INFO:trainer.default_trainer:-------------------------------------------------------
INFO:trainer.default_trainer:Training on rank: 0
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm
INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros
INFO:base_dir.pipeline.XDecoderPipeline:GeneralizedSEEM(
  (backbone): D2FocalNet(
    (patch_embed): PatchEmbed(
      (proj): Conv2d(3, 96, kernel_size=(7, 7), stride=(4, 4), padding=(3, 3))
      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
    )
    (pos_drop): Dropout(p=0.0, inplace=False)
    (layers): ModuleList(
      (0): BasicLayer(
        (blocks): ModuleList(
          (0): FocalModulationBlock(
            (dw1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96)
            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (modulation): FocalModulation(
              (f): Linear(in_features=96, out_features=196, bias=True)
              (h): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1))
              (act): GELU(approximate='none')
              (proj): Linear(in_features=96, out_features=96, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (focal_layers): ModuleList(
                (0): Sequential(
                  (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
                  (1): GELU(approximate='none')
                )
                (1): Sequential(
                  (0): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)
                  (1): GELU(approximate='none')
                )
                (2): Sequential(
                  (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96, bias=False)
                  (1): GELU(approximate='none')
                )
              )
            )
            (dw2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96)
            (drop_path): Identity()
            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=96, out_features=384, bias=True)
              (act): GELU(approximate='none')
              (fc2): Linear(in_features=384, out_features=96, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): FocalModulationBlock(
            (dw1): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96)
            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (modulation): FocalModulation(
              (f): Linear(in_features=96, out_features=196, bias=True)
              (h): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1))
              (act): GELU(approximate='none')
              (proj): Linear(in_features=96, out_features=96, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (focal_layers): ModuleList(
                (0): Sequential(
                  (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
                  (1): GELU(approximate='none')
                )
                (1): Sequential(
                  (0): Conv2d(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)
                  (1): GELU(approximate='none')
                )
                (2): Sequential(
                  (0): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96, bias=False)
                  (1): GELU(approximate='none')
                )
              )
            )
            (dw2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96)
            (drop_path): DropPath()
            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=96, out_features=384, bias=True)
              (act): GELU(approximate='none')
              (fc2): Linear(in_features=384, out_features=96, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchEmbed(
          (proj): Conv2d(96, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (norm): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
        )
      )
      (1): BasicLayer(
        (blocks): ModuleList(
          (0-1): 2 x FocalModulationBlock(
            (dw1): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192)
            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
            (modulation): FocalModulation(
              (f): Linear(in_features=192, out_features=388, bias=True)
              (h): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))
              (act): GELU(approximate='none')
              (proj): Linear(in_features=192, out_features=192, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (focal_layers): ModuleList(
                (0): Sequential(
                  (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
                  (1): GELU(approximate='none')
                )
                (1): Sequential(
                  (0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)
                  (1): GELU(approximate='none')
                )
                (2): Sequential(
                  (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192, bias=False)
                  (1): GELU(approximate='none')
                )
              )
            )
            (dw2): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192)
            (drop_path): DropPath()
            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=192, out_features=768, bias=True)
              (act): GELU(approximate='none')
              (fc2): Linear(in_features=768, out_features=192, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchEmbed(
          (proj): Conv2d(192, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
        )
      )
      (2): BasicLayer(
        (blocks): ModuleList(
          (0-5): 6 x FocalModulationBlock(
            (dw1): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384)
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (modulation): FocalModulation(
              (f): Linear(in_features=384, out_features=772, bias=True)
              (h): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))
              (act): GELU(approximate='none')
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (focal_layers): ModuleList(
                (0): Sequential(
                  (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
                  (1): GELU(approximate='none')
                )
                (1): Sequential(
                  (0): Conv2d(384, 384, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=384, bias=False)
                  (1): GELU(approximate='none')
                )
                (2): Sequential(
                  (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384, bias=False)
                  (1): GELU(approximate='none')
                )
              )
            )
            (dw2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384)
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU(approximate='none')
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchEmbed(
          (proj): Conv2d(384, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        )
      )
      (3): BasicLayer(
        (blocks): ModuleList(
          (0-1): 2 x FocalModulationBlock(
            (dw1): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=768)
            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (modulation): FocalModulation(
              (f): Linear(in_features=768, out_features=1540, bias=True)
              (h): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))
              (act): GELU(approximate='none')
              (proj): Linear(in_features=768, out_features=768, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (focal_layers): ModuleList(
                (0): Sequential(
                  (0): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=768, bias=False)
                  (1): GELU(approximate='none')
                )
                (1): Sequential(
                  (0): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)
                  (1): GELU(approximate='none')
                )
                (2): Sequential(
                  (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768, bias=False)
                  (1): GELU(approximate='none')
                )
              )
            )
            (dw2): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=768)
            (drop_path): DropPath()
            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=768, out_features=3072, bias=True)
              (act): GELU(approximate='none')
              (fc2): Linear(in_features=3072, out_features=768, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
      )
    )
    (norm0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
    (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
    (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
    (norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (sem_seg_head): XdecoderHead(
    (pixel_decoder): TransformerEncoderPixelDecoder(
      (adapter_1): Conv2d(
        96, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
      (layer_1): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
      (adapter_2): Conv2d(
        192, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
      (layer_2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
      (adapter_3): Conv2d(
        384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
      (layer_3): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
      (mask_features): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (input_proj): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1))
      (transformer): TransformerEncoderOnly(
        (encoder): TransformerEncoder(
          (layers): ModuleList(
            (0-5): 6 x TransformerEncoderLayer(
              (self_attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
              )
              (linear1): Linear(in_features=512, out_features=2048, bias=True)
              (dropout): Dropout(p=0.0, inplace=False)
              (linear2): Linear(in_features=2048, out_features=512, bias=True)
              (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
              (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
              (dropout1): Dropout(p=0.0, inplace=False)
              (dropout2): Dropout(p=0.0, inplace=False)
            )
          )
        )
      )
      (pe_layer): Positional encoding PositionEmbeddingSine
          num_pos_feats: 256
          temperature: 10000
          normalize: True
          scale: 6.283185307179586
      (layer_4): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        (norm): GroupNorm(32, 512, eps=1e-05, affine=True)
      )
    )
    (predictor): SEEMDecoder(
      (pe_layer): Positional encoding PositionEmbeddingSine
          num_pos_feats: 256
          temperature: 10000
          normalize: True
          scale: 6.283185307179586
      (transformer_self_attention_layers): ModuleList(
        (0-8): 9 x SelfAttentionLayer(
          (self_attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (transformer_cross_attention_layers): ModuleList(
        (0-8): 9 x CrossAttentionLayer(
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
          )
          (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (transformer_ffn_layers): ModuleList(
        (0-8): 9 x FFNLayer(
          (linear1): Linear(in_features=512, out_features=2048, bias=True)
          (dropout): Dropout(p=0.0, inplace=False)
          (linear2): Linear(in_features=2048, out_features=512, bias=True)
          (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
      )
      (decoder_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      (query_feat): Embedding(101, 512)
      (query_embed): Embedding(101, 512)
      (level_embed): Embedding(3, 512)
      (input_proj): ModuleList(
        (0-2): 3 x Sequential()
      )
      (lang_encoder): LanguageEncoder(
        (lang_encoder): Transformer(
          (token_embedding): Embedding(49408, 512)
          (resblocks): ModuleList(
            (0-11): 12 x ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
              )
              (ln_1): LayerNorm()
              (mlp): Sequential(
                (c_fc): Linear(in_features=512, out_features=2048, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=2048, out_features=512, bias=True)
              )
              (ln_2): LayerNorm()
              (drop_path): Identity()
            )
          )
          (ln_final): LayerNorm()
        )
      )
      (mask_embed): MLP(
        (layers): ModuleList(
          (0-2): 3 x Linear(in_features=512, out_features=512, bias=True)
        )
      )
      (mask_sptial_embed): ParameterList(
          (0): Parameter containing: [torch.float32 of size 512x512]
          (1): Parameter containing: [torch.float32 of size 512x512]
          (2): Parameter containing: [torch.float32 of size 512x512]
      )
      (spatial_embed): Embedding(32, 512)
      (spatial_featured): Embedding(32, 512)
      (pn_indicator): Embedding(2, 512)
      (attention_data): AttentionDataStruct()
    )
  )
  (criterion): Criterion SetCriterion
      matcher: Matcher HungarianMatcher
          cost_class: 2.0
          cost_mask: 5.0
          cost_dice: 5.0
      losses: []
      weight_dict: {'loss_mask_ce_0': 2.0, 'loss_mask_dice_0': 5.0, 'loss_mask_bce_0': 5.0, 'loss_spatial_ce_0': 0.4, 'loss_spatial_dice_0': 1.0, 'loss_spatial_bce_0': 1.0, 'loss_grounding_ce_0': 0.4, 'loss_grounding_dice_0': 1.0, 'loss_grounding_bce_0': 1.0, 'loss_openimage_ce_0': 0.4, 'loss_openimage_dice_0': 1.0, 'loss_openimage_bce_0': 1.0, 'loss_mask_ce_1': 2.0, 'loss_mask_dice_1': 5.0, 'loss_mask_bce_1': 5.0, 'loss_spatial_ce_1': 0.4, 'loss_spatial_dice_1': 1.0, 'loss_spatial_bce_1': 1.0, 'loss_grounding_ce_1': 0.4, 'loss_grounding_dice_1': 1.0, 'loss_grounding_bce_1': 1.0, 'loss_openimage_ce_1': 0.4, 'loss_openimage_dice_1': 1.0, 'loss_openimage_bce_1': 1.0, 'loss_mask_ce_2': 2.0, 'loss_mask_dice_2': 5.0, 'loss_mask_bce_2': 5.0, 'loss_spatial_ce_2': 0.4, 'loss_spatial_dice_2': 1.0, 'loss_spatial_bce_2': 1.0, 'loss_grounding_ce_2': 0.4, 'loss_grounding_dice_2': 1.0, 'loss_grounding_bce_2': 1.0, 'loss_openimage_ce_2': 0.4, 'loss_openimage_dice_2': 1.0, 'loss_openimage_bce_2': 1.0, 'loss_mask_ce_3': 2.0, 'loss_mask_dice_3': 5.0, 'loss_mask_bce_3': 5.0, 'loss_spatial_ce_3': 0.4, 'loss_spatial_dice_3': 1.0, 'loss_spatial_bce_3': 1.0, 'loss_grounding_ce_3': 0.4, 'loss_grounding_dice_3': 1.0, 'loss_grounding_bce_3': 1.0, 'loss_openimage_ce_3': 0.4, 'loss_openimage_dice_3': 1.0, 'loss_openimage_bce_3': 1.0, 'loss_mask_ce_4': 2.0, 'loss_mask_dice_4': 5.0, 'loss_mask_bce_4': 5.0, 'loss_spatial_ce_4': 0.4, 'loss_spatial_dice_4': 1.0, 'loss_spatial_bce_4': 1.0, 'loss_grounding_ce_4': 0.4, 'loss_grounding_dice_4': 1.0, 'loss_grounding_bce_4': 1.0, 'loss_openimage_ce_4': 0.4, 'loss_openimage_dice_4': 1.0, 'loss_openimage_bce_4': 1.0, 'loss_mask_ce_5': 2.0, 'loss_mask_dice_5': 5.0, 'loss_mask_bce_5': 5.0, 'loss_spatial_ce_5': 0.4, 'loss_spatial_dice_5': 1.0, 'loss_spatial_bce_5': 1.0, 'loss_grounding_ce_5': 0.4, 'loss_grounding_dice_5': 1.0, 'loss_grounding_bce_5': 1.0, 'loss_openimage_ce_5': 0.4, 'loss_openimage_dice_5': 1.0, 'loss_openimage_bce_5': 1.0, 'loss_mask_ce_6': 2.0, 'loss_mask_dice_6': 5.0, 'loss_mask_bce_6': 5.0, 'loss_spatial_ce_6': 0.4, 'loss_spatial_dice_6': 1.0, 'loss_spatial_bce_6': 1.0, 'loss_grounding_ce_6': 0.4, 'loss_grounding_dice_6': 1.0, 'loss_grounding_bce_6': 1.0, 'loss_openimage_ce_6': 0.4, 'loss_openimage_dice_6': 1.0, 'loss_openimage_bce_6': 1.0, 'loss_mask_ce_7': 2.0, 'loss_mask_dice_7': 5.0, 'loss_mask_bce_7': 5.0, 'loss_spatial_ce_7': 0.4, 'loss_spatial_dice_7': 1.0, 'loss_spatial_bce_7': 1.0, 'loss_grounding_ce_7': 0.4, 'loss_grounding_dice_7': 1.0, 'loss_grounding_bce_7': 1.0, 'loss_openimage_ce_7': 0.4, 'loss_openimage_dice_7': 1.0, 'loss_openimage_bce_7': 1.0, 'loss_mask_ce_8': 2.0, 'loss_mask_dice_8': 5.0, 'loss_mask_bce_8': 5.0, 'loss_spatial_ce_8': 0.4, 'loss_spatial_dice_8': 1.0, 'loss_spatial_bce_8': 1.0, 'loss_grounding_ce_8': 0.4, 'loss_grounding_dice_8': 1.0, 'loss_grounding_bce_8': 1.0, 'loss_openimage_ce_8': 0.4, 'loss_openimage_dice_8': 1.0, 'loss_openimage_bce_8': 1.0, 'loss_mask_ce_9': 2.0, 'loss_mask_dice_9': 5.0, 'loss_mask_bce_9': 5.0, 'loss_spatial_ce_9': 0.4, 'loss_spatial_dice_9': 1.0, 'loss_spatial_bce_9': 1.0, 'loss_grounding_ce_9': 0.4, 'loss_grounding_dice_9': 1.0, 'loss_grounding_bce_9': 1.0, 'loss_openimage_ce_9': 0.4, 'loss_openimage_dice_9': 1.0, 'loss_openimage_bce_9': 1.0}
      num_classes: 133
      eos_coef: 0.1
      num_points: 12544
      oversample_ratio: 3.0
      importance_sample_ratio: 0.75
)
INFO:datasets.dataset_mappers.coco_panoptic_interactive_dataset_mapper:[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: [RandomFlip(), ResizeScale(min_scale=0.1, max_scale=2.0, target_height=1024, target_width=1024), FixedSizeCrop(crop_size=(1024, 1024))]
INFO:datasets.build:Using training sampler TrainingSampler
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:2 to store for rank: 0
INFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes.
INFO:detectron2.data.common:Serializing 116987 elements to byte tensors and concatenating them all ...
INFO:detectron2.data.common:Serialized dataset takes 1458.79 MiB
INFO:base_dir.pipeline.XDecoderPipeline:num of train samples: 1827
INFO:trainer.xdecoder_trainer:Calculate MAX_ITER @ 91350 and STEPS @ [81200, 87966]
INFO:trainer.xdecoder_trainer:Total number of parameters in default module (on each GPU): 164619249
INFO:trainer.xdecoder_trainer:Number of trainable parameters in default module (on each GPU): 39812608
WARNING:trainer.utils_trainer:PyTorch AMP GradScaler initialized.
INFO:utils.model:Loaded backbone.layers.0.blocks.0.dw1.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.dw1.weight, Model Shape: torch.Size([96, 1, 3, 3]) <-> Ckpt Shape: torch.Size([96, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.dw2.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.dw2.weight, Model Shape: torch.Size([96, 1, 3, 3]) <-> Ckpt Shape: torch.Size([96, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.gamma_1, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.gamma_2, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([384, 96]) <-> Ckpt Shape: torch.Size([384, 96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([96, 384]) <-> Ckpt Shape: torch.Size([96, 384])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.f.bias, Model Shape: torch.Size([196]) <-> Ckpt Shape: torch.Size([196])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.f.weight, Model Shape: torch.Size([196, 96]) <-> Ckpt Shape: torch.Size([196, 96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([96, 1, 3, 3]) <-> Ckpt Shape: torch.Size([96, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([96, 1, 5, 5]) <-> Ckpt Shape: torch.Size([96, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([96, 1, 7, 7]) <-> Ckpt Shape: torch.Size([96, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.h.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.h.weight, Model Shape: torch.Size([96, 96, 1, 1]) <-> Ckpt Shape: torch.Size([96, 96, 1, 1])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.proj.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.proj.weight, Model Shape: torch.Size([96, 96]) <-> Ckpt Shape: torch.Size([96, 96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm1.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm1.weight, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm2.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm2.weight, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.dw1.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.dw1.weight, Model Shape: torch.Size([96, 1, 3, 3]) <-> Ckpt Shape: torch.Size([96, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.dw2.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.dw2.weight, Model Shape: torch.Size([96, 1, 3, 3]) <-> Ckpt Shape: torch.Size([96, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.gamma_1, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.gamma_2, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([384, 96]) <-> Ckpt Shape: torch.Size([384, 96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([96, 384]) <-> Ckpt Shape: torch.Size([96, 384])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.f.bias, Model Shape: torch.Size([196]) <-> Ckpt Shape: torch.Size([196])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.f.weight, Model Shape: torch.Size([196, 96]) <-> Ckpt Shape: torch.Size([196, 96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([96, 1, 3, 3]) <-> Ckpt Shape: torch.Size([96, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([96, 1, 5, 5]) <-> Ckpt Shape: torch.Size([96, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([96, 1, 7, 7]) <-> Ckpt Shape: torch.Size([96, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.h.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.h.weight, Model Shape: torch.Size([96, 96, 1, 1]) <-> Ckpt Shape: torch.Size([96, 96, 1, 1])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.proj.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.proj.weight, Model Shape: torch.Size([96, 96]) <-> Ckpt Shape: torch.Size([96, 96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm1.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm1.weight, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm2.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm2.weight, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.layers.0.downsample.norm.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.0.downsample.norm.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.0.downsample.proj.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.0.downsample.proj.weight, Model Shape: torch.Size([192, 96, 3, 3]) <-> Ckpt Shape: torch.Size([192, 96, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.dw1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.dw1.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.dw2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.dw2.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.gamma_1, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.gamma_2, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([768, 192]) <-> Ckpt Shape: torch.Size([768, 192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([192, 768]) <-> Ckpt Shape: torch.Size([192, 768])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.f.bias, Model Shape: torch.Size([388]) <-> Ckpt Shape: torch.Size([388])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.f.weight, Model Shape: torch.Size([388, 192]) <-> Ckpt Shape: torch.Size([388, 192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([192, 1, 5, 5]) <-> Ckpt Shape: torch.Size([192, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([192, 1, 7, 7]) <-> Ckpt Shape: torch.Size([192, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.h.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.h.weight, Model Shape: torch.Size([192, 192, 1, 1]) <-> Ckpt Shape: torch.Size([192, 192, 1, 1])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.proj.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.proj.weight, Model Shape: torch.Size([192, 192]) <-> Ckpt Shape: torch.Size([192, 192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm1.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm2.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.dw1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.dw1.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.dw2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.dw2.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.gamma_1, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.gamma_2, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([768, 192]) <-> Ckpt Shape: torch.Size([768, 192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([192, 768]) <-> Ckpt Shape: torch.Size([192, 768])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.f.bias, Model Shape: torch.Size([388]) <-> Ckpt Shape: torch.Size([388])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.f.weight, Model Shape: torch.Size([388, 192]) <-> Ckpt Shape: torch.Size([388, 192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([192, 1, 5, 5]) <-> Ckpt Shape: torch.Size([192, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([192, 1, 7, 7]) <-> Ckpt Shape: torch.Size([192, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.h.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.h.weight, Model Shape: torch.Size([192, 192, 1, 1]) <-> Ckpt Shape: torch.Size([192, 192, 1, 1])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.proj.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.proj.weight, Model Shape: torch.Size([192, 192]) <-> Ckpt Shape: torch.Size([192, 192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm1.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm2.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.layers.1.downsample.norm.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.1.downsample.norm.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.1.downsample.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.1.downsample.proj.weight, Model Shape: torch.Size([384, 192, 3, 3]) <-> Ckpt Shape: torch.Size([384, 192, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.dw1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.dw1.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.dw2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.dw2.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.f.bias, Model Shape: torch.Size([772]) <-> Ckpt Shape: torch.Size([772])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.f.weight, Model Shape: torch.Size([772, 384]) <-> Ckpt Shape: torch.Size([772, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.dw1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.dw1.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.dw2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.dw2.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.f.bias, Model Shape: torch.Size([772]) <-> Ckpt Shape: torch.Size([772])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.f.weight, Model Shape: torch.Size([772, 384]) <-> Ckpt Shape: torch.Size([772, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.dw1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.dw1.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.dw2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.dw2.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.f.bias, Model Shape: torch.Size([772]) <-> Ckpt Shape: torch.Size([772])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.f.weight, Model Shape: torch.Size([772, 384]) <-> Ckpt Shape: torch.Size([772, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.dw1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.dw1.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.dw2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.dw2.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.f.bias, Model Shape: torch.Size([772]) <-> Ckpt Shape: torch.Size([772])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.f.weight, Model Shape: torch.Size([772, 384]) <-> Ckpt Shape: torch.Size([772, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.dw1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.dw1.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.dw2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.dw2.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.f.bias, Model Shape: torch.Size([772]) <-> Ckpt Shape: torch.Size([772])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.f.weight, Model Shape: torch.Size([772, 384]) <-> Ckpt Shape: torch.Size([772, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.dw1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.dw1.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.dw2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.dw2.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.f.bias, Model Shape: torch.Size([772]) <-> Ckpt Shape: torch.Size([772])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.f.weight, Model Shape: torch.Size([772, 384]) <-> Ckpt Shape: torch.Size([772, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.layers.2.downsample.norm.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.2.downsample.norm.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.2.downsample.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.2.downsample.proj.weight, Model Shape: torch.Size([768, 384, 3, 3]) <-> Ckpt Shape: torch.Size([768, 384, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.dw1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.dw1.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.dw2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.dw2.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.f.bias, Model Shape: torch.Size([1540]) <-> Ckpt Shape: torch.Size([1540])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.f.weight, Model Shape: torch.Size([1540, 768]) <-> Ckpt Shape: torch.Size([1540, 768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.dw1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.dw1.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.dw2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.dw2.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.f.bias, Model Shape: torch.Size([1540]) <-> Ckpt Shape: torch.Size([1540])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.f.weight, Model Shape: torch.Size([1540, 768]) <-> Ckpt Shape: torch.Size([1540, 768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.norm0.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.norm0.weight, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.norm1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.norm1.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192])
INFO:utils.model:Loaded backbone.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384])
INFO:utils.model:Loaded backbone.norm3.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.norm3.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768])
INFO:utils.model:Loaded backbone.patch_embed.norm.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.patch_embed.norm.weight, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.patch_embed.proj.bias, Model Shape: torch.Size([96]) <-> Ckpt Shape: torch.Size([96])
INFO:utils.model:Loaded backbone.patch_embed.proj.weight, Model Shape: torch.Size([96, 3, 7, 7]) <-> Ckpt Shape: torch.Size([96, 3, 7, 7])
INFO:utils.model:Loaded criterion.empty_weight, Model Shape: torch.Size([134]) <-> Ckpt Shape: torch.Size([134])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_1.weight, Model Shape: torch.Size([512, 96, 1, 1]) <-> Ckpt Shape: torch.Size([512, 96, 1, 1])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_2.weight, Model Shape: torch.Size([512, 192, 1, 1]) <-> Ckpt Shape: torch.Size([512, 192, 1, 1])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_3.weight, Model Shape: torch.Size([512, 384, 1, 1]) <-> Ckpt Shape: torch.Size([512, 384, 1, 1])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.input_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.input_proj.weight, Model Shape: torch.Size([512, 768, 1, 1]) <-> Ckpt Shape: torch.Size([512, 768, 1, 1])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_1.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_2.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_3.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_4.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.mask_features.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.mask_features.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.class_embed, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.decoder_norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.decoder_norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.ln_final.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.ln_final.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.positional_embedding, Model Shape: torch.Size([77, 512]) <-> Ckpt Shape: torch.Size([77, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.token_embedding.weight, Model Shape: torch.Size([49408, 512]) <-> Ckpt Shape: torch.Size([49408, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_proj, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.logit_scale, Model Shape: torch.Size([]) <-> Ckpt Shape: torch.Size([])
INFO:utils.model:Loaded sem_seg_head.predictor.level_embed.weight, Model Shape: torch.Size([3, 512]) <-> Ckpt Shape: torch.Size([3, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.0.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.0.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.1.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.2.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.query_embed.weight, Model Shape: torch.Size([101, 512]) <-> Ckpt Shape: torch.Size([101, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.query_feat.weight, Model Shape: torch.Size([101, 512]) <-> Ckpt Shape: torch.Size([101, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512])
INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512])
WARNING:utils.model:*UNLOADED* dilation_kernel, Model Shape: torch.Size([1, 1, 3, 3])
WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.mask_sptial_embed.0, Model Shape: torch.Size([512, 512])
WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.mask_sptial_embed.1, Model Shape: torch.Size([512, 512])
WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.mask_sptial_embed.2, Model Shape: torch.Size([512, 512])
WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.pn_indicator.weight, Model Shape: torch.Size([2, 512])
WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.spatial_embed.weight, Model Shape: torch.Size([32, 512])
WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.spatial_featured.weight, Model Shape: torch.Size([32, 512])
WARNING:utils.model:$UNUSED$ backbone_proj, Ckpt Shape: torch.Size([768, 512])
WARNING:utils.model:$UNUSED$ sem_seg_head.predictor.caping_embed, Ckpt Shape: torch.Size([512, 512])
WARNING:utils.model:$UNUSED$ sem_seg_head.predictor.pos_embed_caping.weight, Ckpt Shape: torch.Size([77, 512])
WARNING:utils.model:$UNUSED$ sem_seg_head.predictor.self_attn_mask, Ckpt Shape: torch.Size([1, 178, 178])
WARNING:trainer.utils_trainer:Load weights from /mnt/output/xueyanz/mainzvision/mask2former_vlp_focalt_enc6_fpn_dec10_lang_capgTrue_retTrue_grdTrue_topc3_topr3_topg6_capgw8_rw8_cbs32_vbs1024_ep50_lr0.0001_preuTrue_gtw2.0_gcw0.5_bitoken_caplang/vlp_focalt_lang.yaml_conf~/run_1/00175900/default/model_state_dict.pt...
INFO:trainer.default_trainer:***** Running training *****
INFO:trainer.default_trainer:  Num of GPUs = 32
INFO:trainer.default_trainer:  Num Epochs = 50
INFO:trainer.default_trainer:  Num of Mini Batches per Epoch = 1827
INFO:trainer.default_trainer:  Total train batch size (w. parallel, distributed & accumulation) = 91350
INFO:trainer.default_trainer:  Gradient Accumulation steps = 1
INFO:trainer.default_trainer:  Total optimization steps = 91350
INFO:trainer.default_trainer:Start epoch: 0 training.
INFO:trainer.default_trainer:epochs[     0] optim steps[1] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05463/0.05463, loss_mask_bce_0: 0.34289/0.34289, loss_mask_dice_0: 0.55142/0.55142, loss_spatial_bce_0: 0.67336/0.67336, loss_spatial_dice_0: 0.60182/0.60182, loss_spatial_ce_0: 2.24330/2.24330, loss_grounding_bce_0: 0.10153/0.10153, loss_grounding_dice_0: 0.14154/0.14154, loss_grounding_ce_0: 0.05502/0.05502, loss_mask_ce_1: 0.05892/0.05892, loss_mask_bce_1: 0.33797/0.33797, loss_mask_dice_1: 0.55632/0.55632, loss_spatial_bce_1: 0.71281/0.71281, loss_spatial_dice_1: 0.58766/0.58766, loss_spatial_ce_1: 2.18325/2.18325, loss_grounding_bce_1: 0.10472/0.10472, loss_grounding_dice_1: 0.14383/0.14383, loss_grounding_ce_1: 0.03689/0.03689, loss_mask_ce_2: 0.06068/0.06068, loss_mask_bce_2: 0.34374/0.34374, loss_mask_dice_2: 0.54820/0.54820, loss_spatial_bce_2: 0.75255/0.75255, loss_spatial_dice_2: 0.59053/0.59053, loss_spatial_ce_2: 2.28121/2.28121, loss_grounding_bce_2: 0.10182/0.10182, loss_grounding_dice_2: 0.14180/0.14180, loss_grounding_ce_2: 0.05963/0.05963, loss_mask_ce_3: 0.06587/0.06587, loss_mask_bce_3: 0.34822/0.34822, loss_mask_dice_3: 0.55897/0.55897, loss_spatial_bce_3: 0.98537/0.98537, loss_spatial_dice_3: 0.62637/0.62637, loss_spatial_ce_3: 2.73910/2.73910, loss_grounding_bce_3: 0.10788/0.10788, loss_grounding_dice_3: 0.14871/0.14871, loss_grounding_ce_3: 0.02214/0.02214, loss_mask_ce_4: 0.07075/0.07075, loss_mask_bce_4: 0.34878/0.34878, loss_mask_dice_4: 0.55277/0.55277, loss_spatial_bce_4: 1.04908/1.04908, loss_spatial_dice_4: 0.64184/0.64184, loss_spatial_ce_4: 2.66498/2.66498, loss_grounding_bce_4: 0.10537/0.10537, loss_grounding_dice_4: 0.13817/0.13817, loss_grounding_ce_4: 0.03638/0.03638, loss_mask_ce_5: 0.08412/0.08412, loss_mask_bce_5: 0.32786/0.32786, loss_mask_dice_5: 0.54174/0.54174, loss_spatial_bce_5: 1.06558/1.06558, loss_spatial_dice_5: 0.65406/0.65406, loss_spatial_ce_5: 2.83394/2.83394, loss_grounding_bce_5: 0.10157/0.10157, loss_grounding_dice_5: 0.14463/0.14463, loss_grounding_ce_5: 0.04295/0.04295, loss_mask_ce_6: 0.13406/0.13406, loss_mask_bce_6: 0.35071/0.35071, loss_mask_dice_6: 0.57605/0.57605, loss_spatial_bce_6: 0.94506/0.94506, loss_spatial_dice_6: 0.65465/0.65465, loss_spatial_ce_6: 3.15707/3.15707, loss_grounding_bce_6: 0.10013/0.10013, loss_grounding_dice_6: 0.13699/0.13699, loss_grounding_ce_6: 0.09319/0.09319, loss_mask_ce_7: 0.10392/0.10392, loss_mask_bce_7: 0.34500/0.34500, loss_mask_dice_7: 0.58267/0.58267, loss_spatial_bce_7: 1.03591/1.03591, loss_spatial_dice_7: 0.68091/0.68091, loss_spatial_ce_7: 2.92811/2.92811, loss_grounding_bce_7: 0.10105/0.10105, loss_grounding_dice_7: 0.14040/0.14040, loss_grounding_ce_7: 0.16984/0.16984, loss_mask_ce_8: 0.15064/0.15064, loss_mask_bce_8: 0.37174/0.37174, loss_mask_dice_8: 0.59656/0.59656, loss_spatial_bce_8: 1.18503/1.18503, loss_spatial_dice_8: 0.79970/0.79970, loss_spatial_ce_8: 3.81361/3.81361, loss_grounding_bce_8: 0.09974/0.09974, loss_grounding_dice_8: 0.13930/0.13930, loss_grounding_ce_8: 0.62624/0.62624, loss_mask_ce_9: 3.14632/3.14632, loss_mask_bce_9: 0.39442/0.39442, loss_mask_dice_9: 0.60420/0.60420, loss_spatial_bce_9: 1.27721/1.27721, loss_spatial_dice_9: 0.82648/0.82648, loss_spatial_ce_9: 3.35113/3.35113, loss_grounding_bce_9: 0.14483/0.14483, loss_grounding_dice_9: 0.17267/0.17267, loss_grounding_ce_9: 3.16601/3.16601] items per batch[64] items per second[4.77] total items[64] mini batches[     1] memory[6150] epoch remaining[6:48:10]
INFO:trainer.default_trainer:epochs[     0] optim steps[2] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31134/0.18298, loss_mask_bce_0: 0.15713/0.25001, loss_mask_dice_0: 1.56610/1.05876, loss_spatial_bce_0: 0.17899/0.42618, loss_spatial_dice_0: 0.84755/0.72469, loss_spatial_ce_0: 9.15662/5.69996, loss_grounding_bce_0: 0.03346/0.06749, loss_grounding_dice_0: 0.17665/0.15909, loss_grounding_ce_0: 0.19213/0.12357, loss_mask_ce_1: 0.32767/0.19330, loss_mask_bce_1: 0.14861/0.24329, loss_mask_dice_1: 1.37644/0.96638, loss_spatial_bce_1: 0.10850/0.41065, loss_spatial_dice_1: 0.84851/0.71809, loss_spatial_ce_1: 9.80359/5.99342, loss_grounding_bce_1: 0.03178/0.06825, loss_grounding_dice_1: 0.11214/0.12799, loss_grounding_ce_1: 0.00244/0.01967, loss_mask_ce_2: 0.29502/0.17785, loss_mask_bce_2: 0.14387/0.24381, loss_mask_dice_2: 1.59797/1.07309, loss_spatial_bce_2: 0.11411/0.43333, loss_spatial_dice_2: 0.84980/0.72016, loss_spatial_ce_2: 9.59049/5.93585, loss_grounding_bce_2: 0.03114/0.06648, loss_grounding_dice_2: 0.05220/0.09700, loss_grounding_ce_2: 0.00178/0.03071, loss_mask_ce_3: 0.33850/0.20219, loss_mask_bce_3: 0.16836/0.25829, loss_mask_dice_3: 1.75864/1.15881, loss_spatial_bce_3: 0.15926/0.57231, loss_spatial_dice_3: 0.84942/0.73789, loss_spatial_ce_3: 11.41738/7.07824, loss_grounding_bce_3: 0.03385/0.07086, loss_grounding_dice_3: 0.10336/0.12604, loss_grounding_ce_3: 0.00344/0.01279, loss_mask_ce_4: 0.40641/0.23858, loss_mask_bce_4: 0.15012/0.24945, loss_mask_dice_4: 1.65923/1.10600, loss_spatial_bce_4: 0.19954/0.62431, loss_spatial_dice_4: 0.83796/0.73990, loss_spatial_ce_4: 10.17610/6.42054, loss_grounding_bce_4: 0.03258/0.06897, loss_grounding_dice_4: 0.06192/0.10005, loss_grounding_ce_4: 0.00204/0.01921, loss_mask_ce_5: 0.53560/0.30986, loss_mask_bce_5: 0.15384/0.24085, loss_mask_dice_5: 1.68322/1.11248, loss_spatial_bce_5: 0.19333/0.62945, loss_spatial_dice_5: 0.83276/0.74341, loss_spatial_ce_5: 8.60846/5.72120, loss_grounding_bce_5: 0.03471/0.06814, loss_grounding_dice_5: 0.08026/0.11244, loss_grounding_ce_5: 0.00366/0.02330, loss_mask_ce_6: 0.41044/0.27225, loss_mask_bce_6: 0.18144/0.26608, loss_mask_dice_6: 1.59383/1.08494, loss_spatial_bce_6: 0.13526/0.54016, loss_spatial_dice_6: 0.84773/0.75119, loss_spatial_ce_6: 9.84757/6.50232, loss_grounding_bce_6: 0.03268/0.06640, loss_grounding_dice_6: 0.04859/0.09279, loss_grounding_ce_6: 0.00241/0.04780, loss_mask_ce_7: 0.47753/0.29073, loss_mask_bce_7: 0.14038/0.24269, loss_mask_dice_7: 1.62528/1.10398, loss_spatial_bce_7: 0.18593/0.61092, loss_spatial_dice_7: 0.84778/0.76434, loss_spatial_ce_7: 8.59227/5.76019, loss_grounding_bce_7: 0.03385/0.06745, loss_grounding_dice_7: 0.09476/0.11758, loss_grounding_ce_7: 0.01062/0.09023, loss_mask_ce_8: 0.66265/0.40664, loss_mask_bce_8: 0.15741/0.26458, loss_mask_dice_8: 1.83521/1.21589, loss_spatial_bce_8: 0.13830/0.66166, loss_spatial_dice_8: 0.84113/0.82041, loss_spatial_ce_8: 7.13408/5.47384, loss_grounding_bce_8: 0.03447/0.06710, loss_grounding_dice_8: 0.15028/0.14479, loss_grounding_ce_8: 0.07287/0.34956, loss_mask_ce_9: 3.83650/3.49141, loss_mask_bce_9: 0.20589/0.30015, loss_mask_dice_9: 2.34629/1.47525, loss_spatial_bce_9: 0.18241/0.72981, loss_spatial_dice_9: 0.75438/0.79043, loss_spatial_ce_9: 5.21497/4.28305, loss_grounding_bce_9: 0.04142/0.09312, loss_grounding_dice_9: 0.20030/0.18649, loss_grounding_ce_9: 0.31521/1.74061] items per batch[64] items per second[9.86] total items[128] mini batches[     2] memory[6394] epoch remaining[5:02:42]
INFO:trainer.default_trainer:epochs[     0] optim steps[3] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.22219/0.52939, loss_mask_bce_0: 0.33718/0.27906, loss_mask_dice_0: 4.69147/2.26966, loss_spatial_bce_0: 0.38494/0.41243, loss_spatial_dice_0: 0.78383/0.74440, loss_spatial_ce_0: 5.42234/5.60742, loss_grounding_bce_0: 0.02350/0.05283, loss_grounding_dice_0: 0.14868/0.15562, loss_grounding_ce_0: 0.49521/0.24745, loss_mask_ce_1: 1.28228/0.55629, loss_mask_bce_1: 0.34203/0.27620, loss_mask_dice_1: 4.92506/2.28594, loss_spatial_bce_1: 0.33150/0.38427, loss_spatial_dice_1: 0.73367/0.72328, loss_spatial_ce_1: 5.19977/5.72887, loss_grounding_bce_1: 0.02708/0.05453, loss_grounding_dice_1: 0.16883/0.14160, loss_grounding_ce_1: 0.38694/0.14209, loss_mask_ce_2: 1.52750/0.62773, loss_mask_bce_2: 0.36359/0.28374, loss_mask_dice_2: 4.90628/2.35082, loss_spatial_bce_2: 0.37485/0.41384, loss_spatial_dice_2: 0.75200/0.73078, loss_spatial_ce_2: 5.60970/5.82713, loss_grounding_bce_2: 0.02324/0.05206, loss_grounding_dice_2: 0.13572/0.10991, loss_grounding_ce_2: 0.60743/0.22295, loss_mask_ce_3: 1.48410/0.62949, loss_mask_bce_3: 0.35041/0.28900, loss_mask_dice_3: 4.77514/2.36425, loss_spatial_bce_3: 0.40648/0.51704, loss_spatial_dice_3: 0.75056/0.74212, loss_spatial_ce_3: 6.24446/6.80031, loss_grounding_bce_3: 0.02473/0.05549, loss_grounding_dice_3: 0.13473/0.12893, loss_grounding_ce_3: 0.62283/0.21613, loss_mask_ce_4: 1.53658/0.67125, loss_mask_bce_4: 0.36097/0.28663, loss_mask_dice_4: 4.83425/2.34875, loss_spatial_bce_4: 0.45069/0.56644, loss_spatial_dice_4: 0.72196/0.73392, loss_spatial_ce_4: 6.10319/6.31476, loss_grounding_bce_4: 0.02798/0.05531, loss_grounding_dice_4: 0.18161/0.12723, loss_grounding_ce_4: 0.57277/0.20373, loss_mask_ce_5: 1.51689/0.71220, loss_mask_bce_5: 0.34549/0.27573, loss_mask_dice_5: 4.81269/2.34588, loss_spatial_bce_5: 0.45857/0.57249, loss_spatial_dice_5: 0.76304/0.74995, loss_spatial_ce_5: 6.27362/5.90534, loss_grounding_bce_5: 0.02608/0.05412, loss_grounding_dice_5: 0.14932/0.12473, loss_grounding_ce_5: 0.71331/0.25331, loss_mask_ce_6: 1.50553/0.68334, loss_mask_bce_6: 0.35840/0.29685, loss_mask_dice_6: 5.56368/2.57785, loss_spatial_bce_6: 0.38727/0.48919, loss_spatial_dice_6: 0.80988/0.77075, loss_spatial_ce_6: 6.93749/6.64738, loss_grounding_bce_6: 0.02799/0.05360, loss_grounding_dice_6: 0.21159/0.13239, loss_grounding_ce_6: 1.22503/0.44021, loss_mask_ce_7: 1.80108/0.79418, loss_mask_bce_7: 0.43031/0.30523, loss_mask_dice_7: 5.45574/2.55457, loss_spatial_bce_7: 0.40139/0.54108, loss_spatial_dice_7: 0.82472/0.78447, loss_spatial_ce_7: 7.18114/6.23384, loss_grounding_bce_7: 0.02617/0.05369, loss_grounding_dice_7: 0.18125/0.13880, loss_grounding_ce_7: 0.88532/0.35526, loss_mask_ce_8: 2.30369/1.03899, loss_mask_bce_8: 0.43741/0.32219, loss_mask_dice_8: 5.21585/2.54921, loss_spatial_bce_8: 0.42798/0.58377, loss_spatial_dice_8: 0.79139/0.81074, loss_spatial_ce_8: 6.80119/5.91629, loss_grounding_bce_8: 0.03440/0.05620, loss_grounding_dice_8: 0.21193/0.16717, loss_grounding_ce_8: 2.10508/0.93473, loss_mask_ce_9: 5.70957/4.23080, loss_mask_bce_9: 0.43920/0.34650, loss_mask_dice_9: 4.53922/2.49657, loss_spatial_bce_9: 0.27540/0.57834, loss_spatial_dice_9: 0.85617/0.81234, loss_spatial_ce_9: 5.94982/4.83864, loss_grounding_bce_9: 0.04780/0.07802, loss_grounding_dice_9: 0.22495/0.19931, loss_grounding_ce_9: 1.58789/1.68970] items per batch[64] items per second[8.79] total items[192] mini batches[     3] memory[6394] epoch remaining[4:35:30]
INFO:trainer.default_trainer:epochs[     0] optim steps[4] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.54777/0.78398, loss_mask_bce_0: 0.24946/0.27166, loss_mask_dice_0: 0.57875/1.84694, loss_spatial_bce_0: 0.70897/0.48657, loss_spatial_dice_0: 0.81964/0.76321, loss_spatial_ce_0: 5.96694/5.69730, loss_grounding_bce_0: 0.05099/0.05237, loss_grounding_dice_0: 0.07324/0.13503, loss_grounding_ce_0: 0.07119/0.20338, loss_mask_ce_1: 1.54114/0.80250, loss_mask_bce_1: 0.25287/0.27037, loss_mask_dice_1: 0.62676/1.87115, loss_spatial_bce_1: 0.76866/0.48037, loss_spatial_dice_1: 0.82172/0.74789, loss_spatial_ce_1: 6.66672/5.96333, loss_grounding_bce_1: 0.05262/0.05405, loss_grounding_dice_1: 0.05805/0.12071, loss_grounding_ce_1: 0.07729/0.12589, loss_mask_ce_2: 1.53916/0.85559, loss_mask_bce_2: 0.23168/0.27072, loss_mask_dice_2: 0.56082/1.90332, loss_spatial_bce_2: 0.77128/0.50320, loss_spatial_dice_2: 0.81986/0.75305, loss_spatial_ce_2: 6.64567/6.03177, loss_grounding_bce_2: 0.05211/0.05208, loss_grounding_dice_2: 0.07988/0.10240, loss_grounding_ce_2: 0.08314/0.18800, loss_mask_ce_3: 1.59724/0.87143, loss_mask_bce_3: 0.24769/0.27867, loss_mask_dice_3: 0.57034/1.91577, loss_spatial_bce_3: 0.71102/0.56553, loss_spatial_dice_3: 0.79930/0.75641, loss_spatial_ce_3: 7.66004/7.01524, loss_grounding_bce_3: 0.05110/0.05439, loss_grounding_dice_3: 0.06765/0.11361, loss_grounding_ce_3: 0.10138/0.18745, loss_mask_ce_4: 1.58458/0.89958, loss_mask_bce_4: 0.25840/0.27957, loss_mask_dice_4: 0.63031/1.91914, loss_spatial_bce_4: 0.63709/0.58410, loss_spatial_dice_4: 0.83073/0.75812, loss_spatial_ce_4: 6.42490/6.34229, loss_grounding_bce_4: 0.05418/0.05503, loss_grounding_dice_4: 0.07157/0.11332, loss_grounding_ce_4: 0.08989/0.17527, loss_mask_ce_5: 1.56328/0.92497, loss_mask_bce_5: 0.25193/0.26978, loss_mask_dice_5: 0.57855/1.90405, loss_spatial_bce_5: 0.61470/0.58305, loss_spatial_dice_5: 0.82471/0.76864, loss_spatial_ce_5: 4.99869/5.67868, loss_grounding_bce_5: 0.04850/0.05272, loss_grounding_dice_5: 0.07734/0.11289, loss_grounding_ce_5: 0.09648/0.21410, loss_mask_ce_6: 1.59857/0.91215, loss_mask_bce_6: 0.25918/0.28743, loss_mask_dice_6: 0.59398/2.08189, loss_spatial_bce_6: 0.68972/0.53933, loss_spatial_dice_6: 0.83131/0.78589, loss_spatial_ce_6: 5.20899/6.28778, loss_grounding_bce_6: 0.05194/0.05319, loss_grounding_dice_6: 0.05192/0.11227, loss_grounding_ce_6: 0.07100/0.34791, loss_mask_ce_7: 1.75545/1.03450, loss_mask_bce_7: 0.26405/0.29494, loss_mask_dice_7: 0.61973/2.07086, loss_spatial_bce_7: 0.80499/0.60706, loss_spatial_dice_7: 0.90240/0.81395, loss_spatial_ce_7: 7.07181/6.44333, loss_grounding_bce_7: 0.05118/0.05306, loss_grounding_dice_7: 0.06303/0.11986, loss_grounding_ce_7: 0.06700/0.28319, loss_mask_ce_8: 1.73004/1.21175, loss_mask_bce_8: 0.20935/0.29398, loss_mask_dice_8: 0.59859/2.06155, loss_spatial_bce_8: 0.81462/0.64148, loss_spatial_dice_8: 0.92934/0.84039, loss_spatial_ce_8: 4.30251/5.51285, loss_grounding_bce_8: 0.04709/0.05392, loss_grounding_dice_8: 0.06046/0.14049, loss_grounding_ce_8: 0.06872/0.71823, loss_mask_ce_9: 4.05116/4.18589, loss_mask_bce_9: 0.24576/0.32132, loss_mask_dice_9: 0.70691/2.04916, loss_spatial_bce_9: 0.56829/0.57583, loss_spatial_dice_9: 0.83521/0.81806, loss_spatial_ce_9: 3.40782/4.48093, loss_grounding_bce_9: 0.05398/0.07201, loss_grounding_dice_9: 0.09377/0.17292, loss_grounding_ce_9: 0.33590/1.35125] items per batch[64] items per second[10.80] total items[256] mini batches[     4] memory[6478] epoch remaining[4:11:33]
INFO:trainer.default_trainer:epochs[     0] optim steps[5] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.65884/1.15895, loss_mask_bce_0: 0.13469/0.24427, loss_mask_dice_0: 0.47638/1.57282, loss_spatial_bce_0: 0.08324/0.40590, loss_spatial_dice_0: 0.60898/0.73236, loss_spatial_ce_0: 2.57371/5.07258, loss_grounding_bce_0: 0.07178/0.05625, loss_grounding_dice_0: 0.22598/0.15322, loss_grounding_ce_0: 0.02604/0.16792, loss_mask_ce_1: 2.69539/1.18108, loss_mask_bce_1: 0.13688/0.24367, loss_mask_dice_1: 0.48372/1.59366, loss_spatial_bce_1: 0.08274/0.40084, loss_spatial_dice_1: 0.58565/0.71544, loss_spatial_ce_1: 2.91568/5.35380, loss_grounding_bce_1: 0.08502/0.06024, loss_grounding_dice_1: 0.19835/0.13624, loss_grounding_ce_1: 0.17334/0.13538, loss_mask_ce_2: 2.66618/1.21771, loss_mask_bce_2: 0.13176/0.24293, loss_mask_dice_2: 0.43789/1.61023, loss_spatial_bce_2: 0.08793/0.42015, loss_spatial_dice_2: 0.60951/0.72434, loss_spatial_ce_2: 2.88314/5.40204, loss_grounding_bce_2: 0.08456/0.05857, loss_grounding_dice_2: 0.18126/0.11817, loss_grounding_ce_2: 0.15878/0.18215, loss_mask_ce_3: 2.79765/1.25667, loss_mask_bce_3: 0.12237/0.24741, loss_mask_dice_3: 0.47824/1.62827, loss_spatial_bce_3: 0.08975/0.47038, loss_spatial_dice_3: 0.59869/0.72487, loss_spatial_ce_3: 3.53514/6.31922, loss_grounding_bce_3: 0.08001/0.05951, loss_grounding_dice_3: 0.18978/0.12885, loss_grounding_ce_3: 0.17036/0.18403, loss_mask_ce_4: 2.65858/1.25138, loss_mask_bce_4: 0.13548/0.25075, loss_mask_dice_4: 0.55364/1.64604, loss_spatial_bce_4: 0.07947/0.48317, loss_spatial_dice_4: 0.55911/0.71832, loss_spatial_ce_4: 3.73109/5.82005, loss_grounding_bce_4: 0.07276/0.05857, loss_grounding_dice_4: 0.22716/0.13609, loss_grounding_ce_4: 0.02448/0.14511, loss_mask_ce_5: 2.62731/1.26544, loss_mask_bce_5: 0.12306/0.24044, loss_mask_dice_5: 0.57882/1.63901, loss_spatial_bce_5: 0.08241/0.48292, loss_spatial_dice_5: 0.55787/0.72649, loss_spatial_ce_5: 3.59223/5.26139, loss_grounding_bce_5: 0.08336/0.05885, loss_grounding_dice_5: 0.17811/0.12593, loss_grounding_ce_5: 0.16899/0.20508, loss_mask_ce_6: 2.49833/1.22939, loss_mask_bce_6: 0.11632/0.25321, loss_mask_dice_6: 0.56031/1.77757, loss_spatial_bce_6: 0.09614/0.45069, loss_spatial_dice_6: 0.60704/0.75012, loss_spatial_ce_6: 3.40852/5.71193, loss_grounding_bce_6: 0.09224/0.06100, loss_grounding_dice_6: 0.18821/0.12746, loss_grounding_ce_6: 0.15449/0.30923, loss_mask_ce_7: 2.61897/1.35139, loss_mask_bce_7: 0.11497/0.25894, loss_mask_dice_7: 0.54953/1.76659, loss_spatial_bce_7: 0.08702/0.50305, loss_spatial_dice_7: 0.68409/0.78798, loss_spatial_ce_7: 2.70908/5.69648, loss_grounding_bce_7: 0.07837/0.05812, loss_grounding_dice_7: 0.23626/0.14314, loss_grounding_ce_7: 0.04339/0.23523, loss_mask_ce_8: 2.53538/1.47648, loss_mask_bce_8: 0.12424/0.26003, loss_mask_dice_8: 0.61320/1.77188, loss_spatial_bce_8: 0.24001/0.56119, loss_spatial_dice_8: 0.75450/0.82321, loss_spatial_ce_8: 3.69047/5.14837, loss_grounding_bce_8: 0.08140/0.05942, loss_grounding_dice_8: 0.23857/0.16011, loss_grounding_ce_8: 0.13878/0.60234, loss_mask_ce_9: 3.68288/4.08529, loss_mask_bce_9: 0.11750/0.28055, loss_mask_dice_9: 0.51911/1.74315, loss_spatial_bce_9: 1.22933/0.70653, loss_spatial_dice_9: 0.95841/0.84613, loss_spatial_ce_9: 6.95212/4.97517, loss_grounding_bce_9: 0.07784/0.07318, loss_grounding_dice_9: 0.24190/0.18672, loss_grounding_ce_9: 0.31741/1.14448] items per batch[64] items per second[12.28] total items[320] mini batches[     5] memory[6478] epoch remaining[3:52:46]
INFO:trainer.default_trainer:epochs[     0] optim steps[6] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63654/1.07189, loss_mask_bce_0: 0.30198/0.25389, loss_mask_dice_0: 0.47215/1.38938, loss_spatial_bce_0: 0.75901/0.46475, loss_spatial_dice_0: 0.76638/0.73803, loss_spatial_ce_0: 3.62281/4.83095, loss_grounding_bce_0: 0.15153/0.07213, loss_grounding_dice_0: 0.26088/0.17116, loss_grounding_ce_0: 0.36468/0.20071, loss_mask_ce_1: 0.67154/1.09616, loss_mask_bce_1: 0.29792/0.25272, loss_mask_dice_1: 0.48025/1.40809, loss_spatial_bce_1: 0.73659/0.45680, loss_spatial_dice_1: 0.76780/0.72417, loss_spatial_ce_1: 3.87249/5.10692, loss_grounding_bce_1: 0.14196/0.07386, loss_grounding_dice_1: 0.26509/0.15772, loss_grounding_ce_1: 0.35129/0.17137, loss_mask_ce_2: 0.59577/1.11405, loss_mask_bce_2: 0.30576/0.25340, loss_mask_dice_2: 0.49607/1.42454, loss_spatial_bce_2: 0.75998/0.47679, loss_spatial_dice_2: 0.76598/0.73128, loss_spatial_ce_2: 3.71744/5.12127, loss_grounding_bce_2: 0.14270/0.07259, loss_grounding_dice_2: 0.26660/0.14291, loss_grounding_ce_2: 0.36039/0.21186, loss_mask_ce_3: 0.62920/1.15209, loss_mask_bce_3: 0.30703/0.25735, loss_mask_dice_3: 0.48563/1.43783, loss_spatial_bce_3: 0.90401/0.54265, loss_spatial_dice_3: 0.76495/0.73155, loss_spatial_ce_3: 4.16468/5.96013, loss_grounding_bce_3: 0.15163/0.07487, loss_grounding_dice_3: 0.27292/0.15286, loss_grounding_ce_3: 0.35773/0.21298, loss_mask_ce_4: 0.69935/1.15937, loss_mask_bce_4: 0.27999/0.25562, loss_mask_dice_4: 0.44672/1.44615, loss_spatial_bce_4: 0.73404/0.52498, loss_spatial_dice_4: 0.77125/0.72714, loss_spatial_ce_4: 5.13410/5.70573, loss_grounding_bce_4: 0.15116/0.07400, loss_grounding_dice_4: 0.27364/0.15901, loss_grounding_ce_4: 0.33474/0.17672, loss_mask_ce_5: 0.60248/1.15495, loss_mask_bce_5: 0.27887/0.24684, loss_mask_dice_5: 0.46114/1.44269, loss_spatial_bce_5: 0.83878/0.54223, loss_spatial_dice_5: 0.77645/0.73481, loss_spatial_ce_5: 4.37557/5.11375, loss_grounding_bce_5: 0.15056/0.07413, loss_grounding_dice_5: 0.25731/0.14783, loss_grounding_ce_5: 0.33237/0.22629, loss_mask_ce_6: 0.62633/1.12888, loss_mask_bce_6: 0.29547/0.26025, loss_mask_dice_6: 0.47214/1.56000, loss_spatial_bce_6: 0.93029/0.53062, loss_spatial_dice_6: 0.78019/0.75513, loss_spatial_ce_6: 4.52802/5.51461, loss_grounding_bce_6: 0.17684/0.08030, loss_grounding_dice_6: 0.27773/0.15250, loss_grounding_ce_6: 0.31425/0.31006, loss_mask_ce_7: 0.69943/1.24273, loss_mask_bce_7: 0.28218/0.26281, loss_mask_dice_7: 0.48874/1.55362, loss_spatial_bce_7: 0.91968/0.57249, loss_spatial_dice_7: 0.83334/0.79554, loss_spatial_ce_7: 4.48356/5.49433, loss_grounding_bce_7: 0.13574/0.07106, loss_grounding_dice_7: 0.26934/0.16417, loss_grounding_ce_7: 0.40501/0.26353, loss_mask_ce_8: 0.89629/1.37978, loss_mask_bce_8: 0.30246/0.26710, loss_mask_dice_8: 0.49735/1.55946, loss_spatial_bce_8: 1.23495/0.67348, loss_spatial_dice_8: 0.85423/0.82838, loss_spatial_ce_8: 5.10039/5.14037, loss_grounding_bce_8: 0.16769/0.07747, loss_grounding_dice_8: 0.28268/0.18054, loss_grounding_ce_8: 0.44355/0.57587, loss_mask_ce_9: 3.91582/4.05704, loss_mask_bce_9: 0.32204/0.28747, loss_mask_dice_9: 0.46149/1.52954, loss_spatial_bce_9: 1.27590/0.80142, loss_spatial_dice_9: 0.88404/0.85245, loss_spatial_ce_9: 6.02926/5.15085, loss_grounding_bce_9: 0.22971/0.09927, loss_grounding_dice_9: 0.26809/0.20028, loss_grounding_ce_9: 0.48528/1.03461] items per batch[64] items per second[8.72] total items[384] mini batches[     6] memory[6478] epoch remaining[3:50:59]
INFO:trainer.default_trainer:epochs[     0] optim steps[7] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47590/0.98675, loss_mask_bce_0: 0.52842/0.29311, loss_mask_dice_0: 0.37444/1.24439, loss_spatial_bce_0: 0.93655/0.53215, loss_spatial_dice_0: 0.53320/0.70877, loss_spatial_ce_0: 1.17874/4.30921, loss_grounding_bce_0: 0.16231/0.08501, loss_grounding_dice_0: 0.12184/0.16412, loss_grounding_ce_0: 0.25989/0.20916, loss_mask_ce_1: 0.48418/1.00873, loss_mask_bce_1: 0.50409/0.28863, loss_mask_dice_1: 0.37749/1.26086, loss_spatial_bce_1: 0.90576/0.52094, loss_spatial_dice_1: 0.53188/0.69670, loss_spatial_ce_1: 1.27272/4.55918, loss_grounding_bce_1: 0.20824/0.09306, loss_grounding_dice_1: 0.15785/0.15773, loss_grounding_ce_1: 0.13077/0.16557, loss_mask_ce_2: 0.50870/1.02757, loss_mask_bce_2: 0.51554/0.29085, loss_mask_dice_2: 0.37623/1.27478, loss_spatial_bce_2: 1.02438/0.55501, loss_spatial_dice_2: 0.52835/0.70229, loss_spatial_ce_2: 1.36191/4.58422, loss_grounding_bce_2: 0.17495/0.08722, loss_grounding_dice_2: 0.12454/0.14029, loss_grounding_ce_2: 0.23736/0.21550, loss_mask_ce_3: 0.50611/1.05981, loss_mask_bce_3: 0.52396/0.29544, loss_mask_dice_3: 0.38350/1.28721, loss_spatial_bce_3: 1.25326/0.64416, loss_spatial_dice_3: 0.52065/0.70142, loss_spatial_ce_3: 1.64345/5.34346, loss_grounding_bce_3: 0.19822/0.09249, loss_grounding_dice_3: 0.15722/0.15348, loss_grounding_ce_3: 0.14503/0.20327, loss_mask_ce_4: 0.47720/1.06192, loss_mask_bce_4: 0.50089/0.29066, loss_mask_dice_4: 0.37578/1.29324, loss_spatial_bce_4: 1.24691/0.62812, loss_spatial_dice_4: 0.54223/0.70073, loss_spatial_ce_4: 1.79288/5.14675, loss_grounding_bce_4: 0.20281/0.09240, loss_grounding_dice_4: 0.15516/0.15846, loss_grounding_ce_4: 0.13818/0.17121, loss_mask_ce_5: 0.41993/1.04995, loss_mask_bce_5: 0.50147/0.28322, loss_mask_dice_5: 0.36690/1.28901, loss_spatial_bce_5: 1.29361/0.64957, loss_spatial_dice_5: 0.55993/0.70983, loss_spatial_ce_5: 1.86658/4.64987, loss_grounding_bce_5: 0.20485/0.09281, loss_grounding_dice_5: 0.15430/0.14875, loss_grounding_ce_5: 0.12390/0.21167, loss_mask_ce_6: 0.50192/1.03931, loss_mask_bce_6: 0.49371/0.29360, loss_mask_dice_6: 0.36831/1.38976, loss_spatial_bce_6: 1.32749/0.64446, loss_spatial_dice_6: 0.56007/0.72727, loss_spatial_ce_6: 2.13880/5.03235, loss_grounding_bce_6: 0.16637/0.09260, loss_grounding_dice_6: 0.13790/0.15042, loss_grounding_ce_6: 0.26272/0.30330, loss_mask_ce_7: 0.50183/1.13689, loss_mask_bce_7: 0.50458/0.29735, loss_mask_dice_7: 0.38399/1.38653, loss_spatial_bce_7: 1.31178/0.67810, loss_spatial_dice_7: 0.60605/0.76847, loss_spatial_ce_7: 1.58004/4.93514, loss_grounding_bce_7: 0.19814/0.08921, loss_grounding_dice_7: 0.12378/0.15840, loss_grounding_ce_7: 0.26592/0.26387, loss_mask_ce_8: 0.44912/1.24683, loss_mask_bce_8: 0.49759/0.30003, loss_mask_dice_8: 0.41002/1.39525, loss_spatial_bce_8: 1.62998/0.81012, loss_spatial_dice_8: 0.68420/0.80778, loss_spatial_ce_8: 1.77681/4.65987, loss_grounding_bce_8: 0.21435/0.09702, loss_grounding_dice_8: 0.22996/0.18760, loss_grounding_ce_8: 0.10776/0.50900, loss_mask_ce_9: 2.65904/3.85733, loss_mask_bce_9: 0.45903/0.31198, loss_mask_dice_9: 0.41570/1.37042, loss_spatial_bce_9: 2.15483/0.99477, loss_spatial_dice_9: 0.65000/0.82353, loss_spatial_ce_9: 2.33089/4.74800, loss_grounding_bce_9: 0.19848/0.11344, loss_grounding_dice_9: 0.17874/0.19720, loss_grounding_ce_9: 0.45477/0.95178] items per batch[64] items per second[12.37] total items[448] mini batches[     7] memory[6478] epoch remaining[3:40:18]
INFO:trainer.default_trainer:epochs[     0] optim steps[8] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32280/0.90375, loss_mask_bce_0: 0.34459/0.29954, loss_mask_dice_0: 0.83026/1.19262, loss_spatial_bce_0: 0.63530/0.54504, loss_spatial_dice_0: 0.79306/0.71931, loss_spatial_ce_0: 6.01851/4.52287, loss_grounding_bce_0: 0.11350/0.08858, loss_grounding_dice_0: 0.27810/0.17836, loss_grounding_ce_0: 0.24244/0.21332, loss_mask_ce_1: 0.48176/0.94286, loss_mask_bce_1: 0.35269/0.29663, loss_mask_dice_1: 0.94521/1.22141, loss_spatial_bce_1: 0.66870/0.53941, loss_spatial_dice_1: 0.79082/0.70847, loss_spatial_ce_1: 6.28855/4.77535, loss_grounding_bce_1: 0.11556/0.09587, loss_grounding_dice_1: 0.24973/0.16923, loss_grounding_ce_1: 0.23750/0.17456, loss_mask_ce_2: 0.28772/0.93509, loss_mask_bce_2: 0.32894/0.29561, loss_mask_dice_2: 0.80190/1.21567, loss_spatial_bce_2: 0.84172/0.59085, loss_spatial_dice_2: 0.79309/0.71364, loss_spatial_ce_2: 6.76121/4.85635, loss_grounding_bce_2: 0.11521/0.09072, loss_grounding_dice_2: 0.25411/0.15451, loss_grounding_ce_2: 0.26040/0.22111, loss_mask_ce_3: 0.55603/0.99684, loss_mask_bce_3: 0.33781/0.30073, loss_mask_dice_3: 0.84948/1.23249, loss_spatial_bce_3: 0.94175/0.68136, loss_spatial_dice_3: 0.80876/0.71484, loss_spatial_ce_3: 7.89792/5.66277, loss_grounding_bce_3: 0.11472/0.09527, loss_grounding_dice_3: 0.27746/0.16898, loss_grounding_ce_3: 0.27543/0.21229, loss_mask_ce_4: 0.32332/0.96960, loss_mask_bce_4: 0.32854/0.29540, loss_mask_dice_4: 0.85322/1.23824, loss_spatial_bce_4: 0.86560/0.65780, loss_spatial_dice_4: 0.85185/0.71962, loss_spatial_ce_4: 7.87194/5.48740, loss_grounding_bce_4: 0.11673/0.09545, loss_grounding_dice_4: 0.27275/0.17275, loss_grounding_ce_4: 0.26732/0.18323, loss_mask_ce_5: 0.29439/0.95550, loss_mask_bce_5: 0.33222/0.28934, loss_mask_dice_5: 0.87490/1.23725, loss_spatial_bce_5: 1.04607/0.69913, loss_spatial_dice_5: 0.81438/0.72290, loss_spatial_ce_5: 7.94146/5.06132, loss_grounding_bce_5: 0.11659/0.09578, loss_grounding_dice_5: 0.28518/0.16581, loss_grounding_ce_5: 0.29390/0.22195, loss_mask_ce_6: 0.27949/0.94433, loss_mask_bce_6: 0.32904/0.29803, loss_mask_dice_6: 0.79353/1.31523, loss_spatial_bce_6: 1.42649/0.74222, loss_spatial_dice_6: 0.86250/0.74417, loss_spatial_ce_6: 8.55274/5.47240, loss_grounding_bce_6: 0.11099/0.09490, loss_grounding_dice_6: 0.25226/0.16315, loss_grounding_ce_6: 0.40339/0.31581, loss_mask_ce_7: 0.31722/1.03443, loss_mask_bce_7: 0.33132/0.30160, loss_mask_dice_7: 0.75177/1.30718, loss_spatial_bce_7: 1.95432/0.83763, loss_spatial_dice_7: 0.86838/0.78096, loss_spatial_ce_7: 6.82672/5.17159, loss_grounding_bce_7: 0.11678/0.09266, loss_grounding_dice_7: 0.26620/0.17188, loss_grounding_ce_7: 0.25418/0.26266, loss_mask_ce_8: 0.55059/1.15980, loss_mask_bce_8: 0.33055/0.30384, loss_mask_dice_8: 0.74172/1.31356, loss_spatial_bce_8: 1.94400/0.95186, loss_spatial_dice_8: 0.91894/0.82168, loss_spatial_ce_8: 4.79557/4.67683, loss_grounding_bce_8: 0.11349/0.09908, loss_grounding_dice_8: 0.23940/0.19407, loss_grounding_ce_8: 0.32908/0.48651, loss_mask_ce_9: 2.89248/3.73672, loss_mask_bce_9: 0.35887/0.31784, loss_mask_dice_9: 0.84578/1.30484, loss_spatial_bce_9: 1.87297/1.10454, loss_spatial_dice_9: 0.98776/0.84406, loss_spatial_ce_9: 6.64085/4.98461, loss_grounding_bce_9: 0.12062/0.11434, loss_grounding_dice_9: 0.28064/0.20763, loss_grounding_ce_9: 0.43634/0.88735] items per batch[64] items per second[10.25] total items[512] mini batches[     8] memory[6480] epoch remaining[3:36:19]
INFO:trainer.default_trainer:epochs[     0] optim steps[9] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.41836/0.96093, loss_mask_bce_0: 0.45318/0.31661, loss_mask_dice_0: 2.22106/1.30689, loss_spatial_bce_0: 0.38703/0.52749, loss_spatial_dice_0: 0.92704/0.74239, loss_spatial_ce_0: 1.79195/4.21943, loss_grounding_bce_0: 0.12139/0.09222, loss_grounding_dice_0: 0.45790/0.20942, loss_grounding_ce_0: 0.33587/0.22694, loss_mask_ce_1: 1.34647/0.98771, loss_mask_bce_1: 0.47483/0.31643, loss_mask_dice_1: 2.19802/1.32992, loss_spatial_bce_1: 0.32154/0.51520, loss_spatial_dice_1: 0.93375/0.73350, loss_spatial_ce_1: 1.98524/4.46534, loss_grounding_bce_1: 0.11428/0.09792, loss_grounding_dice_1: 0.44067/0.19939, loss_grounding_ce_1: 0.34852/0.19389, loss_mask_ce_2: 1.59365/1.00827, loss_mask_bce_2: 0.44981/0.31274, loss_mask_dice_2: 2.14885/1.31936, loss_spatial_bce_2: 0.30148/0.55870, loss_spatial_dice_2: 0.92656/0.73730, loss_spatial_ce_2: 1.78049/4.51458, loss_grounding_bce_2: 0.09698/0.09141, loss_grounding_dice_2: 0.46846/0.18940, loss_grounding_ce_2: 0.43554/0.24494, loss_mask_ce_3: 1.27059/1.02725, loss_mask_bce_3: 0.45670/0.31806, loss_mask_dice_3: 2.16605/1.33622, loss_spatial_bce_3: 0.37076/0.64685, loss_spatial_dice_3: 0.94551/0.74047, loss_spatial_ce_3: 2.70379/5.33400, loss_grounding_bce_3: 0.11754/0.09774, loss_grounding_dice_3: 0.47582/0.20307, loss_grounding_ce_3: 0.32698/0.22503, loss_mask_ce_4: 1.34231/1.01101, loss_mask_bce_4: 0.45424/0.31305, loss_mask_dice_4: 2.14549/1.33905, loss_spatial_bce_4: 0.29039/0.61698, loss_spatial_dice_4: 0.96252/0.74660, loss_spatial_ce_4: 2.75963/5.18431, loss_grounding_bce_4: 0.09932/0.09588, loss_grounding_dice_4: 0.43550/0.20194, loss_grounding_ce_4: 0.52128/0.22079, loss_mask_ce_5: 1.45936/1.01148, loss_mask_bce_5: 0.44948/0.30714, loss_mask_dice_5: 2.12187/1.33554, loss_spatial_bce_5: 0.39538/0.66538, loss_spatial_dice_5: 0.96063/0.74931, loss_spatial_ce_5: 2.88420/4.81942, loss_grounding_bce_5: 0.09542/0.09574, loss_grounding_dice_5: 0.44230/0.19653, loss_grounding_ce_5: 0.54636/0.25799, loss_mask_ce_6: 1.23941/0.97712, loss_mask_bce_6: 0.48824/0.31917, loss_mask_dice_6: 2.22631/1.41646, loss_spatial_bce_6: 0.43283/0.70784, loss_spatial_dice_6: 0.96326/0.76852, loss_spatial_ce_6: 3.56663/5.26065, loss_grounding_bce_6: 0.10487/0.09601, loss_grounding_dice_6: 0.45617/0.19571, loss_grounding_ce_6: 0.89623/0.38030, loss_mask_ce_7: 1.52075/1.08847, loss_mask_bce_7: 0.46431/0.31968, loss_mask_dice_7: 2.25103/1.41206, loss_spatial_bce_7: 0.35066/0.78352, loss_spatial_dice_7: 0.97113/0.80209, loss_spatial_ce_7: 3.55310/4.99176, loss_grounding_bce_7: 0.11515/0.09516, loss_grounding_dice_7: 0.46080/0.20398, loss_grounding_ce_7: 2.01206/0.45704, loss_mask_ce_8: 1.46151/1.19332, loss_mask_bce_8: 0.52841/0.32879, loss_mask_dice_8: 2.48116/1.44330, loss_spatial_bce_8: 0.37891/0.88820, loss_spatial_dice_8: 0.98261/0.83956, loss_spatial_ce_8: 4.10765/4.61359, loss_grounding_bce_8: 0.13449/0.10301, loss_grounding_dice_8: 0.47600/0.22540, loss_grounding_ce_8: 1.82152/0.63484, loss_mask_ce_9: 4.78592/3.85330, loss_mask_bce_9: 0.58059/0.34703, loss_mask_dice_9: 2.46757/1.43403, loss_spatial_bce_9: 0.53821/1.04162, loss_spatial_dice_9: 0.98419/0.85963, loss_spatial_ce_9: 4.58360/4.94005, loss_grounding_bce_9: 0.11140/0.11401, loss_grounding_dice_9: 0.48174/0.23809, loss_grounding_ce_9: 3.08056/1.13104] items per batch[64] items per second[9.14] total items[576] mini batches[     9] memory[6480] epoch remaining[3:35:45]
INFO:trainer.default_trainer:epochs[     0] optim steps[10] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81759/0.94660, loss_mask_bce_0: 0.09448/0.29440, loss_mask_dice_0: 0.70922/1.24713, loss_spatial_bce_0: 0.20586/0.49532, loss_spatial_dice_0: 0.77915/0.74607, loss_spatial_ce_0: 3.09665/4.10716, loss_grounding_bce_0: 0.06168/0.08917, loss_grounding_dice_0: 0.14668/0.20315, loss_grounding_ce_0: 1.16716/0.32096, loss_mask_ce_1: 0.92796/0.98173, loss_mask_bce_1: 0.08539/0.29333, loss_mask_dice_1: 0.63044/1.25997, loss_spatial_bce_1: 0.17574/0.48125, loss_spatial_dice_1: 0.78370/0.73852, loss_spatial_ce_1: 3.18083/4.33688, loss_grounding_bce_1: 0.06081/0.09421, loss_grounding_dice_1: 0.14768/0.19422, loss_grounding_ce_1: 1.35270/0.30977, loss_mask_ce_2: 0.95423/1.00286, loss_mask_bce_2: 0.09706/0.29118, loss_mask_dice_2: 0.60017/1.24744, loss_spatial_bce_2: 0.18025/0.52085, loss_spatial_dice_2: 0.78747/0.74232, loss_spatial_ce_2: 3.41831/4.40496, loss_grounding_bce_2: 0.06395/0.08867, loss_grounding_dice_2: 0.19098/0.18955, loss_grounding_ce_2: 1.15897/0.33634, loss_mask_ce_3: 1.02752/1.02728, loss_mask_bce_3: 0.10076/0.29633, loss_mask_dice_3: 0.92392/1.29499, loss_spatial_bce_3: 0.45279/0.62745, loss_spatial_dice_3: 0.79217/0.74564, loss_spatial_ce_3: 3.78749/5.17934, loss_grounding_bce_3: 0.06276/0.09424, loss_grounding_dice_3: 0.15954/0.19872, loss_grounding_ce_3: 1.28891/0.33142, loss_mask_ce_4: 0.87141/0.99705, loss_mask_bce_4: 0.09858/0.29160, loss_mask_dice_4: 0.85068/1.29021, loss_spatial_bce_4: 0.59592/0.61487, loss_spatial_dice_4: 0.81833/0.75378, loss_spatial_ce_4: 4.16395/5.08228, loss_grounding_bce_4: 0.06527/0.09282, loss_grounding_dice_4: 0.17890/0.19964, loss_grounding_ce_4: 1.68646/0.36736, loss_mask_ce_5: 1.02877/1.01321, loss_mask_bce_5: 0.09275/0.28570, loss_mask_dice_5: 0.65573/1.26756, loss_spatial_bce_5: 0.32126/0.63097, loss_spatial_dice_5: 0.80179/0.75456, loss_spatial_ce_5: 4.33936/4.77141, loss_grounding_bce_5: 0.05918/0.09208, loss_grounding_dice_5: 0.20153/0.19703, loss_grounding_ce_5: 1.06307/0.33850, loss_mask_ce_6: 1.20694/1.00010, loss_mask_bce_6: 0.09101/0.29635, loss_mask_dice_6: 0.68389/1.34320, loss_spatial_bce_6: 0.62542/0.69960, loss_spatial_dice_6: 0.80979/0.77264, loss_spatial_ce_6: 4.89590/5.22417, loss_grounding_bce_6: 0.06111/0.09252, loss_grounding_dice_6: 0.19881/0.19602, loss_grounding_ce_6: 1.03954/0.44623, loss_mask_ce_7: 0.70832/1.05045, loss_mask_bce_7: 0.09575/0.29728, loss_mask_dice_7: 0.89092/1.35994, loss_spatial_bce_7: 0.60231/0.76540, loss_spatial_dice_7: 0.83725/0.80560, loss_spatial_ce_7: 4.33431/4.92601, loss_grounding_bce_7: 0.06098/0.09174, loss_grounding_dice_7: 0.16296/0.19988, loss_grounding_ce_7: 1.16309/0.52764, loss_mask_ce_8: 0.88880/1.16287, loss_mask_bce_8: 0.09238/0.30515, loss_mask_dice_8: 0.74704/1.37367, loss_spatial_bce_8: 0.34744/0.83412, loss_spatial_dice_8: 0.79507/0.83511, loss_spatial_ce_8: 3.80144/4.53237, loss_grounding_bce_8: 0.05906/0.09862, loss_grounding_dice_8: 0.15732/0.21859, loss_grounding_ce_8: 1.19942/0.69130, loss_mask_ce_9: 2.88144/3.75611, loss_mask_bce_9: 0.11019/0.32335, loss_mask_dice_9: 0.77108/1.36773, loss_spatial_bce_9: 0.74589/1.01204, loss_spatial_dice_9: 0.84316/0.85798, loss_spatial_ce_9: 6.27996/5.07404, loss_grounding_bce_9: 0.06837/0.10945, loss_grounding_dice_9: 0.21608/0.23589, loss_grounding_ce_9: 0.76676/1.09461] items per batch[64] items per second[10.24] total items[640] mini batches[    10] memory[6932] epoch remaining[3:33:00]
INFO:trainer.default_trainer:epochs[     0] optim steps[100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23625/0.90767, loss_mask_bce_0: 0.06451/0.32678, loss_mask_dice_0: 2.60033/1.20305, loss_spatial_bce_0: 0.01199/0.22331, loss_spatial_dice_0: 0.60783/0.57196, loss_spatial_ce_0: 0.19972/0.98093, loss_grounding_bce_0: 0.01340/0.08218, loss_grounding_dice_0: 0.15560/0.18442, loss_grounding_ce_0: 0.29403/0.27010, loss_mask_ce_1: 1.06745/0.91791, loss_mask_bce_1: 0.10076/0.32611, loss_mask_dice_1: 2.82982/1.23078, loss_spatial_bce_1: 0.01333/0.22358, loss_spatial_dice_1: 0.58471/0.58332, loss_spatial_ce_1: 0.31204/1.02169, loss_grounding_bce_1: 0.01010/0.08168, loss_grounding_dice_1: 0.14022/0.18300, loss_grounding_ce_1: 0.43938/0.26770, loss_mask_ce_2: 1.27504/0.94725, loss_mask_bce_2: 0.10728/0.33000, loss_mask_dice_2: 3.07754/1.22525, loss_spatial_bce_2: 0.01383/0.22684, loss_spatial_dice_2: 0.59915/0.59723, loss_spatial_ce_2: 0.37244/1.05550, loss_grounding_bce_2: 0.00963/0.08186, loss_grounding_dice_2: 0.16259/0.18155, loss_grounding_ce_2: 0.30342/0.25823, loss_mask_ce_3: 1.44770/0.94158, loss_mask_bce_3: 0.05634/0.32638, loss_mask_dice_3: 2.84583/1.23013, loss_spatial_bce_3: 0.01329/0.24626, loss_spatial_dice_3: 0.65222/0.60427, loss_spatial_ce_3: 0.69627/1.20053, loss_grounding_bce_3: 0.01162/0.08239, loss_grounding_dice_3: 0.16925/0.18604, loss_grounding_ce_3: 0.30895/0.26475, loss_mask_ce_4: 0.92552/0.92736, loss_mask_bce_4: 0.09564/0.32693, loss_mask_dice_4: 3.10907/1.25072, loss_spatial_bce_4: 0.01257/0.25079, loss_spatial_dice_4: 0.66180/0.62592, loss_spatial_ce_4: 0.76214/1.19773, loss_grounding_bce_4: 0.01140/0.08241, loss_grounding_dice_4: 0.21542/0.18650, loss_grounding_ce_4: 0.30505/0.27444, loss_mask_ce_5: 1.25039/0.93685, loss_mask_bce_5: 0.09675/0.33137, loss_mask_dice_5: 2.83760/1.24355, loss_spatial_bce_5: 0.01087/0.26160, loss_spatial_dice_5: 0.62442/0.63694, loss_spatial_ce_5: 0.81434/1.21304, loss_grounding_bce_5: 0.01745/0.08145, loss_grounding_dice_5: 0.23257/0.18711, loss_grounding_ce_5: 0.70954/0.28553, loss_mask_ce_6: 1.37924/0.94184, loss_mask_bce_6: 0.05700/0.32399, loss_mask_dice_6: 2.56049/1.25926, loss_spatial_bce_6: 0.01173/0.27498, loss_spatial_dice_6: 0.64527/0.64427, loss_spatial_ce_6: 1.01982/1.35213, loss_grounding_bce_6: 0.01237/0.08225, loss_grounding_dice_6: 0.19734/0.18839, loss_grounding_ce_6: 0.36817/0.32713, loss_mask_ce_7: 1.24996/1.00558, loss_mask_bce_7: 0.06478/0.33278, loss_mask_dice_7: 3.07618/1.30316, loss_spatial_bce_7: 0.01314/0.29147, loss_spatial_dice_7: 0.76997/0.67916, loss_spatial_ce_7: 0.64953/1.31097, loss_grounding_bce_7: 0.01199/0.08130, loss_grounding_dice_7: 0.17138/0.18954, loss_grounding_ce_7: 0.77541/0.40287, loss_mask_ce_8: 1.24975/1.09955, loss_mask_bce_8: 0.10060/0.33469, loss_mask_dice_8: 3.38548/1.37560, loss_spatial_bce_8: 0.00958/0.32076, loss_spatial_dice_8: 0.77268/0.73709, loss_spatial_ce_8: 0.74956/1.34303, loss_grounding_bce_8: 0.01224/0.08487, loss_grounding_dice_8: 0.29423/0.20691, loss_grounding_ce_8: 1.13443/0.53382, loss_mask_ce_9: 5.51132/3.91422, loss_mask_bce_9: 0.05417/0.35585, loss_mask_dice_9: 3.60067/1.59039, loss_spatial_bce_9: 0.03856/0.64756, loss_spatial_dice_9: 0.87634/0.87413, loss_spatial_ce_9: 4.12353/3.19676, loss_grounding_bce_9: 0.01051/0.09538, loss_grounding_dice_9: 0.37719/0.24298, loss_grounding_ce_9: 2.17268/1.08048] items per batch[64] items per second[0.17] total items[6400] mini batches[   100] memory[7089] epoch remaining[2:06:13]
INFO:trainer.default_trainer:epochs[     0] optim steps[200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35478/0.89261, loss_mask_bce_0: 0.18568/0.32197, loss_mask_dice_0: 0.21567/1.15213, loss_spatial_bce_0: 0.09869/0.16953, loss_spatial_dice_0: 0.11287/0.43041, loss_spatial_ce_0: 0.01970/0.59006, loss_grounding_bce_0: 0.12187/0.08490, loss_grounding_dice_0: 0.13006/0.18671, loss_grounding_ce_0: 0.04841/0.27833, loss_mask_ce_1: 0.36466/0.89847, loss_mask_bce_1: 0.18477/0.32358, loss_mask_dice_1: 0.20584/1.17188, loss_spatial_bce_1: 0.09897/0.17013, loss_spatial_dice_1: 0.11634/0.43970, loss_spatial_ce_1: 0.01058/0.61431, loss_grounding_bce_1: 0.12907/0.08489, loss_grounding_dice_1: 0.14387/0.18287, loss_grounding_ce_1: 0.03986/0.28657, loss_mask_ce_2: 0.35007/0.91679, loss_mask_bce_2: 0.18804/0.32486, loss_mask_dice_2: 0.21817/1.17356, loss_spatial_bce_2: 0.09797/0.17234, loss_spatial_dice_2: 0.12062/0.45020, loss_spatial_ce_2: 0.01233/0.63068, loss_grounding_bce_2: 0.13034/0.08447, loss_grounding_dice_2: 0.13922/0.18239, loss_grounding_ce_2: 0.03129/0.27693, loss_mask_ce_3: 0.35416/0.92870, loss_mask_bce_3: 0.18056/0.32244, loss_mask_dice_3: 0.20164/1.15960, loss_spatial_bce_3: 0.09465/0.18206, loss_spatial_dice_3: 0.11444/0.45894, loss_spatial_ce_3: 0.03537/0.72530, loss_grounding_bce_3: 0.13141/0.08495, loss_grounding_dice_3: 0.13576/0.18613, loss_grounding_ce_3: 0.03577/0.28665, loss_mask_ce_4: 0.34592/0.91520, loss_mask_bce_4: 0.19210/0.32407, loss_mask_dice_4: 0.21047/1.17853, loss_spatial_bce_4: 0.10846/0.18589, loss_spatial_dice_4: 0.16075/0.47338, loss_spatial_ce_4: 0.09118/0.71684, loss_grounding_bce_4: 0.13277/0.08515, loss_grounding_dice_4: 0.13132/0.18770, loss_grounding_ce_4: 0.01541/0.29699, loss_mask_ce_5: 0.39438/0.91315, loss_mask_bce_5: 0.18671/0.32833, loss_mask_dice_5: 0.20798/1.18544, loss_spatial_bce_5: 0.10510/0.18988, loss_spatial_dice_5: 0.15938/0.48349, loss_spatial_ce_5: 0.16102/0.73742, loss_grounding_bce_5: 0.14228/0.08437, loss_grounding_dice_5: 0.14813/0.18487, loss_grounding_ce_5: 0.03472/0.30233, loss_mask_ce_6: 0.34689/0.92499, loss_mask_bce_6: 0.18272/0.32570, loss_mask_dice_6: 0.20688/1.19740, loss_spatial_bce_6: 0.12049/0.19757, loss_spatial_dice_6: 0.18138/0.49237, loss_spatial_ce_6: 0.17253/0.83770, loss_grounding_bce_6: 0.12752/0.08543, loss_grounding_dice_6: 0.11411/0.18912, loss_grounding_ce_6: 0.03488/0.34168, loss_mask_ce_7: 0.34321/0.97557, loss_mask_bce_7: 0.16748/0.33007, loss_mask_dice_7: 0.19824/1.24927, loss_spatial_bce_7: 0.09745/0.21369, loss_spatial_dice_7: 0.12522/0.52180, loss_spatial_ce_7: 0.12740/0.83237, loss_grounding_bce_7: 0.11091/0.08351, loss_grounding_dice_7: 0.13666/0.19523, loss_grounding_ce_7: 0.04727/0.41417, loss_mask_ce_8: 0.38295/1.07837, loss_mask_bce_8: 0.18591/0.33817, loss_mask_dice_8: 0.25852/1.31062, loss_spatial_bce_8: 0.11118/0.23929, loss_spatial_dice_8: 0.15636/0.59206, loss_spatial_ce_8: 0.18723/0.88351, loss_grounding_bce_8: 0.12385/0.08785, loss_grounding_dice_8: 0.15372/0.20517, loss_grounding_ce_8: 0.09406/0.53535, loss_mask_ce_9: 2.83507/4.00202, loss_mask_bce_9: 0.20079/0.36251, loss_mask_dice_9: 0.33802/1.65996, loss_spatial_bce_9: 0.62613/0.51547, loss_spatial_dice_9: 0.83303/0.85753, loss_spatial_ce_9: 1.99173/2.49000, loss_grounding_bce_9: 0.13399/0.09853, loss_grounding_dice_9: 0.18934/0.26597, loss_grounding_ce_9: 0.37159/1.09001] items per batch[64] items per second[0.19] total items[12800] mini batches[   200] memory[7207] epoch remaining[1:45:45]
INFO:trainer.default_trainer:epochs[     0] optim steps[300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06753/0.89499, loss_mask_bce_0: 0.26544/0.33352, loss_mask_dice_0: 0.28798/1.17822, loss_spatial_bce_0: 0.11861/0.15030, loss_spatial_dice_0: 0.11634/0.37684, loss_spatial_ce_0: 0.01196/0.46561, loss_grounding_bce_0: 0.22995/0.08454, loss_grounding_dice_0: 0.19470/0.18548, loss_grounding_ce_0: 0.00425/0.27246, loss_mask_ce_1: 0.06136/0.90121, loss_mask_bce_1: 0.26417/0.33336, loss_mask_dice_1: 0.28678/1.19395, loss_spatial_bce_1: 0.11820/0.15120, loss_spatial_dice_1: 0.11099/0.38434, loss_spatial_ce_1: 0.01980/0.48209, loss_grounding_bce_1: 0.23268/0.08448, loss_grounding_dice_1: 0.19605/0.18557, loss_grounding_ce_1: 0.00424/0.27852, loss_mask_ce_2: 0.06347/0.92364, loss_mask_bce_2: 0.26187/0.33451, loss_mask_dice_2: 0.28040/1.19142, loss_spatial_bce_2: 0.11928/0.15131, loss_spatial_dice_2: 0.12194/0.39146, loss_spatial_ce_2: 0.03077/0.49766, loss_grounding_bce_2: 0.22910/0.08449, loss_grounding_dice_2: 0.19031/0.18325, loss_grounding_ce_2: 0.00429/0.27608, loss_mask_ce_3: 0.06713/0.92692, loss_mask_bce_3: 0.26869/0.33345, loss_mask_dice_3: 0.29390/1.18194, loss_spatial_bce_3: 0.11953/0.15744, loss_spatial_dice_3: 0.11932/0.39944, loss_spatial_ce_3: 0.04516/0.56387, loss_grounding_bce_3: 0.23945/0.08447, loss_grounding_dice_3: 0.20189/0.18575, loss_grounding_ce_3: 0.00410/0.27971, loss_mask_ce_4: 0.07078/0.91758, loss_mask_bce_4: 0.26560/0.33442, loss_mask_dice_4: 0.28833/1.19842, loss_spatial_bce_4: 0.11846/0.16288, loss_spatial_dice_4: 0.11921/0.41212, loss_spatial_ce_4: 0.07644/0.55580, loss_grounding_bce_4: 0.23332/0.08455, loss_grounding_dice_4: 0.19619/0.18650, loss_grounding_ce_4: 0.00264/0.29029, loss_mask_ce_5: 0.07732/0.91625, loss_mask_bce_5: 0.26958/0.33923, loss_mask_dice_5: 0.29130/1.20837, loss_spatial_bce_5: 0.12635/0.16452, loss_spatial_dice_5: 0.13890/0.42084, loss_spatial_ce_5: 0.08531/0.57533, loss_grounding_bce_5: 0.23998/0.08450, loss_grounding_dice_5: 0.20035/0.18541, loss_grounding_ce_5: 0.00382/0.30400, loss_mask_ce_6: 0.08476/0.94131, loss_mask_bce_6: 0.27409/0.33745, loss_mask_dice_6: 0.30047/1.21112, loss_spatial_bce_6: 0.12567/0.16931, loss_spatial_dice_6: 0.13571/0.42841, loss_spatial_ce_6: 0.07323/0.65489, loss_grounding_bce_6: 0.23896/0.08490, loss_grounding_dice_6: 0.19822/0.18863, loss_grounding_ce_6: 0.00354/0.33838, loss_mask_ce_7: 0.07877/0.98636, loss_mask_bce_7: 0.27718/0.34261, loss_mask_dice_7: 0.30538/1.26663, loss_spatial_bce_7: 0.13942/0.18619, loss_spatial_dice_7: 0.15500/0.45330, loss_spatial_ce_7: 0.10961/0.67430, loss_grounding_bce_7: 0.23654/0.08468, loss_grounding_dice_7: 0.19781/0.19553, loss_grounding_ce_7: 0.00528/0.40717, loss_mask_ce_8: 0.12641/1.09155, loss_mask_bce_8: 0.28214/0.35134, loss_mask_dice_8: 0.31592/1.34181, loss_spatial_bce_8: 0.15228/0.21003, loss_spatial_dice_8: 0.18293/0.52113, loss_spatial_ce_8: 0.15204/0.71799, loss_grounding_bce_8: 0.24503/0.08898, loss_grounding_dice_8: 0.20438/0.20678, loss_grounding_ce_8: 0.00967/0.50721, loss_mask_ce_9: 1.58322/4.01288, loss_mask_bce_9: 0.28443/0.38007, loss_mask_dice_9: 0.35028/1.76891, loss_spatial_bce_9: 0.59006/0.46199, loss_spatial_dice_9: 0.76850/0.85362, loss_spatial_ce_9: 1.88150/2.22163, loss_grounding_bce_9: 0.25229/0.09804, loss_grounding_dice_9: 0.22527/0.27140, loss_grounding_ce_9: 0.07553/1.01689] items per batch[64] items per second[0.19] total items[19200] mini batches[   300] memory[7207] epoch remaining[1:34:16]
INFO:trainer.default_trainer:epochs[     0] optim steps[400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.03162/0.89812, loss_mask_bce_0: 0.95249/0.33145, loss_mask_dice_0: 5.05809/1.17516, loss_spatial_bce_0: 0.04617/0.13962, loss_spatial_dice_0: 0.36274/0.34730, loss_spatial_ce_0: 0.02760/0.38560, loss_grounding_bce_0: 0.01639/0.08521, loss_grounding_dice_0: 0.09357/0.18413, loss_grounding_ce_0: 2.93485/0.25746, loss_mask_ce_1: 1.85655/0.90148, loss_mask_bce_1: 1.02922/0.33140, loss_mask_dice_1: 5.04913/1.18582, loss_spatial_bce_1: 0.04379/0.13977, loss_spatial_dice_1: 0.36696/0.35309, loss_spatial_ce_1: 0.03435/0.40217, loss_grounding_bce_1: 0.01751/0.08510, loss_grounding_dice_1: 0.10787/0.18397, loss_grounding_ce_1: 2.46744/0.26030, loss_mask_ce_2: 1.98684/0.91919, loss_mask_bce_2: 0.95063/0.33177, loss_mask_dice_2: 5.06352/1.18385, loss_spatial_bce_2: 0.03972/0.13956, loss_spatial_dice_2: 0.39529/0.35898, loss_spatial_ce_2: 0.06179/0.41668, loss_grounding_bce_2: 0.01515/0.08499, loss_grounding_dice_2: 0.09119/0.18189, loss_grounding_ce_2: 2.49599/0.26043, loss_mask_ce_3: 2.21586/0.92183, loss_mask_bce_3: 0.85474/0.33107, loss_mask_dice_3: 4.95466/1.17706, loss_spatial_bce_3: 0.04565/0.14444, loss_spatial_dice_3: 0.37522/0.36575, loss_spatial_ce_3: 0.04495/0.46701, loss_grounding_bce_3: 0.01333/0.08491, loss_grounding_dice_3: 0.09420/0.18431, loss_grounding_ce_3: 2.04834/0.26154, loss_mask_ce_4: 1.98256/0.91561, loss_mask_bce_4: 0.95409/0.33228, loss_mask_dice_4: 5.13428/1.19147, loss_spatial_bce_4: 0.04476/0.14992, loss_spatial_dice_4: 0.37468/0.37657, loss_spatial_ce_4: 0.07428/0.46191, loss_grounding_bce_4: 0.01756/0.08527, loss_grounding_dice_4: 0.13594/0.18642, loss_grounding_ce_4: 2.55199/0.27197, loss_mask_ce_5: 2.22506/0.91584, loss_mask_bce_5: 0.97949/0.33665, loss_mask_dice_5: 5.30411/1.20279, loss_spatial_bce_5: 0.04105/0.15040, loss_spatial_dice_5: 0.37118/0.38458, loss_spatial_ce_5: 0.05123/0.48255, loss_grounding_bce_5: 0.02529/0.08523, loss_grounding_dice_5: 0.20235/0.18440, loss_grounding_ce_5: 2.31389/0.28378, loss_mask_ce_6: 2.31205/0.94433, loss_mask_bce_6: 0.87346/0.33522, loss_mask_dice_6: 5.43371/1.20419, loss_spatial_bce_6: 0.05155/0.15468, loss_spatial_dice_6: 0.39582/0.39201, loss_spatial_ce_6: 0.13494/0.55002, loss_grounding_bce_6: 0.01763/0.08569, loss_grounding_dice_6: 0.16667/0.18731, loss_grounding_ce_6: 3.36355/0.31953, loss_mask_ce_7: 1.91879/0.98442, loss_mask_bce_7: 1.01424/0.34187, loss_mask_dice_7: 5.69215/1.26046, loss_spatial_bce_7: 0.03147/0.17180, loss_spatial_dice_7: 0.42461/0.41682, loss_spatial_ce_7: 0.12649/0.57469, loss_grounding_bce_7: 0.04117/0.08618, loss_grounding_dice_7: 0.24565/0.19392, loss_grounding_ce_7: 2.36124/0.37989, loss_mask_ce_8: 2.28847/1.10286, loss_mask_bce_8: 1.08477/0.35090, loss_mask_dice_8: 5.81517/1.33287, loss_spatial_bce_8: 0.03703/0.19564, loss_spatial_dice_8: 0.52763/0.48041, loss_spatial_ce_8: 0.21735/0.61651, loss_grounding_bce_8: 0.02664/0.09039, loss_grounding_dice_8: 0.17757/0.20508, loss_grounding_ce_8: 2.82565/0.47514, loss_mask_ce_9: 7.60845/3.95766, loss_mask_bce_9: 1.02730/0.37858, loss_mask_dice_9: 8.38722/1.81369, loss_spatial_bce_9: 0.09326/0.43215, loss_spatial_dice_9: 0.89423/0.84774, loss_spatial_ce_9: 1.63390/2.05266, loss_grounding_bce_9: 0.03269/0.10063, loss_grounding_dice_9: 0.23626/0.27642, loss_grounding_ce_9: 2.11415/0.94138] items per batch[64] items per second[0.19] total items[25600] mini batches[   400] memory[7207] epoch remaining[1:25:56]
INFO:trainer.default_trainer:epochs[     0] optim steps[500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.92188/0.90646, loss_mask_bce_0: 0.38256/0.32530, loss_mask_dice_0: 0.85861/1.14698, loss_spatial_bce_0: 0.11249/0.13161, loss_spatial_dice_0: 0.20945/0.32658, loss_spatial_ce_0: 0.05652/0.33266, loss_grounding_bce_0: 0.04003/0.08522, loss_grounding_dice_0: 0.07228/0.18004, loss_grounding_ce_0: 0.16278/0.25730, loss_mask_ce_1: 1.85922/0.90892, loss_mask_bce_1: 0.39867/0.32561, loss_mask_dice_1: 0.84994/1.15723, loss_spatial_bce_1: 0.11677/0.13208, loss_spatial_dice_1: 0.21895/0.33132, loss_spatial_ce_1: 0.04826/0.34559, loss_grounding_bce_1: 0.04048/0.08526, loss_grounding_dice_1: 0.07343/0.18057, loss_grounding_ce_1: 0.17203/0.26257, loss_mask_ce_2: 1.88444/0.92147, loss_mask_bce_2: 0.41263/0.32610, loss_mask_dice_2: 0.83572/1.15549, loss_spatial_bce_2: 0.09549/0.13171, loss_spatial_dice_2: 0.19092/0.33676, loss_spatial_ce_2: 0.04480/0.35881, loss_grounding_bce_2: 0.04058/0.08495, loss_grounding_dice_2: 0.07575/0.17860, loss_grounding_ce_2: 0.17918/0.26127, loss_mask_ce_3: 1.92676/0.92666, loss_mask_bce_3: 0.39994/0.32589, loss_mask_dice_3: 0.82268/1.15160, loss_spatial_bce_3: 0.09292/0.13588, loss_spatial_dice_3: 0.18881/0.34299, loss_spatial_ce_3: 0.08451/0.40148, loss_grounding_bce_3: 0.04460/0.08496, loss_grounding_dice_3: 0.07303/0.18026, loss_grounding_ce_3: 0.15265/0.26422, loss_mask_ce_4: 1.95313/0.92222, loss_mask_bce_4: 0.41267/0.32676, loss_mask_dice_4: 0.82989/1.16322, loss_spatial_bce_4: 0.11017/0.14067, loss_spatial_dice_4: 0.20664/0.35303, loss_spatial_ce_4: 0.07494/0.39999, loss_grounding_bce_4: 0.03927/0.08516, loss_grounding_dice_4: 0.07395/0.18336, loss_grounding_ce_4: 0.15483/0.27219, loss_mask_ce_5: 1.72627/0.92614, loss_mask_bce_5: 0.41518/0.33074, loss_mask_dice_5: 0.83299/1.17365, loss_spatial_bce_5: 0.11483/0.14102, loss_spatial_dice_5: 0.20139/0.36003, loss_spatial_ce_5: 0.10363/0.41890, loss_grounding_bce_5: 0.03683/0.08543, loss_grounding_dice_5: 0.07267/0.18162, loss_grounding_ce_5: 0.18478/0.28171, loss_mask_ce_6: 1.85992/0.95716, loss_mask_bce_6: 0.39381/0.32980, loss_mask_dice_6: 0.78854/1.17553, loss_spatial_bce_6: 0.09385/0.14469, loss_spatial_dice_6: 0.18580/0.36763, loss_spatial_ce_6: 0.14682/0.48206, loss_grounding_bce_6: 0.04317/0.08557, loss_grounding_dice_6: 0.08645/0.18389, loss_grounding_ce_6: 0.20842/0.31825, loss_mask_ce_7: 1.97685/0.99292, loss_mask_bce_7: 0.52522/0.33665, loss_mask_dice_7: 0.91819/1.23424, loss_spatial_bce_7: 0.11250/0.16134, loss_spatial_dice_7: 0.22490/0.39162, loss_spatial_ce_7: 0.17263/0.50866, loss_grounding_bce_7: 0.04665/0.08665, loss_grounding_dice_7: 0.08620/0.19141, loss_grounding_ce_7: 0.17789/0.37966, loss_mask_ce_8: 1.80876/1.10824, loss_mask_bce_8: 0.50801/0.34579, loss_mask_dice_8: 1.13628/1.30455, loss_spatial_bce_8: 0.14861/0.18499, loss_spatial_dice_8: 0.26768/0.45293, loss_spatial_ce_8: 0.21572/0.54890, loss_grounding_bce_8: 0.04987/0.09054, loss_grounding_dice_8: 0.09503/0.20221, loss_grounding_ce_8: 0.12229/0.46458, loss_mask_ce_9: 6.48266/3.92466, loss_mask_bce_9: 1.18791/0.37619, loss_mask_dice_9: 4.64396/1.80999, loss_spatial_bce_9: 0.30667/0.41902, loss_spatial_dice_9: 0.80012/0.84515, loss_spatial_ce_9: 1.33238/1.94829, loss_grounding_bce_9: 0.05265/0.10123, loss_grounding_dice_9: 0.13592/0.27610, loss_grounding_ce_9: 0.43235/0.93107] items per batch[64] items per second[0.20] total items[32000] mini batches[   500] memory[7207] epoch remaining[1:18:15]
INFO:trainer.default_trainer:epochs[     0] optim steps[600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43519/0.90384, loss_mask_bce_0: 0.13509/0.32958, loss_mask_dice_0: 0.29450/1.18802, loss_spatial_bce_0: 0.06259/0.12644, loss_spatial_dice_0: 0.11378/0.31555, loss_spatial_ce_0: 0.08800/0.30033, loss_grounding_bce_0: 0.05200/0.08525, loss_grounding_dice_0: 0.08864/0.18198, loss_grounding_ce_0: 0.21817/0.26825, loss_mask_ce_1: 0.42587/0.90721, loss_mask_bce_1: 0.14344/0.33047, loss_mask_dice_1: 0.32239/1.19736, loss_spatial_bce_1: 0.06377/0.12665, loss_spatial_dice_1: 0.12480/0.32073, loss_spatial_ce_1: 0.09809/0.31163, loss_grounding_bce_1: 0.04869/0.08536, loss_grounding_dice_1: 0.08780/0.18252, loss_grounding_ce_1: 0.21322/0.27025, loss_mask_ce_2: 0.42027/0.91817, loss_mask_bce_2: 0.14276/0.33067, loss_mask_dice_2: 0.29310/1.19406, loss_spatial_bce_2: 0.06258/0.12608, loss_spatial_dice_2: 0.12400/0.32598, loss_spatial_ce_2: 0.09402/0.32227, loss_grounding_bce_2: 0.04876/0.08511, loss_grounding_dice_2: 0.08759/0.18003, loss_grounding_ce_2: 0.19921/0.26798, loss_mask_ce_3: 0.43462/0.92173, loss_mask_bce_3: 0.14317/0.33138, loss_mask_dice_3: 0.32191/1.19317, loss_spatial_bce_3: 0.06050/0.12993, loss_spatial_dice_3: 0.13297/0.33134, loss_spatial_ce_3: 0.10151/0.35884, loss_grounding_bce_3: 0.04763/0.08494, loss_grounding_dice_3: 0.08261/0.18256, loss_grounding_ce_3: 0.19436/0.27205, loss_mask_ce_4: 0.39659/0.92110, loss_mask_bce_4: 0.14483/0.33198, loss_mask_dice_4: 0.30766/1.20550, loss_spatial_bce_4: 0.06344/0.13444, loss_spatial_dice_4: 0.12178/0.34161, loss_spatial_ce_4: 0.09469/0.35929, loss_grounding_bce_4: 0.04940/0.08535, loss_grounding_dice_4: 0.09126/0.18476, loss_grounding_ce_4: 0.19354/0.27670, loss_mask_ce_5: 0.39086/0.92331, loss_mask_bce_5: 0.13899/0.33555, loss_mask_dice_5: 0.31142/1.21349, loss_spatial_bce_5: 0.06338/0.13563, loss_spatial_dice_5: 0.12234/0.34891, loss_spatial_ce_5: 0.12421/0.37879, loss_grounding_bce_5: 0.04910/0.08540, loss_grounding_dice_5: 0.08655/0.18394, loss_grounding_ce_5: 0.20874/0.28830, loss_mask_ce_6: 0.41077/0.95859, loss_mask_bce_6: 0.13887/0.33567, loss_mask_dice_6: 0.29306/1.21646, loss_spatial_bce_6: 0.06040/0.13843, loss_spatial_dice_6: 0.13193/0.35514, loss_spatial_ce_6: 0.15261/0.43436, loss_grounding_bce_6: 0.04960/0.08652, loss_grounding_dice_6: 0.09110/0.18554, loss_grounding_ce_6: 0.21471/0.32660, loss_mask_ce_7: 0.37678/0.99353, loss_mask_bce_7: 0.13696/0.34153, loss_mask_dice_7: 0.31472/1.27628, loss_spatial_bce_7: 0.08640/0.15512, loss_spatial_dice_7: 0.16129/0.37880, loss_spatial_ce_7: 0.21933/0.46757, loss_grounding_bce_7: 0.05200/0.08675, loss_grounding_dice_7: 0.09231/0.19172, loss_grounding_ce_7: 0.19656/0.39040, loss_mask_ce_8: 0.42982/1.09634, loss_mask_bce_8: 0.13246/0.35100, loss_mask_dice_8: 0.36051/1.35221, loss_spatial_bce_8: 0.11133/0.17814, loss_spatial_dice_8: 0.20449/0.43703, loss_spatial_ce_8: 0.26446/0.50507, loss_grounding_bce_8: 0.05217/0.09075, loss_grounding_dice_8: 0.09847/0.20358, loss_grounding_ce_8: 0.21125/0.46717, loss_mask_ce_9: 2.94851/3.92169, loss_mask_bce_9: 0.16494/0.38042, loss_mask_dice_9: 0.59064/1.87911, loss_spatial_bce_9: 0.58119/0.40404, loss_spatial_dice_9: 0.79274/0.84392, loss_spatial_ce_9: 1.42853/1.88523, loss_grounding_bce_9: 0.06470/0.10113, loss_grounding_dice_9: 0.14154/0.27967, loss_grounding_ce_9: 0.43179/0.91416] items per batch[64] items per second[0.20] total items[38400] mini batches[   600] memory[7207] epoch remaining[1:11:05]
INFO:trainer.default_trainer:epochs[     0] optim steps[700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34855/0.90807, loss_mask_bce_0: 0.12355/0.33059, loss_mask_dice_0: 1.10793/1.17086, loss_spatial_bce_0: 0.03775/0.12329, loss_spatial_dice_0: 0.27897/0.30695, loss_spatial_ce_0: 0.06244/0.27715, loss_grounding_bce_0: 0.03773/0.08696, loss_grounding_dice_0: 0.11252/0.18208, loss_grounding_ce_0: 0.07322/0.26549, loss_mask_ce_1: 1.28717/0.91251, loss_mask_bce_1: 0.11826/0.33139, loss_mask_dice_1: 1.39575/1.18114, loss_spatial_bce_1: 0.03586/0.12373, loss_spatial_dice_1: 0.23198/0.31244, loss_spatial_ce_1: 0.30327/0.28835, loss_grounding_bce_1: 0.03838/0.08732, loss_grounding_dice_1: 0.08967/0.18253, loss_grounding_ce_1: 0.13385/0.26677, loss_mask_ce_2: 1.13905/0.92030, loss_mask_bce_2: 0.12303/0.33152, loss_mask_dice_2: 1.27535/1.17491, loss_spatial_bce_2: 0.03668/0.12318, loss_spatial_dice_2: 0.24931/0.31703, loss_spatial_ce_2: 0.07094/0.29992, loss_grounding_bce_2: 0.03773/0.08721, loss_grounding_dice_2: 0.11277/0.18069, loss_grounding_ce_2: 0.09952/0.26369, loss_mask_ce_3: 1.23789/0.92221, loss_mask_bce_3: 0.12602/0.33198, loss_mask_dice_3: 1.13273/1.17588, loss_spatial_bce_3: 0.03561/0.12626, loss_spatial_dice_3: 0.27293/0.32169, loss_spatial_ce_3: 0.11443/0.33122, loss_grounding_bce_3: 0.03512/0.08677, loss_grounding_dice_3: 0.09322/0.18261, loss_grounding_ce_3: 0.03573/0.26876, loss_mask_ce_4: 1.43609/0.92336, loss_mask_bce_4: 0.11969/0.33290, loss_mask_dice_4: 1.29900/1.19085, loss_spatial_bce_4: 0.05430/0.13104, loss_spatial_dice_4: 0.29209/0.33184, loss_spatial_ce_4: 0.10546/0.33354, loss_grounding_bce_4: 0.04072/0.08697, loss_grounding_dice_4: 0.09551/0.18415, loss_grounding_ce_4: 0.07721/0.27471, loss_mask_ce_5: 1.69070/0.92674, loss_mask_bce_5: 0.12084/0.33693, loss_mask_dice_5: 1.24988/1.19921, loss_spatial_bce_5: 0.03435/0.13218, loss_spatial_dice_5: 0.28319/0.33903, loss_spatial_ce_5: 0.21701/0.35272, loss_grounding_bce_5: 0.03947/0.08744, loss_grounding_dice_5: 0.09110/0.18334, loss_grounding_ce_5: 0.14007/0.28345, loss_mask_ce_6: 1.27609/0.96008, loss_mask_bce_6: 0.12394/0.33653, loss_mask_dice_6: 1.26520/1.19813, loss_spatial_bce_6: 0.04316/0.13516, loss_spatial_dice_6: 0.33058/0.34500, loss_spatial_ce_6: 0.14984/0.40286, loss_grounding_bce_6: 0.03767/0.08859, loss_grounding_dice_6: 0.11986/0.18526, loss_grounding_ce_6: 0.02950/0.31890, loss_mask_ce_7: 1.69168/0.99916, loss_mask_bce_7: 0.13014/0.34268, loss_mask_dice_7: 1.35571/1.26028, loss_spatial_bce_7: 0.07062/0.15145, loss_spatial_dice_7: 0.35207/0.36824, loss_spatial_ce_7: 0.33783/0.43996, loss_grounding_bce_7: 0.03870/0.08902, loss_grounding_dice_7: 0.12034/0.19151, loss_grounding_ce_7: 0.19867/0.37621, loss_mask_ce_8: 1.55609/1.09890, loss_mask_bce_8: 0.14194/0.35330, loss_mask_dice_8: 1.20603/1.33607, loss_spatial_bce_8: 0.06409/0.17431, loss_spatial_dice_8: 0.38149/0.42486, loss_spatial_ce_8: 0.72945/0.47790, loss_grounding_bce_8: 0.03519/0.09243, loss_grounding_dice_8: 0.11962/0.20326, loss_grounding_ce_8: 0.57303/0.46388, loss_mask_ce_9: 3.75758/3.89457, loss_mask_bce_9: 0.15739/0.38251, loss_mask_dice_9: 1.50531/1.85901, loss_spatial_bce_9: 0.18216/0.39518, loss_spatial_dice_9: 0.80869/0.84260, loss_spatial_ce_9: 2.11513/1.84063, loss_grounding_bce_9: 0.03644/0.10286, loss_grounding_dice_9: 0.13355/0.28050, loss_grounding_ce_9: 1.39582/0.91513] items per batch[64] items per second[0.20] total items[44800] mini batches[   700] memory[7207] epoch remaining[1:04:26]
INFO:trainer.default_trainer:epochs[     0] optim steps[800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.37173/0.90844, loss_mask_bce_0: 0.56029/0.32973, loss_mask_dice_0: 1.32975/1.18595, loss_spatial_bce_0: 0.38740/0.12046, loss_spatial_dice_0: 0.34911/0.29817, loss_spatial_ce_0: 0.13955/0.25586, loss_grounding_bce_0: 0.11312/0.08590, loss_grounding_dice_0: 0.05827/0.18172, loss_grounding_ce_0: 0.04129/0.26652, loss_mask_ce_1: 1.42861/0.91212, loss_mask_bce_1: 0.55200/0.33009, loss_mask_dice_1: 1.11895/1.19540, loss_spatial_bce_1: 0.32960/0.12103, loss_spatial_dice_1: 0.36224/0.30353, loss_spatial_ce_1: 0.17213/0.26682, loss_grounding_bce_1: 0.11174/0.08625, loss_grounding_dice_1: 0.05467/0.18202, loss_grounding_ce_1: 0.03351/0.26919, loss_mask_ce_2: 1.41601/0.92008, loss_mask_bce_2: 0.55995/0.33005, loss_mask_dice_2: 1.12474/1.19015, loss_spatial_bce_2: 0.33064/0.12047, loss_spatial_dice_2: 0.36588/0.30792, loss_spatial_ce_2: 0.16824/0.27636, loss_grounding_bce_2: 0.11002/0.08624, loss_grounding_dice_2: 0.05248/0.18068, loss_grounding_ce_2: 0.04483/0.26569, loss_mask_ce_3: 1.37491/0.91958, loss_mask_bce_3: 0.54058/0.33090, loss_mask_dice_3: 1.13457/1.18988, loss_spatial_bce_3: 0.27694/0.12309, loss_spatial_dice_3: 0.37803/0.31235, loss_spatial_ce_3: 0.18843/0.30590, loss_grounding_bce_3: 0.11145/0.08570, loss_grounding_dice_3: 0.05680/0.18261, loss_grounding_ce_3: 0.05766/0.26940, loss_mask_ce_4: 1.25717/0.92138, loss_mask_bce_4: 0.66947/0.33255, loss_mask_dice_4: 1.19338/1.20666, loss_spatial_bce_4: 0.25056/0.12789, loss_spatial_dice_4: 0.35509/0.32198, loss_spatial_ce_4: 0.36584/0.30895, loss_grounding_bce_4: 0.11845/0.08583, loss_grounding_dice_4: 0.05425/0.18355, loss_grounding_ce_4: 0.03948/0.27582, loss_mask_ce_5: 1.18428/0.92558, loss_mask_bce_5: 0.64350/0.33626, loss_mask_dice_5: 1.23803/1.21287, loss_spatial_bce_5: 0.29229/0.12914, loss_spatial_dice_5: 0.36475/0.32907, loss_spatial_ce_5: 0.30977/0.32728, loss_grounding_bce_5: 0.12149/0.08633, loss_grounding_dice_5: 0.05414/0.18309, loss_grounding_ce_5: 0.06720/0.28465, loss_mask_ce_6: 1.30510/0.95574, loss_mask_bce_6: 0.64271/0.33645, loss_mask_dice_6: 1.44197/1.21382, loss_spatial_bce_6: 0.26030/0.13189, loss_spatial_dice_6: 0.36883/0.33428, loss_spatial_ce_6: 0.40679/0.37449, loss_grounding_bce_6: 0.12925/0.08745, loss_grounding_dice_6: 0.05827/0.18487, loss_grounding_ce_6: 0.15202/0.31797, loss_mask_ce_7: 1.37546/0.99894, loss_mask_bce_7: 0.65423/0.34275, loss_mask_dice_7: 1.33022/1.27368, loss_spatial_bce_7: 0.31976/0.14858, loss_spatial_dice_7: 0.37866/0.35766, loss_spatial_ce_7: 0.28442/0.41266, loss_grounding_bce_7: 0.11283/0.08817, loss_grounding_dice_7: 0.05270/0.19110, loss_grounding_ce_7: 0.26181/0.37457, loss_mask_ce_8: 1.43571/1.10379, loss_mask_bce_8: 0.65006/0.35324, loss_mask_dice_8: 1.21938/1.34837, loss_spatial_bce_8: 0.27494/0.17094, loss_spatial_dice_8: 0.44095/0.41334, loss_spatial_ce_8: 0.39889/0.45031, loss_grounding_bce_8: 0.13183/0.09200, loss_grounding_dice_8: 0.06246/0.20309, loss_grounding_ce_8: 0.13898/0.46022, loss_mask_ce_9: 4.82615/3.89789, loss_mask_bce_9: 0.62955/0.38140, loss_mask_dice_9: 2.05807/1.89072, loss_spatial_bce_9: 0.48517/0.38847, loss_spatial_dice_9: 0.85962/0.84167, loss_spatial_ce_9: 1.24401/1.80575, loss_grounding_bce_9: 0.13410/0.10234, loss_grounding_dice_9: 0.06833/0.28137, loss_grounding_ce_9: 0.47917/0.90844] items per batch[64] items per second[0.20] total items[51200] mini batches[   800] memory[7207] epoch remaining[0:58:06]
INFO:trainer.default_trainer:epochs[     0] optim steps[900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11213/0.90820, loss_mask_bce_0: 0.30208/0.33228, loss_mask_dice_0: 1.32083/1.17440, loss_spatial_bce_0: 0.04368/0.11861, loss_spatial_dice_0: 0.29941/0.29109, loss_spatial_ce_0: 0.10520/0.24078, loss_grounding_bce_0: 0.02587/0.08715, loss_grounding_dice_0: 0.12185/0.18148, loss_grounding_ce_0: 0.19673/0.27125, loss_mask_ce_1: 1.34816/0.91057, loss_mask_bce_1: 0.31533/0.33279, loss_mask_dice_1: 1.49953/1.18308, loss_spatial_bce_1: 0.04244/0.11908, loss_spatial_dice_1: 0.30099/0.29629, loss_spatial_ce_1: 0.11312/0.25143, loss_grounding_bce_1: 0.02631/0.08745, loss_grounding_dice_1: 0.15066/0.18158, loss_grounding_ce_1: 0.27467/0.27859, loss_mask_ce_2: 1.26646/0.91781, loss_mask_bce_2: 0.30176/0.33235, loss_mask_dice_2: 1.39012/1.17773, loss_spatial_bce_2: 0.04045/0.11876, loss_spatial_dice_2: 0.30501/0.30066, loss_spatial_ce_2: 0.15000/0.26010, loss_grounding_bce_2: 0.02367/0.08733, loss_grounding_dice_2: 0.12552/0.18062, loss_grounding_ce_2: 0.27102/0.27571, loss_mask_ce_3: 1.34534/0.91978, loss_mask_bce_3: 0.29075/0.33331, loss_mask_dice_3: 1.39781/1.17787, loss_spatial_bce_3: 0.04128/0.12120, loss_spatial_dice_3: 0.30555/0.30510, loss_spatial_ce_3: 0.13433/0.28692, loss_grounding_bce_3: 0.02690/0.08699, loss_grounding_dice_3: 0.23242/0.18256, loss_grounding_ce_3: 0.43119/0.27920, loss_mask_ce_4: 1.31608/0.92127, loss_mask_bce_4: 0.30907/0.33421, loss_mask_dice_4: 1.34160/1.19424, loss_spatial_bce_4: 0.03815/0.12601, loss_spatial_dice_4: 0.31076/0.31461, loss_spatial_ce_4: 0.15093/0.29092, loss_grounding_bce_4: 0.02575/0.08714, loss_grounding_dice_4: 0.15248/0.18365, loss_grounding_ce_4: 0.35367/0.28504, loss_mask_ce_5: 1.17821/0.92545, loss_mask_bce_5: 0.29899/0.33788, loss_mask_dice_5: 1.42039/1.19919, loss_spatial_bce_5: 0.03844/0.12754, loss_spatial_dice_5: 0.31566/0.32153, loss_spatial_ce_5: 0.22936/0.30849, loss_grounding_bce_5: 0.02889/0.08795, loss_grounding_dice_5: 0.17036/0.18330, loss_grounding_ce_5: 0.39267/0.29039, loss_mask_ce_6: 1.18992/0.95982, loss_mask_bce_6: 0.31329/0.33868, loss_mask_dice_6: 1.23085/1.20148, loss_spatial_bce_6: 0.04455/0.13016, loss_spatial_dice_6: 0.30381/0.32640, loss_spatial_ce_6: 0.41477/0.35188, loss_grounding_bce_6: 0.02602/0.08915, loss_grounding_dice_6: 0.15993/0.18477, loss_grounding_ce_6: 0.60299/0.32804, loss_mask_ce_7: 1.45134/0.99938, loss_mask_bce_7: 0.32284/0.34491, loss_mask_dice_7: 1.31351/1.26004, loss_spatial_bce_7: 0.06454/0.14657, loss_spatial_dice_7: 0.36913/0.34987, loss_spatial_ce_7: 0.44151/0.38992, loss_grounding_bce_7: 0.02931/0.09030, loss_grounding_dice_7: 0.16859/0.19122, loss_grounding_ce_7: 0.66641/0.37914, loss_mask_ce_8: 1.41719/1.10384, loss_mask_bce_8: 0.36383/0.35610, loss_mask_dice_8: 1.29981/1.33636, loss_spatial_bce_8: 0.05727/0.16841, loss_spatial_dice_8: 0.39133/0.40368, loss_spatial_ce_8: 0.46418/0.43033, loss_grounding_bce_8: 0.02784/0.09451, loss_grounding_dice_8: 0.13626/0.20310, loss_grounding_ce_8: 1.79280/0.46527, loss_mask_ce_9: 4.61411/3.88453, loss_mask_bce_9: 0.40276/0.38339, loss_mask_dice_9: 1.97089/1.87634, loss_spatial_bce_9: 0.13547/0.38601, loss_spatial_dice_9: 0.85045/0.83988, loss_spatial_ce_9: 1.59023/1.77963, loss_grounding_bce_9: 0.03225/0.10509, loss_grounding_dice_9: 0.24535/0.28228, loss_grounding_ce_9: 2.97346/0.90678] items per batch[64] items per second[0.20] total items[57600] mini batches[   900] memory[7207] epoch remaining[0:52:09]
INFO:trainer.default_trainer:epochs[     0] optim steps[1000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67483/0.90608, loss_mask_bce_0: 0.14263/0.32873, loss_mask_dice_0: 0.10178/1.16873, loss_spatial_bce_0: 0.49085/0.11640, loss_spatial_dice_0: 0.24909/0.28615, loss_spatial_ce_0: 0.15451/0.22993, loss_grounding_bce_0: 0.12307/0.08609, loss_grounding_dice_0: 0.09117/0.18094, loss_grounding_ce_0: 0.19498/0.26968, loss_mask_ce_1: 0.65189/0.90886, loss_mask_bce_1: 0.15225/0.32924, loss_mask_dice_1: 0.10648/1.17734, loss_spatial_bce_1: 0.46058/0.11685, loss_spatial_dice_1: 0.24330/0.29150, loss_spatial_ce_1: 0.15660/0.23857, loss_grounding_bce_1: 0.12708/0.08644, loss_grounding_dice_1: 0.09336/0.18159, loss_grounding_ce_1: 0.18109/0.27635, loss_mask_ce_2: 0.34881/0.91677, loss_mask_bce_2: 0.20153/0.32906, loss_mask_dice_2: 0.12949/1.17306, loss_spatial_bce_2: 0.46250/0.11640, loss_spatial_dice_2: 0.25010/0.29548, loss_spatial_ce_2: 0.16552/0.24743, loss_grounding_bce_2: 0.13365/0.08619, loss_grounding_dice_2: 0.10817/0.18049, loss_grounding_ce_2: 0.15529/0.27528, loss_mask_ce_3: 0.40072/0.91893, loss_mask_bce_3: 0.18391/0.32970, loss_mask_dice_3: 0.13237/1.17288, loss_spatial_bce_3: 0.48232/0.11859, loss_spatial_dice_3: 0.27068/0.29966, loss_spatial_ce_3: 0.16700/0.27212, loss_grounding_bce_3: 0.13705/0.08596, loss_grounding_dice_3: 0.11199/0.18191, loss_grounding_ce_3: 0.18502/0.27684, loss_mask_ce_4: 0.39223/0.91827, loss_mask_bce_4: 0.17523/0.33064, loss_mask_dice_4: 0.13534/1.18934, loss_spatial_bce_4: 0.44685/0.12309, loss_spatial_dice_4: 0.25974/0.30932, loss_spatial_ce_4: 0.15313/0.27586, loss_grounding_bce_4: 0.13769/0.08604, loss_grounding_dice_4: 0.11819/0.18301, loss_grounding_ce_4: 0.26841/0.28436, loss_mask_ce_5: 0.35716/0.92354, loss_mask_bce_5: 0.16373/0.33488, loss_mask_dice_5: 0.11418/1.19407, loss_spatial_bce_5: 0.37588/0.12466, loss_spatial_dice_5: 0.14852/0.31535, loss_spatial_ce_5: 0.20552/0.29308, loss_grounding_bce_5: 0.12154/0.08697, loss_grounding_dice_5: 0.08362/0.18312, loss_grounding_ce_5: 0.17815/0.29278, loss_mask_ce_6: 0.43180/0.95753, loss_mask_bce_6: 0.15954/0.33557, loss_mask_dice_6: 0.11888/1.19553, loss_spatial_bce_6: 0.40105/0.12758, loss_spatial_dice_6: 0.18218/0.31997, loss_spatial_ce_6: 0.16192/0.33473, loss_grounding_bce_6: 0.13980/0.08798, loss_grounding_dice_6: 0.10405/0.18450, loss_grounding_ce_6: 0.20328/0.32715, loss_mask_ce_7: 0.45461/0.99681, loss_mask_bce_7: 0.16048/0.34203, loss_mask_dice_7: 0.12587/1.25429, loss_spatial_bce_7: 0.40083/0.14366, loss_spatial_dice_7: 0.19877/0.34344, loss_spatial_ce_7: 0.16545/0.37364, loss_grounding_bce_7: 0.14508/0.08907, loss_grounding_dice_7: 0.12107/0.19059, loss_grounding_ce_7: 0.26650/0.37795, loss_mask_ce_8: 0.48919/1.10438, loss_mask_bce_8: 0.23007/0.35280, loss_mask_dice_8: 0.13233/1.32818, loss_spatial_bce_8: 0.41165/0.16483, loss_spatial_dice_8: 0.18674/0.39641, loss_spatial_ce_8: 0.15384/0.41564, loss_grounding_bce_8: 0.17338/0.09353, loss_grounding_dice_8: 0.09992/0.20258, loss_grounding_ce_8: 0.28138/0.46305, loss_mask_ce_9: 2.40322/3.87241, loss_mask_bce_9: 0.26222/0.38097, loss_mask_dice_9: 0.18223/1.87337, loss_spatial_bce_9: 0.65697/0.37886, loss_spatial_dice_9: 0.49933/0.83829, loss_spatial_ce_9: 0.49784/1.75259, loss_grounding_bce_9: 0.26222/0.10460, loss_grounding_dice_9: 0.17187/0.28258, loss_grounding_ce_9: 0.55640/0.89323] items per batch[64] items per second[0.21] total items[64000] mini batches[  1000] memory[7207] epoch remaining[0:46:07]
INFO:trainer.default_trainer:epochs[     0] optim steps[1100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04871/0.90578, loss_mask_bce_0: 0.19339/0.32929, loss_mask_dice_0: 0.93825/1.16578, loss_spatial_bce_0: 0.04316/0.11516, loss_spatial_dice_0: 0.26524/0.28213, loss_spatial_ce_0: 0.08648/0.21989, loss_grounding_bce_0: 0.12294/0.08634, loss_grounding_dice_0: 0.51626/0.18104, loss_grounding_ce_0: 0.32593/0.26993, loss_mask_ce_1: 1.01126/0.90848, loss_mask_bce_1: 0.17721/0.32963, loss_mask_dice_1: 0.98833/1.17280, loss_spatial_bce_1: 0.04617/0.11565, loss_spatial_dice_1: 0.28504/0.28730, loss_spatial_ce_1: 0.08953/0.22842, loss_grounding_bce_1: 0.13895/0.08667, loss_grounding_dice_1: 0.52822/0.18190, loss_grounding_ce_1: 0.26146/0.27558, loss_mask_ce_2: 0.99315/0.91612, loss_mask_bce_2: 0.19431/0.32940, loss_mask_dice_2: 0.91568/1.16911, loss_spatial_bce_2: 0.04858/0.11508, loss_spatial_dice_2: 0.27990/0.29109, loss_spatial_ce_2: 0.09589/0.23608, loss_grounding_bce_2: 0.09331/0.08625, loss_grounding_dice_2: 0.43953/0.18072, loss_grounding_ce_2: 0.72985/0.27618, loss_mask_ce_3: 1.02623/0.91835, loss_mask_bce_3: 0.17995/0.33024, loss_mask_dice_3: 0.87624/1.16868, loss_spatial_bce_3: 0.05098/0.11736, loss_spatial_dice_3: 0.29028/0.29497, loss_spatial_ce_3: 0.08946/0.25949, loss_grounding_bce_3: 0.09688/0.08617, loss_grounding_dice_3: 0.44577/0.18166, loss_grounding_ce_3: 0.27453/0.27782, loss_mask_ce_4: 0.94075/0.91832, loss_mask_bce_4: 0.17347/0.33067, loss_mask_dice_4: 0.85363/1.18435, loss_spatial_bce_4: 0.05394/0.12160, loss_spatial_dice_4: 0.29045/0.30420, loss_spatial_ce_4: 0.13601/0.26440, loss_grounding_bce_4: 0.09846/0.08613, loss_grounding_dice_4: 0.45113/0.18281, loss_grounding_ce_4: 0.26644/0.28370, loss_mask_ce_5: 1.00015/0.92392, loss_mask_bce_5: 0.18938/0.33487, loss_mask_dice_5: 0.96120/1.19145, loss_spatial_bce_5: 0.04835/0.12314, loss_spatial_dice_5: 0.29411/0.31012, loss_spatial_ce_5: 0.18986/0.28104, loss_grounding_bce_5: 0.10340/0.08691, loss_grounding_dice_5: 0.41857/0.18293, loss_grounding_ce_5: 0.22989/0.29263, loss_mask_ce_6: 0.91348/0.95771, loss_mask_bce_6: 0.16925/0.33570, loss_mask_dice_6: 0.93747/1.19086, loss_spatial_bce_6: 0.05563/0.12614, loss_spatial_dice_6: 0.31038/0.31466, loss_spatial_ce_6: 0.22000/0.32038, loss_grounding_bce_6: 0.09734/0.08802, loss_grounding_dice_6: 0.42725/0.18488, loss_grounding_ce_6: 0.20100/0.32498, loss_mask_ce_7: 1.19436/0.99715, loss_mask_bce_7: 0.20844/0.34187, loss_mask_dice_7: 0.98915/1.25052, loss_spatial_bce_7: 0.07052/0.14202, loss_spatial_dice_7: 0.35078/0.33794, loss_spatial_ce_7: 0.26504/0.36002, loss_grounding_bce_7: 0.12529/0.08931, loss_grounding_dice_7: 0.49116/0.19091, loss_grounding_ce_7: 0.22444/0.37497, loss_mask_ce_8: 1.08697/1.10251, loss_mask_bce_8: 0.19419/0.35339, loss_mask_dice_8: 1.09867/1.32393, loss_spatial_bce_8: 0.06023/0.16297, loss_spatial_dice_8: 0.34778/0.38977, loss_spatial_ce_8: 0.23252/0.40266, loss_grounding_bce_8: 0.07134/0.09367, loss_grounding_dice_8: 0.38420/0.20257, loss_grounding_ce_8: 0.38086/0.45934, loss_mask_ce_9: 4.72939/3.85800, loss_mask_bce_9: 0.19520/0.38118, loss_mask_dice_9: 1.36333/1.86668, loss_spatial_bce_9: 0.17238/0.37508, loss_spatial_dice_9: 0.86446/0.83752, loss_spatial_ce_9: 1.57214/1.73336, loss_grounding_bce_9: 0.06650/0.10456, loss_grounding_dice_9: 0.45483/0.28255, loss_grounding_ce_9: 0.57098/0.87432] items per batch[64] items per second[0.21] total items[70400] mini batches[  1100] memory[7266] epoch remaining[0:40:15]
INFO:trainer.default_trainer:epochs[     0] optim steps[1200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95061/0.91009, loss_mask_bce_0: 0.15255/0.33052, loss_mask_dice_0: 2.71778/1.16002, loss_spatial_bce_0: 0.01427/0.11398, loss_spatial_dice_0: 0.33740/0.27813, loss_spatial_ce_0: 0.16497/0.21160, loss_grounding_bce_0: 0.02477/0.08630, loss_grounding_dice_0: 0.33618/0.18000, loss_grounding_ce_0: 0.40586/0.27303, loss_mask_ce_1: 1.26696/0.91201, loss_mask_bce_1: 0.09807/0.33070, loss_mask_dice_1: 2.87550/1.16818, loss_spatial_bce_1: 0.01343/0.11440, loss_spatial_dice_1: 0.36912/0.28331, loss_spatial_ce_1: 0.08907/0.21938, loss_grounding_bce_1: 0.01996/0.08657, loss_grounding_dice_1: 0.18237/0.18067, loss_grounding_ce_1: 0.32257/0.27814, loss_mask_ce_2: 1.18266/0.92001, loss_mask_bce_2: 0.07745/0.33010, loss_mask_dice_2: 2.97972/1.16291, loss_spatial_bce_2: 0.01450/0.11380, loss_spatial_dice_2: 0.35335/0.28722, loss_spatial_ce_2: 0.05941/0.22622, loss_grounding_bce_2: 0.01943/0.08624, loss_grounding_dice_2: 0.13753/0.17966, loss_grounding_ce_2: 0.26416/0.27969, loss_mask_ce_3: 1.44503/0.92316, loss_mask_bce_3: 0.06945/0.33123, loss_mask_dice_3: 2.81877/1.16284, loss_spatial_bce_3: 0.01196/0.11595, loss_spatial_dice_3: 0.34297/0.29103, loss_spatial_ce_3: 0.44622/0.24873, loss_grounding_bce_3: 0.02076/0.08613, loss_grounding_dice_3: 0.15196/0.18056, loss_grounding_ce_3: 0.33771/0.28079, loss_mask_ce_4: 1.32141/0.92377, loss_mask_bce_4: 0.12582/0.33142, loss_mask_dice_4: 2.80873/1.17911, loss_spatial_bce_4: 0.01640/0.12015, loss_spatial_dice_4: 0.34504/0.30003, loss_spatial_ce_4: 0.17390/0.25441, loss_grounding_bce_4: 0.02134/0.08607, loss_grounding_dice_4: 0.21233/0.18187, loss_grounding_ce_4: 0.37165/0.28565, loss_mask_ce_5: 1.11948/0.92769, loss_mask_bce_5: 0.09950/0.33567, loss_mask_dice_5: 2.65805/1.18545, loss_spatial_bce_5: 0.01591/0.12183, loss_spatial_dice_5: 0.35439/0.30561, loss_spatial_ce_5: 0.12097/0.26972, loss_grounding_bce_5: 0.05467/0.08692, loss_grounding_dice_5: 0.22147/0.18189, loss_grounding_ce_5: 0.15748/0.29567, loss_mask_ce_6: 1.34675/0.96132, loss_mask_bce_6: 0.07621/0.33678, loss_mask_dice_6: 2.56065/1.18644, loss_spatial_bce_6: 0.01317/0.12489, loss_spatial_dice_6: 0.35528/0.31027, loss_spatial_ce_6: 0.13522/0.30782, loss_grounding_bce_6: 0.07246/0.08802, loss_grounding_dice_6: 0.26949/0.18422, loss_grounding_ce_6: 0.11548/0.32783, loss_mask_ce_7: 1.14437/1.00037, loss_mask_bce_7: 0.09445/0.34261, loss_mask_dice_7: 2.90048/1.24420, loss_spatial_bce_7: 0.01679/0.14035, loss_spatial_dice_7: 0.40343/0.33336, loss_spatial_ce_7: 0.21103/0.34924, loss_grounding_bce_7: 0.02792/0.08933, loss_grounding_dice_7: 0.17328/0.19013, loss_grounding_ce_7: 0.22519/0.37682, loss_mask_ce_8: 1.41101/1.10547, loss_mask_bce_8: 0.10695/0.35472, loss_mask_dice_8: 2.69709/1.31951, loss_spatial_bce_8: 0.02939/0.16110, loss_spatial_dice_8: 0.45675/0.38431, loss_spatial_ce_8: 0.38032/0.39207, loss_grounding_bce_8: 0.03123/0.09373, loss_grounding_dice_8: 0.17582/0.20116, loss_grounding_ce_8: 0.35927/0.46021, loss_mask_ce_9: 3.87897/3.84750, loss_mask_bce_9: 0.07316/0.38324, loss_mask_dice_9: 3.41151/1.86720, loss_spatial_bce_9: 0.07573/0.37339, loss_spatial_dice_9: 0.87132/0.83626, loss_spatial_ce_9: 1.79451/1.71990, loss_grounding_bce_9: 0.02999/0.10468, loss_grounding_dice_9: 0.25862/0.28066, loss_grounding_ce_9: 0.15202/0.87637] items per batch[64] items per second[0.20] total items[76800] mini batches[  1200] memory[7266] epoch remaining[0:34:38]
INFO:trainer.default_trainer:epochs[     0] optim steps[1300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71253/0.90939, loss_mask_bce_0: 0.41694/0.33184, loss_mask_dice_0: 0.70929/1.16055, loss_spatial_bce_0: 0.47007/0.11359, loss_spatial_dice_0: 0.27747/0.27545, loss_spatial_ce_0: 0.11507/0.20660, loss_grounding_bce_0: 0.12487/0.08672, loss_grounding_dice_0: 0.09198/0.17957, loss_grounding_ce_0: 0.18554/0.27173, loss_mask_ce_1: 0.68081/0.91127, loss_mask_bce_1: 0.41553/0.33218, loss_mask_dice_1: 0.71363/1.16965, loss_spatial_bce_1: 0.41614/0.11403, loss_spatial_dice_1: 0.26036/0.28067, loss_spatial_ce_1: 0.23787/0.21313, loss_grounding_bce_1: 0.12149/0.08691, loss_grounding_dice_1: 0.09689/0.18013, loss_grounding_ce_1: 0.18244/0.27795, loss_mask_ce_2: 0.69718/0.91933, loss_mask_bce_2: 0.43410/0.33135, loss_mask_dice_2: 0.72023/1.16412, loss_spatial_bce_2: 0.49218/0.11343, loss_spatial_dice_2: 0.29623/0.28435, loss_spatial_ce_2: 0.10110/0.21856, loss_grounding_bce_2: 0.13745/0.08660, loss_grounding_dice_2: 0.11012/0.17910, loss_grounding_ce_2: 0.19169/0.27878, loss_mask_ce_3: 0.72375/0.92260, loss_mask_bce_3: 0.41948/0.33258, loss_mask_dice_3: 0.71796/1.16496, loss_spatial_bce_3: 0.43487/0.11538, loss_spatial_dice_3: 0.27096/0.28807, loss_spatial_ce_3: 0.21610/0.24128, loss_grounding_bce_3: 0.12168/0.08659, loss_grounding_dice_3: 0.09269/0.18011, loss_grounding_ce_3: 0.16341/0.28115, loss_mask_ce_4: 0.65803/0.92490, loss_mask_bce_4: 0.48193/0.33228, loss_mask_dice_4: 0.73910/1.18036, loss_spatial_bce_4: 0.32229/0.11937, loss_spatial_dice_4: 0.26139/0.29690, loss_spatial_ce_4: 0.42152/0.24708, loss_grounding_bce_4: 0.12136/0.08637, loss_grounding_dice_4: 0.09851/0.18108, loss_grounding_ce_4: 0.19289/0.28541, loss_mask_ce_5: 0.65704/0.92722, loss_mask_bce_5: 0.46140/0.33660, loss_mask_dice_5: 0.77575/1.18558, loss_spatial_bce_5: 0.31175/0.12104, loss_spatial_dice_5: 0.28222/0.30252, loss_spatial_ce_5: 0.50896/0.26210, loss_grounding_bce_5: 0.12223/0.08721, loss_grounding_dice_5: 0.10014/0.18170, loss_grounding_ce_5: 0.27380/0.29518, loss_mask_ce_6: 0.69652/0.96319, loss_mask_bce_6: 0.44061/0.33777, loss_mask_dice_6: 0.76880/1.18815, loss_spatial_bce_6: 0.42360/0.12427, loss_spatial_dice_6: 0.31250/0.30723, loss_spatial_ce_6: 0.42557/0.29832, loss_grounding_bce_6: 0.12217/0.08858, loss_grounding_dice_6: 0.09118/0.18430, loss_grounding_ce_6: 0.26516/0.32665, loss_mask_ce_7: 0.79313/1.00289, loss_mask_bce_7: 0.45150/0.34372, loss_mask_dice_7: 0.75544/1.24443, loss_spatial_bce_7: 0.38846/0.13932, loss_spatial_dice_7: 0.34135/0.32999, loss_spatial_ce_7: 0.49044/0.33962, loss_grounding_bce_7: 0.11508/0.09002, loss_grounding_dice_7: 0.08480/0.19002, loss_grounding_ce_7: 0.62963/0.37298, loss_mask_ce_8: 0.82450/1.10775, loss_mask_bce_8: 0.53399/0.35586, loss_mask_dice_8: 0.83059/1.31820, loss_spatial_bce_8: 0.43700/0.15982, loss_spatial_dice_8: 0.39681/0.38015, loss_spatial_ce_8: 0.22725/0.38161, loss_grounding_bce_8: 0.13525/0.09443, loss_grounding_dice_8: 0.09287/0.20067, loss_grounding_ce_8: 0.53593/0.45835, loss_mask_ce_9: 4.55842/3.83679, loss_mask_bce_9: 0.63292/0.38354, loss_mask_dice_9: 1.22605/1.86709, loss_spatial_bce_9: 0.46094/0.36975, loss_spatial_dice_9: 0.77475/0.83579, loss_spatial_ce_9: 1.54680/1.70920, loss_grounding_bce_9: 0.12543/0.10554, loss_grounding_dice_9: 0.16047/0.28082, loss_grounding_ce_9: 1.36475/0.86353] items per batch[64] items per second[0.20] total items[83200] mini batches[  1300] memory[7266] epoch remaining[0:28:59]
INFO:trainer.default_trainer:epochs[     0] optim steps[1400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71108/0.91405, loss_mask_bce_0: 0.31901/0.33093, loss_mask_dice_0: 2.73461/1.15900, loss_spatial_bce_0: 0.07991/0.11270, loss_spatial_dice_0: 0.29598/0.27240, loss_spatial_ce_0: 0.13424/0.20019, loss_grounding_bce_0: 0.03222/0.08641, loss_grounding_dice_0: 0.41670/0.17845, loss_grounding_ce_0: 0.40200/0.27080, loss_mask_ce_1: 0.65771/0.91495, loss_mask_bce_1: 0.31220/0.33136, loss_mask_dice_1: 2.47174/1.16875, loss_spatial_bce_1: 0.07853/0.11307, loss_spatial_dice_1: 0.34992/0.27763, loss_spatial_ce_1: 0.18256/0.20678, loss_grounding_bce_1: 0.03072/0.08654, loss_grounding_dice_1: 0.27545/0.17906, loss_grounding_ce_1: 0.43175/0.27819, loss_mask_ce_2: 0.77650/0.92352, loss_mask_bce_2: 0.31991/0.33041, loss_mask_dice_2: 2.53183/1.16190, loss_spatial_bce_2: 0.07670/0.11246, loss_spatial_dice_2: 0.35440/0.28140, loss_spatial_ce_2: 0.15550/0.21199, loss_grounding_bce_2: 0.03469/0.08627, loss_grounding_dice_2: 0.36178/0.17792, loss_grounding_ce_2: 0.44103/0.27849, loss_mask_ce_3: 0.96497/0.92723, loss_mask_bce_3: 0.32497/0.33215, loss_mask_dice_3: 2.42573/1.16179, loss_spatial_bce_3: 0.07730/0.11441, loss_spatial_dice_3: 0.39893/0.28496, loss_spatial_ce_3: 0.17868/0.23366, loss_grounding_bce_3: 0.03257/0.08646, loss_grounding_dice_3: 0.42136/0.17883, loss_grounding_ce_3: 0.38965/0.28105, loss_mask_ce_4: 0.90750/0.92894, loss_mask_bce_4: 0.30487/0.33162, loss_mask_dice_4: 2.02883/1.17890, loss_spatial_bce_4: 0.07977/0.11827, loss_spatial_dice_4: 0.36169/0.29341, loss_spatial_ce_4: 0.22639/0.23969, loss_grounding_bce_4: 0.02961/0.08628, loss_grounding_dice_4: 0.35862/0.18049, loss_grounding_ce_4: 0.40866/0.28481, loss_mask_ce_5: 0.95066/0.93121, loss_mask_bce_5: 0.30328/0.33598, loss_mask_dice_5: 2.49845/1.18445, loss_spatial_bce_5: 0.08227/0.11994, loss_spatial_dice_5: 0.43816/0.29917, loss_spatial_ce_5: 0.34391/0.25401, loss_grounding_bce_5: 0.03112/0.08703, loss_grounding_dice_5: 0.38976/0.18086, loss_grounding_ce_5: 0.40055/0.29584, loss_mask_ce_6: 0.75632/0.96775, loss_mask_bce_6: 0.30621/0.33740, loss_mask_dice_6: 2.26405/1.18631, loss_spatial_bce_6: 0.07872/0.12304, loss_spatial_dice_6: 0.39576/0.30368, loss_spatial_ce_6: 0.40157/0.28913, loss_grounding_bce_6: 0.03191/0.08839, loss_grounding_dice_6: 0.37392/0.18347, loss_grounding_ce_6: 0.35943/0.32680, loss_mask_ce_7: 0.91592/1.00599, loss_mask_bce_7: 0.32368/0.34391, loss_mask_dice_7: 2.55449/1.24305, loss_spatial_bce_7: 0.13111/0.13778, loss_spatial_dice_7: 0.51097/0.32655, loss_spatial_ce_7: 0.26348/0.33006, loss_grounding_bce_7: 0.03280/0.08987, loss_grounding_dice_7: 0.41572/0.18985, loss_grounding_ce_7: 0.39989/0.37075, loss_mask_ce_8: 1.33489/1.11290, loss_mask_bce_8: 0.30242/0.35497, loss_mask_dice_8: 2.72941/1.31711, loss_spatial_bce_8: 0.11313/0.15821, loss_spatial_dice_8: 0.46276/0.37627, loss_spatial_ce_8: 0.75289/0.37301, loss_grounding_bce_8: 0.03229/0.09396, loss_grounding_dice_8: 0.46961/0.19993, loss_grounding_ce_8: 0.40929/0.45482, loss_mask_ce_9: 3.26804/3.83435, loss_mask_bce_9: 0.25664/0.38302, loss_mask_dice_9: 3.12275/1.86644, loss_spatial_bce_9: 0.18669/0.36791, loss_spatial_dice_9: 0.85746/0.83538, loss_spatial_ce_9: 2.47281/1.69904, loss_grounding_bce_9: 0.02600/0.10532, loss_grounding_dice_9: 0.41234/0.28057, loss_grounding_ce_9: 0.62084/0.85705] items per batch[64] items per second[0.21] total items[89600] mini batches[  1400] memory[7266] epoch remaining[0:23:22]
INFO:trainer.default_trainer:epochs[     0] optim steps[1500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75362/0.91304, loss_mask_bce_0: 0.75901/0.33223, loss_mask_dice_0: 2.56572/1.16739, loss_spatial_bce_0: 0.11540/0.11163, loss_spatial_dice_0: 0.26042/0.27034, loss_spatial_ce_0: 0.10073/0.19468, loss_grounding_bce_0: 0.08721/0.08647, loss_grounding_dice_0: 0.36734/0.17982, loss_grounding_ce_0: 0.28278/0.27138, loss_mask_ce_1: 0.71676/0.91263, loss_mask_bce_1: 0.76726/0.33285, loss_mask_dice_1: 2.56916/1.17715, loss_spatial_bce_1: 0.10720/0.11214, loss_spatial_dice_1: 0.26463/0.27581, loss_spatial_ce_1: 0.13101/0.20060, loss_grounding_bce_1: 0.08435/0.08665, loss_grounding_dice_1: 0.37054/0.18017, loss_grounding_ce_1: 0.29057/0.27897, loss_mask_ce_2: 0.66213/0.92105, loss_mask_bce_2: 0.85132/0.33205, loss_mask_dice_2: 2.58175/1.16970, loss_spatial_bce_2: 0.11532/0.11156, loss_spatial_dice_2: 0.26603/0.27946, loss_spatial_ce_2: 0.11958/0.20588, loss_grounding_bce_2: 0.08428/0.08628, loss_grounding_dice_2: 0.36281/0.17900, loss_grounding_ce_2: 0.28412/0.27867, loss_mask_ce_3: 0.70940/0.92410, loss_mask_bce_3: 0.76486/0.33365, loss_mask_dice_3: 2.64044/1.17060, loss_spatial_bce_3: 0.10724/0.11342, loss_spatial_dice_3: 0.25139/0.28276, loss_spatial_ce_3: 0.11631/0.22690, loss_grounding_bce_3: 0.08776/0.08652, loss_grounding_dice_3: 0.38622/0.18026, loss_grounding_ce_3: 0.28339/0.28127, loss_mask_ce_4: 0.69804/0.92661, loss_mask_bce_4: 0.78915/0.33295, loss_mask_dice_4: 2.76056/1.18671, loss_spatial_bce_4: 0.19934/0.11723, loss_spatial_dice_4: 0.27042/0.29127, loss_spatial_ce_4: 0.12600/0.23412, loss_grounding_bce_4: 0.08229/0.08644, loss_grounding_dice_4: 0.40003/0.18209, loss_grounding_ce_4: 0.26618/0.28614, loss_mask_ce_5: 0.68682/0.92965, loss_mask_bce_5: 0.80491/0.33702, loss_mask_dice_5: 2.65881/1.19335, loss_spatial_bce_5: 0.19690/0.11894, loss_spatial_dice_5: 0.29837/0.29682, loss_spatial_ce_5: 0.11179/0.24686, loss_grounding_bce_5: 0.10884/0.08711, loss_grounding_dice_5: 0.41561/0.18238, loss_grounding_ce_5: 0.27504/0.29503, loss_mask_ce_6: 0.85020/0.96685, loss_mask_bce_6: 0.79077/0.33872, loss_mask_dice_6: 2.69505/1.19306, loss_spatial_bce_6: 0.20846/0.12206, loss_spatial_dice_6: 0.29330/0.30115, loss_spatial_ce_6: 0.14381/0.28162, loss_grounding_bce_6: 0.10533/0.08840, loss_grounding_dice_6: 0.41970/0.18480, loss_grounding_ce_6: 0.29550/0.32549, loss_mask_ce_7: 0.82489/1.00581, loss_mask_bce_7: 0.86396/0.34507, loss_mask_dice_7: 3.02852/1.25179, loss_spatial_bce_7: 0.23396/0.13679, loss_spatial_dice_7: 0.35320/0.32412, loss_spatial_ce_7: 0.25740/0.32303, loss_grounding_bce_7: 0.09568/0.08975, loss_grounding_dice_7: 0.49260/0.19114, loss_grounding_ce_7: 0.57861/0.37057, loss_mask_ce_8: 0.85629/1.11343, loss_mask_bce_8: 0.95327/0.35686, loss_mask_dice_8: 3.41698/1.32514, loss_spatial_bce_8: 0.24268/0.15681, loss_spatial_dice_8: 0.42369/0.37338, loss_spatial_ce_8: 0.28162/0.36422, loss_grounding_bce_8: 0.11209/0.09395, loss_grounding_dice_8: 0.48752/0.20124, loss_grounding_ce_8: 0.53389/0.45166, loss_mask_ce_9: 3.96380/3.82582, loss_mask_bce_9: 0.90445/0.38514, loss_mask_dice_9: 4.45607/1.88278, loss_spatial_bce_9: 0.34337/0.36469, loss_spatial_dice_9: 0.93491/0.83512, loss_spatial_ce_9: 1.56489/1.68861, loss_grounding_bce_9: 0.10036/0.10545, loss_grounding_dice_9: 0.59220/0.28258, loss_grounding_ce_9: 1.58147/0.84643] items per batch[64] items per second[0.20] total items[96000] mini batches[  1500] memory[7266] epoch remaining[0:17:50]
INFO:trainer.default_trainer:epochs[     0] optim steps[1600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53763/0.91131, loss_mask_bce_0: 0.35828/0.33368, loss_mask_dice_0: 0.42998/1.17200, loss_spatial_bce_0: 0.12163/0.11127, loss_spatial_dice_0: 0.15816/0.26773, loss_spatial_ce_0: 0.07435/0.18856, loss_grounding_bce_0: 0.11035/0.08644, loss_grounding_dice_0: 0.09042/0.17968, loss_grounding_ce_0: 0.06755/0.27556, loss_mask_ce_1: 0.56537/0.91336, loss_mask_bce_1: 0.36146/0.33422, loss_mask_dice_1: 0.44050/1.18153, loss_spatial_bce_1: 0.11675/0.11171, loss_spatial_dice_1: 0.15378/0.27293, loss_spatial_ce_1: 0.06515/0.19399, loss_grounding_bce_1: 0.10934/0.08662, loss_grounding_dice_1: 0.09399/0.17996, loss_grounding_ce_1: 0.05996/0.28112, loss_mask_ce_2: 0.54145/0.92003, loss_mask_bce_2: 0.36557/0.33350, loss_mask_dice_2: 0.43033/1.17448, loss_spatial_bce_2: 0.11384/0.11126, loss_spatial_dice_2: 0.15780/0.27649, loss_spatial_ce_2: 0.06894/0.19900, loss_grounding_bce_2: 0.10785/0.08619, loss_grounding_dice_2: 0.08996/0.17885, loss_grounding_ce_2: 0.05752/0.28198, loss_mask_ce_3: 0.53063/0.92299, loss_mask_bce_3: 0.35102/0.33490, loss_mask_dice_3: 0.43025/1.17477, loss_spatial_bce_3: 0.12124/0.11305, loss_spatial_dice_3: 0.16032/0.27979, loss_spatial_ce_3: 0.08053/0.21917, loss_grounding_bce_3: 0.10812/0.08643, loss_grounding_dice_3: 0.08834/0.18026, loss_grounding_ce_3: 0.05523/0.28371, loss_mask_ce_4: 0.52873/0.92621, loss_mask_bce_4: 0.35819/0.33449, loss_mask_dice_4: 0.44437/1.19073, loss_spatial_bce_4: 0.13153/0.11667, loss_spatial_dice_4: 0.17026/0.28817, loss_spatial_ce_4: 0.06528/0.22663, loss_grounding_bce_4: 0.09988/0.08646, loss_grounding_dice_4: 0.09687/0.18176, loss_grounding_ce_4: 0.04185/0.28968, loss_mask_ce_5: 0.52627/0.92921, loss_mask_bce_5: 0.36713/0.33860, loss_mask_dice_5: 0.43236/1.19763, loss_spatial_bce_5: 0.11894/0.11825, loss_spatial_dice_5: 0.16869/0.29352, loss_spatial_ce_5: 0.11627/0.23944, loss_grounding_bce_5: 0.10901/0.08718, loss_grounding_dice_5: 0.09342/0.18221, loss_grounding_ce_5: 0.06465/0.29858, loss_mask_ce_6: 0.52977/0.96616, loss_mask_bce_6: 0.36841/0.34030, loss_mask_dice_6: 0.42199/1.19724, loss_spatial_bce_6: 0.12205/0.12146, loss_spatial_dice_6: 0.15695/0.29761, loss_spatial_ce_6: 0.25038/0.27266, loss_grounding_bce_6: 0.11538/0.08864, loss_grounding_dice_6: 0.09326/0.18451, loss_grounding_ce_6: 0.03852/0.32714, loss_mask_ce_7: 0.65467/1.00436, loss_mask_bce_7: 0.35151/0.34682, loss_mask_dice_7: 0.43299/1.25653, loss_spatial_bce_7: 0.15511/0.13597, loss_spatial_dice_7: 0.17327/0.32061, loss_spatial_ce_7: 0.16763/0.31527, loss_grounding_bce_7: 0.10869/0.08991, loss_grounding_dice_7: 0.09363/0.19094, loss_grounding_ce_7: 0.04314/0.37140, loss_mask_ce_8: 0.60031/1.11307, loss_mask_bce_8: 0.35763/0.35824, loss_mask_dice_8: 0.44262/1.33100, loss_spatial_bce_8: 0.26554/0.15629, loss_spatial_dice_8: 0.19535/0.36908, loss_spatial_ce_8: 0.14824/0.35570, loss_grounding_bce_8: 0.10604/0.09394, loss_grounding_dice_8: 0.09853/0.20140, loss_grounding_ce_8: 0.07272/0.45354, loss_mask_ce_9: 2.56450/3.82142, loss_mask_bce_9: 0.33771/0.38668, loss_mask_dice_9: 0.49169/1.89535, loss_spatial_bce_9: 0.52242/0.36376, loss_spatial_dice_9: 0.78447/0.83498, loss_spatial_ce_9: 1.88124/1.67993, loss_grounding_bce_9: 0.09877/0.10556, loss_grounding_dice_9: 0.12047/0.28319, loss_grounding_ce_9: 0.24636/0.84900] items per batch[64] items per second[0.21] total items[102400] mini batches[  1600] memory[7266] epoch remaining[0:12:20]
INFO:trainer.default_trainer:epochs[     0] optim steps[1700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38742/0.91057, loss_mask_bce_0: 0.01396/0.33032, loss_mask_dice_0: 1.40099/1.16728, loss_spatial_bce_0: 0.00338/0.10989, loss_spatial_dice_0: 0.35247/0.26633, loss_spatial_ce_0: 0.12628/0.18407, loss_grounding_bce_0: 0.00627/0.08584, loss_grounding_dice_0: 0.06532/0.17893, loss_grounding_ce_0: 0.01153/0.27459, loss_mask_ce_1: 0.38225/0.91219, loss_mask_bce_1: 0.01366/0.33109, loss_mask_dice_1: 1.29642/1.17623, loss_spatial_bce_1: 0.00384/0.11041, loss_spatial_dice_1: 0.25004/0.27152, loss_spatial_ce_1: 0.14971/0.19000, loss_grounding_bce_1: 0.00622/0.08617, loss_grounding_dice_1: 0.06114/0.17923, loss_grounding_ce_1: 0.01390/0.27942, loss_mask_ce_2: 0.33041/0.91805, loss_mask_bce_2: 0.01333/0.33054, loss_mask_dice_2: 1.32402/1.17046, loss_spatial_bce_2: 0.00491/0.10988, loss_spatial_dice_2: 0.32722/0.27471, loss_spatial_ce_2: 0.37343/0.19419, loss_grounding_bce_2: 0.00615/0.08561, loss_grounding_dice_2: 0.05681/0.17829, loss_grounding_ce_2: 0.00965/0.28042, loss_mask_ce_3: 0.33386/0.92044, loss_mask_bce_3: 0.01651/0.33168, loss_mask_dice_3: 1.28986/1.17034, loss_spatial_bce_3: 0.00332/0.11161, loss_spatial_dice_3: 0.21269/0.27775, loss_spatial_ce_3: 0.20271/0.21399, loss_grounding_bce_3: 0.00701/0.08587, loss_grounding_dice_3: 0.07050/0.17957, loss_grounding_ce_3: 0.01295/0.28264, loss_mask_ce_4: 0.35569/0.92453, loss_mask_bce_4: 0.01898/0.33129, loss_mask_dice_4: 1.46759/1.18575, loss_spatial_bce_4: 0.00281/0.11520, loss_spatial_dice_4: 0.26440/0.28624, loss_spatial_ce_4: 0.18104/0.22075, loss_grounding_bce_4: 0.00840/0.08588, loss_grounding_dice_4: 0.09408/0.18109, loss_grounding_ce_4: 0.02245/0.28822, loss_mask_ce_5: 0.29720/0.92740, loss_mask_bce_5: 0.01709/0.33519, loss_mask_dice_5: 1.50005/1.19160, loss_spatial_bce_5: 0.00354/0.11675, loss_spatial_dice_5: 0.29682/0.29180, loss_spatial_ce_5: 0.15536/0.23343, loss_grounding_bce_5: 0.00673/0.08657, loss_grounding_dice_5: 0.08644/0.18158, loss_grounding_ce_5: 0.01373/0.29837, loss_mask_ce_6: 0.32297/0.96478, loss_mask_bce_6: 0.01555/0.33700, loss_mask_dice_6: 1.05402/1.19249, loss_spatial_bce_6: 0.00363/0.12007, loss_spatial_dice_6: 0.29920/0.29567, loss_spatial_ce_6: 0.25771/0.26613, loss_grounding_bce_6: 0.00761/0.08810, loss_grounding_dice_6: 0.09877/0.18357, loss_grounding_ce_6: 0.01668/0.32532, loss_mask_ce_7: 0.41719/1.00291, loss_mask_bce_7: 0.01966/0.34345, loss_mask_dice_7: 1.62390/1.25090, loss_spatial_bce_7: 0.00398/0.13412, loss_spatial_dice_7: 0.29554/0.31898, loss_spatial_ce_7: 0.29911/0.30915, loss_grounding_bce_7: 0.00842/0.08920, loss_grounding_dice_7: 0.07535/0.19006, loss_grounding_ce_7: 0.12173/0.36878, loss_mask_ce_8: 0.37339/1.11252, loss_mask_bce_8: 0.02722/0.35477, loss_mask_dice_8: 1.04143/1.32392, loss_spatial_bce_8: 0.00439/0.15449, loss_spatial_dice_8: 0.34672/0.36667, loss_spatial_ce_8: 0.47833/0.35092, loss_grounding_bce_8: 0.01052/0.09317, loss_grounding_dice_8: 0.13498/0.20050, loss_grounding_ce_8: 0.01624/0.45173, loss_mask_ce_9: 2.39348/3.80738, loss_mask_bce_9: 0.01715/0.38271, loss_mask_dice_9: 1.14424/1.88324, loss_spatial_bce_9: 0.08023/0.36093, loss_spatial_dice_9: 0.70718/0.83437, loss_spatial_ce_9: 3.31600/1.67233, loss_grounding_bce_9: 0.00986/0.10480, loss_grounding_dice_9: 0.11732/0.28149, loss_grounding_ce_9: 0.06833/0.84851] items per batch[64] items per second[0.21] total items[108800] mini batches[  1700] memory[7266] epoch remaining[0:06:53]
INFO:trainer.default_trainer:epochs[     0] optim steps[1800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12736/0.91359, loss_mask_bce_0: 0.18955/0.33152, loss_mask_dice_0: 1.95665/1.16498, loss_spatial_bce_0: 0.04397/0.10921, loss_spatial_dice_0: 0.19808/0.26495, loss_spatial_ce_0: 0.01095/0.18047, loss_grounding_bce_0: 0.01597/0.08641, loss_grounding_dice_0: 0.28239/0.17965, loss_grounding_ce_0: 0.59854/0.27647, loss_mask_ce_1: 1.18635/0.91648, loss_mask_bce_1: 0.17070/0.33237, loss_mask_dice_1: 1.96612/1.17489, loss_spatial_bce_1: 0.05072/0.10978, loss_spatial_dice_1: 0.20325/0.27026, loss_spatial_ce_1: 0.02171/0.18656, loss_grounding_bce_1: 0.01419/0.08663, loss_grounding_dice_1: 0.26332/0.17986, loss_grounding_ce_1: 0.59815/0.28153, loss_mask_ce_2: 1.26275/0.92135, loss_mask_bce_2: 0.18650/0.33171, loss_mask_dice_2: 2.01818/1.16966, loss_spatial_bce_2: 0.04800/0.10933, loss_spatial_dice_2: 0.19805/0.27321, loss_spatial_ce_2: 0.01813/0.19044, loss_grounding_bce_2: 0.01331/0.08609, loss_grounding_dice_2: 0.27687/0.17918, loss_grounding_ce_2: 0.56355/0.28219, loss_mask_ce_3: 1.21720/0.92345, loss_mask_bce_3: 0.18844/0.33281, loss_mask_dice_3: 2.15118/1.16991, loss_spatial_bce_3: 0.05137/0.11112, loss_spatial_dice_3: 0.20854/0.27636, loss_spatial_ce_3: 0.05888/0.20953, loss_grounding_bce_3: 0.01796/0.08636, loss_grounding_dice_3: 0.28985/0.18011, loss_grounding_ce_3: 0.61120/0.28482, loss_mask_ce_4: 1.25419/0.92739, loss_mask_bce_4: 0.21040/0.33271, loss_mask_dice_4: 2.37981/1.18493, loss_spatial_bce_4: 0.05457/0.11468, loss_spatial_dice_4: 0.22771/0.28479, loss_spatial_ce_4: 0.09555/0.21713, loss_grounding_bce_4: 0.01534/0.08657, loss_grounding_dice_4: 0.24242/0.18192, loss_grounding_ce_4: 0.62733/0.28961, loss_mask_ce_5: 1.15720/0.93129, loss_mask_bce_5: 0.20680/0.33643, loss_mask_dice_5: 2.18744/1.19062, loss_spatial_bce_5: 0.04867/0.11618, loss_spatial_dice_5: 0.22409/0.29022, loss_spatial_ce_5: 0.05650/0.22912, loss_grounding_bce_5: 0.01455/0.08711, loss_grounding_dice_5: 0.25396/0.18238, loss_grounding_ce_5: 0.65640/0.30043, loss_mask_ce_6: 1.10947/0.96781, loss_mask_bce_6: 0.22137/0.33832, loss_mask_dice_6: 2.32681/1.19237, loss_spatial_bce_6: 0.05349/0.11951, loss_spatial_dice_6: 0.21041/0.29399, loss_spatial_ce_6: 0.06927/0.26190, loss_grounding_bce_6: 0.01711/0.08864, loss_grounding_dice_6: 0.26157/0.18407, loss_grounding_ce_6: 0.66214/0.32672, loss_mask_ce_7: 1.18796/1.00673, loss_mask_bce_7: 0.21349/0.34500, loss_mask_dice_7: 2.21154/1.25038, loss_spatial_bce_7: 0.04221/0.13343, loss_spatial_dice_7: 0.25244/0.31773, loss_spatial_ce_7: 0.20565/0.30427, loss_grounding_bce_7: 0.01686/0.08973, loss_grounding_dice_7: 0.27287/0.19060, loss_grounding_ce_7: 0.63016/0.37038, loss_mask_ce_8: 1.17746/1.11522, loss_mask_bce_8: 0.23506/0.35652, loss_mask_dice_8: 2.32005/1.32262, loss_spatial_bce_8: 0.04850/0.15388, loss_spatial_dice_8: 0.27621/0.36497, loss_spatial_ce_8: 0.46387/0.34599, loss_grounding_bce_8: 0.01510/0.09377, loss_grounding_dice_8: 0.31343/0.20120, loss_grounding_ce_8: 0.64852/0.45047, loss_mask_ce_9: 5.65801/3.80501, loss_mask_bce_9: 0.30357/0.38511, loss_mask_dice_9: 3.19357/1.88533, loss_spatial_bce_9: 0.15960/0.36005, loss_spatial_dice_9: 0.87217/0.83443, loss_spatial_ce_9: 1.32870/1.66535, loss_grounding_bce_9: 0.02017/0.10528, loss_grounding_dice_9: 0.40506/0.28210, loss_grounding_ce_9: 0.55216/0.84129] items per batch[64] items per second[0.21] total items[115200] mini batches[  1800] memory[7266] epoch remaining[0:01:27]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00001827.
INFO:trainer.default_trainer:Evaluation start ...
INFO:detectron2.data.dataset_mapper:[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
INFO:detectron2.data.common:Serializing 5000 elements to byte tensors and concatenating them all ...
INFO:detectron2.data.common:Serialized dataset takes 3.69 MiB
INFO:detectron2.data.common:Serializing 1581 elements to byte tensors and concatenating them all ...
INFO:detectron2.data.common:Serialized dataset takes 0.53 MiB
INFO:detectron2.data.common:Serializing 1300 elements to byte tensors and concatenating them all ...
INFO:detectron2.data.common:Serialized dataset takes 3.11 MiB
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0021 s/iter. Inference: 0.2270 s/iter. Eval: 0.0825 s/iter. Total: 0.3116 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0028 s/iter. Inference: 0.2342 s/iter. Eval: 0.0691 s/iter. Total: 0.3062 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2366 s/iter. Eval: 0.0661 s/iter. Total: 0.3059 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 63/157. Dataloading: 0.0030 s/iter. Inference: 0.2342 s/iter. Eval: 0.0646 s/iter. Total: 0.3020 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 81/157. Dataloading: 0.0031 s/iter. Inference: 0.2300 s/iter. Eval: 0.0652 s/iter. Total: 0.2983 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 97/157. Dataloading: 0.0031 s/iter. Inference: 0.2318 s/iter. Eval: 0.0662 s/iter. Total: 0.3012 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 114/157. Dataloading: 0.0031 s/iter. Inference: 0.2319 s/iter. Eval: 0.0664 s/iter. Total: 0.3015 s/iter. ETA=0:00:12
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 131/157. Dataloading: 0.0031 s/iter. Inference: 0.2317 s/iter. Eval: 0.0660 s/iter. Total: 0.3009 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 148/157. Dataloading: 0.0031 s/iter. Inference: 0.2329 s/iter. Eval: 0.0659 s/iter. Total: 0.3021 s/iter. ETA=0:00:02
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalw24u9dvc ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.613 | 81.968 | 59.596 |      133      |
| Things | 54.950 | 82.850 | 65.668 |      80       |
| Stuff  | 41.556 | 80.637 | 50.430 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.33s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.31 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.98 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.06s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.81 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.412
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.607
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.502
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.283
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.712
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.987 | 61.142 | 41.217 | 19.061 | 41.868 | 60.727 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.775 | bicycle      | 18.644 | car            | 37.056 |
| motorcycle    | 34.035 | airplane     | 56.468 | bus            | 64.199 |
| train         | 67.744 | truck        | 34.840 | boat           | 24.233 |
| traffic light | 23.759 | fire hydrant | 64.816 | stop sign      | 64.858 |
| parking meter | 43.314 | bench        | 19.803 | bird           | 29.991 |
| cat           | 74.083 | dog          | 65.183 | horse          | 46.126 |
| sheep         | 47.166 | cow          | 51.173 | elephant       | 60.876 |
| bear          | 77.271 | zebra        | 60.757 | giraffe        | 57.091 |
| backpack      | 14.783 | umbrella     | 47.728 | handbag        | 15.026 |
| tie           | 32.036 | suitcase     | 40.097 | frisbee        | 67.501 |
| skis          | 5.585  | snowboard    | 23.846 | sports ball    | 44.859 |
| kite          | 33.752 | baseball bat | 29.779 | baseball glove | 42.671 |
| skateboard    | 36.006 | surfboard    | 35.278 | tennis racket  | 55.466 |
| bottle        | 33.194 | wine glass   | 26.916 | cup            | 39.462 |
| fork          | 16.323 | knife        | 12.500 | spoon          | 14.328 |
| bowl          | 33.132 | banana       | 20.776 | apple          | 19.814 |
| sandwich      | 42.730 | orange       | 27.665 | broccoli       | 21.829 |
| carrot        | 19.587 | hot dog      | 28.187 | pizza          | 51.362 |
| donut         | 45.226 | cake         | 42.624 | chair          | 19.921 |
| couch         | 43.596 | potted plant | 17.416 | bed            | 40.371 |
| dining table  | 13.852 | toilet       | 66.446 | tv             | 62.669 |
| laptop        | 63.035 | mouse        | 60.193 | remote         | 31.571 |
| keyboard      | 49.417 | cell phone   | 35.875 | microwave      | 54.653 |
| oven          | 34.715 | toaster      | 41.755 | sink           | 36.933 |
| refrigerator  | 57.978 | book         | 8.741  | clock          | 51.492 |
| vase          | 34.027 | scissors     | 24.285 | teddy bear     | 51.358 |
| hair drier    | 10.308 | toothbrush   | 20.004 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.85737225192888, 'fwIoU': 69.20804187226483, 'IoU-person': 87.65461422296335, 'IoU-bicycle': 72.94922237039108, 'IoU-car': 70.28613904910844, 'IoU-motorcycle': 85.2899249144394, 'IoU-airplane': 82.11197047534989, 'IoU-bus': 82.94966384277917, 'IoU-train': 84.98188530182507, 'IoU-truck': 64.99939297083792, 'IoU-boat': 66.4976161411003, 'IoU-traffic light': 75.53037752529984, 'IoU-fire hydrant': 90.27338559525685, 'IoU-stop sign': 91.76014093615504, 'IoU-parking meter': 87.93132184582107, 'IoU-bench': 55.87171668413059, 'IoU-bird': 66.87171215351596, 'IoU-cat': 88.96529671556871, 'IoU-dog': 79.25524208933601, 'IoU-horse': 86.03964212524097, 'IoU-sheep': 90.90436941198628, 'IoU-cow': 86.32758698957359, 'IoU-elephant': 88.63271581006087, 'IoU-bear': 83.43589442437705, 'IoU-zebra': 92.43850680145947, 'IoU-giraffe': 88.125213195182, 'IoU-backpack': 38.48165539491949, 'IoU-umbrella': 73.44256685206557, 'IoU-handbag': 38.491599487176046, 'IoU-tie': 70.06793635423735, 'IoU-suitcase': 80.98463660433029, 'IoU-frisbee': 83.46767485328994, 'IoU-skis': 51.5297340967368, 'IoU-snowboard': 70.30063692041553, 'IoU-sports ball': 70.54028167320239, 'IoU-kite': 72.47383925430883, 'IoU-baseball bat': 58.61049102645725, 'IoU-baseball glove': 78.33210411529538, 'IoU-skateboard': 61.02358878849853, 'IoU-surfboard': 81.07033203509782, 'IoU-tennis racket': 83.05781180710312, 'IoU-bottle': 69.57840606750284, 'IoU-wine glass': 70.04744936553018, 'IoU-cup': 65.36031972016858, 'IoU-fork': 54.273612602009806, 'IoU-knife': 52.13477795667001, 'IoU-spoon': 52.17395216045681, 'IoU-bowl': 55.654949311057344, 'IoU-banana': 83.6119624145325, 'IoU-apple': 59.85942052788778, 'IoU-sandwich': 64.08136235211839, 'IoU-orange': 78.598732866439, 'IoU-broccoli': 68.64722047331064, 'IoU-carrot': 64.13144411618157, 'IoU-hot dog': 57.321373811714196, 'IoU-pizza': 82.50163607338736, 'IoU-donut': 63.53185950443188, 'IoU-cake': 67.54010598915843, 'IoU-chair': 52.55823339603831, 'IoU-couch': 66.5453868880677, 'IoU-potted plant': 33.41652567924678, 'IoU-bed': 67.23032692930083, 'IoU-dining table': 51.10063142366368, 'IoU-toilet': 83.21130640842658, 'IoU-tv': 76.49365276119644, 'IoU-laptop': 74.83501386186698, 'IoU-mouse': 72.34425486939453, 'IoU-remote': 49.12326790517491, 'IoU-keyboard': 59.515622212234334, 'IoU-cell phone': 69.9164303302115, 'IoU-microwave': 59.3862321937541, 'IoU-oven': 71.03173389760023, 'IoU-toaster': 46.76391301743246, 'IoU-sink': 72.05095098574763, 'IoU-refrigerator': 84.52401679248453, 'IoU-book': 51.40958433530647, 'IoU-clock': 73.47198061290358, 'IoU-vase': 66.64178926973142, 'IoU-scissors': 62.74498705043655, 'IoU-teddy bear': 81.08775967028453, 'IoU-hair drier': 36.17576823155851, 'IoU-toothbrush': 49.985936823885766, 'IoU-banner': 33.47231619490399, 'IoU-blanket': 9.771212668647642, 'IoU-bridge': 40.12993600926543, 'IoU-cardboard': 41.94754198310756, 'IoU-counter': 31.04870001693768, 'IoU-curtain': 63.80975550554783, 'IoU-door-stuff': 43.47044613327743, 'IoU-floor-wood': 62.05515677242863, 'IoU-flower': 48.88201290936377, 'IoU-fruit': 40.870526731481654, 'IoU-gravel': 22.77948344599445, 'IoU-house': 21.046902503185162, 'IoU-light': 40.732238089132686, 'IoU-mirror-stuff': 56.22930243146177, 'IoU-net': 47.55630856124894, 'IoU-pillow': 14.13600969996005, 'IoU-platform': 29.26857999515989, 'IoU-playingfield': 69.01529489209602, 'IoU-railroad': 60.15414118926278, 'IoU-river': 32.53795408438413, 'IoU-road': 66.46868217030726, 'IoU-roof': 13.314256571574818, 'IoU-sand': 57.73856045208737, 'IoU-sea': 85.18388222947631, 'IoU-shelf': 35.5662755405504, 'IoU-snow': 88.96977813127187, 'IoU-stairs': 27.623068722971432, 'IoU-tent': 9.167490506396248, 'IoU-towel': 34.51983821957459, 'IoU-wall-brick': 43.85426588936919, 'IoU-wall-stone': 27.58003007246132, 'IoU-wall-tile': 67.4748109203142, 'IoU-wall-wood': 37.70468203608542, 'IoU-water-other': 26.334058979109297, 'IoU-window-blind': 45.31057737316094, 'IoU-window-other': 47.72590609764228, 'IoU-tree-merged': 80.60554487240984, 'IoU-fence-merged': 51.48689943871149, 'IoU-ceiling-merged': 66.89041779350148, 'IoU-sky-other-merged': 93.72042783928563, 'IoU-cabinet-merged': 60.93887336274467, 'IoU-table-merged': 39.088384673009564, 'IoU-floor-other-merged': 51.524497561213636, 'IoU-pavement-merged': 53.25484073125423, 'IoU-mountain-merged': 56.363131259094224, 'IoU-grass-merged': 73.20637584671029, 'IoU-dirt-merged': 42.110717741393636, 'IoU-paper-merged': 30.866275576525076, 'IoU-food-other-merged': 41.469024910567036, 'IoU-building-other-merged': 58.049112430473826, 'IoU-rock-merged': 59.234940893518896, 'IoU-wall-other-merged': 66.03413524047393, 'IoU-rug-merged': 63.16459389305553, 'mACC': 72.68325199365377, 'pACC': 80.47730505467321, 'ACC-person': 92.7610646115872, 'ACC-bicycle': 85.98203701620649, 'ACC-car': 86.10086595588012, 'ACC-motorcycle': 90.34365541194718, 'ACC-airplane': 90.50842417211408, 'ACC-bus': 90.10678336505967, 'ACC-train': 95.85207863992208, 'ACC-truck': 76.05213225375232, 'ACC-boat': 79.38309998510348, 'ACC-traffic light': 89.07889817967703, 'ACC-fire hydrant': 95.44338498721108, 'ACC-stop sign': 94.28451904211045, 'ACC-parking meter': 92.30317631674781, 'ACC-bench': 73.21771601207465, 'ACC-bird': 70.44129731395408, 'ACC-cat': 94.64255973314388, 'ACC-dog': 81.86496773462689, 'ACC-horse': 92.26734060863497, 'ACC-sheep': 94.91175681026736, 'ACC-cow': 91.21068506036688, 'ACC-elephant': 91.48978082786014, 'ACC-bear': 85.15875879175725, 'ACC-zebra': 95.13734008985504, 'ACC-giraffe': 92.69835259387823, 'ACC-backpack': 61.25658407901772, 'ACC-umbrella': 80.30462786242785, 'ACC-handbag': 51.437428577066804, 'ACC-tie': 80.35400481105202, 'ACC-suitcase': 90.96279971610181, 'ACC-frisbee': 93.04581818181819, 'ACC-skis': 69.97686518090475, 'ACC-snowboard': 78.8263819157013, 'ACC-sports ball': 84.88334832180772, 'ACC-kite': 82.97755594652894, 'ACC-baseball bat': 81.73583378911957, 'ACC-baseball glove': 89.15573233032865, 'ACC-skateboard': 69.71062565363951, 'ACC-surfboard': 90.3557486896563, 'ACC-tennis racket': 89.12256749770366, 'ACC-bottle': 82.38176454497219, 'ACC-wine glass': 85.8836648474189, 'ACC-cup': 79.64107410187546, 'ACC-fork': 64.87467941335156, 'ACC-knife': 65.78515424635451, 'ACC-spoon': 71.50643041221223, 'ACC-bowl': 66.81871074035347, 'ACC-banana': 91.09396707732088, 'ACC-apple': 71.26440943712896, 'ACC-sandwich': 74.16495171758746, 'ACC-orange': 87.95272207170768, 'ACC-broccoli': 77.74781746368424, 'ACC-carrot': 74.18258321895927, 'ACC-hot dog': 68.83053569171906, 'ACC-pizza': 88.96296533966172, 'ACC-donut': 79.55519620681085, 'ACC-cake': 73.96292473193857, 'ACC-chair': 65.10043045238278, 'ACC-couch': 81.82709753857262, 'ACC-potted plant': 42.92365531989466, 'ACC-bed': 84.0729091228082, 'ACC-dining table': 76.9912945426199, 'ACC-toilet': 92.93656033434755, 'ACC-tv': 87.49934432233967, 'ACC-laptop': 88.72476020201708, 'ACC-mouse': 86.42143653344588, 'ACC-remote': 72.61425037927691, 'ACC-keyboard': 66.60131392826146, 'ACC-cell phone': 77.97217835113263, 'ACC-microwave': 66.00495175541268, 'ACC-oven': 84.90489402947502, 'ACC-toaster': 53.417317877622516, 'ACC-sink': 82.89605620088207, 'ACC-refrigerator': 90.08260569672538, 'ACC-book': 70.029174021462, 'ACC-clock': 78.92782093554771, 'ACC-vase': 77.36905029748795, 'ACC-scissors': 67.42980378113839, 'ACC-teddy bear': 85.82501318535608, 'ACC-hair drier': 39.23803831155893, 'ACC-toothbrush': 80.27623349548297, 'ACC-banner': 52.837416473042985, 'ACC-blanket': 15.447516810719833, 'ACC-bridge': 56.051534898367585, 'ACC-cardboard': 50.42256798022393, 'ACC-counter': 45.227484087965394, 'ACC-curtain': 73.55675120536516, 'ACC-door-stuff': 63.580388322088446, 'ACC-floor-wood': 77.80326396851797, 'ACC-flower': 67.28070153416665, 'ACC-fruit': 62.82645810195396, 'ACC-gravel': 25.258955122593214, 'ACC-house': 22.997852429382505, 'ACC-light': 56.246171955747805, 'ACC-mirror-stuff': 71.63482303525993, 'ACC-net': 61.0787424094286, 'ACC-pillow': 26.623248053686314, 'ACC-platform': 61.105121557005994, 'ACC-playingfield': 85.7473729510722, 'ACC-railroad': 79.19218978771926, 'ACC-river': 38.95740986626537, 'ACC-road': 85.99531807509439, 'ACC-roof': 17.466252694283842, 'ACC-sand': 76.58446950539106, 'ACC-sea': 89.90803017940692, 'ACC-shelf': 60.30216381121072, 'ACC-snow': 93.73585916393819, 'ACC-stairs': 50.25785485426903, 'ACC-tent': 11.295172534931474, 'ACC-towel': 41.509381166178336, 'ACC-wall-brick': 53.982928108760106, 'ACC-wall-stone': 34.20727287082095, 'ACC-wall-tile': 78.34887404088083, 'ACC-wall-wood': 52.29276082205314, 'ACC-water-other': 60.86811432355991, 'ACC-window-blind': 51.919143442070116, 'ACC-window-other': 73.03567318641241, 'ACC-tree-merged': 90.12105609557887, 'ACC-fence-merged': 68.9833536497175, 'ACC-ceiling-merged': 77.0846067174572, 'ACC-sky-other-merged': 96.73931274619714, 'ACC-cabinet-merged': 74.89769211273902, 'ACC-table-merged': 55.391548350250666, 'ACC-floor-other-merged': 59.79103036306248, 'ACC-pavement-merged': 64.42247412536463, 'ACC-mountain-merged': 67.98967159092616, 'ACC-grass-merged': 84.61392009767185, 'ACC-dirt-merged': 58.53123434471902, 'ACC-paper-merged': 39.86044146947228, 'ACC-food-other-merged': 58.36766712610976, 'ACC-building-other-merged': 78.22853542477093, 'ACC-rock-merged': 84.34976159548614, 'ACC-wall-other-merged': 82.11202157270885, 'ACC-rug-merged': 79.7606084989626})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1666 s/iter. Inference: 0.5935 s/iter. Eval: 0.0000 s/iter. Total: 0.7602 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1691 s/iter. Inference: 0.6874 s/iter. Eval: 0.0000 s/iter. Total: 0.8566 s/iter. ETA=0:00:27
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/50. Dataloading: 0.1821 s/iter. Inference: 0.8042 s/iter. Eval: 0.0000 s/iter. Total: 0.9865 s/iter. ETA=0:00:27
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 27/50. Dataloading: 0.1846 s/iter. Inference: 0.8345 s/iter. Eval: 0.0000 s/iter. Total: 1.0192 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 32/50. Dataloading: 0.1831 s/iter. Inference: 0.8359 s/iter. Eval: 0.0000 s/iter. Total: 1.0191 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1855 s/iter. Inference: 0.7682 s/iter. Eval: 0.0000 s/iter. Total: 0.9539 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1834 s/iter. Inference: 0.7952 s/iter. Eval: 0.0000 s/iter. Total: 0.9788 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.7079309335674568, 'noc@0.8': 3.4296166227685103, 'noc@0.85': 4.175592625109745, 'noc@0.9': 5.329236172080773, 'miou@iter1': 0.8404605743792241}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1149 s/iter. Eval: 0.0008 s/iter. Total: 0.1174 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.22930145263672, 'precision@0.6': 67.00349426269531, 'precision@0.7': 61.52351379394531, 'precision@0.8': 51.34084701538086, 'precision@0.9': 25.10687828063965, 'cIoU': 55.67923355102539, 'mIoU': 61.61784744262695}
INFO:trainer.default_trainer:This epoch takes 1:42:19.677688
INFO:trainer.default_trainer:PROGRESS: 2.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 1 training.
INFO:trainer.default_trainer:epochs[     1] optim steps[1900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86458/0.91688, loss_mask_bce_0: 0.36291/0.33224, loss_mask_dice_0: 2.33155/1.16960, loss_spatial_bce_0: 0.03718/0.10850, loss_spatial_dice_0: 0.24648/0.26350, loss_spatial_ce_0: 0.11477/0.17659, loss_grounding_bce_0: 0.10317/0.08664, loss_grounding_dice_0: 0.32325/0.18009, loss_grounding_ce_0: 0.06150/0.27701, loss_mask_ce_1: 0.97209/0.91964, loss_mask_bce_1: 0.37285/0.33297, loss_mask_dice_1: 2.53402/1.17937, loss_spatial_bce_1: 0.03307/0.10905, loss_spatial_dice_1: 0.27406/0.26871, loss_spatial_ce_1: 0.15855/0.18266, loss_grounding_bce_1: 0.09949/0.08677, loss_grounding_dice_1: 0.32014/0.18021, loss_grounding_ce_1: 0.07039/0.28267, loss_mask_ce_2: 0.93975/0.92411, loss_mask_bce_2: 0.35266/0.33247, loss_mask_dice_2: 2.61665/1.17515, loss_spatial_bce_2: 0.03420/0.10857, loss_spatial_dice_2: 0.25660/0.27162, loss_spatial_ce_2: 0.18637/0.18715, loss_grounding_bce_2: 0.09385/0.08622, loss_grounding_dice_2: 0.30416/0.17955, loss_grounding_ce_2: 0.06174/0.28302, loss_mask_ce_3: 1.01483/0.92700, loss_mask_bce_3: 0.35207/0.33357, loss_mask_dice_3: 2.54096/1.17369, loss_spatial_bce_3: 0.03586/0.11036, loss_spatial_dice_3: 0.30463/0.27461, loss_spatial_ce_3: 0.13364/0.20535, loss_grounding_bce_3: 0.08696/0.08650, loss_grounding_dice_3: 0.29021/0.18051, loss_grounding_ce_3: 0.05003/0.28464, loss_mask_ce_4: 1.05022/0.93004, loss_mask_bce_4: 0.34781/0.33367, loss_mask_dice_4: 2.67732/1.18973, loss_spatial_bce_4: 0.03789/0.11384, loss_spatial_dice_4: 0.30549/0.28295, loss_spatial_ce_4: 0.12187/0.21302, loss_grounding_bce_4: 0.08532/0.08671, loss_grounding_dice_4: 0.30407/0.18225, loss_grounding_ce_4: 0.08722/0.28994, loss_mask_ce_5: 1.14990/0.93442, loss_mask_bce_5: 0.35018/0.33704, loss_mask_dice_5: 2.45942/1.19524, loss_spatial_bce_5: 0.03773/0.11526, loss_spatial_dice_5: 0.26873/0.28811, loss_spatial_ce_5: 0.19100/0.22510, loss_grounding_bce_5: 0.08147/0.08729, loss_grounding_dice_5: 0.28949/0.18277, loss_grounding_ce_5: 0.07680/0.30079, loss_mask_ce_6: 1.34765/0.97096, loss_mask_bce_6: 0.35066/0.33903, loss_mask_dice_6: 2.54765/1.19754, loss_spatial_bce_6: 0.05552/0.11884, loss_spatial_dice_6: 0.33093/0.29197, loss_spatial_ce_6: 0.16240/0.25641, loss_grounding_bce_6: 0.07610/0.08893, loss_grounding_dice_6: 0.26158/0.18435, loss_grounding_ce_6: 0.05411/0.32770, loss_mask_ce_7: 1.38625/1.00914, loss_mask_bce_7: 0.33020/0.34590, loss_mask_dice_7: 2.51955/1.25511, loss_spatial_bce_7: 0.06274/0.13247, loss_spatial_dice_7: 0.35815/0.31570, loss_spatial_ce_7: 0.21803/0.29957, loss_grounding_bce_7: 0.07098/0.08992, loss_grounding_dice_7: 0.22763/0.19091, loss_grounding_ce_7: 0.07760/0.36909, loss_mask_ce_8: 1.25156/1.11821, loss_mask_bce_8: 0.39054/0.35748, loss_mask_dice_8: 2.95781/1.32854, loss_spatial_bce_8: 0.09410/0.15303, loss_spatial_dice_8: 0.40595/0.36289, loss_spatial_ce_8: 0.20910/0.34169, loss_grounding_bce_8: 0.07431/0.09382, loss_grounding_dice_8: 0.23088/0.20149, loss_grounding_ce_8: 0.77290/0.45020, loss_mask_ce_9: 4.18171/3.79675, loss_mask_bce_9: 0.37806/0.38559, loss_mask_dice_9: 3.78469/1.89510, loss_spatial_bce_9: 0.14977/0.35736, loss_spatial_dice_9: 0.90392/0.83403, loss_spatial_ce_9: 1.22582/1.65801, loss_grounding_bce_9: 0.08550/0.10505, loss_grounding_dice_9: 0.37332/0.28244, loss_grounding_ce_9: 0.77900/0.83267] items per batch[64] items per second[0.12] total items[121600] mini batches[  1900] memory[7266] epoch remaining[1:32:53]
INFO:trainer.default_trainer:epochs[     1] optim steps[2000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.33737/0.91549, loss_mask_bce_0: 0.32591/0.33185, loss_mask_dice_0: 0.93422/1.16229, loss_spatial_bce_0: 0.03924/0.10771, loss_spatial_dice_0: 0.17176/0.26149, loss_spatial_ce_0: 0.00351/0.17263, loss_grounding_bce_0: 0.04823/0.08653, loss_grounding_dice_0: 0.07686/0.17979, loss_grounding_ce_0: 0.51824/0.27531, loss_mask_ce_1: 1.26434/0.91749, loss_mask_bce_1: 0.33472/0.33255, loss_mask_dice_1: 0.75883/1.17281, loss_spatial_bce_1: 0.03825/0.10822, loss_spatial_dice_1: 0.18309/0.26679, loss_spatial_ce_1: 0.00323/0.17844, loss_grounding_bce_1: 0.05577/0.08670, loss_grounding_dice_1: 0.08657/0.17983, loss_grounding_ce_1: 0.57487/0.28108, loss_mask_ce_2: 1.28947/0.92220, loss_mask_bce_2: 0.33303/0.33225, loss_mask_dice_2: 0.96111/1.16844, loss_spatial_bce_2: 0.03815/0.10774, loss_spatial_dice_2: 0.19981/0.26951, loss_spatial_ce_2: 0.00569/0.18356, loss_grounding_bce_2: 0.05749/0.08612, loss_grounding_dice_2: 0.08801/0.17906, loss_grounding_ce_2: 0.50654/0.28102, loss_mask_ce_3: 1.40125/0.92586, loss_mask_bce_3: 0.32952/0.33328, loss_mask_dice_3: 0.80893/1.16722, loss_spatial_bce_3: 0.03729/0.10951, loss_spatial_dice_3: 0.19571/0.27252, loss_spatial_ce_3: 0.03621/0.20022, loss_grounding_bce_3: 0.05821/0.08643, loss_grounding_dice_3: 0.08543/0.18017, loss_grounding_ce_3: 0.50760/0.28225, loss_mask_ce_4: 1.47322/0.92905, loss_mask_bce_4: 0.31216/0.33326, loss_mask_dice_4: 0.93869/1.18353, loss_spatial_bce_4: 0.03619/0.11296, loss_spatial_dice_4: 0.17324/0.28077, loss_spatial_ce_4: 0.08770/0.20836, loss_grounding_bce_4: 0.05720/0.08655, loss_grounding_dice_4: 0.09812/0.18209, loss_grounding_ce_4: 0.48509/0.28764, loss_mask_ce_5: 1.39242/0.93319, loss_mask_bce_5: 0.31605/0.33675, loss_mask_dice_5: 0.92073/1.18919, loss_spatial_bce_5: 0.03831/0.11440, loss_spatial_dice_5: 0.19764/0.28581, loss_spatial_ce_5: 0.01769/0.22027, loss_grounding_bce_5: 0.05571/0.08717, loss_grounding_dice_5: 0.09067/0.18262, loss_grounding_ce_5: 0.58686/0.29768, loss_mask_ce_6: 1.26656/0.97103, loss_mask_bce_6: 0.32655/0.33874, loss_mask_dice_6: 1.10175/1.19137, loss_spatial_bce_6: 0.03877/0.11791, loss_spatial_dice_6: 0.18911/0.28963, loss_spatial_ce_6: 0.06543/0.25120, loss_grounding_bce_6: 0.05394/0.08875, loss_grounding_dice_6: 0.08490/0.18413, loss_grounding_ce_6: 0.78651/0.32566, loss_mask_ce_7: 1.40313/1.00869, loss_mask_bce_7: 0.32462/0.34567, loss_mask_dice_7: 0.94293/1.24835, loss_spatial_bce_7: 0.04325/0.13150, loss_spatial_dice_7: 0.21509/0.31340, loss_spatial_ce_7: 0.14308/0.29441, loss_grounding_bce_7: 0.06209/0.08976, loss_grounding_dice_7: 0.11704/0.19059, loss_grounding_ce_7: 0.98390/0.36779, loss_mask_ce_8: 1.43453/1.11748, loss_mask_bce_8: 0.33479/0.35729, loss_mask_dice_8: 1.09028/1.32073, loss_spatial_bce_8: 0.05091/0.15175, loss_spatial_dice_8: 0.27470/0.35981, loss_spatial_ce_8: 0.21176/0.33642, loss_grounding_bce_8: 0.05426/0.09365, loss_grounding_dice_8: 0.07429/0.20096, loss_grounding_ce_8: 0.99324/0.44817, loss_mask_ce_9: 4.60357/3.79082, loss_mask_bce_9: 0.30228/0.38524, loss_mask_dice_9: 2.49036/1.88651, loss_spatial_bce_9: 0.20515/0.35641, loss_spatial_dice_9: 0.89516/0.83313, loss_spatial_ce_9: 1.58361/1.65395, loss_grounding_bce_9: 0.06009/0.10510, loss_grounding_dice_9: 0.13643/0.28234, loss_grounding_ce_9: 1.75458/0.82509] items per batch[64] items per second[0.21] total items[128000] mini batches[  2000] memory[7266] epoch remaining[1:24:59]
INFO:trainer.default_trainer:epochs[     1] optim steps[2100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.35293/0.91785, loss_mask_bce_0: 0.27112/0.33158, loss_mask_dice_0: 0.60129/1.16687, loss_spatial_bce_0: 0.06976/0.10669, loss_spatial_dice_0: 0.19252/0.26019, loss_spatial_ce_0: 0.09776/0.16951, loss_grounding_bce_0: 0.04976/0.08625, loss_grounding_dice_0: 0.13015/0.18021, loss_grounding_ce_0: 0.05450/0.27676, loss_mask_ce_1: 1.32701/0.92035, loss_mask_bce_1: 0.27773/0.33236, loss_mask_dice_1: 0.57759/1.17736, loss_spatial_bce_1: 0.07535/0.10727, loss_spatial_dice_1: 0.17164/0.26563, loss_spatial_ce_1: 0.09881/0.17535, loss_grounding_bce_1: 0.04717/0.08639, loss_grounding_dice_1: 0.13125/0.18074, loss_grounding_ce_1: 0.12167/0.28230, loss_mask_ce_2: 1.30684/0.92522, loss_mask_bce_2: 0.28338/0.33200, loss_mask_dice_2: 0.70637/1.17200, loss_spatial_bce_2: 0.07386/0.10682, loss_spatial_dice_2: 0.19452/0.26834, loss_spatial_ce_2: 0.10979/0.18008, loss_grounding_bce_2: 0.04976/0.08580, loss_grounding_dice_2: 0.12545/0.17964, loss_grounding_ce_2: 0.11914/0.28308, loss_mask_ce_3: 1.21318/0.92859, loss_mask_bce_3: 0.29503/0.33305, loss_mask_dice_3: 0.75201/1.17077, loss_spatial_bce_3: 0.06933/0.10860, loss_spatial_dice_3: 0.16735/0.27134, loss_spatial_ce_3: 0.08993/0.19635, loss_grounding_bce_3: 0.04850/0.08611, loss_grounding_dice_3: 0.12942/0.18062, loss_grounding_ce_3: 0.22434/0.28319, loss_mask_ce_4: 1.22620/0.93200, loss_mask_bce_4: 0.27647/0.33284, loss_mask_dice_4: 0.80897/1.18666, loss_spatial_bce_4: 0.07090/0.11200, loss_spatial_dice_4: 0.20013/0.27942, loss_spatial_ce_4: 0.06626/0.20463, loss_grounding_bce_4: 0.04547/0.08632, loss_grounding_dice_4: 0.13239/0.18269, loss_grounding_ce_4: 0.11783/0.28953, loss_mask_ce_5: 1.18925/0.93648, loss_mask_bce_5: 0.26643/0.33627, loss_mask_dice_5: 0.79463/1.19302, loss_spatial_bce_5: 0.07179/0.11345, loss_spatial_dice_5: 0.16832/0.28430, loss_spatial_ce_5: 0.06489/0.21598, loss_grounding_bce_5: 0.04365/0.08679, loss_grounding_dice_5: 0.12432/0.18339, loss_grounding_ce_5: 0.12675/0.29965, loss_mask_ce_6: 1.12948/0.97387, loss_mask_bce_6: 0.26444/0.33808, loss_mask_dice_6: 0.76025/1.19525, loss_spatial_bce_6: 0.07188/0.11691, loss_spatial_dice_6: 0.19893/0.28804, loss_spatial_ce_6: 0.09957/0.24607, loss_grounding_bce_6: 0.05050/0.08840, loss_grounding_dice_6: 0.13752/0.18486, loss_grounding_ce_6: 0.09351/0.32684, loss_mask_ce_7: 1.39010/1.01195, loss_mask_bce_7: 0.28495/0.34490, loss_mask_dice_7: 0.67331/1.25216, loss_spatial_bce_7: 0.08335/0.13029, loss_spatial_dice_7: 0.24816/0.31198, loss_spatial_ce_7: 0.13160/0.29046, loss_grounding_bce_7: 0.04780/0.08943, loss_grounding_dice_7: 0.12843/0.19128, loss_grounding_ce_7: 0.20983/0.36932, loss_mask_ce_8: 1.27127/1.12224, loss_mask_bce_8: 0.27318/0.35673, loss_mask_dice_8: 0.85404/1.32549, loss_spatial_bce_8: 0.10741/0.15054, loss_spatial_dice_8: 0.33790/0.35817, loss_spatial_ce_8: 0.23307/0.33307, loss_grounding_bce_8: 0.05556/0.09317, loss_grounding_dice_8: 0.12910/0.20169, loss_grounding_ce_8: 0.04623/0.44915, loss_mask_ce_9: 2.81160/3.79460, loss_mask_bce_9: 0.28496/0.38483, loss_mask_dice_9: 1.01966/1.89426, loss_spatial_bce_9: 0.31588/0.35476, loss_spatial_dice_9: 0.79397/0.83325, loss_spatial_ce_9: 1.90241/1.64952, loss_grounding_bce_9: 0.04321/0.10431, loss_grounding_dice_9: 0.16245/0.28267, loss_grounding_ce_9: 0.16726/0.82452] items per batch[64] items per second[0.21] total items[134400] mini batches[  2100] memory[7266] epoch remaining[1:20:08]
INFO:trainer.default_trainer:epochs[     1] optim steps[2200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37541/0.91608, loss_mask_bce_0: 0.26422/0.33306, loss_mask_dice_0: 0.70396/1.16280, loss_spatial_bce_0: 0.05466/0.10647, loss_spatial_dice_0: 0.12459/0.25858, loss_spatial_ce_0: 0.03559/0.16630, loss_grounding_bce_0: 0.07789/0.08638, loss_grounding_dice_0: 0.07945/0.18052, loss_grounding_ce_0: 0.22591/0.27631, loss_mask_ce_1: 0.35298/0.91855, loss_mask_bce_1: 0.27033/0.33371, loss_mask_dice_1: 0.70043/1.17260, loss_spatial_bce_1: 0.05187/0.10718, loss_spatial_dice_1: 0.12132/0.26390, loss_spatial_ce_1: 0.03547/0.17232, loss_grounding_bce_1: 0.07726/0.08650, loss_grounding_dice_1: 0.08451/0.18105, loss_grounding_ce_1: 0.17499/0.28112, loss_mask_ce_2: 0.35011/0.92363, loss_mask_bce_2: 0.28285/0.33350, loss_mask_dice_2: 0.71903/1.16815, loss_spatial_bce_2: 0.05179/0.10669, loss_spatial_dice_2: 0.12548/0.26645, loss_spatial_ce_2: 0.02801/0.17666, loss_grounding_bce_2: 0.07902/0.08591, loss_grounding_dice_2: 0.08348/0.17994, loss_grounding_ce_2: 0.23270/0.28244, loss_mask_ce_3: 0.36235/0.92670, loss_mask_bce_3: 0.26719/0.33454, loss_mask_dice_3: 0.72612/1.16606, loss_spatial_bce_3: 0.05306/0.10842, loss_spatial_dice_3: 0.13594/0.26946, loss_spatial_ce_3: 0.02849/0.19227, loss_grounding_bce_3: 0.07611/0.08625, loss_grounding_dice_3: 0.08213/0.18085, loss_grounding_ce_3: 0.27743/0.28196, loss_mask_ce_4: 0.36150/0.92983, loss_mask_bce_4: 0.27287/0.33438, loss_mask_dice_4: 0.71316/1.18244, loss_spatial_bce_4: 0.05818/0.11175, loss_spatial_dice_4: 0.13438/0.27732, loss_spatial_ce_4: 0.02304/0.20111, loss_grounding_bce_4: 0.07870/0.08653, loss_grounding_dice_4: 0.08470/0.18281, loss_grounding_ce_4: 0.33393/0.28768, loss_mask_ce_5: 0.37052/0.93488, loss_mask_bce_5: 0.26150/0.33769, loss_mask_dice_5: 0.70020/1.18830, loss_spatial_bce_5: 0.05758/0.11322, loss_spatial_dice_5: 0.13391/0.28208, loss_spatial_ce_5: 0.02622/0.21258, loss_grounding_bce_5: 0.07575/0.08691, loss_grounding_dice_5: 0.07629/0.18364, loss_grounding_ce_5: 0.51053/0.29774, loss_mask_ce_6: 0.31263/0.97258, loss_mask_bce_6: 0.27292/0.33973, loss_mask_dice_6: 0.70929/1.19114, loss_spatial_bce_6: 0.06885/0.11662, loss_spatial_dice_6: 0.15375/0.28574, loss_spatial_ce_6: 0.05845/0.24231, loss_grounding_bce_6: 0.08124/0.08856, loss_grounding_dice_6: 0.08884/0.18496, loss_grounding_ce_6: 1.00205/0.32434, loss_mask_ce_7: 0.39467/1.01081, loss_mask_bce_7: 0.25323/0.34661, loss_mask_dice_7: 0.67668/1.24785, loss_spatial_bce_7: 0.06667/0.12987, loss_spatial_dice_7: 0.15022/0.30963, loss_spatial_ce_7: 0.17583/0.28616, loss_grounding_bce_7: 0.07855/0.08951, loss_grounding_dice_7: 0.08795/0.19131, loss_grounding_ce_7: 0.39257/0.36592, loss_mask_ce_8: 0.46368/1.12114, loss_mask_bce_8: 0.24658/0.35825, loss_mask_dice_8: 0.62220/1.32071, loss_spatial_bce_8: 0.10269/0.15041, loss_spatial_dice_8: 0.21555/0.35545, loss_spatial_ce_8: 0.14918/0.32892, loss_grounding_bce_8: 0.08085/0.09322, loss_grounding_dice_8: 0.08462/0.20178, loss_grounding_ce_8: 0.83723/0.44574, loss_mask_ce_9: 2.51009/3.78404, loss_mask_bce_9: 0.32386/0.38613, loss_mask_dice_9: 1.17348/1.89102, loss_spatial_bce_9: 0.40823/0.35500, loss_spatial_dice_9: 0.84055/0.83294, loss_spatial_ce_9: 1.22495/1.64401, loss_grounding_bce_9: 0.13943/0.10446, loss_grounding_dice_9: 0.18920/0.28306, loss_grounding_ce_9: 3.30644/0.81682] items per batch[64] items per second[0.21] total items[140800] mini batches[  2200] memory[7266] epoch remaining[1:14:24]
INFO:trainer.default_trainer:epochs[     1] optim steps[2300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63979/0.91674, loss_mask_bce_0: 0.51327/0.33289, loss_mask_dice_0: 3.82147/1.15807, loss_spatial_bce_0: 0.06320/0.10639, loss_spatial_dice_0: 0.23075/0.25713, loss_spatial_ce_0: 0.07063/0.16349, loss_grounding_bce_0: 0.10403/0.08688, loss_grounding_dice_0: 0.31203/0.18084, loss_grounding_ce_0: 0.14614/0.27776, loss_mask_ce_1: 1.71854/0.91922, loss_mask_bce_1: 0.46651/0.33351, loss_mask_dice_1: 3.88554/1.16778, loss_spatial_bce_1: 0.06600/0.10721, loss_spatial_dice_1: 0.22393/0.26235, loss_spatial_ce_1: 0.08888/0.16952, loss_grounding_bce_1: 0.08222/0.08697, loss_grounding_dice_1: 0.32444/0.18125, loss_grounding_ce_1: 0.26071/0.28233, loss_mask_ce_2: 1.70948/0.92377, loss_mask_bce_2: 0.47796/0.33349, loss_mask_dice_2: 3.79306/1.16372, loss_spatial_bce_2: 0.06026/0.10669, loss_spatial_dice_2: 0.22911/0.26477, loss_spatial_ce_2: 0.09911/0.17424, loss_grounding_bce_2: 0.09108/0.08644, loss_grounding_dice_2: 0.31953/0.18003, loss_grounding_ce_2: 0.25665/0.28403, loss_mask_ce_3: 1.68965/0.92719, loss_mask_bce_3: 0.48117/0.33440, loss_mask_dice_3: 3.69612/1.16187, loss_spatial_bce_3: 0.06561/0.10830, loss_spatial_dice_3: 0.24742/0.26771, loss_spatial_ce_3: 0.10053/0.18918, loss_grounding_bce_3: 0.11376/0.08668, loss_grounding_dice_3: 0.32814/0.18089, loss_grounding_ce_3: 0.14138/0.28371, loss_mask_ce_4: 1.73303/0.92982, loss_mask_bce_4: 0.51339/0.33431, loss_mask_dice_4: 3.99105/1.17909, loss_spatial_bce_4: 0.06789/0.11147, loss_spatial_dice_4: 0.24871/0.27543, loss_spatial_ce_4: 0.10096/0.19855, loss_grounding_bce_4: 0.08870/0.08708, loss_grounding_dice_4: 0.33032/0.18295, loss_grounding_ce_4: 0.27336/0.28929, loss_mask_ce_5: 1.77497/0.93505, loss_mask_bce_5: 0.49686/0.33750, loss_mask_dice_5: 3.78457/1.18391, loss_spatial_bce_5: 0.06979/0.11310, loss_spatial_dice_5: 0.25805/0.28025, loss_spatial_ce_5: 0.11066/0.20970, loss_grounding_bce_5: 0.07990/0.08753, loss_grounding_dice_5: 0.31747/0.18386, loss_grounding_ce_5: 0.23407/0.29940, loss_mask_ce_6: 1.67850/0.97281, loss_mask_bce_6: 0.52150/0.33963, loss_mask_dice_6: 3.76197/1.18734, loss_spatial_bce_6: 0.03558/0.11662, loss_spatial_dice_6: 0.26061/0.28375, loss_spatial_ce_6: 0.22317/0.23849, loss_grounding_bce_6: 0.07225/0.08904, loss_grounding_dice_6: 0.30978/0.18512, loss_grounding_ce_6: 0.19421/0.32757, loss_mask_ce_7: 1.77095/1.01054, loss_mask_bce_7: 0.54777/0.34670, loss_mask_dice_7: 4.29636/1.24379, loss_spatial_bce_7: 0.07516/0.12985, loss_spatial_dice_7: 0.32676/0.30779, loss_spatial_ce_7: 0.26632/0.28166, loss_grounding_bce_7: 0.09206/0.09016, loss_grounding_dice_7: 0.31876/0.19165, loss_grounding_ce_7: 0.23543/0.36756, loss_mask_ce_8: 2.28620/1.12126, loss_mask_bce_8: 0.62257/0.35849, loss_mask_dice_8: 4.68579/1.31747, loss_spatial_bce_8: 0.06367/0.15053, loss_spatial_dice_8: 0.36204/0.35338, loss_spatial_ce_8: 0.27537/0.32527, loss_grounding_bce_8: 0.08625/0.09378, loss_grounding_dice_8: 0.30089/0.20201, loss_grounding_ce_8: 0.57763/0.44774, loss_mask_ce_9: 5.96648/3.77942, loss_mask_bce_9: 0.48396/0.38651, loss_mask_dice_9: 6.24368/1.88786, loss_spatial_bce_9: 0.19794/0.35515, loss_spatial_dice_9: 0.93365/0.83228, loss_spatial_ce_9: 1.27277/1.63359, loss_grounding_bce_9: 0.12763/0.10496, loss_grounding_dice_9: 0.43260/0.28334, loss_grounding_ce_9: 1.53743/0.81768] items per batch[64] items per second[0.21] total items[147200] mini batches[  2300] memory[7266] epoch remaining[1:09:17]
INFO:trainer.default_trainer:epochs[     1] optim steps[2400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52865/0.91781, loss_mask_bce_0: 0.41332/0.33316, loss_mask_dice_0: 0.53769/1.16811, loss_spatial_bce_0: 0.10911/0.10575, loss_spatial_dice_0: 0.15960/0.25644, loss_spatial_ce_0: 0.01243/0.16092, loss_grounding_bce_0: 0.11002/0.08631, loss_grounding_dice_0: 0.07526/0.18099, loss_grounding_ce_0: 0.00677/0.27848, loss_mask_ce_1: 0.51306/0.91955, loss_mask_bce_1: 0.41664/0.33395, loss_mask_dice_1: 0.37265/1.17770, loss_spatial_bce_1: 0.11136/0.10657, loss_spatial_dice_1: 0.16142/0.26167, loss_spatial_ce_1: 0.01605/0.16699, loss_grounding_bce_1: 0.11449/0.08640, loss_grounding_dice_1: 0.07899/0.18116, loss_grounding_ce_1: 0.00711/0.28277, loss_mask_ce_2: 0.51631/0.92482, loss_mask_bce_2: 0.41563/0.33395, loss_mask_dice_2: 0.39628/1.17323, loss_spatial_bce_2: 0.11053/0.10600, loss_spatial_dice_2: 0.16047/0.26404, loss_spatial_ce_2: 0.01812/0.17207, loss_grounding_bce_2: 0.10483/0.08591, loss_grounding_dice_2: 0.08148/0.18024, loss_grounding_ce_2: 0.00914/0.28394, loss_mask_ce_3: 0.52443/0.92800, loss_mask_bce_3: 0.42154/0.33483, loss_mask_dice_3: 0.38311/1.17121, loss_spatial_bce_3: 0.11125/0.10760, loss_spatial_dice_3: 0.16338/0.26695, loss_spatial_ce_3: 0.02209/0.18626, loss_grounding_bce_3: 0.10727/0.08614, loss_grounding_dice_3: 0.08329/0.18064, loss_grounding_ce_3: 0.00826/0.28386, loss_mask_ce_4: 0.56836/0.93017, loss_mask_bce_4: 0.42433/0.33478, loss_mask_dice_4: 0.38988/1.18954, loss_spatial_bce_4: 0.10886/0.11068, loss_spatial_dice_4: 0.15963/0.27458, loss_spatial_ce_4: 0.03299/0.19553, loss_grounding_bce_4: 0.10391/0.08645, loss_grounding_dice_4: 0.07820/0.18320, loss_grounding_ce_4: 0.01103/0.28944, loss_mask_ce_5: 0.57039/0.93612, loss_mask_bce_5: 0.42388/0.33797, loss_mask_dice_5: 0.40349/1.19473, loss_spatial_bce_5: 0.11091/0.11239, loss_spatial_dice_5: 0.16297/0.27917, loss_spatial_ce_5: 0.03776/0.20728, loss_grounding_bce_5: 0.10481/0.08685, loss_grounding_dice_5: 0.08197/0.18407, loss_grounding_ce_5: 0.01693/0.29921, loss_mask_ce_6: 0.71394/0.97433, loss_mask_bce_6: 0.44128/0.34029, loss_mask_dice_6: 0.40935/1.19888, loss_spatial_bce_6: 0.11139/0.11594, loss_spatial_dice_6: 0.16347/0.28281, loss_spatial_ce_6: 0.03947/0.23461, loss_grounding_bce_6: 0.11065/0.08856, loss_grounding_dice_6: 0.08527/0.18524, loss_grounding_ce_6: 0.03249/0.32716, loss_mask_ce_7: 0.74303/1.01223, loss_mask_bce_7: 0.42619/0.34707, loss_mask_dice_7: 0.54401/1.25405, loss_spatial_bce_7: 0.12505/0.12899, loss_spatial_dice_7: 0.17747/0.30688, loss_spatial_ce_7: 0.10649/0.27832, loss_grounding_bce_7: 0.10795/0.08960, loss_grounding_dice_7: 0.09203/0.19194, loss_grounding_ce_7: 0.06930/0.36726, loss_mask_ce_8: 0.94839/1.12294, loss_mask_bce_8: 0.49546/0.35916, loss_mask_dice_8: 0.52797/1.32891, loss_spatial_bce_8: 0.17744/0.14972, loss_spatial_dice_8: 0.25643/0.35256, loss_spatial_ce_8: 0.21604/0.32231, loss_grounding_bce_8: 0.12688/0.09314, loss_grounding_dice_8: 0.10833/0.20207, loss_grounding_ce_8: 0.02459/0.44620, loss_mask_ce_9: 4.16826/3.78002, loss_mask_bce_9: 0.80549/0.38726, loss_mask_dice_9: 1.12983/1.90394, loss_spatial_bce_9: 0.43128/0.35384, loss_spatial_dice_9: 0.80920/0.83252, loss_spatial_ce_9: 1.34195/1.62916, loss_grounding_bce_9: 0.17607/0.10428, loss_grounding_dice_9: 0.26526/0.28326, loss_grounding_ce_9: 0.27841/0.81180] items per batch[64] items per second[0.21] total items[153600] mini batches[  2400] memory[7266] epoch remaining[1:03:58]
INFO:trainer.default_trainer:epochs[     1] optim steps[2500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29423/0.91871, loss_mask_bce_0: 0.17797/0.33312, loss_mask_dice_0: 0.10862/1.16650, loss_spatial_bce_0: 0.13979/0.10555, loss_spatial_dice_0: 0.07710/0.25507, loss_spatial_ce_0: 0.00253/0.15813, loss_grounding_bce_0: 0.08449/0.08652, loss_grounding_dice_0: 0.03743/0.18025, loss_grounding_ce_0: 0.03986/0.27816, loss_mask_ce_1: 0.29594/0.92074, loss_mask_bce_1: 0.18133/0.33382, loss_mask_dice_1: 0.10809/1.17558, loss_spatial_bce_1: 0.14362/0.10635, loss_spatial_dice_1: 0.07743/0.26030, loss_spatial_ce_1: 0.01033/0.16404, loss_grounding_bce_1: 0.07926/0.08655, loss_grounding_dice_1: 0.03455/0.18069, loss_grounding_ce_1: 0.03537/0.28256, loss_mask_ce_2: 0.25259/0.92570, loss_mask_bce_2: 0.18226/0.33380, loss_mask_dice_2: 0.10779/1.17142, loss_spatial_bce_2: 0.13865/0.10578, loss_spatial_dice_2: 0.07780/0.26253, loss_spatial_ce_2: 0.01793/0.16956, loss_grounding_bce_2: 0.08180/0.08608, loss_grounding_dice_2: 0.03756/0.17979, loss_grounding_ce_2: 0.04617/0.28380, loss_mask_ce_3: 0.25325/0.92915, loss_mask_bce_3: 0.18689/0.33477, loss_mask_dice_3: 0.10720/1.16872, loss_spatial_bce_3: 0.13407/0.10738, loss_spatial_dice_3: 0.07655/0.26542, loss_spatial_ce_3: 0.04687/0.18343, loss_grounding_bce_3: 0.07752/0.08631, loss_grounding_dice_3: 0.03593/0.18003, loss_grounding_ce_3: 0.03846/0.28432, loss_mask_ce_4: 0.23587/0.93111, loss_mask_bce_4: 0.18112/0.33492, loss_mask_dice_4: 0.10802/1.18730, loss_spatial_bce_4: 0.13429/0.11037, loss_spatial_dice_4: 0.07407/0.27299, loss_spatial_ce_4: 0.02456/0.19259, loss_grounding_bce_4: 0.08020/0.08670, loss_grounding_dice_4: 0.03529/0.18281, loss_grounding_ce_4: 0.04318/0.28903, loss_mask_ce_5: 0.27293/0.93750, loss_mask_bce_5: 0.18276/0.33797, loss_mask_dice_5: 0.10670/1.19251, loss_spatial_bce_5: 0.14279/0.11207, loss_spatial_dice_5: 0.08177/0.27750, loss_spatial_ce_5: 0.04638/0.20452, loss_grounding_bce_5: 0.08218/0.08706, loss_grounding_dice_5: 0.03472/0.18381, loss_grounding_ce_5: 0.05312/0.29811, loss_mask_ce_6: 0.30706/0.97656, loss_mask_bce_6: 0.18265/0.34021, loss_mask_dice_6: 0.10822/1.19688, loss_spatial_bce_6: 0.13567/0.11578, loss_spatial_dice_6: 0.07660/0.28119, loss_spatial_ce_6: 0.05659/0.23169, loss_grounding_bce_6: 0.07854/0.08867, loss_grounding_dice_6: 0.03699/0.18478, loss_grounding_ce_6: 0.05876/0.32590, loss_mask_ce_7: 0.26356/1.01366, loss_mask_bce_7: 0.18100/0.34735, loss_mask_dice_7: 0.10645/1.25081, loss_spatial_bce_7: 0.15997/0.12868, loss_spatial_dice_7: 0.08374/0.30521, loss_spatial_ce_7: 0.09228/0.27570, loss_grounding_bce_7: 0.08128/0.08978, loss_grounding_dice_7: 0.03542/0.19140, loss_grounding_ce_7: 0.05741/0.36641, loss_mask_ce_8: 0.33409/1.12514, loss_mask_bce_8: 0.18305/0.35961, loss_mask_dice_8: 0.11386/1.32590, loss_spatial_bce_8: 0.17999/0.14934, loss_spatial_dice_8: 0.09875/0.35055, loss_spatial_ce_8: 0.07677/0.31933, loss_grounding_bce_8: 0.07453/0.09327, loss_grounding_dice_8: 0.04026/0.20150, loss_grounding_ce_8: 0.03488/0.44579, loss_mask_ce_9: 1.80722/3.77903, loss_mask_bce_9: 0.19793/0.38778, loss_mask_dice_9: 0.15597/1.90461, loss_spatial_bce_9: 0.36061/0.35360, loss_spatial_dice_9: 0.58244/0.83198, loss_spatial_ce_9: 0.73543/1.62327, loss_grounding_bce_9: 0.07907/0.10472, loss_grounding_dice_9: 0.04251/0.28221, loss_grounding_ce_9: 0.28991/0.81257] items per batch[64] items per second[0.20] total items[160000] mini batches[  2500] memory[7266] epoch remaining[0:59:16]
INFO:trainer.default_trainer:epochs[     1] optim steps[2600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44586/0.91912, loss_mask_bce_0: 0.56179/0.33349, loss_mask_dice_0: 1.79647/1.16506, loss_spatial_bce_0: 0.09006/0.10513, loss_spatial_dice_0: 0.18477/0.25397, loss_spatial_ce_0: 0.10423/0.15565, loss_grounding_bce_0: 0.05448/0.08652, loss_grounding_dice_0: 0.30069/0.18028, loss_grounding_ce_0: 2.14924/0.27754, loss_mask_ce_1: 1.33146/0.92097, loss_mask_bce_1: 0.64641/0.33424, loss_mask_dice_1: 1.86593/1.17403, loss_spatial_bce_1: 0.08667/0.10598, loss_spatial_dice_1: 0.19207/0.25912, loss_spatial_ce_1: 0.11837/0.16142, loss_grounding_bce_1: 0.05527/0.08654, loss_grounding_dice_1: 0.29878/0.18073, loss_grounding_ce_1: 2.12085/0.28197, loss_mask_ce_2: 1.38491/0.92618, loss_mask_bce_2: 0.66238/0.33404, loss_mask_dice_2: 1.90186/1.17005, loss_spatial_bce_2: 0.09194/0.10537, loss_spatial_dice_2: 0.20070/0.26128, loss_spatial_ce_2: 0.10628/0.16723, loss_grounding_bce_2: 0.05404/0.08610, loss_grounding_dice_2: 0.30750/0.17969, loss_grounding_ce_2: 1.81427/0.28288, loss_mask_ce_3: 1.53457/0.92940, loss_mask_bce_3: 0.67519/0.33489, loss_mask_dice_3: 1.85481/1.16773, loss_spatial_bce_3: 0.08865/0.10694, loss_spatial_dice_3: 0.19610/0.26417, loss_spatial_ce_3: 0.11669/0.18051, loss_grounding_bce_3: 0.06074/0.08635, loss_grounding_dice_3: 0.30548/0.18006, loss_grounding_ce_3: 1.86885/0.28288, loss_mask_ce_4: 1.31438/0.93119, loss_mask_bce_4: 0.67954/0.33544, loss_mask_dice_4: 1.90292/1.18602, loss_spatial_bce_4: 0.08178/0.10990, loss_spatial_dice_4: 0.20254/0.27170, loss_spatial_ce_4: 0.15686/0.19036, loss_grounding_bce_4: 0.06050/0.08668, loss_grounding_dice_4: 0.30155/0.18279, loss_grounding_ce_4: 2.15023/0.28799, loss_mask_ce_5: 1.45851/0.93820, loss_mask_bce_5: 0.72264/0.33834, loss_mask_dice_5: 1.96813/1.19117, loss_spatial_bce_5: 0.09579/0.11158, loss_spatial_dice_5: 0.20887/0.27627, loss_spatial_ce_5: 0.16862/0.20234, loss_grounding_bce_5: 0.05540/0.08706, loss_grounding_dice_5: 0.30206/0.18356, loss_grounding_ce_5: 2.62475/0.29767, loss_mask_ce_6: 1.81083/0.97656, loss_mask_bce_6: 0.57986/0.34029, loss_mask_dice_6: 1.80177/1.19455, loss_spatial_bce_6: 0.10211/0.11537, loss_spatial_dice_6: 0.20695/0.27995, loss_spatial_ce_6: 0.21709/0.22863, loss_grounding_bce_6: 0.05070/0.08862, loss_grounding_dice_6: 0.27922/0.18448, loss_grounding_ce_6: 1.73767/0.32432, loss_mask_ce_7: 1.70551/1.01448, loss_mask_bce_7: 0.63372/0.34765, loss_mask_dice_7: 1.95624/1.24895, loss_spatial_bce_7: 0.16939/0.12810, loss_spatial_dice_7: 0.25518/0.30403, loss_spatial_ce_7: 0.16652/0.27339, loss_grounding_bce_7: 0.06199/0.08979, loss_grounding_dice_7: 0.31067/0.19125, loss_grounding_ce_7: 1.89740/0.36586, loss_mask_ce_8: 2.07627/1.12672, loss_mask_bce_8: 0.49670/0.35957, loss_mask_dice_8: 2.05918/1.32394, loss_spatial_bce_8: 0.21987/0.14874, loss_spatial_dice_8: 0.29377/0.34922, loss_spatial_ce_8: 0.29115/0.31651, loss_grounding_bce_8: 0.05675/0.09328, loss_grounding_dice_8: 0.31662/0.20156, loss_grounding_ce_8: 1.66750/0.44536, loss_mask_ce_9: 7.09496/3.77927, loss_mask_bce_9: 0.81060/0.38829, loss_mask_dice_9: 5.26942/1.90633, loss_spatial_bce_9: 0.42048/0.35274, loss_spatial_dice_9: 0.81259/0.83166, loss_spatial_ce_9: 1.30615/1.62063, loss_grounding_bce_9: 0.07470/0.10477, loss_grounding_dice_9: 0.46350/0.28278, loss_grounding_ce_9: 1.18977/0.81116] items per batch[64] items per second[0.21] total items[166400] mini batches[  2600] memory[7266] epoch remaining[0:53:55]
INFO:trainer.default_trainer:epochs[     1] optim steps[2700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64272/0.91689, loss_mask_bce_0: 0.22522/0.33389, loss_mask_dice_0: 0.31499/1.16312, loss_spatial_bce_0: 0.07177/0.10513, loss_spatial_dice_0: 0.09254/0.25283, loss_spatial_ce_0: 0.17861/0.15373, loss_grounding_bce_0: 0.12189/0.08647, loss_grounding_dice_0: 0.08261/0.17955, loss_grounding_ce_0: 0.19495/0.28001, loss_mask_ce_1: 0.64296/0.91929, loss_mask_bce_1: 0.22097/0.33451, loss_mask_dice_1: 0.30681/1.17170, loss_spatial_bce_1: 0.07594/0.10593, loss_spatial_dice_1: 0.09697/0.25797, loss_spatial_ce_1: 0.11981/0.15940, loss_grounding_bce_1: 0.11993/0.08649, loss_grounding_dice_1: 0.08231/0.17997, loss_grounding_ce_1: 0.21352/0.28287, loss_mask_ce_2: 0.59759/0.92366, loss_mask_bce_2: 0.21428/0.33426, loss_mask_dice_2: 0.30163/1.16803, loss_spatial_bce_2: 0.08094/0.10528, loss_spatial_dice_2: 0.10083/0.26007, loss_spatial_ce_2: 0.09592/0.16510, loss_grounding_bce_2: 0.12042/0.08612, loss_grounding_dice_2: 0.08042/0.17899, loss_grounding_ce_2: 0.18477/0.28517, loss_mask_ce_3: 0.62505/0.92767, loss_mask_bce_3: 0.22604/0.33508, loss_mask_dice_3: 0.31133/1.16665, loss_spatial_bce_3: 0.07214/0.10684, loss_spatial_dice_3: 0.08925/0.26297, loss_spatial_ce_3: 0.11448/0.17789, loss_grounding_bce_3: 0.11993/0.08634, loss_grounding_dice_3: 0.08068/0.17920, loss_grounding_ce_3: 0.20093/0.28461, loss_mask_ce_4: 0.67558/0.92887, loss_mask_bce_4: 0.22562/0.33561, loss_mask_dice_4: 0.31055/1.18374, loss_spatial_bce_4: 0.08028/0.10966, loss_spatial_dice_4: 0.09473/0.27033, loss_spatial_ce_4: 0.20759/0.18869, loss_grounding_bce_4: 0.12138/0.08664, loss_grounding_dice_4: 0.07901/0.18197, loss_grounding_ce_4: 0.24341/0.28898, loss_mask_ce_5: 0.67427/0.93653, loss_mask_bce_5: 0.23345/0.33861, loss_mask_dice_5: 0.31111/1.18898, loss_spatial_bce_5: 0.09062/0.11147, loss_spatial_dice_5: 0.11809/0.27487, loss_spatial_ce_5: 0.20199/0.19979, loss_grounding_bce_5: 0.12228/0.08706, loss_grounding_dice_5: 0.08252/0.18282, loss_grounding_ce_5: 0.24784/0.29925, loss_mask_ce_6: 0.89293/0.97373, loss_mask_bce_6: 0.23506/0.34041, loss_mask_dice_6: 0.30565/1.19239, loss_spatial_bce_6: 0.08539/0.11516, loss_spatial_dice_6: 0.12368/0.27844, loss_spatial_ce_6: 0.16649/0.22580, loss_grounding_bce_6: 0.12720/0.08858, loss_grounding_dice_6: 0.08626/0.18369, loss_grounding_ce_6: 0.26644/0.32532, loss_mask_ce_7: 0.95329/1.01244, loss_mask_bce_7: 0.27964/0.34775, loss_mask_dice_7: 0.32047/1.24681, loss_spatial_bce_7: 0.23937/0.12777, loss_spatial_dice_7: 0.16838/0.30265, loss_spatial_ce_7: 0.09038/0.27016, loss_grounding_bce_7: 0.14051/0.08984, loss_grounding_dice_7: 0.09463/0.19044, loss_grounding_ce_7: 0.29074/0.36701, loss_mask_ce_8: 0.89901/1.12494, loss_mask_bce_8: 0.25678/0.35991, loss_mask_dice_8: 0.31888/1.32174, loss_spatial_bce_8: 0.22476/0.14847, loss_spatial_dice_8: 0.23141/0.34745, loss_spatial_ce_8: 0.23929/0.31383, loss_grounding_bce_8: 0.15193/0.09332, loss_grounding_dice_8: 0.10341/0.20076, loss_grounding_ce_8: 0.23972/0.44666, loss_mask_ce_9: 3.88297/3.77553, loss_mask_bce_9: 0.24008/0.38821, loss_mask_dice_9: 0.52980/1.90071, loss_spatial_bce_9: 0.59586/0.35235, loss_spatial_dice_9: 0.87258/0.83144, loss_spatial_ce_9: 1.55263/1.61515, loss_grounding_bce_9: 0.16244/0.10480, loss_grounding_dice_9: 0.13459/0.28176, loss_grounding_ce_9: 0.40463/0.81100] items per batch[64] items per second[0.22] total items[172800] mini batches[  2700] memory[7266] epoch remaining[0:48:37]
INFO:trainer.default_trainer:epochs[     1] optim steps[2800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23983/0.91713, loss_mask_bce_0: 0.60786/0.33438, loss_mask_dice_0: 3.10069/1.16071, loss_spatial_bce_0: 0.04458/0.10506, loss_spatial_dice_0: 0.19157/0.25179, loss_spatial_ce_0: 0.02292/0.15200, loss_grounding_bce_0: 0.03928/0.08657, loss_grounding_dice_0: 0.35479/0.17980, loss_grounding_ce_0: 0.32856/0.28025, loss_mask_ce_1: 1.23655/0.91906, loss_mask_bce_1: 0.63454/0.33511, loss_mask_dice_1: 3.26277/1.16955, loss_spatial_bce_1: 0.04457/0.10581, loss_spatial_dice_1: 0.19295/0.25683, loss_spatial_ce_1: 0.02832/0.15751, loss_grounding_bce_1: 0.03969/0.08653, loss_grounding_dice_1: 0.34752/0.18010, loss_grounding_ce_1: 0.33783/0.28345, loss_mask_ce_2: 1.21814/0.92360, loss_mask_bce_2: 0.64386/0.33493, loss_mask_dice_2: 3.25420/1.16605, loss_spatial_bce_2: 0.04446/0.10518, loss_spatial_dice_2: 0.19846/0.25897, loss_spatial_ce_2: 0.03471/0.16323, loss_grounding_bce_2: 0.04040/0.08614, loss_grounding_dice_2: 0.34380/0.17915, loss_grounding_ce_2: 0.33475/0.28610, loss_mask_ce_3: 1.24547/0.92764, loss_mask_bce_3: 0.62012/0.33568, loss_mask_dice_3: 3.13208/1.16453, loss_spatial_bce_3: 0.04781/0.10666, loss_spatial_dice_3: 0.21280/0.26171, loss_spatial_ce_3: 0.04233/0.17572, loss_grounding_bce_3: 0.04003/0.08636, loss_grounding_dice_3: 0.33304/0.17930, loss_grounding_ce_3: 0.33245/0.28521, loss_mask_ce_4: 1.27236/0.92879, loss_mask_bce_4: 0.70556/0.33616, loss_mask_dice_4: 3.14189/1.18158, loss_spatial_bce_4: 0.04988/0.10946, loss_spatial_dice_4: 0.20723/0.26900, loss_spatial_ce_4: 0.06997/0.18687, loss_grounding_bce_4: 0.04028/0.08683, loss_grounding_dice_4: 0.35330/0.18231, loss_grounding_ce_4: 0.35154/0.28898, loss_mask_ce_5: 1.32444/0.93656, loss_mask_bce_5: 0.73202/0.33924, loss_mask_dice_5: 3.13101/1.18692, loss_spatial_bce_5: 0.04615/0.11130, loss_spatial_dice_5: 0.20021/0.27352, loss_spatial_ce_5: 0.06110/0.19753, loss_grounding_bce_5: 0.04264/0.08728, loss_grounding_dice_5: 0.36243/0.18292, loss_grounding_ce_5: 0.33763/0.29941, loss_mask_ce_6: 1.38791/0.97512, loss_mask_bce_6: 0.76386/0.34123, loss_mask_dice_6: 3.30192/1.19039, loss_spatial_bce_6: 0.05686/0.11495, loss_spatial_dice_6: 0.22996/0.27699, loss_spatial_ce_6: 0.06361/0.22308, loss_grounding_bce_6: 0.04108/0.08871, loss_grounding_dice_6: 0.36366/0.18379, loss_grounding_ce_6: 0.34843/0.32620, loss_mask_ce_7: 1.25168/1.01340, loss_mask_bce_7: 0.83002/0.34861, loss_mask_dice_7: 3.43171/1.24484, loss_spatial_bce_7: 0.06205/0.12746, loss_spatial_dice_7: 0.26375/0.30123, loss_spatial_ce_7: 0.05680/0.26706, loss_grounding_bce_7: 0.04633/0.08997, loss_grounding_dice_7: 0.40706/0.19069, loss_grounding_ce_7: 0.35522/0.36764, loss_mask_ce_8: 1.72029/1.12604, loss_mask_bce_8: 0.86485/0.36069, loss_mask_dice_8: 4.07014/1.31935, loss_spatial_bce_8: 0.07618/0.14820, loss_spatial_dice_8: 0.28515/0.34584, loss_spatial_ce_8: 0.13478/0.31196, loss_grounding_bce_8: 0.04800/0.09338, loss_grounding_dice_8: 0.43342/0.20098, loss_grounding_ce_8: 0.45155/0.44779, loss_mask_ce_9: 6.40824/3.77468, loss_mask_bce_9: 0.88163/0.38877, loss_mask_dice_9: 6.17017/1.89633, loss_spatial_bce_9: 0.25035/0.35201, loss_spatial_dice_9: 0.96217/0.83110, loss_spatial_ce_9: 1.48608/1.61025, loss_grounding_bce_9: 0.03846/0.10505, loss_grounding_dice_9: 0.58281/0.28218, loss_grounding_ce_9: 0.60233/0.80688] items per batch[64] items per second[0.21] total items[179200] mini batches[  2800] memory[7266] epoch remaining[0:43:24]
INFO:trainer.default_trainer:epochs[     1] optim steps[2900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.15940/0.91550, loss_mask_bce_0: 0.23324/0.33433, loss_mask_dice_0: 3.49680/1.15711, loss_spatial_bce_0: 0.01721/0.10485, loss_spatial_dice_0: 0.37345/0.25069, loss_spatial_ce_0: 0.13192/0.14992, loss_grounding_bce_0: 0.06467/0.08663, loss_grounding_dice_0: 0.31539/0.18010, loss_grounding_ce_0: 0.65577/0.28116, loss_mask_ce_1: 1.37679/0.91763, loss_mask_bce_1: 0.23350/0.33525, loss_mask_dice_1: 3.47245/1.16600, loss_spatial_bce_1: 0.01933/0.10567, loss_spatial_dice_1: 0.38275/0.25576, loss_spatial_ce_1: 0.11653/0.15546, loss_grounding_bce_1: 0.05833/0.08665, loss_grounding_dice_1: 0.30330/0.18037, loss_grounding_ce_1: 0.62329/0.28456, loss_mask_ce_2: 1.40968/0.92226, loss_mask_bce_2: 0.21082/0.33507, loss_mask_dice_2: 3.15533/1.16214, loss_spatial_bce_2: 0.01759/0.10506, loss_spatial_dice_2: 0.30632/0.25779, loss_spatial_ce_2: 0.11923/0.16122, loss_grounding_bce_2: 0.04504/0.08632, loss_grounding_dice_2: 0.26003/0.17940, loss_grounding_ce_2: 0.99224/0.28770, loss_mask_ce_3: 1.34747/0.92632, loss_mask_bce_3: 0.21493/0.33568, loss_mask_dice_3: 3.52223/1.16077, loss_spatial_bce_3: 0.01860/0.10648, loss_spatial_dice_3: 0.36169/0.26035, loss_spatial_ce_3: 0.13816/0.17333, loss_grounding_bce_3: 0.05319/0.08649, loss_grounding_dice_3: 0.29419/0.17939, loss_grounding_ce_3: 0.95322/0.28627, loss_mask_ce_4: 1.34725/0.92654, loss_mask_bce_4: 0.20368/0.33628, loss_mask_dice_4: 3.59064/1.17771, loss_spatial_bce_4: 0.01859/0.10930, loss_spatial_dice_4: 0.36055/0.26772, loss_spatial_ce_4: 0.14393/0.18469, loss_grounding_bce_4: 0.05894/0.08699, loss_grounding_dice_4: 0.30522/0.18263, loss_grounding_ce_4: 1.26454/0.29079, loss_mask_ce_5: 1.34661/0.93467, loss_mask_bce_5: 0.19660/0.33923, loss_mask_dice_5: 3.67148/1.18322, loss_spatial_bce_5: 0.02167/0.11109, loss_spatial_dice_5: 0.35191/0.27210, loss_spatial_ce_5: 0.16802/0.19533, loss_grounding_bce_5: 0.03543/0.08741, loss_grounding_dice_5: 0.30208/0.18325, loss_grounding_ce_5: 1.40654/0.30108, loss_mask_ce_6: 1.41357/0.97320, loss_mask_bce_6: 0.19932/0.34123, loss_mask_dice_6: 3.57280/1.18698, loss_spatial_bce_6: 0.02270/0.11488, loss_spatial_dice_6: 0.36988/0.27551, loss_spatial_ce_6: 0.18062/0.22044, loss_grounding_bce_6: 0.04710/0.08877, loss_grounding_dice_6: 0.29898/0.18405, loss_grounding_ce_6: 1.16676/0.32721, loss_mask_ce_7: 1.45863/1.01077, loss_mask_bce_7: 0.19017/0.34871, loss_mask_dice_7: 3.59549/1.24155, loss_spatial_bce_7: 0.02139/0.12736, loss_spatial_dice_7: 0.43896/0.29988, loss_spatial_ce_7: 0.26027/0.26454, loss_grounding_bce_7: 0.03971/0.09003, loss_grounding_dice_7: 0.27777/0.19086, loss_grounding_ce_7: 1.67836/0.36811, loss_mask_ce_8: 1.55862/1.12358, loss_mask_bce_8: 0.19671/0.36102, loss_mask_dice_8: 3.44988/1.31586, loss_spatial_bce_8: 0.03316/0.14803, loss_spatial_dice_8: 0.50211/0.34424, loss_spatial_ce_8: 0.23291/0.30934, loss_grounding_bce_8: 0.06886/0.09344, loss_grounding_dice_8: 0.35121/0.20111, loss_grounding_ce_8: 0.56454/0.44656, loss_mask_ce_9: 4.34063/3.76699, loss_mask_bce_9: 0.14660/0.38901, loss_mask_dice_9: 4.39467/1.89167, loss_spatial_bce_9: 0.08154/0.35198, loss_spatial_dice_9: 0.80620/0.83071, loss_spatial_ce_9: 2.24127/1.60595, loss_grounding_bce_9: 0.03460/0.10502, loss_grounding_dice_9: 0.38411/0.28259, loss_grounding_ce_9: 1.36637/0.80250] items per batch[64] items per second[0.21] total items[185600] mini batches[  2900] memory[7266] epoch remaining[0:38:20]
INFO:trainer.default_trainer:epochs[     1] optim steps[3000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23065/0.91802, loss_mask_bce_0: 0.46898/0.33565, loss_mask_dice_0: 1.70058/1.15821, loss_spatial_bce_0: 0.08237/0.10459, loss_spatial_dice_0: 0.30804/0.24984, loss_spatial_ce_0: 0.48650/0.14840, loss_grounding_bce_0: 0.08754/0.08665, loss_grounding_dice_0: 0.27692/0.17980, loss_grounding_ce_0: 0.09797/0.28272, loss_mask_ce_1: 1.24656/0.91967, loss_mask_bce_1: 0.46172/0.33655, loss_mask_dice_1: 1.74241/1.16693, loss_spatial_bce_1: 0.09984/0.10535, loss_spatial_dice_1: 0.35976/0.25475, loss_spatial_ce_1: 0.15796/0.15378, loss_grounding_bce_1: 0.08299/0.08671, loss_grounding_dice_1: 0.28507/0.17997, loss_grounding_ce_1: 0.10871/0.28545, loss_mask_ce_2: 1.19420/0.92465, loss_mask_bce_2: 0.46655/0.33640, loss_mask_dice_2: 1.71623/1.16318, loss_spatial_bce_2: 0.09370/0.10471, loss_spatial_dice_2: 0.32580/0.25679, loss_spatial_ce_2: 0.37481/0.15932, loss_grounding_bce_2: 0.08236/0.08636, loss_grounding_dice_2: 0.27761/0.17912, loss_grounding_ce_2: 0.11189/0.28844, loss_mask_ce_3: 1.20573/0.92830, loss_mask_bce_3: 0.48882/0.33705, loss_mask_dice_3: 1.77479/1.16197, loss_spatial_bce_3: 0.10300/0.10612, loss_spatial_dice_3: 0.32725/0.25929, loss_spatial_ce_3: 0.33478/0.17128, loss_grounding_bce_3: 0.10224/0.08653, loss_grounding_dice_3: 0.28704/0.17902, loss_grounding_ce_3: 0.12119/0.28763, loss_mask_ce_4: 1.13632/0.92800, loss_mask_bce_4: 0.50446/0.33778, loss_mask_dice_4: 1.63702/1.17865, loss_spatial_bce_4: 0.08780/0.10894, loss_spatial_dice_4: 0.29534/0.26645, loss_spatial_ce_4: 0.40955/0.18257, loss_grounding_bce_4: 0.07774/0.08699, loss_grounding_dice_4: 0.27276/0.18224, loss_grounding_ce_4: 0.13422/0.29148, loss_mask_ce_5: 1.16548/0.93731, loss_mask_bce_5: 0.50085/0.34058, loss_mask_dice_5: 1.60668/1.18411, loss_spatial_bce_5: 0.10390/0.11076, loss_spatial_dice_5: 0.35731/0.27094, loss_spatial_ce_5: 0.19850/0.19321, loss_grounding_bce_5: 0.07661/0.08750, loss_grounding_dice_5: 0.26425/0.18284, loss_grounding_ce_5: 0.15050/0.30273, loss_mask_ce_6: 1.33694/0.97590, loss_mask_bce_6: 0.51567/0.34255, loss_mask_dice_6: 1.54784/1.18835, loss_spatial_bce_6: 0.10763/0.11453, loss_spatial_dice_6: 0.35664/0.27438, loss_spatial_ce_6: 0.12827/0.21810, loss_grounding_bce_6: 0.08733/0.08880, loss_grounding_dice_6: 0.26794/0.18359, loss_grounding_ce_6: 0.27476/0.32836, loss_mask_ce_7: 1.57318/1.01355, loss_mask_bce_7: 0.52048/0.34994, loss_mask_dice_7: 1.62964/1.24246, loss_spatial_bce_7: 0.13483/0.12697, loss_spatial_dice_7: 0.36961/0.29889, loss_spatial_ce_7: 0.15036/0.26149, loss_grounding_bce_7: 0.09262/0.09004, loss_grounding_dice_7: 0.31228/0.19039, loss_grounding_ce_7: 0.73071/0.37038, loss_mask_ce_8: 1.75244/1.12684, loss_mask_bce_8: 0.59221/0.36234, loss_mask_dice_8: 1.86668/1.31661, loss_spatial_bce_8: 0.17848/0.14774, loss_spatial_dice_8: 0.37456/0.34291, loss_spatial_ce_8: 0.18573/0.30724, loss_grounding_bce_8: 0.10975/0.09348, loss_grounding_dice_8: 0.37555/0.20063, loss_grounding_ce_8: 1.21095/0.44931, loss_mask_ce_9: 4.50504/3.77231, loss_mask_bce_9: 0.47333/0.39039, loss_mask_dice_9: 2.96832/1.89518, loss_spatial_bce_9: 0.23442/0.35179, loss_spatial_dice_9: 0.76484/0.83110, loss_spatial_ce_9: 2.49210/1.60224, loss_grounding_bce_9: 0.13607/0.10511, loss_grounding_dice_9: 0.57201/0.28209, loss_grounding_ce_9: 0.71404/0.80219] items per batch[64] items per second[0.21] total items[192000] mini batches[  3000] memory[7266] epoch remaining[0:33:12]
INFO:trainer.default_trainer:epochs[     1] optim steps[3100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61862/0.91459, loss_mask_bce_0: 0.12496/0.33616, loss_mask_dice_0: 0.57187/1.15821, loss_spatial_bce_0: 0.04217/0.10445, loss_spatial_dice_0: 0.33401/0.24875, loss_spatial_ce_0: 0.04244/0.14694, loss_grounding_bce_0: 0.09661/0.08653, loss_grounding_dice_0: 0.06648/0.17946, loss_grounding_ce_0: 0.00620/0.28016, loss_mask_ce_1: 0.64348/0.91671, loss_mask_bce_1: 0.12375/0.33688, loss_mask_dice_1: 0.57620/1.16577, loss_spatial_bce_1: 0.04359/0.10516, loss_spatial_dice_1: 0.32862/0.25361, loss_spatial_ce_1: 0.05728/0.15214, loss_grounding_bce_1: 0.09062/0.08664, loss_grounding_dice_1: 0.06543/0.17965, loss_grounding_ce_1: 0.00769/0.28286, loss_mask_ce_2: 0.67858/0.92135, loss_mask_bce_2: 0.12694/0.33693, loss_mask_dice_2: 0.60915/1.16313, loss_spatial_bce_2: 0.04068/0.10456, loss_spatial_dice_2: 0.30843/0.25563, loss_spatial_ce_2: 0.05821/0.15769, loss_grounding_bce_2: 0.09672/0.08634, loss_grounding_dice_2: 0.06941/0.17886, loss_grounding_ce_2: 0.00713/0.28552, loss_mask_ce_3: 0.65120/0.92455, loss_mask_bce_3: 0.12109/0.33747, loss_mask_dice_3: 0.53899/1.16168, loss_spatial_bce_3: 0.04203/0.10598, loss_spatial_dice_3: 0.31393/0.25814, loss_spatial_ce_3: 0.08715/0.16919, loss_grounding_bce_3: 0.09231/0.08647, loss_grounding_dice_3: 0.06379/0.17869, loss_grounding_ce_3: 0.00862/0.28502, loss_mask_ce_4: 0.67374/0.92451, loss_mask_bce_4: 0.12423/0.33815, loss_mask_dice_4: 0.56582/1.17825, loss_spatial_bce_4: 0.04223/0.10879, loss_spatial_dice_4: 0.30994/0.26518, loss_spatial_ce_4: 0.10088/0.18044, loss_grounding_bce_4: 0.09949/0.08698, loss_grounding_dice_4: 0.07111/0.18181, loss_grounding_ce_4: 0.01018/0.28853, loss_mask_ce_5: 0.85502/0.93403, loss_mask_bce_5: 0.11962/0.34088, loss_mask_dice_5: 0.62690/1.18352, loss_spatial_bce_5: 0.04097/0.11052, loss_spatial_dice_5: 0.32829/0.26972, loss_spatial_ce_5: 0.11516/0.19115, loss_grounding_bce_5: 0.09378/0.08758, loss_grounding_dice_5: 0.07203/0.18253, loss_grounding_ce_5: 0.00675/0.29953, loss_mask_ce_6: 0.89424/0.97233, loss_mask_bce_6: 0.12640/0.34282, loss_mask_dice_6: 0.53122/1.18768, loss_spatial_bce_6: 0.04389/0.11439, loss_spatial_dice_6: 0.31561/0.27308, loss_spatial_ce_6: 0.07376/0.21579, loss_grounding_bce_6: 0.09950/0.08879, loss_grounding_dice_6: 0.06851/0.18310, loss_grounding_ce_6: 0.01081/0.32616, loss_mask_ce_7: 0.83490/1.01007, loss_mask_bce_7: 0.11710/0.35031, loss_mask_dice_7: 0.55350/1.24139, loss_spatial_bce_7: 0.05944/0.12679, loss_spatial_dice_7: 0.37798/0.29788, loss_spatial_ce_7: 0.21242/0.25878, loss_grounding_bce_7: 0.09519/0.09011, loss_grounding_dice_7: 0.06666/0.18988, loss_grounding_ce_7: 0.01412/0.36805, loss_mask_ce_8: 0.95744/1.12345, loss_mask_bce_8: 0.11766/0.36255, loss_mask_dice_8: 0.51302/1.31557, loss_spatial_bce_8: 0.06688/0.14744, loss_spatial_dice_8: 0.41624/0.34157, loss_spatial_ce_8: 0.28890/0.30509, loss_grounding_bce_8: 0.09317/0.09339, loss_grounding_dice_8: 0.06334/0.20033, loss_grounding_ce_8: 0.01975/0.44570, loss_mask_ce_9: 3.75423/3.76649, loss_mask_bce_9: 0.12598/0.39064, loss_mask_dice_9: 0.72531/1.89267, loss_spatial_bce_9: 0.19100/0.35180, loss_spatial_dice_9: 0.68461/0.83080, loss_spatial_ce_9: 2.70533/1.59947, loss_grounding_bce_9: 0.11703/0.10504, loss_grounding_dice_9: 0.10232/0.28161, loss_grounding_ce_9: 0.23319/0.79889] items per batch[64] items per second[0.21] total items[198400] mini batches[  3100] memory[7324] epoch remaining[0:28:07]
INFO:trainer.default_trainer:epochs[     1] optim steps[3200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.23227/0.91415, loss_mask_bce_0: 0.39105/0.33595, loss_mask_dice_0: 0.53924/1.15814, loss_spatial_bce_0: 0.08665/0.10398, loss_spatial_dice_0: 0.13266/0.24828, loss_spatial_ce_0: 0.03589/0.14531, loss_grounding_bce_0: 0.05414/0.08641, loss_grounding_dice_0: 0.08734/0.17971, loss_grounding_ce_0: 0.26916/0.27948, loss_mask_ce_1: 0.22279/0.91656, loss_mask_bce_1: 0.38704/0.33657, loss_mask_dice_1: 0.55151/1.16559, loss_spatial_bce_1: 0.08929/0.10473, loss_spatial_dice_1: 0.12432/0.25309, loss_spatial_ce_1: 0.02719/0.15046, loss_grounding_bce_1: 0.05413/0.08656, loss_grounding_dice_1: 0.09225/0.18023, loss_grounding_ce_1: 0.23287/0.28207, loss_mask_ce_2: 0.21869/0.92154, loss_mask_bce_2: 0.39225/0.33668, loss_mask_dice_2: 0.53110/1.16294, loss_spatial_bce_2: 0.09064/0.10411, loss_spatial_dice_2: 0.12631/0.25498, loss_spatial_ce_2: 0.03217/0.15583, loss_grounding_bce_2: 0.05168/0.08626, loss_grounding_dice_2: 0.08672/0.17910, loss_grounding_ce_2: 0.23682/0.28493, loss_mask_ce_3: 0.23725/0.92437, loss_mask_bce_3: 0.40402/0.33723, loss_mask_dice_3: 0.51149/1.16155, loss_spatial_bce_3: 0.09284/0.10556, loss_spatial_dice_3: 0.13688/0.25756, loss_spatial_ce_3: 0.04076/0.16707, loss_grounding_bce_3: 0.05380/0.08642, loss_grounding_dice_3: 0.08989/0.17914, loss_grounding_ce_3: 0.22166/0.28417, loss_mask_ce_4: 0.27942/0.92439, loss_mask_bce_4: 0.36678/0.33780, loss_mask_dice_4: 0.48408/1.17800, loss_spatial_bce_4: 0.09633/0.10834, loss_spatial_dice_4: 0.13148/0.26452, loss_spatial_ce_4: 0.06521/0.17856, loss_grounding_bce_4: 0.05279/0.08692, loss_grounding_dice_4: 0.08733/0.18223, loss_grounding_ce_4: 0.19785/0.28795, loss_mask_ce_5: 0.30907/0.93458, loss_mask_bce_5: 0.35470/0.34052, loss_mask_dice_5: 0.45209/1.18342, loss_spatial_bce_5: 0.09279/0.11010, loss_spatial_dice_5: 0.14671/0.26901, loss_spatial_ce_5: 0.04478/0.18936, loss_grounding_bce_5: 0.05502/0.08749, loss_grounding_dice_5: 0.09165/0.18298, loss_grounding_ce_5: 0.23393/0.29885, loss_mask_ce_6: 0.37611/0.97210, loss_mask_bce_6: 0.35894/0.34245, loss_mask_dice_6: 0.47506/1.18742, loss_spatial_bce_6: 0.10107/0.11398, loss_spatial_dice_6: 0.13872/0.27238, loss_spatial_ce_6: 0.06780/0.21383, loss_grounding_bce_6: 0.05627/0.08871, loss_grounding_dice_6: 0.09927/0.18338, loss_grounding_ce_6: 0.30975/0.32539, loss_mask_ce_7: 0.39846/1.01082, loss_mask_bce_7: 0.35831/0.35007, loss_mask_dice_7: 0.47188/1.24144, loss_spatial_bce_7: 0.12995/0.12645, loss_spatial_dice_7: 0.17601/0.29730, loss_spatial_ce_7: 0.11769/0.25676, loss_grounding_bce_7: 0.05600/0.09006, loss_grounding_dice_7: 0.09227/0.19045, loss_grounding_ce_7: 0.37100/0.36678, loss_mask_ce_8: 0.51650/1.12418, loss_mask_bce_8: 0.41722/0.36261, loss_mask_dice_8: 0.58439/1.31537, loss_spatial_bce_8: 0.13260/0.14693, loss_spatial_dice_8: 0.16583/0.34084, loss_spatial_ce_8: 0.13655/0.30381, loss_grounding_bce_8: 0.06361/0.09340, loss_grounding_dice_8: 0.08673/0.20086, loss_grounding_ce_8: 0.59250/0.44405, loss_mask_ce_9: 2.92575/3.76507, loss_mask_bce_9: 0.53975/0.39074, loss_mask_dice_9: 0.98573/1.89165, loss_spatial_bce_9: 0.42848/0.35091, loss_spatial_dice_9: 0.82811/0.83071, loss_spatial_ce_9: 1.25915/1.59771, loss_grounding_bce_9: 0.06865/0.10491, loss_grounding_dice_9: 0.15617/0.28237, loss_grounding_ce_9: 2.04567/0.79597] items per batch[64] items per second[0.21] total items[204800] mini batches[  3200] memory[7324] epoch remaining[0:23:02]
INFO:trainer.default_trainer:epochs[     1] optim steps[3300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52138/0.91389, loss_mask_bce_0: 0.34226/0.33621, loss_mask_dice_0: 0.80591/1.16102, loss_spatial_bce_0: 0.06238/0.10375, loss_spatial_dice_0: 0.16558/0.24752, loss_spatial_ce_0: 0.06203/0.14355, loss_grounding_bce_0: 0.06795/0.08653, loss_grounding_dice_0: 0.09314/0.17986, loss_grounding_ce_0: 0.00269/0.27882, loss_mask_ce_1: 0.55544/0.91610, loss_mask_bce_1: 0.34399/0.33674, loss_mask_dice_1: 0.81137/1.16788, loss_spatial_bce_1: 0.06557/0.10454, loss_spatial_dice_1: 0.16518/0.25229, loss_spatial_ce_1: 0.02914/0.14897, loss_grounding_bce_1: 0.06221/0.08667, loss_grounding_dice_1: 0.08713/0.18041, loss_grounding_ce_1: 0.00219/0.28135, loss_mask_ce_2: 0.51370/0.92170, loss_mask_bce_2: 0.34660/0.33690, loss_mask_dice_2: 0.81088/1.16513, loss_spatial_bce_2: 0.07097/0.10384, loss_spatial_dice_2: 0.16781/0.25421, loss_spatial_ce_2: 0.02595/0.15423, loss_grounding_bce_2: 0.06495/0.08636, loss_grounding_dice_2: 0.09183/0.17936, loss_grounding_ce_2: 0.00209/0.28460, loss_mask_ce_3: 0.54973/0.92383, loss_mask_bce_3: 0.34327/0.33763, loss_mask_dice_3: 0.82458/1.16446, loss_spatial_bce_3: 0.06414/0.10531, loss_spatial_dice_3: 0.16299/0.25679, loss_spatial_ce_3: 0.02693/0.16506, loss_grounding_bce_3: 0.06353/0.08653, loss_grounding_dice_3: 0.08981/0.17937, loss_grounding_ce_3: 0.00247/0.28394, loss_mask_ce_4: 0.58956/0.92419, loss_mask_bce_4: 0.35464/0.33811, loss_mask_dice_4: 0.81451/1.18123, loss_spatial_bce_4: 0.06907/0.10809, loss_spatial_dice_4: 0.16797/0.26363, loss_spatial_ce_4: 0.03319/0.17675, loss_grounding_bce_4: 0.06416/0.08706, loss_grounding_dice_4: 0.09180/0.18241, loss_grounding_ce_4: 0.00229/0.28744, loss_mask_ce_5: 0.52013/0.93400, loss_mask_bce_5: 0.36907/0.34083, loss_mask_dice_5: 0.87471/1.18663, loss_spatial_bce_5: 0.06894/0.10981, loss_spatial_dice_5: 0.17099/0.26809, loss_spatial_ce_5: 0.04574/0.18774, loss_grounding_bce_5: 0.06544/0.08763, loss_grounding_dice_5: 0.09171/0.18323, loss_grounding_ce_5: 0.00094/0.29836, loss_mask_ce_6: 0.58756/0.97224, loss_mask_bce_6: 0.43476/0.34287, loss_mask_dice_6: 0.84059/1.19044, loss_spatial_bce_6: 0.06624/0.11377, loss_spatial_dice_6: 0.16570/0.27145, loss_spatial_ce_6: 0.07080/0.21127, loss_grounding_bce_6: 0.06432/0.08897, loss_grounding_dice_6: 0.09709/0.18359, loss_grounding_ce_6: 0.00122/0.32453, loss_mask_ce_7: 0.77043/1.01109, loss_mask_bce_7: 0.38006/0.35051, loss_mask_dice_7: 0.82471/1.24537, loss_spatial_bce_7: 0.08211/0.12621, loss_spatial_dice_7: 0.19680/0.29657, loss_spatial_ce_7: 0.05347/0.25415, loss_grounding_bce_7: 0.06635/0.09017, loss_grounding_dice_7: 0.10102/0.19060, loss_grounding_ce_7: 0.00182/0.36612, loss_mask_ce_8: 0.94036/1.12497, loss_mask_bce_8: 0.35525/0.36329, loss_mask_dice_8: 0.93352/1.31912, loss_spatial_bce_8: 0.15185/0.14666, loss_spatial_dice_8: 0.31491/0.33999, loss_spatial_ce_8: 0.08763/0.30203, loss_grounding_bce_8: 0.07091/0.09353, loss_grounding_dice_8: 0.08934/0.20108, loss_grounding_ce_8: 0.00293/0.44314, loss_mask_ce_9: 3.92330/3.76271, loss_mask_bce_9: 0.52266/0.39111, loss_mask_dice_9: 1.85358/1.89569, loss_spatial_bce_9: 0.33060/0.35039, loss_spatial_dice_9: 0.87553/0.83092, loss_spatial_ce_9: 1.29792/1.59378, loss_grounding_bce_9: 0.06609/0.10510, loss_grounding_dice_9: 0.10900/0.28277, loss_grounding_ce_9: 0.11516/0.79274] items per batch[64] items per second[0.22] total items[211200] mini batches[  3300] memory[7324] epoch remaining[0:17:55]
INFO:trainer.default_trainer:epochs[     1] optim steps[3400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82310/0.91326, loss_mask_bce_0: 0.55222/0.33634, loss_mask_dice_0: 3.79577/1.16683, loss_spatial_bce_0: 0.07386/0.10318, loss_spatial_dice_0: 0.39738/0.24735, loss_spatial_ce_0: 0.22942/0.14210, loss_grounding_bce_0: 0.08288/0.08650, loss_grounding_dice_0: 0.22351/0.17994, loss_grounding_ce_0: 0.63865/0.27956, loss_mask_ce_1: 0.75828/0.91574, loss_mask_bce_1: 0.53237/0.33686, loss_mask_dice_1: 3.72861/1.17390, loss_spatial_bce_1: 0.07477/0.10402, loss_spatial_dice_1: 0.38689/0.25217, loss_spatial_ce_1: 0.10639/0.14765, loss_grounding_bce_1: 0.08612/0.08665, loss_grounding_dice_1: 0.22361/0.18044, loss_grounding_ce_1: 0.55749/0.28168, loss_mask_ce_2: 0.86169/0.92056, loss_mask_bce_2: 0.54377/0.33706, loss_mask_dice_2: 4.01492/1.17128, loss_spatial_bce_2: 0.07662/0.10333, loss_spatial_dice_2: 0.38035/0.25407, loss_spatial_ce_2: 0.22927/0.15272, loss_grounding_bce_2: 0.08778/0.08638, loss_grounding_dice_2: 0.22733/0.17957, loss_grounding_ce_2: 0.36123/0.28484, loss_mask_ce_3: 0.86003/0.92258, loss_mask_bce_3: 0.53065/0.33776, loss_mask_dice_3: 3.76691/1.17040, loss_spatial_bce_3: 0.07896/0.10481, loss_spatial_dice_3: 0.38347/0.25656, loss_spatial_ce_3: 0.32480/0.16342, loss_grounding_bce_3: 0.08874/0.08652, loss_grounding_dice_3: 0.22518/0.17952, loss_grounding_ce_3: 0.28485/0.28441, loss_mask_ce_4: 0.93507/0.92340, loss_mask_bce_4: 0.51032/0.33824, loss_mask_dice_4: 3.70183/1.18768, loss_spatial_bce_4: 0.07125/0.10762, loss_spatial_dice_4: 0.40882/0.26336, loss_spatial_ce_4: 0.13454/0.17530, loss_grounding_bce_4: 0.08688/0.08703, loss_grounding_dice_4: 0.23034/0.18248, loss_grounding_ce_4: 0.13044/0.28822, loss_mask_ce_5: 0.74074/0.93303, loss_mask_bce_5: 0.52787/0.34110, loss_mask_dice_5: 4.28777/1.19279, loss_spatial_bce_5: 0.08107/0.10933, loss_spatial_dice_5: 0.37964/0.26782, loss_spatial_ce_5: 0.20951/0.18621, loss_grounding_bce_5: 0.08259/0.08755, loss_grounding_dice_5: 0.21148/0.18329, loss_grounding_ce_5: 0.53645/0.29942, loss_mask_ce_6: 0.76810/0.97065, loss_mask_bce_6: 0.51215/0.34308, loss_mask_dice_6: 3.88135/1.19640, loss_spatial_bce_6: 0.08501/0.11319, loss_spatial_dice_6: 0.41322/0.27114, loss_spatial_ce_6: 0.14707/0.20914, loss_grounding_bce_6: 0.07832/0.08891, loss_grounding_dice_6: 0.19925/0.18373, loss_grounding_ce_6: 0.50089/0.32355, loss_mask_ce_7: 0.87417/1.01036, loss_mask_bce_7: 0.47258/0.35059, loss_mask_dice_7: 4.26396/1.25136, loss_spatial_bce_7: 0.08878/0.12554, loss_spatial_dice_7: 0.43975/0.29630, loss_spatial_ce_7: 0.15173/0.25223, loss_grounding_bce_7: 0.07808/0.09006, loss_grounding_dice_7: 0.20106/0.19060, loss_grounding_ce_7: 0.84480/0.36630, loss_mask_ce_8: 1.10246/1.12415, loss_mask_bce_8: 0.54962/0.36352, loss_mask_dice_8: 3.95314/1.32550, loss_spatial_bce_8: 0.11084/0.14599, loss_spatial_dice_8: 0.45135/0.33949, loss_spatial_ce_8: 0.26276/0.30080, loss_grounding_bce_8: 0.11814/0.09355, loss_grounding_dice_8: 0.22088/0.20135, loss_grounding_ce_8: 1.00860/0.44373, loss_mask_ce_9: 4.63862/3.76012, loss_mask_bce_9: 0.52689/0.39105, loss_mask_dice_9: 5.16104/1.90403, loss_spatial_bce_9: 0.29250/0.34936, loss_spatial_dice_9: 0.88980/0.83102, loss_spatial_ce_9: 1.64731/1.59309, loss_grounding_bce_9: 0.09762/0.10492, loss_grounding_dice_9: 0.31909/0.28265, loss_grounding_ce_9: 1.30640/0.78898] items per batch[64] items per second[0.21] total items[217600] mini batches[  3400] memory[7324] epoch remaining[0:12:52]
INFO:trainer.default_trainer:epochs[     1] optim steps[3500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95376/0.91170, loss_mask_bce_0: 0.36268/0.33581, loss_mask_dice_0: 1.90861/1.16527, loss_spatial_bce_0: 0.02569/0.10305, loss_spatial_dice_0: 0.20578/0.24695, loss_spatial_ce_0: 0.07794/0.14069, loss_grounding_bce_0: 0.04558/0.08644, loss_grounding_dice_0: 0.18254/0.17951, loss_grounding_ce_0: 0.41351/0.27852, loss_mask_ce_1: 0.88847/0.91412, loss_mask_bce_1: 0.38407/0.33639, loss_mask_dice_1: 1.85166/1.17263, loss_spatial_bce_1: 0.02830/0.10387, loss_spatial_dice_1: 0.22068/0.25176, loss_spatial_ce_1: 0.06912/0.14645, loss_grounding_bce_1: 0.04511/0.08660, loss_grounding_dice_1: 0.19258/0.17998, loss_grounding_ce_1: 0.37270/0.28061, loss_mask_ce_2: 0.83745/0.91937, loss_mask_bce_2: 0.38367/0.33655, loss_mask_dice_2: 1.89864/1.16996, loss_spatial_bce_2: 0.02496/0.10322, loss_spatial_dice_2: 0.22501/0.25366, loss_spatial_ce_2: 0.08547/0.15151, loss_grounding_bce_2: 0.04450/0.08629, loss_grounding_dice_2: 0.20407/0.17907, loss_grounding_ce_2: 0.40116/0.28396, loss_mask_ce_3: 0.96175/0.92111, loss_mask_bce_3: 0.39539/0.33719, loss_mask_dice_3: 1.90480/1.16871, loss_spatial_bce_3: 0.02679/0.10467, loss_spatial_dice_3: 0.23780/0.25609, loss_spatial_ce_3: 0.08824/0.16212, loss_grounding_bce_3: 0.04460/0.08648, loss_grounding_dice_3: 0.20055/0.17905, loss_grounding_ce_3: 0.42578/0.28342, loss_mask_ce_4: 0.84524/0.92187, loss_mask_bce_4: 0.42029/0.33782, loss_mask_dice_4: 1.87973/1.18617, loss_spatial_bce_4: 0.02874/0.10745, loss_spatial_dice_4: 0.24322/0.26284, loss_spatial_ce_4: 0.12999/0.17378, loss_grounding_bce_4: 0.04728/0.08704, loss_grounding_dice_4: 0.22316/0.18200, loss_grounding_ce_4: 0.38670/0.28734, loss_mask_ce_5: 0.88422/0.93207, loss_mask_bce_5: 0.40521/0.34072, loss_mask_dice_5: 2.01024/1.19155, loss_spatial_bce_5: 0.02469/0.10916, loss_spatial_dice_5: 0.23182/0.26734, loss_spatial_ce_5: 0.14595/0.18473, loss_grounding_bce_5: 0.04524/0.08752, loss_grounding_dice_5: 0.19450/0.18277, loss_grounding_ce_5: 0.39329/0.29857, loss_mask_ce_6: 0.91911/0.97018, loss_mask_bce_6: 0.40320/0.34270, loss_mask_dice_6: 2.01896/1.19479, loss_spatial_bce_6: 0.03680/0.11301, loss_spatial_dice_6: 0.25372/0.27064, loss_spatial_ce_6: 0.17442/0.20750, loss_grounding_bce_6: 0.04479/0.08895, loss_grounding_dice_6: 0.21165/0.18329, loss_grounding_ce_6: 0.40485/0.32226, loss_mask_ce_7: 0.90542/1.00914, loss_mask_bce_7: 0.37818/0.35006, loss_mask_dice_7: 2.23419/1.24935, loss_spatial_bce_7: 0.08539/0.12523, loss_spatial_dice_7: 0.33581/0.29601, loss_spatial_ce_7: 0.36601/0.25034, loss_grounding_bce_7: 0.04700/0.09007, loss_grounding_dice_7: 0.24969/0.19010, loss_grounding_ce_7: 0.42011/0.36518, loss_mask_ce_8: 1.01804/1.12357, loss_mask_bce_8: 0.39097/0.36274, loss_mask_dice_8: 2.48862/1.32324, loss_spatial_bce_8: 0.04224/0.14572, loss_spatial_dice_8: 0.38440/0.33892, loss_spatial_ce_8: 0.24050/0.29912, loss_grounding_bce_8: 0.05004/0.09348, loss_grounding_dice_8: 0.24752/0.20079, loss_grounding_ce_8: 0.50014/0.44217, loss_mask_ce_9: 5.88560/3.75374, loss_mask_bce_9: 0.59696/0.39017, loss_mask_dice_9: 3.75819/1.89871, loss_spatial_bce_9: 0.18787/0.34866, loss_spatial_dice_9: 0.94894/0.83096, loss_spatial_ce_9: 1.26854/1.59054, loss_grounding_bce_9: 0.07979/0.10489, loss_grounding_dice_9: 0.45219/0.28220, loss_grounding_ce_9: 0.52912/0.78823] items per batch[64] items per second[0.22] total items[224000] mini batches[  3500] memory[7324] epoch remaining[0:07:47]
INFO:trainer.default_trainer:epochs[     1] optim steps[3600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85200/0.91312, loss_mask_bce_0: 0.43791/0.33608, loss_mask_dice_0: 0.55702/1.16257, loss_spatial_bce_0: 0.13846/0.10292, loss_spatial_dice_0: 0.14837/0.24675, loss_spatial_ce_0: 0.08843/0.13917, loss_grounding_bce_0: 0.38157/0.08688, loss_grounding_dice_0: 0.23954/0.17974, loss_grounding_ce_0: 0.35537/0.27868, loss_mask_ce_1: 0.89713/0.91558, loss_mask_bce_1: 0.44252/0.33670, loss_mask_dice_1: 0.57290/1.16963, loss_spatial_bce_1: 0.14437/0.10375, loss_spatial_dice_1: 0.15092/0.25154, loss_spatial_ce_1: 0.13576/0.14488, loss_grounding_bce_1: 0.34018/0.08702, loss_grounding_dice_1: 0.23879/0.18028, loss_grounding_ce_1: 0.33768/0.28105, loss_mask_ce_2: 1.02033/0.92039, loss_mask_bce_2: 0.42586/0.33687, loss_mask_dice_2: 0.54195/1.16750, loss_spatial_bce_2: 0.13798/0.10310, loss_spatial_dice_2: 0.15380/0.25345, loss_spatial_ce_2: 0.15077/0.15009, loss_grounding_bce_2: 0.36919/0.08673, loss_grounding_dice_2: 0.22684/0.17917, loss_grounding_ce_2: 0.31638/0.28463, loss_mask_ce_3: 1.18618/0.92252, loss_mask_bce_3: 0.39267/0.33758, loss_mask_dice_3: 0.48342/1.16601, loss_spatial_bce_3: 0.14347/0.10452, loss_spatial_dice_3: 0.16473/0.25581, loss_spatial_ce_3: 0.18652/0.16094, loss_grounding_bce_3: 0.27417/0.08690, loss_grounding_dice_3: 0.30220/0.17919, loss_grounding_ce_3: 1.93441/0.28437, loss_mask_ce_4: 0.95954/0.92297, loss_mask_bce_4: 0.43250/0.33813, loss_mask_dice_4: 0.56277/1.18369, loss_spatial_bce_4: 0.13373/0.10726, loss_spatial_dice_4: 0.15132/0.26246, loss_spatial_ce_4: 0.15344/0.17234, loss_grounding_bce_4: 0.40892/0.08750, loss_grounding_dice_4: 0.22021/0.18236, loss_grounding_ce_4: 0.26625/0.28777, loss_mask_ce_5: 1.01331/0.93325, loss_mask_bce_5: 0.40698/0.34106, loss_mask_dice_5: 0.53056/1.18866, loss_spatial_bce_5: 0.14582/0.10904, loss_spatial_dice_5: 0.17276/0.26699, loss_spatial_ce_5: 0.21668/0.18341, loss_grounding_bce_5: 0.39242/0.08795, loss_grounding_dice_5: 0.31608/0.18293, loss_grounding_ce_5: 0.39221/0.29888, loss_mask_ce_6: 0.92069/0.97155, loss_mask_bce_6: 0.39289/0.34309, loss_mask_dice_6: 0.52093/1.19176, loss_spatial_bce_6: 0.15495/0.11289, loss_spatial_dice_6: 0.20711/0.27020, loss_spatial_ce_6: 0.18886/0.20606, loss_grounding_bce_6: 0.34966/0.08927, loss_grounding_dice_6: 0.34433/0.18353, loss_grounding_ce_6: 0.17012/0.32211, loss_mask_ce_7: 1.00466/1.01072, loss_mask_bce_7: 0.40283/0.35040, loss_mask_dice_7: 0.56599/1.24624, loss_spatial_bce_7: 0.16603/0.12515, loss_spatial_dice_7: 0.21400/0.29566, loss_spatial_ce_7: 0.30517/0.24900, loss_grounding_bce_7: 0.37834/0.09039, loss_grounding_dice_7: 0.29806/0.19033, loss_grounding_ce_7: 0.87266/0.36513, loss_mask_ce_8: 1.03640/1.12609, loss_mask_bce_8: 0.46936/0.36305, loss_mask_dice_8: 0.63321/1.31969, loss_spatial_bce_8: 0.21252/0.14547, loss_spatial_dice_8: 0.21554/0.33833, loss_spatial_ce_8: 0.40994/0.29829, loss_grounding_bce_8: 0.39020/0.09380, loss_grounding_dice_8: 0.33277/0.20094, loss_grounding_ce_8: 0.35380/0.44189, loss_mask_ce_9: 3.69573/3.74944, loss_mask_bce_9: 0.57577/0.39046, loss_mask_dice_9: 0.82592/1.89445, loss_spatial_bce_9: 0.54909/0.34796, loss_spatial_dice_9: 0.83965/0.83102, loss_spatial_ce_9: 1.59610/1.58926, loss_grounding_bce_9: 0.40377/0.10504, loss_grounding_dice_9: 0.32938/0.28187, loss_grounding_ce_9: 0.71048/0.78507] items per batch[64] items per second[0.22] total items[230400] mini batches[  3600] memory[7324] epoch remaining[0:02:43]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00003654.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0022 s/iter. Inference: 0.2150 s/iter. Eval: 0.1293 s/iter. Total: 0.3466 s/iter. ETA=0:00:50
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0027 s/iter. Inference: 0.2211 s/iter. Eval: 0.0900 s/iter. Total: 0.3141 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0029 s/iter. Inference: 0.2234 s/iter. Eval: 0.0806 s/iter. Total: 0.3071 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 63/157. Dataloading: 0.0031 s/iter. Inference: 0.2231 s/iter. Eval: 0.0759 s/iter. Total: 0.3022 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 81/157. Dataloading: 0.0032 s/iter. Inference: 0.2221 s/iter. Eval: 0.0744 s/iter. Total: 0.2999 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 97/157. Dataloading: 0.0032 s/iter. Inference: 0.2239 s/iter. Eval: 0.0759 s/iter. Total: 0.3031 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 114/157. Dataloading: 0.0033 s/iter. Inference: 0.2247 s/iter. Eval: 0.0761 s/iter. Total: 0.3041 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 131/157. Dataloading: 0.0033 s/iter. Inference: 0.2248 s/iter. Eval: 0.0751 s/iter. Total: 0.3033 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 147/157. Dataloading: 0.0033 s/iter. Inference: 0.2263 s/iter. Eval: 0.0746 s/iter. Total: 0.3043 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evali_erejri ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.827 | 81.856 | 59.957 |      133      |
| Things | 54.734 | 82.629 | 65.558 |      80       |
| Stuff  | 42.422 | 80.690 | 51.501 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.37s)
creating index...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 9.80 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.30s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 15.02 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.383
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.605
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.405
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.415
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.297
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.711
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.271 | 60.488 | 40.483 | 18.898 | 41.517 | 60.457 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.988 | bicycle      | 18.619 | car            | 36.594 |
| motorcycle    | 34.626 | airplane     | 55.388 | bus            | 62.612 |
| train         | 67.537 | truck        | 33.336 | boat           | 22.828 |
| traffic light | 25.035 | fire hydrant | 64.141 | stop sign      | 63.644 |
| parking meter | 44.318 | bench        | 19.509 | bird           | 29.330 |
| cat           | 72.975 | dog          | 65.589 | horse          | 45.308 |
| sheep         | 44.999 | cow          | 49.606 | elephant       | 59.920 |
| bear          | 77.129 | zebra        | 59.956 | giraffe        | 56.737 |
| backpack      | 15.422 | umbrella     | 47.631 | handbag        | 15.626 |
| tie           | 32.977 | suitcase     | 39.282 | frisbee        | 68.032 |
| skis          | 4.923  | snowboard    | 22.401 | sports ball    | 46.063 |
| kite          | 33.375 | baseball bat | 28.778 | baseball glove | 42.357 |
| skateboard    | 35.231 | surfboard    | 33.964 | tennis racket  | 56.609 |
| bottle        | 33.581 | wine glass   | 26.570 | cup            | 39.865 |
| fork          | 14.617 | knife        | 11.549 | spoon          | 14.955 |
| bowl          | 31.939 | banana       | 19.911 | apple          | 20.047 |
| sandwich      | 40.130 | orange       | 29.136 | broccoli       | 20.597 |
| carrot        | 19.081 | hot dog      | 21.281 | pizza          | 48.028 |
| donut         | 46.105 | cake         | 43.442 | chair          | 19.871 |
| couch         | 40.778 | potted plant | 17.659 | bed            | 40.202 |
| dining table  | 13.017 | toilet       | 66.092 | tv             | 62.641 |
| laptop        | 60.883 | mouse        | 57.235 | remote         | 30.375 |
| keyboard      | 48.444 | cell phone   | 36.856 | microwave      | 53.631 |
| oven          | 31.998 | toaster      | 34.756 | sink           | 36.558 |
| refrigerator  | 59.408 | book         | 7.854  | clock          | 52.042 |
| vase          | 33.583 | scissors     | 23.537 | teddy bear     | 50.984 |
| hair drier    | 4.852  | toothbrush   | 21.207 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.65395775296673, 'fwIoU': 68.96340046784523, 'IoU-person': 87.18917638948149, 'IoU-bicycle': 74.91272685679427, 'IoU-car': 67.1088472866953, 'IoU-motorcycle': 79.1443572235283, 'IoU-airplane': 84.0861930890634, 'IoU-bus': 82.34951324274776, 'IoU-train': 85.50448730723308, 'IoU-truck': 60.65910333458548, 'IoU-boat': 65.9011490478912, 'IoU-traffic light': 76.0527883518281, 'IoU-fire hydrant': 89.83837165442637, 'IoU-stop sign': 91.92055601181772, 'IoU-parking meter': 88.21476386476411, 'IoU-bench': 54.129532529504154, 'IoU-bird': 74.76209255671199, 'IoU-cat': 85.95473787349039, 'IoU-dog': 74.06956497026023, 'IoU-horse': 86.05298195653252, 'IoU-sheep': 86.36531768865696, 'IoU-cow': 85.47009828052407, 'IoU-elephant': 87.65616690832671, 'IoU-bear': 81.823053255882, 'IoU-zebra': 92.25539511227916, 'IoU-giraffe': 85.89677486513116, 'IoU-backpack': 38.728665102663705, 'IoU-umbrella': 77.82729143426721, 'IoU-handbag': 36.27133416664812, 'IoU-tie': 69.94987009961802, 'IoU-suitcase': 81.00321307279239, 'IoU-frisbee': 83.44291595152986, 'IoU-skis': 51.22032832253601, 'IoU-snowboard': 69.93923803610237, 'IoU-sports ball': 66.27411530451482, 'IoU-kite': 64.74624850294894, 'IoU-baseball bat': 58.23986269040978, 'IoU-baseball glove': 76.03629247464863, 'IoU-skateboard': 63.6392570176354, 'IoU-surfboard': 75.50627766403372, 'IoU-tennis racket': 83.04589429360611, 'IoU-bottle': 68.13070404412144, 'IoU-wine glass': 70.48217927477978, 'IoU-cup': 60.388585702482956, 'IoU-fork': 53.88529654953879, 'IoU-knife': 47.98141064619422, 'IoU-spoon': 50.06349291549015, 'IoU-bowl': 57.24423158092201, 'IoU-banana': 83.0449240399035, 'IoU-apple': 58.113450385884455, 'IoU-sandwich': 66.72627669285805, 'IoU-orange': 76.54574593557713, 'IoU-broccoli': 66.0861177559997, 'IoU-carrot': 64.31288221625213, 'IoU-hot dog': 65.58478067679548, 'IoU-pizza': 84.17604165111585, 'IoU-donut': 64.68212404130087, 'IoU-cake': 70.8937496658016, 'IoU-chair': 52.1482830947436, 'IoU-couch': 69.52233501965, 'IoU-potted plant': 35.11065821492137, 'IoU-bed': 68.95996477079916, 'IoU-dining table': 51.631628335038336, 'IoU-toilet': 88.10861838388317, 'IoU-tv': 75.23998876785592, 'IoU-laptop': 76.49778024917575, 'IoU-mouse': 65.66574574244362, 'IoU-remote': 47.45272516013222, 'IoU-keyboard': 66.99313181941768, 'IoU-cell phone': 66.47795840963204, 'IoU-microwave': 52.666956958218904, 'IoU-oven': 65.12017596114191, 'IoU-toaster': 43.59814388028235, 'IoU-sink': 72.22610735188843, 'IoU-refrigerator': 79.11493656782743, 'IoU-book': 50.41093178930417, 'IoU-clock': 73.16679399309265, 'IoU-vase': 65.90412846981023, 'IoU-scissors': 56.93947501657652, 'IoU-teddy bear': 81.98511968789228, 'IoU-hair drier': 32.20243112550464, 'IoU-toothbrush': 63.90815311523988, 'IoU-banner': 36.09035936511054, 'IoU-blanket': 10.644379428174117, 'IoU-bridge': 41.4274281646346, 'IoU-cardboard': 47.001083295999216, 'IoU-counter': 31.409136022988783, 'IoU-curtain': 65.66993375472987, 'IoU-door-stuff': 42.40577322268796, 'IoU-floor-wood': 61.99812554296058, 'IoU-flower': 44.06405530004225, 'IoU-fruit': 38.63119302775805, 'IoU-gravel': 27.997866810392907, 'IoU-house': 25.390046612874894, 'IoU-light': 39.56068807477504, 'IoU-mirror-stuff': 55.108637954204376, 'IoU-net': 47.12590039315307, 'IoU-pillow': 14.24728164584187, 'IoU-platform': 33.10719987642588, 'IoU-playingfield': 67.39374876342262, 'IoU-railroad': 60.8544119196215, 'IoU-river': 51.54103848641809, 'IoU-road': 65.30134700556629, 'IoU-roof': 12.301491839810309, 'IoU-sand': 62.91172470159524, 'IoU-sea': 84.12595516939247, 'IoU-shelf': 36.262371143715285, 'IoU-snow': 88.30252722827991, 'IoU-stairs': 24.259945456307364, 'IoU-tent': 8.823547780685374, 'IoU-towel': 33.57761703297637, 'IoU-wall-brick': 45.6651605021528, 'IoU-wall-stone': 21.69041918059868, 'IoU-wall-tile': 67.28544654251682, 'IoU-wall-wood': 36.52408054034287, 'IoU-water-other': 24.549128145903783, 'IoU-window-blind': 48.89661677771603, 'IoU-window-other': 48.073114286585074, 'IoU-tree-merged': 80.35622284265459, 'IoU-fence-merged': 51.74829785156432, 'IoU-ceiling-merged': 65.86681231236895, 'IoU-sky-other-merged': 92.74883649540409, 'IoU-cabinet-merged': 58.14772759201144, 'IoU-table-merged': 38.7962402578433, 'IoU-floor-other-merged': 50.32290916332662, 'IoU-pavement-merged': 53.090683005257176, 'IoU-mountain-merged': 54.42768994566434, 'IoU-grass-merged': 71.55181807924205, 'IoU-dirt-merged': 45.47016345484386, 'IoU-paper-merged': 34.38451456868495, 'IoU-food-other-merged': 39.087902658774, 'IoU-building-other-merged': 56.911721137978546, 'IoU-rock-merged': 60.757844740065146, 'IoU-wall-other-merged': 66.65932525504368, 'IoU-rug-merged': 63.846171335888144, 'mACC': 72.82551557019274, 'pACC': 80.38215928641253, 'ACC-person': 92.26116924683652, 'ACC-bicycle': 85.00449939863806, 'ACC-car': 83.59453645891122, 'ACC-motorcycle': 86.56732714510954, 'ACC-airplane': 90.04278679517283, 'ACC-bus': 86.16669169460968, 'ACC-train': 95.05317060815727, 'ACC-truck': 80.15501601655153, 'ACC-boat': 78.55029232130147, 'ACC-traffic light': 89.95961871274345, 'ACC-fire hydrant': 95.09259694176022, 'ACC-stop sign': 95.18279417839675, 'ACC-parking meter': 91.88172370489505, 'ACC-bench': 66.87007081122069, 'ACC-bird': 79.30961429818704, 'ACC-cat': 90.22877466632008, 'ACC-dog': 76.37435294580516, 'ACC-horse': 91.92860484543844, 'ACC-sheep': 89.39112547374084, 'ACC-cow': 91.64687249590871, 'ACC-elephant': 90.00557513696272, 'ACC-bear': 83.46927754865867, 'ACC-zebra': 94.85594719502564, 'ACC-giraffe': 89.84310073095585, 'ACC-backpack': 59.12064749651624, 'ACC-umbrella': 84.24646997379129, 'ACC-handbag': 55.54511059005882, 'ACC-tie': 80.48980283986089, 'ACC-suitcase': 90.86887871569046, 'ACC-frisbee': 94.052, 'ACC-skis': 69.36629675942173, 'ACC-snowboard': 77.24131174842644, 'ACC-sports ball': 82.40094685810566, 'ACC-kite': 75.7924132706098, 'ACC-baseball bat': 76.26939588968224, 'ACC-baseball glove': 87.76939538704308, 'ACC-skateboard': 68.43353187516551, 'ACC-surfboard': 82.6461713912006, 'ACC-tennis racket': 89.49018466187152, 'ACC-bottle': 81.2139037865053, 'ACC-wine glass': 84.15930808144911, 'ACC-cup': 83.48287405483738, 'ACC-fork': 67.29962078406334, 'ACC-knife': 58.68853960577416, 'ACC-spoon': 72.83813610525407, 'ACC-bowl': 70.50135025207207, 'ACC-banana': 89.47447298120416, 'ACC-apple': 68.88756520117538, 'ACC-sandwich': 80.0211834712575, 'ACC-orange': 84.94348476015065, 'ACC-broccoli': 74.3501521057041, 'ACC-carrot': 75.589420629249, 'ACC-hot dog': 73.32506460286126, 'ACC-pizza': 90.45697530425552, 'ACC-donut': 81.16878473492586, 'ACC-cake': 76.99382907455833, 'ACC-chair': 66.15348358200728, 'ACC-couch': 86.55290849468213, 'ACC-potted plant': 53.52141128308766, 'ACC-bed': 87.95326261318183, 'ACC-dining table': 78.71585481501808, 'ACC-toilet': 92.55103556391958, 'ACC-tv': 88.90061596492066, 'ACC-laptop': 90.07305530814384, 'ACC-mouse': 86.16928021817473, 'ACC-remote': 67.87586088977568, 'ACC-keyboard': 74.19641498225165, 'ACC-cell phone': 80.16336050049138, 'ACC-microwave': 58.17706028394849, 'ACC-oven': 81.15963813449478, 'ACC-toaster': 57.734470158343484, 'ACC-sink': 81.93770448584347, 'ACC-refrigerator': 90.94020134971021, 'ACC-book': 62.972563111148474, 'ACC-clock': 79.07659730135931, 'ACC-vase': 78.55284547692912, 'ACC-scissors': 61.699331552029676, 'ACC-teddy bear': 87.43332728863695, 'ACC-hair drier': 41.32154954031822, 'ACC-toothbrush': 79.80802640722725, 'ACC-banner': 74.18121734711988, 'ACC-blanket': 12.19885028498947, 'ACC-bridge': 55.641081641152745, 'ACC-cardboard': 61.96716591537007, 'ACC-counter': 52.17161669523755, 'ACC-curtain': 77.56256045633961, 'ACC-door-stuff': 68.42804351415252, 'ACC-floor-wood': 77.21917597994029, 'ACC-flower': 60.7996947231797, 'ACC-fruit': 64.28223301302044, 'ACC-gravel': 36.48866309493102, 'ACC-house': 31.57365185956688, 'ACC-light': 55.03686885120393, 'ACC-mirror-stuff': 73.82066372752743, 'ACC-net': 62.57706965700262, 'ACC-pillow': 25.79456325750476, 'ACC-platform': 53.33083303104269, 'ACC-playingfield': 85.37479539181204, 'ACC-railroad': 76.41095054624904, 'ACC-river': 72.5599507984451, 'ACC-road': 83.80925735659184, 'ACC-roof': 15.980882299013555, 'ACC-sand': 70.41458141323956, 'ACC-sea': 89.07485640595476, 'ACC-shelf': 59.74673874430597, 'ACC-snow': 95.30692139611304, 'ACC-stairs': 40.0741761476002, 'ACC-tent': 11.078536746531945, 'ACC-towel': 39.243747844940415, 'ACC-wall-brick': 62.60145436004888, 'ACC-wall-stone': 24.427960323270604, 'ACC-wall-tile': 80.91641807872291, 'ACC-wall-wood': 45.9850766375506, 'ACC-water-other': 43.63980169590081, 'ACC-window-blind': 59.055000334937056, 'ACC-window-other': 69.26887193874977, 'ACC-tree-merged': 89.09440164672297, 'ACC-fence-merged': 72.90240635245084, 'ACC-ceiling-merged': 80.14361159451158, 'ACC-sky-other-merged': 96.70832246775993, 'ACC-cabinet-merged': 74.81684328235463, 'ACC-table-merged': 49.094119214562156, 'ACC-floor-other-merged': 61.141092846234976, 'ACC-pavement-merged': 67.92395077486754, 'ACC-mountain-merged': 64.47115585510834, 'ACC-grass-merged': 82.9605079320189, 'ACC-dirt-merged': 69.20856327771406, 'ACC-paper-merged': 46.235276100537014, 'ACC-food-other-merged': 49.86851347727034, 'ACC-building-other-merged': 73.27946731191568, 'ACC-rock-merged': 82.4619009560986, 'ACC-wall-other-merged': 81.54214454816785, 'ACC-rug-merged': 81.69474001838549})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1549 s/iter. Inference: 0.5940 s/iter. Eval: 0.0000 s/iter. Total: 0.7490 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1542 s/iter. Inference: 0.5777 s/iter. Eval: 0.0000 s/iter. Total: 0.7321 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1673 s/iter. Inference: 0.6173 s/iter. Eval: 0.0000 s/iter. Total: 0.7847 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1698 s/iter. Inference: 0.7351 s/iter. Eval: 0.0000 s/iter. Total: 0.9051 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 36/50. Dataloading: 0.1680 s/iter. Inference: 0.6713 s/iter. Eval: 0.0000 s/iter. Total: 0.8394 s/iter. ETA=0:00:11
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 41/50. Dataloading: 0.1672 s/iter. Inference: 0.7376 s/iter. Eval: 0.0000 s/iter. Total: 0.9050 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 47/50. Dataloading: 0.1681 s/iter. Inference: 0.7341 s/iter. Eval: 0.0000 s/iter. Total: 0.9024 s/iter. ETA=0:00:02
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.6695932104184958, 'noc@0.8': 3.270412642669008, 'noc@0.85': 3.9669300556043314, 'noc@0.9': 5.083406496927129, 'miou@iter1': 0.8391177484714953}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0985 s/iter. Eval: 0.0008 s/iter. Total: 0.1009 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.54022216796875, 'precision@0.6': 67.23668670654297, 'precision@0.7': 62.10649108886719, 'precision@0.8': 51.263118743896484, 'precision@0.9': 26.19510269165039, 'cIoU': 56.56468200683594, 'mIoU': 61.90000915527344}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.82729760506833, 'SQ': 81.85618263535429, 'RQ': 59.95650330641944, 'PQ_th': 54.73352610574855, 'SQ_th': 82.62853247108805, 'RQ_th': 65.55847782974446, 'PQ_st': 42.42166967951332, 'SQ_st': 80.69037156254866, 'RQ_st': 51.5006927051742}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.27114551541778, 'AP50': 60.48753718564864, 'AP75': 40.4834375473166, 'APs': 18.897587775830203, 'APm': 41.5172055649572, 'APl': 60.45688757299652, 'AP-person': 43.98816741180438, 'AP-bicycle': 18.61921056871737, 'AP-car': 36.594226118386366, 'AP-motorcycle': 34.62613306850717, 'AP-airplane': 55.38774927300298, 'AP-bus': 62.61169332665998, 'AP-train': 67.53654378051898, 'AP-truck': 33.33644046647037, 'AP-boat': 22.827773976285798, 'AP-traffic light': 25.034505342242408, 'AP-fire hydrant': 64.14068555171174, 'AP-stop sign': 63.644094566822496, 'AP-parking meter': 44.31825439500693, 'AP-bench': 19.508913900810665, 'AP-bird': 29.33037402383411, 'AP-cat': 72.97490980659308, 'AP-dog': 65.5893157548293, 'AP-horse': 45.3078840556466, 'AP-sheep': 44.99932314490629, 'AP-cow': 49.60608095739544, 'AP-elephant': 59.91992617272766, 'AP-bear': 77.12901240169589, 'AP-zebra': 59.95648185820318, 'AP-giraffe': 56.736760960534816, 'AP-backpack': 15.421943869119293, 'AP-umbrella': 47.63055216581391, 'AP-handbag': 15.625806301836212, 'AP-tie': 32.97734199577497, 'AP-suitcase': 39.282172103906525, 'AP-frisbee': 68.03162384750003, 'AP-skis': 4.922980145238841, 'AP-snowboard': 22.400793693280747, 'AP-sports ball': 46.06295823757799, 'AP-kite': 33.374637935787746, 'AP-baseball bat': 28.777653571513202, 'AP-baseball glove': 42.35691283784073, 'AP-skateboard': 35.231488387604045, 'AP-surfboard': 33.963968250260365, 'AP-tennis racket': 56.60867545056867, 'AP-bottle': 33.58109994236156, 'AP-wine glass': 26.57047826634844, 'AP-cup': 39.86519733080066, 'AP-fork': 14.61687647793437, 'AP-knife': 11.549340046431746, 'AP-spoon': 14.955161703278439, 'AP-bowl': 31.938719169461194, 'AP-banana': 19.91070325644827, 'AP-apple': 20.047085796893256, 'AP-sandwich': 40.13024827002765, 'AP-orange': 29.13615357160494, 'AP-broccoli': 20.596988874931302, 'AP-carrot': 19.08077943591836, 'AP-hot dog': 21.280862650539984, 'AP-pizza': 48.02849247250538, 'AP-donut': 46.10510514192366, 'AP-cake': 43.44185581240655, 'AP-chair': 19.87053569068307, 'AP-couch': 40.77765067938636, 'AP-potted plant': 17.6591835976219, 'AP-bed': 40.202174572966904, 'AP-dining table': 13.016731684248828, 'AP-toilet': 66.09201979613135, 'AP-tv': 62.64144687609728, 'AP-laptop': 60.88270636512279, 'AP-mouse': 57.23484631669762, 'AP-remote': 30.375445423205523, 'AP-keyboard': 48.443599121166876, 'AP-cell phone': 36.85593561523861, 'AP-microwave': 53.63084711438317, 'AP-oven': 31.998339002037472, 'AP-toaster': 34.75637048628512, 'AP-sink': 36.55774160012755, 'AP-refrigerator': 59.40806725818288, 'AP-book': 7.854194686845705, 'AP-clock': 52.04235925556738, 'AP-vase': 33.583351702563334, 'AP-scissors': 23.536804933569837, 'AP-teddy bear': 50.98367008110508, 'AP-hair drier': 4.851734494332286, 'AP-toothbrush': 21.206766983101897}), ('sem_seg', {'mIoU': 60.65395775296673, 'fwIoU': 68.96340046784523, 'IoU-person': 87.18917638948149, 'IoU-bicycle': 74.91272685679427, 'IoU-car': 67.1088472866953, 'IoU-motorcycle': 79.1443572235283, 'IoU-airplane': 84.0861930890634, 'IoU-bus': 82.34951324274776, 'IoU-train': 85.50448730723308, 'IoU-truck': 60.65910333458548, 'IoU-boat': 65.9011490478912, 'IoU-traffic light': 76.0527883518281, 'IoU-fire hydrant': 89.83837165442637, 'IoU-stop sign': 91.92055601181772, 'IoU-parking meter': 88.21476386476411, 'IoU-bench': 54.129532529504154, 'IoU-bird': 74.76209255671199, 'IoU-cat': 85.95473787349039, 'IoU-dog': 74.06956497026023, 'IoU-horse': 86.05298195653252, 'IoU-sheep': 86.36531768865696, 'IoU-cow': 85.47009828052407, 'IoU-elephant': 87.65616690832671, 'IoU-bear': 81.823053255882, 'IoU-zebra': 92.25539511227916, 'IoU-giraffe': 85.89677486513116, 'IoU-backpack': 38.728665102663705, 'IoU-umbrella': 77.82729143426721, 'IoU-handbag': 36.27133416664812, 'IoU-tie': 69.94987009961802, 'IoU-suitcase': 81.00321307279239, 'IoU-frisbee': 83.44291595152986, 'IoU-skis': 51.22032832253601, 'IoU-snowboard': 69.93923803610237, 'IoU-sports ball': 66.27411530451482, 'IoU-kite': 64.74624850294894, 'IoU-baseball bat': 58.23986269040978, 'IoU-baseball glove': 76.03629247464863, 'IoU-skateboard': 63.6392570176354, 'IoU-surfboard': 75.50627766403372, 'IoU-tennis racket': 83.04589429360611, 'IoU-bottle': 68.13070404412144, 'IoU-wine glass': 70.48217927477978, 'IoU-cup': 60.388585702482956, 'IoU-fork': 53.88529654953879, 'IoU-knife': 47.98141064619422, 'IoU-spoon': 50.06349291549015, 'IoU-bowl': 57.24423158092201, 'IoU-banana': 83.0449240399035, 'IoU-apple': 58.113450385884455, 'IoU-sandwich': 66.72627669285805, 'IoU-orange': 76.54574593557713, 'IoU-broccoli': 66.0861177559997, 'IoU-carrot': 64.31288221625213, 'IoU-hot dog': 65.58478067679548, 'IoU-pizza': 84.17604165111585, 'IoU-donut': 64.68212404130087, 'IoU-cake': 70.8937496658016, 'IoU-chair': 52.1482830947436, 'IoU-couch': 69.52233501965, 'IoU-potted plant': 35.11065821492137, 'IoU-bed': 68.95996477079916, 'IoU-dining table': 51.631628335038336, 'IoU-toilet': 88.10861838388317, 'IoU-tv': 75.23998876785592, 'IoU-laptop': 76.49778024917575, 'IoU-mouse': 65.66574574244362, 'IoU-remote': 47.45272516013222, 'IoU-keyboard': 66.99313181941768, 'IoU-cell phone': 66.47795840963204, 'IoU-microwave': 52.666956958218904, 'IoU-oven': 65.12017596114191, 'IoU-toaster': 43.59814388028235, 'IoU-sink': 72.22610735188843, 'IoU-refrigerator': 79.11493656782743, 'IoU-book': 50.41093178930417, 'IoU-clock': 73.16679399309265, 'IoU-vase': 65.90412846981023, 'IoU-scissors': 56.93947501657652, 'IoU-teddy bear': 81.98511968789228, 'IoU-hair drier': 32.20243112550464, 'IoU-toothbrush': 63.90815311523988, 'IoU-banner': 36.09035936511054, 'IoU-blanket': 10.644379428174117, 'IoU-bridge': 41.4274281646346, 'IoU-cardboard': 47.001083295999216, 'IoU-counter': 31.409136022988783, 'IoU-curtain': 65.66993375472987, 'IoU-door-stuff': 42.40577322268796, 'IoU-floor-wood': 61.99812554296058, 'IoU-flower': 44.06405530004225, 'IoU-fruit': 38.63119302775805, 'IoU-gravel': 27.997866810392907, 'IoU-house': 25.390046612874894, 'IoU-light': 39.56068807477504, 'IoU-mirror-stuff': 55.108637954204376, 'IoU-net': 47.12590039315307, 'IoU-pillow': 14.24728164584187, 'IoU-platform': 33.10719987642588, 'IoU-playingfield': 67.39374876342262, 'IoU-railroad': 60.8544119196215, 'IoU-river': 51.54103848641809, 'IoU-road': 65.30134700556629, 'IoU-roof': 12.301491839810309, 'IoU-sand': 62.91172470159524, 'IoU-sea': 84.12595516939247, 'IoU-shelf': 36.262371143715285, 'IoU-snow': 88.30252722827991, 'IoU-stairs': 24.259945456307364, 'IoU-tent': 8.823547780685374, 'IoU-towel': 33.57761703297637, 'IoU-wall-brick': 45.6651605021528, 'IoU-wall-stone': 21.69041918059868, 'IoU-wall-tile': 67.28544654251682, 'IoU-wall-wood': 36.52408054034287, 'IoU-water-other': 24.549128145903783, 'IoU-window-blind': 48.89661677771603, 'IoU-window-other': 48.073114286585074, 'IoU-tree-merged': 80.35622284265459, 'IoU-fence-merged': 51.74829785156432, 'IoU-ceiling-merged': 65.86681231236895, 'IoU-sky-other-merged': 92.74883649540409, 'IoU-cabinet-merged': 58.14772759201144, 'IoU-table-merged': 38.7962402578433, 'IoU-floor-other-merged': 50.32290916332662, 'IoU-pavement-merged': 53.090683005257176, 'IoU-mountain-merged': 54.42768994566434, 'IoU-grass-merged': 71.55181807924205, 'IoU-dirt-merged': 45.47016345484386, 'IoU-paper-merged': 34.38451456868495, 'IoU-food-other-merged': 39.087902658774, 'IoU-building-other-merged': 56.911721137978546, 'IoU-rock-merged': 60.757844740065146, 'IoU-wall-other-merged': 66.65932525504368, 'IoU-rug-merged': 63.846171335888144, 'mACC': 72.82551557019274, 'pACC': 80.38215928641253, 'ACC-person': 92.26116924683652, 'ACC-bicycle': 85.00449939863806, 'ACC-car': 83.59453645891122, 'ACC-motorcycle': 86.56732714510954, 'ACC-airplane': 90.04278679517283, 'ACC-bus': 86.16669169460968, 'ACC-train': 95.05317060815727, 'ACC-truck': 80.15501601655153, 'ACC-boat': 78.55029232130147, 'ACC-traffic light': 89.95961871274345, 'ACC-fire hydrant': 95.09259694176022, 'ACC-stop sign': 95.18279417839675, 'ACC-parking meter': 91.88172370489505, 'ACC-bench': 66.87007081122069, 'ACC-bird': 79.30961429818704, 'ACC-cat': 90.22877466632008, 'ACC-dog': 76.37435294580516, 'ACC-horse': 91.92860484543844, 'ACC-sheep': 89.39112547374084, 'ACC-cow': 91.64687249590871, 'ACC-elephant': 90.00557513696272, 'ACC-bear': 83.46927754865867, 'ACC-zebra': 94.85594719502564, 'ACC-giraffe': 89.84310073095585, 'ACC-backpack': 59.12064749651624, 'ACC-umbrella': 84.24646997379129, 'ACC-handbag': 55.54511059005882, 'ACC-tie': 80.48980283986089, 'ACC-suitcase': 90.86887871569046, 'ACC-frisbee': 94.052, 'ACC-skis': 69.36629675942173, 'ACC-snowboard': 77.24131174842644, 'ACC-sports ball': 82.40094685810566, 'ACC-kite': 75.7924132706098, 'ACC-baseball bat': 76.26939588968224, 'ACC-baseball glove': 87.76939538704308, 'ACC-skateboard': 68.43353187516551, 'ACC-surfboard': 82.6461713912006, 'ACC-tennis racket': 89.49018466187152, 'ACC-bottle': 81.2139037865053, 'ACC-wine glass': 84.15930808144911, 'ACC-cup': 83.48287405483738, 'ACC-fork': 67.29962078406334, 'ACC-knife': 58.68853960577416, 'ACC-spoon': 72.83813610525407, 'ACC-bowl': 70.50135025207207, 'ACC-banana': 89.47447298120416, 'ACC-apple': 68.88756520117538, 'ACC-sandwich': 80.0211834712575, 'ACC-orange': 84.94348476015065, 'ACC-broccoli': 74.3501521057041, 'ACC-carrot': 75.589420629249, 'ACC-hot dog': 73.32506460286126, 'ACC-pizza': 90.45697530425552, 'ACC-donut': 81.16878473492586, 'ACC-cake': 76.99382907455833, 'ACC-chair': 66.15348358200728, 'ACC-couch': 86.55290849468213, 'ACC-potted plant': 53.52141128308766, 'ACC-bed': 87.95326261318183, 'ACC-dining table': 78.71585481501808, 'ACC-toilet': 92.55103556391958, 'ACC-tv': 88.90061596492066, 'ACC-laptop': 90.07305530814384, 'ACC-mouse': 86.16928021817473, 'ACC-remote': 67.87586088977568, 'ACC-keyboard': 74.19641498225165, 'ACC-cell phone': 80.16336050049138, 'ACC-microwave': 58.17706028394849, 'ACC-oven': 81.15963813449478, 'ACC-toaster': 57.734470158343484, 'ACC-sink': 81.93770448584347, 'ACC-refrigerator': 90.94020134971021, 'ACC-book': 62.972563111148474, 'ACC-clock': 79.07659730135931, 'ACC-vase': 78.55284547692912, 'ACC-scissors': 61.699331552029676, 'ACC-teddy bear': 87.43332728863695, 'ACC-hair drier': 41.32154954031822, 'ACC-toothbrush': 79.80802640722725, 'ACC-banner': 74.18121734711988, 'ACC-blanket': 12.19885028498947, 'ACC-bridge': 55.641081641152745, 'ACC-cardboard': 61.96716591537007, 'ACC-counter': 52.17161669523755, 'ACC-curtain': 77.56256045633961, 'ACC-door-stuff': 68.42804351415252, 'ACC-floor-wood': 77.21917597994029, 'ACC-flower': 60.7996947231797, 'ACC-fruit': 64.28223301302044, 'ACC-gravel': 36.48866309493102, 'ACC-house': 31.57365185956688, 'ACC-light': 55.03686885120393, 'ACC-mirror-stuff': 73.82066372752743, 'ACC-net': 62.57706965700262, 'ACC-pillow': 25.79456325750476, 'ACC-platform': 53.33083303104269, 'ACC-playingfield': 85.37479539181204, 'ACC-railroad': 76.41095054624904, 'ACC-river': 72.5599507984451, 'ACC-road': 83.80925735659184, 'ACC-roof': 15.980882299013555, 'ACC-sand': 70.41458141323956, 'ACC-sea': 89.07485640595476, 'ACC-shelf': 59.74673874430597, 'ACC-snow': 95.30692139611304, 'ACC-stairs': 40.0741761476002, 'ACC-tent': 11.078536746531945, 'ACC-towel': 39.243747844940415, 'ACC-wall-brick': 62.60145436004888, 'ACC-wall-stone': 24.427960323270604, 'ACC-wall-tile': 80.91641807872291, 'ACC-wall-wood': 45.9850766375506, 'ACC-water-other': 43.63980169590081, 'ACC-window-blind': 59.055000334937056, 'ACC-window-other': 69.26887193874977, 'ACC-tree-merged': 89.09440164672297, 'ACC-fence-merged': 72.90240635245084, 'ACC-ceiling-merged': 80.14361159451158, 'ACC-sky-other-merged': 96.70832246775993, 'ACC-cabinet-merged': 74.81684328235463, 'ACC-table-merged': 49.094119214562156, 'ACC-floor-other-merged': 61.141092846234976, 'ACC-pavement-merged': 67.92395077486754, 'ACC-mountain-merged': 64.47115585510834, 'ACC-grass-merged': 82.9605079320189, 'ACC-dirt-merged': 69.20856327771406, 'ACC-paper-merged': 46.235276100537014, 'ACC-food-other-merged': 49.86851347727034, 'ACC-building-other-merged': 73.27946731191568, 'ACC-rock-merged': 82.4619009560986, 'ACC-wall-other-merged': 81.54214454816785, 'ACC-rug-merged': 81.69474001838549})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.6695932104184958, 'noc@0.8': 3.270412642669008, 'noc@0.85': 3.9669300556043314, 'noc@0.9': 5.083406496927129, 'miou@iter1': 0.8391177484714953}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.54022216796875, 'precision@0.6': 67.23668670654297, 'precision@0.7': 62.10649108886719, 'precision@0.8': 51.263118743896484, 'precision@0.9': 26.19510269165039, 'cIoU': 56.56468200683594, 'mIoU': 61.90000915527344}}}
INFO:trainer.default_trainer:epochs[     2] optim steps[3700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28883/0.91420, loss_mask_bce_0: 0.45611/0.33638, loss_mask_dice_0: 0.66111/1.16547, loss_spatial_bce_0: 0.13369/0.10265, loss_spatial_dice_0: 0.18630/0.24620, loss_spatial_ce_0: 0.04272/0.13794, loss_grounding_bce_0: 0.31248/0.08690, loss_grounding_dice_0: 0.19662/0.18003, loss_grounding_ce_0: 0.00516/0.27890, loss_mask_ce_1: 0.32807/0.91632, loss_mask_bce_1: 0.45686/0.33682, loss_mask_dice_1: 0.77813/1.17241, loss_spatial_bce_1: 0.11141/0.10349, loss_spatial_dice_1: 0.18011/0.25098, loss_spatial_ce_1: 0.09243/0.14368, loss_grounding_bce_1: 0.30078/0.08705, loss_grounding_dice_1: 0.19321/0.18065, loss_grounding_ce_1: 0.00556/0.28122, loss_mask_ce_2: 0.29390/0.92111, loss_mask_bce_2: 0.46660/0.33703, loss_mask_dice_2: 0.69103/1.17054, loss_spatial_bce_2: 0.10746/0.10290, loss_spatial_dice_2: 0.16648/0.25280, loss_spatial_ce_2: 0.10537/0.14886, loss_grounding_bce_2: 0.30730/0.08677, loss_grounding_dice_2: 0.18985/0.17956, loss_grounding_ce_2: 0.00485/0.28485, loss_mask_ce_3: 0.31668/0.92281, loss_mask_bce_3: 0.46244/0.33772, loss_mask_dice_3: 0.64305/1.16862, loss_spatial_bce_3: 0.10814/0.10428, loss_spatial_dice_3: 0.17640/0.25517, loss_spatial_ce_3: 0.12622/0.15940, loss_grounding_bce_3: 0.31605/0.08697, loss_grounding_dice_3: 0.19571/0.17952, loss_grounding_ce_3: 0.00930/0.28476, loss_mask_ce_4: 0.29777/0.92375, loss_mask_bce_4: 0.44570/0.33825, loss_mask_dice_4: 0.67997/1.18668, loss_spatial_bce_4: 0.11619/0.10705, loss_spatial_dice_4: 0.19095/0.26175, loss_spatial_ce_4: 0.08933/0.17115, loss_grounding_bce_4: 0.30439/0.08756, loss_grounding_dice_4: 0.19245/0.18268, loss_grounding_ce_4: 0.00833/0.28820, loss_mask_ce_5: 0.30077/0.93451, loss_mask_bce_5: 0.48322/0.34108, loss_mask_dice_5: 0.71489/1.19181, loss_spatial_bce_5: 0.11560/0.10867, loss_spatial_dice_5: 0.19474/0.26635, loss_spatial_ce_5: 0.08793/0.18211, loss_grounding_bce_5: 0.31738/0.08801, loss_grounding_dice_5: 0.19684/0.18330, loss_grounding_ce_5: 0.00752/0.29890, loss_mask_ce_6: 0.36871/0.97229, loss_mask_bce_6: 0.47092/0.34326, loss_mask_dice_6: 0.72117/1.19519, loss_spatial_bce_6: 0.11229/0.11264, loss_spatial_dice_6: 0.20052/0.26951, loss_spatial_ce_6: 0.15451/0.20454, loss_grounding_bce_6: 0.30138/0.08930, loss_grounding_dice_6: 0.19295/0.18388, loss_grounding_ce_6: 0.00587/0.32216, loss_mask_ce_7: 0.41752/1.01189, loss_mask_bce_7: 0.45593/0.35068, loss_mask_dice_7: 0.71304/1.24924, loss_spatial_bce_7: 0.12144/0.12473, loss_spatial_dice_7: 0.22622/0.29492, loss_spatial_ce_7: 0.12318/0.24747, loss_grounding_bce_7: 0.29400/0.09049, loss_grounding_dice_7: 0.19163/0.19071, loss_grounding_ce_7: 0.00635/0.36532, loss_mask_ce_8: 0.29495/1.12699, loss_mask_bce_8: 0.47842/0.36340, loss_mask_dice_8: 0.87894/1.32396, loss_spatial_bce_8: 0.14774/0.14518, loss_spatial_dice_8: 0.23441/0.33754, loss_spatial_ce_8: 0.42152/0.29675, loss_grounding_bce_8: 0.30470/0.09391, loss_grounding_dice_8: 0.20995/0.20131, loss_grounding_ce_8: 0.00529/0.44217, loss_mask_ce_9: 1.89257/3.75001, loss_mask_bce_9: 0.42978/0.39074, loss_mask_dice_9: 0.96562/1.90070, loss_spatial_bce_9: 0.74265/0.34773, loss_spatial_dice_9: 0.93152/0.83114, loss_spatial_ce_9: 2.83273/1.58949, loss_grounding_bce_9: 0.28029/0.10513, loss_grounding_dice_9: 0.20906/0.28222, loss_grounding_ce_9: 0.08256/0.78415] items per batch[64] items per second[0.13] total items[236800] mini batches[  3700] memory[7324] epoch remaining[1:26:11]
INFO:trainer.default_trainer:epochs[     2] optim steps[3800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16439/0.91143, loss_mask_bce_0: 0.16744/0.33622, loss_mask_dice_0: 0.34571/1.16420, loss_spatial_bce_0: 0.04269/0.10225, loss_spatial_dice_0: 0.08627/0.24524, loss_spatial_ce_0: 0.00868/0.13613, loss_grounding_bce_0: 0.01559/0.08690, loss_grounding_dice_0: 0.07739/0.17983, loss_grounding_ce_0: 0.23878/0.27880, loss_mask_ce_1: 0.17507/0.91336, loss_mask_bce_1: 0.15973/0.33662, loss_mask_dice_1: 0.32970/1.17130, loss_spatial_bce_1: 0.04428/0.10309, loss_spatial_dice_1: 0.08101/0.25004, loss_spatial_ce_1: 0.00939/0.14211, loss_grounding_bce_1: 0.01512/0.08704, loss_grounding_dice_1: 0.08405/0.18051, loss_grounding_ce_1: 0.23723/0.28114, loss_mask_ce_2: 0.17639/0.91829, loss_mask_bce_2: 0.16171/0.33674, loss_mask_dice_2: 0.32670/1.16958, loss_spatial_bce_2: 0.04590/0.10251, loss_spatial_dice_2: 0.09301/0.25181, loss_spatial_ce_2: 0.01325/0.14724, loss_grounding_bce_2: 0.01460/0.08677, loss_grounding_dice_2: 0.07501/0.17942, loss_grounding_ce_2: 0.27032/0.28485, loss_mask_ce_3: 0.16247/0.92010, loss_mask_bce_3: 0.16071/0.33747, loss_mask_dice_3: 0.34876/1.16700, loss_spatial_bce_3: 0.04438/0.10388, loss_spatial_dice_3: 0.08561/0.25415, loss_spatial_ce_3: 0.01469/0.15773, loss_grounding_bce_3: 0.01350/0.08700, loss_grounding_dice_3: 0.07039/0.17940, loss_grounding_ce_3: 0.28862/0.28477, loss_mask_ce_4: 0.17689/0.92127, loss_mask_bce_4: 0.15438/0.33801, loss_mask_dice_4: 0.32513/1.18547, loss_spatial_bce_4: 0.04212/0.10662, loss_spatial_dice_4: 0.08043/0.26068, loss_spatial_ce_4: 0.02610/0.16956, loss_grounding_bce_4: 0.01365/0.08758, loss_grounding_dice_4: 0.06861/0.18254, loss_grounding_ce_4: 0.28469/0.28778, loss_mask_ce_5: 0.16884/0.93184, loss_mask_bce_5: 0.15548/0.34089, loss_mask_dice_5: 0.32163/1.19041, loss_spatial_bce_5: 0.04169/0.10818, loss_spatial_dice_5: 0.08522/0.26514, loss_spatial_ce_5: 0.02212/0.18034, loss_grounding_bce_5: 0.01539/0.08804, loss_grounding_dice_5: 0.07918/0.18325, loss_grounding_ce_5: 0.28716/0.29849, loss_mask_ce_6: 0.26638/0.96898, loss_mask_bce_6: 0.15896/0.34296, loss_mask_dice_6: 0.33351/1.19397, loss_spatial_bce_6: 0.04320/0.11214, loss_spatial_dice_6: 0.08911/0.26833, loss_spatial_ce_6: 0.03168/0.20303, loss_grounding_bce_6: 0.01567/0.08921, loss_grounding_dice_6: 0.07949/0.18380, loss_grounding_ce_6: 0.33309/0.32192, loss_mask_ce_7: 0.31822/1.00956, loss_mask_bce_7: 0.17011/0.35042, loss_mask_dice_7: 0.35016/1.24796, loss_spatial_bce_7: 0.05379/0.12419, loss_spatial_dice_7: 0.10481/0.29379, loss_spatial_ce_7: 0.09596/0.24553, loss_grounding_bce_7: 0.01467/0.09050, loss_grounding_dice_7: 0.07994/0.19058, loss_grounding_ce_7: 0.37412/0.36495, loss_mask_ce_8: 0.40302/1.12409, loss_mask_bce_8: 0.17186/0.36327, loss_mask_dice_8: 0.38077/1.32268, loss_spatial_bce_8: 0.11111/0.14463, loss_spatial_dice_8: 0.15110/0.33627, loss_spatial_ce_8: 0.09018/0.29474, loss_grounding_bce_8: 0.01629/0.09389, loss_grounding_dice_8: 0.08073/0.20124, loss_grounding_ce_8: 0.38344/0.44109, loss_mask_ce_9: 3.77425/3.74400, loss_mask_bce_9: 0.15661/0.39076, loss_mask_dice_9: 0.53365/1.89805, loss_spatial_bce_9: 0.42465/0.34746, loss_spatial_dice_9: 0.85915/0.83093, loss_spatial_ce_9: 1.82621/1.58734, loss_grounding_bce_9: 0.01758/0.10525, loss_grounding_dice_9: 0.12895/0.28214, loss_grounding_ce_9: 0.55533/0.78115] items per batch[64] items per second[0.22] total items[243200] mini batches[  3800] memory[7324] epoch remaining[1:21:33]
INFO:trainer.default_trainer:epochs[     2] optim steps[3900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35275/0.91188, loss_mask_bce_0: 0.08076/0.33627, loss_mask_dice_0: 0.26017/1.16646, loss_spatial_bce_0: 0.04814/0.10190, loss_spatial_dice_0: 0.10811/0.24464, loss_spatial_ce_0: 0.00479/0.13472, loss_grounding_bce_0: 0.03505/0.08667, loss_grounding_dice_0: 0.10901/0.17998, loss_grounding_ce_0: 0.15290/0.27815, loss_mask_ce_1: 0.54753/0.91445, loss_mask_bce_1: 0.08068/0.33664, loss_mask_dice_1: 0.22583/1.17330, loss_spatial_bce_1: 0.04969/0.10276, loss_spatial_dice_1: 0.12740/0.24938, loss_spatial_ce_1: 0.01691/0.14059, loss_grounding_bce_1: 0.03493/0.08682, loss_grounding_dice_1: 0.10485/0.18072, loss_grounding_ce_1: 0.16126/0.28035, loss_mask_ce_2: 0.42425/0.91957, loss_mask_bce_2: 0.08032/0.33677, loss_mask_dice_2: 0.24256/1.17141, loss_spatial_bce_2: 0.04751/0.10213, loss_spatial_dice_2: 0.11119/0.25107, loss_spatial_ce_2: 0.02106/0.14602, loss_grounding_bce_2: 0.03552/0.08654, loss_grounding_dice_2: 0.11998/0.17955, loss_grounding_ce_2: 0.16894/0.28413, loss_mask_ce_3: 0.59782/0.92145, loss_mask_bce_3: 0.08311/0.33758, loss_mask_dice_3: 0.26995/1.16891, loss_spatial_bce_3: 0.04196/0.10346, loss_spatial_dice_3: 0.11668/0.25345, loss_spatial_ce_3: 0.02948/0.15623, loss_grounding_bce_3: 0.03623/0.08675, loss_grounding_dice_3: 0.10056/0.17954, loss_grounding_ce_3: 0.15335/0.28395, loss_mask_ce_4: 0.43173/0.92200, loss_mask_bce_4: 0.08659/0.33805, loss_mask_dice_4: 0.25561/1.18746, loss_spatial_bce_4: 0.04156/0.10619, loss_spatial_dice_4: 0.10260/0.25984, loss_spatial_ce_4: 0.05508/0.16826, loss_grounding_bce_4: 0.03595/0.08738, loss_grounding_dice_4: 0.09113/0.18272, loss_grounding_ce_4: 0.16720/0.28696, loss_mask_ce_5: 0.42502/0.93262, loss_mask_bce_5: 0.08006/0.34095, loss_mask_dice_5: 0.24303/1.19213, loss_spatial_bce_5: 0.04389/0.10771, loss_spatial_dice_5: 0.09703/0.26429, loss_spatial_ce_5: 0.07035/0.17935, loss_grounding_bce_5: 0.03849/0.08782, loss_grounding_dice_5: 0.11506/0.18330, loss_grounding_ce_5: 0.16611/0.29790, loss_mask_ce_6: 0.79927/0.96970, loss_mask_bce_6: 0.08652/0.34302, loss_mask_dice_6: 0.21689/1.19621, loss_spatial_bce_6: 0.04827/0.11184, loss_spatial_dice_6: 0.11736/0.26753, loss_spatial_ce_6: 0.05830/0.20125, loss_grounding_bce_6: 0.03745/0.08893, loss_grounding_dice_6: 0.09601/0.18387, loss_grounding_ce_6: 0.13886/0.32153, loss_mask_ce_7: 0.51714/1.01065, loss_mask_bce_7: 0.08350/0.35039, loss_mask_dice_7: 0.25263/1.25039, loss_spatial_bce_7: 0.04841/0.12383, loss_spatial_dice_7: 0.13731/0.29286, loss_spatial_ce_7: 0.13085/0.24380, loss_grounding_bce_7: 0.03968/0.09027, loss_grounding_dice_7: 0.10606/0.19059, loss_grounding_ce_7: 0.20998/0.36424, loss_mask_ce_8: 0.63291/1.12510, loss_mask_bce_8: 0.09185/0.36318, loss_mask_dice_8: 0.25968/1.32501, loss_spatial_bce_8: 0.05832/0.14404, loss_spatial_dice_8: 0.17801/0.33529, loss_spatial_ce_8: 0.14689/0.29329, loss_grounding_bce_8: 0.04165/0.09369, loss_grounding_dice_8: 0.12405/0.20132, loss_grounding_ce_8: 0.17376/0.44059, loss_mask_ce_9: 2.74425/3.74745, loss_mask_bce_9: 0.09094/0.39089, loss_mask_dice_9: 0.34549/1.90192, loss_spatial_bce_9: 0.37540/0.34688, loss_spatial_dice_9: 0.79775/0.83094, loss_spatial_ce_9: 1.21312/1.58680, loss_grounding_bce_9: 0.03915/0.10494, loss_grounding_dice_9: 0.16397/0.28219, loss_grounding_ce_9: 0.29722/0.77954] items per batch[64] items per second[0.21] total items[249600] mini batches[  3900] memory[7324] epoch remaining[1:17:56]
INFO:trainer.default_trainer:epochs[     2] optim steps[4000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77788/0.91336, loss_mask_bce_0: 0.48971/0.33735, loss_mask_dice_0: 0.72008/1.16371, loss_spatial_bce_0: 0.11412/0.10207, loss_spatial_dice_0: 0.21949/0.24421, loss_spatial_ce_0: 0.09204/0.13352, loss_grounding_bce_0: 0.05439/0.08692, loss_grounding_dice_0: 0.14310/0.17983, loss_grounding_ce_0: 0.25819/0.27766, loss_mask_ce_1: 0.82652/0.91619, loss_mask_bce_1: 0.50588/0.33777, loss_mask_dice_1: 0.70319/1.17121, loss_spatial_bce_1: 0.11495/0.10295, loss_spatial_dice_1: 0.20372/0.24898, loss_spatial_ce_1: 0.08693/0.13912, loss_grounding_bce_1: 0.05190/0.08703, loss_grounding_dice_1: 0.14205/0.18051, loss_grounding_ce_1: 0.25993/0.28023, loss_mask_ce_2: 0.87754/0.92156, loss_mask_bce_2: 0.49738/0.33784, loss_mask_dice_2: 0.67239/1.16920, loss_spatial_bce_2: 0.11650/0.10230, loss_spatial_dice_2: 0.22097/0.25063, loss_spatial_ce_2: 0.07127/0.14457, loss_grounding_bce_2: 0.05396/0.08675, loss_grounding_dice_2: 0.14107/0.17931, loss_grounding_ce_2: 0.30273/0.28374, loss_mask_ce_3: 0.80740/0.92308, loss_mask_bce_3: 0.48593/0.33863, loss_mask_dice_3: 0.71598/1.16668, loss_spatial_bce_3: 0.10847/0.10366, loss_spatial_dice_3: 0.21379/0.25296, loss_spatial_ce_3: 0.10238/0.15473, loss_grounding_bce_3: 0.05112/0.08698, loss_grounding_dice_3: 0.13965/0.17930, loss_grounding_ce_3: 0.28145/0.28367, loss_mask_ce_4: 0.94993/0.92338, loss_mask_bce_4: 0.41768/0.33916, loss_mask_dice_4: 0.63049/1.18498, loss_spatial_bce_4: 0.12419/0.10643, loss_spatial_dice_4: 0.21693/0.25940, loss_spatial_ce_4: 0.14998/0.16699, loss_grounding_bce_4: 0.05033/0.08759, loss_grounding_dice_4: 0.11777/0.18244, loss_grounding_ce_4: 0.31383/0.28686, loss_mask_ce_5: 0.97989/0.93473, loss_mask_bce_5: 0.43004/0.34208, loss_mask_dice_5: 0.71258/1.18934, loss_spatial_bce_5: 0.11589/0.10789, loss_spatial_dice_5: 0.23912/0.26374, loss_spatial_ce_5: 0.11291/0.17805, loss_grounding_bce_5: 0.05271/0.08806, loss_grounding_dice_5: 0.12382/0.18317, loss_grounding_ce_5: 0.37682/0.29752, loss_mask_ce_6: 1.13877/0.97155, loss_mask_bce_6: 0.41926/0.34403, loss_mask_dice_6: 0.69797/1.19372, loss_spatial_bce_6: 0.11775/0.11204, loss_spatial_dice_6: 0.24220/0.26702, loss_spatial_ce_6: 0.19888/0.19989, loss_grounding_bce_6: 0.05139/0.08911, loss_grounding_dice_6: 0.14177/0.18363, loss_grounding_ce_6: 0.33463/0.32105, loss_mask_ce_7: 1.13397/1.01223, loss_mask_bce_7: 0.40925/0.35154, loss_mask_dice_7: 0.71638/1.24847, loss_spatial_bce_7: 0.12764/0.12389, loss_spatial_dice_7: 0.25921/0.29217, loss_spatial_ce_7: 0.19741/0.24253, loss_grounding_bce_7: 0.05265/0.09050, loss_grounding_dice_7: 0.11909/0.19048, loss_grounding_ce_7: 0.35245/0.36434, loss_mask_ce_8: 1.12561/1.12649, loss_mask_bce_8: 0.49295/0.36420, loss_mask_dice_8: 0.74681/1.32241, loss_spatial_bce_8: 0.14677/0.14414, loss_spatial_dice_8: 0.29406/0.33452, loss_spatial_ce_8: 0.33876/0.29187, loss_grounding_bce_8: 0.05822/0.09395, loss_grounding_dice_8: 0.13750/0.20121, loss_grounding_ce_8: 0.30880/0.43950, loss_mask_ce_9: 3.10071/3.74705, loss_mask_bce_9: 0.50476/0.39245, loss_mask_dice_9: 0.99242/1.90134, loss_spatial_bce_9: 0.50687/0.34734, loss_spatial_dice_9: 0.89591/0.83116, loss_spatial_ce_9: 2.45869/1.58381, loss_grounding_bce_9: 0.04758/0.10542, loss_grounding_dice_9: 0.15294/0.28223, loss_grounding_ce_9: 0.46932/0.77685] items per batch[64] items per second[0.21] total items[256000] mini batches[  4000] memory[7324] epoch remaining[1:13:27]
INFO:trainer.default_trainer:epochs[     2] optim steps[4100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.33126/0.91496, loss_mask_bce_0: 0.99376/0.33645, loss_mask_dice_0: 5.56470/1.16281, loss_spatial_bce_0: 0.12198/0.10181, loss_spatial_dice_0: 0.29925/0.24361, loss_spatial_ce_0: 0.06367/0.13286, loss_grounding_bce_0: 0.14463/0.08686, loss_grounding_dice_0: 0.11892/0.17970, loss_grounding_ce_0: 0.24780/0.27690, loss_mask_ce_1: 1.21951/0.91753, loss_mask_bce_1: 0.96884/0.33692, loss_mask_dice_1: 5.07724/1.17042, loss_spatial_bce_1: 0.12200/0.10272, loss_spatial_dice_1: 0.28053/0.24833, loss_spatial_ce_1: 0.05952/0.13839, loss_grounding_bce_1: 0.15111/0.08697, loss_grounding_dice_1: 0.11428/0.18032, loss_grounding_ce_1: 0.25317/0.27972, loss_mask_ce_2: 1.23847/0.92324, loss_mask_bce_2: 0.97189/0.33693, loss_mask_dice_2: 5.17780/1.16865, loss_spatial_bce_2: 0.13275/0.10209, loss_spatial_dice_2: 0.28923/0.25000, loss_spatial_ce_2: 0.06283/0.14382, loss_grounding_bce_2: 0.16278/0.08667, loss_grounding_dice_2: 0.10880/0.17909, loss_grounding_ce_2: 0.26477/0.28333, loss_mask_ce_3: 1.22167/0.92458, loss_mask_bce_3: 0.92602/0.33779, loss_mask_dice_3: 5.05940/1.16599, loss_spatial_bce_3: 0.13061/0.10346, loss_spatial_dice_3: 0.32787/0.25227, loss_spatial_ce_3: 0.06876/0.15377, loss_grounding_bce_3: 0.12826/0.08691, loss_grounding_dice_3: 0.11552/0.17915, loss_grounding_ce_3: 0.25539/0.28315, loss_mask_ce_4: 1.23763/0.92505, loss_mask_bce_4: 0.96625/0.33829, loss_mask_dice_4: 5.39106/1.18433, loss_spatial_bce_4: 0.14546/0.10622, loss_spatial_dice_4: 0.32066/0.25868, loss_spatial_ce_4: 0.05895/0.16617, loss_grounding_bce_4: 0.16051/0.08751, loss_grounding_dice_4: 0.11959/0.18227, loss_grounding_ce_4: 0.26215/0.28658, loss_mask_ce_5: 1.28219/0.93607, loss_mask_bce_5: 0.96907/0.34119, loss_mask_dice_5: 5.46701/1.18873, loss_spatial_bce_5: 0.17007/0.10763, loss_spatial_dice_5: 0.29323/0.26303, loss_spatial_ce_5: 0.08018/0.17762, loss_grounding_bce_5: 0.17771/0.08797, loss_grounding_dice_5: 0.11580/0.18290, loss_grounding_ce_5: 0.27143/0.29711, loss_mask_ce_6: 1.16030/0.97295, loss_mask_bce_6: 0.88235/0.34319, loss_mask_dice_6: 5.63701/1.19305, loss_spatial_bce_6: 0.15023/0.11182, loss_spatial_dice_6: 0.32615/0.26637, loss_spatial_ce_6: 0.14211/0.19898, loss_grounding_bce_6: 0.15464/0.08901, loss_grounding_dice_6: 0.11864/0.18340, loss_grounding_ce_6: 0.26473/0.32054, loss_mask_ce_7: 1.30531/1.01378, loss_mask_bce_7: 0.89577/0.35074, loss_mask_dice_7: 6.25646/1.24816, loss_spatial_bce_7: 0.11311/0.12356, loss_spatial_dice_7: 0.37506/0.29156, loss_spatial_ce_7: 0.13305/0.24176, loss_grounding_bce_7: 0.15196/0.09046, loss_grounding_dice_7: 0.11941/0.19020, loss_grounding_ce_7: 0.26852/0.36427, loss_mask_ce_8: 1.54527/1.12820, loss_mask_bce_8: 0.92345/0.36326, loss_mask_dice_8: 6.03813/1.32141, loss_spatial_bce_8: 0.11116/0.14402, loss_spatial_dice_8: 0.43489/0.33390, loss_spatial_ce_8: 0.10550/0.29103, loss_grounding_bce_8: 0.16204/0.09383, loss_grounding_dice_8: 0.10991/0.20088, loss_grounding_ce_8: 0.42227/0.43826, loss_mask_ce_9: 6.42971/3.74372, loss_mask_bce_9: 0.88179/0.39157, loss_mask_dice_9: 8.40174/1.90156, loss_spatial_bce_9: 0.22736/0.34734, loss_spatial_dice_9: 0.89981/0.83092, loss_spatial_ce_9: 1.65901/1.58282, loss_grounding_bce_9: 0.14646/0.10526, loss_grounding_dice_9: 0.16281/0.28203, loss_grounding_ce_9: 2.34931/0.77309] items per batch[64] items per second[0.21] total items[262400] mini batches[  4100] memory[7324] epoch remaining[1:08:59]
INFO:trainer.default_trainer:epochs[     2] optim steps[4200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66529/0.91577, loss_mask_bce_0: 0.53496/0.33714, loss_mask_dice_0: 0.45500/1.16377, loss_spatial_bce_0: 0.15584/0.10190, loss_spatial_dice_0: 0.25051/0.24335, loss_spatial_ce_0: 0.14484/0.13208, loss_grounding_bce_0: 0.31230/0.08727, loss_grounding_dice_0: 0.15370/0.17984, loss_grounding_ce_0: 0.04525/0.27713, loss_mask_ce_1: 0.65685/0.91831, loss_mask_bce_1: 0.53319/0.33753, loss_mask_dice_1: 0.45392/1.17166, loss_spatial_bce_1: 0.16495/0.10277, loss_spatial_dice_1: 0.25871/0.24795, loss_spatial_ce_1: 0.12682/0.13733, loss_grounding_bce_1: 0.17313/0.08733, loss_grounding_dice_1: 0.13334/0.18050, loss_grounding_ce_1: 0.16237/0.27994, loss_mask_ce_2: 0.55814/0.92417, loss_mask_bce_2: 0.52048/0.33750, loss_mask_dice_2: 0.44836/1.16967, loss_spatial_bce_2: 0.15596/0.10214, loss_spatial_dice_2: 0.26134/0.24966, loss_spatial_ce_2: 0.17729/0.14264, loss_grounding_bce_2: 0.27587/0.08704, loss_grounding_dice_2: 0.15103/0.17937, loss_grounding_ce_2: 0.03855/0.28391, loss_mask_ce_3: 0.64633/0.92542, loss_mask_bce_3: 0.50197/0.33839, loss_mask_dice_3: 0.46196/1.16686, loss_spatial_bce_3: 0.16781/0.10350, loss_spatial_dice_3: 0.27607/0.25184, loss_spatial_ce_3: 0.28336/0.15270, loss_grounding_bce_3: 0.16827/0.08725, loss_grounding_dice_3: 0.13812/0.17929, loss_grounding_ce_3: 0.15216/0.28356, loss_mask_ce_4: 1.19034/0.92643, loss_mask_bce_4: 0.27222/0.33893, loss_mask_dice_4: 0.40668/1.18548, loss_spatial_bce_4: 0.22455/0.10624, loss_spatial_dice_4: 0.27625/0.25819, loss_spatial_ce_4: 0.22142/0.16504, loss_grounding_bce_4: 0.15770/0.08785, loss_grounding_dice_4: 0.13120/0.18239, loss_grounding_ce_4: 0.15208/0.28731, loss_mask_ce_5: 0.82537/0.93743, loss_mask_bce_5: 0.39076/0.34189, loss_mask_dice_5: 0.44742/1.18974, loss_spatial_bce_5: 0.19721/0.10758, loss_spatial_dice_5: 0.26266/0.26249, loss_spatial_ce_5: 0.20038/0.17657, loss_grounding_bce_5: 0.26248/0.08836, loss_grounding_dice_5: 0.14285/0.18294, loss_grounding_ce_5: 0.05902/0.29783, loss_mask_ce_6: 0.82893/0.97400, loss_mask_bce_6: 0.40117/0.34393, loss_mask_dice_6: 0.46588/1.19392, loss_spatial_bce_6: 0.22781/0.11185, loss_spatial_dice_6: 0.27529/0.26599, loss_spatial_ce_6: 0.42076/0.19773, loss_grounding_bce_6: 0.24820/0.08951, loss_grounding_dice_6: 0.14017/0.18357, loss_grounding_ce_6: 0.04573/0.32107, loss_mask_ce_7: 1.00992/1.01546, loss_mask_bce_7: 0.41283/0.35136, loss_mask_dice_7: 0.42174/1.24929, loss_spatial_bce_7: 0.26865/0.12361, loss_spatial_dice_7: 0.35360/0.29115, loss_spatial_ce_7: 0.57918/0.24056, loss_grounding_bce_7: 0.28873/0.09096, loss_grounding_dice_7: 0.14496/0.19035, loss_grounding_ce_7: 0.04318/0.36509, loss_mask_ce_8: 1.10475/1.12950, loss_mask_bce_8: 0.33552/0.36400, loss_mask_dice_8: 0.45257/1.32224, loss_spatial_bce_8: 0.22075/0.14397, loss_spatial_dice_8: 0.36204/0.33342, loss_spatial_ce_8: 0.26572/0.29015, loss_grounding_bce_8: 0.19241/0.09438, loss_grounding_dice_8: 0.16026/0.20113, loss_grounding_ce_8: 0.23580/0.43893, loss_mask_ce_9: 1.90363/3.74169, loss_mask_bce_9: 0.48940/0.39231, loss_mask_dice_9: 0.50020/1.90234, loss_spatial_bce_9: 0.40774/0.34730, loss_spatial_dice_9: 0.72240/0.83050, loss_spatial_ce_9: 1.88458/1.58205, loss_grounding_bce_9: 0.24873/0.10580, loss_grounding_dice_9: 0.14263/0.28212, loss_grounding_ce_9: 0.08783/0.76977] items per batch[64] items per second[0.21] total items[268800] mini batches[  4200] memory[7324] epoch remaining[1:04:01]
INFO:trainer.default_trainer:epochs[     2] optim steps[4300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83842/0.91632, loss_mask_bce_0: 0.54897/0.33683, loss_mask_dice_0: 0.85706/1.16678, loss_spatial_bce_0: 0.10175/0.10151, loss_spatial_dice_0: 0.15565/0.24279, loss_spatial_ce_0: 0.10737/0.13089, loss_grounding_bce_0: 0.16608/0.08726, loss_grounding_dice_0: 0.12725/0.17943, loss_grounding_ce_0: 0.15141/0.27623, loss_mask_ce_1: 0.82933/0.91888, loss_mask_bce_1: 0.52295/0.33730, loss_mask_dice_1: 0.86158/1.17432, loss_spatial_bce_1: 0.11497/0.10241, loss_spatial_dice_1: 0.16633/0.24739, loss_spatial_ce_1: 0.11186/0.13625, loss_grounding_bce_1: 0.16281/0.08727, loss_grounding_dice_1: 0.12406/0.18012, loss_grounding_ce_1: 0.16238/0.27915, loss_mask_ce_2: 0.81044/0.92498, loss_mask_bce_2: 0.53255/0.33728, loss_mask_dice_2: 0.86010/1.17284, loss_spatial_bce_2: 0.10646/0.10175, loss_spatial_dice_2: 0.16065/0.24910, loss_spatial_ce_2: 0.11924/0.14154, loss_grounding_bce_2: 0.16961/0.08700, loss_grounding_dice_2: 0.12691/0.17896, loss_grounding_ce_2: 0.13606/0.28322, loss_mask_ce_3: 0.88340/0.92620, loss_mask_bce_3: 0.52057/0.33820, loss_mask_dice_3: 0.86113/1.16987, loss_spatial_bce_3: 0.10222/0.10309, loss_spatial_dice_3: 0.17665/0.25122, loss_spatial_ce_3: 0.07370/0.15136, loss_grounding_bce_3: 0.16995/0.08724, loss_grounding_dice_3: 0.13278/0.17887, loss_grounding_ce_3: 0.19682/0.28324, loss_mask_ce_4: 0.89696/0.92733, loss_mask_bce_4: 0.55967/0.33879, loss_mask_dice_4: 0.90063/1.18861, loss_spatial_bce_4: 0.11393/0.10584, loss_spatial_dice_4: 0.18935/0.25753, loss_spatial_ce_4: 0.04815/0.16380, loss_grounding_bce_4: 0.16760/0.08787, loss_grounding_dice_4: 0.13238/0.18196, loss_grounding_ce_4: 0.12458/0.28673, loss_mask_ce_5: 0.90990/0.93822, loss_mask_bce_5: 0.56930/0.34169, loss_mask_dice_5: 0.91742/1.19288, loss_spatial_bce_5: 0.09972/0.10716, loss_spatial_dice_5: 0.17855/0.26188, loss_spatial_ce_5: 0.04356/0.17512, loss_grounding_bce_5: 0.16300/0.08839, loss_grounding_dice_5: 0.12410/0.18256, loss_grounding_ce_5: 0.13916/0.29710, loss_mask_ce_6: 0.86090/0.97455, loss_mask_bce_6: 0.54983/0.34379, loss_mask_dice_6: 0.88681/1.19693, loss_spatial_bce_6: 0.10969/0.11142, loss_spatial_dice_6: 0.17514/0.26535, loss_spatial_ce_6: 0.04717/0.19637, loss_grounding_bce_6: 0.16066/0.08954, loss_grounding_dice_6: 0.11907/0.18328, loss_grounding_ce_6: 0.13997/0.32072, loss_mask_ce_7: 0.83194/1.01640, loss_mask_bce_7: 0.56671/0.35120, loss_mask_dice_7: 0.91823/1.25230, loss_spatial_bce_7: 0.09311/0.12312, loss_spatial_dice_7: 0.18139/0.29056, loss_spatial_ce_7: 0.11357/0.23923, loss_grounding_bce_7: 0.16162/0.09107, loss_grounding_dice_7: 0.11462/0.19002, loss_grounding_ce_7: 0.12501/0.36411, loss_mask_ce_8: 0.85116/1.13048, loss_mask_bce_8: 0.51306/0.36390, loss_mask_dice_8: 0.96532/1.32578, loss_spatial_bce_8: 0.10509/0.14339, loss_spatial_dice_8: 0.20537/0.33285, loss_spatial_ce_8: 0.17315/0.28897, loss_grounding_bce_8: 0.15584/0.09450, loss_grounding_dice_8: 0.10829/0.20082, loss_grounding_ce_8: 0.11427/0.43853, loss_mask_ce_9: 4.15962/3.74429, loss_mask_bce_9: 0.54671/0.39201, loss_mask_dice_9: 1.44782/1.90827, loss_spatial_bce_9: 0.26492/0.34690, loss_spatial_dice_9: 0.86993/0.83045, loss_spatial_ce_9: 1.20573/1.58076, loss_grounding_bce_9: 0.15864/0.10579, loss_grounding_dice_9: 0.14296/0.28161, loss_grounding_ce_9: 0.53186/0.76896] items per batch[64] items per second[0.22] total items[275200] mini batches[  4300] memory[7324] epoch remaining[0:58:47]
INFO:trainer.default_trainer:epochs[     2] optim steps[4400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.85948/0.91501, loss_mask_bce_0: 0.61342/0.33685, loss_mask_dice_0: 5.78360/1.16458, loss_spatial_bce_0: 0.04403/0.10131, loss_spatial_dice_0: 0.42459/0.24203, loss_spatial_ce_0: 0.06766/0.12943, loss_grounding_bce_0: 0.01815/0.08703, loss_grounding_dice_0: 0.36937/0.17955, loss_grounding_ce_0: 0.48780/0.27713, loss_mask_ce_1: 1.88190/0.91768, loss_mask_bce_1: 0.60786/0.33735, loss_mask_dice_1: 5.58343/1.17238, loss_spatial_bce_1: 0.04245/0.10222, loss_spatial_dice_1: 0.42730/0.24663, loss_spatial_ce_1: 0.10311/0.13495, loss_grounding_bce_1: 0.02153/0.08709, loss_grounding_dice_1: 0.39854/0.18025, loss_grounding_ce_1: 0.34868/0.28009, loss_mask_ce_2: 1.95186/0.92379, loss_mask_bce_2: 0.61610/0.33729, loss_mask_dice_2: 5.74609/1.17054, loss_spatial_bce_2: 0.03981/0.10157, loss_spatial_dice_2: 0.41547/0.24830, loss_spatial_ce_2: 0.18441/0.14011, loss_grounding_bce_2: 0.02100/0.08685, loss_grounding_dice_2: 0.38443/0.17924, loss_grounding_ce_2: 0.62074/0.28391, loss_mask_ce_3: 2.12026/0.92476, loss_mask_bce_3: 0.58341/0.33827, loss_mask_dice_3: 5.77509/1.16815, loss_spatial_bce_3: 0.04089/0.10292, loss_spatial_dice_3: 0.46826/0.25049, loss_spatial_ce_3: 0.06947/0.14975, loss_grounding_bce_3: 0.02198/0.08709, loss_grounding_dice_3: 0.35944/0.17897, loss_grounding_ce_3: 0.68744/0.28360, loss_mask_ce_4: 1.83826/0.92571, loss_mask_bce_4: 0.69263/0.33876, loss_mask_dice_4: 5.98939/1.18660, loss_spatial_bce_4: 0.04640/0.10566, loss_spatial_dice_4: 0.48299/0.25674, loss_spatial_ce_4: 0.18735/0.16232, loss_grounding_bce_4: 0.02110/0.08768, loss_grounding_dice_4: 0.35382/0.18219, loss_grounding_ce_4: 0.64731/0.28674, loss_mask_ce_5: 1.85227/0.93681, loss_mask_bce_5: 0.64666/0.34161, loss_mask_dice_5: 6.03020/1.19079, loss_spatial_bce_5: 0.04911/0.10697, loss_spatial_dice_5: 0.51310/0.26113, loss_spatial_ce_5: 0.15301/0.17378, loss_grounding_bce_5: 0.01364/0.08816, loss_grounding_dice_5: 0.32335/0.18279, loss_grounding_ce_5: 0.59558/0.29742, loss_mask_ce_6: 1.96804/0.97335, loss_mask_bce_6: 0.67213/0.34375, loss_mask_dice_6: 6.16444/1.19505, loss_spatial_bce_6: 0.05470/0.11126, loss_spatial_dice_6: 0.46988/0.26456, loss_spatial_ce_6: 0.16247/0.19479, loss_grounding_bce_6: 0.01258/0.08935, loss_grounding_dice_6: 0.33641/0.18352, loss_grounding_ce_6: 0.64249/0.32107, loss_mask_ce_7: 1.93192/1.01509, loss_mask_bce_7: 0.58373/0.35117, loss_mask_dice_7: 5.93479/1.25006, loss_spatial_bce_7: 0.05417/0.12288, loss_spatial_dice_7: 0.54974/0.28974, loss_spatial_ce_7: 0.26126/0.23786, loss_grounding_bce_7: 0.01880/0.09088, loss_grounding_dice_7: 0.34001/0.19019, loss_grounding_ce_7: 0.35552/0.36446, loss_mask_ce_8: 2.17769/1.12947, loss_mask_bce_8: 0.69706/0.36391, loss_mask_dice_8: 6.08474/1.32347, loss_spatial_bce_8: 0.06333/0.14314, loss_spatial_dice_8: 0.60002/0.33202, loss_spatial_ce_8: 0.34741/0.28758, loss_grounding_bce_8: 0.01693/0.09435, loss_grounding_dice_8: 0.31777/0.20120, loss_grounding_ce_8: 0.23801/0.43943, loss_mask_ce_9: 6.32739/3.74081, loss_mask_bce_9: 0.46385/0.39150, loss_mask_dice_9: 7.96737/1.90360, loss_spatial_bce_9: 0.08405/0.34663, loss_spatial_dice_9: 0.91410/0.83044, loss_spatial_ce_9: 2.03998/1.57811, loss_grounding_bce_9: 0.02373/0.10557, loss_grounding_dice_9: 0.39353/0.28170, loss_grounding_ce_9: 1.05026/0.76807] items per batch[64] items per second[0.22] total items[281600] mini batches[  4400] memory[7324] epoch remaining[0:53:45]
INFO:trainer.default_trainer:epochs[     2] optim steps[4500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90207/0.91363, loss_mask_bce_0: 0.10387/0.33690, loss_mask_dice_0: 1.91324/1.16542, loss_spatial_bce_0: 0.02046/0.10103, loss_spatial_dice_0: 0.20336/0.24156, loss_spatial_ce_0: 0.04058/0.12814, loss_grounding_bce_0: 0.03994/0.08698, loss_grounding_dice_0: 0.06770/0.17935, loss_grounding_ce_0: 0.00250/0.27734, loss_mask_ce_1: 0.98961/0.91630, loss_mask_bce_1: 0.11616/0.33740, loss_mask_dice_1: 2.28560/1.17332, loss_spatial_bce_1: 0.02237/0.10193, loss_spatial_dice_1: 0.21506/0.24618, loss_spatial_ce_1: 0.02129/0.13389, loss_grounding_bce_1: 0.03914/0.08706, loss_grounding_dice_1: 0.06301/0.18006, loss_grounding_ce_1: 0.00249/0.28079, loss_mask_ce_2: 1.04569/0.92188, loss_mask_bce_2: 0.11600/0.33740, loss_mask_dice_2: 1.96678/1.17152, loss_spatial_bce_2: 0.02409/0.10129, loss_spatial_dice_2: 0.21438/0.24775, loss_spatial_ce_2: 0.06808/0.13890, loss_grounding_bce_2: 0.03939/0.08684, loss_grounding_dice_2: 0.06606/0.17909, loss_grounding_ce_2: 0.00225/0.28422, loss_mask_ce_3: 0.93561/0.92331, loss_mask_bce_3: 0.11963/0.33830, loss_mask_dice_3: 2.18174/1.16884, loss_spatial_bce_3: 0.02262/0.10263, loss_spatial_dice_3: 0.21642/0.24997, loss_spatial_ce_3: 0.03020/0.14831, loss_grounding_bce_3: 0.04242/0.08709, loss_grounding_dice_3: 0.04546/0.17883, loss_grounding_ce_3: 0.00256/0.28393, loss_mask_ce_4: 0.87379/0.92368, loss_mask_bce_4: 0.12525/0.33880, loss_mask_dice_4: 2.00458/1.18779, loss_spatial_bce_4: 0.02207/0.10538, loss_spatial_dice_4: 0.22687/0.25621, loss_spatial_ce_4: 0.03592/0.16132, loss_grounding_bce_4: 0.03924/0.08766, loss_grounding_dice_4: 0.04333/0.18197, loss_grounding_ce_4: 0.00207/0.28704, loss_mask_ce_5: 0.94879/0.93487, loss_mask_bce_5: 0.12897/0.34175, loss_mask_dice_5: 2.10383/1.19181, loss_spatial_bce_5: 0.02039/0.10668, loss_spatial_dice_5: 0.20574/0.26055, loss_spatial_ce_5: 0.02943/0.17280, loss_grounding_bce_5: 0.04233/0.08809, loss_grounding_dice_5: 0.04514/0.18262, loss_grounding_ce_5: 0.00507/0.29825, loss_mask_ce_6: 0.90151/0.97118, loss_mask_bce_6: 0.13293/0.34383, loss_mask_dice_6: 2.05847/1.19587, loss_spatial_bce_6: 0.02048/0.11102, loss_spatial_dice_6: 0.19915/0.26401, loss_spatial_ce_6: 0.04626/0.19354, loss_grounding_bce_6: 0.03823/0.08928, loss_grounding_dice_6: 0.05591/0.18325, loss_grounding_ce_6: 0.00128/0.32105, loss_mask_ce_7: 1.23893/1.01311, loss_mask_bce_7: 0.14146/0.35117, loss_mask_dice_7: 2.26991/1.25048, loss_spatial_bce_7: 0.03014/0.12261, loss_spatial_dice_7: 0.32903/0.28930, loss_spatial_ce_7: 0.22398/0.23677, loss_grounding_bce_7: 0.03805/0.09081, loss_grounding_dice_7: 0.04489/0.18981, loss_grounding_ce_7: 0.00252/0.36469, loss_mask_ce_8: 1.22878/1.12725, loss_mask_bce_8: 0.11631/0.36366, loss_mask_dice_8: 2.02953/1.32425, loss_spatial_bce_8: 0.03871/0.14280, loss_spatial_dice_8: 0.36801/0.33141, loss_spatial_ce_8: 0.37116/0.28701, loss_grounding_bce_8: 0.03925/0.09419, loss_grounding_dice_8: 0.04764/0.20084, loss_grounding_ce_8: 0.01825/0.44014, loss_mask_ce_9: 4.18675/3.73887, loss_mask_bce_9: 0.09914/0.39136, loss_mask_dice_9: 3.02414/1.90339, loss_spatial_bce_9: 0.14870/0.34616, loss_spatial_dice_9: 0.86603/0.83035, loss_spatial_ce_9: 1.11431/1.57618, loss_grounding_bce_9: 0.04109/0.10542, loss_grounding_dice_9: 0.05176/0.28137, loss_grounding_ce_9: 0.38648/0.76891] items per batch[64] items per second[0.22] total items[288000] mini batches[  4500] memory[7324] epoch remaining[0:48:36]
INFO:trainer.default_trainer:epochs[     2] optim steps[4600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10102/0.91271, loss_mask_bce_0: 0.15918/0.33708, loss_mask_dice_0: 0.21636/1.16542, loss_spatial_bce_0: 0.06775/0.10070, loss_spatial_dice_0: 0.08795/0.24095, loss_spatial_ce_0: 0.01899/0.12711, loss_grounding_bce_0: 0.05398/0.08682, loss_grounding_dice_0: 0.05186/0.17909, loss_grounding_ce_0: 0.03954/0.27823, loss_mask_ce_1: 0.10173/0.91490, loss_mask_bce_1: 0.16048/0.33762, loss_mask_dice_1: 0.21141/1.17351, loss_spatial_bce_1: 0.06229/0.10163, loss_spatial_dice_1: 0.07977/0.24550, loss_spatial_ce_1: 0.02097/0.13274, loss_grounding_bce_1: 0.05070/0.08690, loss_grounding_dice_1: 0.05057/0.17990, loss_grounding_ce_1: 0.03348/0.28172, loss_mask_ce_2: 0.09422/0.92064, loss_mask_bce_2: 0.16426/0.33769, loss_mask_dice_2: 0.21400/1.17168, loss_spatial_bce_2: 0.06711/0.10105, loss_spatial_dice_2: 0.08441/0.24710, loss_spatial_ce_2: 0.01825/0.13763, loss_grounding_bce_2: 0.05288/0.08672, loss_grounding_dice_2: 0.04928/0.17901, loss_grounding_ce_2: 0.02692/0.28466, loss_mask_ce_3: 0.08973/0.92238, loss_mask_bce_3: 0.16710/0.33865, loss_mask_dice_3: 0.22452/1.16895, loss_spatial_bce_3: 0.06480/0.10234, loss_spatial_dice_3: 0.07864/0.24927, loss_spatial_ce_3: 0.03038/0.14685, loss_grounding_bce_3: 0.05319/0.08693, loss_grounding_dice_3: 0.05279/0.17866, loss_grounding_ce_3: 0.04309/0.28449, loss_mask_ce_4: 0.09262/0.92231, loss_mask_bce_4: 0.16100/0.33917, loss_mask_dice_4: 0.22905/1.18792, loss_spatial_bce_4: 0.06393/0.10514, loss_spatial_dice_4: 0.07706/0.25558, loss_spatial_ce_4: 0.04890/0.16031, loss_grounding_bce_4: 0.05437/0.08751, loss_grounding_dice_4: 0.05347/0.18180, loss_grounding_ce_4: 0.02515/0.28828, loss_mask_ce_5: 0.09535/0.93395, loss_mask_bce_5: 0.16325/0.34209, loss_mask_dice_5: 0.22032/1.19174, loss_spatial_bce_5: 0.06752/0.10641, loss_spatial_dice_5: 0.08703/0.25982, loss_spatial_ce_5: 0.07150/0.17182, loss_grounding_bce_5: 0.05460/0.08796, loss_grounding_dice_5: 0.05249/0.18260, loss_grounding_ce_5: 0.02766/0.29948, loss_mask_ce_6: 0.11224/0.97025, loss_mask_bce_6: 0.16059/0.34418, loss_mask_dice_6: 0.21588/1.19584, loss_spatial_bce_6: 0.06701/0.11075, loss_spatial_dice_6: 0.09165/0.26338, loss_spatial_ce_6: 0.07706/0.19249, loss_grounding_bce_6: 0.05199/0.08913, loss_grounding_dice_6: 0.05205/0.18310, loss_grounding_ce_6: 0.04584/0.32202, loss_mask_ce_7: 0.14554/1.01241, loss_mask_bce_7: 0.16549/0.35149, loss_mask_dice_7: 0.22176/1.25050, loss_spatial_bce_7: 0.06240/0.12230, loss_spatial_dice_7: 0.08776/0.28870, loss_spatial_ce_7: 0.10056/0.23581, loss_grounding_bce_7: 0.05259/0.09065, loss_grounding_dice_7: 0.05244/0.18980, loss_grounding_ce_7: 0.02749/0.36620, loss_mask_ce_8: 0.13177/1.12640, loss_mask_bce_8: 0.16278/0.36406, loss_mask_dice_8: 0.23067/1.32448, loss_spatial_bce_8: 0.08041/0.14258, loss_spatial_dice_8: 0.12832/0.33081, loss_spatial_ce_8: 0.15041/0.28599, loss_grounding_bce_8: 0.05279/0.09408, loss_grounding_dice_8: 0.05429/0.20087, loss_grounding_ce_8: 0.10470/0.44179, loss_mask_ce_9: 1.74682/3.73881, loss_mask_bce_9: 0.21690/0.39194, loss_mask_dice_9: 0.35222/1.90280, loss_spatial_bce_9: 0.46338/0.34614, loss_spatial_dice_9: 0.74755/0.83028, loss_spatial_ce_9: 1.03818/1.57455, loss_grounding_bce_9: 0.06692/0.10534, loss_grounding_dice_9: 0.07605/0.28154, loss_grounding_ce_9: 0.52369/0.77046] items per batch[64] items per second[0.21] total items[294400] mini batches[  4600] memory[7324] epoch remaining[0:43:42]
INFO:trainer.default_trainer:epochs[     2] optim steps[4700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29427/0.91347, loss_mask_bce_0: 0.07415/0.33653, loss_mask_dice_0: 0.39459/1.16246, loss_spatial_bce_0: 0.02988/0.10066, loss_spatial_dice_0: 0.17915/0.24026, loss_spatial_ce_0: 0.03686/0.12632, loss_grounding_bce_0: 0.01415/0.08673, loss_grounding_dice_0: 0.23256/0.17899, loss_grounding_ce_0: 0.28226/0.27840, loss_mask_ce_1: 0.27945/0.91530, loss_mask_bce_1: 0.07686/0.33707, loss_mask_dice_1: 0.46780/1.17059, loss_spatial_bce_1: 0.03203/0.10163, loss_spatial_dice_1: 0.17578/0.24481, loss_spatial_ce_1: 0.04264/0.13185, loss_grounding_bce_1: 0.01657/0.08684, loss_grounding_dice_1: 0.40412/0.17993, loss_grounding_ce_1: 0.07936/0.28130, loss_mask_ce_2: 0.27305/0.92098, loss_mask_bce_2: 0.07044/0.33722, loss_mask_dice_2: 0.41062/1.16863, loss_spatial_bce_2: 0.03218/0.10105, loss_spatial_dice_2: 0.18846/0.24646, loss_spatial_ce_2: 0.04681/0.13685, loss_grounding_bce_2: 0.01380/0.08668, loss_grounding_dice_2: 0.23244/0.17896, loss_grounding_ce_2: 0.22710/0.28409, loss_mask_ce_3: 0.27464/0.92293, loss_mask_bce_3: 0.07457/0.33810, loss_mask_dice_3: 0.43612/1.16591, loss_spatial_bce_3: 0.02944/0.10232, loss_spatial_dice_3: 0.16819/0.24861, loss_spatial_ce_3: 0.08818/0.14575, loss_grounding_bce_3: 0.01756/0.08688, loss_grounding_dice_3: 0.40794/0.17869, loss_grounding_ce_3: 0.07725/0.28409, loss_mask_ce_4: 0.25196/0.92237, loss_mask_bce_4: 0.06996/0.33867, loss_mask_dice_4: 0.40417/1.18488, loss_spatial_bce_4: 0.03243/0.10516, loss_spatial_dice_4: 0.21318/0.25487, loss_spatial_ce_4: 0.06130/0.15953, loss_grounding_bce_4: 0.01371/0.08743, loss_grounding_dice_4: 0.26693/0.18176, loss_grounding_ce_4: 0.23710/0.28774, loss_mask_ce_5: 0.23431/0.93434, loss_mask_bce_5: 0.07698/0.34157, loss_mask_dice_5: 0.45376/1.18903, loss_spatial_bce_5: 0.03094/0.10642, loss_spatial_dice_5: 0.18371/0.25913, loss_spatial_ce_5: 0.16389/0.17111, loss_grounding_bce_5: 0.01384/0.08788, loss_grounding_dice_5: 0.22364/0.18253, loss_grounding_ce_5: 0.12233/0.29892, loss_mask_ce_6: 0.28135/0.97033, loss_mask_bce_6: 0.07038/0.34364, loss_mask_dice_6: 0.41862/1.19303, loss_spatial_bce_6: 0.02970/0.11076, loss_spatial_dice_6: 0.19012/0.26264, loss_spatial_ce_6: 0.14241/0.19166, loss_grounding_bce_6: 0.01468/0.08904, loss_grounding_dice_6: 0.30473/0.18319, loss_grounding_ce_6: 0.28250/0.32158, loss_mask_ce_7: 0.25741/1.01270, loss_mask_bce_7: 0.07573/0.35091, loss_mask_dice_7: 0.46364/1.24736, loss_spatial_bce_7: 0.03583/0.12236, loss_spatial_dice_7: 0.22093/0.28795, loss_spatial_ce_7: 0.19879/0.23492, loss_grounding_bce_7: 0.01936/0.09055, loss_grounding_dice_7: 0.41378/0.18975, loss_grounding_ce_7: 0.18181/0.36617, loss_mask_ce_8: 0.45838/1.12687, loss_mask_bce_8: 0.07632/0.36365, loss_mask_dice_8: 0.44005/1.32153, loss_spatial_bce_8: 0.05425/0.14268, loss_spatial_dice_8: 0.30429/0.33004, loss_spatial_ce_8: 0.30640/0.28499, loss_grounding_bce_8: 0.01767/0.09394, loss_grounding_dice_8: 0.26963/0.20078, loss_grounding_ce_8: 0.45227/0.44095, loss_mask_ce_9: 2.60075/3.73538, loss_mask_bce_9: 0.09004/0.39122, loss_mask_dice_9: 0.69319/1.89906, loss_spatial_bce_9: 0.69109/0.34610, loss_spatial_dice_9: 0.88638/0.82983, loss_spatial_ce_9: 2.05824/1.57256, loss_grounding_bce_9: 0.01902/0.10516, loss_grounding_dice_9: 0.52665/0.28139, loss_grounding_ce_9: 0.39809/0.76789] items per batch[64] items per second[0.22] total items[300800] mini batches[  4700] memory[7324] epoch remaining[0:38:43]
INFO:trainer.default_trainer:epochs[     2] optim steps[4800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.44038/0.91677, loss_mask_bce_0: 0.47593/0.33633, loss_mask_dice_0: 1.05181/1.16306, loss_spatial_bce_0: 0.10776/0.10029, loss_spatial_dice_0: 0.17591/0.24012, loss_spatial_ce_0: 0.14412/0.12566, loss_grounding_bce_0: 0.12983/0.08659, loss_grounding_dice_0: 0.20229/0.17897, loss_grounding_ce_0: 0.39412/0.27978, loss_mask_ce_1: 2.41057/0.91795, loss_mask_bce_1: 0.51780/0.33691, loss_mask_dice_1: 1.07194/1.17110, loss_spatial_bce_1: 0.12146/0.10128, loss_spatial_dice_1: 0.18711/0.24465, loss_spatial_ce_1: 0.11301/0.13106, loss_grounding_bce_1: 0.13421/0.08673, loss_grounding_dice_1: 0.20219/0.17986, loss_grounding_ce_1: 0.40116/0.28296, loss_mask_ce_2: 2.47039/0.92404, loss_mask_bce_2: 0.53808/0.33697, loss_mask_dice_2: 1.03108/1.16932, loss_spatial_bce_2: 0.14029/0.10069, loss_spatial_dice_2: 0.18083/0.24633, loss_spatial_ce_2: 0.08389/0.13611, loss_grounding_bce_2: 0.13092/0.08657, loss_grounding_dice_2: 0.19796/0.17898, loss_grounding_ce_2: 0.40567/0.28563, loss_mask_ce_3: 2.58597/0.92581, loss_mask_bce_3: 0.57808/0.33787, loss_mask_dice_3: 1.20331/1.16684, loss_spatial_bce_3: 0.10636/0.10195, loss_spatial_dice_3: 0.16525/0.24840, loss_spatial_ce_3: 0.16299/0.14472, loss_grounding_bce_3: 0.12186/0.08676, loss_grounding_dice_3: 0.19986/0.17865, loss_grounding_ce_3: 0.40301/0.28594, loss_mask_ce_4: 2.83520/0.92527, loss_mask_bce_4: 0.59532/0.33846, loss_mask_dice_4: 1.16171/1.18589, loss_spatial_bce_4: 0.11390/0.10477, loss_spatial_dice_4: 0.16446/0.25469, loss_spatial_ce_4: 0.18123/0.15879, loss_grounding_bce_4: 0.13877/0.08729, loss_grounding_dice_4: 0.22179/0.18166, loss_grounding_ce_4: 0.39078/0.28936, loss_mask_ce_5: 2.74086/0.93676, loss_mask_bce_5: 0.67097/0.34139, loss_mask_dice_5: 1.19535/1.19021, loss_spatial_bce_5: 0.15925/0.10603, loss_spatial_dice_5: 0.19464/0.25892, loss_spatial_ce_5: 0.10036/0.17047, loss_grounding_bce_5: 0.14885/0.08776, loss_grounding_dice_5: 0.21582/0.18260, loss_grounding_ce_5: 0.37988/0.30008, loss_mask_ce_6: 2.83713/0.97338, loss_mask_bce_6: 0.56789/0.34341, loss_mask_dice_6: 1.19230/1.19404, loss_spatial_bce_6: 0.16750/0.11035, loss_spatial_dice_6: 0.20047/0.26233, loss_spatial_ce_6: 0.07232/0.19077, loss_grounding_bce_6: 0.15836/0.08888, loss_grounding_dice_6: 0.22523/0.18317, loss_grounding_ce_6: 0.38413/0.32369, loss_mask_ce_7: 2.79075/1.01561, loss_mask_bce_7: 0.61204/0.35088, loss_mask_dice_7: 1.22094/1.24835, loss_spatial_bce_7: 0.12119/0.12184, loss_spatial_dice_7: 0.18805/0.28769, loss_spatial_ce_7: 0.14495/0.23399, loss_grounding_bce_7: 0.14850/0.09046, loss_grounding_dice_7: 0.22074/0.18980, loss_grounding_ce_7: 0.40610/0.36818, loss_mask_ce_8: 2.76335/1.12973, loss_mask_bce_8: 0.67917/0.36361, loss_mask_dice_8: 1.23416/1.32302, loss_spatial_bce_8: 0.16825/0.14222, loss_spatial_dice_8: 0.22725/0.32982, loss_spatial_ce_8: 0.15569/0.28445, loss_grounding_bce_8: 0.16510/0.09384, loss_grounding_dice_8: 0.21497/0.20076, loss_grounding_ce_8: 0.43353/0.44250, loss_mask_ce_9: 5.17116/3.73787, loss_mask_bce_9: 1.01445/0.39124, loss_mask_dice_9: 2.78381/1.90109, loss_spatial_bce_9: 0.36762/0.34535, loss_spatial_dice_9: 0.83722/0.82986, loss_spatial_ce_9: 1.35773/1.57330, loss_grounding_bce_9: 0.19387/0.10508, loss_grounding_dice_9: 0.41760/0.28144, loss_grounding_ce_9: 0.59006/0.76777] items per batch[64] items per second[0.22] total items[307200] mini batches[  4800] memory[7324] epoch remaining[0:33:42]
INFO:trainer.default_trainer:epochs[     2] optim steps[4900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43761/0.91550, loss_mask_bce_0: 0.43773/0.33614, loss_mask_dice_0: 4.84114/1.16226, loss_spatial_bce_0: 0.10762/0.10033, loss_spatial_dice_0: 0.35310/0.23967, loss_spatial_ce_0: 0.15719/0.12504, loss_grounding_bce_0: 0.09190/0.08672, loss_grounding_dice_0: 0.49730/0.17898, loss_grounding_ce_0: 0.35192/0.28006, loss_mask_ce_1: 0.44645/0.91680, loss_mask_bce_1: 0.43093/0.33670, loss_mask_dice_1: 4.85153/1.17039, loss_spatial_bce_1: 0.10708/0.10134, loss_spatial_dice_1: 0.35583/0.24422, loss_spatial_ce_1: 0.45867/0.13058, loss_grounding_bce_1: 0.09639/0.08687, loss_grounding_dice_1: 0.59522/0.17983, loss_grounding_ce_1: 0.34350/0.28330, loss_mask_ce_2: 0.76993/0.92270, loss_mask_bce_2: 0.43312/0.33680, loss_mask_dice_2: 4.96321/1.16863, loss_spatial_bce_2: 0.13889/0.10076, loss_spatial_dice_2: 0.35525/0.24588, loss_spatial_ce_2: 0.15254/0.13567, loss_grounding_bce_2: 0.09618/0.08672, loss_grounding_dice_2: 0.53375/0.17905, loss_grounding_ce_2: 0.36263/0.28529, loss_mask_ce_3: 0.45075/0.92443, loss_mask_bce_3: 0.45901/0.33767, loss_mask_dice_3: 5.04989/1.16615, loss_spatial_bce_3: 0.14532/0.10201, loss_spatial_dice_3: 0.36749/0.24797, loss_spatial_ce_3: 0.14657/0.14409, loss_grounding_bce_3: 0.09892/0.08693, loss_grounding_dice_3: 0.52854/0.17863, loss_grounding_ce_3: 0.33140/0.28611, loss_mask_ce_4: 0.58847/0.92412, loss_mask_bce_4: 0.28331/0.33828, loss_mask_dice_4: 4.84514/1.18530, loss_spatial_bce_4: 0.14834/0.10489, loss_spatial_dice_4: 0.37790/0.25425, loss_spatial_ce_4: 0.24909/0.15810, loss_grounding_bce_4: 0.08909/0.08747, loss_grounding_dice_4: 0.41524/0.18164, loss_grounding_ce_4: 0.34341/0.28988, loss_mask_ce_5: 0.76126/0.93577, loss_mask_bce_5: 0.43917/0.34120, loss_mask_dice_5: 4.81920/1.18974, loss_spatial_bce_5: 0.11809/0.10619, loss_spatial_dice_5: 0.38178/0.25850, loss_spatial_ce_5: 0.34185/0.17008, loss_grounding_bce_5: 0.08868/0.08793, loss_grounding_dice_5: 0.56625/0.18265, loss_grounding_ce_5: 0.33764/0.30007, loss_mask_ce_6: 0.54616/0.97192, loss_mask_bce_6: 0.51463/0.34325, loss_mask_dice_6: 4.62102/1.19323, loss_spatial_bce_6: 0.11545/0.11041, loss_spatial_dice_6: 0.36677/0.26179, loss_spatial_ce_6: 0.25270/0.19014, loss_grounding_bce_6: 0.09519/0.08898, loss_grounding_dice_6: 0.45917/0.18317, loss_grounding_ce_6: 0.36720/0.32384, loss_mask_ce_7: 0.73780/1.01432, loss_mask_bce_7: 0.48829/0.35064, loss_mask_dice_7: 4.65176/1.24726, loss_spatial_bce_7: 0.17737/0.12197, loss_spatial_dice_7: 0.49106/0.28726, loss_spatial_ce_7: 0.33517/0.23354, loss_grounding_bce_7: 0.08860/0.09057, loss_grounding_dice_7: 0.60715/0.18990, loss_grounding_ce_7: 0.38503/0.36949, loss_mask_ce_8: 0.91018/1.12890, loss_mask_bce_8: 0.40687/0.36336, loss_mask_dice_8: 5.05411/1.32220, loss_spatial_bce_8: 0.17351/0.14239, loss_spatial_dice_8: 0.48988/0.32927, loss_spatial_ce_8: 0.13249/0.28371, loss_grounding_bce_8: 0.06831/0.09397, loss_grounding_dice_8: 0.58968/0.20078, loss_grounding_ce_8: 0.38180/0.44373, loss_mask_ce_9: 5.35123/3.73441, loss_mask_bce_9: 0.37965/0.39093, loss_mask_dice_9: 6.10617/1.89925, loss_spatial_bce_9: 0.15783/0.34546, loss_spatial_dice_9: 0.86147/0.82955, loss_spatial_ce_9: 1.29912/1.57274, loss_grounding_bce_9: 0.06137/0.10522, loss_grounding_dice_9: 0.59370/0.28136, loss_grounding_ce_9: 0.41932/0.76718] items per batch[64] items per second[0.22] total items[313600] mini batches[  4900] memory[7324] epoch remaining[0:28:41]
INFO:trainer.default_trainer:epochs[     2] optim steps[5000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34113/0.91617, loss_mask_bce_0: 0.41836/0.33645, loss_mask_dice_0: 0.57731/1.16267, loss_spatial_bce_0: 0.19393/0.10022, loss_spatial_dice_0: 0.21904/0.23928, loss_spatial_ce_0: 0.02374/0.12382, loss_grounding_bce_0: 0.12456/0.08664, loss_grounding_dice_0: 0.22869/0.17928, loss_grounding_ce_0: 0.06918/0.28024, loss_mask_ce_1: 0.33068/0.91731, loss_mask_bce_1: 0.41973/0.33703, loss_mask_dice_1: 0.59859/1.17032, loss_spatial_bce_1: 0.17193/0.10122, loss_spatial_dice_1: 0.20491/0.24374, loss_spatial_ce_1: 0.03222/0.12940, loss_grounding_bce_1: 0.12101/0.08676, loss_grounding_dice_1: 0.22129/0.18007, loss_grounding_ce_1: 0.07771/0.28339, loss_mask_ce_2: 0.35060/0.92327, loss_mask_bce_2: 0.42562/0.33721, loss_mask_dice_2: 0.59175/1.16894, loss_spatial_bce_2: 0.18437/0.10068, loss_spatial_dice_2: 0.21639/0.24541, loss_spatial_ce_2: 0.02935/0.13449, loss_grounding_bce_2: 0.13174/0.08664, loss_grounding_dice_2: 0.22307/0.17940, loss_grounding_ce_2: 0.08141/0.28532, loss_mask_ce_3: 0.33516/0.92510, loss_mask_bce_3: 0.42943/0.33796, loss_mask_dice_3: 0.59402/1.16644, loss_spatial_bce_3: 0.15154/0.10190, loss_spatial_dice_3: 0.19478/0.24746, loss_spatial_ce_3: 0.04753/0.14297, loss_grounding_bce_3: 0.12643/0.08682, loss_grounding_dice_3: 0.21852/0.17895, loss_grounding_ce_3: 0.06216/0.28589, loss_mask_ce_4: 0.30917/0.92496, loss_mask_bce_4: 0.43674/0.33870, loss_mask_dice_4: 0.59697/1.18543, loss_spatial_bce_4: 0.19659/0.10480, loss_spatial_dice_4: 0.21920/0.25379, loss_spatial_ce_4: 0.05047/0.15693, loss_grounding_bce_4: 0.13756/0.08741, loss_grounding_dice_4: 0.22750/0.18207, loss_grounding_ce_4: 0.08054/0.28962, loss_mask_ce_5: 0.33873/0.93678, loss_mask_bce_5: 0.45913/0.34164, loss_mask_dice_5: 0.62786/1.19025, loss_spatial_bce_5: 0.23293/0.10611, loss_spatial_dice_5: 0.23016/0.25797, loss_spatial_ce_5: 0.08788/0.16896, loss_grounding_bce_5: 0.14621/0.08787, loss_grounding_dice_5: 0.22111/0.18303, loss_grounding_ce_5: 0.09801/0.29979, loss_mask_ce_6: 0.36655/0.97261, loss_mask_bce_6: 0.44447/0.34364, loss_mask_dice_6: 0.63495/1.19342, loss_spatial_bce_6: 0.22312/0.11035, loss_spatial_dice_6: 0.22458/0.26127, loss_spatial_ce_6: 0.17305/0.18914, loss_grounding_bce_6: 0.13594/0.08888, loss_grounding_dice_6: 0.21636/0.18365, loss_grounding_ce_6: 0.09014/0.32388, loss_mask_ce_7: 0.39413/1.01520, loss_mask_bce_7: 0.41823/0.35095, loss_mask_dice_7: 0.61048/1.24758, loss_spatial_bce_7: 0.26444/0.12183, loss_spatial_dice_7: 0.23459/0.28674, loss_spatial_ce_7: 0.14418/0.23244, loss_grounding_bce_7: 0.11999/0.09048, loss_grounding_dice_7: 0.22241/0.19038, loss_grounding_ce_7: 0.08349/0.36904, loss_mask_ce_8: 0.62727/1.13026, loss_mask_bce_8: 0.43157/0.36387, loss_mask_dice_8: 0.64870/1.32247, loss_spatial_bce_8: 0.23419/0.14223, loss_spatial_dice_8: 0.25161/0.32858, loss_spatial_ce_8: 0.21149/0.28288, loss_grounding_bce_8: 0.13071/0.09387, loss_grounding_dice_8: 0.26969/0.20104, loss_grounding_ce_8: 0.01544/0.44430, loss_mask_ce_9: 2.00216/3.73454, loss_mask_bce_9: 0.40796/0.39122, loss_mask_dice_9: 0.76120/1.89982, loss_spatial_bce_9: 0.40434/0.34519, loss_spatial_dice_9: 0.87708/0.82966, loss_spatial_ce_9: 1.51224/1.57100, loss_grounding_bce_9: 0.12189/0.10513, loss_grounding_dice_9: 0.27952/0.28168, loss_grounding_ce_9: 0.07182/0.76471] items per batch[64] items per second[0.21] total items[320000] mini batches[  5000] memory[7341] epoch remaining[0:23:46]
INFO:trainer.default_trainer:epochs[     2] optim steps[5100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86507/0.91628, loss_mask_bce_0: 0.27128/0.33595, loss_mask_dice_0: 0.59674/1.16380, loss_spatial_bce_0: 0.41155/0.10014, loss_spatial_dice_0: 0.15581/0.23918, loss_spatial_ce_0: 0.05411/0.12290, loss_grounding_bce_0: 0.03222/0.08636, loss_grounding_dice_0: 0.09335/0.17954, loss_grounding_ce_0: 0.71262/0.28091, loss_mask_ce_1: 0.83476/0.91723, loss_mask_bce_1: 0.30703/0.33645, loss_mask_dice_1: 0.62740/1.17114, loss_spatial_bce_1: 0.46526/0.10117, loss_spatial_dice_1: 0.16862/0.24371, loss_spatial_ce_1: 0.08761/0.12862, loss_grounding_bce_1: 0.03355/0.08644, loss_grounding_dice_1: 0.09531/0.18036, loss_grounding_ce_1: 0.73460/0.28399, loss_mask_ce_2: 0.84145/0.92327, loss_mask_bce_2: 0.26404/0.33663, loss_mask_dice_2: 0.60484/1.17019, loss_spatial_bce_2: 0.43631/0.10061, loss_spatial_dice_2: 0.14012/0.24528, loss_spatial_ce_2: 0.11404/0.13374, loss_grounding_bce_2: 0.02688/0.08633, loss_grounding_dice_2: 0.09185/0.17953, loss_grounding_ce_2: 0.95378/0.28620, loss_mask_ce_3: 0.83202/0.92558, loss_mask_bce_3: 0.24677/0.33738, loss_mask_dice_3: 0.58855/1.16737, loss_spatial_bce_3: 0.45803/0.10186, loss_spatial_dice_3: 0.15216/0.24737, loss_spatial_ce_3: 0.07581/0.14214, loss_grounding_bce_3: 0.02823/0.08650, loss_grounding_dice_3: 0.09516/0.17911, loss_grounding_ce_3: 0.87794/0.28672, loss_mask_ce_4: 0.83877/0.92498, loss_mask_bce_4: 0.26831/0.33816, loss_mask_dice_4: 0.61930/1.18665, loss_spatial_bce_4: 0.41899/0.10470, loss_spatial_dice_4: 0.14941/0.25367, loss_spatial_ce_4: 0.09484/0.15617, loss_grounding_bce_4: 0.02722/0.08712, loss_grounding_dice_4: 0.09961/0.18215, loss_grounding_ce_4: 0.90631/0.29026, loss_mask_ce_5: 0.80459/0.93680, loss_mask_bce_5: 0.25311/0.34107, loss_mask_dice_5: 0.61625/1.19147, loss_spatial_bce_5: 0.43613/0.10600, loss_spatial_dice_5: 0.15084/0.25784, loss_spatial_ce_5: 0.15022/0.16817, loss_grounding_bce_5: 0.02675/0.08756, loss_grounding_dice_5: 0.10027/0.18315, loss_grounding_ce_5: 0.87497/0.30025, loss_mask_ce_6: 0.81086/0.97252, loss_mask_bce_6: 0.27051/0.34313, loss_mask_dice_6: 0.63175/1.19460, loss_spatial_bce_6: 0.40159/0.11027, loss_spatial_dice_6: 0.14455/0.26111, loss_spatial_ce_6: 0.15365/0.18837, loss_grounding_bce_6: 0.03161/0.08854, loss_grounding_dice_6: 0.10129/0.18393, loss_grounding_ce_6: 0.89761/0.32460, loss_mask_ce_7: 0.97070/1.01573, loss_mask_bce_7: 0.26923/0.35050, loss_mask_dice_7: 0.62330/1.24855, loss_spatial_bce_7: 0.30520/0.12167, loss_spatial_dice_7: 0.14046/0.28671, loss_spatial_ce_7: 0.08604/0.23128, loss_grounding_bce_7: 0.03166/0.09018, loss_grounding_dice_7: 0.09041/0.19051, loss_grounding_ce_7: 0.82202/0.37012, loss_mask_ce_8: 1.07038/1.13050, loss_mask_bce_8: 0.48549/0.36349, loss_mask_dice_8: 0.86778/1.32351, loss_spatial_bce_8: 0.32348/0.14201, loss_spatial_dice_8: 0.16499/0.32834, loss_spatial_ce_8: 0.15958/0.28214, loss_grounding_bce_8: 0.03235/0.09353, loss_grounding_dice_8: 0.10355/0.20123, loss_grounding_ce_8: 0.82221/0.44468, loss_mask_ce_9: 3.95747/3.73385, loss_mask_bce_9: 0.64574/0.39063, loss_mask_dice_9: 2.96568/1.90002, loss_spatial_bce_9: 0.45315/0.34490, loss_spatial_dice_9: 0.82552/0.82944, loss_spatial_ce_9: 1.69555/1.57247, loss_grounding_bce_9: 0.09420/0.10473, loss_grounding_dice_9: 0.24596/0.28190, loss_grounding_ce_9: 0.61742/0.76302] items per batch[64] items per second[0.22] total items[326400] mini batches[  5100] memory[7341] epoch remaining[0:18:48]
INFO:trainer.default_trainer:epochs[     2] optim steps[5200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66432/0.91686, loss_mask_bce_0: 0.14324/0.33562, loss_mask_dice_0: 4.16841/1.16824, loss_spatial_bce_0: 0.01993/0.09987, loss_spatial_dice_0: 0.30716/0.23915, loss_spatial_ce_0: 0.04728/0.12205, loss_grounding_bce_0: 0.01093/0.08632, loss_grounding_dice_0: 0.47759/0.17989, loss_grounding_ce_0: 0.55880/0.28063, loss_mask_ce_1: 0.88013/0.91776, loss_mask_bce_1: 0.13080/0.33613, loss_mask_dice_1: 4.19703/1.17580, loss_spatial_bce_1: 0.01819/0.10090, loss_spatial_dice_1: 0.34020/0.24372, loss_spatial_ce_1: 0.21452/0.12778, loss_grounding_bce_1: 0.01061/0.08640, loss_grounding_dice_1: 0.48285/0.18072, loss_grounding_ce_1: 0.55978/0.28342, loss_mask_ce_2: 0.72913/0.92366, loss_mask_bce_2: 0.14159/0.33633, loss_mask_dice_2: 3.43892/1.17450, loss_spatial_bce_2: 0.01755/0.10035, loss_spatial_dice_2: 0.33856/0.24531, loss_spatial_ce_2: 0.44709/0.13295, loss_grounding_bce_2: 0.01094/0.08630, loss_grounding_dice_2: 0.49738/0.17996, loss_grounding_ce_2: 0.51760/0.28572, loss_mask_ce_3: 0.86080/0.92597, loss_mask_bce_3: 0.13086/0.33713, loss_mask_dice_3: 3.89119/1.17195, loss_spatial_bce_3: 0.01719/0.10159, loss_spatial_dice_3: 0.29638/0.24743, loss_spatial_ce_3: 0.04708/0.14099, loss_grounding_bce_3: 0.00921/0.08647, loss_grounding_dice_3: 0.40970/0.17960, loss_grounding_ce_3: 0.52768/0.28655, loss_mask_ce_4: 0.82181/0.92573, loss_mask_bce_4: 0.13401/0.33788, loss_mask_dice_4: 3.25433/1.19099, loss_spatial_bce_4: 0.01774/0.10450, loss_spatial_dice_4: 0.33850/0.25368, loss_spatial_ce_4: 0.23000/0.15535, loss_grounding_bce_4: 0.00989/0.08708, loss_grounding_dice_4: 0.45761/0.18259, loss_grounding_ce_4: 0.51458/0.29008, loss_mask_ce_5: 0.81010/0.93758, loss_mask_bce_5: 0.13388/0.34076, loss_mask_dice_5: 3.67618/1.19591, loss_spatial_bce_5: 0.01802/0.10575, loss_spatial_dice_5: 0.32016/0.25793, loss_spatial_ce_5: 0.25533/0.16732, loss_grounding_bce_5: 0.01072/0.08753, loss_grounding_dice_5: 0.53094/0.18365, loss_grounding_ce_5: 0.44404/0.30009, loss_mask_ce_6: 0.81518/0.97357, loss_mask_bce_6: 0.14681/0.34284, loss_mask_dice_6: 4.22113/1.19921, loss_spatial_bce_6: 0.01757/0.11005, loss_spatial_dice_6: 0.34155/0.26126, loss_spatial_ce_6: 0.35390/0.18770, loss_grounding_bce_6: 0.00996/0.08855, loss_grounding_dice_6: 0.46721/0.18436, loss_grounding_ce_6: 0.52294/0.32407, loss_mask_ce_7: 1.06449/1.01660, loss_mask_bce_7: 0.14841/0.35032, loss_mask_dice_7: 3.72065/1.25356, loss_spatial_bce_7: 0.03034/0.12136, loss_spatial_dice_7: 0.40605/0.28691, loss_spatial_ce_7: 0.15428/0.23032, loss_grounding_bce_7: 0.01215/0.09021, loss_grounding_dice_7: 0.48166/0.19093, loss_grounding_ce_7: 0.59117/0.36965, loss_mask_ce_8: 1.06506/1.13131, loss_mask_bce_8: 0.17349/0.36325, loss_mask_dice_8: 3.62815/1.32885, loss_spatial_bce_8: 0.03456/0.14177, loss_spatial_dice_8: 0.41809/0.32855, loss_spatial_ce_8: 0.27109/0.28119, loss_grounding_bce_8: 0.01642/0.09360, loss_grounding_dice_8: 0.50493/0.20184, loss_grounding_ce_8: 0.36831/0.44416, loss_mask_ce_9: 3.83747/3.73290, loss_mask_bce_9: 0.16650/0.39058, loss_mask_dice_9: 4.63005/1.90692, loss_spatial_bce_9: 0.07517/0.34416, loss_spatial_dice_9: 0.85223/0.82963, loss_spatial_ce_9: 2.13300/1.57331, loss_grounding_bce_9: 0.01558/0.10483, loss_grounding_dice_9: 0.67569/0.28226, loss_grounding_ce_9: 0.45784/0.76119] items per batch[64] items per second[0.22] total items[332800] mini batches[  5200] memory[7341] epoch remaining[0:13:51]
INFO:trainer.default_trainer:epochs[     2] optim steps[5300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48858/0.91584, loss_mask_bce_0: 0.25335/0.33498, loss_mask_dice_0: 0.38678/1.16523, loss_spatial_bce_0: 0.07082/0.09963, loss_spatial_dice_0: 0.14204/0.23876, loss_spatial_ce_0: 0.02053/0.12139, loss_grounding_bce_0: 0.06639/0.08626, loss_grounding_dice_0: 0.10396/0.17954, loss_grounding_ce_0: 0.16711/0.27977, loss_mask_ce_1: 0.47290/0.91646, loss_mask_bce_1: 0.25646/0.33553, loss_mask_dice_1: 0.39400/1.17286, loss_spatial_bce_1: 0.07230/0.10064, loss_spatial_dice_1: 0.13527/0.24329, loss_spatial_ce_1: 0.04391/0.12698, loss_grounding_bce_1: 0.06648/0.08632, loss_grounding_dice_1: 0.10141/0.18031, loss_grounding_ce_1: 0.15953/0.28260, loss_mask_ce_2: 0.46931/0.92246, loss_mask_bce_2: 0.25347/0.33568, loss_mask_dice_2: 0.39302/1.17130, loss_spatial_bce_2: 0.07381/0.10009, loss_spatial_dice_2: 0.14894/0.24484, loss_spatial_ce_2: 0.03344/0.13220, loss_grounding_bce_2: 0.06563/0.08623, loss_grounding_dice_2: 0.10099/0.17961, loss_grounding_ce_2: 0.16514/0.28449, loss_mask_ce_3: 0.51330/0.92479, loss_mask_bce_3: 0.25263/0.33653, loss_mask_dice_3: 0.38690/1.16871, loss_spatial_bce_3: 0.07144/0.10133, loss_spatial_dice_3: 0.14889/0.24695, loss_spatial_ce_3: 0.04954/0.13992, loss_grounding_bce_3: 0.06485/0.08640, loss_grounding_dice_3: 0.09944/0.17925, loss_grounding_ce_3: 0.15177/0.28541, loss_mask_ce_4: 0.52042/0.92443, loss_mask_bce_4: 0.24724/0.33734, loss_mask_dice_4: 0.41058/1.18797, loss_spatial_bce_4: 0.07137/0.10429, loss_spatial_dice_4: 0.15171/0.25328, loss_spatial_ce_4: 0.06776/0.15446, loss_grounding_bce_4: 0.06018/0.08703, loss_grounding_dice_4: 0.09671/0.18225, loss_grounding_ce_4: 0.16761/0.28884, loss_mask_ce_5: 0.68879/0.93622, loss_mask_bce_5: 0.25102/0.34013, loss_mask_dice_5: 0.40973/1.19264, loss_spatial_bce_5: 0.06732/0.10554, loss_spatial_dice_5: 0.13257/0.25743, loss_spatial_ce_5: 0.05718/0.16621, loss_grounding_bce_5: 0.06536/0.08748, loss_grounding_dice_5: 0.09993/0.18328, loss_grounding_ce_5: 0.20680/0.29942, loss_mask_ce_6: 0.81072/0.97231, loss_mask_bce_6: 0.25132/0.34222, loss_mask_dice_6: 0.39901/1.19614, loss_spatial_bce_6: 0.06501/0.10986, loss_spatial_dice_6: 0.14693/0.26079, loss_spatial_ce_6: 0.08102/0.18678, loss_grounding_bce_6: 0.06769/0.08849, loss_grounding_dice_6: 0.10313/0.18394, loss_grounding_ce_6: 0.24000/0.32340, loss_mask_ce_7: 0.86353/1.01569, loss_mask_bce_7: 0.24456/0.34972, loss_mask_dice_7: 0.41834/1.25014, loss_spatial_bce_7: 0.12313/0.12106, loss_spatial_dice_7: 0.19217/0.28661, loss_spatial_ce_7: 0.13387/0.22977, loss_grounding_bce_7: 0.06609/0.09012, loss_grounding_dice_7: 0.10003/0.19058, loss_grounding_ce_7: 0.23613/0.36838, loss_mask_ce_8: 0.58427/1.12982, loss_mask_bce_8: 0.26066/0.36260, loss_mask_dice_8: 0.46462/1.32516, loss_spatial_bce_8: 0.18439/0.14156, loss_spatial_dice_8: 0.27742/0.32814, loss_spatial_ce_8: 0.11536/0.28029, loss_grounding_bce_8: 0.07092/0.09351, loss_grounding_dice_8: 0.13310/0.20137, loss_grounding_ce_8: 0.19528/0.44263, loss_mask_ce_9: 2.70941/3.72802, loss_mask_bce_9: 0.23183/0.38986, loss_mask_dice_9: 0.66364/1.90136, loss_spatial_bce_9: 0.34316/0.34382, loss_spatial_dice_9: 0.77005/0.82937, loss_spatial_ce_9: 1.33953/1.57251, loss_grounding_bce_9: 0.05838/0.10479, loss_grounding_dice_9: 0.20515/0.28180, loss_grounding_ce_9: 0.43384/0.75886] items per batch[64] items per second[0.23] total items[339200] mini batches[  5300] memory[7341] epoch remaining[0:08:54]
INFO:trainer.default_trainer:epochs[     2] optim steps[5400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.29648/0.91692, loss_mask_bce_0: 0.61598/0.33496, loss_mask_dice_0: 1.31558/1.16524, loss_spatial_bce_0: 0.15420/0.09948, loss_spatial_dice_0: 0.26834/0.23879, loss_spatial_ce_0: 0.09344/0.12104, loss_grounding_bce_0: 0.23305/0.08625, loss_grounding_dice_0: 0.35803/0.17980, loss_grounding_ce_0: 1.14174/0.27980, loss_mask_ce_1: 1.31830/0.91713, loss_mask_bce_1: 0.62413/0.33552, loss_mask_dice_1: 1.22654/1.17317, loss_spatial_bce_1: 0.13765/0.10048, loss_spatial_dice_1: 0.25743/0.24328, loss_spatial_ce_1: 0.13583/0.12657, loss_grounding_bce_1: 0.23514/0.08629, loss_grounding_dice_1: 0.36521/0.18051, loss_grounding_ce_1: 1.14635/0.28258, loss_mask_ce_2: 1.38808/0.92307, loss_mask_bce_2: 0.61641/0.33565, loss_mask_dice_2: 1.23856/1.17157, loss_spatial_bce_2: 0.12490/0.09994, loss_spatial_dice_2: 0.24327/0.24480, loss_spatial_ce_2: 0.10628/0.13176, loss_grounding_bce_2: 0.23465/0.08622, loss_grounding_dice_2: 0.34935/0.17989, loss_grounding_ce_2: 1.19688/0.28433, loss_mask_ce_3: 1.30913/0.92577, loss_mask_bce_3: 0.62650/0.33651, loss_mask_dice_3: 1.27454/1.16887, loss_spatial_bce_3: 0.11144/0.10115, loss_spatial_dice_3: 0.23882/0.24688, loss_spatial_ce_3: 0.11444/0.13935, loss_grounding_bce_3: 0.22976/0.08636, loss_grounding_dice_3: 0.39621/0.17951, loss_grounding_ce_3: 1.16111/0.28554, loss_mask_ce_4: 1.50344/0.92486, loss_mask_bce_4: 0.62520/0.33730, loss_mask_dice_4: 1.23394/1.18836, loss_spatial_bce_4: 0.12649/0.10410, loss_spatial_dice_4: 0.26027/0.25321, loss_spatial_ce_4: 0.17580/0.15403, loss_grounding_bce_4: 0.21268/0.08698, loss_grounding_dice_4: 0.34923/0.18241, loss_grounding_ce_4: 1.32796/0.28868, loss_mask_ce_5: 1.37074/0.93704, loss_mask_bce_5: 0.61534/0.34008, loss_mask_dice_5: 1.30090/1.19263, loss_spatial_bce_5: 0.15507/0.10539, loss_spatial_dice_5: 0.30952/0.25739, loss_spatial_ce_5: 0.17893/0.16574, loss_grounding_bce_5: 0.22500/0.08746, loss_grounding_dice_5: 0.34476/0.18352, loss_grounding_ce_5: 1.23831/0.29902, loss_mask_ce_6: 1.56842/0.97310, loss_mask_bce_6: 0.59461/0.34218, loss_mask_dice_6: 1.17439/1.19623, loss_spatial_bce_6: 0.16160/0.10976, loss_spatial_dice_6: 0.32431/0.26077, loss_spatial_ce_6: 0.25067/0.18626, loss_grounding_bce_6: 0.27440/0.08848, loss_grounding_dice_6: 0.36871/0.18412, loss_grounding_ce_6: 0.36883/0.32276, loss_mask_ce_7: 1.31126/1.01647, loss_mask_bce_7: 0.72978/0.34980, loss_mask_dice_7: 1.43283/1.25073, loss_spatial_bce_7: 0.15863/0.12089, loss_spatial_dice_7: 0.30305/0.28663, loss_spatial_ce_7: 0.37794/0.22932, loss_grounding_bce_7: 0.21721/0.09009, loss_grounding_dice_7: 0.33283/0.19080, loss_grounding_ce_7: 1.35679/0.36780, loss_mask_ce_8: 1.36514/1.13074, loss_mask_bce_8: 0.72470/0.36260, loss_mask_dice_8: 1.59843/1.32555, loss_spatial_bce_8: 0.19598/0.14140, loss_spatial_dice_8: 0.34141/0.32816, loss_spatial_ce_8: 0.40008/0.27946, loss_grounding_bce_8: 0.24691/0.09346, loss_grounding_dice_8: 0.38839/0.20156, loss_grounding_ce_8: 1.49441/0.44269, loss_mask_ce_9: 4.74257/3.72629, loss_mask_bce_9: 0.77218/0.38962, loss_mask_dice_9: 2.71085/1.90085, loss_spatial_bce_9: 0.36663/0.34371, loss_spatial_dice_9: 0.91151/0.82939, loss_spatial_ce_9: 1.33854/1.57085, loss_grounding_bce_9: 0.26667/0.10473, loss_grounding_dice_9: 0.50767/0.28197, loss_grounding_ce_9: 0.92025/0.75858] items per batch[64] items per second[0.22] total items[345600] mini batches[  5400] memory[7341] epoch remaining[0:03:59]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00005481.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0034 s/iter. Inference: 0.2254 s/iter. Eval: 0.0891 s/iter. Total: 0.3179 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2260 s/iter. Eval: 0.0888 s/iter. Total: 0.3178 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0030 s/iter. Inference: 0.2269 s/iter. Eval: 0.0852 s/iter. Total: 0.3152 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2272 s/iter. Eval: 0.0788 s/iter. Total: 0.3092 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2245 s/iter. Eval: 0.0768 s/iter. Total: 0.3046 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2268 s/iter. Eval: 0.0762 s/iter. Total: 0.3063 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0032 s/iter. Inference: 0.2276 s/iter. Eval: 0.0756 s/iter. Total: 0.3065 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 131/157. Dataloading: 0.0032 s/iter. Inference: 0.2269 s/iter. Eval: 0.0742 s/iter. Total: 0.3044 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 147/157. Dataloading: 0.0032 s/iter. Inference: 0.2282 s/iter. Eval: 0.0739 s/iter. Total: 0.3054 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalvoi5srgc ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.974 | 81.813 | 60.181 |      133      |
| Things | 55.236 | 82.511 | 66.245 |      80       |
| Stuff  | 42.031 | 80.759 | 51.029 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
DONE (t=0.34s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.99 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.97 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.20s)
creating index...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.00 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.531 | 60.661 | 40.682 | 19.297 | 41.599 | 60.007 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.779 | bicycle      | 17.628 | car            | 36.628 |
| motorcycle    | 35.433 | airplane     | 55.270 | bus            | 64.318 |
| train         | 68.735 | truck        | 33.913 | boat           | 22.736 |
| traffic light | 24.484 | fire hydrant | 63.324 | stop sign      | 64.227 |
| parking meter | 43.492 | bench        | 19.858 | bird           | 28.812 |
| cat           | 73.632 | dog          | 65.664 | horse          | 43.772 |
| sheep         | 44.524 | cow          | 49.680 | elephant       | 60.548 |
| bear          | 78.078 | zebra        | 59.546 | giraffe        | 57.419 |
| backpack      | 16.498 | umbrella     | 47.274 | handbag        | 15.088 |
| tie           | 32.748 | suitcase     | 38.568 | frisbee        | 68.037 |
| skis          | 5.677  | snowboard    | 22.818 | sports ball    | 46.836 |
| kite          | 32.534 | baseball bat | 28.953 | baseball glove | 43.012 |
| skateboard    | 36.203 | surfboard    | 36.119 | tennis racket  | 56.375 |
| bottle        | 33.084 | wine glass   | 25.813 | cup            | 40.119 |
| fork          | 16.938 | knife        | 11.759 | spoon          | 15.419 |
| bowl          | 30.412 | banana       | 18.799 | apple          | 20.024 |
| sandwich      | 42.256 | orange       | 28.658 | broccoli       | 21.772 |
| carrot        | 19.833 | hot dog      | 26.704 | pizza          | 51.221 |
| donut         | 44.645 | cake         | 43.233 | chair          | 20.651 |
| couch         | 40.139 | potted plant | 16.688 | bed            | 40.396 |
| dining table  | 13.061 | toilet       | 66.642 | tv             | 61.837 |
| laptop        | 62.025 | mouse        | 59.168 | remote         | 30.770 |
| keyboard      | 46.236 | cell phone   | 37.396 | microwave      | 52.652 |
| oven          | 31.804 | toaster      | 34.103 | sink           | 37.456 |
| refrigerator  | 58.052 | book         | 8.564  | clock          | 51.076 |
| vase          | 32.983 | scissors     | 25.104 | teddy bear     | 51.790 |
| hair drier    | 11.019 | toothbrush   | 19.959 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.385
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.486
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.501
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.297
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 59.8753357834349, 'fwIoU': 68.58922898053854, 'IoU-person': 87.29874437725685, 'IoU-bicycle': 73.85221526953511, 'IoU-car': 69.17611785393898, 'IoU-motorcycle': 84.91519345540378, 'IoU-airplane': 83.84841426003108, 'IoU-bus': 83.84021690955508, 'IoU-train': 85.35872937119147, 'IoU-truck': 64.87915749228488, 'IoU-boat': 66.71297159716157, 'IoU-traffic light': 73.14510993708144, 'IoU-fire hydrant': 80.60423072314717, 'IoU-stop sign': 93.4972385083205, 'IoU-parking meter': 88.11654719938542, 'IoU-bench': 51.834910972194734, 'IoU-bird': 74.50138605482529, 'IoU-cat': 83.83685444562514, 'IoU-dog': 76.89948588989081, 'IoU-horse': 86.88134226245171, 'IoU-sheep': 83.1511418868057, 'IoU-cow': 85.75203884270212, 'IoU-elephant': 84.19301730330181, 'IoU-bear': 69.94223653753693, 'IoU-zebra': 84.93361432030365, 'IoU-giraffe': 87.78681224050597, 'IoU-backpack': 35.7521140959431, 'IoU-umbrella': 74.05550660126859, 'IoU-handbag': 36.33662897662544, 'IoU-tie': 68.87651161043928, 'IoU-suitcase': 78.64956947888396, 'IoU-frisbee': 83.37711872652616, 'IoU-skis': 51.67091548282313, 'IoU-snowboard': 70.67039848365411, 'IoU-sports ball': 65.0356522354384, 'IoU-kite': 65.80516963404935, 'IoU-baseball bat': 58.91349988394911, 'IoU-baseball glove': 76.61703998632112, 'IoU-skateboard': 64.52017231363747, 'IoU-surfboard': 75.46810414624792, 'IoU-tennis racket': 74.62172507088103, 'IoU-bottle': 68.78645073306335, 'IoU-wine glass': 75.21988726094825, 'IoU-cup': 63.63696401073169, 'IoU-fork': 54.35285667210936, 'IoU-knife': 47.25628492013824, 'IoU-spoon': 47.75267434093433, 'IoU-bowl': 49.16413126457937, 'IoU-banana': 83.72094333558279, 'IoU-apple': 58.55129108578099, 'IoU-sandwich': 65.48060005234514, 'IoU-orange': 79.504227834255, 'IoU-broccoli': 68.9824048295831, 'IoU-carrot': 63.67464588916162, 'IoU-hot dog': 65.25685861341032, 'IoU-pizza': 75.568245965608, 'IoU-donut': 64.48265358766426, 'IoU-cake': 69.14379205204179, 'IoU-chair': 53.98661424893437, 'IoU-couch': 66.22645157445338, 'IoU-potted plant': 36.187576635366035, 'IoU-bed': 68.30373544756526, 'IoU-dining table': 50.974917306292674, 'IoU-toilet': 79.6312406567505, 'IoU-tv': 73.49182639910752, 'IoU-laptop': 75.22201206881023, 'IoU-mouse': 68.31131520478858, 'IoU-remote': 48.38139889092904, 'IoU-keyboard': 68.30629272482001, 'IoU-cell phone': 71.73211673915768, 'IoU-microwave': 29.26685352947822, 'IoU-oven': 69.77025886733716, 'IoU-toaster': 43.95469263662654, 'IoU-sink': 70.08885299986035, 'IoU-refrigerator': 70.4029495389952, 'IoU-book': 49.91660236083043, 'IoU-clock': 68.00146422398295, 'IoU-vase': 54.76172482633969, 'IoU-scissors': 60.96379062152315, 'IoU-teddy bear': 79.23193255108978, 'IoU-hair drier': 37.89816904941058, 'IoU-toothbrush': 51.67645679931607, 'IoU-banner': 30.43927890600426, 'IoU-blanket': 12.809740001338485, 'IoU-bridge': 37.712070369258065, 'IoU-cardboard': 45.03582272506323, 'IoU-counter': 31.29756781695363, 'IoU-curtain': 62.77736799204564, 'IoU-door-stuff': 44.72840533924212, 'IoU-floor-wood': 63.618692208796176, 'IoU-flower': 43.39795678269011, 'IoU-fruit': 41.76922119244765, 'IoU-gravel': 28.98921686375472, 'IoU-house': 22.20543595296771, 'IoU-light': 39.19705097715338, 'IoU-mirror-stuff': 56.824681994004344, 'IoU-net': 45.12423727644026, 'IoU-pillow': 13.970639157989675, 'IoU-platform': 33.70881907901381, 'IoU-playingfield': 69.99575062060622, 'IoU-railroad': 60.07088437852065, 'IoU-river': 47.14476293974012, 'IoU-road': 66.78552630124476, 'IoU-roof': 12.64225212995164, 'IoU-sand': 55.916220868813085, 'IoU-sea': 83.25137440448525, 'IoU-shelf': 36.936404305023665, 'IoU-snow': 88.8728966758824, 'IoU-stairs': 26.173052301032453, 'IoU-tent': 9.133047423046945, 'IoU-towel': 35.774974483537505, 'IoU-wall-brick': 45.95911878830701, 'IoU-wall-stone': 25.507894034388695, 'IoU-wall-tile': 66.09551332915471, 'IoU-wall-wood': 38.60463329293243, 'IoU-water-other': 28.24155848312506, 'IoU-window-blind': 45.72757450971233, 'IoU-window-other': 47.73488278351136, 'IoU-tree-merged': 80.54944129481224, 'IoU-fence-merged': 50.840630836630865, 'IoU-ceiling-merged': 66.50541429081333, 'IoU-sky-other-merged': 93.74625067228234, 'IoU-cabinet-merged': 59.97493226446231, 'IoU-table-merged': 38.10126656682944, 'IoU-floor-other-merged': 49.80164662342851, 'IoU-pavement-merged': 55.00889465285878, 'IoU-mountain-merged': 55.75120171288888, 'IoU-grass-merged': 71.1564860034983, 'IoU-dirt-merged': 41.939426919743894, 'IoU-paper-merged': 36.164928645434046, 'IoU-food-other-merged': 39.332823061797754, 'IoU-building-other-merged': 57.99697759225659, 'IoU-rock-merged': 61.866334460796814, 'IoU-wall-other-merged': 63.988546628477, 'IoU-rug-merged': 63.96791749482298, 'mACC': 72.13534934746951, 'pACC': 80.03954429182924, 'ACC-person': 92.30321202207527, 'ACC-bicycle': 85.54762873039084, 'ACC-car': 83.33338797042536, 'ACC-motorcycle': 90.69167546941264, 'ACC-airplane': 90.30116942091917, 'ACC-bus': 89.71737013250942, 'ACC-train': 95.48006982952596, 'ACC-truck': 76.23026805396977, 'ACC-boat': 78.02236685541727, 'ACC-traffic light': 89.72397162680095, 'ACC-fire hydrant': 95.51400163117508, 'ACC-stop sign': 96.85240359467176, 'ACC-parking meter': 92.16346970064164, 'ACC-bench': 74.90069107900402, 'ACC-bird': 80.63107741427422, 'ACC-cat': 91.05143250190353, 'ACC-dog': 80.06201409460574, 'ACC-horse': 92.89782489285331, 'ACC-sheep': 86.2975636338501, 'ACC-cow': 91.1712850553948, 'ACC-elephant': 86.28125093742617, 'ACC-bear': 71.34616182596528, 'ACC-zebra': 87.26691629058344, 'ACC-giraffe': 91.69209158710233, 'ACC-backpack': 70.24390671422687, 'ACC-umbrella': 82.57120171099996, 'ACC-handbag': 51.64185289701284, 'ACC-tie': 81.51259584838212, 'ACC-suitcase': 87.09803838895888, 'ACC-frisbee': 93.47999999999999, 'ACC-skis': 68.50700347874084, 'ACC-snowboard': 78.1651196394896, 'ACC-sports ball': 85.51286424576963, 'ACC-kite': 76.22785358130778, 'ACC-baseball bat': 83.67748316088381, 'ACC-baseball glove': 90.40372379902438, 'ACC-skateboard': 69.54544842475684, 'ACC-surfboard': 83.75572409748769, 'ACC-tennis racket': 79.71279281630686, 'ACC-bottle': 83.9716044583641, 'ACC-wine glass': 82.82125437435404, 'ACC-cup': 84.68028893210884, 'ACC-fork': 68.08263289949463, 'ACC-knife': 61.41862680442588, 'ACC-spoon': 69.19875707468648, 'ACC-bowl': 58.95823637614326, 'ACC-banana': 90.05798783933106, 'ACC-apple': 72.95947403329055, 'ACC-sandwich': 78.79281910886637, 'ACC-orange': 88.67831198671796, 'ACC-broccoli': 80.11884331001269, 'ACC-carrot': 76.13543247951819, 'ACC-hot dog': 73.90414809198226, 'ACC-pizza': 84.32332313751442, 'ACC-donut': 80.81729726948343, 'ACC-cake': 75.71031429783088, 'ACC-chair': 68.21801262952803, 'ACC-couch': 74.62135340114976, 'ACC-potted plant': 50.37380398409174, 'ACC-bed': 86.76625572612758, 'ACC-dining table': 70.54997463337509, 'ACC-toilet': 91.67041559922251, 'ACC-tv': 82.90820313102645, 'ACC-laptop': 88.65077566446885, 'ACC-mouse': 86.28976692675924, 'ACC-remote': 68.3052009117136, 'ACC-keyboard': 74.49732124689532, 'ACC-cell phone': 82.14266742127211, 'ACC-microwave': 32.75634048529937, 'ACC-oven': 85.09164283668126, 'ACC-toaster': 48.673856218095224, 'ACC-sink': 82.74636348313896, 'ACC-refrigerator': 74.99307757444336, 'ACC-book': 67.76120030259729, 'ACC-clock': 76.8729636908292, 'ACC-vase': 62.98206833236984, 'ACC-scissors': 66.34897024121055, 'ACC-teddy bear': 83.74534794952505, 'ACC-hair drier': 40.857598269433296, 'ACC-toothbrush': 81.38637943015983, 'ACC-banner': 56.80143895651328, 'ACC-blanket': 16.244044085960304, 'ACC-bridge': 55.16879056916185, 'ACC-cardboard': 55.77539624836412, 'ACC-counter': 54.570507965554825, 'ACC-curtain': 73.12998927372678, 'ACC-door-stuff': 66.28317382102179, 'ACC-floor-wood': 80.62532255731132, 'ACC-flower': 66.02886573091958, 'ACC-fruit': 59.16048386304153, 'ACC-gravel': 35.67881524212135, 'ACC-house': 26.018108753240256, 'ACC-light': 55.866896508823636, 'ACC-mirror-stuff': 66.76981147470468, 'ACC-net': 60.89295492990846, 'ACC-pillow': 22.899842394130832, 'ACC-platform': 56.13278702813274, 'ACC-playingfield': 87.47516083266318, 'ACC-railroad': 77.94276913087155, 'ACC-river': 59.666825948290935, 'ACC-road': 83.69450078167621, 'ACC-roof': 17.23066329295473, 'ACC-sand': 74.64365045069631, 'ACC-sea': 88.92201526084337, 'ACC-shelf': 60.53392213405381, 'ACC-snow': 94.19560447987924, 'ACC-stairs': 44.755539708123045, 'ACC-tent': 11.172896688149018, 'ACC-towel': 45.14802438579674, 'ACC-wall-brick': 57.14895247100259, 'ACC-wall-stone': 29.292331175325014, 'ACC-wall-tile': 76.97886343690803, 'ACC-wall-wood': 54.244206846626696, 'ACC-water-other': 57.606687955391614, 'ACC-window-blind': 54.347528852762586, 'ACC-window-other': 70.44103062570632, 'ACC-tree-merged': 88.96968277858883, 'ACC-fence-merged': 72.34489159182083, 'ACC-ceiling-merged': 78.87113946141038, 'ACC-sky-other-merged': 96.64910796953431, 'ACC-cabinet-merged': 74.16668354369045, 'ACC-table-merged': 56.70906246798778, 'ACC-floor-other-merged': 59.87109587176853, 'ACC-pavement-merged': 69.02764667834772, 'ACC-mountain-merged': 66.27987279688156, 'ACC-grass-merged': 83.37067385929254, 'ACC-dirt-merged': 61.411466306372105, 'ACC-paper-merged': 48.009907764473574, 'ACC-food-other-merged': 54.10925838418154, 'ACC-building-other-merged': 75.74392751544482, 'ACC-rock-merged': 83.44377301688041, 'ACC-wall-other-merged': 82.63466126653553, 'ACC-rug-merged': 77.52638236209127})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1528 s/iter. Inference: 0.5612 s/iter. Eval: 0.0000 s/iter. Total: 0.7140 s/iter. ETA=0:00:27
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1548 s/iter. Inference: 0.5676 s/iter. Eval: 0.0000 s/iter. Total: 0.7225 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1687 s/iter. Inference: 0.6096 s/iter. Eval: 0.0000 s/iter. Total: 0.7785 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1716 s/iter. Inference: 0.7181 s/iter. Eval: 0.0000 s/iter. Total: 0.8898 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1687 s/iter. Inference: 0.6265 s/iter. Eval: 0.0000 s/iter. Total: 0.7954 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 43/50. Dataloading: 0.1674 s/iter. Inference: 0.6558 s/iter. Eval: 0.0000 s/iter. Total: 0.8234 s/iter. ETA=0:00:05
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 48/50. Dataloading: 0.1681 s/iter. Inference: 0.7039 s/iter. Eval: 0.0000 s/iter. Total: 0.8722 s/iter. ETA=0:00:01
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.6423763535264853, 'noc@0.8': 3.1594966344746855, 'noc@0.85': 3.839332748024583, 'noc@0.9': 4.950834064969271, 'miou@iter1': 0.8379005809467998}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.0986 s/iter. Eval: 0.0008 s/iter. Total: 0.1011 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.15157318115234, 'precision@0.6': 66.88690185546875, 'precision@0.7': 62.067626953125, 'precision@0.8': 51.4185791015625, 'precision@0.9': 26.2728328704834, 'cIoU': 56.838958740234375, 'mIoU': 61.99746322631836}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.97387505554578, 'SQ': 81.81292581586358, 'RQ': 60.18134247526569, 'PQ_th': 55.23606066290129, 'SQ_th': 82.51085661445525, 'RQ_th': 66.24479980974304, 'PQ_st': 42.03095338406578, 'SQ_st': 80.75944536515904, 'RQ_st': 51.0289540458659}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.531308938499, 'AP50': 60.661176756550574, 'AP75': 40.682150237897616, 'APs': 19.297251771638702, 'APm': 41.59916095654337, 'APl': 60.00716265942954, 'AP-person': 43.779128918268455, 'AP-bicycle': 17.628035331507576, 'AP-car': 36.6284158233293, 'AP-motorcycle': 35.432738260664784, 'AP-airplane': 55.26987705346593, 'AP-bus': 64.317937859795, 'AP-train': 68.7349584524389, 'AP-truck': 33.912885660697896, 'AP-boat': 22.736293889025283, 'AP-traffic light': 24.48406726466386, 'AP-fire hydrant': 63.32399133194042, 'AP-stop sign': 64.22682302408779, 'AP-parking meter': 43.49243073591394, 'AP-bench': 19.857748373732633, 'AP-bird': 28.812442623138658, 'AP-cat': 73.6318407342007, 'AP-dog': 65.66370724764838, 'AP-horse': 43.77245428664062, 'AP-sheep': 44.52371653588441, 'AP-cow': 49.67967421496755, 'AP-elephant': 60.547855392364625, 'AP-bear': 78.07765682805648, 'AP-zebra': 59.545577198872046, 'AP-giraffe': 57.418949970160746, 'AP-backpack': 16.497795407218156, 'AP-umbrella': 47.27415807826173, 'AP-handbag': 15.088085390596248, 'AP-tie': 32.74789033573498, 'AP-suitcase': 38.56750231854373, 'AP-frisbee': 68.03699743679907, 'AP-skis': 5.676859495445689, 'AP-snowboard': 22.817747115544364, 'AP-sports ball': 46.83600027262295, 'AP-kite': 32.53351718313059, 'AP-baseball bat': 28.953359218759406, 'AP-baseball glove': 43.011679670540495, 'AP-skateboard': 36.203104454352726, 'AP-surfboard': 36.11909535570135, 'AP-tennis racket': 56.375165728713824, 'AP-bottle': 33.08436723345706, 'AP-wine glass': 25.813086007614334, 'AP-cup': 40.11910271605123, 'AP-fork': 16.93829254390052, 'AP-knife': 11.759401540686845, 'AP-spoon': 15.419435648101887, 'AP-bowl': 30.412491165066957, 'AP-banana': 18.7992585771978, 'AP-apple': 20.024470313425564, 'AP-sandwich': 42.256318645290705, 'AP-orange': 28.657887944793725, 'AP-broccoli': 21.77164043730506, 'AP-carrot': 19.832927790370668, 'AP-hot dog': 26.70428766675027, 'AP-pizza': 51.221455530276735, 'AP-donut': 44.64523994081375, 'AP-cake': 43.23285778307082, 'AP-chair': 20.651286484574566, 'AP-couch': 40.13929441305742, 'AP-potted plant': 16.688396252706013, 'AP-bed': 40.39600920837885, 'AP-dining table': 13.060964975905717, 'AP-toilet': 66.64235863724402, 'AP-tv': 61.83720769560368, 'AP-laptop': 62.02460443163403, 'AP-mouse': 59.16837349299101, 'AP-remote': 30.77028927440697, 'AP-keyboard': 46.23623598747108, 'AP-cell phone': 37.39647740172892, 'AP-microwave': 52.65224904867015, 'AP-oven': 31.803795209177515, 'AP-toaster': 34.1032326835168, 'AP-sink': 37.45565114597628, 'AP-refrigerator': 58.05201110137222, 'AP-book': 8.563518988209614, 'AP-clock': 51.07595875170762, 'AP-vase': 32.98280766353581, 'AP-scissors': 25.10418154061968, 'AP-teddy bear': 51.790455721757326, 'AP-hair drier': 11.019199892962272, 'AP-toothbrush': 19.959469119108064}), ('sem_seg', {'mIoU': 59.8753357834349, 'fwIoU': 68.58922898053854, 'IoU-person': 87.29874437725685, 'IoU-bicycle': 73.85221526953511, 'IoU-car': 69.17611785393898, 'IoU-motorcycle': 84.91519345540378, 'IoU-airplane': 83.84841426003108, 'IoU-bus': 83.84021690955508, 'IoU-train': 85.35872937119147, 'IoU-truck': 64.87915749228488, 'IoU-boat': 66.71297159716157, 'IoU-traffic light': 73.14510993708144, 'IoU-fire hydrant': 80.60423072314717, 'IoU-stop sign': 93.4972385083205, 'IoU-parking meter': 88.11654719938542, 'IoU-bench': 51.834910972194734, 'IoU-bird': 74.50138605482529, 'IoU-cat': 83.83685444562514, 'IoU-dog': 76.89948588989081, 'IoU-horse': 86.88134226245171, 'IoU-sheep': 83.1511418868057, 'IoU-cow': 85.75203884270212, 'IoU-elephant': 84.19301730330181, 'IoU-bear': 69.94223653753693, 'IoU-zebra': 84.93361432030365, 'IoU-giraffe': 87.78681224050597, 'IoU-backpack': 35.7521140959431, 'IoU-umbrella': 74.05550660126859, 'IoU-handbag': 36.33662897662544, 'IoU-tie': 68.87651161043928, 'IoU-suitcase': 78.64956947888396, 'IoU-frisbee': 83.37711872652616, 'IoU-skis': 51.67091548282313, 'IoU-snowboard': 70.67039848365411, 'IoU-sports ball': 65.0356522354384, 'IoU-kite': 65.80516963404935, 'IoU-baseball bat': 58.91349988394911, 'IoU-baseball glove': 76.61703998632112, 'IoU-skateboard': 64.52017231363747, 'IoU-surfboard': 75.46810414624792, 'IoU-tennis racket': 74.62172507088103, 'IoU-bottle': 68.78645073306335, 'IoU-wine glass': 75.21988726094825, 'IoU-cup': 63.63696401073169, 'IoU-fork': 54.35285667210936, 'IoU-knife': 47.25628492013824, 'IoU-spoon': 47.75267434093433, 'IoU-bowl': 49.16413126457937, 'IoU-banana': 83.72094333558279, 'IoU-apple': 58.55129108578099, 'IoU-sandwich': 65.48060005234514, 'IoU-orange': 79.504227834255, 'IoU-broccoli': 68.9824048295831, 'IoU-carrot': 63.67464588916162, 'IoU-hot dog': 65.25685861341032, 'IoU-pizza': 75.568245965608, 'IoU-donut': 64.48265358766426, 'IoU-cake': 69.14379205204179, 'IoU-chair': 53.98661424893437, 'IoU-couch': 66.22645157445338, 'IoU-potted plant': 36.187576635366035, 'IoU-bed': 68.30373544756526, 'IoU-dining table': 50.974917306292674, 'IoU-toilet': 79.6312406567505, 'IoU-tv': 73.49182639910752, 'IoU-laptop': 75.22201206881023, 'IoU-mouse': 68.31131520478858, 'IoU-remote': 48.38139889092904, 'IoU-keyboard': 68.30629272482001, 'IoU-cell phone': 71.73211673915768, 'IoU-microwave': 29.26685352947822, 'IoU-oven': 69.77025886733716, 'IoU-toaster': 43.95469263662654, 'IoU-sink': 70.08885299986035, 'IoU-refrigerator': 70.4029495389952, 'IoU-book': 49.91660236083043, 'IoU-clock': 68.00146422398295, 'IoU-vase': 54.76172482633969, 'IoU-scissors': 60.96379062152315, 'IoU-teddy bear': 79.23193255108978, 'IoU-hair drier': 37.89816904941058, 'IoU-toothbrush': 51.67645679931607, 'IoU-banner': 30.43927890600426, 'IoU-blanket': 12.809740001338485, 'IoU-bridge': 37.712070369258065, 'IoU-cardboard': 45.03582272506323, 'IoU-counter': 31.29756781695363, 'IoU-curtain': 62.77736799204564, 'IoU-door-stuff': 44.72840533924212, 'IoU-floor-wood': 63.618692208796176, 'IoU-flower': 43.39795678269011, 'IoU-fruit': 41.76922119244765, 'IoU-gravel': 28.98921686375472, 'IoU-house': 22.20543595296771, 'IoU-light': 39.19705097715338, 'IoU-mirror-stuff': 56.824681994004344, 'IoU-net': 45.12423727644026, 'IoU-pillow': 13.970639157989675, 'IoU-platform': 33.70881907901381, 'IoU-playingfield': 69.99575062060622, 'IoU-railroad': 60.07088437852065, 'IoU-river': 47.14476293974012, 'IoU-road': 66.78552630124476, 'IoU-roof': 12.64225212995164, 'IoU-sand': 55.916220868813085, 'IoU-sea': 83.25137440448525, 'IoU-shelf': 36.936404305023665, 'IoU-snow': 88.8728966758824, 'IoU-stairs': 26.173052301032453, 'IoU-tent': 9.133047423046945, 'IoU-towel': 35.774974483537505, 'IoU-wall-brick': 45.95911878830701, 'IoU-wall-stone': 25.507894034388695, 'IoU-wall-tile': 66.09551332915471, 'IoU-wall-wood': 38.60463329293243, 'IoU-water-other': 28.24155848312506, 'IoU-window-blind': 45.72757450971233, 'IoU-window-other': 47.73488278351136, 'IoU-tree-merged': 80.54944129481224, 'IoU-fence-merged': 50.840630836630865, 'IoU-ceiling-merged': 66.50541429081333, 'IoU-sky-other-merged': 93.74625067228234, 'IoU-cabinet-merged': 59.97493226446231, 'IoU-table-merged': 38.10126656682944, 'IoU-floor-other-merged': 49.80164662342851, 'IoU-pavement-merged': 55.00889465285878, 'IoU-mountain-merged': 55.75120171288888, 'IoU-grass-merged': 71.1564860034983, 'IoU-dirt-merged': 41.939426919743894, 'IoU-paper-merged': 36.164928645434046, 'IoU-food-other-merged': 39.332823061797754, 'IoU-building-other-merged': 57.99697759225659, 'IoU-rock-merged': 61.866334460796814, 'IoU-wall-other-merged': 63.988546628477, 'IoU-rug-merged': 63.96791749482298, 'mACC': 72.13534934746951, 'pACC': 80.03954429182924, 'ACC-person': 92.30321202207527, 'ACC-bicycle': 85.54762873039084, 'ACC-car': 83.33338797042536, 'ACC-motorcycle': 90.69167546941264, 'ACC-airplane': 90.30116942091917, 'ACC-bus': 89.71737013250942, 'ACC-train': 95.48006982952596, 'ACC-truck': 76.23026805396977, 'ACC-boat': 78.02236685541727, 'ACC-traffic light': 89.72397162680095, 'ACC-fire hydrant': 95.51400163117508, 'ACC-stop sign': 96.85240359467176, 'ACC-parking meter': 92.16346970064164, 'ACC-bench': 74.90069107900402, 'ACC-bird': 80.63107741427422, 'ACC-cat': 91.05143250190353, 'ACC-dog': 80.06201409460574, 'ACC-horse': 92.89782489285331, 'ACC-sheep': 86.2975636338501, 'ACC-cow': 91.1712850553948, 'ACC-elephant': 86.28125093742617, 'ACC-bear': 71.34616182596528, 'ACC-zebra': 87.26691629058344, 'ACC-giraffe': 91.69209158710233, 'ACC-backpack': 70.24390671422687, 'ACC-umbrella': 82.57120171099996, 'ACC-handbag': 51.64185289701284, 'ACC-tie': 81.51259584838212, 'ACC-suitcase': 87.09803838895888, 'ACC-frisbee': 93.47999999999999, 'ACC-skis': 68.50700347874084, 'ACC-snowboard': 78.1651196394896, 'ACC-sports ball': 85.51286424576963, 'ACC-kite': 76.22785358130778, 'ACC-baseball bat': 83.67748316088381, 'ACC-baseball glove': 90.40372379902438, 'ACC-skateboard': 69.54544842475684, 'ACC-surfboard': 83.75572409748769, 'ACC-tennis racket': 79.71279281630686, 'ACC-bottle': 83.9716044583641, 'ACC-wine glass': 82.82125437435404, 'ACC-cup': 84.68028893210884, 'ACC-fork': 68.08263289949463, 'ACC-knife': 61.41862680442588, 'ACC-spoon': 69.19875707468648, 'ACC-bowl': 58.95823637614326, 'ACC-banana': 90.05798783933106, 'ACC-apple': 72.95947403329055, 'ACC-sandwich': 78.79281910886637, 'ACC-orange': 88.67831198671796, 'ACC-broccoli': 80.11884331001269, 'ACC-carrot': 76.13543247951819, 'ACC-hot dog': 73.90414809198226, 'ACC-pizza': 84.32332313751442, 'ACC-donut': 80.81729726948343, 'ACC-cake': 75.71031429783088, 'ACC-chair': 68.21801262952803, 'ACC-couch': 74.62135340114976, 'ACC-potted plant': 50.37380398409174, 'ACC-bed': 86.76625572612758, 'ACC-dining table': 70.54997463337509, 'ACC-toilet': 91.67041559922251, 'ACC-tv': 82.90820313102645, 'ACC-laptop': 88.65077566446885, 'ACC-mouse': 86.28976692675924, 'ACC-remote': 68.3052009117136, 'ACC-keyboard': 74.49732124689532, 'ACC-cell phone': 82.14266742127211, 'ACC-microwave': 32.75634048529937, 'ACC-oven': 85.09164283668126, 'ACC-toaster': 48.673856218095224, 'ACC-sink': 82.74636348313896, 'ACC-refrigerator': 74.99307757444336, 'ACC-book': 67.76120030259729, 'ACC-clock': 76.8729636908292, 'ACC-vase': 62.98206833236984, 'ACC-scissors': 66.34897024121055, 'ACC-teddy bear': 83.74534794952505, 'ACC-hair drier': 40.857598269433296, 'ACC-toothbrush': 81.38637943015983, 'ACC-banner': 56.80143895651328, 'ACC-blanket': 16.244044085960304, 'ACC-bridge': 55.16879056916185, 'ACC-cardboard': 55.77539624836412, 'ACC-counter': 54.570507965554825, 'ACC-curtain': 73.12998927372678, 'ACC-door-stuff': 66.28317382102179, 'ACC-floor-wood': 80.62532255731132, 'ACC-flower': 66.02886573091958, 'ACC-fruit': 59.16048386304153, 'ACC-gravel': 35.67881524212135, 'ACC-house': 26.018108753240256, 'ACC-light': 55.866896508823636, 'ACC-mirror-stuff': 66.76981147470468, 'ACC-net': 60.89295492990846, 'ACC-pillow': 22.899842394130832, 'ACC-platform': 56.13278702813274, 'ACC-playingfield': 87.47516083266318, 'ACC-railroad': 77.94276913087155, 'ACC-river': 59.666825948290935, 'ACC-road': 83.69450078167621, 'ACC-roof': 17.23066329295473, 'ACC-sand': 74.64365045069631, 'ACC-sea': 88.92201526084337, 'ACC-shelf': 60.53392213405381, 'ACC-snow': 94.19560447987924, 'ACC-stairs': 44.755539708123045, 'ACC-tent': 11.172896688149018, 'ACC-towel': 45.14802438579674, 'ACC-wall-brick': 57.14895247100259, 'ACC-wall-stone': 29.292331175325014, 'ACC-wall-tile': 76.97886343690803, 'ACC-wall-wood': 54.244206846626696, 'ACC-water-other': 57.606687955391614, 'ACC-window-blind': 54.347528852762586, 'ACC-window-other': 70.44103062570632, 'ACC-tree-merged': 88.96968277858883, 'ACC-fence-merged': 72.34489159182083, 'ACC-ceiling-merged': 78.87113946141038, 'ACC-sky-other-merged': 96.64910796953431, 'ACC-cabinet-merged': 74.16668354369045, 'ACC-table-merged': 56.70906246798778, 'ACC-floor-other-merged': 59.87109587176853, 'ACC-pavement-merged': 69.02764667834772, 'ACC-mountain-merged': 66.27987279688156, 'ACC-grass-merged': 83.37067385929254, 'ACC-dirt-merged': 61.411466306372105, 'ACC-paper-merged': 48.009907764473574, 'ACC-food-other-merged': 54.10925838418154, 'ACC-building-other-merged': 75.74392751544482, 'ACC-rock-merged': 83.44377301688041, 'ACC-wall-other-merged': 82.63466126653553, 'ACC-rug-merged': 77.52638236209127})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.6423763535264853, 'noc@0.8': 3.1594966344746855, 'noc@0.85': 3.839332748024583, 'noc@0.9': 4.950834064969271, 'miou@iter1': 0.8379005809467998}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.15157318115234, 'precision@0.6': 66.88690185546875, 'precision@0.7': 62.067626953125, 'precision@0.8': 51.4185791015625, 'precision@0.9': 26.2728328704834, 'cIoU': 56.838958740234375, 'mIoU': 61.99746322631836}}}
INFO:trainer.default_trainer:This epoch takes 1:33:06.136446
INFO:trainer.default_trainer:PROGRESS: 6.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 3 training.
INFO:trainer.default_trainer:epochs[     3] optim steps[5500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82932/0.91853, loss_mask_bce_0: 0.35120/0.33548, loss_mask_dice_0: 0.79731/1.16712, loss_spatial_bce_0: 0.07538/0.09949, loss_spatial_dice_0: 0.17789/0.23855, loss_spatial_ce_0: 0.02684/0.12044, loss_grounding_bce_0: 0.08607/0.08635, loss_grounding_dice_0: 0.16199/0.17990, loss_grounding_ce_0: 0.27704/0.28053, loss_mask_ce_1: 0.97173/0.91854, loss_mask_bce_1: 0.33242/0.33599, loss_mask_dice_1: 0.68514/1.17514, loss_spatial_bce_1: 0.08561/0.10050, loss_spatial_dice_1: 0.18935/0.24309, loss_spatial_ce_1: 0.03491/0.12594, loss_grounding_bce_1: 0.08189/0.08640, loss_grounding_dice_1: 0.16256/0.18068, loss_grounding_ce_1: 0.22207/0.28300, loss_mask_ce_2: 0.76999/0.92443, loss_mask_bce_2: 0.34431/0.33609, loss_mask_dice_2: 0.79818/1.17361, loss_spatial_bce_2: 0.07731/0.09997, loss_spatial_dice_2: 0.18828/0.24459, loss_spatial_ce_2: 0.04902/0.13118, loss_grounding_bce_2: 0.08193/0.08632, loss_grounding_dice_2: 0.15573/0.18001, loss_grounding_ce_2: 0.24436/0.28484, loss_mask_ce_3: 0.80498/0.92676, loss_mask_bce_3: 0.34388/0.33704, loss_mask_dice_3: 0.78411/1.17119, loss_spatial_bce_3: 0.09009/0.10114, loss_spatial_dice_3: 0.17378/0.24662, loss_spatial_ce_3: 0.07454/0.13882, loss_grounding_bce_3: 0.07877/0.08648, loss_grounding_dice_3: 0.15319/0.17962, loss_grounding_ce_3: 0.25388/0.28618, loss_mask_ce_4: 0.99033/0.92644, loss_mask_bce_4: 0.33319/0.33786, loss_mask_dice_4: 0.72699/1.19066, loss_spatial_bce_4: 0.13232/0.10412, loss_spatial_dice_4: 0.20770/0.25300, loss_spatial_ce_4: 0.07095/0.15348, loss_grounding_bce_4: 0.07873/0.08705, loss_grounding_dice_4: 0.16003/0.18245, loss_grounding_ce_4: 0.22056/0.28953, loss_mask_ce_5: 0.87272/0.93848, loss_mask_bce_5: 0.32321/0.34070, loss_mask_dice_5: 0.71848/1.19463, loss_spatial_bce_5: 0.12758/0.10540, loss_spatial_dice_5: 0.19877/0.25709, loss_spatial_ce_5: 0.10834/0.16516, loss_grounding_bce_5: 0.08176/0.08752, loss_grounding_dice_5: 0.16844/0.18362, loss_grounding_ce_5: 0.28243/0.29958, loss_mask_ce_6: 0.91076/0.97468, loss_mask_bce_6: 0.38933/0.34283, loss_mask_dice_6: 0.84084/1.19866, loss_spatial_bce_6: 0.12481/0.10980, loss_spatial_dice_6: 0.21324/0.26056, loss_spatial_ce_6: 0.12308/0.18542, loss_grounding_bce_6: 0.08524/0.08858, loss_grounding_dice_6: 0.15968/0.18426, loss_grounding_ce_6: 0.37489/0.32328, loss_mask_ce_7: 1.34728/1.01795, loss_mask_bce_7: 0.40308/0.35048, loss_mask_dice_7: 0.85340/1.25340, loss_spatial_bce_7: 0.15108/0.12094, loss_spatial_dice_7: 0.23493/0.28646, loss_spatial_ce_7: 0.14638/0.22860, loss_grounding_bce_7: 0.08549/0.09021, loss_grounding_dice_7: 0.14996/0.19084, loss_grounding_ce_7: 0.49509/0.36794, loss_mask_ce_8: 1.77295/1.13223, loss_mask_bce_8: 0.46459/0.36324, loss_mask_dice_8: 0.88738/1.32811, loss_spatial_bce_8: 0.28015/0.14138, loss_spatial_dice_8: 0.26623/0.32783, loss_spatial_ce_8: 0.17729/0.27861, loss_grounding_bce_8: 0.09217/0.09358, loss_grounding_dice_8: 0.14208/0.20156, loss_grounding_ce_8: 1.24662/0.44291, loss_mask_ce_9: 3.70555/3.72982, loss_mask_bce_9: 0.43371/0.39048, loss_mask_dice_9: 1.27935/1.90585, loss_spatial_bce_9: 0.40119/0.34384, loss_spatial_dice_9: 0.85076/0.82952, loss_spatial_ce_9: 1.31304/1.56979, loss_grounding_bce_9: 0.09525/0.10498, loss_grounding_dice_9: 0.20943/0.28220, loss_grounding_ce_9: 1.11440/0.75837] items per batch[64] items per second[0.13] total items[352000] mini batches[  5500] memory[7341] epoch remaining[1:33:57]
INFO:trainer.default_trainer:epochs[     3] optim steps[5600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98953/0.91803, loss_mask_bce_0: 0.30610/0.33559, loss_mask_dice_0: 1.89170/1.16479, loss_spatial_bce_0: 0.02171/0.09940, loss_spatial_dice_0: 0.22034/0.23823, loss_spatial_ce_0: 0.03455/0.11965, loss_grounding_bce_0: 0.01109/0.08652, loss_grounding_dice_0: 0.30523/0.17994, loss_grounding_ce_0: 0.48695/0.27998, loss_mask_ce_1: 1.05782/0.91807, loss_mask_bce_1: 0.30189/0.33605, loss_mask_dice_1: 2.11549/1.17284, loss_spatial_bce_1: 0.02270/0.10039, loss_spatial_dice_1: 0.23375/0.24272, loss_spatial_ce_1: 0.03038/0.12534, loss_grounding_bce_1: 0.01058/0.08654, loss_grounding_dice_1: 0.29037/0.18067, loss_grounding_ce_1: 0.43098/0.28275, loss_mask_ce_2: 0.79219/0.92401, loss_mask_bce_2: 0.29509/0.33617, loss_mask_dice_2: 2.21366/1.17139, loss_spatial_bce_2: 0.02252/0.09989, loss_spatial_dice_2: 0.21263/0.24420, loss_spatial_ce_2: 0.07888/0.13046, loss_grounding_bce_2: 0.01212/0.08646, loss_grounding_dice_2: 0.32130/0.18004, loss_grounding_ce_2: 0.38774/0.28430, loss_mask_ce_3: 1.14183/0.92655, loss_mask_bce_3: 0.33210/0.33712, loss_mask_dice_3: 1.99053/1.16875, loss_spatial_bce_3: 0.02269/0.10109, loss_spatial_dice_3: 0.24970/0.24626, loss_spatial_ce_3: 0.05075/0.13784, loss_grounding_bce_3: 0.01132/0.08664, loss_grounding_dice_3: 0.28444/0.17968, loss_grounding_ce_3: 0.37900/0.28582, loss_mask_ce_4: 1.02064/0.92624, loss_mask_bce_4: 0.32548/0.33800, loss_mask_dice_4: 1.98867/1.18857, loss_spatial_bce_4: 0.02514/0.10406, loss_spatial_dice_4: 0.26008/0.25263, loss_spatial_ce_4: 0.02375/0.15254, loss_grounding_bce_4: 0.01091/0.08719, loss_grounding_dice_4: 0.33367/0.18256, loss_grounding_ce_4: 0.37373/0.28873, loss_mask_ce_5: 1.04164/0.93840, loss_mask_bce_5: 0.30134/0.34073, loss_mask_dice_5: 1.90526/1.19221, loss_spatial_bce_5: 0.02393/0.10530, loss_spatial_dice_5: 0.25399/0.25665, loss_spatial_ce_5: 0.03543/0.16408, loss_grounding_bce_5: 0.01037/0.08767, loss_grounding_dice_5: 0.34101/0.18372, loss_grounding_ce_5: 0.39698/0.29876, loss_mask_ce_6: 1.11434/0.97405, loss_mask_bce_6: 0.33054/0.34304, loss_mask_dice_6: 2.03950/1.19651, loss_spatial_bce_6: 0.02182/0.10968, loss_spatial_dice_6: 0.24904/0.26011, loss_spatial_ce_6: 0.06700/0.18460, loss_grounding_bce_6: 0.00887/0.08872, loss_grounding_dice_6: 0.26970/0.18433, loss_grounding_ce_6: 0.42596/0.32234, loss_mask_ce_7: 1.43511/1.01759, loss_mask_bce_7: 0.35557/0.35070, loss_mask_dice_7: 2.29966/1.25156, loss_spatial_bce_7: 0.02551/0.12083, loss_spatial_dice_7: 0.28489/0.28605, loss_spatial_ce_7: 0.11241/0.22749, loss_grounding_bce_7: 0.01252/0.09034, loss_grounding_dice_7: 0.36682/0.19094, loss_grounding_ce_7: 0.45293/0.36677, loss_mask_ce_8: 1.69547/1.13195, loss_mask_bce_8: 0.21238/0.36341, loss_mask_dice_8: 2.37598/1.32574, loss_spatial_bce_8: 0.02944/0.14134, loss_spatial_dice_8: 0.40857/0.32742, loss_spatial_ce_8: 0.28449/0.27774, loss_grounding_bce_8: 0.01564/0.09377, loss_grounding_dice_8: 0.40101/0.20166, loss_grounding_ce_8: 0.52516/0.44139, loss_mask_ce_9: 3.94854/3.72594, loss_mask_bce_9: 0.21202/0.39056, loss_mask_dice_9: 3.08373/1.90262, loss_spatial_bce_9: 0.18522/0.34444, loss_spatial_dice_9: 0.87697/0.82943, loss_spatial_ce_9: 2.20176/1.56953, loss_grounding_bce_9: 0.01166/0.10515, loss_grounding_dice_9: 0.44260/0.28208, loss_grounding_ce_9: 0.68691/0.75593] items per batch[64] items per second[0.22] total items[358400] mini batches[  5600] memory[7341] epoch remaining[1:23:40]
INFO:trainer.default_trainer:epochs[     3] optim steps[5700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02053/0.91824, loss_mask_bce_0: 0.29578/0.33576, loss_mask_dice_0: 0.76412/1.16591, loss_spatial_bce_0: 0.05735/0.09925, loss_spatial_dice_0: 0.15977/0.23792, loss_spatial_ce_0: 0.02420/0.11901, loss_grounding_bce_0: 0.05181/0.08657, loss_grounding_dice_0: 0.08707/0.17993, loss_grounding_ce_0: 0.04426/0.28097, loss_mask_ce_1: 1.01055/0.91803, loss_mask_bce_1: 0.29277/0.33626, loss_mask_dice_1: 0.73976/1.17387, loss_spatial_bce_1: 0.05830/0.10026, loss_spatial_dice_1: 0.15997/0.24247, loss_spatial_ce_1: 0.01687/0.12462, loss_grounding_bce_1: 0.05718/0.08661, loss_grounding_dice_1: 0.09038/0.18062, loss_grounding_ce_1: 0.04394/0.28383, loss_mask_ce_2: 1.00920/0.92411, loss_mask_bce_2: 0.29960/0.33626, loss_mask_dice_2: 0.74490/1.17246, loss_spatial_bce_2: 0.05416/0.09975, loss_spatial_dice_2: 0.15561/0.24388, loss_spatial_ce_2: 0.01325/0.12990, loss_grounding_bce_2: 0.05723/0.08649, loss_grounding_dice_2: 0.09211/0.18009, loss_grounding_ce_2: 0.04227/0.28519, loss_mask_ce_3: 1.00267/0.92712, loss_mask_bce_3: 0.30826/0.33722, loss_mask_dice_3: 0.73071/1.16963, loss_spatial_bce_3: 0.05730/0.10096, loss_spatial_dice_3: 0.16812/0.24599, loss_spatial_ce_3: 0.05290/0.13711, loss_grounding_bce_3: 0.05967/0.08669, loss_grounding_dice_3: 0.09206/0.17963, loss_grounding_ce_3: 0.04076/0.28642, loss_mask_ce_4: 1.14838/0.92663, loss_mask_bce_4: 0.24733/0.33810, loss_mask_dice_4: 0.82707/1.18965, loss_spatial_bce_4: 0.05818/0.10403, loss_spatial_dice_4: 0.15389/0.25232, loss_spatial_ce_4: 0.07013/0.15175, loss_grounding_bce_4: 0.05713/0.08723, loss_grounding_dice_4: 0.08857/0.18253, loss_grounding_ce_4: 0.04434/0.28930, loss_mask_ce_5: 1.25354/0.93881, loss_mask_bce_5: 0.25176/0.34088, loss_mask_dice_5: 0.79167/1.19332, loss_spatial_bce_5: 0.07255/0.10522, loss_spatial_dice_5: 0.18112/0.25631, loss_spatial_ce_5: 0.07212/0.16348, loss_grounding_bce_5: 0.05769/0.08776, loss_grounding_dice_5: 0.09787/0.18369, loss_grounding_ce_5: 0.03927/0.29925, loss_mask_ce_6: 1.27300/0.97455, loss_mask_bce_6: 0.26414/0.34318, loss_mask_dice_6: 0.83453/1.19750, loss_spatial_bce_6: 0.10869/0.10957, loss_spatial_dice_6: 0.19094/0.25980, loss_spatial_ce_6: 0.03232/0.18378, loss_grounding_bce_6: 0.05520/0.08873, loss_grounding_dice_6: 0.09565/0.18424, loss_grounding_ce_6: 0.05440/0.32347, loss_mask_ce_7: 1.04415/1.01742, loss_mask_bce_7: 0.36954/0.35099, loss_mask_dice_7: 0.88129/1.25255, loss_spatial_bce_7: 0.07791/0.12070, loss_spatial_dice_7: 0.18448/0.28576, loss_spatial_ce_7: 0.12515/0.22682, loss_grounding_bce_7: 0.06272/0.09039, loss_grounding_dice_7: 0.10454/0.19098, loss_grounding_ce_7: 0.08672/0.36799, loss_mask_ce_8: 1.33310/1.13201, loss_mask_bce_8: 0.29951/0.36358, loss_mask_dice_8: 0.88877/1.32677, loss_spatial_bce_8: 0.12729/0.14117, loss_spatial_dice_8: 0.22238/0.32704, loss_spatial_ce_8: 0.15232/0.27697, loss_grounding_bce_8: 0.06206/0.09385, loss_grounding_dice_8: 0.09961/0.20164, loss_grounding_ce_8: 0.12682/0.44186, loss_mask_ce_9: 3.91325/3.72767, loss_mask_bce_9: 0.36211/0.39083, loss_mask_dice_9: 1.27317/1.90326, loss_spatial_bce_9: 0.40975/0.34445, loss_spatial_dice_9: 0.82813/0.82939, loss_spatial_ce_9: 1.30021/1.57002, loss_grounding_bce_9: 0.07160/0.10529, loss_grounding_dice_9: 0.16464/0.28195, loss_grounding_ce_9: 0.34576/0.75542] items per batch[64] items per second[0.22] total items[364800] mini batches[  5700] memory[7341] epoch remaining[1:19:10]
INFO:trainer.default_trainer:epochs[     3] optim steps[5800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67596/0.91879, loss_mask_bce_0: 0.15723/0.33608, loss_mask_dice_0: 2.40090/1.16607, loss_spatial_bce_0: 0.02264/0.09918, loss_spatial_dice_0: 0.40658/0.23769, loss_spatial_ce_0: 0.13865/0.11831, loss_grounding_bce_0: 0.03146/0.08667, loss_grounding_dice_0: 0.49193/0.17977, loss_grounding_ce_0: 0.18244/0.28036, loss_mask_ce_1: 0.92020/0.91865, loss_mask_bce_1: 0.12862/0.33662, loss_mask_dice_1: 2.19177/1.17398, loss_spatial_bce_1: 0.01822/0.10019, loss_spatial_dice_1: 0.40965/0.24222, loss_spatial_ce_1: 0.30368/0.12398, loss_grounding_bce_1: 0.03556/0.08670, loss_grounding_dice_1: 0.52704/0.18044, loss_grounding_ce_1: 0.18307/0.28312, loss_mask_ce_2: 0.86928/0.92466, loss_mask_bce_2: 0.16714/0.33662, loss_mask_dice_2: 2.27491/1.17272, loss_spatial_bce_2: 0.02334/0.09971, loss_spatial_dice_2: 0.43801/0.24363, loss_spatial_ce_2: 0.09710/0.12931, loss_grounding_bce_2: 0.03598/0.08659, loss_grounding_dice_2: 0.61083/0.17996, loss_grounding_ce_2: 0.18521/0.28477, loss_mask_ce_3: 0.89184/0.92791, loss_mask_bce_3: 0.12972/0.33755, loss_mask_dice_3: 2.12304/1.16990, loss_spatial_bce_3: 0.02500/0.10089, loss_spatial_dice_3: 0.45503/0.24574, loss_spatial_ce_3: 0.09666/0.13617, loss_grounding_bce_3: 0.03984/0.08676, loss_grounding_dice_3: 0.51271/0.17942, loss_grounding_ce_3: 0.19725/0.28586, loss_mask_ce_4: 0.94720/0.92732, loss_mask_bce_4: 0.14708/0.33849, loss_mask_dice_4: 2.33737/1.18961, loss_spatial_bce_4: 0.02175/0.10399, loss_spatial_dice_4: 0.43834/0.25204, loss_spatial_ce_4: 0.26388/0.15100, loss_grounding_bce_4: 0.04090/0.08730, loss_grounding_dice_4: 0.50657/0.18230, loss_grounding_ce_4: 0.17802/0.28889, loss_mask_ce_5: 1.30017/0.93939, loss_mask_bce_5: 0.15032/0.34134, loss_mask_dice_5: 2.60094/1.19367, loss_spatial_bce_5: 0.02089/0.10513, loss_spatial_dice_5: 0.39886/0.25599, loss_spatial_ce_5: 0.18548/0.16284, loss_grounding_bce_5: 0.03592/0.08783, loss_grounding_dice_5: 0.60158/0.18354, loss_grounding_ce_5: 0.17209/0.29899, loss_mask_ce_6: 0.95113/0.97510, loss_mask_bce_6: 0.12539/0.34360, loss_mask_dice_6: 2.60262/1.19792, loss_spatial_bce_6: 0.03976/0.10954, loss_spatial_dice_6: 0.45799/0.25952, loss_spatial_ce_6: 0.14825/0.18317, loss_grounding_bce_6: 0.03442/0.08878, loss_grounding_dice_6: 0.49376/0.18398, loss_grounding_ce_6: 0.22965/0.32273, loss_mask_ce_7: 0.88662/1.01803, loss_mask_bce_7: 0.12820/0.35145, loss_mask_dice_7: 2.39416/1.25277, loss_spatial_bce_7: 0.04017/0.12058, loss_spatial_dice_7: 0.52007/0.28542, loss_spatial_ce_7: 0.20438/0.22603, loss_grounding_bce_7: 0.03942/0.09048, loss_grounding_dice_7: 0.53541/0.19076, loss_grounding_ce_7: 0.31276/0.36727, loss_mask_ce_8: 0.71546/1.13215, loss_mask_bce_8: 0.14630/0.36407, loss_mask_dice_8: 2.14014/1.32709, loss_spatial_bce_8: 0.02804/0.14111, loss_spatial_dice_8: 0.50111/0.32661, loss_spatial_ce_8: 0.21717/0.27605, loss_grounding_bce_8: 0.04728/0.09401, loss_grounding_dice_8: 0.54138/0.20154, loss_grounding_ce_8: 0.24045/0.44128, loss_mask_ce_9: 3.60192/3.72714, loss_mask_bce_9: 0.10894/0.39127, loss_mask_dice_9: 2.50295/1.90295, loss_spatial_bce_9: 0.04551/0.34454, loss_spatial_dice_9: 0.87000/0.82940, loss_spatial_ce_9: 2.12780/1.56849, loss_grounding_bce_9: 0.03420/0.10541, loss_grounding_dice_9: 0.56748/0.28192, loss_grounding_ce_9: 0.26012/0.75368] items per batch[64] items per second[0.21] total items[371200] mini batches[  5800] memory[7341] epoch remaining[1:14:31]
INFO:trainer.default_trainer:epochs[     3] optim steps[5900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.10557/0.91821, loss_mask_bce_0: 0.63693/0.33591, loss_mask_dice_0: 2.02744/1.16588, loss_spatial_bce_0: 0.09578/0.09900, loss_spatial_dice_0: 0.24330/0.23726, loss_spatial_ce_0: 0.01902/0.11719, loss_grounding_bce_0: 0.08952/0.08641, loss_grounding_dice_0: 0.11842/0.17954, loss_grounding_ce_0: 0.38017/0.28023, loss_mask_ce_1: 1.13134/0.91810, loss_mask_bce_1: 0.63769/0.33647, loss_mask_dice_1: 1.99229/1.17357, loss_spatial_bce_1: 0.09633/0.10003, loss_spatial_dice_1: 0.25664/0.24181, loss_spatial_ce_1: 0.02391/0.12300, loss_grounding_bce_1: 0.08541/0.08647, loss_grounding_dice_1: 0.11311/0.18024, loss_grounding_ce_1: 0.37992/0.28272, loss_mask_ce_2: 1.10745/0.92404, loss_mask_bce_2: 0.61318/0.33647, loss_mask_dice_2: 2.02644/1.17265, loss_spatial_bce_2: 0.09493/0.09955, loss_spatial_dice_2: 0.24981/0.24324, loss_spatial_ce_2: 0.02662/0.12837, loss_grounding_bce_2: 0.07851/0.08634, loss_grounding_dice_2: 0.11194/0.17976, loss_grounding_ce_2: 0.36116/0.28472, loss_mask_ce_3: 1.25043/0.92751, loss_mask_bce_3: 0.64045/0.33740, loss_mask_dice_3: 2.02746/1.16958, loss_spatial_bce_3: 0.09102/0.10073, loss_spatial_dice_3: 0.24876/0.24535, loss_spatial_ce_3: 0.03604/0.13514, loss_grounding_bce_3: 0.07714/0.08653, loss_grounding_dice_3: 0.10591/0.17929, loss_grounding_ce_3: 0.31798/0.28564, loss_mask_ce_4: 1.26312/0.92710, loss_mask_bce_4: 0.63316/0.33836, loss_mask_dice_4: 2.02639/1.18963, loss_spatial_bce_4: 0.10281/0.10385, loss_spatial_dice_4: 0.26689/0.25161, loss_spatial_ce_4: 0.06473/0.15005, loss_grounding_bce_4: 0.08189/0.08709, loss_grounding_dice_4: 0.10524/0.18216, loss_grounding_ce_4: 0.32315/0.28885, loss_mask_ce_5: 1.27945/0.93921, loss_mask_bce_5: 0.59057/0.34115, loss_mask_dice_5: 2.00638/1.19371, loss_spatial_bce_5: 0.09187/0.10494, loss_spatial_dice_5: 0.26057/0.25560, loss_spatial_ce_5: 0.06998/0.16200, loss_grounding_bce_5: 0.08329/0.08761, loss_grounding_dice_5: 0.11585/0.18340, loss_grounding_ce_5: 0.48514/0.29926, loss_mask_ce_6: 1.26537/0.97494, loss_mask_bce_6: 0.59781/0.34355, loss_mask_dice_6: 1.94748/1.19805, loss_spatial_bce_6: 0.10910/0.10938, loss_spatial_dice_6: 0.27058/0.25909, loss_spatial_ce_6: 0.09802/0.18214, loss_grounding_bce_6: 0.08370/0.08855, loss_grounding_dice_6: 0.10686/0.18383, loss_grounding_ce_6: 0.38393/0.32263, loss_mask_ce_7: 1.46589/1.01804, loss_mask_bce_7: 0.69024/0.35130, loss_mask_dice_7: 2.19977/1.25269, loss_spatial_bce_7: 0.09572/0.12047, loss_spatial_dice_7: 0.27908/0.28509, loss_spatial_ce_7: 0.09949/0.22485, loss_grounding_bce_7: 0.09048/0.09025, loss_grounding_dice_7: 0.11850/0.19057, loss_grounding_ce_7: 0.64709/0.36649, loss_mask_ce_8: 1.32313/1.13193, loss_mask_bce_8: 0.73637/0.36380, loss_mask_dice_8: 2.36339/1.32732, loss_spatial_bce_8: 0.10870/0.14097, loss_spatial_dice_8: 0.33248/0.32622, loss_spatial_ce_8: 0.16463/0.27529, loss_grounding_bce_8: 0.10426/0.09375, loss_grounding_dice_8: 0.14861/0.20142, loss_grounding_ce_8: 1.22346/0.44031, loss_mask_ce_9: 5.11450/3.72464, loss_mask_bce_9: 0.84253/0.39129, loss_mask_dice_9: 3.42106/1.90365, loss_spatial_bce_9: 0.27032/0.34452, loss_spatial_dice_9: 0.88423/0.82949, loss_spatial_ce_9: 1.14530/1.56796, loss_grounding_bce_9: 0.24230/0.10527, loss_grounding_dice_9: 0.48263/0.28196, loss_grounding_ce_9: 3.15764/0.75140] items per batch[64] items per second[0.22] total items[377600] mini batches[  5900] memory[7341] epoch remaining[1:09:11]
INFO:trainer.default_trainer:epochs[     3] optim steps[6000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45198/0.91761, loss_mask_bce_0: 0.44170/0.33575, loss_mask_dice_0: 0.50780/1.16454, loss_spatial_bce_0: 0.10430/0.09887, loss_spatial_dice_0: 0.11748/0.23686, loss_spatial_ce_0: 0.02271/0.11644, loss_grounding_bce_0: 0.03494/0.08637, loss_grounding_dice_0: 0.06326/0.17920, loss_grounding_ce_0: 0.04683/0.27959, loss_mask_ce_1: 0.35503/0.91767, loss_mask_bce_1: 0.42968/0.33631, loss_mask_dice_1: 0.50741/1.17231, loss_spatial_bce_1: 0.10665/0.09987, loss_spatial_dice_1: 0.12254/0.24139, loss_spatial_ce_1: 0.03026/0.12216, loss_grounding_bce_1: 0.03554/0.08643, loss_grounding_dice_1: 0.06442/0.17990, loss_grounding_ce_1: 0.05327/0.28200, loss_mask_ce_2: 0.37878/0.92347, loss_mask_bce_2: 0.42907/0.33628, loss_mask_dice_2: 0.49152/1.17111, loss_spatial_bce_2: 0.10598/0.09940, loss_spatial_dice_2: 0.12086/0.24281, loss_spatial_ce_2: 0.02507/0.12738, loss_grounding_bce_2: 0.03479/0.08631, loss_grounding_dice_2: 0.06564/0.17944, loss_grounding_ce_2: 0.06056/0.28406, loss_mask_ce_3: 0.34990/0.92712, loss_mask_bce_3: 0.43547/0.33726, loss_mask_dice_3: 0.49421/1.16800, loss_spatial_bce_3: 0.11077/0.10058, loss_spatial_dice_3: 0.11880/0.24486, loss_spatial_ce_3: 0.01787/0.13420, loss_grounding_bce_3: 0.03492/0.08651, loss_grounding_dice_3: 0.06194/0.17899, loss_grounding_ce_3: 0.05489/0.28497, loss_mask_ce_4: 0.36922/0.92668, loss_mask_bce_4: 0.43297/0.33822, loss_mask_dice_4: 0.48603/1.18829, loss_spatial_bce_4: 0.11030/0.10373, loss_spatial_dice_4: 0.12056/0.25117, loss_spatial_ce_4: 0.02522/0.14923, loss_grounding_bce_4: 0.03911/0.08702, loss_grounding_dice_4: 0.06827/0.18182, loss_grounding_ce_4: 0.06535/0.28846, loss_mask_ce_5: 0.38525/0.93910, loss_mask_bce_5: 0.43833/0.34098, loss_mask_dice_5: 0.49522/1.19215, loss_spatial_bce_5: 0.11306/0.10481, loss_spatial_dice_5: 0.12314/0.25509, loss_spatial_ce_5: 0.02988/0.16127, loss_grounding_bce_5: 0.04345/0.08754, loss_grounding_dice_5: 0.07837/0.18302, loss_grounding_ce_5: 0.06808/0.29883, loss_mask_ce_6: 0.40834/0.97500, loss_mask_bce_6: 0.45793/0.34343, loss_mask_dice_6: 0.50615/1.19672, loss_spatial_bce_6: 0.12417/0.10934, loss_spatial_dice_6: 0.13243/0.25865, loss_spatial_ce_6: 0.05203/0.18135, loss_grounding_bce_6: 0.02890/0.08848, loss_grounding_dice_6: 0.06156/0.18345, loss_grounding_ce_6: 0.10884/0.32193, loss_mask_ce_7: 0.52476/1.01780, loss_mask_bce_7: 0.47509/0.35119, loss_mask_dice_7: 0.52601/1.25157, loss_spatial_bce_7: 0.19681/0.12042, loss_spatial_dice_7: 0.19020/0.28470, loss_spatial_ce_7: 0.13903/0.22394, loss_grounding_bce_7: 0.02950/0.09014, loss_grounding_dice_7: 0.06247/0.19022, loss_grounding_ce_7: 0.37374/0.36596, loss_mask_ce_8: 0.70509/1.13176, loss_mask_bce_8: 0.48665/0.36372, loss_mask_dice_8: 0.55731/1.32588, loss_spatial_bce_8: 0.25009/0.14100, loss_spatial_dice_8: 0.22386/0.32590, loss_spatial_ce_8: 0.18158/0.27440, loss_grounding_bce_8: 0.03830/0.09362, loss_grounding_dice_8: 0.07668/0.20109, loss_grounding_ce_8: 1.56656/0.44033, loss_mask_ce_9: 3.10622/3.72411, loss_mask_bce_9: 0.55315/0.39128, loss_mask_dice_9: 0.94438/1.90306, loss_spatial_bce_9: 0.39317/0.34435, loss_spatial_dice_9: 0.79910/0.82928, loss_spatial_ce_9: 1.55170/1.56654, loss_grounding_bce_9: 0.07104/0.10514, loss_grounding_dice_9: 0.22193/0.28164, loss_grounding_ce_9: 1.62892/0.75010] items per batch[64] items per second[0.22] total items[384000] mini batches[  6000] memory[7341] epoch remaining[1:03:59]
INFO:trainer.default_trainer:epochs[     3] optim steps[6100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88742/0.91815, loss_mask_bce_0: 0.68308/0.33611, loss_mask_dice_0: 1.11981/1.16770, loss_spatial_bce_0: 0.12103/0.09869, loss_spatial_dice_0: 0.19689/0.23668, loss_spatial_ce_0: 0.01983/0.11562, loss_grounding_bce_0: 0.12086/0.08649, loss_grounding_dice_0: 0.19761/0.17921, loss_grounding_ce_0: 0.34206/0.27947, loss_mask_ce_1: 0.89080/0.91784, loss_mask_bce_1: 0.66714/0.33673, loss_mask_dice_1: 1.13985/1.17596, loss_spatial_bce_1: 0.11797/0.09971, loss_spatial_dice_1: 0.19378/0.24120, loss_spatial_ce_1: 0.02168/0.12148, loss_grounding_bce_1: 0.11970/0.08654, loss_grounding_dice_1: 0.19909/0.17992, loss_grounding_ce_1: 0.30587/0.28187, loss_mask_ce_2: 0.62377/0.92413, loss_mask_bce_2: 0.68693/0.33669, loss_mask_dice_2: 1.19954/1.17473, loss_spatial_bce_2: 0.12005/0.09922, loss_spatial_dice_2: 0.18533/0.24260, loss_spatial_ce_2: 0.02859/0.12669, loss_grounding_bce_2: 0.12342/0.08642, loss_grounding_dice_2: 0.20254/0.17944, loss_grounding_ce_2: 0.34378/0.28400, loss_mask_ce_3: 0.65575/0.92774, loss_mask_bce_3: 0.68079/0.33763, loss_mask_dice_3: 1.20492/1.17154, loss_spatial_bce_3: 0.12315/0.10039, loss_spatial_dice_3: 0.19156/0.24460, loss_spatial_ce_3: 0.03237/0.13345, loss_grounding_bce_3: 0.11710/0.08664, loss_grounding_dice_3: 0.19885/0.17900, loss_grounding_ce_3: 0.34165/0.28487, loss_mask_ce_4: 0.69268/0.92732, loss_mask_bce_4: 0.67242/0.33865, loss_mask_dice_4: 1.17141/1.19216, loss_spatial_bce_4: 0.12266/0.10356, loss_spatial_dice_4: 0.19010/0.25100, loss_spatial_ce_4: 0.03112/0.14841, loss_grounding_bce_4: 0.12232/0.08714, loss_grounding_dice_4: 0.21199/0.18183, loss_grounding_ce_4: 0.44093/0.28824, loss_mask_ce_5: 0.67425/0.93999, loss_mask_bce_5: 0.66498/0.34133, loss_mask_dice_5: 1.17969/1.19576, loss_spatial_bce_5: 0.12258/0.10462, loss_spatial_dice_5: 0.19133/0.25482, loss_spatial_ce_5: 0.04254/0.16061, loss_grounding_bce_5: 0.11930/0.08767, loss_grounding_dice_5: 0.19700/0.18306, loss_grounding_ce_5: 0.53908/0.29873, loss_mask_ce_6: 0.75965/0.97572, loss_mask_bce_6: 0.65959/0.34388, loss_mask_dice_6: 1.17157/1.20043, loss_spatial_bce_6: 0.12662/0.10917, loss_spatial_dice_6: 0.19330/0.25836, loss_spatial_ce_6: 0.07660/0.18048, loss_grounding_bce_6: 0.12308/0.08863, loss_grounding_dice_6: 0.20728/0.18342, loss_grounding_ce_6: 0.52418/0.32161, loss_mask_ce_7: 0.86010/1.01900, loss_mask_bce_7: 0.62314/0.35163, loss_mask_dice_7: 1.12116/1.25570, loss_spatial_bce_7: 0.14818/0.12019, loss_spatial_dice_7: 0.21418/0.28447, loss_spatial_ce_7: 0.10750/0.22310, loss_grounding_bce_7: 0.12524/0.09032, loss_grounding_dice_7: 0.21234/0.19030, loss_grounding_ce_7: 0.61702/0.36548, loss_mask_ce_8: 0.89412/1.13235, loss_mask_bce_8: 0.65534/0.36420, loss_mask_dice_8: 1.24212/1.32981, loss_spatial_bce_8: 0.23636/0.14081, loss_spatial_dice_8: 0.29630/0.32578, loss_spatial_ce_8: 0.13762/0.27339, loss_grounding_bce_8: 0.12791/0.09378, loss_grounding_dice_8: 0.21570/0.20109, loss_grounding_ce_8: 0.95366/0.43964, loss_mask_ce_9: 3.21407/3.72561, loss_mask_bce_9: 0.70664/0.39198, loss_mask_dice_9: 1.84472/1.90890, loss_spatial_bce_9: 0.36420/0.34389, loss_spatial_dice_9: 0.89588/0.82927, loss_spatial_ce_9: 1.37459/1.56509, loss_grounding_bce_9: 0.14590/0.10527, loss_grounding_dice_9: 0.42770/0.28177, loss_grounding_ce_9: 1.06918/0.74826] items per batch[64] items per second[0.22] total items[390400] mini batches[  6100] memory[7341] epoch remaining[0:59:08]
INFO:trainer.default_trainer:epochs[     3] optim steps[6200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50945/0.91788, loss_mask_bce_0: 0.23072/0.33573, loss_mask_dice_0: 0.70914/1.16452, loss_spatial_bce_0: 0.03028/0.09859, loss_spatial_dice_0: 0.14878/0.23639, loss_spatial_ce_0: 0.01316/0.11481, loss_grounding_bce_0: 0.05816/0.08658, loss_grounding_dice_0: 0.07704/0.17916, loss_grounding_ce_0: 0.57598/0.27987, loss_mask_ce_1: 0.46276/0.91729, loss_mask_bce_1: 0.24551/0.33635, loss_mask_dice_1: 0.73596/1.17284, loss_spatial_bce_1: 0.02905/0.09959, loss_spatial_dice_1: 0.13854/0.24096, loss_spatial_ce_1: 0.02486/0.12066, loss_grounding_bce_1: 0.06631/0.08659, loss_grounding_dice_1: 0.08911/0.17991, loss_grounding_ce_1: 0.45146/0.28214, loss_mask_ce_2: 0.52399/0.92339, loss_mask_bce_2: 0.23504/0.33632, loss_mask_dice_2: 0.76005/1.17144, loss_spatial_bce_2: 0.02537/0.09910, loss_spatial_dice_2: 0.15458/0.24231, loss_spatial_ce_2: 0.02465/0.12590, loss_grounding_bce_2: 0.06529/0.08650, loss_grounding_dice_2: 0.08804/0.17937, loss_grounding_ce_2: 0.51823/0.28453, loss_mask_ce_3: 0.54151/0.92735, loss_mask_bce_3: 0.23831/0.33724, loss_mask_dice_3: 0.87248/1.16839, loss_spatial_bce_3: 0.02494/0.10026, loss_spatial_dice_3: 0.15299/0.24431, loss_spatial_ce_3: 0.02225/0.13256, loss_grounding_bce_3: 0.06260/0.08669, loss_grounding_dice_3: 0.07878/0.17903, loss_grounding_ce_3: 0.78090/0.28530, loss_mask_ce_4: 0.58124/0.92660, loss_mask_bce_4: 0.24314/0.33831, loss_mask_dice_4: 1.13759/1.18924, loss_spatial_bce_4: 0.02532/0.10346, loss_spatial_dice_4: 0.15933/0.25068, loss_spatial_ce_4: 0.02064/0.14732, loss_grounding_bce_4: 0.06505/0.08719, loss_grounding_dice_4: 0.09027/0.18182, loss_grounding_ce_4: 0.73214/0.28885, loss_mask_ce_5: 0.59872/0.93967, loss_mask_bce_5: 0.22146/0.34090, loss_mask_dice_5: 0.84855/1.19260, loss_spatial_bce_5: 0.03596/0.10447, loss_spatial_dice_5: 0.16116/0.25442, loss_spatial_ce_5: 0.04667/0.15963, loss_grounding_bce_5: 0.05827/0.08771, loss_grounding_dice_5: 0.07840/0.18302, loss_grounding_ce_5: 0.56247/0.29945, loss_mask_ce_6: 0.64568/0.97535, loss_mask_bce_6: 0.22450/0.34340, loss_mask_dice_6: 0.76078/1.19702, loss_spatial_bce_6: 0.04151/0.10903, loss_spatial_dice_6: 0.16591/0.25797, loss_spatial_ce_6: 0.03931/0.17945, loss_grounding_bce_6: 0.06265/0.08864, loss_grounding_dice_6: 0.08176/0.18330, loss_grounding_ce_6: 0.61123/0.32218, loss_mask_ce_7: 0.81269/1.01854, loss_mask_bce_7: 0.23336/0.35115, loss_mask_dice_7: 0.82963/1.25227, loss_spatial_bce_7: 0.06818/0.11996, loss_spatial_dice_7: 0.24198/0.28409, loss_spatial_ce_7: 0.09674/0.22206, loss_grounding_bce_7: 0.06941/0.09034, loss_grounding_dice_7: 0.10837/0.19021, loss_grounding_ce_7: 0.58203/0.36622, loss_mask_ce_8: 1.02724/1.13165, loss_mask_bce_8: 0.28334/0.36383, loss_mask_dice_8: 1.23140/1.32635, loss_spatial_bce_8: 0.07517/0.14065, loss_spatial_dice_8: 0.32215/0.32532, loss_spatial_ce_8: 0.12089/0.27241, loss_grounding_bce_8: 0.07923/0.09383, loss_grounding_dice_8: 0.12327/0.20102, loss_grounding_ce_8: 0.63692/0.43993, loss_mask_ce_9: 5.72822/3.72341, loss_mask_bce_9: 0.27063/0.39160, loss_mask_dice_9: 2.05775/1.90359, loss_spatial_bce_9: 0.15064/0.34390, loss_spatial_dice_9: 0.80797/0.82906, loss_spatial_ce_9: 1.63408/1.56417, loss_grounding_bce_9: 0.07393/0.10529, loss_grounding_dice_9: 0.28028/0.28157, loss_grounding_ce_9: 0.83097/0.74834] items per batch[64] items per second[0.22] total items[396800] mini batches[  6200] memory[7341] epoch remaining[0:54:16]
INFO:trainer.default_trainer:epochs[     3] optim steps[6300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72652/0.91775, loss_mask_bce_0: 0.34007/0.33623, loss_mask_dice_0: 1.49161/1.16670, loss_spatial_bce_0: 0.05829/0.09843, loss_spatial_dice_0: 0.21662/0.23603, loss_spatial_ce_0: 0.01399/0.11418, loss_grounding_bce_0: 0.10633/0.08644, loss_grounding_dice_0: 0.16746/0.17920, loss_grounding_ce_0: 0.53721/0.28005, loss_mask_ce_1: 0.73001/0.91699, loss_mask_bce_1: 0.34138/0.33682, loss_mask_dice_1: 1.55647/1.17483, loss_spatial_bce_1: 0.05593/0.09944, loss_spatial_dice_1: 0.20693/0.24062, loss_spatial_ce_1: 0.01641/0.12005, loss_grounding_bce_1: 0.10912/0.08646, loss_grounding_dice_1: 0.16172/0.18000, loss_grounding_ce_1: 0.45686/0.28235, loss_mask_ce_2: 0.69667/0.92291, loss_mask_bce_2: 0.35047/0.33684, loss_mask_dice_2: 1.49005/1.17352, loss_spatial_bce_2: 0.05554/0.09897, loss_spatial_dice_2: 0.21058/0.24189, loss_spatial_ce_2: 0.02648/0.12535, loss_grounding_bce_2: 0.11314/0.08638, loss_grounding_dice_2: 0.16061/0.17944, loss_grounding_ce_2: 0.36398/0.28471, loss_mask_ce_3: 0.79319/0.92691, loss_mask_bce_3: 0.34877/0.33774, loss_mask_dice_3: 1.57072/1.17071, loss_spatial_bce_3: 0.06882/0.10011, loss_spatial_dice_3: 0.22904/0.24391, loss_spatial_ce_3: 0.03563/0.13185, loss_grounding_bce_3: 0.10157/0.08655, loss_grounding_dice_3: 0.15981/0.17912, loss_grounding_ce_3: 0.35568/0.28576, loss_mask_ce_4: 0.66087/0.92616, loss_mask_bce_4: 0.35847/0.33884, loss_mask_dice_4: 1.71118/1.19163, loss_spatial_bce_4: 0.07572/0.10332, loss_spatial_dice_4: 0.22738/0.25032, loss_spatial_ce_4: 0.04834/0.14669, loss_grounding_bce_4: 0.10572/0.08709, loss_grounding_dice_4: 0.15059/0.18192, loss_grounding_ce_4: 0.36264/0.28911, loss_mask_ce_5: 0.71742/0.93915, loss_mask_bce_5: 0.35636/0.34135, loss_mask_dice_5: 1.68514/1.19492, loss_spatial_bce_5: 0.09007/0.10431, loss_spatial_dice_5: 0.24543/0.25407, loss_spatial_ce_5: 0.06211/0.15918, loss_grounding_bce_5: 0.10746/0.08758, loss_grounding_dice_5: 0.15745/0.18310, loss_grounding_ce_5: 0.35026/0.29945, loss_mask_ce_6: 0.85376/0.97518, loss_mask_bce_6: 0.34574/0.34376, loss_mask_dice_6: 1.61998/1.19913, loss_spatial_bce_6: 0.08231/0.10892, loss_spatial_dice_6: 0.23794/0.25757, loss_spatial_ce_6: 0.07378/0.17903, loss_grounding_bce_6: 0.09910/0.08849, loss_grounding_dice_6: 0.15003/0.18332, loss_grounding_ce_6: 0.40573/0.32206, loss_mask_ce_7: 0.88968/1.01810, loss_mask_bce_7: 0.37548/0.35157, loss_mask_dice_7: 1.77517/1.25476, loss_spatial_bce_7: 0.06129/0.11980, loss_spatial_dice_7: 0.23192/0.28374, loss_spatial_ce_7: 0.11848/0.22134, loss_grounding_bce_7: 0.10723/0.09018, loss_grounding_dice_7: 0.16623/0.19028, loss_grounding_ce_7: 0.40401/0.36579, loss_mask_ce_8: 0.95917/1.13162, loss_mask_bce_8: 0.37593/0.36432, loss_mask_dice_8: 1.79022/1.32860, loss_spatial_bce_8: 0.08382/0.14049, loss_spatial_dice_8: 0.28066/0.32497, loss_spatial_ce_8: 0.19448/0.27191, loss_grounding_bce_8: 0.11561/0.09364, loss_grounding_dice_8: 0.17989/0.20101, loss_grounding_ce_8: 0.33276/0.44084, loss_mask_ce_9: 3.93802/3.72485, loss_mask_bce_9: 0.45190/0.39208, loss_mask_dice_9: 2.84722/1.90669, loss_spatial_bce_9: 0.21004/0.34383, loss_spatial_dice_9: 0.85702/0.82908, loss_spatial_ce_9: 1.36128/1.56448, loss_grounding_bce_9: 0.12850/0.10521, loss_grounding_dice_9: 0.18252/0.28164, loss_grounding_ce_9: 0.81019/0.74836] items per batch[64] items per second[0.23] total items[403200] mini batches[  6300] memory[7341] epoch remaining[0:49:10]
INFO:trainer.default_trainer:epochs[     3] optim steps[6400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.58960/0.91810, loss_mask_bce_0: 0.25347/0.33633, loss_mask_dice_0: 1.37361/1.16622, loss_spatial_bce_0: 0.12871/0.09843, loss_spatial_dice_0: 0.31304/0.23577, loss_spatial_ce_0: 0.03465/0.11361, loss_grounding_bce_0: 0.02839/0.08641, loss_grounding_dice_0: 0.38847/0.17900, loss_grounding_ce_0: 0.52659/0.28097, loss_mask_ce_1: 1.56604/0.91755, loss_mask_bce_1: 0.30561/0.33692, loss_mask_dice_1: 1.21358/1.17398, loss_spatial_bce_1: 0.08922/0.09945, loss_spatial_dice_1: 0.31544/0.24033, loss_spatial_ce_1: 0.04109/0.11960, loss_grounding_bce_1: 0.02720/0.08644, loss_grounding_dice_1: 0.34205/0.17983, loss_grounding_ce_1: 0.58369/0.28326, loss_mask_ce_2: 1.57466/0.92331, loss_mask_bce_2: 0.33940/0.33695, loss_mask_dice_2: 1.26457/1.17289, loss_spatial_bce_2: 0.09328/0.09895, loss_spatial_dice_2: 0.28635/0.24152, loss_spatial_ce_2: 0.04799/0.12487, loss_grounding_bce_2: 0.02575/0.08635, loss_grounding_dice_2: 0.39508/0.17937, loss_grounding_ce_2: 0.56972/0.28536, loss_mask_ce_3: 1.97411/0.92745, loss_mask_bce_3: 0.33619/0.33779, loss_mask_dice_3: 1.48475/1.17020, loss_spatial_bce_3: 0.10998/0.10010, loss_spatial_dice_3: 0.28541/0.24357, loss_spatial_ce_3: 0.11605/0.13136, loss_grounding_bce_3: 0.02675/0.08651, loss_grounding_dice_3: 0.34134/0.17890, loss_grounding_ce_3: 0.55728/0.28660, loss_mask_ce_4: 1.57348/0.92665, loss_mask_bce_4: 0.45375/0.33896, loss_mask_dice_4: 1.40274/1.19086, loss_spatial_bce_4: 0.07518/0.10331, loss_spatial_dice_4: 0.30216/0.24997, loss_spatial_ce_4: 0.11429/0.14625, loss_grounding_bce_4: 0.02407/0.08705, loss_grounding_dice_4: 0.38375/0.18175, loss_grounding_ce_4: 0.55369/0.29044, loss_mask_ce_5: 1.83107/0.93966, loss_mask_bce_5: 0.49978/0.34143, loss_mask_dice_5: 1.36066/1.19448, loss_spatial_bce_5: 0.12219/0.10430, loss_spatial_dice_5: 0.33568/0.25371, loss_spatial_ce_5: 0.25049/0.15897, loss_grounding_bce_5: 0.02256/0.08750, loss_grounding_dice_5: 0.37792/0.18292, loss_grounding_ce_5: 0.60413/0.30081, loss_mask_ce_6: 1.93014/0.97568, loss_mask_bce_6: 0.51200/0.34396, loss_mask_dice_6: 1.47054/1.19872, loss_spatial_bce_6: 0.08653/0.10894, loss_spatial_dice_6: 0.27879/0.25721, loss_spatial_ce_6: 0.19238/0.17859, loss_grounding_bce_6: 0.02442/0.08843, loss_grounding_dice_6: 0.33405/0.18313, loss_grounding_ce_6: 0.68398/0.32362, loss_mask_ce_7: 1.93206/1.01890, loss_mask_bce_7: 0.45617/0.35171, loss_mask_dice_7: 1.34505/1.25401, loss_spatial_bce_7: 0.09138/0.11976, loss_spatial_dice_7: 0.34485/0.28343, loss_spatial_ce_7: 0.19699/0.22080, loss_grounding_bce_7: 0.02770/0.09017, loss_grounding_dice_7: 0.38455/0.19001, loss_grounding_ce_7: 0.66325/0.36678, loss_mask_ce_8: 1.72775/1.13185, loss_mask_bce_8: 0.35360/0.36441, loss_mask_dice_8: 1.53526/1.32802, loss_spatial_bce_8: 0.09392/0.14048, loss_spatial_dice_8: 0.38507/0.32452, loss_spatial_ce_8: 0.26342/0.27157, loss_grounding_bce_8: 0.02536/0.09361, loss_grounding_dice_8: 0.38426/0.20087, loss_grounding_ce_8: 0.45745/0.44152, loss_mask_ce_9: 4.77433/3.72570, loss_mask_bce_9: 0.36091/0.39226, loss_mask_dice_9: 1.94629/1.90596, loss_spatial_bce_9: 0.21180/0.34394, loss_spatial_dice_9: 0.86488/0.82890, loss_spatial_ce_9: 1.52893/1.56350, loss_grounding_bce_9: 0.02774/0.10513, loss_grounding_dice_9: 0.47486/0.28110, loss_grounding_ce_9: 0.57977/0.74802] items per batch[64] items per second[0.22] total items[409600] mini batches[  6400] memory[7341] epoch remaining[0:44:14]
INFO:trainer.default_trainer:epochs[     3] optim steps[6500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17271/0.91816, loss_mask_bce_0: 0.19205/0.33637, loss_mask_dice_0: 0.35984/1.16561, loss_spatial_bce_0: 0.06568/0.09828, loss_spatial_dice_0: 0.13061/0.23548, loss_spatial_ce_0: 0.00742/0.11301, loss_grounding_bce_0: 0.07321/0.08648, loss_grounding_dice_0: 0.12856/0.17909, loss_grounding_ce_0: 0.23346/0.28041, loss_mask_ce_1: 0.18546/0.91778, loss_mask_bce_1: 0.18919/0.33699, loss_mask_dice_1: 0.36375/1.17349, loss_spatial_bce_1: 0.06376/0.09930, loss_spatial_dice_1: 0.12673/0.24004, loss_spatial_ce_1: 0.00860/0.11901, loss_grounding_bce_1: 0.07479/0.08649, loss_grounding_dice_1: 0.11974/0.17986, loss_grounding_ce_1: 0.23807/0.28282, loss_mask_ce_2: 0.19866/0.92345, loss_mask_bce_2: 0.19613/0.33696, loss_mask_dice_2: 0.36380/1.17229, loss_spatial_bce_2: 0.06218/0.09881, loss_spatial_dice_2: 0.13701/0.24121, loss_spatial_ce_2: 0.00651/0.12420, loss_grounding_bce_2: 0.07529/0.08640, loss_grounding_dice_2: 0.13064/0.17942, loss_grounding_ce_2: 0.23294/0.28502, loss_mask_ce_3: 0.18922/0.92767, loss_mask_bce_3: 0.19723/0.33784, loss_mask_dice_3: 0.39744/1.16979, loss_spatial_bce_3: 0.06555/0.09997, loss_spatial_dice_3: 0.13069/0.24325, loss_spatial_ce_3: 0.00804/0.13065, loss_grounding_bce_3: 0.07697/0.08657, loss_grounding_dice_3: 0.13408/0.17892, loss_grounding_ce_3: 0.22663/0.28620, loss_mask_ce_4: 0.19310/0.92679, loss_mask_bce_4: 0.18922/0.33896, loss_mask_dice_4: 0.40875/1.19029, loss_spatial_bce_4: 0.06792/0.10321, loss_spatial_dice_4: 0.13722/0.24964, loss_spatial_ce_4: 0.01121/0.14579, loss_grounding_bce_4: 0.07807/0.08709, loss_grounding_dice_4: 0.14014/0.18180, loss_grounding_ce_4: 0.22709/0.29000, loss_mask_ce_5: 0.20436/0.93996, loss_mask_bce_5: 0.18200/0.34142, loss_mask_dice_5: 0.37016/1.19403, loss_spatial_bce_5: 0.06602/0.10421, loss_spatial_dice_5: 0.13481/0.25337, loss_spatial_ce_5: 0.02889/0.15837, loss_grounding_bce_5: 0.07453/0.08754, loss_grounding_dice_5: 0.14770/0.18301, loss_grounding_ce_5: 0.22715/0.30071, loss_mask_ce_6: 0.25080/0.97589, loss_mask_bce_6: 0.18977/0.34394, loss_mask_dice_6: 0.40176/1.19806, loss_spatial_bce_6: 0.07509/0.10887, loss_spatial_dice_6: 0.11983/0.25689, loss_spatial_ce_6: 0.02189/0.17819, loss_grounding_bce_6: 0.07288/0.08851, loss_grounding_dice_6: 0.14700/0.18316, loss_grounding_ce_6: 0.20579/0.32277, loss_mask_ce_7: 0.19590/1.01965, loss_mask_bce_7: 0.19852/0.35167, loss_mask_dice_7: 0.42042/1.25327, loss_spatial_bce_7: 0.07854/0.11963, loss_spatial_dice_7: 0.14538/0.28308, loss_spatial_ce_7: 0.06737/0.22024, loss_grounding_bce_7: 0.08418/0.09021, loss_grounding_dice_7: 0.15140/0.19008, loss_grounding_ce_7: 0.19102/0.36603, loss_mask_ce_8: 0.29698/1.13307, loss_mask_bce_8: 0.21466/0.36437, loss_mask_dice_8: 0.44360/1.32760, loss_spatial_bce_8: 0.08456/0.14036, loss_spatial_dice_8: 0.16011/0.32411, loss_spatial_ce_8: 0.11253/0.27087, loss_grounding_bce_8: 0.08164/0.09373, loss_grounding_dice_8: 0.15827/0.20097, loss_grounding_ce_8: 0.21266/0.44003, loss_mask_ce_9: 2.41722/3.72442, loss_mask_bce_9: 0.19398/0.39207, loss_mask_dice_9: 0.68306/1.90451, loss_spatial_bce_9: 0.41722/0.34378, loss_spatial_dice_9: 0.77534/0.82882, loss_spatial_ce_9: 1.32919/1.56213, loss_grounding_bce_9: 0.07478/0.10512, loss_grounding_dice_9: 0.23947/0.28107, loss_grounding_ce_9: 0.47451/0.74613] items per batch[64] items per second[0.21] total items[416000] mini batches[  6500] memory[7341] epoch remaining[0:39:29]
INFO:trainer.default_trainer:epochs[     3] optim steps[6600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74488/0.91877, loss_mask_bce_0: 0.03411/0.33676, loss_mask_dice_0: 0.30130/1.16545, loss_spatial_bce_0: 0.02280/0.09824, loss_spatial_dice_0: 0.18229/0.23526, loss_spatial_ce_0: 0.07844/0.11245, loss_grounding_bce_0: 0.02591/0.08655, loss_grounding_dice_0: 0.09263/0.17898, loss_grounding_ce_0: 0.08684/0.28109, loss_mask_ce_1: 0.73497/0.91829, loss_mask_bce_1: 0.03509/0.33738, loss_mask_dice_1: 0.28101/1.17328, loss_spatial_bce_1: 0.02215/0.09928, loss_spatial_dice_1: 0.19150/0.23986, loss_spatial_ce_1: 0.09374/0.11844, loss_grounding_bce_1: 0.02545/0.08657, loss_grounding_dice_1: 0.18034/0.17984, loss_grounding_ce_1: 0.08471/0.28349, loss_mask_ce_2: 0.59993/0.92424, loss_mask_bce_2: 0.04801/0.33739, loss_mask_dice_2: 0.33994/1.17181, loss_spatial_bce_2: 0.02099/0.09880, loss_spatial_dice_2: 0.18475/0.24102, loss_spatial_ce_2: 0.11206/0.12361, loss_grounding_bce_2: 0.02600/0.08651, loss_grounding_dice_2: 0.18504/0.17940, loss_grounding_ce_2: 0.08943/0.28587, loss_mask_ce_3: 0.75135/0.92835, loss_mask_bce_3: 0.03748/0.33824, loss_mask_dice_3: 0.23526/1.16924, loss_spatial_bce_3: 0.02345/0.09996, loss_spatial_dice_3: 0.18336/0.24306, loss_spatial_ce_3: 0.10738/0.12998, loss_grounding_bce_3: 0.02827/0.08667, loss_grounding_dice_3: 0.17407/0.17891, loss_grounding_ce_3: 0.08245/0.28694, loss_mask_ce_4: 0.79884/0.92787, loss_mask_bce_4: 0.03937/0.33931, loss_mask_dice_4: 0.29992/1.18980, loss_spatial_bce_4: 0.02484/0.10322, loss_spatial_dice_4: 0.18522/0.24946, loss_spatial_ce_4: 0.10474/0.14518, loss_grounding_bce_4: 0.02814/0.08720, loss_grounding_dice_4: 0.17267/0.18179, loss_grounding_ce_4: 0.09423/0.29063, loss_mask_ce_5: 0.65119/0.94118, loss_mask_bce_5: 0.04719/0.34181, loss_mask_dice_5: 0.33473/1.19359, loss_spatial_bce_5: 0.02799/0.10421, loss_spatial_dice_5: 0.18750/0.25313, loss_spatial_ce_5: 0.14046/0.15767, loss_grounding_bce_5: 0.03293/0.08762, loss_grounding_dice_5: 0.21407/0.18298, loss_grounding_ce_5: 0.01564/0.30116, loss_mask_ce_6: 0.82183/0.97697, loss_mask_bce_6: 0.03372/0.34434, loss_mask_dice_6: 0.26990/1.19754, loss_spatial_bce_6: 0.02970/0.10890, loss_spatial_dice_6: 0.19578/0.25668, loss_spatial_ce_6: 0.13628/0.17767, loss_grounding_bce_6: 0.02142/0.08855, loss_grounding_dice_6: 0.17272/0.18313, loss_grounding_ce_6: 0.10502/0.32349, loss_mask_ce_7: 0.96820/1.02076, loss_mask_bce_7: 0.03548/0.35214, loss_mask_dice_7: 0.29528/1.25299, loss_spatial_bce_7: 0.02391/0.11957, loss_spatial_dice_7: 0.19062/0.28290, loss_spatial_ce_7: 0.26118/0.21959, loss_grounding_bce_7: 0.02083/0.09029, loss_grounding_dice_7: 0.16473/0.19009, loss_grounding_ce_7: 0.12651/0.36685, loss_mask_ce_8: 0.79894/1.13414, loss_mask_bce_8: 0.03466/0.36489, loss_mask_dice_8: 0.24033/1.32711, loss_spatial_bce_8: 0.02469/0.14026, loss_spatial_dice_8: 0.18220/0.32380, loss_spatial_ce_8: 0.41212/0.27034, loss_grounding_bce_8: 0.02481/0.09375, loss_grounding_dice_8: 0.15232/0.20096, loss_grounding_ce_8: 0.14144/0.44084, loss_mask_ce_9: 2.23549/3.72529, loss_mask_bce_9: 0.04092/0.39238, loss_mask_dice_9: 0.33872/1.90411, loss_spatial_bce_9: 0.44357/0.34375, loss_spatial_dice_9: 0.73526/0.82879, loss_spatial_ce_9: 1.77003/1.56156, loss_grounding_bce_9: 0.02253/0.10508, loss_grounding_dice_9: 0.10059/0.28093, loss_grounding_ce_9: 0.26830/0.74587] items per batch[64] items per second[0.22] total items[422400] mini batches[  6600] memory[7341] epoch remaining[0:34:31]
INFO:trainer.default_trainer:epochs[     3] optim steps[6700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28330/0.91915, loss_mask_bce_0: 0.45283/0.33639, loss_mask_dice_0: 1.31980/1.16447, loss_spatial_bce_0: 0.10431/0.09811, loss_spatial_dice_0: 0.24048/0.23491, loss_spatial_ce_0: 0.02026/0.11183, loss_grounding_bce_0: 0.06457/0.08646, loss_grounding_dice_0: 0.22372/0.17880, loss_grounding_ce_0: 0.34748/0.28049, loss_mask_ce_1: 1.06148/0.91864, loss_mask_bce_1: 0.45546/0.33701, loss_mask_dice_1: 1.41047/1.17248, loss_spatial_bce_1: 0.11012/0.09915, loss_spatial_dice_1: 0.23701/0.23953, loss_spatial_ce_1: 0.05197/0.11777, loss_grounding_bce_1: 0.06621/0.08647, loss_grounding_dice_1: 0.22114/0.17965, loss_grounding_ce_1: 0.37796/0.28315, loss_mask_ce_2: 1.15130/0.92478, loss_mask_bce_2: 0.46458/0.33699, loss_mask_dice_2: 1.37136/1.17072, loss_spatial_bce_2: 0.10175/0.09868, loss_spatial_dice_2: 0.22995/0.24067, loss_spatial_ce_2: 0.05965/0.12291, loss_grounding_bce_2: 0.06674/0.08640, loss_grounding_dice_2: 0.19889/0.17923, loss_grounding_ce_2: 0.40440/0.28562, loss_mask_ce_3: 1.15339/0.92899, loss_mask_bce_3: 0.46746/0.33793, loss_mask_dice_3: 1.27836/1.16813, loss_spatial_bce_3: 0.10734/0.09982, loss_spatial_dice_3: 0.23639/0.24267, loss_spatial_ce_3: 0.05252/0.12916, loss_grounding_bce_3: 0.06364/0.08654, loss_grounding_dice_3: 0.20881/0.17877, loss_grounding_ce_3: 0.39273/0.28675, loss_mask_ce_4: 1.16358/0.92834, loss_mask_bce_4: 0.45150/0.33895, loss_mask_dice_4: 1.31121/1.18890, loss_spatial_bce_4: 0.13595/0.10310, loss_spatial_dice_4: 0.25032/0.24911, loss_spatial_ce_4: 0.03350/0.14440, loss_grounding_bce_4: 0.06571/0.08712, loss_grounding_dice_4: 0.21150/0.18165, loss_grounding_ce_4: 0.45708/0.29040, loss_mask_ce_5: 1.21791/0.94158, loss_mask_bce_5: 0.47432/0.34145, loss_mask_dice_5: 1.34904/1.19254, loss_spatial_bce_5: 0.11451/0.10406, loss_spatial_dice_5: 0.25101/0.25271, loss_spatial_ce_5: 0.02914/0.15691, loss_grounding_bce_5: 0.06783/0.08753, loss_grounding_dice_5: 0.20734/0.18284, loss_grounding_ce_5: 0.38730/0.30117, loss_mask_ce_6: 1.38880/0.97768, loss_mask_bce_6: 0.45160/0.34397, loss_mask_dice_6: 1.35118/1.19671, loss_spatial_bce_6: 0.13575/0.10878, loss_spatial_dice_6: 0.25131/0.25630, loss_spatial_ce_6: 0.08791/0.17697, loss_grounding_bce_6: 0.06163/0.08844, loss_grounding_dice_6: 0.19180/0.18298, loss_grounding_ce_6: 0.43797/0.32363, loss_mask_ce_7: 1.07792/1.02095, loss_mask_bce_7: 0.44581/0.35170, loss_mask_dice_7: 1.42647/1.25219, loss_spatial_bce_7: 0.11294/0.11939, loss_spatial_dice_7: 0.27178/0.28253, loss_spatial_ce_7: 0.18693/0.21893, loss_grounding_bce_7: 0.06597/0.09021, loss_grounding_dice_7: 0.22382/0.19002, loss_grounding_ce_7: 0.42138/0.36697, loss_mask_ce_8: 1.47013/1.13452, loss_mask_bce_8: 0.41892/0.36453, loss_mask_dice_8: 1.48819/1.32601, loss_spatial_bce_8: 0.17326/0.14006, loss_spatial_dice_8: 0.34503/0.32338, loss_spatial_ce_8: 0.13753/0.26961, loss_grounding_bce_8: 0.06473/0.09369, loss_grounding_dice_8: 0.22718/0.20097, loss_grounding_ce_8: 0.44459/0.44105, loss_mask_ce_9: 4.71704/3.72487, loss_mask_bce_9: 0.65650/0.39191, loss_mask_dice_9: 2.46426/1.90213, loss_spatial_bce_9: 0.34027/0.34347, loss_spatial_dice_9: 0.87402/0.82846, loss_spatial_ce_9: 1.18618/1.56007, loss_grounding_bce_9: 0.11759/0.10500, loss_grounding_dice_9: 0.47467/0.28087, loss_grounding_ce_9: 0.38002/0.74575] items per batch[64] items per second[0.22] total items[428800] mini batches[  6700] memory[7341] epoch remaining[0:29:39]
INFO:trainer.default_trainer:epochs[     3] optim steps[6800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28854/0.91854, loss_mask_bce_0: 0.15677/0.33665, loss_mask_dice_0: 0.96498/1.16414, loss_spatial_bce_0: 0.03571/0.09798, loss_spatial_dice_0: 0.17211/0.23464, loss_spatial_ce_0: 0.01288/0.11124, loss_grounding_bce_0: 0.06324/0.08652, loss_grounding_dice_0: 0.22591/0.17870, loss_grounding_ce_0: 0.23237/0.27999, loss_mask_ce_1: 1.28016/0.91797, loss_mask_bce_1: 0.15606/0.33737, loss_mask_dice_1: 0.94803/1.17217, loss_spatial_bce_1: 0.03614/0.09902, loss_spatial_dice_1: 0.16902/0.23935, loss_spatial_ce_1: 0.02525/0.11723, loss_grounding_bce_1: 0.05915/0.08649, loss_grounding_dice_1: 0.21879/0.17961, loss_grounding_ce_1: 0.32245/0.28263, loss_mask_ce_2: 1.25593/0.92428, loss_mask_bce_2: 0.14439/0.33729, loss_mask_dice_2: 0.98186/1.17034, loss_spatial_bce_2: 0.03507/0.09854, loss_spatial_dice_2: 0.20039/0.24040, loss_spatial_ce_2: 0.02492/0.12256, loss_grounding_bce_2: 0.05470/0.08644, loss_grounding_dice_2: 0.20626/0.17915, loss_grounding_ce_2: 0.30309/0.28511, loss_mask_ce_3: 1.04137/0.92865, loss_mask_bce_3: 0.15343/0.33820, loss_mask_dice_3: 1.05947/1.16769, loss_spatial_bce_3: 0.03859/0.09965, loss_spatial_dice_3: 0.18518/0.24237, loss_spatial_ce_3: 0.02710/0.12861, loss_grounding_bce_3: 0.05477/0.08657, loss_grounding_dice_3: 0.20467/0.17871, loss_grounding_ce_3: 0.28679/0.28619, loss_mask_ce_4: 1.19752/0.92782, loss_mask_bce_4: 0.14939/0.33924, loss_mask_dice_4: 0.98987/1.18852, loss_spatial_bce_4: 0.03249/0.10294, loss_spatial_dice_4: 0.17656/0.24876, loss_spatial_ce_4: 0.02321/0.14383, loss_grounding_bce_4: 0.05723/0.08717, loss_grounding_dice_4: 0.22652/0.18160, loss_grounding_ce_4: 0.20397/0.28973, loss_mask_ce_5: 0.97615/0.94108, loss_mask_bce_5: 0.16579/0.34169, loss_mask_dice_5: 1.17652/1.19216, loss_spatial_bce_5: 0.03179/0.10392, loss_spatial_dice_5: 0.22104/0.25240, loss_spatial_ce_5: 0.01834/0.15637, loss_grounding_bce_5: 0.05692/0.08757, loss_grounding_dice_5: 0.22801/0.18277, loss_grounding_ce_5: 0.14150/0.30070, loss_mask_ce_6: 1.03166/0.97720, loss_mask_bce_6: 0.14829/0.34423, loss_mask_dice_6: 0.97519/1.19646, loss_spatial_bce_6: 0.03440/0.10864, loss_spatial_dice_6: 0.20336/0.25597, loss_spatial_ce_6: 0.01934/0.17639, loss_grounding_bce_6: 0.05966/0.08847, loss_grounding_dice_6: 0.21467/0.18296, loss_grounding_ce_6: 0.21230/0.32344, loss_mask_ce_7: 0.99622/1.02037, loss_mask_bce_7: 0.16207/0.35211, loss_mask_dice_7: 1.26780/1.25206, loss_spatial_bce_7: 0.04393/0.11922, loss_spatial_dice_7: 0.26258/0.28218, loss_spatial_ce_7: 0.05996/0.21845, loss_grounding_bce_7: 0.06821/0.09028, loss_grounding_dice_7: 0.32120/0.18998, loss_grounding_ce_7: 0.22263/0.36642, loss_mask_ce_8: 1.63908/1.13386, loss_mask_bce_8: 0.16623/0.36485, loss_mask_dice_8: 1.12458/1.32560, loss_spatial_bce_8: 0.05242/0.13992, loss_spatial_dice_8: 0.28194/0.32294, loss_spatial_ce_8: 0.15942/0.26922, loss_grounding_bce_8: 0.06559/0.09375, loss_grounding_dice_8: 0.28143/0.20085, loss_grounding_ce_8: 0.31985/0.44009, loss_mask_ce_9: 3.62896/3.72408, loss_mask_bce_9: 0.20991/0.39218, loss_mask_dice_9: 1.71208/1.90165, loss_spatial_bce_9: 0.25511/0.34362, loss_spatial_dice_9: 0.85917/0.82843, loss_spatial_ce_9: 1.08825/1.55937, loss_grounding_bce_9: 0.13154/0.10502, loss_grounding_dice_9: 0.50938/0.28083, loss_grounding_ce_9: 0.24742/0.74412] items per batch[64] items per second[0.22] total items[435200] mini batches[  6800] memory[7341] epoch remaining[0:24:44]
INFO:trainer.default_trainer:epochs[     3] optim steps[6900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.50573/0.91890, loss_mask_bce_0: 0.46395/0.33676, loss_mask_dice_0: 1.02979/1.16463, loss_spatial_bce_0: 0.07854/0.09784, loss_spatial_dice_0: 0.13943/0.23433, loss_spatial_ce_0: 0.05236/0.11080, loss_grounding_bce_0: 0.11264/0.08665, loss_grounding_dice_0: 0.20639/0.17882, loss_grounding_ce_0: 0.19582/0.27987, loss_mask_ce_1: 1.49564/0.91834, loss_mask_bce_1: 0.47847/0.33743, loss_mask_dice_1: 1.03035/1.17256, loss_spatial_bce_1: 0.08043/0.09888, loss_spatial_dice_1: 0.14479/0.23901, loss_spatial_ce_1: 0.05506/0.11683, loss_grounding_bce_1: 0.13268/0.08664, loss_grounding_dice_1: 0.19253/0.17977, loss_grounding_ce_1: 0.17142/0.28264, loss_mask_ce_2: 1.49734/0.92491, loss_mask_bce_2: 0.46077/0.33736, loss_mask_dice_2: 0.98988/1.17050, loss_spatial_bce_2: 0.08001/0.09840, loss_spatial_dice_2: 0.14430/0.24006, loss_spatial_ce_2: 0.05044/0.12216, loss_grounding_bce_2: 0.11861/0.08658, loss_grounding_dice_2: 0.20315/0.17931, loss_grounding_ce_2: 0.19740/0.28526, loss_mask_ce_3: 1.49931/0.92909, loss_mask_bce_3: 0.46587/0.33831, loss_mask_dice_3: 1.01759/1.16813, loss_spatial_bce_3: 0.07909/0.09951, loss_spatial_dice_3: 0.14556/0.24196, loss_spatial_ce_3: 0.05848/0.12816, loss_grounding_bce_3: 0.12169/0.08677, loss_grounding_dice_3: 0.20792/0.17883, loss_grounding_ce_3: 0.19536/0.28615, loss_mask_ce_4: 1.56891/0.92831, loss_mask_bce_4: 0.47770/0.33932, loss_mask_dice_4: 1.02491/1.18889, loss_spatial_bce_4: 0.08764/0.10282, loss_spatial_dice_4: 0.15150/0.24842, loss_spatial_ce_4: 0.06965/0.14322, loss_grounding_bce_4: 0.12709/0.08730, loss_grounding_dice_4: 0.21183/0.18169, loss_grounding_ce_4: 0.19221/0.28952, loss_mask_ce_5: 1.53358/0.94166, loss_mask_bce_5: 0.48051/0.34183, loss_mask_dice_5: 1.01904/1.19246, loss_spatial_bce_5: 0.08554/0.10380, loss_spatial_dice_5: 0.15698/0.25205, loss_spatial_ce_5: 0.08175/0.15579, loss_grounding_bce_5: 0.12594/0.08769, loss_grounding_dice_5: 0.21292/0.18290, loss_grounding_ce_5: 0.19042/0.30036, loss_mask_ce_6: 1.42465/0.97760, loss_mask_bce_6: 0.50805/0.34435, loss_mask_dice_6: 1.06448/1.19682, loss_spatial_bce_6: 0.08799/0.10854, loss_spatial_dice_6: 0.15561/0.25564, loss_spatial_ce_6: 0.12655/0.17579, loss_grounding_bce_6: 0.11566/0.08863, loss_grounding_dice_6: 0.20775/0.18305, loss_grounding_ce_6: 0.18675/0.32327, loss_mask_ce_7: 1.62168/1.02104, loss_mask_bce_7: 0.48193/0.35225, loss_mask_dice_7: 1.04465/1.25234, loss_spatial_bce_7: 0.08639/0.11916, loss_spatial_dice_7: 0.14638/0.28190, loss_spatial_ce_7: 0.23257/0.21792, loss_grounding_bce_7: 0.12935/0.09044, loss_grounding_dice_7: 0.17240/0.19010, loss_grounding_ce_7: 0.19273/0.36619, loss_mask_ce_8: 1.74089/1.13432, loss_mask_bce_8: 0.41659/0.36512, loss_mask_dice_8: 1.08277/1.32622, loss_spatial_bce_8: 0.10369/0.13977, loss_spatial_dice_8: 0.16821/0.32253, loss_spatial_ce_8: 0.27904/0.26895, loss_grounding_bce_8: 0.10899/0.09393, loss_grounding_dice_8: 0.19453/0.20100, loss_grounding_ce_8: 0.17863/0.43977, loss_mask_ce_9: 3.32932/3.72503, loss_mask_bce_9: 0.66080/0.39240, loss_mask_dice_9: 1.92186/1.90226, loss_spatial_bce_9: 0.36397/0.34361, loss_spatial_dice_9: 0.84639/0.82830, loss_spatial_ce_9: 1.48595/1.55816, loss_grounding_bce_9: 0.16298/0.10522, loss_grounding_dice_9: 0.31259/0.28080, loss_grounding_ce_9: 0.29398/0.74339] items per batch[64] items per second[0.23] total items[441600] mini batches[  6900] memory[7341] epoch remaining[0:19:49]
INFO:trainer.default_trainer:epochs[     3] optim steps[7000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85171/0.91850, loss_mask_bce_0: 0.12421/0.33711, loss_mask_dice_0: 1.33430/1.16827, loss_spatial_bce_0: 0.04700/0.09783, loss_spatial_dice_0: 0.33627/0.23439, loss_spatial_ce_0: 0.13987/0.11035, loss_grounding_bce_0: 0.00870/0.08678, loss_grounding_dice_0: 0.38011/0.17906, loss_grounding_ce_0: 0.84073/0.27955, loss_mask_ce_1: 0.88854/0.91804, loss_mask_bce_1: 0.12600/0.33777, loss_mask_dice_1: 1.26079/1.17601, loss_spatial_bce_1: 0.04452/0.09889, loss_spatial_dice_1: 0.36149/0.23900, loss_spatial_ce_1: 0.10721/0.11665, loss_grounding_bce_1: 0.00960/0.08678, loss_grounding_dice_1: 0.42929/0.18001, loss_grounding_ce_1: 0.74248/0.28231, loss_mask_ce_2: 0.98983/0.92473, loss_mask_bce_2: 0.12239/0.33771, loss_mask_dice_2: 1.24692/1.17388, loss_spatial_bce_2: 0.05098/0.09842, loss_spatial_dice_2: 0.36795/0.24004, loss_spatial_ce_2: 0.06773/0.12179, loss_grounding_bce_2: 0.00870/0.08672, loss_grounding_dice_2: 0.28989/0.17952, loss_grounding_ce_2: 0.82429/0.28496, loss_mask_ce_3: 1.00871/0.92928, loss_mask_bce_3: 0.11862/0.33865, loss_mask_dice_3: 1.22969/1.17140, loss_spatial_bce_3: 0.04527/0.09951, loss_spatial_dice_3: 0.34786/0.24199, loss_spatial_ce_3: 0.08647/0.12766, loss_grounding_bce_3: 0.01024/0.08691, loss_grounding_dice_3: 0.36505/0.17913, loss_grounding_ce_3: 0.73176/0.28605, loss_mask_ce_4: 1.07868/0.92802, loss_mask_bce_4: 0.10918/0.33968, loss_mask_dice_4: 1.22648/1.19217, loss_spatial_bce_4: 0.04319/0.10282, loss_spatial_dice_4: 0.39433/0.24844, loss_spatial_ce_4: 0.11229/0.14320, loss_grounding_bce_4: 0.00865/0.08746, loss_grounding_dice_4: 0.34217/0.18190, loss_grounding_ce_4: 0.65908/0.28920, loss_mask_ce_5: 1.10696/0.94129, loss_mask_bce_5: 0.11990/0.34211, loss_mask_dice_5: 1.10602/1.19566, loss_spatial_bce_5: 0.04422/0.10377, loss_spatial_dice_5: 0.37999/0.25204, loss_spatial_ce_5: 0.24787/0.15559, loss_grounding_bce_5: 0.01170/0.08789, loss_grounding_dice_5: 0.39009/0.18316, loss_grounding_ce_5: 0.65836/0.29999, loss_mask_ce_6: 1.18778/0.97732, loss_mask_bce_6: 0.12053/0.34468, loss_mask_dice_6: 1.36637/1.19986, loss_spatial_bce_6: 0.04120/0.10851, loss_spatial_dice_6: 0.39273/0.25564, loss_spatial_ce_6: 0.17134/0.17536, loss_grounding_bce_6: 0.00798/0.08881, loss_grounding_dice_6: 0.33690/0.18328, loss_grounding_ce_6: 0.83121/0.32284, loss_mask_ce_7: 1.34352/1.02046, loss_mask_bce_7: 0.14694/0.35266, loss_mask_dice_7: 1.39715/1.25574, loss_spatial_bce_7: 0.06229/0.11914, loss_spatial_dice_7: 0.46580/0.28193, loss_spatial_ce_7: 0.21548/0.21752, loss_grounding_bce_7: 0.01056/0.09059, loss_grounding_dice_7: 0.45870/0.19032, loss_grounding_ce_7: 0.91766/0.36565, loss_mask_ce_8: 1.29380/1.13407, loss_mask_bce_8: 0.15910/0.36567, loss_mask_dice_8: 1.21245/1.32958, loss_spatial_bce_8: 0.07938/0.13983, loss_spatial_dice_8: 0.57131/0.32266, loss_spatial_ce_8: 0.37804/0.26870, loss_grounding_bce_8: 0.04254/0.09408, loss_grounding_dice_8: 0.36742/0.20118, loss_grounding_ce_8: 0.87381/0.43909, loss_mask_ce_9: 4.33860/3.72460, loss_mask_bce_9: 0.26243/0.39290, loss_mask_dice_9: 1.66948/1.90647, loss_spatial_bce_9: 0.16555/0.34338, loss_spatial_dice_9: 0.85374/0.82842, loss_spatial_ce_9: 1.30002/1.55744, loss_grounding_bce_9: 0.13829/0.10537, loss_grounding_dice_9: 0.40543/0.28094, loss_grounding_ce_9: 0.91671/0.74295] items per batch[64] items per second[0.22] total items[448000] mini batches[  7000] memory[7341] epoch remaining[0:14:58]
INFO:trainer.default_trainer:epochs[     3] optim steps[7100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72783/0.91761, loss_mask_bce_0: 0.35479/0.33694, loss_mask_dice_0: 1.86324/1.16764, loss_spatial_bce_0: 0.02621/0.09765, loss_spatial_dice_0: 0.22355/0.23410, loss_spatial_ce_0: 0.03060/0.10991, loss_grounding_bce_0: 0.06262/0.08685, loss_grounding_dice_0: 0.22303/0.17896, loss_grounding_ce_0: 0.31440/0.27979, loss_mask_ce_1: 0.74140/0.91720, loss_mask_bce_1: 0.36830/0.33761, loss_mask_dice_1: 2.27021/1.17552, loss_spatial_bce_1: 0.02899/0.09871, loss_spatial_dice_1: 0.24588/0.23870, loss_spatial_ce_1: 0.03358/0.11599, loss_grounding_bce_1: 0.06272/0.08685, loss_grounding_dice_1: 0.22129/0.17991, loss_grounding_ce_1: 0.31680/0.28260, loss_mask_ce_2: 0.49219/0.92369, loss_mask_bce_2: 0.37996/0.33759, loss_mask_dice_2: 2.05772/1.17337, loss_spatial_bce_2: 0.02664/0.09825, loss_spatial_dice_2: 0.24075/0.23974, loss_spatial_ce_2: 0.01648/0.12109, loss_grounding_bce_2: 0.05938/0.08679, loss_grounding_dice_2: 0.21341/0.17946, loss_grounding_ce_2: 0.31197/0.28508, loss_mask_ce_3: 0.72094/0.92821, loss_mask_bce_3: 0.36728/0.33847, loss_mask_dice_3: 1.96512/1.17077, loss_spatial_bce_3: 0.03138/0.09934, loss_spatial_dice_3: 0.24109/0.24164, loss_spatial_ce_3: 0.09365/0.12699, loss_grounding_bce_3: 0.06810/0.08699, loss_grounding_dice_3: 0.21960/0.17907, loss_grounding_ce_3: 0.31653/0.28659, loss_mask_ce_4: 0.60595/0.92720, loss_mask_bce_4: 0.38287/0.33951, loss_mask_dice_4: 1.93686/1.19161, loss_spatial_bce_4: 0.02932/0.10264, loss_spatial_dice_4: 0.25066/0.24814, loss_spatial_ce_4: 0.01679/0.14272, loss_grounding_bce_4: 0.06863/0.08750, loss_grounding_dice_4: 0.22726/0.18190, loss_grounding_ce_4: 0.31786/0.28967, loss_mask_ce_5: 0.86749/0.94020, loss_mask_bce_5: 0.34618/0.34191, loss_mask_dice_5: 2.09962/1.19505, loss_spatial_bce_5: 0.03123/0.10358, loss_spatial_dice_5: 0.24453/0.25170, loss_spatial_ce_5: 0.02431/0.15511, loss_grounding_bce_5: 0.06490/0.08792, loss_grounding_dice_5: 0.22157/0.18305, loss_grounding_ce_5: 0.32845/0.30039, loss_mask_ce_6: 0.79736/0.97631, loss_mask_bce_6: 0.36117/0.34447, loss_mask_dice_6: 1.89298/1.19921, loss_spatial_bce_6: 0.02878/0.10831, loss_spatial_dice_6: 0.25194/0.25525, loss_spatial_ce_6: 0.13714/0.17497, loss_grounding_bce_6: 0.06469/0.08884, loss_grounding_dice_6: 0.23256/0.18317, loss_grounding_ce_6: 0.34572/0.32314, loss_mask_ce_7: 0.82672/1.01942, loss_mask_bce_7: 0.36674/0.35247, loss_mask_dice_7: 2.11504/1.25507, loss_spatial_bce_7: 0.02959/0.11892, loss_spatial_dice_7: 0.33703/0.28159, loss_spatial_ce_7: 0.14511/0.21696, loss_grounding_bce_7: 0.07498/0.09061, loss_grounding_dice_7: 0.24126/0.19018, loss_grounding_ce_7: 0.34304/0.36575, loss_mask_ce_8: 0.88559/1.13340, loss_mask_bce_8: 0.41546/0.36549, loss_mask_dice_8: 2.09413/1.32853, loss_spatial_bce_8: 0.03899/0.13964, loss_spatial_dice_8: 0.37500/0.32248, loss_spatial_ce_8: 0.12373/0.26825, loss_grounding_bce_8: 0.07485/0.09415, loss_grounding_dice_8: 0.26178/0.20112, loss_grounding_ce_8: 0.37879/0.43861, loss_mask_ce_9: 5.65313/3.72277, loss_mask_bce_9: 0.41215/0.39252, loss_mask_dice_9: 2.99059/1.90489, loss_spatial_bce_9: 0.16810/0.34306, loss_spatial_dice_9: 0.94277/0.82835, loss_spatial_ce_9: 1.63915/1.55689, loss_grounding_bce_9: 0.07897/0.10541, loss_grounding_dice_9: 0.40527/0.28076, loss_grounding_ce_9: 0.33089/0.74201] items per batch[64] items per second[0.23] total items[454400] mini batches[  7100] memory[7341] epoch remaining[0:10:05]
INFO:trainer.default_trainer:epochs[     3] optim steps[7200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89410/0.91822, loss_mask_bce_0: 0.54271/0.33669, loss_mask_dice_0: 1.50782/1.16758, loss_spatial_bce_0: 0.12730/0.09741, loss_spatial_dice_0: 0.29266/0.23394, loss_spatial_ce_0: 0.16955/0.10952, loss_grounding_bce_0: 0.13210/0.08675, loss_grounding_dice_0: 0.17907/0.17905, loss_grounding_ce_0: 0.23111/0.27957, loss_mask_ce_1: 0.91377/0.91787, loss_mask_bce_1: 0.53381/0.33738, loss_mask_dice_1: 1.30479/1.17548, loss_spatial_bce_1: 0.12078/0.09850, loss_spatial_dice_1: 0.30214/0.23855, loss_spatial_ce_1: 0.19533/0.11552, loss_grounding_bce_1: 0.13082/0.08675, loss_grounding_dice_1: 0.18580/0.17992, loss_grounding_ce_1: 0.27587/0.28222, loss_mask_ce_2: 0.96536/0.92442, loss_mask_bce_2: 0.55315/0.33739, loss_mask_dice_2: 1.25624/1.17326, loss_spatial_bce_2: 0.12692/0.09800, loss_spatial_dice_2: 0.30991/0.23958, loss_spatial_ce_2: 0.19357/0.12063, loss_grounding_bce_2: 0.12817/0.08670, loss_grounding_dice_2: 0.18750/0.17948, loss_grounding_ce_2: 0.27557/0.28467, loss_mask_ce_3: 0.93954/0.92870, loss_mask_bce_3: 0.55235/0.33827, loss_mask_dice_3: 1.48903/1.17074, loss_spatial_bce_3: 0.12378/0.09910, loss_spatial_dice_3: 0.30286/0.24142, loss_spatial_ce_3: 0.16519/0.12660, loss_grounding_bce_3: 0.12922/0.08688, loss_grounding_dice_3: 0.17498/0.17909, loss_grounding_ce_3: 0.24153/0.28634, loss_mask_ce_4: 1.01371/0.92759, loss_mask_bce_4: 0.54225/0.33934, loss_mask_dice_4: 1.37877/1.19140, loss_spatial_bce_4: 0.15187/0.10244, loss_spatial_dice_4: 0.33053/0.24799, loss_spatial_ce_4: 0.13746/0.14217, loss_grounding_bce_4: 0.13134/0.08742, loss_grounding_dice_4: 0.18741/0.18195, loss_grounding_ce_4: 0.26648/0.28930, loss_mask_ce_5: 1.02960/0.94088, loss_mask_bce_5: 0.54214/0.34167, loss_mask_dice_5: 1.49976/1.19484, loss_spatial_bce_5: 0.19353/0.10335, loss_spatial_dice_5: 0.34252/0.25147, loss_spatial_ce_5: 0.40150/0.15488, loss_grounding_bce_5: 0.13285/0.08785, loss_grounding_dice_5: 0.20754/0.18314, loss_grounding_ce_5: 0.31468/0.29988, loss_mask_ce_6: 0.89362/0.97685, loss_mask_bce_6: 0.53365/0.34426, loss_mask_dice_6: 1.40238/1.19894, loss_spatial_bce_6: 0.19992/0.10808, loss_spatial_dice_6: 0.34755/0.25502, loss_spatial_ce_6: 0.16070/0.17470, loss_grounding_bce_6: 0.13766/0.08875, loss_grounding_dice_6: 0.21019/0.18322, loss_grounding_ce_6: 0.32527/0.32285, loss_mask_ce_7: 0.90975/1.02017, loss_mask_bce_7: 0.54617/0.35217, loss_mask_dice_7: 1.36507/1.25489, loss_spatial_bce_7: 0.27023/0.11864, loss_spatial_dice_7: 0.37925/0.28142, loss_spatial_ce_7: 0.09928/0.21652, loss_grounding_bce_7: 0.13806/0.09052, loss_grounding_dice_7: 0.21610/0.19030, loss_grounding_ce_7: 0.17821/0.36534, loss_mask_ce_8: 1.14673/1.13371, loss_mask_bce_8: 0.56619/0.36528, loss_mask_dice_8: 1.64304/1.32874, loss_spatial_bce_8: 0.23542/0.13929, loss_spatial_dice_8: 0.40423/0.32238, loss_spatial_ce_8: 0.28441/0.26783, loss_grounding_bce_8: 0.14181/0.09413, loss_grounding_dice_8: 0.22688/0.20127, loss_grounding_ce_8: 0.26219/0.43802, loss_mask_ce_9: 3.49421/3.72138, loss_mask_bce_9: 0.55310/0.39227, loss_mask_dice_9: 1.94195/1.90428, loss_spatial_bce_9: 0.30136/0.34256, loss_spatial_dice_9: 0.81194/0.82833, loss_spatial_ce_9: 1.79125/1.55622, loss_grounding_bce_9: 0.15344/0.10535, loss_grounding_dice_9: 0.26153/0.28084, loss_grounding_ce_9: 1.11943/0.74143] items per batch[64] items per second[0.22] total items[460800] mini batches[  7200] memory[7341] epoch remaining[0:05:14]
INFO:trainer.default_trainer:epochs[     3] optim steps[7300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84639/0.91851, loss_mask_bce_0: 0.05903/0.33671, loss_mask_dice_0: 1.70950/1.16782, loss_spatial_bce_0: 0.01496/0.09735, loss_spatial_dice_0: 0.27297/0.23389, loss_spatial_ce_0: 0.11600/0.10891, loss_grounding_bce_0: 0.00858/0.08669, loss_grounding_dice_0: 0.28329/0.17924, loss_grounding_ce_0: 0.34576/0.27973, loss_mask_ce_1: 0.86367/0.91849, loss_mask_bce_1: 0.06625/0.33736, loss_mask_dice_1: 1.86428/1.17570, loss_spatial_bce_1: 0.01505/0.09844, loss_spatial_dice_1: 0.28140/0.23854, loss_spatial_ce_1: 0.08579/0.11487, loss_grounding_bce_1: 0.00974/0.08676, loss_grounding_dice_1: 0.27154/0.18008, loss_grounding_ce_1: 0.34873/0.28192, loss_mask_ce_2: 0.93389/0.92499, loss_mask_bce_2: 0.06886/0.33734, loss_mask_dice_2: 1.98905/1.17347, loss_spatial_bce_2: 0.01652/0.09793, loss_spatial_dice_2: 0.29360/0.23957, loss_spatial_ce_2: 0.02533/0.12004, loss_grounding_bce_2: 0.00952/0.08671, loss_grounding_dice_2: 0.28280/0.17972, loss_grounding_ce_2: 0.33657/0.28441, loss_mask_ce_3: 0.94861/0.92918, loss_mask_bce_3: 0.06402/0.33825, loss_mask_dice_3: 1.57916/1.17112, loss_spatial_bce_3: 0.01444/0.09905, loss_spatial_dice_3: 0.29965/0.24138, loss_spatial_ce_3: 0.06238/0.12611, loss_grounding_bce_3: 0.01007/0.08688, loss_grounding_dice_3: 0.25682/0.17928, loss_grounding_ce_3: 0.35290/0.28632, loss_mask_ce_4: 0.77076/0.92831, loss_mask_bce_4: 0.07265/0.33935, loss_mask_dice_4: 1.80901/1.19187, loss_spatial_bce_4: 0.01288/0.10240, loss_spatial_dice_4: 0.29488/0.24806, loss_spatial_ce_4: 0.03272/0.14161, loss_grounding_bce_4: 0.01017/0.08737, loss_grounding_dice_4: 0.29041/0.18213, loss_grounding_ce_4: 0.33658/0.28920, loss_mask_ce_5: 1.04984/0.94158, loss_mask_bce_5: 0.06391/0.34160, loss_mask_dice_5: 1.66290/1.19525, loss_spatial_bce_5: 0.01698/0.10332, loss_spatial_dice_5: 0.36456/0.25147, loss_spatial_ce_5: 0.05257/0.15448, loss_grounding_bce_5: 0.01133/0.08781, loss_grounding_dice_5: 0.30793/0.18334, loss_grounding_ce_5: 0.36599/0.29973, loss_mask_ce_6: 1.06273/0.97763, loss_mask_bce_6: 0.07657/0.34427, loss_mask_dice_6: 1.76969/1.19937, loss_spatial_bce_6: 0.01413/0.10808, loss_spatial_dice_6: 0.32134/0.25502, loss_spatial_ce_6: 0.07669/0.17447, loss_grounding_bce_6: 0.01118/0.08873, loss_grounding_dice_6: 0.29157/0.18345, loss_grounding_ce_6: 0.35648/0.32257, loss_mask_ce_7: 0.90777/1.02126, loss_mask_bce_7: 0.08645/0.35209, loss_mask_dice_7: 2.22962/1.25544, loss_spatial_bce_7: 0.02279/0.11862, loss_spatial_dice_7: 0.35675/0.28145, loss_spatial_ce_7: 0.07679/0.21610, loss_grounding_bce_7: 0.01141/0.09046, loss_grounding_dice_7: 0.32198/0.19046, loss_grounding_ce_7: 0.37079/0.36526, loss_mask_ce_8: 1.27680/1.13404, loss_mask_bce_8: 0.09189/0.36524, loss_mask_dice_8: 2.20080/1.32952, loss_spatial_bce_8: 0.02391/0.13929, loss_spatial_dice_8: 0.44533/0.32233, loss_spatial_ce_8: 0.07056/0.26735, loss_grounding_bce_8: 0.01254/0.09413, loss_grounding_dice_8: 0.26687/0.20146, loss_grounding_ce_8: 0.48126/0.43776, loss_mask_ce_9: 3.17276/3.72162, loss_mask_bce_9: 0.08372/0.39229, loss_mask_dice_9: 2.77597/1.90458, loss_spatial_bce_9: 0.22093/0.34233, loss_spatial_dice_9: 0.93313/0.82833, loss_spatial_ce_9: 1.45754/1.55605, loss_grounding_bce_9: 0.01378/0.10533, loss_grounding_dice_9: 0.40807/0.28115, loss_grounding_ce_9: 0.59348/0.74111] items per batch[64] items per second[0.22] total items[467200] mini batches[  7300] memory[7341] epoch remaining[0:00:23]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00007308.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0030 s/iter. Inference: 0.2190 s/iter. Eval: 0.0955 s/iter. Total: 0.3176 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2232 s/iter. Eval: 0.0904 s/iter. Total: 0.3167 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2256 s/iter. Eval: 0.0847 s/iter. Total: 0.3135 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2253 s/iter. Eval: 0.0810 s/iter. Total: 0.3094 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2222 s/iter. Eval: 0.0785 s/iter. Total: 0.3038 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2242 s/iter. Eval: 0.0781 s/iter. Total: 0.3055 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2262 s/iter. Eval: 0.0774 s/iter. Total: 0.3068 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2259 s/iter. Eval: 0.0767 s/iter. Total: 0.3058 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2264 s/iter. Eval: 0.0768 s/iter. Total: 0.3064 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaloe_1t_86 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.605 | 82.245 | 59.513 |      133      |
| Things | 54.717 | 82.915 | 65.371 |      80       |
| Stuff  | 41.888 | 81.234 | 50.671 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.35s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.62 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.98 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.17s)
creating index...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.79 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.497 | 60.947 | 40.383 | 19.092 | 41.745 | 59.834 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.884 | bicycle      | 16.785 | car            | 36.389 |
| motorcycle    | 34.445 | airplane     | 55.781 | bus            | 64.188 |
| train         | 68.626 | truck        | 35.200 | boat           | 23.714 |
| traffic light | 23.909 | fire hydrant | 63.100 | stop sign      | 65.486 |
| parking meter | 44.735 | bench        | 19.968 | bird           | 29.638 |
| cat           | 72.480 | dog          | 65.681 | horse          | 44.307 |
| sheep         | 45.814 | cow          | 49.553 | elephant       | 60.636 |
| bear          | 76.518 | zebra        | 60.084 | giraffe        | 56.583 |
| backpack      | 16.008 | umbrella     | 48.033 | handbag        | 15.045 |
| tie           | 32.432 | suitcase     | 40.186 | frisbee        | 68.256 |
| skis          | 5.821  | snowboard    | 24.589 | sports ball    | 45.756 |
| kite          | 31.535 | baseball bat | 27.197 | baseball glove | 43.260 |
| skateboard    | 35.227 | surfboard    | 34.232 | tennis racket  | 55.489 |
| bottle        | 32.895 | wine glass   | 26.007 | cup            | 39.678 |
| fork          | 16.220 | knife        | 11.656 | spoon          | 13.844 |
| bowl          | 32.069 | banana       | 18.947 | apple          | 21.297 |
| sandwich      | 41.946 | orange       | 26.864 | broccoli       | 21.777 |
| carrot        | 19.379 | hot dog      | 24.301 | pizza          | 47.117 |
| donut         | 45.487 | cake         | 43.079 | chair          | 20.047 |
| couch         | 39.171 | potted plant | 17.514 | bed            | 39.991 |
| dining table  | 12.743 | toilet       | 66.700 | tv             | 61.735 |
| laptop        | 63.582 | mouse        | 59.248 | remote         | 30.800 |
| keyboard      | 48.194 | cell phone   | 37.026 | microwave      | 53.848 |
| oven          | 34.471 | toaster      | 35.538 | sink           | 38.878 |
| refrigerator  | 57.679 | book         | 7.638  | clock          | 49.955 |
| vase          | 33.841 | scissors     | 26.537 | teddy bear     | 49.204 |
| hair drier    | 12.636 | toothbrush   | 19.675 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.385
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.609
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.404
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.598
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.833901514584774, 'fwIoU': 69.27903128435537, 'IoU-person': 87.57042330612731, 'IoU-bicycle': 70.49667225852438, 'IoU-car': 68.91088055714802, 'IoU-motorcycle': 82.97635787391702, 'IoU-airplane': 82.9521634355612, 'IoU-bus': 85.11510154347603, 'IoU-train': 86.50124992847849, 'IoU-truck': 65.14790524168609, 'IoU-boat': 67.72146702440658, 'IoU-traffic light': 74.44964984685436, 'IoU-fire hydrant': 81.93716569883975, 'IoU-stop sign': 94.76147870457677, 'IoU-parking meter': 85.22642181993909, 'IoU-bench': 53.710216788355616, 'IoU-bird': 75.95865383747393, 'IoU-cat': 89.7275757800051, 'IoU-dog': 78.91329759557867, 'IoU-horse': 85.28137851271543, 'IoU-sheep': 89.81873424607572, 'IoU-cow': 85.57496301503102, 'IoU-elephant': 92.52407032225032, 'IoU-bear': 92.1854511439483, 'IoU-zebra': 92.32330988364451, 'IoU-giraffe': 88.36389933689692, 'IoU-backpack': 39.07769106284281, 'IoU-umbrella': 79.79645290951409, 'IoU-handbag': 37.020958845771986, 'IoU-tie': 68.92856092121754, 'IoU-suitcase': 79.54427201445557, 'IoU-frisbee': 82.66083725841814, 'IoU-skis': 51.69825649134487, 'IoU-snowboard': 69.21589592800498, 'IoU-sports ball': 66.38635343408649, 'IoU-kite': 66.22917093445014, 'IoU-baseball bat': 56.89677089629873, 'IoU-baseball glove': 76.73161353445778, 'IoU-skateboard': 61.01859187713673, 'IoU-surfboard': 81.40487206297273, 'IoU-tennis racket': 75.0752480556774, 'IoU-bottle': 67.73976131877703, 'IoU-wine glass': 76.61146434827742, 'IoU-cup': 66.9017497404398, 'IoU-fork': 55.486749359813246, 'IoU-knife': 51.62933564902863, 'IoU-spoon': 52.41999109733073, 'IoU-bowl': 56.30219564445845, 'IoU-banana': 83.92873056716039, 'IoU-apple': 58.10557627398285, 'IoU-sandwich': 65.64757605893598, 'IoU-orange': 80.51711784289627, 'IoU-broccoli': 67.99891440861539, 'IoU-carrot': 63.572532960484715, 'IoU-hot dog': 61.91388418156015, 'IoU-pizza': 82.76028371601329, 'IoU-donut': 66.12679655905826, 'IoU-cake': 70.42064550607215, 'IoU-chair': 52.07895597513685, 'IoU-couch': 67.11323472020993, 'IoU-potted plant': 34.60097956071702, 'IoU-bed': 71.40166601641053, 'IoU-dining table': 49.91922856724975, 'IoU-toilet': 82.23163637868781, 'IoU-tv': 75.57607293951916, 'IoU-laptop': 75.06450064282568, 'IoU-mouse': 67.34046377090522, 'IoU-remote': 63.06852152984321, 'IoU-keyboard': 62.76923965566006, 'IoU-cell phone': 78.27007516894943, 'IoU-microwave': 43.2563164011825, 'IoU-oven': 65.14696565210494, 'IoU-toaster': 41.18657229984405, 'IoU-sink': 66.40214387966611, 'IoU-refrigerator': 79.63875480553553, 'IoU-book': 51.39565458251496, 'IoU-clock': 70.91601586924726, 'IoU-vase': 66.49968831229931, 'IoU-scissors': 62.87698891414494, 'IoU-teddy bear': 83.69412512241757, 'IoU-hair drier': 39.55809173517957, 'IoU-toothbrush': 56.26526946107785, 'IoU-banner': 34.19349055534903, 'IoU-blanket': 11.726685982715841, 'IoU-bridge': 39.128564771668216, 'IoU-cardboard': 38.19419910620368, 'IoU-counter': 30.070812892895653, 'IoU-curtain': 64.13837606880381, 'IoU-door-stuff': 42.270038144105506, 'IoU-floor-wood': 63.10923666653983, 'IoU-flower': 44.41118557400346, 'IoU-fruit': 41.19947718548625, 'IoU-gravel': 26.466309800240733, 'IoU-house': 24.61363464746659, 'IoU-light': 39.965345821078394, 'IoU-mirror-stuff': 57.16682326088297, 'IoU-net': 45.97563970744338, 'IoU-pillow': 11.245259294402548, 'IoU-platform': 28.057504807128947, 'IoU-playingfield': 69.07521810749077, 'IoU-railroad': 60.97107643734127, 'IoU-river': 49.28025212927426, 'IoU-road': 66.62952842900852, 'IoU-roof': 13.8890115378774, 'IoU-sand': 64.17495955815123, 'IoU-sea': 85.22648507250176, 'IoU-shelf': 37.30397389108214, 'IoU-snow': 89.62409464546528, 'IoU-stairs': 18.033662664294493, 'IoU-tent': 7.47438120763046, 'IoU-towel': 33.827856659696025, 'IoU-wall-brick': 43.525684369595204, 'IoU-wall-stone': 24.511230371877005, 'IoU-wall-tile': 68.31523008346365, 'IoU-wall-wood': 38.842579949862674, 'IoU-water-other': 21.631180129293494, 'IoU-window-blind': 46.568853582392386, 'IoU-window-other': 48.94089722110922, 'IoU-tree-merged': 81.2503130320915, 'IoU-fence-merged': 50.63777508605744, 'IoU-ceiling-merged': 67.43105024447486, 'IoU-sky-other-merged': 92.94079234691587, 'IoU-cabinet-merged': 59.03814399827041, 'IoU-table-merged': 39.042012792896955, 'IoU-floor-other-merged': 48.621075248971486, 'IoU-pavement-merged': 52.43575144208153, 'IoU-mountain-merged': 55.62965617100147, 'IoU-grass-merged': 72.16379003408746, 'IoU-dirt-merged': 44.6377486488606, 'IoU-paper-merged': 27.306884687193, 'IoU-food-other-merged': 37.626319845479294, 'IoU-building-other-merged': 57.630842103109394, 'IoU-rock-merged': 60.139972828512214, 'IoU-wall-other-merged': 66.11797439517136, 'IoU-rug-merged': 64.29155707838243, 'mACC': 72.63972643304916, 'pACC': 80.54997533724739, 'ACC-person': 92.63790645779778, 'ACC-bicycle': 84.12244421178323, 'ACC-car': 84.94855707642805, 'ACC-motorcycle': 91.21904407154607, 'ACC-airplane': 90.63886783961273, 'ACC-bus': 90.09667895221085, 'ACC-train': 95.28100254030247, 'ACC-truck': 77.94589703643375, 'ACC-boat': 77.66860198877795, 'ACC-traffic light': 89.90921675112777, 'ACC-fire hydrant': 95.46126599683159, 'ACC-stop sign': 97.83519685652803, 'ACC-parking meter': 92.33829736773555, 'ACC-bench': 72.81772374695902, 'ACC-bird': 81.02921224727372, 'ACC-cat': 95.30252393626051, 'ACC-dog': 83.51884877094467, 'ACC-horse': 91.62860937616927, 'ACC-sheep': 93.42445303796946, 'ACC-cow': 92.01982907579742, 'ACC-elephant': 95.22750028580698, 'ACC-bear': 94.4242508420031, 'ACC-zebra': 94.89717692320576, 'ACC-giraffe': 92.89594156858507, 'ACC-backpack': 58.4366044118807, 'ACC-umbrella': 84.54660678916126, 'ACC-handbag': 51.542555958802986, 'ACC-tie': 80.09991343388496, 'ACC-suitcase': 88.87753728596147, 'ACC-frisbee': 93.36436363636363, 'ACC-skis': 67.73016419247897, 'ACC-snowboard': 76.10002204207588, 'ACC-sports ball': 80.25604448992671, 'ACC-kite': 76.08513990722354, 'ACC-baseball bat': 82.15990229941035, 'ACC-baseball glove': 88.71907942615451, 'ACC-skateboard': 69.65698793936919, 'ACC-surfboard': 89.99801370945839, 'ACC-tennis racket': 79.54926240657736, 'ACC-bottle': 83.46713973841295, 'ACC-wine glass': 85.64214610795815, 'ACC-cup': 84.41484769661284, 'ACC-fork': 68.23428835561272, 'ACC-knife': 65.89341979922327, 'ACC-spoon': 70.18397267537208, 'ACC-bowl': 66.79119936032242, 'ACC-banana': 90.02892366977704, 'ACC-apple': 72.90196000330721, 'ACC-sandwich': 79.76837621959561, 'ACC-orange': 88.22933708823962, 'ACC-broccoli': 80.18513921943669, 'ACC-carrot': 74.41530958136909, 'ACC-hot dog': 73.69394367240575, 'ACC-pizza': 92.19943084772272, 'ACC-donut': 80.51908153184972, 'ACC-cake': 79.96873929216825, 'ACC-chair': 67.40604208230285, 'ACC-couch': 84.55268153186368, 'ACC-potted plant': 52.3870129098826, 'ACC-bed': 84.17351066850274, 'ACC-dining table': 77.14523594373648, 'ACC-toilet': 91.67775606640247, 'ACC-tv': 87.61791988372227, 'ACC-laptop': 91.34079237011412, 'ACC-mouse': 85.8298251499771, 'ACC-remote': 71.99132336128463, 'ACC-keyboard': 68.76559151047199, 'ACC-cell phone': 88.50178469635644, 'ACC-microwave': 45.978022954410704, 'ACC-oven': 86.86394041582277, 'ACC-toaster': 44.63931352557172, 'ACC-sink': 84.01165056626535, 'ACC-refrigerator': 90.05081807176268, 'ACC-book': 67.90959205401921, 'ACC-clock': 76.64784026961921, 'ACC-vase': 76.7842147363713, 'ACC-scissors': 68.1968432360962, 'ACC-teddy bear': 88.72594601912326, 'ACC-hair drier': 41.633222326587536, 'ACC-toothbrush': 81.62178596247394, 'ACC-banner': 67.70176878495023, 'ACC-blanket': 14.2616699325945, 'ACC-bridge': 53.31124356226445, 'ACC-cardboard': 44.520895739421256, 'ACC-counter': 53.46565583474624, 'ACC-curtain': 73.85345647391655, 'ACC-door-stuff': 64.23130590984914, 'ACC-floor-wood': 77.05951148498241, 'ACC-flower': 59.4999083667439, 'ACC-fruit': 54.854095855666685, 'ACC-gravel': 37.508981334904576, 'ACC-house': 28.986483243134735, 'ACC-light': 55.722268211920536, 'ACC-mirror-stuff': 70.51430945748314, 'ACC-net': 61.215588855411674, 'ACC-pillow': 20.399004205463143, 'ACC-platform': 46.93593017574329, 'ACC-playingfield': 87.88552752014955, 'ACC-railroad': 75.07529173669106, 'ACC-river': 71.61894537838802, 'ACC-road': 87.09058829302369, 'ACC-roof': 18.07180954010765, 'ACC-sand': 70.21241877845395, 'ACC-sea': 90.44234847217487, 'ACC-shelf': 56.951806574202855, 'ACC-snow': 94.95145441728631, 'ACC-stairs': 28.304408475289723, 'ACC-tent': 9.17027029287653, 'ACC-towel': 39.45316096467237, 'ACC-wall-brick': 57.023857540569786, 'ACC-wall-stone': 32.107670738651315, 'ACC-wall-tile': 80.79279123315965, 'ACC-wall-wood': 52.44529079140404, 'ACC-water-other': 38.145949882023174, 'ACC-window-blind': 54.19143900887996, 'ACC-window-other': 68.24465634404146, 'ACC-tree-merged': 89.22932787835518, 'ACC-fence-merged': 68.3252441646181, 'ACC-ceiling-merged': 80.58496884585091, 'ACC-sky-other-merged': 96.90188578067558, 'ACC-cabinet-merged': 74.63463767175053, 'ACC-table-merged': 52.65459879023664, 'ACC-floor-other-merged': 56.94845211754087, 'ACC-pavement-merged': 67.01171615949025, 'ACC-mountain-merged': 67.68814157594281, 'ACC-grass-merged': 82.77831748430505, 'ACC-dirt-merged': 69.76654507901863, 'ACC-paper-merged': 34.78621764080293, 'ACC-food-other-merged': 48.973444320114275, 'ACC-building-other-merged': 74.61249013117768, 'ACC-rock-merged': 84.39007044140985, 'ACC-wall-other-merged': 81.52229512162906, 'ACC-rug-merged': 78.65233292242773})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1515 s/iter. Inference: 0.5543 s/iter. Eval: 0.0000 s/iter. Total: 0.7059 s/iter. ETA=0:00:27
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1539 s/iter. Inference: 0.5611 s/iter. Eval: 0.0000 s/iter. Total: 0.7150 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1683 s/iter. Inference: 0.5854 s/iter. Eval: 0.0000 s/iter. Total: 0.7538 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1715 s/iter. Inference: 0.7084 s/iter. Eval: 0.0000 s/iter. Total: 0.8801 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1693 s/iter. Inference: 0.6283 s/iter. Eval: 0.0000 s/iter. Total: 0.7977 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1689 s/iter. Inference: 0.6621 s/iter. Eval: 0.0000 s/iter. Total: 0.8312 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1712 s/iter. Inference: 0.7208 s/iter. Eval: 0.0000 s/iter. Total: 0.8922 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5929177641205736, 'noc@0.8': 3.1109160081943226, 'noc@0.85': 3.807433421129646, 'noc@0.9': 4.828504536142815, 'miou@iter1': 0.8288588497350535}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.0994 s/iter. Eval: 0.0008 s/iter. Total: 0.1016 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.00660705566406, 'precision@0.6': 68.16944885253906, 'precision@0.7': 62.80606460571289, 'precision@0.8': 51.88496017456055, 'precision@0.9': 25.80645179748535, 'cIoU': 57.2438850402832, 'mIoU': 62.454891204833984}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.605089512224815, 'SQ': 82.24513840120949, 'RQ': 59.51342195893403, 'PQ_th': 54.71747129033292, 'SQ_th': 82.91504193376149, 'RQ_th': 65.37141500752432, 'PQ_st': 41.88828682828801, 'SQ_st': 81.23396325773479, 'RQ_st': 50.67116830068464}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.49734348985629, 'AP50': 60.94677169095583, 'AP75': 40.38280682522174, 'APs': 19.092226857201265, 'APm': 41.74517973725347, 'APl': 59.83403825854287, 'AP-person': 43.8840109607987, 'AP-bicycle': 16.784520943960164, 'AP-car': 36.38893051911212, 'AP-motorcycle': 34.44508423923063, 'AP-airplane': 55.780564752010584, 'AP-bus': 64.18810505463296, 'AP-train': 68.62616749598426, 'AP-truck': 35.20044316784309, 'AP-boat': 23.713815982053973, 'AP-traffic light': 23.909027739474038, 'AP-fire hydrant': 63.09977805593977, 'AP-stop sign': 65.48635104345134, 'AP-parking meter': 44.7350405230225, 'AP-bench': 19.967837656195535, 'AP-bird': 29.638154468010143, 'AP-cat': 72.48021141027775, 'AP-dog': 65.68067738416477, 'AP-horse': 44.30730521074172, 'AP-sheep': 45.81382843774087, 'AP-cow': 49.55343172575864, 'AP-elephant': 60.6364860087529, 'AP-bear': 76.51825555234765, 'AP-zebra': 60.08363938676386, 'AP-giraffe': 56.58312871450221, 'AP-backpack': 16.007711240289762, 'AP-umbrella': 48.03335250897536, 'AP-handbag': 15.045301712535315, 'AP-tie': 32.432427494934146, 'AP-suitcase': 40.185878217896565, 'AP-frisbee': 68.2560326287703, 'AP-skis': 5.821231953293386, 'AP-snowboard': 24.589331026120224, 'AP-sports ball': 45.75626827375399, 'AP-kite': 31.53489036466748, 'AP-baseball bat': 27.197002796563797, 'AP-baseball glove': 43.260309776297554, 'AP-skateboard': 35.22663858102854, 'AP-surfboard': 34.23187759914233, 'AP-tennis racket': 55.48864047731373, 'AP-bottle': 32.89480892194033, 'AP-wine glass': 26.007487954522457, 'AP-cup': 39.67802786449781, 'AP-fork': 16.220332516670556, 'AP-knife': 11.656080881307384, 'AP-spoon': 13.844369329311256, 'AP-bowl': 32.068683394103495, 'AP-banana': 18.946717006835208, 'AP-apple': 21.297266283752826, 'AP-sandwich': 41.94557393571484, 'AP-orange': 26.863839375192526, 'AP-broccoli': 21.776677277688286, 'AP-carrot': 19.37896376882578, 'AP-hot dog': 24.300607172624115, 'AP-pizza': 47.11693268185702, 'AP-donut': 45.486522807345104, 'AP-cake': 43.07906633592864, 'AP-chair': 20.047427712989013, 'AP-couch': 39.17104308617813, 'AP-potted plant': 17.51377754155112, 'AP-bed': 39.990948561762465, 'AP-dining table': 12.74344679432223, 'AP-toilet': 66.69994480098845, 'AP-tv': 61.735174857517606, 'AP-laptop': 63.58233828882551, 'AP-mouse': 59.24817835469444, 'AP-remote': 30.79979700802425, 'AP-keyboard': 48.193522531983106, 'AP-cell phone': 37.02606625367326, 'AP-microwave': 53.847571150283514, 'AP-oven': 34.47124036510711, 'AP-toaster': 35.537855959508995, 'AP-sink': 38.878085188395175, 'AP-refrigerator': 57.679497082500106, 'AP-book': 7.638325493663449, 'AP-clock': 49.955100741516354, 'AP-vase': 33.84140264288101, 'AP-scissors': 26.53741118461807, 'AP-teddy bear': 49.204437939904224, 'AP-hair drier': 12.636138613861386, 'AP-toothbrush': 19.675098441283698}), ('sem_seg', {'mIoU': 60.833901514584774, 'fwIoU': 69.27903128435537, 'IoU-person': 87.57042330612731, 'IoU-bicycle': 70.49667225852438, 'IoU-car': 68.91088055714802, 'IoU-motorcycle': 82.97635787391702, 'IoU-airplane': 82.9521634355612, 'IoU-bus': 85.11510154347603, 'IoU-train': 86.50124992847849, 'IoU-truck': 65.14790524168609, 'IoU-boat': 67.72146702440658, 'IoU-traffic light': 74.44964984685436, 'IoU-fire hydrant': 81.93716569883975, 'IoU-stop sign': 94.76147870457677, 'IoU-parking meter': 85.22642181993909, 'IoU-bench': 53.710216788355616, 'IoU-bird': 75.95865383747393, 'IoU-cat': 89.7275757800051, 'IoU-dog': 78.91329759557867, 'IoU-horse': 85.28137851271543, 'IoU-sheep': 89.81873424607572, 'IoU-cow': 85.57496301503102, 'IoU-elephant': 92.52407032225032, 'IoU-bear': 92.1854511439483, 'IoU-zebra': 92.32330988364451, 'IoU-giraffe': 88.36389933689692, 'IoU-backpack': 39.07769106284281, 'IoU-umbrella': 79.79645290951409, 'IoU-handbag': 37.020958845771986, 'IoU-tie': 68.92856092121754, 'IoU-suitcase': 79.54427201445557, 'IoU-frisbee': 82.66083725841814, 'IoU-skis': 51.69825649134487, 'IoU-snowboard': 69.21589592800498, 'IoU-sports ball': 66.38635343408649, 'IoU-kite': 66.22917093445014, 'IoU-baseball bat': 56.89677089629873, 'IoU-baseball glove': 76.73161353445778, 'IoU-skateboard': 61.01859187713673, 'IoU-surfboard': 81.40487206297273, 'IoU-tennis racket': 75.0752480556774, 'IoU-bottle': 67.73976131877703, 'IoU-wine glass': 76.61146434827742, 'IoU-cup': 66.9017497404398, 'IoU-fork': 55.486749359813246, 'IoU-knife': 51.62933564902863, 'IoU-spoon': 52.41999109733073, 'IoU-bowl': 56.30219564445845, 'IoU-banana': 83.92873056716039, 'IoU-apple': 58.10557627398285, 'IoU-sandwich': 65.64757605893598, 'IoU-orange': 80.51711784289627, 'IoU-broccoli': 67.99891440861539, 'IoU-carrot': 63.572532960484715, 'IoU-hot dog': 61.91388418156015, 'IoU-pizza': 82.76028371601329, 'IoU-donut': 66.12679655905826, 'IoU-cake': 70.42064550607215, 'IoU-chair': 52.07895597513685, 'IoU-couch': 67.11323472020993, 'IoU-potted plant': 34.60097956071702, 'IoU-bed': 71.40166601641053, 'IoU-dining table': 49.91922856724975, 'IoU-toilet': 82.23163637868781, 'IoU-tv': 75.57607293951916, 'IoU-laptop': 75.06450064282568, 'IoU-mouse': 67.34046377090522, 'IoU-remote': 63.06852152984321, 'IoU-keyboard': 62.76923965566006, 'IoU-cell phone': 78.27007516894943, 'IoU-microwave': 43.2563164011825, 'IoU-oven': 65.14696565210494, 'IoU-toaster': 41.18657229984405, 'IoU-sink': 66.40214387966611, 'IoU-refrigerator': 79.63875480553553, 'IoU-book': 51.39565458251496, 'IoU-clock': 70.91601586924726, 'IoU-vase': 66.49968831229931, 'IoU-scissors': 62.87698891414494, 'IoU-teddy bear': 83.69412512241757, 'IoU-hair drier': 39.55809173517957, 'IoU-toothbrush': 56.26526946107785, 'IoU-banner': 34.19349055534903, 'IoU-blanket': 11.726685982715841, 'IoU-bridge': 39.128564771668216, 'IoU-cardboard': 38.19419910620368, 'IoU-counter': 30.070812892895653, 'IoU-curtain': 64.13837606880381, 'IoU-door-stuff': 42.270038144105506, 'IoU-floor-wood': 63.10923666653983, 'IoU-flower': 44.41118557400346, 'IoU-fruit': 41.19947718548625, 'IoU-gravel': 26.466309800240733, 'IoU-house': 24.61363464746659, 'IoU-light': 39.965345821078394, 'IoU-mirror-stuff': 57.16682326088297, 'IoU-net': 45.97563970744338, 'IoU-pillow': 11.245259294402548, 'IoU-platform': 28.057504807128947, 'IoU-playingfield': 69.07521810749077, 'IoU-railroad': 60.97107643734127, 'IoU-river': 49.28025212927426, 'IoU-road': 66.62952842900852, 'IoU-roof': 13.8890115378774, 'IoU-sand': 64.17495955815123, 'IoU-sea': 85.22648507250176, 'IoU-shelf': 37.30397389108214, 'IoU-snow': 89.62409464546528, 'IoU-stairs': 18.033662664294493, 'IoU-tent': 7.47438120763046, 'IoU-towel': 33.827856659696025, 'IoU-wall-brick': 43.525684369595204, 'IoU-wall-stone': 24.511230371877005, 'IoU-wall-tile': 68.31523008346365, 'IoU-wall-wood': 38.842579949862674, 'IoU-water-other': 21.631180129293494, 'IoU-window-blind': 46.568853582392386, 'IoU-window-other': 48.94089722110922, 'IoU-tree-merged': 81.2503130320915, 'IoU-fence-merged': 50.63777508605744, 'IoU-ceiling-merged': 67.43105024447486, 'IoU-sky-other-merged': 92.94079234691587, 'IoU-cabinet-merged': 59.03814399827041, 'IoU-table-merged': 39.042012792896955, 'IoU-floor-other-merged': 48.621075248971486, 'IoU-pavement-merged': 52.43575144208153, 'IoU-mountain-merged': 55.62965617100147, 'IoU-grass-merged': 72.16379003408746, 'IoU-dirt-merged': 44.6377486488606, 'IoU-paper-merged': 27.306884687193, 'IoU-food-other-merged': 37.626319845479294, 'IoU-building-other-merged': 57.630842103109394, 'IoU-rock-merged': 60.139972828512214, 'IoU-wall-other-merged': 66.11797439517136, 'IoU-rug-merged': 64.29155707838243, 'mACC': 72.63972643304916, 'pACC': 80.54997533724739, 'ACC-person': 92.63790645779778, 'ACC-bicycle': 84.12244421178323, 'ACC-car': 84.94855707642805, 'ACC-motorcycle': 91.21904407154607, 'ACC-airplane': 90.63886783961273, 'ACC-bus': 90.09667895221085, 'ACC-train': 95.28100254030247, 'ACC-truck': 77.94589703643375, 'ACC-boat': 77.66860198877795, 'ACC-traffic light': 89.90921675112777, 'ACC-fire hydrant': 95.46126599683159, 'ACC-stop sign': 97.83519685652803, 'ACC-parking meter': 92.33829736773555, 'ACC-bench': 72.81772374695902, 'ACC-bird': 81.02921224727372, 'ACC-cat': 95.30252393626051, 'ACC-dog': 83.51884877094467, 'ACC-horse': 91.62860937616927, 'ACC-sheep': 93.42445303796946, 'ACC-cow': 92.01982907579742, 'ACC-elephant': 95.22750028580698, 'ACC-bear': 94.4242508420031, 'ACC-zebra': 94.89717692320576, 'ACC-giraffe': 92.89594156858507, 'ACC-backpack': 58.4366044118807, 'ACC-umbrella': 84.54660678916126, 'ACC-handbag': 51.542555958802986, 'ACC-tie': 80.09991343388496, 'ACC-suitcase': 88.87753728596147, 'ACC-frisbee': 93.36436363636363, 'ACC-skis': 67.73016419247897, 'ACC-snowboard': 76.10002204207588, 'ACC-sports ball': 80.25604448992671, 'ACC-kite': 76.08513990722354, 'ACC-baseball bat': 82.15990229941035, 'ACC-baseball glove': 88.71907942615451, 'ACC-skateboard': 69.65698793936919, 'ACC-surfboard': 89.99801370945839, 'ACC-tennis racket': 79.54926240657736, 'ACC-bottle': 83.46713973841295, 'ACC-wine glass': 85.64214610795815, 'ACC-cup': 84.41484769661284, 'ACC-fork': 68.23428835561272, 'ACC-knife': 65.89341979922327, 'ACC-spoon': 70.18397267537208, 'ACC-bowl': 66.79119936032242, 'ACC-banana': 90.02892366977704, 'ACC-apple': 72.90196000330721, 'ACC-sandwich': 79.76837621959561, 'ACC-orange': 88.22933708823962, 'ACC-broccoli': 80.18513921943669, 'ACC-carrot': 74.41530958136909, 'ACC-hot dog': 73.69394367240575, 'ACC-pizza': 92.19943084772272, 'ACC-donut': 80.51908153184972, 'ACC-cake': 79.96873929216825, 'ACC-chair': 67.40604208230285, 'ACC-couch': 84.55268153186368, 'ACC-potted plant': 52.3870129098826, 'ACC-bed': 84.17351066850274, 'ACC-dining table': 77.14523594373648, 'ACC-toilet': 91.67775606640247, 'ACC-tv': 87.61791988372227, 'ACC-laptop': 91.34079237011412, 'ACC-mouse': 85.8298251499771, 'ACC-remote': 71.99132336128463, 'ACC-keyboard': 68.76559151047199, 'ACC-cell phone': 88.50178469635644, 'ACC-microwave': 45.978022954410704, 'ACC-oven': 86.86394041582277, 'ACC-toaster': 44.63931352557172, 'ACC-sink': 84.01165056626535, 'ACC-refrigerator': 90.05081807176268, 'ACC-book': 67.90959205401921, 'ACC-clock': 76.64784026961921, 'ACC-vase': 76.7842147363713, 'ACC-scissors': 68.1968432360962, 'ACC-teddy bear': 88.72594601912326, 'ACC-hair drier': 41.633222326587536, 'ACC-toothbrush': 81.62178596247394, 'ACC-banner': 67.70176878495023, 'ACC-blanket': 14.2616699325945, 'ACC-bridge': 53.31124356226445, 'ACC-cardboard': 44.520895739421256, 'ACC-counter': 53.46565583474624, 'ACC-curtain': 73.85345647391655, 'ACC-door-stuff': 64.23130590984914, 'ACC-floor-wood': 77.05951148498241, 'ACC-flower': 59.4999083667439, 'ACC-fruit': 54.854095855666685, 'ACC-gravel': 37.508981334904576, 'ACC-house': 28.986483243134735, 'ACC-light': 55.722268211920536, 'ACC-mirror-stuff': 70.51430945748314, 'ACC-net': 61.215588855411674, 'ACC-pillow': 20.399004205463143, 'ACC-platform': 46.93593017574329, 'ACC-playingfield': 87.88552752014955, 'ACC-railroad': 75.07529173669106, 'ACC-river': 71.61894537838802, 'ACC-road': 87.09058829302369, 'ACC-roof': 18.07180954010765, 'ACC-sand': 70.21241877845395, 'ACC-sea': 90.44234847217487, 'ACC-shelf': 56.951806574202855, 'ACC-snow': 94.95145441728631, 'ACC-stairs': 28.304408475289723, 'ACC-tent': 9.17027029287653, 'ACC-towel': 39.45316096467237, 'ACC-wall-brick': 57.023857540569786, 'ACC-wall-stone': 32.107670738651315, 'ACC-wall-tile': 80.79279123315965, 'ACC-wall-wood': 52.44529079140404, 'ACC-water-other': 38.145949882023174, 'ACC-window-blind': 54.19143900887996, 'ACC-window-other': 68.24465634404146, 'ACC-tree-merged': 89.22932787835518, 'ACC-fence-merged': 68.3252441646181, 'ACC-ceiling-merged': 80.58496884585091, 'ACC-sky-other-merged': 96.90188578067558, 'ACC-cabinet-merged': 74.63463767175053, 'ACC-table-merged': 52.65459879023664, 'ACC-floor-other-merged': 56.94845211754087, 'ACC-pavement-merged': 67.01171615949025, 'ACC-mountain-merged': 67.68814157594281, 'ACC-grass-merged': 82.77831748430505, 'ACC-dirt-merged': 69.76654507901863, 'ACC-paper-merged': 34.78621764080293, 'ACC-food-other-merged': 48.973444320114275, 'ACC-building-other-merged': 74.61249013117768, 'ACC-rock-merged': 84.39007044140985, 'ACC-wall-other-merged': 81.52229512162906, 'ACC-rug-merged': 78.65233292242773})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5929177641205736, 'noc@0.8': 3.1109160081943226, 'noc@0.85': 3.807433421129646, 'noc@0.9': 4.828504536142815, 'miou@iter1': 0.8288588497350535}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.00660705566406, 'precision@0.6': 68.16944885253906, 'precision@0.7': 62.80606460571289, 'precision@0.8': 51.88496017456055, 'precision@0.9': 25.80645179748535, 'cIoU': 57.2438850402832, 'mIoU': 62.454891204833984}}}
INFO:trainer.default_trainer:This epoch takes 1:31:43.728333
INFO:trainer.default_trainer:PROGRESS: 8.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 4 training.
INFO:trainer.default_trainer:epochs[     4] optim steps[7400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20924/0.91876, loss_mask_bce_0: 0.67580/0.33698, loss_mask_dice_0: 3.33085/1.16749, loss_spatial_bce_0: 0.08564/0.09733, loss_spatial_dice_0: 0.32863/0.23358, loss_spatial_ce_0: 0.07503/0.10827, loss_grounding_bce_0: 0.19657/0.08671, loss_grounding_dice_0: 0.46818/0.17931, loss_grounding_ce_0: 0.58828/0.27979, loss_mask_ce_1: 1.17359/0.91875, loss_mask_bce_1: 0.67933/0.33763, loss_mask_dice_1: 3.36068/1.17540, loss_spatial_bce_1: 0.08785/0.09841, loss_spatial_dice_1: 0.32863/0.23823, loss_spatial_ce_1: 0.08654/0.11418, loss_grounding_bce_1: 0.19425/0.08677, loss_grounding_dice_1: 0.46392/0.18007, loss_grounding_ce_1: 0.54310/0.28202, loss_mask_ce_2: 1.25307/0.92524, loss_mask_bce_2: 0.64852/0.33761, loss_mask_dice_2: 3.09050/1.17337, loss_spatial_bce_2: 0.08772/0.09790, loss_spatial_dice_2: 0.32977/0.23926, loss_spatial_ce_2: 0.10101/0.11952, loss_grounding_bce_2: 0.18168/0.08672, loss_grounding_dice_2: 0.46954/0.17973, loss_grounding_ce_2: 0.60442/0.28450, loss_mask_ce_3: 1.21772/0.92950, loss_mask_bce_3: 0.67309/0.33852, loss_mask_dice_3: 3.38769/1.17090, loss_spatial_bce_3: 0.09128/0.09901, loss_spatial_dice_3: 0.33163/0.24103, loss_spatial_ce_3: 0.10803/0.12545, loss_grounding_bce_3: 0.17795/0.08690, loss_grounding_dice_3: 0.45143/0.17928, loss_grounding_ce_3: 0.63880/0.28646, loss_mask_ce_4: 1.23329/0.92865, loss_mask_bce_4: 0.66045/0.33971, loss_mask_dice_4: 3.20097/1.19157, loss_spatial_bce_4: 0.08631/0.10236, loss_spatial_dice_4: 0.33403/0.24779, loss_spatial_ce_4: 0.12958/0.14093, loss_grounding_bce_4: 0.19656/0.08741, loss_grounding_dice_4: 0.46384/0.18217, loss_grounding_ce_4: 0.59571/0.28929, loss_mask_ce_5: 1.36237/0.94177, loss_mask_bce_5: 0.66938/0.34191, loss_mask_dice_5: 3.35549/1.19487, loss_spatial_bce_5: 0.08551/0.10329, loss_spatial_dice_5: 0.32764/0.25120, loss_spatial_ce_5: 0.12521/0.15403, loss_grounding_bce_5: 0.23616/0.08784, loss_grounding_dice_5: 0.49042/0.18334, loss_grounding_ce_5: 0.54662/0.29993, loss_mask_ce_6: 1.45295/0.97775, loss_mask_bce_6: 0.66414/0.34461, loss_mask_dice_6: 3.33692/1.19931, loss_spatial_bce_6: 0.08016/0.10812, loss_spatial_dice_6: 0.33544/0.25474, loss_spatial_ce_6: 0.14115/0.17393, loss_grounding_bce_6: 0.24254/0.08875, loss_grounding_dice_6: 0.49598/0.18343, loss_grounding_ce_6: 0.66208/0.32272, loss_mask_ce_7: 1.36191/1.02134, loss_mask_bce_7: 0.66064/0.35246, loss_mask_dice_7: 3.41911/1.25527, loss_spatial_bce_7: 0.09800/0.11860, loss_spatial_dice_7: 0.35496/0.28114, loss_spatial_ce_7: 0.11523/0.21554, loss_grounding_bce_7: 0.21239/0.09047, loss_grounding_dice_7: 0.46097/0.19043, loss_grounding_ce_7: 0.76503/0.36514, loss_mask_ce_8: 1.41993/1.13396, loss_mask_bce_8: 0.65890/0.36554, loss_mask_dice_8: 3.52545/1.32921, loss_spatial_bce_8: 0.09858/0.13922, loss_spatial_dice_8: 0.41354/0.32197, loss_spatial_ce_8: 0.14043/0.26700, loss_grounding_bce_8: 0.20642/0.09414, loss_grounding_dice_8: 0.48952/0.20153, loss_grounding_ce_8: 0.70331/0.43741, loss_mask_ce_9: 5.83041/3.72008, loss_mask_bce_9: 0.62704/0.39266, loss_mask_dice_9: 4.39926/1.90442, loss_spatial_bce_9: 0.16593/0.34215, loss_spatial_dice_9: 0.92194/0.82827, loss_spatial_ce_9: 1.62745/1.55481, loss_grounding_bce_9: 0.14450/0.10530, loss_grounding_dice_9: 0.49376/0.28121, loss_grounding_ce_9: 0.49365/0.73992] items per batch[64] items per second[0.13] total items[473600] mini batches[  7400] memory[7341] epoch remaining[1:22:24]
INFO:trainer.default_trainer:epochs[     4] optim steps[7500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07467/0.91872, loss_mask_bce_0: 0.42159/0.33659, loss_mask_dice_0: 0.67898/1.16682, loss_spatial_bce_0: 0.11997/0.09716, loss_spatial_dice_0: 0.27898/0.23334, loss_spatial_ce_0: 0.39228/0.10791, loss_grounding_bce_0: 0.20741/0.08656, loss_grounding_dice_0: 0.21641/0.17921, loss_grounding_ce_0: 1.80752/0.27971, loss_mask_ce_1: 1.03295/0.91895, loss_mask_bce_1: 0.42767/0.33728, loss_mask_dice_1: 0.84534/1.17495, loss_spatial_bce_1: 0.12938/0.09826, loss_spatial_dice_1: 0.29886/0.23799, loss_spatial_ce_1: 0.01978/0.11366, loss_grounding_bce_1: 0.22343/0.08662, loss_grounding_dice_1: 0.22588/0.17995, loss_grounding_ce_1: 1.84881/0.28201, loss_mask_ce_2: 1.16363/0.92548, loss_mask_bce_2: 0.45709/0.33727, loss_mask_dice_2: 0.82961/1.17281, loss_spatial_bce_2: 0.13208/0.09776, loss_spatial_dice_2: 0.27940/0.23900, loss_spatial_ce_2: 0.04352/0.11901, loss_grounding_bce_2: 0.21112/0.08657, loss_grounding_dice_2: 0.22230/0.17958, loss_grounding_ce_2: 1.60414/0.28433, loss_mask_ce_3: 1.16162/0.92980, loss_mask_bce_3: 0.41854/0.33813, loss_mask_dice_3: 0.74529/1.17045, loss_spatial_bce_3: 0.13030/0.09884, loss_spatial_dice_3: 0.25454/0.24076, loss_spatial_ce_3: 0.03468/0.12477, loss_grounding_bce_3: 0.18649/0.08673, loss_grounding_dice_3: 0.22071/0.17913, loss_grounding_ce_3: 1.60692/0.28620, loss_mask_ce_4: 1.09260/0.92891, loss_mask_bce_4: 0.45555/0.33932, loss_mask_dice_4: 0.85109/1.19089, loss_spatial_bce_4: 0.14351/0.10220, loss_spatial_dice_4: 0.30683/0.24757, loss_spatial_ce_4: 0.03558/0.14039, loss_grounding_bce_4: 0.14833/0.08725, loss_grounding_dice_4: 0.18855/0.18207, loss_grounding_ce_4: 1.53822/0.28910, loss_mask_ce_5: 0.96089/0.94189, loss_mask_bce_5: 0.42755/0.34148, loss_mask_dice_5: 0.69756/1.19437, loss_spatial_bce_5: 0.13642/0.10317, loss_spatial_dice_5: 0.29058/0.25099, loss_spatial_ce_5: 0.01532/0.15342, loss_grounding_bce_5: 0.14303/0.08770, loss_grounding_dice_5: 0.18325/0.18327, loss_grounding_ce_5: 1.51027/0.29961, loss_mask_ce_6: 1.03088/0.97779, loss_mask_bce_6: 0.41214/0.34412, loss_mask_dice_6: 0.67785/1.19889, loss_spatial_bce_6: 0.13499/0.10801, loss_spatial_dice_6: 0.30912/0.25447, loss_spatial_ce_6: 0.11140/0.17338, loss_grounding_bce_6: 0.19240/0.08860, loss_grounding_dice_6: 0.20868/0.18341, loss_grounding_ce_6: 1.49976/0.32240, loss_mask_ce_7: 0.95770/1.02134, loss_mask_bce_7: 0.43562/0.35205, loss_mask_dice_7: 0.77313/1.25486, loss_spatial_bce_7: 0.15481/0.11845, loss_spatial_dice_7: 0.33222/0.28103, loss_spatial_ce_7: 0.09828/0.21504, loss_grounding_bce_7: 0.21745/0.09035, loss_grounding_dice_7: 0.20995/0.19037, loss_grounding_ce_7: 1.37077/0.36441, loss_mask_ce_8: 0.83166/1.13377, loss_mask_bce_8: 0.43203/0.36518, loss_mask_dice_8: 0.86871/1.32889, loss_spatial_bce_8: 0.14422/0.13912, loss_spatial_dice_8: 0.34703/0.32182, loss_spatial_ce_8: 0.15557/0.26691, loss_grounding_bce_8: 0.15084/0.09399, loss_grounding_dice_8: 0.17339/0.20145, loss_grounding_ce_8: 1.38230/0.43746, loss_mask_ce_9: 2.96429/3.71996, loss_mask_bce_9: 0.39283/0.39217, loss_mask_dice_9: 1.07242/1.90361, loss_spatial_bce_9: 0.37261/0.34175, loss_spatial_dice_9: 0.81661/0.82813, loss_spatial_ce_9: 1.35538/1.55468, loss_grounding_bce_9: 0.20778/0.10517, loss_grounding_dice_9: 0.24828/0.28122, loss_grounding_ce_9: 1.11942/0.74003] items per batch[64] items per second[0.21] total items[480000] mini batches[  7500] memory[7341] epoch remaining[1:20:05]
INFO:trainer.default_trainer:epochs[     4] optim steps[7600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44123/0.91872, loss_mask_bce_0: 0.24983/0.33638, loss_mask_dice_0: 3.12359/1.16834, loss_spatial_bce_0: 0.05648/0.09695, loss_spatial_dice_0: 0.32440/0.23318, loss_spatial_ce_0: 0.05778/0.10835, loss_grounding_bce_0: 0.03928/0.08631, loss_grounding_dice_0: 0.37378/0.17929, loss_grounding_ce_0: 0.34798/0.27979, loss_mask_ce_1: 1.32269/0.91897, loss_mask_bce_1: 0.25431/0.33706, loss_mask_dice_1: 2.61626/1.17615, loss_spatial_bce_1: 0.05647/0.09805, loss_spatial_dice_1: 0.31004/0.23782, loss_spatial_ce_1: 0.08152/0.11405, loss_grounding_bce_1: 0.03787/0.08638, loss_grounding_dice_1: 0.28381/0.18000, loss_grounding_ce_1: 0.37321/0.28217, loss_mask_ce_2: 1.60091/0.92550, loss_mask_bce_2: 0.25942/0.33704, loss_mask_dice_2: 2.87817/1.17433, loss_spatial_bce_2: 0.05646/0.09755, loss_spatial_dice_2: 0.37432/0.23884, loss_spatial_ce_2: 0.07685/0.11935, loss_grounding_bce_2: 0.03995/0.08631, loss_grounding_dice_2: 0.31939/0.17955, loss_grounding_ce_2: 0.39106/0.28448, loss_mask_ce_3: 1.33492/0.93010, loss_mask_bce_3: 0.26040/0.33790, loss_mask_dice_3: 2.93423/1.17196, loss_spatial_bce_3: 0.06157/0.09865, loss_spatial_dice_3: 0.36312/0.24055, loss_spatial_ce_3: 0.10553/0.12532, loss_grounding_bce_3: 0.04050/0.08648, loss_grounding_dice_3: 0.28289/0.17920, loss_grounding_ce_3: 0.53860/0.28617, loss_mask_ce_4: 1.48181/0.92935, loss_mask_bce_4: 0.24937/0.33913, loss_mask_dice_4: 2.89798/1.19241, loss_spatial_bce_4: 0.06836/0.10200, loss_spatial_dice_4: 0.39249/0.24740, loss_spatial_ce_4: 0.06188/0.14101, loss_grounding_bce_4: 0.03714/0.08702, loss_grounding_dice_4: 0.29660/0.18205, loss_grounding_ce_4: 0.30062/0.28909, loss_mask_ce_5: 1.49035/0.94208, loss_mask_bce_5: 0.26159/0.34127, loss_mask_dice_5: 2.85230/1.19594, loss_spatial_bce_5: 0.05399/0.10296, loss_spatial_dice_5: 0.37827/0.25075, loss_spatial_ce_5: 0.39273/0.15385, loss_grounding_bce_5: 0.03821/0.08749, loss_grounding_dice_5: 0.34701/0.18338, loss_grounding_ce_5: 0.33095/0.29958, loss_mask_ce_6: 1.30201/0.97806, loss_mask_bce_6: 0.24376/0.34389, loss_mask_dice_6: 2.84742/1.20047, loss_spatial_bce_6: 0.06628/0.10781, loss_spatial_dice_6: 0.37282/0.25425, loss_spatial_ce_6: 0.08336/0.17413, loss_grounding_bce_6: 0.03921/0.08842, loss_grounding_dice_6: 0.40710/0.18345, loss_grounding_ce_6: 0.29967/0.32220, loss_mask_ce_7: 1.55137/1.02136, loss_mask_bce_7: 0.26074/0.35182, loss_mask_dice_7: 3.03109/1.25652, loss_spatial_bce_7: 0.07211/0.11821, loss_spatial_dice_7: 0.39122/0.28082, loss_spatial_ce_7: 0.31198/0.21516, loss_grounding_bce_7: 0.03838/0.09014, loss_grounding_dice_7: 0.33405/0.19048, loss_grounding_ce_7: 0.32969/0.36403, loss_mask_ce_8: 1.52514/1.13399, loss_mask_bce_8: 0.27303/0.36504, loss_mask_dice_8: 2.94955/1.33019, loss_spatial_bce_8: 0.06904/0.13892, loss_spatial_dice_8: 0.41535/0.32160, loss_spatial_ce_8: 0.16968/0.26700, loss_grounding_bce_8: 0.03654/0.09373, loss_grounding_dice_8: 0.34785/0.20150, loss_grounding_ce_8: 0.33854/0.43695, loss_mask_ce_9: 4.99396/3.72008, loss_mask_bce_9: 0.20485/0.39196, loss_mask_dice_9: 3.23159/1.90486, loss_spatial_bce_9: 0.18874/0.34140, loss_spatial_dice_9: 0.91261/0.82823, loss_spatial_ce_9: 1.83255/1.55373, loss_grounding_bce_9: 0.03417/0.10497, loss_grounding_dice_9: 0.39369/0.28128, loss_grounding_ce_9: 0.55920/0.73951] items per batch[64] items per second[0.22] total items[486400] mini batches[  7600] memory[7341] epoch remaining[1:14:53]
INFO:trainer.default_trainer:epochs[     4] optim steps[7700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.66229/0.91905, loss_mask_bce_0: 0.44779/0.33649, loss_mask_dice_0: 1.00272/1.16736, loss_spatial_bce_0: 0.10397/0.09694, loss_spatial_dice_0: 0.23057/0.23286, loss_spatial_ce_0: 0.00891/0.10794, loss_grounding_bce_0: 0.03315/0.08642, loss_grounding_dice_0: 0.23328/0.17918, loss_grounding_ce_0: 0.21541/0.27948, loss_mask_ce_1: 1.67454/0.91954, loss_mask_bce_1: 0.45731/0.33714, loss_mask_dice_1: 0.98550/1.17512, loss_spatial_bce_1: 0.10646/0.09804, loss_spatial_dice_1: 0.24952/0.23748, loss_spatial_ce_1: 0.01003/0.11356, loss_grounding_bce_1: 0.03109/0.08650, loss_grounding_dice_1: 0.20451/0.17985, loss_grounding_ce_1: 0.26991/0.28162, loss_mask_ce_2: 1.65145/0.92588, loss_mask_bce_2: 0.45894/0.33714, loss_mask_dice_2: 1.04501/1.17338, loss_spatial_bce_2: 0.10565/0.09752, loss_spatial_dice_2: 0.24083/0.23846, loss_spatial_ce_2: 0.01252/0.11898, loss_grounding_bce_2: 0.03278/0.08643, loss_grounding_dice_2: 0.21156/0.17941, loss_grounding_ce_2: 0.26241/0.28416, loss_mask_ce_3: 1.67232/0.93043, loss_mask_bce_3: 0.44049/0.33797, loss_mask_dice_3: 0.98183/1.17087, loss_spatial_bce_3: 0.10153/0.09864, loss_spatial_dice_3: 0.22193/0.24017, loss_spatial_ce_3: 0.02092/0.12503, loss_grounding_bce_3: 0.03242/0.08660, loss_grounding_dice_3: 0.20618/0.17900, loss_grounding_ce_3: 0.27357/0.28596, loss_mask_ce_4: 1.65599/0.92981, loss_mask_bce_4: 0.45006/0.33917, loss_mask_dice_4: 0.98300/1.19147, loss_spatial_bce_4: 0.11160/0.10200, loss_spatial_dice_4: 0.24689/0.24708, loss_spatial_ce_4: 0.05306/0.14062, loss_grounding_bce_4: 0.03632/0.08718, loss_grounding_dice_4: 0.21370/0.18186, loss_grounding_ce_4: 0.26464/0.28861, loss_mask_ce_5: 1.75258/0.94231, loss_mask_bce_5: 0.45347/0.34142, loss_mask_dice_5: 0.95832/1.19516, loss_spatial_bce_5: 0.11185/0.10295, loss_spatial_dice_5: 0.22652/0.25039, loss_spatial_ce_5: 0.04958/0.15329, loss_grounding_bce_5: 0.03638/0.08763, loss_grounding_dice_5: 0.22197/0.18324, loss_grounding_ce_5: 0.24729/0.29915, loss_mask_ce_6: 1.75205/0.97830, loss_mask_bce_6: 0.47116/0.34403, loss_mask_dice_6: 1.01204/1.19976, loss_spatial_bce_6: 0.10633/0.10782, loss_spatial_dice_6: 0.23129/0.25388, loss_spatial_ce_6: 0.06950/0.17352, loss_grounding_bce_6: 0.03698/0.08856, loss_grounding_dice_6: 0.21203/0.18325, loss_grounding_ce_6: 0.25987/0.32176, loss_mask_ce_7: 1.71472/1.02113, loss_mask_bce_7: 0.40937/0.35197, loss_mask_dice_7: 0.93494/1.25571, loss_spatial_bce_7: 0.11597/0.11822, loss_spatial_dice_7: 0.28089/0.28045, loss_spatial_ce_7: 0.25360/0.21455, loss_grounding_bce_7: 0.03088/0.09027, loss_grounding_dice_7: 0.19228/0.19029, loss_grounding_ce_7: 0.23349/0.36330, loss_mask_ce_8: 2.04705/1.13404, loss_mask_bce_8: 0.49850/0.36508, loss_mask_dice_8: 1.08934/1.32933, loss_spatial_bce_8: 0.14212/0.13888, loss_spatial_dice_8: 0.31428/0.32111, loss_spatial_ce_8: 0.28349/0.26639, loss_grounding_bce_8: 0.02479/0.09383, loss_grounding_dice_8: 0.24845/0.20140, loss_grounding_ce_8: 0.34855/0.43558, loss_mask_ce_9: 5.12860/3.71819, loss_mask_bce_9: 0.55259/0.39209, loss_mask_dice_9: 1.53978/1.90392, loss_spatial_bce_9: 0.56666/0.34164, loss_spatial_dice_9: 0.93594/0.82827, loss_spatial_ce_9: 2.06735/1.55263, loss_grounding_bce_9: 0.05270/0.10513, loss_grounding_dice_9: 0.36939/0.28110, loss_grounding_ce_9: 0.44237/0.73735] items per batch[64] items per second[0.22] total items[492800] mini batches[  7700] memory[7341] epoch remaining[1:09:41]
INFO:trainer.default_trainer:epochs[     4] optim steps[7800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.45200/0.91912, loss_mask_bce_0: 0.70814/0.33660, loss_mask_dice_0: 1.81704/1.16654, loss_spatial_bce_0: 0.08001/0.09687, loss_spatial_dice_0: 0.29300/0.23250, loss_spatial_ce_0: 0.11342/0.10741, loss_grounding_bce_0: 0.23652/0.08648, loss_grounding_dice_0: 0.24393/0.17894, loss_grounding_ce_0: 0.39574/0.27931, loss_mask_ce_1: 1.45365/0.91987, loss_mask_bce_1: 0.73114/0.33720, loss_mask_dice_1: 2.04663/1.17414, loss_spatial_bce_1: 0.08509/0.09797, loss_spatial_dice_1: 0.30287/0.23713, loss_spatial_ce_1: 0.48077/0.11315, loss_grounding_bce_1: 0.21394/0.08656, loss_grounding_dice_1: 0.24592/0.17961, loss_grounding_ce_1: 0.45454/0.28152, loss_mask_ce_2: 1.43279/0.92602, loss_mask_bce_2: 0.72755/0.33725, loss_mask_dice_2: 1.93332/1.17246, loss_spatial_bce_2: 0.09473/0.09746, loss_spatial_dice_2: 0.33569/0.23811, loss_spatial_ce_2: 0.16147/0.11835, loss_grounding_bce_2: 0.25792/0.08649, loss_grounding_dice_2: 0.24740/0.17918, loss_grounding_ce_2: 0.73353/0.28420, loss_mask_ce_3: 1.35971/0.93071, loss_mask_bce_3: 0.75714/0.33812, loss_mask_dice_3: 2.01568/1.17004, loss_spatial_bce_3: 0.07560/0.09857, loss_spatial_dice_3: 0.31000/0.23979, loss_spatial_ce_3: 0.20085/0.12439, loss_grounding_bce_3: 0.26437/0.08666, loss_grounding_dice_3: 0.25727/0.17878, loss_grounding_ce_3: 0.70358/0.28583, loss_mask_ce_4: 1.44107/0.92999, loss_mask_bce_4: 0.73552/0.33931, loss_mask_dice_4: 1.84298/1.19070, loss_spatial_bce_4: 0.08088/0.10198, loss_spatial_dice_4: 0.34499/0.24672, loss_spatial_ce_4: 0.14516/0.14009, loss_grounding_bce_4: 0.03511/0.08721, loss_grounding_dice_4: 0.15009/0.18158, loss_grounding_ce_4: 2.58901/0.28883, loss_mask_ce_5: 1.43688/0.94258, loss_mask_bce_5: 0.68013/0.34154, loss_mask_dice_5: 2.22501/1.19439, loss_spatial_bce_5: 0.05984/0.10290, loss_spatial_dice_5: 0.32580/0.25002, loss_spatial_ce_5: 0.27907/0.15273, loss_grounding_bce_5: 0.03740/0.08768, loss_grounding_dice_5: 0.15537/0.18304, loss_grounding_ce_5: 2.52856/0.29924, loss_mask_ce_6: 1.52724/0.97877, loss_mask_bce_6: 0.66652/0.34414, loss_mask_dice_6: 1.91979/1.19882, loss_spatial_bce_6: 0.05251/0.10781, loss_spatial_dice_6: 0.29733/0.25352, loss_spatial_ce_6: 0.29734/0.17291, loss_grounding_bce_6: 0.25323/0.08860, loss_grounding_dice_6: 0.23847/0.18298, loss_grounding_ce_6: 0.88523/0.32145, loss_mask_ce_7: 1.43645/1.02148, loss_mask_bce_7: 0.67464/0.35205, loss_mask_dice_7: 2.05004/1.25484, loss_spatial_bce_7: 0.10571/0.11824, loss_spatial_dice_7: 0.41264/0.28013, loss_spatial_ce_7: 0.28765/0.21398, loss_grounding_bce_7: 0.20106/0.09030, loss_grounding_dice_7: 0.24465/0.19007, loss_grounding_ce_7: 0.83064/0.36313, loss_mask_ce_8: 1.64192/1.13385, loss_mask_bce_8: 0.67872/0.36521, loss_mask_dice_8: 2.06342/1.32854, loss_spatial_bce_8: 0.18593/0.13882, loss_spatial_dice_8: 0.41993/0.32076, loss_spatial_ce_8: 0.15930/0.26621, loss_grounding_bce_8: 0.27551/0.09389, loss_grounding_dice_8: 0.24706/0.20118, loss_grounding_ce_8: 0.69271/0.43539, loss_mask_ce_9: 3.97478/3.71755, loss_mask_bce_9: 0.75646/0.39229, loss_mask_dice_9: 3.09087/1.90313, loss_spatial_bce_9: 0.19172/0.34199, loss_spatial_dice_9: 0.85041/0.82818, loss_spatial_ce_9: 1.31231/1.55173, loss_grounding_bce_9: 0.09700/0.10520, loss_grounding_dice_9: 0.24329/0.28099, loss_grounding_ce_9: 1.70459/0.73697] items per batch[64] items per second[0.22] total items[499200] mini batches[  7800] memory[7341] epoch remaining[1:04:43]
INFO:trainer.default_trainer:epochs[     4] optim steps[7900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.76193/0.91925, loss_mask_bce_0: 0.54928/0.33694, loss_mask_dice_0: 1.25784/1.16940, loss_spatial_bce_0: 0.13353/0.09688, loss_spatial_dice_0: 0.21034/0.23250, loss_spatial_ce_0: 0.00240/0.10698, loss_grounding_bce_0: 0.23406/0.08649, loss_grounding_dice_0: 0.32103/0.17911, loss_grounding_ce_0: 0.06681/0.28043, loss_mask_ce_1: 0.94414/0.92000, loss_mask_bce_1: 0.55921/0.33751, loss_mask_dice_1: 1.18544/1.17700, loss_spatial_bce_1: 0.13971/0.09799, loss_spatial_dice_1: 0.23265/0.23712, loss_spatial_ce_1: 0.00079/0.11278, loss_grounding_bce_1: 0.24258/0.08657, loss_grounding_dice_1: 0.32902/0.17982, loss_grounding_ce_1: 0.07786/0.28228, loss_mask_ce_2: 0.96938/0.92619, loss_mask_bce_2: 0.55320/0.33758, loss_mask_dice_2: 1.21684/1.17517, loss_spatial_bce_2: 0.14693/0.09749, loss_spatial_dice_2: 0.21771/0.23807, loss_spatial_ce_2: 0.00286/0.11803, loss_grounding_bce_2: 0.22881/0.08652, loss_grounding_dice_2: 0.32753/0.17943, loss_grounding_ce_2: 0.07060/0.28487, loss_mask_ce_3: 0.99742/0.93129, loss_mask_bce_3: 0.54285/0.33844, loss_mask_dice_3: 1.20579/1.17271, loss_spatial_bce_3: 0.14452/0.09860, loss_spatial_dice_3: 0.23230/0.23978, loss_spatial_ce_3: 0.00742/0.12393, loss_grounding_bce_3: 0.22657/0.08668, loss_grounding_dice_3: 0.32206/0.17898, loss_grounding_ce_3: 0.07648/0.28658, loss_mask_ce_4: 1.05414/0.93014, loss_mask_bce_4: 0.58229/0.33963, loss_mask_dice_4: 1.14732/1.19360, loss_spatial_bce_4: 0.14963/0.10199, loss_spatial_dice_4: 0.21627/0.24672, loss_spatial_ce_4: 0.02477/0.13963, loss_grounding_bce_4: 0.25414/0.08722, loss_grounding_dice_4: 0.31465/0.18171, loss_grounding_ce_4: 0.06698/0.28939, loss_mask_ce_5: 0.94525/0.94275, loss_mask_bce_5: 0.57130/0.34188, loss_mask_dice_5: 1.19366/1.19749, loss_spatial_bce_5: 0.13782/0.10287, loss_spatial_dice_5: 0.20438/0.24998, loss_spatial_ce_5: 0.02679/0.15214, loss_grounding_bce_5: 0.25195/0.08769, loss_grounding_dice_5: 0.33223/0.18323, loss_grounding_ce_5: 0.05888/0.29985, loss_mask_ce_6: 0.95312/0.97890, loss_mask_bce_6: 0.55594/0.34446, loss_mask_dice_6: 1.10566/1.20166, loss_spatial_bce_6: 0.15553/0.10778, loss_spatial_dice_6: 0.21697/0.25348, loss_spatial_ce_6: 0.06547/0.17234, loss_grounding_bce_6: 0.23169/0.08861, loss_grounding_dice_6: 0.31174/0.18326, loss_grounding_ce_6: 0.10430/0.32175, loss_mask_ce_7: 0.93421/1.02175, loss_mask_bce_7: 0.57193/0.35233, loss_mask_dice_7: 1.19954/1.25807, loss_spatial_bce_7: 0.14780/0.11825, loss_spatial_dice_7: 0.27722/0.28007, loss_spatial_ce_7: 0.14793/0.21341, loss_grounding_bce_7: 0.23941/0.09031, loss_grounding_dice_7: 0.29561/0.19031, loss_grounding_ce_7: 0.14752/0.36310, loss_mask_ce_8: 1.11330/1.13400, loss_mask_bce_8: 0.59564/0.36554, loss_mask_dice_8: 1.35196/1.33175, loss_spatial_bce_8: 0.19625/0.13876, loss_spatial_dice_8: 0.28607/0.32067, loss_spatial_ce_8: 0.15502/0.26583, loss_grounding_bce_8: 0.23797/0.09387, loss_grounding_dice_8: 0.31705/0.20128, loss_grounding_ce_8: 0.19349/0.43542, loss_mask_ce_9: 3.29858/3.71833, loss_mask_bce_9: 0.66959/0.39264, loss_mask_dice_9: 1.98712/1.90746, loss_spatial_bce_9: 0.44646/0.34176, loss_spatial_dice_9: 0.72374/0.82816, loss_spatial_ce_9: 1.05689/1.55138, loss_grounding_bce_9: 0.30526/0.10515, loss_grounding_dice_9: 0.44378/0.28126, loss_grounding_ce_9: 0.46038/0.73594] items per batch[64] items per second[0.23] total items[505600] mini batches[  7900] memory[7341] epoch remaining[0:59:36]
INFO:trainer.default_trainer:epochs[     4] optim steps[8000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18587/0.91921, loss_mask_bce_0: 0.22405/0.33655, loss_mask_dice_0: 0.86595/1.16829, loss_spatial_bce_0: 0.06919/0.09668, loss_spatial_dice_0: 0.21020/0.23224, loss_spatial_ce_0: 0.04308/0.10646, loss_grounding_bce_0: 0.27731/0.08649, loss_grounding_dice_0: 0.36309/0.17898, loss_grounding_ce_0: 0.09887/0.28006, loss_mask_ce_1: 1.04139/0.91989, loss_mask_bce_1: 0.18083/0.33712, loss_mask_dice_1: 0.76010/1.17605, loss_spatial_bce_1: 0.08204/0.09779, loss_spatial_dice_1: 0.23869/0.23688, loss_spatial_ce_1: 0.05975/0.11217, loss_grounding_bce_1: 0.27716/0.08656, loss_grounding_dice_1: 0.36018/0.17969, loss_grounding_ce_1: 0.07207/0.28186, loss_mask_ce_2: 1.12272/0.92637, loss_mask_bce_2: 0.19962/0.33715, loss_mask_dice_2: 0.75386/1.17438, loss_spatial_bce_2: 0.08522/0.09728, loss_spatial_dice_2: 0.23239/0.23780, loss_spatial_ce_2: 0.23088/0.11740, loss_grounding_bce_2: 0.29478/0.08653, loss_grounding_dice_2: 0.33334/0.17925, loss_grounding_ce_2: 0.06393/0.28463, loss_mask_ce_3: 1.02056/0.93136, loss_mask_bce_3: 0.20525/0.33804, loss_mask_dice_3: 0.84233/1.17170, loss_spatial_bce_3: 0.07727/0.09838, loss_spatial_dice_3: 0.24841/0.23948, loss_spatial_ce_3: 0.04625/0.12325, loss_grounding_bce_3: 0.34222/0.08674, loss_grounding_dice_3: 0.50604/0.17888, loss_grounding_ce_3: 0.62282/0.28595, loss_mask_ce_4: 1.21849/0.93018, loss_mask_bce_4: 0.20573/0.33926, loss_mask_dice_4: 0.82403/1.19260, loss_spatial_bce_4: 0.08787/0.10180, loss_spatial_dice_4: 0.25952/0.24645, loss_spatial_ce_4: 0.07682/0.13903, loss_grounding_bce_4: 0.35680/0.08727, loss_grounding_dice_4: 0.48822/0.18160, loss_grounding_ce_4: 0.69773/0.28894, loss_mask_ce_5: 1.05906/0.94285, loss_mask_bce_5: 0.18420/0.34147, loss_mask_dice_5: 0.84613/1.19648, loss_spatial_bce_5: 0.07981/0.10268, loss_spatial_dice_5: 0.27614/0.24970, loss_spatial_ce_5: 0.13014/0.15152, loss_grounding_bce_5: 0.36568/0.08772, loss_grounding_dice_5: 0.56040/0.18310, loss_grounding_ce_5: 0.69024/0.29959, loss_mask_ce_6: 1.09688/0.97899, loss_mask_bce_6: 0.16430/0.34405, loss_mask_dice_6: 0.58620/1.20074, loss_spatial_bce_6: 0.07677/0.10762, loss_spatial_dice_6: 0.22646/0.25320, loss_spatial_ce_6: 0.11728/0.17174, loss_grounding_bce_6: 0.28842/0.08863, loss_grounding_dice_6: 0.46116/0.18311, loss_grounding_ce_6: 0.53589/0.32181, loss_mask_ce_7: 1.02019/1.02210, loss_mask_bce_7: 0.17133/0.35190, loss_mask_dice_7: 0.73244/1.25684, loss_spatial_bce_7: 0.09432/0.11804, loss_spatial_dice_7: 0.30483/0.27980, loss_spatial_ce_7: 0.13697/0.21266, loss_grounding_bce_7: 0.31212/0.09034, loss_grounding_dice_7: 0.51504/0.19019, loss_grounding_ce_7: 0.51081/0.36277, loss_mask_ce_8: 1.20520/1.13425, loss_mask_bce_8: 0.19493/0.36520, loss_mask_dice_8: 0.72094/1.33068, loss_spatial_bce_8: 0.09523/0.13858, loss_spatial_dice_8: 0.42617/0.32031, loss_spatial_ce_8: 0.55613/0.26529, loss_grounding_bce_8: 0.32310/0.09391, loss_grounding_dice_8: 0.46854/0.20111, loss_grounding_ce_8: 0.45903/0.43550, loss_mask_ce_9: 4.42807/3.71728, loss_mask_bce_9: 0.22188/0.39239, loss_mask_dice_9: 1.06509/1.90591, loss_spatial_bce_9: 0.17090/0.34159, loss_spatial_dice_9: 0.79052/0.82803, loss_spatial_ce_9: 1.50333/1.55023, loss_grounding_bce_9: 0.37476/0.10522, loss_grounding_dice_9: 0.50816/0.28119, loss_grounding_ce_9: 1.36010/0.73600] items per batch[64] items per second[0.22] total items[512000] mini batches[  8000] memory[7341] epoch remaining[0:54:40]
INFO:trainer.default_trainer:epochs[     4] optim steps[8100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.51781/0.91844, loss_mask_bce_0: 0.47722/0.33672, loss_mask_dice_0: 6.36426/1.17030, loss_spatial_bce_0: 0.03632/0.09667, loss_spatial_dice_0: 0.31016/0.23202, loss_spatial_ce_0: 0.02678/0.10603, loss_grounding_bce_0: 0.07552/0.08657, loss_grounding_dice_0: 0.12177/0.17910, loss_grounding_ce_0: 0.27452/0.27943, loss_mask_ce_1: 1.56454/0.91905, loss_mask_bce_1: 0.48046/0.33728, loss_mask_dice_1: 6.60593/1.17783, loss_spatial_bce_1: 0.03572/0.09776, loss_spatial_dice_1: 0.31027/0.23667, loss_spatial_ce_1: 0.06743/0.11168, loss_grounding_bce_1: 0.07491/0.08666, loss_grounding_dice_1: 0.14496/0.17984, loss_grounding_ce_1: 0.30403/0.28132, loss_mask_ce_2: 1.57539/0.92538, loss_mask_bce_2: 0.49081/0.33740, loss_mask_dice_2: 6.57374/1.17629, loss_spatial_bce_2: 0.03605/0.09727, loss_spatial_dice_2: 0.31249/0.23759, loss_spatial_ce_2: 0.03641/0.11692, loss_grounding_bce_2: 0.07566/0.08662, loss_grounding_dice_2: 0.12872/0.17935, loss_grounding_ce_2: 0.18028/0.28418, loss_mask_ce_3: 1.55499/0.93053, loss_mask_bce_3: 0.48364/0.33827, loss_mask_dice_3: 6.25140/1.17322, loss_spatial_bce_3: 0.03809/0.09837, loss_spatial_dice_3: 0.31389/0.23926, loss_spatial_ce_3: 0.03550/0.12271, loss_grounding_bce_3: 0.07312/0.08681, loss_grounding_dice_3: 0.13617/0.17907, loss_grounding_ce_3: 0.13305/0.28561, loss_mask_ce_4: 1.69958/0.92945, loss_mask_bce_4: 0.50095/0.33948, loss_mask_dice_4: 6.48974/1.19455, loss_spatial_bce_4: 0.03722/0.10180, loss_spatial_dice_4: 0.33257/0.24621, loss_spatial_ce_4: 0.13229/0.13861, loss_grounding_bce_4: 0.07880/0.08736, loss_grounding_dice_4: 0.11719/0.18175, loss_grounding_ce_4: 0.21244/0.28847, loss_mask_ce_5: 1.55207/0.94198, loss_mask_bce_5: 0.52557/0.34168, loss_mask_dice_5: 6.68479/1.19838, loss_spatial_bce_5: 0.04652/0.10265, loss_spatial_dice_5: 0.38101/0.24942, loss_spatial_ce_5: 0.10955/0.15112, loss_grounding_bce_5: 0.07899/0.08780, loss_grounding_dice_5: 0.14397/0.18317, loss_grounding_ce_5: 0.12707/0.29918, loss_mask_ce_6: 1.61081/0.97804, loss_mask_bce_6: 0.53132/0.34426, loss_mask_dice_6: 6.53334/1.20274, loss_spatial_bce_6: 0.05363/0.10759, loss_spatial_dice_6: 0.35803/0.25290, loss_spatial_ce_6: 0.07483/0.17130, loss_grounding_bce_6: 0.08464/0.08869, loss_grounding_dice_6: 0.16139/0.18319, loss_grounding_ce_6: 0.18471/0.32133, loss_mask_ce_7: 1.84173/1.02129, loss_mask_bce_7: 0.53090/0.35213, loss_mask_dice_7: 6.76363/1.25871, loss_spatial_bce_7: 0.04255/0.11798, loss_spatial_dice_7: 0.37706/0.27957, loss_spatial_ce_7: 0.14774/0.21200, loss_grounding_bce_7: 0.08164/0.09038, loss_grounding_dice_7: 0.14449/0.19026, loss_grounding_ce_7: 0.52540/0.36244, loss_mask_ce_8: 2.34354/1.13390, loss_mask_bce_8: 0.52894/0.36533, loss_mask_dice_8: 7.11971/1.33264, loss_spatial_bce_8: 0.05341/0.13851, loss_spatial_dice_8: 0.40899/0.32004, loss_spatial_ce_8: 0.33799/0.26479, loss_grounding_bce_8: 0.08110/0.09397, loss_grounding_dice_8: 0.16970/0.20121, loss_grounding_ce_8: 0.85670/0.43505, loss_mask_ce_9: 7.34251/3.71807, loss_mask_bce_9: 0.74206/0.39261, loss_mask_dice_9: 9.16453/1.90822, loss_spatial_bce_9: 0.12991/0.34156, loss_spatial_dice_9: 0.89161/0.82800, loss_spatial_ce_9: 1.86759/1.54965, loss_grounding_bce_9: 0.17806/0.10534, loss_grounding_dice_9: 0.25379/0.28117, loss_grounding_ce_9: 2.36270/0.73542] items per batch[64] items per second[0.22] total items[518400] mini batches[  8100] memory[7341] epoch remaining[0:49:57]
INFO:trainer.default_trainer:epochs[     4] optim steps[8200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49287/0.91790, loss_mask_bce_0: 0.15433/0.33694, loss_mask_dice_0: 0.21582/1.16864, loss_spatial_bce_0: 0.08468/0.09667, loss_spatial_dice_0: 0.10757/0.23181, loss_spatial_ce_0: 0.00081/0.10552, loss_grounding_bce_0: 0.06051/0.08662, loss_grounding_dice_0: 0.13607/0.17908, loss_grounding_ce_0: 0.19428/0.28081, loss_mask_ce_1: 1.40336/0.91872, loss_mask_bce_1: 0.14935/0.33747, loss_mask_dice_1: 0.21005/1.17636, loss_spatial_bce_1: 0.07802/0.09777, loss_spatial_dice_1: 0.08624/0.23641, loss_spatial_ce_1: 0.00049/0.11136, loss_grounding_bce_1: 0.05997/0.08669, loss_grounding_dice_1: 0.14384/0.17979, loss_grounding_ce_1: 0.17450/0.28243, loss_mask_ce_2: 1.36723/0.92491, loss_mask_bce_2: 0.18501/0.33759, loss_mask_dice_2: 0.24174/1.17477, loss_spatial_bce_2: 0.06896/0.09727, loss_spatial_dice_2: 0.07676/0.23733, loss_spatial_ce_2: 0.00096/0.11650, loss_grounding_bce_2: 0.06046/0.08666, loss_grounding_dice_2: 0.15505/0.17928, loss_grounding_ce_2: 0.19978/0.28542, loss_mask_ce_3: 1.38228/0.93004, loss_mask_bce_3: 0.18299/0.33855, loss_mask_dice_3: 0.23896/1.17178, loss_spatial_bce_3: 0.07756/0.09837, loss_spatial_dice_3: 0.08796/0.23898, loss_spatial_ce_3: 0.00422/0.12224, loss_grounding_bce_3: 0.05891/0.08685, loss_grounding_dice_3: 0.15681/0.17901, loss_grounding_ce_3: 0.19962/0.28685, loss_mask_ce_4: 1.20203/0.92896, loss_mask_bce_4: 0.16363/0.33966, loss_mask_dice_4: 0.21163/1.19301, loss_spatial_bce_4: 0.07709/0.10180, loss_spatial_dice_4: 0.08720/0.24593, loss_spatial_ce_4: 0.00402/0.13819, loss_grounding_bce_4: 0.05635/0.08741, loss_grounding_dice_4: 0.12973/0.18179, loss_grounding_ce_4: 0.15851/0.28896, loss_mask_ce_5: 1.47848/0.94154, loss_mask_bce_5: 0.20054/0.34193, loss_mask_dice_5: 0.22735/1.19688, loss_spatial_bce_5: 0.07391/0.10266, loss_spatial_dice_5: 0.09130/0.24912, loss_spatial_ce_5: 0.00664/0.15072, loss_grounding_bce_5: 0.05955/0.08784, loss_grounding_dice_5: 0.12995/0.18314, loss_grounding_ce_5: 0.14763/0.29955, loss_mask_ce_6: 1.49434/0.97766, loss_mask_bce_6: 0.21431/0.34452, loss_mask_dice_6: 0.22527/1.20145, loss_spatial_bce_6: 0.07867/0.10761, loss_spatial_dice_6: 0.08129/0.25262, loss_spatial_ce_6: 0.01873/0.17093, loss_grounding_bce_6: 0.06171/0.08871, loss_grounding_dice_6: 0.13431/0.18320, loss_grounding_ce_6: 0.13304/0.32191, loss_mask_ce_7: 1.50537/1.02097, loss_mask_bce_7: 0.19671/0.35244, loss_mask_dice_7: 0.24576/1.25733, loss_spatial_bce_7: 0.11024/0.11803, loss_spatial_dice_7: 0.12399/0.27929, loss_spatial_ce_7: 0.04921/0.21150, loss_grounding_bce_7: 0.05760/0.09044, loss_grounding_dice_7: 0.12416/0.19029, loss_grounding_ce_7: 0.23674/0.36262, loss_mask_ce_8: 1.67778/1.13418, loss_mask_bce_8: 0.29948/0.36566, loss_mask_dice_8: 0.28580/1.33129, loss_spatial_bce_8: 0.18333/0.13867, loss_spatial_dice_8: 0.18360/0.31976, loss_spatial_ce_8: 0.10824/0.26453, loss_grounding_bce_8: 0.05492/0.09404, loss_grounding_dice_8: 0.15970/0.20126, loss_grounding_ce_8: 0.34490/0.43598, loss_mask_ce_9: 4.24713/3.71738, loss_mask_bce_9: 0.27528/0.39291, loss_mask_dice_9: 1.52825/1.90621, loss_spatial_bce_9: 0.60441/0.34170, loss_spatial_dice_9: 0.71774/0.82800, loss_spatial_ce_9: 1.51012/1.54862, loss_grounding_bce_9: 0.06198/0.10537, loss_grounding_dice_9: 0.20316/0.28117, loss_grounding_ce_9: 0.35014/0.73692] items per batch[64] items per second[0.22] total items[524800] mini batches[  8200] memory[7341] epoch remaining[0:45:04]
INFO:trainer.default_trainer:epochs[     4] optim steps[8300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67560/0.91748, loss_mask_bce_0: 0.27723/0.33669, loss_mask_dice_0: 0.66902/1.16731, loss_spatial_bce_0: 0.10606/0.09654, loss_spatial_dice_0: 0.20844/0.23164, loss_spatial_ce_0: 0.10197/0.10500, loss_grounding_bce_0: 0.12897/0.08657, loss_grounding_dice_0: 0.16678/0.17904, loss_grounding_ce_0: 0.01961/0.28098, loss_mask_ce_1: 0.68628/0.91844, loss_mask_bce_1: 0.27797/0.33722, loss_mask_dice_1: 0.66880/1.17491, loss_spatial_bce_1: 0.11652/0.09762, loss_spatial_dice_1: 0.26804/0.23625, loss_spatial_ce_1: 0.04378/0.11088, loss_grounding_bce_1: 0.12312/0.08663, loss_grounding_dice_1: 0.16913/0.17976, loss_grounding_ce_1: 0.01459/0.28252, loss_mask_ce_2: 0.66880/0.92452, loss_mask_bce_2: 0.27305/0.33734, loss_mask_dice_2: 0.68333/1.17350, loss_spatial_bce_2: 0.10736/0.09711, loss_spatial_dice_2: 0.24412/0.23712, loss_spatial_ce_2: 0.05187/0.11596, loss_grounding_bce_2: 0.11834/0.08661, loss_grounding_dice_2: 0.17463/0.17928, loss_grounding_ce_2: 0.01114/0.28551, loss_mask_ce_3: 0.77422/0.92980, loss_mask_bce_3: 0.26359/0.33829, loss_mask_dice_3: 0.56617/1.17051, loss_spatial_bce_3: 0.10879/0.09820, loss_spatial_dice_3: 0.24053/0.23875, loss_spatial_ce_3: 0.13232/0.12159, loss_grounding_bce_3: 0.11981/0.08676, loss_grounding_dice_3: 0.17996/0.17900, loss_grounding_ce_3: 0.01572/0.28691, loss_mask_ce_4: 0.81238/0.92863, loss_mask_bce_4: 0.26643/0.33936, loss_mask_dice_4: 0.60578/1.19180, loss_spatial_bce_4: 0.11439/0.10164, loss_spatial_dice_4: 0.27021/0.24571, loss_spatial_ce_4: 0.04522/0.13759, loss_grounding_bce_4: 0.11747/0.08735, loss_grounding_dice_4: 0.18010/0.18176, loss_grounding_ce_4: 0.01002/0.28890, loss_mask_ce_5: 0.63453/0.94113, loss_mask_bce_5: 0.25861/0.34163, loss_mask_dice_5: 0.62672/1.19570, loss_spatial_bce_5: 0.10920/0.10251, loss_spatial_dice_5: 0.27594/0.24891, loss_spatial_ce_5: 0.05544/0.15014, loss_grounding_bce_5: 0.11685/0.08778, loss_grounding_dice_5: 0.18603/0.18315, loss_grounding_ce_5: 0.01625/0.29947, loss_mask_ce_6: 0.76520/0.97742, loss_mask_bce_6: 0.25815/0.34420, loss_mask_dice_6: 0.62246/1.20022, loss_spatial_bce_6: 0.11621/0.10744, loss_spatial_dice_6: 0.27093/0.25239, loss_spatial_ce_6: 0.07549/0.17057, loss_grounding_bce_6: 0.11829/0.08862, loss_grounding_dice_6: 0.17401/0.18313, loss_grounding_ce_6: 0.01543/0.32190, loss_mask_ce_7: 0.60677/1.02087, loss_mask_bce_7: 0.28349/0.35212, loss_mask_dice_7: 0.66365/1.25589, loss_spatial_bce_7: 0.12586/0.11783, loss_spatial_dice_7: 0.32973/0.27902, loss_spatial_ce_7: 0.15886/0.21091, loss_grounding_bce_7: 0.12214/0.09035, loss_grounding_dice_7: 0.18610/0.19027, loss_grounding_ce_7: 0.00747/0.36265, loss_mask_ce_8: 0.78232/1.13385, loss_mask_bce_8: 0.26772/0.36538, loss_mask_dice_8: 0.67059/1.32978, loss_spatial_bce_8: 0.23256/0.13850, loss_spatial_dice_8: 0.33064/0.31944, loss_spatial_ce_8: 0.44505/0.26401, loss_grounding_bce_8: 0.12506/0.09400, loss_grounding_dice_8: 0.17784/0.20131, loss_grounding_ce_8: 0.01004/0.43608, loss_mask_ce_9: 2.26043/3.71648, loss_mask_bce_9: 0.24033/0.39255, loss_mask_dice_9: 0.89960/1.90417, loss_spatial_bce_9: 0.49710/0.34146, loss_spatial_dice_9: 0.80902/0.82795, loss_spatial_ce_9: 1.20629/1.54846, loss_grounding_bce_9: 0.10331/0.10528, loss_grounding_dice_9: 0.22419/0.28101, loss_grounding_ce_9: 0.29578/0.73630] items per batch[64] items per second[0.23] total items[531200] mini batches[  8300] memory[7341] epoch remaining[0:40:04]
INFO:trainer.default_trainer:epochs[     4] optim steps[8400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.51787/0.91751, loss_mask_bce_0: 0.98027/0.33665, loss_mask_dice_0: 3.98807/1.16614, loss_spatial_bce_0: 0.15838/0.09651, loss_spatial_dice_0: 0.31328/0.23143, loss_spatial_ce_0: 0.03019/0.10461, loss_grounding_bce_0: 0.18640/0.08656, loss_grounding_dice_0: 0.24215/0.17894, loss_grounding_ce_0: 0.11114/0.28074, loss_mask_ce_1: 1.61447/0.91864, loss_mask_bce_1: 0.97532/0.33719, loss_mask_dice_1: 4.02705/1.17360, loss_spatial_bce_1: 0.15730/0.09759, loss_spatial_dice_1: 0.32955/0.23602, loss_spatial_ce_1: 0.04362/0.11066, loss_grounding_bce_1: 0.18843/0.08662, loss_grounding_dice_1: 0.24587/0.17963, loss_grounding_ce_1: 0.09004/0.28225, loss_mask_ce_2: 1.60061/0.92464, loss_mask_bce_2: 1.01965/0.33730, loss_mask_dice_2: 4.02291/1.17227, loss_spatial_bce_2: 0.16703/0.09709, loss_spatial_dice_2: 0.32103/0.23686, loss_spatial_ce_2: 0.05958/0.11576, loss_grounding_bce_2: 0.18600/0.08661, loss_grounding_dice_2: 0.24076/0.17925, loss_grounding_ce_2: 0.13133/0.28538, loss_mask_ce_3: 1.47030/0.93003, loss_mask_bce_3: 1.01433/0.33824, loss_mask_dice_3: 4.02079/1.16927, loss_spatial_bce_3: 0.16137/0.09817, loss_spatial_dice_3: 0.33181/0.23851, loss_spatial_ce_3: 0.06721/0.12125, loss_grounding_bce_3: 0.18744/0.08675, loss_grounding_dice_3: 0.23553/0.17894, loss_grounding_ce_3: 0.11379/0.28659, loss_mask_ce_4: 1.62002/0.92911, loss_mask_bce_4: 1.01632/0.33927, loss_mask_dice_4: 4.03545/1.19041, loss_spatial_bce_4: 0.16668/0.10163, loss_spatial_dice_4: 0.34956/0.24549, loss_spatial_ce_4: 0.12069/0.13728, loss_grounding_bce_4: 0.18315/0.08734, loss_grounding_dice_4: 0.24587/0.18168, loss_grounding_ce_4: 0.08382/0.28874, loss_mask_ce_5: 1.73893/0.94169, loss_mask_bce_5: 1.01055/0.34162, loss_mask_dice_5: 3.98701/1.19454, loss_spatial_bce_5: 0.16793/0.10248, loss_spatial_dice_5: 0.35330/0.24869, loss_spatial_ce_5: 0.18462/0.14975, loss_grounding_bce_5: 0.19322/0.08779, loss_grounding_dice_5: 0.25580/0.18305, loss_grounding_ce_5: 0.15817/0.29919, loss_mask_ce_6: 1.59721/0.97802, loss_mask_bce_6: 1.06805/0.34420, loss_mask_dice_6: 4.00402/1.19917, loss_spatial_bce_6: 0.19381/0.10747, loss_spatial_dice_6: 0.36992/0.25217, loss_spatial_ce_6: 0.17360/0.17015, loss_grounding_bce_6: 0.17956/0.08862, loss_grounding_dice_6: 0.22535/0.18307, loss_grounding_ce_6: 0.19044/0.32140, loss_mask_ce_7: 1.70685/1.02148, loss_mask_bce_7: 0.93317/0.35204, loss_mask_dice_7: 4.10344/1.25458, loss_spatial_bce_7: 0.19517/0.11781, loss_spatial_dice_7: 0.39377/0.27879, loss_spatial_ce_7: 0.16757/0.21052, loss_grounding_bce_7: 0.18522/0.09035, loss_grounding_dice_7: 0.24123/0.19013, loss_grounding_ce_7: 0.06396/0.36243, loss_mask_ce_8: 2.07279/1.13475, loss_mask_bce_8: 0.96492/0.36536, loss_mask_dice_8: 4.46982/1.32885, loss_spatial_bce_8: 0.22020/0.13843, loss_spatial_dice_8: 0.39780/0.31922, loss_spatial_ce_8: 0.19017/0.26355, loss_grounding_bce_8: 0.17642/0.09401, loss_grounding_dice_8: 0.22764/0.20125, loss_grounding_ce_8: 0.16766/0.43530, loss_mask_ce_9: 5.38651/3.71623, loss_mask_bce_9: 1.05910/0.39262, loss_mask_dice_9: 6.58398/1.90322, loss_spatial_bce_9: 0.24481/0.34153, loss_spatial_dice_9: 0.94514/0.82787, loss_spatial_ce_9: 1.34248/1.54764, loss_grounding_bce_9: 0.20741/0.10526, loss_grounding_dice_9: 0.26773/0.28093, loss_grounding_ce_9: 0.39682/0.73509] items per batch[64] items per second[0.23] total items[537600] mini batches[  8400] memory[7341] epoch remaining[0:35:12]
INFO:trainer.default_trainer:epochs[     4] optim steps[8500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07645/0.91804, loss_mask_bce_0: 0.11831/0.33689, loss_mask_dice_0: 0.32012/1.16544, loss_spatial_bce_0: 0.04635/0.09650, loss_spatial_dice_0: 0.11121/0.23129, loss_spatial_ce_0: 0.10829/0.10419, loss_grounding_bce_0: 0.07362/0.08658, loss_grounding_dice_0: 0.08759/0.17884, loss_grounding_ce_0: 0.15395/0.28102, loss_mask_ce_1: 0.07367/0.91920, loss_mask_bce_1: 0.11722/0.33743, loss_mask_dice_1: 0.30135/1.17312, loss_spatial_bce_1: 0.05202/0.09758, loss_spatial_dice_1: 0.11470/0.23588, loss_spatial_ce_1: 0.07382/0.11030, loss_grounding_bce_1: 0.07949/0.08665, loss_grounding_dice_1: 0.09575/0.17952, loss_grounding_ce_1: 0.22808/0.28227, loss_mask_ce_2: 0.08181/0.92504, loss_mask_bce_2: 0.11355/0.33752, loss_mask_dice_2: 0.34816/1.17163, loss_spatial_bce_2: 0.04644/0.09709, loss_spatial_dice_2: 0.11546/0.23671, loss_spatial_ce_2: 0.05519/0.11529, loss_grounding_bce_2: 0.07472/0.08664, loss_grounding_dice_2: 0.09211/0.17917, loss_grounding_ce_2: 0.41843/0.28549, loss_mask_ce_3: 0.12428/0.93062, loss_mask_bce_3: 0.11509/0.33846, loss_mask_dice_3: 0.48382/1.16866, loss_spatial_bce_3: 0.05152/0.09816, loss_spatial_dice_3: 0.11746/0.23836, loss_spatial_ce_3: 0.07420/0.12075, loss_grounding_bce_3: 0.06719/0.08676, loss_grounding_dice_3: 0.09296/0.17882, loss_grounding_ce_3: 0.76681/0.28689, loss_mask_ce_4: 0.14020/0.92955, loss_mask_bce_4: 0.12018/0.33962, loss_mask_dice_4: 0.51223/1.19005, loss_spatial_bce_4: 0.04639/0.10163, loss_spatial_dice_4: 0.11765/0.24538, loss_spatial_ce_4: 0.12424/0.13686, loss_grounding_bce_4: 0.07752/0.08738, loss_grounding_dice_4: 0.09919/0.18159, loss_grounding_ce_4: 0.24593/0.28903, loss_mask_ce_5: 0.09214/0.94250, loss_mask_bce_5: 0.12373/0.34201, loss_mask_dice_5: 0.37101/1.19405, loss_spatial_bce_5: 0.04126/0.10248, loss_spatial_dice_5: 0.10065/0.24856, loss_spatial_ce_5: 0.15411/0.14930, loss_grounding_bce_5: 0.09391/0.08785, loss_grounding_dice_5: 0.09874/0.18297, loss_grounding_ce_5: 0.15058/0.29937, loss_mask_ce_6: 0.21953/0.97828, loss_mask_bce_6: 0.11357/0.34451, loss_mask_dice_6: 0.49447/1.19854, loss_spatial_bce_6: 0.04444/0.10754, loss_spatial_dice_6: 0.10282/0.25204, loss_spatial_ce_6: 0.16723/0.16990, loss_grounding_bce_6: 0.06818/0.08872, loss_grounding_dice_6: 0.08832/0.18302, loss_grounding_ce_6: 0.67348/0.32169, loss_mask_ce_7: 0.19810/1.02200, loss_mask_bce_7: 0.12271/0.35233, loss_mask_dice_7: 0.34496/1.25402, loss_spatial_bce_7: 0.05661/0.11782, loss_spatial_dice_7: 0.13639/0.27862, loss_spatial_ce_7: 0.25937/0.21022, loss_grounding_bce_7: 0.07504/0.09038, loss_grounding_dice_7: 0.08823/0.19006, loss_grounding_ce_7: 0.40891/0.36319, loss_mask_ce_8: 0.19487/1.13502, loss_mask_bce_8: 0.13061/0.36564, loss_mask_dice_8: 0.34745/1.32846, loss_spatial_bce_8: 0.13819/0.13844, loss_spatial_dice_8: 0.20539/0.31905, loss_spatial_ce_8: 0.20091/0.26326, loss_grounding_bce_8: 0.08446/0.09410, loss_grounding_dice_8: 0.09183/0.20115, loss_grounding_ce_8: 0.11025/0.43622, loss_mask_ce_9: 3.60847/3.71668, loss_mask_bce_9: 0.14770/0.39288, loss_mask_dice_9: 0.47462/1.90235, loss_spatial_bce_9: 0.32609/0.34156, loss_spatial_dice_9: 0.78259/0.82779, loss_spatial_ce_9: 1.39432/1.54642, loss_grounding_bce_9: 0.09599/0.10540, loss_grounding_dice_9: 0.14043/0.28090, loss_grounding_ce_9: 0.76217/0.73550] items per batch[64] items per second[0.22] total items[544000] mini batches[  8500] memory[7341] epoch remaining[0:30:27]
INFO:trainer.default_trainer:epochs[     4] optim steps[8600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25901/0.91736, loss_mask_bce_0: 0.49397/0.33695, loss_mask_dice_0: 2.23415/1.16475, loss_spatial_bce_0: 0.04292/0.09643, loss_spatial_dice_0: 0.25712/0.23106, loss_spatial_ce_0: 0.02488/0.10388, loss_grounding_bce_0: 0.00249/0.08651, loss_grounding_dice_0: 0.24236/0.17874, loss_grounding_ce_0: 1.59423/0.28056, loss_mask_ce_1: 1.36711/0.91861, loss_mask_bce_1: 0.51720/0.33751, loss_mask_dice_1: 2.17451/1.17228, loss_spatial_bce_1: 0.04234/0.09750, loss_spatial_dice_1: 0.29285/0.23564, loss_spatial_ce_1: 0.01929/0.11001, loss_grounding_bce_1: 0.00328/0.08658, loss_grounding_dice_1: 0.36907/0.17948, loss_grounding_ce_1: 1.94144/0.28194, loss_mask_ce_2: 1.52448/0.92440, loss_mask_bce_2: 0.49966/0.33761, loss_mask_dice_2: 2.25612/1.17092, loss_spatial_bce_2: 0.04565/0.09703, loss_spatial_dice_2: 0.27731/0.23646, loss_spatial_ce_2: 0.02170/0.11500, loss_grounding_bce_2: 0.00329/0.08657, loss_grounding_dice_2: 0.29027/0.17916, loss_grounding_ce_2: 1.84581/0.28511, loss_mask_ce_3: 1.47696/0.93012, loss_mask_bce_3: 0.46492/0.33855, loss_mask_dice_3: 2.04101/1.16785, loss_spatial_bce_3: 0.05188/0.09810, loss_spatial_dice_3: 0.28610/0.23811, loss_spatial_ce_3: 0.01443/0.12033, loss_grounding_bce_3: 0.00393/0.08669, loss_grounding_dice_3: 0.26818/0.17874, loss_grounding_ce_3: 1.86339/0.28646, loss_mask_ce_4: 1.74455/0.92886, loss_mask_bce_4: 0.50555/0.33973, loss_mask_dice_4: 2.51733/1.18930, loss_spatial_bce_4: 0.04468/0.10158, loss_spatial_dice_4: 0.30093/0.24515, loss_spatial_ce_4: 0.25396/0.13646, loss_grounding_bce_4: 0.00310/0.08732, loss_grounding_dice_4: 0.24393/0.18152, loss_grounding_ce_4: 2.59776/0.28858, loss_mask_ce_5: 1.45646/0.94194, loss_mask_bce_5: 0.50206/0.34206, loss_mask_dice_5: 2.25566/1.19345, loss_spatial_bce_5: 0.04037/0.10245, loss_spatial_dice_5: 0.30842/0.24830, loss_spatial_ce_5: 0.09597/0.14908, loss_grounding_bce_5: 0.00233/0.08783, loss_grounding_dice_5: 0.21942/0.18291, loss_grounding_ce_5: 2.55352/0.29900, loss_mask_ce_6: 1.32196/0.97763, loss_mask_bce_6: 0.57173/0.34457, loss_mask_dice_6: 2.68896/1.19784, loss_spatial_bce_6: 0.04868/0.10752, loss_spatial_dice_6: 0.29215/0.25181, loss_spatial_ce_6: 0.49873/0.16950, loss_grounding_bce_6: 0.00207/0.08868, loss_grounding_dice_6: 0.24903/0.18296, loss_grounding_ce_6: 2.27740/0.32103, loss_mask_ce_7: 1.37079/1.02135, loss_mask_bce_7: 0.51646/0.35242, loss_mask_dice_7: 2.47092/1.25337, loss_spatial_bce_7: 0.05549/0.11778, loss_spatial_dice_7: 0.30882/0.27834, loss_spatial_ce_7: 0.33574/0.20988, loss_grounding_bce_7: 0.00239/0.09033, loss_grounding_dice_7: 0.18092/0.19000, loss_grounding_ce_7: 2.54625/0.36270, loss_mask_ce_8: 1.90788/1.13431, loss_mask_bce_8: 0.54177/0.36566, loss_mask_dice_8: 2.55835/1.32787, loss_spatial_bce_8: 0.05939/0.13837, loss_spatial_dice_8: 0.43246/0.31885, loss_spatial_ce_8: 0.28523/0.26303, loss_grounding_bce_8: 0.00347/0.09403, loss_grounding_dice_8: 0.21057/0.20107, loss_grounding_ce_8: 2.90308/0.43570, loss_mask_ce_9: 4.72265/3.71569, loss_mask_bce_9: 0.56057/0.39292, loss_mask_dice_9: 3.72510/1.90173, loss_spatial_bce_9: 0.11937/0.34157, loss_spatial_dice_9: 0.83824/0.82769, loss_spatial_ce_9: 2.33060/1.54631, loss_grounding_bce_9: 0.00207/0.10534, loss_grounding_dice_9: 0.29340/0.28079, loss_grounding_ce_9: 1.56233/0.73393] items per batch[64] items per second[0.22] total items[550400] mini batches[  8600] memory[7341] epoch remaining[0:25:40]
INFO:trainer.default_trainer:epochs[     4] optim steps[8700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24314/0.91719, loss_mask_bce_0: 0.40606/0.33693, loss_mask_dice_0: 0.30727/1.16359, loss_spatial_bce_0: 0.15551/0.09646, loss_spatial_dice_0: 0.13060/0.23078, loss_spatial_ce_0: 0.00071/0.10351, loss_grounding_bce_0: 0.08851/0.08642, loss_grounding_dice_0: 0.10962/0.17858, loss_grounding_ce_0: 0.00082/0.28032, loss_mask_ce_1: 0.23040/0.91830, loss_mask_bce_1: 0.42428/0.33755, loss_mask_dice_1: 0.36922/1.17112, loss_spatial_bce_1: 0.17169/0.09752, loss_spatial_dice_1: 0.12401/0.23541, loss_spatial_ce_1: 0.00075/0.10967, loss_grounding_bce_1: 0.08692/0.08651, loss_grounding_dice_1: 0.11155/0.17934, loss_grounding_ce_1: 0.00051/0.28182, loss_mask_ce_2: 0.20682/0.92421, loss_mask_bce_2: 0.41737/0.33766, loss_mask_dice_2: 0.34572/1.16977, loss_spatial_bce_2: 0.16782/0.09706, loss_spatial_dice_2: 0.12119/0.23622, loss_spatial_ce_2: 0.00169/0.11450, loss_grounding_bce_2: 0.08845/0.08648, loss_grounding_dice_2: 0.09999/0.17897, loss_grounding_ce_2: 0.00058/0.28488, loss_mask_ce_3: 0.22537/0.93003, loss_mask_bce_3: 0.40561/0.33859, loss_mask_dice_3: 0.34526/1.16668, loss_spatial_bce_3: 0.16432/0.09812, loss_spatial_dice_3: 0.11940/0.23783, loss_spatial_ce_3: 0.00826/0.11989, loss_grounding_bce_3: 0.08890/0.08660, loss_grounding_dice_3: 0.11761/0.17861, loss_grounding_ce_3: 0.00072/0.28613, loss_mask_ce_4: 0.24050/0.92887, loss_mask_bce_4: 0.41872/0.33979, loss_mask_dice_4: 0.33227/1.18811, loss_spatial_bce_4: 0.17287/0.10158, loss_spatial_dice_4: 0.12693/0.24491, loss_spatial_ce_4: 0.01921/0.13599, loss_grounding_bce_4: 0.09290/0.08722, loss_grounding_dice_4: 0.11054/0.18138, loss_grounding_ce_4: 0.00147/0.28866, loss_mask_ce_5: 0.25890/0.94170, loss_mask_bce_5: 0.40810/0.34215, loss_mask_dice_5: 0.35879/1.19220, loss_spatial_bce_5: 0.16154/0.10244, loss_spatial_dice_5: 0.12532/0.24802, loss_spatial_ce_5: 0.02746/0.14874, loss_grounding_bce_5: 0.09213/0.08774, loss_grounding_dice_5: 0.09246/0.18275, loss_grounding_ce_5: 0.00164/0.29887, loss_mask_ce_6: 0.31448/0.97751, loss_mask_bce_6: 0.41325/0.34464, loss_mask_dice_6: 0.33463/1.19663, loss_spatial_bce_6: 0.18607/0.10754, loss_spatial_dice_6: 0.13411/0.25149, loss_spatial_ce_6: 0.05859/0.16918, loss_grounding_bce_6: 0.10106/0.08858, loss_grounding_dice_6: 0.10009/0.18279, loss_grounding_ce_6: 0.00215/0.32081, loss_mask_ce_7: 0.25676/1.02136, loss_mask_bce_7: 0.43504/0.35246, loss_mask_dice_7: 0.32930/1.25211, loss_spatial_bce_7: 0.18800/0.11777, loss_spatial_dice_7: 0.13368/0.27803, loss_spatial_ce_7: 0.04541/0.20937, loss_grounding_bce_7: 0.09770/0.09024, loss_grounding_dice_7: 0.11598/0.18982, loss_grounding_ce_7: 0.00255/0.36230, loss_mask_ce_8: 0.34826/1.13438, loss_mask_bce_8: 0.42695/0.36565, loss_mask_dice_8: 0.34588/1.32644, loss_spatial_bce_8: 0.21710/0.13841, loss_spatial_dice_8: 0.14963/0.31853, loss_spatial_ce_8: 0.10166/0.26271, loss_grounding_bce_8: 0.10136/0.09391, loss_grounding_dice_8: 0.11847/0.20092, loss_grounding_ce_8: 0.00300/0.43546, loss_mask_ce_9: 2.88530/3.71556, loss_mask_bce_9: 0.38275/0.39306, loss_mask_dice_9: 0.39761/1.90079, loss_spatial_bce_9: 0.41040/0.34167, loss_spatial_dice_9: 0.71740/0.82762, loss_spatial_ce_9: 0.96610/1.54535, loss_grounding_bce_9: 0.10563/0.10520, loss_grounding_dice_9: 0.14332/0.28062, loss_grounding_ce_9: 0.14854/0.73340] items per batch[64] items per second[0.22] total items[556800] mini batches[  8700] memory[7341] epoch remaining[0:20:52]
INFO:trainer.default_trainer:epochs[     4] optim steps[8800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60046/0.91730, loss_mask_bce_0: 0.42080/0.33735, loss_mask_dice_0: 1.77661/1.16458, loss_spatial_bce_0: 0.06958/0.09644, loss_spatial_dice_0: 0.27445/0.23079, loss_spatial_ce_0: 0.13547/0.10315, loss_grounding_bce_0: 0.15452/0.08657, loss_grounding_dice_0: 0.11012/0.17874, loss_grounding_ce_0: 0.00522/0.28020, loss_mask_ce_1: 0.62506/0.91860, loss_mask_bce_1: 0.40740/0.33794, loss_mask_dice_1: 1.71232/1.17222, loss_spatial_bce_1: 0.07063/0.09750, loss_spatial_dice_1: 0.28911/0.23538, loss_spatial_ce_1: 0.20450/0.10934, loss_grounding_bce_1: 0.15654/0.08667, loss_grounding_dice_1: 0.11047/0.17945, loss_grounding_ce_1: 0.00475/0.28178, loss_mask_ce_2: 0.66974/0.92431, loss_mask_bce_2: 0.40570/0.33798, loss_mask_dice_2: 1.73395/1.17090, loss_spatial_bce_2: 0.07403/0.09705, loss_spatial_dice_2: 0.28069/0.23620, loss_spatial_ce_2: 0.26748/0.11424, loss_grounding_bce_2: 0.16444/0.08660, loss_grounding_dice_2: 0.10995/0.17907, loss_grounding_ce_2: 0.00508/0.28494, loss_mask_ce_3: 0.82127/0.93025, loss_mask_bce_3: 0.43352/0.33894, loss_mask_dice_3: 1.75369/1.16766, loss_spatial_bce_3: 0.07715/0.09811, loss_spatial_dice_3: 0.28190/0.23777, loss_spatial_ce_3: 0.24767/0.11951, loss_grounding_bce_3: 0.16121/0.08674, loss_grounding_dice_3: 0.11052/0.17874, loss_grounding_ce_3: 0.00541/0.28607, loss_mask_ce_4: 0.62295/0.92893, loss_mask_bce_4: 0.42401/0.34020, loss_mask_dice_4: 1.73932/1.18906, loss_spatial_bce_4: 0.08633/0.10152, loss_spatial_dice_4: 0.31415/0.24488, loss_spatial_ce_4: 0.28840/0.13571, loss_grounding_bce_4: 0.16817/0.08737, loss_grounding_dice_4: 0.11372/0.18152, loss_grounding_ce_4: 0.00504/0.28885, loss_mask_ce_5: 0.61709/0.94187, loss_mask_bce_5: 0.43533/0.34256, loss_mask_dice_5: 1.83574/1.19343, loss_spatial_bce_5: 0.09144/0.10237, loss_spatial_dice_5: 0.31404/0.24798, loss_spatial_ce_5: 0.22821/0.14849, loss_grounding_bce_5: 0.16369/0.08789, loss_grounding_dice_5: 0.11972/0.18293, loss_grounding_ce_5: 0.00283/0.29885, loss_mask_ce_6: 0.60028/0.97792, loss_mask_bce_6: 0.43636/0.34507, loss_mask_dice_6: 1.71090/1.19782, loss_spatial_bce_6: 0.10542/0.10750, loss_spatial_dice_6: 0.34364/0.25144, loss_spatial_ce_6: 0.21930/0.16884, loss_grounding_bce_6: 0.18334/0.08871, loss_grounding_dice_6: 0.12568/0.18291, loss_grounding_ce_6: 0.00350/0.32077, loss_mask_ce_7: 0.80733/1.02164, loss_mask_bce_7: 0.43241/0.35286, loss_mask_dice_7: 1.86047/1.25323, loss_spatial_bce_7: 0.14146/0.11769, loss_spatial_dice_7: 0.37397/0.27801, loss_spatial_ce_7: 0.17261/0.20903, loss_grounding_bce_7: 0.16665/0.09035, loss_grounding_dice_7: 0.12478/0.18994, loss_grounding_ce_7: 0.00395/0.36205, loss_mask_ce_8: 0.85223/1.13444, loss_mask_bce_8: 0.42272/0.36610, loss_mask_dice_8: 1.81566/1.32774, loss_spatial_bce_8: 0.18366/0.13837, loss_spatial_dice_8: 0.41975/0.31849, loss_spatial_ce_8: 0.20610/0.26244, loss_grounding_bce_8: 0.14498/0.09405, loss_grounding_dice_8: 0.11850/0.20105, loss_grounding_ce_8: 0.00747/0.43539, loss_mask_ce_9: 3.54350/3.71478, loss_mask_bce_9: 0.42511/0.39351, loss_mask_dice_9: 2.24261/1.90270, loss_spatial_bce_9: 0.26794/0.34141, loss_spatial_dice_9: 0.93092/0.82771, loss_spatial_ce_9: 1.75237/1.54523, loss_grounding_bce_9: 0.15934/0.10531, loss_grounding_dice_9: 0.13271/0.28063, loss_grounding_ce_9: 0.05384/0.73259] items per batch[64] items per second[0.22] total items[563200] mini batches[  8800] memory[7341] epoch remaining[0:16:05]
INFO:trainer.default_trainer:epochs[     4] optim steps[8900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84121/0.91704, loss_mask_bce_0: 0.20498/0.33703, loss_mask_dice_0: 0.49088/1.16656, loss_spatial_bce_0: 0.05914/0.09618, loss_spatial_dice_0: 0.14394/0.23067, loss_spatial_ce_0: 0.07776/0.10265, loss_grounding_bce_0: 0.04499/0.08650, loss_grounding_dice_0: 0.13592/0.17869, loss_grounding_ce_0: 0.23403/0.27995, loss_mask_ce_1: 0.79224/0.91811, loss_mask_bce_1: 0.19429/0.33760, loss_mask_dice_1: 0.47903/1.17440, loss_spatial_bce_1: 0.05779/0.09723, loss_spatial_dice_1: 0.15144/0.23524, loss_spatial_ce_1: 0.06973/0.10896, loss_grounding_bce_1: 0.04885/0.08659, loss_grounding_dice_1: 0.12896/0.17944, loss_grounding_ce_1: 0.18854/0.28151, loss_mask_ce_2: 0.81731/0.92401, loss_mask_bce_2: 0.20191/0.33770, loss_mask_dice_2: 0.49503/1.17279, loss_spatial_bce_2: 0.05672/0.09680, loss_spatial_dice_2: 0.15587/0.23607, loss_spatial_ce_2: 0.06437/0.11386, loss_grounding_bce_2: 0.04672/0.08655, loss_grounding_dice_2: 0.12319/0.17904, loss_grounding_ce_2: 0.24928/0.28476, loss_mask_ce_3: 0.82739/0.93009, loss_mask_bce_3: 0.19785/0.33860, loss_mask_dice_3: 0.49553/1.16965, loss_spatial_bce_3: 0.06130/0.09787, loss_spatial_dice_3: 0.15751/0.23763, loss_spatial_ce_3: 0.10770/0.11904, loss_grounding_bce_3: 0.04746/0.08666, loss_grounding_dice_3: 0.11173/0.17873, loss_grounding_ce_3: 0.22565/0.28571, loss_mask_ce_4: 0.81373/0.92861, loss_mask_bce_4: 0.21697/0.33986, loss_mask_dice_4: 0.48721/1.19093, loss_spatial_bce_4: 0.06266/0.10129, loss_spatial_dice_4: 0.17151/0.24477, loss_spatial_ce_4: 0.08978/0.13535, loss_grounding_bce_4: 0.05096/0.08730, loss_grounding_dice_4: 0.13852/0.18151, loss_grounding_ce_4: 0.25936/0.28846, loss_mask_ce_5: 0.93163/0.94169, loss_mask_bce_5: 0.18629/0.34227, loss_mask_dice_5: 0.52040/1.19527, loss_spatial_bce_5: 0.06455/0.10213, loss_spatial_dice_5: 0.17448/0.24785, loss_spatial_ce_5: 0.10291/0.14815, loss_grounding_bce_5: 0.04676/0.08784, loss_grounding_dice_5: 0.11496/0.18292, loss_grounding_ce_5: 0.24230/0.29844, loss_mask_ce_6: 1.01868/0.97796, loss_mask_bce_6: 0.22993/0.34476, loss_mask_dice_6: 0.50093/1.19970, loss_spatial_bce_6: 0.06695/0.10727, loss_spatial_dice_6: 0.16018/0.25133, loss_spatial_ce_6: 0.07577/0.16846, loss_grounding_bce_6: 0.05195/0.08864, loss_grounding_dice_6: 0.12489/0.18290, loss_grounding_ce_6: 0.28629/0.32078, loss_mask_ce_7: 1.08828/1.02153, loss_mask_bce_7: 0.22927/0.35252, loss_mask_dice_7: 0.60916/1.25535, loss_spatial_bce_7: 0.07804/0.11744, loss_spatial_dice_7: 0.19695/0.27793, loss_spatial_ce_7: 0.14158/0.20884, loss_grounding_bce_7: 0.04599/0.09028, loss_grounding_dice_7: 0.10872/0.18995, loss_grounding_ce_7: 0.36052/0.36181, loss_mask_ce_8: 1.32345/1.13401, loss_mask_bce_8: 0.21969/0.36581, loss_mask_dice_8: 0.61326/1.32983, loss_spatial_bce_8: 0.08756/0.13807, loss_spatial_dice_8: 0.23552/0.31842, loss_spatial_ce_8: 0.22544/0.26233, loss_grounding_bce_8: 0.04780/0.09398, loss_grounding_dice_8: 0.12183/0.20099, loss_grounding_ce_8: 0.35695/0.43561, loss_mask_ce_9: 4.07795/3.71470, loss_mask_bce_9: 0.26067/0.39305, loss_mask_dice_9: 0.97435/1.90516, loss_spatial_bce_9: 0.33584/0.34113, loss_spatial_dice_9: 0.80518/0.82775, loss_spatial_ce_9: 1.51198/1.54550, loss_grounding_bce_9: 0.05344/0.10524, loss_grounding_dice_9: 0.22839/0.28054, loss_grounding_ce_9: 2.47099/0.73225] items per batch[64] items per second[0.22] total items[569600] mini batches[  8900] memory[7341] epoch remaining[0:11:18]
INFO:trainer.default_trainer:epochs[     4] optim steps[9000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73379/0.91681, loss_mask_bce_0: 0.45751/0.33694, loss_mask_dice_0: 2.24851/1.16576, loss_spatial_bce_0: 0.04445/0.09608, loss_spatial_dice_0: 0.17292/0.23048, loss_spatial_ce_0: 0.01819/0.10213, loss_grounding_bce_0: 0.03787/0.08644, loss_grounding_dice_0: 0.18263/0.17873, loss_grounding_ce_0: 0.78737/0.28010, loss_mask_ce_1: 0.54801/0.91771, loss_mask_bce_1: 0.46722/0.33749, loss_mask_dice_1: 2.21851/1.17371, loss_spatial_bce_1: 0.04828/0.09712, loss_spatial_dice_1: 0.16664/0.23503, loss_spatial_ce_1: 0.02620/0.10840, loss_grounding_bce_1: 0.03856/0.08652, loss_grounding_dice_1: 0.18249/0.17944, loss_grounding_ce_1: 0.79537/0.28167, loss_mask_ce_2: 0.69213/0.92364, loss_mask_bce_2: 0.46787/0.33761, loss_mask_dice_2: 2.25630/1.17199, loss_spatial_bce_2: 0.04753/0.09668, loss_spatial_dice_2: 0.17618/0.23585, loss_spatial_ce_2: 0.03056/0.11335, loss_grounding_bce_2: 0.03712/0.08653, loss_grounding_dice_2: 0.17608/0.17907, loss_grounding_ce_2: 1.03461/0.28477, loss_mask_ce_3: 0.87997/0.92973, loss_mask_bce_3: 0.45201/0.33848, loss_mask_dice_3: 2.13920/1.16895, loss_spatial_bce_3: 0.05617/0.09778, loss_spatial_dice_3: 0.18299/0.23739, loss_spatial_ce_3: 0.03614/0.11851, loss_grounding_bce_3: 0.03620/0.08663, loss_grounding_dice_3: 0.17395/0.17876, loss_grounding_ce_3: 1.09068/0.28586, loss_mask_ce_4: 0.69911/0.92830, loss_mask_bce_4: 0.46991/0.33979, loss_mask_dice_4: 2.23550/1.19017, loss_spatial_bce_4: 0.05181/0.10120, loss_spatial_dice_4: 0.17350/0.24456, loss_spatial_ce_4: 0.06924/0.13473, loss_grounding_bce_4: 0.04046/0.08726, loss_grounding_dice_4: 0.19631/0.18149, loss_grounding_ce_4: 1.07710/0.28842, loss_mask_ce_5: 0.57119/0.94148, loss_mask_bce_5: 0.42260/0.34214, loss_mask_dice_5: 2.10331/1.19432, loss_spatial_bce_5: 0.04734/0.10204, loss_spatial_dice_5: 0.16799/0.24763, loss_spatial_ce_5: 0.06489/0.14759, loss_grounding_bce_5: 0.03423/0.08779, loss_grounding_dice_5: 0.16396/0.18297, loss_grounding_ce_5: 0.90739/0.29831, loss_mask_ce_6: 0.56752/0.97756, loss_mask_bce_6: 0.44795/0.34467, loss_mask_dice_6: 1.98801/1.19888, loss_spatial_bce_6: 0.06197/0.10719, loss_spatial_dice_6: 0.16978/0.25111, loss_spatial_ce_6: 0.08281/0.16792, loss_grounding_bce_6: 0.04136/0.08859, loss_grounding_dice_6: 0.18867/0.18286, loss_grounding_ce_6: 0.87512/0.32066, loss_mask_ce_7: 0.81718/1.02145, loss_mask_bce_7: 0.42986/0.35241, loss_mask_dice_7: 2.09039/1.25456, loss_spatial_bce_7: 0.05996/0.11728, loss_spatial_dice_7: 0.20897/0.27768, loss_spatial_ce_7: 0.09784/0.20838, loss_grounding_bce_7: 0.03243/0.09023, loss_grounding_dice_7: 0.16765/0.19001, loss_grounding_ce_7: 1.15803/0.36182, loss_mask_ce_8: 1.06994/1.13409, loss_mask_bce_8: 0.45584/0.36565, loss_mask_dice_8: 2.11598/1.32899, loss_spatial_bce_8: 0.05979/0.13793, loss_spatial_dice_8: 0.23282/0.31813, loss_spatial_ce_8: 0.12334/0.26205, loss_grounding_bce_8: 0.03638/0.09389, loss_grounding_dice_8: 0.19452/0.20100, loss_grounding_ce_8: 1.09974/0.43536, loss_mask_ce_9: 4.18856/3.71247, loss_mask_bce_9: 0.51387/0.39295, loss_mask_dice_9: 3.13222/1.90361, loss_spatial_bce_9: 0.22970/0.34101, loss_spatial_dice_9: 0.86593/0.82782, loss_spatial_ce_9: 1.16693/1.54497, loss_grounding_bce_9: 0.05241/0.10517, loss_grounding_dice_9: 0.30748/0.28057, loss_grounding_ce_9: 1.74696/0.73230] items per batch[64] items per second[0.22] total items[576000] mini batches[  9000] memory[7341] epoch remaining[0:06:30]
INFO:trainer.default_trainer:epochs[     4] optim steps[9100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.93724/0.91709, loss_mask_bce_0: 0.57671/0.33671, loss_mask_dice_0: 2.71002/1.16507, loss_spatial_bce_0: 0.17271/0.09594, loss_spatial_dice_0: 0.30046/0.23028, loss_spatial_ce_0: 0.17750/0.10175, loss_grounding_bce_0: 0.10500/0.08649, loss_grounding_dice_0: 0.20156/0.17864, loss_grounding_ce_0: 0.21618/0.28045, loss_mask_ce_1: 1.81792/0.91792, loss_mask_bce_1: 0.55144/0.33728, loss_mask_dice_1: 2.64539/1.17283, loss_spatial_bce_1: 0.11448/0.09697, loss_spatial_dice_1: 0.27970/0.23485, loss_spatial_ce_1: 0.18297/0.10793, loss_grounding_bce_1: 0.11090/0.08657, loss_grounding_dice_1: 0.21115/0.17934, loss_grounding_ce_1: 0.24363/0.28197, loss_mask_ce_2: 1.77206/0.92376, loss_mask_bce_2: 0.55952/0.33742, loss_mask_dice_2: 2.56853/1.17124, loss_spatial_bce_2: 0.12315/0.09653, loss_spatial_dice_2: 0.28810/0.23566, loss_spatial_ce_2: 0.18851/0.11284, loss_grounding_bce_2: 0.11104/0.08657, loss_grounding_dice_2: 0.22652/0.17897, loss_grounding_ce_2: 0.33208/0.28505, loss_mask_ce_3: 1.81080/0.92993, loss_mask_bce_3: 0.57279/0.33827, loss_mask_dice_3: 2.82386/1.16842, loss_spatial_bce_3: 0.15273/0.09765, loss_spatial_dice_3: 0.30190/0.23722, loss_spatial_ce_3: 0.24256/0.11810, loss_grounding_bce_3: 0.11955/0.08670, loss_grounding_dice_3: 0.21705/0.17873, loss_grounding_ce_3: 0.33642/0.28612, loss_mask_ce_4: 1.79789/0.92850, loss_mask_bce_4: 0.65752/0.33958, loss_mask_dice_4: 2.80739/1.18951, loss_spatial_bce_4: 0.10113/0.10104, loss_spatial_dice_4: 0.31505/0.24435, loss_spatial_ce_4: 0.31365/0.13429, loss_grounding_bce_4: 0.12326/0.08731, loss_grounding_dice_4: 0.22239/0.18144, loss_grounding_ce_4: 0.26198/0.28866, loss_mask_ce_5: 1.63739/0.94148, loss_mask_bce_5: 0.72050/0.34187, loss_mask_dice_5: 2.60847/1.19370, loss_spatial_bce_5: 0.10901/0.10189, loss_spatial_dice_5: 0.31447/0.24741, loss_spatial_ce_5: 0.34850/0.14712, loss_grounding_bce_5: 0.11594/0.08784, loss_grounding_dice_5: 0.21415/0.18285, loss_grounding_ce_5: 0.34517/0.29852, loss_mask_ce_6: 1.78840/0.97781, loss_mask_bce_6: 0.63607/0.34441, loss_mask_dice_6: 2.82873/1.19821, loss_spatial_bce_6: 0.10998/0.10704, loss_spatial_dice_6: 0.33250/0.25090, loss_spatial_ce_6: 0.37502/0.16747, loss_grounding_bce_6: 0.11754/0.08864, loss_grounding_dice_6: 0.21536/0.18278, loss_grounding_ce_6: 0.52152/0.32064, loss_mask_ce_7: 2.08347/1.02181, loss_mask_bce_7: 0.59684/0.35215, loss_mask_dice_7: 2.99641/1.25376, loss_spatial_bce_7: 0.11552/0.11708, loss_spatial_dice_7: 0.38580/0.27751, loss_spatial_ce_7: 0.35305/0.20802, loss_grounding_bce_7: 0.11457/0.09026, loss_grounding_dice_7: 0.23369/0.18990, loss_grounding_ce_7: 0.87540/0.36186, loss_mask_ce_8: 2.13335/1.13453, loss_mask_bce_8: 0.76616/0.36541, loss_mask_dice_8: 3.24692/1.32826, loss_spatial_bce_8: 0.23925/0.13785, loss_spatial_dice_8: 0.41609/0.31795, loss_spatial_ce_8: 0.27014/0.26157, loss_grounding_bce_8: 0.13694/0.09390, loss_grounding_dice_8: 0.25600/0.20102, loss_grounding_ce_8: 1.57894/0.43549, loss_mask_ce_9: 6.23932/3.71224, loss_mask_bce_9: 1.01946/0.39265, loss_mask_dice_9: 7.21575/1.90255, loss_spatial_bce_9: 0.28524/0.34069, loss_spatial_dice_9: 0.91215/0.82783, loss_spatial_ce_9: 1.21777/1.54424, loss_grounding_bce_9: 0.12822/0.10518, loss_grounding_dice_9: 0.40438/0.28050, loss_grounding_ce_9: 4.21719/0.73242] items per batch[64] items per second[0.22] total items[582400] mini batches[  9100] memory[7341] epoch remaining[0:01:41]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00009135.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0029 s/iter. Inference: 0.2190 s/iter. Eval: 0.1092 s/iter. Total: 0.3312 s/iter. ETA=0:00:48
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0030 s/iter. Inference: 0.2280 s/iter. Eval: 0.0914 s/iter. Total: 0.3225 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0032 s/iter. Inference: 0.2274 s/iter. Eval: 0.0814 s/iter. Total: 0.3121 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0032 s/iter. Inference: 0.2282 s/iter. Eval: 0.0785 s/iter. Total: 0.3100 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0032 s/iter. Inference: 0.2264 s/iter. Eval: 0.0766 s/iter. Total: 0.3063 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2276 s/iter. Eval: 0.0757 s/iter. Total: 0.3066 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0032 s/iter. Inference: 0.2287 s/iter. Eval: 0.0752 s/iter. Total: 0.3072 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0032 s/iter. Inference: 0.2285 s/iter. Eval: 0.0738 s/iter. Total: 0.3056 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2293 s/iter. Eval: 0.0747 s/iter. Total: 0.3072 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evals128ffbj ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.594 | 82.019 | 59.592 |      133      |
| Things | 54.753 | 82.734 | 65.488 |      80       |
| Stuff  | 41.806 | 80.940 | 50.693 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.03 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.94 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.10s)
creating index...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.37 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.386
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.597
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.285
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.710
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.580 | 60.657 | 40.781 | 18.852 | 41.690 | 59.685 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.183 | bicycle      | 18.092 | car            | 36.290 |
| motorcycle    | 34.339 | airplane     | 57.522 | bus            | 64.324 |
| train         | 68.625 | truck        | 34.952 | boat           | 23.943 |
| traffic light | 25.059 | fire hydrant | 63.465 | stop sign      | 63.839 |
| parking meter | 43.502 | bench        | 19.619 | bird           | 29.396 |
| cat           | 73.840 | dog          | 66.432 | horse          | 46.521 |
| sheep         | 46.291 | cow          | 50.748 | elephant       | 60.937 |
| bear          | 76.807 | zebra        | 60.433 | giraffe        | 57.665 |
| backpack      | 16.820 | umbrella     | 48.016 | handbag        | 15.111 |
| tie           | 33.017 | suitcase     | 40.168 | frisbee        | 68.181 |
| skis          | 5.710  | snowboard    | 22.937 | sports ball    | 47.074 |
| kite          | 32.931 | baseball bat | 28.764 | baseball glove | 43.459 |
| skateboard    | 35.135 | surfboard    | 34.951 | tennis racket  | 56.359 |
| bottle        | 33.776 | wine glass   | 27.155 | cup            | 39.889 |
| fork          | 14.917 | knife        | 13.185 | spoon          | 14.232 |
| bowl          | 32.090 | banana       | 19.633 | apple          | 19.406 |
| sandwich      | 41.899 | orange       | 28.507 | broccoli       | 21.718 |
| carrot        | 19.248 | hot dog      | 19.617 | pizza          | 49.877 |
| donut         | 46.002 | cake         | 42.946 | chair          | 21.014 |
| couch         | 40.735 | potted plant | 17.468 | bed            | 37.629 |
| dining table  | 12.640 | toilet       | 66.130 | tv             | 62.379 |
| laptop        | 62.753 | mouse        | 58.379 | remote         | 30.211 |
| keyboard      | 46.675 | cell phone   | 39.340 | microwave      | 52.428 |
| oven          | 33.770 | toaster      | 37.971 | sink           | 34.833 |
| refrigerator  | 57.101 | book         | 8.894  | clock          | 51.771 |
| vase          | 32.627 | scissors     | 24.956 | teddy bear     | 50.607 |
| hair drier    | 6.622  | toothbrush   | 19.914 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.75476888700374, 'fwIoU': 69.00975844163841, 'IoU-person': 87.3514484397781, 'IoU-bicycle': 73.1768915859729, 'IoU-car': 68.1663686556287, 'IoU-motorcycle': 82.53243533538209, 'IoU-airplane': 83.92702432522493, 'IoU-bus': 83.052474712521, 'IoU-train': 86.94620243708067, 'IoU-truck': 61.92250217604879, 'IoU-boat': 67.66263423948364, 'IoU-traffic light': 76.86843540074274, 'IoU-fire hydrant': 89.7459448140171, 'IoU-stop sign': 93.51507659846263, 'IoU-parking meter': 88.3513127395334, 'IoU-bench': 52.753685672030784, 'IoU-bird': 76.5102924897009, 'IoU-cat': 86.843246666965, 'IoU-dog': 76.28782349657695, 'IoU-horse': 85.46948399823606, 'IoU-sheep': 85.13581225195274, 'IoU-cow': 86.34862677515555, 'IoU-elephant': 90.686667989496, 'IoU-bear': 80.0814428136834, 'IoU-zebra': 85.91997547305802, 'IoU-giraffe': 88.19142349792084, 'IoU-backpack': 40.196904832698735, 'IoU-umbrella': 77.17521452484213, 'IoU-handbag': 33.99546813482635, 'IoU-tie': 69.569984738843, 'IoU-suitcase': 81.00045793995781, 'IoU-frisbee': 82.18952053874638, 'IoU-skis': 52.71609614443761, 'IoU-snowboard': 70.06179913957261, 'IoU-sports ball': 67.24977221341072, 'IoU-kite': 64.0387505152994, 'IoU-baseball bat': 58.842248577005726, 'IoU-baseball glove': 77.15570724354399, 'IoU-skateboard': 61.03742805241962, 'IoU-surfboard': 75.38214697310922, 'IoU-tennis racket': 82.64745464858912, 'IoU-bottle': 66.96173546818349, 'IoU-wine glass': 72.57797994658772, 'IoU-cup': 66.04219688290519, 'IoU-fork': 56.40155347821053, 'IoU-knife': 51.5670800132286, 'IoU-spoon': 50.60882591636614, 'IoU-bowl': 54.418839508373026, 'IoU-banana': 83.28952561744772, 'IoU-apple': 56.977810290173124, 'IoU-sandwich': 66.13288849392895, 'IoU-orange': 79.54495501872937, 'IoU-broccoli': 68.0759825467645, 'IoU-carrot': 64.46295394294319, 'IoU-hot dog': 65.50451031524673, 'IoU-pizza': 85.66698636081891, 'IoU-donut': 63.614409671561454, 'IoU-cake': 66.81668690593233, 'IoU-chair': 53.13010471705532, 'IoU-couch': 65.47491579139049, 'IoU-potted plant': 34.54399112754529, 'IoU-bed': 66.42310422631203, 'IoU-dining table': 50.594368334130365, 'IoU-toilet': 83.10338474943245, 'IoU-tv': 73.87835089312442, 'IoU-laptop': 75.9888714776026, 'IoU-mouse': 71.62410101647073, 'IoU-remote': 50.56978339264605, 'IoU-keyboard': 63.302622217444934, 'IoU-cell phone': 66.30614636535367, 'IoU-microwave': 49.66415692490588, 'IoU-oven': 68.3221187138247, 'IoU-toaster': 62.46083414073079, 'IoU-sink': 71.2876642084128, 'IoU-refrigerator': 82.30394803225718, 'IoU-book': 48.08093744735139, 'IoU-clock': 68.76157685988467, 'IoU-vase': 62.16936982617198, 'IoU-scissors': 55.10821802167891, 'IoU-teddy bear': 77.8236862423019, 'IoU-hair drier': 39.09452509437308, 'IoU-toothbrush': 59.228714799566085, 'IoU-banner': 34.624058582956515, 'IoU-blanket': 12.169565942529232, 'IoU-bridge': 40.62495605657237, 'IoU-cardboard': 47.76264061135723, 'IoU-counter': 30.54044128615408, 'IoU-curtain': 64.16489996344752, 'IoU-door-stuff': 43.60453706755663, 'IoU-floor-wood': 59.929995144900836, 'IoU-flower': 46.454222652272406, 'IoU-fruit': 39.191940925298304, 'IoU-gravel': 26.032524296040656, 'IoU-house': 25.71665145550061, 'IoU-light': 40.48423219356363, 'IoU-mirror-stuff': 55.613693028311694, 'IoU-net': 47.049677570656534, 'IoU-pillow': 13.270835368694097, 'IoU-platform': 26.482987070682423, 'IoU-playingfield': 66.36303933952826, 'IoU-railroad': 59.38506812612394, 'IoU-river': 47.75874910187491, 'IoU-road': 66.27682686441548, 'IoU-roof': 15.065804121416026, 'IoU-sand': 59.01543075868151, 'IoU-sea': 84.61057618357732, 'IoU-shelf': 35.24315737356299, 'IoU-snow': 89.72438053508235, 'IoU-stairs': 27.714315633028768, 'IoU-tent': 9.179285306856816, 'IoU-towel': 32.68321753948764, 'IoU-wall-brick': 42.715779604891765, 'IoU-wall-stone': 25.760064754120116, 'IoU-wall-tile': 66.75735164857915, 'IoU-wall-wood': 39.070376301923346, 'IoU-water-other': 27.766443999176825, 'IoU-window-blind': 48.95540448186439, 'IoU-window-other': 47.27047638889264, 'IoU-tree-merged': 80.802470931402, 'IoU-fence-merged': 51.64367133855584, 'IoU-ceiling-merged': 66.54509496070985, 'IoU-sky-other-merged': 93.78481883491628, 'IoU-cabinet-merged': 59.79578203584709, 'IoU-table-merged': 40.20736782370189, 'IoU-floor-other-merged': 49.81334371041882, 'IoU-pavement-merged': 54.87883658459518, 'IoU-mountain-merged': 53.80039346281471, 'IoU-grass-merged': 71.33987445706083, 'IoU-dirt-merged': 42.65622191605617, 'IoU-paper-merged': 36.20067746689924, 'IoU-food-other-merged': 38.15079132739706, 'IoU-building-other-merged': 58.934085657336176, 'IoU-rock-merged': 59.381080559763674, 'IoU-wall-other-merged': 65.07428928595506, 'IoU-rug-merged': 63.72525253916494, 'mACC': 72.81646823248596, 'pACC': 80.35538405420107, 'ACC-person': 92.60760684677794, 'ACC-bicycle': 85.39131789116648, 'ACC-car': 83.21455229526221, 'ACC-motorcycle': 89.68858893995983, 'ACC-airplane': 90.12122792774669, 'ACC-bus': 88.22560566602328, 'ACC-train': 92.34798833853083, 'ACC-truck': 76.03447392770649, 'ACC-boat': 76.7443554126797, 'ACC-traffic light': 89.24431693995598, 'ACC-fire hydrant': 95.3909696097563, 'ACC-stop sign': 96.33017192335365, 'ACC-parking meter': 91.92902696545048, 'ACC-bench': 67.1566935344184, 'ACC-bird': 80.56912630750004, 'ACC-cat': 91.87035234685297, 'ACC-dog': 81.15726630583355, 'ACC-horse': 91.4133233012432, 'ACC-sheep': 88.31121337132012, 'ACC-cow': 91.77411266492945, 'ACC-elephant': 93.31601488159296, 'ACC-bear': 82.11282265259217, 'ACC-zebra': 88.20623625407305, 'ACC-giraffe': 92.71173436618805, 'ACC-backpack': 61.018965653237046, 'ACC-umbrella': 84.62167611589524, 'ACC-handbag': 49.88670999732697, 'ACC-tie': 80.60782267758704, 'ACC-suitcase': 91.69284292246476, 'ACC-frisbee': 93.46472727272727, 'ACC-skis': 68.28519541937396, 'ACC-snowboard': 77.85506110553257, 'ACC-sports ball': 82.39784579444083, 'ACC-kite': 72.72583117500925, 'ACC-baseball bat': 80.6299590167005, 'ACC-baseball glove': 90.73049292780816, 'ACC-skateboard': 69.33897688865349, 'ACC-surfboard': 83.83037889505495, 'ACC-tennis racket': 89.51407676593267, 'ACC-bottle': 84.59517987928102, 'ACC-wine glass': 85.69191855088756, 'ACC-cup': 82.25594067005322, 'ACC-fork': 69.28366372373664, 'ACC-knife': 66.84014801787939, 'ACC-spoon': 68.17860475057235, 'ACC-bowl': 71.18573382948816, 'ACC-banana': 89.4695968124733, 'ACC-apple': 70.50930393539599, 'ACC-sandwich': 79.55081108197349, 'ACC-orange': 88.63288192420119, 'ACC-broccoli': 78.69012700007458, 'ACC-carrot': 74.83367851742099, 'ACC-hot dog': 73.18861420870755, 'ACC-pizza': 93.45038873569024, 'ACC-donut': 79.84187225908825, 'ACC-cake': 72.83155625738097, 'ACC-chair': 67.72131343941747, 'ACC-couch': 86.98832833295029, 'ACC-potted plant': 46.16791839592398, 'ACC-bed': 77.42558083445125, 'ACC-dining table': 71.68172304654856, 'ACC-toilet': 92.36602144961779, 'ACC-tv': 81.48010542389265, 'ACC-laptop': 92.40638944842321, 'ACC-mouse': 86.04302169121493, 'ACC-remote': 71.09815701122544, 'ACC-keyboard': 67.46590119953369, 'ACC-cell phone': 77.45306360956114, 'ACC-microwave': 55.67760278993313, 'ACC-oven': 83.6182118807421, 'ACC-toaster': 75.43486293777116, 'ACC-sink': 83.96753447360442, 'ACC-refrigerator': 89.18715213136026, 'ACC-book': 64.26851307052198, 'ACC-clock': 77.22639326030331, 'ACC-vase': 75.93043358592158, 'ACC-scissors': 59.116326474396054, 'ACC-teddy bear': 82.12816957903229, 'ACC-hair drier': 43.92166908604446, 'ACC-toothbrush': 81.10145934676859, 'ACC-banner': 69.95376455781278, 'ACC-blanket': 17.316667615276412, 'ACC-bridge': 56.859870686590206, 'ACC-cardboard': 62.171470117783926, 'ACC-counter': 53.77870561063, 'ACC-curtain': 74.46218568505927, 'ACC-door-stuff': 63.64130509925205, 'ACC-floor-wood': 78.0163698247736, 'ACC-flower': 66.77187336032958, 'ACC-fruit': 58.55279323197641, 'ACC-gravel': 30.068126279659314, 'ACC-house': 30.98923525657802, 'ACC-light': 55.45556119128737, 'ACC-mirror-stuff': 66.24712538169344, 'ACC-net': 60.38004403030243, 'ACC-pillow': 24.743593466616332, 'ACC-platform': 38.785655980345744, 'ACC-playingfield': 80.40298160759266, 'ACC-railroad': 80.35032713772722, 'ACC-river': 62.570787286818565, 'ACC-road': 83.93773565036261, 'ACC-roof': 19.851279606563253, 'ACC-sand': 76.81159200547862, 'ACC-sea': 90.73985555949704, 'ACC-shelf': 59.25223416280703, 'ACC-snow': 95.27704915102166, 'ACC-stairs': 51.10777223399957, 'ACC-tent': 12.10598843215076, 'ACC-towel': 39.799956312090536, 'ACC-wall-brick': 57.16103930727129, 'ACC-wall-stone': 34.834309461263516, 'ACC-wall-tile': 79.32731485326724, 'ACC-wall-wood': 57.021456342922505, 'ACC-water-other': 48.633272117925294, 'ACC-window-blind': 57.582057284330354, 'ACC-window-other': 67.59585295838743, 'ACC-tree-merged': 89.69109805546623, 'ACC-fence-merged': 71.58233205920747, 'ACC-ceiling-merged': 79.71989523921218, 'ACC-sky-other-merged': 96.71560106667212, 'ACC-cabinet-merged': 73.24143147193325, 'ACC-table-merged': 53.13063038432963, 'ACC-floor-other-merged': 59.10742067411486, 'ACC-pavement-merged': 70.84984684064277, 'ACC-mountain-merged': 63.154353685022635, 'ACC-grass-merged': 85.2414705901629, 'ACC-dirt-merged': 62.8652455567116, 'ACC-paper-merged': 49.25792900226281, 'ACC-food-other-merged': 55.52574469569181, 'ACC-building-other-merged': 74.8798416628337, 'ACC-rock-merged': 82.88335149374146, 'ACC-wall-other-merged': 81.6428287743327, 'ACC-rug-merged': 81.16851086872619})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1565 s/iter. Inference: 0.5831 s/iter. Eval: 0.0000 s/iter. Total: 0.7396 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1587 s/iter. Inference: 0.5345 s/iter. Eval: 0.0000 s/iter. Total: 0.6933 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1715 s/iter. Inference: 0.6144 s/iter. Eval: 0.0000 s/iter. Total: 0.7862 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1742 s/iter. Inference: 0.6985 s/iter. Eval: 0.0000 s/iter. Total: 0.8729 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 36/50. Dataloading: 0.1713 s/iter. Inference: 0.6449 s/iter. Eval: 0.0000 s/iter. Total: 0.8164 s/iter. ETA=0:00:11
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 41/50. Dataloading: 0.1704 s/iter. Inference: 0.6956 s/iter. Eval: 0.0000 s/iter. Total: 0.8662 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 47/50. Dataloading: 0.1710 s/iter. Inference: 0.6963 s/iter. Eval: 0.0000 s/iter. Total: 0.8674 s/iter. ETA=0:00:02
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.6148668422592918, 'noc@0.8': 3.1717881182323677, 'noc@0.85': 3.8481123792800704, 'noc@0.9': 4.8978636230611645, 'miou@iter1': 0.8343045941074585}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0999 s/iter. Eval: 0.0008 s/iter. Total: 0.1023 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 69.8795166015625, 'precision@0.6': 66.77030944824219, 'precision@0.7': 61.7567024230957, 'precision@0.8': 51.4185791015625, 'precision@0.9': 25.728721618652344, 'cIoU': 55.717079162597656, 'mIoU': 61.6783561706543}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.59365083245963, 'SQ': 82.01868016648042, 'RQ': 59.59232869640667, 'PQ_th': 54.75326942672669, 'SQ_th': 82.73360221080468, 'RQ_th': 65.48817692658048, 'PQ_st': 41.80554729394332, 'SQ_st': 80.93955255240594, 'RQ_st': 50.69293514142738}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.5799533183167, 'AP50': 60.657204018578796, 'AP75': 40.78089813647543, 'APs': 18.852337224048405, 'APm': 41.68978875665549, 'APl': 59.68479536349226, 'AP-person': 44.18275166921875, 'AP-bicycle': 18.092239977354645, 'AP-car': 36.29000467355322, 'AP-motorcycle': 34.3392781337472, 'AP-airplane': 57.52232458879399, 'AP-bus': 64.32429319068234, 'AP-train': 68.6253179114566, 'AP-truck': 34.95190438165853, 'AP-boat': 23.943013868164435, 'AP-traffic light': 25.05885334412121, 'AP-fire hydrant': 63.46465220045342, 'AP-stop sign': 63.83925175134909, 'AP-parking meter': 43.501578605063266, 'AP-bench': 19.618803527659527, 'AP-bird': 29.396410472166323, 'AP-cat': 73.84016109810378, 'AP-dog': 66.43180106883064, 'AP-horse': 46.520862153206096, 'AP-sheep': 46.291333345876154, 'AP-cow': 50.74780173287583, 'AP-elephant': 60.93714247703437, 'AP-bear': 76.80671163204032, 'AP-zebra': 60.43251839217619, 'AP-giraffe': 57.664650459058464, 'AP-backpack': 16.820204732035208, 'AP-umbrella': 48.015992178187474, 'AP-handbag': 15.110913460355738, 'AP-tie': 33.01679727851166, 'AP-suitcase': 40.16779504115282, 'AP-frisbee': 68.18066642207683, 'AP-skis': 5.710003696066136, 'AP-snowboard': 22.93678358220051, 'AP-sports ball': 47.07360370299161, 'AP-kite': 32.93149641611671, 'AP-baseball bat': 28.76364549101009, 'AP-baseball glove': 43.459208991572304, 'AP-skateboard': 35.13515248027089, 'AP-surfboard': 34.95110705891061, 'AP-tennis racket': 56.359006889207876, 'AP-bottle': 33.775917951004246, 'AP-wine glass': 27.154672209407437, 'AP-cup': 39.88865966787935, 'AP-fork': 14.916562721490239, 'AP-knife': 13.185399260077938, 'AP-spoon': 14.23177129999342, 'AP-bowl': 32.089820506941884, 'AP-banana': 19.6326189080343, 'AP-apple': 19.40574961367489, 'AP-sandwich': 41.89877864518392, 'AP-orange': 28.506707068675315, 'AP-broccoli': 21.71789534572094, 'AP-carrot': 19.247892693020976, 'AP-hot dog': 19.61674024586039, 'AP-pizza': 49.87746372893262, 'AP-donut': 46.00231982416545, 'AP-cake': 42.946383689171235, 'AP-chair': 21.0138269709992, 'AP-couch': 40.73521713938339, 'AP-potted plant': 17.46798798845314, 'AP-bed': 37.62900619179382, 'AP-dining table': 12.640043013897598, 'AP-toilet': 66.1295810677223, 'AP-tv': 62.37927899158615, 'AP-laptop': 62.75293841587235, 'AP-mouse': 58.37878009687289, 'AP-remote': 30.210581092445132, 'AP-keyboard': 46.67481116258846, 'AP-cell phone': 39.33992729847091, 'AP-microwave': 52.428343787443296, 'AP-oven': 33.77045746357556, 'AP-toaster': 37.97072372559837, 'AP-sink': 34.832661112634625, 'AP-refrigerator': 57.10089087310036, 'AP-book': 8.89363795580641, 'AP-clock': 51.77078664148388, 'AP-vase': 32.62706319318092, 'AP-scissors': 24.955679079587057, 'AP-teddy bear': 50.60710346039134, 'AP-hair drier': 6.621913096287005, 'AP-toothbrush': 19.913634191616254}), ('sem_seg', {'mIoU': 60.75476888700374, 'fwIoU': 69.00975844163841, 'IoU-person': 87.3514484397781, 'IoU-bicycle': 73.1768915859729, 'IoU-car': 68.1663686556287, 'IoU-motorcycle': 82.53243533538209, 'IoU-airplane': 83.92702432522493, 'IoU-bus': 83.052474712521, 'IoU-train': 86.94620243708067, 'IoU-truck': 61.92250217604879, 'IoU-boat': 67.66263423948364, 'IoU-traffic light': 76.86843540074274, 'IoU-fire hydrant': 89.7459448140171, 'IoU-stop sign': 93.51507659846263, 'IoU-parking meter': 88.3513127395334, 'IoU-bench': 52.753685672030784, 'IoU-bird': 76.5102924897009, 'IoU-cat': 86.843246666965, 'IoU-dog': 76.28782349657695, 'IoU-horse': 85.46948399823606, 'IoU-sheep': 85.13581225195274, 'IoU-cow': 86.34862677515555, 'IoU-elephant': 90.686667989496, 'IoU-bear': 80.0814428136834, 'IoU-zebra': 85.91997547305802, 'IoU-giraffe': 88.19142349792084, 'IoU-backpack': 40.196904832698735, 'IoU-umbrella': 77.17521452484213, 'IoU-handbag': 33.99546813482635, 'IoU-tie': 69.569984738843, 'IoU-suitcase': 81.00045793995781, 'IoU-frisbee': 82.18952053874638, 'IoU-skis': 52.71609614443761, 'IoU-snowboard': 70.06179913957261, 'IoU-sports ball': 67.24977221341072, 'IoU-kite': 64.0387505152994, 'IoU-baseball bat': 58.842248577005726, 'IoU-baseball glove': 77.15570724354399, 'IoU-skateboard': 61.03742805241962, 'IoU-surfboard': 75.38214697310922, 'IoU-tennis racket': 82.64745464858912, 'IoU-bottle': 66.96173546818349, 'IoU-wine glass': 72.57797994658772, 'IoU-cup': 66.04219688290519, 'IoU-fork': 56.40155347821053, 'IoU-knife': 51.5670800132286, 'IoU-spoon': 50.60882591636614, 'IoU-bowl': 54.418839508373026, 'IoU-banana': 83.28952561744772, 'IoU-apple': 56.977810290173124, 'IoU-sandwich': 66.13288849392895, 'IoU-orange': 79.54495501872937, 'IoU-broccoli': 68.0759825467645, 'IoU-carrot': 64.46295394294319, 'IoU-hot dog': 65.50451031524673, 'IoU-pizza': 85.66698636081891, 'IoU-donut': 63.614409671561454, 'IoU-cake': 66.81668690593233, 'IoU-chair': 53.13010471705532, 'IoU-couch': 65.47491579139049, 'IoU-potted plant': 34.54399112754529, 'IoU-bed': 66.42310422631203, 'IoU-dining table': 50.594368334130365, 'IoU-toilet': 83.10338474943245, 'IoU-tv': 73.87835089312442, 'IoU-laptop': 75.9888714776026, 'IoU-mouse': 71.62410101647073, 'IoU-remote': 50.56978339264605, 'IoU-keyboard': 63.302622217444934, 'IoU-cell phone': 66.30614636535367, 'IoU-microwave': 49.66415692490588, 'IoU-oven': 68.3221187138247, 'IoU-toaster': 62.46083414073079, 'IoU-sink': 71.2876642084128, 'IoU-refrigerator': 82.30394803225718, 'IoU-book': 48.08093744735139, 'IoU-clock': 68.76157685988467, 'IoU-vase': 62.16936982617198, 'IoU-scissors': 55.10821802167891, 'IoU-teddy bear': 77.8236862423019, 'IoU-hair drier': 39.09452509437308, 'IoU-toothbrush': 59.228714799566085, 'IoU-banner': 34.624058582956515, 'IoU-blanket': 12.169565942529232, 'IoU-bridge': 40.62495605657237, 'IoU-cardboard': 47.76264061135723, 'IoU-counter': 30.54044128615408, 'IoU-curtain': 64.16489996344752, 'IoU-door-stuff': 43.60453706755663, 'IoU-floor-wood': 59.929995144900836, 'IoU-flower': 46.454222652272406, 'IoU-fruit': 39.191940925298304, 'IoU-gravel': 26.032524296040656, 'IoU-house': 25.71665145550061, 'IoU-light': 40.48423219356363, 'IoU-mirror-stuff': 55.613693028311694, 'IoU-net': 47.049677570656534, 'IoU-pillow': 13.270835368694097, 'IoU-platform': 26.482987070682423, 'IoU-playingfield': 66.36303933952826, 'IoU-railroad': 59.38506812612394, 'IoU-river': 47.75874910187491, 'IoU-road': 66.27682686441548, 'IoU-roof': 15.065804121416026, 'IoU-sand': 59.01543075868151, 'IoU-sea': 84.61057618357732, 'IoU-shelf': 35.24315737356299, 'IoU-snow': 89.72438053508235, 'IoU-stairs': 27.714315633028768, 'IoU-tent': 9.179285306856816, 'IoU-towel': 32.68321753948764, 'IoU-wall-brick': 42.715779604891765, 'IoU-wall-stone': 25.760064754120116, 'IoU-wall-tile': 66.75735164857915, 'IoU-wall-wood': 39.070376301923346, 'IoU-water-other': 27.766443999176825, 'IoU-window-blind': 48.95540448186439, 'IoU-window-other': 47.27047638889264, 'IoU-tree-merged': 80.802470931402, 'IoU-fence-merged': 51.64367133855584, 'IoU-ceiling-merged': 66.54509496070985, 'IoU-sky-other-merged': 93.78481883491628, 'IoU-cabinet-merged': 59.79578203584709, 'IoU-table-merged': 40.20736782370189, 'IoU-floor-other-merged': 49.81334371041882, 'IoU-pavement-merged': 54.87883658459518, 'IoU-mountain-merged': 53.80039346281471, 'IoU-grass-merged': 71.33987445706083, 'IoU-dirt-merged': 42.65622191605617, 'IoU-paper-merged': 36.20067746689924, 'IoU-food-other-merged': 38.15079132739706, 'IoU-building-other-merged': 58.934085657336176, 'IoU-rock-merged': 59.381080559763674, 'IoU-wall-other-merged': 65.07428928595506, 'IoU-rug-merged': 63.72525253916494, 'mACC': 72.81646823248596, 'pACC': 80.35538405420107, 'ACC-person': 92.60760684677794, 'ACC-bicycle': 85.39131789116648, 'ACC-car': 83.21455229526221, 'ACC-motorcycle': 89.68858893995983, 'ACC-airplane': 90.12122792774669, 'ACC-bus': 88.22560566602328, 'ACC-train': 92.34798833853083, 'ACC-truck': 76.03447392770649, 'ACC-boat': 76.7443554126797, 'ACC-traffic light': 89.24431693995598, 'ACC-fire hydrant': 95.3909696097563, 'ACC-stop sign': 96.33017192335365, 'ACC-parking meter': 91.92902696545048, 'ACC-bench': 67.1566935344184, 'ACC-bird': 80.56912630750004, 'ACC-cat': 91.87035234685297, 'ACC-dog': 81.15726630583355, 'ACC-horse': 91.4133233012432, 'ACC-sheep': 88.31121337132012, 'ACC-cow': 91.77411266492945, 'ACC-elephant': 93.31601488159296, 'ACC-bear': 82.11282265259217, 'ACC-zebra': 88.20623625407305, 'ACC-giraffe': 92.71173436618805, 'ACC-backpack': 61.018965653237046, 'ACC-umbrella': 84.62167611589524, 'ACC-handbag': 49.88670999732697, 'ACC-tie': 80.60782267758704, 'ACC-suitcase': 91.69284292246476, 'ACC-frisbee': 93.46472727272727, 'ACC-skis': 68.28519541937396, 'ACC-snowboard': 77.85506110553257, 'ACC-sports ball': 82.39784579444083, 'ACC-kite': 72.72583117500925, 'ACC-baseball bat': 80.6299590167005, 'ACC-baseball glove': 90.73049292780816, 'ACC-skateboard': 69.33897688865349, 'ACC-surfboard': 83.83037889505495, 'ACC-tennis racket': 89.51407676593267, 'ACC-bottle': 84.59517987928102, 'ACC-wine glass': 85.69191855088756, 'ACC-cup': 82.25594067005322, 'ACC-fork': 69.28366372373664, 'ACC-knife': 66.84014801787939, 'ACC-spoon': 68.17860475057235, 'ACC-bowl': 71.18573382948816, 'ACC-banana': 89.4695968124733, 'ACC-apple': 70.50930393539599, 'ACC-sandwich': 79.55081108197349, 'ACC-orange': 88.63288192420119, 'ACC-broccoli': 78.69012700007458, 'ACC-carrot': 74.83367851742099, 'ACC-hot dog': 73.18861420870755, 'ACC-pizza': 93.45038873569024, 'ACC-donut': 79.84187225908825, 'ACC-cake': 72.83155625738097, 'ACC-chair': 67.72131343941747, 'ACC-couch': 86.98832833295029, 'ACC-potted plant': 46.16791839592398, 'ACC-bed': 77.42558083445125, 'ACC-dining table': 71.68172304654856, 'ACC-toilet': 92.36602144961779, 'ACC-tv': 81.48010542389265, 'ACC-laptop': 92.40638944842321, 'ACC-mouse': 86.04302169121493, 'ACC-remote': 71.09815701122544, 'ACC-keyboard': 67.46590119953369, 'ACC-cell phone': 77.45306360956114, 'ACC-microwave': 55.67760278993313, 'ACC-oven': 83.6182118807421, 'ACC-toaster': 75.43486293777116, 'ACC-sink': 83.96753447360442, 'ACC-refrigerator': 89.18715213136026, 'ACC-book': 64.26851307052198, 'ACC-clock': 77.22639326030331, 'ACC-vase': 75.93043358592158, 'ACC-scissors': 59.116326474396054, 'ACC-teddy bear': 82.12816957903229, 'ACC-hair drier': 43.92166908604446, 'ACC-toothbrush': 81.10145934676859, 'ACC-banner': 69.95376455781278, 'ACC-blanket': 17.316667615276412, 'ACC-bridge': 56.859870686590206, 'ACC-cardboard': 62.171470117783926, 'ACC-counter': 53.77870561063, 'ACC-curtain': 74.46218568505927, 'ACC-door-stuff': 63.64130509925205, 'ACC-floor-wood': 78.0163698247736, 'ACC-flower': 66.77187336032958, 'ACC-fruit': 58.55279323197641, 'ACC-gravel': 30.068126279659314, 'ACC-house': 30.98923525657802, 'ACC-light': 55.45556119128737, 'ACC-mirror-stuff': 66.24712538169344, 'ACC-net': 60.38004403030243, 'ACC-pillow': 24.743593466616332, 'ACC-platform': 38.785655980345744, 'ACC-playingfield': 80.40298160759266, 'ACC-railroad': 80.35032713772722, 'ACC-river': 62.570787286818565, 'ACC-road': 83.93773565036261, 'ACC-roof': 19.851279606563253, 'ACC-sand': 76.81159200547862, 'ACC-sea': 90.73985555949704, 'ACC-shelf': 59.25223416280703, 'ACC-snow': 95.27704915102166, 'ACC-stairs': 51.10777223399957, 'ACC-tent': 12.10598843215076, 'ACC-towel': 39.799956312090536, 'ACC-wall-brick': 57.16103930727129, 'ACC-wall-stone': 34.834309461263516, 'ACC-wall-tile': 79.32731485326724, 'ACC-wall-wood': 57.021456342922505, 'ACC-water-other': 48.633272117925294, 'ACC-window-blind': 57.582057284330354, 'ACC-window-other': 67.59585295838743, 'ACC-tree-merged': 89.69109805546623, 'ACC-fence-merged': 71.58233205920747, 'ACC-ceiling-merged': 79.71989523921218, 'ACC-sky-other-merged': 96.71560106667212, 'ACC-cabinet-merged': 73.24143147193325, 'ACC-table-merged': 53.13063038432963, 'ACC-floor-other-merged': 59.10742067411486, 'ACC-pavement-merged': 70.84984684064277, 'ACC-mountain-merged': 63.154353685022635, 'ACC-grass-merged': 85.2414705901629, 'ACC-dirt-merged': 62.8652455567116, 'ACC-paper-merged': 49.25792900226281, 'ACC-food-other-merged': 55.52574469569181, 'ACC-building-other-merged': 74.8798416628337, 'ACC-rock-merged': 82.88335149374146, 'ACC-wall-other-merged': 81.6428287743327, 'ACC-rug-merged': 81.16851086872619})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.6148668422592918, 'noc@0.8': 3.1717881182323677, 'noc@0.85': 3.8481123792800704, 'noc@0.9': 4.8978636230611645, 'miou@iter1': 0.8343045941074585}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 69.8795166015625, 'precision@0.6': 66.77030944824219, 'precision@0.7': 61.7567024230957, 'precision@0.8': 51.4185791015625, 'precision@0.9': 25.728721618652344, 'cIoU': 55.717079162597656, 'mIoU': 61.6783561706543}}}
INFO:trainer.default_trainer:This epoch takes 1:31:09.810521
INFO:trainer.default_trainer:PROGRESS: 10.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 5 training.
INFO:trainer.default_trainer:epochs[     5] optim steps[9200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89705/0.91821, loss_mask_bce_0: 0.05904/0.33686, loss_mask_dice_0: 0.37506/1.16572, loss_spatial_bce_0: 0.02472/0.09590, loss_spatial_dice_0: 0.10530/0.23014, loss_spatial_ce_0: 0.00132/0.10137, loss_grounding_bce_0: 0.01827/0.08642, loss_grounding_dice_0: 0.03559/0.17876, loss_grounding_ce_0: 0.06736/0.28102, loss_mask_ce_1: 0.98785/0.91875, loss_mask_bce_1: 0.06345/0.33741, loss_mask_dice_1: 0.40931/1.17354, loss_spatial_bce_1: 0.02299/0.09693, loss_spatial_dice_1: 0.10303/0.23471, loss_spatial_ce_1: 0.00108/0.10757, loss_grounding_bce_1: 0.01856/0.08653, loss_grounding_dice_1: 0.03775/0.17942, loss_grounding_ce_1: 0.03479/0.28248, loss_mask_ce_2: 0.89382/0.92464, loss_mask_bce_2: 0.05876/0.33757, loss_mask_dice_2: 0.36473/1.17203, loss_spatial_bce_2: 0.02398/0.09650, loss_spatial_dice_2: 0.12234/0.23555, loss_spatial_ce_2: 0.00218/0.11244, loss_grounding_bce_2: 0.01755/0.08652, loss_grounding_dice_2: 0.03372/0.17902, loss_grounding_ce_2: 0.02701/0.28562, loss_mask_ce_3: 0.97304/0.93075, loss_mask_bce_3: 0.06015/0.33840, loss_mask_dice_3: 0.36111/1.16935, loss_spatial_bce_3: 0.02569/0.09761, loss_spatial_dice_3: 0.10001/0.23706, loss_spatial_ce_3: 0.00174/0.11763, loss_grounding_bce_3: 0.01882/0.08665, loss_grounding_dice_3: 0.03930/0.17880, loss_grounding_ce_3: 0.04041/0.28672, loss_mask_ce_4: 0.90530/0.92936, loss_mask_bce_4: 0.06876/0.33973, loss_mask_dice_4: 0.39243/1.19043, loss_spatial_bce_4: 0.02564/0.10100, loss_spatial_dice_4: 0.11360/0.24425, loss_spatial_ce_4: 0.00318/0.13394, loss_grounding_bce_4: 0.01691/0.08725, loss_grounding_dice_4: 0.03472/0.18151, loss_grounding_ce_4: 0.08081/0.28890, loss_mask_ce_5: 0.93406/0.94234, loss_mask_bce_5: 0.05797/0.34197, loss_mask_dice_5: 0.38530/1.19455, loss_spatial_bce_5: 0.03113/0.10186, loss_spatial_dice_5: 0.13271/0.24731, loss_spatial_ce_5: 0.00560/0.14677, loss_grounding_bce_5: 0.01755/0.08776, loss_grounding_dice_5: 0.03643/0.18296, loss_grounding_ce_5: 0.07245/0.29930, loss_mask_ce_6: 0.81442/0.97869, loss_mask_bce_6: 0.05515/0.34453, loss_mask_dice_6: 0.36318/1.19903, loss_spatial_bce_6: 0.03097/0.10701, loss_spatial_dice_6: 0.12020/0.25076, loss_spatial_ce_6: 0.02092/0.16713, loss_grounding_bce_6: 0.01712/0.08859, loss_grounding_dice_6: 0.03727/0.18288, loss_grounding_ce_6: 0.06176/0.32102, loss_mask_ce_7: 0.73310/1.02270, loss_mask_bce_7: 0.06247/0.35226, loss_mask_dice_7: 0.38865/1.25469, loss_spatial_bce_7: 0.02383/0.11699, loss_spatial_dice_7: 0.12599/0.27739, loss_spatial_ce_7: 0.05023/0.20787, loss_grounding_bce_7: 0.01752/0.09020, loss_grounding_dice_7: 0.03483/0.19002, loss_grounding_ce_7: 0.10132/0.36243, loss_mask_ce_8: 0.92580/1.13515, loss_mask_bce_8: 0.07509/0.36557, loss_mask_dice_8: 0.38540/1.32931, loss_spatial_bce_8: 0.03100/0.13778, loss_spatial_dice_8: 0.17596/0.31785, loss_spatial_ce_8: 0.08633/0.26133, loss_grounding_bce_8: 0.01903/0.09382, loss_grounding_dice_8: 0.04297/0.20119, loss_grounding_ce_8: 0.40745/0.43675, loss_mask_ce_9: 2.12004/3.71188, loss_mask_bce_9: 0.09583/0.39286, loss_mask_dice_9: 0.51760/1.90485, loss_spatial_bce_9: 0.36987/0.34050, loss_spatial_dice_9: 0.74735/0.82773, loss_spatial_ce_9: 1.04880/1.54361, loss_grounding_bce_9: 0.02405/0.10515, loss_grounding_dice_9: 0.04779/0.28081, loss_grounding_ce_9: 0.70626/0.73197] items per batch[64] items per second[0.13] total items[588800] mini batches[  9200] memory[7341] epoch remaining[1:24:43]
INFO:trainer.default_trainer:epochs[     5] optim steps[9300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86820/0.91761, loss_mask_bce_0: 0.40672/0.33670, loss_mask_dice_0: 0.64494/1.16410, loss_spatial_bce_0: 0.10165/0.09581, loss_spatial_dice_0: 0.18627/0.22987, loss_spatial_ce_0: 0.09795/0.10108, loss_grounding_bce_0: 0.07640/0.08641, loss_grounding_dice_0: 0.24152/0.17865, loss_grounding_ce_0: 0.34681/0.28043, loss_mask_ce_1: 0.84322/0.91809, loss_mask_bce_1: 0.39350/0.33723, loss_mask_dice_1: 0.63515/1.17192, loss_spatial_bce_1: 0.12820/0.09685, loss_spatial_dice_1: 0.20346/0.23443, loss_spatial_ce_1: 0.04787/0.10706, loss_grounding_bce_1: 0.06826/0.08651, loss_grounding_dice_1: 0.24303/0.17929, loss_grounding_ce_1: 0.28487/0.28187, loss_mask_ce_2: 0.85556/0.92393, loss_mask_bce_2: 0.40972/0.33742, loss_mask_dice_2: 0.64900/1.17050, loss_spatial_bce_2: 0.12134/0.09641, loss_spatial_dice_2: 0.19762/0.23527, loss_spatial_ce_2: 0.06945/0.11196, loss_grounding_bce_2: 0.08050/0.08654, loss_grounding_dice_2: 0.25192/0.17891, loss_grounding_ce_2: 0.29546/0.28483, loss_mask_ce_3: 0.85855/0.93017, loss_mask_bce_3: 0.42235/0.33826, loss_mask_dice_3: 0.65399/1.16777, loss_spatial_bce_3: 0.12735/0.09754, loss_spatial_dice_3: 0.20114/0.23675, loss_spatial_ce_3: 0.07793/0.11706, loss_grounding_bce_3: 0.09286/0.08667, loss_grounding_dice_3: 0.24555/0.17870, loss_grounding_ce_3: 0.32157/0.28589, loss_mask_ce_4: 0.87894/0.92864, loss_mask_bce_4: 0.39703/0.33954, loss_mask_dice_4: 0.64505/1.18892, loss_spatial_bce_4: 0.12727/0.10093, loss_spatial_dice_4: 0.19803/0.24398, loss_spatial_ce_4: 0.10147/0.13344, loss_grounding_bce_4: 0.06941/0.08724, loss_grounding_dice_4: 0.23647/0.18136, loss_grounding_ce_4: 0.33952/0.28811, loss_mask_ce_5: 0.91212/0.94171, loss_mask_bce_5: 0.47559/0.34180, loss_mask_dice_5: 0.65876/1.19316, loss_spatial_bce_5: 0.11912/0.10178, loss_spatial_dice_5: 0.21062/0.24704, loss_spatial_ce_5: 0.18760/0.14628, loss_grounding_bce_5: 0.06769/0.08772, loss_grounding_dice_5: 0.23258/0.18279, loss_grounding_ce_5: 0.34983/0.29850, loss_mask_ce_6: 1.00107/0.97797, loss_mask_bce_6: 0.40094/0.34433, loss_mask_dice_6: 0.66006/1.19740, loss_spatial_bce_6: 0.15024/0.10697, loss_spatial_dice_6: 0.22267/0.25045, loss_spatial_ce_6: 0.25229/0.16691, loss_grounding_bce_6: 0.07518/0.08856, loss_grounding_dice_6: 0.24509/0.18274, loss_grounding_ce_6: 0.36123/0.32034, loss_mask_ce_7: 0.83819/1.02221, loss_mask_bce_7: 0.47246/0.35197, loss_mask_dice_7: 0.74836/1.25315, loss_spatial_bce_7: 0.10700/0.11687, loss_spatial_dice_7: 0.21909/0.27703, loss_spatial_ce_7: 0.32202/0.20753, loss_grounding_bce_7: 0.09272/0.09019, loss_grounding_dice_7: 0.24431/0.18986, loss_grounding_ce_7: 0.46694/0.36149, loss_mask_ce_8: 0.99827/1.13466, loss_mask_bce_8: 0.54428/0.36536, loss_mask_dice_8: 0.78232/1.32755, loss_spatial_bce_8: 0.23450/0.13769, loss_spatial_dice_8: 0.26606/0.31748, loss_spatial_ce_8: 0.21015/0.26099, loss_grounding_bce_8: 0.07393/0.09382, loss_grounding_dice_8: 0.26661/0.20103, loss_grounding_ce_8: 0.45924/0.43556, loss_mask_ce_9: 4.08001/3.71060, loss_mask_bce_9: 0.57155/0.39256, loss_mask_dice_9: 1.04297/1.90194, loss_spatial_bce_9: 0.52670/0.34061, loss_spatial_dice_9: 0.84552/0.82761, loss_spatial_ce_9: 1.19300/1.54308, loss_grounding_bce_9: 0.10111/0.10510, loss_grounding_dice_9: 0.32364/0.28062, loss_grounding_ce_9: 0.32836/0.73081] items per batch[64] items per second[0.23] total items[595200] mini batches[  9300] memory[7341] epoch remaining[1:19:10]
INFO:trainer.default_trainer:epochs[     5] optim steps[9400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49425/0.91818, loss_mask_bce_0: 0.49230/0.33668, loss_mask_dice_0: 1.24350/1.16596, loss_spatial_bce_0: 0.08936/0.09566, loss_spatial_dice_0: 0.26596/0.22983, loss_spatial_ce_0: 0.02827/0.10081, loss_grounding_bce_0: 0.16000/0.08642, loss_grounding_dice_0: 0.20013/0.17874, loss_grounding_ce_0: 0.08999/0.28019, loss_mask_ce_1: 1.38678/0.91891, loss_mask_bce_1: 0.49688/0.33723, loss_mask_dice_1: 1.14484/1.17395, loss_spatial_bce_1: 0.08700/0.09669, loss_spatial_dice_1: 0.26915/0.23440, loss_spatial_ce_1: 0.01273/0.10677, loss_grounding_bce_1: 0.16120/0.08651, loss_grounding_dice_1: 0.19075/0.17936, loss_grounding_ce_1: 0.09647/0.28188, loss_mask_ce_2: 1.50777/0.92466, loss_mask_bce_2: 0.48980/0.33743, loss_mask_dice_2: 1.10255/1.17248, loss_spatial_bce_2: 0.08725/0.09627, loss_spatial_dice_2: 0.26254/0.23522, loss_spatial_ce_2: 0.03727/0.11154, loss_grounding_bce_2: 0.15862/0.08654, loss_grounding_dice_2: 0.18818/0.17903, loss_grounding_ce_2: 0.09151/0.28471, loss_mask_ce_3: 1.49380/0.93095, loss_mask_bce_3: 0.47403/0.33827, loss_mask_dice_3: 1.10587/1.16975, loss_spatial_bce_3: 0.08594/0.09739, loss_spatial_dice_3: 0.26378/0.23671, loss_spatial_ce_3: 0.03047/0.11664, loss_grounding_bce_3: 0.15223/0.08668, loss_grounding_dice_3: 0.18652/0.17880, loss_grounding_ce_3: 0.09359/0.28572, loss_mask_ce_4: 1.52299/0.92950, loss_mask_bce_4: 0.47377/0.33953, loss_mask_dice_4: 1.14514/1.19096, loss_spatial_bce_4: 0.09018/0.10077, loss_spatial_dice_4: 0.27632/0.24392, loss_spatial_ce_4: 0.07084/0.13311, loss_grounding_bce_4: 0.15357/0.08726, loss_grounding_dice_4: 0.19020/0.18147, loss_grounding_ce_4: 0.10760/0.28801, loss_mask_ce_5: 1.46083/0.94285, loss_mask_bce_5: 0.47012/0.34182, loss_mask_dice_5: 1.12050/1.19525, loss_spatial_bce_5: 0.08886/0.10162, loss_spatial_dice_5: 0.27505/0.24703, loss_spatial_ce_5: 0.11541/0.14590, loss_grounding_bce_5: 0.14581/0.08771, loss_grounding_dice_5: 0.18558/0.18293, loss_grounding_ce_5: 0.07295/0.29841, loss_mask_ce_6: 1.40614/0.97895, loss_mask_bce_6: 0.45794/0.34432, loss_mask_dice_6: 1.20116/1.19954, loss_spatial_bce_6: 0.09855/0.10684, loss_spatial_dice_6: 0.30406/0.25040, loss_spatial_ce_6: 0.14634/0.16669, loss_grounding_bce_6: 0.14308/0.08856, loss_grounding_dice_6: 0.18386/0.18289, loss_grounding_ce_6: 0.08270/0.32055, loss_mask_ce_7: 1.43708/1.02344, loss_mask_bce_7: 0.44335/0.35199, loss_mask_dice_7: 1.31589/1.25528, loss_spatial_bce_7: 0.09330/0.11674, loss_spatial_dice_7: 0.33803/0.27702, loss_spatial_ce_7: 0.16213/0.20729, loss_grounding_bce_7: 0.13497/0.09021, loss_grounding_dice_7: 0.17394/0.19000, loss_grounding_ce_7: 0.05305/0.36199, loss_mask_ce_8: 1.39346/1.13575, loss_mask_bce_8: 0.47489/0.36541, loss_mask_dice_8: 1.19983/1.32985, loss_spatial_bce_8: 0.11071/0.13753, loss_spatial_dice_8: 0.33445/0.31745, loss_spatial_ce_8: 0.22800/0.26088, loss_grounding_bce_8: 0.15354/0.09383, loss_grounding_dice_8: 0.18354/0.20119, loss_grounding_ce_8: 0.04997/0.43590, loss_mask_ce_9: 3.92545/3.71255, loss_mask_bce_9: 0.52476/0.39248, loss_mask_dice_9: 1.76093/1.90448, loss_spatial_bce_9: 0.37470/0.34029, loss_spatial_dice_9: 0.80668/0.82758, loss_spatial_ce_9: 1.46078/1.54365, loss_grounding_bce_9: 0.16824/0.10509, loss_grounding_dice_9: 0.19021/0.28066, loss_grounding_ce_9: 0.05267/0.73012] items per batch[64] items per second[0.23] total items[601600] mini batches[  9400] memory[7341] epoch remaining[1:14:07]
INFO:trainer.default_trainer:epochs[     5] optim steps[9500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90866/0.91796, loss_mask_bce_0: 0.30435/0.33670, loss_mask_dice_0: 0.39071/1.16516, loss_spatial_bce_0: 0.14634/0.09565, loss_spatial_dice_0: 0.17125/0.22961, loss_spatial_ce_0: 0.01904/0.10047, loss_grounding_bce_0: 0.15842/0.08642, loss_grounding_dice_0: 0.12221/0.17864, loss_grounding_ce_0: 0.31132/0.27976, loss_mask_ce_1: 0.87856/0.91859, loss_mask_bce_1: 0.31433/0.33723, loss_mask_dice_1: 0.41711/1.17307, loss_spatial_bce_1: 0.16051/0.09668, loss_spatial_dice_1: 0.16511/0.23419, loss_spatial_ce_1: 0.03436/0.10635, loss_grounding_bce_1: 0.16025/0.08652, loss_grounding_dice_1: 0.12086/0.17930, loss_grounding_ce_1: 0.29092/0.28149, loss_mask_ce_2: 0.92444/0.92446, loss_mask_bce_2: 0.30888/0.33743, loss_mask_dice_2: 0.41209/1.17168, loss_spatial_bce_2: 0.15969/0.09628, loss_spatial_dice_2: 0.18466/0.23501, loss_spatial_ce_2: 0.03901/0.11112, loss_grounding_bce_2: 0.16112/0.08655, loss_grounding_dice_2: 0.12396/0.17896, loss_grounding_ce_2: 0.28330/0.28422, loss_mask_ce_3: 0.99357/0.93080, loss_mask_bce_3: 0.31489/0.33827, loss_mask_dice_3: 0.41221/1.16876, loss_spatial_bce_3: 0.16327/0.09741, loss_spatial_dice_3: 0.18559/0.23647, loss_spatial_ce_3: 0.03695/0.11619, loss_grounding_bce_3: 0.16269/0.08669, loss_grounding_dice_3: 0.12568/0.17871, loss_grounding_ce_3: 0.30654/0.28529, loss_mask_ce_4: 1.03913/0.92920, loss_mask_bce_4: 0.30414/0.33954, loss_mask_dice_4: 0.40978/1.19011, loss_spatial_bce_4: 0.18125/0.10079, loss_spatial_dice_4: 0.19220/0.24373, loss_spatial_ce_4: 0.06973/0.13267, loss_grounding_bce_4: 0.16224/0.08726, loss_grounding_dice_4: 0.13974/0.18141, loss_grounding_ce_4: 0.34636/0.28755, loss_mask_ce_5: 1.05797/0.94253, loss_mask_bce_5: 0.36703/0.34182, loss_mask_dice_5: 0.43006/1.19451, loss_spatial_bce_5: 0.17298/0.10164, loss_spatial_dice_5: 0.20947/0.24683, loss_spatial_ce_5: 0.11080/0.14540, loss_grounding_bce_5: 0.18197/0.08773, loss_grounding_dice_5: 0.13263/0.18282, loss_grounding_ce_5: 0.36866/0.29784, loss_mask_ce_6: 1.18050/0.97862, loss_mask_bce_6: 0.38267/0.34432, loss_mask_dice_6: 0.45130/1.19879, loss_spatial_bce_6: 0.19283/0.10684, loss_spatial_dice_6: 0.19769/0.25013, loss_spatial_ce_6: 0.15624/0.16631, loss_grounding_bce_6: 0.21649/0.08858, loss_grounding_dice_6: 0.13741/0.18285, loss_grounding_ce_6: 0.38659/0.31998, loss_mask_ce_7: 1.14280/1.02306, loss_mask_bce_7: 0.39008/0.35198, loss_mask_dice_7: 0.45072/1.25453, loss_spatial_bce_7: 0.19838/0.11667, loss_spatial_dice_7: 0.21395/0.27674, loss_spatial_ce_7: 0.20635/0.20697, loss_grounding_bce_7: 0.23817/0.09024, loss_grounding_dice_7: 0.15373/0.18989, loss_grounding_ce_7: 0.50898/0.36104, loss_mask_ce_8: 0.93835/1.13527, loss_mask_bce_8: 0.36253/0.36539, loss_mask_dice_8: 0.41195/1.32882, loss_spatial_bce_8: 0.24935/0.13749, loss_spatial_dice_8: 0.25863/0.31712, loss_spatial_ce_8: 0.21871/0.26041, loss_grounding_bce_8: 0.23055/0.09382, loss_grounding_dice_8: 0.12705/0.20115, loss_grounding_ce_8: 0.27710/0.43485, loss_mask_ce_9: 3.39585/3.71097, loss_mask_bce_9: 0.43082/0.39252, loss_mask_dice_9: 0.58549/1.90352, loss_spatial_bce_9: 0.52766/0.34027, loss_spatial_dice_9: 0.82915/0.82750, loss_spatial_ce_9: 1.31846/1.54272, loss_grounding_bce_9: 0.21447/0.10508, loss_grounding_dice_9: 0.16944/0.28060, loss_grounding_ce_9: 1.31819/0.72880] items per batch[64] items per second[0.22] total items[608000] mini batches[  9500] memory[7341] epoch remaining[1:09:54]
INFO:trainer.default_trainer:epochs[     5] optim steps[9600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.33903/0.91846, loss_mask_bce_0: 0.34024/0.33687, loss_mask_dice_0: 1.07239/1.16487, loss_spatial_bce_0: 0.04262/0.09563, loss_spatial_dice_0: 0.17485/0.22938, loss_spatial_ce_0: 0.01860/0.10027, loss_grounding_bce_0: 0.25838/0.08660, loss_grounding_dice_0: 0.11711/0.17849, loss_grounding_ce_0: 0.20738/0.27949, loss_mask_ce_1: 1.29876/0.91904, loss_mask_bce_1: 0.33071/0.33737, loss_mask_dice_1: 1.07779/1.17287, loss_spatial_bce_1: 0.04533/0.09665, loss_spatial_dice_1: 0.17648/0.23398, loss_spatial_ce_1: 0.03395/0.10615, loss_grounding_bce_1: 0.25656/0.08666, loss_grounding_dice_1: 0.11337/0.17913, loss_grounding_ce_1: 0.21963/0.28127, loss_mask_ce_2: 1.36778/0.92505, loss_mask_bce_2: 0.35395/0.33757, loss_mask_dice_2: 1.09338/1.17147, loss_spatial_bce_2: 0.04124/0.09626, loss_spatial_dice_2: 0.16023/0.23480, loss_spatial_ce_2: 0.01742/0.11093, loss_grounding_bce_2: 0.27176/0.08668, loss_grounding_dice_2: 0.11907/0.17880, loss_grounding_ce_2: 0.20512/0.28403, loss_mask_ce_3: 1.39586/0.93138, loss_mask_bce_3: 0.36924/0.33841, loss_mask_dice_3: 1.06921/1.16846, loss_spatial_bce_3: 0.04034/0.09740, loss_spatial_dice_3: 0.14823/0.23628, loss_spatial_ce_3: 0.01352/0.11588, loss_grounding_bce_3: 0.27251/0.08682, loss_grounding_dice_3: 0.11303/0.17855, loss_grounding_ce_3: 0.15702/0.28505, loss_mask_ce_4: 1.58825/0.92972, loss_mask_bce_4: 0.35128/0.33965, loss_mask_dice_4: 0.98044/1.18980, loss_spatial_bce_4: 0.04455/0.10079, loss_spatial_dice_4: 0.18231/0.24350, loss_spatial_ce_4: 0.05925/0.13243, loss_grounding_bce_4: 0.27692/0.08739, loss_grounding_dice_4: 0.12022/0.18126, loss_grounding_ce_4: 0.16692/0.28737, loss_mask_ce_5: 1.28941/0.94301, loss_mask_bce_5: 0.39517/0.34190, loss_mask_dice_5: 1.01667/1.19429, loss_spatial_bce_5: 0.05066/0.10164, loss_spatial_dice_5: 0.19761/0.24662, loss_spatial_ce_5: 0.03872/0.14509, loss_grounding_bce_5: 0.26158/0.08786, loss_grounding_dice_5: 0.10077/0.18263, loss_grounding_ce_5: 0.14936/0.29768, loss_mask_ce_6: 1.36423/0.97934, loss_mask_bce_6: 0.33191/0.34446, loss_mask_dice_6: 1.05967/1.19846, loss_spatial_bce_6: 0.06109/0.10687, loss_spatial_dice_6: 0.17419/0.24992, loss_spatial_ce_6: 0.18578/0.16605, loss_grounding_bce_6: 0.27444/0.08869, loss_grounding_dice_6: 0.11040/0.18269, loss_grounding_ce_6: 0.09938/0.31984, loss_mask_ce_7: 1.34303/1.02340, loss_mask_bce_7: 0.32224/0.35209, loss_mask_dice_7: 0.97748/1.25428, loss_spatial_bce_7: 0.07638/0.11671, loss_spatial_dice_7: 0.24794/0.27654, loss_spatial_ce_7: 0.10957/0.20661, loss_grounding_bce_7: 0.26460/0.09036, loss_grounding_dice_7: 0.11524/0.18972, loss_grounding_ce_7: 0.16050/0.36075, loss_mask_ce_8: 1.67206/1.13607, loss_mask_bce_8: 0.34633/0.36540, loss_mask_dice_8: 1.00009/1.32862, loss_spatial_bce_8: 0.06604/0.13756, loss_spatial_dice_8: 0.22610/0.31692, loss_spatial_ce_8: 0.19125/0.26003, loss_grounding_bce_8: 0.25792/0.09395, loss_grounding_dice_8: 0.09943/0.20103, loss_grounding_ce_8: 0.33651/0.43428, loss_mask_ce_9: 3.64132/3.71085, loss_mask_bce_9: 0.32502/0.39272, loss_mask_dice_9: 1.67214/1.90338, loss_spatial_bce_9: 0.31954/0.34033, loss_spatial_dice_9: 0.85957/0.82746, loss_spatial_ce_9: 1.40458/1.54145, loss_grounding_bce_9: 0.19978/0.10531, loss_grounding_dice_9: 0.07121/0.28056, loss_grounding_ce_9: 0.96463/0.72830] items per batch[64] items per second[0.22] total items[614400] mini batches[  9600] memory[7341] epoch remaining[1:05:25]
INFO:trainer.default_trainer:epochs[     5] optim steps[9700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28308/0.91848, loss_mask_bce_0: 0.24992/0.33700, loss_mask_dice_0: 1.26008/1.16711, loss_spatial_bce_0: 0.04077/0.09558, loss_spatial_dice_0: 0.15793/0.22937, loss_spatial_ce_0: 0.06888/0.09991, loss_grounding_bce_0: 0.25938/0.08656, loss_grounding_dice_0: 0.15073/0.17860, loss_grounding_ce_0: 0.02479/0.27943, loss_mask_ce_1: 1.20873/0.91904, loss_mask_bce_1: 0.27524/0.33750, loss_mask_dice_1: 1.34084/1.17513, loss_spatial_bce_1: 0.04407/0.09660, loss_spatial_dice_1: 0.15760/0.23398, loss_spatial_ce_1: 0.03952/0.10572, loss_grounding_bce_1: 0.25575/0.08661, loss_grounding_dice_1: 0.14356/0.17926, loss_grounding_ce_1: 0.03118/0.28117, loss_mask_ce_2: 1.28202/0.92527, loss_mask_bce_2: 0.28037/0.33768, loss_mask_dice_2: 1.35045/1.17385, loss_spatial_bce_2: 0.04284/0.09620, loss_spatial_dice_2: 0.16140/0.23478, loss_spatial_ce_2: 0.02835/0.11055, loss_grounding_bce_2: 0.20367/0.08663, loss_grounding_dice_2: 0.13796/0.17888, loss_grounding_ce_2: 0.04706/0.28399, loss_mask_ce_3: 1.25484/0.93146, loss_mask_bce_3: 0.28156/0.33856, loss_mask_dice_3: 1.27688/1.17082, loss_spatial_bce_3: 0.04504/0.09733, loss_spatial_dice_3: 0.14938/0.23624, loss_spatial_ce_3: 0.07817/0.11545, loss_grounding_bce_3: 0.23260/0.08678, loss_grounding_dice_3: 0.14531/0.17861, loss_grounding_ce_3: 0.02231/0.28499, loss_mask_ce_4: 1.17774/0.92986, loss_mask_bce_4: 0.27750/0.33974, loss_mask_dice_4: 1.39020/1.19206, loss_spatial_bce_4: 0.04763/0.10071, loss_spatial_dice_4: 0.17889/0.24349, loss_spatial_ce_4: 0.11817/0.13201, loss_grounding_bce_4: 0.23067/0.08733, loss_grounding_dice_4: 0.13899/0.18140, loss_grounding_ce_4: 0.03485/0.28750, loss_mask_ce_5: 1.31970/0.94300, loss_mask_bce_5: 0.27848/0.34204, loss_mask_dice_5: 1.38045/1.19663, loss_spatial_bce_5: 0.04730/0.10157, loss_spatial_dice_5: 0.17640/0.24664, loss_spatial_ce_5: 0.06634/0.14472, loss_grounding_bce_5: 0.16725/0.08781, loss_grounding_dice_5: 0.13604/0.18272, loss_grounding_ce_5: 0.06357/0.29779, loss_mask_ce_6: 1.14551/0.97958, loss_mask_bce_6: 0.26884/0.34458, loss_mask_dice_6: 1.38371/1.20092, loss_spatial_bce_6: 0.04696/0.10685, loss_spatial_dice_6: 0.16508/0.24988, loss_spatial_ce_6: 0.10352/0.16559, loss_grounding_bce_6: 0.25066/0.08863, loss_grounding_dice_6: 0.15170/0.18278, loss_grounding_ce_6: 0.11167/0.31967, loss_mask_ce_7: 1.32735/1.02375, loss_mask_bce_7: 0.28378/0.35217, loss_mask_dice_7: 1.56347/1.25652, loss_spatial_bce_7: 0.05064/0.11662, loss_spatial_dice_7: 0.18549/0.27649, loss_spatial_ce_7: 0.12030/0.20619, loss_grounding_bce_7: 0.24130/0.09030, loss_grounding_dice_7: 0.14449/0.18984, loss_grounding_ce_7: 0.08337/0.36038, loss_mask_ce_8: 1.27904/1.13598, loss_mask_bce_8: 0.29444/0.36555, loss_mask_dice_8: 1.74076/1.33157, loss_spatial_bce_8: 0.04993/0.13747, loss_spatial_dice_8: 0.22343/0.31690, loss_spatial_ce_8: 0.20843/0.25955, loss_grounding_bce_8: 0.20779/0.09391, loss_grounding_dice_8: 0.14177/0.20114, loss_grounding_ce_8: 0.22135/0.43408, loss_mask_ce_9: 3.58100/3.71128, loss_mask_bce_9: 0.33269/0.39289, loss_mask_dice_9: 2.74204/1.90710, loss_spatial_bce_9: 0.31478/0.34002, loss_spatial_dice_9: 0.85607/0.82744, loss_spatial_ce_9: 1.49056/1.54149, loss_grounding_bce_9: 0.18041/0.10528, loss_grounding_dice_9: 0.16793/0.28068, loss_grounding_ce_9: 0.90253/0.72774] items per batch[64] items per second[0.23] total items[620800] mini batches[  9700] memory[7341] epoch remaining[1:00:25]
INFO:trainer.default_trainer:epochs[     5] optim steps[9800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44793/0.91859, loss_mask_bce_0: 0.45202/0.33708, loss_mask_dice_0: 0.64714/1.16641, loss_spatial_bce_0: 0.13688/0.09564, loss_spatial_dice_0: 0.21362/0.22914, loss_spatial_ce_0: 0.00472/0.09944, loss_grounding_bce_0: 0.08781/0.08663, loss_grounding_dice_0: 0.12913/0.17866, loss_grounding_ce_0: 0.39267/0.27905, loss_mask_ce_1: 0.46540/0.91921, loss_mask_bce_1: 0.43725/0.33756, loss_mask_dice_1: 0.66215/1.17454, loss_spatial_bce_1: 0.12756/0.09667, loss_spatial_dice_1: 0.21419/0.23376, loss_spatial_ce_1: 0.00426/0.10532, loss_grounding_bce_1: 0.09455/0.08667, loss_grounding_dice_1: 0.13843/0.17927, loss_grounding_ce_1: 0.34835/0.28081, loss_mask_ce_2: 0.70164/0.92544, loss_mask_bce_2: 0.40483/0.33772, loss_mask_dice_2: 0.63666/1.17312, loss_spatial_bce_2: 0.12600/0.09627, loss_spatial_dice_2: 0.22713/0.23455, loss_spatial_ce_2: 0.00626/0.11004, loss_grounding_bce_2: 0.08912/0.08669, loss_grounding_dice_2: 0.13314/0.17889, loss_grounding_ce_2: 0.33521/0.28371, loss_mask_ce_3: 0.66362/0.93158, loss_mask_bce_3: 0.43174/0.33860, loss_mask_dice_3: 0.66929/1.16993, loss_spatial_bce_3: 0.13498/0.09738, loss_spatial_dice_3: 0.23587/0.23600, loss_spatial_ce_3: 0.00658/0.11497, loss_grounding_bce_3: 0.09372/0.08686, loss_grounding_dice_3: 0.13387/0.17862, loss_grounding_ce_3: 0.34575/0.28460, loss_mask_ce_4: 0.41393/0.92980, loss_mask_bce_4: 0.47447/0.33979, loss_mask_dice_4: 0.66371/1.19131, loss_spatial_bce_4: 0.15280/0.10077, loss_spatial_dice_4: 0.23906/0.24326, loss_spatial_ce_4: 0.01109/0.13153, loss_grounding_bce_4: 0.09411/0.08740, loss_grounding_dice_4: 0.13828/0.18142, loss_grounding_ce_4: 0.35600/0.28730, loss_mask_ce_5: 0.42641/0.94307, loss_mask_bce_5: 0.46602/0.34213, loss_mask_dice_5: 0.65803/1.19592, loss_spatial_bce_5: 0.13807/0.10168, loss_spatial_dice_5: 0.23034/0.24642, loss_spatial_ce_5: 0.01661/0.14425, loss_grounding_bce_5: 0.09336/0.08788, loss_grounding_dice_5: 0.13366/0.18274, loss_grounding_ce_5: 0.31844/0.29743, loss_mask_ce_6: 0.45421/0.97967, loss_mask_bce_6: 0.44680/0.34467, loss_mask_dice_6: 0.64460/1.20023, loss_spatial_bce_6: 0.16574/0.10695, loss_spatial_dice_6: 0.23615/0.24964, loss_spatial_ce_6: 0.03262/0.16519, loss_grounding_bce_6: 0.10028/0.08869, loss_grounding_dice_6: 0.15004/0.18282, loss_grounding_ce_6: 0.34670/0.31942, loss_mask_ce_7: 0.52456/1.02371, loss_mask_bce_7: 0.44505/0.35226, loss_mask_dice_7: 0.65273/1.25589, loss_spatial_bce_7: 0.16839/0.11672, loss_spatial_dice_7: 0.23993/0.27628, loss_spatial_ce_7: 0.06889/0.20585, loss_grounding_bce_7: 0.10790/0.09037, loss_grounding_dice_7: 0.15711/0.18988, loss_grounding_ce_7: 0.24482/0.36012, loss_mask_ce_8: 0.65044/1.13603, loss_mask_bce_8: 0.51038/0.36565, loss_mask_dice_8: 0.67487/1.33092, loss_spatial_bce_8: 0.18690/0.13762, loss_spatial_dice_8: 0.25152/0.31668, loss_spatial_ce_8: 0.14649/0.25918, loss_grounding_bce_8: 0.10767/0.09401, loss_grounding_dice_8: 0.16092/0.20117, loss_grounding_ce_8: 0.56531/0.43388, loss_mask_ce_9: 3.14075/3.71119, loss_mask_bce_9: 0.43848/0.39314, loss_mask_dice_9: 0.78401/1.90682, loss_spatial_bce_9: 0.48056/0.34006, loss_spatial_dice_9: 0.85204/0.82736, loss_spatial_ce_9: 1.23437/1.54023, loss_grounding_bce_9: 0.11359/0.10539, loss_grounding_dice_9: 0.21274/0.28071, loss_grounding_ce_9: 1.14802/0.72754] items per batch[64] items per second[0.22] total items[627200] mini batches[  9800] memory[7341] epoch remaining[0:55:38]
INFO:trainer.default_trainer:epochs[     5] optim steps[9900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77647/0.91770, loss_mask_bce_0: 0.60906/0.33733, loss_mask_dice_0: 0.92700/1.16620, loss_spatial_bce_0: 0.11768/0.09564, loss_spatial_dice_0: 0.20532/0.22891, loss_spatial_ce_0: 0.00376/0.09904, loss_grounding_bce_0: 0.07445/0.08659, loss_grounding_dice_0: 0.31069/0.17863, loss_grounding_ce_0: 0.03453/0.27901, loss_mask_ce_1: 0.84888/0.91843, loss_mask_bce_1: 0.58202/0.33781, loss_mask_dice_1: 0.89503/1.17433, loss_spatial_bce_1: 0.12826/0.09668, loss_spatial_dice_1: 0.21983/0.23353, loss_spatial_ce_1: 0.00764/0.10487, loss_grounding_bce_1: 0.07403/0.08665, loss_grounding_dice_1: 0.30408/0.17925, loss_grounding_ce_1: 0.03393/0.28063, loss_mask_ce_2: 0.63626/0.92468, loss_mask_bce_2: 0.59418/0.33797, loss_mask_dice_2: 0.90269/1.17276, loss_spatial_bce_2: 0.12911/0.09629, loss_spatial_dice_2: 0.21488/0.23432, loss_spatial_ce_2: 0.01604/0.10957, loss_grounding_bce_2: 0.07169/0.08666, loss_grounding_dice_2: 0.30267/0.17890, loss_grounding_ce_2: 0.03319/0.28351, loss_mask_ce_3: 0.69010/0.93080, loss_mask_bce_3: 0.57174/0.33887, loss_mask_dice_3: 0.90237/1.16963, loss_spatial_bce_3: 0.13170/0.09741, loss_spatial_dice_3: 0.22390/0.23576, loss_spatial_ce_3: 0.02439/0.11455, loss_grounding_bce_3: 0.07743/0.08685, loss_grounding_dice_3: 0.29738/0.17859, loss_grounding_ce_3: 0.05174/0.28429, loss_mask_ce_4: 0.82475/0.92895, loss_mask_bce_4: 0.56498/0.34006, loss_mask_dice_4: 0.87356/1.19107, loss_spatial_bce_4: 0.11964/0.10078, loss_spatial_dice_4: 0.20534/0.24305, loss_spatial_ce_4: 0.05494/0.13106, loss_grounding_bce_4: 0.07980/0.08737, loss_grounding_dice_4: 0.31227/0.18142, loss_grounding_ce_4: 0.08149/0.28700, loss_mask_ce_5: 0.78049/0.94238, loss_mask_bce_5: 0.55749/0.34238, loss_mask_dice_5: 0.88475/1.19552, loss_spatial_bce_5: 0.11307/0.10170, loss_spatial_dice_5: 0.20934/0.24616, loss_spatial_ce_5: 0.03542/0.14381, loss_grounding_bce_5: 0.06200/0.08785, loss_grounding_dice_5: 0.27211/0.18270, loss_grounding_ce_5: 0.23898/0.29731, loss_mask_ce_6: 0.86875/0.97899, loss_mask_bce_6: 0.54632/0.34495, loss_mask_dice_6: 0.85403/1.19988, loss_spatial_bce_6: 0.13177/0.10704, loss_spatial_dice_6: 0.20673/0.24939, loss_spatial_ce_6: 0.08527/0.16484, loss_grounding_bce_6: 0.07042/0.08865, loss_grounding_dice_6: 0.27951/0.18282, loss_grounding_ce_6: 0.17304/0.31909, loss_mask_ce_7: 1.14529/1.02323, loss_mask_bce_7: 0.54977/0.35262, loss_mask_dice_7: 0.89734/1.25550, loss_spatial_bce_7: 0.13702/0.11678, loss_spatial_dice_7: 0.23753/0.27603, loss_spatial_ce_7: 0.09393/0.20566, loss_grounding_bce_7: 0.09091/0.09035, loss_grounding_dice_7: 0.29933/0.18986, loss_grounding_ce_7: 0.21264/0.35965, loss_mask_ce_8: 1.27473/1.13533, loss_mask_bce_8: 0.47873/0.36596, loss_mask_dice_8: 0.84827/1.33053, loss_spatial_bce_8: 0.17144/0.13772, loss_spatial_dice_8: 0.25906/0.31648, loss_spatial_ce_8: 0.14172/0.25869, loss_grounding_bce_8: 0.06950/0.09398, loss_grounding_dice_8: 0.27444/0.20115, loss_grounding_ce_8: 0.23159/0.43338, loss_mask_ce_9: 2.96448/3.71066, loss_mask_bce_9: 0.67184/0.39345, loss_mask_dice_9: 1.33670/1.90629, loss_spatial_bce_9: 0.36646/0.34009, loss_spatial_dice_9: 0.86295/0.82734, loss_spatial_ce_9: 1.33531/1.53954, loss_grounding_bce_9: 0.08135/0.10533, loss_grounding_dice_9: 0.44250/0.28070, loss_grounding_ce_9: 0.26051/0.72720] items per batch[64] items per second[0.22] total items[633600] mini batches[  9900] memory[7341] epoch remaining[0:50:55]
INFO:trainer.default_trainer:epochs[     5] optim steps[10000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96093/0.91741, loss_mask_bce_0: 0.20846/0.33691, loss_mask_dice_0: 0.43353/1.16673, loss_spatial_bce_0: 0.05004/0.09550, loss_spatial_dice_0: 0.10871/0.22873, loss_spatial_ce_0: 0.00250/0.09870, loss_grounding_bce_0: 0.05378/0.08652, loss_grounding_dice_0: 0.09903/0.17861, loss_grounding_ce_0: 0.42469/0.27877, loss_mask_ce_1: 1.02979/0.91827, loss_mask_bce_1: 0.21566/0.33738, loss_mask_dice_1: 0.46548/1.17486, loss_spatial_bce_1: 0.05041/0.09656, loss_spatial_dice_1: 0.10533/0.23340, loss_spatial_ce_1: 0.00163/0.10447, loss_grounding_bce_1: 0.05306/0.08658, loss_grounding_dice_1: 0.09393/0.17922, loss_grounding_ce_1: 0.48882/0.28059, loss_mask_ce_2: 1.03156/0.92427, loss_mask_bce_2: 0.21805/0.33756, loss_mask_dice_2: 0.43936/1.17342, loss_spatial_bce_2: 0.05083/0.09618, loss_spatial_dice_2: 0.10975/0.23416, loss_spatial_ce_2: 0.00228/0.10917, loss_grounding_bce_2: 0.05623/0.08662, loss_grounding_dice_2: 0.15710/0.17892, loss_grounding_ce_2: 0.49101/0.28321, loss_mask_ce_3: 1.16946/0.93039, loss_mask_bce_3: 0.21676/0.33849, loss_mask_dice_3: 0.55703/1.17016, loss_spatial_bce_3: 0.06078/0.09729, loss_spatial_dice_3: 0.12240/0.23559, loss_spatial_ce_3: 0.00288/0.11420, loss_grounding_bce_3: 0.05453/0.08682, loss_grounding_dice_3: 0.10083/0.17858, loss_grounding_ce_3: 0.50588/0.28394, loss_mask_ce_4: 1.14429/0.92862, loss_mask_bce_4: 0.21851/0.33966, loss_mask_dice_4: 0.48344/1.19157, loss_spatial_bce_4: 0.06750/0.10067, loss_spatial_dice_4: 0.14422/0.24292, loss_spatial_ce_4: 0.00547/0.13071, loss_grounding_bce_4: 0.05550/0.08732, loss_grounding_dice_4: 0.11416/0.18143, loss_grounding_ce_4: 0.49624/0.28703, loss_mask_ce_5: 0.92729/0.94205, loss_mask_bce_5: 0.22521/0.34197, loss_mask_dice_5: 0.68082/1.19607, loss_spatial_bce_5: 0.07053/0.10158, loss_spatial_dice_5: 0.13737/0.24601, loss_spatial_ce_5: 0.00648/0.14357, loss_grounding_bce_5: 0.05563/0.08777, loss_grounding_dice_5: 0.13342/0.18269, loss_grounding_ce_5: 0.46886/0.29761, loss_mask_ce_6: 0.76437/0.97865, loss_mask_bce_6: 0.21768/0.34453, loss_mask_dice_6: 0.64144/1.20030, loss_spatial_bce_6: 0.07279/0.10694, loss_spatial_dice_6: 0.14171/0.24920, loss_spatial_ce_6: 0.01769/0.16466, loss_grounding_bce_6: 0.05503/0.08856, loss_grounding_dice_6: 0.11204/0.18278, loss_grounding_ce_6: 0.45735/0.31895, loss_mask_ce_7: 0.97063/1.02290, loss_mask_bce_7: 0.21158/0.35222, loss_mask_dice_7: 0.70451/1.25588, loss_spatial_bce_7: 0.07898/0.11667, loss_spatial_dice_7: 0.18083/0.27595, loss_spatial_ce_7: 0.06744/0.20532, loss_grounding_bce_7: 0.05620/0.09028, loss_grounding_dice_7: 0.17573/0.18984, loss_grounding_ce_7: 0.48597/0.35934, loss_mask_ce_8: 1.01525/1.13503, loss_mask_bce_8: 0.19671/0.36556, loss_mask_dice_8: 0.61284/1.33099, loss_spatial_bce_8: 0.07545/0.13760, loss_spatial_dice_8: 0.20578/0.31635, loss_spatial_ce_8: 0.20003/0.25847, loss_grounding_bce_8: 0.05409/0.09391, loss_grounding_dice_8: 0.14916/0.20122, loss_grounding_ce_8: 0.56761/0.43338, loss_mask_ce_9: 4.43066/3.70995, loss_mask_bce_9: 0.26462/0.39301, loss_mask_dice_9: 1.23617/1.90610, loss_spatial_bce_9: 0.35794/0.33987, loss_spatial_dice_9: 0.81315/0.82726, loss_spatial_ce_9: 2.24195/1.53885, loss_grounding_bce_9: 0.09655/0.10532, loss_grounding_dice_9: 0.35245/0.28068, loss_grounding_ce_9: 0.40555/0.72702] items per batch[64] items per second[0.23] total items[640000] mini batches[ 10000] memory[7341] epoch remaining[0:46:00]
INFO:trainer.default_trainer:epochs[     5] optim steps[10100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54875/0.91735, loss_mask_bce_0: 0.33996/0.33666, loss_mask_dice_0: 0.58210/1.16569, loss_spatial_bce_0: 0.19511/0.09539, loss_spatial_dice_0: 0.29125/0.22851, loss_spatial_ce_0: 0.04701/0.09867, loss_grounding_bce_0: 0.18267/0.08652, loss_grounding_dice_0: 0.15588/0.17846, loss_grounding_ce_0: 0.88193/0.27882, loss_mask_ce_1: 0.79881/0.91811, loss_mask_bce_1: 0.32499/0.33711, loss_mask_dice_1: 0.60458/1.17406, loss_spatial_bce_1: 0.18713/0.09645, loss_spatial_dice_1: 0.29282/0.23317, loss_spatial_ce_1: 0.05624/0.10431, loss_grounding_bce_1: 0.16486/0.08659, loss_grounding_dice_1: 0.14546/0.17907, loss_grounding_ce_1: 0.93750/0.28051, loss_mask_ce_2: 0.41454/0.92407, loss_mask_bce_2: 0.34808/0.33728, loss_mask_dice_2: 0.58186/1.17249, loss_spatial_bce_2: 0.17954/0.09608, loss_spatial_dice_2: 0.28709/0.23394, loss_spatial_ce_2: 0.06544/0.10895, loss_grounding_bce_2: 0.16083/0.08662, loss_grounding_dice_2: 0.14349/0.17876, loss_grounding_ce_2: 0.94119/0.28322, loss_mask_ce_3: 0.63545/0.93026, loss_mask_bce_3: 0.34366/0.33819, loss_mask_dice_3: 0.60182/1.16926, loss_spatial_bce_3: 0.19346/0.09719, loss_spatial_dice_3: 0.28773/0.23538, loss_spatial_ce_3: 0.10740/0.11394, loss_grounding_bce_3: 0.16785/0.08683, loss_grounding_dice_3: 0.15724/0.17847, loss_grounding_ce_3: 1.08292/0.28395, loss_mask_ce_4: 0.64054/0.92873, loss_mask_bce_4: 0.33385/0.33939, loss_mask_dice_4: 0.60433/1.19060, loss_spatial_bce_4: 0.19118/0.10057, loss_spatial_dice_4: 0.29669/0.24276, loss_spatial_ce_4: 0.10478/0.13040, loss_grounding_bce_4: 0.15855/0.08732, loss_grounding_dice_4: 0.14688/0.18127, loss_grounding_ce_4: 1.01987/0.28700, loss_mask_ce_5: 0.90623/0.94193, loss_mask_bce_5: 0.33511/0.34169, loss_mask_dice_5: 0.60401/1.19521, loss_spatial_bce_5: 0.18620/0.10145, loss_spatial_dice_5: 0.28748/0.24582, loss_spatial_ce_5: 0.13229/0.14324, loss_grounding_bce_5: 0.17432/0.08777, loss_grounding_dice_5: 0.15502/0.18245, loss_grounding_ce_5: 1.05486/0.29790, loss_mask_ce_6: 0.47572/0.97856, loss_mask_bce_6: 0.37653/0.34424, loss_mask_dice_6: 0.59574/1.19922, loss_spatial_bce_6: 0.17431/0.10679, loss_spatial_dice_6: 0.31021/0.24898, loss_spatial_ce_6: 0.12851/0.16436, loss_grounding_bce_6: 0.17747/0.08854, loss_grounding_dice_6: 0.13957/0.18264, loss_grounding_ce_6: 1.22313/0.31913, loss_mask_ce_7: 0.42603/1.02300, loss_mask_bce_7: 0.38235/0.35193, loss_mask_dice_7: 0.70916/1.25479, loss_spatial_bce_7: 0.16402/0.11651, loss_spatial_dice_7: 0.30025/0.27576, loss_spatial_ce_7: 0.19979/0.20494, loss_grounding_bce_7: 0.20090/0.09022, loss_grounding_dice_7: 0.15188/0.18969, loss_grounding_ce_7: 1.03688/0.35988, loss_mask_ce_8: 0.73539/1.13504, loss_mask_bce_8: 0.38122/0.36520, loss_mask_dice_8: 0.66977/1.32998, loss_spatial_bce_8: 0.24609/0.13748, loss_spatial_dice_8: 0.31657/0.31611, loss_spatial_ce_8: 0.38901/0.25841, loss_grounding_bce_8: 0.20161/0.09387, loss_grounding_dice_8: 0.15804/0.20103, loss_grounding_ce_8: 1.26282/0.43374, loss_mask_ce_9: 3.64020/3.70921, loss_mask_bce_9: 0.49004/0.39268, loss_mask_dice_9: 0.95435/1.90445, loss_spatial_bce_9: 0.34545/0.33982, loss_spatial_dice_9: 0.74077/0.82721, loss_spatial_ce_9: 1.25958/1.53870, loss_grounding_bce_9: 0.26087/0.10526, loss_grounding_dice_9: 0.15174/0.28046, loss_grounding_ce_9: 1.74861/0.72677] items per batch[64] items per second[0.22] total items[646400] mini batches[ 10100] memory[7341] epoch remaining[0:41:11]
INFO:trainer.default_trainer:epochs[     5] optim steps[10200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95776/0.91669, loss_mask_bce_0: 0.32318/0.33675, loss_mask_dice_0: 0.88541/1.16451, loss_spatial_bce_0: 0.08389/0.09547, loss_spatial_dice_0: 0.25821/0.22837, loss_spatial_ce_0: 0.03377/0.09836, loss_grounding_bce_0: 0.10914/0.08653, loss_grounding_dice_0: 0.25095/0.17849, loss_grounding_ce_0: 0.10499/0.27885, loss_mask_ce_1: 0.84049/0.91740, loss_mask_bce_1: 0.32562/0.33722, loss_mask_dice_1: 0.79779/1.17303, loss_spatial_bce_1: 0.07234/0.09653, loss_spatial_dice_1: 0.23704/0.23302, loss_spatial_ce_1: 0.07973/0.10390, loss_grounding_bce_1: 0.10796/0.08662, loss_grounding_dice_1: 0.22198/0.17910, loss_grounding_ce_1: 0.07509/0.28053, loss_mask_ce_2: 0.85714/0.92344, loss_mask_bce_2: 0.32695/0.33739, loss_mask_dice_2: 0.73528/1.17147, loss_spatial_bce_2: 0.07434/0.09615, loss_spatial_dice_2: 0.23594/0.23378, loss_spatial_ce_2: 0.10723/0.10853, loss_grounding_bce_2: 0.10952/0.08663, loss_grounding_dice_2: 0.25038/0.17881, loss_grounding_ce_2: 0.07924/0.28334, loss_mask_ce_3: 0.97295/0.92967, loss_mask_bce_3: 0.31944/0.33827, loss_mask_dice_3: 0.90737/1.16820, loss_spatial_bce_3: 0.07196/0.09726, loss_spatial_dice_3: 0.23373/0.23521, loss_spatial_ce_3: 0.09346/0.11350, loss_grounding_bce_3: 0.10273/0.08683, loss_grounding_dice_3: 0.19004/0.17851, loss_grounding_ce_3: 0.12778/0.28411, loss_mask_ce_4: 0.87904/0.92815, loss_mask_bce_4: 0.33770/0.33946, loss_mask_dice_4: 0.85208/1.18958, loss_spatial_bce_4: 0.08386/0.10065, loss_spatial_dice_4: 0.24913/0.24263, loss_spatial_ce_4: 0.08562/0.13007, loss_grounding_bce_4: 0.11685/0.08732, loss_grounding_dice_4: 0.25006/0.18133, loss_grounding_ce_4: 0.10740/0.28723, loss_mask_ce_5: 1.14174/0.94141, loss_mask_bce_5: 0.35768/0.34173, loss_mask_dice_5: 0.76196/1.19407, loss_spatial_bce_5: 0.08454/0.10151, loss_spatial_dice_5: 0.25617/0.24568, loss_spatial_ce_5: 0.07458/0.14286, loss_grounding_bce_5: 0.12273/0.08777, loss_grounding_dice_5: 0.20950/0.18248, loss_grounding_ce_5: 0.11440/0.29826, loss_mask_ce_6: 1.31033/0.97804, loss_mask_bce_6: 0.36934/0.34430, loss_mask_dice_6: 0.77036/1.19809, loss_spatial_bce_6: 0.08219/0.10681, loss_spatial_dice_6: 0.24767/0.24877, loss_spatial_ce_6: 0.15164/0.16408, loss_grounding_bce_6: 0.13619/0.08853, loss_grounding_dice_6: 0.20576/0.18268, loss_grounding_ce_6: 0.15290/0.31931, loss_mask_ce_7: 1.03570/1.02230, loss_mask_bce_7: 0.37409/0.35206, loss_mask_dice_7: 0.84001/1.25373, loss_spatial_bce_7: 0.09666/0.11652, loss_spatial_dice_7: 0.28992/0.27554, loss_spatial_ce_7: 0.18746/0.20473, loss_grounding_bce_7: 0.11918/0.09024, loss_grounding_dice_7: 0.18036/0.18974, loss_grounding_ce_7: 0.16551/0.35983, loss_mask_ce_8: 1.37581/1.13397, loss_mask_bce_8: 0.36756/0.36530, loss_mask_dice_8: 0.98190/1.32894, loss_spatial_bce_8: 0.11056/0.13752, loss_spatial_dice_8: 0.39211/0.31594, loss_spatial_ce_8: 0.32948/0.25807, loss_grounding_bce_8: 0.13478/0.09385, loss_grounding_dice_8: 0.20424/0.20105, loss_grounding_ce_8: 0.15705/0.43337, loss_mask_ce_9: 4.25091/3.70750, loss_mask_bce_9: 0.42284/0.39268, loss_mask_dice_9: 1.39069/1.90288, loss_spatial_bce_9: 0.29692/0.34002, loss_spatial_dice_9: 0.86304/0.82725, loss_spatial_ce_9: 1.31070/1.53817, loss_grounding_bce_9: 0.11857/0.10529, loss_grounding_dice_9: 0.40165/0.28056, loss_grounding_ce_9: 0.25397/0.72572] items per batch[64] items per second[0.23] total items[652800] mini batches[ 10200] memory[7341] epoch remaining[0:36:21]
INFO:trainer.default_trainer:epochs[     5] optim steps[10300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19468/0.91713, loss_mask_bce_0: 0.40328/0.33677, loss_mask_dice_0: 3.31605/1.16549, loss_spatial_bce_0: 0.02814/0.09536, loss_spatial_dice_0: 0.30957/0.22838, loss_spatial_ce_0: 0.04928/0.09819, loss_grounding_bce_0: 0.02779/0.08647, loss_grounding_dice_0: 0.16834/0.17859, loss_grounding_ce_0: 0.50878/0.27946, loss_mask_ce_1: 1.17835/0.91790, loss_mask_bce_1: 0.39367/0.33727, loss_mask_dice_1: 3.36858/1.17399, loss_spatial_bce_1: 0.03550/0.09643, loss_spatial_dice_1: 0.32296/0.23303, loss_spatial_ce_1: 0.04466/0.10363, loss_grounding_bce_1: 0.02915/0.08658, loss_grounding_dice_1: 0.18239/0.17923, loss_grounding_ce_1: 0.49260/0.28113, loss_mask_ce_2: 1.17754/0.92389, loss_mask_bce_2: 0.40935/0.33744, loss_mask_dice_2: 3.80967/1.17246, loss_spatial_bce_2: 0.02045/0.09605, loss_spatial_dice_2: 0.31597/0.23377, loss_spatial_ce_2: 0.29527/0.10832, loss_grounding_bce_2: 0.02664/0.08659, loss_grounding_dice_2: 0.15994/0.17893, loss_grounding_ce_2: 0.52969/0.28399, loss_mask_ce_3: 1.21021/0.93009, loss_mask_bce_3: 0.39510/0.33832, loss_mask_dice_3: 3.39567/1.16907, loss_spatial_bce_3: 0.02072/0.09715, loss_spatial_dice_3: 0.33591/0.23520, loss_spatial_ce_3: 0.24067/0.11337, loss_grounding_bce_3: 0.02810/0.08679, loss_grounding_dice_3: 0.18610/0.17861, loss_grounding_ce_3: 0.32466/0.28486, loss_mask_ce_4: 1.29304/0.92862, loss_mask_bce_4: 0.39191/0.33952, loss_mask_dice_4: 3.47736/1.19061, loss_spatial_bce_4: 0.03332/0.10056, loss_spatial_dice_4: 0.35888/0.24265, loss_spatial_ce_4: 0.09575/0.12982, loss_grounding_bce_4: 0.02906/0.08726, loss_grounding_dice_4: 0.18957/0.18149, loss_grounding_ce_4: 0.57097/0.28794, loss_mask_ce_5: 1.29327/0.94204, loss_mask_bce_5: 0.39410/0.34176, loss_mask_dice_5: 3.59776/1.19522, loss_spatial_bce_5: 0.02724/0.10144, loss_spatial_dice_5: 0.38249/0.24568, loss_spatial_ce_5: 0.16636/0.14255, loss_grounding_bce_5: 0.02942/0.08769, loss_grounding_dice_5: 0.18757/0.18260, loss_grounding_ce_5: 0.61342/0.29911, loss_mask_ce_6: 1.43664/0.97867, loss_mask_bce_6: 0.38937/0.34436, loss_mask_dice_6: 3.62842/1.19919, loss_spatial_bce_6: 0.04105/0.10676, loss_spatial_dice_6: 0.33061/0.24873, loss_spatial_ce_6: 0.09090/0.16382, loss_grounding_bce_6: 0.02934/0.08847, loss_grounding_dice_6: 0.18759/0.18281, loss_grounding_ce_6: 0.39564/0.32003, loss_mask_ce_7: 1.45118/1.02291, loss_mask_bce_7: 0.39962/0.35212, loss_mask_dice_7: 3.57850/1.25473, loss_spatial_bce_7: 0.03300/0.11643, loss_spatial_dice_7: 0.38860/0.27551, loss_spatial_ce_7: 0.11662/0.20444, loss_grounding_bce_7: 0.03126/0.09016, loss_grounding_dice_7: 0.18132/0.18989, loss_grounding_ce_7: 0.93741/0.36033, loss_mask_ce_8: 1.58197/1.13455, loss_mask_bce_8: 0.41576/0.36537, loss_mask_dice_8: 3.44268/1.32993, loss_spatial_bce_8: 0.02914/0.13743, loss_spatial_dice_8: 0.42046/0.31592, loss_spatial_ce_8: 0.17371/0.25777, loss_grounding_bce_8: 0.03597/0.09378, loss_grounding_dice_8: 0.18860/0.20116, loss_grounding_ce_8: 1.68024/0.43409, loss_mask_ce_9: 5.21821/3.70981, loss_mask_bce_9: 0.41120/0.39271, loss_mask_dice_9: 6.56522/1.90366, loss_spatial_bce_9: 0.13157/0.33962, loss_spatial_dice_9: 0.93154/0.82719, loss_spatial_ce_9: 1.34234/1.53780, loss_grounding_bce_9: 0.03379/0.10522, loss_grounding_dice_9: 0.28463/0.28069, loss_grounding_ce_9: 1.96069/0.72591] items per batch[64] items per second[0.22] total items[659200] mini batches[ 10300] memory[7341] epoch remaining[0:31:37]
INFO:trainer.default_trainer:epochs[     5] optim steps[10400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.06391/0.91672, loss_mask_bce_0: 0.60090/0.33706, loss_mask_dice_0: 2.19469/1.16515, loss_spatial_bce_0: 0.04323/0.09537, loss_spatial_dice_0: 0.20772/0.22822, loss_spatial_ce_0: 0.01055/0.09785, loss_grounding_bce_0: 0.04698/0.08647, loss_grounding_dice_0: 0.10985/0.17850, loss_grounding_ce_0: 0.37959/0.27880, loss_mask_ce_1: 2.03929/0.91738, loss_mask_bce_1: 0.56719/0.33761, loss_mask_dice_1: 2.19180/1.17361, loss_spatial_bce_1: 0.05064/0.09646, loss_spatial_dice_1: 0.18864/0.23284, loss_spatial_ce_1: 0.00591/0.10323, loss_grounding_bce_1: 0.03571/0.08658, loss_grounding_dice_1: 0.12117/0.17910, loss_grounding_ce_1: 0.36492/0.28051, loss_mask_ce_2: 2.09709/0.92355, loss_mask_bce_2: 0.57849/0.33775, loss_mask_dice_2: 2.25305/1.17189, loss_spatial_bce_2: 0.05140/0.09609, loss_spatial_dice_2: 0.18452/0.23363, loss_spatial_ce_2: 0.00509/0.10797, loss_grounding_bce_2: 0.03386/0.08661, loss_grounding_dice_2: 0.11663/0.17884, loss_grounding_ce_2: 0.36028/0.28332, loss_mask_ce_3: 2.12944/0.92966, loss_mask_bce_3: 0.60564/0.33863, loss_mask_dice_3: 2.10712/1.16871, loss_spatial_bce_3: 0.05006/0.09717, loss_spatial_dice_3: 0.17911/0.23502, loss_spatial_ce_3: 0.01113/0.11293, loss_grounding_bce_3: 0.03397/0.08681, loss_grounding_dice_3: 0.12332/0.17856, loss_grounding_ce_3: 0.38404/0.28422, loss_mask_ce_4: 2.03439/0.92826, loss_mask_bce_4: 0.61336/0.33983, loss_mask_dice_4: 2.18302/1.19011, loss_spatial_bce_4: 0.05275/0.10060, loss_spatial_dice_4: 0.17465/0.24249, loss_spatial_ce_4: 0.01122/0.12945, loss_grounding_bce_4: 0.03539/0.08727, loss_grounding_dice_4: 0.13176/0.18138, loss_grounding_ce_4: 0.39743/0.28726, loss_mask_ce_5: 2.06418/0.94161, loss_mask_bce_5: 0.57362/0.34206, loss_mask_dice_5: 2.17311/1.19499, loss_spatial_bce_5: 0.04577/0.10149, loss_spatial_dice_5: 0.18715/0.24549, loss_spatial_ce_5: 0.02290/0.14222, loss_grounding_bce_5: 0.03183/0.08771, loss_grounding_dice_5: 0.13867/0.18251, loss_grounding_ce_5: 0.37321/0.29851, loss_mask_ce_6: 2.23536/0.97833, loss_mask_bce_6: 0.60121/0.34464, loss_mask_dice_6: 2.31231/1.19857, loss_spatial_bce_6: 0.04778/0.10679, loss_spatial_dice_6: 0.21527/0.24853, loss_spatial_ce_6: 0.05316/0.16356, loss_grounding_bce_6: 0.03861/0.08848, loss_grounding_dice_6: 0.11626/0.18273, loss_grounding_ce_6: 0.40080/0.31942, loss_mask_ce_7: 2.35694/1.02282, loss_mask_bce_7: 0.59187/0.35244, loss_mask_dice_7: 2.44139/1.25406, loss_spatial_bce_7: 0.05353/0.11646, loss_spatial_dice_7: 0.30293/0.27533, loss_spatial_ce_7: 0.10114/0.20417, loss_grounding_bce_7: 0.04126/0.09017, loss_grounding_dice_7: 0.11786/0.18978, loss_grounding_ce_7: 0.50235/0.35981, loss_mask_ce_8: 2.27342/1.13428, loss_mask_bce_8: 0.62289/0.36565, loss_mask_dice_8: 2.68259/1.32935, loss_spatial_bce_8: 0.07315/0.13747, loss_spatial_dice_8: 0.37428/0.31571, loss_spatial_ce_8: 0.16090/0.25751, loss_grounding_bce_8: 0.04006/0.09379, loss_grounding_dice_8: 0.12944/0.20105, loss_grounding_ce_8: 0.44061/0.43359, loss_mask_ce_9: 6.48221/3.70903, loss_mask_bce_9: 0.78874/0.39292, loss_mask_dice_9: 7.07538/1.90438, loss_spatial_bce_9: 0.35305/0.33966, loss_spatial_dice_9: 0.80624/0.82708, loss_spatial_ce_9: 1.34104/1.53779, loss_grounding_bce_9: 0.04358/0.10519, loss_grounding_dice_9: 0.29009/0.28065, loss_grounding_ce_9: 0.54581/0.72604] items per batch[64] items per second[0.23] total items[665600] mini batches[ 10400] memory[7341] epoch remaining[0:26:47]
INFO:trainer.default_trainer:epochs[     5] optim steps[10500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35511/0.91676, loss_mask_bce_0: 0.50975/0.33686, loss_mask_dice_0: 0.56019/1.16465, loss_spatial_bce_0: 0.13209/0.09531, loss_spatial_dice_0: 0.16809/0.22819, loss_spatial_ce_0: 0.13786/0.09768, loss_grounding_bce_0: 0.19033/0.08637, loss_grounding_dice_0: 0.21784/0.17856, loss_grounding_ce_0: 0.08784/0.27886, loss_mask_ce_1: 0.36463/0.91747, loss_mask_bce_1: 0.47307/0.33740, loss_mask_dice_1: 0.55737/1.17305, loss_spatial_bce_1: 0.12812/0.09641, loss_spatial_dice_1: 0.16628/0.23279, loss_spatial_ce_1: 0.12606/0.10319, loss_grounding_bce_1: 0.16976/0.08648, loss_grounding_dice_1: 0.22024/0.17912, loss_grounding_ce_1: 0.08916/0.28038, loss_mask_ce_2: 0.35427/0.92364, loss_mask_bce_2: 0.47715/0.33756, loss_mask_dice_2: 0.57782/1.17149, loss_spatial_bce_2: 0.13047/0.09605, loss_spatial_dice_2: 0.18319/0.23362, loss_spatial_ce_2: 0.14004/0.10779, loss_grounding_bce_2: 0.17169/0.08652, loss_grounding_dice_2: 0.21918/0.17890, loss_grounding_ce_2: 0.09353/0.28327, loss_mask_ce_3: 0.33400/0.92972, loss_mask_bce_3: 0.48618/0.33847, loss_mask_dice_3: 0.55886/1.16828, loss_spatial_bce_3: 0.13272/0.09712, loss_spatial_dice_3: 0.18524/0.23498, loss_spatial_ce_3: 0.13478/0.11286, loss_grounding_bce_3: 0.16428/0.08671, loss_grounding_dice_3: 0.20839/0.17856, loss_grounding_ce_3: 0.09332/0.28419, loss_mask_ce_4: 0.38338/0.92848, loss_mask_bce_4: 0.51131/0.33963, loss_mask_dice_4: 0.58498/1.18965, loss_spatial_bce_4: 0.13807/0.10059, loss_spatial_dice_4: 0.16755/0.24245, loss_spatial_ce_4: 0.12542/0.12940, loss_grounding_bce_4: 0.18063/0.08714, loss_grounding_dice_4: 0.22351/0.18142, loss_grounding_ce_4: 0.09226/0.28729, loss_mask_ce_5: 0.41797/0.94169, loss_mask_bce_5: 0.48341/0.34184, loss_mask_dice_5: 0.57417/1.19461, loss_spatial_bce_5: 0.13001/0.10147, loss_spatial_dice_5: 0.18477/0.24546, loss_spatial_ce_5: 0.12794/0.14201, loss_grounding_bce_5: 0.17039/0.08759, loss_grounding_dice_5: 0.21204/0.18256, loss_grounding_ce_5: 0.09800/0.29829, loss_mask_ce_6: 0.35770/0.97840, loss_mask_bce_6: 0.49836/0.34443, loss_mask_dice_6: 0.57940/1.19810, loss_spatial_bce_6: 0.13963/0.10679, loss_spatial_dice_6: 0.18252/0.24848, loss_spatial_ce_6: 0.13452/0.16342, loss_grounding_bce_6: 0.17180/0.08837, loss_grounding_dice_6: 0.21294/0.18279, loss_grounding_ce_6: 0.11052/0.31947, loss_mask_ce_7: 0.53180/1.02288, loss_mask_bce_7: 0.44797/0.35222, loss_mask_dice_7: 0.52108/1.25355, loss_spatial_bce_7: 0.14579/0.11644, loss_spatial_dice_7: 0.17438/0.27529, loss_spatial_ce_7: 0.12047/0.20394, loss_grounding_bce_7: 0.13889/0.09007, loss_grounding_dice_7: 0.21355/0.18979, loss_grounding_ce_7: 0.13234/0.35939, loss_mask_ce_8: 0.65436/1.13453, loss_mask_bce_8: 0.46893/0.36544, loss_mask_dice_8: 0.53281/1.32877, loss_spatial_bce_8: 0.17971/0.13741, loss_spatial_dice_8: 0.21505/0.31569, loss_spatial_ce_8: 0.23043/0.25735, loss_grounding_bce_8: 0.16092/0.09367, loss_grounding_dice_8: 0.21295/0.20113, loss_grounding_ce_8: 0.11783/0.43343, loss_mask_ce_9: 2.28709/3.70820, loss_mask_bce_9: 0.39701/0.39276, loss_mask_dice_9: 0.66832/1.90365, loss_spatial_bce_9: 0.47499/0.33958, loss_spatial_dice_9: 0.80091/0.82706, loss_spatial_ce_9: 1.03166/1.53753, loss_grounding_bce_9: 0.10952/0.10506, loss_grounding_dice_9: 0.30276/0.28074, loss_grounding_ce_9: 0.23472/0.72546] items per batch[64] items per second[0.23] total items[672000] mini batches[ 10500] memory[7341] epoch remaining[0:22:00]
INFO:trainer.default_trainer:epochs[     5] optim steps[10600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18186/0.91660, loss_mask_bce_0: 0.68122/0.33677, loss_mask_dice_0: 1.17878/1.16539, loss_spatial_bce_0: 0.20184/0.09528, loss_spatial_dice_0: 0.31964/0.22810, loss_spatial_ce_0: 0.11579/0.09744, loss_grounding_bce_0: 0.08867/0.08638, loss_grounding_dice_0: 0.18217/0.17862, loss_grounding_ce_0: 0.27084/0.27865, loss_mask_ce_1: 1.33188/0.91722, loss_mask_bce_1: 0.63162/0.33731, loss_mask_dice_1: 1.10487/1.17388, loss_spatial_bce_1: 0.20530/0.09637, loss_spatial_dice_1: 0.31025/0.23268, loss_spatial_ce_1: 0.10180/0.10292, loss_grounding_bce_1: 0.08678/0.08649, loss_grounding_dice_1: 0.15691/0.17913, loss_grounding_ce_1: 0.26556/0.28037, loss_mask_ce_2: 1.21569/0.92357, loss_mask_bce_2: 0.66112/0.33747, loss_mask_dice_2: 1.15431/1.17232, loss_spatial_bce_2: 0.18008/0.09604, loss_spatial_dice_2: 0.31825/0.23350, loss_spatial_ce_2: 0.09448/0.10743, loss_grounding_bce_2: 0.08339/0.08653, loss_grounding_dice_2: 0.17861/0.17894, loss_grounding_ce_2: 0.27511/0.28329, loss_mask_ce_3: 1.37334/0.92972, loss_mask_bce_3: 0.64493/0.33834, loss_mask_dice_3: 1.13097/1.16921, loss_spatial_bce_3: 0.18179/0.09710, loss_spatial_dice_3: 0.31413/0.23486, loss_spatial_ce_3: 0.08777/0.11254, loss_grounding_bce_3: 0.08938/0.08671, loss_grounding_dice_3: 0.17318/0.17862, loss_grounding_ce_3: 0.32141/0.28407, loss_mask_ce_4: 1.31880/0.92829, loss_mask_bce_4: 0.68104/0.33952, loss_mask_dice_4: 1.16884/1.19044, loss_spatial_bce_4: 0.17056/0.10056, loss_spatial_dice_4: 0.30094/0.24238, loss_spatial_ce_4: 0.15944/0.12907, loss_grounding_bce_4: 0.08714/0.08712, loss_grounding_dice_4: 0.18077/0.18146, loss_grounding_ce_4: 0.35726/0.28718, loss_mask_ce_5: 1.33526/0.94167, loss_mask_bce_5: 0.62044/0.34174, loss_mask_dice_5: 1.13080/1.19553, loss_spatial_bce_5: 0.18860/0.10144, loss_spatial_dice_5: 0.31295/0.24537, loss_spatial_ce_5: 0.14386/0.14169, loss_grounding_bce_5: 0.07961/0.08758, loss_grounding_dice_5: 0.17227/0.18261, loss_grounding_ce_5: 0.38388/0.29817, loss_mask_ce_6: 1.48549/0.97828, loss_mask_bce_6: 0.61852/0.34432, loss_mask_dice_6: 1.14503/1.19896, loss_spatial_bce_6: 0.21215/0.10678, loss_spatial_dice_6: 0.31747/0.24838, loss_spatial_ce_6: 0.22617/0.16300, loss_grounding_bce_6: 0.08394/0.08834, loss_grounding_dice_6: 0.17531/0.18280, loss_grounding_ce_6: 0.39718/0.31935, loss_mask_ce_7: 1.49647/1.02296, loss_mask_bce_7: 0.61097/0.35211, loss_mask_dice_7: 1.15730/1.25460, loss_spatial_bce_7: 0.20408/0.11640, loss_spatial_dice_7: 0.34498/0.27523, loss_spatial_ce_7: 0.28882/0.20366, loss_grounding_bce_7: 0.07876/0.09005, loss_grounding_dice_7: 0.21503/0.18976, loss_grounding_ce_7: 0.52608/0.35953, loss_mask_ce_8: 1.66369/1.13488, loss_mask_bce_8: 0.65289/0.36535, loss_mask_dice_8: 1.14649/1.32975, loss_spatial_bce_8: 0.26864/0.13736, loss_spatial_dice_8: 0.38082/0.31568, loss_spatial_ce_8: 0.20209/0.25738, loss_grounding_bce_8: 0.10406/0.09368, loss_grounding_dice_8: 0.21241/0.20114, loss_grounding_ce_8: 0.70290/0.43332, loss_mask_ce_9: 4.18660/3.70836, loss_mask_bce_9: 0.67820/0.39264, loss_mask_dice_9: 1.59622/1.90513, loss_spatial_bce_9: 0.44102/0.33937, loss_spatial_dice_9: 0.83777/0.82708, loss_spatial_ce_9: 1.44028/1.53782, loss_grounding_bce_9: 0.11536/0.10507, loss_grounding_dice_9: 0.33956/0.28082, loss_grounding_ce_9: 1.36710/0.72460] items per batch[64] items per second[0.22] total items[678400] mini batches[ 10600] memory[7341] epoch remaining[0:17:14]
INFO:trainer.default_trainer:epochs[     5] optim steps[10700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82370/0.91623, loss_mask_bce_0: 0.18269/0.33694, loss_mask_dice_0: 0.46618/1.16630, loss_spatial_bce_0: 0.06617/0.09521, loss_spatial_dice_0: 0.16327/0.22803, loss_spatial_ce_0: 0.00138/0.09704, loss_grounding_bce_0: 0.04580/0.08642, loss_grounding_dice_0: 0.38162/0.17868, loss_grounding_ce_0: 0.46922/0.27877, loss_mask_ce_1: 0.79874/0.91678, loss_mask_bce_1: 0.18360/0.33750, loss_mask_dice_1: 0.46451/1.17499, loss_spatial_bce_1: 0.07180/0.09630, loss_spatial_dice_1: 0.15961/0.23259, loss_spatial_ce_1: 0.00698/0.10253, loss_grounding_bce_1: 0.05737/0.08653, loss_grounding_dice_1: 0.35770/0.17920, loss_grounding_ce_1: 0.45564/0.28059, loss_mask_ce_2: 1.07345/0.92307, loss_mask_bce_2: 0.18079/0.33764, loss_mask_dice_2: 0.46632/1.17327, loss_spatial_bce_2: 0.06874/0.09598, loss_spatial_dice_2: 0.16685/0.23339, loss_spatial_ce_2: 0.00154/0.10695, loss_grounding_bce_2: 0.05360/0.08657, loss_grounding_dice_2: 0.40962/0.17904, loss_grounding_ce_2: 0.05568/0.28336, loss_mask_ce_3: 1.11207/0.92937, loss_mask_bce_3: 0.18510/0.33849, loss_mask_dice_3: 0.44344/1.17016, loss_spatial_bce_3: 0.06803/0.09703, loss_spatial_dice_3: 0.15948/0.23477, loss_spatial_ce_3: 0.00080/0.11206, loss_grounding_bce_3: 0.05597/0.08674, loss_grounding_dice_3: 0.40224/0.17872, loss_grounding_ce_3: 0.04354/0.28419, loss_mask_ce_4: 1.12017/0.92798, loss_mask_bce_4: 0.18819/0.33969, loss_mask_dice_4: 0.40373/1.19142, loss_spatial_bce_4: 0.07085/0.10049, loss_spatial_dice_4: 0.15536/0.24230, loss_spatial_ce_4: 0.00291/0.12862, loss_grounding_bce_4: 0.04757/0.08715, loss_grounding_dice_4: 0.40417/0.18154, loss_grounding_ce_4: 0.03387/0.28715, loss_mask_ce_5: 0.80252/0.94130, loss_mask_bce_5: 0.18724/0.34194, loss_mask_dice_5: 0.46098/1.19658, loss_spatial_bce_5: 0.06593/0.10138, loss_spatial_dice_5: 0.18020/0.24530, loss_spatial_ce_5: 0.01402/0.14117, loss_grounding_bce_5: 0.05290/0.08763, loss_grounding_dice_5: 0.40053/0.18272, loss_grounding_ce_5: 0.03387/0.29840, loss_mask_ce_6: 0.54990/0.97771, loss_mask_bce_6: 0.19807/0.34455, loss_mask_dice_6: 0.49514/1.20010, loss_spatial_bce_6: 0.07022/0.10672, loss_spatial_dice_6: 0.17439/0.24827, loss_spatial_ce_6: 0.03453/0.16261, loss_grounding_bce_6: 0.04495/0.08841, loss_grounding_dice_6: 0.38778/0.18293, loss_grounding_ce_6: 0.04075/0.31942, loss_mask_ce_7: 0.53274/1.02229, loss_mask_bce_7: 0.20626/0.35230, loss_mask_dice_7: 0.48910/1.25573, loss_spatial_bce_7: 0.07604/0.11630, loss_spatial_dice_7: 0.17576/0.27516, loss_spatial_ce_7: 0.05425/0.20330, loss_grounding_bce_7: 0.04464/0.09009, loss_grounding_dice_7: 0.39737/0.18986, loss_grounding_ce_7: 0.03155/0.35929, loss_mask_ce_8: 1.05802/1.13408, loss_mask_bce_8: 0.19356/0.36559, loss_mask_dice_8: 0.46871/1.33080, loss_spatial_bce_8: 0.07871/0.13725, loss_spatial_dice_8: 0.18182/0.31564, loss_spatial_ce_8: 0.09774/0.25711, loss_grounding_bce_8: 0.03709/0.09373, loss_grounding_dice_8: 0.37909/0.20120, loss_grounding_ce_8: 0.04515/0.43299, loss_mask_ce_9: 2.42162/3.70775, loss_mask_bce_9: 0.20218/0.39284, loss_mask_dice_9: 0.62687/1.90715, loss_spatial_bce_9: 0.32689/0.33931, loss_spatial_dice_9: 0.69679/0.82714, loss_spatial_ce_9: 1.26420/1.53722, loss_grounding_bce_9: 0.06023/0.10510, loss_grounding_dice_9: 0.55210/0.28099, loss_grounding_ce_9: 0.35155/0.72395] items per batch[64] items per second[0.22] total items[684800] mini batches[ 10700] memory[7341] epoch remaining[0:12:28]
INFO:trainer.default_trainer:epochs[     5] optim steps[10800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.47345/0.91589, loss_mask_bce_0: 0.52275/0.33654, loss_mask_dice_0: 0.63169/1.16555, loss_spatial_bce_0: 0.18350/0.09506, loss_spatial_dice_0: 0.20183/0.22786, loss_spatial_ce_0: 0.08940/0.09664, loss_grounding_bce_0: 0.26918/0.08634, loss_grounding_dice_0: 0.20639/0.17855, loss_grounding_ce_0: 0.39380/0.27851, loss_mask_ce_1: 1.54809/0.91634, loss_mask_bce_1: 0.56113/0.33710, loss_mask_dice_1: 0.63684/1.17422, loss_spatial_bce_1: 0.18819/0.09614, loss_spatial_dice_1: 0.19843/0.23246, loss_spatial_ce_1: 0.08822/0.10215, loss_grounding_bce_1: 0.28881/0.08646, loss_grounding_dice_1: 0.22386/0.17907, loss_grounding_ce_1: 0.38108/0.28025, loss_mask_ce_2: 1.45275/0.92245, loss_mask_bce_2: 0.66050/0.33726, loss_mask_dice_2: 0.70359/1.17250, loss_spatial_bce_2: 0.18785/0.09583, loss_spatial_dice_2: 0.20066/0.23321, loss_spatial_ce_2: 0.09071/0.10664, loss_grounding_bce_2: 0.31260/0.08650, loss_grounding_dice_2: 0.21731/0.17889, loss_grounding_ce_2: 0.37891/0.28345, loss_mask_ce_3: 1.54878/0.92865, loss_mask_bce_3: 0.52331/0.33810, loss_mask_dice_3: 0.63792/1.16941, loss_spatial_bce_3: 0.17784/0.09688, loss_spatial_dice_3: 0.19469/0.23461, loss_spatial_ce_3: 0.09562/0.11168, loss_grounding_bce_3: 0.29939/0.08665, loss_grounding_dice_3: 0.22656/0.17856, loss_grounding_ce_3: 0.37633/0.28389, loss_mask_ce_4: 1.63230/0.92730, loss_mask_bce_4: 0.52060/0.33932, loss_mask_dice_4: 0.67596/1.19053, loss_spatial_bce_4: 0.18014/0.10036, loss_spatial_dice_4: 0.20820/0.24214, loss_spatial_ce_4: 0.20366/0.12827, loss_grounding_bce_4: 0.34726/0.08707, loss_grounding_dice_4: 0.23691/0.18135, loss_grounding_ce_4: 0.41673/0.28692, loss_mask_ce_5: 1.67101/0.94079, loss_mask_bce_5: 0.48442/0.34155, loss_mask_dice_5: 0.65162/1.19569, loss_spatial_bce_5: 0.18719/0.10125, loss_spatial_dice_5: 0.22457/0.24515, loss_spatial_ce_5: 0.20701/0.14085, loss_grounding_bce_5: 0.34399/0.08755, loss_grounding_dice_5: 0.26354/0.18256, loss_grounding_ce_5: 0.40441/0.29808, loss_mask_ce_6: 1.60395/0.97701, loss_mask_bce_6: 0.44533/0.34413, loss_mask_dice_6: 0.64067/1.19923, loss_spatial_bce_6: 0.19931/0.10658, loss_spatial_dice_6: 0.23468/0.24809, loss_spatial_ce_6: 0.17842/0.16225, loss_grounding_bce_6: 0.38046/0.08831, loss_grounding_dice_6: 0.21434/0.18271, loss_grounding_ce_6: 0.38106/0.31892, loss_mask_ce_7: 1.74437/1.02166, loss_mask_bce_7: 0.45005/0.35186, loss_mask_dice_7: 0.74994/1.25478, loss_spatial_bce_7: 0.20410/0.11615, loss_spatial_dice_7: 0.27902/0.27495, loss_spatial_ce_7: 0.35199/0.20299, loss_grounding_bce_7: 0.41866/0.09000, loss_grounding_dice_7: 0.25050/0.18963, loss_grounding_ce_7: 0.52506/0.35887, loss_mask_ce_8: 1.24731/1.13338, loss_mask_bce_8: 0.57763/0.36513, loss_mask_dice_8: 0.97134/1.32968, loss_spatial_bce_8: 0.24632/0.13714, loss_spatial_dice_8: 0.36074/0.31547, loss_spatial_ce_8: 0.30821/0.25677, loss_grounding_bce_8: 0.29257/0.09365, loss_grounding_dice_8: 0.28539/0.20098, loss_grounding_ce_8: 0.37373/0.43205, loss_mask_ce_9: 2.55693/3.70580, loss_mask_bce_9: 0.56755/0.39231, loss_mask_dice_9: 1.16004/1.90570, loss_spatial_bce_9: 0.40354/0.33931, loss_spatial_dice_9: 0.82533/0.82720, loss_spatial_ce_9: 1.67638/1.53703, loss_grounding_bce_9: 0.34417/0.10498, loss_grounding_dice_9: 0.31153/0.28076, loss_grounding_ce_9: 0.35236/0.72336] items per batch[64] items per second[0.23] total items[691200] mini batches[ 10800] memory[7341] epoch remaining[0:07:42]
INFO:trainer.default_trainer:epochs[     5] optim steps[10900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23047/0.91624, loss_mask_bce_0: 0.41436/0.33652, loss_mask_dice_0: 0.88388/1.16473, loss_spatial_bce_0: 0.11048/0.09508, loss_spatial_dice_0: 0.25387/0.22788, loss_spatial_ce_0: 0.11559/0.09644, loss_grounding_bce_0: 0.01529/0.08630, loss_grounding_dice_0: 0.29234/0.17874, loss_grounding_ce_0: 0.28399/0.27886, loss_mask_ce_1: 1.45812/0.91667, loss_mask_bce_1: 0.42632/0.33712, loss_mask_dice_1: 0.90955/1.17337, loss_spatial_bce_1: 0.13724/0.09615, loss_spatial_dice_1: 0.26200/0.23245, loss_spatial_ce_1: 0.07922/0.10200, loss_grounding_bce_1: 0.01715/0.08642, loss_grounding_dice_1: 0.28201/0.17925, loss_grounding_ce_1: 0.29583/0.28062, loss_mask_ce_2: 1.26273/0.92281, loss_mask_bce_2: 0.46370/0.33729, loss_mask_dice_2: 0.92161/1.17180, loss_spatial_bce_2: 0.14386/0.09584, loss_spatial_dice_2: 0.28819/0.23322, loss_spatial_ce_2: 0.19216/0.10651, loss_grounding_bce_2: 0.01447/0.08647, loss_grounding_dice_2: 0.27203/0.17909, loss_grounding_ce_2: 0.26768/0.28383, loss_mask_ce_3: 1.15999/0.92907, loss_mask_bce_3: 0.43187/0.33813, loss_mask_dice_3: 0.96407/1.16846, loss_spatial_bce_3: 0.14013/0.09691, loss_spatial_dice_3: 0.27816/0.23459, loss_spatial_ce_3: 0.13699/0.11151, loss_grounding_bce_3: 0.04642/0.08662, loss_grounding_dice_3: 0.29836/0.17875, loss_grounding_ce_3: 0.23297/0.28430, loss_mask_ce_4: 1.23446/0.92773, loss_mask_bce_4: 0.34357/0.33938, loss_mask_dice_4: 0.93571/1.18959, loss_spatial_bce_4: 0.09453/0.10036, loss_spatial_dice_4: 0.24983/0.24218, loss_spatial_ce_4: 0.11916/0.12808, loss_grounding_bce_4: 0.01503/0.08705, loss_grounding_dice_4: 0.27801/0.18156, loss_grounding_ce_4: 0.27009/0.28733, loss_mask_ce_5: 1.28186/0.94135, loss_mask_bce_5: 0.32731/0.34156, loss_mask_dice_5: 1.00541/1.19484, loss_spatial_bce_5: 0.09574/0.10126, loss_spatial_dice_5: 0.27192/0.24515, loss_spatial_ce_5: 0.17373/0.14070, loss_grounding_bce_5: 0.04128/0.08752, loss_grounding_dice_5: 0.28116/0.18274, loss_grounding_ce_5: 0.22777/0.29845, loss_mask_ce_6: 1.48193/0.97734, loss_mask_bce_6: 0.31761/0.34416, loss_mask_dice_6: 1.01036/1.19841, loss_spatial_bce_6: 0.12873/0.10662, loss_spatial_dice_6: 0.26966/0.24808, loss_spatial_ce_6: 0.19227/0.16216, loss_grounding_bce_6: 0.02884/0.08829, loss_grounding_dice_6: 0.27451/0.18294, loss_grounding_ce_6: 0.16922/0.31898, loss_mask_ce_7: 1.70616/1.02207, loss_mask_bce_7: 0.40416/0.35190, loss_mask_dice_7: 1.09781/1.25387, loss_spatial_bce_7: 0.12397/0.11620, loss_spatial_dice_7: 0.29052/0.27496, loss_spatial_ce_7: 0.36208/0.20295, loss_grounding_bce_7: 0.02542/0.08999, loss_grounding_dice_7: 0.32158/0.18984, loss_grounding_ce_7: 0.29767/0.35883, loss_mask_ce_8: 2.06091/1.13377, loss_mask_bce_8: 0.42032/0.36517, loss_mask_dice_8: 1.40047/1.32871, loss_spatial_bce_8: 0.12095/0.13722, loss_spatial_dice_8: 0.34029/0.31541, loss_spatial_ce_8: 0.42562/0.25667, loss_grounding_bce_8: 0.02168/0.09360, loss_grounding_dice_8: 0.41888/0.20117, loss_grounding_ce_8: 0.34122/0.43152, loss_mask_ce_9: 6.18177/3.70539, loss_mask_bce_9: 0.35480/0.39227, loss_mask_dice_9: 1.81412/1.90460, loss_spatial_bce_9: 0.22330/0.33922, loss_spatial_dice_9: 0.93353/0.82708, loss_spatial_ce_9: 1.34711/1.53636, loss_grounding_bce_9: 0.02837/0.10494, loss_grounding_dice_9: 0.39351/0.28090, loss_grounding_ce_9: 0.52181/0.72258] items per batch[64] items per second[0.22] total items[697600] mini batches[ 10900] memory[7341] epoch remaining[0:02:57]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00010962.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2243 s/iter. Eval: 0.0980 s/iter. Total: 0.3254 s/iter. ETA=0:00:47
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2270 s/iter. Eval: 0.0926 s/iter. Total: 0.3227 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2284 s/iter. Eval: 0.0853 s/iter. Total: 0.3169 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2282 s/iter. Eval: 0.0814 s/iter. Total: 0.3128 s/iter. ETA=0:00:30
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2255 s/iter. Eval: 0.0786 s/iter. Total: 0.3074 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2277 s/iter. Eval: 0.0775 s/iter. Total: 0.3085 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2293 s/iter. Eval: 0.0775 s/iter. Total: 0.3100 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2284 s/iter. Eval: 0.0766 s/iter. Total: 0.3083 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0033 s/iter. Inference: 0.2288 s/iter. Eval: 0.0760 s/iter. Total: 0.3082 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval5gk9376p ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.926 | 82.128 | 59.940 |      133      |
| Things | 55.040 | 82.868 | 65.764 |      80       |
| Stuff  | 42.207 | 81.010 | 51.149 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.38s)
creating index...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.29 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.01 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.20s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.27 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.610
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.412
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.285
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.711
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.882 | 61.044 | 41.158 | 18.698 | 42.127 | 60.219 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.386 | bicycle      | 18.255 | car            | 38.161 |
| motorcycle    | 34.449 | airplane     | 57.238 | bus            | 63.586 |
| train         | 68.968 | truck        | 32.232 | boat           | 23.918 |
| traffic light | 24.655 | fire hydrant | 64.276 | stop sign      | 63.586 |
| parking meter | 43.858 | bench        | 20.244 | bird           | 29.551 |
| cat           | 74.667 | dog          | 65.894 | horse          | 45.803 |
| sheep         | 46.571 | cow          | 50.132 | elephant       | 61.115 |
| bear          | 76.578 | zebra        | 60.774 | giraffe        | 57.057 |
| backpack      | 16.818 | umbrella     | 47.927 | handbag        | 15.686 |
| tie           | 32.859 | suitcase     | 39.236 | frisbee        | 67.031 |
| skis          | 5.324  | snowboard    | 23.530 | sports ball    | 47.760 |
| kite          | 33.356 | baseball bat | 28.834 | baseball glove | 42.231 |
| skateboard    | 35.299 | surfboard    | 35.725 | tennis racket  | 56.554 |
| bottle        | 33.696 | wine glass   | 26.845 | cup            | 40.138 |
| fork          | 16.153 | knife        | 13.265 | spoon          | 14.766 |
| bowl          | 32.195 | banana       | 21.312 | apple          | 21.853 |
| sandwich      | 43.070 | orange       | 29.124 | broccoli       | 20.215 |
| carrot        | 19.596 | hot dog      | 25.046 | pizza          | 50.276 |
| donut         | 45.574 | cake         | 44.019 | chair          | 20.304 |
| couch         | 39.939 | potted plant | 16.755 | bed            | 41.265 |
| dining table  | 13.287 | toilet       | 66.806 | tv             | 62.682 |
| laptop        | 62.739 | mouse        | 58.757 | remote         | 30.364 |
| keyboard      | 47.731 | cell phone   | 38.156 | microwave      | 55.485 |
| oven          | 34.299 | toaster      | 32.533 | sink           | 37.820 |
| refrigerator  | 59.268 | book         | 7.782  | clock          | 51.665 |
| vase          | 35.048 | scissors     | 25.273 | teddy bear     | 49.959 |
| hair drier    | 11.366 | toothbrush   | 20.008 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.405775903895865, 'fwIoU': 68.62538006100274, 'IoU-person': 87.42531463152126, 'IoU-bicycle': 72.28892706134921, 'IoU-car': 67.2899774286878, 'IoU-motorcycle': 84.56905547814327, 'IoU-airplane': 81.10696578685959, 'IoU-bus': 85.64024612978746, 'IoU-train': 88.79313214926168, 'IoU-truck': 61.26449024594641, 'IoU-boat': 67.69852503287485, 'IoU-traffic light': 76.44435385490598, 'IoU-fire hydrant': 87.74752991832526, 'IoU-stop sign': 91.17734171821226, 'IoU-parking meter': 86.69066541808299, 'IoU-bench': 54.23549525159746, 'IoU-bird': 73.07634297217263, 'IoU-cat': 88.68693498845333, 'IoU-dog': 78.46146693546996, 'IoU-horse': 86.28950366259754, 'IoU-sheep': 88.54640591747763, 'IoU-cow': 82.62572531470994, 'IoU-elephant': 85.4540753775969, 'IoU-bear': 72.51674846939511, 'IoU-zebra': 91.43549961046547, 'IoU-giraffe': 86.56158669734472, 'IoU-backpack': 39.84701518292489, 'IoU-umbrella': 71.37141779079838, 'IoU-handbag': 37.10434553465826, 'IoU-tie': 71.4540025476222, 'IoU-suitcase': 80.81218680586379, 'IoU-frisbee': 82.68162140386104, 'IoU-skis': 52.14073327292914, 'IoU-snowboard': 69.34518870478902, 'IoU-sports ball': 67.58207785421928, 'IoU-kite': 64.9394209726043, 'IoU-baseball bat': 61.270873000240734, 'IoU-baseball glove': 76.50458627553563, 'IoU-skateboard': 68.13010452295796, 'IoU-surfboard': 79.89085111306075, 'IoU-tennis racket': 74.53179614387192, 'IoU-bottle': 68.2434173476776, 'IoU-wine glass': 76.18324705844674, 'IoU-cup': 65.35436836234534, 'IoU-fork': 55.155891330121335, 'IoU-knife': 49.07712477571336, 'IoU-spoon': 48.06735502601834, 'IoU-bowl': 51.03072437185021, 'IoU-banana': 84.5517119988142, 'IoU-apple': 57.031383235328626, 'IoU-sandwich': 62.5621197320433, 'IoU-orange': 77.37008446617472, 'IoU-broccoli': 68.74048394677223, 'IoU-carrot': 63.954468079972926, 'IoU-hot dog': 62.45654215017189, 'IoU-pizza': 80.20934663117416, 'IoU-donut': 64.66566946645199, 'IoU-cake': 69.48456021063096, 'IoU-chair': 52.77581380808145, 'IoU-couch': 66.35020197521973, 'IoU-potted plant': 34.71135655144417, 'IoU-bed': 70.47292677656921, 'IoU-dining table': 49.89997076798196, 'IoU-toilet': 80.49213355338154, 'IoU-tv': 74.2861385728477, 'IoU-laptop': 72.60237645331512, 'IoU-mouse': 72.35625160307187, 'IoU-remote': 50.160640465675954, 'IoU-keyboard': 59.255107401213536, 'IoU-cell phone': 68.4245476800135, 'IoU-microwave': 50.40597482537552, 'IoU-oven': 67.55812976218789, 'IoU-toaster': 46.301433335568795, 'IoU-sink': 67.60762744354408, 'IoU-refrigerator': 82.95285136669402, 'IoU-book': 47.78977678148581, 'IoU-clock': 74.25436014762349, 'IoU-vase': 66.4584254734242, 'IoU-scissors': 54.655902368559104, 'IoU-teddy bear': 79.35234912290771, 'IoU-hair drier': 38.21428571428571, 'IoU-toothbrush': 57.5471756576705, 'IoU-banner': 36.2965002890722, 'IoU-blanket': 11.88705812383506, 'IoU-bridge': 39.933271937712014, 'IoU-cardboard': 44.28353678513979, 'IoU-counter': 27.92419924361646, 'IoU-curtain': 62.986602751437246, 'IoU-door-stuff': 43.55641102668366, 'IoU-floor-wood': 60.044601264901274, 'IoU-flower': 48.60279584487991, 'IoU-fruit': 39.136308158098316, 'IoU-gravel': 29.915858782934528, 'IoU-house': 23.310202905147055, 'IoU-light': 40.34634487153369, 'IoU-mirror-stuff': 54.23668826027611, 'IoU-net': 47.52612872122661, 'IoU-pillow': 12.039506918904118, 'IoU-platform': 28.701178993551686, 'IoU-playingfield': 65.01168226826059, 'IoU-railroad': 60.5552002078272, 'IoU-river': 50.600696667505716, 'IoU-road': 66.50668831441646, 'IoU-roof': 16.595072063219213, 'IoU-sand': 60.74819902067947, 'IoU-sea': 83.87440229946965, 'IoU-shelf': 35.49339620448814, 'IoU-snow': 88.69356896252793, 'IoU-stairs': 29.75397159670322, 'IoU-tent': 5.7038204719279975, 'IoU-towel': 34.10762747902733, 'IoU-wall-brick': 46.99392903395647, 'IoU-wall-stone': 26.808447520457037, 'IoU-wall-tile': 67.4799646925095, 'IoU-wall-wood': 38.42578545386437, 'IoU-water-other': 24.663773376570617, 'IoU-window-blind': 45.93728701739847, 'IoU-window-other': 48.01272514941406, 'IoU-tree-merged': 80.60317630832503, 'IoU-fence-merged': 50.89753416625695, 'IoU-ceiling-merged': 65.9841933831004, 'IoU-sky-other-merged': 92.22950167455036, 'IoU-cabinet-merged': 57.80751941672242, 'IoU-table-merged': 36.27235280984223, 'IoU-floor-other-merged': 49.11633462530556, 'IoU-pavement-merged': 54.667393471892126, 'IoU-mountain-merged': 55.16767927997436, 'IoU-grass-merged': 71.9588812149135, 'IoU-dirt-merged': 42.32504632694054, 'IoU-paper-merged': 32.35390942802759, 'IoU-food-other-merged': 39.216684005895495, 'IoU-building-other-merged': 56.85340356176651, 'IoU-rock-merged': 60.73786905570834, 'IoU-wall-other-merged': 65.45784261546648, 'IoU-rug-merged': 62.96865421926216, 'mACC': 72.55438711046128, 'pACC': 80.071054440799, 'ACC-person': 92.2396359196459, 'ACC-bicycle': 86.00825466596291, 'ACC-car': 86.45811688639184, 'ACC-motorcycle': 90.99707270191462, 'ACC-airplane': 87.71106151168347, 'ACC-bus': 90.98758048785433, 'ACC-train': 93.59687441141075, 'ACC-truck': 70.0618970033516, 'ACC-boat': 78.35663750503447, 'ACC-traffic light': 90.03593827073497, 'ACC-fire hydrant': 93.13374568186961, 'ACC-stop sign': 93.71338338953692, 'ACC-parking meter': 92.38093510122249, 'ACC-bench': 71.01563968933742, 'ACC-bird': 78.5380608768596, 'ACC-cat': 95.10664894280735, 'ACC-dog': 82.65423309620732, 'ACC-horse': 92.31420058308979, 'ACC-sheep': 91.85323826277433, 'ACC-cow': 88.59737740107121, 'ACC-elephant': 87.81765528235587, 'ACC-bear': 79.6787651543269, 'ACC-zebra': 94.07830666561384, 'ACC-giraffe': 91.10391040058697, 'ACC-backpack': 56.28686426937538, 'ACC-umbrella': 79.10293325315948, 'ACC-handbag': 54.309684456396354, 'ACC-tie': 80.47134087219806, 'ACC-suitcase': 88.92064634945964, 'ACC-frisbee': 94.03636363636365, 'ACC-skis': 69.60548148703394, 'ACC-snowboard': 77.77815875192869, 'ACC-sports ball': 80.21883172594868, 'ACC-kite': 74.38843289311073, 'ACC-baseball bat': 83.90412755021765, 'ACC-baseball glove': 89.03540058940936, 'ACC-skateboard': 74.40785309999058, 'ACC-surfboard': 89.57515001898462, 'ACC-tennis racket': 79.62967238705208, 'ACC-bottle': 82.21222844112116, 'ACC-wine glass': 86.25990462548276, 'ACC-cup': 83.91759604429467, 'ACC-fork': 67.60478680891258, 'ACC-knife': 60.27735033340661, 'ACC-spoon': 67.50494251881445, 'ACC-bowl': 60.59783292783497, 'ACC-banana': 90.51673338569476, 'ACC-apple': 65.99532766400702, 'ACC-sandwich': 75.63322987528446, 'ACC-orange': 86.4532072373398, 'ACC-broccoli': 79.01450341404203, 'ACC-carrot': 73.30167245692961, 'ACC-hot dog': 70.11371410891628, 'ACC-pizza': 91.2732739956226, 'ACC-donut': 81.7178089126476, 'ACC-cake': 75.96259858169645, 'ACC-chair': 68.82132092016579, 'ACC-couch': 82.01894879255521, 'ACC-potted plant': 47.38558879512363, 'ACC-bed': 81.91168058658515, 'ACC-dining table': 79.09642340407648, 'ACC-toilet': 91.52345601214311, 'ACC-tv': 86.25534456700498, 'ACC-laptop': 87.33231273280782, 'ACC-mouse': 86.49971682016097, 'ACC-remote': 72.12173376281825, 'ACC-keyboard': 67.07122084697804, 'ACC-cell phone': 80.73687425807915, 'ACC-microwave': 57.62003051702956, 'ACC-oven': 86.30435646703695, 'ACC-toaster': 51.96800284662443, 'ACC-sink': 80.17159160333226, 'ACC-refrigerator': 89.73832273937326, 'ACC-book': 66.26519991034155, 'ACC-clock': 82.90394032099064, 'ACC-vase': 81.96946396939425, 'ACC-scissors': 59.66793377957744, 'ACC-teddy bear': 84.90208654035376, 'ACC-hair drier': 41.724304784675375, 'ACC-toothbrush': 80.66452397498263, 'ACC-banner': 74.68875312958627, 'ACC-blanket': 13.569828518461302, 'ACC-bridge': 55.912812490476725, 'ACC-cardboard': 54.049701904900395, 'ACC-counter': 57.63930887015016, 'ACC-curtain': 75.40587683241836, 'ACC-door-stuff': 62.04703143896706, 'ACC-floor-wood': 75.61984870202426, 'ACC-flower': 69.64800277159219, 'ACC-fruit': 58.32188119882312, 'ACC-gravel': 42.830209190325824, 'ACC-house': 26.60132236758073, 'ACC-light': 54.60902509283008, 'ACC-mirror-stuff': 67.51273346157701, 'ACC-net': 61.30582215393756, 'ACC-pillow': 26.928691924085186, 'ACC-platform': 52.7595850848409, 'ACC-playingfield': 77.7579257263322, 'ACC-railroad': 77.26368615690743, 'ACC-river': 70.12297216187264, 'ACC-road': 84.1683480539143, 'ACC-roof': 23.711857293614802, 'ACC-sand': 73.54374131558058, 'ACC-sea': 88.52212421102702, 'ACC-shelf': 54.86519574270672, 'ACC-snow': 94.97754486513853, 'ACC-stairs': 46.30978323930743, 'ACC-tent': 6.79255659505418, 'ACC-towel': 39.81837022434283, 'ACC-wall-brick': 64.49073014244681, 'ACC-wall-stone': 35.836186354305724, 'ACC-wall-tile': 81.74773252104033, 'ACC-wall-wood': 51.469691145317, 'ACC-water-other': 45.48780696439812, 'ACC-window-blind': 57.98462409509643, 'ACC-window-other': 70.86283511495083, 'ACC-tree-merged': 89.82415389743443, 'ACC-fence-merged': 68.15143335069396, 'ACC-ceiling-merged': 81.77256050092303, 'ACC-sky-other-merged': 96.42708595148166, 'ACC-cabinet-merged': 75.26734625236381, 'ACC-table-merged': 48.2011716444109, 'ACC-floor-other-merged': 59.68041557742904, 'ACC-pavement-merged': 68.05191960289699, 'ACC-mountain-merged': 63.70188740066245, 'ACC-grass-merged': 84.60868783211369, 'ACC-dirt-merged': 67.04792315406411, 'ACC-paper-merged': 44.88379424735862, 'ACC-food-other-merged': 48.94979757323664, 'ACC-building-other-merged': 70.3746379087119, 'ACC-rock-merged': 82.6335727764491, 'ACC-wall-other-merged': 83.03468340825142, 'ACC-rug-merged': 78.79706604040598})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1582 s/iter. Inference: 0.3848 s/iter. Eval: 0.0000 s/iter. Total: 0.5430 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1586 s/iter. Inference: 0.4484 s/iter. Eval: 0.0000 s/iter. Total: 0.6071 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1690 s/iter. Inference: 0.5217 s/iter. Eval: 0.0000 s/iter. Total: 0.6909 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 30/50. Dataloading: 0.1725 s/iter. Inference: 0.6327 s/iter. Eval: 0.0000 s/iter. Total: 0.8053 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1683 s/iter. Inference: 0.5936 s/iter. Eval: 0.0000 s/iter. Total: 0.7621 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 44/50. Dataloading: 0.1674 s/iter. Inference: 0.6092 s/iter. Eval: 0.0000 s/iter. Total: 0.7768 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 48/50. Dataloading: 0.1675 s/iter. Inference: 0.6727 s/iter. Eval: 0.0000 s/iter. Total: 0.8403 s/iter. ETA=0:00:01
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.562774363476734, 'noc@0.8': 3.015218027509511, 'noc@0.85': 3.68510389230319, 'noc@0.9': 4.732513901082821, 'miou@iter1': 0.8314173259963967}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1008 s/iter. Eval: 0.0008 s/iter. Total: 0.1032 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.384765625, 'precision@0.6': 67.31442260742188, 'precision@0.7': 62.2619514465332, 'precision@0.8': 51.65176773071289, 'precision@0.9': 26.2728328704834, 'cIoU': 56.249755859375, 'mIoU': 62.19520568847656}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.92603609865054, 'SQ': 82.1278148295625, 'RQ': 59.940213317733416, 'PQ_th': 55.04007117525804, 'SQ_th': 82.8681046776149, 'RQ_th': 65.76438780950431, 'PQ_st': 42.206737869809004, 'SQ_st': 81.0103961909928, 'RQ_st': 51.1490065377019}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.8819729870491, 'AP50': 61.04445556241727, 'AP75': 41.157936421768575, 'APs': 18.69849528515608, 'APm': 42.12687566471121, 'APl': 60.21926611850862, 'AP-person': 44.38606945485731, 'AP-bicycle': 18.25458479109003, 'AP-car': 38.16135463535718, 'AP-motorcycle': 34.44893255087172, 'AP-airplane': 57.23766439864873, 'AP-bus': 63.58598301326867, 'AP-train': 68.96810952194204, 'AP-truck': 32.23189138454966, 'AP-boat': 23.918018305305267, 'AP-traffic light': 24.654633319484148, 'AP-fire hydrant': 64.27558315466034, 'AP-stop sign': 63.585732076714486, 'AP-parking meter': 43.85786871677498, 'AP-bench': 20.244462582558466, 'AP-bird': 29.551413067378974, 'AP-cat': 74.66673858915372, 'AP-dog': 65.89443711085418, 'AP-horse': 45.802672386560076, 'AP-sheep': 46.57135797133758, 'AP-cow': 50.13222116040051, 'AP-elephant': 61.11456110861371, 'AP-bear': 76.5781292034305, 'AP-zebra': 60.7737413320094, 'AP-giraffe': 57.05728003512072, 'AP-backpack': 16.81761512622998, 'AP-umbrella': 47.92684020629055, 'AP-handbag': 15.685999722791644, 'AP-tie': 32.85866109652563, 'AP-suitcase': 39.23552944102377, 'AP-frisbee': 67.03099478095076, 'AP-skis': 5.324411764053364, 'AP-snowboard': 23.53013250758785, 'AP-sports ball': 47.75988137672422, 'AP-kite': 33.35618732441315, 'AP-baseball bat': 28.83368770550186, 'AP-baseball glove': 42.23108447176706, 'AP-skateboard': 35.29932063609736, 'AP-surfboard': 35.724776269207005, 'AP-tennis racket': 56.5537995095292, 'AP-bottle': 33.69604276959778, 'AP-wine glass': 26.845208635918294, 'AP-cup': 40.13796404890152, 'AP-fork': 16.15284273615756, 'AP-knife': 13.264725679476733, 'AP-spoon': 14.76626561070773, 'AP-bowl': 32.195382852157856, 'AP-banana': 21.312071436843613, 'AP-apple': 21.8528668377621, 'AP-sandwich': 43.070311274818515, 'AP-orange': 29.12379626760811, 'AP-broccoli': 20.214770921688206, 'AP-carrot': 19.596054363312593, 'AP-hot dog': 25.04592973637202, 'AP-pizza': 50.27639388069959, 'AP-donut': 45.57363924762285, 'AP-cake': 44.01922222976754, 'AP-chair': 20.30407632441368, 'AP-couch': 39.93929453441883, 'AP-potted plant': 16.75453796602981, 'AP-bed': 41.264940412501886, 'AP-dining table': 13.286992316518328, 'AP-toilet': 66.80635668676294, 'AP-tv': 62.68215515132967, 'AP-laptop': 62.73916984126999, 'AP-mouse': 58.756960065353184, 'AP-remote': 30.364445931506907, 'AP-keyboard': 47.73110035684374, 'AP-cell phone': 38.155709457751705, 'AP-microwave': 55.48547645704895, 'AP-oven': 34.29900946376157, 'AP-toaster': 32.533239038189535, 'AP-sink': 37.81972197984905, 'AP-refrigerator': 59.26772390020888, 'AP-book': 7.781628750593765, 'AP-clock': 51.664811138656766, 'AP-vase': 35.04822108953054, 'AP-scissors': 25.27298965505802, 'AP-teddy bear': 49.95875204815679, 'AP-hair drier': 11.366336633663368, 'AP-toothbrush': 20.008339425461955}), ('sem_seg', {'mIoU': 60.405775903895865, 'fwIoU': 68.62538006100274, 'IoU-person': 87.42531463152126, 'IoU-bicycle': 72.28892706134921, 'IoU-car': 67.2899774286878, 'IoU-motorcycle': 84.56905547814327, 'IoU-airplane': 81.10696578685959, 'IoU-bus': 85.64024612978746, 'IoU-train': 88.79313214926168, 'IoU-truck': 61.26449024594641, 'IoU-boat': 67.69852503287485, 'IoU-traffic light': 76.44435385490598, 'IoU-fire hydrant': 87.74752991832526, 'IoU-stop sign': 91.17734171821226, 'IoU-parking meter': 86.69066541808299, 'IoU-bench': 54.23549525159746, 'IoU-bird': 73.07634297217263, 'IoU-cat': 88.68693498845333, 'IoU-dog': 78.46146693546996, 'IoU-horse': 86.28950366259754, 'IoU-sheep': 88.54640591747763, 'IoU-cow': 82.62572531470994, 'IoU-elephant': 85.4540753775969, 'IoU-bear': 72.51674846939511, 'IoU-zebra': 91.43549961046547, 'IoU-giraffe': 86.56158669734472, 'IoU-backpack': 39.84701518292489, 'IoU-umbrella': 71.37141779079838, 'IoU-handbag': 37.10434553465826, 'IoU-tie': 71.4540025476222, 'IoU-suitcase': 80.81218680586379, 'IoU-frisbee': 82.68162140386104, 'IoU-skis': 52.14073327292914, 'IoU-snowboard': 69.34518870478902, 'IoU-sports ball': 67.58207785421928, 'IoU-kite': 64.9394209726043, 'IoU-baseball bat': 61.270873000240734, 'IoU-baseball glove': 76.50458627553563, 'IoU-skateboard': 68.13010452295796, 'IoU-surfboard': 79.89085111306075, 'IoU-tennis racket': 74.53179614387192, 'IoU-bottle': 68.2434173476776, 'IoU-wine glass': 76.18324705844674, 'IoU-cup': 65.35436836234534, 'IoU-fork': 55.155891330121335, 'IoU-knife': 49.07712477571336, 'IoU-spoon': 48.06735502601834, 'IoU-bowl': 51.03072437185021, 'IoU-banana': 84.5517119988142, 'IoU-apple': 57.031383235328626, 'IoU-sandwich': 62.5621197320433, 'IoU-orange': 77.37008446617472, 'IoU-broccoli': 68.74048394677223, 'IoU-carrot': 63.954468079972926, 'IoU-hot dog': 62.45654215017189, 'IoU-pizza': 80.20934663117416, 'IoU-donut': 64.66566946645199, 'IoU-cake': 69.48456021063096, 'IoU-chair': 52.77581380808145, 'IoU-couch': 66.35020197521973, 'IoU-potted plant': 34.71135655144417, 'IoU-bed': 70.47292677656921, 'IoU-dining table': 49.89997076798196, 'IoU-toilet': 80.49213355338154, 'IoU-tv': 74.2861385728477, 'IoU-laptop': 72.60237645331512, 'IoU-mouse': 72.35625160307187, 'IoU-remote': 50.160640465675954, 'IoU-keyboard': 59.255107401213536, 'IoU-cell phone': 68.4245476800135, 'IoU-microwave': 50.40597482537552, 'IoU-oven': 67.55812976218789, 'IoU-toaster': 46.301433335568795, 'IoU-sink': 67.60762744354408, 'IoU-refrigerator': 82.95285136669402, 'IoU-book': 47.78977678148581, 'IoU-clock': 74.25436014762349, 'IoU-vase': 66.4584254734242, 'IoU-scissors': 54.655902368559104, 'IoU-teddy bear': 79.35234912290771, 'IoU-hair drier': 38.21428571428571, 'IoU-toothbrush': 57.5471756576705, 'IoU-banner': 36.2965002890722, 'IoU-blanket': 11.88705812383506, 'IoU-bridge': 39.933271937712014, 'IoU-cardboard': 44.28353678513979, 'IoU-counter': 27.92419924361646, 'IoU-curtain': 62.986602751437246, 'IoU-door-stuff': 43.55641102668366, 'IoU-floor-wood': 60.044601264901274, 'IoU-flower': 48.60279584487991, 'IoU-fruit': 39.136308158098316, 'IoU-gravel': 29.915858782934528, 'IoU-house': 23.310202905147055, 'IoU-light': 40.34634487153369, 'IoU-mirror-stuff': 54.23668826027611, 'IoU-net': 47.52612872122661, 'IoU-pillow': 12.039506918904118, 'IoU-platform': 28.701178993551686, 'IoU-playingfield': 65.01168226826059, 'IoU-railroad': 60.5552002078272, 'IoU-river': 50.600696667505716, 'IoU-road': 66.50668831441646, 'IoU-roof': 16.595072063219213, 'IoU-sand': 60.74819902067947, 'IoU-sea': 83.87440229946965, 'IoU-shelf': 35.49339620448814, 'IoU-snow': 88.69356896252793, 'IoU-stairs': 29.75397159670322, 'IoU-tent': 5.7038204719279975, 'IoU-towel': 34.10762747902733, 'IoU-wall-brick': 46.99392903395647, 'IoU-wall-stone': 26.808447520457037, 'IoU-wall-tile': 67.4799646925095, 'IoU-wall-wood': 38.42578545386437, 'IoU-water-other': 24.663773376570617, 'IoU-window-blind': 45.93728701739847, 'IoU-window-other': 48.01272514941406, 'IoU-tree-merged': 80.60317630832503, 'IoU-fence-merged': 50.89753416625695, 'IoU-ceiling-merged': 65.9841933831004, 'IoU-sky-other-merged': 92.22950167455036, 'IoU-cabinet-merged': 57.80751941672242, 'IoU-table-merged': 36.27235280984223, 'IoU-floor-other-merged': 49.11633462530556, 'IoU-pavement-merged': 54.667393471892126, 'IoU-mountain-merged': 55.16767927997436, 'IoU-grass-merged': 71.9588812149135, 'IoU-dirt-merged': 42.32504632694054, 'IoU-paper-merged': 32.35390942802759, 'IoU-food-other-merged': 39.216684005895495, 'IoU-building-other-merged': 56.85340356176651, 'IoU-rock-merged': 60.73786905570834, 'IoU-wall-other-merged': 65.45784261546648, 'IoU-rug-merged': 62.96865421926216, 'mACC': 72.55438711046128, 'pACC': 80.071054440799, 'ACC-person': 92.2396359196459, 'ACC-bicycle': 86.00825466596291, 'ACC-car': 86.45811688639184, 'ACC-motorcycle': 90.99707270191462, 'ACC-airplane': 87.71106151168347, 'ACC-bus': 90.98758048785433, 'ACC-train': 93.59687441141075, 'ACC-truck': 70.0618970033516, 'ACC-boat': 78.35663750503447, 'ACC-traffic light': 90.03593827073497, 'ACC-fire hydrant': 93.13374568186961, 'ACC-stop sign': 93.71338338953692, 'ACC-parking meter': 92.38093510122249, 'ACC-bench': 71.01563968933742, 'ACC-bird': 78.5380608768596, 'ACC-cat': 95.10664894280735, 'ACC-dog': 82.65423309620732, 'ACC-horse': 92.31420058308979, 'ACC-sheep': 91.85323826277433, 'ACC-cow': 88.59737740107121, 'ACC-elephant': 87.81765528235587, 'ACC-bear': 79.6787651543269, 'ACC-zebra': 94.07830666561384, 'ACC-giraffe': 91.10391040058697, 'ACC-backpack': 56.28686426937538, 'ACC-umbrella': 79.10293325315948, 'ACC-handbag': 54.309684456396354, 'ACC-tie': 80.47134087219806, 'ACC-suitcase': 88.92064634945964, 'ACC-frisbee': 94.03636363636365, 'ACC-skis': 69.60548148703394, 'ACC-snowboard': 77.77815875192869, 'ACC-sports ball': 80.21883172594868, 'ACC-kite': 74.38843289311073, 'ACC-baseball bat': 83.90412755021765, 'ACC-baseball glove': 89.03540058940936, 'ACC-skateboard': 74.40785309999058, 'ACC-surfboard': 89.57515001898462, 'ACC-tennis racket': 79.62967238705208, 'ACC-bottle': 82.21222844112116, 'ACC-wine glass': 86.25990462548276, 'ACC-cup': 83.91759604429467, 'ACC-fork': 67.60478680891258, 'ACC-knife': 60.27735033340661, 'ACC-spoon': 67.50494251881445, 'ACC-bowl': 60.59783292783497, 'ACC-banana': 90.51673338569476, 'ACC-apple': 65.99532766400702, 'ACC-sandwich': 75.63322987528446, 'ACC-orange': 86.4532072373398, 'ACC-broccoli': 79.01450341404203, 'ACC-carrot': 73.30167245692961, 'ACC-hot dog': 70.11371410891628, 'ACC-pizza': 91.2732739956226, 'ACC-donut': 81.7178089126476, 'ACC-cake': 75.96259858169645, 'ACC-chair': 68.82132092016579, 'ACC-couch': 82.01894879255521, 'ACC-potted plant': 47.38558879512363, 'ACC-bed': 81.91168058658515, 'ACC-dining table': 79.09642340407648, 'ACC-toilet': 91.52345601214311, 'ACC-tv': 86.25534456700498, 'ACC-laptop': 87.33231273280782, 'ACC-mouse': 86.49971682016097, 'ACC-remote': 72.12173376281825, 'ACC-keyboard': 67.07122084697804, 'ACC-cell phone': 80.73687425807915, 'ACC-microwave': 57.62003051702956, 'ACC-oven': 86.30435646703695, 'ACC-toaster': 51.96800284662443, 'ACC-sink': 80.17159160333226, 'ACC-refrigerator': 89.73832273937326, 'ACC-book': 66.26519991034155, 'ACC-clock': 82.90394032099064, 'ACC-vase': 81.96946396939425, 'ACC-scissors': 59.66793377957744, 'ACC-teddy bear': 84.90208654035376, 'ACC-hair drier': 41.724304784675375, 'ACC-toothbrush': 80.66452397498263, 'ACC-banner': 74.68875312958627, 'ACC-blanket': 13.569828518461302, 'ACC-bridge': 55.912812490476725, 'ACC-cardboard': 54.049701904900395, 'ACC-counter': 57.63930887015016, 'ACC-curtain': 75.40587683241836, 'ACC-door-stuff': 62.04703143896706, 'ACC-floor-wood': 75.61984870202426, 'ACC-flower': 69.64800277159219, 'ACC-fruit': 58.32188119882312, 'ACC-gravel': 42.830209190325824, 'ACC-house': 26.60132236758073, 'ACC-light': 54.60902509283008, 'ACC-mirror-stuff': 67.51273346157701, 'ACC-net': 61.30582215393756, 'ACC-pillow': 26.928691924085186, 'ACC-platform': 52.7595850848409, 'ACC-playingfield': 77.7579257263322, 'ACC-railroad': 77.26368615690743, 'ACC-river': 70.12297216187264, 'ACC-road': 84.1683480539143, 'ACC-roof': 23.711857293614802, 'ACC-sand': 73.54374131558058, 'ACC-sea': 88.52212421102702, 'ACC-shelf': 54.86519574270672, 'ACC-snow': 94.97754486513853, 'ACC-stairs': 46.30978323930743, 'ACC-tent': 6.79255659505418, 'ACC-towel': 39.81837022434283, 'ACC-wall-brick': 64.49073014244681, 'ACC-wall-stone': 35.836186354305724, 'ACC-wall-tile': 81.74773252104033, 'ACC-wall-wood': 51.469691145317, 'ACC-water-other': 45.48780696439812, 'ACC-window-blind': 57.98462409509643, 'ACC-window-other': 70.86283511495083, 'ACC-tree-merged': 89.82415389743443, 'ACC-fence-merged': 68.15143335069396, 'ACC-ceiling-merged': 81.77256050092303, 'ACC-sky-other-merged': 96.42708595148166, 'ACC-cabinet-merged': 75.26734625236381, 'ACC-table-merged': 48.2011716444109, 'ACC-floor-other-merged': 59.68041557742904, 'ACC-pavement-merged': 68.05191960289699, 'ACC-mountain-merged': 63.70188740066245, 'ACC-grass-merged': 84.60868783211369, 'ACC-dirt-merged': 67.04792315406411, 'ACC-paper-merged': 44.88379424735862, 'ACC-food-other-merged': 48.94979757323664, 'ACC-building-other-merged': 70.3746379087119, 'ACC-rock-merged': 82.6335727764491, 'ACC-wall-other-merged': 83.03468340825142, 'ACC-rug-merged': 78.79706604040598})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.562774363476734, 'noc@0.8': 3.015218027509511, 'noc@0.85': 3.68510389230319, 'noc@0.9': 4.732513901082821, 'miou@iter1': 0.8314173259963967}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.384765625, 'precision@0.6': 67.31442260742188, 'precision@0.7': 62.2619514465332, 'precision@0.8': 51.65176773071289, 'precision@0.9': 26.2728328704834, 'cIoU': 56.249755859375, 'mIoU': 62.19520568847656}}}
INFO:trainer.default_trainer:This epoch takes 1:30:28.996627
INFO:trainer.default_trainer:PROGRESS: 12.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 6 training.
INFO:trainer.default_trainer:epochs[     6] optim steps[11000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05723/0.91766, loss_mask_bce_0: 0.76775/0.33667, loss_mask_dice_0: 1.97134/1.16657, loss_spatial_bce_0: 0.06485/0.09506, loss_spatial_dice_0: 0.19440/0.22782, loss_spatial_ce_0: 0.04190/0.09611, loss_grounding_bce_0: 0.08074/0.08636, loss_grounding_dice_0: 0.11812/0.17886, loss_grounding_ce_0: 0.13082/0.27959, loss_mask_ce_1: 0.88301/0.91800, loss_mask_bce_1: 0.79052/0.33728, loss_mask_dice_1: 2.11993/1.17520, loss_spatial_bce_1: 0.06406/0.09614, loss_spatial_dice_1: 0.19058/0.23239, loss_spatial_ce_1: 0.20980/0.10176, loss_grounding_bce_1: 0.08190/0.08648, loss_grounding_dice_1: 0.11070/0.17936, loss_grounding_ce_1: 0.12504/0.28132, loss_mask_ce_2: 0.99459/0.92424, loss_mask_bce_2: 0.75436/0.33748, loss_mask_dice_2: 2.03034/1.17351, loss_spatial_bce_2: 0.06824/0.09582, loss_spatial_dice_2: 0.21964/0.23318, loss_spatial_ce_2: 0.02493/0.10625, loss_grounding_bce_2: 0.08581/0.08653, loss_grounding_dice_2: 0.11590/0.17923, loss_grounding_ce_2: 0.12453/0.28455, loss_mask_ce_3: 0.98224/0.93053, loss_mask_bce_3: 0.81830/0.33833, loss_mask_dice_3: 2.00084/1.17016, loss_spatial_bce_3: 0.06900/0.09689, loss_spatial_dice_3: 0.22219/0.23453, loss_spatial_ce_3: 0.03409/0.11113, loss_grounding_bce_3: 0.08834/0.08668, loss_grounding_dice_3: 0.13119/0.17885, loss_grounding_ce_3: 0.11544/0.28510, loss_mask_ce_4: 1.02662/0.92909, loss_mask_bce_4: 0.67239/0.33959, loss_mask_dice_4: 1.95171/1.19142, loss_spatial_bce_4: 0.07229/0.10037, loss_spatial_dice_4: 0.23640/0.24218, loss_spatial_ce_4: 0.04593/0.12765, loss_grounding_bce_4: 0.08792/0.08712, loss_grounding_dice_4: 0.10299/0.18167, loss_grounding_ce_4: 0.11860/0.28806, loss_mask_ce_5: 1.03607/0.94291, loss_mask_bce_5: 0.73014/0.34178, loss_mask_dice_5: 1.94972/1.19673, loss_spatial_bce_5: 0.07082/0.10124, loss_spatial_dice_5: 0.21440/0.24511, loss_spatial_ce_5: 0.10032/0.14032, loss_grounding_bce_5: 0.08507/0.08762, loss_grounding_dice_5: 0.12025/0.18286, loss_grounding_ce_5: 0.13993/0.29908, loss_mask_ce_6: 0.99708/0.97876, loss_mask_bce_6: 0.71723/0.34434, loss_mask_dice_6: 2.03149/1.20010, loss_spatial_bce_6: 0.06888/0.10661, loss_spatial_dice_6: 0.21919/0.24806, loss_spatial_ce_6: 0.15135/0.16175, loss_grounding_bce_6: 0.08656/0.08838, loss_grounding_dice_6: 0.13171/0.18309, loss_grounding_ce_6: 0.14224/0.32007, loss_mask_ce_7: 1.08890/1.02337, loss_mask_bce_7: 0.60624/0.35211, loss_mask_dice_7: 1.87912/1.25552, loss_spatial_bce_7: 0.08167/0.11612, loss_spatial_dice_7: 0.24883/0.27492, loss_spatial_ce_7: 0.27849/0.20270, loss_grounding_bce_7: 0.09391/0.09010, loss_grounding_dice_7: 0.16461/0.18995, loss_grounding_ce_7: 0.20227/0.35953, loss_mask_ce_8: 0.89486/1.13469, loss_mask_bce_8: 0.68857/0.36543, loss_mask_dice_8: 2.16319/1.33084, loss_spatial_bce_8: 0.11526/0.13719, loss_spatial_dice_8: 0.27461/0.31542, loss_spatial_ce_8: 0.16185/0.25647, loss_grounding_bce_8: 0.09699/0.09375, loss_grounding_dice_8: 0.23371/0.20136, loss_grounding_ce_8: 0.11644/0.43199, loss_mask_ce_9: 3.26885/3.70681, loss_mask_bce_9: 0.67517/0.39258, loss_mask_dice_9: 3.11417/1.90769, loss_spatial_bce_9: 0.24981/0.33907, loss_spatial_dice_9: 0.82346/0.82715, loss_spatial_ce_9: 1.44984/1.53642, loss_grounding_bce_9: 0.09423/0.10511, loss_grounding_dice_9: 0.29218/0.28114, loss_grounding_ce_9: 0.23955/0.72227] items per batch[64] items per second[0.13] total items[704000] mini batches[ 11000] memory[7341] epoch remaining[1:27:14]
INFO:trainer.default_trainer:epochs[     6] optim steps[11100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70486/0.91713, loss_mask_bce_0: 0.46491/0.33670, loss_mask_dice_0: 0.93277/1.16583, loss_spatial_bce_0: 0.09338/0.09504, loss_spatial_dice_0: 0.16747/0.22764, loss_spatial_ce_0: 0.00605/0.09607, loss_grounding_bce_0: 0.04352/0.08627, loss_grounding_dice_0: 0.29666/0.17879, loss_grounding_ce_0: 0.27121/0.27945, loss_mask_ce_1: 0.69157/0.91752, loss_mask_bce_1: 0.45402/0.33728, loss_mask_dice_1: 0.92768/1.17431, loss_spatial_bce_1: 0.09231/0.09609, loss_spatial_dice_1: 0.16826/0.23219, loss_spatial_ce_1: 0.01379/0.10180, loss_grounding_bce_1: 0.05051/0.08638, loss_grounding_dice_1: 0.28888/0.17933, loss_grounding_ce_1: 0.26447/0.28121, loss_mask_ce_2: 0.71305/0.92370, loss_mask_bce_2: 0.45009/0.33749, loss_mask_dice_2: 0.90099/1.17274, loss_spatial_bce_2: 0.09390/0.09580, loss_spatial_dice_2: 0.16221/0.23298, loss_spatial_ce_2: 0.01073/0.10621, loss_grounding_bce_2: 0.05602/0.08644, loss_grounding_dice_2: 0.30571/0.17913, loss_grounding_ce_2: 0.26760/0.28429, loss_mask_ce_3: 0.70091/0.92994, loss_mask_bce_3: 0.44114/0.33836, loss_mask_dice_3: 0.94099/1.16931, loss_spatial_bce_3: 0.09008/0.09685, loss_spatial_dice_3: 0.16468/0.23433, loss_spatial_ce_3: 0.02483/0.11110, loss_grounding_bce_3: 0.07169/0.08658, loss_grounding_dice_3: 0.31042/0.17878, loss_grounding_ce_3: 0.18432/0.28483, loss_mask_ce_4: 0.70457/0.92891, loss_mask_bce_4: 0.46031/0.33958, loss_mask_dice_4: 0.95295/1.19053, loss_spatial_bce_4: 0.10065/0.10033, loss_spatial_dice_4: 0.18015/0.24197, loss_spatial_ce_4: 0.02282/0.12757, loss_grounding_bce_4: 0.06742/0.08703, loss_grounding_dice_4: 0.32012/0.18161, loss_grounding_ce_4: 0.23780/0.28782, loss_mask_ce_5: 1.02106/0.94247, loss_mask_bce_5: 0.40028/0.34176, loss_mask_dice_5: 0.86970/1.19587, loss_spatial_bce_5: 0.09409/0.10120, loss_spatial_dice_5: 0.18503/0.24487, loss_spatial_ce_5: 0.06529/0.14024, loss_grounding_bce_5: 0.07174/0.08752, loss_grounding_dice_5: 0.33303/0.18275, loss_grounding_ce_5: 0.31055/0.29885, loss_mask_ce_6: 0.77599/0.97833, loss_mask_bce_6: 0.44008/0.34431, loss_mask_dice_6: 0.96248/1.19932, loss_spatial_bce_6: 0.10730/0.10658, loss_spatial_dice_6: 0.20378/0.24785, loss_spatial_ce_6: 0.17270/0.16173, loss_grounding_bce_6: 0.08801/0.08827, loss_grounding_dice_6: 0.35870/0.18299, loss_grounding_ce_6: 0.28258/0.31995, loss_mask_ce_7: 1.06407/1.02286, loss_mask_bce_7: 0.40674/0.35203, loss_mask_dice_7: 0.95660/1.25476, loss_spatial_bce_7: 0.11309/0.11602, loss_spatial_dice_7: 0.22508/0.27469, loss_spatial_ce_7: 0.18805/0.20266, loss_grounding_bce_7: 0.07382/0.08999, loss_grounding_dice_7: 0.32642/0.18988, loss_grounding_ce_7: 0.30302/0.35910, loss_mask_ce_8: 1.21250/1.13378, loss_mask_bce_8: 0.43276/0.36537, loss_mask_dice_8: 1.03502/1.32978, loss_spatial_bce_8: 0.11079/0.13713, loss_spatial_dice_8: 0.24334/0.31513, loss_spatial_ce_8: 0.25845/0.25652, loss_grounding_bce_8: 0.04254/0.09361, loss_grounding_dice_8: 0.33859/0.20133, loss_grounding_ce_8: 0.32512/0.43158, loss_mask_ce_9: 3.64088/3.70516, loss_mask_bce_9: 0.56047/0.39256, loss_mask_dice_9: 1.61820/1.90622, loss_spatial_bce_9: 0.55979/0.33917, loss_spatial_dice_9: 0.88962/0.82703, loss_spatial_ce_9: 1.78518/1.53587, loss_grounding_bce_9: 0.09075/0.10501, loss_grounding_dice_9: 0.51471/0.28107, loss_grounding_ce_9: 0.35261/0.72175] items per batch[64] items per second[0.22] total items[710400] mini batches[ 11100] memory[7341] epoch remaining[1:21:39]
INFO:trainer.default_trainer:epochs[     6] optim steps[11200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.49015/0.91669, loss_mask_bce_0: 0.49326/0.33668, loss_mask_dice_0: 3.18955/1.16708, loss_spatial_bce_0: 0.10602/0.09497, loss_spatial_dice_0: 0.32039/0.22759, loss_spatial_ce_0: 0.02946/0.09575, loss_grounding_bce_0: 0.09770/0.08625, loss_grounding_dice_0: 0.38023/0.17885, loss_grounding_ce_0: 0.09936/0.27949, loss_mask_ce_1: 0.54270/0.91704, loss_mask_bce_1: 0.48023/0.33721, loss_mask_dice_1: 3.13545/1.17550, loss_spatial_bce_1: 0.09706/0.09602, loss_spatial_dice_1: 0.30707/0.23211, loss_spatial_ce_1: 0.04208/0.10150, loss_grounding_bce_1: 0.09942/0.08636, loss_grounding_dice_1: 0.39026/0.17947, loss_grounding_ce_1: 0.08654/0.28114, loss_mask_ce_2: 0.50972/0.92330, loss_mask_bce_2: 0.47965/0.33742, loss_mask_dice_2: 3.72676/1.17404, loss_spatial_bce_2: 0.09570/0.09573, loss_spatial_dice_2: 0.30258/0.23290, loss_spatial_ce_2: 0.03153/0.10595, loss_grounding_bce_2: 0.10447/0.08641, loss_grounding_dice_2: 0.32456/0.17925, loss_grounding_ce_2: 0.19504/0.28424, loss_mask_ce_3: 0.45264/0.92946, loss_mask_bce_3: 0.50345/0.33830, loss_mask_dice_3: 3.54775/1.17057, loss_spatial_bce_3: 0.09528/0.09678, loss_spatial_dice_3: 0.30818/0.23427, loss_spatial_ce_3: 0.03197/0.11078, loss_grounding_bce_3: 0.09961/0.08655, loss_grounding_dice_3: 0.18375/0.17885, loss_grounding_ce_3: 0.21455/0.28488, loss_mask_ce_4: 0.59052/0.92854, loss_mask_bce_4: 0.47002/0.33957, loss_mask_dice_4: 3.09403/1.19163, loss_spatial_bce_4: 0.09732/0.10026, loss_spatial_dice_4: 0.30195/0.24190, loss_spatial_ce_4: 0.06697/0.12713, loss_grounding_bce_4: 0.10832/0.08699, loss_grounding_dice_4: 0.25441/0.18167, loss_grounding_ce_4: 0.23472/0.28788, loss_mask_ce_5: 0.52142/0.94207, loss_mask_bce_5: 0.45292/0.34174, loss_mask_dice_5: 2.82201/1.19703, loss_spatial_bce_5: 0.09895/0.10113, loss_spatial_dice_5: 0.30030/0.24478, loss_spatial_ce_5: 0.09841/0.13987, loss_grounding_bce_5: 0.10441/0.08749, loss_grounding_dice_5: 0.37922/0.18282, loss_grounding_ce_5: 0.06777/0.29887, loss_mask_ce_6: 0.60031/0.97803, loss_mask_bce_6: 0.46972/0.34421, loss_mask_dice_6: 3.04031/1.20056, loss_spatial_bce_6: 0.09974/0.10650, loss_spatial_dice_6: 0.34624/0.24777, loss_spatial_ce_6: 0.08619/0.16147, loss_grounding_bce_6: 0.10253/0.08822, loss_grounding_dice_6: 0.20047/0.18299, loss_grounding_ce_6: 0.21511/0.32012, loss_mask_ce_7: 0.57365/1.02243, loss_mask_bce_7: 0.46528/0.35198, loss_mask_dice_7: 3.25346/1.25596, loss_spatial_bce_7: 0.11067/0.11595, loss_spatial_dice_7: 0.37868/0.27463, loss_spatial_ce_7: 0.12364/0.20235, loss_grounding_bce_7: 0.10289/0.08996, loss_grounding_dice_7: 0.38040/0.18994, loss_grounding_ce_7: 0.04490/0.35898, loss_mask_ce_8: 0.60965/1.13322, loss_mask_bce_8: 0.51364/0.36534, loss_mask_dice_8: 3.02134/1.33119, loss_spatial_bce_8: 0.11914/0.13706, loss_spatial_dice_8: 0.42050/0.31503, loss_spatial_ce_8: 0.18703/0.25633, loss_grounding_bce_8: 0.10963/0.09358, loss_grounding_dice_8: 0.38167/0.20137, loss_grounding_ce_8: 0.07285/0.43149, loss_mask_ce_9: 4.84989/3.70668, loss_mask_bce_9: 0.47301/0.39248, loss_mask_dice_9: 4.15855/1.90730, loss_spatial_bce_9: 0.37922/0.33921, loss_spatial_dice_9: 0.83518/0.82702, loss_spatial_ce_9: 1.68563/1.53562, loss_grounding_bce_9: 0.10824/0.10499, loss_grounding_dice_9: 0.42620/0.28106, loss_grounding_ce_9: 0.21701/0.72191] items per batch[64] items per second[0.23] total items[716800] mini batches[ 11200] memory[7341] epoch remaining[1:15:43]
INFO:trainer.default_trainer:epochs[     6] optim steps[11300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.16326/0.91680, loss_mask_bce_0: 0.46494/0.33659, loss_mask_dice_0: 2.09521/1.16711, loss_spatial_bce_0: 0.07991/0.09490, loss_spatial_dice_0: 0.23905/0.22747, loss_spatial_ce_0: 0.04069/0.09549, loss_grounding_bce_0: 0.08068/0.08623, loss_grounding_dice_0: 0.29501/0.17883, loss_grounding_ce_0: 0.43741/0.27945, loss_mask_ce_1: 1.21646/0.91737, loss_mask_bce_1: 0.46531/0.33712, loss_mask_dice_1: 2.07454/1.17539, loss_spatial_bce_1: 0.08932/0.09596, loss_spatial_dice_1: 0.24284/0.23201, loss_spatial_ce_1: 0.02741/0.10121, loss_grounding_bce_1: 0.07971/0.08636, loss_grounding_dice_1: 0.28870/0.17945, loss_grounding_ce_1: 0.44134/0.28106, loss_mask_ce_2: 1.18431/0.92363, loss_mask_bce_2: 0.52879/0.33738, loss_mask_dice_2: 2.05174/1.17388, loss_spatial_bce_2: 0.09442/0.09566, loss_spatial_dice_2: 0.24698/0.23279, loss_spatial_ce_2: 0.02629/0.10564, loss_grounding_bce_2: 0.08031/0.08643, loss_grounding_dice_2: 0.29637/0.17926, loss_grounding_ce_2: 0.43045/0.28423, loss_mask_ce_3: 1.23779/0.92977, loss_mask_bce_3: 0.46867/0.33822, loss_mask_dice_3: 2.06761/1.17044, loss_spatial_bce_3: 0.10053/0.09673, loss_spatial_dice_3: 0.26187/0.23416, loss_spatial_ce_3: 0.01504/0.11044, loss_grounding_bce_3: 0.07783/0.08655, loss_grounding_dice_3: 0.31111/0.17886, loss_grounding_ce_3: 0.43281/0.28475, loss_mask_ce_4: 1.25862/0.92875, loss_mask_bce_4: 0.48357/0.33952, loss_mask_dice_4: 2.12303/1.19158, loss_spatial_bce_4: 0.08454/0.10020, loss_spatial_dice_4: 0.26731/0.24181, loss_spatial_ce_4: 0.03703/0.12676, loss_grounding_bce_4: 0.07720/0.08699, loss_grounding_dice_4: 0.30627/0.18168, loss_grounding_ce_4: 0.43543/0.28781, loss_mask_ce_5: 1.26541/0.94235, loss_mask_bce_5: 0.48717/0.34167, loss_mask_dice_5: 2.18437/1.19705, loss_spatial_bce_5: 0.09593/0.10108, loss_spatial_dice_5: 0.25126/0.24471, loss_spatial_ce_5: 0.05555/0.13952, loss_grounding_bce_5: 0.08099/0.08748, loss_grounding_dice_5: 0.31783/0.18282, loss_grounding_ce_5: 0.45771/0.29887, loss_mask_ce_6: 1.17295/0.97843, loss_mask_bce_6: 0.50690/0.34415, loss_mask_dice_6: 2.22686/1.20060, loss_spatial_bce_6: 0.12022/0.10646, loss_spatial_dice_6: 0.27331/0.24768, loss_spatial_ce_6: 0.05971/0.16118, loss_grounding_bce_6: 0.08894/0.08825, loss_grounding_dice_6: 0.31545/0.18304, loss_grounding_ce_6: 0.45578/0.31997, loss_mask_ce_7: 1.16723/1.02273, loss_mask_bce_7: 0.47511/0.35193, loss_mask_dice_7: 2.18187/1.25587, loss_spatial_bce_7: 0.08379/0.11588, loss_spatial_dice_7: 0.27803/0.27450, loss_spatial_ce_7: 0.16979/0.20207, loss_grounding_bce_7: 0.08978/0.08998, loss_grounding_dice_7: 0.30899/0.19000, loss_grounding_ce_7: 0.44565/0.35871, loss_mask_ce_8: 1.55667/1.13338, loss_mask_bce_8: 0.47135/0.36531, loss_mask_dice_8: 2.25509/1.33115, loss_spatial_bce_8: 0.10604/0.13705, loss_spatial_dice_8: 0.35719/0.31493, loss_spatial_ce_8: 0.38779/0.25616, loss_grounding_bce_8: 0.08808/0.09357, loss_grounding_dice_8: 0.32050/0.20139, loss_grounding_ce_8: 0.48611/0.43109, loss_mask_ce_9: 5.34864/3.70569, loss_mask_bce_9: 0.59745/0.39238, loss_mask_dice_9: 3.49197/1.90707, loss_spatial_bce_9: 0.29075/0.33907, loss_spatial_dice_9: 0.87903/0.82704, loss_spatial_ce_9: 1.46114/1.53540, loss_grounding_bce_9: 0.10381/0.10497, loss_grounding_dice_9: 0.40507/0.28109, loss_grounding_ce_9: 0.59030/0.72079] items per batch[64] items per second[0.22] total items[723200] mini batches[ 11300] memory[7341] epoch remaining[1:11:28]
INFO:trainer.default_trainer:epochs[     6] optim steps[11400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.76381/0.91717, loss_mask_bce_0: 0.15746/0.33630, loss_mask_dice_0: 1.09313/1.16589, loss_spatial_bce_0: 0.06071/0.09486, loss_spatial_dice_0: 0.23277/0.22734, loss_spatial_ce_0: 0.08285/0.09521, loss_grounding_bce_0: 0.05711/0.08614, loss_grounding_dice_0: 0.11005/0.17886, loss_grounding_ce_0: 0.25311/0.27911, loss_mask_ce_1: 1.46225/0.91774, loss_mask_bce_1: 0.16698/0.33684, loss_mask_dice_1: 1.18823/1.17419, loss_spatial_bce_1: 0.05649/0.09590, loss_spatial_dice_1: 0.26392/0.23190, loss_spatial_ce_1: 0.02138/0.10093, loss_grounding_bce_1: 0.07622/0.08626, loss_grounding_dice_1: 0.12641/0.17949, loss_grounding_ce_1: 0.18120/0.28077, loss_mask_ce_2: 1.48812/0.92391, loss_mask_bce_2: 0.15922/0.33708, loss_mask_dice_2: 0.96452/1.17278, loss_spatial_bce_2: 0.05124/0.09560, loss_spatial_dice_2: 0.21311/0.23266, loss_spatial_ce_2: 0.16732/0.10541, loss_grounding_bce_2: 0.06843/0.08632, loss_grounding_dice_2: 0.11066/0.17929, loss_grounding_ce_2: 0.16076/0.28390, loss_mask_ce_3: 1.65527/0.92998, loss_mask_bce_3: 0.18402/0.33796, loss_mask_dice_3: 1.22883/1.16925, loss_spatial_bce_3: 0.05786/0.09666, loss_spatial_dice_3: 0.25970/0.23403, loss_spatial_ce_3: 0.06283/0.11014, loss_grounding_bce_3: 0.07804/0.08648, loss_grounding_dice_3: 0.11880/0.17891, loss_grounding_ce_3: 0.15290/0.28435, loss_mask_ce_4: 1.48483/0.92897, loss_mask_bce_4: 0.16511/0.33929, loss_mask_dice_4: 0.96054/1.19042, loss_spatial_bce_4: 0.05873/0.10013, loss_spatial_dice_4: 0.27215/0.24169, loss_spatial_ce_4: 0.05534/0.12647, loss_grounding_bce_4: 0.07565/0.08692, loss_grounding_dice_4: 0.12207/0.18170, loss_grounding_ce_4: 0.11564/0.28748, loss_mask_ce_5: 1.91651/0.94251, loss_mask_bce_5: 0.15451/0.34141, loss_mask_dice_5: 1.10439/1.19588, loss_spatial_bce_5: 0.05539/0.10101, loss_spatial_dice_5: 0.25148/0.24459, loss_spatial_ce_5: 0.04031/0.13922, loss_grounding_bce_5: 0.06413/0.08738, loss_grounding_dice_5: 0.10699/0.18284, loss_grounding_ce_5: 0.21325/0.29850, loss_mask_ce_6: 1.70593/0.97860, loss_mask_bce_6: 0.16134/0.34387, loss_mask_dice_6: 1.00901/1.19935, loss_spatial_bce_6: 0.05981/0.10636, loss_spatial_dice_6: 0.27695/0.24757, loss_spatial_ce_6: 0.03848/0.16099, loss_grounding_bce_6: 0.06744/0.08815, loss_grounding_dice_6: 0.11626/0.18307, loss_grounding_ce_6: 0.18850/0.31966, loss_mask_ce_7: 1.62684/1.02260, loss_mask_bce_7: 0.22306/0.35170, loss_mask_dice_7: 1.24894/1.25475, loss_spatial_bce_7: 0.06315/0.11576, loss_spatial_dice_7: 0.28862/0.27439, loss_spatial_ce_7: 0.04498/0.20178, loss_grounding_bce_7: 0.15278/0.08986, loss_grounding_dice_7: 0.14888/0.19003, loss_grounding_ce_7: 0.17330/0.35819, loss_mask_ce_8: 1.95950/1.13329, loss_mask_bce_8: 0.25826/0.36508, loss_mask_dice_8: 1.18029/1.32988, loss_spatial_bce_8: 0.08758/0.13692, loss_spatial_dice_8: 0.34561/0.31479, loss_spatial_ce_8: 0.17494/0.25599, loss_grounding_bce_8: 0.15816/0.09343, loss_grounding_dice_8: 0.13711/0.20138, loss_grounding_ce_8: 0.29621/0.43058, loss_mask_ce_9: 3.54352/3.70401, loss_mask_bce_9: 0.34882/0.39214, loss_mask_dice_9: 1.74008/1.90705, loss_spatial_bce_9: 0.19493/0.33903, loss_spatial_dice_9: 0.88125/0.82693, loss_spatial_ce_9: 1.56869/1.53523, loss_grounding_bce_9: 0.16230/0.10488, loss_grounding_dice_9: 0.19295/0.28118, loss_grounding_ce_9: 0.49914/0.72003] items per batch[64] items per second[0.22] total items[729600] mini batches[ 11400] memory[7341] epoch remaining[1:06:57]
INFO:trainer.default_trainer:epochs[     6] optim steps[11500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58306/0.91686, loss_mask_bce_0: 0.73206/0.33626, loss_mask_dice_0: 1.48834/1.16662, loss_spatial_bce_0: 0.18659/0.09481, loss_spatial_dice_0: 0.21357/0.22727, loss_spatial_ce_0: 0.09934/0.09503, loss_grounding_bce_0: 0.11472/0.08608, loss_grounding_dice_0: 0.14651/0.17883, loss_grounding_ce_0: 1.02899/0.27918, loss_mask_ce_1: 0.58090/0.91728, loss_mask_bce_1: 0.74275/0.33682, loss_mask_dice_1: 1.47567/1.17478, loss_spatial_bce_1: 0.18234/0.09583, loss_spatial_dice_1: 0.21219/0.23181, loss_spatial_ce_1: 0.07863/0.10071, loss_grounding_bce_1: 0.12048/0.08622, loss_grounding_dice_1: 0.14935/0.17946, loss_grounding_ce_1: 1.06651/0.28097, loss_mask_ce_2: 0.58365/0.92367, loss_mask_bce_2: 0.74781/0.33710, loss_mask_dice_2: 1.46637/1.17344, loss_spatial_bce_2: 0.19854/0.09553, loss_spatial_dice_2: 0.21801/0.23259, loss_spatial_ce_2: 0.07208/0.10519, loss_grounding_bce_2: 0.11284/0.08628, loss_grounding_dice_2: 0.13979/0.17929, loss_grounding_ce_2: 1.13959/0.28405, loss_mask_ce_3: 0.47936/0.92958, loss_mask_bce_3: 0.81314/0.33796, loss_mask_dice_3: 1.53243/1.16999, loss_spatial_bce_3: 0.21883/0.09661, loss_spatial_dice_3: 0.21935/0.23396, loss_spatial_ce_3: 0.08019/0.10981, loss_grounding_bce_3: 0.11510/0.08645, loss_grounding_dice_3: 0.14797/0.17893, loss_grounding_ce_3: 1.16278/0.28453, loss_mask_ce_4: 0.58820/0.92870, loss_mask_bce_4: 0.82369/0.33927, loss_mask_dice_4: 1.61411/1.19128, loss_spatial_bce_4: 0.22718/0.10007, loss_spatial_dice_4: 0.21567/0.24162, loss_spatial_ce_4: 0.10484/0.12619, loss_grounding_bce_4: 0.11515/0.08688, loss_grounding_dice_4: 0.15158/0.18166, loss_grounding_ce_4: 1.05796/0.28759, loss_mask_ce_5: 0.63372/0.94209, loss_mask_bce_5: 0.81250/0.34144, loss_mask_dice_5: 1.61815/1.19673, loss_spatial_bce_5: 0.19720/0.10097, loss_spatial_dice_5: 0.21852/0.24453, loss_spatial_ce_5: 0.07814/0.13899, loss_grounding_bce_5: 0.11678/0.08732, loss_grounding_dice_5: 0.14557/0.18285, loss_grounding_ce_5: 1.16425/0.29864, loss_mask_ce_6: 0.77350/0.97834, loss_mask_bce_6: 0.82394/0.34390, loss_mask_dice_6: 1.58790/1.20007, loss_spatial_bce_6: 0.25786/0.10632, loss_spatial_dice_6: 0.21032/0.24749, loss_spatial_ce_6: 0.09632/0.16070, loss_grounding_bce_6: 0.13330/0.08811, loss_grounding_dice_6: 0.15687/0.18306, loss_grounding_ce_6: 1.52661/0.31978, loss_mask_ce_7: 0.68636/1.02216, loss_mask_bce_7: 0.88097/0.35173, loss_mask_dice_7: 1.73436/1.25555, loss_spatial_bce_7: 0.27205/0.11572, loss_spatial_dice_7: 0.22987/0.27435, loss_spatial_ce_7: 0.14446/0.20127, loss_grounding_bce_7: 0.13657/0.08982, loss_grounding_dice_7: 0.15856/0.19004, loss_grounding_ce_7: 0.95685/0.35794, loss_mask_ce_8: 0.76911/1.13291, loss_mask_bce_8: 0.84153/0.36512, loss_mask_dice_8: 1.73716/1.33082, loss_spatial_bce_8: 0.19020/0.13690, loss_spatial_dice_8: 0.24886/0.31476, loss_spatial_ce_8: 0.12892/0.25562, loss_grounding_bce_8: 0.14511/0.09338, loss_grounding_dice_8: 0.15448/0.20135, loss_grounding_ce_8: 1.30833/0.43020, loss_mask_ce_9: 4.03223/3.70414, loss_mask_bce_9: 1.06390/0.39215, loss_mask_dice_9: 3.04315/1.90792, loss_spatial_bce_9: 0.36481/0.33905, loss_spatial_dice_9: 0.85653/0.82694, loss_spatial_ce_9: 1.28030/1.53489, loss_grounding_bce_9: 0.19761/0.10485, loss_grounding_dice_9: 0.34295/0.28122, loss_grounding_ce_9: 1.34566/0.71980] items per batch[64] items per second[0.23] total items[736000] mini batches[ 11500] memory[7341] epoch remaining[1:01:33]
INFO:trainer.default_trainer:epochs[     6] optim steps[11600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85359/0.91718, loss_mask_bce_0: 0.31481/0.33608, loss_mask_dice_0: 4.62813/1.16870, loss_spatial_bce_0: 0.03424/0.09473, loss_spatial_dice_0: 0.27691/0.22720, loss_spatial_ce_0: 0.07873/0.09478, loss_grounding_bce_0: 0.05055/0.08601, loss_grounding_dice_0: 0.19638/0.17882, loss_grounding_ce_0: 0.66845/0.27899, loss_mask_ce_1: 0.83536/0.91756, loss_mask_bce_1: 0.30762/0.33666, loss_mask_dice_1: 4.49189/1.17689, loss_spatial_bce_1: 0.03571/0.09575, loss_spatial_dice_1: 0.31243/0.23175, loss_spatial_ce_1: 0.08143/0.10043, loss_grounding_bce_1: 0.05055/0.08614, loss_grounding_dice_1: 0.19744/0.17946, loss_grounding_ce_1: 0.59475/0.28083, loss_mask_ce_2: 0.99672/0.92408, loss_mask_bce_2: 0.31453/0.33695, loss_mask_dice_2: 4.46000/1.17548, loss_spatial_bce_2: 0.03481/0.09544, loss_spatial_dice_2: 0.33645/0.23251, loss_spatial_ce_2: 0.23116/0.10488, loss_grounding_bce_2: 0.05066/0.08620, loss_grounding_dice_2: 0.19622/0.17932, loss_grounding_ce_2: 0.60587/0.28400, loss_mask_ce_3: 0.99929/0.92989, loss_mask_bce_3: 0.30160/0.33781, loss_mask_dice_3: 3.76168/1.17203, loss_spatial_bce_3: 0.03612/0.09652, loss_spatial_dice_3: 0.29012/0.23387, loss_spatial_ce_3: 0.12940/0.10948, loss_grounding_bce_3: 0.04950/0.08637, loss_grounding_dice_3: 0.24403/0.17893, loss_grounding_ce_3: 0.67063/0.28448, loss_mask_ce_4: 1.19680/0.92896, loss_mask_bce_4: 0.30413/0.33913, loss_mask_dice_4: 4.36397/1.19344, loss_spatial_bce_4: 0.03679/0.09998, loss_spatial_dice_4: 0.29449/0.24157, loss_spatial_ce_4: 0.06173/0.12597, loss_grounding_bce_4: 0.04834/0.08681, loss_grounding_dice_4: 0.21235/0.18170, loss_grounding_ce_4: 0.69586/0.28741, loss_mask_ce_5: 0.87963/0.94246, loss_mask_bce_5: 0.28164/0.34131, loss_mask_dice_5: 3.67537/1.19879, loss_spatial_bce_5: 0.04011/0.10088, loss_spatial_dice_5: 0.30237/0.24450, loss_spatial_ce_5: 0.10565/0.13873, loss_grounding_bce_5: 0.04798/0.08725, loss_grounding_dice_5: 0.19069/0.18286, loss_grounding_ce_5: 0.55377/0.29851, loss_mask_ce_6: 1.00344/0.97891, loss_mask_bce_6: 0.29880/0.34376, loss_mask_dice_6: 4.43974/1.20197, loss_spatial_bce_6: 0.04352/0.10622, loss_spatial_dice_6: 0.30820/0.24743, loss_spatial_ce_6: 0.11588/0.16048, loss_grounding_bce_6: 0.04647/0.08807, loss_grounding_dice_6: 0.19506/0.18304, loss_grounding_ce_6: 0.51889/0.31951, loss_mask_ce_7: 1.08056/1.02260, loss_mask_bce_7: 0.30959/0.35162, loss_mask_dice_7: 4.16088/1.25766, loss_spatial_bce_7: 0.09605/0.11561, loss_spatial_dice_7: 0.38792/0.27437, loss_spatial_ce_7: 0.18717/0.20106, loss_grounding_bce_7: 0.05240/0.08977, loss_grounding_dice_7: 0.23359/0.19008, loss_grounding_ce_7: 0.68547/0.35763, loss_mask_ce_8: 0.98416/1.13380, loss_mask_bce_8: 0.36071/0.36502, loss_mask_dice_8: 4.55951/1.33303, loss_spatial_bce_8: 0.14404/0.13686, loss_spatial_dice_8: 0.40053/0.31477, loss_spatial_ce_8: 0.22626/0.25540, loss_grounding_bce_8: 0.05678/0.09338, loss_grounding_dice_8: 0.24983/0.20141, loss_grounding_ce_8: 0.61676/0.43002, loss_mask_ce_9: 5.40599/3.70563, loss_mask_bce_9: 0.31614/0.39207, loss_mask_dice_9: 5.47480/1.91076, loss_spatial_bce_9: 0.15908/0.33881, loss_spatial_dice_9: 0.89841/0.82683, loss_spatial_ce_9: 1.86516/1.53441, loss_grounding_bce_9: 0.05130/0.10482, loss_grounding_dice_9: 0.34174/0.28131, loss_grounding_ce_9: 0.48403/0.71960] items per batch[64] items per second[0.22] total items[742400] mini batches[ 11600] memory[7341] epoch remaining[0:56:44]
INFO:trainer.default_trainer:epochs[     6] optim steps[11700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49902/0.91717, loss_mask_bce_0: 0.49735/0.33570, loss_mask_dice_0: 0.47885/1.16691, loss_spatial_bce_0: 0.18229/0.09464, loss_spatial_dice_0: 0.19008/0.22700, loss_spatial_ce_0: 0.00563/0.09454, loss_grounding_bce_0: 0.19403/0.08598, loss_grounding_dice_0: 0.23123/0.17884, loss_grounding_ce_0: 0.42134/0.27898, loss_mask_ce_1: 1.49497/0.91757, loss_mask_bce_1: 0.52095/0.33632, loss_mask_dice_1: 0.49086/1.17508, loss_spatial_bce_1: 0.19045/0.09565, loss_spatial_dice_1: 0.18412/0.23153, loss_spatial_ce_1: 0.00374/0.10023, loss_grounding_bce_1: 0.19493/0.08611, loss_grounding_dice_1: 0.22442/0.17948, loss_grounding_ce_1: 0.42361/0.28077, loss_mask_ce_2: 1.37858/0.92405, loss_mask_bce_2: 0.55552/0.33660, loss_mask_dice_2: 0.47620/1.17367, loss_spatial_bce_2: 0.19094/0.09538, loss_spatial_dice_2: 0.17810/0.23230, loss_spatial_ce_2: 0.00820/0.10462, loss_grounding_bce_2: 0.18321/0.08616, loss_grounding_dice_2: 0.19569/0.17936, loss_grounding_ce_2: 0.49789/0.28390, loss_mask_ce_3: 1.47412/0.92996, loss_mask_bce_3: 0.54097/0.33744, loss_mask_dice_3: 0.45471/1.17031, loss_spatial_bce_3: 0.21239/0.09646, loss_spatial_dice_3: 0.18406/0.23367, loss_spatial_ce_3: 0.01730/0.10920, loss_grounding_bce_3: 0.18490/0.08634, loss_grounding_dice_3: 0.20047/0.17893, loss_grounding_ce_3: 0.46607/0.28427, loss_mask_ce_4: 1.37912/0.92906, loss_mask_bce_4: 0.53960/0.33878, loss_mask_dice_4: 0.48901/1.19166, loss_spatial_bce_4: 0.20877/0.09996, loss_spatial_dice_4: 0.17608/0.24135, loss_spatial_ce_4: 0.03172/0.12561, loss_grounding_bce_4: 0.20181/0.08678, loss_grounding_dice_4: 0.20723/0.18171, loss_grounding_ce_4: 0.42895/0.28727, loss_mask_ce_5: 1.48458/0.94240, loss_mask_bce_5: 0.53872/0.34099, loss_mask_dice_5: 0.47459/1.19709, loss_spatial_bce_5: 0.21283/0.10085, loss_spatial_dice_5: 0.19919/0.24429, loss_spatial_ce_5: 0.05842/0.13852, loss_grounding_bce_5: 0.21556/0.08721, loss_grounding_dice_5: 0.21381/0.18286, loss_grounding_ce_5: 0.49511/0.29843, loss_mask_ce_6: 1.19435/0.97901, loss_mask_bce_6: 0.65158/0.34345, loss_mask_dice_6: 0.50231/1.20025, loss_spatial_bce_6: 0.19758/0.10617, loss_spatial_dice_6: 0.17162/0.24723, loss_spatial_ce_6: 0.08724/0.16024, loss_grounding_bce_6: 0.33314/0.08803, loss_grounding_dice_6: 0.21471/0.18307, loss_grounding_ce_6: 0.05670/0.31922, loss_mask_ce_7: 1.19389/1.02273, loss_mask_bce_7: 0.65519/0.35133, loss_mask_dice_7: 0.55894/1.25573, loss_spatial_bce_7: 0.23371/0.11555, loss_spatial_dice_7: 0.24134/0.27415, loss_spatial_ce_7: 0.13582/0.20060, loss_grounding_bce_7: 0.29092/0.08974, loss_grounding_dice_7: 0.23572/0.19006, loss_grounding_ce_7: 0.03690/0.35753, loss_mask_ce_8: 1.23307/1.13391, loss_mask_bce_8: 0.57824/0.36466, loss_mask_dice_8: 0.56147/1.33120, loss_spatial_bce_8: 0.25494/0.13679, loss_spatial_dice_8: 0.28871/0.31453, loss_spatial_ce_8: 0.27179/0.25521, loss_grounding_bce_8: 0.22009/0.09331, loss_grounding_dice_8: 0.23787/0.20140, loss_grounding_ce_8: 0.04926/0.43009, loss_mask_ce_9: 3.11150/3.70400, loss_mask_bce_9: 0.52626/0.39175, loss_mask_dice_9: 0.65821/1.90732, loss_spatial_bce_9: 0.51024/0.33876, loss_spatial_dice_9: 0.72072/0.82675, loss_spatial_ce_9: 1.14726/1.53381, loss_grounding_bce_9: 0.19231/0.10477, loss_grounding_dice_9: 0.32075/0.28123, loss_grounding_ce_9: 0.07161/0.71975] items per batch[64] items per second[0.23] total items[748800] mini batches[ 11700] memory[7341] epoch remaining[0:51:50]
INFO:trainer.default_trainer:epochs[     6] optim steps[11800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.65872/0.91714, loss_mask_bce_0: 0.54272/0.33585, loss_mask_dice_0: 0.59317/1.16709, loss_spatial_bce_0: 0.18688/0.09465, loss_spatial_dice_0: 0.16287/0.22687, loss_spatial_ce_0: 0.37417/0.09440, loss_grounding_bce_0: 0.21966/0.08598, loss_grounding_dice_0: 0.13306/0.17871, loss_grounding_ce_0: 0.45739/0.27896, loss_mask_ce_1: 1.65941/0.91752, loss_mask_bce_1: 0.49703/0.33651, loss_mask_dice_1: 0.58179/1.17532, loss_spatial_bce_1: 0.17828/0.09566, loss_spatial_dice_1: 0.16748/0.23137, loss_spatial_ce_1: 0.40938/0.10003, loss_grounding_bce_1: 0.21772/0.08613, loss_grounding_dice_1: 0.13521/0.17942, loss_grounding_ce_1: 0.46122/0.28079, loss_mask_ce_2: 1.55195/0.92387, loss_mask_bce_2: 0.53507/0.33678, loss_mask_dice_2: 0.63553/1.17397, loss_spatial_bce_2: 0.15321/0.09538, loss_spatial_dice_2: 0.16749/0.23214, loss_spatial_ce_2: 0.41887/0.10441, loss_grounding_bce_2: 0.23968/0.08618, loss_grounding_dice_2: 0.14048/0.17926, loss_grounding_ce_2: 0.46000/0.28387, loss_mask_ce_3: 1.42389/0.92986, loss_mask_bce_3: 0.52997/0.33761, loss_mask_dice_3: 0.61434/1.17054, loss_spatial_bce_3: 0.18502/0.09647, loss_spatial_dice_3: 0.17558/0.23350, loss_spatial_ce_3: 0.45556/0.10900, loss_grounding_bce_3: 0.21448/0.08636, loss_grounding_dice_3: 0.12300/0.17884, loss_grounding_ce_3: 0.04892/0.28437, loss_mask_ce_4: 1.65200/0.92901, loss_mask_bce_4: 0.52264/0.33894, loss_mask_dice_4: 0.67123/1.19196, loss_spatial_bce_4: 0.18910/0.09997, loss_spatial_dice_4: 0.18689/0.24123, loss_spatial_ce_4: 0.51561/0.12540, loss_grounding_bce_4: 0.20120/0.08678, loss_grounding_dice_4: 0.14935/0.18164, loss_grounding_ce_4: 0.45064/0.28735, loss_mask_ce_5: 1.60565/0.94215, loss_mask_bce_5: 0.62982/0.34118, loss_mask_dice_5: 0.61544/1.19736, loss_spatial_bce_5: 0.16610/0.10088, loss_spatial_dice_5: 0.17964/0.24414, loss_spatial_ce_5: 0.56059/0.13841, loss_grounding_bce_5: 0.26296/0.08722, loss_grounding_dice_5: 0.15244/0.18276, loss_grounding_ce_5: 0.44476/0.29846, loss_mask_ce_6: 1.74275/0.97887, loss_mask_bce_6: 0.49687/0.34367, loss_mask_dice_6: 0.64480/1.20062, loss_spatial_bce_6: 0.20892/0.10622, loss_spatial_dice_6: 0.23722/0.24711, loss_spatial_ce_6: 0.52284/0.16020, loss_grounding_bce_6: 0.21065/0.08802, loss_grounding_dice_6: 0.14912/0.18296, loss_grounding_ce_6: 0.46426/0.31971, loss_mask_ce_7: 1.67266/1.02274, loss_mask_bce_7: 0.58979/0.35153, loss_mask_dice_7: 0.60413/1.25596, loss_spatial_bce_7: 0.13306/0.11564, loss_spatial_dice_7: 0.19183/0.27405, loss_spatial_ce_7: 0.70354/0.20047, loss_grounding_bce_7: 0.26049/0.08975, loss_grounding_dice_7: 0.16155/0.18997, loss_grounding_ce_7: 0.42694/0.35778, loss_mask_ce_8: 1.97068/1.13401, loss_mask_bce_8: 0.50609/0.36487, loss_mask_dice_8: 0.66795/1.33162, loss_spatial_bce_8: 0.18209/0.13685, loss_spatial_dice_8: 0.19956/0.31441, loss_spatial_ce_8: 0.83465/0.25518, loss_grounding_bce_8: 0.19087/0.09330, loss_grounding_dice_8: 0.17479/0.20130, loss_grounding_ce_8: 0.36760/0.43039, loss_mask_ce_9: 4.65049/3.70459, loss_mask_bce_9: 0.55166/0.39200, loss_mask_dice_9: 2.02352/1.90815, loss_spatial_bce_9: 0.47331/0.33881, loss_spatial_dice_9: 0.82260/0.82673, loss_spatial_ce_9: 1.81852/1.53374, loss_grounding_bce_9: 0.19538/0.10478, loss_grounding_dice_9: 0.16826/0.28107, loss_grounding_ce_9: 0.27558/0.71982] items per batch[64] items per second[0.22] total items[755200] mini batches[ 11800] memory[7341] epoch remaining[0:47:05]
INFO:trainer.default_trainer:epochs[     6] optim steps[11900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18805/0.91682, loss_mask_bce_0: 0.15832/0.33608, loss_mask_dice_0: 0.34373/1.16677, loss_spatial_bce_0: 0.05581/0.09465, loss_spatial_dice_0: 0.14096/0.22671, loss_spatial_ce_0: 0.03013/0.09412, loss_grounding_bce_0: 0.04382/0.08600, loss_grounding_dice_0: 0.08220/0.17876, loss_grounding_ce_0: 0.00999/0.27951, loss_mask_ce_1: 0.21093/0.91721, loss_mask_bce_1: 0.15630/0.33671, loss_mask_dice_1: 0.32988/1.17475, loss_spatial_bce_1: 0.05860/0.09567, loss_spatial_dice_1: 0.15430/0.23121, loss_spatial_ce_1: 0.04561/0.09976, loss_grounding_bce_1: 0.04354/0.08615, loss_grounding_dice_1: 0.10367/0.17942, loss_grounding_ce_1: 0.01061/0.28137, loss_mask_ce_2: 0.22887/0.92353, loss_mask_bce_2: 0.17100/0.33697, loss_mask_dice_2: 0.38693/1.17347, loss_spatial_bce_2: 0.05658/0.09539, loss_spatial_dice_2: 0.14561/0.23197, loss_spatial_ce_2: 0.02630/0.10409, loss_grounding_bce_2: 0.04515/0.08619, loss_grounding_dice_2: 0.10565/0.17929, loss_grounding_ce_2: 0.01267/0.28468, loss_mask_ce_3: 0.23774/0.92964, loss_mask_bce_3: 0.16178/0.33784, loss_mask_dice_3: 0.33897/1.17004, loss_spatial_bce_3: 0.05596/0.09646, loss_spatial_dice_3: 0.15649/0.23332, loss_spatial_ce_3: 0.02421/0.10869, loss_grounding_bce_3: 0.04450/0.08637, loss_grounding_dice_3: 0.11649/0.17889, loss_grounding_ce_3: 0.00899/0.28510, loss_mask_ce_4: 0.23007/0.92871, loss_mask_bce_4: 0.15908/0.33917, loss_mask_dice_4: 0.34144/1.19156, loss_spatial_bce_4: 0.05381/0.09997, loss_spatial_dice_4: 0.16880/0.24107, loss_spatial_ce_4: 0.03873/0.12507, loss_grounding_bce_4: 0.04265/0.08680, loss_grounding_dice_4: 0.10036/0.18167, loss_grounding_ce_4: 0.00901/0.28802, loss_mask_ce_5: 0.24557/0.94191, loss_mask_bce_5: 0.15828/0.34138, loss_mask_dice_5: 0.33762/1.19686, loss_spatial_bce_5: 0.05531/0.10087, loss_spatial_dice_5: 0.18157/0.24394, loss_spatial_ce_5: 0.04516/0.13822, loss_grounding_bce_5: 0.04249/0.08723, loss_grounding_dice_5: 0.10400/0.18282, loss_grounding_ce_5: 0.00729/0.29932, loss_mask_ce_6: 0.24200/0.97896, loss_mask_bce_6: 0.16256/0.34386, loss_mask_dice_6: 0.37022/1.20004, loss_spatial_bce_6: 0.05801/0.10620, loss_spatial_dice_6: 0.16965/0.24692, loss_spatial_ce_6: 0.04315/0.15995, loss_grounding_bce_6: 0.04336/0.08802, loss_grounding_dice_6: 0.11421/0.18298, loss_grounding_ce_6: 0.02211/0.32067, loss_mask_ce_7: 0.31504/1.02256, loss_mask_bce_7: 0.15464/0.35176, loss_mask_dice_7: 0.31088/1.25548, loss_spatial_bce_7: 0.06178/0.11560, loss_spatial_dice_7: 0.16472/0.27384, loss_spatial_ce_7: 0.15221/0.20023, loss_grounding_bce_7: 0.04822/0.08975, loss_grounding_dice_7: 0.11063/0.18995, loss_grounding_ce_7: 0.05564/0.35870, loss_mask_ce_8: 0.37803/1.13376, loss_mask_bce_8: 0.15164/0.36514, loss_mask_dice_8: 0.34799/1.33123, loss_spatial_bce_8: 0.06277/0.13684, loss_spatial_dice_8: 0.20871/0.31419, loss_spatial_ce_8: 0.38125/0.25491, loss_grounding_bce_8: 0.05357/0.09334, loss_grounding_dice_8: 0.10629/0.20134, loss_grounding_ce_8: 0.00705/0.43077, loss_mask_ce_9: 2.84235/3.70504, loss_mask_bce_9: 0.17503/0.39228, loss_mask_dice_9: 0.63966/1.90784, loss_spatial_bce_9: 0.36002/0.33885, loss_spatial_dice_9: 0.79127/0.82667, loss_spatial_ce_9: 1.33184/1.53323, loss_grounding_bce_9: 0.04548/0.10483, loss_grounding_dice_9: 0.13155/0.28120, loss_grounding_ce_9: 0.50502/0.72018] items per batch[64] items per second[0.23] total items[761600] mini batches[ 11900] memory[7341] epoch remaining[0:42:09]
INFO:trainer.default_trainer:epochs[     6] optim steps[12000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.93330/0.91701, loss_mask_bce_0: 0.26282/0.33596, loss_mask_dice_0: 0.78989/1.16564, loss_spatial_bce_0: 0.05934/0.09465, loss_spatial_dice_0: 0.14470/0.22654, loss_spatial_ce_0: 0.02031/0.09385, loss_grounding_bce_0: 0.05785/0.08602, loss_grounding_dice_0: 0.12572/0.17873, loss_grounding_ce_0: 0.16389/0.27955, loss_mask_ce_1: 1.04667/0.91730, loss_mask_bce_1: 0.27403/0.33660, loss_mask_dice_1: 0.90487/1.17363, loss_spatial_bce_1: 0.06241/0.09567, loss_spatial_dice_1: 0.14995/0.23103, loss_spatial_ce_1: 0.02360/0.09952, loss_grounding_bce_1: 0.05845/0.08617, loss_grounding_dice_1: 0.14328/0.17941, loss_grounding_ce_1: 0.18722/0.28145, loss_mask_ce_2: 1.09563/0.92359, loss_mask_bce_2: 0.26320/0.33687, loss_mask_dice_2: 0.81166/1.17231, loss_spatial_bce_2: 0.06659/0.09540, loss_spatial_dice_2: 0.16543/0.23179, loss_spatial_ce_2: 0.02367/0.10376, loss_grounding_bce_2: 0.05118/0.08620, loss_grounding_dice_2: 0.11503/0.17928, loss_grounding_ce_2: 0.26930/0.28456, loss_mask_ce_3: 1.07563/0.92967, loss_mask_bce_3: 0.26341/0.33769, loss_mask_dice_3: 0.80302/1.16880, loss_spatial_bce_3: 0.06567/0.09646, loss_spatial_dice_3: 0.16133/0.23312, loss_spatial_ce_3: 0.02625/0.10839, loss_grounding_bce_3: 0.05183/0.08640, loss_grounding_dice_3: 0.11029/0.17886, loss_grounding_ce_3: 0.25331/0.28501, loss_mask_ce_4: 1.03637/0.92886, loss_mask_bce_4: 0.26253/0.33899, loss_mask_dice_4: 0.87984/1.19042, loss_spatial_bce_4: 0.07213/0.09997, loss_spatial_dice_4: 0.17580/0.24088, loss_spatial_ce_4: 0.05686/0.12481, loss_grounding_bce_4: 0.06242/0.08682, loss_grounding_dice_4: 0.13940/0.18158, loss_grounding_ce_4: 0.41110/0.28806, loss_mask_ce_5: 1.02583/0.94215, loss_mask_bce_5: 0.26266/0.34121, loss_mask_dice_5: 0.82020/1.19571, loss_spatial_bce_5: 0.08667/0.10086, loss_spatial_dice_5: 0.18005/0.24377, loss_spatial_ce_5: 0.09445/0.13791, loss_grounding_bce_5: 0.05125/0.08727, loss_grounding_dice_5: 0.11430/0.18281, loss_grounding_ce_5: 0.32258/0.29929, loss_mask_ce_6: 0.96895/0.97916, loss_mask_bce_6: 0.28314/0.34369, loss_mask_dice_6: 0.86499/1.19900, loss_spatial_bce_6: 0.07996/0.10619, loss_spatial_dice_6: 0.14581/0.24672, loss_spatial_ce_6: 0.05109/0.15964, loss_grounding_bce_6: 0.06987/0.08805, loss_grounding_dice_6: 0.14823/0.18294, loss_grounding_ce_6: 0.26443/0.32055, loss_mask_ce_7: 1.15782/1.02267, loss_mask_bce_7: 0.27758/0.35163, loss_mask_dice_7: 0.93378/1.25426, loss_spatial_bce_7: 0.09426/0.11557, loss_spatial_dice_7: 0.19730/0.27361, loss_spatial_ce_7: 0.08841/0.20002, loss_grounding_bce_7: 0.05967/0.08980, loss_grounding_dice_7: 0.13825/0.18989, loss_grounding_ce_7: 0.15139/0.35862, loss_mask_ce_8: 1.63569/1.13381, loss_mask_bce_8: 0.30861/0.36498, loss_mask_dice_8: 0.94875/1.33010, loss_spatial_bce_8: 0.09200/0.13681, loss_spatial_dice_8: 0.21954/0.31397, loss_spatial_ce_8: 0.11340/0.25462, loss_grounding_bce_8: 0.05486/0.09331, loss_grounding_dice_8: 0.13153/0.20130, loss_grounding_ce_8: 0.16110/0.43064, loss_mask_ce_9: 4.73253/3.70434, loss_mask_bce_9: 0.34324/0.39211, loss_mask_dice_9: 1.41648/1.90613, loss_spatial_bce_9: 0.29088/0.33886, loss_spatial_dice_9: 0.84852/0.82655, loss_spatial_ce_9: 1.37473/1.53226, loss_grounding_bce_9: 0.05993/0.10484, loss_grounding_dice_9: 0.23130/0.28116, loss_grounding_ce_9: 1.04875/0.71899] items per batch[64] items per second[0.22] total items[768000] mini batches[ 12000] memory[7341] epoch remaining[0:37:25]
INFO:trainer.default_trainer:epochs[     6] optim steps[12100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04583/0.91732, loss_mask_bce_0: 0.59666/0.33617, loss_mask_dice_0: 1.17209/1.16555, loss_spatial_bce_0: 0.09820/0.09462, loss_spatial_dice_0: 0.25541/0.22644, loss_spatial_ce_0: 0.09443/0.09364, loss_grounding_bce_0: 0.09099/0.08608, loss_grounding_dice_0: 0.13528/0.17879, loss_grounding_ce_0: 0.09115/0.28020, loss_mask_ce_1: 0.88285/0.91765, loss_mask_bce_1: 0.61682/0.33680, loss_mask_dice_1: 1.32443/1.17364, loss_spatial_bce_1: 0.10784/0.09565, loss_spatial_dice_1: 0.24288/0.23097, loss_spatial_ce_1: 0.06312/0.09934, loss_grounding_bce_1: 0.09333/0.08622, loss_grounding_dice_1: 0.12274/0.17945, loss_grounding_ce_1: 0.09851/0.28195, loss_mask_ce_2: 1.00146/0.92412, loss_mask_bce_2: 0.62344/0.33705, loss_mask_dice_2: 1.22803/1.17235, loss_spatial_bce_2: 0.11445/0.09538, loss_spatial_dice_2: 0.23360/0.23174, loss_spatial_ce_2: 0.05410/0.10351, loss_grounding_bce_2: 0.09592/0.08624, loss_grounding_dice_2: 0.12645/0.17934, loss_grounding_ce_2: 0.10646/0.28505, loss_mask_ce_3: 1.10055/0.93020, loss_mask_bce_3: 0.59507/0.33787, loss_mask_dice_3: 1.10033/1.16894, loss_spatial_bce_3: 0.10838/0.09642, loss_spatial_dice_3: 0.23956/0.23304, loss_spatial_ce_3: 0.13617/0.10817, loss_grounding_bce_3: 0.09679/0.08643, loss_grounding_dice_3: 0.13428/0.17888, loss_grounding_ce_3: 0.10876/0.28554, loss_mask_ce_4: 1.14608/0.92927, loss_mask_bce_4: 0.58111/0.33918, loss_mask_dice_4: 1.23994/1.19041, loss_spatial_bce_4: 0.12941/0.09995, loss_spatial_dice_4: 0.29464/0.24082, loss_spatial_ce_4: 0.21327/0.12461, loss_grounding_bce_4: 0.09848/0.08687, loss_grounding_dice_4: 0.13835/0.18159, loss_grounding_ce_4: 0.10430/0.28848, loss_mask_ce_5: 1.15695/0.94267, loss_mask_bce_5: 0.59243/0.34140, loss_mask_dice_5: 1.19260/1.19568, loss_spatial_bce_5: 0.11173/0.10084, loss_spatial_dice_5: 0.27550/0.24371, loss_spatial_ce_5: 0.13973/0.13767, loss_grounding_bce_5: 0.09660/0.08730, loss_grounding_dice_5: 0.13420/0.18284, loss_grounding_ce_5: 0.12393/0.29983, loss_mask_ce_6: 1.17179/0.97955, loss_mask_bce_6: 0.59849/0.34391, loss_mask_dice_6: 1.16395/1.19895, loss_spatial_bce_6: 0.10900/0.10619, loss_spatial_dice_6: 0.26472/0.24667, loss_spatial_ce_6: 0.39204/0.15943, loss_grounding_bce_6: 0.10484/0.08809, loss_grounding_dice_6: 0.15124/0.18299, loss_grounding_ce_6: 0.12320/0.32108, loss_mask_ce_7: 1.15734/1.02302, loss_mask_bce_7: 0.65143/0.35187, loss_mask_dice_7: 1.21540/1.25411, loss_spatial_bce_7: 0.12024/0.11555, loss_spatial_dice_7: 0.31243/0.27355, loss_spatial_ce_7: 0.15579/0.19979, loss_grounding_bce_7: 0.09303/0.08982, loss_grounding_dice_7: 0.12559/0.18992, loss_grounding_ce_7: 0.11004/0.35904, loss_mask_ce_8: 1.29161/1.13423, loss_mask_bce_8: 0.62488/0.36525, loss_mask_dice_8: 1.34077/1.33027, loss_spatial_bce_8: 0.14529/0.13679, loss_spatial_dice_8: 0.39357/0.31393, loss_spatial_ce_8: 0.45659/0.25457, loss_grounding_bce_8: 0.09898/0.09336, loss_grounding_dice_8: 0.13919/0.20135, loss_grounding_ce_8: 0.10993/0.43108, loss_mask_ce_9: 3.21631/3.70480, loss_mask_bce_9: 0.58564/0.39240, loss_mask_dice_9: 2.14147/1.90634, loss_spatial_bce_9: 0.17201/0.33882, loss_spatial_dice_9: 0.86409/0.82651, loss_spatial_ce_9: 1.47380/1.53197, loss_grounding_bce_9: 0.07933/0.10483, loss_grounding_dice_9: 0.12487/0.28123, loss_grounding_ce_9: 0.20737/0.71886] items per batch[64] items per second[0.22] total items[774400] mini batches[ 12100] memory[7341] epoch remaining[0:32:43]
INFO:trainer.default_trainer:epochs[     6] optim steps[12200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85745/0.91666, loss_mask_bce_0: 0.17454/0.33616, loss_mask_dice_0: 0.71457/1.16526, loss_spatial_bce_0: 0.06124/0.09459, loss_spatial_dice_0: 0.27369/0.22632, loss_spatial_ce_0: 0.01261/0.09337, loss_grounding_bce_0: 0.07820/0.08617, loss_grounding_dice_0: 0.26689/0.17880, loss_grounding_ce_0: 0.27905/0.28011, loss_mask_ce_1: 0.75893/0.91701, loss_mask_bce_1: 0.17374/0.33680, loss_mask_dice_1: 0.69444/1.17332, loss_spatial_bce_1: 0.06375/0.09561, loss_spatial_dice_1: 0.28729/0.23084, loss_spatial_ce_1: 0.00921/0.09911, loss_grounding_bce_1: 0.08045/0.08630, loss_grounding_dice_1: 0.28540/0.17944, loss_grounding_ce_1: 0.28642/0.28182, loss_mask_ce_2: 0.80919/0.92356, loss_mask_bce_2: 0.17806/0.33705, loss_mask_dice_2: 0.77816/1.17204, loss_spatial_bce_2: 0.06402/0.09535, loss_spatial_dice_2: 0.25543/0.23158, loss_spatial_ce_2: 0.01920/0.10331, loss_grounding_bce_2: 0.07563/0.08630, loss_grounding_dice_2: 0.26323/0.17929, loss_grounding_ce_2: 0.30629/0.28495, loss_mask_ce_3: 0.88732/0.92945, loss_mask_bce_3: 0.19551/0.33788, loss_mask_dice_3: 0.83302/1.16861, loss_spatial_bce_3: 0.06258/0.09639, loss_spatial_dice_3: 0.26864/0.23289, loss_spatial_ce_3: 0.03673/0.10791, loss_grounding_bce_3: 0.07893/0.08650, loss_grounding_dice_3: 0.27658/0.17882, loss_grounding_ce_3: 0.32019/0.28532, loss_mask_ce_4: 0.85401/0.92859, loss_mask_bce_4: 0.18569/0.33914, loss_mask_dice_4: 0.70626/1.19007, loss_spatial_bce_4: 0.06758/0.09993, loss_spatial_dice_4: 0.28152/0.24067, loss_spatial_ce_4: 0.02104/0.12440, loss_grounding_bce_4: 0.08090/0.08695, loss_grounding_dice_4: 0.26177/0.18159, loss_grounding_ce_4: 0.33826/0.28813, loss_mask_ce_5: 0.98934/0.94194, loss_mask_bce_5: 0.18354/0.34140, loss_mask_dice_5: 0.73193/1.19539, loss_spatial_bce_5: 0.07648/0.10082, loss_spatial_dice_5: 0.30932/0.24358, loss_spatial_ce_5: 0.03925/0.13742, loss_grounding_bce_5: 0.07881/0.08739, loss_grounding_dice_5: 0.32078/0.18279, loss_grounding_ce_5: 0.25449/0.29961, loss_mask_ce_6: 1.19074/0.97882, loss_mask_bce_6: 0.17778/0.34393, loss_mask_dice_6: 0.72621/1.19862, loss_spatial_bce_6: 0.06844/0.10621, loss_spatial_dice_6: 0.31311/0.24653, loss_spatial_ce_6: 0.06589/0.15924, loss_grounding_bce_6: 0.08448/0.08819, loss_grounding_dice_6: 0.31991/0.18296, loss_grounding_ce_6: 0.33549/0.32082, loss_mask_ce_7: 1.42634/1.02256, loss_mask_bce_7: 0.19618/0.35189, loss_mask_dice_7: 0.70202/1.25369, loss_spatial_bce_7: 0.07642/0.11551, loss_spatial_dice_7: 0.30916/0.27342, loss_spatial_ce_7: 0.14473/0.19948, loss_grounding_bce_7: 0.08833/0.08993, loss_grounding_dice_7: 0.27541/0.18990, loss_grounding_ce_7: 0.36437/0.35857, loss_mask_ce_8: 0.89881/1.13354, loss_mask_bce_8: 0.15989/0.36528, loss_mask_dice_8: 0.71329/1.32977, loss_spatial_bce_8: 0.12216/0.13677, loss_spatial_dice_8: 0.40899/0.31387, loss_spatial_ce_8: 0.12436/0.25428, loss_grounding_bce_8: 0.08259/0.09344, loss_grounding_dice_8: 0.33251/0.20134, loss_grounding_ce_8: 0.23203/0.43082, loss_mask_ce_9: 2.20473/3.70386, loss_mask_bce_9: 0.15472/0.39237, loss_mask_dice_9: 0.91209/1.90529, loss_spatial_bce_9: 0.21185/0.33877, loss_spatial_dice_9: 0.86326/0.82647, loss_spatial_ce_9: 1.45837/1.53147, loss_grounding_bce_9: 0.06974/0.10493, loss_grounding_dice_9: 0.37679/0.28118, loss_grounding_ce_9: 0.35791/0.71886] items per batch[64] items per second[0.23] total items[780800] mini batches[ 12200] memory[7341] epoch remaining[0:27:56]
INFO:trainer.default_trainer:epochs[     6] optim steps[12300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98822/0.91679, loss_mask_bce_0: 0.58578/0.33601, loss_mask_dice_0: 1.44711/1.16556, loss_spatial_bce_0: 0.08204/0.09452, loss_spatial_dice_0: 0.16762/0.22624, loss_spatial_ce_0: 0.10723/0.09309, loss_grounding_bce_0: 0.05782/0.08608, loss_grounding_dice_0: 0.13662/0.17880, loss_grounding_ce_0: 0.48500/0.27980, loss_mask_ce_1: 0.91505/0.91713, loss_mask_bce_1: 0.56499/0.33663, loss_mask_dice_1: 1.50831/1.17345, loss_spatial_bce_1: 0.09822/0.09554, loss_spatial_dice_1: 0.19118/0.23077, loss_spatial_ce_1: 0.03609/0.09878, loss_grounding_bce_1: 0.05299/0.08622, loss_grounding_dice_1: 0.13184/0.17939, loss_grounding_ce_1: 0.45817/0.28149, loss_mask_ce_2: 0.87847/0.92371, loss_mask_bce_2: 0.58957/0.33689, loss_mask_dice_2: 1.52306/1.17223, loss_spatial_bce_2: 0.11384/0.09528, loss_spatial_dice_2: 0.19586/0.23150, loss_spatial_ce_2: 0.08615/0.10301, loss_grounding_bce_2: 0.05833/0.08624, loss_grounding_dice_2: 0.13685/0.17927, loss_grounding_ce_2: 0.51340/0.28459, loss_mask_ce_3: 0.90583/0.92953, loss_mask_bce_3: 0.57773/0.33777, loss_mask_dice_3: 1.51774/1.16877, loss_spatial_bce_3: 0.11942/0.09633, loss_spatial_dice_3: 0.18932/0.23282, loss_spatial_ce_3: 0.12237/0.10754, loss_grounding_bce_3: 0.05797/0.08643, loss_grounding_dice_3: 0.13322/0.17878, loss_grounding_ce_3: 0.48768/0.28509, loss_mask_ce_4: 0.93072/0.92863, loss_mask_bce_4: 0.59049/0.33903, loss_mask_dice_4: 1.55636/1.19031, loss_spatial_bce_4: 0.11464/0.09988, loss_spatial_dice_4: 0.20168/0.24064, loss_spatial_ce_4: 0.07441/0.12414, loss_grounding_bce_4: 0.05952/0.08689, loss_grounding_dice_4: 0.13806/0.18155, loss_grounding_ce_4: 0.51039/0.28783, loss_mask_ce_5: 0.93867/0.94199, loss_mask_bce_5: 0.58525/0.34131, loss_mask_dice_5: 1.53289/1.19566, loss_spatial_bce_5: 0.10157/0.10077, loss_spatial_dice_5: 0.19377/0.24353, loss_spatial_ce_5: 0.05101/0.13707, loss_grounding_bce_5: 0.06220/0.08734, loss_grounding_dice_5: 0.14639/0.18275, loss_grounding_ce_5: 0.51152/0.29942, loss_mask_ce_6: 1.01209/0.97880, loss_mask_bce_6: 0.61291/0.34381, loss_mask_dice_6: 1.55949/1.19901, loss_spatial_bce_6: 0.09986/0.10620, loss_spatial_dice_6: 0.16748/0.24649, loss_spatial_ce_6: 0.13553/0.15911, loss_grounding_bce_6: 0.06124/0.08813, loss_grounding_dice_6: 0.13439/0.18294, loss_grounding_ce_6: 0.49829/0.32060, loss_mask_ce_7: 1.10887/1.02255, loss_mask_bce_7: 0.55069/0.35168, loss_mask_dice_7: 1.52837/1.25396, loss_spatial_bce_7: 0.09583/0.11548, loss_spatial_dice_7: 0.18601/0.27340, loss_spatial_ce_7: 0.14508/0.19914, loss_grounding_bce_7: 0.06814/0.08987, loss_grounding_dice_7: 0.14792/0.18987, loss_grounding_ce_7: 0.52818/0.35837, loss_mask_ce_8: 1.46170/1.13359, loss_mask_bce_8: 0.61751/0.36514, loss_mask_dice_8: 1.72509/1.32998, loss_spatial_bce_8: 0.09324/0.13669, loss_spatial_dice_8: 0.22129/0.31388, loss_spatial_ce_8: 0.12527/0.25418, loss_grounding_bce_8: 0.06715/0.09338, loss_grounding_dice_8: 0.17862/0.20131, loss_grounding_ce_8: 0.55083/0.43048, loss_mask_ce_9: 6.20207/3.70436, loss_mask_bce_9: 0.69632/0.39226, loss_mask_dice_9: 2.89372/1.90573, loss_spatial_bce_9: 0.16897/0.33867, loss_spatial_dice_9: 0.92827/0.82649, loss_spatial_ce_9: 1.27475/1.53129, loss_grounding_bce_9: 0.09145/0.10489, loss_grounding_dice_9: 0.37599/0.28115, loss_grounding_ce_9: 0.55551/0.71869] items per batch[64] items per second[0.22] total items[787200] mini batches[ 12300] memory[7341] epoch remaining[0:23:12]
INFO:trainer.default_trainer:epochs[     6] optim steps[12400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80085/0.91650, loss_mask_bce_0: 0.21309/0.33610, loss_mask_dice_0: 0.94021/1.16561, loss_spatial_bce_0: 0.06781/0.09453, loss_spatial_dice_0: 0.23629/0.22616, loss_spatial_ce_0: 0.27367/0.09289, loss_grounding_bce_0: 0.02110/0.08608, loss_grounding_dice_0: 0.24573/0.17882, loss_grounding_ce_0: 0.59324/0.27978, loss_mask_ce_1: 0.73313/0.91692, loss_mask_bce_1: 0.23251/0.33674, loss_mask_dice_1: 1.30685/1.17341, loss_spatial_bce_1: 0.06947/0.09554, loss_spatial_dice_1: 0.29709/0.23068, loss_spatial_ce_1: 0.44546/0.09853, loss_grounding_bce_1: 0.02562/0.08622, loss_grounding_dice_1: 0.24720/0.17942, loss_grounding_ce_1: 0.75602/0.28149, loss_mask_ce_2: 0.80993/0.92355, loss_mask_bce_2: 0.21722/0.33698, loss_mask_dice_2: 0.73730/1.17223, loss_spatial_bce_2: 0.06607/0.09528, loss_spatial_dice_2: 0.31694/0.23141, loss_spatial_ce_2: 0.29029/0.10280, loss_grounding_bce_2: 0.02768/0.08623, loss_grounding_dice_2: 0.28279/0.17929, loss_grounding_ce_2: 1.28461/0.28469, loss_mask_ce_3: 0.83483/0.92940, loss_mask_bce_3: 0.22365/0.33788, loss_mask_dice_3: 1.03344/1.16885, loss_spatial_bce_3: 0.07110/0.09633, loss_spatial_dice_3: 0.31022/0.23273, loss_spatial_ce_3: 0.30944/0.10728, loss_grounding_bce_3: 0.02393/0.08642, loss_grounding_dice_3: 0.24102/0.17883, loss_grounding_ce_3: 0.69436/0.28508, loss_mask_ce_4: 0.81043/0.92836, loss_mask_bce_4: 0.22062/0.33915, loss_mask_dice_4: 0.87634/1.19019, loss_spatial_bce_4: 0.09706/0.09988, loss_spatial_dice_4: 0.34045/0.24057, loss_spatial_ce_4: 0.38635/0.12386, loss_grounding_bce_4: 0.01966/0.08689, loss_grounding_dice_4: 0.29197/0.18157, loss_grounding_ce_4: 0.61229/0.28794, loss_mask_ce_5: 0.78035/0.94163, loss_mask_bce_5: 0.23011/0.34143, loss_mask_dice_5: 1.05257/1.19554, loss_spatial_bce_5: 0.09968/0.10075, loss_spatial_dice_5: 0.35177/0.24348, loss_spatial_ce_5: 0.28926/0.13685, loss_grounding_bce_5: 0.02307/0.08734, loss_grounding_dice_5: 0.28115/0.18278, loss_grounding_ce_5: 0.51312/0.29947, loss_mask_ce_6: 0.72717/0.97848, loss_mask_bce_6: 0.32316/0.34392, loss_mask_dice_6: 1.12160/1.19887, loss_spatial_bce_6: 0.08653/0.10619, loss_spatial_dice_6: 0.34648/0.24641, loss_spatial_ce_6: 0.45292/0.15900, loss_grounding_bce_6: 0.03584/0.08813, loss_grounding_dice_6: 0.29130/0.18297, loss_grounding_ce_6: 0.45843/0.32057, loss_mask_ce_7: 0.82115/1.02223, loss_mask_bce_7: 0.33957/0.35177, loss_mask_dice_7: 1.07361/1.25394, loss_spatial_bce_7: 0.18394/0.11547, loss_spatial_dice_7: 0.42102/0.27328, loss_spatial_ce_7: 0.26417/0.19883, loss_grounding_bce_7: 0.03188/0.08986, loss_grounding_dice_7: 0.29432/0.18992, loss_grounding_ce_7: 0.38152/0.35818, loss_mask_ce_8: 0.80097/1.13362, loss_mask_bce_8: 0.24391/0.36523, loss_mask_dice_8: 1.00890/1.32986, loss_spatial_bce_8: 0.14806/0.13669, loss_spatial_dice_8: 0.43368/0.31381, loss_spatial_ce_8: 0.57451/0.25399, loss_grounding_bce_8: 0.03452/0.09335, loss_grounding_dice_8: 0.25684/0.20134, loss_grounding_ce_8: 0.49255/0.43043, loss_mask_ce_9: 3.03198/3.70413, loss_mask_bce_9: 0.28187/0.39241, loss_mask_dice_9: 1.34498/1.90597, loss_spatial_bce_9: 0.28665/0.33872, loss_spatial_dice_9: 0.81122/0.82649, loss_spatial_ce_9: 1.62733/1.53117, loss_grounding_bce_9: 0.02911/0.10488, loss_grounding_dice_9: 0.44833/0.28127, loss_grounding_ce_9: 1.27501/0.71788] items per batch[64] items per second[0.23] total items[793600] mini batches[ 12400] memory[7341] epoch remaining[0:18:27]
INFO:trainer.default_trainer:epochs[     6] optim steps[12500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50699/0.91649, loss_mask_bce_0: 0.29734/0.33624, loss_mask_dice_0: 0.70598/1.16690, loss_spatial_bce_0: 0.12447/0.09453, loss_spatial_dice_0: 0.17284/0.22614, loss_spatial_ce_0: 0.09549/0.09262, loss_grounding_bce_0: 0.20393/0.08613, loss_grounding_dice_0: 0.17865/0.17882, loss_grounding_ce_0: 0.18787/0.27960, loss_mask_ce_1: 0.42598/0.91689, loss_mask_bce_1: 0.28278/0.33689, loss_mask_dice_1: 0.60370/1.17457, loss_spatial_bce_1: 0.11675/0.09553, loss_spatial_dice_1: 0.16238/0.23067, loss_spatial_ce_1: 0.13223/0.09831, loss_grounding_bce_1: 0.21513/0.08627, loss_grounding_dice_1: 0.18190/0.17943, loss_grounding_ce_1: 0.14976/0.28135, loss_mask_ce_2: 0.47636/0.92353, loss_mask_bce_2: 0.27981/0.33712, loss_mask_dice_2: 0.46611/1.17342, loss_spatial_bce_2: 0.11710/0.09527, loss_spatial_dice_2: 0.16440/0.23140, loss_spatial_ce_2: 0.14947/0.10256, loss_grounding_bce_2: 0.21020/0.08630, loss_grounding_dice_2: 0.17718/0.17933, loss_grounding_ce_2: 0.13716/0.28460, loss_mask_ce_3: 0.46751/0.92962, loss_mask_bce_3: 0.28175/0.33805, loss_mask_dice_3: 0.51036/1.17017, loss_spatial_bce_3: 0.12206/0.09633, loss_spatial_dice_3: 0.15655/0.23268, loss_spatial_ce_3: 0.08962/0.10701, loss_grounding_bce_3: 0.21044/0.08649, loss_grounding_dice_3: 0.17880/0.17887, loss_grounding_ce_3: 0.25817/0.28504, loss_mask_ce_4: 0.47903/0.92838, loss_mask_bce_4: 0.27424/0.33931, loss_mask_dice_4: 0.44123/1.19136, loss_spatial_bce_4: 0.12485/0.09988, loss_spatial_dice_4: 0.18075/0.24055, loss_spatial_ce_4: 0.22613/0.12366, loss_grounding_bce_4: 0.21803/0.08696, loss_grounding_dice_4: 0.18893/0.18159, loss_grounding_ce_4: 0.10829/0.28782, loss_mask_ce_5: 0.55521/0.94163, loss_mask_bce_5: 0.28258/0.34161, loss_mask_dice_5: 0.65552/1.19674, loss_spatial_bce_5: 0.11711/0.10074, loss_spatial_dice_5: 0.16565/0.24342, loss_spatial_ce_5: 0.14268/0.13661, loss_grounding_bce_5: 0.21378/0.08741, loss_grounding_dice_5: 0.18217/0.18280, loss_grounding_ce_5: 0.20301/0.29953, loss_mask_ce_6: 0.52048/0.97846, loss_mask_bce_6: 0.27563/0.34410, loss_mask_dice_6: 0.42040/1.20008, loss_spatial_bce_6: 0.12435/0.10619, loss_spatial_dice_6: 0.18848/0.24633, loss_spatial_ce_6: 0.12611/0.15883, loss_grounding_bce_6: 0.22827/0.08820, loss_grounding_dice_6: 0.18269/0.18299, loss_grounding_ce_6: 0.16288/0.32033, loss_mask_ce_7: 0.49959/1.02207, loss_mask_bce_7: 0.27886/0.35189, loss_mask_dice_7: 0.51144/1.25521, loss_spatial_bce_7: 0.18029/0.11545, loss_spatial_dice_7: 0.23137/0.27325, loss_spatial_ce_7: 0.30211/0.19864, loss_grounding_bce_7: 0.22098/0.08993, loss_grounding_dice_7: 0.18779/0.18992, loss_grounding_ce_7: 0.09951/0.35802, loss_mask_ce_8: 0.54032/1.13355, loss_mask_bce_8: 0.30377/0.36536, loss_mask_dice_8: 0.80109/1.33105, loss_spatial_bce_8: 0.23184/0.13672, loss_spatial_dice_8: 0.28256/0.31378, loss_spatial_ce_8: 0.30965/0.25370, loss_grounding_bce_8: 0.19812/0.09339, loss_grounding_dice_8: 0.17090/0.20133, loss_grounding_ce_8: 0.11510/0.43051, loss_mask_ce_9: 2.35837/3.70394, loss_mask_bce_9: 0.29000/0.39257, loss_mask_dice_9: 0.89219/1.90754, loss_spatial_bce_9: 0.52609/0.33876, loss_spatial_dice_9: 0.80909/0.82657, loss_spatial_ce_9: 1.32619/1.53097, loss_grounding_bce_9: 0.20711/0.10494, loss_grounding_dice_9: 0.19229/0.28123, loss_grounding_ce_9: 0.63358/0.71754] items per batch[64] items per second[0.22] total items[800000] mini batches[ 12500] memory[7341] epoch remaining[0:13:42]
INFO:trainer.default_trainer:epochs[     6] optim steps[12600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28660/0.91568, loss_mask_bce_0: 0.06704/0.33609, loss_mask_dice_0: 0.21833/1.16640, loss_spatial_bce_0: 0.02966/0.09442, loss_spatial_dice_0: 0.10535/0.22600, loss_spatial_ce_0: 0.00097/0.09243, loss_grounding_bce_0: 0.06697/0.08610, loss_grounding_dice_0: 0.16237/0.17880, loss_grounding_ce_0: 0.09037/0.27948, loss_mask_ce_1: 0.28481/0.91612, loss_mask_bce_1: 0.06651/0.33672, loss_mask_dice_1: 0.19972/1.17403, loss_spatial_bce_1: 0.02932/0.09541, loss_spatial_dice_1: 0.10479/0.23051, loss_spatial_ce_1: 0.00033/0.09803, loss_grounding_bce_1: 0.06422/0.08624, loss_grounding_dice_1: 0.14092/0.17936, loss_grounding_ce_1: 0.09723/0.28134, loss_mask_ce_2: 0.34964/0.92273, loss_mask_bce_2: 0.07045/0.33696, loss_mask_dice_2: 0.20483/1.17275, loss_spatial_bce_2: 0.03063/0.09515, loss_spatial_dice_2: 0.09551/0.23125, loss_spatial_ce_2: 0.00036/0.10228, loss_grounding_bce_2: 0.06498/0.08626, loss_grounding_dice_2: 0.16920/0.17922, loss_grounding_ce_2: 0.08776/0.28448, loss_mask_ce_3: 0.29473/0.92882, loss_mask_bce_3: 0.07021/0.33790, loss_mask_dice_3: 0.20753/1.16954, loss_spatial_bce_3: 0.03063/0.09621, loss_spatial_dice_3: 0.10146/0.23250, loss_spatial_ce_3: 0.00079/0.10672, loss_grounding_bce_3: 0.07284/0.08646, loss_grounding_dice_3: 0.18113/0.17876, loss_grounding_ce_3: 0.08648/0.28510, loss_mask_ce_4: 0.33619/0.92754, loss_mask_bce_4: 0.06863/0.33913, loss_mask_dice_4: 0.19801/1.19088, loss_spatial_bce_4: 0.04084/0.09977, loss_spatial_dice_4: 0.10877/0.24041, loss_spatial_ce_4: 0.02929/0.12343, loss_grounding_bce_4: 0.06687/0.08691, loss_grounding_dice_4: 0.15228/0.18148, loss_grounding_ce_4: 0.11141/0.28783, loss_mask_ce_5: 0.39483/0.94083, loss_mask_bce_5: 0.06576/0.34145, loss_mask_dice_5: 0.21392/1.19630, loss_spatial_bce_5: 0.04281/0.10062, loss_spatial_dice_5: 0.12852/0.24327, loss_spatial_ce_5: 0.10681/0.13635, loss_grounding_bce_5: 0.05738/0.08735, loss_grounding_dice_5: 0.15010/0.18273, loss_grounding_ce_5: 0.19677/0.29971, loss_mask_ce_6: 0.32951/0.97773, loss_mask_bce_6: 0.06792/0.34395, loss_mask_dice_6: 0.20309/1.19949, loss_spatial_bce_6: 0.03597/0.10608, loss_spatial_dice_6: 0.15713/0.24618, loss_spatial_ce_6: 0.07635/0.15860, loss_grounding_bce_6: 0.06025/0.08815, loss_grounding_dice_6: 0.14990/0.18292, loss_grounding_ce_6: 0.14719/0.32032, loss_mask_ce_7: 0.46077/1.02128, loss_mask_bce_7: 0.06799/0.35171, loss_mask_dice_7: 0.21292/1.25452, loss_spatial_bce_7: 0.04247/0.11533, loss_spatial_dice_7: 0.16471/0.27312, loss_spatial_ce_7: 0.02920/0.19838, loss_grounding_bce_7: 0.05641/0.08988, loss_grounding_dice_7: 0.13229/0.18983, loss_grounding_ce_7: 0.14596/0.35803, loss_mask_ce_8: 0.68117/1.13285, loss_mask_bce_8: 0.06106/0.36518, loss_mask_dice_8: 0.16918/1.33015, loss_spatial_bce_8: 0.07974/0.13659, loss_spatial_dice_8: 0.23554/0.31369, loss_spatial_ce_8: 0.07341/0.25351, loss_grounding_bce_8: 0.06033/0.09336, loss_grounding_dice_8: 0.09884/0.20126, loss_grounding_ce_8: 0.39659/0.43039, loss_mask_ce_9: 3.48238/3.70258, loss_mask_bce_9: 0.07638/0.39229, loss_mask_dice_9: 0.40458/1.90618, loss_spatial_bce_9: 0.19859/0.33863, loss_spatial_dice_9: 0.72194/0.82653, loss_spatial_ce_9: 1.57131/1.53131, loss_grounding_bce_9: 0.05370/0.10487, loss_grounding_dice_9: 0.17231/0.28119, loss_grounding_ce_9: 1.47122/0.71692] items per batch[64] items per second[0.22] total items[806400] mini batches[ 12600] memory[7341] epoch remaining[0:08:59]
INFO:trainer.default_trainer:epochs[     6] optim steps[12700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05508/0.91575, loss_mask_bce_0: 0.21959/0.33623, loss_mask_dice_0: 1.94931/1.16628, loss_spatial_bce_0: 0.03133/0.09443, loss_spatial_dice_0: 0.18387/0.22597, loss_spatial_ce_0: 0.03506/0.09210, loss_grounding_bce_0: 0.08804/0.08613, loss_grounding_dice_0: 0.16175/0.17882, loss_grounding_ce_0: 0.02251/0.27914, loss_mask_ce_1: 1.13015/0.91616, loss_mask_bce_1: 0.21709/0.33685, loss_mask_dice_1: 1.63322/1.17392, loss_spatial_bce_1: 0.03211/0.09541, loss_spatial_dice_1: 0.18794/0.23050, loss_spatial_ce_1: 0.17911/0.09780, loss_grounding_bce_1: 0.07961/0.08625, loss_grounding_dice_1: 0.15000/0.17939, loss_grounding_ce_1: 0.01665/0.28103, loss_mask_ce_2: 0.98995/0.92274, loss_mask_bce_2: 0.22071/0.33709, loss_mask_dice_2: 1.47137/1.17271, loss_spatial_bce_2: 0.03300/0.09515, loss_spatial_dice_2: 0.19753/0.23125, loss_spatial_ce_2: 0.09062/0.10197, loss_grounding_bce_2: 0.08423/0.08627, loss_grounding_dice_2: 0.15513/0.17923, loss_grounding_ce_2: 0.02039/0.28419, loss_mask_ce_3: 1.03281/0.92881, loss_mask_bce_3: 0.22730/0.33803, loss_mask_dice_3: 1.85734/1.16954, loss_spatial_bce_3: 0.03404/0.09622, loss_spatial_dice_3: 0.20851/0.23250, loss_spatial_ce_3: 0.02804/0.10635, loss_grounding_bce_3: 0.08310/0.08648, loss_grounding_dice_3: 0.15444/0.17876, loss_grounding_ce_3: 0.01855/0.28484, loss_mask_ce_4: 1.06832/0.92762, loss_mask_bce_4: 0.21878/0.33926, loss_mask_dice_4: 1.96980/1.19083, loss_spatial_bce_4: 0.03327/0.09977, loss_spatial_dice_4: 0.19346/0.24039, loss_spatial_ce_4: 0.04838/0.12313, loss_grounding_bce_4: 0.09071/0.08694, loss_grounding_dice_4: 0.18447/0.18148, loss_grounding_ce_4: 0.03098/0.28751, loss_mask_ce_5: 1.05667/0.94103, loss_mask_bce_5: 0.22574/0.34158, loss_mask_dice_5: 2.05227/1.19631, loss_spatial_bce_5: 0.03575/0.10059, loss_spatial_dice_5: 0.21198/0.24326, loss_spatial_ce_5: 0.04306/0.13600, loss_grounding_bce_5: 0.10157/0.08740, loss_grounding_dice_5: 0.19932/0.18277, loss_grounding_ce_5: 0.06498/0.29927, loss_mask_ce_6: 1.22204/0.97786, loss_mask_bce_6: 0.23375/0.34410, loss_mask_dice_6: 1.74333/1.19947, loss_spatial_bce_6: 0.04082/0.10606, loss_spatial_dice_6: 0.18827/0.24614, loss_spatial_ce_6: 0.18567/0.15838, loss_grounding_bce_6: 0.09884/0.08818, loss_grounding_dice_6: 0.21975/0.18294, loss_grounding_ce_6: 0.08635/0.32005, loss_mask_ce_7: 1.19720/1.02113, loss_mask_bce_7: 0.24295/0.35192, loss_mask_dice_7: 1.84631/1.25454, loss_spatial_bce_7: 0.03786/0.11531, loss_spatial_dice_7: 0.21425/0.27310, loss_spatial_ce_7: 0.37942/0.19822, loss_grounding_bce_7: 0.09834/0.08989, loss_grounding_dice_7: 0.26999/0.18984, loss_grounding_ce_7: 0.10579/0.35781, loss_mask_ce_8: 1.65903/1.13315, loss_mask_bce_8: 0.26882/0.36538, loss_mask_dice_8: 2.10682/1.33020, loss_spatial_bce_8: 0.04018/0.13659, loss_spatial_dice_8: 0.28344/0.31367, loss_spatial_ce_8: 0.20417/0.25328, loss_grounding_bce_8: 0.12625/0.09338, loss_grounding_dice_8: 0.30435/0.20129, loss_grounding_ce_8: 0.14854/0.42976, loss_mask_ce_9: 4.91751/3.70120, loss_mask_bce_9: 0.34350/0.39247, loss_mask_dice_9: 2.75823/1.90631, loss_spatial_bce_9: 0.23195/0.33857, loss_spatial_dice_9: 0.85631/0.82650, loss_spatial_ce_9: 1.56417/1.53104, loss_grounding_bce_9: 0.12677/0.10491, loss_grounding_dice_9: 0.36236/0.28127, loss_grounding_ce_9: 0.15275/0.71623] items per batch[64] items per second[0.23] total items[812800] mini batches[ 12700] memory[7341] epoch remaining[0:04:13]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00012789.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0033 s/iter. Inference: 0.2300 s/iter. Eval: 0.1008 s/iter. Total: 0.3342 s/iter. ETA=0:00:48
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2294 s/iter. Eval: 0.0940 s/iter. Total: 0.3264 s/iter. ETA=0:00:42
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0030 s/iter. Inference: 0.2284 s/iter. Eval: 0.0880 s/iter. Total: 0.3196 s/iter. ETA=0:00:36
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0030 s/iter. Inference: 0.2287 s/iter. Eval: 0.0820 s/iter. Total: 0.3139 s/iter. ETA=0:00:30
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0031 s/iter. Inference: 0.2269 s/iter. Eval: 0.0792 s/iter. Total: 0.3093 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 94/157. Dataloading: 0.0031 s/iter. Inference: 0.2279 s/iter. Eval: 0.0797 s/iter. Total: 0.3109 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 110/157. Dataloading: 0.0032 s/iter. Inference: 0.2296 s/iter. Eval: 0.0789 s/iter. Total: 0.3118 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2286 s/iter. Eval: 0.0775 s/iter. Total: 0.3094 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2289 s/iter. Eval: 0.0767 s/iter. Total: 0.3089 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval79qhk_7b ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.106 | 81.973 | 60.249 |      133      |
| Things | 55.268 | 82.699 | 66.142 |      80       |
| Stuff  | 42.313 | 80.878 | 51.354 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.36s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.39 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.22s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.12 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.502
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.281
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.16 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.846 | 61.167 | 40.960 | 18.705 | 41.997 | 60.229 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.250 | bicycle      | 18.676 | car            | 37.284 |
| motorcycle    | 34.513 | airplane     | 55.477 | bus            | 64.819 |
| train         | 69.280 | truck        | 35.418 | boat           | 24.063 |
| traffic light | 24.066 | fire hydrant | 63.666 | stop sign      | 64.994 |
| parking meter | 43.743 | bench        | 20.186 | bird           | 29.987 |
| cat           | 73.862 | dog          | 65.738 | horse          | 45.983 |
| sheep         | 47.041 | cow          | 50.455 | elephant       | 61.266 |
| bear          | 77.501 | zebra        | 60.250 | giraffe        | 57.324 |
| backpack      | 16.715 | umbrella     | 48.503 | handbag        | 15.899 |
| tie           | 32.747 | suitcase     | 39.915 | frisbee        | 67.632 |
| skis          | 4.793  | snowboard    | 22.582 | sports ball    | 46.032 |
| kite          | 33.117 | baseball bat | 28.717 | baseball glove | 41.918 |
| skateboard    | 36.124 | surfboard    | 35.907 | tennis racket  | 55.865 |
| bottle        | 33.709 | wine glass   | 26.754 | cup            | 39.146 |
| fork          | 15.418 | knife        | 11.368 | spoon          | 15.068 |
| bowl          | 32.096 | banana       | 18.354 | apple          | 19.894 |
| sandwich      | 42.228 | orange       | 29.671 | broccoli       | 21.550 |
| carrot        | 19.178 | hot dog      | 25.756 | pizza          | 49.245 |
| donut         | 45.631 | cake         | 43.412 | chair          | 20.969 |
| couch         | 42.614 | potted plant | 16.810 | bed            | 42.300 |
| dining table  | 13.109 | toilet       | 67.211 | tv             | 62.986 |
| laptop        | 61.118 | mouse        | 58.626 | remote         | 29.521 |
| keyboard      | 48.566 | cell phone   | 37.475 | microwave      | 56.644 |
| oven          | 32.738 | toaster      | 35.883 | sink           | 36.611 |
| refrigerator  | 59.079 | book         | 8.769  | clock          | 51.827 |
| vase          | 34.012 | scissors     | 24.349 | teddy bear     | 51.661 |
| hair drier    | 10.969 | toothbrush   | 19.073 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 59.77782176671671, 'fwIoU': 68.2263669517883, 'IoU-person': 87.20515801339957, 'IoU-bicycle': 70.71813816901002, 'IoU-car': 67.26208926622775, 'IoU-motorcycle': 84.52807439282815, 'IoU-airplane': 85.22646139606539, 'IoU-bus': 80.20529241505614, 'IoU-train': 78.31179312457104, 'IoU-truck': 62.928826093042936, 'IoU-boat': 68.52487642574549, 'IoU-traffic light': 76.33583153192612, 'IoU-fire hydrant': 88.39270240213186, 'IoU-stop sign': 85.28383751630244, 'IoU-parking meter': 87.67179618505664, 'IoU-bench': 52.91399567789507, 'IoU-bird': 67.46890025575448, 'IoU-cat': 86.0453548353758, 'IoU-dog': 75.36600502067674, 'IoU-horse': 86.17536302167605, 'IoU-sheep': 86.91446029821464, 'IoU-cow': 79.70951198837348, 'IoU-elephant': 89.96818482935133, 'IoU-bear': 71.09927489058542, 'IoU-zebra': 70.47525065285929, 'IoU-giraffe': 85.98019265379581, 'IoU-backpack': 41.421636181171664, 'IoU-umbrella': 73.2345988471751, 'IoU-handbag': 35.23147606464625, 'IoU-tie': 70.54889707936802, 'IoU-suitcase': 82.19292316800743, 'IoU-frisbee': 83.32700879218416, 'IoU-skis': 53.31518559163061, 'IoU-snowboard': 66.82816481478271, 'IoU-sports ball': 68.2418304301717, 'IoU-kite': 65.18853354596502, 'IoU-baseball bat': 60.63690515153499, 'IoU-baseball glove': 52.208155222452355, 'IoU-skateboard': 64.53253174513989, 'IoU-surfboard': 80.79929084870162, 'IoU-tennis racket': 74.64508340936356, 'IoU-bottle': 67.53527065893135, 'IoU-wine glass': 73.80232617717887, 'IoU-cup': 66.07073255487053, 'IoU-fork': 54.36918103502201, 'IoU-knife': 50.177012886037495, 'IoU-spoon': 48.167219484055956, 'IoU-bowl': 56.25587734859816, 'IoU-banana': 82.36372499097362, 'IoU-apple': 56.73016157232836, 'IoU-sandwich': 66.3806587999468, 'IoU-orange': 77.4270851072486, 'IoU-broccoli': 68.7236129972087, 'IoU-carrot': 65.65931632328791, 'IoU-hot dog': 62.31771908977839, 'IoU-pizza': 82.50674901739369, 'IoU-donut': 64.96224510997001, 'IoU-cake': 74.00873290260385, 'IoU-chair': 52.02428036973007, 'IoU-couch': 66.09026140450419, 'IoU-potted plant': 33.511637535912456, 'IoU-bed': 67.89214114693824, 'IoU-dining table': 50.83360671696839, 'IoU-toilet': 80.15502329623983, 'IoU-tv': 74.80879849579051, 'IoU-laptop': 73.91497300195067, 'IoU-mouse': 67.08733090080072, 'IoU-remote': 49.390874143804986, 'IoU-keyboard': 65.93446988184391, 'IoU-cell phone': 64.11430686402218, 'IoU-microwave': 64.25693190504148, 'IoU-oven': 68.62321377618227, 'IoU-toaster': 49.57913390187175, 'IoU-sink': 68.33144011235952, 'IoU-refrigerator': 82.77105395112547, 'IoU-book': 49.305360776230174, 'IoU-clock': 66.1169268944629, 'IoU-vase': 58.77912948629926, 'IoU-scissors': 55.115887225394864, 'IoU-teddy bear': 82.22215943590211, 'IoU-hair drier': 33.07688935647904, 'IoU-toothbrush': 56.301451294275886, 'IoU-banner': 38.876283879360294, 'IoU-blanket': 9.48380525626111, 'IoU-bridge': 39.06893249608239, 'IoU-cardboard': 42.13217223514063, 'IoU-counter': 31.728960141165548, 'IoU-curtain': 62.2337463411826, 'IoU-door-stuff': 41.61858757296678, 'IoU-floor-wood': 61.316977313826996, 'IoU-flower': 43.32630820734646, 'IoU-fruit': 36.84110244753112, 'IoU-gravel': 29.972210514614954, 'IoU-house': 27.32986680378517, 'IoU-light': 39.67683528567934, 'IoU-mirror-stuff': 51.47131989873925, 'IoU-net': 44.295107507448996, 'IoU-pillow': 11.598193375354306, 'IoU-platform': 31.107762418691316, 'IoU-playingfield': 65.89458694388271, 'IoU-railroad': 59.466617709714754, 'IoU-river': 48.89710116451742, 'IoU-road': 65.79975605784884, 'IoU-roof': 13.684960639139105, 'IoU-sand': 60.91691859416494, 'IoU-sea': 84.65649787448193, 'IoU-shelf': 36.2959123644334, 'IoU-snow': 88.27107616555881, 'IoU-stairs': 24.50782427908761, 'IoU-tent': 9.129907937244846, 'IoU-towel': 31.86713485138682, 'IoU-wall-brick': 46.31785055375205, 'IoU-wall-stone': 27.6860200828997, 'IoU-wall-tile': 67.74627400387233, 'IoU-wall-wood': 40.27439571058727, 'IoU-water-other': 20.91548212259903, 'IoU-window-blind': 47.78119699175886, 'IoU-window-other': 48.31778076697228, 'IoU-tree-merged': 80.46033667606343, 'IoU-fence-merged': 51.79329894459232, 'IoU-ceiling-merged': 65.50064594955397, 'IoU-sky-other-merged': 91.32573646429435, 'IoU-cabinet-merged': 58.1499344270451, 'IoU-table-merged': 36.801216905293856, 'IoU-floor-other-merged': 48.94129664763016, 'IoU-pavement-merged': 53.38326889253128, 'IoU-mountain-merged': 56.13918219754856, 'IoU-grass-merged': 70.60947381668846, 'IoU-dirt-merged': 43.967042241190576, 'IoU-paper-merged': 36.09475289681615, 'IoU-food-other-merged': 36.426145861315796, 'IoU-building-other-merged': 55.332497333626094, 'IoU-rock-merged': 59.570464809687394, 'IoU-wall-other-merged': 65.30364264186413, 'IoU-rug-merged': 65.3873918807177, 'mACC': 72.21164205601487, 'pACC': 79.8507192967431, 'ACC-person': 92.50127016358486, 'ACC-bicycle': 85.88966955378079, 'ACC-car': 81.99865844924803, 'ACC-motorcycle': 89.44414035676357, 'ACC-airplane': 89.03140110254915, 'ACC-bus': 85.67065767665285, 'ACC-train': 95.29630075991435, 'ACC-truck': 69.8568166579681, 'ACC-boat': 80.57026102020963, 'ACC-traffic light': 90.13112870535038, 'ACC-fire hydrant': 93.37596664204247, 'ACC-stop sign': 87.76097671790782, 'ACC-parking meter': 92.28593978618926, 'ACC-bench': 71.18780258106509, 'ACC-bird': 70.56525302586752, 'ACC-cat': 93.45259344253621, 'ACC-dog': 77.78351505005152, 'ACC-horse': 92.22440047995202, 'ACC-sheep': 90.62859843441144, 'ACC-cow': 84.60657626010331, 'ACC-elephant': 92.4435121676362, 'ACC-bear': 72.70008091528791, 'ACC-zebra': 72.17968872659591, 'ACC-giraffe': 90.19341353994002, 'ACC-backpack': 60.09401758091515, 'ACC-umbrella': 80.7524681441228, 'ACC-handbag': 54.559586235847725, 'ACC-tie': 79.4196924919964, 'ACC-suitcase': 89.8275042852301, 'ACC-frisbee': 94.22254545454545, 'ACC-skis': 72.33736631038819, 'ACC-snowboard': 75.53574489970856, 'ACC-sports ball': 82.14717648153317, 'ACC-kite': 73.35910737253303, 'ACC-baseball bat': 83.28675572604874, 'ACC-baseball glove': 59.75443678260306, 'ACC-skateboard': 69.59628081924315, 'ACC-surfboard': 89.78661564467289, 'ACC-tennis racket': 80.32485230563825, 'ACC-bottle': 84.16294709090022, 'ACC-wine glass': 86.18737647549455, 'ACC-cup': 81.88394550906047, 'ACC-fork': 67.18398543118228, 'ACC-knife': 58.75668645123471, 'ACC-spoon': 70.01319375084772, 'ACC-bowl': 71.90175169402558, 'ACC-banana': 87.60535761463335, 'ACC-apple': 65.01046779485898, 'ACC-sandwich': 78.1834600354188, 'ACC-orange': 86.76706199428291, 'ACC-broccoli': 81.01991521226726, 'ACC-carrot': 77.13438018958684, 'ACC-hot dog': 73.38885039589678, 'ACC-pizza': 89.19952159241382, 'ACC-donut': 82.80738222048517, 'ACC-cake': 82.12533812372752, 'ACC-chair': 64.61963769554704, 'ACC-couch': 82.49451658351204, 'ACC-potted plant': 46.51183897339938, 'ACC-bed': 81.18040669483956, 'ACC-dining table': 75.73886351403682, 'ACC-toilet': 90.5568967767193, 'ACC-tv': 86.05652676517047, 'ACC-laptop': 86.3467709091979, 'ACC-mouse': 86.62164648333929, 'ACC-remote': 72.01102982196083, 'ACC-keyboard': 75.83236095222978, 'ACC-cell phone': 72.73194412378253, 'ACC-microwave': 79.29011169642904, 'ACC-oven': 84.22165609984425, 'ACC-toaster': 55.137609656625926, 'ACC-sink': 81.82286388940695, 'ACC-refrigerator': 90.30887230911053, 'ACC-book': 68.76908744501415, 'ACC-clock': 70.98405428819225, 'ACC-vase': 74.06316079197934, 'ACC-scissors': 59.019227061124425, 'ACC-teddy bear': 88.23505101202315, 'ACC-hair drier': 42.953917968861184, 'ACC-toothbrush': 81.4497915218902, 'ACC-banner': 76.25010022784379, 'ACC-blanket': 12.913204241098653, 'ACC-bridge': 55.3529707546499, 'ACC-cardboard': 53.721797295332266, 'ACC-counter': 55.260931100280374, 'ACC-curtain': 71.64356566869053, 'ACC-door-stuff': 66.18294715205256, 'ACC-floor-wood': 75.1453111068474, 'ACC-flower': 63.18135602156018, 'ACC-fruit': 59.35959779448366, 'ACC-gravel': 41.626243959107505, 'ACC-house': 32.92903086408906, 'ACC-light': 57.04228553382077, 'ACC-mirror-stuff': 72.85061031040114, 'ACC-net': 59.97665462817008, 'ACC-pillow': 28.72360365687856, 'ACC-platform': 56.12913422391834, 'ACC-playingfield': 78.67151989354404, 'ACC-railroad': 78.4071585770365, 'ACC-river': 83.57673005753023, 'ACC-road': 85.17102251409457, 'ACC-roof': 17.48051355392846, 'ACC-sand': 66.01178247156095, 'ACC-sea': 88.88968715122631, 'ACC-shelf': 61.845385469711985, 'ACC-snow': 94.57401699735577, 'ACC-stairs': 40.25500062251049, 'ACC-tent': 11.205935736958986, 'ACC-towel': 40.54951085788973, 'ACC-wall-brick': 62.98354755911888, 'ACC-wall-stone': 34.523378024680724, 'ACC-wall-tile': 83.68640993644621, 'ACC-wall-wood': 51.473007228765844, 'ACC-water-other': 30.073145825012816, 'ACC-window-blind': 58.41742700436886, 'ACC-window-other': 69.20267162435705, 'ACC-tree-merged': 89.92137094598304, 'ACC-fence-merged': 69.50321475527929, 'ACC-ceiling-merged': 78.99397236716977, 'ACC-sky-other-merged': 96.64704363000271, 'ACC-cabinet-merged': 76.10256965463174, 'ACC-table-merged': 47.236431896634265, 'ACC-floor-other-merged': 61.846691413781244, 'ACC-pavement-merged': 66.6222461738075, 'ACC-mountain-merged': 66.71790800313788, 'ACC-grass-merged': 83.96327036240949, 'ACC-dirt-merged': 72.95673415623017, 'ACC-paper-merged': 50.79034146565504, 'ACC-food-other-merged': 48.7698190095481, 'ACC-building-other-merged': 70.44240898033706, 'ACC-rock-merged': 82.51948286095097, 'ACC-wall-other-merged': 80.82579330890418, 'ACC-rug-merged': 77.9296614590034})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1523 s/iter. Inference: 0.5758 s/iter. Eval: 0.0000 s/iter. Total: 0.7281 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 20/50. Dataloading: 0.1538 s/iter. Inference: 0.5029 s/iter. Eval: 0.0000 s/iter. Total: 0.6568 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1689 s/iter. Inference: 0.6159 s/iter. Eval: 0.0000 s/iter. Total: 0.7850 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 30/50. Dataloading: 0.1696 s/iter. Inference: 0.6934 s/iter. Eval: 0.0000 s/iter. Total: 0.8631 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1663 s/iter. Inference: 0.6406 s/iter. Eval: 0.0000 s/iter. Total: 0.8071 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 44/50. Dataloading: 0.1656 s/iter. Inference: 0.6487 s/iter. Eval: 0.0000 s/iter. Total: 0.8145 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 48/50. Dataloading: 0.1659 s/iter. Inference: 0.7091 s/iter. Eval: 0.0000 s/iter. Total: 0.8751 s/iter. ETA=0:00:01
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5993561603745976, 'noc@0.8': 3.0433128475270705, 'noc@0.85': 3.739537606087211, 'noc@0.9': 4.796605209247878, 'miou@iter1': 0.8273292353950749}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0020 s/iter. Inference: 0.1018 s/iter. Eval: 0.0008 s/iter. Total: 0.1047 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.73455047607422, 'precision@0.6': 67.70307159423828, 'precision@0.7': 62.145355224609375, 'precision@0.8': 52.42906951904297, 'precision@0.9': 26.389429092407227, 'cIoU': 56.86830520629883, 'mIoU': 62.34375}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.1056491203049, 'SQ': 81.97338013820736, 'RQ': 60.249373467353706, 'PQ_th': 55.26812014345881, 'SQ_th': 82.69933798165286, 'RQ_th': 66.14237182478723, 'PQ_st': 42.313240028751885, 'SQ_st': 80.8775947141388, 'RQ_st': 51.3542816070767}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.84637162291293, 'AP50': 61.16688057556713, 'AP75': 40.95999011720951, 'APs': 18.705400502493152, 'APm': 41.99655432440335, 'APl': 60.22922719020376, 'AP-person': 44.24973531454434, 'AP-bicycle': 18.675712397274612, 'AP-car': 37.284143898119396, 'AP-motorcycle': 34.51299259110517, 'AP-airplane': 55.47652091163573, 'AP-bus': 64.8193818821153, 'AP-train': 69.27991105399555, 'AP-truck': 35.418029643470994, 'AP-boat': 24.062983090746396, 'AP-traffic light': 24.06645602237073, 'AP-fire hydrant': 63.66574653547987, 'AP-stop sign': 64.99356142444518, 'AP-parking meter': 43.74299638173039, 'AP-bench': 20.185729650369634, 'AP-bird': 29.98652395322068, 'AP-cat': 73.86154557207243, 'AP-dog': 65.73824054868888, 'AP-horse': 45.983342559769284, 'AP-sheep': 47.04113613590133, 'AP-cow': 50.455465970210554, 'AP-elephant': 61.266482238932184, 'AP-bear': 77.50138035951522, 'AP-zebra': 60.25042792898756, 'AP-giraffe': 57.323646793861435, 'AP-backpack': 16.71516326115295, 'AP-umbrella': 48.503244598770415, 'AP-handbag': 15.898558728081039, 'AP-tie': 32.74704685404073, 'AP-suitcase': 39.91527433205398, 'AP-frisbee': 67.63228809047447, 'AP-skis': 4.792984897391088, 'AP-snowboard': 22.582271005361942, 'AP-sports ball': 46.03245712946107, 'AP-kite': 33.11724998969701, 'AP-baseball bat': 28.716813252501804, 'AP-baseball glove': 41.918315209275406, 'AP-skateboard': 36.12378507533771, 'AP-surfboard': 35.907089500035596, 'AP-tennis racket': 55.86507086176533, 'AP-bottle': 33.708806793952036, 'AP-wine glass': 26.753567263883422, 'AP-cup': 39.14563338790971, 'AP-fork': 15.418460825876648, 'AP-knife': 11.36817281517766, 'AP-spoon': 15.067944218316926, 'AP-bowl': 32.09645732307285, 'AP-banana': 18.35449522679121, 'AP-apple': 19.89413085981474, 'AP-sandwich': 42.22755065267923, 'AP-orange': 29.671193415045472, 'AP-broccoli': 21.549900860285653, 'AP-carrot': 19.17817307185298, 'AP-hot dog': 25.756482253334127, 'AP-pizza': 49.24531538134081, 'AP-donut': 45.63070898813333, 'AP-cake': 43.41246978103822, 'AP-chair': 20.969132464238548, 'AP-couch': 42.61448980431122, 'AP-potted plant': 16.80983345022088, 'AP-bed': 42.30036192912046, 'AP-dining table': 13.10919074591181, 'AP-toilet': 67.21127479903213, 'AP-tv': 62.98631614249207, 'AP-laptop': 61.11795184053055, 'AP-mouse': 58.625939298642535, 'AP-remote': 29.520524748508155, 'AP-keyboard': 48.565707558595136, 'AP-cell phone': 37.47544735584151, 'AP-microwave': 56.64370382724716, 'AP-oven': 32.738493933405884, 'AP-toaster': 35.882526134386836, 'AP-sink': 36.61070872438846, 'AP-refrigerator': 59.07904523739455, 'AP-book': 8.768760771536673, 'AP-clock': 51.826635825958256, 'AP-vase': 34.01211579014916, 'AP-scissors': 24.348839498602647, 'AP-teddy bear': 51.661456843671935, 'AP-hair drier': 10.968974448465255, 'AP-toothbrush': 19.073129897914647}), ('sem_seg', {'mIoU': 59.77782176671671, 'fwIoU': 68.2263669517883, 'IoU-person': 87.20515801339957, 'IoU-bicycle': 70.71813816901002, 'IoU-car': 67.26208926622775, 'IoU-motorcycle': 84.52807439282815, 'IoU-airplane': 85.22646139606539, 'IoU-bus': 80.20529241505614, 'IoU-train': 78.31179312457104, 'IoU-truck': 62.928826093042936, 'IoU-boat': 68.52487642574549, 'IoU-traffic light': 76.33583153192612, 'IoU-fire hydrant': 88.39270240213186, 'IoU-stop sign': 85.28383751630244, 'IoU-parking meter': 87.67179618505664, 'IoU-bench': 52.91399567789507, 'IoU-bird': 67.46890025575448, 'IoU-cat': 86.0453548353758, 'IoU-dog': 75.36600502067674, 'IoU-horse': 86.17536302167605, 'IoU-sheep': 86.91446029821464, 'IoU-cow': 79.70951198837348, 'IoU-elephant': 89.96818482935133, 'IoU-bear': 71.09927489058542, 'IoU-zebra': 70.47525065285929, 'IoU-giraffe': 85.98019265379581, 'IoU-backpack': 41.421636181171664, 'IoU-umbrella': 73.2345988471751, 'IoU-handbag': 35.23147606464625, 'IoU-tie': 70.54889707936802, 'IoU-suitcase': 82.19292316800743, 'IoU-frisbee': 83.32700879218416, 'IoU-skis': 53.31518559163061, 'IoU-snowboard': 66.82816481478271, 'IoU-sports ball': 68.2418304301717, 'IoU-kite': 65.18853354596502, 'IoU-baseball bat': 60.63690515153499, 'IoU-baseball glove': 52.208155222452355, 'IoU-skateboard': 64.53253174513989, 'IoU-surfboard': 80.79929084870162, 'IoU-tennis racket': 74.64508340936356, 'IoU-bottle': 67.53527065893135, 'IoU-wine glass': 73.80232617717887, 'IoU-cup': 66.07073255487053, 'IoU-fork': 54.36918103502201, 'IoU-knife': 50.177012886037495, 'IoU-spoon': 48.167219484055956, 'IoU-bowl': 56.25587734859816, 'IoU-banana': 82.36372499097362, 'IoU-apple': 56.73016157232836, 'IoU-sandwich': 66.3806587999468, 'IoU-orange': 77.4270851072486, 'IoU-broccoli': 68.7236129972087, 'IoU-carrot': 65.65931632328791, 'IoU-hot dog': 62.31771908977839, 'IoU-pizza': 82.50674901739369, 'IoU-donut': 64.96224510997001, 'IoU-cake': 74.00873290260385, 'IoU-chair': 52.02428036973007, 'IoU-couch': 66.09026140450419, 'IoU-potted plant': 33.511637535912456, 'IoU-bed': 67.89214114693824, 'IoU-dining table': 50.83360671696839, 'IoU-toilet': 80.15502329623983, 'IoU-tv': 74.80879849579051, 'IoU-laptop': 73.91497300195067, 'IoU-mouse': 67.08733090080072, 'IoU-remote': 49.390874143804986, 'IoU-keyboard': 65.93446988184391, 'IoU-cell phone': 64.11430686402218, 'IoU-microwave': 64.25693190504148, 'IoU-oven': 68.62321377618227, 'IoU-toaster': 49.57913390187175, 'IoU-sink': 68.33144011235952, 'IoU-refrigerator': 82.77105395112547, 'IoU-book': 49.305360776230174, 'IoU-clock': 66.1169268944629, 'IoU-vase': 58.77912948629926, 'IoU-scissors': 55.115887225394864, 'IoU-teddy bear': 82.22215943590211, 'IoU-hair drier': 33.07688935647904, 'IoU-toothbrush': 56.301451294275886, 'IoU-banner': 38.876283879360294, 'IoU-blanket': 9.48380525626111, 'IoU-bridge': 39.06893249608239, 'IoU-cardboard': 42.13217223514063, 'IoU-counter': 31.728960141165548, 'IoU-curtain': 62.2337463411826, 'IoU-door-stuff': 41.61858757296678, 'IoU-floor-wood': 61.316977313826996, 'IoU-flower': 43.32630820734646, 'IoU-fruit': 36.84110244753112, 'IoU-gravel': 29.972210514614954, 'IoU-house': 27.32986680378517, 'IoU-light': 39.67683528567934, 'IoU-mirror-stuff': 51.47131989873925, 'IoU-net': 44.295107507448996, 'IoU-pillow': 11.598193375354306, 'IoU-platform': 31.107762418691316, 'IoU-playingfield': 65.89458694388271, 'IoU-railroad': 59.466617709714754, 'IoU-river': 48.89710116451742, 'IoU-road': 65.79975605784884, 'IoU-roof': 13.684960639139105, 'IoU-sand': 60.91691859416494, 'IoU-sea': 84.65649787448193, 'IoU-shelf': 36.2959123644334, 'IoU-snow': 88.27107616555881, 'IoU-stairs': 24.50782427908761, 'IoU-tent': 9.129907937244846, 'IoU-towel': 31.86713485138682, 'IoU-wall-brick': 46.31785055375205, 'IoU-wall-stone': 27.6860200828997, 'IoU-wall-tile': 67.74627400387233, 'IoU-wall-wood': 40.27439571058727, 'IoU-water-other': 20.91548212259903, 'IoU-window-blind': 47.78119699175886, 'IoU-window-other': 48.31778076697228, 'IoU-tree-merged': 80.46033667606343, 'IoU-fence-merged': 51.79329894459232, 'IoU-ceiling-merged': 65.50064594955397, 'IoU-sky-other-merged': 91.32573646429435, 'IoU-cabinet-merged': 58.1499344270451, 'IoU-table-merged': 36.801216905293856, 'IoU-floor-other-merged': 48.94129664763016, 'IoU-pavement-merged': 53.38326889253128, 'IoU-mountain-merged': 56.13918219754856, 'IoU-grass-merged': 70.60947381668846, 'IoU-dirt-merged': 43.967042241190576, 'IoU-paper-merged': 36.09475289681615, 'IoU-food-other-merged': 36.426145861315796, 'IoU-building-other-merged': 55.332497333626094, 'IoU-rock-merged': 59.570464809687394, 'IoU-wall-other-merged': 65.30364264186413, 'IoU-rug-merged': 65.3873918807177, 'mACC': 72.21164205601487, 'pACC': 79.8507192967431, 'ACC-person': 92.50127016358486, 'ACC-bicycle': 85.88966955378079, 'ACC-car': 81.99865844924803, 'ACC-motorcycle': 89.44414035676357, 'ACC-airplane': 89.03140110254915, 'ACC-bus': 85.67065767665285, 'ACC-train': 95.29630075991435, 'ACC-truck': 69.8568166579681, 'ACC-boat': 80.57026102020963, 'ACC-traffic light': 90.13112870535038, 'ACC-fire hydrant': 93.37596664204247, 'ACC-stop sign': 87.76097671790782, 'ACC-parking meter': 92.28593978618926, 'ACC-bench': 71.18780258106509, 'ACC-bird': 70.56525302586752, 'ACC-cat': 93.45259344253621, 'ACC-dog': 77.78351505005152, 'ACC-horse': 92.22440047995202, 'ACC-sheep': 90.62859843441144, 'ACC-cow': 84.60657626010331, 'ACC-elephant': 92.4435121676362, 'ACC-bear': 72.70008091528791, 'ACC-zebra': 72.17968872659591, 'ACC-giraffe': 90.19341353994002, 'ACC-backpack': 60.09401758091515, 'ACC-umbrella': 80.7524681441228, 'ACC-handbag': 54.559586235847725, 'ACC-tie': 79.4196924919964, 'ACC-suitcase': 89.8275042852301, 'ACC-frisbee': 94.22254545454545, 'ACC-skis': 72.33736631038819, 'ACC-snowboard': 75.53574489970856, 'ACC-sports ball': 82.14717648153317, 'ACC-kite': 73.35910737253303, 'ACC-baseball bat': 83.28675572604874, 'ACC-baseball glove': 59.75443678260306, 'ACC-skateboard': 69.59628081924315, 'ACC-surfboard': 89.78661564467289, 'ACC-tennis racket': 80.32485230563825, 'ACC-bottle': 84.16294709090022, 'ACC-wine glass': 86.18737647549455, 'ACC-cup': 81.88394550906047, 'ACC-fork': 67.18398543118228, 'ACC-knife': 58.75668645123471, 'ACC-spoon': 70.01319375084772, 'ACC-bowl': 71.90175169402558, 'ACC-banana': 87.60535761463335, 'ACC-apple': 65.01046779485898, 'ACC-sandwich': 78.1834600354188, 'ACC-orange': 86.76706199428291, 'ACC-broccoli': 81.01991521226726, 'ACC-carrot': 77.13438018958684, 'ACC-hot dog': 73.38885039589678, 'ACC-pizza': 89.19952159241382, 'ACC-donut': 82.80738222048517, 'ACC-cake': 82.12533812372752, 'ACC-chair': 64.61963769554704, 'ACC-couch': 82.49451658351204, 'ACC-potted plant': 46.51183897339938, 'ACC-bed': 81.18040669483956, 'ACC-dining table': 75.73886351403682, 'ACC-toilet': 90.5568967767193, 'ACC-tv': 86.05652676517047, 'ACC-laptop': 86.3467709091979, 'ACC-mouse': 86.62164648333929, 'ACC-remote': 72.01102982196083, 'ACC-keyboard': 75.83236095222978, 'ACC-cell phone': 72.73194412378253, 'ACC-microwave': 79.29011169642904, 'ACC-oven': 84.22165609984425, 'ACC-toaster': 55.137609656625926, 'ACC-sink': 81.82286388940695, 'ACC-refrigerator': 90.30887230911053, 'ACC-book': 68.76908744501415, 'ACC-clock': 70.98405428819225, 'ACC-vase': 74.06316079197934, 'ACC-scissors': 59.019227061124425, 'ACC-teddy bear': 88.23505101202315, 'ACC-hair drier': 42.953917968861184, 'ACC-toothbrush': 81.4497915218902, 'ACC-banner': 76.25010022784379, 'ACC-blanket': 12.913204241098653, 'ACC-bridge': 55.3529707546499, 'ACC-cardboard': 53.721797295332266, 'ACC-counter': 55.260931100280374, 'ACC-curtain': 71.64356566869053, 'ACC-door-stuff': 66.18294715205256, 'ACC-floor-wood': 75.1453111068474, 'ACC-flower': 63.18135602156018, 'ACC-fruit': 59.35959779448366, 'ACC-gravel': 41.626243959107505, 'ACC-house': 32.92903086408906, 'ACC-light': 57.04228553382077, 'ACC-mirror-stuff': 72.85061031040114, 'ACC-net': 59.97665462817008, 'ACC-pillow': 28.72360365687856, 'ACC-platform': 56.12913422391834, 'ACC-playingfield': 78.67151989354404, 'ACC-railroad': 78.4071585770365, 'ACC-river': 83.57673005753023, 'ACC-road': 85.17102251409457, 'ACC-roof': 17.48051355392846, 'ACC-sand': 66.01178247156095, 'ACC-sea': 88.88968715122631, 'ACC-shelf': 61.845385469711985, 'ACC-snow': 94.57401699735577, 'ACC-stairs': 40.25500062251049, 'ACC-tent': 11.205935736958986, 'ACC-towel': 40.54951085788973, 'ACC-wall-brick': 62.98354755911888, 'ACC-wall-stone': 34.523378024680724, 'ACC-wall-tile': 83.68640993644621, 'ACC-wall-wood': 51.473007228765844, 'ACC-water-other': 30.073145825012816, 'ACC-window-blind': 58.41742700436886, 'ACC-window-other': 69.20267162435705, 'ACC-tree-merged': 89.92137094598304, 'ACC-fence-merged': 69.50321475527929, 'ACC-ceiling-merged': 78.99397236716977, 'ACC-sky-other-merged': 96.64704363000271, 'ACC-cabinet-merged': 76.10256965463174, 'ACC-table-merged': 47.236431896634265, 'ACC-floor-other-merged': 61.846691413781244, 'ACC-pavement-merged': 66.6222461738075, 'ACC-mountain-merged': 66.71790800313788, 'ACC-grass-merged': 83.96327036240949, 'ACC-dirt-merged': 72.95673415623017, 'ACC-paper-merged': 50.79034146565504, 'ACC-food-other-merged': 48.7698190095481, 'ACC-building-other-merged': 70.44240898033706, 'ACC-rock-merged': 82.51948286095097, 'ACC-wall-other-merged': 80.82579330890418, 'ACC-rug-merged': 77.9296614590034})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5993561603745976, 'noc@0.8': 3.0433128475270705, 'noc@0.85': 3.739537606087211, 'noc@0.9': 4.796605209247878, 'miou@iter1': 0.8273292353950749}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.73455047607422, 'precision@0.6': 67.70307159423828, 'precision@0.7': 62.145355224609375, 'precision@0.8': 52.42906951904297, 'precision@0.9': 26.389429092407227, 'cIoU': 56.86830520629883, 'mIoU': 62.34375}}}
INFO:trainer.default_trainer:This epoch takes 1:30:09.927121
INFO:trainer.default_trainer:PROGRESS: 14.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 7 training.
INFO:trainer.default_trainer:epochs[     7] optim steps[12800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21456/0.91607, loss_mask_bce_0: 0.23145/0.33602, loss_mask_dice_0: 2.12443/1.16767, loss_spatial_bce_0: 0.10218/0.09433, loss_spatial_dice_0: 0.33979/0.22594, loss_spatial_ce_0: 0.12743/0.09194, loss_grounding_bce_0: 0.03395/0.08606, loss_grounding_dice_0: 0.32265/0.17884, loss_grounding_ce_0: 0.45077/0.27924, loss_mask_ce_1: 1.19587/0.91644, loss_mask_bce_1: 0.23794/0.33664, loss_mask_dice_1: 2.30283/1.17537, loss_spatial_bce_1: 0.09328/0.09531, loss_spatial_dice_1: 0.32264/0.23048, loss_spatial_ce_1: 0.12828/0.09766, loss_grounding_bce_1: 0.03394/0.08620, loss_grounding_dice_1: 0.33350/0.17943, loss_grounding_ce_1: 0.45752/0.28110, loss_mask_ce_2: 1.27298/0.92321, loss_mask_bce_2: 0.23290/0.33688, loss_mask_dice_2: 2.21685/1.17422, loss_spatial_bce_2: 0.11008/0.09507, loss_spatial_dice_2: 0.34671/0.23123, loss_spatial_ce_2: 0.12758/0.10189, loss_grounding_bce_2: 0.03360/0.08622, loss_grounding_dice_2: 0.34333/0.17923, loss_grounding_ce_2: 0.46547/0.28420, loss_mask_ce_3: 1.10086/0.92908, loss_mask_bce_3: 0.21892/0.33781, loss_mask_dice_3: 2.22106/1.17103, loss_spatial_bce_3: 0.11042/0.09613, loss_spatial_dice_3: 0.36171/0.23246, loss_spatial_ce_3: 0.12859/0.10627, loss_grounding_bce_3: 0.03191/0.08640, loss_grounding_dice_3: 0.33116/0.17878, loss_grounding_ce_3: 0.46586/0.28494, loss_mask_ce_4: 1.17658/0.92795, loss_mask_bce_4: 0.22975/0.33905, loss_mask_dice_4: 2.42327/1.19218, loss_spatial_bce_4: 0.12124/0.09968, loss_spatial_dice_4: 0.41360/0.24041, loss_spatial_ce_4: 0.16943/0.12294, loss_grounding_bce_4: 0.03440/0.08688, loss_grounding_dice_4: 0.34378/0.18149, loss_grounding_ce_4: 0.45761/0.28748, loss_mask_ce_5: 1.30344/0.94154, loss_mask_bce_5: 0.25479/0.34137, loss_mask_dice_5: 2.38507/1.19763, loss_spatial_bce_5: 0.14772/0.10049, loss_spatial_dice_5: 0.39806/0.24328, loss_spatial_ce_5: 0.16945/0.13592, loss_grounding_bce_5: 0.03367/0.08734, loss_grounding_dice_5: 0.33473/0.18276, loss_grounding_ce_5: 0.50013/0.29935, loss_mask_ce_6: 1.19688/0.97820, loss_mask_bce_6: 0.23388/0.34389, loss_mask_dice_6: 2.41385/1.20067, loss_spatial_bce_6: 0.15091/0.10595, loss_spatial_dice_6: 0.43538/0.24613, loss_spatial_ce_6: 0.17704/0.15833, loss_grounding_bce_6: 0.03472/0.08813, loss_grounding_dice_6: 0.32160/0.18294, loss_grounding_ce_6: 0.48850/0.32013, loss_mask_ce_7: 1.27115/1.02144, loss_mask_bce_7: 0.22136/0.35171, loss_mask_dice_7: 2.39765/1.25598, loss_spatial_bce_7: 0.17898/0.11518, loss_spatial_dice_7: 0.46367/0.27314, loss_spatial_ce_7: 0.26126/0.19814, loss_grounding_bce_7: 0.03224/0.08984, loss_grounding_dice_7: 0.34684/0.18986, loss_grounding_ce_7: 0.58513/0.35805, loss_mask_ce_8: 1.42005/1.13342, loss_mask_bce_8: 0.25821/0.36521, loss_mask_dice_8: 2.65926/1.33168, loss_spatial_bce_8: 0.18940/0.13648, loss_spatial_dice_8: 0.48659/0.31371, loss_spatial_ce_8: 0.32280/0.25342, loss_grounding_bce_8: 0.03330/0.09334, loss_grounding_dice_8: 0.37816/0.20131, loss_grounding_ce_8: 0.68625/0.42986, loss_mask_ce_9: 6.86667/3.70262, loss_mask_bce_9: 0.22438/0.39231, loss_mask_dice_9: 3.54875/1.90782, loss_spatial_bce_9: 0.58578/0.33837, loss_spatial_dice_9: 0.88071/0.82646, loss_spatial_ce_9: 1.52320/1.53130, loss_grounding_bce_9: 0.03499/0.10488, loss_grounding_dice_9: 0.53336/0.28139, loss_grounding_ce_9: 0.57386/0.71584] items per batch[64] items per second[0.13] total items[819200] mini batches[ 12800] memory[7341] epoch remaining[1:28:14]
INFO:trainer.default_trainer:epochs[     7] optim steps[12900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75527/0.91585, loss_mask_bce_0: 0.57223/0.33609, loss_mask_dice_0: 2.18483/1.16735, loss_spatial_bce_0: 0.22854/0.09426, loss_spatial_dice_0: 0.41716/0.22575, loss_spatial_ce_0: 0.44067/0.09166, loss_grounding_bce_0: 0.11391/0.08605, loss_grounding_dice_0: 0.28237/0.17884, loss_grounding_ce_0: 0.17250/0.27874, loss_mask_ce_1: 0.77812/0.91622, loss_mask_bce_1: 0.55528/0.33671, loss_mask_dice_1: 2.14775/1.17500, loss_spatial_bce_1: 0.25882/0.09524, loss_spatial_dice_1: 0.43195/0.23029, loss_spatial_ce_1: 0.41262/0.09736, loss_grounding_bce_1: 0.10765/0.08620, loss_grounding_dice_1: 0.25761/0.17949, loss_grounding_ce_1: 0.19486/0.28061, loss_mask_ce_2: 0.72066/0.92305, loss_mask_bce_2: 0.68546/0.33695, loss_mask_dice_2: 2.17207/1.17392, loss_spatial_bce_2: 0.27119/0.09500, loss_spatial_dice_2: 0.43516/0.23105, loss_spatial_ce_2: 0.39640/0.10153, loss_grounding_bce_2: 0.11157/0.08620, loss_grounding_dice_2: 0.27338/0.17923, loss_grounding_ce_2: 0.20487/0.28367, loss_mask_ce_3: 0.75638/0.92892, loss_mask_bce_3: 0.68371/0.33788, loss_mask_dice_3: 2.21578/1.17065, loss_spatial_bce_3: 0.23704/0.09606, loss_spatial_dice_3: 0.44272/0.23226, loss_spatial_ce_3: 0.43318/0.10593, loss_grounding_bce_3: 0.11508/0.08638, loss_grounding_dice_3: 0.27647/0.17878, loss_grounding_ce_3: 0.22292/0.28446, loss_mask_ce_4: 0.71276/0.92789, loss_mask_bce_4: 0.72137/0.33909, loss_mask_dice_4: 2.29026/1.19183, loss_spatial_bce_4: 0.21888/0.09961, loss_spatial_dice_4: 0.42990/0.24023, loss_spatial_ce_4: 0.49752/0.12265, loss_grounding_bce_4: 0.11519/0.08686, loss_grounding_dice_4: 0.27885/0.18151, loss_grounding_ce_4: 0.23556/0.28696, loss_mask_ce_5: 0.79794/0.94139, loss_mask_bce_5: 0.60010/0.34143, loss_mask_dice_5: 2.36520/1.19730, loss_spatial_bce_5: 0.21052/0.10040, loss_spatial_dice_5: 0.44530/0.24310, loss_spatial_ce_5: 0.50333/0.13572, loss_grounding_bce_5: 0.12601/0.08733, loss_grounding_dice_5: 0.29036/0.18277, loss_grounding_ce_5: 0.24312/0.29892, loss_mask_ce_6: 0.83130/0.97796, loss_mask_bce_6: 0.63336/0.34396, loss_mask_dice_6: 2.19988/1.20035, loss_spatial_bce_6: 0.30490/0.10589, loss_spatial_dice_6: 0.41984/0.24595, loss_spatial_ce_6: 0.41280/0.15810, loss_grounding_bce_6: 0.13943/0.08812, loss_grounding_dice_6: 0.26410/0.18297, loss_grounding_ce_6: 0.22800/0.31992, loss_mask_ce_7: 0.85846/1.02113, loss_mask_bce_7: 0.54708/0.35174, loss_mask_dice_7: 2.29142/1.25563, loss_spatial_bce_7: 0.26906/0.11511, loss_spatial_dice_7: 0.49685/0.27297, loss_spatial_ce_7: 0.38158/0.19790, loss_grounding_bce_7: 0.12449/0.08982, loss_grounding_dice_7: 0.26209/0.18986, loss_grounding_ce_7: 0.19156/0.35750, loss_mask_ce_8: 0.84736/1.13285, loss_mask_bce_8: 0.70088/0.36529, loss_mask_dice_8: 2.45628/1.33134, loss_spatial_bce_8: 0.32962/0.13641, loss_spatial_dice_8: 0.52817/0.31353, loss_spatial_ce_8: 0.31826/0.25317, loss_grounding_bce_8: 0.10461/0.09332, loss_grounding_dice_8: 0.26965/0.20129, loss_grounding_ce_8: 0.10841/0.42939, loss_mask_ce_9: 4.13568/3.70109, loss_mask_bce_9: 0.62407/0.39226, loss_mask_dice_9: 3.21093/1.90736, loss_spatial_bce_9: 0.33310/0.33835, loss_spatial_dice_9: 0.90042/0.82638, loss_spatial_ce_9: 1.26771/1.53067, loss_grounding_bce_9: 0.13834/0.10486, loss_grounding_dice_9: 0.51706/0.28134, loss_grounding_ce_9: 0.50476/0.71528] items per batch[64] items per second[0.23] total items[825600] mini batches[ 12900] memory[7341] epoch remaining[1:20:49]
INFO:trainer.default_trainer:epochs[     7] optim steps[13000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15793/0.91572, loss_mask_bce_0: 0.04915/0.33617, loss_mask_dice_0: 0.93568/1.16720, loss_spatial_bce_0: 0.01721/0.09423, loss_spatial_dice_0: 0.23743/0.22565, loss_spatial_ce_0: 0.17131/0.09147, loss_grounding_bce_0: 0.01096/0.08612, loss_grounding_dice_0: 0.26998/0.17889, loss_grounding_ce_0: 0.18131/0.27871, loss_mask_ce_1: 0.16510/0.91618, loss_mask_bce_1: 0.04751/0.33680, loss_mask_dice_1: 0.84799/1.17479, loss_spatial_bce_1: 0.01913/0.09522, loss_spatial_dice_1: 0.25456/0.23022, loss_spatial_ce_1: 0.05539/0.09719, loss_grounding_bce_1: 0.00837/0.08627, loss_grounding_dice_1: 0.22526/0.17950, loss_grounding_ce_1: 0.25431/0.28063, loss_mask_ce_2: 0.19981/0.92298, loss_mask_bce_2: 0.05008/0.33707, loss_mask_dice_2: 0.97747/1.17387, loss_spatial_bce_2: 0.01988/0.09498, loss_spatial_dice_2: 0.26932/0.23100, loss_spatial_ce_2: 0.03777/0.10129, loss_grounding_bce_2: 0.00851/0.08627, loss_grounding_dice_2: 0.31696/0.17925, loss_grounding_ce_2: 0.19410/0.28366, loss_mask_ce_3: 0.39688/0.92879, loss_mask_bce_3: 0.05136/0.33799, loss_mask_dice_3: 0.96151/1.17059, loss_spatial_bce_3: 0.02008/0.09606, loss_spatial_dice_3: 0.23035/0.23219, loss_spatial_ce_3: 0.05791/0.10567, loss_grounding_bce_3: 0.00886/0.08644, loss_grounding_dice_3: 0.19671/0.17881, loss_grounding_ce_3: 0.24006/0.28441, loss_mask_ce_4: 0.23496/0.92788, loss_mask_bce_4: 0.05467/0.33920, loss_mask_dice_4: 1.02665/1.19180, loss_spatial_bce_4: 0.01925/0.09961, loss_spatial_dice_4: 0.24348/0.24017, loss_spatial_ce_4: 0.07418/0.12247, loss_grounding_bce_4: 0.01028/0.08692, loss_grounding_dice_4: 0.24672/0.18153, loss_grounding_ce_4: 0.16040/0.28692, loss_mask_ce_5: 0.41344/0.94124, loss_mask_bce_5: 0.05052/0.34156, loss_mask_dice_5: 0.79477/1.19724, loss_spatial_bce_5: 0.01714/0.10038, loss_spatial_dice_5: 0.26251/0.24306, loss_spatial_ce_5: 0.06089/0.13559, loss_grounding_bce_5: 0.01254/0.08740, loss_grounding_dice_5: 0.25285/0.18280, loss_grounding_ce_5: 0.17913/0.29905, loss_mask_ce_6: 0.47061/0.97793, loss_mask_bce_6: 0.05383/0.34407, loss_mask_dice_6: 1.28921/1.20032, loss_spatial_bce_6: 0.01604/0.10588, loss_spatial_dice_6: 0.26333/0.24593, loss_spatial_ce_6: 0.07524/0.15798, loss_grounding_bce_6: 0.01623/0.08820, loss_grounding_dice_6: 0.34767/0.18298, loss_grounding_ce_6: 0.21664/0.31987, loss_mask_ce_7: 0.28470/1.02115, loss_mask_bce_7: 0.05763/0.35183, loss_mask_dice_7: 1.08238/1.25555, loss_spatial_bce_7: 0.02249/0.11510, loss_spatial_dice_7: 0.29288/0.27291, loss_spatial_ce_7: 0.07290/0.19775, loss_grounding_bce_7: 0.01296/0.08992, loss_grounding_dice_7: 0.30772/0.18993, loss_grounding_ce_7: 0.19242/0.35737, loss_mask_ce_8: 0.28218/1.13279, loss_mask_bce_8: 0.06654/0.36538, loss_mask_dice_8: 1.10606/1.33114, loss_spatial_bce_8: 0.03761/0.13637, loss_spatial_dice_8: 0.31877/0.31351, loss_spatial_ce_8: 0.12717/0.25304, loss_grounding_bce_8: 0.01761/0.09341, loss_grounding_dice_8: 0.32869/0.20136, loss_grounding_ce_8: 0.18352/0.42912, loss_mask_ce_9: 2.39094/3.70130, loss_mask_bce_9: 0.06076/0.39222, loss_mask_dice_9: 1.48040/1.90653, loss_spatial_bce_9: 0.21955/0.33830, loss_spatial_dice_9: 0.80207/0.82633, loss_spatial_ce_9: 1.86398/1.53092, loss_grounding_bce_9: 0.01540/0.10491, loss_grounding_dice_9: 0.24123/0.28141, loss_grounding_ce_9: 0.51234/0.71503] items per batch[64] items per second[0.22] total items[832000] mini batches[ 13000] memory[7341] epoch remaining[1:16:58]
INFO:trainer.default_trainer:epochs[     7] optim steps[13100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44244/0.91554, loss_mask_bce_0: 0.27591/0.33630, loss_mask_dice_0: 0.90161/1.16757, loss_spatial_bce_0: 0.07723/0.09417, loss_spatial_dice_0: 0.12245/0.22550, loss_spatial_ce_0: 0.02477/0.09132, loss_grounding_bce_0: 0.07732/0.08615, loss_grounding_dice_0: 0.23246/0.17894, loss_grounding_ce_0: 0.00723/0.27859, loss_mask_ce_1: 0.39591/0.91597, loss_mask_bce_1: 0.27689/0.33693, loss_mask_dice_1: 0.87284/1.17520, loss_spatial_bce_1: 0.07500/0.09516, loss_spatial_dice_1: 0.14687/0.23005, loss_spatial_ce_1: 0.02456/0.09701, loss_grounding_bce_1: 0.07850/0.08629, loss_grounding_dice_1: 0.19890/0.17950, loss_grounding_ce_1: 0.00893/0.28060, loss_mask_ce_2: 0.39972/0.92280, loss_mask_bce_2: 0.27216/0.33721, loss_mask_dice_2: 0.76030/1.17424, loss_spatial_bce_2: 0.07900/0.09491, loss_spatial_dice_2: 0.12637/0.23085, loss_spatial_ce_2: 0.01834/0.10107, loss_grounding_bce_2: 0.07868/0.08630, loss_grounding_dice_2: 0.21724/0.17927, loss_grounding_ce_2: 0.00888/0.28358, loss_mask_ce_3: 0.47429/0.92871, loss_mask_bce_3: 0.27267/0.33812, loss_mask_dice_3: 1.00398/1.17098, loss_spatial_bce_3: 0.07733/0.09599, loss_spatial_dice_3: 0.13469/0.23202, loss_spatial_ce_3: 0.01828/0.10548, loss_grounding_bce_3: 0.07907/0.08646, loss_grounding_dice_3: 0.23114/0.17888, loss_grounding_ce_3: 0.00803/0.28430, loss_mask_ce_4: 0.43384/0.92770, loss_mask_bce_4: 0.26873/0.33929, loss_mask_dice_4: 1.03068/1.19225, loss_spatial_bce_4: 0.08973/0.09956, loss_spatial_dice_4: 0.14233/0.24003, loss_spatial_ce_4: 0.04097/0.12229, loss_grounding_bce_4: 0.07619/0.08693, loss_grounding_dice_4: 0.15720/0.18155, loss_grounding_ce_4: 0.00674/0.28691, loss_mask_ce_5: 0.42303/0.94128, loss_mask_bce_5: 0.27229/0.34165, loss_mask_dice_5: 0.89097/1.19766, loss_spatial_bce_5: 0.08213/0.10034, loss_spatial_dice_5: 0.14148/0.24293, loss_spatial_ce_5: 0.02065/0.13533, loss_grounding_bce_5: 0.07588/0.08741, loss_grounding_dice_5: 0.21175/0.18287, loss_grounding_ce_5: 0.00578/0.29888, loss_mask_ce_6: 0.54320/0.97785, loss_mask_bce_6: 0.27536/0.34417, loss_mask_dice_6: 0.76021/1.20079, loss_spatial_bce_6: 0.08215/0.10585, loss_spatial_dice_6: 0.15280/0.24579, loss_spatial_ce_6: 0.04905/0.15780, loss_grounding_bce_6: 0.07707/0.08820, loss_grounding_dice_6: 0.18772/0.18298, loss_grounding_ce_6: 0.00474/0.31977, loss_mask_ce_7: 0.32864/1.02101, loss_mask_bce_7: 0.27115/0.35190, loss_mask_dice_7: 1.17924/1.25604, loss_spatial_bce_7: 0.10013/0.11503, loss_spatial_dice_7: 0.17815/0.27273, loss_spatial_ce_7: 0.11458/0.19747, loss_grounding_bce_7: 0.07239/0.08992, loss_grounding_dice_7: 0.20043/0.18996, loss_grounding_ce_7: 0.00629/0.35717, loss_mask_ce_8: 0.40167/1.13240, loss_mask_bce_8: 0.26504/0.36553, loss_mask_dice_8: 1.16618/1.33163, loss_spatial_bce_8: 0.10414/0.13630, loss_spatial_dice_8: 0.19576/0.31332, loss_spatial_ce_8: 0.17301/0.25275, loss_grounding_bce_8: 0.07762/0.09345, loss_grounding_dice_8: 0.20719/0.20139, loss_grounding_ce_8: 0.00385/0.42895, loss_mask_ce_9: 3.94857/3.70138, loss_mask_bce_9: 0.29073/0.39237, loss_mask_dice_9: 1.57585/1.90715, loss_spatial_bce_9: 0.35125/0.33824, loss_spatial_dice_9: 0.88154/0.82632, loss_spatial_ce_9: 1.56260/1.53079, loss_grounding_bce_9: 0.07018/0.10492, loss_grounding_dice_9: 0.26788/0.28144, loss_grounding_ce_9: 0.05976/0.71450] items per batch[64] items per second[0.23] total items[838400] mini batches[ 13100] memory[7341] epoch remaining[1:11:35]
INFO:trainer.default_trainer:epochs[     7] optim steps[13200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33771/0.91574, loss_mask_bce_0: 0.09113/0.33624, loss_mask_dice_0: 0.18442/1.16739, loss_spatial_bce_0: 0.04884/0.09418, loss_spatial_dice_0: 0.10033/0.22547, loss_spatial_ce_0: 0.00158/0.09113, loss_grounding_bce_0: 0.03315/0.08615, loss_grounding_dice_0: 0.07877/0.17889, loss_grounding_ce_0: 0.04884/0.27831, loss_mask_ce_1: 0.16052/0.91622, loss_mask_bce_1: 0.11420/0.33688, loss_mask_dice_1: 0.23722/1.17502, loss_spatial_bce_1: 0.04996/0.09518, loss_spatial_dice_1: 0.10323/0.23004, loss_spatial_ce_1: 0.00242/0.09678, loss_grounding_bce_1: 0.03472/0.08628, loss_grounding_dice_1: 0.08505/0.17945, loss_grounding_ce_1: 0.05809/0.28035, loss_mask_ce_2: 0.31948/0.92299, loss_mask_bce_2: 0.09544/0.33717, loss_mask_dice_2: 0.18070/1.17412, loss_spatial_bce_2: 0.04503/0.09492, loss_spatial_dice_2: 0.09377/0.23081, loss_spatial_ce_2: 0.00686/0.10082, loss_grounding_bce_2: 0.03458/0.08630, loss_grounding_dice_2: 0.07977/0.17924, loss_grounding_ce_2: 0.06811/0.28332, loss_mask_ce_3: 0.28781/0.92902, loss_mask_bce_3: 0.08882/0.33807, loss_mask_dice_3: 0.17261/1.17092, loss_spatial_bce_3: 0.04535/0.09600, loss_spatial_dice_3: 0.09222/0.23201, loss_spatial_ce_3: 0.00783/0.10531, loss_grounding_bce_3: 0.03188/0.08646, loss_grounding_dice_3: 0.08256/0.17882, loss_grounding_ce_3: 0.10951/0.28409, loss_mask_ce_4: 0.28276/0.92799, loss_mask_bce_4: 0.09721/0.33925, loss_mask_dice_4: 0.18799/1.19212, loss_spatial_bce_4: 0.05434/0.09957, loss_spatial_dice_4: 0.10880/0.24001, loss_spatial_ce_4: 0.00927/0.12213, loss_grounding_bce_4: 0.03288/0.08695, loss_grounding_dice_4: 0.07991/0.18152, loss_grounding_ce_4: 0.06143/0.28665, loss_mask_ce_5: 0.16147/0.94177, loss_mask_bce_5: 0.11006/0.34162, loss_mask_dice_5: 0.25004/1.19761, loss_spatial_bce_5: 0.05011/0.10034, loss_spatial_dice_5: 0.11577/0.24287, loss_spatial_ce_5: 0.02663/0.13520, loss_grounding_bce_5: 0.02978/0.08742, loss_grounding_dice_5: 0.06804/0.18280, loss_grounding_ce_5: 0.11572/0.29864, loss_mask_ce_6: 0.36247/0.97814, loss_mask_bce_6: 0.09353/0.34413, loss_mask_dice_6: 0.18313/1.20067, loss_spatial_bce_6: 0.04911/0.10585, loss_spatial_dice_6: 0.10471/0.24573, loss_spatial_ce_6: 0.06416/0.15761, loss_grounding_bce_6: 0.03237/0.08820, loss_grounding_dice_6: 0.07555/0.18292, loss_grounding_ce_6: 0.13643/0.31937, loss_mask_ce_7: 0.21727/1.02144, loss_mask_bce_7: 0.09901/0.35183, loss_mask_dice_7: 0.21785/1.25598, loss_spatial_bce_7: 0.05322/0.11502, loss_spatial_dice_7: 0.12191/0.27265, loss_spatial_ce_7: 0.07844/0.19736, loss_grounding_bce_7: 0.03027/0.08992, loss_grounding_dice_7: 0.06944/0.18991, loss_grounding_ce_7: 0.07220/0.35677, loss_mask_ce_8: 0.23282/1.13280, loss_mask_bce_8: 0.11741/0.36543, loss_mask_dice_8: 0.24991/1.33151, loss_spatial_bce_8: 0.06756/0.13628, loss_spatial_dice_8: 0.18771/0.31326, loss_spatial_ce_8: 0.15677/0.25275, loss_grounding_bce_8: 0.03317/0.09344, loss_grounding_dice_8: 0.09130/0.20127, loss_grounding_ce_8: 0.05863/0.42840, loss_mask_ce_9: 1.77864/3.70072, loss_mask_bce_9: 0.10049/0.39234, loss_mask_dice_9: 0.30273/1.90683, loss_spatial_bce_9: 0.31175/0.33818, loss_spatial_dice_9: 0.78708/0.82628, loss_spatial_ce_9: 1.13885/1.53080, loss_grounding_bce_9: 0.04085/0.10494, loss_grounding_dice_9: 0.13060/0.28130, loss_grounding_ce_9: 0.20901/0.71355] items per batch[64] items per second[0.23] total items[844800] mini batches[ 13200] memory[7341] epoch remaining[1:06:32]
INFO:trainer.default_trainer:epochs[     7] optim steps[13300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97020/0.91616, loss_mask_bce_0: 0.47108/0.33598, loss_mask_dice_0: 1.23856/1.16745, loss_spatial_bce_0: 0.08798/0.09403, loss_spatial_dice_0: 0.25950/0.22537, loss_spatial_ce_0: 0.03494/0.09087, loss_grounding_bce_0: 0.09613/0.08611, loss_grounding_dice_0: 0.24194/0.17884, loss_grounding_ce_0: 0.31646/0.27803, loss_mask_ce_1: 1.02172/0.91676, loss_mask_bce_1: 0.47021/0.33661, loss_mask_dice_1: 1.38850/1.17515, loss_spatial_bce_1: 0.08735/0.09505, loss_spatial_dice_1: 0.27244/0.22993, loss_spatial_ce_1: 0.06929/0.09655, loss_grounding_bce_1: 0.10139/0.08624, loss_grounding_dice_1: 0.25327/0.17939, loss_grounding_ce_1: 0.32570/0.28008, loss_mask_ce_2: 1.00482/0.92349, loss_mask_bce_2: 0.47850/0.33690, loss_mask_dice_2: 1.34943/1.17419, loss_spatial_bce_2: 0.08298/0.09480, loss_spatial_dice_2: 0.27361/0.23072, loss_spatial_ce_2: 0.07514/0.10058, loss_grounding_bce_2: 0.10004/0.08627, loss_grounding_dice_2: 0.24816/0.17918, loss_grounding_ce_2: 0.33424/0.28306, loss_mask_ce_3: 0.99114/0.92964, loss_mask_bce_3: 0.54969/0.33781, loss_mask_dice_3: 1.25126/1.17083, loss_spatial_bce_3: 0.08576/0.09586, loss_spatial_dice_3: 0.27981/0.23191, loss_spatial_ce_3: 0.05015/0.10509, loss_grounding_bce_3: 0.09941/0.08642, loss_grounding_dice_3: 0.24733/0.17878, loss_grounding_ce_3: 0.46581/0.28383, loss_mask_ce_4: 1.01500/0.92867, loss_mask_bce_4: 0.50198/0.33896, loss_mask_dice_4: 1.16133/1.19200, loss_spatial_bce_4: 0.09099/0.09945, loss_spatial_dice_4: 0.28125/0.23992, loss_spatial_ce_4: 0.03917/0.12183, loss_grounding_bce_4: 0.10594/0.08691, loss_grounding_dice_4: 0.24151/0.18146, loss_grounding_ce_4: 0.36521/0.28635, loss_mask_ce_5: 0.99378/0.94247, loss_mask_bce_5: 0.52564/0.34135, loss_mask_dice_5: 1.45177/1.19752, loss_spatial_bce_5: 0.08483/0.10021, loss_spatial_dice_5: 0.28163/0.24279, loss_spatial_ce_5: 0.13153/0.13496, loss_grounding_bce_5: 0.11182/0.08738, loss_grounding_dice_5: 0.25242/0.18273, loss_grounding_ce_5: 0.36625/0.29830, loss_mask_ce_6: 1.09636/0.97879, loss_mask_bce_6: 0.51560/0.34384, loss_mask_dice_6: 1.32838/1.20059, loss_spatial_bce_6: 0.09509/0.10572, loss_spatial_dice_6: 0.28635/0.24565, loss_spatial_ce_6: 0.21865/0.15737, loss_grounding_bce_6: 0.10712/0.08816, loss_grounding_dice_6: 0.24706/0.18286, loss_grounding_ce_6: 0.35110/0.31896, loss_mask_ce_7: 1.13503/1.02211, loss_mask_bce_7: 0.51767/0.35154, loss_mask_dice_7: 1.43128/1.25608, loss_spatial_bce_7: 0.10516/0.11489, loss_spatial_dice_7: 0.30467/0.27257, loss_spatial_ce_7: 0.25294/0.19717, loss_grounding_bce_7: 0.10437/0.08988, loss_grounding_dice_7: 0.27257/0.18984, loss_grounding_ce_7: 0.43501/0.35646, loss_mask_ce_8: 1.33523/1.13365, loss_mask_bce_8: 0.56891/0.36519, loss_mask_dice_8: 1.52557/1.33149, loss_spatial_bce_8: 0.14788/0.13616, loss_spatial_dice_8: 0.34485/0.31323, loss_spatial_ce_8: 0.23113/0.25251, loss_grounding_bce_8: 0.10958/0.09340, loss_grounding_dice_8: 0.26876/0.20121, loss_grounding_ce_8: 0.45357/0.42821, loss_mask_ce_9: 5.25475/3.70087, loss_mask_bce_9: 0.73099/0.39208, loss_mask_dice_9: 2.39868/1.90715, loss_spatial_bce_9: 0.45874/0.33799, loss_spatial_dice_9: 0.85693/0.82630, loss_spatial_ce_9: 1.99097/1.53109, loss_grounding_bce_9: 0.14574/0.10487, loss_grounding_dice_9: 0.44827/0.28120, loss_grounding_ce_9: 0.57376/0.71317] items per batch[64] items per second[0.22] total items[851200] mini batches[ 13300] memory[7341] epoch remaining[1:02:08]
INFO:trainer.default_trainer:epochs[     7] optim steps[13400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61811/0.91637, loss_mask_bce_0: 0.59868/0.33611, loss_mask_dice_0: 2.25891/1.16865, loss_spatial_bce_0: 0.14388/0.09401, loss_spatial_dice_0: 0.25378/0.22532, loss_spatial_ce_0: 0.02742/0.09065, loss_grounding_bce_0: 0.05690/0.08612, loss_grounding_dice_0: 0.07712/0.17877, loss_grounding_ce_0: 0.18949/0.27808, loss_mask_ce_1: 0.65056/0.91707, loss_mask_bce_1: 0.60715/0.33673, loss_mask_dice_1: 2.21808/1.17611, loss_spatial_bce_1: 0.13207/0.09503, loss_spatial_dice_1: 0.25414/0.22987, loss_spatial_ce_1: 0.02962/0.09629, loss_grounding_bce_1: 0.05321/0.08625, loss_grounding_dice_1: 0.07789/0.17931, loss_grounding_ce_1: 0.19469/0.28005, loss_mask_ce_2: 0.72500/0.92377, loss_mask_bce_2: 0.61450/0.33704, loss_mask_dice_2: 2.24911/1.17511, loss_spatial_bce_2: 0.13479/0.09478, loss_spatial_dice_2: 0.27243/0.23067, loss_spatial_ce_2: 0.02957/0.10033, loss_grounding_bce_2: 0.05433/0.08626, loss_grounding_dice_2: 0.07978/0.17910, loss_grounding_ce_2: 0.31317/0.28307, loss_mask_ce_3: 0.64012/0.93009, loss_mask_bce_3: 0.62767/0.33796, loss_mask_dice_3: 2.43309/1.17192, loss_spatial_bce_3: 0.13869/0.09582, loss_spatial_dice_3: 0.24328/0.23182, loss_spatial_ce_3: 0.04439/0.10490, loss_grounding_bce_3: 0.05758/0.08643, loss_grounding_dice_3: 0.08003/0.17872, loss_grounding_ce_3: 0.27020/0.28384, loss_mask_ce_4: 0.62518/0.92889, loss_mask_bce_4: 0.62850/0.33910, loss_mask_dice_4: 2.35424/1.19301, loss_spatial_bce_4: 0.14711/0.09942, loss_spatial_dice_4: 0.24345/0.23986, loss_spatial_ce_4: 0.04438/0.12157, loss_grounding_bce_4: 0.05440/0.08691, loss_grounding_dice_4: 0.08113/0.18140, loss_grounding_ce_4: 0.19824/0.28636, loss_mask_ce_5: 0.71305/0.94272, loss_mask_bce_5: 0.62763/0.34148, loss_mask_dice_5: 2.42009/1.19859, loss_spatial_bce_5: 0.13749/0.10021, loss_spatial_dice_5: 0.27257/0.24272, loss_spatial_ce_5: 0.08135/0.13477, loss_grounding_bce_5: 0.05671/0.08737, loss_grounding_dice_5: 0.07985/0.18267, loss_grounding_ce_5: 0.22372/0.29833, loss_mask_ce_6: 0.81630/0.97907, loss_mask_bce_6: 0.61481/0.34399, loss_mask_dice_6: 2.27619/1.20180, loss_spatial_bce_6: 0.14113/0.10573, loss_spatial_dice_6: 0.24472/0.24557, loss_spatial_ce_6: 0.12483/0.15709, loss_grounding_bce_6: 0.05098/0.08816, loss_grounding_dice_6: 0.07201/0.18279, loss_grounding_ce_6: 0.35591/0.31912, loss_mask_ce_7: 1.00700/1.02235, loss_mask_bce_7: 0.61571/0.35170, loss_mask_dice_7: 2.45324/1.25714, loss_spatial_bce_7: 0.14777/0.11486, loss_spatial_dice_7: 0.31450/0.27250, loss_spatial_ce_7: 0.13804/0.19690, loss_grounding_bce_7: 0.05358/0.08990, loss_grounding_dice_7: 0.07493/0.18976, loss_grounding_ce_7: 0.18237/0.35633, loss_mask_ce_8: 0.89779/1.13370, loss_mask_bce_8: 0.75678/0.36536, loss_mask_dice_8: 3.06101/1.33266, loss_spatial_bce_8: 0.14376/0.13613, loss_spatial_dice_8: 0.29084/0.31318, loss_spatial_ce_8: 0.10339/0.25235, loss_grounding_bce_8: 0.05783/0.09340, loss_grounding_dice_8: 0.06669/0.20114, loss_grounding_ce_8: 0.27194/0.42813, loss_mask_ce_9: 5.00498/3.70088, loss_mask_bce_9: 0.62816/0.39229, loss_mask_dice_9: 3.97170/1.90909, loss_spatial_bce_9: 0.34151/0.33797, loss_spatial_dice_9: 0.89841/0.82620, loss_spatial_ce_9: 1.33711/1.53031, loss_grounding_bce_9: 0.05571/0.10487, loss_grounding_dice_9: 0.07669/0.28109, loss_grounding_ce_9: 0.45476/0.71302] items per batch[64] items per second[0.23] total items[857600] mini batches[ 13400] memory[7341] epoch remaining[0:57:20]
INFO:trainer.default_trainer:epochs[     7] optim steps[13500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54671/0.91679, loss_mask_bce_0: 0.31459/0.33587, loss_mask_dice_0: 1.00233/1.16747, loss_spatial_bce_0: 0.06216/0.09396, loss_spatial_dice_0: 0.21428/0.22515, loss_spatial_ce_0: 0.03265/0.09041, loss_grounding_bce_0: 0.06056/0.08613, loss_grounding_dice_0: 0.24262/0.17872, loss_grounding_ce_0: 0.21523/0.27799, loss_mask_ce_1: 0.53746/0.91755, loss_mask_bce_1: 0.30894/0.33648, loss_mask_dice_1: 0.89283/1.17493, loss_spatial_bce_1: 0.06138/0.09497, loss_spatial_dice_1: 0.22576/0.22971, loss_spatial_ce_1: 0.02341/0.09597, loss_grounding_bce_1: 0.06928/0.08625, loss_grounding_dice_1: 0.27333/0.17928, loss_grounding_ce_1: 0.17256/0.28003, loss_mask_ce_2: 0.53985/0.92420, loss_mask_bce_2: 0.30327/0.33677, loss_mask_dice_2: 0.89470/1.17386, loss_spatial_bce_2: 0.06339/0.09473, loss_spatial_dice_2: 0.22565/0.23050, loss_spatial_ce_2: 0.02319/0.10003, loss_grounding_bce_2: 0.06101/0.08627, loss_grounding_dice_2: 0.22656/0.17901, loss_grounding_ce_2: 0.21784/0.28310, loss_mask_ce_3: 0.60276/0.93057, loss_mask_bce_3: 0.30760/0.33769, loss_mask_dice_3: 0.95527/1.17079, loss_spatial_bce_3: 0.06713/0.09576, loss_spatial_dice_3: 0.22694/0.23165, loss_spatial_ce_3: 0.03115/0.10462, loss_grounding_bce_3: 0.06219/0.08642, loss_grounding_dice_3: 0.21197/0.17867, loss_grounding_ce_3: 0.22864/0.28385, loss_mask_ce_4: 0.54275/0.92927, loss_mask_bce_4: 0.34514/0.33884, loss_mask_dice_4: 0.94425/1.19181, loss_spatial_bce_4: 0.06881/0.09934, loss_spatial_dice_4: 0.22284/0.23967, loss_spatial_ce_4: 0.24496/0.12131, loss_grounding_bce_4: 0.06639/0.08689, loss_grounding_dice_4: 0.26068/0.18133, loss_grounding_ce_4: 0.21273/0.28642, loss_mask_ce_5: 0.58213/0.94319, loss_mask_bce_5: 0.34214/0.34121, loss_mask_dice_5: 0.98927/1.19738, loss_spatial_bce_5: 0.07673/0.10014, loss_spatial_dice_5: 0.24637/0.24254, loss_spatial_ce_5: 0.05578/0.13447, loss_grounding_bce_5: 0.06708/0.08737, loss_grounding_dice_5: 0.24351/0.18263, loss_grounding_ce_5: 0.28383/0.29836, loss_mask_ce_6: 0.67733/0.97945, loss_mask_bce_6: 0.34118/0.34375, loss_mask_dice_6: 0.94706/1.20054, loss_spatial_bce_6: 0.06686/0.10565, loss_spatial_dice_6: 0.22095/0.24540, loss_spatial_ce_6: 0.15127/0.15680, loss_grounding_bce_6: 0.06973/0.08816, loss_grounding_dice_6: 0.25824/0.18280, loss_grounding_ce_6: 0.29197/0.31902, loss_mask_ce_7: 0.93195/1.02289, loss_mask_bce_7: 0.34235/0.35148, loss_mask_dice_7: 0.99271/1.25586, loss_spatial_bce_7: 0.09331/0.11477, loss_spatial_dice_7: 0.25208/0.27231, loss_spatial_ce_7: 0.09925/0.19668, loss_grounding_bce_7: 0.08331/0.08991, loss_grounding_dice_7: 0.30957/0.18974, loss_grounding_ce_7: 0.22390/0.35630, loss_mask_ce_8: 0.87430/1.13418, loss_mask_bce_8: 0.34387/0.36515, loss_mask_dice_8: 1.11483/1.33139, loss_spatial_bce_8: 0.13181/0.13608, loss_spatial_dice_8: 0.30024/0.31301, loss_spatial_ce_8: 0.21903/0.25226, loss_grounding_bce_8: 0.07376/0.09341, loss_grounding_dice_8: 0.29954/0.20113, loss_grounding_ce_8: 0.36665/0.42836, loss_mask_ce_9: 3.21962/3.70065, loss_mask_bce_9: 0.43363/0.39207, loss_mask_dice_9: 1.39481/1.90707, loss_spatial_bce_9: 0.31556/0.33798, loss_spatial_dice_9: 0.87485/0.82614, loss_spatial_ce_9: 1.64674/1.53009, loss_grounding_bce_9: 0.09764/0.10491, loss_grounding_dice_9: 0.43397/0.28106, loss_grounding_ce_9: 0.42400/0.71343] items per batch[64] items per second[0.22] total items[864000] mini batches[ 13500] memory[7341] epoch remaining[0:52:43]
INFO:trainer.default_trainer:epochs[     7] optim steps[13600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94664/0.91648, loss_mask_bce_0: 0.35423/0.33594, loss_mask_dice_0: 1.15966/1.16835, loss_spatial_bce_0: 0.08153/0.09391, loss_spatial_dice_0: 0.22573/0.22509, loss_spatial_ce_0: 0.01594/0.09016, loss_grounding_bce_0: 0.16934/0.08617, loss_grounding_dice_0: 0.24986/0.17882, loss_grounding_ce_0: 0.49238/0.27786, loss_mask_ce_1: 0.97697/0.91732, loss_mask_bce_1: 0.34974/0.33654, loss_mask_dice_1: 1.13272/1.17587, loss_spatial_bce_1: 0.08425/0.09492, loss_spatial_dice_1: 0.22438/0.22964, loss_spatial_ce_1: 0.03473/0.09572, loss_grounding_bce_1: 0.17313/0.08628, loss_grounding_dice_1: 0.27736/0.17937, loss_grounding_ce_1: 0.49075/0.27998, loss_mask_ce_2: 1.02873/0.92387, loss_mask_bce_2: 0.35646/0.33682, loss_mask_dice_2: 1.27119/1.17481, loss_spatial_bce_2: 0.08401/0.09467, loss_spatial_dice_2: 0.19879/0.23041, loss_spatial_ce_2: 0.02922/0.09972, loss_grounding_bce_2: 0.17033/0.08630, loss_grounding_dice_2: 0.22197/0.17910, loss_grounding_ce_2: 0.51179/0.28307, loss_mask_ce_3: 0.96387/0.93032, loss_mask_bce_3: 0.35270/0.33778, loss_mask_dice_3: 1.24778/1.17169, loss_spatial_bce_3: 0.08222/0.09570, loss_spatial_dice_3: 0.23027/0.23154, loss_spatial_ce_3: 0.02650/0.10432, loss_grounding_bce_3: 0.17763/0.08644, loss_grounding_dice_3: 0.24042/0.17877, loss_grounding_ce_3: 0.48472/0.28393, loss_mask_ce_4: 1.11232/0.92902, loss_mask_bce_4: 0.35765/0.33896, loss_mask_dice_4: 1.26068/1.19277, loss_spatial_bce_4: 0.08223/0.09929, loss_spatial_dice_4: 0.21925/0.23958, loss_spatial_ce_4: 0.15816/0.12101, loss_grounding_bce_4: 0.17752/0.08695, loss_grounding_dice_4: 0.28670/0.18141, loss_grounding_ce_4: 0.45945/0.28640, loss_mask_ce_5: 1.21182/0.94305, loss_mask_bce_5: 0.35786/0.34131, loss_mask_dice_5: 1.25096/1.19830, loss_spatial_bce_5: 0.07851/0.10009, loss_spatial_dice_5: 0.19974/0.24246, loss_spatial_ce_5: 0.13672/0.13416, loss_grounding_bce_5: 0.17859/0.08740, loss_grounding_dice_5: 0.25887/0.18272, loss_grounding_ce_5: 0.44530/0.29842, loss_mask_ce_6: 1.12580/0.97924, loss_mask_bce_6: 0.36049/0.34384, loss_mask_dice_6: 1.17552/1.20140, loss_spatial_bce_6: 0.08656/0.10560, loss_spatial_dice_6: 0.21411/0.24530, loss_spatial_ce_6: 0.33339/0.15653, loss_grounding_bce_6: 0.17215/0.08820, loss_grounding_dice_6: 0.21595/0.18289, loss_grounding_ce_6: 0.43299/0.31884, loss_mask_ce_7: 1.09559/1.02261, loss_mask_bce_7: 0.37985/0.35156, loss_mask_dice_7: 1.36244/1.25681, loss_spatial_bce_7: 0.09256/0.11473, loss_spatial_dice_7: 0.20968/0.27220, loss_spatial_ce_7: 0.22785/0.19642, loss_grounding_bce_7: 0.18071/0.08997, loss_grounding_dice_7: 0.26831/0.18983, loss_grounding_ce_7: 0.68126/0.35616, loss_mask_ce_8: 1.30552/1.13407, loss_mask_bce_8: 0.39795/0.36524, loss_mask_dice_8: 1.44997/1.33248, loss_spatial_bce_8: 0.10125/0.13600, loss_spatial_dice_8: 0.26633/0.31291, loss_spatial_ce_8: 0.23333/0.25203, loss_grounding_bce_8: 0.20978/0.09345, loss_grounding_dice_8: 0.27890/0.20125, loss_grounding_ce_8: 0.68154/0.42829, loss_mask_ce_9: 3.08558/3.70166, loss_mask_bce_9: 0.39218/0.39217, loss_mask_dice_9: 1.70588/1.90829, loss_spatial_bce_9: 0.24360/0.33790, loss_spatial_dice_9: 0.89909/0.82611, loss_spatial_ce_9: 1.37401/1.53025, loss_grounding_bce_9: 0.21704/0.10494, loss_grounding_dice_9: 0.29596/0.28112, loss_grounding_ce_9: 0.21668/0.71359] items per batch[64] items per second[0.22] total items[870400] mini batches[ 13600] memory[7341] epoch remaining[0:48:10]
INFO:trainer.default_trainer:epochs[     7] optim steps[13700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14709/0.91614, loss_mask_bce_0: 0.16733/0.33618, loss_mask_dice_0: 0.11016/1.16801, loss_spatial_bce_0: 0.14637/0.09391, loss_spatial_dice_0: 0.09796/0.22495, loss_spatial_ce_0: 0.00013/0.08995, loss_grounding_bce_0: 0.25019/0.08630, loss_grounding_dice_0: 0.16037/0.17885, loss_grounding_ce_0: 0.00867/0.27833, loss_mask_ce_1: 0.15326/0.91701, loss_mask_bce_1: 0.16258/0.33677, loss_mask_dice_1: 0.11238/1.17545, loss_spatial_bce_1: 0.14384/0.09492, loss_spatial_dice_1: 0.09838/0.22946, loss_spatial_ce_1: 0.00005/0.09553, loss_grounding_bce_1: 0.24209/0.08643, loss_grounding_dice_1: 0.15708/0.17939, loss_grounding_ce_1: 0.00872/0.28031, loss_mask_ce_2: 0.14924/0.92358, loss_mask_bce_2: 0.16371/0.33706, loss_mask_dice_2: 0.11069/1.17450, loss_spatial_bce_2: 0.14930/0.09467, loss_spatial_dice_2: 0.09971/0.23026, loss_spatial_ce_2: 0.00006/0.09951, loss_grounding_bce_2: 0.23780/0.08644, loss_grounding_dice_2: 0.15835/0.17911, loss_grounding_ce_2: 0.00372/0.28346, loss_mask_ce_3: 0.15873/0.93007, loss_mask_bce_3: 0.16405/0.33803, loss_mask_dice_3: 0.11607/1.17134, loss_spatial_bce_3: 0.14929/0.09570, loss_spatial_dice_3: 0.10655/0.23138, loss_spatial_ce_3: 0.00020/0.10409, loss_grounding_bce_3: 0.23591/0.08656, loss_grounding_dice_3: 0.16237/0.17879, loss_grounding_ce_3: 0.00288/0.28426, loss_mask_ce_4: 0.15993/0.92865, loss_mask_bce_4: 0.16914/0.33924, loss_mask_dice_4: 0.11896/1.19240, loss_spatial_bce_4: 0.15764/0.09930, loss_spatial_dice_4: 0.10388/0.23941, loss_spatial_ce_4: 0.00013/0.12077, loss_grounding_bce_4: 0.24532/0.08710, loss_grounding_dice_4: 0.16651/0.18144, loss_grounding_ce_4: 0.01190/0.28673, loss_mask_ce_5: 0.15920/0.94272, loss_mask_bce_5: 0.16807/0.34161, loss_mask_dice_5: 0.11177/1.19804, loss_spatial_bce_5: 0.14686/0.10010, loss_spatial_dice_5: 0.09252/0.24230, loss_spatial_ce_5: 0.00015/0.13394, loss_grounding_bce_5: 0.24201/0.08755, loss_grounding_dice_5: 0.16198/0.18272, loss_grounding_ce_5: 0.00959/0.29868, loss_mask_ce_6: 0.17513/0.97886, loss_mask_bce_6: 0.16479/0.34413, loss_mask_dice_6: 0.11434/1.20106, loss_spatial_bce_6: 0.16014/0.10560, loss_spatial_dice_6: 0.10517/0.24512, loss_spatial_ce_6: 0.00361/0.15653, loss_grounding_bce_6: 0.23758/0.08836, loss_grounding_dice_6: 0.16561/0.18288, loss_grounding_ce_6: 0.00908/0.31907, loss_mask_ce_7: 0.16786/1.02222, loss_mask_bce_7: 0.16836/0.35188, loss_mask_dice_7: 0.11311/1.25638, loss_spatial_bce_7: 0.14691/0.11473, loss_spatial_dice_7: 0.10466/0.27204, loss_spatial_ce_7: 0.04202/0.19621, loss_grounding_bce_7: 0.23999/0.09015, loss_grounding_dice_7: 0.16059/0.18984, loss_grounding_ce_7: 0.00452/0.35646, loss_mask_ce_8: 0.23380/1.13361, loss_mask_bce_8: 0.16521/0.36553, loss_mask_dice_8: 0.10269/1.33218, loss_spatial_bce_8: 0.15814/0.13599, loss_spatial_dice_8: 0.10171/0.31274, loss_spatial_ce_8: 0.06001/0.25179, loss_grounding_bce_8: 0.24484/0.09362, loss_grounding_dice_8: 0.14395/0.20126, loss_grounding_ce_8: 0.03712/0.42902, loss_mask_ce_9: 1.65019/3.70141, loss_mask_bce_9: 0.15214/0.39249, loss_mask_dice_9: 0.13896/1.90801, loss_spatial_bce_9: 0.63930/0.33801, loss_spatial_dice_9: 0.77592/0.82613, loss_spatial_ce_9: 1.27850/1.52994, loss_grounding_bce_9: 0.22234/0.10511, loss_grounding_dice_9: 0.15978/0.28116, loss_grounding_ce_9: 0.01935/0.71429] items per batch[64] items per second[0.22] total items[876800] mini batches[ 13700] memory[7341] epoch remaining[0:43:28]
INFO:trainer.default_trainer:epochs[     7] optim steps[13800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18155/0.91660, loss_mask_bce_0: 0.29466/0.33607, loss_mask_dice_0: 0.54947/1.16712, loss_spatial_bce_0: 0.06568/0.09384, loss_spatial_dice_0: 0.15659/0.22477, loss_spatial_ce_0: 0.00551/0.08970, loss_grounding_bce_0: 0.04703/0.08632, loss_grounding_dice_0: 0.15829/0.17884, loss_grounding_ce_0: 0.25408/0.27814, loss_mask_ce_1: 1.14218/0.91731, loss_mask_bce_1: 0.27446/0.33668, loss_mask_dice_1: 0.55992/1.17457, loss_spatial_bce_1: 0.06576/0.09485, loss_spatial_dice_1: 0.17512/0.22928, loss_spatial_ce_1: 0.00657/0.09523, loss_grounding_bce_1: 0.04372/0.08646, loss_grounding_dice_1: 0.12664/0.17935, loss_grounding_ce_1: 0.32518/0.28015, loss_mask_ce_2: 0.99715/0.92386, loss_mask_bce_2: 0.28157/0.33697, loss_mask_dice_2: 0.53150/1.17362, loss_spatial_bce_2: 0.05794/0.09461, loss_spatial_dice_2: 0.16037/0.23008, loss_spatial_ce_2: 0.00661/0.09920, loss_grounding_bce_2: 0.04661/0.08646, loss_grounding_dice_2: 0.22487/0.17908, loss_grounding_ce_2: 0.14686/0.28328, loss_mask_ce_3: 1.08944/0.93055, loss_mask_bce_3: 0.27497/0.33794, loss_mask_dice_3: 0.50438/1.17054, loss_spatial_bce_3: 0.06140/0.09564, loss_spatial_dice_3: 0.19214/0.23120, loss_spatial_ce_3: 0.00699/0.10380, loss_grounding_bce_3: 0.04567/0.08659, loss_grounding_dice_3: 0.16298/0.17874, loss_grounding_ce_3: 0.35097/0.28421, loss_mask_ce_4: 1.08024/0.92906, loss_mask_bce_4: 0.27901/0.33912, loss_mask_dice_4: 0.57689/1.19154, loss_spatial_bce_4: 0.05369/0.09923, loss_spatial_dice_4: 0.16418/0.23926, loss_spatial_ce_4: 0.02572/0.12039, loss_grounding_bce_4: 0.04262/0.08713, loss_grounding_dice_4: 0.18266/0.18141, loss_grounding_ce_4: 0.29875/0.28667, loss_mask_ce_5: 0.89752/0.94310, loss_mask_bce_5: 0.30296/0.34148, loss_mask_dice_5: 0.69371/1.19715, loss_spatial_bce_5: 0.06021/0.10003, loss_spatial_dice_5: 0.18998/0.24213, loss_spatial_ce_5: 0.08552/0.13365, loss_grounding_bce_5: 0.04627/0.08759, loss_grounding_dice_5: 0.15944/0.18268, loss_grounding_ce_5: 0.27495/0.29848, loss_mask_ce_6: 1.15814/0.97933, loss_mask_bce_6: 0.30914/0.34401, loss_mask_dice_6: 0.56387/1.20020, loss_spatial_bce_6: 0.05985/0.10553, loss_spatial_dice_6: 0.16914/0.24494, loss_spatial_ce_6: 0.10045/0.15623, loss_grounding_bce_6: 0.04607/0.08842, loss_grounding_dice_6: 0.13118/0.18283, loss_grounding_ce_6: 0.32977/0.31877, loss_mask_ce_7: 1.10079/1.02266, loss_mask_bce_7: 0.30268/0.35173, loss_mask_dice_7: 0.58669/1.25552, loss_spatial_bce_7: 0.07401/0.11463, loss_spatial_dice_7: 0.20632/0.27185, loss_spatial_ce_7: 0.13061/0.19585, loss_grounding_bce_7: 0.03960/0.09016, loss_grounding_dice_7: 0.14809/0.18979, loss_grounding_ce_7: 0.39707/0.35596, loss_mask_ce_8: 1.33444/1.13416, loss_mask_bce_8: 0.32715/0.36535, loss_mask_dice_8: 0.68284/1.33134, loss_spatial_bce_8: 0.08226/0.13589, loss_spatial_dice_8: 0.24851/0.31255, loss_spatial_ce_8: 0.11397/0.25141, loss_grounding_bce_8: 0.06847/0.09362, loss_grounding_dice_8: 0.21034/0.20120, loss_grounding_ce_8: 0.28029/0.42824, loss_mask_ce_9: 4.70248/3.70166, loss_mask_bce_9: 0.31992/0.39234, loss_mask_dice_9: 1.19539/1.90690, loss_spatial_bce_9: 0.23017/0.33802, loss_spatial_dice_9: 0.89086/0.82608, loss_spatial_ce_9: 1.61836/1.52977, loss_grounding_bce_9: 0.05018/0.10515, loss_grounding_dice_9: 0.24967/0.28109, loss_grounding_ce_9: 0.66839/0.71321] items per batch[64] items per second[0.22] total items[883200] mini batches[ 13800] memory[7341] epoch remaining[0:38:43]
INFO:trainer.default_trainer:epochs[     7] optim steps[13900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78048/0.91610, loss_mask_bce_0: 0.30105/0.33601, loss_mask_dice_0: 1.04590/1.16720, loss_spatial_bce_0: 0.06803/0.09376, loss_spatial_dice_0: 0.21953/0.22465, loss_spatial_ce_0: 0.03721/0.08951, loss_grounding_bce_0: 0.05093/0.08628, loss_grounding_dice_0: 0.05807/0.17881, loss_grounding_ce_0: 0.01226/0.27801, loss_mask_ce_1: 0.70486/0.91686, loss_mask_bce_1: 0.28582/0.33661, loss_mask_dice_1: 1.03222/1.17471, loss_spatial_bce_1: 0.07578/0.09477, loss_spatial_dice_1: 0.21834/0.22912, loss_spatial_ce_1: 0.04268/0.09508, loss_grounding_bce_1: 0.05034/0.08641, loss_grounding_dice_1: 0.05649/0.17929, loss_grounding_ce_1: 0.01996/0.28000, loss_mask_ce_2: 0.68444/0.92354, loss_mask_bce_2: 0.31468/0.33689, loss_mask_dice_2: 1.03205/1.17380, loss_spatial_bce_2: 0.07904/0.09453, loss_spatial_dice_2: 0.22787/0.22994, loss_spatial_ce_2: 0.05575/0.09900, loss_grounding_bce_2: 0.04990/0.08641, loss_grounding_dice_2: 0.06196/0.17899, loss_grounding_ce_2: 0.02097/0.28318, loss_mask_ce_3: 0.70803/0.93018, loss_mask_bce_3: 0.31557/0.33786, loss_mask_dice_3: 1.11426/1.17080, loss_spatial_bce_3: 0.08247/0.09556, loss_spatial_dice_3: 0.21721/0.23106, loss_spatial_ce_3: 0.05017/0.10353, loss_grounding_bce_3: 0.05094/0.08655, loss_grounding_dice_3: 0.05975/0.17871, loss_grounding_ce_3: 0.00989/0.28412, loss_mask_ce_4: 0.64761/0.92882, loss_mask_bce_4: 0.29284/0.33905, loss_mask_dice_4: 1.25726/1.19174, loss_spatial_bce_4: 0.10151/0.09915, loss_spatial_dice_4: 0.21495/0.23912, loss_spatial_ce_4: 0.04257/0.12019, loss_grounding_bce_4: 0.04973/0.08708, loss_grounding_dice_4: 0.06333/0.18140, loss_grounding_ce_4: 0.02460/0.28663, loss_mask_ce_5: 0.47140/0.94280, loss_mask_bce_5: 0.28875/0.34142, loss_mask_dice_5: 1.15289/1.19731, loss_spatial_bce_5: 0.12374/0.09997, loss_spatial_dice_5: 0.24721/0.24201, loss_spatial_ce_5: 0.03366/0.13339, loss_grounding_bce_5: 0.04737/0.08754, loss_grounding_dice_5: 0.05496/0.18262, loss_grounding_ce_5: 0.02198/0.29841, loss_mask_ce_6: 0.60394/0.97916, loss_mask_bce_6: 0.28929/0.34393, loss_mask_dice_6: 1.05966/1.20037, loss_spatial_bce_6: 0.08951/0.10544, loss_spatial_dice_6: 0.23229/0.24480, loss_spatial_ce_6: 0.06433/0.15592, loss_grounding_bce_6: 0.05454/0.08839, loss_grounding_dice_6: 0.07396/0.18276, loss_grounding_ce_6: 0.07371/0.31873, loss_mask_ce_7: 0.74323/1.02243, loss_mask_bce_7: 0.27547/0.35167, loss_mask_dice_7: 1.12380/1.25555, loss_spatial_bce_7: 0.08932/0.11455, loss_spatial_dice_7: 0.25405/0.27171, loss_spatial_ce_7: 0.16401/0.19559, loss_grounding_bce_7: 0.04902/0.09015, loss_grounding_dice_7: 0.05646/0.18977, loss_grounding_ce_7: 0.07738/0.35581, loss_mask_ce_8: 0.83145/1.13380, loss_mask_bce_8: 0.28506/0.36530, loss_mask_dice_8: 1.38133/1.33153, loss_spatial_bce_8: 0.10212/0.13580, loss_spatial_dice_8: 0.31926/0.31244, loss_spatial_ce_8: 0.30925/0.25115, loss_grounding_bce_8: 0.04935/0.09360, loss_grounding_dice_8: 0.05867/0.20122, loss_grounding_ce_8: 0.16700/0.42817, loss_mask_ce_9: 4.70701/3.70171, loss_mask_bce_9: 0.27379/0.39228, loss_mask_dice_9: 1.90788/1.90706, loss_spatial_bce_9: 0.18855/0.33789, loss_spatial_dice_9: 0.87206/0.82608, loss_spatial_ce_9: 1.26851/1.52947, loss_grounding_bce_9: 0.05509/0.10511, loss_grounding_dice_9: 0.10932/0.28116, loss_grounding_ce_9: 1.45812/0.71303] items per batch[64] items per second[0.23] total items[889600] mini batches[ 13900] memory[7341] epoch remaining[0:33:58]
INFO:trainer.default_trainer:epochs[     7] optim steps[14000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98134/0.91618, loss_mask_bce_0: 0.11506/0.33589, loss_mask_dice_0: 1.29235/1.16626, loss_spatial_bce_0: 0.03355/0.09372, loss_spatial_dice_0: 0.32873/0.22457, loss_spatial_ce_0: 0.02345/0.08924, loss_grounding_bce_0: 0.02167/0.08628, loss_grounding_dice_0: 0.10118/0.17866, loss_grounding_ce_0: 0.12631/0.27786, loss_mask_ce_1: 0.75371/0.91688, loss_mask_bce_1: 0.11580/0.33651, loss_mask_dice_1: 1.27943/1.17379, loss_spatial_bce_1: 0.02931/0.09472, loss_spatial_dice_1: 0.28782/0.22903, loss_spatial_ce_1: 0.03709/0.09481, loss_grounding_bce_1: 0.02418/0.08640, loss_grounding_dice_1: 0.11913/0.17914, loss_grounding_ce_1: 0.13388/0.27979, loss_mask_ce_2: 0.84856/0.92357, loss_mask_bce_2: 0.11880/0.33678, loss_mask_dice_2: 1.56250/1.17285, loss_spatial_bce_2: 0.03285/0.09450, loss_spatial_dice_2: 0.31098/0.22987, loss_spatial_ce_2: 0.02468/0.09869, loss_grounding_bce_2: 0.02210/0.08640, loss_grounding_dice_2: 0.10087/0.17882, loss_grounding_ce_2: 0.12104/0.28295, loss_mask_ce_3: 0.84632/0.93041, loss_mask_bce_3: 0.12305/0.33775, loss_mask_dice_3: 1.73993/1.17003, loss_spatial_bce_3: 0.03328/0.09552, loss_spatial_dice_3: 0.28164/0.23095, loss_spatial_ce_3: 0.02507/0.10327, loss_grounding_bce_3: 0.02440/0.08654, loss_grounding_dice_3: 0.24430/0.17857, loss_grounding_ce_3: 0.06731/0.28391, loss_mask_ce_4: 0.89915/0.92887, loss_mask_bce_4: 0.11950/0.33895, loss_mask_dice_4: 1.48150/1.19090, loss_spatial_bce_4: 0.02848/0.09910, loss_spatial_dice_4: 0.28016/0.23904, loss_spatial_ce_4: 0.03755/0.11990, loss_grounding_bce_4: 0.02058/0.08707, loss_grounding_dice_4: 0.06535/0.18126, loss_grounding_ce_4: 0.15737/0.28650, loss_mask_ce_5: 0.78881/0.94286, loss_mask_bce_5: 0.11967/0.34131, loss_mask_dice_5: 1.17560/1.19645, loss_spatial_bce_5: 0.02862/0.09993, loss_spatial_dice_5: 0.33937/0.24193, loss_spatial_ce_5: 0.07099/0.13315, loss_grounding_bce_5: 0.02391/0.08754, loss_grounding_dice_5: 0.13575/0.18249, loss_grounding_ce_5: 0.16522/0.29848, loss_mask_ce_6: 0.76038/0.97926, loss_mask_bce_6: 0.12451/0.34382, loss_mask_dice_6: 1.81620/1.19946, loss_spatial_bce_6: 0.03472/0.10544, loss_spatial_dice_6: 0.30033/0.24472, loss_spatial_ce_6: 0.11118/0.15577, loss_grounding_bce_6: 0.02197/0.08839, loss_grounding_dice_6: 0.20007/0.18264, loss_grounding_ce_6: 0.13083/0.31857, loss_mask_ce_7: 0.78181/1.02245, loss_mask_bce_7: 0.13063/0.35160, loss_mask_dice_7: 1.91594/1.25455, loss_spatial_bce_7: 0.03219/0.11455, loss_spatial_dice_7: 0.35045/0.27165, loss_spatial_ce_7: 0.29854/0.19531, loss_grounding_bce_7: 0.02654/0.09018, loss_grounding_dice_7: 0.21894/0.18965, loss_grounding_ce_7: 0.16696/0.35556, loss_mask_ce_8: 0.81603/1.13392, loss_mask_bce_8: 0.12432/0.36519, loss_mask_dice_8: 1.95172/1.33020, loss_spatial_bce_8: 0.05481/0.13580, loss_spatial_dice_8: 0.39691/0.31236, loss_spatial_ce_8: 0.16617/0.25104, loss_grounding_bce_8: 0.02381/0.09361, loss_grounding_dice_8: 0.16315/0.20107, loss_grounding_ce_8: 0.24666/0.42790, loss_mask_ce_9: 2.37017/3.70158, loss_mask_bce_9: 0.10397/0.39214, loss_mask_dice_9: 1.89043/1.90556, loss_spatial_bce_9: 0.19638/0.33793, loss_spatial_dice_9: 0.87641/0.82595, loss_spatial_ce_9: 1.93540/1.52912, loss_grounding_bce_9: 0.02526/0.10513, loss_grounding_dice_9: 0.26410/0.28099, loss_grounding_ce_9: 0.17353/0.71293] items per batch[64] items per second[0.22] total items[896000] mini batches[ 14000] memory[7341] epoch remaining[0:29:15]
INFO:trainer.default_trainer:epochs[     7] optim steps[14100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42906/0.91590, loss_mask_bce_0: 0.27570/0.33610, loss_mask_dice_0: 0.54899/1.16574, loss_spatial_bce_0: 0.07420/0.09373, loss_spatial_dice_0: 0.12792/0.22440, loss_spatial_ce_0: 0.02012/0.08906, loss_grounding_bce_0: 0.02700/0.08623, loss_grounding_dice_0: 0.28071/0.17860, loss_grounding_ce_0: 0.55658/0.27780, loss_mask_ce_1: 0.65320/0.91667, loss_mask_bce_1: 0.26458/0.33670, loss_mask_dice_1: 0.51818/1.17322, loss_spatial_bce_1: 0.07503/0.09473, loss_spatial_dice_1: 0.13627/0.22887, loss_spatial_ce_1: 0.01312/0.09459, loss_grounding_bce_1: 0.02766/0.08635, loss_grounding_dice_1: 0.28795/0.17907, loss_grounding_ce_1: 0.60553/0.27974, loss_mask_ce_2: 0.47024/0.92326, loss_mask_bce_2: 0.27077/0.33698, loss_mask_dice_2: 0.55109/1.17230, loss_spatial_bce_2: 0.06932/0.09452, loss_spatial_dice_2: 0.12775/0.22970, loss_spatial_ce_2: 0.01559/0.09848, loss_grounding_bce_2: 0.03033/0.08635, loss_grounding_dice_2: 0.28842/0.17875, loss_grounding_ce_2: 0.61547/0.28287, loss_mask_ce_3: 0.55809/0.93010, loss_mask_bce_3: 0.27788/0.33797, loss_mask_dice_3: 0.57247/1.16959, loss_spatial_bce_3: 0.07503/0.09553, loss_spatial_dice_3: 0.14186/0.23079, loss_spatial_ce_3: 0.02196/0.10305, loss_grounding_bce_3: 0.03200/0.08649, loss_grounding_dice_3: 0.30919/0.17850, loss_grounding_ce_3: 0.60525/0.28384, loss_mask_ce_4: 0.48003/0.92858, loss_mask_bce_4: 0.27807/0.33915, loss_mask_dice_4: 0.57976/1.19045, loss_spatial_bce_4: 0.08154/0.09913, loss_spatial_dice_4: 0.14106/0.23890, loss_spatial_ce_4: 0.07474/0.11960, loss_grounding_bce_4: 0.03432/0.08703, loss_grounding_dice_4: 0.28314/0.18120, loss_grounding_ce_4: 0.54137/0.28634, loss_mask_ce_5: 0.54792/0.94258, loss_mask_bce_5: 0.28555/0.34151, loss_mask_dice_5: 0.55240/1.19589, loss_spatial_bce_5: 0.07768/0.09997, loss_spatial_dice_5: 0.13658/0.24180, loss_spatial_ce_5: 0.09511/0.13290, loss_grounding_bce_5: 0.03561/0.08751, loss_grounding_dice_5: 0.28207/0.18242, loss_grounding_ce_5: 0.59382/0.29828, loss_mask_ce_6: 0.51768/0.97906, loss_mask_bce_6: 0.28945/0.34406, loss_mask_dice_6: 0.56494/1.19895, loss_spatial_bce_6: 0.07658/0.10545, loss_spatial_dice_6: 0.15043/0.24457, loss_spatial_ce_6: 0.10805/0.15564, loss_grounding_bce_6: 0.03210/0.08836, loss_grounding_dice_6: 0.33784/0.18257, loss_grounding_ce_6: 0.56707/0.31837, loss_mask_ce_7: 0.47344/1.02222, loss_mask_bce_7: 0.29826/0.35184, loss_mask_dice_7: 0.58710/1.25408, loss_spatial_bce_7: 0.07753/0.11453, loss_spatial_dice_7: 0.15746/0.27150, loss_spatial_ce_7: 0.18461/0.19513, loss_grounding_bce_7: 0.03610/0.09013, loss_grounding_dice_7: 0.33795/0.18960, loss_grounding_ce_7: 0.51830/0.35534, loss_mask_ce_8: 0.84306/1.13378, loss_mask_bce_8: 0.29531/0.36540, loss_mask_dice_8: 0.61298/1.32958, loss_spatial_bce_8: 0.10154/0.13581, loss_spatial_dice_8: 0.20116/0.31229, loss_spatial_ce_8: 0.14552/0.25077, loss_grounding_bce_8: 0.03504/0.09355, loss_grounding_dice_8: 0.40652/0.20098, loss_grounding_ce_8: 0.67561/0.42745, loss_mask_ce_9: 2.45611/3.70102, loss_mask_bce_9: 0.31335/0.39238, loss_mask_dice_9: 0.84332/1.90503, loss_spatial_bce_9: 0.48003/0.33799, loss_spatial_dice_9: 0.79622/0.82600, loss_spatial_ce_9: 1.66608/1.52918, loss_grounding_bce_9: 0.08706/0.10505, loss_grounding_dice_9: 0.52468/0.28090, loss_grounding_ce_9: 0.08812/0.71291] items per batch[64] items per second[0.22] total items[902400] mini batches[ 14100] memory[7341] epoch remaining[0:24:33]
INFO:trainer.default_trainer:epochs[     7] optim steps[14200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68708/0.91593, loss_mask_bce_0: 0.14408/0.33598, loss_mask_dice_0: 0.13195/1.16587, loss_spatial_bce_0: 0.12194/0.09370, loss_spatial_dice_0: 0.11883/0.22438, loss_spatial_ce_0: 0.00357/0.08910, loss_grounding_bce_0: 0.03386/0.08615, loss_grounding_dice_0: 0.08748/0.17856, loss_grounding_ce_0: 0.17689/0.27774, loss_mask_ce_1: 0.71693/0.91659, loss_mask_bce_1: 0.15470/0.33659, loss_mask_dice_1: 0.12445/1.17342, loss_spatial_bce_1: 0.12176/0.09470, loss_spatial_dice_1: 0.11501/0.22885, loss_spatial_ce_1: 0.00345/0.09477, loss_grounding_bce_1: 0.03139/0.08627, loss_grounding_dice_1: 0.08511/0.17904, loss_grounding_ce_1: 0.17180/0.27965, loss_mask_ce_2: 0.74781/0.92325, loss_mask_bce_2: 0.15464/0.33686, loss_mask_dice_2: 0.13021/1.17263, loss_spatial_bce_2: 0.12787/0.09449, loss_spatial_dice_2: 0.12230/0.22968, loss_spatial_ce_2: 0.00393/0.09859, loss_grounding_bce_2: 0.02595/0.08627, loss_grounding_dice_2: 0.06812/0.17872, loss_grounding_ce_2: 0.22862/0.28274, loss_mask_ce_3: 0.75316/0.92992, loss_mask_bce_3: 0.15440/0.33787, loss_mask_dice_3: 0.13101/1.16988, loss_spatial_bce_3: 0.11398/0.09549, loss_spatial_dice_3: 0.11115/0.23077, loss_spatial_ce_3: 0.00575/0.10316, loss_grounding_bce_3: 0.02609/0.08640, loss_grounding_dice_3: 0.06581/0.17850, loss_grounding_ce_3: 0.23515/0.28364, loss_mask_ce_4: 0.71915/0.92862, loss_mask_bce_4: 0.15301/0.33907, loss_mask_dice_4: 0.13578/1.19074, loss_spatial_bce_4: 0.10694/0.09910, loss_spatial_dice_4: 0.11032/0.23889, loss_spatial_ce_4: 0.04181/0.11965, loss_grounding_bce_4: 0.02703/0.08694, loss_grounding_dice_4: 0.07422/0.18115, loss_grounding_ce_4: 0.23825/0.28620, loss_mask_ce_5: 0.68081/0.94268, loss_mask_bce_5: 0.15781/0.34143, loss_mask_dice_5: 0.13672/1.19612, loss_spatial_bce_5: 0.10967/0.09992, loss_spatial_dice_5: 0.12895/0.24178, loss_spatial_ce_5: 0.06017/0.13314, loss_grounding_bce_5: 0.02661/0.08742, loss_grounding_dice_5: 0.07337/0.18240, loss_grounding_ce_5: 0.21083/0.29815, loss_mask_ce_6: 0.87557/0.97909, loss_mask_bce_6: 0.14202/0.34397, loss_mask_dice_6: 0.11359/1.19926, loss_spatial_bce_6: 0.13647/0.10539, loss_spatial_dice_6: 0.11638/0.24455, loss_spatial_ce_6: 0.04349/0.15593, loss_grounding_bce_6: 0.02695/0.08826, loss_grounding_dice_6: 0.06590/0.18253, loss_grounding_ce_6: 0.24910/0.31827, loss_mask_ce_7: 0.86468/1.02244, loss_mask_bce_7: 0.19982/0.35175, loss_mask_dice_7: 0.15356/1.25417, loss_spatial_bce_7: 0.10047/0.11448, loss_spatial_dice_7: 0.11298/0.27148, loss_spatial_ce_7: 0.10844/0.19518, loss_grounding_bce_7: 0.03374/0.09006, loss_grounding_dice_7: 0.07528/0.18958, loss_grounding_ce_7: 0.30143/0.35515, loss_mask_ce_8: 1.01108/1.13424, loss_mask_bce_8: 0.17710/0.36530, loss_mask_dice_8: 0.19076/1.32969, loss_spatial_bce_8: 0.12035/0.13574, loss_spatial_dice_8: 0.16010/0.31228, loss_spatial_ce_8: 0.12223/0.25090, loss_grounding_bce_8: 0.04836/0.09345, loss_grounding_dice_8: 0.11162/0.20090, loss_grounding_ce_8: 0.34224/0.42747, loss_mask_ce_9: 3.16529/3.70159, loss_mask_bce_9: 0.15814/0.39229, loss_mask_dice_9: 0.30663/1.90541, loss_spatial_bce_9: 0.40861/0.33777, loss_spatial_dice_9: 0.77345/0.82595, loss_spatial_ce_9: 0.91263/1.52959, loss_grounding_bce_9: 0.05503/0.10497, loss_grounding_dice_9: 0.22059/0.28084, loss_grounding_ce_9: 0.40476/0.71245] items per batch[64] items per second[0.22] total items[908800] mini batches[ 14200] memory[7341] epoch remaining[0:19:49]
INFO:trainer.default_trainer:epochs[     7] optim steps[14300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97343/0.91615, loss_mask_bce_0: 0.22442/0.33605, loss_mask_dice_0: 0.64215/1.16679, loss_spatial_bce_0: 0.10917/0.09360, loss_spatial_dice_0: 0.21326/0.22434, loss_spatial_ce_0: 0.03600/0.08884, loss_grounding_bce_0: 0.06348/0.08611, loss_grounding_dice_0: 0.11031/0.17855, loss_grounding_ce_0: 0.19443/0.27781, loss_mask_ce_1: 0.99786/0.91680, loss_mask_bce_1: 0.22362/0.33666, loss_mask_dice_1: 0.62456/1.17433, loss_spatial_bce_1: 0.12024/0.09460, loss_spatial_dice_1: 0.21568/0.22880, loss_spatial_ce_1: 0.03829/0.09460, loss_grounding_bce_1: 0.06280/0.08623, loss_grounding_dice_1: 0.09598/0.17898, loss_grounding_ce_1: 0.18272/0.27973, loss_mask_ce_2: 0.96480/0.92337, loss_mask_bce_2: 0.22506/0.33692, loss_mask_dice_2: 0.66453/1.17363, loss_spatial_bce_2: 0.14369/0.09439, loss_spatial_dice_2: 0.22675/0.22963, loss_spatial_ce_2: 0.03233/0.09837, loss_grounding_bce_2: 0.05866/0.08623, loss_grounding_dice_2: 0.10857/0.17869, loss_grounding_ce_2: 0.20792/0.28281, loss_mask_ce_3: 0.93478/0.93008, loss_mask_bce_3: 0.22862/0.33793, loss_mask_dice_3: 0.67908/1.17088, loss_spatial_bce_3: 0.13453/0.09540, loss_spatial_dice_3: 0.20725/0.23074, loss_spatial_ce_3: 0.04322/0.10290, loss_grounding_bce_3: 0.05940/0.08637, loss_grounding_dice_3: 0.10488/0.17845, loss_grounding_ce_3: 0.20728/0.28373, loss_mask_ce_4: 0.94813/0.92885, loss_mask_bce_4: 0.23977/0.33916, loss_mask_dice_4: 0.67014/1.19168, loss_spatial_bce_4: 0.17412/0.09901, loss_spatial_dice_4: 0.25252/0.23884, loss_spatial_ce_4: 0.04617/0.11934, loss_grounding_bce_4: 0.05864/0.08691, loss_grounding_dice_4: 0.09835/0.18110, loss_grounding_ce_4: 0.31618/0.28630, loss_mask_ce_5: 0.91888/0.94284, loss_mask_bce_5: 0.24801/0.34152, loss_mask_dice_5: 0.73083/1.19719, loss_spatial_bce_5: 0.19440/0.09984, loss_spatial_dice_5: 0.26742/0.24173, loss_spatial_ce_5: 0.10580/0.13288, loss_grounding_bce_5: 0.05963/0.08738, loss_grounding_dice_5: 0.09449/0.18237, loss_grounding_ce_5: 0.27132/0.29828, loss_mask_ce_6: 1.01292/0.97936, loss_mask_bce_6: 0.24498/0.34405, loss_mask_dice_6: 0.72729/1.20023, loss_spatial_bce_6: 0.18851/0.10532, loss_spatial_dice_6: 0.26482/0.24452, loss_spatial_ce_6: 0.08397/0.15567, loss_grounding_bce_6: 0.06328/0.08822, loss_grounding_dice_6: 0.11268/0.18249, loss_grounding_ce_6: 0.32261/0.31841, loss_mask_ce_7: 1.12068/1.02272, loss_mask_bce_7: 0.23347/0.35183, loss_mask_dice_7: 0.76761/1.25518, loss_spatial_bce_7: 0.23209/0.11440, loss_spatial_dice_7: 0.30291/0.27146, loss_spatial_ce_7: 0.11884/0.19493, loss_grounding_bce_7: 0.06795/0.09002, loss_grounding_dice_7: 0.23605/0.18957, loss_grounding_ce_7: 0.40469/0.35510, loss_mask_ce_8: 1.29359/1.13453, loss_mask_bce_8: 0.20241/0.36538, loss_mask_dice_8: 1.06789/1.33067, loss_spatial_bce_8: 0.25526/0.13568, loss_spatial_dice_8: 0.30531/0.31236, loss_spatial_ce_8: 0.19143/0.25069, loss_grounding_bce_8: 0.06729/0.09342, loss_grounding_dice_8: 0.26980/0.20087, loss_grounding_ce_8: 0.02698/0.42751, loss_mask_ce_9: 3.23241/3.70146, loss_mask_bce_9: 0.22711/0.39237, loss_mask_dice_9: 1.22719/1.90748, loss_spatial_bce_9: 0.27839/0.33755, loss_spatial_dice_9: 0.81432/0.82597, loss_spatial_ce_9: 1.53877/1.52992, loss_grounding_bce_9: 0.08391/0.10492, loss_grounding_dice_9: 0.33654/0.28078, loss_grounding_ce_9: 0.16993/0.71214] items per batch[64] items per second[0.23] total items[915200] mini batches[ 14300] memory[7341] epoch remaining[0:15:01]
INFO:trainer.default_trainer:epochs[     7] optim steps[14400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02901/0.91563, loss_mask_bce_0: 0.50755/0.33613, loss_mask_dice_0: 1.40863/1.16636, loss_spatial_bce_0: 0.08982/0.09358, loss_spatial_dice_0: 0.23704/0.22418, loss_spatial_ce_0: 0.08042/0.08859, loss_grounding_bce_0: 0.11249/0.08612, loss_grounding_dice_0: 0.09784/0.17846, loss_grounding_ce_0: 0.24193/0.27782, loss_mask_ce_1: 1.01691/0.91637, loss_mask_bce_1: 0.51821/0.33674, loss_mask_dice_1: 1.42635/1.17392, loss_spatial_bce_1: 0.08953/0.09458, loss_spatial_dice_1: 0.23450/0.22864, loss_spatial_ce_1: 0.13003/0.09443, loss_grounding_bce_1: 0.11181/0.08623, loss_grounding_dice_1: 0.09018/0.17892, loss_grounding_ce_1: 0.26606/0.27985, loss_mask_ce_2: 1.05118/0.92300, loss_mask_bce_2: 0.51192/0.33702, loss_mask_dice_2: 1.39636/1.17312, loss_spatial_bce_2: 0.09127/0.09437, loss_spatial_dice_2: 0.23400/0.22948, loss_spatial_ce_2: 0.12985/0.09813, loss_grounding_bce_2: 0.11084/0.08624, loss_grounding_dice_2: 0.09167/0.17864, loss_grounding_ce_2: 0.28373/0.28280, loss_mask_ce_3: 1.01176/0.92970, loss_mask_bce_3: 0.50872/0.33800, loss_mask_dice_3: 1.38245/1.17044, loss_spatial_bce_3: 0.08979/0.09536, loss_spatial_dice_3: 0.22774/0.23058, loss_spatial_ce_3: 0.12943/0.10268, loss_grounding_bce_3: 0.11614/0.08638, loss_grounding_dice_3: 0.09934/0.17841, loss_grounding_ce_3: 0.33044/0.28375, loss_mask_ce_4: 1.00765/0.92834, loss_mask_bce_4: 0.53034/0.33927, loss_mask_dice_4: 1.41517/1.19118, loss_spatial_bce_4: 0.10755/0.09899, loss_spatial_dice_4: 0.24207/0.23869, loss_spatial_ce_4: 0.08888/0.11912, loss_grounding_bce_4: 0.10952/0.08693, loss_grounding_dice_4: 0.08682/0.18104, loss_grounding_ce_4: 0.32752/0.28637, loss_mask_ce_5: 1.00134/0.94250, loss_mask_bce_5: 0.54847/0.34161, loss_mask_dice_5: 1.42910/1.19671, loss_spatial_bce_5: 0.10565/0.09982, loss_spatial_dice_5: 0.24881/0.24160, loss_spatial_ce_5: 0.14088/0.13272, loss_grounding_bce_5: 0.11466/0.08740, loss_grounding_dice_5: 0.09470/0.18231, loss_grounding_ce_5: 0.24317/0.29824, loss_mask_ce_6: 1.03977/0.97906, loss_mask_bce_6: 0.57951/0.34415, loss_mask_dice_6: 1.46856/1.19971, loss_spatial_bce_6: 0.09947/0.10531, loss_spatial_dice_6: 0.23275/0.24436, loss_spatial_ce_6: 0.26325/0.15548, loss_grounding_bce_6: 0.12223/0.08824, loss_grounding_dice_6: 0.10114/0.18239, loss_grounding_ce_6: 0.43458/0.31854, loss_mask_ce_7: 1.03793/1.02239, loss_mask_bce_7: 0.56854/0.35189, loss_mask_dice_7: 1.48767/1.25468, loss_spatial_bce_7: 0.10195/0.11436, loss_spatial_dice_7: 0.27659/0.27127, loss_spatial_ce_7: 0.19912/0.19469, loss_grounding_bce_7: 0.11548/0.09003, loss_grounding_dice_7: 0.09999/0.18949, loss_grounding_ce_7: 0.31175/0.35506, loss_mask_ce_8: 1.22483/1.13415, loss_mask_bce_8: 0.61467/0.36544, loss_mask_dice_8: 1.63114/1.32999, loss_spatial_bce_8: 0.17671/0.13568, loss_spatial_dice_8: 0.36903/0.31218, loss_spatial_ce_8: 0.24798/0.25050, loss_grounding_bce_8: 0.16249/0.09341, loss_grounding_dice_8: 0.12370/0.20077, loss_grounding_ce_8: 0.49274/0.42734, loss_mask_ce_9: 4.46404/3.70100, loss_mask_bce_9: 0.56863/0.39250, loss_mask_dice_9: 2.15595/1.90704, loss_spatial_bce_9: 0.43232/0.33758, loss_spatial_dice_9: 0.83579/0.82588, loss_spatial_ce_9: 1.39423/1.52989, loss_grounding_bce_9: 0.14576/0.10492, loss_grounding_dice_9: 0.21265/0.28060, loss_grounding_ce_9: 1.84239/0.71185] items per batch[64] items per second[0.22] total items[921600] mini batches[ 14400] memory[7341] epoch remaining[0:10:17]
INFO:trainer.default_trainer:epochs[     7] optim steps[14500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38893/0.91528, loss_mask_bce_0: 0.51701/0.33619, loss_mask_dice_0: 1.16495/1.16559, loss_spatial_bce_0: 0.11688/0.09364, loss_spatial_dice_0: 0.27749/0.22414, loss_spatial_ce_0: 0.05658/0.08838, loss_grounding_bce_0: 0.09182/0.08618, loss_grounding_dice_0: 0.19460/0.17845, loss_grounding_ce_0: 0.33392/0.27756, loss_mask_ce_1: 1.16373/0.91595, loss_mask_bce_1: 0.54953/0.33679, loss_mask_dice_1: 1.20479/1.17308, loss_spatial_bce_1: 0.10811/0.09463, loss_spatial_dice_1: 0.26447/0.22855, loss_spatial_ce_1: 0.09429/0.09426, loss_grounding_bce_1: 0.09741/0.08628, loss_grounding_dice_1: 0.19110/0.17890, loss_grounding_ce_1: 0.35409/0.27973, loss_mask_ce_2: 0.99660/0.92261, loss_mask_bce_2: 0.55669/0.33707, loss_mask_dice_2: 1.28165/1.17228, loss_spatial_bce_2: 0.10944/0.09442, loss_spatial_dice_2: 0.27707/0.22942, loss_spatial_ce_2: 0.09480/0.09792, loss_grounding_bce_2: 0.09669/0.08630, loss_grounding_dice_2: 0.18412/0.17861, loss_grounding_ce_2: 0.34289/0.28261, loss_mask_ce_3: 0.96761/0.92924, loss_mask_bce_3: 0.59290/0.33806, loss_mask_dice_3: 1.26543/1.16962, loss_spatial_bce_3: 0.11446/0.09541, loss_spatial_dice_3: 0.26668/0.23050, loss_spatial_ce_3: 0.10946/0.10255, loss_grounding_bce_3: 0.09825/0.08643, loss_grounding_dice_3: 0.21187/0.17838, loss_grounding_ce_3: 0.31523/0.28351, loss_mask_ce_4: 1.17188/0.92792, loss_mask_bce_4: 0.52862/0.33935, loss_mask_dice_4: 1.27965/1.19038, loss_spatial_bce_4: 0.11034/0.09907, loss_spatial_dice_4: 0.26080/0.23864, loss_spatial_ce_4: 0.18473/0.11906, loss_grounding_bce_4: 0.08630/0.08699, loss_grounding_dice_4: 0.22680/0.18102, loss_grounding_ce_4: 0.29675/0.28613, loss_mask_ce_5: 1.11787/0.94207, loss_mask_bce_5: 0.52301/0.34170, loss_mask_dice_5: 1.28236/1.19601, loss_spatial_bce_5: 0.11686/0.09990, loss_spatial_dice_5: 0.26498/0.24154, loss_spatial_ce_5: 0.16903/0.13260, loss_grounding_bce_5: 0.08290/0.08746, loss_grounding_dice_5: 0.22439/0.18228, loss_grounding_ce_5: 0.32783/0.29797, loss_mask_ce_6: 1.07195/0.97862, loss_mask_bce_6: 0.53082/0.34423, loss_mask_dice_6: 1.31773/1.19897, loss_spatial_bce_6: 0.11758/0.10541, loss_spatial_dice_6: 0.26884/0.24432, loss_spatial_ce_6: 0.22211/0.15544, loss_grounding_bce_6: 0.08705/0.08831, loss_grounding_dice_6: 0.23102/0.18238, loss_grounding_ce_6: 0.31818/0.31828, loss_mask_ce_7: 1.05881/1.02184, loss_mask_bce_7: 0.54121/0.35199, loss_mask_dice_7: 1.31536/1.25391, loss_spatial_bce_7: 0.14878/0.11444, loss_spatial_dice_7: 0.30937/0.27122, loss_spatial_ce_7: 0.26685/0.19462, loss_grounding_bce_7: 0.08788/0.09010, loss_grounding_dice_7: 0.21722/0.18949, loss_grounding_ce_7: 0.33263/0.35461, loss_mask_ce_8: 1.39407/1.13365, loss_mask_bce_8: 0.55285/0.36552, loss_mask_dice_8: 1.47399/1.32910, loss_spatial_bce_8: 0.19289/0.13576, loss_spatial_dice_8: 0.35732/0.31208, loss_spatial_ce_8: 0.31679/0.25040, loss_grounding_bce_8: 0.09357/0.09346, loss_grounding_dice_8: 0.25863/0.20074, loss_grounding_ce_8: 0.38383/0.42702, loss_mask_ce_9: 5.22557/3.69981, loss_mask_bce_9: 0.53174/0.39252, loss_mask_dice_9: 1.99599/1.90557, loss_spatial_bce_9: 0.37668/0.33758, loss_spatial_dice_9: 0.94852/0.82590, loss_spatial_ce_9: 1.76714/1.52974, loss_grounding_bce_9: 0.11673/0.10498, loss_grounding_dice_9: 0.34046/0.28058, loss_grounding_ce_9: 0.34943/0.71167] items per batch[64] items per second[0.23] total items[928000] mini batches[ 14500] memory[7341] epoch remaining[0:05:31]
INFO:trainer.default_trainer:epochs[     7] optim steps[14600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05913/0.91551, loss_mask_bce_0: 0.37430/0.33618, loss_mask_dice_0: 0.80304/1.16558, loss_spatial_bce_0: 0.04000/0.09362, loss_spatial_dice_0: 0.15091/0.22407, loss_spatial_ce_0: 0.02722/0.08817, loss_grounding_bce_0: 0.02558/0.08617, loss_grounding_dice_0: 0.07977/0.17851, loss_grounding_ce_0: 0.00486/0.27775, loss_mask_ce_1: 0.88488/0.91610, loss_mask_bce_1: 0.35463/0.33677, loss_mask_dice_1: 0.80454/1.17308, loss_spatial_bce_1: 0.04367/0.09459, loss_spatial_dice_1: 0.16853/0.22847, loss_spatial_ce_1: 0.03699/0.09408, loss_grounding_bce_1: 0.02753/0.08629, loss_grounding_dice_1: 0.07834/0.17897, loss_grounding_ce_1: 0.00417/0.27980, loss_mask_ce_2: 1.09320/0.92289, loss_mask_bce_2: 0.36744/0.33705, loss_mask_dice_2: 0.80058/1.17224, loss_spatial_bce_2: 0.04555/0.09440, loss_spatial_dice_2: 0.17821/0.22934, loss_spatial_ce_2: 0.02744/0.09770, loss_grounding_bce_2: 0.02536/0.08630, loss_grounding_dice_2: 0.08106/0.17863, loss_grounding_ce_2: 0.00550/0.28271, loss_mask_ce_3: 0.97824/0.92959, loss_mask_bce_3: 0.36573/0.33803, loss_mask_dice_3: 0.80410/1.16950, loss_spatial_bce_3: 0.04243/0.09540, loss_spatial_dice_3: 0.16492/0.23042, loss_spatial_ce_3: 0.02989/0.10232, loss_grounding_bce_3: 0.02711/0.08643, loss_grounding_dice_3: 0.08082/0.17843, loss_grounding_ce_3: 0.00761/0.28363, loss_mask_ce_4: 1.07662/0.92814, loss_mask_bce_4: 0.37833/0.33934, loss_mask_dice_4: 0.87679/1.19039, loss_spatial_bce_4: 0.04473/0.09906, loss_spatial_dice_4: 0.17084/0.23858, loss_spatial_ce_4: 0.03419/0.11882, loss_grounding_bce_4: 0.02548/0.08697, loss_grounding_dice_4: 0.08106/0.18105, loss_grounding_ce_4: 0.00559/0.28630, loss_mask_ce_5: 1.03446/0.94234, loss_mask_bce_5: 0.37686/0.34169, loss_mask_dice_5: 0.88442/1.19599, loss_spatial_bce_5: 0.04220/0.09987, loss_spatial_dice_5: 0.15915/0.24149, loss_spatial_ce_5: 0.04602/0.13236, loss_grounding_bce_5: 0.02326/0.08744, loss_grounding_dice_5: 0.07901/0.18233, loss_grounding_ce_5: 0.00618/0.29829, loss_mask_ce_6: 0.96310/0.97880, loss_mask_bce_6: 0.36083/0.34418, loss_mask_dice_6: 0.86841/1.19894, loss_spatial_bce_6: 0.04748/0.10537, loss_spatial_dice_6: 0.17118/0.24424, loss_spatial_ce_6: 0.08089/0.15527, loss_grounding_bce_6: 0.02341/0.08828, loss_grounding_dice_6: 0.08082/0.18241, loss_grounding_ce_6: 0.00442/0.31851, loss_mask_ce_7: 0.95181/1.02189, loss_mask_bce_7: 0.37491/0.35202, loss_mask_dice_7: 0.92150/1.25397, loss_spatial_bce_7: 0.04846/0.11440, loss_spatial_dice_7: 0.21009/0.27116, loss_spatial_ce_7: 0.15641/0.19457, loss_grounding_bce_7: 0.02959/0.09008, loss_grounding_dice_7: 0.08960/0.18954, loss_grounding_ce_7: 0.00742/0.35481, loss_mask_ce_8: 1.46113/1.13402, loss_mask_bce_8: 0.40283/0.36552, loss_mask_dice_8: 1.13216/1.32916, loss_spatial_bce_8: 0.10604/0.13570, loss_spatial_dice_8: 0.26433/0.31200, loss_spatial_ce_8: 0.11285/0.25031, loss_grounding_bce_8: 0.03014/0.09344, loss_grounding_dice_8: 0.10655/0.20080, loss_grounding_ce_8: 0.01192/0.42682, loss_mask_ce_9: 3.70479/3.70007, loss_mask_bce_9: 0.45087/0.39253, loss_mask_dice_9: 1.41844/1.90569, loss_spatial_bce_9: 0.27314/0.33751, loss_spatial_dice_9: 0.89186/0.82586, loss_spatial_ce_9: 1.94319/1.52949, loss_grounding_bce_9: 0.02922/0.10500, loss_grounding_dice_9: 0.12482/0.28073, loss_grounding_ce_9: 0.05006/0.71127] items per batch[64] items per second[0.22] total items[934400] mini batches[ 14600] memory[7341] epoch remaining[0:00:45]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00014616.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0034 s/iter. Inference: 0.2185 s/iter. Eval: 0.0948 s/iter. Total: 0.3167 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0030 s/iter. Inference: 0.2249 s/iter. Eval: 0.0919 s/iter. Total: 0.3200 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2254 s/iter. Eval: 0.0846 s/iter. Total: 0.3133 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0032 s/iter. Inference: 0.2267 s/iter. Eval: 0.0813 s/iter. Total: 0.3113 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0032 s/iter. Inference: 0.2232 s/iter. Eval: 0.0789 s/iter. Total: 0.3054 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0032 s/iter. Inference: 0.2253 s/iter. Eval: 0.0782 s/iter. Total: 0.3069 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0032 s/iter. Inference: 0.2266 s/iter. Eval: 0.0780 s/iter. Total: 0.3079 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0032 s/iter. Inference: 0.2264 s/iter. Eval: 0.0771 s/iter. Total: 0.3069 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0033 s/iter. Inference: 0.2268 s/iter. Eval: 0.0773 s/iter. Total: 0.3075 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalv_8mnmxk ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.815 | 81.801 | 60.002 |      133      |
| Things | 54.902 | 82.566 | 65.805 |      80       |
| Stuff  | 42.136 | 80.647 | 51.242 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.50 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.30s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.10 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.609
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.286
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.714
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.688 | 60.919 | 40.793 | 18.896 | 41.909 | 60.313 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.349 | bicycle      | 18.391 | car            | 37.564 |
| motorcycle    | 34.811 | airplane     | 56.987 | bus            | 64.340 |
| train         | 68.954 | truck        | 35.056 | boat           | 22.705 |
| traffic light | 24.559 | fire hydrant | 63.468 | stop sign      | 63.366 |
| parking meter | 43.199 | bench        | 20.653 | bird           | 29.300 |
| cat           | 74.135 | dog          | 65.900 | horse          | 45.849 |
| sheep         | 45.785 | cow          | 49.562 | elephant       | 60.965 |
| bear          | 77.599 | zebra        | 60.701 | giraffe        | 56.653 |
| backpack      | 15.905 | umbrella     | 48.440 | handbag        | 14.850 |
| tie           | 31.427 | suitcase     | 40.814 | frisbee        | 67.681 |
| skis          | 4.786  | snowboard    | 23.764 | sports ball    | 47.121 |
| kite          | 31.602 | baseball bat | 26.873 | baseball glove | 42.766 |
| skateboard    | 35.040 | surfboard    | 35.490 | tennis racket  | 56.328 |
| bottle        | 33.547 | wine glass   | 27.233 | cup            | 39.806 |
| fork          | 14.941 | knife        | 11.576 | spoon          | 14.075 |
| bowl          | 31.531 | banana       | 21.519 | apple          | 19.332 |
| sandwich      | 42.555 | orange       | 28.322 | broccoli       | 21.072 |
| carrot        | 19.061 | hot dog      | 21.608 | pizza          | 50.775 |
| donut         | 46.202 | cake         | 43.049 | chair          | 20.352 |
| couch         | 41.284 | potted plant | 17.019 | bed            | 40.247 |
| dining table  | 13.182 | toilet       | 67.487 | tv             | 62.186 |
| laptop        | 63.473 | mouse        | 56.913 | remote         | 30.805 |
| keyboard      | 48.659 | cell phone   | 37.187 | microwave      | 57.587 |
| oven          | 33.511 | toaster      | 38.787 | sink           | 36.356 |
| refrigerator  | 58.317 | book         | 7.904  | clock          | 51.210 |
| vase          | 33.702 | scissors     | 25.412 | teddy bear     | 51.284 |
| hair drier    | 11.422 | toothbrush   | 17.785 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.97032599875675, 'fwIoU': 69.09234925057308, 'IoU-person': 87.34074829742386, 'IoU-bicycle': 75.60158811961702, 'IoU-car': 69.04339744263015, 'IoU-motorcycle': 85.5798667278464, 'IoU-airplane': 76.99335171433935, 'IoU-bus': 86.7237104153108, 'IoU-train': 89.11714173437808, 'IoU-truck': 64.24409431014838, 'IoU-boat': 68.40699118529065, 'IoU-traffic light': 76.41395612494784, 'IoU-fire hydrant': 87.9850032888408, 'IoU-stop sign': 85.93820545528773, 'IoU-parking meter': 88.11424756534602, 'IoU-bench': 52.09889666055092, 'IoU-bird': 75.69427298916256, 'IoU-cat': 84.37743250565673, 'IoU-dog': 78.86245615034042, 'IoU-horse': 86.21371704073862, 'IoU-sheep': 89.59834858310087, 'IoU-cow': 80.32495753296583, 'IoU-elephant': 90.80969241743226, 'IoU-bear': 86.3623044145085, 'IoU-zebra': 92.43372613364087, 'IoU-giraffe': 88.2126117120874, 'IoU-backpack': 36.67856023170058, 'IoU-umbrella': 75.17511079445384, 'IoU-handbag': 35.554676427882065, 'IoU-tie': 71.06470691061648, 'IoU-suitcase': 79.38601657575394, 'IoU-frisbee': 82.71987502480937, 'IoU-skis': 52.43269178681154, 'IoU-snowboard': 69.96640919548274, 'IoU-sports ball': 67.0687788789999, 'IoU-kite': 65.78055892719394, 'IoU-baseball bat': 60.222640797613934, 'IoU-baseball glove': 76.33056282354704, 'IoU-skateboard': 59.82399434539848, 'IoU-surfboard': 74.97506046316977, 'IoU-tennis racket': 83.08462474995378, 'IoU-bottle': 65.51423585984865, 'IoU-wine glass': 74.0637498291259, 'IoU-cup': 65.16907409629205, 'IoU-fork': 54.27033125972594, 'IoU-knife': 49.79236120553974, 'IoU-spoon': 46.78317539484622, 'IoU-bowl': 56.21874560368452, 'IoU-banana': 84.76889595842837, 'IoU-apple': 56.85372686952421, 'IoU-sandwich': 66.10322545359969, 'IoU-orange': 81.13237615237067, 'IoU-broccoli': 68.20531792195034, 'IoU-carrot': 62.22551208953483, 'IoU-hot dog': 59.49106007801587, 'IoU-pizza': 86.2102169936749, 'IoU-donut': 63.8558044782395, 'IoU-cake': 69.65784120479914, 'IoU-chair': 54.25316844982603, 'IoU-couch': 64.82615529625672, 'IoU-potted plant': 34.61738650847188, 'IoU-bed': 66.53006342149558, 'IoU-dining table': 51.2550438783408, 'IoU-toilet': 85.7188270357288, 'IoU-tv': 76.05552679551867, 'IoU-laptop': 74.69705836423663, 'IoU-mouse': 70.9282213894494, 'IoU-remote': 50.88413840328192, 'IoU-keyboard': 67.8034972378213, 'IoU-cell phone': 68.50216515616309, 'IoU-microwave': 70.75565258069545, 'IoU-oven': 64.73587913613027, 'IoU-toaster': 56.94966484123177, 'IoU-sink': 67.87974512844717, 'IoU-refrigerator': 76.91511773270021, 'IoU-book': 48.33561882814973, 'IoU-clock': 72.46643469515513, 'IoU-vase': 67.77995516990575, 'IoU-scissors': 55.51533527546966, 'IoU-teddy bear': 81.07837873588709, 'IoU-hair drier': 43.29686089127317, 'IoU-toothbrush': 60.47426205318547, 'IoU-banner': 33.146541382919544, 'IoU-blanket': 10.202901021078052, 'IoU-bridge': 37.611606877734495, 'IoU-cardboard': 45.421231904897034, 'IoU-counter': 29.88626600112414, 'IoU-curtain': 64.17177012986016, 'IoU-door-stuff': 43.57342973641594, 'IoU-floor-wood': 62.108382721837806, 'IoU-flower': 43.802974677679494, 'IoU-fruit': 40.47985678938743, 'IoU-gravel': 27.074402010828802, 'IoU-house': 23.69937979486386, 'IoU-light': 39.62509573083409, 'IoU-mirror-stuff': 52.02348177622822, 'IoU-net': 47.64103927606409, 'IoU-pillow': 13.719239788750787, 'IoU-platform': 29.334232793066327, 'IoU-playingfield': 70.07934484320893, 'IoU-railroad': 61.59924317279841, 'IoU-river': 47.64837547830626, 'IoU-road': 66.16325086575353, 'IoU-roof': 15.425663699396939, 'IoU-sand': 62.37559625538217, 'IoU-sea': 85.06273788947657, 'IoU-shelf': 36.6109371696959, 'IoU-snow': 89.18522634481275, 'IoU-stairs': 28.823493229088786, 'IoU-tent': 9.02984129237303, 'IoU-towel': 33.558617858404396, 'IoU-wall-brick': 47.283725825748725, 'IoU-wall-stone': 28.610424755853632, 'IoU-wall-tile': 65.75969369450421, 'IoU-wall-wood': 39.76401520412018, 'IoU-water-other': 20.754205045471195, 'IoU-window-blind': 48.23102620274273, 'IoU-window-other': 47.22596126424644, 'IoU-tree-merged': 81.08013112577393, 'IoU-fence-merged': 49.965697091330505, 'IoU-ceiling-merged': 66.18305178276319, 'IoU-sky-other-merged': 92.92491365980538, 'IoU-cabinet-merged': 59.893563142907155, 'IoU-table-merged': 40.10929144773966, 'IoU-floor-other-merged': 50.14823758047432, 'IoU-pavement-merged': 54.48722246549157, 'IoU-mountain-merged': 54.69484468520579, 'IoU-grass-merged': 71.93666530821011, 'IoU-dirt-merged': 44.46799511532821, 'IoU-paper-merged': 33.728909199680636, 'IoU-food-other-merged': 37.559683357495224, 'IoU-building-other-merged': 55.9391788623459, 'IoU-rock-merged': 61.16800215723887, 'IoU-wall-other-merged': 64.80435586918803, 'IoU-rug-merged': 63.92360856971337, 'mACC': 72.81706461401386, 'pACC': 80.41198079093404, 'ACC-person': 92.55758100074769, 'ACC-bicycle': 85.10815862110736, 'ACC-car': 83.96189945430153, 'ACC-motorcycle': 90.84494987766197, 'ACC-airplane': 87.72966792326207, 'ACC-bus': 91.45649167443581, 'ACC-train': 94.7227218099018, 'ACC-truck': 75.85552558331602, 'ACC-boat': 78.91126637008483, 'ACC-traffic light': 89.03363196296532, 'ACC-fire hydrant': 93.24514170299804, 'ACC-stop sign': 88.47463441491657, 'ACC-parking meter': 92.0250590642767, 'ACC-bench': 68.4622638443116, 'ACC-bird': 79.54848449831373, 'ACC-cat': 94.2158075875212, 'ACC-dog': 81.92481113241861, 'ACC-horse': 92.35133730374682, 'ACC-sheep': 93.2736436397228, 'ACC-cow': 85.41260644467565, 'ACC-elephant': 93.48447166073419, 'ACC-bear': 88.5791874814137, 'ACC-zebra': 95.06399166717743, 'ACC-giraffe': 92.58140287635698, 'ACC-backpack': 53.83037834900658, 'ACC-umbrella': 83.8024932101597, 'ACC-handbag': 51.35230410158247, 'ACC-tie': 80.05847657313059, 'ACC-suitcase': 91.62235487381072, 'ACC-frisbee': 93.96436363636363, 'ACC-skis': 69.25420021329009, 'ACC-snowboard': 78.35566113982023, 'ACC-sports ball': 82.29706122533362, 'ACC-kite': 73.72797313861852, 'ACC-baseball bat': 82.52440641647124, 'ACC-baseball glove': 88.07634940878926, 'ACC-skateboard': 65.15231764299276, 'ACC-surfboard': 83.2736770561823, 'ACC-tennis racket': 88.85282765059304, 'ACC-bottle': 81.00465241060465, 'ACC-wine glass': 83.99475984116334, 'ACC-cup': 83.38212243138989, 'ACC-fork': 66.47564159842686, 'ACC-knife': 58.36044551916172, 'ACC-spoon': 72.28839648659869, 'ACC-bowl': 72.0150841400789, 'ACC-banana': 91.31849123074774, 'ACC-apple': 68.17844678322763, 'ACC-sandwich': 79.07234446731043, 'ACC-orange': 90.17419564379765, 'ACC-broccoli': 77.91923974376631, 'ACC-carrot': 72.61484587518356, 'ACC-hot dog': 67.04082944408857, 'ACC-pizza': 94.26657916332607, 'ACC-donut': 81.59318031079967, 'ACC-cake': 76.63037084482556, 'ACC-chair': 70.52930512847482, 'ACC-couch': 77.65436529925907, 'ACC-potted plant': 53.18581372578952, 'ACC-bed': 78.97480711851775, 'ACC-dining table': 74.8426823379687, 'ACC-toilet': 90.3434007912251, 'ACC-tv': 87.14318203227222, 'ACC-laptop': 88.18455948474137, 'ACC-mouse': 85.20935467463178, 'ACC-remote': 71.92684266274856, 'ACC-keyboard': 72.9130355801474, 'ACC-cell phone': 75.04157821505846, 'ACC-microwave': 77.53239230615449, 'ACC-oven': 81.40758572512678, 'ACC-toaster': 69.87983960366229, 'ACC-sink': 82.1697526846132, 'ACC-refrigerator': 91.61476921992785, 'ACC-book': 62.58857274943262, 'ACC-clock': 76.35983704139734, 'ACC-vase': 78.38227032449568, 'ACC-scissors': 59.51367655565656, 'ACC-teddy bear': 88.54182418121884, 'ACC-hair drier': 46.40366606893804, 'ACC-toothbrush': 80.81393328700487, 'ACC-banner': 67.96096474592579, 'ACC-blanket': 12.897399725174205, 'ACC-bridge': 50.26112826711905, 'ACC-cardboard': 55.48806165479133, 'ACC-counter': 47.93595409979193, 'ACC-curtain': 74.96795803995884, 'ACC-door-stuff': 63.58162183938304, 'ACC-floor-wood': 82.61618182726949, 'ACC-flower': 66.69439932517756, 'ACC-fruit': 58.2555677030728, 'ACC-gravel': 34.95873249483955, 'ACC-house': 28.389789079528516, 'ACC-light': 55.21032710638135, 'ACC-mirror-stuff': 61.68152643673268, 'ACC-net': 60.48114079720121, 'ACC-pillow': 26.34816377282579, 'ACC-platform': 47.8562560058792, 'ACC-playingfield': 88.546686012068, 'ACC-railroad': 76.9520629321547, 'ACC-river': 71.68768373346862, 'ACC-road': 84.58865581187503, 'ACC-roof': 21.715662905874254, 'ACC-sand': 71.23328954346262, 'ACC-sea': 90.246398130745, 'ACC-shelf': 56.35064061943537, 'ACC-snow': 95.39921306114174, 'ACC-stairs': 48.81591729672915, 'ACC-tent': 11.439666096499115, 'ACC-towel': 41.762843252474596, 'ACC-wall-brick': 64.41926098558586, 'ACC-wall-stone': 37.4503562388451, 'ACC-wall-tile': 79.97004936401181, 'ACC-wall-wood': 56.71430411347321, 'ACC-water-other': 34.279999237019645, 'ACC-window-blind': 56.34600190684712, 'ACC-window-other': 66.5352036797433, 'ACC-tree-merged': 89.03230315860725, 'ACC-fence-merged': 72.31029298819547, 'ACC-ceiling-merged': 79.06591618810292, 'ACC-sky-other-merged': 96.66548286304214, 'ACC-cabinet-merged': 76.3171034408876, 'ACC-table-merged': 54.30745700849708, 'ACC-floor-other-merged': 61.98645280866412, 'ACC-pavement-merged': 67.53961590392018, 'ACC-mountain-merged': 64.131030162882, 'ACC-grass-merged': 83.57128195399012, 'ACC-dirt-merged': 67.7546312768673, 'ACC-paper-merged': 45.306624688291244, 'ACC-food-other-merged': 51.3000503979275, 'ACC-building-other-merged': 70.67123039540378, 'ACC-rock-merged': 81.17717560378568, 'ACC-wall-other-merged': 82.00260888263375, 'ACC-rug-merged': 79.03501313416497})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1554 s/iter. Inference: 0.5806 s/iter. Eval: 0.0000 s/iter. Total: 0.7361 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1572 s/iter. Inference: 0.5224 s/iter. Eval: 0.0000 s/iter. Total: 0.6797 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1696 s/iter. Inference: 0.6123 s/iter. Eval: 0.0000 s/iter. Total: 0.7821 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1715 s/iter. Inference: 0.7285 s/iter. Eval: 0.0000 s/iter. Total: 0.9003 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1685 s/iter. Inference: 0.6373 s/iter. Eval: 0.0000 s/iter. Total: 0.8060 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1672 s/iter. Inference: 0.6718 s/iter. Eval: 0.0000 s/iter. Total: 0.8391 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1676 s/iter. Inference: 0.6896 s/iter. Eval: 0.0000 s/iter. Total: 0.8573 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5390693590869184, 'noc@0.8': 2.9862452443664034, 'noc@0.85': 3.6704711735440445, 'noc@0.9': 4.7126134035703835, 'miou@iter1': 0.8321298493744512}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1003 s/iter. Eval: 0.0008 s/iter. Total: 0.1029 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.15157318115234, 'precision@0.6': 66.6537094116211, 'precision@0.7': 61.717838287353516, 'precision@0.8': 50.835601806640625, 'precision@0.9': 25.4566650390625, 'cIoU': 56.19596862792969, 'mIoU': 61.61571502685547}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.8148896527274, 'SQ': 81.80141396857925, 'RQ': 60.001787379980634, 'PQ_th': 54.90191600624108, 'SQ_th': 82.5664268230922, 'RQ_th': 65.80535864545031, 'PQ_st': 42.13635930780105, 'SQ_st': 80.64667758440869, 'RQ_st': 51.241679809460294}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.687621560698744, 'AP50': 60.919112343821645, 'AP75': 40.79328307959882, 'APs': 18.896293324404255, 'APm': 41.90879199551109, 'APl': 60.312730260504864, 'AP-person': 43.34919657360775, 'AP-bicycle': 18.390583535724527, 'AP-car': 37.56399191456518, 'AP-motorcycle': 34.811071430958776, 'AP-airplane': 56.98657451401081, 'AP-bus': 64.33956125786254, 'AP-train': 68.95382072394598, 'AP-truck': 35.055575076574186, 'AP-boat': 22.704786710054744, 'AP-traffic light': 24.558883097940466, 'AP-fire hydrant': 63.4678345410878, 'AP-stop sign': 63.36641316314628, 'AP-parking meter': 43.19900384122784, 'AP-bench': 20.6527941674733, 'AP-bird': 29.29991671691824, 'AP-cat': 74.13515475884589, 'AP-dog': 65.89979766059713, 'AP-horse': 45.84894591168712, 'AP-sheep': 45.78457987184015, 'AP-cow': 49.561914370782894, 'AP-elephant': 60.96538636851315, 'AP-bear': 77.59925031682118, 'AP-zebra': 60.70105389325583, 'AP-giraffe': 56.65312007082283, 'AP-backpack': 15.904655369669394, 'AP-umbrella': 48.439624874102456, 'AP-handbag': 14.84994878940158, 'AP-tie': 31.427164832653894, 'AP-suitcase': 40.81418444498324, 'AP-frisbee': 67.68113142670475, 'AP-skis': 4.7864768462630725, 'AP-snowboard': 23.76375870221608, 'AP-sports ball': 47.120895002802996, 'AP-kite': 31.601788255622044, 'AP-baseball bat': 26.872978679593633, 'AP-baseball glove': 42.7663057940574, 'AP-skateboard': 35.03950522365959, 'AP-surfboard': 35.489584666182935, 'AP-tennis racket': 56.32800573981566, 'AP-bottle': 33.54699591348844, 'AP-wine glass': 27.23299769189062, 'AP-cup': 39.806123748138155, 'AP-fork': 14.941290868172377, 'AP-knife': 11.576321141971952, 'AP-spoon': 14.075188707706026, 'AP-bowl': 31.531304634882062, 'AP-banana': 21.51885239088197, 'AP-apple': 19.331619869634984, 'AP-sandwich': 42.555015146173886, 'AP-orange': 28.321549598288843, 'AP-broccoli': 21.07175299616661, 'AP-carrot': 19.061036085081515, 'AP-hot dog': 21.608086236921586, 'AP-pizza': 50.77545015931312, 'AP-donut': 46.20234747756524, 'AP-cake': 43.04934736319554, 'AP-chair': 20.35240971165378, 'AP-couch': 41.28394315613659, 'AP-potted plant': 17.018647169033457, 'AP-bed': 40.24692501503828, 'AP-dining table': 13.181945372613797, 'AP-toilet': 67.48651911684965, 'AP-tv': 62.186109595171516, 'AP-laptop': 63.47273615632562, 'AP-mouse': 56.91309743632661, 'AP-remote': 30.804530129987363, 'AP-keyboard': 48.65925118992283, 'AP-cell phone': 37.186636441608165, 'AP-microwave': 57.58687275860868, 'AP-oven': 33.51085771133489, 'AP-toaster': 38.78689297501178, 'AP-sink': 36.35600301706394, 'AP-refrigerator': 58.317040585791545, 'AP-book': 7.9044346857298065, 'AP-clock': 51.2097542851824, 'AP-vase': 33.70222435800174, 'AP-scissors': 25.41169013502244, 'AP-teddy bear': 51.284098971864566, 'AP-hair drier': 11.421521959888297, 'AP-toothbrush': 17.78508375626532}), ('sem_seg', {'mIoU': 60.97032599875675, 'fwIoU': 69.09234925057308, 'IoU-person': 87.34074829742386, 'IoU-bicycle': 75.60158811961702, 'IoU-car': 69.04339744263015, 'IoU-motorcycle': 85.5798667278464, 'IoU-airplane': 76.99335171433935, 'IoU-bus': 86.7237104153108, 'IoU-train': 89.11714173437808, 'IoU-truck': 64.24409431014838, 'IoU-boat': 68.40699118529065, 'IoU-traffic light': 76.41395612494784, 'IoU-fire hydrant': 87.9850032888408, 'IoU-stop sign': 85.93820545528773, 'IoU-parking meter': 88.11424756534602, 'IoU-bench': 52.09889666055092, 'IoU-bird': 75.69427298916256, 'IoU-cat': 84.37743250565673, 'IoU-dog': 78.86245615034042, 'IoU-horse': 86.21371704073862, 'IoU-sheep': 89.59834858310087, 'IoU-cow': 80.32495753296583, 'IoU-elephant': 90.80969241743226, 'IoU-bear': 86.3623044145085, 'IoU-zebra': 92.43372613364087, 'IoU-giraffe': 88.2126117120874, 'IoU-backpack': 36.67856023170058, 'IoU-umbrella': 75.17511079445384, 'IoU-handbag': 35.554676427882065, 'IoU-tie': 71.06470691061648, 'IoU-suitcase': 79.38601657575394, 'IoU-frisbee': 82.71987502480937, 'IoU-skis': 52.43269178681154, 'IoU-snowboard': 69.96640919548274, 'IoU-sports ball': 67.0687788789999, 'IoU-kite': 65.78055892719394, 'IoU-baseball bat': 60.222640797613934, 'IoU-baseball glove': 76.33056282354704, 'IoU-skateboard': 59.82399434539848, 'IoU-surfboard': 74.97506046316977, 'IoU-tennis racket': 83.08462474995378, 'IoU-bottle': 65.51423585984865, 'IoU-wine glass': 74.0637498291259, 'IoU-cup': 65.16907409629205, 'IoU-fork': 54.27033125972594, 'IoU-knife': 49.79236120553974, 'IoU-spoon': 46.78317539484622, 'IoU-bowl': 56.21874560368452, 'IoU-banana': 84.76889595842837, 'IoU-apple': 56.85372686952421, 'IoU-sandwich': 66.10322545359969, 'IoU-orange': 81.13237615237067, 'IoU-broccoli': 68.20531792195034, 'IoU-carrot': 62.22551208953483, 'IoU-hot dog': 59.49106007801587, 'IoU-pizza': 86.2102169936749, 'IoU-donut': 63.8558044782395, 'IoU-cake': 69.65784120479914, 'IoU-chair': 54.25316844982603, 'IoU-couch': 64.82615529625672, 'IoU-potted plant': 34.61738650847188, 'IoU-bed': 66.53006342149558, 'IoU-dining table': 51.2550438783408, 'IoU-toilet': 85.7188270357288, 'IoU-tv': 76.05552679551867, 'IoU-laptop': 74.69705836423663, 'IoU-mouse': 70.9282213894494, 'IoU-remote': 50.88413840328192, 'IoU-keyboard': 67.8034972378213, 'IoU-cell phone': 68.50216515616309, 'IoU-microwave': 70.75565258069545, 'IoU-oven': 64.73587913613027, 'IoU-toaster': 56.94966484123177, 'IoU-sink': 67.87974512844717, 'IoU-refrigerator': 76.91511773270021, 'IoU-book': 48.33561882814973, 'IoU-clock': 72.46643469515513, 'IoU-vase': 67.77995516990575, 'IoU-scissors': 55.51533527546966, 'IoU-teddy bear': 81.07837873588709, 'IoU-hair drier': 43.29686089127317, 'IoU-toothbrush': 60.47426205318547, 'IoU-banner': 33.146541382919544, 'IoU-blanket': 10.202901021078052, 'IoU-bridge': 37.611606877734495, 'IoU-cardboard': 45.421231904897034, 'IoU-counter': 29.88626600112414, 'IoU-curtain': 64.17177012986016, 'IoU-door-stuff': 43.57342973641594, 'IoU-floor-wood': 62.108382721837806, 'IoU-flower': 43.802974677679494, 'IoU-fruit': 40.47985678938743, 'IoU-gravel': 27.074402010828802, 'IoU-house': 23.69937979486386, 'IoU-light': 39.62509573083409, 'IoU-mirror-stuff': 52.02348177622822, 'IoU-net': 47.64103927606409, 'IoU-pillow': 13.719239788750787, 'IoU-platform': 29.334232793066327, 'IoU-playingfield': 70.07934484320893, 'IoU-railroad': 61.59924317279841, 'IoU-river': 47.64837547830626, 'IoU-road': 66.16325086575353, 'IoU-roof': 15.425663699396939, 'IoU-sand': 62.37559625538217, 'IoU-sea': 85.06273788947657, 'IoU-shelf': 36.6109371696959, 'IoU-snow': 89.18522634481275, 'IoU-stairs': 28.823493229088786, 'IoU-tent': 9.02984129237303, 'IoU-towel': 33.558617858404396, 'IoU-wall-brick': 47.283725825748725, 'IoU-wall-stone': 28.610424755853632, 'IoU-wall-tile': 65.75969369450421, 'IoU-wall-wood': 39.76401520412018, 'IoU-water-other': 20.754205045471195, 'IoU-window-blind': 48.23102620274273, 'IoU-window-other': 47.22596126424644, 'IoU-tree-merged': 81.08013112577393, 'IoU-fence-merged': 49.965697091330505, 'IoU-ceiling-merged': 66.18305178276319, 'IoU-sky-other-merged': 92.92491365980538, 'IoU-cabinet-merged': 59.893563142907155, 'IoU-table-merged': 40.10929144773966, 'IoU-floor-other-merged': 50.14823758047432, 'IoU-pavement-merged': 54.48722246549157, 'IoU-mountain-merged': 54.69484468520579, 'IoU-grass-merged': 71.93666530821011, 'IoU-dirt-merged': 44.46799511532821, 'IoU-paper-merged': 33.728909199680636, 'IoU-food-other-merged': 37.559683357495224, 'IoU-building-other-merged': 55.9391788623459, 'IoU-rock-merged': 61.16800215723887, 'IoU-wall-other-merged': 64.80435586918803, 'IoU-rug-merged': 63.92360856971337, 'mACC': 72.81706461401386, 'pACC': 80.41198079093404, 'ACC-person': 92.55758100074769, 'ACC-bicycle': 85.10815862110736, 'ACC-car': 83.96189945430153, 'ACC-motorcycle': 90.84494987766197, 'ACC-airplane': 87.72966792326207, 'ACC-bus': 91.45649167443581, 'ACC-train': 94.7227218099018, 'ACC-truck': 75.85552558331602, 'ACC-boat': 78.91126637008483, 'ACC-traffic light': 89.03363196296532, 'ACC-fire hydrant': 93.24514170299804, 'ACC-stop sign': 88.47463441491657, 'ACC-parking meter': 92.0250590642767, 'ACC-bench': 68.4622638443116, 'ACC-bird': 79.54848449831373, 'ACC-cat': 94.2158075875212, 'ACC-dog': 81.92481113241861, 'ACC-horse': 92.35133730374682, 'ACC-sheep': 93.2736436397228, 'ACC-cow': 85.41260644467565, 'ACC-elephant': 93.48447166073419, 'ACC-bear': 88.5791874814137, 'ACC-zebra': 95.06399166717743, 'ACC-giraffe': 92.58140287635698, 'ACC-backpack': 53.83037834900658, 'ACC-umbrella': 83.8024932101597, 'ACC-handbag': 51.35230410158247, 'ACC-tie': 80.05847657313059, 'ACC-suitcase': 91.62235487381072, 'ACC-frisbee': 93.96436363636363, 'ACC-skis': 69.25420021329009, 'ACC-snowboard': 78.35566113982023, 'ACC-sports ball': 82.29706122533362, 'ACC-kite': 73.72797313861852, 'ACC-baseball bat': 82.52440641647124, 'ACC-baseball glove': 88.07634940878926, 'ACC-skateboard': 65.15231764299276, 'ACC-surfboard': 83.2736770561823, 'ACC-tennis racket': 88.85282765059304, 'ACC-bottle': 81.00465241060465, 'ACC-wine glass': 83.99475984116334, 'ACC-cup': 83.38212243138989, 'ACC-fork': 66.47564159842686, 'ACC-knife': 58.36044551916172, 'ACC-spoon': 72.28839648659869, 'ACC-bowl': 72.0150841400789, 'ACC-banana': 91.31849123074774, 'ACC-apple': 68.17844678322763, 'ACC-sandwich': 79.07234446731043, 'ACC-orange': 90.17419564379765, 'ACC-broccoli': 77.91923974376631, 'ACC-carrot': 72.61484587518356, 'ACC-hot dog': 67.04082944408857, 'ACC-pizza': 94.26657916332607, 'ACC-donut': 81.59318031079967, 'ACC-cake': 76.63037084482556, 'ACC-chair': 70.52930512847482, 'ACC-couch': 77.65436529925907, 'ACC-potted plant': 53.18581372578952, 'ACC-bed': 78.97480711851775, 'ACC-dining table': 74.8426823379687, 'ACC-toilet': 90.3434007912251, 'ACC-tv': 87.14318203227222, 'ACC-laptop': 88.18455948474137, 'ACC-mouse': 85.20935467463178, 'ACC-remote': 71.92684266274856, 'ACC-keyboard': 72.9130355801474, 'ACC-cell phone': 75.04157821505846, 'ACC-microwave': 77.53239230615449, 'ACC-oven': 81.40758572512678, 'ACC-toaster': 69.87983960366229, 'ACC-sink': 82.1697526846132, 'ACC-refrigerator': 91.61476921992785, 'ACC-book': 62.58857274943262, 'ACC-clock': 76.35983704139734, 'ACC-vase': 78.38227032449568, 'ACC-scissors': 59.51367655565656, 'ACC-teddy bear': 88.54182418121884, 'ACC-hair drier': 46.40366606893804, 'ACC-toothbrush': 80.81393328700487, 'ACC-banner': 67.96096474592579, 'ACC-blanket': 12.897399725174205, 'ACC-bridge': 50.26112826711905, 'ACC-cardboard': 55.48806165479133, 'ACC-counter': 47.93595409979193, 'ACC-curtain': 74.96795803995884, 'ACC-door-stuff': 63.58162183938304, 'ACC-floor-wood': 82.61618182726949, 'ACC-flower': 66.69439932517756, 'ACC-fruit': 58.2555677030728, 'ACC-gravel': 34.95873249483955, 'ACC-house': 28.389789079528516, 'ACC-light': 55.21032710638135, 'ACC-mirror-stuff': 61.68152643673268, 'ACC-net': 60.48114079720121, 'ACC-pillow': 26.34816377282579, 'ACC-platform': 47.8562560058792, 'ACC-playingfield': 88.546686012068, 'ACC-railroad': 76.9520629321547, 'ACC-river': 71.68768373346862, 'ACC-road': 84.58865581187503, 'ACC-roof': 21.715662905874254, 'ACC-sand': 71.23328954346262, 'ACC-sea': 90.246398130745, 'ACC-shelf': 56.35064061943537, 'ACC-snow': 95.39921306114174, 'ACC-stairs': 48.81591729672915, 'ACC-tent': 11.439666096499115, 'ACC-towel': 41.762843252474596, 'ACC-wall-brick': 64.41926098558586, 'ACC-wall-stone': 37.4503562388451, 'ACC-wall-tile': 79.97004936401181, 'ACC-wall-wood': 56.71430411347321, 'ACC-water-other': 34.279999237019645, 'ACC-window-blind': 56.34600190684712, 'ACC-window-other': 66.5352036797433, 'ACC-tree-merged': 89.03230315860725, 'ACC-fence-merged': 72.31029298819547, 'ACC-ceiling-merged': 79.06591618810292, 'ACC-sky-other-merged': 96.66548286304214, 'ACC-cabinet-merged': 76.3171034408876, 'ACC-table-merged': 54.30745700849708, 'ACC-floor-other-merged': 61.98645280866412, 'ACC-pavement-merged': 67.53961590392018, 'ACC-mountain-merged': 64.131030162882, 'ACC-grass-merged': 83.57128195399012, 'ACC-dirt-merged': 67.7546312768673, 'ACC-paper-merged': 45.306624688291244, 'ACC-food-other-merged': 51.3000503979275, 'ACC-building-other-merged': 70.67123039540378, 'ACC-rock-merged': 81.17717560378568, 'ACC-wall-other-merged': 82.00260888263375, 'ACC-rug-merged': 79.03501313416497})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5390693590869184, 'noc@0.8': 2.9862452443664034, 'noc@0.85': 3.6704711735440445, 'noc@0.9': 4.7126134035703835, 'miou@iter1': 0.8321298493744512}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.15157318115234, 'precision@0.6': 66.6537094116211, 'precision@0.7': 61.717838287353516, 'precision@0.8': 50.835601806640625, 'precision@0.9': 25.4566650390625, 'cIoU': 56.19596862792969, 'mIoU': 61.61571502685547}}}
INFO:trainer.default_trainer:This epoch takes 1:30:11.050106
INFO:trainer.default_trainer:PROGRESS: 16.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 8 training.
INFO:trainer.default_trainer:epochs[     8] optim steps[14700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.46841/0.91562, loss_mask_bce_0: 0.32421/0.33654, loss_mask_dice_0: 1.77792/1.16606, loss_spatial_bce_0: 0.04529/0.09366, loss_spatial_dice_0: 0.20496/0.22397, loss_spatial_ce_0: 0.00826/0.08800, loss_grounding_bce_0: 0.05086/0.08618, loss_grounding_dice_0: 0.12242/0.17847, loss_grounding_ce_0: 0.05354/0.27775, loss_mask_ce_1: 0.50520/0.91634, loss_mask_bce_1: 0.33193/0.33713, loss_mask_dice_1: 1.66685/1.17355, loss_spatial_bce_1: 0.04768/0.09463, loss_spatial_dice_1: 0.22481/0.22839, loss_spatial_ce_1: 0.00353/0.09384, loss_grounding_bce_1: 0.05499/0.08627, loss_grounding_dice_1: 0.12511/0.17895, loss_grounding_ce_1: 0.05511/0.27990, loss_mask_ce_2: 0.55351/0.92312, loss_mask_bce_2: 0.33016/0.33741, loss_mask_dice_2: 1.60950/1.17270, loss_spatial_bce_2: 0.04823/0.09444, loss_spatial_dice_2: 0.21677/0.22923, loss_spatial_ce_2: 0.00713/0.09747, loss_grounding_bce_2: 0.05368/0.08629, loss_grounding_dice_2: 0.13073/0.17861, loss_grounding_ce_2: 0.06237/0.28279, loss_mask_ce_3: 0.56996/0.92968, loss_mask_bce_3: 0.30396/0.33836, loss_mask_dice_3: 1.54573/1.17000, loss_spatial_bce_3: 0.04990/0.09544, loss_spatial_dice_3: 0.20603/0.23031, loss_spatial_ce_3: 0.01522/0.10215, loss_grounding_bce_3: 0.05039/0.08643, loss_grounding_dice_3: 0.11794/0.17841, loss_grounding_ce_3: 0.06278/0.28369, loss_mask_ce_4: 0.63202/0.92840, loss_mask_bce_4: 0.31304/0.33967, loss_mask_dice_4: 1.77356/1.19089, loss_spatial_bce_4: 0.04786/0.09909, loss_spatial_dice_4: 0.24217/0.23847, loss_spatial_ce_4: 0.01507/0.11856, loss_grounding_bce_4: 0.04838/0.08697, loss_grounding_dice_4: 0.12797/0.18099, loss_grounding_ce_4: 0.07187/0.28637, loss_mask_ce_5: 0.55012/0.94258, loss_mask_bce_5: 0.35126/0.34199, loss_mask_dice_5: 1.86000/1.19632, loss_spatial_bce_5: 0.05069/0.09992, loss_spatial_dice_5: 0.22910/0.24138, loss_spatial_ce_5: 0.02352/0.13211, loss_grounding_bce_5: 0.07058/0.08744, loss_grounding_dice_5: 0.16731/0.18230, loss_grounding_ce_5: 0.07887/0.29835, loss_mask_ce_6: 0.69561/0.97882, loss_mask_bce_6: 0.34573/0.34454, loss_mask_dice_6: 2.01410/1.19943, loss_spatial_bce_6: 0.05840/0.10541, loss_spatial_dice_6: 0.23435/0.24411, loss_spatial_ce_6: 0.06266/0.15498, loss_grounding_bce_6: 0.06237/0.08828, loss_grounding_dice_6: 0.14936/0.18241, loss_grounding_ce_6: 0.05328/0.31853, loss_mask_ce_7: 0.76313/1.02203, loss_mask_bce_7: 0.33740/0.35236, loss_mask_dice_7: 2.02009/1.25440, loss_spatial_bce_7: 0.05287/0.11445, loss_spatial_dice_7: 0.23835/0.27106, loss_spatial_ce_7: 0.04784/0.19426, loss_grounding_bce_7: 0.06377/0.09010, loss_grounding_dice_7: 0.16949/0.18950, loss_grounding_ce_7: 0.11541/0.35450, loss_mask_ce_8: 0.80108/1.13408, loss_mask_bce_8: 0.31427/0.36584, loss_mask_dice_8: 1.92581/1.32956, loss_spatial_bce_8: 0.06068/0.13575, loss_spatial_dice_8: 0.27856/0.31187, loss_spatial_ce_8: 0.11587/0.25011, loss_grounding_bce_8: 0.09470/0.09345, loss_grounding_dice_8: 0.19539/0.20075, loss_grounding_ce_8: 0.25621/0.42689, loss_mask_ce_9: 4.50828/3.69938, loss_mask_bce_9: 0.37241/0.39285, loss_mask_dice_9: 2.53134/1.90639, loss_spatial_bce_9: 0.31209/0.33759, loss_spatial_dice_9: 0.93605/0.82588, loss_spatial_ce_9: 1.33184/1.52879, loss_grounding_bce_9: 0.09430/0.10501, loss_grounding_dice_9: 0.22682/0.28067, loss_grounding_ce_9: 0.73542/0.71080] items per batch[64] items per second[0.13] total items[940800] mini batches[ 14700] memory[7341] epoch remaining[1:21:01]
INFO:trainer.default_trainer:epochs[     8] optim steps[14800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54382/0.91511, loss_mask_bce_0: 0.61301/0.33641, loss_mask_dice_0: 0.66946/1.16525, loss_spatial_bce_0: 0.17844/0.09362, loss_spatial_dice_0: 0.22851/0.22384, loss_spatial_ce_0: 0.06266/0.08779, loss_grounding_bce_0: 0.25646/0.08618, loss_grounding_dice_0: 0.27547/0.17837, loss_grounding_ce_0: 0.16919/0.27765, loss_mask_ce_1: 0.60033/0.91591, loss_mask_bce_1: 0.60800/0.33700, loss_mask_dice_1: 0.66137/1.17270, loss_spatial_bce_1: 0.17748/0.09458, loss_spatial_dice_1: 0.22050/0.22828, loss_spatial_ce_1: 0.05240/0.09363, loss_grounding_bce_1: 0.25946/0.08627, loss_grounding_dice_1: 0.28817/0.17887, loss_grounding_ce_1: 0.18602/0.27981, loss_mask_ce_2: 0.57819/0.92265, loss_mask_bce_2: 0.61938/0.33729, loss_mask_dice_2: 0.65604/1.17189, loss_spatial_bce_2: 0.18773/0.09441, loss_spatial_dice_2: 0.21614/0.22911, loss_spatial_ce_2: 0.05134/0.09728, loss_grounding_bce_2: 0.26204/0.08628, loss_grounding_dice_2: 0.36964/0.17849, loss_grounding_ce_2: 0.05065/0.28272, loss_mask_ce_3: 0.60226/0.92923, loss_mask_bce_3: 0.60933/0.33826, loss_mask_dice_3: 0.61827/1.16918, loss_spatial_bce_3: 0.18973/0.09541, loss_spatial_dice_3: 0.21790/0.23018, loss_spatial_ce_3: 0.05445/0.10188, loss_grounding_bce_3: 0.25706/0.08642, loss_grounding_dice_3: 0.27961/0.17829, loss_grounding_ce_3: 0.17364/0.28367, loss_mask_ce_4: 0.60728/0.92788, loss_mask_bce_4: 0.62624/0.33960, loss_mask_dice_4: 0.64728/1.19026, loss_spatial_bce_4: 0.17962/0.09906, loss_spatial_dice_4: 0.20180/0.23835, loss_spatial_ce_4: 0.04819/0.11841, loss_grounding_bce_4: 0.27611/0.08695, loss_grounding_dice_4: 0.36753/0.18089, loss_grounding_ce_4: 0.04145/0.28629, loss_mask_ce_5: 0.64976/0.94200, loss_mask_bce_5: 0.62819/0.34190, loss_mask_dice_5: 0.67769/1.19565, loss_spatial_bce_5: 0.18814/0.09989, loss_spatial_dice_5: 0.22795/0.24128, loss_spatial_ce_5: 0.09752/0.13184, loss_grounding_bce_5: 0.26411/0.08744, loss_grounding_dice_5: 0.32069/0.18218, loss_grounding_ce_5: 0.16585/0.29822, loss_mask_ce_6: 0.66488/0.97821, loss_mask_bce_6: 0.63269/0.34444, loss_mask_dice_6: 0.67693/1.19863, loss_spatial_bce_6: 0.18565/0.10539, loss_spatial_dice_6: 0.23138/0.24398, loss_spatial_ce_6: 0.15486/0.15480, loss_grounding_bce_6: 0.27334/0.08828, loss_grounding_dice_6: 0.32061/0.18233, loss_grounding_ce_6: 0.16780/0.31828, loss_mask_ce_7: 0.86868/1.02127, loss_mask_bce_7: 0.64928/0.35225, loss_mask_dice_7: 0.74423/1.25375, loss_spatial_bce_7: 0.19291/0.11442, loss_spatial_dice_7: 0.24842/0.27096, loss_spatial_ce_7: 0.06367/0.19406, loss_grounding_bce_7: 0.27265/0.09009, loss_grounding_dice_7: 0.33034/0.18940, loss_grounding_ce_7: 0.23712/0.35410, loss_mask_ce_8: 0.84380/1.13355, loss_mask_bce_8: 0.61259/0.36569, loss_mask_dice_8: 0.77843/1.32874, loss_spatial_bce_8: 0.22796/0.13571, loss_spatial_dice_8: 0.28652/0.31169, loss_spatial_ce_8: 0.19071/0.24984, loss_grounding_bce_8: 0.26430/0.09342, loss_grounding_dice_8: 0.36067/0.20064, loss_grounding_ce_8: 0.09628/0.42662, loss_mask_ce_9: 3.25206/3.69925, loss_mask_bce_9: 0.63403/0.39265, loss_mask_dice_9: 0.91963/1.90530, loss_spatial_bce_9: 0.45587/0.33759, loss_spatial_dice_9: 0.72692/0.82581, loss_spatial_ce_9: 1.09513/1.52865, loss_grounding_bce_9: 0.26258/0.10500, loss_grounding_dice_9: 0.38855/0.28058, loss_grounding_ce_9: 0.31697/0.71069] items per batch[64] items per second[0.23] total items[947200] mini batches[ 14800] memory[7341] epoch remaining[1:16:40]
INFO:trainer.default_trainer:epochs[     8] optim steps[14900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77754/0.91554, loss_mask_bce_0: 0.35795/0.33640, loss_mask_dice_0: 0.64882/1.16618, loss_spatial_bce_0: 0.11332/0.09355, loss_spatial_dice_0: 0.19136/0.22385, loss_spatial_ce_0: 0.02512/0.08769, loss_grounding_bce_0: 0.15806/0.08615, loss_grounding_dice_0: 0.27205/0.17845, loss_grounding_ce_0: 0.31842/0.27765, loss_mask_ce_1: 0.69402/0.91644, loss_mask_bce_1: 0.35329/0.33702, loss_mask_dice_1: 0.68372/1.17370, loss_spatial_bce_1: 0.10893/0.09451, loss_spatial_dice_1: 0.21676/0.22828, loss_spatial_ce_1: 0.02699/0.09347, loss_grounding_bce_1: 0.16158/0.08623, loss_grounding_dice_1: 0.26573/0.17888, loss_grounding_ce_1: 0.58494/0.27987, loss_mask_ce_2: 0.74427/0.92307, loss_mask_bce_2: 0.35248/0.33732, loss_mask_dice_2: 0.67448/1.17286, loss_spatial_bce_2: 0.11667/0.09435, loss_spatial_dice_2: 0.21916/0.22914, loss_spatial_ce_2: 0.02602/0.09712, loss_grounding_bce_2: 0.14591/0.08624, loss_grounding_dice_2: 0.15666/0.17851, loss_grounding_ce_2: 0.43511/0.28276, loss_mask_ce_3: 0.98995/0.92966, loss_mask_bce_3: 0.34993/0.33828, loss_mask_dice_3: 0.62767/1.17002, loss_spatial_bce_3: 0.12429/0.09534, loss_spatial_dice_3: 0.22155/0.23021, loss_spatial_ce_3: 0.02752/0.10176, loss_grounding_bce_3: 0.16707/0.08639, loss_grounding_dice_3: 0.35082/0.17836, loss_grounding_ce_3: 0.39641/0.28370, loss_mask_ce_4: 0.56622/0.92834, loss_mask_bce_4: 0.33300/0.33962, loss_mask_dice_4: 0.68453/1.19123, loss_spatial_bce_4: 0.12787/0.09900, loss_spatial_dice_4: 0.22418/0.23839, loss_spatial_ce_4: 0.03591/0.11832, loss_grounding_bce_4: 0.15015/0.08691, loss_grounding_dice_4: 0.13345/0.18095, loss_grounding_ce_4: 0.45615/0.28636, loss_mask_ce_5: 0.73230/0.94254, loss_mask_bce_5: 0.35394/0.34189, loss_mask_dice_5: 0.64636/1.19644, loss_spatial_bce_5: 0.11827/0.09983, loss_spatial_dice_5: 0.19997/0.24131, loss_spatial_ce_5: 0.05473/0.13166, loss_grounding_bce_5: 0.16894/0.08740, loss_grounding_dice_5: 0.26285/0.18224, loss_grounding_ce_5: 0.25204/0.29851, loss_mask_ce_6: 0.70568/0.97858, loss_mask_bce_6: 0.35968/0.34441, loss_mask_dice_6: 0.62376/1.19959, loss_spatial_bce_6: 0.11546/0.10535, loss_spatial_dice_6: 0.19959/0.24403, loss_spatial_ce_6: 0.07659/0.15466, loss_grounding_bce_6: 0.16554/0.08824, loss_grounding_dice_6: 0.16430/0.18240, loss_grounding_ce_6: 0.27167/0.31836, loss_mask_ce_7: 1.05454/1.02185, loss_mask_bce_7: 0.42076/0.35223, loss_mask_dice_7: 0.61023/1.25461, loss_spatial_bce_7: 0.12528/0.11435, loss_spatial_dice_7: 0.19538/0.27102, loss_spatial_ce_7: 0.12751/0.19400, loss_grounding_bce_7: 0.21867/0.09005, loss_grounding_dice_7: 0.15905/0.18941, loss_grounding_ce_7: 0.47666/0.35442, loss_mask_ce_8: 0.86401/1.13420, loss_mask_bce_8: 0.42542/0.36568, loss_mask_dice_8: 0.81492/1.32964, loss_spatial_bce_8: 0.15334/0.13563, loss_spatial_dice_8: 0.21663/0.31180, loss_spatial_ce_8: 0.27442/0.24993, loss_grounding_bce_8: 0.20630/0.09339, loss_grounding_dice_8: 0.21686/0.20073, loss_grounding_ce_8: 0.59395/0.42683, loss_mask_ce_9: 4.21568/3.70024, loss_mask_bce_9: 0.62723/0.39266, loss_mask_dice_9: 1.14737/1.90635, loss_spatial_bce_9: 0.46839/0.33739, loss_spatial_dice_9: 0.85927/0.82587, loss_spatial_ce_9: 1.32937/1.52875, loss_grounding_bce_9: 0.12155/0.10493, loss_grounding_dice_9: 0.17302/0.28063, loss_grounding_ce_9: 0.41645/0.71058] items per batch[64] items per second[0.23] total items[953600] mini batches[ 14900] memory[7341] epoch remaining[1:11:59]
INFO:trainer.default_trainer:epochs[     8] optim steps[15000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74082/0.91557, loss_mask_bce_0: 0.69634/0.33657, loss_mask_dice_0: 1.16824/1.16594, loss_spatial_bce_0: 0.22213/0.09356, loss_spatial_dice_0: 0.25273/0.22373, loss_spatial_ce_0: 0.16560/0.08749, loss_grounding_bce_0: 0.25648/0.08617, loss_grounding_dice_0: 0.28669/0.17840, loss_grounding_ce_0: 0.00514/0.27739, loss_mask_ce_1: 0.83117/0.91650, loss_mask_bce_1: 0.68662/0.33718, loss_mask_dice_1: 1.15206/1.17340, loss_spatial_bce_1: 0.22888/0.09452, loss_spatial_dice_1: 0.24300/0.22815, loss_spatial_ce_1: 0.14305/0.09327, loss_grounding_bce_1: 0.26029/0.08624, loss_grounding_dice_1: 0.29000/0.17880, loss_grounding_ce_1: 0.00480/0.27967, loss_mask_ce_2: 0.78109/0.92314, loss_mask_bce_2: 0.67881/0.33749, loss_mask_dice_2: 1.17533/1.17253, loss_spatial_bce_2: 0.22011/0.09437, loss_spatial_dice_2: 0.24928/0.22900, loss_spatial_ce_2: 0.15421/0.09696, loss_grounding_bce_2: 0.25932/0.08627, loss_grounding_dice_2: 0.28644/0.17844, loss_grounding_ce_2: 0.00517/0.28249, loss_mask_ce_3: 0.75030/0.92981, loss_mask_bce_3: 0.67862/0.33846, loss_mask_dice_3: 1.14529/1.16978, loss_spatial_bce_3: 0.21252/0.09535, loss_spatial_dice_3: 0.24515/0.23009, loss_spatial_ce_3: 0.20435/0.10156, loss_grounding_bce_3: 0.26154/0.08640, loss_grounding_dice_3: 0.28249/0.17830, loss_grounding_ce_3: 0.00572/0.28350, loss_mask_ce_4: 0.71396/0.92847, loss_mask_bce_4: 0.71894/0.33975, loss_mask_dice_4: 1.22145/1.19093, loss_spatial_bce_4: 0.22415/0.09902, loss_spatial_dice_4: 0.23479/0.23828, loss_spatial_ce_4: 0.12823/0.11813, loss_grounding_bce_4: 0.26593/0.08693, loss_grounding_dice_4: 0.28064/0.18090, loss_grounding_ce_4: 0.00503/0.28616, loss_mask_ce_5: 1.16759/0.94276, loss_mask_bce_5: 0.66711/0.34199, loss_mask_dice_5: 1.15312/1.19602, loss_spatial_bce_5: 0.23190/0.09985, loss_spatial_dice_5: 0.22314/0.24120, loss_spatial_ce_5: 0.20941/0.13145, loss_grounding_bce_5: 0.27957/0.08743, loss_grounding_dice_5: 0.29757/0.18216, loss_grounding_ce_5: 0.00759/0.29825, loss_mask_ce_6: 0.75969/0.97882, loss_mask_bce_6: 0.82899/0.34453, loss_mask_dice_6: 1.29197/1.19915, loss_spatial_bce_6: 0.28216/0.10537, loss_spatial_dice_6: 0.23497/0.24392, loss_spatial_ce_6: 0.21293/0.15445, loss_grounding_bce_6: 0.25641/0.08826, loss_grounding_dice_6: 0.28910/0.18234, loss_grounding_ce_6: 0.01048/0.31798, loss_mask_ce_7: 0.64518/1.02201, loss_mask_bce_7: 0.80238/0.35238, loss_mask_dice_7: 1.35411/1.25430, loss_spatial_bce_7: 0.41090/0.11437, loss_spatial_dice_7: 0.25544/0.27094, loss_spatial_ce_7: 0.20021/0.19379, loss_grounding_bce_7: 0.25271/0.09008, loss_grounding_dice_7: 0.28729/0.18937, loss_grounding_ce_7: 0.02246/0.35399, loss_mask_ce_8: 1.11336/1.13410, loss_mask_bce_8: 0.79749/0.36584, loss_mask_dice_8: 1.38687/1.32937, loss_spatial_bce_8: 0.42534/0.13564, loss_spatial_dice_8: 0.29097/0.31169, loss_spatial_ce_8: 0.44047/0.24975, loss_grounding_bce_8: 0.27146/0.09343, loss_grounding_dice_8: 0.27460/0.20070, loss_grounding_ce_8: 0.03650/0.42617, loss_mask_ce_9: 3.03867/3.69934, loss_mask_bce_9: 0.83576/0.39284, loss_mask_dice_9: 1.94432/1.90590, loss_spatial_bce_9: 0.47190/0.33751, loss_spatial_dice_9: 0.74973/0.82584, loss_spatial_ce_9: 1.21019/1.52851, loss_grounding_bce_9: 0.27603/0.10500, loss_grounding_dice_9: 0.35172/0.28061, loss_grounding_ce_9: 0.27321/0.70935] items per batch[64] items per second[0.22] total items[960000] mini batches[ 15000] memory[7341] epoch remaining[1:07:57]
INFO:trainer.default_trainer:epochs[     8] optim steps[15100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34728/0.91515, loss_mask_bce_0: 0.21950/0.33644, loss_mask_dice_0: 0.66725/1.16525, loss_spatial_bce_0: 0.05934/0.09349, loss_spatial_dice_0: 0.11768/0.22358, loss_spatial_ce_0: 0.00218/0.08731, loss_grounding_bce_0: 0.02476/0.08614, loss_grounding_dice_0: 0.10757/0.17842, loss_grounding_ce_0: 0.21025/0.27739, loss_mask_ce_1: 0.30454/0.91614, loss_mask_bce_1: 0.22025/0.33704, loss_mask_dice_1: 0.70714/1.17272, loss_spatial_bce_1: 0.05811/0.09444, loss_spatial_dice_1: 0.12736/0.22802, loss_spatial_ce_1: 0.00202/0.09310, loss_grounding_bce_1: 0.02523/0.08620, loss_grounding_dice_1: 0.10965/0.17882, loss_grounding_ce_1: 0.20202/0.27975, loss_mask_ce_2: 0.30921/0.92265, loss_mask_bce_2: 0.22954/0.33733, loss_mask_dice_2: 0.71367/1.17191, loss_spatial_bce_2: 0.05804/0.09430, loss_spatial_dice_2: 0.11567/0.22887, loss_spatial_ce_2: 0.00177/0.09681, loss_grounding_bce_2: 0.02314/0.08622, loss_grounding_dice_2: 0.10712/0.17846, loss_grounding_ce_2: 0.20075/0.28252, loss_mask_ce_3: 0.35525/0.92948, loss_mask_bce_3: 0.22443/0.33832, loss_mask_dice_3: 0.68482/1.16910, loss_spatial_bce_3: 0.06009/0.09528, loss_spatial_dice_3: 0.12238/0.22997, loss_spatial_ce_3: 0.00316/0.10134, loss_grounding_bce_3: 0.02304/0.08636, loss_grounding_dice_3: 0.10874/0.17834, loss_grounding_ce_3: 0.19423/0.28355, loss_mask_ce_4: 0.34414/0.92801, loss_mask_bce_4: 0.22040/0.33964, loss_mask_dice_4: 0.67475/1.19034, loss_spatial_bce_4: 0.05722/0.09895, loss_spatial_dice_4: 0.13871/0.23815, loss_spatial_ce_4: 0.00332/0.11793, loss_grounding_bce_4: 0.02196/0.08690, loss_grounding_dice_4: 0.10653/0.18093, loss_grounding_ce_4: 0.18970/0.28607, loss_mask_ce_5: 0.31563/0.94242, loss_mask_bce_5: 0.21634/0.34186, loss_mask_dice_5: 0.69543/1.19532, loss_spatial_bce_5: 0.05568/0.09977, loss_spatial_dice_5: 0.14613/0.24109, loss_spatial_ce_5: 0.00806/0.13128, loss_grounding_bce_5: 0.02548/0.08740, loss_grounding_dice_5: 0.12322/0.18222, loss_grounding_ce_5: 0.17588/0.29823, loss_mask_ce_6: 0.39366/0.97858, loss_mask_bce_6: 0.21426/0.34441, loss_mask_dice_6: 0.71905/1.19857, loss_spatial_bce_6: 0.06076/0.10531, loss_spatial_dice_6: 0.12254/0.24381, loss_spatial_ce_6: 0.05452/0.15431, loss_grounding_bce_6: 0.02445/0.08822, loss_grounding_dice_6: 0.12270/0.18237, loss_grounding_ce_6: 0.17083/0.31815, loss_mask_ce_7: 0.42812/1.02179, loss_mask_bce_7: 0.22823/0.35225, loss_mask_dice_7: 0.70742/1.25360, loss_spatial_bce_7: 0.06854/0.11430, loss_spatial_dice_7: 0.16684/0.27084, loss_spatial_ce_7: 0.10318/0.19363, loss_grounding_bce_7: 0.02338/0.09004, loss_grounding_dice_7: 0.11586/0.18939, loss_grounding_ce_7: 0.30251/0.35386, loss_mask_ce_8: 0.50699/1.13371, loss_mask_bce_8: 0.21784/0.36573, loss_mask_dice_8: 0.70466/1.32876, loss_spatial_bce_8: 0.07485/0.13556, loss_spatial_dice_8: 0.16830/0.31154, loss_spatial_ce_8: 0.09950/0.24963, loss_grounding_bce_8: 0.02775/0.09339, loss_grounding_dice_8: 0.11935/0.20073, loss_grounding_ce_8: 0.31485/0.42598, loss_mask_ce_9: 2.55641/3.69860, loss_mask_bce_9: 0.30901/0.39265, loss_mask_dice_9: 1.02579/1.90514, loss_spatial_bce_9: 0.37505/0.33741, loss_spatial_dice_9: 0.81680/0.82570, loss_spatial_ce_9: 1.27159/1.52795, loss_grounding_bce_9: 0.06104/0.10496, loss_grounding_dice_9: 0.21137/0.28061, loss_grounding_ce_9: 0.30338/0.70937] items per batch[64] items per second[0.23] total items[966400] mini batches[ 15100] memory[7341] epoch remaining[1:03:12]
INFO:trainer.default_trainer:epochs[     8] optim steps[15200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.49772/0.91498, loss_mask_bce_0: 0.36131/0.33657, loss_mask_dice_0: 0.52716/1.16634, loss_spatial_bce_0: 0.11048/0.09349, loss_spatial_dice_0: 0.14272/0.22349, loss_spatial_ce_0: 0.05118/0.08711, loss_grounding_bce_0: 0.12213/0.08621, loss_grounding_dice_0: 0.17940/0.17850, loss_grounding_ce_0: 0.06857/0.27741, loss_mask_ce_1: 0.46932/0.91595, loss_mask_bce_1: 0.37255/0.33718, loss_mask_dice_1: 0.55311/1.17398, loss_spatial_bce_1: 0.11033/0.09445, loss_spatial_dice_1: 0.14502/0.22792, loss_spatial_ce_1: 0.05090/0.09285, loss_grounding_bce_1: 0.12462/0.08628, loss_grounding_dice_1: 0.18144/0.17889, loss_grounding_ce_1: 0.06353/0.27973, loss_mask_ce_2: 0.46895/0.92247, loss_mask_bce_2: 0.36284/0.33749, loss_mask_dice_2: 0.53004/1.17299, loss_spatial_bce_2: 0.11268/0.09430, loss_spatial_dice_2: 0.15628/0.22878, loss_spatial_ce_2: 0.05111/0.09660, loss_grounding_bce_2: 0.12239/0.08630, loss_grounding_dice_2: 0.18873/0.17851, loss_grounding_ce_2: 0.06406/0.28258, loss_mask_ce_3: 0.50848/0.92943, loss_mask_bce_3: 0.37311/0.33844, loss_mask_dice_3: 0.53891/1.17026, loss_spatial_bce_3: 0.11131/0.09529, loss_spatial_dice_3: 0.15799/0.22987, loss_spatial_ce_3: 0.05354/0.10106, loss_grounding_bce_3: 0.12410/0.08643, loss_grounding_dice_3: 0.19168/0.17844, loss_grounding_ce_3: 0.06162/0.28362, loss_mask_ce_4: 0.47721/0.92783, loss_mask_bce_4: 0.38110/0.33980, loss_mask_dice_4: 0.55719/1.19166, loss_spatial_bce_4: 0.12370/0.09895, loss_spatial_dice_4: 0.16815/0.23808, loss_spatial_ce_4: 0.05587/0.11768, loss_grounding_bce_4: 0.12368/0.08697, loss_grounding_dice_4: 0.18853/0.18097, loss_grounding_ce_4: 0.05627/0.28614, loss_mask_ce_5: 0.56161/0.94216, loss_mask_bce_5: 0.35505/0.34201, loss_mask_dice_5: 0.55835/1.19645, loss_spatial_bce_5: 0.13120/0.09978, loss_spatial_dice_5: 0.18254/0.24101, loss_spatial_ce_5: 0.06657/0.13108, loss_grounding_bce_5: 0.14399/0.08748, loss_grounding_dice_5: 0.21978/0.18227, loss_grounding_ce_5: 0.05686/0.29838, loss_mask_ce_6: 0.66189/0.97856, loss_mask_bce_6: 0.40298/0.34456, loss_mask_dice_6: 0.56824/1.19970, loss_spatial_bce_6: 0.12704/0.10533, loss_spatial_dice_6: 0.19144/0.24373, loss_spatial_ce_6: 0.10100/0.15406, loss_grounding_bce_6: 0.14031/0.08829, loss_grounding_dice_6: 0.20606/0.18246, loss_grounding_ce_6: 0.12894/0.31823, loss_mask_ce_7: 0.62478/1.02151, loss_mask_bce_7: 0.35409/0.35243, loss_mask_dice_7: 0.59974/1.25489, loss_spatial_bce_7: 0.12114/0.11433, loss_spatial_dice_7: 0.16013/0.27079, loss_spatial_ce_7: 0.13025/0.19336, loss_grounding_bce_7: 0.13900/0.09013, loss_grounding_dice_7: 0.23634/0.18947, loss_grounding_ce_7: 0.19527/0.35381, loss_mask_ce_8: 0.62483/1.13343, loss_mask_bce_8: 0.40325/0.36591, loss_mask_dice_8: 0.69139/1.33000, loss_spatial_bce_8: 0.12405/0.13558, loss_spatial_dice_8: 0.19654/0.31143, loss_spatial_ce_8: 0.22365/0.24933, loss_grounding_bce_8: 0.15616/0.09349, loss_grounding_dice_8: 0.25327/0.20087, loss_grounding_ce_8: 0.26932/0.42589, loss_mask_ce_9: 3.42801/3.69861, loss_mask_bce_9: 0.29092/0.39286, loss_mask_dice_9: 0.75303/1.90717, loss_spatial_bce_9: 0.39498/0.33742, loss_spatial_dice_9: 0.89366/0.82571, loss_spatial_ce_9: 1.44598/1.52713, loss_grounding_bce_9: 0.11782/0.10503, loss_grounding_dice_9: 0.21034/0.28078, loss_grounding_ce_9: 0.70008/0.70886] items per batch[64] items per second[0.23] total items[972800] mini batches[ 15200] memory[7341] epoch remaining[0:58:29]
INFO:trainer.default_trainer:epochs[     8] optim steps[15300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70206/0.91521, loss_mask_bce_0: 0.32158/0.33653, loss_mask_dice_0: 0.64345/1.16568, loss_spatial_bce_0: 0.08646/0.09342, loss_spatial_dice_0: 0.13219/0.22334, loss_spatial_ce_0: 0.02639/0.08689, loss_grounding_bce_0: 0.07566/0.08618, loss_grounding_dice_0: 0.08090/0.17848, loss_grounding_ce_0: 0.05978/0.27734, loss_mask_ce_1: 0.68502/0.91603, loss_mask_bce_1: 0.31592/0.33716, loss_mask_dice_1: 0.64218/1.17341, loss_spatial_bce_1: 0.08400/0.09437, loss_spatial_dice_1: 0.12106/0.22778, loss_spatial_ce_1: 0.02426/0.09268, loss_grounding_bce_1: 0.07286/0.08625, loss_grounding_dice_1: 0.08121/0.17888, loss_grounding_ce_1: 0.06846/0.27969, loss_mask_ce_2: 0.66651/0.92267, loss_mask_bce_2: 0.32180/0.33746, loss_mask_dice_2: 0.64729/1.17228, loss_spatial_bce_2: 0.08717/0.09424, loss_spatial_dice_2: 0.13707/0.22865, loss_spatial_ce_2: 0.02225/0.09645, loss_grounding_bce_2: 0.07750/0.08628, loss_grounding_dice_2: 0.08286/0.17846, loss_grounding_ce_2: 0.07565/0.28249, loss_mask_ce_3: 0.66174/0.92979, loss_mask_bce_3: 0.32127/0.33843, loss_mask_dice_3: 0.60242/1.16967, loss_spatial_bce_3: 0.08880/0.09523, loss_spatial_dice_3: 0.11052/0.22973, loss_spatial_ce_3: 0.02935/0.10082, loss_grounding_bce_3: 0.07615/0.08641, loss_grounding_dice_3: 0.08411/0.17842, loss_grounding_ce_3: 0.12620/0.28351, loss_mask_ce_4: 0.68380/0.92798, loss_mask_bce_4: 0.32501/0.33980, loss_mask_dice_4: 0.60193/1.19099, loss_spatial_bce_4: 0.09286/0.09887, loss_spatial_dice_4: 0.13190/0.23795, loss_spatial_ce_4: 0.03678/0.11755, loss_grounding_bce_4: 0.07953/0.08695, loss_grounding_dice_4: 0.08635/0.18096, loss_grounding_ce_4: 0.05529/0.28604, loss_mask_ce_5: 0.57533/0.94244, loss_mask_bce_5: 0.33726/0.34203, loss_mask_dice_5: 0.63892/1.19579, loss_spatial_bce_5: 0.09836/0.09972, loss_spatial_dice_5: 0.14111/0.24088, loss_spatial_ce_5: 0.06815/0.13094, loss_grounding_bce_5: 0.08689/0.08746, loss_grounding_dice_5: 0.08746/0.18227, loss_grounding_ce_5: 0.15068/0.29805, loss_mask_ce_6: 0.63141/0.97871, loss_mask_bce_6: 0.34416/0.34457, loss_mask_dice_6: 0.64086/1.19908, loss_spatial_bce_6: 0.10792/0.10526, loss_spatial_dice_6: 0.13498/0.24358, loss_spatial_ce_6: 0.07736/0.15391, loss_grounding_bce_6: 0.08070/0.08827, loss_grounding_dice_6: 0.08218/0.18242, loss_grounding_ce_6: 0.38056/0.31791, loss_mask_ce_7: 0.66969/1.02167, loss_mask_bce_7: 0.37322/0.35241, loss_mask_dice_7: 0.66271/1.25423, loss_spatial_bce_7: 0.13019/0.11424, loss_spatial_dice_7: 0.17550/0.27065, loss_spatial_ce_7: 0.09886/0.19310, loss_grounding_bce_7: 0.08411/0.09009, loss_grounding_dice_7: 0.08217/0.18944, loss_grounding_ce_7: 0.24390/0.35342, loss_mask_ce_8: 0.92074/1.13347, loss_mask_bce_8: 0.38571/0.36590, loss_mask_dice_8: 0.56604/1.32927, loss_spatial_bce_8: 0.14993/0.13552, loss_spatial_dice_8: 0.23282/0.31130, loss_spatial_ce_8: 0.16888/0.24928, loss_grounding_bce_8: 0.09814/0.09346, loss_grounding_dice_8: 0.09029/0.20086, loss_grounding_ce_8: 0.40061/0.42597, loss_mask_ce_9: 3.52945/3.69856, loss_mask_bce_9: 0.41274/0.39290, loss_mask_dice_9: 1.13872/1.90662, loss_spatial_bce_9: 0.36282/0.33737, loss_spatial_dice_9: 0.77847/0.82564, loss_spatial_ce_9: 1.22469/1.52666, loss_grounding_bce_9: 0.14025/0.10503, loss_grounding_dice_9: 0.19652/0.28085, loss_grounding_ce_9: 1.02130/0.70903] items per batch[64] items per second[0.23] total items[979200] mini batches[ 15300] memory[7341] epoch remaining[0:53:33]
INFO:trainer.default_trainer:epochs[     8] optim steps[15400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55907/0.91547, loss_mask_bce_0: 0.28041/0.33674, loss_mask_dice_0: 0.31336/1.16588, loss_spatial_bce_0: 0.15121/0.09344, loss_spatial_dice_0: 0.21850/0.22327, loss_spatial_ce_0: 0.05007/0.08663, loss_grounding_bce_0: 0.19354/0.08621, loss_grounding_dice_0: 0.15844/0.17853, loss_grounding_ce_0: 1.11220/0.27720, loss_mask_ce_1: 0.52484/0.91632, loss_mask_bce_1: 0.28350/0.33739, loss_mask_dice_1: 0.43788/1.17363, loss_spatial_bce_1: 0.14147/0.09440, loss_spatial_dice_1: 0.22144/0.22771, loss_spatial_ce_1: 0.04412/0.09242, loss_grounding_bce_1: 0.24861/0.08629, loss_grounding_dice_1: 0.17675/0.17894, loss_grounding_ce_1: 1.12932/0.27949, loss_mask_ce_2: 0.55246/0.92296, loss_mask_bce_2: 0.26545/0.33770, loss_mask_dice_2: 0.31993/1.17249, loss_spatial_bce_2: 0.14463/0.09427, loss_spatial_dice_2: 0.20504/0.22858, loss_spatial_ce_2: 0.06181/0.09623, loss_grounding_bce_2: 0.22924/0.08632, loss_grounding_dice_2: 0.39380/0.17854, loss_grounding_ce_2: 1.12401/0.28230, loss_mask_ce_3: 0.52239/0.93020, loss_mask_bce_3: 0.26232/0.33862, loss_mask_dice_3: 0.30807/1.16987, loss_spatial_bce_3: 0.13423/0.09525, loss_spatial_dice_3: 0.20354/0.22966, loss_spatial_ce_3: 0.05491/0.10062, loss_grounding_bce_3: 0.23913/0.08645, loss_grounding_dice_3: 0.34408/0.17849, loss_grounding_ce_3: 0.86245/0.28334, loss_mask_ce_4: 0.70608/0.92827, loss_mask_bce_4: 0.24622/0.34002, loss_mask_dice_4: 0.29430/1.19119, loss_spatial_bce_4: 0.11672/0.09890, loss_spatial_dice_4: 0.20958/0.23788, loss_spatial_ce_4: 0.08994/0.11731, loss_grounding_bce_4: 0.18559/0.08697, loss_grounding_dice_4: 0.15466/0.18099, loss_grounding_ce_4: 1.15288/0.28591, loss_mask_ce_5: 0.54492/0.94275, loss_mask_bce_5: 0.28967/0.34222, loss_mask_dice_5: 0.33736/1.19593, loss_spatial_bce_5: 0.13886/0.09975, loss_spatial_dice_5: 0.19169/0.24082, loss_spatial_ce_5: 0.09513/0.13076, loss_grounding_bce_5: 0.19179/0.08748, loss_grounding_dice_5: 0.14618/0.18228, loss_grounding_ce_5: 1.02836/0.29781, loss_mask_ce_6: 0.87682/0.97907, loss_mask_bce_6: 0.29103/0.34477, loss_mask_dice_6: 0.43456/1.19920, loss_spatial_bce_6: 0.14217/0.10527, loss_spatial_dice_6: 0.21424/0.24351, loss_spatial_ce_6: 0.16444/0.15370, loss_grounding_bce_6: 0.25030/0.08828, loss_grounding_dice_6: 0.39800/0.18249, loss_grounding_ce_6: 1.18350/0.31772, loss_mask_ce_7: 0.88218/1.02206, loss_mask_bce_7: 0.25906/0.35255, loss_mask_dice_7: 0.42723/1.25432, loss_spatial_bce_7: 0.18693/0.11425, loss_spatial_dice_7: 0.27007/0.27060, loss_spatial_ce_7: 0.19861/0.19284, loss_grounding_bce_7: 0.20794/0.09010, loss_grounding_dice_7: 0.16995/0.18946, loss_grounding_ce_7: 1.20928/0.35325, loss_mask_ce_8: 0.78073/1.13369, loss_mask_bce_8: 0.34998/0.36608, loss_mask_dice_8: 0.50323/1.32936, loss_spatial_bce_8: 0.20638/0.13553, loss_spatial_dice_8: 0.27036/0.31120, loss_spatial_ce_8: 0.18957/0.24909, loss_grounding_bce_8: 0.30289/0.09347, loss_grounding_dice_8: 0.21515/0.20090, loss_grounding_ce_8: 1.07853/0.42585, loss_mask_ce_9: 2.41871/3.69894, loss_mask_bce_9: 0.39573/0.39307, loss_mask_dice_9: 0.59264/1.90660, loss_spatial_bce_9: 0.51487/0.33741, loss_spatial_dice_9: 0.70892/0.82558, loss_spatial_ce_9: 2.10253/1.52655, loss_grounding_bce_9: 0.36965/0.10504, loss_grounding_dice_9: 0.59050/0.28088, loss_grounding_ce_9: 1.79532/0.70921] items per batch[64] items per second[0.23] total items[985600] mini batches[ 15400] memory[7341] epoch remaining[0:48:47]
INFO:trainer.default_trainer:epochs[     8] optim steps[15500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56300/0.91496, loss_mask_bce_0: 0.39968/0.33663, loss_mask_dice_0: 1.61520/1.16566, loss_spatial_bce_0: 0.06821/0.09343, loss_spatial_dice_0: 0.21953/0.22315, loss_spatial_ce_0: 0.04991/0.08640, loss_grounding_bce_0: 0.07101/0.08620, loss_grounding_dice_0: 0.11863/0.17851, loss_grounding_ce_0: 0.30754/0.27711, loss_mask_ce_1: 0.55634/0.91590, loss_mask_bce_1: 0.39439/0.33727, loss_mask_dice_1: 1.31156/1.17347, loss_spatial_bce_1: 0.07317/0.09438, loss_spatial_dice_1: 0.23160/0.22758, loss_spatial_ce_1: 0.05789/0.09226, loss_grounding_bce_1: 0.06788/0.08627, loss_grounding_dice_1: 0.11973/0.17889, loss_grounding_ce_1: 0.33604/0.27948, loss_mask_ce_2: 0.52846/0.92259, loss_mask_bce_2: 0.39946/0.33758, loss_mask_dice_2: 1.63128/1.17227, loss_spatial_bce_2: 0.06844/0.09425, loss_spatial_dice_2: 0.22319/0.22844, loss_spatial_ce_2: 0.05686/0.09605, loss_grounding_bce_2: 0.07176/0.08631, loss_grounding_dice_2: 0.12306/0.17850, loss_grounding_ce_2: 0.34545/0.28230, loss_mask_ce_3: 0.53937/0.92977, loss_mask_bce_3: 0.41637/0.33850, loss_mask_dice_3: 1.59645/1.16966, loss_spatial_bce_3: 0.08216/0.09523, loss_spatial_dice_3: 0.22349/0.22952, loss_spatial_ce_3: 0.05295/0.10038, loss_grounding_bce_3: 0.07290/0.08642, loss_grounding_dice_3: 0.11920/0.17843, loss_grounding_ce_3: 0.33308/0.28333, loss_mask_ce_4: 0.59737/0.92786, loss_mask_bce_4: 0.40440/0.33992, loss_mask_dice_4: 1.43670/1.19100, loss_spatial_bce_4: 0.06940/0.09888, loss_spatial_dice_4: 0.24354/0.23776, loss_spatial_ce_4: 0.11911/0.11706, loss_grounding_bce_4: 0.07297/0.08695, loss_grounding_dice_4: 0.11706/0.18099, loss_grounding_ce_4: 0.33440/0.28593, loss_mask_ce_5: 0.56737/0.94233, loss_mask_bce_5: 0.39285/0.34211, loss_mask_dice_5: 1.47517/1.19581, loss_spatial_bce_5: 0.08670/0.09973, loss_spatial_dice_5: 0.25605/0.24068, loss_spatial_ce_5: 0.07238/0.13054, loss_grounding_bce_5: 0.07482/0.08745, loss_grounding_dice_5: 0.11382/0.18225, loss_grounding_ce_5: 0.32659/0.29816, loss_mask_ce_6: 0.76242/0.97881, loss_mask_bce_6: 0.40174/0.34465, loss_mask_dice_6: 1.27937/1.19913, loss_spatial_bce_6: 0.12289/0.10527, loss_spatial_dice_6: 0.28232/0.24339, loss_spatial_ce_6: 0.09976/0.15347, loss_grounding_bce_6: 0.07434/0.08825, loss_grounding_dice_6: 0.12185/0.18246, loss_grounding_ce_6: 0.34811/0.31793, loss_mask_ce_7: 0.84115/1.02157, loss_mask_bce_7: 0.37051/0.35244, loss_mask_dice_7: 1.62123/1.25424, loss_spatial_bce_7: 0.10508/0.11424, loss_spatial_dice_7: 0.25841/0.27045, loss_spatial_ce_7: 0.13086/0.19261, loss_grounding_bce_7: 0.06864/0.09009, loss_grounding_dice_7: 0.11676/0.18943, loss_grounding_ce_7: 0.41476/0.35315, loss_mask_ce_8: 1.19887/1.13308, loss_mask_bce_8: 0.42728/0.36607, loss_mask_dice_8: 2.19038/1.32915, loss_spatial_bce_8: 0.11324/0.13551, loss_spatial_dice_8: 0.31653/0.31108, loss_spatial_ce_8: 0.26819/0.24890, loss_grounding_bce_8: 0.07584/0.09348, loss_grounding_dice_8: 0.14923/0.20087, loss_grounding_ce_8: 0.49754/0.42598, loss_mask_ce_9: 4.43990/3.69887, loss_mask_bce_9: 0.51104/0.39301, loss_mask_dice_9: 3.12752/1.90617, loss_spatial_bce_9: 0.27366/0.33742, loss_spatial_dice_9: 0.82411/0.82550, loss_spatial_ce_9: 1.56911/1.52612, loss_grounding_bce_9: 0.09544/0.10507, loss_grounding_dice_9: 0.31241/0.28084, loss_grounding_ce_9: 1.23495/0.70979] items per batch[64] items per second[0.22] total items[992000] mini batches[ 15500] memory[7341] epoch remaining[0:44:13]
INFO:trainer.default_trainer:epochs[     8] optim steps[15600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15749/0.91464, loss_mask_bce_0: 0.04639/0.33656, loss_mask_dice_0: 0.39258/1.16536, loss_spatial_bce_0: 0.02268/0.09338, loss_spatial_dice_0: 0.14034/0.22305, loss_spatial_ce_0: 0.00168/0.08625, loss_grounding_bce_0: 0.01579/0.08628, loss_grounding_dice_0: 0.16179/0.17848, loss_grounding_ce_0: 0.22703/0.27726, loss_mask_ce_1: 0.09452/0.91555, loss_mask_bce_1: 0.04983/0.33721, loss_mask_dice_1: 0.23478/1.17314, loss_spatial_bce_1: 0.02130/0.09435, loss_spatial_dice_1: 0.13558/0.22749, loss_spatial_ce_1: 0.00514/0.09209, loss_grounding_bce_1: 0.01603/0.08633, loss_grounding_dice_1: 0.13234/0.17885, loss_grounding_ce_1: 0.16083/0.27966, loss_mask_ce_2: 0.08781/0.92223, loss_mask_bce_2: 0.04984/0.33752, loss_mask_dice_2: 0.34338/1.17189, loss_spatial_bce_2: 0.02270/0.09422, loss_spatial_dice_2: 0.14994/0.22834, loss_spatial_ce_2: 0.03164/0.09588, loss_grounding_bce_2: 0.01603/0.08637, loss_grounding_dice_2: 0.15262/0.17850, loss_grounding_ce_2: 0.15492/0.28249, loss_mask_ce_3: 0.11950/0.92947, loss_mask_bce_3: 0.04609/0.33845, loss_mask_dice_3: 0.20043/1.16937, loss_spatial_bce_3: 0.02110/0.09517, loss_spatial_dice_3: 0.15956/0.22942, loss_spatial_ce_3: 0.02870/0.10024, loss_grounding_bce_3: 0.01489/0.08648, loss_grounding_dice_3: 0.11451/0.17843, loss_grounding_ce_3: 0.16091/0.28360, loss_mask_ce_4: 0.10902/0.92750, loss_mask_bce_4: 0.04905/0.33986, loss_mask_dice_4: 0.22074/1.19066, loss_spatial_bce_4: 0.02383/0.09884, loss_spatial_dice_4: 0.18873/0.23768, loss_spatial_ce_4: 0.01714/0.11690, loss_grounding_bce_4: 0.01612/0.08702, loss_grounding_dice_4: 0.11163/0.18096, loss_grounding_ce_4: 0.16667/0.28610, loss_mask_ce_5: 0.18389/0.94191, loss_mask_bce_5: 0.04703/0.34210, loss_mask_dice_5: 0.27997/1.19545, loss_spatial_bce_5: 0.02050/0.09970, loss_spatial_dice_5: 0.09181/0.24059, loss_spatial_ce_5: 0.02240/0.13038, loss_grounding_bce_5: 0.01574/0.08752, loss_grounding_dice_5: 0.16288/0.18225, loss_grounding_ce_5: 0.18361/0.29838, loss_mask_ce_6: 0.17331/0.97848, loss_mask_bce_6: 0.04745/0.34465, loss_mask_dice_6: 0.36523/1.19891, loss_spatial_bce_6: 0.02250/0.10525, loss_spatial_dice_6: 0.06870/0.24331, loss_spatial_ce_6: 0.05501/0.15325, loss_grounding_bce_6: 0.01656/0.08832, loss_grounding_dice_6: 0.16776/0.18247, loss_grounding_ce_6: 0.23445/0.31816, loss_mask_ce_7: 0.71973/1.02121, loss_mask_bce_7: 0.05182/0.35244, loss_mask_dice_7: 0.17608/1.25396, loss_spatial_bce_7: 0.02065/0.11422, loss_spatial_dice_7: 0.15067/0.27035, loss_spatial_ce_7: 0.05602/0.19250, loss_grounding_bce_7: 0.01840/0.09016, loss_grounding_dice_7: 0.15373/0.18942, loss_grounding_ce_7: 0.21780/0.35334, loss_mask_ce_8: 0.40556/1.13278, loss_mask_bce_8: 0.05766/0.36600, loss_mask_dice_8: 0.28012/1.32863, loss_spatial_bce_8: 0.02585/0.13548, loss_spatial_dice_8: 0.18605/0.31099, loss_spatial_ce_8: 0.06687/0.24878, loss_grounding_bce_8: 0.01775/0.09354, loss_grounding_dice_8: 0.07539/0.20085, loss_grounding_ce_8: 0.24706/0.42630, loss_mask_ce_9: 2.10210/3.69854, loss_mask_bce_9: 0.05279/0.39293, loss_mask_dice_9: 0.52118/1.90529, loss_spatial_bce_9: 0.31404/0.33741, loss_spatial_dice_9: 0.84084/0.82544, loss_spatial_ce_9: 2.05449/1.52581, loss_grounding_bce_9: 0.01719/0.10517, loss_grounding_dice_9: 0.14650/0.28079, loss_grounding_ce_9: 0.29012/0.70947] items per batch[64] items per second[0.22] total items[998400] mini batches[ 15600] memory[7341] epoch remaining[0:39:36]
INFO:trainer.default_trainer:epochs[     8] optim steps[15700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43861/0.91451, loss_mask_bce_0: 0.13214/0.33653, loss_mask_dice_0: 0.41027/1.16448, loss_spatial_bce_0: 0.04697/0.09333, loss_spatial_dice_0: 0.17182/0.22291, loss_spatial_ce_0: 0.16572/0.08607, loss_grounding_bce_0: 0.03856/0.08623, loss_grounding_dice_0: 0.18253/0.17846, loss_grounding_ce_0: 0.01564/0.27709, loss_mask_ce_1: 0.41886/0.91554, loss_mask_bce_1: 0.13091/0.33717, loss_mask_dice_1: 0.30117/1.17219, loss_spatial_bce_1: 0.05004/0.09430, loss_spatial_dice_1: 0.16224/0.22734, loss_spatial_ce_1: 0.09296/0.09187, loss_grounding_bce_1: 0.03986/0.08628, loss_grounding_dice_1: 0.19316/0.17883, loss_grounding_ce_1: 0.01377/0.27946, loss_mask_ce_2: 0.55866/0.92215, loss_mask_bce_2: 0.12977/0.33751, loss_mask_dice_2: 0.36384/1.17098, loss_spatial_bce_2: 0.04606/0.09418, loss_spatial_dice_2: 0.16321/0.22820, loss_spatial_ce_2: 0.14173/0.09564, loss_grounding_bce_2: 0.04672/0.08632, loss_grounding_dice_2: 0.21000/0.17847, loss_grounding_ce_2: 0.01948/0.28231, loss_mask_ce_3: 0.47861/0.92938, loss_mask_bce_3: 0.13094/0.33844, loss_mask_dice_3: 0.42985/1.16845, loss_spatial_bce_3: 0.04388/0.09513, loss_spatial_dice_3: 0.16083/0.22928, loss_spatial_ce_3: 0.18316/0.10001, loss_grounding_bce_3: 0.04051/0.08643, loss_grounding_dice_3: 0.14029/0.17842, loss_grounding_ce_3: 0.19916/0.28344, loss_mask_ce_4: 0.58153/0.92746, loss_mask_bce_4: 0.11933/0.33985, loss_mask_dice_4: 0.29985/1.18985, loss_spatial_bce_4: 0.05292/0.09882, loss_spatial_dice_4: 0.17558/0.23755, loss_spatial_ce_4: 0.19014/0.11660, loss_grounding_bce_4: 0.04346/0.08696, loss_grounding_dice_4: 0.17186/0.18092, loss_grounding_ce_4: 0.21913/0.28588, loss_mask_ce_5: 0.52674/0.94192, loss_mask_bce_5: 0.12507/0.34209, loss_mask_dice_5: 0.27895/1.19461, loss_spatial_bce_5: 0.05644/0.09967, loss_spatial_dice_5: 0.17055/0.24046, loss_spatial_ce_5: 0.20587/0.13011, loss_grounding_bce_5: 0.04144/0.08748, loss_grounding_dice_5: 0.15277/0.18224, loss_grounding_ce_5: 0.24018/0.29825, loss_mask_ce_6: 0.66404/0.97847, loss_mask_bce_6: 0.12370/0.34465, loss_mask_dice_6: 0.34960/1.19795, loss_spatial_bce_6: 0.05323/0.10521, loss_spatial_dice_6: 0.15698/0.24318, loss_spatial_ce_6: 0.15279/0.15299, loss_grounding_bce_6: 0.03920/0.08829, loss_grounding_dice_6: 0.15723/0.18247, loss_grounding_ce_6: 0.22291/0.31785, loss_mask_ce_7: 0.67120/1.02122, loss_mask_bce_7: 0.15715/0.35243, loss_mask_dice_7: 0.39290/1.25301, loss_spatial_bce_7: 0.07420/0.11419, loss_spatial_dice_7: 0.22322/0.27022, loss_spatial_ce_7: 0.25589/0.19230, loss_grounding_bce_7: 0.07828/0.09012, loss_grounding_dice_7: 0.26570/0.18940, loss_grounding_ce_7: 0.01902/0.35297, loss_mask_ce_8: 0.54844/1.13269, loss_mask_bce_8: 0.18660/0.36600, loss_mask_dice_8: 0.57962/1.32771, loss_spatial_bce_8: 0.06466/0.13546, loss_spatial_dice_8: 0.21562/0.31086, loss_spatial_ce_8: 0.14807/0.24847, loss_grounding_bce_8: 0.08440/0.09350, loss_grounding_dice_8: 0.27077/0.20084, loss_grounding_ce_8: 0.29834/0.42600, loss_mask_ce_9: 3.15014/3.69827, loss_mask_bce_9: 0.19578/0.39290, loss_mask_dice_9: 0.64772/1.90435, loss_spatial_bce_9: 0.31831/0.33741, loss_spatial_dice_9: 0.71590/0.82541, loss_spatial_ce_9: 1.49045/1.52494, loss_grounding_bce_9: 0.04291/0.10510, loss_grounding_dice_9: 0.30246/0.28079, loss_grounding_ce_9: 0.93006/0.70894] items per batch[64] items per second[0.23] total items[1004800] mini batches[ 15700] memory[7341] epoch remaining[0:34:55]
INFO:trainer.default_trainer:epochs[     8] optim steps[15800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67709/0.91437, loss_mask_bce_0: 0.46646/0.33660, loss_mask_dice_0: 1.70697/1.16428, loss_spatial_bce_0: 0.08385/0.09328, loss_spatial_dice_0: 0.19556/0.22278, loss_spatial_ce_0: 0.03355/0.08583, loss_grounding_bce_0: 0.04910/0.08619, loss_grounding_dice_0: 0.06442/0.17843, loss_grounding_ce_0: 0.02556/0.27758, loss_mask_ce_1: 0.62126/0.91538, loss_mask_bce_1: 0.46697/0.33725, loss_mask_dice_1: 1.73088/1.17220, loss_spatial_bce_1: 0.08349/0.09424, loss_spatial_dice_1: 0.20714/0.22720, loss_spatial_ce_1: 0.25815/0.09167, loss_grounding_bce_1: 0.04917/0.08625, loss_grounding_dice_1: 0.06685/0.17883, loss_grounding_ce_1: 0.00867/0.27969, loss_mask_ce_2: 0.59230/0.92211, loss_mask_bce_2: 0.47705/0.33760, loss_mask_dice_2: 1.62727/1.17084, loss_spatial_bce_2: 0.09292/0.09413, loss_spatial_dice_2: 0.21905/0.22807, loss_spatial_ce_2: 0.02890/0.09541, loss_grounding_bce_2: 0.04873/0.08630, loss_grounding_dice_2: 0.06188/0.17844, loss_grounding_ce_2: 0.00721/0.28262, loss_mask_ce_3: 0.55466/0.92933, loss_mask_bce_3: 0.48001/0.33852, loss_mask_dice_3: 1.72108/1.16826, loss_spatial_bce_3: 0.09355/0.09508, loss_spatial_dice_3: 0.21727/0.22913, loss_spatial_ce_3: 0.12750/0.09979, loss_grounding_bce_3: 0.05097/0.08641, loss_grounding_dice_3: 0.06367/0.17837, loss_grounding_ce_3: 0.03450/0.28364, loss_mask_ce_4: 0.59789/0.92756, loss_mask_bce_4: 0.49453/0.33992, loss_mask_dice_4: 1.64553/1.18959, loss_spatial_bce_4: 0.11244/0.09879, loss_spatial_dice_4: 0.24706/0.23743, loss_spatial_ce_4: 0.03498/0.11640, loss_grounding_bce_4: 0.05552/0.08695, loss_grounding_dice_4: 0.07031/0.18095, loss_grounding_ce_4: 0.02712/0.28606, loss_mask_ce_5: 0.62576/0.94196, loss_mask_bce_5: 0.51048/0.34217, loss_mask_dice_5: 1.71855/1.19442, loss_spatial_bce_5: 0.11793/0.09966, loss_spatial_dice_5: 0.23617/0.24035, loss_spatial_ce_5: 0.06125/0.12991, loss_grounding_bce_5: 0.04677/0.08746, loss_grounding_dice_5: 0.05642/0.18222, loss_grounding_ce_5: 0.03679/0.29848, loss_mask_ce_6: 0.66375/0.97864, loss_mask_bce_6: 0.52746/0.34471, loss_mask_dice_6: 1.66742/1.19781, loss_spatial_bce_6: 0.13105/0.10520, loss_spatial_dice_6: 0.26409/0.24307, loss_spatial_ce_6: 0.08194/0.15287, loss_grounding_bce_6: 0.05173/0.08827, loss_grounding_dice_6: 0.06175/0.18244, loss_grounding_ce_6: 0.20719/0.31818, loss_mask_ce_7: 0.74656/1.02123, loss_mask_bce_7: 0.47040/0.35249, loss_mask_dice_7: 1.85200/1.25281, loss_spatial_bce_7: 0.10136/0.11419, loss_spatial_dice_7: 0.29372/0.27011, loss_spatial_ce_7: 0.15995/0.19220, loss_grounding_bce_7: 0.05373/0.09008, loss_grounding_dice_7: 0.06962/0.18937, loss_grounding_ce_7: 0.13467/0.35339, loss_mask_ce_8: 1.29311/1.13265, loss_mask_bce_8: 0.55734/0.36605, loss_mask_dice_8: 1.95361/1.32764, loss_spatial_bce_8: 0.16050/0.13548, loss_spatial_dice_8: 0.35700/0.31075, loss_spatial_ce_8: 0.20507/0.24825, loss_grounding_bce_8: 0.05882/0.09348, loss_grounding_dice_8: 0.07879/0.20080, loss_grounding_ce_8: 0.36453/0.42645, loss_mask_ce_9: 4.38243/3.69859, loss_mask_bce_9: 0.48169/0.39305, loss_mask_dice_9: 2.48817/1.90424, loss_spatial_bce_9: 0.33584/0.33739, loss_spatial_dice_9: 0.86184/0.82539, loss_spatial_ce_9: 1.27265/1.52451, loss_grounding_bce_9: 0.05110/0.10510, loss_grounding_dice_9: 0.09855/0.28079, loss_grounding_ce_9: 0.81827/0.70937] items per batch[64] items per second[0.23] total items[1011200] mini batches[ 15800] memory[7341] epoch remaining[0:30:12]
INFO:trainer.default_trainer:epochs[     8] optim steps[15900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88066/0.91388, loss_mask_bce_0: 0.53672/0.33665, loss_mask_dice_0: 0.47584/1.16440, loss_spatial_bce_0: 0.32314/0.09323, loss_spatial_dice_0: 0.29343/0.22266, loss_spatial_ce_0: 0.05354/0.08567, loss_grounding_bce_0: 0.23244/0.08622, loss_grounding_dice_0: 0.19477/0.17842, loss_grounding_ce_0: 0.36358/0.27720, loss_mask_ce_1: 0.96100/0.91481, loss_mask_bce_1: 0.49298/0.33730, loss_mask_dice_1: 0.44102/1.17226, loss_spatial_bce_1: 0.31711/0.09419, loss_spatial_dice_1: 0.29373/0.22709, loss_spatial_ce_1: 0.07463/0.09152, loss_grounding_bce_1: 0.20687/0.08627, loss_grounding_dice_1: 0.17702/0.17882, loss_grounding_ce_1: 0.36782/0.27923, loss_mask_ce_2: 0.94531/0.92165, loss_mask_bce_2: 0.48327/0.33765, loss_mask_dice_2: 0.41122/1.17085, loss_spatial_bce_2: 0.33025/0.09408, loss_spatial_dice_2: 0.30054/0.22796, loss_spatial_ce_2: 0.10054/0.09526, loss_grounding_bce_2: 0.21794/0.08632, loss_grounding_dice_2: 0.18382/0.17845, loss_grounding_ce_2: 0.36541/0.28216, loss_mask_ce_3: 0.94988/0.92890, loss_mask_bce_3: 0.54572/0.33859, loss_mask_dice_3: 0.46431/1.16837, loss_spatial_bce_3: 0.35780/0.09502, loss_spatial_dice_3: 0.32207/0.22901, loss_spatial_ce_3: 0.13999/0.09964, loss_grounding_bce_3: 0.22223/0.08643, loss_grounding_dice_3: 0.19413/0.17835, loss_grounding_ce_3: 0.36155/0.28325, loss_mask_ce_4: 0.94014/0.92712, loss_mask_bce_4: 0.50591/0.33998, loss_mask_dice_4: 0.41408/1.18966, loss_spatial_bce_4: 0.37141/0.09872, loss_spatial_dice_4: 0.32265/0.23731, loss_spatial_ce_4: 0.11806/0.11618, loss_grounding_bce_4: 0.20605/0.08696, loss_grounding_dice_4: 0.18870/0.18092, loss_grounding_ce_4: 0.36623/0.28568, loss_mask_ce_5: 0.97306/0.94164, loss_mask_bce_5: 0.55378/0.34221, loss_mask_dice_5: 0.47625/1.19454, loss_spatial_bce_5: 0.36109/0.09960, loss_spatial_dice_5: 0.30388/0.24022, loss_spatial_ce_5: 0.16665/0.12973, loss_grounding_bce_5: 0.24344/0.08746, loss_grounding_dice_5: 0.19677/0.18220, loss_grounding_ce_5: 0.36754/0.29816, loss_mask_ce_6: 1.16132/0.97845, loss_mask_bce_6: 0.57348/0.34477, loss_mask_dice_6: 0.44518/1.19786, loss_spatial_bce_6: 0.36602/0.10513, loss_spatial_dice_6: 0.28210/0.24297, loss_spatial_ce_6: 0.15267/0.15262, loss_grounding_bce_6: 0.25951/0.08827, loss_grounding_dice_6: 0.19774/0.18241, loss_grounding_ce_6: 0.37389/0.31786, loss_mask_ce_7: 1.00990/1.02112, loss_mask_bce_7: 0.53467/0.35254, loss_mask_dice_7: 0.43300/1.25272, loss_spatial_bce_7: 0.35473/0.11412, loss_spatial_dice_7: 0.29770/0.27001, loss_spatial_ce_7: 0.34214/0.19203, loss_grounding_bce_7: 0.20688/0.09011, loss_grounding_dice_7: 0.20383/0.18934, loss_grounding_ce_7: 0.35281/0.35293, loss_mask_ce_8: 1.08557/1.13223, loss_mask_bce_8: 0.45189/0.36606, loss_mask_dice_8: 0.45327/1.32761, loss_spatial_bce_8: 0.32972/0.13541, loss_spatial_dice_8: 0.29202/0.31067, loss_spatial_ce_8: 0.29144/0.24804, loss_grounding_bce_8: 0.23559/0.09351, loss_grounding_dice_8: 0.21084/0.20077, loss_grounding_ce_8: 0.35544/0.42575, loss_mask_ce_9: 1.96114/3.69806, loss_mask_bce_9: 0.59295/0.39303, loss_mask_dice_9: 0.66204/1.90394, loss_spatial_bce_9: 0.52634/0.33735, loss_spatial_dice_9: 0.80032/0.82537, loss_spatial_ce_9: 1.26318/1.52479, loss_grounding_bce_9: 0.23471/0.10507, loss_grounding_dice_9: 0.29024/0.28074, loss_grounding_ce_9: 0.43708/0.70871] items per batch[64] items per second[0.23] total items[1017600] mini batches[ 15900] memory[7341] epoch remaining[0:25:31]
INFO:trainer.default_trainer:epochs[     8] optim steps[16000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05714/0.91397, loss_mask_bce_0: 0.55126/0.33639, loss_mask_dice_0: 0.77784/1.16478, loss_spatial_bce_0: 0.19815/0.09314, loss_spatial_dice_0: 0.30938/0.22270, loss_spatial_ce_0: 0.03206/0.08555, loss_grounding_bce_0: 0.12133/0.08614, loss_grounding_dice_0: 0.10604/0.17849, loss_grounding_ce_0: 0.85821/0.27716, loss_mask_ce_1: 1.08395/0.91485, loss_mask_bce_1: 0.55076/0.33705, loss_mask_dice_1: 0.77990/1.17275, loss_spatial_bce_1: 0.20391/0.09410, loss_spatial_dice_1: 0.29950/0.22714, loss_spatial_ce_1: 0.04543/0.09140, loss_grounding_bce_1: 0.12604/0.08620, loss_grounding_dice_1: 0.11242/0.17892, loss_grounding_ce_1: 0.92703/0.27917, loss_mask_ce_2: 1.08685/0.92189, loss_mask_bce_2: 0.53667/0.33738, loss_mask_dice_2: 0.78755/1.17141, loss_spatial_bce_2: 0.20197/0.09400, loss_spatial_dice_2: 0.29689/0.22800, loss_spatial_ce_2: 0.06637/0.09522, loss_grounding_bce_2: 0.12571/0.08624, loss_grounding_dice_2: 0.12575/0.17851, loss_grounding_ce_2: 0.86567/0.28211, loss_mask_ce_3: 1.22746/0.92902, loss_mask_bce_3: 0.54961/0.33834, loss_mask_dice_3: 0.77856/1.16882, loss_spatial_bce_3: 0.22072/0.09495, loss_spatial_dice_3: 0.32085/0.22902, loss_spatial_ce_3: 0.04689/0.09956, loss_grounding_bce_3: 0.13248/0.08636, loss_grounding_dice_3: 0.12796/0.17838, loss_grounding_ce_3: 1.01412/0.28328, loss_mask_ce_4: 1.12189/0.92727, loss_mask_bce_4: 0.54977/0.33971, loss_mask_dice_4: 0.77203/1.19004, loss_spatial_bce_4: 0.22334/0.09865, loss_spatial_dice_4: 0.30848/0.23736, loss_spatial_ce_4: 0.04733/0.11623, loss_grounding_bce_4: 0.14374/0.08690, loss_grounding_dice_4: 0.12561/0.18097, loss_grounding_ce_4: 1.84925/0.28582, loss_mask_ce_5: 1.06227/0.94182, loss_mask_bce_5: 0.56317/0.34196, loss_mask_dice_5: 0.82904/1.19499, loss_spatial_bce_5: 0.20398/0.09953, loss_spatial_dice_5: 0.31105/0.24025, loss_spatial_ce_5: 0.04855/0.12972, loss_grounding_bce_5: 0.16213/0.08741, loss_grounding_dice_5: 0.15510/0.18230, loss_grounding_ce_5: 1.99700/0.29828, loss_mask_ce_6: 1.23782/0.97873, loss_mask_bce_6: 0.59878/0.34452, loss_mask_dice_6: 0.78209/1.19834, loss_spatial_bce_6: 0.18039/0.10503, loss_spatial_dice_6: 0.32787/0.24302, loss_spatial_ce_6: 0.13409/0.15264, loss_grounding_bce_6: 0.12944/0.08822, loss_grounding_dice_6: 0.12842/0.18250, loss_grounding_ce_6: 0.60208/0.31796, loss_mask_ce_7: 1.13615/1.02130, loss_mask_bce_7: 0.51933/0.35230, loss_mask_dice_7: 0.74309/1.25318, loss_spatial_bce_7: 0.24961/0.11404, loss_spatial_dice_7: 0.29858/0.27010, loss_spatial_ce_7: 0.14158/0.19199, loss_grounding_bce_7: 0.13561/0.09005, loss_grounding_dice_7: 0.13871/0.18943, loss_grounding_ce_7: 0.50353/0.35313, loss_mask_ce_8: 1.44499/1.13251, loss_mask_bce_8: 0.61526/0.36583, loss_mask_dice_8: 0.93680/1.32807, loss_spatial_bce_8: 0.18584/0.13534, loss_spatial_dice_8: 0.27169/0.31073, loss_spatial_ce_8: 0.25002/0.24815, loss_grounding_bce_8: 0.12458/0.09344, loss_grounding_dice_8: 0.16038/0.20085, loss_grounding_ce_8: 0.86452/0.42564, loss_mask_ce_9: 5.81135/3.69843, loss_mask_bce_9: 0.67215/0.39270, loss_mask_dice_9: 2.82388/1.90404, loss_spatial_bce_9: 0.36732/0.33707, loss_spatial_dice_9: 0.84621/0.82532, loss_spatial_ce_9: 1.85967/1.52543, loss_grounding_bce_9: 0.19758/0.10498, loss_grounding_dice_9: 0.29498/0.28082, loss_grounding_ce_9: 1.60492/0.70836] items per batch[64] items per second[0.22] total items[1024000] mini batches[ 16000] memory[7341] epoch remaining[0:20:51]
INFO:trainer.default_trainer:epochs[     8] optim steps[16100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.29241/0.91406, loss_mask_bce_0: 0.47985/0.33651, loss_mask_dice_0: 2.18778/1.16568, loss_spatial_bce_0: 0.05662/0.09310, loss_spatial_dice_0: 0.35464/0.22271, loss_spatial_ce_0: 0.11275/0.08541, loss_grounding_bce_0: 0.04941/0.08619, loss_grounding_dice_0: 0.60301/0.17867, loss_grounding_ce_0: 0.47450/0.27727, loss_mask_ce_1: 1.87040/0.91495, loss_mask_bce_1: 0.49787/0.33717, loss_mask_dice_1: 2.35649/1.17367, loss_spatial_bce_1: 0.05843/0.09406, loss_spatial_dice_1: 0.37168/0.22714, loss_spatial_ce_1: 0.08843/0.09127, loss_grounding_bce_1: 0.03453/0.08625, loss_grounding_dice_1: 0.38282/0.17905, loss_grounding_ce_1: 0.76585/0.27924, loss_mask_ce_2: 2.07239/0.92207, loss_mask_bce_2: 0.49998/0.33748, loss_mask_dice_2: 2.24022/1.17231, loss_spatial_bce_2: 0.06089/0.09397, loss_spatial_dice_2: 0.37387/0.22801, loss_spatial_ce_2: 0.07399/0.09510, loss_grounding_bce_2: 0.03672/0.08629, loss_grounding_dice_2: 0.53717/0.17866, loss_grounding_ce_2: 0.35399/0.28221, loss_mask_ce_3: 1.99715/0.92920, loss_mask_bce_3: 0.47741/0.33845, loss_mask_dice_3: 2.32068/1.16962, loss_spatial_bce_3: 0.06128/0.09493, loss_spatial_dice_3: 0.35366/0.22901, loss_spatial_ce_3: 0.09310/0.09941, loss_grounding_bce_3: 0.03378/0.08640, loss_grounding_dice_3: 0.45202/0.17850, loss_grounding_ce_3: 0.74957/0.28341, loss_mask_ce_4: 2.16661/0.92742, loss_mask_bce_4: 0.48179/0.33980, loss_mask_dice_4: 2.37570/1.19096, loss_spatial_bce_4: 0.06233/0.09864, loss_spatial_dice_4: 0.36480/0.23737, loss_spatial_ce_4: 0.13314/0.11611, loss_grounding_bce_4: 0.04850/0.08693, loss_grounding_dice_4: 0.59249/0.18113, loss_grounding_ce_4: 0.08725/0.28594, loss_mask_ce_5: 2.15371/0.94202, loss_mask_bce_5: 0.44825/0.34208, loss_mask_dice_5: 2.32284/1.19589, loss_spatial_bce_5: 0.06408/0.09951, loss_spatial_dice_5: 0.38137/0.24027, loss_spatial_ce_5: 0.17883/0.12956, loss_grounding_bce_5: 0.03123/0.08745, loss_grounding_dice_5: 0.45398/0.18242, loss_grounding_ce_5: 0.39733/0.29842, loss_mask_ce_6: 2.49552/0.97891, loss_mask_bce_6: 0.45715/0.34462, loss_mask_dice_6: 2.18689/1.19929, loss_spatial_bce_6: 0.09065/0.10499, loss_spatial_dice_6: 0.37924/0.24305, loss_spatial_ce_6: 0.09615/0.15243, loss_grounding_bce_6: 0.03299/0.08825, loss_grounding_dice_6: 0.41369/0.18261, loss_grounding_ce_6: 0.72746/0.31795, loss_mask_ce_7: 2.12128/1.02149, loss_mask_bce_7: 0.48531/0.35241, loss_mask_dice_7: 2.48399/1.25405, loss_spatial_bce_7: 0.07427/0.11400, loss_spatial_dice_7: 0.40184/0.27011, loss_spatial_ce_7: 0.15655/0.19174, loss_grounding_bce_7: 0.03581/0.09010, loss_grounding_dice_7: 0.40978/0.18958, loss_grounding_ce_7: 0.73783/0.35300, loss_mask_ce_8: 1.72646/1.13284, loss_mask_bce_8: 0.50220/0.36595, loss_mask_dice_8: 2.58256/1.32902, loss_spatial_bce_8: 0.06934/0.13528, loss_spatial_dice_8: 0.45537/0.31075, loss_spatial_ce_8: 0.23362/0.24798, loss_grounding_bce_8: 0.03449/0.09350, loss_grounding_dice_8: 0.41321/0.20098, loss_grounding_ce_8: 0.75296/0.42555, loss_mask_ce_9: 4.19906/3.69849, loss_mask_bce_9: 0.54319/0.39284, loss_mask_dice_9: 3.80260/1.90549, loss_spatial_bce_9: 0.13699/0.33695, loss_spatial_dice_9: 0.82107/0.82530, loss_spatial_ce_9: 1.29455/1.52549, loss_grounding_bce_9: 0.08810/0.10505, loss_grounding_dice_9: 0.72149/0.28097, loss_grounding_ce_9: 0.03696/0.70803] items per batch[64] items per second[0.22] total items[1030400] mini batches[ 16100] memory[7341] epoch remaining[0:16:09]
INFO:trainer.default_trainer:epochs[     8] optim steps[16200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94217/0.91354, loss_mask_bce_0: 0.47339/0.33644, loss_mask_dice_0: 4.06879/1.16603, loss_spatial_bce_0: 0.02205/0.09305, loss_spatial_dice_0: 0.33223/0.22264, loss_spatial_ce_0: 0.02508/0.08563, loss_grounding_bce_0: 0.02368/0.08621, loss_grounding_dice_0: 0.48710/0.17867, loss_grounding_ce_0: 0.68356/0.27729, loss_mask_ce_1: 0.96311/0.91445, loss_mask_bce_1: 0.45417/0.33709, loss_mask_dice_1: 4.10687/1.17398, loss_spatial_bce_1: 0.02167/0.09401, loss_spatial_dice_1: 0.35220/0.22707, loss_spatial_ce_1: 0.03851/0.09158, loss_grounding_bce_1: 0.02383/0.08628, loss_grounding_dice_1: 0.48305/0.17903, loss_grounding_ce_1: 0.68373/0.27935, loss_mask_ce_2: 0.81779/0.92150, loss_mask_bce_2: 0.46015/0.33741, loss_mask_dice_2: 4.06587/1.17262, loss_spatial_bce_2: 0.02040/0.09392, loss_spatial_dice_2: 0.33917/0.22795, loss_spatial_ce_2: 0.23129/0.09544, loss_grounding_bce_2: 0.02320/0.08633, loss_grounding_dice_2: 0.50749/0.17863, loss_grounding_ce_2: 0.77155/0.28219, loss_mask_ce_3: 0.67650/0.92876, loss_mask_bce_3: 0.48727/0.33839, loss_mask_dice_3: 4.34533/1.16991, loss_spatial_bce_3: 0.02111/0.09488, loss_spatial_dice_3: 0.31332/0.22894, loss_spatial_ce_3: 0.04274/0.09967, loss_grounding_bce_3: 0.02351/0.08644, loss_grounding_dice_3: 0.48100/0.17846, loss_grounding_ce_3: 0.73798/0.28344, loss_mask_ce_4: 0.79147/0.92685, loss_mask_bce_4: 0.44436/0.33976, loss_mask_dice_4: 4.48408/1.19133, loss_spatial_bce_4: 0.02122/0.09858, loss_spatial_dice_4: 0.36021/0.23734, loss_spatial_ce_4: 0.38128/0.11649, loss_grounding_bce_4: 0.02164/0.08696, loss_grounding_dice_4: 0.48901/0.18112, loss_grounding_ce_4: 0.69988/0.28600, loss_mask_ce_5: 0.88899/0.94151, loss_mask_bce_5: 0.45124/0.34201, loss_mask_dice_5: 3.91743/1.19619, loss_spatial_bce_5: 0.02287/0.09946, loss_spatial_dice_5: 0.34097/0.24021, loss_spatial_ce_5: 0.06507/0.12975, loss_grounding_bce_5: 0.02339/0.08747, loss_grounding_dice_5: 0.49964/0.18242, loss_grounding_ce_5: 0.64795/0.29853, loss_mask_ce_6: 0.88210/0.97833, loss_mask_bce_6: 0.48200/0.34455, loss_mask_dice_6: 4.13291/1.19963, loss_spatial_bce_6: 0.03294/0.10494, loss_spatial_dice_6: 0.35946/0.24298, loss_spatial_ce_6: 0.12347/0.15275, loss_grounding_bce_6: 0.02224/0.08828, loss_grounding_dice_6: 0.50018/0.18260, loss_grounding_ce_6: 0.68183/0.31795, loss_mask_ce_7: 0.82549/1.02078, loss_mask_bce_7: 0.45838/0.35236, loss_mask_dice_7: 4.09318/1.25435, loss_spatial_bce_7: 0.02541/0.11392, loss_spatial_dice_7: 0.42878/0.27003, loss_spatial_ce_7: 0.15640/0.19208, loss_grounding_bce_7: 0.02231/0.09014, loss_grounding_dice_7: 0.51694/0.18956, loss_grounding_ce_7: 0.63383/0.35286, loss_mask_ce_8: 0.97882/1.13210, loss_mask_bce_8: 0.49383/0.36586, loss_mask_dice_8: 4.24749/1.32944, loss_spatial_bce_8: 0.03112/0.13523, loss_spatial_dice_8: 0.47880/0.31070, loss_spatial_ce_8: 0.52726/0.24840, loss_grounding_bce_8: 0.02490/0.09354, loss_grounding_dice_8: 0.53888/0.20096, loss_grounding_ce_8: 0.51842/0.42523, loss_mask_ce_9: 5.64752/3.69828, loss_mask_bce_9: 0.32383/0.39275, loss_mask_dice_9: 4.96402/1.90642, loss_spatial_bce_9: 0.09216/0.33720, loss_spatial_dice_9: 0.80870/0.82530, loss_spatial_ce_9: 2.47367/1.52578, loss_grounding_bce_9: 0.02087/0.10505, loss_grounding_dice_9: 0.65181/0.28095, loss_grounding_ce_9: 0.89019/0.70774] items per batch[64] items per second[0.22] total items[1036800] mini batches[ 16200] memory[7341] epoch remaining[0:11:28]
INFO:trainer.default_trainer:epochs[     8] optim steps[16300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68864/0.91336, loss_mask_bce_0: 0.84504/0.33626, loss_mask_dice_0: 1.48370/1.16565, loss_spatial_bce_0: 0.09719/0.09298, loss_spatial_dice_0: 0.26713/0.22259, loss_spatial_ce_0: 0.02786/0.08555, loss_grounding_bce_0: 0.20117/0.08619, loss_grounding_dice_0: 0.29592/0.17865, loss_grounding_ce_0: 0.70728/0.27731, loss_mask_ce_1: 0.73588/0.91416, loss_mask_bce_1: 0.93282/0.33692, loss_mask_dice_1: 1.59375/1.17362, loss_spatial_bce_1: 0.09214/0.09394, loss_spatial_dice_1: 0.26324/0.22704, loss_spatial_ce_1: 0.03170/0.09143, loss_grounding_bce_1: 0.19667/0.08626, loss_grounding_dice_1: 0.30769/0.17900, loss_grounding_ce_1: 0.80629/0.27907, loss_mask_ce_2: 0.81810/0.92130, loss_mask_bce_2: 0.98228/0.33725, loss_mask_dice_2: 1.51800/1.17231, loss_spatial_bce_2: 0.09388/0.09386, loss_spatial_dice_2: 0.25955/0.22792, loss_spatial_ce_2: 0.00799/0.09531, loss_grounding_bce_2: 0.20459/0.08631, loss_grounding_dice_2: 0.40347/0.17861, loss_grounding_ce_2: 0.81241/0.28218, loss_mask_ce_3: 0.78559/0.92865, loss_mask_bce_3: 0.97723/0.33821, loss_mask_dice_3: 1.58973/1.16954, loss_spatial_bce_3: 0.09051/0.09482, loss_spatial_dice_3: 0.26280/0.22890, loss_spatial_ce_3: 0.02723/0.09949, loss_grounding_bce_3: 0.22200/0.08641, loss_grounding_dice_3: 0.37409/0.17842, loss_grounding_ce_3: 1.02571/0.28331, loss_mask_ce_4: 0.97827/0.92682, loss_mask_bce_4: 1.00897/0.33960, loss_mask_dice_4: 1.63320/1.19087, loss_spatial_bce_4: 0.15128/0.09853, loss_spatial_dice_4: 0.27743/0.23734, loss_spatial_ce_4: 0.01565/0.11629, loss_grounding_bce_4: 0.25366/0.08694, loss_grounding_dice_4: 0.42089/0.18111, loss_grounding_ce_4: 1.12448/0.28586, loss_mask_ce_5: 0.97681/0.94133, loss_mask_bce_5: 1.06551/0.34187, loss_mask_dice_5: 1.70920/1.19590, loss_spatial_bce_5: 0.11523/0.09943, loss_spatial_dice_5: 0.26875/0.24018, loss_spatial_ce_5: 0.04969/0.12966, loss_grounding_bce_5: 0.27511/0.08745, loss_grounding_dice_5: 0.39650/0.18238, loss_grounding_ce_5: 1.81224/0.29849, loss_mask_ce_6: 0.98241/0.97816, loss_mask_bce_6: 1.02801/0.34438, loss_mask_dice_6: 1.64460/1.19922, loss_spatial_bce_6: 0.14694/0.10490, loss_spatial_dice_6: 0.26601/0.24295, loss_spatial_ce_6: 0.04995/0.15264, loss_grounding_bce_6: 0.27299/0.08826, loss_grounding_dice_6: 0.33290/0.18254, loss_grounding_ce_6: 1.77344/0.31770, loss_mask_ce_7: 0.63103/1.02048, loss_mask_bce_7: 1.03445/0.35217, loss_mask_dice_7: 1.63589/1.25401, loss_spatial_bce_7: 0.14634/0.11387, loss_spatial_dice_7: 0.28684/0.27003, loss_spatial_ce_7: 0.08571/0.19204, loss_grounding_bce_7: 0.35082/0.09013, loss_grounding_dice_7: 0.39202/0.18955, loss_grounding_ce_7: 1.96181/0.35270, loss_mask_ce_8: 1.21895/1.13199, loss_mask_bce_8: 0.81355/0.36564, loss_mask_dice_8: 1.61040/1.32911, loss_spatial_bce_8: 0.17411/0.13519, loss_spatial_dice_8: 0.32765/0.31074, loss_spatial_ce_8: 0.18275/0.24826, loss_grounding_bce_8: 0.26677/0.09351, loss_grounding_dice_8: 0.34543/0.20096, loss_grounding_ce_8: 2.45386/0.42518, loss_mask_ce_9: 4.71932/3.69731, loss_mask_bce_9: 0.97668/0.39250, loss_mask_dice_9: 2.63416/1.90569, loss_spatial_bce_9: 0.24929/0.33716, loss_spatial_dice_9: 0.88514/0.82540, loss_spatial_ce_9: 1.26870/1.52590, loss_grounding_bce_9: 0.30478/0.10503, loss_grounding_dice_9: 0.53883/0.28100, loss_grounding_ce_9: 1.68066/0.70757] items per batch[64] items per second[0.22] total items[1043200] mini batches[ 16300] memory[7341] epoch remaining[0:06:45]
INFO:trainer.default_trainer:epochs[     8] optim steps[16400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65214/0.91346, loss_mask_bce_0: 0.25656/0.33626, loss_mask_dice_0: 2.00352/1.16493, loss_spatial_bce_0: 0.03916/0.09302, loss_spatial_dice_0: 0.21781/0.22250, loss_spatial_ce_0: 0.01925/0.08538, loss_grounding_bce_0: 0.01501/0.08622, loss_grounding_dice_0: 0.31052/0.17879, loss_grounding_ce_0: 0.56289/0.27737, loss_mask_ce_1: 0.60789/0.91410, loss_mask_bce_1: 0.25625/0.33693, loss_mask_dice_1: 2.07324/1.17286, loss_spatial_bce_1: 0.04103/0.09397, loss_spatial_dice_1: 0.21126/0.22694, loss_spatial_ce_1: 0.16652/0.09128, loss_grounding_bce_1: 0.01547/0.08629, loss_grounding_dice_1: 0.35342/0.17918, loss_grounding_ce_1: 0.55201/0.27906, loss_mask_ce_2: 0.72362/0.92130, loss_mask_bce_2: 0.25222/0.33726, loss_mask_dice_2: 1.79940/1.17171, loss_spatial_bce_2: 0.03924/0.09390, loss_spatial_dice_2: 0.23829/0.22782, loss_spatial_ce_2: 0.04189/0.09514, loss_grounding_bce_2: 0.01545/0.08633, loss_grounding_dice_2: 0.27145/0.17877, loss_grounding_ce_2: 0.63905/0.28211, loss_mask_ce_3: 0.72936/0.92869, loss_mask_bce_3: 0.24557/0.33820, loss_mask_dice_3: 2.09628/1.16877, loss_spatial_bce_3: 0.03836/0.09485, loss_spatial_dice_3: 0.22167/0.22881, loss_spatial_ce_3: 0.03996/0.09937, loss_grounding_bce_3: 0.01680/0.08643, loss_grounding_dice_3: 0.31212/0.17858, loss_grounding_ce_3: 0.62182/0.28323, loss_mask_ce_4: 0.55912/0.92682, loss_mask_bce_4: 0.24357/0.33960, loss_mask_dice_4: 2.08008/1.19018, loss_spatial_bce_4: 0.04316/0.09855, loss_spatial_dice_4: 0.23933/0.23724, loss_spatial_ce_4: 0.04643/0.11612, loss_grounding_bce_4: 0.01676/0.08697, loss_grounding_dice_4: 0.34410/0.18127, loss_grounding_ce_4: 0.61085/0.28580, loss_mask_ce_5: 0.84848/0.94149, loss_mask_bce_5: 0.25210/0.34187, loss_mask_dice_5: 1.82590/1.19532, loss_spatial_bce_5: 0.04369/0.09944, loss_spatial_dice_5: 0.24208/0.24004, loss_spatial_ce_5: 0.09478/0.12960, loss_grounding_bce_5: 0.01384/0.08745, loss_grounding_dice_5: 0.22957/0.18249, loss_grounding_ce_5: 0.67602/0.29847, loss_mask_ce_6: 0.69171/0.97820, loss_mask_bce_6: 0.25850/0.34437, loss_mask_dice_6: 2.09789/1.19855, loss_spatial_bce_6: 0.04323/0.10492, loss_spatial_dice_6: 0.23978/0.24282, loss_spatial_ce_6: 0.25442/0.15258, loss_grounding_bce_6: 0.01522/0.08828, loss_grounding_dice_6: 0.26473/0.18270, loss_grounding_ce_6: 0.73312/0.31766, loss_mask_ce_7: 0.80883/1.02044, loss_mask_bce_7: 0.31951/0.35217, loss_mask_dice_7: 2.37285/1.25340, loss_spatial_bce_7: 0.04547/0.11389, loss_spatial_dice_7: 0.26727/0.26990, loss_spatial_ce_7: 0.09717/0.19199, loss_grounding_bce_7: 0.01383/0.09014, loss_grounding_dice_7: 0.31702/0.18970, loss_grounding_ce_7: 0.55608/0.35278, loss_mask_ce_8: 0.61209/1.13201, loss_mask_bce_8: 0.36656/0.36566, loss_mask_dice_8: 2.60108/1.32853, loss_spatial_bce_8: 0.06626/0.13522, loss_spatial_dice_8: 0.32842/0.31061, loss_spatial_ce_8: 0.24112/0.24825, loss_grounding_bce_8: 0.01411/0.09354, loss_grounding_dice_8: 0.21039/0.20110, loss_grounding_ce_8: 0.70338/0.42536, loss_mask_ce_9: 3.84961/3.69739, loss_mask_bce_9: 0.47317/0.39261, loss_mask_dice_9: 3.86474/1.90491, loss_spatial_bce_9: 0.27979/0.33727, loss_spatial_dice_9: 0.86827/0.82536, loss_spatial_ce_9: 1.22819/1.52591, loss_grounding_bce_9: 0.01636/0.10509, loss_grounding_dice_9: 0.34117/0.28120, loss_grounding_ce_9: 0.99475/0.70711] items per batch[64] items per second[0.23] total items[1049600] mini batches[ 16400] memory[7341] epoch remaining[0:02:01]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00016443.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0027 s/iter. Inference: 0.2259 s/iter. Eval: 0.0998 s/iter. Total: 0.3283 s/iter. ETA=0:00:47
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0028 s/iter. Inference: 0.2307 s/iter. Eval: 0.0895 s/iter. Total: 0.3231 s/iter. ETA=0:00:42
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 43/157. Dataloading: 0.0030 s/iter. Inference: 0.2315 s/iter. Eval: 0.0854 s/iter. Total: 0.3201 s/iter. ETA=0:00:36
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 60/157. Dataloading: 0.0031 s/iter. Inference: 0.2325 s/iter. Eval: 0.0804 s/iter. Total: 0.3161 s/iter. ETA=0:00:30
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0031 s/iter. Inference: 0.2309 s/iter. Eval: 0.0760 s/iter. Total: 0.3101 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2322 s/iter. Eval: 0.0748 s/iter. Total: 0.3102 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 109/157. Dataloading: 0.0077 s/iter. Inference: 0.2334 s/iter. Eval: 0.0755 s/iter. Total: 0.3167 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 126/157. Dataloading: 0.0071 s/iter. Inference: 0.2327 s/iter. Eval: 0.0757 s/iter. Total: 0.3157 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 142/157. Dataloading: 0.0067 s/iter. Inference: 0.2333 s/iter. Eval: 0.0756 s/iter. Total: 0.3158 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalg548dfa6 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.851 | 82.270 | 59.819 |      133      |
| Things | 55.012 | 82.986 | 65.717 |      80       |
| Stuff  | 42.061 | 81.189 | 50.917 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.34s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.96 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.06s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.47 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.01 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.614
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.413
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.296
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.036 | 61.386 | 41.301 | 19.752 | 42.124 | 60.096 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.990 | bicycle      | 18.590 | car            | 36.854 |
| motorcycle    | 34.638 | airplane     | 56.514 | bus            | 64.527 |
| train         | 69.588 | truck        | 35.816 | boat           | 22.671 |
| traffic light | 25.104 | fire hydrant | 64.923 | stop sign      | 64.252 |
| parking meter | 43.581 | bench        | 20.782 | bird           | 29.910 |
| cat           | 73.855 | dog          | 66.139 | horse          | 43.321 |
| sheep         | 46.308 | cow          | 51.289 | elephant       | 59.995 |
| bear          | 77.115 | zebra        | 60.006 | giraffe        | 57.346 |
| backpack      | 16.547 | umbrella     | 48.722 | handbag        | 14.442 |
| tie           | 33.517 | suitcase     | 40.443 | frisbee        | 68.087 |
| skis          | 4.950  | snowboard    | 23.938 | sports ball    | 47.525 |
| kite          | 33.288 | baseball bat | 29.005 | baseball glove | 44.291 |
| skateboard    | 36.755 | surfboard    | 35.798 | tennis racket  | 57.082 |
| bottle        | 32.999 | wine glass   | 27.930 | cup            | 39.995 |
| fork          | 14.874 | knife        | 13.329 | spoon          | 14.658 |
| bowl          | 32.448 | banana       | 19.889 | apple          | 19.584 |
| sandwich      | 43.500 | orange       | 29.018 | broccoli       | 21.948 |
| carrot        | 20.181 | hot dog      | 23.439 | pizza          | 50.008 |
| donut         | 46.449 | cake         | 43.886 | chair          | 20.751 |
| couch         | 41.826 | potted plant | 18.178 | bed            | 41.903 |
| dining table  | 13.772 | toilet       | 66.961 | tv             | 62.531 |
| laptop        | 63.475 | mouse        | 57.566 | remote         | 30.010 |
| keyboard      | 46.997 | cell phone   | 38.566 | microwave      | 56.733 |
| oven          | 32.766 | toaster      | 38.221 | sink           | 37.125 |
| refrigerator  | 59.441 | book         | 8.527  | clock          | 51.596 |
| vase          | 33.082 | scissors     | 25.920 | teddy bear     | 51.556 |
| hair drier    | 10.373 | toothbrush   | 19.362 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.497034554951924, 'fwIoU': 68.97419497500447, 'IoU-person': 87.63402543114368, 'IoU-bicycle': 71.12346810573898, 'IoU-car': 68.70368681356167, 'IoU-motorcycle': 85.17148072958595, 'IoU-airplane': 76.02712525731505, 'IoU-bus': 85.3762153549923, 'IoU-train': 87.4385047010329, 'IoU-truck': 62.57947950053831, 'IoU-boat': 67.34455088380295, 'IoU-traffic light': 77.41852911976422, 'IoU-fire hydrant': 88.32547474045397, 'IoU-stop sign': 91.87873686784023, 'IoU-parking meter': 86.4065587167598, 'IoU-bench': 55.46495962537607, 'IoU-bird': 75.1433906358739, 'IoU-cat': 87.15457252507208, 'IoU-dog': 76.78512163346988, 'IoU-horse': 84.44800837355353, 'IoU-sheep': 81.77004085881492, 'IoU-cow': 78.93562885812884, 'IoU-elephant': 91.18467460282396, 'IoU-bear': 65.1295853235781, 'IoU-zebra': 79.89848047722909, 'IoU-giraffe': 85.68536884376805, 'IoU-backpack': 40.15428380673159, 'IoU-umbrella': 73.96659795097756, 'IoU-handbag': 36.96447592219205, 'IoU-tie': 70.10777216816703, 'IoU-suitcase': 80.13674451442692, 'IoU-frisbee': 83.82366264997864, 'IoU-skis': 52.75912289606517, 'IoU-snowboard': 68.29035126722988, 'IoU-sports ball': 62.62737875997545, 'IoU-kite': 65.54347404736986, 'IoU-baseball bat': 60.369085532302094, 'IoU-baseball glove': 52.855905779057856, 'IoU-skateboard': 64.0681746041561, 'IoU-surfboard': 81.69966746814109, 'IoU-tennis racket': 83.02740602082876, 'IoU-bottle': 67.64449222546017, 'IoU-wine glass': 73.04600669536107, 'IoU-cup': 64.30339102188172, 'IoU-fork': 54.695053992933914, 'IoU-knife': 47.536632551460464, 'IoU-spoon': 51.613326628037484, 'IoU-bowl': 56.066865528863616, 'IoU-banana': 83.79288027219303, 'IoU-apple': 57.20523649527733, 'IoU-sandwich': 67.12802975677987, 'IoU-orange': 80.53637177275448, 'IoU-broccoli': 69.16343839259433, 'IoU-carrot': 64.79348172161278, 'IoU-hot dog': 63.94191936403067, 'IoU-pizza': 87.20538894947292, 'IoU-donut': 66.72478453039702, 'IoU-cake': 75.68582155305621, 'IoU-chair': 53.63653696695564, 'IoU-couch': 67.0060809571958, 'IoU-potted plant': 35.308309656115064, 'IoU-bed': 68.67274617325556, 'IoU-dining table': 50.35046717029391, 'IoU-toilet': 83.29009381845734, 'IoU-tv': 75.85216943256601, 'IoU-laptop': 72.21123025573459, 'IoU-mouse': 72.07333034405032, 'IoU-remote': 65.45824995550186, 'IoU-keyboard': 61.913510860498114, 'IoU-cell phone': 75.06234890958419, 'IoU-microwave': 53.84779367975451, 'IoU-oven': 69.3164271111047, 'IoU-toaster': 59.533707431336715, 'IoU-sink': 69.88963143503595, 'IoU-refrigerator': 82.86946925237814, 'IoU-book': 48.23221370336711, 'IoU-clock': 60.48691976630578, 'IoU-vase': 52.81336345642481, 'IoU-scissors': 60.86249836151527, 'IoU-teddy bear': 81.97867007883647, 'IoU-hair drier': 37.74189654941194, 'IoU-toothbrush': 56.414671740282266, 'IoU-banner': 33.89283244778715, 'IoU-blanket': 11.599872053632723, 'IoU-bridge': 37.959351029011835, 'IoU-cardboard': 43.548329101760224, 'IoU-counter': 32.06826409239786, 'IoU-curtain': 63.86767611442051, 'IoU-door-stuff': 43.133886469169575, 'IoU-floor-wood': 61.919082641696875, 'IoU-flower': 43.217955309588376, 'IoU-fruit': 39.29171705845233, 'IoU-gravel': 26.284405812334587, 'IoU-house': 25.590477264743395, 'IoU-light': 38.58672470505322, 'IoU-mirror-stuff': 58.40137802937442, 'IoU-net': 47.361325428409, 'IoU-pillow': 15.364583333333334, 'IoU-platform': 31.677453076968092, 'IoU-playingfield': 70.50768008664552, 'IoU-railroad': 60.27013685794742, 'IoU-river': 47.767206507554064, 'IoU-road': 66.43244267251917, 'IoU-roof': 15.852803137692709, 'IoU-sand': 62.36376013646252, 'IoU-sea': 85.34006079054951, 'IoU-shelf': 36.78532586685645, 'IoU-snow': 87.74131377465234, 'IoU-stairs': 26.371646210094752, 'IoU-tent': 8.117545849630778, 'IoU-towel': 31.30744849445325, 'IoU-wall-brick': 48.37447391884024, 'IoU-wall-stone': 28.888670874625095, 'IoU-wall-tile': 66.80311456102564, 'IoU-wall-wood': 37.56283105602561, 'IoU-water-other': 22.654724596504614, 'IoU-window-blind': 47.65235316923717, 'IoU-window-other': 48.63955793597147, 'IoU-tree-merged': 81.16044001840507, 'IoU-fence-merged': 50.61128614889054, 'IoU-ceiling-merged': 66.05479941216541, 'IoU-sky-other-merged': 93.3222188347524, 'IoU-cabinet-merged': 58.54846316199873, 'IoU-table-merged': 36.26459832444108, 'IoU-floor-other-merged': 49.38841287067995, 'IoU-pavement-merged': 53.99800747999074, 'IoU-mountain-merged': 55.25503823904552, 'IoU-grass-merged': 70.71915888545169, 'IoU-dirt-merged': 45.148097221178745, 'IoU-paper-merged': 29.25014510002718, 'IoU-food-other-merged': 39.68098720060875, 'IoU-building-other-merged': 58.412774878148014, 'IoU-rock-merged': 57.058745443007595, 'IoU-wall-other-merged': 64.45240549780148, 'IoU-rug-merged': 64.25437674064328, 'mACC': 72.1667726550782, 'pACC': 80.34579433507702, 'ACC-person': 92.38334377226056, 'ACC-bicycle': 79.8666187884504, 'ACC-car': 83.67473950715662, 'ACC-motorcycle': 90.20310529600685, 'ACC-airplane': 87.07327507035052, 'ACC-bus': 91.1944237626421, 'ACC-train': 94.18560648825877, 'ACC-truck': 73.37360940682389, 'ACC-boat': 77.24947632280217, 'ACC-traffic light': 89.22425170405211, 'ACC-fire hydrant': 93.19651603205985, 'ACC-stop sign': 94.47627240368632, 'ACC-parking meter': 89.88928445070546, 'ACC-bench': 74.65654850533645, 'ACC-bird': 79.83149084963964, 'ACC-cat': 93.28304504214451, 'ACC-dog': 79.91470518206381, 'ACC-horse': 92.41242580928271, 'ACC-sheep': 84.55997359428976, 'ACC-cow': 84.4414930015396, 'ACC-elephant': 93.57977291097212, 'ACC-bear': 66.50409648053078, 'ACC-zebra': 81.84934841384266, 'ACC-giraffe': 89.61228527149578, 'ACC-backpack': 60.6171286842359, 'ACC-umbrella': 80.69652908561767, 'ACC-handbag': 54.661394749730455, 'ACC-tie': 82.20963769414126, 'ACC-suitcase': 89.76317853530807, 'ACC-frisbee': 94.20072727272726, 'ACC-skis': 70.47738137016732, 'ACC-snowboard': 75.22960495701795, 'ACC-sports ball': 79.09262877166869, 'ACC-kite': 76.60439618524084, 'ACC-baseball bat': 79.53382433374043, 'ACC-baseball glove': 60.03364965449659, 'ACC-skateboard': 69.75977485625542, 'ACC-surfboard': 89.6679111384958, 'ACC-tennis racket': 89.27605920824779, 'ACC-bottle': 80.02725640509718, 'ACC-wine glass': 86.54602817718627, 'ACC-cup': 82.73846123512985, 'ACC-fork': 67.82477224627857, 'ACC-knife': 56.76137612662123, 'ACC-spoon': 71.92341048183907, 'ACC-bowl': 67.34808631087499, 'ACC-banana': 89.91657894613597, 'ACC-apple': 69.65522360766862, 'ACC-sandwich': 80.17886566713727, 'ACC-orange': 87.6778016045769, 'ACC-broccoli': 80.94399061123644, 'ACC-carrot': 74.93621492912689, 'ACC-hot dog': 72.05544951825658, 'ACC-pizza': 94.44613948266077, 'ACC-donut': 81.43211657125302, 'ACC-cake': 84.84014245179694, 'ACC-chair': 67.27899310252506, 'ACC-couch': 82.61156380765526, 'ACC-potted plant': 52.16191494709863, 'ACC-bed': 83.13006530291514, 'ACC-dining table': 77.69480590869966, 'ACC-toilet': 92.63072899853707, 'ACC-tv': 86.78495061408637, 'ACC-laptop': 87.55808382096151, 'ACC-mouse': 85.8164778199842, 'ACC-remote': 70.34061747876122, 'ACC-keyboard': 67.57598619627969, 'ACC-cell phone': 88.19817178443074, 'ACC-microwave': 60.640379190324765, 'ACC-oven': 83.55452311322026, 'ACC-toaster': 63.3052320409476, 'ACC-sink': 81.62432106720951, 'ACC-refrigerator': 90.79007184386225, 'ACC-book': 62.50854557171276, 'ACC-clock': 64.30454394754923, 'ACC-vase': 59.79107259991751, 'ACC-scissors': 66.61524759202638, 'ACC-teddy bear': 87.50066868246668, 'ACC-hair drier': 40.05208778071898, 'ACC-toothbrush': 81.39159138290479, 'ACC-banner': 77.58891898977541, 'ACC-blanket': 15.756949921536423, 'ACC-bridge': 52.95812043517568, 'ACC-cardboard': 52.48151810382434, 'ACC-counter': 53.80913188492177, 'ACC-curtain': 74.64445709706806, 'ACC-door-stuff': 58.475348224643284, 'ACC-floor-wood': 78.45559742560972, 'ACC-flower': 59.00292975369483, 'ACC-fruit': 56.48094455393912, 'ACC-gravel': 30.948699129622614, 'ACC-house': 32.29575169276425, 'ACC-light': 53.83881062665085, 'ACC-mirror-stuff': 74.88740985915368, 'ACC-net': 64.6442879218185, 'ACC-pillow': 26.977267267346544, 'ACC-platform': 57.36041857519659, 'ACC-playingfield': 89.19885694852671, 'ACC-railroad': 78.35615808114751, 'ACC-river': 71.0284753545804, 'ACC-road': 86.66672777460599, 'ACC-roof': 21.106479727169383, 'ACC-sand': 71.4878917216928, 'ACC-sea': 91.14116469491661, 'ACC-shelf': 59.65900695791434, 'ACC-snow': 94.31867321024468, 'ACC-stairs': 41.88343724035596, 'ACC-tent': 10.097507015570175, 'ACC-towel': 37.44658611482699, 'ACC-wall-brick': 60.41119340487134, 'ACC-wall-stone': 33.90372818665097, 'ACC-wall-tile': 78.60839451715566, 'ACC-wall-wood': 55.30374485501264, 'ACC-water-other': 36.38975789335619, 'ACC-window-blind': 57.80284044975165, 'ACC-window-other': 69.87082961740265, 'ACC-tree-merged': 90.07908091711234, 'ACC-fence-merged': 72.62464785128827, 'ACC-ceiling-merged': 78.49916973449471, 'ACC-sky-other-merged': 96.52090527636933, 'ACC-cabinet-merged': 73.90122530978172, 'ACC-table-merged': 47.95584004413585, 'ACC-floor-other-merged': 60.12854727307222, 'ACC-pavement-merged': 66.35355364449568, 'ACC-mountain-merged': 66.5280996085472, 'ACC-grass-merged': 83.82353384579805, 'ACC-dirt-merged': 64.47629462243945, 'ACC-paper-merged': 38.75093548744732, 'ACC-food-other-merged': 53.843573871278, 'ACC-building-other-merged': 75.91138877675456, 'ACC-rock-merged': 82.89173089360703, 'ACC-wall-other-merged': 80.86814429406877, 'ACC-rug-merged': 78.16398291505949})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1534 s/iter. Inference: 0.5850 s/iter. Eval: 0.0000 s/iter. Total: 0.7384 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1560 s/iter. Inference: 0.5336 s/iter. Eval: 0.0000 s/iter. Total: 0.6898 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/50. Dataloading: 0.1666 s/iter. Inference: 0.7042 s/iter. Eval: 0.0000 s/iter. Total: 0.8709 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 27/50. Dataloading: 0.1718 s/iter. Inference: 0.7296 s/iter. Eval: 0.0000 s/iter. Total: 0.9016 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 36/50. Dataloading: 0.1713 s/iter. Inference: 0.6319 s/iter. Eval: 0.0000 s/iter. Total: 0.8034 s/iter. ETA=0:00:11
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 41/50. Dataloading: 0.1705 s/iter. Inference: 0.6952 s/iter. Eval: 0.0000 s/iter. Total: 0.8659 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 47/50. Dataloading: 0.1713 s/iter. Inference: 0.6957 s/iter. Eval: 0.0000 s/iter. Total: 0.8672 s/iter. ETA=0:00:02
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5835528241147205, 'noc@0.8': 3.0222417325139013, 'noc@0.85': 3.6414983904009364, 'noc@0.9': 4.679543459174715, 'miou@iter1': 0.825649315061226}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1023 s/iter. Eval: 0.0008 s/iter. Total: 0.1049 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.00660705566406, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.68946838378906, 'precision@0.8': 51.88496017456055, 'precision@0.9': 26.467159271240234, 'cIoU': 57.620887756347656, 'mIoU': 62.68373489379883}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.851359159297786, 'SQ': 82.2701296508038, 'RQ': 59.8193487476128, 'PQ_th': 55.012366877331175, 'SQ_th': 82.98634602371955, 'RQ_th': 65.71688676293816, 'PQ_st': 42.061158830190806, 'SQ_st': 81.18904833319513, 'RQ_st': 50.917404573536814}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.03597287231151, 'AP50': 61.386167380967024, 'AP75': 41.30053859802071, 'APs': 19.751727610414967, 'APm': 42.12373282531252, 'APl': 60.095764613482835, 'AP-person': 43.989841973546085, 'AP-bicycle': 18.589924394720647, 'AP-car': 36.85366769587641, 'AP-motorcycle': 34.63832152957577, 'AP-airplane': 56.51399762349286, 'AP-bus': 64.52702918224915, 'AP-train': 69.58756930987249, 'AP-truck': 35.81611758024429, 'AP-boat': 22.67069690439354, 'AP-traffic light': 25.10372824238355, 'AP-fire hydrant': 64.92324757552348, 'AP-stop sign': 64.25170103162851, 'AP-parking meter': 43.58134914603699, 'AP-bench': 20.782294950861022, 'AP-bird': 29.909660168207807, 'AP-cat': 73.85502226800709, 'AP-dog': 66.13867493357284, 'AP-horse': 43.32137352897667, 'AP-sheep': 46.30826710381321, 'AP-cow': 51.288574588564664, 'AP-elephant': 59.99509819859089, 'AP-bear': 77.11531281022026, 'AP-zebra': 60.006007106182714, 'AP-giraffe': 57.34608860637188, 'AP-backpack': 16.54653011980734, 'AP-umbrella': 48.721772157514835, 'AP-handbag': 14.441519297462001, 'AP-tie': 33.51704408577876, 'AP-suitcase': 40.44325240411116, 'AP-frisbee': 68.08732614007165, 'AP-skis': 4.949633609297763, 'AP-snowboard': 23.938455460437194, 'AP-sports ball': 47.52466419024661, 'AP-kite': 33.28842338671407, 'AP-baseball bat': 29.00543574391842, 'AP-baseball glove': 44.290644594513026, 'AP-skateboard': 36.75467950286119, 'AP-surfboard': 35.79808987797911, 'AP-tennis racket': 57.08227478373495, 'AP-bottle': 32.999343278567764, 'AP-wine glass': 27.930278905600037, 'AP-cup': 39.99509892219871, 'AP-fork': 14.87396162520653, 'AP-knife': 13.329157951165401, 'AP-spoon': 14.65800557734884, 'AP-bowl': 32.44774589890293, 'AP-banana': 19.889468934397712, 'AP-apple': 19.58440091240094, 'AP-sandwich': 43.50019377547783, 'AP-orange': 29.01751932857867, 'AP-broccoli': 21.948333649631625, 'AP-carrot': 20.18055273199745, 'AP-hot dog': 23.438522512938974, 'AP-pizza': 50.007839997258806, 'AP-donut': 46.448633191733265, 'AP-cake': 43.88579474413952, 'AP-chair': 20.75146064995062, 'AP-couch': 41.82644446488707, 'AP-potted plant': 18.178276527702018, 'AP-bed': 41.902711602892104, 'AP-dining table': 13.772169690774321, 'AP-toilet': 66.96135931449432, 'AP-tv': 62.53126405199944, 'AP-laptop': 63.47458974630518, 'AP-mouse': 57.56585955844764, 'AP-remote': 30.009766015743704, 'AP-keyboard': 46.99721684318934, 'AP-cell phone': 38.56631162693967, 'AP-microwave': 56.73333553255411, 'AP-oven': 32.76550716456689, 'AP-toaster': 38.22087428523072, 'AP-sink': 37.12452884249315, 'AP-refrigerator': 59.441324020769606, 'AP-book': 8.526821610497448, 'AP-clock': 51.59567068631033, 'AP-vase': 33.08248315483128, 'AP-scissors': 25.92048167124376, 'AP-teddy bear': 51.556302166335314, 'AP-hair drier': 10.372663210040976, 'AP-toothbrush': 19.36224360181502}), ('sem_seg', {'mIoU': 60.497034554951924, 'fwIoU': 68.97419497500447, 'IoU-person': 87.63402543114368, 'IoU-bicycle': 71.12346810573898, 'IoU-car': 68.70368681356167, 'IoU-motorcycle': 85.17148072958595, 'IoU-airplane': 76.02712525731505, 'IoU-bus': 85.3762153549923, 'IoU-train': 87.4385047010329, 'IoU-truck': 62.57947950053831, 'IoU-boat': 67.34455088380295, 'IoU-traffic light': 77.41852911976422, 'IoU-fire hydrant': 88.32547474045397, 'IoU-stop sign': 91.87873686784023, 'IoU-parking meter': 86.4065587167598, 'IoU-bench': 55.46495962537607, 'IoU-bird': 75.1433906358739, 'IoU-cat': 87.15457252507208, 'IoU-dog': 76.78512163346988, 'IoU-horse': 84.44800837355353, 'IoU-sheep': 81.77004085881492, 'IoU-cow': 78.93562885812884, 'IoU-elephant': 91.18467460282396, 'IoU-bear': 65.1295853235781, 'IoU-zebra': 79.89848047722909, 'IoU-giraffe': 85.68536884376805, 'IoU-backpack': 40.15428380673159, 'IoU-umbrella': 73.96659795097756, 'IoU-handbag': 36.96447592219205, 'IoU-tie': 70.10777216816703, 'IoU-suitcase': 80.13674451442692, 'IoU-frisbee': 83.82366264997864, 'IoU-skis': 52.75912289606517, 'IoU-snowboard': 68.29035126722988, 'IoU-sports ball': 62.62737875997545, 'IoU-kite': 65.54347404736986, 'IoU-baseball bat': 60.369085532302094, 'IoU-baseball glove': 52.855905779057856, 'IoU-skateboard': 64.0681746041561, 'IoU-surfboard': 81.69966746814109, 'IoU-tennis racket': 83.02740602082876, 'IoU-bottle': 67.64449222546017, 'IoU-wine glass': 73.04600669536107, 'IoU-cup': 64.30339102188172, 'IoU-fork': 54.695053992933914, 'IoU-knife': 47.536632551460464, 'IoU-spoon': 51.613326628037484, 'IoU-bowl': 56.066865528863616, 'IoU-banana': 83.79288027219303, 'IoU-apple': 57.20523649527733, 'IoU-sandwich': 67.12802975677987, 'IoU-orange': 80.53637177275448, 'IoU-broccoli': 69.16343839259433, 'IoU-carrot': 64.79348172161278, 'IoU-hot dog': 63.94191936403067, 'IoU-pizza': 87.20538894947292, 'IoU-donut': 66.72478453039702, 'IoU-cake': 75.68582155305621, 'IoU-chair': 53.63653696695564, 'IoU-couch': 67.0060809571958, 'IoU-potted plant': 35.308309656115064, 'IoU-bed': 68.67274617325556, 'IoU-dining table': 50.35046717029391, 'IoU-toilet': 83.29009381845734, 'IoU-tv': 75.85216943256601, 'IoU-laptop': 72.21123025573459, 'IoU-mouse': 72.07333034405032, 'IoU-remote': 65.45824995550186, 'IoU-keyboard': 61.913510860498114, 'IoU-cell phone': 75.06234890958419, 'IoU-microwave': 53.84779367975451, 'IoU-oven': 69.3164271111047, 'IoU-toaster': 59.533707431336715, 'IoU-sink': 69.88963143503595, 'IoU-refrigerator': 82.86946925237814, 'IoU-book': 48.23221370336711, 'IoU-clock': 60.48691976630578, 'IoU-vase': 52.81336345642481, 'IoU-scissors': 60.86249836151527, 'IoU-teddy bear': 81.97867007883647, 'IoU-hair drier': 37.74189654941194, 'IoU-toothbrush': 56.414671740282266, 'IoU-banner': 33.89283244778715, 'IoU-blanket': 11.599872053632723, 'IoU-bridge': 37.959351029011835, 'IoU-cardboard': 43.548329101760224, 'IoU-counter': 32.06826409239786, 'IoU-curtain': 63.86767611442051, 'IoU-door-stuff': 43.133886469169575, 'IoU-floor-wood': 61.919082641696875, 'IoU-flower': 43.217955309588376, 'IoU-fruit': 39.29171705845233, 'IoU-gravel': 26.284405812334587, 'IoU-house': 25.590477264743395, 'IoU-light': 38.58672470505322, 'IoU-mirror-stuff': 58.40137802937442, 'IoU-net': 47.361325428409, 'IoU-pillow': 15.364583333333334, 'IoU-platform': 31.677453076968092, 'IoU-playingfield': 70.50768008664552, 'IoU-railroad': 60.27013685794742, 'IoU-river': 47.767206507554064, 'IoU-road': 66.43244267251917, 'IoU-roof': 15.852803137692709, 'IoU-sand': 62.36376013646252, 'IoU-sea': 85.34006079054951, 'IoU-shelf': 36.78532586685645, 'IoU-snow': 87.74131377465234, 'IoU-stairs': 26.371646210094752, 'IoU-tent': 8.117545849630778, 'IoU-towel': 31.30744849445325, 'IoU-wall-brick': 48.37447391884024, 'IoU-wall-stone': 28.888670874625095, 'IoU-wall-tile': 66.80311456102564, 'IoU-wall-wood': 37.56283105602561, 'IoU-water-other': 22.654724596504614, 'IoU-window-blind': 47.65235316923717, 'IoU-window-other': 48.63955793597147, 'IoU-tree-merged': 81.16044001840507, 'IoU-fence-merged': 50.61128614889054, 'IoU-ceiling-merged': 66.05479941216541, 'IoU-sky-other-merged': 93.3222188347524, 'IoU-cabinet-merged': 58.54846316199873, 'IoU-table-merged': 36.26459832444108, 'IoU-floor-other-merged': 49.38841287067995, 'IoU-pavement-merged': 53.99800747999074, 'IoU-mountain-merged': 55.25503823904552, 'IoU-grass-merged': 70.71915888545169, 'IoU-dirt-merged': 45.148097221178745, 'IoU-paper-merged': 29.25014510002718, 'IoU-food-other-merged': 39.68098720060875, 'IoU-building-other-merged': 58.412774878148014, 'IoU-rock-merged': 57.058745443007595, 'IoU-wall-other-merged': 64.45240549780148, 'IoU-rug-merged': 64.25437674064328, 'mACC': 72.1667726550782, 'pACC': 80.34579433507702, 'ACC-person': 92.38334377226056, 'ACC-bicycle': 79.8666187884504, 'ACC-car': 83.67473950715662, 'ACC-motorcycle': 90.20310529600685, 'ACC-airplane': 87.07327507035052, 'ACC-bus': 91.1944237626421, 'ACC-train': 94.18560648825877, 'ACC-truck': 73.37360940682389, 'ACC-boat': 77.24947632280217, 'ACC-traffic light': 89.22425170405211, 'ACC-fire hydrant': 93.19651603205985, 'ACC-stop sign': 94.47627240368632, 'ACC-parking meter': 89.88928445070546, 'ACC-bench': 74.65654850533645, 'ACC-bird': 79.83149084963964, 'ACC-cat': 93.28304504214451, 'ACC-dog': 79.91470518206381, 'ACC-horse': 92.41242580928271, 'ACC-sheep': 84.55997359428976, 'ACC-cow': 84.4414930015396, 'ACC-elephant': 93.57977291097212, 'ACC-bear': 66.50409648053078, 'ACC-zebra': 81.84934841384266, 'ACC-giraffe': 89.61228527149578, 'ACC-backpack': 60.6171286842359, 'ACC-umbrella': 80.69652908561767, 'ACC-handbag': 54.661394749730455, 'ACC-tie': 82.20963769414126, 'ACC-suitcase': 89.76317853530807, 'ACC-frisbee': 94.20072727272726, 'ACC-skis': 70.47738137016732, 'ACC-snowboard': 75.22960495701795, 'ACC-sports ball': 79.09262877166869, 'ACC-kite': 76.60439618524084, 'ACC-baseball bat': 79.53382433374043, 'ACC-baseball glove': 60.03364965449659, 'ACC-skateboard': 69.75977485625542, 'ACC-surfboard': 89.6679111384958, 'ACC-tennis racket': 89.27605920824779, 'ACC-bottle': 80.02725640509718, 'ACC-wine glass': 86.54602817718627, 'ACC-cup': 82.73846123512985, 'ACC-fork': 67.82477224627857, 'ACC-knife': 56.76137612662123, 'ACC-spoon': 71.92341048183907, 'ACC-bowl': 67.34808631087499, 'ACC-banana': 89.91657894613597, 'ACC-apple': 69.65522360766862, 'ACC-sandwich': 80.17886566713727, 'ACC-orange': 87.6778016045769, 'ACC-broccoli': 80.94399061123644, 'ACC-carrot': 74.93621492912689, 'ACC-hot dog': 72.05544951825658, 'ACC-pizza': 94.44613948266077, 'ACC-donut': 81.43211657125302, 'ACC-cake': 84.84014245179694, 'ACC-chair': 67.27899310252506, 'ACC-couch': 82.61156380765526, 'ACC-potted plant': 52.16191494709863, 'ACC-bed': 83.13006530291514, 'ACC-dining table': 77.69480590869966, 'ACC-toilet': 92.63072899853707, 'ACC-tv': 86.78495061408637, 'ACC-laptop': 87.55808382096151, 'ACC-mouse': 85.8164778199842, 'ACC-remote': 70.34061747876122, 'ACC-keyboard': 67.57598619627969, 'ACC-cell phone': 88.19817178443074, 'ACC-microwave': 60.640379190324765, 'ACC-oven': 83.55452311322026, 'ACC-toaster': 63.3052320409476, 'ACC-sink': 81.62432106720951, 'ACC-refrigerator': 90.79007184386225, 'ACC-book': 62.50854557171276, 'ACC-clock': 64.30454394754923, 'ACC-vase': 59.79107259991751, 'ACC-scissors': 66.61524759202638, 'ACC-teddy bear': 87.50066868246668, 'ACC-hair drier': 40.05208778071898, 'ACC-toothbrush': 81.39159138290479, 'ACC-banner': 77.58891898977541, 'ACC-blanket': 15.756949921536423, 'ACC-bridge': 52.95812043517568, 'ACC-cardboard': 52.48151810382434, 'ACC-counter': 53.80913188492177, 'ACC-curtain': 74.64445709706806, 'ACC-door-stuff': 58.475348224643284, 'ACC-floor-wood': 78.45559742560972, 'ACC-flower': 59.00292975369483, 'ACC-fruit': 56.48094455393912, 'ACC-gravel': 30.948699129622614, 'ACC-house': 32.29575169276425, 'ACC-light': 53.83881062665085, 'ACC-mirror-stuff': 74.88740985915368, 'ACC-net': 64.6442879218185, 'ACC-pillow': 26.977267267346544, 'ACC-platform': 57.36041857519659, 'ACC-playingfield': 89.19885694852671, 'ACC-railroad': 78.35615808114751, 'ACC-river': 71.0284753545804, 'ACC-road': 86.66672777460599, 'ACC-roof': 21.106479727169383, 'ACC-sand': 71.4878917216928, 'ACC-sea': 91.14116469491661, 'ACC-shelf': 59.65900695791434, 'ACC-snow': 94.31867321024468, 'ACC-stairs': 41.88343724035596, 'ACC-tent': 10.097507015570175, 'ACC-towel': 37.44658611482699, 'ACC-wall-brick': 60.41119340487134, 'ACC-wall-stone': 33.90372818665097, 'ACC-wall-tile': 78.60839451715566, 'ACC-wall-wood': 55.30374485501264, 'ACC-water-other': 36.38975789335619, 'ACC-window-blind': 57.80284044975165, 'ACC-window-other': 69.87082961740265, 'ACC-tree-merged': 90.07908091711234, 'ACC-fence-merged': 72.62464785128827, 'ACC-ceiling-merged': 78.49916973449471, 'ACC-sky-other-merged': 96.52090527636933, 'ACC-cabinet-merged': 73.90122530978172, 'ACC-table-merged': 47.95584004413585, 'ACC-floor-other-merged': 60.12854727307222, 'ACC-pavement-merged': 66.35355364449568, 'ACC-mountain-merged': 66.5280996085472, 'ACC-grass-merged': 83.82353384579805, 'ACC-dirt-merged': 64.47629462243945, 'ACC-paper-merged': 38.75093548744732, 'ACC-food-other-merged': 53.843573871278, 'ACC-building-other-merged': 75.91138877675456, 'ACC-rock-merged': 82.89173089360703, 'ACC-wall-other-merged': 80.86814429406877, 'ACC-rug-merged': 78.16398291505949})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5835528241147205, 'noc@0.8': 3.0222417325139013, 'noc@0.85': 3.6414983904009364, 'noc@0.9': 4.679543459174715, 'miou@iter1': 0.825649315061226}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.00660705566406, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.68946838378906, 'precision@0.8': 51.88496017456055, 'precision@0.9': 26.467159271240234, 'cIoU': 57.620887756347656, 'mIoU': 62.68373489379883}}}
INFO:trainer.default_trainer:This epoch takes 1:29:38.263929
INFO:trainer.default_trainer:PROGRESS: 18.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 9 training.
INFO:trainer.default_trainer:epochs[     9] optim steps[16500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65636/0.91410, loss_mask_bce_0: 0.55578/0.33647, loss_mask_dice_0: 0.45308/1.16587, loss_spatial_bce_0: 0.25056/0.09300, loss_spatial_dice_0: 0.23572/0.22249, loss_spatial_ce_0: 0.07183/0.08524, loss_grounding_bce_0: 0.45414/0.08626, loss_grounding_dice_0: 0.35147/0.17894, loss_grounding_ce_0: 0.99979/0.27788, loss_mask_ce_1: 0.54679/0.91480, loss_mask_bce_1: 0.54743/0.33712, loss_mask_dice_1: 0.50365/1.17378, loss_spatial_bce_1: 0.32714/0.09396, loss_spatial_dice_1: 0.27164/0.22693, loss_spatial_ce_1: 0.01157/0.09111, loss_grounding_bce_1: 0.40722/0.08634, loss_grounding_dice_1: 0.36700/0.17934, loss_grounding_ce_1: 0.91699/0.27927, loss_mask_ce_2: 0.70706/0.92198, loss_mask_bce_2: 0.59453/0.33746, loss_mask_dice_2: 0.45939/1.17259, loss_spatial_bce_2: 0.33455/0.09389, loss_spatial_dice_2: 0.26228/0.22780, loss_spatial_ce_2: 0.00675/0.09496, loss_grounding_bce_2: 0.44927/0.08639, loss_grounding_dice_2: 0.34692/0.17894, loss_grounding_ce_2: 0.96439/0.28241, loss_mask_ce_3: 0.72593/0.92931, loss_mask_bce_3: 0.58815/0.33840, loss_mask_dice_3: 0.45511/1.16972, loss_spatial_bce_3: 0.29393/0.09483, loss_spatial_dice_3: 0.25317/0.22880, loss_spatial_ce_3: 0.40124/0.09918, loss_grounding_bce_3: 0.41077/0.08648, loss_grounding_dice_3: 0.36532/0.17874, loss_grounding_ce_3: 1.01098/0.28359, loss_mask_ce_4: 0.68541/0.92747, loss_mask_bce_4: 0.64206/0.33980, loss_mask_dice_4: 0.52277/1.19112, loss_spatial_bce_4: 0.25728/0.09856, loss_spatial_dice_4: 0.25953/0.23725, loss_spatial_ce_4: 0.12465/0.11599, loss_grounding_bce_4: 0.44263/0.08701, loss_grounding_dice_4: 0.37637/0.18143, loss_grounding_ce_4: 1.07028/0.28626, loss_mask_ce_5: 0.62744/0.94227, loss_mask_bce_5: 0.59508/0.34207, loss_mask_dice_5: 0.53091/1.19632, loss_spatial_bce_5: 0.35749/0.09946, loss_spatial_dice_5: 0.28706/0.24005, loss_spatial_ce_5: 0.04122/0.12934, loss_grounding_bce_5: 0.42240/0.08749, loss_grounding_dice_5: 0.38430/0.18261, loss_grounding_ce_5: 0.94220/0.29891, loss_mask_ce_6: 0.55641/0.97894, loss_mask_bce_6: 0.60310/0.34458, loss_mask_dice_6: 0.53326/1.19960, loss_spatial_bce_6: 0.31784/0.10494, loss_spatial_dice_6: 0.30760/0.24283, loss_spatial_ce_6: 0.27881/0.15242, loss_grounding_bce_6: 0.42826/0.08832, loss_grounding_dice_6: 0.38163/0.18283, loss_grounding_ce_6: 1.07855/0.31804, loss_mask_ce_7: 0.93841/1.02119, loss_mask_bce_7: 0.47189/0.35238, loss_mask_dice_7: 0.48411/1.25445, loss_spatial_bce_7: 0.38773/0.11389, loss_spatial_dice_7: 0.34239/0.26989, loss_spatial_ce_7: 0.19604/0.19183, loss_grounding_bce_7: 0.33481/0.09019, loss_grounding_dice_7: 0.31749/0.18985, loss_grounding_ce_7: 1.26602/0.35314, loss_mask_ce_8: 0.59800/1.13255, loss_mask_bce_8: 0.50291/0.36584, loss_mask_dice_8: 0.53522/1.32969, loss_spatial_bce_8: 0.32052/0.13525, loss_spatial_dice_8: 0.30035/0.31059, loss_spatial_ce_8: 0.06730/0.24811, loss_grounding_bce_8: 0.33669/0.09359, loss_grounding_dice_8: 0.32200/0.20128, loss_grounding_ce_8: 0.93004/0.42571, loss_mask_ce_9: 2.77798/3.69723, loss_mask_bce_9: 0.47754/0.39282, loss_mask_dice_9: 0.53439/1.90621, loss_spatial_bce_9: 0.56328/0.33728, loss_spatial_dice_9: 0.59425/0.82539, loss_spatial_ce_9: 0.62852/1.52577, loss_grounding_bce_9: 0.36294/0.10513, loss_grounding_dice_9: 0.34274/0.28139, loss_grounding_ce_9: 1.35365/0.70712] items per batch[64] items per second[0.13] total items[1056000] mini batches[ 16500] memory[7341] epoch remaining[1:23:03]
INFO:trainer.default_trainer:epochs[     9] optim steps[16600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.21533/0.91394, loss_mask_bce_0: 0.17044/0.33634, loss_mask_dice_0: 0.26291/1.16566, loss_spatial_bce_0: 0.06519/0.09295, loss_spatial_dice_0: 0.07916/0.22241, loss_spatial_ce_0: 0.02817/0.08508, loss_grounding_bce_0: 0.07408/0.08626, loss_grounding_dice_0: 0.08469/0.17902, loss_grounding_ce_0: 0.01128/0.27763, loss_mask_ce_1: 0.21397/0.91467, loss_mask_bce_1: 0.16699/0.33697, loss_mask_dice_1: 0.24828/1.17352, loss_spatial_bce_1: 0.06498/0.09390, loss_spatial_dice_1: 0.08183/0.22686, loss_spatial_ce_1: 0.02655/0.09088, loss_grounding_bce_1: 0.07556/0.08634, loss_grounding_dice_1: 0.08432/0.17944, loss_grounding_ce_1: 0.01038/0.27901, loss_mask_ce_2: 0.22035/0.92191, loss_mask_bce_2: 0.15623/0.33731, loss_mask_dice_2: 0.25664/1.17235, loss_spatial_bce_2: 0.06834/0.09384, loss_spatial_dice_2: 0.08350/0.22771, loss_spatial_ce_2: 0.02530/0.09480, loss_grounding_bce_2: 0.07425/0.08637, loss_grounding_dice_2: 0.08479/0.17904, loss_grounding_ce_2: 0.00933/0.28219, loss_mask_ce_3: 0.21631/0.92914, loss_mask_bce_3: 0.14903/0.33828, loss_mask_dice_3: 0.24813/1.16946, loss_spatial_bce_3: 0.06648/0.09478, loss_spatial_dice_3: 0.08671/0.22872, loss_spatial_ce_3: 0.02376/0.09902, loss_grounding_bce_3: 0.07014/0.08647, loss_grounding_dice_3: 0.08353/0.17881, loss_grounding_ce_3: 0.00897/0.28340, loss_mask_ce_4: 0.25188/0.92736, loss_mask_bce_4: 0.15630/0.33964, loss_mask_dice_4: 0.24505/1.19074, loss_spatial_bce_4: 0.06575/0.09850, loss_spatial_dice_4: 0.08013/0.23718, loss_spatial_ce_4: 0.02982/0.11581, loss_grounding_bce_4: 0.07479/0.08701, loss_grounding_dice_4: 0.08151/0.18150, loss_grounding_ce_4: 0.00910/0.28595, loss_mask_ce_5: 0.36540/0.94204, loss_mask_bce_5: 0.14365/0.34191, loss_mask_dice_5: 0.19375/1.19597, loss_spatial_bce_5: 0.07103/0.09940, loss_spatial_dice_5: 0.09029/0.23999, loss_spatial_ce_5: 0.03697/0.12917, loss_grounding_bce_5: 0.07749/0.08750, loss_grounding_dice_5: 0.08446/0.18269, loss_grounding_ce_5: 0.00662/0.29863, loss_mask_ce_6: 0.29270/0.97878, loss_mask_bce_6: 0.14168/0.34443, loss_mask_dice_6: 0.18893/1.19921, loss_spatial_bce_6: 0.07871/0.10487, loss_spatial_dice_6: 0.09690/0.24275, loss_spatial_ce_6: 0.05391/0.15227, loss_grounding_bce_6: 0.06936/0.08831, loss_grounding_dice_6: 0.08296/0.18291, loss_grounding_ce_6: 0.00498/0.31773, loss_mask_ce_7: 0.28528/1.02097, loss_mask_bce_7: 0.15062/0.35220, loss_mask_dice_7: 0.20918/1.25413, loss_spatial_bce_7: 0.10207/0.11383, loss_spatial_dice_7: 0.11667/0.26985, loss_spatial_ce_7: 0.08892/0.19165, loss_grounding_bce_7: 0.08125/0.09018, loss_grounding_dice_7: 0.09373/0.18993, loss_grounding_ce_7: 0.01353/0.35286, loss_mask_ce_8: 0.27280/1.13234, loss_mask_bce_8: 0.15890/0.36561, loss_mask_dice_8: 0.23950/1.32929, loss_spatial_bce_8: 0.11111/0.13515, loss_spatial_dice_8: 0.15924/0.31055, loss_spatial_ce_8: 0.14393/0.24789, loss_grounding_bce_8: 0.08327/0.09356, loss_grounding_dice_8: 0.09149/0.20137, loss_grounding_ce_8: 0.00654/0.42527, loss_mask_ce_9: 2.14606/3.69656, loss_mask_bce_9: 0.16482/0.39255, loss_mask_dice_9: 0.28396/1.90535, loss_spatial_bce_9: 0.43389/0.33727, loss_spatial_dice_9: 0.74071/0.82535, loss_spatial_ce_9: 1.38142/1.52586, loss_grounding_bce_9: 0.08920/0.10508, loss_grounding_dice_9: 0.08658/0.28140, loss_grounding_ce_9: 0.13238/0.70659] items per batch[64] items per second[0.22] total items[1062400] mini batches[ 16600] memory[7341] epoch remaining[1:19:34]
INFO:trainer.default_trainer:epochs[     9] optim steps[16700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72600/0.91417, loss_mask_bce_0: 0.14667/0.33642, loss_mask_dice_0: 2.02998/1.16628, loss_spatial_bce_0: 0.01779/0.09292, loss_spatial_dice_0: 0.20997/0.22241, loss_spatial_ce_0: 0.03820/0.08492, loss_grounding_bce_0: 0.03531/0.08631, loss_grounding_dice_0: 0.06666/0.17901, loss_grounding_ce_0: 0.05836/0.27771, loss_mask_ce_1: 0.70673/0.91482, loss_mask_bce_1: 0.16292/0.33707, loss_mask_dice_1: 1.95258/1.17410, loss_spatial_bce_1: 0.01483/0.09388, loss_spatial_dice_1: 0.19033/0.22685, loss_spatial_ce_1: 0.03003/0.09075, loss_grounding_bce_1: 0.03439/0.08638, loss_grounding_dice_1: 0.06454/0.17945, loss_grounding_ce_1: 0.06219/0.27909, loss_mask_ce_2: 0.67678/0.92218, loss_mask_bce_2: 0.16794/0.33739, loss_mask_dice_2: 2.16124/1.17301, loss_spatial_bce_2: 0.01566/0.09382, loss_spatial_dice_2: 0.20945/0.22771, loss_spatial_ce_2: 0.00871/0.09476, loss_grounding_bce_2: 0.03656/0.08641, loss_grounding_dice_2: 0.07379/0.17905, loss_grounding_ce_2: 0.07010/0.28226, loss_mask_ce_3: 0.78952/0.92938, loss_mask_bce_3: 0.15284/0.33836, loss_mask_dice_3: 1.92377/1.17013, loss_spatial_bce_3: 0.02080/0.09475, loss_spatial_dice_3: 0.25319/0.22871, loss_spatial_ce_3: 0.01386/0.09887, loss_grounding_bce_3: 0.03410/0.08650, loss_grounding_dice_3: 0.06570/0.17882, loss_grounding_ce_3: 0.06220/0.28346, loss_mask_ce_4: 0.72255/0.92757, loss_mask_bce_4: 0.16381/0.33972, loss_mask_dice_4: 1.97841/1.19137, loss_spatial_bce_4: 0.01685/0.09848, loss_spatial_dice_4: 0.23205/0.23719, loss_spatial_ce_4: 0.04941/0.11578, loss_grounding_bce_4: 0.04070/0.08705, loss_grounding_dice_4: 0.07463/0.18152, loss_grounding_ce_4: 0.10877/0.28600, loss_mask_ce_5: 0.77940/0.94225, loss_mask_bce_5: 0.16820/0.34197, loss_mask_dice_5: 2.09505/1.19657, loss_spatial_bce_5: 0.02383/0.09941, loss_spatial_dice_5: 0.23304/0.24002, loss_spatial_ce_5: 0.09587/0.12901, loss_grounding_bce_5: 0.03595/0.08753, loss_grounding_dice_5: 0.07238/0.18270, loss_grounding_ce_5: 0.12566/0.29868, loss_mask_ce_6: 0.88541/0.97903, loss_mask_bce_6: 0.16278/0.34453, loss_mask_dice_6: 2.02826/1.19979, loss_spatial_bce_6: 0.02064/0.10487, loss_spatial_dice_6: 0.23074/0.24276, loss_spatial_ce_6: 0.07236/0.15217, loss_grounding_bce_6: 0.03300/0.08835, loss_grounding_dice_6: 0.06283/0.18289, loss_grounding_ce_6: 0.24989/0.31770, loss_mask_ce_7: 0.86735/1.02123, loss_mask_bce_7: 0.16757/0.35227, loss_mask_dice_7: 2.20323/1.25483, loss_spatial_bce_7: 0.02105/0.11381, loss_spatial_dice_7: 0.27367/0.26988, loss_spatial_ce_7: 0.14920/0.19153, loss_grounding_bce_7: 0.03673/0.09020, loss_grounding_dice_7: 0.06963/0.18996, loss_grounding_ce_7: 0.39793/0.35293, loss_mask_ce_8: 0.97626/1.13273, loss_mask_bce_8: 0.14949/0.36567, loss_mask_dice_8: 2.43461/1.32998, loss_spatial_bce_8: 0.03925/0.13513, loss_spatial_dice_8: 0.27079/0.31056, loss_spatial_ce_8: 0.22988/0.24781, loss_grounding_bce_8: 0.03760/0.09358, loss_grounding_dice_8: 0.07243/0.20138, loss_grounding_ce_8: 0.12140/0.42534, loss_mask_ce_9: 3.55332/3.69729, loss_mask_bce_9: 0.15964/0.39262, loss_mask_dice_9: 2.91412/1.90632, loss_spatial_bce_9: 0.11988/0.33723, loss_spatial_dice_9: 0.85993/0.82539, loss_spatial_ce_9: 1.70614/1.52562, loss_grounding_bce_9: 0.03418/0.10509, loss_grounding_dice_9: 0.08975/0.28144, loss_grounding_ce_9: 1.11715/0.70656] items per batch[64] items per second[0.23] total items[1068800] mini batches[ 16700] memory[7341] epoch remaining[1:14:05]
INFO:trainer.default_trainer:epochs[     9] optim steps[16800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.51377/0.91377, loss_mask_bce_0: 0.47317/0.33654, loss_mask_dice_0: 0.59917/1.16574, loss_spatial_bce_0: 0.10915/0.09293, loss_spatial_dice_0: 0.11936/0.22230, loss_spatial_ce_0: 0.03596/0.08473, loss_grounding_bce_0: 0.22804/0.08628, loss_grounding_dice_0: 0.14811/0.17893, loss_grounding_ce_0: 0.58925/0.27762, loss_mask_ce_1: 1.46082/0.91440, loss_mask_bce_1: 0.45334/0.33717, loss_mask_dice_1: 0.61885/1.17347, loss_spatial_bce_1: 0.11184/0.09388, loss_spatial_dice_1: 0.11551/0.22676, loss_spatial_ce_1: 0.04027/0.09056, loss_grounding_bce_1: 0.24236/0.08636, loss_grounding_dice_1: 0.13314/0.17939, loss_grounding_ce_1: 0.48889/0.27897, loss_mask_ce_2: 1.49036/0.92187, loss_mask_bce_2: 0.47862/0.33751, loss_mask_dice_2: 0.62240/1.17246, loss_spatial_bce_2: 0.11780/0.09383, loss_spatial_dice_2: 0.11468/0.22762, loss_spatial_ce_2: 0.03209/0.09459, loss_grounding_bce_2: 0.25906/0.08638, loss_grounding_dice_2: 0.13801/0.17900, loss_grounding_ce_2: 0.45732/0.28224, loss_mask_ce_3: 1.60442/0.92905, loss_mask_bce_3: 0.45624/0.33847, loss_mask_dice_3: 0.60118/1.16958, loss_spatial_bce_3: 0.12013/0.09477, loss_spatial_dice_3: 0.12150/0.22861, loss_spatial_ce_3: 0.03243/0.09872, loss_grounding_bce_3: 0.20493/0.08648, loss_grounding_dice_3: 0.13187/0.17873, loss_grounding_ce_3: 0.46380/0.28342, loss_mask_ce_4: 1.43593/0.92720, loss_mask_bce_4: 0.49717/0.33986, loss_mask_dice_4: 0.61494/1.19094, loss_spatial_bce_4: 0.12318/0.09848, loss_spatial_dice_4: 0.12502/0.23711, loss_spatial_ce_4: 0.03866/0.11566, loss_grounding_bce_4: 0.24009/0.08703, loss_grounding_dice_4: 0.13884/0.18145, loss_grounding_ce_4: 0.43842/0.28593, loss_mask_ce_5: 1.39542/0.94183, loss_mask_bce_5: 0.49198/0.34211, loss_mask_dice_5: 0.58460/1.19610, loss_spatial_bce_5: 0.14796/0.09945, loss_spatial_dice_5: 0.14848/0.23993, loss_spatial_ce_5: 0.08098/0.12883, loss_grounding_bce_5: 0.23297/0.08752, loss_grounding_dice_5: 0.13347/0.18267, loss_grounding_ce_5: 0.40971/0.29848, loss_mask_ce_6: 1.29737/0.97867, loss_mask_bce_6: 0.52544/0.34469, loss_mask_dice_6: 0.60743/1.19929, loss_spatial_bce_6: 0.16225/0.10490, loss_spatial_dice_6: 0.15619/0.24266, loss_spatial_ce_6: 0.09284/0.15202, loss_grounding_bce_6: 0.27192/0.08834, loss_grounding_dice_6: 0.13525/0.18284, loss_grounding_ce_6: 0.33224/0.31741, loss_mask_ce_7: 1.36120/1.02076, loss_mask_bce_7: 0.47246/0.35240, loss_mask_dice_7: 0.62277/1.25432, loss_spatial_bce_7: 0.15355/0.11382, loss_spatial_dice_7: 0.16014/0.26979, loss_spatial_ce_7: 0.18057/0.19151, loss_grounding_bce_7: 0.20184/0.09019, loss_grounding_dice_7: 0.12861/0.18994, loss_grounding_ce_7: 0.36109/0.35255, loss_mask_ce_8: 1.46392/1.13219, loss_mask_bce_8: 0.50259/0.36581, loss_mask_dice_8: 0.66836/1.32951, loss_spatial_bce_8: 0.14821/0.13515, loss_spatial_dice_8: 0.14355/0.31048, loss_spatial_ce_8: 0.09602/0.24761, loss_grounding_bce_8: 0.19785/0.09357, loss_grounding_dice_8: 0.13423/0.20136, loss_grounding_ce_8: 0.27338/0.42480, loss_mask_ce_9: 2.63291/3.69640, loss_mask_bce_9: 0.65090/0.39284, loss_mask_dice_9: 1.14823/1.90597, loss_spatial_bce_9: 0.49128/0.33728, loss_spatial_dice_9: 0.83970/0.82540, loss_spatial_ce_9: 1.32373/1.52507, loss_grounding_bce_9: 0.19456/0.10511, loss_grounding_dice_9: 0.13866/0.28138, loss_grounding_ce_9: 0.43480/0.70586] items per batch[64] items per second[0.23] total items[1075200] mini batches[ 16800] memory[7341] epoch remaining[1:08:52]
INFO:trainer.default_trainer:epochs[     9] optim steps[16900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.46045/0.91366, loss_mask_bce_0: 0.16425/0.33645, loss_mask_dice_0: 2.69732/1.16549, loss_spatial_bce_0: 0.03078/0.09293, loss_spatial_dice_0: 0.26502/0.22218, loss_spatial_ce_0: 0.07531/0.08452, loss_grounding_bce_0: 0.04780/0.08631, loss_grounding_dice_0: 0.38278/0.17889, loss_grounding_ce_0: 0.20232/0.27752, loss_mask_ce_1: 1.54591/0.91421, loss_mask_bce_1: 0.16739/0.33710, loss_mask_dice_1: 2.79203/1.17321, loss_spatial_bce_1: 0.03671/0.09387, loss_spatial_dice_1: 0.26376/0.22662, loss_spatial_ce_1: 0.09502/0.09042, loss_grounding_bce_1: 0.04806/0.08639, loss_grounding_dice_1: 0.32848/0.17937, loss_grounding_ce_1: 0.21833/0.27890, loss_mask_ce_2: 1.45153/0.92165, loss_mask_bce_2: 0.19839/0.33742, loss_mask_dice_2: 2.76637/1.17227, loss_spatial_bce_2: 0.04411/0.09382, loss_spatial_dice_2: 0.31197/0.22747, loss_spatial_ce_2: 0.09267/0.09442, loss_grounding_bce_2: 0.05182/0.08641, loss_grounding_dice_2: 0.44768/0.17898, loss_grounding_ce_2: 0.19812/0.28218, loss_mask_ce_3: 1.51377/0.92893, loss_mask_bce_3: 0.17492/0.33837, loss_mask_dice_3: 2.56084/1.16934, loss_spatial_bce_3: 0.03715/0.09476, loss_spatial_dice_3: 0.29388/0.22845, loss_spatial_ce_3: 0.10599/0.09851, loss_grounding_bce_3: 0.06107/0.08650, loss_grounding_dice_3: 0.46051/0.17870, loss_grounding_ce_3: 0.29066/0.28339, loss_mask_ce_4: 1.27167/0.92699, loss_mask_bce_4: 0.19564/0.33979, loss_mask_dice_4: 2.85566/1.19070, loss_spatial_bce_4: 0.03924/0.09846, loss_spatial_dice_4: 0.31527/0.23696, loss_spatial_ce_4: 0.12029/0.11542, loss_grounding_bce_4: 0.05590/0.08706, loss_grounding_dice_4: 0.48961/0.18143, loss_grounding_ce_4: 0.22541/0.28583, loss_mask_ce_5: 1.57989/0.94167, loss_mask_bce_5: 0.18193/0.34202, loss_mask_dice_5: 2.56419/1.19571, loss_spatial_bce_5: 0.04405/0.09944, loss_spatial_dice_5: 0.32433/0.23978, loss_spatial_ce_5: 0.09001/0.12856, loss_grounding_bce_5: 0.05133/0.08754, loss_grounding_dice_5: 0.31139/0.18265, loss_grounding_ce_5: 0.20351/0.29843, loss_mask_ce_6: 1.57972/0.97854, loss_mask_bce_6: 0.18218/0.34460, loss_mask_dice_6: 2.83282/1.19903, loss_spatial_bce_6: 0.04991/0.10488, loss_spatial_dice_6: 0.32672/0.24249, loss_spatial_ce_6: 0.15828/0.15179, loss_grounding_bce_6: 0.05462/0.08835, loss_grounding_dice_6: 0.39286/0.18280, loss_grounding_ce_6: 0.16253/0.31740, loss_mask_ce_7: 1.58303/1.02036, loss_mask_bce_7: 0.21721/0.35229, loss_mask_dice_7: 3.01243/1.25410, loss_spatial_bce_7: 0.04925/0.11382, loss_spatial_dice_7: 0.35897/0.26963, loss_spatial_ce_7: 0.13209/0.19134, loss_grounding_bce_7: 0.06276/0.09020, loss_grounding_dice_7: 0.41683/0.18990, loss_grounding_ce_7: 0.22980/0.35237, loss_mask_ce_8: 1.83150/1.13191, loss_mask_bce_8: 0.24915/0.36569, loss_mask_dice_8: 3.06616/1.32926, loss_spatial_bce_8: 0.05243/0.13519, loss_spatial_dice_8: 0.38618/0.31031, loss_spatial_ce_8: 0.26902/0.24742, loss_grounding_bce_8: 0.07615/0.09358, loss_grounding_dice_8: 0.54054/0.20128, loss_grounding_ce_8: 0.30644/0.42449, loss_mask_ce_9: 5.71470/3.69599, loss_mask_bce_9: 0.39024/0.39281, loss_mask_dice_9: 4.35093/1.90564, loss_spatial_bce_9: 0.17606/0.33737, loss_spatial_dice_9: 0.89947/0.82529, loss_spatial_ce_9: 1.35345/1.52432, loss_grounding_bce_9: 0.18509/0.10513, loss_grounding_dice_9: 0.55423/0.28134, loss_grounding_ce_9: 0.23484/0.70545] items per batch[64] items per second[0.23] total items[1081600] mini batches[ 16900] memory[7341] epoch remaining[1:04:11]
INFO:trainer.default_trainer:epochs[     9] optim steps[17000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53714/0.91382, loss_mask_bce_0: 0.25740/0.33653, loss_mask_dice_0: 1.87972/1.16631, loss_spatial_bce_0: 0.07154/0.09288, loss_spatial_dice_0: 0.19275/0.22217, loss_spatial_ce_0: 0.01559/0.08440, loss_grounding_bce_0: 0.09912/0.08634, loss_grounding_dice_0: 0.16460/0.17895, loss_grounding_ce_0: 0.05016/0.27769, loss_mask_ce_1: 1.90340/0.91446, loss_mask_bce_1: 0.26101/0.33717, loss_mask_dice_1: 1.87479/1.17389, loss_spatial_bce_1: 0.07663/0.09382, loss_spatial_dice_1: 0.21750/0.22662, loss_spatial_ce_1: 0.01053/0.09030, loss_grounding_bce_1: 0.09388/0.08641, loss_grounding_dice_1: 0.16541/0.17943, loss_grounding_ce_1: 0.05365/0.27910, loss_mask_ce_2: 1.54613/0.92182, loss_mask_bce_2: 0.25498/0.33750, loss_mask_dice_2: 1.82071/1.17302, loss_spatial_bce_2: 0.06769/0.09377, loss_spatial_dice_2: 0.23023/0.22749, loss_spatial_ce_2: 0.01535/0.09427, loss_grounding_bce_2: 0.09353/0.08643, loss_grounding_dice_2: 0.17320/0.17902, loss_grounding_ce_2: 0.06407/0.28245, loss_mask_ce_3: 1.49194/0.92920, loss_mask_bce_3: 0.25412/0.33845, loss_mask_dice_3: 2.09935/1.17013, loss_spatial_bce_3: 0.07377/0.09470, loss_spatial_dice_3: 0.20461/0.22845, loss_spatial_ce_3: 0.02382/0.09834, loss_grounding_bce_3: 0.09695/0.08653, loss_grounding_dice_3: 0.18167/0.17876, loss_grounding_ce_3: 0.05760/0.28362, loss_mask_ce_4: 1.55322/0.92705, loss_mask_bce_4: 0.24773/0.33987, loss_mask_dice_4: 1.94631/1.19147, loss_spatial_bce_4: 0.06820/0.09842, loss_spatial_dice_4: 0.22184/0.23699, loss_spatial_ce_4: 0.04811/0.11528, loss_grounding_bce_4: 0.09624/0.08710, loss_grounding_dice_4: 0.15976/0.18147, loss_grounding_ce_4: 0.03758/0.28598, loss_mask_ce_5: 1.67566/0.94188, loss_mask_bce_5: 0.24607/0.34209, loss_mask_dice_5: 1.94401/1.19654, loss_spatial_bce_5: 0.10636/0.09940, loss_spatial_dice_5: 0.22936/0.23983, loss_spatial_ce_5: 0.05067/0.12843, loss_grounding_bce_5: 0.08847/0.08758, loss_grounding_dice_5: 0.16498/0.18272, loss_grounding_ce_5: 0.05272/0.29852, loss_mask_ce_6: 1.69434/0.97866, loss_mask_bce_6: 0.25415/0.34469, loss_mask_dice_6: 2.00122/1.19978, loss_spatial_bce_6: 0.08892/0.10484, loss_spatial_dice_6: 0.21732/0.24251, loss_spatial_ce_6: 0.10749/0.15170, loss_grounding_bce_6: 0.09370/0.08839, loss_grounding_dice_6: 0.15361/0.18286, loss_grounding_ce_6: 0.05379/0.31739, loss_mask_ce_7: 1.66687/1.02040, loss_mask_bce_7: 0.25345/0.35237, loss_mask_dice_7: 2.02176/1.25489, loss_spatial_bce_7: 0.08121/0.11378, loss_spatial_dice_7: 0.27146/0.26966, loss_spatial_ce_7: 0.16924/0.19130, loss_grounding_bce_7: 0.08895/0.09025, loss_grounding_dice_7: 0.15139/0.18996, loss_grounding_ce_7: 0.07692/0.35233, loss_mask_ce_8: 1.97222/1.13201, loss_mask_bce_8: 0.27604/0.36577, loss_mask_dice_8: 2.12994/1.33018, loss_spatial_bce_8: 0.12418/0.13515, loss_spatial_dice_8: 0.35905/0.31034, loss_spatial_ce_8: 0.17273/0.24733, loss_grounding_bce_8: 0.10299/0.09360, loss_grounding_dice_8: 0.16864/0.20132, loss_grounding_ce_8: 0.16887/0.42457, loss_mask_ce_9: 4.15474/3.69604, loss_mask_bce_9: 0.30708/0.39287, loss_mask_dice_9: 3.13688/1.90675, loss_spatial_bce_9: 0.26645/0.33733, loss_spatial_dice_9: 0.87870/0.82529, loss_spatial_ce_9: 2.52085/1.52424, loss_grounding_bce_9: 0.12896/0.10512, loss_grounding_dice_9: 0.47653/0.28134, loss_grounding_ce_9: 0.86300/0.70496] items per batch[64] items per second[0.23] total items[1088000] mini batches[ 17000] memory[7341] epoch remaining[0:59:33]
INFO:trainer.default_trainer:epochs[     9] optim steps[17100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25123/0.91420, loss_mask_bce_0: 0.32228/0.33644, loss_mask_dice_0: 0.29752/1.16665, loss_spatial_bce_0: 0.11599/0.09284, loss_spatial_dice_0: 0.11995/0.22210, loss_spatial_ce_0: 0.02751/0.08423, loss_grounding_bce_0: 0.10371/0.08635, loss_grounding_dice_0: 0.16547/0.17892, loss_grounding_ce_0: 0.03274/0.27735, loss_mask_ce_1: 0.49804/0.91489, loss_mask_bce_1: 0.31213/0.33710, loss_mask_dice_1: 0.29675/1.17431, loss_spatial_bce_1: 0.11774/0.09378, loss_spatial_dice_1: 0.12795/0.22654, loss_spatial_ce_1: 0.03011/0.09009, loss_grounding_bce_1: 0.10090/0.08643, loss_grounding_dice_1: 0.13658/0.17940, loss_grounding_ce_1: 0.03270/0.27875, loss_mask_ce_2: 0.48068/0.92228, loss_mask_bce_2: 0.31417/0.33742, loss_mask_dice_2: 0.30512/1.17337, loss_spatial_bce_2: 0.11975/0.09373, loss_spatial_dice_2: 0.12948/0.22740, loss_spatial_ce_2: 0.03871/0.09408, loss_grounding_bce_2: 0.09418/0.08645, loss_grounding_dice_2: 0.14844/0.17900, loss_grounding_ce_2: 0.03375/0.28213, loss_mask_ce_3: 0.49674/0.92967, loss_mask_bce_3: 0.31861/0.33836, loss_mask_dice_3: 0.29668/1.17049, loss_spatial_bce_3: 0.11766/0.09465, loss_spatial_dice_3: 0.12347/0.22835, loss_spatial_ce_3: 0.03469/0.09818, loss_grounding_bce_3: 0.09249/0.08655, loss_grounding_dice_3: 0.14147/0.17876, loss_grounding_ce_3: 0.02092/0.28327, loss_mask_ce_4: 0.27843/0.92752, loss_mask_bce_4: 0.30752/0.33980, loss_mask_dice_4: 0.28306/1.19182, loss_spatial_bce_4: 0.12052/0.09838, loss_spatial_dice_4: 0.14010/0.23693, loss_spatial_ce_4: 0.04086/0.11506, loss_grounding_bce_4: 0.08992/0.08711, loss_grounding_dice_4: 0.13131/0.18144, loss_grounding_ce_4: 0.02060/0.28575, loss_mask_ce_5: 0.32308/0.94222, loss_mask_bce_5: 0.31198/0.34200, loss_mask_dice_5: 0.30891/1.19696, loss_spatial_bce_5: 0.12020/0.09937, loss_spatial_dice_5: 0.16407/0.23977, loss_spatial_ce_5: 0.05586/0.12821, loss_grounding_bce_5: 0.09482/0.08759, loss_grounding_dice_5: 0.13616/0.18271, loss_grounding_ce_5: 0.02160/0.29830, loss_mask_ce_6: 0.38116/0.97906, loss_mask_bce_6: 0.32957/0.34462, loss_mask_dice_6: 0.29344/1.20018, loss_spatial_bce_6: 0.13328/0.10482, loss_spatial_dice_6: 0.13950/0.24242, loss_spatial_ce_6: 0.11988/0.15151, loss_grounding_bce_6: 0.09678/0.08842, loss_grounding_dice_6: 0.12527/0.18284, loss_grounding_ce_6: 0.03934/0.31699, loss_mask_ce_7: 0.38874/1.02083, loss_mask_bce_7: 0.31721/0.35230, loss_mask_dice_7: 0.30098/1.25533, loss_spatial_bce_7: 0.14726/0.11372, loss_spatial_dice_7: 0.16230/0.26961, loss_spatial_ce_7: 0.09549/0.19106, loss_grounding_bce_7: 0.09684/0.09027, loss_grounding_dice_7: 0.13581/0.18997, loss_grounding_ce_7: 0.02356/0.35194, loss_mask_ce_8: 0.73309/1.13227, loss_mask_bce_8: 0.30222/0.36571, loss_mask_dice_8: 0.28150/1.33071, loss_spatial_bce_8: 0.15377/0.13513, loss_spatial_dice_8: 0.16249/0.31028, loss_spatial_ce_8: 0.19868/0.24709, loss_grounding_bce_8: 0.08596/0.09365, loss_grounding_dice_8: 0.13923/0.20128, loss_grounding_ce_8: 0.03323/0.42425, loss_mask_ce_9: 2.79156/3.69687, loss_mask_bce_9: 0.31534/0.39285, loss_mask_dice_9: 0.49545/1.90782, loss_spatial_bce_9: 0.47680/0.33731, loss_spatial_dice_9: 0.68276/0.82529, loss_spatial_ce_9: 1.01564/1.52404, loss_grounding_bce_9: 0.09795/0.10515, loss_grounding_dice_9: 0.24807/0.28132, loss_grounding_ce_9: 0.24301/0.70490] items per batch[64] items per second[0.22] total items[1094400] mini batches[ 17100] memory[7341] epoch remaining[0:55:01]
INFO:trainer.default_trainer:epochs[     9] optim steps[17200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.90988/0.91407, loss_mask_bce_0: 0.57118/0.33656, loss_mask_dice_0: 0.67130/1.16670, loss_spatial_bce_0: 0.25727/0.09282, loss_spatial_dice_0: 0.20513/0.22201, loss_spatial_ce_0: 0.09864/0.08417, loss_grounding_bce_0: 0.37099/0.08647, loss_grounding_dice_0: 0.19825/0.17894, loss_grounding_ce_0: 0.23283/0.27725, loss_mask_ce_1: 1.91208/0.91477, loss_mask_bce_1: 0.52196/0.33721, loss_mask_dice_1: 0.67810/1.17426, loss_spatial_bce_1: 0.25969/0.09377, loss_spatial_dice_1: 0.19417/0.22644, loss_spatial_ce_1: 0.12433/0.08995, loss_grounding_bce_1: 0.31726/0.08655, loss_grounding_dice_1: 0.19890/0.17941, loss_grounding_ce_1: 0.24399/0.27872, loss_mask_ce_2: 1.93951/0.92216, loss_mask_bce_2: 0.52824/0.33751, loss_mask_dice_2: 0.67479/1.17342, loss_spatial_bce_2: 0.26054/0.09372, loss_spatial_dice_2: 0.19888/0.22731, loss_spatial_ce_2: 0.10519/0.09395, loss_grounding_bce_2: 0.31802/0.08656, loss_grounding_dice_2: 0.19909/0.17901, loss_grounding_ce_2: 0.24569/0.28209, loss_mask_ce_3: 1.90253/0.92961, loss_mask_bce_3: 0.53905/0.33845, loss_mask_dice_3: 0.68230/1.17053, loss_spatial_bce_3: 0.26666/0.09464, loss_spatial_dice_3: 0.21480/0.22825, loss_spatial_ce_3: 0.10751/0.09814, loss_grounding_bce_3: 0.34411/0.08666, loss_grounding_dice_3: 0.19492/0.17878, loss_grounding_ce_3: 0.25404/0.28313, loss_mask_ce_4: 1.88782/0.92751, loss_mask_bce_4: 0.48077/0.33989, loss_mask_dice_4: 0.68129/1.19191, loss_spatial_bce_4: 0.28810/0.09838, loss_spatial_dice_4: 0.20555/0.23684, loss_spatial_ce_4: 0.05170/0.11495, loss_grounding_bce_4: 0.28427/0.08721, loss_grounding_dice_4: 0.19655/0.18145, loss_grounding_ce_4: 0.26227/0.28572, loss_mask_ce_5: 1.83668/0.94223, loss_mask_bce_5: 0.45124/0.34208, loss_mask_dice_5: 0.68754/1.19705, loss_spatial_bce_5: 0.28748/0.09935, loss_spatial_dice_5: 0.21723/0.23969, loss_spatial_ce_5: 0.06529/0.12813, loss_grounding_bce_5: 0.24792/0.08769, loss_grounding_dice_5: 0.18380/0.18272, loss_grounding_ce_5: 0.29986/0.29816, loss_mask_ce_6: 1.74290/0.97890, loss_mask_bce_6: 0.41601/0.34473, loss_mask_dice_6: 0.66770/1.20027, loss_spatial_bce_6: 0.27770/0.10480, loss_spatial_dice_6: 0.23041/0.24234, loss_spatial_ce_6: 0.09341/0.15152, loss_grounding_bce_6: 0.20529/0.08852, loss_grounding_dice_6: 0.18671/0.18287, loss_grounding_ce_6: 0.30093/0.31662, loss_mask_ce_7: 1.92717/1.02082, loss_mask_bce_7: 0.51878/0.35243, loss_mask_dice_7: 0.68060/1.25547, loss_spatial_bce_7: 0.30782/0.11370, loss_spatial_dice_7: 0.24991/0.26953, loss_spatial_ce_7: 0.14766/0.19094, loss_grounding_bce_7: 0.28956/0.09037, loss_grounding_dice_7: 0.20272/0.18998, loss_grounding_ce_7: 0.28436/0.35174, loss_mask_ce_8: 2.07305/1.13235, loss_mask_bce_8: 0.58881/0.36583, loss_mask_dice_8: 0.76701/1.33091, loss_spatial_bce_8: 0.22493/0.13512, loss_spatial_dice_8: 0.25278/0.31022, loss_spatial_ce_8: 0.20708/0.24699, loss_grounding_bce_8: 0.31586/0.09375, loss_grounding_dice_8: 0.19883/0.20132, loss_grounding_ce_8: 0.30605/0.42409, loss_mask_ce_9: 4.14654/3.69667, loss_mask_bce_9: 0.62171/0.39297, loss_mask_dice_9: 1.38016/1.90753, loss_spatial_bce_9: 0.43021/0.33735, loss_spatial_dice_9: 0.82412/0.82530, loss_spatial_ce_9: 1.44371/1.52357, loss_grounding_bce_9: 0.29333/0.10526, loss_grounding_dice_9: 0.31439/0.28129, loss_grounding_ce_9: 0.79736/0.70461] items per batch[64] items per second[0.23] total items[1100800] mini batches[ 17200] memory[7341] epoch remaining[0:50:20]
INFO:trainer.default_trainer:epochs[     9] optim steps[17300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15936/0.91377, loss_mask_bce_0: 0.33504/0.33656, loss_mask_dice_0: 0.36137/1.16649, loss_spatial_bce_0: 0.10568/0.09281, loss_spatial_dice_0: 0.12243/0.22191, loss_spatial_ce_0: 0.01279/0.08399, loss_grounding_bce_0: 0.06450/0.08646, loss_grounding_dice_0: 0.08106/0.17894, loss_grounding_ce_0: 0.05057/0.27750, loss_mask_ce_1: 0.15312/0.91434, loss_mask_bce_1: 0.33961/0.33722, loss_mask_dice_1: 0.37599/1.17406, loss_spatial_bce_1: 0.10704/0.09375, loss_spatial_dice_1: 0.12349/0.22635, loss_spatial_ce_1: 0.01594/0.08975, loss_grounding_bce_1: 0.05833/0.08654, loss_grounding_dice_1: 0.07740/0.17941, loss_grounding_ce_1: 0.03526/0.27898, loss_mask_ce_2: 0.17805/0.92180, loss_mask_bce_2: 0.33709/0.33752, loss_mask_dice_2: 0.37923/1.17323, loss_spatial_bce_2: 0.10311/0.09371, loss_spatial_dice_2: 0.11338/0.22721, loss_spatial_ce_2: 0.01901/0.09378, loss_grounding_bce_2: 0.06370/0.08654, loss_grounding_dice_2: 0.07875/0.17900, loss_grounding_ce_2: 0.04143/0.28240, loss_mask_ce_3: 0.16524/0.92931, loss_mask_bce_3: 0.32033/0.33847, loss_mask_dice_3: 0.36770/1.17022, loss_spatial_bce_3: 0.10442/0.09461, loss_spatial_dice_3: 0.11769/0.22813, loss_spatial_ce_3: 0.02286/0.09800, loss_grounding_bce_3: 0.05928/0.08665, loss_grounding_dice_3: 0.07512/0.17880, loss_grounding_ce_3: 0.04279/0.28339, loss_mask_ce_4: 0.09857/0.92715, loss_mask_bce_4: 0.38472/0.33991, loss_mask_dice_4: 0.45822/1.19166, loss_spatial_bce_4: 0.11102/0.09836, loss_spatial_dice_4: 0.12926/0.23676, loss_spatial_ce_4: 0.02173/0.11472, loss_grounding_bce_4: 0.05887/0.08720, loss_grounding_dice_4: 0.07429/0.18146, loss_grounding_ce_4: 0.02627/0.28590, loss_mask_ce_5: 0.09261/0.94193, loss_mask_bce_5: 0.38149/0.34210, loss_mask_dice_5: 0.43857/1.19676, loss_spatial_bce_5: 0.11035/0.09933, loss_spatial_dice_5: 0.14406/0.23964, loss_spatial_ce_5: 0.02429/0.12795, loss_grounding_bce_5: 0.05811/0.08769, loss_grounding_dice_5: 0.07337/0.18271, loss_grounding_ce_5: 0.02373/0.29813, loss_mask_ce_6: 0.09709/0.97867, loss_mask_bce_6: 0.38465/0.34472, loss_mask_dice_6: 0.44993/1.20001, loss_spatial_bce_6: 0.11991/0.10478, loss_spatial_dice_6: 0.16192/0.24225, loss_spatial_ce_6: 0.02706/0.15137, loss_grounding_bce_6: 0.05825/0.08851, loss_grounding_dice_6: 0.06852/0.18285, loss_grounding_ce_6: 0.04486/0.31685, loss_mask_ce_7: 0.13141/1.02038, loss_mask_bce_7: 0.38210/0.35243, loss_mask_dice_7: 0.42547/1.25523, loss_spatial_bce_7: 0.12247/0.11368, loss_spatial_dice_7: 0.14673/0.26946, loss_spatial_ce_7: 0.06763/0.19066, loss_grounding_bce_7: 0.06235/0.09037, loss_grounding_dice_7: 0.07632/0.18997, loss_grounding_ce_7: 0.07487/0.35185, loss_mask_ce_8: 0.24629/1.13202, loss_mask_bce_8: 0.45718/0.36578, loss_mask_dice_8: 0.44769/1.33055, loss_spatial_bce_8: 0.13578/0.13510, loss_spatial_dice_8: 0.14544/0.31011, loss_spatial_ce_8: 0.12325/0.24671, loss_grounding_bce_8: 0.07044/0.09374, loss_grounding_dice_8: 0.08060/0.20133, loss_grounding_ce_8: 0.07617/0.42438, loss_mask_ce_9: 3.03849/3.69634, loss_mask_bce_9: 0.39350/0.39298, loss_mask_dice_9: 0.60031/1.90689, loss_spatial_bce_9: 0.43852/0.33746, loss_spatial_dice_9: 0.85066/0.82526, loss_spatial_ce_9: 1.37873/1.52323, loss_grounding_bce_9: 0.06850/0.10523, loss_grounding_dice_9: 0.10644/0.28120, loss_grounding_ce_9: 1.22345/0.70494] items per batch[64] items per second[0.24] total items[1107200] mini batches[ 17300] memory[7341] epoch remaining[0:45:24]
INFO:trainer.default_trainer:epochs[     9] optim steps[17400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96912/0.91365, loss_mask_bce_0: 0.23809/0.33651, loss_mask_dice_0: 0.65673/1.16644, loss_spatial_bce_0: 0.07774/0.09278, loss_spatial_dice_0: 0.17923/0.22181, loss_spatial_ce_0: 0.07079/0.08391, loss_grounding_bce_0: 0.13934/0.08648, loss_grounding_dice_0: 0.15091/0.17903, loss_grounding_ce_0: 0.21070/0.27753, loss_mask_ce_1: 0.96587/0.91411, loss_mask_bce_1: 0.23872/0.33717, loss_mask_dice_1: 0.73993/1.17406, loss_spatial_bce_1: 0.07558/0.09373, loss_spatial_dice_1: 0.18061/0.22624, loss_spatial_ce_1: 0.05361/0.08964, loss_grounding_bce_1: 0.13683/0.08656, loss_grounding_dice_1: 0.14313/0.17951, loss_grounding_ce_1: 0.21467/0.27895, loss_mask_ce_2: 1.00484/0.92172, loss_mask_bce_2: 0.22922/0.33746, loss_mask_dice_2: 0.55245/1.17305, loss_spatial_bce_2: 0.07246/0.09369, loss_spatial_dice_2: 0.17995/0.22710, loss_spatial_ce_2: 0.13868/0.09366, loss_grounding_bce_2: 0.14155/0.08656, loss_grounding_dice_2: 0.14957/0.17909, loss_grounding_ce_2: 0.22426/0.28242, loss_mask_ce_3: 1.09581/0.92929, loss_mask_bce_3: 0.22561/0.33840, loss_mask_dice_3: 0.57104/1.17009, loss_spatial_bce_3: 0.07496/0.09459, loss_spatial_dice_3: 0.18569/0.22801, loss_spatial_ce_3: 0.12434/0.09786, loss_grounding_bce_3: 0.13564/0.08667, loss_grounding_dice_3: 0.14064/0.17887, loss_grounding_ce_3: 0.22178/0.28341, loss_mask_ce_4: 1.03548/0.92718, loss_mask_bce_4: 0.23993/0.33985, loss_mask_dice_4: 0.72227/1.19151, loss_spatial_bce_4: 0.07275/0.09834, loss_spatial_dice_4: 0.18167/0.23670, loss_spatial_ce_4: 0.12020/0.11465, loss_grounding_bce_4: 0.13261/0.08722, loss_grounding_dice_4: 0.14919/0.18154, loss_grounding_ce_4: 0.22526/0.28591, loss_mask_ce_5: 1.01474/0.94181, loss_mask_bce_5: 0.22875/0.34202, loss_mask_dice_5: 0.68807/1.19666, loss_spatial_bce_5: 0.08041/0.09932, loss_spatial_dice_5: 0.20399/0.23957, loss_spatial_ce_5: 0.11927/0.12786, loss_grounding_bce_5: 0.13643/0.08771, loss_grounding_dice_5: 0.17000/0.18280, loss_grounding_ce_5: 0.21883/0.29818, loss_mask_ce_6: 1.00367/0.97863, loss_mask_bce_6: 0.23440/0.34466, loss_mask_dice_6: 0.75351/1.19986, loss_spatial_bce_6: 0.08129/0.10478, loss_spatial_dice_6: 0.19922/0.24219, loss_spatial_ce_6: 0.08188/0.15128, loss_grounding_bce_6: 0.13816/0.08853, loss_grounding_dice_6: 0.16069/0.18291, loss_grounding_ce_6: 0.23975/0.31687, loss_mask_ce_7: 1.39745/1.02044, loss_mask_bce_7: 0.23586/0.35238, loss_mask_dice_7: 0.59881/1.25503, loss_spatial_bce_7: 0.07702/0.11364, loss_spatial_dice_7: 0.26314/0.26938, loss_spatial_ce_7: 0.13944/0.19060, loss_grounding_bce_7: 0.13710/0.09039, loss_grounding_dice_7: 0.15542/0.19007, loss_grounding_ce_7: 0.26580/0.35185, loss_mask_ce_8: 0.93318/1.13188, loss_mask_bce_8: 0.26851/0.36573, loss_mask_dice_8: 0.76578/1.33050, loss_spatial_bce_8: 0.09358/0.13508, loss_spatial_dice_8: 0.29974/0.31001, loss_spatial_ce_8: 0.24191/0.24671, loss_grounding_bce_8: 0.15389/0.09375, loss_grounding_dice_8: 0.17512/0.20143, loss_grounding_ce_8: 0.27806/0.42476, loss_mask_ce_9: 3.11737/3.69633, loss_mask_bce_9: 0.32909/0.39286, loss_mask_dice_9: 0.98010/1.90654, loss_spatial_bce_9: 0.26512/0.33738, loss_spatial_dice_9: 0.78277/0.82521, loss_spatial_ce_9: 1.18153/1.52291, loss_grounding_bce_9: 0.20921/0.10523, loss_grounding_dice_9: 0.27908/0.28132, loss_grounding_ce_9: 0.46249/0.70473] items per batch[64] items per second[0.22] total items[1113600] mini batches[ 17400] memory[7341] epoch remaining[0:40:48]
INFO:trainer.default_trainer:epochs[     9] optim steps[17500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87199/0.91382, loss_mask_bce_0: 0.35100/0.33649, loss_mask_dice_0: 1.00224/1.16566, loss_spatial_bce_0: 0.10161/0.09280, loss_spatial_dice_0: 0.22308/0.22173, loss_spatial_ce_0: 0.05590/0.08373, loss_grounding_bce_0: 0.17462/0.08647, loss_grounding_dice_0: 0.16619/0.17903, loss_grounding_ce_0: 0.21202/0.27770, loss_mask_ce_1: 0.94897/0.91430, loss_mask_bce_1: 0.30508/0.33714, loss_mask_dice_1: 0.93631/1.17320, loss_spatial_bce_1: 0.10089/0.09375, loss_spatial_dice_1: 0.23593/0.22616, loss_spatial_ce_1: 0.05710/0.08946, loss_grounding_bce_1: 0.17359/0.08653, loss_grounding_dice_1: 0.16965/0.17949, loss_grounding_ce_1: 0.22498/0.27926, loss_mask_ce_2: 0.90846/0.92189, loss_mask_bce_2: 0.33040/0.33743, loss_mask_dice_2: 0.95585/1.17222, loss_spatial_bce_2: 0.09574/0.09370, loss_spatial_dice_2: 0.21839/0.22700, loss_spatial_ce_2: 0.06163/0.09344, loss_grounding_bce_2: 0.17521/0.08655, loss_grounding_dice_2: 0.15644/0.17911, loss_grounding_ce_2: 0.22223/0.28265, loss_mask_ce_3: 0.94673/0.92943, loss_mask_bce_3: 0.29868/0.33836, loss_mask_dice_3: 0.71891/1.16925, loss_spatial_bce_3: 0.09373/0.09460, loss_spatial_dice_3: 0.20132/0.22792, loss_spatial_ce_3: 0.08510/0.09771, loss_grounding_bce_3: 0.12562/0.08666, loss_grounding_dice_3: 0.12032/0.17886, loss_grounding_ce_3: 0.30650/0.28364, loss_mask_ce_4: 0.99705/0.92739, loss_mask_bce_4: 0.26312/0.33978, loss_mask_dice_4: 0.85545/1.19070, loss_spatial_bce_4: 0.09959/0.09836, loss_spatial_dice_4: 0.25041/0.23662, loss_spatial_ce_4: 0.07223/0.11445, loss_grounding_bce_4: 0.12203/0.08721, loss_grounding_dice_4: 0.14052/0.18151, loss_grounding_ce_4: 0.44007/0.28609, loss_mask_ce_5: 0.98003/0.94209, loss_mask_bce_5: 0.24320/0.34194, loss_mask_dice_5: 0.72166/1.19578, loss_spatial_bce_5: 0.09006/0.09935, loss_spatial_dice_5: 0.24992/0.23950, loss_spatial_ce_5: 0.09331/0.12770, loss_grounding_bce_5: 0.11495/0.08770, loss_grounding_dice_5: 0.14024/0.18279, loss_grounding_ce_5: 0.47666/0.29842, loss_mask_ce_6: 0.96960/0.97889, loss_mask_bce_6: 0.23826/0.34458, loss_mask_dice_6: 0.71917/1.19898, loss_spatial_bce_6: 0.10271/0.10479, loss_spatial_dice_6: 0.25168/0.24210, loss_spatial_ce_6: 0.12079/0.15116, loss_grounding_bce_6: 0.11519/0.08851, loss_grounding_dice_6: 0.15018/0.18289, loss_grounding_ce_6: 0.31157/0.31700, loss_mask_ce_7: 1.04295/1.02070, loss_mask_bce_7: 0.28083/0.35233, loss_mask_dice_7: 0.82840/1.25418, loss_spatial_bce_7: 0.09916/0.11369, loss_spatial_dice_7: 0.25684/0.26932, loss_spatial_ce_7: 0.16998/0.19051, loss_grounding_bce_7: 0.15906/0.09038, loss_grounding_dice_7: 0.16672/0.19006, loss_grounding_ce_7: 0.26900/0.35194, loss_mask_ce_8: 1.04233/1.13214, loss_mask_bce_8: 0.29248/0.36569, loss_mask_dice_8: 0.88728/1.32958, loss_spatial_bce_8: 0.10220/0.13510, loss_spatial_dice_8: 0.28146/0.30990, loss_spatial_ce_8: 0.12053/0.24649, loss_grounding_bce_8: 0.16514/0.09375, loss_grounding_dice_8: 0.13814/0.20142, loss_grounding_ce_8: 0.24917/0.42494, loss_mask_ce_9: 4.75626/3.69599, loss_mask_bce_9: 0.30234/0.39280, loss_mask_dice_9: 1.82333/1.90550, loss_spatial_bce_9: 0.56112/0.33750, loss_spatial_dice_9: 0.89688/0.82515, loss_spatial_ce_9: 2.20173/1.52290, loss_grounding_bce_9: 0.14396/0.10522, loss_grounding_dice_9: 0.29699/0.28132, loss_grounding_ce_9: 0.29473/0.70453] items per batch[64] items per second[0.23] total items[1120000] mini batches[ 17500] memory[7341] epoch remaining[0:36:08]
INFO:trainer.default_trainer:epochs[     9] optim steps[17600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38581/0.91390, loss_mask_bce_0: 0.24463/0.33650, loss_mask_dice_0: 1.46040/1.16607, loss_spatial_bce_0: 0.03608/0.09276, loss_spatial_dice_0: 0.14793/0.22169, loss_spatial_ce_0: 0.06192/0.08361, loss_grounding_bce_0: 0.04071/0.08642, loss_grounding_dice_0: 0.04508/0.17906, loss_grounding_ce_0: 0.00085/0.27780, loss_mask_ce_1: 0.36966/0.91433, loss_mask_bce_1: 0.24498/0.33715, loss_mask_dice_1: 1.73875/1.17366, loss_spatial_bce_1: 0.03338/0.09371, loss_spatial_dice_1: 0.14766/0.22612, loss_spatial_ce_1: 0.05343/0.08929, loss_grounding_bce_1: 0.04031/0.08648, loss_grounding_dice_1: 0.04368/0.17951, loss_grounding_ce_1: 0.00076/0.27935, loss_mask_ce_2: 0.31104/0.92194, loss_mask_bce_2: 0.24514/0.33745, loss_mask_dice_2: 1.72756/1.17279, loss_spatial_bce_2: 0.03244/0.09367, loss_spatial_dice_2: 0.15189/0.22697, loss_spatial_ce_2: 0.04651/0.09328, loss_grounding_bce_2: 0.03994/0.08650, loss_grounding_dice_2: 0.04232/0.17915, loss_grounding_ce_2: 0.00072/0.28275, loss_mask_ce_3: 0.30093/0.92949, loss_mask_bce_3: 0.24547/0.33839, loss_mask_dice_3: 1.52507/1.16977, loss_spatial_bce_3: 0.03699/0.09456, loss_spatial_dice_3: 0.15544/0.22787, loss_spatial_ce_3: 0.06235/0.09754, loss_grounding_bce_3: 0.04222/0.08661, loss_grounding_dice_3: 0.04148/0.17886, loss_grounding_ce_3: 0.00083/0.28374, loss_mask_ce_4: 0.30019/0.92752, loss_mask_bce_4: 0.23787/0.33981, loss_mask_dice_4: 1.55756/1.19125, loss_spatial_bce_4: 0.03801/0.09832, loss_spatial_dice_4: 0.17125/0.23661, loss_spatial_ce_4: 0.05552/0.11426, loss_grounding_bce_4: 0.03990/0.08716, loss_grounding_dice_4: 0.04217/0.18154, loss_grounding_ce_4: 0.00129/0.28617, loss_mask_ce_5: 0.27990/0.94226, loss_mask_bce_5: 0.24222/0.34198, loss_mask_dice_5: 1.63460/1.19622, loss_spatial_bce_5: 0.03754/0.09931, loss_spatial_dice_5: 0.19764/0.23949, loss_spatial_ce_5: 0.05206/0.12752, loss_grounding_bce_5: 0.03908/0.08764, loss_grounding_dice_5: 0.04406/0.18281, loss_grounding_ce_5: 0.00129/0.29859, loss_mask_ce_6: 0.32281/0.97898, loss_mask_bce_6: 0.24395/0.34462, loss_mask_dice_6: 1.53296/1.19948, loss_spatial_bce_6: 0.03445/0.10476, loss_spatial_dice_6: 0.18291/0.24208, loss_spatial_ce_6: 0.13752/0.15103, loss_grounding_bce_6: 0.03659/0.08846, loss_grounding_dice_6: 0.04119/0.18289, loss_grounding_ce_6: 0.00191/0.31706, loss_mask_ce_7: 0.43963/1.02074, loss_mask_bce_7: 0.26050/0.35242, loss_mask_dice_7: 1.79689/1.25472, loss_spatial_bce_7: 0.03582/0.11366, loss_spatial_dice_7: 0.20034/0.26932, loss_spatial_ce_7: 0.08137/0.19040, loss_grounding_bce_7: 0.04027/0.09033, loss_grounding_dice_7: 0.04016/0.19009, loss_grounding_ce_7: 0.00097/0.35196, loss_mask_ce_8: 0.54496/1.13234, loss_mask_bce_8: 0.33727/0.36573, loss_mask_dice_8: 1.86854/1.33003, loss_spatial_bce_8: 0.03812/0.13506, loss_spatial_dice_8: 0.20922/0.30992, loss_spatial_ce_8: 0.14398/0.24647, loss_grounding_bce_8: 0.04077/0.09370, loss_grounding_dice_8: 0.03698/0.20146, loss_grounding_ce_8: 0.00050/0.42495, loss_mask_ce_9: 3.07372/3.69657, loss_mask_bce_9: 0.42171/0.39291, loss_mask_dice_9: 2.99161/1.90582, loss_spatial_bce_9: 0.24759/0.33736, loss_spatial_dice_9: 0.83523/0.82512, loss_spatial_ce_9: 1.59248/1.52282, loss_grounding_bce_9: 0.05900/0.10518, loss_grounding_dice_9: 0.04960/0.28135, loss_grounding_ce_9: 0.04344/0.70450] items per batch[64] items per second[0.23] total items[1126400] mini batches[ 17600] memory[7341] epoch remaining[0:31:27]
INFO:trainer.default_trainer:epochs[     9] optim steps[17700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65956/0.91343, loss_mask_bce_0: 0.27106/0.33642, loss_mask_dice_0: 0.90935/1.16564, loss_spatial_bce_0: 0.06349/0.09273, loss_spatial_dice_0: 0.19986/0.22158, loss_spatial_ce_0: 0.00112/0.08350, loss_grounding_bce_0: 0.04320/0.08641, loss_grounding_dice_0: 0.16799/0.17900, loss_grounding_ce_0: 0.21163/0.27754, loss_mask_ce_1: 0.68112/0.91372, loss_mask_bce_1: 0.27269/0.33710, loss_mask_dice_1: 0.93131/1.17335, loss_spatial_bce_1: 0.07239/0.09368, loss_spatial_dice_1: 0.20870/0.22600, loss_spatial_ce_1: 0.00222/0.08914, loss_grounding_bce_1: 0.04172/0.08647, loss_grounding_dice_1: 0.14820/0.17944, loss_grounding_ce_1: 0.40837/0.27916, loss_mask_ce_2: 0.63728/0.92137, loss_mask_bce_2: 0.28005/0.33741, loss_mask_dice_2: 0.94765/1.17245, loss_spatial_bce_2: 0.07268/0.09364, loss_spatial_dice_2: 0.21970/0.22688, loss_spatial_ce_2: 0.00625/0.09316, loss_grounding_bce_2: 0.04610/0.08649, loss_grounding_dice_2: 0.15341/0.17907, loss_grounding_ce_2: 0.39083/0.28256, loss_mask_ce_3: 0.62262/0.92892, loss_mask_bce_3: 0.27582/0.33831, loss_mask_dice_3: 0.97843/1.16945, loss_spatial_bce_3: 0.07227/0.09453, loss_spatial_dice_3: 0.20695/0.22778, loss_spatial_ce_3: 0.01165/0.09743, loss_grounding_bce_3: 0.04321/0.08659, loss_grounding_dice_3: 0.17663/0.17878, loss_grounding_ce_3: 0.31464/0.28355, loss_mask_ce_4: 0.67047/0.92698, loss_mask_bce_4: 0.27994/0.33973, loss_mask_dice_4: 0.90145/1.19097, loss_spatial_bce_4: 0.06724/0.09829, loss_spatial_dice_4: 0.20677/0.23652, loss_spatial_ce_4: 0.01003/0.11411, loss_grounding_bce_4: 0.04678/0.08715, loss_grounding_dice_4: 0.17220/0.18146, loss_grounding_ce_4: 0.26990/0.28593, loss_mask_ce_5: 0.68500/0.94175, loss_mask_bce_5: 0.28187/0.34190, loss_mask_dice_5: 0.96397/1.19605, loss_spatial_bce_5: 0.06814/0.09929, loss_spatial_dice_5: 0.20948/0.23941, loss_spatial_ce_5: 0.00932/0.12734, loss_grounding_bce_5: 0.04793/0.08764, loss_grounding_dice_5: 0.18115/0.18275, loss_grounding_ce_5: 0.20951/0.29832, loss_mask_ce_6: 0.76385/0.97851, loss_mask_bce_6: 0.28503/0.34456, loss_mask_dice_6: 0.91849/1.19915, loss_spatial_bce_6: 0.07383/0.10473, loss_spatial_dice_6: 0.20364/0.24200, loss_spatial_ce_6: 0.05300/0.15087, loss_grounding_bce_6: 0.04534/0.08845, loss_grounding_dice_6: 0.16688/0.18281, loss_grounding_ce_6: 0.22825/0.31677, loss_mask_ce_7: 1.11929/1.02035, loss_mask_bce_7: 0.34025/0.35234, loss_mask_dice_7: 0.92162/1.25435, loss_spatial_bce_7: 0.07102/0.11362, loss_spatial_dice_7: 0.23276/0.26919, loss_spatial_ce_7: 0.11167/0.19030, loss_grounding_bce_7: 0.04246/0.09033, loss_grounding_dice_7: 0.18525/0.19002, loss_grounding_ce_7: 0.42374/0.35152, loss_mask_ce_8: 1.84972/1.13169, loss_mask_bce_8: 0.34212/0.36564, loss_mask_dice_8: 1.06434/1.32968, loss_spatial_bce_8: 0.09478/0.13500, loss_spatial_dice_8: 0.30180/0.30981, loss_spatial_ce_8: 0.27302/0.24640, loss_grounding_bce_8: 0.04566/0.09370, loss_grounding_dice_8: 0.15574/0.20137, loss_grounding_ce_8: 0.46604/0.42455, loss_mask_ce_9: 3.31186/3.69590, loss_mask_bce_9: 0.46793/0.39273, loss_mask_dice_9: 1.32915/1.90518, loss_spatial_bce_9: 0.22871/0.33730, loss_spatial_dice_9: 0.85385/0.82508, loss_spatial_ce_9: 1.60771/1.52282, loss_grounding_bce_9: 0.07471/0.10515, loss_grounding_dice_9: 0.19724/0.28125, loss_grounding_ce_9: 0.61286/0.70452] items per batch[64] items per second[0.23] total items[1132800] mini batches[ 17700] memory[7341] epoch remaining[0:26:44]
INFO:trainer.default_trainer:epochs[     9] optim steps[17800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18250/0.91328, loss_mask_bce_0: 0.20606/0.33635, loss_mask_dice_0: 0.51153/1.16630, loss_spatial_bce_0: 0.09738/0.09274, loss_spatial_dice_0: 0.23863/0.22153, loss_spatial_ce_0: 0.01641/0.08337, loss_grounding_bce_0: 0.04080/0.08637, loss_grounding_dice_0: 0.09874/0.17907, loss_grounding_ce_0: 0.02871/0.27744, loss_mask_ce_1: 0.95341/0.91351, loss_mask_bce_1: 0.20960/0.33702, loss_mask_dice_1: 0.64410/1.17405, loss_spatial_bce_1: 0.09561/0.09370, loss_spatial_dice_1: 0.24514/0.22596, loss_spatial_ce_1: 0.00621/0.08897, loss_grounding_bce_1: 0.04108/0.08642, loss_grounding_dice_1: 0.09193/0.17948, loss_grounding_ce_1: 0.03644/0.27906, loss_mask_ce_2: 0.68051/0.92114, loss_mask_bce_2: 0.21846/0.33735, loss_mask_dice_2: 0.71394/1.17309, loss_spatial_bce_2: 0.08505/0.09366, loss_spatial_dice_2: 0.23675/0.22684, loss_spatial_ce_2: 0.00695/0.09301, loss_grounding_bce_2: 0.04225/0.08645, loss_grounding_dice_2: 0.12207/0.17911, loss_grounding_ce_2: 0.03035/0.28246, loss_mask_ce_3: 1.03328/0.92878, loss_mask_bce_3: 0.22364/0.33825, loss_mask_dice_3: 0.50810/1.17008, loss_spatial_bce_3: 0.07939/0.09454, loss_spatial_dice_3: 0.23070/0.22772, loss_spatial_ce_3: 0.00828/0.09737, loss_grounding_bce_3: 0.04139/0.08655, loss_grounding_dice_3: 0.09939/0.17882, loss_grounding_ce_3: 0.02595/0.28354, loss_mask_ce_4: 1.05971/0.92682, loss_mask_bce_4: 0.23733/0.33967, loss_mask_dice_4: 0.51334/1.19167, loss_spatial_bce_4: 0.07663/0.09831, loss_spatial_dice_4: 0.20467/0.23648, loss_spatial_ce_4: 0.01133/0.11394, loss_grounding_bce_4: 0.04394/0.08713, loss_grounding_dice_4: 0.37457/0.18156, loss_grounding_ce_4: 0.24249/0.28584, loss_mask_ce_5: 0.99504/0.94172, loss_mask_bce_5: 0.27802/0.34186, loss_mask_dice_5: 0.54880/1.19669, loss_spatial_bce_5: 0.07597/0.09931, loss_spatial_dice_5: 0.19751/0.23937, loss_spatial_ce_5: 0.04275/0.12725, loss_grounding_bce_5: 0.04066/0.08761, loss_grounding_dice_5: 0.09604/0.18278, loss_grounding_ce_5: 0.08068/0.29822, loss_mask_ce_6: 1.17795/0.97860, loss_mask_bce_6: 0.28268/0.34450, loss_mask_dice_6: 0.55256/1.19975, loss_spatial_bce_6: 0.07640/0.10475, loss_spatial_dice_6: 0.20289/0.24196, loss_spatial_ce_6: 0.04510/0.15077, loss_grounding_bce_6: 0.04067/0.08842, loss_grounding_dice_6: 0.10298/0.18286, loss_grounding_ce_6: 0.13952/0.31664, loss_mask_ce_7: 0.87386/1.02032, loss_mask_bce_7: 0.31697/0.35229, loss_mask_dice_7: 0.67718/1.25501, loss_spatial_bce_7: 0.08641/0.11362, loss_spatial_dice_7: 0.26739/0.26912, loss_spatial_ce_7: 0.07030/0.19023, loss_grounding_bce_7: 0.03858/0.09031, loss_grounding_dice_7: 0.09723/0.19010, loss_grounding_ce_7: 0.36428/0.35146, loss_mask_ce_8: 1.10043/1.13176, loss_mask_bce_8: 0.28985/0.36559, loss_mask_dice_8: 0.63685/1.33027, loss_spatial_bce_8: 0.15756/0.13501, loss_spatial_dice_8: 0.27667/0.30976, loss_spatial_ce_8: 0.25795/0.24626, loss_grounding_bce_8: 0.04429/0.09367, loss_grounding_dice_8: 0.07399/0.20145, loss_grounding_ce_8: 0.78689/0.42452, loss_mask_ce_9: 2.76730/3.69600, loss_mask_bce_9: 0.32301/0.39271, loss_mask_dice_9: 0.85855/1.90570, loss_spatial_bce_9: 0.22880/0.33721, loss_spatial_dice_9: 0.80868/0.82503, loss_spatial_ce_9: 1.27226/1.52287, loss_grounding_bce_9: 0.05321/0.10514, loss_grounding_dice_9: 0.11303/0.28134, loss_grounding_ce_9: 0.72814/0.70418] items per batch[64] items per second[0.24] total items[1139200] mini batches[ 17800] memory[7341] epoch remaining[0:21:59]
INFO:trainer.default_trainer:epochs[     9] optim steps[17900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39595/0.91349, loss_mask_bce_0: 0.62246/0.33632, loss_mask_dice_0: 4.58052/1.16671, loss_spatial_bce_0: 0.05397/0.09274, loss_spatial_dice_0: 0.24383/0.22152, loss_spatial_ce_0: 0.03910/0.08322, loss_grounding_bce_0: 0.10363/0.08636, loss_grounding_dice_0: 0.19773/0.17910, loss_grounding_ce_0: 0.36786/0.27734, loss_mask_ce_1: 1.32573/0.91367, loss_mask_bce_1: 0.64121/0.33702, loss_mask_dice_1: 4.48245/1.17446, loss_spatial_bce_1: 0.05672/0.09371, loss_spatial_dice_1: 0.25351/0.22596, loss_spatial_ce_1: 0.06639/0.08884, loss_grounding_bce_1: 0.10832/0.08640, loss_grounding_dice_1: 0.20272/0.17954, loss_grounding_ce_1: 0.61263/0.27902, loss_mask_ce_2: 1.27570/0.92130, loss_mask_bce_2: 0.63721/0.33734, loss_mask_dice_2: 4.56912/1.17367, loss_spatial_bce_2: 0.05759/0.09367, loss_spatial_dice_2: 0.27191/0.22684, loss_spatial_ce_2: 0.19720/0.09290, loss_grounding_bce_2: 0.13129/0.08644, loss_grounding_dice_2: 0.21006/0.17914, loss_grounding_ce_2: 0.35008/0.28241, loss_mask_ce_3: 1.24494/0.92897, loss_mask_bce_3: 0.61945/0.33823, loss_mask_dice_3: 4.33229/1.17057, loss_spatial_bce_3: 0.05954/0.09456, loss_spatial_dice_3: 0.24938/0.22770, loss_spatial_ce_3: 0.20832/0.09727, loss_grounding_bce_3: 0.11887/0.08654, loss_grounding_dice_3: 0.19925/0.17884, loss_grounding_ce_3: 0.35368/0.28346, loss_mask_ce_4: 1.39950/0.92688, loss_mask_bce_4: 0.70896/0.33971, loss_mask_dice_4: 4.57940/1.19220, loss_spatial_bce_4: 0.06604/0.09832, loss_spatial_dice_4: 0.29385/0.23649, loss_spatial_ce_4: 0.11754/0.11384, loss_grounding_bce_4: 0.12240/0.08715, loss_grounding_dice_4: 0.19288/0.18164, loss_grounding_ce_4: 0.56608/0.28578, loss_mask_ce_5: 1.31521/0.94190, loss_mask_bce_5: 0.68795/0.34189, loss_mask_dice_5: 4.52878/1.19710, loss_spatial_bce_5: 0.05550/0.09932, loss_spatial_dice_5: 0.25296/0.23938, loss_spatial_ce_5: 0.12989/0.12715, loss_grounding_bce_5: 0.12242/0.08762, loss_grounding_dice_5: 0.19646/0.18280, loss_grounding_ce_5: 0.52546/0.29819, loss_mask_ce_6: 1.33223/0.97871, loss_mask_bce_6: 0.71182/0.34453, loss_mask_dice_6: 4.77559/1.20018, loss_spatial_bce_6: 0.05966/0.10475, loss_spatial_dice_6: 0.25754/0.24197, loss_spatial_ce_6: 0.16402/0.15065, loss_grounding_bce_6: 0.12705/0.08841, loss_grounding_dice_6: 0.19321/0.18289, loss_grounding_ce_6: 0.53849/0.31667, loss_mask_ce_7: 1.51178/1.02046, loss_mask_bce_7: 0.73160/0.35232, loss_mask_dice_7: 5.09901/1.25551, loss_spatial_bce_7: 0.07225/0.11364, loss_spatial_dice_7: 0.30996/0.26914, loss_spatial_ce_7: 0.13933/0.19011, loss_grounding_bce_7: 0.11071/0.09032, loss_grounding_dice_7: 0.20100/0.19014, loss_grounding_ce_7: 0.63013/0.35122, loss_mask_ce_8: 1.76527/1.13226, loss_mask_bce_8: 0.61567/0.36565, loss_mask_dice_8: 5.16159/1.33080, loss_spatial_bce_8: 0.08427/0.13502, loss_spatial_dice_8: 0.32894/0.30976, loss_spatial_ce_8: 0.28311/0.24627, loss_grounding_bce_8: 0.10700/0.09369, loss_grounding_dice_8: 0.21879/0.20150, loss_grounding_ce_8: 0.58218/0.42433, loss_mask_ce_9: 6.66769/3.69590, loss_mask_bce_9: 0.70552/0.39272, loss_mask_dice_9: 6.83192/1.90643, loss_spatial_bce_9: 0.17549/0.33716, loss_spatial_dice_9: 0.92608/0.82500, loss_spatial_ce_9: 1.25801/1.52290, loss_grounding_bce_9: 0.14277/0.10515, loss_grounding_dice_9: 0.42254/0.28148, loss_grounding_ce_9: 0.50888/0.70373] items per batch[64] items per second[0.23] total items[1145600] mini batches[ 17900] memory[7341] epoch remaining[0:17:18]
INFO:trainer.default_trainer:epochs[     9] optim steps[18000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27961/0.91330, loss_mask_bce_0: 0.23971/0.33637, loss_mask_dice_0: 0.27945/1.16765, loss_spatial_bce_0: 0.12789/0.09272, loss_spatial_dice_0: 0.12153/0.22156, loss_spatial_ce_0: 0.00783/0.08310, loss_grounding_bce_0: 0.10088/0.08644, loss_grounding_dice_0: 0.19579/0.17916, loss_grounding_ce_0: 0.02423/0.27735, loss_mask_ce_1: 0.28645/0.91346, loss_mask_bce_1: 0.24331/0.33705, loss_mask_dice_1: 0.27938/1.17531, loss_spatial_bce_1: 0.14941/0.09369, loss_spatial_dice_1: 0.12884/0.22600, loss_spatial_ce_1: 0.00807/0.08873, loss_grounding_bce_1: 0.10421/0.08647, loss_grounding_dice_1: 0.19921/0.17957, loss_grounding_ce_1: 0.02683/0.27912, loss_mask_ce_2: 0.29398/0.92112, loss_mask_bce_2: 0.27380/0.33737, loss_mask_dice_2: 0.30272/1.17453, loss_spatial_bce_2: 0.16525/0.09365, loss_spatial_dice_2: 0.13634/0.22689, loss_spatial_ce_2: 0.01514/0.09284, loss_grounding_bce_2: 0.10390/0.08650, loss_grounding_dice_2: 0.20795/0.17918, loss_grounding_ce_2: 0.03606/0.28248, loss_mask_ce_3: 0.28267/0.92878, loss_mask_bce_3: 0.28147/0.33829, loss_mask_dice_3: 0.30152/1.17146, loss_spatial_bce_3: 0.18065/0.09455, loss_spatial_dice_3: 0.13370/0.22774, loss_spatial_ce_3: 0.01691/0.09718, loss_grounding_bce_3: 0.10651/0.08661, loss_grounding_dice_3: 0.19481/0.17889, loss_grounding_ce_3: 0.04080/0.28357, loss_mask_ce_4: 0.46548/0.92685, loss_mask_bce_4: 0.26191/0.33976, loss_mask_dice_4: 0.27976/1.19305, loss_spatial_bce_4: 0.18687/0.09831, loss_spatial_dice_4: 0.14398/0.23657, loss_spatial_ce_4: 0.01831/0.11377, loss_grounding_bce_4: 0.11124/0.08723, loss_grounding_dice_4: 0.20302/0.18167, loss_grounding_ce_4: 0.04368/0.28581, loss_mask_ce_5: 0.44727/0.94176, loss_mask_bce_5: 0.29714/0.34192, loss_mask_dice_5: 0.29102/1.19801, loss_spatial_bce_5: 0.13068/0.09932, loss_spatial_dice_5: 0.13226/0.23945, loss_spatial_ce_5: 0.05662/0.12697, loss_grounding_bce_5: 0.11760/0.08769, loss_grounding_dice_5: 0.20592/0.18283, loss_grounding_ce_5: 0.09384/0.29819, loss_mask_ce_6: 0.46698/0.97854, loss_mask_bce_6: 0.26979/0.34457, loss_mask_dice_6: 0.29045/1.20107, loss_spatial_bce_6: 0.11122/0.10475, loss_spatial_dice_6: 0.12144/0.24204, loss_spatial_ce_6: 0.08466/0.15061, loss_grounding_bce_6: 0.10998/0.08849, loss_grounding_dice_6: 0.20183/0.18292, loss_grounding_ce_6: 0.05279/0.31677, loss_mask_ce_7: 0.41663/1.02051, loss_mask_bce_7: 0.26166/0.35232, loss_mask_dice_7: 0.28669/1.25637, loss_spatial_bce_7: 0.12675/0.11363, loss_spatial_dice_7: 0.13948/0.26922, loss_spatial_ce_7: 0.07903/0.19000, loss_grounding_bce_7: 0.11801/0.09037, loss_grounding_dice_7: 0.20967/0.19016, loss_grounding_ce_7: 0.03195/0.35112, loss_mask_ce_8: 0.71637/1.13207, loss_mask_bce_8: 0.26132/0.36569, loss_mask_dice_8: 0.32029/1.33192, loss_spatial_bce_8: 0.15228/0.13498, loss_spatial_dice_8: 0.15534/0.30983, loss_spatial_ce_8: 0.19458/0.24611, loss_grounding_bce_8: 0.13642/0.09377, loss_grounding_dice_8: 0.23835/0.20155, loss_grounding_ce_8: 0.06212/0.42435, loss_mask_ce_9: 2.70186/3.69574, loss_mask_bce_9: 0.33233/0.39272, loss_mask_dice_9: 0.55383/1.90750, loss_spatial_bce_9: 0.40590/0.33704, loss_spatial_dice_9: 0.72391/0.82504, loss_spatial_ce_9: 1.27485/1.52323, loss_grounding_bce_9: 0.16412/0.10521, loss_grounding_dice_9: 0.33724/0.28144, loss_grounding_ce_9: 0.27220/0.70309] items per batch[64] items per second[0.23] total items[1152000] mini batches[ 18000] memory[7341] epoch remaining[0:12:37]
INFO:trainer.default_trainer:epochs[     9] optim steps[18100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61475/0.91324, loss_mask_bce_0: 0.41755/0.33625, loss_mask_dice_0: 0.71953/1.16676, loss_spatial_bce_0: 0.08005/0.09269, loss_spatial_dice_0: 0.15663/0.22145, loss_spatial_ce_0: 0.01836/0.08316, loss_grounding_bce_0: 0.02644/0.08642, loss_grounding_dice_0: 0.07855/0.17908, loss_grounding_ce_0: 0.28343/0.27711, loss_mask_ce_1: 0.47309/0.91334, loss_mask_bce_1: 0.56249/0.33694, loss_mask_dice_1: 0.92257/1.17440, loss_spatial_bce_1: 0.07775/0.09365, loss_spatial_dice_1: 0.13920/0.22589, loss_spatial_ce_1: 0.03092/0.08878, loss_grounding_bce_1: 0.02645/0.08644, loss_grounding_dice_1: 0.07709/0.17948, loss_grounding_ce_1: 0.28432/0.27888, loss_mask_ce_2: 0.50630/0.92113, loss_mask_bce_2: 0.53439/0.33725, loss_mask_dice_2: 0.79684/1.17370, loss_spatial_bce_2: 0.07865/0.09362, loss_spatial_dice_2: 0.15305/0.22677, loss_spatial_ce_2: 0.03602/0.09287, loss_grounding_bce_2: 0.02577/0.08648, loss_grounding_dice_2: 0.07976/0.17908, loss_grounding_ce_2: 0.28335/0.28227, loss_mask_ce_3: 0.49153/0.92860, loss_mask_bce_3: 0.54603/0.33819, loss_mask_dice_3: 0.76236/1.17066, loss_spatial_bce_3: 0.07828/0.09453, loss_spatial_dice_3: 0.14663/0.22764, loss_spatial_ce_3: 0.05262/0.09712, loss_grounding_bce_3: 0.02486/0.08658, loss_grounding_dice_3: 0.07858/0.17881, loss_grounding_ce_3: 0.29261/0.28331, loss_mask_ce_4: 0.50194/0.92679, loss_mask_bce_4: 0.53411/0.33964, loss_mask_dice_4: 0.80850/1.19226, loss_spatial_bce_4: 0.07759/0.09827, loss_spatial_dice_4: 0.15022/0.23648, loss_spatial_ce_4: 0.07405/0.11377, loss_grounding_bce_4: 0.02444/0.08721, loss_grounding_dice_4: 0.08112/0.18158, loss_grounding_ce_4: 0.28151/0.28558, loss_mask_ce_5: 0.49190/0.94160, loss_mask_bce_5: 0.55006/0.34180, loss_mask_dice_5: 0.78410/1.19711, loss_spatial_bce_5: 0.07714/0.09929, loss_spatial_dice_5: 0.15685/0.23937, loss_spatial_ce_5: 0.08333/0.12696, loss_grounding_bce_5: 0.02395/0.08765, loss_grounding_dice_5: 0.07311/0.18279, loss_grounding_ce_5: 0.29189/0.29783, loss_mask_ce_6: 0.50307/0.97843, loss_mask_bce_6: 0.54903/0.34444, loss_mask_dice_6: 0.89400/1.20021, loss_spatial_bce_6: 0.08006/0.10472, loss_spatial_dice_6: 0.15603/0.24192, loss_spatial_ce_6: 0.05171/0.15073, loss_grounding_bce_6: 0.02476/0.08845, loss_grounding_dice_6: 0.08411/0.18286, loss_grounding_ce_6: 0.30767/0.31642, loss_mask_ce_7: 0.57719/1.02040, loss_mask_bce_7: 0.53184/0.35217, loss_mask_dice_7: 0.84186/1.25537, loss_spatial_bce_7: 0.08697/0.11359, loss_spatial_dice_7: 0.19566/0.26910, loss_spatial_ce_7: 0.25287/0.18997, loss_grounding_bce_7: 0.02409/0.09033, loss_grounding_dice_7: 0.08009/0.19008, loss_grounding_ce_7: 0.32119/0.35076, loss_mask_ce_8: 0.82105/1.13180, loss_mask_bce_8: 0.49616/0.36553, loss_mask_dice_8: 0.93901/1.33082, loss_spatial_bce_8: 0.09196/0.13493, loss_spatial_dice_8: 0.25402/0.30969, loss_spatial_ce_8: 0.20352/0.24621, loss_grounding_bce_8: 0.02402/0.09373, loss_grounding_dice_8: 0.07992/0.20144, loss_grounding_ce_8: 0.37249/0.42393, loss_mask_ce_9: 4.89092/3.69475, loss_mask_bce_9: 0.56712/0.39254, loss_mask_dice_9: 1.52265/1.90622, loss_spatial_bce_9: 0.39132/0.33705, loss_spatial_dice_9: 0.82420/0.82499, loss_spatial_ce_9: 1.69620/1.52313, loss_grounding_bce_9: 0.02686/0.10518, loss_grounding_dice_9: 0.19614/0.28136, loss_grounding_ce_9: 0.37589/0.70275] items per batch[64] items per second[0.23] total items[1158400] mini batches[ 18100] memory[7341] epoch remaining[0:07:57]
INFO:trainer.default_trainer:epochs[     9] optim steps[18200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43766/0.91341, loss_mask_bce_0: 0.31570/0.33610, loss_mask_dice_0: 0.73161/1.16663, loss_spatial_bce_0: 0.07667/0.09262, loss_spatial_dice_0: 0.16383/0.22146, loss_spatial_ce_0: 0.01080/0.08306, loss_grounding_bce_0: 0.08535/0.08644, loss_grounding_dice_0: 0.13953/0.17917, loss_grounding_ce_0: 0.16249/0.27708, loss_mask_ce_1: 0.45705/0.91344, loss_mask_bce_1: 0.31774/0.33677, loss_mask_dice_1: 0.72312/1.17417, loss_spatial_bce_1: 0.07652/0.09357, loss_spatial_dice_1: 0.16533/0.22590, loss_spatial_ce_1: 0.01196/0.08870, loss_grounding_bce_1: 0.08250/0.08647, loss_grounding_dice_1: 0.14569/0.17957, loss_grounding_ce_1: 0.16787/0.27887, loss_mask_ce_2: 0.46621/0.92121, loss_mask_bce_2: 0.30989/0.33710, loss_mask_dice_2: 0.68331/1.17360, loss_spatial_bce_2: 0.07498/0.09355, loss_spatial_dice_2: 0.16230/0.22677, loss_spatial_ce_2: 0.01258/0.09274, loss_grounding_bce_2: 0.08196/0.08651, loss_grounding_dice_2: 0.14435/0.17919, loss_grounding_ce_2: 0.17172/0.28226, loss_mask_ce_3: 0.41537/0.92867, loss_mask_bce_3: 0.30772/0.33803, loss_mask_dice_3: 0.64572/1.17053, loss_spatial_bce_3: 0.07269/0.09446, loss_spatial_dice_3: 0.16696/0.22762, loss_spatial_ce_3: 0.02203/0.09702, loss_grounding_bce_3: 0.08275/0.08660, loss_grounding_dice_3: 0.15247/0.17888, loss_grounding_ce_3: 0.18135/0.28328, loss_mask_ce_4: 0.42504/0.92686, loss_mask_bce_4: 0.30827/0.33950, loss_mask_dice_4: 0.66815/1.19217, loss_spatial_bce_4: 0.07687/0.09819, loss_spatial_dice_4: 0.17056/0.23651, loss_spatial_ce_4: 0.05220/0.11362, loss_grounding_bce_4: 0.08397/0.08723, loss_grounding_dice_4: 0.13826/0.18162, loss_grounding_ce_4: 0.18782/0.28565, loss_mask_ce_5: 0.47423/0.94166, loss_mask_bce_5: 0.30337/0.34167, loss_mask_dice_5: 0.63451/1.19703, loss_spatial_bce_5: 0.08212/0.09922, loss_spatial_dice_5: 0.18191/0.23940, loss_spatial_ce_5: 0.08065/0.12687, loss_grounding_bce_5: 0.08530/0.08767, loss_grounding_dice_5: 0.15771/0.18285, loss_grounding_ce_5: 0.19641/0.29784, loss_mask_ce_6: 0.47758/0.97853, loss_mask_bce_6: 0.31861/0.34428, loss_mask_dice_6: 0.67929/1.20011, loss_spatial_bce_6: 0.08528/0.10464, loss_spatial_dice_6: 0.18265/0.24193, loss_spatial_ce_6: 0.13621/0.15067, loss_grounding_bce_6: 0.08991/0.08846, loss_grounding_dice_6: 0.14958/0.18295, loss_grounding_ce_6: 0.20960/0.31624, loss_mask_ce_7: 0.56269/1.02068, loss_mask_bce_7: 0.29796/0.35197, loss_mask_dice_7: 0.64564/1.25525, loss_spatial_bce_7: 0.12166/0.11353, loss_spatial_dice_7: 0.22955/0.26913, loss_spatial_ce_7: 0.09380/0.18989, loss_grounding_bce_7: 0.08967/0.09034, loss_grounding_dice_7: 0.16481/0.19019, loss_grounding_ce_7: 0.23223/0.35064, loss_mask_ce_8: 0.64997/1.13202, loss_mask_bce_8: 0.42323/0.36534, loss_mask_dice_8: 0.80662/1.33069, loss_spatial_bce_8: 0.16816/0.13489, loss_spatial_dice_8: 0.31254/0.30973, loss_spatial_ce_8: 0.16479/0.24609, loss_grounding_bce_8: 0.11174/0.09375, loss_grounding_dice_8: 0.21460/0.20155, loss_grounding_ce_8: 0.26811/0.42382, loss_mask_ce_9: 2.39680/3.69463, loss_mask_bce_9: 0.37127/0.39231, loss_mask_dice_9: 1.13732/1.90578, loss_spatial_bce_9: 0.32707/0.33684, loss_spatial_dice_9: 0.85868/0.82499, loss_spatial_ce_9: 1.59285/1.52312, loss_grounding_bce_9: 0.11471/0.10517, loss_grounding_dice_9: 0.29106/0.28148, loss_grounding_ce_9: 0.31975/0.70267] items per batch[64] items per second[0.23] total items[1164800] mini batches[ 18200] memory[7341] epoch remaining[0:03:16]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00018270.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2177 s/iter. Eval: 0.0915 s/iter. Total: 0.3123 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2189 s/iter. Eval: 0.0839 s/iter. Total: 0.3059 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2230 s/iter. Eval: 0.0793 s/iter. Total: 0.3055 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0030 s/iter. Inference: 0.2226 s/iter. Eval: 0.0779 s/iter. Total: 0.3036 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2212 s/iter. Eval: 0.0761 s/iter. Total: 0.3006 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2239 s/iter. Eval: 0.0758 s/iter. Total: 0.3028 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0031 s/iter. Inference: 0.2251 s/iter. Eval: 0.0755 s/iter. Total: 0.3039 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 131/157. Dataloading: 0.0031 s/iter. Inference: 0.2250 s/iter. Eval: 0.0743 s/iter. Total: 0.3025 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 148/157. Dataloading: 0.0032 s/iter. Inference: 0.2262 s/iter. Eval: 0.0741 s/iter. Total: 0.3036 s/iter. ETA=0:00:02
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalcc9xme4q ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.451 | 82.033 | 59.448 |      133      |
| Things | 54.407 | 82.717 | 65.148 |      80       |
| Stuff  | 41.970 | 80.999 | 50.844 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.74 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.32s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.82 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.384
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.606
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.406
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.599
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.712
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.418 | 60.560 | 40.608 | 19.794 | 41.420 | 59.905 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.293 | bicycle      | 18.617 | car            | 37.560 |
| motorcycle    | 35.389 | airplane     | 57.038 | bus            | 66.656 |
| train         | 68.637 | truck        | 34.417 | boat           | 23.147 |
| traffic light | 24.726 | fire hydrant | 63.913 | stop sign      | 63.504 |
| parking meter | 40.946 | bench        | 20.576 | bird           | 29.545 |
| cat           | 73.040 | dog          | 65.065 | horse          | 45.842 |
| sheep         | 46.050 | cow          | 50.481 | elephant       | 60.479 |
| bear          | 75.817 | zebra        | 59.337 | giraffe        | 56.045 |
| backpack      | 16.750 | umbrella     | 47.880 | handbag        | 15.207 |
| tie           | 32.989 | suitcase     | 39.550 | frisbee        | 67.639 |
| skis          | 4.418  | snowboard    | 23.873 | sports ball    | 46.379 |
| kite          | 34.101 | baseball bat | 27.734 | baseball glove | 43.771 |
| skateboard    | 36.029 | surfboard    | 36.411 | tennis racket  | 56.545 |
| bottle        | 32.792 | wine glass   | 27.039 | cup            | 38.658 |
| fork          | 15.010 | knife        | 12.985 | spoon          | 13.821 |
| bowl          | 30.156 | banana       | 19.686 | apple          | 19.713 |
| sandwich      | 41.455 | orange       | 28.219 | broccoli       | 22.052 |
| carrot        | 18.952 | hot dog      | 21.057 | pizza          | 46.833 |
| donut         | 45.333 | cake         | 43.283 | chair          | 21.446 |
| couch         | 39.825 | potted plant | 17.147 | bed            | 41.413 |
| dining table  | 13.273 | toilet       | 67.839 | tv             | 61.977 |
| laptop        | 61.473 | mouse        | 59.302 | remote         | 31.615 |
| keyboard      | 47.837 | cell phone   | 36.295 | microwave      | 54.697 |
| oven          | 31.582 | toaster      | 26.829 | sink           | 37.315 |
| refrigerator  | 59.204 | book         | 7.715  | clock          | 51.724 |
| vase          | 32.798 | scissors     | 24.087 | teddy bear     | 51.348 |
| hair drier    | 11.618 | toothbrush   | 17.674 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.23414374899673, 'fwIoU': 68.9367541801435, 'IoU-person': 87.41110576021136, 'IoU-bicycle': 76.20085349196827, 'IoU-car': 68.6842150974086, 'IoU-motorcycle': 83.03835206010075, 'IoU-airplane': 82.86194516366243, 'IoU-bus': 84.7814975649968, 'IoU-train': 87.25246797356155, 'IoU-truck': 62.99287922542395, 'IoU-boat': 65.66417403620254, 'IoU-traffic light': 75.90230851443542, 'IoU-fire hydrant': 89.89323037840965, 'IoU-stop sign': 88.22830568667688, 'IoU-parking meter': 83.78148268934424, 'IoU-bench': 54.035607870035676, 'IoU-bird': 75.55754839167628, 'IoU-cat': 83.43712250491387, 'IoU-dog': 77.03716095464374, 'IoU-horse': 86.65592309141029, 'IoU-sheep': 86.53534554759372, 'IoU-cow': 83.04657950631749, 'IoU-elephant': 88.7645385536926, 'IoU-bear': 91.01548485744651, 'IoU-zebra': 91.27014836982022, 'IoU-giraffe': 85.10153147377468, 'IoU-backpack': 36.73508866487718, 'IoU-umbrella': 77.49769195433326, 'IoU-handbag': 36.19052042451406, 'IoU-tie': 62.44389347240627, 'IoU-suitcase': 80.01452010612489, 'IoU-frisbee': 83.73696732885854, 'IoU-skis': 51.882922827509425, 'IoU-snowboard': 68.16402569593147, 'IoU-sports ball': 65.98254425549635, 'IoU-kite': 63.96501642495749, 'IoU-baseball bat': 58.26225107949542, 'IoU-baseball glove': 78.42219121379271, 'IoU-skateboard': 64.49978202325704, 'IoU-surfboard': 80.70959689903874, 'IoU-tennis racket': 82.77380829490173, 'IoU-bottle': 67.56267764900517, 'IoU-wine glass': 74.33230947767805, 'IoU-cup': 60.83050414024752, 'IoU-fork': 55.618493607519014, 'IoU-knife': 49.905737208299634, 'IoU-spoon': 51.328253328890405, 'IoU-bowl': 55.52907293435618, 'IoU-banana': 83.27768976539849, 'IoU-apple': 56.15589958047343, 'IoU-sandwich': 66.63662034415073, 'IoU-orange': 77.66397560747588, 'IoU-broccoli': 67.5637296027137, 'IoU-carrot': 62.97859975386299, 'IoU-hot dog': 64.42433563015986, 'IoU-pizza': 84.45271639759862, 'IoU-donut': 64.21692592562175, 'IoU-cake': 70.78296899916847, 'IoU-chair': 53.92147371279547, 'IoU-couch': 68.39293463206317, 'IoU-potted plant': 35.6435472794377, 'IoU-bed': 67.98794782683431, 'IoU-dining table': 51.54420038330289, 'IoU-toilet': 83.4900396297281, 'IoU-tv': 76.7920723190612, 'IoU-laptop': 73.21419119817254, 'IoU-mouse': 68.48135087249545, 'IoU-remote': 48.86072883229286, 'IoU-keyboard': 64.78255318431367, 'IoU-cell phone': 67.38275811233599, 'IoU-microwave': 59.55268359307768, 'IoU-oven': 64.47934855155354, 'IoU-toaster': 50.18533900301253, 'IoU-sink': 69.20017833861368, 'IoU-refrigerator': 79.24156789613409, 'IoU-book': 49.69620341905069, 'IoU-clock': 68.54996704178441, 'IoU-vase': 58.380777964638106, 'IoU-scissors': 55.29571077325035, 'IoU-teddy bear': 81.04225505398959, 'IoU-hair drier': 32.52974934112198, 'IoU-toothbrush': 52.17758821676989, 'IoU-banner': 30.83370712762855, 'IoU-blanket': 12.04379548810896, 'IoU-bridge': 39.28625491451419, 'IoU-cardboard': 45.777512799959055, 'IoU-counter': 30.854223920460587, 'IoU-curtain': 62.740227974705974, 'IoU-door-stuff': 43.1684866703922, 'IoU-floor-wood': 61.36297838159586, 'IoU-flower': 44.52998277212994, 'IoU-fruit': 38.97597187983856, 'IoU-gravel': 33.69246128247792, 'IoU-house': 22.20869879209452, 'IoU-light': 38.56907280246325, 'IoU-mirror-stuff': 47.33755958665547, 'IoU-net': 32.49690836788604, 'IoU-pillow': 11.05807530760619, 'IoU-platform': 33.06018546789321, 'IoU-playingfield': 66.93130484563692, 'IoU-railroad': 59.793023753758575, 'IoU-river': 46.59744133332791, 'IoU-road': 67.16514068559096, 'IoU-roof': 11.401799742528665, 'IoU-sand': 61.607993872520254, 'IoU-sea': 84.67865753992363, 'IoU-shelf': 36.289136298907074, 'IoU-snow': 89.47922979344885, 'IoU-stairs': 20.41345382667399, 'IoU-tent': 9.197901562626406, 'IoU-towel': 29.74028137468655, 'IoU-wall-brick': 44.39008975087984, 'IoU-wall-stone': 26.87261750967111, 'IoU-wall-tile': 61.826783228597435, 'IoU-wall-wood': 38.19237629593715, 'IoU-water-other': 24.066866373133998, 'IoU-window-blind': 46.095148134529666, 'IoU-window-other': 47.325249736763126, 'IoU-tree-merged': 81.26623929805253, 'IoU-fence-merged': 49.66065818032654, 'IoU-ceiling-merged': 65.85227104312973, 'IoU-sky-other-merged': 93.82467534029193, 'IoU-cabinet-merged': 58.42203620198252, 'IoU-table-merged': 39.03616196011422, 'IoU-floor-other-merged': 49.5585496434756, 'IoU-pavement-merged': 55.85343871590447, 'IoU-mountain-merged': 57.17545047639653, 'IoU-grass-merged': 72.69855905921327, 'IoU-dirt-merged': 46.37254317775497, 'IoU-paper-merged': 33.18028284163134, 'IoU-food-other-merged': 38.58136295466058, 'IoU-building-other-merged': 57.621805813041995, 'IoU-rock-merged': 59.05621955462398, 'IoU-wall-other-merged': 62.77281274457944, 'IoU-rug-merged': 65.63114585816061, 'mACC': 72.6052561364207, 'pACC': 80.26163576810237, 'ACC-person': 92.47396839690111, 'ACC-bicycle': 86.93975997438804, 'ACC-car': 83.25977919890255, 'ACC-motorcycle': 90.61899772384447, 'ACC-airplane': 90.62996220672042, 'ACC-bus': 92.08173281240248, 'ACC-train': 91.54402019618901, 'ACC-truck': 72.3092345790361, 'ACC-boat': 79.90914959236856, 'ACC-traffic light': 89.65660976340936, 'ACC-fire hydrant': 95.38371045361185, 'ACC-stop sign': 90.91688745472126, 'ACC-parking meter': 87.23006361964549, 'ACC-bench': 69.94561306570085, 'ACC-bird': 80.86427678770137, 'ACC-cat': 91.0686443957399, 'ACC-dog': 79.59432389916842, 'ACC-horse': 92.75640500253772, 'ACC-sheep': 89.77092189043023, 'ACC-cow': 88.53221295690132, 'ACC-elephant': 91.3636663361397, 'ACC-bear': 93.2070924209347, 'ACC-zebra': 93.95029204084025, 'ACC-giraffe': 89.45744288007921, 'ACC-backpack': 60.33097869431469, 'ACC-umbrella': 84.9705869199074, 'ACC-handbag': 53.119107888321, 'ACC-tie': 71.91551803597486, 'ACC-suitcase': 89.12478600877101, 'ACC-frisbee': 94.44981818181817, 'ACC-skis': 72.40278435554708, 'ACC-snowboard': 77.96184271754304, 'ACC-sports ball': 80.25656133387085, 'ACC-kite': 76.21161895700719, 'ACC-baseball bat': 82.29064426945583, 'ACC-baseball glove': 90.50820345431357, 'ACC-skateboard': 70.2264678555238, 'ACC-surfboard': 90.87549471471058, 'ACC-tennis racket': 89.3500444212439, 'ACC-bottle': 80.88030954736179, 'ACC-wine glass': 86.64058675273341, 'ACC-cup': 83.48312755347061, 'ACC-fork': 70.38652817220392, 'ACC-knife': 64.884223638895, 'ACC-spoon': 71.8597023391164, 'ACC-bowl': 69.33010264613687, 'ACC-banana': 91.4081355982632, 'ACC-apple': 71.1208355889334, 'ACC-sandwich': 80.20737453174668, 'ACC-orange': 85.1329918681802, 'ACC-broccoli': 80.75283740573057, 'ACC-carrot': 75.07644748083537, 'ACC-hot dog': 72.38069714440101, 'ACC-pizza': 92.32447545730108, 'ACC-donut': 76.91828351531355, 'ACC-cake': 78.30993744644074, 'ACC-chair': 70.6741841625246, 'ACC-couch': 80.8755064693513, 'ACC-potted plant': 51.859358525177555, 'ACC-bed': 81.03930026217249, 'ACC-dining table': 74.23437911519525, 'ACC-toilet': 92.07285349286043, 'ACC-tv': 88.62277198915994, 'ACC-laptop': 91.1857656207839, 'ACC-mouse': 86.72589995274323, 'ACC-remote': 69.56453068919, 'ACC-keyboard': 73.3502115781268, 'ACC-cell phone': 75.64344075564064, 'ACC-microwave': 73.31067053739703, 'ACC-oven': 75.43233205514149, 'ACC-toaster': 55.4010592727422, 'ACC-sink': 83.6117081906158, 'ACC-refrigerator': 91.34447866188651, 'ACC-book': 70.50888179092767, 'ACC-clock': 73.13313329980396, 'ACC-vase': 67.11887793419366, 'ACC-scissors': 59.75562166579438, 'ACC-teddy bear': 86.85942875108655, 'ACC-hair drier': 40.57723507813167, 'ACC-toothbrush': 82.03874218207088, 'ACC-banner': 78.24839567880602, 'ACC-blanket': 16.880239374893282, 'ACC-bridge': 53.879846713326494, 'ACC-cardboard': 60.60395521302894, 'ACC-counter': 53.90469477725027, 'ACC-curtain': 73.62286000764813, 'ACC-door-stuff': 58.543747436385075, 'ACC-floor-wood': 79.9045530837908, 'ACC-flower': 63.03464239180372, 'ACC-fruit': 56.23494287615565, 'ACC-gravel': 42.33195013807295, 'ACC-house': 24.561501792158236, 'ACC-light': 53.72008144164147, 'ACC-mirror-stuff': 58.20413625757289, 'ACC-net': 63.89609425056479, 'ACC-pillow': 22.060333744287778, 'ACC-platform': 55.02052622803841, 'ACC-playingfield': 80.68797229444729, 'ACC-railroad': 76.13817239422363, 'ACC-river': 64.01752571229932, 'ACC-road': 83.02865282355896, 'ACC-roof': 14.53589050919418, 'ACC-sand': 71.42624655827139, 'ACC-sea': 90.34400665966936, 'ACC-shelf': 57.6287582415559, 'ACC-snow': 95.1204142249514, 'ACC-stairs': 32.85280040789259, 'ACC-tent': 11.589962402398886, 'ACC-towel': 36.58059065331478, 'ACC-wall-brick': 53.03064988355901, 'ACC-wall-stone': 31.315463971787988, 'ACC-wall-tile': 71.01500433798887, 'ACC-wall-wood': 52.05678595520228, 'ACC-water-other': 43.580356087649136, 'ACC-window-blind': 58.84082414703461, 'ACC-window-other': 68.28262664105868, 'ACC-tree-merged': 89.05879886811286, 'ACC-fence-merged': 65.30870506241301, 'ACC-ceiling-merged': 78.38442223942027, 'ACC-sky-other-merged': 96.56206700814855, 'ACC-cabinet-merged': 74.16528290844559, 'ACC-table-merged': 49.05883463584239, 'ACC-floor-other-merged': 62.400631695514264, 'ACC-pavement-merged': 69.49439488960458, 'ACC-mountain-merged': 71.20739834876372, 'ACC-grass-merged': 83.31353419732397, 'ACC-dirt-merged': 71.40735415737105, 'ACC-paper-merged': 45.76026671334535, 'ACC-food-other-merged': 55.365326029399775, 'ACC-building-other-merged': 76.43268226738658, 'ACC-rock-merged': 83.49187137711958, 'ACC-wall-other-merged': 83.54678517578643, 'ACC-rug-merged': 81.31931108205403})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1528 s/iter. Inference: 0.5785 s/iter. Eval: 0.0000 s/iter. Total: 0.7314 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1558 s/iter. Inference: 0.5316 s/iter. Eval: 0.0000 s/iter. Total: 0.6875 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/50. Dataloading: 0.1671 s/iter. Inference: 0.7035 s/iter. Eval: 0.0000 s/iter. Total: 0.8706 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1705 s/iter. Inference: 0.7255 s/iter. Eval: 0.0000 s/iter. Total: 0.8961 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1678 s/iter. Inference: 0.6455 s/iter. Eval: 0.0000 s/iter. Total: 0.8134 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1677 s/iter. Inference: 0.6796 s/iter. Eval: 0.0000 s/iter. Total: 0.8475 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1701 s/iter. Inference: 0.7364 s/iter. Eval: 0.0000 s/iter. Total: 0.9066 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5700907228563068, 'noc@0.8': 3.0146327187591453, 'noc@0.85': 3.6669593210418494, 'noc@0.9': 4.688323090430202, 'miou@iter1': 0.8279773443108732}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.1004 s/iter. Eval: 0.0008 s/iter. Total: 0.1026 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 69.95724487304688, 'precision@0.6': 67.04236602783203, 'precision@0.7': 61.795570373535156, 'precision@0.8': 51.65176773071289, 'precision@0.9': 26.2728328704834, 'cIoU': 55.80817794799805, 'mIoU': 61.758758544921875}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.45098693147171, 'SQ': 82.03257595748502, 'RQ': 59.44816218556511, 'PQ_th': 54.40682401654681, 'SQ_th': 82.71717510970153, 'RQ_th': 65.14845525158346, 'PQ_st': 41.970478123811155, 'SQ_st': 80.99921874659204, 'RQ_st': 50.843946236858095}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.418440363260316, 'AP50': 60.559890068884826, 'AP75': 40.60791922501304, 'APs': 19.79392217762477, 'APm': 41.42012601034813, 'APl': 59.90516899688691, 'AP-person': 44.29302859034297, 'AP-bicycle': 18.616741159878117, 'AP-car': 37.55986668402361, 'AP-motorcycle': 35.38876841302601, 'AP-airplane': 57.03846700418155, 'AP-bus': 66.65611891110136, 'AP-train': 68.63665307978215, 'AP-truck': 34.41696992395371, 'AP-boat': 23.146692238543608, 'AP-traffic light': 24.725812829162493, 'AP-fire hydrant': 63.913081487430325, 'AP-stop sign': 63.50418514357442, 'AP-parking meter': 40.94596304273062, 'AP-bench': 20.57641873798873, 'AP-bird': 29.54469666622628, 'AP-cat': 73.04042277600166, 'AP-dog': 65.06489127103393, 'AP-horse': 45.842133302587904, 'AP-sheep': 46.049599075862, 'AP-cow': 50.4806409858264, 'AP-elephant': 60.47934468461001, 'AP-bear': 75.81714199571539, 'AP-zebra': 59.3367987382554, 'AP-giraffe': 56.044642838235895, 'AP-backpack': 16.750029676319393, 'AP-umbrella': 47.880060301376695, 'AP-handbag': 15.207149078057578, 'AP-tie': 32.98898441769123, 'AP-suitcase': 39.549563288974376, 'AP-frisbee': 67.63917822468707, 'AP-skis': 4.418122363773064, 'AP-snowboard': 23.872774334850046, 'AP-sports ball': 46.378592072678124, 'AP-kite': 34.101390893527025, 'AP-baseball bat': 27.734422927896972, 'AP-baseball glove': 43.77095375982844, 'AP-skateboard': 36.02887622169686, 'AP-surfboard': 36.411460745335255, 'AP-tennis racket': 56.54522818123352, 'AP-bottle': 32.79215881839672, 'AP-wine glass': 27.03852023293416, 'AP-cup': 38.65824967953767, 'AP-fork': 15.009835390880767, 'AP-knife': 12.985375170743316, 'AP-spoon': 13.820761301556933, 'AP-bowl': 30.156334897902966, 'AP-banana': 19.68604783831991, 'AP-apple': 19.712855414799343, 'AP-sandwich': 41.45522596541617, 'AP-orange': 28.2187513513505, 'AP-broccoli': 22.051837814395878, 'AP-carrot': 18.95170462641822, 'AP-hot dog': 21.057205768152123, 'AP-pizza': 46.83294449631343, 'AP-donut': 45.333036219746056, 'AP-cake': 43.283203934878856, 'AP-chair': 21.445711740127297, 'AP-couch': 39.82487899719575, 'AP-potted plant': 17.146829725864208, 'AP-bed': 41.412962522033375, 'AP-dining table': 13.273339041292465, 'AP-toilet': 67.83881919032329, 'AP-tv': 61.97688725666558, 'AP-laptop': 61.47281502862756, 'AP-mouse': 59.3015870969636, 'AP-remote': 31.614902179614486, 'AP-keyboard': 47.8373894999147, 'AP-cell phone': 36.29521975554002, 'AP-microwave': 54.697336486943584, 'AP-oven': 31.58228660351624, 'AP-toaster': 26.82941978408367, 'AP-sink': 37.314867469022985, 'AP-refrigerator': 59.20445390597203, 'AP-book': 7.715306597509568, 'AP-clock': 51.72428832341714, 'AP-vase': 32.79826186707548, 'AP-scissors': 24.08732564619124, 'AP-teddy bear': 51.348180829109594, 'AP-hair drier': 11.617938264414676, 'AP-toothbrush': 17.674306259663044}), ('sem_seg', {'mIoU': 60.23414374899673, 'fwIoU': 68.9367541801435, 'IoU-person': 87.41110576021136, 'IoU-bicycle': 76.20085349196827, 'IoU-car': 68.6842150974086, 'IoU-motorcycle': 83.03835206010075, 'IoU-airplane': 82.86194516366243, 'IoU-bus': 84.7814975649968, 'IoU-train': 87.25246797356155, 'IoU-truck': 62.99287922542395, 'IoU-boat': 65.66417403620254, 'IoU-traffic light': 75.90230851443542, 'IoU-fire hydrant': 89.89323037840965, 'IoU-stop sign': 88.22830568667688, 'IoU-parking meter': 83.78148268934424, 'IoU-bench': 54.035607870035676, 'IoU-bird': 75.55754839167628, 'IoU-cat': 83.43712250491387, 'IoU-dog': 77.03716095464374, 'IoU-horse': 86.65592309141029, 'IoU-sheep': 86.53534554759372, 'IoU-cow': 83.04657950631749, 'IoU-elephant': 88.7645385536926, 'IoU-bear': 91.01548485744651, 'IoU-zebra': 91.27014836982022, 'IoU-giraffe': 85.10153147377468, 'IoU-backpack': 36.73508866487718, 'IoU-umbrella': 77.49769195433326, 'IoU-handbag': 36.19052042451406, 'IoU-tie': 62.44389347240627, 'IoU-suitcase': 80.01452010612489, 'IoU-frisbee': 83.73696732885854, 'IoU-skis': 51.882922827509425, 'IoU-snowboard': 68.16402569593147, 'IoU-sports ball': 65.98254425549635, 'IoU-kite': 63.96501642495749, 'IoU-baseball bat': 58.26225107949542, 'IoU-baseball glove': 78.42219121379271, 'IoU-skateboard': 64.49978202325704, 'IoU-surfboard': 80.70959689903874, 'IoU-tennis racket': 82.77380829490173, 'IoU-bottle': 67.56267764900517, 'IoU-wine glass': 74.33230947767805, 'IoU-cup': 60.83050414024752, 'IoU-fork': 55.618493607519014, 'IoU-knife': 49.905737208299634, 'IoU-spoon': 51.328253328890405, 'IoU-bowl': 55.52907293435618, 'IoU-banana': 83.27768976539849, 'IoU-apple': 56.15589958047343, 'IoU-sandwich': 66.63662034415073, 'IoU-orange': 77.66397560747588, 'IoU-broccoli': 67.5637296027137, 'IoU-carrot': 62.97859975386299, 'IoU-hot dog': 64.42433563015986, 'IoU-pizza': 84.45271639759862, 'IoU-donut': 64.21692592562175, 'IoU-cake': 70.78296899916847, 'IoU-chair': 53.92147371279547, 'IoU-couch': 68.39293463206317, 'IoU-potted plant': 35.6435472794377, 'IoU-bed': 67.98794782683431, 'IoU-dining table': 51.54420038330289, 'IoU-toilet': 83.4900396297281, 'IoU-tv': 76.7920723190612, 'IoU-laptop': 73.21419119817254, 'IoU-mouse': 68.48135087249545, 'IoU-remote': 48.86072883229286, 'IoU-keyboard': 64.78255318431367, 'IoU-cell phone': 67.38275811233599, 'IoU-microwave': 59.55268359307768, 'IoU-oven': 64.47934855155354, 'IoU-toaster': 50.18533900301253, 'IoU-sink': 69.20017833861368, 'IoU-refrigerator': 79.24156789613409, 'IoU-book': 49.69620341905069, 'IoU-clock': 68.54996704178441, 'IoU-vase': 58.380777964638106, 'IoU-scissors': 55.29571077325035, 'IoU-teddy bear': 81.04225505398959, 'IoU-hair drier': 32.52974934112198, 'IoU-toothbrush': 52.17758821676989, 'IoU-banner': 30.83370712762855, 'IoU-blanket': 12.04379548810896, 'IoU-bridge': 39.28625491451419, 'IoU-cardboard': 45.777512799959055, 'IoU-counter': 30.854223920460587, 'IoU-curtain': 62.740227974705974, 'IoU-door-stuff': 43.1684866703922, 'IoU-floor-wood': 61.36297838159586, 'IoU-flower': 44.52998277212994, 'IoU-fruit': 38.97597187983856, 'IoU-gravel': 33.69246128247792, 'IoU-house': 22.20869879209452, 'IoU-light': 38.56907280246325, 'IoU-mirror-stuff': 47.33755958665547, 'IoU-net': 32.49690836788604, 'IoU-pillow': 11.05807530760619, 'IoU-platform': 33.06018546789321, 'IoU-playingfield': 66.93130484563692, 'IoU-railroad': 59.793023753758575, 'IoU-river': 46.59744133332791, 'IoU-road': 67.16514068559096, 'IoU-roof': 11.401799742528665, 'IoU-sand': 61.607993872520254, 'IoU-sea': 84.67865753992363, 'IoU-shelf': 36.289136298907074, 'IoU-snow': 89.47922979344885, 'IoU-stairs': 20.41345382667399, 'IoU-tent': 9.197901562626406, 'IoU-towel': 29.74028137468655, 'IoU-wall-brick': 44.39008975087984, 'IoU-wall-stone': 26.87261750967111, 'IoU-wall-tile': 61.826783228597435, 'IoU-wall-wood': 38.19237629593715, 'IoU-water-other': 24.066866373133998, 'IoU-window-blind': 46.095148134529666, 'IoU-window-other': 47.325249736763126, 'IoU-tree-merged': 81.26623929805253, 'IoU-fence-merged': 49.66065818032654, 'IoU-ceiling-merged': 65.85227104312973, 'IoU-sky-other-merged': 93.82467534029193, 'IoU-cabinet-merged': 58.42203620198252, 'IoU-table-merged': 39.03616196011422, 'IoU-floor-other-merged': 49.5585496434756, 'IoU-pavement-merged': 55.85343871590447, 'IoU-mountain-merged': 57.17545047639653, 'IoU-grass-merged': 72.69855905921327, 'IoU-dirt-merged': 46.37254317775497, 'IoU-paper-merged': 33.18028284163134, 'IoU-food-other-merged': 38.58136295466058, 'IoU-building-other-merged': 57.621805813041995, 'IoU-rock-merged': 59.05621955462398, 'IoU-wall-other-merged': 62.77281274457944, 'IoU-rug-merged': 65.63114585816061, 'mACC': 72.6052561364207, 'pACC': 80.26163576810237, 'ACC-person': 92.47396839690111, 'ACC-bicycle': 86.93975997438804, 'ACC-car': 83.25977919890255, 'ACC-motorcycle': 90.61899772384447, 'ACC-airplane': 90.62996220672042, 'ACC-bus': 92.08173281240248, 'ACC-train': 91.54402019618901, 'ACC-truck': 72.3092345790361, 'ACC-boat': 79.90914959236856, 'ACC-traffic light': 89.65660976340936, 'ACC-fire hydrant': 95.38371045361185, 'ACC-stop sign': 90.91688745472126, 'ACC-parking meter': 87.23006361964549, 'ACC-bench': 69.94561306570085, 'ACC-bird': 80.86427678770137, 'ACC-cat': 91.0686443957399, 'ACC-dog': 79.59432389916842, 'ACC-horse': 92.75640500253772, 'ACC-sheep': 89.77092189043023, 'ACC-cow': 88.53221295690132, 'ACC-elephant': 91.3636663361397, 'ACC-bear': 93.2070924209347, 'ACC-zebra': 93.95029204084025, 'ACC-giraffe': 89.45744288007921, 'ACC-backpack': 60.33097869431469, 'ACC-umbrella': 84.9705869199074, 'ACC-handbag': 53.119107888321, 'ACC-tie': 71.91551803597486, 'ACC-suitcase': 89.12478600877101, 'ACC-frisbee': 94.44981818181817, 'ACC-skis': 72.40278435554708, 'ACC-snowboard': 77.96184271754304, 'ACC-sports ball': 80.25656133387085, 'ACC-kite': 76.21161895700719, 'ACC-baseball bat': 82.29064426945583, 'ACC-baseball glove': 90.50820345431357, 'ACC-skateboard': 70.2264678555238, 'ACC-surfboard': 90.87549471471058, 'ACC-tennis racket': 89.3500444212439, 'ACC-bottle': 80.88030954736179, 'ACC-wine glass': 86.64058675273341, 'ACC-cup': 83.48312755347061, 'ACC-fork': 70.38652817220392, 'ACC-knife': 64.884223638895, 'ACC-spoon': 71.8597023391164, 'ACC-bowl': 69.33010264613687, 'ACC-banana': 91.4081355982632, 'ACC-apple': 71.1208355889334, 'ACC-sandwich': 80.20737453174668, 'ACC-orange': 85.1329918681802, 'ACC-broccoli': 80.75283740573057, 'ACC-carrot': 75.07644748083537, 'ACC-hot dog': 72.38069714440101, 'ACC-pizza': 92.32447545730108, 'ACC-donut': 76.91828351531355, 'ACC-cake': 78.30993744644074, 'ACC-chair': 70.6741841625246, 'ACC-couch': 80.8755064693513, 'ACC-potted plant': 51.859358525177555, 'ACC-bed': 81.03930026217249, 'ACC-dining table': 74.23437911519525, 'ACC-toilet': 92.07285349286043, 'ACC-tv': 88.62277198915994, 'ACC-laptop': 91.1857656207839, 'ACC-mouse': 86.72589995274323, 'ACC-remote': 69.56453068919, 'ACC-keyboard': 73.3502115781268, 'ACC-cell phone': 75.64344075564064, 'ACC-microwave': 73.31067053739703, 'ACC-oven': 75.43233205514149, 'ACC-toaster': 55.4010592727422, 'ACC-sink': 83.6117081906158, 'ACC-refrigerator': 91.34447866188651, 'ACC-book': 70.50888179092767, 'ACC-clock': 73.13313329980396, 'ACC-vase': 67.11887793419366, 'ACC-scissors': 59.75562166579438, 'ACC-teddy bear': 86.85942875108655, 'ACC-hair drier': 40.57723507813167, 'ACC-toothbrush': 82.03874218207088, 'ACC-banner': 78.24839567880602, 'ACC-blanket': 16.880239374893282, 'ACC-bridge': 53.879846713326494, 'ACC-cardboard': 60.60395521302894, 'ACC-counter': 53.90469477725027, 'ACC-curtain': 73.62286000764813, 'ACC-door-stuff': 58.543747436385075, 'ACC-floor-wood': 79.9045530837908, 'ACC-flower': 63.03464239180372, 'ACC-fruit': 56.23494287615565, 'ACC-gravel': 42.33195013807295, 'ACC-house': 24.561501792158236, 'ACC-light': 53.72008144164147, 'ACC-mirror-stuff': 58.20413625757289, 'ACC-net': 63.89609425056479, 'ACC-pillow': 22.060333744287778, 'ACC-platform': 55.02052622803841, 'ACC-playingfield': 80.68797229444729, 'ACC-railroad': 76.13817239422363, 'ACC-river': 64.01752571229932, 'ACC-road': 83.02865282355896, 'ACC-roof': 14.53589050919418, 'ACC-sand': 71.42624655827139, 'ACC-sea': 90.34400665966936, 'ACC-shelf': 57.6287582415559, 'ACC-snow': 95.1204142249514, 'ACC-stairs': 32.85280040789259, 'ACC-tent': 11.589962402398886, 'ACC-towel': 36.58059065331478, 'ACC-wall-brick': 53.03064988355901, 'ACC-wall-stone': 31.315463971787988, 'ACC-wall-tile': 71.01500433798887, 'ACC-wall-wood': 52.05678595520228, 'ACC-water-other': 43.580356087649136, 'ACC-window-blind': 58.84082414703461, 'ACC-window-other': 68.28262664105868, 'ACC-tree-merged': 89.05879886811286, 'ACC-fence-merged': 65.30870506241301, 'ACC-ceiling-merged': 78.38442223942027, 'ACC-sky-other-merged': 96.56206700814855, 'ACC-cabinet-merged': 74.16528290844559, 'ACC-table-merged': 49.05883463584239, 'ACC-floor-other-merged': 62.400631695514264, 'ACC-pavement-merged': 69.49439488960458, 'ACC-mountain-merged': 71.20739834876372, 'ACC-grass-merged': 83.31353419732397, 'ACC-dirt-merged': 71.40735415737105, 'ACC-paper-merged': 45.76026671334535, 'ACC-food-other-merged': 55.365326029399775, 'ACC-building-other-merged': 76.43268226738658, 'ACC-rock-merged': 83.49187137711958, 'ACC-wall-other-merged': 83.54678517578643, 'ACC-rug-merged': 81.31931108205403})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5700907228563068, 'noc@0.8': 3.0146327187591453, 'noc@0.85': 3.6669593210418494, 'noc@0.9': 4.688323090430202, 'miou@iter1': 0.8279773443108732}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 69.95724487304688, 'precision@0.6': 67.04236602783203, 'precision@0.7': 61.795570373535156, 'precision@0.8': 51.65176773071289, 'precision@0.9': 26.2728328704834, 'cIoU': 55.80817794799805, 'mIoU': 61.758758544921875}}}
INFO:trainer.default_trainer:This epoch takes 1:28:45.162823
INFO:trainer.default_trainer:PROGRESS: 20.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 10 training.
INFO:trainer.default_trainer:epochs[    10] optim steps[18300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65881/0.91383, loss_mask_bce_0: 0.20672/0.33616, loss_mask_dice_0: 1.08609/1.16744, loss_spatial_bce_0: 0.10935/0.09256, loss_spatial_dice_0: 0.26910/0.22141, loss_spatial_ce_0: 0.09569/0.08299, loss_grounding_bce_0: 0.03958/0.08641, loss_grounding_dice_0: 0.10202/0.17921, loss_grounding_ce_0: 0.10277/0.27732, loss_mask_ce_1: 0.68893/0.91386, loss_mask_bce_1: 0.21375/0.33683, loss_mask_dice_1: 0.88976/1.17503, loss_spatial_bce_1: 0.11316/0.09352, loss_spatial_dice_1: 0.30098/0.22587, loss_spatial_ce_1: 0.37131/0.08862, loss_grounding_bce_1: 0.03974/0.08644, loss_grounding_dice_1: 0.10790/0.17963, loss_grounding_ce_1: 0.11726/0.27907, loss_mask_ce_2: 0.68550/0.92162, loss_mask_bce_2: 0.21278/0.33716, loss_mask_dice_2: 0.98893/1.17447, loss_spatial_bce_2: 0.08968/0.09350, loss_spatial_dice_2: 0.34571/0.22673, loss_spatial_ce_2: 0.31934/0.09264, loss_grounding_bce_2: 0.03876/0.08648, loss_grounding_dice_2: 0.10500/0.17925, loss_grounding_ce_2: 0.11443/0.28251, loss_mask_ce_3: 0.94954/0.92913, loss_mask_bce_3: 0.22169/0.33808, loss_mask_dice_3: 1.10820/1.17137, loss_spatial_bce_3: 0.08545/0.09440, loss_spatial_dice_3: 0.28778/0.22757, loss_spatial_ce_3: 0.05653/0.09695, loss_grounding_bce_3: 0.03945/0.08657, loss_grounding_dice_3: 0.09600/0.17892, loss_grounding_ce_3: 0.12637/0.28349, loss_mask_ce_4: 1.00209/0.92733, loss_mask_bce_4: 0.22560/0.33958, loss_mask_dice_4: 1.41675/1.19321, loss_spatial_bce_4: 0.07529/0.09814, loss_spatial_dice_4: 0.31350/0.23648, loss_spatial_ce_4: 0.05450/0.11345, loss_grounding_bce_4: 0.04239/0.08720, loss_grounding_dice_4: 0.10843/0.18169, loss_grounding_ce_4: 0.10347/0.28581, loss_mask_ce_5: 0.66724/0.94203, loss_mask_bce_5: 0.22816/0.34174, loss_mask_dice_5: 1.20397/1.19796, loss_spatial_bce_5: 0.08275/0.09919, loss_spatial_dice_5: 0.27927/0.23939, loss_spatial_ce_5: 0.06032/0.12671, loss_grounding_bce_5: 0.04112/0.08765, loss_grounding_dice_5: 0.11155/0.18290, loss_grounding_ce_5: 0.11873/0.29806, loss_mask_ce_6: 0.74195/0.97881, loss_mask_bce_6: 0.20632/0.34435, loss_mask_dice_6: 1.02728/1.20106, loss_spatial_bce_6: 0.08594/0.10462, loss_spatial_dice_6: 0.29733/0.24192, loss_spatial_ce_6: 0.08331/0.15057, loss_grounding_bce_6: 0.04813/0.08842, loss_grounding_dice_6: 0.12515/0.18305, loss_grounding_ce_6: 0.08951/0.31648, loss_mask_ce_7: 0.85268/1.02096, loss_mask_bce_7: 0.21601/0.35207, loss_mask_dice_7: 1.34810/1.25630, loss_spatial_bce_7: 0.08698/0.11352, loss_spatial_dice_7: 0.36960/0.26914, loss_spatial_ce_7: 0.32317/0.18986, loss_grounding_bce_7: 0.05122/0.09030, loss_grounding_dice_7: 0.13667/0.19028, loss_grounding_ce_7: 0.08976/0.35090, loss_mask_ce_8: 0.64697/1.13246, loss_mask_bce_8: 0.24824/0.36545, loss_mask_dice_8: 1.02977/1.33164, loss_spatial_bce_8: 0.11313/0.13489, loss_spatial_dice_8: 0.46494/0.30978, loss_spatial_ce_8: 0.24107/0.24596, loss_grounding_bce_8: 0.05373/0.09372, loss_grounding_dice_8: 0.14703/0.20166, loss_grounding_ce_8: 0.05829/0.42420, loss_mask_ce_9: 2.66478/3.69548, loss_mask_bce_9: 0.31690/0.39252, loss_mask_dice_9: 1.32814/1.90728, loss_spatial_bce_9: 0.17614/0.33665, loss_spatial_dice_9: 0.78087/0.82503, loss_spatial_ce_9: 1.50647/1.52295, loss_grounding_bce_9: 0.17981/0.10521, loss_grounding_dice_9: 0.32586/0.28170, loss_grounding_ce_9: 0.56821/0.70240] items per batch[64] items per second[0.13] total items[1171200] mini batches[ 18300] memory[7341] epoch remaining[1:32:00]
INFO:trainer.default_trainer:epochs[    10] optim steps[18400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.47025/0.91342, loss_mask_bce_0: 0.11690/0.33609, loss_mask_dice_0: 1.03189/1.16722, loss_spatial_bce_0: 0.03163/0.09253, loss_spatial_dice_0: 0.17579/0.22134, loss_spatial_ce_0: 0.00705/0.08280, loss_grounding_bce_0: 0.02430/0.08641, loss_grounding_dice_0: 0.05018/0.17923, loss_grounding_ce_0: 0.12761/0.27710, loss_mask_ce_1: 1.42364/0.91345, loss_mask_bce_1: 0.13006/0.33678, loss_mask_dice_1: 1.06685/1.17491, loss_spatial_bce_1: 0.03417/0.09349, loss_spatial_dice_1: 0.18428/0.22581, loss_spatial_ce_1: 0.00715/0.08839, loss_grounding_bce_1: 0.02527/0.08644, loss_grounding_dice_1: 0.05161/0.17967, loss_grounding_ce_1: 0.11931/0.27883, loss_mask_ce_2: 1.34535/0.92121, loss_mask_bce_2: 0.11484/0.33711, loss_mask_dice_2: 1.03129/1.17431, loss_spatial_bce_2: 0.03088/0.09347, loss_spatial_dice_2: 0.17775/0.22667, loss_spatial_ce_2: 0.01375/0.09241, loss_grounding_bce_2: 0.02407/0.08648, loss_grounding_dice_2: 0.05115/0.17928, loss_grounding_ce_2: 0.12424/0.28224, loss_mask_ce_3: 1.42501/0.92881, loss_mask_bce_3: 0.12298/0.33801, loss_mask_dice_3: 1.03909/1.17123, loss_spatial_bce_3: 0.02929/0.09437, loss_spatial_dice_3: 0.14788/0.22749, loss_spatial_ce_3: 0.07340/0.09677, loss_grounding_bce_3: 0.02380/0.08658, loss_grounding_dice_3: 0.04481/0.17896, loss_grounding_ce_3: 0.12024/0.28320, loss_mask_ce_4: 1.37808/0.92687, loss_mask_bce_4: 0.12391/0.33952, loss_mask_dice_4: 1.12548/1.19313, loss_spatial_bce_4: 0.02965/0.09811, loss_spatial_dice_4: 0.17855/0.23643, loss_spatial_ce_4: 0.11923/0.11323, loss_grounding_bce_4: 0.02375/0.08720, loss_grounding_dice_4: 0.05257/0.18172, loss_grounding_ce_4: 0.11560/0.28560, loss_mask_ce_5: 1.40328/0.94168, loss_mask_bce_5: 0.12015/0.34165, loss_mask_dice_5: 1.11341/1.19789, loss_spatial_bce_5: 0.02786/0.09917, loss_spatial_dice_5: 0.17603/0.23934, loss_spatial_ce_5: 0.03197/0.12652, loss_grounding_bce_5: 0.02395/0.08764, loss_grounding_dice_5: 0.04782/0.18293, loss_grounding_ce_5: 0.14179/0.29780, loss_mask_ce_6: 1.53560/0.97840, loss_mask_bce_6: 0.12254/0.34426, loss_mask_dice_6: 1.08274/1.20092, loss_spatial_bce_6: 0.03257/0.10459, loss_spatial_dice_6: 0.16290/0.24186, loss_spatial_ce_6: 0.02902/0.15041, loss_grounding_bce_6: 0.02323/0.08841, loss_grounding_dice_6: 0.05418/0.18309, loss_grounding_ce_6: 0.09440/0.31626, loss_mask_ce_7: 1.63504/1.02065, loss_mask_bce_7: 0.11089/0.35194, loss_mask_dice_7: 1.03168/1.25621, loss_spatial_bce_7: 0.03524/0.11350, loss_spatial_dice_7: 0.22772/0.26904, loss_spatial_ce_7: 0.04022/0.18961, loss_grounding_bce_7: 0.02157/0.09031, loss_grounding_dice_7: 0.05134/0.19033, loss_grounding_ce_7: 0.13956/0.35068, loss_mask_ce_8: 1.69829/1.13221, loss_mask_bce_8: 0.13457/0.36535, loss_mask_dice_8: 1.15473/1.33158, loss_spatial_bce_8: 0.04966/0.13483, loss_spatial_dice_8: 0.26821/0.30967, loss_spatial_ce_8: 0.05503/0.24567, loss_grounding_bce_8: 0.02331/0.09373, loss_grounding_dice_8: 0.04881/0.20171, loss_grounding_ce_8: 0.35824/0.42395, loss_mask_ce_9: 3.44464/3.69425, loss_mask_bce_9: 0.11777/0.39237, loss_mask_dice_9: 1.70823/1.90690, loss_spatial_bce_9: 0.38407/0.33677, loss_spatial_dice_9: 0.92792/0.82503, loss_spatial_ce_9: 1.63794/1.52259, loss_grounding_bce_9: 0.02874/0.10521, loss_grounding_dice_9: 0.09870/0.28179, loss_grounding_ce_9: 1.52191/0.70160] items per batch[64] items per second[0.23] total items[1177600] mini batches[ 18400] memory[7341] epoch remaining[1:21:32]
INFO:trainer.default_trainer:epochs[    10] optim steps[18500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30354/0.91346, loss_mask_bce_0: 0.08509/0.33614, loss_mask_dice_0: 0.92909/1.16745, loss_spatial_bce_0: 0.02032/0.09254, loss_spatial_dice_0: 0.24343/0.22130, loss_spatial_ce_0: 0.04017/0.08269, loss_grounding_bce_0: 0.05477/0.08640, loss_grounding_dice_0: 0.06245/0.17927, loss_grounding_ce_0: 0.04591/0.27727, loss_mask_ce_1: 0.53052/0.91361, loss_mask_bce_1: 0.08984/0.33684, loss_mask_dice_1: 0.92314/1.17507, loss_spatial_bce_1: 0.02111/0.09350, loss_spatial_dice_1: 0.26038/0.22578, loss_spatial_ce_1: 0.03414/0.08830, loss_grounding_bce_1: 0.05780/0.08644, loss_grounding_dice_1: 0.05933/0.17973, loss_grounding_ce_1: 0.04946/0.27894, loss_mask_ce_2: 0.33921/0.92123, loss_mask_bce_2: 0.09140/0.33717, loss_mask_dice_2: 0.75661/1.17449, loss_spatial_bce_2: 0.02236/0.09349, loss_spatial_dice_2: 0.25053/0.22664, loss_spatial_ce_2: 0.03552/0.09232, loss_grounding_bce_2: 0.05338/0.08648, loss_grounding_dice_2: 0.05564/0.17930, loss_grounding_ce_2: 0.04687/0.28237, loss_mask_ce_3: 0.36817/0.92892, loss_mask_bce_3: 0.08805/0.33806, loss_mask_dice_3: 1.07948/1.17137, loss_spatial_bce_3: 0.02191/0.09438, loss_spatial_dice_3: 0.25043/0.22748, loss_spatial_ce_3: 0.08555/0.09671, loss_grounding_bce_3: 0.05614/0.08658, loss_grounding_dice_3: 0.06963/0.17903, loss_grounding_ce_3: 0.05300/0.28331, loss_mask_ce_4: 0.33024/0.92708, loss_mask_bce_4: 0.09240/0.33957, loss_mask_dice_4: 1.26909/1.19327, loss_spatial_bce_4: 0.02067/0.09813, loss_spatial_dice_4: 0.23987/0.23643, loss_spatial_ce_4: 0.12423/0.11308, loss_grounding_bce_4: 0.05624/0.08721, loss_grounding_dice_4: 0.06223/0.18176, loss_grounding_ce_4: 0.05201/0.28572, loss_mask_ce_5: 0.47368/0.94187, loss_mask_bce_5: 0.08867/0.34172, loss_mask_dice_5: 0.76781/1.19809, loss_spatial_bce_5: 0.02355/0.09917, loss_spatial_dice_5: 0.23696/0.23934, loss_spatial_ce_5: 0.03430/0.12646, loss_grounding_bce_5: 0.05219/0.08764, loss_grounding_dice_5: 0.05975/0.18297, loss_grounding_ce_5: 0.06100/0.29807, loss_mask_ce_6: 0.48115/0.97860, loss_mask_bce_6: 0.08921/0.34433, loss_mask_dice_6: 0.89418/1.20108, loss_spatial_bce_6: 0.02242/0.10460, loss_spatial_dice_6: 0.23775/0.24184, loss_spatial_ce_6: 0.10126/0.15034, loss_grounding_bce_6: 0.05463/0.08841, loss_grounding_dice_6: 0.06567/0.18314, loss_grounding_ce_6: 0.05979/0.31651, loss_mask_ce_7: 0.81597/1.02099, loss_mask_bce_7: 0.08538/0.35203, loss_mask_dice_7: 1.07114/1.25649, loss_spatial_bce_7: 0.04272/0.11352, loss_spatial_dice_7: 0.34337/0.26903, loss_spatial_ce_7: 0.19695/0.18940, loss_grounding_bce_7: 0.05297/0.09033, loss_grounding_dice_7: 0.05857/0.19037, loss_grounding_ce_7: 0.05575/0.35089, loss_mask_ce_8: 0.64418/1.13253, loss_mask_bce_8: 0.09668/0.36546, loss_mask_dice_8: 1.11262/1.33179, loss_spatial_bce_8: 0.03678/0.13482, loss_spatial_dice_8: 0.37657/0.30969, loss_spatial_ce_8: 0.33474/0.24558, loss_grounding_bce_8: 0.05478/0.09375, loss_grounding_dice_8: 0.06085/0.20176, loss_grounding_ce_8: 0.24878/0.42402, loss_mask_ce_9: 3.19811/3.69527, loss_mask_bce_9: 0.09353/0.39243, loss_mask_dice_9: 1.45211/1.90697, loss_spatial_bce_9: 0.14153/0.33679, loss_spatial_dice_9: 0.77391/0.82498, loss_spatial_ce_9: 1.74300/1.52253, loss_grounding_bce_9: 0.05667/0.10521, loss_grounding_dice_9: 0.11202/0.28183, loss_grounding_ce_9: 0.96827/0.70168] items per batch[64] items per second[0.22] total items[1184000] mini batches[ 18500] memory[7341] epoch remaining[1:16:30]
INFO:trainer.default_trainer:epochs[    10] optim steps[18600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70311/0.91325, loss_mask_bce_0: 0.63331/0.33616, loss_mask_dice_0: 0.78865/1.16735, loss_spatial_bce_0: 0.21025/0.09252, loss_spatial_dice_0: 0.25065/0.22126, loss_spatial_ce_0: 0.12031/0.08261, loss_grounding_bce_0: 0.00878/0.08640, loss_grounding_dice_0: 0.02216/0.17928, loss_grounding_ce_0: 0.20984/0.27718, loss_mask_ce_1: 0.41795/0.91348, loss_mask_bce_1: 0.64185/0.33687, loss_mask_dice_1: 0.74214/1.17480, loss_spatial_bce_1: 0.21806/0.09347, loss_spatial_dice_1: 0.25430/0.22573, loss_spatial_ce_1: 0.13509/0.08828, loss_grounding_bce_1: 0.00621/0.08645, loss_grounding_dice_1: 0.02010/0.17979, loss_grounding_ce_1: 0.21199/0.27876, loss_mask_ce_2: 0.44710/0.92114, loss_mask_bce_2: 0.64222/0.33720, loss_mask_dice_2: 0.76052/1.17429, loss_spatial_bce_2: 0.20962/0.09347, loss_spatial_dice_2: 0.23438/0.22662, loss_spatial_ce_2: 0.10137/0.09232, loss_grounding_bce_2: 0.00865/0.08650, loss_grounding_dice_2: 0.02295/0.17937, loss_grounding_ce_2: 0.15488/0.28222, loss_mask_ce_3: 0.45066/0.92882, loss_mask_bce_3: 0.65504/0.33810, loss_mask_dice_3: 0.76187/1.17118, loss_spatial_bce_3: 0.20858/0.09436, loss_spatial_dice_3: 0.24153/0.22742, loss_spatial_ce_3: 0.10235/0.09667, loss_grounding_bce_3: 0.00716/0.08660, loss_grounding_dice_3: 0.01884/0.17908, loss_grounding_ce_3: 0.18417/0.28316, loss_mask_ce_4: 0.53125/0.92696, loss_mask_bce_4: 0.60716/0.33959, loss_mask_dice_4: 0.75464/1.19309, loss_spatial_bce_4: 0.19288/0.09811, loss_spatial_dice_4: 0.23673/0.23641, loss_spatial_ce_4: 0.12849/0.11302, loss_grounding_bce_4: 0.00692/0.08720, loss_grounding_dice_4: 0.02113/0.18179, loss_grounding_ce_4: 0.14955/0.28564, loss_mask_ce_5: 0.51983/0.94180, loss_mask_bce_5: 0.62295/0.34175, loss_mask_dice_5: 0.77435/1.19796, loss_spatial_bce_5: 0.19277/0.09917, loss_spatial_dice_5: 0.24895/0.23932, loss_spatial_ce_5: 0.16457/0.12641, loss_grounding_bce_5: 0.00858/0.08764, loss_grounding_dice_5: 0.02340/0.18301, loss_grounding_ce_5: 0.20792/0.29788, loss_mask_ce_6: 0.89487/0.97855, loss_mask_bce_6: 0.65429/0.34437, loss_mask_dice_6: 0.75807/1.20098, loss_spatial_bce_6: 0.19756/0.10460, loss_spatial_dice_6: 0.23118/0.24181, loss_spatial_ce_6: 0.16305/0.15033, loss_grounding_bce_6: 0.00653/0.08841, loss_grounding_dice_6: 0.01908/0.18320, loss_grounding_ce_6: 0.57776/0.31639, loss_mask_ce_7: 0.90861/1.02095, loss_mask_bce_7: 0.64140/0.35207, loss_mask_dice_7: 0.81575/1.25639, loss_spatial_bce_7: 0.17303/0.11353, loss_spatial_dice_7: 0.23865/0.26898, loss_spatial_ce_7: 0.15132/0.18945, loss_grounding_bce_7: 0.00615/0.09032, loss_grounding_dice_7: 0.02077/0.19044, loss_grounding_ce_7: 0.49247/0.35073, loss_mask_ce_8: 1.01688/1.13253, loss_mask_bce_8: 0.60740/0.36547, loss_mask_dice_8: 0.88088/1.33156, loss_spatial_bce_8: 0.25559/0.13481, loss_spatial_dice_8: 0.34002/0.30964, loss_spatial_ce_8: 0.31307/0.24556, loss_grounding_bce_8: 0.00553/0.09375, loss_grounding_dice_8: 0.02585/0.20182, loss_grounding_ce_8: 1.30038/0.42360, loss_mask_ce_9: 4.18612/3.69500, loss_mask_bce_9: 0.57793/0.39252, loss_mask_dice_9: 1.06271/1.90683, loss_spatial_bce_9: 0.48729/0.33676, loss_spatial_dice_9: 0.81729/0.82491, loss_spatial_ce_9: 1.33561/1.52259, loss_grounding_bce_9: 0.00521/0.10521, loss_grounding_dice_9: 0.02863/0.28186, loss_grounding_ce_9: 1.80934/0.70108] items per batch[64] items per second[0.23] total items[1190400] mini batches[ 18600] memory[7341] epoch remaining[1:11:24]
INFO:trainer.default_trainer:epochs[    10] optim steps[18700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63112/0.91324, loss_mask_bce_0: 0.11132/0.33596, loss_mask_dice_0: 0.23446/1.16732, loss_spatial_bce_0: 0.14559/0.09246, loss_spatial_dice_0: 0.25972/0.22117, loss_spatial_ce_0: 0.13130/0.08244, loss_grounding_bce_0: 0.05338/0.08639, loss_grounding_dice_0: 0.12046/0.17929, loss_grounding_ce_0: 0.34086/0.27696, loss_mask_ce_1: 0.50675/0.91337, loss_mask_bce_1: 0.11511/0.33666, loss_mask_dice_1: 0.27018/1.17480, loss_spatial_bce_1: 0.18007/0.09342, loss_spatial_dice_1: 0.24960/0.22563, loss_spatial_ce_1: 0.10656/0.08808, loss_grounding_bce_1: 0.05219/0.08644, loss_grounding_dice_1: 0.12422/0.17981, loss_grounding_ce_1: 0.29932/0.27857, loss_mask_ce_2: 0.57776/0.92097, loss_mask_bce_2: 0.10081/0.33699, loss_mask_dice_2: 0.23472/1.17429, loss_spatial_bce_2: 0.21857/0.09343, loss_spatial_dice_2: 0.24969/0.22653, loss_spatial_ce_2: 0.07139/0.09213, loss_grounding_bce_2: 0.05606/0.08649, loss_grounding_dice_2: 0.12604/0.17939, loss_grounding_ce_2: 0.29876/0.28199, loss_mask_ce_3: 0.64764/0.92875, loss_mask_bce_3: 0.10917/0.33789, loss_mask_dice_3: 0.24703/1.17122, loss_spatial_bce_3: 0.17838/0.09432, loss_spatial_dice_3: 0.24653/0.22733, loss_spatial_ce_3: 0.07120/0.09647, loss_grounding_bce_3: 0.05799/0.08659, loss_grounding_dice_3: 0.12529/0.17911, loss_grounding_ce_3: 0.30404/0.28297, loss_mask_ce_4: 0.79394/0.92688, loss_mask_bce_4: 0.09295/0.33939, loss_mask_dice_4: 0.22826/1.19303, loss_spatial_bce_4: 0.17188/0.09807, loss_spatial_dice_4: 0.23730/0.23635, loss_spatial_ce_4: 0.07770/0.11283, loss_grounding_bce_4: 0.04789/0.08718, loss_grounding_dice_4: 0.11451/0.18183, loss_grounding_ce_4: 0.33295/0.28552, loss_mask_ce_5: 0.81230/0.94175, loss_mask_bce_5: 0.09444/0.34154, loss_mask_dice_5: 0.24713/1.19788, loss_spatial_bce_5: 0.21075/0.09914, loss_spatial_dice_5: 0.25914/0.23927, loss_spatial_ce_5: 0.07581/0.12621, loss_grounding_bce_5: 0.04418/0.08762, loss_grounding_dice_5: 0.10946/0.18303, loss_grounding_ce_5: 0.33106/0.29774, loss_mask_ce_6: 0.88052/0.97862, loss_mask_bce_6: 0.09534/0.34421, loss_mask_dice_6: 0.24116/1.20101, loss_spatial_bce_6: 0.18225/0.10456, loss_spatial_dice_6: 0.22763/0.24174, loss_spatial_ce_6: 0.23753/0.15023, loss_grounding_bce_6: 0.04629/0.08840, loss_grounding_dice_6: 0.11633/0.18322, loss_grounding_ce_6: 0.32360/0.31626, loss_mask_ce_7: 0.85077/1.02114, loss_mask_bce_7: 0.11225/0.35192, loss_mask_dice_7: 0.29359/1.25638, loss_spatial_bce_7: 0.13611/0.11346, loss_spatial_dice_7: 0.26268/0.26890, loss_spatial_ce_7: 0.25555/0.18937, loss_grounding_bce_7: 0.05423/0.09032, loss_grounding_dice_7: 0.13962/0.19047, loss_grounding_ce_7: 0.49121/0.35065, loss_mask_ce_8: 0.76746/1.13261, loss_mask_bce_8: 0.20087/0.36533, loss_mask_dice_8: 0.39275/1.33163, loss_spatial_bce_8: 0.19497/0.13476, loss_spatial_dice_8: 0.32661/0.30959, loss_spatial_ce_8: 0.30499/0.24540, loss_grounding_bce_8: 0.06345/0.09375, loss_grounding_dice_8: 0.13491/0.20185, loss_grounding_ce_8: 0.64543/0.42354, loss_mask_ce_9: 3.49952/3.69449, loss_mask_bce_9: 0.19762/0.39238, loss_mask_dice_9: 0.61313/1.90696, loss_spatial_bce_9: 0.41667/0.33669, loss_spatial_dice_9: 0.68905/0.82483, loss_spatial_ce_9: 1.09144/1.52199, loss_grounding_bce_9: 0.06726/0.10520, loss_grounding_dice_9: 0.28594/0.28186, loss_grounding_ce_9: 0.85287/0.70094] items per batch[64] items per second[0.23] total items[1196800] mini batches[ 18700] memory[7341] epoch remaining[1:06:27]
INFO:trainer.default_trainer:epochs[    10] optim steps[18800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27597/0.91329, loss_mask_bce_0: 0.21158/0.33594, loss_mask_dice_0: 0.30709/1.16771, loss_spatial_bce_0: 0.06695/0.09243, loss_spatial_dice_0: 0.09713/0.22113, loss_spatial_ce_0: 0.00014/0.08234, loss_grounding_bce_0: 0.15307/0.08639, loss_grounding_dice_0: 0.19665/0.17935, loss_grounding_ce_0: 0.01242/0.27682, loss_mask_ce_1: 0.28272/0.91350, loss_mask_bce_1: 0.19849/0.33663, loss_mask_dice_1: 0.29135/1.17527, loss_spatial_bce_1: 0.06846/0.09339, loss_spatial_dice_1: 0.10342/0.22559, loss_spatial_ce_1: 0.00033/0.08797, loss_grounding_bce_1: 0.09728/0.08643, loss_grounding_dice_1: 0.14109/0.17986, loss_grounding_ce_1: 0.13182/0.27841, loss_mask_ce_2: 0.28082/0.92114, loss_mask_bce_2: 0.19588/0.33695, loss_mask_dice_2: 0.28469/1.17472, loss_spatial_bce_2: 0.06629/0.09341, loss_spatial_dice_2: 0.11557/0.22649, loss_spatial_ce_2: 0.00040/0.09199, loss_grounding_bce_2: 0.08771/0.08648, loss_grounding_dice_2: 0.13222/0.17945, loss_grounding_ce_2: 0.26419/0.28195, loss_mask_ce_3: 0.30240/0.92895, loss_mask_bce_3: 0.21084/0.33782, loss_mask_dice_3: 0.29811/1.17156, loss_spatial_bce_3: 0.06306/0.09430, loss_spatial_dice_3: 0.11175/0.22730, loss_spatial_ce_3: 0.00073/0.09640, loss_grounding_bce_3: 0.09326/0.08658, loss_grounding_dice_3: 0.13542/0.17918, loss_grounding_ce_3: 0.26239/0.28299, loss_mask_ce_4: 0.34783/0.92715, loss_mask_bce_4: 0.20467/0.33934, loss_mask_dice_4: 0.28837/1.19347, loss_spatial_bce_4: 0.07182/0.09808, loss_spatial_dice_4: 0.11129/0.23635, loss_spatial_ce_4: 0.00141/0.11271, loss_grounding_bce_4: 0.08762/0.08718, loss_grounding_dice_4: 0.13401/0.18191, loss_grounding_ce_4: 0.26909/0.28552, loss_mask_ce_5: 0.26713/0.94188, loss_mask_bce_5: 0.19443/0.34151, loss_mask_dice_5: 0.29193/1.19839, loss_spatial_bce_5: 0.06993/0.09915, loss_spatial_dice_5: 0.11946/0.23928, loss_spatial_ce_5: 0.00714/0.12612, loss_grounding_bce_5: 0.09342/0.08762, loss_grounding_dice_5: 0.14193/0.18310, loss_grounding_ce_5: 0.13154/0.29775, loss_mask_ce_6: 0.35491/0.97888, loss_mask_bce_6: 0.19247/0.34418, loss_mask_dice_6: 0.29468/1.20137, loss_spatial_bce_6: 0.09362/0.10456, loss_spatial_dice_6: 0.12147/0.24175, loss_spatial_ce_6: 0.04571/0.15022, loss_grounding_bce_6: 0.09650/0.08840, loss_grounding_dice_6: 0.13672/0.18330, loss_grounding_ce_6: 0.17416/0.31615, loss_mask_ce_7: 0.21834/1.02135, loss_mask_bce_7: 0.24619/0.35194, loss_mask_dice_7: 0.35104/1.25687, loss_spatial_bce_7: 0.10095/0.11345, loss_spatial_dice_7: 0.12286/0.26891, loss_spatial_ce_7: 0.06143/0.18918, loss_grounding_bce_7: 0.12955/0.09032, loss_grounding_dice_7: 0.17852/0.19054, loss_grounding_ce_7: 0.00430/0.35059, loss_mask_ce_8: 0.24645/1.13263, loss_mask_bce_8: 0.22172/0.36538, loss_mask_dice_8: 0.34680/1.33220, loss_spatial_bce_8: 0.15219/0.13473, loss_spatial_dice_8: 0.15639/0.30960, loss_spatial_ce_8: 0.07680/0.24526, loss_grounding_bce_8: 0.11140/0.09375, loss_grounding_dice_8: 0.16294/0.20193, loss_grounding_ce_8: 0.04897/0.42323, loss_mask_ce_9: 1.47514/3.69460, loss_mask_bce_9: 0.22527/0.39237, loss_mask_dice_9: 0.41521/1.90746, loss_spatial_bce_9: 0.39957/0.33672, loss_spatial_dice_9: 0.79129/0.82486, loss_spatial_ce_9: 1.12444/1.52201, loss_grounding_bce_9: 0.11988/0.10519, loss_grounding_dice_9: 0.17870/0.28199, loss_grounding_ce_9: 0.09956/0.70078] items per batch[64] items per second[0.23] total items[1203200] mini batches[ 18800] memory[7341] epoch remaining[1:01:38]
INFO:trainer.default_trainer:epochs[    10] optim steps[18900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70064/0.91322, loss_mask_bce_0: 0.24735/0.33604, loss_mask_dice_0: 0.30863/1.16874, loss_spatial_bce_0: 0.13078/0.09239, loss_spatial_dice_0: 0.15443/0.22108, loss_spatial_ce_0: 0.06282/0.08215, loss_grounding_bce_0: 0.02946/0.08640, loss_grounding_dice_0: 0.10987/0.17938, loss_grounding_ce_0: 0.27070/0.27677, loss_mask_ce_1: 0.69102/0.91343, loss_mask_bce_1: 0.23882/0.33674, loss_mask_dice_1: 0.28780/1.17631, loss_spatial_bce_1: 0.12464/0.09334, loss_spatial_dice_1: 0.16818/0.22554, loss_spatial_ce_1: 0.04860/0.08781, loss_grounding_bce_1: 0.03074/0.08645, loss_grounding_dice_1: 0.12827/0.17992, loss_grounding_ce_1: 0.27401/0.27838, loss_mask_ce_2: 0.88237/0.92108, loss_mask_bce_2: 0.24363/0.33706, loss_mask_dice_2: 0.31892/1.17571, loss_spatial_bce_2: 0.12417/0.09336, loss_spatial_dice_2: 0.16532/0.22645, loss_spatial_ce_2: 0.06751/0.09184, loss_grounding_bce_2: 0.03336/0.08650, loss_grounding_dice_2: 0.10377/0.17950, loss_grounding_ce_2: 0.27816/0.28196, loss_mask_ce_3: 0.94681/0.92895, loss_mask_bce_3: 0.25601/0.33794, loss_mask_dice_3: 0.29791/1.17260, loss_spatial_bce_3: 0.13294/0.09426, loss_spatial_dice_3: 0.17193/0.22726, loss_spatial_ce_3: 0.06691/0.09622, loss_grounding_bce_3: 0.03628/0.08661, loss_grounding_dice_3: 0.12151/0.17925, loss_grounding_ce_3: 0.23478/0.28308, loss_mask_ce_4: 0.75098/0.92716, loss_mask_bce_4: 0.25305/0.33944, loss_mask_dice_4: 0.33712/1.19458, loss_spatial_bce_4: 0.15386/0.09804, loss_spatial_dice_4: 0.18491/0.23632, loss_spatial_ce_4: 0.09061/0.11248, loss_grounding_bce_4: 0.03514/0.08719, loss_grounding_dice_4: 0.12856/0.18198, loss_grounding_ce_4: 0.30818/0.28557, loss_mask_ce_5: 0.67464/0.94190, loss_mask_bce_5: 0.24335/0.34164, loss_mask_dice_5: 0.32592/1.19941, loss_spatial_bce_5: 0.19311/0.09912, loss_spatial_dice_5: 0.16838/0.23925, loss_spatial_ce_5: 0.16365/0.12597, loss_grounding_bce_5: 0.03449/0.08762, loss_grounding_dice_5: 0.10759/0.18315, loss_grounding_ce_5: 0.24614/0.29772, loss_mask_ce_6: 0.72587/0.97890, loss_mask_bce_6: 0.23751/0.34430, loss_mask_dice_6: 0.30457/1.20240, loss_spatial_bce_6: 0.17725/0.10453, loss_spatial_dice_6: 0.16199/0.24171, loss_spatial_ce_6: 0.22531/0.15008, loss_grounding_bce_6: 0.03148/0.08840, loss_grounding_dice_6: 0.10893/0.18335, loss_grounding_ce_6: 0.18737/0.31622, loss_mask_ce_7: 0.75052/1.02153, loss_mask_bce_7: 0.27968/0.35205, loss_mask_dice_7: 0.39072/1.25795, loss_spatial_bce_7: 0.20360/0.11342, loss_spatial_dice_7: 0.15298/0.26888, loss_spatial_ce_7: 0.27199/0.18892, loss_grounding_bce_7: 0.03840/0.09033, loss_grounding_dice_7: 0.12602/0.19062, loss_grounding_ce_7: 0.20473/0.35072, loss_mask_ce_8: 0.90882/1.13277, loss_mask_bce_8: 0.27876/0.36544, loss_mask_dice_8: 0.40188/1.33334, loss_spatial_bce_8: 0.19886/0.13468, loss_spatial_dice_8: 0.16098/0.30954, loss_spatial_ce_8: 0.37355/0.24510, loss_grounding_bce_8: 0.03686/0.09375, loss_grounding_dice_8: 0.13239/0.20201, loss_grounding_ce_8: 0.22683/0.42307, loss_mask_ce_9: 3.58846/3.69493, loss_mask_bce_9: 0.42002/0.39246, loss_mask_dice_9: 0.56251/1.90924, loss_spatial_bce_9: 0.53160/0.33672, loss_spatial_dice_9: 0.60827/0.82487, loss_spatial_ce_9: 0.93719/1.52169, loss_grounding_bce_9: 0.03467/0.10520, loss_grounding_dice_9: 0.18506/0.28201, loss_grounding_ce_9: 0.34846/0.70044] items per batch[64] items per second[0.23] total items[1209600] mini batches[ 18900] memory[7341] epoch remaining[0:56:45]
INFO:trainer.default_trainer:epochs[    10] optim steps[19000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19102/0.91278, loss_mask_bce_0: 0.79356/0.33587, loss_mask_dice_0: 2.63225/1.16771, loss_spatial_bce_0: 0.06742/0.09232, loss_spatial_dice_0: 0.25131/0.22096, loss_spatial_ce_0: 0.05387/0.08205, loss_grounding_bce_0: 0.03495/0.08640, loss_grounding_dice_0: 0.22124/0.17936, loss_grounding_ce_0: 0.41760/0.27669, loss_mask_ce_1: 1.20417/0.91302, loss_mask_bce_1: 0.80304/0.33658, loss_mask_dice_1: 2.57663/1.17532, loss_spatial_bce_1: 0.06949/0.09326, loss_spatial_dice_1: 0.26221/0.22542, loss_spatial_ce_1: 0.08410/0.08769, loss_grounding_bce_1: 0.03605/0.08644, loss_grounding_dice_1: 0.22756/0.17989, loss_grounding_ce_1: 0.42570/0.27832, loss_mask_ce_2: 1.16319/0.92063, loss_mask_bce_2: 0.81617/0.33691, loss_mask_dice_2: 2.81004/1.17471, loss_spatial_bce_2: 0.06973/0.09330, loss_spatial_dice_2: 0.23887/0.22633, loss_spatial_ce_2: 0.05472/0.09170, loss_grounding_bce_2: 0.03560/0.08651, loss_grounding_dice_2: 0.21672/0.17946, loss_grounding_ce_2: 0.40835/0.28188, loss_mask_ce_3: 1.24094/0.92852, loss_mask_bce_3: 0.83263/0.33778, loss_mask_dice_3: 2.89293/1.17163, loss_spatial_bce_3: 0.06619/0.09418, loss_spatial_dice_3: 0.24672/0.22714, loss_spatial_ce_3: 0.07791/0.09608, loss_grounding_bce_3: 0.03639/0.08661, loss_grounding_dice_3: 0.21338/0.17921, loss_grounding_ce_3: 0.43048/0.28298, loss_mask_ce_4: 1.31776/0.92686, loss_mask_bce_4: 0.82833/0.33928, loss_mask_dice_4: 2.96863/1.19350, loss_spatial_bce_4: 0.08054/0.09797, loss_spatial_dice_4: 0.27378/0.23622, loss_spatial_ce_4: 0.05071/0.11233, loss_grounding_bce_4: 0.03516/0.08718, loss_grounding_dice_4: 0.22083/0.18195, loss_grounding_ce_4: 0.39711/0.28549, loss_mask_ce_5: 1.32035/0.94162, loss_mask_bce_5: 0.80749/0.34148, loss_mask_dice_5: 3.19523/1.19830, loss_spatial_bce_5: 0.07880/0.09905, loss_spatial_dice_5: 0.26148/0.23916, loss_spatial_ce_5: 0.03593/0.12585, loss_grounding_bce_5: 0.03515/0.08762, loss_grounding_dice_5: 0.22183/0.18313, loss_grounding_ce_5: 0.42192/0.29768, loss_mask_ce_6: 1.27441/0.97858, loss_mask_bce_6: 0.82236/0.34415, loss_mask_dice_6: 2.91757/1.20141, loss_spatial_bce_6: 0.08639/0.10446, loss_spatial_dice_6: 0.25755/0.24161, loss_spatial_ce_6: 0.05379/0.14994, loss_grounding_bce_6: 0.03499/0.08840, loss_grounding_dice_6: 0.22477/0.18335, loss_grounding_ce_6: 0.46282/0.31617, loss_mask_ce_7: 1.21330/1.02121, loss_mask_bce_7: 0.91458/0.35192, loss_mask_dice_7: 3.19544/1.25688, loss_spatial_bce_7: 0.09077/0.11337, loss_spatial_dice_7: 0.29354/0.26876, loss_spatial_ce_7: 0.07591/0.18876, loss_grounding_bce_7: 0.03709/0.09032, loss_grounding_dice_7: 0.21312/0.19059, loss_grounding_ce_7: 0.47383/0.35060, loss_mask_ce_8: 1.22101/1.13237, loss_mask_bce_8: 0.77844/0.36533, loss_mask_dice_8: 3.22566/1.33229, loss_spatial_bce_8: 0.08693/0.13462, loss_spatial_dice_8: 0.31008/0.30942, loss_spatial_ce_8: 0.16242/0.24496, loss_grounding_bce_8: 0.03618/0.09377, loss_grounding_dice_8: 0.23081/0.20202, loss_grounding_ce_8: 0.44497/0.42303, loss_mask_ce_9: 5.45860/3.69434, loss_mask_bce_9: 0.82581/0.39232, loss_mask_dice_9: 5.23887/1.90763, loss_spatial_bce_9: 0.22746/0.33674, loss_spatial_dice_9: 0.96987/0.82485, loss_spatial_ce_9: 1.43072/1.52161, loss_grounding_bce_9: 0.05521/0.10524, loss_grounding_dice_9: 0.50303/0.28198, loss_grounding_ce_9: 0.40621/0.70062] items per batch[64] items per second[0.23] total items[1216000] mini batches[ 19000] memory[7341] epoch remaining[0:51:50]
INFO:trainer.default_trainer:epochs[    10] optim steps[19100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.50081/0.91266, loss_mask_bce_0: 0.26309/0.33590, loss_mask_dice_0: 1.36653/1.16786, loss_spatial_bce_0: 0.05892/0.09226, loss_spatial_dice_0: 0.23784/0.22083, loss_spatial_ce_0: 0.04416/0.08187, loss_grounding_bce_0: 0.10064/0.08638, loss_grounding_dice_0: 0.33583/0.17925, loss_grounding_ce_0: 1.59835/0.27675, loss_mask_ce_1: 0.76385/0.91294, loss_mask_bce_1: 0.27520/0.33663, loss_mask_dice_1: 1.44980/1.17551, loss_spatial_bce_1: 0.06155/0.09321, loss_spatial_dice_1: 0.27424/0.22529, loss_spatial_ce_1: 0.04660/0.08749, loss_grounding_bce_1: 0.09931/0.08643, loss_grounding_dice_1: 0.32627/0.17980, loss_grounding_ce_1: 1.58091/0.27828, loss_mask_ce_2: 0.84078/0.92051, loss_mask_bce_2: 0.26683/0.33697, loss_mask_dice_2: 1.46230/1.17487, loss_spatial_bce_2: 0.06341/0.09325, loss_spatial_dice_2: 0.25630/0.22621, loss_spatial_ce_2: 0.05736/0.09149, loss_grounding_bce_2: 0.11024/0.08648, loss_grounding_dice_2: 0.34865/0.17937, loss_grounding_ce_2: 0.82157/0.28189, loss_mask_ce_3: 1.14654/0.92848, loss_mask_bce_3: 0.25878/0.33783, loss_mask_dice_3: 1.37766/1.17184, loss_spatial_bce_3: 0.06986/0.09413, loss_spatial_dice_3: 0.29048/0.22701, loss_spatial_ce_3: 0.08964/0.09586, loss_grounding_bce_3: 0.11221/0.08658, loss_grounding_dice_3: 0.33487/0.17914, loss_grounding_ce_3: 1.48710/0.28316, loss_mask_ce_4: 1.09372/0.92686, loss_mask_bce_4: 0.28039/0.33932, loss_mask_dice_4: 1.49828/1.19360, loss_spatial_bce_4: 0.06747/0.09791, loss_spatial_dice_4: 0.31529/0.23612, loss_spatial_ce_4: 0.10989/0.11215, loss_grounding_bce_4: 0.09742/0.08716, loss_grounding_dice_4: 0.32042/0.18188, loss_grounding_ce_4: 1.62449/0.28556, loss_mask_ce_5: 1.40040/0.94163, loss_mask_bce_5: 0.27176/0.34153, loss_mask_dice_5: 1.41361/1.19852, loss_spatial_bce_5: 0.07441/0.09901, loss_spatial_dice_5: 0.32723/0.23908, loss_spatial_ce_5: 0.08963/0.12561, loss_grounding_bce_5: 0.10982/0.08761, loss_grounding_dice_5: 0.31823/0.18305, loss_grounding_ce_5: 2.62934/0.29782, loss_mask_ce_6: 1.50239/0.97852, loss_mask_bce_6: 0.27728/0.34421, loss_mask_dice_6: 1.43243/1.20152, loss_spatial_bce_6: 0.07317/0.10442, loss_spatial_dice_6: 0.28233/0.24151, loss_spatial_ce_6: 0.16135/0.14970, loss_grounding_bce_6: 0.10702/0.08837, loss_grounding_dice_6: 0.26943/0.18326, loss_grounding_ce_6: 2.87440/0.31626, loss_mask_ce_7: 1.11588/1.02115, loss_mask_bce_7: 0.31179/0.35197, loss_mask_dice_7: 1.68567/1.25704, loss_spatial_bce_7: 0.06941/0.11331, loss_spatial_dice_7: 0.30798/0.26867, loss_spatial_ce_7: 0.09339/0.18849, loss_grounding_bce_7: 0.09849/0.09032, loss_grounding_dice_7: 0.27164/0.19048, loss_grounding_ce_7: 2.18340/0.35079, loss_mask_ce_8: 0.90886/1.13233, loss_mask_bce_8: 0.31623/0.36538, loss_mask_dice_8: 1.80363/1.33243, loss_spatial_bce_8: 0.07416/0.13453, loss_spatial_dice_8: 0.36061/0.30929, loss_spatial_ce_8: 0.23734/0.24481, loss_grounding_bce_8: 0.10294/0.09377, loss_grounding_dice_8: 0.31936/0.20190, loss_grounding_ce_8: 3.61365/0.42311, loss_mask_ce_9: 3.26825/3.69456, loss_mask_bce_9: 0.23795/0.39237, loss_mask_dice_9: 1.92072/1.90770, loss_spatial_bce_9: 0.18453/0.33669, loss_spatial_dice_9: 0.86050/0.82480, loss_spatial_ce_9: 1.21410/1.52141, loss_grounding_bce_9: 0.08752/0.10524, loss_grounding_dice_9: 0.36902/0.28181, loss_grounding_ce_9: 1.63145/0.70089] items per batch[64] items per second[0.23] total items[1222400] mini batches[ 19100] memory[7341] epoch remaining[0:46:55]
INFO:trainer.default_trainer:epochs[    10] optim steps[19200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42144/0.91248, loss_mask_bce_0: 0.38038/0.33587, loss_mask_dice_0: 1.03660/1.16785, loss_spatial_bce_0: 0.08026/0.09224, loss_spatial_dice_0: 0.24131/0.22073, loss_spatial_ce_0: 0.01910/0.08178, loss_grounding_bce_0: 0.11220/0.08636, loss_grounding_dice_0: 0.25430/0.17932, loss_grounding_ce_0: 0.23117/0.27688, loss_mask_ce_1: 0.43715/0.91277, loss_mask_bce_1: 0.38023/0.33659, loss_mask_dice_1: 0.95903/1.17542, loss_spatial_bce_1: 0.08115/0.09318, loss_spatial_dice_1: 0.20941/0.22520, loss_spatial_ce_1: 0.02661/0.08743, loss_grounding_bce_1: 0.11794/0.08640, loss_grounding_dice_1: 0.32879/0.17987, loss_grounding_ce_1: 0.24108/0.27846, loss_mask_ce_2: 0.40097/0.92034, loss_mask_bce_2: 0.38747/0.33691, loss_mask_dice_2: 1.06316/1.17480, loss_spatial_bce_2: 0.08363/0.09322, loss_spatial_dice_2: 0.23049/0.22612, loss_spatial_ce_2: 0.04943/0.09138, loss_grounding_bce_2: 0.11647/0.08646, loss_grounding_dice_2: 0.20267/0.17941, loss_grounding_ce_2: 0.21766/0.28202, loss_mask_ce_3: 0.55992/0.92829, loss_mask_bce_3: 0.37881/0.33778, loss_mask_dice_3: 0.98749/1.17183, loss_spatial_bce_3: 0.07849/0.09410, loss_spatial_dice_3: 0.21912/0.22691, loss_spatial_ce_3: 0.04519/0.09573, loss_grounding_bce_3: 0.11866/0.08656, loss_grounding_dice_3: 0.21741/0.17917, loss_grounding_ce_3: 0.22246/0.28326, loss_mask_ce_4: 0.44766/0.92674, loss_mask_bce_4: 0.37789/0.33927, loss_mask_dice_4: 0.97931/1.19352, loss_spatial_bce_4: 0.07486/0.09789, loss_spatial_dice_4: 0.23552/0.23604, loss_spatial_ce_4: 0.03733/0.11204, loss_grounding_bce_4: 0.11383/0.08714, loss_grounding_dice_4: 0.22798/0.18191, loss_grounding_ce_4: 0.21547/0.28572, loss_mask_ce_5: 0.44172/0.94140, loss_mask_bce_5: 0.43364/0.34148, loss_mask_dice_5: 1.03668/1.19846, loss_spatial_bce_5: 0.07599/0.09898, loss_spatial_dice_5: 0.25382/0.23899, loss_spatial_ce_5: 0.07631/0.12545, loss_grounding_bce_5: 0.11641/0.08759, loss_grounding_dice_5: 0.27482/0.18310, loss_grounding_ce_5: 0.24477/0.29796, loss_mask_ce_6: 0.52831/0.97842, loss_mask_bce_6: 0.44208/0.34419, loss_mask_dice_6: 1.07375/1.20147, loss_spatial_bce_6: 0.08221/0.10440, loss_spatial_dice_6: 0.22703/0.24141, loss_spatial_ce_6: 0.03531/0.14960, loss_grounding_bce_6: 0.12172/0.08836, loss_grounding_dice_6: 0.28250/0.18332, loss_grounding_ce_6: 0.19272/0.31624, loss_mask_ce_7: 0.60588/1.02089, loss_mask_bce_7: 0.49965/0.35196, loss_mask_dice_7: 1.19106/1.25697, loss_spatial_bce_7: 0.08817/0.11330, loss_spatial_dice_7: 0.26274/0.26858, loss_spatial_ce_7: 0.11546/0.18836, loss_grounding_bce_7: 0.11749/0.09029, loss_grounding_dice_7: 0.30861/0.19055, loss_grounding_ce_7: 0.27043/0.35097, loss_mask_ce_8: 0.61755/1.13219, loss_mask_bce_8: 0.42864/0.36539, loss_mask_dice_8: 1.17699/1.33239, loss_spatial_bce_8: 0.11158/0.13453, loss_spatial_dice_8: 0.31961/0.30923, loss_spatial_ce_8: 0.15890/0.24468, loss_grounding_bce_8: 0.11735/0.09374, loss_grounding_dice_8: 0.22050/0.20195, loss_grounding_ce_8: 0.24245/0.42341, loss_mask_ce_9: 3.45813/3.69507, loss_mask_bce_9: 0.45633/0.39240, loss_mask_dice_9: 1.76476/1.90770, loss_spatial_bce_9: 0.31468/0.33671, loss_spatial_dice_9: 0.90075/0.82481, loss_spatial_ce_9: 1.30442/1.52112, loss_grounding_bce_9: 0.10450/0.10521, loss_grounding_dice_9: 0.31109/0.28188, loss_grounding_ce_9: 0.26713/0.70119] items per batch[64] items per second[0.23] total items[1228800] mini batches[ 19200] memory[7341] epoch remaining[0:42:10]
INFO:trainer.default_trainer:epochs[    10] optim steps[19300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38675/0.91285, loss_mask_bce_0: 0.20113/0.33575, loss_mask_dice_0: 1.90978/1.16719, loss_spatial_bce_0: 0.01938/0.09224, loss_spatial_dice_0: 0.24358/0.22065, loss_spatial_ce_0: 0.02715/0.08163, loss_grounding_bce_0: 0.01131/0.08636, loss_grounding_dice_0: 0.22650/0.17934, loss_grounding_ce_0: 0.26399/0.27693, loss_mask_ce_1: 1.65276/0.91314, loss_mask_bce_1: 0.21262/0.33648, loss_mask_dice_1: 1.81329/1.17471, loss_spatial_bce_1: 0.02004/0.09317, loss_spatial_dice_1: 0.20955/0.22511, loss_spatial_ce_1: 0.02544/0.08731, loss_grounding_bce_1: 0.01275/0.08641, loss_grounding_dice_1: 0.23039/0.17993, loss_grounding_ce_1: 0.42377/0.27852, loss_mask_ce_2: 1.45643/0.92063, loss_mask_bce_2: 0.20572/0.33681, loss_mask_dice_2: 1.90631/1.17417, loss_spatial_bce_2: 0.02097/0.09322, loss_spatial_dice_2: 0.22640/0.22604, loss_spatial_ce_2: 0.03999/0.09124, loss_grounding_bce_2: 0.01161/0.08646, loss_grounding_dice_2: 0.22172/0.17945, loss_grounding_ce_2: 0.31297/0.28205, loss_mask_ce_3: 1.68475/0.92868, loss_mask_bce_3: 0.21026/0.33766, loss_mask_dice_3: 1.84849/1.17120, loss_spatial_bce_3: 0.02105/0.09411, loss_spatial_dice_3: 0.20061/0.22681, loss_spatial_ce_3: 0.03816/0.09554, loss_grounding_bce_3: 0.01000/0.08655, loss_grounding_dice_3: 0.20378/0.17923, loss_grounding_ce_3: 0.53506/0.28329, loss_mask_ce_4: 1.60144/0.92709, loss_mask_bce_4: 0.22731/0.33918, loss_mask_dice_4: 1.94957/1.19299, loss_spatial_bce_4: 0.01773/0.09789, loss_spatial_dice_4: 0.24261/0.23596, loss_spatial_ce_4: 0.04399/0.11182, loss_grounding_bce_4: 0.01139/0.08714, loss_grounding_dice_4: 0.22046/0.18195, loss_grounding_ce_4: 0.28424/0.28568, loss_mask_ce_5: 1.28596/0.94170, loss_mask_bce_5: 0.21446/0.34140, loss_mask_dice_5: 2.04089/1.19789, loss_spatial_bce_5: 0.02322/0.09898, loss_spatial_dice_5: 0.26706/0.23892, loss_spatial_ce_5: 0.04438/0.12528, loss_grounding_bce_5: 0.01038/0.08758, loss_grounding_dice_5: 0.20465/0.18317, loss_grounding_ce_5: 0.36186/0.29794, loss_mask_ce_6: 1.49381/0.97876, loss_mask_bce_6: 0.25353/0.34413, loss_mask_dice_6: 2.02889/1.20095, loss_spatial_bce_6: 0.02292/0.10441, loss_spatial_dice_6: 0.22802/0.24132, loss_spatial_ce_6: 0.08060/0.14944, loss_grounding_bce_6: 0.01884/0.08836, loss_grounding_dice_6: 0.20429/0.18336, loss_grounding_ce_6: 0.32830/0.31632, loss_mask_ce_7: 1.55772/1.02115, loss_mask_bce_7: 0.23430/0.35194, loss_mask_dice_7: 2.01869/1.25642, loss_spatial_bce_7: 0.02502/0.11330, loss_spatial_dice_7: 0.25461/0.26851, loss_spatial_ce_7: 0.09708/0.18826, loss_grounding_bce_7: 0.01933/0.09030, loss_grounding_dice_7: 0.28096/0.19059, loss_grounding_ce_7: 0.35685/0.35094, loss_mask_ce_8: 1.90732/1.13247, loss_mask_bce_8: 0.19226/0.36534, loss_mask_dice_8: 2.01131/1.33180, loss_spatial_bce_8: 0.03674/0.13453, loss_spatial_dice_8: 0.32215/0.30914, loss_spatial_ce_8: 0.19749/0.24455, loss_grounding_bce_8: 0.01876/0.09375, loss_grounding_dice_8: 0.24878/0.20203, loss_grounding_ce_8: 0.69520/0.42303, loss_mask_ce_9: 3.31277/3.69455, loss_mask_bce_9: 0.22240/0.39229, loss_mask_dice_9: 3.09385/1.90651, loss_spatial_bce_9: 0.11774/0.33676, loss_spatial_dice_9: 0.87903/0.82476, loss_spatial_ce_9: 2.38666/1.52112, loss_grounding_bce_9: 0.01441/0.10520, loss_grounding_dice_9: 0.39556/0.28187, loss_grounding_ce_9: 0.34361/0.70080] items per batch[64] items per second[0.23] total items[1235200] mini batches[ 19300] memory[7341] epoch remaining[0:37:28]
INFO:trainer.default_trainer:epochs[    10] optim steps[19400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95712/0.91344, loss_mask_bce_0: 0.22700/0.33599, loss_mask_dice_0: 0.83493/1.16679, loss_spatial_bce_0: 0.05108/0.09226, loss_spatial_dice_0: 0.22306/0.22057, loss_spatial_ce_0: 0.03381/0.08151, loss_grounding_bce_0: 0.04153/0.08646, loss_grounding_dice_0: 0.17790/0.17941, loss_grounding_ce_0: 0.18372/0.27685, loss_mask_ce_1: 1.04203/0.91369, loss_mask_bce_1: 0.20307/0.33672, loss_mask_dice_1: 0.73921/1.17422, loss_spatial_bce_1: 0.05046/0.09319, loss_spatial_dice_1: 0.20692/0.22504, loss_spatial_ce_1: 0.19602/0.08719, loss_grounding_bce_1: 0.05009/0.08651, loss_grounding_dice_1: 0.19445/0.18000, loss_grounding_ce_1: 0.18175/0.27845, loss_mask_ce_2: 1.06953/0.92125, loss_mask_bce_2: 0.20977/0.33705, loss_mask_dice_2: 0.74011/1.17374, loss_spatial_bce_2: 0.05206/0.09325, loss_spatial_dice_2: 0.20739/0.22595, loss_spatial_ce_2: 0.18506/0.09111, loss_grounding_bce_2: 0.04941/0.08656, loss_grounding_dice_2: 0.18980/0.17951, loss_grounding_ce_2: 0.20214/0.28202, loss_mask_ce_3: 0.98521/0.92925, loss_mask_bce_3: 0.21128/0.33790, loss_mask_dice_3: 0.72407/1.17085, loss_spatial_bce_3: 0.05576/0.09414, loss_spatial_dice_3: 0.23449/0.22673, loss_spatial_ce_3: 0.20254/0.09538, loss_grounding_bce_3: 0.04811/0.08665, loss_grounding_dice_3: 0.17579/0.17929, loss_grounding_ce_3: 0.26276/0.28322, loss_mask_ce_4: 0.94678/0.92764, loss_mask_bce_4: 0.21205/0.33939, loss_mask_dice_4: 0.77431/1.19261, loss_spatial_bce_4: 0.06041/0.09793, loss_spatial_dice_4: 0.22796/0.23590, loss_spatial_ce_4: 0.23903/0.11162, loss_grounding_bce_4: 0.05060/0.08722, loss_grounding_dice_4: 0.19406/0.18203, loss_grounding_ce_4: 0.15453/0.28567, loss_mask_ce_5: 1.11640/0.94234, loss_mask_bce_5: 0.21820/0.34159, loss_mask_dice_5: 0.76168/1.19748, loss_spatial_bce_5: 0.06387/0.09900, loss_spatial_dice_5: 0.22454/0.23884, loss_spatial_ce_5: 0.29853/0.12514, loss_grounding_bce_5: 0.05680/0.08765, loss_grounding_dice_5: 0.19142/0.18322, loss_grounding_ce_5: 0.21838/0.29807, loss_mask_ce_6: 1.09397/0.97948, loss_mask_bce_6: 0.22525/0.34432, loss_mask_dice_6: 0.71281/1.20050, loss_spatial_bce_6: 0.08814/0.10444, loss_spatial_dice_6: 0.25711/0.24124, loss_spatial_ce_6: 0.48084/0.14931, loss_grounding_bce_6: 0.05153/0.08843, loss_grounding_dice_6: 0.18063/0.18341, loss_grounding_ce_6: 0.38867/0.31642, loss_mask_ce_7: 1.03092/1.02189, loss_mask_bce_7: 0.40074/0.35216, loss_mask_dice_7: 0.92785/1.25604, loss_spatial_bce_7: 0.10700/0.11333, loss_spatial_dice_7: 0.32580/0.26844, loss_spatial_ce_7: 0.20582/0.18807, loss_grounding_bce_7: 0.05024/0.09038, loss_grounding_dice_7: 0.18545/0.19063, loss_grounding_ce_7: 0.45066/0.35130, loss_mask_ce_8: 1.33480/1.13300, loss_mask_bce_8: 0.34347/0.36559, loss_mask_dice_8: 0.85901/1.33139, loss_spatial_bce_8: 0.11897/0.13455, loss_spatial_dice_8: 0.33434/0.30904, loss_spatial_ce_8: 0.28011/0.24446, loss_grounding_bce_8: 0.03986/0.09385, loss_grounding_dice_8: 0.19124/0.20206, loss_grounding_ce_8: 0.48338/0.42317, loss_mask_ce_9: 3.20721/3.69514, loss_mask_bce_9: 0.43367/0.39247, loss_mask_dice_9: 1.45019/1.90640, loss_spatial_bce_9: 0.22185/0.33687, loss_spatial_dice_9: 0.79682/0.82474, loss_spatial_ce_9: 1.11316/1.52082, loss_grounding_bce_9: 0.07338/0.10528, loss_grounding_dice_9: 0.38256/0.28188, loss_grounding_ce_9: 0.67544/0.70095] items per batch[64] items per second[0.23] total items[1241600] mini batches[ 19400] memory[7341] epoch remaining[0:32:43]
INFO:trainer.default_trainer:epochs[    10] optim steps[19500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03022/0.91340, loss_mask_bce_0: 0.32375/0.33586, loss_mask_dice_0: 1.41224/1.16650, loss_spatial_bce_0: 0.05017/0.09222, loss_spatial_dice_0: 0.31605/0.22049, loss_spatial_ce_0: 0.04400/0.08137, loss_grounding_bce_0: 0.04683/0.08640, loss_grounding_dice_0: 0.21802/0.17936, loss_grounding_ce_0: 0.22113/0.27682, loss_mask_ce_1: 1.05399/0.91370, loss_mask_bce_1: 0.28828/0.33658, loss_mask_dice_1: 1.40638/1.17393, loss_spatial_bce_1: 0.04750/0.09316, loss_spatial_dice_1: 0.31975/0.22497, loss_spatial_ce_1: 0.05650/0.08704, loss_grounding_bce_1: 0.04668/0.08646, loss_grounding_dice_1: 0.16632/0.17998, loss_grounding_ce_1: 0.17668/0.27835, loss_mask_ce_2: 1.29884/0.92116, loss_mask_bce_2: 0.28716/0.33693, loss_mask_dice_2: 1.42407/1.17338, loss_spatial_bce_2: 0.04351/0.09323, loss_spatial_dice_2: 0.30687/0.22586, loss_spatial_ce_2: 0.08069/0.09095, loss_grounding_bce_2: 0.04520/0.08650, loss_grounding_dice_2: 0.14995/0.17946, loss_grounding_ce_2: 0.16986/0.28198, loss_mask_ce_3: 0.96956/0.92917, loss_mask_bce_3: 0.32129/0.33777, loss_mask_dice_3: 1.56035/1.17046, loss_spatial_bce_3: 0.04426/0.09411, loss_spatial_dice_3: 0.32665/0.22664, loss_spatial_ce_3: 0.06182/0.09527, loss_grounding_bce_3: 0.04487/0.08659, loss_grounding_dice_3: 0.21971/0.17925, loss_grounding_ce_3: 0.17226/0.28328, loss_mask_ce_4: 0.85194/0.92766, loss_mask_bce_4: 0.39031/0.33925, loss_mask_dice_4: 1.61173/1.19227, loss_spatial_bce_4: 0.06130/0.09790, loss_spatial_dice_4: 0.34051/0.23581, loss_spatial_ce_4: 0.16595/0.11147, loss_grounding_bce_4: 0.04664/0.08717, loss_grounding_dice_4: 0.20739/0.18201, loss_grounding_ce_4: 0.17020/0.28558, loss_mask_ce_5: 1.23096/0.94224, loss_mask_bce_5: 0.31340/0.34146, loss_mask_dice_5: 1.37552/1.19718, loss_spatial_bce_5: 0.04771/0.09898, loss_spatial_dice_5: 0.30575/0.23876, loss_spatial_ce_5: 0.14603/0.12494, loss_grounding_bce_5: 0.04143/0.08760, loss_grounding_dice_5: 0.15250/0.18319, loss_grounding_ce_5: 0.15048/0.29799, loss_mask_ce_6: 1.05392/0.97940, loss_mask_bce_6: 0.33478/0.34422, loss_mask_dice_6: 1.58098/1.20015, loss_spatial_bce_6: 0.05045/0.10443, loss_spatial_dice_6: 0.35402/0.24116, loss_spatial_ce_6: 0.16003/0.14920, loss_grounding_bce_6: 0.03568/0.08838, loss_grounding_dice_6: 0.21171/0.18336, loss_grounding_ce_6: 0.16698/0.31636, loss_mask_ce_7: 1.30060/1.02178, loss_mask_bce_7: 0.36722/0.35206, loss_mask_dice_7: 1.52137/1.25570, loss_spatial_bce_7: 0.05154/0.11332, loss_spatial_dice_7: 0.33403/0.26836, loss_spatial_ce_7: 0.16162/0.18802, loss_grounding_bce_7: 0.03164/0.09034, loss_grounding_dice_7: 0.13342/0.19060, loss_grounding_ce_7: 0.20460/0.35111, loss_mask_ce_8: 1.35849/1.13281, loss_mask_bce_8: 0.41064/0.36547, loss_mask_dice_8: 1.58903/1.33090, loss_spatial_bce_8: 0.10466/0.13454, loss_spatial_dice_8: 0.40292/0.30896, loss_spatial_ce_8: 0.25898/0.24433, loss_grounding_bce_8: 0.03652/0.09381, loss_grounding_dice_8: 0.17903/0.20206, loss_grounding_ce_8: 0.18574/0.42299, loss_mask_ce_9: 4.28993/3.69469, loss_mask_bce_9: 0.54015/0.39231, loss_mask_dice_9: 1.99743/1.90539, loss_spatial_bce_9: 0.20372/0.33681, loss_spatial_dice_9: 0.85277/0.82468, loss_spatial_ce_9: 1.74259/1.52061, loss_grounding_bce_9: 0.06982/0.10519, loss_grounding_dice_9: 0.28830/0.28184, loss_grounding_ce_9: 0.44680/0.70067] items per batch[64] items per second[0.23] total items[1248000] mini batches[ 19500] memory[7341] epoch remaining[0:28:01]
INFO:trainer.default_trainer:epochs[    10] optim steps[19600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31055/0.91341, loss_mask_bce_0: 0.27332/0.33592, loss_mask_dice_0: 0.19489/1.16608, loss_spatial_bce_0: 0.12486/0.09224, loss_spatial_dice_0: 0.09312/0.22038, loss_spatial_ce_0: 0.00008/0.08122, loss_grounding_bce_0: 0.11205/0.08639, loss_grounding_dice_0: 0.09435/0.17929, loss_grounding_ce_0: 0.06811/0.27686, loss_mask_ce_1: 0.27316/0.91380, loss_mask_bce_1: 0.27205/0.33663, loss_mask_dice_1: 0.19475/1.17344, loss_spatial_bce_1: 0.12340/0.09317, loss_spatial_dice_1: 0.09246/0.22485, loss_spatial_ce_1: 0.00008/0.08695, loss_grounding_bce_1: 0.11649/0.08646, loss_grounding_dice_1: 0.09389/0.17992, loss_grounding_ce_1: 0.04806/0.27836, loss_mask_ce_2: 0.27317/0.92117, loss_mask_bce_2: 0.26763/0.33698, loss_mask_dice_2: 0.19822/1.17294, loss_spatial_bce_2: 0.12178/0.09325, loss_spatial_dice_2: 0.08555/0.22574, loss_spatial_ce_2: 0.00017/0.09087, loss_grounding_bce_2: 0.11124/0.08650, loss_grounding_dice_2: 0.09353/0.17939, loss_grounding_ce_2: 0.05588/0.28192, loss_mask_ce_3: 0.25570/0.92921, loss_mask_bce_3: 0.26639/0.33783, loss_mask_dice_3: 0.19370/1.17000, loss_spatial_bce_3: 0.12083/0.09412, loss_spatial_dice_3: 0.08371/0.22652, loss_spatial_ce_3: 0.00040/0.09515, loss_grounding_bce_3: 0.11409/0.08660, loss_grounding_dice_3: 0.09556/0.17918, loss_grounding_ce_3: 0.17228/0.28320, loss_mask_ce_4: 0.25890/0.92774, loss_mask_bce_4: 0.28078/0.33930, loss_mask_dice_4: 0.21626/1.19183, loss_spatial_bce_4: 0.13084/0.09792, loss_spatial_dice_4: 0.09713/0.23568, loss_spatial_ce_4: 0.00539/0.11141, loss_grounding_bce_4: 0.11351/0.08718, loss_grounding_dice_4: 0.09265/0.18196, loss_grounding_ce_4: 0.20386/0.28551, loss_mask_ce_5: 0.25351/0.94236, loss_mask_bce_5: 0.26784/0.34150, loss_mask_dice_5: 0.19344/1.19668, loss_spatial_bce_5: 0.12111/0.09900, loss_spatial_dice_5: 0.09023/0.23864, loss_spatial_ce_5: 0.00596/0.12481, loss_grounding_bce_5: 0.11086/0.08760, loss_grounding_dice_5: 0.08912/0.18312, loss_grounding_ce_5: 0.21339/0.29801, loss_mask_ce_6: 0.24768/0.97959, loss_mask_bce_6: 0.25552/0.34424, loss_mask_dice_6: 0.20494/1.19973, loss_spatial_bce_6: 0.13183/0.10444, loss_spatial_dice_6: 0.09628/0.24104, loss_spatial_ce_6: 0.02462/0.14911, loss_grounding_bce_6: 0.11389/0.08837, loss_grounding_dice_6: 0.09530/0.18329, loss_grounding_ce_6: 0.21776/0.31641, loss_mask_ce_7: 0.18006/1.02190, loss_mask_bce_7: 0.26659/0.35208, loss_mask_dice_7: 0.20462/1.25524, loss_spatial_bce_7: 0.14213/0.11334, loss_spatial_dice_7: 0.11179/0.26822, loss_spatial_ce_7: 0.28210/0.18791, loss_grounding_bce_7: 0.11014/0.09033, loss_grounding_dice_7: 0.09381/0.19056, loss_grounding_ce_7: 0.14070/0.35105, loss_mask_ce_8: 0.16515/1.13282, loss_mask_bce_8: 0.27607/0.36552, loss_mask_dice_8: 0.21808/1.33045, loss_spatial_bce_8: 0.17302/0.13460, loss_spatial_dice_8: 0.11604/0.30885, loss_spatial_ce_8: 0.21848/0.24421, loss_grounding_bce_8: 0.12958/0.09380, loss_grounding_dice_8: 0.09474/0.20198, loss_grounding_ce_8: 0.17753/0.42335, loss_mask_ce_9: 1.87795/3.69430, loss_mask_bce_9: 0.23223/0.39237, loss_mask_dice_9: 0.29145/1.90494, loss_spatial_bce_9: 0.36162/0.33692, loss_spatial_dice_9: 0.66891/0.82462, loss_spatial_ce_9: 1.01328/1.52028, loss_grounding_bce_9: 0.15756/0.10519, loss_grounding_dice_9: 0.17178/0.28179, loss_grounding_ce_9: 0.35049/0.70092] items per batch[64] items per second[0.23] total items[1254400] mini batches[ 19600] memory[7341] epoch remaining[0:23:20]
INFO:trainer.default_trainer:epochs[    10] optim steps[19700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47259/0.91350, loss_mask_bce_0: 0.36697/0.33605, loss_mask_dice_0: 0.46165/1.16649, loss_spatial_bce_0: 0.12356/0.09221, loss_spatial_dice_0: 0.18444/0.22029, loss_spatial_ce_0: 0.06285/0.08108, loss_grounding_bce_0: 0.27808/0.08637, loss_grounding_dice_0: 0.24491/0.17939, loss_grounding_ce_0: 0.01099/0.27689, loss_mask_ce_1: 0.61319/0.91396, loss_mask_bce_1: 0.37661/0.33675, loss_mask_dice_1: 0.41826/1.17390, loss_spatial_bce_1: 0.11867/0.09315, loss_spatial_dice_1: 0.17541/0.22475, loss_spatial_ce_1: 0.03677/0.08681, loss_grounding_bce_1: 0.26780/0.08645, loss_grounding_dice_1: 0.23237/0.18004, loss_grounding_ce_1: 0.01277/0.27831, loss_mask_ce_2: 0.44677/0.92134, loss_mask_bce_2: 0.35139/0.33710, loss_mask_dice_2: 0.47621/1.17334, loss_spatial_bce_2: 0.12245/0.09324, loss_spatial_dice_2: 0.16880/0.22565, loss_spatial_ce_2: 0.01204/0.09070, loss_grounding_bce_2: 0.25713/0.08649, loss_grounding_dice_2: 0.25048/0.17946, loss_grounding_ce_2: 0.01194/0.28193, loss_mask_ce_3: 0.63808/0.92939, loss_mask_bce_3: 0.35319/0.33794, loss_mask_dice_3: 0.42108/1.17047, loss_spatial_bce_3: 0.12315/0.09410, loss_spatial_dice_3: 0.17419/0.22642, loss_spatial_ce_3: 0.03600/0.09502, loss_grounding_bce_3: 0.25205/0.08658, loss_grounding_dice_3: 0.24753/0.17928, loss_grounding_ce_3: 0.01436/0.28310, loss_mask_ce_4: 0.47310/0.92788, loss_mask_bce_4: 0.36503/0.33942, loss_mask_dice_4: 0.43362/1.19222, loss_spatial_bce_4: 0.13714/0.09791, loss_spatial_dice_4: 0.22718/0.23562, loss_spatial_ce_4: 0.00359/0.11125, loss_grounding_bce_4: 0.25265/0.08716, loss_grounding_dice_4: 0.24900/0.18205, loss_grounding_ce_4: 0.02072/0.28546, loss_mask_ce_5: 0.48336/0.94247, loss_mask_bce_5: 0.37645/0.34164, loss_mask_dice_5: 0.46894/1.19714, loss_spatial_bce_5: 0.11864/0.09899, loss_spatial_dice_5: 0.19131/0.23858, loss_spatial_ce_5: 0.02974/0.12463, loss_grounding_bce_5: 0.25687/0.08758, loss_grounding_dice_5: 0.24851/0.18323, loss_grounding_ce_5: 0.02582/0.29797, loss_mask_ce_6: 0.45265/0.97980, loss_mask_bce_6: 0.34700/0.34438, loss_mask_dice_6: 0.46608/1.20020, loss_spatial_bce_6: 0.13963/0.10441, loss_spatial_dice_6: 0.21148/0.24096, loss_spatial_ce_6: 0.02656/0.14896, loss_grounding_bce_6: 0.24610/0.08835, loss_grounding_dice_6: 0.25037/0.18339, loss_grounding_ce_6: 0.01965/0.31637, loss_mask_ce_7: 0.56027/1.02205, loss_mask_bce_7: 0.37613/0.35217, loss_mask_dice_7: 0.47010/1.25568, loss_spatial_bce_7: 0.15595/0.11332, loss_spatial_dice_7: 0.20871/0.26817, loss_spatial_ce_7: 0.05961/0.18774, loss_grounding_bce_7: 0.24349/0.09030, loss_grounding_dice_7: 0.24148/0.19066, loss_grounding_ce_7: 0.04607/0.35091, loss_mask_ce_8: 0.71681/1.13302, loss_mask_bce_8: 0.37928/0.36565, loss_mask_dice_8: 0.45932/1.33094, loss_spatial_bce_8: 0.18872/0.13457, loss_spatial_dice_8: 0.21440/0.30879, loss_spatial_ce_8: 0.11802/0.24405, loss_grounding_bce_8: 0.24939/0.09378, loss_grounding_dice_8: 0.24273/0.20211, loss_grounding_ce_8: 0.10584/0.42321, loss_mask_ce_9: 2.56025/3.69492, loss_mask_bce_9: 0.37249/0.39252, loss_mask_dice_9: 0.65629/1.90572, loss_spatial_bce_9: 0.57295/0.33696, loss_spatial_dice_9: 0.75670/0.82468, loss_spatial_ce_9: 1.21513/1.52021, loss_grounding_bce_9: 0.29272/0.10515, loss_grounding_dice_9: 0.32044/0.28195, loss_grounding_ce_9: 0.11807/0.70040] items per batch[64] items per second[0.23] total items[1260800] mini batches[ 19700] memory[7341] epoch remaining[0:18:38]
INFO:trainer.default_trainer:epochs[    10] optim steps[19800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38431/0.91344, loss_mask_bce_0: 0.07034/0.33609, loss_mask_dice_0: 2.80572/1.16741, loss_spatial_bce_0: 0.00760/0.09218, loss_spatial_dice_0: 0.35398/0.22031, loss_spatial_ce_0: 0.13361/0.08100, loss_grounding_bce_0: 0.00951/0.08641, loss_grounding_dice_0: 0.48372/0.17939, loss_grounding_ce_0: 2.20599/0.27677, loss_mask_ce_1: 1.51344/0.91385, loss_mask_bce_1: 0.05809/0.33678, loss_mask_dice_1: 2.49777/1.17474, loss_spatial_bce_1: 0.00843/0.09312, loss_spatial_dice_1: 0.39098/0.22476, loss_spatial_ce_1: 0.08639/0.08667, loss_grounding_bce_1: 0.01248/0.08650, loss_grounding_dice_1: 0.52751/0.18006, loss_grounding_ce_1: 2.28951/0.27819, loss_mask_ce_2: 1.48205/0.92130, loss_mask_bce_2: 0.05448/0.33713, loss_mask_dice_2: 2.59609/1.17411, loss_spatial_bce_2: 0.00713/0.09320, loss_spatial_dice_2: 0.32771/0.22566, loss_spatial_ce_2: 0.06292/0.09056, loss_grounding_bce_2: 0.00939/0.08654, loss_grounding_dice_2: 0.45022/0.17948, loss_grounding_ce_2: 2.60380/0.28181, loss_mask_ce_3: 1.67633/0.92940, loss_mask_bce_3: 0.05948/0.33797, loss_mask_dice_3: 2.46062/1.17128, loss_spatial_bce_3: 0.00847/0.09407, loss_spatial_dice_3: 0.35309/0.22642, loss_spatial_ce_3: 0.11643/0.09491, loss_grounding_bce_3: 0.00650/0.08664, loss_grounding_dice_3: 0.46687/0.17930, loss_grounding_ce_3: 2.04184/0.28298, loss_mask_ce_4: 1.40619/0.92793, loss_mask_bce_4: 0.06464/0.33944, loss_mask_dice_4: 2.58540/1.19309, loss_spatial_bce_4: 0.00796/0.09788, loss_spatial_dice_4: 0.35790/0.23563, loss_spatial_ce_4: 0.21968/0.11109, loss_grounding_bce_4: 0.00741/0.08721, loss_grounding_dice_4: 0.51775/0.18208, loss_grounding_ce_4: 1.62800/0.28527, loss_mask_ce_5: 1.45445/0.94253, loss_mask_bce_5: 0.05245/0.34165, loss_mask_dice_5: 2.40367/1.19789, loss_spatial_bce_5: 0.00777/0.09896, loss_spatial_dice_5: 0.36244/0.23860, loss_spatial_ce_5: 0.10335/0.12446, loss_grounding_bce_5: 0.00997/0.08762, loss_grounding_dice_5: 0.55525/0.18324, loss_grounding_ce_5: 1.95837/0.29795, loss_mask_ce_6: 1.80069/0.97992, loss_mask_bce_6: 0.06387/0.34440, loss_mask_dice_6: 2.66055/1.20101, loss_spatial_bce_6: 0.01230/0.10438, loss_spatial_dice_6: 0.33726/0.24100, loss_spatial_ce_6: 0.16922/0.14876, loss_grounding_bce_6: 0.01193/0.08839, loss_grounding_dice_6: 0.52063/0.18340, loss_grounding_ce_6: 0.51966/0.31615, loss_mask_ce_7: 1.47184/1.02203, loss_mask_bce_7: 0.06312/0.35218, loss_mask_dice_7: 2.87344/1.25660, loss_spatial_bce_7: 0.01581/0.11329, loss_spatial_dice_7: 0.43259/0.26822, loss_spatial_ce_7: 0.30155/0.18759, loss_grounding_bce_7: 0.01025/0.09035, loss_grounding_dice_7: 0.61121/0.19067, loss_grounding_ce_7: 3.44621/0.35084, loss_mask_ce_8: 1.58267/1.13288, loss_mask_bce_8: 0.11857/0.36568, loss_mask_dice_8: 3.20865/1.33191, loss_spatial_bce_8: 0.01276/0.13454, loss_spatial_dice_8: 0.45912/0.30884, loss_spatial_ce_8: 0.43258/0.24403, loss_grounding_bce_8: 0.01711/0.09383, loss_grounding_dice_8: 0.64046/0.20212, loss_grounding_ce_8: 3.08356/0.42312, loss_mask_ce_9: 4.50555/3.69476, loss_mask_bce_9: 0.04240/0.39249, loss_mask_dice_9: 3.09583/1.90689, loss_spatial_bce_9: 0.01604/0.33688, loss_spatial_dice_9: 0.78704/0.82469, loss_spatial_ce_9: 1.42885/1.52027, loss_grounding_bce_9: 0.00750/0.10516, loss_grounding_dice_9: 0.51631/0.28190, loss_grounding_ce_9: 2.13942/0.70010] items per batch[64] items per second[0.22] total items[1267200] mini batches[ 19800] memory[7341] epoch remaining[0:13:59]
INFO:trainer.default_trainer:epochs[    10] optim steps[19900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44523/0.91308, loss_mask_bce_0: 0.26164/0.33598, loss_mask_dice_0: 1.37165/1.16681, loss_spatial_bce_0: 0.05421/0.09218, loss_spatial_dice_0: 0.18412/0.22022, loss_spatial_ce_0: 0.10308/0.08093, loss_grounding_bce_0: 0.11287/0.08641, loss_grounding_dice_0: 0.17469/0.17935, loss_grounding_ce_0: 0.07298/0.27667, loss_mask_ce_1: 0.53618/0.91347, loss_mask_bce_1: 0.25536/0.33669, loss_mask_dice_1: 1.27248/1.17425, loss_spatial_bce_1: 0.05719/0.09311, loss_spatial_dice_1: 0.17978/0.22466, loss_spatial_ce_1: 0.30905/0.08660, loss_grounding_bce_1: 0.11578/0.08649, loss_grounding_dice_1: 0.15546/0.18001, loss_grounding_ce_1: 0.07415/0.27812, loss_mask_ce_2: 0.54775/0.92100, loss_mask_bce_2: 0.24761/0.33702, loss_mask_dice_2: 1.25747/1.17355, loss_spatial_bce_2: 0.06077/0.09320, loss_spatial_dice_2: 0.18046/0.22556, loss_spatial_ce_2: 0.10906/0.09047, loss_grounding_bce_2: 0.11659/0.08653, loss_grounding_dice_2: 0.17856/0.17945, loss_grounding_ce_2: 0.07533/0.28167, loss_mask_ce_3: 0.49675/0.92899, loss_mask_bce_3: 0.27529/0.33789, loss_mask_dice_3: 1.25237/1.17072, loss_spatial_bce_3: 0.05713/0.09405, loss_spatial_dice_3: 0.16156/0.22631, loss_spatial_ce_3: 0.14843/0.09482, loss_grounding_bce_3: 0.11201/0.08663, loss_grounding_dice_3: 0.15698/0.17927, loss_grounding_ce_3: 0.07313/0.28297, loss_mask_ce_4: 0.50035/0.92761, loss_mask_bce_4: 0.25548/0.33933, loss_mask_dice_4: 1.26490/1.19250, loss_spatial_bce_4: 0.05785/0.09785, loss_spatial_dice_4: 0.18321/0.23552, loss_spatial_ce_4: 0.06994/0.11094, loss_grounding_bce_4: 0.11851/0.08719, loss_grounding_dice_4: 0.16494/0.18206, loss_grounding_ce_4: 0.08124/0.28523, loss_mask_ce_5: 0.53005/0.94222, loss_mask_bce_5: 0.24474/0.34154, loss_mask_dice_5: 1.19721/1.19737, loss_spatial_bce_5: 0.05460/0.09893, loss_spatial_dice_5: 0.18806/0.23849, loss_spatial_ce_5: 0.09025/0.12433, loss_grounding_bce_5: 0.11125/0.08760, loss_grounding_dice_5: 0.17187/0.18318, loss_grounding_ce_5: 0.38328/0.29800, loss_mask_ce_6: 0.49760/0.97959, loss_mask_bce_6: 0.25098/0.34428, loss_mask_dice_6: 1.29167/1.20037, loss_spatial_bce_6: 0.06202/0.10435, loss_spatial_dice_6: 0.20297/0.24090, loss_spatial_ce_6: 0.09440/0.14856, loss_grounding_bce_6: 0.11040/0.08838, loss_grounding_dice_6: 0.15457/0.18335, loss_grounding_ce_6: 0.09262/0.31638, loss_mask_ce_7: 0.58808/1.02168, loss_mask_bce_7: 0.24182/0.35207, loss_mask_dice_7: 1.25857/1.25591, loss_spatial_bce_7: 0.06042/0.11326, loss_spatial_dice_7: 0.19400/0.26811, loss_spatial_ce_7: 0.16792/0.18739, loss_grounding_bce_7: 0.11157/0.09034, loss_grounding_dice_7: 0.16193/0.19063, loss_grounding_ce_7: 0.10952/0.35075, loss_mask_ce_8: 0.55923/1.13243, loss_mask_bce_8: 0.25888/0.36555, loss_mask_dice_8: 1.30002/1.33113, loss_spatial_bce_8: 0.06621/0.13448, loss_spatial_dice_8: 0.21570/0.30873, loss_spatial_ce_8: 0.19835/0.24388, loss_grounding_bce_8: 0.11058/0.09381, loss_grounding_dice_8: 0.12816/0.20208, loss_grounding_ce_8: 0.13513/0.42314, loss_mask_ce_9: 2.37789/3.69416, loss_mask_bce_9: 0.29419/0.39237, loss_mask_dice_9: 1.97130/1.90602, loss_spatial_bce_9: 0.26599/0.33696, loss_spatial_dice_9: 0.90855/0.82464, loss_spatial_ce_9: 1.70757/1.52014, loss_grounding_bce_9: 0.11395/0.10514, loss_grounding_dice_9: 0.40751/0.28187, loss_grounding_ce_9: 0.21051/0.70026] items per batch[64] items per second[0.23] total items[1273600] mini batches[ 19900] memory[7341] epoch remaining[0:09:16]
INFO:trainer.default_trainer:epochs[    10] optim steps[20000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83793/0.91313, loss_mask_bce_0: 0.41945/0.33599, loss_mask_dice_0: 0.86460/1.16699, loss_spatial_bce_0: 0.06025/0.09217, loss_spatial_dice_0: 0.09334/0.22021, loss_spatial_ce_0: 0.00446/0.08081, loss_grounding_bce_0: 0.13211/0.08644, loss_grounding_dice_0: 0.19321/0.17948, loss_grounding_ce_0: 0.21124/0.27668, loss_mask_ce_1: 0.84367/0.91351, loss_mask_bce_1: 0.43134/0.33671, loss_mask_dice_1: 0.94526/1.17441, loss_spatial_bce_1: 0.06811/0.09310, loss_spatial_dice_1: 0.10644/0.22465, loss_spatial_ce_1: 0.00701/0.08647, loss_grounding_bce_1: 0.13665/0.08651, loss_grounding_dice_1: 0.20252/0.18012, loss_grounding_ce_1: 0.22401/0.27811, loss_mask_ce_2: 0.82264/0.92110, loss_mask_bce_2: 0.42333/0.33703, loss_mask_dice_2: 0.92336/1.17372, loss_spatial_bce_2: 0.06347/0.09320, loss_spatial_dice_2: 0.09584/0.22556, loss_spatial_ce_2: 0.00702/0.09033, loss_grounding_bce_2: 0.13280/0.08655, loss_grounding_dice_2: 0.19943/0.17957, loss_grounding_ce_2: 0.20700/0.28163, loss_mask_ce_3: 0.83751/0.92911, loss_mask_bce_3: 0.43365/0.33789, loss_mask_dice_3: 0.98617/1.17087, loss_spatial_bce_3: 0.06495/0.09404, loss_spatial_dice_3: 0.09198/0.22631, loss_spatial_ce_3: 0.00925/0.09466, loss_grounding_bce_3: 0.13629/0.08664, loss_grounding_dice_3: 0.19438/0.17939, loss_grounding_ce_3: 0.22518/0.28299, loss_mask_ce_4: 0.84784/0.92767, loss_mask_bce_4: 0.42555/0.33936, loss_mask_dice_4: 0.89719/1.19270, loss_spatial_bce_4: 0.07092/0.09785, loss_spatial_dice_4: 0.10216/0.23552, loss_spatial_ce_4: 0.01074/0.11083, loss_grounding_bce_4: 0.13041/0.08723, loss_grounding_dice_4: 0.18780/0.18220, loss_grounding_ce_4: 0.22679/0.28517, loss_mask_ce_5: 0.95467/0.94235, loss_mask_bce_5: 0.43579/0.34153, loss_mask_dice_5: 0.92702/1.19757, loss_spatial_bce_5: 0.07627/0.09892, loss_spatial_dice_5: 0.10648/0.23849, loss_spatial_ce_5: 0.01918/0.12420, loss_grounding_bce_5: 0.12531/0.08764, loss_grounding_dice_5: 0.19180/0.18332, loss_grounding_ce_5: 0.23439/0.29812, loss_mask_ce_6: 1.04020/0.97974, loss_mask_bce_6: 0.43237/0.34427, loss_mask_dice_6: 0.87567/1.20057, loss_spatial_bce_6: 0.07253/0.10433, loss_spatial_dice_6: 0.10624/0.24089, loss_spatial_ce_6: 0.04223/0.14842, loss_grounding_bce_6: 0.13200/0.08841, loss_grounding_dice_6: 0.21481/0.18348, loss_grounding_ce_6: 0.28922/0.31648, loss_mask_ce_7: 1.04145/1.02183, loss_mask_bce_7: 0.47765/0.35206, loss_mask_dice_7: 1.02290/1.25609, loss_spatial_bce_7: 0.06879/0.11325, loss_spatial_dice_7: 0.11656/0.26810, loss_spatial_ce_7: 0.10700/0.18724, loss_grounding_bce_7: 0.14652/0.09036, loss_grounding_dice_7: 0.25732/0.19075, loss_grounding_ce_7: 0.39663/0.35077, loss_mask_ce_8: 1.10851/1.13261, loss_mask_bce_8: 0.49717/0.36555, loss_mask_dice_8: 1.20703/1.33128, loss_spatial_bce_8: 0.07964/0.13444, loss_spatial_dice_8: 0.14838/0.30873, loss_spatial_ce_8: 0.11548/0.24383, loss_grounding_bce_8: 0.14751/0.09383, loss_grounding_dice_8: 0.27656/0.20219, loss_grounding_ce_8: 0.38053/0.42294, loss_mask_ce_9: 3.79716/3.69366, loss_mask_bce_9: 0.63079/0.39232, loss_mask_dice_9: 1.72840/1.90586, loss_spatial_bce_9: 0.33618/0.33692, loss_spatial_dice_9: 0.89056/0.82463, loss_spatial_ce_9: 1.55708/1.52009, loss_grounding_bce_9: 0.19089/0.10514, loss_grounding_dice_9: 0.37354/0.28195, loss_grounding_ce_9: 0.39274/0.70002] items per batch[64] items per second[0.22] total items[1280000] mini batches[ 20000] memory[7341] epoch remaining[0:04:34]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00020097.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0032 s/iter. Inference: 0.2215 s/iter. Eval: 0.0990 s/iter. Total: 0.3236 s/iter. ETA=0:00:47
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0033 s/iter. Inference: 0.2276 s/iter. Eval: 0.0879 s/iter. Total: 0.3189 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 43/157. Dataloading: 0.0033 s/iter. Inference: 0.2267 s/iter. Eval: 0.0861 s/iter. Total: 0.3162 s/iter. ETA=0:00:36
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 60/157. Dataloading: 0.0033 s/iter. Inference: 0.2277 s/iter. Eval: 0.0811 s/iter. Total: 0.3122 s/iter. ETA=0:00:30
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0033 s/iter. Inference: 0.2254 s/iter. Eval: 0.0782 s/iter. Total: 0.3070 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2272 s/iter. Eval: 0.0774 s/iter. Total: 0.3080 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2295 s/iter. Eval: 0.0766 s/iter. Total: 0.3095 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2291 s/iter. Eval: 0.0759 s/iter. Total: 0.3083 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2296 s/iter. Eval: 0.0750 s/iter. Total: 0.3080 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval6hx3ckjy ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.697 | 82.009 | 59.707 |      133      |
| Things | 54.786 | 82.920 | 65.452 |      80       |
| Stuff  | 42.015 | 80.634 | 51.037 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.35s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.90 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.19s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.53 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.821 | 61.112 | 40.953 | 19.397 | 41.781 | 60.034 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.174 | bicycle      | 18.720 | car            | 35.821 |
| motorcycle    | 34.094 | airplane     | 56.129 | bus            | 62.763 |
| train         | 69.209 | truck        | 35.837 | boat           | 22.545 |
| traffic light | 23.857 | fire hydrant | 62.865 | stop sign      | 63.703 |
| parking meter | 43.517 | bench        | 20.861 | bird           | 29.433 |
| cat           | 73.671 | dog          | 66.104 | horse          | 45.573 |
| sheep         | 47.594 | cow          | 50.773 | elephant       | 60.599 |
| bear          | 77.199 | zebra        | 60.009 | giraffe        | 56.651 |
| backpack      | 16.479 | umbrella     | 48.548 | handbag        | 14.359 |
| tie           | 32.480 | suitcase     | 41.377 | frisbee        | 68.075 |
| skis          | 5.495  | snowboard    | 23.040 | sports ball    | 46.448 |
| kite          | 33.579 | baseball bat | 28.942 | baseball glove | 43.520 |
| skateboard    | 35.788 | surfboard    | 35.406 | tennis racket  | 56.932 |
| bottle        | 34.003 | wine glass   | 27.171 | cup            | 39.990 |
| fork          | 16.066 | knife        | 13.007 | spoon          | 14.297 |
| bowl          | 31.067 | banana       | 19.869 | apple          | 19.758 |
| sandwich      | 41.830 | orange       | 30.095 | broccoli       | 20.470 |
| carrot        | 19.397 | hot dog      | 23.773 | pizza          | 51.657 |
| donut         | 46.046 | cake         | 43.723 | chair          | 20.898 |
| couch         | 43.283 | potted plant | 17.468 | bed            | 42.157 |
| dining table  | 13.896 | toilet       | 66.466 | tv             | 62.532 |
| laptop        | 62.127 | mouse        | 59.503 | remote         | 31.791 |
| keyboard      | 47.813 | cell phone   | 35.705 | microwave      | 57.029 |
| oven          | 34.729 | toaster      | 33.880 | sink           | 38.061 |
| refrigerator  | 59.730 | book         | 8.951  | clock          | 51.497 |
| vase          | 32.014 | scissors     | 24.024 | teddy bear     | 52.300 |
| hair drier    | 5.783  | toothbrush   | 19.636 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 58.58495926142802, 'fwIoU': 68.07627039481675, 'IoU-person': 87.343328790986, 'IoU-bicycle': 71.04094939679071, 'IoU-car': 68.68032557111098, 'IoU-motorcycle': 83.75185663748402, 'IoU-airplane': 83.7935541291093, 'IoU-bus': 78.81800349161246, 'IoU-train': 81.91595754850785, 'IoU-truck': 62.146066492716656, 'IoU-boat': 67.17075571177504, 'IoU-traffic light': 75.10517722990788, 'IoU-fire hydrant': 89.3233181735236, 'IoU-stop sign': 91.83321083001346, 'IoU-parking meter': 83.25279459352096, 'IoU-bench': 58.64852693959358, 'IoU-bird': 75.87912104612087, 'IoU-cat': 77.67680407758039, 'IoU-dog': 73.23677153170382, 'IoU-horse': 85.85758272895552, 'IoU-sheep': 79.4920749744875, 'IoU-cow': 80.42925756248705, 'IoU-elephant': 87.10312210209248, 'IoU-bear': 74.90644498808228, 'IoU-zebra': 85.3153715604387, 'IoU-giraffe': 86.6556712481758, 'IoU-backpack': 37.49728478812993, 'IoU-umbrella': 71.60057473322236, 'IoU-handbag': 35.28533858550234, 'IoU-tie': 69.60543822210879, 'IoU-suitcase': 80.05134480182137, 'IoU-frisbee': 83.94844357976655, 'IoU-skis': 51.08029356879997, 'IoU-snowboard': 66.8779357167764, 'IoU-sports ball': 56.02711363499686, 'IoU-kite': 65.99426087450635, 'IoU-baseball bat': 58.73446669784366, 'IoU-baseball glove': 51.62603884957828, 'IoU-skateboard': 63.94625439039212, 'IoU-surfboard': 75.42270818187686, 'IoU-tennis racket': 66.70007727369448, 'IoU-bottle': 67.97235654026754, 'IoU-wine glass': 72.34194934066802, 'IoU-cup': 65.04736459659618, 'IoU-fork': 56.49681455488654, 'IoU-knife': 49.52687526492119, 'IoU-spoon': 53.18014032470829, 'IoU-bowl': 49.09252963713773, 'IoU-banana': 81.09049345637965, 'IoU-apple': 57.347719965495614, 'IoU-sandwich': 53.35288939820466, 'IoU-orange': 78.34075491883567, 'IoU-broccoli': 68.24064370225324, 'IoU-carrot': 59.50781618728463, 'IoU-hot dog': 48.296205947744745, 'IoU-pizza': 75.07543705211916, 'IoU-donut': 64.85941595436354, 'IoU-cake': 59.96964756449403, 'IoU-chair': 53.20748078602979, 'IoU-couch': 68.2001239379921, 'IoU-potted plant': 34.48793053852373, 'IoU-bed': 63.978199137812375, 'IoU-dining table': 49.334065301484756, 'IoU-toilet': 85.72609223706462, 'IoU-tv': 72.81947129458636, 'IoU-laptop': 70.35337172514032, 'IoU-mouse': 72.12628678255803, 'IoU-remote': 59.68246079150031, 'IoU-keyboard': 54.710308926932186, 'IoU-cell phone': 65.91747969098917, 'IoU-microwave': 54.97592487603108, 'IoU-oven': 66.97729977488987, 'IoU-toaster': 4.06064658321261, 'IoU-sink': 68.74412973562794, 'IoU-refrigerator': 81.54118812151363, 'IoU-book': 46.53596584905079, 'IoU-clock': 70.12847494182417, 'IoU-vase': 59.25829511533615, 'IoU-scissors': 33.76738109643463, 'IoU-teddy bear': 75.53415521682666, 'IoU-hair drier': 27.85373887789808, 'IoU-toothbrush': 61.967350300196586, 'IoU-banner': 36.34389007306825, 'IoU-blanket': 11.214621556366696, 'IoU-bridge': 38.0556345125197, 'IoU-cardboard': 42.765958572076826, 'IoU-counter': 30.161351833662454, 'IoU-curtain': 63.52513889679632, 'IoU-door-stuff': 44.37094146088765, 'IoU-floor-wood': 61.37804939781491, 'IoU-flower': 45.3337639128178, 'IoU-fruit': 38.47710111571164, 'IoU-gravel': 26.159733460056078, 'IoU-house': 24.121629009815745, 'IoU-light': 39.706167318486116, 'IoU-mirror-stuff': 54.29516575646396, 'IoU-net': 48.36578167593611, 'IoU-pillow': 12.005006917451743, 'IoU-platform': 27.928838129678752, 'IoU-playingfield': 67.92026627544413, 'IoU-railroad': 60.55701714151923, 'IoU-river': 49.34350767292302, 'IoU-road': 66.48112350968574, 'IoU-roof': 15.746794504949477, 'IoU-sand': 62.83675195941877, 'IoU-sea': 85.14350520502391, 'IoU-shelf': 36.088624677277735, 'IoU-snow': 88.56267513139218, 'IoU-stairs': 20.586651974374, 'IoU-tent': 8.157523220027581, 'IoU-towel': 32.41299400672501, 'IoU-wall-brick': 48.3920256694535, 'IoU-wall-stone': 29.657671240749533, 'IoU-wall-tile': 63.70763352682245, 'IoU-wall-wood': 39.27363289886374, 'IoU-water-other': 18.764220597175324, 'IoU-window-blind': 47.082588865501094, 'IoU-window-other': 47.71945893656377, 'IoU-tree-merged': 81.13700820811397, 'IoU-fence-merged': 52.80093384561968, 'IoU-ceiling-merged': 65.86327753304309, 'IoU-sky-other-merged': 92.64264472133881, 'IoU-cabinet-merged': 58.80261511171885, 'IoU-table-merged': 35.13492734571378, 'IoU-floor-other-merged': 50.288976835600664, 'IoU-pavement-merged': 54.48322914318051, 'IoU-mountain-merged': 56.015094141172796, 'IoU-grass-merged': 70.62378170483771, 'IoU-dirt-merged': 45.75437178802028, 'IoU-paper-merged': 29.53315069111797, 'IoU-food-other-merged': 35.337547242337685, 'IoU-building-other-merged': 56.972557287858514, 'IoU-rock-merged': 58.1954107549182, 'IoU-wall-other-merged': 64.05274835203136, 'IoU-rug-merged': 66.11736914819251, 'mACC': 70.28641349056511, 'pACC': 79.65805021725244, 'ACC-person': 92.39633387201171, 'ACC-bicycle': 79.83425772901506, 'ACC-car': 84.24069992636602, 'ACC-motorcycle': 88.99982797325482, 'ACC-airplane': 90.28359669887273, 'ACC-bus': 82.24342614417179, 'ACC-train': 94.95537807686235, 'ACC-truck': 77.7155117439476, 'ACC-boat': 79.07604767623417, 'ACC-traffic light': 89.33819357936338, 'ACC-fire hydrant': 94.84220943092487, 'ACC-stop sign': 94.63627418439354, 'ACC-parking meter': 87.45698967233744, 'ACC-bench': 73.46253744527466, 'ACC-bird': 80.13573605190626, 'ACC-cat': 83.48073762015524, 'ACC-dog': 75.6032807368973, 'ACC-horse': 92.18176579379677, 'ACC-sheep': 82.4337191489088, 'ACC-cow': 86.38198719160832, 'ACC-elephant': 89.46347497333043, 'ACC-bear': 76.95672069471884, 'ACC-zebra': 87.70691479600579, 'ACC-giraffe': 91.38691209033742, 'ACC-backpack': 59.003974548864605, 'ACC-umbrella': 77.25610137309432, 'ACC-handbag': 49.75234069882799, 'ACC-tie': 80.55913778508355, 'ACC-suitcase': 88.55004354150552, 'ACC-frisbee': 94.14436363636364, 'ACC-skis': 66.77785462883777, 'ACC-snowboard': 79.85354265141682, 'ACC-sports ball': 67.41195563411584, 'ACC-kite': 76.00421848532214, 'ACC-baseball bat': 81.39605451453147, 'ACC-baseball glove': 60.246932261155905, 'ACC-skateboard': 69.66562832430394, 'ACC-surfboard': 83.68985221728127, 'ACC-tennis racket': 71.74678385174849, 'ACC-bottle': 81.93060467291787, 'ACC-wine glass': 85.53135935885116, 'ACC-cup': 82.92841621095593, 'ACC-fork': 68.4227368785566, 'ACC-knife': 60.14691873671869, 'ACC-spoon': 66.52856385405492, 'ACC-bowl': 56.688367027649036, 'ACC-banana': 86.70070434466633, 'ACC-apple': 70.6147362652815, 'ACC-sandwich': 61.36248973597306, 'ACC-orange': 85.7384705077798, 'ACC-broccoli': 78.11354989734, 'ACC-carrot': 69.5494510772509, 'ACC-hot dog': 54.78464260346263, 'ACC-pizza': 84.4841341470304, 'ACC-donut': 80.02976103770905, 'ACC-cake': 65.65091509572039, 'ACC-chair': 68.42271515166513, 'ACC-couch': 80.30627747401834, 'ACC-potted plant': 47.08545817807834, 'ACC-bed': 83.47775807815975, 'ACC-dining table': 76.3362886575436, 'ACC-toilet': 91.41332754108944, 'ACC-tv': 83.14196015766755, 'ACC-laptop': 84.41311714379835, 'ACC-mouse': 85.73278645354227, 'ACC-remote': 69.25183552640158, 'ACC-keyboard': 58.7492751464845, 'ACC-cell phone': 70.15736383900752, 'ACC-microwave': 64.70157044803305, 'ACC-oven': 86.52290397022253, 'ACC-toaster': 4.488223460017244, 'ACC-sink': 83.09721089165521, 'ACC-refrigerator': 88.12148157907134, 'ACC-book': 58.14697682889244, 'ACC-clock': 75.63657234532376, 'ACC-vase': 68.42526268709283, 'ACC-scissors': 36.19248638771818, 'ACC-teddy bear': 81.68126760179858, 'ACC-hair drier': 40.675433353257624, 'ACC-toothbrush': 81.04847116052815, 'ACC-banner': 74.65622975735403, 'ACC-blanket': 15.607137339718832, 'ACC-bridge': 56.100294078807025, 'ACC-cardboard': 58.79159517231351, 'ACC-counter': 53.36949880139868, 'ACC-curtain': 73.22699153567225, 'ACC-door-stuff': 63.555162215657326, 'ACC-floor-wood': 77.66514604544919, 'ACC-flower': 67.31328782633365, 'ACC-fruit': 54.08672093928124, 'ACC-gravel': 31.886649603470445, 'ACC-house': 27.630738624415503, 'ACC-light': 56.37823948244842, 'ACC-mirror-stuff': 76.65166942931565, 'ACC-net': 63.30087595027636, 'ACC-pillow': 24.053242800157342, 'ACC-platform': 45.62920275920534, 'ACC-playingfield': 82.56908198131258, 'ACC-railroad': 77.48175183435791, 'ACC-river': 82.73271370112532, 'ACC-road': 84.61178149124139, 'ACC-roof': 21.86234603364748, 'ACC-sand': 69.07643083312306, 'ACC-sea': 90.5846600286726, 'ACC-shelf': 63.52636871242432, 'ACC-snow': 95.55334875070898, 'ACC-stairs': 35.887009688295876, 'ACC-tent': 9.901834294532664, 'ACC-towel': 40.70359197658906, 'ACC-wall-brick': 63.55011800921392, 'ACC-wall-stone': 34.26047543172129, 'ACC-wall-tile': 73.50695462507001, 'ACC-wall-wood': 49.71896686235836, 'ACC-water-other': 24.254358771058897, 'ACC-window-blind': 57.03078759760204, 'ACC-window-other': 73.48526266301428, 'ACC-tree-merged': 89.1274171268346, 'ACC-fence-merged': 72.88637171342472, 'ACC-ceiling-merged': 82.74367549375725, 'ACC-sky-other-merged': 96.53110538903174, 'ACC-cabinet-merged': 75.85861306448203, 'ACC-table-merged': 54.83100784674575, 'ACC-floor-other-merged': 61.50780793415878, 'ACC-pavement-merged': 67.36682951908269, 'ACC-mountain-merged': 66.65295405138309, 'ACC-grass-merged': 83.0723480596934, 'ACC-dirt-merged': 76.52758803209257, 'ACC-paper-merged': 41.37256634489903, 'ACC-food-other-merged': 45.473607892335096, 'ACC-building-other-merged': 71.29159299199975, 'ACC-rock-merged': 81.86292511841575, 'ACC-wall-other-merged': 82.35804240949378, 'ACC-rug-merged': 80.72485269384882})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1552 s/iter. Inference: 0.3892 s/iter. Eval: 0.0000 s/iter. Total: 0.5444 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1585 s/iter. Inference: 0.4455 s/iter. Eval: 0.0000 s/iter. Total: 0.6042 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1732 s/iter. Inference: 0.5151 s/iter. Eval: 0.0000 s/iter. Total: 0.6884 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 32/50. Dataloading: 0.1707 s/iter. Inference: 0.5534 s/iter. Eval: 0.0000 s/iter. Total: 0.7243 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1713 s/iter. Inference: 0.5453 s/iter. Eval: 0.0000 s/iter. Total: 0.7169 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1696 s/iter. Inference: 0.5962 s/iter. Eval: 0.0000 s/iter. Total: 0.7660 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.565993561603746, 'noc@0.8': 2.9622475856014048, 'noc@0.85': 3.614281533508926, 'noc@0.9': 4.671934445419959, 'miou@iter1': 0.8342226557052027}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0998 s/iter. Eval: 0.0008 s/iter. Total: 0.1023 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.8900146484375, 'precision@0.6': 67.78079986572266, 'precision@0.7': 62.92265701293945, 'precision@0.8': 52.39020538330078, 'precision@0.9': 26.661483764648438, 'cIoU': 56.48704147338867, 'mIoU': 62.42222595214844}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.69661334483087, 'SQ': 82.00931292875367, 'RQ': 59.70733700052755, 'PQ_th': 54.78567017278764, 'SQ_th': 82.9203203725414, 'RQ_th': 65.45154817334065, 'PQ_st': 42.01501813282066, 'SQ_st': 80.63420735322494, 'RQ_st': 51.036829569866285}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.82075831314066, 'AP50': 61.11167427885418, 'AP75': 40.95309526438786, 'APs': 19.397258749714968, 'APm': 41.78127724909518, 'APl': 60.03350586517442, 'AP-person': 44.17367733425638, 'AP-bicycle': 18.719997472298754, 'AP-car': 35.82096707828746, 'AP-motorcycle': 34.09402427390528, 'AP-airplane': 56.12898476132154, 'AP-bus': 62.76326629986284, 'AP-train': 69.209348732862, 'AP-truck': 35.83728741880368, 'AP-boat': 22.54481958057782, 'AP-traffic light': 23.856880909004698, 'AP-fire hydrant': 62.8651181249401, 'AP-stop sign': 63.70274246698171, 'AP-parking meter': 43.51702259885347, 'AP-bench': 20.86142088732484, 'AP-bird': 29.433302618862182, 'AP-cat': 73.67052438432889, 'AP-dog': 66.10434609238402, 'AP-horse': 45.57286544633247, 'AP-sheep': 47.59351252044737, 'AP-cow': 50.77304592355123, 'AP-elephant': 60.59923373356321, 'AP-bear': 77.19920617550277, 'AP-zebra': 60.00933468521556, 'AP-giraffe': 56.6513929462348, 'AP-backpack': 16.47873459916428, 'AP-umbrella': 48.54785073906777, 'AP-handbag': 14.35909196450815, 'AP-tie': 32.47975626271809, 'AP-suitcase': 41.3772213226605, 'AP-frisbee': 68.07549371561046, 'AP-skis': 5.494664333779793, 'AP-snowboard': 23.04014020973441, 'AP-sports ball': 46.44761856464588, 'AP-kite': 33.57852436163406, 'AP-baseball bat': 28.941849129213704, 'AP-baseball glove': 43.52032461398862, 'AP-skateboard': 35.7875826891314, 'AP-surfboard': 35.40618807231931, 'AP-tennis racket': 56.93203617879087, 'AP-bottle': 34.00261648595701, 'AP-wine glass': 27.171395347862482, 'AP-cup': 39.989559649418844, 'AP-fork': 16.06649857086118, 'AP-knife': 13.00676052666476, 'AP-spoon': 14.296748231119318, 'AP-bowl': 31.066610933729294, 'AP-banana': 19.86931003709197, 'AP-apple': 19.757653686060817, 'AP-sandwich': 41.83020117292307, 'AP-orange': 30.094592919390628, 'AP-broccoli': 20.470338309328145, 'AP-carrot': 19.397317219610162, 'AP-hot dog': 23.772829280664794, 'AP-pizza': 51.65686748004404, 'AP-donut': 46.04632224955999, 'AP-cake': 43.72334968202846, 'AP-chair': 20.897706104906412, 'AP-couch': 43.282580364205415, 'AP-potted plant': 17.4675930488125, 'AP-bed': 42.156591944770234, 'AP-dining table': 13.89600612416641, 'AP-toilet': 66.46630437475709, 'AP-tv': 62.53209309131713, 'AP-laptop': 62.12682864378366, 'AP-mouse': 59.50327500213507, 'AP-remote': 31.7911362764488, 'AP-keyboard': 47.8129579993913, 'AP-cell phone': 35.7052710836494, 'AP-microwave': 57.028887283508936, 'AP-oven': 34.72879118805305, 'AP-toaster': 33.87964635291368, 'AP-sink': 38.061121466263145, 'AP-refrigerator': 59.729936117195805, 'AP-book': 8.951467220062007, 'AP-clock': 51.49690137895981, 'AP-vase': 32.01387892348766, 'AP-scissors': 24.02352466011167, 'AP-teddy bear': 52.300369727350656, 'AP-hair drier': 5.783259838588902, 'AP-toothbrush': 19.636165831428116}), ('sem_seg', {'mIoU': 58.58495926142802, 'fwIoU': 68.07627039481675, 'IoU-person': 87.343328790986, 'IoU-bicycle': 71.04094939679071, 'IoU-car': 68.68032557111098, 'IoU-motorcycle': 83.75185663748402, 'IoU-airplane': 83.7935541291093, 'IoU-bus': 78.81800349161246, 'IoU-train': 81.91595754850785, 'IoU-truck': 62.146066492716656, 'IoU-boat': 67.17075571177504, 'IoU-traffic light': 75.10517722990788, 'IoU-fire hydrant': 89.3233181735236, 'IoU-stop sign': 91.83321083001346, 'IoU-parking meter': 83.25279459352096, 'IoU-bench': 58.64852693959358, 'IoU-bird': 75.87912104612087, 'IoU-cat': 77.67680407758039, 'IoU-dog': 73.23677153170382, 'IoU-horse': 85.85758272895552, 'IoU-sheep': 79.4920749744875, 'IoU-cow': 80.42925756248705, 'IoU-elephant': 87.10312210209248, 'IoU-bear': 74.90644498808228, 'IoU-zebra': 85.3153715604387, 'IoU-giraffe': 86.6556712481758, 'IoU-backpack': 37.49728478812993, 'IoU-umbrella': 71.60057473322236, 'IoU-handbag': 35.28533858550234, 'IoU-tie': 69.60543822210879, 'IoU-suitcase': 80.05134480182137, 'IoU-frisbee': 83.94844357976655, 'IoU-skis': 51.08029356879997, 'IoU-snowboard': 66.8779357167764, 'IoU-sports ball': 56.02711363499686, 'IoU-kite': 65.99426087450635, 'IoU-baseball bat': 58.73446669784366, 'IoU-baseball glove': 51.62603884957828, 'IoU-skateboard': 63.94625439039212, 'IoU-surfboard': 75.42270818187686, 'IoU-tennis racket': 66.70007727369448, 'IoU-bottle': 67.97235654026754, 'IoU-wine glass': 72.34194934066802, 'IoU-cup': 65.04736459659618, 'IoU-fork': 56.49681455488654, 'IoU-knife': 49.52687526492119, 'IoU-spoon': 53.18014032470829, 'IoU-bowl': 49.09252963713773, 'IoU-banana': 81.09049345637965, 'IoU-apple': 57.347719965495614, 'IoU-sandwich': 53.35288939820466, 'IoU-orange': 78.34075491883567, 'IoU-broccoli': 68.24064370225324, 'IoU-carrot': 59.50781618728463, 'IoU-hot dog': 48.296205947744745, 'IoU-pizza': 75.07543705211916, 'IoU-donut': 64.85941595436354, 'IoU-cake': 59.96964756449403, 'IoU-chair': 53.20748078602979, 'IoU-couch': 68.2001239379921, 'IoU-potted plant': 34.48793053852373, 'IoU-bed': 63.978199137812375, 'IoU-dining table': 49.334065301484756, 'IoU-toilet': 85.72609223706462, 'IoU-tv': 72.81947129458636, 'IoU-laptop': 70.35337172514032, 'IoU-mouse': 72.12628678255803, 'IoU-remote': 59.68246079150031, 'IoU-keyboard': 54.710308926932186, 'IoU-cell phone': 65.91747969098917, 'IoU-microwave': 54.97592487603108, 'IoU-oven': 66.97729977488987, 'IoU-toaster': 4.06064658321261, 'IoU-sink': 68.74412973562794, 'IoU-refrigerator': 81.54118812151363, 'IoU-book': 46.53596584905079, 'IoU-clock': 70.12847494182417, 'IoU-vase': 59.25829511533615, 'IoU-scissors': 33.76738109643463, 'IoU-teddy bear': 75.53415521682666, 'IoU-hair drier': 27.85373887789808, 'IoU-toothbrush': 61.967350300196586, 'IoU-banner': 36.34389007306825, 'IoU-blanket': 11.214621556366696, 'IoU-bridge': 38.0556345125197, 'IoU-cardboard': 42.765958572076826, 'IoU-counter': 30.161351833662454, 'IoU-curtain': 63.52513889679632, 'IoU-door-stuff': 44.37094146088765, 'IoU-floor-wood': 61.37804939781491, 'IoU-flower': 45.3337639128178, 'IoU-fruit': 38.47710111571164, 'IoU-gravel': 26.159733460056078, 'IoU-house': 24.121629009815745, 'IoU-light': 39.706167318486116, 'IoU-mirror-stuff': 54.29516575646396, 'IoU-net': 48.36578167593611, 'IoU-pillow': 12.005006917451743, 'IoU-platform': 27.928838129678752, 'IoU-playingfield': 67.92026627544413, 'IoU-railroad': 60.55701714151923, 'IoU-river': 49.34350767292302, 'IoU-road': 66.48112350968574, 'IoU-roof': 15.746794504949477, 'IoU-sand': 62.83675195941877, 'IoU-sea': 85.14350520502391, 'IoU-shelf': 36.088624677277735, 'IoU-snow': 88.56267513139218, 'IoU-stairs': 20.586651974374, 'IoU-tent': 8.157523220027581, 'IoU-towel': 32.41299400672501, 'IoU-wall-brick': 48.3920256694535, 'IoU-wall-stone': 29.657671240749533, 'IoU-wall-tile': 63.70763352682245, 'IoU-wall-wood': 39.27363289886374, 'IoU-water-other': 18.764220597175324, 'IoU-window-blind': 47.082588865501094, 'IoU-window-other': 47.71945893656377, 'IoU-tree-merged': 81.13700820811397, 'IoU-fence-merged': 52.80093384561968, 'IoU-ceiling-merged': 65.86327753304309, 'IoU-sky-other-merged': 92.64264472133881, 'IoU-cabinet-merged': 58.80261511171885, 'IoU-table-merged': 35.13492734571378, 'IoU-floor-other-merged': 50.288976835600664, 'IoU-pavement-merged': 54.48322914318051, 'IoU-mountain-merged': 56.015094141172796, 'IoU-grass-merged': 70.62378170483771, 'IoU-dirt-merged': 45.75437178802028, 'IoU-paper-merged': 29.53315069111797, 'IoU-food-other-merged': 35.337547242337685, 'IoU-building-other-merged': 56.972557287858514, 'IoU-rock-merged': 58.1954107549182, 'IoU-wall-other-merged': 64.05274835203136, 'IoU-rug-merged': 66.11736914819251, 'mACC': 70.28641349056511, 'pACC': 79.65805021725244, 'ACC-person': 92.39633387201171, 'ACC-bicycle': 79.83425772901506, 'ACC-car': 84.24069992636602, 'ACC-motorcycle': 88.99982797325482, 'ACC-airplane': 90.28359669887273, 'ACC-bus': 82.24342614417179, 'ACC-train': 94.95537807686235, 'ACC-truck': 77.7155117439476, 'ACC-boat': 79.07604767623417, 'ACC-traffic light': 89.33819357936338, 'ACC-fire hydrant': 94.84220943092487, 'ACC-stop sign': 94.63627418439354, 'ACC-parking meter': 87.45698967233744, 'ACC-bench': 73.46253744527466, 'ACC-bird': 80.13573605190626, 'ACC-cat': 83.48073762015524, 'ACC-dog': 75.6032807368973, 'ACC-horse': 92.18176579379677, 'ACC-sheep': 82.4337191489088, 'ACC-cow': 86.38198719160832, 'ACC-elephant': 89.46347497333043, 'ACC-bear': 76.95672069471884, 'ACC-zebra': 87.70691479600579, 'ACC-giraffe': 91.38691209033742, 'ACC-backpack': 59.003974548864605, 'ACC-umbrella': 77.25610137309432, 'ACC-handbag': 49.75234069882799, 'ACC-tie': 80.55913778508355, 'ACC-suitcase': 88.55004354150552, 'ACC-frisbee': 94.14436363636364, 'ACC-skis': 66.77785462883777, 'ACC-snowboard': 79.85354265141682, 'ACC-sports ball': 67.41195563411584, 'ACC-kite': 76.00421848532214, 'ACC-baseball bat': 81.39605451453147, 'ACC-baseball glove': 60.246932261155905, 'ACC-skateboard': 69.66562832430394, 'ACC-surfboard': 83.68985221728127, 'ACC-tennis racket': 71.74678385174849, 'ACC-bottle': 81.93060467291787, 'ACC-wine glass': 85.53135935885116, 'ACC-cup': 82.92841621095593, 'ACC-fork': 68.4227368785566, 'ACC-knife': 60.14691873671869, 'ACC-spoon': 66.52856385405492, 'ACC-bowl': 56.688367027649036, 'ACC-banana': 86.70070434466633, 'ACC-apple': 70.6147362652815, 'ACC-sandwich': 61.36248973597306, 'ACC-orange': 85.7384705077798, 'ACC-broccoli': 78.11354989734, 'ACC-carrot': 69.5494510772509, 'ACC-hot dog': 54.78464260346263, 'ACC-pizza': 84.4841341470304, 'ACC-donut': 80.02976103770905, 'ACC-cake': 65.65091509572039, 'ACC-chair': 68.42271515166513, 'ACC-couch': 80.30627747401834, 'ACC-potted plant': 47.08545817807834, 'ACC-bed': 83.47775807815975, 'ACC-dining table': 76.3362886575436, 'ACC-toilet': 91.41332754108944, 'ACC-tv': 83.14196015766755, 'ACC-laptop': 84.41311714379835, 'ACC-mouse': 85.73278645354227, 'ACC-remote': 69.25183552640158, 'ACC-keyboard': 58.7492751464845, 'ACC-cell phone': 70.15736383900752, 'ACC-microwave': 64.70157044803305, 'ACC-oven': 86.52290397022253, 'ACC-toaster': 4.488223460017244, 'ACC-sink': 83.09721089165521, 'ACC-refrigerator': 88.12148157907134, 'ACC-book': 58.14697682889244, 'ACC-clock': 75.63657234532376, 'ACC-vase': 68.42526268709283, 'ACC-scissors': 36.19248638771818, 'ACC-teddy bear': 81.68126760179858, 'ACC-hair drier': 40.675433353257624, 'ACC-toothbrush': 81.04847116052815, 'ACC-banner': 74.65622975735403, 'ACC-blanket': 15.607137339718832, 'ACC-bridge': 56.100294078807025, 'ACC-cardboard': 58.79159517231351, 'ACC-counter': 53.36949880139868, 'ACC-curtain': 73.22699153567225, 'ACC-door-stuff': 63.555162215657326, 'ACC-floor-wood': 77.66514604544919, 'ACC-flower': 67.31328782633365, 'ACC-fruit': 54.08672093928124, 'ACC-gravel': 31.886649603470445, 'ACC-house': 27.630738624415503, 'ACC-light': 56.37823948244842, 'ACC-mirror-stuff': 76.65166942931565, 'ACC-net': 63.30087595027636, 'ACC-pillow': 24.053242800157342, 'ACC-platform': 45.62920275920534, 'ACC-playingfield': 82.56908198131258, 'ACC-railroad': 77.48175183435791, 'ACC-river': 82.73271370112532, 'ACC-road': 84.61178149124139, 'ACC-roof': 21.86234603364748, 'ACC-sand': 69.07643083312306, 'ACC-sea': 90.5846600286726, 'ACC-shelf': 63.52636871242432, 'ACC-snow': 95.55334875070898, 'ACC-stairs': 35.887009688295876, 'ACC-tent': 9.901834294532664, 'ACC-towel': 40.70359197658906, 'ACC-wall-brick': 63.55011800921392, 'ACC-wall-stone': 34.26047543172129, 'ACC-wall-tile': 73.50695462507001, 'ACC-wall-wood': 49.71896686235836, 'ACC-water-other': 24.254358771058897, 'ACC-window-blind': 57.03078759760204, 'ACC-window-other': 73.48526266301428, 'ACC-tree-merged': 89.1274171268346, 'ACC-fence-merged': 72.88637171342472, 'ACC-ceiling-merged': 82.74367549375725, 'ACC-sky-other-merged': 96.53110538903174, 'ACC-cabinet-merged': 75.85861306448203, 'ACC-table-merged': 54.83100784674575, 'ACC-floor-other-merged': 61.50780793415878, 'ACC-pavement-merged': 67.36682951908269, 'ACC-mountain-merged': 66.65295405138309, 'ACC-grass-merged': 83.0723480596934, 'ACC-dirt-merged': 76.52758803209257, 'ACC-paper-merged': 41.37256634489903, 'ACC-food-other-merged': 45.473607892335096, 'ACC-building-other-merged': 71.29159299199975, 'ACC-rock-merged': 81.86292511841575, 'ACC-wall-other-merged': 82.35804240949378, 'ACC-rug-merged': 80.72485269384882})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.565993561603746, 'noc@0.8': 2.9622475856014048, 'noc@0.85': 3.614281533508926, 'noc@0.9': 4.671934445419959, 'miou@iter1': 0.8342226557052027}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.8900146484375, 'precision@0.6': 67.78079986572266, 'precision@0.7': 62.92265701293945, 'precision@0.8': 52.39020538330078, 'precision@0.9': 26.661483764648438, 'cIoU': 56.48704147338867, 'mIoU': 62.42222595214844}}}
INFO:trainer.default_trainer:This epoch takes 1:29:28.042164
INFO:trainer.default_trainer:PROGRESS: 22.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 11 training.
INFO:trainer.default_trainer:epochs[    11] optim steps[20100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20357/0.91345, loss_mask_bce_0: 0.46829/0.33603, loss_mask_dice_0: 0.94065/1.16719, loss_spatial_bce_0: 0.10394/0.09219, loss_spatial_dice_0: 0.34827/0.22022, loss_spatial_ce_0: 0.35232/0.08073, loss_grounding_bce_0: 0.00152/0.08645, loss_grounding_dice_0: 0.07514/0.17953, loss_grounding_ce_0: 0.02839/0.27667, loss_mask_ce_1: 1.21888/0.91381, loss_mask_bce_1: 0.48351/0.33673, loss_mask_dice_1: 0.94543/1.17470, loss_spatial_bce_1: 0.10177/0.09311, loss_spatial_dice_1: 0.36153/0.22465, loss_spatial_ce_1: 0.02987/0.08640, loss_grounding_bce_1: 0.00262/0.08653, loss_grounding_dice_1: 0.09694/0.18019, loss_grounding_ce_1: 0.11716/0.27807, loss_mask_ce_2: 1.17685/0.92142, loss_mask_bce_2: 0.48825/0.33708, loss_mask_dice_2: 0.93501/1.17396, loss_spatial_bce_2: 0.09761/0.09321, loss_spatial_dice_2: 0.35933/0.22556, loss_spatial_ce_2: 0.02463/0.09026, loss_grounding_bce_2: 0.00226/0.08657, loss_grounding_dice_2: 0.12892/0.17963, loss_grounding_ce_2: 0.02545/0.28156, loss_mask_ce_3: 1.30267/0.92940, loss_mask_bce_3: 0.47836/0.33793, loss_mask_dice_3: 0.86625/1.17114, loss_spatial_bce_3: 0.09086/0.09405, loss_spatial_dice_3: 0.36720/0.22631, loss_spatial_ce_3: 0.02483/0.09454, loss_grounding_bce_3: 0.00185/0.08667, loss_grounding_dice_3: 0.19183/0.17947, loss_grounding_ce_3: 0.03347/0.28301, loss_mask_ce_4: 1.21817/0.92803, loss_mask_bce_4: 0.49516/0.33939, loss_mask_dice_4: 0.90437/1.19295, loss_spatial_bce_4: 0.10748/0.09786, loss_spatial_dice_4: 0.38204/0.23555, loss_spatial_ce_4: 0.01834/0.11074, loss_grounding_bce_4: 0.00296/0.08724, loss_grounding_dice_4: 0.13458/0.18223, loss_grounding_ce_4: 0.20482/0.28521, loss_mask_ce_5: 1.08121/0.94267, loss_mask_bce_5: 0.51828/0.34156, loss_mask_dice_5: 1.15262/1.19784, loss_spatial_bce_5: 0.11041/0.09892, loss_spatial_dice_5: 0.36508/0.23851, loss_spatial_ce_5: 0.02475/0.12415, loss_grounding_bce_5: 0.00224/0.08764, loss_grounding_dice_5: 0.18917/0.18338, loss_grounding_ce_5: 0.18139/0.29813, loss_mask_ce_6: 1.36560/0.98010, loss_mask_bce_6: 0.51497/0.34433, loss_mask_dice_6: 1.03636/1.20086, loss_spatial_bce_6: 0.11638/0.10434, loss_spatial_dice_6: 0.38145/0.24090, loss_spatial_ce_6: 0.14207/0.14839, loss_grounding_bce_6: 0.00156/0.08842, loss_grounding_dice_6: 0.11545/0.18352, loss_grounding_ce_6: 0.16829/0.31648, loss_mask_ce_7: 0.93564/1.02217, loss_mask_bce_7: 0.51295/0.35209, loss_mask_dice_7: 1.04539/1.25638, loss_spatial_bce_7: 0.10201/0.11325, loss_spatial_dice_7: 0.36845/0.26812, loss_spatial_ce_7: 0.07325/0.18721, loss_grounding_bce_7: 0.00250/0.09036, loss_grounding_dice_7: 0.16469/0.19079, loss_grounding_ce_7: 0.19965/0.35070, loss_mask_ce_8: 1.19727/1.13286, loss_mask_bce_8: 0.51189/0.36562, loss_mask_dice_8: 0.99845/1.33157, loss_spatial_bce_8: 0.10067/0.13443, loss_spatial_dice_8: 0.35169/0.30873, loss_spatial_ce_8: 0.19690/0.24384, loss_grounding_bce_8: 0.00205/0.09384, loss_grounding_dice_8: 0.13393/0.20224, loss_grounding_ce_8: 0.03131/0.42291, loss_mask_ce_9: 3.27152/3.69443, loss_mask_bce_9: 0.57430/0.39242, loss_mask_dice_9: 1.36819/1.90661, loss_spatial_bce_9: 0.20056/0.33685, loss_spatial_dice_9: 0.82515/0.82465, loss_spatial_ce_9: 1.27249/1.51997, loss_grounding_bce_9: 0.00306/0.10515, loss_grounding_dice_9: 0.26926/0.28201, loss_grounding_ce_9: 0.46221/0.69975] items per batch[64] items per second[0.13] total items[1286400] mini batches[ 20100] memory[7341] epoch remaining[1:55:50]
INFO:trainer.default_trainer:epochs[    11] optim steps[20200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74410/0.91317, loss_mask_bce_0: 0.34696/0.33608, loss_mask_dice_0: 1.07140/1.16707, loss_spatial_bce_0: 0.09107/0.09220, loss_spatial_dice_0: 0.23716/0.22012, loss_spatial_ce_0: 0.02656/0.08060, loss_grounding_bce_0: 0.03376/0.08646, loss_grounding_dice_0: 0.38180/0.17951, loss_grounding_ce_0: 0.37171/0.27631, loss_mask_ce_1: 0.49764/0.91362, loss_mask_bce_1: 0.35791/0.33677, loss_mask_dice_1: 1.22595/1.17457, loss_spatial_bce_1: 0.08799/0.09312, loss_spatial_dice_1: 0.25708/0.22455, loss_spatial_ce_1: 0.20350/0.08626, loss_grounding_bce_1: 0.03169/0.08654, loss_grounding_dice_1: 0.27790/0.18016, loss_grounding_ce_1: 0.49394/0.27771, loss_mask_ce_2: 0.47749/0.92120, loss_mask_bce_2: 0.36086/0.33712, loss_mask_dice_2: 1.34941/1.17376, loss_spatial_bce_2: 0.08647/0.09321, loss_spatial_dice_2: 0.25995/0.22546, loss_spatial_ce_2: 0.32499/0.09010, loss_grounding_bce_2: 0.03374/0.08659, loss_grounding_dice_2: 0.35945/0.17959, loss_grounding_ce_2: 0.36273/0.28124, loss_mask_ce_3: 0.55284/0.92920, loss_mask_bce_3: 0.35820/0.33798, loss_mask_dice_3: 1.17839/1.17097, loss_spatial_bce_3: 0.08554/0.09405, loss_spatial_dice_3: 0.29617/0.22621, loss_spatial_ce_3: 0.11062/0.09441, loss_grounding_bce_3: 0.03176/0.08669, loss_grounding_dice_3: 0.24485/0.17944, loss_grounding_ce_3: 0.41829/0.28268, loss_mask_ce_4: 0.72174/0.92787, loss_mask_bce_4: 0.36122/0.33942, loss_mask_dice_4: 0.98845/1.19274, loss_spatial_bce_4: 0.09130/0.09786, loss_spatial_dice_4: 0.28433/0.23545, loss_spatial_ce_4: 0.03601/0.11056, loss_grounding_bce_4: 0.03049/0.08725, loss_grounding_dice_4: 0.40060/0.18220, loss_grounding_ce_4: 0.38321/0.28492, loss_mask_ce_5: 0.53064/0.94249, loss_mask_bce_5: 0.36692/0.34159, loss_mask_dice_5: 1.22978/1.19766, loss_spatial_bce_5: 0.08896/0.09894, loss_spatial_dice_5: 0.22871/0.23841, loss_spatial_ce_5: 0.07636/0.12393, loss_grounding_bce_5: 0.03551/0.08766, loss_grounding_dice_5: 0.39759/0.18333, loss_grounding_ce_5: 0.36999/0.29791, loss_mask_ce_6: 0.60797/0.97982, loss_mask_bce_6: 0.35057/0.34438, loss_mask_dice_6: 1.35852/1.20069, loss_spatial_bce_6: 0.09297/0.10435, loss_spatial_dice_6: 0.26462/0.24080, loss_spatial_ce_6: 0.14426/0.14813, loss_grounding_bce_6: 0.03491/0.08843, loss_grounding_dice_6: 0.33859/0.18348, loss_grounding_ce_6: 0.39831/0.31621, loss_mask_ce_7: 0.54493/1.02181, loss_mask_bce_7: 0.35635/0.35214, loss_mask_dice_7: 1.42838/1.25624, loss_spatial_bce_7: 0.09579/0.11327, loss_spatial_dice_7: 0.26628/0.26799, loss_spatial_ce_7: 0.14060/0.18696, loss_grounding_bce_7: 0.03492/0.09036, loss_grounding_dice_7: 0.39197/0.19075, loss_grounding_ce_7: 0.33744/0.35035, loss_mask_ce_8: 0.90185/1.13256, loss_mask_bce_8: 0.34597/0.36565, loss_mask_dice_8: 1.27672/1.33139, loss_spatial_bce_8: 0.12202/0.13444, loss_spatial_dice_8: 0.38309/0.30859, loss_spatial_ce_8: 0.19568/0.24360, loss_grounding_bce_8: 0.03457/0.09386, loss_grounding_dice_8: 0.32926/0.20218, loss_grounding_ce_8: 0.45572/0.42244, loss_mask_ce_9: 4.09288/3.69355, loss_mask_bce_9: 0.30306/0.39249, loss_mask_dice_9: 1.59113/1.90641, loss_spatial_bce_9: 0.26877/0.33687, loss_spatial_dice_9: 0.84098/0.82465, loss_spatial_ce_9: 1.82535/1.51998, loss_grounding_bce_9: 0.02783/0.10520, loss_grounding_dice_9: 0.37795/0.28195, loss_grounding_ce_9: 0.61915/0.69901] items per batch[64] items per second[0.23] total items[1292800] mini batches[ 20200] memory[7341] epoch remaining[1:22:14]
INFO:trainer.default_trainer:epochs[    11] optim steps[20300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83663/0.91309, loss_mask_bce_0: 0.77034/0.33612, loss_mask_dice_0: 1.21412/1.16697, loss_spatial_bce_0: 0.11147/0.09217, loss_spatial_dice_0: 0.19274/0.22006, loss_spatial_ce_0: 0.02197/0.08043, loss_grounding_bce_0: 0.10260/0.08644, loss_grounding_dice_0: 0.09969/0.17944, loss_grounding_ce_0: 0.49046/0.27619, loss_mask_ce_1: 0.92941/0.91356, loss_mask_bce_1: 0.73463/0.33682, loss_mask_dice_1: 1.23604/1.17450, loss_spatial_bce_1: 0.12153/0.09308, loss_spatial_dice_1: 0.18949/0.22448, loss_spatial_ce_1: 0.02219/0.08611, loss_grounding_bce_1: 0.11537/0.08652, loss_grounding_dice_1: 0.10427/0.18008, loss_grounding_ce_1: 0.55922/0.27757, loss_mask_ce_2: 0.95213/0.92099, loss_mask_bce_2: 0.73960/0.33717, loss_mask_dice_2: 1.25913/1.17369, loss_spatial_bce_2: 0.11753/0.09317, loss_spatial_dice_2: 0.19346/0.22540, loss_spatial_ce_2: 0.02297/0.08992, loss_grounding_bce_2: 0.10747/0.08658, loss_grounding_dice_2: 0.10538/0.17952, loss_grounding_ce_2: 0.49240/0.28104, loss_mask_ce_3: 0.98840/0.92911, loss_mask_bce_3: 0.74880/0.33801, loss_mask_dice_3: 1.26294/1.17089, loss_spatial_bce_3: 0.11219/0.09402, loss_spatial_dice_3: 0.19037/0.22614, loss_spatial_ce_3: 0.02327/0.09425, loss_grounding_bce_3: 0.08758/0.08667, loss_grounding_dice_3: 0.09160/0.17937, loss_grounding_ce_3: 0.42045/0.28248, loss_mask_ce_4: 0.95692/0.92779, loss_mask_bce_4: 0.74125/0.33944, loss_mask_dice_4: 1.25398/1.19264, loss_spatial_bce_4: 0.11977/0.09782, loss_spatial_dice_4: 0.20262/0.23540, loss_spatial_ce_4: 0.04706/0.11043, loss_grounding_bce_4: 0.09745/0.08723, loss_grounding_dice_4: 0.09691/0.18211, loss_grounding_ce_4: 0.45146/0.28477, loss_mask_ce_5: 0.74887/0.94236, loss_mask_bce_5: 0.85270/0.34165, loss_mask_dice_5: 1.30707/1.19755, loss_spatial_bce_5: 0.11738/0.09891, loss_spatial_dice_5: 0.21121/0.23836, loss_spatial_ce_5: 0.06105/0.12380, loss_grounding_bce_5: 0.10145/0.08766, loss_grounding_dice_5: 0.10073/0.18325, loss_grounding_ce_5: 0.41444/0.29782, loss_mask_ce_6: 0.86004/0.97969, loss_mask_bce_6: 0.82636/0.34443, loss_mask_dice_6: 1.30619/1.20064, loss_spatial_bce_6: 0.11037/0.10432, loss_spatial_dice_6: 0.20528/0.24074, loss_spatial_ce_6: 0.07851/0.14799, loss_grounding_bce_6: 0.08845/0.08844, loss_grounding_dice_6: 0.09774/0.18342, loss_grounding_ce_6: 0.36358/0.31605, loss_mask_ce_7: 1.12184/1.02163, loss_mask_bce_7: 0.77002/0.35217, loss_mask_dice_7: 1.31159/1.25609, loss_spatial_bce_7: 0.11999/0.11324, loss_spatial_dice_7: 0.20288/0.26794, loss_spatial_ce_7: 0.06813/0.18674, loss_grounding_bce_7: 0.08774/0.09037, loss_grounding_dice_7: 0.09907/0.19067, loss_grounding_ce_7: 0.31288/0.35010, loss_mask_ce_8: 1.22973/1.13242, loss_mask_bce_8: 0.84197/0.36566, loss_mask_dice_8: 1.40848/1.33118, loss_spatial_bce_8: 0.13835/0.13440, loss_spatial_dice_8: 0.26536/0.30856, loss_spatial_ce_8: 0.15842/0.24341, loss_grounding_bce_8: 0.08398/0.09384, loss_grounding_dice_8: 0.09062/0.20210, loss_grounding_ce_8: 0.24409/0.42221, loss_mask_ce_9: 3.46716/3.69347, loss_mask_bce_9: 0.91619/0.39247, loss_mask_dice_9: 1.92315/1.90586, loss_spatial_bce_9: 0.26192/0.33694, loss_spatial_dice_9: 0.81380/0.82465, loss_spatial_ce_9: 1.67986/1.51975, loss_grounding_bce_9: 0.08442/0.10518, loss_grounding_dice_9: 0.11467/0.28183, loss_grounding_ce_9: 0.50736/0.69887] items per batch[64] items per second[0.23] total items[1299200] mini batches[ 20300] memory[7341] epoch remaining[1:15:50]
INFO:trainer.default_trainer:epochs[    11] optim steps[20400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62372/0.91333, loss_mask_bce_0: 0.20539/0.33621, loss_mask_dice_0: 0.51671/1.16728, loss_spatial_bce_0: 0.07602/0.09213, loss_spatial_dice_0: 0.17894/0.22001, loss_spatial_ce_0: 0.00354/0.08027, loss_grounding_bce_0: 0.06005/0.08641, loss_grounding_dice_0: 0.16161/0.17946, loss_grounding_ce_0: 0.35248/0.27635, loss_mask_ce_1: 0.65376/0.91378, loss_mask_bce_1: 0.20447/0.33689, loss_mask_dice_1: 0.52407/1.17476, loss_spatial_bce_1: 0.08389/0.09305, loss_spatial_dice_1: 0.19422/0.22444, loss_spatial_ce_1: 0.00587/0.08597, loss_grounding_bce_1: 0.05715/0.08649, loss_grounding_dice_1: 0.16610/0.18012, loss_grounding_ce_1: 0.36861/0.27776, loss_mask_ce_2: 0.64768/0.92125, loss_mask_bce_2: 0.20023/0.33726, loss_mask_dice_2: 0.53619/1.17394, loss_spatial_bce_2: 0.08366/0.09315, loss_spatial_dice_2: 0.18809/0.22537, loss_spatial_ce_2: 0.00458/0.08975, loss_grounding_bce_2: 0.05734/0.08654, loss_grounding_dice_2: 0.16560/0.17955, loss_grounding_ce_2: 0.37936/0.28125, loss_mask_ce_3: 0.64232/0.92933, loss_mask_bce_3: 0.20332/0.33811, loss_mask_dice_3: 0.50370/1.17113, loss_spatial_bce_3: 0.07935/0.09400, loss_spatial_dice_3: 0.18071/0.22610, loss_spatial_ce_3: 0.00715/0.09404, loss_grounding_bce_3: 0.05925/0.08663, loss_grounding_dice_3: 0.15797/0.17940, loss_grounding_ce_3: 0.43686/0.28271, loss_mask_ce_4: 0.67683/0.92806, loss_mask_bce_4: 0.21515/0.33954, loss_mask_dice_4: 0.53670/1.19295, loss_spatial_bce_4: 0.08582/0.09779, loss_spatial_dice_4: 0.20450/0.23540, loss_spatial_ce_4: 0.01269/0.11028, loss_grounding_bce_4: 0.05685/0.08721, loss_grounding_dice_4: 0.17320/0.18213, loss_grounding_ce_4: 0.42669/0.28497, loss_mask_ce_5: 0.66409/0.94264, loss_mask_bce_5: 0.19938/0.34175, loss_mask_dice_5: 0.51445/1.19791, loss_spatial_bce_5: 0.08878/0.09889, loss_spatial_dice_5: 0.20997/0.23834, loss_spatial_ce_5: 0.05481/0.12367, loss_grounding_bce_5: 0.06219/0.08764, loss_grounding_dice_5: 0.17151/0.18327, loss_grounding_ce_5: 0.39594/0.29805, loss_mask_ce_6: 0.68712/0.98001, loss_mask_bce_6: 0.20860/0.34453, loss_mask_dice_6: 0.49829/1.20085, loss_spatial_bce_6: 0.08201/0.10430, loss_spatial_dice_6: 0.18718/0.24074, loss_spatial_ce_6: 0.09882/0.14786, loss_grounding_bce_6: 0.06169/0.08841, loss_grounding_dice_6: 0.17344/0.18342, loss_grounding_ce_6: 0.45378/0.31628, loss_mask_ce_7: 0.81582/1.02205, loss_mask_bce_7: 0.21945/0.35225, loss_mask_dice_7: 0.56318/1.25629, loss_spatial_bce_7: 0.08549/0.11323, loss_spatial_dice_7: 0.19869/0.26793, loss_spatial_ce_7: 0.06322/0.18660, loss_grounding_bce_7: 0.06773/0.09033, loss_grounding_dice_7: 0.17264/0.19069, loss_grounding_ce_7: 0.43250/0.35033, loss_mask_ce_8: 1.24249/1.13275, loss_mask_bce_8: 0.22115/0.36577, loss_mask_dice_8: 0.56762/1.33153, loss_spatial_bce_8: 0.11722/0.13439, loss_spatial_dice_8: 0.22785/0.30856, loss_spatial_ce_8: 0.15641/0.24334, loss_grounding_bce_8: 0.06572/0.09380, loss_grounding_dice_8: 0.18624/0.20216, loss_grounding_ce_8: 0.50444/0.42225, loss_mask_ce_9: 2.56957/3.69383, loss_mask_bce_9: 0.47232/0.39268, loss_mask_dice_9: 1.07861/1.90639, loss_spatial_bce_9: 0.47582/0.33688, loss_spatial_dice_9: 0.87206/0.82469, loss_spatial_ce_9: 1.59074/1.51939, loss_grounding_bce_9: 0.23280/0.10517, loss_grounding_dice_9: 0.34002/0.28192, loss_grounding_ce_9: 0.13566/0.69890] items per batch[64] items per second[0.23] total items[1305600] mini batches[ 20400] memory[7341] epoch remaining[1:11:06]
INFO:trainer.default_trainer:epochs[    11] optim steps[20500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77121/0.91341, loss_mask_bce_0: 0.22693/0.33610, loss_mask_dice_0: 1.41966/1.16719, loss_spatial_bce_0: 0.03099/0.09208, loss_spatial_dice_0: 0.20798/0.21992, loss_spatial_ce_0: 0.02852/0.08014, loss_grounding_bce_0: 0.02925/0.08640, loss_grounding_dice_0: 0.15005/0.17944, loss_grounding_ce_0: 0.57892/0.27639, loss_mask_ce_1: 0.79578/0.91390, loss_mask_bce_1: 0.22431/0.33679, loss_mask_dice_1: 1.38654/1.17469, loss_spatial_bce_1: 0.03053/0.09299, loss_spatial_dice_1: 0.23169/0.22434, loss_spatial_ce_1: 0.07541/0.08583, loss_grounding_bce_1: 0.03082/0.08648, loss_grounding_dice_1: 0.15863/0.18011, loss_grounding_ce_1: 0.49014/0.27779, loss_mask_ce_2: 0.79460/0.92140, loss_mask_bce_2: 0.21751/0.33715, loss_mask_dice_2: 1.23522/1.17377, loss_spatial_bce_2: 0.03118/0.09309, loss_spatial_dice_2: 0.25100/0.22527, loss_spatial_ce_2: 0.23191/0.08958, loss_grounding_bce_2: 0.02776/0.08653, loss_grounding_dice_2: 0.11841/0.17954, loss_grounding_ce_2: 0.51640/0.28136, loss_mask_ce_3: 0.78264/0.92946, loss_mask_bce_3: 0.21434/0.33801, loss_mask_dice_3: 1.45879/1.17113, loss_spatial_bce_3: 0.03049/0.09395, loss_spatial_dice_3: 0.19791/0.22600, loss_spatial_ce_3: 0.02425/0.09385, loss_grounding_bce_3: 0.02777/0.08662, loss_grounding_dice_3: 0.14323/0.17939, loss_grounding_ce_3: 0.51209/0.28274, loss_mask_ce_4: 0.80450/0.92816, loss_mask_bce_4: 0.20666/0.33946, loss_mask_dice_4: 1.36945/1.19294, loss_spatial_bce_4: 0.03137/0.09775, loss_spatial_dice_4: 0.24498/0.23531, loss_spatial_ce_4: 0.08688/0.11016, loss_grounding_bce_4: 0.02727/0.08720, loss_grounding_dice_4: 0.12503/0.18212, loss_grounding_ce_4: 0.49781/0.28502, loss_mask_ce_5: 0.85407/0.94261, loss_mask_bce_5: 0.21527/0.34169, loss_mask_dice_5: 1.53553/1.19795, loss_spatial_bce_5: 0.03329/0.09886, loss_spatial_dice_5: 0.21325/0.23828, loss_spatial_ce_5: 0.04103/0.12348, loss_grounding_bce_5: 0.02791/0.08763, loss_grounding_dice_5: 0.12938/0.18327, loss_grounding_ce_5: 0.52933/0.29810, loss_mask_ce_6: 0.81545/0.98009, loss_mask_bce_6: 0.20753/0.34448, loss_mask_dice_6: 1.55617/1.20086, loss_spatial_bce_6: 0.03306/0.10427, loss_spatial_dice_6: 0.23983/0.24066, loss_spatial_ce_6: 0.13289/0.14772, loss_grounding_bce_6: 0.02934/0.08841, loss_grounding_dice_6: 0.13714/0.18344, loss_grounding_ce_6: 0.51841/0.31635, loss_mask_ce_7: 0.95728/1.02216, loss_mask_bce_7: 0.22913/0.35219, loss_mask_dice_7: 1.50377/1.25622, loss_spatial_bce_7: 0.03439/0.11321, loss_spatial_dice_7: 0.31390/0.26786, loss_spatial_ce_7: 0.19450/0.18648, loss_grounding_bce_7: 0.02830/0.09034, loss_grounding_dice_7: 0.15040/0.19069, loss_grounding_ce_7: 0.51934/0.35023, loss_mask_ce_8: 1.24583/1.13282, loss_mask_bce_8: 0.22067/0.36572, loss_mask_dice_8: 1.40181/1.33149, loss_spatial_bce_8: 0.04504/0.13436, loss_spatial_dice_8: 0.34745/0.30847, loss_spatial_ce_8: 0.17980/0.24332, loss_grounding_bce_8: 0.02854/0.09380, loss_grounding_dice_8: 0.12479/0.20219, loss_grounding_ce_8: 0.47239/0.42201, loss_mask_ce_9: 3.21764/3.69352, loss_mask_bce_9: 0.26325/0.39260, loss_mask_dice_9: 2.34610/1.90630, loss_spatial_bce_9: 0.14247/0.33691, loss_spatial_dice_9: 0.84144/0.82469, loss_spatial_ce_9: 2.21718/1.51938, loss_grounding_bce_9: 0.03012/0.10517, loss_grounding_dice_9: 0.26600/0.28196, loss_grounding_ce_9: 0.54397/0.69849] items per batch[64] items per second[0.22] total items[1312000] mini batches[ 20500] memory[7341] epoch remaining[1:06:43]
INFO:trainer.default_trainer:epochs[    11] optim steps[20600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38997/0.91313, loss_mask_bce_0: 0.22262/0.33610, loss_mask_dice_0: 0.77178/1.16646, loss_spatial_bce_0: 0.03827/0.09205, loss_spatial_dice_0: 0.12506/0.21980, loss_spatial_ce_0: 0.00086/0.08014, loss_grounding_bce_0: 0.08136/0.08640, loss_grounding_dice_0: 0.13048/0.17942, loss_grounding_ce_0: 0.00378/0.27641, loss_mask_ce_1: 0.37298/0.91362, loss_mask_bce_1: 0.22129/0.33680, loss_mask_dice_1: 0.80558/1.17394, loss_spatial_bce_1: 0.03654/0.09295, loss_spatial_dice_1: 0.13335/0.22421, loss_spatial_ce_1: 0.00068/0.08582, loss_grounding_bce_1: 0.07724/0.08648, loss_grounding_dice_1: 0.13407/0.18010, loss_grounding_ce_1: 0.00313/0.27780, loss_mask_ce_2: 0.36635/0.92101, loss_mask_bce_2: 0.22492/0.33717, loss_mask_dice_2: 0.77388/1.17306, loss_spatial_bce_2: 0.03750/0.09306, loss_spatial_dice_2: 0.13644/0.22515, loss_spatial_ce_2: 0.00140/0.08958, loss_grounding_bce_2: 0.08130/0.08652, loss_grounding_dice_2: 0.13254/0.17952, loss_grounding_ce_2: 0.00654/0.28141, loss_mask_ce_3: 0.39775/0.92911, loss_mask_bce_3: 0.21900/0.33802, loss_mask_dice_3: 0.72249/1.17042, loss_spatial_bce_3: 0.04018/0.09391, loss_spatial_dice_3: 0.13463/0.22585, loss_spatial_ce_3: 0.00111/0.09386, loss_grounding_bce_3: 0.07988/0.08662, loss_grounding_dice_3: 0.13083/0.17938, loss_grounding_ce_3: 0.00464/0.28279, loss_mask_ce_4: 0.38811/0.92776, loss_mask_bce_4: 0.22644/0.33948, loss_mask_dice_4: 0.76422/1.19221, loss_spatial_bce_4: 0.04183/0.09773, loss_spatial_dice_4: 0.13564/0.23520, loss_spatial_ce_4: 0.01589/0.11010, loss_grounding_bce_4: 0.08588/0.08719, loss_grounding_dice_4: 0.13615/0.18210, loss_grounding_ce_4: 0.00885/0.28508, loss_mask_ce_5: 0.36889/0.94222, loss_mask_bce_5: 0.22748/0.34170, loss_mask_dice_5: 0.79562/1.19729, loss_spatial_bce_5: 0.04133/0.09885, loss_spatial_dice_5: 0.14500/0.23817, loss_spatial_ce_5: 0.02696/0.12344, loss_grounding_bce_5: 0.08493/0.08762, loss_grounding_dice_5: 0.13448/0.18327, loss_grounding_ce_5: 0.00970/0.29815, loss_mask_ce_6: 0.41447/0.97984, loss_mask_bce_6: 0.23686/0.34448, loss_mask_dice_6: 0.80433/1.20016, loss_spatial_bce_6: 0.04798/0.10428, loss_spatial_dice_6: 0.18963/0.24058, loss_spatial_ce_6: 0.08448/0.14771, loss_grounding_bce_6: 0.08771/0.08840, loss_grounding_dice_6: 0.13358/0.18343, loss_grounding_ce_6: 0.00730/0.31639, loss_mask_ce_7: 0.41151/1.02180, loss_mask_bce_7: 0.22230/0.35220, loss_mask_dice_7: 0.79328/1.25546, loss_spatial_bce_7: 0.05712/0.11321, loss_spatial_dice_7: 0.21956/0.26773, loss_spatial_ce_7: 0.07164/0.18645, loss_grounding_bce_7: 0.08358/0.09033, loss_grounding_dice_7: 0.12692/0.19069, loss_grounding_ce_7: 0.01218/0.35026, loss_mask_ce_8: 0.59488/1.13247, loss_mask_bce_8: 0.24828/0.36577, loss_mask_dice_8: 0.93466/1.33076, loss_spatial_bce_8: 0.05985/0.13442, loss_spatial_dice_8: 0.19989/0.30838, loss_spatial_ce_8: 0.13581/0.24326, loss_grounding_bce_8: 0.08976/0.09378, loss_grounding_dice_8: 0.13086/0.20220, loss_grounding_ce_8: 0.03456/0.42198, loss_mask_ce_9: 3.29546/3.69332, loss_mask_bce_9: 0.29716/0.39265, loss_mask_dice_9: 1.39944/1.90569, loss_spatial_bce_9: 0.23805/0.33703, loss_spatial_dice_9: 0.83029/0.82464, loss_spatial_ce_9: 1.48960/1.51895, loss_grounding_bce_9: 0.14313/0.10517, loss_grounding_dice_9: 0.23944/0.28196, loss_grounding_ce_9: 0.18336/0.69832] items per batch[64] items per second[0.22] total items[1318400] mini batches[ 20600] memory[7341] epoch remaining[1:02:31]
INFO:trainer.default_trainer:epochs[    11] optim steps[20700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20258/0.91325, loss_mask_bce_0: 0.07656/0.33624, loss_mask_dice_0: 0.08142/1.16738, loss_spatial_bce_0: 0.05265/0.09203, loss_spatial_dice_0: 0.04668/0.21974, loss_spatial_ce_0: 0.06653/0.08004, loss_grounding_bce_0: 0.02167/0.08641, loss_grounding_dice_0: 0.03554/0.17937, loss_grounding_ce_0: 0.15987/0.27644, loss_mask_ce_1: 1.33239/0.91365, loss_mask_bce_1: 0.07644/0.33694, loss_mask_dice_1: 0.08346/1.17483, loss_spatial_bce_1: 0.05258/0.09294, loss_spatial_dice_1: 0.04842/0.22415, loss_spatial_ce_1: 0.07210/0.08571, loss_grounding_bce_1: 0.02193/0.08649, loss_grounding_dice_1: 0.03754/0.18006, loss_grounding_ce_1: 0.17361/0.27784, loss_mask_ce_2: 1.32977/0.92115, loss_mask_bce_2: 0.06975/0.33731, loss_mask_dice_2: 0.08407/1.17392, loss_spatial_bce_2: 0.05414/0.09305, loss_spatial_dice_2: 0.04809/0.22509, loss_spatial_ce_2: 0.07332/0.08948, loss_grounding_bce_2: 0.02141/0.08653, loss_grounding_dice_2: 0.03979/0.17946, loss_grounding_ce_2: 0.19415/0.28156, loss_mask_ce_3: 1.35573/0.92920, loss_mask_bce_3: 0.07297/0.33817, loss_mask_dice_3: 0.08353/1.17132, loss_spatial_bce_3: 0.05636/0.09390, loss_spatial_dice_3: 0.05111/0.22580, loss_spatial_ce_3: 0.06689/0.09376, loss_grounding_bce_3: 0.02157/0.08663, loss_grounding_dice_3: 0.03726/0.17932, loss_grounding_ce_3: 0.22023/0.28286, loss_mask_ce_4: 1.43108/0.92782, loss_mask_bce_4: 0.07182/0.33966, loss_mask_dice_4: 0.07212/1.19316, loss_spatial_bce_4: 0.05707/0.09773, loss_spatial_dice_4: 0.04683/0.23517, loss_spatial_ce_4: 0.06631/0.11001, loss_grounding_bce_4: 0.02410/0.08720, loss_grounding_dice_4: 0.03892/0.18208, loss_grounding_ce_4: 0.21513/0.28523, loss_mask_ce_5: 1.35509/0.94221, loss_mask_bce_5: 0.07337/0.34187, loss_mask_dice_5: 0.08252/1.19824, loss_spatial_bce_5: 0.06159/0.09886, loss_spatial_dice_5: 0.05468/0.23816, loss_spatial_ce_5: 0.08079/0.12337, loss_grounding_bce_5: 0.02203/0.08763, loss_grounding_dice_5: 0.03563/0.18326, loss_grounding_ce_5: 0.23985/0.29823, loss_mask_ce_6: 1.22817/0.97984, loss_mask_bce_6: 0.07259/0.34465, loss_mask_dice_6: 0.07901/1.20109, loss_spatial_bce_6: 0.06089/0.10428, loss_spatial_dice_6: 0.05159/0.24055, loss_spatial_ce_6: 0.09488/0.14769, loss_grounding_bce_6: 0.02345/0.08840, loss_grounding_dice_6: 0.04069/0.18338, loss_grounding_ce_6: 0.20364/0.31631, loss_mask_ce_7: 1.21477/1.02195, loss_mask_bce_7: 0.06773/0.35234, loss_mask_dice_7: 0.07329/1.25640, loss_spatial_bce_7: 0.06166/0.11327, loss_spatial_dice_7: 0.05010/0.26771, loss_spatial_ce_7: 0.09632/0.18637, loss_grounding_bce_7: 0.02160/0.09033, loss_grounding_dice_7: 0.03266/0.19066, loss_grounding_ce_7: 0.24966/0.35008, loss_mask_ce_8: 0.80464/1.13267, loss_mask_bce_8: 0.13779/0.36596, loss_mask_dice_8: 0.10573/1.33178, loss_spatial_bce_8: 0.06738/0.13449, loss_spatial_dice_8: 0.06227/0.30839, loss_spatial_ce_8: 0.17035/0.24317, loss_grounding_bce_8: 0.02229/0.09381, loss_grounding_dice_8: 0.04050/0.20214, loss_grounding_ce_8: 0.22298/0.42187, loss_mask_ce_9: 2.68084/3.69348, loss_mask_bce_9: 0.14686/0.39279, loss_mask_dice_9: 0.13767/1.90732, loss_spatial_bce_9: 0.41894/0.33709, loss_spatial_dice_9: 0.72495/0.82463, loss_spatial_ce_9: 1.67649/1.51889, loss_grounding_bce_9: 0.02905/0.10518, loss_grounding_dice_9: 0.05979/0.28188, loss_grounding_ce_9: 0.56800/0.69809] items per batch[64] items per second[0.23] total items[1324800] mini batches[ 20700] memory[7341] epoch remaining[0:57:46]
INFO:trainer.default_trainer:epochs[    11] optim steps[20800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.36437/0.91311, loss_mask_bce_0: 0.29897/0.33617, loss_mask_dice_0: 0.52049/1.16663, loss_spatial_bce_0: 0.10907/0.09204, loss_spatial_dice_0: 0.19591/0.21963, loss_spatial_ce_0: 0.28920/0.07992, loss_grounding_bce_0: 0.10492/0.08640, loss_grounding_dice_0: 0.11090/0.17929, loss_grounding_ce_0: 0.15654/0.27637, loss_mask_ce_1: 1.10588/0.91348, loss_mask_bce_1: 0.30217/0.33688, loss_mask_dice_1: 0.55096/1.17402, loss_spatial_bce_1: 0.11203/0.09295, loss_spatial_dice_1: 0.20101/0.22404, loss_spatial_ce_1: 0.32303/0.08558, loss_grounding_bce_1: 0.10210/0.08647, loss_grounding_dice_1: 0.11287/0.17998, loss_grounding_ce_1: 0.15296/0.27775, loss_mask_ce_2: 1.07367/0.92105, loss_mask_bce_2: 0.29202/0.33725, loss_mask_dice_2: 0.51425/1.17318, loss_spatial_bce_2: 0.10855/0.09305, loss_spatial_dice_2: 0.19921/0.22497, loss_spatial_ce_2: 0.23110/0.08936, loss_grounding_bce_2: 0.09640/0.08651, loss_grounding_dice_2: 0.11241/0.17938, loss_grounding_ce_2: 0.14745/0.28146, loss_mask_ce_3: 1.26391/0.92902, loss_mask_bce_3: 0.27632/0.33811, loss_mask_dice_3: 0.49317/1.17048, loss_spatial_bce_3: 0.11424/0.09390, loss_spatial_dice_3: 0.20865/0.22567, loss_spatial_ce_3: 0.21852/0.09362, loss_grounding_bce_3: 0.10150/0.08662, loss_grounding_dice_3: 0.11486/0.17924, loss_grounding_ce_3: 0.15742/0.28272, loss_mask_ce_4: 1.04948/0.92755, loss_mask_bce_4: 0.27503/0.33961, loss_mask_dice_4: 0.50764/1.19236, loss_spatial_bce_4: 0.10377/0.09773, loss_spatial_dice_4: 0.20100/0.23507, loss_spatial_ce_4: 0.27282/0.10986, loss_grounding_bce_4: 0.09335/0.08719, loss_grounding_dice_4: 0.10579/0.18200, loss_grounding_ce_4: 0.14808/0.28511, loss_mask_ce_5: 1.04497/0.94200, loss_mask_bce_5: 0.27906/0.34183, loss_mask_dice_5: 0.52513/1.19751, loss_spatial_bce_5: 0.12291/0.09886, loss_spatial_dice_5: 0.20047/0.23806, loss_spatial_ce_5: 0.17606/0.12319, loss_grounding_bce_5: 0.08974/0.08761, loss_grounding_dice_5: 0.10585/0.18317, loss_grounding_ce_5: 0.16633/0.29807, loss_mask_ce_6: 1.32957/0.97964, loss_mask_bce_6: 0.29197/0.34460, loss_mask_dice_6: 0.57139/1.20030, loss_spatial_bce_6: 0.11140/0.10427, loss_spatial_dice_6: 0.18062/0.24044, loss_spatial_ce_6: 0.23526/0.14755, loss_grounding_bce_6: 0.09765/0.08838, loss_grounding_dice_6: 0.11523/0.18329, loss_grounding_ce_6: 0.16617/0.31613, loss_mask_ce_7: 1.13882/1.02183, loss_mask_bce_7: 0.29197/0.35230, loss_mask_dice_7: 0.52797/1.25558, loss_spatial_bce_7: 0.14125/0.11327, loss_spatial_dice_7: 0.20917/0.26761, loss_spatial_ce_7: 0.25927/0.18619, loss_grounding_bce_7: 0.10304/0.09031, loss_grounding_dice_7: 0.11280/0.19057, loss_grounding_ce_7: 0.15655/0.35008, loss_mask_ce_8: 1.19998/1.13251, loss_mask_bce_8: 0.29937/0.36592, loss_mask_dice_8: 0.68720/1.33095, loss_spatial_bce_8: 0.13656/0.13447, loss_spatial_dice_8: 0.21902/0.30825, loss_spatial_ce_8: 0.32977/0.24303, loss_grounding_bce_8: 0.10381/0.09381, loss_grounding_dice_8: 0.12362/0.20204, loss_grounding_ce_8: 0.16770/0.42196, loss_mask_ce_9: 3.58062/3.69306, loss_mask_bce_9: 0.31516/0.39282, loss_mask_dice_9: 0.86937/1.90627, loss_spatial_bce_9: 0.49227/0.33716, loss_spatial_dice_9: 0.86761/0.82466, loss_spatial_ce_9: 1.26356/1.51862, loss_grounding_bce_9: 0.12727/0.10520, loss_grounding_dice_9: 0.23622/0.28181, loss_grounding_ce_9: 0.37272/0.69845] items per batch[64] items per second[0.23] total items[1331200] mini batches[ 20800] memory[7341] epoch remaining[0:52:54]
INFO:trainer.default_trainer:epochs[    11] optim steps[20900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.51084/0.91299, loss_mask_bce_0: 0.07327/0.33623, loss_mask_dice_0: 0.64254/1.16770, loss_spatial_bce_0: 0.04698/0.09203, loss_spatial_dice_0: 0.23194/0.21961, loss_spatial_ce_0: 0.02989/0.07981, loss_grounding_bce_0: 0.06700/0.08639, loss_grounding_dice_0: 0.04344/0.17935, loss_grounding_ce_0: 0.00088/0.27603, loss_mask_ce_1: 1.46618/0.91344, loss_mask_bce_1: 0.07159/0.33693, loss_mask_dice_1: 0.58682/1.17514, loss_spatial_bce_1: 0.04785/0.09295, loss_spatial_dice_1: 0.24105/0.22402, loss_spatial_ce_1: 0.02565/0.08547, loss_grounding_bce_1: 0.06960/0.08646, loss_grounding_dice_1: 0.04524/0.18003, loss_grounding_ce_1: 0.00106/0.27753, loss_mask_ce_2: 1.46099/0.92099, loss_mask_bce_2: 0.07504/0.33731, loss_mask_dice_2: 0.52223/1.17430, loss_spatial_bce_2: 0.05950/0.09305, loss_spatial_dice_2: 0.20157/0.22496, loss_spatial_ce_2: 0.02491/0.08927, loss_grounding_bce_2: 0.06932/0.08650, loss_grounding_dice_2: 0.04123/0.17943, loss_grounding_ce_2: 0.00083/0.28116, loss_mask_ce_3: 1.49664/0.92894, loss_mask_bce_3: 0.07308/0.33816, loss_mask_dice_3: 0.52809/1.17159, loss_spatial_bce_3: 0.06828/0.09389, loss_spatial_dice_3: 0.24739/0.22565, loss_spatial_ce_3: 0.01396/0.09350, loss_grounding_bce_3: 0.06956/0.08661, loss_grounding_dice_3: 0.04455/0.17928, loss_grounding_ce_3: 0.00141/0.28246, loss_mask_ce_4: 1.41795/0.92746, loss_mask_bce_4: 0.06941/0.33968, loss_mask_dice_4: 0.57034/1.19350, loss_spatial_bce_4: 0.07176/0.09774, loss_spatial_dice_4: 0.26786/0.23509, loss_spatial_ce_4: 0.00038/0.10973, loss_grounding_bce_4: 0.06938/0.08718, loss_grounding_dice_4: 0.04470/0.18205, loss_grounding_ce_4: 0.00104/0.28490, loss_mask_ce_5: 1.85547/0.94194, loss_mask_bce_5: 0.07129/0.34189, loss_mask_dice_5: 0.72107/1.19861, loss_spatial_bce_5: 0.08150/0.09885, loss_spatial_dice_5: 0.27466/0.23805, loss_spatial_ce_5: 0.00050/0.12314, loss_grounding_bce_5: 0.06536/0.08759, loss_grounding_dice_5: 0.04096/0.18320, loss_grounding_ce_5: 0.00139/0.29789, loss_mask_ce_6: 2.20626/0.97954, loss_mask_bce_6: 0.06619/0.34463, loss_mask_dice_6: 0.35431/1.20131, loss_spatial_bce_6: 0.12650/0.10426, loss_spatial_dice_6: 0.22322/0.24043, loss_spatial_ce_6: 0.00141/0.14753, loss_grounding_bce_6: 0.06861/0.08836, loss_grounding_dice_6: 0.04475/0.18331, loss_grounding_ce_6: 0.00395/0.31578, loss_mask_ce_7: 2.04609/1.02181, loss_mask_bce_7: 0.06525/0.35235, loss_mask_dice_7: 0.38673/1.25671, loss_spatial_bce_7: 0.06414/0.11324, loss_spatial_dice_7: 0.25891/0.26765, loss_spatial_ce_7: 0.01738/0.18608, loss_grounding_bce_7: 0.06431/0.09028, loss_grounding_dice_7: 0.03926/0.19062, loss_grounding_ce_7: 0.00747/0.34994, loss_mask_ce_8: 2.23575/1.13251, loss_mask_bce_8: 0.07253/0.36597, loss_mask_dice_8: 0.65787/1.33213, loss_spatial_bce_8: 0.06974/0.13450, loss_spatial_dice_8: 0.27434/0.30828, loss_spatial_ce_8: 0.11818/0.24292, loss_grounding_bce_8: 0.06907/0.09378, loss_grounding_dice_8: 0.04002/0.20207, loss_grounding_ce_8: 0.00341/0.42167, loss_mask_ce_9: 3.96067/3.69356, loss_mask_bce_9: 0.06323/0.39286, loss_mask_dice_9: 0.90699/1.90748, loss_spatial_bce_9: 0.22387/0.33718, loss_spatial_dice_9: 0.67967/0.82470, loss_spatial_ce_9: 2.20299/1.51873, loss_grounding_bce_9: 0.06734/0.10516, loss_grounding_dice_9: 0.05387/0.28182, loss_grounding_ce_9: 0.12988/0.69825] items per batch[64] items per second[0.22] total items[1337600] mini batches[ 20900] memory[7341] epoch remaining[0:48:17]
INFO:trainer.default_trainer:epochs[    11] optim steps[21000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58040/0.91262, loss_mask_bce_0: 0.61482/0.33626, loss_mask_dice_0: 1.43326/1.16727, loss_spatial_bce_0: 0.05748/0.09202, loss_spatial_dice_0: 0.11241/0.21952, loss_spatial_ce_0: 0.00306/0.07969, loss_grounding_bce_0: 0.12079/0.08642, loss_grounding_dice_0: 0.24189/0.17935, loss_grounding_ce_0: 0.83738/0.27625, loss_mask_ce_1: 0.56194/0.91303, loss_mask_bce_1: 0.61078/0.33698, loss_mask_dice_1: 1.44113/1.17471, loss_spatial_bce_1: 0.05849/0.09293, loss_spatial_dice_1: 0.11648/0.22393, loss_spatial_ce_1: 0.00285/0.08536, loss_grounding_bce_1: 0.10900/0.08650, loss_grounding_dice_1: 0.23138/0.18004, loss_grounding_ce_1: 0.90201/0.27778, loss_mask_ce_2: 0.55877/0.92061, loss_mask_bce_2: 0.60517/0.33736, loss_mask_dice_2: 1.45385/1.17383, loss_spatial_bce_2: 0.05721/0.09303, loss_spatial_dice_2: 0.10885/0.22486, loss_spatial_ce_2: 0.00578/0.08918, loss_grounding_bce_2: 0.11499/0.08655, loss_grounding_dice_2: 0.22968/0.17943, loss_grounding_ce_2: 0.70836/0.28129, loss_mask_ce_3: 0.58479/0.92860, loss_mask_bce_3: 0.58724/0.33818, loss_mask_dice_3: 1.48828/1.17110, loss_spatial_bce_3: 0.05639/0.09388, loss_spatial_dice_3: 0.11703/0.22555, loss_spatial_ce_3: 0.00693/0.09338, loss_grounding_bce_3: 0.12548/0.08665, loss_grounding_dice_3: 0.24741/0.17929, loss_grounding_ce_3: 0.69308/0.28277, loss_mask_ce_4: 0.64128/0.92710, loss_mask_bce_4: 0.59845/0.33972, loss_mask_dice_4: 1.45683/1.19309, loss_spatial_bce_4: 0.06405/0.09775, loss_spatial_dice_4: 0.12731/0.23501, loss_spatial_ce_4: 0.00894/0.10960, loss_grounding_bce_4: 0.07981/0.08721, loss_grounding_dice_4: 0.23593/0.18205, loss_grounding_ce_4: 0.95027/0.28517, loss_mask_ce_5: 0.70554/0.94157, loss_mask_bce_5: 0.61131/0.34194, loss_mask_dice_5: 1.50102/1.19822, loss_spatial_bce_5: 0.06190/0.09887, loss_spatial_dice_5: 0.12804/0.23798, loss_spatial_ce_5: 0.01226/0.12301, loss_grounding_bce_5: 0.08978/0.08763, loss_grounding_dice_5: 0.22351/0.18322, loss_grounding_ce_5: 0.68390/0.29802, loss_mask_ce_6: 0.67945/0.97914, loss_mask_bce_6: 0.58874/0.34467, loss_mask_dice_6: 1.45755/1.20089, loss_spatial_bce_6: 0.06355/0.10427, loss_spatial_dice_6: 0.12692/0.24034, loss_spatial_ce_6: 0.03515/0.14745, loss_grounding_bce_6: 0.12509/0.08841, loss_grounding_dice_6: 0.22750/0.18332, loss_grounding_ce_6: 1.03615/0.31586, loss_mask_ce_7: 0.70725/1.02152, loss_mask_bce_7: 0.58791/0.35243, loss_mask_dice_7: 1.61825/1.25627, loss_spatial_bce_7: 0.07245/0.11324, loss_spatial_dice_7: 0.15420/0.26756, loss_spatial_ce_7: 0.12625/0.18600, loss_grounding_bce_7: 0.09368/0.09033, loss_grounding_dice_7: 0.26159/0.19066, loss_grounding_ce_7: 0.92532/0.35016, loss_mask_ce_8: 1.14383/1.13230, loss_mask_bce_8: 0.61211/0.36605, loss_mask_dice_8: 1.73120/1.33169, loss_spatial_bce_8: 0.17586/0.13449, loss_spatial_dice_8: 0.21235/0.30818, loss_spatial_ce_8: 0.16850/0.24297, loss_grounding_bce_8: 0.12048/0.09382, loss_grounding_dice_8: 0.25114/0.20207, loss_grounding_ce_8: 1.13643/0.42210, loss_mask_ce_9: 4.93467/3.69324, loss_mask_bce_9: 0.95256/0.39294, loss_mask_dice_9: 3.17607/1.90664, loss_spatial_bce_9: 0.31122/0.33721, loss_spatial_dice_9: 0.91861/0.82469, loss_spatial_ce_9: 1.39565/1.51850, loss_grounding_bce_9: 0.18997/0.10521, loss_grounding_dice_9: 0.51485/0.28183, loss_grounding_ce_9: 1.34612/0.69874] items per batch[64] items per second[0.23] total items[1344000] mini batches[ 21000] memory[7341] epoch remaining[0:43:32]
INFO:trainer.default_trainer:epochs[    11] optim steps[21100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39857/0.91258, loss_mask_bce_0: 0.39699/0.33609, loss_mask_dice_0: 1.21185/1.16707, loss_spatial_bce_0: 0.06663/0.09197, loss_spatial_dice_0: 0.23698/0.21944, loss_spatial_ce_0: 0.12037/0.07956, loss_grounding_bce_0: 0.05086/0.08638, loss_grounding_dice_0: 0.28582/0.17925, loss_grounding_ce_0: 0.40297/0.27605, loss_mask_ce_1: 1.38422/0.91300, loss_mask_bce_1: 0.38897/0.33680, loss_mask_dice_1: 1.21976/1.17444, loss_spatial_bce_1: 0.07599/0.09288, loss_spatial_dice_1: 0.25662/0.22386, loss_spatial_ce_1: 0.12220/0.08524, loss_grounding_bce_1: 0.04773/0.08645, loss_grounding_dice_1: 0.29374/0.17993, loss_grounding_ce_1: 0.40906/0.27760, loss_mask_ce_2: 1.23277/0.92058, loss_mask_bce_2: 0.36775/0.33718, loss_mask_dice_2: 1.18313/1.17358, loss_spatial_bce_2: 0.06247/0.09297, loss_spatial_dice_2: 0.24336/0.22479, loss_spatial_ce_2: 0.13263/0.08906, loss_grounding_bce_2: 0.04758/0.08651, loss_grounding_dice_2: 0.29214/0.17933, loss_grounding_ce_2: 0.41759/0.28119, loss_mask_ce_3: 1.26543/0.92864, loss_mask_bce_3: 0.38920/0.33801, loss_mask_dice_3: 1.21090/1.17081, loss_spatial_bce_3: 0.06584/0.09382, loss_spatial_dice_3: 0.24119/0.22547, loss_spatial_ce_3: 0.10087/0.09324, loss_grounding_bce_3: 0.04907/0.08661, loss_grounding_dice_3: 0.28604/0.17917, loss_grounding_ce_3: 0.41313/0.28280, loss_mask_ce_4: 1.24539/0.92713, loss_mask_bce_4: 0.33834/0.33954, loss_mask_dice_4: 1.19697/1.19287, loss_spatial_bce_4: 0.07550/0.09769, loss_spatial_dice_4: 0.24484/0.23495, loss_spatial_ce_4: 0.07978/0.10944, loss_grounding_bce_4: 0.04962/0.08717, loss_grounding_dice_4: 0.29933/0.18195, loss_grounding_ce_4: 0.39526/0.28509, loss_mask_ce_5: 1.17683/0.94155, loss_mask_bce_5: 0.36794/0.34175, loss_mask_dice_5: 1.21819/1.19797, loss_spatial_bce_5: 0.07231/0.09881, loss_spatial_dice_5: 0.24398/0.23790, loss_spatial_ce_5: 0.11056/0.12284, loss_grounding_bce_5: 0.05186/0.08758, loss_grounding_dice_5: 0.28391/0.18311, loss_grounding_ce_5: 0.39233/0.29794, loss_mask_ce_6: 1.16341/0.97919, loss_mask_bce_6: 0.37088/0.34449, loss_mask_dice_6: 1.20284/1.20064, loss_spatial_bce_6: 0.09419/0.10421, loss_spatial_dice_6: 0.25687/0.24026, loss_spatial_ce_6: 0.09872/0.14736, loss_grounding_bce_6: 0.05169/0.08837, loss_grounding_dice_6: 0.28805/0.18322, loss_grounding_ce_6: 0.42537/0.31577, loss_mask_ce_7: 1.18151/1.02163, loss_mask_bce_7: 0.39374/0.35223, loss_mask_dice_7: 1.20372/1.25606, loss_spatial_bce_7: 0.09463/0.11317, loss_spatial_dice_7: 0.29443/0.26749, loss_spatial_ce_7: 0.26004/0.18593, loss_grounding_bce_7: 0.04783/0.09029, loss_grounding_dice_7: 0.27801/0.19054, loss_grounding_ce_7: 0.39438/0.34996, loss_mask_ce_8: 1.58022/1.13248, loss_mask_bce_8: 0.37315/0.36586, loss_mask_dice_8: 1.33478/1.33147, loss_spatial_bce_8: 0.10974/0.13443, loss_spatial_dice_8: 0.30827/0.30813, loss_spatial_ce_8: 0.18544/0.24285, loss_grounding_bce_8: 0.05586/0.09378, loss_grounding_dice_8: 0.31424/0.20197, loss_grounding_ce_8: 0.51289/0.42202, loss_mask_ce_9: 4.45696/3.69335, loss_mask_bce_9: 0.45136/0.39273, loss_mask_dice_9: 2.08888/1.90620, loss_spatial_bce_9: 0.31073/0.33712, loss_spatial_dice_9: 0.85607/0.82468, loss_spatial_ce_9: 1.71427/1.51852, loss_grounding_bce_9: 0.06401/0.10517, loss_grounding_dice_9: 0.52476/0.28171, loss_grounding_ce_9: 0.50215/0.69835] items per batch[64] items per second[0.22] total items[1350400] mini batches[ 21100] memory[7341] epoch remaining[0:38:51]
INFO:trainer.default_trainer:epochs[    11] optim steps[21200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56941/0.91265, loss_mask_bce_0: 0.19637/0.33622, loss_mask_dice_0: 0.54807/1.16664, loss_spatial_bce_0: 0.05286/0.09195, loss_spatial_dice_0: 0.13094/0.21939, loss_spatial_ce_0: 0.01153/0.07942, loss_grounding_bce_0: 0.04890/0.08641, loss_grounding_dice_0: 0.17419/0.17928, loss_grounding_ce_0: 0.30232/0.27616, loss_mask_ce_1: 0.60710/0.91309, loss_mask_bce_1: 0.19906/0.33692, loss_mask_dice_1: 0.53097/1.17402, loss_spatial_bce_1: 0.05296/0.09287, loss_spatial_dice_1: 0.12321/0.22381, loss_spatial_ce_1: 0.01844/0.08513, loss_grounding_bce_1: 0.04864/0.08648, loss_grounding_dice_1: 0.16202/0.17996, loss_grounding_ce_1: 0.30636/0.27770, loss_mask_ce_2: 0.64027/0.92068, loss_mask_bce_2: 0.20709/0.33731, loss_mask_dice_2: 0.51633/1.17313, loss_spatial_bce_2: 0.05947/0.09297, loss_spatial_dice_2: 0.12438/0.22474, loss_spatial_ce_2: 0.02763/0.08889, loss_grounding_bce_2: 0.04864/0.08654, loss_grounding_dice_2: 0.13829/0.17934, loss_grounding_ce_2: 0.32515/0.28122, loss_mask_ce_3: 0.61534/0.92875, loss_mask_bce_3: 0.19598/0.33813, loss_mask_dice_3: 0.53061/1.17043, loss_spatial_bce_3: 0.05401/0.09381, loss_spatial_dice_3: 0.12509/0.22542, loss_spatial_ce_3: 0.03279/0.09308, loss_grounding_bce_3: 0.04834/0.08664, loss_grounding_dice_3: 0.16600/0.17919, loss_grounding_ce_3: 0.38311/0.28283, loss_mask_ce_4: 0.60444/0.92723, loss_mask_bce_4: 0.19543/0.33967, loss_mask_dice_4: 0.49295/1.19254, loss_spatial_bce_4: 0.06305/0.09768, loss_spatial_dice_4: 0.13106/0.23492, loss_spatial_ce_4: 0.07988/0.10929, loss_grounding_bce_4: 0.04567/0.08719, loss_grounding_dice_4: 0.16175/0.18195, loss_grounding_ce_4: 0.45940/0.28509, loss_mask_ce_5: 0.59028/0.94162, loss_mask_bce_5: 0.20146/0.34189, loss_mask_dice_5: 0.51442/1.19752, loss_spatial_bce_5: 0.06032/0.09881, loss_spatial_dice_5: 0.14942/0.23788, loss_spatial_ce_5: 0.15037/0.12270, loss_grounding_bce_5: 0.04607/0.08762, loss_grounding_dice_5: 0.13631/0.18311, loss_grounding_ce_5: 0.39797/0.29796, loss_mask_ce_6: 0.55498/0.97932, loss_mask_bce_6: 0.20318/0.34465, loss_mask_dice_6: 0.52474/1.20025, loss_spatial_bce_6: 0.09169/0.10420, loss_spatial_dice_6: 0.16270/0.24022, loss_spatial_ce_6: 0.06986/0.14726, loss_grounding_bce_6: 0.05227/0.08840, loss_grounding_dice_6: 0.14969/0.18322, loss_grounding_ce_6: 0.48395/0.31571, loss_mask_ce_7: 0.60855/1.02169, loss_mask_bce_7: 0.20348/0.35237, loss_mask_dice_7: 0.53302/1.25566, loss_spatial_bce_7: 0.06206/0.11314, loss_spatial_dice_7: 0.13910/0.26745, loss_spatial_ce_7: 0.10029/0.18580, loss_grounding_bce_7: 0.04617/0.09033, loss_grounding_dice_7: 0.14623/0.19053, loss_grounding_ce_7: 0.44410/0.34976, loss_mask_ce_8: 0.81087/1.13255, loss_mask_bce_8: 0.23869/0.36600, loss_mask_dice_8: 0.61684/1.33110, loss_spatial_bce_8: 0.07066/0.13441, loss_spatial_dice_8: 0.16229/0.30812, loss_spatial_ce_8: 0.11928/0.24280, loss_grounding_bce_8: 0.05806/0.09380, loss_grounding_dice_8: 0.16578/0.20198, loss_grounding_ce_8: 0.46664/0.42165, loss_mask_ce_9: 3.59984/3.69348, loss_mask_bce_9: 0.24571/0.39284, loss_mask_dice_9: 0.98061/1.90588, loss_spatial_bce_9: 0.34704/0.33708, loss_spatial_dice_9: 0.86274/0.82471, loss_spatial_ce_9: 1.31476/1.51829, loss_grounding_bce_9: 0.03816/0.10518, loss_grounding_dice_9: 0.33250/0.28169, loss_grounding_ce_9: 1.29449/0.69785] items per batch[64] items per second[0.23] total items[1356800] mini batches[ 21200] memory[7341] epoch remaining[0:34:05]
INFO:trainer.default_trainer:epochs[    11] optim steps[21300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50594/0.91263, loss_mask_bce_0: 0.07022/0.33613, loss_mask_dice_0: 0.51832/1.16655, loss_spatial_bce_0: 0.03323/0.09193, loss_spatial_dice_0: 0.21587/0.21940, loss_spatial_ce_0: 0.06913/0.07932, loss_grounding_bce_0: 0.03597/0.08639, loss_grounding_dice_0: 0.25418/0.17936, loss_grounding_ce_0: 0.10032/0.27613, loss_mask_ce_1: 0.55411/0.91308, loss_mask_bce_1: 0.07884/0.33684, loss_mask_dice_1: 0.60663/1.17394, loss_spatial_bce_1: 0.03345/0.09284, loss_spatial_dice_1: 0.22409/0.22380, loss_spatial_ce_1: 0.05644/0.08499, loss_grounding_bce_1: 0.03792/0.08647, loss_grounding_dice_1: 0.24507/0.18006, loss_grounding_ce_1: 0.09227/0.27768, loss_mask_ce_2: 0.81136/0.92056, loss_mask_bce_2: 0.07272/0.33723, loss_mask_dice_2: 0.55812/1.17302, loss_spatial_bce_2: 0.03571/0.09294, loss_spatial_dice_2: 0.21028/0.22473, loss_spatial_ce_2: 0.05370/0.08876, loss_grounding_bce_2: 0.03417/0.08652, loss_grounding_dice_2: 0.22698/0.17945, loss_grounding_ce_2: 0.20211/0.28126, loss_mask_ce_3: 0.45487/0.92868, loss_mask_bce_3: 0.07458/0.33805, loss_mask_dice_3: 0.55388/1.17028, loss_spatial_bce_3: 0.03862/0.09379, loss_spatial_dice_3: 0.19828/0.22540, loss_spatial_ce_3: 0.06033/0.09294, loss_grounding_bce_3: 0.03634/0.08663, loss_grounding_dice_3: 0.25746/0.17929, loss_grounding_ce_3: 0.11256/0.28284, loss_mask_ce_4: 0.64912/0.92722, loss_mask_bce_4: 0.06790/0.33958, loss_mask_dice_4: 0.52522/1.19240, loss_spatial_bce_4: 0.03532/0.09766, loss_spatial_dice_4: 0.20684/0.23492, loss_spatial_ce_4: 0.09473/0.10916, loss_grounding_bce_4: 0.04207/0.08718, loss_grounding_dice_4: 0.24875/0.18204, loss_grounding_ce_4: 0.10493/0.28511, loss_mask_ce_5: 0.64614/0.94162, loss_mask_bce_5: 0.06912/0.34181, loss_mask_dice_5: 0.56405/1.19741, loss_spatial_bce_5: 0.03155/0.09879, loss_spatial_dice_5: 0.21175/0.23788, loss_spatial_ce_5: 0.09386/0.12264, loss_grounding_bce_5: 0.03523/0.08761, loss_grounding_dice_5: 0.21621/0.18320, loss_grounding_ce_5: 0.19969/0.29794, loss_mask_ce_6: 0.49402/0.97932, loss_mask_bce_6: 0.06788/0.34455, loss_mask_dice_6: 0.53001/1.20012, loss_spatial_bce_6: 0.03396/0.10416, loss_spatial_dice_6: 0.19053/0.24022, loss_spatial_ce_6: 0.15738/0.14714, loss_grounding_bce_6: 0.03266/0.08839, loss_grounding_dice_6: 0.22827/0.18331, loss_grounding_ce_6: 0.11287/0.31569, loss_mask_ce_7: 0.72094/1.02183, loss_mask_bce_7: 0.06047/0.35226, loss_mask_dice_7: 0.58620/1.25545, loss_spatial_bce_7: 0.03720/0.11310, loss_spatial_dice_7: 0.23058/0.26742, loss_spatial_ce_7: 0.22758/0.18575, loss_grounding_bce_7: 0.03196/0.09030, loss_grounding_dice_7: 0.24588/0.19059, loss_grounding_ce_7: 0.17615/0.34981, loss_mask_ce_8: 1.21360/1.13266, loss_mask_bce_8: 0.05856/0.36590, loss_mask_dice_8: 0.57123/1.33081, loss_spatial_bce_8: 0.03676/0.13439, loss_spatial_dice_8: 0.34401/0.30811, loss_spatial_ce_8: 0.19776/0.24276, loss_grounding_bce_8: 0.05859/0.09379, loss_grounding_dice_8: 0.29543/0.20206, loss_grounding_ce_8: 0.17003/0.42172, loss_mask_ce_9: 2.63530/3.69345, loss_mask_bce_9: 0.05783/0.39277, loss_mask_dice_9: 0.64940/1.90574, loss_spatial_bce_9: 0.08786/0.33700, loss_spatial_dice_9: 0.68615/0.82470, loss_spatial_ce_9: 1.36644/1.51788, loss_grounding_bce_9: 0.03458/0.10518, loss_grounding_dice_9: 0.29395/0.28176, loss_grounding_ce_9: 0.18434/0.69793] items per batch[64] items per second[0.23] total items[1363200] mini batches[ 21300] memory[7341] epoch remaining[0:29:18]
INFO:trainer.default_trainer:epochs[    11] optim steps[21400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.67331/0.91239, loss_mask_bce_0: 0.29499/0.33624, loss_mask_dice_0: 0.73385/1.16632, loss_spatial_bce_0: 0.10792/0.09194, loss_spatial_dice_0: 0.25133/0.21932, loss_spatial_ce_0: 0.01255/0.07924, loss_grounding_bce_0: 0.08847/0.08644, loss_grounding_dice_0: 0.12567/0.17932, loss_grounding_ce_0: 1.31268/0.27604, loss_mask_ce_1: 1.73514/0.91284, loss_mask_bce_1: 0.28906/0.33697, loss_mask_dice_1: 0.84311/1.17372, loss_spatial_bce_1: 0.11451/0.09286, loss_spatial_dice_1: 0.25372/0.22372, loss_spatial_ce_1: 0.01136/0.08487, loss_grounding_bce_1: 0.08095/0.08651, loss_grounding_dice_1: 0.12004/0.18005, loss_grounding_ce_1: 1.37436/0.27759, loss_mask_ce_2: 1.90161/0.92038, loss_mask_bce_2: 0.28344/0.33736, loss_mask_dice_2: 0.80658/1.17278, loss_spatial_bce_2: 0.11750/0.09295, loss_spatial_dice_2: 0.25923/0.22464, loss_spatial_ce_2: 0.01604/0.08863, loss_grounding_bce_2: 0.09481/0.08656, loss_grounding_dice_2: 0.12377/0.17942, loss_grounding_ce_2: 1.22174/0.28129, loss_mask_ce_3: 2.01545/0.92852, loss_mask_bce_3: 0.29694/0.33816, loss_mask_dice_3: 0.78740/1.17002, loss_spatial_bce_3: 0.12277/0.09380, loss_spatial_dice_3: 0.24953/0.22532, loss_spatial_ce_3: 0.03279/0.09281, loss_grounding_bce_3: 0.10600/0.08667, loss_grounding_dice_3: 0.12277/0.17926, loss_grounding_ce_3: 1.27500/0.28276, loss_mask_ce_4: 2.09987/0.92703, loss_mask_bce_4: 0.30055/0.33974, loss_mask_dice_4: 0.92003/1.19216, loss_spatial_bce_4: 0.12617/0.09767, loss_spatial_dice_4: 0.28919/0.23485, loss_spatial_ce_4: 0.04835/0.10909, loss_grounding_bce_4: 0.09438/0.08722, loss_grounding_dice_4: 0.12594/0.18203, loss_grounding_ce_4: 1.86429/0.28512, loss_mask_ce_5: 2.11289/0.94150, loss_mask_bce_5: 0.29364/0.34194, loss_mask_dice_5: 0.79787/1.19721, loss_spatial_bce_5: 0.09822/0.09881, loss_spatial_dice_5: 0.27706/0.23782, loss_spatial_ce_5: 0.20029/0.12255, loss_grounding_bce_5: 0.08534/0.08765, loss_grounding_dice_5: 0.12190/0.18315, loss_grounding_ce_5: 1.39517/0.29789, loss_mask_ce_6: 2.16675/0.97912, loss_mask_bce_6: 0.31283/0.34468, loss_mask_dice_6: 0.82135/1.19993, loss_spatial_bce_6: 0.10909/0.10417, loss_spatial_dice_6: 0.25275/0.24016, loss_spatial_ce_6: 0.12811/0.14700, loss_grounding_bce_6: 0.08700/0.08842, loss_grounding_dice_6: 0.12183/0.18327, loss_grounding_ce_6: 1.35764/0.31580, loss_mask_ce_7: 2.41980/1.02162, loss_mask_bce_7: 0.34966/0.35236, loss_mask_dice_7: 0.95301/1.25527, loss_spatial_bce_7: 0.12596/0.11311, loss_spatial_dice_7: 0.28377/0.26737, loss_spatial_ce_7: 0.29697/0.18567, loss_grounding_bce_7: 0.08248/0.09031, loss_grounding_dice_7: 0.12077/0.19057, loss_grounding_ce_7: 1.60504/0.34968, loss_mask_ce_8: 1.97806/1.13249, loss_mask_bce_8: 0.40935/0.36602, loss_mask_dice_8: 1.11427/1.33049, loss_spatial_bce_8: 0.16561/0.13439, loss_spatial_dice_8: 0.36385/0.30807, loss_spatial_ce_8: 0.16727/0.24269, loss_grounding_bce_8: 0.08882/0.09382, loss_grounding_dice_8: 0.12748/0.20201, loss_grounding_ce_8: 1.57068/0.42167, loss_mask_ce_9: 3.86384/3.69324, loss_mask_bce_9: 0.35423/0.39296, loss_mask_dice_9: 1.55810/1.90558, loss_spatial_bce_9: 0.21526/0.33694, loss_spatial_dice_9: 0.85777/0.82465, loss_spatial_ce_9: 1.50282/1.51767, loss_grounding_bce_9: 0.11152/0.10521, loss_grounding_dice_9: 0.22540/0.28172, loss_grounding_ce_9: 1.81043/0.69771] items per batch[64] items per second[0.23] total items[1369600] mini batches[ 21400] memory[7341] epoch remaining[0:24:33]
INFO:trainer.default_trainer:epochs[    11] optim steps[21500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24517/0.91246, loss_mask_bce_0: 0.15939/0.33607, loss_mask_dice_0: 0.88125/1.16633, loss_spatial_bce_0: 0.04275/0.09189, loss_spatial_dice_0: 0.20415/0.21930, loss_spatial_ce_0: 0.03064/0.07911, loss_grounding_bce_0: 0.09013/0.08642, loss_grounding_dice_0: 0.19987/0.17931, loss_grounding_ce_0: 0.05388/0.27592, loss_mask_ce_1: 1.21300/0.91299, loss_mask_bce_1: 0.16002/0.33679, loss_mask_dice_1: 0.73883/1.17378, loss_spatial_bce_1: 0.04224/0.09280, loss_spatial_dice_1: 0.21602/0.22369, loss_spatial_ce_1: 0.03295/0.08483, loss_grounding_bce_1: 0.08910/0.08649, loss_grounding_dice_1: 0.20419/0.18005, loss_grounding_ce_1: 0.05306/0.27753, loss_mask_ce_2: 1.45658/0.92043, loss_mask_bce_2: 0.15431/0.33720, loss_mask_dice_2: 0.66219/1.17283, loss_spatial_bce_2: 0.04235/0.09290, loss_spatial_dice_2: 0.21838/0.22461, loss_spatial_ce_2: 0.03429/0.08863, loss_grounding_bce_2: 0.08614/0.08655, loss_grounding_dice_2: 0.20903/0.17943, loss_grounding_ce_2: 0.05213/0.28127, loss_mask_ce_3: 1.23981/0.92856, loss_mask_bce_3: 0.15412/0.33800, loss_mask_dice_3: 0.71462/1.17006, loss_spatial_bce_3: 0.04244/0.09375, loss_spatial_dice_3: 0.24079/0.22530, loss_spatial_ce_3: 0.04251/0.09274, loss_grounding_bce_3: 0.08572/0.08665, loss_grounding_dice_3: 0.20606/0.17927, loss_grounding_ce_3: 0.05582/0.28265, loss_mask_ce_4: 1.30897/0.92710, loss_mask_bce_4: 0.15146/0.33960, loss_mask_dice_4: 0.68706/1.19218, loss_spatial_bce_4: 0.04697/0.09763, loss_spatial_dice_4: 0.22789/0.23481, loss_spatial_ce_4: 0.08191/0.10901, loss_grounding_bce_4: 0.08904/0.08721, loss_grounding_dice_4: 0.19192/0.18202, loss_grounding_ce_4: 0.03632/0.28502, loss_mask_ce_5: 1.35417/0.94165, loss_mask_bce_5: 0.15970/0.34180, loss_mask_dice_5: 0.64804/1.19722, loss_spatial_bce_5: 0.04969/0.09876, loss_spatial_dice_5: 0.24759/0.23780, loss_spatial_ce_5: 0.13911/0.12244, loss_grounding_bce_5: 0.08761/0.08764, loss_grounding_dice_5: 0.21497/0.18316, loss_grounding_ce_5: 0.03352/0.29785, loss_mask_ce_6: 1.23858/0.97925, loss_mask_bce_6: 0.15513/0.34451, loss_mask_dice_6: 0.89158/1.19989, loss_spatial_bce_6: 0.05148/0.10411, loss_spatial_dice_6: 0.22295/0.24012, loss_spatial_ce_6: 0.24040/0.14690, loss_grounding_bce_6: 0.08866/0.08839, loss_grounding_dice_6: 0.21376/0.18327, loss_grounding_ce_6: 0.03751/0.31557, loss_mask_ce_7: 1.53838/1.02171, loss_mask_bce_7: 0.16163/0.35221, loss_mask_dice_7: 0.57628/1.25538, loss_spatial_bce_7: 0.05311/0.11305, loss_spatial_dice_7: 0.25028/0.26736, loss_spatial_ce_7: 0.28369/0.18563, loss_grounding_bce_7: 0.09230/0.09029, loss_grounding_dice_7: 0.18250/0.19054, loss_grounding_ce_7: 0.17940/0.34942, loss_mask_ce_8: 1.57634/1.13259, loss_mask_bce_8: 0.14514/0.36586, loss_mask_dice_8: 0.60964/1.33049, loss_spatial_bce_8: 0.10242/0.13434, loss_spatial_dice_8: 0.34368/0.30807, loss_spatial_ce_8: 0.48109/0.24261, loss_grounding_bce_8: 0.10300/0.09380, loss_grounding_dice_8: 0.21928/0.20202, loss_grounding_ce_8: 0.05582/0.42148, loss_mask_ce_9: 2.79595/3.69362, loss_mask_bce_9: 0.19342/0.39277, loss_mask_dice_9: 0.96955/1.90544, loss_spatial_bce_9: 0.22773/0.33679, loss_spatial_dice_9: 0.81800/0.82461, loss_spatial_ce_9: 1.74286/1.51797, loss_grounding_bce_9: 0.11538/0.10520, loss_grounding_dice_9: 0.23590/0.28163, loss_grounding_ce_9: 0.06210/0.69728] items per batch[64] items per second[0.22] total items[1376000] mini batches[ 21500] memory[7341] epoch remaining[0:19:54]
INFO:trainer.default_trainer:epochs[    11] optim steps[21600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63633/0.91254, loss_mask_bce_0: 0.23456/0.33611, loss_mask_dice_0: 0.24312/1.16666, loss_spatial_bce_0: 0.15204/0.09189, loss_spatial_dice_0: 0.14135/0.21925, loss_spatial_ce_0: 0.01576/0.07917, loss_grounding_bce_0: 0.08557/0.08644, loss_grounding_dice_0: 0.08446/0.17938, loss_grounding_ce_0: 0.17585/0.27620, loss_mask_ce_1: 0.56984/0.91310, loss_mask_bce_1: 0.22587/0.33679, loss_mask_dice_1: 0.23590/1.17399, loss_spatial_bce_1: 0.16372/0.09280, loss_spatial_dice_1: 0.14617/0.22363, loss_spatial_ce_1: 0.01897/0.08491, loss_grounding_bce_1: 0.08811/0.08651, loss_grounding_dice_1: 0.09019/0.18010, loss_grounding_ce_1: 0.15971/0.27784, loss_mask_ce_2: 0.53149/0.92052, loss_mask_bce_2: 0.22935/0.33722, loss_mask_dice_2: 0.22935/1.17307, loss_spatial_bce_2: 0.14885/0.09290, loss_spatial_dice_2: 0.13247/0.22458, loss_spatial_ce_2: 0.05043/0.08867, loss_grounding_bce_2: 0.08868/0.08656, loss_grounding_dice_2: 0.08781/0.17949, loss_grounding_ce_2: 0.13709/0.28168, loss_mask_ce_3: 0.55080/0.92861, loss_mask_bce_3: 0.24428/0.33803, loss_mask_dice_3: 0.23730/1.17032, loss_spatial_bce_3: 0.14611/0.09376, loss_spatial_dice_3: 0.12579/0.22525, loss_spatial_ce_3: 0.04199/0.09278, loss_grounding_bce_3: 0.08585/0.08666, loss_grounding_dice_3: 0.08235/0.17931, loss_grounding_ce_3: 0.13496/0.28300, loss_mask_ce_4: 0.55430/0.92722, loss_mask_bce_4: 0.26844/0.33961, loss_mask_dice_4: 0.28846/1.19242, loss_spatial_bce_4: 0.15140/0.09763, loss_spatial_dice_4: 0.14509/0.23479, loss_spatial_ce_4: 0.03355/0.10907, loss_grounding_bce_4: 0.10718/0.08723, loss_grounding_dice_4: 0.11585/0.18209, loss_grounding_ce_4: 0.13348/0.28534, loss_mask_ce_5: 0.52025/0.94174, loss_mask_bce_5: 0.27314/0.34183, loss_mask_dice_5: 0.28795/1.19750, loss_spatial_bce_5: 0.18812/0.09878, loss_spatial_dice_5: 0.15289/0.23779, loss_spatial_ce_5: 0.04848/0.12244, loss_grounding_bce_5: 0.11405/0.08765, loss_grounding_dice_5: 0.12312/0.18319, loss_grounding_ce_5: 0.15244/0.29818, loss_mask_ce_6: 0.29167/0.97929, loss_mask_bce_6: 0.37544/0.34457, loss_mask_dice_6: 0.36845/1.20023, loss_spatial_bce_6: 0.14806/0.10414, loss_spatial_dice_6: 0.14364/0.24010, loss_spatial_ce_6: 0.21332/0.14693, loss_grounding_bce_6: 0.16299/0.08842, loss_grounding_dice_6: 0.16114/0.18333, loss_grounding_ce_6: 0.02299/0.31594, loss_mask_ce_7: 0.43394/1.02171, loss_mask_bce_7: 0.38925/0.35225, loss_mask_dice_7: 0.35033/1.25567, loss_spatial_bce_7: 0.14556/0.11305, loss_spatial_dice_7: 0.14864/0.26734, loss_spatial_ce_7: 0.12794/0.18559, loss_grounding_bce_7: 0.16636/0.09031, loss_grounding_dice_7: 0.15287/0.19061, loss_grounding_ce_7: 0.02435/0.34957, loss_mask_ce_8: 0.43551/1.13269, loss_mask_bce_8: 0.30892/0.36588, loss_mask_dice_8: 0.33473/1.33081, loss_spatial_bce_8: 0.17611/0.13435, loss_spatial_dice_8: 0.14943/0.30803, loss_spatial_ce_8: 0.09994/0.24259, loss_grounding_bce_8: 0.13214/0.09383, loss_grounding_dice_8: 0.14084/0.20210, loss_grounding_ce_8: 0.02665/0.42160, loss_mask_ce_9: 1.68469/3.69343, loss_mask_bce_9: 0.51663/0.39283, loss_mask_dice_9: 0.54294/1.90601, loss_spatial_bce_9: 0.52169/0.33681, loss_spatial_dice_9: 0.74316/0.82463, loss_spatial_ce_9: 0.97029/1.51803, loss_grounding_bce_9: 0.23370/0.10522, loss_grounding_dice_9: 0.23715/0.28166, loss_grounding_ce_9: 0.13170/0.69710] items per batch[64] items per second[0.23] total items[1382400] mini batches[ 21600] memory[7341] epoch remaining[0:15:13]
INFO:trainer.default_trainer:epochs[    11] optim steps[21700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27310/0.91221, loss_mask_bce_0: 0.12925/0.33614, loss_mask_dice_0: 0.41650/1.16703, loss_spatial_bce_0: 0.04646/0.09184, loss_spatial_dice_0: 0.12095/0.21920, loss_spatial_ce_0: 0.11273/0.07906, loss_grounding_bce_0: 0.03583/0.08642, loss_grounding_dice_0: 0.05118/0.17933, loss_grounding_ce_0: 0.13872/0.27623, loss_mask_ce_1: 0.28011/0.91279, loss_mask_bce_1: 0.12908/0.33680, loss_mask_dice_1: 0.39229/1.17442, loss_spatial_bce_1: 0.04427/0.09275, loss_spatial_dice_1: 0.13794/0.22356, loss_spatial_ce_1: 0.11206/0.08484, loss_grounding_bce_1: 0.03351/0.08649, loss_grounding_dice_1: 0.04702/0.18005, loss_grounding_ce_1: 0.13538/0.27785, loss_mask_ce_2: 0.31462/0.92015, loss_mask_bce_2: 0.12958/0.33722, loss_mask_dice_2: 0.42328/1.17338, loss_spatial_bce_2: 0.04333/0.09286, loss_spatial_dice_2: 0.09394/0.22451, loss_spatial_ce_2: 0.12192/0.08858, loss_grounding_bce_2: 0.03876/0.08654, loss_grounding_dice_2: 0.05571/0.17945, loss_grounding_ce_2: 0.16259/0.28168, loss_mask_ce_3: 0.35547/0.92819, loss_mask_bce_3: 0.13440/0.33804, loss_mask_dice_3: 0.39189/1.17074, loss_spatial_bce_3: 0.04337/0.09372, loss_spatial_dice_3: 0.11885/0.22519, loss_spatial_ce_3: 0.15498/0.09269, loss_grounding_bce_3: 0.03773/0.08664, loss_grounding_dice_3: 0.05319/0.17926, loss_grounding_ce_3: 0.17644/0.28296, loss_mask_ce_4: 0.29444/0.92680, loss_mask_bce_4: 0.13636/0.33962, loss_mask_dice_4: 0.42920/1.19284, loss_spatial_bce_4: 0.05847/0.09759, loss_spatial_dice_4: 0.15266/0.23474, loss_spatial_ce_4: 0.09868/0.10896, loss_grounding_bce_4: 0.03652/0.08721, loss_grounding_dice_4: 0.05323/0.18203, loss_grounding_ce_4: 0.25516/0.28524, loss_mask_ce_5: 0.35886/0.94130, loss_mask_bce_5: 0.12837/0.34184, loss_mask_dice_5: 0.41945/1.19796, loss_spatial_bce_5: 0.07372/0.09876, loss_spatial_dice_5: 0.19156/0.23775, loss_spatial_ce_5: 0.16458/0.12232, loss_grounding_bce_5: 0.03491/0.08764, loss_grounding_dice_5: 0.05107/0.18314, loss_grounding_ce_5: 0.31463/0.29800, loss_mask_ce_6: 0.51025/0.97880, loss_mask_bce_6: 0.13335/0.34458, loss_mask_dice_6: 0.33052/1.20074, loss_spatial_bce_6: 0.07409/0.10411, loss_spatial_dice_6: 0.16703/0.24004, loss_spatial_ce_6: 0.09648/0.14690, loss_grounding_bce_6: 0.03420/0.08841, loss_grounding_dice_6: 0.05193/0.18327, loss_grounding_ce_6: 0.26487/0.31581, loss_mask_ce_7: 0.46472/1.02127, loss_mask_bce_7: 0.14160/0.35225, loss_mask_dice_7: 0.40691/1.25607, loss_spatial_bce_7: 0.06627/0.11301, loss_spatial_dice_7: 0.17650/0.26727, loss_spatial_ce_7: 0.11948/0.18558, loss_grounding_bce_7: 0.03965/0.09031, loss_grounding_dice_7: 0.05207/0.19056, loss_grounding_ce_7: 0.36317/0.34950, loss_mask_ce_8: 0.56388/1.13217, loss_mask_bce_8: 0.13780/0.36589, loss_mask_dice_8: 0.34009/1.33125, loss_spatial_bce_8: 0.09675/0.13432, loss_spatial_dice_8: 0.22107/0.30797, loss_spatial_ce_8: 0.36081/0.24252, loss_grounding_bce_8: 0.04181/0.09383, loss_grounding_dice_8: 0.06029/0.20203, loss_grounding_ce_8: 0.38410/0.42146, loss_mask_ce_9: 2.84122/3.69341, loss_mask_bce_9: 0.18695/0.39282, loss_mask_dice_9: 0.71568/1.90665, loss_spatial_bce_9: 0.38539/0.33674, loss_spatial_dice_9: 0.70018/0.82459, loss_spatial_ce_9: 1.68695/1.51790, loss_grounding_bce_9: 0.04230/0.10522, loss_grounding_dice_9: 0.12826/0.28159, loss_grounding_ce_9: 1.43559/0.69708] items per batch[64] items per second[0.22] total items[1388800] mini batches[ 21700] memory[7341] epoch remaining[0:10:31]
INFO:trainer.default_trainer:epochs[    11] optim steps[21800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88308/0.91212, loss_mask_bce_0: 0.54053/0.33597, loss_mask_dice_0: 0.90595/1.16703, loss_spatial_bce_0: 0.23750/0.09178, loss_spatial_dice_0: 0.31408/0.21917, loss_spatial_ce_0: 0.01550/0.07898, loss_grounding_bce_0: 0.01834/0.08640, loss_grounding_dice_0: 0.09324/0.17931, loss_grounding_ce_0: 0.06067/0.27608, loss_mask_ce_1: 0.87572/0.91264, loss_mask_bce_1: 0.57380/0.33665, loss_mask_dice_1: 0.91349/1.17435, loss_spatial_bce_1: 0.26440/0.09268, loss_spatial_dice_1: 0.31422/0.22355, loss_spatial_ce_1: 0.01822/0.08471, loss_grounding_bce_1: 0.01262/0.08646, loss_grounding_dice_1: 0.07120/0.18003, loss_grounding_ce_1: 0.05703/0.27767, loss_mask_ce_2: 0.98606/0.91994, loss_mask_bce_2: 0.59589/0.33705, loss_mask_dice_2: 0.91322/1.17337, loss_spatial_bce_2: 0.28245/0.09279, loss_spatial_dice_2: 0.31680/0.22448, loss_spatial_ce_2: 0.01912/0.08847, loss_grounding_bce_2: 0.01436/0.08652, loss_grounding_dice_2: 0.08431/0.17942, loss_grounding_ce_2: 0.08623/0.28156, loss_mask_ce_3: 0.91478/0.92812, loss_mask_bce_3: 0.55444/0.33787, loss_mask_dice_3: 0.96096/1.17070, loss_spatial_bce_3: 0.28541/0.09365, loss_spatial_dice_3: 0.31969/0.22515, loss_spatial_ce_3: 0.01830/0.09263, loss_grounding_bce_3: 0.01211/0.08661, loss_grounding_dice_3: 0.07898/0.17923, loss_grounding_ce_3: 0.08876/0.28278, loss_mask_ce_4: 0.92527/0.92670, loss_mask_bce_4: 0.50921/0.33946, loss_mask_dice_4: 0.88861/1.19280, loss_spatial_bce_4: 0.18193/0.09753, loss_spatial_dice_4: 0.30391/0.23475, loss_spatial_ce_4: 0.06679/0.10889, loss_grounding_bce_4: 0.01324/0.08717, loss_grounding_dice_4: 0.11715/0.18202, loss_grounding_ce_4: 0.16057/0.28524, loss_mask_ce_5: 1.19134/0.94115, loss_mask_bce_5: 0.54168/0.34173, loss_mask_dice_5: 0.87231/1.19803, loss_spatial_bce_5: 0.15352/0.09870, loss_spatial_dice_5: 0.31063/0.23776, loss_spatial_ce_5: 0.43182/0.12231, loss_grounding_bce_5: 0.01358/0.08760, loss_grounding_dice_5: 0.09156/0.18312, loss_grounding_ce_5: 0.19035/0.29792, loss_mask_ce_6: 0.93576/0.97866, loss_mask_bce_6: 0.50863/0.34443, loss_mask_dice_6: 0.91739/1.20070, loss_spatial_bce_6: 0.17179/0.10406, loss_spatial_dice_6: 0.30001/0.24003, loss_spatial_ce_6: 0.37250/0.14684, loss_grounding_bce_6: 0.01017/0.08837, loss_grounding_dice_6: 0.07857/0.18326, loss_grounding_ce_6: 0.16144/0.31574, loss_mask_ce_7: 0.85300/1.02114, loss_mask_bce_7: 0.56183/0.35213, loss_mask_dice_7: 0.99670/1.25609, loss_spatial_bce_7: 0.16364/0.11294, loss_spatial_dice_7: 0.27748/0.26725, loss_spatial_ce_7: 0.20388/0.18550, loss_grounding_bce_7: 0.01288/0.09028, loss_grounding_dice_7: 0.06856/0.19054, loss_grounding_ce_7: 0.20355/0.34934, loss_mask_ce_8: 0.89284/1.13209, loss_mask_bce_8: 0.57240/0.36572, loss_mask_dice_8: 1.08365/1.33108, loss_spatial_bce_8: 0.27492/0.13423, loss_spatial_dice_8: 0.30690/0.30794, loss_spatial_ce_8: 0.30705/0.24240, loss_grounding_bce_8: 0.01854/0.09381, loss_grounding_dice_8: 0.10205/0.20202, loss_grounding_ce_8: 0.84701/0.42119, loss_mask_ce_9: 4.52169/3.69261, loss_mask_bce_9: 0.36949/0.39260, loss_mask_dice_9: 1.78481/1.90607, loss_spatial_bce_9: 0.34987/0.33658, loss_spatial_dice_9: 0.93174/0.82459, loss_spatial_ce_9: 1.42983/1.51800, loss_grounding_bce_9: 0.02398/0.10516, loss_grounding_dice_9: 0.18337/0.28149, loss_grounding_ce_9: 1.13525/0.69685] items per batch[64] items per second[0.23] total items[1395200] mini batches[ 21800] memory[7341] epoch remaining[0:05:49]
INFO:trainer.default_trainer:epochs[    11] optim steps[21900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02353/0.91215, loss_mask_bce_0: 0.03323/0.33591, loss_mask_dice_0: 0.20622/1.16672, loss_spatial_bce_0: 0.01594/0.09176, loss_spatial_dice_0: 0.09335/0.21913, loss_spatial_ce_0: 0.00003/0.07884, loss_grounding_bce_0: 0.01952/0.08642, loss_grounding_dice_0: 0.04733/0.17939, loss_grounding_ce_0: 0.00498/0.27591, loss_mask_ce_1: 0.02483/0.91264, loss_mask_bce_1: 0.03529/0.33658, loss_mask_dice_1: 0.19951/1.17405, loss_spatial_bce_1: 0.01597/0.09266, loss_spatial_dice_1: 0.08078/0.22349, loss_spatial_ce_1: 0.00002/0.08459, loss_grounding_bce_1: 0.02070/0.08647, loss_grounding_dice_1: 0.04768/0.18008, loss_grounding_ce_1: 0.00504/0.27755, loss_mask_ce_2: 0.02803/0.91999, loss_mask_bce_2: 0.03540/0.33699, loss_mask_dice_2: 0.21038/1.17301, loss_spatial_bce_2: 0.01616/0.09277, loss_spatial_dice_2: 0.09161/0.22442, loss_spatial_ce_2: 0.00001/0.08841, loss_grounding_bce_2: 0.01967/0.08654, loss_grounding_dice_2: 0.04434/0.17950, loss_grounding_ce_2: 0.00739/0.28147, loss_mask_ce_3: 0.03507/0.92818, loss_mask_bce_3: 0.03463/0.33781, loss_mask_dice_3: 0.20028/1.17030, loss_spatial_bce_3: 0.01675/0.09363, loss_spatial_dice_3: 0.08388/0.22510, loss_spatial_ce_3: 0.00004/0.09255, loss_grounding_bce_3: 0.02192/0.08664, loss_grounding_dice_3: 0.04605/0.17930, loss_grounding_ce_3: 0.00557/0.28267, loss_mask_ce_4: 0.03864/0.92681, loss_mask_bce_4: 0.03437/0.33942, loss_mask_dice_4: 0.18075/1.19249, loss_spatial_bce_4: 0.01709/0.09753, loss_spatial_dice_4: 0.06952/0.23471, loss_spatial_ce_4: 0.00015/0.10883, loss_grounding_bce_4: 0.02230/0.08720, loss_grounding_dice_4: 0.04597/0.18209, loss_grounding_ce_4: 0.00240/0.28526, loss_mask_ce_5: 0.04312/0.94125, loss_mask_bce_5: 0.03458/0.34167, loss_mask_dice_5: 0.19667/1.19766, loss_spatial_bce_5: 0.01840/0.09869, loss_spatial_dice_5: 0.09824/0.23774, loss_spatial_ce_5: 0.00025/0.12218, loss_grounding_bce_5: 0.02070/0.08762, loss_grounding_dice_5: 0.04457/0.18318, loss_grounding_ce_5: 0.00493/0.29784, loss_mask_ce_6: 0.05111/0.97874, loss_mask_bce_6: 0.03374/0.34439, loss_mask_dice_6: 0.17837/1.20031, loss_spatial_bce_6: 0.01769/0.10404, loss_spatial_dice_6: 0.08999/0.24000, loss_spatial_ce_6: 0.02202/0.14680, loss_grounding_bce_6: 0.02104/0.08838, loss_grounding_dice_6: 0.04595/0.18333, loss_grounding_ce_6: 0.00297/0.31569, loss_mask_ce_7: 0.06540/1.02131, loss_mask_bce_7: 0.03025/0.35205, loss_mask_dice_7: 0.17233/1.25584, loss_spatial_bce_7: 0.01984/0.11292, loss_spatial_dice_7: 0.08955/0.26722, loss_spatial_ce_7: 0.03538/0.18544, loss_grounding_bce_7: 0.02147/0.09032, loss_grounding_dice_7: 0.04534/0.19061, loss_grounding_ce_7: 0.00194/0.34916, loss_mask_ce_8: 0.05994/1.13244, loss_mask_bce_8: 0.03465/0.36565, loss_mask_dice_8: 0.18453/1.33077, loss_spatial_bce_8: 0.01728/0.13420, loss_spatial_dice_8: 0.11858/0.30790, loss_spatial_ce_8: 0.06701/0.24232, loss_grounding_bce_8: 0.02223/0.09382, loss_grounding_dice_8: 0.04741/0.20209, loss_grounding_ce_8: 0.00180/0.42096, loss_mask_ce_9: 1.78521/3.69276, loss_mask_bce_9: 0.02890/0.39256, loss_mask_dice_9: 0.20267/1.90573, loss_spatial_bce_9: 0.89236/0.33653, loss_spatial_dice_9: 0.86901/0.82460, loss_spatial_ce_9: 2.77154/1.51831, loss_grounding_bce_9: 0.02044/0.10519, loss_grounding_dice_9: 0.05151/0.28154, loss_grounding_ce_9: 0.06750/0.69656] items per batch[64] items per second[0.23] total items[1401600] mini batches[ 21900] memory[7341] epoch remaining[0:01:07]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00021924.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0025 s/iter. Inference: 0.2271 s/iter. Eval: 0.0854 s/iter. Total: 0.3150 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0028 s/iter. Inference: 0.2258 s/iter. Eval: 0.0816 s/iter. Total: 0.3103 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2274 s/iter. Eval: 0.0790 s/iter. Total: 0.3094 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2261 s/iter. Eval: 0.0757 s/iter. Total: 0.3050 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2242 s/iter. Eval: 0.0753 s/iter. Total: 0.3028 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2260 s/iter. Eval: 0.0759 s/iter. Total: 0.3051 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0031 s/iter. Inference: 0.2272 s/iter. Eval: 0.0763 s/iter. Total: 0.3067 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0031 s/iter. Inference: 0.2274 s/iter. Eval: 0.0752 s/iter. Total: 0.3057 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0031 s/iter. Inference: 0.2277 s/iter. Eval: 0.0753 s/iter. Total: 0.3063 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalqvmd4gvq ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.825 | 82.046 | 59.854 |      133      |
| Things | 54.945 | 82.826 | 65.690 |      80       |
| Stuff  | 42.096 | 80.870 | 51.046 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.35s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.17 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.24s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.88 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.821 | 61.156 | 40.834 | 19.264 | 41.885 | 60.344 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.149 | bicycle      | 17.750 | car            | 37.227 |
| motorcycle    | 35.064 | airplane     | 54.928 | bus            | 62.864 |
| train         | 69.276 | truck        | 35.232 | boat           | 22.080 |
| traffic light | 24.892 | fire hydrant | 64.935 | stop sign      | 64.338 |
| parking meter | 44.403 | bench        | 20.184 | bird           | 30.761 |
| cat           | 73.111 | dog          | 66.891 | horse          | 46.884 |
| sheep         | 45.826 | cow          | 50.099 | elephant       | 60.460 |
| bear          | 77.480 | zebra        | 60.675 | giraffe        | 56.981 |
| backpack      | 17.264 | umbrella     | 48.664 | handbag        | 15.313 |
| tie           | 33.978 | suitcase     | 40.341 | frisbee        | 66.868 |
| skis          | 5.403  | snowboard    | 23.880 | sports ball    | 47.233 |
| kite          | 33.072 | baseball bat | 27.791 | baseball glove | 43.450 |
| skateboard    | 36.911 | surfboard    | 35.629 | tennis racket  | 56.478 |
| bottle        | 33.362 | wine glass   | 27.446 | cup            | 40.465 |
| fork          | 17.048 | knife        | 13.255 | spoon          | 14.398 |
| bowl          | 31.519 | banana       | 20.246 | apple          | 18.950 |
| sandwich      | 42.068 | orange       | 28.009 | broccoli       | 20.584 |
| carrot        | 20.323 | hot dog      | 24.762 | pizza          | 49.378 |
| donut         | 46.313 | cake         | 43.954 | chair          | 20.443 |
| couch         | 42.048 | potted plant | 17.556 | bed            | 41.937 |
| dining table  | 13.322 | toilet       | 66.168 | tv             | 61.755 |
| laptop        | 63.279 | mouse        | 58.637 | remote         | 31.450 |
| keyboard      | 48.674 | cell phone   | 37.123 | microwave      | 54.688 |
| oven          | 32.695 | toaster      | 23.964 | sink           | 38.557 |
| refrigerator  | 59.828 | book         | 8.812  | clock          | 52.265 |
| vase          | 34.608 | scissors     | 25.906 | teddy bear     | 49.117 |
| hair drier    | 11.373 | toothbrush   | 20.602 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 59.927554701302235, 'fwIoU': 68.79923965055136, 'IoU-person': 87.37603552351673, 'IoU-bicycle': 74.89575035076321, 'IoU-car': 67.94965860846446, 'IoU-motorcycle': 85.41527780312234, 'IoU-airplane': 77.86543302821676, 'IoU-bus': 80.56641951821364, 'IoU-train': 77.78757124527304, 'IoU-truck': 62.72705522140396, 'IoU-boat': 68.42842771025244, 'IoU-traffic light': 76.56035275740658, 'IoU-fire hydrant': 80.88764065286466, 'IoU-stop sign': 91.02590249566931, 'IoU-parking meter': 81.17922333749085, 'IoU-bench': 54.86873953307468, 'IoU-bird': 75.62335778855956, 'IoU-cat': 88.34620623092955, 'IoU-dog': 76.7991441545573, 'IoU-horse': 86.76073039277948, 'IoU-sheep': 86.18829392517168, 'IoU-cow': 80.92743471865782, 'IoU-elephant': 85.38393489458726, 'IoU-bear': 70.73697696469439, 'IoU-zebra': 92.34569133723141, 'IoU-giraffe': 88.2844826332297, 'IoU-backpack': 39.561513968394614, 'IoU-umbrella': 73.53780542041584, 'IoU-handbag': 38.745336184431245, 'IoU-tie': 69.13646462957212, 'IoU-suitcase': 81.93613693740504, 'IoU-frisbee': 80.2624395086239, 'IoU-skis': 51.8811786401701, 'IoU-snowboard': 69.48280472341783, 'IoU-sports ball': 60.439659145050804, 'IoU-kite': 64.66998918395778, 'IoU-baseball bat': 58.646503501941496, 'IoU-baseball glove': 76.48305348065475, 'IoU-skateboard': 64.11535499117896, 'IoU-surfboard': 77.1701353063371, 'IoU-tennis racket': 74.46540313384695, 'IoU-bottle': 68.29668081359878, 'IoU-wine glass': 73.97951339433789, 'IoU-cup': 64.52663641315, 'IoU-fork': 54.41338592631924, 'IoU-knife': 49.49556320123029, 'IoU-spoon': 48.96685934146189, 'IoU-bowl': 53.719580155217706, 'IoU-banana': 82.47653564293194, 'IoU-apple': 56.49622428816309, 'IoU-sandwich': 61.91538356049955, 'IoU-orange': 79.5689323731951, 'IoU-broccoli': 67.05794504082783, 'IoU-carrot': 63.564048613110046, 'IoU-hot dog': 60.42234900793182, 'IoU-pizza': 80.31631248790035, 'IoU-donut': 66.72753830398797, 'IoU-cake': 70.1121441243997, 'IoU-chair': 52.46734648874269, 'IoU-couch': 66.76214399678906, 'IoU-potted plant': 35.686635271097536, 'IoU-bed': 66.66925213832504, 'IoU-dining table': 50.8435141247986, 'IoU-toilet': 81.60051494485272, 'IoU-tv': 75.57351730238034, 'IoU-laptop': 74.56919078809125, 'IoU-mouse': 67.5830698991289, 'IoU-remote': 49.76887610867629, 'IoU-keyboard': 56.77803588420625, 'IoU-cell phone': 71.91667412095325, 'IoU-microwave': 41.768417873698304, 'IoU-oven': 65.65642435257809, 'IoU-toaster': 34.051988517873355, 'IoU-sink': 69.57116678911324, 'IoU-refrigerator': 82.86846547921813, 'IoU-book': 49.88184339500779, 'IoU-clock': 72.0809819547528, 'IoU-vase': 59.86296771372125, 'IoU-scissors': 56.30823120989559, 'IoU-teddy bear': 79.56236930751415, 'IoU-hair drier': 36.462432325366436, 'IoU-toothbrush': 63.83427109974424, 'IoU-banner': 36.160234694893205, 'IoU-blanket': 12.534262159099093, 'IoU-bridge': 39.12361637386482, 'IoU-cardboard': 43.81443083181736, 'IoU-counter': 31.747064043822444, 'IoU-curtain': 63.03667522145873, 'IoU-door-stuff': 41.44743943192164, 'IoU-floor-wood': 59.952555047566015, 'IoU-flower': 43.19238588271395, 'IoU-fruit': 37.754456124502795, 'IoU-gravel': 30.120539805300613, 'IoU-house': 23.860967934741566, 'IoU-light': 40.66812059540699, 'IoU-mirror-stuff': 58.87959671331713, 'IoU-net': 49.84768624783927, 'IoU-pillow': 10.207355345670539, 'IoU-platform': 28.546314085525736, 'IoU-playingfield': 71.56955399832276, 'IoU-railroad': 60.9800546576886, 'IoU-river': 52.122212370315005, 'IoU-road': 66.59595672732095, 'IoU-roof': 11.969305375675603, 'IoU-sand': 64.25680455300723, 'IoU-sea': 84.37880283511792, 'IoU-shelf': 37.11221992129631, 'IoU-snow': 88.45977217034861, 'IoU-stairs': 27.317155384903764, 'IoU-tent': 6.924683438309091, 'IoU-towel': 33.41887417218543, 'IoU-wall-brick': 45.03603554377204, 'IoU-wall-stone': 21.422289147386177, 'IoU-wall-tile': 67.92282269174741, 'IoU-wall-wood': 37.11954949224174, 'IoU-water-other': 21.03043857216254, 'IoU-window-blind': 48.67881233784953, 'IoU-window-other': 46.9875682404806, 'IoU-tree-merged': 80.51349003366244, 'IoU-fence-merged': 52.2659787427884, 'IoU-ceiling-merged': 65.52661088194424, 'IoU-sky-other-merged': 93.44455006301922, 'IoU-cabinet-merged': 57.465792554234994, 'IoU-table-merged': 36.81722639324751, 'IoU-floor-other-merged': 50.11676639401229, 'IoU-pavement-merged': 53.698585305973815, 'IoU-mountain-merged': 55.99138341866765, 'IoU-grass-merged': 71.75350034749265, 'IoU-dirt-merged': 44.2721635766851, 'IoU-paper-merged': 34.2812692362348, 'IoU-food-other-merged': 37.34083079129248, 'IoU-building-other-merged': 57.82805564639123, 'IoU-rock-merged': 60.6064486917114, 'IoU-wall-other-merged': 65.39563140085718, 'IoU-rug-merged': 65.20239626907083, 'mACC': 72.24186388738416, 'pACC': 80.26734856739648, 'ACC-person': 92.5065824276042, 'ACC-bicycle': 86.0261657336183, 'ACC-car': 84.3768975882347, 'ACC-motorcycle': 91.16946909134336, 'ACC-airplane': 90.8745092956519, 'ACC-bus': 83.24160402833921, 'ACC-train': 96.0609396915527, 'ACC-truck': 75.39696047538477, 'ACC-boat': 79.28272315821029, 'ACC-traffic light': 90.55990368686766, 'ACC-fire hydrant': 95.38760691242469, 'ACC-stop sign': 93.50225174355458, 'ACC-parking meter': 85.02547248181416, 'ACC-bench': 70.22949977926778, 'ACC-bird': 80.75936649375996, 'ACC-cat': 93.82462526108365, 'ACC-dog': 81.84954222039221, 'ACC-horse': 93.19260238560796, 'ACC-sheep': 90.20282779008103, 'ACC-cow': 86.29721063533951, 'ACC-elephant': 87.76719842663083, 'ACC-bear': 72.29585026752761, 'ACC-zebra': 95.0047607451758, 'ACC-giraffe': 92.7786432277371, 'ACC-backpack': 59.57747960140577, 'ACC-umbrella': 81.26713558068325, 'ACC-handbag': 51.27830231836374, 'ACC-tie': 79.14973749817091, 'ACC-suitcase': 89.67517658499459, 'ACC-frisbee': 94.08509090909091, 'ACC-skis': 70.16528277972178, 'ACC-snowboard': 78.9140603952879, 'ACC-sports ball': 79.98831932686244, 'ACC-kite': 73.66643258603722, 'ACC-baseball bat': 84.75750923435405, 'ACC-baseball glove': 88.68161086011975, 'ACC-skateboard': 69.77582128541997, 'ACC-surfboard': 83.37650475020708, 'ACC-tennis racket': 79.58520094966094, 'ACC-bottle': 81.17394270817422, 'ACC-wine glass': 85.93842360066, 'ACC-cup': 83.92525733632085, 'ACC-fork': 66.53655112922252, 'ACC-knife': 64.61914706528907, 'ACC-spoon': 67.79306773203122, 'ACC-bowl': 68.31979091351177, 'ACC-banana': 89.03630651982328, 'ACC-apple': 75.52479289215907, 'ACC-sandwich': 75.56900136121472, 'ACC-orange': 86.50569792236139, 'ACC-broccoli': 79.98419947492062, 'ACC-carrot': 72.63645929908466, 'ACC-hot dog': 73.76502860828224, 'ACC-pizza': 90.23877467502672, 'ACC-donut': 81.65004636683412, 'ACC-cake': 78.09598974491065, 'ACC-chair': 67.18092217940634, 'ACC-couch': 86.32804091785637, 'ACC-potted plant': 53.91426620029871, 'ACC-bed': 77.04828017767012, 'ACC-dining table': 78.22030180537162, 'ACC-toilet': 88.70280637650501, 'ACC-tv': 87.58742746934558, 'ACC-laptop': 92.78186144202624, 'ACC-mouse': 86.45354227315852, 'ACC-remote': 72.77871238565542, 'ACC-keyboard': 61.747700243681706, 'ACC-cell phone': 78.84695539178529, 'ACC-microwave': 46.31907553562854, 'ACC-oven': 86.20128483179357, 'ACC-toaster': 37.98943464396666, 'ACC-sink': 84.05470558731743, 'ACC-refrigerator': 90.82904040819906, 'ACC-book': 64.37603317362921, 'ACC-clock': 77.79446628315208, 'ACC-vase': 70.39493489579208, 'ACC-scissors': 61.99177753904635, 'ACC-teddy bear': 85.75813528262238, 'ACC-hair drier': 39.297811174679076, 'ACC-toothbrush': 81.30385684503128, 'ACC-banner': 75.93378565746313, 'ACC-blanket': 17.86138982168847, 'ACC-bridge': 54.913249291468155, 'ACC-cardboard': 54.78894866947797, 'ACC-counter': 56.02218209859363, 'ACC-curtain': 73.73758198972128, 'ACC-door-stuff': 57.8292020444397, 'ACC-floor-wood': 79.47989506966066, 'ACC-flower': 61.061891360615775, 'ACC-fruit': 52.96580894659827, 'ACC-gravel': 39.72721828519381, 'ACC-house': 28.02398617836807, 'ACC-light': 56.6828082532634, 'ACC-mirror-stuff': 68.55440289976151, 'ACC-net': 62.39887552017168, 'ACC-pillow': 23.51812203499009, 'ACC-platform': 55.15575231672778, 'ACC-playingfield': 92.262944667068, 'ACC-railroad': 76.42250473342635, 'ACC-river': 82.82151459149098, 'ACC-road': 84.47468491046493, 'ACC-roof': 16.11289711400946, 'ACC-sand': 72.36680821661149, 'ACC-sea': 91.08179902581762, 'ACC-shelf': 58.19875662030303, 'ACC-snow': 95.34339970746196, 'ACC-stairs': 42.83007665772002, 'ACC-tent': 8.084268394176764, 'ACC-towel': 41.17684757432812, 'ACC-wall-brick': 65.2581868712861, 'ACC-wall-stone': 24.64217614324199, 'ACC-wall-tile': 80.36545513191923, 'ACC-wall-wood': 56.9629215484728, 'ACC-water-other': 27.39148620116606, 'ACC-window-blind': 58.109330795764556, 'ACC-window-other': 69.09112023208064, 'ACC-tree-merged': 89.0698317294188, 'ACC-fence-merged': 71.84489124133691, 'ACC-ceiling-merged': 82.11505523070535, 'ACC-sky-other-merged': 96.39351720267454, 'ACC-cabinet-merged': 75.68012670827815, 'ACC-table-merged': 47.18840826824135, 'ACC-floor-other-merged': 61.11626253421516, 'ACC-pavement-merged': 67.0073991254662, 'ACC-mountain-merged': 66.32582738325219, 'ACC-grass-merged': 82.79948848014187, 'ACC-dirt-merged': 64.75909901851479, 'ACC-paper-merged': 45.89660721267856, 'ACC-food-other-merged': 49.10632587319567, 'ACC-building-other-merged': 74.69696793805353, 'ACC-rock-merged': 81.6706678048937, 'ACC-wall-other-merged': 80.81430339359375, 'ACC-rug-merged': 79.26010968138831})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1553 s/iter. Inference: 0.5706 s/iter. Eval: 0.0000 s/iter. Total: 0.7259 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1567 s/iter. Inference: 0.5701 s/iter. Eval: 0.0000 s/iter. Total: 0.7270 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1728 s/iter. Inference: 0.5953 s/iter. Eval: 0.0000 s/iter. Total: 0.7682 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1730 s/iter. Inference: 0.6700 s/iter. Eval: 0.0000 s/iter. Total: 0.8432 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1707 s/iter. Inference: 0.6180 s/iter. Eval: 0.0000 s/iter. Total: 0.7889 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1690 s/iter. Inference: 0.6647 s/iter. Eval: 0.0000 s/iter. Total: 0.8339 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5861867134913668, 'noc@0.8': 3.0005853087503658, 'noc@0.85': 3.684811237928007, 'noc@0.9': 4.688323090430202, 'miou@iter1': 0.8297221703176301}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1004 s/iter. Eval: 0.0008 s/iter. Total: 0.1029 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.65682220458984, 'precision@0.6': 67.39215087890625, 'precision@0.7': 62.2619514465332, 'precision@0.8': 52.27360916137695, 'precision@0.9': 26.894676208496094, 'cIoU': 56.954647064208984, 'mIoU': 62.30198287963867}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.82473144134064, 'SQ': 82.04642953703906, 'RQ': 59.85409403860116, 'PQ_th': 54.94509830855592, 'SQ_th': 82.82592306673084, 'RQ_th': 65.68956982299785, 'PQ_st': 42.09587579271382, 'SQ_st': 80.86983552995714, 'RQ_st': 51.045828703662764}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.820654786013804, 'AP50': 61.156376114140855, 'AP75': 40.833550800354416, 'APs': 19.264232583102523, 'APm': 41.88515076200277, 'APl': 60.343813158226, 'AP-person': 44.148820323042536, 'AP-bicycle': 17.750323217153774, 'AP-car': 37.22727793239116, 'AP-motorcycle': 35.06404843587583, 'AP-airplane': 54.92809644741946, 'AP-bus': 62.863686067837385, 'AP-train': 69.27634757522655, 'AP-truck': 35.23233993782388, 'AP-boat': 22.080235560401437, 'AP-traffic light': 24.89211935652431, 'AP-fire hydrant': 64.93452059605181, 'AP-stop sign': 64.33798833911851, 'AP-parking meter': 44.40347811864562, 'AP-bench': 20.183787202742604, 'AP-bird': 30.76073849778923, 'AP-cat': 73.11144608954667, 'AP-dog': 66.89082597866917, 'AP-horse': 46.88365980468884, 'AP-sheep': 45.82569597845575, 'AP-cow': 50.09934153975165, 'AP-elephant': 60.46037711631806, 'AP-bear': 77.47986842130352, 'AP-zebra': 60.675371603463425, 'AP-giraffe': 56.980995043779906, 'AP-backpack': 17.263844852273756, 'AP-umbrella': 48.66395145223829, 'AP-handbag': 15.31267465979716, 'AP-tie': 33.977894712019506, 'AP-suitcase': 40.34078023498235, 'AP-frisbee': 66.86770943169562, 'AP-skis': 5.40323574410513, 'AP-snowboard': 23.88022311019383, 'AP-sports ball': 47.2333835818235, 'AP-kite': 33.072036197878866, 'AP-baseball bat': 27.790561391710167, 'AP-baseball glove': 43.45018159270689, 'AP-skateboard': 36.911125885505555, 'AP-surfboard': 35.62886468247427, 'AP-tennis racket': 56.47826923901952, 'AP-bottle': 33.36153848330228, 'AP-wine glass': 27.446357970013064, 'AP-cup': 40.464811576401644, 'AP-fork': 17.048136952060435, 'AP-knife': 13.254951060512585, 'AP-spoon': 14.398232132625388, 'AP-bowl': 31.518985164837076, 'AP-banana': 20.24550669119027, 'AP-apple': 18.950417399978075, 'AP-sandwich': 42.068140193691995, 'AP-orange': 28.009493036181276, 'AP-broccoli': 20.584015008823776, 'AP-carrot': 20.323037554331933, 'AP-hot dog': 24.762181616657458, 'AP-pizza': 49.378000866090126, 'AP-donut': 46.31263068724644, 'AP-cake': 43.95375335115341, 'AP-chair': 20.442725419225198, 'AP-couch': 42.04763592322707, 'AP-potted plant': 17.556256441443054, 'AP-bed': 41.937149610402635, 'AP-dining table': 13.322286451641066, 'AP-toilet': 66.16808143235149, 'AP-tv': 61.75523363735238, 'AP-laptop': 63.278558525469045, 'AP-mouse': 58.63726010818463, 'AP-remote': 31.45003317622363, 'AP-keyboard': 48.67423467852838, 'AP-cell phone': 37.123127840230964, 'AP-microwave': 54.68817032470514, 'AP-oven': 32.69453384501075, 'AP-toaster': 23.96366341703295, 'AP-sink': 38.557353366362925, 'AP-refrigerator': 59.827631176937004, 'AP-book': 8.81190607274823, 'AP-clock': 52.26464270858276, 'AP-vase': 34.60841987009643, 'AP-scissors': 25.906025178155883, 'AP-teddy bear': 49.11669653973704, 'AP-hair drier': 11.372642854347546, 'AP-toothbrush': 20.60179858556519}), ('sem_seg', {'mIoU': 59.927554701302235, 'fwIoU': 68.79923965055136, 'IoU-person': 87.37603552351673, 'IoU-bicycle': 74.89575035076321, 'IoU-car': 67.94965860846446, 'IoU-motorcycle': 85.41527780312234, 'IoU-airplane': 77.86543302821676, 'IoU-bus': 80.56641951821364, 'IoU-train': 77.78757124527304, 'IoU-truck': 62.72705522140396, 'IoU-boat': 68.42842771025244, 'IoU-traffic light': 76.56035275740658, 'IoU-fire hydrant': 80.88764065286466, 'IoU-stop sign': 91.02590249566931, 'IoU-parking meter': 81.17922333749085, 'IoU-bench': 54.86873953307468, 'IoU-bird': 75.62335778855956, 'IoU-cat': 88.34620623092955, 'IoU-dog': 76.7991441545573, 'IoU-horse': 86.76073039277948, 'IoU-sheep': 86.18829392517168, 'IoU-cow': 80.92743471865782, 'IoU-elephant': 85.38393489458726, 'IoU-bear': 70.73697696469439, 'IoU-zebra': 92.34569133723141, 'IoU-giraffe': 88.2844826332297, 'IoU-backpack': 39.561513968394614, 'IoU-umbrella': 73.53780542041584, 'IoU-handbag': 38.745336184431245, 'IoU-tie': 69.13646462957212, 'IoU-suitcase': 81.93613693740504, 'IoU-frisbee': 80.2624395086239, 'IoU-skis': 51.8811786401701, 'IoU-snowboard': 69.48280472341783, 'IoU-sports ball': 60.439659145050804, 'IoU-kite': 64.66998918395778, 'IoU-baseball bat': 58.646503501941496, 'IoU-baseball glove': 76.48305348065475, 'IoU-skateboard': 64.11535499117896, 'IoU-surfboard': 77.1701353063371, 'IoU-tennis racket': 74.46540313384695, 'IoU-bottle': 68.29668081359878, 'IoU-wine glass': 73.97951339433789, 'IoU-cup': 64.52663641315, 'IoU-fork': 54.41338592631924, 'IoU-knife': 49.49556320123029, 'IoU-spoon': 48.96685934146189, 'IoU-bowl': 53.719580155217706, 'IoU-banana': 82.47653564293194, 'IoU-apple': 56.49622428816309, 'IoU-sandwich': 61.91538356049955, 'IoU-orange': 79.5689323731951, 'IoU-broccoli': 67.05794504082783, 'IoU-carrot': 63.564048613110046, 'IoU-hot dog': 60.42234900793182, 'IoU-pizza': 80.31631248790035, 'IoU-donut': 66.72753830398797, 'IoU-cake': 70.1121441243997, 'IoU-chair': 52.46734648874269, 'IoU-couch': 66.76214399678906, 'IoU-potted plant': 35.686635271097536, 'IoU-bed': 66.66925213832504, 'IoU-dining table': 50.8435141247986, 'IoU-toilet': 81.60051494485272, 'IoU-tv': 75.57351730238034, 'IoU-laptop': 74.56919078809125, 'IoU-mouse': 67.5830698991289, 'IoU-remote': 49.76887610867629, 'IoU-keyboard': 56.77803588420625, 'IoU-cell phone': 71.91667412095325, 'IoU-microwave': 41.768417873698304, 'IoU-oven': 65.65642435257809, 'IoU-toaster': 34.051988517873355, 'IoU-sink': 69.57116678911324, 'IoU-refrigerator': 82.86846547921813, 'IoU-book': 49.88184339500779, 'IoU-clock': 72.0809819547528, 'IoU-vase': 59.86296771372125, 'IoU-scissors': 56.30823120989559, 'IoU-teddy bear': 79.56236930751415, 'IoU-hair drier': 36.462432325366436, 'IoU-toothbrush': 63.83427109974424, 'IoU-banner': 36.160234694893205, 'IoU-blanket': 12.534262159099093, 'IoU-bridge': 39.12361637386482, 'IoU-cardboard': 43.81443083181736, 'IoU-counter': 31.747064043822444, 'IoU-curtain': 63.03667522145873, 'IoU-door-stuff': 41.44743943192164, 'IoU-floor-wood': 59.952555047566015, 'IoU-flower': 43.19238588271395, 'IoU-fruit': 37.754456124502795, 'IoU-gravel': 30.120539805300613, 'IoU-house': 23.860967934741566, 'IoU-light': 40.66812059540699, 'IoU-mirror-stuff': 58.87959671331713, 'IoU-net': 49.84768624783927, 'IoU-pillow': 10.207355345670539, 'IoU-platform': 28.546314085525736, 'IoU-playingfield': 71.56955399832276, 'IoU-railroad': 60.9800546576886, 'IoU-river': 52.122212370315005, 'IoU-road': 66.59595672732095, 'IoU-roof': 11.969305375675603, 'IoU-sand': 64.25680455300723, 'IoU-sea': 84.37880283511792, 'IoU-shelf': 37.11221992129631, 'IoU-snow': 88.45977217034861, 'IoU-stairs': 27.317155384903764, 'IoU-tent': 6.924683438309091, 'IoU-towel': 33.41887417218543, 'IoU-wall-brick': 45.03603554377204, 'IoU-wall-stone': 21.422289147386177, 'IoU-wall-tile': 67.92282269174741, 'IoU-wall-wood': 37.11954949224174, 'IoU-water-other': 21.03043857216254, 'IoU-window-blind': 48.67881233784953, 'IoU-window-other': 46.9875682404806, 'IoU-tree-merged': 80.51349003366244, 'IoU-fence-merged': 52.2659787427884, 'IoU-ceiling-merged': 65.52661088194424, 'IoU-sky-other-merged': 93.44455006301922, 'IoU-cabinet-merged': 57.465792554234994, 'IoU-table-merged': 36.81722639324751, 'IoU-floor-other-merged': 50.11676639401229, 'IoU-pavement-merged': 53.698585305973815, 'IoU-mountain-merged': 55.99138341866765, 'IoU-grass-merged': 71.75350034749265, 'IoU-dirt-merged': 44.2721635766851, 'IoU-paper-merged': 34.2812692362348, 'IoU-food-other-merged': 37.34083079129248, 'IoU-building-other-merged': 57.82805564639123, 'IoU-rock-merged': 60.6064486917114, 'IoU-wall-other-merged': 65.39563140085718, 'IoU-rug-merged': 65.20239626907083, 'mACC': 72.24186388738416, 'pACC': 80.26734856739648, 'ACC-person': 92.5065824276042, 'ACC-bicycle': 86.0261657336183, 'ACC-car': 84.3768975882347, 'ACC-motorcycle': 91.16946909134336, 'ACC-airplane': 90.8745092956519, 'ACC-bus': 83.24160402833921, 'ACC-train': 96.0609396915527, 'ACC-truck': 75.39696047538477, 'ACC-boat': 79.28272315821029, 'ACC-traffic light': 90.55990368686766, 'ACC-fire hydrant': 95.38760691242469, 'ACC-stop sign': 93.50225174355458, 'ACC-parking meter': 85.02547248181416, 'ACC-bench': 70.22949977926778, 'ACC-bird': 80.75936649375996, 'ACC-cat': 93.82462526108365, 'ACC-dog': 81.84954222039221, 'ACC-horse': 93.19260238560796, 'ACC-sheep': 90.20282779008103, 'ACC-cow': 86.29721063533951, 'ACC-elephant': 87.76719842663083, 'ACC-bear': 72.29585026752761, 'ACC-zebra': 95.0047607451758, 'ACC-giraffe': 92.7786432277371, 'ACC-backpack': 59.57747960140577, 'ACC-umbrella': 81.26713558068325, 'ACC-handbag': 51.27830231836374, 'ACC-tie': 79.14973749817091, 'ACC-suitcase': 89.67517658499459, 'ACC-frisbee': 94.08509090909091, 'ACC-skis': 70.16528277972178, 'ACC-snowboard': 78.9140603952879, 'ACC-sports ball': 79.98831932686244, 'ACC-kite': 73.66643258603722, 'ACC-baseball bat': 84.75750923435405, 'ACC-baseball glove': 88.68161086011975, 'ACC-skateboard': 69.77582128541997, 'ACC-surfboard': 83.37650475020708, 'ACC-tennis racket': 79.58520094966094, 'ACC-bottle': 81.17394270817422, 'ACC-wine glass': 85.93842360066, 'ACC-cup': 83.92525733632085, 'ACC-fork': 66.53655112922252, 'ACC-knife': 64.61914706528907, 'ACC-spoon': 67.79306773203122, 'ACC-bowl': 68.31979091351177, 'ACC-banana': 89.03630651982328, 'ACC-apple': 75.52479289215907, 'ACC-sandwich': 75.56900136121472, 'ACC-orange': 86.50569792236139, 'ACC-broccoli': 79.98419947492062, 'ACC-carrot': 72.63645929908466, 'ACC-hot dog': 73.76502860828224, 'ACC-pizza': 90.23877467502672, 'ACC-donut': 81.65004636683412, 'ACC-cake': 78.09598974491065, 'ACC-chair': 67.18092217940634, 'ACC-couch': 86.32804091785637, 'ACC-potted plant': 53.91426620029871, 'ACC-bed': 77.04828017767012, 'ACC-dining table': 78.22030180537162, 'ACC-toilet': 88.70280637650501, 'ACC-tv': 87.58742746934558, 'ACC-laptop': 92.78186144202624, 'ACC-mouse': 86.45354227315852, 'ACC-remote': 72.77871238565542, 'ACC-keyboard': 61.747700243681706, 'ACC-cell phone': 78.84695539178529, 'ACC-microwave': 46.31907553562854, 'ACC-oven': 86.20128483179357, 'ACC-toaster': 37.98943464396666, 'ACC-sink': 84.05470558731743, 'ACC-refrigerator': 90.82904040819906, 'ACC-book': 64.37603317362921, 'ACC-clock': 77.79446628315208, 'ACC-vase': 70.39493489579208, 'ACC-scissors': 61.99177753904635, 'ACC-teddy bear': 85.75813528262238, 'ACC-hair drier': 39.297811174679076, 'ACC-toothbrush': 81.30385684503128, 'ACC-banner': 75.93378565746313, 'ACC-blanket': 17.86138982168847, 'ACC-bridge': 54.913249291468155, 'ACC-cardboard': 54.78894866947797, 'ACC-counter': 56.02218209859363, 'ACC-curtain': 73.73758198972128, 'ACC-door-stuff': 57.8292020444397, 'ACC-floor-wood': 79.47989506966066, 'ACC-flower': 61.061891360615775, 'ACC-fruit': 52.96580894659827, 'ACC-gravel': 39.72721828519381, 'ACC-house': 28.02398617836807, 'ACC-light': 56.6828082532634, 'ACC-mirror-stuff': 68.55440289976151, 'ACC-net': 62.39887552017168, 'ACC-pillow': 23.51812203499009, 'ACC-platform': 55.15575231672778, 'ACC-playingfield': 92.262944667068, 'ACC-railroad': 76.42250473342635, 'ACC-river': 82.82151459149098, 'ACC-road': 84.47468491046493, 'ACC-roof': 16.11289711400946, 'ACC-sand': 72.36680821661149, 'ACC-sea': 91.08179902581762, 'ACC-shelf': 58.19875662030303, 'ACC-snow': 95.34339970746196, 'ACC-stairs': 42.83007665772002, 'ACC-tent': 8.084268394176764, 'ACC-towel': 41.17684757432812, 'ACC-wall-brick': 65.2581868712861, 'ACC-wall-stone': 24.64217614324199, 'ACC-wall-tile': 80.36545513191923, 'ACC-wall-wood': 56.9629215484728, 'ACC-water-other': 27.39148620116606, 'ACC-window-blind': 58.109330795764556, 'ACC-window-other': 69.09112023208064, 'ACC-tree-merged': 89.0698317294188, 'ACC-fence-merged': 71.84489124133691, 'ACC-ceiling-merged': 82.11505523070535, 'ACC-sky-other-merged': 96.39351720267454, 'ACC-cabinet-merged': 75.68012670827815, 'ACC-table-merged': 47.18840826824135, 'ACC-floor-other-merged': 61.11626253421516, 'ACC-pavement-merged': 67.0073991254662, 'ACC-mountain-merged': 66.32582738325219, 'ACC-grass-merged': 82.79948848014187, 'ACC-dirt-merged': 64.75909901851479, 'ACC-paper-merged': 45.89660721267856, 'ACC-food-other-merged': 49.10632587319567, 'ACC-building-other-merged': 74.69696793805353, 'ACC-rock-merged': 81.6706678048937, 'ACC-wall-other-merged': 80.81430339359375, 'ACC-rug-merged': 79.26010968138831})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5861867134913668, 'noc@0.8': 3.0005853087503658, 'noc@0.85': 3.684811237928007, 'noc@0.9': 4.688323090430202, 'miou@iter1': 0.8297221703176301}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.65682220458984, 'precision@0.6': 67.39215087890625, 'precision@0.7': 62.2619514465332, 'precision@0.8': 52.27360916137695, 'precision@0.9': 26.894676208496094, 'cIoU': 56.954647064208984, 'mIoU': 62.30198287963867}}}
INFO:trainer.default_trainer:This epoch takes 1:29:03.239974
INFO:trainer.default_trainer:PROGRESS: 24.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 12 training.
INFO:trainer.default_trainer:epochs[    12] optim steps[22000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.66406/0.91231, loss_mask_bce_0: 0.17340/0.33605, loss_mask_dice_0: 3.21102/1.16678, loss_spatial_bce_0: 0.04063/0.09175, loss_spatial_dice_0: 0.49425/0.21909, loss_spatial_ce_0: 0.03614/0.07872, loss_grounding_bce_0: 0.03979/0.08652, loss_grounding_dice_0: 0.21751/0.17943, loss_grounding_ce_0: 0.87977/0.27598, loss_mask_ce_1: 1.68327/0.91285, loss_mask_bce_1: 0.18347/0.33673, loss_mask_dice_1: 3.08427/1.17412, loss_spatial_bce_1: 0.03713/0.09265, loss_spatial_dice_1: 0.43462/0.22344, loss_spatial_ce_1: 0.03948/0.08451, loss_grounding_bce_1: 0.04069/0.08656, loss_grounding_dice_1: 0.23323/0.18012, loss_grounding_ce_1: 1.19555/0.27786, loss_mask_ce_2: 1.52717/0.92020, loss_mask_bce_2: 0.17793/0.33714, loss_mask_dice_2: 3.62087/1.17310, loss_spatial_bce_2: 0.03774/0.09275, loss_spatial_dice_2: 0.46136/0.22436, loss_spatial_ce_2: 0.06348/0.08833, loss_grounding_bce_2: 0.04162/0.08664, loss_grounding_dice_2: 0.22880/0.17954, loss_grounding_ce_2: 0.86095/0.28164, loss_mask_ce_3: 1.93918/0.92835, loss_mask_bce_3: 0.14388/0.33796, loss_mask_dice_3: 3.43811/1.17045, loss_spatial_bce_3: 0.04010/0.09362, loss_spatial_dice_3: 0.46739/0.22506, loss_spatial_ce_3: 0.26842/0.09242, loss_grounding_bce_3: 0.04601/0.08673, loss_grounding_dice_3: 0.20867/0.17934, loss_grounding_ce_3: 0.77652/0.28277, loss_mask_ce_4: 2.11066/0.92700, loss_mask_bce_4: 0.15011/0.33956, loss_mask_dice_4: 3.21783/1.19264, loss_spatial_bce_4: 0.04657/0.09752, loss_spatial_dice_4: 0.52705/0.23467, loss_spatial_ce_4: 0.07174/0.10874, loss_grounding_bce_4: 0.04193/0.08728, loss_grounding_dice_4: 0.23853/0.18213, loss_grounding_ce_4: 0.80551/0.28545, loss_mask_ce_5: 1.96085/0.94132, loss_mask_bce_5: 0.14586/0.34182, loss_mask_dice_5: 3.52635/1.19783, loss_spatial_bce_5: 0.03745/0.09867, loss_spatial_dice_5: 0.47621/0.23769, loss_spatial_ce_5: 0.04740/0.12209, loss_grounding_bce_5: 0.03921/0.08770, loss_grounding_dice_5: 0.24200/0.18322, loss_grounding_ce_5: 0.76949/0.29790, loss_mask_ce_6: 2.00437/0.97874, loss_mask_bce_6: 0.16780/0.34454, loss_mask_dice_6: 3.23417/1.20050, loss_spatial_bce_6: 0.04314/0.10402, loss_spatial_dice_6: 0.48966/0.23995, loss_spatial_ce_6: 0.07181/0.14675, loss_grounding_bce_6: 0.05101/0.08848, loss_grounding_dice_6: 0.19049/0.18339, loss_grounding_ce_6: 1.08452/0.31579, loss_mask_ce_7: 2.09210/1.02152, loss_mask_bce_7: 0.15045/0.35219, loss_mask_dice_7: 3.47224/1.25603, loss_spatial_bce_7: 0.04238/0.11290, loss_spatial_dice_7: 0.53940/0.26718, loss_spatial_ce_7: 0.20910/0.18538, loss_grounding_bce_7: 0.05906/0.09040, loss_grounding_dice_7: 0.26120/0.19065, loss_grounding_ce_7: 0.52329/0.34916, loss_mask_ce_8: 1.87957/1.13267, loss_mask_bce_8: 0.14923/0.36580, loss_mask_dice_8: 3.57296/1.33106, loss_spatial_bce_8: 0.05513/0.13421, loss_spatial_dice_8: 0.62207/0.30783, loss_spatial_ce_8: 0.11519/0.24219, loss_grounding_bce_8: 0.05373/0.09390, loss_grounding_dice_8: 0.26726/0.20212, loss_grounding_ce_8: 0.78593/0.42121, loss_mask_ce_9: 4.32279/3.69252, loss_mask_bce_9: 0.14749/0.39274, loss_mask_dice_9: 4.32701/1.90648, loss_spatial_bce_9: 0.10122/0.33649, loss_spatial_dice_9: 0.84188/0.82457, loss_spatial_ce_9: 2.85717/1.51803, loss_grounding_bce_9: 0.05053/0.10528, loss_grounding_dice_9: 0.32366/0.28159, loss_grounding_ce_9: 0.50029/0.69607] items per batch[64] items per second[0.13] total items[1408000] mini batches[ 22000] memory[7341] epoch remaining[1:23:42]
INFO:trainer.default_trainer:epochs[    12] optim steps[22100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05352/0.91193, loss_mask_bce_0: 0.52572/0.33584, loss_mask_dice_0: 0.66025/1.16669, loss_spatial_bce_0: 0.15676/0.09167, loss_spatial_dice_0: 0.16909/0.21904, loss_spatial_ce_0: 0.05149/0.07862, loss_grounding_bce_0: 0.18639/0.08647, loss_grounding_dice_0: 0.21665/0.17942, loss_grounding_ce_0: 0.18615/0.27592, loss_mask_ce_1: 1.04577/0.91245, loss_mask_bce_1: 0.50177/0.33651, loss_mask_dice_1: 0.64918/1.17400, loss_spatial_bce_1: 0.15860/0.09257, loss_spatial_dice_1: 0.16492/0.22339, loss_spatial_ce_1: 0.03770/0.08443, loss_grounding_bce_1: 0.17398/0.08652, loss_grounding_dice_1: 0.22393/0.18011, loss_grounding_ce_1: 0.16955/0.27789, loss_mask_ce_2: 1.06731/0.91979, loss_mask_bce_2: 0.49454/0.33692, loss_mask_dice_2: 0.62447/1.17298, loss_spatial_bce_2: 0.14446/0.09267, loss_spatial_dice_2: 0.17083/0.22432, loss_spatial_ce_2: 0.03409/0.08820, loss_grounding_bce_2: 0.17165/0.08660, loss_grounding_dice_2: 0.19662/0.17954, loss_grounding_ce_2: 0.15243/0.28162, loss_mask_ce_3: 0.91364/0.92798, loss_mask_bce_3: 0.46594/0.33773, loss_mask_dice_3: 0.63223/1.17031, loss_spatial_bce_3: 0.14911/0.09354, loss_spatial_dice_3: 0.16655/0.22502, loss_spatial_ce_3: 0.11936/0.09227, loss_grounding_bce_3: 0.16293/0.08670, loss_grounding_dice_3: 0.17744/0.17934, loss_grounding_ce_3: 0.17541/0.28275, loss_mask_ce_4: 1.02634/0.92671, loss_mask_bce_4: 0.49951/0.33934, loss_mask_dice_4: 0.63557/1.19252, loss_spatial_bce_4: 0.17489/0.09745, loss_spatial_dice_4: 0.19155/0.23465, loss_spatial_ce_4: 0.05080/0.10858, loss_grounding_bce_4: 0.18474/0.08725, loss_grounding_dice_4: 0.19704/0.18214, loss_grounding_ce_4: 0.09387/0.28536, loss_mask_ce_5: 1.06685/0.94093, loss_mask_bce_5: 0.47800/0.34159, loss_mask_dice_5: 0.64076/1.19776, loss_spatial_bce_5: 0.16647/0.09860, loss_spatial_dice_5: 0.21278/0.23767, loss_spatial_ce_5: 0.08126/0.12197, loss_grounding_bce_5: 0.16169/0.08767, loss_grounding_dice_5: 0.17810/0.18323, loss_grounding_ce_5: 0.14485/0.29786, loss_mask_ce_6: 1.09267/0.97839, loss_mask_bce_6: 0.50217/0.34432, loss_mask_dice_6: 0.63367/1.20037, loss_spatial_bce_6: 0.19207/0.10395, loss_spatial_dice_6: 0.24400/0.23993, loss_spatial_ce_6: 0.10857/0.14667, loss_grounding_bce_6: 0.16537/0.08844, loss_grounding_dice_6: 0.15901/0.18340, loss_grounding_ce_6: 0.19195/0.31573, loss_mask_ce_7: 1.19322/1.02121, loss_mask_bce_7: 0.51393/0.35196, loss_mask_dice_7: 0.65872/1.25589, loss_spatial_bce_7: 0.18833/0.11281, loss_spatial_dice_7: 0.22149/0.26716, loss_spatial_ce_7: 0.12269/0.18526, loss_grounding_bce_7: 0.16305/0.09035, loss_grounding_dice_7: 0.17548/0.19063, loss_grounding_ce_7: 0.27441/0.34911, loss_mask_ce_8: 1.38989/1.13242, loss_mask_bce_8: 0.51481/0.36557, loss_mask_dice_8: 0.66490/1.33096, loss_spatial_bce_8: 0.27208/0.13411, loss_spatial_dice_8: 0.22357/0.30779, loss_spatial_ce_8: 0.14869/0.24211, loss_grounding_bce_8: 0.16885/0.09385, loss_grounding_dice_8: 0.17363/0.20212, loss_grounding_ce_8: 0.74492/0.42114, loss_mask_ce_9: 3.11387/3.69233, loss_mask_bce_9: 0.58640/0.39248, loss_mask_dice_9: 0.92961/1.90604, loss_spatial_bce_9: 0.42265/0.33642, loss_spatial_dice_9: 0.80302/0.82457, loss_spatial_ce_9: 1.47210/1.51779, loss_grounding_bce_9: 0.21471/0.10523, loss_grounding_dice_9: 0.26213/0.28162, loss_grounding_ce_9: 0.83944/0.69587] items per batch[64] items per second[0.23] total items[1414400] mini batches[ 22100] memory[7341] epoch remaining[1:18:03]
INFO:trainer.default_trainer:epochs[    12] optim steps[22200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94762/0.91187, loss_mask_bce_0: 0.57117/0.33588, loss_mask_dice_0: 1.57578/1.16710, loss_spatial_bce_0: 0.07240/0.09161, loss_spatial_dice_0: 0.24770/0.21898, loss_spatial_ce_0: 0.02043/0.07847, loss_grounding_bce_0: 0.10729/0.08642, loss_grounding_dice_0: 0.16466/0.17946, loss_grounding_ce_0: 1.07673/0.27599, loss_mask_ce_1: 1.02510/0.91244, loss_mask_bce_1: 0.56585/0.33656, loss_mask_dice_1: 1.67509/1.17432, loss_spatial_bce_1: 0.07609/0.09251, loss_spatial_dice_1: 0.26121/0.22333, loss_spatial_ce_1: 0.01650/0.08427, loss_grounding_bce_1: 0.10220/0.08647, loss_grounding_dice_1: 0.15940/0.18017, loss_grounding_ce_1: 1.09993/0.27798, loss_mask_ce_2: 1.02128/0.91974, loss_mask_bce_2: 0.58152/0.33698, loss_mask_dice_2: 1.46730/1.17339, loss_spatial_bce_2: 0.07710/0.09261, loss_spatial_dice_2: 0.23663/0.22425, loss_spatial_ce_2: 0.02401/0.08806, loss_grounding_bce_2: 0.10258/0.08655, loss_grounding_dice_2: 0.16100/0.17959, loss_grounding_ce_2: 0.99725/0.28172, loss_mask_ce_3: 1.03715/0.92803, loss_mask_bce_3: 0.58143/0.33780, loss_mask_dice_3: 1.53491/1.17070, loss_spatial_bce_3: 0.08178/0.09349, loss_spatial_dice_3: 0.25881/0.22496, loss_spatial_ce_3: 0.05668/0.09212, loss_grounding_bce_3: 0.09692/0.08665, loss_grounding_dice_3: 0.16111/0.17941, loss_grounding_ce_3: 1.20211/0.28289, loss_mask_ce_4: 0.99029/0.92675, loss_mask_bce_4: 0.58313/0.33942, loss_mask_dice_4: 1.40195/1.19300, loss_spatial_bce_4: 0.07332/0.09741, loss_spatial_dice_4: 0.25054/0.23462, loss_spatial_ce_4: 0.10793/0.10843, loss_grounding_bce_4: 0.10138/0.08721, loss_grounding_dice_4: 0.16853/0.18219, loss_grounding_ce_4: 1.14307/0.28557, loss_mask_ce_5: 0.99897/0.94097, loss_mask_bce_5: 0.58302/0.34167, loss_mask_dice_5: 1.61311/1.19814, loss_spatial_bce_5: 0.08415/0.09856, loss_spatial_dice_5: 0.26807/0.23765, loss_spatial_ce_5: 0.12151/0.12190, loss_grounding_bce_5: 0.13220/0.08763, loss_grounding_dice_5: 0.19746/0.18329, loss_grounding_ce_5: 0.66876/0.29792, loss_mask_ce_6: 0.92892/0.97838, loss_mask_bce_6: 0.61065/0.34438, loss_mask_dice_6: 1.46850/1.20079, loss_spatial_bce_6: 0.09495/0.10392, loss_spatial_dice_6: 0.27226/0.23990, loss_spatial_ce_6: 0.25236/0.14659, loss_grounding_bce_6: 0.10374/0.08840, loss_grounding_dice_6: 0.17165/0.18348, loss_grounding_ce_6: 1.44782/0.31581, loss_mask_ce_7: 1.03762/1.02134, loss_mask_bce_7: 0.61679/0.35204, loss_mask_dice_7: 1.44708/1.25639, loss_spatial_bce_7: 0.08072/0.11276, loss_spatial_dice_7: 0.25531/0.26712, loss_spatial_ce_7: 0.26360/0.18519, loss_grounding_bce_7: 0.11972/0.09032, loss_grounding_dice_7: 0.17627/0.19069, loss_grounding_ce_7: 0.62186/0.34917, loss_mask_ce_8: 1.22461/1.13252, loss_mask_bce_8: 0.75595/0.36566, loss_mask_dice_8: 1.71628/1.33144, loss_spatial_bce_8: 0.10225/0.13405, loss_spatial_dice_8: 0.35815/0.30775, loss_spatial_ce_8: 0.23078/0.24201, loss_grounding_bce_8: 0.11806/0.09384, loss_grounding_dice_8: 0.17045/0.20220, loss_grounding_ce_8: 0.54826/0.42127, loss_mask_ce_9: 3.93093/3.69273, loss_mask_bce_9: 0.84005/0.39259, loss_mask_dice_9: 3.05454/1.90668, loss_spatial_bce_9: 0.36510/0.33641, loss_spatial_dice_9: 0.89077/0.82462, loss_spatial_ce_9: 1.50469/1.51779, loss_grounding_bce_9: 0.11259/0.10519, loss_grounding_dice_9: 0.25198/0.28171, loss_grounding_ce_9: 1.41072/0.69586] items per batch[64] items per second[0.23] total items[1420800] mini batches[ 22200] memory[7341] epoch remaining[1:13:15]
INFO:trainer.default_trainer:epochs[    12] optim steps[22300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04678/0.91167, loss_mask_bce_0: 0.21667/0.33591, loss_mask_dice_0: 0.18557/1.16681, loss_spatial_bce_0: 0.11250/0.09162, loss_spatial_dice_0: 0.08816/0.21890, loss_spatial_ce_0: 0.03298/0.07835, loss_grounding_bce_0: 0.10909/0.08644, loss_grounding_dice_0: 0.08836/0.17942, loss_grounding_ce_0: 0.00399/0.27573, loss_mask_ce_1: 0.04188/0.91227, loss_mask_bce_1: 0.21582/0.33659, loss_mask_dice_1: 0.17998/1.17400, loss_spatial_bce_1: 0.10460/0.09252, loss_spatial_dice_1: 0.08511/0.22327, loss_spatial_ce_1: 0.03536/0.08414, loss_grounding_bce_1: 0.10869/0.08649, loss_grounding_dice_1: 0.08617/0.18013, loss_grounding_ce_1: 0.00285/0.27771, loss_mask_ce_2: 0.04283/0.91954, loss_mask_bce_2: 0.21030/0.33701, loss_mask_dice_2: 0.17930/1.17303, loss_spatial_bce_2: 0.11553/0.09263, loss_spatial_dice_2: 0.08766/0.22418, loss_spatial_ce_2: 0.03623/0.08801, loss_grounding_bce_2: 0.10531/0.08658, loss_grounding_dice_2: 0.08418/0.17957, loss_grounding_ce_2: 0.00216/0.28143, loss_mask_ce_3: 0.04220/0.92786, loss_mask_bce_3: 0.21767/0.33780, loss_mask_dice_3: 0.18586/1.17037, loss_spatial_bce_3: 0.11511/0.09351, loss_spatial_dice_3: 0.08470/0.22488, loss_spatial_ce_3: 0.05313/0.09201, loss_grounding_bce_3: 0.10467/0.08667, loss_grounding_dice_3: 0.08423/0.17938, loss_grounding_ce_3: 0.00256/0.28257, loss_mask_ce_4: 0.04000/0.92655, loss_mask_bce_4: 0.21982/0.33943, loss_mask_dice_4: 0.17890/1.19274, loss_spatial_bce_4: 0.11984/0.09744, loss_spatial_dice_4: 0.08631/0.23455, loss_spatial_ce_4: 0.06755/0.10828, loss_grounding_bce_4: 0.10920/0.08723, loss_grounding_dice_4: 0.08396/0.18215, loss_grounding_ce_4: 0.00204/0.28523, loss_mask_ce_5: 0.04805/0.94074, loss_mask_bce_5: 0.21938/0.34171, loss_mask_dice_5: 0.17910/1.19784, loss_spatial_bce_5: 0.10936/0.09859, loss_spatial_dice_5: 0.10555/0.23758, loss_spatial_ce_5: 0.05652/0.12181, loss_grounding_bce_5: 0.11181/0.08765, loss_grounding_dice_5: 0.08512/0.18325, loss_grounding_ce_5: 0.00336/0.29763, loss_mask_ce_6: 0.06840/0.97816, loss_mask_bce_6: 0.22587/0.34440, loss_mask_dice_6: 0.18714/1.20049, loss_spatial_bce_6: 0.11362/0.10394, loss_spatial_dice_6: 0.10368/0.23985, loss_spatial_ce_6: 0.09683/0.14647, loss_grounding_bce_6: 0.10874/0.08842, loss_grounding_dice_6: 0.08801/0.18345, loss_grounding_ce_6: 0.00577/0.31544, loss_mask_ce_7: 0.06550/1.02104, loss_mask_bce_7: 0.21624/0.35206, loss_mask_dice_7: 0.17953/1.25611, loss_spatial_bce_7: 0.11685/0.11278, loss_spatial_dice_7: 0.10720/0.26707, loss_spatial_ce_7: 0.17542/0.18513, loss_grounding_bce_7: 0.10903/0.09033, loss_grounding_dice_7: 0.08474/0.19068, loss_grounding_ce_7: 0.00362/0.34878, loss_mask_ce_8: 0.07337/1.13221, loss_mask_bce_8: 0.21273/0.36567, loss_mask_dice_8: 0.18097/1.33115, loss_spatial_bce_8: 0.11440/0.13407, loss_spatial_dice_8: 0.10159/0.30770, loss_spatial_ce_8: 0.13233/0.24195, loss_grounding_bce_8: 0.10596/0.09385, loss_grounding_dice_8: 0.08486/0.20218, loss_grounding_ce_8: 0.00579/0.42070, loss_mask_ce_9: 1.81515/3.69207, loss_mask_bce_9: 0.24382/0.39261, loss_mask_dice_9: 0.23973/1.90611, loss_spatial_bce_9: 0.53699/0.33646, loss_spatial_dice_9: 0.75244/0.82459, loss_spatial_ce_9: 1.19672/1.51767, loss_grounding_bce_9: 0.13586/0.10519, loss_grounding_dice_9: 0.12182/0.28170, loss_grounding_ce_9: 0.11874/0.69522] items per batch[64] items per second[0.23] total items[1427200] mini batches[ 22300] memory[7341] epoch remaining[1:08:27]
INFO:trainer.default_trainer:epochs[    12] optim steps[22400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69421/0.91150, loss_mask_bce_0: 0.24992/0.33585, loss_mask_dice_0: 0.69897/1.16655, loss_spatial_bce_0: 0.06101/0.09166, loss_spatial_dice_0: 0.15385/0.21883, loss_spatial_ce_0: 0.00143/0.07822, loss_grounding_bce_0: 0.04488/0.08642, loss_grounding_dice_0: 0.09950/0.17940, loss_grounding_ce_0: 0.33024/0.27572, loss_mask_ce_1: 0.64408/0.91212, loss_mask_bce_1: 0.26155/0.33654, loss_mask_dice_1: 0.73770/1.17380, loss_spatial_bce_1: 0.06006/0.09255, loss_spatial_dice_1: 0.14058/0.22320, loss_spatial_ce_1: 0.00442/0.08403, loss_grounding_bce_1: 0.04604/0.08647, loss_grounding_dice_1: 0.10640/0.18013, loss_grounding_ce_1: 0.31813/0.27773, loss_mask_ce_2: 0.63255/0.91945, loss_mask_bce_2: 0.25535/0.33695, loss_mask_dice_2: 0.67708/1.17283, loss_spatial_bce_2: 0.05764/0.09266, loss_spatial_dice_2: 0.13520/0.22412, loss_spatial_ce_2: 0.02614/0.08787, loss_grounding_bce_2: 0.04632/0.08656, loss_grounding_dice_2: 0.13364/0.17957, loss_grounding_ce_2: 0.27090/0.28146, loss_mask_ce_3: 0.62879/0.92769, loss_mask_bce_3: 0.25470/0.33774, loss_mask_dice_3: 0.74725/1.17019, loss_spatial_bce_3: 0.05839/0.09354, loss_spatial_dice_3: 0.13175/0.22481, loss_spatial_ce_3: 0.01208/0.09187, loss_grounding_bce_3: 0.04412/0.08665, loss_grounding_dice_3: 0.10845/0.17937, loss_grounding_ce_3: 0.30055/0.28257, loss_mask_ce_4: 0.61207/0.92651, loss_mask_bce_4: 0.24996/0.33938, loss_mask_dice_4: 0.66036/1.19253, loss_spatial_bce_4: 0.06100/0.09748, loss_spatial_dice_4: 0.16028/0.23451, loss_spatial_ce_4: 0.03203/0.10820, loss_grounding_bce_4: 0.04534/0.08720, loss_grounding_dice_4: 0.09816/0.18215, loss_grounding_ce_4: 0.30748/0.28521, loss_mask_ce_5: 0.68206/0.94067, loss_mask_bce_5: 0.25366/0.34166, loss_mask_dice_5: 0.77347/1.19765, loss_spatial_bce_5: 0.06698/0.09862, loss_spatial_dice_5: 0.15699/0.23752, loss_spatial_ce_5: 0.01392/0.12172, loss_grounding_bce_5: 0.04339/0.08762, loss_grounding_dice_5: 0.11663/0.18326, loss_grounding_ce_5: 0.28709/0.29758, loss_mask_ce_6: 0.69373/0.97811, loss_mask_bce_6: 0.26917/0.34435, loss_mask_dice_6: 0.71410/1.20033, loss_spatial_bce_6: 0.06457/0.10397, loss_spatial_dice_6: 0.14589/0.23979, loss_spatial_ce_6: 0.11547/0.14644, loss_grounding_bce_6: 0.04503/0.08838, loss_grounding_dice_6: 0.11876/0.18346, loss_grounding_ce_6: 0.29646/0.31547, loss_mask_ce_7: 0.69436/1.02098, loss_mask_bce_7: 0.26611/0.35199, loss_mask_dice_7: 0.70303/1.25594, loss_spatial_bce_7: 0.08195/0.11281, loss_spatial_dice_7: 0.23816/0.26702, loss_spatial_ce_7: 0.18262/0.18503, loss_grounding_bce_7: 0.04887/0.09029, loss_grounding_dice_7: 0.10789/0.19068, loss_grounding_ce_7: 0.30155/0.34886, loss_mask_ce_8: 0.83751/1.13216, loss_mask_bce_8: 0.28474/0.36563, loss_mask_dice_8: 0.81720/1.33095, loss_spatial_bce_8: 0.11694/0.13408, loss_spatial_dice_8: 0.27131/0.30764, loss_spatial_ce_8: 0.04615/0.24184, loss_grounding_bce_8: 0.05731/0.09382, loss_grounding_dice_8: 0.14269/0.20215, loss_grounding_ce_8: 0.29260/0.42067, loss_mask_ce_9: 3.30630/3.69202, loss_mask_bce_9: 0.35154/0.39261, loss_mask_dice_9: 1.36705/1.90591, loss_spatial_bce_9: 0.31978/0.33641, loss_spatial_dice_9: 0.90058/0.82451, loss_spatial_ce_9: 1.55576/1.51713, loss_grounding_bce_9: 0.07887/0.10518, loss_grounding_dice_9: 0.25913/0.28169, loss_grounding_ce_9: 0.41695/0.69530] items per batch[64] items per second[0.23] total items[1433600] mini batches[ 22400] memory[7341] epoch remaining[1:03:37]
INFO:trainer.default_trainer:epochs[    12] optim steps[22500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61395/0.91145, loss_mask_bce_0: 0.14678/0.33575, loss_mask_dice_0: 2.84761/1.16788, loss_spatial_bce_0: 0.01153/0.09159, loss_spatial_dice_0: 0.23071/0.21883, loss_spatial_ce_0: 0.01569/0.07808, loss_grounding_bce_0: 0.01678/0.08635, loss_grounding_dice_0: 0.29239/0.17955, loss_grounding_ce_0: 0.35441/0.27592, loss_mask_ce_1: 0.62078/0.91218, loss_mask_bce_1: 0.15114/0.33643, loss_mask_dice_1: 2.62714/1.17506, loss_spatial_bce_1: 0.01026/0.09248, loss_spatial_dice_1: 0.20758/0.22321, loss_spatial_ce_1: 0.00519/0.08390, loss_grounding_bce_1: 0.01873/0.08640, loss_grounding_dice_1: 0.26343/0.18027, loss_grounding_ce_1: 0.33965/0.27790, loss_mask_ce_2: 0.77366/0.91942, loss_mask_bce_2: 0.15315/0.33685, loss_mask_dice_2: 2.57079/1.17427, loss_spatial_bce_2: 0.01144/0.09259, loss_spatial_dice_2: 0.19992/0.22413, loss_spatial_ce_2: 0.01164/0.08778, loss_grounding_bce_2: 0.01711/0.08648, loss_grounding_dice_2: 0.23582/0.17972, loss_grounding_ce_2: 0.34046/0.28166, loss_mask_ce_3: 0.83591/0.92773, loss_mask_bce_3: 0.13856/0.33761, loss_mask_dice_3: 2.59850/1.17156, loss_spatial_bce_3: 0.01154/0.09347, loss_spatial_dice_3: 0.19950/0.22481, loss_spatial_ce_3: 0.01606/0.09174, loss_grounding_bce_3: 0.01920/0.08658, loss_grounding_dice_3: 0.31776/0.17950, loss_grounding_ce_3: 0.35093/0.28275, loss_mask_ce_4: 0.68851/0.92656, loss_mask_bce_4: 0.16095/0.33927, loss_mask_dice_4: 2.68746/1.19388, loss_spatial_bce_4: 0.01099/0.09741, loss_spatial_dice_4: 0.23873/0.23454, loss_spatial_ce_4: 0.03956/0.10805, loss_grounding_bce_4: 0.02157/0.08713, loss_grounding_dice_4: 0.25484/0.18230, loss_grounding_ce_4: 0.34010/0.28538, loss_mask_ce_5: 0.68768/0.94074, loss_mask_bce_5: 0.16216/0.34156, loss_mask_dice_5: 2.86250/1.19908, loss_spatial_bce_5: 0.01023/0.09856, loss_spatial_dice_5: 0.20122/0.23756, loss_spatial_ce_5: 0.02356/0.12154, loss_grounding_bce_5: 0.01847/0.08755, loss_grounding_dice_5: 0.23710/0.18343, loss_grounding_ce_5: 0.34457/0.29785, loss_mask_ce_6: 0.62742/0.97816, loss_mask_bce_6: 0.16726/0.34428, loss_mask_dice_6: 2.87772/1.20167, loss_spatial_bce_6: 0.01247/0.10391, loss_spatial_dice_6: 0.21817/0.23983, loss_spatial_ce_6: 0.05336/0.14631, loss_grounding_bce_6: 0.01845/0.08832, loss_grounding_dice_6: 0.27075/0.18361, loss_grounding_ce_6: 0.34763/0.31568, loss_mask_ce_7: 0.71678/1.02101, loss_mask_bce_7: 0.18502/0.35192, loss_mask_dice_7: 2.98291/1.25735, loss_spatial_bce_7: 0.01418/0.11275, loss_spatial_dice_7: 0.24565/0.26707, loss_spatial_ce_7: 0.07404/0.18490, loss_grounding_bce_7: 0.02152/0.09023, loss_grounding_dice_7: 0.32763/0.19086, loss_grounding_ce_7: 0.38090/0.34894, loss_mask_ce_8: 0.69275/1.13227, loss_mask_bce_8: 0.20219/0.36553, loss_mask_dice_8: 3.51397/1.33260, loss_spatial_bce_8: 0.01627/0.13400, loss_spatial_dice_8: 0.27132/0.30766, loss_spatial_ce_8: 0.12153/0.24182, loss_grounding_bce_8: 0.02716/0.09377, loss_grounding_dice_8: 0.36082/0.20234, loss_grounding_ce_8: 0.36954/0.42065, loss_mask_ce_9: 3.97958/3.69221, loss_mask_bce_9: 0.18620/0.39250, loss_mask_dice_9: 5.33339/1.90777, loss_spatial_bce_9: 0.12422/0.33627, loss_spatial_dice_9: 0.86812/0.82453, loss_spatial_ce_9: 2.28509/1.51704, loss_grounding_bce_9: 0.01650/0.10509, loss_grounding_dice_9: 0.50795/0.28191, loss_grounding_ce_9: 0.47152/0.69488] items per batch[64] items per second[0.23] total items[1440000] mini batches[ 22500] memory[7341] epoch remaining[0:58:49]
INFO:trainer.default_trainer:epochs[    12] optim steps[22600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25395/0.91163, loss_mask_bce_0: 0.05713/0.33554, loss_mask_dice_0: 1.51434/1.16781, loss_spatial_bce_0: 0.01090/0.09154, loss_spatial_dice_0: 0.26562/0.21875, loss_spatial_ce_0: 0.02868/0.07798, loss_grounding_bce_0: 0.01007/0.08628, loss_grounding_dice_0: 0.37368/0.17948, loss_grounding_ce_0: 0.28710/0.27582, loss_mask_ce_1: 0.47442/0.91229, loss_mask_bce_1: 0.06268/0.33621, loss_mask_dice_1: 1.33769/1.17500, loss_spatial_bce_1: 0.01067/0.09243, loss_spatial_dice_1: 0.21641/0.22314, loss_spatial_ce_1: 0.17965/0.08381, loss_grounding_bce_1: 0.00981/0.08633, loss_grounding_dice_1: 0.22463/0.18018, loss_grounding_ce_1: 0.30552/0.27789, loss_mask_ce_2: 0.73698/0.91960, loss_mask_bce_2: 0.05826/0.33664, loss_mask_dice_2: 1.43052/1.17416, loss_spatial_bce_2: 0.01120/0.09254, loss_spatial_dice_2: 0.25962/0.22406, loss_spatial_ce_2: 0.04926/0.08769, loss_grounding_bce_2: 0.00958/0.08642, loss_grounding_dice_2: 0.37667/0.17965, loss_grounding_ce_2: 0.28716/0.28153, loss_mask_ce_3: 0.97343/0.92782, loss_mask_bce_3: 0.05976/0.33741, loss_mask_dice_3: 1.49918/1.17152, loss_spatial_bce_3: 0.01268/0.09342, loss_spatial_dice_3: 0.23505/0.22474, loss_spatial_ce_3: 0.10190/0.09164, loss_grounding_bce_3: 0.01063/0.08652, loss_grounding_dice_3: 0.23500/0.17942, loss_grounding_ce_3: 0.34492/0.28269, loss_mask_ce_4: 0.58244/0.92674, loss_mask_bce_4: 0.06017/0.33907, loss_mask_dice_4: 1.42080/1.19380, loss_spatial_bce_4: 0.01059/0.09736, loss_spatial_dice_4: 0.17425/0.23449, loss_spatial_ce_4: 0.13934/0.10795, loss_grounding_bce_4: 0.01029/0.08706, loss_grounding_dice_4: 0.39889/0.18221, loss_grounding_ce_4: 0.30923/0.28531, loss_mask_ce_5: 0.55184/0.94088, loss_mask_bce_5: 0.05579/0.34135, loss_mask_dice_5: 1.38295/1.19904, loss_spatial_bce_5: 0.01210/0.09852, loss_spatial_dice_5: 0.22942/0.23751, loss_spatial_ce_5: 0.12588/0.12146, loss_grounding_bce_5: 0.00927/0.08750, loss_grounding_dice_5: 0.37705/0.18335, loss_grounding_ce_5: 0.37596/0.29779, loss_mask_ce_6: 1.16932/0.97836, loss_mask_bce_6: 0.05867/0.34408, loss_mask_dice_6: 1.13848/1.20152, loss_spatial_bce_6: 0.01127/0.10386, loss_spatial_dice_6: 0.29047/0.23978, loss_spatial_ce_6: 0.33931/0.14623, loss_grounding_bce_6: 0.00984/0.08826, loss_grounding_dice_6: 0.33639/0.18352, loss_grounding_ce_6: 0.29560/0.31553, loss_mask_ce_7: 0.48999/1.02109, loss_mask_bce_7: 0.05419/0.35174, loss_mask_dice_7: 1.20022/1.25728, loss_spatial_bce_7: 0.01228/0.11272, loss_spatial_dice_7: 0.27787/0.26701, loss_spatial_ce_7: 0.09708/0.18474, loss_grounding_bce_7: 0.01099/0.09016, loss_grounding_dice_7: 0.37135/0.19079, loss_grounding_ce_7: 0.27078/0.34879, loss_mask_ce_8: 0.97942/1.13231, loss_mask_bce_8: 0.06008/0.36537, loss_mask_dice_8: 1.55949/1.33262, loss_spatial_bce_8: 0.01535/0.13395, loss_spatial_dice_8: 0.34422/0.30762, loss_spatial_ce_8: 0.26731/0.24176, loss_grounding_bce_8: 0.01095/0.09370, loss_grounding_dice_8: 0.25314/0.20226, loss_grounding_ce_8: 0.36228/0.42037, loss_mask_ce_9: 3.10449/3.69229, loss_mask_bce_9: 0.05969/0.39234, loss_mask_dice_9: 1.91847/1.90758, loss_spatial_bce_9: 0.15472/0.33619, loss_spatial_dice_9: 0.87229/0.82452, loss_spatial_ce_9: 2.54461/1.51669, loss_grounding_bce_9: 0.00981/0.10504, loss_grounding_dice_9: 0.41569/0.28179, loss_grounding_ce_9: 0.41673/0.69501] items per batch[64] items per second[0.23] total items[1446400] mini batches[ 22600] memory[7341] epoch remaining[0:54:09]
INFO:trainer.default_trainer:epochs[    12] optim steps[22700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.41972/0.91166, loss_mask_bce_0: 0.50372/0.33565, loss_mask_dice_0: 2.20024/1.16789, loss_spatial_bce_0: 0.06617/0.09153, loss_spatial_dice_0: 0.20674/0.21867, loss_spatial_ce_0: 0.01814/0.07789, loss_grounding_bce_0: 0.11319/0.08630, loss_grounding_dice_0: 0.33933/0.17949, loss_grounding_ce_0: 0.15309/0.27590, loss_mask_ce_1: 1.47103/0.91241, loss_mask_bce_1: 0.52737/0.33630, loss_mask_dice_1: 2.16419/1.17505, loss_spatial_bce_1: 0.05846/0.09242, loss_spatial_dice_1: 0.22130/0.22305, loss_spatial_ce_1: 0.02549/0.08370, loss_grounding_bce_1: 0.11172/0.08635, loss_grounding_dice_1: 0.24621/0.18022, loss_grounding_ce_1: 0.15036/0.27797, loss_mask_ce_2: 1.47813/0.91967, loss_mask_bce_2: 0.49927/0.33674, loss_mask_dice_2: 2.29240/1.17419, loss_spatial_bce_2: 0.05762/0.09253, loss_spatial_dice_2: 0.20062/0.22398, loss_spatial_ce_2: 0.04157/0.08757, loss_grounding_bce_2: 0.11118/0.08643, loss_grounding_dice_2: 0.28346/0.17968, loss_grounding_ce_2: 0.15442/0.28162, loss_mask_ce_3: 1.58632/0.92801, loss_mask_bce_3: 0.47567/0.33749, loss_mask_dice_3: 2.04696/1.17155, loss_spatial_bce_3: 0.05411/0.09340, loss_spatial_dice_3: 0.19940/0.22465, loss_spatial_ce_3: 0.05971/0.09154, loss_grounding_bce_3: 0.10836/0.08653, loss_grounding_dice_3: 0.39797/0.17946, loss_grounding_ce_3: 0.09997/0.28288, loss_mask_ce_4: 1.43910/0.92685, loss_mask_bce_4: 0.50245/0.33918, loss_mask_dice_4: 2.34828/1.19387, loss_spatial_bce_4: 0.06018/0.09735, loss_spatial_dice_4: 0.21344/0.23442, loss_spatial_ce_4: 0.06324/0.10783, loss_grounding_bce_4: 0.09434/0.08707, loss_grounding_dice_4: 0.06978/0.18224, loss_grounding_ce_4: 0.26203/0.28548, loss_mask_ce_5: 1.37812/0.94098, loss_mask_bce_5: 0.51337/0.34143, loss_mask_dice_5: 2.04615/1.19907, loss_spatial_bce_5: 0.07065/0.09852, loss_spatial_dice_5: 0.23500/0.23743, loss_spatial_ce_5: 0.06365/0.12141, loss_grounding_bce_5: 0.10649/0.08750, loss_grounding_dice_5: 0.22013/0.18338, loss_grounding_ce_5: 0.13430/0.29790, loss_mask_ce_6: 1.41354/0.97857, loss_mask_bce_6: 0.52114/0.34417, loss_mask_dice_6: 2.07892/1.20158, loss_spatial_bce_6: 0.05705/0.10386, loss_spatial_dice_6: 0.26979/0.23972, loss_spatial_ce_6: 0.10378/0.14619, loss_grounding_bce_6: 0.07892/0.08826, loss_grounding_dice_6: 0.18928/0.18356, loss_grounding_ce_6: 0.14881/0.31566, loss_mask_ce_7: 1.60814/1.02136, loss_mask_bce_7: 0.51646/0.35186, loss_mask_dice_7: 2.41052/1.25737, loss_spatial_bce_7: 0.07935/0.11272, loss_spatial_dice_7: 0.33401/0.26696, loss_spatial_ce_7: 0.09168/0.18461, loss_grounding_bce_7: 0.08239/0.09019, loss_grounding_dice_7: 0.35968/0.19083, loss_grounding_ce_7: 0.30185/0.34889, loss_mask_ce_8: 1.50270/1.13253, loss_mask_bce_8: 0.57420/0.36549, loss_mask_dice_8: 2.33394/1.33270, loss_spatial_bce_8: 0.09412/0.13395, loss_spatial_dice_8: 0.41426/0.30755, loss_spatial_ce_8: 0.22058/0.24165, loss_grounding_bce_8: 0.06196/0.09371, loss_grounding_dice_8: 0.27203/0.20228, loss_grounding_ce_8: 0.25236/0.42028, loss_mask_ce_9: 4.74609/3.69268, loss_mask_bce_9: 0.72556/0.39249, loss_mask_dice_9: 3.91416/1.90771, loss_spatial_bce_9: 0.11555/0.33624, loss_spatial_dice_9: 0.91091/0.82456, loss_spatial_ce_9: 1.79912/1.51654, loss_grounding_bce_9: 0.08028/0.10507, loss_grounding_dice_9: 0.25292/0.28187, loss_grounding_ce_9: 0.25096/0.69507] items per batch[64] items per second[0.23] total items[1452800] mini batches[ 22700] memory[7341] epoch remaining[0:49:23]
INFO:trainer.default_trainer:epochs[    12] optim steps[22800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96253/0.91129, loss_mask_bce_0: 0.43738/0.33575, loss_mask_dice_0: 0.56301/1.16729, loss_spatial_bce_0: 0.11266/0.09156, loss_spatial_dice_0: 0.14008/0.21856, loss_spatial_ce_0: 0.01119/0.07781, loss_grounding_bce_0: 0.04564/0.08633, loss_grounding_dice_0: 0.09347/0.17942, loss_grounding_ce_0: 1.59217/0.27603, loss_mask_ce_1: 0.95309/0.91210, loss_mask_bce_1: 0.40377/0.33637, loss_mask_dice_1: 0.56535/1.17441, loss_spatial_bce_1: 0.10888/0.09244, loss_spatial_dice_1: 0.15028/0.22294, loss_spatial_ce_1: 0.00978/0.08361, loss_grounding_bce_1: 0.04339/0.08638, loss_grounding_dice_1: 0.09262/0.18013, loss_grounding_ce_1: 1.68935/0.27806, loss_mask_ce_2: 0.93543/0.91936, loss_mask_bce_2: 0.39990/0.33686, loss_mask_dice_2: 0.52581/1.17353, loss_spatial_bce_2: 0.10404/0.09256, loss_spatial_dice_2: 0.15012/0.22388, loss_spatial_ce_2: 0.01322/0.08746, loss_grounding_bce_2: 0.04424/0.08646, loss_grounding_dice_2: 0.08706/0.17959, loss_grounding_ce_2: 1.73512/0.28177, loss_mask_ce_3: 1.05475/0.92769, loss_mask_bce_3: 0.39623/0.33761, loss_mask_dice_3: 0.53357/1.17095, loss_spatial_bce_3: 0.10958/0.09342, loss_spatial_dice_3: 0.14838/0.22455, loss_spatial_ce_3: 0.03327/0.09139, loss_grounding_bce_3: 0.04460/0.08657, loss_grounding_dice_3: 0.10065/0.17940, loss_grounding_ce_3: 1.80954/0.28294, loss_mask_ce_4: 0.93191/0.92649, loss_mask_bce_4: 0.45271/0.33928, loss_mask_dice_4: 0.60248/1.19328, loss_spatial_bce_4: 0.10221/0.09739, loss_spatial_dice_4: 0.15816/0.23432, loss_spatial_ce_4: 0.04068/0.10769, loss_grounding_bce_4: 0.05182/0.08710, loss_grounding_dice_4: 0.10866/0.18216, loss_grounding_ce_4: 1.93212/0.28558, loss_mask_ce_5: 0.84058/0.94061, loss_mask_bce_5: 0.46597/0.34153, loss_mask_dice_5: 0.68354/1.19846, loss_spatial_bce_5: 0.10374/0.09854, loss_spatial_dice_5: 0.15036/0.23733, loss_spatial_ce_5: 0.06055/0.12131, loss_grounding_bce_5: 0.05116/0.08753, loss_grounding_dice_5: 0.10621/0.18331, loss_grounding_ce_5: 2.11814/0.29803, loss_mask_ce_6: 0.85928/0.97820, loss_mask_bce_6: 0.40021/0.34426, loss_mask_dice_6: 0.64234/1.20093, loss_spatial_bce_6: 0.10752/0.10390, loss_spatial_dice_6: 0.15613/0.23962, loss_spatial_ce_6: 0.09947/0.14610, loss_grounding_bce_6: 0.05689/0.08828, loss_grounding_dice_6: 0.11144/0.18348, loss_grounding_ce_6: 2.08396/0.31581, loss_mask_ce_7: 0.99466/1.02103, loss_mask_bce_7: 0.39254/0.35198, loss_mask_dice_7: 0.69579/1.25672, loss_spatial_bce_7: 0.11869/0.11275, loss_spatial_dice_7: 0.19270/0.26684, loss_spatial_ce_7: 0.10812/0.18450, loss_grounding_bce_7: 0.07269/0.09021, loss_grounding_dice_7: 0.11760/0.19074, loss_grounding_ce_7: 1.45061/0.34897, loss_mask_ce_8: 0.99899/1.13213, loss_mask_bce_8: 0.45507/0.36558, loss_mask_dice_8: 0.71553/1.33199, loss_spatial_bce_8: 0.17805/0.13397, loss_spatial_dice_8: 0.23579/0.30742, loss_spatial_ce_8: 0.06828/0.24153, loss_grounding_bce_8: 0.06416/0.09374, loss_grounding_dice_8: 0.11896/0.20222, loss_grounding_ce_8: 1.81214/0.42035, loss_mask_ce_9: 6.03539/3.69227, loss_mask_bce_9: 0.48678/0.39259, loss_mask_dice_9: 1.87747/1.90684, loss_spatial_bce_9: 0.33018/0.33631, loss_spatial_dice_9: 0.84669/0.82450, loss_spatial_ce_9: 1.22046/1.51621, loss_grounding_bce_9: 0.07687/0.10512, loss_grounding_dice_9: 0.33316/0.28180, loss_grounding_ce_9: 2.22477/0.69546] items per batch[64] items per second[0.23] total items[1459200] mini batches[ 22800] memory[7341] epoch remaining[0:44:33]
INFO:trainer.default_trainer:epochs[    12] optim steps[22900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96161/0.91112, loss_mask_bce_0: 0.18984/0.33573, loss_mask_dice_0: 0.79548/1.16686, loss_spatial_bce_0: 0.03717/0.09154, loss_spatial_dice_0: 0.11574/0.21849, loss_spatial_ce_0: 0.01861/0.07776, loss_grounding_bce_0: 0.01808/0.08633, loss_grounding_dice_0: 0.26553/0.17938, loss_grounding_ce_0: 0.22695/0.27620, loss_mask_ce_1: 0.91321/0.91194, loss_mask_bce_1: 0.19375/0.33637, loss_mask_dice_1: 1.05022/1.17401, loss_spatial_bce_1: 0.03579/0.09242, loss_spatial_dice_1: 0.14174/0.22286, loss_spatial_ce_1: 0.02173/0.08354, loss_grounding_bce_1: 0.01849/0.08640, loss_grounding_dice_1: 0.26102/0.18009, loss_grounding_ce_1: 0.30740/0.27818, loss_mask_ce_2: 0.96455/0.91926, loss_mask_bce_2: 0.19949/0.33685, loss_mask_dice_2: 1.21345/1.17319, loss_spatial_bce_2: 0.03772/0.09253, loss_spatial_dice_2: 0.16360/0.22379, loss_spatial_ce_2: 0.02242/0.08740, loss_grounding_bce_2: 0.01892/0.08648, loss_grounding_dice_2: 0.21848/0.17959, loss_grounding_ce_2: 0.26818/0.28183, loss_mask_ce_3: 1.09552/0.92766, loss_mask_bce_3: 0.19802/0.33759, loss_mask_dice_3: 1.24099/1.17049, loss_spatial_bce_3: 0.03790/0.09341, loss_spatial_dice_3: 0.13987/0.22447, loss_spatial_ce_3: 0.04240/0.09134, loss_grounding_bce_3: 0.02005/0.08659, loss_grounding_dice_3: 0.16677/0.17938, loss_grounding_ce_3: 0.25936/0.28303, loss_mask_ce_4: 1.21360/0.92641, loss_mask_bce_4: 0.19822/0.33927, loss_mask_dice_4: 1.16463/1.19281, loss_spatial_bce_4: 0.03762/0.09737, loss_spatial_dice_4: 0.15359/0.23426, loss_spatial_ce_4: 0.03135/0.10762, loss_grounding_bce_4: 0.01837/0.08712, loss_grounding_dice_4: 0.23875/0.18215, loss_grounding_ce_4: 0.27226/0.28558, loss_mask_ce_5: 1.12062/0.94059, loss_mask_bce_5: 0.20131/0.34152, loss_mask_dice_5: 1.00088/1.19800, loss_spatial_bce_5: 0.03751/0.09854, loss_spatial_dice_5: 0.14367/0.23725, loss_spatial_ce_5: 0.06420/0.12125, loss_grounding_bce_5: 0.02066/0.08754, loss_grounding_dice_5: 0.26110/0.18328, loss_grounding_ce_5: 0.24926/0.29808, loss_mask_ce_6: 1.02935/0.97827, loss_mask_bce_6: 0.20788/0.34424, loss_mask_dice_6: 1.18452/1.20057, loss_spatial_bce_6: 0.04064/0.10389, loss_spatial_dice_6: 0.17134/0.23952, loss_spatial_ce_6: 0.04439/0.14603, loss_grounding_bce_6: 0.01864/0.08829, loss_grounding_dice_6: 0.28275/0.18347, loss_grounding_ce_6: 0.26651/0.31582, loss_mask_ce_7: 1.40807/1.02104, loss_mask_bce_7: 0.19777/0.35195, loss_mask_dice_7: 0.88430/1.25618, loss_spatial_bce_7: 0.03809/0.11273, loss_spatial_dice_7: 0.18350/0.26676, loss_spatial_ce_7: 0.07109/0.18441, loss_grounding_bce_7: 0.02055/0.09023, loss_grounding_dice_7: 0.42500/0.19075, loss_grounding_ce_7: 0.23536/0.34899, loss_mask_ce_8: 1.29935/1.13223, loss_mask_bce_8: 0.20527/0.36557, loss_mask_dice_8: 1.22523/1.33156, loss_spatial_bce_8: 0.04790/0.13396, loss_spatial_dice_8: 0.23365/0.30736, loss_spatial_ce_8: 0.11642/0.24141, loss_grounding_bce_8: 0.02041/0.09376, loss_grounding_dice_8: 0.24887/0.20220, loss_grounding_ce_8: 0.32112/0.42033, loss_mask_ce_9: 3.52537/3.69234, loss_mask_bce_9: 0.24264/0.39258, loss_mask_dice_9: 1.86384/1.90636, loss_spatial_bce_9: 0.22363/0.33626, loss_spatial_dice_9: 0.87750/0.82447, loss_spatial_ce_9: 2.57637/1.51592, loss_grounding_bce_9: 0.02384/0.10515, loss_grounding_dice_9: 0.35925/0.28181, loss_grounding_ce_9: 0.92725/0.69541] items per batch[64] items per second[0.23] total items[1465600] mini batches[ 22900] memory[7341] epoch remaining[0:39:54]
INFO:trainer.default_trainer:epochs[    12] optim steps[23000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.69843/0.91105, loss_mask_bce_0: 0.23123/0.33575, loss_mask_dice_0: 1.73884/1.16613, loss_spatial_bce_0: 0.02657/0.09154, loss_spatial_dice_0: 0.22720/0.21841, loss_spatial_ce_0: 0.02215/0.07764, loss_grounding_bce_0: 0.01471/0.08636, loss_grounding_dice_0: 0.06143/0.17937, loss_grounding_ce_0: 0.24239/0.27617, loss_mask_ce_1: 1.70970/0.91191, loss_mask_bce_1: 0.23599/0.33639, loss_mask_dice_1: 1.88940/1.17328, loss_spatial_bce_1: 0.02738/0.09242, loss_spatial_dice_1: 0.23868/0.22278, loss_spatial_ce_1: 0.02355/0.08345, loss_grounding_bce_1: 0.01421/0.08644, loss_grounding_dice_1: 0.08965/0.18008, loss_grounding_ce_1: 0.24622/0.27815, loss_mask_ce_2: 1.42811/0.91926, loss_mask_bce_2: 0.25246/0.33688, loss_mask_dice_2: 1.88517/1.17252, loss_spatial_bce_2: 0.03056/0.09254, loss_spatial_dice_2: 0.24364/0.22371, loss_spatial_ce_2: 0.02402/0.08732, loss_grounding_bce_2: 0.01398/0.08651, loss_grounding_dice_2: 0.06762/0.17958, loss_grounding_ce_2: 0.24886/0.28176, loss_mask_ce_3: 1.68401/0.92766, loss_mask_bce_3: 0.17941/0.33761, loss_mask_dice_3: 1.80197/1.16983, loss_spatial_bce_3: 0.02631/0.09342, loss_spatial_dice_3: 0.24679/0.22439, loss_spatial_ce_3: 0.02676/0.09125, loss_grounding_bce_3: 0.01495/0.08662, loss_grounding_dice_3: 0.10566/0.17936, loss_grounding_ce_3: 0.20953/0.28291, loss_mask_ce_4: 1.55357/0.92642, loss_mask_bce_4: 0.21479/0.33930, loss_mask_dice_4: 1.86931/1.19211, loss_spatial_bce_4: 0.03680/0.09739, loss_spatial_dice_4: 0.27177/0.23420, loss_spatial_ce_4: 0.03727/0.10750, loss_grounding_bce_4: 0.01496/0.08715, loss_grounding_dice_4: 0.10738/0.18213, loss_grounding_ce_4: 0.24909/0.28543, loss_mask_ce_5: 1.40235/0.94055, loss_mask_bce_5: 0.22735/0.34154, loss_mask_dice_5: 1.92909/1.19728, loss_spatial_bce_5: 0.03749/0.09854, loss_spatial_dice_5: 0.29979/0.23718, loss_spatial_ce_5: 0.21746/0.12116, loss_grounding_bce_5: 0.01529/0.08757, loss_grounding_dice_5: 0.08012/0.18327, loss_grounding_ce_5: 0.29442/0.29792, loss_mask_ce_6: 1.68195/0.97845, loss_mask_bce_6: 0.20530/0.34424, loss_mask_dice_6: 1.91374/1.19988, loss_spatial_bce_6: 0.04113/0.10391, loss_spatial_dice_6: 0.27873/0.23945, loss_spatial_ce_6: 0.10324/0.14596, loss_grounding_bce_6: 0.01434/0.08831, loss_grounding_dice_6: 0.08498/0.18346, loss_grounding_ce_6: 0.28977/0.31561, loss_mask_ce_7: 1.56272/1.02108, loss_mask_bce_7: 0.19052/0.35195, loss_mask_dice_7: 1.83284/1.25543, loss_spatial_bce_7: 0.04212/0.11275, loss_spatial_dice_7: 0.33900/0.26669, loss_spatial_ce_7: 0.16845/0.18431, loss_grounding_bce_7: 0.01332/0.09026, loss_grounding_dice_7: 0.14794/0.19077, loss_grounding_ce_7: 0.27249/0.34882, loss_mask_ce_8: 1.91034/1.13225, loss_mask_bce_8: 0.19709/0.36559, loss_mask_dice_8: 2.28095/1.33083, loss_spatial_bce_8: 0.04627/0.13398, loss_spatial_dice_8: 0.38007/0.30728, loss_spatial_ce_8: 0.28558/0.24138, loss_grounding_bce_8: 0.01661/0.09380, loss_grounding_dice_8: 0.21747/0.20220, loss_grounding_ce_8: 0.29839/0.42007, loss_mask_ce_9: 4.62448/3.69190, loss_mask_bce_9: 0.22477/0.39257, loss_mask_dice_9: 2.89411/1.90553, loss_spatial_bce_9: 0.11221/0.33627, loss_spatial_dice_9: 0.86726/0.82447, loss_spatial_ce_9: 1.46966/1.51555, loss_grounding_bce_9: 0.02537/0.10517, loss_grounding_dice_9: 0.47878/0.28177, loss_grounding_ce_9: 0.34928/0.69492] items per batch[64] items per second[0.23] total items[1472000] mini batches[ 23000] memory[7341] epoch remaining[0:35:10]
INFO:trainer.default_trainer:epochs[    12] optim steps[23100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.33139/0.91142, loss_mask_bce_0: 0.42971/0.33569, loss_mask_dice_0: 2.58306/1.16603, loss_spatial_bce_0: 0.08193/0.09153, loss_spatial_dice_0: 0.20252/0.21841, loss_spatial_ce_0: 0.04765/0.07757, loss_grounding_bce_0: 0.04085/0.08634, loss_grounding_dice_0: 0.22726/0.17932, loss_grounding_ce_0: 0.24965/0.27618, loss_mask_ce_1: 1.15295/0.91229, loss_mask_bce_1: 0.43994/0.33635, loss_mask_dice_1: 2.64889/1.17321, loss_spatial_bce_1: 0.07903/0.09241, loss_spatial_dice_1: 0.20546/0.22278, loss_spatial_ce_1: 0.06013/0.08337, loss_grounding_bce_1: 0.04079/0.08642, loss_grounding_dice_1: 0.25627/0.18001, loss_grounding_ce_1: 0.24210/0.27820, loss_mask_ce_2: 1.33561/0.91960, loss_mask_bce_2: 0.42915/0.33684, loss_mask_dice_2: 2.62571/1.17247, loss_spatial_bce_2: 0.07727/0.09253, loss_spatial_dice_2: 0.19736/0.22371, loss_spatial_ce_2: 0.05161/0.08721, loss_grounding_bce_2: 0.03730/0.08650, loss_grounding_dice_2: 0.20734/0.17952, loss_grounding_ce_2: 0.24347/0.28184, loss_mask_ce_3: 1.24996/0.92802, loss_mask_bce_3: 0.44122/0.33756, loss_mask_dice_3: 2.72699/1.16979, loss_spatial_bce_3: 0.07778/0.09340, loss_spatial_dice_3: 0.20223/0.22439, loss_spatial_ce_3: 0.05253/0.09118, loss_grounding_bce_3: 0.03941/0.08660, loss_grounding_dice_3: 0.21785/0.17929, loss_grounding_ce_3: 0.24486/0.28304, loss_mask_ce_4: 1.21635/0.92688, loss_mask_bce_4: 0.44154/0.33923, loss_mask_dice_4: 2.87909/1.19211, loss_spatial_bce_4: 0.07951/0.09736, loss_spatial_dice_4: 0.19791/0.23420, loss_spatial_ce_4: 0.10050/0.10743, loss_grounding_bce_4: 0.04483/0.08714, loss_grounding_dice_4: 0.26492/0.18208, loss_grounding_ce_4: 0.25424/0.28551, loss_mask_ce_5: 1.32669/0.94097, loss_mask_bce_5: 0.44526/0.34149, loss_mask_dice_5: 2.74106/1.19727, loss_spatial_bce_5: 0.08381/0.09853, loss_spatial_dice_5: 0.22110/0.23718, loss_spatial_ce_5: 0.05588/0.12116, loss_grounding_bce_5: 0.04174/0.08757, loss_grounding_dice_5: 0.27542/0.18323, loss_grounding_ce_5: 0.28804/0.29803, loss_mask_ce_6: 1.56097/0.97886, loss_mask_bce_6: 0.43843/0.34419, loss_mask_dice_6: 2.74297/1.19986, loss_spatial_bce_6: 0.10043/0.10390, loss_spatial_dice_6: 0.21954/0.23946, loss_spatial_ce_6: 0.07219/0.14594, loss_grounding_bce_6: 0.04295/0.08830, loss_grounding_dice_6: 0.26705/0.18341, loss_grounding_ce_6: 0.24962/0.31577, loss_mask_ce_7: 1.42424/1.02148, loss_mask_bce_7: 0.48273/0.35191, loss_mask_dice_7: 2.90303/1.25533, loss_spatial_bce_7: 0.10452/0.11275, loss_spatial_dice_7: 0.26770/0.26671, loss_spatial_ce_7: 0.08135/0.18424, loss_grounding_bce_7: 0.04348/0.09026, loss_grounding_dice_7: 0.26771/0.19071, loss_grounding_ce_7: 0.30912/0.34891, loss_mask_ce_8: 1.34139/1.13267, loss_mask_bce_8: 0.50317/0.36553, loss_mask_dice_8: 3.04411/1.33074, loss_spatial_bce_8: 0.11848/0.13397, loss_spatial_dice_8: 0.28442/0.30731, loss_spatial_ce_8: 0.14101/0.24133, loss_grounding_bce_8: 0.04116/0.09380, loss_grounding_dice_8: 0.27034/0.20216, loss_grounding_ce_8: 0.67736/0.42032, loss_mask_ce_9: 4.53302/3.69289, loss_mask_bce_9: 0.53757/0.39249, loss_mask_dice_9: 4.31833/1.90559, loss_spatial_bce_9: 0.27846/0.33622, loss_spatial_dice_9: 0.84333/0.82449, loss_spatial_ce_9: 1.10166/1.51543, loss_grounding_bce_9: 0.05158/0.10519, loss_grounding_dice_9: 0.31101/0.28177, loss_grounding_ce_9: 1.22912/0.69517] items per batch[64] items per second[0.23] total items[1478400] mini batches[ 23100] memory[7341] epoch remaining[0:30:29]
INFO:trainer.default_trainer:epochs[    12] optim steps[23200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50382/0.91103, loss_mask_bce_0: 0.12195/0.33572, loss_mask_dice_0: 0.09245/1.16588, loss_spatial_bce_0: 0.08877/0.09150, loss_spatial_dice_0: 0.07142/0.21835, loss_spatial_ce_0: 0.00061/0.07749, loss_grounding_bce_0: 0.04549/0.08633, loss_grounding_dice_0: 0.04717/0.17926, loss_grounding_ce_0: 0.07954/0.27597, loss_mask_ce_1: 0.51349/0.91187, loss_mask_bce_1: 0.12239/0.33639, loss_mask_dice_1: 0.08808/1.17305, loss_spatial_bce_1: 0.08902/0.09238, loss_spatial_dice_1: 0.07426/0.22272, loss_spatial_ce_1: 0.00035/0.08325, loss_grounding_bce_1: 0.04293/0.08641, loss_grounding_dice_1: 0.04598/0.17995, loss_grounding_ce_1: 0.06341/0.27798, loss_mask_ce_2: 0.55986/0.91916, loss_mask_bce_2: 0.12140/0.33687, loss_mask_dice_2: 0.08671/1.17234, loss_spatial_bce_2: 0.09107/0.09250, loss_spatial_dice_2: 0.08156/0.22365, loss_spatial_ce_2: 0.00035/0.08713, loss_grounding_bce_2: 0.04322/0.08649, loss_grounding_dice_2: 0.04279/0.17946, loss_grounding_ce_2: 0.06066/0.28161, loss_mask_ce_3: 0.55767/0.92758, loss_mask_bce_3: 0.12138/0.33758, loss_mask_dice_3: 0.08929/1.16965, loss_spatial_bce_3: 0.08716/0.09338, loss_spatial_dice_3: 0.07496/0.22431, loss_spatial_ce_3: 0.00065/0.09109, loss_grounding_bce_3: 0.04608/0.08658, loss_grounding_dice_3: 0.04729/0.17922, loss_grounding_ce_3: 0.05613/0.28287, loss_mask_ce_4: 0.52504/0.92641, loss_mask_bce_4: 0.12255/0.33927, loss_mask_dice_4: 0.09300/1.19196, loss_spatial_bce_4: 0.09122/0.09734, loss_spatial_dice_4: 0.08748/0.23414, loss_spatial_ce_4: 0.00135/0.10731, loss_grounding_bce_4: 0.04523/0.08713, loss_grounding_dice_4: 0.05298/0.18203, loss_grounding_ce_4: 0.07639/0.28531, loss_mask_ce_5: 0.49833/0.94062, loss_mask_bce_5: 0.11765/0.34150, loss_mask_dice_5: 0.08659/1.19710, loss_spatial_bce_5: 0.08828/0.09851, loss_spatial_dice_5: 0.08629/0.23713, loss_spatial_ce_5: 0.00264/0.12107, loss_grounding_bce_5: 0.04648/0.08755, loss_grounding_dice_5: 0.05011/0.18316, loss_grounding_ce_5: 0.09651/0.29778, loss_mask_ce_6: 0.53413/0.97848, loss_mask_bce_6: 0.11792/0.34422, loss_mask_dice_6: 0.08358/1.19966, loss_spatial_bce_6: 0.08496/0.10388, loss_spatial_dice_6: 0.06092/0.23941, loss_spatial_ce_6: 0.00215/0.14586, loss_grounding_bce_6: 0.04112/0.08829, loss_grounding_dice_6: 0.04209/0.18335, loss_grounding_ce_6: 0.09801/0.31553, loss_mask_ce_7: 0.57710/1.02124, loss_mask_bce_7: 0.12131/0.35194, loss_mask_dice_7: 0.08765/1.25520, loss_spatial_bce_7: 0.09968/0.11273, loss_spatial_dice_7: 0.06658/0.26666, loss_spatial_ce_7: 0.01234/0.18412, loss_grounding_bce_7: 0.04436/0.09026, loss_grounding_dice_7: 0.04152/0.19064, loss_grounding_ce_7: 0.11499/0.34857, loss_mask_ce_8: 0.60099/1.13239, loss_mask_bce_8: 0.14137/0.36555, loss_mask_dice_8: 0.10034/1.33052, loss_spatial_bce_8: 0.10291/0.13395, loss_spatial_dice_8: 0.08180/0.30726, loss_spatial_ce_8: 0.09871/0.24128, loss_grounding_bce_8: 0.04731/0.09379, loss_grounding_dice_8: 0.04660/0.20208, loss_grounding_ce_8: 0.10932/0.41995, loss_mask_ce_9: 2.30881/3.69234, loss_mask_bce_9: 0.14310/0.39245, loss_mask_dice_9: 0.13485/1.90515, loss_spatial_bce_9: 0.61855/0.33620, loss_spatial_dice_9: 0.64501/0.82447, loss_spatial_ce_9: 1.35806/1.51529, loss_grounding_bce_9: 0.05879/0.10515, loss_grounding_dice_9: 0.08916/0.28167, loss_grounding_ce_9: 0.45481/0.69489] items per batch[64] items per second[0.23] total items[1484800] mini batches[ 23200] memory[7341] epoch remaining[0:25:46]
INFO:trainer.default_trainer:epochs[    12] optim steps[23300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.45184/0.91107, loss_mask_bce_0: 0.12319/0.33564, loss_mask_dice_0: 2.81291/1.16563, loss_spatial_bce_0: 0.01460/0.09145, loss_spatial_dice_0: 0.28344/0.21829, loss_spatial_ce_0: 0.05065/0.07741, loss_grounding_bce_0: 0.01179/0.08627, loss_grounding_dice_0: 0.35677/0.17926, loss_grounding_ce_0: 0.54009/0.27589, loss_mask_ce_1: 1.67163/0.91205, loss_mask_bce_1: 0.14022/0.33630, loss_mask_dice_1: 2.54845/1.17276, loss_spatial_bce_1: 0.02638/0.09234, loss_spatial_dice_1: 0.30049/0.22266, loss_spatial_ce_1: 0.04044/0.08315, loss_grounding_bce_1: 0.01349/0.08635, loss_grounding_dice_1: 0.37317/0.17996, loss_grounding_ce_1: 0.49503/0.27785, loss_mask_ce_2: 1.65826/0.91930, loss_mask_bce_2: 0.12235/0.33679, loss_mask_dice_2: 2.57282/1.17203, loss_spatial_bce_2: 0.02868/0.09246, loss_spatial_dice_2: 0.32944/0.22359, loss_spatial_ce_2: 0.04439/0.08703, loss_grounding_bce_2: 0.01401/0.08643, loss_grounding_dice_2: 0.39129/0.17945, loss_grounding_ce_2: 0.59385/0.28153, loss_mask_ce_3: 1.74874/0.92776, loss_mask_bce_3: 0.12289/0.33749, loss_mask_dice_3: 2.49112/1.16932, loss_spatial_bce_3: 0.02942/0.09334, loss_spatial_dice_3: 0.30667/0.22426, loss_spatial_ce_3: 0.10172/0.09102, loss_grounding_bce_3: 0.01159/0.08652, loss_grounding_dice_3: 0.38603/0.17920, loss_grounding_ce_3: 0.64462/0.28276, loss_mask_ce_4: 1.64376/0.92657, loss_mask_bce_4: 0.11457/0.33919, loss_mask_dice_4: 2.30137/1.19170, loss_spatial_bce_4: 0.02781/0.09729, loss_spatial_dice_4: 0.34001/0.23407, loss_spatial_ce_4: 0.08950/0.10721, loss_grounding_bce_4: 0.01052/0.08708, loss_grounding_dice_4: 0.35783/0.18204, loss_grounding_ce_4: 0.58646/0.28523, loss_mask_ce_5: 2.16829/0.94082, loss_mask_bce_5: 0.12322/0.34143, loss_mask_dice_5: 2.46344/1.19678, loss_spatial_bce_5: 0.02457/0.09848, loss_spatial_dice_5: 0.31308/0.23706, loss_spatial_ce_5: 0.09314/0.12101, loss_grounding_bce_5: 0.01531/0.08749, loss_grounding_dice_5: 0.42510/0.18314, loss_grounding_ce_5: 0.55168/0.29781, loss_mask_ce_6: 1.55622/0.97868, loss_mask_bce_6: 0.15062/0.34416, loss_mask_dice_6: 2.98787/1.19932, loss_spatial_bce_6: 0.03139/0.10387, loss_spatial_dice_6: 0.34959/0.23935, loss_spatial_ce_6: 0.04866/0.14575, loss_grounding_bce_6: 0.01654/0.08823, loss_grounding_dice_6: 0.41639/0.18333, loss_grounding_ce_6: 0.55477/0.31560, loss_mask_ce_7: 1.66694/1.02148, loss_mask_bce_7: 0.12506/0.35189, loss_mask_dice_7: 2.73404/1.25490, loss_spatial_bce_7: 0.03030/0.11271, loss_spatial_dice_7: 0.36128/0.26661, loss_spatial_ce_7: 0.12053/0.18406, loss_grounding_bce_7: 0.01343/0.09020, loss_grounding_dice_7: 0.44207/0.19065, loss_grounding_ce_7: 0.61377/0.34867, loss_mask_ce_8: 1.53875/1.13252, loss_mask_bce_8: 0.12248/0.36548, loss_mask_dice_8: 2.76031/1.33016, loss_spatial_bce_8: 0.04232/0.13392, loss_spatial_dice_8: 0.40799/0.30720, loss_spatial_ce_8: 0.08981/0.24122, loss_grounding_bce_8: 0.01618/0.09372, loss_grounding_dice_8: 0.42391/0.20207, loss_grounding_ce_8: 0.47983/0.41996, loss_mask_ce_9: 4.56110/3.69246, loss_mask_bce_9: 0.11949/0.39242, loss_mask_dice_9: 3.50972/1.90475, loss_spatial_bce_9: 0.08050/0.33619, loss_spatial_dice_9: 0.86388/0.82445, loss_spatial_ce_9: 1.19483/1.51537, loss_grounding_bce_9: 0.01719/0.10508, loss_grounding_dice_9: 0.51575/0.28166, loss_grounding_ce_9: 0.55812/0.69465] items per batch[64] items per second[0.23] total items[1491200] mini batches[ 23300] memory[7341] epoch remaining[0:21:04]
INFO:trainer.default_trainer:epochs[    12] optim steps[23400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23712/0.91089, loss_mask_bce_0: 0.41479/0.33566, loss_mask_dice_0: 3.20881/1.16627, loss_spatial_bce_0: 0.07098/0.09145, loss_spatial_dice_0: 0.28463/0.21829, loss_spatial_ce_0: 0.06321/0.07730, loss_grounding_bce_0: 0.02779/0.08628, loss_grounding_dice_0: 0.38028/0.17936, loss_grounding_ce_0: 0.66722/0.27592, loss_mask_ce_1: 1.15420/0.91187, loss_mask_bce_1: 0.41852/0.33632, loss_mask_dice_1: 3.05534/1.17333, loss_spatial_bce_1: 0.07312/0.09234, loss_spatial_dice_1: 0.32880/0.22267, loss_spatial_ce_1: 0.17391/0.08311, loss_grounding_bce_1: 0.03027/0.08635, loss_grounding_dice_1: 0.33969/0.18006, loss_grounding_ce_1: 0.62965/0.27791, loss_mask_ce_2: 1.13602/0.91903, loss_mask_bce_2: 0.41817/0.33684, loss_mask_dice_2: 2.99614/1.17262, loss_spatial_bce_2: 0.07607/0.09246, loss_spatial_dice_2: 0.32474/0.22361, loss_spatial_ce_2: 0.30754/0.08692, loss_grounding_bce_2: 0.02905/0.08643, loss_grounding_dice_2: 0.35922/0.17955, loss_grounding_ce_2: 0.59820/0.28155, loss_mask_ce_3: 1.22420/0.92756, loss_mask_bce_3: 0.38055/0.33753, loss_mask_dice_3: 2.82209/1.16984, loss_spatial_bce_3: 0.06902/0.09335, loss_spatial_dice_3: 0.27166/0.22426, loss_spatial_ce_3: 0.04699/0.09091, loss_grounding_bce_3: 0.03248/0.08653, loss_grounding_dice_3: 0.40062/0.17930, loss_grounding_ce_3: 0.62073/0.28280, loss_mask_ce_4: 1.06598/0.92640, loss_mask_bce_4: 0.44005/0.33924, loss_mask_dice_4: 3.15475/1.19239, loss_spatial_bce_4: 0.07763/0.09730, loss_spatial_dice_4: 0.32312/0.23410, loss_spatial_ce_4: 0.25520/0.10714, loss_grounding_bce_4: 0.03204/0.08708, loss_grounding_dice_4: 0.42129/0.18218, loss_grounding_ce_4: 0.64513/0.28528, loss_mask_ce_5: 1.26399/0.94066, loss_mask_bce_5: 0.34926/0.34146, loss_mask_dice_5: 3.24090/1.19742, loss_spatial_bce_5: 0.09489/0.09847, loss_spatial_dice_5: 0.30283/0.23708, loss_spatial_ce_5: 0.19985/0.12101, loss_grounding_bce_5: 0.03223/0.08748, loss_grounding_dice_5: 0.42881/0.18326, loss_grounding_ce_5: 0.62387/0.29786, loss_mask_ce_6: 1.17746/0.97850, loss_mask_bce_6: 0.37216/0.34420, loss_mask_dice_6: 2.99729/1.19991, loss_spatial_bce_6: 0.08882/0.10385, loss_spatial_dice_6: 0.29229/0.23937, loss_spatial_ce_6: 0.14793/0.14574, loss_grounding_bce_6: 0.02836/0.08823, loss_grounding_dice_6: 0.31035/0.18344, loss_grounding_ce_6: 0.64555/0.31560, loss_mask_ce_7: 1.22399/1.02140, loss_mask_bce_7: 0.38151/0.35192, loss_mask_dice_7: 3.15906/1.25551, loss_spatial_bce_7: 0.08819/0.11269, loss_spatial_dice_7: 0.38840/0.26663, loss_spatial_ce_7: 0.17857/0.18397, loss_grounding_bce_7: 0.02925/0.09019, loss_grounding_dice_7: 0.35644/0.19077, loss_grounding_ce_7: 0.63514/0.34869, loss_mask_ce_8: 1.31970/1.13245, loss_mask_bce_8: 0.37385/0.36551, loss_mask_dice_8: 3.08880/1.33076, loss_spatial_bce_8: 0.16408/0.13389, loss_spatial_dice_8: 0.48703/0.30721, loss_spatial_ce_8: 0.29141/0.24113, loss_grounding_bce_8: 0.03279/0.09373, loss_grounding_dice_8: 0.43439/0.20220, loss_grounding_ce_8: 0.58910/0.41989, loss_mask_ce_9: 4.24301/3.69242, loss_mask_bce_9: 0.56995/0.39246, loss_mask_dice_9: 3.95945/1.90535, loss_spatial_bce_9: 0.31461/0.33614, loss_spatial_dice_9: 0.88583/0.82444, loss_spatial_ce_9: 1.27029/1.51535, loss_grounding_bce_9: 0.06908/0.10511, loss_grounding_dice_9: 0.42020/0.28182, loss_grounding_ce_9: 0.90521/0.69438] items per batch[64] items per second[0.23] total items[1497600] mini batches[ 23400] memory[7341] epoch remaining[0:16:23]
INFO:trainer.default_trainer:epochs[    12] optim steps[23500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62259/0.91067, loss_mask_bce_0: 0.22938/0.33557, loss_mask_dice_0: 0.43019/1.16686, loss_spatial_bce_0: 0.08364/0.09143, loss_spatial_dice_0: 0.13236/0.21823, loss_spatial_ce_0: 0.00644/0.07723, loss_grounding_bce_0: 0.04340/0.08628, loss_grounding_dice_0: 0.15256/0.17932, loss_grounding_ce_0: 0.13949/0.27585, loss_mask_ce_1: 0.62532/0.91161, loss_mask_bce_1: 0.23124/0.33624, loss_mask_dice_1: 0.44746/1.17395, loss_spatial_bce_1: 0.08533/0.09231, loss_spatial_dice_1: 0.14018/0.22259, loss_spatial_ce_1: 0.01427/0.08301, loss_grounding_bce_1: 0.03779/0.08635, loss_grounding_dice_1: 0.14256/0.17999, loss_grounding_ce_1: 0.14657/0.27783, loss_mask_ce_2: 0.61316/0.91886, loss_mask_bce_2: 0.22379/0.33675, loss_mask_dice_2: 0.41565/1.17316, loss_spatial_bce_2: 0.08476/0.09243, loss_spatial_dice_2: 0.12444/0.22354, loss_spatial_ce_2: 0.00452/0.08682, loss_grounding_bce_2: 0.04340/0.08643, loss_grounding_dice_2: 0.16564/0.17950, loss_grounding_ce_2: 0.16008/0.28144, loss_mask_ce_3: 0.64353/0.92741, loss_mask_bce_3: 0.23879/0.33744, loss_mask_dice_3: 0.47388/1.17040, loss_spatial_bce_3: 0.08718/0.09332, loss_spatial_dice_3: 0.12695/0.22419, loss_spatial_ce_3: 0.00421/0.09083, loss_grounding_bce_3: 0.04142/0.08653, loss_grounding_dice_3: 0.16393/0.17925, loss_grounding_ce_3: 0.15831/0.28275, loss_mask_ce_4: 0.82157/0.92614, loss_mask_bce_4: 0.21991/0.33918, loss_mask_dice_4: 0.42930/1.19295, loss_spatial_bce_4: 0.08626/0.09727, loss_spatial_dice_4: 0.15170/0.23404, loss_spatial_ce_4: 0.01058/0.10713, loss_grounding_bce_4: 0.04432/0.08708, loss_grounding_dice_4: 0.18302/0.18212, loss_grounding_ce_4: 0.15503/0.28525, loss_mask_ce_5: 0.80434/0.94039, loss_mask_bce_5: 0.23481/0.34141, loss_mask_dice_5: 0.45888/1.19798, loss_spatial_bce_5: 0.09118/0.09846, loss_spatial_dice_5: 0.15926/0.23704, loss_spatial_ce_5: 0.01939/0.12098, loss_grounding_bce_5: 0.04322/0.08748, loss_grounding_dice_5: 0.17926/0.18320, loss_grounding_ce_5: 0.14794/0.29786, loss_mask_ce_6: 0.78611/0.97825, loss_mask_bce_6: 0.22970/0.34414, loss_mask_dice_6: 0.44306/1.20056, loss_spatial_bce_6: 0.12747/0.10383, loss_spatial_dice_6: 0.18068/0.23932, loss_spatial_ce_6: 0.08573/0.14573, loss_grounding_bce_6: 0.04177/0.08822, loss_grounding_dice_6: 0.16074/0.18338, loss_grounding_ce_6: 0.14735/0.31559, loss_mask_ce_7: 0.79141/1.02114, loss_mask_bce_7: 0.24200/0.35185, loss_mask_dice_7: 0.43175/1.25613, loss_spatial_bce_7: 0.08517/0.11264, loss_spatial_dice_7: 0.16320/0.26658, loss_spatial_ce_7: 0.06579/0.18389, loss_grounding_bce_7: 0.05284/0.09019, loss_grounding_dice_7: 0.19731/0.19071, loss_grounding_ce_7: 0.05205/0.34866, loss_mask_ce_8: 0.80456/1.13212, loss_mask_bce_8: 0.23208/0.36544, loss_mask_dice_8: 0.45775/1.33138, loss_spatial_bce_8: 0.08922/0.13383, loss_spatial_dice_8: 0.16410/0.30714, loss_spatial_ce_8: 0.08375/0.24104, loss_grounding_bce_8: 0.05244/0.09373, loss_grounding_dice_8: 0.19199/0.20212, loss_grounding_ce_8: 0.04794/0.42006, loss_mask_ce_9: 2.14929/3.69226, loss_mask_bce_9: 0.23476/0.39237, loss_mask_dice_9: 0.60759/1.90606, loss_spatial_bce_9: 0.31831/0.33607, loss_spatial_dice_9: 0.79255/0.82443, loss_spatial_ce_9: 1.53440/1.51553, loss_grounding_bce_9: 0.05176/0.10512, loss_grounding_dice_9: 0.25961/0.28172, loss_grounding_ce_9: 0.06490/0.69404] items per batch[64] items per second[0.22] total items[1504000] mini batches[ 23500] memory[7341] epoch remaining[0:11:44]
INFO:trainer.default_trainer:epochs[    12] optim steps[23600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53249/0.91033, loss_mask_bce_0: 0.16958/0.33552, loss_mask_dice_0: 1.94035/1.16663, loss_spatial_bce_0: 0.02972/0.09142, loss_spatial_dice_0: 0.27111/0.21819, loss_spatial_ce_0: 0.22453/0.07720, loss_grounding_bce_0: 0.07845/0.08627, loss_grounding_dice_0: 0.28475/0.17934, loss_grounding_ce_0: 0.11581/0.27569, loss_mask_ce_1: 1.58538/0.91127, loss_mask_bce_1: 0.16863/0.33620, loss_mask_dice_1: 1.86083/1.17365, loss_spatial_bce_1: 0.02860/0.09230, loss_spatial_dice_1: 0.33537/0.22253, loss_spatial_ce_1: 0.28535/0.08298, loss_grounding_bce_1: 0.07488/0.08635, loss_grounding_dice_1: 0.29470/0.17999, loss_grounding_ce_1: 0.12270/0.27766, loss_mask_ce_2: 1.96401/0.91855, loss_mask_bce_2: 0.17189/0.33671, loss_mask_dice_2: 2.14963/1.17294, loss_spatial_bce_2: 0.03081/0.09241, loss_spatial_dice_2: 0.33778/0.22349, loss_spatial_ce_2: 0.22649/0.08674, loss_grounding_bce_2: 0.07400/0.08643, loss_grounding_dice_2: 0.17263/0.17951, loss_grounding_ce_2: 0.83295/0.28130, loss_mask_ce_3: 1.82250/0.92705, loss_mask_bce_3: 0.16435/0.33742, loss_mask_dice_3: 2.03227/1.17020, loss_spatial_bce_3: 0.03028/0.09330, loss_spatial_dice_3: 0.35182/0.22414, loss_spatial_ce_3: 0.22807/0.09078, loss_grounding_bce_3: 0.07608/0.08652, loss_grounding_dice_3: 0.19727/0.17924, loss_grounding_ce_3: 0.78954/0.28257, loss_mask_ce_4: 1.69374/0.92582, loss_mask_bce_4: 0.18105/0.33915, loss_mask_dice_4: 2.38100/1.19273, loss_spatial_bce_4: 0.03176/0.09726, loss_spatial_dice_4: 0.33561/0.23400, loss_spatial_ce_4: 0.13679/0.10712, loss_grounding_bce_4: 0.07438/0.08708, loss_grounding_dice_4: 0.28820/0.18211, loss_grounding_ce_4: 0.15333/0.28511, loss_mask_ce_5: 1.68638/0.94007, loss_mask_bce_5: 0.20037/0.34138, loss_mask_dice_5: 2.15150/1.19769, loss_spatial_bce_5: 0.03431/0.09844, loss_spatial_dice_5: 0.37678/0.23699, loss_spatial_ce_5: 0.25577/0.12104, loss_grounding_bce_5: 0.08500/0.08748, loss_grounding_dice_5: 0.29816/0.18325, loss_grounding_ce_5: 0.16502/0.29772, loss_mask_ce_6: 1.46745/0.97783, loss_mask_bce_6: 0.18696/0.34411, loss_mask_dice_6: 2.24743/1.20032, loss_spatial_bce_6: 0.03790/0.10381, loss_spatial_dice_6: 0.34315/0.23928, loss_spatial_ce_6: 0.37686/0.14580, loss_grounding_bce_6: 0.07848/0.08822, loss_grounding_dice_6: 0.29193/0.18340, loss_grounding_ce_6: 0.14388/0.31540, loss_mask_ce_7: 1.79374/1.02083, loss_mask_bce_7: 0.21868/0.35182, loss_mask_dice_7: 2.23914/1.25587, loss_spatial_bce_7: 0.05620/0.11263, loss_spatial_dice_7: 0.40994/0.26655, loss_spatial_ce_7: 0.34239/0.18386, loss_grounding_bce_7: 0.07593/0.09019, loss_grounding_dice_7: 0.29469/0.19073, loss_grounding_ce_7: 0.12347/0.34840, loss_mask_ce_8: 2.48601/1.13185, loss_mask_bce_8: 0.19248/0.36538, loss_mask_dice_8: 2.05920/1.33100, loss_spatial_bce_8: 0.14274/0.13380, loss_spatial_dice_8: 0.50230/0.30708, loss_spatial_ce_8: 0.46290/0.24102, loss_grounding_bce_8: 0.07051/0.09373, loss_grounding_dice_8: 0.26341/0.20211, loss_grounding_ce_8: 0.70169/0.41983, loss_mask_ce_9: 5.41407/3.69167, loss_mask_bce_9: 0.27743/0.39227, loss_mask_dice_9: 2.85433/1.90529, loss_spatial_bce_9: 0.22036/0.33615, loss_spatial_dice_9: 0.90011/0.82442, loss_spatial_ce_9: 1.41849/1.51544, loss_grounding_bce_9: 0.09810/0.10509, loss_grounding_dice_9: 0.36162/0.28170, loss_grounding_ce_9: 1.54134/0.69394] items per batch[64] items per second[0.23] total items[1510400] mini batches[ 23600] memory[7341] epoch remaining[0:07:03]
INFO:trainer.default_trainer:epochs[    12] optim steps[23700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73116/0.91032, loss_mask_bce_0: 0.46447/0.33530, loss_mask_dice_0: 2.09406/1.16641, loss_spatial_bce_0: 0.09080/0.09136, loss_spatial_dice_0: 0.17846/0.21818, loss_spatial_ce_0: 0.05216/0.07716, loss_grounding_bce_0: 0.09748/0.08626, loss_grounding_dice_0: 0.20803/0.17940, loss_grounding_ce_0: 0.33219/0.27558, loss_mask_ce_1: 0.70105/0.91124, loss_mask_bce_1: 0.45889/0.33597, loss_mask_dice_1: 2.05703/1.17339, loss_spatial_bce_1: 0.09610/0.09224, loss_spatial_dice_1: 0.17979/0.22252, loss_spatial_ce_1: 0.04909/0.08291, loss_grounding_bce_1: 0.09865/0.08634, loss_grounding_dice_1: 0.20278/0.18007, loss_grounding_ce_1: 0.40081/0.27755, loss_mask_ce_2: 0.75542/0.91858, loss_mask_bce_2: 0.46293/0.33648, loss_mask_dice_2: 2.04515/1.17265, loss_spatial_bce_2: 0.09609/0.09235, loss_spatial_dice_2: 0.17298/0.22349, loss_spatial_ce_2: 0.02680/0.08666, loss_grounding_bce_2: 0.09384/0.08642, loss_grounding_dice_2: 0.20034/0.17959, loss_grounding_ce_2: 0.31266/0.28118, loss_mask_ce_3: 0.78088/0.92707, loss_mask_bce_3: 0.45158/0.33716, loss_mask_dice_3: 2.08039/1.16995, loss_spatial_bce_3: 0.07028/0.09324, loss_spatial_dice_3: 0.17106/0.22410, loss_spatial_ce_3: 0.14167/0.09071, loss_grounding_bce_3: 0.09005/0.08651, loss_grounding_dice_3: 0.20966/0.17931, loss_grounding_ce_3: 0.20236/0.28255, loss_mask_ce_4: 0.70949/0.92576, loss_mask_bce_4: 0.44482/0.33892, loss_mask_dice_4: 2.12538/1.19258, loss_spatial_bce_4: 0.10677/0.09719, loss_spatial_dice_4: 0.19047/0.23400, loss_spatial_ce_4: 0.01737/0.10710, loss_grounding_bce_4: 0.09321/0.08707, loss_grounding_dice_4: 0.21000/0.18217, loss_grounding_ce_4: 0.29555/0.28516, loss_mask_ce_5: 0.71147/0.94012, loss_mask_bce_5: 0.46087/0.34114, loss_mask_dice_5: 2.18544/1.19750, loss_spatial_bce_5: 0.10417/0.09838, loss_spatial_dice_5: 0.18878/0.23700, loss_spatial_ce_5: 0.01716/0.12100, loss_grounding_bce_5: 0.09661/0.08746, loss_grounding_dice_5: 0.19719/0.18332, loss_grounding_ce_5: 0.25531/0.29778, loss_mask_ce_6: 0.70503/0.97783, loss_mask_bce_6: 0.45216/0.34385, loss_mask_dice_6: 2.01675/1.20016, loss_spatial_bce_6: 0.11045/0.10373, loss_spatial_dice_6: 0.19585/0.23930, loss_spatial_ce_6: 0.04395/0.14575, loss_grounding_bce_6: 0.09153/0.08820, loss_grounding_dice_6: 0.21179/0.18346, loss_grounding_ce_6: 0.16426/0.31540, loss_mask_ce_7: 0.78947/1.02073, loss_mask_bce_7: 0.47010/0.35158, loss_mask_dice_7: 2.14488/1.25571, loss_spatial_bce_7: 0.13803/0.11257, loss_spatial_dice_7: 0.24741/0.26660, loss_spatial_ce_7: 0.13647/0.18378, loss_grounding_bce_7: 0.10207/0.09017, loss_grounding_dice_7: 0.24276/0.19078, loss_grounding_ce_7: 0.43563/0.34841, loss_mask_ce_8: 1.04119/1.13183, loss_mask_bce_8: 0.61035/0.36516, loss_mask_dice_8: 2.40970/1.33082, loss_spatial_bce_8: 0.12691/0.13373, loss_spatial_dice_8: 0.32542/0.30711, loss_spatial_ce_8: 0.14868/0.24098, loss_grounding_bce_8: 0.11519/0.09371, loss_grounding_dice_8: 0.27327/0.20218, loss_grounding_ce_8: 0.83662/0.41975, loss_mask_ce_9: 4.88413/3.69120, loss_mask_bce_9: 0.65929/0.39203, loss_mask_dice_9: 3.61843/1.90485, loss_spatial_bce_9: 0.28185/0.33600, loss_spatial_dice_9: 0.91274/0.82441, loss_spatial_ce_9: 1.41701/1.51546, loss_grounding_bce_9: 0.17415/0.10511, loss_grounding_dice_9: 0.40015/0.28175, loss_grounding_ce_9: 0.81560/0.69388] items per batch[64] items per second[0.23] total items[1516800] mini batches[ 23700] memory[7341] epoch remaining[0:02:23]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00023751.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0029 s/iter. Inference: 0.2248 s/iter. Eval: 0.0842 s/iter. Total: 0.3119 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2267 s/iter. Eval: 0.0837 s/iter. Total: 0.3135 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 42/157. Dataloading: 0.0031 s/iter. Inference: 0.2272 s/iter. Eval: 0.0936 s/iter. Total: 0.3240 s/iter. ETA=0:00:37
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 59/157. Dataloading: 0.0031 s/iter. Inference: 0.2276 s/iter. Eval: 0.0871 s/iter. Total: 0.3179 s/iter. ETA=0:00:31
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 77/157. Dataloading: 0.0032 s/iter. Inference: 0.2254 s/iter. Eval: 0.0824 s/iter. Total: 0.3111 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 93/157. Dataloading: 0.0032 s/iter. Inference: 0.2278 s/iter. Eval: 0.0810 s/iter. Total: 0.3121 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 109/157. Dataloading: 0.0031 s/iter. Inference: 0.2297 s/iter. Eval: 0.0802 s/iter. Total: 0.3132 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 126/157. Dataloading: 0.0032 s/iter. Inference: 0.2291 s/iter. Eval: 0.0795 s/iter. Total: 0.3119 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 142/157. Dataloading: 0.0032 s/iter. Inference: 0.2304 s/iter. Eval: 0.0796 s/iter. Total: 0.3134 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalp6qvbzak ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.041 | 82.088 | 60.129 |      133      |
| Things | 55.028 | 82.750 | 65.858 |      80       |
| Stuff  | 42.513 | 81.087 | 51.481 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.34s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.80 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.01 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.17s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.99 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.610
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.289
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.714
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.790 | 61.014 | 40.870 | 19.176 | 41.858 | 60.391 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.852 | bicycle      | 18.548 | car            | 37.459 |
| motorcycle    | 34.906 | airplane     | 55.611 | bus            | 64.343 |
| train         | 69.162 | truck        | 35.022 | boat           | 23.456 |
| traffic light | 24.096 | fire hydrant | 65.788 | stop sign      | 62.608 |
| parking meter | 45.256 | bench        | 20.198 | bird           | 29.552 |
| cat           | 74.050 | dog          | 66.558 | horse          | 43.903 |
| sheep         | 46.227 | cow          | 50.952 | elephant       | 60.910 |
| bear          | 77.404 | zebra        | 59.857 | giraffe        | 56.280 |
| backpack      | 17.600 | umbrella     | 48.577 | handbag        | 14.700 |
| tie           | 32.944 | suitcase     | 42.016 | frisbee        | 66.654 |
| skis          | 5.138  | snowboard    | 23.289 | sports ball    | 46.015 |
| kite          | 33.567 | baseball bat | 28.333 | baseball glove | 42.744 |
| skateboard    | 36.113 | surfboard    | 35.126 | tennis racket  | 55.988 |
| bottle        | 33.162 | wine glass   | 27.354 | cup            | 40.112 |
| fork          | 17.320 | knife        | 13.384 | spoon          | 14.636 |
| bowl          | 31.031 | banana       | 20.408 | apple          | 20.081 |
| sandwich      | 43.450 | orange       | 27.820 | broccoli       | 21.517 |
| carrot        | 20.866 | hot dog      | 23.672 | pizza          | 51.194 |
| donut         | 45.671 | cake         | 43.902 | chair          | 20.341 |
| couch         | 39.992 | potted plant | 17.403 | bed            | 42.440 |
| dining table  | 12.424 | toilet       | 67.427 | tv             | 62.252 |
| laptop        | 63.563 | mouse        | 58.686 | remote         | 30.996 |
| keyboard      | 49.027 | cell phone   | 37.825 | microwave      | 55.339 |
| oven          | 32.074 | toaster      | 30.045 | sink           | 38.941 |
| refrigerator  | 57.972 | book         | 8.640  | clock          | 50.803 |
| vase          | 32.762 | scissors     | 23.886 | teddy bear     | 51.319 |
| hair drier    | 11.002 | toothbrush   | 17.687 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.841944216131616, 'fwIoU': 69.22490685148748, 'IoU-person': 87.81019983028705, 'IoU-bicycle': 70.70451704888086, 'IoU-car': 68.50233156469089, 'IoU-motorcycle': 83.73556143220478, 'IoU-airplane': 83.75210057470842, 'IoU-bus': 85.41354722595008, 'IoU-train': 87.4828160916442, 'IoU-truck': 63.358231615333295, 'IoU-boat': 65.22885085317075, 'IoU-traffic light': 76.57544597726971, 'IoU-fire hydrant': 90.05334660569048, 'IoU-stop sign': 91.57214292885824, 'IoU-parking meter': 88.23543283241241, 'IoU-bench': 55.33753127064806, 'IoU-bird': 70.765671278095, 'IoU-cat': 84.15897614997945, 'IoU-dog': 79.30329176755745, 'IoU-horse': 85.35942068971433, 'IoU-sheep': 85.23876910363558, 'IoU-cow': 82.38299348160533, 'IoU-elephant': 90.85327877698131, 'IoU-bear': 92.64120606461053, 'IoU-zebra': 90.65958007793984, 'IoU-giraffe': 88.2825664621677, 'IoU-backpack': 39.64661185189413, 'IoU-umbrella': 77.22514057608974, 'IoU-handbag': 36.70592031973885, 'IoU-tie': 68.9152202665936, 'IoU-suitcase': 80.90293345269556, 'IoU-frisbee': 84.00866520730892, 'IoU-skis': 51.626795354413076, 'IoU-snowboard': 68.7319810763874, 'IoU-sports ball': 67.37053902361538, 'IoU-kite': 64.10495104502057, 'IoU-baseball bat': 60.99410646647796, 'IoU-baseball glove': 72.93775362656841, 'IoU-skateboard': 64.15900809139492, 'IoU-surfboard': 74.97756954049906, 'IoU-tennis racket': 74.54263696941521, 'IoU-bottle': 69.86829750388888, 'IoU-wine glass': 75.11364840228136, 'IoU-cup': 62.55758238903212, 'IoU-fork': 54.14982966926169, 'IoU-knife': 47.04071980210771, 'IoU-spoon': 48.44810578481339, 'IoU-bowl': 54.643302873571606, 'IoU-banana': 84.0072105700425, 'IoU-apple': 58.541409062651574, 'IoU-sandwich': 62.99493276460497, 'IoU-orange': 80.58241617538175, 'IoU-broccoli': 68.34899358086925, 'IoU-carrot': 63.806119014075726, 'IoU-hot dog': 61.5940682189442, 'IoU-pizza': 83.60872123727941, 'IoU-donut': 64.82919222248395, 'IoU-cake': 69.05616188763757, 'IoU-chair': 55.155657346870555, 'IoU-couch': 67.93594109275084, 'IoU-potted plant': 34.60147918248754, 'IoU-bed': 69.49497330895308, 'IoU-dining table': 51.75415891642332, 'IoU-toilet': 88.98954393534565, 'IoU-tv': 74.94811395852847, 'IoU-laptop': 73.96971763172455, 'IoU-mouse': 71.81593585643482, 'IoU-remote': 50.04264297969833, 'IoU-keyboard': 61.50255592038154, 'IoU-cell phone': 68.02217131378919, 'IoU-microwave': 64.77261997718931, 'IoU-oven': 68.23690277756572, 'IoU-toaster': 39.30633727828653, 'IoU-sink': 70.69958347142168, 'IoU-refrigerator': 82.8237781443164, 'IoU-book': 49.037185017528614, 'IoU-clock': 72.4546226042382, 'IoU-vase': 54.143233708419935, 'IoU-scissors': 55.21036890174057, 'IoU-teddy bear': 82.98902499776784, 'IoU-hair drier': 38.01312395121096, 'IoU-toothbrush': 59.84387939169453, 'IoU-banner': 34.944353263702226, 'IoU-blanket': 11.6283857586319, 'IoU-bridge': 39.223773561218735, 'IoU-cardboard': 48.94152548753169, 'IoU-counter': 30.522254226898717, 'IoU-curtain': 64.46982093723824, 'IoU-door-stuff': 44.62756920224994, 'IoU-floor-wood': 60.68985979976554, 'IoU-flower': 46.40947277498721, 'IoU-fruit': 39.71345974523619, 'IoU-gravel': 27.075034970487934, 'IoU-house': 24.417724081714887, 'IoU-light': 40.21147053394814, 'IoU-mirror-stuff': 58.39037797458244, 'IoU-net': 44.84277376721021, 'IoU-pillow': 10.933600964016644, 'IoU-platform': 33.8984301499751, 'IoU-playingfield': 68.8122808552146, 'IoU-railroad': 61.439337362514635, 'IoU-river': 49.216906106098634, 'IoU-road': 65.99128836925503, 'IoU-roof': 12.331876469079516, 'IoU-sand': 63.640097085550764, 'IoU-sea': 84.30627829615823, 'IoU-shelf': 36.416210419273774, 'IoU-snow': 87.65028079988909, 'IoU-stairs': 20.48269568725782, 'IoU-tent': 9.56873366043453, 'IoU-towel': 36.136260833773306, 'IoU-wall-brick': 48.90388567023247, 'IoU-wall-stone': 29.043487611144904, 'IoU-wall-tile': 67.35633400140826, 'IoU-wall-wood': 38.99326646665551, 'IoU-water-other': 21.613349917761447, 'IoU-window-blind': 47.231396259126115, 'IoU-window-other': 47.91342762226323, 'IoU-tree-merged': 81.00100355740592, 'IoU-fence-merged': 51.31500996175189, 'IoU-ceiling-merged': 65.46987205679685, 'IoU-sky-other-merged': 93.00063613376841, 'IoU-cabinet-merged': 59.48654650914135, 'IoU-table-merged': 38.06110952925909, 'IoU-floor-other-merged': 48.45034070962445, 'IoU-pavement-merged': 54.65470601995859, 'IoU-mountain-merged': 54.89151238542346, 'IoU-grass-merged': 72.12500911764741, 'IoU-dirt-merged': 45.779553191730024, 'IoU-paper-merged': 34.9074166305986, 'IoU-food-other-merged': 36.382674154995165, 'IoU-building-other-merged': 57.03525233333549, 'IoU-rock-merged': 63.63585144408833, 'IoU-wall-other-merged': 65.12208525858608, 'IoU-rug-merged': 63.460815663061496, 'mACC': 72.8128004460031, 'pACC': 80.52627929344816, 'ACC-person': 92.46449101600977, 'ACC-bicycle': 79.63944242067646, 'ACC-car': 85.02816752236758, 'ACC-motorcycle': 89.44913055408252, 'ACC-airplane': 90.59215302421777, 'ACC-bus': 89.70514169516035, 'ACC-train': 95.65114328120241, 'ACC-truck': 75.87108000034243, 'ACC-boat': 77.45560008165502, 'ACC-traffic light': 89.56631620184194, 'ACC-fire hydrant': 95.32019283734793, 'ACC-stop sign': 94.93879800953596, 'ACC-parking meter': 92.13469895038601, 'ACC-bench': 70.65557670763, 'ACC-bird': 75.35143076027596, 'ACC-cat': 92.19200162115261, 'ACC-dog': 83.4411242581474, 'ACC-horse': 91.19101204818051, 'ACC-sheep': 88.32166205942035, 'ACC-cow': 88.40113072238616, 'ACC-elephant': 93.56928064974053, 'ACC-bear': 94.98653717146894, 'ACC-zebra': 93.28765300324518, 'ACC-giraffe': 92.6161847574909, 'ACC-backpack': 63.928558908335596, 'ACC-umbrella': 85.52365538449146, 'ACC-handbag': 53.05237173472738, 'ACC-tie': 81.16031415431196, 'ACC-suitcase': 89.20203096392947, 'ACC-frisbee': 94.05854545454545, 'ACC-skis': 69.1100760825494, 'ACC-snowboard': 78.70637505816659, 'ACC-sports ball': 82.20144509566781, 'ACC-kite': 73.49087211394945, 'ACC-baseball bat': 83.55048737909178, 'ACC-baseball glove': 90.13856163939387, 'ACC-skateboard': 69.4463645299855, 'ACC-surfboard': 83.38907107812338, 'ACC-tennis racket': 79.67605117728844, 'ACC-bottle': 83.56061635573928, 'ACC-wine glass': 86.13506554731555, 'ACC-cup': 84.63998264942688, 'ACC-fork': 65.4632138898465, 'ACC-knife': 58.21224444932952, 'ACC-spoon': 67.82738792504634, 'ACC-bowl': 71.4526018156181, 'ACC-banana': 90.42116598836498, 'ACC-apple': 72.40304238959027, 'ACC-sandwich': 75.2646202841288, 'ACC-orange': 87.60275727395417, 'ACC-broccoli': 79.27499109148717, 'ACC-carrot': 74.48735432770607, 'ACC-hot dog': 73.65210977910833, 'ACC-pizza': 94.92239163637086, 'ACC-donut': 82.26176319581938, 'ACC-cake': 76.55590487298892, 'ACC-chair': 70.7798708249128, 'ACC-couch': 80.42668229031513, 'ACC-potted plant': 47.337198170273936, 'ACC-bed': 81.55344968931708, 'ACC-dining table': 77.41177303066283, 'ACC-toilet': 93.51083384273028, 'ACC-tv': 85.35790562125844, 'ACC-laptop': 89.57466060314997, 'ACC-mouse': 85.4625932058483, 'ACC-remote': 72.09883949232679, 'ACC-keyboard': 69.72858975009396, 'ACC-cell phone': 77.0769186762804, 'ACC-microwave': 73.84625636696984, 'ACC-oven': 84.73646602268215, 'ACC-toaster': 44.20342416072479, 'ACC-sink': 83.49078683765156, 'ACC-refrigerator': 91.17435742082105, 'ACC-book': 62.069149085203556, 'ACC-clock': 78.85460807613745, 'ACC-vase': 61.555719632032094, 'ACC-scissors': 60.02281721437163, 'ACC-teddy bear': 89.21203230610189, 'ACC-hair drier': 43.20012523837987, 'ACC-toothbrush': 80.97897845726199, 'ACC-banner': 74.62685962065676, 'ACC-blanket': 16.160041305178595, 'ACC-bridge': 54.35137592312302, 'ACC-cardboard': 62.19880761960157, 'ACC-counter': 52.36439982063196, 'ACC-curtain': 75.68350178805903, 'ACC-door-stuff': 65.55286380694885, 'ACC-floor-wood': 76.84115823230752, 'ACC-flower': 67.76763814654794, 'ACC-fruit': 54.04711784896826, 'ACC-gravel': 32.831836604994294, 'ACC-house': 29.274021118111904, 'ACC-light': 57.07488372315584, 'ACC-mirror-stuff': 71.64497787663598, 'ACC-net': 60.14431785178238, 'ACC-pillow': 25.36662504322942, 'ACC-platform': 47.18612918124021, 'ACC-playingfield': 84.85594118811186, 'ACC-railroad': 77.22646180726832, 'ACC-river': 70.92654619376088, 'ACC-road': 84.09680696773859, 'ACC-roof': 16.43657788262905, 'ACC-sand': 70.5321245451792, 'ACC-sea': 89.84726460528935, 'ACC-shelf': 56.83645510589117, 'ACC-snow': 96.24867675529526, 'ACC-stairs': 32.03294986122675, 'ACC-tent': 11.972107096704041, 'ACC-towel': 44.85313099693282, 'ACC-wall-brick': 64.54083696368727, 'ACC-wall-stone': 33.441323466785335, 'ACC-wall-tile': 81.43028046323917, 'ACC-wall-wood': 56.549792818804164, 'ACC-water-other': 37.82250913954105, 'ACC-window-blind': 59.598791290168364, 'ACC-window-other': 70.22736583625465, 'ACC-tree-merged': 88.84479437869012, 'ACC-fence-merged': 74.53952255212106, 'ACC-ceiling-merged': 78.34584911349597, 'ACC-sky-other-merged': 96.5415773310056, 'ACC-cabinet-merged': 76.31899619121852, 'ACC-table-merged': 49.642023330672295, 'ACC-floor-other-merged': 60.13766976984544, 'ACC-pavement-merged': 69.42177364375553, 'ACC-mountain-merged': 65.67694656526261, 'ACC-grass-merged': 83.76498230264713, 'ACC-dirt-merged': 68.64471375002653, 'ACC-paper-merged': 48.338660979653255, 'ACC-food-other-merged': 46.45547348017829, 'ACC-building-other-merged': 72.77136872408708, 'ACC-rock-merged': 83.3679500357864, 'ACC-wall-other-merged': 81.41836889458325, 'ACC-rug-merged': 81.01331018383152})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1531 s/iter. Inference: 0.5711 s/iter. Eval: 0.0000 s/iter. Total: 0.7242 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1542 s/iter. Inference: 0.5283 s/iter. Eval: 0.0000 s/iter. Total: 0.6826 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1691 s/iter. Inference: 0.5990 s/iter. Eval: 0.0000 s/iter. Total: 0.7682 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1689 s/iter. Inference: 0.6737 s/iter. Eval: 0.0000 s/iter. Total: 0.8427 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1674 s/iter. Inference: 0.6272 s/iter. Eval: 0.0000 s/iter. Total: 0.7947 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1666 s/iter. Inference: 0.6700 s/iter. Eval: 0.0000 s/iter. Total: 0.8367 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5390693590869184, 'noc@0.8': 3.002633889376646, 'noc@0.85': 3.63681592039801, 'noc@0.9': 4.654667837284167, 'miou@iter1': 0.8320497619432422}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0024 s/iter. Inference: 0.1012 s/iter. Eval: 0.0008 s/iter. Total: 0.1044 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.04547119140625, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.883792877197266, 'precision@0.8': 51.88496017456055, 'precision@0.9': 26.117372512817383, 'cIoU': 57.1826057434082, 'mIoU': 62.681495666503906}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.041103957917535, 'SQ': 82.08768069906472, 'RQ': 60.12889826420681, 'PQ_th': 55.02828448205735, 'SQ_th': 82.75030295963684, 'RQ_th': 65.85839853423727, 'PQ_st': 42.51328429883857, 'SQ_st': 81.08749615480487, 'RQ_st': 51.48059596982119}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.79040426174938, 'AP50': 61.01387240565054, 'AP75': 40.86992059661099, 'APs': 19.175915503621397, 'APm': 41.85815257276493, 'APl': 60.39104131679118, 'AP-person': 43.85205024386707, 'AP-bicycle': 18.547733498630667, 'AP-car': 37.45944007936702, 'AP-motorcycle': 34.90631245598762, 'AP-airplane': 55.611109071743634, 'AP-bus': 64.34348370589402, 'AP-train': 69.16237157483664, 'AP-truck': 35.02241725764645, 'AP-boat': 23.45623226719156, 'AP-traffic light': 24.09624952761792, 'AP-fire hydrant': 65.78806925314458, 'AP-stop sign': 62.608279121130614, 'AP-parking meter': 45.256162075555466, 'AP-bench': 20.197837820523347, 'AP-bird': 29.551713791106458, 'AP-cat': 74.04997697825434, 'AP-dog': 66.55792330340114, 'AP-horse': 43.9030785508434, 'AP-sheep': 46.22709543840132, 'AP-cow': 50.952317273917394, 'AP-elephant': 60.91025405329115, 'AP-bear': 77.40414374528449, 'AP-zebra': 59.85658065987531, 'AP-giraffe': 56.28037540560784, 'AP-backpack': 17.600431358219478, 'AP-umbrella': 48.577430630808465, 'AP-handbag': 14.70021066864634, 'AP-tie': 32.94415297123072, 'AP-suitcase': 42.01629257438695, 'AP-frisbee': 66.6542369069547, 'AP-skis': 5.138179668983497, 'AP-snowboard': 23.28895892536118, 'AP-sports ball': 46.014590918215845, 'AP-kite': 33.56742112504442, 'AP-baseball bat': 28.333402118592993, 'AP-baseball glove': 42.74382326457689, 'AP-skateboard': 36.11260560501861, 'AP-surfboard': 35.126300401126045, 'AP-tennis racket': 55.98783936833508, 'AP-bottle': 33.16247496760554, 'AP-wine glass': 27.35362627030276, 'AP-cup': 40.11222802741599, 'AP-fork': 17.319926163660238, 'AP-knife': 13.384469114301176, 'AP-spoon': 14.635585763804684, 'AP-bowl': 31.03130662696928, 'AP-banana': 20.40753694082239, 'AP-apple': 20.08096185321712, 'AP-sandwich': 43.44975549636712, 'AP-orange': 27.81960203391996, 'AP-broccoli': 21.51716261195977, 'AP-carrot': 20.865722216053065, 'AP-hot dog': 23.67231633859742, 'AP-pizza': 51.19425016881964, 'AP-donut': 45.67135629640586, 'AP-cake': 43.901622367635674, 'AP-chair': 20.34083012780904, 'AP-couch': 39.99218483564352, 'AP-potted plant': 17.403010478593792, 'AP-bed': 42.44038868884973, 'AP-dining table': 12.4236452140405, 'AP-toilet': 67.42706631163009, 'AP-tv': 62.252384861701934, 'AP-laptop': 63.56317396498642, 'AP-mouse': 58.68587936344579, 'AP-remote': 30.995779229331905, 'AP-keyboard': 49.02654932406698, 'AP-cell phone': 37.824551052071925, 'AP-microwave': 55.338919039177284, 'AP-oven': 32.07363636684159, 'AP-toaster': 30.04544887261835, 'AP-sink': 38.94085203230294, 'AP-refrigerator': 57.97225707875696, 'AP-book': 8.639611208500556, 'AP-clock': 50.803122790582755, 'AP-vase': 32.761682157771446, 'AP-scissors': 23.88649912931042, 'AP-teddy bear': 51.31914857362704, 'AP-hair drier': 11.001980198019801, 'AP-toothbrush': 17.686753123791064}), ('sem_seg', {'mIoU': 60.841944216131616, 'fwIoU': 69.22490685148748, 'IoU-person': 87.81019983028705, 'IoU-bicycle': 70.70451704888086, 'IoU-car': 68.50233156469089, 'IoU-motorcycle': 83.73556143220478, 'IoU-airplane': 83.75210057470842, 'IoU-bus': 85.41354722595008, 'IoU-train': 87.4828160916442, 'IoU-truck': 63.358231615333295, 'IoU-boat': 65.22885085317075, 'IoU-traffic light': 76.57544597726971, 'IoU-fire hydrant': 90.05334660569048, 'IoU-stop sign': 91.57214292885824, 'IoU-parking meter': 88.23543283241241, 'IoU-bench': 55.33753127064806, 'IoU-bird': 70.765671278095, 'IoU-cat': 84.15897614997945, 'IoU-dog': 79.30329176755745, 'IoU-horse': 85.35942068971433, 'IoU-sheep': 85.23876910363558, 'IoU-cow': 82.38299348160533, 'IoU-elephant': 90.85327877698131, 'IoU-bear': 92.64120606461053, 'IoU-zebra': 90.65958007793984, 'IoU-giraffe': 88.2825664621677, 'IoU-backpack': 39.64661185189413, 'IoU-umbrella': 77.22514057608974, 'IoU-handbag': 36.70592031973885, 'IoU-tie': 68.9152202665936, 'IoU-suitcase': 80.90293345269556, 'IoU-frisbee': 84.00866520730892, 'IoU-skis': 51.626795354413076, 'IoU-snowboard': 68.7319810763874, 'IoU-sports ball': 67.37053902361538, 'IoU-kite': 64.10495104502057, 'IoU-baseball bat': 60.99410646647796, 'IoU-baseball glove': 72.93775362656841, 'IoU-skateboard': 64.15900809139492, 'IoU-surfboard': 74.97756954049906, 'IoU-tennis racket': 74.54263696941521, 'IoU-bottle': 69.86829750388888, 'IoU-wine glass': 75.11364840228136, 'IoU-cup': 62.55758238903212, 'IoU-fork': 54.14982966926169, 'IoU-knife': 47.04071980210771, 'IoU-spoon': 48.44810578481339, 'IoU-bowl': 54.643302873571606, 'IoU-banana': 84.0072105700425, 'IoU-apple': 58.541409062651574, 'IoU-sandwich': 62.99493276460497, 'IoU-orange': 80.58241617538175, 'IoU-broccoli': 68.34899358086925, 'IoU-carrot': 63.806119014075726, 'IoU-hot dog': 61.5940682189442, 'IoU-pizza': 83.60872123727941, 'IoU-donut': 64.82919222248395, 'IoU-cake': 69.05616188763757, 'IoU-chair': 55.155657346870555, 'IoU-couch': 67.93594109275084, 'IoU-potted plant': 34.60147918248754, 'IoU-bed': 69.49497330895308, 'IoU-dining table': 51.75415891642332, 'IoU-toilet': 88.98954393534565, 'IoU-tv': 74.94811395852847, 'IoU-laptop': 73.96971763172455, 'IoU-mouse': 71.81593585643482, 'IoU-remote': 50.04264297969833, 'IoU-keyboard': 61.50255592038154, 'IoU-cell phone': 68.02217131378919, 'IoU-microwave': 64.77261997718931, 'IoU-oven': 68.23690277756572, 'IoU-toaster': 39.30633727828653, 'IoU-sink': 70.69958347142168, 'IoU-refrigerator': 82.8237781443164, 'IoU-book': 49.037185017528614, 'IoU-clock': 72.4546226042382, 'IoU-vase': 54.143233708419935, 'IoU-scissors': 55.21036890174057, 'IoU-teddy bear': 82.98902499776784, 'IoU-hair drier': 38.01312395121096, 'IoU-toothbrush': 59.84387939169453, 'IoU-banner': 34.944353263702226, 'IoU-blanket': 11.6283857586319, 'IoU-bridge': 39.223773561218735, 'IoU-cardboard': 48.94152548753169, 'IoU-counter': 30.522254226898717, 'IoU-curtain': 64.46982093723824, 'IoU-door-stuff': 44.62756920224994, 'IoU-floor-wood': 60.68985979976554, 'IoU-flower': 46.40947277498721, 'IoU-fruit': 39.71345974523619, 'IoU-gravel': 27.075034970487934, 'IoU-house': 24.417724081714887, 'IoU-light': 40.21147053394814, 'IoU-mirror-stuff': 58.39037797458244, 'IoU-net': 44.84277376721021, 'IoU-pillow': 10.933600964016644, 'IoU-platform': 33.8984301499751, 'IoU-playingfield': 68.8122808552146, 'IoU-railroad': 61.439337362514635, 'IoU-river': 49.216906106098634, 'IoU-road': 65.99128836925503, 'IoU-roof': 12.331876469079516, 'IoU-sand': 63.640097085550764, 'IoU-sea': 84.30627829615823, 'IoU-shelf': 36.416210419273774, 'IoU-snow': 87.65028079988909, 'IoU-stairs': 20.48269568725782, 'IoU-tent': 9.56873366043453, 'IoU-towel': 36.136260833773306, 'IoU-wall-brick': 48.90388567023247, 'IoU-wall-stone': 29.043487611144904, 'IoU-wall-tile': 67.35633400140826, 'IoU-wall-wood': 38.99326646665551, 'IoU-water-other': 21.613349917761447, 'IoU-window-blind': 47.231396259126115, 'IoU-window-other': 47.91342762226323, 'IoU-tree-merged': 81.00100355740592, 'IoU-fence-merged': 51.31500996175189, 'IoU-ceiling-merged': 65.46987205679685, 'IoU-sky-other-merged': 93.00063613376841, 'IoU-cabinet-merged': 59.48654650914135, 'IoU-table-merged': 38.06110952925909, 'IoU-floor-other-merged': 48.45034070962445, 'IoU-pavement-merged': 54.65470601995859, 'IoU-mountain-merged': 54.89151238542346, 'IoU-grass-merged': 72.12500911764741, 'IoU-dirt-merged': 45.779553191730024, 'IoU-paper-merged': 34.9074166305986, 'IoU-food-other-merged': 36.382674154995165, 'IoU-building-other-merged': 57.03525233333549, 'IoU-rock-merged': 63.63585144408833, 'IoU-wall-other-merged': 65.12208525858608, 'IoU-rug-merged': 63.460815663061496, 'mACC': 72.8128004460031, 'pACC': 80.52627929344816, 'ACC-person': 92.46449101600977, 'ACC-bicycle': 79.63944242067646, 'ACC-car': 85.02816752236758, 'ACC-motorcycle': 89.44913055408252, 'ACC-airplane': 90.59215302421777, 'ACC-bus': 89.70514169516035, 'ACC-train': 95.65114328120241, 'ACC-truck': 75.87108000034243, 'ACC-boat': 77.45560008165502, 'ACC-traffic light': 89.56631620184194, 'ACC-fire hydrant': 95.32019283734793, 'ACC-stop sign': 94.93879800953596, 'ACC-parking meter': 92.13469895038601, 'ACC-bench': 70.65557670763, 'ACC-bird': 75.35143076027596, 'ACC-cat': 92.19200162115261, 'ACC-dog': 83.4411242581474, 'ACC-horse': 91.19101204818051, 'ACC-sheep': 88.32166205942035, 'ACC-cow': 88.40113072238616, 'ACC-elephant': 93.56928064974053, 'ACC-bear': 94.98653717146894, 'ACC-zebra': 93.28765300324518, 'ACC-giraffe': 92.6161847574909, 'ACC-backpack': 63.928558908335596, 'ACC-umbrella': 85.52365538449146, 'ACC-handbag': 53.05237173472738, 'ACC-tie': 81.16031415431196, 'ACC-suitcase': 89.20203096392947, 'ACC-frisbee': 94.05854545454545, 'ACC-skis': 69.1100760825494, 'ACC-snowboard': 78.70637505816659, 'ACC-sports ball': 82.20144509566781, 'ACC-kite': 73.49087211394945, 'ACC-baseball bat': 83.55048737909178, 'ACC-baseball glove': 90.13856163939387, 'ACC-skateboard': 69.4463645299855, 'ACC-surfboard': 83.38907107812338, 'ACC-tennis racket': 79.67605117728844, 'ACC-bottle': 83.56061635573928, 'ACC-wine glass': 86.13506554731555, 'ACC-cup': 84.63998264942688, 'ACC-fork': 65.4632138898465, 'ACC-knife': 58.21224444932952, 'ACC-spoon': 67.82738792504634, 'ACC-bowl': 71.4526018156181, 'ACC-banana': 90.42116598836498, 'ACC-apple': 72.40304238959027, 'ACC-sandwich': 75.2646202841288, 'ACC-orange': 87.60275727395417, 'ACC-broccoli': 79.27499109148717, 'ACC-carrot': 74.48735432770607, 'ACC-hot dog': 73.65210977910833, 'ACC-pizza': 94.92239163637086, 'ACC-donut': 82.26176319581938, 'ACC-cake': 76.55590487298892, 'ACC-chair': 70.7798708249128, 'ACC-couch': 80.42668229031513, 'ACC-potted plant': 47.337198170273936, 'ACC-bed': 81.55344968931708, 'ACC-dining table': 77.41177303066283, 'ACC-toilet': 93.51083384273028, 'ACC-tv': 85.35790562125844, 'ACC-laptop': 89.57466060314997, 'ACC-mouse': 85.4625932058483, 'ACC-remote': 72.09883949232679, 'ACC-keyboard': 69.72858975009396, 'ACC-cell phone': 77.0769186762804, 'ACC-microwave': 73.84625636696984, 'ACC-oven': 84.73646602268215, 'ACC-toaster': 44.20342416072479, 'ACC-sink': 83.49078683765156, 'ACC-refrigerator': 91.17435742082105, 'ACC-book': 62.069149085203556, 'ACC-clock': 78.85460807613745, 'ACC-vase': 61.555719632032094, 'ACC-scissors': 60.02281721437163, 'ACC-teddy bear': 89.21203230610189, 'ACC-hair drier': 43.20012523837987, 'ACC-toothbrush': 80.97897845726199, 'ACC-banner': 74.62685962065676, 'ACC-blanket': 16.160041305178595, 'ACC-bridge': 54.35137592312302, 'ACC-cardboard': 62.19880761960157, 'ACC-counter': 52.36439982063196, 'ACC-curtain': 75.68350178805903, 'ACC-door-stuff': 65.55286380694885, 'ACC-floor-wood': 76.84115823230752, 'ACC-flower': 67.76763814654794, 'ACC-fruit': 54.04711784896826, 'ACC-gravel': 32.831836604994294, 'ACC-house': 29.274021118111904, 'ACC-light': 57.07488372315584, 'ACC-mirror-stuff': 71.64497787663598, 'ACC-net': 60.14431785178238, 'ACC-pillow': 25.36662504322942, 'ACC-platform': 47.18612918124021, 'ACC-playingfield': 84.85594118811186, 'ACC-railroad': 77.22646180726832, 'ACC-river': 70.92654619376088, 'ACC-road': 84.09680696773859, 'ACC-roof': 16.43657788262905, 'ACC-sand': 70.5321245451792, 'ACC-sea': 89.84726460528935, 'ACC-shelf': 56.83645510589117, 'ACC-snow': 96.24867675529526, 'ACC-stairs': 32.03294986122675, 'ACC-tent': 11.972107096704041, 'ACC-towel': 44.85313099693282, 'ACC-wall-brick': 64.54083696368727, 'ACC-wall-stone': 33.441323466785335, 'ACC-wall-tile': 81.43028046323917, 'ACC-wall-wood': 56.549792818804164, 'ACC-water-other': 37.82250913954105, 'ACC-window-blind': 59.598791290168364, 'ACC-window-other': 70.22736583625465, 'ACC-tree-merged': 88.84479437869012, 'ACC-fence-merged': 74.53952255212106, 'ACC-ceiling-merged': 78.34584911349597, 'ACC-sky-other-merged': 96.5415773310056, 'ACC-cabinet-merged': 76.31899619121852, 'ACC-table-merged': 49.642023330672295, 'ACC-floor-other-merged': 60.13766976984544, 'ACC-pavement-merged': 69.42177364375553, 'ACC-mountain-merged': 65.67694656526261, 'ACC-grass-merged': 83.76498230264713, 'ACC-dirt-merged': 68.64471375002653, 'ACC-paper-merged': 48.338660979653255, 'ACC-food-other-merged': 46.45547348017829, 'ACC-building-other-merged': 72.77136872408708, 'ACC-rock-merged': 83.3679500357864, 'ACC-wall-other-merged': 81.41836889458325, 'ACC-rug-merged': 81.01331018383152})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5390693590869184, 'noc@0.8': 3.002633889376646, 'noc@0.85': 3.63681592039801, 'noc@0.9': 4.654667837284167, 'miou@iter1': 0.8320497619432422}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.04547119140625, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.883792877197266, 'precision@0.8': 51.88496017456055, 'precision@0.9': 26.117372512817383, 'cIoU': 57.1826057434082, 'mIoU': 62.681495666503906}}}
INFO:trainer.default_trainer:This epoch takes 1:28:35.979332
INFO:trainer.default_trainer:PROGRESS: 26.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 13 training.
INFO:trainer.default_trainer:epochs[    13] optim steps[23800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59334/0.91066, loss_mask_bce_0: 0.28119/0.33538, loss_mask_dice_0: 0.22971/1.16724, loss_spatial_bce_0: 0.16433/0.09135, loss_spatial_dice_0: 0.13270/0.21819, loss_spatial_ce_0: 0.00329/0.07710, loss_grounding_bce_0: 0.18427/0.08628, loss_grounding_dice_0: 0.13955/0.17949, loss_grounding_ce_0: 0.17507/0.27559, loss_mask_ce_1: 0.57881/0.91154, loss_mask_bce_1: 0.28274/0.33605, loss_mask_dice_1: 0.24352/1.17420, loss_spatial_bce_1: 0.16943/0.09223, loss_spatial_dice_1: 0.13044/0.22254, loss_spatial_ce_1: 0.00325/0.08283, loss_grounding_bce_1: 0.18326/0.08637, loss_grounding_dice_1: 0.14818/0.18017, loss_grounding_ce_1: 0.17621/0.27754, loss_mask_ce_2: 0.58177/0.91892, loss_mask_bce_2: 0.28753/0.33657, loss_mask_dice_2: 0.24676/1.17356, loss_spatial_bce_2: 0.17131/0.09234, loss_spatial_dice_2: 0.13519/0.22350, loss_spatial_ce_2: 0.00654/0.08660, loss_grounding_bce_2: 0.18687/0.08644, loss_grounding_dice_2: 0.14462/0.17968, loss_grounding_ce_2: 0.15456/0.28113, loss_mask_ce_3: 0.60190/0.92745, loss_mask_bce_3: 0.26251/0.33725, loss_mask_dice_3: 0.21193/1.17079, loss_spatial_bce_3: 0.17152/0.09324, loss_spatial_dice_3: 0.15009/0.22414, loss_spatial_ce_3: 0.00933/0.09064, loss_grounding_bce_3: 0.18439/0.08654, loss_grounding_dice_3: 0.13584/0.17938, loss_grounding_ce_3: 0.14631/0.28256, loss_mask_ce_4: 0.59317/0.92612, loss_mask_bce_4: 0.27511/0.33900, loss_mask_dice_4: 0.22180/1.19335, loss_spatial_bce_4: 0.20310/0.09720, loss_spatial_dice_4: 0.16984/0.23406, loss_spatial_ce_4: 0.04221/0.10707, loss_grounding_bce_4: 0.18932/0.08710, loss_grounding_dice_4: 0.13925/0.18227, loss_grounding_ce_4: 0.14498/0.28521, loss_mask_ce_5: 0.58696/0.94037, loss_mask_bce_5: 0.27817/0.34122, loss_mask_dice_5: 0.22047/1.19837, loss_spatial_bce_5: 0.21265/0.09839, loss_spatial_dice_5: 0.18106/0.23707, loss_spatial_ce_5: 0.04956/0.12098, loss_grounding_bce_5: 0.18647/0.08748, loss_grounding_dice_5: 0.13592/0.18344, loss_grounding_ce_5: 0.15543/0.29775, loss_mask_ce_6: 0.64119/0.97796, loss_mask_bce_6: 0.27790/0.34396, loss_mask_dice_6: 0.20667/1.20103, loss_spatial_bce_6: 0.22715/0.10375, loss_spatial_dice_6: 0.20077/0.23936, loss_spatial_ce_6: 0.07824/0.14580, loss_grounding_bce_6: 0.19140/0.08823, loss_grounding_dice_6: 0.13481/0.18357, loss_grounding_ce_6: 0.21124/0.31542, loss_mask_ce_7: 0.60393/1.02103, loss_mask_bce_7: 0.36495/0.35172, loss_mask_dice_7: 0.29473/1.25667, loss_spatial_bce_7: 0.17765/0.11256, loss_spatial_dice_7: 0.16424/0.26666, loss_spatial_ce_7: 0.19776/0.18378, loss_grounding_bce_7: 0.25882/0.09021, loss_grounding_dice_7: 0.20499/0.19088, loss_grounding_ce_7: 0.06202/0.34832, loss_mask_ce_8: 0.55888/1.13210, loss_mask_bce_8: 0.34663/0.36529, loss_mask_dice_8: 0.28051/1.33180, loss_spatial_bce_8: 0.27204/0.13373, loss_spatial_dice_8: 0.21978/0.30717, loss_spatial_ce_8: 0.12937/0.24092, loss_grounding_bce_8: 0.25521/0.09374, loss_grounding_dice_8: 0.20490/0.20229, loss_grounding_ce_8: 0.08126/0.41951, loss_mask_ce_9: 2.32410/3.69148, loss_mask_bce_9: 0.32486/0.39213, loss_mask_dice_9: 0.77525/1.90597, loss_spatial_bce_9: 0.59564/0.33591, loss_spatial_dice_9: 0.72775/0.82441, loss_spatial_ce_9: 1.57234/1.51542, loss_grounding_bce_9: 0.22954/0.10514, loss_grounding_dice_9: 0.20556/0.28188, loss_grounding_ce_9: 0.28369/0.69346] items per batch[64] items per second[0.13] total items[1523200] mini batches[ 23800] memory[7341] epoch remaining[1:31:08]
INFO:trainer.default_trainer:epochs[    13] optim steps[23900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19505/0.91051, loss_mask_bce_0: 0.24170/0.33522, loss_mask_dice_0: 1.45548/1.16692, loss_spatial_bce_0: 0.02441/0.09129, loss_spatial_dice_0: 0.23958/0.21813, loss_spatial_ce_0: 0.00543/0.07727, loss_grounding_bce_0: 0.03937/0.08624, loss_grounding_dice_0: 0.11658/0.17949, loss_grounding_ce_0: 0.07316/0.27546, loss_mask_ce_1: 1.23037/0.91143, loss_mask_bce_1: 0.23366/0.33589, loss_mask_dice_1: 1.45289/1.17395, loss_spatial_bce_1: 0.02463/0.09217, loss_spatial_dice_1: 0.25687/0.22247, loss_spatial_ce_1: 0.00798/0.08303, loss_grounding_bce_1: 0.04002/0.08633, loss_grounding_dice_1: 0.11657/0.18017, loss_grounding_ce_1: 0.08126/0.27740, loss_mask_ce_2: 0.94081/0.91879, loss_mask_bce_2: 0.24169/0.33642, loss_mask_dice_2: 1.64511/1.17333, loss_spatial_bce_2: 0.02299/0.09229, loss_spatial_dice_2: 0.26593/0.22343, loss_spatial_ce_2: 0.01122/0.08680, loss_grounding_bce_2: 0.04095/0.08640, loss_grounding_dice_2: 0.12997/0.17968, loss_grounding_ce_2: 0.07238/0.28099, loss_mask_ce_3: 0.96877/0.92730, loss_mask_bce_3: 0.23250/0.33707, loss_mask_dice_3: 1.65711/1.17052, loss_spatial_bce_3: 0.02260/0.09318, loss_spatial_dice_3: 0.25066/0.22407, loss_spatial_ce_3: 0.01693/0.09085, loss_grounding_bce_3: 0.04068/0.08650, loss_grounding_dice_3: 0.12482/0.17937, loss_grounding_ce_3: 0.06935/0.28252, loss_mask_ce_4: 0.95764/0.92605, loss_mask_bce_4: 0.23866/0.33880, loss_mask_dice_4: 1.56222/1.19314, loss_spatial_bce_4: 0.02638/0.09715, loss_spatial_dice_4: 0.24707/0.23400, loss_spatial_ce_4: 0.00753/0.10725, loss_grounding_bce_4: 0.03952/0.08705, loss_grounding_dice_4: 0.11496/0.18226, loss_grounding_ce_4: 0.07938/0.28509, loss_mask_ce_5: 1.19069/0.94034, loss_mask_bce_5: 0.26741/0.34102, loss_mask_dice_5: 1.64077/1.19808, loss_spatial_bce_5: 0.02586/0.09834, loss_spatial_dice_5: 0.25446/0.23703, loss_spatial_ce_5: 0.01322/0.12118, loss_grounding_bce_5: 0.04004/0.08744, loss_grounding_dice_5: 0.12391/0.18345, loss_grounding_ce_5: 0.15411/0.29763, loss_mask_ce_6: 1.20986/0.97791, loss_mask_bce_6: 0.26344/0.34377, loss_mask_dice_6: 1.72365/1.20074, loss_spatial_bce_6: 0.02428/0.10371, loss_spatial_dice_6: 0.24803/0.23933, loss_spatial_ce_6: 0.03209/0.14603, loss_grounding_bce_6: 0.04229/0.08818, loss_grounding_dice_6: 0.12532/0.18356, loss_grounding_ce_6: 0.12835/0.31535, loss_mask_ce_7: 0.98699/1.02096, loss_mask_bce_7: 0.28414/0.35154, loss_mask_dice_7: 1.83486/1.25648, loss_spatial_bce_7: 0.02570/0.11250, loss_spatial_dice_7: 0.27980/0.26662, loss_spatial_ce_7: 0.10916/0.18407, loss_grounding_bce_7: 0.03952/0.09016, loss_grounding_dice_7: 0.12890/0.19088, loss_grounding_ce_7: 0.19615/0.34820, loss_mask_ce_8: 0.95237/1.13194, loss_mask_bce_8: 0.28284/0.36510, loss_mask_dice_8: 1.91407/1.33156, loss_spatial_bce_8: 0.03131/0.13366, loss_spatial_dice_8: 0.28081/0.30711, loss_spatial_ce_8: 0.25912/0.24112, loss_grounding_bce_8: 0.03930/0.09370, loss_grounding_dice_8: 0.12596/0.20232, loss_grounding_ce_8: 0.16541/0.41929, loss_mask_ce_9: 4.61300/3.69096, loss_mask_bce_9: 0.24488/0.39191, loss_mask_dice_9: 2.50949/1.90530, loss_spatial_bce_9: 0.15040/0.33593, loss_spatial_dice_9: 0.91958/0.82441, loss_spatial_ce_9: 1.71094/1.51542, loss_grounding_bce_9: 0.04209/0.10509, loss_grounding_dice_9: 0.16137/0.28183, loss_grounding_ce_9: 0.39928/0.69309] items per batch[64] items per second[0.22] total items[1529600] mini batches[ 23900] memory[7341] epoch remaining[1:22:25]
INFO:trainer.default_trainer:epochs[    13] optim steps[24000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68287/0.91059, loss_mask_bce_0: 0.13185/0.33514, loss_mask_dice_0: 1.44439/1.16767, loss_spatial_bce_0: 0.02698/0.09124, loss_spatial_dice_0: 0.18955/0.21814, loss_spatial_ce_0: 0.02334/0.07723, loss_grounding_bce_0: 0.01871/0.08619, loss_grounding_dice_0: 0.35844/0.17949, loss_grounding_ce_0: 0.39538/0.27561, loss_mask_ce_1: 0.75598/0.91150, loss_mask_bce_1: 0.12725/0.33582, loss_mask_dice_1: 1.00654/1.17464, loss_spatial_bce_1: 0.02874/0.09212, loss_spatial_dice_1: 0.24716/0.22249, loss_spatial_ce_1: 0.02995/0.08296, loss_grounding_bce_1: 0.01972/0.08628, loss_grounding_dice_1: 0.31290/0.18017, loss_grounding_ce_1: 0.48732/0.27749, loss_mask_ce_2: 0.66366/0.91882, loss_mask_bce_2: 0.12932/0.33634, loss_mask_dice_2: 1.23555/1.17402, loss_spatial_bce_2: 0.02880/0.09225, loss_spatial_dice_2: 0.18495/0.22344, loss_spatial_ce_2: 0.02694/0.08673, loss_grounding_bce_2: 0.02246/0.08636, loss_grounding_dice_2: 0.38244/0.17968, loss_grounding_ce_2: 0.51866/0.28109, loss_mask_ce_3: 0.71473/0.92738, loss_mask_bce_3: 0.11356/0.33699, loss_mask_dice_3: 1.01234/1.17127, loss_spatial_bce_3: 0.02858/0.09313, loss_spatial_dice_3: 0.18090/0.22407, loss_spatial_ce_3: 0.02511/0.09082, loss_grounding_bce_3: 0.01752/0.08645, loss_grounding_dice_3: 0.25410/0.17934, loss_grounding_ce_3: 0.41156/0.28262, loss_mask_ce_4: 0.68953/0.92608, loss_mask_bce_4: 0.12025/0.33874, loss_mask_dice_4: 1.18797/1.19379, loss_spatial_bce_4: 0.03142/0.09712, loss_spatial_dice_4: 0.21661/0.23404, loss_spatial_ce_4: 0.02782/0.10724, loss_grounding_bce_4: 0.01882/0.08700, loss_grounding_dice_4: 0.41980/0.18225, loss_grounding_ce_4: 0.49711/0.28531, loss_mask_ce_5: 0.72422/0.94042, loss_mask_bce_5: 0.11742/0.34096, loss_mask_dice_5: 0.89036/1.19875, loss_spatial_bce_5: 0.03169/0.09832, loss_spatial_dice_5: 0.23401/0.23708, loss_spatial_ce_5: 0.03222/0.12118, loss_grounding_bce_5: 0.01954/0.08741, loss_grounding_dice_5: 0.44897/0.18346, loss_grounding_ce_5: 0.45565/0.29775, loss_mask_ce_6: 0.75912/0.97800, loss_mask_bce_6: 0.11416/0.34372, loss_mask_dice_6: 1.13273/1.20143, loss_spatial_bce_6: 0.03557/0.10368, loss_spatial_dice_6: 0.21839/0.23937, loss_spatial_ce_6: 0.09369/0.14603, loss_grounding_bce_6: 0.01824/0.08814, loss_grounding_dice_6: 0.42480/0.18356, loss_grounding_ce_6: 0.60850/0.31551, loss_mask_ce_7: 0.47642/1.02103, loss_mask_bce_7: 0.11554/0.35145, loss_mask_dice_7: 1.17569/1.25705, loss_spatial_bce_7: 0.05677/0.11248, loss_spatial_dice_7: 0.27835/0.26667, loss_spatial_ce_7: 0.12823/0.18406, loss_grounding_bce_7: 0.01795/0.09011, loss_grounding_dice_7: 0.42623/0.19090, loss_grounding_ce_7: 0.79132/0.34839, loss_mask_ce_8: 0.73513/1.13204, loss_mask_bce_8: 0.11431/0.36503, loss_mask_dice_8: 1.25700/1.33223, loss_spatial_bce_8: 0.08396/0.13363, loss_spatial_dice_8: 0.37403/0.30714, loss_spatial_ce_8: 0.18587/0.24108, loss_grounding_bce_8: 0.01353/0.09365, loss_grounding_dice_8: 0.40856/0.20230, loss_grounding_ce_8: 0.29899/0.41942, loss_mask_ce_9: 2.08487/3.69174, loss_mask_bce_9: 0.12958/0.39180, loss_mask_dice_9: 1.39436/1.90593, loss_spatial_bce_9: 0.30530/0.33583, loss_spatial_dice_9: 0.86553/0.82441, loss_spatial_ce_9: 2.52893/1.51553, loss_grounding_bce_9: 0.01460/0.10503, loss_grounding_dice_9: 0.40555/0.28182, loss_grounding_ce_9: 0.69376/0.69318] items per batch[64] items per second[0.23] total items[1536000] mini batches[ 24000] memory[7341] epoch remaining[1:16:05]
INFO:trainer.default_trainer:epochs[    13] optim steps[24100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20371/0.91088, loss_mask_bce_0: 0.23872/0.33518, loss_mask_dice_0: 2.17421/1.16717, loss_spatial_bce_0: 0.02835/0.09126, loss_spatial_dice_0: 0.26012/0.21808, loss_spatial_ce_0: 0.00267/0.07714, loss_grounding_bce_0: 0.01671/0.08624, loss_grounding_dice_0: 0.09987/0.17945, loss_grounding_ce_0: 0.41292/0.27556, loss_mask_ce_1: 1.40984/0.91180, loss_mask_bce_1: 0.23000/0.33587, loss_mask_dice_1: 1.62772/1.17418, loss_spatial_bce_1: 0.02887/0.09213, loss_spatial_dice_1: 0.24917/0.22242, loss_spatial_ce_1: 0.00312/0.08292, loss_grounding_bce_1: 0.01669/0.08633, loss_grounding_dice_1: 0.06400/0.18014, loss_grounding_ce_1: 0.41399/0.27752, loss_mask_ce_2: 1.34006/0.91907, loss_mask_bce_2: 0.23721/0.33640, loss_mask_dice_2: 1.97346/1.17356, loss_spatial_bce_2: 0.02590/0.09226, loss_spatial_dice_2: 0.26787/0.22338, loss_spatial_ce_2: 0.01692/0.08669, loss_grounding_bce_2: 0.01597/0.08640, loss_grounding_dice_2: 0.13090/0.17965, loss_grounding_ce_2: 0.41088/0.28111, loss_mask_ce_3: 1.38483/0.92767, loss_mask_bce_3: 0.22963/0.33706, loss_mask_dice_3: 1.51789/1.17084, loss_spatial_bce_3: 0.02556/0.09314, loss_spatial_dice_3: 0.24223/0.22400, loss_spatial_ce_3: 0.00819/0.09076, loss_grounding_bce_3: 0.01469/0.08649, loss_grounding_dice_3: 0.06609/0.17931, loss_grounding_ce_3: 0.40811/0.28266, loss_mask_ce_4: 1.32273/0.92637, loss_mask_bce_4: 0.22013/0.33879, loss_mask_dice_4: 1.66152/1.19330, loss_spatial_bce_4: 0.03001/0.09715, loss_spatial_dice_4: 0.31156/0.23399, loss_spatial_ce_4: 0.00660/0.10713, loss_grounding_bce_4: 0.01569/0.08706, loss_grounding_dice_4: 0.10257/0.18222, loss_grounding_ce_4: 0.39563/0.28537, loss_mask_ce_5: 1.44802/0.94073, loss_mask_bce_5: 0.22608/0.34103, loss_mask_dice_5: 1.57479/1.19823, loss_spatial_bce_5: 0.03117/0.09834, loss_spatial_dice_5: 0.27083/0.23703, loss_spatial_ce_5: 0.02521/0.12108, loss_grounding_bce_5: 0.01472/0.08746, loss_grounding_dice_5: 0.06524/0.18345, loss_grounding_ce_5: 0.39277/0.29774, loss_mask_ce_6: 1.36315/0.97824, loss_mask_bce_6: 0.23068/0.34378, loss_mask_dice_6: 1.80036/1.20093, loss_spatial_bce_6: 0.03570/0.10370, loss_spatial_dice_6: 0.27784/0.23931, loss_spatial_ce_6: 0.01884/0.14598, loss_grounding_bce_6: 0.01525/0.08818, loss_grounding_dice_6: 0.07985/0.18352, loss_grounding_ce_6: 0.38085/0.31547, loss_mask_ce_7: 1.44911/1.02137, loss_mask_bce_7: 0.23398/0.35150, loss_mask_dice_7: 1.79298/1.25659, loss_spatial_bce_7: 0.02862/0.11250, loss_spatial_dice_7: 0.34265/0.26662, loss_spatial_ce_7: 0.12405/0.18396, loss_grounding_bce_7: 0.01647/0.09015, loss_grounding_dice_7: 0.07655/0.19087, loss_grounding_ce_7: 0.37435/0.34829, loss_mask_ce_8: 1.35466/1.13227, loss_mask_bce_8: 0.27652/0.36508, loss_mask_dice_8: 1.97029/1.33174, loss_spatial_bce_8: 0.03086/0.13367, loss_spatial_dice_8: 0.34910/0.30708, loss_spatial_ce_8: 0.09992/0.24092, loss_grounding_bce_8: 0.02223/0.09371, loss_grounding_dice_8: 0.10980/0.20229, loss_grounding_ce_8: 0.32657/0.41927, loss_mask_ce_9: 3.94604/3.69172, loss_mask_bce_9: 0.28871/0.39191, loss_mask_dice_9: 4.99840/1.90584, loss_spatial_bce_9: 0.18801/0.33585, loss_spatial_dice_9: 0.91430/0.82439, loss_spatial_ce_9: 1.72558/1.51518, loss_grounding_bce_9: 0.02007/0.10510, loss_grounding_dice_9: 0.39959/0.28180, loss_grounding_ce_9: 0.47428/0.69265] items per batch[64] items per second[0.23] total items[1542400] mini batches[ 24100] memory[7341] epoch remaining[1:10:28]
INFO:trainer.default_trainer:epochs[    13] optim steps[24200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60746/0.91104, loss_mask_bce_0: 0.09727/0.33509, loss_mask_dice_0: 0.19090/1.16705, loss_spatial_bce_0: 0.04215/0.09124, loss_spatial_dice_0: 0.08744/0.21803, loss_spatial_ce_0: 0.00008/0.07708, loss_grounding_bce_0: 0.04159/0.08618, loss_grounding_dice_0: 0.07032/0.17940, loss_grounding_ce_0: 1.60578/0.27547, loss_mask_ce_1: 0.65332/0.91203, loss_mask_bce_1: 0.13533/0.33576, loss_mask_dice_1: 0.20505/1.17415, loss_spatial_bce_1: 0.03824/0.09211, loss_spatial_dice_1: 0.08953/0.22238, loss_spatial_ce_1: 0.00006/0.08289, loss_grounding_bce_1: 0.04685/0.08628, loss_grounding_dice_1: 0.07186/0.18008, loss_grounding_ce_1: 1.57249/0.27740, loss_mask_ce_2: 0.62046/0.91936, loss_mask_bce_2: 0.12392/0.33630, loss_mask_dice_2: 0.20194/1.17354, loss_spatial_bce_2: 0.03941/0.09225, loss_spatial_dice_2: 0.09411/0.22333, loss_spatial_ce_2: 0.00113/0.08665, loss_grounding_bce_2: 0.04884/0.08634, loss_grounding_dice_2: 0.07451/0.17960, loss_grounding_ce_2: 1.52311/0.28101, loss_mask_ce_3: 0.61997/0.92786, loss_mask_bce_3: 0.11929/0.33697, loss_mask_dice_3: 0.20525/1.17082, loss_spatial_bce_3: 0.04087/0.09312, loss_spatial_dice_3: 0.10008/0.22396, loss_spatial_ce_3: 0.00559/0.09077, loss_grounding_bce_3: 0.05464/0.08644, loss_grounding_dice_3: 0.09233/0.17927, loss_grounding_ce_3: 0.42801/0.28251, loss_mask_ce_4: 0.50938/0.92656, loss_mask_bce_4: 0.13872/0.33871, loss_mask_dice_4: 0.24802/1.19318, loss_spatial_bce_4: 0.03652/0.09714, loss_spatial_dice_4: 0.09653/0.23398, loss_spatial_ce_4: 0.04079/0.10708, loss_grounding_bce_4: 0.04368/0.08700, loss_grounding_dice_4: 0.07643/0.18217, loss_grounding_ce_4: 1.92292/0.28525, loss_mask_ce_5: 0.60034/0.94089, loss_mask_bce_5: 0.09217/0.34095, loss_mask_dice_5: 0.23416/1.19813, loss_spatial_bce_5: 0.04099/0.09834, loss_spatial_dice_5: 0.09664/0.23701, loss_spatial_ce_5: 0.01285/0.12101, loss_grounding_bce_5: 0.05289/0.08741, loss_grounding_dice_5: 0.07931/0.18340, loss_grounding_ce_5: 1.56666/0.29765, loss_mask_ce_6: 0.47161/0.97846, loss_mask_bce_6: 0.11090/0.34368, loss_mask_dice_6: 0.25346/1.20089, loss_spatial_bce_6: 0.04372/0.10369, loss_spatial_dice_6: 0.10337/0.23929, loss_spatial_ce_6: 0.14487/0.14594, loss_grounding_bce_6: 0.07441/0.08813, loss_grounding_dice_6: 0.10128/0.18348, loss_grounding_ce_6: 0.42291/0.31538, loss_mask_ce_7: 0.59914/1.02159, loss_mask_bce_7: 0.09795/0.35141, loss_mask_dice_7: 0.23744/1.25656, loss_spatial_bce_7: 0.04825/0.11248, loss_spatial_dice_7: 0.10858/0.26660, loss_spatial_ce_7: 0.07950/0.18386, loss_grounding_bce_7: 0.06458/0.09010, loss_grounding_dice_7: 0.08515/0.19082, loss_grounding_ce_7: 1.79036/0.34816, loss_mask_ce_8: 0.88212/1.13249, loss_mask_bce_8: 0.11281/0.36498, loss_mask_dice_8: 0.21565/1.33161, loss_spatial_bce_8: 0.08733/0.13360, loss_spatial_dice_8: 0.12017/0.30701, loss_spatial_ce_8: 0.18417/0.24084, loss_grounding_bce_8: 0.05559/0.09367, loss_grounding_dice_8: 0.08368/0.20225, loss_grounding_ce_8: 1.53856/0.41906, loss_mask_ce_9: 2.62559/3.69135, loss_mask_bce_9: 0.08701/0.39185, loss_mask_dice_9: 0.38825/1.90598, loss_spatial_bce_9: 0.23907/0.33578, loss_spatial_dice_9: 0.63355/0.82435, loss_spatial_ce_9: 0.92606/1.51507, loss_grounding_bce_9: 0.04100/0.10511, loss_grounding_dice_9: 0.13877/0.28186, loss_grounding_ce_9: 1.01455/0.69235] items per batch[64] items per second[0.23] total items[1548800] mini batches[ 24200] memory[7341] epoch remaining[1:05:11]
INFO:trainer.default_trainer:epochs[    13] optim steps[24300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03472/0.91104, loss_mask_bce_0: 0.44705/0.33505, loss_mask_dice_0: 0.29474/1.16748, loss_spatial_bce_0: 0.26861/0.09121, loss_spatial_dice_0: 0.18089/0.21803, loss_spatial_ce_0: 0.01047/0.07699, loss_grounding_bce_0: 0.29711/0.08620, loss_grounding_dice_0: 0.20140/0.17946, loss_grounding_ce_0: 0.00296/0.27546, loss_mask_ce_1: 0.02697/0.91196, loss_mask_bce_1: 0.44270/0.33573, loss_mask_dice_1: 0.29799/1.17470, loss_spatial_bce_1: 0.26046/0.09208, loss_spatial_dice_1: 0.17596/0.22236, loss_spatial_ce_1: 0.00675/0.08281, loss_grounding_bce_1: 0.28904/0.08630, loss_grounding_dice_1: 0.19886/0.18018, loss_grounding_ce_1: 0.00259/0.27730, loss_mask_ce_2: 0.03158/0.91934, loss_mask_bce_2: 0.45424/0.33625, loss_mask_dice_2: 0.30258/1.17404, loss_spatial_bce_2: 0.26195/0.09222, loss_spatial_dice_2: 0.17613/0.22333, loss_spatial_ce_2: 0.00785/0.08660, loss_grounding_bce_2: 0.29390/0.08636, loss_grounding_dice_2: 0.20373/0.17969, loss_grounding_ce_2: 0.00273/0.28092, loss_mask_ce_3: 0.03042/0.92790, loss_mask_bce_3: 0.44625/0.33694, loss_mask_dice_3: 0.29016/1.17131, loss_spatial_bce_3: 0.25544/0.09310, loss_spatial_dice_3: 0.17613/0.22394, loss_spatial_ce_3: 0.01794/0.09067, loss_grounding_bce_3: 0.29194/0.08647, loss_grounding_dice_3: 0.19780/0.17937, loss_grounding_ce_3: 0.00252/0.28249, loss_mask_ce_4: 0.02998/0.92651, loss_mask_bce_4: 0.45740/0.33868, loss_mask_dice_4: 0.29709/1.19364, loss_spatial_bce_4: 0.27065/0.09712, loss_spatial_dice_4: 0.17865/0.23399, loss_spatial_ce_4: 0.03030/0.10698, loss_grounding_bce_4: 0.29559/0.08702, loss_grounding_dice_4: 0.19628/0.18225, loss_grounding_ce_4: 0.00204/0.28522, loss_mask_ce_5: 0.03573/0.94082, loss_mask_bce_5: 0.44846/0.34094, loss_mask_dice_5: 0.28882/1.19865, loss_spatial_bce_5: 0.25437/0.09832, loss_spatial_dice_5: 0.16682/0.23702, loss_spatial_ce_5: 0.07272/0.12095, loss_grounding_bce_5: 0.29163/0.08743, loss_grounding_dice_5: 0.19349/0.18348, loss_grounding_ce_5: 0.00257/0.29755, loss_mask_ce_6: 0.03966/0.97839, loss_mask_bce_6: 0.46340/0.34363, loss_mask_dice_6: 0.31596/1.20143, loss_spatial_bce_6: 0.24583/0.10367, loss_spatial_dice_6: 0.16601/0.23931, loss_spatial_ce_6: 0.05779/0.14586, loss_grounding_bce_6: 0.29876/0.08816, loss_grounding_dice_6: 0.20985/0.18357, loss_grounding_ce_6: 0.00249/0.31532, loss_mask_ce_7: 0.07565/1.02160, loss_mask_bce_7: 0.44600/0.35137, loss_mask_dice_7: 0.31397/1.25713, loss_spatial_bce_7: 0.25912/0.11245, loss_spatial_dice_7: 0.16592/0.26661, loss_spatial_ce_7: 0.06307/0.18380, loss_grounding_bce_7: 0.29450/0.09013, loss_grounding_dice_7: 0.21028/0.19092, loss_grounding_ce_7: 0.01040/0.34806, loss_mask_ce_8: 0.08053/1.13243, loss_mask_bce_8: 0.45262/0.36493, loss_mask_dice_8: 0.29734/1.33208, loss_spatial_bce_8: 0.41169/0.13357, loss_spatial_dice_8: 0.22771/0.30703, loss_spatial_ce_8: 0.16579/0.24083, loss_grounding_bce_8: 0.29639/0.09368, loss_grounding_dice_8: 0.20297/0.20234, loss_grounding_ce_8: 0.00980/0.41897, loss_mask_ce_9: 2.11366/3.69099, loss_mask_bce_9: 0.47672/0.39180, loss_mask_dice_9: 0.35224/1.90636, loss_spatial_bce_9: 0.68653/0.33570, loss_spatial_dice_9: 0.69523/0.82439, loss_spatial_ce_9: 1.29858/1.51488, loss_grounding_bce_9: 0.31024/0.10511, loss_grounding_dice_9: 0.24231/0.28192, loss_grounding_ce_9: 0.07055/0.69212] items per batch[64] items per second[0.23] total items[1555200] mini batches[ 24300] memory[7341] epoch remaining[1:00:08]
INFO:trainer.default_trainer:epochs[    13] optim steps[24400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.83533/0.91119, loss_mask_bce_0: 0.24884/0.33512, loss_mask_dice_0: 0.23093/1.16755, loss_spatial_bce_0: 0.17156/0.09119, loss_spatial_dice_0: 0.16513/0.21798, loss_spatial_ce_0: 0.11696/0.07690, loss_grounding_bce_0: 0.15160/0.08620, loss_grounding_dice_0: 0.10810/0.17939, loss_grounding_ce_0: 0.21066/0.27566, loss_mask_ce_1: 1.68628/0.91210, loss_mask_bce_1: 0.26186/0.33580, loss_mask_dice_1: 0.28568/1.17478, loss_spatial_bce_1: 0.17067/0.09206, loss_spatial_dice_1: 0.16092/0.22231, loss_spatial_ce_1: 0.08860/0.08269, loss_grounding_bce_1: 0.14426/0.08630, loss_grounding_dice_1: 0.10134/0.18011, loss_grounding_ce_1: 0.22200/0.27735, loss_mask_ce_2: 1.69473/0.91947, loss_mask_bce_2: 0.25461/0.33635, loss_mask_dice_2: 0.24018/1.17415, loss_spatial_bce_2: 0.20035/0.09219, loss_spatial_dice_2: 0.16113/0.22327, loss_spatial_ce_2: 0.09721/0.08646, loss_grounding_bce_2: 0.17613/0.08636, loss_grounding_dice_2: 0.13077/0.17962, loss_grounding_ce_2: 0.16971/0.28096, loss_mask_ce_3: 1.63875/0.92801, loss_mask_bce_3: 0.24824/0.33703, loss_mask_dice_3: 0.24897/1.17141, loss_spatial_bce_3: 0.19701/0.09307, loss_spatial_dice_3: 0.21079/0.22389, loss_spatial_ce_3: 0.12435/0.09053, loss_grounding_bce_3: 0.16972/0.08647, loss_grounding_dice_3: 0.13107/0.17931, loss_grounding_ce_3: 0.17970/0.28262, loss_mask_ce_4: 1.68048/0.92671, loss_mask_bce_4: 0.22256/0.33876, loss_mask_dice_4: 0.22292/1.19378, loss_spatial_bce_4: 0.16695/0.09710, loss_spatial_dice_4: 0.14857/0.23394, loss_spatial_ce_4: 0.08682/0.10688, loss_grounding_bce_4: 0.14583/0.08701, loss_grounding_dice_4: 0.11130/0.18218, loss_grounding_ce_4: 0.26128/0.28530, loss_mask_ce_5: 1.84225/0.94098, loss_mask_bce_5: 0.21082/0.34101, loss_mask_dice_5: 0.22036/1.19884, loss_spatial_bce_5: 0.17339/0.09830, loss_spatial_dice_5: 0.17174/0.23698, loss_spatial_ce_5: 0.09993/0.12083, loss_grounding_bce_5: 0.13585/0.08742, loss_grounding_dice_5: 0.10713/0.18341, loss_grounding_ce_5: 0.29616/0.29777, loss_mask_ce_6: 1.99319/0.97855, loss_mask_bce_6: 0.22760/0.34370, loss_mask_dice_6: 0.21991/1.20162, loss_spatial_bce_6: 0.19122/0.10365, loss_spatial_dice_6: 0.17567/0.23928, loss_spatial_ce_6: 0.08268/0.14577, loss_grounding_bce_6: 0.13997/0.08816, loss_grounding_dice_6: 0.11035/0.18351, loss_grounding_ce_6: 0.30714/0.31543, loss_mask_ce_7: 1.96758/1.02183, loss_mask_bce_7: 0.26613/0.35148, loss_mask_dice_7: 0.25517/1.25734, loss_spatial_bce_7: 0.21428/0.11242, loss_spatial_dice_7: 0.20993/0.26656, loss_spatial_ce_7: 0.12989/0.18365, loss_grounding_bce_7: 0.15790/0.09013, loss_grounding_dice_7: 0.11752/0.19085, loss_grounding_ce_7: 0.34852/0.34814, loss_mask_ce_8: 2.00234/1.13262, loss_mask_bce_8: 0.25925/0.36506, loss_mask_dice_8: 0.29697/1.33231, loss_spatial_bce_8: 0.19174/0.13354, loss_spatial_dice_8: 0.19914/0.30697, loss_spatial_ce_8: 0.20041/0.24073, loss_grounding_bce_8: 0.17859/0.09368, loss_grounding_dice_8: 0.18162/0.20226, loss_grounding_ce_8: 0.26771/0.41900, loss_mask_ce_9: 2.85526/3.69148, loss_mask_bce_9: 0.37986/0.39195, loss_mask_dice_9: 0.46956/1.90719, loss_spatial_bce_9: 0.53081/0.33566, loss_spatial_dice_9: 0.73224/0.82437, loss_spatial_ce_9: 1.06972/1.51461, loss_grounding_bce_9: 0.23739/0.10512, loss_grounding_dice_9: 0.25018/0.28182, loss_grounding_ce_9: 0.51619/0.69218] items per batch[64] items per second[0.23] total items[1561600] mini batches[ 24400] memory[7341] epoch remaining[0:55:21]
INFO:trainer.default_trainer:epochs[    13] optim steps[24500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63159/0.91150, loss_mask_bce_0: 0.46507/0.33511, loss_mask_dice_0: 6.17808/1.16738, loss_spatial_bce_0: 0.02648/0.09117, loss_spatial_dice_0: 0.24678/0.21789, loss_spatial_ce_0: 0.13065/0.07679, loss_grounding_bce_0: 0.06051/0.08623, loss_grounding_dice_0: 0.37721/0.17943, loss_grounding_ce_0: 0.67708/0.27567, loss_mask_ce_1: 1.73476/0.91237, loss_mask_bce_1: 0.48664/0.33580, loss_mask_dice_1: 6.01527/1.17458, loss_spatial_bce_1: 0.02463/0.09203, loss_spatial_dice_1: 0.28258/0.22222, loss_spatial_ce_1: 0.08465/0.08256, loss_grounding_bce_1: 0.04340/0.08632, loss_grounding_dice_1: 0.33196/0.18015, loss_grounding_ce_1: 0.72776/0.27739, loss_mask_ce_2: 1.89575/0.91974, loss_mask_bce_2: 0.47570/0.33634, loss_mask_dice_2: 6.06911/1.17391, loss_spatial_bce_2: 0.02309/0.09218, loss_spatial_dice_2: 0.27544/0.22317, loss_spatial_ce_2: 0.10324/0.08635, loss_grounding_bce_2: 0.04476/0.08639, loss_grounding_dice_2: 0.32134/0.17966, loss_grounding_ce_2: 0.73120/0.28087, loss_mask_ce_3: 2.04647/0.92822, loss_mask_bce_3: 0.43727/0.33702, loss_mask_dice_3: 6.22986/1.17115, loss_spatial_bce_3: 0.02267/0.09305, loss_spatial_dice_3: 0.27043/0.22380, loss_spatial_ce_3: 0.13272/0.09042, loss_grounding_bce_3: 0.04228/0.08650, loss_grounding_dice_3: 0.33011/0.17935, loss_grounding_ce_3: 0.69735/0.28254, loss_mask_ce_4: 2.09134/0.92691, loss_mask_bce_4: 0.44589/0.33876, loss_mask_dice_4: 6.19184/1.19361, loss_spatial_bce_4: 0.02616/0.09708, loss_spatial_dice_4: 0.32141/0.23387, loss_spatial_ce_4: 0.04472/0.10677, loss_grounding_bce_4: 0.06220/0.08704, loss_grounding_dice_4: 0.38671/0.18219, loss_grounding_ce_4: 0.60579/0.28518, loss_mask_ce_5: 1.82274/0.94127, loss_mask_bce_5: 0.51580/0.34099, loss_mask_dice_5: 6.23163/1.19856, loss_spatial_bce_5: 0.02461/0.09829, loss_spatial_dice_5: 0.28668/0.23691, loss_spatial_ce_5: 0.21292/0.12074, loss_grounding_bce_5: 0.06055/0.08745, loss_grounding_dice_5: 0.37819/0.18343, loss_grounding_ce_5: 0.77982/0.29765, loss_mask_ce_6: 2.25301/0.97881, loss_mask_bce_6: 0.44597/0.34368, loss_mask_dice_6: 6.26263/1.20142, loss_spatial_bce_6: 0.03152/0.10364, loss_spatial_dice_6: 0.30768/0.23920, loss_spatial_ce_6: 0.06033/0.14566, loss_grounding_bce_6: 0.06749/0.08819, loss_grounding_dice_6: 0.40914/0.18354, loss_grounding_ce_6: 0.81375/0.31546, loss_mask_ce_7: 2.12344/1.02210, loss_mask_bce_7: 0.44638/0.35144, loss_mask_dice_7: 6.59597/1.25712, loss_spatial_bce_7: 0.03342/0.11240, loss_spatial_dice_7: 0.31970/0.26649, loss_spatial_ce_7: 0.15395/0.18352, loss_grounding_bce_7: 0.05445/0.09015, loss_grounding_dice_7: 0.41523/0.19086, loss_grounding_ce_7: 0.82406/0.34805, loss_mask_ce_8: 2.46722/1.13275, loss_mask_bce_8: 0.56316/0.36503, loss_mask_dice_8: 6.32604/1.33215, loss_spatial_bce_8: 0.05295/0.13351, loss_spatial_dice_8: 0.42442/0.30690, loss_spatial_ce_8: 0.20363/0.24062, loss_grounding_bce_8: 0.06357/0.09371, loss_grounding_dice_8: 0.41214/0.20226, loss_grounding_ce_8: 0.96820/0.41894, loss_mask_ce_9: 6.73559/3.69120, loss_mask_bce_9: 0.48148/0.39192, loss_mask_dice_9: 8.62301/1.90683, loss_spatial_bce_9: 0.15754/0.33571, loss_spatial_dice_9: 0.95210/0.82438, loss_spatial_ce_9: 1.28525/1.51425, loss_grounding_bce_9: 0.04755/0.10516, loss_grounding_dice_9: 0.54504/0.28185, loss_grounding_ce_9: 0.80032/0.69218] items per batch[64] items per second[0.23] total items[1568000] mini batches[ 24500] memory[7341] epoch remaining[0:50:40]
INFO:trainer.default_trainer:epochs[    13] optim steps[24600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50205/0.91129, loss_mask_bce_0: 0.52891/0.33510, loss_mask_dice_0: 1.16714/1.16754, loss_spatial_bce_0: 0.08819/0.09114, loss_spatial_dice_0: 0.17555/0.21782, loss_spatial_ce_0: 0.00464/0.07670, loss_grounding_bce_0: 0.07737/0.08619, loss_grounding_dice_0: 0.23862/0.17939, loss_grounding_ce_0: 0.55322/0.27587, loss_mask_ce_1: 0.48272/0.91212, loss_mask_bce_1: 0.49378/0.33578, loss_mask_dice_1: 1.30680/1.17477, loss_spatial_bce_1: 0.09907/0.09200, loss_spatial_dice_1: 0.21604/0.22215, loss_spatial_ce_1: 0.00697/0.08251, loss_grounding_bce_1: 0.07860/0.08629, loss_grounding_dice_1: 0.24428/0.18014, loss_grounding_ce_1: 0.49949/0.27750, loss_mask_ce_2: 0.49003/0.91954, loss_mask_bce_2: 0.47361/0.33630, loss_mask_dice_2: 1.20338/1.17404, loss_spatial_bce_2: 0.09962/0.09215, loss_spatial_dice_2: 0.21010/0.22312, loss_spatial_ce_2: 0.01505/0.08630, loss_grounding_bce_2: 0.09256/0.08635, loss_grounding_dice_2: 0.25050/0.17963, loss_grounding_ce_2: 0.38857/0.28100, loss_mask_ce_3: 0.52108/0.92803, loss_mask_bce_3: 0.46618/0.33699, loss_mask_dice_3: 1.01152/1.17120, loss_spatial_bce_3: 0.10049/0.09304, loss_spatial_dice_3: 0.20763/0.22374, loss_spatial_ce_3: 0.01110/0.09036, loss_grounding_bce_3: 0.07644/0.08646, loss_grounding_dice_3: 0.22679/0.17933, loss_grounding_ce_3: 0.35701/0.28268, loss_mask_ce_4: 0.57193/0.92673, loss_mask_bce_4: 0.45884/0.33872, loss_mask_dice_4: 1.18350/1.19371, loss_spatial_bce_4: 0.10366/0.09708, loss_spatial_dice_4: 0.21674/0.23384, loss_spatial_ce_4: 0.01996/0.10665, loss_grounding_bce_4: 0.07751/0.08700, loss_grounding_dice_4: 0.28813/0.18216, loss_grounding_ce_4: 0.34804/0.28523, loss_mask_ce_5: 0.55294/0.94107, loss_mask_bce_5: 0.46425/0.34094, loss_mask_dice_5: 1.19913/1.19874, loss_spatial_bce_5: 0.11379/0.09828, loss_spatial_dice_5: 0.24423/0.23691, loss_spatial_ce_5: 0.05868/0.12063, loss_grounding_bce_5: 0.08031/0.08742, loss_grounding_dice_5: 0.28725/0.18339, loss_grounding_ce_5: 0.37847/0.29783, loss_mask_ce_6: 0.58071/0.97863, loss_mask_bce_6: 0.42465/0.34365, loss_mask_dice_6: 1.28846/1.20154, loss_spatial_bce_6: 0.16938/0.10362, loss_spatial_dice_6: 0.22132/0.23918, loss_spatial_ce_6: 0.06079/0.14564, loss_grounding_bce_6: 0.06656/0.08815, loss_grounding_dice_6: 0.22645/0.18349, loss_grounding_ce_6: 0.35750/0.31566, loss_mask_ce_7: 0.78198/1.02185, loss_mask_bce_7: 0.44611/0.35142, loss_mask_dice_7: 1.19742/1.25717, loss_spatial_bce_7: 0.13750/0.11237, loss_spatial_dice_7: 0.26100/0.26645, loss_spatial_ce_7: 0.05891/0.18348, loss_grounding_bce_7: 0.06783/0.09011, loss_grounding_dice_7: 0.24418/0.19083, loss_grounding_ce_7: 0.43017/0.34816, loss_mask_ce_8: 0.69071/1.13254, loss_mask_bce_8: 0.44849/0.36499, loss_mask_dice_8: 1.85396/1.33226, loss_spatial_bce_8: 0.21670/0.13347, loss_spatial_dice_8: 0.30163/0.30687, loss_spatial_ce_8: 0.12865/0.24060, loss_grounding_bce_8: 0.05467/0.09367, loss_grounding_dice_8: 0.25330/0.20222, loss_grounding_ce_8: 0.51939/0.41915, loss_mask_ce_9: 3.13538/3.69102, loss_mask_bce_9: 0.51651/0.39188, loss_mask_dice_9: 2.06749/1.90679, loss_spatial_bce_9: 0.33726/0.33575, loss_spatial_dice_9: 0.91321/0.82437, loss_spatial_ce_9: 1.71105/1.51393, loss_grounding_bce_9: 0.06822/0.10513, loss_grounding_dice_9: 0.37022/0.28181, loss_grounding_ce_9: 0.49789/0.69244] items per batch[64] items per second[0.23] total items[1574400] mini batches[ 24600] memory[7341] epoch remaining[0:45:50]
INFO:trainer.default_trainer:epochs[    13] optim steps[24700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09988/0.91127, loss_mask_bce_0: 0.14548/0.33513, loss_mask_dice_0: 0.35270/1.16717, loss_spatial_bce_0: 0.04247/0.09113, loss_spatial_dice_0: 0.09984/0.21774, loss_spatial_ce_0: 0.00678/0.07662, loss_grounding_bce_0: 0.09512/0.08626, loss_grounding_dice_0: 0.09217/0.17938, loss_grounding_ce_0: 0.05085/0.27612, loss_mask_ce_1: 0.09349/0.91207, loss_mask_bce_1: 0.14187/0.33580, loss_mask_dice_1: 0.35120/1.17436, loss_spatial_bce_1: 0.04318/0.09199, loss_spatial_dice_1: 0.11123/0.22207, loss_spatial_ce_1: 0.00706/0.08244, loss_grounding_bce_1: 0.09319/0.08636, loss_grounding_dice_1: 0.08746/0.18014, loss_grounding_ce_1: 0.04028/0.27777, loss_mask_ce_2: 0.10433/0.91956, loss_mask_bce_2: 0.13892/0.33631, loss_mask_dice_2: 0.29309/1.17368, loss_spatial_bce_2: 0.04423/0.09214, loss_spatial_dice_2: 0.10052/0.22303, loss_spatial_ce_2: 0.00816/0.08621, loss_grounding_bce_2: 0.08904/0.08642, loss_grounding_dice_2: 0.08852/0.17965, loss_grounding_ce_2: 0.06058/0.28120, loss_mask_ce_3: 0.08609/0.92805, loss_mask_bce_3: 0.14267/0.33702, loss_mask_dice_3: 0.34068/1.17082, loss_spatial_bce_3: 0.04240/0.09301, loss_spatial_dice_3: 0.11062/0.22365, loss_spatial_ce_3: 0.01163/0.09028, loss_grounding_bce_3: 0.09681/0.08653, loss_grounding_dice_3: 0.09025/0.17932, loss_grounding_ce_3: 0.05311/0.28288, loss_mask_ce_4: 0.08969/0.92670, loss_mask_bce_4: 0.14002/0.33875, loss_mask_dice_4: 0.34482/1.19329, loss_spatial_bce_4: 0.04232/0.09706, loss_spatial_dice_4: 0.10464/0.23376, loss_spatial_ce_4: 0.01808/0.10660, loss_grounding_bce_4: 0.09045/0.08707, loss_grounding_dice_4: 0.08578/0.18215, loss_grounding_ce_4: 0.08625/0.28541, loss_mask_ce_5: 0.09693/0.94112, loss_mask_bce_5: 0.14566/0.34098, loss_mask_dice_5: 0.28233/1.19835, loss_spatial_bce_5: 0.04668/0.09826, loss_spatial_dice_5: 0.11792/0.23683, loss_spatial_ce_5: 0.01326/0.12058, loss_grounding_bce_5: 0.10175/0.08749, loss_grounding_dice_5: 0.09022/0.18339, loss_grounding_ce_5: 0.08450/0.29805, loss_mask_ce_6: 0.12259/0.97870, loss_mask_bce_6: 0.15562/0.34367, loss_mask_dice_6: 0.35088/1.20112, loss_spatial_bce_6: 0.05034/0.10360, loss_spatial_dice_6: 0.12095/0.23910, loss_spatial_ce_6: 0.02532/0.14556, loss_grounding_bce_6: 0.09725/0.08822, loss_grounding_dice_6: 0.08679/0.18351, loss_grounding_ce_6: 0.17084/0.31587, loss_mask_ce_7: 0.12087/1.02188, loss_mask_bce_7: 0.13891/0.35145, loss_mask_dice_7: 0.31855/1.25675, loss_spatial_bce_7: 0.04915/0.11234, loss_spatial_dice_7: 0.10231/0.26637, loss_spatial_ce_7: 0.04196/0.18340, loss_grounding_bce_7: 0.09402/0.09017, loss_grounding_dice_7: 0.08723/0.19084, loss_grounding_ce_7: 0.15682/0.34840, loss_mask_ce_8: 0.21112/1.13253, loss_mask_bce_8: 0.13725/0.36501, loss_mask_dice_8: 0.34766/1.33184, loss_spatial_bce_8: 0.06284/0.13345, loss_spatial_dice_8: 0.15779/0.30677, loss_spatial_ce_8: 0.16356/0.24050, loss_grounding_bce_8: 0.08697/0.09375, loss_grounding_dice_8: 0.08472/0.20223, loss_grounding_ce_8: 0.22962/0.41932, loss_mask_ce_9: 2.16341/3.69129, loss_mask_bce_9: 0.15667/0.39196, loss_mask_dice_9: 0.49730/1.90641, loss_spatial_bce_9: 0.35711/0.33575, loss_spatial_dice_9: 0.85365/0.82436, loss_spatial_ce_9: 1.74044/1.51396, loss_grounding_bce_9: 0.10101/0.10521, loss_grounding_dice_9: 0.09755/0.28182, loss_grounding_ce_9: 0.50381/0.69239] items per batch[64] items per second[0.23] total items[1580800] mini batches[ 24700] memory[7341] epoch remaining[0:41:10]
INFO:trainer.default_trainer:epochs[    13] optim steps[24800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71762/0.91130, loss_mask_bce_0: 0.08919/0.33501, loss_mask_dice_0: 0.91869/1.16653, loss_spatial_bce_0: 0.01438/0.09111, loss_spatial_dice_0: 0.26798/0.21771, loss_spatial_ce_0: 0.04066/0.07653, loss_grounding_bce_0: 0.01593/0.08626, loss_grounding_dice_0: 0.17628/0.17937, loss_grounding_ce_0: 0.34095/0.27624, loss_mask_ce_1: 0.67667/0.91211, loss_mask_bce_1: 0.08292/0.33569, loss_mask_dice_1: 0.99212/1.17371, loss_spatial_bce_1: 0.01537/0.09196, loss_spatial_dice_1: 0.31162/0.22202, loss_spatial_ce_1: 0.48724/0.08240, loss_grounding_bce_1: 0.01696/0.08636, loss_grounding_dice_1: 0.24112/0.18013, loss_grounding_ce_1: 0.28615/0.27787, loss_mask_ce_2: 0.60832/0.91959, loss_mask_bce_2: 0.10422/0.33619, loss_mask_dice_2: 1.33714/1.17309, loss_spatial_bce_2: 0.01408/0.09213, loss_spatial_dice_2: 0.28055/0.22299, loss_spatial_ce_2: 0.06526/0.08607, loss_grounding_bce_2: 0.01617/0.08642, loss_grounding_dice_2: 0.17826/0.17964, loss_grounding_ce_2: 0.33318/0.28134, loss_mask_ce_3: 0.71215/0.92805, loss_mask_bce_3: 0.09429/0.33690, loss_mask_dice_3: 0.96233/1.17014, loss_spatial_bce_3: 0.01555/0.09299, loss_spatial_dice_3: 0.24684/0.22360, loss_spatial_ce_3: 0.03718/0.09016, loss_grounding_bce_3: 0.01951/0.08654, loss_grounding_dice_3: 0.31523/0.17936, loss_grounding_ce_3: 0.48806/0.28302, loss_mask_ce_4: 0.74233/0.92673, loss_mask_bce_4: 0.09777/0.33863, loss_mask_dice_4: 1.16149/1.19270, loss_spatial_bce_4: 0.01646/0.09703, loss_spatial_dice_4: 0.29613/0.23372, loss_spatial_ce_4: 0.11531/0.10647, loss_grounding_bce_4: 0.01684/0.08708, loss_grounding_dice_4: 0.17960/0.18216, loss_grounding_ce_4: 0.30722/0.28550, loss_mask_ce_5: 0.72007/0.94107, loss_mask_bce_5: 0.19504/0.34085, loss_mask_dice_5: 1.04336/1.19773, loss_spatial_bce_5: 0.01876/0.09823, loss_spatial_dice_5: 0.31769/0.23679, loss_spatial_ce_5: 0.08710/0.12045, loss_grounding_bce_5: 0.01747/0.08750, loss_grounding_dice_5: 0.22339/0.18342, loss_grounding_ce_5: 0.27004/0.29808, loss_mask_ce_6: 0.95885/0.97871, loss_mask_bce_6: 0.19864/0.34354, loss_mask_dice_6: 1.10630/1.20049, loss_spatial_bce_6: 0.02166/0.10358, loss_spatial_dice_6: 0.30109/0.23906, loss_spatial_ce_6: 0.13446/0.14546, loss_grounding_bce_6: 0.01669/0.08824, loss_grounding_dice_6: 0.18173/0.18355, loss_grounding_ce_6: 0.30181/0.31588, loss_mask_ce_7: 1.10695/1.02193, loss_mask_bce_7: 0.17165/0.35133, loss_mask_dice_7: 1.05786/1.25610, loss_spatial_bce_7: 0.01972/0.11232, loss_spatial_dice_7: 0.39810/0.26633, loss_spatial_ce_7: 0.22642/0.18328, loss_grounding_bce_7: 0.01678/0.09019, loss_grounding_dice_7: 0.20016/0.19085, loss_grounding_ce_7: 0.38143/0.34828, loss_mask_ce_8: 1.29305/1.13260, loss_mask_bce_8: 0.22243/0.36488, loss_mask_dice_8: 1.55662/1.33120, loss_spatial_bce_8: 0.02663/0.13343, loss_spatial_dice_8: 0.44806/0.30672, loss_spatial_ce_8: 0.22284/0.24029, loss_grounding_bce_8: 0.02133/0.09377, loss_grounding_dice_8: 0.34877/0.20225, loss_grounding_ce_8: 0.37074/0.41921, loss_mask_ce_9: 3.12542/3.69061, loss_mask_bce_9: 0.15512/0.39186, loss_mask_dice_9: 1.46606/1.90548, loss_spatial_bce_9: 0.38746/0.33575, loss_spatial_dice_9: 0.82741/0.82432, loss_spatial_ce_9: 1.91575/1.51384, loss_grounding_bce_9: 0.01767/0.10523, loss_grounding_dice_9: 0.38361/0.28182, loss_grounding_ce_9: 0.44371/0.69214] items per batch[64] items per second[0.23] total items[1587200] mini batches[ 24800] memory[7341] epoch remaining[0:36:26]
INFO:trainer.default_trainer:epochs[    13] optim steps[24900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24109/0.91132, loss_mask_bce_0: 0.06363/0.33508, loss_mask_dice_0: 0.12566/1.16677, loss_spatial_bce_0: 0.04812/0.09111, loss_spatial_dice_0: 0.10841/0.21769, loss_spatial_ce_0: 0.00163/0.07644, loss_grounding_bce_0: 0.07809/0.08624, loss_grounding_dice_0: 0.12575/0.17938, loss_grounding_ce_0: 0.01034/0.27640, loss_mask_ce_1: 0.25439/0.91216, loss_mask_bce_1: 0.06425/0.33576, loss_mask_dice_1: 0.14211/1.17392, loss_spatial_bce_1: 0.05119/0.09195, loss_spatial_dice_1: 0.13179/0.22200, loss_spatial_ce_1: 0.01134/0.08233, loss_grounding_bce_1: 0.07739/0.08634, loss_grounding_dice_1: 0.13438/0.18014, loss_grounding_ce_1: 0.00871/0.27805, loss_mask_ce_2: 0.26673/0.91965, loss_mask_bce_2: 0.06324/0.33626, loss_mask_dice_2: 0.13755/1.17330, loss_spatial_bce_2: 0.04817/0.09212, loss_spatial_dice_2: 0.11360/0.22298, loss_spatial_ce_2: 0.00500/0.08599, loss_grounding_bce_2: 0.07199/0.08641, loss_grounding_dice_2: 0.12912/0.17965, loss_grounding_ce_2: 0.01175/0.28146, loss_mask_ce_3: 0.31246/0.92809, loss_mask_bce_3: 0.06092/0.33696, loss_mask_dice_3: 0.12635/1.17042, loss_spatial_bce_3: 0.04940/0.09298, loss_spatial_dice_3: 0.11020/0.22358, loss_spatial_ce_3: 0.01885/0.09012, loss_grounding_bce_3: 0.06955/0.08653, loss_grounding_dice_3: 0.12563/0.17939, loss_grounding_ce_3: 0.00536/0.28312, loss_mask_ce_4: 0.36767/0.92677, loss_mask_bce_4: 0.06658/0.33869, loss_mask_dice_4: 0.13239/1.19295, loss_spatial_bce_4: 0.05556/0.09703, loss_spatial_dice_4: 0.14694/0.23372, loss_spatial_ce_4: 0.06699/0.10642, loss_grounding_bce_4: 0.07673/0.08706, loss_grounding_dice_4: 0.13212/0.18218, loss_grounding_ce_4: 0.00761/0.28557, loss_mask_ce_5: 0.36370/0.94115, loss_mask_bce_5: 0.07652/0.34091, loss_mask_dice_5: 0.13137/1.19798, loss_spatial_bce_5: 0.05045/0.09824, loss_spatial_dice_5: 0.14791/0.23680, loss_spatial_ce_5: 0.04887/0.12035, loss_grounding_bce_5: 0.08899/0.08749, loss_grounding_dice_5: 0.12212/0.18343, loss_grounding_ce_5: 0.00712/0.29823, loss_mask_ce_6: 0.43989/0.97875, loss_mask_bce_6: 0.07533/0.34359, loss_mask_dice_6: 0.13308/1.20078, loss_spatial_bce_6: 0.07124/0.10360, loss_spatial_dice_6: 0.17301/0.23906, loss_spatial_ce_6: 0.03203/0.14535, loss_grounding_bce_6: 0.08470/0.08822, loss_grounding_dice_6: 0.11455/0.18355, loss_grounding_ce_6: 0.02079/0.31592, loss_mask_ce_7: 0.51073/1.02198, loss_mask_bce_7: 0.06777/0.35141, loss_mask_dice_7: 0.15746/1.25638, loss_spatial_bce_7: 0.05635/0.11233, loss_spatial_dice_7: 0.16649/0.26634, loss_spatial_ce_7: 0.18327/0.18328, loss_grounding_bce_7: 0.08000/0.09017, loss_grounding_dice_7: 0.12422/0.19088, loss_grounding_ce_7: 0.11468/0.34838, loss_mask_ce_8: 0.49664/1.13269, loss_mask_bce_8: 0.06948/0.36492, loss_mask_dice_8: 0.16473/1.33137, loss_spatial_bce_8: 0.13870/0.13343, loss_spatial_dice_8: 0.23096/0.30673, loss_spatial_ce_8: 0.04458/0.24031, loss_grounding_bce_8: 0.06270/0.09375, loss_grounding_dice_8: 0.11866/0.20225, loss_grounding_ce_8: 0.37354/0.41935, loss_mask_ce_9: 2.64981/3.69111, loss_mask_bce_9: 0.10225/0.39193, loss_mask_dice_9: 0.27992/1.90558, loss_spatial_bce_9: 0.27461/0.33572, loss_spatial_dice_9: 0.67180/0.82432, loss_spatial_ce_9: 0.66852/1.51399, loss_grounding_bce_9: 0.08155/0.10520, loss_grounding_dice_9: 0.16678/0.28185, loss_grounding_ce_9: 0.72412/0.69228] items per batch[64] items per second[0.23] total items[1593600] mini batches[ 24900] memory[7341] epoch remaining[0:31:46]
INFO:trainer.default_trainer:epochs[    13] optim steps[25000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19850/0.91114, loss_mask_bce_0: 0.07715/0.33512, loss_mask_dice_0: 0.42568/1.16631, loss_spatial_bce_0: 0.02786/0.09108, loss_spatial_dice_0: 0.13300/0.21760, loss_spatial_ce_0: 0.11199/0.07632, loss_grounding_bce_0: 0.06038/0.08625, loss_grounding_dice_0: 0.15821/0.17939, loss_grounding_ce_0: 0.04024/0.27620, loss_mask_ce_1: 0.90577/0.91193, loss_mask_bce_1: 0.07379/0.33581, loss_mask_dice_1: 0.28495/1.17346, loss_spatial_bce_1: 0.02391/0.09192, loss_spatial_dice_1: 0.14766/0.22192, loss_spatial_ce_1: 0.11518/0.08224, loss_grounding_bce_1: 0.06052/0.08635, loss_grounding_dice_1: 0.15560/0.18015, loss_grounding_ce_1: 0.04306/0.27788, loss_mask_ce_2: 0.91541/0.91935, loss_mask_bce_2: 0.07487/0.33629, loss_mask_dice_2: 0.28615/1.17279, loss_spatial_bce_2: 0.02365/0.09209, loss_spatial_dice_2: 0.14439/0.22290, loss_spatial_ce_2: 0.12535/0.08593, loss_grounding_bce_2: 0.05947/0.08641, loss_grounding_dice_2: 0.15332/0.17967, loss_grounding_ce_2: 0.04615/0.28129, loss_mask_ce_3: 0.95982/0.92771, loss_mask_bce_3: 0.07988/0.33699, loss_mask_dice_3: 0.34378/1.16997, loss_spatial_bce_3: 0.02606/0.09295, loss_spatial_dice_3: 0.17347/0.22350, loss_spatial_ce_3: 0.03776/0.09000, loss_grounding_bce_3: 0.06201/0.08653, loss_grounding_dice_3: 0.15517/0.17939, loss_grounding_ce_3: 0.05348/0.28290, loss_mask_ce_4: 0.99619/0.92645, loss_mask_bce_4: 0.07198/0.33874, loss_mask_dice_4: 0.39858/1.19254, loss_spatial_bce_4: 0.02518/0.09701, loss_spatial_dice_4: 0.13421/0.23365, loss_spatial_ce_4: 0.42406/0.10632, loss_grounding_bce_4: 0.05767/0.08708, loss_grounding_dice_4: 0.15399/0.18221, loss_grounding_ce_4: 0.05420/0.28537, loss_mask_ce_5: 1.01854/0.94081, loss_mask_bce_5: 0.07139/0.34095, loss_mask_dice_5: 0.47372/1.19754, loss_spatial_bce_5: 0.02873/0.09822, loss_spatial_dice_5: 0.19798/0.23675, loss_spatial_ce_5: 0.00381/0.12033, loss_grounding_bce_5: 0.05975/0.08750, loss_grounding_dice_5: 0.15786/0.18343, loss_grounding_ce_5: 0.07179/0.29800, loss_mask_ce_6: 0.96824/0.97846, loss_mask_bce_6: 0.08274/0.34362, loss_mask_dice_6: 0.47780/1.20032, loss_spatial_bce_6: 0.03366/0.10360, loss_spatial_dice_6: 0.19182/0.23901, loss_spatial_ce_6: 0.02189/0.14528, loss_grounding_bce_6: 0.06135/0.08824, loss_grounding_dice_6: 0.14746/0.18355, loss_grounding_ce_6: 0.05158/0.31564, loss_mask_ce_7: 1.17088/1.02166, loss_mask_bce_7: 0.08939/0.35146, loss_mask_dice_7: 0.39786/1.25585, loss_spatial_bce_7: 0.03608/0.11232, loss_spatial_dice_7: 0.25047/0.26627, loss_spatial_ce_7: 0.08511/0.18319, loss_grounding_bce_7: 0.07081/0.09019, loss_grounding_dice_7: 0.16113/0.19088, loss_grounding_ce_7: 0.03691/0.34810, loss_mask_ce_8: 1.20151/1.13234, loss_mask_bce_8: 0.08938/0.36497, loss_mask_dice_8: 0.49138/1.33087, loss_spatial_bce_8: 0.04395/0.13340, loss_spatial_dice_8: 0.33489/0.30663, loss_spatial_ce_8: 0.21023/0.24024, loss_grounding_bce_8: 0.06819/0.09377, loss_grounding_dice_8: 0.14056/0.20224, loss_grounding_ce_8: 0.06222/0.41902, loss_mask_ce_9: 2.58453/3.69033, loss_mask_bce_9: 0.12438/0.39203, loss_mask_dice_9: 0.73325/1.90499, loss_spatial_bce_9: 0.25812/0.33576, loss_spatial_dice_9: 0.83846/0.82427, loss_spatial_ce_9: 1.65914/1.51393, loss_grounding_bce_9: 0.10114/0.10521, loss_grounding_dice_9: 0.28202/0.28185, loss_grounding_ce_9: 0.08654/0.69196] items per batch[64] items per second[0.23] total items[1600000] mini batches[ 25000] memory[7341] epoch remaining[0:27:04]
INFO:trainer.default_trainer:epochs[    13] optim steps[25100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.93758/0.91133, loss_mask_bce_0: 0.27552/0.33503, loss_mask_dice_0: 1.62192/1.16655, loss_spatial_bce_0: 0.16466/0.09105, loss_spatial_dice_0: 0.20314/0.21762, loss_spatial_ce_0: 0.01298/0.07627, loss_grounding_bce_0: 0.02511/0.08620, loss_grounding_dice_0: 0.27564/0.17942, loss_grounding_ce_0: 0.97473/0.27638, loss_mask_ce_1: 1.04045/0.91198, loss_mask_bce_1: 0.26332/0.33572, loss_mask_dice_1: 1.50293/1.17377, loss_spatial_bce_1: 0.14301/0.09188, loss_spatial_dice_1: 0.20815/0.22193, loss_spatial_ce_1: 0.01312/0.08221, loss_grounding_bce_1: 0.02474/0.08630, loss_grounding_dice_1: 0.26893/0.18021, loss_grounding_ce_1: 0.85022/0.27804, loss_mask_ce_2: 1.01615/0.91946, loss_mask_bce_2: 0.27636/0.33621, loss_mask_dice_2: 1.59532/1.17304, loss_spatial_bce_2: 0.16673/0.09206, loss_spatial_dice_2: 0.21710/0.22292, loss_spatial_ce_2: 0.02066/0.08584, loss_grounding_bce_2: 0.02507/0.08636, loss_grounding_dice_2: 0.30295/0.17970, loss_grounding_ce_2: 0.71758/0.28149, loss_mask_ce_3: 1.15100/0.92782, loss_mask_bce_3: 0.27265/0.33692, loss_mask_dice_3: 1.55539/1.17021, loss_spatial_bce_3: 0.16604/0.09291, loss_spatial_dice_3: 0.20475/0.22353, loss_spatial_ce_3: 0.02543/0.08994, loss_grounding_bce_3: 0.02515/0.08649, loss_grounding_dice_3: 0.26529/0.17944, loss_grounding_ce_3: 0.67661/0.28303, loss_mask_ce_4: 1.06599/0.92656, loss_mask_bce_4: 0.26506/0.33868, loss_mask_dice_4: 1.62491/1.19286, loss_spatial_bce_4: 0.17282/0.09697, loss_spatial_dice_4: 0.20486/0.23368, loss_spatial_ce_4: 0.02491/0.10627, loss_grounding_bce_4: 0.02450/0.08704, loss_grounding_dice_4: 0.26591/0.18226, loss_grounding_ce_4: 0.61846/0.28539, loss_mask_ce_5: 1.06173/0.94107, loss_mask_bce_5: 0.27832/0.34089, loss_mask_dice_5: 1.62455/1.19778, loss_spatial_bce_5: 0.15829/0.09820, loss_spatial_dice_5: 0.18862/0.23680, loss_spatial_ce_5: 0.03507/0.12027, loss_grounding_bce_5: 0.02803/0.08745, loss_grounding_dice_5: 0.32599/0.18349, loss_grounding_ce_5: 0.79420/0.29802, loss_mask_ce_6: 1.17957/0.97864, loss_mask_bce_6: 0.28215/0.34356, loss_mask_dice_6: 1.75064/1.20054, loss_spatial_bce_6: 0.16488/0.10358, loss_spatial_dice_6: 0.18596/0.23905, loss_spatial_ce_6: 0.06419/0.14526, loss_grounding_bce_6: 0.02898/0.08819, loss_grounding_dice_6: 0.35745/0.18361, loss_grounding_ce_6: 0.68722/0.31558, loss_mask_ce_7: 1.32924/1.02183, loss_mask_bce_7: 0.26315/0.35142, loss_mask_dice_7: 1.71620/1.25616, loss_spatial_bce_7: 0.17274/0.11229, loss_spatial_dice_7: 0.21535/0.26630, loss_spatial_ce_7: 0.08638/0.18320, loss_grounding_bce_7: 0.02726/0.09015, loss_grounding_dice_7: 0.33595/0.19093, loss_grounding_ce_7: 0.69093/0.34815, loss_mask_ce_8: 1.10621/1.13260, loss_mask_bce_8: 0.30511/0.36488, loss_mask_dice_8: 1.86689/1.33109, loss_spatial_bce_8: 0.17025/0.13334, loss_spatial_dice_8: 0.24856/0.30665, loss_spatial_ce_8: 0.11947/0.24020, loss_grounding_bce_8: 0.02648/0.09370, loss_grounding_dice_8: 0.34715/0.20226, loss_grounding_ce_8: 0.78726/0.41910, loss_mask_ce_9: 3.13909/3.69046, loss_mask_bce_9: 0.40606/0.39191, loss_mask_dice_9: 2.34736/1.90533, loss_spatial_bce_9: 0.39765/0.33569, loss_spatial_dice_9: 0.81787/0.82429, loss_spatial_ce_9: 1.37240/1.51409, loss_grounding_bce_9: 0.03815/0.10513, loss_grounding_dice_9: 0.42886/0.28191, loss_grounding_ce_9: 2.28663/0.69191] items per batch[64] items per second[0.23] total items[1606400] mini batches[ 25100] memory[7341] epoch remaining[0:22:22]
INFO:trainer.default_trainer:epochs[    13] optim steps[25200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.22294/0.91112, loss_mask_bce_0: 0.07069/0.33512, loss_mask_dice_0: 2.12841/1.16671, loss_spatial_bce_0: 0.01409/0.09104, loss_spatial_dice_0: 0.31786/0.21758, loss_spatial_ce_0: 0.03222/0.07621, loss_grounding_bce_0: 0.01219/0.08622, loss_grounding_dice_0: 0.30333/0.17940, loss_grounding_ce_0: 0.12994/0.27644, loss_mask_ce_1: 1.07311/0.91179, loss_mask_bce_1: 0.07469/0.33582, loss_mask_dice_1: 2.34972/1.17391, loss_spatial_bce_1: 0.01224/0.09187, loss_spatial_dice_1: 0.31746/0.22188, loss_spatial_ce_1: 0.04699/0.08217, loss_grounding_bce_1: 0.00796/0.08631, loss_grounding_dice_1: 0.28545/0.18017, loss_grounding_ce_1: 0.12060/0.27809, loss_mask_ce_2: 1.01085/0.91928, loss_mask_bce_2: 0.07680/0.33630, loss_mask_dice_2: 1.95510/1.17316, loss_spatial_bce_2: 0.01216/0.09206, loss_spatial_dice_2: 0.34630/0.22288, loss_spatial_ce_2: 0.21866/0.08581, loss_grounding_bce_2: 0.02075/0.08638, loss_grounding_dice_2: 0.28769/0.17968, loss_grounding_ce_2: 0.13129/0.28147, loss_mask_ce_3: 1.26710/0.92768, loss_mask_bce_3: 0.05455/0.33701, loss_mask_dice_3: 2.13994/1.17034, loss_spatial_bce_3: 0.01751/0.09292, loss_spatial_dice_3: 0.32366/0.22349, loss_spatial_ce_3: 0.01011/0.08987, loss_grounding_bce_3: 0.00846/0.08650, loss_grounding_dice_3: 0.28168/0.17941, loss_grounding_ce_3: 0.11136/0.28302, loss_mask_ce_4: 1.29143/0.92645, loss_mask_bce_4: 0.07576/0.33877, loss_mask_dice_4: 1.95516/1.19297, loss_spatial_bce_4: 0.01007/0.09699, loss_spatial_dice_4: 0.31974/0.23368, loss_spatial_ce_4: 0.09654/0.10618, loss_grounding_bce_4: 0.01965/0.08706, loss_grounding_dice_4: 0.33877/0.18224, loss_grounding_ce_4: 0.27339/0.28539, loss_mask_ce_5: 1.49534/0.94097, loss_mask_bce_5: 0.06271/0.34098, loss_mask_dice_5: 2.07949/1.19798, loss_spatial_bce_5: 0.01040/0.09822, loss_spatial_dice_5: 0.32411/0.23679, loss_spatial_ce_5: 0.20292/0.12015, loss_grounding_bce_5: 0.01176/0.08746, loss_grounding_dice_5: 0.26656/0.18347, loss_grounding_ce_5: 0.29096/0.29807, loss_mask_ce_6: 1.67600/0.97854, loss_mask_bce_6: 0.05116/0.34365, loss_mask_dice_6: 2.00088/1.20073, loss_spatial_bce_6: 0.01902/0.10361, loss_spatial_dice_6: 0.33171/0.23904, loss_spatial_ce_6: 0.06018/0.14516, loss_grounding_bce_6: 0.01273/0.08821, loss_grounding_dice_6: 0.32360/0.18360, loss_grounding_ce_6: 0.10767/0.31555, loss_mask_ce_7: 1.38256/1.02172, loss_mask_bce_7: 0.06941/0.35149, loss_mask_dice_7: 2.14575/1.25628, loss_spatial_bce_7: 0.01834/0.11230, loss_spatial_dice_7: 0.36176/0.26626, loss_spatial_ce_7: 0.55687/0.18319, loss_grounding_bce_7: 0.01810/0.09017, loss_grounding_dice_7: 0.27509/0.19092, loss_grounding_ce_7: 0.23174/0.34794, loss_mask_ce_8: 1.60915/1.13262, loss_mask_bce_8: 0.05837/0.36501, loss_mask_dice_8: 2.32688/1.33128, loss_spatial_bce_8: 0.02265/0.13335, loss_spatial_dice_8: 0.41625/0.30662, loss_spatial_ce_8: 0.17487/0.24014, loss_grounding_bce_8: 0.01484/0.09373, loss_grounding_dice_8: 0.25614/0.20222, loss_grounding_ce_8: 0.31860/0.41913, loss_mask_ce_9: 4.50855/3.69039, loss_mask_bce_9: 0.04616/0.39204, loss_mask_dice_9: 2.70480/1.90555, loss_spatial_bce_9: 0.03029/0.33566, loss_spatial_dice_9: 0.87253/0.82423, loss_spatial_ce_9: 1.74349/1.51392, loss_grounding_bce_9: 0.00761/0.10518, loss_grounding_dice_9: 0.34709/0.28191, loss_grounding_ce_9: 0.43173/0.69187] items per batch[64] items per second[0.23] total items[1612800] mini batches[ 25200] memory[7341] epoch remaining[0:17:41]
INFO:trainer.default_trainer:epochs[    13] optim steps[25300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61813/0.91132, loss_mask_bce_0: 0.23530/0.33511, loss_mask_dice_0: 1.44887/1.16741, loss_spatial_bce_0: 0.04670/0.09101, loss_spatial_dice_0: 0.28872/0.21757, loss_spatial_ce_0: 0.03100/0.07616, loss_grounding_bce_0: 0.04177/0.08622, loss_grounding_dice_0: 0.12685/0.17942, loss_grounding_ce_0: 0.25387/0.27645, loss_mask_ce_1: 0.71830/0.91204, loss_mask_bce_1: 0.24536/0.33580, loss_mask_dice_1: 1.44303/1.17465, loss_spatial_bce_1: 0.04366/0.09185, loss_spatial_dice_1: 0.27902/0.22189, loss_spatial_ce_1: 0.04159/0.08211, loss_grounding_bce_1: 0.04355/0.08630, loss_grounding_dice_1: 0.12519/0.18018, loss_grounding_ce_1: 0.26701/0.27811, loss_mask_ce_2: 0.62067/0.91955, loss_mask_bce_2: 0.25154/0.33628, loss_mask_dice_2: 1.50428/1.17388, loss_spatial_bce_2: 0.04692/0.09203, loss_spatial_dice_2: 0.29514/0.22289, loss_spatial_ce_2: 0.06962/0.08573, loss_grounding_bce_2: 0.04361/0.08637, loss_grounding_dice_2: 0.13403/0.17971, loss_grounding_ce_2: 0.26533/0.28152, loss_mask_ce_3: 0.56744/0.92787, loss_mask_bce_3: 0.24648/0.33700, loss_mask_dice_3: 1.57480/1.17105, loss_spatial_bce_3: 0.05714/0.09289, loss_spatial_dice_3: 0.27742/0.22349, loss_spatial_ce_3: 0.03173/0.08978, loss_grounding_bce_3: 0.04098/0.08650, loss_grounding_dice_3: 0.12339/0.17945, loss_grounding_ce_3: 0.26011/0.28306, loss_mask_ce_4: 0.59868/0.92669, loss_mask_bce_4: 0.25583/0.33875, loss_mask_dice_4: 1.54325/1.19369, loss_spatial_bce_4: 0.07073/0.09696, loss_spatial_dice_4: 0.28577/0.23369, loss_spatial_ce_4: 0.25068/0.10610, loss_grounding_bce_4: 0.04463/0.08705, loss_grounding_dice_4: 0.12969/0.18229, loss_grounding_ce_4: 0.27258/0.28544, loss_mask_ce_5: 0.73433/0.94121, loss_mask_bce_5: 0.26468/0.34098, loss_mask_dice_5: 1.47157/1.19868, loss_spatial_bce_5: 0.09166/0.09820, loss_spatial_dice_5: 0.32509/0.23680, loss_spatial_ce_5: 0.06633/0.12008, loss_grounding_bce_5: 0.04914/0.08745, loss_grounding_dice_5: 0.13700/0.18350, loss_grounding_ce_5: 0.28314/0.29815, loss_mask_ce_6: 0.50951/0.97872, loss_mask_bce_6: 0.28488/0.34366, loss_mask_dice_6: 1.60253/1.20152, loss_spatial_bce_6: 0.08933/0.10360, loss_spatial_dice_6: 0.28045/0.23904, loss_spatial_ce_6: 0.08799/0.14510, loss_grounding_bce_6: 0.04372/0.08821, loss_grounding_dice_6: 0.12928/0.18366, loss_grounding_ce_6: 0.27948/0.31555, loss_mask_ce_7: 0.60288/1.02188, loss_mask_bce_7: 0.31177/0.35149, loss_mask_dice_7: 1.66176/1.25707, loss_spatial_bce_7: 0.10298/0.11227, loss_spatial_dice_7: 0.37733/0.26627, loss_spatial_ce_7: 0.28546/0.18317, loss_grounding_bce_7: 0.05273/0.09016, loss_grounding_dice_7: 0.13568/0.19096, loss_grounding_ce_7: 0.26980/0.34796, loss_mask_ce_8: 1.03788/1.13286, loss_mask_bce_8: 0.29726/0.36501, loss_mask_dice_8: 1.65331/1.33203, loss_spatial_bce_8: 0.12002/0.13334, loss_spatial_dice_8: 0.43646/0.30663, loss_spatial_ce_8: 0.30058/0.24011, loss_grounding_bce_8: 0.05319/0.09372, loss_grounding_dice_8: 0.13433/0.20228, loss_grounding_ce_8: 0.28278/0.41911, loss_mask_ce_9: 3.67949/3.69043, loss_mask_bce_9: 0.45450/0.39204, loss_mask_dice_9: 2.23790/1.90663, loss_spatial_bce_9: 0.17020/0.33561, loss_spatial_dice_9: 0.82103/0.82424, loss_spatial_ce_9: 1.24769/1.51386, loss_grounding_bce_9: 0.06077/0.10517, loss_grounding_dice_9: 0.20047/0.28196, loss_grounding_ce_9: 0.30544/0.69176] items per batch[64] items per second[0.22] total items[1619200] mini batches[ 25300] memory[7341] epoch remaining[0:13:01]
INFO:trainer.default_trainer:epochs[    13] optim steps[25400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57804/0.91097, loss_mask_bce_0: 0.13708/0.33509, loss_mask_dice_0: 0.57273/1.16700, loss_spatial_bce_0: 0.05278/0.09099, loss_spatial_dice_0: 0.19917/0.21750, loss_spatial_ce_0: 0.02763/0.07607, loss_grounding_bce_0: 0.08688/0.08622, loss_grounding_dice_0: 0.26272/0.17940, loss_grounding_ce_0: 0.17659/0.27638, loss_mask_ce_1: 0.71488/0.91168, loss_mask_bce_1: 0.13514/0.33576, loss_mask_dice_1: 0.52708/1.17426, loss_spatial_bce_1: 0.05737/0.09182, loss_spatial_dice_1: 0.19154/0.22183, loss_spatial_ce_1: 0.09738/0.08205, loss_grounding_bce_1: 0.08865/0.08630, loss_grounding_dice_1: 0.27427/0.18016, loss_grounding_ce_1: 0.19494/0.27810, loss_mask_ce_2: 0.97197/0.91922, loss_mask_bce_2: 0.13520/0.33624, loss_mask_dice_2: 0.56730/1.17347, loss_spatial_bce_2: 0.05511/0.09201, loss_spatial_dice_2: 0.17783/0.22283, loss_spatial_ce_2: 0.04937/0.08567, loss_grounding_bce_2: 0.08100/0.08637, loss_grounding_dice_2: 0.26479/0.17969, loss_grounding_ce_2: 0.17455/0.28150, loss_mask_ce_3: 0.80410/0.92752, loss_mask_bce_3: 0.12980/0.33697, loss_mask_dice_3: 0.51302/1.17063, loss_spatial_bce_3: 0.05248/0.09286, loss_spatial_dice_3: 0.19363/0.22343, loss_spatial_ce_3: 0.05805/0.08970, loss_grounding_bce_3: 0.08654/0.08650, loss_grounding_dice_3: 0.27588/0.17944, loss_grounding_ce_3: 0.17551/0.28302, loss_mask_ce_4: 0.68453/0.92643, loss_mask_bce_4: 0.13689/0.33871, loss_mask_dice_4: 0.56848/1.19328, loss_spatial_bce_4: 0.05884/0.09694, loss_spatial_dice_4: 0.20867/0.23365, loss_spatial_ce_4: 0.01369/0.10603, loss_grounding_bce_4: 0.08779/0.08705, loss_grounding_dice_4: 0.26734/0.18228, loss_grounding_ce_4: 0.16988/0.28541, loss_mask_ce_5: 0.93394/0.94092, loss_mask_bce_5: 0.11533/0.34094, loss_mask_dice_5: 0.58683/1.19830, loss_spatial_bce_5: 0.05165/0.09818, loss_spatial_dice_5: 0.21829/0.23677, loss_spatial_ce_5: 0.01522/0.12003, loss_grounding_bce_5: 0.09044/0.08745, loss_grounding_dice_5: 0.26430/0.18349, loss_grounding_ce_5: 0.17739/0.29820, loss_mask_ce_6: 0.71551/0.97840, loss_mask_bce_6: 0.13451/0.34360, loss_mask_dice_6: 0.56446/1.20107, loss_spatial_bce_6: 0.04820/0.10359, loss_spatial_dice_6: 0.21021/0.23900, loss_spatial_ce_6: 0.01083/0.14507, loss_grounding_bce_6: 0.08315/0.08822, loss_grounding_dice_6: 0.26356/0.18365, loss_grounding_ce_6: 0.19806/0.31553, loss_mask_ce_7: 0.82731/1.02154, loss_mask_bce_7: 0.13064/0.35144, loss_mask_dice_7: 0.53869/1.25663, loss_spatial_bce_7: 0.07936/0.11224, loss_spatial_dice_7: 0.23492/0.26624, loss_spatial_ce_7: 0.06652/0.18316, loss_grounding_bce_7: 0.09547/0.09016, loss_grounding_dice_7: 0.31197/0.19094, loss_grounding_ce_7: 0.11014/0.34783, loss_mask_ce_8: 0.66466/1.13249, loss_mask_bce_8: 0.14281/0.36496, loss_mask_dice_8: 0.60756/1.33151, loss_spatial_bce_8: 0.08378/0.13329, loss_spatial_dice_8: 0.27953/0.30659, loss_spatial_ce_8: 0.09077/0.24007, loss_grounding_bce_8: 0.08863/0.09373, loss_grounding_dice_8: 0.32375/0.20225, loss_grounding_ce_8: 0.04861/0.41896, loss_mask_ce_9: 2.91905/3.68991, loss_mask_bce_9: 0.11965/0.39194, loss_mask_dice_9: 0.79076/1.90572, loss_spatial_bce_9: 0.30578/0.33563, loss_spatial_dice_9: 0.84057/0.82424, loss_spatial_ce_9: 1.18180/1.51386, loss_grounding_bce_9: 0.06667/0.10517, loss_grounding_dice_9: 0.37180/0.28189, loss_grounding_ce_9: 1.22912/0.69169] items per batch[64] items per second[0.23] total items[1625600] mini batches[ 25400] memory[7341] epoch remaining[0:08:19]
INFO:trainer.default_trainer:epochs[    13] optim steps[25500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.86985/0.91097, loss_mask_bce_0: 0.35770/0.33512, loss_mask_dice_0: 0.90077/1.16711, loss_spatial_bce_0: 0.05794/0.09096, loss_spatial_dice_0: 0.18806/0.21747, loss_spatial_ce_0: 0.00261/0.07600, loss_grounding_bce_0: 0.09885/0.08621, loss_grounding_dice_0: 0.24260/0.17943, loss_grounding_ce_0: 0.09462/0.27664, loss_mask_ce_1: 1.80751/0.91167, loss_mask_bce_1: 0.34552/0.33579, loss_mask_dice_1: 1.00510/1.17430, loss_spatial_bce_1: 0.05757/0.09179, loss_spatial_dice_1: 0.17706/0.22180, loss_spatial_ce_1: 0.00670/0.08198, loss_grounding_bce_1: 0.09247/0.08629, loss_grounding_dice_1: 0.21382/0.18020, loss_grounding_ce_1: 0.18428/0.27839, loss_mask_ce_2: 1.75456/0.91933, loss_mask_bce_2: 0.37178/0.33626, loss_mask_dice_2: 1.03542/1.17352, loss_spatial_bce_2: 0.05811/0.09199, loss_spatial_dice_2: 0.17415/0.22281, loss_spatial_ce_2: 0.00478/0.08560, loss_grounding_bce_2: 0.10132/0.08636, loss_grounding_dice_2: 0.25202/0.17973, loss_grounding_ce_2: 0.08893/0.28177, loss_mask_ce_3: 1.87324/0.92753, loss_mask_bce_3: 0.37245/0.33699, loss_mask_dice_3: 1.08404/1.17074, loss_spatial_bce_3: 0.06434/0.09284, loss_spatial_dice_3: 0.18073/0.22341, loss_spatial_ce_3: 0.01191/0.08960, loss_grounding_bce_3: 0.08681/0.08649, loss_grounding_dice_3: 0.20765/0.17947, loss_grounding_ce_3: 0.17799/0.28330, loss_mask_ce_4: 1.76577/0.92645, loss_mask_bce_4: 0.33789/0.33874, loss_mask_dice_4: 1.07040/1.19335, loss_spatial_bce_4: 0.06016/0.09691, loss_spatial_dice_4: 0.19233/0.23363, loss_spatial_ce_4: 0.02386/0.10596, loss_grounding_bce_4: 0.09955/0.08705, loss_grounding_dice_4: 0.24923/0.18233, loss_grounding_ce_4: 0.08561/0.28574, loss_mask_ce_5: 1.61969/0.94095, loss_mask_bce_5: 0.44961/0.34096, loss_mask_dice_5: 1.11652/1.19842, loss_spatial_bce_5: 0.07447/0.09817, loss_spatial_dice_5: 0.20664/0.23677, loss_spatial_ce_5: 0.04884/0.11998, loss_grounding_bce_5: 0.09324/0.08745, loss_grounding_dice_5: 0.25428/0.18352, loss_grounding_ce_5: 0.08836/0.29861, loss_mask_ce_6: 1.74271/0.97847, loss_mask_bce_6: 0.39700/0.34362, loss_mask_dice_6: 1.12972/1.20112, loss_spatial_bce_6: 0.08928/0.10358, loss_spatial_dice_6: 0.22474/0.23901, loss_spatial_ce_6: 0.12680/0.14499, loss_grounding_bce_6: 0.07150/0.08821, loss_grounding_dice_6: 0.19531/0.18370, loss_grounding_ce_6: 0.16092/0.31579, loss_mask_ce_7: 1.90080/1.02169, loss_mask_bce_7: 0.41735/0.35145, loss_mask_dice_7: 1.22715/1.25669, loss_spatial_bce_7: 0.09380/0.11224, loss_spatial_dice_7: 0.24603/0.26624, loss_spatial_ce_7: 0.14636/0.18311, loss_grounding_bce_7: 0.09438/0.09015, loss_grounding_dice_7: 0.24907/0.19098, loss_grounding_ce_7: 0.11400/0.34817, loss_mask_ce_8: 2.12076/1.13264, loss_mask_bce_8: 0.43123/0.36499, loss_mask_dice_8: 1.22912/1.33160, loss_spatial_bce_8: 0.09855/0.13328, loss_spatial_dice_8: 0.27953/0.30661, loss_spatial_ce_8: 0.22198/0.24006, loss_grounding_bce_8: 0.06021/0.09372, loss_grounding_dice_8: 0.21375/0.20231, loss_grounding_ce_8: 0.20162/0.41917, loss_mask_ce_9: 4.89691/3.68967, loss_mask_bce_9: 0.56226/0.39197, loss_mask_dice_9: 1.77420/1.90581, loss_spatial_bce_9: 0.36563/0.33560, loss_spatial_dice_9: 0.90848/0.82426, loss_spatial_ce_9: 1.39603/1.51368, loss_grounding_bce_9: 0.09805/0.10516, loss_grounding_dice_9: 0.38863/0.28198, loss_grounding_ce_9: 0.52147/0.69174] items per batch[64] items per second[0.23] total items[1632000] mini batches[ 25500] memory[7341] epoch remaining[0:03:38]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00025578.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0030 s/iter. Inference: 0.2257 s/iter. Eval: 0.0980 s/iter. Total: 0.3268 s/iter. ETA=0:00:47
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2265 s/iter. Eval: 0.0895 s/iter. Total: 0.3190 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0030 s/iter. Inference: 0.2276 s/iter. Eval: 0.0840 s/iter. Total: 0.3147 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0030 s/iter. Inference: 0.2288 s/iter. Eval: 0.0797 s/iter. Total: 0.3117 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2266 s/iter. Eval: 0.0767 s/iter. Total: 0.3065 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 94/157. Dataloading: 0.0032 s/iter. Inference: 0.2330 s/iter. Eval: 0.0775 s/iter. Total: 0.3138 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 110/157. Dataloading: 0.0032 s/iter. Inference: 0.2346 s/iter. Eval: 0.0776 s/iter. Total: 0.3155 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 127/157. Dataloading: 0.0032 s/iter. Inference: 0.2331 s/iter. Eval: 0.0781 s/iter. Total: 0.3145 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 143/157. Dataloading: 0.0032 s/iter. Inference: 0.2333 s/iter. Eval: 0.0776 s/iter. Total: 0.3143 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalnigg0a8_ ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.060 | 82.168 | 60.076 |      133      |
| Things | 55.306 | 82.886 | 66.109 |      80       |
| Stuff  | 42.142 | 81.085 | 50.969 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.35s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.03 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.21s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.85 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.507
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.996 | 61.258 | 41.023 | 19.719 | 41.923 | 60.273 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.505 | bicycle      | 18.741 | car            | 37.356 |
| motorcycle    | 35.150 | airplane     | 56.465 | bus            | 65.336 |
| train         | 68.911 | truck        | 35.712 | boat           | 23.515 |
| traffic light | 25.498 | fire hydrant | 63.986 | stop sign      | 63.679 |
| parking meter | 43.939 | bench        | 20.534 | bird           | 30.229 |
| cat           | 74.042 | dog          | 64.667 | horse          | 46.690 |
| sheep         | 47.431 | cow          | 50.619 | elephant       | 60.758 |
| bear          | 78.652 | zebra        | 60.114 | giraffe        | 56.858 |
| backpack      | 15.897 | umbrella     | 48.613 | handbag        | 14.859 |
| tie           | 33.377 | suitcase     | 41.433 | frisbee        | 68.180 |
| skis          | 5.374  | snowboard    | 23.712 | sports ball    | 47.678 |
| kite          | 34.437 | baseball bat | 29.540 | baseball glove | 42.961 |
| skateboard    | 35.954 | surfboard    | 35.494 | tennis racket  | 56.383 |
| bottle        | 34.103 | wine glass   | 26.760 | cup            | 40.314 |
| fork          | 16.127 | knife        | 12.514 | spoon          | 13.987 |
| bowl          | 32.374 | banana       | 19.547 | apple          | 20.351 |
| sandwich      | 43.799 | orange       | 29.175 | broccoli       | 21.598 |
| carrot        | 21.066 | hot dog      | 23.858 | pizza          | 51.208 |
| donut         | 46.575 | cake         | 44.004 | chair          | 19.966 |
| couch         | 43.056 | potted plant | 16.812 | bed            | 41.534 |
| dining table  | 13.138 | toilet       | 66.114 | tv             | 62.054 |
| laptop        | 63.174 | mouse        | 58.829 | remote         | 31.660 |
| keyboard      | 47.967 | cell phone   | 37.807 | microwave      | 57.708 |
| oven          | 31.882 | toaster      | 27.863 | sink           | 37.700 |
| refrigerator  | 60.413 | book         | 9.072  | clock          | 51.641 |
| vase          | 32.438 | scissors     | 24.932 | teddy bear     | 50.613 |
| hair drier    | 10.197 | toothbrush   | 18.490 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.46935613812083, 'fwIoU': 68.88475970274024, 'IoU-person': 87.76835311448113, 'IoU-bicycle': 75.37012236301409, 'IoU-car': 69.39408326846026, 'IoU-motorcycle': 83.75358941986306, 'IoU-airplane': 78.45256549851092, 'IoU-bus': 86.23187190602366, 'IoU-train': 84.57566003454386, 'IoU-truck': 64.77259270926447, 'IoU-boat': 65.43809970338292, 'IoU-traffic light': 76.18306102808485, 'IoU-fire hydrant': 90.0399862695919, 'IoU-stop sign': 91.24025220419932, 'IoU-parking meter': 87.75385747317144, 'IoU-bench': 54.612604822187386, 'IoU-bird': 75.25804870702399, 'IoU-cat': 84.30624331079562, 'IoU-dog': 76.81862062467724, 'IoU-horse': 85.59677824199355, 'IoU-sheep': 86.10814799296091, 'IoU-cow': 78.76146208628595, 'IoU-elephant': 85.25469363895583, 'IoU-bear': 64.60096452404379, 'IoU-zebra': 90.70382705626466, 'IoU-giraffe': 86.81797584720505, 'IoU-backpack': 39.57801395558915, 'IoU-umbrella': 73.8414788025485, 'IoU-handbag': 36.96899205994275, 'IoU-tie': 70.46263599752982, 'IoU-suitcase': 80.49938012995374, 'IoU-frisbee': 84.09680459345763, 'IoU-skis': 52.49794773998813, 'IoU-snowboard': 69.32890845934324, 'IoU-sports ball': 67.27859662520122, 'IoU-kite': 65.63141437168636, 'IoU-baseball bat': 58.42396425187866, 'IoU-baseball glove': 76.52606943100992, 'IoU-skateboard': 64.31270132530456, 'IoU-surfboard': 75.7847994281855, 'IoU-tennis racket': 82.87100602298534, 'IoU-bottle': 67.6290953535214, 'IoU-wine glass': 73.1630638603353, 'IoU-cup': 62.49214002575556, 'IoU-fork': 54.19128714683807, 'IoU-knife': 48.009145863246424, 'IoU-spoon': 48.59326060722932, 'IoU-bowl': 52.7204547210405, 'IoU-banana': 84.03042889476319, 'IoU-apple': 58.857121542350235, 'IoU-sandwich': 64.67228587076093, 'IoU-orange': 79.96729204204094, 'IoU-broccoli': 68.52712372771775, 'IoU-carrot': 65.70159440893242, 'IoU-hot dog': 61.73317796827781, 'IoU-pizza': 86.54024922060103, 'IoU-donut': 64.57333923562513, 'IoU-cake': 68.1007084073423, 'IoU-chair': 52.9616568198949, 'IoU-couch': 67.13738289674941, 'IoU-potted plant': 34.464423442051476, 'IoU-bed': 67.16270842910725, 'IoU-dining table': 49.81692971774842, 'IoU-toilet': 86.25456279948828, 'IoU-tv': 74.85094274656032, 'IoU-laptop': 71.14886751893098, 'IoU-mouse': 67.54985192497533, 'IoU-remote': 49.61277938132774, 'IoU-keyboard': 65.42799242695432, 'IoU-cell phone': 68.46349630756153, 'IoU-microwave': 66.72974985504602, 'IoU-oven': 71.41904252915737, 'IoU-toaster': 60.56401457066144, 'IoU-sink': 71.13812151608907, 'IoU-refrigerator': 81.9672777442454, 'IoU-book': 52.09764451871044, 'IoU-clock': 71.59888827553277, 'IoU-vase': 52.96555604515849, 'IoU-scissors': 52.07007571930146, 'IoU-teddy bear': 83.24943324300996, 'IoU-hair drier': 41.20502676866631, 'IoU-toothbrush': 54.95540488137346, 'IoU-banner': 40.23721111221918, 'IoU-blanket': 11.842134202980555, 'IoU-bridge': 39.40528479047582, 'IoU-cardboard': 38.96406278715231, 'IoU-counter': 32.245974362968056, 'IoU-curtain': 65.58830901292959, 'IoU-door-stuff': 44.024679501944355, 'IoU-floor-wood': 58.731259994278936, 'IoU-flower': 43.32338414398861, 'IoU-fruit': 41.36245947756846, 'IoU-gravel': 26.62596746660173, 'IoU-house': 24.964059375662558, 'IoU-light': 41.437983438541416, 'IoU-mirror-stuff': 50.475258308060155, 'IoU-net': 43.574648981431594, 'IoU-pillow': 10.250705416103735, 'IoU-platform': 28.773140409710322, 'IoU-playingfield': 67.42204640181373, 'IoU-railroad': 60.64848923917382, 'IoU-river': 48.70188249670645, 'IoU-road': 66.88811350894561, 'IoU-roof': 14.566292783390622, 'IoU-sand': 63.711635063524255, 'IoU-sea': 85.13704048649787, 'IoU-shelf': 37.593526970288124, 'IoU-snow': 88.25346325210519, 'IoU-stairs': 20.720359725007906, 'IoU-tent': 9.178320402195173, 'IoU-towel': 35.179266507159944, 'IoU-wall-brick': 44.52959755873272, 'IoU-wall-stone': 26.72968241385394, 'IoU-wall-tile': 65.92618224832859, 'IoU-wall-wood': 37.4529328906076, 'IoU-water-other': 21.5386187052906, 'IoU-window-blind': 48.67618696412359, 'IoU-window-other': 47.616121266797634, 'IoU-tree-merged': 80.7413053459895, 'IoU-fence-merged': 52.911409824365606, 'IoU-ceiling-merged': 67.04641568661646, 'IoU-sky-other-merged': 93.72378912961378, 'IoU-cabinet-merged': 59.89462680326155, 'IoU-table-merged': 35.63320730032203, 'IoU-floor-other-merged': 49.781241798295696, 'IoU-pavement-merged': 54.37855641275207, 'IoU-mountain-merged': 54.36089346416363, 'IoU-grass-merged': 70.88041780533123, 'IoU-dirt-merged': 46.403296595054364, 'IoU-paper-merged': 29.59113748638042, 'IoU-food-other-merged': 37.52187669371791, 'IoU-building-other-merged': 58.5134748334572, 'IoU-rock-merged': 59.33097165597358, 'IoU-wall-other-merged': 64.61865999099949, 'IoU-rug-merged': 62.56702986234498, 'mACC': 72.27120501484188, 'pACC': 80.24220978179474, 'ACC-person': 92.23077823301034, 'ACC-bicycle': 85.46123162385028, 'ACC-car': 85.45297511574658, 'ACC-motorcycle': 89.01248118013191, 'ACC-airplane': 90.86437118677895, 'ACC-bus': 91.24144074941879, 'ACC-train': 95.7368296339663, 'ACC-truck': 74.0494865518861, 'ACC-boat': 79.10874646667317, 'ACC-traffic light': 90.10270295448655, 'ACC-fire hydrant': 94.92681062569656, 'ACC-stop sign': 94.65133163511477, 'ACC-parking meter': 92.12484950435254, 'ACC-bench': 68.15583076566715, 'ACC-bird': 79.9350636844538, 'ACC-cat': 90.22320516738111, 'ACC-dog': 79.53176713035917, 'ACC-horse': 91.92247054134089, 'ACC-sheep': 90.07513946319463, 'ACC-cow': 83.6344583554001, 'ACC-elephant': 87.49990150545287, 'ACC-bear': 66.068081800515, 'ACC-zebra': 93.33742317511977, 'ACC-giraffe': 91.19948682646309, 'ACC-backpack': 56.44604341767382, 'ACC-umbrella': 82.83275017625837, 'ACC-handbag': 57.392105400067095, 'ACC-tie': 80.82813549169691, 'ACC-suitcase': 88.92434913422406, 'ACC-frisbee': 94.4290909090909, 'ACC-skis': 70.81571532247348, 'ACC-snowboard': 78.8944674389557, 'ACC-sports ball': 80.14130513432775, 'ACC-kite': 75.65158734353723, 'ACC-baseball bat': 84.78410717095356, 'ACC-baseball glove': 90.35400589409366, 'ACC-skateboard': 69.96691966910691, 'ACC-surfboard': 83.2373968513917, 'ACC-tennis racket': 89.31079310742915, 'ACC-bottle': 81.14096955127361, 'ACC-wine glass': 85.74422947906656, 'ACC-cup': 83.97860471535624, 'ACC-fork': 65.89870157576357, 'ACC-knife': 58.31244962262768, 'ACC-spoon': 66.28667842184655, 'ACC-bowl': 63.91862559377225, 'ACC-banana': 90.1395879058894, 'ACC-apple': 68.56354334705101, 'ACC-sandwich': 78.27077738707422, 'ACC-orange': 88.02853298420236, 'ACC-broccoli': 78.59218268627079, 'ACC-carrot': 73.72178793228667, 'ACC-hot dog': 67.4419010018346, 'ACC-pizza': 94.70377763653188, 'ACC-donut': 81.18543214986609, 'ACC-cake': 75.81700957085957, 'ACC-chair': 67.89666667979078, 'ACC-couch': 79.78915622668067, 'ACC-potted plant': 46.38644995585454, 'ACC-bed': 81.60263302972068, 'ACC-dining table': 82.08173020631425, 'ACC-toilet': 92.76240667733539, 'ACC-tv': 87.90810602720686, 'ACC-laptop': 84.70343150080232, 'ACC-mouse': 86.39618482805392, 'ACC-remote': 71.82961446338292, 'ACC-keyboard': 71.78883755589487, 'ACC-cell phone': 76.43419302507564, 'ACC-microwave': 77.47942321788919, 'ACC-oven': 83.55283266530182, 'ACC-toaster': 72.6997769231822, 'ACC-sink': 83.12790034741933, 'ACC-refrigerator': 91.1728254870879, 'ACC-book': 68.78099520887618, 'ACC-clock': 76.6663026428618, 'ACC-vase': 61.705651931623265, 'ACC-scissors': 56.18842336262384, 'ACC-teddy bear': 88.9804219429817, 'ACC-hair drier': 46.99000939287849, 'ACC-toothbrush': 81.24826268241834, 'ACC-banner': 74.15454998373832, 'ACC-blanket': 19.973960662509047, 'ACC-bridge': 54.994070172587186, 'ACC-cardboard': 45.50501672240802, 'ACC-counter': 51.20920205610317, 'ACC-curtain': 76.69495045601468, 'ACC-door-stuff': 65.16040198024258, 'ACC-floor-wood': 73.42992549136811, 'ACC-flower': 60.88329437873907, 'ACC-fruit': 59.138629894956416, 'ACC-gravel': 34.74437046390324, 'ACC-house': 29.759556771805904, 'ACC-light': 56.59129407035945, 'ACC-mirror-stuff': 59.640358975859854, 'ACC-net': 61.994488452576526, 'ACC-pillow': 22.854435008038692, 'ACC-platform': 44.669093912873024, 'ACC-playingfield': 83.38613754351083, 'ACC-railroad': 77.38071804829389, 'ACC-river': 69.32450440779037, 'ACC-road': 86.51690190466556, 'ACC-roof': 19.65961365293957, 'ACC-sand': 70.52572032171295, 'ACC-sea': 90.79096118296701, 'ACC-shelf': 58.88486993824419, 'ACC-snow': 95.40634620715385, 'ACC-stairs': 32.4088276221878, 'ACC-tent': 11.503287176248687, 'ACC-towel': 42.68236542950451, 'ACC-wall-brick': 55.51893167667178, 'ACC-wall-stone': 34.34591653149209, 'ACC-wall-tile': 78.02353321438471, 'ACC-wall-wood': 51.524791424766114, 'ACC-water-other': 36.85759463365243, 'ACC-window-blind': 58.98801292398213, 'ACC-window-other': 68.80864372567711, 'ACC-tree-merged': 89.91065831134351, 'ACC-fence-merged': 69.4460917188362, 'ACC-ceiling-merged': 80.38116749410091, 'ACC-sky-other-merged': 96.49549140690662, 'ACC-cabinet-merged': 75.23407170154658, 'ACC-table-merged': 47.27684512569545, 'ACC-floor-other-merged': 64.05787324329411, 'ACC-pavement-merged': 63.902055950142845, 'ACC-mountain-merged': 64.93224554590446, 'ACC-grass-merged': 83.09537147252907, 'ACC-dirt-merged': 73.72207740479205, 'ACC-paper-merged': 42.542174686473885, 'ACC-food-other-merged': 48.390901258376324, 'ACC-building-other-merged': 74.6968720660698, 'ACC-rock-merged': 82.87680225160827, 'ACC-wall-other-merged': 83.1812965496012, 'ACC-rug-merged': 77.52032988624855})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1525 s/iter. Inference: 0.4203 s/iter. Eval: 0.0000 s/iter. Total: 0.5728 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1574 s/iter. Inference: 0.4655 s/iter. Eval: 0.0000 s/iter. Total: 0.6230 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1688 s/iter. Inference: 0.5245 s/iter. Eval: 0.0000 s/iter. Total: 0.6935 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1726 s/iter. Inference: 0.6626 s/iter. Eval: 0.0000 s/iter. Total: 0.8354 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1688 s/iter. Inference: 0.5874 s/iter. Eval: 0.0000 s/iter. Total: 0.7564 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1681 s/iter. Inference: 0.6298 s/iter. Eval: 0.0000 s/iter. Total: 0.7981 s/iter. ETA=0:00:03
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1697 s/iter. Inference: 0.6842 s/iter. Eval: 0.0000 s/iter. Total: 0.8541 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5156570090722856, 'noc@0.8': 2.919227392449517, 'noc@0.85': 3.54228855721393, 'noc@0.9': 4.571553994732222, 'miou@iter1': 0.837410140602708}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0986 s/iter. Eval: 0.0008 s/iter. Total: 0.1010 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.7734146118164, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.37854766845703, 'precision@0.8': 51.72949981689453, 'precision@0.9': 26.700349807739258, 'cIoU': 56.503360748291016, 'mIoU': 62.4311408996582}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.059887969552044, 'SQ': 82.16802577875555, 'RQ': 60.075912093719595, 'PQ_th': 55.3055040063157, 'SQ_th': 82.88554062429569, 'RQ_th': 66.10932174052233, 'PQ_st': 42.1419769706635, 'SQ_st': 81.0849845024686, 'RQ_st': 50.968878664583315}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.99622658326089, 'AP50': 61.257633094734985, 'AP75': 41.022569345376716, 'APs': 19.718673759963266, 'APm': 41.92256855379705, 'APl': 60.273376566194315, 'AP-person': 44.50498087643459, 'AP-bicycle': 18.74064552921316, 'AP-car': 37.35593703109023, 'AP-motorcycle': 35.14984222819425, 'AP-airplane': 56.46516692037385, 'AP-bus': 65.33637391071564, 'AP-train': 68.91118856808919, 'AP-truck': 35.71244832550988, 'AP-boat': 23.515045423310347, 'AP-traffic light': 25.498411790303376, 'AP-fire hydrant': 63.98579230898807, 'AP-stop sign': 63.67861526223935, 'AP-parking meter': 43.93890218049802, 'AP-bench': 20.533833476803682, 'AP-bird': 30.229498565491888, 'AP-cat': 74.04176676765296, 'AP-dog': 64.66700131138478, 'AP-horse': 46.690242906778, 'AP-sheep': 47.43120519098923, 'AP-cow': 50.618851354263285, 'AP-elephant': 60.75753822610079, 'AP-bear': 78.65160969561838, 'AP-zebra': 60.113508286885256, 'AP-giraffe': 56.858085043139305, 'AP-backpack': 15.896973113756957, 'AP-umbrella': 48.6134437581664, 'AP-handbag': 14.859111718372548, 'AP-tie': 33.37740063244997, 'AP-suitcase': 41.43251204914489, 'AP-frisbee': 68.17973518935067, 'AP-skis': 5.373533915598209, 'AP-snowboard': 23.712187241733705, 'AP-sports ball': 47.67752098029993, 'AP-kite': 34.436910813970115, 'AP-baseball bat': 29.539987535493616, 'AP-baseball glove': 42.96051969865815, 'AP-skateboard': 35.95388919665056, 'AP-surfboard': 35.49434374698456, 'AP-tennis racket': 56.38294192955865, 'AP-bottle': 34.10327224341729, 'AP-wine glass': 26.76011402396413, 'AP-cup': 40.31424198273578, 'AP-fork': 16.12733077198463, 'AP-knife': 12.513971327263837, 'AP-spoon': 13.98686534053688, 'AP-bowl': 32.3741438218968, 'AP-banana': 19.546870319359257, 'AP-apple': 20.351242758924123, 'AP-sandwich': 43.79932977539678, 'AP-orange': 29.174983718950227, 'AP-broccoli': 21.59825831433849, 'AP-carrot': 21.065889636861034, 'AP-hot dog': 23.857819183386002, 'AP-pizza': 51.20763032474495, 'AP-donut': 46.57513650266195, 'AP-cake': 44.00394749233843, 'AP-chair': 19.966131988945218, 'AP-couch': 43.05569306352036, 'AP-potted plant': 16.81157067787468, 'AP-bed': 41.534010742277864, 'AP-dining table': 13.137691865615716, 'AP-toilet': 66.11384265761487, 'AP-tv': 62.05429040587028, 'AP-laptop': 63.17422140070572, 'AP-mouse': 58.82947915126315, 'AP-remote': 31.660492428595404, 'AP-keyboard': 47.96692426163688, 'AP-cell phone': 37.80681097681522, 'AP-microwave': 57.70772179470662, 'AP-oven': 31.881775118376154, 'AP-toaster': 27.86295772434386, 'AP-sink': 37.699970576532, 'AP-refrigerator': 60.412699086470276, 'AP-book': 9.071819020502813, 'AP-clock': 51.64087655312043, 'AP-vase': 32.43764997181256, 'AP-scissors': 24.932456497441283, 'AP-teddy bear': 50.613229492742065, 'AP-hair drier': 10.197026391602371, 'AP-toothbrush': 18.490234573468296}), ('sem_seg', {'mIoU': 60.46935613812083, 'fwIoU': 68.88475970274024, 'IoU-person': 87.76835311448113, 'IoU-bicycle': 75.37012236301409, 'IoU-car': 69.39408326846026, 'IoU-motorcycle': 83.75358941986306, 'IoU-airplane': 78.45256549851092, 'IoU-bus': 86.23187190602366, 'IoU-train': 84.57566003454386, 'IoU-truck': 64.77259270926447, 'IoU-boat': 65.43809970338292, 'IoU-traffic light': 76.18306102808485, 'IoU-fire hydrant': 90.0399862695919, 'IoU-stop sign': 91.24025220419932, 'IoU-parking meter': 87.75385747317144, 'IoU-bench': 54.612604822187386, 'IoU-bird': 75.25804870702399, 'IoU-cat': 84.30624331079562, 'IoU-dog': 76.81862062467724, 'IoU-horse': 85.59677824199355, 'IoU-sheep': 86.10814799296091, 'IoU-cow': 78.76146208628595, 'IoU-elephant': 85.25469363895583, 'IoU-bear': 64.60096452404379, 'IoU-zebra': 90.70382705626466, 'IoU-giraffe': 86.81797584720505, 'IoU-backpack': 39.57801395558915, 'IoU-umbrella': 73.8414788025485, 'IoU-handbag': 36.96899205994275, 'IoU-tie': 70.46263599752982, 'IoU-suitcase': 80.49938012995374, 'IoU-frisbee': 84.09680459345763, 'IoU-skis': 52.49794773998813, 'IoU-snowboard': 69.32890845934324, 'IoU-sports ball': 67.27859662520122, 'IoU-kite': 65.63141437168636, 'IoU-baseball bat': 58.42396425187866, 'IoU-baseball glove': 76.52606943100992, 'IoU-skateboard': 64.31270132530456, 'IoU-surfboard': 75.7847994281855, 'IoU-tennis racket': 82.87100602298534, 'IoU-bottle': 67.6290953535214, 'IoU-wine glass': 73.1630638603353, 'IoU-cup': 62.49214002575556, 'IoU-fork': 54.19128714683807, 'IoU-knife': 48.009145863246424, 'IoU-spoon': 48.59326060722932, 'IoU-bowl': 52.7204547210405, 'IoU-banana': 84.03042889476319, 'IoU-apple': 58.857121542350235, 'IoU-sandwich': 64.67228587076093, 'IoU-orange': 79.96729204204094, 'IoU-broccoli': 68.52712372771775, 'IoU-carrot': 65.70159440893242, 'IoU-hot dog': 61.73317796827781, 'IoU-pizza': 86.54024922060103, 'IoU-donut': 64.57333923562513, 'IoU-cake': 68.1007084073423, 'IoU-chair': 52.9616568198949, 'IoU-couch': 67.13738289674941, 'IoU-potted plant': 34.464423442051476, 'IoU-bed': 67.16270842910725, 'IoU-dining table': 49.81692971774842, 'IoU-toilet': 86.25456279948828, 'IoU-tv': 74.85094274656032, 'IoU-laptop': 71.14886751893098, 'IoU-mouse': 67.54985192497533, 'IoU-remote': 49.61277938132774, 'IoU-keyboard': 65.42799242695432, 'IoU-cell phone': 68.46349630756153, 'IoU-microwave': 66.72974985504602, 'IoU-oven': 71.41904252915737, 'IoU-toaster': 60.56401457066144, 'IoU-sink': 71.13812151608907, 'IoU-refrigerator': 81.9672777442454, 'IoU-book': 52.09764451871044, 'IoU-clock': 71.59888827553277, 'IoU-vase': 52.96555604515849, 'IoU-scissors': 52.07007571930146, 'IoU-teddy bear': 83.24943324300996, 'IoU-hair drier': 41.20502676866631, 'IoU-toothbrush': 54.95540488137346, 'IoU-banner': 40.23721111221918, 'IoU-blanket': 11.842134202980555, 'IoU-bridge': 39.40528479047582, 'IoU-cardboard': 38.96406278715231, 'IoU-counter': 32.245974362968056, 'IoU-curtain': 65.58830901292959, 'IoU-door-stuff': 44.024679501944355, 'IoU-floor-wood': 58.731259994278936, 'IoU-flower': 43.32338414398861, 'IoU-fruit': 41.36245947756846, 'IoU-gravel': 26.62596746660173, 'IoU-house': 24.964059375662558, 'IoU-light': 41.437983438541416, 'IoU-mirror-stuff': 50.475258308060155, 'IoU-net': 43.574648981431594, 'IoU-pillow': 10.250705416103735, 'IoU-platform': 28.773140409710322, 'IoU-playingfield': 67.42204640181373, 'IoU-railroad': 60.64848923917382, 'IoU-river': 48.70188249670645, 'IoU-road': 66.88811350894561, 'IoU-roof': 14.566292783390622, 'IoU-sand': 63.711635063524255, 'IoU-sea': 85.13704048649787, 'IoU-shelf': 37.593526970288124, 'IoU-snow': 88.25346325210519, 'IoU-stairs': 20.720359725007906, 'IoU-tent': 9.178320402195173, 'IoU-towel': 35.179266507159944, 'IoU-wall-brick': 44.52959755873272, 'IoU-wall-stone': 26.72968241385394, 'IoU-wall-tile': 65.92618224832859, 'IoU-wall-wood': 37.4529328906076, 'IoU-water-other': 21.5386187052906, 'IoU-window-blind': 48.67618696412359, 'IoU-window-other': 47.616121266797634, 'IoU-tree-merged': 80.7413053459895, 'IoU-fence-merged': 52.911409824365606, 'IoU-ceiling-merged': 67.04641568661646, 'IoU-sky-other-merged': 93.72378912961378, 'IoU-cabinet-merged': 59.89462680326155, 'IoU-table-merged': 35.63320730032203, 'IoU-floor-other-merged': 49.781241798295696, 'IoU-pavement-merged': 54.37855641275207, 'IoU-mountain-merged': 54.36089346416363, 'IoU-grass-merged': 70.88041780533123, 'IoU-dirt-merged': 46.403296595054364, 'IoU-paper-merged': 29.59113748638042, 'IoU-food-other-merged': 37.52187669371791, 'IoU-building-other-merged': 58.5134748334572, 'IoU-rock-merged': 59.33097165597358, 'IoU-wall-other-merged': 64.61865999099949, 'IoU-rug-merged': 62.56702986234498, 'mACC': 72.27120501484188, 'pACC': 80.24220978179474, 'ACC-person': 92.23077823301034, 'ACC-bicycle': 85.46123162385028, 'ACC-car': 85.45297511574658, 'ACC-motorcycle': 89.01248118013191, 'ACC-airplane': 90.86437118677895, 'ACC-bus': 91.24144074941879, 'ACC-train': 95.7368296339663, 'ACC-truck': 74.0494865518861, 'ACC-boat': 79.10874646667317, 'ACC-traffic light': 90.10270295448655, 'ACC-fire hydrant': 94.92681062569656, 'ACC-stop sign': 94.65133163511477, 'ACC-parking meter': 92.12484950435254, 'ACC-bench': 68.15583076566715, 'ACC-bird': 79.9350636844538, 'ACC-cat': 90.22320516738111, 'ACC-dog': 79.53176713035917, 'ACC-horse': 91.92247054134089, 'ACC-sheep': 90.07513946319463, 'ACC-cow': 83.6344583554001, 'ACC-elephant': 87.49990150545287, 'ACC-bear': 66.068081800515, 'ACC-zebra': 93.33742317511977, 'ACC-giraffe': 91.19948682646309, 'ACC-backpack': 56.44604341767382, 'ACC-umbrella': 82.83275017625837, 'ACC-handbag': 57.392105400067095, 'ACC-tie': 80.82813549169691, 'ACC-suitcase': 88.92434913422406, 'ACC-frisbee': 94.4290909090909, 'ACC-skis': 70.81571532247348, 'ACC-snowboard': 78.8944674389557, 'ACC-sports ball': 80.14130513432775, 'ACC-kite': 75.65158734353723, 'ACC-baseball bat': 84.78410717095356, 'ACC-baseball glove': 90.35400589409366, 'ACC-skateboard': 69.96691966910691, 'ACC-surfboard': 83.2373968513917, 'ACC-tennis racket': 89.31079310742915, 'ACC-bottle': 81.14096955127361, 'ACC-wine glass': 85.74422947906656, 'ACC-cup': 83.97860471535624, 'ACC-fork': 65.89870157576357, 'ACC-knife': 58.31244962262768, 'ACC-spoon': 66.28667842184655, 'ACC-bowl': 63.91862559377225, 'ACC-banana': 90.1395879058894, 'ACC-apple': 68.56354334705101, 'ACC-sandwich': 78.27077738707422, 'ACC-orange': 88.02853298420236, 'ACC-broccoli': 78.59218268627079, 'ACC-carrot': 73.72178793228667, 'ACC-hot dog': 67.4419010018346, 'ACC-pizza': 94.70377763653188, 'ACC-donut': 81.18543214986609, 'ACC-cake': 75.81700957085957, 'ACC-chair': 67.89666667979078, 'ACC-couch': 79.78915622668067, 'ACC-potted plant': 46.38644995585454, 'ACC-bed': 81.60263302972068, 'ACC-dining table': 82.08173020631425, 'ACC-toilet': 92.76240667733539, 'ACC-tv': 87.90810602720686, 'ACC-laptop': 84.70343150080232, 'ACC-mouse': 86.39618482805392, 'ACC-remote': 71.82961446338292, 'ACC-keyboard': 71.78883755589487, 'ACC-cell phone': 76.43419302507564, 'ACC-microwave': 77.47942321788919, 'ACC-oven': 83.55283266530182, 'ACC-toaster': 72.6997769231822, 'ACC-sink': 83.12790034741933, 'ACC-refrigerator': 91.1728254870879, 'ACC-book': 68.78099520887618, 'ACC-clock': 76.6663026428618, 'ACC-vase': 61.705651931623265, 'ACC-scissors': 56.18842336262384, 'ACC-teddy bear': 88.9804219429817, 'ACC-hair drier': 46.99000939287849, 'ACC-toothbrush': 81.24826268241834, 'ACC-banner': 74.15454998373832, 'ACC-blanket': 19.973960662509047, 'ACC-bridge': 54.994070172587186, 'ACC-cardboard': 45.50501672240802, 'ACC-counter': 51.20920205610317, 'ACC-curtain': 76.69495045601468, 'ACC-door-stuff': 65.16040198024258, 'ACC-floor-wood': 73.42992549136811, 'ACC-flower': 60.88329437873907, 'ACC-fruit': 59.138629894956416, 'ACC-gravel': 34.74437046390324, 'ACC-house': 29.759556771805904, 'ACC-light': 56.59129407035945, 'ACC-mirror-stuff': 59.640358975859854, 'ACC-net': 61.994488452576526, 'ACC-pillow': 22.854435008038692, 'ACC-platform': 44.669093912873024, 'ACC-playingfield': 83.38613754351083, 'ACC-railroad': 77.38071804829389, 'ACC-river': 69.32450440779037, 'ACC-road': 86.51690190466556, 'ACC-roof': 19.65961365293957, 'ACC-sand': 70.52572032171295, 'ACC-sea': 90.79096118296701, 'ACC-shelf': 58.88486993824419, 'ACC-snow': 95.40634620715385, 'ACC-stairs': 32.4088276221878, 'ACC-tent': 11.503287176248687, 'ACC-towel': 42.68236542950451, 'ACC-wall-brick': 55.51893167667178, 'ACC-wall-stone': 34.34591653149209, 'ACC-wall-tile': 78.02353321438471, 'ACC-wall-wood': 51.524791424766114, 'ACC-water-other': 36.85759463365243, 'ACC-window-blind': 58.98801292398213, 'ACC-window-other': 68.80864372567711, 'ACC-tree-merged': 89.91065831134351, 'ACC-fence-merged': 69.4460917188362, 'ACC-ceiling-merged': 80.38116749410091, 'ACC-sky-other-merged': 96.49549140690662, 'ACC-cabinet-merged': 75.23407170154658, 'ACC-table-merged': 47.27684512569545, 'ACC-floor-other-merged': 64.05787324329411, 'ACC-pavement-merged': 63.902055950142845, 'ACC-mountain-merged': 64.93224554590446, 'ACC-grass-merged': 83.09537147252907, 'ACC-dirt-merged': 73.72207740479205, 'ACC-paper-merged': 42.542174686473885, 'ACC-food-other-merged': 48.390901258376324, 'ACC-building-other-merged': 74.6968720660698, 'ACC-rock-merged': 82.87680225160827, 'ACC-wall-other-merged': 83.1812965496012, 'ACC-rug-merged': 77.52032988624855})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5156570090722856, 'noc@0.8': 2.919227392449517, 'noc@0.85': 3.54228855721393, 'noc@0.9': 4.571553994732222, 'miou@iter1': 0.837410140602708}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.7734146118164, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.37854766845703, 'precision@0.8': 51.72949981689453, 'precision@0.9': 26.700349807739258, 'cIoU': 56.503360748291016, 'mIoU': 62.4311408996582}}}
INFO:trainer.default_trainer:This epoch takes 1:28:44.040553
INFO:trainer.default_trainer:PROGRESS: 28.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 14 training.
INFO:trainer.default_trainer:epochs[    14] optim steps[25600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43333/0.91122, loss_mask_bce_0: 0.44204/0.33520, loss_mask_dice_0: 0.51346/1.16737, loss_spatial_bce_0: 0.07751/0.09093, loss_spatial_dice_0: 0.12325/0.21742, loss_spatial_ce_0: 0.00092/0.07592, loss_grounding_bce_0: 0.15417/0.08620, loss_grounding_dice_0: 0.08425/0.17945, loss_grounding_ce_0: 0.03524/0.27668, loss_mask_ce_1: 0.48819/0.91200, loss_mask_bce_1: 0.43431/0.33587, loss_mask_dice_1: 0.48378/1.17456, loss_spatial_bce_1: 0.07714/0.09175, loss_spatial_dice_1: 0.12239/0.22175, loss_spatial_ce_1: 0.00130/0.08189, loss_grounding_bce_1: 0.15425/0.08629, loss_grounding_dice_1: 0.08501/0.18023, loss_grounding_ce_1: 0.02238/0.27839, loss_mask_ce_2: 0.49038/0.91958, loss_mask_bce_2: 0.44114/0.33635, loss_mask_dice_2: 0.46986/1.17376, loss_spatial_bce_2: 0.08481/0.09197, loss_spatial_dice_2: 0.12135/0.22276, loss_spatial_ce_2: 0.00186/0.08552, loss_grounding_bce_2: 0.15380/0.08636, loss_grounding_dice_2: 0.08691/0.17974, loss_grounding_ce_2: 0.03917/0.28176, loss_mask_ce_3: 0.46824/0.92780, loss_mask_bce_3: 0.43039/0.33707, loss_mask_dice_3: 0.49603/1.17100, loss_spatial_bce_3: 0.07340/0.09282, loss_spatial_dice_3: 0.11192/0.22338, loss_spatial_ce_3: 0.00450/0.08952, loss_grounding_bce_3: 0.15630/0.08649, loss_grounding_dice_3: 0.08339/0.17949, loss_grounding_ce_3: 0.06566/0.28325, loss_mask_ce_4: 0.48386/0.92668, loss_mask_bce_4: 0.42237/0.33883, loss_mask_dice_4: 0.49869/1.19371, loss_spatial_bce_4: 0.08911/0.09690, loss_spatial_dice_4: 0.13946/0.23361, loss_spatial_ce_4: 0.00654/0.10591, loss_grounding_bce_4: 0.15616/0.08706, loss_grounding_dice_4: 0.08778/0.18234, loss_grounding_ce_4: 0.06149/0.28579, loss_mask_ce_5: 0.43022/0.94125, loss_mask_bce_5: 0.45277/0.34105, loss_mask_dice_5: 0.51612/1.19872, loss_spatial_bce_5: 0.09515/0.09815, loss_spatial_dice_5: 0.13851/0.23675, loss_spatial_ce_5: 0.01233/0.11994, loss_grounding_bce_5: 0.15951/0.08744, loss_grounding_dice_5: 0.08594/0.18353, loss_grounding_ce_5: 0.08159/0.29867, loss_mask_ce_6: 0.41418/0.97870, loss_mask_bce_6: 0.48354/0.34369, loss_mask_dice_6: 0.54185/1.20146, loss_spatial_bce_6: 0.10175/0.10357, loss_spatial_dice_6: 0.14567/0.23898, loss_spatial_ce_6: 0.02817/0.14494, loss_grounding_bce_6: 0.15722/0.08820, loss_grounding_dice_6: 0.08310/0.18372, loss_grounding_ce_6: 0.03921/0.31585, loss_mask_ce_7: 0.53079/1.02199, loss_mask_bce_7: 0.49173/0.35151, loss_mask_dice_7: 0.52872/1.25704, loss_spatial_bce_7: 0.11518/0.11222, loss_spatial_dice_7: 0.18194/0.26619, loss_spatial_ce_7: 0.10245/0.18303, loss_grounding_bce_7: 0.16196/0.09014, loss_grounding_dice_7: 0.09101/0.19101, loss_grounding_ce_7: 0.11828/0.34815, loss_mask_ce_8: 0.57652/1.13292, loss_mask_bce_8: 0.45355/0.36503, loss_mask_dice_8: 0.50423/1.33195, loss_spatial_bce_8: 0.15150/0.13329, loss_spatial_dice_8: 0.20450/0.30657, loss_spatial_ce_8: 0.20767/0.24002, loss_grounding_bce_8: 0.15964/0.09370, loss_grounding_dice_8: 0.08420/0.20231, loss_grounding_ce_8: 0.35731/0.41926, loss_mask_ce_9: 3.09809/3.69043, loss_mask_bce_9: 0.47100/0.39205, loss_mask_dice_9: 0.86386/1.90658, loss_spatial_bce_9: 0.78094/0.33552, loss_spatial_dice_9: 0.85892/0.82426, loss_spatial_ce_9: 1.44432/1.51373, loss_grounding_bce_9: 0.17785/0.10518, loss_grounding_dice_9: 0.15131/0.28202, loss_grounding_ce_9: 1.26797/0.69166] items per batch[64] items per second[0.13] total items[1638400] mini batches[ 25600] memory[7341] epoch remaining[1:29:33]
INFO:trainer.default_trainer:epochs[    14] optim steps[25700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.71619/0.91109, loss_mask_bce_0: 0.36262/0.33527, loss_mask_dice_0: 1.43608/1.16684, loss_spatial_bce_0: 0.06909/0.09093, loss_spatial_dice_0: 0.30512/0.21734, loss_spatial_ce_0: 0.20515/0.07588, loss_grounding_bce_0: 0.10028/0.08625, loss_grounding_dice_0: 0.18575/0.17946, loss_grounding_ce_0: 1.75903/0.27668, loss_mask_ce_1: 2.13370/0.91188, loss_mask_bce_1: 0.41455/0.33594, loss_mask_dice_1: 1.57022/1.17404, loss_spatial_bce_1: 0.06406/0.09175, loss_spatial_dice_1: 0.30666/0.22167, loss_spatial_ce_1: 0.15336/0.08186, loss_grounding_bce_1: 0.14184/0.08635, loss_grounding_dice_1: 0.13970/0.18025, loss_grounding_ce_1: 1.24331/0.27838, loss_mask_ce_2: 2.35725/0.91952, loss_mask_bce_2: 0.45849/0.33641, loss_mask_dice_2: 1.46373/1.17319, loss_spatial_bce_2: 0.06395/0.09197, loss_spatial_dice_2: 0.29076/0.22269, loss_spatial_ce_2: 0.17643/0.08546, loss_grounding_bce_2: 0.15351/0.08642, loss_grounding_dice_2: 0.13239/0.17973, loss_grounding_ce_2: 0.88889/0.28172, loss_mask_ce_3: 2.57822/0.92768, loss_mask_bce_3: 0.49257/0.33714, loss_mask_dice_3: 1.51875/1.17046, loss_spatial_bce_3: 0.06060/0.09282, loss_spatial_dice_3: 0.30153/0.22330, loss_spatial_ce_3: 0.28625/0.08943, loss_grounding_bce_3: 0.16046/0.08655, loss_grounding_dice_3: 0.13526/0.17951, loss_grounding_ce_3: 0.70539/0.28320, loss_mask_ce_4: 2.62394/0.92662, loss_mask_bce_4: 0.51007/0.33890, loss_mask_dice_4: 1.43349/1.19306, loss_spatial_bce_4: 0.06266/0.09691, loss_spatial_dice_4: 0.32834/0.23355, loss_spatial_ce_4: 0.17714/0.10583, loss_grounding_bce_4: 0.16275/0.08710, loss_grounding_dice_4: 0.13469/0.18235, loss_grounding_ce_4: 0.80819/0.28570, loss_mask_ce_5: 2.48044/0.94119, loss_mask_bce_5: 0.42721/0.34110, loss_mask_dice_5: 1.44983/1.19817, loss_spatial_bce_5: 0.06702/0.09816, loss_spatial_dice_5: 0.31978/0.23669, loss_spatial_ce_5: 0.17114/0.11983, loss_grounding_bce_5: 0.13653/0.08748, loss_grounding_dice_5: 0.15106/0.18353, loss_grounding_ce_5: 1.11099/0.29865, loss_mask_ce_6: 2.53175/0.97857, loss_mask_bce_6: 0.42898/0.34375, loss_mask_dice_6: 1.54838/1.20088, loss_spatial_bce_6: 0.07686/0.10357, loss_spatial_dice_6: 0.31711/0.23892, loss_spatial_ce_6: 0.29575/0.14492, loss_grounding_bce_6: 0.15917/0.08824, loss_grounding_dice_6: 0.14531/0.18372, loss_grounding_ce_6: 0.60789/0.31590, loss_mask_ce_7: 2.51442/1.02182, loss_mask_bce_7: 0.43310/0.35157, loss_mask_dice_7: 1.58902/1.25645, loss_spatial_bce_7: 0.09263/0.11224, loss_spatial_dice_7: 0.38310/0.26610, loss_spatial_ce_7: 0.08838/0.18292, loss_grounding_bce_7: 0.19717/0.09018, loss_grounding_dice_7: 0.14022/0.19100, loss_grounding_ce_7: 0.98884/0.34813, loss_mask_ce_8: 2.51466/1.13270, loss_mask_bce_8: 0.46343/0.36510, loss_mask_dice_8: 1.78675/1.33133, loss_spatial_bce_8: 0.12461/0.13332, loss_spatial_dice_8: 0.41580/0.30647, loss_spatial_ce_8: 0.25816/0.23991, loss_grounding_bce_8: 0.14310/0.09374, loss_grounding_dice_8: 0.12428/0.20230, loss_grounding_ce_8: 2.13807/0.41900, loss_mask_ce_9: 5.10084/3.68927, loss_mask_bce_9: 0.43212/0.39211, loss_mask_dice_9: 2.45799/1.90586, loss_spatial_bce_9: 0.21485/0.33557, loss_spatial_dice_9: 0.91143/0.82424, loss_spatial_ce_9: 1.26436/1.51335, loss_grounding_bce_9: 0.19311/0.10522, loss_grounding_dice_9: 0.22734/0.28198, loss_grounding_ce_9: 2.49944/0.69122] items per batch[64] items per second[0.23] total items[1644800] mini batches[ 25700] memory[7341] epoch remaining[1:20:20]
INFO:trainer.default_trainer:epochs[    14] optim steps[25800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.57675/0.91105, loss_mask_bce_0: 0.19483/0.33521, loss_mask_dice_0: 1.05834/1.16686, loss_spatial_bce_0: 0.03227/0.09090, loss_spatial_dice_0: 0.18306/0.21729, loss_spatial_ce_0: 0.02212/0.07581, loss_grounding_bce_0: 0.04006/0.08621, loss_grounding_dice_0: 0.07323/0.17941, loss_grounding_ce_0: 0.41751/0.27669, loss_mask_ce_1: 1.24024/0.91186, loss_mask_bce_1: 0.26521/0.33591, loss_mask_dice_1: 1.20464/1.17405, loss_spatial_bce_1: 0.03425/0.09172, loss_spatial_dice_1: 0.21586/0.22162, loss_spatial_ce_1: 0.04878/0.08176, loss_grounding_bce_1: 0.03874/0.08631, loss_grounding_dice_1: 0.07166/0.18023, loss_grounding_ce_1: 0.32809/0.27843, loss_mask_ce_2: 1.58111/0.91949, loss_mask_bce_2: 0.20749/0.33638, loss_mask_dice_2: 1.17818/1.17322, loss_spatial_bce_2: 0.03522/0.09194, loss_spatial_dice_2: 0.23882/0.22263, loss_spatial_ce_2: 0.03760/0.08538, loss_grounding_bce_2: 0.04232/0.08638, loss_grounding_dice_2: 0.07626/0.17970, loss_grounding_ce_2: 0.31445/0.28178, loss_mask_ce_3: 1.16483/0.92767, loss_mask_bce_3: 0.29822/0.33710, loss_mask_dice_3: 1.22589/1.17048, loss_spatial_bce_3: 0.03521/0.09280, loss_spatial_dice_3: 0.20154/0.22324, loss_spatial_ce_3: 0.03535/0.08933, loss_grounding_bce_3: 0.04184/0.08651, loss_grounding_dice_3: 0.08105/0.17948, loss_grounding_ce_3: 0.25154/0.28323, loss_mask_ce_4: 1.52727/0.92660, loss_mask_bce_4: 0.19431/0.33887, loss_mask_dice_4: 1.22831/1.19310, loss_spatial_bce_4: 0.03663/0.09688, loss_spatial_dice_4: 0.24670/0.23352, loss_spatial_ce_4: 0.03141/0.10576, loss_grounding_bce_4: 0.04607/0.08706, loss_grounding_dice_4: 0.08319/0.18230, loss_grounding_ce_4: 0.41697/0.28564, loss_mask_ce_5: 1.18463/0.94114, loss_mask_bce_5: 0.26150/0.34106, loss_mask_dice_5: 1.20194/1.19827, loss_spatial_bce_5: 0.04089/0.09814, loss_spatial_dice_5: 0.23824/0.23665, loss_spatial_ce_5: 0.02592/0.11981, loss_grounding_bce_5: 0.04665/0.08744, loss_grounding_dice_5: 0.09502/0.18351, loss_grounding_ce_5: 0.31531/0.29864, loss_mask_ce_6: 1.33821/0.97849, loss_mask_bce_6: 0.19451/0.34373, loss_mask_dice_6: 1.18310/1.20093, loss_spatial_bce_6: 0.03534/0.10355, loss_spatial_dice_6: 0.21197/0.23887, loss_spatial_ce_6: 0.06731/0.14493, loss_grounding_bce_6: 0.04885/0.08820, loss_grounding_dice_6: 0.09611/0.18372, loss_grounding_ce_6: 0.45503/0.31584, loss_mask_ce_7: 1.58274/1.02170, loss_mask_bce_7: 0.20602/0.35156, loss_mask_dice_7: 1.13741/1.25651, loss_spatial_bce_7: 0.04694/0.11221, loss_spatial_dice_7: 0.27204/0.26607, loss_spatial_ce_7: 0.08701/0.18286, loss_grounding_bce_7: 0.04959/0.09014, loss_grounding_dice_7: 0.08731/0.19098, loss_grounding_ce_7: 0.67202/0.34807, loss_mask_ce_8: 1.46050/1.13266, loss_mask_bce_8: 0.32846/0.36508, loss_mask_dice_8: 1.32520/1.33133, loss_spatial_bce_8: 0.05435/0.13331, loss_spatial_dice_8: 0.29304/0.30646, loss_spatial_ce_8: 0.17978/0.23981, loss_grounding_bce_8: 0.07287/0.09371, loss_grounding_dice_8: 0.12739/0.20229, loss_grounding_ce_8: 0.68222/0.41893, loss_mask_ce_9: 4.17760/3.68965, loss_mask_bce_9: 0.35888/0.39204, loss_mask_dice_9: 1.88187/1.90581, loss_spatial_bce_9: 0.24145/0.33554, loss_spatial_dice_9: 0.87264/0.82423, loss_spatial_ce_9: 1.26503/1.51333, loss_grounding_bce_9: 0.14023/0.10522, loss_grounding_dice_9: 0.22651/0.28196, loss_grounding_ce_9: 0.99446/0.69120] items per batch[64] items per second[0.23] total items[1651200] mini batches[ 25800] memory[7341] epoch remaining[1:15:29]
INFO:trainer.default_trainer:epochs[    14] optim steps[25900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07970/0.91118, loss_mask_bce_0: 0.49299/0.33513, loss_mask_dice_0: 0.60899/1.16704, loss_spatial_bce_0: 0.09590/0.09089, loss_spatial_dice_0: 0.11564/0.21729, loss_spatial_ce_0: 0.00368/0.07575, loss_grounding_bce_0: 0.06646/0.08619, loss_grounding_dice_0: 0.08982/0.17947, loss_grounding_ce_0: 0.48035/0.27672, loss_mask_ce_1: 0.99118/0.91203, loss_mask_bce_1: 0.49328/0.33583, loss_mask_dice_1: 0.58513/1.17424, loss_spatial_bce_1: 0.08379/0.09171, loss_spatial_dice_1: 0.10850/0.22163, loss_spatial_ce_1: 0.00228/0.08170, loss_grounding_bce_1: 0.06682/0.08629, loss_grounding_dice_1: 0.09197/0.18026, loss_grounding_ce_1: 0.43036/0.27845, loss_mask_ce_2: 1.03646/0.91968, loss_mask_bce_2: 0.50571/0.33630, loss_mask_dice_2: 0.59676/1.17341, loss_spatial_bce_2: 0.09104/0.09192, loss_spatial_dice_2: 0.10541/0.22264, loss_spatial_ce_2: 0.00392/0.08530, loss_grounding_bce_2: 0.06667/0.08635, loss_grounding_dice_2: 0.09246/0.17972, loss_grounding_ce_2: 0.44317/0.28179, loss_mask_ce_3: 1.01631/0.92783, loss_mask_bce_3: 0.48987/0.33701, loss_mask_dice_3: 0.57955/1.17071, loss_spatial_bce_3: 0.08524/0.09277, loss_spatial_dice_3: 0.10277/0.22325, loss_spatial_ce_3: 0.00309/0.08927, loss_grounding_bce_3: 0.06650/0.08649, loss_grounding_dice_3: 0.08998/0.17953, loss_grounding_ce_3: 0.43568/0.28323, loss_mask_ce_4: 0.87861/0.92679, loss_mask_bce_4: 0.49052/0.33877, loss_mask_dice_4: 0.59946/1.19323, loss_spatial_bce_4: 0.09425/0.09686, loss_spatial_dice_4: 0.11735/0.23352, loss_spatial_ce_4: 0.00884/0.10574, loss_grounding_bce_4: 0.06264/0.08704, loss_grounding_dice_4: 0.08591/0.18235, loss_grounding_ce_4: 0.47063/0.28564, loss_mask_ce_5: 0.87345/0.94131, loss_mask_bce_5: 0.48378/0.34097, loss_mask_dice_5: 0.60944/1.19846, loss_spatial_bce_5: 0.10274/0.09812, loss_spatial_dice_5: 0.13850/0.23667, loss_spatial_ce_5: 0.06596/0.11974, loss_grounding_bce_5: 0.06420/0.08741, loss_grounding_dice_5: 0.09350/0.18356, loss_grounding_ce_5: 0.46879/0.29867, loss_mask_ce_6: 0.88841/0.97864, loss_mask_bce_6: 0.50253/0.34364, loss_mask_dice_6: 0.63132/1.20105, loss_spatial_bce_6: 0.11663/0.10355, loss_spatial_dice_6: 0.15056/0.23889, loss_spatial_ce_6: 0.04007/0.14488, loss_grounding_bce_6: 0.06537/0.08818, loss_grounding_dice_6: 0.10435/0.18378, loss_grounding_ce_6: 0.43868/0.31590, loss_mask_ce_7: 0.86578/1.02190, loss_mask_bce_7: 0.51449/0.35148, loss_mask_dice_7: 0.61542/1.25668, loss_spatial_bce_7: 0.09474/0.11221, loss_spatial_dice_7: 0.14316/0.26607, loss_spatial_ce_7: 0.18719/0.18277, loss_grounding_bce_7: 0.06317/0.09011, loss_grounding_dice_7: 0.09476/0.19102, loss_grounding_ce_7: 0.44700/0.34801, loss_mask_ce_8: 0.92221/1.13283, loss_mask_bce_8: 0.58078/0.36502, loss_mask_dice_8: 0.74574/1.33153, loss_spatial_bce_8: 0.14864/0.13332, loss_spatial_dice_8: 0.20600/0.30646, loss_spatial_ce_8: 0.21069/0.23971, loss_grounding_bce_8: 0.08154/0.09369, loss_grounding_dice_8: 0.14915/0.20235, loss_grounding_ce_8: 0.38722/0.41864, loss_mask_ce_9: 5.16413/3.68958, loss_mask_bce_9: 0.63819/0.39199, loss_mask_dice_9: 1.14609/1.90593, loss_spatial_bce_9: 0.53779/0.33556, loss_spatial_dice_9: 0.80289/0.82427, loss_spatial_ce_9: 1.39551/1.51337, loss_grounding_bce_9: 0.08633/0.10519, loss_grounding_dice_9: 0.26895/0.28202, loss_grounding_ce_9: 0.59354/0.69099] items per batch[64] items per second[0.23] total items[1657600] mini batches[ 25900] memory[7341] epoch remaining[1:10:47]
INFO:trainer.default_trainer:epochs[    14] optim steps[26000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68337/0.91091, loss_mask_bce_0: 0.40347/0.33515, loss_mask_dice_0: 1.33666/1.16704, loss_spatial_bce_0: 0.06759/0.09089, loss_spatial_dice_0: 0.19404/0.21721, loss_spatial_ce_0: 0.00377/0.07563, loss_grounding_bce_0: 0.03826/0.08620, loss_grounding_dice_0: 0.09238/0.17943, loss_grounding_ce_0: 0.24105/0.27656, loss_mask_ce_1: 0.63114/0.91173, loss_mask_bce_1: 0.38962/0.33587, loss_mask_dice_1: 1.31340/1.17427, loss_spatial_bce_1: 0.06274/0.09170, loss_spatial_dice_1: 0.19137/0.22154, loss_spatial_ce_1: 0.00417/0.08156, loss_grounding_bce_1: 0.04124/0.08631, loss_grounding_dice_1: 0.08664/0.18020, loss_grounding_ce_1: 0.32318/0.27831, loss_mask_ce_2: 0.65375/0.91946, loss_mask_bce_2: 0.39198/0.33633, loss_mask_dice_2: 1.39035/1.17344, loss_spatial_bce_2: 0.06822/0.09191, loss_spatial_dice_2: 0.18830/0.22255, loss_spatial_ce_2: 0.00836/0.08517, loss_grounding_bce_2: 0.03929/0.08636, loss_grounding_dice_2: 0.09024/0.17968, loss_grounding_ce_2: 0.26568/0.28168, loss_mask_ce_3: 0.71310/0.92760, loss_mask_bce_3: 0.39378/0.33704, loss_mask_dice_3: 1.31279/1.17068, loss_spatial_bce_3: 0.07409/0.09276, loss_spatial_dice_3: 0.19597/0.22316, loss_spatial_ce_3: 0.01512/0.08912, loss_grounding_bce_3: 0.03962/0.08651, loss_grounding_dice_3: 0.09356/0.17949, loss_grounding_ce_3: 0.21554/0.28313, loss_mask_ce_4: 0.70422/0.92656, loss_mask_bce_4: 0.39772/0.33880, loss_mask_dice_4: 1.56456/1.19322, loss_spatial_bce_4: 0.07509/0.09686, loss_spatial_dice_4: 0.20113/0.23344, loss_spatial_ce_4: 0.01918/0.10562, loss_grounding_bce_4: 0.03691/0.08705, loss_grounding_dice_4: 0.08418/0.18229, loss_grounding_ce_4: 0.21170/0.28557, loss_mask_ce_5: 0.68839/0.94102, loss_mask_bce_5: 0.39696/0.34101, loss_mask_dice_5: 1.46234/1.19848, loss_spatial_bce_5: 0.07787/0.09811, loss_spatial_dice_5: 0.21612/0.23659, loss_spatial_ce_5: 0.03857/0.11962, loss_grounding_bce_5: 0.03565/0.08742, loss_grounding_dice_5: 0.08625/0.18352, loss_grounding_ce_5: 0.23047/0.29857, loss_mask_ce_6: 0.71722/0.97835, loss_mask_bce_6: 0.39928/0.34367, loss_mask_dice_6: 1.44688/1.20105, loss_spatial_bce_6: 0.09063/0.10354, loss_spatial_dice_6: 0.22477/0.23880, loss_spatial_ce_6: 0.07589/0.14479, loss_grounding_bce_6: 0.03931/0.08819, loss_grounding_dice_6: 0.08966/0.18374, loss_grounding_ce_6: 0.20089/0.31595, loss_mask_ce_7: 0.87826/1.02167, loss_mask_bce_7: 0.39684/0.35151, loss_mask_dice_7: 1.52536/1.25667, loss_spatial_bce_7: 0.07479/0.11221, loss_spatial_dice_7: 0.22280/0.26599, loss_spatial_ce_7: 0.05382/0.18268, loss_grounding_bce_7: 0.04262/0.09012, loss_grounding_dice_7: 0.09994/0.19099, loss_grounding_ce_7: 0.34520/0.34786, loss_mask_ce_8: 1.11209/1.13253, loss_mask_bce_8: 0.41493/0.36507, loss_mask_dice_8: 1.52840/1.33156, loss_spatial_bce_8: 0.09552/0.13334, loss_spatial_dice_8: 0.26045/0.30636, loss_spatial_ce_8: 0.09237/0.23958, loss_grounding_bce_8: 0.04531/0.09370, loss_grounding_dice_8: 0.09426/0.20231, loss_grounding_ce_8: 1.26645/0.41850, loss_mask_ce_9: 3.70391/3.68900, loss_mask_bce_9: 0.48126/0.39203, loss_mask_dice_9: 2.44082/1.90604, loss_spatial_bce_9: 0.25541/0.33560, loss_spatial_dice_9: 0.95221/0.82426, loss_spatial_ce_9: 1.49682/1.51311, loss_grounding_bce_9: 0.05813/0.10520, loss_grounding_dice_9: 0.17968/0.28199, loss_grounding_ce_9: 1.48965/0.69086] items per batch[64] items per second[0.23] total items[1664000] mini batches[ 26000] memory[7341] epoch remaining[1:05:55]
INFO:trainer.default_trainer:epochs[    14] optim steps[26100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81057/0.91093, loss_mask_bce_0: 0.53384/0.33515, loss_mask_dice_0: 0.69174/1.16698, loss_spatial_bce_0: 0.20807/0.09088, loss_spatial_dice_0: 0.23907/0.21721, loss_spatial_ce_0: 0.08410/0.07556, loss_grounding_bce_0: 0.13727/0.08620, loss_grounding_dice_0: 0.23283/0.17946, loss_grounding_ce_0: 0.24698/0.27655, loss_mask_ce_1: 0.69693/0.91182, loss_mask_bce_1: 0.50921/0.33585, loss_mask_dice_1: 0.67423/1.17419, loss_spatial_bce_1: 0.18725/0.09169, loss_spatial_dice_1: 0.23288/0.22152, loss_spatial_ce_1: 0.08208/0.08147, loss_grounding_bce_1: 0.13030/0.08630, loss_grounding_dice_1: 0.25328/0.18023, loss_grounding_ce_1: 0.24832/0.27831, loss_mask_ce_2: 0.78280/0.91952, loss_mask_bce_2: 0.44899/0.33631, loss_mask_dice_2: 0.71030/1.17342, loss_spatial_bce_2: 0.17623/0.09190, loss_spatial_dice_2: 0.21991/0.22254, loss_spatial_ce_2: 0.07140/0.08509, loss_grounding_bce_2: 0.13658/0.08636, loss_grounding_dice_2: 0.25279/0.17971, loss_grounding_ce_2: 0.28112/0.28164, loss_mask_ce_3: 0.79952/0.92768, loss_mask_bce_3: 0.48768/0.33704, loss_mask_dice_3: 0.70178/1.17064, loss_spatial_bce_3: 0.22440/0.09276, loss_spatial_dice_3: 0.23756/0.22313, loss_spatial_ce_3: 0.10452/0.08905, loss_grounding_bce_3: 0.14623/0.08651, loss_grounding_dice_3: 0.25011/0.17952, loss_grounding_ce_3: 0.27396/0.28314, loss_mask_ce_4: 0.73356/0.92661, loss_mask_bce_4: 0.50584/0.33879, loss_mask_dice_4: 0.70676/1.19325, loss_spatial_bce_4: 0.21432/0.09685, loss_spatial_dice_4: 0.23835/0.23344, loss_spatial_ce_4: 0.09185/0.10553, loss_grounding_bce_4: 0.14104/0.08705, loss_grounding_dice_4: 0.25994/0.18232, loss_grounding_ce_4: 0.21684/0.28556, loss_mask_ce_5: 0.73030/0.94105, loss_mask_bce_5: 0.54518/0.34101, loss_mask_dice_5: 0.70866/1.19850, loss_spatial_bce_5: 0.15760/0.09810, loss_spatial_dice_5: 0.23578/0.23660, loss_spatial_ce_5: 0.14238/0.11954, loss_grounding_bce_5: 0.15568/0.08742, loss_grounding_dice_5: 0.25686/0.18356, loss_grounding_ce_5: 0.23985/0.29853, loss_mask_ce_6: 0.78445/0.97836, loss_mask_bce_6: 0.52599/0.34369, loss_mask_dice_6: 0.67881/1.20105, loss_spatial_bce_6: 0.19395/0.10354, loss_spatial_dice_6: 0.23331/0.23882, loss_spatial_ce_6: 0.15236/0.14469, loss_grounding_bce_6: 0.14786/0.08819, loss_grounding_dice_6: 0.24936/0.18376, loss_grounding_ce_6: 0.25833/0.31593, loss_mask_ce_7: 0.76384/1.02182, loss_mask_bce_7: 0.46477/0.35149, loss_mask_dice_7: 0.69657/1.25671, loss_spatial_bce_7: 0.24983/0.11221, loss_spatial_dice_7: 0.26246/0.26602, loss_spatial_ce_7: 0.27811/0.18261, loss_grounding_bce_7: 0.13403/0.09011, loss_grounding_dice_7: 0.27085/0.19103, loss_grounding_ce_7: 0.21608/0.34793, loss_mask_ce_8: 0.77871/1.13270, loss_mask_bce_8: 0.47938/0.36506, loss_mask_dice_8: 0.75201/1.33151, loss_spatial_bce_8: 0.27170/0.13333, loss_spatial_dice_8: 0.30113/0.30637, loss_spatial_ce_8: 0.16207/0.23948, loss_grounding_bce_8: 0.13211/0.09370, loss_grounding_dice_8: 0.27834/0.20232, loss_grounding_ce_8: 0.27607/0.41850, loss_mask_ce_9: 2.68119/3.68917, loss_mask_bce_9: 0.54502/0.39204, loss_mask_dice_9: 0.93673/1.90609, loss_spatial_bce_9: 0.33772/0.33556, loss_spatial_dice_9: 0.76018/0.82422, loss_spatial_ce_9: 1.18002/1.51295, loss_grounding_bce_9: 0.17414/0.10519, loss_grounding_dice_9: 0.37244/0.28199, loss_grounding_ce_9: 0.28283/0.69076] items per batch[64] items per second[0.23] total items[1670400] mini batches[ 26100] memory[7341] epoch remaining[1:00:55]
INFO:trainer.default_trainer:epochs[    14] optim steps[26200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68904/0.91104, loss_mask_bce_0: 0.05302/0.33513, loss_mask_dice_0: 0.72991/1.16779, loss_spatial_bce_0: 0.00613/0.09087, loss_spatial_dice_0: 0.20199/0.21719, loss_spatial_ce_0: 0.02888/0.07547, loss_grounding_bce_0: 0.00527/0.08622, loss_grounding_dice_0: 0.07044/0.17950, loss_grounding_ce_0: 0.04130/0.27668, loss_mask_ce_1: 0.58184/0.91193, loss_mask_bce_1: 0.03139/0.33585, loss_mask_dice_1: 0.59768/1.17500, loss_spatial_bce_1: 0.00626/0.09167, loss_spatial_dice_1: 0.21029/0.22149, loss_spatial_ce_1: 0.03785/0.08141, loss_grounding_bce_1: 0.00408/0.08632, loss_grounding_dice_1: 0.06839/0.18027, loss_grounding_ce_1: 0.04282/0.27850, loss_mask_ce_2: 0.75828/0.91968, loss_mask_bce_2: 0.06007/0.33630, loss_mask_dice_2: 0.70507/1.17418, loss_spatial_bce_2: 0.00702/0.09188, loss_spatial_dice_2: 0.21159/0.22250, loss_spatial_ce_2: 0.03685/0.08502, loss_grounding_bce_2: 0.00447/0.08637, loss_grounding_dice_2: 0.06841/0.17975, loss_grounding_ce_2: 0.05282/0.28186, loss_mask_ce_3: 0.78347/0.92784, loss_mask_bce_3: 0.04278/0.33702, loss_mask_dice_3: 0.55835/1.17140, loss_spatial_bce_3: 0.00807/0.09273, loss_spatial_dice_3: 0.23446/0.22310, loss_spatial_ce_3: 0.04711/0.08898, loss_grounding_bce_3: 0.00494/0.08653, loss_grounding_dice_3: 0.07458/0.17958, loss_grounding_ce_3: 0.07076/0.28324, loss_mask_ce_4: 0.50777/0.92667, loss_mask_bce_4: 0.03958/0.33877, loss_mask_dice_4: 0.71826/1.19406, loss_spatial_bce_4: 0.00683/0.09683, loss_spatial_dice_4: 0.22246/0.23342, loss_spatial_ce_4: 0.07167/0.10553, loss_grounding_bce_4: 0.00474/0.08707, loss_grounding_dice_4: 0.07008/0.18237, loss_grounding_ce_4: 0.03867/0.28561, loss_mask_ce_5: 0.93813/0.94114, loss_mask_bce_5: 0.04823/0.34101, loss_mask_dice_5: 0.58444/1.19932, loss_spatial_bce_5: 0.00774/0.09807, loss_spatial_dice_5: 0.26764/0.23659, loss_spatial_ce_5: 0.11578/0.11949, loss_grounding_bce_5: 0.00543/0.08744, loss_grounding_dice_5: 0.07441/0.18360, loss_grounding_ce_5: 0.04011/0.29857, loss_mask_ce_6: 0.63502/0.97844, loss_mask_bce_6: 0.03736/0.34368, loss_mask_dice_6: 0.64413/1.20180, loss_spatial_bce_6: 0.00772/0.10351, loss_spatial_dice_6: 0.25528/0.23881, loss_spatial_ce_6: 0.06785/0.14466, loss_grounding_bce_6: 0.00594/0.08821, loss_grounding_dice_6: 0.07394/0.18381, loss_grounding_ce_6: 0.03846/0.31599, loss_mask_ce_7: 0.79365/1.02193, loss_mask_bce_7: 0.03077/0.35148, loss_mask_dice_7: 0.61255/1.25753, loss_spatial_bce_7: 0.01069/0.11219, loss_spatial_dice_7: 0.26040/0.26603, loss_spatial_ce_7: 0.09234/0.18261, loss_grounding_bce_7: 0.00532/0.09013, loss_grounding_dice_7: 0.07444/0.19108, loss_grounding_ce_7: 0.03037/0.34794, loss_mask_ce_8: 0.98019/1.13293, loss_mask_bce_8: 0.03762/0.36506, loss_mask_dice_8: 0.67217/1.33235, loss_spatial_bce_8: 0.00949/0.13329, loss_spatial_dice_8: 0.33486/0.30636, loss_spatial_ce_8: 0.15915/0.23943, loss_grounding_bce_8: 0.00704/0.09373, loss_grounding_dice_8: 0.07029/0.20236, loss_grounding_ce_8: 0.08196/0.41861, loss_mask_ce_9: 2.52396/3.68947, loss_mask_bce_9: 0.02425/0.39206, loss_mask_dice_9: 0.88109/1.90735, loss_spatial_bce_9: 0.03247/0.33556, loss_spatial_dice_9: 0.81210/0.82422, loss_spatial_ce_9: 1.41673/1.51282, loss_grounding_bce_9: 0.00577/0.10521, loss_grounding_dice_9: 0.12079/0.28207, loss_grounding_ce_9: 0.77252/0.69070] items per batch[64] items per second[0.23] total items[1676800] mini batches[ 26200] memory[7341] epoch remaining[0:56:18]
INFO:trainer.default_trainer:epochs[    14] optim steps[26300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79801/0.91083, loss_mask_bce_0: 0.20167/0.33499, loss_mask_dice_0: 0.71062/1.16696, loss_spatial_bce_0: 0.05726/0.09084, loss_spatial_dice_0: 0.16714/0.21712, loss_spatial_ce_0: 0.13914/0.07537, loss_grounding_bce_0: 0.07358/0.08619, loss_grounding_dice_0: 0.22327/0.17951, loss_grounding_ce_0: 0.36210/0.27655, loss_mask_ce_1: 0.66008/0.91173, loss_mask_bce_1: 0.24300/0.33570, loss_mask_dice_1: 0.70747/1.17413, loss_spatial_bce_1: 0.05643/0.09164, loss_spatial_dice_1: 0.16983/0.22142, loss_spatial_ce_1: 0.10559/0.08130, loss_grounding_bce_1: 0.07387/0.08629, loss_grounding_dice_1: 0.20838/0.18026, loss_grounding_ce_1: 0.36727/0.27834, loss_mask_ce_2: 0.46095/0.91948, loss_mask_bce_2: 0.27177/0.33615, loss_mask_dice_2: 0.77565/1.17335, loss_spatial_bce_2: 0.06431/0.09186, loss_spatial_dice_2: 0.19495/0.22243, loss_spatial_ce_2: 0.11914/0.08492, loss_grounding_bce_2: 0.07174/0.08634, loss_grounding_dice_2: 0.21310/0.17974, loss_grounding_ce_2: 0.35544/0.28173, loss_mask_ce_3: 0.68082/0.92763, loss_mask_bce_3: 0.24177/0.33687, loss_mask_dice_3: 0.72580/1.17053, loss_spatial_bce_3: 0.06393/0.09270, loss_spatial_dice_3: 0.17316/0.22302, loss_spatial_ce_3: 0.10467/0.08887, loss_grounding_bce_3: 0.07774/0.08650, loss_grounding_dice_3: 0.22026/0.17957, loss_grounding_ce_3: 0.31911/0.28311, loss_mask_ce_4: 0.76026/0.92643, loss_mask_bce_4: 0.24354/0.33863, loss_mask_dice_4: 0.69491/1.19323, loss_spatial_bce_4: 0.06349/0.09680, loss_spatial_dice_4: 0.19732/0.23336, loss_spatial_ce_4: 0.19183/0.10542, loss_grounding_bce_4: 0.09282/0.08704, loss_grounding_dice_4: 0.23663/0.18238, loss_grounding_ce_4: 0.29535/0.28545, loss_mask_ce_5: 0.56356/0.94088, loss_mask_bce_5: 0.27590/0.34087, loss_mask_dice_5: 0.77755/1.19845, loss_spatial_bce_5: 0.06470/0.09805, loss_spatial_dice_5: 0.20325/0.23653, loss_spatial_ce_5: 0.15669/0.11937, loss_grounding_bce_5: 0.08613/0.08741, loss_grounding_dice_5: 0.23987/0.18358, loss_grounding_ce_5: 0.31724/0.29840, loss_mask_ce_6: 0.57075/0.97820, loss_mask_bce_6: 0.24024/0.34355, loss_mask_dice_6: 0.68007/1.20090, loss_spatial_bce_6: 0.07012/0.10349, loss_spatial_dice_6: 0.20061/0.23874, loss_spatial_ce_6: 0.16295/0.14454, loss_grounding_bce_6: 0.08620/0.08817, loss_grounding_dice_6: 0.22246/0.18378, loss_grounding_ce_6: 0.33581/0.31588, loss_mask_ce_7: 0.73905/1.02170, loss_mask_bce_7: 0.22393/0.35134, loss_mask_dice_7: 0.69093/1.25665, loss_spatial_bce_7: 0.10414/0.11217, loss_spatial_dice_7: 0.26280/0.26594, loss_spatial_ce_7: 0.06696/0.18247, loss_grounding_bce_7: 0.09111/0.09010, loss_grounding_dice_7: 0.23335/0.19108, loss_grounding_ce_7: 0.32747/0.34787, loss_mask_ce_8: 0.74367/1.13268, loss_mask_bce_8: 0.25591/0.36492, loss_mask_dice_8: 0.69550/1.33146, loss_spatial_bce_8: 0.07873/0.13326, loss_spatial_dice_8: 0.23818/0.30628, loss_spatial_ce_8: 0.29483/0.23926, loss_grounding_bce_8: 0.07822/0.09370, loss_grounding_dice_8: 0.22555/0.20234, loss_grounding_ce_8: 0.33694/0.41846, loss_mask_ce_9: 4.30536/3.68866, loss_mask_bce_9: 0.25466/0.39193, loss_mask_dice_9: 1.16889/1.90620, loss_spatial_bce_9: 0.34948/0.33550, loss_spatial_dice_9: 0.78954/0.82418, loss_spatial_ce_9: 1.34432/1.51242, loss_grounding_bce_9: 0.07705/0.10519, loss_grounding_dice_9: 0.35878/0.28205, loss_grounding_ce_9: 0.36002/0.69069] items per batch[64] items per second[0.23] total items[1683200] mini batches[ 26300] memory[7341] epoch remaining[0:51:35]
INFO:trainer.default_trainer:epochs[    14] optim steps[26400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.75024/0.91086, loss_mask_bce_0: 0.07318/0.33498, loss_mask_dice_0: 0.98337/1.16755, loss_spatial_bce_0: 0.02174/0.09081, loss_spatial_dice_0: 0.26405/0.21710, loss_spatial_ce_0: 0.06738/0.07531, loss_grounding_bce_0: 0.03324/0.08620, loss_grounding_dice_0: 0.13675/0.17955, loss_grounding_ce_0: 0.04654/0.27659, loss_mask_ce_1: 1.68881/0.91169, loss_mask_bce_1: 0.08328/0.33569, loss_mask_dice_1: 1.10086/1.17477, loss_spatial_bce_1: 0.02000/0.09162, loss_spatial_dice_1: 0.23784/0.22141, loss_spatial_ce_1: 0.06339/0.08127, loss_grounding_bce_1: 0.02987/0.08630, loss_grounding_dice_1: 0.11560/0.18031, loss_grounding_ce_1: 0.03452/0.27835, loss_mask_ce_2: 1.62549/0.91949, loss_mask_bce_2: 0.08192/0.33615, loss_mask_dice_2: 1.11514/1.17395, loss_spatial_bce_2: 0.02097/0.09183, loss_spatial_dice_2: 0.27768/0.22242, loss_spatial_ce_2: 0.06260/0.08486, loss_grounding_bce_2: 0.03024/0.08635, loss_grounding_dice_2: 0.11705/0.17979, loss_grounding_ce_2: 0.03936/0.28177, loss_mask_ce_3: 2.06947/0.92760, loss_mask_bce_3: 0.07386/0.33685, loss_mask_dice_3: 1.16217/1.17110, loss_spatial_bce_3: 0.02133/0.09267, loss_spatial_dice_3: 0.26513/0.22300, loss_spatial_ce_3: 0.05884/0.08883, loss_grounding_bce_3: 0.03104/0.08652, loss_grounding_dice_3: 0.11540/0.17961, loss_grounding_ce_3: 0.03211/0.28314, loss_mask_ce_4: 2.14578/0.92639, loss_mask_bce_4: 0.06071/0.33861, loss_mask_dice_4: 0.88544/1.19382, loss_spatial_bce_4: 0.02247/0.09678, loss_spatial_dice_4: 0.25935/0.23337, loss_spatial_ce_4: 0.08501/0.10542, loss_grounding_bce_4: 0.02909/0.08706, loss_grounding_dice_4: 0.09842/0.18242, loss_grounding_ce_4: 0.00701/0.28545, loss_mask_ce_5: 1.62302/0.94084, loss_mask_bce_5: 0.08161/0.34083, loss_mask_dice_5: 1.29915/1.19905, loss_spatial_bce_5: 0.02635/0.09805, loss_spatial_dice_5: 0.28882/0.23656, loss_spatial_ce_5: 0.03376/0.11933, loss_grounding_bce_5: 0.03489/0.08744, loss_grounding_dice_5: 0.11949/0.18362, loss_grounding_ce_5: 0.01359/0.29831, loss_mask_ce_6: 1.53851/0.97820, loss_mask_bce_6: 0.07050/0.34352, loss_mask_dice_6: 0.93898/1.20151, loss_spatial_bce_6: 0.02539/0.10349, loss_spatial_dice_6: 0.25625/0.23874, loss_spatial_ce_6: 0.05274/0.14451, loss_grounding_bce_6: 0.02927/0.08819, loss_grounding_dice_6: 0.12075/0.18384, loss_grounding_ce_6: 0.03336/0.31568, loss_mask_ce_7: 1.43773/1.02176, loss_mask_bce_7: 0.07516/0.35129, loss_mask_dice_7: 1.31712/1.25727, loss_spatial_bce_7: 0.02483/0.11217, loss_spatial_dice_7: 0.33809/0.26597, loss_spatial_ce_7: 0.11125/0.18244, loss_grounding_bce_7: 0.03116/0.09011, loss_grounding_dice_7: 0.11636/0.19112, loss_grounding_ce_7: 0.02721/0.34771, loss_mask_ce_8: 1.82942/1.13280, loss_mask_bce_8: 0.09435/0.36487, loss_mask_dice_8: 1.39638/1.33211, loss_spatial_bce_8: 0.02502/0.13327, loss_spatial_dice_8: 0.35642/0.30630, loss_spatial_ce_8: 0.47843/0.23921, loss_grounding_bce_8: 0.04261/0.09371, loss_grounding_dice_8: 0.16274/0.20238, loss_grounding_ce_8: 0.16673/0.41826, loss_mask_ce_9: 4.36344/3.68906, loss_mask_bce_9: 0.06540/0.39184, loss_mask_dice_9: 1.27704/1.90681, loss_spatial_bce_9: 0.11396/0.33544, loss_spatial_dice_9: 0.65475/0.82417, loss_spatial_ce_9: 1.77960/1.51217, loss_grounding_bce_9: 0.02920/0.10519, loss_grounding_dice_9: 0.12646/0.28205, loss_grounding_ce_9: 0.71871/0.69053] items per batch[64] items per second[0.23] total items[1689600] mini batches[ 26400] memory[7341] epoch remaining[0:46:55]
INFO:trainer.default_trainer:epochs[    14] optim steps[26500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.70402/0.91072, loss_mask_bce_0: 0.31057/0.33495, loss_mask_dice_0: 2.41194/1.16758, loss_spatial_bce_0: 0.02593/0.09078, loss_spatial_dice_0: 0.19918/0.21706, loss_spatial_ce_0: 0.00793/0.07524, loss_grounding_bce_0: 0.03579/0.08621, loss_grounding_dice_0: 0.25368/0.17953, loss_grounding_ce_0: 0.30004/0.27680, loss_mask_ce_1: 1.65110/0.91156, loss_mask_bce_1: 0.31988/0.33567, loss_mask_dice_1: 2.54269/1.17480, loss_spatial_bce_1: 0.02839/0.09159, loss_spatial_dice_1: 0.19036/0.22137, loss_spatial_ce_1: 0.00695/0.08118, loss_grounding_bce_1: 0.02574/0.08630, loss_grounding_dice_1: 0.21996/0.18027, loss_grounding_ce_1: 0.31919/0.27843, loss_mask_ce_2: 1.81871/0.91927, loss_mask_bce_2: 0.32037/0.33612, loss_mask_dice_2: 2.61190/1.17403, loss_spatial_bce_2: 0.03058/0.09180, loss_spatial_dice_2: 0.20360/0.22238, loss_spatial_ce_2: 0.02498/0.08485, loss_grounding_bce_2: 0.03007/0.08636, loss_grounding_dice_2: 0.22278/0.17979, loss_grounding_ce_2: 0.35597/0.28180, loss_mask_ce_3: 1.71444/0.92744, loss_mask_bce_3: 0.33403/0.33681, loss_mask_dice_3: 2.58195/1.17108, loss_spatial_bce_3: 0.02462/0.09264, loss_spatial_dice_3: 0.20606/0.22296, loss_spatial_ce_3: 0.02718/0.08874, loss_grounding_bce_3: 0.02880/0.08653, loss_grounding_dice_3: 0.24155/0.17959, loss_grounding_ce_3: 0.31825/0.28322, loss_mask_ce_4: 1.57844/0.92623, loss_mask_bce_4: 0.34776/0.33858, loss_mask_dice_4: 2.69840/1.19378, loss_spatial_bce_4: 0.02559/0.09675, loss_spatial_dice_4: 0.20002/0.23334, loss_spatial_ce_4: 0.06749/0.10538, loss_grounding_bce_4: 0.03134/0.08707, loss_grounding_dice_4: 0.27860/0.18240, loss_grounding_ce_4: 0.35821/0.28556, loss_mask_ce_5: 1.54698/0.94073, loss_mask_bce_5: 0.34930/0.34078, loss_mask_dice_5: 2.55674/1.19907, loss_spatial_bce_5: 0.03130/0.09803, loss_spatial_dice_5: 0.22861/0.23654, loss_spatial_ce_5: 0.03383/0.11928, loss_grounding_bce_5: 0.02699/0.08746, loss_grounding_dice_5: 0.26573/0.18362, loss_grounding_ce_5: 0.44379/0.29837, loss_mask_ce_6: 1.56477/0.97805, loss_mask_bce_6: 0.37025/0.34349, loss_mask_dice_6: 2.70223/1.20160, loss_spatial_bce_6: 0.03550/0.10347, loss_spatial_dice_6: 0.21675/0.23873, loss_spatial_ce_6: 0.09019/0.14447, loss_grounding_bce_6: 0.02578/0.08819, loss_grounding_dice_6: 0.27648/0.18383, loss_grounding_ce_6: 0.39545/0.31576, loss_mask_ce_7: 1.91111/1.02166, loss_mask_bce_7: 0.35302/0.35125, loss_mask_dice_7: 2.49711/1.25725, loss_spatial_bce_7: 0.03835/0.11215, loss_spatial_dice_7: 0.26560/0.26595, loss_spatial_ce_7: 0.09582/0.18234, loss_grounding_bce_7: 0.03177/0.09012, loss_grounding_dice_7: 0.32012/0.19111, loss_grounding_ce_7: 0.37328/0.34780, loss_mask_ce_8: 2.43224/1.13270, loss_mask_bce_8: 0.32196/0.36484, loss_mask_dice_8: 2.79643/1.33213, loss_spatial_bce_8: 0.04557/0.13324, loss_spatial_dice_8: 0.34179/0.30625, loss_spatial_ce_8: 0.22164/0.23911, loss_grounding_bce_8: 0.02963/0.09372, loss_grounding_dice_8: 0.30177/0.20235, loss_grounding_ce_8: 1.04177/0.41846, loss_mask_ce_9: 5.43467/3.68927, loss_mask_bce_9: 0.41615/0.39180, loss_mask_dice_9: 3.87380/1.90668, loss_spatial_bce_9: 0.19851/0.33545, loss_spatial_dice_9: 0.91309/0.82413, loss_spatial_ce_9: 1.31189/1.51212, loss_grounding_bce_9: 0.02869/0.10519, loss_grounding_dice_9: 0.43533/0.28200, loss_grounding_ce_9: 1.21539/0.69074] items per batch[64] items per second[0.23] total items[1696000] mini batches[ 26500] memory[7341] epoch remaining[0:42:18]
INFO:trainer.default_trainer:epochs[    14] optim steps[26600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.46720/0.91089, loss_mask_bce_0: 0.11315/0.33505, loss_mask_dice_0: 0.65419/1.16685, loss_spatial_bce_0: 0.02911/0.09081, loss_spatial_dice_0: 0.15837/0.21697, loss_spatial_ce_0: 0.00132/0.07531, loss_grounding_bce_0: 0.04100/0.08626, loss_grounding_dice_0: 0.17267/0.17955, loss_grounding_ce_0: 0.31113/0.27675, loss_mask_ce_1: 1.61114/0.91174, loss_mask_bce_1: 0.11656/0.33574, loss_mask_dice_1: 0.68533/1.17405, loss_spatial_bce_1: 0.03019/0.09162, loss_spatial_dice_1: 0.15376/0.22128, loss_spatial_ce_1: 0.00060/0.08125, loss_grounding_bce_1: 0.03837/0.08636, loss_grounding_dice_1: 0.18142/0.18031, loss_grounding_ce_1: 0.19887/0.27839, loss_mask_ce_2: 1.44017/0.91942, loss_mask_bce_2: 0.11582/0.33621, loss_mask_dice_2: 0.69731/1.17329, loss_spatial_bce_2: 0.02906/0.09184, loss_spatial_dice_2: 0.16258/0.22229, loss_spatial_ce_2: 0.00070/0.08491, loss_grounding_bce_2: 0.03644/0.08643, loss_grounding_dice_2: 0.16032/0.17979, loss_grounding_ce_2: 0.22100/0.28172, loss_mask_ce_3: 1.82331/0.92761, loss_mask_bce_3: 0.11556/0.33690, loss_mask_dice_3: 0.58251/1.17028, loss_spatial_bce_3: 0.03132/0.09268, loss_spatial_dice_3: 0.16821/0.22288, loss_spatial_ce_3: 0.00538/0.08879, loss_grounding_bce_3: 0.03757/0.08659, loss_grounding_dice_3: 0.16309/0.17962, loss_grounding_ce_3: 0.21820/0.28317, loss_mask_ce_4: 1.51868/0.92643, loss_mask_bce_4: 0.11512/0.33867, loss_mask_dice_4: 0.68974/1.19300, loss_spatial_bce_4: 0.02927/0.09679, loss_spatial_dice_4: 0.16560/0.23326, loss_spatial_ce_4: 0.00050/0.10545, loss_grounding_bce_4: 0.03699/0.08713, loss_grounding_dice_4: 0.17524/0.18242, loss_grounding_ce_4: 0.22871/0.28556, loss_mask_ce_5: 1.70259/0.94094, loss_mask_bce_5: 0.11507/0.34088, loss_mask_dice_5: 0.70453/1.19835, loss_spatial_bce_5: 0.03180/0.09808, loss_spatial_dice_5: 0.17962/0.23646, loss_spatial_ce_5: 0.01116/0.11937, loss_grounding_bce_5: 0.04100/0.08752, loss_grounding_dice_5: 0.16640/0.18363, loss_grounding_ce_5: 0.17012/0.29831, loss_mask_ce_6: 1.29859/0.97829, loss_mask_bce_6: 0.12114/0.34358, loss_mask_dice_6: 0.71340/1.20078, loss_spatial_bce_6: 0.03415/0.10353, loss_spatial_dice_6: 0.18367/0.23865, loss_spatial_ce_6: 0.07953/0.14459, loss_grounding_bce_6: 0.04120/0.08825, loss_grounding_dice_6: 0.17149/0.18383, loss_grounding_ce_6: 0.25050/0.31586, loss_mask_ce_7: 1.53389/1.02188, loss_mask_bce_7: 0.11122/0.35134, loss_mask_dice_7: 0.69286/1.25650, loss_spatial_bce_7: 0.03379/0.11221, loss_spatial_dice_7: 0.17803/0.26584, loss_spatial_ce_7: 0.03521/0.18239, loss_grounding_bce_7: 0.03839/0.09019, loss_grounding_dice_7: 0.16574/0.19113, loss_grounding_ce_7: 0.24150/0.34776, loss_mask_ce_8: 1.74979/1.13293, loss_mask_bce_8: 0.11441/0.36493, loss_mask_dice_8: 0.72795/1.33132, loss_spatial_bce_8: 0.03994/0.13329, loss_spatial_dice_8: 0.18350/0.30612, loss_spatial_ce_8: 0.12784/0.23912, loss_grounding_bce_8: 0.04175/0.09378, loss_grounding_dice_8: 0.18065/0.20236, loss_grounding_ce_8: 0.13665/0.41837, loss_mask_ce_9: 3.53962/3.68903, loss_mask_bce_9: 0.10672/0.39188, loss_mask_dice_9: 0.99130/1.90574, loss_spatial_bce_9: 0.23403/0.33565, loss_spatial_dice_9: 0.85904/0.82410, loss_spatial_ce_9: 1.60845/1.51191, loss_grounding_bce_9: 0.02891/0.10523, loss_grounding_dice_9: 0.27080/0.28201, loss_grounding_ce_9: 0.34835/0.69042] items per batch[64] items per second[0.23] total items[1702400] mini batches[ 26600] memory[7341] epoch remaining[0:37:37]
INFO:trainer.default_trainer:epochs[    14] optim steps[26700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17733/0.91093, loss_mask_bce_0: 0.11174/0.33512, loss_mask_dice_0: 0.13425/1.16684, loss_spatial_bce_0: 0.07297/0.09080, loss_spatial_dice_0: 0.09276/0.21694, loss_spatial_ce_0: 0.00641/0.07527, loss_grounding_bce_0: 0.05138/0.08626, loss_grounding_dice_0: 0.09302/0.17959, loss_grounding_ce_0: 0.07922/0.27678, loss_mask_ce_1: 0.16810/0.91177, loss_mask_bce_1: 0.11561/0.33581, loss_mask_dice_1: 0.14911/1.17402, loss_spatial_bce_1: 0.07916/0.09161, loss_spatial_dice_1: 0.10688/0.22125, loss_spatial_ce_1: 0.00909/0.08117, loss_grounding_bce_1: 0.05268/0.08636, loss_grounding_dice_1: 0.10488/0.18035, loss_grounding_ce_1: 0.07262/0.27845, loss_mask_ce_2: 0.15143/0.91945, loss_mask_bce_2: 0.11821/0.33628, loss_mask_dice_2: 0.13826/1.17332, loss_spatial_bce_2: 0.08030/0.09183, loss_spatial_dice_2: 0.11130/0.22227, loss_spatial_ce_2: 0.01391/0.08481, loss_grounding_bce_2: 0.05300/0.08643, loss_grounding_dice_2: 0.10157/0.17984, loss_grounding_ce_2: 0.06113/0.28179, loss_mask_ce_3: 0.15601/0.92762, loss_mask_bce_3: 0.11516/0.33699, loss_mask_dice_3: 0.14386/1.17027, loss_spatial_bce_3: 0.07963/0.09266, loss_spatial_dice_3: 0.08361/0.22285, loss_spatial_ce_3: 0.01449/0.08871, loss_grounding_bce_3: 0.05134/0.08660, loss_grounding_dice_3: 0.09566/0.17966, loss_grounding_ce_3: 0.06415/0.28327, loss_mask_ce_4: 0.14452/0.92647, loss_mask_bce_4: 0.12075/0.33876, loss_mask_dice_4: 0.13607/1.19297, loss_spatial_bce_4: 0.08056/0.09677, loss_spatial_dice_4: 0.11283/0.23324, loss_spatial_ce_4: 0.02126/0.10533, loss_grounding_bce_4: 0.05747/0.08714, loss_grounding_dice_4: 0.10118/0.18247, loss_grounding_ce_4: 0.07524/0.28556, loss_mask_ce_5: 0.16695/0.94098, loss_mask_bce_5: 0.13370/0.34095, loss_mask_dice_5: 0.15612/1.19827, loss_spatial_bce_5: 0.09461/0.09806, loss_spatial_dice_5: 0.11332/0.23645, loss_spatial_ce_5: 0.04965/0.11930, loss_grounding_bce_5: 0.06225/0.08752, loss_grounding_dice_5: 0.10272/0.18368, loss_grounding_ce_5: 0.08129/0.29836, loss_mask_ce_6: 0.20977/0.97827, loss_mask_bce_6: 0.11371/0.34368, loss_mask_dice_6: 0.14244/1.20083, loss_spatial_bce_6: 0.08298/0.10351, loss_spatial_dice_6: 0.10771/0.23863, loss_spatial_ce_6: 0.03636/0.14451, loss_grounding_bce_6: 0.05415/0.08826, loss_grounding_dice_6: 0.10673/0.18390, loss_grounding_ce_6: 0.08146/0.31586, loss_mask_ce_7: 0.22066/1.02195, loss_mask_bce_7: 0.11358/0.35142, loss_mask_dice_7: 0.14603/1.25642, loss_spatial_bce_7: 0.09402/0.11220, loss_spatial_dice_7: 0.14206/0.26581, loss_spatial_ce_7: 0.05054/0.18228, loss_grounding_bce_7: 0.05106/0.09019, loss_grounding_dice_7: 0.09820/0.19119, loss_grounding_ce_7: 0.08616/0.34775, loss_mask_ce_8: 0.36974/1.13296, loss_mask_bce_8: 0.12760/0.36499, loss_mask_dice_8: 0.15444/1.33128, loss_spatial_bce_8: 0.17516/0.13327, loss_spatial_dice_8: 0.24693/0.30607, loss_spatial_ce_8: 0.12207/0.23904, loss_grounding_bce_8: 0.05804/0.09379, loss_grounding_dice_8: 0.10445/0.20243, loss_grounding_ce_8: 0.13495/0.41837, loss_mask_ce_9: 3.18884/3.68913, loss_mask_bce_9: 0.19262/0.39199, loss_mask_dice_9: 0.28146/1.90578, loss_spatial_bce_9: 0.58925/0.33567, loss_spatial_dice_9: 0.57986/0.82410, loss_spatial_ce_9: 0.93266/1.51193, loss_grounding_bce_9: 0.13684/0.10523, loss_grounding_dice_9: 0.18358/0.28204, loss_grounding_ce_9: 0.32099/0.69028] items per batch[64] items per second[0.23] total items[1708800] mini batches[ 26700] memory[7341] epoch remaining[0:32:55]
INFO:trainer.default_trainer:epochs[    14] optim steps[26800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38749/0.91089, loss_mask_bce_0: 0.23344/0.33518, loss_mask_dice_0: 1.50235/1.16642, loss_spatial_bce_0: 0.04884/0.09081, loss_spatial_dice_0: 0.15440/0.21689, loss_spatial_ce_0: 0.06683/0.07518, loss_grounding_bce_0: 0.12722/0.08633, loss_grounding_dice_0: 0.11347/0.17962, loss_grounding_ce_0: 0.01604/0.27685, loss_mask_ce_1: 0.50640/0.91172, loss_mask_bce_1: 0.22921/0.33585, loss_mask_dice_1: 1.52153/1.17357, loss_spatial_bce_1: 0.05036/0.09162, loss_spatial_dice_1: 0.16664/0.22118, loss_spatial_ce_1: 0.10684/0.08111, loss_grounding_bce_1: 0.12586/0.08642, loss_grounding_dice_1: 0.11197/0.18036, loss_grounding_ce_1: 0.01394/0.27851, loss_mask_ce_2: 0.43480/0.91937, loss_mask_bce_2: 0.23334/0.33632, loss_mask_dice_2: 1.37386/1.17290, loss_spatial_bce_2: 0.05240/0.09184, loss_spatial_dice_2: 0.12913/0.22220, loss_spatial_ce_2: 0.14053/0.08470, loss_grounding_bce_2: 0.12855/0.08649, loss_grounding_dice_2: 0.10924/0.17985, loss_grounding_ce_2: 0.01449/0.28184, loss_mask_ce_3: 0.36591/0.92752, loss_mask_bce_3: 0.23704/0.33704, loss_mask_dice_3: 1.69583/1.16990, loss_spatial_bce_3: 0.05441/0.09267, loss_spatial_dice_3: 0.14858/0.22279, loss_spatial_ce_3: 0.11392/0.08864, loss_grounding_bce_3: 0.13085/0.08666, loss_grounding_dice_3: 0.11273/0.17969, loss_grounding_ce_3: 0.01158/0.28346, loss_mask_ce_4: 0.51632/0.92639, loss_mask_bce_4: 0.22616/0.33879, loss_mask_dice_4: 1.37022/1.19257, loss_spatial_bce_4: 0.05240/0.09678, loss_spatial_dice_4: 0.17308/0.23318, loss_spatial_ce_4: 0.16176/0.10530, loss_grounding_bce_4: 0.12658/0.08719, loss_grounding_dice_4: 0.11296/0.18250, loss_grounding_ce_4: 0.01469/0.28557, loss_mask_ce_5: 0.36991/0.94091, loss_mask_bce_5: 0.23721/0.34099, loss_mask_dice_5: 1.12361/1.19783, loss_spatial_bce_5: 0.05208/0.09808, loss_spatial_dice_5: 0.14205/0.23640, loss_spatial_ce_5: 0.15899/0.11926, loss_grounding_bce_5: 0.12824/0.08758, loss_grounding_dice_5: 0.11645/0.18369, loss_grounding_ce_5: 0.03012/0.29842, loss_mask_ce_6: 0.36615/0.97821, loss_mask_bce_6: 0.24755/0.34374, loss_mask_dice_6: 1.78796/1.20037, loss_spatial_bce_6: 0.05545/0.10353, loss_spatial_dice_6: 0.19930/0.23859, loss_spatial_ce_6: 0.11888/0.14446, loss_grounding_bce_6: 0.13278/0.08833, loss_grounding_dice_6: 0.11592/0.18391, loss_grounding_ce_6: 0.01275/0.31591, loss_mask_ce_7: 0.55899/1.02188, loss_mask_bce_7: 0.24342/0.35146, loss_mask_dice_7: 1.65762/1.25595, loss_spatial_bce_7: 0.06119/0.11221, loss_spatial_dice_7: 0.20944/0.26575, loss_spatial_ce_7: 0.14499/0.18224, loss_grounding_bce_7: 0.12867/0.09026, loss_grounding_dice_7: 0.11465/0.19121, loss_grounding_ce_7: 0.02007/0.34769, loss_mask_ce_8: 0.72590/1.13291, loss_mask_bce_8: 0.28283/0.36501, loss_mask_dice_8: 1.45035/1.33074, loss_spatial_bce_8: 0.06332/0.13328, loss_spatial_dice_8: 0.25151/0.30599, loss_spatial_ce_8: 0.14195/0.23899, loss_grounding_bce_8: 0.14225/0.09385, loss_grounding_dice_8: 0.11107/0.20243, loss_grounding_ce_8: 0.23098/0.41828, loss_mask_ce_9: 2.89091/3.68897, loss_mask_bce_9: 0.30826/0.39204, loss_mask_dice_9: 1.84620/1.90516, loss_spatial_bce_9: 0.26561/0.33568, loss_spatial_dice_9: 0.85806/0.82405, loss_spatial_ce_9: 1.60104/1.51168, loss_grounding_bce_9: 0.17902/0.10530, loss_grounding_dice_9: 0.14889/0.28209, loss_grounding_ce_9: 0.27068/0.69027] items per batch[64] items per second[0.23] total items[1715200] mini batches[ 26800] memory[7341] epoch remaining[0:28:15]
INFO:trainer.default_trainer:epochs[    14] optim steps[26900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89283/0.91066, loss_mask_bce_0: 0.25402/0.33518, loss_mask_dice_0: 1.19659/1.16636, loss_spatial_bce_0: 0.06154/0.09079, loss_spatial_dice_0: 0.28123/0.21687, loss_spatial_ce_0: 0.00117/0.07509, loss_grounding_bce_0: 0.13012/0.08632, loss_grounding_dice_0: 0.11586/0.17957, loss_grounding_ce_0: 0.12009/0.27673, loss_mask_ce_1: 0.48988/0.91141, loss_mask_bce_1: 0.26960/0.33585, loss_mask_dice_1: 1.29387/1.17353, loss_spatial_bce_1: 0.06426/0.09160, loss_spatial_dice_1: 0.27780/0.22115, loss_spatial_ce_1: 0.00252/0.08097, loss_grounding_bce_1: 0.12722/0.08641, loss_grounding_dice_1: 0.13088/0.18033, loss_grounding_ce_1: 0.10466/0.27839, loss_mask_ce_2: 0.61145/0.91905, loss_mask_bce_2: 0.28426/0.33633, loss_mask_dice_2: 1.25146/1.17285, loss_spatial_bce_2: 0.06248/0.09182, loss_spatial_dice_2: 0.24655/0.22217, loss_spatial_ce_2: 0.00346/0.08458, loss_grounding_bce_2: 0.13138/0.08647, loss_grounding_dice_2: 0.12092/0.17981, loss_grounding_ce_2: 0.12703/0.28170, loss_mask_ce_3: 0.67687/0.92720, loss_mask_bce_3: 0.28681/0.33704, loss_mask_dice_3: 1.38253/1.16988, loss_spatial_bce_3: 0.06448/0.09265, loss_spatial_dice_3: 0.25726/0.22276, loss_spatial_ce_3: 0.00400/0.08851, loss_grounding_bce_3: 0.12671/0.08664, loss_grounding_dice_3: 0.11693/0.17962, loss_grounding_ce_3: 0.09957/0.28333, loss_mask_ce_4: 0.64258/0.92611, loss_mask_bce_4: 0.27897/0.33878, loss_mask_dice_4: 1.38380/1.19254, loss_spatial_bce_4: 0.06204/0.09677, loss_spatial_dice_4: 0.32540/0.23317, loss_spatial_ce_4: 0.03962/0.10517, loss_grounding_bce_4: 0.13677/0.08717, loss_grounding_dice_4: 0.13050/0.18243, loss_grounding_ce_4: 0.18593/0.28545, loss_mask_ce_5: 0.49353/0.94063, loss_mask_bce_5: 0.27406/0.34101, loss_mask_dice_5: 1.38458/1.19784, loss_spatial_bce_5: 0.06592/0.09807, loss_spatial_dice_5: 0.30251/0.23638, loss_spatial_ce_5: 0.08636/0.11918, loss_grounding_bce_5: 0.13045/0.08755, loss_grounding_dice_5: 0.14150/0.18363, loss_grounding_ce_5: 0.29571/0.29832, loss_mask_ce_6: 0.70135/0.97790, loss_mask_bce_6: 0.28857/0.34376, loss_mask_dice_6: 1.17169/1.20035, loss_spatial_bce_6: 0.07643/0.10352, loss_spatial_dice_6: 0.28291/0.23857, loss_spatial_ce_6: 0.07498/0.14431, loss_grounding_bce_6: 0.14157/0.08830, loss_grounding_dice_6: 0.11918/0.18386, loss_grounding_ce_6: 0.24665/0.31582, loss_mask_ce_7: 0.81515/1.02161, loss_mask_bce_7: 0.25565/0.35147, loss_mask_dice_7: 1.24932/1.25590, loss_spatial_bce_7: 0.08217/0.11220, loss_spatial_dice_7: 0.40235/0.26575, loss_spatial_ce_7: 0.09961/0.18213, loss_grounding_bce_7: 0.14213/0.09025, loss_grounding_dice_7: 0.13466/0.19116, loss_grounding_ce_7: 0.51641/0.34754, loss_mask_ce_8: 0.85470/1.13273, loss_mask_bce_8: 0.29739/0.36497, loss_mask_dice_8: 1.60382/1.33068, loss_spatial_bce_8: 0.12309/0.13327, loss_spatial_dice_8: 0.47519/0.30597, loss_spatial_ce_8: 0.14816/0.23892, loss_grounding_bce_8: 0.13187/0.09382, loss_grounding_dice_8: 0.12126/0.20238, loss_grounding_ce_8: 1.35332/0.41816, loss_mask_ce_9: 2.49553/3.68859, loss_mask_bce_9: 0.41056/0.39200, loss_mask_dice_9: 1.67590/1.90499, loss_spatial_bce_9: 0.19459/0.33562, loss_spatial_dice_9: 0.83678/0.82406, loss_spatial_ce_9: 1.89847/1.51173, loss_grounding_bce_9: 0.29501/0.10527, loss_grounding_dice_9: 0.33243/0.28203, loss_grounding_ce_9: 2.09958/0.69018] items per batch[64] items per second[0.23] total items[1721600] mini batches[ 26900] memory[7341] epoch remaining[0:23:36]
INFO:trainer.default_trainer:epochs[    14] optim steps[27000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.56361/0.91057, loss_mask_bce_0: 0.22358/0.33516, loss_mask_dice_0: 1.99548/1.16650, loss_spatial_bce_0: 0.01368/0.09075, loss_spatial_dice_0: 0.24665/0.21684, loss_spatial_ce_0: 0.04446/0.07500, loss_grounding_bce_0: 0.01214/0.08631, loss_grounding_dice_0: 0.08889/0.17956, loss_grounding_ce_0: 0.38462/0.27672, loss_mask_ce_1: 1.43893/0.91123, loss_mask_bce_1: 0.27932/0.33583, loss_mask_dice_1: 1.84286/1.17375, loss_spatial_bce_1: 0.01420/0.09156, loss_spatial_dice_1: 0.23059/0.22111, loss_spatial_ce_1: 0.07838/0.08089, loss_grounding_bce_1: 0.01123/0.08640, loss_grounding_dice_1: 0.09374/0.18034, loss_grounding_ce_1: 0.35729/0.27832, loss_mask_ce_2: 1.35538/0.91894, loss_mask_bce_2: 0.26275/0.33630, loss_mask_dice_2: 1.91839/1.17305, loss_spatial_bce_2: 0.01312/0.09178, loss_spatial_dice_2: 0.21590/0.22213, loss_spatial_ce_2: 0.09945/0.08453, loss_grounding_bce_2: 0.01275/0.08644, loss_grounding_dice_2: 0.10545/0.17981, loss_grounding_ce_2: 0.28081/0.28170, loss_mask_ce_3: 1.34875/0.92702, loss_mask_bce_3: 0.27827/0.33701, loss_mask_dice_3: 2.05471/1.17010, loss_spatial_bce_3: 0.01385/0.09261, loss_spatial_dice_3: 0.24639/0.22272, loss_spatial_ce_3: 0.06045/0.08843, loss_grounding_bce_3: 0.01276/0.08662, loss_grounding_dice_3: 0.09092/0.17964, loss_grounding_ce_3: 0.24428/0.28339, loss_mask_ce_4: 1.45776/0.92598, loss_mask_bce_4: 0.30212/0.33877, loss_mask_dice_4: 1.97380/1.19274, loss_spatial_bce_4: 0.01421/0.09674, loss_spatial_dice_4: 0.26216/0.23316, loss_spatial_ce_4: 0.09023/0.10508, loss_grounding_bce_4: 0.01611/0.08714, loss_grounding_dice_4: 0.12710/0.18243, loss_grounding_ce_4: 0.22037/0.28550, loss_mask_ce_5: 1.55441/0.94058, loss_mask_bce_5: 0.28276/0.34098, loss_mask_dice_5: 1.76588/1.19802, loss_spatial_bce_5: 0.01884/0.09804, loss_spatial_dice_5: 0.28629/0.23636, loss_spatial_ce_5: 0.14360/0.11912, loss_grounding_bce_5: 0.01299/0.08753, loss_grounding_dice_5: 0.09245/0.18363, loss_grounding_ce_5: 0.39488/0.29838, loss_mask_ce_6: 1.54355/0.97779, loss_mask_bce_6: 0.27982/0.34373, loss_mask_dice_6: 2.07303/1.20054, loss_spatial_bce_6: 0.02020/0.10351, loss_spatial_dice_6: 0.25403/0.23856, loss_spatial_ce_6: 0.18319/0.14432, loss_grounding_bce_6: 0.01156/0.08827, loss_grounding_dice_6: 0.09624/0.18385, loss_grounding_ce_6: 0.29798/0.31580, loss_mask_ce_7: 1.55736/1.02151, loss_mask_bce_7: 0.29709/0.35144, loss_mask_dice_7: 2.08976/1.25611, loss_spatial_bce_7: 0.02451/0.11219, loss_spatial_dice_7: 0.32594/0.26574, loss_spatial_ce_7: 0.13724/0.18208, loss_grounding_bce_7: 0.01458/0.09022, loss_grounding_dice_7: 0.12973/0.19116, loss_grounding_ce_7: 0.26017/0.34752, loss_mask_ce_8: 1.73068/1.13278, loss_mask_bce_8: 0.27358/0.36494, loss_mask_dice_8: 2.56352/1.33096, loss_spatial_bce_8: 0.02220/0.13328, loss_spatial_dice_8: 0.38035/0.30598, loss_spatial_ce_8: 0.17696/0.23887, loss_grounding_bce_8: 0.01414/0.09379, loss_grounding_dice_8: 0.11876/0.20239, loss_grounding_ce_8: 0.14036/0.41817, loss_mask_ce_9: 3.73627/3.68880, loss_mask_bce_9: 0.25244/0.39200, loss_mask_dice_9: 2.93279/1.90525, loss_spatial_bce_9: 0.17869/0.33555, loss_spatial_dice_9: 0.90469/0.82404, loss_spatial_ce_9: 1.18483/1.51174, loss_grounding_bce_9: 0.00901/0.10524, loss_grounding_dice_9: 0.12649/0.28202, loss_grounding_ce_9: 0.60335/0.68994] items per batch[64] items per second[0.23] total items[1728000] mini batches[ 27000] memory[7341] epoch remaining[0:18:55]
INFO:trainer.default_trainer:epochs[    14] optim steps[27100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28744/0.91054, loss_mask_bce_0: 0.03425/0.33523, loss_mask_dice_0: 0.13386/1.16695, loss_spatial_bce_0: 0.01743/0.09074, loss_spatial_dice_0: 0.12006/0.21683, loss_spatial_ce_0: 0.00212/0.07497, loss_grounding_bce_0: 0.02353/0.08632, loss_grounding_dice_0: 0.05981/0.17956, loss_grounding_ce_0: 0.25499/0.27661, loss_mask_ce_1: 0.29017/0.91125, loss_mask_bce_1: 0.02719/0.33590, loss_mask_dice_1: 0.12360/1.17414, loss_spatial_bce_1: 0.02083/0.09154, loss_spatial_dice_1: 0.14291/0.22110, loss_spatial_ce_1: 0.00127/0.08084, loss_grounding_bce_1: 0.03481/0.08642, loss_grounding_dice_1: 0.05503/0.18034, loss_grounding_ce_1: 0.08826/0.27825, loss_mask_ce_2: 0.28266/0.91889, loss_mask_bce_2: 0.02411/0.33638, loss_mask_dice_2: 0.15169/1.17347, loss_spatial_bce_2: 0.01729/0.09177, loss_spatial_dice_2: 0.10883/0.22212, loss_spatial_ce_2: 0.00294/0.08448, loss_grounding_bce_2: 0.03401/0.08646, loss_grounding_dice_2: 0.06097/0.17981, loss_grounding_ce_2: 0.07716/0.28157, loss_mask_ce_3: 0.28451/0.92703, loss_mask_bce_3: 0.02601/0.33710, loss_mask_dice_3: 0.08680/1.17048, loss_spatial_bce_3: 0.01932/0.09260, loss_spatial_dice_3: 0.11259/0.22272, loss_spatial_ce_3: 0.00517/0.08839, loss_grounding_bce_3: 0.02516/0.08663, loss_grounding_dice_3: 0.05960/0.17963, loss_grounding_ce_3: 0.26059/0.28327, loss_mask_ce_4: 0.28522/0.92602, loss_mask_bce_4: 0.02401/0.33884, loss_mask_dice_4: 0.12638/1.19314, loss_spatial_bce_4: 0.01935/0.09673, loss_spatial_dice_4: 0.15923/0.23317, loss_spatial_ce_4: 0.01657/0.10506, loss_grounding_bce_4: 0.02633/0.08716, loss_grounding_dice_4: 0.05255/0.18242, loss_grounding_ce_4: 0.27560/0.28549, loss_mask_ce_5: 0.26471/0.94058, loss_mask_bce_5: 0.02457/0.34107, loss_mask_dice_5: 0.11774/1.19848, loss_spatial_bce_5: 0.02322/0.09804, loss_spatial_dice_5: 0.15274/0.23639, loss_spatial_ce_5: 0.02787/0.11907, loss_grounding_bce_5: 0.02329/0.08755, loss_grounding_dice_5: 0.05229/0.18363, loss_grounding_ce_5: 0.28658/0.29840, loss_mask_ce_6: 0.26703/0.97778, loss_mask_bce_6: 0.02364/0.34382, loss_mask_dice_6: 0.10304/1.20099, loss_spatial_bce_6: 0.02010/0.10350, loss_spatial_dice_6: 0.12847/0.23857, loss_spatial_ce_6: 0.02021/0.14428, loss_grounding_bce_6: 0.02324/0.08829, loss_grounding_dice_6: 0.05190/0.18386, loss_grounding_ce_6: 0.28919/0.31576, loss_mask_ce_7: 0.29821/1.02156, loss_mask_bce_7: 0.02550/0.35152, loss_mask_dice_7: 0.11708/1.25656, loss_spatial_bce_7: 0.02084/0.11220, loss_spatial_dice_7: 0.13090/0.26576, loss_spatial_ce_7: 0.06532/0.18207, loss_grounding_bce_7: 0.03168/0.09024, loss_grounding_dice_7: 0.05978/0.19116, loss_grounding_ce_7: 0.14142/0.34760, loss_mask_ce_8: 0.37739/1.13270, loss_mask_bce_8: 0.03133/0.36504, loss_mask_dice_8: 0.12749/1.33137, loss_spatial_bce_8: 0.02543/0.13330, loss_spatial_dice_8: 0.09437/0.30600, loss_spatial_ce_8: 0.10880/0.23886, loss_grounding_bce_8: 0.04352/0.09381, loss_grounding_dice_8: 0.06875/0.20240, loss_grounding_ce_8: 0.20026/0.41820, loss_mask_ce_9: 2.54188/3.68874, loss_mask_bce_9: 0.03196/0.39213, loss_mask_dice_9: 0.19473/1.90628, loss_spatial_bce_9: 0.35860/0.33556, loss_spatial_dice_9: 0.73783/0.82409, loss_spatial_ce_9: 1.03048/1.51171, loss_grounding_bce_9: 0.04107/0.10524, loss_grounding_dice_9: 0.11141/0.28202, loss_grounding_ce_9: 0.39943/0.68992] items per batch[64] items per second[0.22] total items[1734400] mini batches[ 27100] memory[7341] epoch remaining[0:14:16]
INFO:trainer.default_trainer:epochs[    14] optim steps[27200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03109/0.91022, loss_mask_bce_0: 0.08179/0.33511, loss_mask_dice_0: 0.70286/1.16715, loss_spatial_bce_0: 0.01463/0.09068, loss_spatial_dice_0: 0.13342/0.21680, loss_spatial_ce_0: 0.00357/0.07492, loss_grounding_bce_0: 0.03497/0.08630, loss_grounding_dice_0: 0.12880/0.17958, loss_grounding_ce_0: 0.00097/0.27654, loss_mask_ce_1: 0.95371/0.91085, loss_mask_bce_1: 0.07671/0.33577, loss_mask_dice_1: 0.65312/1.17426, loss_spatial_bce_1: 0.01253/0.09148, loss_spatial_dice_1: 0.12502/0.22108, loss_spatial_ce_1: 0.00237/0.08081, loss_grounding_bce_1: 0.03461/0.08640, loss_grounding_dice_1: 0.12139/0.18035, loss_grounding_ce_1: 0.00093/0.27817, loss_mask_ce_2: 0.93851/0.91854, loss_mask_bce_2: 0.08297/0.33624, loss_mask_dice_2: 0.73687/1.17357, loss_spatial_bce_2: 0.01317/0.09171, loss_spatial_dice_2: 0.14058/0.22209, loss_spatial_ce_2: 0.00115/0.08444, loss_grounding_bce_2: 0.03636/0.08644, loss_grounding_dice_2: 0.11981/0.17983, loss_grounding_ce_2: 0.00066/0.28147, loss_mask_ce_3: 0.88865/0.92668, loss_mask_bce_3: 0.08091/0.33696, loss_mask_dice_3: 0.67123/1.17063, loss_spatial_bce_3: 0.01482/0.09254, loss_spatial_dice_3: 0.13091/0.22269, loss_spatial_ce_3: 0.10312/0.08835, loss_grounding_bce_3: 0.03346/0.08661, loss_grounding_dice_3: 0.11160/0.17965, loss_grounding_ce_3: 0.00122/0.28316, loss_mask_ce_4: 1.01397/0.92568, loss_mask_bce_4: 0.08065/0.33870, loss_mask_dice_4: 0.71089/1.19323, loss_spatial_bce_4: 0.01561/0.09667, loss_spatial_dice_4: 0.18772/0.23316, loss_spatial_ce_4: 0.00839/0.10498, loss_grounding_bce_4: 0.03378/0.08713, loss_grounding_dice_4: 0.11504/0.18247, loss_grounding_ce_4: 0.00128/0.28546, loss_mask_ce_5: 1.10073/0.94028, loss_mask_bce_5: 0.07878/0.34094, loss_mask_dice_5: 0.69644/1.19854, loss_spatial_bce_5: 0.01578/0.09798, loss_spatial_dice_5: 0.18106/0.23638, loss_spatial_ce_5: 0.03526/0.11902, loss_grounding_bce_5: 0.03063/0.08753, loss_grounding_dice_5: 0.12356/0.18365, loss_grounding_ce_5: 0.00126/0.29841, loss_mask_ce_6: 1.00226/0.97751, loss_mask_bce_6: 0.08433/0.34369, loss_mask_dice_6: 0.74827/1.20102, loss_spatial_bce_6: 0.02106/0.10345, loss_spatial_dice_6: 0.21066/0.23856, loss_spatial_ce_6: 0.10543/0.14423, loss_grounding_bce_6: 0.03101/0.08828, loss_grounding_dice_6: 0.10730/0.18387, loss_grounding_ce_6: 0.00180/0.31567, loss_mask_ce_7: 0.89655/1.02125, loss_mask_bce_7: 0.09429/0.35135, loss_mask_dice_7: 0.94447/1.25668, loss_spatial_bce_7: 0.02077/0.11214, loss_spatial_dice_7: 0.20421/0.26575, loss_spatial_ce_7: 0.06494/0.18198, loss_grounding_bce_7: 0.03815/0.09022, loss_grounding_dice_7: 0.11280/0.19117, loss_grounding_ce_7: 0.00178/0.34758, loss_mask_ce_8: 1.39482/1.13256, loss_mask_bce_8: 0.09791/0.36487, loss_mask_dice_8: 0.83847/1.33140, loss_spatial_bce_8: 0.02480/0.13322, loss_spatial_dice_8: 0.27706/0.30598, loss_spatial_ce_8: 0.12975/0.23878, loss_grounding_bce_8: 0.04304/0.09380, loss_grounding_dice_8: 0.12389/0.20238, loss_grounding_ce_8: 0.00948/0.41822, loss_mask_ce_9: 3.80497/3.68831, loss_mask_bce_9: 0.10139/0.39194, loss_mask_dice_9: 1.36905/1.90613, loss_spatial_bce_9: 0.11142/0.33545, loss_spatial_dice_9: 0.82039/0.82406, loss_spatial_ce_9: 2.10231/1.51180, loss_grounding_bce_9: 0.03003/0.10520, loss_grounding_dice_9: 0.12883/0.28198, loss_grounding_ce_9: 0.04404/0.68985] items per batch[64] items per second[0.23] total items[1740800] mini batches[ 27200] memory[7341] epoch remaining[0:09:35]
INFO:trainer.default_trainer:epochs[    14] optim steps[27300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61134/0.91027, loss_mask_bce_0: 0.04847/0.33521, loss_mask_dice_0: 0.41181/1.16717, loss_spatial_bce_0: 0.04106/0.09068, loss_spatial_dice_0: 0.25349/0.21681, loss_spatial_ce_0: 0.06196/0.07482, loss_grounding_bce_0: 0.03103/0.08630, loss_grounding_dice_0: 0.26416/0.17957, loss_grounding_ce_0: 0.26152/0.27652, loss_mask_ce_1: 0.53205/0.91091, loss_mask_bce_1: 0.04388/0.33587, loss_mask_dice_1: 0.39027/1.17428, loss_spatial_bce_1: 0.04347/0.09148, loss_spatial_dice_1: 0.25391/0.22109, loss_spatial_ce_1: 0.07813/0.08072, loss_grounding_bce_1: 0.02945/0.08640, loss_grounding_dice_1: 0.23839/0.18035, loss_grounding_ce_1: 0.31345/0.27814, loss_mask_ce_2: 0.53929/0.91863, loss_mask_bce_2: 0.04419/0.33632, loss_mask_dice_2: 0.39986/1.17359, loss_spatial_bce_2: 0.04411/0.09171, loss_spatial_dice_2: 0.25410/0.22210, loss_spatial_ce_2: 0.07503/0.08434, loss_grounding_bce_2: 0.02788/0.08644, loss_grounding_dice_2: 0.24025/0.17981, loss_grounding_ce_2: 0.30507/0.28151, loss_mask_ce_3: 0.62134/0.92678, loss_mask_bce_3: 0.04591/0.33704, loss_mask_dice_3: 0.38533/1.17068, loss_spatial_bce_3: 0.04253/0.09253, loss_spatial_dice_3: 0.24075/0.22270, loss_spatial_ce_3: 0.07191/0.08827, loss_grounding_bce_3: 0.03493/0.08662, loss_grounding_dice_3: 0.28999/0.17963, loss_grounding_ce_3: 0.26754/0.28317, loss_mask_ce_4: 0.60449/0.92577, loss_mask_bce_4: 0.05157/0.33880, loss_mask_dice_4: 0.42386/1.19328, loss_spatial_bce_4: 0.04554/0.09667, loss_spatial_dice_4: 0.27868/0.23320, loss_spatial_ce_4: 0.09920/0.10488, loss_grounding_bce_4: 0.03469/0.08715, loss_grounding_dice_4: 0.27593/0.18247, loss_grounding_ce_4: 0.28437/0.28546, loss_mask_ce_5: 0.59188/0.94023, loss_mask_bce_5: 0.05788/0.34103, loss_mask_dice_5: 0.43371/1.19865, loss_spatial_bce_5: 0.04951/0.09799, loss_spatial_dice_5: 0.29116/0.23642, loss_spatial_ce_5: 0.12162/0.11897, loss_grounding_bce_5: 0.05465/0.08753, loss_grounding_dice_5: 0.35094/0.18365, loss_grounding_ce_5: 0.16669/0.29836, loss_mask_ce_6: 0.72258/0.97749, loss_mask_bce_6: 0.06000/0.34377, loss_mask_dice_6: 0.46363/1.20107, loss_spatial_bce_6: 0.04502/0.10346, loss_spatial_dice_6: 0.27953/0.23858, loss_spatial_ce_6: 0.14705/0.14418, loss_grounding_bce_6: 0.03698/0.08828, loss_grounding_dice_6: 0.28666/0.18385, loss_grounding_ce_6: 0.31892/0.31563, loss_mask_ce_7: 0.71677/1.02121, loss_mask_bce_7: 0.05917/0.35143, loss_mask_dice_7: 0.45243/1.25673, loss_spatial_bce_7: 0.15717/0.11214, loss_spatial_dice_7: 0.31840/0.26576, loss_spatial_ce_7: 0.15387/0.18187, loss_grounding_bce_7: 0.04190/0.09022, loss_grounding_dice_7: 0.32480/0.19116, loss_grounding_ce_7: 0.30542/0.34772, loss_mask_ce_8: 0.63642/1.13265, loss_mask_bce_8: 0.05530/0.36492, loss_mask_dice_8: 0.48602/1.33134, loss_spatial_bce_8: 0.31512/0.13323, loss_spatial_dice_8: 0.30948/0.30598, loss_spatial_ce_8: 0.46437/0.23878, loss_grounding_bce_8: 0.03972/0.09380, loss_grounding_dice_8: 0.33983/0.20236, loss_grounding_ce_8: 0.23071/0.41822, loss_mask_ce_9: 2.48121/3.68769, loss_mask_bce_9: 0.05431/0.39200, loss_mask_dice_9: 0.58153/1.90585, loss_spatial_bce_9: 0.33265/0.33543, loss_spatial_dice_9: 0.78608/0.82408, loss_spatial_ce_9: 1.12641/1.51190, loss_grounding_bce_9: 0.03630/0.10521, loss_grounding_dice_9: 0.41421/0.28198, loss_grounding_ce_9: 0.27194/0.68982] items per batch[64] items per second[0.23] total items[1747200] mini batches[ 27300] memory[7341] epoch remaining[0:04:54]
INFO:trainer.default_trainer:epochs[    14] optim steps[27400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.22974/0.91038, loss_mask_bce_0: 0.23417/0.33522, loss_mask_dice_0: 0.60677/1.16717, loss_spatial_bce_0: 0.05293/0.09069, loss_spatial_dice_0: 0.17650/0.21678, loss_spatial_ce_0: 0.00915/0.07474, loss_grounding_bce_0: 0.16664/0.08633, loss_grounding_dice_0: 0.19930/0.17965, loss_grounding_ce_0: 0.02950/0.27679, loss_mask_ce_1: 1.24394/0.91099, loss_mask_bce_1: 0.23369/0.33590, loss_mask_dice_1: 0.78389/1.17429, loss_spatial_bce_1: 0.05324/0.09148, loss_spatial_dice_1: 0.16749/0.22107, loss_spatial_ce_1: 0.08907/0.08067, loss_grounding_bce_1: 0.17311/0.08644, loss_grounding_dice_1: 0.21164/0.18044, loss_grounding_ce_1: 0.03046/0.27837, loss_mask_ce_2: 1.52842/0.91872, loss_mask_bce_2: 0.24642/0.33635, loss_mask_dice_2: 0.89133/1.17355, loss_spatial_bce_2: 0.05516/0.09171, loss_spatial_dice_2: 0.15078/0.22208, loss_spatial_ce_2: 0.13910/0.08429, loss_grounding_bce_2: 0.16771/0.08647, loss_grounding_dice_2: 0.20105/0.17989, loss_grounding_ce_2: 0.02271/0.28181, loss_mask_ce_3: 1.23384/0.92694, loss_mask_bce_3: 0.22270/0.33707, loss_mask_dice_3: 0.72031/1.17070, loss_spatial_bce_3: 0.05661/0.09254, loss_spatial_dice_3: 0.16477/0.22268, loss_spatial_ce_3: 0.07270/0.08822, loss_grounding_bce_3: 0.16128/0.08665, loss_grounding_dice_3: 0.18896/0.17972, loss_grounding_ce_3: 0.02176/0.28345, loss_mask_ce_4: 1.31374/0.92581, loss_mask_bce_4: 0.22577/0.33884, loss_mask_dice_4: 0.78275/1.19333, loss_spatial_bce_4: 0.05744/0.09667, loss_spatial_dice_4: 0.19941/0.23320, loss_spatial_ce_4: 0.06428/0.10484, loss_grounding_bce_4: 0.15816/0.08718, loss_grounding_dice_4: 0.19349/0.18255, loss_grounding_ce_4: 0.02272/0.28574, loss_mask_ce_5: 1.32459/0.94037, loss_mask_bce_5: 0.24014/0.34104, loss_mask_dice_5: 0.88556/1.19871, loss_spatial_bce_5: 0.06843/0.09800, loss_spatial_dice_5: 0.19470/0.23642, loss_spatial_ce_5: 0.15629/0.11896, loss_grounding_bce_5: 0.15524/0.08756, loss_grounding_dice_5: 0.19004/0.18374, loss_grounding_ce_5: 0.02643/0.29855, loss_mask_ce_6: 1.29677/0.97760, loss_mask_bce_6: 0.23471/0.34379, loss_mask_dice_6: 0.72636/1.20115, loss_spatial_bce_6: 0.05989/0.10347, loss_spatial_dice_6: 0.16080/0.23858, loss_spatial_ce_6: 0.05333/0.14423, loss_grounding_bce_6: 0.16496/0.08831, loss_grounding_dice_6: 0.18901/0.18393, loss_grounding_ce_6: 0.03023/0.31582, loss_mask_ce_7: 1.49886/1.02139, loss_mask_bce_7: 0.23706/0.35146, loss_mask_dice_7: 0.70600/1.25683, loss_spatial_bce_7: 0.07685/0.11216, loss_spatial_dice_7: 0.21691/0.26578, loss_spatial_ce_7: 0.12601/0.18194, loss_grounding_bce_7: 0.16117/0.09025, loss_grounding_dice_7: 0.20321/0.19126, loss_grounding_ce_7: 0.01944/0.34800, loss_mask_ce_8: 1.66996/1.13295, loss_mask_bce_8: 0.26209/0.36496, loss_mask_dice_8: 0.86624/1.33149, loss_spatial_bce_8: 0.10737/0.13323, loss_spatial_dice_8: 0.26537/0.30598, loss_spatial_ce_8: 0.09149/0.23874, loss_grounding_bce_8: 0.15597/0.09383, loss_grounding_dice_8: 0.18054/0.20246, loss_grounding_ce_8: 0.02678/0.41848, loss_mask_ce_9: 4.07842/3.68803, loss_mask_bce_9: 0.27350/0.39207, loss_mask_dice_9: 1.39771/1.90617, loss_spatial_bce_9: 0.21129/0.33540, loss_spatial_dice_9: 0.84479/0.82412, loss_spatial_ce_9: 1.42884/1.51184, loss_grounding_bce_9: 0.15744/0.10526, loss_grounding_dice_9: 0.20478/0.28213, loss_grounding_ce_9: 0.09925/0.68983] items per batch[64] items per second[0.23] total items[1753600] mini batches[ 27400] memory[7341] epoch remaining[0:00:14]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00027405.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0026 s/iter. Inference: 0.2219 s/iter. Eval: 0.0947 s/iter. Total: 0.3193 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2225 s/iter. Eval: 0.0845 s/iter. Total: 0.3102 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2256 s/iter. Eval: 0.0827 s/iter. Total: 0.3115 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0032 s/iter. Inference: 0.2264 s/iter. Eval: 0.0804 s/iter. Total: 0.3101 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0033 s/iter. Inference: 0.2237 s/iter. Eval: 0.0798 s/iter. Total: 0.3069 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 94/157. Dataloading: 0.0032 s/iter. Inference: 0.2248 s/iter. Eval: 0.0799 s/iter. Total: 0.3080 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 110/157. Dataloading: 0.0032 s/iter. Inference: 0.2268 s/iter. Eval: 0.0798 s/iter. Total: 0.3099 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 127/157. Dataloading: 0.0032 s/iter. Inference: 0.2261 s/iter. Eval: 0.0787 s/iter. Total: 0.3081 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0032 s/iter. Inference: 0.2266 s/iter. Eval: 0.0778 s/iter. Total: 0.3078 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalt18w_ort ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.093 | 81.916 | 60.281 |      133      |
| Things | 54.927 | 82.646 | 65.784 |      80       |
| Stuff  | 42.796 | 80.815 | 51.974 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.55s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.77 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.00 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.21s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.90 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.14 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.768 | 61.106 | 40.934 | 20.014 | 41.838 | 60.005 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.818 | bicycle      | 18.776 | car            | 36.759 |
| motorcycle    | 34.909 | airplane     | 56.453 | bus            | 63.715 |
| train         | 67.395 | truck        | 35.280 | boat           | 22.164 |
| traffic light | 25.493 | fire hydrant | 64.837 | stop sign      | 64.446 |
| parking meter | 43.580 | bench        | 20.423 | bird           | 29.268 |
| cat           | 73.991 | dog          | 64.925 | horse          | 46.010 |
| sheep         | 46.915 | cow          | 49.732 | elephant       | 60.370 |
| bear          | 78.197 | zebra        | 60.465 | giraffe        | 57.274 |
| backpack      | 16.820 | umbrella     | 48.585 | handbag        | 15.453 |
| tie           | 32.910 | suitcase     | 38.082 | frisbee        | 67.339 |
| skis          | 5.230  | snowboard    | 24.150 | sports ball    | 47.948 |
| kite          | 33.251 | baseball bat | 27.924 | baseball glove | 42.553 |
| skateboard    | 34.119 | surfboard    | 34.985 | tennis racket  | 56.338 |
| bottle        | 33.753 | wine glass   | 25.587 | cup            | 41.136 |
| fork          | 16.298 | knife        | 12.824 | spoon          | 13.723 |
| bowl          | 32.023 | banana       | 20.415 | apple          | 19.306 |
| sandwich      | 42.969 | orange       | 28.674 | broccoli       | 22.514 |
| carrot        | 19.401 | hot dog      | 22.314 | pizza          | 51.880 |
| donut         | 44.948 | cake         | 42.091 | chair          | 20.400 |
| couch         | 41.136 | potted plant | 17.037 | bed            | 41.681 |
| dining table  | 12.827 | toilet       | 66.804 | tv             | 63.144 |
| laptop        | 63.184 | mouse        | 60.055 | remote         | 31.096 |
| keyboard      | 49.309 | cell phone   | 38.700 | microwave      | 55.681 |
| oven          | 32.432 | toaster      | 38.079 | sink           | 37.715 |
| refrigerator  | 58.243 | book         | 9.633  | clock          | 51.041 |
| vase          | 32.359 | scissors     | 21.712 | teddy bear     | 50.408 |
| hair drier    | 9.538  | toothbrush   | 18.487 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.728622009881725, 'fwIoU': 68.9960405682507, 'IoU-person': 87.19165328282237, 'IoU-bicycle': 70.510380361703, 'IoU-car': 69.27545104953818, 'IoU-motorcycle': 80.76253533869657, 'IoU-airplane': 75.51440927563442, 'IoU-bus': 86.20154317356452, 'IoU-train': 84.58462346028558, 'IoU-truck': 65.6328893239634, 'IoU-boat': 66.42857387813153, 'IoU-traffic light': 75.79998976591025, 'IoU-fire hydrant': 90.46714543172432, 'IoU-stop sign': 91.48581402729292, 'IoU-parking meter': 87.39789677822651, 'IoU-bench': 53.242819914820615, 'IoU-bird': 75.18016384402526, 'IoU-cat': 84.77793662901787, 'IoU-dog': 78.91838067662441, 'IoU-horse': 84.63832355915115, 'IoU-sheep': 85.51311863931306, 'IoU-cow': 81.36692208772664, 'IoU-elephant': 88.82284627314695, 'IoU-bear': 85.07777264822633, 'IoU-zebra': 89.32512568094303, 'IoU-giraffe': 88.30768462827311, 'IoU-backpack': 40.7723960189194, 'IoU-umbrella': 76.51088208999597, 'IoU-handbag': 35.288147824733564, 'IoU-tie': 69.70081993010662, 'IoU-suitcase': 80.25821349112853, 'IoU-frisbee': 82.05799640696515, 'IoU-skis': 51.69701153758951, 'IoU-snowboard': 70.0699103833341, 'IoU-sports ball': 66.64317753144661, 'IoU-kite': 64.6886318622999, 'IoU-baseball bat': 60.29929904927912, 'IoU-baseball glove': 76.40565331106718, 'IoU-skateboard': 64.16688130389541, 'IoU-surfboard': 75.23972960614577, 'IoU-tennis racket': 76.96254755509632, 'IoU-bottle': 66.73333051371444, 'IoU-wine glass': 74.04845749952554, 'IoU-cup': 58.89201483973847, 'IoU-fork': 54.943055358695695, 'IoU-knife': 46.60550080125449, 'IoU-spoon': 48.81189506103307, 'IoU-bowl': 58.2929516926606, 'IoU-banana': 82.27448849169208, 'IoU-apple': 55.21794664220051, 'IoU-sandwich': 65.27791014456984, 'IoU-orange': 75.88327986699252, 'IoU-broccoli': 68.45290112231818, 'IoU-carrot': 62.35822106687476, 'IoU-hot dog': 60.55673003047208, 'IoU-pizza': 84.79463815910083, 'IoU-donut': 64.10608431232419, 'IoU-cake': 70.44037112185073, 'IoU-chair': 52.861913959174785, 'IoU-couch': 67.7539078540715, 'IoU-potted plant': 33.041718286790065, 'IoU-bed': 62.64493503359321, 'IoU-dining table': 50.71761426024848, 'IoU-toilet': 84.16786824998745, 'IoU-tv': 75.09167592136978, 'IoU-laptop': 76.26713572715059, 'IoU-mouse': 70.79838980207984, 'IoU-remote': 64.0026662266607, 'IoU-keyboard': 66.87425172113373, 'IoU-cell phone': 73.0901807417509, 'IoU-microwave': 54.79115442832355, 'IoU-oven': 64.29705850143618, 'IoU-toaster': 59.94684612196133, 'IoU-sink': 71.63860697198953, 'IoU-refrigerator': 76.56383050481493, 'IoU-book': 52.27856463563968, 'IoU-clock': 76.06540168115188, 'IoU-vase': 61.82992202729045, 'IoU-scissors': 73.21807940848373, 'IoU-teddy bear': 79.20111990595598, 'IoU-hair drier': 36.18409940885511, 'IoU-toothbrush': 51.312813077438804, 'IoU-banner': 32.36661683408596, 'IoU-blanket': 12.694358662240731, 'IoU-bridge': 37.39120406873304, 'IoU-cardboard': 45.44226367315964, 'IoU-counter': 30.88509142286072, 'IoU-curtain': 64.04351863695005, 'IoU-door-stuff': 42.36184684939854, 'IoU-floor-wood': 60.57340052837091, 'IoU-flower': 44.533226482091784, 'IoU-fruit': 37.13118682711307, 'IoU-gravel': 30.585830730715557, 'IoU-house': 23.624432183546755, 'IoU-light': 40.63110846800121, 'IoU-mirror-stuff': 55.48090096923921, 'IoU-net': 41.949052443926135, 'IoU-pillow': 10.796680397675795, 'IoU-platform': 31.578749478841985, 'IoU-playingfield': 69.78029851660513, 'IoU-railroad': 61.23308650680514, 'IoU-river': 48.475006834326955, 'IoU-road': 65.99936062440999, 'IoU-roof': 15.569687198905275, 'IoU-sand': 62.24405819586766, 'IoU-sea': 84.83137187897475, 'IoU-shelf': 36.09659569470877, 'IoU-snow': 89.30278506220007, 'IoU-stairs': 29.066969023194705, 'IoU-tent': 9.387712288815315, 'IoU-towel': 33.2874335255539, 'IoU-wall-brick': 45.9623975585898, 'IoU-wall-stone': 26.72649304438067, 'IoU-wall-tile': 67.95085113565936, 'IoU-wall-wood': 38.67942741331594, 'IoU-water-other': 23.32844275424253, 'IoU-window-blind': 46.498940507914085, 'IoU-window-other': 48.17243344048773, 'IoU-tree-merged': 80.96196380471903, 'IoU-fence-merged': 51.423313588052075, 'IoU-ceiling-merged': 67.23647438707714, 'IoU-sky-other-merged': 93.9406184844218, 'IoU-cabinet-merged': 58.79254547796984, 'IoU-table-merged': 40.238874162517966, 'IoU-floor-other-merged': 48.836061073803485, 'IoU-pavement-merged': 54.78990587359611, 'IoU-mountain-merged': 54.60215363868939, 'IoU-grass-merged': 71.14173476235383, 'IoU-dirt-merged': 44.28820258096725, 'IoU-paper-merged': 28.002500656663354, 'IoU-food-other-merged': 41.32839187575284, 'IoU-building-other-merged': 57.60309999941584, 'IoU-rock-merged': 59.75175420400859, 'IoU-wall-other-merged': 64.0845752155233, 'IoU-rug-merged': 65.70690885369133, 'mACC': 73.13167005691463, 'pACC': 80.27397317499187, 'ACC-person': 92.27602094502853, 'ACC-bicycle': 79.83244066418045, 'ACC-car': 84.37293850048921, 'ACC-motorcycle': 85.76542660500243, 'ACC-airplane': 87.75610652091113, 'ACC-bus': 91.00532439623073, 'ACC-train': 94.42614310663767, 'ACC-truck': 75.8997366921588, 'ACC-boat': 78.37208575248597, 'ACC-traffic light': 90.23085770522974, 'ACC-fire hydrant': 95.33214909452703, 'ACC-stop sign': 94.36170484385096, 'ACC-parking meter': 92.04644273000724, 'ACC-bench': 67.21429373717109, 'ACC-bird': 80.95875490045024, 'ACC-cat': 94.52637141681734, 'ACC-dog': 82.2240552550874, 'ACC-horse': 90.61497289452102, 'ACC-sheep': 88.36468922118695, 'ACC-cow': 88.50147643294603, 'ACC-elephant': 91.40274620768346, 'ACC-bear': 87.24996369185799, 'ACC-zebra': 92.00412002783743, 'ACC-giraffe': 92.98779040658329, 'ACC-backpack': 64.0561870623395, 'ACC-umbrella': 84.9056329859211, 'ACC-handbag': 54.300714543278936, 'ACC-tie': 81.77256770413808, 'ACC-suitcase': 86.74454406827394, 'ACC-frisbee': 94.00981818181819, 'ACC-skis': 69.14312582411405, 'ACC-snowboard': 80.46533271288972, 'ACC-sports ball': 81.62878200556123, 'ACC-kite': 74.39862672697389, 'ACC-baseball bat': 85.01374851089018, 'ACC-baseball glove': 89.67092511330638, 'ACC-skateboard': 69.88870735352864, 'ACC-surfboard': 84.15399561935106, 'ACC-tennis racket': 83.44608465635022, 'ACC-bottle': 81.44668233506135, 'ACC-wine glass': 82.7742017370492, 'ACC-cup': 85.47576764315279, 'ACC-fork': 66.51738163222083, 'ACC-knife': 62.0676705503041, 'ACC-spoon': 73.02473931039019, 'ACC-bowl': 70.36586430524788, 'ACC-banana': 90.08559191316343, 'ACC-apple': 66.68634093085824, 'ACC-sandwich': 82.09827153666839, 'ACC-orange': 84.6340277623677, 'ACC-broccoli': 80.64711910730979, 'ACC-carrot': 71.46831559383064, 'ACC-hot dog': 68.43556491007891, 'ACC-pizza': 94.00667731325638, 'ACC-donut': 81.2147164661473, 'ACC-cake': 77.02850128820774, 'ACC-chair': 68.03943085566824, 'ACC-couch': 79.98437808673988, 'ACC-potted plant': 46.92807363070634, 'ACC-bed': 72.02870023276037, 'ACC-dining table': 73.53721302707778, 'ACC-toilet': 87.85180584077969, 'ACC-tv': 84.61841012188435, 'ACC-laptop': 90.39878838913893, 'ACC-mouse': 85.26995876757249, 'ACC-remote': 70.81878895105118, 'ACC-keyboard': 74.07427409636746, 'ACC-cell phone': 91.10859575963501, 'ACC-microwave': 63.397505668546785, 'ACC-oven': 81.41217734717551, 'ACC-toaster': 67.29529622685408, 'ACC-sink': 81.9524370570281, 'ACC-refrigerator': 83.2479790921684, 'ACC-book': 69.48267770586423, 'ACC-clock': 81.19056352354148, 'ACC-vase': 70.30113666971933, 'ACC-scissors': 79.62656897042486, 'ACC-teddy bear': 85.43210792081177, 'ACC-hair drier': 42.68494008482054, 'ACC-toothbrush': 81.77466990965948, 'ACC-banner': 73.78827915323913, 'ACC-blanket': 21.64380178392838, 'ACC-bridge': 54.34509056001951, 'ACC-cardboard': 53.567369492511276, 'ACC-counter': 56.640682899433195, 'ACC-curtain': 75.09878303779391, 'ACC-door-stuff': 60.00935575424974, 'ACC-floor-wood': 79.62582545723072, 'ACC-flower': 71.4846344837282, 'ACC-fruit': 59.525526070685444, 'ACC-gravel': 38.354529390915935, 'ACC-house': 28.649434129472006, 'ACC-light': 54.84163859434214, 'ACC-mirror-stuff': 68.85634313962863, 'ACC-net': 66.82900900781061, 'ACC-pillow': 23.59494499634365, 'ACC-platform': 51.33459361106459, 'ACC-playingfield': 91.7748304504429, 'ACC-railroad': 78.54442336633073, 'ACC-river': 68.68594775008788, 'ACC-road': 81.90481579264394, 'ACC-roof': 21.33946142844919, 'ACC-sand': 71.37290450306439, 'ACC-sea': 89.72359420825963, 'ACC-shelf': 57.701984081183, 'ACC-snow': 94.9644293639118, 'ACC-stairs': 46.768577703283555, 'ACC-tent': 12.44537056011643, 'ACC-towel': 45.53932002115795, 'ACC-wall-brick': 59.390238234985304, 'ACC-wall-stone': 35.83681437779022, 'ACC-wall-tile': 80.85157340073977, 'ACC-wall-wood': 55.444204675381584, 'ACC-water-other': 42.19790648417278, 'ACC-window-blind': 53.16056698878741, 'ACC-window-other': 70.8215984655365, 'ACC-tree-merged': 89.06406543332197, 'ACC-fence-merged': 69.20322389122661, 'ACC-ceiling-merged': 80.07606166342542, 'ACC-sky-other-merged': 96.79657406831568, 'ACC-cabinet-merged': 75.27522009374036, 'ACC-table-merged': 53.65689061401193, 'ACC-floor-other-merged': 60.47899244730495, 'ACC-pavement-merged': 68.99106796816307, 'ACC-mountain-merged': 67.5777682312085, 'ACC-grass-merged': 82.31302138835693, 'ACC-dirt-merged': 66.60394590377655, 'ACC-paper-merged': 37.112005583192875, 'ACC-food-other-merged': 57.03590434807383, 'ACC-building-other-merged': 74.39715748738621, 'ACC-rock-merged': 78.99136226051404, 'ACC-wall-other-merged': 81.21072027546099, 'ACC-rug-merged': 79.31041540451591})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1579 s/iter. Inference: 0.5869 s/iter. Eval: 0.0000 s/iter. Total: 0.7448 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1581 s/iter. Inference: 0.5722 s/iter. Eval: 0.0000 s/iter. Total: 0.7304 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1697 s/iter. Inference: 0.5614 s/iter. Eval: 0.0000 s/iter. Total: 0.7313 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1725 s/iter. Inference: 0.6951 s/iter. Eval: 0.0000 s/iter. Total: 0.8678 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1690 s/iter. Inference: 0.6195 s/iter. Eval: 0.0000 s/iter. Total: 0.7886 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1678 s/iter. Inference: 0.6561 s/iter. Eval: 0.0000 s/iter. Total: 0.8241 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1684 s/iter. Inference: 0.6819 s/iter. Eval: 0.0000 s/iter. Total: 0.8505 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.55633596722271, 'noc@0.8': 2.9733684518583554, 'noc@0.85': 3.60462393912789, 'noc@0.9': 4.611354989757097, 'miou@iter1': 0.8255540324750615}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0998 s/iter. Eval: 0.0008 s/iter. Total: 0.1023 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.50135803222656, 'precision@0.6': 67.19782257080078, 'precision@0.7': 62.223087310791016, 'precision@0.8': 52.15701675415039, 'precision@0.9': 26.700349807739258, 'cIoU': 56.60593795776367, 'mIoU': 62.319496154785156}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.09299384535317, 'SQ': 81.9161534599535, 'RQ': 60.280880326956954, 'PQ_th': 54.92722201639981, 'SQ_th': 82.64578821338675, 'RQ_th': 65.78386687240413, 'PQ_st': 42.79604566264129, 'SQ_st': 80.81481798307313, 'RQ_st': 51.97448554137627}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.767996565313, 'AP50': 61.10556702323826, 'AP75': 40.93416163024538, 'APs': 20.014184209036017, 'APm': 41.838033076346726, 'APl': 60.004575375685256, 'AP-person': 43.81776428910256, 'AP-bicycle': 18.775620755119824, 'AP-car': 36.75892622746498, 'AP-motorcycle': 34.9091496092528, 'AP-airplane': 56.452771990661276, 'AP-bus': 63.714505458574614, 'AP-train': 67.39499703071506, 'AP-truck': 35.279657113495425, 'AP-boat': 22.164202982714524, 'AP-traffic light': 25.4927446243334, 'AP-fire hydrant': 64.83720741540752, 'AP-stop sign': 64.4463436882827, 'AP-parking meter': 43.58015538117664, 'AP-bench': 20.422843457929858, 'AP-bird': 29.268252735098688, 'AP-cat': 73.9908144205741, 'AP-dog': 64.92458403090245, 'AP-horse': 46.0104446922682, 'AP-sheep': 46.915225529802576, 'AP-cow': 49.73155112052318, 'AP-elephant': 60.369883999169915, 'AP-bear': 78.19749179066923, 'AP-zebra': 60.46548166458386, 'AP-giraffe': 57.274112947745614, 'AP-backpack': 16.819792768565257, 'AP-umbrella': 48.585156809871094, 'AP-handbag': 15.453237027937416, 'AP-tie': 32.910157701765215, 'AP-suitcase': 38.08238804843997, 'AP-frisbee': 67.33940600419776, 'AP-skis': 5.229897820763653, 'AP-snowboard': 24.149614742113673, 'AP-sports ball': 47.94760560202402, 'AP-kite': 33.251225871079264, 'AP-baseball bat': 27.92426297760988, 'AP-baseball glove': 42.553012142172406, 'AP-skateboard': 34.11936988448734, 'AP-surfboard': 34.98531556855779, 'AP-tennis racket': 56.338464869025096, 'AP-bottle': 33.75305174000706, 'AP-wine glass': 25.586592695653405, 'AP-cup': 41.135699944651535, 'AP-fork': 16.297631283482755, 'AP-knife': 12.82418110048983, 'AP-spoon': 13.722940427059626, 'AP-bowl': 32.02303579340534, 'AP-banana': 20.41532951978864, 'AP-apple': 19.30646413610843, 'AP-sandwich': 42.96920892962845, 'AP-orange': 28.67379009552829, 'AP-broccoli': 22.513570370322007, 'AP-carrot': 19.40079227413437, 'AP-hot dog': 22.31409892071163, 'AP-pizza': 51.87992466068947, 'AP-donut': 44.94778907052904, 'AP-cake': 42.09094571474639, 'AP-chair': 20.400427271924613, 'AP-couch': 41.136385276954144, 'AP-potted plant': 17.03713378818657, 'AP-bed': 41.68068836764361, 'AP-dining table': 12.826755037317133, 'AP-toilet': 66.80382891358414, 'AP-tv': 63.14402978876772, 'AP-laptop': 63.18445760373961, 'AP-mouse': 60.054538010039906, 'AP-remote': 31.096154796996146, 'AP-keyboard': 49.30926909042341, 'AP-cell phone': 38.69972063730501, 'AP-microwave': 55.68142740007871, 'AP-oven': 32.43206009486917, 'AP-toaster': 38.07915643781127, 'AP-sink': 37.71503745532867, 'AP-refrigerator': 58.24258852392038, 'AP-book': 9.63311287046646, 'AP-clock': 51.04063065374733, 'AP-vase': 32.35894292240003, 'AP-scissors': 21.712330823379116, 'AP-teddy bear': 50.40762350681448, 'AP-hair drier': 9.537857037302878, 'AP-toothbrush': 18.486881444924602}), ('sem_seg', {'mIoU': 60.728622009881725, 'fwIoU': 68.9960405682507, 'IoU-person': 87.19165328282237, 'IoU-bicycle': 70.510380361703, 'IoU-car': 69.27545104953818, 'IoU-motorcycle': 80.76253533869657, 'IoU-airplane': 75.51440927563442, 'IoU-bus': 86.20154317356452, 'IoU-train': 84.58462346028558, 'IoU-truck': 65.6328893239634, 'IoU-boat': 66.42857387813153, 'IoU-traffic light': 75.79998976591025, 'IoU-fire hydrant': 90.46714543172432, 'IoU-stop sign': 91.48581402729292, 'IoU-parking meter': 87.39789677822651, 'IoU-bench': 53.242819914820615, 'IoU-bird': 75.18016384402526, 'IoU-cat': 84.77793662901787, 'IoU-dog': 78.91838067662441, 'IoU-horse': 84.63832355915115, 'IoU-sheep': 85.51311863931306, 'IoU-cow': 81.36692208772664, 'IoU-elephant': 88.82284627314695, 'IoU-bear': 85.07777264822633, 'IoU-zebra': 89.32512568094303, 'IoU-giraffe': 88.30768462827311, 'IoU-backpack': 40.7723960189194, 'IoU-umbrella': 76.51088208999597, 'IoU-handbag': 35.288147824733564, 'IoU-tie': 69.70081993010662, 'IoU-suitcase': 80.25821349112853, 'IoU-frisbee': 82.05799640696515, 'IoU-skis': 51.69701153758951, 'IoU-snowboard': 70.0699103833341, 'IoU-sports ball': 66.64317753144661, 'IoU-kite': 64.6886318622999, 'IoU-baseball bat': 60.29929904927912, 'IoU-baseball glove': 76.40565331106718, 'IoU-skateboard': 64.16688130389541, 'IoU-surfboard': 75.23972960614577, 'IoU-tennis racket': 76.96254755509632, 'IoU-bottle': 66.73333051371444, 'IoU-wine glass': 74.04845749952554, 'IoU-cup': 58.89201483973847, 'IoU-fork': 54.943055358695695, 'IoU-knife': 46.60550080125449, 'IoU-spoon': 48.81189506103307, 'IoU-bowl': 58.2929516926606, 'IoU-banana': 82.27448849169208, 'IoU-apple': 55.21794664220051, 'IoU-sandwich': 65.27791014456984, 'IoU-orange': 75.88327986699252, 'IoU-broccoli': 68.45290112231818, 'IoU-carrot': 62.35822106687476, 'IoU-hot dog': 60.55673003047208, 'IoU-pizza': 84.79463815910083, 'IoU-donut': 64.10608431232419, 'IoU-cake': 70.44037112185073, 'IoU-chair': 52.861913959174785, 'IoU-couch': 67.7539078540715, 'IoU-potted plant': 33.041718286790065, 'IoU-bed': 62.64493503359321, 'IoU-dining table': 50.71761426024848, 'IoU-toilet': 84.16786824998745, 'IoU-tv': 75.09167592136978, 'IoU-laptop': 76.26713572715059, 'IoU-mouse': 70.79838980207984, 'IoU-remote': 64.0026662266607, 'IoU-keyboard': 66.87425172113373, 'IoU-cell phone': 73.0901807417509, 'IoU-microwave': 54.79115442832355, 'IoU-oven': 64.29705850143618, 'IoU-toaster': 59.94684612196133, 'IoU-sink': 71.63860697198953, 'IoU-refrigerator': 76.56383050481493, 'IoU-book': 52.27856463563968, 'IoU-clock': 76.06540168115188, 'IoU-vase': 61.82992202729045, 'IoU-scissors': 73.21807940848373, 'IoU-teddy bear': 79.20111990595598, 'IoU-hair drier': 36.18409940885511, 'IoU-toothbrush': 51.312813077438804, 'IoU-banner': 32.36661683408596, 'IoU-blanket': 12.694358662240731, 'IoU-bridge': 37.39120406873304, 'IoU-cardboard': 45.44226367315964, 'IoU-counter': 30.88509142286072, 'IoU-curtain': 64.04351863695005, 'IoU-door-stuff': 42.36184684939854, 'IoU-floor-wood': 60.57340052837091, 'IoU-flower': 44.533226482091784, 'IoU-fruit': 37.13118682711307, 'IoU-gravel': 30.585830730715557, 'IoU-house': 23.624432183546755, 'IoU-light': 40.63110846800121, 'IoU-mirror-stuff': 55.48090096923921, 'IoU-net': 41.949052443926135, 'IoU-pillow': 10.796680397675795, 'IoU-platform': 31.578749478841985, 'IoU-playingfield': 69.78029851660513, 'IoU-railroad': 61.23308650680514, 'IoU-river': 48.475006834326955, 'IoU-road': 65.99936062440999, 'IoU-roof': 15.569687198905275, 'IoU-sand': 62.24405819586766, 'IoU-sea': 84.83137187897475, 'IoU-shelf': 36.09659569470877, 'IoU-snow': 89.30278506220007, 'IoU-stairs': 29.066969023194705, 'IoU-tent': 9.387712288815315, 'IoU-towel': 33.2874335255539, 'IoU-wall-brick': 45.9623975585898, 'IoU-wall-stone': 26.72649304438067, 'IoU-wall-tile': 67.95085113565936, 'IoU-wall-wood': 38.67942741331594, 'IoU-water-other': 23.32844275424253, 'IoU-window-blind': 46.498940507914085, 'IoU-window-other': 48.17243344048773, 'IoU-tree-merged': 80.96196380471903, 'IoU-fence-merged': 51.423313588052075, 'IoU-ceiling-merged': 67.23647438707714, 'IoU-sky-other-merged': 93.9406184844218, 'IoU-cabinet-merged': 58.79254547796984, 'IoU-table-merged': 40.238874162517966, 'IoU-floor-other-merged': 48.836061073803485, 'IoU-pavement-merged': 54.78990587359611, 'IoU-mountain-merged': 54.60215363868939, 'IoU-grass-merged': 71.14173476235383, 'IoU-dirt-merged': 44.28820258096725, 'IoU-paper-merged': 28.002500656663354, 'IoU-food-other-merged': 41.32839187575284, 'IoU-building-other-merged': 57.60309999941584, 'IoU-rock-merged': 59.75175420400859, 'IoU-wall-other-merged': 64.0845752155233, 'IoU-rug-merged': 65.70690885369133, 'mACC': 73.13167005691463, 'pACC': 80.27397317499187, 'ACC-person': 92.27602094502853, 'ACC-bicycle': 79.83244066418045, 'ACC-car': 84.37293850048921, 'ACC-motorcycle': 85.76542660500243, 'ACC-airplane': 87.75610652091113, 'ACC-bus': 91.00532439623073, 'ACC-train': 94.42614310663767, 'ACC-truck': 75.8997366921588, 'ACC-boat': 78.37208575248597, 'ACC-traffic light': 90.23085770522974, 'ACC-fire hydrant': 95.33214909452703, 'ACC-stop sign': 94.36170484385096, 'ACC-parking meter': 92.04644273000724, 'ACC-bench': 67.21429373717109, 'ACC-bird': 80.95875490045024, 'ACC-cat': 94.52637141681734, 'ACC-dog': 82.2240552550874, 'ACC-horse': 90.61497289452102, 'ACC-sheep': 88.36468922118695, 'ACC-cow': 88.50147643294603, 'ACC-elephant': 91.40274620768346, 'ACC-bear': 87.24996369185799, 'ACC-zebra': 92.00412002783743, 'ACC-giraffe': 92.98779040658329, 'ACC-backpack': 64.0561870623395, 'ACC-umbrella': 84.9056329859211, 'ACC-handbag': 54.300714543278936, 'ACC-tie': 81.77256770413808, 'ACC-suitcase': 86.74454406827394, 'ACC-frisbee': 94.00981818181819, 'ACC-skis': 69.14312582411405, 'ACC-snowboard': 80.46533271288972, 'ACC-sports ball': 81.62878200556123, 'ACC-kite': 74.39862672697389, 'ACC-baseball bat': 85.01374851089018, 'ACC-baseball glove': 89.67092511330638, 'ACC-skateboard': 69.88870735352864, 'ACC-surfboard': 84.15399561935106, 'ACC-tennis racket': 83.44608465635022, 'ACC-bottle': 81.44668233506135, 'ACC-wine glass': 82.7742017370492, 'ACC-cup': 85.47576764315279, 'ACC-fork': 66.51738163222083, 'ACC-knife': 62.0676705503041, 'ACC-spoon': 73.02473931039019, 'ACC-bowl': 70.36586430524788, 'ACC-banana': 90.08559191316343, 'ACC-apple': 66.68634093085824, 'ACC-sandwich': 82.09827153666839, 'ACC-orange': 84.6340277623677, 'ACC-broccoli': 80.64711910730979, 'ACC-carrot': 71.46831559383064, 'ACC-hot dog': 68.43556491007891, 'ACC-pizza': 94.00667731325638, 'ACC-donut': 81.2147164661473, 'ACC-cake': 77.02850128820774, 'ACC-chair': 68.03943085566824, 'ACC-couch': 79.98437808673988, 'ACC-potted plant': 46.92807363070634, 'ACC-bed': 72.02870023276037, 'ACC-dining table': 73.53721302707778, 'ACC-toilet': 87.85180584077969, 'ACC-tv': 84.61841012188435, 'ACC-laptop': 90.39878838913893, 'ACC-mouse': 85.26995876757249, 'ACC-remote': 70.81878895105118, 'ACC-keyboard': 74.07427409636746, 'ACC-cell phone': 91.10859575963501, 'ACC-microwave': 63.397505668546785, 'ACC-oven': 81.41217734717551, 'ACC-toaster': 67.29529622685408, 'ACC-sink': 81.9524370570281, 'ACC-refrigerator': 83.2479790921684, 'ACC-book': 69.48267770586423, 'ACC-clock': 81.19056352354148, 'ACC-vase': 70.30113666971933, 'ACC-scissors': 79.62656897042486, 'ACC-teddy bear': 85.43210792081177, 'ACC-hair drier': 42.68494008482054, 'ACC-toothbrush': 81.77466990965948, 'ACC-banner': 73.78827915323913, 'ACC-blanket': 21.64380178392838, 'ACC-bridge': 54.34509056001951, 'ACC-cardboard': 53.567369492511276, 'ACC-counter': 56.640682899433195, 'ACC-curtain': 75.09878303779391, 'ACC-door-stuff': 60.00935575424974, 'ACC-floor-wood': 79.62582545723072, 'ACC-flower': 71.4846344837282, 'ACC-fruit': 59.525526070685444, 'ACC-gravel': 38.354529390915935, 'ACC-house': 28.649434129472006, 'ACC-light': 54.84163859434214, 'ACC-mirror-stuff': 68.85634313962863, 'ACC-net': 66.82900900781061, 'ACC-pillow': 23.59494499634365, 'ACC-platform': 51.33459361106459, 'ACC-playingfield': 91.7748304504429, 'ACC-railroad': 78.54442336633073, 'ACC-river': 68.68594775008788, 'ACC-road': 81.90481579264394, 'ACC-roof': 21.33946142844919, 'ACC-sand': 71.37290450306439, 'ACC-sea': 89.72359420825963, 'ACC-shelf': 57.701984081183, 'ACC-snow': 94.9644293639118, 'ACC-stairs': 46.768577703283555, 'ACC-tent': 12.44537056011643, 'ACC-towel': 45.53932002115795, 'ACC-wall-brick': 59.390238234985304, 'ACC-wall-stone': 35.83681437779022, 'ACC-wall-tile': 80.85157340073977, 'ACC-wall-wood': 55.444204675381584, 'ACC-water-other': 42.19790648417278, 'ACC-window-blind': 53.16056698878741, 'ACC-window-other': 70.8215984655365, 'ACC-tree-merged': 89.06406543332197, 'ACC-fence-merged': 69.20322389122661, 'ACC-ceiling-merged': 80.07606166342542, 'ACC-sky-other-merged': 96.79657406831568, 'ACC-cabinet-merged': 75.27522009374036, 'ACC-table-merged': 53.65689061401193, 'ACC-floor-other-merged': 60.47899244730495, 'ACC-pavement-merged': 68.99106796816307, 'ACC-mountain-merged': 67.5777682312085, 'ACC-grass-merged': 82.31302138835693, 'ACC-dirt-merged': 66.60394590377655, 'ACC-paper-merged': 37.112005583192875, 'ACC-food-other-merged': 57.03590434807383, 'ACC-building-other-merged': 74.39715748738621, 'ACC-rock-merged': 78.99136226051404, 'ACC-wall-other-merged': 81.21072027546099, 'ACC-rug-merged': 79.31041540451591})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.55633596722271, 'noc@0.8': 2.9733684518583554, 'noc@0.85': 3.60462393912789, 'noc@0.9': 4.611354989757097, 'miou@iter1': 0.8255540324750615}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.50135803222656, 'precision@0.6': 67.19782257080078, 'precision@0.7': 62.223087310791016, 'precision@0.8': 52.15701675415039, 'precision@0.9': 26.700349807739258, 'cIoU': 56.60593795776367, 'mIoU': 62.319496154785156}}}
INFO:trainer.default_trainer:This epoch takes 1:28:39.520506
INFO:trainer.default_trainer:PROGRESS: 30.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 15 training.
INFO:trainer.default_trainer:epochs[    15] optim steps[27500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28109/0.91032, loss_mask_bce_0: 0.10721/0.33527, loss_mask_dice_0: 0.24338/1.16707, loss_spatial_bce_0: 0.04751/0.09067, loss_spatial_dice_0: 0.10563/0.21672, loss_spatial_ce_0: 0.00056/0.07466, loss_grounding_bce_0: 0.06253/0.08633, loss_grounding_dice_0: 0.13987/0.17967, loss_grounding_ce_0: 0.01015/0.27669, loss_mask_ce_1: 0.26755/0.91092, loss_mask_bce_1: 0.11082/0.33596, loss_mask_dice_1: 0.22562/1.17414, loss_spatial_bce_1: 0.04464/0.09146, loss_spatial_dice_1: 0.10083/0.22101, loss_spatial_ce_1: 0.00080/0.08059, loss_grounding_bce_1: 0.06043/0.08644, loss_grounding_dice_1: 0.13933/0.18048, loss_grounding_ce_1: 0.01088/0.27827, loss_mask_ce_2: 0.28425/0.91874, loss_mask_bce_2: 0.10843/0.33640, loss_mask_dice_2: 0.22887/1.17332, loss_spatial_bce_2: 0.04612/0.09169, loss_spatial_dice_2: 0.10887/0.22202, loss_spatial_ce_2: 0.00350/0.08421, loss_grounding_bce_2: 0.05973/0.08647, loss_grounding_dice_2: 0.13041/0.17991, loss_grounding_ce_2: 0.00941/0.28169, loss_mask_ce_3: 0.27396/0.92693, loss_mask_bce_3: 0.11123/0.33711, loss_mask_dice_3: 0.21889/1.17053, loss_spatial_bce_3: 0.05005/0.09252, loss_spatial_dice_3: 0.11180/0.22262, loss_spatial_ce_3: 0.00348/0.08814, loss_grounding_bce_3: 0.06411/0.08665, loss_grounding_dice_3: 0.13031/0.17976, loss_grounding_ce_3: 0.00862/0.28341, loss_mask_ce_4: 0.20820/0.92577, loss_mask_bce_4: 0.10964/0.33890, loss_mask_dice_4: 0.26828/1.19317, loss_spatial_bce_4: 0.04186/0.09665, loss_spatial_dice_4: 0.10133/0.23315, loss_spatial_ce_4: 0.01501/0.10475, loss_grounding_bce_4: 0.06213/0.08717, loss_grounding_dice_4: 0.11897/0.18258, loss_grounding_ce_4: 0.00532/0.28567, loss_mask_ce_5: 0.19017/0.94029, loss_mask_bce_5: 0.10405/0.34111, loss_mask_dice_5: 0.22812/1.19863, loss_spatial_bce_5: 0.04137/0.09799, loss_spatial_dice_5: 0.10713/0.23638, loss_spatial_ce_5: 0.02715/0.11890, loss_grounding_bce_5: 0.06143/0.08755, loss_grounding_dice_5: 0.12387/0.18376, loss_grounding_ce_5: 0.00387/0.29843, loss_mask_ce_6: 0.23297/0.97755, loss_mask_bce_6: 0.10776/0.34386, loss_mask_dice_6: 0.22773/1.20103, loss_spatial_bce_6: 0.04329/0.10347, loss_spatial_dice_6: 0.10016/0.23854, loss_spatial_ce_6: 0.03779/0.14419, loss_grounding_bce_6: 0.06590/0.08830, loss_grounding_dice_6: 0.13082/0.18396, loss_grounding_ce_6: 0.00590/0.31569, loss_mask_ce_7: 0.22509/1.02140, loss_mask_bce_7: 0.12309/0.35153, loss_mask_dice_7: 0.24655/1.25667, loss_spatial_bce_7: 0.04732/0.11217, loss_spatial_dice_7: 0.14383/0.26574, loss_spatial_ce_7: 0.11367/0.18190, loss_grounding_bce_7: 0.07215/0.09025, loss_grounding_dice_7: 0.12991/0.19129, loss_grounding_ce_7: 0.01536/0.34785, loss_mask_ce_8: 0.27773/1.13289, loss_mask_bce_8: 0.13221/0.36498, loss_mask_dice_8: 0.29721/1.33139, loss_spatial_bce_8: 0.04688/0.13322, loss_spatial_dice_8: 0.11404/0.30592, loss_spatial_ce_8: 0.12833/0.23861, loss_grounding_bce_8: 0.07450/0.09382, loss_grounding_dice_8: 0.17474/0.20248, loss_grounding_ce_8: 0.00916/0.41846, loss_mask_ce_9: 1.67093/3.68744, loss_mask_bce_9: 0.14674/0.39214, loss_mask_dice_9: 0.35839/1.90608, loss_spatial_bce_9: 0.42107/0.33543, loss_spatial_dice_9: 0.77224/0.82414, loss_spatial_ce_9: 1.55358/1.51188, loss_grounding_bce_9: 0.08306/0.10527, loss_grounding_dice_9: 0.18784/0.28215, loss_grounding_ce_9: 0.09248/0.68949] items per batch[64] items per second[0.13] total items[1760000] mini batches[ 27500] memory[7341] epoch remaining[1:21:44]
INFO:trainer.default_trainer:epochs[    15] optim steps[27600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86475/0.91009, loss_mask_bce_0: 0.64521/0.33526, loss_mask_dice_0: 1.09981/1.16669, loss_spatial_bce_0: 0.09611/0.09065, loss_spatial_dice_0: 0.17225/0.21663, loss_spatial_ce_0: 0.00339/0.07458, loss_grounding_bce_0: 0.07712/0.08630, loss_grounding_dice_0: 0.21692/0.17963, loss_grounding_ce_0: 0.11535/0.27656, loss_mask_ce_1: 0.83947/0.91071, loss_mask_bce_1: 0.66653/0.33596, loss_mask_dice_1: 1.11140/1.17372, loss_spatial_bce_1: 0.10324/0.09143, loss_spatial_dice_1: 0.18576/0.22092, loss_spatial_ce_1: 0.00389/0.08047, loss_grounding_bce_1: 0.07181/0.08642, loss_grounding_dice_1: 0.20359/0.18044, loss_grounding_ce_1: 0.14279/0.27817, loss_mask_ce_2: 0.79494/0.91845, loss_mask_bce_2: 0.63737/0.33639, loss_mask_dice_2: 1.08556/1.17293, loss_spatial_bce_2: 0.09594/0.09167, loss_spatial_dice_2: 0.18041/0.22193, loss_spatial_ce_2: 0.00424/0.08412, loss_grounding_bce_2: 0.07853/0.08645, loss_grounding_dice_2: 0.21046/0.17987, loss_grounding_ce_2: 0.16597/0.28158, loss_mask_ce_3: 0.80426/0.92667, loss_mask_bce_3: 0.61541/0.33710, loss_mask_dice_3: 1.06290/1.17015, loss_spatial_bce_3: 0.09518/0.09250, loss_spatial_dice_3: 0.17590/0.22253, loss_spatial_ce_3: 0.00740/0.08803, loss_grounding_bce_3: 0.08016/0.08663, loss_grounding_dice_3: 0.20901/0.17972, loss_grounding_ce_3: 0.16901/0.28326, loss_mask_ce_4: 0.78291/0.92556, loss_mask_bce_4: 0.60808/0.33888, loss_mask_dice_4: 1.10387/1.19277, loss_spatial_bce_4: 0.09780/0.09663, loss_spatial_dice_4: 0.17717/0.23306, loss_spatial_ce_4: 0.02232/0.10464, loss_grounding_bce_4: 0.08279/0.08714, loss_grounding_dice_4: 0.21152/0.18254, loss_grounding_ce_4: 0.17709/0.28560, loss_mask_ce_5: 0.85534/0.94007, loss_mask_bce_5: 0.64044/0.34112, loss_mask_dice_5: 1.10447/1.19825, loss_spatial_bce_5: 0.09905/0.09797, loss_spatial_dice_5: 0.17866/0.23631, loss_spatial_ce_5: 0.06642/0.11883, loss_grounding_bce_5: 0.08626/0.08754, loss_grounding_dice_5: 0.20952/0.18371, loss_grounding_ce_5: 0.15975/0.29832, loss_mask_ce_6: 0.93777/0.97734, loss_mask_bce_6: 0.62136/0.34386, loss_mask_dice_6: 1.10021/1.20062, loss_spatial_bce_6: 0.11693/0.10346, loss_spatial_dice_6: 0.19669/0.23848, loss_spatial_ce_6: 0.10056/0.14414, loss_grounding_bce_6: 0.08323/0.08828, loss_grounding_dice_6: 0.20636/0.18392, loss_grounding_ce_6: 0.10456/0.31563, loss_mask_ce_7: 0.87961/1.02121, loss_mask_bce_7: 0.68158/0.35153, loss_mask_dice_7: 1.15032/1.25629, loss_spatial_bce_7: 0.16096/0.11219, loss_spatial_dice_7: 0.24953/0.26570, loss_spatial_ce_7: 0.14255/0.18187, loss_grounding_bce_7: 0.08460/0.09023, loss_grounding_dice_7: 0.22216/0.19124, loss_grounding_ce_7: 0.12079/0.34785, loss_mask_ce_8: 0.81230/1.13273, loss_mask_bce_8: 0.54011/0.36498, loss_mask_dice_8: 1.07982/1.33091, loss_spatial_bce_8: 0.18762/0.13323, loss_spatial_dice_8: 0.28493/0.30586, loss_spatial_ce_8: 0.11883/0.23858, loss_grounding_bce_8: 0.09268/0.09381, loss_grounding_dice_8: 0.23273/0.20244, loss_grounding_ce_8: 0.24231/0.41852, loss_mask_ce_9: 3.55577/3.68746, loss_mask_bce_9: 0.78170/0.39212, loss_mask_dice_9: 1.82156/1.90564, loss_spatial_bce_9: 0.42028/0.33553, loss_spatial_dice_9: 0.83845/0.82403, loss_spatial_ce_9: 1.29416/1.51166, loss_grounding_bce_9: 0.10791/0.10525, loss_grounding_dice_9: 0.31135/0.28209, loss_grounding_ce_9: 0.94970/0.68961] items per batch[64] items per second[0.23] total items[1766400] mini batches[ 27600] memory[7341] epoch remaining[1:16:40]
INFO:trainer.default_trainer:epochs[    15] optim steps[27700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85216/0.91055, loss_mask_bce_0: 0.32101/0.33527, loss_mask_dice_0: 0.35960/1.16668, loss_spatial_bce_0: 0.16461/0.09061, loss_spatial_dice_0: 0.12868/0.21661, loss_spatial_ce_0: 0.06293/0.07452, loss_grounding_bce_0: 0.16772/0.08626, loss_grounding_dice_0: 0.20943/0.17959, loss_grounding_ce_0: 0.35762/0.27654, loss_mask_ce_1: 0.81900/0.91112, loss_mask_bce_1: 0.31846/0.33598, loss_mask_dice_1: 0.34975/1.17374, loss_spatial_bce_1: 0.17047/0.09141, loss_spatial_dice_1: 0.13713/0.22090, loss_spatial_ce_1: 0.06282/0.08037, loss_grounding_bce_1: 0.16412/0.08638, loss_grounding_dice_1: 0.20424/0.18041, loss_grounding_ce_1: 0.35848/0.27811, loss_mask_ce_2: 0.77259/0.91888, loss_mask_bce_2: 0.31892/0.33642, loss_mask_dice_2: 0.34550/1.17294, loss_spatial_bce_2: 0.16985/0.09165, loss_spatial_dice_2: 0.13502/0.22191, loss_spatial_ce_2: 0.06345/0.08404, loss_grounding_bce_2: 0.16559/0.08641, loss_grounding_dice_2: 0.20132/0.17984, loss_grounding_ce_2: 0.32944/0.28152, loss_mask_ce_3: 0.81697/0.92703, loss_mask_bce_3: 0.32421/0.33715, loss_mask_dice_3: 0.34675/1.17018, loss_spatial_bce_3: 0.16331/0.09248, loss_spatial_dice_3: 0.14957/0.22252, loss_spatial_ce_3: 0.06429/0.08797, loss_grounding_bce_3: 0.16801/0.08661, loss_grounding_dice_3: 0.20281/0.17967, loss_grounding_ce_3: 0.34354/0.28316, loss_mask_ce_4: 0.40839/0.92582, loss_mask_bce_4: 0.34218/0.33894, loss_mask_dice_4: 0.47292/1.19283, loss_spatial_bce_4: 0.16828/0.09662, loss_spatial_dice_4: 0.16305/0.23306, loss_spatial_ce_4: 0.09623/0.10454, loss_grounding_bce_4: 0.16431/0.08711, loss_grounding_dice_4: 0.16642/0.18250, loss_grounding_ce_4: 0.35951/0.28550, loss_mask_ce_5: 0.91280/0.94046, loss_mask_bce_5: 0.31666/0.34114, loss_mask_dice_5: 0.35770/1.19827, loss_spatial_bce_5: 0.17072/0.09797, loss_spatial_dice_5: 0.16614/0.23632, loss_spatial_ce_5: 0.16662/0.11877, loss_grounding_bce_5: 0.16472/0.08751, loss_grounding_dice_5: 0.17369/0.18368, loss_grounding_ce_5: 0.43859/0.29817, loss_mask_ce_6: 1.06346/0.97770, loss_mask_bce_6: 0.31738/0.34389, loss_mask_dice_6: 0.38122/1.20069, loss_spatial_bce_6: 0.17845/0.10347, loss_spatial_dice_6: 0.19883/0.23850, loss_spatial_ce_6: 0.21294/0.14411, loss_grounding_bce_6: 0.16462/0.08826, loss_grounding_dice_6: 0.18298/0.18390, loss_grounding_ce_6: 0.49694/0.31551, loss_mask_ce_7: 0.92915/1.02156, loss_mask_bce_7: 0.31935/0.35156, loss_mask_dice_7: 0.38409/1.25635, loss_spatial_bce_7: 0.17897/0.11221, loss_spatial_dice_7: 0.20153/0.26573, loss_spatial_ce_7: 0.25542/0.18190, loss_grounding_bce_7: 0.16633/0.09020, loss_grounding_dice_7: 0.19246/0.19119, loss_grounding_ce_7: 0.30524/0.34777, loss_mask_ce_8: 1.24806/1.13314, loss_mask_bce_8: 0.33383/0.36501, loss_mask_dice_8: 0.41916/1.33095, loss_spatial_bce_8: 0.19015/0.13328, loss_spatial_dice_8: 0.20410/0.30585, loss_spatial_ce_8: 0.23174/0.23853, loss_grounding_bce_8: 0.19179/0.09378, loss_grounding_dice_8: 0.31045/0.20239, loss_grounding_ce_8: 0.22057/0.41827, loss_mask_ce_9: 1.65367/3.68768, loss_mask_bce_9: 0.42347/0.39218, loss_mask_dice_9: 0.59720/1.90584, loss_spatial_bce_9: 0.53609/0.33545, loss_spatial_dice_9: 0.70919/0.82404, loss_spatial_ce_9: 1.88702/1.51173, loss_grounding_bce_9: 0.24841/0.10524, loss_grounding_dice_9: 0.32139/0.28203, loss_grounding_ce_9: 0.10363/0.68953] items per batch[64] items per second[0.23] total items[1772800] mini batches[ 27700] memory[7341] epoch remaining[1:11:56]
INFO:trainer.default_trainer:epochs[    15] optim steps[27800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12388/0.91060, loss_mask_bce_0: 0.37353/0.33527, loss_mask_dice_0: 0.90772/1.16634, loss_spatial_bce_0: 0.07374/0.09062, loss_spatial_dice_0: 0.15607/0.21656, loss_spatial_ce_0: 0.05322/0.07447, loss_grounding_bce_0: 0.09792/0.08628, loss_grounding_dice_0: 0.14855/0.17955, loss_grounding_ce_0: 0.11300/0.27634, loss_mask_ce_1: 1.13468/0.91116, loss_mask_bce_1: 0.38700/0.33598, loss_mask_dice_1: 0.91988/1.17335, loss_spatial_bce_1: 0.07241/0.09140, loss_spatial_dice_1: 0.15731/0.22084, loss_spatial_ce_1: 0.08151/0.08030, loss_grounding_bce_1: 0.09169/0.08639, loss_grounding_dice_1: 0.14519/0.18039, loss_grounding_ce_1: 0.11090/0.27801, loss_mask_ce_2: 1.00095/0.91890, loss_mask_bce_2: 0.38355/0.33640, loss_mask_dice_2: 0.93147/1.17259, loss_spatial_bce_2: 0.07193/0.09164, loss_spatial_dice_2: 0.16245/0.22185, loss_spatial_ce_2: 0.05930/0.08399, loss_grounding_bce_2: 0.07524/0.08643, loss_grounding_dice_2: 0.10635/0.17981, loss_grounding_ce_2: 0.66080/0.28146, loss_mask_ce_3: 1.00802/0.92705, loss_mask_bce_3: 0.39568/0.33714, loss_mask_dice_3: 0.94570/1.16985, loss_spatial_bce_3: 0.07264/0.09248, loss_spatial_dice_3: 0.16345/0.22245, loss_spatial_ce_3: 0.05071/0.08789, loss_grounding_bce_3: 0.07439/0.08662, loss_grounding_dice_3: 0.11270/0.17964, loss_grounding_ce_3: 0.61156/0.28311, loss_mask_ce_4: 0.92679/0.92588, loss_mask_bce_4: 0.38899/0.33893, loss_mask_dice_4: 0.96631/1.19236, loss_spatial_bce_4: 0.07060/0.09662, loss_spatial_dice_4: 0.16820/0.23300, loss_spatial_ce_4: 0.05003/0.10446, loss_grounding_bce_4: 0.06463/0.08713, loss_grounding_dice_4: 0.10842/0.18246, loss_grounding_ce_4: 0.59910/0.28538, loss_mask_ce_5: 1.07698/0.94050, loss_mask_bce_5: 0.38575/0.34115, loss_mask_dice_5: 0.98278/1.19790, loss_spatial_bce_5: 0.07727/0.09797, loss_spatial_dice_5: 0.18593/0.23627, loss_spatial_ce_5: 0.08357/0.11865, loss_grounding_bce_5: 0.08093/0.08754, loss_grounding_dice_5: 0.12406/0.18366, loss_grounding_ce_5: 0.32369/0.29787, loss_mask_ce_6: 1.05780/0.97775, loss_mask_bce_6: 0.40003/0.34388, loss_mask_dice_6: 0.97026/1.20035, loss_spatial_bce_6: 0.08238/0.10346, loss_spatial_dice_6: 0.19654/0.23846, loss_spatial_ce_6: 0.10057/0.14401, loss_grounding_bce_6: 0.07398/0.08827, loss_grounding_dice_6: 0.11613/0.18385, loss_grounding_ce_6: 0.39621/0.31529, loss_mask_ce_7: 1.21329/1.02160, loss_mask_bce_7: 0.38319/0.35154, loss_mask_dice_7: 1.05949/1.25597, loss_spatial_bce_7: 0.07740/0.11220, loss_spatial_dice_7: 0.19133/0.26567, loss_spatial_ce_7: 0.11825/0.18189, loss_grounding_bce_7: 0.09108/0.09022, loss_grounding_dice_7: 0.16361/0.19114, loss_grounding_ce_7: 0.14686/0.34755, loss_mask_ce_8: 1.08478/1.13333, loss_mask_bce_8: 0.39736/0.36499, loss_mask_dice_8: 1.06489/1.33050, loss_spatial_bce_8: 0.09487/0.13328, loss_spatial_dice_8: 0.20704/0.30575, loss_spatial_ce_8: 0.15649/0.23849, loss_grounding_bce_8: 0.08095/0.09379, loss_grounding_dice_8: 0.13247/0.20234, loss_grounding_ce_8: 0.22006/0.41787, loss_mask_ce_9: 3.50906/3.68731, loss_mask_bce_9: 0.49989/0.39219, loss_mask_dice_9: 1.42670/1.90545, loss_spatial_bce_9: 0.37865/0.33547, loss_spatial_dice_9: 0.80299/0.82400, loss_spatial_ce_9: 1.24260/1.51162, loss_grounding_bce_9: 0.10653/0.10526, loss_grounding_dice_9: 0.16955/0.28200, loss_grounding_ce_9: 1.12512/0.68920] items per batch[64] items per second[0.23] total items[1779200] mini batches[ 27800] memory[7341] epoch remaining[1:07:10]
INFO:trainer.default_trainer:epochs[    15] optim steps[27900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.46929/0.91077, loss_mask_bce_0: 1.10579/0.33522, loss_mask_dice_0: 3.40982/1.16643, loss_spatial_bce_0: 0.20191/0.09062, loss_spatial_dice_0: 0.41520/0.21656, loss_spatial_ce_0: 0.08909/0.07442, loss_grounding_bce_0: 0.07686/0.08629, loss_grounding_dice_0: 0.24321/0.17956, loss_grounding_ce_0: 0.33359/0.27621, loss_mask_ce_1: 1.52628/0.91118, loss_mask_bce_1: 1.10720/0.33593, loss_mask_dice_1: 3.30626/1.17347, loss_spatial_bce_1: 0.21229/0.09141, loss_spatial_dice_1: 0.40463/0.22083, loss_spatial_ce_1: 0.09937/0.08023, loss_grounding_bce_1: 0.07654/0.08640, loss_grounding_dice_1: 0.25731/0.18041, loss_grounding_ce_1: 0.33090/0.27788, loss_mask_ce_2: 1.49771/0.91897, loss_mask_bce_2: 1.09355/0.33636, loss_mask_dice_2: 3.32372/1.17263, loss_spatial_bce_2: 0.22522/0.09166, loss_spatial_dice_2: 0.40440/0.22185, loss_spatial_ce_2: 0.10910/0.08393, loss_grounding_bce_2: 0.07589/0.08644, loss_grounding_dice_2: 0.24329/0.17984, loss_grounding_ce_2: 0.33137/0.28137, loss_mask_ce_3: 1.55501/0.92723, loss_mask_bce_3: 1.07834/0.33709, loss_mask_dice_3: 3.24524/1.16992, loss_spatial_bce_3: 0.19948/0.09249, loss_spatial_dice_3: 0.41604/0.22244, loss_spatial_ce_3: 0.10400/0.08783, loss_grounding_bce_3: 0.08443/0.08662, loss_grounding_dice_3: 0.25911/0.17966, loss_grounding_ce_3: 0.34716/0.28298, loss_mask_ce_4: 1.57953/0.92604, loss_mask_bce_4: 1.17430/0.33888, loss_mask_dice_4: 3.37435/1.19244, loss_spatial_bce_4: 0.16217/0.09663, loss_spatial_dice_4: 0.40017/0.23301, loss_spatial_ce_4: 0.17592/0.10441, loss_grounding_bce_4: 0.07479/0.08713, loss_grounding_dice_4: 0.22513/0.18246, loss_grounding_ce_4: 0.36429/0.28529, loss_mask_ce_5: 1.60228/0.94067, loss_mask_bce_5: 1.13362/0.34110, loss_mask_dice_5: 3.31782/1.19802, loss_spatial_bce_5: 0.17972/0.09798, loss_spatial_dice_5: 0.43248/0.23629, loss_spatial_ce_5: 0.21282/0.11860, loss_grounding_bce_5: 0.07118/0.08754, loss_grounding_dice_5: 0.23416/0.18368, loss_grounding_ce_5: 0.34519/0.29779, loss_mask_ce_6: 1.52062/0.97797, loss_mask_bce_6: 1.15916/0.34383, loss_mask_dice_6: 3.40871/1.20048, loss_spatial_bce_6: 0.23056/0.10347, loss_spatial_dice_6: 0.45924/0.23848, loss_spatial_ce_6: 0.20748/0.14395, loss_grounding_bce_6: 0.06996/0.08828, loss_grounding_dice_6: 0.21651/0.18387, loss_grounding_ce_6: 0.35092/0.31512, loss_mask_ce_7: 1.58695/1.02185, loss_mask_bce_7: 1.16039/0.35150, loss_mask_dice_7: 3.50640/1.25610, loss_spatial_bce_7: 0.15201/0.11220, loss_spatial_dice_7: 0.47339/0.26570, loss_spatial_ce_7: 0.21081/0.18190, loss_grounding_bce_7: 0.07032/0.09022, loss_grounding_dice_7: 0.22788/0.19116, loss_grounding_ce_7: 0.33814/0.34741, loss_mask_ce_8: 1.64871/1.13354, loss_mask_bce_8: 1.13863/0.36494, loss_mask_dice_8: 3.66189/1.33052, loss_spatial_bce_8: 0.19923/0.13330, loss_spatial_dice_8: 0.47621/0.30575, loss_spatial_ce_8: 0.21266/0.23838, loss_grounding_bce_8: 0.08082/0.09378, loss_grounding_dice_8: 0.27209/0.20234, loss_grounding_ce_8: 0.35529/0.41780, loss_mask_ce_9: 5.65536/3.68751, loss_mask_bce_9: 1.13476/0.39212, loss_mask_dice_9: 5.14437/1.90551, loss_spatial_bce_9: 0.21011/0.33548, loss_spatial_dice_9: 0.91639/0.82395, loss_spatial_ce_9: 1.73887/1.51166, loss_grounding_bce_9: 0.11317/0.10527, loss_grounding_dice_9: 0.40049/0.28201, loss_grounding_ce_9: 0.56949/0.68903] items per batch[64] items per second[0.24] total items[1785600] mini batches[ 27900] memory[7341] epoch remaining[1:01:58]
INFO:trainer.default_trainer:epochs[    15] optim steps[28000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62010/0.91062, loss_mask_bce_0: 0.20539/0.33541, loss_mask_dice_0: 0.16351/1.16699, loss_spatial_bce_0: 0.10045/0.09064, loss_spatial_dice_0: 0.09086/0.21656, loss_spatial_ce_0: 0.00227/0.07435, loss_grounding_bce_0: 0.14060/0.08632, loss_grounding_dice_0: 0.06398/0.17957, loss_grounding_ce_0: 0.65070/0.27622, loss_mask_ce_1: 0.56340/0.91099, loss_mask_bce_1: 0.20912/0.33614, loss_mask_dice_1: 0.16404/1.17414, loss_spatial_bce_1: 0.10062/0.09143, loss_spatial_dice_1: 0.09800/0.22084, loss_spatial_ce_1: 0.00226/0.08017, loss_grounding_bce_1: 0.13325/0.08641, loss_grounding_dice_1: 0.05986/0.18042, loss_grounding_ce_1: 0.60090/0.27788, loss_mask_ce_2: 0.57030/0.91884, loss_mask_bce_2: 0.22123/0.33656, loss_mask_dice_2: 0.15223/1.17324, loss_spatial_bce_2: 0.10444/0.09168, loss_spatial_dice_2: 0.08962/0.22184, loss_spatial_ce_2: 0.00288/0.08387, loss_grounding_bce_2: 0.13715/0.08646, loss_grounding_dice_2: 0.05953/0.17986, loss_grounding_ce_2: 0.55907/0.28136, loss_mask_ce_3: 0.52538/0.92708, loss_mask_bce_3: 0.21206/0.33730, loss_mask_dice_3: 0.16238/1.17047, loss_spatial_bce_3: 0.10298/0.09251, loss_spatial_dice_3: 0.09247/0.22243, loss_spatial_ce_3: 0.00525/0.08774, loss_grounding_bce_3: 0.13900/0.08664, loss_grounding_dice_3: 0.05895/0.17968, loss_grounding_ce_3: 0.56887/0.28297, loss_mask_ce_4: 0.61683/0.92593, loss_mask_bce_4: 0.21906/0.33909, loss_mask_dice_4: 0.16595/1.19311, loss_spatial_bce_4: 0.09903/0.09665, loss_spatial_dice_4: 0.09380/0.23301, loss_spatial_ce_4: 0.00692/0.10437, loss_grounding_bce_4: 0.13183/0.08714, loss_grounding_dice_4: 0.05756/0.18249, loss_grounding_ce_4: 0.57330/0.28526, loss_mask_ce_5: 0.50048/0.94054, loss_mask_bce_5: 0.22240/0.34131, loss_mask_dice_5: 0.17798/1.19865, loss_spatial_bce_5: 0.09534/0.09801, loss_spatial_dice_5: 0.09936/0.23629, loss_spatial_ce_5: 0.03974/0.11854, loss_grounding_bce_5: 0.12936/0.08756, loss_grounding_dice_5: 0.06096/0.18371, loss_grounding_ce_5: 0.78996/0.29773, loss_mask_ce_6: 0.41820/0.97782, loss_mask_bce_6: 0.20373/0.34406, loss_mask_dice_6: 0.16333/1.20118, loss_spatial_bce_6: 0.09485/0.10351, loss_spatial_dice_6: 0.10408/0.23850, loss_spatial_ce_6: 0.05663/0.14390, loss_grounding_bce_6: 0.13339/0.08830, loss_grounding_dice_6: 0.05684/0.18389, loss_grounding_ce_6: 0.90958/0.31504, loss_mask_ce_7: 0.39507/1.02167, loss_mask_bce_7: 0.17153/0.35172, loss_mask_dice_7: 0.15657/1.25680, loss_spatial_bce_7: 0.10821/0.11226, loss_spatial_dice_7: 0.11074/0.26574, loss_spatial_ce_7: 0.10101/0.18185, loss_grounding_bce_7: 0.12993/0.09024, loss_grounding_dice_7: 0.05751/0.19118, loss_grounding_ce_7: 0.64988/0.34737, loss_mask_ce_8: 0.48503/1.13327, loss_mask_bce_8: 0.18703/0.36519, loss_mask_dice_8: 0.22138/1.33122, loss_spatial_bce_8: 0.15283/0.13335, loss_spatial_dice_8: 0.14305/0.30577, loss_spatial_ce_8: 0.11858/0.23831, loss_grounding_bce_8: 0.13971/0.09381, loss_grounding_dice_8: 0.06308/0.20235, loss_grounding_ce_8: 0.84206/0.41773, loss_mask_ce_9: 3.46025/3.68750, loss_mask_bce_9: 0.21069/0.39237, loss_mask_dice_9: 0.36547/1.90663, loss_spatial_bce_9: 0.70274/0.33554, loss_spatial_dice_9: 0.83782/0.82397, loss_spatial_ce_9: 1.69687/1.51160, loss_grounding_bce_9: 0.15752/0.10529, loss_grounding_dice_9: 0.08298/0.28203, loss_grounding_ce_9: 1.18133/0.68884] items per batch[64] items per second[0.23] total items[1792000] mini batches[ 28000] memory[7341] epoch remaining[0:57:05]
INFO:trainer.default_trainer:epochs[    15] optim steps[28100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.23944/0.91061, loss_mask_bce_0: 0.08107/0.33532, loss_mask_dice_0: 0.91428/1.16659, loss_spatial_bce_0: 0.02550/0.09060, loss_spatial_dice_0: 0.26247/0.21648, loss_spatial_ce_0: 0.01117/0.07425, loss_grounding_bce_0: 0.04826/0.08628, loss_grounding_dice_0: 0.10131/0.17950, loss_grounding_ce_0: 0.01456/0.27613, loss_mask_ce_1: 0.26598/0.91098, loss_mask_bce_1: 0.07244/0.33605, loss_mask_dice_1: 0.97623/1.17376, loss_spatial_bce_1: 0.02497/0.09139, loss_spatial_dice_1: 0.25561/0.22076, loss_spatial_ce_1: 0.23362/0.08009, loss_grounding_bce_1: 0.04354/0.08638, loss_grounding_dice_1: 0.09979/0.18035, loss_grounding_ce_1: 0.01262/0.27776, loss_mask_ce_2: 0.39897/0.91886, loss_mask_bce_2: 0.06764/0.33646, loss_mask_dice_2: 0.94070/1.17288, loss_spatial_bce_2: 0.02484/0.09164, loss_spatial_dice_2: 0.26498/0.22176, loss_spatial_ce_2: 0.22030/0.08381, loss_grounding_bce_2: 0.04422/0.08643, loss_grounding_dice_2: 0.10846/0.17981, loss_grounding_ce_2: 0.02154/0.28126, loss_mask_ce_3: 0.75442/0.92708, loss_mask_bce_3: 0.07019/0.33720, loss_mask_dice_3: 0.79913/1.17006, loss_spatial_bce_3: 0.02218/0.09248, loss_spatial_dice_3: 0.25291/0.22235, loss_spatial_ce_3: 0.01898/0.08766, loss_grounding_bce_3: 0.04715/0.08661, loss_grounding_dice_3: 0.10606/0.17962, loss_grounding_ce_3: 0.01194/0.28286, loss_mask_ce_4: 0.35391/0.92594, loss_mask_bce_4: 0.06716/0.33900, loss_mask_dice_4: 0.88149/1.19270, loss_spatial_bce_4: 0.02647/0.09663, loss_spatial_dice_4: 0.25344/0.23294, loss_spatial_ce_4: 0.57656/0.10433, loss_grounding_bce_4: 0.04083/0.08711, loss_grounding_dice_4: 0.10288/0.18242, loss_grounding_ce_4: 0.02333/0.28519, loss_mask_ce_5: 0.42386/0.94054, loss_mask_bce_5: 0.07070/0.34122, loss_mask_dice_5: 0.95537/1.19828, loss_spatial_bce_5: 0.03072/0.09799, loss_spatial_dice_5: 0.29945/0.23623, loss_spatial_ce_5: 0.37366/0.11846, loss_grounding_bce_5: 0.04406/0.08753, loss_grounding_dice_5: 0.10429/0.18364, loss_grounding_ce_5: 0.04029/0.29766, loss_mask_ce_6: 0.43603/0.97781, loss_mask_bce_6: 0.06825/0.34397, loss_mask_dice_6: 0.82454/1.20079, loss_spatial_bce_6: 0.03892/0.10348, loss_spatial_dice_6: 0.29529/0.23843, loss_spatial_ce_6: 0.02904/0.14386, loss_grounding_bce_6: 0.04533/0.08826, loss_grounding_dice_6: 0.10470/0.18385, loss_grounding_ce_6: 0.03053/0.31494, loss_mask_ce_7: 0.59727/1.02164, loss_mask_bce_7: 0.07257/0.35165, loss_mask_dice_7: 0.93545/1.25639, loss_spatial_bce_7: 0.04887/0.11223, loss_spatial_dice_7: 0.33508/0.26570, loss_spatial_ce_7: 0.11420/0.18182, loss_grounding_bce_7: 0.04829/0.09022, loss_grounding_dice_7: 0.09236/0.19113, loss_grounding_ce_7: 0.12791/0.34728, loss_mask_ce_8: 0.88799/1.13315, loss_mask_bce_8: 0.07718/0.36515, loss_mask_dice_8: 0.95862/1.33088, loss_spatial_bce_8: 0.11190/0.13331, loss_spatial_dice_8: 0.41472/0.30568, loss_spatial_ce_8: 0.33978/0.23819, loss_grounding_bce_8: 0.05082/0.09378, loss_grounding_dice_8: 0.09526/0.20229, loss_grounding_ce_8: 0.44860/0.41787, loss_mask_ce_9: 3.07517/3.68736, loss_mask_bce_9: 0.08903/0.39237, loss_mask_dice_9: 1.12117/1.90618, loss_spatial_bce_9: 0.62412/0.33554, loss_spatial_dice_9: 0.85018/0.82392, loss_spatial_ce_9: 1.82464/1.51139, loss_grounding_bce_9: 0.04699/0.10526, loss_grounding_dice_9: 0.12454/0.28195, loss_grounding_ce_9: 0.62696/0.68904] items per batch[64] items per second[0.22] total items[1798400] mini batches[ 28100] memory[7341] epoch remaining[0:52:44]
INFO:trainer.default_trainer:epochs[    15] optim steps[28200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.41720/0.91040, loss_mask_bce_0: 0.51586/0.33540, loss_mask_dice_0: 0.84524/1.16710, loss_spatial_bce_0: 0.16710/0.09058, loss_spatial_dice_0: 0.25885/0.21644, loss_spatial_ce_0: 0.02999/0.07415, loss_grounding_bce_0: 0.18998/0.08630, loss_grounding_dice_0: 0.18546/0.17953, loss_grounding_ce_0: 1.50241/0.27614, loss_mask_ce_1: 2.33624/0.91076, loss_mask_bce_1: 0.52746/0.33613, loss_mask_dice_1: 0.85179/1.17421, loss_spatial_bce_1: 0.16375/0.09137, loss_spatial_dice_1: 0.23824/0.22071, loss_spatial_ce_1: 0.06151/0.08000, loss_grounding_bce_1: 0.28325/0.08641, loss_grounding_dice_1: 0.20028/0.18040, loss_grounding_ce_1: 0.77598/0.27773, loss_mask_ce_2: 2.40350/0.91870, loss_mask_bce_2: 0.51440/0.33655, loss_mask_dice_2: 0.81325/1.17337, loss_spatial_bce_2: 0.15882/0.09162, loss_spatial_dice_2: 0.22846/0.22171, loss_spatial_ce_2: 0.06362/0.08368, loss_grounding_bce_2: 0.26518/0.08645, loss_grounding_dice_2: 0.18437/0.17983, loss_grounding_ce_2: 0.75165/0.28123, loss_mask_ce_3: 2.43071/0.92687, loss_mask_bce_3: 0.52330/0.33728, loss_mask_dice_3: 0.80859/1.17046, loss_spatial_bce_3: 0.16092/0.09245, loss_spatial_dice_3: 0.24319/0.22231, loss_spatial_ce_3: 0.06712/0.08755, loss_grounding_bce_3: 0.26687/0.08663, loss_grounding_dice_3: 0.17836/0.17968, loss_grounding_ce_3: 0.83130/0.28285, loss_mask_ce_4: 2.39734/0.92578, loss_mask_bce_4: 0.53955/0.33908, loss_mask_dice_4: 0.84638/1.19318, loss_spatial_bce_4: 0.16887/0.09662, loss_spatial_dice_4: 0.23413/0.23292, loss_spatial_ce_4: 0.18830/0.10421, loss_grounding_bce_4: 0.27825/0.08712, loss_grounding_dice_4: 0.18783/0.18246, loss_grounding_ce_4: 0.70694/0.28516, loss_mask_ce_5: 2.05001/0.94035, loss_mask_bce_5: 0.54221/0.34131, loss_mask_dice_5: 0.97000/1.19873, loss_spatial_bce_5: 0.16041/0.09799, loss_spatial_dice_5: 0.23714/0.23620, loss_spatial_ce_5: 0.10945/0.11832, loss_grounding_bce_5: 0.27062/0.08754, loss_grounding_dice_5: 0.18842/0.18367, loss_grounding_ce_5: 0.65262/0.29769, loss_mask_ce_6: 1.90528/0.97764, loss_mask_bce_6: 0.54986/0.34404, loss_mask_dice_6: 1.09073/1.20132, loss_spatial_bce_6: 0.17961/0.10347, loss_spatial_dice_6: 0.25398/0.23841, loss_spatial_ce_6: 0.16520/0.14373, loss_grounding_bce_6: 0.26691/0.08827, loss_grounding_dice_6: 0.18105/0.18387, loss_grounding_ce_6: 0.73195/0.31492, loss_mask_ce_7: 2.83523/1.02159, loss_mask_bce_7: 0.51466/0.35169, loss_mask_dice_7: 0.91881/1.25699, loss_spatial_bce_7: 0.18282/0.11221, loss_spatial_dice_7: 0.29745/0.26567, loss_spatial_ce_7: 0.29215/0.18172, loss_grounding_bce_7: 0.28765/0.09022, loss_grounding_dice_7: 0.21689/0.19116, loss_grounding_ce_7: 0.38671/0.34730, loss_mask_ce_8: 2.66145/1.13301, loss_mask_bce_8: 0.56754/0.36523, loss_mask_dice_8: 1.24131/1.33146, loss_spatial_bce_8: 0.25450/0.13330, loss_spatial_dice_8: 0.31845/0.30565, loss_spatial_ce_8: 0.41313/0.23817, loss_grounding_bce_8: 0.26658/0.09379, loss_grounding_dice_8: 0.26794/0.20233, loss_grounding_ce_8: 0.36539/0.41806, loss_mask_ce_9: 3.99033/3.68774, loss_mask_bce_9: 0.60870/0.39243, loss_mask_dice_9: 1.77410/1.90700, loss_spatial_bce_9: 0.43025/0.33551, loss_spatial_dice_9: 0.83057/0.82395, loss_spatial_ce_9: 1.44454/1.51123, loss_grounding_bce_9: 0.24808/0.10528, loss_grounding_dice_9: 0.22678/0.28199, loss_grounding_ce_9: 1.67857/0.68906] items per batch[64] items per second[0.22] total items[1804800] mini batches[ 28200] memory[7341] epoch remaining[0:48:13]
INFO:trainer.default_trainer:epochs[    15] optim steps[28300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72016/0.91006, loss_mask_bce_0: 0.10007/0.33553, loss_mask_dice_0: 1.51596/1.16647, loss_spatial_bce_0: 0.00758/0.09059, loss_spatial_dice_0: 0.17705/0.21635, loss_spatial_ce_0: 0.00828/0.07404, loss_grounding_bce_0: 0.01176/0.08636, loss_grounding_dice_0: 0.18370/0.17956, loss_grounding_ce_0: 0.25612/0.27631, loss_mask_ce_1: 0.86116/0.91048, loss_mask_bce_1: 0.11027/0.33626, loss_mask_dice_1: 1.62234/1.17357, loss_spatial_bce_1: 0.00699/0.09138, loss_spatial_dice_1: 0.14359/0.22061, loss_spatial_ce_1: 0.01898/0.07989, loss_grounding_bce_1: 0.01741/0.08647, loss_grounding_dice_1: 0.19916/0.18041, loss_grounding_ce_1: 0.25970/0.27788, loss_mask_ce_2: 0.71985/0.91842, loss_mask_bce_2: 0.10234/0.33667, loss_mask_dice_2: 1.49287/1.17273, loss_spatial_bce_2: 0.00725/0.09164, loss_spatial_dice_2: 0.14541/0.22161, loss_spatial_ce_2: 0.01627/0.08358, loss_grounding_bce_2: 0.01551/0.08651, loss_grounding_dice_2: 0.18332/0.17986, loss_grounding_ce_2: 0.23796/0.28139, loss_mask_ce_3: 0.82475/0.92665, loss_mask_bce_3: 0.09364/0.33741, loss_mask_dice_3: 1.50318/1.16981, loss_spatial_bce_3: 0.00830/0.09247, loss_spatial_dice_3: 0.15269/0.22221, loss_spatial_ce_3: 0.02668/0.08743, loss_grounding_bce_3: 0.01522/0.08669, loss_grounding_dice_3: 0.18000/0.17968, loss_grounding_ce_3: 0.25433/0.28311, loss_mask_ce_4: 0.81735/0.92549, loss_mask_bce_4: 0.09304/0.33921, loss_mask_dice_4: 1.43566/1.19252, loss_spatial_bce_4: 0.00924/0.09664, loss_spatial_dice_4: 0.20364/0.23284, loss_spatial_ce_4: 0.00641/0.10409, loss_grounding_bce_4: 0.01507/0.08719, loss_grounding_dice_4: 0.20439/0.18247, loss_grounding_ce_4: 0.28180/0.28546, loss_mask_ce_5: 0.77513/0.94006, loss_mask_bce_5: 0.11025/0.34144, loss_mask_dice_5: 1.46953/1.19811, loss_spatial_bce_5: 0.01096/0.09802, loss_spatial_dice_5: 0.21339/0.23613, loss_spatial_ce_5: 0.00497/0.11819, loss_grounding_bce_5: 0.01340/0.08760, loss_grounding_dice_5: 0.17530/0.18371, loss_grounding_ce_5: 0.27248/0.29800, loss_mask_ce_6: 1.04199/0.97737, loss_mask_bce_6: 0.10164/0.34418, loss_mask_dice_6: 1.50922/1.20074, loss_spatial_bce_6: 0.01231/0.10349, loss_spatial_dice_6: 0.21238/0.23833, loss_spatial_ce_6: 0.01603/0.14364, loss_grounding_bce_6: 0.01299/0.08833, loss_grounding_dice_6: 0.17515/0.18389, loss_grounding_ce_6: 0.25215/0.31507, loss_mask_ce_7: 0.94229/1.02127, loss_mask_bce_7: 0.09411/0.35182, loss_mask_dice_7: 1.63691/1.25638, loss_spatial_bce_7: 0.00914/0.11224, loss_spatial_dice_7: 0.20708/0.26558, loss_spatial_ce_7: 0.05381/0.18159, loss_grounding_bce_7: 0.01752/0.09028, loss_grounding_dice_7: 0.19232/0.19118, loss_grounding_ce_7: 0.32207/0.34759, loss_mask_ce_8: 1.27619/1.13264, loss_mask_bce_8: 0.08968/0.36535, loss_mask_dice_8: 1.70100/1.33078, loss_spatial_bce_8: 0.01530/0.13332, loss_spatial_dice_8: 0.27621/0.30554, loss_spatial_ce_8: 0.10811/0.23800, loss_grounding_bce_8: 0.01148/0.09385, loss_grounding_dice_8: 0.19746/0.20232, loss_grounding_ce_8: 0.30982/0.41827, loss_mask_ce_9: 4.42584/3.68748, loss_mask_bce_9: 0.09214/0.39256, loss_mask_dice_9: 2.32109/1.90609, loss_spatial_bce_9: 0.10893/0.33557, loss_spatial_dice_9: 0.87102/0.82390, loss_spatial_ce_9: 1.51713/1.51103, loss_grounding_bce_9: 0.01623/0.10535, loss_grounding_dice_9: 0.36221/0.28198, loss_grounding_ce_9: 0.34229/0.68931] items per batch[64] items per second[0.23] total items[1811200] mini batches[ 28300] memory[7341] epoch remaining[0:43:30]
INFO:trainer.default_trainer:epochs[    15] optim steps[28400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59926/0.91001, loss_mask_bce_0: 0.08683/0.33550, loss_mask_dice_0: 0.42202/1.16633, loss_spatial_bce_0: 0.02908/0.09057, loss_spatial_dice_0: 0.12421/0.21630, loss_spatial_ce_0: 0.02724/0.07396, loss_grounding_bce_0: 0.02772/0.08634, loss_grounding_dice_0: 0.07339/0.17954, loss_grounding_ce_0: 0.50844/0.27626, loss_mask_ce_1: 0.62241/0.91040, loss_mask_bce_1: 0.08989/0.33622, loss_mask_dice_1: 0.41135/1.17337, loss_spatial_bce_1: 0.03134/0.09136, loss_spatial_dice_1: 0.14488/0.22056, loss_spatial_ce_1: 0.00625/0.07980, loss_grounding_bce_1: 0.02810/0.08645, loss_grounding_dice_1: 0.07673/0.18040, loss_grounding_ce_1: 0.55310/0.27777, loss_mask_ce_2: 0.51738/0.91841, loss_mask_bce_2: 0.09568/0.33664, loss_mask_dice_2: 0.50854/1.17256, loss_spatial_bce_2: 0.03478/0.09162, loss_spatial_dice_2: 0.15195/0.22158, loss_spatial_ce_2: 0.04035/0.08352, loss_grounding_bce_2: 0.03110/0.08649, loss_grounding_dice_2: 0.08203/0.17984, loss_grounding_ce_2: 0.42624/0.28132, loss_mask_ce_3: 0.54704/0.92661, loss_mask_bce_3: 0.09344/0.33738, loss_mask_dice_3: 0.43533/1.16962, loss_spatial_bce_3: 0.03258/0.09245, loss_spatial_dice_3: 0.13348/0.22218, loss_spatial_ce_3: 0.01800/0.08736, loss_grounding_bce_3: 0.03157/0.08668, loss_grounding_dice_3: 0.08313/0.17967, loss_grounding_ce_3: 0.52200/0.28305, loss_mask_ce_4: 0.48127/0.92549, loss_mask_bce_4: 0.09421/0.33919, loss_mask_dice_4: 0.43790/1.19236, loss_spatial_bce_4: 0.03297/0.09663, loss_spatial_dice_4: 0.11806/0.23282, loss_spatial_ce_4: 0.03870/0.10398, loss_grounding_bce_4: 0.03013/0.08717, loss_grounding_dice_4: 0.08055/0.18246, loss_grounding_ce_4: 0.51291/0.28545, loss_mask_ce_5: 0.69857/0.93999, loss_mask_bce_5: 0.09166/0.34142, loss_mask_dice_5: 0.44300/1.19798, loss_spatial_bce_5: 0.03604/0.09800, loss_spatial_dice_5: 0.16522/0.23611, loss_spatial_ce_5: 0.15788/0.11814, loss_grounding_bce_5: 0.03130/0.08759, loss_grounding_dice_5: 0.08212/0.18369, loss_grounding_ce_5: 0.50012/0.29797, loss_mask_ce_6: 0.65268/0.97748, loss_mask_bce_6: 0.10072/0.34417, loss_mask_dice_6: 0.49521/1.20058, loss_spatial_bce_6: 0.04899/0.10349, loss_spatial_dice_6: 0.26843/0.23832, loss_spatial_ce_6: 0.18541/0.14358, loss_grounding_bce_6: 0.03383/0.08832, loss_grounding_dice_6: 0.09241/0.18385, loss_grounding_ce_6: 0.53650/0.31504, loss_mask_ce_7: 0.58426/1.02127, loss_mask_bce_7: 0.10126/0.35181, loss_mask_dice_7: 0.50739/1.25626, loss_spatial_bce_7: 0.05709/0.11221, loss_spatial_dice_7: 0.23469/0.26556, loss_spatial_ce_7: 0.14411/0.18157, loss_grounding_bce_7: 0.03379/0.09025, loss_grounding_dice_7: 0.09941/0.19116, loss_grounding_ce_7: 0.70759/0.34759, loss_mask_ce_8: 0.72694/1.13264, loss_mask_bce_8: 0.08726/0.36535, loss_mask_dice_8: 0.46869/1.33063, loss_spatial_bce_8: 0.05012/0.13331, loss_spatial_dice_8: 0.23144/0.30550, loss_spatial_ce_8: 0.29090/0.23792, loss_grounding_bce_8: 0.02730/0.09383, loss_grounding_dice_8: 0.09108/0.20230, loss_grounding_ce_8: 0.68061/0.41836, loss_mask_ce_9: 2.69212/3.68731, loss_mask_bce_9: 0.12850/0.39253, loss_mask_dice_9: 0.92050/1.90583, loss_spatial_bce_9: 0.29735/0.33555, loss_spatial_dice_9: 0.84888/0.82386, loss_spatial_ce_9: 1.08256/1.51103, loss_grounding_bce_9: 0.11194/0.10533, loss_grounding_dice_9: 0.27235/0.28198, loss_grounding_ce_9: 0.60697/0.68914] items per batch[64] items per second[0.24] total items[1817600] mini batches[ 28400] memory[7341] epoch remaining[0:38:43]
INFO:trainer.default_trainer:epochs[    15] optim steps[28500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84814/0.91010, loss_mask_bce_0: 0.65456/0.33545, loss_mask_dice_0: 1.04330/1.16601, loss_spatial_bce_0: 0.09230/0.09054, loss_spatial_dice_0: 0.18416/0.21627, loss_spatial_ce_0: 0.01948/0.07387, loss_grounding_bce_0: 0.12608/0.08633, loss_grounding_dice_0: 0.10247/0.17952, loss_grounding_ce_0: 0.43112/0.27630, loss_mask_ce_1: 0.89497/0.91050, loss_mask_bce_1: 0.69211/0.33617, loss_mask_dice_1: 1.06236/1.17315, loss_spatial_bce_1: 0.09353/0.09134, loss_spatial_dice_1: 0.18899/0.22053, loss_spatial_ce_1: 0.20468/0.07970, loss_grounding_bce_1: 0.11932/0.08645, loss_grounding_dice_1: 0.09582/0.18039, loss_grounding_ce_1: 0.38419/0.27777, loss_mask_ce_2: 0.90218/0.91854, loss_mask_bce_2: 0.68570/0.33659, loss_mask_dice_2: 1.06692/1.17235, loss_spatial_bce_2: 0.09846/0.09159, loss_spatial_dice_2: 0.18406/0.22154, loss_spatial_ce_2: 0.01715/0.08347, loss_grounding_bce_2: 0.12140/0.08649, loss_grounding_dice_2: 0.09990/0.17983, loss_grounding_ce_2: 0.42424/0.28126, loss_mask_ce_3: 0.85194/0.92674, loss_mask_bce_3: 0.62171/0.33733, loss_mask_dice_3: 1.03338/1.16933, loss_spatial_bce_3: 0.09789/0.09243, loss_spatial_dice_3: 0.19362/0.22215, loss_spatial_ce_3: 0.02107/0.08728, loss_grounding_bce_3: 0.12338/0.08668, loss_grounding_dice_3: 0.10436/0.17966, loss_grounding_ce_3: 0.45326/0.28306, loss_mask_ce_4: 0.93328/0.92565, loss_mask_bce_4: 0.67726/0.33913, loss_mask_dice_4: 1.07147/1.19215, loss_spatial_bce_4: 0.11429/0.09660, loss_spatial_dice_4: 0.19700/0.23280, loss_spatial_ce_4: 0.04685/0.10389, loss_grounding_bce_4: 0.12322/0.08716, loss_grounding_dice_4: 0.09091/0.18244, loss_grounding_ce_4: 0.54231/0.28540, loss_mask_ce_5: 1.03681/0.94010, loss_mask_bce_5: 0.76577/0.34138, loss_mask_dice_5: 1.32306/1.19774, loss_spatial_bce_5: 0.10957/0.09799, loss_spatial_dice_5: 0.23473/0.23610, loss_spatial_ce_5: 0.05659/0.11807, loss_grounding_bce_5: 0.11564/0.08758, loss_grounding_dice_5: 0.09460/0.18367, loss_grounding_ce_5: 0.57428/0.29792, loss_mask_ce_6: 0.79841/0.97767, loss_mask_bce_6: 0.73057/0.34412, loss_mask_dice_6: 1.19294/1.20036, loss_spatial_bce_6: 0.11423/0.10348, loss_spatial_dice_6: 0.23383/0.23832, loss_spatial_ce_6: 0.08222/0.14354, loss_grounding_bce_6: 0.11617/0.08831, loss_grounding_dice_6: 0.08842/0.18384, loss_grounding_ce_6: 0.58424/0.31501, loss_mask_ce_7: 1.10392/1.02138, loss_mask_bce_7: 0.86848/0.35179, loss_mask_dice_7: 1.34143/1.25605, loss_spatial_bce_7: 0.14195/0.11220, loss_spatial_dice_7: 0.25352/0.26555, loss_spatial_ce_7: 0.12138/0.18153, loss_grounding_bce_7: 0.11718/0.09025, loss_grounding_dice_7: 0.10771/0.19112, loss_grounding_ce_7: 0.91168/0.34757, loss_mask_ce_8: 1.25068/1.13280, loss_mask_bce_8: 0.69683/0.36532, loss_mask_dice_8: 1.32391/1.33040, loss_spatial_bce_8: 0.21228/0.13331, loss_spatial_dice_8: 0.33428/0.30547, loss_spatial_ce_8: 0.18943/0.23784, loss_grounding_bce_8: 0.13962/0.09382, loss_grounding_dice_8: 0.12220/0.20226, loss_grounding_ce_8: 1.10367/0.41822, loss_mask_ce_9: 3.58961/3.68743, loss_mask_bce_9: 0.83115/0.39249, loss_mask_dice_9: 2.15083/1.90558, loss_spatial_bce_9: 0.39509/0.33548, loss_spatial_dice_9: 0.90376/0.82385, loss_spatial_ce_9: 1.74448/1.51087, loss_grounding_bce_9: 0.21333/0.10533, loss_grounding_dice_9: 0.27950/0.28200, loss_grounding_ce_9: 1.65338/0.68902] items per batch[64] items per second[0.23] total items[1824000] mini batches[ 28500] memory[7341] epoch remaining[0:34:00]
INFO:trainer.default_trainer:epochs[    15] optim steps[28600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.32529/0.91014, loss_mask_bce_0: 0.68230/0.33549, loss_mask_dice_0: 0.79076/1.16589, loss_spatial_bce_0: 0.30475/0.09055, loss_spatial_dice_0: 0.28279/0.21623, loss_spatial_ce_0: 0.07533/0.07379, loss_grounding_bce_0: 0.05478/0.08632, loss_grounding_dice_0: 0.04689/0.17949, loss_grounding_ce_0: 0.63832/0.27629, loss_mask_ce_1: 1.37567/0.91055, loss_mask_bce_1: 0.70067/0.33623, loss_mask_dice_1: 0.79852/1.17305, loss_spatial_bce_1: 0.31557/0.09134, loss_spatial_dice_1: 0.29182/0.22048, loss_spatial_ce_1: 0.08847/0.07966, loss_grounding_bce_1: 0.05864/0.08644, loss_grounding_dice_1: 0.04778/0.18038, loss_grounding_ce_1: 0.70682/0.27777, loss_mask_ce_2: 1.35298/0.91849, loss_mask_bce_2: 0.70787/0.33666, loss_mask_dice_2: 0.80959/1.17229, loss_spatial_bce_2: 0.31105/0.09160, loss_spatial_dice_2: 0.29338/0.22150, loss_spatial_ce_2: 0.07676/0.08339, loss_grounding_bce_2: 0.06117/0.08649, loss_grounding_dice_2: 0.04579/0.17982, loss_grounding_ce_2: 0.72923/0.28127, loss_mask_ce_3: 1.31052/0.92674, loss_mask_bce_3: 0.67995/0.33740, loss_mask_dice_3: 0.77383/1.16917, loss_spatial_bce_3: 0.28307/0.09243, loss_spatial_dice_3: 0.28366/0.22211, loss_spatial_ce_3: 0.08161/0.08718, loss_grounding_bce_3: 0.05976/0.08668, loss_grounding_dice_3: 0.04586/0.17967, loss_grounding_ce_3: 0.61466/0.28305, loss_mask_ce_4: 1.20486/0.92563, loss_mask_bce_4: 0.66287/0.33919, loss_mask_dice_4: 0.76798/1.19208, loss_spatial_bce_4: 0.27005/0.09661, loss_spatial_dice_4: 0.28564/0.23275, loss_spatial_ce_4: 0.15110/0.10380, loss_grounding_bce_4: 0.06152/0.08716, loss_grounding_dice_4: 0.04383/0.18244, loss_grounding_ce_4: 0.49142/0.28538, loss_mask_ce_5: 1.23844/0.94009, loss_mask_bce_5: 0.71036/0.34146, loss_mask_dice_5: 0.77872/1.19770, loss_spatial_bce_5: 0.29978/0.09800, loss_spatial_dice_5: 0.30140/0.23606, loss_spatial_ce_5: 0.09184/0.11798, loss_grounding_bce_5: 0.05701/0.08758, loss_grounding_dice_5: 0.04332/0.18367, loss_grounding_ce_5: 0.40700/0.29790, loss_mask_ce_6: 1.20386/0.97765, loss_mask_bce_6: 0.72684/0.34420, loss_mask_dice_6: 0.81509/1.20035, loss_spatial_bce_6: 0.34571/0.10349, loss_spatial_dice_6: 0.32107/0.23829, loss_spatial_ce_6: 0.17980/0.14344, loss_grounding_bce_6: 0.05711/0.08831, loss_grounding_dice_6: 0.04664/0.18383, loss_grounding_ce_6: 0.62039/0.31503, loss_mask_ce_7: 1.22367/1.02137, loss_mask_bce_7: 0.74756/0.35186, loss_mask_dice_7: 0.82240/1.25596, loss_spatial_bce_7: 0.35843/0.11220, loss_spatial_dice_7: 0.36342/0.26551, loss_spatial_ce_7: 0.19668/0.18144, loss_grounding_bce_7: 0.05976/0.09025, loss_grounding_dice_7: 0.05432/0.19114, loss_grounding_ce_7: 0.68380/0.34757, loss_mask_ce_8: 1.25819/1.13279, loss_mask_bce_8: 0.70478/0.36541, loss_mask_dice_8: 0.83987/1.33034, loss_spatial_bce_8: 0.45345/0.13332, loss_spatial_dice_8: 0.39056/0.30540, loss_spatial_ce_8: 0.22334/0.23772, loss_grounding_bce_8: 0.06227/0.09382, loss_grounding_dice_8: 0.07032/0.20227, loss_grounding_ce_8: 1.04664/0.41841, loss_mask_ce_9: 3.12954/3.68741, loss_mask_bce_9: 0.86765/0.39260, loss_mask_dice_9: 1.62886/1.90550, loss_spatial_bce_9: 0.51053/0.33549, loss_spatial_dice_9: 0.80830/0.82381, loss_spatial_ce_9: 1.89346/1.51064, loss_grounding_bce_9: 0.08722/0.10532, loss_grounding_dice_9: 0.18676/0.28202, loss_grounding_ce_9: 1.59203/0.68937] items per batch[64] items per second[0.22] total items[1830400] mini batches[ 28600] memory[7341] epoch remaining[0:29:25]
INFO:trainer.default_trainer:epochs[    15] optim steps[28700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71516/0.90991, loss_mask_bce_0: 0.65377/0.33547, loss_mask_dice_0: 1.11577/1.16606, loss_spatial_bce_0: 0.11358/0.09054, loss_spatial_dice_0: 0.19736/0.21620, loss_spatial_ce_0: 0.03258/0.07374, loss_grounding_bce_0: 0.26704/0.08630, loss_grounding_dice_0: 0.27828/0.17948, loss_grounding_ce_0: 0.34513/0.27624, loss_mask_ce_1: 0.69503/0.91036, loss_mask_bce_1: 0.67343/0.33620, loss_mask_dice_1: 1.11938/1.17324, loss_spatial_bce_1: 0.12064/0.09132, loss_spatial_dice_1: 0.21821/0.22047, loss_spatial_ce_1: 0.03438/0.07959, loss_grounding_bce_1: 0.26568/0.08641, loss_grounding_dice_1: 0.27629/0.18037, loss_grounding_ce_1: 0.36212/0.27780, loss_mask_ce_2: 0.79412/0.91834, loss_mask_bce_2: 0.71597/0.33664, loss_mask_dice_2: 1.08940/1.17243, loss_spatial_bce_2: 0.12250/0.09159, loss_spatial_dice_2: 0.22295/0.22148, loss_spatial_ce_2: 0.02792/0.08336, loss_grounding_bce_2: 0.31634/0.08647, loss_grounding_dice_2: 0.29672/0.17981, loss_grounding_ce_2: 0.51748/0.28125, loss_mask_ce_3: 0.90034/0.92657, loss_mask_bce_3: 0.70464/0.33739, loss_mask_dice_3: 1.07515/1.16930, loss_spatial_bce_3: 0.12589/0.09242, loss_spatial_dice_3: 0.22176/0.22209, loss_spatial_ce_3: 0.03161/0.08712, loss_grounding_bce_3: 0.29631/0.08665, loss_grounding_dice_3: 0.27419/0.17967, loss_grounding_ce_3: 0.46612/0.28303, loss_mask_ce_4: 0.79647/0.92547, loss_mask_bce_4: 0.74738/0.33917, loss_mask_dice_4: 1.20470/1.19227, loss_spatial_bce_4: 0.13893/0.09660, loss_spatial_dice_4: 0.22711/0.23274, loss_spatial_ce_4: 0.04478/0.10376, loss_grounding_bce_4: 0.33146/0.08714, loss_grounding_dice_4: 0.33055/0.18244, loss_grounding_ce_4: 0.41169/0.28529, loss_mask_ce_5: 0.75935/0.93987, loss_mask_bce_5: 0.79081/0.34145, loss_mask_dice_5: 1.25540/1.19792, loss_spatial_bce_5: 0.14402/0.09799, loss_spatial_dice_5: 0.24014/0.23606, loss_spatial_ce_5: 0.05806/0.11798, loss_grounding_bce_5: 0.37405/0.08756, loss_grounding_dice_5: 0.32846/0.18367, loss_grounding_ce_5: 0.41954/0.29786, loss_mask_ce_6: 1.09977/0.97748, loss_mask_bce_6: 0.83931/0.34420, loss_mask_dice_6: 1.27117/1.20056, loss_spatial_bce_6: 0.15726/0.10348, loss_spatial_dice_6: 0.23519/0.23827, loss_spatial_ce_6: 0.10692/0.14344, loss_grounding_bce_6: 0.37982/0.08831, loss_grounding_dice_6: 0.32668/0.18382, loss_grounding_ce_6: 0.40401/0.31502, loss_mask_ce_7: 1.19025/1.02114, loss_mask_bce_7: 0.84284/0.35184, loss_mask_dice_7: 1.38775/1.25607, loss_spatial_bce_7: 0.14401/0.11218, loss_spatial_dice_7: 0.23837/0.26550, loss_spatial_ce_7: 0.17194/0.18144, loss_grounding_bce_7: 0.42157/0.09024, loss_grounding_dice_7: 0.35238/0.19113, loss_grounding_ce_7: 0.34206/0.34747, loss_mask_ce_8: 1.21909/1.13249, loss_mask_bce_8: 0.85350/0.36539, loss_mask_dice_8: 1.45563/1.33040, loss_spatial_bce_8: 0.19132/0.13330, loss_spatial_dice_8: 0.26379/0.30538, loss_spatial_ce_8: 0.13348/0.23768, loss_grounding_bce_8: 0.33594/0.09381, loss_grounding_dice_8: 0.34048/0.20226, loss_grounding_ce_8: 0.45041/0.41824, loss_mask_ce_9: 5.02188/3.68704, loss_mask_bce_9: 0.97237/0.39259, loss_mask_dice_9: 2.37206/1.90552, loss_spatial_bce_9: 0.27575/0.33547, loss_spatial_dice_9: 0.88827/0.82383, loss_spatial_ce_9: 1.60650/1.51060, loss_grounding_bce_9: 0.35865/0.10530, loss_grounding_dice_9: 0.40517/0.28200, loss_grounding_ce_9: 0.59965/0.68910] items per batch[64] items per second[0.23] total items[1836800] mini batches[ 28700] memory[7341] epoch remaining[0:24:46]
INFO:trainer.default_trainer:epochs[    15] optim steps[28800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.48689/0.90989, loss_mask_bce_0: 0.50020/0.33546, loss_mask_dice_0: 1.19617/1.16601, loss_spatial_bce_0: 0.06659/0.09050, loss_spatial_dice_0: 0.20473/0.21616, loss_spatial_ce_0: 0.13545/0.07366, loss_grounding_bce_0: 0.06707/0.08627, loss_grounding_dice_0: 0.07357/0.17946, loss_grounding_ce_0: 0.09336/0.27618, loss_mask_ce_1: 1.39852/0.91030, loss_mask_bce_1: 0.50659/0.33621, loss_mask_dice_1: 1.42713/1.17319, loss_spatial_bce_1: 0.06330/0.09128, loss_spatial_dice_1: 0.18932/0.22043, loss_spatial_ce_1: 0.14245/0.07951, loss_grounding_bce_1: 0.06978/0.08640, loss_grounding_dice_1: 0.07153/0.18036, loss_grounding_ce_1: 0.11320/0.27770, loss_mask_ce_2: 1.42064/0.91828, loss_mask_bce_2: 0.50013/0.33664, loss_mask_dice_2: 1.19041/1.17231, loss_spatial_bce_2: 0.06803/0.09155, loss_spatial_dice_2: 0.18726/0.22144, loss_spatial_ce_2: 0.16915/0.08328, loss_grounding_bce_2: 0.06965/0.08645, loss_grounding_dice_2: 0.07514/0.17980, loss_grounding_ce_2: 0.09815/0.28109, loss_mask_ce_3: 1.45372/0.92650, loss_mask_bce_3: 0.52995/0.33741, loss_mask_dice_3: 1.34025/1.16928, loss_spatial_bce_3: 0.06965/0.09239, loss_spatial_dice_3: 0.18952/0.22205, loss_spatial_ce_3: 0.13901/0.08708, loss_grounding_bce_3: 0.06981/0.08664, loss_grounding_dice_3: 0.07416/0.17966, loss_grounding_ce_3: 0.06514/0.28285, loss_mask_ce_4: 1.47654/0.92544, loss_mask_bce_4: 0.50619/0.33917, loss_mask_dice_4: 1.27869/1.19222, loss_spatial_bce_4: 0.07766/0.09657, loss_spatial_dice_4: 0.23848/0.23272, loss_spatial_ce_4: 0.29196/0.10367, loss_grounding_bce_4: 0.06805/0.08714, loss_grounding_dice_4: 0.06238/0.18245, loss_grounding_ce_4: 0.06610/0.28510, loss_mask_ce_5: 1.45574/0.93984, loss_mask_bce_5: 0.52707/0.34145, loss_mask_dice_5: 1.31062/1.19793, loss_spatial_bce_5: 0.07227/0.09797, loss_spatial_dice_5: 0.24316/0.23604, loss_spatial_ce_5: 0.20152/0.11791, loss_grounding_bce_5: 0.07169/0.08756, loss_grounding_dice_5: 0.06820/0.18366, loss_grounding_ce_5: 0.06914/0.29763, loss_mask_ce_6: 1.42480/0.97747, loss_mask_bce_6: 0.52118/0.34422, loss_mask_dice_6: 1.35837/1.20052, loss_spatial_bce_6: 0.10336/0.10346, loss_spatial_dice_6: 0.28490/0.23827, loss_spatial_ce_6: 0.23567/0.14342, loss_grounding_bce_6: 0.07719/0.08830, loss_grounding_dice_6: 0.07141/0.18382, loss_grounding_ce_6: 0.06183/0.31481, loss_mask_ce_7: 1.71765/1.02113, loss_mask_bce_7: 0.49771/0.35186, loss_mask_dice_7: 1.32856/1.25605, loss_spatial_bce_7: 0.08849/0.11215, loss_spatial_dice_7: 0.31092/0.26548, loss_spatial_ce_7: 0.18862/0.18138, loss_grounding_bce_7: 0.07630/0.09022, loss_grounding_dice_7: 0.07238/0.19113, loss_grounding_ce_7: 0.09183/0.34729, loss_mask_ce_8: 2.01032/1.13250, loss_mask_bce_8: 0.52708/0.36540, loss_mask_dice_8: 1.35287/1.33033, loss_spatial_bce_8: 0.08895/0.13325, loss_spatial_dice_8: 0.32925/0.30537, loss_spatial_ce_8: 0.20934/0.23763, loss_grounding_bce_8: 0.09519/0.09380, loss_grounding_dice_8: 0.11206/0.20223, loss_grounding_ce_8: 0.12694/0.41816, loss_mask_ce_9: 4.48620/3.68725, loss_mask_bce_9: 0.61601/0.39260, loss_mask_dice_9: 2.28008/1.90527, loss_spatial_bce_9: 0.35278/0.33548, loss_spatial_dice_9: 0.89694/0.82383, loss_spatial_ce_9: 1.36179/1.51062, loss_grounding_bce_9: 0.16957/0.10530, loss_grounding_dice_9: 0.22803/0.28198, loss_grounding_ce_9: 0.85591/0.68900] items per batch[64] items per second[0.24] total items[1843200] mini batches[ 28800] memory[7341] epoch remaining[0:20:04]
INFO:trainer.default_trainer:epochs[    15] optim steps[28900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34738/0.90993, loss_mask_bce_0: 0.19555/0.33550, loss_mask_dice_0: 0.64431/1.16634, loss_spatial_bce_0: 0.05017/0.09047, loss_spatial_dice_0: 0.11530/0.21614, loss_spatial_ce_0: 0.00191/0.07362, loss_grounding_bce_0: 0.06281/0.08629, loss_grounding_dice_0: 0.05537/0.17954, loss_grounding_ce_0: 0.40962/0.27620, loss_mask_ce_1: 1.28852/0.91029, loss_mask_bce_1: 0.17864/0.33624, loss_mask_dice_1: 0.61774/1.17351, loss_spatial_bce_1: 0.04905/0.09125, loss_spatial_dice_1: 0.12016/0.22042, loss_spatial_ce_1: 0.01485/0.07946, loss_grounding_bce_1: 0.06293/0.08642, loss_grounding_dice_1: 0.04862/0.18042, loss_grounding_ce_1: 0.39059/0.27779, loss_mask_ce_2: 1.29435/0.91826, loss_mask_bce_2: 0.19465/0.33668, loss_mask_dice_2: 0.63083/1.17267, loss_spatial_bce_2: 0.05628/0.09153, loss_spatial_dice_2: 0.11702/0.22144, loss_spatial_ce_2: 0.02047/0.08321, loss_grounding_bce_2: 0.05988/0.08647, loss_grounding_dice_2: 0.04642/0.17988, loss_grounding_ce_2: 0.45194/0.28115, loss_mask_ce_3: 1.35618/0.92650, loss_mask_bce_3: 0.21088/0.33746, loss_mask_dice_3: 0.62320/1.16965, loss_spatial_bce_3: 0.05344/0.09237, loss_spatial_dice_3: 0.12467/0.22204, loss_spatial_ce_3: 0.00582/0.08703, loss_grounding_bce_3: 0.05865/0.08666, loss_grounding_dice_3: 0.04656/0.17971, loss_grounding_ce_3: 0.50660/0.28292, loss_mask_ce_4: 1.34871/0.92547, loss_mask_bce_4: 0.18511/0.33921, loss_mask_dice_4: 0.62233/1.19257, loss_spatial_bce_4: 0.05502/0.09655, loss_spatial_dice_4: 0.17221/0.23273, loss_spatial_ce_4: 0.00089/0.10359, loss_grounding_bce_4: 0.06210/0.08717, loss_grounding_dice_4: 0.04893/0.18253, loss_grounding_ce_4: 0.52890/0.28512, loss_mask_ce_5: 1.23383/0.93994, loss_mask_bce_5: 0.18577/0.34148, loss_mask_dice_5: 0.61893/1.19820, loss_spatial_bce_5: 0.06126/0.09796, loss_spatial_dice_5: 0.15531/0.23604, loss_spatial_ce_5: 0.00505/0.11784, loss_grounding_bce_5: 0.05908/0.08758, loss_grounding_dice_5: 0.04426/0.18375, loss_grounding_ce_5: 0.51349/0.29763, loss_mask_ce_6: 1.50445/0.97757, loss_mask_bce_6: 0.20899/0.34423, loss_mask_dice_6: 0.62665/1.20083, loss_spatial_bce_6: 0.05887/0.10346, loss_spatial_dice_6: 0.17851/0.23827, loss_spatial_ce_6: 0.06594/0.14333, loss_grounding_bce_6: 0.06314/0.08833, loss_grounding_dice_6: 0.05300/0.18389, loss_grounding_ce_6: 0.50878/0.31488, loss_mask_ce_7: 1.53920/1.02122, loss_mask_bce_7: 0.16516/0.35188, loss_mask_dice_7: 0.61175/1.25642, loss_spatial_bce_7: 0.06396/0.11214, loss_spatial_dice_7: 0.19607/0.26550, loss_spatial_ce_7: 0.05557/0.18128, loss_grounding_bce_7: 0.06761/0.09025, loss_grounding_dice_7: 0.07651/0.19122, loss_grounding_ce_7: 0.48639/0.34733, loss_mask_ce_8: 1.53166/1.13275, loss_mask_bce_8: 0.14319/0.36543, loss_mask_dice_8: 0.61859/1.33067, loss_spatial_bce_8: 0.08588/0.13321, loss_spatial_dice_8: 0.23282/0.30536, loss_spatial_ce_8: 0.11210/0.23757, loss_grounding_bce_8: 0.07809/0.09381, loss_grounding_dice_8: 0.08286/0.20229, loss_grounding_ce_8: 0.45859/0.41822, loss_mask_ce_9: 3.32274/3.68715, loss_mask_bce_9: 0.15794/0.39264, loss_mask_dice_9: 0.72216/1.90592, loss_spatial_bce_9: 0.32253/0.33544, loss_spatial_dice_9: 0.72083/0.82384, loss_spatial_ce_9: 1.26818/1.51072, loss_grounding_bce_9: 0.08613/0.10532, loss_grounding_dice_9: 0.06541/0.28208, loss_grounding_ce_9: 0.46736/0.68868] items per batch[64] items per second[0.23] total items[1849600] mini batches[ 28900] memory[7341] epoch remaining[0:15:26]
INFO:trainer.default_trainer:epochs[    15] optim steps[29000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06320/0.90981, loss_mask_bce_0: 0.48727/0.33549, loss_mask_dice_0: 0.44818/1.16658, loss_spatial_bce_0: 0.12097/0.09045, loss_spatial_dice_0: 0.14784/0.21610, loss_spatial_ce_0: 0.00111/0.07364, loss_grounding_bce_0: 0.27423/0.08628, loss_grounding_dice_0: 0.27580/0.17953, loss_grounding_ce_0: 1.47600/0.27637, loss_mask_ce_1: 1.19966/0.91022, loss_mask_bce_1: 0.30426/0.33624, loss_mask_dice_1: 0.40262/1.17375, loss_spatial_bce_1: 0.12511/0.09123, loss_spatial_dice_1: 0.15165/0.22038, loss_spatial_ce_1: 0.00308/0.07947, loss_grounding_bce_1: 0.26743/0.08642, loss_grounding_dice_1: 0.26427/0.18038, loss_grounding_ce_1: 1.54157/0.27792, loss_mask_ce_2: 1.14313/0.91820, loss_mask_bce_2: 0.30264/0.33666, loss_mask_dice_2: 0.42689/1.17291, loss_spatial_bce_2: 0.12252/0.09151, loss_spatial_dice_2: 0.15266/0.22141, loss_spatial_ce_2: 0.00567/0.08322, loss_grounding_bce_2: 0.24969/0.08645, loss_grounding_dice_2: 0.27836/0.17985, loss_grounding_ce_2: 1.44575/0.28123, loss_mask_ce_3: 1.09805/0.92640, loss_mask_bce_3: 0.30098/0.33746, loss_mask_dice_3: 0.42006/1.16987, loss_spatial_bce_3: 0.11812/0.09235, loss_spatial_dice_3: 0.15088/0.22200, loss_spatial_ce_3: 0.01720/0.08707, loss_grounding_bce_3: 0.25735/0.08664, loss_grounding_dice_3: 0.28612/0.17970, loss_grounding_ce_3: 1.49396/0.28308, loss_mask_ce_4: 1.08384/0.92536, loss_mask_bce_4: 0.30370/0.33920, loss_mask_dice_4: 0.45676/1.19283, loss_spatial_bce_4: 0.11258/0.09654, loss_spatial_dice_4: 0.18006/0.23269, loss_spatial_ce_4: 0.06115/0.10360, loss_grounding_bce_4: 0.24138/0.08716, loss_grounding_dice_4: 0.27668/0.18250, loss_grounding_ce_4: 1.46308/0.28522, loss_mask_ce_5: 0.99117/0.93991, loss_mask_bce_5: 0.41247/0.34148, loss_mask_dice_5: 0.53742/1.19847, loss_spatial_bce_5: 0.11930/0.09794, loss_spatial_dice_5: 0.18408/0.23602, loss_spatial_ce_5: 0.04087/0.11786, loss_grounding_bce_5: 0.16461/0.08757, loss_grounding_dice_5: 0.32345/0.18371, loss_grounding_ce_5: 2.85160/0.29779, loss_mask_ce_6: 1.01963/0.97755, loss_mask_bce_6: 0.37849/0.34423, loss_mask_dice_6: 0.55016/1.20109, loss_spatial_bce_6: 0.13448/0.10345, loss_spatial_dice_6: 0.20262/0.23824, loss_spatial_ce_6: 0.05140/0.14332, loss_grounding_bce_6: 0.15631/0.08832, loss_grounding_dice_6: 0.31565/0.18389, loss_grounding_ce_6: 2.31842/0.31507, loss_mask_ce_7: 1.10629/1.02132, loss_mask_bce_7: 0.30261/0.35187, loss_mask_dice_7: 0.47132/1.25660, loss_spatial_bce_7: 0.15396/0.11213, loss_spatial_dice_7: 0.19929/0.26547, loss_spatial_ce_7: 0.09352/0.18129, loss_grounding_bce_7: 0.11120/0.09023, loss_grounding_dice_7: 0.29986/0.19121, loss_grounding_ce_7: 1.95371/0.34750, loss_mask_ce_8: 1.04356/1.13279, loss_mask_bce_8: 0.29713/0.36542, loss_mask_dice_8: 0.50825/1.33094, loss_spatial_bce_8: 0.17180/0.13322, loss_spatial_dice_8: 0.23361/0.30533, loss_spatial_ce_8: 0.11510/0.23757, loss_grounding_bce_8: 0.08591/0.09379, loss_grounding_dice_8: 0.27105/0.20228, loss_grounding_ce_8: 2.01261/0.41835, loss_mask_ce_9: 3.54481/3.68739, loss_mask_bce_9: 0.39004/0.39264, loss_mask_dice_9: 0.80047/1.90634, loss_spatial_bce_9: 0.48198/0.33549, loss_spatial_dice_9: 0.80833/0.82384, loss_spatial_ce_9: 1.51496/1.51078, loss_grounding_bce_9: 0.13095/0.10531, loss_grounding_dice_9: 0.31709/0.28204, loss_grounding_ce_9: 1.21005/0.68869] items per batch[64] items per second[0.23] total items[1856000] mini batches[ 29000] memory[7341] epoch remaining[0:10:47]
INFO:trainer.default_trainer:epochs[    15] optim steps[29100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65645/0.90964, loss_mask_bce_0: 0.14134/0.33539, loss_mask_dice_0: 0.61015/1.16642, loss_spatial_bce_0: 0.04382/0.09041, loss_spatial_dice_0: 0.16955/0.21606, loss_spatial_ce_0: 0.01026/0.07358, loss_grounding_bce_0: 0.01274/0.08625, loss_grounding_dice_0: 0.14511/0.17953, loss_grounding_ce_0: 0.12979/0.27642, loss_mask_ce_1: 0.62630/0.91005, loss_mask_bce_1: 0.13852/0.33613, loss_mask_dice_1: 0.54440/1.17345, loss_spatial_bce_1: 0.04380/0.09118, loss_spatial_dice_1: 0.18046/0.22034, loss_spatial_ce_1: 0.00759/0.07941, loss_grounding_bce_1: 0.01189/0.08638, loss_grounding_dice_1: 0.14001/0.18037, loss_grounding_ce_1: 0.16820/0.27790, loss_mask_ce_2: 0.68199/0.91797, loss_mask_bce_2: 0.13882/0.33655, loss_mask_dice_2: 0.59747/1.17268, loss_spatial_bce_2: 0.04251/0.09146, loss_spatial_dice_2: 0.16992/0.22136, loss_spatial_ce_2: 0.03187/0.08316, loss_grounding_bce_2: 0.01243/0.08642, loss_grounding_dice_2: 0.13523/0.17984, loss_grounding_ce_2: 0.23552/0.28124, loss_mask_ce_3: 0.58356/0.92619, loss_mask_bce_3: 0.14430/0.33735, loss_mask_dice_3: 0.59014/1.16973, loss_spatial_bce_3: 0.04338/0.09230, loss_spatial_dice_3: 0.17555/0.22196, loss_spatial_ce_3: 0.02796/0.08699, loss_grounding_bce_3: 0.01321/0.08662, loss_grounding_dice_3: 0.14053/0.17971, loss_grounding_ce_3: 0.22535/0.28308, loss_mask_ce_4: 0.59437/0.92511, loss_mask_bce_4: 0.13833/0.33909, loss_mask_dice_4: 0.65979/1.19263, loss_spatial_bce_4: 0.04812/0.09649, loss_spatial_dice_4: 0.16865/0.23265, loss_spatial_ce_4: 0.01493/0.10355, loss_grounding_bce_4: 0.01251/0.08713, loss_grounding_dice_4: 0.14455/0.18249, loss_grounding_ce_4: 0.38968/0.28531, loss_mask_ce_5: 0.55189/0.93968, loss_mask_bce_5: 0.15572/0.34136, loss_mask_dice_5: 0.66632/1.19825, loss_spatial_bce_5: 0.04672/0.09789, loss_spatial_dice_5: 0.18142/0.23597, loss_spatial_ce_5: 0.04346/0.11784, loss_grounding_bce_5: 0.01407/0.08754, loss_grounding_dice_5: 0.14680/0.18369, loss_grounding_ce_5: 0.34906/0.29789, loss_mask_ce_6: 0.80530/0.97734, loss_mask_bce_6: 0.14778/0.34412, loss_mask_dice_6: 0.61968/1.20090, loss_spatial_bce_6: 0.05091/0.10339, loss_spatial_dice_6: 0.17357/0.23822, loss_spatial_ce_6: 0.07416/0.14327, loss_grounding_bce_6: 0.01417/0.08829, loss_grounding_dice_6: 0.14424/0.18389, loss_grounding_ce_6: 0.36605/0.31511, loss_mask_ce_7: 0.74663/1.02118, loss_mask_bce_7: 0.14618/0.35174, loss_mask_dice_7: 0.57846/1.25635, loss_spatial_bce_7: 0.04382/0.11207, loss_spatial_dice_7: 0.20097/0.26544, loss_spatial_ce_7: 0.12559/0.18128, loss_grounding_bce_7: 0.01368/0.09020, loss_grounding_dice_7: 0.13657/0.19119, loss_grounding_ce_7: 0.28543/0.34765, loss_mask_ce_8: 0.75720/1.13265, loss_mask_bce_8: 0.14852/0.36526, loss_mask_dice_8: 0.68767/1.33068, loss_spatial_bce_8: 0.06276/0.13315, loss_spatial_dice_8: 0.27668/0.30531, loss_spatial_ce_8: 0.13590/0.23756, loss_grounding_bce_8: 0.01429/0.09375, loss_grounding_dice_8: 0.17131/0.20227, loss_grounding_ce_8: 0.65493/0.41830, loss_mask_ce_9: 2.98509/3.68687, loss_mask_bce_9: 0.15970/0.39246, loss_mask_dice_9: 0.98238/1.90568, loss_spatial_bce_9: 0.26968/0.33539, loss_spatial_dice_9: 0.85199/0.82383, loss_spatial_ce_9: 1.63511/1.51089, loss_grounding_bce_9: 0.02304/0.10526, loss_grounding_dice_9: 0.23745/0.28201, loss_grounding_ce_9: 1.31188/0.68871] items per batch[64] items per second[0.22] total items[1862400] mini batches[ 29100] memory[7341] epoch remaining[0:06:08]
INFO:trainer.default_trainer:epochs[    15] optim steps[29200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65324/0.90959, loss_mask_bce_0: 0.12910/0.33527, loss_mask_dice_0: 0.65027/1.16632, loss_spatial_bce_0: 0.02914/0.09037, loss_spatial_dice_0: 0.19600/0.21603, loss_spatial_ce_0: 0.03187/0.07349, loss_grounding_bce_0: 0.08192/0.08624, loss_grounding_dice_0: 0.06468/0.17954, loss_grounding_ce_0: 0.05863/0.27642, loss_mask_ce_1: 0.37193/0.91002, loss_mask_bce_1: 0.12376/0.33602, loss_mask_dice_1: 0.83396/1.17336, loss_spatial_bce_1: 0.02946/0.09113, loss_spatial_dice_1: 0.19987/0.22032, loss_spatial_ce_1: 0.05104/0.07932, loss_grounding_bce_1: 0.07367/0.08637, loss_grounding_dice_1: 0.06142/0.18036, loss_grounding_ce_1: 0.08643/0.27793, loss_mask_ce_2: 0.37953/0.91790, loss_mask_bce_2: 0.12273/0.33644, loss_mask_dice_2: 0.71421/1.17261, loss_spatial_bce_2: 0.03091/0.09142, loss_spatial_dice_2: 0.21468/0.22134, loss_spatial_ce_2: 0.07608/0.08309, loss_grounding_bce_2: 0.07987/0.08641, loss_grounding_dice_2: 0.06589/0.17985, loss_grounding_ce_2: 0.15169/0.28125, loss_mask_ce_3: 0.41562/0.92616, loss_mask_bce_3: 0.13687/0.33724, loss_mask_dice_3: 0.74237/1.16961, loss_spatial_bce_3: 0.03222/0.09227, loss_spatial_dice_3: 0.16474/0.22194, loss_spatial_ce_3: 0.04627/0.08690, loss_grounding_bce_3: 0.07300/0.08660, loss_grounding_dice_3: 0.05642/0.17972, loss_grounding_ce_3: 0.17406/0.28309, loss_mask_ce_4: 0.78488/0.92507, loss_mask_bce_4: 0.12091/0.33898, loss_mask_dice_4: 0.77264/1.19259, loss_spatial_bce_4: 0.03226/0.09646, loss_spatial_dice_4: 0.19523/0.23263, loss_spatial_ce_4: 0.09417/0.10348, loss_grounding_bce_4: 0.08125/0.08712, loss_grounding_dice_4: 0.06517/0.18249, loss_grounding_ce_4: 0.15794/0.28541, loss_mask_ce_5: 0.41848/0.93969, loss_mask_bce_5: 0.12832/0.34124, loss_mask_dice_5: 0.72297/1.19814, loss_spatial_bce_5: 0.03378/0.09786, loss_spatial_dice_5: 0.22594/0.23595, loss_spatial_ce_5: 0.11725/0.11776, loss_grounding_bce_5: 0.07965/0.08752, loss_grounding_dice_5: 0.06767/0.18370, loss_grounding_ce_5: 0.12021/0.29794, loss_mask_ce_6: 0.58189/0.97728, loss_mask_bce_6: 0.12770/0.34399, loss_mask_dice_6: 0.62619/1.20080, loss_spatial_bce_6: 0.04547/0.10336, loss_spatial_dice_6: 0.22095/0.23820, loss_spatial_ce_6: 0.12636/0.14321, loss_grounding_bce_6: 0.08786/0.08828, loss_grounding_dice_6: 0.07131/0.18390, loss_grounding_ce_6: 0.14424/0.31509, loss_mask_ce_7: 0.49247/1.02113, loss_mask_bce_7: 0.12724/0.35165, loss_mask_dice_7: 0.83986/1.25632, loss_spatial_bce_7: 0.03833/0.11203, loss_spatial_dice_7: 0.23930/0.26543, loss_spatial_ce_7: 0.06943/0.18117, loss_grounding_bce_7: 0.07668/0.09019, loss_grounding_dice_7: 0.06119/0.19119, loss_grounding_ce_7: 0.14397/0.34770, loss_mask_ce_8: 0.58482/1.13265, loss_mask_bce_8: 0.13881/0.36517, loss_mask_dice_8: 0.75223/1.33058, loss_spatial_bce_8: 0.05649/0.13312, loss_spatial_dice_8: 0.27132/0.30527, loss_spatial_ce_8: 0.19061/0.23751, loss_grounding_bce_8: 0.08531/0.09376, loss_grounding_dice_8: 0.06422/0.20227, loss_grounding_ce_8: 0.20165/0.41822, loss_mask_ce_9: 2.82319/3.68693, loss_mask_bce_9: 0.11634/0.39235, loss_mask_dice_9: 0.92047/1.90531, loss_spatial_bce_9: 0.16911/0.33532, loss_spatial_dice_9: 0.74488/0.82381, loss_spatial_ce_9: 1.37364/1.51057, loss_grounding_bce_9: 0.09286/0.10526, loss_grounding_dice_9: 0.11013/0.28200, loss_grounding_ce_9: 1.22055/0.68872] items per batch[64] items per second[0.23] total items[1868800] mini batches[ 29200] memory[7341] epoch remaining[0:01:29]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00029232.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0021 s/iter. Inference: 0.2203 s/iter. Eval: 0.0989 s/iter. Total: 0.3213 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0028 s/iter. Inference: 0.2218 s/iter. Eval: 0.0849 s/iter. Total: 0.3095 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0029 s/iter. Inference: 0.2248 s/iter. Eval: 0.0813 s/iter. Total: 0.3091 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0029 s/iter. Inference: 0.2249 s/iter. Eval: 0.0781 s/iter. Total: 0.3060 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0030 s/iter. Inference: 0.2238 s/iter. Eval: 0.0762 s/iter. Total: 0.3032 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0030 s/iter. Inference: 0.2256 s/iter. Eval: 0.0809 s/iter. Total: 0.3096 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0030 s/iter. Inference: 0.2277 s/iter. Eval: 0.0799 s/iter. Total: 0.3107 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0030 s/iter. Inference: 0.2275 s/iter. Eval: 0.0794 s/iter. Total: 0.3100 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0031 s/iter. Inference: 0.2284 s/iter. Eval: 0.0782 s/iter. Total: 0.3098 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval1nts6ktm ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.698 | 81.973 | 59.716 |      133      |
| Things | 54.650 | 82.810 | 65.341 |      80       |
| Stuff  | 42.225 | 80.710 | 51.225 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.65 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.01 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.18s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.89 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.639 | 60.933 | 40.664 | 19.523 | 41.761 | 60.122 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.976 | bicycle      | 17.725 | car            | 37.106 |
| motorcycle    | 34.599 | airplane     | 57.235 | bus            | 66.028 |
| train         | 68.460 | truck        | 35.971 | boat           | 22.854 |
| traffic light | 25.247 | fire hydrant | 62.688 | stop sign      | 63.568 |
| parking meter | 42.242 | bench        | 20.117 | bird           | 28.954 |
| cat           | 72.507 | dog          | 65.082 | horse          | 46.039 |
| sheep         | 46.244 | cow          | 50.429 | elephant       | 59.326 |
| bear          | 78.355 | zebra        | 60.076 | giraffe        | 56.701 |
| backpack      | 16.827 | umbrella     | 48.576 | handbag        | 14.979 |
| tie           | 32.702 | suitcase     | 40.334 | frisbee        | 67.823 |
| skis          | 5.009  | snowboard    | 23.478 | sports ball    | 46.376 |
| kite          | 32.952 | baseball bat | 28.534 | baseball glove | 42.474 |
| skateboard    | 34.990 | surfboard    | 35.424 | tennis racket  | 55.944 |
| bottle        | 33.676 | wine glass   | 27.106 | cup            | 40.729 |
| fork          | 15.530 | knife        | 12.381 | spoon          | 13.832 |
| bowl          | 30.765 | banana       | 21.010 | apple          | 20.032 |
| sandwich      | 43.892 | orange       | 28.845 | broccoli       | 21.250 |
| carrot        | 20.060 | hot dog      | 24.481 | pizza          | 48.591 |
| donut         | 46.845 | cake         | 44.156 | chair          | 20.315 |
| couch         | 42.503 | potted plant | 15.950 | bed            | 41.016 |
| dining table  | 13.244 | toilet       | 67.430 | tv             | 62.402 |
| laptop        | 62.599 | mouse        | 59.376 | remote         | 31.300 |
| keyboard      | 46.749 | cell phone   | 38.641 | microwave      | 56.140 |
| oven          | 31.955 | toaster      | 34.666 | sink           | 37.634 |
| refrigerator  | 59.540 | book         | 8.804  | clock          | 51.541 |
| vase          | 31.245 | scissors     | 21.692 | teddy bear     | 49.588 |
| hair drier    | 6.536  | toothbrush   | 19.094 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.386
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.609
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.290
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.92307018369926, 'fwIoU': 69.08699783315481, 'IoU-person': 87.46970233971251, 'IoU-bicycle': 74.0283288044761, 'IoU-car': 69.55132280854583, 'IoU-motorcycle': 85.08163625184638, 'IoU-airplane': 84.34556061933108, 'IoU-bus': 85.06605299419, 'IoU-train': 84.84040307937086, 'IoU-truck': 63.295719541847596, 'IoU-boat': 66.95303558193122, 'IoU-traffic light': 75.7346387759562, 'IoU-fire hydrant': 85.92060585692597, 'IoU-stop sign': 85.99302023394364, 'IoU-parking meter': 87.94120196653911, 'IoU-bench': 56.97813474168433, 'IoU-bird': 75.18431086261711, 'IoU-cat': 81.19054090036313, 'IoU-dog': 79.37759195366701, 'IoU-horse': 86.16777027251855, 'IoU-sheep': 89.15816756294817, 'IoU-cow': 82.1125450584124, 'IoU-elephant': 92.3776694386781, 'IoU-bear': 92.12268699618261, 'IoU-zebra': 89.6892632607233, 'IoU-giraffe': 88.0644116882256, 'IoU-backpack': 40.94514762455644, 'IoU-umbrella': 74.53798235535926, 'IoU-handbag': 37.9378607389974, 'IoU-tie': 69.18260562946575, 'IoU-suitcase': 79.71842571578803, 'IoU-frisbee': 83.60499610684616, 'IoU-skis': 51.28476274775515, 'IoU-snowboard': 69.23063734210041, 'IoU-sports ball': 67.09428043246876, 'IoU-kite': 66.16800024565929, 'IoU-baseball bat': 61.209865509950525, 'IoU-baseball glove': 78.61748939915194, 'IoU-skateboard': 81.77332203863773, 'IoU-surfboard': 80.47347795439798, 'IoU-tennis racket': 83.06485039526265, 'IoU-bottle': 67.51633850568751, 'IoU-wine glass': 75.47631941217077, 'IoU-cup': 63.32350528925351, 'IoU-fork': 54.58962358729087, 'IoU-knife': 48.49182814549569, 'IoU-spoon': 52.19709519242621, 'IoU-bowl': 58.99105228346227, 'IoU-banana': 84.05435491042877, 'IoU-apple': 58.13470398550672, 'IoU-sandwich': 65.43030051038703, 'IoU-orange': 80.4330812880349, 'IoU-broccoli': 67.82141319463915, 'IoU-carrot': 63.60290268482673, 'IoU-hot dog': 65.49201407554875, 'IoU-pizza': 82.91655132947582, 'IoU-donut': 63.7618518890571, 'IoU-cake': 73.45961445828146, 'IoU-chair': 53.13089009612809, 'IoU-couch': 62.54863748901156, 'IoU-potted plant': 33.75046830317737, 'IoU-bed': 67.65452298153618, 'IoU-dining table': 50.85228861823633, 'IoU-toilet': 78.99318453499417, 'IoU-tv': 74.26885625911822, 'IoU-laptop': 75.11659077783368, 'IoU-mouse': 71.73356002185926, 'IoU-remote': 49.110943275430024, 'IoU-keyboard': 60.641469326058065, 'IoU-cell phone': 71.53008135359804, 'IoU-microwave': 65.00577724683683, 'IoU-oven': 65.01504888217136, 'IoU-toaster': 44.692860486246865, 'IoU-sink': 71.16483443340498, 'IoU-refrigerator': 81.02438807997189, 'IoU-book': 50.527775266484134, 'IoU-clock': 73.84209829187571, 'IoU-vase': 67.41745454459956, 'IoU-scissors': 61.70576132561926, 'IoU-teddy bear': 82.50841063278173, 'IoU-hair drier': 29.39884849282079, 'IoU-toothbrush': 53.89043168001293, 'IoU-banner': 36.773214918390806, 'IoU-blanket': 11.972456922871446, 'IoU-bridge': 36.802678565902745, 'IoU-cardboard': 38.827812027252314, 'IoU-counter': 30.30600819641863, 'IoU-curtain': 64.55879154859677, 'IoU-door-stuff': 42.63948732804076, 'IoU-floor-wood': 61.67928413350583, 'IoU-flower': 44.53280869492547, 'IoU-fruit': 40.64176976203702, 'IoU-gravel': 24.143115562396275, 'IoU-house': 20.181819231179265, 'IoU-light': 41.36540173967289, 'IoU-mirror-stuff': 49.7147660535338, 'IoU-net': 43.35383658790957, 'IoU-pillow': 11.048700530602588, 'IoU-platform': 33.18287582840897, 'IoU-playingfield': 68.89598472807043, 'IoU-railroad': 59.87638807904238, 'IoU-river': 48.75020024478649, 'IoU-road': 66.58510016999503, 'IoU-roof': 16.18429873539734, 'IoU-sand': 61.542916584666415, 'IoU-sea': 84.71738253305247, 'IoU-shelf': 36.28913800288551, 'IoU-snow': 87.98831535990027, 'IoU-stairs': 28.76052177247294, 'IoU-tent': 8.073440368079055, 'IoU-towel': 34.23038192882618, 'IoU-wall-brick': 44.617984033692224, 'IoU-wall-stone': 28.37950081036943, 'IoU-wall-tile': 65.19322294094665, 'IoU-wall-wood': 37.49233115523178, 'IoU-water-other': 24.17247172650331, 'IoU-window-blind': 48.13861766656814, 'IoU-window-other': 47.9220520158366, 'IoU-tree-merged': 81.27703431367776, 'IoU-fence-merged': 52.76454892291989, 'IoU-ceiling-merged': 66.22135030466903, 'IoU-sky-other-merged': 93.66029060155215, 'IoU-cabinet-merged': 58.66596078126093, 'IoU-table-merged': 38.18655358734455, 'IoU-floor-other-merged': 50.94736581491309, 'IoU-pavement-merged': 54.5648827844701, 'IoU-mountain-merged': 55.19909486065524, 'IoU-grass-merged': 72.20966937042861, 'IoU-dirt-merged': 44.50138005097335, 'IoU-paper-merged': 28.541696027189378, 'IoU-food-other-merged': 35.78918155536665, 'IoU-building-other-merged': 57.249921834004894, 'IoU-rock-merged': 61.273401547824456, 'IoU-wall-other-merged': 64.99831668926393, 'IoU-rug-merged': 62.47684992870405, 'mACC': 73.24579691263101, 'pACC': 80.45013684615353, 'ACC-person': 92.07327861933395, 'ACC-bicycle': 84.8027619385486, 'ACC-car': 83.03051775789548, 'ACC-motorcycle': 90.20283517136565, 'ACC-airplane': 90.35671035462707, 'ACC-bus': 91.59983273525582, 'ACC-train': 93.86687393169775, 'ACC-truck': 71.23233786515335, 'ACC-boat': 78.14672524740185, 'ACC-traffic light': 89.81856488177634, 'ACC-fire hydrant': 94.9776247187077, 'ACC-stop sign': 88.51378378679173, 'ACC-parking meter': 92.0319277569053, 'ACC-bench': 71.57950453128453, 'ACC-bird': 80.88193124818106, 'ACC-cat': 94.64473397984506, 'ACC-dog': 82.23642822692736, 'ACC-horse': 92.28029646251737, 'ACC-sheep': 93.58062073780597, 'ACC-cow': 86.9939554014042, 'ACC-elephant': 95.0490212545012, 'ACC-bear': 94.5321667085459, 'ACC-zebra': 92.20191228633016, 'ACC-giraffe': 92.63718260863035, 'ACC-backpack': 55.29224108465307, 'ACC-umbrella': 82.79217152896268, 'ACC-handbag': 58.06843684912067, 'ACC-tie': 78.34794108033964, 'ACC-suitcase': 87.48504696590574, 'ACC-frisbee': 94.48945454545455, 'ACC-skis': 66.92231947856367, 'ACC-snowboard': 79.1129289020597, 'ACC-sports ball': 80.4731189464653, 'ACC-kite': 75.93034465226441, 'ACC-baseball bat': 84.15811911380172, 'ACC-baseball glove': 88.50507627014836, 'ACC-skateboard': 89.25764505747539, 'ACC-surfboard': 89.31794566211711, 'ACC-tennis racket': 89.12186478876067, 'ACC-bottle': 80.6836996373655, 'ACC-wine glass': 85.544595746224, 'ACC-cup': 84.44307054444464, 'ACC-fork': 69.02657603410933, 'ACC-knife': 57.15725067780465, 'ACC-spoon': 71.36072372450133, 'ACC-bowl': 71.24688395147015, 'ACC-banana': 91.23749724165879, 'ACC-apple': 72.77739656376302, 'ACC-sandwich': 81.91076725377259, 'ACC-orange': 89.9819449795948, 'ACC-broccoli': 81.61788063816127, 'ACC-carrot': 73.8286906922554, 'ACC-hot dog': 72.46605353866804, 'ACC-pizza': 92.80805017610572, 'ACC-donut': 81.41168565291728, 'ACC-cake': 82.58211989514334, 'ACC-chair': 65.76999679958962, 'ACC-couch': 72.62432786340794, 'ACC-potted plant': 48.56724853918449, 'ACC-bed': 85.22746153034902, 'ACC-dining table': 78.49471334683666, 'ACC-toilet': 89.77979456994306, 'ACC-tv': 86.53008485058807, 'ACC-laptop': 90.29387298905166, 'ACC-mouse': 85.23388490272681, 'ACC-remote': 71.13742503213167, 'ACC-keyboard': 65.39234281929967, 'ACC-cell phone': 79.91026460907284, 'ACC-microwave': 73.7322019591728, 'ACC-oven': 79.2788777618835, 'ACC-toaster': 55.53654764674486, 'ACC-sink': 82.03144608144214, 'ACC-refrigerator': 90.93727152644558, 'ACC-book': 65.76892633997366, 'ACC-clock': 80.8119624425377, 'ACC-vase': 77.88258653135337, 'ACC-scissors': 66.64153100294742, 'ACC-teddy bear': 88.91466414373014, 'ACC-hair drier': 41.34858964506304, 'ACC-toothbrush': 81.0823488533704, 'ACC-banner': 74.94691302745747, 'ACC-blanket': 14.258519193085448, 'ACC-bridge': 56.63055016608596, 'ACC-cardboard': 48.152799185691435, 'ACC-counter': 58.25730934171052, 'ACC-curtain': 75.45998040426252, 'ACC-door-stuff': 59.01960045425969, 'ACC-floor-wood': 76.3093243449439, 'ACC-flower': 61.27006203445913, 'ACC-fruit': 54.312024943587275, 'ACC-gravel': 32.23721916644207, 'ACC-house': 21.857973509010563, 'ACC-light': 59.016419917314245, 'ACC-mirror-stuff': 71.42731395133303, 'ACC-net': 65.38821380192572, 'ACC-pillow': 25.89171394402748, 'ACC-platform': 61.84078185923395, 'ACC-playingfield': 86.98334890167932, 'ACC-railroad': 82.08711491161962, 'ACC-river': 64.91538459562888, 'ACC-road': 84.36240940977726, 'ACC-roof': 23.09631784603976, 'ACC-sand': 67.96120065832818, 'ACC-sea': 92.29648630809564, 'ACC-shelf': 60.51874253651075, 'ACC-snow': 95.6764701890499, 'ACC-stairs': 49.42419897298474, 'ACC-tent': 9.622622965902865, 'ACC-towel': 42.5886711112796, 'ACC-wall-brick': 59.260056123464736, 'ACC-wall-stone': 32.93289359727067, 'ACC-wall-tile': 82.95228745939286, 'ACC-wall-wood': 49.45640411214086, 'ACC-water-other': 38.48384398939614, 'ACC-window-blind': 58.50391417536203, 'ACC-window-other': 70.2598446307842, 'ACC-tree-merged': 89.27289089354475, 'ACC-fence-merged': 70.92596331087665, 'ACC-ceiling-merged': 81.38381558860647, 'ACC-sky-other-merged': 96.7387213220402, 'ACC-cabinet-merged': 75.17129863682229, 'ACC-table-merged': 50.7084210519268, 'ACC-floor-other-merged': 63.882637764011584, 'ACC-pavement-merged': 66.93900370869704, 'ACC-mountain-merged': 65.23136719062785, 'ACC-grass-merged': 83.41728218758243, 'ACC-dirt-merged': 69.1778024859039, 'ACC-paper-merged': 40.378672239720814, 'ACC-food-other-merged': 43.22729465300585, 'ACC-building-other-merged': 72.29787575403576, 'ACC-rock-merged': 84.13309209535213, 'ACC-wall-other-merged': 82.09991104254766, 'ACC-rug-merged': 79.48656584275025})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1572 s/iter. Inference: 0.5689 s/iter. Eval: 0.0000 s/iter. Total: 0.7261 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1584 s/iter. Inference: 0.5273 s/iter. Eval: 0.0000 s/iter. Total: 0.6858 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1732 s/iter. Inference: 0.5972 s/iter. Eval: 0.0000 s/iter. Total: 0.7706 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1725 s/iter. Inference: 0.6489 s/iter. Eval: 0.0000 s/iter. Total: 0.8215 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1700 s/iter. Inference: 0.6053 s/iter. Eval: 0.0000 s/iter. Total: 0.7754 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1678 s/iter. Inference: 0.6528 s/iter. Eval: 0.0000 s/iter. Total: 0.8207 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5870646766169154, 'noc@0.8': 2.9888791337430494, 'noc@0.85': 3.66900790166813, 'noc@0.9': 4.725782850453614, 'miou@iter1': 0.8361964662555788}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0999 s/iter. Eval: 0.0008 s/iter. Total: 0.1023 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.46249389648438, 'precision@0.6': 67.1589584350586, 'precision@0.7': 62.02875900268555, 'precision@0.8': 52.04042053222656, 'precision@0.9': 26.58375358581543, 'cIoU': 56.52067565917969, 'mIoU': 62.13801193237305}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.69848753265891, 'SQ': 81.97320382414566, 'RQ': 59.7157976093352, 'PQ_th': 54.649918375497066, 'SQ_th': 82.81009424243629, 'RQ_th': 65.34101517582707, 'PQ_st': 42.22462965667681, 'SQ_st': 80.70997300408432, 'RQ_st': 51.22490316934753}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.63864027563046, 'AP50': 60.932546033599344, 'AP75': 40.66354994098726, 'APs': 19.5230711323296, 'APm': 41.760546492834855, 'APl': 60.12194225026918, 'AP-person': 43.97628323847426, 'AP-bicycle': 17.725135929203866, 'AP-car': 37.10572109312398, 'AP-motorcycle': 34.59870945637892, 'AP-airplane': 57.235466019133305, 'AP-bus': 66.02802894397296, 'AP-train': 68.45989261721968, 'AP-truck': 35.97057934108071, 'AP-boat': 22.853503302907058, 'AP-traffic light': 25.24657788768387, 'AP-fire hydrant': 62.68792291179549, 'AP-stop sign': 63.567680391213656, 'AP-parking meter': 42.241787328253444, 'AP-bench': 20.11731878295519, 'AP-bird': 28.954489399676348, 'AP-cat': 72.50703598074313, 'AP-dog': 65.08173415898659, 'AP-horse': 46.038504064991855, 'AP-sheep': 46.243620881380316, 'AP-cow': 50.42877049709903, 'AP-elephant': 59.326084681346046, 'AP-bear': 78.35489999913182, 'AP-zebra': 60.07622562607974, 'AP-giraffe': 56.70068772663722, 'AP-backpack': 16.8268732698293, 'AP-umbrella': 48.57629761702461, 'AP-handbag': 14.978712249642054, 'AP-tie': 32.702385350623906, 'AP-suitcase': 40.33388509614589, 'AP-frisbee': 67.82275440551064, 'AP-skis': 5.009217958371361, 'AP-snowboard': 23.477886640205178, 'AP-sports ball': 46.37561299134345, 'AP-kite': 32.95198872254112, 'AP-baseball bat': 28.53425161036205, 'AP-baseball glove': 42.473646885668835, 'AP-skateboard': 34.99020090709614, 'AP-surfboard': 35.42414839652739, 'AP-tennis racket': 55.94365977579309, 'AP-bottle': 33.67626708680394, 'AP-wine glass': 27.10638252848941, 'AP-cup': 40.729276872511754, 'AP-fork': 15.530196010027982, 'AP-knife': 12.380635479072609, 'AP-spoon': 13.8315977226615, 'AP-bowl': 30.76466724922165, 'AP-banana': 21.00958449758333, 'AP-apple': 20.032487959189872, 'AP-sandwich': 43.89190582486104, 'AP-orange': 28.845266449057448, 'AP-broccoli': 21.249682053992096, 'AP-carrot': 20.060351719650267, 'AP-hot dog': 24.481287338202637, 'AP-pizza': 48.591394669710645, 'AP-donut': 46.84535539246221, 'AP-cake': 44.15636742371241, 'AP-chair': 20.314533930227615, 'AP-couch': 42.50326320266918, 'AP-potted plant': 15.950334929151595, 'AP-bed': 41.016298533359965, 'AP-dining table': 13.24446550781245, 'AP-toilet': 67.42981324609698, 'AP-tv': 62.40198821813997, 'AP-laptop': 62.59901925895136, 'AP-mouse': 59.37566676901211, 'AP-remote': 31.299670820762653, 'AP-keyboard': 46.748506739789136, 'AP-cell phone': 38.64053451532208, 'AP-microwave': 56.13966920083774, 'AP-oven': 31.95452819971183, 'AP-toaster': 34.66591230551626, 'AP-sink': 37.633717183644926, 'AP-refrigerator': 59.539889691477775, 'AP-book': 8.804436984025612, 'AP-clock': 51.54136609482072, 'AP-vase': 31.245338237230975, 'AP-scissors': 21.692472458318836, 'AP-teddy bear': 49.58818144779608, 'AP-hair drier': 6.536250399233472, 'AP-toothbrush': 19.094475763163263}), ('sem_seg', {'mIoU': 60.92307018369926, 'fwIoU': 69.08699783315481, 'IoU-person': 87.46970233971251, 'IoU-bicycle': 74.0283288044761, 'IoU-car': 69.55132280854583, 'IoU-motorcycle': 85.08163625184638, 'IoU-airplane': 84.34556061933108, 'IoU-bus': 85.06605299419, 'IoU-train': 84.84040307937086, 'IoU-truck': 63.295719541847596, 'IoU-boat': 66.95303558193122, 'IoU-traffic light': 75.7346387759562, 'IoU-fire hydrant': 85.92060585692597, 'IoU-stop sign': 85.99302023394364, 'IoU-parking meter': 87.94120196653911, 'IoU-bench': 56.97813474168433, 'IoU-bird': 75.18431086261711, 'IoU-cat': 81.19054090036313, 'IoU-dog': 79.37759195366701, 'IoU-horse': 86.16777027251855, 'IoU-sheep': 89.15816756294817, 'IoU-cow': 82.1125450584124, 'IoU-elephant': 92.3776694386781, 'IoU-bear': 92.12268699618261, 'IoU-zebra': 89.6892632607233, 'IoU-giraffe': 88.0644116882256, 'IoU-backpack': 40.94514762455644, 'IoU-umbrella': 74.53798235535926, 'IoU-handbag': 37.9378607389974, 'IoU-tie': 69.18260562946575, 'IoU-suitcase': 79.71842571578803, 'IoU-frisbee': 83.60499610684616, 'IoU-skis': 51.28476274775515, 'IoU-snowboard': 69.23063734210041, 'IoU-sports ball': 67.09428043246876, 'IoU-kite': 66.16800024565929, 'IoU-baseball bat': 61.209865509950525, 'IoU-baseball glove': 78.61748939915194, 'IoU-skateboard': 81.77332203863773, 'IoU-surfboard': 80.47347795439798, 'IoU-tennis racket': 83.06485039526265, 'IoU-bottle': 67.51633850568751, 'IoU-wine glass': 75.47631941217077, 'IoU-cup': 63.32350528925351, 'IoU-fork': 54.58962358729087, 'IoU-knife': 48.49182814549569, 'IoU-spoon': 52.19709519242621, 'IoU-bowl': 58.99105228346227, 'IoU-banana': 84.05435491042877, 'IoU-apple': 58.13470398550672, 'IoU-sandwich': 65.43030051038703, 'IoU-orange': 80.4330812880349, 'IoU-broccoli': 67.82141319463915, 'IoU-carrot': 63.60290268482673, 'IoU-hot dog': 65.49201407554875, 'IoU-pizza': 82.91655132947582, 'IoU-donut': 63.7618518890571, 'IoU-cake': 73.45961445828146, 'IoU-chair': 53.13089009612809, 'IoU-couch': 62.54863748901156, 'IoU-potted plant': 33.75046830317737, 'IoU-bed': 67.65452298153618, 'IoU-dining table': 50.85228861823633, 'IoU-toilet': 78.99318453499417, 'IoU-tv': 74.26885625911822, 'IoU-laptop': 75.11659077783368, 'IoU-mouse': 71.73356002185926, 'IoU-remote': 49.110943275430024, 'IoU-keyboard': 60.641469326058065, 'IoU-cell phone': 71.53008135359804, 'IoU-microwave': 65.00577724683683, 'IoU-oven': 65.01504888217136, 'IoU-toaster': 44.692860486246865, 'IoU-sink': 71.16483443340498, 'IoU-refrigerator': 81.02438807997189, 'IoU-book': 50.527775266484134, 'IoU-clock': 73.84209829187571, 'IoU-vase': 67.41745454459956, 'IoU-scissors': 61.70576132561926, 'IoU-teddy bear': 82.50841063278173, 'IoU-hair drier': 29.39884849282079, 'IoU-toothbrush': 53.89043168001293, 'IoU-banner': 36.773214918390806, 'IoU-blanket': 11.972456922871446, 'IoU-bridge': 36.802678565902745, 'IoU-cardboard': 38.827812027252314, 'IoU-counter': 30.30600819641863, 'IoU-curtain': 64.55879154859677, 'IoU-door-stuff': 42.63948732804076, 'IoU-floor-wood': 61.67928413350583, 'IoU-flower': 44.53280869492547, 'IoU-fruit': 40.64176976203702, 'IoU-gravel': 24.143115562396275, 'IoU-house': 20.181819231179265, 'IoU-light': 41.36540173967289, 'IoU-mirror-stuff': 49.7147660535338, 'IoU-net': 43.35383658790957, 'IoU-pillow': 11.048700530602588, 'IoU-platform': 33.18287582840897, 'IoU-playingfield': 68.89598472807043, 'IoU-railroad': 59.87638807904238, 'IoU-river': 48.75020024478649, 'IoU-road': 66.58510016999503, 'IoU-roof': 16.18429873539734, 'IoU-sand': 61.542916584666415, 'IoU-sea': 84.71738253305247, 'IoU-shelf': 36.28913800288551, 'IoU-snow': 87.98831535990027, 'IoU-stairs': 28.76052177247294, 'IoU-tent': 8.073440368079055, 'IoU-towel': 34.23038192882618, 'IoU-wall-brick': 44.617984033692224, 'IoU-wall-stone': 28.37950081036943, 'IoU-wall-tile': 65.19322294094665, 'IoU-wall-wood': 37.49233115523178, 'IoU-water-other': 24.17247172650331, 'IoU-window-blind': 48.13861766656814, 'IoU-window-other': 47.9220520158366, 'IoU-tree-merged': 81.27703431367776, 'IoU-fence-merged': 52.76454892291989, 'IoU-ceiling-merged': 66.22135030466903, 'IoU-sky-other-merged': 93.66029060155215, 'IoU-cabinet-merged': 58.66596078126093, 'IoU-table-merged': 38.18655358734455, 'IoU-floor-other-merged': 50.94736581491309, 'IoU-pavement-merged': 54.5648827844701, 'IoU-mountain-merged': 55.19909486065524, 'IoU-grass-merged': 72.20966937042861, 'IoU-dirt-merged': 44.50138005097335, 'IoU-paper-merged': 28.541696027189378, 'IoU-food-other-merged': 35.78918155536665, 'IoU-building-other-merged': 57.249921834004894, 'IoU-rock-merged': 61.273401547824456, 'IoU-wall-other-merged': 64.99831668926393, 'IoU-rug-merged': 62.47684992870405, 'mACC': 73.24579691263101, 'pACC': 80.45013684615353, 'ACC-person': 92.07327861933395, 'ACC-bicycle': 84.8027619385486, 'ACC-car': 83.03051775789548, 'ACC-motorcycle': 90.20283517136565, 'ACC-airplane': 90.35671035462707, 'ACC-bus': 91.59983273525582, 'ACC-train': 93.86687393169775, 'ACC-truck': 71.23233786515335, 'ACC-boat': 78.14672524740185, 'ACC-traffic light': 89.81856488177634, 'ACC-fire hydrant': 94.9776247187077, 'ACC-stop sign': 88.51378378679173, 'ACC-parking meter': 92.0319277569053, 'ACC-bench': 71.57950453128453, 'ACC-bird': 80.88193124818106, 'ACC-cat': 94.64473397984506, 'ACC-dog': 82.23642822692736, 'ACC-horse': 92.28029646251737, 'ACC-sheep': 93.58062073780597, 'ACC-cow': 86.9939554014042, 'ACC-elephant': 95.0490212545012, 'ACC-bear': 94.5321667085459, 'ACC-zebra': 92.20191228633016, 'ACC-giraffe': 92.63718260863035, 'ACC-backpack': 55.29224108465307, 'ACC-umbrella': 82.79217152896268, 'ACC-handbag': 58.06843684912067, 'ACC-tie': 78.34794108033964, 'ACC-suitcase': 87.48504696590574, 'ACC-frisbee': 94.48945454545455, 'ACC-skis': 66.92231947856367, 'ACC-snowboard': 79.1129289020597, 'ACC-sports ball': 80.4731189464653, 'ACC-kite': 75.93034465226441, 'ACC-baseball bat': 84.15811911380172, 'ACC-baseball glove': 88.50507627014836, 'ACC-skateboard': 89.25764505747539, 'ACC-surfboard': 89.31794566211711, 'ACC-tennis racket': 89.12186478876067, 'ACC-bottle': 80.6836996373655, 'ACC-wine glass': 85.544595746224, 'ACC-cup': 84.44307054444464, 'ACC-fork': 69.02657603410933, 'ACC-knife': 57.15725067780465, 'ACC-spoon': 71.36072372450133, 'ACC-bowl': 71.24688395147015, 'ACC-banana': 91.23749724165879, 'ACC-apple': 72.77739656376302, 'ACC-sandwich': 81.91076725377259, 'ACC-orange': 89.9819449795948, 'ACC-broccoli': 81.61788063816127, 'ACC-carrot': 73.8286906922554, 'ACC-hot dog': 72.46605353866804, 'ACC-pizza': 92.80805017610572, 'ACC-donut': 81.41168565291728, 'ACC-cake': 82.58211989514334, 'ACC-chair': 65.76999679958962, 'ACC-couch': 72.62432786340794, 'ACC-potted plant': 48.56724853918449, 'ACC-bed': 85.22746153034902, 'ACC-dining table': 78.49471334683666, 'ACC-toilet': 89.77979456994306, 'ACC-tv': 86.53008485058807, 'ACC-laptop': 90.29387298905166, 'ACC-mouse': 85.23388490272681, 'ACC-remote': 71.13742503213167, 'ACC-keyboard': 65.39234281929967, 'ACC-cell phone': 79.91026460907284, 'ACC-microwave': 73.7322019591728, 'ACC-oven': 79.2788777618835, 'ACC-toaster': 55.53654764674486, 'ACC-sink': 82.03144608144214, 'ACC-refrigerator': 90.93727152644558, 'ACC-book': 65.76892633997366, 'ACC-clock': 80.8119624425377, 'ACC-vase': 77.88258653135337, 'ACC-scissors': 66.64153100294742, 'ACC-teddy bear': 88.91466414373014, 'ACC-hair drier': 41.34858964506304, 'ACC-toothbrush': 81.0823488533704, 'ACC-banner': 74.94691302745747, 'ACC-blanket': 14.258519193085448, 'ACC-bridge': 56.63055016608596, 'ACC-cardboard': 48.152799185691435, 'ACC-counter': 58.25730934171052, 'ACC-curtain': 75.45998040426252, 'ACC-door-stuff': 59.01960045425969, 'ACC-floor-wood': 76.3093243449439, 'ACC-flower': 61.27006203445913, 'ACC-fruit': 54.312024943587275, 'ACC-gravel': 32.23721916644207, 'ACC-house': 21.857973509010563, 'ACC-light': 59.016419917314245, 'ACC-mirror-stuff': 71.42731395133303, 'ACC-net': 65.38821380192572, 'ACC-pillow': 25.89171394402748, 'ACC-platform': 61.84078185923395, 'ACC-playingfield': 86.98334890167932, 'ACC-railroad': 82.08711491161962, 'ACC-river': 64.91538459562888, 'ACC-road': 84.36240940977726, 'ACC-roof': 23.09631784603976, 'ACC-sand': 67.96120065832818, 'ACC-sea': 92.29648630809564, 'ACC-shelf': 60.51874253651075, 'ACC-snow': 95.6764701890499, 'ACC-stairs': 49.42419897298474, 'ACC-tent': 9.622622965902865, 'ACC-towel': 42.5886711112796, 'ACC-wall-brick': 59.260056123464736, 'ACC-wall-stone': 32.93289359727067, 'ACC-wall-tile': 82.95228745939286, 'ACC-wall-wood': 49.45640411214086, 'ACC-water-other': 38.48384398939614, 'ACC-window-blind': 58.50391417536203, 'ACC-window-other': 70.2598446307842, 'ACC-tree-merged': 89.27289089354475, 'ACC-fence-merged': 70.92596331087665, 'ACC-ceiling-merged': 81.38381558860647, 'ACC-sky-other-merged': 96.7387213220402, 'ACC-cabinet-merged': 75.17129863682229, 'ACC-table-merged': 50.7084210519268, 'ACC-floor-other-merged': 63.882637764011584, 'ACC-pavement-merged': 66.93900370869704, 'ACC-mountain-merged': 65.23136719062785, 'ACC-grass-merged': 83.41728218758243, 'ACC-dirt-merged': 69.1778024859039, 'ACC-paper-merged': 40.378672239720814, 'ACC-food-other-merged': 43.22729465300585, 'ACC-building-other-merged': 72.29787575403576, 'ACC-rock-merged': 84.13309209535213, 'ACC-wall-other-merged': 82.09991104254766, 'ACC-rug-merged': 79.48656584275025})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5870646766169154, 'noc@0.8': 2.9888791337430494, 'noc@0.85': 3.66900790166813, 'noc@0.9': 4.725782850453614, 'miou@iter1': 0.8361964662555788}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.46249389648438, 'precision@0.6': 67.1589584350586, 'precision@0.7': 62.02875900268555, 'precision@0.8': 52.04042053222656, 'precision@0.9': 26.58375358581543, 'cIoU': 56.52067565917969, 'mIoU': 62.13801193237305}}}
INFO:trainer.default_trainer:This epoch takes 1:28:10.797672
INFO:trainer.default_trainer:PROGRESS: 32.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 16 training.
INFO:trainer.default_trainer:epochs[    16] optim steps[29300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92578/0.90968, loss_mask_bce_0: 0.30098/0.33540, loss_mask_dice_0: 0.56892/1.16662, loss_spatial_bce_0: 0.07388/0.09039, loss_spatial_dice_0: 0.21493/0.21603, loss_spatial_ce_0: 0.09319/0.07345, loss_grounding_bce_0: 0.05085/0.08625, loss_grounding_dice_0: 0.11761/0.17956, loss_grounding_ce_0: 0.01701/0.27643, loss_mask_ce_1: 0.99711/0.91006, loss_mask_bce_1: 0.30002/0.33614, loss_mask_dice_1: 0.51846/1.17353, loss_spatial_bce_1: 0.06402/0.09115, loss_spatial_dice_1: 0.20808/0.22031, loss_spatial_ce_1: 0.09266/0.07930, loss_grounding_bce_1: 0.05344/0.08638, loss_grounding_dice_1: 0.11905/0.18039, loss_grounding_ce_1: 0.01769/0.27791, loss_mask_ce_2: 1.03995/0.91799, loss_mask_bce_2: 0.31632/0.33657, loss_mask_dice_2: 0.59454/1.17283, loss_spatial_bce_2: 0.05912/0.09145, loss_spatial_dice_2: 0.22232/0.22133, loss_spatial_ce_2: 0.09305/0.08303, loss_grounding_bce_2: 0.05647/0.08642, loss_grounding_dice_2: 0.14140/0.17988, loss_grounding_ce_2: 0.01973/0.28125, loss_mask_ce_3: 1.00596/0.92623, loss_mask_bce_3: 0.29853/0.33736, loss_mask_dice_3: 0.58752/1.16983, loss_spatial_bce_3: 0.05774/0.09230, loss_spatial_dice_3: 0.21523/0.22194, loss_spatial_ce_3: 0.09335/0.08684, loss_grounding_bce_3: 0.05470/0.08662, loss_grounding_dice_3: 0.11366/0.17974, loss_grounding_ce_3: 0.02688/0.28311, loss_mask_ce_4: 1.07482/0.92519, loss_mask_bce_4: 0.29750/0.33911, loss_mask_dice_4: 0.56491/1.19283, loss_spatial_bce_4: 0.05786/0.09648, loss_spatial_dice_4: 0.23065/0.23264, loss_spatial_ce_4: 0.09447/0.10344, loss_grounding_bce_4: 0.05082/0.08713, loss_grounding_dice_4: 0.11350/0.18250, loss_grounding_ce_4: 0.01704/0.28546, loss_mask_ce_5: 1.04080/0.93972, loss_mask_bce_5: 0.30924/0.34139, loss_mask_dice_5: 0.73484/1.19846, loss_spatial_bce_5: 0.05577/0.09788, loss_spatial_dice_5: 0.21956/0.23595, loss_spatial_ce_5: 0.12734/0.11772, loss_grounding_bce_5: 0.05314/0.08753, loss_grounding_dice_5: 0.11051/0.18371, loss_grounding_ce_5: 0.02603/0.29807, loss_mask_ce_6: 1.11571/0.97731, loss_mask_bce_6: 0.31039/0.34414, loss_mask_dice_6: 0.54840/1.20108, loss_spatial_bce_6: 0.05133/0.10339, loss_spatial_dice_6: 0.21896/0.23819, loss_spatial_ce_6: 0.13587/0.14314, loss_grounding_bce_6: 0.05021/0.08830, loss_grounding_dice_6: 0.09971/0.18392, loss_grounding_ce_6: 0.05918/0.31517, loss_mask_ce_7: 1.05291/1.02115, loss_mask_bce_7: 0.31686/0.35180, loss_mask_dice_7: 0.74310/1.25664, loss_spatial_bce_7: 0.07573/0.11206, loss_spatial_dice_7: 0.26024/0.26547, loss_spatial_ce_7: 0.18715/0.18111, loss_grounding_bce_7: 0.06064/0.09022, loss_grounding_dice_7: 0.13493/0.19125, loss_grounding_ce_7: 0.06156/0.34766, loss_mask_ce_8: 1.18204/1.13264, loss_mask_bce_8: 0.28858/0.36532, loss_mask_dice_8: 0.63805/1.33093, loss_spatial_bce_8: 0.06662/0.13314, loss_spatial_dice_8: 0.26938/0.30529, loss_spatial_ce_8: 0.21308/0.23748, loss_grounding_bce_8: 0.05958/0.09380, loss_grounding_dice_8: 0.14575/0.20231, loss_grounding_ce_8: 0.08745/0.41816, loss_mask_ce_9: 3.32937/3.68691, loss_mask_bce_9: 0.28299/0.39247, loss_mask_dice_9: 1.64400/1.90567, loss_spatial_bce_9: 0.41723/0.33534, loss_spatial_dice_9: 0.91184/0.82382, loss_spatial_ce_9: 2.37428/1.51057, loss_grounding_bce_9: 0.07099/0.10529, loss_grounding_dice_9: 0.24015/0.28201, loss_grounding_ce_9: 0.03253/0.68859] items per batch[64] items per second[0.14] total items[1875200] mini batches[ 29300] memory[7341] epoch remaining[1:22:59]
INFO:trainer.default_trainer:epochs[    16] optim steps[29400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.31695/0.90955, loss_mask_bce_0: 0.67887/0.33530, loss_mask_dice_0: 1.82080/1.16623, loss_spatial_bce_0: 0.17208/0.09035, loss_spatial_dice_0: 0.23789/0.21595, loss_spatial_ce_0: 0.02216/0.07336, loss_grounding_bce_0: 0.12566/0.08624, loss_grounding_dice_0: 0.18493/0.17953, loss_grounding_ce_0: 0.55526/0.27634, loss_mask_ce_1: 1.38913/0.90990, loss_mask_bce_1: 0.69025/0.33604, loss_mask_dice_1: 1.82500/1.17314, loss_spatial_bce_1: 0.16486/0.09111, loss_spatial_dice_1: 0.23341/0.22023, loss_spatial_ce_1: 0.02037/0.07920, loss_grounding_bce_1: 0.12720/0.08637, loss_grounding_dice_1: 0.18800/0.18037, loss_grounding_ce_1: 0.56523/0.27779, loss_mask_ce_2: 1.44077/0.91782, loss_mask_bce_2: 0.67103/0.33647, loss_mask_dice_2: 1.84448/1.17246, loss_spatial_bce_2: 0.16517/0.09142, loss_spatial_dice_2: 0.24746/0.22125, loss_spatial_ce_2: 0.02464/0.08292, loss_grounding_bce_2: 0.12412/0.08641, loss_grounding_dice_2: 0.19022/0.17987, loss_grounding_ce_2: 0.56717/0.28114, loss_mask_ce_3: 1.20261/0.92605, loss_mask_bce_3: 0.68652/0.33727, loss_mask_dice_3: 1.86732/1.16941, loss_spatial_bce_3: 0.14727/0.09226, loss_spatial_dice_3: 0.24871/0.22185, loss_spatial_ce_3: 0.03709/0.08678, loss_grounding_bce_3: 0.12518/0.08660, loss_grounding_dice_3: 0.19028/0.17971, loss_grounding_ce_3: 0.57931/0.28301, loss_mask_ce_4: 1.16142/0.92504, loss_mask_bce_4: 0.69048/0.33900, loss_mask_dice_4: 1.76358/1.19241, loss_spatial_bce_4: 0.16998/0.09644, loss_spatial_dice_4: 0.24767/0.23256, loss_spatial_ce_4: 0.06563/0.10336, loss_grounding_bce_4: 0.12377/0.08712, loss_grounding_dice_4: 0.19097/0.18249, loss_grounding_ce_4: 0.53651/0.28537, loss_mask_ce_5: 1.25317/0.93960, loss_mask_bce_5: 0.68687/0.34128, loss_mask_dice_5: 1.81588/1.19803, loss_spatial_bce_5: 0.17577/0.09785, loss_spatial_dice_5: 0.24446/0.23588, loss_spatial_ce_5: 0.09462/0.11765, loss_grounding_bce_5: 0.14907/0.08751, loss_grounding_dice_5: 0.21356/0.18368, loss_grounding_ce_5: 0.50702/0.29801, loss_mask_ce_6: 1.16471/0.97715, loss_mask_bce_6: 0.72222/0.34402, loss_mask_dice_6: 1.88436/1.20067, loss_spatial_bce_6: 0.18027/0.10337, loss_spatial_dice_6: 0.24158/0.23813, loss_spatial_ce_6: 0.14631/0.14302, loss_grounding_bce_6: 0.13825/0.08828, loss_grounding_dice_6: 0.20097/0.18389, loss_grounding_ce_6: 0.55247/0.31509, loss_mask_ce_7: 1.30529/1.02112, loss_mask_bce_7: 0.69214/0.35168, loss_mask_dice_7: 1.87894/1.25616, loss_spatial_bce_7: 0.17572/0.11204, loss_spatial_dice_7: 0.24463/0.26541, loss_spatial_ce_7: 0.17096/0.18105, loss_grounding_bce_7: 0.13838/0.09020, loss_grounding_dice_7: 0.20846/0.19122, loss_grounding_ce_7: 0.56877/0.34755, loss_mask_ce_8: 1.23883/1.13261, loss_mask_bce_8: 0.89160/0.36520, loss_mask_dice_8: 2.00246/1.33047, loss_spatial_bce_8: 0.19293/0.13310, loss_spatial_dice_8: 0.27821/0.30526, loss_spatial_ce_8: 0.19135/0.23741, loss_grounding_bce_8: 0.14093/0.09378, loss_grounding_dice_8: 0.22853/0.20228, loss_grounding_ce_8: 0.48398/0.41802, loss_mask_ce_9: 3.90263/3.68645, loss_mask_bce_9: 0.74816/0.39234, loss_mask_dice_9: 2.82908/1.90491, loss_spatial_bce_9: 0.37619/0.33529, loss_spatial_dice_9: 0.87830/0.82376, loss_spatial_ce_9: 1.50859/1.51058, loss_grounding_bce_9: 0.15816/0.10526, loss_grounding_dice_9: 0.35520/0.28194, loss_grounding_ce_9: 0.51128/0.68841] items per batch[64] items per second[0.23] total items[1881600] mini batches[ 29400] memory[7341] epoch remaining[1:17:33]
INFO:trainer.default_trainer:epochs[    16] optim steps[29500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63369/0.90966, loss_mask_bce_0: 0.19374/0.33537, loss_mask_dice_0: 0.48784/1.16656, loss_spatial_bce_0: 0.13226/0.09033, loss_spatial_dice_0: 0.22248/0.21597, loss_spatial_ce_0: 0.00419/0.07328, loss_grounding_bce_0: 0.10103/0.08623, loss_grounding_dice_0: 0.26143/0.17956, loss_grounding_ce_0: 0.40916/0.27630, loss_mask_ce_1: 0.60054/0.90998, loss_mask_bce_1: 0.19679/0.33611, loss_mask_dice_1: 0.50228/1.17340, loss_spatial_bce_1: 0.12346/0.09110, loss_spatial_dice_1: 0.22789/0.22024, loss_spatial_ce_1: 0.00500/0.07915, loss_grounding_bce_1: 0.09742/0.08636, loss_grounding_dice_1: 0.27248/0.18040, loss_grounding_ce_1: 0.53280/0.27778, loss_mask_ce_2: 0.57070/0.91791, loss_mask_bce_2: 0.18930/0.33654, loss_mask_dice_2: 0.45437/1.17271, loss_spatial_bce_2: 0.13491/0.09140, loss_spatial_dice_2: 0.24428/0.22126, loss_spatial_ce_2: 0.01032/0.08288, loss_grounding_bce_2: 0.09626/0.08640, loss_grounding_dice_2: 0.26776/0.17990, loss_grounding_ce_2: 1.33927/0.28115, loss_mask_ce_3: 0.51272/0.92620, loss_mask_bce_3: 0.18773/0.33733, loss_mask_dice_3: 0.49501/1.16970, loss_spatial_bce_3: 0.14813/0.09224, loss_spatial_dice_3: 0.24715/0.22186, loss_spatial_ce_3: 0.02336/0.08672, loss_grounding_bce_3: 0.08742/0.08659, loss_grounding_dice_3: 0.28023/0.17973, loss_grounding_ce_3: 0.57909/0.28301, loss_mask_ce_4: 0.50363/0.92514, loss_mask_bce_4: 0.19770/0.33907, loss_mask_dice_4: 0.52151/1.19278, loss_spatial_bce_4: 0.13633/0.09643, loss_spatial_dice_4: 0.24317/0.23257, loss_spatial_ce_4: 0.09551/0.10332, loss_grounding_bce_4: 0.11235/0.08711, loss_grounding_dice_4: 0.30804/0.18252, loss_grounding_ce_4: 0.50133/0.28540, loss_mask_ce_5: 0.50284/0.93973, loss_mask_bce_5: 0.21122/0.34138, loss_mask_dice_5: 0.52401/1.19846, loss_spatial_bce_5: 0.13182/0.09785, loss_spatial_dice_5: 0.23528/0.23591, loss_spatial_ce_5: 0.04828/0.11757, loss_grounding_bce_5: 0.10856/0.08750, loss_grounding_dice_5: 0.27418/0.18370, loss_grounding_ce_5: 0.82588/0.29804, loss_mask_ce_6: 0.60640/0.97739, loss_mask_bce_6: 0.20307/0.34412, loss_mask_dice_6: 0.49718/1.20105, loss_spatial_bce_6: 0.13865/0.10338, loss_spatial_dice_6: 0.27550/0.23816, loss_spatial_ce_6: 0.09728/0.14298, loss_grounding_bce_6: 0.10843/0.08828, loss_grounding_dice_6: 0.31872/0.18393, loss_grounding_ce_6: 0.68373/0.31511, loss_mask_ce_7: 0.65366/1.02127, loss_mask_bce_7: 0.18271/0.35177, loss_mask_dice_7: 0.48669/1.25657, loss_spatial_bce_7: 0.12726/0.11204, loss_spatial_dice_7: 0.24243/0.26545, loss_spatial_ce_7: 0.14914/0.18100, loss_grounding_bce_7: 0.10258/0.09019, loss_grounding_dice_7: 0.29658/0.19126, loss_grounding_ce_7: 0.53033/0.34755, loss_mask_ce_8: 0.57595/1.13274, loss_mask_bce_8: 0.24293/0.36528, loss_mask_dice_8: 0.55662/1.33084, loss_spatial_bce_8: 0.12274/0.13310, loss_spatial_dice_8: 0.34341/0.30530, loss_spatial_ce_8: 0.27465/0.23746, loss_grounding_bce_8: 0.12952/0.09376, loss_grounding_dice_8: 0.34845/0.20232, loss_grounding_ce_8: 0.54314/0.41797, loss_mask_ce_9: 2.21898/3.68688, loss_mask_bce_9: 0.25771/0.39245, loss_mask_dice_9: 0.63818/1.90543, loss_spatial_bce_9: 0.21565/0.33525, loss_spatial_dice_9: 0.67565/0.82377, loss_spatial_ce_9: 0.93193/1.51060, loss_grounding_bce_9: 0.18895/0.10524, loss_grounding_dice_9: 0.40838/0.28194, loss_grounding_ce_9: 1.07216/0.68849] items per batch[64] items per second[0.23] total items[1888000] mini batches[ 29500] memory[7341] epoch remaining[1:12:48]
INFO:trainer.default_trainer:epochs[    16] optim steps[29600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70426/0.90969, loss_mask_bce_0: 0.31307/0.33531, loss_mask_dice_0: 0.34093/1.16633, loss_spatial_bce_0: 0.08875/0.09032, loss_spatial_dice_0: 0.08965/0.21594, loss_spatial_ce_0: 0.00954/0.07322, loss_grounding_bce_0: 0.14351/0.08622, loss_grounding_dice_0: 0.07146/0.17955, loss_grounding_ce_0: 0.00146/0.27610, loss_mask_ce_1: 0.70604/0.90992, loss_mask_bce_1: 0.32102/0.33605, loss_mask_dice_1: 0.41523/1.17320, loss_spatial_bce_1: 0.09380/0.09110, loss_spatial_dice_1: 0.09703/0.22021, loss_spatial_ce_1: 0.02883/0.07913, loss_grounding_bce_1: 0.13918/0.08635, loss_grounding_dice_1: 0.07409/0.18038, loss_grounding_ce_1: 0.00061/0.27758, loss_mask_ce_2: 0.69033/0.91792, loss_mask_bce_2: 0.30538/0.33647, loss_mask_dice_2: 0.36210/1.17247, loss_spatial_bce_2: 0.08950/0.09139, loss_spatial_dice_2: 0.09286/0.22123, loss_spatial_ce_2: 0.02100/0.08284, loss_grounding_bce_2: 0.13799/0.08638, loss_grounding_dice_2: 0.07077/0.17989, loss_grounding_ce_2: 0.00093/0.28092, loss_mask_ce_3: 0.72202/0.92624, loss_mask_bce_3: 0.30780/0.33726, loss_mask_dice_3: 0.35439/1.16946, loss_spatial_bce_3: 0.08628/0.09224, loss_spatial_dice_3: 0.09059/0.22184, loss_spatial_ce_3: 0.01483/0.08665, loss_grounding_bce_3: 0.13242/0.08657, loss_grounding_dice_3: 0.06928/0.17972, loss_grounding_ce_3: 0.00110/0.28288, loss_mask_ce_4: 0.80309/0.92515, loss_mask_bce_4: 0.32456/0.33902, loss_mask_dice_4: 0.36822/1.19255, loss_spatial_bce_4: 0.08726/0.09643, loss_spatial_dice_4: 0.09786/0.23256, loss_spatial_ce_4: 0.05677/0.10327, loss_grounding_bce_4: 0.13528/0.08709, loss_grounding_dice_4: 0.06985/0.18250, loss_grounding_ce_4: 0.00153/0.28520, loss_mask_ce_5: 0.86280/0.93977, loss_mask_bce_5: 0.32965/0.34131, loss_mask_dice_5: 0.40276/1.19824, loss_spatial_bce_5: 0.08787/0.09785, loss_spatial_dice_5: 0.10271/0.23591, loss_spatial_ce_5: 0.02865/0.11753, loss_grounding_bce_5: 0.13394/0.08748, loss_grounding_dice_5: 0.06910/0.18369, loss_grounding_ce_5: 0.00072/0.29782, loss_mask_ce_6: 0.87119/0.97746, loss_mask_bce_6: 0.34233/0.34406, loss_mask_dice_6: 0.41701/1.20079, loss_spatial_bce_6: 0.09781/0.10339, loss_spatial_dice_6: 0.12022/0.23815, loss_spatial_ce_6: 0.06325/0.14293, loss_grounding_bce_6: 0.13904/0.08827, loss_grounding_dice_6: 0.06888/0.18393, loss_grounding_ce_6: 0.00195/0.31484, loss_mask_ce_7: 0.68395/1.02127, loss_mask_bce_7: 0.40846/0.35172, loss_mask_dice_7: 0.49648/1.25636, loss_spatial_bce_7: 0.11000/0.11206, loss_spatial_dice_7: 0.13225/0.26545, loss_spatial_ce_7: 0.06650/0.18095, loss_grounding_bce_7: 0.15807/0.09018, loss_grounding_dice_7: 0.07638/0.19125, loss_grounding_ce_7: 0.00330/0.34723, loss_mask_ce_8: 0.94893/1.13274, loss_mask_bce_8: 0.36140/0.36523, loss_mask_dice_8: 0.47654/1.33055, loss_spatial_bce_8: 0.11354/0.13309, loss_spatial_dice_8: 0.14371/0.30529, loss_spatial_ce_8: 0.13312/0.23750, loss_grounding_bce_8: 0.14855/0.09374, loss_grounding_dice_8: 0.07724/0.20229, loss_grounding_ce_8: 0.03597/0.41763, loss_mask_ce_9: 3.44536/3.68651, loss_mask_bce_9: 0.34855/0.39238, loss_mask_dice_9: 0.66253/1.90509, loss_spatial_bce_9: 0.52015/0.33527, loss_spatial_dice_9: 0.81511/0.82374, loss_spatial_ce_9: 1.36920/1.51056, loss_grounding_bce_9: 0.13865/0.10523, loss_grounding_dice_9: 0.11013/0.28191, loss_grounding_ce_9: 1.01093/0.68801] items per batch[64] items per second[0.23] total items[1894400] mini batches[ 29600] memory[7341] epoch remaining[1:08:24]
INFO:trainer.default_trainer:epochs[    16] optim steps[29700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45391/0.90967, loss_mask_bce_0: 0.06346/0.33523, loss_mask_dice_0: 0.30611/1.16600, loss_spatial_bce_0: 0.02338/0.09030, loss_spatial_dice_0: 0.18697/0.21588, loss_spatial_ce_0: 0.00324/0.07315, loss_grounding_bce_0: 0.01935/0.08620, loss_grounding_dice_0: 0.13214/0.17951, loss_grounding_ce_0: 0.16966/0.27610, loss_mask_ce_1: 0.38130/0.90990, loss_mask_bce_1: 0.05289/0.33598, loss_mask_dice_1: 0.30197/1.17291, loss_spatial_bce_1: 0.02339/0.09108, loss_spatial_dice_1: 0.19342/0.22015, loss_spatial_ce_1: 0.00459/0.07902, loss_grounding_bce_1: 0.02049/0.08633, loss_grounding_dice_1: 0.12881/0.18034, loss_grounding_ce_1: 0.16410/0.27752, loss_mask_ce_2: 0.73102/0.91783, loss_mask_bce_2: 0.05069/0.33641, loss_mask_dice_2: 0.28132/1.17215, loss_spatial_bce_2: 0.02472/0.09138, loss_spatial_dice_2: 0.16500/0.22117, loss_spatial_ce_2: 0.00225/0.08273, loss_grounding_bce_2: 0.02041/0.08637, loss_grounding_dice_2: 0.13144/0.17985, loss_grounding_ce_2: 0.17277/0.28092, loss_mask_ce_3: 0.74011/0.92625, loss_mask_bce_3: 0.05491/0.33718, loss_mask_dice_3: 0.28270/1.16914, loss_spatial_bce_3: 0.02348/0.09222, loss_spatial_dice_3: 0.15685/0.22178, loss_spatial_ce_3: 0.01196/0.08654, loss_grounding_bce_3: 0.02013/0.08655, loss_grounding_dice_3: 0.14482/0.17970, loss_grounding_ce_3: 0.17459/0.28281, loss_mask_ce_4: 0.37794/0.92517, loss_mask_bce_4: 0.05596/0.33896, loss_mask_dice_4: 0.27531/1.19223, loss_spatial_bce_4: 0.02412/0.09641, loss_spatial_dice_4: 0.16562/0.23250, loss_spatial_ce_4: 0.01586/0.10315, loss_grounding_bce_4: 0.02330/0.08707, loss_grounding_dice_4: 0.15739/0.18248, loss_grounding_ce_4: 0.13093/0.28513, loss_mask_ce_5: 0.46011/0.93978, loss_mask_bce_5: 0.05893/0.34125, loss_mask_dice_5: 0.28458/1.19797, loss_spatial_bce_5: 0.02387/0.09784, loss_spatial_dice_5: 0.18389/0.23585, loss_spatial_ce_5: 0.04506/0.11740, loss_grounding_bce_5: 0.02282/0.08746, loss_grounding_dice_5: 0.14330/0.18365, loss_grounding_ce_5: 0.13607/0.29778, loss_mask_ce_6: 0.50134/0.97745, loss_mask_bce_6: 0.05876/0.34401, loss_mask_dice_6: 0.35033/1.20042, loss_spatial_bce_6: 0.02992/0.10338, loss_spatial_dice_6: 0.19229/0.23809, loss_spatial_ce_6: 0.05328/0.14282, loss_grounding_bce_6: 0.02537/0.08824, loss_grounding_dice_6: 0.14274/0.18389, loss_grounding_ce_6: 0.13576/0.31481, loss_mask_ce_7: 0.45557/1.02126, loss_mask_bce_7: 0.06291/0.35168, loss_mask_dice_7: 0.36263/1.25607, loss_spatial_bce_7: 0.02921/0.11204, loss_spatial_dice_7: 0.18596/0.26540, loss_spatial_ce_7: 0.15766/0.18089, loss_grounding_bce_7: 0.02618/0.09016, loss_grounding_dice_7: 0.15516/0.19123, loss_grounding_ce_7: 0.15606/0.34714, loss_mask_ce_8: 0.73725/1.13278, loss_mask_bce_8: 0.05379/0.36518, loss_mask_dice_8: 0.30133/1.33021, loss_spatial_bce_8: 0.04256/0.13309, loss_spatial_dice_8: 0.24583/0.30524, loss_spatial_ce_8: 0.18729/0.23742, loss_grounding_bce_8: 0.02696/0.09372, loss_grounding_dice_8: 0.18903/0.20226, loss_grounding_ce_8: 0.22302/0.41750, loss_mask_ce_9: 2.07566/3.68637, loss_mask_bce_9: 0.04613/0.39233, loss_mask_dice_9: 0.42845/1.90481, loss_spatial_bce_9: 0.23023/0.33525, loss_spatial_dice_9: 0.68662/0.82367, loss_spatial_ce_9: 1.45303/1.51048, loss_grounding_bce_9: 0.01869/0.10520, loss_grounding_dice_9: 0.19487/0.28188, loss_grounding_ce_9: 0.22811/0.68794] items per batch[64] items per second[0.23] total items[1900800] mini batches[ 29700] memory[7341] epoch remaining[1:03:40]
INFO:trainer.default_trainer:epochs[    16] optim steps[29800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24414/0.90982, loss_mask_bce_0: 0.19825/0.33526, loss_mask_dice_0: 0.12256/1.16678, loss_spatial_bce_0: 0.22346/0.09030, loss_spatial_dice_0: 0.17577/0.21588, loss_spatial_ce_0: 0.00012/0.07311, loss_grounding_bce_0: 0.41729/0.08622, loss_grounding_dice_0: 0.25038/0.17954, loss_grounding_ce_0: 0.19687/0.27602, loss_mask_ce_1: 0.21722/0.91011, loss_mask_bce_1: 0.19762/0.33602, loss_mask_dice_1: 0.12236/1.17373, loss_spatial_bce_1: 0.22031/0.09107, loss_spatial_dice_1: 0.17368/0.22015, loss_spatial_ce_1: 0.00008/0.07896, loss_grounding_bce_1: 0.41398/0.08636, loss_grounding_dice_1: 0.23321/0.18037, loss_grounding_ce_1: 0.18219/0.27747, loss_mask_ce_2: 0.22126/0.91794, loss_mask_bce_2: 0.21460/0.33644, loss_mask_dice_2: 0.13082/1.17299, loss_spatial_bce_2: 0.22670/0.09138, loss_spatial_dice_2: 0.17590/0.22117, loss_spatial_ce_2: 0.00013/0.08266, loss_grounding_bce_2: 0.44214/0.08640, loss_grounding_dice_2: 0.25116/0.17988, loss_grounding_ce_2: 0.18442/0.28076, loss_mask_ce_3: 0.24630/0.92645, loss_mask_bce_3: 0.19568/0.33722, loss_mask_dice_3: 0.12839/1.16995, loss_spatial_bce_3: 0.20496/0.09222, loss_spatial_dice_3: 0.16199/0.22177, loss_spatial_ce_3: 0.00045/0.08648, loss_grounding_bce_3: 0.40845/0.08659, loss_grounding_dice_3: 0.25427/0.17974, loss_grounding_ce_3: 0.19337/0.28273, loss_mask_ce_4: 0.26440/0.92537, loss_mask_bce_4: 0.18097/0.33900, loss_mask_dice_4: 0.11704/1.19311, loss_spatial_bce_4: 0.24582/0.09641, loss_spatial_dice_4: 0.17523/0.23252, loss_spatial_ce_4: 0.00092/0.10307, loss_grounding_bce_4: 0.35392/0.08710, loss_grounding_dice_4: 0.21624/0.18252, loss_grounding_ce_4: 0.17816/0.28510, loss_mask_ce_5: 0.28438/0.94001, loss_mask_bce_5: 0.17230/0.34130, loss_mask_dice_5: 0.11511/1.19880, loss_spatial_bce_5: 0.24824/0.09784, loss_spatial_dice_5: 0.18203/0.23587, loss_spatial_ce_5: 0.00595/0.11736, loss_grounding_bce_5: 0.34025/0.08748, loss_grounding_dice_5: 0.21660/0.18369, loss_grounding_ce_5: 0.17747/0.29767, loss_mask_ce_6: 0.32795/0.97767, loss_mask_bce_6: 0.14190/0.34406, loss_mask_dice_6: 0.10216/1.20128, loss_spatial_bce_6: 0.25140/0.10337, loss_spatial_dice_6: 0.17437/0.23812, loss_spatial_ce_6: 0.01258/0.14278, loss_grounding_bce_6: 0.26066/0.08826, loss_grounding_dice_6: 0.18335/0.18393, loss_grounding_ce_6: 0.21949/0.31467, loss_mask_ce_7: 0.30957/1.02149, loss_mask_bce_7: 0.15491/0.35173, loss_mask_dice_7: 0.10868/1.25695, loss_spatial_bce_7: 0.23977/0.11204, loss_spatial_dice_7: 0.16796/0.26542, loss_spatial_ce_7: 0.13535/0.18084, loss_grounding_bce_7: 0.30073/0.09018, loss_grounding_dice_7: 0.19999/0.19128, loss_grounding_ce_7: 0.16487/0.34698, loss_mask_ce_8: 0.31396/1.13304, loss_mask_bce_8: 0.16215/0.36523, loss_mask_dice_8: 0.11381/1.33123, loss_spatial_bce_8: 0.36673/0.13310, loss_spatial_dice_8: 0.21360/0.30527, loss_spatial_ce_8: 0.16729/0.23732, loss_grounding_bce_8: 0.33115/0.09373, loss_grounding_dice_8: 0.21710/0.20230, loss_grounding_ce_8: 0.11999/0.41738, loss_mask_ce_9: 1.62768/3.68658, loss_mask_bce_9: 0.14858/0.39241, loss_mask_dice_9: 0.12490/1.90614, loss_spatial_bce_9: 0.79289/0.33523, loss_spatial_dice_9: 0.54301/0.82368, loss_spatial_ce_9: 0.35664/1.51033, loss_grounding_bce_9: 0.30127/0.10521, loss_grounding_dice_9: 0.22725/0.28195, loss_grounding_ce_9: 0.09131/0.68774] items per batch[64] items per second[0.23] total items[1907200] mini batches[ 29800] memory[7341] epoch remaining[0:58:44]
INFO:trainer.default_trainer:epochs[    16] optim steps[29900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.46111/0.90995, loss_mask_bce_0: 0.22446/0.33531, loss_mask_dice_0: 1.12871/1.16669, loss_spatial_bce_0: 0.02715/0.09030, loss_spatial_dice_0: 0.19704/0.21585, loss_spatial_ce_0: 0.04109/0.07304, loss_grounding_bce_0: 0.03027/0.08625, loss_grounding_dice_0: 0.11297/0.17952, loss_grounding_ce_0: 0.20470/0.27595, loss_mask_ce_1: 1.40278/0.91029, loss_mask_bce_1: 0.21716/0.33607, loss_mask_dice_1: 1.05854/1.17372, loss_spatial_bce_1: 0.02558/0.09108, loss_spatial_dice_1: 0.18705/0.22013, loss_spatial_ce_1: 0.10638/0.07890, loss_grounding_bce_1: 0.02955/0.08639, loss_grounding_dice_1: 0.15901/0.18037, loss_grounding_ce_1: 0.15214/0.27734, loss_mask_ce_2: 1.42861/0.91812, loss_mask_bce_2: 0.21036/0.33649, loss_mask_dice_2: 1.13432/1.17295, loss_spatial_bce_2: 0.02363/0.09139, loss_spatial_dice_2: 0.19513/0.22114, loss_spatial_ce_2: 0.04941/0.08259, loss_grounding_bce_2: 0.02794/0.08643, loss_grounding_dice_2: 0.14179/0.17987, loss_grounding_ce_2: 0.20961/0.28071, loss_mask_ce_3: 1.53410/0.92659, loss_mask_bce_3: 0.20870/0.33725, loss_mask_dice_3: 1.18578/1.16994, loss_spatial_bce_3: 0.02425/0.09222, loss_spatial_dice_3: 0.20532/0.22174, loss_spatial_ce_3: 0.06140/0.08642, loss_grounding_bce_3: 0.02968/0.08662, loss_grounding_dice_3: 0.12722/0.17972, loss_grounding_ce_3: 0.15689/0.28269, loss_mask_ce_4: 1.40908/0.92541, loss_mask_bce_4: 0.22275/0.33908, loss_mask_dice_4: 1.08586/1.19315, loss_spatial_bce_4: 0.02919/0.09642, loss_spatial_dice_4: 0.21538/0.23250, loss_spatial_ce_4: 0.04482/0.10303, loss_grounding_bce_4: 0.03753/0.08713, loss_grounding_dice_4: 0.12541/0.18250, loss_grounding_ce_4: 0.15966/0.28505, loss_mask_ce_5: 1.54322/0.94014, loss_mask_bce_5: 0.20451/0.34135, loss_mask_dice_5: 1.28469/1.19885, loss_spatial_bce_5: 0.02584/0.09785, loss_spatial_dice_5: 0.24904/0.23585, loss_spatial_ce_5: 0.07108/0.11726, loss_grounding_bce_5: 0.03801/0.08750, loss_grounding_dice_5: 0.12183/0.18367, loss_grounding_ce_5: 0.23791/0.29767, loss_mask_ce_6: 1.47548/0.97777, loss_mask_bce_6: 0.20932/0.34411, loss_mask_dice_6: 1.25389/1.20127, loss_spatial_bce_6: 0.03232/0.10338, loss_spatial_dice_6: 0.22205/0.23811, loss_spatial_ce_6: 0.09670/0.14267, loss_grounding_bce_6: 0.03838/0.08828, loss_grounding_dice_6: 0.14024/0.18390, loss_grounding_ce_6: 0.16968/0.31462, loss_mask_ce_7: 1.44498/1.02160, loss_mask_bce_7: 0.21072/0.35178, loss_mask_dice_7: 1.20229/1.25693, loss_spatial_bce_7: 0.02832/0.11204, loss_spatial_dice_7: 0.24203/0.26541, loss_spatial_ce_7: 0.17127/0.18076, loss_grounding_bce_7: 0.04020/0.09020, loss_grounding_dice_7: 0.18383/0.19126, loss_grounding_ce_7: 0.18743/0.34699, loss_mask_ce_8: 1.72791/1.13315, loss_mask_bce_8: 0.24175/0.36529, loss_mask_dice_8: 1.22545/1.33126, loss_spatial_bce_8: 0.04374/0.13308, loss_spatial_dice_8: 0.25192/0.30526, loss_spatial_ce_8: 0.13571/0.23722, loss_grounding_bce_8: 0.02753/0.09375, loss_grounding_dice_8: 0.13203/0.20227, loss_grounding_ce_8: 0.31588/0.41745, loss_mask_ce_9: 3.68937/3.68666, loss_mask_bce_9: 0.21058/0.39246, loss_mask_dice_9: 1.83258/1.90618, loss_spatial_bce_9: 0.19037/0.33524, loss_spatial_dice_9: 0.83320/0.82367, loss_spatial_ce_9: 1.67628/1.51039, loss_grounding_bce_9: 0.01766/0.10523, loss_grounding_dice_9: 0.15193/0.28189, loss_grounding_ce_9: 0.79766/0.68774] items per batch[64] items per second[0.23] total items[1913600] mini batches[ 29900] memory[7341] epoch remaining[0:54:01]
INFO:trainer.default_trainer:epochs[    16] optim steps[30000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54673/0.90995, loss_mask_bce_0: 0.49271/0.33521, loss_mask_dice_0: 1.05121/1.16678, loss_spatial_bce_0: 0.10604/0.09026, loss_spatial_dice_0: 0.23854/0.21582, loss_spatial_ce_0: 0.04402/0.07298, loss_grounding_bce_0: 0.02768/0.08624, loss_grounding_dice_0: 0.12935/0.17948, loss_grounding_ce_0: 0.07351/0.27591, loss_mask_ce_1: 0.55647/0.91025, loss_mask_bce_1: 0.49729/0.33598, loss_mask_dice_1: 1.08952/1.17381, loss_spatial_bce_1: 0.10701/0.09104, loss_spatial_dice_1: 0.24316/0.22010, loss_spatial_ce_1: 0.08550/0.07883, loss_grounding_bce_1: 0.02860/0.08638, loss_grounding_dice_1: 0.12398/0.18032, loss_grounding_ce_1: 0.10433/0.27737, loss_mask_ce_2: 0.64646/0.91810, loss_mask_bce_2: 0.50943/0.33639, loss_mask_dice_2: 1.18925/1.17296, loss_spatial_bce_2: 0.11687/0.09135, loss_spatial_dice_2: 0.25583/0.22111, loss_spatial_ce_2: 0.00550/0.08252, loss_grounding_bce_2: 0.02932/0.08642, loss_grounding_dice_2: 0.11586/0.17984, loss_grounding_ce_2: 0.06646/0.28077, loss_mask_ce_3: 0.63452/0.92663, loss_mask_bce_3: 0.49906/0.33717, loss_mask_dice_3: 1.15551/1.17007, loss_spatial_bce_3: 0.11205/0.09219, loss_spatial_dice_3: 0.25844/0.22171, loss_spatial_ce_3: 0.00188/0.08635, loss_grounding_bce_3: 0.02895/0.08661, loss_grounding_dice_3: 0.14820/0.17970, loss_grounding_ce_3: 0.10131/0.28263, loss_mask_ce_4: 0.89200/0.92540, loss_mask_bce_4: 0.49221/0.33900, loss_mask_dice_4: 1.00588/1.19327, loss_spatial_bce_4: 0.11712/0.09638, loss_spatial_dice_4: 0.26382/0.23248, loss_spatial_ce_4: 0.01772/0.10294, loss_grounding_bce_4: 0.02707/0.08712, loss_grounding_dice_4: 0.12535/0.18246, loss_grounding_ce_4: 0.09939/0.28501, loss_mask_ce_5: 0.69030/0.94013, loss_mask_bce_5: 0.50780/0.34127, loss_mask_dice_5: 1.09021/1.19892, loss_spatial_bce_5: 0.11961/0.09780, loss_spatial_dice_5: 0.27781/0.23584, loss_spatial_ce_5: 0.01848/0.11716, loss_grounding_bce_5: 0.02943/0.08750, loss_grounding_dice_5: 0.14661/0.18365, loss_grounding_ce_5: 0.05820/0.29763, loss_mask_ce_6: 0.61008/0.97775, loss_mask_bce_6: 0.53310/0.34401, loss_mask_dice_6: 1.10845/1.20142, loss_spatial_bce_6: 0.10929/0.10334, loss_spatial_dice_6: 0.23387/0.23809, loss_spatial_ce_6: 0.06503/0.14256, loss_grounding_bce_6: 0.03119/0.08827, loss_grounding_dice_6: 0.11347/0.18388, loss_grounding_ce_6: 0.19313/0.31452, loss_mask_ce_7: 0.92323/1.02163, loss_mask_bce_7: 0.50606/0.35170, loss_mask_dice_7: 1.00512/1.25701, loss_spatial_bce_7: 0.12907/0.11199, loss_spatial_dice_7: 0.28169/0.26540, loss_spatial_ce_7: 0.05322/0.18065, loss_grounding_bce_7: 0.02959/0.09019, loss_grounding_dice_7: 0.11231/0.19124, loss_grounding_ce_7: 0.75169/0.34686, loss_mask_ce_8: 0.99951/1.13321, loss_mask_bce_8: 0.50082/0.36520, loss_mask_dice_8: 0.99968/1.33138, loss_spatial_bce_8: 0.14247/0.13302, loss_spatial_dice_8: 0.32084/0.30525, loss_spatial_ce_8: 0.07421/0.23716, loss_grounding_bce_8: 0.03104/0.09374, loss_grounding_dice_8: 0.12973/0.20226, loss_grounding_ce_8: 0.27305/0.41734, loss_mask_ce_9: 4.16438/3.68669, loss_mask_bce_9: 0.46879/0.39233, loss_mask_dice_9: 1.50324/1.90602, loss_spatial_bce_9: 0.19830/0.33519, loss_spatial_dice_9: 0.83729/0.82367, loss_spatial_ce_9: 1.32222/1.51049, loss_grounding_bce_9: 0.04696/0.10521, loss_grounding_dice_9: 0.23750/0.28183, loss_grounding_ce_9: 1.40319/0.68745] items per batch[64] items per second[0.23] total items[1920000] mini batches[ 30000] memory[7341] epoch remaining[0:49:18]
INFO:trainer.default_trainer:epochs[    16] optim steps[30100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08222/0.90971, loss_mask_bce_0: 0.18692/0.33518, loss_mask_dice_0: 1.50696/1.16660, loss_spatial_bce_0: 0.03501/0.09026, loss_spatial_dice_0: 0.14517/0.21576, loss_spatial_ce_0: 0.20150/0.07291, loss_grounding_bce_0: 0.06797/0.08625, loss_grounding_dice_0: 0.04856/0.17948, loss_grounding_ce_0: 0.00048/0.27599, loss_mask_ce_1: 0.76475/0.91002, loss_mask_bce_1: 0.18117/0.33595, loss_mask_dice_1: 1.43639/1.17367, loss_spatial_bce_1: 0.03178/0.09104, loss_spatial_dice_1: 0.17189/0.22004, loss_spatial_ce_1: 0.19888/0.07875, loss_grounding_bce_1: 0.06752/0.08639, loss_grounding_dice_1: 0.04982/0.18033, loss_grounding_ce_1: 0.00039/0.27741, loss_mask_ce_2: 0.86442/0.91790, loss_mask_bce_2: 0.18357/0.33638, loss_mask_dice_2: 1.42782/1.17279, loss_spatial_bce_2: 0.03316/0.09136, loss_spatial_dice_2: 0.14526/0.22105, loss_spatial_ce_2: 0.20958/0.08243, loss_grounding_bce_2: 0.06820/0.08643, loss_grounding_dice_2: 0.05008/0.17985, loss_grounding_ce_2: 0.00052/0.28086, loss_mask_ce_3: 0.89365/0.92641, loss_mask_bce_3: 0.18003/0.33715, loss_mask_dice_3: 1.06623/1.16985, loss_spatial_bce_3: 0.03256/0.09219, loss_spatial_dice_3: 0.15953/0.22164, loss_spatial_ce_3: 0.21465/0.08626, loss_grounding_bce_3: 0.06657/0.08662, loss_grounding_dice_3: 0.04969/0.17969, loss_grounding_ce_3: 0.00050/0.28266, loss_mask_ce_4: 0.69661/0.92517, loss_mask_bce_4: 0.18362/0.33898, loss_mask_dice_4: 1.42575/1.19312, loss_spatial_bce_4: 0.02925/0.09639, loss_spatial_dice_4: 0.15320/0.23242, loss_spatial_ce_4: 0.23950/0.10290, loss_grounding_bce_4: 0.06710/0.08714, loss_grounding_dice_4: 0.05036/0.18247, loss_grounding_ce_4: 0.00129/0.28502, loss_mask_ce_5: 0.86874/0.93992, loss_mask_bce_5: 0.18491/0.34125, loss_mask_dice_5: 1.14444/1.19870, loss_spatial_bce_5: 0.02932/0.09780, loss_spatial_dice_5: 0.15365/0.23579, loss_spatial_ce_5: 0.19634/0.11710, loss_grounding_bce_5: 0.07033/0.08752, loss_grounding_dice_5: 0.05308/0.18366, loss_grounding_ce_5: 0.00073/0.29772, loss_mask_ce_6: 0.66565/0.97753, loss_mask_bce_6: 0.18203/0.34400, loss_mask_dice_6: 1.33755/1.20129, loss_spatial_bce_6: 0.02940/0.10334, loss_spatial_dice_6: 0.17183/0.23803, loss_spatial_ce_6: 0.21537/0.14251, loss_grounding_bce_6: 0.06365/0.08829, loss_grounding_dice_6: 0.04825/0.18389, loss_grounding_ce_6: 0.00203/0.31461, loss_mask_ce_7: 0.82698/1.02144, loss_mask_bce_7: 0.17942/0.35169, loss_mask_dice_7: 1.36619/1.25682, loss_spatial_bce_7: 0.03663/0.11199, loss_spatial_dice_7: 0.19059/0.26536, loss_spatial_ce_7: 0.21334/0.18058, loss_grounding_bce_7: 0.06799/0.09019, loss_grounding_dice_7: 0.05117/0.19125, loss_grounding_ce_7: 0.23677/0.34701, loss_mask_ce_8: 0.73472/1.13298, loss_mask_bce_8: 0.19112/0.36520, loss_mask_dice_8: 1.65260/1.33119, loss_spatial_bce_8: 0.03968/0.13304, loss_spatial_dice_8: 0.23725/0.30517, loss_spatial_ce_8: 0.39220/0.23711, loss_grounding_bce_8: 0.06483/0.09375, loss_grounding_dice_8: 0.04866/0.20227, loss_grounding_ce_8: 0.27498/0.41740, loss_mask_ce_9: 2.78456/3.68650, loss_mask_bce_9: 0.18128/0.39234, loss_mask_dice_9: 1.82982/1.90546, loss_spatial_bce_9: 0.24101/0.33519, loss_spatial_dice_9: 0.70168/0.82364, loss_spatial_ce_9: 1.32781/1.51019, loss_grounding_bce_9: 0.08234/0.10523, loss_grounding_dice_9: 0.07699/0.28183, loss_grounding_ce_9: 0.08044/0.68806] items per batch[64] items per second[0.23] total items[1926400] mini batches[ 30100] memory[7341] epoch remaining[0:44:39]
INFO:trainer.default_trainer:epochs[    16] optim steps[30200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.10324/0.90979, loss_mask_bce_0: 0.73186/0.33521, loss_mask_dice_0: 1.36875/1.16623, loss_spatial_bce_0: 0.09525/0.09025, loss_spatial_dice_0: 0.17760/0.21568, loss_spatial_ce_0: 0.02230/0.07285, loss_grounding_bce_0: 0.23839/0.08629, loss_grounding_dice_0: 0.15049/0.17947, loss_grounding_ce_0: 0.00460/0.27613, loss_mask_ce_1: 1.08001/0.91010, loss_mask_bce_1: 0.76204/0.33598, loss_mask_dice_1: 1.44679/1.17333, loss_spatial_bce_1: 0.10503/0.09103, loss_spatial_dice_1: 0.19429/0.21996, loss_spatial_ce_1: 0.03047/0.07866, loss_grounding_bce_1: 0.24037/0.08644, loss_grounding_dice_1: 0.14844/0.18034, loss_grounding_ce_1: 0.00410/0.27756, loss_mask_ce_2: 1.20871/0.91794, loss_mask_bce_2: 0.74183/0.33638, loss_mask_dice_2: 1.33702/1.17248, loss_spatial_bce_2: 0.11072/0.09135, loss_spatial_dice_2: 0.19939/0.22097, loss_spatial_ce_2: 0.02812/0.08238, loss_grounding_bce_2: 0.21671/0.08648, loss_grounding_dice_2: 0.14063/0.17985, loss_grounding_ce_2: 0.00451/0.28096, loss_mask_ce_3: 1.15834/0.92655, loss_mask_bce_3: 0.69319/0.33716, loss_mask_dice_3: 1.44454/1.16951, loss_spatial_bce_3: 0.11968/0.09218, loss_spatial_dice_3: 0.19113/0.22156, loss_spatial_ce_3: 0.03560/0.08618, loss_grounding_bce_3: 0.21545/0.08666, loss_grounding_dice_3: 0.14456/0.17970, loss_grounding_ce_3: 0.00374/0.28268, loss_mask_ce_4: 1.11273/0.92524, loss_mask_bce_4: 0.67093/0.33901, loss_mask_dice_4: 1.62934/1.19273, loss_spatial_bce_4: 0.11229/0.09638, loss_spatial_dice_4: 0.19133/0.23235, loss_spatial_ce_4: 0.01664/0.10278, loss_grounding_bce_4: 0.22435/0.08719, loss_grounding_dice_4: 0.14453/0.18246, loss_grounding_ce_4: 0.00350/0.28505, loss_mask_ce_5: 1.29768/0.94000, loss_mask_bce_5: 0.64714/0.34127, loss_mask_dice_5: 1.45325/1.19833, loss_spatial_bce_5: 0.13621/0.09781, loss_spatial_dice_5: 0.19668/0.23572, loss_spatial_ce_5: 0.01417/0.11702, loss_grounding_bce_5: 0.20853/0.08757, loss_grounding_dice_5: 0.14807/0.18367, loss_grounding_ce_5: 0.00175/0.29786, loss_mask_ce_6: 1.30964/0.97764, loss_mask_bce_6: 0.59424/0.34401, loss_mask_dice_6: 1.36715/1.20094, loss_spatial_bce_6: 0.21484/0.10336, loss_spatial_dice_6: 0.21544/0.23798, loss_spatial_ce_6: 0.01864/0.14241, loss_grounding_bce_6: 0.20135/0.08833, loss_grounding_dice_6: 0.14206/0.18389, loss_grounding_ce_6: 0.00237/0.31472, loss_mask_ce_7: 1.47643/1.02157, loss_mask_bce_7: 0.60479/0.35171, loss_mask_dice_7: 1.34947/1.25647, loss_spatial_bce_7: 0.20703/0.11201, loss_spatial_dice_7: 0.20747/0.26531, loss_spatial_ce_7: 0.07023/0.18043, loss_grounding_bce_7: 0.21464/0.09024, loss_grounding_dice_7: 0.14687/0.19125, loss_grounding_ce_7: 0.01136/0.34718, loss_mask_ce_8: 1.24128/1.13316, loss_mask_bce_8: 0.64190/0.36521, loss_mask_dice_8: 1.45737/1.33086, loss_spatial_bce_8: 0.18884/0.13307, loss_spatial_dice_8: 0.24006/0.30512, loss_spatial_ce_8: 0.21817/0.23706, loss_grounding_bce_8: 0.21574/0.09379, loss_grounding_dice_8: 0.15075/0.20225, loss_grounding_ce_8: 0.03399/0.41765, loss_mask_ce_9: 5.51705/3.68659, loss_mask_bce_9: 0.66064/0.39236, loss_mask_dice_9: 4.75400/1.90504, loss_spatial_bce_9: 0.37783/0.33525, loss_spatial_dice_9: 0.85128/0.82361, loss_spatial_ce_9: 1.81796/1.51017, loss_grounding_bce_9: 0.17518/0.10527, loss_grounding_dice_9: 0.15368/0.28178, loss_grounding_ce_9: 0.05783/0.68795] items per batch[64] items per second[0.23] total items[1932800] mini batches[ 30200] memory[7341] epoch remaining[0:39:58]
INFO:trainer.default_trainer:epochs[    16] optim steps[30300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39492/0.90978, loss_mask_bce_0: 0.03533/0.33520, loss_mask_dice_0: 1.21062/1.16563, loss_spatial_bce_0: 0.00982/0.09026, loss_spatial_dice_0: 0.33008/0.21566, loss_spatial_ce_0: 0.01539/0.07279, loss_grounding_bce_0: 0.00372/0.08631, loss_grounding_dice_0: 0.26865/0.17949, loss_grounding_ce_0: 0.31277/0.27612, loss_mask_ce_1: 0.45274/0.91013, loss_mask_bce_1: 0.04577/0.33597, loss_mask_dice_1: 1.52227/1.17279, loss_spatial_bce_1: 0.01009/0.09104, loss_spatial_dice_1: 0.28546/0.21994, loss_spatial_ce_1: 0.09874/0.07862, loss_grounding_bce_1: 0.00545/0.08646, loss_grounding_dice_1: 0.33265/0.18035, loss_grounding_ce_1: 0.38245/0.27754, loss_mask_ce_2: 0.41910/0.91792, loss_mask_bce_2: 0.03537/0.33637, loss_mask_dice_2: 1.15356/1.17191, loss_spatial_bce_2: 0.00903/0.09136, loss_spatial_dice_2: 0.32947/0.22095, loss_spatial_ce_2: 0.02406/0.08232, loss_grounding_bce_2: 0.00483/0.08650, loss_grounding_dice_2: 0.26762/0.17987, loss_grounding_ce_2: 0.29943/0.28090, loss_mask_ce_3: 0.52727/0.92647, loss_mask_bce_3: 0.03555/0.33715, loss_mask_dice_3: 1.18363/1.16897, loss_spatial_bce_3: 0.00940/0.09219, loss_spatial_dice_3: 0.28394/0.22154, loss_spatial_ce_3: 0.10722/0.08611, loss_grounding_bce_3: 0.00466/0.08668, loss_grounding_dice_3: 0.31216/0.17972, loss_grounding_ce_3: 0.28827/0.28258, loss_mask_ce_4: 0.35407/0.92516, loss_mask_bce_4: 0.03309/0.33900, loss_mask_dice_4: 1.03125/1.19216, loss_spatial_bce_4: 0.00884/0.09640, loss_spatial_dice_4: 0.22745/0.23233, loss_spatial_ce_4: 0.00573/0.10268, loss_grounding_bce_4: 0.00489/0.08721, loss_grounding_dice_4: 0.36521/0.18248, loss_grounding_ce_4: 0.29155/0.28500, loss_mask_ce_5: 0.39328/0.93998, loss_mask_bce_5: 0.03612/0.34126, loss_mask_dice_5: 0.93921/1.19776, loss_spatial_bce_5: 0.00933/0.09783, loss_spatial_dice_5: 0.29871/0.23571, loss_spatial_ce_5: 0.01128/0.11693, loss_grounding_bce_5: 0.00412/0.08760, loss_grounding_dice_5: 0.29269/0.18369, loss_grounding_ce_5: 0.31861/0.29777, loss_mask_ce_6: 0.51247/0.97763, loss_mask_bce_6: 0.04503/0.34401, loss_mask_dice_6: 1.33063/1.20036, loss_spatial_bce_6: 0.01266/0.10338, loss_spatial_dice_6: 0.29762/0.23795, loss_spatial_ce_6: 0.08579/0.14236, loss_grounding_bce_6: 0.00473/0.08835, loss_grounding_dice_6: 0.40294/0.18390, loss_grounding_ce_6: 0.25674/0.31469, loss_mask_ce_7: 0.96488/1.02163, loss_mask_bce_7: 0.03941/0.35169, loss_mask_dice_7: 1.32393/1.25586, loss_spatial_bce_7: 0.01196/0.11201, loss_spatial_dice_7: 0.38649/0.26529, loss_spatial_ce_7: 0.10187/0.18033, loss_grounding_bce_7: 0.00617/0.09026, loss_grounding_dice_7: 0.32626/0.19127, loss_grounding_ce_7: 0.28207/0.34720, loss_mask_ce_8: 0.61085/1.13304, loss_mask_bce_8: 0.03981/0.36520, loss_mask_dice_8: 1.67431/1.33033, loss_spatial_bce_8: 0.01420/0.13311, loss_spatial_dice_8: 0.38359/0.30510, loss_spatial_ce_8: 0.15512/0.23702, loss_grounding_bce_8: 0.00446/0.09382, loss_grounding_dice_8: 0.27552/0.20225, loss_grounding_ce_8: 0.71057/0.41758, loss_mask_ce_9: 2.50622/3.68613, loss_mask_bce_9: 0.03420/0.39230, loss_mask_dice_9: 1.28305/1.90425, loss_spatial_bce_9: 0.10010/0.33529, loss_spatial_dice_9: 0.77860/0.82358, loss_spatial_ce_9: 1.95383/1.51011, loss_grounding_bce_9: 0.00197/0.10528, loss_grounding_dice_9: 0.35011/0.28176, loss_grounding_ce_9: 1.05584/0.68780] items per batch[64] items per second[0.24] total items[1939200] mini batches[ 30300] memory[7341] epoch remaining[0:35:11]
INFO:trainer.default_trainer:epochs[    16] optim steps[30400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62312/0.90997, loss_mask_bce_0: 0.15730/0.33526, loss_mask_dice_0: 0.23849/1.16587, loss_spatial_bce_0: 0.08145/0.09026, loss_spatial_dice_0: 0.13230/0.21567, loss_spatial_ce_0: 0.08614/0.07274, loss_grounding_bce_0: 0.04368/0.08632, loss_grounding_dice_0: 0.07640/0.17950, loss_grounding_ce_0: 0.12375/0.27624, loss_mask_ce_1: 0.56277/0.91029, loss_mask_bce_1: 0.15208/0.33603, loss_mask_dice_1: 0.22737/1.17307, loss_spatial_bce_1: 0.07674/0.09104, loss_spatial_dice_1: 0.13709/0.21993, loss_spatial_ce_1: 0.08867/0.07853, loss_grounding_bce_1: 0.04750/0.08646, loss_grounding_dice_1: 0.07536/0.18035, loss_grounding_ce_1: 0.12345/0.27769, loss_mask_ce_2: 0.60132/0.91816, loss_mask_bce_2: 0.15704/0.33643, loss_mask_dice_2: 0.23099/1.17218, loss_spatial_bce_2: 0.07565/0.09136, loss_spatial_dice_2: 0.13955/0.22096, loss_spatial_ce_2: 0.08651/0.08225, loss_grounding_bce_2: 0.03456/0.08650, loss_grounding_dice_2: 0.07261/0.17989, loss_grounding_ce_2: 0.44912/0.28099, loss_mask_ce_3: 0.57567/0.92665, loss_mask_bce_3: 0.15870/0.33723, loss_mask_dice_3: 0.23718/1.16926, loss_spatial_bce_3: 0.07980/0.09220, loss_spatial_dice_3: 0.14323/0.22154, loss_spatial_ce_3: 0.09263/0.08606, loss_grounding_bce_3: 0.04498/0.08669, loss_grounding_dice_3: 0.07329/0.17972, loss_grounding_ce_3: 0.17368/0.28269, loss_mask_ce_4: 0.55798/0.92539, loss_mask_bce_4: 0.16708/0.33907, loss_mask_dice_4: 0.24390/1.19241, loss_spatial_bce_4: 0.08539/0.09639, loss_spatial_dice_4: 0.14008/0.23234, loss_spatial_ce_4: 0.10697/0.10263, loss_grounding_bce_4: 0.05395/0.08721, loss_grounding_dice_4: 0.06834/0.18248, loss_grounding_ce_4: 0.14803/0.28512, loss_mask_ce_5: 0.56590/0.94021, loss_mask_bce_5: 0.16127/0.34133, loss_mask_dice_5: 0.24735/1.19802, loss_spatial_bce_5: 0.07667/0.09782, loss_spatial_dice_5: 0.13527/0.23571, loss_spatial_ce_5: 0.12224/0.11685, loss_grounding_bce_5: 0.05177/0.08760, loss_grounding_dice_5: 0.07557/0.18369, loss_grounding_ce_5: 0.15331/0.29785, loss_mask_ce_6: 0.68624/0.97782, loss_mask_bce_6: 0.15665/0.34409, loss_mask_dice_6: 0.25382/1.20066, loss_spatial_bce_6: 0.07834/0.10338, loss_spatial_dice_6: 0.14136/0.23795, loss_spatial_ce_6: 0.18564/0.14229, loss_grounding_bce_6: 0.04108/0.08836, loss_grounding_dice_6: 0.07485/0.18391, loss_grounding_ce_6: 0.21506/0.31476, loss_mask_ce_7: 0.72224/1.02186, loss_mask_bce_7: 0.15214/0.35177, loss_mask_dice_7: 0.22594/1.25612, loss_spatial_bce_7: 0.08484/0.11201, loss_spatial_dice_7: 0.15715/0.26531, loss_spatial_ce_7: 0.17868/0.18027, loss_grounding_bce_7: 0.04285/0.09027, loss_grounding_dice_7: 0.06527/0.19127, loss_grounding_ce_7: 0.27036/0.34736, loss_mask_ce_8: 0.78002/1.13335, loss_mask_bce_8: 0.15767/0.36529, loss_mask_dice_8: 0.23417/1.33062, loss_spatial_bce_8: 0.11498/0.13312, loss_spatial_dice_8: 0.22917/0.30511, loss_spatial_ce_8: 0.31531/0.23697, loss_grounding_bce_8: 0.05348/0.09383, loss_grounding_dice_8: 0.08029/0.20227, loss_grounding_ce_8: 0.31332/0.41782, loss_mask_ce_9: 2.82254/3.68692, loss_mask_bce_9: 0.16817/0.39241, loss_mask_dice_9: 0.64338/1.90465, loss_spatial_bce_9: 0.63745/0.33526, loss_spatial_dice_9: 0.65946/0.82360, loss_spatial_ce_9: 1.34656/1.51006, loss_grounding_bce_9: 0.04256/0.10530, loss_grounding_dice_9: 0.09578/0.28181, loss_grounding_ce_9: 0.66984/0.68775] items per batch[64] items per second[0.23] total items[1945600] mini batches[ 30400] memory[7341] epoch remaining[0:30:34]
INFO:trainer.default_trainer:epochs[    16] optim steps[30500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.30885/0.90979, loss_mask_bce_0: 0.02548/0.33530, loss_mask_dice_0: 1.74297/1.16581, loss_spatial_bce_0: 0.00375/0.09024, loss_spatial_dice_0: 0.25266/0.21561, loss_spatial_ce_0: 0.04407/0.07272, loss_grounding_bce_0: 0.00455/0.08631, loss_grounding_dice_0: 0.23878/0.17948, loss_grounding_ce_0: 0.48658/0.27615, loss_mask_ce_1: 1.02065/0.91017, loss_mask_bce_1: 0.02479/0.33607, loss_mask_dice_1: 1.81937/1.17307, loss_spatial_bce_1: 0.00330/0.09102, loss_spatial_dice_1: 0.33410/0.21988, loss_spatial_ce_1: 0.12077/0.07850, loss_grounding_bce_1: 0.00681/0.08645, loss_grounding_dice_1: 0.36108/0.18032, loss_grounding_ce_1: 0.41685/0.27759, loss_mask_ce_2: 1.02608/0.91800, loss_mask_bce_2: 0.01798/0.33647, loss_mask_dice_2: 1.75892/1.17214, loss_spatial_bce_2: 0.00367/0.09135, loss_spatial_dice_2: 0.30259/0.22090, loss_spatial_ce_2: 0.08363/0.08218, loss_grounding_bce_2: 0.00420/0.08649, loss_grounding_dice_2: 0.35107/0.17986, loss_grounding_ce_2: 0.49052/0.28092, loss_mask_ce_3: 1.23154/0.92652, loss_mask_bce_3: 0.02313/0.33726, loss_mask_dice_3: 1.59125/1.16926, loss_spatial_bce_3: 0.00349/0.09218, loss_spatial_dice_3: 0.27866/0.22148, loss_spatial_ce_3: 0.03715/0.08600, loss_grounding_bce_3: 0.00474/0.08668, loss_grounding_dice_3: 0.36323/0.17969, loss_grounding_ce_3: 0.47254/0.28264, loss_mask_ce_4: 1.02793/0.92523, loss_mask_bce_4: 0.02864/0.33912, loss_mask_dice_4: 1.81456/1.19246, loss_spatial_bce_4: 0.00469/0.09638, loss_spatial_dice_4: 0.35201/0.23229, loss_spatial_ce_4: 0.10139/0.10254, loss_grounding_bce_4: 0.00547/0.08720, loss_grounding_dice_4: 0.38713/0.18246, loss_grounding_ce_4: 0.45403/0.28506, loss_mask_ce_5: 1.33277/0.94009, loss_mask_bce_5: 0.02413/0.34137, loss_mask_dice_5: 1.81276/1.19803, loss_spatial_bce_5: 0.00407/0.09781, loss_spatial_dice_5: 0.36916/0.23567, loss_spatial_ce_5: 0.77735/0.11679, loss_grounding_bce_5: 0.00702/0.08759, loss_grounding_dice_5: 0.42243/0.18368, loss_grounding_ce_5: 0.50754/0.29786, loss_mask_ce_6: 1.13731/0.97763, loss_mask_bce_6: 0.03391/0.34412, loss_mask_dice_6: 1.95488/1.20074, loss_spatial_bce_6: 0.00518/0.10337, loss_spatial_dice_6: 0.32394/0.23792, loss_spatial_ce_6: 0.16850/0.14223, loss_grounding_bce_6: 0.00721/0.08835, loss_grounding_dice_6: 0.30449/0.18390, loss_grounding_ce_6: 0.29458/0.31464, loss_mask_ce_7: 1.28146/1.02174, loss_mask_bce_7: 0.02627/0.35181, loss_mask_dice_7: 1.91288/1.25614, loss_spatial_bce_7: 0.00740/0.11201, loss_spatial_dice_7: 0.37169/0.26527, loss_spatial_ce_7: 0.17225/0.18019, loss_grounding_bce_7: 0.00609/0.09027, loss_grounding_dice_7: 0.41097/0.19124, loss_grounding_ce_7: 0.40499/0.34716, loss_mask_ce_8: 1.59571/1.13308, loss_mask_bce_8: 0.03625/0.36533, loss_mask_dice_8: 2.10313/1.33063, loss_spatial_bce_8: 0.00714/0.13313, loss_spatial_dice_8: 0.50872/0.30505, loss_spatial_ce_8: 0.26261/0.23689, loss_grounding_bce_8: 0.00726/0.09382, loss_grounding_dice_8: 0.37870/0.20225, loss_grounding_ce_8: 0.78361/0.41763, loss_mask_ce_9: 3.64064/3.68652, loss_mask_bce_9: 0.02557/0.39246, loss_mask_dice_9: 2.52868/1.90472, loss_spatial_bce_9: 0.02234/0.33532, loss_spatial_dice_9: 0.91189/0.82359, loss_spatial_ce_9: 1.81517/1.51001, loss_grounding_bce_9: 0.00595/0.10529, loss_grounding_dice_9: 0.47582/0.28176, loss_grounding_ce_9: 0.66595/0.68772] items per batch[64] items per second[0.23] total items[1952000] mini batches[ 30500] memory[7341] epoch remaining[0:25:55]
INFO:trainer.default_trainer:epochs[    16] optim steps[30600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60796/0.90985, loss_mask_bce_0: 0.61757/0.33529, loss_mask_dice_0: 0.38219/1.16564, loss_spatial_bce_0: 0.21724/0.09024, loss_spatial_dice_0: 0.20709/0.21557, loss_spatial_ce_0: 0.03763/0.07269, loss_grounding_bce_0: 0.17470/0.08631, loss_grounding_dice_0: 0.08732/0.17947, loss_grounding_ce_0: 0.04885/0.27606, loss_mask_ce_1: 0.64157/0.91025, loss_mask_bce_1: 0.62592/0.33606, loss_mask_dice_1: 0.39757/1.17287, loss_spatial_bce_1: 0.20428/0.09101, loss_spatial_dice_1: 0.20635/0.21986, loss_spatial_ce_1: 0.04884/0.07844, loss_grounding_bce_1: 0.17537/0.08646, loss_grounding_dice_1: 0.08733/0.18031, loss_grounding_ce_1: 0.05746/0.27746, loss_mask_ce_2: 0.64530/0.91807, loss_mask_bce_2: 0.59991/0.33646, loss_mask_dice_2: 0.39416/1.17197, loss_spatial_bce_2: 0.21176/0.09134, loss_spatial_dice_2: 0.21287/0.22087, loss_spatial_ce_2: 0.06242/0.08216, loss_grounding_bce_2: 0.17656/0.08650, loss_grounding_dice_2: 0.23381/0.17986, loss_grounding_ce_2: 0.04695/0.28080, loss_mask_ce_3: 0.69327/0.92660, loss_mask_bce_3: 0.61124/0.33726, loss_mask_dice_3: 0.38364/1.16915, loss_spatial_bce_3: 0.20605/0.09217, loss_spatial_dice_3: 0.21605/0.22146, loss_spatial_ce_3: 0.05686/0.08596, loss_grounding_bce_3: 0.17494/0.08668, loss_grounding_dice_3: 0.08994/0.17968, loss_grounding_ce_3: 0.06759/0.28253, loss_mask_ce_4: 0.73015/0.92538, loss_mask_bce_4: 0.63812/0.33912, loss_mask_dice_4: 0.39960/1.19226, loss_spatial_bce_4: 0.24721/0.09637, loss_spatial_dice_4: 0.22688/0.23227, loss_spatial_ce_4: 0.06050/0.10250, loss_grounding_bce_4: 0.16324/0.08720, loss_grounding_dice_4: 0.23583/0.18245, loss_grounding_ce_4: 0.08275/0.28496, loss_mask_ce_5: 0.73692/0.94019, loss_mask_bce_5: 0.62430/0.34136, loss_mask_dice_5: 0.39327/1.19786, loss_spatial_bce_5: 0.26681/0.09781, loss_spatial_dice_5: 0.25984/0.23565, loss_spatial_ce_5: 0.09912/0.11672, loss_grounding_bce_5: 0.16890/0.08759, loss_grounding_dice_5: 0.20122/0.18366, loss_grounding_ce_5: 0.09087/0.29779, loss_mask_ce_6: 0.72803/0.97780, loss_mask_bce_6: 0.59240/0.34412, loss_mask_dice_6: 0.39241/1.20055, loss_spatial_bce_6: 0.24484/0.10337, loss_spatial_dice_6: 0.23335/0.23792, loss_spatial_ce_6: 0.08191/0.14219, loss_grounding_bce_6: 0.17157/0.08835, loss_grounding_dice_6: 0.24526/0.18389, loss_grounding_ce_6: 0.05453/0.31458, loss_mask_ce_7: 0.77230/1.02193, loss_mask_bce_7: 0.49460/0.35178, loss_mask_dice_7: 0.41136/1.25589, loss_spatial_bce_7: 0.25518/0.11201, loss_spatial_dice_7: 0.25799/0.26524, loss_spatial_ce_7: 0.10734/0.18012, loss_grounding_bce_7: 0.17368/0.09026, loss_grounding_dice_7: 0.20003/0.19123, loss_grounding_ce_7: 0.08544/0.34704, loss_mask_ce_8: 0.81672/1.13312, loss_mask_bce_8: 0.49464/0.36528, loss_mask_dice_8: 0.51554/1.33035, loss_spatial_bce_8: 0.30637/0.13311, loss_spatial_dice_8: 0.26238/0.30502, loss_spatial_ce_8: 0.18086/0.23685, loss_grounding_bce_8: 0.16972/0.09380, loss_grounding_dice_8: 0.07810/0.20222, loss_grounding_ce_8: 0.08986/0.41755, loss_mask_ce_9: 3.16888/3.68641, loss_mask_bce_9: 0.41023/0.39241, loss_mask_dice_9: 0.59370/1.90430, loss_spatial_bce_9: 0.49656/0.33535, loss_spatial_dice_9: 0.74067/0.82358, loss_spatial_ce_9: 1.67276/1.51007, loss_grounding_bce_9: 0.16886/0.10528, loss_grounding_dice_9: 0.17432/0.28173, loss_grounding_ce_9: 0.29641/0.68741] items per batch[64] items per second[0.23] total items[1958400] mini batches[ 30600] memory[7341] epoch remaining[0:21:18]
INFO:trainer.default_trainer:epochs[    16] optim steps[30700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34292/0.90967, loss_mask_bce_0: 0.78326/0.33524, loss_mask_dice_0: 1.45055/1.16581, loss_spatial_bce_0: 0.05080/0.09022, loss_spatial_dice_0: 0.20431/0.21553, loss_spatial_ce_0: 0.00590/0.07263, loss_grounding_bce_0: 0.36155/0.08633, loss_grounding_dice_0: 0.34160/0.17947, loss_grounding_ce_0: 2.72943/0.27624, loss_mask_ce_1: 1.19908/0.91006, loss_mask_bce_1: 0.86521/0.33601, loss_mask_dice_1: 1.65760/1.17297, loss_spatial_bce_1: 0.04878/0.09100, loss_spatial_dice_1: 0.19303/0.21982, loss_spatial_ce_1: 0.01274/0.07838, loss_grounding_bce_1: 0.35204/0.08649, loss_grounding_dice_1: 0.30702/0.18032, loss_grounding_ce_1: 2.10966/0.27765, loss_mask_ce_2: 1.48388/0.91789, loss_mask_bce_2: 1.01003/0.33642, loss_mask_dice_2: 1.65370/1.17216, loss_spatial_bce_2: 0.05002/0.09132, loss_spatial_dice_2: 0.18892/0.22082, loss_spatial_ce_2: 0.01198/0.08211, loss_grounding_bce_2: 0.35336/0.08653, loss_grounding_dice_2: 0.32712/0.17987, loss_grounding_ce_2: 2.41576/0.28091, loss_mask_ce_3: 1.45246/0.92643, loss_mask_bce_3: 0.84185/0.33721, loss_mask_dice_3: 1.56836/1.16927, loss_spatial_bce_3: 0.04917/0.09215, loss_spatial_dice_3: 0.20936/0.22142, loss_spatial_ce_3: 0.01439/0.08594, loss_grounding_bce_3: 0.33690/0.08672, loss_grounding_dice_3: 0.31123/0.17970, loss_grounding_ce_3: 2.67004/0.28254, loss_mask_ce_4: 1.35918/0.92518, loss_mask_bce_4: 0.81580/0.33907, loss_mask_dice_4: 1.55978/1.19243, loss_spatial_bce_4: 0.05418/0.09635, loss_spatial_dice_4: 0.20239/0.23223, loss_spatial_ce_4: 0.04176/0.10243, loss_grounding_bce_4: 0.32979/0.08723, loss_grounding_dice_4: 0.36965/0.18247, loss_grounding_ce_4: 3.20923/0.28511, loss_mask_ce_5: 1.36780/0.94002, loss_mask_bce_5: 0.89888/0.34132, loss_mask_dice_5: 1.71117/1.19803, loss_spatial_bce_5: 0.05659/0.09779, loss_spatial_dice_5: 0.23826/0.23563, loss_spatial_ce_5: 0.05005/0.11668, loss_grounding_bce_5: 0.28364/0.08763, loss_grounding_dice_5: 0.34598/0.18368, loss_grounding_ce_5: 3.08678/0.29786, loss_mask_ce_6: 1.68534/0.97760, loss_mask_bce_6: 0.96692/0.34408, loss_mask_dice_6: 1.75790/1.20072, loss_spatial_bce_6: 0.06318/0.10337, loss_spatial_dice_6: 0.20738/0.23790, loss_spatial_ce_6: 0.06194/0.14211, loss_grounding_bce_6: 0.33989/0.08838, loss_grounding_dice_6: 0.32537/0.18390, loss_grounding_ce_6: 2.91695/0.31461, loss_mask_ce_7: 1.66820/1.02177, loss_mask_bce_7: 0.93978/0.35176, loss_mask_dice_7: 1.73048/1.25609, loss_spatial_bce_7: 0.06357/0.11200, loss_spatial_dice_7: 0.28361/0.26521, loss_spatial_ce_7: 0.21352/0.18004, loss_grounding_bce_7: 0.27893/0.09029, loss_grounding_dice_7: 0.39849/0.19123, loss_grounding_ce_7: 3.71181/0.34713, loss_mask_ce_8: 1.44549/1.13294, loss_mask_bce_8: 1.02170/0.36527, loss_mask_dice_8: 1.84514/1.33053, loss_spatial_bce_8: 0.06844/0.13310, loss_spatial_dice_8: 0.32877/0.30498, loss_spatial_ce_8: 0.16586/0.23682, loss_grounding_bce_8: 0.34153/0.09385, loss_grounding_dice_8: 0.29049/0.20223, loss_grounding_ce_8: 0.91228/0.41738, loss_mask_ce_9: 2.56442/3.68637, loss_mask_bce_9: 0.81384/0.39238, loss_mask_dice_9: 2.13741/1.90446, loss_spatial_bce_9: 0.17977/0.33526, loss_spatial_dice_9: 0.84579/0.82357, loss_spatial_ce_9: 1.47985/1.50997, loss_grounding_bce_9: 0.33612/0.10533, loss_grounding_dice_9: 0.34232/0.28173, loss_grounding_ce_9: 1.90153/0.68718] items per batch[64] items per second[0.23] total items[1964800] mini batches[ 30700] memory[7341] epoch remaining[0:16:40]
INFO:trainer.default_trainer:epochs[    16] optim steps[30800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.20633/0.90968, loss_mask_bce_0: 0.21566/0.33520, loss_mask_dice_0: 1.56733/1.16627, loss_spatial_bce_0: 0.05094/0.09019, loss_spatial_dice_0: 0.30927/0.21552, loss_spatial_ce_0: 0.07462/0.07258, loss_grounding_bce_0: 0.05142/0.08632, loss_grounding_dice_0: 0.28341/0.17943, loss_grounding_ce_0: 0.13192/0.27615, loss_mask_ce_1: 2.19025/0.91008, loss_mask_bce_1: 0.20573/0.33598, loss_mask_dice_1: 1.65803/1.17341, loss_spatial_bce_1: 0.05040/0.09096, loss_spatial_dice_1: 0.26694/0.21981, loss_spatial_ce_1: 0.12889/0.07835, loss_grounding_bce_1: 0.04889/0.08648, loss_grounding_dice_1: 0.28834/0.18028, loss_grounding_ce_1: 0.15911/0.27758, loss_mask_ce_2: 2.20240/0.91789, loss_mask_bce_2: 0.20934/0.33638, loss_mask_dice_2: 1.55355/1.17258, loss_spatial_bce_2: 0.05234/0.09129, loss_spatial_dice_2: 0.26876/0.22081, loss_spatial_ce_2: 0.21087/0.08210, loss_grounding_bce_2: 0.04804/0.08653, loss_grounding_dice_2: 0.27580/0.17983, loss_grounding_ce_2: 0.17109/0.28079, loss_mask_ce_3: 2.39084/0.92648, loss_mask_bce_3: 0.21118/0.33718, loss_mask_dice_3: 1.54806/1.16972, loss_spatial_bce_3: 0.05197/0.09212, loss_spatial_dice_3: 0.26324/0.22141, loss_spatial_ce_3: 0.07761/0.08593, loss_grounding_bce_3: 0.04648/0.08672, loss_grounding_dice_3: 0.28096/0.17967, loss_grounding_ce_3: 0.14900/0.28240, loss_mask_ce_4: 2.19015/0.92518, loss_mask_bce_4: 0.21286/0.33904, loss_mask_dice_4: 1.57331/1.19281, loss_spatial_bce_4: 0.06326/0.09633, loss_spatial_dice_4: 0.34239/0.23225, loss_spatial_ce_4: 0.15517/0.10241, loss_grounding_bce_4: 0.05627/0.08722, loss_grounding_dice_4: 0.28244/0.18244, loss_grounding_ce_4: 0.21327/0.28497, loss_mask_ce_5: 2.32968/0.94002, loss_mask_bce_5: 0.21145/0.34128, loss_mask_dice_5: 1.36644/1.19847, loss_spatial_bce_5: 0.05439/0.09778, loss_spatial_dice_5: 0.33560/0.23564, loss_spatial_ce_5: 0.10612/0.11670, loss_grounding_bce_5: 0.05315/0.08762, loss_grounding_dice_5: 0.26209/0.18364, loss_grounding_ce_5: 0.24027/0.29775, loss_mask_ce_6: 2.24336/0.97758, loss_mask_bce_6: 0.22166/0.34405, loss_mask_dice_6: 1.46295/1.20114, loss_spatial_bce_6: 0.06346/0.10336, loss_spatial_dice_6: 0.34380/0.23790, loss_spatial_ce_6: 0.10542/0.14205, loss_grounding_bce_6: 0.05671/0.08837, loss_grounding_dice_6: 0.31127/0.18386, loss_grounding_ce_6: 0.23681/0.31456, loss_mask_ce_7: 2.23876/1.02184, loss_mask_bce_7: 0.21456/0.35171, loss_mask_dice_7: 1.50061/1.25655, loss_spatial_bce_7: 0.09738/0.11198, loss_spatial_dice_7: 0.34359/0.26522, loss_spatial_ce_7: 0.27849/0.17993, loss_grounding_bce_7: 0.05827/0.09027, loss_grounding_dice_7: 0.30373/0.19117, loss_grounding_ce_7: 0.27532/0.34714, loss_mask_ce_8: 2.23263/1.13313, loss_mask_bce_8: 0.19583/0.36522, loss_mask_dice_8: 1.63989/1.33093, loss_spatial_bce_8: 0.06533/0.13307, loss_spatial_dice_8: 0.37484/0.30497, loss_spatial_ce_8: 0.30421/0.23680, loss_grounding_bce_8: 0.07029/0.09384, loss_grounding_dice_8: 0.33830/0.20216, loss_grounding_ce_8: 0.27855/0.41737, loss_mask_ce_9: 4.28764/3.68627, loss_mask_bce_9: 0.21450/0.39234, loss_mask_dice_9: 2.02992/1.90512, loss_spatial_bce_9: 0.19570/0.33524, loss_spatial_dice_9: 0.85682/0.82357, loss_spatial_ce_9: 1.33818/1.51002, loss_grounding_bce_9: 0.05837/0.10532, loss_grounding_dice_9: 0.38785/0.28165, loss_grounding_ce_9: 0.26684/0.68696] items per batch[64] items per second[0.23] total items[1971200] mini batches[ 30800] memory[7341] epoch remaining[0:12:01]
INFO:trainer.default_trainer:epochs[    16] optim steps[30900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84531/0.90965, loss_mask_bce_0: 0.29181/0.33517, loss_mask_dice_0: 1.19997/1.16603, loss_spatial_bce_0: 0.06391/0.09018, loss_spatial_dice_0: 0.20739/0.21551, loss_spatial_ce_0: 0.06600/0.07256, loss_grounding_bce_0: 0.02064/0.08633, loss_grounding_dice_0: 0.19331/0.17940, loss_grounding_ce_0: 0.88675/0.27605, loss_mask_ce_1: 0.78993/0.91007, loss_mask_bce_1: 0.29556/0.33595, loss_mask_dice_1: 1.25591/1.17313, loss_spatial_bce_1: 0.07617/0.09096, loss_spatial_dice_1: 0.22399/0.21979, loss_spatial_ce_1: 0.00706/0.07829, loss_grounding_bce_1: 0.02280/0.08649, loss_grounding_dice_1: 0.27127/0.18027, loss_grounding_ce_1: 1.08704/0.27752, loss_mask_ce_2: 0.77866/0.91785, loss_mask_bce_2: 0.30036/0.33635, loss_mask_dice_2: 1.19144/1.17228, loss_spatial_bce_2: 0.07865/0.09130, loss_spatial_dice_2: 0.22413/0.22079, loss_spatial_ce_2: 0.00372/0.08206, loss_grounding_bce_2: 0.02189/0.08653, loss_grounding_dice_2: 0.11123/0.17980, loss_grounding_ce_2: 0.90672/0.28076, loss_mask_ce_3: 0.73183/0.92640, loss_mask_bce_3: 0.29536/0.33715, loss_mask_dice_3: 1.17858/1.16948, loss_spatial_bce_3: 0.08319/0.09212, loss_spatial_dice_3: 0.20076/0.22139, loss_spatial_ce_3: 0.00378/0.08587, loss_grounding_bce_3: 0.02668/0.08673, loss_grounding_dice_3: 0.17113/0.17965, loss_grounding_ce_3: 1.12889/0.28239, loss_mask_ce_4: 0.87873/0.92516, loss_mask_bce_4: 0.29299/0.33902, loss_mask_dice_4: 1.21258/1.19256, loss_spatial_bce_4: 0.09054/0.09632, loss_spatial_dice_4: 0.23025/0.23224, loss_spatial_ce_4: 0.04365/0.10237, loss_grounding_bce_4: 0.03245/0.08724, loss_grounding_dice_4: 0.29882/0.18241, loss_grounding_ce_4: 1.35262/0.28499, loss_mask_ce_5: 0.85215/0.93996, loss_mask_bce_5: 0.30956/0.34125, loss_mask_dice_5: 1.27193/1.19824, loss_spatial_bce_5: 0.08794/0.09777, loss_spatial_dice_5: 0.24930/0.23564, loss_spatial_ce_5: 0.00792/0.11666, loss_grounding_bce_5: 0.02763/0.08763, loss_grounding_dice_5: 0.11283/0.18363, loss_grounding_ce_5: 1.15235/0.29774, loss_mask_ce_6: 0.86481/0.97751, loss_mask_bce_6: 0.29932/0.34402, loss_mask_dice_6: 1.34717/1.20087, loss_spatial_bce_6: 0.09513/0.10336, loss_spatial_dice_6: 0.25193/0.23791, loss_spatial_ce_6: 0.03559/0.14199, loss_grounding_bce_6: 0.02456/0.08837, loss_grounding_dice_6: 0.23876/0.18386, loss_grounding_ce_6: 1.47917/0.31453, loss_mask_ce_7: 1.19322/1.02187, loss_mask_bce_7: 0.28495/0.35167, loss_mask_dice_7: 1.24383/1.25624, loss_spatial_bce_7: 0.08928/0.11199, loss_spatial_dice_7: 0.26847/0.26523, loss_spatial_ce_7: 0.02356/0.17984, loss_grounding_bce_7: 0.03164/0.09028, loss_grounding_dice_7: 0.50951/0.19117, loss_grounding_ce_7: 1.63940/0.34716, loss_mask_ce_8: 1.38759/1.13313, loss_mask_bce_8: 0.29314/0.36519, loss_mask_dice_8: 1.34953/1.33059, loss_spatial_bce_8: 0.14241/0.13306, loss_spatial_dice_8: 0.31022/0.30497, loss_spatial_ce_8: 0.07887/0.23674, loss_grounding_bce_8: 0.01334/0.09385, loss_grounding_dice_8: 0.46104/0.20216, loss_grounding_ce_8: 3.00269/0.41742, loss_mask_ce_9: 4.40799/3.68577, loss_mask_bce_9: 0.33923/0.39227, loss_mask_dice_9: 1.70169/1.90431, loss_spatial_bce_9: 0.34222/0.33519, loss_spatial_dice_9: 0.89547/0.82355, loss_spatial_ce_9: 1.26932/1.50998, loss_grounding_bce_9: 0.03516/0.10532, loss_grounding_dice_9: 0.61396/0.28163, loss_grounding_ce_9: 1.39895/0.68693] items per batch[64] items per second[0.23] total items[1977600] mini batches[ 30900] memory[7341] epoch remaining[0:07:23]
INFO:trainer.default_trainer:epochs[    16] optim steps[31000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31048/0.90958, loss_mask_bce_0: 0.12701/0.33515, loss_mask_dice_0: 0.40704/1.16588, loss_spatial_bce_0: 0.04348/0.09016, loss_spatial_dice_0: 0.13623/0.21552, loss_spatial_ce_0: 0.02769/0.07247, loss_grounding_bce_0: 0.04395/0.08637, loss_grounding_dice_0: 0.27061/0.17943, loss_grounding_ce_0: 0.07920/0.27599, loss_mask_ce_1: 0.31929/0.91002, loss_mask_bce_1: 0.12977/0.33593, loss_mask_dice_1: 0.36898/1.17296, loss_spatial_bce_1: 0.04207/0.09093, loss_spatial_dice_1: 0.14901/0.21979, loss_spatial_ce_1: 0.03429/0.07822, loss_grounding_bce_1: 0.04381/0.08654, loss_grounding_dice_1: 0.24120/0.18031, loss_grounding_ce_1: 0.07779/0.27745, loss_mask_ce_2: 0.36066/0.91781, loss_mask_bce_2: 0.12658/0.33634, loss_mask_dice_2: 0.35552/1.17210, loss_spatial_bce_2: 0.04246/0.09127, loss_spatial_dice_2: 0.14793/0.22080, loss_spatial_ce_2: 0.03624/0.08196, loss_grounding_bce_2: 0.04524/0.08657, loss_grounding_dice_2: 0.28895/0.17984, loss_grounding_ce_2: 0.09639/0.28073, loss_mask_ce_3: 0.39708/0.92642, loss_mask_bce_3: 0.12043/0.33714, loss_mask_dice_3: 0.38892/1.16925, loss_spatial_bce_3: 0.04161/0.09210, loss_spatial_dice_3: 0.15049/0.22139, loss_spatial_ce_3: 0.02588/0.08578, loss_grounding_bce_3: 0.04105/0.08677, loss_grounding_dice_3: 0.28819/0.17968, loss_grounding_ce_3: 0.10114/0.28238, loss_mask_ce_4: 0.38478/0.92519, loss_mask_bce_4: 0.12294/0.33901, loss_mask_dice_4: 0.37821/1.19242, loss_spatial_bce_4: 0.04076/0.09630, loss_spatial_dice_4: 0.14619/0.23225, loss_spatial_ce_4: 0.06362/0.10228, loss_grounding_bce_4: 0.04621/0.08727, loss_grounding_dice_4: 0.30376/0.18245, loss_grounding_ce_4: 0.09205/0.28493, loss_mask_ce_5: 0.34275/0.94008, loss_mask_bce_5: 0.12147/0.34124, loss_mask_dice_5: 0.37777/1.19809, loss_spatial_bce_5: 0.04241/0.09775, loss_spatial_dice_5: 0.14489/0.23565, loss_spatial_ce_5: 0.07373/0.11656, loss_grounding_bce_5: 0.04324/0.08767, loss_grounding_dice_5: 0.25591/0.18366, loss_grounding_ce_5: 0.24532/0.29767, loss_mask_ce_6: 0.33262/0.97752, loss_mask_bce_6: 0.12690/0.34401, loss_mask_dice_6: 0.40929/1.20068, loss_spatial_bce_6: 0.03997/0.10334, loss_spatial_dice_6: 0.15872/0.23793, loss_spatial_ce_6: 0.04921/0.14192, loss_grounding_bce_6: 0.04166/0.08840, loss_grounding_dice_6: 0.26179/0.18390, loss_grounding_ce_6: 0.16677/0.31445, loss_mask_ce_7: 0.38986/1.02193, loss_mask_bce_7: 0.12082/0.35168, loss_mask_dice_7: 0.23102/1.25606, loss_spatial_bce_7: 0.06992/0.11196, loss_spatial_dice_7: 0.17348/0.26524, loss_spatial_ce_7: 0.08991/0.17976, loss_grounding_bce_7: 0.03711/0.09031, loss_grounding_dice_7: 0.26497/0.19119, loss_grounding_ce_7: 0.18245/0.34717, loss_mask_ce_8: 0.65707/1.13321, loss_mask_bce_8: 0.12520/0.36520, loss_mask_dice_8: 0.36751/1.33044, loss_spatial_bce_8: 0.06605/0.13303, loss_spatial_dice_8: 0.21382/0.30497, loss_spatial_ce_8: 0.21630/0.23670, loss_grounding_bce_8: 0.04132/0.09389, loss_grounding_dice_8: 0.24297/0.20219, loss_grounding_ce_8: 0.13821/0.41727, loss_mask_ce_9: 3.47165/3.68542, loss_mask_bce_9: 0.12800/0.39223, loss_mask_dice_9: 0.64434/1.90393, loss_spatial_bce_9: 0.44610/0.33512, loss_spatial_dice_9: 0.77752/0.82354, loss_spatial_ce_9: 1.72795/1.51002, loss_grounding_bce_9: 0.03481/0.10536, loss_grounding_dice_9: 0.35907/0.28170, loss_grounding_ce_9: 0.47306/0.68685] items per batch[64] items per second[0.22] total items[1984000] mini batches[ 31000] memory[7341] epoch remaining[0:02:44]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00031059.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0030 s/iter. Inference: 0.2268 s/iter. Eval: 0.0869 s/iter. Total: 0.3167 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2241 s/iter. Eval: 0.0826 s/iter. Total: 0.3099 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2263 s/iter. Eval: 0.0824 s/iter. Total: 0.3120 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2277 s/iter. Eval: 0.0789 s/iter. Total: 0.3098 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2261 s/iter. Eval: 0.0760 s/iter. Total: 0.3054 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2280 s/iter. Eval: 0.0758 s/iter. Total: 0.3071 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2295 s/iter. Eval: 0.0761 s/iter. Total: 0.3088 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 127/157. Dataloading: 0.0031 s/iter. Inference: 0.2290 s/iter. Eval: 0.0793 s/iter. Total: 0.3116 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0032 s/iter. Inference: 0.2296 s/iter. Eval: 0.0783 s/iter. Total: 0.3112 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalknjhexo9 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.731 | 81.335 | 59.765 |      133      |
| Things | 54.486 | 81.781 | 65.132 |      80       |
| Stuff  | 42.554 | 80.662 | 51.664 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.91 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.94 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.12s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.90 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.022 | 61.234 | 41.219 | 19.129 | 42.017 | 60.346 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.279 | bicycle      | 18.523 | car            | 37.428 |
| motorcycle    | 34.995 | airplane     | 57.008 | bus            | 64.924 |
| train         | 68.240 | truck        | 34.810 | boat           | 23.381 |
| traffic light | 24.977 | fire hydrant | 64.616 | stop sign      | 62.644 |
| parking meter | 42.983 | bench        | 19.976 | bird           | 29.592 |
| cat           | 74.322 | dog          | 65.783 | horse          | 46.337 |
| sheep         | 47.684 | cow          | 51.190 | elephant       | 60.653 |
| bear          | 78.114 | zebra        | 60.969 | giraffe        | 57.335 |
| backpack      | 17.407 | umbrella     | 48.786 | handbag        | 15.095 |
| tie           | 34.265 | suitcase     | 41.407 | frisbee        | 67.413 |
| skis          | 6.009  | snowboard    | 23.009 | sports ball    | 47.186 |
| kite          | 32.911 | baseball bat | 28.537 | baseball glove | 42.777 |
| skateboard    | 36.391 | surfboard    | 35.625 | tennis racket  | 56.028 |
| bottle        | 34.122 | wine glass   | 27.327 | cup            | 40.301 |
| fork          | 17.096 | knife        | 13.453 | spoon          | 13.988 |
| bowl          | 33.103 | banana       | 21.592 | apple          | 21.030 |
| sandwich      | 42.677 | orange       | 28.468 | broccoli       | 22.406 |
| carrot        | 19.662 | hot dog      | 26.919 | pizza          | 51.579 |
| donut         | 46.109 | cake         | 43.422 | chair          | 20.915 |
| couch         | 41.489 | potted plant | 17.819 | bed            | 40.821 |
| dining table  | 13.068 | toilet       | 67.086 | tv             | 62.797 |
| laptop        | 63.046 | mouse        | 60.652 | remote         | 31.041 |
| keyboard      | 48.837 | cell phone   | 36.837 | microwave      | 54.068 |
| oven          | 33.289 | toaster      | 27.169 | sink           | 36.437 |
| refrigerator  | 58.272 | book         | 8.420  | clock          | 52.286 |
| vase          | 32.946 | scissors     | 24.774 | teddy bear     | 50.009 |
| hair drier    | 12.889 | toothbrush   | 19.899 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.412
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.290
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.714
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 61.56216742733868, 'fwIoU': 69.55556928461573, 'IoU-person': 87.55798179220871, 'IoU-bicycle': 75.19652687382283, 'IoU-car': 68.76576833960057, 'IoU-motorcycle': 84.27176200056947, 'IoU-airplane': 83.71527791868762, 'IoU-bus': 86.52544554959724, 'IoU-train': 88.43200541931131, 'IoU-truck': 63.56050210595643, 'IoU-boat': 67.11700396768761, 'IoU-traffic light': 75.54799073421292, 'IoU-fire hydrant': 90.19711555948932, 'IoU-stop sign': 91.47936307558449, 'IoU-parking meter': 87.62400402244643, 'IoU-bench': 56.84002870011089, 'IoU-bird': 75.5550083491724, 'IoU-cat': 88.00983126417182, 'IoU-dog': 78.89446908562893, 'IoU-horse': 86.52396383332845, 'IoU-sheep': 86.56036017295281, 'IoU-cow': 81.67399746534736, 'IoU-elephant': 87.57089573425696, 'IoU-bear': 83.87668395094022, 'IoU-zebra': 91.70930279719818, 'IoU-giraffe': 88.2097879330502, 'IoU-backpack': 40.340619372312545, 'IoU-umbrella': 78.99323760544378, 'IoU-handbag': 36.00988571712916, 'IoU-tie': 70.16144007935668, 'IoU-suitcase': 81.40936097865553, 'IoU-frisbee': 83.95131450827652, 'IoU-skis': 49.951583898187856, 'IoU-snowboard': 66.17032297006847, 'IoU-sports ball': 68.02966101694915, 'IoU-kite': 65.93965738507993, 'IoU-baseball bat': 61.30732693170212, 'IoU-baseball glove': 74.62160462400048, 'IoU-skateboard': 63.934161890021265, 'IoU-surfboard': 82.88524765343678, 'IoU-tennis racket': 83.4454340048779, 'IoU-bottle': 67.40981990641532, 'IoU-wine glass': 73.26573494842181, 'IoU-cup': 58.50710330738976, 'IoU-fork': 55.06479110605077, 'IoU-knife': 48.7634434501945, 'IoU-spoon': 54.70269584811427, 'IoU-bowl': 57.56432688872009, 'IoU-banana': 84.30922974820608, 'IoU-apple': 56.22186804001682, 'IoU-sandwich': 63.04259828510914, 'IoU-orange': 78.02845355178552, 'IoU-broccoli': 67.37489742104997, 'IoU-carrot': 64.74235817321326, 'IoU-hot dog': 60.371677703478845, 'IoU-pizza': 85.69178209607769, 'IoU-donut': 65.00892790890235, 'IoU-cake': 68.74570209369436, 'IoU-chair': 53.99565963960706, 'IoU-couch': 68.3062182877253, 'IoU-potted plant': 32.88007654071631, 'IoU-bed': 69.66461001871114, 'IoU-dining table': 51.69684700421418, 'IoU-toilet': 87.74745715163958, 'IoU-tv': 73.10428002301059, 'IoU-laptop': 75.47113093294588, 'IoU-mouse': 72.15124849921732, 'IoU-remote': 49.391125277735355, 'IoU-keyboard': 64.75896012115093, 'IoU-cell phone': 65.90301480685986, 'IoU-microwave': 57.325542447829434, 'IoU-oven': 67.59492744031745, 'IoU-toaster': 63.871490901609086, 'IoU-sink': 69.82097335992397, 'IoU-refrigerator': 82.10341989405012, 'IoU-book': 46.57360250368192, 'IoU-clock': 73.37921084156525, 'IoU-vase': 66.74792621971436, 'IoU-scissors': 63.16058524524068, 'IoU-teddy bear': 79.93142973021513, 'IoU-hair drier': 55.95195075450773, 'IoU-toothbrush': 58.752132919827126, 'IoU-banner': 42.86600667786733, 'IoU-blanket': 12.45137385309032, 'IoU-bridge': 38.09964756127188, 'IoU-cardboard': 49.04809635681798, 'IoU-counter': 32.53996281236311, 'IoU-curtain': 62.84588129337575, 'IoU-door-stuff': 42.02273177479648, 'IoU-floor-wood': 60.52576801478651, 'IoU-flower': 44.51542709188607, 'IoU-fruit': 38.01626833048755, 'IoU-gravel': 32.07216202821962, 'IoU-house': 22.423588437732427, 'IoU-light': 40.24653774284974, 'IoU-mirror-stuff': 59.594970827232316, 'IoU-net': 44.549566388764454, 'IoU-pillow': 13.171026960200383, 'IoU-platform': 30.468809108049133, 'IoU-playingfield': 70.00321411540295, 'IoU-railroad': 60.113576827589114, 'IoU-river': 51.13912359968714, 'IoU-road': 65.63967787169204, 'IoU-roof': 16.931864580080905, 'IoU-sand': 65.00611824989858, 'IoU-sea': 85.5761854032727, 'IoU-shelf': 36.447040971845, 'IoU-snow': 89.4682347873885, 'IoU-stairs': 28.763493670886074, 'IoU-tent': 8.065298468493767, 'IoU-towel': 35.25226906944325, 'IoU-wall-brick': 43.07475609725269, 'IoU-wall-stone': 26.88887895777346, 'IoU-wall-tile': 66.75683767058526, 'IoU-wall-wood': 38.37753136822958, 'IoU-water-other': 26.582379057288396, 'IoU-window-blind': 47.90685656658486, 'IoU-window-other': 48.397011097842615, 'IoU-tree-merged': 81.04442295536958, 'IoU-fence-merged': 52.619910504028056, 'IoU-ceiling-merged': 66.33259192183823, 'IoU-sky-other-merged': 93.88608103505489, 'IoU-cabinet-merged': 59.4128576194519, 'IoU-table-merged': 38.08492562721655, 'IoU-floor-other-merged': 48.452178839049076, 'IoU-pavement-merged': 53.646260693539205, 'IoU-mountain-merged': 57.19344566602492, 'IoU-grass-merged': 72.36624289937708, 'IoU-dirt-merged': 46.79690011872973, 'IoU-paper-merged': 31.776969013099603, 'IoU-food-other-merged': 38.92239434370725, 'IoU-building-other-merged': 58.635565621914786, 'IoU-rock-merged': 61.46097392559766, 'IoU-wall-other-merged': 65.25833347033992, 'IoU-rug-merged': 64.36487357099124, 'mACC': 73.51648363113219, 'pACC': 80.84232816988093, 'ACC-person': 92.59671817301455, 'ACC-bicycle': 84.99948083861868, 'ACC-car': 85.40006959924953, 'ACC-motorcycle': 89.24589730429825, 'ACC-airplane': 90.2153334324615, 'ACC-bus': 91.02618004420418, 'ACC-train': 95.36755038142624, 'ACC-truck': 75.47587882818942, 'ACC-boat': 77.83632581825137, 'ACC-traffic light': 89.59080056708179, 'ACC-fire hydrant': 95.29804173587773, 'ACC-stop sign': 95.26200946262577, 'ACC-parking meter': 92.82454896665055, 'ACC-bench': 73.49761185445087, 'ACC-bird': 78.79185712084639, 'ACC-cat': 94.79607440293633, 'ACC-dog': 82.08709785297735, 'ACC-horse': 92.58601897918052, 'ACC-sheep': 89.85477931031157, 'ACC-cow': 86.64570857606775, 'ACC-elephant': 90.39636002860419, 'ACC-bear': 85.93013391364886, 'ACC-zebra': 94.47436974194423, 'ACC-giraffe': 92.71095666799769, 'ACC-backpack': 58.6763043268683, 'ACC-umbrella': 85.65538964282126, 'ACC-handbag': 52.57893972038987, 'ACC-tie': 81.63786371785737, 'ACC-suitcase': 90.17802556705311, 'ACC-frisbee': 94.056, 'ACC-skis': 67.6684940561573, 'ACC-snowboard': 79.47148000293895, 'ACC-sports ball': 81.31970932696582, 'ACC-kite': 75.33230639895898, 'ACC-baseball bat': 85.00363380260585, 'ACC-baseball glove': 89.76928009914758, 'ACC-skateboard': 69.85268704750192, 'ACC-surfboard': 90.13374356313498, 'ACC-tennis racket': 89.60894247323432, 'ACC-bottle': 82.12882988133526, 'ACC-wine glass': 85.72945186850647, 'ACC-cup': 83.34699878743153, 'ACC-fork': 67.88691851881626, 'ACC-knife': 59.33868249432109, 'ACC-spoon': 69.14861260105961, 'ACC-bowl': 71.57940495810927, 'ACC-banana': 90.18730273211459, 'ACC-apple': 65.3559744282846, 'ACC-sandwich': 75.37631581900548, 'ACC-orange': 85.30305200451073, 'ACC-broccoli': 79.02038322981832, 'ACC-carrot': 73.93653949434777, 'ACC-hot dog': 73.05957189982526, 'ACC-pizza': 93.80223602005202, 'ACC-donut': 83.53078806970575, 'ACC-cake': 76.77850991340304, 'ACC-chair': 69.82121011509665, 'ACC-couch': 83.27882668551777, 'ACC-potted plant': 45.03172158117308, 'ACC-bed': 83.18029266779166, 'ACC-dining table': 79.61391285412631, 'ACC-toilet': 92.57910963137977, 'ACC-tv': 84.62628732893165, 'ACC-laptop': 90.6066452751478, 'ACC-mouse': 85.62997593873214, 'ACC-remote': 72.63468134218385, 'ACC-keyboard': 72.78211189719872, 'ACC-cell phone': 77.36066190066586, 'ACC-microwave': 63.75103161571097, 'ACC-oven': 82.24513413704233, 'ACC-toaster': 72.22419904473854, 'ACC-sink': 82.9839210921855, 'ACC-refrigerator': 91.07713707628149, 'ACC-book': 65.13399736628281, 'ACC-clock': 79.25269993844243, 'ACC-vase': 77.28290260240281, 'ACC-scissors': 67.78480199065274, 'ACC-teddy bear': 85.86483479535806, 'ACC-hair drier': 63.90288332906384, 'ACC-toothbrush': 81.9518763029882, 'ACC-banner': 72.63622202742233, 'ACC-blanket': 14.790587623082113, 'ACC-bridge': 54.961246609713236, 'ACC-cardboard': 65.1920023265959, 'ACC-counter': 53.44973910955147, 'ACC-curtain': 72.22292208269299, 'ACC-door-stuff': 60.24310050400703, 'ACC-floor-wood': 76.41803925902659, 'ACC-flower': 67.40236037225696, 'ACC-fruit': 60.293768208026655, 'ACC-gravel': 42.16917650888035, 'ACC-house': 26.223914164165258, 'ACC-light': 53.85023494621598, 'ACC-mirror-stuff': 71.29938068770892, 'ACC-net': 61.295568370014166, 'ACC-pillow': 27.000234956823384, 'ACC-platform': 50.976727297188894, 'ACC-playingfield': 87.6446307834203, 'ACC-railroad': 77.59316347632554, 'ACC-river': 71.2879739755077, 'ACC-road': 85.90173333359064, 'ACC-roof': 24.128274395237685, 'ACC-sand': 70.74871292797559, 'ACC-sea': 92.09046392276596, 'ACC-shelf': 55.068526002695506, 'ACC-snow': 95.46999108752058, 'ACC-stairs': 48.1136238325811, 'ACC-tent': 9.083647343702298, 'ACC-towel': 41.06419775819645, 'ACC-wall-brick': 55.397719068648065, 'ACC-wall-stone': 34.90763867954772, 'ACC-wall-tile': 80.12459751435405, 'ACC-wall-wood': 49.22517637665576, 'ACC-water-other': 40.72334863423567, 'ACC-window-blind': 61.42048181383556, 'ACC-window-other': 69.50040510326838, 'ACC-tree-merged': 89.2730825863243, 'ACC-fence-merged': 73.32038178446314, 'ACC-ceiling-merged': 79.4920173312003, 'ACC-sky-other-merged': 96.7637339610176, 'ACC-cabinet-merged': 75.45422695628523, 'ACC-table-merged': 47.12344982749389, 'ACC-floor-other-merged': 59.561946884172286, 'ACC-pavement-merged': 66.48168000833779, 'ACC-mountain-merged': 68.2930233218754, 'ACC-grass-merged': 83.3784446222195, 'ACC-dirt-merged': 70.69230269651524, 'ACC-paper-merged': 41.35487933979869, 'ACC-food-other-merged': 50.59968621641243, 'ACC-building-other-merged': 73.91923356434799, 'ACC-rock-merged': 81.51732781240793, 'ACC-wall-other-merged': 84.20770469081998, 'ACC-rug-merged': 82.37663767715918})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1528 s/iter. Inference: 0.5693 s/iter. Eval: 0.0000 s/iter. Total: 0.7221 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1539 s/iter. Inference: 0.5667 s/iter. Eval: 0.0000 s/iter. Total: 0.7208 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1673 s/iter. Inference: 0.6010 s/iter. Eval: 0.0000 s/iter. Total: 0.7684 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1695 s/iter. Inference: 0.6889 s/iter. Eval: 0.0000 s/iter. Total: 0.8586 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1668 s/iter. Inference: 0.6069 s/iter. Eval: 0.0000 s/iter. Total: 0.7739 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1661 s/iter. Inference: 0.6461 s/iter. Eval: 0.0000 s/iter. Total: 0.8124 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1683 s/iter. Inference: 0.6848 s/iter. Eval: 0.0000 s/iter. Total: 0.8533 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.521217442200761, 'noc@0.8': 2.952004682470003, 'noc@0.85': 3.616330114135206, 'noc@0.9': 4.60257535850161, 'miou@iter1': 0.8366046131860801}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0018 s/iter. Inference: 0.1018 s/iter. Eval: 0.0008 s/iter. Total: 0.1043 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.8122787475586, 'precision@0.6': 67.78079986572266, 'precision@0.7': 62.80606460571289, 'precision@0.8': 52.50680160522461, 'precision@0.9': 27.633113861083984, 'cIoU': 56.910457611083984, 'mIoU': 62.596160888671875}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.730935889899676, 'SQ': 81.33480287758779, 'RQ': 59.765067321284214, 'PQ_th': 54.48568219008576, 'SQ_th': 81.78065029059567, 'RQ_th': 65.13186187334091, 'PQ_st': 42.553960342448924, 'SQ_st': 80.66182565040617, 'RQ_st': 51.664245355915604}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.021620845270796, 'AP50': 61.23428507140072, 'AP75': 41.21864773055914, 'APs': 19.129428014384164, 'APm': 42.01673810675657, 'APl': 60.34604429869106, 'AP-person': 44.27900066225017, 'AP-bicycle': 18.523045378807353, 'AP-car': 37.428438396759184, 'AP-motorcycle': 34.99467454580312, 'AP-airplane': 57.00841393341967, 'AP-bus': 64.92355690763235, 'AP-train': 68.24047353169833, 'AP-truck': 34.809806114178635, 'AP-boat': 23.3806487415001, 'AP-traffic light': 24.977282981528575, 'AP-fire hydrant': 64.61641556167069, 'AP-stop sign': 62.643956496986384, 'AP-parking meter': 42.983309991247076, 'AP-bench': 19.97642653295381, 'AP-bird': 29.59196846983708, 'AP-cat': 74.3217386466954, 'AP-dog': 65.78338032392345, 'AP-horse': 46.33711347718074, 'AP-sheep': 47.68369942712242, 'AP-cow': 51.19013425041257, 'AP-elephant': 60.65328193572768, 'AP-bear': 78.11423601405484, 'AP-zebra': 60.968904848258454, 'AP-giraffe': 57.334929114259566, 'AP-backpack': 17.40728833398744, 'AP-umbrella': 48.786440253177595, 'AP-handbag': 15.095272100585985, 'AP-tie': 34.264828904787755, 'AP-suitcase': 41.40728585255743, 'AP-frisbee': 67.41253519789758, 'AP-skis': 6.00926047564783, 'AP-snowboard': 23.00873517338811, 'AP-sports ball': 47.185675019390985, 'AP-kite': 32.91065662458167, 'AP-baseball bat': 28.53689115453651, 'AP-baseball glove': 42.77658617291078, 'AP-skateboard': 36.39059529557659, 'AP-surfboard': 35.62527782327261, 'AP-tennis racket': 56.02804587576199, 'AP-bottle': 34.12237115425547, 'AP-wine glass': 27.327358096823968, 'AP-cup': 40.30137741087765, 'AP-fork': 17.095991853790526, 'AP-knife': 13.452866926470296, 'AP-spoon': 13.987750904259483, 'AP-bowl': 33.102737584441186, 'AP-banana': 21.591647724750867, 'AP-apple': 21.029813654142643, 'AP-sandwich': 42.67713997057282, 'AP-orange': 28.467968458885444, 'AP-broccoli': 22.406196983994107, 'AP-carrot': 19.66232200428567, 'AP-hot dog': 26.91882905922165, 'AP-pizza': 51.57888721318944, 'AP-donut': 46.10895239228435, 'AP-cake': 43.42167771970915, 'AP-chair': 20.91479383588549, 'AP-couch': 41.489426973420436, 'AP-potted plant': 17.8188114080507, 'AP-bed': 40.82095104266607, 'AP-dining table': 13.06781851024487, 'AP-toilet': 67.08605711710423, 'AP-tv': 62.79719794040947, 'AP-laptop': 63.04561916279987, 'AP-mouse': 60.65240162185999, 'AP-remote': 31.040556904813897, 'AP-keyboard': 48.836745341549765, 'AP-cell phone': 36.8371074179697, 'AP-microwave': 54.067811147184806, 'AP-oven': 33.28940952759701, 'AP-toaster': 27.16910441044104, 'AP-sink': 36.43701627326856, 'AP-refrigerator': 58.27247534293046, 'AP-book': 8.41954434083302, 'AP-clock': 52.28585563855057, 'AP-vase': 32.945650033153846, 'AP-scissors': 24.77429698433309, 'AP-teddy bear': 50.00904555272952, 'AP-hair drier': 12.88896746817539, 'AP-toothbrush': 19.898903971768586}), ('sem_seg', {'mIoU': 61.56216742733868, 'fwIoU': 69.55556928461573, 'IoU-person': 87.55798179220871, 'IoU-bicycle': 75.19652687382283, 'IoU-car': 68.76576833960057, 'IoU-motorcycle': 84.27176200056947, 'IoU-airplane': 83.71527791868762, 'IoU-bus': 86.52544554959724, 'IoU-train': 88.43200541931131, 'IoU-truck': 63.56050210595643, 'IoU-boat': 67.11700396768761, 'IoU-traffic light': 75.54799073421292, 'IoU-fire hydrant': 90.19711555948932, 'IoU-stop sign': 91.47936307558449, 'IoU-parking meter': 87.62400402244643, 'IoU-bench': 56.84002870011089, 'IoU-bird': 75.5550083491724, 'IoU-cat': 88.00983126417182, 'IoU-dog': 78.89446908562893, 'IoU-horse': 86.52396383332845, 'IoU-sheep': 86.56036017295281, 'IoU-cow': 81.67399746534736, 'IoU-elephant': 87.57089573425696, 'IoU-bear': 83.87668395094022, 'IoU-zebra': 91.70930279719818, 'IoU-giraffe': 88.2097879330502, 'IoU-backpack': 40.340619372312545, 'IoU-umbrella': 78.99323760544378, 'IoU-handbag': 36.00988571712916, 'IoU-tie': 70.16144007935668, 'IoU-suitcase': 81.40936097865553, 'IoU-frisbee': 83.95131450827652, 'IoU-skis': 49.951583898187856, 'IoU-snowboard': 66.17032297006847, 'IoU-sports ball': 68.02966101694915, 'IoU-kite': 65.93965738507993, 'IoU-baseball bat': 61.30732693170212, 'IoU-baseball glove': 74.62160462400048, 'IoU-skateboard': 63.934161890021265, 'IoU-surfboard': 82.88524765343678, 'IoU-tennis racket': 83.4454340048779, 'IoU-bottle': 67.40981990641532, 'IoU-wine glass': 73.26573494842181, 'IoU-cup': 58.50710330738976, 'IoU-fork': 55.06479110605077, 'IoU-knife': 48.7634434501945, 'IoU-spoon': 54.70269584811427, 'IoU-bowl': 57.56432688872009, 'IoU-banana': 84.30922974820608, 'IoU-apple': 56.22186804001682, 'IoU-sandwich': 63.04259828510914, 'IoU-orange': 78.02845355178552, 'IoU-broccoli': 67.37489742104997, 'IoU-carrot': 64.74235817321326, 'IoU-hot dog': 60.371677703478845, 'IoU-pizza': 85.69178209607769, 'IoU-donut': 65.00892790890235, 'IoU-cake': 68.74570209369436, 'IoU-chair': 53.99565963960706, 'IoU-couch': 68.3062182877253, 'IoU-potted plant': 32.88007654071631, 'IoU-bed': 69.66461001871114, 'IoU-dining table': 51.69684700421418, 'IoU-toilet': 87.74745715163958, 'IoU-tv': 73.10428002301059, 'IoU-laptop': 75.47113093294588, 'IoU-mouse': 72.15124849921732, 'IoU-remote': 49.391125277735355, 'IoU-keyboard': 64.75896012115093, 'IoU-cell phone': 65.90301480685986, 'IoU-microwave': 57.325542447829434, 'IoU-oven': 67.59492744031745, 'IoU-toaster': 63.871490901609086, 'IoU-sink': 69.82097335992397, 'IoU-refrigerator': 82.10341989405012, 'IoU-book': 46.57360250368192, 'IoU-clock': 73.37921084156525, 'IoU-vase': 66.74792621971436, 'IoU-scissors': 63.16058524524068, 'IoU-teddy bear': 79.93142973021513, 'IoU-hair drier': 55.95195075450773, 'IoU-toothbrush': 58.752132919827126, 'IoU-banner': 42.86600667786733, 'IoU-blanket': 12.45137385309032, 'IoU-bridge': 38.09964756127188, 'IoU-cardboard': 49.04809635681798, 'IoU-counter': 32.53996281236311, 'IoU-curtain': 62.84588129337575, 'IoU-door-stuff': 42.02273177479648, 'IoU-floor-wood': 60.52576801478651, 'IoU-flower': 44.51542709188607, 'IoU-fruit': 38.01626833048755, 'IoU-gravel': 32.07216202821962, 'IoU-house': 22.423588437732427, 'IoU-light': 40.24653774284974, 'IoU-mirror-stuff': 59.594970827232316, 'IoU-net': 44.549566388764454, 'IoU-pillow': 13.171026960200383, 'IoU-platform': 30.468809108049133, 'IoU-playingfield': 70.00321411540295, 'IoU-railroad': 60.113576827589114, 'IoU-river': 51.13912359968714, 'IoU-road': 65.63967787169204, 'IoU-roof': 16.931864580080905, 'IoU-sand': 65.00611824989858, 'IoU-sea': 85.5761854032727, 'IoU-shelf': 36.447040971845, 'IoU-snow': 89.4682347873885, 'IoU-stairs': 28.763493670886074, 'IoU-tent': 8.065298468493767, 'IoU-towel': 35.25226906944325, 'IoU-wall-brick': 43.07475609725269, 'IoU-wall-stone': 26.88887895777346, 'IoU-wall-tile': 66.75683767058526, 'IoU-wall-wood': 38.37753136822958, 'IoU-water-other': 26.582379057288396, 'IoU-window-blind': 47.90685656658486, 'IoU-window-other': 48.397011097842615, 'IoU-tree-merged': 81.04442295536958, 'IoU-fence-merged': 52.619910504028056, 'IoU-ceiling-merged': 66.33259192183823, 'IoU-sky-other-merged': 93.88608103505489, 'IoU-cabinet-merged': 59.4128576194519, 'IoU-table-merged': 38.08492562721655, 'IoU-floor-other-merged': 48.452178839049076, 'IoU-pavement-merged': 53.646260693539205, 'IoU-mountain-merged': 57.19344566602492, 'IoU-grass-merged': 72.36624289937708, 'IoU-dirt-merged': 46.79690011872973, 'IoU-paper-merged': 31.776969013099603, 'IoU-food-other-merged': 38.92239434370725, 'IoU-building-other-merged': 58.635565621914786, 'IoU-rock-merged': 61.46097392559766, 'IoU-wall-other-merged': 65.25833347033992, 'IoU-rug-merged': 64.36487357099124, 'mACC': 73.51648363113219, 'pACC': 80.84232816988093, 'ACC-person': 92.59671817301455, 'ACC-bicycle': 84.99948083861868, 'ACC-car': 85.40006959924953, 'ACC-motorcycle': 89.24589730429825, 'ACC-airplane': 90.2153334324615, 'ACC-bus': 91.02618004420418, 'ACC-train': 95.36755038142624, 'ACC-truck': 75.47587882818942, 'ACC-boat': 77.83632581825137, 'ACC-traffic light': 89.59080056708179, 'ACC-fire hydrant': 95.29804173587773, 'ACC-stop sign': 95.26200946262577, 'ACC-parking meter': 92.82454896665055, 'ACC-bench': 73.49761185445087, 'ACC-bird': 78.79185712084639, 'ACC-cat': 94.79607440293633, 'ACC-dog': 82.08709785297735, 'ACC-horse': 92.58601897918052, 'ACC-sheep': 89.85477931031157, 'ACC-cow': 86.64570857606775, 'ACC-elephant': 90.39636002860419, 'ACC-bear': 85.93013391364886, 'ACC-zebra': 94.47436974194423, 'ACC-giraffe': 92.71095666799769, 'ACC-backpack': 58.6763043268683, 'ACC-umbrella': 85.65538964282126, 'ACC-handbag': 52.57893972038987, 'ACC-tie': 81.63786371785737, 'ACC-suitcase': 90.17802556705311, 'ACC-frisbee': 94.056, 'ACC-skis': 67.6684940561573, 'ACC-snowboard': 79.47148000293895, 'ACC-sports ball': 81.31970932696582, 'ACC-kite': 75.33230639895898, 'ACC-baseball bat': 85.00363380260585, 'ACC-baseball glove': 89.76928009914758, 'ACC-skateboard': 69.85268704750192, 'ACC-surfboard': 90.13374356313498, 'ACC-tennis racket': 89.60894247323432, 'ACC-bottle': 82.12882988133526, 'ACC-wine glass': 85.72945186850647, 'ACC-cup': 83.34699878743153, 'ACC-fork': 67.88691851881626, 'ACC-knife': 59.33868249432109, 'ACC-spoon': 69.14861260105961, 'ACC-bowl': 71.57940495810927, 'ACC-banana': 90.18730273211459, 'ACC-apple': 65.3559744282846, 'ACC-sandwich': 75.37631581900548, 'ACC-orange': 85.30305200451073, 'ACC-broccoli': 79.02038322981832, 'ACC-carrot': 73.93653949434777, 'ACC-hot dog': 73.05957189982526, 'ACC-pizza': 93.80223602005202, 'ACC-donut': 83.53078806970575, 'ACC-cake': 76.77850991340304, 'ACC-chair': 69.82121011509665, 'ACC-couch': 83.27882668551777, 'ACC-potted plant': 45.03172158117308, 'ACC-bed': 83.18029266779166, 'ACC-dining table': 79.61391285412631, 'ACC-toilet': 92.57910963137977, 'ACC-tv': 84.62628732893165, 'ACC-laptop': 90.6066452751478, 'ACC-mouse': 85.62997593873214, 'ACC-remote': 72.63468134218385, 'ACC-keyboard': 72.78211189719872, 'ACC-cell phone': 77.36066190066586, 'ACC-microwave': 63.75103161571097, 'ACC-oven': 82.24513413704233, 'ACC-toaster': 72.22419904473854, 'ACC-sink': 82.9839210921855, 'ACC-refrigerator': 91.07713707628149, 'ACC-book': 65.13399736628281, 'ACC-clock': 79.25269993844243, 'ACC-vase': 77.28290260240281, 'ACC-scissors': 67.78480199065274, 'ACC-teddy bear': 85.86483479535806, 'ACC-hair drier': 63.90288332906384, 'ACC-toothbrush': 81.9518763029882, 'ACC-banner': 72.63622202742233, 'ACC-blanket': 14.790587623082113, 'ACC-bridge': 54.961246609713236, 'ACC-cardboard': 65.1920023265959, 'ACC-counter': 53.44973910955147, 'ACC-curtain': 72.22292208269299, 'ACC-door-stuff': 60.24310050400703, 'ACC-floor-wood': 76.41803925902659, 'ACC-flower': 67.40236037225696, 'ACC-fruit': 60.293768208026655, 'ACC-gravel': 42.16917650888035, 'ACC-house': 26.223914164165258, 'ACC-light': 53.85023494621598, 'ACC-mirror-stuff': 71.29938068770892, 'ACC-net': 61.295568370014166, 'ACC-pillow': 27.000234956823384, 'ACC-platform': 50.976727297188894, 'ACC-playingfield': 87.6446307834203, 'ACC-railroad': 77.59316347632554, 'ACC-river': 71.2879739755077, 'ACC-road': 85.90173333359064, 'ACC-roof': 24.128274395237685, 'ACC-sand': 70.74871292797559, 'ACC-sea': 92.09046392276596, 'ACC-shelf': 55.068526002695506, 'ACC-snow': 95.46999108752058, 'ACC-stairs': 48.1136238325811, 'ACC-tent': 9.083647343702298, 'ACC-towel': 41.06419775819645, 'ACC-wall-brick': 55.397719068648065, 'ACC-wall-stone': 34.90763867954772, 'ACC-wall-tile': 80.12459751435405, 'ACC-wall-wood': 49.22517637665576, 'ACC-water-other': 40.72334863423567, 'ACC-window-blind': 61.42048181383556, 'ACC-window-other': 69.50040510326838, 'ACC-tree-merged': 89.2730825863243, 'ACC-fence-merged': 73.32038178446314, 'ACC-ceiling-merged': 79.4920173312003, 'ACC-sky-other-merged': 96.7637339610176, 'ACC-cabinet-merged': 75.45422695628523, 'ACC-table-merged': 47.12344982749389, 'ACC-floor-other-merged': 59.561946884172286, 'ACC-pavement-merged': 66.48168000833779, 'ACC-mountain-merged': 68.2930233218754, 'ACC-grass-merged': 83.3784446222195, 'ACC-dirt-merged': 70.69230269651524, 'ACC-paper-merged': 41.35487933979869, 'ACC-food-other-merged': 50.59968621641243, 'ACC-building-other-merged': 73.91923356434799, 'ACC-rock-merged': 81.51732781240793, 'ACC-wall-other-merged': 84.20770469081998, 'ACC-rug-merged': 82.37663767715918})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.521217442200761, 'noc@0.8': 2.952004682470003, 'noc@0.85': 3.616330114135206, 'noc@0.9': 4.60257535850161, 'miou@iter1': 0.8366046131860801}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.8122787475586, 'precision@0.6': 67.78079986572266, 'precision@0.7': 62.80606460571289, 'precision@0.8': 52.50680160522461, 'precision@0.9': 27.633113861083984, 'cIoU': 56.910457611083984, 'mIoU': 62.596160888671875}}}
INFO:trainer.default_trainer:This epoch takes 1:28:15.518621
INFO:trainer.default_trainer:PROGRESS: 34.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 17 training.
INFO:trainer.default_trainer:epochs[    17] optim steps[31100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05594/0.90984, loss_mask_bce_0: 0.02630/0.33525, loss_mask_dice_0: 0.16424/1.16646, loss_spatial_bce_0: 0.05525/0.09016, loss_spatial_dice_0: 0.26208/0.21549, loss_spatial_ce_0: 0.00433/0.07239, loss_grounding_bce_0: 0.04091/0.08637, loss_grounding_dice_0: 0.25819/0.17945, loss_grounding_ce_0: 0.00072/0.27601, loss_mask_ce_1: 0.05559/0.91030, loss_mask_bce_1: 0.02306/0.33604, loss_mask_dice_1: 0.15511/1.17352, loss_spatial_bce_1: 0.05267/0.09092, loss_spatial_dice_1: 0.26680/0.21977, loss_spatial_ce_1: 0.00378/0.07814, loss_grounding_bce_1: 0.03696/0.08654, loss_grounding_dice_1: 0.25434/0.18033, loss_grounding_ce_1: 0.00093/0.27746, loss_mask_ce_2: 0.05556/0.91814, loss_mask_bce_2: 0.02092/0.33644, loss_mask_dice_2: 0.15605/1.17267, loss_spatial_bce_2: 0.05731/0.09127, loss_spatial_dice_2: 0.28247/0.22078, loss_spatial_ce_2: 0.00462/0.08186, loss_grounding_bce_2: 0.03083/0.08657, loss_grounding_dice_2: 0.24567/0.17985, loss_grounding_ce_2: 0.00124/0.28074, loss_mask_ce_3: 0.04949/0.92673, loss_mask_bce_3: 0.02079/0.33724, loss_mask_dice_3: 0.14938/1.16983, loss_spatial_bce_3: 0.05378/0.09210, loss_spatial_dice_3: 0.27565/0.22136, loss_spatial_ce_3: 0.00209/0.08568, loss_grounding_bce_3: 0.03239/0.08677, loss_grounding_dice_3: 0.24558/0.17969, loss_grounding_ce_3: 0.00145/0.28244, loss_mask_ce_4: 0.04996/0.92551, loss_mask_bce_4: 0.02058/0.33913, loss_mask_dice_4: 0.16102/1.19301, loss_spatial_bce_4: 0.05159/0.09629, loss_spatial_dice_4: 0.25503/0.23224, loss_spatial_ce_4: 0.01114/0.10215, loss_grounding_bce_4: 0.03349/0.08728, loss_grounding_dice_4: 0.25939/0.18246, loss_grounding_ce_4: 0.00072/0.28496, loss_mask_ce_5: 0.07038/0.94039, loss_mask_bce_5: 0.02231/0.34136, loss_mask_dice_5: 0.16493/1.19865, loss_spatial_bce_5: 0.05977/0.09775, loss_spatial_dice_5: 0.26474/0.23565, loss_spatial_ce_5: 0.00943/0.11642, loss_grounding_bce_5: 0.03564/0.08767, loss_grounding_dice_5: 0.25740/0.18368, loss_grounding_ce_5: 0.00112/0.29769, loss_mask_ce_6: 0.05415/0.97789, loss_mask_bce_6: 0.02454/0.34413, loss_mask_dice_6: 0.16268/1.20123, loss_spatial_bce_6: 0.08356/0.10334, loss_spatial_dice_6: 0.27916/0.23793, loss_spatial_ce_6: 0.00919/0.14180, loss_grounding_bce_6: 0.03861/0.08841, loss_grounding_dice_6: 0.26374/0.18390, loss_grounding_ce_6: 0.00187/0.31447, loss_mask_ce_7: 0.08814/1.02234, loss_mask_bce_7: 0.02107/0.35183, loss_mask_dice_7: 0.16524/1.25668, loss_spatial_bce_7: 0.06609/0.11196, loss_spatial_dice_7: 0.25601/0.26522, loss_spatial_ce_7: 0.07837/0.17970, loss_grounding_bce_7: 0.03275/0.09031, loss_grounding_dice_7: 0.27468/0.19120, loss_grounding_ce_7: 0.01155/0.34720, loss_mask_ce_8: 0.15040/1.13361, loss_mask_bce_8: 0.01575/0.36534, loss_mask_dice_8: 0.14447/1.33117, loss_spatial_bce_8: 0.04177/0.13302, loss_spatial_dice_8: 0.28274/0.30495, loss_spatial_ce_8: 0.13652/0.23660, loss_grounding_bce_8: 0.02755/0.09390, loss_grounding_dice_8: 0.25188/0.20220, loss_grounding_ce_8: 0.01506/0.41727, loss_mask_ce_9: 1.47671/3.68590, loss_mask_bce_9: 0.02173/0.39241, loss_mask_dice_9: 0.15791/1.90510, loss_spatial_bce_9: 0.15289/0.33515, loss_spatial_dice_9: 0.50978/0.82358, loss_spatial_ce_9: 0.20186/1.50987, loss_grounding_bce_9: 0.03680/0.10538, loss_grounding_dice_9: 0.26407/0.28171, loss_grounding_ce_9: 0.12835/0.68690] items per batch[64] items per second[0.13] total items[1990400] mini batches[ 31100] memory[7341] epoch remaining[1:27:13]
INFO:trainer.default_trainer:epochs[    17] optim steps[31200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02266/0.90976, loss_mask_bce_0: 0.32094/0.33521, loss_mask_dice_0: 0.62231/1.16634, loss_spatial_bce_0: 0.08017/0.09015, loss_spatial_dice_0: 0.15470/0.21549, loss_spatial_ce_0: 0.02565/0.07236, loss_grounding_bce_0: 0.11806/0.08638, loss_grounding_dice_0: 0.18503/0.17943, loss_grounding_ce_0: 0.26592/0.27594, loss_mask_ce_1: 1.00620/0.91024, loss_mask_bce_1: 0.31786/0.33602, loss_mask_dice_1: 0.63276/1.17344, loss_spatial_bce_1: 0.07322/0.09092, loss_spatial_dice_1: 0.14900/0.21976, loss_spatial_ce_1: 0.02593/0.07811, loss_grounding_bce_1: 0.11625/0.08655, loss_grounding_dice_1: 0.18580/0.18030, loss_grounding_ce_1: 0.26900/0.27739, loss_mask_ce_2: 0.98280/0.91805, loss_mask_bce_2: 0.30758/0.33641, loss_mask_dice_2: 0.58956/1.17261, loss_spatial_bce_2: 0.07891/0.09128, loss_spatial_dice_2: 0.16045/0.22077, loss_spatial_ce_2: 0.02632/0.08184, loss_grounding_bce_2: 0.11211/0.08658, loss_grounding_dice_2: 0.17675/0.17983, loss_grounding_ce_2: 0.25282/0.28068, loss_mask_ce_3: 1.02953/0.92667, loss_mask_bce_3: 0.32304/0.33722, loss_mask_dice_3: 0.61254/1.16974, loss_spatial_bce_3: 0.07861/0.09211, loss_spatial_dice_3: 0.16110/0.22135, loss_spatial_ce_3: 0.02629/0.08562, loss_grounding_bce_3: 0.11536/0.08679, loss_grounding_dice_3: 0.17787/0.17966, loss_grounding_ce_3: 0.27362/0.28237, loss_mask_ce_4: 0.97290/0.92545, loss_mask_bce_4: 0.32162/0.33911, loss_mask_dice_4: 0.61483/1.19285, loss_spatial_bce_4: 0.07554/0.09630, loss_spatial_dice_4: 0.15427/0.23224, loss_spatial_ce_4: 0.02726/0.10212, loss_grounding_bce_4: 0.11461/0.08728, loss_grounding_dice_4: 0.18440/0.18244, loss_grounding_ce_4: 0.25566/0.28492, loss_mask_ce_5: 1.02175/0.94034, loss_mask_bce_5: 0.31398/0.34135, loss_mask_dice_5: 0.61329/1.19856, loss_spatial_bce_5: 0.08349/0.09775, loss_spatial_dice_5: 0.17161/0.23565, loss_spatial_ce_5: 0.03021/0.11635, loss_grounding_bce_5: 0.11022/0.08767, loss_grounding_dice_5: 0.18630/0.18364, loss_grounding_ce_5: 0.24742/0.29763, loss_mask_ce_6: 1.11603/0.97776, loss_mask_bce_6: 0.30907/0.34410, loss_mask_dice_6: 0.64400/1.20117, loss_spatial_bce_6: 0.07694/0.10335, loss_spatial_dice_6: 0.16450/0.23793, loss_spatial_ce_6: 0.09769/0.14175, loss_grounding_bce_6: 0.11217/0.08841, loss_grounding_dice_6: 0.17526/0.18386, loss_grounding_ce_6: 0.28628/0.31445, loss_mask_ce_7: 1.06656/1.02232, loss_mask_bce_7: 0.32176/0.35178, loss_mask_dice_7: 0.62101/1.25657, loss_spatial_bce_7: 0.09537/0.11196, loss_spatial_dice_7: 0.18023/0.26522, loss_spatial_ce_7: 0.08583/0.17966, loss_grounding_bce_7: 0.11968/0.09031, loss_grounding_dice_7: 0.17988/0.19117, loss_grounding_ce_7: 0.30265/0.34713, loss_mask_ce_8: 1.25453/1.13347, loss_mask_bce_8: 0.30679/0.36531, loss_mask_dice_8: 0.63201/1.33100, loss_spatial_bce_8: 0.10168/0.13300, loss_spatial_dice_8: 0.21751/0.30493, loss_spatial_ce_8: 0.22520/0.23654, loss_grounding_bce_8: 0.11128/0.09390, loss_grounding_dice_8: 0.17683/0.20220, loss_grounding_ce_8: 0.28835/0.41717, loss_mask_ce_9: 4.36370/3.68549, loss_mask_bce_9: 0.28845/0.39237, loss_mask_dice_9: 0.99354/1.90465, loss_spatial_bce_9: 0.31964/0.33518, loss_spatial_dice_9: 0.79270/0.82356, loss_spatial_ce_9: 1.64858/1.50989, loss_grounding_bce_9: 0.09277/0.10538, loss_grounding_dice_9: 0.23593/0.28166, loss_grounding_ce_9: 0.41690/0.68664] items per batch[64] items per second[0.23] total items[1996800] mini batches[ 31200] memory[7341] epoch remaining[1:20:19]
INFO:trainer.default_trainer:epochs[    17] optim steps[31300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.27820/0.90976, loss_mask_bce_0: 0.04380/0.33520, loss_mask_dice_0: 0.63487/1.16670, loss_spatial_bce_0: 0.02055/0.09014, loss_spatial_dice_0: 0.23691/0.21544, loss_spatial_ce_0: 0.05936/0.07229, loss_grounding_bce_0: 0.02187/0.08633, loss_grounding_dice_0: 0.02994/0.17941, loss_grounding_ce_0: 0.00119/0.27599, loss_mask_ce_1: 1.08880/0.91026, loss_mask_bce_1: 0.03812/0.33602, loss_mask_dice_1: 0.60073/1.17379, loss_spatial_bce_1: 0.01633/0.09091, loss_spatial_dice_1: 0.21279/0.21970, loss_spatial_ce_1: 0.09871/0.07805, loss_grounding_bce_1: 0.02258/0.08651, loss_grounding_dice_1: 0.03088/0.18028, loss_grounding_ce_1: 0.00104/0.27747, loss_mask_ce_2: 1.07660/0.91805, loss_mask_bce_2: 0.04192/0.33640, loss_mask_dice_2: 0.73342/1.17300, loss_spatial_bce_2: 0.01664/0.09126, loss_spatial_dice_2: 0.21310/0.22071, loss_spatial_ce_2: 0.09252/0.08174, loss_grounding_bce_2: 0.02073/0.08654, loss_grounding_dice_2: 0.02916/0.17981, loss_grounding_ce_2: 0.00093/0.28075, loss_mask_ce_3: 0.96171/0.92670, loss_mask_bce_3: 0.03791/0.33722, loss_mask_dice_3: 0.64076/1.17011, loss_spatial_bce_3: 0.01543/0.09209, loss_spatial_dice_3: 0.23433/0.22130, loss_spatial_ce_3: 0.03679/0.08554, loss_grounding_bce_3: 0.02106/0.08675, loss_grounding_dice_3: 0.02612/0.17965, loss_grounding_ce_3: 0.00056/0.28242, loss_mask_ce_4: 1.07884/0.92553, loss_mask_bce_4: 0.03778/0.33910, loss_mask_dice_4: 0.69510/1.19318, loss_spatial_bce_4: 0.01723/0.09628, loss_spatial_dice_4: 0.24784/0.23219, loss_spatial_ce_4: 0.16296/0.10205, loss_grounding_bce_4: 0.02046/0.08725, loss_grounding_dice_4: 0.02607/0.18242, loss_grounding_ce_4: 0.00100/0.28494, loss_mask_ce_5: 1.30200/0.94044, loss_mask_bce_5: 0.03716/0.34135, loss_mask_dice_5: 0.57994/1.19896, loss_spatial_bce_5: 0.01783/0.09774, loss_spatial_dice_5: 0.26754/0.23562, loss_spatial_ce_5: 0.14018/0.11628, loss_grounding_bce_5: 0.02144/0.08763, loss_grounding_dice_5: 0.02932/0.18362, loss_grounding_ce_5: 0.00182/0.29781, loss_mask_ce_6: 1.16863/0.97791, loss_mask_bce_6: 0.04019/0.34409, loss_mask_dice_6: 0.58137/1.20158, loss_spatial_bce_6: 0.02587/0.10333, loss_spatial_dice_6: 0.25359/0.23789, loss_spatial_ce_6: 0.52443/0.14169, loss_grounding_bce_6: 0.02052/0.08837, loss_grounding_dice_6: 0.02655/0.18385, loss_grounding_ce_6: 0.00096/0.31456, loss_mask_ce_7: 1.37536/1.02239, loss_mask_bce_7: 0.04684/0.35180, loss_mask_dice_7: 0.67803/1.25691, loss_spatial_bce_7: 0.03607/0.11195, loss_spatial_dice_7: 0.29596/0.26519, loss_spatial_ce_7: 0.13894/0.17957, loss_grounding_bce_7: 0.02143/0.09027, loss_grounding_dice_7: 0.02694/0.19116, loss_grounding_ce_7: 0.00153/0.34729, loss_mask_ce_8: 1.34526/1.13354, loss_mask_bce_8: 0.04042/0.36533, loss_mask_dice_8: 0.63067/1.33142, loss_spatial_bce_8: 0.05187/0.13299, loss_spatial_dice_8: 0.38008/0.30489, loss_spatial_ce_8: 0.22824/0.23642, loss_grounding_bce_8: 0.02224/0.09386, loss_grounding_dice_8: 0.03025/0.20218, loss_grounding_ce_8: 0.00219/0.41731, loss_mask_ce_9: 2.61929/3.68614, loss_mask_bce_9: 0.04563/0.39234, loss_mask_dice_9: 0.88718/1.90518, loss_spatial_bce_9: 0.22171/0.33513, loss_spatial_dice_9: 0.76293/0.82356, loss_spatial_ce_9: 1.07128/1.50959, loss_grounding_bce_9: 0.03156/0.10533, loss_grounding_dice_9: 0.04943/0.28162, loss_grounding_ce_9: 0.03806/0.68688] items per batch[64] items per second[0.23] total items[2003200] mini batches[ 31300] memory[7341] epoch remaining[1:15:03]
INFO:trainer.default_trainer:epochs[    17] optim steps[31400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59879/0.90960, loss_mask_bce_0: 0.18004/0.33526, loss_mask_dice_0: 0.66709/1.16636, loss_spatial_bce_0: 0.02627/0.09013, loss_spatial_dice_0: 0.11715/0.21537, loss_spatial_ce_0: 0.01861/0.07223, loss_grounding_bce_0: 0.04847/0.08634, loss_grounding_dice_0: 0.11270/0.17942, loss_grounding_ce_0: 0.08320/0.27595, loss_mask_ce_1: 0.46697/0.91005, loss_mask_bce_1: 0.16974/0.33607, loss_mask_dice_1: 0.71685/1.17343, loss_spatial_bce_1: 0.02390/0.09089, loss_spatial_dice_1: 0.12914/0.21964, loss_spatial_ce_1: 0.02234/0.07798, loss_grounding_bce_1: 0.04905/0.08652, loss_grounding_dice_1: 0.11048/0.18029, loss_grounding_ce_1: 0.08713/0.27744, loss_mask_ce_2: 0.51447/0.91791, loss_mask_bce_2: 0.16536/0.33645, loss_mask_dice_2: 0.65157/1.17265, loss_spatial_bce_2: 0.02276/0.09125, loss_spatial_dice_2: 0.11820/0.22066, loss_spatial_ce_2: 0.03083/0.08169, loss_grounding_bce_2: 0.04678/0.08655, loss_grounding_dice_2: 0.10326/0.17983, loss_grounding_ce_2: 0.08655/0.28072, loss_mask_ce_3: 0.43482/0.92653, loss_mask_bce_3: 0.16991/0.33726, loss_mask_dice_3: 0.86782/1.16978, loss_spatial_bce_3: 0.02326/0.09208, loss_spatial_dice_3: 0.13565/0.22123, loss_spatial_ce_3: 0.01814/0.08544, loss_grounding_bce_3: 0.04866/0.08676, loss_grounding_dice_3: 0.11050/0.17966, loss_grounding_ce_3: 0.08509/0.28240, loss_mask_ce_4: 0.37082/0.92542, loss_mask_bce_4: 0.16063/0.33915, loss_mask_dice_4: 0.75245/1.19285, loss_spatial_bce_4: 0.02922/0.09628, loss_spatial_dice_4: 0.14841/0.23214, loss_spatial_ce_4: 0.01818/0.10195, loss_grounding_bce_4: 0.04221/0.08725, loss_grounding_dice_4: 0.10304/0.18243, loss_grounding_ce_4: 0.08715/0.28485, loss_mask_ce_5: 0.46865/0.94029, loss_mask_bce_5: 0.15219/0.34141, loss_mask_dice_5: 0.82336/1.19862, loss_spatial_bce_5: 0.03597/0.09775, loss_spatial_dice_5: 0.16390/0.23558, loss_spatial_ce_5: 0.09042/0.11619, loss_grounding_bce_5: 0.04268/0.08764, loss_grounding_dice_5: 0.11012/0.18365, loss_grounding_ce_5: 0.08219/0.29780, loss_mask_ce_6: 0.59766/0.97782, loss_mask_bce_6: 0.16355/0.34416, loss_mask_dice_6: 0.75588/1.20121, loss_spatial_bce_6: 0.03595/0.10334, loss_spatial_dice_6: 0.14631/0.23785, loss_spatial_ce_6: 0.06947/0.14162, loss_grounding_bce_6: 0.04729/0.08838, loss_grounding_dice_6: 0.10542/0.18386, loss_grounding_ce_6: 0.08644/0.31448, loss_mask_ce_7: 0.63717/1.02229, loss_mask_bce_7: 0.15160/0.35187, loss_mask_dice_7: 0.79025/1.25657, loss_spatial_bce_7: 0.03924/0.11196, loss_spatial_dice_7: 0.20360/0.26514, loss_spatial_ce_7: 0.10868/0.17949, loss_grounding_bce_7: 0.04008/0.09028, loss_grounding_dice_7: 0.10790/0.19118, loss_grounding_ce_7: 0.08868/0.34710, loss_mask_ce_8: 0.45391/1.13351, loss_mask_bce_8: 0.15314/0.36542, loss_mask_dice_8: 0.80390/1.33110, loss_spatial_bce_8: 0.05058/0.13298, loss_spatial_dice_8: 0.23041/0.30482, loss_spatial_ce_8: 0.28197/0.23632, loss_grounding_bce_8: 0.03959/0.09387, loss_grounding_dice_8: 0.11192/0.20221, loss_grounding_ce_8: 0.10478/0.41713, loss_mask_ce_9: 3.52668/3.68576, loss_mask_bce_9: 0.17030/0.39247, loss_mask_dice_9: 1.40357/1.90479, loss_spatial_bce_9: 0.20633/0.33516, loss_spatial_dice_9: 0.84294/0.82357, loss_spatial_ce_9: 1.55768/1.50936, loss_grounding_bce_9: 0.05427/0.10536, loss_grounding_dice_9: 0.17644/0.28169, loss_grounding_ce_9: 0.18058/0.68653] items per batch[64] items per second[0.22] total items[2009600] mini batches[ 31400] memory[7341] epoch remaining[1:10:33]
INFO:trainer.default_trainer:epochs[    17] optim steps[31500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19414/0.90976, loss_mask_bce_0: 0.15792/0.33515, loss_mask_dice_0: 2.06605/1.16640, loss_spatial_bce_0: 0.04143/0.09010, loss_spatial_dice_0: 0.34018/0.21532, loss_spatial_ce_0: 0.29766/0.07220, loss_grounding_bce_0: 0.02273/0.08630, loss_grounding_dice_0: 0.24066/0.17938, loss_grounding_ce_0: 0.29684/0.27597, loss_mask_ce_1: 1.04318/0.91022, loss_mask_bce_1: 0.21672/0.33596, loss_mask_dice_1: 2.07786/1.17350, loss_spatial_bce_1: 0.05119/0.09086, loss_spatial_dice_1: 0.39911/0.21958, loss_spatial_ce_1: 0.24949/0.07794, loss_grounding_bce_1: 0.02922/0.08648, loss_grounding_dice_1: 0.25244/0.18024, loss_grounding_ce_1: 0.23612/0.27741, loss_mask_ce_2: 1.02235/0.91799, loss_mask_bce_2: 0.18545/0.33635, loss_mask_dice_2: 1.94593/1.17266, loss_spatial_bce_2: 0.05036/0.09123, loss_spatial_dice_2: 0.37370/0.22060, loss_spatial_ce_2: 0.06868/0.08168, loss_grounding_bce_2: 0.02463/0.08651, loss_grounding_dice_2: 0.23282/0.17978, loss_grounding_ce_2: 0.30664/0.28070, loss_mask_ce_3: 1.36742/0.92669, loss_mask_bce_3: 0.18774/0.33716, loss_mask_dice_3: 1.91951/1.16987, loss_spatial_bce_3: 0.05410/0.09205, loss_spatial_dice_3: 0.39102/0.22118, loss_spatial_ce_3: 0.09550/0.08540, loss_grounding_bce_3: 0.02974/0.08673, loss_grounding_dice_3: 0.22841/0.17962, loss_grounding_ce_3: 0.25508/0.28236, loss_mask_ce_4: 1.15814/0.92552, loss_mask_bce_4: 0.17487/0.33904, loss_mask_dice_4: 1.63287/1.19290, loss_spatial_bce_4: 0.06251/0.09625, loss_spatial_dice_4: 0.35638/0.23210, loss_spatial_ce_4: 0.22285/0.10189, loss_grounding_bce_4: 0.02004/0.08721, loss_grounding_dice_4: 0.23129/0.18238, loss_grounding_ce_4: 0.31384/0.28481, loss_mask_ce_5: 1.30230/0.94044, loss_mask_bce_5: 0.15108/0.34130, loss_mask_dice_5: 2.08932/1.19869, loss_spatial_bce_5: 0.06711/0.09772, loss_spatial_dice_5: 0.36904/0.23554, loss_spatial_ce_5: 0.13505/0.11613, loss_grounding_bce_5: 0.02511/0.08760, loss_grounding_dice_5: 0.24813/0.18360, loss_grounding_ce_5: 0.34606/0.29770, loss_mask_ce_6: 1.07436/0.97792, loss_mask_bce_6: 0.22104/0.34406, loss_mask_dice_6: 2.14846/1.20129, loss_spatial_bce_6: 0.04886/0.10331, loss_spatial_dice_6: 0.41091/0.23780, loss_spatial_ce_6: 0.06718/0.14156, loss_grounding_bce_6: 0.03722/0.08834, loss_grounding_dice_6: 0.23526/0.18382, loss_grounding_ce_6: 0.24350/0.31441, loss_mask_ce_7: 1.10102/1.02240, loss_mask_bce_7: 0.24095/0.35176, loss_mask_dice_7: 1.91280/1.25659, loss_spatial_bce_7: 0.05355/0.11194, loss_spatial_dice_7: 0.44826/0.26511, loss_spatial_ce_7: 0.14068/0.17945, loss_grounding_bce_7: 0.04124/0.09025, loss_grounding_dice_7: 0.26183/0.19114, loss_grounding_ce_7: 0.24357/0.34704, loss_mask_ce_8: 1.17672/1.13368, loss_mask_bce_8: 0.20830/0.36531, loss_mask_dice_8: 1.95641/1.33106, loss_spatial_bce_8: 0.09045/0.13297, loss_spatial_dice_8: 0.51078/0.30480, loss_spatial_ce_8: 0.32641/0.23624, loss_grounding_bce_8: 0.02934/0.09382, loss_grounding_dice_8: 0.28832/0.20215, loss_grounding_ce_8: 0.28684/0.41700, loss_mask_ce_9: 3.26378/3.68539, loss_mask_bce_9: 0.28463/0.39240, loss_mask_dice_9: 2.29253/1.90463, loss_spatial_bce_9: 0.21661/0.33518, loss_spatial_dice_9: 0.73812/0.82354, loss_spatial_ce_9: 1.63911/1.50931, loss_grounding_bce_9: 0.07649/0.10534, loss_grounding_dice_9: 0.35494/0.28162, loss_grounding_ce_9: 0.27404/0.68628] items per batch[64] items per second[0.23] total items[2016000] mini batches[ 31500] memory[7341] epoch remaining[1:05:29]
INFO:trainer.default_trainer:epochs[    17] optim steps[31600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.12976/0.90968, loss_mask_bce_0: 0.37035/0.33511, loss_mask_dice_0: 5.31922/1.16660, loss_spatial_bce_0: 0.06520/0.09005, loss_spatial_dice_0: 0.37820/0.21526, loss_spatial_ce_0: 0.31533/0.07213, loss_grounding_bce_0: 0.01890/0.08627, loss_grounding_dice_0: 0.48424/0.17941, loss_grounding_ce_0: 0.21655/0.27593, loss_mask_ce_1: 2.16501/0.91013, loss_mask_bce_1: 0.37925/0.33592, loss_mask_dice_1: 4.80066/1.17366, loss_spatial_bce_1: 0.06367/0.09081, loss_spatial_dice_1: 0.40117/0.21952, loss_spatial_ce_1: 0.15019/0.07785, loss_grounding_bce_1: 0.01906/0.08645, loss_grounding_dice_1: 0.43788/0.18028, loss_grounding_ce_1: 0.23469/0.27737, loss_mask_ce_2: 1.92159/0.91795, loss_mask_bce_2: 0.36938/0.33631, loss_mask_dice_2: 4.98875/1.17285, loss_spatial_bce_2: 0.06744/0.09119, loss_spatial_dice_2: 0.39815/0.22055, loss_spatial_ce_2: 0.12808/0.08159, loss_grounding_bce_2: 0.01902/0.08648, loss_grounding_dice_2: 0.46698/0.17981, loss_grounding_ce_2: 0.24673/0.28067, loss_mask_ce_3: 2.14127/0.92664, loss_mask_bce_3: 0.38684/0.33712, loss_mask_dice_3: 4.98499/1.17003, loss_spatial_bce_3: 0.06793/0.09201, loss_spatial_dice_3: 0.39129/0.22112, loss_spatial_ce_3: 0.25226/0.08530, loss_grounding_bce_3: 0.01900/0.08669, loss_grounding_dice_3: 0.44958/0.17965, loss_grounding_ce_3: 0.25757/0.28236, loss_mask_ce_4: 2.01812/0.92546, loss_mask_bce_4: 0.35718/0.33900, loss_mask_dice_4: 4.87248/1.19311, loss_spatial_bce_4: 0.07301/0.09620, loss_spatial_dice_4: 0.34103/0.23205, loss_spatial_ce_4: 0.19780/0.10179, loss_grounding_bce_4: 0.02360/0.08718, loss_grounding_dice_4: 0.42261/0.18243, loss_grounding_ce_4: 0.39034/0.28481, loss_mask_ce_5: 2.08379/0.94049, loss_mask_bce_5: 0.35400/0.34126, loss_mask_dice_5: 5.05330/1.19887, loss_spatial_bce_5: 0.08063/0.09767, loss_spatial_dice_5: 0.40805/0.23549, loss_spatial_ce_5: 0.10195/0.11604, loss_grounding_bce_5: 0.01907/0.08757, loss_grounding_dice_5: 0.30994/0.18364, loss_grounding_ce_5: 0.23183/0.29761, loss_mask_ce_6: 2.65507/0.97790, loss_mask_bce_6: 0.38092/0.34404, loss_mask_dice_6: 4.69747/1.20146, loss_spatial_bce_6: 0.08831/0.10327, loss_spatial_dice_6: 0.41955/0.23776, loss_spatial_ce_6: 0.37893/0.14144, loss_grounding_bce_6: 0.02159/0.08832, loss_grounding_dice_6: 0.46793/0.18385, loss_grounding_ce_6: 0.26095/0.31430, loss_mask_ce_7: 2.27964/1.02240, loss_mask_bce_7: 0.37421/0.35175, loss_mask_dice_7: 5.22932/1.25681, loss_spatial_bce_7: 0.11380/0.11190, loss_spatial_dice_7: 0.48871/0.26507, loss_spatial_ce_7: 0.30684/0.17936, loss_grounding_bce_7: 0.02128/0.09023, loss_grounding_dice_7: 0.51132/0.19114, loss_grounding_ce_7: 0.24816/0.34695, loss_mask_ce_8: 2.27735/1.13356, loss_mask_bce_8: 0.38942/0.36527, loss_mask_dice_8: 5.64715/1.33132, loss_spatial_bce_8: 0.09526/0.13292, loss_spatial_dice_8: 0.53416/0.30474, loss_spatial_ce_8: 0.27070/0.23616, loss_grounding_bce_8: 0.02068/0.09380, loss_grounding_dice_8: 0.38373/0.20218, loss_grounding_ce_8: 0.28881/0.41683, loss_mask_ce_9: 6.36583/3.68552, loss_mask_bce_9: 0.37094/0.39236, loss_mask_dice_9: 6.34880/1.90510, loss_spatial_bce_9: 0.21230/0.33514, loss_spatial_dice_9: 0.83030/0.82353, loss_spatial_ce_9: 2.19481/1.50936, loss_grounding_bce_9: 0.01795/0.10530, loss_grounding_dice_9: 0.54101/0.28164, loss_grounding_ce_9: 0.84882/0.68610] items per batch[64] items per second[0.22] total items[2022400] mini batches[ 31600] memory[7341] epoch remaining[1:00:48]
INFO:trainer.default_trainer:epochs[    17] optim steps[31700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99517/0.90973, loss_mask_bce_0: 0.71254/0.33512, loss_mask_dice_0: 0.83038/1.16691, loss_spatial_bce_0: 0.13319/0.09003, loss_spatial_dice_0: 0.17174/0.21520, loss_spatial_ce_0: 0.01676/0.07206, loss_grounding_bce_0: 0.06291/0.08630, loss_grounding_dice_0: 0.05320/0.17939, loss_grounding_ce_0: 0.25759/0.27591, loss_mask_ce_1: 1.18520/0.91018, loss_mask_bce_1: 0.67356/0.33593, loss_mask_dice_1: 0.87015/1.17392, loss_spatial_bce_1: 0.14575/0.09080, loss_spatial_dice_1: 0.19440/0.21945, loss_spatial_ce_1: 0.01686/0.07780, loss_grounding_bce_1: 0.06514/0.08647, loss_grounding_dice_1: 0.05610/0.18026, loss_grounding_ce_1: 0.24099/0.27732, loss_mask_ce_2: 0.84479/0.91803, loss_mask_bce_2: 0.75205/0.33633, loss_mask_dice_2: 0.87697/1.17311, loss_spatial_bce_2: 0.15583/0.09117, loss_spatial_dice_2: 0.19498/0.22047, loss_spatial_ce_2: 0.02546/0.08152, loss_grounding_bce_2: 0.06887/0.08650, loss_grounding_dice_2: 0.06715/0.17979, loss_grounding_ce_2: 0.15071/0.28066, loss_mask_ce_3: 0.89103/0.92666, loss_mask_bce_3: 0.76090/0.33715, loss_mask_dice_3: 0.86935/1.17032, loss_spatial_bce_3: 0.15134/0.09199, loss_spatial_dice_3: 0.20351/0.22105, loss_spatial_ce_3: 0.03787/0.08522, loss_grounding_bce_3: 0.06579/0.08672, loss_grounding_dice_3: 0.06086/0.17964, loss_grounding_ce_3: 0.14518/0.28234, loss_mask_ce_4: 1.00693/0.92549, loss_mask_bce_4: 0.78880/0.33904, loss_mask_dice_4: 0.94697/1.19339, loss_spatial_bce_4: 0.16731/0.09619, loss_spatial_dice_4: 0.18071/0.23199, loss_spatial_ce_4: 0.03194/0.10172, loss_grounding_bce_4: 0.05781/0.08720, loss_grounding_dice_4: 0.05132/0.18241, loss_grounding_ce_4: 0.15140/0.28479, loss_mask_ce_5: 1.03304/0.94049, loss_mask_bce_5: 0.81174/0.34129, loss_mask_dice_5: 0.93702/1.19920, loss_spatial_bce_5: 0.15942/0.09765, loss_spatial_dice_5: 0.19582/0.23544, loss_spatial_ce_5: 0.07076/0.11597, loss_grounding_bce_5: 0.06874/0.08759, loss_grounding_dice_5: 0.06722/0.18363, loss_grounding_ce_5: 0.35556/0.29751, loss_mask_ce_6: 1.11785/0.97802, loss_mask_bce_6: 0.77944/0.34406, loss_mask_dice_6: 0.91964/1.20179, loss_spatial_bce_6: 0.15166/0.10326, loss_spatial_dice_6: 0.19199/0.23770, loss_spatial_ce_6: 0.13818/0.14138, loss_grounding_bce_6: 0.06723/0.08833, loss_grounding_dice_6: 0.06463/0.18382, loss_grounding_ce_6: 0.39834/0.31425, loss_mask_ce_7: 1.07790/1.02246, loss_mask_bce_7: 0.76198/0.35176, loss_mask_dice_7: 0.89542/1.25716, loss_spatial_bce_7: 0.15403/0.11188, loss_spatial_dice_7: 0.20238/0.26501, loss_spatial_ce_7: 0.12421/0.17929, loss_grounding_bce_7: 0.06531/0.09023, loss_grounding_dice_7: 0.05797/0.19111, loss_grounding_ce_7: 0.73825/0.34698, loss_mask_ce_8: 1.24092/1.13364, loss_mask_bce_8: 0.90558/0.36529, loss_mask_dice_8: 1.08968/1.33165, loss_spatial_bce_8: 0.21168/0.13291, loss_spatial_dice_8: 0.26003/0.30468, loss_spatial_ce_8: 0.10415/0.23603, loss_grounding_bce_8: 0.16519/0.09381, loss_grounding_dice_8: 0.16401/0.20216, loss_grounding_ce_8: 2.02021/0.41687, loss_mask_ce_9: 4.82264/3.68574, loss_mask_bce_9: 0.86749/0.39245, loss_mask_dice_9: 1.58904/1.90581, loss_spatial_bce_9: 0.39121/0.33514, loss_spatial_dice_9: 0.87770/0.82353, loss_spatial_ce_9: 1.38803/1.50905, loss_grounding_bce_9: 0.13564/0.10532, loss_grounding_dice_9: 0.18830/0.28162, loss_grounding_ce_9: 3.51021/0.68621] items per batch[64] items per second[0.23] total items[2028800] mini batches[ 31700] memory[7341] epoch remaining[0:55:43]
INFO:trainer.default_trainer:epochs[    17] optim steps[31800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.25690/0.90969, loss_mask_bce_0: 0.30057/0.33507, loss_mask_dice_0: 0.67451/1.16613, loss_spatial_bce_0: 0.06550/0.09001, loss_spatial_dice_0: 0.24367/0.21511, loss_spatial_ce_0: 0.08492/0.07197, loss_grounding_bce_0: 0.14455/0.08630, loss_grounding_dice_0: 0.14416/0.17931, loss_grounding_ce_0: 0.30736/0.27581, loss_mask_ce_1: 2.25214/0.91015, loss_mask_bce_1: 0.31393/0.33587, loss_mask_dice_1: 0.69317/1.17313, loss_spatial_bce_1: 0.06840/0.09078, loss_spatial_dice_1: 0.25813/0.21936, loss_spatial_ce_1: 0.09434/0.07773, loss_grounding_bce_1: 0.14574/0.08647, loss_grounding_dice_1: 0.13284/0.18017, loss_grounding_ce_1: 0.34020/0.27723, loss_mask_ce_2: 2.30760/0.91795, loss_mask_bce_2: 0.31518/0.33629, loss_mask_dice_2: 0.89548/1.17235, loss_spatial_bce_2: 0.07062/0.09115, loss_spatial_dice_2: 0.25559/0.22038, loss_spatial_ce_2: 0.10578/0.08145, loss_grounding_bce_2: 0.14747/0.08650, loss_grounding_dice_2: 0.16928/0.17972, loss_grounding_ce_2: 0.34227/0.28053, loss_mask_ce_3: 2.30311/0.92657, loss_mask_bce_3: 0.31369/0.33711, loss_mask_dice_3: 0.72287/1.16963, loss_spatial_bce_3: 0.07394/0.09198, loss_spatial_dice_3: 0.25332/0.22096, loss_spatial_ce_3: 0.04425/0.08516, loss_grounding_bce_3: 0.16388/0.08672, loss_grounding_dice_3: 0.15855/0.17956, loss_grounding_ce_3: 0.32887/0.28227, loss_mask_ce_4: 2.20846/0.92540, loss_mask_bce_4: 0.36477/0.33901, loss_mask_dice_4: 1.04175/1.19265, loss_spatial_bce_4: 0.07548/0.09617, loss_spatial_dice_4: 0.25031/0.23191, loss_spatial_ce_4: 0.01255/0.10164, loss_grounding_bce_4: 0.14976/0.08721, loss_grounding_dice_4: 0.15213/0.18233, loss_grounding_ce_4: 0.44174/0.28472, loss_mask_ce_5: 1.94369/0.94040, loss_mask_bce_5: 0.35853/0.34125, loss_mask_dice_5: 0.97550/1.19848, loss_spatial_bce_5: 0.07124/0.09764, loss_spatial_dice_5: 0.24750/0.23534, loss_spatial_ce_5: 0.03119/0.11587, loss_grounding_bce_5: 0.21783/0.08760, loss_grounding_dice_5: 0.32957/0.18356, loss_grounding_ce_5: 0.04456/0.29739, loss_mask_ce_6: 2.27262/0.97792, loss_mask_bce_6: 0.35238/0.34401, loss_mask_dice_6: 0.97251/1.20105, loss_spatial_bce_6: 0.07543/0.10325, loss_spatial_dice_6: 0.24936/0.23761, loss_spatial_ce_6: 0.05124/0.14129, loss_grounding_bce_6: 0.21617/0.08834, loss_grounding_dice_6: 0.34004/0.18375, loss_grounding_ce_6: 0.09671/0.31411, loss_mask_ce_7: 2.00103/1.02229, loss_mask_bce_7: 0.41888/0.35173, loss_mask_dice_7: 0.97093/1.25638, loss_spatial_bce_7: 0.08781/0.11186, loss_spatial_dice_7: 0.25356/0.26492, loss_spatial_ce_7: 0.04560/0.17923, loss_grounding_bce_7: 0.21254/0.09024, loss_grounding_dice_7: 0.31814/0.19103, loss_grounding_ce_7: 0.04810/0.34679, loss_mask_ce_8: 2.13937/1.13350, loss_mask_bce_8: 0.40235/0.36526, loss_mask_dice_8: 1.13592/1.33084, loss_spatial_bce_8: 0.07697/0.13290, loss_spatial_dice_8: 0.25569/0.30459, loss_spatial_ce_8: 0.13721/0.23595, loss_grounding_bce_8: 0.21468/0.09383, loss_grounding_dice_8: 0.33387/0.20208, loss_grounding_ce_8: 0.04268/0.41673, loss_mask_ce_9: 3.75186/3.68529, loss_mask_bce_9: 0.69511/0.39239, loss_mask_dice_9: 1.68714/1.90462, loss_spatial_bce_9: 0.38697/0.33515, loss_spatial_dice_9: 0.88420/0.82347, loss_spatial_ce_9: 1.36374/1.50883, loss_grounding_bce_9: 0.24829/0.10535, loss_grounding_dice_9: 0.36958/0.28154, loss_grounding_ce_9: 0.36678/0.68614] items per batch[64] items per second[0.23] total items[2035200] mini batches[ 31800] memory[7341] epoch remaining[0:50:55]
INFO:trainer.default_trainer:epochs[    17] optim steps[31900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78470/0.90957, loss_mask_bce_0: 0.07749/0.33517, loss_mask_dice_0: 0.62257/1.16643, loss_spatial_bce_0: 0.02760/0.09002, loss_spatial_dice_0: 0.24196/0.21508, loss_spatial_ce_0: 0.13991/0.07193, loss_grounding_bce_0: 0.02087/0.08633, loss_grounding_dice_0: 0.14195/0.17927, loss_grounding_ce_0: 0.14690/0.27596, loss_mask_ce_1: 0.69789/0.91001, loss_mask_bce_1: 0.08309/0.33599, loss_mask_dice_1: 0.64235/1.17345, loss_spatial_bce_1: 0.02787/0.09078, loss_spatial_dice_1: 0.23601/0.21931, loss_spatial_ce_1: 0.18050/0.07769, loss_grounding_bce_1: 0.02152/0.08651, loss_grounding_dice_1: 0.20107/0.18013, loss_grounding_ce_1: 0.14581/0.27734, loss_mask_ce_2: 0.67197/0.91782, loss_mask_bce_2: 0.08776/0.33638, loss_mask_dice_2: 0.56447/1.17270, loss_spatial_bce_2: 0.02585/0.09115, loss_spatial_dice_2: 0.22818/0.22033, loss_spatial_ce_2: 0.26244/0.08140, loss_grounding_bce_2: 0.02116/0.08654, loss_grounding_dice_2: 0.25981/0.17968, loss_grounding_ce_2: 0.28337/0.28064, loss_mask_ce_3: 0.65119/0.92645, loss_mask_bce_3: 0.08608/0.33721, loss_mask_dice_3: 0.51247/1.16998, loss_spatial_bce_3: 0.02792/0.09198, loss_spatial_dice_3: 0.23939/0.22091, loss_spatial_ce_3: 0.21955/0.08512, loss_grounding_bce_3: 0.01918/0.08675, loss_grounding_dice_3: 0.18675/0.17953, loss_grounding_ce_3: 0.28293/0.28246, loss_mask_ce_4: 0.68859/0.92533, loss_mask_bce_4: 0.08669/0.33912, loss_mask_dice_4: 0.46608/1.19299, loss_spatial_bce_4: 0.02804/0.09618, loss_spatial_dice_4: 0.24631/0.23188, loss_spatial_ce_4: 0.19872/0.10158, loss_grounding_bce_4: 0.01984/0.08725, loss_grounding_dice_4: 0.14441/0.18230, loss_grounding_ce_4: 0.25683/0.28480, loss_mask_ce_5: 0.84646/0.94037, loss_mask_bce_5: 0.07970/0.34136, loss_mask_dice_5: 0.61271/1.19887, loss_spatial_bce_5: 0.02954/0.09765, loss_spatial_dice_5: 0.27172/0.23531, loss_spatial_ce_5: 0.21003/0.11584, loss_grounding_bce_5: 0.01552/0.08763, loss_grounding_dice_5: 0.13870/0.18351, loss_grounding_ce_5: 0.38554/0.29746, loss_mask_ce_6: 0.95082/0.97788, loss_mask_bce_6: 0.07727/0.34413, loss_mask_dice_6: 0.61423/1.20139, loss_spatial_bce_6: 0.03229/0.10326, loss_spatial_dice_6: 0.27302/0.23757, loss_spatial_ce_6: 0.10420/0.14124, loss_grounding_bce_6: 0.01402/0.08837, loss_grounding_dice_6: 0.13211/0.18370, loss_grounding_ce_6: 0.33386/0.31420, loss_mask_ce_7: 0.71830/1.02221, loss_mask_bce_7: 0.11235/0.35185, loss_mask_dice_7: 0.69755/1.25671, loss_spatial_bce_7: 0.03921/0.11188, loss_spatial_dice_7: 0.30787/0.26490, loss_spatial_ce_7: 0.42432/0.17918, loss_grounding_bce_7: 0.01489/0.09027, loss_grounding_dice_7: 0.16691/0.19097, loss_grounding_ce_7: 0.32335/0.34682, loss_mask_ce_8: 0.97926/1.13341, loss_mask_bce_8: 0.08513/0.36538, loss_mask_dice_8: 0.81911/1.33114, loss_spatial_bce_8: 0.06751/0.13290, loss_spatial_dice_8: 0.36036/0.30453, loss_spatial_ce_8: 0.21175/0.23586, loss_grounding_bce_8: 0.01815/0.09386, loss_grounding_dice_8: 0.24491/0.20203, loss_grounding_ce_8: 0.31521/0.41681, loss_mask_ce_9: 3.69961/3.68569, loss_mask_bce_9: 0.07269/0.39249, loss_mask_dice_9: 0.78298/1.90510, loss_spatial_bce_9: 0.15078/0.33513, loss_spatial_dice_9: 0.75380/0.82347, loss_spatial_ce_9: 1.80510/1.50878, loss_grounding_bce_9: 0.01604/0.10540, loss_grounding_dice_9: 0.29047/0.28153, loss_grounding_ce_9: 0.32536/0.68620] items per batch[64] items per second[0.23] total items[2041600] mini batches[ 31900] memory[7341] epoch remaining[0:46:08]
INFO:trainer.default_trainer:epochs[    17] optim steps[32000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86397/0.90936, loss_mask_bce_0: 0.24490/0.33518, loss_mask_dice_0: 0.79183/1.16636, loss_spatial_bce_0: 0.06824/0.08997, loss_spatial_dice_0: 0.23695/0.21504, loss_spatial_ce_0: 0.07577/0.07189, loss_grounding_bce_0: 0.10383/0.08631, loss_grounding_dice_0: 0.40825/0.17923, loss_grounding_ce_0: 0.45699/0.27591, loss_mask_ce_1: 0.83303/0.90988, loss_mask_bce_1: 0.24288/0.33599, loss_mask_dice_1: 0.79642/1.17336, loss_spatial_bce_1: 0.07719/0.09074, loss_spatial_dice_1: 0.24256/0.21927, loss_spatial_ce_1: 0.05977/0.07766, loss_grounding_bce_1: 0.10605/0.08648, loss_grounding_dice_1: 0.28521/0.18008, loss_grounding_ce_1: 0.55478/0.27727, loss_mask_ce_2: 0.86513/0.91758, loss_mask_bce_2: 0.25555/0.33640, loss_mask_dice_2: 0.79872/1.17266, loss_spatial_bce_2: 0.08072/0.09111, loss_spatial_dice_2: 0.24847/0.22030, loss_spatial_ce_2: 0.06384/0.08135, loss_grounding_bce_2: 0.11246/0.08652, loss_grounding_dice_2: 0.36753/0.17964, loss_grounding_ce_2: 0.46243/0.28057, loss_mask_ce_3: 0.89288/0.92631, loss_mask_bce_3: 0.24638/0.33723, loss_mask_dice_3: 0.81396/1.16991, loss_spatial_bce_3: 0.09702/0.09195, loss_spatial_dice_3: 0.25947/0.22087, loss_spatial_ce_3: 0.05126/0.08507, loss_grounding_bce_3: 0.10685/0.08674, loss_grounding_dice_3: 0.32888/0.17950, loss_grounding_ce_3: 0.70615/0.28239, loss_mask_ce_4: 0.88924/0.92517, loss_mask_bce_4: 0.25672/0.33913, loss_mask_dice_4: 0.83134/1.19287, loss_spatial_bce_4: 0.10414/0.09615, loss_spatial_dice_4: 0.26147/0.23185, loss_spatial_ce_4: 0.12414/0.10150, loss_grounding_bce_4: 0.10590/0.08723, loss_grounding_dice_4: 0.34406/0.18226, loss_grounding_ce_4: 0.58032/0.28485, loss_mask_ce_5: 0.94571/0.94021, loss_mask_bce_5: 0.25748/0.34136, loss_mask_dice_5: 0.69920/1.19883, loss_spatial_bce_5: 0.08839/0.09762, loss_spatial_dice_5: 0.28042/0.23528, loss_spatial_ce_5: 0.23153/0.11577, loss_grounding_bce_5: 0.11852/0.08762, loss_grounding_dice_5: 0.37209/0.18347, loss_grounding_ce_5: 0.63300/0.29740, loss_mask_ce_6: 1.13184/0.97780, loss_mask_bce_6: 0.25496/0.34413, loss_mask_dice_6: 0.80831/1.20133, loss_spatial_bce_6: 0.10878/0.10324, loss_spatial_dice_6: 0.24480/0.23758, loss_spatial_ce_6: 0.17188/0.14113, loss_grounding_bce_6: 0.12597/0.08836, loss_grounding_dice_6: 0.41601/0.18366, loss_grounding_ce_6: 0.68737/0.31411, loss_mask_ce_7: 1.08607/1.02219, loss_mask_bce_7: 0.27499/0.35184, loss_mask_dice_7: 0.81282/1.25663, loss_spatial_bce_7: 0.15254/0.11187, loss_spatial_dice_7: 0.30653/0.26489, loss_spatial_ce_7: 0.31050/0.17911, loss_grounding_bce_7: 0.10770/0.09027, loss_grounding_dice_7: 0.38592/0.19093, loss_grounding_ce_7: 0.68169/0.34694, loss_mask_ce_8: 0.87351/1.13337, loss_mask_bce_8: 0.34380/0.36539, loss_mask_dice_8: 0.98930/1.33107, loss_spatial_bce_8: 0.14572/0.13287, loss_spatial_dice_8: 0.33354/0.30451, loss_spatial_ce_8: 0.36113/0.23577, loss_grounding_bce_8: 0.09352/0.09386, loss_grounding_dice_8: 0.36867/0.20200, loss_grounding_ce_8: 0.83870/0.41699, loss_mask_ce_9: 2.54416/3.68576, loss_mask_bce_9: 0.43511/0.39251, loss_mask_dice_9: 1.15233/1.90502, loss_spatial_bce_9: 0.26356/0.33504, loss_spatial_dice_9: 0.79726/0.82347, loss_spatial_ce_9: 1.43689/1.50884, loss_grounding_bce_9: 0.17349/0.10540, loss_grounding_dice_9: 0.50758/0.28151, loss_grounding_ce_9: 0.57987/0.68612] items per batch[64] items per second[0.23] total items[2048000] mini batches[ 32000] memory[7341] epoch remaining[0:41:28]
INFO:trainer.default_trainer:epochs[    17] optim steps[32100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43262/0.90930, loss_mask_bce_0: 0.30862/0.33507, loss_mask_dice_0: 0.89095/1.16599, loss_spatial_bce_0: 0.07873/0.08995, loss_spatial_dice_0: 0.13635/0.21498, loss_spatial_ce_0: 0.00585/0.07187, loss_grounding_bce_0: 0.04977/0.08631, loss_grounding_dice_0: 0.11977/0.17926, loss_grounding_ce_0: 0.16785/0.27591, loss_mask_ce_1: 0.40587/0.90983, loss_mask_bce_1: 0.28463/0.33589, loss_mask_dice_1: 0.86693/1.17294, loss_spatial_bce_1: 0.07355/0.09071, loss_spatial_dice_1: 0.14329/0.21921, loss_spatial_ce_1: 0.01302/0.07761, loss_grounding_bce_1: 0.04848/0.08648, loss_grounding_dice_1: 0.12393/0.18011, loss_grounding_ce_1: 0.17728/0.27730, loss_mask_ce_2: 0.39685/0.91748, loss_mask_bce_2: 0.30002/0.33631, loss_mask_dice_2: 0.83183/1.17233, loss_spatial_bce_2: 0.08033/0.09108, loss_spatial_dice_2: 0.13450/0.22024, loss_spatial_ce_2: 0.01836/0.08128, loss_grounding_bce_2: 0.05429/0.08652, loss_grounding_dice_2: 0.14049/0.17968, loss_grounding_ce_2: 0.16938/0.28058, loss_mask_ce_3: 0.40848/0.92621, loss_mask_bce_3: 0.30302/0.33713, loss_mask_dice_3: 0.84493/1.16955, loss_spatial_bce_3: 0.08010/0.09192, loss_spatial_dice_3: 0.14709/0.22081, loss_spatial_ce_3: 0.02573/0.08500, loss_grounding_bce_3: 0.05523/0.08674, loss_grounding_dice_3: 0.13972/0.17954, loss_grounding_ce_3: 0.17297/0.28238, loss_mask_ce_4: 0.43925/0.92513, loss_mask_bce_4: 0.31610/0.33903, loss_mask_dice_4: 0.94785/1.19252, loss_spatial_bce_4: 0.08840/0.09612, loss_spatial_dice_4: 0.15176/0.23180, loss_spatial_ce_4: 0.05734/0.10144, loss_grounding_bce_4: 0.05631/0.08722, loss_grounding_dice_4: 0.13902/0.18230, loss_grounding_ce_4: 0.19283/0.28488, loss_mask_ce_5: 0.44444/0.94018, loss_mask_bce_5: 0.29436/0.34126, loss_mask_dice_5: 0.87502/1.19844, loss_spatial_bce_5: 0.09585/0.09760, loss_spatial_dice_5: 0.18552/0.23524, loss_spatial_ce_5: 0.05280/0.11571, loss_grounding_bce_5: 0.05161/0.08762, loss_grounding_dice_5: 0.14148/0.18352, loss_grounding_ce_5: 0.18019/0.29737, loss_mask_ce_6: 0.41755/0.97772, loss_mask_bce_6: 0.29898/0.34403, loss_mask_dice_6: 0.82505/1.20100, loss_spatial_bce_6: 0.12020/0.10322, loss_spatial_dice_6: 0.17293/0.23754, loss_spatial_ce_6: 0.11614/0.14108, loss_grounding_bce_6: 0.05818/0.08836, loss_grounding_dice_6: 0.13479/0.18370, loss_grounding_ce_6: 0.19448/0.31409, loss_mask_ce_7: 0.44341/1.02209, loss_mask_bce_7: 0.30829/0.35175, loss_mask_dice_7: 0.93000/1.25632, loss_spatial_bce_7: 0.08716/0.11186, loss_spatial_dice_7: 0.23150/0.26486, loss_spatial_ce_7: 0.14414/0.17904, loss_grounding_bce_7: 0.05908/0.09026, loss_grounding_dice_7: 0.15886/0.19098, loss_grounding_ce_7: 0.18933/0.34692, loss_mask_ce_8: 0.75378/1.13328, loss_mask_bce_8: 0.32952/0.36531, loss_mask_dice_8: 1.09331/1.33073, loss_spatial_bce_8: 0.10142/0.13289, loss_spatial_dice_8: 0.27107/0.30447, loss_spatial_ce_8: 0.27636/0.23571, loss_grounding_bce_8: 0.05746/0.09386, loss_grounding_dice_8: 0.25665/0.20204, loss_grounding_ce_8: 0.31101/0.41691, loss_mask_ce_9: 4.25348/3.68549, loss_mask_bce_9: 0.35238/0.39235, loss_mask_dice_9: 1.43375/1.90450, loss_spatial_bce_9: 0.33023/0.33501, loss_spatial_dice_9: 0.83958/0.82345, loss_spatial_ce_9: 1.70419/1.50878, loss_grounding_bce_9: 0.06540/0.10538, loss_grounding_dice_9: 0.31342/0.28157, loss_grounding_ce_9: 0.30556/0.68593] items per batch[64] items per second[0.23] total items[2054400] mini batches[ 32100] memory[7341] epoch remaining[0:36:44]
INFO:trainer.default_trainer:epochs[    17] optim steps[32200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81814/0.90950, loss_mask_bce_0: 0.39400/0.33510, loss_mask_dice_0: 1.01285/1.16607, loss_spatial_bce_0: 0.03343/0.08991, loss_spatial_dice_0: 0.11078/0.21498, loss_spatial_ce_0: 0.00207/0.07182, loss_grounding_bce_0: 0.05949/0.08632, loss_grounding_dice_0: 0.27844/0.17931, loss_grounding_ce_0: 0.02377/0.27599, loss_mask_ce_1: 0.80169/0.90998, loss_mask_bce_1: 0.40169/0.33592, loss_mask_dice_1: 1.01202/1.17305, loss_spatial_bce_1: 0.03401/0.09068, loss_spatial_dice_1: 0.11900/0.21921, loss_spatial_ce_1: 0.00610/0.07753, loss_grounding_bce_1: 0.05831/0.08649, loss_grounding_dice_1: 0.27736/0.18015, loss_grounding_ce_1: 0.02311/0.27736, loss_mask_ce_2: 0.85579/0.91757, loss_mask_bce_2: 0.39960/0.33635, loss_mask_dice_2: 1.12415/1.17242, loss_spatial_bce_2: 0.03178/0.09105, loss_spatial_dice_2: 0.10686/0.22023, loss_spatial_ce_2: 0.00408/0.08121, loss_grounding_bce_2: 0.05811/0.08652, loss_grounding_dice_2: 0.28587/0.17973, loss_grounding_ce_2: 0.01936/0.28065, loss_mask_ce_3: 0.83176/0.92635, loss_mask_bce_3: 0.40451/0.33717, loss_mask_dice_3: 1.01758/1.16965, loss_spatial_bce_3: 0.03428/0.09189, loss_spatial_dice_3: 0.11415/0.22081, loss_spatial_ce_3: 0.00280/0.08495, loss_grounding_bce_3: 0.05756/0.08675, loss_grounding_dice_3: 0.28457/0.17959, loss_grounding_ce_3: 0.02046/0.28248, loss_mask_ce_4: 0.87159/0.92527, loss_mask_bce_4: 0.38820/0.33907, loss_mask_dice_4: 1.17659/1.19271, loss_spatial_bce_4: 0.03117/0.09609, loss_spatial_dice_4: 0.10368/0.23180, loss_spatial_ce_4: 0.01469/0.10136, loss_grounding_bce_4: 0.05048/0.08723, loss_grounding_dice_4: 0.27681/0.18234, loss_grounding_ce_4: 0.01779/0.28499, loss_mask_ce_5: 0.95300/0.94028, loss_mask_bce_5: 0.37634/0.34131, loss_mask_dice_5: 1.20153/1.19865, loss_spatial_bce_5: 0.03053/0.09756, loss_spatial_dice_5: 0.10728/0.23524, loss_spatial_ce_5: 0.01072/0.11566, loss_grounding_bce_5: 0.05180/0.08763, loss_grounding_dice_5: 0.30456/0.18355, loss_grounding_ce_5: 0.01677/0.29749, loss_mask_ce_6: 0.84866/0.97782, loss_mask_bce_6: 0.38839/0.34407, loss_mask_dice_6: 1.19084/1.20115, loss_spatial_bce_6: 0.03079/0.10320, loss_spatial_dice_6: 0.11345/0.23755, loss_spatial_ce_6: 0.00810/0.14102, loss_grounding_bce_6: 0.04312/0.08837, loss_grounding_dice_6: 0.27472/0.18374, loss_grounding_ce_6: 0.03466/0.31412, loss_mask_ce_7: 0.99685/1.02221, loss_mask_bce_7: 0.38507/0.35180, loss_mask_dice_7: 1.15336/1.25656, loss_spatial_bce_7: 0.03354/0.11184, loss_spatial_dice_7: 0.12031/0.26489, loss_spatial_ce_7: 0.06372/0.17894, loss_grounding_bce_7: 0.04114/0.09028, loss_grounding_dice_7: 0.25849/0.19101, loss_grounding_ce_7: 0.06625/0.34703, loss_mask_ce_8: 1.68505/1.13346, loss_mask_bce_8: 0.41326/0.36537, loss_mask_dice_8: 1.31122/1.33101, loss_spatial_bce_8: 0.03731/0.13286, loss_spatial_dice_8: 0.15922/0.30449, loss_spatial_ce_8: 0.08397/0.23558, loss_grounding_bce_8: 0.05122/0.09389, loss_grounding_dice_8: 0.26231/0.20209, loss_grounding_ce_8: 0.10274/0.41711, loss_mask_ce_9: 4.05029/3.68569, loss_mask_bce_9: 0.41510/0.39241, loss_mask_dice_9: 2.07534/1.90466, loss_spatial_bce_9: 0.38174/0.33494, loss_spatial_dice_9: 0.90120/0.82346, loss_spatial_ce_9: 1.75060/1.50885, loss_grounding_bce_9: 0.05239/0.10539, loss_grounding_dice_9: 0.36589/0.28160, loss_grounding_ce_9: 0.43259/0.68595] items per batch[64] items per second[0.23] total items[2060800] mini batches[ 32200] memory[7341] epoch remaining[0:32:03]
INFO:trainer.default_trainer:epochs[    17] optim steps[32300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79921/0.90925, loss_mask_bce_0: 0.49916/0.33514, loss_mask_dice_0: 1.27362/1.16578, loss_spatial_bce_0: 0.09293/0.08993, loss_spatial_dice_0: 0.23037/0.21493, loss_spatial_ce_0: 0.01130/0.07175, loss_grounding_bce_0: 0.02907/0.08634, loss_grounding_dice_0: 0.25106/0.17928, loss_grounding_ce_0: 0.18920/0.27595, loss_mask_ce_1: 1.05336/0.90975, loss_mask_bce_1: 0.48043/0.33597, loss_mask_dice_1: 1.11422/1.17277, loss_spatial_bce_1: 0.09307/0.09070, loss_spatial_dice_1: 0.22763/0.21914, loss_spatial_ce_1: 0.00321/0.07745, loss_grounding_bce_1: 0.03145/0.08651, loss_grounding_dice_1: 0.23252/0.18013, loss_grounding_ce_1: 0.18507/0.27736, loss_mask_ce_2: 0.95048/0.91737, loss_mask_bce_2: 0.49241/0.33639, loss_mask_dice_2: 1.13006/1.17214, loss_spatial_bce_2: 0.09998/0.09107, loss_spatial_dice_2: 0.21477/0.22016, loss_spatial_ce_2: 0.00215/0.08112, loss_grounding_bce_2: 0.03434/0.08654, loss_grounding_dice_2: 0.25526/0.17970, loss_grounding_ce_2: 0.14017/0.28062, loss_mask_ce_3: 0.74509/0.92619, loss_mask_bce_3: 0.47700/0.33721, loss_mask_dice_3: 1.23271/1.16937, loss_spatial_bce_3: 0.10636/0.09190, loss_spatial_dice_3: 0.26247/0.22075, loss_spatial_ce_3: 0.00453/0.08485, loss_grounding_bce_3: 0.03449/0.08677, loss_grounding_dice_3: 0.21589/0.17956, loss_grounding_ce_3: 0.22087/0.28241, loss_mask_ce_4: 0.73787/0.92510, loss_mask_bce_4: 0.48094/0.33911, loss_mask_dice_4: 1.37739/1.19246, loss_spatial_bce_4: 0.10468/0.09611, loss_spatial_dice_4: 0.23725/0.23175, loss_spatial_ce_4: 0.01390/0.10126, loss_grounding_bce_4: 0.03244/0.08725, loss_grounding_dice_4: 0.20530/0.18232, loss_grounding_ce_4: 0.22071/0.28492, loss_mask_ce_5: 1.07717/0.94008, loss_mask_bce_5: 0.47536/0.34137, loss_mask_dice_5: 1.15638/1.19842, loss_spatial_bce_5: 0.12066/0.09759, loss_spatial_dice_5: 0.25186/0.23519, loss_spatial_ce_5: 0.05093/0.11557, loss_grounding_bce_5: 0.02932/0.08765, loss_grounding_dice_5: 0.16926/0.18353, loss_grounding_ce_5: 0.25231/0.29748, loss_mask_ce_6: 0.81979/0.97764, loss_mask_bce_6: 0.47915/0.34411, loss_mask_dice_6: 1.37917/1.20094, loss_spatial_bce_6: 0.12087/0.10322, loss_spatial_dice_6: 0.25146/0.23750, loss_spatial_ce_6: 0.11807/0.14095, loss_grounding_bce_6: 0.03086/0.08839, loss_grounding_dice_6: 0.18782/0.18371, loss_grounding_ce_6: 0.25009/0.31419, loss_mask_ce_7: 0.90541/1.02203, loss_mask_bce_7: 0.46716/0.35185, loss_mask_dice_7: 1.40969/1.25630, loss_spatial_bce_7: 0.13179/0.11187, loss_spatial_dice_7: 0.33013/0.26483, loss_spatial_ce_7: 0.06019/0.17884, loss_grounding_bce_7: 0.09537/0.09030, loss_grounding_dice_7: 0.29861/0.19100, loss_grounding_ce_7: 0.17307/0.34706, loss_mask_ce_8: 0.91741/1.13325, loss_mask_bce_8: 0.39951/0.36542, loss_mask_dice_8: 1.62762/1.33076, loss_spatial_bce_8: 0.14878/0.13290, loss_spatial_dice_8: 0.31046/0.30440, loss_spatial_ce_8: 0.12829/0.23552, loss_grounding_bce_8: 0.04482/0.09390, loss_grounding_dice_8: 0.28740/0.20207, loss_grounding_ce_8: 0.13875/0.41702, loss_mask_ce_9: 2.83905/3.68533, loss_mask_bce_9: 0.47277/0.39242, loss_mask_dice_9: 2.26154/1.90422, loss_spatial_bce_9: 0.27686/0.33504, loss_spatial_dice_9: 0.81117/0.82343, loss_spatial_ce_9: 1.21617/1.50881, loss_grounding_bce_9: 0.06075/0.10541, loss_grounding_dice_9: 0.45934/0.28154, loss_grounding_ce_9: 0.19814/0.68600] items per batch[64] items per second[0.22] total items[2067200] mini batches[ 32300] memory[7341] epoch remaining[0:27:25]
INFO:trainer.default_trainer:epochs[    17] optim steps[32400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.20494/0.90920, loss_mask_bce_0: 0.18339/0.33513, loss_mask_dice_0: 2.84918/1.16589, loss_spatial_bce_0: 0.04344/0.08993, loss_spatial_dice_0: 0.29001/0.21489, loss_spatial_ce_0: 0.08586/0.07170, loss_grounding_bce_0: 0.04526/0.08632, loss_grounding_dice_0: 0.08834/0.17928, loss_grounding_ce_0: 0.01225/0.27603, loss_mask_ce_1: 2.08459/0.90971, loss_mask_bce_1: 0.19939/0.33596, loss_mask_dice_1: 3.23477/1.17290, loss_spatial_bce_1: 0.04500/0.09069, loss_spatial_dice_1: 0.33729/0.21911, loss_spatial_ce_1: 0.02156/0.07741, loss_grounding_bce_1: 0.04855/0.08648, loss_grounding_dice_1: 0.09672/0.18014, loss_grounding_ce_1: 0.01218/0.27756, loss_mask_ce_2: 2.42415/0.91732, loss_mask_bce_2: 0.19395/0.33638, loss_mask_dice_2: 3.32763/1.17232, loss_spatial_bce_2: 0.04557/0.09107, loss_spatial_dice_2: 0.30870/0.22014, loss_spatial_ce_2: 0.03286/0.08108, loss_grounding_bce_2: 0.04822/0.08652, loss_grounding_dice_2: 0.10718/0.17973, loss_grounding_ce_2: 0.01372/0.28079, loss_mask_ce_3: 2.27454/0.92620, loss_mask_bce_3: 0.20681/0.33721, loss_mask_dice_3: 3.16942/1.16957, loss_spatial_bce_3: 0.04377/0.09190, loss_spatial_dice_3: 0.31241/0.22072, loss_spatial_ce_3: 0.03716/0.08482, loss_grounding_bce_3: 0.04784/0.08675, loss_grounding_dice_3: 0.13684/0.17958, loss_grounding_ce_3: 0.01797/0.28259, loss_mask_ce_4: 2.44462/0.92511, loss_mask_bce_4: 0.18591/0.33912, loss_mask_dice_4: 2.90885/1.19268, loss_spatial_bce_4: 0.04869/0.09612, loss_spatial_dice_4: 0.30897/0.23173, loss_spatial_ce_4: 0.05713/0.10120, loss_grounding_bce_4: 0.04498/0.08723, loss_grounding_dice_4: 0.10607/0.18233, loss_grounding_ce_4: 0.07217/0.28500, loss_mask_ce_5: 2.76135/0.94012, loss_mask_bce_5: 0.19947/0.34137, loss_mask_dice_5: 3.09884/1.19862, loss_spatial_bce_5: 0.04756/0.09759, loss_spatial_dice_5: 0.32499/0.23516, loss_spatial_ce_5: 0.07982/0.11551, loss_grounding_bce_5: 0.04630/0.08764, loss_grounding_dice_5: 0.12102/0.18355, loss_grounding_ce_5: 0.03586/0.29759, loss_mask_ce_6: 2.30191/0.97769, loss_mask_bce_6: 0.20103/0.34410, loss_mask_dice_6: 3.05112/1.20112, loss_spatial_bce_6: 0.05603/0.10323, loss_spatial_dice_6: 0.32194/0.23746, loss_spatial_ce_6: 0.08628/0.14090, loss_grounding_bce_6: 0.04574/0.08837, loss_grounding_dice_6: 0.14401/0.18373, loss_grounding_ce_6: 0.03598/0.31438, loss_mask_ce_7: 2.76916/1.02214, loss_mask_bce_7: 0.19187/0.35187, loss_mask_dice_7: 3.35902/1.25649, loss_spatial_bce_7: 0.05764/0.11187, loss_spatial_dice_7: 0.38697/0.26480, loss_spatial_ce_7: 0.11685/0.17882, loss_grounding_bce_7: 0.04662/0.09029, loss_grounding_dice_7: 0.10612/0.19101, loss_grounding_ce_7: 0.12383/0.34730, loss_mask_ce_8: 3.00226/1.13341, loss_mask_bce_8: 0.22326/0.36541, loss_mask_dice_8: 3.24502/1.33093, loss_spatial_bce_8: 0.07788/0.13290, loss_spatial_dice_8: 0.50270/0.30438, loss_spatial_ce_8: 0.14974/0.23542, loss_grounding_bce_8: 0.04663/0.09387, loss_grounding_dice_8: 0.22241/0.20208, loss_grounding_ce_8: 0.38008/0.41700, loss_mask_ce_9: 6.23552/3.68563, loss_mask_bce_9: 0.24065/0.39244, loss_mask_dice_9: 4.15390/1.90445, loss_spatial_bce_9: 0.19907/0.33508, loss_spatial_dice_9: 0.91285/0.82342, loss_spatial_ce_9: 1.32992/1.50876, loss_grounding_bce_9: 0.04378/0.10539, loss_grounding_dice_9: 0.15509/0.28154, loss_grounding_ce_9: 0.89154/0.68599] items per batch[64] items per second[0.23] total items[2073600] mini batches[ 32400] memory[7341] epoch remaining[0:22:45]
INFO:trainer.default_trainer:epochs[    17] optim steps[32500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55301/0.90908, loss_mask_bce_0: 0.06571/0.33519, loss_mask_dice_0: 0.71443/1.16585, loss_spatial_bce_0: 0.02103/0.08993, loss_spatial_dice_0: 0.17907/0.21485, loss_spatial_ce_0: 0.08126/0.07166, loss_grounding_bce_0: 0.01568/0.08629, loss_grounding_dice_0: 0.18650/0.17923, loss_grounding_ce_0: 0.14297/0.27603, loss_mask_ce_1: 0.52658/0.90964, loss_mask_bce_1: 0.06691/0.33601, loss_mask_dice_1: 0.71825/1.17291, loss_spatial_bce_1: 0.02035/0.09069, loss_spatial_dice_1: 0.19020/0.21908, loss_spatial_ce_1: 0.08436/0.07733, loss_grounding_bce_1: 0.01631/0.08645, loss_grounding_dice_1: 0.18765/0.18011, loss_grounding_ce_1: 0.15284/0.27756, loss_mask_ce_2: 0.33727/0.91723, loss_mask_bce_2: 0.06082/0.33643, loss_mask_dice_2: 0.73120/1.17230, loss_spatial_bce_2: 0.02129/0.09107, loss_spatial_dice_2: 0.17655/0.22009, loss_spatial_ce_2: 0.10230/0.08102, loss_grounding_bce_2: 0.01680/0.08649, loss_grounding_dice_2: 0.20430/0.17968, loss_grounding_ce_2: 0.14939/0.28081, loss_mask_ce_3: 0.34270/0.92615, loss_mask_bce_3: 0.06267/0.33727, loss_mask_dice_3: 0.75722/1.16955, loss_spatial_bce_3: 0.02089/0.09190, loss_spatial_dice_3: 0.20359/0.22068, loss_spatial_ce_3: 0.09688/0.08477, loss_grounding_bce_3: 0.01541/0.08673, loss_grounding_dice_3: 0.17946/0.17954, loss_grounding_ce_3: 0.14899/0.28262, loss_mask_ce_4: 0.53597/0.92502, loss_mask_bce_4: 0.06131/0.33916, loss_mask_dice_4: 0.55787/1.19267, loss_spatial_bce_4: 0.02214/0.09612, loss_spatial_dice_4: 0.17392/0.23168, loss_spatial_ce_4: 0.17817/0.10119, loss_grounding_bce_4: 0.01539/0.08720, loss_grounding_dice_4: 0.20207/0.18231, loss_grounding_ce_4: 0.16664/0.28504, loss_mask_ce_5: 0.74128/0.94008, loss_mask_bce_5: 0.06104/0.34142, loss_mask_dice_5: 0.61086/1.19860, loss_spatial_bce_5: 0.02269/0.09761, loss_spatial_dice_5: 0.18054/0.23512, loss_spatial_ce_5: 0.16549/0.11545, loss_grounding_bce_5: 0.01626/0.08761, loss_grounding_dice_5: 0.18462/0.18350, loss_grounding_ce_5: 0.15634/0.29764, loss_mask_ce_6: 0.43532/0.97762, loss_mask_bce_6: 0.06739/0.34416, loss_mask_dice_6: 0.70423/1.20114, loss_spatial_bce_6: 0.02491/0.10325, loss_spatial_dice_6: 0.20971/0.23742, loss_spatial_ce_6: 0.23173/0.14087, loss_grounding_bce_6: 0.01670/0.08834, loss_grounding_dice_6: 0.19857/0.18368, loss_grounding_ce_6: 0.32931/0.31448, loss_mask_ce_7: 1.00785/1.02210, loss_mask_bce_7: 0.06592/0.35195, loss_mask_dice_7: 0.64597/1.25647, loss_spatial_bce_7: 0.04032/0.11191, loss_spatial_dice_7: 0.29319/0.26477, loss_spatial_ce_7: 0.19243/0.17879, loss_grounding_bce_7: 0.01589/0.09026, loss_grounding_dice_7: 0.21282/0.19097, loss_grounding_ce_7: 0.25172/0.34745, loss_mask_ce_8: 0.75829/1.13330, loss_mask_bce_8: 0.06702/0.36550, loss_mask_dice_8: 0.75824/1.33089, loss_spatial_bce_8: 0.03495/0.13292, loss_spatial_dice_8: 0.27373/0.30433, loss_spatial_ce_8: 0.23190/0.23537, loss_grounding_bce_8: 0.01688/0.09385, loss_grounding_dice_8: 0.21232/0.20204, loss_grounding_ce_8: 0.21938/0.41718, loss_mask_ce_9: 2.77984/3.68558, loss_mask_bce_9: 0.06606/0.39251, loss_mask_dice_9: 0.81575/1.90449, loss_spatial_bce_9: 0.13769/0.33507, loss_spatial_dice_9: 0.62156/0.82339, loss_spatial_ce_9: 2.09733/1.50874, loss_grounding_bce_9: 0.01758/0.10538, loss_grounding_dice_9: 0.18702/0.28151, loss_grounding_ce_9: 0.44803/0.68590] items per batch[64] items per second[0.23] total items[2080000] mini batches[ 32500] memory[7341] epoch remaining[0:18:03]
INFO:trainer.default_trainer:epochs[    17] optim steps[32600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44185/0.90910, loss_mask_bce_0: 0.17107/0.33529, loss_mask_dice_0: 0.19688/1.16660, loss_spatial_bce_0: 0.14123/0.08991, loss_spatial_dice_0: 0.13302/0.21486, loss_spatial_ce_0: 0.00125/0.07163, loss_grounding_bce_0: 0.04366/0.08634, loss_grounding_dice_0: 0.03597/0.17926, loss_grounding_ce_0: 0.16768/0.27613, loss_mask_ce_1: 1.48290/0.90966, loss_mask_bce_1: 0.16717/0.33610, loss_mask_dice_1: 0.18898/1.17365, loss_spatial_bce_1: 0.12312/0.09067, loss_spatial_dice_1: 0.12300/0.21908, loss_spatial_ce_1: 0.00124/0.07730, loss_grounding_bce_1: 0.04226/0.08651, loss_grounding_dice_1: 0.03725/0.18015, loss_grounding_ce_1: 0.16510/0.27765, loss_mask_ce_2: 1.34828/0.91726, loss_mask_bce_2: 0.17269/0.33653, loss_mask_dice_2: 0.19346/1.17307, loss_spatial_bce_2: 0.14674/0.09105, loss_spatial_dice_2: 0.13060/0.22011, loss_spatial_ce_2: 0.00105/0.08099, loss_grounding_bce_2: 0.04361/0.08655, loss_grounding_dice_2: 0.03736/0.17971, loss_grounding_ce_2: 0.16583/0.28091, loss_mask_ce_3: 1.30921/0.92623, loss_mask_bce_3: 0.16940/0.33737, loss_mask_dice_3: 0.18704/1.17024, loss_spatial_bce_3: 0.12776/0.09189, loss_spatial_dice_3: 0.12694/0.22069, loss_spatial_ce_3: 0.00222/0.08474, loss_grounding_bce_3: 0.04301/0.08679, loss_grounding_dice_3: 0.03636/0.17957, loss_grounding_ce_3: 0.18343/0.28267, loss_mask_ce_4: 1.40388/0.92507, loss_mask_bce_4: 0.16322/0.33927, loss_mask_dice_4: 0.16500/1.19336, loss_spatial_bce_4: 0.14205/0.09611, loss_spatial_dice_4: 0.13648/0.23171, loss_spatial_ce_4: 0.00257/0.10111, loss_grounding_bce_4: 0.05403/0.08727, loss_grounding_dice_4: 0.05158/0.18236, loss_grounding_ce_4: 0.17499/0.28512, loss_mask_ce_5: 1.24728/0.94013, loss_mask_bce_5: 0.15173/0.34151, loss_mask_dice_5: 0.16767/1.19935, loss_spatial_bce_5: 0.16052/0.09760, loss_spatial_dice_5: 0.16271/0.23515, loss_spatial_ce_5: 0.00338/0.11543, loss_grounding_bce_5: 0.04127/0.08767, loss_grounding_dice_5: 0.03639/0.18356, loss_grounding_ce_5: 0.17304/0.29770, loss_mask_ce_6: 1.19710/0.97775, loss_mask_bce_6: 0.17266/0.34425, loss_mask_dice_6: 0.19354/1.20184, loss_spatial_bce_6: 0.15955/0.10324, loss_spatial_dice_6: 0.15767/0.23745, loss_spatial_ce_6: 0.05538/0.14085, loss_grounding_bce_6: 0.04123/0.08841, loss_grounding_dice_6: 0.03662/0.18372, loss_grounding_ce_6: 0.15558/0.31458, loss_mask_ce_7: 1.46954/1.02227, loss_mask_bce_7: 0.18905/0.35204, loss_mask_dice_7: 0.20558/1.25725, loss_spatial_bce_7: 0.11919/0.11189, loss_spatial_dice_7: 0.11486/0.26479, loss_spatial_ce_7: 0.11177/0.17878, loss_grounding_bce_7: 0.04226/0.09033, loss_grounding_dice_7: 0.03513/0.19102, loss_grounding_ce_7: 0.15267/0.34751, loss_mask_ce_8: 1.42539/1.13341, loss_mask_bce_8: 0.15967/0.36562, loss_mask_dice_8: 0.19670/1.33171, loss_spatial_bce_8: 0.15501/0.13289, loss_spatial_dice_8: 0.12633/0.30436, loss_spatial_ce_8: 0.19320/0.23529, loss_grounding_bce_8: 0.05519/0.09391, loss_grounding_dice_8: 0.05763/0.20211, loss_grounding_ce_8: 0.18969/0.41729, loss_mask_ce_9: 3.47622/3.68569, loss_mask_bce_9: 0.23530/0.39262, loss_mask_dice_9: 0.30961/1.90548, loss_spatial_bce_9: 0.49822/0.33504, loss_spatial_dice_9: 0.69367/0.82343, loss_spatial_ce_9: 1.27016/1.50878, loss_grounding_bce_9: 0.12087/0.10544, loss_grounding_dice_9: 0.15041/0.28157, loss_grounding_ce_9: 0.29399/0.68572] items per batch[64] items per second[0.23] total items[2086400] mini batches[ 32600] memory[7341] epoch remaining[0:13:23]
INFO:trainer.default_trainer:epochs[    17] optim steps[32700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44396/0.90875, loss_mask_bce_0: 0.30237/0.33514, loss_mask_dice_0: 0.31180/1.16633, loss_spatial_bce_0: 0.08388/0.08986, loss_spatial_dice_0: 0.11569/0.21479, loss_spatial_ce_0: 0.00086/0.07156, loss_grounding_bce_0: 0.14675/0.08634, loss_grounding_dice_0: 0.15137/0.17925, loss_grounding_ce_0: 0.03124/0.27605, loss_mask_ce_1: 0.43118/0.90928, loss_mask_bce_1: 0.27925/0.33596, loss_mask_dice_1: 0.29872/1.17333, loss_spatial_bce_1: 0.08639/0.09061, loss_spatial_dice_1: 0.11472/0.21901, loss_spatial_ce_1: 0.00036/0.07724, loss_grounding_bce_1: 0.14586/0.08651, loss_grounding_dice_1: 0.15184/0.18014, loss_grounding_ce_1: 0.03225/0.27757, loss_mask_ce_2: 0.43139/0.91685, loss_mask_bce_2: 0.28757/0.33638, loss_mask_dice_2: 0.28635/1.17283, loss_spatial_bce_2: 0.09328/0.09100, loss_spatial_dice_2: 0.12677/0.22005, loss_spatial_ce_2: 0.00038/0.08093, loss_grounding_bce_2: 0.14787/0.08654, loss_grounding_dice_2: 0.15218/0.17971, loss_grounding_ce_2: 0.03537/0.28090, loss_mask_ce_3: 0.42369/0.92582, loss_mask_bce_3: 0.32000/0.33722, loss_mask_dice_3: 0.31472/1.17001, loss_spatial_bce_3: 0.10154/0.09184, loss_spatial_dice_3: 0.14074/0.22062, loss_spatial_ce_3: 0.00259/0.08467, loss_grounding_bce_3: 0.15200/0.08678, loss_grounding_dice_3: 0.16214/0.17957, loss_grounding_ce_3: 0.03829/0.28263, loss_mask_ce_4: 0.47282/0.92463, loss_mask_bce_4: 0.29971/0.33912, loss_mask_dice_4: 0.31877/1.19311, loss_spatial_bce_4: 0.12048/0.09605, loss_spatial_dice_4: 0.15395/0.23164, loss_spatial_ce_4: 0.00405/0.10101, loss_grounding_bce_4: 0.15759/0.08727, loss_grounding_dice_4: 0.16750/0.18234, loss_grounding_ce_4: 0.04915/0.28512, loss_mask_ce_5: 0.49614/0.93975, loss_mask_bce_5: 0.34228/0.34136, loss_mask_dice_5: 0.31758/1.19906, loss_spatial_bce_5: 0.11670/0.09754, loss_spatial_dice_5: 0.14468/0.23509, loss_spatial_ce_5: 0.03249/0.11536, loss_grounding_bce_5: 0.18223/0.08766, loss_grounding_dice_5: 0.16768/0.18354, loss_grounding_ce_5: 0.06160/0.29770, loss_mask_ce_6: 0.52691/0.97737, loss_mask_bce_6: 0.29991/0.34409, loss_mask_dice_6: 0.31376/1.20156, loss_spatial_bce_6: 0.12987/0.10318, loss_spatial_dice_6: 0.15557/0.23738, loss_spatial_ce_6: 0.06655/0.14079, loss_grounding_bce_6: 0.16501/0.08839, loss_grounding_dice_6: 0.16565/0.18371, loss_grounding_ce_6: 0.10864/0.31449, loss_mask_ce_7: 0.53984/1.02192, loss_mask_bce_7: 0.28014/0.35187, loss_mask_dice_7: 0.31910/1.25697, loss_spatial_bce_7: 0.11053/0.11183, loss_spatial_dice_7: 0.19854/0.26474, loss_spatial_ce_7: 0.04788/0.17871, loss_grounding_bce_7: 0.15749/0.09032, loss_grounding_dice_7: 0.16595/0.19100, loss_grounding_ce_7: 0.09730/0.34749, loss_mask_ce_8: 0.64058/1.13299, loss_mask_bce_8: 0.24803/0.36544, loss_mask_dice_8: 0.31114/1.33137, loss_spatial_bce_8: 0.12173/0.13284, loss_spatial_dice_8: 0.15515/0.30428, loss_spatial_ce_8: 0.24078/0.23525, loss_grounding_bce_8: 0.12605/0.09388, loss_grounding_dice_8: 0.16829/0.20209, loss_grounding_ce_8: 0.11154/0.41734, loss_mask_ce_9: 3.36276/3.68519, loss_mask_bce_9: 0.44992/0.39243, loss_mask_dice_9: 0.69887/1.90477, loss_spatial_bce_9: 0.56147/0.33499, loss_spatial_dice_9: 0.82508/0.82340, loss_spatial_ce_9: 0.96510/1.50881, loss_grounding_bce_9: 0.26845/0.10542, loss_grounding_dice_9: 0.41177/0.28151, loss_grounding_ce_9: 0.34370/0.68576] items per batch[64] items per second[0.22] total items[2092800] mini batches[ 32700] memory[7341] epoch remaining[0:08:43]
INFO:trainer.default_trainer:epochs[    17] optim steps[32800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01781/0.90889, loss_mask_bce_0: 0.42240/0.33503, loss_mask_dice_0: 0.96476/1.16663, loss_spatial_bce_0: 0.07359/0.08981, loss_spatial_dice_0: 0.19857/0.21483, loss_spatial_ce_0: 0.04695/0.07154, loss_grounding_bce_0: 0.04899/0.08631, loss_grounding_dice_0: 0.08281/0.17931, loss_grounding_ce_0: 0.49382/0.27608, loss_mask_ce_1: 1.05325/0.90939, loss_mask_bce_1: 0.43120/0.33587, loss_mask_dice_1: 1.01986/1.17367, loss_spatial_bce_1: 0.07591/0.09056, loss_spatial_dice_1: 0.18987/0.21905, loss_spatial_ce_1: 0.03155/0.07724, loss_grounding_bce_1: 0.05375/0.08648, loss_grounding_dice_1: 0.08614/0.18019, loss_grounding_ce_1: 0.47230/0.27759, loss_mask_ce_2: 1.11469/0.91694, loss_mask_bce_2: 0.42132/0.33629, loss_mask_dice_2: 1.00730/1.17315, loss_spatial_bce_2: 0.07966/0.09094, loss_spatial_dice_2: 0.21942/0.22008, loss_spatial_ce_2: 0.03496/0.08092, loss_grounding_bce_2: 0.05623/0.08651, loss_grounding_dice_2: 0.08600/0.17978, loss_grounding_ce_2: 0.55462/0.28090, loss_mask_ce_3: 1.01195/0.92593, loss_mask_bce_3: 0.43254/0.33713, loss_mask_dice_3: 0.99437/1.17027, loss_spatial_bce_3: 0.08823/0.09178, loss_spatial_dice_3: 0.23512/0.22066, loss_spatial_ce_3: 0.02860/0.08464, loss_grounding_bce_3: 0.05225/0.08676, loss_grounding_dice_3: 0.08421/0.17963, loss_grounding_ce_3: 0.36617/0.28260, loss_mask_ce_4: 1.09919/0.92476, loss_mask_bce_4: 0.43900/0.33902, loss_mask_dice_4: 0.99739/1.19343, loss_spatial_bce_4: 0.07699/0.09600, loss_spatial_dice_4: 0.22944/0.23169, loss_spatial_ce_4: 0.02072/0.10095, loss_grounding_bce_4: 0.05947/0.08724, loss_grounding_dice_4: 0.08623/0.18240, loss_grounding_ce_4: 0.30126/0.28510, loss_mask_ce_5: 1.19528/0.93983, loss_mask_bce_5: 0.43715/0.34126, loss_mask_dice_5: 1.04861/1.19941, loss_spatial_bce_5: 0.07826/0.09748, loss_spatial_dice_5: 0.22976/0.23514, loss_spatial_ce_5: 0.10663/0.11532, loss_grounding_bce_5: 0.05722/0.08763, loss_grounding_dice_5: 0.07775/0.18360, loss_grounding_ce_5: 0.52480/0.29771, loss_mask_ce_6: 1.34801/0.97756, loss_mask_bce_6: 0.46676/0.34398, loss_mask_dice_6: 1.08234/1.20185, loss_spatial_bce_6: 0.08069/0.10313, loss_spatial_dice_6: 0.22814/0.23743, loss_spatial_ce_6: 0.12936/0.14077, loss_grounding_bce_6: 0.05308/0.08836, loss_grounding_dice_6: 0.07740/0.18377, loss_grounding_ce_6: 0.46064/0.31442, loss_mask_ce_7: 1.45817/1.02212, loss_mask_bce_7: 0.45008/0.35176, loss_mask_dice_7: 1.10744/1.25725, loss_spatial_bce_7: 0.09711/0.11178, loss_spatial_dice_7: 0.26608/0.26480, loss_spatial_ce_7: 0.13159/0.17870, loss_grounding_bce_7: 0.05349/0.09028, loss_grounding_dice_7: 0.08016/0.19106, loss_grounding_ce_7: 0.70350/0.34748, loss_mask_ce_8: 1.45070/1.13314, loss_mask_bce_8: 0.53062/0.36534, loss_mask_dice_8: 1.36240/1.33167, loss_spatial_bce_8: 0.10429/0.13278, loss_spatial_dice_8: 0.30108/0.30434, loss_spatial_ce_8: 0.20482/0.23518, loss_grounding_bce_8: 0.05356/0.09385, loss_grounding_dice_8: 0.06931/0.20212, loss_grounding_ce_8: 0.90718/0.41730, loss_mask_ce_9: 3.49921/3.68491, loss_mask_bce_9: 0.55264/0.39226, loss_mask_dice_9: 1.99917/1.90488, loss_spatial_bce_9: 0.27704/0.33486, loss_spatial_dice_9: 0.90742/0.82344, loss_spatial_ce_9: 1.67741/1.50895, loss_grounding_bce_9: 0.07970/0.10537, loss_grounding_dice_9: 0.09941/0.28155, loss_grounding_ce_9: 3.31876/0.68567] items per batch[64] items per second[0.23] total items[2099200] mini batches[ 32800] memory[7341] epoch remaining[0:04:01]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00032886.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0028 s/iter. Inference: 0.2193 s/iter. Eval: 0.0960 s/iter. Total: 0.3181 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2237 s/iter. Eval: 0.0841 s/iter. Total: 0.3108 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2261 s/iter. Eval: 0.0804 s/iter. Total: 0.3096 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2254 s/iter. Eval: 0.0772 s/iter. Total: 0.3058 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2239 s/iter. Eval: 0.0764 s/iter. Total: 0.3036 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2265 s/iter. Eval: 0.0757 s/iter. Total: 0.3055 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0031 s/iter. Inference: 0.2277 s/iter. Eval: 0.0754 s/iter. Total: 0.3064 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 130/157. Dataloading: 0.0032 s/iter. Inference: 0.2276 s/iter. Eval: 0.0748 s/iter. Total: 0.3056 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2285 s/iter. Eval: 0.0747 s/iter. Total: 0.3065 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalg49ox0nk ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.663 | 82.025 | 59.715 |      133      |
| Things | 54.714 | 82.732 | 65.425 |      80       |
| Stuff  | 42.039 | 80.957 | 51.097 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.34s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.77 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=1.98s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.57 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.10 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.748 | 61.120 | 40.835 | 19.299 | 41.801 | 60.067 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.417 | bicycle      | 17.707 | car            | 37.056 |
| motorcycle    | 34.479 | airplane     | 57.149 | bus            | 65.551 |
| train         | 67.744 | truck        | 34.508 | boat           | 23.177 |
| traffic light | 25.362 | fire hydrant | 64.329 | stop sign      | 64.354 |
| parking meter | 43.346 | bench        | 20.325 | bird           | 29.502 |
| cat           | 73.412 | dog          | 65.675 | horse          | 47.015 |
| sheep         | 46.460 | cow          | 49.880 | elephant       | 60.462 |
| bear          | 77.532 | zebra        | 60.161 | giraffe        | 56.285 |
| backpack      | 17.577 | umbrella     | 49.195 | handbag        | 15.537 |
| tie           | 33.191 | suitcase     | 41.535 | frisbee        | 68.533 |
| skis          | 4.800  | snowboard    | 22.251 | sports ball    | 47.000 |
| kite          | 33.495 | baseball bat | 28.376 | baseball glove | 43.577 |
| skateboard    | 35.291 | surfboard    | 35.143 | tennis racket  | 55.821 |
| bottle        | 34.408 | wine glass   | 27.294 | cup            | 39.218 |
| fork          | 15.624 | knife        | 11.838 | spoon          | 13.830 |
| bowl          | 32.102 | banana       | 20.274 | apple          | 19.744 |
| sandwich      | 43.011 | orange       | 30.603 | broccoli       | 21.577 |
| carrot        | 20.224 | hot dog      | 21.785 | pizza          | 51.006 |
| donut         | 46.317 | cake         | 43.370 | chair          | 20.532 |
| couch         | 43.247 | potted plant | 17.689 | bed            | 40.559 |
| dining table  | 12.872 | toilet       | 65.551 | tv             | 61.205 |
| laptop        | 62.180 | mouse        | 59.425 | remote         | 32.037 |
| keyboard      | 47.666 | cell phone   | 37.432 | microwave      | 54.065 |
| oven          | 31.490 | toaster      | 33.328 | sink           | 36.672 |
| refrigerator  | 59.602 | book         | 8.890  | clock          | 52.334 |
| vase          | 33.733 | scissors     | 25.674 | teddy bear     | 50.773 |
| hair drier    | 6.891  | toothbrush   | 16.553 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.710
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.72454954986747, 'fwIoU': 69.17825659914884, 'IoU-person': 87.60095350930118, 'IoU-bicycle': 75.20093746690408, 'IoU-car': 69.50435840733611, 'IoU-motorcycle': 83.3691033610039, 'IoU-airplane': 83.72848557979718, 'IoU-bus': 85.39627414326564, 'IoU-train': 87.49503022162524, 'IoU-truck': 62.57707653470375, 'IoU-boat': 67.07492741006145, 'IoU-traffic light': 76.65876269515906, 'IoU-fire hydrant': 90.3064991586645, 'IoU-stop sign': 90.59257142604827, 'IoU-parking meter': 87.84962566209005, 'IoU-bench': 54.17604340095389, 'IoU-bird': 75.92917609375397, 'IoU-cat': 83.29549073261198, 'IoU-dog': 82.24101869655478, 'IoU-horse': 86.2979461749993, 'IoU-sheep': 85.24501461369475, 'IoU-cow': 81.94525192765393, 'IoU-elephant': 90.75866187085322, 'IoU-bear': 84.6156436149169, 'IoU-zebra': 91.58151100698954, 'IoU-giraffe': 88.17098475467739, 'IoU-backpack': 36.547231671001185, 'IoU-umbrella': 74.67645449147474, 'IoU-handbag': 34.76490633151227, 'IoU-tie': 70.65116464881368, 'IoU-suitcase': 79.47208854294868, 'IoU-frisbee': 81.06081059868772, 'IoU-skis': 48.82611478356159, 'IoU-snowboard': 68.66974920628157, 'IoU-sports ball': 67.46100238083879, 'IoU-kite': 64.85990841536648, 'IoU-baseball bat': 60.599868020520674, 'IoU-baseball glove': 77.6947046357948, 'IoU-skateboard': 63.918505290086344, 'IoU-surfboard': 80.43470095225632, 'IoU-tennis racket': 75.87827653890749, 'IoU-bottle': 68.44687472431714, 'IoU-wine glass': 71.82722091738822, 'IoU-cup': 67.60549855745452, 'IoU-fork': 54.86124575635913, 'IoU-knife': 47.107133704300495, 'IoU-spoon': 48.1919699940329, 'IoU-bowl': 55.222793869522214, 'IoU-banana': 83.24887682068865, 'IoU-apple': 55.2104045481304, 'IoU-sandwich': 62.79571325418184, 'IoU-orange': 80.4613587640397, 'IoU-broccoli': 67.85727372727317, 'IoU-carrot': 64.66504808379975, 'IoU-hot dog': 65.02499643464348, 'IoU-pizza': 86.13713332520439, 'IoU-donut': 65.13548009246455, 'IoU-cake': 68.12229156923712, 'IoU-chair': 53.981028784913335, 'IoU-couch': 67.82136561492203, 'IoU-potted plant': 34.38729416915782, 'IoU-bed': 65.43151875260365, 'IoU-dining table': 51.19237023904947, 'IoU-toilet': 87.43715211076243, 'IoU-tv': 71.79341547597839, 'IoU-laptop': 70.80192161463815, 'IoU-mouse': 69.86456080500889, 'IoU-remote': 48.3026061799179, 'IoU-keyboard': 65.76846515585876, 'IoU-cell phone': 66.69525094158453, 'IoU-microwave': 56.10412455887791, 'IoU-oven': 68.33741917736997, 'IoU-toaster': 58.43987749447024, 'IoU-sink': 70.91786680483652, 'IoU-refrigerator': 80.14298446519612, 'IoU-book': 50.15722283470478, 'IoU-clock': 74.74786180127728, 'IoU-vase': 69.35301909766359, 'IoU-scissors': 54.4012099746088, 'IoU-teddy bear': 77.01522116156679, 'IoU-hair drier': 29.774889765606872, 'IoU-toothbrush': 49.87773903989651, 'IoU-banner': 41.187732757171254, 'IoU-blanket': 15.53968872256256, 'IoU-bridge': 39.74080851756325, 'IoU-cardboard': 48.77290959232005, 'IoU-counter': 29.920040360948846, 'IoU-curtain': 64.08955680026396, 'IoU-door-stuff': 42.479989853879076, 'IoU-floor-wood': 61.344193812599535, 'IoU-flower': 44.41006337627293, 'IoU-fruit': 36.95279408549108, 'IoU-gravel': 27.521622692136315, 'IoU-house': 23.51674150114233, 'IoU-light': 40.79466169892669, 'IoU-mirror-stuff': 55.49685223044703, 'IoU-net': 37.136109113032525, 'IoU-pillow': 12.976203967498872, 'IoU-platform': 30.812301130629503, 'IoU-playingfield': 69.31781371039766, 'IoU-railroad': 60.3144905595704, 'IoU-river': 49.65736948578153, 'IoU-road': 66.63015339713975, 'IoU-roof': 16.0149800390979, 'IoU-sand': 63.59798511143696, 'IoU-sea': 85.01644220365439, 'IoU-shelf': 36.25774283928431, 'IoU-snow': 88.73166027561555, 'IoU-stairs': 23.41113482575709, 'IoU-tent': 7.8445408803090855, 'IoU-towel': 35.09965487155039, 'IoU-wall-brick': 46.38122460679007, 'IoU-wall-stone': 28.973249618594348, 'IoU-wall-tile': 64.79633338347354, 'IoU-wall-wood': 37.58688809438563, 'IoU-water-other': 20.401918880246786, 'IoU-window-blind': 43.66722055055626, 'IoU-window-other': 45.00203525791183, 'IoU-tree-merged': 80.90099230558863, 'IoU-fence-merged': 50.111725296625366, 'IoU-ceiling-merged': 66.73108213058104, 'IoU-sky-other-merged': 93.3365067793608, 'IoU-cabinet-merged': 58.07517145684077, 'IoU-table-merged': 39.00257915473148, 'IoU-floor-other-merged': 50.17016457759497, 'IoU-pavement-merged': 55.79078986913796, 'IoU-mountain-merged': 56.15111241776178, 'IoU-grass-merged': 71.45150882941115, 'IoU-dirt-merged': 47.09634382938363, 'IoU-paper-merged': 34.59622105733081, 'IoU-food-other-merged': 41.0369134238678, 'IoU-building-other-merged': 57.82881213348593, 'IoU-rock-merged': 61.35697250400823, 'IoU-wall-other-merged': 64.46545155267755, 'IoU-rug-merged': 65.07309887436524, 'mACC': 73.33996990573439, 'pACC': 80.44179971380046, 'ACC-person': 92.48850899012847, 'ACC-bicycle': 85.40135501120524, 'ACC-car': 86.17707209070093, 'ACC-motorcycle': 90.5544095004258, 'ACC-airplane': 90.48508664306144, 'ACC-bus': 91.66401673486563, 'ACC-train': 94.5135070946287, 'ACC-truck': 73.48183738872679, 'ACC-boat': 78.44215458914098, 'ACC-traffic light': 89.73591521960087, 'ACC-fire hydrant': 95.24861542271775, 'ACC-stop sign': 94.02225753085305, 'ACC-parking meter': 91.91127204299544, 'ACC-bench': 68.75333001172163, 'ACC-bird': 79.89515390409669, 'ACC-cat': 92.17073041943576, 'ACC-dog': 85.94484666401209, 'ACC-horse': 92.64318763894454, 'ACC-sheep': 88.59568560272587, 'ACC-cow': 86.85717284749536, 'ACC-elephant': 93.4572940776231, 'ACC-bear': 86.90065631289089, 'ACC-zebra': 94.17168095626465, 'ACC-giraffe': 92.68880567816221, 'ACC-backpack': 58.59381162303573, 'ACC-umbrella': 82.74943867765592, 'ACC-handbag': 48.84889104964129, 'ACC-tie': 79.81163322771265, 'ACC-suitcase': 88.65896487435667, 'ACC-frisbee': 93.892, 'ACC-skis': 70.44058371976544, 'ACC-snowboard': 78.92875511253705, 'ACC-sports ball': 80.39972710639749, 'ACC-kite': 74.42052459230956, 'ACC-baseball bat': 85.31718975941979, 'ACC-baseball glove': 89.29155588220459, 'ACC-skateboard': 70.20503521237393, 'ACC-surfboard': 89.69790817932825, 'ACC-tennis racket': 81.9219089590371, 'ACC-bottle': 84.73233978434361, 'ACC-wine glass': 87.30730177150006, 'ACC-cup': 82.89163074262424, 'ACC-fork': 68.67534137936899, 'ACC-knife': 58.590166336923865, 'ACC-spoon': 69.70986078743265, 'ACC-bowl': 67.43922725701377, 'ACC-banana': 90.2724566165615, 'ACC-apple': 64.75041747460904, 'ACC-sandwich': 77.18221151906167, 'ACC-orange': 89.16024990569026, 'ACC-broccoli': 77.4606535750661, 'ACC-carrot': 75.99891132383313, 'ACC-hot dog': 72.7699484484419, 'ACC-pizza': 93.93131591415828, 'ACC-donut': 83.3154310564336, 'ACC-cake': 77.13174648311679, 'ACC-chair': 68.00013724079629, 'ACC-couch': 83.32621037943655, 'ACC-potted plant': 49.14061679853147, 'ACC-bed': 73.99678424149461, 'ACC-dining table': 69.82593366934225, 'ACC-toilet': 91.74388466108505, 'ACC-tv': 84.3376621066592, 'ACC-laptop': 84.49451317038361, 'ACC-mouse': 86.5841296638998, 'ACC-remote': 73.06083355430651, 'ACC-keyboard': 73.95087852518836, 'ACC-cell phone': 77.30912330239315, 'ACC-microwave': 66.75753997884654, 'ACC-oven': 77.46539264764806, 'ACC-toaster': 65.8083455364108, 'ACC-sink': 81.77556458191943, 'ACC-refrigerator': 90.45243364907765, 'ACC-book': 68.56301308453111, 'ACC-clock': 81.10471593655409, 'ACC-vase': 82.12378740215583, 'ACC-scissors': 60.1489546318486, 'ACC-teddy bear': 85.46694700153125, 'ACC-hair drier': 45.647966299490506, 'ACC-toothbrush': 82.39315496872828, 'ACC-banner': 75.25800223609446, 'ACC-blanket': 27.430236529064047, 'ACC-bridge': 54.41943561248641, 'ACC-cardboard': 60.3016140759052, 'ACC-counter': 57.93456568604213, 'ACC-curtain': 75.86237972159711, 'ACC-door-stuff': 61.747178295300785, 'ACC-floor-wood': 81.1157196020011, 'ACC-flower': 70.46999073625439, 'ACC-fruit': 56.86408277219319, 'ACC-gravel': 36.13577784310307, 'ACC-house': 27.647521495461135, 'ACC-light': 57.99863865176281, 'ACC-mirror-stuff': 69.7514325421426, 'ACC-net': 63.09607740110364, 'ACC-pillow': 25.996256530611706, 'ACC-platform': 52.72566102193888, 'ACC-playingfield': 86.65558572989538, 'ACC-railroad': 77.27633825327354, 'ACC-river': 81.42759139444138, 'ACC-road': 84.28303571010136, 'ACC-roof': 21.780162736781204, 'ACC-sand': 71.72773602758365, 'ACC-sea': 91.01089777575395, 'ACC-shelf': 58.797901622734905, 'ACC-snow': 94.315721563619, 'ACC-stairs': 37.61581024186438, 'ACC-tent': 9.303419750659735, 'ACC-towel': 44.99069375317053, 'ACC-wall-brick': 63.3159546812489, 'ACC-wall-stone': 36.75175487705095, 'ACC-wall-tile': 78.23823114125632, 'ACC-wall-wood': 54.65361485171136, 'ACC-water-other': 27.04861698995993, 'ACC-window-blind': 52.46201676153491, 'ACC-window-other': 68.63973384182071, 'ACC-tree-merged': 88.55459810367752, 'ACC-fence-merged': 69.76459397490115, 'ACC-ceiling-merged': 81.95008018440618, 'ACC-sky-other-merged': 97.03966502916863, 'ACC-cabinet-merged': 75.64555562348426, 'ACC-table-merged': 55.64031244148529, 'ACC-floor-other-merged': 61.969089639282856, 'ACC-pavement-merged': 67.82489391414005, 'ACC-mountain-merged': 66.71685820071687, 'ACC-grass-merged': 83.21926675449338, 'ACC-dirt-merged': 71.6205727964936, 'ACC-paper-merged': 51.42071666893013, 'ACC-food-other-merged': 59.8553098097159, 'ACC-building-other-merged': 70.50910461840564, 'ACC-rock-merged': 81.21034532202609, 'ACC-wall-other-merged': 82.06750469865226, 'ACC-rug-merged': 79.97792746853297})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1558 s/iter. Inference: 0.3996 s/iter. Eval: 0.0000 s/iter. Total: 0.5555 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1573 s/iter. Inference: 0.4543 s/iter. Eval: 0.0000 s/iter. Total: 0.6117 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1714 s/iter. Inference: 0.5396 s/iter. Eval: 0.0000 s/iter. Total: 0.7111 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1706 s/iter. Inference: 0.6308 s/iter. Eval: 0.0000 s/iter. Total: 0.8015 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1689 s/iter. Inference: 0.5927 s/iter. Eval: 0.0000 s/iter. Total: 0.7617 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1678 s/iter. Inference: 0.6372 s/iter. Eval: 0.0000 s/iter. Total: 0.8052 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4869768803043606, 'noc@0.8': 2.883523558677202, 'noc@0.85': 3.539654667837284, 'noc@0.9': 4.533801580333626, 'miou@iter1': 0.8334861244065461}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0018 s/iter. Inference: 0.1036 s/iter. Eval: 0.0008 s/iter. Total: 0.1062 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 68.67469787597656, 'precision@0.6': 65.91527557373047, 'precision@0.7': 61.173728942871094, 'precision@0.8': 50.87446594238281, 'precision@0.9': 26.23396873474121, 'cIoU': 54.395751953125, 'mIoU': 60.593421936035156}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.663216882245855, 'SQ': 82.02454279149893, 'RQ': 59.715081640290656, 'PQ_th': 54.71449871073315, 'SQ_th': 82.73211958399165, 'RQ_th': 65.42478074623097, 'PQ_st': 42.03864053735942, 'SQ_st': 80.95650235000058, 'RQ_st': 51.0966678954751}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.74795670429665, 'AP50': 61.1203896150747, 'AP75': 40.835176796553, 'APs': 19.299121367777357, 'APm': 41.80143858526929, 'APl': 60.06725858121767, 'AP-person': 44.41688663603165, 'AP-bicycle': 17.70695307057033, 'AP-car': 37.05589317762231, 'AP-motorcycle': 34.47934731519805, 'AP-airplane': 57.1487112025768, 'AP-bus': 65.55128720063689, 'AP-train': 67.7436676225676, 'AP-truck': 34.50842001686775, 'AP-boat': 23.176818472373263, 'AP-traffic light': 25.361560715828503, 'AP-fire hydrant': 64.32911257060192, 'AP-stop sign': 64.35402308741884, 'AP-parking meter': 43.34625125106517, 'AP-bench': 20.32506784629346, 'AP-bird': 29.501815541617688, 'AP-cat': 73.41173471591546, 'AP-dog': 65.67473754693629, 'AP-horse': 47.015181888535075, 'AP-sheep': 46.459554351445234, 'AP-cow': 49.879879047052476, 'AP-elephant': 60.46203233337387, 'AP-bear': 77.5317418040318, 'AP-zebra': 60.1608582102955, 'AP-giraffe': 56.285056701153394, 'AP-backpack': 17.57749666118129, 'AP-umbrella': 49.19476394142877, 'AP-handbag': 15.53737815074046, 'AP-tie': 33.19115580070829, 'AP-suitcase': 41.53538015181672, 'AP-frisbee': 68.53275941366796, 'AP-skis': 4.800225990016263, 'AP-snowboard': 22.25113695168163, 'AP-sports ball': 46.9997774498564, 'AP-kite': 33.4945853488861, 'AP-baseball bat': 28.37570522387146, 'AP-baseball glove': 43.577287457201344, 'AP-skateboard': 35.29062426690416, 'AP-surfboard': 35.143112702288335, 'AP-tennis racket': 55.82125069971622, 'AP-bottle': 34.40765123367875, 'AP-wine glass': 27.293846936417783, 'AP-cup': 39.218289761087505, 'AP-fork': 15.623799600425665, 'AP-knife': 11.838251979887927, 'AP-spoon': 13.829602743936723, 'AP-bowl': 32.10179845367241, 'AP-banana': 20.27407557328663, 'AP-apple': 19.743831374413194, 'AP-sandwich': 43.01084518434162, 'AP-orange': 30.602960915764164, 'AP-broccoli': 21.577351345232138, 'AP-carrot': 20.223789460681857, 'AP-hot dog': 21.785494049786713, 'AP-pizza': 51.00598980174721, 'AP-donut': 46.3168643251001, 'AP-cake': 43.37034899033871, 'AP-chair': 20.53216194903163, 'AP-couch': 43.247421503125025, 'AP-potted plant': 17.689279697797748, 'AP-bed': 40.55923236302989, 'AP-dining table': 12.871867919936792, 'AP-toilet': 65.55079356106, 'AP-tv': 61.20531708643043, 'AP-laptop': 62.18040754662131, 'AP-mouse': 59.425416330412205, 'AP-remote': 32.03681900607432, 'AP-keyboard': 47.66594440348623, 'AP-cell phone': 37.432237098834705, 'AP-microwave': 54.064576488106674, 'AP-oven': 31.489959650527727, 'AP-toaster': 33.32835076735484, 'AP-sink': 36.67163343019044, 'AP-refrigerator': 59.6016801871754, 'AP-book': 8.890405888605418, 'AP-clock': 52.334109495210555, 'AP-vase': 33.7334955734098, 'AP-scissors': 25.674221519597186, 'AP-teddy bear': 50.77286832267561, 'AP-hair drier': 6.8910514890498344, 'AP-toothbrush': 16.553260800214343}), ('sem_seg', {'mIoU': 60.72454954986747, 'fwIoU': 69.17825659914884, 'IoU-person': 87.60095350930118, 'IoU-bicycle': 75.20093746690408, 'IoU-car': 69.50435840733611, 'IoU-motorcycle': 83.3691033610039, 'IoU-airplane': 83.72848557979718, 'IoU-bus': 85.39627414326564, 'IoU-train': 87.49503022162524, 'IoU-truck': 62.57707653470375, 'IoU-boat': 67.07492741006145, 'IoU-traffic light': 76.65876269515906, 'IoU-fire hydrant': 90.3064991586645, 'IoU-stop sign': 90.59257142604827, 'IoU-parking meter': 87.84962566209005, 'IoU-bench': 54.17604340095389, 'IoU-bird': 75.92917609375397, 'IoU-cat': 83.29549073261198, 'IoU-dog': 82.24101869655478, 'IoU-horse': 86.2979461749993, 'IoU-sheep': 85.24501461369475, 'IoU-cow': 81.94525192765393, 'IoU-elephant': 90.75866187085322, 'IoU-bear': 84.6156436149169, 'IoU-zebra': 91.58151100698954, 'IoU-giraffe': 88.17098475467739, 'IoU-backpack': 36.547231671001185, 'IoU-umbrella': 74.67645449147474, 'IoU-handbag': 34.76490633151227, 'IoU-tie': 70.65116464881368, 'IoU-suitcase': 79.47208854294868, 'IoU-frisbee': 81.06081059868772, 'IoU-skis': 48.82611478356159, 'IoU-snowboard': 68.66974920628157, 'IoU-sports ball': 67.46100238083879, 'IoU-kite': 64.85990841536648, 'IoU-baseball bat': 60.599868020520674, 'IoU-baseball glove': 77.6947046357948, 'IoU-skateboard': 63.918505290086344, 'IoU-surfboard': 80.43470095225632, 'IoU-tennis racket': 75.87827653890749, 'IoU-bottle': 68.44687472431714, 'IoU-wine glass': 71.82722091738822, 'IoU-cup': 67.60549855745452, 'IoU-fork': 54.86124575635913, 'IoU-knife': 47.107133704300495, 'IoU-spoon': 48.1919699940329, 'IoU-bowl': 55.222793869522214, 'IoU-banana': 83.24887682068865, 'IoU-apple': 55.2104045481304, 'IoU-sandwich': 62.79571325418184, 'IoU-orange': 80.4613587640397, 'IoU-broccoli': 67.85727372727317, 'IoU-carrot': 64.66504808379975, 'IoU-hot dog': 65.02499643464348, 'IoU-pizza': 86.13713332520439, 'IoU-donut': 65.13548009246455, 'IoU-cake': 68.12229156923712, 'IoU-chair': 53.981028784913335, 'IoU-couch': 67.82136561492203, 'IoU-potted plant': 34.38729416915782, 'IoU-bed': 65.43151875260365, 'IoU-dining table': 51.19237023904947, 'IoU-toilet': 87.43715211076243, 'IoU-tv': 71.79341547597839, 'IoU-laptop': 70.80192161463815, 'IoU-mouse': 69.86456080500889, 'IoU-remote': 48.3026061799179, 'IoU-keyboard': 65.76846515585876, 'IoU-cell phone': 66.69525094158453, 'IoU-microwave': 56.10412455887791, 'IoU-oven': 68.33741917736997, 'IoU-toaster': 58.43987749447024, 'IoU-sink': 70.91786680483652, 'IoU-refrigerator': 80.14298446519612, 'IoU-book': 50.15722283470478, 'IoU-clock': 74.74786180127728, 'IoU-vase': 69.35301909766359, 'IoU-scissors': 54.4012099746088, 'IoU-teddy bear': 77.01522116156679, 'IoU-hair drier': 29.774889765606872, 'IoU-toothbrush': 49.87773903989651, 'IoU-banner': 41.187732757171254, 'IoU-blanket': 15.53968872256256, 'IoU-bridge': 39.74080851756325, 'IoU-cardboard': 48.77290959232005, 'IoU-counter': 29.920040360948846, 'IoU-curtain': 64.08955680026396, 'IoU-door-stuff': 42.479989853879076, 'IoU-floor-wood': 61.344193812599535, 'IoU-flower': 44.41006337627293, 'IoU-fruit': 36.95279408549108, 'IoU-gravel': 27.521622692136315, 'IoU-house': 23.51674150114233, 'IoU-light': 40.79466169892669, 'IoU-mirror-stuff': 55.49685223044703, 'IoU-net': 37.136109113032525, 'IoU-pillow': 12.976203967498872, 'IoU-platform': 30.812301130629503, 'IoU-playingfield': 69.31781371039766, 'IoU-railroad': 60.3144905595704, 'IoU-river': 49.65736948578153, 'IoU-road': 66.63015339713975, 'IoU-roof': 16.0149800390979, 'IoU-sand': 63.59798511143696, 'IoU-sea': 85.01644220365439, 'IoU-shelf': 36.25774283928431, 'IoU-snow': 88.73166027561555, 'IoU-stairs': 23.41113482575709, 'IoU-tent': 7.8445408803090855, 'IoU-towel': 35.09965487155039, 'IoU-wall-brick': 46.38122460679007, 'IoU-wall-stone': 28.973249618594348, 'IoU-wall-tile': 64.79633338347354, 'IoU-wall-wood': 37.58688809438563, 'IoU-water-other': 20.401918880246786, 'IoU-window-blind': 43.66722055055626, 'IoU-window-other': 45.00203525791183, 'IoU-tree-merged': 80.90099230558863, 'IoU-fence-merged': 50.111725296625366, 'IoU-ceiling-merged': 66.73108213058104, 'IoU-sky-other-merged': 93.3365067793608, 'IoU-cabinet-merged': 58.07517145684077, 'IoU-table-merged': 39.00257915473148, 'IoU-floor-other-merged': 50.17016457759497, 'IoU-pavement-merged': 55.79078986913796, 'IoU-mountain-merged': 56.15111241776178, 'IoU-grass-merged': 71.45150882941115, 'IoU-dirt-merged': 47.09634382938363, 'IoU-paper-merged': 34.59622105733081, 'IoU-food-other-merged': 41.0369134238678, 'IoU-building-other-merged': 57.82881213348593, 'IoU-rock-merged': 61.35697250400823, 'IoU-wall-other-merged': 64.46545155267755, 'IoU-rug-merged': 65.07309887436524, 'mACC': 73.33996990573439, 'pACC': 80.44179971380046, 'ACC-person': 92.48850899012847, 'ACC-bicycle': 85.40135501120524, 'ACC-car': 86.17707209070093, 'ACC-motorcycle': 90.5544095004258, 'ACC-airplane': 90.48508664306144, 'ACC-bus': 91.66401673486563, 'ACC-train': 94.5135070946287, 'ACC-truck': 73.48183738872679, 'ACC-boat': 78.44215458914098, 'ACC-traffic light': 89.73591521960087, 'ACC-fire hydrant': 95.24861542271775, 'ACC-stop sign': 94.02225753085305, 'ACC-parking meter': 91.91127204299544, 'ACC-bench': 68.75333001172163, 'ACC-bird': 79.89515390409669, 'ACC-cat': 92.17073041943576, 'ACC-dog': 85.94484666401209, 'ACC-horse': 92.64318763894454, 'ACC-sheep': 88.59568560272587, 'ACC-cow': 86.85717284749536, 'ACC-elephant': 93.4572940776231, 'ACC-bear': 86.90065631289089, 'ACC-zebra': 94.17168095626465, 'ACC-giraffe': 92.68880567816221, 'ACC-backpack': 58.59381162303573, 'ACC-umbrella': 82.74943867765592, 'ACC-handbag': 48.84889104964129, 'ACC-tie': 79.81163322771265, 'ACC-suitcase': 88.65896487435667, 'ACC-frisbee': 93.892, 'ACC-skis': 70.44058371976544, 'ACC-snowboard': 78.92875511253705, 'ACC-sports ball': 80.39972710639749, 'ACC-kite': 74.42052459230956, 'ACC-baseball bat': 85.31718975941979, 'ACC-baseball glove': 89.29155588220459, 'ACC-skateboard': 70.20503521237393, 'ACC-surfboard': 89.69790817932825, 'ACC-tennis racket': 81.9219089590371, 'ACC-bottle': 84.73233978434361, 'ACC-wine glass': 87.30730177150006, 'ACC-cup': 82.89163074262424, 'ACC-fork': 68.67534137936899, 'ACC-knife': 58.590166336923865, 'ACC-spoon': 69.70986078743265, 'ACC-bowl': 67.43922725701377, 'ACC-banana': 90.2724566165615, 'ACC-apple': 64.75041747460904, 'ACC-sandwich': 77.18221151906167, 'ACC-orange': 89.16024990569026, 'ACC-broccoli': 77.4606535750661, 'ACC-carrot': 75.99891132383313, 'ACC-hot dog': 72.7699484484419, 'ACC-pizza': 93.93131591415828, 'ACC-donut': 83.3154310564336, 'ACC-cake': 77.13174648311679, 'ACC-chair': 68.00013724079629, 'ACC-couch': 83.32621037943655, 'ACC-potted plant': 49.14061679853147, 'ACC-bed': 73.99678424149461, 'ACC-dining table': 69.82593366934225, 'ACC-toilet': 91.74388466108505, 'ACC-tv': 84.3376621066592, 'ACC-laptop': 84.49451317038361, 'ACC-mouse': 86.5841296638998, 'ACC-remote': 73.06083355430651, 'ACC-keyboard': 73.95087852518836, 'ACC-cell phone': 77.30912330239315, 'ACC-microwave': 66.75753997884654, 'ACC-oven': 77.46539264764806, 'ACC-toaster': 65.8083455364108, 'ACC-sink': 81.77556458191943, 'ACC-refrigerator': 90.45243364907765, 'ACC-book': 68.56301308453111, 'ACC-clock': 81.10471593655409, 'ACC-vase': 82.12378740215583, 'ACC-scissors': 60.1489546318486, 'ACC-teddy bear': 85.46694700153125, 'ACC-hair drier': 45.647966299490506, 'ACC-toothbrush': 82.39315496872828, 'ACC-banner': 75.25800223609446, 'ACC-blanket': 27.430236529064047, 'ACC-bridge': 54.41943561248641, 'ACC-cardboard': 60.3016140759052, 'ACC-counter': 57.93456568604213, 'ACC-curtain': 75.86237972159711, 'ACC-door-stuff': 61.747178295300785, 'ACC-floor-wood': 81.1157196020011, 'ACC-flower': 70.46999073625439, 'ACC-fruit': 56.86408277219319, 'ACC-gravel': 36.13577784310307, 'ACC-house': 27.647521495461135, 'ACC-light': 57.99863865176281, 'ACC-mirror-stuff': 69.7514325421426, 'ACC-net': 63.09607740110364, 'ACC-pillow': 25.996256530611706, 'ACC-platform': 52.72566102193888, 'ACC-playingfield': 86.65558572989538, 'ACC-railroad': 77.27633825327354, 'ACC-river': 81.42759139444138, 'ACC-road': 84.28303571010136, 'ACC-roof': 21.780162736781204, 'ACC-sand': 71.72773602758365, 'ACC-sea': 91.01089777575395, 'ACC-shelf': 58.797901622734905, 'ACC-snow': 94.315721563619, 'ACC-stairs': 37.61581024186438, 'ACC-tent': 9.303419750659735, 'ACC-towel': 44.99069375317053, 'ACC-wall-brick': 63.3159546812489, 'ACC-wall-stone': 36.75175487705095, 'ACC-wall-tile': 78.23823114125632, 'ACC-wall-wood': 54.65361485171136, 'ACC-water-other': 27.04861698995993, 'ACC-window-blind': 52.46201676153491, 'ACC-window-other': 68.63973384182071, 'ACC-tree-merged': 88.55459810367752, 'ACC-fence-merged': 69.76459397490115, 'ACC-ceiling-merged': 81.95008018440618, 'ACC-sky-other-merged': 97.03966502916863, 'ACC-cabinet-merged': 75.64555562348426, 'ACC-table-merged': 55.64031244148529, 'ACC-floor-other-merged': 61.969089639282856, 'ACC-pavement-merged': 67.82489391414005, 'ACC-mountain-merged': 66.71685820071687, 'ACC-grass-merged': 83.21926675449338, 'ACC-dirt-merged': 71.6205727964936, 'ACC-paper-merged': 51.42071666893013, 'ACC-food-other-merged': 59.8553098097159, 'ACC-building-other-merged': 70.50910461840564, 'ACC-rock-merged': 81.21034532202609, 'ACC-wall-other-merged': 82.06750469865226, 'ACC-rug-merged': 79.97792746853297})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4869768803043606, 'noc@0.8': 2.883523558677202, 'noc@0.85': 3.539654667837284, 'noc@0.9': 4.533801580333626, 'miou@iter1': 0.8334861244065461}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 68.67469787597656, 'precision@0.6': 65.91527557373047, 'precision@0.7': 61.173728942871094, 'precision@0.8': 50.87446594238281, 'precision@0.9': 26.23396873474121, 'cIoU': 54.395751953125, 'mIoU': 60.593421936035156}}}
INFO:trainer.default_trainer:This epoch takes 1:28:49.642339
INFO:trainer.default_trainer:PROGRESS: 36.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 18 training.
INFO:trainer.default_trainer:epochs[    18] optim steps[32900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65164/0.90891, loss_mask_bce_0: 0.04269/0.33504, loss_mask_dice_0: 0.23891/1.16695, loss_spatial_bce_0: 0.02329/0.08977, loss_spatial_dice_0: 0.11715/0.21481, loss_spatial_ce_0: 0.03403/0.07147, loss_grounding_bce_0: 0.01710/0.08631, loss_grounding_dice_0: 0.10755/0.17942, loss_grounding_ce_0: 0.17518/0.27609, loss_mask_ce_1: 0.63647/0.90940, loss_mask_bce_1: 0.04220/0.33588, loss_mask_dice_1: 0.23976/1.17399, loss_spatial_bce_1: 0.02200/0.09052, loss_spatial_dice_1: 0.10067/0.21903, loss_spatial_ce_1: 0.02408/0.07719, loss_grounding_bce_1: 0.01678/0.08647, loss_grounding_dice_1: 0.11567/0.18029, loss_grounding_ce_1: 0.17137/0.27770, loss_mask_ce_2: 0.68217/0.91700, loss_mask_bce_2: 0.04124/0.33629, loss_mask_dice_2: 0.20517/1.17350, loss_spatial_bce_2: 0.02157/0.09090, loss_spatial_dice_2: 0.09576/0.22004, loss_spatial_ce_2: 0.05246/0.08087, loss_grounding_bce_2: 0.01457/0.08652, loss_grounding_dice_2: 0.09163/0.17988, loss_grounding_ce_2: 0.18584/0.28096, loss_mask_ce_3: 0.77236/0.92599, loss_mask_bce_3: 0.04300/0.33713, loss_mask_dice_3: 0.22302/1.17061, loss_spatial_bce_3: 0.02273/0.09174, loss_spatial_dice_3: 0.11136/0.22063, loss_spatial_ce_3: 0.08236/0.08456, loss_grounding_bce_3: 0.01717/0.08676, loss_grounding_dice_3: 0.11888/0.17973, loss_grounding_ce_3: 0.21794/0.28263, loss_mask_ce_4: 0.78017/0.92485, loss_mask_bce_4: 0.04381/0.33903, loss_mask_dice_4: 0.21959/1.19372, loss_spatial_bce_4: 0.02506/0.09597, loss_spatial_dice_4: 0.10879/0.23167, loss_spatial_ce_4: 0.07889/0.10089, loss_grounding_bce_4: 0.02261/0.08724, loss_grounding_dice_4: 0.12538/0.18248, loss_grounding_ce_4: 0.23294/0.28522, loss_mask_ce_5: 0.83221/0.93992, loss_mask_bce_5: 0.04208/0.34128, loss_mask_dice_5: 0.22277/1.19981, loss_spatial_bce_5: 0.01984/0.09745, loss_spatial_dice_5: 0.09610/0.23512, loss_spatial_ce_5: 0.02722/0.11528, loss_grounding_bce_5: 0.01619/0.08764, loss_grounding_dice_5: 0.11289/0.18371, loss_grounding_ce_5: 0.21159/0.29777, loss_mask_ce_6: 0.66661/0.97768, loss_mask_bce_6: 0.04095/0.34400, loss_mask_dice_6: 0.20791/1.20220, loss_spatial_bce_6: 0.02153/0.10312, loss_spatial_dice_6: 0.10998/0.23742, loss_spatial_ce_6: 0.08427/0.14072, loss_grounding_bce_6: 0.01713/0.08837, loss_grounding_dice_6: 0.11091/0.18388, loss_grounding_ce_6: 0.29378/0.31441, loss_mask_ce_7: 0.60343/1.02221, loss_mask_bce_7: 0.04256/0.35178, loss_mask_dice_7: 0.23366/1.25766, loss_spatial_bce_7: 0.02652/0.11175, loss_spatial_dice_7: 0.12483/0.26479, loss_spatial_ce_7: 0.21582/0.17868, loss_grounding_bce_7: 0.01801/0.09028, loss_grounding_dice_7: 0.08592/0.19115, loss_grounding_ce_7: 0.17751/0.34740, loss_mask_ce_8: 0.71770/1.13325, loss_mask_bce_8: 0.05751/0.36537, loss_mask_dice_8: 0.30913/1.33215, loss_spatial_bce_8: 0.05349/0.13274, loss_spatial_dice_8: 0.18502/0.30433, loss_spatial_ce_8: 0.10036/0.23515, loss_grounding_bce_8: 0.02797/0.09385, loss_grounding_dice_8: 0.15603/0.20223, loss_grounding_ce_8: 0.17735/0.41727, loss_mask_ce_9: 2.81179/3.68540, loss_mask_bce_9: 0.04912/0.39231, loss_mask_dice_9: 0.32786/1.90550, loss_spatial_bce_9: 0.18557/0.33481, loss_spatial_dice_9: 0.68660/0.82345, loss_spatial_ce_9: 1.79825/1.50894, loss_grounding_bce_9: 0.02600/0.10538, loss_grounding_dice_9: 0.19317/0.28168, loss_grounding_ce_9: 0.41225/0.68553] items per batch[64] items per second[0.13] total items[2105600] mini batches[ 32900] memory[7341] epoch remaining[1:30:31]
INFO:trainer.default_trainer:epochs[    18] optim steps[33000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24052/0.90886, loss_mask_bce_0: 0.08358/0.33503, loss_mask_dice_0: 0.78596/1.16674, loss_spatial_bce_0: 0.03265/0.08977, loss_spatial_dice_0: 0.28210/0.21476, loss_spatial_ce_0: 0.03703/0.07141, loss_grounding_bce_0: 0.03304/0.08630, loss_grounding_dice_0: 0.34045/0.17941, loss_grounding_ce_0: 0.03377/0.27613, loss_mask_ce_1: 1.30184/0.90932, loss_mask_bce_1: 0.08092/0.33588, loss_mask_dice_1: 0.78792/1.17372, loss_spatial_bce_1: 0.02356/0.09051, loss_spatial_dice_1: 0.26580/0.21897, loss_spatial_ce_1: 0.09579/0.07716, loss_grounding_bce_1: 0.04333/0.08646, loss_grounding_dice_1: 0.37893/0.18027, loss_grounding_ce_1: 0.05283/0.27775, loss_mask_ce_2: 1.01943/0.91693, loss_mask_bce_2: 0.08338/0.33630, loss_mask_dice_2: 0.73490/1.17326, loss_spatial_bce_2: 0.02209/0.09089, loss_spatial_dice_2: 0.29946/0.21999, loss_spatial_ce_2: 0.07031/0.08083, loss_grounding_bce_2: 0.04483/0.08651, loss_grounding_dice_2: 0.34655/0.17987, loss_grounding_ce_2: 0.06443/0.28101, loss_mask_ce_3: 1.32062/0.92594, loss_mask_bce_3: 0.08776/0.33713, loss_mask_dice_3: 0.78240/1.17036, loss_spatial_bce_3: 0.01598/0.09174, loss_spatial_dice_3: 0.27502/0.22058, loss_spatial_ce_3: 0.06265/0.08449, loss_grounding_bce_3: 0.04150/0.08675, loss_grounding_dice_3: 0.33105/0.17973, loss_grounding_ce_3: 0.07335/0.28262, loss_mask_ce_4: 1.24872/0.92484, loss_mask_bce_4: 0.09047/0.33903, loss_mask_dice_4: 0.79610/1.19346, loss_spatial_bce_4: 0.02073/0.09596, loss_spatial_dice_4: 0.27338/0.23162, loss_spatial_ce_4: 0.06054/0.10083, loss_grounding_bce_4: 0.04300/0.08723, loss_grounding_dice_4: 0.35130/0.18247, loss_grounding_ce_4: 0.10325/0.28518, loss_mask_ce_5: 1.16908/0.93980, loss_mask_bce_5: 0.08533/0.34130, loss_mask_dice_5: 0.79495/1.19954, loss_spatial_bce_5: 0.02494/0.09744, loss_spatial_dice_5: 0.28540/0.23507, loss_spatial_ce_5: 0.08968/0.11522, loss_grounding_bce_5: 0.03894/0.08763, loss_grounding_dice_5: 0.40795/0.18370, loss_grounding_ce_5: 0.02752/0.29776, loss_mask_ce_6: 1.15418/0.97762, loss_mask_bce_6: 0.08636/0.34402, loss_mask_dice_6: 0.82985/1.20195, loss_spatial_bce_6: 0.03241/0.10312, loss_spatial_dice_6: 0.35086/0.23737, loss_spatial_ce_6: 0.19268/0.14067, loss_grounding_bce_6: 0.03221/0.08836, loss_grounding_dice_6: 0.34242/0.18388, loss_grounding_ce_6: 0.04502/0.31447, loss_mask_ce_7: 1.26527/1.02223, loss_mask_bce_7: 0.08045/0.35177, loss_mask_dice_7: 0.83576/1.25742, loss_spatial_bce_7: 0.02643/0.11174, loss_spatial_dice_7: 0.32937/0.26475, loss_spatial_ce_7: 0.14391/0.17860, loss_grounding_bce_7: 0.03749/0.09027, loss_grounding_dice_7: 0.37485/0.19116, loss_grounding_ce_7: 0.15551/0.34739, loss_mask_ce_8: 1.38225/1.13321, loss_mask_bce_8: 0.08892/0.36537, loss_mask_dice_8: 1.07224/1.33190, loss_spatial_bce_8: 0.03174/0.13273, loss_spatial_dice_8: 0.37014/0.30427, loss_spatial_ce_8: 0.24633/0.23505, loss_grounding_bce_8: 0.04011/0.09385, loss_grounding_dice_8: 0.38687/0.20225, loss_grounding_ce_8: 0.72747/0.41710, loss_mask_ce_9: 4.56865/3.68487, loss_mask_bce_9: 0.04733/0.39227, loss_mask_dice_9: 1.27953/1.90496, loss_spatial_bce_9: 0.04943/0.33481, loss_spatial_dice_9: 0.81286/0.82343, loss_spatial_ce_9: 1.62508/1.50890, loss_grounding_bce_9: 0.02070/0.10536, loss_grounding_dice_9: 0.44346/0.28164, loss_grounding_ce_9: 1.47533/0.68522] items per batch[64] items per second[0.23] total items[2112000] mini batches[ 33000] memory[7341] epoch remaining[1:20:26]
INFO:trainer.default_trainer:epochs[    18] optim steps[33100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84878/0.90881, loss_mask_bce_0: 0.56514/0.33501, loss_mask_dice_0: 0.50144/1.16655, loss_spatial_bce_0: 0.19068/0.08978, loss_spatial_dice_0: 0.19227/0.21474, loss_spatial_ce_0: 0.03865/0.07138, loss_grounding_bce_0: 0.02939/0.08630, loss_grounding_dice_0: 0.04272/0.17941, loss_grounding_ce_0: 0.07050/0.27617, loss_mask_ce_1: 0.98980/0.90927, loss_mask_bce_1: 0.46424/0.33585, loss_mask_dice_1: 0.48347/1.17354, loss_spatial_bce_1: 0.18308/0.09053, loss_spatial_dice_1: 0.19338/0.21895, loss_spatial_ce_1: 0.04190/0.07712, loss_grounding_bce_1: 0.03251/0.08646, loss_grounding_dice_1: 0.04032/0.18027, loss_grounding_ce_1: 0.06907/0.27780, loss_mask_ce_2: 1.24048/0.91683, loss_mask_bce_2: 0.38170/0.33626, loss_mask_dice_2: 0.45945/1.17306, loss_spatial_bce_2: 0.22519/0.09091, loss_spatial_dice_2: 0.19817/0.21997, loss_spatial_ce_2: 0.04502/0.08080, loss_grounding_bce_2: 0.03047/0.08650, loss_grounding_dice_2: 0.03923/0.17986, loss_grounding_ce_2: 0.05715/0.28105, loss_mask_ce_3: 1.28492/0.92586, loss_mask_bce_3: 0.37370/0.33709, loss_mask_dice_3: 0.45829/1.17018, loss_spatial_bce_3: 0.25344/0.09175, loss_spatial_dice_3: 0.19754/0.22056, loss_spatial_ce_3: 0.04699/0.08443, loss_grounding_bce_3: 0.03129/0.08675, loss_grounding_dice_3: 0.04089/0.17974, loss_grounding_ce_3: 0.06975/0.28267, loss_mask_ce_4: 1.31897/0.92482, loss_mask_bce_4: 0.51367/0.33900, loss_mask_dice_4: 0.45540/1.19322, loss_spatial_bce_4: 0.16235/0.09597, loss_spatial_dice_4: 0.20134/0.23161, loss_spatial_ce_4: 0.24944/0.10081, loss_grounding_bce_4: 0.03066/0.08722, loss_grounding_dice_4: 0.03966/0.18248, loss_grounding_ce_4: 0.11095/0.28522, loss_mask_ce_5: 1.15553/0.93975, loss_mask_bce_5: 0.58966/0.34127, loss_mask_dice_5: 0.49930/1.19936, loss_spatial_bce_5: 0.22699/0.09745, loss_spatial_dice_5: 0.21739/0.23507, loss_spatial_ce_5: 0.15486/0.11519, loss_grounding_bce_5: 0.03295/0.08762, loss_grounding_dice_5: 0.03936/0.18370, loss_grounding_ce_5: 0.08536/0.29786, loss_mask_ce_6: 1.16710/0.97753, loss_mask_bce_6: 0.60375/0.34399, loss_mask_dice_6: 0.51279/1.20180, loss_spatial_bce_6: 0.14765/0.10313, loss_spatial_dice_6: 0.18503/0.23737, loss_spatial_ce_6: 0.26003/0.14065, loss_grounding_bce_6: 0.03389/0.08836, loss_grounding_dice_6: 0.04435/0.18388, loss_grounding_ce_6: 0.07132/0.31453, loss_mask_ce_7: 1.38593/1.02227, loss_mask_bce_7: 0.46731/0.35174, loss_mask_dice_7: 0.46017/1.25726, loss_spatial_bce_7: 0.31144/0.11176, loss_spatial_dice_7: 0.23255/0.26475, loss_spatial_ce_7: 0.16480/0.17852, loss_grounding_bce_7: 0.03429/0.09027, loss_grounding_dice_7: 0.04799/0.19116, loss_grounding_ce_7: 0.14625/0.34754, loss_mask_ce_8: 1.08467/1.13322, loss_mask_bce_8: 0.53456/0.36534, loss_mask_dice_8: 0.52112/1.33167, loss_spatial_bce_8: 0.31867/0.13275, loss_spatial_dice_8: 0.25412/0.30426, loss_spatial_ce_8: 0.17148/0.23498, loss_grounding_bce_8: 0.03560/0.09385, loss_grounding_dice_8: 0.04828/0.20225, loss_grounding_ce_8: 0.07412/0.41718, loss_mask_ce_9: 3.43637/3.68516, loss_mask_bce_9: 0.54780/0.39225, loss_mask_dice_9: 0.81712/1.90474, loss_spatial_bce_9: 0.41747/0.33482, loss_spatial_dice_9: 0.83279/0.82339, loss_spatial_ce_9: 1.25580/1.50872, loss_grounding_bce_9: 0.05024/0.10536, loss_grounding_dice_9: 0.09028/0.28163, loss_grounding_ce_9: 0.52831/0.68528] items per batch[64] items per second[0.23] total items[2118400] mini batches[ 33100] memory[7341] epoch remaining[1:15:26]
INFO:trainer.default_trainer:epochs[    18] optim steps[33200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59121/0.90898, loss_mask_bce_0: 0.13226/0.33503, loss_mask_dice_0: 0.38919/1.16653, loss_spatial_bce_0: 0.04829/0.08975, loss_spatial_dice_0: 0.16350/0.21470, loss_spatial_ce_0: 0.01789/0.07132, loss_grounding_bce_0: 0.00491/0.08627, loss_grounding_dice_0: 0.03774/0.17939, loss_grounding_ce_0: 0.02758/0.27616, loss_mask_ce_1: 0.48683/0.90942, loss_mask_bce_1: 0.13593/0.33586, loss_mask_dice_1: 0.36559/1.17345, loss_spatial_bce_1: 0.04525/0.09051, loss_spatial_dice_1: 0.16537/0.21892, loss_spatial_ce_1: 0.01065/0.07706, loss_grounding_bce_1: 0.00602/0.08643, loss_grounding_dice_1: 0.04901/0.18027, loss_grounding_ce_1: 0.02589/0.27780, loss_mask_ce_2: 0.47083/0.91693, loss_mask_bce_2: 0.14107/0.33628, loss_mask_dice_2: 0.39157/1.17303, loss_spatial_bce_2: 0.04448/0.09088, loss_spatial_dice_2: 0.16753/0.21994, loss_spatial_ce_2: 0.03190/0.08076, loss_grounding_bce_2: 0.00698/0.08646, loss_grounding_dice_2: 0.05233/0.17987, loss_grounding_ce_2: 0.02069/0.28104, loss_mask_ce_3: 0.56899/0.92603, loss_mask_bce_3: 0.13956/0.33710, loss_mask_dice_3: 0.36763/1.17012, loss_spatial_bce_3: 0.04917/0.09173, loss_spatial_dice_3: 0.17161/0.22053, loss_spatial_ce_3: 0.04786/0.08437, loss_grounding_bce_3: 0.00600/0.08672, loss_grounding_dice_3: 0.03941/0.17975, loss_grounding_ce_3: 0.01596/0.28263, loss_mask_ce_4: 0.52300/0.92495, loss_mask_bce_4: 0.14258/0.33901, loss_mask_dice_4: 0.37820/1.19316, loss_spatial_bce_4: 0.05877/0.09595, loss_spatial_dice_4: 0.17977/0.23158, loss_spatial_ce_4: 0.04392/0.10078, loss_grounding_bce_4: 0.00640/0.08719, loss_grounding_dice_4: 0.04263/0.18247, loss_grounding_ce_4: 0.03747/0.28516, loss_mask_ce_5: 0.48453/0.93984, loss_mask_bce_5: 0.15449/0.34129, loss_mask_dice_5: 0.38584/1.19934, loss_spatial_bce_5: 0.06496/0.09744, loss_spatial_dice_5: 0.18376/0.23505, loss_spatial_ce_5: 0.09065/0.11513, loss_grounding_bce_5: 0.00732/0.08758, loss_grounding_dice_5: 0.04926/0.18371, loss_grounding_ce_5: 0.00828/0.29779, loss_mask_ce_6: 0.66308/0.97768, loss_mask_bce_6: 0.14396/0.34400, loss_mask_dice_6: 0.36902/1.20175, loss_spatial_bce_6: 0.06567/0.10312, loss_spatial_dice_6: 0.17794/0.23734, loss_spatial_ce_6: 0.12118/0.14060, loss_grounding_bce_6: 0.00669/0.08833, loss_grounding_dice_6: 0.04905/0.18388, loss_grounding_ce_6: 0.01480/0.31448, loss_mask_ce_7: 0.86429/1.02253, loss_mask_bce_7: 0.17376/0.35176, loss_mask_dice_7: 0.42056/1.25721, loss_spatial_bce_7: 0.07928/0.11174, loss_spatial_dice_7: 0.21471/0.26473, loss_spatial_ce_7: 0.20729/0.17849, loss_grounding_bce_7: 0.00618/0.09024, loss_grounding_dice_7: 0.04532/0.19115, loss_grounding_ce_7: 0.05934/0.34753, loss_mask_ce_8: 0.77959/1.13333, loss_mask_bce_8: 0.19558/0.36536, loss_mask_dice_8: 0.44675/1.33166, loss_spatial_bce_8: 0.07987/0.13272, loss_spatial_dice_8: 0.20591/0.30421, loss_spatial_ce_8: 0.35955/0.23498, loss_grounding_bce_8: 0.00625/0.09382, loss_grounding_dice_8: 0.05375/0.20226, loss_grounding_ce_8: 0.06550/0.41697, loss_mask_ce_9: 3.52283/3.68522, loss_mask_bce_9: 0.17042/0.39227, loss_mask_dice_9: 0.62865/1.90489, loss_spatial_bce_9: 0.27343/0.33479, loss_spatial_dice_9: 0.72144/0.82339, loss_spatial_ce_9: 1.17495/1.50852, loss_grounding_bce_9: 0.01137/0.10532, loss_grounding_dice_9: 0.15184/0.28166, loss_grounding_ce_9: 1.72072/0.68500] items per batch[64] items per second[0.23] total items[2124800] mini batches[ 33200] memory[7341] epoch remaining[1:10:43]
INFO:trainer.default_trainer:epochs[    18] optim steps[33300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.86850/0.90899, loss_mask_bce_0: 0.20121/0.33502, loss_mask_dice_0: 0.57433/1.16648, loss_spatial_bce_0: 0.05663/0.08978, loss_spatial_dice_0: 0.29946/0.21468, loss_spatial_ce_0: 0.09524/0.07126, loss_grounding_bce_0: 0.09955/0.08629, loss_grounding_dice_0: 0.17084/0.17936, loss_grounding_ce_0: 0.48258/0.27606, loss_mask_ce_1: 1.86586/0.90948, loss_mask_bce_1: 0.20795/0.33585, loss_mask_dice_1: 0.55904/1.17340, loss_spatial_bce_1: 0.05788/0.09053, loss_spatial_dice_1: 0.30904/0.21890, loss_spatial_ce_1: 0.07879/0.07701, loss_grounding_bce_1: 0.09753/0.08644, loss_grounding_dice_1: 0.17903/0.18025, loss_grounding_ce_1: 0.46977/0.27769, loss_mask_ce_2: 1.96318/0.91691, loss_mask_bce_2: 0.20522/0.33628, loss_mask_dice_2: 0.54638/1.17295, loss_spatial_bce_2: 0.06570/0.09091, loss_spatial_dice_2: 0.30995/0.21992, loss_spatial_ce_2: 0.05356/0.08072, loss_grounding_bce_2: 0.09882/0.08648, loss_grounding_dice_2: 0.16997/0.17985, loss_grounding_ce_2: 0.52382/0.28090, loss_mask_ce_3: 1.97134/0.92602, loss_mask_bce_3: 0.20388/0.33711, loss_mask_dice_3: 0.55925/1.17006, loss_spatial_bce_3: 0.05804/0.09176, loss_spatial_dice_3: 0.32277/0.22051, loss_spatial_ce_3: 0.06553/0.08434, loss_grounding_bce_3: 0.09645/0.08673, loss_grounding_dice_3: 0.14864/0.17972, loss_grounding_ce_3: 0.54370/0.28254, loss_mask_ce_4: 1.99390/0.92499, loss_mask_bce_4: 0.22005/0.33903, loss_mask_dice_4: 0.59006/1.19309, loss_spatial_bce_4: 0.07022/0.09597, loss_spatial_dice_4: 0.31560/0.23156, loss_spatial_ce_4: 0.05749/0.10073, loss_grounding_bce_4: 0.09711/0.08720, loss_grounding_dice_4: 0.16722/0.18244, loss_grounding_ce_4: 0.56807/0.28509, loss_mask_ce_5: 2.17960/0.93986, loss_mask_bce_5: 0.22198/0.34130, loss_mask_dice_5: 0.59848/1.19926, loss_spatial_bce_5: 0.07367/0.09747, loss_spatial_dice_5: 0.33027/0.23504, loss_spatial_ce_5: 0.04099/0.11508, loss_grounding_bce_5: 0.10102/0.08759, loss_grounding_dice_5: 0.16603/0.18368, loss_grounding_ce_5: 0.63804/0.29772, loss_mask_ce_6: 2.00391/0.97774, loss_mask_bce_6: 0.22831/0.34402, loss_mask_dice_6: 0.64916/1.20166, loss_spatial_bce_6: 0.04275/0.10315, loss_spatial_dice_6: 0.26834/0.23732, loss_spatial_ce_6: 0.14468/0.14058, loss_grounding_bce_6: 0.10594/0.08834, loss_grounding_dice_6: 0.19510/0.18386, loss_grounding_ce_6: 0.55358/0.31433, loss_mask_ce_7: 1.80180/1.02266, loss_mask_bce_7: 0.24259/0.35176, loss_mask_dice_7: 0.64803/1.25714, loss_spatial_bce_7: 0.05774/0.11177, loss_spatial_dice_7: 0.34514/0.26469, loss_spatial_ce_7: 0.22696/0.17847, loss_grounding_bce_7: 0.11186/0.09024, loss_grounding_dice_7: 0.22799/0.19114, loss_grounding_ce_7: 0.58369/0.34737, loss_mask_ce_8: 1.91834/1.13335, loss_mask_bce_8: 0.23201/0.36537, loss_mask_dice_8: 0.75243/1.33159, loss_spatial_bce_8: 0.09550/0.13274, loss_spatial_dice_8: 0.43963/0.30415, loss_spatial_ce_8: 0.16935/0.23490, loss_grounding_bce_8: 0.10490/0.09382, loss_grounding_dice_8: 0.22293/0.20224, loss_grounding_ce_8: 0.56888/0.41672, loss_mask_ce_9: 4.92627/3.68478, loss_mask_bce_9: 0.24401/0.39228, loss_mask_dice_9: 1.15784/1.90490, loss_spatial_bce_9: 0.18448/0.33480, loss_spatial_dice_9: 0.86562/0.82337, loss_spatial_ce_9: 1.32306/1.50834, loss_grounding_bce_9: 0.12959/0.10534, loss_grounding_dice_9: 0.43488/0.28164, loss_grounding_ce_9: 0.67856/0.68467] items per batch[64] items per second[0.23] total items[2131200] mini batches[ 33300] memory[7341] epoch remaining[1:05:42]
INFO:trainer.default_trainer:epochs[    18] optim steps[33400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09103/0.90896, loss_mask_bce_0: 0.24973/0.33504, loss_mask_dice_0: 0.13520/1.16644, loss_spatial_bce_0: 0.15017/0.08977, loss_spatial_dice_0: 0.13091/0.21466, loss_spatial_ce_0: 0.07309/0.07118, loss_grounding_bce_0: 0.19283/0.08628, loss_grounding_dice_0: 0.09868/0.17931, loss_grounding_ce_0: 0.01043/0.27614, loss_mask_ce_1: 0.08593/0.90947, loss_mask_bce_1: 0.25326/0.33587, loss_mask_dice_1: 0.13309/1.17337, loss_spatial_bce_1: 0.15296/0.09051, loss_spatial_dice_1: 0.13344/0.21887, loss_spatial_ce_1: 0.09128/0.07693, loss_grounding_bce_1: 0.19145/0.08644, loss_grounding_dice_1: 0.09804/0.18020, loss_grounding_ce_1: 0.00557/0.27773, loss_mask_ce_2: 0.08232/0.91688, loss_mask_bce_2: 0.24442/0.33630, loss_mask_dice_2: 0.12834/1.17292, loss_spatial_bce_2: 0.14936/0.09090, loss_spatial_dice_2: 0.15036/0.21989, loss_spatial_ce_2: 0.11639/0.08064, loss_grounding_bce_2: 0.18534/0.08647, loss_grounding_dice_2: 0.09469/0.17979, loss_grounding_ce_2: 0.00881/0.28097, loss_mask_ce_3: 0.09827/0.92596, loss_mask_bce_3: 0.23493/0.33713, loss_mask_dice_3: 0.13771/1.17007, loss_spatial_bce_3: 0.14891/0.09175, loss_spatial_dice_3: 0.17040/0.22048, loss_spatial_ce_3: 0.17224/0.08426, loss_grounding_bce_3: 0.18517/0.08673, loss_grounding_dice_3: 0.09838/0.17968, loss_grounding_ce_3: 0.01251/0.28255, loss_mask_ce_4: 0.08678/0.92498, loss_mask_bce_4: 0.24654/0.33904, loss_mask_dice_4: 0.14657/1.19310, loss_spatial_bce_4: 0.15961/0.09596, loss_spatial_dice_4: 0.17273/0.23154, loss_spatial_ce_4: 0.24848/0.10065, loss_grounding_bce_4: 0.18896/0.08718, loss_grounding_dice_4: 0.10365/0.18240, loss_grounding_ce_4: 0.05862/0.28518, loss_mask_ce_5: 0.07460/0.93990, loss_mask_bce_5: 0.24181/0.34132, loss_mask_dice_5: 0.14022/1.19927, loss_spatial_bce_5: 0.14724/0.09747, loss_spatial_dice_5: 0.18706/0.23503, loss_spatial_ce_5: 0.29618/0.11501, loss_grounding_bce_5: 0.19580/0.08757, loss_grounding_dice_5: 0.11041/0.18363, loss_grounding_ce_5: 0.05428/0.29781, loss_mask_ce_6: 0.09141/0.97776, loss_mask_bce_6: 0.23864/0.34404, loss_mask_dice_6: 0.14877/1.20168, loss_spatial_bce_6: 0.16863/0.10315, loss_spatial_dice_6: 0.19569/0.23730, loss_spatial_ce_6: 0.36807/0.14054, loss_grounding_bce_6: 0.18707/0.08833, loss_grounding_dice_6: 0.09914/0.18381, loss_grounding_ce_6: 0.03202/0.31444, loss_mask_ce_7: 0.10492/1.02265, loss_mask_bce_7: 0.25437/0.35178, loss_mask_dice_7: 0.14356/1.25712, loss_spatial_bce_7: 0.20340/0.11176, loss_spatial_dice_7: 0.17730/0.26468, loss_spatial_ce_7: 0.18818/0.17837, loss_grounding_bce_7: 0.18750/0.09023, loss_grounding_dice_7: 0.10075/0.19109, loss_grounding_ce_7: 0.22632/0.34741, loss_mask_ce_8: 0.10427/1.13330, loss_mask_bce_8: 0.24741/0.36539, loss_mask_dice_8: 0.14161/1.33161, loss_spatial_bce_8: 0.18100/0.13272, loss_spatial_dice_8: 0.18373/0.30414, loss_spatial_ce_8: 0.38712/0.23482, loss_grounding_bce_8: 0.19350/0.09381, loss_grounding_dice_8: 0.09653/0.20219, loss_grounding_ce_8: 0.28712/0.41675, loss_mask_ce_9: 1.42395/3.68491, loss_mask_bce_9: 0.21501/0.39231, loss_mask_dice_9: 0.15237/1.90511, loss_spatial_bce_9: 0.35502/0.33482, loss_spatial_dice_9: 0.74813/0.82340, loss_spatial_ce_9: 0.69535/1.50816, loss_grounding_bce_9: 0.17332/0.10532, loss_grounding_dice_9: 0.09927/0.28160, loss_grounding_ce_9: 0.91469/0.68462] items per batch[64] items per second[0.23] total items[2137600] mini batches[ 33400] memory[7341] epoch remaining[1:01:04]
INFO:trainer.default_trainer:epochs[    18] optim steps[33500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70221/0.90895, loss_mask_bce_0: 0.19951/0.33512, loss_mask_dice_0: 2.03582/1.16705, loss_spatial_bce_0: 0.01983/0.08976, loss_spatial_dice_0: 0.25256/0.21463, loss_spatial_ce_0: 0.30762/0.07110, loss_grounding_bce_0: 0.03537/0.08630, loss_grounding_dice_0: 0.02708/0.17935, loss_grounding_ce_0: 0.00137/0.27619, loss_mask_ce_1: 0.60582/0.90944, loss_mask_bce_1: 0.19430/0.33594, loss_mask_dice_1: 2.35702/1.17401, loss_spatial_bce_1: 0.02126/0.09050, loss_spatial_dice_1: 0.27879/0.21885, loss_spatial_ce_1: 0.42303/0.07688, loss_grounding_bce_1: 0.03308/0.08646, loss_grounding_dice_1: 0.03042/0.18023, loss_grounding_ce_1: 0.00125/0.27778, loss_mask_ce_2: 0.86218/0.91684, loss_mask_bce_2: 0.18666/0.33637, loss_mask_dice_2: 2.16417/1.17354, loss_spatial_bce_2: 0.02234/0.09090, loss_spatial_dice_2: 0.27755/0.21986, loss_spatial_ce_2: 0.13418/0.08055, loss_grounding_bce_2: 0.03357/0.08649, loss_grounding_dice_2: 0.02775/0.17983, loss_grounding_ce_2: 0.00080/0.28103, loss_mask_ce_3: 0.68371/0.92599, loss_mask_bce_3: 0.18941/0.33719, loss_mask_dice_3: 2.14106/1.17061, loss_spatial_bce_3: 0.02582/0.09175, loss_spatial_dice_3: 0.26409/0.22045, loss_spatial_ce_3: 0.08352/0.08419, loss_grounding_bce_3: 0.02904/0.08675, loss_grounding_dice_3: 0.02729/0.17971, loss_grounding_ce_3: 0.00069/0.28260, loss_mask_ce_4: 0.60123/0.92498, loss_mask_bce_4: 0.19975/0.33911, loss_mask_dice_4: 2.34207/1.19370, loss_spatial_bce_4: 0.02837/0.09595, loss_spatial_dice_4: 0.30978/0.23152, loss_spatial_ce_4: 0.16061/0.10059, loss_grounding_bce_4: 0.03475/0.08720, loss_grounding_dice_4: 0.02771/0.18243, loss_grounding_ce_4: 0.00103/0.28525, loss_mask_ce_5: 0.75732/0.93983, loss_mask_bce_5: 0.19398/0.34138, loss_mask_dice_5: 2.22255/1.19991, loss_spatial_bce_5: 0.02766/0.09746, loss_spatial_dice_5: 0.30818/0.23502, loss_spatial_ce_5: 0.11714/0.11497, loss_grounding_bce_5: 0.03260/0.08759, loss_grounding_dice_5: 0.02763/0.18366, loss_grounding_ce_5: 0.00166/0.29787, loss_mask_ce_6: 0.63746/0.97778, loss_mask_bce_6: 0.21023/0.34410, loss_mask_dice_6: 2.34323/1.20229, loss_spatial_bce_6: 0.03324/0.10315, loss_spatial_dice_6: 0.30830/0.23728, loss_spatial_ce_6: 0.08945/0.14048, loss_grounding_bce_6: 0.03891/0.08834, loss_grounding_dice_6: 0.02992/0.18385, loss_grounding_ce_6: 0.00192/0.31453, loss_mask_ce_7: 0.64581/1.02268, loss_mask_bce_7: 0.22826/0.35186, loss_mask_dice_7: 2.43268/1.25785, loss_spatial_bce_7: 0.05835/0.11174, loss_spatial_dice_7: 0.33409/0.26467, loss_spatial_ce_7: 0.24924/0.17829, loss_grounding_bce_7: 0.07982/0.09025, loss_grounding_dice_7: 0.05226/0.19112, loss_grounding_ce_7: 0.00257/0.34748, loss_mask_ce_8: 0.72089/1.13333, loss_mask_bce_8: 0.24524/0.36547, loss_mask_dice_8: 2.37545/1.33233, loss_spatial_bce_8: 0.07771/0.13272, loss_spatial_dice_8: 0.37895/0.30414, loss_spatial_ce_8: 0.26401/0.23480, loss_grounding_bce_8: 0.11165/0.09383, loss_grounding_dice_8: 0.05903/0.20222, loss_grounding_ce_8: 0.10174/0.41675, loss_mask_ce_9: 3.68763/3.68512, loss_mask_bce_9: 0.28963/0.39238, loss_mask_dice_9: 2.57976/1.90605, loss_spatial_bce_9: 0.33092/0.33483, loss_spatial_dice_9: 0.87540/0.82339, loss_spatial_ce_9: 1.63621/1.50809, loss_grounding_bce_9: 0.20508/0.10535, loss_grounding_dice_9: 0.10181/0.28164, loss_grounding_ce_9: 0.20004/0.68451] items per batch[64] items per second[0.23] total items[2144000] mini batches[ 33500] memory[7341] epoch remaining[0:56:13]
INFO:trainer.default_trainer:epochs[    18] optim steps[33600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56219/0.90899, loss_mask_bce_0: 0.35548/0.33499, loss_mask_dice_0: 2.28175/1.16652, loss_spatial_bce_0: 0.05300/0.08973, loss_spatial_dice_0: 0.21262/0.21459, loss_spatial_ce_0: 0.00267/0.07104, loss_grounding_bce_0: 0.05887/0.08628, loss_grounding_dice_0: 0.34529/0.17934, loss_grounding_ce_0: 0.35150/0.27616, loss_mask_ce_1: 0.83370/0.90955, loss_mask_bce_1: 0.34640/0.33581, loss_mask_dice_1: 2.17534/1.17344, loss_spatial_bce_1: 0.04901/0.09047, loss_spatial_dice_1: 0.18313/0.21879, loss_spatial_ce_1: 0.01279/0.07682, loss_grounding_bce_1: 0.06018/0.08644, loss_grounding_dice_1: 0.33833/0.18022, loss_grounding_ce_1: 0.31067/0.27774, loss_mask_ce_2: 0.91740/0.91695, loss_mask_bce_2: 0.34453/0.33624, loss_mask_dice_2: 1.92738/1.17298, loss_spatial_bce_2: 0.05020/0.09087, loss_spatial_dice_2: 0.19106/0.21981, loss_spatial_ce_2: 0.00202/0.08047, loss_grounding_bce_2: 0.05863/0.08647, loss_grounding_dice_2: 0.35449/0.17982, loss_grounding_ce_2: 0.27506/0.28105, loss_mask_ce_3: 0.57925/0.92605, loss_mask_bce_3: 0.35792/0.33706, loss_mask_dice_3: 2.25895/1.17010, loss_spatial_bce_3: 0.05460/0.09172, loss_spatial_dice_3: 0.20326/0.22040, loss_spatial_ce_3: 0.00611/0.08413, loss_grounding_bce_3: 0.06000/0.08673, loss_grounding_dice_3: 0.42146/0.17970, loss_grounding_ce_3: 0.36178/0.28258, loss_mask_ce_4: 0.69063/0.92505, loss_mask_bce_4: 0.35312/0.33898, loss_mask_dice_4: 2.18683/1.19315, loss_spatial_bce_4: 0.05377/0.09593, loss_spatial_dice_4: 0.25942/0.23149, loss_spatial_ce_4: 0.03733/0.10054, loss_grounding_bce_4: 0.05954/0.08719, loss_grounding_dice_4: 0.31204/0.18241, loss_grounding_ce_4: 0.31271/0.28524, loss_mask_ce_5: 0.71390/0.93989, loss_mask_bce_5: 0.34580/0.34126, loss_mask_dice_5: 2.33803/1.19938, loss_spatial_bce_5: 0.05974/0.09744, loss_spatial_dice_5: 0.27954/0.23500, loss_spatial_ce_5: 0.08562/0.11494, loss_grounding_bce_5: 0.06315/0.08757, loss_grounding_dice_5: 0.37682/0.18363, loss_grounding_ce_5: 0.35107/0.29789, loss_mask_ce_6: 0.83033/0.97788, loss_mask_bce_6: 0.32341/0.34396, loss_mask_dice_6: 2.27432/1.20172, loss_spatial_bce_6: 0.06698/0.10314, loss_spatial_dice_6: 0.29802/0.23726, loss_spatial_ce_6: 0.11424/0.14046, loss_grounding_bce_6: 0.05400/0.08832, loss_grounding_dice_6: 0.38930/0.18384, loss_grounding_ce_6: 0.35389/0.31456, loss_mask_ce_7: 1.00559/1.02284, loss_mask_bce_7: 0.35685/0.35171, loss_mask_dice_7: 2.29909/1.25731, loss_spatial_bce_7: 0.06565/0.11174, loss_spatial_dice_7: 0.24082/0.26465, loss_spatial_ce_7: 0.05699/0.17818, loss_grounding_bce_7: 0.06367/0.09024, loss_grounding_dice_7: 0.29187/0.19111, loss_grounding_ce_7: 0.41171/0.34742, loss_mask_ce_8: 0.95654/1.13336, loss_mask_bce_8: 0.36144/0.36535, loss_mask_dice_8: 2.53447/1.33172, loss_spatial_bce_8: 0.07208/0.13270, loss_spatial_dice_8: 0.30649/0.30412, loss_spatial_ce_8: 0.07382/0.23471, loss_grounding_bce_8: 0.06099/0.09382, loss_grounding_dice_8: 0.37179/0.20218, loss_grounding_ce_8: 0.37379/0.41675, loss_mask_ce_9: 3.64835/3.68461, loss_mask_bce_9: 0.42939/0.39226, loss_mask_dice_9: 4.21717/1.90531, loss_spatial_bce_9: 0.17890/0.33478, loss_spatial_dice_9: 0.88607/0.82341, loss_spatial_ce_9: 1.26353/1.50798, loss_grounding_bce_9: 0.08076/0.10534, loss_grounding_dice_9: 0.55580/0.28161, loss_grounding_ce_9: 0.37719/0.68463] items per batch[64] items per second[0.24] total items[2150400] mini batches[ 33600] memory[7341] epoch remaining[0:51:23]
INFO:trainer.default_trainer:epochs[    18] optim steps[33700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.21158/0.90882, loss_mask_bce_0: 0.13277/0.33500, loss_mask_dice_0: 0.37665/1.16667, loss_spatial_bce_0: 0.07279/0.08970, loss_spatial_dice_0: 0.23855/0.21451, loss_spatial_ce_0: 0.00114/0.07096, loss_grounding_bce_0: 0.04153/0.08627, loss_grounding_dice_0: 0.16892/0.17928, loss_grounding_ce_0: 0.33340/0.27603, loss_mask_ce_1: 0.17513/0.90939, loss_mask_bce_1: 0.12311/0.33581, loss_mask_dice_1: 0.30366/1.17351, loss_spatial_bce_1: 0.07763/0.09044, loss_spatial_dice_1: 0.20304/0.21871, loss_spatial_ce_1: 0.00101/0.07674, loss_grounding_bce_1: 0.04061/0.08642, loss_grounding_dice_1: 0.23497/0.18017, loss_grounding_ce_1: 0.51237/0.27758, loss_mask_ce_2: 0.17520/0.91679, loss_mask_bce_2: 0.12850/0.33625, loss_mask_dice_2: 0.31470/1.17308, loss_spatial_bce_2: 0.06795/0.09085, loss_spatial_dice_2: 0.24773/0.21974, loss_spatial_ce_2: 0.00261/0.08042, loss_grounding_bce_2: 0.03985/0.08645, loss_grounding_dice_2: 0.29784/0.17976, loss_grounding_ce_2: 0.30062/0.28088, loss_mask_ce_3: 0.97069/0.92587, loss_mask_bce_3: 0.11653/0.33706, loss_mask_dice_3: 0.42861/1.17022, loss_spatial_bce_3: 0.07013/0.09169, loss_spatial_dice_3: 0.23076/0.22032, loss_spatial_ce_3: 0.00772/0.08406, loss_grounding_bce_3: 0.04152/0.08672, loss_grounding_dice_3: 0.18740/0.17965, loss_grounding_ce_3: 0.27528/0.28243, loss_mask_ce_4: 0.27352/0.92489, loss_mask_bce_4: 0.12910/0.33899, loss_mask_dice_4: 0.40135/1.19331, loss_spatial_bce_4: 0.08127/0.09591, loss_spatial_dice_4: 0.26272/0.23143, loss_spatial_ce_4: 0.00121/0.10045, loss_grounding_bce_4: 0.04479/0.08717, loss_grounding_dice_4: 0.38376/0.18236, loss_grounding_ce_4: 0.25649/0.28507, loss_mask_ce_5: 0.27255/0.93972, loss_mask_bce_5: 0.12804/0.34127, loss_mask_dice_5: 0.43077/1.19951, loss_spatial_bce_5: 0.06328/0.09743, loss_spatial_dice_5: 0.23658/0.23493, loss_spatial_ce_5: 0.35761/0.11487, loss_grounding_bce_5: 0.04032/0.08756, loss_grounding_dice_5: 0.30007/0.18359, loss_grounding_ce_5: 0.52336/0.29773, loss_mask_ce_6: 0.29346/0.97771, loss_mask_bce_6: 0.13306/0.34399, loss_mask_dice_6: 0.44604/1.20188, loss_spatial_bce_6: 0.10649/0.10312, loss_spatial_dice_6: 0.26811/0.23720, loss_spatial_ce_6: 0.07800/0.14039, loss_grounding_bce_6: 0.05050/0.08831, loss_grounding_dice_6: 0.35035/0.18381, loss_grounding_ce_6: 0.28606/0.31441, loss_mask_ce_7: 0.19316/1.02264, loss_mask_bce_7: 0.14393/0.35175, loss_mask_dice_7: 0.53181/1.25750, loss_spatial_bce_7: 0.07406/0.11172, loss_spatial_dice_7: 0.25876/0.26459, loss_spatial_ce_7: 0.04275/0.17810, loss_grounding_bce_7: 0.03772/0.09023, loss_grounding_dice_7: 0.31139/0.19106, loss_grounding_ce_7: 0.42676/0.34727, loss_mask_ce_8: 0.67877/1.13319, loss_mask_bce_8: 0.13782/0.36540, loss_mask_dice_8: 0.43011/1.33196, loss_spatial_bce_8: 0.07154/0.13269, loss_spatial_dice_8: 0.19565/0.30407, loss_spatial_ce_8: 0.15120/0.23463, loss_grounding_bce_8: 0.05209/0.09383, loss_grounding_dice_8: 0.20736/0.20216, loss_grounding_ce_8: 0.36612/0.41660, loss_mask_ce_9: 2.12548/3.68481, loss_mask_bce_9: 0.16280/0.39228, loss_mask_dice_9: 0.71737/1.90554, loss_spatial_bce_9: 0.43846/0.33479, loss_spatial_dice_9: 0.80527/0.82337, loss_spatial_ce_9: 1.20530/1.50778, loss_grounding_bce_9: 0.04901/0.10534, loss_grounding_dice_9: 0.35790/0.28156, loss_grounding_ce_9: 0.17403/0.68459] items per batch[64] items per second[0.23] total items[2156800] mini batches[ 33700] memory[7341] epoch remaining[0:46:54]
INFO:trainer.default_trainer:epochs[    18] optim steps[33800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71705/0.90889, loss_mask_bce_0: 0.07295/0.33499, loss_mask_dice_0: 1.41545/1.16659, loss_spatial_bce_0: 0.01080/0.08969, loss_spatial_dice_0: 0.23219/0.21447, loss_spatial_ce_0: 0.10083/0.07091, loss_grounding_bce_0: 0.01772/0.08629, loss_grounding_dice_0: 0.18278/0.17929, loss_grounding_ce_0: 0.19220/0.27606, loss_mask_ce_1: 0.56081/0.90949, loss_mask_bce_1: 0.07587/0.33581, loss_mask_dice_1: 1.60136/1.17348, loss_spatial_bce_1: 0.01111/0.09043, loss_spatial_dice_1: 0.25827/0.21868, loss_spatial_ce_1: 0.10937/0.07670, loss_grounding_bce_1: 0.01846/0.08644, loss_grounding_dice_1: 0.13310/0.18017, loss_grounding_ce_1: 0.19091/0.27775, loss_mask_ce_2: 0.49204/0.91689, loss_mask_bce_2: 0.08445/0.33624, loss_mask_dice_2: 1.74891/1.17301, loss_spatial_bce_2: 0.01085/0.09084, loss_spatial_dice_2: 0.23992/0.21970, loss_spatial_ce_2: 0.03111/0.08035, loss_grounding_bce_2: 0.01645/0.08646, loss_grounding_dice_2: 0.16973/0.17977, loss_grounding_ce_2: 0.19486/0.28100, loss_mask_ce_3: 0.52676/0.92595, loss_mask_bce_3: 0.09589/0.33706, loss_mask_dice_3: 1.85244/1.17016, loss_spatial_bce_3: 0.01098/0.09168, loss_spatial_dice_3: 0.26719/0.22028, loss_spatial_ce_3: 0.08735/0.08402, loss_grounding_bce_3: 0.01871/0.08673, loss_grounding_dice_3: 0.17926/0.17965, loss_grounding_ce_3: 0.18645/0.28256, loss_mask_ce_4: 0.89200/0.92496, loss_mask_bce_4: 0.08661/0.33899, loss_mask_dice_4: 1.70488/1.19330, loss_spatial_bce_4: 0.01245/0.09591, loss_spatial_dice_4: 0.31296/0.23140, loss_spatial_ce_4: 0.09692/0.10038, loss_grounding_bce_4: 0.01897/0.08718, loss_grounding_dice_4: 0.13606/0.18236, loss_grounding_ce_4: 0.31565/0.28530, loss_mask_ce_5: 0.87540/0.93978, loss_mask_bce_5: 0.08332/0.34129, loss_mask_dice_5: 1.75389/1.19946, loss_spatial_bce_5: 0.01476/0.09742, loss_spatial_dice_5: 0.32041/0.23490, loss_spatial_ce_5: 0.06928/0.11480, loss_grounding_bce_5: 0.02144/0.08757, loss_grounding_dice_5: 0.18772/0.18357, loss_grounding_ce_5: 0.29009/0.29797, loss_mask_ce_6: 0.73020/0.97780, loss_mask_bce_6: 0.08852/0.34400, loss_mask_dice_6: 1.78547/1.20188, loss_spatial_bce_6: 0.01156/0.10312, loss_spatial_dice_6: 0.28728/0.23717, loss_spatial_ce_6: 0.10830/0.14035, loss_grounding_bce_6: 0.02146/0.08832, loss_grounding_dice_6: 0.18434/0.18381, loss_grounding_ce_6: 0.40445/0.31455, loss_mask_ce_7: 0.77134/1.02267, loss_mask_bce_7: 0.09375/0.35179, loss_mask_dice_7: 1.86509/1.25750, loss_spatial_bce_7: 0.02185/0.11171, loss_spatial_dice_7: 0.39693/0.26458, loss_spatial_ce_7: 0.11239/0.17806, loss_grounding_bce_7: 0.02275/0.09024, loss_grounding_dice_7: 0.20846/0.19106, loss_grounding_ce_7: 0.28226/0.34755, loss_mask_ce_8: 1.10369/1.13320, loss_mask_bce_8: 0.11931/0.36544, loss_mask_dice_8: 2.00896/1.33196, loss_spatial_bce_8: 0.03105/0.13266, loss_spatial_dice_8: 0.44690/0.30404, loss_spatial_ce_8: 0.18439/0.23459, loss_grounding_bce_8: 0.02658/0.09383, loss_grounding_dice_8: 0.19955/0.20214, loss_grounding_ce_8: 0.47187/0.41701, loss_mask_ce_9: 2.35900/3.68489, loss_mask_bce_9: 0.07569/0.39231, loss_mask_dice_9: 2.46617/1.90552, loss_spatial_bce_9: 0.09412/0.33478, loss_spatial_dice_9: 0.87416/0.82336, loss_spatial_ce_9: 1.42391/1.50790, loss_grounding_bce_9: 0.03260/0.10536, loss_grounding_dice_9: 0.31746/0.28157, loss_grounding_ce_9: 0.32808/0.68470] items per batch[64] items per second[0.23] total items[2163200] mini batches[ 33800] memory[7341] epoch remaining[0:42:13]
INFO:trainer.default_trainer:epochs[    18] optim steps[33900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97147/0.90896, loss_mask_bce_0: 0.05983/0.33495, loss_mask_dice_0: 0.85755/1.16634, loss_spatial_bce_0: 0.01440/0.08966, loss_spatial_dice_0: 0.24309/0.21443, loss_spatial_ce_0: 0.15027/0.07087, loss_grounding_bce_0: 0.00928/0.08628, loss_grounding_dice_0: 0.11520/0.17926, loss_grounding_ce_0: 0.23969/0.27598, loss_mask_ce_1: 0.91067/0.90951, loss_mask_bce_1: 0.06180/0.33576, loss_mask_dice_1: 0.82426/1.17320, loss_spatial_bce_1: 0.01570/0.09040, loss_spatial_dice_1: 0.25338/0.21864, loss_spatial_ce_1: 0.03654/0.07667, loss_grounding_bce_1: 0.00950/0.08643, loss_grounding_dice_1: 0.11169/0.18015, loss_grounding_ce_1: 0.24085/0.27764, loss_mask_ce_2: 1.01738/0.91694, loss_mask_bce_2: 0.06322/0.33619, loss_mask_dice_2: 0.77920/1.17274, loss_spatial_bce_2: 0.01589/0.09081, loss_spatial_dice_2: 0.25748/0.21966, loss_spatial_ce_2: 0.04852/0.08032, loss_grounding_bce_2: 0.00932/0.08646, loss_grounding_dice_2: 0.12676/0.17975, loss_grounding_ce_2: 0.23044/0.28089, loss_mask_ce_3: 0.88586/0.92601, loss_mask_bce_3: 0.05944/0.33702, loss_mask_dice_3: 0.74643/1.16990, loss_spatial_bce_3: 0.01654/0.09166, loss_spatial_dice_3: 0.26183/0.22025, loss_spatial_ce_3: 0.05287/0.08400, loss_grounding_bce_3: 0.01178/0.08672, loss_grounding_dice_3: 0.11374/0.17961, loss_grounding_ce_3: 0.15822/0.28245, loss_mask_ce_4: 1.10456/0.92502, loss_mask_bce_4: 0.06379/0.33895, loss_mask_dice_4: 0.72761/1.19302, loss_spatial_bce_4: 0.02019/0.09589, loss_spatial_dice_4: 0.29200/0.23137, loss_spatial_ce_4: 0.04500/0.10034, loss_grounding_bce_4: 0.00879/0.08717, loss_grounding_dice_4: 0.10739/0.18232, loss_grounding_ce_4: 0.19825/0.28522, loss_mask_ce_5: 1.22753/0.93987, loss_mask_bce_5: 0.05744/0.34124, loss_mask_dice_5: 0.81493/1.19923, loss_spatial_bce_5: 0.02014/0.09741, loss_spatial_dice_5: 0.29494/0.23489, loss_spatial_ce_5: 0.06525/0.11475, loss_grounding_bce_5: 0.00830/0.08756, loss_grounding_dice_5: 0.10707/0.18355, loss_grounding_ce_5: 0.35518/0.29791, loss_mask_ce_6: 1.18367/0.97796, loss_mask_bce_6: 0.06174/0.34393, loss_mask_dice_6: 0.82320/1.20163, loss_spatial_bce_6: 0.02075/0.10311, loss_spatial_dice_6: 0.27742/0.23714, loss_spatial_ce_6: 0.10040/0.14033, loss_grounding_bce_6: 0.00917/0.08831, loss_grounding_dice_6: 0.11260/0.18377, loss_grounding_ce_6: 0.30795/0.31451, loss_mask_ce_7: 1.13973/1.02276, loss_mask_bce_7: 0.06731/0.35176, loss_mask_dice_7: 0.84550/1.25728, loss_spatial_bce_7: 0.03194/0.11168, loss_spatial_dice_7: 0.28917/0.26455, loss_spatial_ce_7: 0.18796/0.17803, loss_grounding_bce_7: 0.01002/0.09023, loss_grounding_dice_7: 0.10625/0.19103, loss_grounding_ce_7: 0.31470/0.34746, loss_mask_ce_8: 1.27314/1.13325, loss_mask_bce_8: 0.06871/0.36541, loss_mask_dice_8: 0.94379/1.33172, loss_spatial_bce_8: 0.04354/0.13264, loss_spatial_dice_8: 0.33570/0.30403, loss_spatial_ce_8: 0.21627/0.23450, loss_grounding_bce_8: 0.00924/0.09382, loss_grounding_dice_8: 0.11540/0.20211, loss_grounding_ce_8: 0.51247/0.41690, loss_mask_ce_9: 3.98716/3.68495, loss_mask_bce_9: 0.08754/0.39225, loss_mask_dice_9: 1.26581/1.90515, loss_spatial_bce_9: 0.14842/0.33476, loss_spatial_dice_9: 0.85190/0.82334, loss_spatial_ce_9: 2.48017/1.50779, loss_grounding_bce_9: 0.01212/0.10535, loss_grounding_dice_9: 0.15177/0.28154, loss_grounding_ce_9: 0.66424/0.68455] items per batch[64] items per second[0.23] total items[2169600] mini batches[ 33900] memory[7341] epoch remaining[0:37:35]
INFO:trainer.default_trainer:epochs[    18] optim steps[34000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44435/0.90908, loss_mask_bce_0: 0.24391/0.33495, loss_mask_dice_0: 1.68857/1.16636, loss_spatial_bce_0: 0.06059/0.08967, loss_spatial_dice_0: 0.21102/0.21443, loss_spatial_ce_0: 0.14429/0.07083, loss_grounding_bce_0: 0.03646/0.08626, loss_grounding_dice_0: 0.14851/0.17926, loss_grounding_ce_0: 0.12687/0.27601, loss_mask_ce_1: 0.58939/0.90962, loss_mask_bce_1: 0.23465/0.33576, loss_mask_dice_1: 1.35969/1.17324, loss_spatial_bce_1: 0.11344/0.09040, loss_spatial_dice_1: 0.21367/0.21864, loss_spatial_ce_1: 0.10648/0.07665, loss_grounding_bce_1: 0.03233/0.08641, loss_grounding_dice_1: 0.11733/0.18017, loss_grounding_ce_1: 0.13949/0.27762, loss_mask_ce_2: 0.57470/0.91707, loss_mask_bce_2: 0.22217/0.33619, loss_mask_dice_2: 1.33075/1.17277, loss_spatial_bce_2: 0.10238/0.09082, loss_spatial_dice_2: 0.16033/0.21966, loss_spatial_ce_2: 0.09788/0.08030, loss_grounding_bce_2: 0.03319/0.08644, loss_grounding_dice_2: 0.12321/0.17977, loss_grounding_ce_2: 0.16802/0.28088, loss_mask_ce_3: 0.48086/0.92621, loss_mask_bce_3: 0.23815/0.33702, loss_mask_dice_3: 1.41381/1.16992, loss_spatial_bce_3: 0.06161/0.09166, loss_spatial_dice_3: 0.18456/0.22025, loss_spatial_ce_3: 0.16652/0.08395, loss_grounding_bce_3: 0.03369/0.08670, loss_grounding_dice_3: 0.12056/0.17962, loss_grounding_ce_3: 0.17841/0.28250, loss_mask_ce_4: 0.54257/0.92517, loss_mask_bce_4: 0.21165/0.33894, loss_mask_dice_4: 1.25908/1.19305, loss_spatial_bce_4: 0.06103/0.09589, loss_spatial_dice_4: 0.19590/0.23137, loss_spatial_ce_4: 0.20809/0.10030, loss_grounding_bce_4: 0.02870/0.08715, loss_grounding_dice_4: 0.10008/0.18235, loss_grounding_ce_4: 0.17469/0.28523, loss_mask_ce_5: 0.52734/0.93998, loss_mask_bce_5: 0.21646/0.34124, loss_mask_dice_5: 1.37917/1.19923, loss_spatial_bce_5: 0.05513/0.09741, loss_spatial_dice_5: 0.16917/0.23489, loss_spatial_ce_5: 0.16334/0.11472, loss_grounding_bce_5: 0.03058/0.08754, loss_grounding_dice_5: 0.11588/0.18358, loss_grounding_ce_5: 0.16126/0.29794, loss_mask_ce_6: 0.56730/0.97802, loss_mask_bce_6: 0.23232/0.34393, loss_mask_dice_6: 1.34215/1.20162, loss_spatial_bce_6: 0.06237/0.10312, loss_spatial_dice_6: 0.21117/0.23715, loss_spatial_ce_6: 0.22026/0.14028, loss_grounding_bce_6: 0.02965/0.08829, loss_grounding_dice_6: 0.11749/0.18379, loss_grounding_ce_6: 0.17460/0.31450, loss_mask_ce_7: 0.71211/1.02291, loss_mask_bce_7: 0.23462/0.35176, loss_mask_dice_7: 1.02096/1.25729, loss_spatial_bce_7: 0.07876/0.11168, loss_spatial_dice_7: 0.24916/0.26456, loss_spatial_ce_7: 0.09797/0.17799, loss_grounding_bce_7: 0.03100/0.09021, loss_grounding_dice_7: 0.12431/0.19105, loss_grounding_ce_7: 0.10881/0.34741, loss_mask_ce_8: 0.83524/1.13336, loss_mask_bce_8: 0.22245/0.36539, loss_mask_dice_8: 1.23067/1.33172, loss_spatial_bce_8: 0.14971/0.13266, loss_spatial_dice_8: 0.24819/0.30402, loss_spatial_ce_8: 0.18377/0.23451, loss_grounding_bce_8: 0.03663/0.09380, loss_grounding_dice_8: 0.15974/0.20213, loss_grounding_ce_8: 0.07426/0.41680, loss_mask_ce_9: 4.26408/3.68511, loss_mask_bce_9: 0.26030/0.39225, loss_mask_dice_9: 1.82532/1.90527, loss_spatial_bce_9: 0.32931/0.33474, loss_spatial_dice_9: 0.81648/0.82332, loss_spatial_ce_9: 1.23468/1.50774, loss_grounding_bce_9: 0.05219/0.10532, loss_grounding_dice_9: 0.26008/0.28155, loss_grounding_ce_9: 0.21640/0.68450] items per batch[64] items per second[0.23] total items[2176000] mini batches[ 34000] memory[7341] epoch remaining[0:32:55]
INFO:trainer.default_trainer:epochs[    18] optim steps[34100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47750/0.90896, loss_mask_bce_0: 0.75815/0.33498, loss_mask_dice_0: 1.16531/1.16609, loss_spatial_bce_0: 0.10659/0.08967, loss_spatial_dice_0: 0.13896/0.21439, loss_spatial_ce_0: 0.02187/0.07078, loss_grounding_bce_0: 0.02611/0.08627, loss_grounding_dice_0: 0.22176/0.17924, loss_grounding_ce_0: 0.21284/0.27600, loss_mask_ce_1: 0.33284/0.90944, loss_mask_bce_1: 0.75510/0.33579, loss_mask_dice_1: 1.17670/1.17300, loss_spatial_bce_1: 0.09969/0.09041, loss_spatial_dice_1: 0.15104/0.21860, loss_spatial_ce_1: 0.02352/0.07656, loss_grounding_bce_1: 0.02863/0.08642, loss_grounding_dice_1: 0.17176/0.18016, loss_grounding_ce_1: 0.03053/0.27758, loss_mask_ce_2: 0.44464/0.91693, loss_mask_bce_2: 0.76719/0.33621, loss_mask_dice_2: 1.23941/1.17253, loss_spatial_bce_2: 0.10174/0.09082, loss_spatial_dice_2: 0.14090/0.21962, loss_spatial_ce_2: 0.02374/0.08023, loss_grounding_bce_2: 0.02513/0.08645, loss_grounding_dice_2: 0.21344/0.17976, loss_grounding_ce_2: 0.26732/0.28087, loss_mask_ce_3: 0.42819/0.92607, loss_mask_bce_3: 0.76320/0.33706, loss_mask_dice_3: 1.21685/1.16970, loss_spatial_bce_3: 0.10880/0.09166, loss_spatial_dice_3: 0.14199/0.22021, loss_spatial_ce_3: 0.02356/0.08390, loss_grounding_bce_3: 0.02937/0.08672, loss_grounding_dice_3: 0.21876/0.17962, loss_grounding_ce_3: 0.31388/0.28254, loss_mask_ce_4: 0.33962/0.92501, loss_mask_bce_4: 0.75173/0.33898, loss_mask_dice_4: 1.19474/1.19278, loss_spatial_bce_4: 0.07667/0.09589, loss_spatial_dice_4: 0.16622/0.23134, loss_spatial_ce_4: 0.03817/0.10022, loss_grounding_bce_4: 0.02869/0.08716, loss_grounding_dice_4: 0.21381/0.18234, loss_grounding_ce_4: 0.02149/0.28526, loss_mask_ce_5: 0.49155/0.93987, loss_mask_bce_5: 0.80181/0.34127, loss_mask_dice_5: 1.22662/1.19901, loss_spatial_bce_5: 0.06768/0.09742, loss_spatial_dice_5: 0.15905/0.23487, loss_spatial_ce_5: 0.05981/0.11463, loss_grounding_bce_5: 0.02964/0.08755, loss_grounding_dice_5: 0.19663/0.18356, loss_grounding_ce_5: 0.03869/0.29790, loss_mask_ce_6: 0.44187/0.97790, loss_mask_bce_6: 0.80962/0.34395, loss_mask_dice_6: 1.19814/1.20135, loss_spatial_bce_6: 0.08695/0.10312, loss_spatial_dice_6: 0.15228/0.23712, loss_spatial_ce_6: 0.08243/0.14023, loss_grounding_bce_6: 0.02951/0.08830, loss_grounding_dice_6: 0.19475/0.18378, loss_grounding_ce_6: 0.03185/0.31440, loss_mask_ce_7: 1.11841/1.02285, loss_mask_bce_7: 0.62272/0.35179, loss_mask_dice_7: 1.14333/1.25700, loss_spatial_bce_7: 0.14936/0.11168, loss_spatial_dice_7: 0.15412/0.26454, loss_spatial_ce_7: 0.07540/0.17796, loss_grounding_bce_7: 0.02789/0.09021, loss_grounding_dice_7: 0.18534/0.19104, loss_grounding_ce_7: 0.03031/0.34738, loss_mask_ce_8: 1.35525/1.13318, loss_mask_bce_8: 0.48869/0.36541, loss_mask_dice_8: 1.36240/1.33146, loss_spatial_bce_8: 0.08671/0.13266, loss_spatial_dice_8: 0.19906/0.30399, loss_spatial_ce_8: 0.18922/0.23452, loss_grounding_bce_8: 0.03406/0.09382, loss_grounding_dice_8: 0.21881/0.20212, loss_grounding_ce_8: 0.51033/0.41685, loss_mask_ce_9: 6.14276/3.68492, loss_mask_bce_9: 0.69128/0.39227, loss_mask_dice_9: 1.70175/1.90475, loss_spatial_bce_9: 0.41299/0.33478, loss_spatial_dice_9: 0.93065/0.82330, loss_spatial_ce_9: 2.43006/1.50747, loss_grounding_bce_9: 0.03709/0.10533, loss_grounding_dice_9: 0.24227/0.28154, loss_grounding_ce_9: 0.08543/0.68436] items per batch[64] items per second[0.23] total items[2182400] mini batches[ 34100] memory[7341] epoch remaining[0:28:20]
INFO:trainer.default_trainer:epochs[    18] optim steps[34200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.95734/0.90890, loss_mask_bce_0: 0.59673/0.33502, loss_mask_dice_0: 1.52960/1.16592, loss_spatial_bce_0: 0.13535/0.08968, loss_spatial_dice_0: 0.26591/0.21437, loss_spatial_ce_0: 0.23254/0.07075, loss_grounding_bce_0: 0.17310/0.08627, loss_grounding_dice_0: 0.31080/0.17923, loss_grounding_ce_0: 0.20293/0.27599, loss_mask_ce_1: 2.28434/0.90941, loss_mask_bce_1: 0.52515/0.33582, loss_mask_dice_1: 1.41142/1.17282, loss_spatial_bce_1: 0.11832/0.09041, loss_spatial_dice_1: 0.27423/0.21858, loss_spatial_ce_1: 0.03869/0.07652, loss_grounding_bce_1: 0.16829/0.08642, loss_grounding_dice_1: 0.30594/0.18014, loss_grounding_ce_1: 0.21264/0.27752, loss_mask_ce_2: 2.05763/0.91688, loss_mask_bce_2: 0.61344/0.33626, loss_mask_dice_2: 1.31318/1.17230, loss_spatial_bce_2: 0.12857/0.09083, loss_spatial_dice_2: 0.27080/0.21961, loss_spatial_ce_2: 0.03450/0.08017, loss_grounding_bce_2: 0.17186/0.08645, loss_grounding_dice_2: 0.31412/0.17975, loss_grounding_ce_2: 0.21296/0.28077, loss_mask_ce_3: 2.07661/0.92597, loss_mask_bce_3: 0.58522/0.33710, loss_mask_dice_3: 1.53412/1.16955, loss_spatial_bce_3: 0.12720/0.09168, loss_spatial_dice_3: 0.26612/0.22019, loss_spatial_ce_3: 0.14966/0.08384, loss_grounding_bce_3: 0.19281/0.08671, loss_grounding_dice_3: 0.34748/0.17960, loss_grounding_ce_3: 0.09628/0.28239, loss_mask_ce_4: 2.13639/0.92496, loss_mask_bce_4: 0.60984/0.33902, loss_mask_dice_4: 1.40789/1.19261, loss_spatial_bce_4: 0.14031/0.09590, loss_spatial_dice_4: 0.30067/0.23133, loss_spatial_ce_4: 0.07671/0.10016, loss_grounding_bce_4: 0.28892/0.08716, loss_grounding_dice_4: 0.37393/0.18234, loss_grounding_ce_4: 0.07697/0.28514, loss_mask_ce_5: 2.25897/0.93979, loss_mask_bce_5: 0.50640/0.34133, loss_mask_dice_5: 1.39450/1.19885, loss_spatial_bce_5: 0.11509/0.09744, loss_spatial_dice_5: 0.31127/0.23486, loss_spatial_ce_5: 0.12688/0.11458, loss_grounding_bce_5: 0.20532/0.08754, loss_grounding_dice_5: 0.35597/0.18355, loss_grounding_ce_5: 0.14463/0.29783, loss_mask_ce_6: 2.59469/0.97790, loss_mask_bce_6: 0.52169/0.34399, loss_mask_dice_6: 1.51813/1.20118, loss_spatial_bce_6: 0.12381/0.10313, loss_spatial_dice_6: 0.29745/0.23711, loss_spatial_ce_6: 0.17592/0.14025, loss_grounding_bce_6: 0.19554/0.08829, loss_grounding_dice_6: 0.35932/0.18377, loss_grounding_ce_6: 0.15489/0.31427, loss_mask_ce_7: 2.39059/1.02290, loss_mask_bce_7: 0.63270/0.35184, loss_mask_dice_7: 1.47713/1.25682, loss_spatial_bce_7: 0.16217/0.11170, loss_spatial_dice_7: 0.32813/0.26456, loss_spatial_ce_7: 0.15522/0.17795, loss_grounding_bce_7: 0.26121/0.09021, loss_grounding_dice_7: 0.38334/0.19103, loss_grounding_ce_7: 0.05655/0.34719, loss_mask_ce_8: 2.30283/1.13320, loss_mask_bce_8: 0.82247/0.36546, loss_mask_dice_8: 1.81728/1.33133, loss_spatial_bce_8: 0.18392/0.13268, loss_spatial_dice_8: 0.32518/0.30398, loss_spatial_ce_8: 0.20643/0.23446, loss_grounding_bce_8: 0.27730/0.09382, loss_grounding_dice_8: 0.35521/0.20211, loss_grounding_ce_8: 0.20934/0.41662, loss_mask_ce_9: 5.20728/3.68483, loss_mask_bce_9: 0.77051/0.39228, loss_mask_dice_9: 2.47587/1.90451, loss_spatial_bce_9: 0.33929/0.33481, loss_spatial_dice_9: 0.92805/0.82332, loss_spatial_ce_9: 1.53417/1.50741, loss_grounding_bce_9: 0.31907/0.10532, loss_grounding_dice_9: 0.45721/0.28152, loss_grounding_ce_9: 0.74666/0.68421] items per batch[64] items per second[0.23] total items[2188800] mini batches[ 34200] memory[7341] epoch remaining[0:23:45]
INFO:trainer.default_trainer:epochs[    18] optim steps[34300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08339/0.90901, loss_mask_bce_0: 0.13398/0.33498, loss_mask_dice_0: 0.06246/1.16617, loss_spatial_bce_0: 0.09576/0.08967, loss_spatial_dice_0: 0.04041/0.21433, loss_spatial_ce_0: 0.00005/0.07070, loss_grounding_bce_0: 0.19795/0.08626, loss_grounding_dice_0: 0.06802/0.17925, loss_grounding_ce_0: 0.01407/0.27604, loss_mask_ce_1: 0.08350/0.90948, loss_mask_bce_1: 0.13749/0.33579, loss_mask_dice_1: 0.06575/1.17313, loss_spatial_bce_1: 0.09112/0.09040, loss_spatial_dice_1: 0.04475/0.21854, loss_spatial_ce_1: 0.00006/0.07644, loss_grounding_bce_1: 0.20325/0.08642, loss_grounding_dice_1: 0.06703/0.18016, loss_grounding_ce_1: 0.01503/0.27759, loss_mask_ce_2: 0.08200/0.91692, loss_mask_bce_2: 0.13492/0.33623, loss_mask_dice_2: 0.06618/1.17256, loss_spatial_bce_2: 0.09135/0.09083, loss_spatial_dice_2: 0.04010/0.21956, loss_spatial_ce_2: 0.00008/0.08009, loss_grounding_bce_2: 0.19463/0.08644, loss_grounding_dice_2: 0.06724/0.17978, loss_grounding_ce_2: 0.01334/0.28086, loss_mask_ce_3: 0.09321/0.92598, loss_mask_bce_3: 0.12958/0.33706, loss_mask_dice_3: 0.06406/1.16987, loss_spatial_bce_3: 0.08940/0.09168, loss_spatial_dice_3: 0.04578/0.22015, loss_spatial_ce_3: 0.00031/0.08377, loss_grounding_bce_3: 0.19294/0.08670, loss_grounding_dice_3: 0.06813/0.17962, loss_grounding_ce_3: 0.01355/0.28247, loss_mask_ce_4: 0.08094/0.92502, loss_mask_bce_4: 0.14165/0.33899, loss_mask_dice_4: 0.06560/1.19288, loss_spatial_bce_4: 0.09397/0.09590, loss_spatial_dice_4: 0.04670/0.23129, loss_spatial_ce_4: 0.00874/0.10007, loss_grounding_bce_4: 0.20567/0.08715, loss_grounding_dice_4: 0.06671/0.18235, loss_grounding_ce_4: 0.00778/0.28519, loss_mask_ce_5: 0.09133/0.93981, loss_mask_bce_5: 0.14593/0.34130, loss_mask_dice_5: 0.06168/1.19913, loss_spatial_bce_5: 0.09073/0.09743, loss_spatial_dice_5: 0.04534/0.23482, loss_spatial_ce_5: 0.03985/0.11451, loss_grounding_bce_5: 0.21323/0.08754, loss_grounding_dice_5: 0.06309/0.18357, loss_grounding_ce_5: 0.00452/0.29788, loss_mask_ce_6: 0.09059/0.97796, loss_mask_bce_6: 0.13657/0.34395, loss_mask_dice_6: 0.06410/1.20146, loss_spatial_bce_6: 0.09697/0.10313, loss_spatial_dice_6: 0.09558/0.23707, loss_spatial_ce_6: 0.03673/0.14020, loss_grounding_bce_6: 0.19847/0.08828, loss_grounding_dice_6: 0.06558/0.18378, loss_grounding_ce_6: 0.00852/0.31436, loss_mask_ce_7: 0.08963/1.02295, loss_mask_bce_7: 0.14715/0.35180, loss_mask_dice_7: 0.06532/1.25711, loss_spatial_bce_7: 0.08328/0.11170, loss_spatial_dice_7: 0.04687/0.26455, loss_spatial_ce_7: 0.04432/0.17784, loss_grounding_bce_7: 0.20646/0.09019, loss_grounding_dice_7: 0.06349/0.19104, loss_grounding_ce_7: 0.02486/0.34726, loss_mask_ce_8: 0.13207/1.13335, loss_mask_bce_8: 0.15559/0.36544, loss_mask_dice_8: 0.06007/1.33155, loss_spatial_bce_8: 0.07851/0.13268, loss_spatial_dice_8: 0.05423/0.30397, loss_spatial_ce_8: 0.04522/0.23436, loss_grounding_bce_8: 0.23110/0.09382, loss_grounding_dice_8: 0.06599/0.20214, loss_grounding_ce_8: 0.01309/0.41661, loss_mask_ce_9: 1.82207/3.68516, loss_mask_bce_9: 0.11555/0.39227, loss_mask_dice_9: 0.12504/1.90476, loss_spatial_bce_9: 0.48660/0.33482, loss_spatial_dice_9: 0.62145/0.82328, loss_spatial_ce_9: 0.80155/1.50741, loss_grounding_bce_9: 0.14564/0.10532, loss_grounding_dice_9: 0.07189/0.28156, loss_grounding_ce_9: 0.19447/0.68408] items per batch[64] items per second[0.23] total items[2195200] mini batches[ 34300] memory[7341] epoch remaining[0:19:07]
INFO:trainer.default_trainer:epochs[    18] optim steps[34400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90288/0.90898, loss_mask_bce_0: 0.36988/0.33496, loss_mask_dice_0: 1.10789/1.16716, loss_spatial_bce_0: 0.07773/0.08963, loss_spatial_dice_0: 0.20526/0.21437, loss_spatial_ce_0: 0.11265/0.07068, loss_grounding_bce_0: 0.09036/0.08626, loss_grounding_dice_0: 0.11309/0.17931, loss_grounding_ce_0: 0.15900/0.27606, loss_mask_ce_1: 0.64696/0.90947, loss_mask_bce_1: 0.38670/0.33578, loss_mask_dice_1: 1.14912/1.17413, loss_spatial_bce_1: 0.08077/0.09035, loss_spatial_dice_1: 0.21744/0.21858, loss_spatial_ce_1: 0.12060/0.07643, loss_grounding_bce_1: 0.09378/0.08641, loss_grounding_dice_1: 0.11408/0.18023, loss_grounding_ce_1: 0.16362/0.27761, loss_mask_ce_2: 0.89053/0.91699, loss_mask_bce_2: 0.35464/0.33621, loss_mask_dice_2: 1.07976/1.17356, loss_spatial_bce_2: 0.11573/0.09079, loss_spatial_dice_2: 0.22518/0.21961, loss_spatial_ce_2: 0.04869/0.08008, loss_grounding_bce_2: 0.09190/0.08644, loss_grounding_dice_2: 0.11533/0.17984, loss_grounding_ce_2: 0.16346/0.28092, loss_mask_ce_3: 0.92851/0.92598, loss_mask_bce_3: 0.35509/0.33705, loss_mask_dice_3: 1.03406/1.17081, loss_spatial_bce_3: 0.09934/0.09163, loss_spatial_dice_3: 0.21870/0.22019, loss_spatial_ce_3: 0.05108/0.08377, loss_grounding_bce_3: 0.10055/0.08671, loss_grounding_dice_3: 0.12542/0.17968, loss_grounding_ce_3: 0.17195/0.28252, loss_mask_ce_4: 0.64818/0.92503, loss_mask_bce_4: 0.37763/0.33897, loss_mask_dice_4: 1.17211/1.19393, loss_spatial_bce_4: 0.07750/0.09584, loss_spatial_dice_4: 0.20342/0.23134, loss_spatial_ce_4: 0.05841/0.10005, loss_grounding_bce_4: 0.09143/0.08716, loss_grounding_dice_4: 0.10881/0.18241, loss_grounding_ce_4: 0.16836/0.28523, loss_mask_ce_5: 0.70215/0.93986, loss_mask_bce_5: 0.38035/0.34127, loss_mask_dice_5: 1.18584/1.20012, loss_spatial_bce_5: 0.08753/0.09739, loss_spatial_dice_5: 0.23753/0.23488, loss_spatial_ce_5: 0.10640/0.11446, loss_grounding_bce_5: 0.08980/0.08755, loss_grounding_dice_5: 0.11387/0.18364, loss_grounding_ce_5: 0.15690/0.29792, loss_mask_ce_6: 0.74943/0.97806, loss_mask_bce_6: 0.39544/0.34392, loss_mask_dice_6: 1.22961/1.20242, loss_spatial_bce_6: 0.07606/0.10308, loss_spatial_dice_6: 0.22007/0.23713, loss_spatial_ce_6: 0.11654/0.14022, loss_grounding_bce_6: 0.08734/0.08829, loss_grounding_dice_6: 0.10773/0.18384, loss_grounding_ce_6: 0.19579/0.31432, loss_mask_ce_7: 0.67981/1.02316, loss_mask_bce_7: 0.41571/0.35176, loss_mask_dice_7: 1.30361/1.25814, loss_spatial_bce_7: 0.07377/0.11165, loss_spatial_dice_7: 0.23975/0.26464, loss_spatial_ce_7: 0.17994/0.17777, loss_grounding_bce_7: 0.09300/0.09019, loss_grounding_dice_7: 0.11455/0.19109, loss_grounding_ce_7: 0.21697/0.34731, loss_mask_ce_8: 0.66548/1.13340, loss_mask_bce_8: 0.47109/0.36542, loss_mask_dice_8: 1.50490/1.33261, loss_spatial_bce_8: 0.07903/0.13263, loss_spatial_dice_8: 0.24481/0.30407, loss_spatial_ce_8: 0.14478/0.23437, loss_grounding_bce_8: 0.08720/0.09382, loss_grounding_dice_8: 0.10669/0.20220, loss_grounding_ce_8: 0.30854/0.41659, loss_mask_ce_9: 3.43377/3.68510, loss_mask_bce_9: 0.46661/0.39221, loss_mask_dice_9: 2.29270/1.90598, loss_spatial_bce_9: 0.22515/0.33467, loss_spatial_dice_9: 0.85982/0.82333, loss_spatial_ce_9: 1.56925/1.50753, loss_grounding_bce_9: 0.12185/0.10531, loss_grounding_dice_9: 0.19451/0.28160, loss_grounding_ce_9: 0.30940/0.68397] items per batch[64] items per second[0.23] total items[2201600] mini batches[ 34400] memory[7341] epoch remaining[0:14:30]
INFO:trainer.default_trainer:epochs[    18] optim steps[34500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71501/0.90874, loss_mask_bce_0: 0.41849/0.33495, loss_mask_dice_0: 1.67704/1.16707, loss_spatial_bce_0: 0.03972/0.08963, loss_spatial_dice_0: 0.12851/0.21432, loss_spatial_ce_0: 0.07004/0.07066, loss_grounding_bce_0: 0.05543/0.08626, loss_grounding_dice_0: 0.11496/0.17925, loss_grounding_ce_0: 0.01144/0.27599, loss_mask_ce_1: 0.63191/0.90922, loss_mask_bce_1: 0.42015/0.33577, loss_mask_dice_1: 1.69161/1.17405, loss_spatial_bce_1: 0.04190/0.09035, loss_spatial_dice_1: 0.16861/0.21855, loss_spatial_ce_1: 0.09828/0.07637, loss_grounding_bce_1: 0.05698/0.08641, loss_grounding_dice_1: 0.11834/0.18018, loss_grounding_ce_1: 0.00961/0.27758, loss_mask_ce_2: 0.60931/0.91675, loss_mask_bce_2: 0.42857/0.33619, loss_mask_dice_2: 1.60866/1.17346, loss_spatial_bce_2: 0.04270/0.09079, loss_spatial_dice_2: 0.16702/0.21957, loss_spatial_ce_2: 0.08141/0.08002, loss_grounding_bce_2: 0.05544/0.08644, loss_grounding_dice_2: 0.11733/0.17979, loss_grounding_ce_2: 0.01013/0.28085, loss_mask_ce_3: 0.58657/0.92572, loss_mask_bce_3: 0.42926/0.33702, loss_mask_dice_3: 1.76390/1.17072, loss_spatial_bce_3: 0.05484/0.09163, loss_spatial_dice_3: 0.18161/0.22014, loss_spatial_ce_3: 0.08812/0.08372, loss_grounding_bce_3: 0.05639/0.08671, loss_grounding_dice_3: 0.11506/0.17964, loss_grounding_ce_3: 0.00738/0.28247, loss_mask_ce_4: 0.63538/0.92479, loss_mask_bce_4: 0.44303/0.33894, loss_mask_dice_4: 2.07077/1.19384, loss_spatial_bce_4: 0.05084/0.09584, loss_spatial_dice_4: 0.18784/0.23130, loss_spatial_ce_4: 0.17318/0.10001, loss_grounding_bce_4: 0.06046/0.08715, loss_grounding_dice_4: 0.12255/0.18237, loss_grounding_ce_4: 0.01533/0.28521, loss_mask_ce_5: 0.68035/0.93959, loss_mask_bce_5: 0.42013/0.34125, loss_mask_dice_5: 1.83710/1.20005, loss_spatial_bce_5: 0.06172/0.09739, loss_spatial_dice_5: 0.19877/0.23484, loss_spatial_ce_5: 0.10389/0.11444, loss_grounding_bce_5: 0.05908/0.08755, loss_grounding_dice_5: 0.12469/0.18360, loss_grounding_ce_5: 0.02326/0.29790, loss_mask_ce_6: 0.59848/0.97780, loss_mask_bce_6: 0.38638/0.34390, loss_mask_dice_6: 1.69199/1.20236, loss_spatial_bce_6: 0.06563/0.10310, loss_spatial_dice_6: 0.18101/0.23710, loss_spatial_ce_6: 0.15362/0.14021, loss_grounding_bce_6: 0.05693/0.08829, loss_grounding_dice_6: 0.11142/0.18381, loss_grounding_ce_6: 0.02010/0.31425, loss_mask_ce_7: 0.82271/1.02291, loss_mask_bce_7: 0.39355/0.35172, loss_mask_dice_7: 2.05405/1.25807, loss_spatial_bce_7: 0.04771/0.11164, loss_spatial_dice_7: 0.18257/0.26462, loss_spatial_ce_7: 0.23233/0.17775, loss_grounding_bce_7: 0.07041/0.09019, loss_grounding_dice_7: 0.12663/0.19104, loss_grounding_ce_7: 0.13153/0.34730, loss_mask_ce_8: 1.01123/1.13316, loss_mask_bce_8: 0.40082/0.36539, loss_mask_dice_8: 2.31646/1.33248, loss_spatial_bce_8: 0.06435/0.13264, loss_spatial_dice_8: 0.33695/0.30404, loss_spatial_ce_8: 0.37947/0.23437, loss_grounding_bce_8: 0.07477/0.09383, loss_grounding_dice_8: 0.12218/0.20216, loss_grounding_ce_8: 0.17989/0.41657, loss_mask_ce_9: 4.28043/3.68473, loss_mask_bce_9: 0.38680/0.39217, loss_mask_dice_9: 3.07934/1.90574, loss_spatial_bce_9: 0.22269/0.33472, loss_spatial_dice_9: 0.88029/0.82332, loss_spatial_ce_9: 1.27058/1.50749, loss_grounding_bce_9: 0.05449/0.10532, loss_grounding_dice_9: 0.18353/0.28154, loss_grounding_ce_9: 1.25159/0.68394] items per batch[64] items per second[0.23] total items[2208000] mini batches[ 34500] memory[7341] epoch remaining[0:09:52]
INFO:trainer.default_trainer:epochs[    18] optim steps[34600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.65578/0.90870, loss_mask_bce_0: 0.17790/0.33493, loss_mask_dice_0: 3.16258/1.16696, loss_spatial_bce_0: 0.01960/0.08964, loss_spatial_dice_0: 0.25477/0.21432, loss_spatial_ce_0: 0.00678/0.07063, loss_grounding_bce_0: 0.01911/0.08625, loss_grounding_dice_0: 0.17257/0.17924, loss_grounding_ce_0: 0.15380/0.27601, loss_mask_ce_1: 1.73129/0.90918, loss_mask_bce_1: 0.19430/0.33575, loss_mask_dice_1: 3.27248/1.17390, loss_spatial_bce_1: 0.01901/0.09037, loss_spatial_dice_1: 0.26096/0.21855, loss_spatial_ce_1: 0.00423/0.07634, loss_grounding_bce_1: 0.01964/0.08640, loss_grounding_dice_1: 0.14262/0.18017, loss_grounding_ce_1: 0.18877/0.27760, loss_mask_ce_2: 1.67530/0.91670, loss_mask_bce_2: 0.20225/0.33616, loss_mask_dice_2: 3.07357/1.17332, loss_spatial_bce_2: 0.02020/0.09080, loss_spatial_dice_2: 0.28056/0.21957, loss_spatial_ce_2: 0.00707/0.07998, loss_grounding_bce_2: 0.01715/0.08643, loss_grounding_dice_2: 0.16479/0.17981, loss_grounding_ce_2: 0.17188/0.28088, loss_mask_ce_3: 1.75852/0.92572, loss_mask_bce_3: 0.21447/0.33701, loss_mask_dice_3: 3.06749/1.17060, loss_spatial_bce_3: 0.02057/0.09165, loss_spatial_dice_3: 0.23722/0.22015, loss_spatial_ce_3: 0.00786/0.08370, loss_grounding_bce_3: 0.01701/0.08670, loss_grounding_dice_3: 0.17475/0.17964, loss_grounding_ce_3: 0.16407/0.28249, loss_mask_ce_4: 1.80043/0.92478, loss_mask_bce_4: 0.20703/0.33892, loss_mask_dice_4: 3.13764/1.19373, loss_spatial_bce_4: 0.01986/0.09586, loss_spatial_dice_4: 0.26406/0.23130, loss_spatial_ce_4: 0.02997/0.09996, loss_grounding_bce_4: 0.01699/0.08714, loss_grounding_dice_4: 0.14610/0.18237, loss_grounding_ce_4: 0.18371/0.28522, loss_mask_ce_5: 1.98249/0.93957, loss_mask_bce_5: 0.17144/0.34123, loss_mask_dice_5: 2.95823/1.19994, loss_spatial_bce_5: 0.02806/0.09741, loss_spatial_dice_5: 0.27028/0.23485, loss_spatial_ce_5: 0.09023/0.11442, loss_grounding_bce_5: 0.01655/0.08753, loss_grounding_dice_5: 0.15211/0.18360, loss_grounding_ce_5: 0.20301/0.29786, loss_mask_ce_6: 2.04085/0.97780, loss_mask_bce_6: 0.19296/0.34387, loss_mask_dice_6: 2.98759/1.20222, loss_spatial_bce_6: 0.03622/0.10312, loss_spatial_dice_6: 0.30272/0.23712, loss_spatial_ce_6: 0.08468/0.14016, loss_grounding_bce_6: 0.01790/0.08828, loss_grounding_dice_6: 0.17577/0.18381, loss_grounding_ce_6: 0.16459/0.31422, loss_mask_ce_7: 2.02856/1.02285, loss_mask_bce_7: 0.18082/0.35170, loss_mask_dice_7: 3.01135/1.25793, loss_spatial_bce_7: 0.02447/0.11169, loss_spatial_dice_7: 0.32980/0.26465, loss_spatial_ce_7: 0.11954/0.17766, loss_grounding_bce_7: 0.02852/0.09017, loss_grounding_dice_7: 0.18464/0.19105, loss_grounding_ce_7: 0.19217/0.34725, loss_mask_ce_8: 2.57268/1.13312, loss_mask_bce_8: 0.22272/0.36537, loss_mask_dice_8: 3.10421/1.33230, loss_spatial_bce_8: 0.02988/0.13267, loss_spatial_dice_8: 0.39589/0.30407, loss_spatial_ce_8: 0.25392/0.23434, loss_grounding_bce_8: 0.01804/0.09380, loss_grounding_dice_8: 0.13038/0.20216, loss_grounding_ce_8: 0.05917/0.41642, loss_mask_ce_9: 4.53175/3.68437, loss_mask_bce_9: 0.23035/0.39213, loss_mask_dice_9: 5.05867/1.90541, loss_spatial_bce_9: 0.09497/0.33473, loss_spatial_dice_9: 0.91976/0.82333, loss_spatial_ce_9: 1.87401/1.50745, loss_grounding_bce_9: 0.02320/0.10529, loss_grounding_dice_9: 0.20898/0.28154, loss_grounding_ce_9: 1.59816/0.68386] items per batch[64] items per second[0.23] total items[2214400] mini batches[ 34600] memory[7341] epoch remaining[0:05:14]
INFO:trainer.default_trainer:epochs[    18] optim steps[34700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08647/0.90862, loss_mask_bce_0: 0.28063/0.33496, loss_mask_dice_0: 3.25137/1.16696, loss_spatial_bce_0: 0.05064/0.08964, loss_spatial_dice_0: 0.29821/0.21429, loss_spatial_ce_0: 0.17076/0.07060, loss_grounding_bce_0: 0.02582/0.08629, loss_grounding_dice_0: 0.44069/0.17929, loss_grounding_ce_0: 0.43875/0.27600, loss_mask_ce_1: 1.17125/0.90917, loss_mask_bce_1: 0.28605/0.33579, loss_mask_dice_1: 3.83567/1.17388, loss_spatial_bce_1: 0.04890/0.09036, loss_spatial_dice_1: 0.30981/0.21852, loss_spatial_ce_1: 0.15335/0.07628, loss_grounding_bce_1: 0.02139/0.08644, loss_grounding_dice_1: 0.42281/0.18021, loss_grounding_ce_1: 0.44952/0.27758, loss_mask_ce_2: 0.97978/0.91663, loss_mask_bce_2: 0.27709/0.33619, loss_mask_dice_2: 3.52321/1.17330, loss_spatial_bce_2: 0.04710/0.09080, loss_spatial_dice_2: 0.32039/0.21954, loss_spatial_ce_2: 0.02910/0.07991, loss_grounding_bce_2: 0.02409/0.08647, loss_grounding_dice_2: 0.45254/0.17983, loss_grounding_ce_2: 0.50720/0.28087, loss_mask_ce_3: 1.06163/0.92567, loss_mask_bce_3: 0.27223/0.33705, loss_mask_dice_3: 3.49941/1.17060, loss_spatial_bce_3: 0.04982/0.09164, loss_spatial_dice_3: 0.27976/0.22013, loss_spatial_ce_3: 0.02492/0.08366, loss_grounding_bce_3: 0.01824/0.08674, loss_grounding_dice_3: 0.40328/0.17967, loss_grounding_ce_3: 0.43311/0.28247, loss_mask_ce_4: 1.02476/0.92472, loss_mask_bce_4: 0.28202/0.33896, loss_mask_dice_4: 3.70383/1.19375, loss_spatial_bce_4: 0.05988/0.09586, loss_spatial_dice_4: 0.35264/0.23129, loss_spatial_ce_4: 0.12981/0.09988, loss_grounding_bce_4: 0.01945/0.08718, loss_grounding_dice_4: 0.45763/0.18241, loss_grounding_ce_4: 0.46413/0.28524, loss_mask_ce_5: 1.03839/0.93954, loss_mask_bce_5: 0.28048/0.34127, loss_mask_dice_5: 3.72067/1.19994, loss_spatial_bce_5: 0.05759/0.09741, loss_spatial_dice_5: 0.35281/0.23484, loss_spatial_ce_5: 0.32213/0.11435, loss_grounding_bce_5: 0.01585/0.08757, loss_grounding_dice_5: 0.35257/0.18364, loss_grounding_ce_5: 0.45540/0.29785, loss_mask_ce_6: 1.05415/0.97778, loss_mask_bce_6: 0.26445/0.34392, loss_mask_dice_6: 3.35851/1.20222, loss_spatial_bce_6: 0.05603/0.10313, loss_spatial_dice_6: 0.36220/0.23711, loss_spatial_ce_6: 0.05949/0.14008, loss_grounding_bce_6: 0.01744/0.08831, loss_grounding_dice_6: 0.35508/0.18385, loss_grounding_ce_6: 0.44026/0.31422, loss_mask_ce_7: 1.23438/1.02282, loss_mask_bce_7: 0.28005/0.35174, loss_mask_dice_7: 3.66701/1.25794, loss_spatial_bce_7: 0.05374/0.11170, loss_spatial_dice_7: 0.42246/0.26467, loss_spatial_ce_7: 0.19846/0.17761, loss_grounding_bce_7: 0.01820/0.09022, loss_grounding_dice_7: 0.46993/0.19108, loss_grounding_ce_7: 0.50386/0.34720, loss_mask_ce_8: 1.00851/1.13307, loss_mask_bce_8: 0.31126/0.36538, loss_mask_dice_8: 3.97792/1.33233, loss_spatial_bce_8: 0.07604/0.13268, loss_spatial_dice_8: 0.40380/0.30408, loss_spatial_ce_8: 0.13083/0.23433, loss_grounding_bce_8: 0.02992/0.09384, loss_grounding_dice_8: 0.49755/0.20221, loss_grounding_ce_8: 0.62190/0.41641, loss_mask_ce_9: 3.43600/3.68446, loss_mask_bce_9: 0.28501/0.39217, loss_mask_dice_9: 5.52187/1.90551, loss_spatial_bce_9: 0.19958/0.33474, loss_spatial_dice_9: 0.88983/0.82333, loss_spatial_ce_9: 1.58935/1.50741, loss_grounding_bce_9: 0.01441/0.10533, loss_grounding_dice_9: 0.56938/0.28161, loss_grounding_ce_9: 0.34057/0.68375] items per batch[64] items per second[0.22] total items[2220800] mini batches[ 34700] memory[7341] epoch remaining[0:00:36]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00034713.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0025 s/iter. Inference: 0.2188 s/iter. Eval: 0.0929 s/iter. Total: 0.3143 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2201 s/iter. Eval: 0.0875 s/iter. Total: 0.3107 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2232 s/iter. Eval: 0.0825 s/iter. Total: 0.3089 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2226 s/iter. Eval: 0.0812 s/iter. Total: 0.3070 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2216 s/iter. Eval: 0.0799 s/iter. Total: 0.3047 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2237 s/iter. Eval: 0.0797 s/iter. Total: 0.3066 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2258 s/iter. Eval: 0.0797 s/iter. Total: 0.3088 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2251 s/iter. Eval: 0.0786 s/iter. Total: 0.3070 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 143/157. Dataloading: 0.0069 s/iter. Inference: 0.2255 s/iter. Eval: 0.0788 s/iter. Total: 0.3113 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaltfd8q27i ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.698 | 81.965 | 59.719 |      133      |
| Things | 54.689 | 82.701 | 65.400 |      80       |
| Stuff  | 42.165 | 80.855 | 51.145 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.41s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.66 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.33s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.42 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.712
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.11 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.809 | 61.100 | 40.654 | 19.016 | 41.789 | 60.230 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.551 | bicycle      | 18.944 | car            | 37.171 |
| motorcycle    | 34.816 | airplane     | 56.918 | bus            | 65.165 |
| train         | 68.530 | truck        | 35.427 | boat           | 21.766 |
| traffic light | 25.303 | fire hydrant | 64.795 | stop sign      | 63.796 |
| parking meter | 41.424 | bench        | 20.181 | bird           | 29.288 |
| cat           | 74.029 | dog          | 65.505 | horse          | 45.664 |
| sheep         | 47.631 | cow          | 51.627 | elephant       | 60.462 |
| bear          | 76.920 | zebra        | 60.929 | giraffe        | 56.603 |
| backpack      | 15.569 | umbrella     | 48.312 | handbag        | 14.845 |
| tie           | 33.259 | suitcase     | 40.977 | frisbee        | 68.702 |
| skis          | 5.183  | snowboard    | 23.493 | sports ball    | 46.469 |
| kite          | 34.762 | baseball bat | 28.789 | baseball glove | 43.491 |
| skateboard    | 35.501 | surfboard    | 35.345 | tennis racket  | 56.694 |
| bottle        | 34.277 | wine glass   | 27.011 | cup            | 40.701 |
| fork          | 15.391 | knife        | 12.779 | spoon          | 14.265 |
| bowl          | 32.159 | banana       | 21.135 | apple          | 19.542 |
| sandwich      | 44.040 | orange       | 28.400 | broccoli       | 21.725 |
| carrot        | 20.158 | hot dog      | 26.580 | pizza          | 52.124 |
| donut         | 45.406 | cake         | 43.073 | chair          | 21.004 |
| couch         | 38.924 | potted plant | 17.982 | bed            | 40.586 |
| dining table  | 13.157 | toilet       | 66.026 | tv             | 61.804 |
| laptop        | 63.142 | mouse        | 60.593 | remote         | 31.127 |
| keyboard      | 47.855 | cell phone   | 36.965 | microwave      | 55.905 |
| oven          | 33.957 | toaster      | 28.600 | sink           | 37.744 |
| refrigerator  | 57.528 | book         | 8.290  | clock          | 52.050 |
| vase          | 33.445 | scissors     | 24.289 | teddy bear     | 50.752 |
| hair drier    | 8.315  | toothbrush   | 17.109 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 61.123129879608754, 'fwIoU': 69.30904030219229, 'IoU-person': 87.5439196071816, 'IoU-bicycle': 74.76695780119414, 'IoU-car': 69.03717696833799, 'IoU-motorcycle': 85.10497158231554, 'IoU-airplane': 80.47112716817784, 'IoU-bus': 87.09871804773918, 'IoU-train': 85.87389615530333, 'IoU-truck': 64.55322474178769, 'IoU-boat': 65.09657031131263, 'IoU-traffic light': 76.81823468556982, 'IoU-fire hydrant': 89.67867912279075, 'IoU-stop sign': 91.0207153500352, 'IoU-parking meter': 83.05388276880376, 'IoU-bench': 54.72933275609695, 'IoU-bird': 75.63578787855903, 'IoU-cat': 85.9196331890845, 'IoU-dog': 80.20050010682972, 'IoU-horse': 86.22405792183353, 'IoU-sheep': 86.94025113257406, 'IoU-cow': 83.03261353294465, 'IoU-elephant': 88.95079883815134, 'IoU-bear': 84.17281162887453, 'IoU-zebra': 87.48044191205348, 'IoU-giraffe': 88.16939401687549, 'IoU-backpack': 39.839852125693156, 'IoU-umbrella': 76.86659606205946, 'IoU-handbag': 37.2558947428615, 'IoU-tie': 70.18785678457974, 'IoU-suitcase': 82.00034235294147, 'IoU-frisbee': 83.9425363040425, 'IoU-skis': 51.54918062665007, 'IoU-snowboard': 67.83941605839416, 'IoU-sports ball': 65.5583363630983, 'IoU-kite': 66.00773608063376, 'IoU-baseball bat': 58.78151194591005, 'IoU-baseball glove': 73.89641000130287, 'IoU-skateboard': 64.01508879413834, 'IoU-surfboard': 75.42050634128003, 'IoU-tennis racket': 83.27879590337658, 'IoU-bottle': 66.78599108084414, 'IoU-wine glass': 76.36257745396969, 'IoU-cup': 57.74191537210898, 'IoU-fork': 55.64747228980805, 'IoU-knife': 50.07765234250016, 'IoU-spoon': 48.91729950967726, 'IoU-bowl': 54.97710444328061, 'IoU-banana': 83.9889699898783, 'IoU-apple': 59.04373064806027, 'IoU-sandwich': 64.9748943053172, 'IoU-orange': 73.24199626771907, 'IoU-broccoli': 64.14248132779792, 'IoU-carrot': 62.32715213457656, 'IoU-hot dog': 62.381030737384734, 'IoU-pizza': 85.34154327042205, 'IoU-donut': 64.24493671825155, 'IoU-cake': 68.86208648801664, 'IoU-chair': 54.14524874838848, 'IoU-couch': 68.43950487858297, 'IoU-potted plant': 34.33193272249556, 'IoU-bed': 66.55229946725805, 'IoU-dining table': 50.737959244015954, 'IoU-toilet': 85.736608067429, 'IoU-tv': 75.18230247585937, 'IoU-laptop': 75.42383609027603, 'IoU-mouse': 71.08731344387415, 'IoU-remote': 49.29952360745097, 'IoU-keyboard': 61.360158405414744, 'IoU-cell phone': 68.13063463872467, 'IoU-microwave': 63.52459802118619, 'IoU-oven': 64.88232341356571, 'IoU-toaster': 72.01004873028845, 'IoU-sink': 70.6725521212827, 'IoU-refrigerator': 79.62563387015219, 'IoU-book': 51.17156627619561, 'IoU-clock': 76.70291667533567, 'IoU-vase': 65.79445069919817, 'IoU-scissors': 62.1537977339488, 'IoU-teddy bear': 82.55995523642125, 'IoU-hair drier': 31.464970844249134, 'IoU-toothbrush': 59.58372267480884, 'IoU-banner': 35.76374843019052, 'IoU-blanket': 10.866539287206232, 'IoU-bridge': 38.7763856963819, 'IoU-cardboard': 49.108530705266, 'IoU-counter': 32.05995723684659, 'IoU-curtain': 65.2628465651753, 'IoU-door-stuff': 42.09860303813345, 'IoU-floor-wood': 60.37330786182827, 'IoU-flower': 45.17953317016225, 'IoU-fruit': 36.79567376830001, 'IoU-gravel': 30.481920742511438, 'IoU-house': 22.692202559156637, 'IoU-light': 39.12421768632574, 'IoU-mirror-stuff': 56.668997911003146, 'IoU-net': 46.60038915548825, 'IoU-pillow': 13.015519022150295, 'IoU-platform': 30.2934016134919, 'IoU-playingfield': 67.98363526943356, 'IoU-railroad': 62.1321144040146, 'IoU-river': 49.63198413190874, 'IoU-road': 66.64025544804531, 'IoU-roof': 16.755435824515224, 'IoU-sand': 64.17912029936554, 'IoU-sea': 84.72232186699323, 'IoU-shelf': 36.55711079811574, 'IoU-snow': 87.84506244233332, 'IoU-stairs': 30.130615556341468, 'IoU-tent': 7.7047934233254995, 'IoU-towel': 30.966972981740987, 'IoU-wall-brick': 46.35959145419793, 'IoU-wall-stone': 28.316532726453225, 'IoU-wall-tile': 67.52442068087338, 'IoU-wall-wood': 39.02406202499536, 'IoU-water-other': 26.270574221299942, 'IoU-window-blind': 46.201750924967804, 'IoU-window-other': 46.45927730093344, 'IoU-tree-merged': 81.03407512869917, 'IoU-fence-merged': 53.54384869575763, 'IoU-ceiling-merged': 67.66600168969076, 'IoU-sky-other-merged': 92.86156062022017, 'IoU-cabinet-merged': 59.28325386927804, 'IoU-table-merged': 38.21334462996505, 'IoU-floor-other-merged': 48.549678780892975, 'IoU-pavement-merged': 54.273591097861775, 'IoU-mountain-merged': 56.311938292168286, 'IoU-grass-merged': 71.82584246086701, 'IoU-dirt-merged': 45.69853648966019, 'IoU-paper-merged': 33.225428436747265, 'IoU-food-other-merged': 39.31934961914292, 'IoU-building-other-merged': 58.20931665891652, 'IoU-rock-merged': 61.31510195258373, 'IoU-wall-other-merged': 66.46945729208326, 'IoU-rug-merged': 63.362091866552774, 'mACC': 73.34437434120396, 'pACC': 80.59778589499794, 'ACC-person': 92.45256253050464, 'ACC-bicycle': 86.52927637556135, 'ACC-car': 85.75239058291893, 'ACC-motorcycle': 90.99806789796114, 'ACC-airplane': 87.77471293248973, 'ACC-bus': 91.57097047640909, 'ACC-train': 95.45371735401196, 'ACC-truck': 77.19187055358779, 'ACC-boat': 79.08502237237265, 'ACC-traffic light': 89.92772931996765, 'ACC-fire hydrant': 95.48202931845064, 'ACC-stop sign': 94.29898728823824, 'ACC-parking meter': 87.33102044148846, 'ACC-bench': 69.3316154758579, 'ACC-bird': 80.37407126838204, 'ACC-cat': 91.42804844650072, 'ACC-dog': 83.26297788399117, 'ACC-horse': 92.4840011749356, 'ACC-sheep': 90.44446040027584, 'ACC-cow': 88.50418847917739, 'ACC-elephant': 91.49188204486568, 'ACC-bear': 86.341107502069, 'ACC-zebra': 89.91235553719851, 'ACC-giraffe': 92.47381235429891, 'ACC-backpack': 59.03037659617356, 'ACC-umbrella': 84.9562801954425, 'ACC-handbag': 56.10196279638835, 'ACC-tie': 80.37260353403087, 'ACC-suitcase': 90.76063307348974, 'ACC-frisbee': 94.10690909090908, 'ACC-skis': 67.45792972330212, 'ACC-snowboard': 77.39119786436775, 'ACC-sports ball': 80.1485409495457, 'ACC-kite': 75.93965753752212, 'ACC-baseball bat': 84.22330278941178, 'ACC-baseball glove': 89.9101474964513, 'ACC-skateboard': 69.41090528791558, 'ACC-surfboard': 83.76511506297352, 'ACC-tennis racket': 89.34321810579785, 'ACC-bottle': 81.21688857706606, 'ACC-wine glass': 85.34931370238074, 'ACC-cup': 84.258748871579, 'ACC-fork': 65.23379829669743, 'ACC-knife': 62.79072323587601, 'ACC-spoon': 65.7525575736651, 'ACC-bowl': 71.64025108694176, 'ACC-banana': 89.5651366608272, 'ACC-apple': 72.42156999421239, 'ACC-sandwich': 77.08539121253645, 'ACC-orange': 80.55314934023527, 'ACC-broccoli': 72.36466854215689, 'ACC-carrot': 70.13563333153829, 'ACC-hot dog': 73.72145163609743, 'ACC-pizza': 93.72806552199752, 'ACC-donut': 81.91125944125979, 'ACC-cake': 76.31581372235358, 'ACC-chair': 70.2584682914702, 'ACC-couch': 82.30833939999047, 'ACC-potted plant': 51.256587837767185, 'ACC-bed': 78.27567776152019, 'ACC-dining table': 77.95901220859166, 'ACC-toilet': 90.18643928102914, 'ACC-tv': 88.17235546822464, 'ACC-laptop': 92.09194119746944, 'ACC-mouse': 86.6854972241161, 'ACC-remote': 72.89086533097435, 'ACC-keyboard': 67.1294818858902, 'ACC-cell phone': 78.22093253704752, 'ACC-microwave': 73.60131705074951, 'ACC-oven': 81.81930116883052, 'ACC-toaster': 81.40045710219108, 'ACC-sink': 85.18409388361596, 'ACC-refrigerator': 91.27912253899633, 'ACC-book': 64.37767924686895, 'ACC-clock': 83.0491907163156, 'ACC-vase': 76.27040688602112, 'ACC-scissors': 66.6383173107824, 'ACC-teddy bear': 87.93478603030111, 'ACC-hair drier': 42.39034525944269, 'ACC-toothbrush': 78.65444753300903, 'ACC-banner': 68.57935928055778, 'ACC-blanket': 16.563488417475018, 'ACC-bridge': 56.23933392927887, 'ACC-cardboard': 61.80866656972517, 'ACC-counter': 51.46865405795189, 'ACC-curtain': 78.09442417872619, 'ACC-door-stuff': 60.18482697818898, 'ACC-floor-wood': 78.02525228910339, 'ACC-flower': 65.56356962997738, 'ACC-fruit': 57.249013247089174, 'ACC-gravel': 40.00323553192981, 'ACC-house': 25.573970061858386, 'ACC-light': 59.50467499904298, 'ACC-mirror-stuff': 68.86252916745377, 'ACC-net': 60.58351235885805, 'ACC-pillow': 31.33822430720737, 'ACC-platform': 52.261899552224065, 'ACC-playingfield': 87.19350167101663, 'ACC-railroad': 78.32742945737407, 'ACC-river': 71.65745396456394, 'ACC-road': 85.9414856562207, 'ACC-roof': 23.218513040537513, 'ACC-sand': 69.9402501112797, 'ACC-sea': 90.49326622960339, 'ACC-shelf': 58.46857621213353, 'ACC-snow': 94.18761482926591, 'ACC-stairs': 47.11896217761791, 'ACC-tent': 9.11950934921438, 'ACC-towel': 35.670726753789744, 'ACC-wall-brick': 60.76224714940955, 'ACC-wall-stone': 35.904162420031675, 'ACC-wall-tile': 82.32158623958574, 'ACC-wall-wood': 52.517889332623405, 'ACC-water-other': 43.177274354952225, 'ACC-window-blind': 56.95063211339776, 'ACC-window-other': 70.21116842295385, 'ACC-tree-merged': 89.58655014316348, 'ACC-fence-merged': 73.24051234205734, 'ACC-ceiling-merged': 81.09176892124941, 'ACC-sky-other-merged': 96.7014633370824, 'ACC-cabinet-merged': 76.24091077631722, 'ACC-table-merged': 49.36377284765744, 'ACC-floor-other-merged': 59.07994488959948, 'ACC-pavement-merged': 67.53272025738367, 'ACC-mountain-merged': 66.43595371565216, 'ACC-grass-merged': 81.88866984669733, 'ACC-dirt-merged': 71.9922200479563, 'ACC-paper-merged': 46.211262531273526, 'ACC-food-other-merged': 50.302623344752284, 'ACC-building-other-merged': 75.31248691529787, 'ACC-rock-merged': 82.02822919987925, 'ACC-wall-other-merged': 80.74847380844197, 'ACC-rug-merged': 77.06219839957335})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1530 s/iter. Inference: 0.5805 s/iter. Eval: 0.0000 s/iter. Total: 0.7335 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1563 s/iter. Inference: 0.5336 s/iter. Eval: 0.0000 s/iter. Total: 0.6900 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1666 s/iter. Inference: 0.5841 s/iter. Eval: 0.0000 s/iter. Total: 0.7509 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1698 s/iter. Inference: 0.7101 s/iter. Eval: 0.0000 s/iter. Total: 0.8801 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1671 s/iter. Inference: 0.6205 s/iter. Eval: 0.0000 s/iter. Total: 0.7878 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1664 s/iter. Inference: 0.6581 s/iter. Eval: 0.0000 s/iter. Total: 0.8247 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1685 s/iter. Inference: 0.7073 s/iter. Eval: 0.0000 s/iter. Total: 0.8760 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5291191103306994, 'noc@0.8': 2.8864501024290314, 'noc@0.85': 3.523266022827041, 'noc@0.9': 4.561603745976003, 'miou@iter1': 0.8275621362272535}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.0998 s/iter. Eval: 0.0008 s/iter. Total: 0.1023 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 69.56859588623047, 'precision@0.6': 66.57598114013672, 'precision@0.7': 61.834434509277344, 'precision@0.8': 52.15701675415039, 'precision@0.9': 26.62261962890625, 'cIoU': 56.28203201293945, 'mIoU': 61.42814254760742}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.69845762707562, 'SQ': 81.96537601566496, 'RQ': 59.71948575066759, 'PQ_th': 54.689427348030684, 'SQ_th': 82.70097514520884, 'RQ_th': 65.39991500946158, 'PQ_st': 42.164918425634, 'SQ_st': 80.8550377069196, 'RQ_st': 51.145252907204984}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.809395536489404, 'AP50': 61.10047502014106, 'AP75': 40.65412937716316, 'APs': 19.01572527791794, 'APm': 41.78877731565645, 'APl': 60.23039232702971, 'AP-person': 44.5513965404517, 'AP-bicycle': 18.94449549719926, 'AP-car': 37.17093326477138, 'AP-motorcycle': 34.81618053824423, 'AP-airplane': 56.917826993105734, 'AP-bus': 65.16518509364401, 'AP-train': 68.52998940771707, 'AP-truck': 35.42661327740662, 'AP-boat': 21.766486593020538, 'AP-traffic light': 25.302990983151037, 'AP-fire hydrant': 64.79509138119779, 'AP-stop sign': 63.79634339344098, 'AP-parking meter': 41.42430456852228, 'AP-bench': 20.1806139314537, 'AP-bird': 29.288000172436202, 'AP-cat': 74.0294425474051, 'AP-dog': 65.50526747966613, 'AP-horse': 45.6637983574244, 'AP-sheep': 47.630696440407895, 'AP-cow': 51.62665860622937, 'AP-elephant': 60.46239520804384, 'AP-bear': 76.9196986085879, 'AP-zebra': 60.928744477639654, 'AP-giraffe': 56.60314835280717, 'AP-backpack': 15.568569885654687, 'AP-umbrella': 48.31241099905022, 'AP-handbag': 14.84488074366783, 'AP-tie': 33.259464911379254, 'AP-suitcase': 40.976749778471564, 'AP-frisbee': 68.70175467540192, 'AP-skis': 5.183480398134744, 'AP-snowboard': 23.493250304545327, 'AP-sports ball': 46.46939218466662, 'AP-kite': 34.76201932038786, 'AP-baseball bat': 28.78949209046131, 'AP-baseball glove': 43.49098714211684, 'AP-skateboard': 35.50101201824939, 'AP-surfboard': 35.34524288107138, 'AP-tennis racket': 56.694249155577204, 'AP-bottle': 34.27678327213372, 'AP-wine glass': 27.010954853983122, 'AP-cup': 40.70078254602171, 'AP-fork': 15.39126464499814, 'AP-knife': 12.778809591200224, 'AP-spoon': 14.265241399374743, 'AP-bowl': 32.158594111969045, 'AP-banana': 21.1345536364993, 'AP-apple': 19.542277300685956, 'AP-sandwich': 44.03977354543239, 'AP-orange': 28.399850353674715, 'AP-broccoli': 21.725436815630523, 'AP-carrot': 20.158019548705006, 'AP-hot dog': 26.579579792742038, 'AP-pizza': 52.12395904705007, 'AP-donut': 45.40603285240457, 'AP-cake': 43.07291122002198, 'AP-chair': 21.00404437858508, 'AP-couch': 38.924244626733554, 'AP-potted plant': 17.982242648278113, 'AP-bed': 40.58558234084971, 'AP-dining table': 13.156949173255505, 'AP-toilet': 66.02596405555113, 'AP-tv': 61.80357855163201, 'AP-laptop': 63.142191614908874, 'AP-mouse': 60.59266313907972, 'AP-remote': 31.12690938245926, 'AP-keyboard': 47.85466079418069, 'AP-cell phone': 36.965271029393676, 'AP-microwave': 55.90461647526143, 'AP-oven': 33.957218457630205, 'AP-toaster': 28.59966641825472, 'AP-sink': 37.74397439868257, 'AP-refrigerator': 57.527723524974014, 'AP-book': 8.290043168326559, 'AP-clock': 52.05038611987797, 'AP-vase': 33.44500085388009, 'AP-scissors': 24.289278442909428, 'AP-teddy bear': 50.75152013828416, 'AP-hair drier': 8.315280375963862, 'AP-toothbrush': 17.108550074863548}), ('sem_seg', {'mIoU': 61.123129879608754, 'fwIoU': 69.30904030219229, 'IoU-person': 87.5439196071816, 'IoU-bicycle': 74.76695780119414, 'IoU-car': 69.03717696833799, 'IoU-motorcycle': 85.10497158231554, 'IoU-airplane': 80.47112716817784, 'IoU-bus': 87.09871804773918, 'IoU-train': 85.87389615530333, 'IoU-truck': 64.55322474178769, 'IoU-boat': 65.09657031131263, 'IoU-traffic light': 76.81823468556982, 'IoU-fire hydrant': 89.67867912279075, 'IoU-stop sign': 91.0207153500352, 'IoU-parking meter': 83.05388276880376, 'IoU-bench': 54.72933275609695, 'IoU-bird': 75.63578787855903, 'IoU-cat': 85.9196331890845, 'IoU-dog': 80.20050010682972, 'IoU-horse': 86.22405792183353, 'IoU-sheep': 86.94025113257406, 'IoU-cow': 83.03261353294465, 'IoU-elephant': 88.95079883815134, 'IoU-bear': 84.17281162887453, 'IoU-zebra': 87.48044191205348, 'IoU-giraffe': 88.16939401687549, 'IoU-backpack': 39.839852125693156, 'IoU-umbrella': 76.86659606205946, 'IoU-handbag': 37.2558947428615, 'IoU-tie': 70.18785678457974, 'IoU-suitcase': 82.00034235294147, 'IoU-frisbee': 83.9425363040425, 'IoU-skis': 51.54918062665007, 'IoU-snowboard': 67.83941605839416, 'IoU-sports ball': 65.5583363630983, 'IoU-kite': 66.00773608063376, 'IoU-baseball bat': 58.78151194591005, 'IoU-baseball glove': 73.89641000130287, 'IoU-skateboard': 64.01508879413834, 'IoU-surfboard': 75.42050634128003, 'IoU-tennis racket': 83.27879590337658, 'IoU-bottle': 66.78599108084414, 'IoU-wine glass': 76.36257745396969, 'IoU-cup': 57.74191537210898, 'IoU-fork': 55.64747228980805, 'IoU-knife': 50.07765234250016, 'IoU-spoon': 48.91729950967726, 'IoU-bowl': 54.97710444328061, 'IoU-banana': 83.9889699898783, 'IoU-apple': 59.04373064806027, 'IoU-sandwich': 64.9748943053172, 'IoU-orange': 73.24199626771907, 'IoU-broccoli': 64.14248132779792, 'IoU-carrot': 62.32715213457656, 'IoU-hot dog': 62.381030737384734, 'IoU-pizza': 85.34154327042205, 'IoU-donut': 64.24493671825155, 'IoU-cake': 68.86208648801664, 'IoU-chair': 54.14524874838848, 'IoU-couch': 68.43950487858297, 'IoU-potted plant': 34.33193272249556, 'IoU-bed': 66.55229946725805, 'IoU-dining table': 50.737959244015954, 'IoU-toilet': 85.736608067429, 'IoU-tv': 75.18230247585937, 'IoU-laptop': 75.42383609027603, 'IoU-mouse': 71.08731344387415, 'IoU-remote': 49.29952360745097, 'IoU-keyboard': 61.360158405414744, 'IoU-cell phone': 68.13063463872467, 'IoU-microwave': 63.52459802118619, 'IoU-oven': 64.88232341356571, 'IoU-toaster': 72.01004873028845, 'IoU-sink': 70.6725521212827, 'IoU-refrigerator': 79.62563387015219, 'IoU-book': 51.17156627619561, 'IoU-clock': 76.70291667533567, 'IoU-vase': 65.79445069919817, 'IoU-scissors': 62.1537977339488, 'IoU-teddy bear': 82.55995523642125, 'IoU-hair drier': 31.464970844249134, 'IoU-toothbrush': 59.58372267480884, 'IoU-banner': 35.76374843019052, 'IoU-blanket': 10.866539287206232, 'IoU-bridge': 38.7763856963819, 'IoU-cardboard': 49.108530705266, 'IoU-counter': 32.05995723684659, 'IoU-curtain': 65.2628465651753, 'IoU-door-stuff': 42.09860303813345, 'IoU-floor-wood': 60.37330786182827, 'IoU-flower': 45.17953317016225, 'IoU-fruit': 36.79567376830001, 'IoU-gravel': 30.481920742511438, 'IoU-house': 22.692202559156637, 'IoU-light': 39.12421768632574, 'IoU-mirror-stuff': 56.668997911003146, 'IoU-net': 46.60038915548825, 'IoU-pillow': 13.015519022150295, 'IoU-platform': 30.2934016134919, 'IoU-playingfield': 67.98363526943356, 'IoU-railroad': 62.1321144040146, 'IoU-river': 49.63198413190874, 'IoU-road': 66.64025544804531, 'IoU-roof': 16.755435824515224, 'IoU-sand': 64.17912029936554, 'IoU-sea': 84.72232186699323, 'IoU-shelf': 36.55711079811574, 'IoU-snow': 87.84506244233332, 'IoU-stairs': 30.130615556341468, 'IoU-tent': 7.7047934233254995, 'IoU-towel': 30.966972981740987, 'IoU-wall-brick': 46.35959145419793, 'IoU-wall-stone': 28.316532726453225, 'IoU-wall-tile': 67.52442068087338, 'IoU-wall-wood': 39.02406202499536, 'IoU-water-other': 26.270574221299942, 'IoU-window-blind': 46.201750924967804, 'IoU-window-other': 46.45927730093344, 'IoU-tree-merged': 81.03407512869917, 'IoU-fence-merged': 53.54384869575763, 'IoU-ceiling-merged': 67.66600168969076, 'IoU-sky-other-merged': 92.86156062022017, 'IoU-cabinet-merged': 59.28325386927804, 'IoU-table-merged': 38.21334462996505, 'IoU-floor-other-merged': 48.549678780892975, 'IoU-pavement-merged': 54.273591097861775, 'IoU-mountain-merged': 56.311938292168286, 'IoU-grass-merged': 71.82584246086701, 'IoU-dirt-merged': 45.69853648966019, 'IoU-paper-merged': 33.225428436747265, 'IoU-food-other-merged': 39.31934961914292, 'IoU-building-other-merged': 58.20931665891652, 'IoU-rock-merged': 61.31510195258373, 'IoU-wall-other-merged': 66.46945729208326, 'IoU-rug-merged': 63.362091866552774, 'mACC': 73.34437434120396, 'pACC': 80.59778589499794, 'ACC-person': 92.45256253050464, 'ACC-bicycle': 86.52927637556135, 'ACC-car': 85.75239058291893, 'ACC-motorcycle': 90.99806789796114, 'ACC-airplane': 87.77471293248973, 'ACC-bus': 91.57097047640909, 'ACC-train': 95.45371735401196, 'ACC-truck': 77.19187055358779, 'ACC-boat': 79.08502237237265, 'ACC-traffic light': 89.92772931996765, 'ACC-fire hydrant': 95.48202931845064, 'ACC-stop sign': 94.29898728823824, 'ACC-parking meter': 87.33102044148846, 'ACC-bench': 69.3316154758579, 'ACC-bird': 80.37407126838204, 'ACC-cat': 91.42804844650072, 'ACC-dog': 83.26297788399117, 'ACC-horse': 92.4840011749356, 'ACC-sheep': 90.44446040027584, 'ACC-cow': 88.50418847917739, 'ACC-elephant': 91.49188204486568, 'ACC-bear': 86.341107502069, 'ACC-zebra': 89.91235553719851, 'ACC-giraffe': 92.47381235429891, 'ACC-backpack': 59.03037659617356, 'ACC-umbrella': 84.9562801954425, 'ACC-handbag': 56.10196279638835, 'ACC-tie': 80.37260353403087, 'ACC-suitcase': 90.76063307348974, 'ACC-frisbee': 94.10690909090908, 'ACC-skis': 67.45792972330212, 'ACC-snowboard': 77.39119786436775, 'ACC-sports ball': 80.1485409495457, 'ACC-kite': 75.93965753752212, 'ACC-baseball bat': 84.22330278941178, 'ACC-baseball glove': 89.9101474964513, 'ACC-skateboard': 69.41090528791558, 'ACC-surfboard': 83.76511506297352, 'ACC-tennis racket': 89.34321810579785, 'ACC-bottle': 81.21688857706606, 'ACC-wine glass': 85.34931370238074, 'ACC-cup': 84.258748871579, 'ACC-fork': 65.23379829669743, 'ACC-knife': 62.79072323587601, 'ACC-spoon': 65.7525575736651, 'ACC-bowl': 71.64025108694176, 'ACC-banana': 89.5651366608272, 'ACC-apple': 72.42156999421239, 'ACC-sandwich': 77.08539121253645, 'ACC-orange': 80.55314934023527, 'ACC-broccoli': 72.36466854215689, 'ACC-carrot': 70.13563333153829, 'ACC-hot dog': 73.72145163609743, 'ACC-pizza': 93.72806552199752, 'ACC-donut': 81.91125944125979, 'ACC-cake': 76.31581372235358, 'ACC-chair': 70.2584682914702, 'ACC-couch': 82.30833939999047, 'ACC-potted plant': 51.256587837767185, 'ACC-bed': 78.27567776152019, 'ACC-dining table': 77.95901220859166, 'ACC-toilet': 90.18643928102914, 'ACC-tv': 88.17235546822464, 'ACC-laptop': 92.09194119746944, 'ACC-mouse': 86.6854972241161, 'ACC-remote': 72.89086533097435, 'ACC-keyboard': 67.1294818858902, 'ACC-cell phone': 78.22093253704752, 'ACC-microwave': 73.60131705074951, 'ACC-oven': 81.81930116883052, 'ACC-toaster': 81.40045710219108, 'ACC-sink': 85.18409388361596, 'ACC-refrigerator': 91.27912253899633, 'ACC-book': 64.37767924686895, 'ACC-clock': 83.0491907163156, 'ACC-vase': 76.27040688602112, 'ACC-scissors': 66.6383173107824, 'ACC-teddy bear': 87.93478603030111, 'ACC-hair drier': 42.39034525944269, 'ACC-toothbrush': 78.65444753300903, 'ACC-banner': 68.57935928055778, 'ACC-blanket': 16.563488417475018, 'ACC-bridge': 56.23933392927887, 'ACC-cardboard': 61.80866656972517, 'ACC-counter': 51.46865405795189, 'ACC-curtain': 78.09442417872619, 'ACC-door-stuff': 60.18482697818898, 'ACC-floor-wood': 78.02525228910339, 'ACC-flower': 65.56356962997738, 'ACC-fruit': 57.249013247089174, 'ACC-gravel': 40.00323553192981, 'ACC-house': 25.573970061858386, 'ACC-light': 59.50467499904298, 'ACC-mirror-stuff': 68.86252916745377, 'ACC-net': 60.58351235885805, 'ACC-pillow': 31.33822430720737, 'ACC-platform': 52.261899552224065, 'ACC-playingfield': 87.19350167101663, 'ACC-railroad': 78.32742945737407, 'ACC-river': 71.65745396456394, 'ACC-road': 85.9414856562207, 'ACC-roof': 23.218513040537513, 'ACC-sand': 69.9402501112797, 'ACC-sea': 90.49326622960339, 'ACC-shelf': 58.46857621213353, 'ACC-snow': 94.18761482926591, 'ACC-stairs': 47.11896217761791, 'ACC-tent': 9.11950934921438, 'ACC-towel': 35.670726753789744, 'ACC-wall-brick': 60.76224714940955, 'ACC-wall-stone': 35.904162420031675, 'ACC-wall-tile': 82.32158623958574, 'ACC-wall-wood': 52.517889332623405, 'ACC-water-other': 43.177274354952225, 'ACC-window-blind': 56.95063211339776, 'ACC-window-other': 70.21116842295385, 'ACC-tree-merged': 89.58655014316348, 'ACC-fence-merged': 73.24051234205734, 'ACC-ceiling-merged': 81.09176892124941, 'ACC-sky-other-merged': 96.7014633370824, 'ACC-cabinet-merged': 76.24091077631722, 'ACC-table-merged': 49.36377284765744, 'ACC-floor-other-merged': 59.07994488959948, 'ACC-pavement-merged': 67.53272025738367, 'ACC-mountain-merged': 66.43595371565216, 'ACC-grass-merged': 81.88866984669733, 'ACC-dirt-merged': 71.9922200479563, 'ACC-paper-merged': 46.211262531273526, 'ACC-food-other-merged': 50.302623344752284, 'ACC-building-other-merged': 75.31248691529787, 'ACC-rock-merged': 82.02822919987925, 'ACC-wall-other-merged': 80.74847380844197, 'ACC-rug-merged': 77.06219839957335})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5291191103306994, 'noc@0.8': 2.8864501024290314, 'noc@0.85': 3.523266022827041, 'noc@0.9': 4.561603745976003, 'miou@iter1': 0.8275621362272535}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 69.56859588623047, 'precision@0.6': 66.57598114013672, 'precision@0.7': 61.834434509277344, 'precision@0.8': 52.15701675415039, 'precision@0.9': 26.62261962890625, 'cIoU': 56.28203201293945, 'mIoU': 61.42814254760742}}}
INFO:trainer.default_trainer:This epoch takes 1:28:08.218563
INFO:trainer.default_trainer:PROGRESS: 38.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 19 training.
INFO:trainer.default_trainer:epochs[    19] optim steps[34800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.93554/0.90870, loss_mask_bce_0: 0.05296/0.33500, loss_mask_dice_0: 1.26095/1.16703, loss_spatial_bce_0: 0.01851/0.08964, loss_spatial_dice_0: 0.25442/0.21425, loss_spatial_ce_0: 0.11020/0.07052, loss_grounding_bce_0: 0.02468/0.08628, loss_grounding_dice_0: 0.10140/0.17930, loss_grounding_ce_0: 0.07388/0.27609, loss_mask_ce_1: 0.94240/0.90926, loss_mask_bce_1: 0.05378/0.33582, loss_mask_dice_1: 1.21852/1.17386, loss_spatial_bce_1: 0.02314/0.09035, loss_spatial_dice_1: 0.29357/0.21848, loss_spatial_ce_1: 0.01571/0.07621, loss_grounding_bce_1: 0.02770/0.08643, loss_grounding_dice_1: 0.10337/0.18020, loss_grounding_ce_1: 0.08909/0.27767, loss_mask_ce_2: 0.90300/0.91672, loss_mask_bce_2: 0.05052/0.33623, loss_mask_dice_2: 1.17799/1.17331, loss_spatial_bce_2: 0.02427/0.09079, loss_spatial_dice_2: 0.29562/0.21950, loss_spatial_ce_2: 0.16467/0.07982, loss_grounding_bce_2: 0.02552/0.08646, loss_grounding_dice_2: 0.10549/0.17984, loss_grounding_ce_2: 0.07055/0.28096, loss_mask_ce_3: 0.92610/0.92580, loss_mask_bce_3: 0.05071/0.33709, loss_mask_dice_3: 0.95410/1.17060, loss_spatial_bce_3: 0.02415/0.09164, loss_spatial_dice_3: 0.26777/0.22010, loss_spatial_ce_3: 0.16630/0.08359, loss_grounding_bce_3: 0.03099/0.08672, loss_grounding_dice_3: 0.10108/0.17968, loss_grounding_ce_3: 0.09494/0.28253, loss_mask_ce_4: 1.20585/0.92488, loss_mask_bce_4: 0.05067/0.33899, loss_mask_dice_4: 1.03267/1.19380, loss_spatial_bce_4: 0.02657/0.09586, loss_spatial_dice_4: 0.32266/0.23126, loss_spatial_ce_4: 0.15816/0.09979, loss_grounding_bce_4: 0.02809/0.08717, loss_grounding_dice_4: 0.11009/0.18241, loss_grounding_ce_4: 0.12478/0.28531, loss_mask_ce_5: 1.10199/0.93967, loss_mask_bce_5: 0.06224/0.34130, loss_mask_dice_5: 0.96897/1.19994, loss_spatial_bce_5: 0.02686/0.09742, loss_spatial_dice_5: 0.33345/0.23481, loss_spatial_ce_5: 0.18219/0.11430, loss_grounding_bce_5: 0.04326/0.08756, loss_grounding_dice_5: 0.13025/0.18366, loss_grounding_ce_5: 0.14091/0.29794, loss_mask_ce_6: 1.24268/0.97792, loss_mask_bce_6: 0.08069/0.34395, loss_mask_dice_6: 1.24810/1.20221, loss_spatial_bce_6: 0.02802/0.10314, loss_spatial_dice_6: 0.34520/0.23707, loss_spatial_ce_6: 0.10969/0.14000, loss_grounding_bce_6: 0.05083/0.08830, loss_grounding_dice_6: 0.15348/0.18387, loss_grounding_ce_6: 0.10676/0.31423, loss_mask_ce_7: 1.06200/1.02289, loss_mask_bce_7: 0.07901/0.35177, loss_mask_dice_7: 0.93713/1.25792, loss_spatial_bce_7: 0.04031/0.11171, loss_spatial_dice_7: 0.39427/0.26465, loss_spatial_ce_7: 0.22756/0.17760, loss_grounding_bce_7: 0.04140/0.09021, loss_grounding_dice_7: 0.13042/0.19110, loss_grounding_ce_7: 0.16033/0.34722, loss_mask_ce_8: 1.51002/1.13303, loss_mask_bce_8: 0.05652/0.36540, loss_mask_dice_8: 1.06217/1.33233, loss_spatial_bce_8: 0.04235/0.13269, loss_spatial_dice_8: 0.42292/0.30406, loss_spatial_ce_8: 0.20716/0.23424, loss_grounding_bce_8: 0.03463/0.09383, loss_grounding_dice_8: 0.15096/0.20222, loss_grounding_ce_8: 0.18140/0.41654, loss_mask_ce_9: 3.56625/3.68404, loss_mask_bce_9: 0.08252/0.39225, loss_mask_dice_9: 1.55793/1.90568, loss_spatial_bce_9: 0.08910/0.33476, loss_spatial_dice_9: 0.84269/0.82334, loss_spatial_ce_9: 1.79282/1.50737, loss_grounding_bce_9: 0.06655/0.10533, loss_grounding_dice_9: 0.49239/0.28160, loss_grounding_ce_9: 0.67280/0.68362] items per batch[64] items per second[0.14] total items[2227200] mini batches[ 34800] memory[7341] epoch remaining[1:20:33]
INFO:trainer.default_trainer:epochs[    19] optim steps[34900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97851/0.90858, loss_mask_bce_0: 0.51850/0.33487, loss_mask_dice_0: 2.49161/1.16678, loss_spatial_bce_0: 0.03471/0.08960, loss_spatial_dice_0: 0.26109/0.21419, loss_spatial_ce_0: 0.00138/0.07046, loss_grounding_bce_0: 0.19595/0.08626, loss_grounding_dice_0: 0.25963/0.17931, loss_grounding_ce_0: 0.79131/0.27607, loss_mask_ce_1: 0.95498/0.90911, loss_mask_bce_1: 0.52104/0.33570, loss_mask_dice_1: 2.22546/1.17359, loss_spatial_bce_1: 0.03617/0.09031, loss_spatial_dice_1: 0.26312/0.21843, loss_spatial_ce_1: 0.00199/0.07616, loss_grounding_bce_1: 0.21483/0.08640, loss_grounding_dice_1: 0.23751/0.18019, loss_grounding_ce_1: 0.69567/0.27765, loss_mask_ce_2: 0.89714/0.91658, loss_mask_bce_2: 0.50700/0.33612, loss_mask_dice_2: 2.34901/1.17307, loss_spatial_bce_2: 0.03719/0.09076, loss_spatial_dice_2: 0.27843/0.21945, loss_spatial_ce_2: 0.00372/0.07976, loss_grounding_bce_2: 0.21715/0.08644, loss_grounding_dice_2: 0.23603/0.17985, loss_grounding_ce_2: 0.83148/0.28092, loss_mask_ce_3: 0.98792/0.92571, loss_mask_bce_3: 0.52308/0.33697, loss_mask_dice_3: 2.30813/1.17036, loss_spatial_bce_3: 0.03808/0.09161, loss_spatial_dice_3: 0.26915/0.22005, loss_spatial_ce_3: 0.01809/0.08354, loss_grounding_bce_3: 0.22010/0.08670, loss_grounding_dice_3: 0.24427/0.17968, loss_grounding_ce_3: 0.80730/0.28249, loss_mask_ce_4: 1.06831/0.92478, loss_mask_bce_4: 0.52404/0.33887, loss_mask_dice_4: 2.37669/1.19357, loss_spatial_bce_4: 0.04327/0.09582, loss_spatial_dice_4: 0.30327/0.23123, loss_spatial_ce_4: 0.02727/0.09973, loss_grounding_bce_4: 0.17289/0.08714, loss_grounding_dice_4: 0.22972/0.18241, loss_grounding_ce_4: 0.70327/0.28529, loss_mask_ce_5: 1.05380/0.93962, loss_mask_bce_5: 0.51242/0.34118, loss_mask_dice_5: 2.22530/1.19973, loss_spatial_bce_5: 0.04074/0.09738, loss_spatial_dice_5: 0.29321/0.23477, loss_spatial_ce_5: 0.04274/0.11425, loss_grounding_bce_5: 0.16709/0.08753, loss_grounding_dice_5: 0.22781/0.18366, loss_grounding_ce_5: 0.78886/0.29793, loss_mask_ce_6: 1.02824/0.97784, loss_mask_bce_6: 0.51802/0.34383, loss_mask_dice_6: 2.47416/1.20201, loss_spatial_bce_6: 0.05313/0.10310, loss_spatial_dice_6: 0.30373/0.23703, loss_spatial_ce_6: 0.15716/0.13996, loss_grounding_bce_6: 0.20243/0.08827, loss_grounding_dice_6: 0.21079/0.18387, loss_grounding_ce_6: 0.74703/0.31417, loss_mask_ce_7: 0.96687/1.02281, loss_mask_bce_7: 0.54577/0.35166, loss_mask_dice_7: 2.45653/1.25772, loss_spatial_bce_7: 0.04458/0.11167, loss_spatial_dice_7: 0.30572/0.26463, loss_spatial_ce_7: 0.12307/0.17755, loss_grounding_bce_7: 0.21800/0.09017, loss_grounding_dice_7: 0.22855/0.19110, loss_grounding_ce_7: 0.74072/0.34717, loss_mask_ce_8: 1.24985/1.13294, loss_mask_bce_8: 0.59458/0.36527, loss_mask_dice_8: 2.61420/1.33205, loss_spatial_bce_8: 0.06357/0.13265, loss_spatial_dice_8: 0.40208/0.30405, loss_spatial_ce_8: 0.20026/0.23419, loss_grounding_bce_8: 0.23409/0.09380, loss_grounding_dice_8: 0.23037/0.20223, loss_grounding_ce_8: 0.73235/0.41647, loss_mask_ce_9: 4.71326/3.68412, loss_mask_bce_9: 0.78065/0.39209, loss_mask_dice_9: 5.19898/1.90539, loss_spatial_bce_9: 0.14773/0.33480, loss_spatial_dice_9: 0.94556/0.82332, loss_spatial_ce_9: 1.17848/1.50732, loss_grounding_bce_9: 0.27549/0.10530, loss_grounding_dice_9: 0.25876/0.28161, loss_grounding_ce_9: 1.71836/0.68364] items per batch[64] items per second[0.23] total items[2233600] mini batches[ 34900] memory[7341] epoch remaining[1:16:15]
INFO:trainer.default_trainer:epochs[    19] optim steps[35000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.14629/0.90866, loss_mask_bce_0: 0.57156/0.33493, loss_mask_dice_0: 0.55375/1.16692, loss_spatial_bce_0: 0.13921/0.08957, loss_spatial_dice_0: 0.18631/0.21417, loss_spatial_ce_0: 0.01913/0.07042, loss_grounding_bce_0: 0.04198/0.08625, loss_grounding_dice_0: 0.06342/0.17929, loss_grounding_ce_0: 0.23627/0.27602, loss_mask_ce_1: 1.47924/0.90923, loss_mask_bce_1: 0.39677/0.33576, loss_mask_dice_1: 0.53665/1.17375, loss_spatial_bce_1: 0.12505/0.09029, loss_spatial_dice_1: 0.19128/0.21842, loss_spatial_ce_1: 0.04155/0.07610, loss_grounding_bce_1: 0.04286/0.08640, loss_grounding_dice_1: 0.04774/0.18015, loss_grounding_ce_1: 0.24260/0.27759, loss_mask_ce_2: 1.48645/0.91668, loss_mask_bce_2: 0.38910/0.33617, loss_mask_dice_2: 0.55896/1.17320, loss_spatial_bce_2: 0.14056/0.09073, loss_spatial_dice_2: 0.19430/0.21943, loss_spatial_ce_2: 0.02703/0.07972, loss_grounding_bce_2: 0.04111/0.08643, loss_grounding_dice_2: 0.06956/0.17981, loss_grounding_ce_2: 0.21973/0.28087, loss_mask_ce_3: 1.58733/0.92580, loss_mask_bce_3: 0.38117/0.33702, loss_mask_dice_3: 0.52050/1.17057, loss_spatial_bce_3: 0.13249/0.09158, loss_spatial_dice_3: 0.18431/0.22002, loss_spatial_ce_3: 0.05721/0.08348, loss_grounding_bce_3: 0.03765/0.08670, loss_grounding_dice_3: 0.06835/0.17965, loss_grounding_ce_3: 0.18907/0.28243, loss_mask_ce_4: 1.54785/0.92491, loss_mask_bce_4: 0.36935/0.33891, loss_mask_dice_4: 0.51443/1.19371, loss_spatial_bce_4: 0.15605/0.09580, loss_spatial_dice_4: 0.19970/0.23121, loss_spatial_ce_4: 0.02495/0.09966, loss_grounding_bce_4: 0.03656/0.08714, loss_grounding_dice_4: 0.05746/0.18239, loss_grounding_ce_4: 0.23602/0.28526, loss_mask_ce_5: 1.55188/0.93983, loss_mask_bce_5: 0.36418/0.34123, loss_mask_dice_5: 0.50773/1.19987, loss_spatial_bce_5: 0.18645/0.09736, loss_spatial_dice_5: 0.21279/0.23477, loss_spatial_ce_5: 0.03655/0.11418, loss_grounding_bce_5: 0.03426/0.08754, loss_grounding_dice_5: 0.05665/0.18365, loss_grounding_ce_5: 0.28148/0.29782, loss_mask_ce_6: 1.79141/0.97802, loss_mask_bce_6: 0.37445/0.34388, loss_mask_dice_6: 0.48845/1.20216, loss_spatial_bce_6: 0.25367/0.10308, loss_spatial_dice_6: 0.21848/0.23702, loss_spatial_ce_6: 0.10065/0.13992, loss_grounding_bce_6: 0.04030/0.08827, loss_grounding_dice_6: 0.06543/0.18384, loss_grounding_ce_6: 0.37886/0.31412, loss_mask_ce_7: 1.77985/1.02301, loss_mask_bce_7: 0.37227/0.35171, loss_mask_dice_7: 0.52101/1.25790, loss_spatial_bce_7: 0.20814/0.11165, loss_spatial_dice_7: 0.26041/0.26464, loss_spatial_ce_7: 0.14210/0.17745, loss_grounding_bce_7: 0.03276/0.09017, loss_grounding_dice_7: 0.05923/0.19109, loss_grounding_ce_7: 0.30366/0.34720, loss_mask_ce_8: 1.44158/1.13319, loss_mask_bce_8: 0.56592/0.36531, loss_mask_dice_8: 0.60962/1.33219, loss_spatial_bce_8: 0.36460/0.13264, loss_spatial_dice_8: 0.27828/0.30406, loss_spatial_ce_8: 0.39177/0.23414, loss_grounding_bce_8: 0.03745/0.09380, loss_grounding_dice_8: 0.06825/0.20221, loss_grounding_ce_8: 0.51227/0.41641, loss_mask_ce_9: 4.19916/3.68449, loss_mask_bce_9: 0.41349/0.39220, loss_mask_dice_9: 0.87994/1.90576, loss_spatial_bce_9: 0.41606/0.33482, loss_spatial_dice_9: 0.78082/0.82334, loss_spatial_ce_9: 1.76933/1.50738, loss_grounding_bce_9: 0.06881/0.10530, loss_grounding_dice_9: 0.13307/0.28160, loss_grounding_ce_9: 0.72884/0.68362] items per batch[64] items per second[0.22] total items[2240000] mini batches[ 35000] memory[7341] epoch remaining[1:12:07]
INFO:trainer.default_trainer:epochs[    19] optim steps[35100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08717/0.90855, loss_mask_bce_0: 0.16187/0.33485, loss_mask_dice_0: 0.12608/1.16682, loss_spatial_bce_0: 0.07328/0.08957, loss_spatial_dice_0: 0.10141/0.21413, loss_spatial_ce_0: 0.03399/0.07040, loss_grounding_bce_0: 0.04811/0.08626, loss_grounding_dice_0: 0.05951/0.17930, loss_grounding_ce_0: 0.00547/0.27584, loss_mask_ce_1: 0.09921/0.90911, loss_mask_bce_1: 0.16429/0.33568, loss_mask_dice_1: 0.13151/1.17361, loss_spatial_bce_1: 0.07441/0.09028, loss_spatial_dice_1: 0.11300/0.21838, loss_spatial_ce_1: 0.07700/0.07609, loss_grounding_bce_1: 0.05028/0.08641, loss_grounding_dice_1: 0.07341/0.18018, loss_grounding_ce_1: 0.00503/0.27739, loss_mask_ce_2: 0.08793/0.91658, loss_mask_bce_2: 0.17610/0.33609, loss_mask_dice_2: 0.12825/1.17307, loss_spatial_bce_2: 0.07633/0.09073, loss_spatial_dice_2: 0.11858/0.21940, loss_spatial_ce_2: 0.07497/0.07971, loss_grounding_bce_2: 0.05439/0.08644, loss_grounding_dice_2: 0.07983/0.17984, loss_grounding_ce_2: 0.00506/0.28065, loss_mask_ce_3: 0.11126/0.92568, loss_mask_bce_3: 0.18613/0.33695, loss_mask_dice_3: 0.12126/1.17044, loss_spatial_bce_3: 0.07844/0.09158, loss_spatial_dice_3: 0.13073/0.21999, loss_spatial_ce_3: 0.05517/0.08344, loss_grounding_bce_3: 0.05221/0.08671, loss_grounding_dice_3: 0.07266/0.17965, loss_grounding_ce_3: 0.00543/0.28220, loss_mask_ce_4: 0.10534/0.92481, loss_mask_bce_4: 0.17795/0.33883, loss_mask_dice_4: 0.13325/1.19357, loss_spatial_bce_4: 0.07151/0.09581, loss_spatial_dice_4: 0.09831/0.23118, loss_spatial_ce_4: 0.20230/0.09963, loss_grounding_bce_4: 0.05661/0.08715, loss_grounding_dice_4: 0.07537/0.18241, loss_grounding_ce_4: 0.00711/0.28503, loss_mask_ce_5: 0.09816/0.93972, loss_mask_bce_5: 0.18475/0.34116, loss_mask_dice_5: 0.12689/1.19976, loss_spatial_bce_5: 0.07925/0.09736, loss_spatial_dice_5: 0.08205/0.23476, loss_spatial_ce_5: 0.14613/0.11414, loss_grounding_bce_5: 0.05156/0.08754, loss_grounding_dice_5: 0.06295/0.18366, loss_grounding_ce_5: 0.00778/0.29758, loss_mask_ce_6: 0.11363/0.97794, loss_mask_bce_6: 0.19676/0.34380, loss_mask_dice_6: 0.13788/1.20205, loss_spatial_bce_6: 0.07602/0.10308, loss_spatial_dice_6: 0.09185/0.23700, loss_spatial_ce_6: 0.36546/0.13994, loss_grounding_bce_6: 0.05611/0.08828, loss_grounding_dice_6: 0.06354/0.18385, loss_grounding_ce_6: 0.00778/0.31386, loss_mask_ce_7: 0.14479/1.02296, loss_mask_bce_7: 0.16487/0.35163, loss_mask_dice_7: 0.15063/1.25775, loss_spatial_bce_7: 0.09478/0.11166, loss_spatial_dice_7: 0.09162/0.26462, loss_spatial_ce_7: 0.44076/0.17743, loss_grounding_bce_7: 0.05616/0.09018, loss_grounding_dice_7: 0.07138/0.19110, loss_grounding_ce_7: 0.01412/0.34700, loss_mask_ce_8: 0.42991/1.13318, loss_mask_bce_8: 0.12080/0.36523, loss_mask_dice_8: 0.19234/1.33204, loss_spatial_bce_8: 0.07559/0.13264, loss_spatial_dice_8: 0.11394/0.30402, loss_spatial_ce_8: 0.26394/0.23412, loss_grounding_bce_8: 0.05064/0.09382, loss_grounding_dice_8: 0.07597/0.20222, loss_grounding_ce_8: 0.01129/0.41612, loss_mask_ce_9: 2.22723/3.68405, loss_mask_bce_9: 0.14577/0.39214, loss_mask_dice_9: 0.17314/1.90556, loss_spatial_bce_9: 0.38389/0.33484, loss_spatial_dice_9: 0.66576/0.82332, loss_spatial_ce_9: 0.53922/1.50715, loss_grounding_bce_9: 0.04945/0.10530, loss_grounding_dice_9: 0.11633/0.28160, loss_grounding_ce_9: 0.27633/0.68318] items per batch[64] items per second[0.23] total items[2246400] mini batches[ 35100] memory[7341] epoch remaining[1:07:26]
INFO:trainer.default_trainer:epochs[    19] optim steps[35200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20039/0.90841, loss_mask_bce_0: 0.52814/0.33465, loss_mask_dice_0: 1.76527/1.16680, loss_spatial_bce_0: 0.13641/0.08953, loss_spatial_dice_0: 0.26845/0.21411, loss_spatial_ce_0: 0.02900/0.07034, loss_grounding_bce_0: 0.16127/0.08623, loss_grounding_dice_0: 0.06410/0.17929, loss_grounding_ce_0: 0.22087/0.27571, loss_mask_ce_1: 1.07685/0.90895, loss_mask_bce_1: 0.54576/0.33549, loss_mask_dice_1: 1.88539/1.17368, loss_spatial_bce_1: 0.09816/0.09024, loss_spatial_dice_1: 0.23893/0.21837, loss_spatial_ce_1: 0.02544/0.07603, loss_grounding_bce_1: 0.15541/0.08638, loss_grounding_dice_1: 0.06418/0.18017, loss_grounding_ce_1: 0.22435/0.27725, loss_mask_ce_2: 1.23389/0.91644, loss_mask_bce_2: 0.56401/0.33591, loss_mask_dice_2: 1.82766/1.17312, loss_spatial_bce_2: 0.16131/0.09069, loss_spatial_dice_2: 0.26803/0.21938, loss_spatial_ce_2: 0.31830/0.07966, loss_grounding_bce_2: 0.16117/0.08642, loss_grounding_dice_2: 0.06589/0.17982, loss_grounding_ce_2: 0.23131/0.28048, loss_mask_ce_3: 1.22212/0.92550, loss_mask_bce_3: 0.38645/0.33677, loss_mask_dice_3: 1.84312/1.17041, loss_spatial_bce_3: 0.15734/0.09154, loss_spatial_dice_3: 0.24513/0.21998, loss_spatial_ce_3: 0.02171/0.08341, loss_grounding_bce_3: 0.05665/0.08669, loss_grounding_dice_3: 0.05512/0.17965, loss_grounding_ce_3: 0.45980/0.28203, loss_mask_ce_4: 1.07399/0.92469, loss_mask_bce_4: 0.40206/0.33865, loss_mask_dice_4: 2.00356/1.19357, loss_spatial_bce_4: 0.14879/0.09576, loss_spatial_dice_4: 0.28177/0.23118, loss_spatial_ce_4: 0.02307/0.09961, loss_grounding_bce_4: 0.06003/0.08712, loss_grounding_dice_4: 0.05597/0.18240, loss_grounding_ce_4: 0.53300/0.28489, loss_mask_ce_5: 1.29867/0.93960, loss_mask_bce_5: 0.39451/0.34096, loss_mask_dice_5: 1.68124/1.19977, loss_spatial_bce_5: 0.14551/0.09732, loss_spatial_dice_5: 0.24894/0.23476, loss_spatial_ce_5: 0.04759/0.11409, loss_grounding_bce_5: 0.15029/0.08751, loss_grounding_dice_5: 0.06042/0.18364, loss_grounding_ce_5: 0.21287/0.29747, loss_mask_ce_6: 1.32668/0.97781, loss_mask_bce_6: 0.57342/0.34363, loss_mask_dice_6: 1.91887/1.20205, loss_spatial_bce_6: 0.09816/0.10304, loss_spatial_dice_6: 0.26196/0.23701, loss_spatial_ce_6: 0.07438/0.13991, loss_grounding_bce_6: 0.15730/0.08825, loss_grounding_dice_6: 0.06435/0.18385, loss_grounding_ce_6: 0.21534/0.31369, loss_mask_ce_7: 1.60431/1.02285, loss_mask_bce_7: 0.50893/0.35145, loss_mask_dice_7: 1.77754/1.25773, loss_spatial_bce_7: 0.08481/0.11161, loss_spatial_dice_7: 0.24839/0.26464, loss_spatial_ce_7: 0.07896/0.17742, loss_grounding_bce_7: 0.13817/0.09015, loss_grounding_dice_7: 0.05553/0.19110, loss_grounding_ce_7: 0.27741/0.34685, loss_mask_ce_8: 1.38255/1.13309, loss_mask_bce_8: 0.62794/0.36506, loss_mask_dice_8: 2.03632/1.33199, loss_spatial_bce_8: 0.28392/0.13259, loss_spatial_dice_8: 0.36367/0.30403, loss_spatial_ce_8: 0.19682/0.23410, loss_grounding_bce_8: 0.16351/0.09379, loss_grounding_dice_8: 0.07249/0.20221, loss_grounding_ce_8: 0.25950/0.41606, loss_mask_ce_9: 5.50300/3.68398, loss_mask_bce_9: 0.51850/0.39196, loss_mask_dice_9: 3.45322/1.90556, loss_spatial_bce_9: 0.25685/0.33477, loss_spatial_dice_9: 0.86649/0.82330, loss_spatial_ce_9: 1.10831/1.50721, loss_grounding_bce_9: 0.08419/0.10525, loss_grounding_dice_9: 0.14308/0.28159, loss_grounding_ce_9: 0.67106/0.68311] items per batch[64] items per second[0.23] total items[2252800] mini batches[ 35200] memory[7341] epoch remaining[1:02:35]
INFO:trainer.default_trainer:epochs[    19] optim steps[35300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55766/0.90833, loss_mask_bce_0: 0.09142/0.33467, loss_mask_dice_0: 0.29074/1.16733, loss_spatial_bce_0: 0.03568/0.08950, loss_spatial_dice_0: 0.12069/0.21410, loss_spatial_ce_0: 0.00079/0.07030, loss_grounding_bce_0: 0.02544/0.08623, loss_grounding_dice_0: 0.15918/0.17930, loss_grounding_ce_0: 0.03115/0.27577, loss_mask_ce_1: 0.54889/0.90888, loss_mask_bce_1: 0.08772/0.33551, loss_mask_dice_1: 0.30149/1.17424, loss_spatial_bce_1: 0.03504/0.09021, loss_spatial_dice_1: 0.12985/0.21835, loss_spatial_ce_1: 0.00125/0.07601, loss_grounding_bce_1: 0.02188/0.08638, loss_grounding_dice_1: 0.14221/0.18018, loss_grounding_ce_1: 0.02725/0.27728, loss_mask_ce_2: 0.55057/0.91635, loss_mask_bce_2: 0.08188/0.33592, loss_mask_dice_2: 0.28209/1.17374, loss_spatial_bce_2: 0.03928/0.09065, loss_spatial_dice_2: 0.15653/0.21937, loss_spatial_ce_2: 0.00163/0.07963, loss_grounding_bce_2: 0.02176/0.08642, loss_grounding_dice_2: 0.14962/0.17984, loss_grounding_ce_2: 0.02396/0.28050, loss_mask_ce_3: 0.55753/0.92540, loss_mask_bce_3: 0.08371/0.33678, loss_mask_dice_3: 0.28999/1.17098, loss_spatial_bce_3: 0.03928/0.09151, loss_spatial_dice_3: 0.16668/0.21996, loss_spatial_ce_3: 0.00161/0.08336, loss_grounding_bce_3: 0.02021/0.08669, loss_grounding_dice_3: 0.14442/0.17966, loss_grounding_ce_3: 0.01921/0.28203, loss_mask_ce_4: 0.56698/0.92459, loss_mask_bce_4: 0.08006/0.33865, loss_mask_dice_4: 0.27772/1.19413, loss_spatial_bce_4: 0.07055/0.09573, loss_spatial_dice_4: 0.21520/0.23118, loss_spatial_ce_4: 0.01474/0.09955, loss_grounding_bce_4: 0.02229/0.08713, loss_grounding_dice_4: 0.14049/0.18241, loss_grounding_ce_4: 0.02014/0.28488, loss_mask_ce_5: 0.62678/0.93952, loss_mask_bce_5: 0.08366/0.34097, loss_mask_dice_5: 0.28565/1.20038, loss_spatial_bce_5: 0.03992/0.09729, loss_spatial_dice_5: 0.14388/0.23474, loss_spatial_ce_5: 0.22665/0.11406, loss_grounding_bce_5: 0.01946/0.08752, loss_grounding_dice_5: 0.13655/0.18365, loss_grounding_ce_5: 0.02942/0.29747, loss_mask_ce_6: 0.71547/0.97777, loss_mask_bce_6: 0.07943/0.34363, loss_mask_dice_6: 0.29457/1.20267, loss_spatial_bce_6: 0.05892/0.10299, loss_spatial_dice_6: 0.21373/0.23700, loss_spatial_ce_6: 0.01335/0.13987, loss_grounding_bce_6: 0.01960/0.08825, loss_grounding_dice_6: 0.14664/0.18385, loss_grounding_ce_6: 0.04271/0.31370, loss_mask_ce_7: 0.78842/1.02284, loss_mask_bce_7: 0.08723/0.35145, loss_mask_dice_7: 0.32838/1.25838, loss_spatial_bce_7: 0.05273/0.11157, loss_spatial_dice_7: 0.21995/0.26464, loss_spatial_ce_7: 0.03440/0.17738, loss_grounding_bce_7: 0.01927/0.09015, loss_grounding_dice_7: 0.16186/0.19110, loss_grounding_ce_7: 0.05060/0.34688, loss_mask_ce_8: 0.91346/1.13305, loss_mask_bce_8: 0.11044/0.36507, loss_mask_dice_8: 0.39448/1.33260, loss_spatial_bce_8: 0.04834/0.13255, loss_spatial_dice_8: 0.22988/0.30404, loss_spatial_ce_8: 0.30600/0.23404, loss_grounding_bce_8: 0.02130/0.09379, loss_grounding_dice_8: 0.17445/0.20221, loss_grounding_ce_8: 0.08272/0.41601, loss_mask_ce_9: 3.33376/3.68393, loss_mask_bce_9: 0.12981/0.39194, loss_mask_dice_9: 0.54392/1.90645, loss_spatial_bce_9: 0.21681/0.33469, loss_spatial_dice_9: 0.64456/0.82332, loss_spatial_ce_9: 1.25160/1.50709, loss_grounding_bce_9: 0.02647/0.10525, loss_grounding_dice_9: 0.24345/0.28161, loss_grounding_ce_9: 0.38331/0.68299] items per batch[64] items per second[0.24] total items[2259200] mini batches[ 35300] memory[7341] epoch remaining[0:57:33]
INFO:trainer.default_trainer:epochs[    19] optim steps[35400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21934/0.90843, loss_mask_bce_0: 0.75368/0.33459, loss_mask_dice_0: 1.62336/1.16724, loss_spatial_bce_0: 0.10384/0.08947, loss_spatial_dice_0: 0.31270/0.21405, loss_spatial_ce_0: 0.08249/0.07025, loss_grounding_bce_0: 0.11673/0.08620, loss_grounding_dice_0: 0.13610/0.17924, loss_grounding_ce_0: 0.12330/0.27558, loss_mask_ce_1: 1.22657/0.90895, loss_mask_bce_1: 0.77464/0.33542, loss_mask_dice_1: 1.66724/1.17411, loss_spatial_bce_1: 0.11742/0.09018, loss_spatial_dice_1: 0.33214/0.21830, loss_spatial_ce_1: 0.10586/0.07596, loss_grounding_bce_1: 0.13035/0.08635, loss_grounding_dice_1: 0.14666/0.18012, loss_grounding_ce_1: 0.18353/0.27708, loss_mask_ce_2: 1.17987/0.91638, loss_mask_bce_2: 0.67935/0.33583, loss_mask_dice_2: 1.69872/1.17368, loss_spatial_bce_2: 0.12228/0.09063, loss_spatial_dice_2: 0.32952/0.21933, loss_spatial_ce_2: 0.11761/0.07959, loss_grounding_bce_2: 0.11779/0.08639, loss_grounding_dice_2: 0.13110/0.17979, loss_grounding_ce_2: 0.22037/0.28030, loss_mask_ce_3: 1.04854/0.92544, loss_mask_bce_3: 0.79566/0.33670, loss_mask_dice_3: 1.74387/1.17093, loss_spatial_bce_3: 0.12521/0.09149, loss_spatial_dice_3: 0.33884/0.21992, loss_spatial_ce_3: 0.12642/0.08330, loss_grounding_bce_3: 0.13121/0.08667, loss_grounding_dice_3: 0.14380/0.17961, loss_grounding_ce_3: 0.23211/0.28183, loss_mask_ce_4: 1.13900/0.92465, loss_mask_bce_4: 0.83288/0.33857, loss_mask_dice_4: 1.78012/1.19406, loss_spatial_bce_4: 0.13916/0.09571, loss_spatial_dice_4: 0.35978/0.23114, loss_spatial_ce_4: 0.09504/0.09949, loss_grounding_bce_4: 0.12047/0.08710, loss_grounding_dice_4: 0.13676/0.18237, loss_grounding_ce_4: 0.17164/0.28468, loss_mask_ce_5: 1.20814/0.93967, loss_mask_bce_5: 0.79643/0.34088, loss_mask_dice_5: 1.71513/1.20032, loss_spatial_bce_5: 0.18818/0.09728, loss_spatial_dice_5: 0.38257/0.23471, loss_spatial_ce_5: 0.16891/0.11401, loss_grounding_bce_5: 0.11044/0.08749, loss_grounding_dice_5: 0.13775/0.18360, loss_grounding_ce_5: 0.16259/0.29727, loss_mask_ce_6: 1.26990/0.97790, loss_mask_bce_6: 0.71471/0.34355, loss_mask_dice_6: 1.59940/1.20259, loss_spatial_bce_6: 0.13752/0.10300, loss_spatial_dice_6: 0.32200/0.23697, loss_spatial_ce_6: 0.20185/0.13984, loss_grounding_bce_6: 0.12082/0.08822, loss_grounding_dice_6: 0.14033/0.18380, loss_grounding_ce_6: 0.12414/0.31348, loss_mask_ce_7: 1.25770/1.02295, loss_mask_bce_7: 0.75290/0.35136, loss_mask_dice_7: 1.84735/1.25836, loss_spatial_bce_7: 0.17025/0.11156, loss_spatial_dice_7: 0.36059/0.26464, loss_spatial_ce_7: 0.36628/0.17734, loss_grounding_bce_7: 0.10413/0.09012, loss_grounding_dice_7: 0.13121/0.19104, loss_grounding_ce_7: 0.22498/0.34671, loss_mask_ce_8: 1.28208/1.13314, loss_mask_bce_8: 0.80641/0.36501, loss_mask_dice_8: 1.76120/1.33256, loss_spatial_bce_8: 0.16466/0.13254, loss_spatial_dice_8: 0.40425/0.30404, loss_spatial_ce_8: 0.38236/0.23401, loss_grounding_bce_8: 0.12126/0.09377, loss_grounding_dice_8: 0.17256/0.20216, loss_grounding_ce_8: 0.33514/0.41588, loss_mask_ce_9: 3.65903/3.68404, loss_mask_bce_9: 0.70437/0.39187, loss_mask_dice_9: 2.42895/1.90634, loss_spatial_bce_9: 0.17291/0.33466, loss_spatial_dice_9: 0.82356/0.82330, loss_spatial_ce_9: 1.49801/1.50699, loss_grounding_bce_9: 0.14249/0.10524, loss_grounding_dice_9: 0.22594/0.28156, loss_grounding_ce_9: 0.63138/0.68291] items per batch[64] items per second[0.23] total items[2265600] mini batches[ 35400] memory[7341] epoch remaining[0:53:00]
INFO:trainer.default_trainer:epochs[    19] optim steps[35500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42180/0.90844, loss_mask_bce_0: 0.21548/0.33456, loss_mask_dice_0: 0.22153/1.16740, loss_spatial_bce_0: 0.14344/0.08944, loss_spatial_dice_0: 0.21486/0.21403, loss_spatial_ce_0: 0.00091/0.07017, loss_grounding_bce_0: 0.12055/0.08618, loss_grounding_dice_0: 0.13250/0.17924, loss_grounding_ce_0: 0.11320/0.27554, loss_mask_ce_1: 0.38883/0.90894, loss_mask_bce_1: 0.22058/0.33539, loss_mask_dice_1: 0.21899/1.17425, loss_spatial_bce_1: 0.16729/0.09014, loss_spatial_dice_1: 0.24656/0.21827, loss_spatial_ce_1: 0.00136/0.07588, loss_grounding_bce_1: 0.12522/0.08632, loss_grounding_dice_1: 0.13597/0.18013, loss_grounding_ce_1: 0.10336/0.27704, loss_mask_ce_2: 0.41246/0.91636, loss_mask_bce_2: 0.21231/0.33581, loss_mask_dice_2: 0.23017/1.17383, loss_spatial_bce_2: 0.16622/0.09060, loss_spatial_dice_2: 0.24360/0.21930, loss_spatial_ce_2: 0.00125/0.07950, loss_grounding_bce_2: 0.11869/0.08636, loss_grounding_dice_2: 0.13731/0.17980, loss_grounding_ce_2: 0.09568/0.28025, loss_mask_ce_3: 0.45103/0.92542, loss_mask_bce_3: 0.21454/0.33669, loss_mask_dice_3: 0.22757/1.17107, loss_spatial_bce_3: 0.15519/0.09145, loss_spatial_dice_3: 0.24787/0.21990, loss_spatial_ce_3: 0.00298/0.08322, loss_grounding_bce_3: 0.12590/0.08665, loss_grounding_dice_3: 0.12754/0.17962, loss_grounding_ce_3: 0.07781/0.28180, loss_mask_ce_4: 0.40854/0.92461, loss_mask_bce_4: 0.22459/0.33855, loss_mask_dice_4: 0.25412/1.19422, loss_spatial_bce_4: 0.16432/0.09567, loss_spatial_dice_4: 0.26040/0.23112, loss_spatial_ce_4: 0.01581/0.09944, loss_grounding_bce_4: 0.12819/0.08708, loss_grounding_dice_4: 0.14928/0.18238, loss_grounding_ce_4: 0.09508/0.28464, loss_mask_ce_5: 0.34545/0.93968, loss_mask_bce_5: 0.22217/0.34086, loss_mask_dice_5: 0.25155/1.20045, loss_spatial_bce_5: 0.14108/0.09724, loss_spatial_dice_5: 0.21901/0.23470, loss_spatial_ce_5: 0.07738/0.11396, loss_grounding_bce_5: 0.14324/0.08746, loss_grounding_dice_5: 0.16220/0.18360, loss_grounding_ce_5: 0.06969/0.29722, loss_mask_ce_6: 0.47541/0.97786, loss_mask_bce_6: 0.21226/0.34351, loss_mask_dice_6: 0.23114/1.20277, loss_spatial_bce_6: 0.12742/0.10297, loss_spatial_dice_6: 0.16368/0.23695, loss_spatial_ce_6: 0.10938/0.13982, loss_grounding_bce_6: 0.13063/0.08819, loss_grounding_dice_6: 0.14758/0.18382, loss_grounding_ce_6: 0.10908/0.31349, loss_mask_ce_7: 0.44663/1.02297, loss_mask_bce_7: 0.21600/0.35132, loss_mask_dice_7: 0.25875/1.25849, loss_spatial_bce_7: 0.14022/0.11152, loss_spatial_dice_7: 0.19015/0.26463, loss_spatial_ce_7: 0.19677/0.17733, loss_grounding_bce_7: 0.12993/0.09009, loss_grounding_dice_7: 0.15552/0.19106, loss_grounding_ce_7: 0.16242/0.34679, loss_mask_ce_8: 0.47088/1.13305, loss_mask_bce_8: 0.19734/0.36497, loss_mask_dice_8: 0.25291/1.33272, loss_spatial_bce_8: 0.16983/0.13249, loss_spatial_dice_8: 0.27863/0.30402, loss_spatial_ce_8: 0.20341/0.23395, loss_grounding_bce_8: 0.13143/0.09374, loss_grounding_dice_8: 0.15154/0.20216, loss_grounding_ce_8: 0.12093/0.41591, loss_mask_ce_9: 2.09056/3.68414, loss_mask_bce_9: 0.22959/0.39183, loss_mask_dice_9: 0.38344/1.90657, loss_spatial_bce_9: 0.34270/0.33460, loss_spatial_dice_9: 0.56818/0.82332, loss_spatial_ce_9: 1.03343/1.50699, loss_grounding_bce_9: 0.20522/0.10521, loss_grounding_dice_9: 0.29105/0.28159, loss_grounding_ce_9: 0.07267/0.68298] items per batch[64] items per second[0.22] total items[2272000] mini batches[ 35500] memory[7341] epoch remaining[0:48:35]
INFO:trainer.default_trainer:epochs[    19] optim steps[35600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.00034/0.90821, loss_mask_bce_0: 0.52678/0.33454, loss_mask_dice_0: 2.39536/1.16716, loss_spatial_bce_0: 0.08032/0.08943, loss_spatial_dice_0: 0.26640/0.21395, loss_spatial_ce_0: 0.02565/0.07014, loss_grounding_bce_0: 0.05123/0.08618, loss_grounding_dice_0: 0.50284/0.17920, loss_grounding_ce_0: 0.29927/0.27557, loss_mask_ce_1: 0.96762/0.90869, loss_mask_bce_1: 0.52618/0.33538, loss_mask_dice_1: 2.28893/1.17400, loss_spatial_bce_1: 0.08567/0.09014, loss_spatial_dice_1: 0.26752/0.21819, loss_spatial_ce_1: 0.02215/0.07582, loss_grounding_bce_1: 0.05225/0.08633, loss_grounding_dice_1: 0.48254/0.18009, loss_grounding_ce_1: 0.29933/0.27704, loss_mask_ce_2: 1.06780/0.91610, loss_mask_bce_2: 0.51284/0.33579, loss_mask_dice_2: 2.38022/1.17359, loss_spatial_bce_2: 0.09596/0.09060, loss_spatial_dice_2: 0.29460/0.21922, loss_spatial_ce_2: 0.02126/0.07948, loss_grounding_bce_2: 0.05031/0.08637, loss_grounding_dice_2: 0.49475/0.17978, loss_grounding_ce_2: 0.32447/0.28027, loss_mask_ce_3: 0.94777/0.92519, loss_mask_bce_3: 0.52951/0.33668, loss_mask_dice_3: 2.15534/1.17086, loss_spatial_bce_3: 0.08845/0.09145, loss_spatial_dice_3: 0.27696/0.21983, loss_spatial_ce_3: 0.01890/0.08319, loss_grounding_bce_3: 0.05324/0.08666, loss_grounding_dice_3: 0.47049/0.17959, loss_grounding_ce_3: 0.32491/0.28187, loss_mask_ce_4: 1.02311/0.92439, loss_mask_bce_4: 0.53479/0.33854, loss_mask_dice_4: 2.30873/1.19395, loss_spatial_bce_4: 0.09130/0.09567, loss_spatial_dice_4: 0.28567/0.23105, loss_spatial_ce_4: 0.02694/0.09940, loss_grounding_bce_4: 0.04396/0.08708, loss_grounding_dice_4: 0.46497/0.18234, loss_grounding_ce_4: 0.30657/0.28465, loss_mask_ce_5: 1.10974/0.93946, loss_mask_bce_5: 0.54476/0.34084, loss_mask_dice_5: 2.46727/1.20027, loss_spatial_bce_5: 0.08817/0.09725, loss_spatial_dice_5: 0.28069/0.23463, loss_spatial_ce_5: 0.05192/0.11394, loss_grounding_bce_5: 0.03670/0.08747, loss_grounding_dice_5: 0.46295/0.18357, loss_grounding_ce_5: 0.31521/0.29727, loss_mask_ce_6: 1.22458/0.97775, loss_mask_bce_6: 0.52015/0.34350, loss_mask_dice_6: 2.42671/1.20250, loss_spatial_bce_6: 0.08898/0.10298, loss_spatial_dice_6: 0.27231/0.23689, loss_spatial_ce_6: 0.07207/0.13975, loss_grounding_bce_6: 0.04447/0.08819, loss_grounding_dice_6: 0.46795/0.18378, loss_grounding_ce_6: 0.27456/0.31373, loss_mask_ce_7: 1.27815/1.02285, loss_mask_bce_7: 0.59687/0.35131, loss_mask_dice_7: 2.50890/1.25823, loss_spatial_bce_7: 0.09385/0.11150, loss_spatial_dice_7: 0.32546/0.26456, loss_spatial_ce_7: 0.08881/0.17726, loss_grounding_bce_7: 0.05507/0.09010, loss_grounding_dice_7: 0.49596/0.19102, loss_grounding_ce_7: 0.29343/0.34698, loss_mask_ce_8: 1.39748/1.13271, loss_mask_bce_8: 0.59610/0.36497, loss_mask_dice_8: 2.53905/1.33248, loss_spatial_bce_8: 0.09353/0.13247, loss_spatial_dice_8: 0.34556/0.30394, loss_spatial_ce_8: 0.17169/0.23385, loss_grounding_bce_8: 0.04573/0.09376, loss_grounding_dice_8: 0.48856/0.20213, loss_grounding_ce_8: 0.28043/0.41591, loss_mask_ce_9: 4.69655/3.68404, loss_mask_bce_9: 0.64175/0.39182, loss_mask_dice_9: 4.28289/1.90622, loss_spatial_bce_9: 0.30621/0.33463, loss_spatial_dice_9: 0.91251/0.82331, loss_spatial_ce_9: 1.82227/1.50691, loss_grounding_bce_9: 0.06115/0.10523, loss_grounding_dice_9: 0.61520/0.28152, loss_grounding_ce_9: 0.42052/0.68310] items per batch[64] items per second[0.23] total items[2278400] mini batches[ 35600] memory[7341] epoch remaining[0:43:52]
INFO:trainer.default_trainer:epochs[    19] optim steps[35700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78112/0.90839, loss_mask_bce_0: 0.60444/0.33464, loss_mask_dice_0: 0.86213/1.16686, loss_spatial_bce_0: 0.14956/0.08945, loss_spatial_dice_0: 0.19765/0.21392, loss_spatial_ce_0: 0.02961/0.07012, loss_grounding_bce_0: 0.06754/0.08622, loss_grounding_dice_0: 0.17235/0.17919, loss_grounding_ce_0: 0.39804/0.27563, loss_mask_ce_1: 0.73737/0.90891, loss_mask_bce_1: 0.62289/0.33548, loss_mask_dice_1: 0.87878/1.17372, loss_spatial_bce_1: 0.14941/0.09016, loss_spatial_dice_1: 0.19719/0.21816, loss_spatial_ce_1: 0.02292/0.07583, loss_grounding_bce_1: 0.06795/0.08637, loss_grounding_dice_1: 0.19510/0.18009, loss_grounding_ce_1: 0.22318/0.27714, loss_mask_ce_2: 0.73845/0.91627, loss_mask_bce_2: 0.58857/0.33588, loss_mask_dice_2: 0.87458/1.17330, loss_spatial_bce_2: 0.14878/0.09061, loss_spatial_dice_2: 0.19591/0.21919, loss_spatial_ce_2: 0.02466/0.07945, loss_grounding_bce_2: 0.06643/0.08641, loss_grounding_dice_2: 0.19897/0.17977, loss_grounding_ce_2: 0.36168/0.28038, loss_mask_ce_3: 0.72231/0.92538, loss_mask_bce_3: 0.57244/0.33677, loss_mask_dice_3: 0.85765/1.17063, loss_spatial_bce_3: 0.13895/0.09146, loss_spatial_dice_3: 0.17610/0.21980, loss_spatial_ce_3: 0.04161/0.08320, loss_grounding_bce_3: 0.07042/0.08669, loss_grounding_dice_3: 0.20755/0.17957, loss_grounding_ce_3: 0.42715/0.28194, loss_mask_ce_4: 0.77994/0.92458, loss_mask_bce_4: 0.57071/0.33863, loss_mask_dice_4: 0.87410/1.19367, loss_spatial_bce_4: 0.13836/0.09569, loss_spatial_dice_4: 0.19699/0.23102, loss_spatial_ce_4: 0.07890/0.09938, loss_grounding_bce_4: 0.08047/0.08712, loss_grounding_dice_4: 0.22995/0.18234, loss_grounding_ce_4: 0.42647/0.28478, loss_mask_ce_5: 0.91978/0.93970, loss_mask_bce_5: 0.52882/0.34093, loss_mask_dice_5: 0.89813/1.20001, loss_spatial_bce_5: 0.12671/0.09728, loss_spatial_dice_5: 0.18994/0.23461, loss_spatial_ce_5: 0.13590/0.11394, loss_grounding_bce_5: 0.11392/0.08752, loss_grounding_dice_5: 0.27641/0.18357, loss_grounding_ce_5: 0.46028/0.29733, loss_mask_ce_6: 0.93401/0.97804, loss_mask_bce_6: 0.58314/0.34359, loss_mask_dice_6: 0.87325/1.20227, loss_spatial_bce_6: 0.09394/0.10302, loss_spatial_dice_6: 0.18776/0.23689, loss_spatial_ce_6: 0.14963/0.13972, loss_grounding_bce_6: 0.08637/0.08823, loss_grounding_dice_6: 0.22424/0.18377, loss_grounding_ce_6: 1.65370/0.31386, loss_mask_ce_7: 0.83129/1.02308, loss_mask_bce_7: 0.69403/0.35141, loss_mask_dice_7: 0.96969/1.25797, loss_spatial_bce_7: 0.14074/0.11155, loss_spatial_dice_7: 0.23705/0.26456, loss_spatial_ce_7: 0.09693/0.17728, loss_grounding_bce_7: 0.07651/0.09013, loss_grounding_dice_7: 0.20110/0.19101, loss_grounding_ce_7: 1.41065/0.34708, loss_mask_ce_8: 0.93804/1.13286, loss_mask_bce_8: 0.60313/0.36508, loss_mask_dice_8: 0.92840/1.33211, loss_spatial_bce_8: 0.14893/0.13252, loss_spatial_dice_8: 0.27325/0.30392, loss_spatial_ce_8: 0.15109/0.23384, loss_grounding_bce_8: 0.10010/0.09379, loss_grounding_dice_8: 0.21932/0.20213, loss_grounding_ce_8: 2.37960/0.41589, loss_mask_ce_9: 3.19948/3.68402, loss_mask_bce_9: 0.66752/0.39195, loss_mask_dice_9: 1.60257/1.90582, loss_spatial_bce_9: 0.40613/0.33470, loss_spatial_dice_9: 0.89690/0.82328, loss_spatial_ce_9: 1.52787/1.50694, loss_grounding_bce_9: 0.07311/0.10527, loss_grounding_dice_9: 0.22910/0.28149, loss_grounding_ce_9: 1.91458/0.68319] items per batch[64] items per second[0.24] total items[2284800] mini batches[ 35700] memory[7341] epoch remaining[0:39:05]
INFO:trainer.default_trainer:epochs[    19] optim steps[35800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11529/0.90837, loss_mask_bce_0: 0.28285/0.33461, loss_mask_dice_0: 2.48072/1.16658, loss_spatial_bce_0: 0.03634/0.08944, loss_spatial_dice_0: 0.27052/0.21388, loss_spatial_ce_0: 0.12827/0.07007, loss_grounding_bce_0: 0.04934/0.08622, loss_grounding_dice_0: 0.19639/0.17919, loss_grounding_ce_0: 0.09354/0.27555, loss_mask_ce_1: 1.15808/0.90894, loss_mask_bce_1: 0.27853/0.33545, loss_mask_dice_1: 2.39068/1.17348, loss_spatial_bce_1: 0.03473/0.09015, loss_spatial_dice_1: 0.29137/0.21812, loss_spatial_ce_1: 0.05132/0.07577, loss_grounding_bce_1: 0.04588/0.08636, loss_grounding_dice_1: 0.17944/0.18009, loss_grounding_ce_1: 0.12447/0.27705, loss_mask_ce_2: 1.18317/0.91627, loss_mask_bce_2: 0.28843/0.33585, loss_mask_dice_2: 2.70976/1.17306, loss_spatial_bce_2: 0.03882/0.09061, loss_spatial_dice_2: 0.28306/0.21915, loss_spatial_ce_2: 0.05598/0.07939, loss_grounding_bce_2: 0.04993/0.08640, loss_grounding_dice_2: 0.21186/0.17978, loss_grounding_ce_2: 0.07477/0.28028, loss_mask_ce_3: 0.98046/0.92537, loss_mask_bce_3: 0.28597/0.33676, loss_mask_dice_3: 2.42164/1.17038, loss_spatial_bce_3: 0.05017/0.09145, loss_spatial_dice_3: 0.29264/0.21975, loss_spatial_ce_3: 0.16574/0.08316, loss_grounding_bce_3: 0.05116/0.08668, loss_grounding_dice_3: 0.19079/0.17960, loss_grounding_ce_3: 0.05250/0.28182, loss_mask_ce_4: 1.21541/0.92458, loss_mask_bce_4: 0.32864/0.33861, loss_mask_dice_4: 2.55514/1.19338, loss_spatial_bce_4: 0.04977/0.09570, loss_spatial_dice_4: 0.26543/0.23099, loss_spatial_ce_4: 0.27917/0.09933, loss_grounding_bce_4: 0.04948/0.08711, loss_grounding_dice_4: 0.21355/0.18236, loss_grounding_ce_4: 0.06738/0.28469, loss_mask_ce_5: 1.22900/0.93967, loss_mask_bce_5: 0.33153/0.34091, loss_mask_dice_5: 2.08240/1.19968, loss_spatial_bce_5: 0.05592/0.09729, loss_spatial_dice_5: 0.31233/0.23458, loss_spatial_ce_5: 0.48259/0.11392, loss_grounding_bce_5: 0.04959/0.08752, loss_grounding_dice_5: 0.20487/0.18358, loss_grounding_ce_5: 0.08614/0.29719, loss_mask_ce_6: 1.20075/0.97797, loss_mask_bce_6: 0.31010/0.34358, loss_mask_dice_6: 2.95753/1.20207, loss_spatial_bce_6: 0.04476/0.10304, loss_spatial_dice_6: 0.30407/0.23686, loss_spatial_ce_6: 0.58775/0.13973, loss_grounding_bce_6: 0.05115/0.08822, loss_grounding_dice_6: 0.21569/0.18378, loss_grounding_ce_6: 0.07592/0.31373, loss_mask_ce_7: 1.32372/1.02305, loss_mask_bce_7: 0.30546/0.35140, loss_mask_dice_7: 2.90090/1.25771, loss_spatial_bce_7: 0.04627/0.11157, loss_spatial_dice_7: 0.38438/0.26453, loss_spatial_ce_7: 0.25025/0.17721, loss_grounding_bce_7: 0.04688/0.09011, loss_grounding_dice_7: 0.22188/0.19103, loss_grounding_ce_7: 0.14424/0.34697, loss_mask_ce_8: 1.47456/1.13278, loss_mask_bce_8: 0.32268/0.36505, loss_mask_dice_8: 3.01611/1.33184, loss_spatial_bce_8: 0.09894/0.13255, loss_spatial_dice_8: 0.48768/0.30390, loss_spatial_ce_8: 0.55849/0.23379, loss_grounding_bce_8: 0.05326/0.09378, loss_grounding_dice_8: 0.24962/0.20215, loss_grounding_ce_8: 0.30489/0.41568, loss_mask_ce_9: 3.66077/3.68403, loss_mask_bce_9: 0.34762/0.39196, loss_mask_dice_9: 3.52386/1.90548, loss_spatial_bce_9: 0.18415/0.33470, loss_spatial_dice_9: 0.93691/0.82325, loss_spatial_ce_9: 1.54373/1.50686, loss_grounding_bce_9: 0.04468/0.10528, loss_grounding_dice_9: 0.26089/0.28151, loss_grounding_ce_9: 1.15995/0.68298] items per batch[64] items per second[0.23] total items[2291200] mini batches[ 35800] memory[7341] epoch remaining[0:34:26]
INFO:trainer.default_trainer:epochs[    19] optim steps[35900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.65101/0.90831, loss_mask_bce_0: 0.39087/0.33464, loss_mask_dice_0: 7.03068/1.16656, loss_spatial_bce_0: 0.02556/0.08943, loss_spatial_dice_0: 0.29026/0.21385, loss_spatial_ce_0: 0.03733/0.06999, loss_grounding_bce_0: 0.03535/0.08624, loss_grounding_dice_0: 0.15003/0.17919, loss_grounding_ce_0: 0.55484/0.27558, loss_mask_ce_1: 1.79966/0.90891, loss_mask_bce_1: 0.44605/0.33548, loss_mask_dice_1: 7.34762/1.17346, loss_spatial_bce_1: 0.02631/0.09014, loss_spatial_dice_1: 0.27416/0.21809, loss_spatial_ce_1: 0.05230/0.07570, loss_grounding_bce_1: 0.03333/0.08638, loss_grounding_dice_1: 0.13479/0.18009, loss_grounding_ce_1: 0.57435/0.27712, loss_mask_ce_2: 1.65158/0.91626, loss_mask_bce_2: 0.43397/0.33588, loss_mask_dice_2: 7.07781/1.17302, loss_spatial_bce_2: 0.02616/0.09059, loss_spatial_dice_2: 0.28458/0.21912, loss_spatial_ce_2: 0.04559/0.07932, loss_grounding_bce_2: 0.03823/0.08642, loss_grounding_dice_2: 0.14428/0.17977, loss_grounding_ce_2: 0.61570/0.28035, loss_mask_ce_3: 1.59795/0.92533, loss_mask_bce_3: 0.43227/0.33678, loss_mask_dice_3: 7.22654/1.17034, loss_spatial_bce_3: 0.02873/0.09144, loss_spatial_dice_3: 0.31950/0.21972, loss_spatial_ce_3: 0.06016/0.08307, loss_grounding_bce_3: 0.03670/0.08670, loss_grounding_dice_3: 0.13812/0.17960, loss_grounding_ce_3: 0.52568/0.28190, loss_mask_ce_4: 1.91125/0.92457, loss_mask_bce_4: 0.44183/0.33864, loss_mask_dice_4: 7.15161/1.19338, loss_spatial_bce_4: 0.03018/0.09569, loss_spatial_dice_4: 0.32239/0.23097, loss_spatial_ce_4: 0.05824/0.09925, loss_grounding_bce_4: 0.03744/0.08714, loss_grounding_dice_4: 0.14434/0.18236, loss_grounding_ce_4: 0.42591/0.28470, loss_mask_ce_5: 1.85544/0.93970, loss_mask_bce_5: 0.34245/0.34093, loss_mask_dice_5: 7.16813/1.19968, loss_spatial_bce_5: 0.02815/0.09728, loss_spatial_dice_5: 0.33249/0.23455, loss_spatial_ce_5: 0.36866/0.11384, loss_grounding_bce_5: 0.03286/0.08754, loss_grounding_dice_5: 0.14011/0.18357, loss_grounding_ce_5: 0.51398/0.29729, loss_mask_ce_6: 1.89160/0.97809, loss_mask_bce_6: 0.46302/0.34360, loss_mask_dice_6: 7.06813/1.20209, loss_spatial_bce_6: 0.03429/0.10304, loss_spatial_dice_6: 0.30875/0.23683, loss_spatial_ce_6: 0.07704/0.13970, loss_grounding_bce_6: 0.03230/0.08824, loss_grounding_dice_6: 0.14476/0.18378, loss_grounding_ce_6: 0.47065/0.31377, loss_mask_ce_7: 1.81426/1.02315, loss_mask_bce_7: 0.48740/0.35143, loss_mask_dice_7: 7.64028/1.25774, loss_spatial_bce_7: 0.03833/0.11155, loss_spatial_dice_7: 0.35551/0.26450, loss_spatial_ce_7: 0.10061/0.17709, loss_grounding_bce_7: 0.03328/0.09014, loss_grounding_dice_7: 0.13299/0.19103, loss_grounding_ce_7: 0.41566/0.34706, loss_mask_ce_8: 2.00703/1.13279, loss_mask_bce_8: 0.44054/0.36508, loss_mask_dice_8: 7.71224/1.33183, loss_spatial_bce_8: 0.04916/0.13252, loss_spatial_dice_8: 0.42078/0.30386, loss_spatial_ce_8: 0.11472/0.23371, loss_grounding_bce_8: 0.02846/0.09381, loss_grounding_dice_8: 0.12233/0.20214, loss_grounding_ce_8: 0.92896/0.41570, loss_mask_ce_9: 7.17404/3.68421, loss_mask_bce_9: 0.35507/0.39200, loss_mask_dice_9: 10.26963/1.90560, loss_spatial_bce_9: 0.18704/0.33471, loss_spatial_dice_9: 0.88882/0.82324, loss_spatial_ce_9: 2.53323/1.50669, loss_grounding_bce_9: 0.02059/0.10532, loss_grounding_dice_9: 0.19437/0.28150, loss_grounding_ce_9: 1.08881/0.68312] items per batch[64] items per second[0.23] total items[2297600] mini batches[ 35900] memory[7341] epoch remaining[0:29:45]
INFO:trainer.default_trainer:epochs[    19] optim steps[36000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42338/0.90815, loss_mask_bce_0: 0.44654/0.33467, loss_mask_dice_0: 0.98969/1.16647, loss_spatial_bce_0: 0.16545/0.08943, loss_spatial_dice_0: 0.26904/0.21385, loss_spatial_ce_0: 0.15614/0.07000, loss_grounding_bce_0: 0.13595/0.08625, loss_grounding_dice_0: 0.26418/0.17919, loss_grounding_ce_0: 0.30785/0.27545, loss_mask_ce_1: 1.35377/0.90881, loss_mask_bce_1: 0.44354/0.33552, loss_mask_dice_1: 0.97145/1.17333, loss_spatial_bce_1: 0.12428/0.09014, loss_spatial_dice_1: 0.25748/0.21809, loss_spatial_ce_1: 0.31859/0.07568, loss_grounding_bce_1: 0.13815/0.08638, loss_grounding_dice_1: 0.26934/0.18010, loss_grounding_ce_1: 0.31763/0.27708, loss_mask_ce_2: 1.34700/0.91612, loss_mask_bce_2: 0.43429/0.33591, loss_mask_dice_2: 0.94864/1.17291, loss_spatial_bce_2: 0.16182/0.09060, loss_spatial_dice_2: 0.27141/0.21911, loss_spatial_ce_2: 0.18867/0.07929, loss_grounding_bce_2: 0.13799/0.08643, loss_grounding_dice_2: 0.25977/0.17977, loss_grounding_ce_2: 0.30977/0.28030, loss_mask_ce_3: 1.37359/0.92521, loss_mask_bce_3: 0.43635/0.33681, loss_mask_dice_3: 0.95384/1.17019, loss_spatial_bce_3: 0.16220/0.09145, loss_spatial_dice_3: 0.26512/0.21971, loss_spatial_ce_3: 0.15531/0.08303, loss_grounding_bce_3: 0.13380/0.08670, loss_grounding_dice_3: 0.26655/0.17960, loss_grounding_ce_3: 0.31618/0.28183, loss_mask_ce_4: 1.34277/0.92441, loss_mask_bce_4: 0.43641/0.33867, loss_mask_dice_4: 0.93380/1.19324, loss_spatial_bce_4: 0.17462/0.09570, loss_spatial_dice_4: 0.28480/0.23099, loss_spatial_ce_4: 0.15952/0.09919, loss_grounding_bce_4: 0.14680/0.08715, loss_grounding_dice_4: 0.25996/0.18236, loss_grounding_ce_4: 0.38191/0.28464, loss_mask_ce_5: 1.36270/0.93956, loss_mask_bce_5: 0.45211/0.34097, loss_mask_dice_5: 0.99403/1.19956, loss_spatial_bce_5: 0.18535/0.09729, loss_spatial_dice_5: 0.29996/0.23456, loss_spatial_ce_5: 0.19978/0.11381, loss_grounding_bce_5: 0.13113/0.08755, loss_grounding_dice_5: 0.25129/0.18356, loss_grounding_ce_5: 0.35410/0.29721, loss_mask_ce_6: 1.59145/0.97800, loss_mask_bce_6: 0.45566/0.34364, loss_mask_dice_6: 0.99446/1.20191, loss_spatial_bce_6: 0.18710/0.10303, loss_spatial_dice_6: 0.28866/0.23684, loss_spatial_ce_6: 0.28495/0.13971, loss_grounding_bce_6: 0.13233/0.08825, loss_grounding_dice_6: 0.25313/0.18377, loss_grounding_ce_6: 0.38776/0.31365, loss_mask_ce_7: 1.46450/1.02308, loss_mask_bce_7: 0.46226/0.35145, loss_mask_dice_7: 1.04396/1.25757, loss_spatial_bce_7: 0.23364/0.11154, loss_spatial_dice_7: 0.31742/0.26451, loss_spatial_ce_7: 0.21272/0.17709, loss_grounding_bce_7: 0.13611/0.09015, loss_grounding_dice_7: 0.26697/0.19104, loss_grounding_ce_7: 0.35389/0.34696, loss_mask_ce_8: 1.32948/1.13264, loss_mask_bce_8: 0.49448/0.36509, loss_mask_dice_8: 1.00618/1.33161, loss_spatial_bce_8: 0.19142/0.13251, loss_spatial_dice_8: 0.32462/0.30387, loss_spatial_ce_8: 0.43976/0.23372, loss_grounding_bce_8: 0.12784/0.09382, loss_grounding_dice_8: 0.25319/0.20214, loss_grounding_ce_8: 0.47559/0.41557, loss_mask_ce_9: 6.06852/3.68390, loss_mask_bce_9: 0.49763/0.39201, loss_mask_dice_9: 1.59470/1.90532, loss_spatial_bce_9: 0.41286/0.33468, loss_spatial_dice_9: 0.87018/0.82323, loss_spatial_ce_9: 1.73149/1.50663, loss_grounding_bce_9: 0.11249/0.10532, loss_grounding_dice_9: 0.33725/0.28150, loss_grounding_ce_9: 0.56831/0.68296] items per batch[64] items per second[0.23] total items[2304000] mini batches[ 36000] memory[7341] epoch remaining[0:25:05]
INFO:trainer.default_trainer:epochs[    19] optim steps[36100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.60297/0.90826, loss_mask_bce_0: 0.34626/0.33466, loss_mask_dice_0: 1.56782/1.16626, loss_spatial_bce_0: 0.06766/0.08941, loss_spatial_dice_0: 0.24691/0.21380, loss_spatial_ce_0: 0.00221/0.06993, loss_grounding_bce_0: 0.05907/0.08622, loss_grounding_dice_0: 0.21736/0.17920, loss_grounding_ce_0: 0.11130/0.27546, loss_mask_ce_1: 1.43418/0.90890, loss_mask_bce_1: 0.34989/0.33551, loss_mask_dice_1: 1.42958/1.17313, loss_spatial_bce_1: 0.06409/0.09012, loss_spatial_dice_1: 0.24620/0.21803, loss_spatial_ce_1: 0.00501/0.07562, loss_grounding_bce_1: 0.05779/0.08636, loss_grounding_dice_1: 0.23386/0.18010, loss_grounding_ce_1: 0.06075/0.27710, loss_mask_ce_2: 1.46274/0.91623, loss_mask_bce_2: 0.34571/0.33590, loss_mask_dice_2: 1.46946/1.17269, loss_spatial_bce_2: 0.06861/0.09058, loss_spatial_dice_2: 0.22372/0.21906, loss_spatial_ce_2: 0.00385/0.07922, loss_grounding_bce_2: 0.06023/0.08641, loss_grounding_dice_2: 0.23110/0.17979, loss_grounding_ce_2: 0.06074/0.28029, loss_mask_ce_3: 1.45780/0.92534, loss_mask_bce_3: 0.35081/0.33680, loss_mask_dice_3: 1.56388/1.16999, loss_spatial_bce_3: 0.06826/0.09142, loss_spatial_dice_3: 0.24798/0.21967, loss_spatial_ce_3: 0.00328/0.08295, loss_grounding_bce_3: 0.05598/0.08668, loss_grounding_dice_3: 0.21018/0.17960, loss_grounding_ce_3: 0.11152/0.28180, loss_mask_ce_4: 1.65976/0.92448, loss_mask_bce_4: 0.37117/0.33866, loss_mask_dice_4: 1.46186/1.19306, loss_spatial_bce_4: 0.06996/0.09567, loss_spatial_dice_4: 0.27268/0.23094, loss_spatial_ce_4: 0.00787/0.09915, loss_grounding_bce_4: 0.05634/0.08711, loss_grounding_dice_4: 0.19941/0.18237, loss_grounding_ce_4: 0.07646/0.28465, loss_mask_ce_5: 1.56324/0.93967, loss_mask_bce_5: 0.36248/0.34096, loss_mask_dice_5: 1.51143/1.19934, loss_spatial_bce_5: 0.07334/0.09727, loss_spatial_dice_5: 0.28874/0.23453, loss_spatial_ce_5: 0.01632/0.11373, loss_grounding_bce_5: 0.05732/0.08753, loss_grounding_dice_5: 0.19962/0.18357, loss_grounding_ce_5: 0.16418/0.29715, loss_mask_ce_6: 1.68377/0.97818, loss_mask_bce_6: 0.34633/0.34364, loss_mask_dice_6: 1.45746/1.20170, loss_spatial_bce_6: 0.08006/0.10301, loss_spatial_dice_6: 0.30010/0.23680, loss_spatial_ce_6: 0.04181/0.13968, loss_grounding_bce_6: 0.06061/0.08822, loss_grounding_dice_6: 0.22743/0.18377, loss_grounding_ce_6: 0.09416/0.31365, loss_mask_ce_7: 1.70279/1.02321, loss_mask_bce_7: 0.38750/0.35144, loss_mask_dice_7: 1.33832/1.25733, loss_spatial_bce_7: 0.11051/0.11152, loss_spatial_dice_7: 0.33746/0.26447, loss_spatial_ce_7: 0.08534/0.17707, loss_grounding_bce_7: 0.06203/0.09012, loss_grounding_dice_7: 0.23283/0.19106, loss_grounding_ce_7: 0.08862/0.34688, loss_mask_ce_8: 2.01936/1.13278, loss_mask_bce_8: 0.40063/0.36507, loss_mask_dice_8: 1.62353/1.33134, loss_spatial_bce_8: 0.13440/0.13250, loss_spatial_dice_8: 0.38029/0.30383, loss_spatial_ce_8: 0.11447/0.23365, loss_grounding_bce_8: 0.06992/0.09380, loss_grounding_dice_8: 0.21535/0.20215, loss_grounding_ce_8: 0.11429/0.41551, loss_mask_ce_9: 3.23146/3.68404, loss_mask_bce_9: 0.40299/0.39200, loss_mask_dice_9: 2.17926/1.90529, loss_spatial_bce_9: 0.21757/0.33464, loss_spatial_dice_9: 0.86400/0.82320, loss_spatial_ce_9: 1.76780/1.50670, loss_grounding_bce_9: 0.05476/0.10529, loss_grounding_dice_9: 0.32851/0.28153, loss_grounding_ce_9: 0.58384/0.68283] items per batch[64] items per second[0.23] total items[2310400] mini batches[ 36100] memory[7341] epoch remaining[0:20:27]
INFO:trainer.default_trainer:epochs[    19] optim steps[36200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59619/0.90821, loss_mask_bce_0: 0.15400/0.33463, loss_mask_dice_0: 0.34629/1.16662, loss_spatial_bce_0: 0.05751/0.08937, loss_spatial_dice_0: 0.12645/0.21379, loss_spatial_ce_0: 0.00175/0.06987, loss_grounding_bce_0: 0.05549/0.08620, loss_grounding_dice_0: 0.08755/0.17921, loss_grounding_ce_0: 0.06487/0.27534, loss_mask_ce_1: 0.53047/0.90888, loss_mask_bce_1: 0.15433/0.33548, loss_mask_dice_1: 0.35767/1.17353, loss_spatial_bce_1: 0.05509/0.09008, loss_spatial_dice_1: 0.12156/0.21803, loss_spatial_ce_1: 0.00139/0.07554, loss_grounding_bce_1: 0.05251/0.08634, loss_grounding_dice_1: 0.09299/0.18011, loss_grounding_ce_1: 0.05596/0.27698, loss_mask_ce_2: 0.54966/0.91615, loss_mask_bce_2: 0.15920/0.33587, loss_mask_dice_2: 0.40611/1.17309, loss_spatial_bce_2: 0.05617/0.09054, loss_spatial_dice_2: 0.12274/0.21906, loss_spatial_ce_2: 0.00236/0.07913, loss_grounding_bce_2: 0.05388/0.08639, loss_grounding_dice_2: 0.08919/0.17981, loss_grounding_ce_2: 0.05764/0.28019, loss_mask_ce_3: 0.63631/0.92533, loss_mask_bce_3: 0.16360/0.33677, loss_mask_dice_3: 0.37929/1.17035, loss_spatial_bce_3: 0.05755/0.09139, loss_spatial_dice_3: 0.14675/0.21965, loss_spatial_ce_3: 0.00372/0.08288, loss_grounding_bce_3: 0.05493/0.08666, loss_grounding_dice_3: 0.09616/0.17963, loss_grounding_ce_3: 0.08170/0.28170, loss_mask_ce_4: 0.57408/0.92448, loss_mask_bce_4: 0.16737/0.33864, loss_mask_dice_4: 0.39794/1.19340, loss_spatial_bce_4: 0.05920/0.09565, loss_spatial_dice_4: 0.13321/0.23095, loss_spatial_ce_4: 0.02906/0.09906, loss_grounding_bce_4: 0.05094/0.08709, loss_grounding_dice_4: 0.09035/0.18238, loss_grounding_ce_4: 0.06673/0.28455, loss_mask_ce_5: 0.61659/0.93966, loss_mask_bce_5: 0.15583/0.34093, loss_mask_dice_5: 0.36969/1.19971, loss_spatial_bce_5: 0.06446/0.09726, loss_spatial_dice_5: 0.15739/0.23453, loss_spatial_ce_5: 0.06343/0.11363, loss_grounding_bce_5: 0.05200/0.08750, loss_grounding_dice_5: 0.09717/0.18359, loss_grounding_ce_5: 0.07289/0.29705, loss_mask_ce_6: 0.65337/0.97822, loss_mask_bce_6: 0.17117/0.34361, loss_mask_dice_6: 0.39523/1.20209, loss_spatial_bce_6: 0.07177/0.10299, loss_spatial_dice_6: 0.19853/0.23681, loss_spatial_ce_6: 0.07908/0.13962, loss_grounding_bce_6: 0.06737/0.08821, loss_grounding_dice_6: 0.09884/0.18379, loss_grounding_ce_6: 0.06414/0.31357, loss_mask_ce_7: 0.79138/1.02319, loss_mask_bce_7: 0.18179/0.35143, loss_mask_dice_7: 0.39030/1.25773, loss_spatial_bce_7: 0.08173/0.11149, loss_spatial_dice_7: 0.16319/0.26448, loss_spatial_ce_7: 0.11599/0.17698, loss_grounding_bce_7: 0.06740/0.09011, loss_grounding_dice_7: 0.10373/0.19108, loss_grounding_ce_7: 0.06125/0.34672, loss_mask_ce_8: 0.68656/1.13276, loss_mask_bce_8: 0.19642/0.36507, loss_mask_dice_8: 0.41560/1.33175, loss_spatial_bce_8: 0.07929/0.13247, loss_spatial_dice_8: 0.19584/0.30385, loss_spatial_ce_8: 0.27204/0.23361, loss_grounding_bce_8: 0.06726/0.09378, loss_grounding_dice_8: 0.10020/0.20215, loss_grounding_ce_8: 0.04409/0.41538, loss_mask_ce_9: 2.51805/3.68388, loss_mask_bce_9: 0.23068/0.39199, loss_mask_dice_9: 0.55793/1.90588, loss_spatial_bce_9: 0.44493/0.33456, loss_spatial_dice_9: 0.89528/0.82323, loss_spatial_ce_9: 1.38630/1.50677, loss_grounding_bce_9: 0.10292/0.10527, loss_grounding_dice_9: 0.15166/0.28154, loss_grounding_ce_9: 0.20101/0.68251] items per batch[64] items per second[0.23] total items[2316800] mini batches[ 36200] memory[7341] epoch remaining[0:15:49]
INFO:trainer.default_trainer:epochs[    19] optim steps[36300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47050/0.90793, loss_mask_bce_0: 0.24768/0.33470, loss_mask_dice_0: 0.33786/1.16681, loss_spatial_bce_0: 0.05928/0.08939, loss_spatial_dice_0: 0.08386/0.21376, loss_spatial_ce_0: 0.02854/0.06979, loss_grounding_bce_0: 0.09816/0.08622, loss_grounding_dice_0: 0.11004/0.17916, loss_grounding_ce_0: 0.61664/0.27526, loss_mask_ce_1: 0.47388/0.90864, loss_mask_bce_1: 0.24697/0.33555, loss_mask_dice_1: 0.33558/1.17371, loss_spatial_bce_1: 0.05243/0.09009, loss_spatial_dice_1: 0.08265/0.21799, loss_spatial_ce_1: 0.06553/0.07547, loss_grounding_bce_1: 0.09218/0.08636, loss_grounding_dice_1: 0.11043/0.18006, loss_grounding_ce_1: 0.51991/0.27689, loss_mask_ce_2: 0.48313/0.91587, loss_mask_bce_2: 0.24203/0.33593, loss_mask_dice_2: 0.33498/1.17327, loss_spatial_bce_2: 0.05483/0.09056, loss_spatial_dice_2: 0.08112/0.21902, loss_spatial_ce_2: 0.06071/0.07903, loss_grounding_bce_2: 0.07662/0.08641, loss_grounding_dice_2: 0.09984/0.17976, loss_grounding_ce_2: 0.61282/0.28011, loss_mask_ce_3: 0.47198/0.92507, loss_mask_bce_3: 0.23972/0.33684, loss_mask_dice_3: 0.33096/1.17048, loss_spatial_bce_3: 0.05329/0.09140, loss_spatial_dice_3: 0.08354/0.21962, loss_spatial_ce_3: 0.05237/0.08279, loss_grounding_bce_3: 0.08842/0.08668, loss_grounding_dice_3: 0.10294/0.17956, loss_grounding_ce_3: 0.59765/0.28164, loss_mask_ce_4: 0.48686/0.92423, loss_mask_bce_4: 0.25489/0.33871, loss_mask_dice_4: 0.34168/1.19359, loss_spatial_bce_4: 0.05124/0.09566, loss_spatial_dice_4: 0.07966/0.23093, loss_spatial_ce_4: 0.02076/0.09897, loss_grounding_bce_4: 0.09247/0.08710, loss_grounding_dice_4: 0.10715/0.18232, loss_grounding_ce_4: 0.55664/0.28450, loss_mask_ce_5: 0.52744/0.93939, loss_mask_bce_5: 0.22565/0.34099, loss_mask_dice_5: 0.34128/1.19989, loss_spatial_bce_5: 0.05984/0.09727, loss_spatial_dice_5: 0.08636/0.23451, loss_spatial_ce_5: 0.03856/0.11353, loss_grounding_bce_5: 0.07527/0.08752, loss_grounding_dice_5: 0.10427/0.18353, loss_grounding_ce_5: 0.65228/0.29694, loss_mask_ce_6: 0.54653/0.97799, loss_mask_bce_6: 0.21857/0.34368, loss_mask_dice_6: 0.31982/1.20230, loss_spatial_bce_6: 0.06679/0.10300, loss_spatial_dice_6: 0.09534/0.23680, loss_spatial_ce_6: 0.03791/0.13956, loss_grounding_bce_6: 0.07901/0.08823, loss_grounding_dice_6: 0.10200/0.18374, loss_grounding_ce_6: 0.57005/0.31345, loss_mask_ce_7: 0.72765/1.02296, loss_mask_bce_7: 0.24663/0.35151, loss_mask_dice_7: 0.34190/1.25792, loss_spatial_bce_7: 0.08879/0.11149, loss_spatial_dice_7: 0.10523/0.26446, loss_spatial_ce_7: 0.04375/0.17690, loss_grounding_bce_7: 0.08938/0.09013, loss_grounding_dice_7: 0.10743/0.19102, loss_grounding_ce_7: 0.49169/0.34657, loss_mask_ce_8: 0.90412/1.13251, loss_mask_bce_8: 0.28868/0.36513, loss_mask_dice_8: 0.43556/1.33193, loss_spatial_bce_8: 0.12246/0.13247, loss_spatial_dice_8: 0.14891/0.30382, loss_spatial_ce_8: 0.11977/0.23356, loss_grounding_bce_8: 0.04360/0.09378, loss_grounding_dice_8: 0.07714/0.20209, loss_grounding_ce_8: 0.79006/0.41519, loss_mask_ce_9: 4.22312/3.68396, loss_mask_bce_9: 0.35990/0.39209, loss_mask_dice_9: 0.87869/1.90621, loss_spatial_bce_9: 0.33067/0.33455, loss_spatial_dice_9: 0.77907/0.82321, loss_spatial_ce_9: 1.32703/1.50672, loss_grounding_bce_9: 0.04326/0.10528, loss_grounding_dice_9: 0.09750/0.28146, loss_grounding_ce_9: 1.51981/0.68240] items per batch[64] items per second[0.23] total items[2323200] mini batches[ 36300] memory[7341] epoch remaining[0:11:09]
INFO:trainer.default_trainer:epochs[    19] optim steps[36400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47482/0.90774, loss_mask_bce_0: 0.31340/0.33469, loss_mask_dice_0: 0.64917/1.16664, loss_spatial_bce_0: 0.21711/0.08936, loss_spatial_dice_0: 0.28823/0.21373, loss_spatial_ce_0: 0.15663/0.06977, loss_grounding_bce_0: 0.15509/0.08622, loss_grounding_dice_0: 0.13657/0.17913, loss_grounding_ce_0: 0.03280/0.27516, loss_mask_ce_1: 0.46690/0.90844, loss_mask_bce_1: 0.31594/0.33553, loss_mask_dice_1: 0.65398/1.17353, loss_spatial_bce_1: 0.17719/0.09006, loss_spatial_dice_1: 0.27103/0.21797, loss_spatial_ce_1: 0.18528/0.07543, loss_grounding_bce_1: 0.15805/0.08636, loss_grounding_dice_1: 0.14179/0.18004, loss_grounding_ce_1: 0.02503/0.27681, loss_mask_ce_2: 0.46552/0.91569, loss_mask_bce_2: 0.32262/0.33592, loss_mask_dice_2: 0.64862/1.17307, loss_spatial_bce_2: 0.15956/0.09053, loss_spatial_dice_2: 0.28599/0.21900, loss_spatial_ce_2: 0.22553/0.07899, loss_grounding_bce_2: 0.15254/0.08641, loss_grounding_dice_2: 0.13709/0.17974, loss_grounding_ce_2: 0.02097/0.28002, loss_mask_ce_3: 0.48499/0.92485, loss_mask_bce_3: 0.33113/0.33682, loss_mask_dice_3: 0.61961/1.17026, loss_spatial_bce_3: 0.18532/0.09137, loss_spatial_dice_3: 0.27817/0.21960, loss_spatial_ce_3: 0.18602/0.08275, loss_grounding_bce_3: 0.15692/0.08668, loss_grounding_dice_3: 0.14172/0.17954, loss_grounding_ce_3: 0.01939/0.28152, loss_mask_ce_4: 0.51072/0.92408, loss_mask_bce_4: 0.32707/0.33868, loss_mask_dice_4: 0.68030/1.19339, loss_spatial_bce_4: 0.19512/0.09563, loss_spatial_dice_4: 0.28361/0.23091, loss_spatial_ce_4: 0.14327/0.09894, loss_grounding_bce_4: 0.14695/0.08710, loss_grounding_dice_4: 0.14184/0.18231, loss_grounding_ce_4: 0.03574/0.28439, loss_mask_ce_5: 0.49612/0.93925, loss_mask_bce_5: 0.31452/0.34098, loss_mask_dice_5: 0.68185/1.19966, loss_spatial_bce_5: 0.16181/0.09725, loss_spatial_dice_5: 0.28367/0.23449, loss_spatial_ce_5: 0.12935/0.11352, loss_grounding_bce_5: 0.13867/0.08751, loss_grounding_dice_5: 0.13923/0.18350, loss_grounding_ce_5: 0.03324/0.29688, loss_mask_ce_6: 0.55825/0.97790, loss_mask_bce_6: 0.31556/0.34365, loss_mask_dice_6: 0.65540/1.20205, loss_spatial_bce_6: 0.16280/0.10297, loss_spatial_dice_6: 0.26461/0.23677, loss_spatial_ce_6: 0.17933/0.13958, loss_grounding_bce_6: 0.14173/0.08822, loss_grounding_dice_6: 0.13111/0.18372, loss_grounding_ce_6: 0.03481/0.31349, loss_mask_ce_7: 0.64499/1.02287, loss_mask_bce_7: 0.32104/0.35148, loss_mask_dice_7: 0.70255/1.25765, loss_spatial_bce_7: 0.32881/0.11147, loss_spatial_dice_7: 0.32712/0.26444, loss_spatial_ce_7: 0.15726/0.17685, loss_grounding_bce_7: 0.15363/0.09012, loss_grounding_dice_7: 0.14494/0.19100, loss_grounding_ce_7: 0.03184/0.34655, loss_mask_ce_8: 0.58191/1.13240, loss_mask_bce_8: 0.35814/0.36511, loss_mask_dice_8: 0.74723/1.33168, loss_spatial_bce_8: 0.33012/0.13244, loss_spatial_dice_8: 0.35728/0.30381, loss_spatial_ce_8: 0.16842/0.23354, loss_grounding_bce_8: 0.15468/0.09378, loss_grounding_dice_8: 0.14017/0.20208, loss_grounding_ce_8: 0.01784/0.41512, loss_mask_ce_9: 2.21617/3.68350, loss_mask_bce_9: 0.30823/0.39207, loss_mask_dice_9: 0.89750/1.90588, loss_spatial_bce_9: 0.28085/0.33452, loss_spatial_dice_9: 0.91474/0.82323, loss_spatial_ce_9: 1.49395/1.50684, loss_grounding_bce_9: 0.16264/0.10527, loss_grounding_dice_9: 0.17410/0.28142, loss_grounding_ce_9: 0.91844/0.68227] items per batch[64] items per second[0.23] total items[2329600] mini batches[ 36400] memory[7341] epoch remaining[0:06:30]
INFO:trainer.default_trainer:epochs[    19] optim steps[36500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34656/0.90754, loss_mask_bce_0: 0.21145/0.33464, loss_mask_dice_0: 0.39793/1.16637, loss_spatial_bce_0: 0.08791/0.08934, loss_spatial_dice_0: 0.18741/0.21371, loss_spatial_ce_0: 0.00796/0.06973, loss_grounding_bce_0: 0.06692/0.08622, loss_grounding_dice_0: 0.11401/0.17915, loss_grounding_ce_0: 0.20615/0.27511, loss_mask_ce_1: 0.33026/0.90824, loss_mask_bce_1: 0.20567/0.33548, loss_mask_dice_1: 0.38627/1.17323, loss_spatial_bce_1: 0.08271/0.09004, loss_spatial_dice_1: 0.18543/0.21795, loss_spatial_ce_1: 0.00590/0.07540, loss_grounding_bce_1: 0.06738/0.08636, loss_grounding_dice_1: 0.11101/0.18007, loss_grounding_ce_1: 0.18540/0.27675, loss_mask_ce_2: 0.36394/0.91547, loss_mask_bce_2: 0.20150/0.33587, loss_mask_dice_2: 0.38323/1.17277, loss_spatial_bce_2: 0.08040/0.09051, loss_spatial_dice_2: 0.18339/0.21898, loss_spatial_ce_2: 0.00730/0.07895, loss_grounding_bce_2: 0.06498/0.08641, loss_grounding_dice_2: 0.10768/0.17977, loss_grounding_ce_2: 0.18674/0.27994, loss_mask_ce_3: 0.41163/0.92470, loss_mask_bce_3: 0.21129/0.33677, loss_mask_dice_3: 0.42172/1.16999, loss_spatial_bce_3: 0.08510/0.09136, loss_spatial_dice_3: 0.17708/0.21957, loss_spatial_ce_3: 0.00769/0.08271, loss_grounding_bce_3: 0.06863/0.08668, loss_grounding_dice_3: 0.12604/0.17958, loss_grounding_ce_3: 0.17422/0.28145, loss_mask_ce_4: 0.38979/0.92390, loss_mask_bce_4: 0.20911/0.33864, loss_mask_dice_4: 0.38906/1.19319, loss_spatial_bce_4: 0.08182/0.09562, loss_spatial_dice_4: 0.19197/0.23090, loss_spatial_ce_4: 0.02610/0.09891, loss_grounding_bce_4: 0.06566/0.08710, loss_grounding_dice_4: 0.11652/0.18234, loss_grounding_ce_4: 0.20989/0.28435, loss_mask_ce_5: 0.42850/0.93915, loss_mask_bce_5: 0.19722/0.34092, loss_mask_dice_5: 0.38157/1.19941, loss_spatial_bce_5: 0.07654/0.09723, loss_spatial_dice_5: 0.17598/0.23447, loss_spatial_ce_5: 0.08068/0.11351, loss_grounding_bce_5: 0.06298/0.08752, loss_grounding_dice_5: 0.10594/0.18353, loss_grounding_ce_5: 0.21438/0.29681, loss_mask_ce_6: 0.43162/0.97782, loss_mask_bce_6: 0.19587/0.34361, loss_mask_dice_6: 0.38711/1.20177, loss_spatial_bce_6: 0.07848/0.10297, loss_spatial_dice_6: 0.14803/0.23676, loss_spatial_ce_6: 0.07887/0.13954, loss_grounding_bce_6: 0.06675/0.08822, loss_grounding_dice_6: 0.11780/0.18374, loss_grounding_ce_6: 0.23534/0.31346, loss_mask_ce_7: 0.54294/1.02279, loss_mask_bce_7: 0.20540/0.35145, loss_mask_dice_7: 0.38928/1.25741, loss_spatial_bce_7: 0.08817/0.11147, loss_spatial_dice_7: 0.18809/0.26442, loss_spatial_ce_7: 0.22429/0.17680, loss_grounding_bce_7: 0.06726/0.09012, loss_grounding_dice_7: 0.11162/0.19102, loss_grounding_ce_7: 0.31796/0.34643, loss_mask_ce_8: 0.63634/1.13228, loss_mask_bce_8: 0.20802/0.36508, loss_mask_dice_8: 0.43263/1.33139, loss_spatial_bce_8: 0.19390/0.13243, loss_spatial_dice_8: 0.24748/0.30381, loss_spatial_ce_8: 0.30813/0.23356, loss_grounding_bce_8: 0.06726/0.09378, loss_grounding_dice_8: 0.12328/0.20210, loss_grounding_ce_8: 0.38594/0.41501, loss_mask_ce_9: 2.88623/3.68321, loss_mask_bce_9: 0.24775/0.39202, loss_mask_dice_9: 0.73688/1.90539, loss_spatial_bce_9: 0.38525/0.33450, loss_spatial_dice_9: 0.78006/0.82324, loss_spatial_ce_9: 1.12322/1.50695, loss_grounding_bce_9: 0.08638/0.10526, loss_grounding_dice_9: 0.30015/0.28142, loss_grounding_ce_9: 0.42149/0.68212] items per batch[64] items per second[0.23] total items[2336000] mini batches[ 36500] memory[7341] epoch remaining[0:01:51]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00036540.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0029 s/iter. Inference: 0.2186 s/iter. Eval: 0.0936 s/iter. Total: 0.3151 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0031 s/iter. Inference: 0.2296 s/iter. Eval: 0.0923 s/iter. Total: 0.3252 s/iter. ETA=0:00:42
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 43/157. Dataloading: 0.0033 s/iter. Inference: 0.2299 s/iter. Eval: 0.0938 s/iter. Total: 0.3272 s/iter. ETA=0:00:37
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 59/157. Dataloading: 0.0033 s/iter. Inference: 0.2301 s/iter. Eval: 0.0897 s/iter. Total: 0.3233 s/iter. ETA=0:00:31
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 76/157. Dataloading: 0.0034 s/iter. Inference: 0.2286 s/iter. Eval: 0.0888 s/iter. Total: 0.3210 s/iter. ETA=0:00:26
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 92/157. Dataloading: 0.0034 s/iter. Inference: 0.2304 s/iter. Eval: 0.0876 s/iter. Total: 0.3215 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 108/157. Dataloading: 0.0034 s/iter. Inference: 0.2325 s/iter. Eval: 0.0871 s/iter. Total: 0.3232 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 125/157. Dataloading: 0.0034 s/iter. Inference: 0.2316 s/iter. Eval: 0.0856 s/iter. Total: 0.3208 s/iter. ETA=0:00:10
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 141/157. Dataloading: 0.0035 s/iter. Inference: 0.2321 s/iter. Eval: 0.0841 s/iter. Total: 0.3199 s/iter. ETA=0:00:05
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalv2xf20wp ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.885 | 81.966 | 60.077 |      133      |
| Things | 54.749 | 82.509 | 65.671 |      80       |
| Stuff  | 42.544 | 81.146 | 51.633 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.51s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.93 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.98 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.14s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.19 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.660 | 60.874 | 40.790 | 19.089 | 41.891 | 60.228 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.376 | bicycle      | 18.950 | car            | 36.081 |
| motorcycle    | 34.559 | airplane     | 55.803 | bus            | 64.135 |
| train         | 68.697 | truck        | 35.994 | boat           | 20.935 |
| traffic light | 24.673 | fire hydrant | 64.749 | stop sign      | 61.923 |
| parking meter | 42.368 | bench        | 19.932 | bird           | 29.614 |
| cat           | 71.801 | dog          | 65.917 | horse          | 45.885 |
| sheep         | 45.303 | cow          | 50.149 | elephant       | 59.499 |
| bear          | 77.605 | zebra        | 58.718 | giraffe        | 55.902 |
| backpack      | 18.110 | umbrella     | 48.047 | handbag        | 14.229 |
| tie           | 33.689 | suitcase     | 40.189 | frisbee        | 67.915 |
| skis          | 5.367  | snowboard    | 23.844 | sports ball    | 46.486 |
| kite          | 32.798 | baseball bat | 28.032 | baseball glove | 43.881 |
| skateboard    | 35.233 | surfboard    | 34.773 | tennis racket  | 56.497 |
| bottle        | 34.257 | wine glass   | 27.381 | cup            | 40.180 |
| fork          | 15.921 | knife        | 13.453 | spoon          | 14.586 |
| bowl          | 33.274 | banana       | 20.053 | apple          | 20.015 |
| sandwich      | 44.161 | orange       | 29.387 | broccoli       | 21.364 |
| carrot        | 20.326 | hot dog      | 26.329 | pizza          | 51.894 |
| donut         | 46.790 | cake         | 42.976 | chair          | 21.231 |
| couch         | 40.815 | potted plant | 17.809 | bed            | 40.756 |
| dining table  | 12.352 | toilet       | 66.907 | tv             | 61.804 |
| laptop        | 60.890 | mouse        | 59.101 | remote         | 31.217 |
| keyboard      | 48.442 | cell phone   | 37.964 | microwave      | 55.487 |
| oven          | 32.468 | toaster      | 31.416 | sink           | 38.334 |
| refrigerator  | 59.351 | book         | 9.640  | clock          | 51.172 |
| vase          | 31.609 | scissors     | 25.074 | teddy bear     | 49.934 |
| hair drier    | 6.546  | toothbrush   | 17.449 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.609
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.294
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.710
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.034097283011654, 'fwIoU': 68.95859561727526, 'IoU-person': 87.65578688531343, 'IoU-bicycle': 71.73663058542513, 'IoU-car': 69.84367229002684, 'IoU-motorcycle': 82.17062526099022, 'IoU-airplane': 85.3113987959074, 'IoU-bus': 83.47585325286829, 'IoU-train': 84.79536703452642, 'IoU-truck': 64.149131450842, 'IoU-boat': 66.50032693066721, 'IoU-traffic light': 75.76644347853878, 'IoU-fire hydrant': 89.63623204013093, 'IoU-stop sign': 92.61299924210238, 'IoU-parking meter': 82.10932594824271, 'IoU-bench': 57.69214374082807, 'IoU-bird': 76.2173480833006, 'IoU-cat': 84.91735217431804, 'IoU-dog': 77.93813527756784, 'IoU-horse': 84.96147494228352, 'IoU-sheep': 80.96772008072048, 'IoU-cow': 81.30132307615324, 'IoU-elephant': 87.06491590459518, 'IoU-bear': 84.67230342551339, 'IoU-zebra': 87.12453579802923, 'IoU-giraffe': 88.13172833585004, 'IoU-backpack': 36.94531497849952, 'IoU-umbrella': 77.09052582729143, 'IoU-handbag': 35.68958317856127, 'IoU-tie': 70.34747135203338, 'IoU-suitcase': 81.35059800509666, 'IoU-frisbee': 83.11942740021854, 'IoU-skis': 51.822414816821, 'IoU-snowboard': 68.57174721508508, 'IoU-sports ball': 62.45585683875493, 'IoU-kite': 65.65016337641663, 'IoU-baseball bat': 61.28888888888889, 'IoU-baseball glove': 52.68217650451837, 'IoU-skateboard': 63.96461620141737, 'IoU-surfboard': 69.43708920245054, 'IoU-tennis racket': 74.54747274975982, 'IoU-bottle': 68.10417492390104, 'IoU-wine glass': 74.29936850649996, 'IoU-cup': 64.89108532122063, 'IoU-fork': 54.77922705741957, 'IoU-knife': 47.544074916588734, 'IoU-spoon': 53.29757811903125, 'IoU-bowl': 56.33224471318379, 'IoU-banana': 82.98193383613258, 'IoU-apple': 58.42018417095848, 'IoU-sandwich': 67.61467140093899, 'IoU-orange': 77.47765649018626, 'IoU-broccoli': 71.21701045994814, 'IoU-carrot': 63.745027523297225, 'IoU-hot dog': 67.23279935278296, 'IoU-pizza': 80.6054269280252, 'IoU-donut': 64.86954878022321, 'IoU-cake': 68.45183834846945, 'IoU-chair': 55.2349472978796, 'IoU-couch': 68.0532429271378, 'IoU-potted plant': 36.260006076665896, 'IoU-bed': 68.33758705501512, 'IoU-dining table': 51.65713250989031, 'IoU-toilet': 79.90094101608491, 'IoU-tv': 74.31109204803045, 'IoU-laptop': 76.12601104060008, 'IoU-mouse': 71.67127231911377, 'IoU-remote': 50.988760341340544, 'IoU-keyboard': 63.59616281205408, 'IoU-cell phone': 66.40933013647886, 'IoU-microwave': 26.320549024807573, 'IoU-oven': 66.70113122039902, 'IoU-toaster': 29.034212790711766, 'IoU-sink': 69.86515876164972, 'IoU-refrigerator': 80.90517322137957, 'IoU-book': 53.03593127568973, 'IoU-clock': 71.23865888067463, 'IoU-vase': 50.61003195433847, 'IoU-scissors': 59.6976363774695, 'IoU-teddy bear': 82.68512133129066, 'IoU-hair drier': 36.038751096947024, 'IoU-toothbrush': 56.08694326738408, 'IoU-banner': 30.780840963580964, 'IoU-blanket': 14.30063435007322, 'IoU-bridge': 39.557359958607364, 'IoU-cardboard': 44.585275721041526, 'IoU-counter': 30.599625093987875, 'IoU-curtain': 63.76410752090376, 'IoU-door-stuff': 42.97400628513406, 'IoU-floor-wood': 61.72897508264441, 'IoU-flower': 48.39949718206423, 'IoU-fruit': 37.51611689929151, 'IoU-gravel': 29.55529700818686, 'IoU-house': 23.798011338897084, 'IoU-light': 40.64091979482348, 'IoU-mirror-stuff': 56.37164327273746, 'IoU-net': 41.592967790825405, 'IoU-pillow': 14.074924226091587, 'IoU-platform': 31.719711908263974, 'IoU-playingfield': 70.12704563860665, 'IoU-railroad': 60.73726393502648, 'IoU-river': 52.09123641041109, 'IoU-road': 66.43174078849648, 'IoU-roof': 15.715727407936406, 'IoU-sand': 63.952944851270175, 'IoU-sea': 83.58325413324708, 'IoU-shelf': 35.835840616626875, 'IoU-snow': 88.5712467608767, 'IoU-stairs': 24.64888206873745, 'IoU-tent': 9.394768818725122, 'IoU-towel': 34.51621858159726, 'IoU-wall-brick': 44.42157850480184, 'IoU-wall-stone': 25.15936094895291, 'IoU-wall-tile': 65.6435271248067, 'IoU-wall-wood': 37.41623616190847, 'IoU-water-other': 26.27573194237092, 'IoU-window-blind': 46.01599795443172, 'IoU-window-other': 48.03657632035115, 'IoU-tree-merged': 80.96925036096366, 'IoU-fence-merged': 50.377950029978905, 'IoU-ceiling-merged': 67.6013956978244, 'IoU-sky-other-merged': 93.70510974829313, 'IoU-cabinet-merged': 59.642173777557225, 'IoU-table-merged': 38.33156643739088, 'IoU-floor-other-merged': 49.27388357225789, 'IoU-pavement-merged': 53.62383642530135, 'IoU-mountain-merged': 55.069674165102164, 'IoU-grass-merged': 71.10610083700034, 'IoU-dirt-merged': 46.7967863268438, 'IoU-paper-merged': 33.15341233702332, 'IoU-food-other-merged': 38.25380629149361, 'IoU-building-other-merged': 57.92718631566658, 'IoU-rock-merged': 61.476653569040884, 'IoU-wall-other-merged': 63.439627564049125, 'IoU-rug-merged': 64.90567561603062, 'mACC': 72.25612667305579, 'pACC': 80.3514763965404, 'ACC-person': 92.5985722244347, 'ACC-bicycle': 81.80434538076162, 'ACC-car': 85.15116402223141, 'ACC-motorcycle': 89.3268209599661, 'ACC-airplane': 90.37022783312435, 'ACC-bus': 88.24279016053954, 'ACC-train': 95.58748382555603, 'ACC-truck': 75.75535049455647, 'ACC-boat': 78.14595283502926, 'ACC-traffic light': 90.60528933950738, 'ACC-fire hydrant': 95.51960612672778, 'ACC-stop sign': 95.758905336426, 'ACC-parking meter': 87.45323133108784, 'ACC-bench': 73.78020256345587, 'ACC-bird': 80.55874762467259, 'ACC-cat': 93.44136876005923, 'ACC-dog': 81.64447920574204, 'ACC-horse': 90.4966393685892, 'ACC-sheep': 83.8885248220642, 'ACC-cow': 86.49534067946301, 'ACC-elephant': 89.41021188699932, 'ACC-bear': 86.4619329302169, 'ACC-zebra': 89.41331017106103, 'ACC-giraffe': 92.29767712277413, 'ACC-backpack': 59.83481739862052, 'ACC-umbrella': 84.63937805320369, 'ACC-handbag': 49.98663482945504, 'ACC-tie': 81.73714807728864, 'ACC-suitcase': 90.1372949346445, 'ACC-frisbee': 93.49454545454546, 'ACC-skis': 67.80171517937151, 'ACC-snowboard': 78.30422962944822, 'ACC-sports ball': 78.70137790595507, 'ACC-kite': 75.14806229061739, 'ACC-baseball bat': 83.17249698431846, 'ACC-baseball glove': 60.216308913915974, 'ACC-skateboard': 69.89443020588803, 'ACC-surfboard': 76.92673560581186, 'ACC-tennis racket': 79.68930226021313, 'ACC-bottle': 84.29255723160324, 'ACC-wine glass': 84.64080433718337, 'ACC-cup': 84.18374144266072, 'ACC-fork': 66.91793136392518, 'ACC-knife': 62.67677877921888, 'ACC-spoon': 71.68316913073322, 'ACC-bowl': 68.4918890352806, 'ACC-banana': 90.4940881275209, 'ACC-apple': 71.23701030846952, 'ACC-sandwich': 80.0002435430645, 'ACC-orange': 87.62535126596961, 'ACC-broccoli': 82.30135989484207, 'ACC-carrot': 75.35596654212871, 'ACC-hot dog': 73.69579569372361, 'ACC-pizza': 92.70477641866333, 'ACC-donut': 82.57855593512502, 'ACC-cake': 76.19315380212538, 'ACC-chair': 71.05994329255294, 'ACC-couch': 79.64463661092456, 'ACC-potted plant': 51.61083899807567, 'ACC-bed': 81.54382508870741, 'ACC-dining table': 77.27406021717076, 'ACC-toilet': 89.11350766155897, 'ACC-tv': 88.73962327711052, 'ACC-laptop': 90.71927011358014, 'ACC-mouse': 84.7663676143271, 'ACC-remote': 72.62308930649198, 'ACC-keyboard': 68.75631774959645, 'ACC-cell phone': 81.5898916923253, 'ACC-microwave': 30.187339704432485, 'ACC-oven': 83.54410629902014, 'ACC-toaster': 32.78681793920815, 'ACC-sink': 81.8462482752485, 'ACC-refrigerator': 88.47754127699699, 'ACC-book': 70.3155907651789, 'ACC-clock': 77.08035931598131, 'ACC-vase': 57.68165370980586, 'ACC-scissors': 64.84140429165633, 'ACC-teddy bear': 87.80408154121604, 'ACC-hair drier': 44.4183531153047, 'ACC-toothbrush': 79.90879082696317, 'ACC-banner': 73.64917641992449, 'ACC-blanket': 19.518221438038168, 'ACC-bridge': 55.38281035726258, 'ACC-cardboard': 56.22474916387959, 'ACC-counter': 55.82874229102027, 'ACC-curtain': 72.48141870450118, 'ACC-door-stuff': 64.06361533346039, 'ACC-floor-wood': 74.26056782399118, 'ACC-flower': 70.58170799368358, 'ACC-fruit': 59.28172135265656, 'ACC-gravel': 34.79669859958716, 'ACC-house': 27.138409623060017, 'ACC-light': 58.20995865712207, 'ACC-mirror-stuff': 74.49669235974568, 'ACC-net': 62.79007123331405, 'ACC-pillow': 29.299643868814897, 'ACC-platform': 55.227434078638005, 'ACC-playingfield': 87.37078896407796, 'ACC-railroad': 76.62059891991876, 'ACC-river': 70.47308455286002, 'ACC-road': 87.00648332263256, 'ACC-roof': 21.161037701638367, 'ACC-sand': 70.77908786385213, 'ACC-sea': 88.87197017147837, 'ACC-shelf': 54.92670005878726, 'ACC-snow': 95.92320061450471, 'ACC-stairs': 41.41185378541428, 'ACC-tent': 12.275104449397977, 'ACC-towel': 44.107006493612, 'ACC-wall-brick': 65.26642963462757, 'ACC-wall-stone': 30.33523893602056, 'ACC-wall-tile': 80.4137383990903, 'ACC-wall-wood': 48.11203822180946, 'ACC-water-other': 50.13526186449193, 'ACC-window-blind': 59.78107963518472, 'ACC-window-other': 68.96467292938658, 'ACC-tree-merged': 88.97458368705084, 'ACC-fence-merged': 68.1732217672941, 'ACC-ceiling-merged': 81.05849623121857, 'ACC-sky-other-merged': 96.56616529096283, 'ACC-cabinet-merged': 76.1838632813439, 'ACC-table-merged': 48.80442411929714, 'ACC-floor-other-merged': 63.09791739432201, 'ACC-pavement-merged': 64.46145456574575, 'ACC-mountain-merged': 64.1837570040862, 'ACC-grass-merged': 84.21557822393183, 'ACC-dirt-merged': 71.67674811235926, 'ACC-paper-merged': 45.31758971896824, 'ACC-food-other-merged': 48.86753990327134, 'ACC-building-other-merged': 72.179509285087, 'ACC-rock-merged': 81.04422447595475, 'ACC-wall-other-merged': 82.43168718679377, 'ACC-rug-merged': 80.36158718723027})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1534 s/iter. Inference: 0.5891 s/iter. Eval: 0.0000 s/iter. Total: 0.7425 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1569 s/iter. Inference: 0.5333 s/iter. Eval: 0.0000 s/iter. Total: 0.6904 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/50. Dataloading: 0.1691 s/iter. Inference: 0.6537 s/iter. Eval: 0.0000 s/iter. Total: 0.8230 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1725 s/iter. Inference: 0.7401 s/iter. Eval: 0.0000 s/iter. Total: 0.9129 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1698 s/iter. Inference: 0.6442 s/iter. Eval: 0.0000 s/iter. Total: 0.8142 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1689 s/iter. Inference: 0.6768 s/iter. Eval: 0.0000 s/iter. Total: 0.8458 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1693 s/iter. Inference: 0.6974 s/iter. Eval: 0.0000 s/iter. Total: 0.8669 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.581796897863623, 'noc@0.8': 2.9180567749487856, 'noc@0.85': 3.559847819724905, 'noc@0.9': 4.543751829089845, 'miou@iter1': 0.8297990258060285}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.1005 s/iter. Eval: 0.0008 s/iter. Total: 0.1027 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.92887878417969, 'precision@0.6': 68.24718475341797, 'precision@0.7': 62.883792877197266, 'precision@0.8': 52.62339782714844, 'precision@0.9': 27.205596923828125, 'cIoU': 58.11103439331055, 'mIoU': 62.5361213684082}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.88535140121728, 'SQ': 81.96590609389105, 'RQ': 60.07696134685915, 'PQ_th': 54.74913057334723, 'SQ_th': 82.50907156216579, 'RQ_th': 65.67135012827453, 'PQ_st': 42.54379793385127, 'SQ_st': 81.14603368894804, 'RQ_st': 51.63260092208124}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.65965554991754, 'AP50': 60.87441172724364, 'AP75': 40.78969198012735, 'APs': 19.089056562689123, 'APm': 41.8912596962554, 'APl': 60.228207318136995, 'AP-person': 44.37638851895404, 'AP-bicycle': 18.94968829444553, 'AP-car': 36.081346866833236, 'AP-motorcycle': 34.55912337728212, 'AP-airplane': 55.802918946928195, 'AP-bus': 64.13520872104523, 'AP-train': 68.69702710269303, 'AP-truck': 35.99422731679597, 'AP-boat': 20.935070188484076, 'AP-traffic light': 24.672735456929075, 'AP-fire hydrant': 64.74865962699859, 'AP-stop sign': 61.92290245928783, 'AP-parking meter': 42.367781104112275, 'AP-bench': 19.932045581288314, 'AP-bird': 29.614070654997025, 'AP-cat': 71.80146730877823, 'AP-dog': 65.91739556367588, 'AP-horse': 45.88476097878988, 'AP-sheep': 45.302502308727625, 'AP-cow': 50.14880556200663, 'AP-elephant': 59.4986888947885, 'AP-bear': 77.60454279519008, 'AP-zebra': 58.7183251226088, 'AP-giraffe': 55.902033822541696, 'AP-backpack': 18.109866943217277, 'AP-umbrella': 48.04695683441221, 'AP-handbag': 14.228998425425615, 'AP-tie': 33.68940946144148, 'AP-suitcase': 40.189179011100386, 'AP-frisbee': 67.91460403189487, 'AP-skis': 5.367109774325881, 'AP-snowboard': 23.844016282728614, 'AP-sports ball': 46.48587670061177, 'AP-kite': 32.79766509055323, 'AP-baseball bat': 28.032262524217387, 'AP-baseball glove': 43.881240510237916, 'AP-skateboard': 35.23258167132801, 'AP-surfboard': 34.77292424424023, 'AP-tennis racket': 56.49695040614263, 'AP-bottle': 34.257191257103074, 'AP-wine glass': 27.381024081004867, 'AP-cup': 40.17975781037651, 'AP-fork': 15.92057654433035, 'AP-knife': 13.45302048475063, 'AP-spoon': 14.585996725363762, 'AP-bowl': 33.274424928076805, 'AP-banana': 20.05257234170983, 'AP-apple': 20.015355397340933, 'AP-sandwich': 44.161383949295995, 'AP-orange': 29.386879215585672, 'AP-broccoli': 21.36397651531357, 'AP-carrot': 20.325863519671678, 'AP-hot dog': 26.329275989711764, 'AP-pizza': 51.894306319236314, 'AP-donut': 46.7901058085874, 'AP-cake': 42.97571709379074, 'AP-chair': 21.230698622415098, 'AP-couch': 40.81497079837931, 'AP-potted plant': 17.80857799357031, 'AP-bed': 40.75593720612977, 'AP-dining table': 12.351673077340925, 'AP-toilet': 66.90734690162675, 'AP-tv': 61.80411717406721, 'AP-laptop': 60.890234559543444, 'AP-mouse': 59.101495304802654, 'AP-remote': 31.217063700744713, 'AP-keyboard': 48.44151261568695, 'AP-cell phone': 37.96373213922358, 'AP-microwave': 55.486976927053966, 'AP-oven': 32.4684439527465, 'AP-toaster': 31.415627731776592, 'AP-sink': 38.334171650776064, 'AP-refrigerator': 59.35081144065002, 'AP-book': 9.640365663749103, 'AP-clock': 51.171529202956776, 'AP-vase': 31.608832284299602, 'AP-scissors': 25.074191172902204, 'AP-teddy bear': 49.93374424659384, 'AP-hair drier': 6.5462553678485556, 'AP-toothbrush': 17.449347791209714}), ('sem_seg', {'mIoU': 60.034097283011654, 'fwIoU': 68.95859561727526, 'IoU-person': 87.65578688531343, 'IoU-bicycle': 71.73663058542513, 'IoU-car': 69.84367229002684, 'IoU-motorcycle': 82.17062526099022, 'IoU-airplane': 85.3113987959074, 'IoU-bus': 83.47585325286829, 'IoU-train': 84.79536703452642, 'IoU-truck': 64.149131450842, 'IoU-boat': 66.50032693066721, 'IoU-traffic light': 75.76644347853878, 'IoU-fire hydrant': 89.63623204013093, 'IoU-stop sign': 92.61299924210238, 'IoU-parking meter': 82.10932594824271, 'IoU-bench': 57.69214374082807, 'IoU-bird': 76.2173480833006, 'IoU-cat': 84.91735217431804, 'IoU-dog': 77.93813527756784, 'IoU-horse': 84.96147494228352, 'IoU-sheep': 80.96772008072048, 'IoU-cow': 81.30132307615324, 'IoU-elephant': 87.06491590459518, 'IoU-bear': 84.67230342551339, 'IoU-zebra': 87.12453579802923, 'IoU-giraffe': 88.13172833585004, 'IoU-backpack': 36.94531497849952, 'IoU-umbrella': 77.09052582729143, 'IoU-handbag': 35.68958317856127, 'IoU-tie': 70.34747135203338, 'IoU-suitcase': 81.35059800509666, 'IoU-frisbee': 83.11942740021854, 'IoU-skis': 51.822414816821, 'IoU-snowboard': 68.57174721508508, 'IoU-sports ball': 62.45585683875493, 'IoU-kite': 65.65016337641663, 'IoU-baseball bat': 61.28888888888889, 'IoU-baseball glove': 52.68217650451837, 'IoU-skateboard': 63.96461620141737, 'IoU-surfboard': 69.43708920245054, 'IoU-tennis racket': 74.54747274975982, 'IoU-bottle': 68.10417492390104, 'IoU-wine glass': 74.29936850649996, 'IoU-cup': 64.89108532122063, 'IoU-fork': 54.77922705741957, 'IoU-knife': 47.544074916588734, 'IoU-spoon': 53.29757811903125, 'IoU-bowl': 56.33224471318379, 'IoU-banana': 82.98193383613258, 'IoU-apple': 58.42018417095848, 'IoU-sandwich': 67.61467140093899, 'IoU-orange': 77.47765649018626, 'IoU-broccoli': 71.21701045994814, 'IoU-carrot': 63.745027523297225, 'IoU-hot dog': 67.23279935278296, 'IoU-pizza': 80.6054269280252, 'IoU-donut': 64.86954878022321, 'IoU-cake': 68.45183834846945, 'IoU-chair': 55.2349472978796, 'IoU-couch': 68.0532429271378, 'IoU-potted plant': 36.260006076665896, 'IoU-bed': 68.33758705501512, 'IoU-dining table': 51.65713250989031, 'IoU-toilet': 79.90094101608491, 'IoU-tv': 74.31109204803045, 'IoU-laptop': 76.12601104060008, 'IoU-mouse': 71.67127231911377, 'IoU-remote': 50.988760341340544, 'IoU-keyboard': 63.59616281205408, 'IoU-cell phone': 66.40933013647886, 'IoU-microwave': 26.320549024807573, 'IoU-oven': 66.70113122039902, 'IoU-toaster': 29.034212790711766, 'IoU-sink': 69.86515876164972, 'IoU-refrigerator': 80.90517322137957, 'IoU-book': 53.03593127568973, 'IoU-clock': 71.23865888067463, 'IoU-vase': 50.61003195433847, 'IoU-scissors': 59.6976363774695, 'IoU-teddy bear': 82.68512133129066, 'IoU-hair drier': 36.038751096947024, 'IoU-toothbrush': 56.08694326738408, 'IoU-banner': 30.780840963580964, 'IoU-blanket': 14.30063435007322, 'IoU-bridge': 39.557359958607364, 'IoU-cardboard': 44.585275721041526, 'IoU-counter': 30.599625093987875, 'IoU-curtain': 63.76410752090376, 'IoU-door-stuff': 42.97400628513406, 'IoU-floor-wood': 61.72897508264441, 'IoU-flower': 48.39949718206423, 'IoU-fruit': 37.51611689929151, 'IoU-gravel': 29.55529700818686, 'IoU-house': 23.798011338897084, 'IoU-light': 40.64091979482348, 'IoU-mirror-stuff': 56.37164327273746, 'IoU-net': 41.592967790825405, 'IoU-pillow': 14.074924226091587, 'IoU-platform': 31.719711908263974, 'IoU-playingfield': 70.12704563860665, 'IoU-railroad': 60.73726393502648, 'IoU-river': 52.09123641041109, 'IoU-road': 66.43174078849648, 'IoU-roof': 15.715727407936406, 'IoU-sand': 63.952944851270175, 'IoU-sea': 83.58325413324708, 'IoU-shelf': 35.835840616626875, 'IoU-snow': 88.5712467608767, 'IoU-stairs': 24.64888206873745, 'IoU-tent': 9.394768818725122, 'IoU-towel': 34.51621858159726, 'IoU-wall-brick': 44.42157850480184, 'IoU-wall-stone': 25.15936094895291, 'IoU-wall-tile': 65.6435271248067, 'IoU-wall-wood': 37.41623616190847, 'IoU-water-other': 26.27573194237092, 'IoU-window-blind': 46.01599795443172, 'IoU-window-other': 48.03657632035115, 'IoU-tree-merged': 80.96925036096366, 'IoU-fence-merged': 50.377950029978905, 'IoU-ceiling-merged': 67.6013956978244, 'IoU-sky-other-merged': 93.70510974829313, 'IoU-cabinet-merged': 59.642173777557225, 'IoU-table-merged': 38.33156643739088, 'IoU-floor-other-merged': 49.27388357225789, 'IoU-pavement-merged': 53.62383642530135, 'IoU-mountain-merged': 55.069674165102164, 'IoU-grass-merged': 71.10610083700034, 'IoU-dirt-merged': 46.7967863268438, 'IoU-paper-merged': 33.15341233702332, 'IoU-food-other-merged': 38.25380629149361, 'IoU-building-other-merged': 57.92718631566658, 'IoU-rock-merged': 61.476653569040884, 'IoU-wall-other-merged': 63.439627564049125, 'IoU-rug-merged': 64.90567561603062, 'mACC': 72.25612667305579, 'pACC': 80.3514763965404, 'ACC-person': 92.5985722244347, 'ACC-bicycle': 81.80434538076162, 'ACC-car': 85.15116402223141, 'ACC-motorcycle': 89.3268209599661, 'ACC-airplane': 90.37022783312435, 'ACC-bus': 88.24279016053954, 'ACC-train': 95.58748382555603, 'ACC-truck': 75.75535049455647, 'ACC-boat': 78.14595283502926, 'ACC-traffic light': 90.60528933950738, 'ACC-fire hydrant': 95.51960612672778, 'ACC-stop sign': 95.758905336426, 'ACC-parking meter': 87.45323133108784, 'ACC-bench': 73.78020256345587, 'ACC-bird': 80.55874762467259, 'ACC-cat': 93.44136876005923, 'ACC-dog': 81.64447920574204, 'ACC-horse': 90.4966393685892, 'ACC-sheep': 83.8885248220642, 'ACC-cow': 86.49534067946301, 'ACC-elephant': 89.41021188699932, 'ACC-bear': 86.4619329302169, 'ACC-zebra': 89.41331017106103, 'ACC-giraffe': 92.29767712277413, 'ACC-backpack': 59.83481739862052, 'ACC-umbrella': 84.63937805320369, 'ACC-handbag': 49.98663482945504, 'ACC-tie': 81.73714807728864, 'ACC-suitcase': 90.1372949346445, 'ACC-frisbee': 93.49454545454546, 'ACC-skis': 67.80171517937151, 'ACC-snowboard': 78.30422962944822, 'ACC-sports ball': 78.70137790595507, 'ACC-kite': 75.14806229061739, 'ACC-baseball bat': 83.17249698431846, 'ACC-baseball glove': 60.216308913915974, 'ACC-skateboard': 69.89443020588803, 'ACC-surfboard': 76.92673560581186, 'ACC-tennis racket': 79.68930226021313, 'ACC-bottle': 84.29255723160324, 'ACC-wine glass': 84.64080433718337, 'ACC-cup': 84.18374144266072, 'ACC-fork': 66.91793136392518, 'ACC-knife': 62.67677877921888, 'ACC-spoon': 71.68316913073322, 'ACC-bowl': 68.4918890352806, 'ACC-banana': 90.4940881275209, 'ACC-apple': 71.23701030846952, 'ACC-sandwich': 80.0002435430645, 'ACC-orange': 87.62535126596961, 'ACC-broccoli': 82.30135989484207, 'ACC-carrot': 75.35596654212871, 'ACC-hot dog': 73.69579569372361, 'ACC-pizza': 92.70477641866333, 'ACC-donut': 82.57855593512502, 'ACC-cake': 76.19315380212538, 'ACC-chair': 71.05994329255294, 'ACC-couch': 79.64463661092456, 'ACC-potted plant': 51.61083899807567, 'ACC-bed': 81.54382508870741, 'ACC-dining table': 77.27406021717076, 'ACC-toilet': 89.11350766155897, 'ACC-tv': 88.73962327711052, 'ACC-laptop': 90.71927011358014, 'ACC-mouse': 84.7663676143271, 'ACC-remote': 72.62308930649198, 'ACC-keyboard': 68.75631774959645, 'ACC-cell phone': 81.5898916923253, 'ACC-microwave': 30.187339704432485, 'ACC-oven': 83.54410629902014, 'ACC-toaster': 32.78681793920815, 'ACC-sink': 81.8462482752485, 'ACC-refrigerator': 88.47754127699699, 'ACC-book': 70.3155907651789, 'ACC-clock': 77.08035931598131, 'ACC-vase': 57.68165370980586, 'ACC-scissors': 64.84140429165633, 'ACC-teddy bear': 87.80408154121604, 'ACC-hair drier': 44.4183531153047, 'ACC-toothbrush': 79.90879082696317, 'ACC-banner': 73.64917641992449, 'ACC-blanket': 19.518221438038168, 'ACC-bridge': 55.38281035726258, 'ACC-cardboard': 56.22474916387959, 'ACC-counter': 55.82874229102027, 'ACC-curtain': 72.48141870450118, 'ACC-door-stuff': 64.06361533346039, 'ACC-floor-wood': 74.26056782399118, 'ACC-flower': 70.58170799368358, 'ACC-fruit': 59.28172135265656, 'ACC-gravel': 34.79669859958716, 'ACC-house': 27.138409623060017, 'ACC-light': 58.20995865712207, 'ACC-mirror-stuff': 74.49669235974568, 'ACC-net': 62.79007123331405, 'ACC-pillow': 29.299643868814897, 'ACC-platform': 55.227434078638005, 'ACC-playingfield': 87.37078896407796, 'ACC-railroad': 76.62059891991876, 'ACC-river': 70.47308455286002, 'ACC-road': 87.00648332263256, 'ACC-roof': 21.161037701638367, 'ACC-sand': 70.77908786385213, 'ACC-sea': 88.87197017147837, 'ACC-shelf': 54.92670005878726, 'ACC-snow': 95.92320061450471, 'ACC-stairs': 41.41185378541428, 'ACC-tent': 12.275104449397977, 'ACC-towel': 44.107006493612, 'ACC-wall-brick': 65.26642963462757, 'ACC-wall-stone': 30.33523893602056, 'ACC-wall-tile': 80.4137383990903, 'ACC-wall-wood': 48.11203822180946, 'ACC-water-other': 50.13526186449193, 'ACC-window-blind': 59.78107963518472, 'ACC-window-other': 68.96467292938658, 'ACC-tree-merged': 88.97458368705084, 'ACC-fence-merged': 68.1732217672941, 'ACC-ceiling-merged': 81.05849623121857, 'ACC-sky-other-merged': 96.56616529096283, 'ACC-cabinet-merged': 76.1838632813439, 'ACC-table-merged': 48.80442411929714, 'ACC-floor-other-merged': 63.09791739432201, 'ACC-pavement-merged': 64.46145456574575, 'ACC-mountain-merged': 64.1837570040862, 'ACC-grass-merged': 84.21557822393183, 'ACC-dirt-merged': 71.67674811235926, 'ACC-paper-merged': 45.31758971896824, 'ACC-food-other-merged': 48.86753990327134, 'ACC-building-other-merged': 72.179509285087, 'ACC-rock-merged': 81.04422447595475, 'ACC-wall-other-merged': 82.43168718679377, 'ACC-rug-merged': 80.36158718723027})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.581796897863623, 'noc@0.8': 2.9180567749487856, 'noc@0.85': 3.559847819724905, 'noc@0.9': 4.543751829089845, 'miou@iter1': 0.8297990258060285}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.92887878417969, 'precision@0.6': 68.24718475341797, 'precision@0.7': 62.883792877197266, 'precision@0.8': 52.62339782714844, 'precision@0.9': 27.205596923828125, 'cIoU': 58.11103439331055, 'mIoU': 62.5361213684082}}}
INFO:trainer.default_trainer:This epoch takes 1:28:11.507269
INFO:trainer.default_trainer:PROGRESS: 40.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 20 training.
INFO:trainer.default_trainer:epochs[    20] optim steps[36600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48425/0.90771, loss_mask_bce_0: 0.33952/0.33468, loss_mask_dice_0: 1.09155/1.16670, loss_spatial_bce_0: 0.06422/0.08933, loss_spatial_dice_0: 0.18302/0.21370, loss_spatial_ce_0: 0.00187/0.06968, loss_grounding_bce_0: 0.03943/0.08624, loss_grounding_dice_0: 0.19846/0.17917, loss_grounding_ce_0: 0.32815/0.27519, loss_mask_ce_1: 0.45692/0.90838, loss_mask_bce_1: 0.33865/0.33551, loss_mask_dice_1: 1.08143/1.17359, loss_spatial_bce_1: 0.06570/0.09003, loss_spatial_dice_1: 0.18707/0.21794, loss_spatial_ce_1: 0.16036/0.07537, loss_grounding_bce_1: 0.03910/0.08637, loss_grounding_dice_1: 0.18606/0.18009, loss_grounding_ce_1: 0.31648/0.27680, loss_mask_ce_2: 0.47673/0.91563, loss_mask_bce_2: 0.33592/0.33591, loss_mask_dice_2: 0.97821/1.17318, loss_spatial_bce_2: 0.06690/0.09050, loss_spatial_dice_2: 0.19285/0.21897, loss_spatial_ce_2: 0.00608/0.07893, loss_grounding_bce_2: 0.03860/0.08642, loss_grounding_dice_2: 0.17361/0.17979, loss_grounding_ce_2: 0.31171/0.28001, loss_mask_ce_3: 0.51416/0.92485, loss_mask_bce_3: 0.32455/0.33681, loss_mask_dice_3: 0.95645/1.17040, loss_spatial_bce_3: 0.06764/0.09135, loss_spatial_dice_3: 0.17253/0.21956, loss_spatial_ce_3: 0.05999/0.08267, loss_grounding_bce_3: 0.03838/0.08670, loss_grounding_dice_3: 0.15766/0.17961, loss_grounding_ce_3: 0.29504/0.28147, loss_mask_ce_4: 0.58195/0.92405, loss_mask_bce_4: 0.36219/0.33868, loss_mask_dice_4: 1.27876/1.19364, loss_spatial_bce_4: 0.07368/0.09561, loss_spatial_dice_4: 0.17912/0.23090, loss_spatial_ce_4: 0.07097/0.09885, loss_grounding_bce_4: 0.04004/0.08712, loss_grounding_dice_4: 0.19299/0.18238, loss_grounding_ce_4: 0.32016/0.28440, loss_mask_ce_5: 0.53263/0.93930, loss_mask_bce_5: 0.34788/0.34096, loss_mask_dice_5: 1.05522/1.19988, loss_spatial_bce_5: 0.07023/0.09723, loss_spatial_dice_5: 0.17880/0.23447, loss_spatial_ce_5: 0.01927/0.11347, loss_grounding_bce_5: 0.03881/0.08753, loss_grounding_dice_5: 0.20012/0.18356, loss_grounding_ce_5: 0.30598/0.29699, loss_mask_ce_6: 0.45197/0.97802, loss_mask_bce_6: 0.36832/0.34365, loss_mask_dice_6: 1.10282/1.20222, loss_spatial_bce_6: 0.07831/0.10296, loss_spatial_dice_6: 0.18758/0.23676, loss_spatial_ce_6: 0.04113/0.13949, loss_grounding_bce_6: 0.04400/0.08824, loss_grounding_dice_6: 0.18294/0.18378, loss_grounding_ce_6: 0.33536/0.31358, loss_mask_ce_7: 0.55160/1.02299, loss_mask_bce_7: 0.38002/0.35148, loss_mask_dice_7: 1.07072/1.25790, loss_spatial_bce_7: 0.07455/0.11146, loss_spatial_dice_7: 0.22600/0.26441, loss_spatial_ce_7: 0.06972/0.17675, loss_grounding_bce_7: 0.03799/0.09015, loss_grounding_dice_7: 0.21541/0.19107, loss_grounding_ce_7: 0.35740/0.34646, loss_mask_ce_8: 0.54878/1.13239, loss_mask_bce_8: 0.35356/0.36510, loss_mask_dice_8: 1.24633/1.33193, loss_spatial_bce_8: 0.09081/0.13241, loss_spatial_dice_8: 0.24925/0.30379, loss_spatial_ce_8: 0.07908/0.23351, loss_grounding_bce_8: 0.03900/0.09380, loss_grounding_dice_8: 0.20200/0.20213, loss_grounding_ce_8: 0.36149/0.41499, loss_mask_ce_9: 3.12214/3.68314, loss_mask_bce_9: 0.39366/0.39202, loss_mask_dice_9: 2.19464/1.90601, loss_spatial_bce_9: 0.24514/0.33445, loss_spatial_dice_9: 0.86518/0.82324, loss_spatial_ce_9: 1.10324/1.50697, loss_grounding_bce_9: 0.03692/0.10526, loss_grounding_dice_9: 0.37572/0.28146, loss_grounding_ce_9: 0.55722/0.68212] items per batch[64] items per second[0.13] total items[2342400] mini batches[ 36600] memory[7341] epoch remaining[1:22:32]
INFO:trainer.default_trainer:epochs[    20] optim steps[36700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.81739/0.90749, loss_mask_bce_0: 0.15988/0.33464, loss_mask_dice_0: 1.48591/1.16627, loss_spatial_bce_0: 0.17565/0.08932, loss_spatial_dice_0: 0.34918/0.21364, loss_spatial_ce_0: 0.00269/0.06961, loss_grounding_bce_0: 0.14375/0.08623, loss_grounding_dice_0: 0.12871/0.17912, loss_grounding_ce_0: 1.28479/0.27514, loss_mask_ce_1: 1.78621/0.90815, loss_mask_bce_1: 0.14693/0.33545, loss_mask_dice_1: 1.16749/1.17321, loss_spatial_bce_1: 0.16244/0.09001, loss_spatial_dice_1: 0.28228/0.21788, loss_spatial_ce_1: 0.00621/0.07531, loss_grounding_bce_1: 0.09133/0.08636, loss_grounding_dice_1: 0.10455/0.18003, loss_grounding_ce_1: 1.29808/0.27675, loss_mask_ce_2: 1.81871/0.91545, loss_mask_bce_2: 0.14036/0.33586, loss_mask_dice_2: 1.31463/1.17276, loss_spatial_bce_2: 0.11490/0.09048, loss_spatial_dice_2: 0.31185/0.21891, loss_spatial_ce_2: 0.28980/0.07886, loss_grounding_bce_2: 0.09901/0.08642, loss_grounding_dice_2: 0.12838/0.17974, loss_grounding_ce_2: 0.43211/0.27990, loss_mask_ce_3: 1.71467/0.92464, loss_mask_bce_3: 0.13869/0.33677, loss_mask_dice_3: 1.23356/1.16999, loss_spatial_bce_3: 0.09526/0.09132, loss_spatial_dice_3: 0.27229/0.21951, loss_spatial_ce_3: 0.45333/0.08262, loss_grounding_bce_3: 0.13432/0.08669, loss_grounding_dice_3: 0.12162/0.17956, loss_grounding_ce_3: 1.32946/0.28137, loss_mask_ce_4: 1.51494/0.92393, loss_mask_bce_4: 0.13702/0.33863, loss_mask_dice_4: 1.51430/1.19319, loss_spatial_bce_4: 0.10184/0.09558, loss_spatial_dice_4: 0.31878/0.23084, loss_spatial_ce_4: 0.25859/0.09878, loss_grounding_bce_4: 0.08098/0.08711, loss_grounding_dice_4: 0.11335/0.18233, loss_grounding_ce_4: 1.39675/0.28437, loss_mask_ce_5: 1.35421/0.93920, loss_mask_bce_5: 0.17960/0.34092, loss_mask_dice_5: 1.39148/1.19944, loss_spatial_bce_5: 0.11195/0.09720, loss_spatial_dice_5: 0.28921/0.23441, loss_spatial_ce_5: 0.22349/0.11338, loss_grounding_bce_5: 0.12057/0.08752, loss_grounding_dice_5: 0.13257/0.18351, loss_grounding_ce_5: 0.28547/0.29691, loss_mask_ce_6: 1.69282/0.97787, loss_mask_bce_6: 0.17020/0.34360, loss_mask_dice_6: 1.58155/1.20182, loss_spatial_bce_6: 0.14152/0.10293, loss_spatial_dice_6: 0.30713/0.23672, loss_spatial_ce_6: 0.38748/0.13941, loss_grounding_bce_6: 0.10861/0.08824, loss_grounding_dice_6: 0.12229/0.18373, loss_grounding_ce_6: 0.40247/0.31346, loss_mask_ce_7: 1.65641/1.02283, loss_mask_bce_7: 0.18809/0.35142, loss_mask_dice_7: 1.57552/1.25748, loss_spatial_bce_7: 0.15116/0.11144, loss_spatial_dice_7: 0.36931/0.26436, loss_spatial_ce_7: 0.08628/0.17668, loss_grounding_bce_7: 0.10967/0.09013, loss_grounding_dice_7: 0.12765/0.19101, loss_grounding_ce_7: 1.69890/0.34642, loss_mask_ce_8: 1.84593/1.13214, loss_mask_bce_8: 0.26137/0.36504, loss_mask_dice_8: 1.59803/1.33145, loss_spatial_bce_8: 0.13973/0.13238, loss_spatial_dice_8: 0.40095/0.30371, loss_spatial_ce_8: 0.44101/0.23346, loss_grounding_bce_8: 0.18972/0.09379, loss_grounding_dice_8: 0.16996/0.20208, loss_grounding_ce_8: 1.68595/0.41490, loss_mask_ce_9: 4.64669/3.68278, loss_mask_bce_9: 0.28956/0.39193, loss_mask_dice_9: 1.99804/1.90538, loss_spatial_bce_9: 0.17920/0.33444, loss_spatial_dice_9: 0.79118/0.82320, loss_spatial_ce_9: 1.58015/1.50689, loss_grounding_bce_9: 0.28488/0.10525, loss_grounding_dice_9: 0.21528/0.28138, loss_grounding_ce_9: 1.15326/0.68191] items per batch[64] items per second[0.23] total items[2348800] mini batches[ 36700] memory[7341] epoch remaining[1:18:05]
INFO:trainer.default_trainer:epochs[    20] optim steps[36800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62929/0.90746, loss_mask_bce_0: 0.22883/0.33472, loss_mask_dice_0: 0.53926/1.16644, loss_spatial_bce_0: 0.05620/0.08931, loss_spatial_dice_0: 0.15637/0.21361, loss_spatial_ce_0: 0.00037/0.06957, loss_grounding_bce_0: 0.03380/0.08625, loss_grounding_dice_0: 0.25589/0.17910, loss_grounding_ce_0: 0.33016/0.27526, loss_mask_ce_1: 0.61915/0.90812, loss_mask_bce_1: 0.22951/0.33552, loss_mask_dice_1: 0.54019/1.17336, loss_spatial_bce_1: 0.05818/0.09000, loss_spatial_dice_1: 0.17474/0.21785, loss_spatial_ce_1: 0.00047/0.07527, loss_grounding_bce_1: 0.03727/0.08638, loss_grounding_dice_1: 0.25032/0.18002, loss_grounding_ce_1: 0.34099/0.27687, loss_mask_ce_2: 0.49729/0.91547, loss_mask_bce_2: 0.22832/0.33593, loss_mask_dice_2: 0.55964/1.17293, loss_spatial_bce_2: 0.05818/0.09048, loss_spatial_dice_2: 0.17963/0.21887, loss_spatial_ce_2: 0.00158/0.07884, loss_grounding_bce_2: 0.04198/0.08644, loss_grounding_dice_2: 0.24042/0.17973, loss_grounding_ce_2: 0.34607/0.28002, loss_mask_ce_3: 0.51287/0.92460, loss_mask_bce_3: 0.22977/0.33684, loss_mask_dice_3: 0.53540/1.17012, loss_spatial_bce_3: 0.06186/0.09132, loss_spatial_dice_3: 0.19219/0.21948, loss_spatial_ce_3: 0.00391/0.08258, loss_grounding_bce_3: 0.03878/0.08671, loss_grounding_dice_3: 0.25028/0.17954, loss_grounding_ce_3: 0.29753/0.28151, loss_mask_ce_4: 0.67770/0.92396, loss_mask_bce_4: 0.22826/0.33870, loss_mask_dice_4: 0.52570/1.19337, loss_spatial_bce_4: 0.05781/0.09558, loss_spatial_dice_4: 0.14652/0.23082, loss_spatial_ce_4: 0.00845/0.09873, loss_grounding_bce_4: 0.02953/0.08713, loss_grounding_dice_4: 0.21492/0.18231, loss_grounding_ce_4: 0.30971/0.28449, loss_mask_ce_5: 0.50124/0.93928, loss_mask_bce_5: 0.22223/0.34099, loss_mask_dice_5: 0.53217/1.19966, loss_spatial_bce_5: 0.05608/0.09720, loss_spatial_dice_5: 0.14265/0.23439, loss_spatial_ce_5: 0.02385/0.11332, loss_grounding_bce_5: 0.03488/0.08753, loss_grounding_dice_5: 0.23384/0.18350, loss_grounding_ce_5: 0.23309/0.29702, loss_mask_ce_6: 0.38311/0.97797, loss_mask_bce_6: 0.22440/0.34367, loss_mask_dice_6: 0.51237/1.20197, loss_spatial_bce_6: 0.05554/0.10293, loss_spatial_dice_6: 0.16148/0.23670, loss_spatial_ce_6: 0.05193/0.13936, loss_grounding_bce_6: 0.02726/0.08825, loss_grounding_dice_6: 0.24051/0.18370, loss_grounding_ce_6: 0.17525/0.31360, loss_mask_ce_7: 0.36522/1.02293, loss_mask_bce_7: 0.23023/0.35149, loss_mask_dice_7: 0.53802/1.25764, loss_spatial_bce_7: 0.05798/0.11143, loss_spatial_dice_7: 0.16652/0.26433, loss_spatial_ce_7: 0.07843/0.17662, loss_grounding_bce_7: 0.02527/0.09015, loss_grounding_dice_7: 0.23016/0.19098, loss_grounding_ce_7: 0.12029/0.34662, loss_mask_ce_8: 0.52264/1.13219, loss_mask_bce_8: 0.22124/0.36512, loss_mask_dice_8: 0.57414/1.33168, loss_spatial_bce_8: 0.05746/0.13238, loss_spatial_dice_8: 0.16040/0.30366, loss_spatial_ce_8: 0.17249/0.23342, loss_grounding_bce_8: 0.02656/0.09381, loss_grounding_dice_8: 0.19747/0.20206, loss_grounding_ce_8: 0.15327/0.41512, loss_mask_ce_9: 4.12486/3.68315, loss_mask_bce_9: 0.21743/0.39203, loss_mask_dice_9: 1.09738/1.90567, loss_spatial_bce_9: 0.25323/0.33443, loss_spatial_dice_9: 0.81677/0.82320, loss_spatial_ce_9: 1.43029/1.50681, loss_grounding_bce_9: 0.02085/0.10526, loss_grounding_dice_9: 0.30110/0.28134, loss_grounding_ce_9: 0.38509/0.68191] items per batch[64] items per second[0.23] total items[2355200] mini batches[ 36800] memory[7341] epoch remaining[1:13:33]
INFO:trainer.default_trainer:epochs[    20] optim steps[36900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57473/0.90737, loss_mask_bce_0: 0.33964/0.33475, loss_mask_dice_0: 3.62556/1.16632, loss_spatial_bce_0: 0.04392/0.08930, loss_spatial_dice_0: 0.25250/0.21357, loss_spatial_ce_0: 0.04584/0.06950, loss_grounding_bce_0: 0.05027/0.08624, loss_grounding_dice_0: 0.42292/0.17911, loss_grounding_ce_0: 0.31717/0.27515, loss_mask_ce_1: 0.55213/0.90808, loss_mask_bce_1: 0.34456/0.33555, loss_mask_dice_1: 3.78968/1.17320, loss_spatial_bce_1: 0.04522/0.09000, loss_spatial_dice_1: 0.21594/0.21781, loss_spatial_ce_1: 0.05671/0.07520, loss_grounding_bce_1: 0.04916/0.08637, loss_grounding_dice_1: 0.39780/0.18002, loss_grounding_ce_1: 0.26985/0.27676, loss_mask_ce_2: 0.61735/0.91543, loss_mask_bce_2: 0.32885/0.33596, loss_mask_dice_2: 3.55604/1.17280, loss_spatial_bce_2: 0.04702/0.09047, loss_spatial_dice_2: 0.23334/0.21884, loss_spatial_ce_2: 0.13937/0.07877, loss_grounding_bce_2: 0.04826/0.08643, loss_grounding_dice_2: 0.36895/0.17974, loss_grounding_ce_2: 0.29337/0.27989, loss_mask_ce_3: 0.67833/0.92454, loss_mask_bce_3: 0.33303/0.33688, loss_mask_dice_3: 3.35511/1.17001, loss_spatial_bce_3: 0.04715/0.09132, loss_spatial_dice_3: 0.26372/0.21944, loss_spatial_ce_3: 0.05108/0.08252, loss_grounding_bce_3: 0.05093/0.08670, loss_grounding_dice_3: 0.36331/0.17955, loss_grounding_ce_3: 0.28240/0.28136, loss_mask_ce_4: 0.67361/0.92385, loss_mask_bce_4: 0.34379/0.33874, loss_mask_dice_4: 3.56563/1.19330, loss_spatial_bce_4: 0.05411/0.09556, loss_spatial_dice_4: 0.28186/0.23078, loss_spatial_ce_4: 0.04195/0.09863, loss_grounding_bce_4: 0.04853/0.08712, loss_grounding_dice_4: 0.36029/0.18232, loss_grounding_ce_4: 0.27856/0.28434, loss_mask_ce_5: 0.70804/0.93922, loss_mask_bce_5: 0.33484/0.34103, loss_mask_dice_5: 3.28534/1.19953, loss_spatial_bce_5: 0.05144/0.09719, loss_spatial_dice_5: 0.29683/0.23436, loss_spatial_ce_5: 0.07017/0.11326, loss_grounding_bce_5: 0.05083/0.08753, loss_grounding_dice_5: 0.35758/0.18351, loss_grounding_ce_5: 0.28738/0.29684, loss_mask_ce_6: 0.76606/0.97790, loss_mask_bce_6: 0.35398/0.34370, loss_mask_dice_6: 3.53993/1.20187, loss_spatial_bce_6: 0.06157/0.10292, loss_spatial_dice_6: 0.27772/0.23668, loss_spatial_ce_6: 0.08007/0.13928, loss_grounding_bce_6: 0.05171/0.08824, loss_grounding_dice_6: 0.43627/0.18369, loss_grounding_ce_6: 0.29761/0.31351, loss_mask_ce_7: 1.13710/1.02283, loss_mask_bce_7: 0.36229/0.35153, loss_mask_dice_7: 4.11205/1.25759, loss_spatial_bce_7: 0.04962/0.11142, loss_spatial_dice_7: 0.31767/0.26432, loss_spatial_ce_7: 0.21561/0.17654, loss_grounding_bce_7: 0.05691/0.09015, loss_grounding_dice_7: 0.44931/0.19100, loss_grounding_ce_7: 0.29532/0.34649, loss_mask_ce_8: 0.86783/1.13214, loss_mask_bce_8: 0.35157/0.36516, loss_mask_dice_8: 4.02989/1.33154, loss_spatial_bce_8: 0.04905/0.13240, loss_spatial_dice_8: 0.37917/0.30363, loss_spatial_ce_8: 0.28875/0.23331, loss_grounding_bce_8: 0.04999/0.09381, loss_grounding_dice_8: 0.49859/0.20206, loss_grounding_ce_8: 0.36791/0.41494, loss_mask_ce_9: 3.23573/3.68282, loss_mask_bce_9: 0.32574/0.39209, loss_mask_dice_9: 4.87417/1.90557, loss_spatial_bce_9: 0.26454/0.33441, loss_spatial_dice_9: 0.93477/0.82319, loss_spatial_ce_9: 1.24649/1.50662, loss_grounding_bce_9: 0.04874/0.10525, loss_grounding_dice_9: 0.58825/0.28138, loss_grounding_ce_9: 0.45680/0.68164] items per batch[64] items per second[0.23] total items[2361600] mini batches[ 36900] memory[7341] epoch remaining[1:08:26]
INFO:trainer.default_trainer:epochs[    20] optim steps[37000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55885/0.90728, loss_mask_bce_0: 0.17937/0.33476, loss_mask_dice_0: 0.50416/1.16604, loss_spatial_bce_0: 0.13700/0.08930, loss_spatial_dice_0: 0.24444/0.21353, loss_spatial_ce_0: 0.06725/0.06940, loss_grounding_bce_0: 0.07006/0.08626, loss_grounding_dice_0: 0.24793/0.17910, loss_grounding_ce_0: 0.39603/0.27489, loss_mask_ce_1: 0.63051/0.90793, loss_mask_bce_1: 0.18146/0.33556, loss_mask_dice_1: 0.54466/1.17294, loss_spatial_bce_1: 0.13404/0.08999, loss_spatial_dice_1: 0.25616/0.21776, loss_spatial_ce_1: 0.06985/0.07510, loss_grounding_bce_1: 0.06289/0.08639, loss_grounding_dice_1: 0.22519/0.18000, loss_grounding_ce_1: 0.55838/0.27647, loss_mask_ce_2: 0.58616/0.91535, loss_mask_bce_2: 0.17857/0.33597, loss_mask_dice_2: 0.57348/1.17260, loss_spatial_bce_2: 0.12805/0.09048, loss_spatial_dice_2: 0.24026/0.21880, loss_spatial_ce_2: 0.07156/0.07867, loss_grounding_bce_2: 0.06361/0.08644, loss_grounding_dice_2: 0.21118/0.17972, loss_grounding_ce_2: 0.60467/0.27962, loss_mask_ce_3: 0.58588/0.92446, loss_mask_bce_3: 0.18839/0.33689, loss_mask_dice_3: 0.55548/1.16978, loss_spatial_bce_3: 0.13694/0.09132, loss_spatial_dice_3: 0.26738/0.21939, loss_spatial_ce_3: 0.07764/0.08243, loss_grounding_bce_3: 0.07010/0.08671, loss_grounding_dice_3: 0.22470/0.17953, loss_grounding_ce_3: 0.42711/0.28108, loss_mask_ce_4: 0.50353/0.92378, loss_mask_bce_4: 0.17630/0.33876, loss_mask_dice_4: 0.56032/1.19305, loss_spatial_bce_4: 0.14346/0.09557, loss_spatial_dice_4: 0.26176/0.23074, loss_spatial_ce_4: 0.13709/0.09852, loss_grounding_bce_4: 0.06666/0.08713, loss_grounding_dice_4: 0.22808/0.18230, loss_grounding_ce_4: 0.55161/0.28407, loss_mask_ce_5: 0.52824/0.93908, loss_mask_bce_5: 0.18297/0.34107, loss_mask_dice_5: 0.56370/1.19928, loss_spatial_bce_5: 0.13485/0.09720, loss_spatial_dice_5: 0.27215/0.23431, loss_spatial_ce_5: 0.10701/0.11316, loss_grounding_bce_5: 0.07169/0.08754, loss_grounding_dice_5: 0.25384/0.18350, loss_grounding_ce_5: 0.48044/0.29655, loss_mask_ce_6: 0.56144/0.97783, loss_mask_bce_6: 0.16980/0.34373, loss_mask_dice_6: 0.52954/1.20167, loss_spatial_bce_6: 0.08213/0.10293, loss_spatial_dice_6: 0.26311/0.23665, loss_spatial_ce_6: 0.14815/0.13915, loss_grounding_bce_6: 0.06725/0.08826, loss_grounding_dice_6: 0.24138/0.18367, loss_grounding_ce_6: 0.44326/0.31328, loss_mask_ce_7: 0.66325/1.02278, loss_mask_bce_7: 0.17075/0.35154, loss_mask_dice_7: 0.57425/1.25735, loss_spatial_bce_7: 0.07191/0.11143, loss_spatial_dice_7: 0.24710/0.26425, loss_spatial_ce_7: 0.20617/0.17643, loss_grounding_bce_7: 0.06819/0.09016, loss_grounding_dice_7: 0.25551/0.19096, loss_grounding_ce_7: 0.50454/0.34617, loss_mask_ce_8: 0.68594/1.13203, loss_mask_bce_8: 0.18564/0.36518, loss_mask_dice_8: 0.70722/1.33128, loss_spatial_bce_8: 0.09904/0.13240, loss_spatial_dice_8: 0.29109/0.30354, loss_spatial_ce_8: 0.52804/0.23324, loss_grounding_bce_8: 0.07221/0.09382, loss_grounding_dice_8: 0.22460/0.20202, loss_grounding_ce_8: 0.99073/0.41464, loss_mask_ce_9: 3.60247/3.68274, loss_mask_bce_9: 0.16520/0.39212, loss_mask_dice_9: 0.74787/1.90531, loss_spatial_bce_9: 0.29191/0.33441, loss_spatial_dice_9: 0.76071/0.82318, loss_spatial_ce_9: 1.10334/1.50644, loss_grounding_bce_9: 0.05947/0.10526, loss_grounding_dice_9: 0.24028/0.28134, loss_grounding_ce_9: 0.97904/0.68151] items per batch[64] items per second[0.23] total items[2368000] mini batches[ 37000] memory[7341] epoch remaining[1:03:30]
INFO:trainer.default_trainer:epochs[    20] optim steps[37100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75741/0.90726, loss_mask_bce_0: 0.40319/0.33479, loss_mask_dice_0: 1.10244/1.16654, loss_spatial_bce_0: 0.08001/0.08927, loss_spatial_dice_0: 0.30697/0.21352, loss_spatial_ce_0: 0.00437/0.06936, loss_grounding_bce_0: 0.10599/0.08624, loss_grounding_dice_0: 0.09035/0.17913, loss_grounding_ce_0: 0.02441/0.27480, loss_mask_ce_1: 0.75736/0.90792, loss_mask_bce_1: 0.41366/0.33558, loss_mask_dice_1: 1.23882/1.17342, loss_spatial_bce_1: 0.08899/0.08997, loss_spatial_dice_1: 0.31619/0.21775, loss_spatial_ce_1: 0.00669/0.07503, loss_grounding_bce_1: 0.10794/0.08637, loss_grounding_dice_1: 0.09091/0.18002, loss_grounding_ce_1: 0.02603/0.27637, loss_mask_ce_2: 0.98381/0.91532, loss_mask_bce_2: 0.40075/0.33600, loss_mask_dice_2: 1.14848/1.17310, loss_spatial_bce_2: 0.07695/0.09045, loss_spatial_dice_2: 0.30490/0.21880, loss_spatial_ce_2: 0.01818/0.07861, loss_grounding_bce_2: 0.11229/0.08642, loss_grounding_dice_2: 0.08923/0.17973, loss_grounding_ce_2: 0.04748/0.27955, loss_mask_ce_3: 0.66455/0.92441, loss_mask_bce_3: 0.40406/0.33693, loss_mask_dice_3: 1.18776/1.17023, loss_spatial_bce_3: 0.08615/0.09130, loss_spatial_dice_3: 0.30108/0.21938, loss_spatial_ce_3: 0.02154/0.08238, loss_grounding_bce_3: 0.10191/0.08669, loss_grounding_dice_3: 0.09023/0.17954, loss_grounding_ce_3: 0.04647/0.28101, loss_mask_ce_4: 0.72283/0.92379, loss_mask_bce_4: 0.43125/0.33879, loss_mask_dice_4: 1.13346/1.19357, loss_spatial_bce_4: 0.08868/0.09554, loss_spatial_dice_4: 0.30471/0.23074, loss_spatial_ce_4: 0.01701/0.09843, loss_grounding_bce_4: 0.10018/0.08712, loss_grounding_dice_4: 0.08602/0.18232, loss_grounding_ce_4: 0.04168/0.28402, loss_mask_ce_5: 1.02008/0.93908, loss_mask_bce_5: 0.42127/0.34110, loss_mask_dice_5: 1.24568/1.19984, loss_spatial_bce_5: 0.10970/0.09717, loss_spatial_dice_5: 0.32245/0.23431, loss_spatial_ce_5: 0.03984/0.11308, loss_grounding_bce_5: 0.09423/0.08753, loss_grounding_dice_5: 0.09246/0.18352, loss_grounding_ce_5: 0.14566/0.29651, loss_mask_ce_6: 0.74507/0.97784, loss_mask_bce_6: 0.39793/0.34377, loss_mask_dice_6: 1.17441/1.20223, loss_spatial_bce_6: 0.10916/0.10290, loss_spatial_dice_6: 0.29921/0.23666, loss_spatial_ce_6: 0.06396/0.13910, loss_grounding_bce_6: 0.09145/0.08824, loss_grounding_dice_6: 0.09583/0.18369, loss_grounding_ce_6: 0.13339/0.31323, loss_mask_ce_7: 0.87777/1.02276, loss_mask_bce_7: 0.39676/0.35159, loss_mask_dice_7: 1.27105/1.25790, loss_spatial_bce_7: 0.10123/0.11140, loss_spatial_dice_7: 0.30877/0.26428, loss_spatial_ce_7: 0.07511/0.17634, loss_grounding_bce_7: 0.10328/0.09015, loss_grounding_dice_7: 0.09782/0.19097, loss_grounding_ce_7: 0.22389/0.34614, loss_mask_ce_8: 1.06025/1.13194, loss_mask_bce_8: 0.40249/0.36523, loss_mask_dice_8: 1.23649/1.33184, loss_spatial_bce_8: 0.09569/0.13237, loss_spatial_dice_8: 0.30763/0.30353, loss_spatial_ce_8: 0.22772/0.23318, loss_grounding_bce_8: 0.09600/0.09381, loss_grounding_dice_8: 0.11735/0.20204, loss_grounding_ce_8: 0.85420/0.41457, loss_mask_ce_9: 4.50479/3.68269, loss_mask_bce_9: 0.77754/0.39215, loss_mask_dice_9: 2.03696/1.90580, loss_spatial_bce_9: 0.41552/0.33438, loss_spatial_dice_9: 0.79639/0.82319, loss_spatial_ce_9: 2.10901/1.50640, loss_grounding_bce_9: 0.24406/0.10524, loss_grounding_dice_9: 0.28045/0.28135, loss_grounding_ce_9: 0.95328/0.68140] items per batch[64] items per second[0.23] total items[2374400] mini batches[ 37100] memory[7341] epoch remaining[0:58:43]
INFO:trainer.default_trainer:epochs[    20] optim steps[37200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71667/0.90726, loss_mask_bce_0: 0.36605/0.33475, loss_mask_dice_0: 1.21064/1.16667, loss_spatial_bce_0: 0.05950/0.08927, loss_spatial_dice_0: 0.16979/0.21349, loss_spatial_ce_0: 0.00182/0.06931, loss_grounding_bce_0: 0.10508/0.08624, loss_grounding_dice_0: 0.14253/0.17913, loss_grounding_ce_0: 1.06874/0.27484, loss_mask_ce_1: 0.73644/0.90787, loss_mask_bce_1: 0.38488/0.33555, loss_mask_dice_1: 1.23733/1.17354, loss_spatial_bce_1: 0.05947/0.08996, loss_spatial_dice_1: 0.17657/0.21773, loss_spatial_ce_1: 0.00283/0.07498, loss_grounding_bce_1: 0.09666/0.08637, loss_grounding_dice_1: 0.13725/0.18001, loss_grounding_ce_1: 1.01938/0.27636, loss_mask_ce_2: 0.82510/0.91532, loss_mask_bce_2: 0.39344/0.33596, loss_mask_dice_2: 1.21229/1.17323, loss_spatial_bce_2: 0.06007/0.09045, loss_spatial_dice_2: 0.17408/0.21876, loss_spatial_ce_2: 0.00509/0.07855, loss_grounding_bce_2: 0.10186/0.08642, loss_grounding_dice_2: 0.14448/0.17973, loss_grounding_ce_2: 1.23851/0.27956, loss_mask_ce_3: 0.86459/0.92441, loss_mask_bce_3: 0.39253/0.33690, loss_mask_dice_3: 1.33030/1.17038, loss_spatial_bce_3: 0.05787/0.09129, loss_spatial_dice_3: 0.19020/0.21936, loss_spatial_ce_3: 0.00528/0.08231, loss_grounding_bce_3: 0.09405/0.08669, loss_grounding_dice_3: 0.14573/0.17954, loss_grounding_ce_3: 1.31916/0.28101, loss_mask_ce_4: 0.89962/0.92387, loss_mask_bce_4: 0.38773/0.33875, loss_mask_dice_4: 1.15607/1.19371, loss_spatial_bce_4: 0.06212/0.09554, loss_spatial_dice_4: 0.21020/0.23071, loss_spatial_ce_4: 0.00337/0.09836, loss_grounding_bce_4: 0.09041/0.08711, loss_grounding_dice_4: 0.17178/0.18232, loss_grounding_ce_4: 1.03192/0.28403, loss_mask_ce_5: 1.10248/0.93915, loss_mask_bce_5: 0.36847/0.34106, loss_mask_dice_5: 1.42752/1.19998, loss_spatial_bce_5: 0.06615/0.09717, loss_spatial_dice_5: 0.21304/0.23429, loss_spatial_ce_5: 0.00224/0.11302, loss_grounding_bce_5: 0.08877/0.08752, loss_grounding_dice_5: 0.14229/0.18352, loss_grounding_ce_5: 1.23153/0.29652, loss_mask_ce_6: 0.99521/0.97795, loss_mask_bce_6: 0.37341/0.34373, loss_mask_dice_6: 1.35375/1.20241, loss_spatial_bce_6: 0.06849/0.10290, loss_spatial_dice_6: 0.21388/0.23665, loss_spatial_ce_6: 0.01755/0.13903, loss_grounding_bce_6: 0.09000/0.08824, loss_grounding_dice_6: 0.12522/0.18368, loss_grounding_ce_6: 1.16702/0.31328, loss_mask_ce_7: 1.08418/1.02287, loss_mask_bce_7: 0.37631/0.35157, loss_mask_dice_7: 1.49993/1.25807, loss_spatial_bce_7: 0.07898/0.11139, loss_spatial_dice_7: 0.25030/0.26428, loss_spatial_ce_7: 0.09559/0.17629, loss_grounding_bce_7: 0.09133/0.09015, loss_grounding_dice_7: 0.13596/0.19097, loss_grounding_ce_7: 2.37630/0.34625, loss_mask_ce_8: 1.24566/1.13199, loss_mask_bce_8: 0.41109/0.36518, loss_mask_dice_8: 1.60468/1.33200, loss_spatial_bce_8: 0.08181/0.13238, loss_spatial_dice_8: 0.27689/0.30353, loss_spatial_ce_8: 0.16194/0.23312, loss_grounding_bce_8: 0.10469/0.09380, loss_grounding_dice_8: 0.15064/0.20205, loss_grounding_ce_8: 2.42614/0.41468, loss_mask_ce_9: 4.58023/3.68282, loss_mask_bce_9: 0.42018/0.39211, loss_mask_dice_9: 2.40674/1.90595, loss_spatial_bce_9: 0.28418/0.33437, loss_spatial_dice_9: 0.87565/0.82316, loss_spatial_ce_9: 1.66131/1.50635, loss_grounding_bce_9: 0.15554/0.10523, loss_grounding_dice_9: 0.31350/0.28137, loss_grounding_ce_9: 1.66646/0.68152] items per batch[64] items per second[0.23] total items[2380800] mini batches[ 37200] memory[7341] epoch remaining[0:54:04]
INFO:trainer.default_trainer:epochs[    20] optim steps[37300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38177/0.90740, loss_mask_bce_0: 0.51740/0.33474, loss_mask_dice_0: 0.98632/1.16647, loss_spatial_bce_0: 0.08930/0.08924, loss_spatial_dice_0: 0.20286/0.21345, loss_spatial_ce_0: 0.01966/0.06925, loss_grounding_bce_0: 0.09250/0.08622, loss_grounding_dice_0: 0.16353/0.17910, loss_grounding_ce_0: 1.00651/0.27485, loss_mask_ce_1: 1.63977/0.90808, loss_mask_bce_1: 0.51441/0.33554, loss_mask_dice_1: 1.06207/1.17333, loss_spatial_bce_1: 0.10834/0.08993, loss_spatial_dice_1: 0.21962/0.21767, loss_spatial_ce_1: 0.02319/0.07490, loss_grounding_bce_1: 0.08780/0.08636, loss_grounding_dice_1: 0.14331/0.17998, loss_grounding_ce_1: 0.95443/0.27640, loss_mask_ce_2: 1.33596/0.91552, loss_mask_bce_2: 0.51462/0.33596, loss_mask_dice_2: 1.04696/1.17299, loss_spatial_bce_2: 0.10585/0.09042, loss_spatial_dice_2: 0.19618/0.21871, loss_spatial_ce_2: 0.08552/0.07849, loss_grounding_bce_2: 0.08957/0.08640, loss_grounding_dice_2: 0.13988/0.17969, loss_grounding_ce_2: 0.96073/0.27961, loss_mask_ce_3: 1.45109/0.92461, loss_mask_bce_3: 0.51062/0.33689, loss_mask_dice_3: 1.09851/1.17014, loss_spatial_bce_3: 0.11013/0.09126, loss_spatial_dice_3: 0.20070/0.21931, loss_spatial_ce_3: 0.12622/0.08227, loss_grounding_bce_3: 0.07862/0.08668, loss_grounding_dice_3: 0.14240/0.17950, loss_grounding_ce_3: 0.92874/0.28109, loss_mask_ce_4: 1.41208/0.92410, loss_mask_bce_4: 0.50035/0.33875, loss_mask_dice_4: 1.06335/1.19352, loss_spatial_bce_4: 0.11626/0.09550, loss_spatial_dice_4: 0.26292/0.23067, loss_spatial_ce_4: 0.01920/0.09832, loss_grounding_bce_4: 0.07631/0.08710, loss_grounding_dice_4: 0.14977/0.18229, loss_grounding_ce_4: 1.95235/0.28412, loss_mask_ce_5: 1.39488/0.93931, loss_mask_bce_5: 0.50071/0.34105, loss_mask_dice_5: 0.93891/1.19982, loss_spatial_bce_5: 0.09974/0.09714, loss_spatial_dice_5: 0.24130/0.23425, loss_spatial_ce_5: 0.02460/0.11297, loss_grounding_bce_5: 0.08494/0.08751, loss_grounding_dice_5: 0.13675/0.18350, loss_grounding_ce_5: 0.98334/0.29656, loss_mask_ce_6: 1.37069/0.97813, loss_mask_bce_6: 0.50636/0.34374, loss_mask_dice_6: 1.09505/1.20226, loss_spatial_bce_6: 0.11928/0.10287, loss_spatial_dice_6: 0.26702/0.23661, loss_spatial_ce_6: 0.10097/0.13897, loss_grounding_bce_6: 0.09339/0.08822, loss_grounding_dice_6: 0.13758/0.18364, loss_grounding_ce_6: 1.17273/0.31328, loss_mask_ce_7: 1.51474/1.02302, loss_mask_bce_7: 0.51118/0.35158, loss_mask_dice_7: 0.97464/1.25787, loss_spatial_bce_7: 0.19983/0.11136, loss_spatial_dice_7: 0.27568/0.26426, loss_spatial_ce_7: 0.14402/0.17623, loss_grounding_bce_7: 0.08681/0.09014, loss_grounding_dice_7: 0.15216/0.19094, loss_grounding_ce_7: 0.73319/0.34632, loss_mask_ce_8: 1.72498/1.13214, loss_mask_bce_8: 0.60709/0.36520, loss_mask_dice_8: 1.12854/1.33183, loss_spatial_bce_8: 0.23646/0.13235, loss_spatial_dice_8: 0.41773/0.30351, loss_spatial_ce_8: 0.24360/0.23311, loss_grounding_bce_8: 0.07558/0.09380, loss_grounding_dice_8: 0.14956/0.20204, loss_grounding_ce_8: 1.37686/0.41465, loss_mask_ce_9: 4.95947/3.68284, loss_mask_bce_9: 0.60946/0.39213, loss_mask_dice_9: 1.78989/1.90576, loss_spatial_bce_9: 0.38260/0.33433, loss_spatial_dice_9: 0.83883/0.82316, loss_spatial_ce_9: 1.25294/1.50627, loss_grounding_bce_9: 0.10110/0.10522, loss_grounding_dice_9: 0.42029/0.28136, loss_grounding_ce_9: 1.87398/0.68145] items per batch[64] items per second[0.23] total items[2387200] mini batches[ 37300] memory[7341] epoch remaining[0:49:24]
INFO:trainer.default_trainer:epochs[    20] optim steps[37400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38199/0.90723, loss_mask_bce_0: 0.37649/0.33471, loss_mask_dice_0: 0.36825/1.16632, loss_spatial_bce_0: 0.14731/0.08924, loss_spatial_dice_0: 0.16833/0.21341, loss_spatial_ce_0: 0.00297/0.06920, loss_grounding_bce_0: 0.21026/0.08624, loss_grounding_dice_0: 0.17436/0.17911, loss_grounding_ce_0: 0.10127/0.27486, loss_mask_ce_1: 0.36506/0.90791, loss_mask_bce_1: 0.34935/0.33551, loss_mask_dice_1: 0.36025/1.17318, loss_spatial_bce_1: 0.14127/0.08993, loss_spatial_dice_1: 0.15533/0.21763, loss_spatial_ce_1: 0.00462/0.07489, loss_grounding_bce_1: 0.20837/0.08637, loss_grounding_dice_1: 0.18284/0.17997, loss_grounding_ce_1: 0.15337/0.27649, loss_mask_ce_2: 0.35273/0.91537, loss_mask_bce_2: 0.34933/0.33592, loss_mask_dice_2: 0.35328/1.17283, loss_spatial_bce_2: 0.14677/0.09042, loss_spatial_dice_2: 0.16603/0.21867, loss_spatial_ce_2: 0.00232/0.07845, loss_grounding_bce_2: 0.20893/0.08642, loss_grounding_dice_2: 0.17924/0.17969, loss_grounding_ce_2: 0.17167/0.27969, loss_mask_ce_3: 0.32904/0.92449, loss_mask_bce_3: 0.36048/0.33685, loss_mask_dice_3: 0.37126/1.16995, loss_spatial_bce_3: 0.14462/0.09126, loss_spatial_dice_3: 0.16502/0.21928, loss_spatial_ce_3: 0.00894/0.08221, loss_grounding_bce_3: 0.21852/0.08670, loss_grounding_dice_3: 0.18120/0.17950, loss_grounding_ce_3: 0.17446/0.28114, loss_mask_ce_4: 0.34849/0.92403, loss_mask_bce_4: 0.34689/0.33871, loss_mask_dice_4: 0.35590/1.19333, loss_spatial_bce_4: 0.13873/0.09550, loss_spatial_dice_4: 0.16857/0.23063, loss_spatial_ce_4: 0.00955/0.09828, loss_grounding_bce_4: 0.19527/0.08712, loss_grounding_dice_4: 0.17515/0.18229, loss_grounding_ce_4: 0.17732/0.28420, loss_mask_ce_5: 0.40011/0.93920, loss_mask_bce_5: 0.34320/0.34103, loss_mask_dice_5: 0.36446/1.19965, loss_spatial_bce_5: 0.13224/0.09714, loss_spatial_dice_5: 0.14081/0.23421, loss_spatial_ce_5: 0.05832/0.11293, loss_grounding_bce_5: 0.20078/0.08754, loss_grounding_dice_5: 0.18034/0.18350, loss_grounding_ce_5: 0.35324/0.29663, loss_mask_ce_6: 0.57701/0.97807, loss_mask_bce_6: 0.33536/0.34369, loss_mask_dice_6: 0.34968/1.20210, loss_spatial_bce_6: 0.20482/0.10286, loss_spatial_dice_6: 0.19834/0.23658, loss_spatial_ce_6: 0.05851/0.13890, loss_grounding_bce_6: 0.19822/0.08825, loss_grounding_dice_6: 0.17770/0.18365, loss_grounding_ce_6: 0.66189/0.31337, loss_mask_ce_7: 0.50537/1.02290, loss_mask_bce_7: 0.33952/0.35154, loss_mask_dice_7: 0.32124/1.25771, loss_spatial_bce_7: 0.17894/0.11135, loss_spatial_dice_7: 0.18971/0.26422, loss_spatial_ce_7: 0.06540/0.17616, loss_grounding_bce_7: 0.20297/0.09016, loss_grounding_dice_7: 0.15824/0.19094, loss_grounding_ce_7: 0.35716/0.34638, loss_mask_ce_8: 0.57424/1.13194, loss_mask_bce_8: 0.38898/0.36516, loss_mask_dice_8: 0.40068/1.33165, loss_spatial_bce_8: 0.22665/0.13235, loss_spatial_dice_8: 0.23343/0.30349, loss_spatial_ce_8: 0.13862/0.23308, loss_grounding_bce_8: 0.20357/0.09383, loss_grounding_dice_8: 0.17985/0.20204, loss_grounding_ce_8: 0.45848/0.41475, loss_mask_ce_9: 2.90674/3.68271, loss_mask_bce_9: 0.44823/0.39209, loss_mask_dice_9: 0.56705/1.90550, loss_spatial_bce_9: 0.44810/0.33431, loss_spatial_dice_9: 0.78029/0.82314, loss_spatial_ce_9: 1.37812/1.50621, loss_grounding_bce_9: 0.18738/0.10525, loss_grounding_dice_9: 0.20541/0.28139, loss_grounding_ce_9: 1.08094/0.68170] items per batch[64] items per second[0.23] total items[2393600] mini batches[ 37400] memory[7341] epoch remaining[0:44:43]
INFO:trainer.default_trainer:epochs[    20] optim steps[37500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63703/0.90731, loss_mask_bce_0: 0.37093/0.33472, loss_mask_dice_0: 0.92439/1.16626, loss_spatial_bce_0: 0.05134/0.08924, loss_spatial_dice_0: 0.16192/0.21339, loss_spatial_ce_0: 0.00092/0.06916, loss_grounding_bce_0: 0.04201/0.08625, loss_grounding_dice_0: 0.12931/0.17912, loss_grounding_ce_0: 0.22057/0.27494, loss_mask_ce_1: 0.57266/0.90800, loss_mask_bce_1: 0.38276/0.33552, loss_mask_dice_1: 0.88168/1.17312, loss_spatial_bce_1: 0.05722/0.08993, loss_spatial_dice_1: 0.16616/0.21760, loss_spatial_ce_1: 0.00754/0.07485, loss_grounding_bce_1: 0.03975/0.08638, loss_grounding_dice_1: 0.12374/0.17997, loss_grounding_ce_1: 0.22714/0.27658, loss_mask_ce_2: 0.65238/0.91548, loss_mask_bce_2: 0.38082/0.33595, loss_mask_dice_2: 0.90044/1.17279, loss_spatial_bce_2: 0.06155/0.09042, loss_spatial_dice_2: 0.13927/0.21864, loss_spatial_ce_2: 0.01476/0.07843, loss_grounding_bce_2: 0.03885/0.08643, loss_grounding_dice_2: 0.11727/0.17968, loss_grounding_ce_2: 0.22858/0.27981, loss_mask_ce_3: 0.57843/0.92457, loss_mask_bce_3: 0.35442/0.33686, loss_mask_dice_3: 0.90201/1.16996, loss_spatial_bce_3: 0.06198/0.09126, loss_spatial_dice_3: 0.17683/0.21925, loss_spatial_ce_3: 0.00449/0.08217, loss_grounding_bce_3: 0.03511/0.08671, loss_grounding_dice_3: 0.14606/0.17949, loss_grounding_ce_3: 0.23641/0.28120, loss_mask_ce_4: 0.51517/0.92409, loss_mask_bce_4: 0.36431/0.33874, loss_mask_dice_4: 0.85235/1.19333, loss_spatial_bce_4: 0.06677/0.09550, loss_spatial_dice_4: 0.17169/0.23060, loss_spatial_ce_4: 0.00888/0.09824, loss_grounding_bce_4: 0.03532/0.08713, loss_grounding_dice_4: 0.15301/0.18230, loss_grounding_ce_4: 0.19472/0.28423, loss_mask_ce_5: 0.59240/0.93925, loss_mask_bce_5: 0.39344/0.34104, loss_mask_dice_5: 1.09657/1.19959, loss_spatial_bce_5: 0.07298/0.09714, loss_spatial_dice_5: 0.18356/0.23419, loss_spatial_ce_5: 0.07859/0.11290, loss_grounding_bce_5: 0.03966/0.08755, loss_grounding_dice_5: 0.14165/0.18349, loss_grounding_ce_5: 0.22323/0.29671, loss_mask_ce_6: 0.64696/0.97817, loss_mask_bce_6: 0.37939/0.34370, loss_mask_dice_6: 0.79840/1.20205, loss_spatial_bce_6: 0.10699/0.10286, loss_spatial_dice_6: 0.21873/0.23656, loss_spatial_ce_6: 0.09644/0.13885, loss_grounding_bce_6: 0.03970/0.08826, loss_grounding_dice_6: 0.13133/0.18364, loss_grounding_ce_6: 0.21146/0.31340, loss_mask_ce_7: 0.83360/1.02303, loss_mask_bce_7: 0.42000/0.35155, loss_mask_dice_7: 1.08453/1.25765, loss_spatial_bce_7: 0.09231/0.11135, loss_spatial_dice_7: 0.23091/0.26421, loss_spatial_ce_7: 0.12968/0.17615, loss_grounding_bce_7: 0.04690/0.09017, loss_grounding_dice_7: 0.17697/0.19093, loss_grounding_ce_7: 0.24685/0.34655, loss_mask_ce_8: 1.03416/1.13197, loss_mask_bce_8: 0.40312/0.36518, loss_mask_dice_8: 1.07698/1.33161, loss_spatial_bce_8: 0.10712/0.13233, loss_spatial_dice_8: 0.25169/0.30348, loss_spatial_ce_8: 0.18801/0.23305, loss_grounding_bce_8: 0.04780/0.09384, loss_grounding_dice_8: 0.14288/0.20204, loss_grounding_ce_8: 0.30710/0.41482, loss_mask_ce_9: 4.13671/3.68278, loss_mask_bce_9: 0.40116/0.39210, loss_mask_dice_9: 1.32393/1.90529, loss_spatial_bce_9: 0.22582/0.33431, loss_spatial_dice_9: 0.80934/0.82310, loss_spatial_ce_9: 1.27341/1.50622, loss_grounding_bce_9: 0.05654/0.10526, loss_grounding_dice_9: 0.23248/0.28139, loss_grounding_ce_9: 0.50776/0.68163] items per batch[64] items per second[0.23] total items[2400000] mini batches[ 37500] memory[7341] epoch remaining[0:40:06]
INFO:trainer.default_trainer:epochs[    20] optim steps[37600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24730/0.90723, loss_mask_bce_0: 0.17884/0.33464, loss_mask_dice_0: 0.55856/1.16592, loss_spatial_bce_0: 0.03509/0.08922, loss_spatial_dice_0: 0.12243/0.21336, loss_spatial_ce_0: 0.07788/0.06910, loss_grounding_bce_0: 0.01771/0.08624, loss_grounding_dice_0: 0.06093/0.17911, loss_grounding_ce_0: 0.00381/0.27488, loss_mask_ce_1: 0.33633/0.90788, loss_mask_bce_1: 0.14167/0.33545, loss_mask_dice_1: 0.49671/1.17282, loss_spatial_bce_1: 0.03151/0.08991, loss_spatial_dice_1: 0.12526/0.21758, loss_spatial_ce_1: 0.06537/0.07479, loss_grounding_bce_1: 0.01988/0.08638, loss_grounding_dice_1: 0.06342/0.17997, loss_grounding_ce_1: 0.00321/0.27648, loss_mask_ce_2: 0.24837/0.91533, loss_mask_bce_2: 0.17711/0.33588, loss_mask_dice_2: 0.56425/1.17248, loss_spatial_bce_2: 0.03205/0.09040, loss_spatial_dice_2: 0.12691/0.21861, loss_spatial_ce_2: 0.06593/0.07838, loss_grounding_bce_2: 0.01657/0.08642, loss_grounding_dice_2: 0.06374/0.17968, loss_grounding_ce_2: 0.00250/0.27977, loss_mask_ce_3: 0.39465/0.92451, loss_mask_bce_3: 0.13023/0.33677, loss_mask_dice_3: 0.47975/1.16966, loss_spatial_bce_3: 0.03364/0.09124, loss_spatial_dice_3: 0.12425/0.21923, loss_spatial_ce_3: 0.03203/0.08209, loss_grounding_bce_3: 0.01393/0.08670, loss_grounding_dice_3: 0.05026/0.17950, loss_grounding_ce_3: 0.00343/0.28116, loss_mask_ce_4: 0.29093/0.92402, loss_mask_bce_4: 0.18279/0.33866, loss_mask_dice_4: 0.56793/1.19303, loss_spatial_bce_4: 0.03444/0.09548, loss_spatial_dice_4: 0.13887/0.23058, loss_spatial_ce_4: 0.17002/0.09815, loss_grounding_bce_4: 0.01487/0.08712, loss_grounding_dice_4: 0.05474/0.18230, loss_grounding_ce_4: 0.00396/0.28415, loss_mask_ce_5: 0.31347/0.93920, loss_mask_bce_5: 0.18331/0.34096, loss_mask_dice_5: 0.56812/1.19931, loss_spatial_bce_5: 0.03647/0.09711, loss_spatial_dice_5: 0.14018/0.23416, loss_spatial_ce_5: 0.27400/0.11284, loss_grounding_bce_5: 0.01315/0.08754, loss_grounding_dice_5: 0.05102/0.18348, loss_grounding_ce_5: 0.00753/0.29664, loss_mask_ce_6: 0.34286/0.97813, loss_mask_bce_6: 0.17883/0.34363, loss_mask_dice_6: 0.58345/1.20179, loss_spatial_bce_6: 0.04968/0.10285, loss_spatial_dice_6: 0.15999/0.23654, loss_spatial_ce_6: 0.24034/0.13880, loss_grounding_bce_6: 0.01484/0.08825, loss_grounding_dice_6: 0.05555/0.18363, loss_grounding_ce_6: 0.01084/0.31333, loss_mask_ce_7: 0.63753/1.02294, loss_mask_bce_7: 0.18372/0.35147, loss_mask_dice_7: 0.52480/1.25735, loss_spatial_bce_7: 0.07481/0.11133, loss_spatial_dice_7: 0.21280/0.26420, loss_spatial_ce_7: 0.14439/0.17606, loss_grounding_bce_7: 0.01326/0.09017, loss_grounding_dice_7: 0.04628/0.19093, loss_grounding_ce_7: 0.02947/0.34649, loss_mask_ce_8: 0.48317/1.13190, loss_mask_bce_8: 0.19838/0.36509, loss_mask_dice_8: 0.62376/1.33134, loss_spatial_bce_8: 0.05569/0.13230, loss_spatial_dice_8: 0.18216/0.30346, loss_spatial_ce_8: 0.08444/0.23304, loss_grounding_bce_8: 0.01462/0.09383, loss_grounding_dice_8: 0.05448/0.20203, loss_grounding_ce_8: 0.02997/0.41469, loss_mask_ce_9: 2.87682/3.68251, loss_mask_bce_9: 0.27258/0.39199, loss_mask_dice_9: 0.91692/1.90484, loss_spatial_bce_9: 0.29207/0.33430, loss_spatial_dice_9: 0.78298/0.82310, loss_spatial_ce_9: 1.03401/1.50622, loss_grounding_bce_9: 0.04086/0.10524, loss_grounding_dice_9: 0.20162/0.28137, loss_grounding_ce_9: 0.32823/0.68141] items per batch[64] items per second[0.23] total items[2406400] mini batches[ 37600] memory[7341] epoch remaining[0:35:26]
INFO:trainer.default_trainer:epochs[    20] optim steps[37700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89943/0.90749, loss_mask_bce_0: 0.20327/0.33469, loss_mask_dice_0: 0.83121/1.16585, loss_spatial_bce_0: 0.03883/0.08922, loss_spatial_dice_0: 0.17335/0.21335, loss_spatial_ce_0: 0.10298/0.06907, loss_grounding_bce_0: 0.02543/0.08625, loss_grounding_dice_0: 0.34968/0.17911, loss_grounding_ce_0: 0.19678/0.27514, loss_mask_ce_1: 0.87972/0.90813, loss_mask_bce_1: 0.19599/0.33550, loss_mask_dice_1: 0.83362/1.17278, loss_spatial_bce_1: 0.04046/0.08991, loss_spatial_dice_1: 0.15971/0.21756, loss_spatial_ce_1: 0.10374/0.07477, loss_grounding_bce_1: 0.02485/0.08639, loss_grounding_dice_1: 0.30274/0.17996, loss_grounding_ce_1: 0.17745/0.27675, loss_mask_ce_2: 0.91906/0.91562, loss_mask_bce_2: 0.20592/0.33593, loss_mask_dice_2: 0.85474/1.17242, loss_spatial_bce_2: 0.04052/0.09041, loss_spatial_dice_2: 0.15292/0.21860, loss_spatial_ce_2: 0.09362/0.07833, loss_grounding_bce_2: 0.02654/0.08643, loss_grounding_dice_2: 0.33595/0.17969, loss_grounding_ce_2: 0.18420/0.28011, loss_mask_ce_3: 1.00667/0.92475, loss_mask_bce_3: 0.20465/0.33683, loss_mask_dice_3: 0.89884/1.16963, loss_spatial_bce_3: 0.04380/0.09124, loss_spatial_dice_3: 0.15390/0.21921, loss_spatial_ce_3: 0.08699/0.08206, loss_grounding_bce_3: 0.02408/0.08671, loss_grounding_dice_3: 0.32721/0.17949, loss_grounding_ce_3: 0.19173/0.28142, loss_mask_ce_4: 1.06028/0.92431, loss_mask_bce_4: 0.19732/0.33871, loss_mask_dice_4: 0.89085/1.19300, loss_spatial_bce_4: 0.03948/0.09548, loss_spatial_dice_4: 0.14862/0.23054, loss_spatial_ce_4: 0.06503/0.09810, loss_grounding_bce_4: 0.02585/0.08713, loss_grounding_dice_4: 0.34978/0.18229, loss_grounding_ce_4: 0.21520/0.28432, loss_mask_ce_5: 1.02459/0.93950, loss_mask_bce_5: 0.21053/0.34101, loss_mask_dice_5: 0.94408/1.19929, loss_spatial_bce_5: 0.04185/0.09711, loss_spatial_dice_5: 0.16411/0.23413, loss_spatial_ce_5: 0.04999/0.11282, loss_grounding_bce_5: 0.02938/0.08754, loss_grounding_dice_5: 0.36017/0.18348, loss_grounding_ce_5: 0.34958/0.29686, loss_mask_ce_6: 0.87156/0.97841, loss_mask_bce_6: 0.21425/0.34370, loss_mask_dice_6: 0.95059/1.20179, loss_spatial_bce_6: 0.04382/0.10285, loss_spatial_dice_6: 0.16776/0.23651, loss_spatial_ce_6: 0.07376/0.13878, loss_grounding_bce_6: 0.02559/0.08825, loss_grounding_dice_6: 0.35767/0.18363, loss_grounding_ce_6: 0.23856/0.31356, loss_mask_ce_7: 0.84781/1.02328, loss_mask_bce_7: 0.21831/0.35153, loss_mask_dice_7: 1.04426/1.25731, loss_spatial_bce_7: 0.04340/0.11133, loss_spatial_dice_7: 0.19673/0.26418, loss_spatial_ce_7: 0.18128/0.17606, loss_grounding_bce_7: 0.02752/0.09016, loss_grounding_dice_7: 0.34838/0.19092, loss_grounding_ce_7: 0.25862/0.34677, loss_mask_ce_8: 1.03829/1.13217, loss_mask_bce_8: 0.25891/0.36515, loss_mask_dice_8: 1.07809/1.33131, loss_spatial_bce_8: 0.07217/0.13230, loss_spatial_dice_8: 0.24109/0.30346, loss_spatial_ce_8: 0.11677/0.23301, loss_grounding_bce_8: 0.03555/0.09383, loss_grounding_dice_8: 0.44248/0.20202, loss_grounding_ce_8: 0.15949/0.41490, loss_mask_ce_9: 2.87052/3.68302, loss_mask_bce_9: 0.29908/0.39207, loss_mask_dice_9: 1.97280/1.90485, loss_spatial_bce_9: 0.34777/0.33427, loss_spatial_dice_9: 0.91939/0.82310, loss_spatial_ce_9: 1.75358/1.50622, loss_grounding_bce_9: 0.04543/0.10526, loss_grounding_dice_9: 0.56917/0.28139, loss_grounding_ce_9: 0.25559/0.68157] items per batch[64] items per second[0.23] total items[2412800] mini batches[ 37700] memory[7341] epoch remaining[0:30:48]
INFO:trainer.default_trainer:epochs[    20] optim steps[37800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62964/0.90736, loss_mask_bce_0: 0.11363/0.33476, loss_mask_dice_0: 0.38229/1.16579, loss_spatial_bce_0: 0.04253/0.08923, loss_spatial_dice_0: 0.16397/0.21332, loss_spatial_ce_0: 0.00296/0.06903, loss_grounding_bce_0: 0.03262/0.08628, loss_grounding_dice_0: 0.24185/0.17912, loss_grounding_ce_0: 0.19322/0.27516, loss_mask_ce_1: 0.32435/0.90797, loss_mask_bce_1: 0.11720/0.33558, loss_mask_dice_1: 0.40079/1.17275, loss_spatial_bce_1: 0.04424/0.08991, loss_spatial_dice_1: 0.17761/0.21753, loss_spatial_ce_1: 0.01854/0.07470, loss_grounding_bce_1: 0.02683/0.08642, loss_grounding_dice_1: 0.22042/0.17997, loss_grounding_ce_1: 0.40290/0.27676, loss_mask_ce_2: 0.60863/0.91548, loss_mask_bce_2: 0.11021/0.33601, loss_mask_dice_2: 0.29959/1.17235, loss_spatial_bce_2: 0.04231/0.09041, loss_spatial_dice_2: 0.17133/0.21857, loss_spatial_ce_2: 0.05162/0.07829, loss_grounding_bce_2: 0.02757/0.08647, loss_grounding_dice_2: 0.19657/0.17971, loss_grounding_ce_2: 0.39335/0.28011, loss_mask_ce_3: 0.37334/0.92455, loss_mask_bce_3: 0.15923/0.33691, loss_mask_dice_3: 0.43561/1.16957, loss_spatial_bce_3: 0.04285/0.09125, loss_spatial_dice_3: 0.16414/0.21918, loss_spatial_ce_3: 0.04472/0.08202, loss_grounding_bce_3: 0.02825/0.08675, loss_grounding_dice_3: 0.17144/0.17950, loss_grounding_ce_3: 0.52351/0.28147, loss_mask_ce_4: 0.59839/0.92416, loss_mask_bce_4: 0.15041/0.33880, loss_mask_dice_4: 0.34048/1.19295, loss_spatial_bce_4: 0.05187/0.09549, loss_spatial_dice_4: 0.24076/0.23051, loss_spatial_ce_4: 0.04877/0.09803, loss_grounding_bce_4: 0.05587/0.08717, loss_grounding_dice_4: 0.19822/0.18231, loss_grounding_ce_4: 0.27393/0.28436, loss_mask_ce_5: 0.25350/0.93934, loss_mask_bce_5: 0.15276/0.34109, loss_mask_dice_5: 0.46641/1.19923, loss_spatial_bce_5: 0.08362/0.09712, loss_spatial_dice_5: 0.27782/0.23412, loss_spatial_ce_5: 0.05851/0.11277, loss_grounding_bce_5: 0.02656/0.08758, loss_grounding_dice_5: 0.17340/0.18349, loss_grounding_ce_5: 0.37531/0.29686, loss_mask_ce_6: 0.65629/0.97829, loss_mask_bce_6: 0.14675/0.34376, loss_mask_dice_6: 0.39395/1.20173, loss_spatial_bce_6: 0.03998/0.10286, loss_spatial_dice_6: 0.16798/0.23649, loss_spatial_ce_6: 0.17578/0.13877, loss_grounding_bce_6: 0.05186/0.08829, loss_grounding_dice_6: 0.20061/0.18363, loss_grounding_ce_6: 0.24276/0.31358, loss_mask_ce_7: 0.81955/1.02315, loss_mask_bce_7: 0.13243/0.35161, loss_mask_dice_7: 0.41742/1.25727, loss_spatial_bce_7: 0.06979/0.11134, loss_spatial_dice_7: 0.21786/0.26415, loss_spatial_ce_7: 0.23455/0.17600, loss_grounding_bce_7: 0.06654/0.09020, loss_grounding_dice_7: 0.20444/0.19092, loss_grounding_ce_7: 0.84319/0.34678, loss_mask_ce_8: 0.55911/1.13195, loss_mask_bce_8: 0.10390/0.36523, loss_mask_dice_8: 0.43877/1.33128, loss_spatial_bce_8: 0.18540/0.13231, loss_spatial_dice_8: 0.35800/0.30343, loss_spatial_ce_8: 0.29935/0.23302, loss_grounding_bce_8: 0.04341/0.09387, loss_grounding_dice_8: 0.22309/0.20202, loss_grounding_ce_8: 0.29359/0.41487, loss_mask_ce_9: 2.98692/3.68281, loss_mask_bce_9: 0.19017/0.39218, loss_mask_dice_9: 0.62336/1.90475, loss_spatial_bce_9: 0.31070/0.33434, loss_spatial_dice_9: 0.73077/0.82311, loss_spatial_ce_9: 1.44618/1.50610, loss_grounding_bce_9: 0.03472/0.10528, loss_grounding_dice_9: 0.24918/0.28138, loss_grounding_ce_9: 0.96622/0.68160] items per batch[64] items per second[0.23] total items[2419200] mini batches[ 37800] memory[7341] epoch remaining[0:26:13]
INFO:trainer.default_trainer:epochs[    20] optim steps[37900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.49652/0.90734, loss_mask_bce_0: 0.10070/0.33474, loss_mask_dice_0: 2.52245/1.16600, loss_spatial_bce_0: 0.02431/0.08921, loss_spatial_dice_0: 0.33770/0.21330, loss_spatial_ce_0: 0.08012/0.06900, loss_grounding_bce_0: 0.02574/0.08627, loss_grounding_dice_0: 0.32839/0.17912, loss_grounding_ce_0: 0.53844/0.27507, loss_mask_ce_1: 0.30918/0.90795, loss_mask_bce_1: 0.11448/0.33556, loss_mask_dice_1: 3.35978/1.17302, loss_spatial_bce_1: 0.02247/0.08989, loss_spatial_dice_1: 0.34335/0.21751, loss_spatial_ce_1: 0.22836/0.07466, loss_grounding_bce_1: 0.02199/0.08641, loss_grounding_dice_1: 0.26688/0.17997, loss_grounding_ce_1: 0.51039/0.27667, loss_mask_ce_2: 0.51544/0.91546, loss_mask_bce_2: 0.09379/0.33598, loss_mask_dice_2: 3.01802/1.17259, loss_spatial_bce_2: 0.02606/0.09040, loss_spatial_dice_2: 0.38890/0.21855, loss_spatial_ce_2: 0.24321/0.07825, loss_grounding_bce_2: 0.02145/0.08646, loss_grounding_dice_2: 0.23920/0.17972, loss_grounding_ce_2: 0.43124/0.28000, loss_mask_ce_3: 0.32417/0.92448, loss_mask_bce_3: 0.11243/0.33688, loss_mask_dice_3: 3.21683/1.16983, loss_spatial_bce_3: 0.02593/0.09124, loss_spatial_dice_3: 0.33670/0.21916, loss_spatial_ce_3: 0.07756/0.08197, loss_grounding_bce_3: 0.02343/0.08674, loss_grounding_dice_3: 0.24258/0.17950, loss_grounding_ce_3: 0.49556/0.28134, loss_mask_ce_4: 0.52380/0.92413, loss_mask_bce_4: 0.11174/0.33877, loss_mask_dice_4: 3.37585/1.19326, loss_spatial_bce_4: 0.02709/0.09547, loss_spatial_dice_4: 0.41071/0.23050, loss_spatial_ce_4: 0.27996/0.09798, loss_grounding_bce_4: 0.02413/0.08716, loss_grounding_dice_4: 0.31193/0.18231, loss_grounding_ce_4: 0.53192/0.28428, loss_mask_ce_5: 0.80273/0.93932, loss_mask_bce_5: 0.09677/0.34106, loss_mask_dice_5: 2.67778/1.19951, loss_spatial_bce_5: 0.02741/0.09710, loss_spatial_dice_5: 0.37195/0.23410, loss_spatial_ce_5: 0.44192/0.11271, loss_grounding_bce_5: 0.02253/0.08757, loss_grounding_dice_5: 0.33569/0.18349, loss_grounding_ce_5: 0.67786/0.29681, loss_mask_ce_6: 0.65852/0.97827, loss_mask_bce_6: 0.09747/0.34373, loss_mask_dice_6: 2.84428/1.20203, loss_spatial_bce_6: 0.03783/0.10284, loss_spatial_dice_6: 0.37763/0.23648, loss_spatial_ce_6: 0.21515/0.13868, loss_grounding_bce_6: 0.02316/0.08828, loss_grounding_dice_6: 0.23758/0.18364, loss_grounding_ce_6: 0.60796/0.31346, loss_mask_ce_7: 0.45293/1.02320, loss_mask_bce_7: 0.11530/0.35157, loss_mask_dice_7: 2.87028/1.25753, loss_spatial_bce_7: 0.03382/0.11131, loss_spatial_dice_7: 0.40458/0.26414, loss_spatial_ce_7: 0.35605/0.17588, loss_grounding_bce_7: 0.02216/0.09019, loss_grounding_dice_7: 0.16979/0.19093, loss_grounding_ce_7: 0.61186/0.34666, loss_mask_ce_8: 0.49333/1.13197, loss_mask_bce_8: 0.12018/0.36518, loss_mask_dice_8: 3.18058/1.33156, loss_spatial_bce_8: 0.07051/0.13228, loss_spatial_dice_8: 0.49464/0.30342, loss_spatial_ce_8: 0.27352/0.23295, loss_grounding_bce_8: 0.03340/0.09385, loss_grounding_dice_8: 0.31337/0.20203, loss_grounding_ce_8: 0.26286/0.41471, loss_mask_ce_9: 3.55202/3.68296, loss_mask_bce_9: 0.13111/0.39214, loss_mask_dice_9: 2.97357/1.90501, loss_spatial_bce_9: 0.16720/0.33428, loss_spatial_dice_9: 0.88026/0.82315, loss_spatial_ce_9: 1.58300/1.50626, loss_grounding_bce_9: 0.03673/0.10528, loss_grounding_dice_9: 0.35091/0.28140, loss_grounding_ce_9: 0.47297/0.68142] items per batch[64] items per second[0.23] total items[2425600] mini batches[ 37900] memory[7341] epoch remaining[0:21:36]
INFO:trainer.default_trainer:epochs[    20] optim steps[38000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22025/0.90730, loss_mask_bce_0: 0.20734/0.33468, loss_mask_dice_0: 0.36024/1.16609, loss_spatial_bce_0: 0.11981/0.08919, loss_spatial_dice_0: 0.19599/0.21329, loss_spatial_ce_0: 0.00291/0.06896, loss_grounding_bce_0: 0.13688/0.08626, loss_grounding_dice_0: 0.13353/0.17912, loss_grounding_ce_0: 0.08721/0.27503, loss_mask_ce_1: 0.38821/0.90787, loss_mask_bce_1: 0.15807/0.33551, loss_mask_dice_1: 0.30320/1.17312, loss_spatial_bce_1: 0.13892/0.08987, loss_spatial_dice_1: 0.21652/0.21748, loss_spatial_ce_1: 0.00347/0.07463, loss_grounding_bce_1: 0.14079/0.08640, loss_grounding_dice_1: 0.13754/0.17998, loss_grounding_ce_1: 0.06873/0.27663, loss_mask_ce_2: 0.21486/0.91538, loss_mask_bce_2: 0.19999/0.33591, loss_mask_dice_2: 0.34530/1.17270, loss_spatial_bce_2: 0.13488/0.09038, loss_spatial_dice_2: 0.21114/0.21854, loss_spatial_ce_2: 0.00543/0.07823, loss_grounding_bce_2: 0.13804/0.08646, loss_grounding_dice_2: 0.13970/0.17974, loss_grounding_ce_2: 0.07060/0.27996, loss_mask_ce_3: 0.43559/0.92440, loss_mask_bce_3: 0.16261/0.33682, loss_mask_dice_3: 0.31138/1.16993, loss_spatial_bce_3: 0.15559/0.09122, loss_spatial_dice_3: 0.22992/0.21914, loss_spatial_ce_3: 0.01509/0.08193, loss_grounding_bce_3: 0.14311/0.08674, loss_grounding_dice_3: 0.13811/0.17950, loss_grounding_ce_3: 0.05723/0.28128, loss_mask_ce_4: 0.22114/0.92409, loss_mask_bce_4: 0.19757/0.33871, loss_mask_dice_4: 0.33456/1.19340, loss_spatial_bce_4: 0.13045/0.09545, loss_spatial_dice_4: 0.23430/0.23049, loss_spatial_ce_4: 0.06032/0.09791, loss_grounding_bce_4: 0.14866/0.08715, loss_grounding_dice_4: 0.14386/0.18232, loss_grounding_ce_4: 0.06134/0.28422, loss_mask_ce_5: 0.17320/0.93934, loss_mask_bce_5: 0.20965/0.34099, loss_mask_dice_5: 0.39639/1.19963, loss_spatial_bce_5: 0.10974/0.09709, loss_spatial_dice_5: 0.21538/0.23408, loss_spatial_ce_5: 0.06978/0.11264, loss_grounding_bce_5: 0.15005/0.08758, loss_grounding_dice_5: 0.14670/0.18352, loss_grounding_ce_5: 0.03902/0.29676, loss_mask_ce_6: 0.21667/0.97828, loss_mask_bce_6: 0.21191/0.34367, loss_mask_dice_6: 0.36329/1.20216, loss_spatial_bce_6: 0.17566/0.10283, loss_spatial_dice_6: 0.26467/0.23646, loss_spatial_ce_6: 0.08975/0.13860, loss_grounding_bce_6: 0.14998/0.08829, loss_grounding_dice_6: 0.13937/0.18366, loss_grounding_ce_6: 0.03936/0.31333, loss_mask_ce_7: 0.20370/1.02319, loss_mask_bce_7: 0.22140/0.35152, loss_mask_dice_7: 0.40592/1.25771, loss_spatial_bce_7: 0.19043/0.11130, loss_spatial_dice_7: 0.24566/0.26413, loss_spatial_ce_7: 0.06687/0.17580, loss_grounding_bce_7: 0.16502/0.09019, loss_grounding_dice_7: 0.17532/0.19095, loss_grounding_ce_7: 0.05598/0.34654, loss_mask_ce_8: 0.23931/1.13197, loss_mask_bce_8: 0.20331/0.36512, loss_mask_dice_8: 0.40507/1.33171, loss_spatial_bce_8: 0.14826/0.13226, loss_spatial_dice_8: 0.24659/0.30341, loss_spatial_ce_8: 0.21387/0.23287, loss_grounding_bce_8: 0.15939/0.09386, loss_grounding_dice_8: 0.20884/0.20204, loss_grounding_ce_8: 0.02991/0.41457, loss_mask_ce_9: 2.16232/3.68301, loss_mask_bce_9: 0.19309/0.39208, loss_mask_dice_9: 0.44143/1.90513, loss_spatial_bce_9: 0.40561/0.33426, loss_spatial_dice_9: 0.79711/0.82314, loss_spatial_ce_9: 0.96754/1.50622, loss_grounding_bce_9: 0.16954/0.10528, loss_grounding_dice_9: 0.20499/0.28141, loss_grounding_ce_9: 0.13348/0.68114] items per batch[64] items per second[0.23] total items[2432000] mini batches[ 38000] memory[7341] epoch remaining[0:16:58]
INFO:trainer.default_trainer:epochs[    20] optim steps[38100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.07637/0.90721, loss_mask_bce_0: 0.48093/0.33469, loss_mask_dice_0: 0.89174/1.16662, loss_spatial_bce_0: 0.21722/0.08917, loss_spatial_dice_0: 0.22453/0.21331, loss_spatial_ce_0: 0.03503/0.06890, loss_grounding_bce_0: 0.05470/0.08628, loss_grounding_dice_0: 0.10067/0.17919, loss_grounding_ce_0: 0.43638/0.27517, loss_mask_ce_1: 2.04022/0.90779, loss_mask_bce_1: 0.51469/0.33551, loss_mask_dice_1: 0.91045/1.17369, loss_spatial_bce_1: 0.23013/0.08985, loss_spatial_dice_1: 0.22705/0.21750, loss_spatial_ce_1: 0.03503/0.07459, loss_grounding_bce_1: 0.05456/0.08642, loss_grounding_dice_1: 0.11015/0.18005, loss_grounding_ce_1: 0.42129/0.27680, loss_mask_ce_2: 1.99851/0.91527, loss_mask_bce_2: 0.53009/0.33592, loss_mask_dice_2: 0.88940/1.17325, loss_spatial_bce_2: 0.25768/0.09037, loss_spatial_dice_2: 0.25068/0.21856, loss_spatial_ce_2: 0.03494/0.07817, loss_grounding_bce_2: 0.05357/0.08649, loss_grounding_dice_2: 0.10639/0.17981, loss_grounding_ce_2: 0.41154/0.28010, loss_mask_ce_3: 2.14279/0.92431, loss_mask_bce_3: 0.52006/0.33684, loss_mask_dice_3: 0.89237/1.17045, loss_spatial_bce_3: 0.24595/0.09121, loss_spatial_dice_3: 0.24974/0.21916, loss_spatial_ce_3: 0.03575/0.08189, loss_grounding_bce_3: 0.05243/0.08676, loss_grounding_dice_3: 0.10485/0.17956, loss_grounding_ce_3: 0.43456/0.28146, loss_mask_ce_4: 2.00242/0.92400, loss_mask_bce_4: 0.58989/0.33873, loss_mask_dice_4: 0.96689/1.19398, loss_spatial_bce_4: 0.27477/0.09544, loss_spatial_dice_4: 0.24068/0.23052, loss_spatial_ce_4: 0.03647/0.09785, loss_grounding_bce_4: 0.05354/0.08718, loss_grounding_dice_4: 0.09603/0.18239, loss_grounding_ce_4: 0.43737/0.28438, loss_mask_ce_5: 1.89877/0.93926, loss_mask_bce_5: 0.58933/0.34102, loss_mask_dice_5: 0.98800/1.20025, loss_spatial_bce_5: 0.32493/0.09709, loss_spatial_dice_5: 0.25089/0.23412, loss_spatial_ce_5: 0.04420/0.11259, loss_grounding_bce_5: 0.05647/0.08761, loss_grounding_dice_5: 0.10903/0.18358, loss_grounding_ce_5: 0.45984/0.29689, loss_mask_ce_6: 2.10200/0.97829, loss_mask_bce_6: 0.48993/0.34368, loss_mask_dice_6: 0.93532/1.20277, loss_spatial_bce_6: 0.37219/0.10282, loss_spatial_dice_6: 0.27918/0.23650, loss_spatial_ce_6: 0.07690/0.13855, loss_grounding_bce_6: 0.06711/0.08831, loss_grounding_dice_6: 0.10625/0.18371, loss_grounding_ce_6: 0.46849/0.31346, loss_mask_ce_7: 2.28680/1.02319, loss_mask_bce_7: 0.54486/0.35152, loss_mask_dice_7: 0.92886/1.25832, loss_spatial_bce_7: 0.39799/0.11128, loss_spatial_dice_7: 0.26684/0.26417, loss_spatial_ce_7: 0.09745/0.17571, loss_grounding_bce_7: 0.05744/0.09021, loss_grounding_dice_7: 0.10133/0.19100, loss_grounding_ce_7: 0.47786/0.34659, loss_mask_ce_8: 2.76482/1.13196, loss_mask_bce_8: 0.64147/0.36515, loss_mask_dice_8: 1.04953/1.33233, loss_spatial_bce_8: 0.33765/0.13223, loss_spatial_dice_8: 0.29552/0.30346, loss_spatial_ce_8: 0.15730/0.23281, loss_grounding_bce_8: 0.04599/0.09388, loss_grounding_dice_8: 0.11364/0.20210, loss_grounding_ce_8: 0.65847/0.41471, loss_mask_ce_9: 5.55588/3.68317, loss_mask_bce_9: 0.74315/0.39211, loss_mask_dice_9: 3.89961/1.90591, loss_spatial_bce_9: 0.53019/0.33421, loss_spatial_dice_9: 0.85401/0.82316, loss_spatial_ce_9: 1.40748/1.50630, loss_grounding_bce_9: 0.07003/0.10531, loss_grounding_dice_9: 0.53464/0.28148, loss_grounding_ce_9: 0.51266/0.68110] items per batch[64] items per second[0.22] total items[2438400] mini batches[ 38100] memory[7341] epoch remaining[0:12:22]
INFO:trainer.default_trainer:epochs[    20] optim steps[38200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91104/0.90702, loss_mask_bce_0: 0.34146/0.33458, loss_mask_dice_0: 1.18098/1.16621, loss_spatial_bce_0: 0.02994/0.08915, loss_spatial_dice_0: 0.13842/0.21327, loss_spatial_ce_0: 0.06907/0.06885, loss_grounding_bce_0: 0.06935/0.08626, loss_grounding_dice_0: 0.05831/0.17915, loss_grounding_ce_0: 0.00978/0.27508, loss_mask_ce_1: 0.89501/0.90759, loss_mask_bce_1: 0.33448/0.33540, loss_mask_dice_1: 1.31107/1.17327, loss_spatial_bce_1: 0.03053/0.08983, loss_spatial_dice_1: 0.15124/0.21745, loss_spatial_ce_1: 0.06985/0.07454, loss_grounding_bce_1: 0.07121/0.08640, loss_grounding_dice_1: 0.05357/0.18000, loss_grounding_ce_1: 0.00920/0.27672, loss_mask_ce_2: 0.94465/0.91511, loss_mask_bce_2: 0.33347/0.33581, loss_mask_dice_2: 1.30210/1.17282, loss_spatial_bce_2: 0.02972/0.09034, loss_spatial_dice_2: 0.13848/0.21853, loss_spatial_ce_2: 0.08105/0.07810, loss_grounding_bce_2: 0.07322/0.08646, loss_grounding_dice_2: 0.05354/0.17976, loss_grounding_ce_2: 0.01019/0.28000, loss_mask_ce_3: 0.97969/0.92417, loss_mask_bce_3: 0.33531/0.33673, loss_mask_dice_3: 1.30686/1.17001, loss_spatial_bce_3: 0.03097/0.09119, loss_spatial_dice_3: 0.14399/0.21911, loss_spatial_ce_3: 0.08621/0.08183, loss_grounding_bce_3: 0.07715/0.08674, loss_grounding_dice_3: 0.05656/0.17951, loss_grounding_ce_3: 0.00646/0.28137, loss_mask_ce_4: 0.93080/0.92387, loss_mask_bce_4: 0.35040/0.33861, loss_mask_dice_4: 1.19652/1.19360, loss_spatial_bce_4: 0.03364/0.09542, loss_spatial_dice_4: 0.14356/0.23048, loss_spatial_ce_4: 0.08274/0.09780, loss_grounding_bce_4: 0.06812/0.08716, loss_grounding_dice_4: 0.05817/0.18235, loss_grounding_ce_4: 0.00846/0.28430, loss_mask_ce_5: 0.89133/0.93911, loss_mask_bce_5: 0.35615/0.34092, loss_mask_dice_5: 1.20476/1.19981, loss_spatial_bce_5: 0.03147/0.09707, loss_spatial_dice_5: 0.16400/0.23408, loss_spatial_ce_5: 0.13389/0.11259, loss_grounding_bce_5: 0.07079/0.08759, loss_grounding_dice_5: 0.05647/0.18353, loss_grounding_ce_5: 0.00400/0.29682, loss_mask_ce_6: 0.98445/0.97811, loss_mask_bce_6: 0.34780/0.34357, loss_mask_dice_6: 1.21661/1.20237, loss_spatial_bce_6: 0.03970/0.10280, loss_spatial_dice_6: 0.15552/0.23647, loss_spatial_ce_6: 0.14774/0.13850, loss_grounding_bce_6: 0.07224/0.08828, loss_grounding_dice_6: 0.05152/0.18367, loss_grounding_ce_6: 0.00203/0.31330, loss_mask_ce_7: 1.19961/1.02306, loss_mask_bce_7: 0.32664/0.35141, loss_mask_dice_7: 1.28291/1.25785, loss_spatial_bce_7: 0.03429/0.11125, loss_spatial_dice_7: 0.17931/0.26412, loss_spatial_ce_7: 0.15233/0.17565, loss_grounding_bce_7: 0.07267/0.09018, loss_grounding_dice_7: 0.05681/0.19095, loss_grounding_ce_7: 0.00343/0.34652, loss_mask_ce_8: 1.34868/1.13185, loss_mask_bce_8: 0.32845/0.36504, loss_mask_dice_8: 1.39453/1.33180, loss_spatial_bce_8: 0.05660/0.13219, loss_spatial_dice_8: 0.27159/0.30341, loss_spatial_ce_8: 0.11677/0.23273, loss_grounding_bce_8: 0.06932/0.09385, loss_grounding_dice_8: 0.05813/0.20207, loss_grounding_ce_8: 0.00533/0.41465, loss_mask_ce_9: 4.49519/3.68266, loss_mask_bce_9: 0.33830/0.39199, loss_mask_dice_9: 1.73704/1.90516, loss_spatial_bce_9: 0.14381/0.33416, loss_spatial_dice_9: 0.85396/0.82315, loss_spatial_ce_9: 1.48003/1.50625, loss_grounding_bce_9: 0.06191/0.10529, loss_grounding_dice_9: 0.05295/0.28145, loss_grounding_ce_9: 0.06977/0.68103] items per batch[64] items per second[0.23] total items[2444800] mini batches[ 38200] memory[7341] epoch remaining[0:07:44]
INFO:trainer.default_trainer:epochs[    20] optim steps[38300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78228/0.90717, loss_mask_bce_0: 0.36195/0.33456, loss_mask_dice_0: 0.49651/1.16613, loss_spatial_bce_0: 0.31228/0.08914, loss_spatial_dice_0: 0.46723/0.21328, loss_spatial_ce_0: 0.16817/0.06883, loss_grounding_bce_0: 0.18902/0.08630, loss_grounding_dice_0: 0.13634/0.17916, loss_grounding_ce_0: 0.26575/0.27505, loss_mask_ce_1: 0.74186/0.90770, loss_mask_bce_1: 0.43032/0.33538, loss_mask_dice_1: 0.49172/1.17318, loss_spatial_bce_1: 0.33552/0.08981, loss_spatial_dice_1: 0.48798/0.21746, loss_spatial_ce_1: 0.08508/0.07453, loss_grounding_bce_1: 0.27934/0.08643, loss_grounding_dice_1: 0.14496/0.18002, loss_grounding_ce_1: 0.20071/0.27665, loss_mask_ce_2: 0.70389/0.91526, loss_mask_bce_2: 0.44279/0.33578, loss_mask_dice_2: 0.48631/1.17265, loss_spatial_bce_2: 0.36129/0.09033, loss_spatial_dice_2: 0.46989/0.21854, loss_spatial_ce_2: 0.08085/0.07810, loss_grounding_bce_2: 0.29708/0.08649, loss_grounding_dice_2: 0.14376/0.17978, loss_grounding_ce_2: 0.17971/0.27994, loss_mask_ce_3: 0.79274/0.92427, loss_mask_bce_3: 0.45040/0.33670, loss_mask_dice_3: 0.49454/1.16990, loss_spatial_bce_3: 0.29195/0.09118, loss_spatial_dice_3: 0.47857/0.21912, loss_spatial_ce_3: 0.23088/0.08184, loss_grounding_bce_3: 0.31020/0.08676, loss_grounding_dice_3: 0.15663/0.17953, loss_grounding_ce_3: 0.19328/0.28134, loss_mask_ce_4: 0.77992/0.92392, loss_mask_bce_4: 0.36971/0.33859, loss_mask_dice_4: 0.49632/1.19348, loss_spatial_bce_4: 0.27529/0.09541, loss_spatial_dice_4: 0.45786/0.23049, loss_spatial_ce_4: 0.19499/0.09779, loss_grounding_bce_4: 0.22743/0.08718, loss_grounding_dice_4: 0.15707/0.18236, loss_grounding_ce_4: 0.25071/0.28422, loss_mask_ce_5: 0.93870/0.93928, loss_mask_bce_5: 0.33360/0.34089, loss_mask_dice_5: 0.53977/1.19975, loss_spatial_bce_5: 0.23896/0.09706, loss_spatial_dice_5: 0.42314/0.23410, loss_spatial_ce_5: 0.31333/0.11258, loss_grounding_bce_5: 0.18619/0.08761, loss_grounding_dice_5: 0.22121/0.18355, loss_grounding_ce_5: 0.37517/0.29679, loss_mask_ce_6: 0.94554/0.97827, loss_mask_bce_6: 0.34018/0.34355, loss_mask_dice_6: 0.54549/1.20228, loss_spatial_bce_6: 0.26873/0.10279, loss_spatial_dice_6: 0.42383/0.23649, loss_spatial_ce_6: 0.29321/0.13852, loss_grounding_bce_6: 0.17327/0.08830, loss_grounding_dice_6: 0.20007/0.18368, loss_grounding_ce_6: 0.36407/0.31327, loss_mask_ce_7: 0.88551/1.02318, loss_mask_bce_7: 0.35858/0.35139, loss_mask_dice_7: 0.49933/1.25778, loss_spatial_bce_7: 0.22169/0.11125, loss_spatial_dice_7: 0.38902/0.26414, loss_spatial_ce_7: 0.41995/0.17564, loss_grounding_bce_7: 0.24186/0.09021, loss_grounding_dice_7: 0.16410/0.19097, loss_grounding_ce_7: 0.27746/0.34654, loss_mask_ce_8: 0.97367/1.13206, loss_mask_bce_8: 0.51526/0.36502, loss_mask_dice_8: 0.54054/1.33172, loss_spatial_bce_8: 0.24527/0.13219, loss_spatial_dice_8: 0.41396/0.30347, loss_spatial_ce_8: 0.31005/0.23272, loss_grounding_bce_8: 0.34116/0.09389, loss_grounding_dice_8: 0.19030/0.20211, loss_grounding_ce_8: 0.33385/0.41463, loss_mask_ce_9: 3.22841/3.68243, loss_mask_bce_9: 0.39689/0.39195, loss_mask_dice_9: 0.67101/1.90498, loss_spatial_bce_9: 0.54259/0.33413, loss_spatial_dice_9: 0.74538/0.82316, loss_spatial_ce_9: 1.20406/1.50632, loss_grounding_bce_9: 0.18430/0.10532, loss_grounding_dice_9: 0.21137/0.28147, loss_grounding_ce_9: 0.40286/0.68098] items per batch[64] items per second[0.22] total items[2451200] mini batches[ 38300] memory[7341] epoch remaining[0:03:06]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00038367.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2163 s/iter. Eval: 0.0930 s/iter. Total: 0.3125 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2218 s/iter. Eval: 0.0818 s/iter. Total: 0.3067 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0030 s/iter. Inference: 0.2254 s/iter. Eval: 0.0816 s/iter. Total: 0.3101 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2272 s/iter. Eval: 0.0787 s/iter. Total: 0.3091 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0032 s/iter. Inference: 0.2252 s/iter. Eval: 0.0782 s/iter. Total: 0.3067 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 94/157. Dataloading: 0.0032 s/iter. Inference: 0.2264 s/iter. Eval: 0.0787 s/iter. Total: 0.3085 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 110/157. Dataloading: 0.0032 s/iter. Inference: 0.2291 s/iter. Eval: 0.0790 s/iter. Total: 0.3114 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 127/157. Dataloading: 0.0033 s/iter. Inference: 0.2281 s/iter. Eval: 0.0788 s/iter. Total: 0.3104 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0033 s/iter. Inference: 0.2288 s/iter. Eval: 0.0781 s/iter. Total: 0.3103 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaln9u8f2ga ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.114 | 81.871 | 60.328 |      133      |
| Things | 54.940 | 82.523 | 65.846 |      80       |
| Stuff  | 42.830 | 80.887 | 52.000 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.36s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.47 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.23s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.86 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.608
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.294
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.712
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.743 | 60.828 | 40.813 | 19.181 | 41.761 | 60.511 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.466 | bicycle      | 18.107 | car            | 35.955 |
| motorcycle    | 34.801 | airplane     | 57.091 | bus            | 64.889 |
| train         | 69.581 | truck        | 37.052 | boat           | 22.276 |
| traffic light | 25.244 | fire hydrant | 65.277 | stop sign      | 62.637 |
| parking meter | 42.107 | bench        | 19.877 | bird           | 29.497 |
| cat           | 74.255 | dog          | 66.026 | horse          | 46.190 |
| sheep         | 46.299 | cow          | 49.529 | elephant       | 60.659 |
| bear          | 78.564 | zebra        | 60.247 | giraffe        | 56.857 |
| backpack      | 16.846 | umbrella     | 48.566 | handbag        | 15.294 |
| tie           | 32.843 | suitcase     | 40.978 | frisbee        | 67.802 |
| skis          | 5.499  | snowboard    | 22.971 | sports ball    | 46.788 |
| kite          | 34.103 | baseball bat | 28.942 | baseball glove | 43.166 |
| skateboard    | 35.986 | surfboard    | 35.833 | tennis racket  | 55.683 |
| bottle        | 33.734 | wine glass   | 26.952 | cup            | 39.453 |
| fork          | 14.771 | knife        | 12.113 | spoon          | 13.016 |
| bowl          | 30.852 | banana       | 20.682 | apple          | 20.500 |
| sandwich      | 43.294 | orange       | 28.659 | broccoli       | 22.698 |
| carrot        | 19.764 | hot dog      | 26.434 | pizza          | 51.333 |
| donut         | 46.161 | cake         | 43.843 | chair          | 20.323 |
| couch         | 40.799 | potted plant | 17.584 | bed            | 42.066 |
| dining table  | 12.879 | toilet       | 66.788 | tv             | 61.704 |
| laptop        | 63.238 | mouse        | 58.752 | remote         | 31.889 |
| keyboard      | 48.961 | cell phone   | 37.367 | microwave      | 53.942 |
| oven          | 30.518 | toaster      | 28.236 | sink           | 38.237 |
| refrigerator  | 57.483 | book         | 9.218  | clock          | 51.777 |
| vase          | 32.399 | scissors     | 22.344 | teddy bear     | 50.545 |
| hair drier    | 10.583 | toothbrush   | 18.801 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 59.39937363942769, 'fwIoU': 68.25672463077046, 'IoU-person': 87.26666364305139, 'IoU-bicycle': 71.40626308616503, 'IoU-car': 68.84260945644772, 'IoU-motorcycle': 84.35754659668311, 'IoU-airplane': 80.35347807745619, 'IoU-bus': 84.12747396860047, 'IoU-train': 85.85638652126933, 'IoU-truck': 63.086741595780104, 'IoU-boat': 67.10593908241543, 'IoU-traffic light': 76.63109901797498, 'IoU-fire hydrant': 89.74178981379862, 'IoU-stop sign': 91.41630439392989, 'IoU-parking meter': 82.62518529453374, 'IoU-bench': 56.281078644563884, 'IoU-bird': 74.9392437692466, 'IoU-cat': 82.94988843758195, 'IoU-dog': 75.36768626001408, 'IoU-horse': 83.5102931164969, 'IoU-sheep': 84.5204205035119, 'IoU-cow': 80.9663880461326, 'IoU-elephant': 84.86992098588867, 'IoU-bear': 72.16070439716145, 'IoU-zebra': 85.34390238399881, 'IoU-giraffe': 86.70739562501231, 'IoU-backpack': 39.91390339071373, 'IoU-umbrella': 69.76373581356606, 'IoU-handbag': 36.87802105826185, 'IoU-tie': 69.10955849561469, 'IoU-suitcase': 79.07797816667954, 'IoU-frisbee': 83.99722221320812, 'IoU-skis': 52.12236656634538, 'IoU-snowboard': 68.8059043842256, 'IoU-sports ball': 63.317011419479044, 'IoU-kite': 64.52354048155087, 'IoU-baseball bat': 60.12238119296134, 'IoU-baseball glove': 52.58074405580261, 'IoU-skateboard': 64.32620016366222, 'IoU-surfboard': 75.58614598199355, 'IoU-tennis racket': 76.52314376009578, 'IoU-bottle': 67.16907354569852, 'IoU-wine glass': 73.17359076555955, 'IoU-cup': 61.79796094888922, 'IoU-fork': 52.0987113437533, 'IoU-knife': 49.68466906671514, 'IoU-spoon': 49.05799272416007, 'IoU-bowl': 55.12983375229599, 'IoU-banana': 84.31680211956237, 'IoU-apple': 59.53019136548862, 'IoU-sandwich': 67.98979494299651, 'IoU-orange': 75.7371502069733, 'IoU-broccoli': 69.19880411638016, 'IoU-carrot': 63.129883023265855, 'IoU-hot dog': 62.627875106116804, 'IoU-pizza': 85.77904463888589, 'IoU-donut': 66.32965449821204, 'IoU-cake': 70.74305079231885, 'IoU-chair': 51.51188531763151, 'IoU-couch': 65.8330443496562, 'IoU-potted plant': 33.815983365304035, 'IoU-bed': 68.50243266527791, 'IoU-dining table': 50.47223115572237, 'IoU-toilet': 82.12837553598348, 'IoU-tv': 74.21944942549825, 'IoU-laptop': 72.688243962569, 'IoU-mouse': 71.0480054492833, 'IoU-remote': 46.14249767196858, 'IoU-keyboard': 58.80588370694328, 'IoU-cell phone': 71.26510658512353, 'IoU-microwave': 37.31602894799465, 'IoU-oven': 64.91847740443477, 'IoU-toaster': 29.245411284840245, 'IoU-sink': 69.20970124562382, 'IoU-refrigerator': 82.75521923722233, 'IoU-book': 48.949948454583655, 'IoU-clock': 66.22347251600912, 'IoU-vase': 57.207091730593326, 'IoU-scissors': 55.85857741840798, 'IoU-teddy bear': 75.73868477643897, 'IoU-hair drier': 37.493911830136774, 'IoU-toothbrush': 52.75971486670689, 'IoU-banner': 34.54338476592024, 'IoU-blanket': 8.701036231038556, 'IoU-bridge': 39.992352474187285, 'IoU-cardboard': 41.96382109297439, 'IoU-counter': 30.95521690121114, 'IoU-curtain': 63.20122730759397, 'IoU-door-stuff': 42.97738522327765, 'IoU-floor-wood': 58.51848384137423, 'IoU-flower': 45.13130157936771, 'IoU-fruit': 39.297509533421824, 'IoU-gravel': 28.754820770097485, 'IoU-house': 25.29328829877414, 'IoU-light': 40.877347726855525, 'IoU-mirror-stuff': 53.818527162019656, 'IoU-net': 36.80394807503565, 'IoU-pillow': 11.862129199438911, 'IoU-platform': 31.419222432528898, 'IoU-playingfield': 69.89501706960733, 'IoU-railroad': 60.303862736513494, 'IoU-river': 50.7847843353076, 'IoU-road': 65.8798866185437, 'IoU-roof': 15.097113416487126, 'IoU-sand': 62.687640180419976, 'IoU-sea': 84.28459926546502, 'IoU-shelf': 35.32586678338958, 'IoU-snow': 88.65206385455541, 'IoU-stairs': 26.459431423862505, 'IoU-tent': 7.869927373440873, 'IoU-towel': 35.60713678701899, 'IoU-wall-brick': 44.82701403360803, 'IoU-wall-stone': 28.360502185758723, 'IoU-wall-tile': 66.327642395689, 'IoU-wall-wood': 38.474011964845175, 'IoU-water-other': 22.886727353111002, 'IoU-window-blind': 47.708988814789166, 'IoU-window-other': 46.16495418317507, 'IoU-tree-merged': 80.64010056995801, 'IoU-fence-merged': 50.15308850761827, 'IoU-ceiling-merged': 66.7519485611057, 'IoU-sky-other-merged': 92.08133580547697, 'IoU-cabinet-merged': 57.300513990048444, 'IoU-table-merged': 37.44939509768504, 'IoU-floor-other-merged': 49.25938893821954, 'IoU-pavement-merged': 54.11044699526184, 'IoU-mountain-merged': 55.98209947760536, 'IoU-grass-merged': 70.8808769496174, 'IoU-dirt-merged': 44.182959660239966, 'IoU-paper-merged': 32.371263946716674, 'IoU-food-other-merged': 40.7995174049476, 'IoU-building-other-merged': 54.35471745537412, 'IoU-rock-merged': 61.2739862006229, 'IoU-wall-other-merged': 63.18925524484437, 'IoU-rug-merged': 62.941904124732496, 'mACC': 71.71795186635023, 'pACC': 79.85289490587958, 'ACC-person': 92.1942531684657, 'ACC-bicycle': 81.14985593271669, 'ACC-car': 82.71167250022695, 'ACC-motorcycle': 89.8626345114085, 'ACC-airplane': 87.46707102480775, 'ACC-bus': 88.59398843376881, 'ACC-train': 95.59145574031005, 'ACC-truck': 77.41019827093501, 'ACC-boat': 79.13574411817173, 'ACC-traffic light': 90.01288713663112, 'ACC-fire hydrant': 95.19102255889524, 'ACC-stop sign': 94.53604393633186, 'ACC-parking meter': 87.045256908544, 'ACC-bench': 70.58361759719108, 'ACC-bird': 79.66291750124115, 'ACC-cat': 89.78984459705586, 'ACC-dog': 79.1134195868757, 'ACC-horse': 89.45502944211431, 'ACC-sheep': 87.64911483536886, 'ACC-cow': 88.20546160910223, 'ACC-elephant': 87.05404261019258, 'ACC-bear': 73.86145158799134, 'ACC-zebra': 87.73179988194309, 'ACC-giraffe': 90.93418447483785, 'ACC-backpack': 58.82233722754402, 'ACC-umbrella': 76.56174522369652, 'ACC-handbag': 53.43386213961132, 'ACC-tie': 79.47193302271643, 'ACC-suitcase': 86.06398844514932, 'ACC-frisbee': 94.12545454545455, 'ACC-skis': 69.92371301921314, 'ACC-snowboard': 76.48845240136171, 'ACC-sports ball': 74.45137015329591, 'ACC-kite': 75.59910797661206, 'ACC-baseball bat': 83.81159670035738, 'ACC-baseball glove': 59.937456316695844, 'ACC-skateboard': 69.59672967040859, 'ACC-surfboard': 82.90824013306796, 'ACC-tennis racket': 81.93997861757073, 'ACC-bottle': 84.03842865174201, 'ACC-wine glass': 85.56889267647006, 'ACC-cup': 84.37975221916928, 'ACC-fork': 70.22482418943173, 'ACC-knife': 59.590019784568035, 'ACC-spoon': 70.66918211075352, 'ACC-bowl': 69.56284740123382, 'ACC-banana': 90.02211907273453, 'ACC-apple': 70.32891627997081, 'ACC-sandwich': 80.16143562428564, 'ACC-orange': 82.48562154659909, 'ACC-broccoli': 80.48623314140404, 'ACC-carrot': 74.50234545674186, 'ACC-hot dog': 68.55507475629578, 'ACC-pizza': 93.50198132470321, 'ACC-donut': 80.96655647843612, 'ACC-cake': 78.65931678595865, 'ACC-chair': 64.59490060447821, 'ACC-couch': 84.02417310183473, 'ACC-potted plant': 51.27210462412781, 'ACC-bed': 85.84083406462626, 'ACC-dining table': 71.35416698524512, 'ACC-toilet': 93.65311441856632, 'ACC-tv': 86.41470373261637, 'ACC-laptop': 87.62832537032772, 'ACC-mouse': 86.16495135439325, 'ACC-remote': 69.43020797561036, 'ACC-keyboard': 64.80558651355142, 'ACC-cell phone': 77.93075337273899, 'ACC-microwave': 44.61167677920604, 'ACC-oven': 86.10876707409507, 'ACC-toaster': 32.381721386634545, 'ACC-sink': 83.57404014850106, 'ACC-refrigerator': 90.71952629545103, 'ACC-book': 66.13617578661287, 'ACC-clock': 70.63361199926933, 'ACC-vase': 67.58243673486201, 'ACC-scissors': 59.84721189249741, 'ACC-teddy bear': 79.59982565009936, 'ACC-hair drier': 40.53596333931062, 'ACC-toothbrush': 82.03787352328006, 'ACC-banner': 76.58736129142113, 'ACC-blanket': 10.383617780741055, 'ACC-bridge': 54.91445557327591, 'ACC-cardboard': 50.1179584120983, 'ACC-counter': 59.69982119482384, 'ACC-curtain': 76.95594416575413, 'ACC-door-stuff': 64.83052421231787, 'ACC-floor-wood': 75.85376825357413, 'ACC-flower': 66.73441669783871, 'ACC-fruit': 61.926629638259016, 'ACC-gravel': 33.34920009031187, 'ACC-house': 30.563164982934666, 'ACC-light': 58.73822876392452, 'ACC-mirror-stuff': 68.95554130625848, 'ACC-net': 61.68742919346507, 'ACC-pillow': 26.030840063042348, 'ACC-platform': 47.72186174389213, 'ACC-playingfield': 89.42755003377697, 'ACC-railroad': 79.5012512243648, 'ACC-river': 76.78586401015333, 'ACC-road': 86.1463603605938, 'ACC-roof': 20.574142209292376, 'ACC-sand': 70.47401000777243, 'ACC-sea': 89.78262656663992, 'ACC-shelf': 53.78769132243486, 'ACC-snow': 95.7096191132823, 'ACC-stairs': 42.559305769528834, 'ACC-tent': 9.532183797483178, 'ACC-towel': 45.95191802559534, 'ACC-wall-brick': 64.11823756332441, 'ACC-wall-stone': 34.87055538808262, 'ACC-wall-tile': 81.02638551295176, 'ACC-wall-wood': 51.8768982973984, 'ACC-water-other': 37.52233143197844, 'ACC-window-blind': 58.16278307916352, 'ACC-window-other': 68.3391790405304, 'ACC-tree-merged': 89.10139123084649, 'ACC-fence-merged': 67.40787004617275, 'ACC-ceiling-merged': 79.70655022804125, 'ACC-sky-other-merged': 96.57657122965342, 'ACC-cabinet-merged': 75.99421916193937, 'ACC-table-merged': 51.782250629420204, 'ACC-floor-other-merged': 61.29808848883752, 'ACC-pavement-merged': 65.69206076714579, 'ACC-mountain-merged': 65.8420125576954, 'ACC-grass-merged': 85.21729528449559, 'ACC-dirt-merged': 63.32730530670537, 'ACC-paper-merged': 46.844405606369996, 'ACC-food-other-merged': 60.84965207746544, 'ACC-building-other-merged': 67.42590538948316, 'ACC-rock-merged': 81.99333520080006, 'ACC-wall-other-merged': 80.79520553733295, 'ACC-rug-merged': 78.76868957757812})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1569 s/iter. Inference: 0.5702 s/iter. Eval: 0.0000 s/iter. Total: 0.7272 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1573 s/iter. Inference: 0.5632 s/iter. Eval: 0.0000 s/iter. Total: 0.7206 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/50. Dataloading: 0.1709 s/iter. Inference: 0.6308 s/iter. Eval: 0.0000 s/iter. Total: 0.8019 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1740 s/iter. Inference: 0.7271 s/iter. Eval: 0.0000 s/iter. Total: 0.9014 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1708 s/iter. Inference: 0.6329 s/iter. Eval: 0.0000 s/iter. Total: 0.8038 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1698 s/iter. Inference: 0.6650 s/iter. Eval: 0.0000 s/iter. Total: 0.8350 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1707 s/iter. Inference: 0.6863 s/iter. Eval: 0.0000 s/iter. Total: 0.8572 s/iter. ETA=0:00:00
/tmp/code/datasets/evaluation/interactive_evaluation.py:92: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.figure()
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5232660228270412, 'noc@0.8': 2.919812701199883, 'noc@0.85': 3.5408252853380158, 'noc@0.9': 4.611062335381914, 'miou@iter1': 0.8433414020488093}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.0992 s/iter. Eval: 0.0008 s/iter. Total: 0.1017 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.08433532714844, 'precision@0.6': 67.50874328613281, 'precision@0.7': 62.650604248046875, 'precision@0.8': 51.72949981689453, 'precision@0.9': 26.62261962890625, 'cIoU': 57.75261688232422, 'mIoU': 62.47641372680664}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.114245476072995, 'SQ': 81.87134797296652, 'RQ': 60.32845571230303, 'PQ_th': 54.93997778069015, 'SQ_th': 82.52339693270538, 'RQ_th': 65.84638862609205, 'PQ_st': 42.83012124268863, 'SQ_st': 80.88712312807785, 'RQ_st': 51.999500370734644}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.74341487136899, 'AP50': 60.82848873490689, 'AP75': 40.812869538439166, 'APs': 19.18116754030535, 'APm': 41.760796195767846, 'APl': 60.51122554348157, 'AP-person': 44.465739163168024, 'AP-bicycle': 18.107345707348852, 'AP-car': 35.95533121119102, 'AP-motorcycle': 34.80123895864649, 'AP-airplane': 57.09115123178946, 'AP-bus': 64.88874418065728, 'AP-train': 69.58054070177548, 'AP-truck': 37.0516096751893, 'AP-boat': 22.27606053672418, 'AP-traffic light': 25.244038703513354, 'AP-fire hydrant': 65.2772939065841, 'AP-stop sign': 62.636924724673584, 'AP-parking meter': 42.10661228745526, 'AP-bench': 19.877031330969626, 'AP-bird': 29.497212061328987, 'AP-cat': 74.25518679384912, 'AP-dog': 66.0260279926215, 'AP-horse': 46.19013159090529, 'AP-sheep': 46.299490786451415, 'AP-cow': 49.52888024101361, 'AP-elephant': 60.658682568354415, 'AP-bear': 78.56396219462266, 'AP-zebra': 60.24696874384385, 'AP-giraffe': 56.8565642817239, 'AP-backpack': 16.84554426609422, 'AP-umbrella': 48.5663654750642, 'AP-handbag': 15.294265597165477, 'AP-tie': 32.84316549065327, 'AP-suitcase': 40.977968801829014, 'AP-frisbee': 67.80235439579727, 'AP-skis': 5.498615057450233, 'AP-snowboard': 22.971466141620596, 'AP-sports ball': 46.78758765387017, 'AP-kite': 34.102831705368985, 'AP-baseball bat': 28.942120667816685, 'AP-baseball glove': 43.16573899346377, 'AP-skateboard': 35.98595474852486, 'AP-surfboard': 35.83265342261545, 'AP-tennis racket': 55.68256278708872, 'AP-bottle': 33.73388434671498, 'AP-wine glass': 26.952230467205666, 'AP-cup': 39.4528770175538, 'AP-fork': 14.771103760955311, 'AP-knife': 12.112890810801284, 'AP-spoon': 13.016127029066915, 'AP-bowl': 30.852125992840566, 'AP-banana': 20.681836853813497, 'AP-apple': 20.50038346701561, 'AP-sandwich': 43.29354512731686, 'AP-orange': 28.65884120904953, 'AP-broccoli': 22.698052449080347, 'AP-carrot': 19.76368428747754, 'AP-hot dog': 26.43402087099107, 'AP-pizza': 51.33299772050406, 'AP-donut': 46.16076497196096, 'AP-cake': 43.84331940945715, 'AP-chair': 20.323068617505445, 'AP-couch': 40.79868237498529, 'AP-potted plant': 17.584391936819944, 'AP-bed': 42.066207947864875, 'AP-dining table': 12.878882445467656, 'AP-toilet': 66.78827773320903, 'AP-tv': 61.70389102277548, 'AP-laptop': 63.23846314535179, 'AP-mouse': 58.751756058073504, 'AP-remote': 31.888966124040362, 'AP-keyboard': 48.96051007078851, 'AP-cell phone': 37.36734182946403, 'AP-microwave': 53.94168341607718, 'AP-oven': 30.51803130656171, 'AP-toaster': 28.23559194503078, 'AP-sink': 38.237237803397925, 'AP-refrigerator': 57.482930360792615, 'AP-book': 9.217903405630189, 'AP-clock': 51.77744767543862, 'AP-vase': 32.39888818021371, 'AP-scissors': 22.343827285218165, 'AP-teddy bear': 50.54464398423475, 'AP-hair drier': 10.582743988684584, 'AP-toothbrush': 18.801170481264794}), ('sem_seg', {'mIoU': 59.39937363942769, 'fwIoU': 68.25672463077046, 'IoU-person': 87.26666364305139, 'IoU-bicycle': 71.40626308616503, 'IoU-car': 68.84260945644772, 'IoU-motorcycle': 84.35754659668311, 'IoU-airplane': 80.35347807745619, 'IoU-bus': 84.12747396860047, 'IoU-train': 85.85638652126933, 'IoU-truck': 63.086741595780104, 'IoU-boat': 67.10593908241543, 'IoU-traffic light': 76.63109901797498, 'IoU-fire hydrant': 89.74178981379862, 'IoU-stop sign': 91.41630439392989, 'IoU-parking meter': 82.62518529453374, 'IoU-bench': 56.281078644563884, 'IoU-bird': 74.9392437692466, 'IoU-cat': 82.94988843758195, 'IoU-dog': 75.36768626001408, 'IoU-horse': 83.5102931164969, 'IoU-sheep': 84.5204205035119, 'IoU-cow': 80.9663880461326, 'IoU-elephant': 84.86992098588867, 'IoU-bear': 72.16070439716145, 'IoU-zebra': 85.34390238399881, 'IoU-giraffe': 86.70739562501231, 'IoU-backpack': 39.91390339071373, 'IoU-umbrella': 69.76373581356606, 'IoU-handbag': 36.87802105826185, 'IoU-tie': 69.10955849561469, 'IoU-suitcase': 79.07797816667954, 'IoU-frisbee': 83.99722221320812, 'IoU-skis': 52.12236656634538, 'IoU-snowboard': 68.8059043842256, 'IoU-sports ball': 63.317011419479044, 'IoU-kite': 64.52354048155087, 'IoU-baseball bat': 60.12238119296134, 'IoU-baseball glove': 52.58074405580261, 'IoU-skateboard': 64.32620016366222, 'IoU-surfboard': 75.58614598199355, 'IoU-tennis racket': 76.52314376009578, 'IoU-bottle': 67.16907354569852, 'IoU-wine glass': 73.17359076555955, 'IoU-cup': 61.79796094888922, 'IoU-fork': 52.0987113437533, 'IoU-knife': 49.68466906671514, 'IoU-spoon': 49.05799272416007, 'IoU-bowl': 55.12983375229599, 'IoU-banana': 84.31680211956237, 'IoU-apple': 59.53019136548862, 'IoU-sandwich': 67.98979494299651, 'IoU-orange': 75.7371502069733, 'IoU-broccoli': 69.19880411638016, 'IoU-carrot': 63.129883023265855, 'IoU-hot dog': 62.627875106116804, 'IoU-pizza': 85.77904463888589, 'IoU-donut': 66.32965449821204, 'IoU-cake': 70.74305079231885, 'IoU-chair': 51.51188531763151, 'IoU-couch': 65.8330443496562, 'IoU-potted plant': 33.815983365304035, 'IoU-bed': 68.50243266527791, 'IoU-dining table': 50.47223115572237, 'IoU-toilet': 82.12837553598348, 'IoU-tv': 74.21944942549825, 'IoU-laptop': 72.688243962569, 'IoU-mouse': 71.0480054492833, 'IoU-remote': 46.14249767196858, 'IoU-keyboard': 58.80588370694328, 'IoU-cell phone': 71.26510658512353, 'IoU-microwave': 37.31602894799465, 'IoU-oven': 64.91847740443477, 'IoU-toaster': 29.245411284840245, 'IoU-sink': 69.20970124562382, 'IoU-refrigerator': 82.75521923722233, 'IoU-book': 48.949948454583655, 'IoU-clock': 66.22347251600912, 'IoU-vase': 57.207091730593326, 'IoU-scissors': 55.85857741840798, 'IoU-teddy bear': 75.73868477643897, 'IoU-hair drier': 37.493911830136774, 'IoU-toothbrush': 52.75971486670689, 'IoU-banner': 34.54338476592024, 'IoU-blanket': 8.701036231038556, 'IoU-bridge': 39.992352474187285, 'IoU-cardboard': 41.96382109297439, 'IoU-counter': 30.95521690121114, 'IoU-curtain': 63.20122730759397, 'IoU-door-stuff': 42.97738522327765, 'IoU-floor-wood': 58.51848384137423, 'IoU-flower': 45.13130157936771, 'IoU-fruit': 39.297509533421824, 'IoU-gravel': 28.754820770097485, 'IoU-house': 25.29328829877414, 'IoU-light': 40.877347726855525, 'IoU-mirror-stuff': 53.818527162019656, 'IoU-net': 36.80394807503565, 'IoU-pillow': 11.862129199438911, 'IoU-platform': 31.419222432528898, 'IoU-playingfield': 69.89501706960733, 'IoU-railroad': 60.303862736513494, 'IoU-river': 50.7847843353076, 'IoU-road': 65.8798866185437, 'IoU-roof': 15.097113416487126, 'IoU-sand': 62.687640180419976, 'IoU-sea': 84.28459926546502, 'IoU-shelf': 35.32586678338958, 'IoU-snow': 88.65206385455541, 'IoU-stairs': 26.459431423862505, 'IoU-tent': 7.869927373440873, 'IoU-towel': 35.60713678701899, 'IoU-wall-brick': 44.82701403360803, 'IoU-wall-stone': 28.360502185758723, 'IoU-wall-tile': 66.327642395689, 'IoU-wall-wood': 38.474011964845175, 'IoU-water-other': 22.886727353111002, 'IoU-window-blind': 47.708988814789166, 'IoU-window-other': 46.16495418317507, 'IoU-tree-merged': 80.64010056995801, 'IoU-fence-merged': 50.15308850761827, 'IoU-ceiling-merged': 66.7519485611057, 'IoU-sky-other-merged': 92.08133580547697, 'IoU-cabinet-merged': 57.300513990048444, 'IoU-table-merged': 37.44939509768504, 'IoU-floor-other-merged': 49.25938893821954, 'IoU-pavement-merged': 54.11044699526184, 'IoU-mountain-merged': 55.98209947760536, 'IoU-grass-merged': 70.8808769496174, 'IoU-dirt-merged': 44.182959660239966, 'IoU-paper-merged': 32.371263946716674, 'IoU-food-other-merged': 40.7995174049476, 'IoU-building-other-merged': 54.35471745537412, 'IoU-rock-merged': 61.2739862006229, 'IoU-wall-other-merged': 63.18925524484437, 'IoU-rug-merged': 62.941904124732496, 'mACC': 71.71795186635023, 'pACC': 79.85289490587958, 'ACC-person': 92.1942531684657, 'ACC-bicycle': 81.14985593271669, 'ACC-car': 82.71167250022695, 'ACC-motorcycle': 89.8626345114085, 'ACC-airplane': 87.46707102480775, 'ACC-bus': 88.59398843376881, 'ACC-train': 95.59145574031005, 'ACC-truck': 77.41019827093501, 'ACC-boat': 79.13574411817173, 'ACC-traffic light': 90.01288713663112, 'ACC-fire hydrant': 95.19102255889524, 'ACC-stop sign': 94.53604393633186, 'ACC-parking meter': 87.045256908544, 'ACC-bench': 70.58361759719108, 'ACC-bird': 79.66291750124115, 'ACC-cat': 89.78984459705586, 'ACC-dog': 79.1134195868757, 'ACC-horse': 89.45502944211431, 'ACC-sheep': 87.64911483536886, 'ACC-cow': 88.20546160910223, 'ACC-elephant': 87.05404261019258, 'ACC-bear': 73.86145158799134, 'ACC-zebra': 87.73179988194309, 'ACC-giraffe': 90.93418447483785, 'ACC-backpack': 58.82233722754402, 'ACC-umbrella': 76.56174522369652, 'ACC-handbag': 53.43386213961132, 'ACC-tie': 79.47193302271643, 'ACC-suitcase': 86.06398844514932, 'ACC-frisbee': 94.12545454545455, 'ACC-skis': 69.92371301921314, 'ACC-snowboard': 76.48845240136171, 'ACC-sports ball': 74.45137015329591, 'ACC-kite': 75.59910797661206, 'ACC-baseball bat': 83.81159670035738, 'ACC-baseball glove': 59.937456316695844, 'ACC-skateboard': 69.59672967040859, 'ACC-surfboard': 82.90824013306796, 'ACC-tennis racket': 81.93997861757073, 'ACC-bottle': 84.03842865174201, 'ACC-wine glass': 85.56889267647006, 'ACC-cup': 84.37975221916928, 'ACC-fork': 70.22482418943173, 'ACC-knife': 59.590019784568035, 'ACC-spoon': 70.66918211075352, 'ACC-bowl': 69.56284740123382, 'ACC-banana': 90.02211907273453, 'ACC-apple': 70.32891627997081, 'ACC-sandwich': 80.16143562428564, 'ACC-orange': 82.48562154659909, 'ACC-broccoli': 80.48623314140404, 'ACC-carrot': 74.50234545674186, 'ACC-hot dog': 68.55507475629578, 'ACC-pizza': 93.50198132470321, 'ACC-donut': 80.96655647843612, 'ACC-cake': 78.65931678595865, 'ACC-chair': 64.59490060447821, 'ACC-couch': 84.02417310183473, 'ACC-potted plant': 51.27210462412781, 'ACC-bed': 85.84083406462626, 'ACC-dining table': 71.35416698524512, 'ACC-toilet': 93.65311441856632, 'ACC-tv': 86.41470373261637, 'ACC-laptop': 87.62832537032772, 'ACC-mouse': 86.16495135439325, 'ACC-remote': 69.43020797561036, 'ACC-keyboard': 64.80558651355142, 'ACC-cell phone': 77.93075337273899, 'ACC-microwave': 44.61167677920604, 'ACC-oven': 86.10876707409507, 'ACC-toaster': 32.381721386634545, 'ACC-sink': 83.57404014850106, 'ACC-refrigerator': 90.71952629545103, 'ACC-book': 66.13617578661287, 'ACC-clock': 70.63361199926933, 'ACC-vase': 67.58243673486201, 'ACC-scissors': 59.84721189249741, 'ACC-teddy bear': 79.59982565009936, 'ACC-hair drier': 40.53596333931062, 'ACC-toothbrush': 82.03787352328006, 'ACC-banner': 76.58736129142113, 'ACC-blanket': 10.383617780741055, 'ACC-bridge': 54.91445557327591, 'ACC-cardboard': 50.1179584120983, 'ACC-counter': 59.69982119482384, 'ACC-curtain': 76.95594416575413, 'ACC-door-stuff': 64.83052421231787, 'ACC-floor-wood': 75.85376825357413, 'ACC-flower': 66.73441669783871, 'ACC-fruit': 61.926629638259016, 'ACC-gravel': 33.34920009031187, 'ACC-house': 30.563164982934666, 'ACC-light': 58.73822876392452, 'ACC-mirror-stuff': 68.95554130625848, 'ACC-net': 61.68742919346507, 'ACC-pillow': 26.030840063042348, 'ACC-platform': 47.72186174389213, 'ACC-playingfield': 89.42755003377697, 'ACC-railroad': 79.5012512243648, 'ACC-river': 76.78586401015333, 'ACC-road': 86.1463603605938, 'ACC-roof': 20.574142209292376, 'ACC-sand': 70.47401000777243, 'ACC-sea': 89.78262656663992, 'ACC-shelf': 53.78769132243486, 'ACC-snow': 95.7096191132823, 'ACC-stairs': 42.559305769528834, 'ACC-tent': 9.532183797483178, 'ACC-towel': 45.95191802559534, 'ACC-wall-brick': 64.11823756332441, 'ACC-wall-stone': 34.87055538808262, 'ACC-wall-tile': 81.02638551295176, 'ACC-wall-wood': 51.8768982973984, 'ACC-water-other': 37.52233143197844, 'ACC-window-blind': 58.16278307916352, 'ACC-window-other': 68.3391790405304, 'ACC-tree-merged': 89.10139123084649, 'ACC-fence-merged': 67.40787004617275, 'ACC-ceiling-merged': 79.70655022804125, 'ACC-sky-other-merged': 96.57657122965342, 'ACC-cabinet-merged': 75.99421916193937, 'ACC-table-merged': 51.782250629420204, 'ACC-floor-other-merged': 61.29808848883752, 'ACC-pavement-merged': 65.69206076714579, 'ACC-mountain-merged': 65.8420125576954, 'ACC-grass-merged': 85.21729528449559, 'ACC-dirt-merged': 63.32730530670537, 'ACC-paper-merged': 46.844405606369996, 'ACC-food-other-merged': 60.84965207746544, 'ACC-building-other-merged': 67.42590538948316, 'ACC-rock-merged': 81.99333520080006, 'ACC-wall-other-merged': 80.79520553733295, 'ACC-rug-merged': 78.76868957757812})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5232660228270412, 'noc@0.8': 2.919812701199883, 'noc@0.85': 3.5408252853380158, 'noc@0.9': 4.611062335381914, 'miou@iter1': 0.8433414020488093}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.08433532714844, 'precision@0.6': 67.50874328613281, 'precision@0.7': 62.650604248046875, 'precision@0.8': 51.72949981689453, 'precision@0.9': 26.62261962890625, 'cIoU': 57.75261688232422, 'mIoU': 62.47641372680664}}}
INFO:trainer.default_trainer:This epoch takes 1:28:05.417367
INFO:trainer.default_trainer:PROGRESS: 42.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 21 training.
INFO:trainer.default_trainer:epochs[    21] optim steps[38400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04319/0.90749, loss_mask_bce_0: 0.18531/0.33456, loss_mask_dice_0: 0.31687/1.16645, loss_spatial_bce_0: 0.05080/0.08912, loss_spatial_dice_0: 0.08244/0.21324, loss_spatial_ce_0: 0.02043/0.06881, loss_grounding_bce_0: 0.04763/0.08626, loss_grounding_dice_0: 0.07670/0.17913, loss_grounding_ce_0: 0.05086/0.27515, loss_mask_ce_1: 0.04110/0.90797, loss_mask_bce_1: 0.17656/0.33539, loss_mask_dice_1: 0.30441/1.17352, loss_spatial_bce_1: 0.04824/0.08979, loss_spatial_dice_1: 0.08399/0.21742, loss_spatial_ce_1: 0.02015/0.07449, loss_grounding_bce_1: 0.04911/0.08640, loss_grounding_dice_1: 0.08307/0.17999, loss_grounding_ce_1: 0.04057/0.27675, loss_mask_ce_2: 0.03846/0.91552, loss_mask_bce_2: 0.17923/0.33579, loss_mask_dice_2: 0.29728/1.17296, loss_spatial_bce_2: 0.04827/0.09031, loss_spatial_dice_2: 0.08386/0.21851, loss_spatial_ce_2: 0.02032/0.07805, loss_grounding_bce_2: 0.04967/0.08647, loss_grounding_dice_2: 0.07385/0.17975, loss_grounding_ce_2: 0.03189/0.28005, loss_mask_ce_3: 0.05048/0.92451, loss_mask_bce_3: 0.17709/0.33672, loss_mask_dice_3: 0.29750/1.17025, loss_spatial_bce_3: 0.04995/0.09117, loss_spatial_dice_3: 0.08588/0.21909, loss_spatial_ce_3: 0.02077/0.08179, loss_grounding_bce_3: 0.04998/0.08674, loss_grounding_dice_3: 0.08109/0.17949, loss_grounding_ce_3: 0.03045/0.28145, loss_mask_ce_4: 0.05829/0.92416, loss_mask_bce_4: 0.17054/0.33861, loss_mask_dice_4: 0.29472/1.19380, loss_spatial_bce_4: 0.05122/0.09539, loss_spatial_dice_4: 0.08376/0.23046, loss_spatial_ce_4: 0.02946/0.09775, loss_grounding_bce_4: 0.04819/0.08716, loss_grounding_dice_4: 0.07785/0.18233, loss_grounding_ce_4: 0.02706/0.28436, loss_mask_ce_5: 0.05170/0.93956, loss_mask_bce_5: 0.17258/0.34092, loss_mask_dice_5: 0.29469/1.20012, loss_spatial_bce_5: 0.05435/0.09704, loss_spatial_dice_5: 0.08886/0.23406, loss_spatial_ce_5: 0.03822/0.11260, loss_grounding_bce_5: 0.05067/0.08758, loss_grounding_dice_5: 0.07888/0.18352, loss_grounding_ce_5: 0.01396/0.29690, loss_mask_ce_6: 0.08510/0.97849, loss_mask_bce_6: 0.17043/0.34356, loss_mask_dice_6: 0.29954/1.20264, loss_spatial_bce_6: 0.06159/0.10277, loss_spatial_dice_6: 0.08653/0.23647, loss_spatial_ce_6: 0.06590/0.13847, loss_grounding_bce_6: 0.05408/0.08827, loss_grounding_dice_6: 0.08166/0.18366, loss_grounding_ce_6: 0.01252/0.31333, loss_mask_ce_7: 0.09598/1.02339, loss_mask_bce_7: 0.16878/0.35141, loss_mask_dice_7: 0.31198/1.25816, loss_spatial_bce_7: 0.05605/0.11123, loss_spatial_dice_7: 0.08407/0.26411, loss_spatial_ce_7: 0.04219/0.17562, loss_grounding_bce_7: 0.05637/0.09018, loss_grounding_dice_7: 0.08491/0.19094, loss_grounding_ce_7: 0.02684/0.34660, loss_mask_ce_8: 0.08818/1.13223, loss_mask_bce_8: 0.18019/0.36507, loss_mask_dice_8: 0.30434/1.33216, loss_spatial_bce_8: 0.08605/0.13218, loss_spatial_dice_8: 0.10589/0.30345, loss_spatial_ce_8: 0.08189/0.23269, loss_grounding_bce_8: 0.06538/0.09385, loss_grounding_dice_8: 0.08489/0.20208, loss_grounding_ce_8: 0.14491/0.41471, loss_mask_ce_9: 1.99172/3.68300, loss_mask_bce_9: 0.18701/0.39204, loss_mask_dice_9: 0.42759/1.90581, loss_spatial_bce_9: 0.45996/0.33408, loss_spatial_dice_9: 0.82788/0.82318, loss_spatial_ce_9: 1.47861/1.50630, loss_grounding_bce_9: 0.06579/0.10531, loss_grounding_dice_9: 0.11906/0.28149, loss_grounding_ce_9: 0.60903/0.68104] items per batch[64] items per second[0.13] total items[2457600] mini batches[ 38400] memory[7341] epoch remaining[1:30:00]
INFO:trainer.default_trainer:epochs[    21] optim steps[38500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.84330/0.90731, loss_mask_bce_0: 0.61858/0.33458, loss_mask_dice_0: 1.39166/1.16625, loss_spatial_bce_0: 0.06227/0.08909, loss_spatial_dice_0: 0.11292/0.21322, loss_spatial_ce_0: 0.00785/0.06875, loss_grounding_bce_0: 0.13602/0.08627, loss_grounding_dice_0: 0.13615/0.17912, loss_grounding_ce_0: 0.63077/0.27507, loss_mask_ce_1: 1.87370/0.90779, loss_mask_bce_1: 0.64248/0.33540, loss_mask_dice_1: 1.46979/1.17331, loss_spatial_bce_1: 0.06240/0.08976, loss_spatial_dice_1: 0.11430/0.21739, loss_spatial_ce_1: 0.00227/0.07442, loss_grounding_bce_1: 0.14401/0.08640, loss_grounding_dice_1: 0.13955/0.17996, loss_grounding_ce_1: 0.63072/0.27670, loss_mask_ce_2: 2.05055/0.91539, loss_mask_bce_2: 0.62826/0.33580, loss_mask_dice_2: 1.33743/1.17276, loss_spatial_bce_2: 0.06435/0.09028, loss_spatial_dice_2: 0.10930/0.21848, loss_spatial_ce_2: 0.00177/0.07797, loss_grounding_bce_2: 0.14479/0.08647, loss_grounding_dice_2: 0.14414/0.17972, loss_grounding_ce_2: 0.66090/0.27999, loss_mask_ce_3: 2.02623/0.92435, loss_mask_bce_3: 0.60088/0.33672, loss_mask_dice_3: 1.33780/1.17006, loss_spatial_bce_3: 0.06570/0.09114, loss_spatial_dice_3: 0.10948/0.21906, loss_spatial_ce_3: 0.01006/0.08170, loss_grounding_bce_3: 0.14253/0.08674, loss_grounding_dice_3: 0.13000/0.17947, loss_grounding_ce_3: 0.68467/0.28140, loss_mask_ce_4: 2.09856/0.92401, loss_mask_bce_4: 0.61797/0.33862, loss_mask_dice_4: 1.29097/1.19357, loss_spatial_bce_4: 0.07190/0.09536, loss_spatial_dice_4: 0.11598/0.23045, loss_spatial_ce_4: 0.03521/0.09769, loss_grounding_bce_4: 0.14013/0.08716, loss_grounding_dice_4: 0.13086/0.18232, loss_grounding_ce_4: 0.63871/0.28432, loss_mask_ce_5: 2.19586/0.93937, loss_mask_bce_5: 0.64916/0.34093, loss_mask_dice_5: 1.28374/1.19992, loss_spatial_bce_5: 0.08024/0.09701, loss_spatial_dice_5: 0.12155/0.23405, loss_spatial_ce_5: 0.05595/0.11254, loss_grounding_bce_5: 0.14884/0.08759, loss_grounding_dice_5: 0.13375/0.18350, loss_grounding_ce_5: 0.75499/0.29693, loss_mask_ce_6: 2.22967/0.97831, loss_mask_bce_6: 0.64623/0.34359, loss_mask_dice_6: 1.37500/1.20247, loss_spatial_bce_6: 0.08982/0.10275, loss_spatial_dice_6: 0.13173/0.23645, loss_spatial_ce_6: 0.04300/0.13843, loss_grounding_bce_6: 0.16284/0.08828, loss_grounding_dice_6: 0.15487/0.18363, loss_grounding_ce_6: 0.71256/0.31325, loss_mask_ce_7: 2.12468/1.02321, loss_mask_bce_7: 0.71228/0.35142, loss_mask_dice_7: 1.43208/1.25796, loss_spatial_bce_7: 0.11318/0.11120, loss_spatial_dice_7: 0.17755/0.26409, loss_spatial_ce_7: 0.08524/0.17556, loss_grounding_bce_7: 0.16896/0.09018, loss_grounding_dice_7: 0.18339/0.19091, loss_grounding_ce_7: 0.91020/0.34653, loss_mask_ce_8: 2.44389/1.13205, loss_mask_bce_8: 0.66570/0.36506, loss_mask_dice_8: 1.35523/1.33189, loss_spatial_bce_8: 0.21332/0.13215, loss_spatial_dice_8: 0.24665/0.30343, loss_spatial_ce_8: 0.07241/0.23268, loss_grounding_bce_8: 0.19487/0.09386, loss_grounding_dice_8: 0.20202/0.20205, loss_grounding_ce_8: 0.80012/0.41459, loss_mask_ce_9: 3.92512/3.68263, loss_mask_bce_9: 1.02059/0.39202, loss_mask_dice_9: 2.71584/1.90543, loss_spatial_bce_9: 0.37310/0.33404, loss_spatial_dice_9: 0.88727/0.82317, loss_spatial_ce_9: 1.17659/1.50629, loss_grounding_bce_9: 0.26539/0.10532, loss_grounding_dice_9: 0.29628/0.28146, loss_grounding_ce_9: 1.51152/0.68083] items per batch[64] items per second[0.23] total items[2464000] mini batches[ 38500] memory[7341] epoch remaining[1:19:14]
INFO:trainer.default_trainer:epochs[    21] optim steps[38600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39141/0.90739, loss_mask_bce_0: 0.28927/0.33452, loss_mask_dice_0: 0.53135/1.16619, loss_spatial_bce_0: 0.11297/0.08907, loss_spatial_dice_0: 0.19975/0.21320, loss_spatial_ce_0: 0.03347/0.06869, loss_grounding_bce_0: 0.09103/0.08628, loss_grounding_dice_0: 0.13592/0.17911, loss_grounding_ce_0: 0.00882/0.27513, loss_mask_ce_1: 0.40725/0.90786, loss_mask_bce_1: 0.29362/0.33535, loss_mask_dice_1: 0.54049/1.17325, loss_spatial_bce_1: 0.11076/0.08975, loss_spatial_dice_1: 0.21858/0.21738, loss_spatial_ce_1: 0.03069/0.07437, loss_grounding_bce_1: 0.08876/0.08641, loss_grounding_dice_1: 0.13192/0.17996, loss_grounding_ce_1: 0.01111/0.27676, loss_mask_ce_2: 0.42477/0.91548, loss_mask_bce_2: 0.28382/0.33575, loss_mask_dice_2: 0.51878/1.17272, loss_spatial_bce_2: 0.11222/0.09027, loss_spatial_dice_2: 0.20657/0.21847, loss_spatial_ce_2: 0.03188/0.07793, loss_grounding_bce_2: 0.09007/0.08648, loss_grounding_dice_2: 0.12916/0.17973, loss_grounding_ce_2: 0.00853/0.28001, loss_mask_ce_3: 0.26661/0.92443, loss_mask_bce_3: 0.28385/0.33667, loss_mask_dice_3: 0.62545/1.17003, loss_spatial_bce_3: 0.10879/0.09113, loss_spatial_dice_3: 0.21152/0.21906, loss_spatial_ce_3: 0.03718/0.08165, loss_grounding_bce_3: 0.09126/0.08676, loss_grounding_dice_3: 0.15830/0.17946, loss_grounding_ce_3: 0.00764/0.28136, loss_mask_ce_4: 0.54745/0.92412, loss_mask_bce_4: 0.28719/0.33857, loss_mask_dice_4: 0.55379/1.19350, loss_spatial_bce_4: 0.11102/0.09534, loss_spatial_dice_4: 0.19691/0.23044, loss_spatial_ce_4: 0.05990/0.09764, loss_grounding_bce_4: 0.09120/0.08717, loss_grounding_dice_4: 0.12496/0.18231, loss_grounding_ce_4: 0.00589/0.28431, loss_mask_ce_5: 0.26673/0.93948, loss_mask_bce_5: 0.29475/0.34087, loss_mask_dice_5: 0.60132/1.19987, loss_spatial_bce_5: 0.11046/0.09700, loss_spatial_dice_5: 0.22869/0.23405, loss_spatial_ce_5: 0.07628/0.11250, loss_grounding_bce_5: 0.09210/0.08760, loss_grounding_dice_5: 0.13824/0.18349, loss_grounding_ce_5: 0.00697/0.29692, loss_mask_ce_6: 0.55703/0.97848, loss_mask_bce_6: 0.31482/0.34353, loss_mask_dice_6: 0.57560/1.20232, loss_spatial_bce_6: 0.11842/0.10274, loss_spatial_dice_6: 0.20025/0.23645, loss_spatial_ce_6: 0.06580/0.13837, loss_grounding_bce_6: 0.08982/0.08829, loss_grounding_dice_6: 0.12283/0.18365, loss_grounding_ce_6: 0.00400/0.31327, loss_mask_ce_7: 0.69105/1.02333, loss_mask_bce_7: 0.29729/0.35138, loss_mask_dice_7: 0.57483/1.25789, loss_spatial_bce_7: 0.14532/0.11119, loss_spatial_dice_7: 0.27093/0.26410, loss_spatial_ce_7: 0.16584/0.17550, loss_grounding_bce_7: 0.08807/0.09020, loss_grounding_dice_7: 0.12447/0.19092, loss_grounding_ce_7: 0.07544/0.34658, loss_mask_ce_8: 0.77410/1.13222, loss_mask_bce_8: 0.35400/0.36501, loss_mask_dice_8: 0.67483/1.33178, loss_spatial_bce_8: 0.18714/0.13212, loss_spatial_dice_8: 0.27372/0.30344, loss_spatial_ce_8: 0.16240/0.23263, loss_grounding_bce_8: 0.12851/0.09388, loss_grounding_dice_8: 0.22914/0.20206, loss_grounding_ce_8: 0.08102/0.41459, loss_mask_ce_9: 2.62507/3.68294, loss_mask_bce_9: 0.35688/0.39195, loss_mask_dice_9: 0.76858/1.90515, loss_spatial_bce_9: 0.87600/0.33400, loss_spatial_dice_9: 0.89060/0.82315, loss_spatial_ce_9: 1.97777/1.50639, loss_grounding_bce_9: 0.21715/0.10533, loss_grounding_dice_9: 0.29017/0.28143, loss_grounding_ce_9: 0.25874/0.68111] items per batch[64] items per second[0.23] total items[2470400] mini batches[ 38600] memory[7341] epoch remaining[1:14:34]
INFO:trainer.default_trainer:epochs[    21] optim steps[38700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98118/0.90741, loss_mask_bce_0: 0.47127/0.33450, loss_mask_dice_0: 0.62489/1.16607, loss_spatial_bce_0: 0.14555/0.08907, loss_spatial_dice_0: 0.16822/0.21316, loss_spatial_ce_0: 0.03082/0.06864, loss_grounding_bce_0: 0.11090/0.08626, loss_grounding_dice_0: 0.15322/0.17908, loss_grounding_ce_0: 0.19565/0.27497, loss_mask_ce_1: 1.08214/0.90789, loss_mask_bce_1: 0.50280/0.33533, loss_mask_dice_1: 0.62796/1.17313, loss_spatial_bce_1: 0.17601/0.08974, loss_spatial_dice_1: 0.19207/0.21733, loss_spatial_ce_1: 0.03205/0.07431, loss_grounding_bce_1: 0.11160/0.08639, loss_grounding_dice_1: 0.14657/0.17991, loss_grounding_ce_1: 0.20071/0.27659, loss_mask_ce_2: 1.08578/0.91548, loss_mask_bce_2: 0.49910/0.33572, loss_mask_dice_2: 0.62873/1.17256, loss_spatial_bce_2: 0.16925/0.09026, loss_spatial_dice_2: 0.18396/0.21841, loss_spatial_ce_2: 0.03005/0.07788, loss_grounding_bce_2: 0.11454/0.08646, loss_grounding_dice_2: 0.15648/0.17970, loss_grounding_ce_2: 0.20825/0.27980, loss_mask_ce_3: 0.98465/0.92441, loss_mask_bce_3: 0.66222/0.33665, loss_mask_dice_3: 0.78244/1.16990, loss_spatial_bce_3: 0.15345/0.09113, loss_spatial_dice_3: 0.18091/0.21901, loss_spatial_ce_3: 0.04118/0.08160, loss_grounding_bce_3: 0.11041/0.08674, loss_grounding_dice_3: 0.14895/0.17945, loss_grounding_ce_3: 0.20359/0.28116, loss_mask_ce_4: 0.96486/0.92414, loss_mask_bce_4: 0.65263/0.33856, loss_mask_dice_4: 0.75878/1.19339, loss_spatial_bce_4: 0.15467/0.09534, loss_spatial_dice_4: 0.21315/0.23040, loss_spatial_ce_4: 0.08487/0.09759, loss_grounding_bce_4: 0.10480/0.08715, loss_grounding_dice_4: 0.14686/0.18229, loss_grounding_ce_4: 0.25926/0.28414, loss_mask_ce_5: 1.08499/0.93950, loss_mask_bce_5: 0.68392/0.34086, loss_mask_dice_5: 0.82103/1.19974, loss_spatial_bce_5: 0.17681/0.09700, loss_spatial_dice_5: 0.24359/0.23401, loss_spatial_ce_5: 0.05988/0.11244, loss_grounding_bce_5: 0.11567/0.08758, loss_grounding_dice_5: 0.16341/0.18347, loss_grounding_ce_5: 0.21476/0.29675, loss_mask_ce_6: 1.11184/0.97854, loss_mask_bce_6: 0.68218/0.34353, loss_mask_dice_6: 0.78270/1.20219, loss_spatial_bce_6: 0.25672/0.10274, loss_spatial_dice_6: 0.26470/0.23640, loss_spatial_ce_6: 0.10194/0.13830, loss_grounding_bce_6: 0.10579/0.08828, loss_grounding_dice_6: 0.15352/0.18362, loss_grounding_ce_6: 0.21177/0.31311, loss_mask_ce_7: 1.34603/1.02335, loss_mask_bce_7: 0.65115/0.35138, loss_mask_dice_7: 0.78154/1.25776, loss_spatial_bce_7: 0.23082/0.11117, loss_spatial_dice_7: 0.33509/0.26404, loss_spatial_ce_7: 0.11157/0.17539, loss_grounding_bce_7: 0.11335/0.09018, loss_grounding_dice_7: 0.16189/0.19090, loss_grounding_ce_7: 0.20993/0.34648, loss_mask_ce_8: 1.43655/1.13235, loss_mask_bce_8: 0.66000/0.36501, loss_mask_dice_8: 0.95282/1.33174, loss_spatial_bce_8: 0.32457/0.13210, loss_spatial_dice_8: 0.36610/0.30339, loss_spatial_ce_8: 0.16973/0.23259, loss_grounding_bce_8: 0.12224/0.09386, loss_grounding_dice_8: 0.15937/0.20204, loss_grounding_ce_8: 0.26723/0.41441, loss_mask_ce_9: 4.23139/3.68275, loss_mask_bce_9: 0.85527/0.39193, loss_mask_dice_9: 1.49473/1.90487, loss_spatial_bce_9: 0.42344/0.33400, loss_spatial_dice_9: 0.84401/0.82312, loss_spatial_ce_9: 1.20225/1.50639, loss_grounding_bce_9: 0.19627/0.10531, loss_grounding_dice_9: 0.32220/0.28142, loss_grounding_ce_9: 0.49088/0.68082] items per batch[64] items per second[0.23] total items[2476800] mini batches[ 38700] memory[7341] epoch remaining[1:09:50]
INFO:trainer.default_trainer:epochs[    21] optim steps[38800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72991/0.90739, loss_mask_bce_0: 0.07229/0.33443, loss_mask_dice_0: 0.35851/1.16603, loss_spatial_bce_0: 0.03551/0.08905, loss_spatial_dice_0: 0.14991/0.21311, loss_spatial_ce_0: 0.00091/0.06860, loss_grounding_bce_0: 0.03988/0.08622, loss_grounding_dice_0: 0.09433/0.17908, loss_grounding_ce_0: 0.12671/0.27488, loss_mask_ce_1: 0.35522/0.90781, loss_mask_bce_1: 0.06778/0.33526, loss_mask_dice_1: 0.21740/1.17310, loss_spatial_bce_1: 0.03618/0.08971, loss_spatial_dice_1: 0.13625/0.21728, loss_spatial_ce_1: 0.00060/0.07428, loss_grounding_bce_1: 0.03806/0.08636, loss_grounding_dice_1: 0.08919/0.17991, loss_grounding_ce_1: 0.10954/0.27649, loss_mask_ce_2: 0.48065/0.91542, loss_mask_bce_2: 0.06688/0.33566, loss_mask_dice_2: 0.16936/1.17255, loss_spatial_bce_2: 0.03144/0.09025, loss_spatial_dice_2: 0.10656/0.21837, loss_spatial_ce_2: 0.00117/0.07783, loss_grounding_bce_2: 0.03936/0.08642, loss_grounding_dice_2: 0.09411/0.17970, loss_grounding_ce_2: 0.15903/0.27972, loss_mask_ce_3: 0.48673/0.92437, loss_mask_bce_3: 0.06678/0.33659, loss_mask_dice_3: 0.23109/1.16987, loss_spatial_bce_3: 0.03347/0.09111, loss_spatial_dice_3: 0.14291/0.21897, loss_spatial_ce_3: 0.00131/0.08154, loss_grounding_bce_3: 0.03609/0.08670, loss_grounding_dice_3: 0.08235/0.17943, loss_grounding_ce_3: 0.18035/0.28107, loss_mask_ce_4: 0.87879/0.92413, loss_mask_bce_4: 0.06580/0.33850, loss_mask_dice_4: 0.29591/1.19338, loss_spatial_bce_4: 0.03546/0.09532, loss_spatial_dice_4: 0.22116/0.23038, loss_spatial_ce_4: 0.02667/0.09755, loss_grounding_bce_4: 0.03957/0.08712, loss_grounding_dice_4: 0.12517/0.18228, loss_grounding_ce_4: 0.13056/0.28405, loss_mask_ce_5: 0.43463/0.93951, loss_mask_bce_5: 0.06128/0.34079, loss_mask_dice_5: 0.15525/1.19972, loss_spatial_bce_5: 0.03442/0.09698, loss_spatial_dice_5: 0.12387/0.23397, loss_spatial_ce_5: 0.09782/0.11243, loss_grounding_bce_5: 0.03844/0.08755, loss_grounding_dice_5: 0.13214/0.18347, loss_grounding_ce_5: 0.13295/0.29665, loss_mask_ce_6: 0.46332/0.97854, loss_mask_bce_6: 0.05559/0.34346, loss_mask_dice_6: 0.16846/1.20216, loss_spatial_bce_6: 0.03755/0.10272, loss_spatial_dice_6: 0.18962/0.23637, loss_spatial_ce_6: 0.07497/0.13825, loss_grounding_bce_6: 0.03370/0.08825, loss_grounding_dice_6: 0.12574/0.18362, loss_grounding_ce_6: 0.16303/0.31297, loss_mask_ce_7: 0.34909/1.02337, loss_mask_bce_7: 0.06260/0.35131, loss_mask_dice_7: 0.18370/1.25773, loss_spatial_bce_7: 0.04032/0.11116, loss_spatial_dice_7: 0.17259/0.26402, loss_spatial_ce_7: 0.08216/0.17531, loss_grounding_bce_7: 0.03782/0.09014, loss_grounding_dice_7: 0.19892/0.19089, loss_grounding_ce_7: 0.29993/0.34633, loss_mask_ce_8: 0.43913/1.13234, loss_mask_bce_8: 0.05294/0.36493, loss_mask_dice_8: 0.16404/1.33168, loss_spatial_bce_8: 0.04416/0.13211, loss_spatial_dice_8: 0.21677/0.30336, loss_spatial_ce_8: 0.24840/0.23253, loss_grounding_bce_8: 0.03318/0.09382, loss_grounding_dice_8: 0.10954/0.20203, loss_grounding_ce_8: 0.14850/0.41436, loss_mask_ce_9: 2.84067/3.68238, loss_mask_bce_9: 0.08524/0.39185, loss_mask_dice_9: 0.38058/1.90492, loss_spatial_bce_9: 0.29879/0.33398, loss_spatial_dice_9: 0.82449/0.82308, loss_spatial_ce_9: 1.85188/1.50629, loss_grounding_bce_9: 0.04865/0.10528, loss_grounding_dice_9: 0.18387/0.28141, loss_grounding_ce_9: 0.44851/0.68058] items per batch[64] items per second[0.23] total items[2483200] mini batches[ 38800] memory[7341] epoch remaining[1:05:01]
INFO:trainer.default_trainer:epochs[    21] optim steps[38900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08011/0.90742, loss_mask_bce_0: 0.76283/0.33443, loss_mask_dice_0: 2.15251/1.16626, loss_spatial_bce_0: 0.06654/0.08903, loss_spatial_dice_0: 0.13697/0.21310, loss_spatial_ce_0: 0.00507/0.06858, loss_grounding_bce_0: 0.24999/0.08621, loss_grounding_dice_0: 0.37122/0.17910, loss_grounding_ce_0: 0.13512/0.27483, loss_mask_ce_1: 1.24839/0.90785, loss_mask_bce_1: 0.67266/0.33525, loss_mask_dice_1: 2.11247/1.17338, loss_spatial_bce_1: 0.06340/0.08969, loss_spatial_dice_1: 0.13332/0.21728, loss_spatial_ce_1: 0.00688/0.07424, loss_grounding_bce_1: 0.25511/0.08635, loss_grounding_dice_1: 0.36875/0.17993, loss_grounding_ce_1: 0.12520/0.27646, loss_mask_ce_2: 1.29435/0.91543, loss_mask_bce_2: 0.63867/0.33566, loss_mask_dice_2: 2.03643/1.17280, loss_spatial_bce_2: 0.06883/0.09023, loss_spatial_dice_2: 0.15373/0.21836, loss_spatial_ce_2: 0.00830/0.07779, loss_grounding_bce_2: 0.24917/0.08642, loss_grounding_dice_2: 0.36580/0.17972, loss_grounding_ce_2: 0.13340/0.27965, loss_mask_ce_3: 1.18287/0.92445, loss_mask_bce_3: 0.75063/0.33659, loss_mask_dice_3: 2.15991/1.17015, loss_spatial_bce_3: 0.06397/0.09109, loss_spatial_dice_3: 0.14115/0.21897, loss_spatial_ce_3: 0.03547/0.08150, loss_grounding_bce_3: 0.24296/0.08670, loss_grounding_dice_3: 0.35999/0.17947, loss_grounding_ce_3: 0.15310/0.28102, loss_mask_ce_4: 1.14819/0.92420, loss_mask_bce_4: 0.66994/0.33851, loss_mask_dice_4: 2.08871/1.19362, loss_spatial_bce_4: 0.06756/0.09531, loss_spatial_dice_4: 0.15713/0.23038, loss_spatial_ce_4: 0.02620/0.09751, loss_grounding_bce_4: 0.23692/0.08711, loss_grounding_dice_4: 0.36637/0.18230, loss_grounding_ce_4: 0.17740/0.28399, loss_mask_ce_5: 1.20330/0.93954, loss_mask_bce_5: 0.65077/0.34080, loss_mask_dice_5: 1.98583/1.20008, loss_spatial_bce_5: 0.06438/0.09697, loss_spatial_dice_5: 0.14797/0.23397, loss_spatial_ce_5: 0.04406/0.11240, loss_grounding_bce_5: 0.25387/0.08754, loss_grounding_dice_5: 0.37190/0.18349, loss_grounding_ce_5: 0.26999/0.29663, loss_mask_ce_6: 1.21427/0.97857, loss_mask_bce_6: 0.66867/0.34347, loss_mask_dice_6: 2.08060/1.20244, loss_spatial_bce_6: 0.07186/0.10270, loss_spatial_dice_6: 0.14820/0.23637, loss_spatial_ce_6: 0.05499/0.13820, loss_grounding_bce_6: 0.23544/0.08824, loss_grounding_dice_6: 0.36821/0.18365, loss_grounding_ce_6: 0.19306/0.31292, loss_mask_ce_7: 1.38426/1.02345, loss_mask_bce_7: 0.70692/0.35134, loss_mask_dice_7: 2.28582/1.25804, loss_spatial_bce_7: 0.07983/0.11114, loss_spatial_dice_7: 0.15320/0.26402, loss_spatial_ce_7: 0.08154/0.17524, loss_grounding_bce_7: 0.26010/0.09014, loss_grounding_dice_7: 0.38281/0.19093, loss_grounding_ce_7: 0.19556/0.34625, loss_mask_ce_8: 1.48915/1.13233, loss_mask_bce_8: 0.76370/0.36497, loss_mask_dice_8: 2.58661/1.33200, loss_spatial_bce_8: 0.09739/0.13209, loss_spatial_dice_8: 0.20163/0.30336, loss_spatial_ce_8: 0.22936/0.23244, loss_grounding_bce_8: 0.20789/0.09382, loss_grounding_dice_8: 0.34871/0.20206, loss_grounding_ce_8: 0.67071/0.41426, loss_mask_ce_9: 4.73808/3.68252, loss_mask_bce_9: 0.81521/0.39189, loss_mask_dice_9: 4.46297/1.90521, loss_spatial_bce_9: 0.33594/0.33396, loss_spatial_dice_9: 0.90145/0.82309, loss_spatial_ce_9: 1.17479/1.50633, loss_grounding_bce_9: 0.16479/0.10528, loss_grounding_dice_9: 0.53907/0.28146, loss_grounding_ce_9: 0.93969/0.68058] items per batch[64] items per second[0.23] total items[2489600] mini batches[ 38900] memory[7341] epoch remaining[1:00:04]
INFO:trainer.default_trainer:epochs[    21] optim steps[39000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38443/0.90736, loss_mask_bce_0: 0.42563/0.33447, loss_mask_dice_0: 1.26715/1.16670, loss_spatial_bce_0: 0.07665/0.08902, loss_spatial_dice_0: 0.22709/0.21309, loss_spatial_ce_0: 0.13356/0.06855, loss_grounding_bce_0: 0.08707/0.08621, loss_grounding_dice_0: 0.21305/0.17910, loss_grounding_ce_0: 0.27226/0.27476, loss_mask_ce_1: 0.43878/0.90782, loss_mask_bce_1: 0.40183/0.33530, loss_mask_dice_1: 1.17468/1.17378, loss_spatial_bce_1: 0.07824/0.08968, loss_spatial_dice_1: 0.24659/0.21727, loss_spatial_ce_1: 0.15323/0.07418, loss_grounding_bce_1: 0.08368/0.08634, loss_grounding_dice_1: 0.23948/0.17993, loss_grounding_ce_1: 0.27802/0.27639, loss_mask_ce_2: 0.43456/0.91539, loss_mask_bce_2: 0.40208/0.33569, loss_mask_dice_2: 1.21477/1.17322, loss_spatial_bce_2: 0.07728/0.09021, loss_spatial_dice_2: 0.23936/0.21835, loss_spatial_ce_2: 0.14459/0.07775, loss_grounding_bce_2: 0.08146/0.08641, loss_grounding_dice_2: 0.21306/0.17973, loss_grounding_ce_2: 0.27489/0.27958, loss_mask_ce_3: 0.39133/0.92443, loss_mask_bce_3: 0.40379/0.33664, loss_mask_dice_3: 1.22713/1.17055, loss_spatial_bce_3: 0.07772/0.09108, loss_spatial_dice_3: 0.25828/0.21896, loss_spatial_ce_3: 0.16710/0.08145, loss_grounding_bce_3: 0.08229/0.08669, loss_grounding_dice_3: 0.22413/0.17947, loss_grounding_ce_3: 0.27711/0.28095, loss_mask_ce_4: 0.50661/0.92414, loss_mask_bce_4: 0.40212/0.33855, loss_mask_dice_4: 1.31892/1.19407, loss_spatial_bce_4: 0.07573/0.09529, loss_spatial_dice_4: 0.24986/0.23038, loss_spatial_ce_4: 0.27958/0.09742, loss_grounding_bce_4: 0.08363/0.08710, loss_grounding_dice_4: 0.24584/0.18230, loss_grounding_ce_4: 0.28746/0.28394, loss_mask_ce_5: 0.51702/0.93952, loss_mask_bce_5: 0.40540/0.34084, loss_mask_dice_5: 1.26252/1.20057, loss_spatial_bce_5: 0.07624/0.09695, loss_spatial_dice_5: 0.26550/0.23397, loss_spatial_ce_5: 0.22262/0.11233, loss_grounding_bce_5: 0.08769/0.08753, loss_grounding_dice_5: 0.22694/0.18349, loss_grounding_ce_5: 0.26388/0.29660, loss_mask_ce_6: 0.59662/0.97858, loss_mask_bce_6: 0.40418/0.34352, loss_mask_dice_6: 1.28840/1.20293, loss_spatial_bce_6: 0.08584/0.10268, loss_spatial_dice_6: 0.26680/0.23637, loss_spatial_ce_6: 0.17260/0.13814, loss_grounding_bce_6: 0.08811/0.08823, loss_grounding_dice_6: 0.26078/0.18365, loss_grounding_ce_6: 0.26404/0.31284, loss_mask_ce_7: 0.39416/1.02344, loss_mask_bce_7: 0.39867/0.35138, loss_mask_dice_7: 1.44573/1.25852, loss_spatial_bce_7: 0.09260/0.11112, loss_spatial_dice_7: 0.30641/0.26403, loss_spatial_ce_7: 0.22104/0.17518, loss_grounding_bce_7: 0.08486/0.09014, loss_grounding_dice_7: 0.27901/0.19092, loss_grounding_ce_7: 0.29902/0.34622, loss_mask_ce_8: 0.68961/1.13238, loss_mask_bce_8: 0.42600/0.36501, loss_mask_dice_8: 1.60669/1.33255, loss_spatial_bce_8: 0.09959/0.13206, loss_spatial_dice_8: 0.38519/0.30336, loss_spatial_ce_8: 0.25827/0.23236, loss_grounding_bce_8: 0.09206/0.09381, loss_grounding_dice_8: 0.29059/0.20206, loss_grounding_ce_8: 0.28244/0.41418, loss_mask_ce_9: 2.83327/3.68272, loss_mask_bce_9: 0.47713/0.39194, loss_mask_dice_9: 2.46891/1.90599, loss_spatial_bce_9: 0.33194/0.33395, loss_spatial_dice_9: 0.82558/0.82308, loss_spatial_ce_9: 1.24877/1.50615, loss_grounding_bce_9: 0.08044/0.10529, loss_grounding_dice_9: 0.37938/0.28148, loss_grounding_ce_9: 0.32942/0.68051] items per batch[64] items per second[0.23] total items[2496000] mini batches[ 39000] memory[7341] epoch remaining[0:55:16]
INFO:trainer.default_trainer:epochs[    21] optim steps[39100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35454/0.90738, loss_mask_bce_0: 0.24871/0.33438, loss_mask_dice_0: 0.37044/1.16633, loss_spatial_bce_0: 0.16136/0.08900, loss_spatial_dice_0: 0.20987/0.21307, loss_spatial_ce_0: 0.15914/0.06851, loss_grounding_bce_0: 0.07062/0.08618, loss_grounding_dice_0: 0.10860/0.17905, loss_grounding_ce_0: 0.18420/0.27468, loss_mask_ce_1: 0.35327/0.90779, loss_mask_bce_1: 0.25904/0.33521, loss_mask_dice_1: 0.38404/1.17345, loss_spatial_bce_1: 0.17186/0.08966, loss_spatial_dice_1: 0.21133/0.21724, loss_spatial_ce_1: 0.15839/0.07413, loss_grounding_bce_1: 0.07425/0.08631, loss_grounding_dice_1: 0.11327/0.17990, loss_grounding_ce_1: 0.17883/0.27635, loss_mask_ce_2: 0.32905/0.91538, loss_mask_bce_2: 0.26291/0.33560, loss_mask_dice_2: 0.38490/1.17283, loss_spatial_bce_2: 0.16313/0.09020, loss_spatial_dice_2: 0.19941/0.21832, loss_spatial_ce_2: 0.11248/0.07771, loss_grounding_bce_2: 0.07337/0.08639, loss_grounding_dice_2: 0.10397/0.17969, loss_grounding_ce_2: 0.19767/0.27951, loss_mask_ce_3: 0.32015/0.92446, loss_mask_bce_3: 0.25043/0.33654, loss_mask_dice_3: 0.36026/1.17022, loss_spatial_bce_3: 0.17557/0.09106, loss_spatial_dice_3: 0.22120/0.21893, loss_spatial_ce_3: 0.10865/0.08142, loss_grounding_bce_3: 0.07388/0.08666, loss_grounding_dice_3: 0.11129/0.17944, loss_grounding_ce_3: 0.21385/0.28086, loss_mask_ce_4: 0.30692/0.92415, loss_mask_bce_4: 0.25686/0.33846, loss_mask_dice_4: 0.38885/1.19369, loss_spatial_bce_4: 0.14059/0.09527, loss_spatial_dice_4: 0.20912/0.23036, loss_spatial_ce_4: 0.09725/0.09741, loss_grounding_bce_4: 0.07119/0.08708, loss_grounding_dice_4: 0.11261/0.18225, loss_grounding_ce_4: 0.19577/0.28381, loss_mask_ce_5: 0.30072/0.93952, loss_mask_bce_5: 0.27137/0.34075, loss_mask_dice_5: 0.45687/1.20016, loss_spatial_bce_5: 0.13730/0.09694, loss_spatial_dice_5: 0.21752/0.23394, loss_spatial_ce_5: 0.09977/0.11232, loss_grounding_bce_5: 0.07190/0.08751, loss_grounding_dice_5: 0.10827/0.18346, loss_grounding_ce_5: 0.18863/0.29646, loss_mask_ce_6: 0.40412/0.97861, loss_mask_bce_6: 0.26809/0.34343, loss_mask_dice_6: 0.43058/1.20258, loss_spatial_bce_6: 0.19490/0.10267, loss_spatial_dice_6: 0.23116/0.23634, loss_spatial_ce_6: 0.10400/0.13814, loss_grounding_bce_6: 0.07328/0.08821, loss_grounding_dice_6: 0.10645/0.18361, loss_grounding_ce_6: 0.27892/0.31266, loss_mask_ce_7: 0.56478/1.02353, loss_mask_bce_7: 0.38801/0.35130, loss_mask_dice_7: 0.56713/1.25816, loss_spatial_bce_7: 0.16429/0.11110, loss_spatial_dice_7: 0.22887/0.26402, loss_spatial_ce_7: 0.09223/0.17511, loss_grounding_bce_7: 0.12395/0.09012, loss_grounding_dice_7: 0.16105/0.19087, loss_grounding_ce_7: 0.29920/0.34608, loss_mask_ce_8: 0.39638/1.13249, loss_mask_bce_8: 0.43157/0.36493, loss_mask_dice_8: 0.61579/1.33209, loss_spatial_bce_8: 0.30830/0.13205, loss_spatial_dice_8: 0.29726/0.30335, loss_spatial_ce_8: 0.27065/0.23232, loss_grounding_bce_8: 0.15653/0.09380, loss_grounding_dice_8: 0.19968/0.20203, loss_grounding_ce_8: 0.20480/0.41414, loss_mask_ce_9: 2.82155/3.68239, loss_mask_bce_9: 0.43033/0.39182, loss_mask_dice_9: 0.80889/1.90527, loss_spatial_bce_9: 0.38760/0.33391, loss_spatial_dice_9: 0.80563/0.82305, loss_spatial_ce_9: 1.32103/1.50604, loss_grounding_bce_9: 0.16294/0.10527, loss_grounding_dice_9: 0.33202/0.28143, loss_grounding_ce_9: 0.35450/0.68054] items per batch[64] items per second[0.23] total items[2502400] mini batches[ 39100] memory[7341] epoch remaining[0:50:32]
INFO:trainer.default_trainer:epochs[    21] optim steps[39200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.46520/0.90736, loss_mask_bce_0: 0.30685/0.33444, loss_mask_dice_0: 0.89458/1.16651, loss_spatial_bce_0: 0.05746/0.08898, loss_spatial_dice_0: 0.16908/0.21303, loss_spatial_ce_0: 0.00380/0.06845, loss_grounding_bce_0: 0.04517/0.08619, loss_grounding_dice_0: 0.24933/0.17908, loss_grounding_ce_0: 0.34241/0.27481, loss_mask_ce_1: 0.46226/0.90777, loss_mask_bce_1: 0.28799/0.33528, loss_mask_dice_1: 0.90228/1.17367, loss_spatial_bce_1: 0.05342/0.08964, loss_spatial_dice_1: 0.17453/0.21721, loss_spatial_ce_1: 0.01657/0.07408, loss_grounding_bce_1: 0.03876/0.08632, loss_grounding_dice_1: 0.23295/0.17994, loss_grounding_ce_1: 0.32333/0.27647, loss_mask_ce_2: 0.42414/0.91531, loss_mask_bce_2: 0.31033/0.33567, loss_mask_dice_2: 0.92292/1.17304, loss_spatial_bce_2: 0.05397/0.09018, loss_spatial_dice_2: 0.16743/0.21828, loss_spatial_ce_2: 0.01656/0.07765, loss_grounding_bce_2: 0.04735/0.08641, loss_grounding_dice_2: 0.26107/0.17973, loss_grounding_ce_2: 0.25334/0.27961, loss_mask_ce_3: 0.47371/0.92439, loss_mask_bce_3: 0.30254/0.33661, loss_mask_dice_3: 0.88526/1.17044, loss_spatial_bce_3: 0.05911/0.09104, loss_spatial_dice_3: 0.17176/0.21889, loss_spatial_ce_3: 0.01168/0.08136, loss_grounding_bce_3: 0.04479/0.08667, loss_grounding_dice_3: 0.25899/0.17948, loss_grounding_ce_3: 0.29655/0.28098, loss_mask_ce_4: 0.44473/0.92409, loss_mask_bce_4: 0.31064/0.33854, loss_mask_dice_4: 0.90832/1.19389, loss_spatial_bce_4: 0.06266/0.09525, loss_spatial_dice_4: 0.18549/0.23032, loss_spatial_ce_4: 0.02254/0.09735, loss_grounding_bce_4: 0.05017/0.08708, loss_grounding_dice_4: 0.24030/0.18229, loss_grounding_ce_4: 0.31406/0.28392, loss_mask_ce_5: 0.47545/0.93940, loss_mask_bce_5: 0.29804/0.34083, loss_mask_dice_5: 0.90489/1.20039, loss_spatial_bce_5: 0.06219/0.09692, loss_spatial_dice_5: 0.18866/0.23390, loss_spatial_ce_5: 0.05805/0.11225, loss_grounding_bce_5: 0.04696/0.08751, loss_grounding_dice_5: 0.24363/0.18349, loss_grounding_ce_5: 0.26749/0.29664, loss_mask_ce_6: 0.51879/0.97853, loss_mask_bce_6: 0.30198/0.34351, loss_mask_dice_6: 0.91748/1.20289, loss_spatial_bce_6: 0.06192/0.10265, loss_spatial_dice_6: 0.18526/0.23632, loss_spatial_ce_6: 0.07800/0.13809, loss_grounding_bce_6: 0.04424/0.08821, loss_grounding_dice_6: 0.22993/0.18365, loss_grounding_ce_6: 0.29178/0.31286, loss_mask_ce_7: 0.67020/1.02351, loss_mask_bce_7: 0.32247/0.35137, loss_mask_dice_7: 0.93830/1.25843, loss_spatial_bce_7: 0.05688/0.11108, loss_spatial_dice_7: 0.19278/0.26399, loss_spatial_ce_7: 0.09705/0.17501, loss_grounding_bce_7: 0.04349/0.09012, loss_grounding_dice_7: 0.21563/0.19092, loss_grounding_ce_7: 0.32034/0.34626, loss_mask_ce_8: 0.66420/1.13254, loss_mask_bce_8: 0.31902/0.36501, loss_mask_dice_8: 0.96845/1.33233, loss_spatial_bce_8: 0.05551/0.13203, loss_spatial_dice_8: 0.20968/0.30331, loss_spatial_ce_8: 0.13541/0.23221, loss_grounding_bce_8: 0.04404/0.09380, loss_grounding_dice_8: 0.21552/0.20208, loss_grounding_ce_8: 0.29664/0.41425, loss_mask_ce_9: 3.40296/3.68264, loss_mask_bce_9: 0.34929/0.39190, loss_mask_dice_9: 1.54218/1.90566, loss_spatial_bce_9: 0.28482/0.33392, loss_spatial_dice_9: 0.86225/0.82306, loss_spatial_ce_9: 1.27206/1.50593, loss_grounding_bce_9: 0.04661/0.10528, loss_grounding_dice_9: 0.38630/0.28148, loss_grounding_ce_9: 0.40862/0.68064] items per batch[64] items per second[0.23] total items[2508800] mini batches[ 39200] memory[7341] epoch remaining[0:45:57]
INFO:trainer.default_trainer:epochs[    21] optim steps[39300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98149/0.90725, loss_mask_bce_0: 0.41737/0.33451, loss_mask_dice_0: 0.59314/1.16655, loss_spatial_bce_0: 0.09209/0.08898, loss_spatial_dice_0: 0.14209/0.21301, loss_spatial_ce_0: 0.00025/0.06841, loss_grounding_bce_0: 0.12565/0.08619, loss_grounding_dice_0: 0.12475/0.17908, loss_grounding_ce_0: 0.11309/0.27484, loss_mask_ce_1: 1.00677/0.90765, loss_mask_bce_1: 0.41568/0.33535, loss_mask_dice_1: 0.66630/1.17362, loss_spatial_bce_1: 0.09113/0.08964, loss_spatial_dice_1: 0.15177/0.21718, loss_spatial_ce_1: 0.00007/0.07404, loss_grounding_bce_1: 0.12341/0.08633, loss_grounding_dice_1: 0.12382/0.17992, loss_grounding_ce_1: 0.12342/0.27654, loss_mask_ce_2: 1.05313/0.91520, loss_mask_bce_2: 0.43094/0.33575, loss_mask_dice_2: 0.66615/1.17303, loss_spatial_bce_2: 0.09519/0.09018, loss_spatial_dice_2: 0.15091/0.21826, loss_spatial_ce_2: 0.00014/0.07761, loss_grounding_bce_2: 0.12257/0.08641, loss_grounding_dice_2: 0.11094/0.17973, loss_grounding_ce_2: 0.11786/0.27969, loss_mask_ce_3: 0.74482/0.92429, loss_mask_bce_3: 0.49655/0.33668, loss_mask_dice_3: 0.63353/1.17043, loss_spatial_bce_3: 0.10036/0.09105, loss_spatial_dice_3: 0.14974/0.21887, loss_spatial_ce_3: 0.00030/0.08133, loss_grounding_bce_3: 0.12554/0.08667, loss_grounding_dice_3: 0.11012/0.17947, loss_grounding_ce_3: 0.10550/0.28106, loss_mask_ce_4: 0.73721/0.92399, loss_mask_bce_4: 0.47200/0.33860, loss_mask_dice_4: 0.65624/1.19391, loss_spatial_bce_4: 0.10354/0.09526, loss_spatial_dice_4: 0.16293/0.23031, loss_spatial_ce_4: 0.00228/0.09729, loss_grounding_bce_4: 0.12455/0.08709, loss_grounding_dice_4: 0.12806/0.18227, loss_grounding_ce_4: 0.11605/0.28403, loss_mask_ce_5: 0.70230/0.93934, loss_mask_bce_5: 0.42144/0.34090, loss_mask_dice_5: 0.64485/1.20034, loss_spatial_bce_5: 0.09553/0.09693, loss_spatial_dice_5: 0.15142/0.23388, loss_spatial_ce_5: 0.02034/0.11219, loss_grounding_bce_5: 0.12538/0.08752, loss_grounding_dice_5: 0.11070/0.18348, loss_grounding_ce_5: 0.10192/0.29678, loss_mask_ce_6: 0.75116/0.97846, loss_mask_bce_6: 0.42801/0.34359, loss_mask_dice_6: 0.64264/1.20292, loss_spatial_bce_6: 0.10526/0.10265, loss_spatial_dice_6: 0.15479/0.23629, loss_spatial_ce_6: 0.03410/0.13806, loss_grounding_bce_6: 0.12034/0.08823, loss_grounding_dice_6: 0.12453/0.18364, loss_grounding_ce_6: 0.13223/0.31293, loss_mask_ce_7: 0.84558/1.02342, loss_mask_bce_7: 0.46667/0.35146, loss_mask_dice_7: 0.73519/1.25844, loss_spatial_bce_7: 0.11561/0.11109, loss_spatial_dice_7: 0.22303/0.26398, loss_spatial_ce_7: 0.04929/0.17491, loss_grounding_bce_7: 0.12844/0.09013, loss_grounding_dice_7: 0.13090/0.19091, loss_grounding_ce_7: 0.13840/0.34637, loss_mask_ce_8: 0.69701/1.13241, loss_mask_bce_8: 0.49918/0.36510, loss_mask_dice_8: 0.75365/1.33236, loss_spatial_bce_8: 0.15724/0.13203, loss_spatial_dice_8: 0.25518/0.30329, loss_spatial_ce_8: 0.08879/0.23220, loss_grounding_bce_8: 0.13814/0.09382, loss_grounding_dice_8: 0.11961/0.20207, loss_grounding_ce_8: 0.14143/0.41437, loss_mask_ce_9: 3.12765/3.68278, loss_mask_bce_9: 0.47281/0.39199, loss_mask_dice_9: 1.25301/1.90565, loss_spatial_bce_9: 0.59609/0.33394, loss_spatial_dice_9: 0.89405/0.82310, loss_spatial_ce_9: 2.27443/1.50595, loss_grounding_bce_9: 0.13112/0.10529, loss_grounding_dice_9: 0.15231/0.28150, loss_grounding_ce_9: 0.26205/0.68064] items per batch[64] items per second[0.23] total items[2515200] mini batches[ 39300] memory[7341] epoch remaining[0:41:24]
INFO:trainer.default_trainer:epochs[    21] optim steps[39400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.27725/0.90721, loss_mask_bce_0: 0.09924/0.33447, loss_mask_dice_0: 0.64128/1.16620, loss_spatial_bce_0: 0.04695/0.08897, loss_spatial_dice_0: 0.23180/0.21297, loss_spatial_ce_0: 0.00465/0.06835, loss_grounding_bce_0: 0.01412/0.08621, loss_grounding_dice_0: 0.65115/0.17907, loss_grounding_ce_0: 0.06017/0.27483, loss_mask_ce_1: 1.29426/0.90761, loss_mask_bce_1: 0.12362/0.33530, loss_mask_dice_1: 0.55536/1.17325, loss_spatial_bce_1: 0.05734/0.08963, loss_spatial_dice_1: 0.24675/0.21713, loss_spatial_ce_1: 0.00349/0.07399, loss_grounding_bce_1: 0.01465/0.08634, loss_grounding_dice_1: 0.50671/0.17990, loss_grounding_ce_1: 0.18734/0.27649, loss_mask_ce_2: 1.35514/0.91514, loss_mask_bce_2: 0.12596/0.33570, loss_mask_dice_2: 0.58410/1.17267, loss_spatial_bce_2: 0.06104/0.09017, loss_spatial_dice_2: 0.25539/0.21820, loss_spatial_ce_2: 0.00286/0.07755, loss_grounding_bce_2: 0.01369/0.08643, loss_grounding_dice_2: 0.42446/0.17970, loss_grounding_ce_2: 0.15523/0.27967, loss_mask_ce_3: 1.28550/0.92425, loss_mask_bce_3: 0.13482/0.33663, loss_mask_dice_3: 0.75991/1.17004, loss_spatial_bce_3: 0.05843/0.09104, loss_spatial_dice_3: 0.25320/0.21883, loss_spatial_ce_3: 0.00568/0.08128, loss_grounding_bce_3: 0.00987/0.08668, loss_grounding_dice_3: 0.49410/0.17946, loss_grounding_ce_3: 0.17249/0.28108, loss_mask_ce_4: 1.53500/0.92398, loss_mask_bce_4: 0.13156/0.33855, loss_mask_dice_4: 0.63078/1.19357, loss_spatial_bce_4: 0.05084/0.09524, loss_spatial_dice_4: 0.23447/0.23026, loss_spatial_ce_4: 0.00465/0.09723, loss_grounding_bce_4: 0.01215/0.08710, loss_grounding_dice_4: 0.43973/0.18226, loss_grounding_ce_4: 0.15436/0.28403, loss_mask_ce_5: 1.50571/0.93930, loss_mask_bce_5: 0.13114/0.34085, loss_mask_dice_5: 0.61555/1.20001, loss_spatial_bce_5: 0.05775/0.09692, loss_spatial_dice_5: 0.23521/0.23383, loss_spatial_ce_5: 0.01446/0.11219, loss_grounding_bce_5: 0.01181/0.08753, loss_grounding_dice_5: 0.51490/0.18346, loss_grounding_ce_5: 0.10943/0.29676, loss_mask_ce_6: 1.45042/0.97843, loss_mask_bce_6: 0.15213/0.34354, loss_mask_dice_6: 0.59511/1.20259, loss_spatial_bce_6: 0.05233/0.10264, loss_spatial_dice_6: 0.24058/0.23625, loss_spatial_ce_6: 0.03732/0.13802, loss_grounding_bce_6: 0.01147/0.08824, loss_grounding_dice_6: 0.50491/0.18363, loss_grounding_ce_6: 0.13804/0.31290, loss_mask_ce_7: 1.29745/1.02341, loss_mask_bce_7: 0.18293/0.35140, loss_mask_dice_7: 0.73614/1.25809, loss_spatial_bce_7: 0.05437/0.11107, loss_spatial_dice_7: 0.25470/0.26394, loss_spatial_ce_7: 0.02645/0.17481, loss_grounding_bce_7: 0.01442/0.09014, loss_grounding_dice_7: 0.50190/0.19089, loss_grounding_ce_7: 0.13293/0.34628, loss_mask_ce_8: 1.48560/1.13234, loss_mask_bce_8: 0.19486/0.36505, loss_mask_dice_8: 0.80388/1.33199, loss_spatial_bce_8: 0.06721/0.13202, loss_spatial_dice_8: 0.31398/0.30323, loss_spatial_ce_8: 0.08239/0.23214, loss_grounding_bce_8: 0.01201/0.09384, loss_grounding_dice_8: 0.51828/0.20204, loss_grounding_ce_8: 0.17172/0.41426, loss_mask_ce_9: 3.16599/3.68254, loss_mask_bce_9: 0.19632/0.39193, loss_mask_dice_9: 1.07566/1.90506, loss_spatial_bce_9: 0.23212/0.33393, loss_spatial_dice_9: 0.80814/0.82308, loss_spatial_ce_9: 1.52958/1.50591, loss_grounding_bce_9: 0.00760/0.10531, loss_grounding_dice_9: 0.52717/0.28145, loss_grounding_ce_9: 0.35808/0.68037] items per batch[64] items per second[0.23] total items[2521600] mini batches[ 39400] memory[7341] epoch remaining[0:36:45]
INFO:trainer.default_trainer:epochs[    21] optim steps[39500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66112/0.90705, loss_mask_bce_0: 0.52578/0.33456, loss_mask_dice_0: 0.54244/1.16616, loss_spatial_bce_0: 0.14077/0.08896, loss_spatial_dice_0: 0.20544/0.21294, loss_spatial_ce_0: 0.00081/0.06828, loss_grounding_bce_0: 0.11150/0.08622, loss_grounding_dice_0: 0.08543/0.17908, loss_grounding_ce_0: 0.09917/0.27488, loss_mask_ce_1: 0.62018/0.90744, loss_mask_bce_1: 0.53528/0.33540, loss_mask_dice_1: 0.66406/1.17319, loss_spatial_bce_1: 0.13993/0.08962, loss_spatial_dice_1: 0.20921/0.21711, loss_spatial_ce_1: 0.00089/0.07393, loss_grounding_bce_1: 0.10907/0.08635, loss_grounding_dice_1: 0.08855/0.17990, loss_grounding_ce_1: 0.10173/0.27655, loss_mask_ce_2: 0.65753/0.91498, loss_mask_bce_2: 0.53698/0.33580, loss_mask_dice_2: 0.54985/1.17262, loss_spatial_bce_2: 0.14680/0.09017, loss_spatial_dice_2: 0.21405/0.21818, loss_spatial_ce_2: 0.00298/0.07750, loss_grounding_bce_2: 0.11605/0.08644, loss_grounding_dice_2: 0.09157/0.17971, loss_grounding_ce_2: 0.11606/0.27975, loss_mask_ce_3: 0.63369/0.92416, loss_mask_bce_3: 0.53791/0.33672, loss_mask_dice_3: 0.52744/1.16998, loss_spatial_bce_3: 0.14552/0.09104, loss_spatial_dice_3: 0.21127/0.21880, loss_spatial_ce_3: 0.00506/0.08123, loss_grounding_bce_3: 0.11423/0.08670, loss_grounding_dice_3: 0.08780/0.17947, loss_grounding_ce_3: 0.09387/0.28110, loss_mask_ce_4: 0.65792/0.92389, loss_mask_bce_4: 0.51579/0.33865, loss_mask_dice_4: 0.56393/1.19352, loss_spatial_bce_4: 0.14089/0.09524, loss_spatial_dice_4: 0.21324/0.23023, loss_spatial_ce_4: 0.03104/0.09716, loss_grounding_bce_4: 0.11315/0.08712, loss_grounding_dice_4: 0.08854/0.18227, loss_grounding_ce_4: 0.08408/0.28407, loss_mask_ce_5: 0.69630/0.93926, loss_mask_bce_5: 0.51708/0.34095, loss_mask_dice_5: 0.55353/1.19992, loss_spatial_bce_5: 0.13255/0.09692, loss_spatial_dice_5: 0.21110/0.23381, loss_spatial_ce_5: 0.03449/0.11214, loss_grounding_bce_5: 0.10792/0.08754, loss_grounding_dice_5: 0.08764/0.18349, loss_grounding_ce_5: 0.09045/0.29680, loss_mask_ce_6: 0.55155/0.97837, loss_mask_bce_6: 0.58087/0.34363, loss_mask_dice_6: 0.70119/1.20256, loss_spatial_bce_6: 0.14496/0.10264, loss_spatial_dice_6: 0.21167/0.23622, loss_spatial_ce_6: 0.06405/0.13796, loss_grounding_bce_6: 0.11407/0.08825, loss_grounding_dice_6: 0.08806/0.18365, loss_grounding_ce_6: 0.09226/0.31297, loss_mask_ce_7: 0.59660/1.02336, loss_mask_bce_7: 0.61080/0.35151, loss_mask_dice_7: 0.57756/1.25802, loss_spatial_bce_7: 0.16022/0.11106, loss_spatial_dice_7: 0.21766/0.26392, loss_spatial_ce_7: 0.05947/0.17473, loss_grounding_bce_7: 0.13468/0.09016, loss_grounding_dice_7: 0.09117/0.19092, loss_grounding_ce_7: 0.08494/0.34635, loss_mask_ce_8: 0.79828/1.13227, loss_mask_bce_8: 0.65885/0.36514, loss_mask_dice_8: 0.67183/1.33189, loss_spatial_bce_8: 0.20957/0.13201, loss_spatial_dice_8: 0.22495/0.30321, loss_spatial_ce_8: 0.19409/0.23211, loss_grounding_bce_8: 0.15491/0.09386, loss_grounding_dice_8: 0.11318/0.20204, loss_grounding_ce_8: 1.05139/0.41435, loss_mask_ce_9: 4.49200/3.68274, loss_mask_bce_9: 0.72387/0.39203, loss_mask_dice_9: 1.14674/1.90496, loss_spatial_bce_9: 0.41927/0.33392, loss_spatial_dice_9: 0.84619/0.82309, loss_spatial_ce_9: 1.33776/1.50577, loss_grounding_bce_9: 0.21316/0.10532, loss_grounding_dice_9: 0.20418/0.28145, loss_grounding_ce_9: 0.88744/0.68044] items per batch[64] items per second[0.23] total items[2528000] mini batches[ 39500] memory[7341] epoch remaining[0:32:09]
INFO:trainer.default_trainer:epochs[    21] optim steps[39600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33277/0.90695, loss_mask_bce_0: 0.38919/0.33454, loss_mask_dice_0: 1.02975/1.16600, loss_spatial_bce_0: 0.07319/0.08896, loss_spatial_dice_0: 0.15792/0.21291, loss_spatial_ce_0: 0.01475/0.06825, loss_grounding_bce_0: 0.24457/0.08622, loss_grounding_dice_0: 0.16446/0.17904, loss_grounding_ce_0: 0.03862/0.27482, loss_mask_ce_1: 0.30454/0.90728, loss_mask_bce_1: 0.38097/0.33538, loss_mask_dice_1: 1.09753/1.17303, loss_spatial_bce_1: 0.07186/0.08962, loss_spatial_dice_1: 0.15504/0.21706, loss_spatial_ce_1: 0.20765/0.07391, loss_grounding_bce_1: 0.25111/0.08635, loss_grounding_dice_1: 0.16950/0.17986, loss_grounding_ce_1: 0.02995/0.27650, loss_mask_ce_2: 0.30543/0.91481, loss_mask_bce_2: 0.39442/0.33579, loss_mask_dice_2: 1.19540/1.17246, loss_spatial_bce_2: 0.07749/0.09017, loss_spatial_dice_2: 0.19912/0.21815, loss_spatial_ce_2: 0.02901/0.07746, loss_grounding_bce_2: 0.26055/0.08644, loss_grounding_dice_2: 0.17496/0.17969, loss_grounding_ce_2: 0.03492/0.27965, loss_mask_ce_3: 0.32851/0.92404, loss_mask_bce_3: 0.38842/0.33670, loss_mask_dice_3: 1.07873/1.16985, loss_spatial_bce_3: 0.07777/0.09103, loss_spatial_dice_3: 0.18239/0.21877, loss_spatial_ce_3: 0.04429/0.08119, loss_grounding_bce_3: 0.23352/0.08670, loss_grounding_dice_3: 0.16526/0.17944, loss_grounding_ce_3: 0.03444/0.28103, loss_mask_ce_4: 0.50537/0.92374, loss_mask_bce_4: 0.38287/0.33865, loss_mask_dice_4: 1.07127/1.19333, loss_spatial_bce_4: 0.07365/0.09523, loss_spatial_dice_4: 0.16233/0.23019, loss_spatial_ce_4: 0.01551/0.09712, loss_grounding_bce_4: 0.25820/0.08712, loss_grounding_dice_4: 0.17489/0.18224, loss_grounding_ce_4: 0.02187/0.28397, loss_mask_ce_5: 0.29520/0.93914, loss_mask_bce_5: 0.37008/0.34094, loss_mask_dice_5: 1.15641/1.19975, loss_spatial_bce_5: 0.08333/0.09691, loss_spatial_dice_5: 0.22114/0.23378, loss_spatial_ce_5: 0.05343/0.11209, loss_grounding_bce_5: 0.25954/0.08754, loss_grounding_dice_5: 0.18626/0.18347, loss_grounding_ce_5: 0.03247/0.29667, loss_mask_ce_6: 0.36035/0.97825, loss_mask_bce_6: 0.37107/0.34363, loss_mask_dice_6: 1.08569/1.20241, loss_spatial_bce_6: 0.07591/0.10264, loss_spatial_dice_6: 0.17422/0.23619, loss_spatial_ce_6: 0.03209/0.13791, loss_grounding_bce_6: 0.25424/0.08826, loss_grounding_dice_6: 0.17382/0.18362, loss_grounding_ce_6: 0.02379/0.31286, loss_mask_ce_7: 0.41365/1.02325, loss_mask_bce_7: 0.36158/0.35151, loss_mask_dice_7: 1.04456/1.25783, loss_spatial_bce_7: 0.08203/0.11105, loss_spatial_dice_7: 0.17829/0.26389, loss_spatial_ce_7: 0.07421/0.17465, loss_grounding_bce_7: 0.24844/0.09017, loss_grounding_dice_7: 0.17545/0.19089, loss_grounding_ce_7: 0.02686/0.34618, loss_mask_ce_8: 0.53491/1.13208, loss_mask_bce_8: 0.34408/0.36513, loss_mask_dice_8: 1.15294/1.33167, loss_spatial_bce_8: 0.10426/0.13200, loss_spatial_dice_8: 0.24117/0.30316, loss_spatial_ce_8: 0.10701/0.23210, loss_grounding_bce_8: 0.23666/0.09386, loss_grounding_dice_8: 0.16507/0.20201, loss_grounding_ce_8: 0.02868/0.41420, loss_mask_ce_9: 2.40687/3.68259, loss_mask_bce_9: 0.35771/0.39199, loss_mask_dice_9: 1.51344/1.90461, loss_spatial_bce_9: 0.37782/0.33392, loss_spatial_dice_9: 0.89751/0.82306, loss_spatial_ce_9: 1.71654/1.50559, loss_grounding_bce_9: 0.24525/0.10531, loss_grounding_dice_9: 0.29942/0.28142, loss_grounding_ce_9: 0.14271/0.68031] items per batch[64] items per second[0.23] total items[2534400] mini batches[ 39600] memory[7341] epoch remaining[0:27:33]
INFO:trainer.default_trainer:epochs[    21] optim steps[39700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.40507/0.90674, loss_mask_bce_0: 0.43122/0.33454, loss_mask_dice_0: 6.44845/1.16604, loss_spatial_bce_0: 0.06159/0.08895, loss_spatial_dice_0: 0.25172/0.21287, loss_spatial_ce_0: 0.09952/0.06820, loss_grounding_bce_0: 0.01161/0.08622, loss_grounding_dice_0: 0.58453/0.17904, loss_grounding_ce_0: 0.26190/0.27487, loss_mask_ce_1: 1.31531/0.90708, loss_mask_bce_1: 0.44175/0.33537, loss_mask_dice_1: 6.50654/1.17302, loss_spatial_bce_1: 0.06771/0.08961, loss_spatial_dice_1: 0.29646/0.21703, loss_spatial_ce_1: 0.13429/0.07387, loss_grounding_bce_1: 0.01278/0.08634, loss_grounding_dice_1: 0.61539/0.17984, loss_grounding_ce_1: 0.28404/0.27655, loss_mask_ce_2: 1.40269/0.91463, loss_mask_bce_2: 0.43122/0.33579, loss_mask_dice_2: 6.73651/1.17249, loss_spatial_bce_2: 0.05837/0.09016, loss_spatial_dice_2: 0.27906/0.21813, loss_spatial_ce_2: 0.26456/0.07742, loss_grounding_bce_2: 0.01222/0.08643, loss_grounding_dice_2: 0.59106/0.17968, loss_grounding_ce_2: 0.25248/0.27970, loss_mask_ce_3: 1.51679/0.92389, loss_mask_bce_3: 0.46974/0.33669, loss_mask_dice_3: 6.34210/1.16983, loss_spatial_bce_3: 0.05481/0.09103, loss_spatial_dice_3: 0.25536/0.21874, loss_spatial_ce_3: 0.20074/0.08114, loss_grounding_bce_3: 0.01359/0.08669, loss_grounding_dice_3: 0.54158/0.17943, loss_grounding_ce_3: 0.36482/0.28112, loss_mask_ce_4: 1.35113/0.92358, loss_mask_bce_4: 0.46070/0.33864, loss_mask_dice_4: 6.87334/1.19336, loss_spatial_bce_4: 0.05563/0.09523, loss_spatial_dice_4: 0.30011/0.23017, loss_spatial_ce_4: 0.27059/0.09707, loss_grounding_bce_4: 0.01289/0.08711, loss_grounding_dice_4: 0.60290/0.18223, loss_grounding_ce_4: 0.30984/0.28405, loss_mask_ce_5: 1.60471/0.93901, loss_mask_bce_5: 0.45393/0.34093, loss_mask_dice_5: 6.16347/1.19974, loss_spatial_bce_5: 0.06135/0.09691, loss_spatial_dice_5: 0.34651/0.23377, loss_spatial_ce_5: 0.28077/0.11205, loss_grounding_bce_5: 0.01119/0.08754, loss_grounding_dice_5: 0.54725/0.18345, loss_grounding_ce_5: 0.25216/0.29673, loss_mask_ce_6: 1.40075/0.97815, loss_mask_bce_6: 0.49347/0.34363, loss_mask_dice_6: 6.59180/1.20240, loss_spatial_bce_6: 0.06599/0.10264, loss_spatial_dice_6: 0.32014/0.23618, loss_spatial_ce_6: 0.22259/0.13785, loss_grounding_bce_6: 0.01419/0.08825, loss_grounding_dice_6: 0.60867/0.18361, loss_grounding_ce_6: 0.33825/0.31295, loss_mask_ce_7: 1.61544/1.02316, loss_mask_bce_7: 0.52931/0.35150, loss_mask_dice_7: 6.92398/1.25784, loss_spatial_bce_7: 0.10167/0.11105, loss_spatial_dice_7: 0.33435/0.26387, loss_spatial_ce_7: 0.27182/0.17459, loss_grounding_bce_7: 0.01167/0.09017, loss_grounding_dice_7: 0.63915/0.19089, loss_grounding_ce_7: 0.34827/0.34621, loss_mask_ce_8: 2.15082/1.13202, loss_mask_bce_8: 0.51836/0.36510, loss_mask_dice_8: 6.92644/1.33168, loss_spatial_bce_8: 0.07791/0.13199, loss_spatial_dice_8: 0.37586/0.30313, loss_spatial_ce_8: 0.16341/0.23202, loss_grounding_bce_8: 0.01272/0.09385, loss_grounding_dice_8: 0.58596/0.20200, loss_grounding_ce_8: 0.85331/0.41416, loss_mask_ce_9: 7.00555/3.68246, loss_mask_bce_9: 0.61299/0.39198, loss_mask_dice_9: 9.53965/1.90466, loss_spatial_bce_9: 0.23340/0.33390, loss_spatial_dice_9: 0.93315/0.82305, loss_spatial_ce_9: 1.24516/1.50551, loss_grounding_bce_9: 0.01764/0.10531, loss_grounding_dice_9: 0.67437/0.28143, loss_grounding_ce_9: 1.56888/0.68035] items per batch[64] items per second[0.24] total items[2540800] mini batches[ 39700] memory[7341] epoch remaining[0:22:52]
INFO:trainer.default_trainer:epochs[    21] optim steps[39800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.46567/0.90680, loss_mask_bce_0: 0.14609/0.33455, loss_mask_dice_0: 0.49245/1.16617, loss_spatial_bce_0: 0.03201/0.08893, loss_spatial_dice_0: 0.13496/0.21282, loss_spatial_ce_0: 0.18535/0.06817, loss_grounding_bce_0: 0.03927/0.08622, loss_grounding_dice_0: 0.08497/0.17905, loss_grounding_ce_0: 0.04095/0.27479, loss_mask_ce_1: 0.28882/0.90713, loss_mask_bce_1: 0.15071/0.33538, loss_mask_dice_1: 0.70347/1.17309, loss_spatial_bce_1: 0.03329/0.08959, loss_spatial_dice_1: 0.13473/0.21697, loss_spatial_ce_1: 0.00122/0.07381, loss_grounding_bce_1: 0.03916/0.08633, loss_grounding_dice_1: 0.08554/0.17985, loss_grounding_ce_1: 0.04572/0.27648, loss_mask_ce_2: 0.39529/0.91475, loss_mask_bce_2: 0.15090/0.33580, loss_mask_dice_2: 0.49812/1.17254, loss_spatial_bce_2: 0.03263/0.09014, loss_spatial_dice_2: 0.11592/0.21807, loss_spatial_ce_2: 0.00060/0.07737, loss_grounding_bce_2: 0.05074/0.08643, loss_grounding_dice_2: 0.08328/0.17967, loss_grounding_ce_2: 0.04560/0.27966, loss_mask_ce_3: 0.44153/0.92393, loss_mask_bce_3: 0.14621/0.33672, loss_mask_dice_3: 0.56968/1.16995, loss_spatial_bce_3: 0.03389/0.09101, loss_spatial_dice_3: 0.13163/0.21868, loss_spatial_ce_3: 0.00050/0.08108, loss_grounding_bce_3: 0.06011/0.08669, loss_grounding_dice_3: 0.08322/0.17943, loss_grounding_ce_3: 0.04071/0.28110, loss_mask_ce_4: 0.33111/0.92366, loss_mask_bce_4: 0.14915/0.33866, loss_mask_dice_4: 0.76604/1.19348, loss_spatial_bce_4: 0.03526/0.09521, loss_spatial_dice_4: 0.13662/0.23012, loss_spatial_ce_4: 0.00136/0.09701, loss_grounding_bce_4: 0.03889/0.08711, loss_grounding_dice_4: 0.07269/0.18223, loss_grounding_ce_4: 0.04563/0.28400, loss_mask_ce_5: 0.46446/0.93909, loss_mask_bce_5: 0.14777/0.34093, loss_mask_dice_5: 0.49287/1.19988, loss_spatial_bce_5: 0.03488/0.09690, loss_spatial_dice_5: 0.14304/0.23372, loss_spatial_ce_5: 0.00571/0.11201, loss_grounding_bce_5: 0.04360/0.08753, loss_grounding_dice_5: 0.07945/0.18345, loss_grounding_ce_5: 0.04098/0.29670, loss_mask_ce_6: 0.55102/0.97824, loss_mask_bce_6: 0.14765/0.34363, loss_mask_dice_6: 0.57326/1.20249, loss_spatial_bce_6: 0.03935/0.10261, loss_spatial_dice_6: 0.12460/0.23613, loss_spatial_ce_6: 0.03068/0.13779, loss_grounding_bce_6: 0.05415/0.08825, loss_grounding_dice_6: 0.08564/0.18360, loss_grounding_ce_6: 0.04706/0.31289, loss_mask_ce_7: 0.55096/1.02324, loss_mask_bce_7: 0.17280/0.35153, loss_mask_dice_7: 0.86277/1.25803, loss_spatial_bce_7: 0.04478/0.11104, loss_spatial_dice_7: 0.17210/0.26382, loss_spatial_ce_7: 0.11246/0.17449, loss_grounding_bce_7: 0.06936/0.09016, loss_grounding_dice_7: 0.08991/0.19087, loss_grounding_ce_7: 0.04548/0.34615, loss_mask_ce_8: 0.43863/1.13211, loss_mask_bce_8: 0.19184/0.36512, loss_mask_dice_8: 0.89295/1.33180, loss_spatial_bce_8: 0.05261/0.13197, loss_spatial_dice_8: 0.18713/0.30308, loss_spatial_ce_8: 0.09928/0.23190, loss_grounding_bce_8: 0.05362/0.09384, loss_grounding_dice_8: 0.08392/0.20199, loss_grounding_ce_8: 0.06419/0.41407, loss_mask_ce_9: 3.18159/3.68267, loss_mask_bce_9: 0.17644/0.39201, loss_mask_dice_9: 0.90770/1.90499, loss_spatial_bce_9: 0.27096/0.33386, loss_spatial_dice_9: 0.81364/0.82304, loss_spatial_ce_9: 1.96667/1.50540, loss_grounding_bce_9: 0.03140/0.10530, loss_grounding_dice_9: 0.10457/0.28142, loss_grounding_ce_9: 0.92838/0.68018] items per batch[64] items per second[0.23] total items[2547200] mini batches[ 39800] memory[7341] epoch remaining[0:18:15]
INFO:trainer.default_trainer:epochs[    21] optim steps[39900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02166/0.90668, loss_mask_bce_0: 0.32723/0.33458, loss_mask_dice_0: 0.54833/1.16650, loss_spatial_bce_0: 0.07365/0.08891, loss_spatial_dice_0: 0.14885/0.21284, loss_spatial_ce_0: 0.00745/0.06813, loss_grounding_bce_0: 0.03761/0.08622, loss_grounding_dice_0: 0.04282/0.17905, loss_grounding_ce_0: 0.02410/0.27484, loss_mask_ce_1: 1.04450/0.90709, loss_mask_bce_1: 0.32301/0.33540, loss_mask_dice_1: 0.54963/1.17345, loss_spatial_bce_1: 0.07246/0.08957, loss_spatial_dice_1: 0.15849/0.21698, loss_spatial_ce_1: 0.01624/0.07375, loss_grounding_bce_1: 0.03849/0.08633, loss_grounding_dice_1: 0.04675/0.17986, loss_grounding_ce_1: 0.03302/0.27648, loss_mask_ce_2: 0.99114/0.91466, loss_mask_bce_2: 0.32585/0.33583, loss_mask_dice_2: 0.54836/1.17293, loss_spatial_bce_2: 0.07391/0.09012, loss_spatial_dice_2: 0.15934/0.21808, loss_spatial_ce_2: 0.03307/0.07732, loss_grounding_bce_2: 0.03761/0.08643, loss_grounding_dice_2: 0.04398/0.17968, loss_grounding_ce_2: 0.01291/0.27967, loss_mask_ce_3: 0.98840/0.92384, loss_mask_bce_3: 0.33529/0.33675, loss_mask_dice_3: 0.54891/1.17033, loss_spatial_bce_3: 0.07422/0.09100, loss_spatial_dice_3: 0.15415/0.21869, loss_spatial_ce_3: 0.03410/0.08106, loss_grounding_bce_3: 0.03667/0.08669, loss_grounding_dice_3: 0.04190/0.17944, loss_grounding_ce_3: 0.01645/0.28114, loss_mask_ce_4: 0.94986/0.92359, loss_mask_bce_4: 0.32193/0.33869, loss_mask_dice_4: 0.53825/1.19383, loss_spatial_bce_4: 0.07846/0.09519, loss_spatial_dice_4: 0.16739/0.23014, loss_spatial_ce_4: 0.02817/0.09696, loss_grounding_bce_4: 0.03783/0.08711, loss_grounding_dice_4: 0.04338/0.18224, loss_grounding_ce_4: 0.01073/0.28401, loss_mask_ce_5: 0.92955/0.93902, loss_mask_bce_5: 0.31056/0.34097, loss_mask_dice_5: 0.54119/1.20023, loss_spatial_bce_5: 0.06542/0.09688, loss_spatial_dice_5: 0.16423/0.23375, loss_spatial_ce_5: 0.09485/0.11198, loss_grounding_bce_5: 0.03566/0.08753, loss_grounding_dice_5: 0.03925/0.18344, loss_grounding_ce_5: 0.01287/0.29671, loss_mask_ce_6: 0.92164/0.97823, loss_mask_bce_6: 0.34662/0.34365, loss_mask_dice_6: 0.55133/1.20283, loss_spatial_bce_6: 0.08142/0.10260, loss_spatial_dice_6: 0.17411/0.23615, loss_spatial_ce_6: 0.09915/0.13776, loss_grounding_bce_6: 0.02950/0.08825, loss_grounding_dice_6: 0.03240/0.18361, loss_grounding_ce_6: 0.01521/0.31292, loss_mask_ce_7: 1.01759/1.02323, loss_mask_bce_7: 0.36675/0.35155, loss_mask_dice_7: 0.57856/1.25840, loss_spatial_bce_7: 0.13969/0.11102, loss_spatial_dice_7: 0.20430/0.26385, loss_spatial_ce_7: 0.12753/0.17447, loss_grounding_bce_7: 0.03020/0.09017, loss_grounding_dice_7: 0.03089/0.19088, loss_grounding_ce_7: 0.07108/0.34618, loss_mask_ce_8: 1.04711/1.13205, loss_mask_bce_8: 0.41675/0.36516, loss_mask_dice_8: 0.62318/1.33214, loss_spatial_bce_8: 0.12637/0.13195, loss_spatial_dice_8: 0.23587/0.30311, loss_spatial_ce_8: 0.19505/0.23186, loss_grounding_bce_8: 0.02865/0.09385, loss_grounding_dice_8: 0.02978/0.20200, loss_grounding_ce_8: 0.01949/0.41399, loss_mask_ce_9: 3.90985/3.68255, loss_mask_bce_9: 0.63372/0.39204, loss_mask_dice_9: 1.50549/1.90551, loss_spatial_bce_9: 0.34485/0.33384, loss_spatial_dice_9: 0.77944/0.82307, loss_spatial_ce_9: 1.59039/1.50559, loss_grounding_bce_9: 0.02732/0.10529, loss_grounding_dice_9: 0.03441/0.28141, loss_grounding_ce_9: 0.31770/0.68000] items per batch[64] items per second[0.23] total items[2553600] mini batches[ 39900] memory[7341] epoch remaining[0:13:37]
INFO:trainer.default_trainer:epochs[    21] optim steps[40000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58633/0.90644, loss_mask_bce_0: 0.49252/0.33454, loss_mask_dice_0: 0.78132/1.16620, loss_spatial_bce_0: 0.12191/0.08890, loss_spatial_dice_0: 0.16572/0.21278, loss_spatial_ce_0: 0.04030/0.06807, loss_grounding_bce_0: 0.16490/0.08621, loss_grounding_dice_0: 0.16289/0.17899, loss_grounding_ce_0: 0.14974/0.27482, loss_mask_ce_1: 0.59234/0.90690, loss_mask_bce_1: 0.47593/0.33536, loss_mask_dice_1: 0.77580/1.17316, loss_spatial_bce_1: 0.12462/0.08955, loss_spatial_dice_1: 0.17709/0.21693, loss_spatial_ce_1: 0.03451/0.07370, loss_grounding_bce_1: 0.16479/0.08632, loss_grounding_dice_1: 0.16708/0.17981, loss_grounding_ce_1: 0.20365/0.27642, loss_mask_ce_2: 0.57571/0.91440, loss_mask_bce_2: 0.47392/0.33579, loss_mask_dice_2: 0.78440/1.17266, loss_spatial_bce_2: 0.15423/0.09011, loss_spatial_dice_2: 0.18504/0.21803, loss_spatial_ce_2: 0.04017/0.07725, loss_grounding_bce_2: 0.16537/0.08642, loss_grounding_dice_2: 0.16657/0.17963, loss_grounding_ce_2: 0.19895/0.27967, loss_mask_ce_3: 0.61405/0.92361, loss_mask_bce_3: 0.47072/0.33672, loss_mask_dice_3: 0.77829/1.17004, loss_spatial_bce_3: 0.18134/0.09099, loss_spatial_dice_3: 0.19801/0.21864, loss_spatial_ce_3: 0.04770/0.08099, loss_grounding_bce_3: 0.15547/0.08668, loss_grounding_dice_3: 0.15425/0.17938, loss_grounding_ce_3: 0.15305/0.28110, loss_mask_ce_4: 0.64674/0.92336, loss_mask_bce_4: 0.45880/0.33866, loss_mask_dice_4: 0.74806/1.19354, loss_spatial_bce_4: 0.17210/0.09518, loss_spatial_dice_4: 0.20782/0.23009, loss_spatial_ce_4: 0.05715/0.09691, loss_grounding_bce_4: 0.16378/0.08710, loss_grounding_dice_4: 0.16175/0.18218, loss_grounding_ce_4: 0.10799/0.28398, loss_mask_ce_5: 0.65658/0.93879, loss_mask_bce_5: 0.47871/0.34092, loss_mask_dice_5: 0.75571/1.19995, loss_spatial_bce_5: 0.15124/0.09687, loss_spatial_dice_5: 0.18370/0.23371, loss_spatial_ce_5: 0.06272/0.11193, loss_grounding_bce_5: 0.16796/0.08752, loss_grounding_dice_5: 0.15627/0.18339, loss_grounding_ce_5: 0.08585/0.29665, loss_mask_ce_6: 0.86716/0.97800, loss_mask_bce_6: 0.50612/0.34361, loss_mask_dice_6: 0.76135/1.20253, loss_spatial_bce_6: 0.15842/0.10259, loss_spatial_dice_6: 0.20838/0.23612, loss_spatial_ce_6: 0.10858/0.13770, loss_grounding_bce_6: 0.16517/0.08824, loss_grounding_dice_6: 0.16063/0.18355, loss_grounding_ce_6: 0.11139/0.31297, loss_mask_ce_7: 1.14447/1.02303, loss_mask_bce_7: 0.55288/0.35151, loss_mask_dice_7: 0.80639/1.25809, loss_spatial_bce_7: 0.17301/0.11101, loss_spatial_dice_7: 0.22003/0.26380, loss_spatial_ce_7: 0.09808/0.17444, loss_grounding_bce_7: 0.20921/0.09017, loss_grounding_dice_7: 0.17348/0.19083, loss_grounding_ce_7: 0.23864/0.34617, loss_mask_ce_8: 1.21582/1.13174, loss_mask_bce_8: 0.57848/0.36512, loss_mask_dice_8: 0.94209/1.33184, loss_spatial_bce_8: 0.23917/0.13194, loss_spatial_dice_8: 0.24347/0.30303, loss_spatial_ce_8: 0.14099/0.23179, loss_grounding_bce_8: 0.24293/0.09384, loss_grounding_dice_8: 0.18704/0.20193, loss_grounding_ce_8: 0.20425/0.41387, loss_mask_ce_9: 4.46688/3.68231, loss_mask_bce_9: 0.70303/0.39200, loss_mask_dice_9: 1.57383/1.90495, loss_spatial_bce_9: 0.47857/0.33387, loss_spatial_dice_9: 0.78058/0.82304, loss_spatial_ce_9: 1.25125/1.50551, loss_grounding_bce_9: 0.21177/0.10528, loss_grounding_dice_9: 0.33767/0.28130, loss_grounding_ce_9: 0.75041/0.68009] items per batch[64] items per second[0.23] total items[2560000] mini batches[ 40000] memory[7341] epoch remaining[0:08:59]
INFO:trainer.default_trainer:epochs[    21] optim steps[40100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87860/0.90640, loss_mask_bce_0: 0.09516/0.33455, loss_mask_dice_0: 0.90950/1.16607, loss_spatial_bce_0: 0.02239/0.08889, loss_spatial_dice_0: 0.28639/0.21275, loss_spatial_ce_0: 0.07918/0.06805, loss_grounding_bce_0: 0.03336/0.08624, loss_grounding_dice_0: 0.16979/0.17900, loss_grounding_ce_0: 0.14327/0.27476, loss_mask_ce_1: 0.85007/0.90682, loss_mask_bce_1: 0.09819/0.33537, loss_mask_dice_1: 0.97338/1.17305, loss_spatial_bce_1: 0.02370/0.08954, loss_spatial_dice_1: 0.30627/0.21691, loss_spatial_ce_1: 0.09255/0.07368, loss_grounding_bce_1: 0.03370/0.08635, loss_grounding_dice_1: 0.26814/0.17981, loss_grounding_ce_1: 0.08148/0.27637, loss_mask_ce_2: 0.87713/0.91437, loss_mask_bce_2: 0.10096/0.33580, loss_mask_dice_2: 0.95587/1.17249, loss_spatial_bce_2: 0.02308/0.09011, loss_spatial_dice_2: 0.32713/0.21802, loss_spatial_ce_2: 0.13537/0.07724, loss_grounding_bce_2: 0.03195/0.08645, loss_grounding_dice_2: 0.18572/0.17964, loss_grounding_ce_2: 0.12838/0.27961, loss_mask_ce_3: 1.00786/0.92356, loss_mask_bce_3: 0.09855/0.33674, loss_mask_dice_3: 0.89553/1.16988, loss_spatial_bce_3: 0.02283/0.09098, loss_spatial_dice_3: 0.31344/0.21864, loss_spatial_ce_3: 0.16939/0.08098, loss_grounding_bce_3: 0.03612/0.08671, loss_grounding_dice_3: 0.18289/0.17938, loss_grounding_ce_3: 0.15030/0.28111, loss_mask_ce_4: 0.92962/0.92330, loss_mask_bce_4: 0.10068/0.33866, loss_mask_dice_4: 0.92543/1.19340, loss_spatial_bce_4: 0.02617/0.09517, loss_spatial_dice_4: 0.29748/0.23007, loss_spatial_ce_4: 0.07422/0.09691, loss_grounding_bce_4: 0.03711/0.08712, loss_grounding_dice_4: 0.28538/0.18218, loss_grounding_ce_4: 0.08034/0.28390, loss_mask_ce_5: 0.91553/0.93877, loss_mask_bce_5: 0.10238/0.34093, loss_mask_dice_5: 0.93027/1.19985, loss_spatial_bce_5: 0.02425/0.09688, loss_spatial_dice_5: 0.26706/0.23370, loss_spatial_ce_5: 0.25929/0.11196, loss_grounding_bce_5: 0.03488/0.08755, loss_grounding_dice_5: 0.18334/0.18340, loss_grounding_ce_5: 0.12422/0.29658, loss_mask_ce_6: 1.14295/0.97799, loss_mask_bce_6: 0.09775/0.34362, loss_mask_dice_6: 0.99006/1.20239, loss_spatial_bce_6: 0.02564/0.10259, loss_spatial_dice_6: 0.32928/0.23611, loss_spatial_ce_6: 0.25138/0.13770, loss_grounding_bce_6: 0.03895/0.08826, loss_grounding_dice_6: 0.22106/0.18356, loss_grounding_ce_6: 0.20949/0.31286, loss_mask_ce_7: 1.01284/1.02303, loss_mask_bce_7: 0.11747/0.35152, loss_mask_dice_7: 1.07930/1.25798, loss_spatial_bce_7: 0.03134/0.11102, loss_spatial_dice_7: 0.36945/0.26379, loss_spatial_ce_7: 0.09050/0.17437, loss_grounding_bce_7: 0.04687/0.09019, loss_grounding_dice_7: 0.21704/0.19084, loss_grounding_ce_7: 0.15199/0.34606, loss_mask_ce_8: 1.48721/1.13176, loss_mask_bce_8: 0.10604/0.36513, loss_mask_dice_8: 1.11818/1.33172, loss_spatial_bce_8: 0.03665/0.13195, loss_spatial_dice_8: 0.46929/0.30303, loss_spatial_ce_8: 0.26220/0.23175, loss_grounding_bce_8: 0.04299/0.09386, loss_grounding_dice_8: 0.30791/0.20194, loss_grounding_ce_8: 0.04774/0.41379, loss_mask_ce_9: 3.21815/3.68195, loss_mask_bce_9: 0.09234/0.39200, loss_mask_dice_9: 1.24582/1.90484, loss_spatial_bce_9: 0.20637/0.33384, loss_spatial_dice_9: 0.86220/0.82305, loss_spatial_ce_9: 3.26596/1.50539, loss_grounding_bce_9: 0.03234/0.10530, loss_grounding_dice_9: 0.32847/0.28130, loss_grounding_ce_9: 0.19892/0.67985] items per batch[64] items per second[0.23] total items[2566400] mini batches[ 40100] memory[7341] epoch remaining[0:04:21]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00040194.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0028 s/iter. Inference: 0.2188 s/iter. Eval: 0.0927 s/iter. Total: 0.3143 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2224 s/iter. Eval: 0.0825 s/iter. Total: 0.3079 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0030 s/iter. Inference: 0.2257 s/iter. Eval: 0.0815 s/iter. Total: 0.3104 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2263 s/iter. Eval: 0.0796 s/iter. Total: 0.3092 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0032 s/iter. Inference: 0.2230 s/iter. Eval: 0.0789 s/iter. Total: 0.3051 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2256 s/iter. Eval: 0.0789 s/iter. Total: 0.3077 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2277 s/iter. Eval: 0.0782 s/iter. Total: 0.3092 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2274 s/iter. Eval: 0.0774 s/iter. Total: 0.3082 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0032 s/iter. Inference: 0.2281 s/iter. Eval: 0.0773 s/iter. Total: 0.3087 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval2_u1xqtk ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.571 | 81.356 | 59.609 |      133      |
| Things | 54.609 | 82.621 | 65.384 |      80       |
| Stuff  | 41.967 | 79.446 | 50.893 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.43 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.20s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.03 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.386
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.642 | 61.145 | 40.691 | 18.935 | 41.861 | 60.317 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.288 | bicycle      | 18.030 | car            | 37.099 |
| motorcycle    | 34.156 | airplane     | 56.785 | bus            | 64.997 |
| train         | 67.619 | truck        | 34.522 | boat           | 22.539 |
| traffic light | 25.210 | fire hydrant | 63.544 | stop sign      | 63.443 |
| parking meter | 42.502 | bench        | 20.027 | bird           | 29.475 |
| cat           | 73.142 | dog          | 66.259 | horse          | 45.370 |
| sheep         | 46.781 | cow          | 50.067 | elephant       | 60.833 |
| bear          | 77.534 | zebra        | 59.572 | giraffe        | 56.570 |
| backpack      | 17.106 | umbrella     | 48.248 | handbag        | 15.910 |
| tie           | 33.954 | suitcase     | 41.208 | frisbee        | 67.862 |
| skis          | 4.843  | snowboard    | 22.909 | sports ball    | 46.980 |
| kite          | 34.404 | baseball bat | 29.283 | baseball glove | 43.660 |
| skateboard    | 35.494 | surfboard    | 34.848 | tennis racket  | 56.622 |
| bottle        | 33.457 | wine glass   | 26.191 | cup            | 39.891 |
| fork          | 18.123 | knife        | 12.097 | spoon          | 13.669 |
| bowl          | 32.203 | banana       | 20.558 | apple          | 21.299 |
| sandwich      | 44.752 | orange       | 27.916 | broccoli       | 20.054 |
| carrot        | 20.965 | hot dog      | 24.427 | pizza          | 51.120 |
| donut         | 45.813 | cake         | 43.254 | chair          | 20.933 |
| couch         | 42.586 | potted plant | 17.386 | bed            | 41.607 |
| dining table  | 13.107 | toilet       | 67.233 | tv             | 62.711 |
| laptop        | 62.701 | mouse        | 58.860 | remote         | 30.357 |
| keyboard      | 48.244 | cell phone   | 36.087 | microwave      | 56.007 |
| oven          | 31.774 | toaster      | 20.619 | sink           | 36.541 |
| refrigerator  | 60.328 | book         | 8.982  | clock          | 51.467 |
| vase          | 32.092 | scissors     | 22.331 | teddy bear     | 49.477 |
| hair drier    | 10.868 | toothbrush   | 19.582 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.778320360053385, 'fwIoU': 68.8658477295427, 'IoU-person': 87.68331686310923, 'IoU-bicycle': 75.49606135066429, 'IoU-car': 69.38072930961674, 'IoU-motorcycle': 85.39941553703, 'IoU-airplane': 83.05121401146032, 'IoU-bus': 85.55117053848204, 'IoU-train': 86.51296555600258, 'IoU-truck': 62.471086200921675, 'IoU-boat': 68.41209708488506, 'IoU-traffic light': 75.91056114514716, 'IoU-fire hydrant': 89.82167710945076, 'IoU-stop sign': 92.07313587367324, 'IoU-parking meter': 83.1710491995295, 'IoU-bench': 56.18238352157508, 'IoU-bird': 75.45187682719535, 'IoU-cat': 82.61045913491368, 'IoU-dog': 77.11722886694272, 'IoU-horse': 84.95421496750582, 'IoU-sheep': 86.85967424676656, 'IoU-cow': 79.70712733469676, 'IoU-elephant': 90.05527015793292, 'IoU-bear': 82.14725343550927, 'IoU-zebra': 92.34704937948324, 'IoU-giraffe': 85.60858415171852, 'IoU-backpack': 41.65795089463687, 'IoU-umbrella': 70.4677482280058, 'IoU-handbag': 39.407582866055975, 'IoU-tie': 71.85178224418077, 'IoU-suitcase': 80.72620821151769, 'IoU-frisbee': 83.2641262851588, 'IoU-skis': 50.14694396603516, 'IoU-snowboard': 65.67078590475099, 'IoU-sports ball': 67.58130703742268, 'IoU-kite': 66.53543134622251, 'IoU-baseball bat': 61.315977353102326, 'IoU-baseball glove': 76.94163240691705, 'IoU-skateboard': 81.88030828350699, 'IoU-surfboard': 76.12814842019274, 'IoU-tennis racket': 83.2246224910723, 'IoU-bottle': 68.478452585643, 'IoU-wine glass': 75.65328445691938, 'IoU-cup': 61.230370467370854, 'IoU-fork': 54.834862979514554, 'IoU-knife': 44.32501965486967, 'IoU-spoon': 46.921657750960954, 'IoU-bowl': 54.94052728649339, 'IoU-banana': 82.84524476109483, 'IoU-apple': 57.378464222594175, 'IoU-sandwich': 65.26769013129397, 'IoU-orange': 78.35417899812965, 'IoU-broccoli': 69.26066165566826, 'IoU-carrot': 63.9360122296063, 'IoU-hot dog': 59.75673789475865, 'IoU-pizza': 82.90311464125844, 'IoU-donut': 64.7608641612686, 'IoU-cake': 68.40168641597909, 'IoU-chair': 53.225702170925985, 'IoU-couch': 68.85034648398155, 'IoU-potted plant': 34.326118858480804, 'IoU-bed': 66.41721164533399, 'IoU-dining table': 52.26217022047687, 'IoU-toilet': 86.9836924700544, 'IoU-tv': 75.78076174587355, 'IoU-laptop': 72.2126114266265, 'IoU-mouse': 72.00952317728921, 'IoU-remote': 49.49847327346632, 'IoU-keyboard': 61.51230503558157, 'IoU-cell phone': 67.18640080213223, 'IoU-microwave': 63.57066605189622, 'IoU-oven': 66.61163606705874, 'IoU-toaster': 55.18859777125261, 'IoU-sink': 72.8011419786977, 'IoU-refrigerator': 77.92819838751991, 'IoU-book': 51.463867069288725, 'IoU-clock': 68.70244199278966, 'IoU-vase': 53.98478312568017, 'IoU-scissors': 55.12192356768285, 'IoU-teddy bear': 80.06514354992657, 'IoU-hair drier': 41.81794822380677, 'IoU-toothbrush': 61.58973713998239, 'IoU-banner': 30.693298574565, 'IoU-blanket': 11.49459795279608, 'IoU-bridge': 38.91827122319358, 'IoU-cardboard': 43.650199752944026, 'IoU-counter': 31.424645967899366, 'IoU-curtain': 64.85447090038248, 'IoU-door-stuff': 41.07488919870056, 'IoU-floor-wood': 61.84146380943696, 'IoU-flower': 46.835369833084464, 'IoU-fruit': 38.10794202908, 'IoU-gravel': 23.94838946654284, 'IoU-house': 22.101149381798134, 'IoU-light': 41.515857666513035, 'IoU-mirror-stuff': 55.35597083658352, 'IoU-net': 48.12122115909674, 'IoU-pillow': 14.98111153796901, 'IoU-platform': 28.730289519122298, 'IoU-playingfield': 70.00157835291715, 'IoU-railroad': 60.097075551697344, 'IoU-river': 45.89092409607606, 'IoU-road': 65.99249209960082, 'IoU-roof': 15.640838749299805, 'IoU-sand': 62.54051110508527, 'IoU-sea': 84.46352545496042, 'IoU-shelf': 35.84954555291063, 'IoU-snow': 88.67676842433185, 'IoU-stairs': 23.08931177374923, 'IoU-tent': 9.211752116031972, 'IoU-towel': 34.879339658654644, 'IoU-wall-brick': 46.998104571352165, 'IoU-wall-stone': 26.036076616742253, 'IoU-wall-tile': 63.1541706234403, 'IoU-wall-wood': 38.024250483116035, 'IoU-water-other': 23.75867615044226, 'IoU-window-blind': 47.38690702396967, 'IoU-window-other': 47.029519024347024, 'IoU-tree-merged': 81.1004779283929, 'IoU-fence-merged': 51.15015823553295, 'IoU-ceiling-merged': 68.13453074394944, 'IoU-sky-other-merged': 93.25724985809937, 'IoU-cabinet-merged': 58.784122870394384, 'IoU-table-merged': 38.92425995420021, 'IoU-floor-other-merged': 49.353508341024146, 'IoU-pavement-merged': 54.08427230291443, 'IoU-mountain-merged': 55.295659037998924, 'IoU-grass-merged': 71.1112881041757, 'IoU-dirt-merged': 45.03121716191643, 'IoU-paper-merged': 36.61562069756193, 'IoU-food-other-merged': 39.56699625327531, 'IoU-building-other-merged': 57.410242598146546, 'IoU-rock-merged': 61.294437932221314, 'IoU-wall-other-merged': 63.000980603283594, 'IoU-rug-merged': 63.89468875335945, 'mACC': 72.55592244126848, 'pACC': 80.24034647224542, 'ACC-person': 92.67527762721937, 'ACC-bicycle': 84.60669199020515, 'ACC-car': 84.62199538022372, 'ACC-motorcycle': 90.2606134104089, 'ACC-airplane': 90.53732772172441, 'ACC-bus': 90.12847813382334, 'ACC-train': 93.77384225712852, 'ACC-truck': 77.21421551834588, 'ACC-boat': 77.59400902251214, 'ACC-traffic light': 89.72492711422494, 'ACC-fire hydrant': 95.41733742692806, 'ACC-stop sign': 94.89696448340177, 'ACC-parking meter': 87.60706412637359, 'ACC-bench': 70.53029626664285, 'ACC-bird': 80.88257322856214, 'ACC-cat': 87.62955377495818, 'ACC-dog': 82.40533557276888, 'ACC-horse': 91.15924602530184, 'ACC-sheep': 90.65855134029877, 'ACC-cow': 85.99825776141175, 'ACC-elephant': 92.92281082563589, 'ACC-bear': 84.13175706049282, 'ACC-zebra': 95.03012439045804, 'ACC-giraffe': 89.90614791873215, 'ACC-backpack': 58.383143004881646, 'ACC-umbrella': 77.05864047716555, 'ACC-handbag': 57.901686164267815, 'ACC-tie': 81.56155425151765, 'ACC-suitcase': 90.84598631645349, 'ACC-frisbee': 94.06181818181818, 'ACC-skis': 68.25384927273532, 'ACC-snowboard': 78.53248757071832, 'ACC-sports ball': 79.8399851148944, 'ACC-kite': 76.4596689143441, 'ACC-baseball bat': 83.00803931999191, 'ACC-baseball glove': 90.55359806316335, 'ACC-skateboard': 89.75710419182104, 'ACC-surfboard': 83.04653730105002, 'ACC-tennis racket': 89.27666153019892, 'ACC-bottle': 82.34542033049708, 'ACC-wine glass': 82.81236967598049, 'ACC-cup': 84.15757475040947, 'ACC-fork': 66.75158959962032, 'ACC-knife': 53.08657580420605, 'ACC-spoon': 67.6335918650867, 'ACC-bowl': 68.43754076001898, 'ACC-banana': 88.93905863496478, 'ACC-apple': 71.7696638537297, 'ACC-sandwich': 76.84729205182883, 'ACC-orange': 87.53671019372331, 'ACC-broccoli': 79.89836994932705, 'ACC-carrot': 73.53381663957104, 'ACC-hot dog': 73.83213714544686, 'ACC-pizza': 90.5890275298921, 'ACC-donut': 82.50175081619628, 'ACC-cake': 75.00249112783483, 'ACC-chair': 68.99307440194792, 'ACC-couch': 82.24177318345805, 'ACC-potted plant': 47.16442230920731, 'ACC-bed': 77.16945226805753, 'ACC-dining table': 71.42891313629963, 'ACC-toilet': 91.03475689746354, 'ACC-tv': 88.89134118888109, 'ACC-laptop': 86.0500692639032, 'ACC-mouse': 86.08703180632664, 'ACC-remote': 73.19269296030161, 'ACC-keyboard': 70.38129886112762, 'ACC-cell phone': 74.10111372408001, 'ACC-microwave': 73.9729550087399, 'ACC-oven': 80.93542534639334, 'ACC-toaster': 63.405821894373815, 'ACC-sink': 83.87771068086924, 'ACC-refrigerator': 88.27997927293659, 'ACC-book': 66.11162477935613, 'ACC-clock': 73.2032119632261, 'ACC-vase': 62.069436943554024, 'ACC-scissors': 59.15190663765162, 'ACC-teddy bear': 88.43450668035511, 'ACC-hair drier': 46.322545754703555, 'ACC-toothbrush': 80.82261987491314, 'ACC-banner': 67.11260924834973, 'ACC-blanket': 16.966528982738012, 'ACC-bridge': 51.5726740347206, 'ACC-cardboard': 58.289195870292275, 'ACC-counter': 51.697054373909836, 'ACC-curtain': 75.3184787548239, 'ACC-door-stuff': 60.407944502023646, 'ACC-floor-wood': 76.43797350240699, 'ACC-flower': 69.93997394100828, 'ACC-fruit': 54.067815522445706, 'ACC-gravel': 26.589221883516988, 'ACC-house': 25.077064986036486, 'ACC-light': 57.64962867970753, 'ACC-mirror-stuff': 68.25614323558966, 'ACC-net': 60.81031497407179, 'ACC-pillow': 27.178168551161185, 'ACC-platform': 46.73462811378955, 'ACC-playingfield': 91.47844411643095, 'ACC-railroad': 79.69832592375334, 'ACC-river': 60.21559973912661, 'ACC-road': 84.5605094031335, 'ACC-roof': 20.774446169329373, 'ACC-sand': 70.85554866036568, 'ACC-sea': 91.39139620193649, 'ACC-shelf': 59.51866304571888, 'ACC-snow': 95.04713256973825, 'ACC-stairs': 40.093910153527176, 'ACC-tent': 11.124017715621616, 'ACC-towel': 41.979296985714775, 'ACC-wall-brick': 61.039995761957414, 'ACC-wall-stone': 30.423012694448033, 'ACC-wall-tile': 71.54139201910115, 'ACC-wall-wood': 53.05180837577542, 'ACC-water-other': 42.395907750956134, 'ACC-window-blind': 55.95297850805271, 'ACC-window-other': 70.05829641333516, 'ACC-tree-merged': 89.09509307039023, 'ACC-fence-merged': 66.87147778270793, 'ACC-ceiling-merged': 82.33531325167776, 'ACC-sky-other-merged': 96.62036701351478, 'ACC-cabinet-merged': 75.3363180744218, 'ACC-table-merged': 57.200654092620574, 'ACC-floor-other-merged': 60.115593224516914, 'ACC-pavement-merged': 68.45656277474626, 'ACC-mountain-merged': 65.63000392955455, 'ACC-grass-merged': 82.67874555692998, 'ACC-dirt-merged': 68.67313587889771, 'ACC-paper-merged': 55.419211676647485, 'ACC-food-other-merged': 53.70684124849322, 'ACC-building-other-merged': 74.62309857459411, 'ACC-rock-merged': 82.18258258548954, 'ACC-wall-other-merged': 81.87301083688045, 'ACC-rug-merged': 75.96075013274512})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1550 s/iter. Inference: 0.4171 s/iter. Eval: 0.0000 s/iter. Total: 0.5721 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1568 s/iter. Inference: 0.4533 s/iter. Eval: 0.0000 s/iter. Total: 0.6102 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1680 s/iter. Inference: 0.5217 s/iter. Eval: 0.0000 s/iter. Total: 0.6898 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1713 s/iter. Inference: 0.6615 s/iter. Eval: 0.0000 s/iter. Total: 0.8329 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1677 s/iter. Inference: 0.5912 s/iter. Eval: 0.0000 s/iter. Total: 0.7590 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1670 s/iter. Inference: 0.6340 s/iter. Eval: 0.0000 s/iter. Total: 0.8012 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1688 s/iter. Inference: 0.6838 s/iter. Eval: 0.0000 s/iter. Total: 0.8528 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4890254609306408, 'noc@0.8': 2.87123207491952, 'noc@0.85': 3.453028972783143, 'noc@0.9': 4.514779045946737, 'miou@iter1': 0.8300720563156757}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1005 s/iter. Eval: 0.0008 s/iter. Total: 0.1030 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 72.56121063232422, 'precision@0.6': 69.10221862792969, 'precision@0.7': 63.661094665527344, 'precision@0.8': 52.93431854248047, 'precision@0.9': 26.816946029663086, 'cIoU': 58.29267501831055, 'mIoU': 63.463096618652344}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.571153058559375, 'SQ': 81.35576123068181, 'RQ': 59.60935572331293, 'PQ_th': 54.60873576259936, 'SQ_th': 82.62084586984511, 'RQ_th': 65.38423165890637, 'PQ_st': 41.967254637366985, 'SQ_st': 79.44619951119002, 'RQ_st': 50.89256185826625}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.642083617482186, 'AP50': 61.145471618361746, 'AP75': 40.691093823038116, 'APs': 18.934595566860438, 'APm': 41.86142985461014, 'APl': 60.317197498196066, 'AP-person': 44.28813884036397, 'AP-bicycle': 18.029788030766408, 'AP-car': 37.099372476375734, 'AP-motorcycle': 34.15617019659686, 'AP-airplane': 56.78476103187163, 'AP-bus': 64.99681059748383, 'AP-train': 67.61850080603149, 'AP-truck': 34.522144505273815, 'AP-boat': 22.539113489061656, 'AP-traffic light': 25.209735268927787, 'AP-fire hydrant': 63.544134974452916, 'AP-stop sign': 63.44282349343451, 'AP-parking meter': 42.5016431189861, 'AP-bench': 20.027391546871467, 'AP-bird': 29.47453158360242, 'AP-cat': 73.14185028179531, 'AP-dog': 66.25879724248534, 'AP-horse': 45.370065687826234, 'AP-sheep': 46.78072435040131, 'AP-cow': 50.06705042955374, 'AP-elephant': 60.833234516714526, 'AP-bear': 77.53434528760526, 'AP-zebra': 59.57180159862894, 'AP-giraffe': 56.569828282280184, 'AP-backpack': 17.10639675367713, 'AP-umbrella': 48.24838493084037, 'AP-handbag': 15.909747134834273, 'AP-tie': 33.9539478977387, 'AP-suitcase': 41.20798006317308, 'AP-frisbee': 67.86220893292942, 'AP-skis': 4.843446765942802, 'AP-snowboard': 22.909182306272353, 'AP-sports ball': 46.98009116970703, 'AP-kite': 34.4043705234703, 'AP-baseball bat': 29.28252192335753, 'AP-baseball glove': 43.6598684815814, 'AP-skateboard': 35.49394015859091, 'AP-surfboard': 34.84789403983979, 'AP-tennis racket': 56.621675223145985, 'AP-bottle': 33.45663935880926, 'AP-wine glass': 26.19080635664752, 'AP-cup': 39.89086100475381, 'AP-fork': 18.123456881497972, 'AP-knife': 12.097201970749097, 'AP-spoon': 13.668844907161946, 'AP-bowl': 32.2034390898528, 'AP-banana': 20.55821066532, 'AP-apple': 21.298713621987254, 'AP-sandwich': 44.751823442753754, 'AP-orange': 27.91611887658395, 'AP-broccoli': 20.05397891873732, 'AP-carrot': 20.9654817247727, 'AP-hot dog': 24.427139943945242, 'AP-pizza': 51.11991892125024, 'AP-donut': 45.813169609377276, 'AP-cake': 43.2544098081916, 'AP-chair': 20.93323102241108, 'AP-couch': 42.58639273618978, 'AP-potted plant': 17.38640281824955, 'AP-bed': 41.6070871645663, 'AP-dining table': 13.107300813742985, 'AP-toilet': 67.23310705119957, 'AP-tv': 62.711359407672894, 'AP-laptop': 62.70078919527911, 'AP-mouse': 58.85992130953922, 'AP-remote': 30.356594495646704, 'AP-keyboard': 48.24431943204044, 'AP-cell phone': 36.08660872443045, 'AP-microwave': 56.00718439916454, 'AP-oven': 31.774289727427696, 'AP-toaster': 20.618878536252115, 'AP-sink': 36.54142309693375, 'AP-refrigerator': 60.32780959873656, 'AP-book': 8.982090466901752, 'AP-clock': 51.46749489954586, 'AP-vase': 32.091594406646884, 'AP-scissors': 22.33107004528375, 'AP-teddy bear': 49.47672470099079, 'AP-hair drier': 10.868208669606455, 'AP-toothbrush': 19.582177635232085}), ('sem_seg', {'mIoU': 60.778320360053385, 'fwIoU': 68.8658477295427, 'IoU-person': 87.68331686310923, 'IoU-bicycle': 75.49606135066429, 'IoU-car': 69.38072930961674, 'IoU-motorcycle': 85.39941553703, 'IoU-airplane': 83.05121401146032, 'IoU-bus': 85.55117053848204, 'IoU-train': 86.51296555600258, 'IoU-truck': 62.471086200921675, 'IoU-boat': 68.41209708488506, 'IoU-traffic light': 75.91056114514716, 'IoU-fire hydrant': 89.82167710945076, 'IoU-stop sign': 92.07313587367324, 'IoU-parking meter': 83.1710491995295, 'IoU-bench': 56.18238352157508, 'IoU-bird': 75.45187682719535, 'IoU-cat': 82.61045913491368, 'IoU-dog': 77.11722886694272, 'IoU-horse': 84.95421496750582, 'IoU-sheep': 86.85967424676656, 'IoU-cow': 79.70712733469676, 'IoU-elephant': 90.05527015793292, 'IoU-bear': 82.14725343550927, 'IoU-zebra': 92.34704937948324, 'IoU-giraffe': 85.60858415171852, 'IoU-backpack': 41.65795089463687, 'IoU-umbrella': 70.4677482280058, 'IoU-handbag': 39.407582866055975, 'IoU-tie': 71.85178224418077, 'IoU-suitcase': 80.72620821151769, 'IoU-frisbee': 83.2641262851588, 'IoU-skis': 50.14694396603516, 'IoU-snowboard': 65.67078590475099, 'IoU-sports ball': 67.58130703742268, 'IoU-kite': 66.53543134622251, 'IoU-baseball bat': 61.315977353102326, 'IoU-baseball glove': 76.94163240691705, 'IoU-skateboard': 81.88030828350699, 'IoU-surfboard': 76.12814842019274, 'IoU-tennis racket': 83.2246224910723, 'IoU-bottle': 68.478452585643, 'IoU-wine glass': 75.65328445691938, 'IoU-cup': 61.230370467370854, 'IoU-fork': 54.834862979514554, 'IoU-knife': 44.32501965486967, 'IoU-spoon': 46.921657750960954, 'IoU-bowl': 54.94052728649339, 'IoU-banana': 82.84524476109483, 'IoU-apple': 57.378464222594175, 'IoU-sandwich': 65.26769013129397, 'IoU-orange': 78.35417899812965, 'IoU-broccoli': 69.26066165566826, 'IoU-carrot': 63.9360122296063, 'IoU-hot dog': 59.75673789475865, 'IoU-pizza': 82.90311464125844, 'IoU-donut': 64.7608641612686, 'IoU-cake': 68.40168641597909, 'IoU-chair': 53.225702170925985, 'IoU-couch': 68.85034648398155, 'IoU-potted plant': 34.326118858480804, 'IoU-bed': 66.41721164533399, 'IoU-dining table': 52.26217022047687, 'IoU-toilet': 86.9836924700544, 'IoU-tv': 75.78076174587355, 'IoU-laptop': 72.2126114266265, 'IoU-mouse': 72.00952317728921, 'IoU-remote': 49.49847327346632, 'IoU-keyboard': 61.51230503558157, 'IoU-cell phone': 67.18640080213223, 'IoU-microwave': 63.57066605189622, 'IoU-oven': 66.61163606705874, 'IoU-toaster': 55.18859777125261, 'IoU-sink': 72.8011419786977, 'IoU-refrigerator': 77.92819838751991, 'IoU-book': 51.463867069288725, 'IoU-clock': 68.70244199278966, 'IoU-vase': 53.98478312568017, 'IoU-scissors': 55.12192356768285, 'IoU-teddy bear': 80.06514354992657, 'IoU-hair drier': 41.81794822380677, 'IoU-toothbrush': 61.58973713998239, 'IoU-banner': 30.693298574565, 'IoU-blanket': 11.49459795279608, 'IoU-bridge': 38.91827122319358, 'IoU-cardboard': 43.650199752944026, 'IoU-counter': 31.424645967899366, 'IoU-curtain': 64.85447090038248, 'IoU-door-stuff': 41.07488919870056, 'IoU-floor-wood': 61.84146380943696, 'IoU-flower': 46.835369833084464, 'IoU-fruit': 38.10794202908, 'IoU-gravel': 23.94838946654284, 'IoU-house': 22.101149381798134, 'IoU-light': 41.515857666513035, 'IoU-mirror-stuff': 55.35597083658352, 'IoU-net': 48.12122115909674, 'IoU-pillow': 14.98111153796901, 'IoU-platform': 28.730289519122298, 'IoU-playingfield': 70.00157835291715, 'IoU-railroad': 60.097075551697344, 'IoU-river': 45.89092409607606, 'IoU-road': 65.99249209960082, 'IoU-roof': 15.640838749299805, 'IoU-sand': 62.54051110508527, 'IoU-sea': 84.46352545496042, 'IoU-shelf': 35.84954555291063, 'IoU-snow': 88.67676842433185, 'IoU-stairs': 23.08931177374923, 'IoU-tent': 9.211752116031972, 'IoU-towel': 34.879339658654644, 'IoU-wall-brick': 46.998104571352165, 'IoU-wall-stone': 26.036076616742253, 'IoU-wall-tile': 63.1541706234403, 'IoU-wall-wood': 38.024250483116035, 'IoU-water-other': 23.75867615044226, 'IoU-window-blind': 47.38690702396967, 'IoU-window-other': 47.029519024347024, 'IoU-tree-merged': 81.1004779283929, 'IoU-fence-merged': 51.15015823553295, 'IoU-ceiling-merged': 68.13453074394944, 'IoU-sky-other-merged': 93.25724985809937, 'IoU-cabinet-merged': 58.784122870394384, 'IoU-table-merged': 38.92425995420021, 'IoU-floor-other-merged': 49.353508341024146, 'IoU-pavement-merged': 54.08427230291443, 'IoU-mountain-merged': 55.295659037998924, 'IoU-grass-merged': 71.1112881041757, 'IoU-dirt-merged': 45.03121716191643, 'IoU-paper-merged': 36.61562069756193, 'IoU-food-other-merged': 39.56699625327531, 'IoU-building-other-merged': 57.410242598146546, 'IoU-rock-merged': 61.294437932221314, 'IoU-wall-other-merged': 63.000980603283594, 'IoU-rug-merged': 63.89468875335945, 'mACC': 72.55592244126848, 'pACC': 80.24034647224542, 'ACC-person': 92.67527762721937, 'ACC-bicycle': 84.60669199020515, 'ACC-car': 84.62199538022372, 'ACC-motorcycle': 90.2606134104089, 'ACC-airplane': 90.53732772172441, 'ACC-bus': 90.12847813382334, 'ACC-train': 93.77384225712852, 'ACC-truck': 77.21421551834588, 'ACC-boat': 77.59400902251214, 'ACC-traffic light': 89.72492711422494, 'ACC-fire hydrant': 95.41733742692806, 'ACC-stop sign': 94.89696448340177, 'ACC-parking meter': 87.60706412637359, 'ACC-bench': 70.53029626664285, 'ACC-bird': 80.88257322856214, 'ACC-cat': 87.62955377495818, 'ACC-dog': 82.40533557276888, 'ACC-horse': 91.15924602530184, 'ACC-sheep': 90.65855134029877, 'ACC-cow': 85.99825776141175, 'ACC-elephant': 92.92281082563589, 'ACC-bear': 84.13175706049282, 'ACC-zebra': 95.03012439045804, 'ACC-giraffe': 89.90614791873215, 'ACC-backpack': 58.383143004881646, 'ACC-umbrella': 77.05864047716555, 'ACC-handbag': 57.901686164267815, 'ACC-tie': 81.56155425151765, 'ACC-suitcase': 90.84598631645349, 'ACC-frisbee': 94.06181818181818, 'ACC-skis': 68.25384927273532, 'ACC-snowboard': 78.53248757071832, 'ACC-sports ball': 79.8399851148944, 'ACC-kite': 76.4596689143441, 'ACC-baseball bat': 83.00803931999191, 'ACC-baseball glove': 90.55359806316335, 'ACC-skateboard': 89.75710419182104, 'ACC-surfboard': 83.04653730105002, 'ACC-tennis racket': 89.27666153019892, 'ACC-bottle': 82.34542033049708, 'ACC-wine glass': 82.81236967598049, 'ACC-cup': 84.15757475040947, 'ACC-fork': 66.75158959962032, 'ACC-knife': 53.08657580420605, 'ACC-spoon': 67.6335918650867, 'ACC-bowl': 68.43754076001898, 'ACC-banana': 88.93905863496478, 'ACC-apple': 71.7696638537297, 'ACC-sandwich': 76.84729205182883, 'ACC-orange': 87.53671019372331, 'ACC-broccoli': 79.89836994932705, 'ACC-carrot': 73.53381663957104, 'ACC-hot dog': 73.83213714544686, 'ACC-pizza': 90.5890275298921, 'ACC-donut': 82.50175081619628, 'ACC-cake': 75.00249112783483, 'ACC-chair': 68.99307440194792, 'ACC-couch': 82.24177318345805, 'ACC-potted plant': 47.16442230920731, 'ACC-bed': 77.16945226805753, 'ACC-dining table': 71.42891313629963, 'ACC-toilet': 91.03475689746354, 'ACC-tv': 88.89134118888109, 'ACC-laptop': 86.0500692639032, 'ACC-mouse': 86.08703180632664, 'ACC-remote': 73.19269296030161, 'ACC-keyboard': 70.38129886112762, 'ACC-cell phone': 74.10111372408001, 'ACC-microwave': 73.9729550087399, 'ACC-oven': 80.93542534639334, 'ACC-toaster': 63.405821894373815, 'ACC-sink': 83.87771068086924, 'ACC-refrigerator': 88.27997927293659, 'ACC-book': 66.11162477935613, 'ACC-clock': 73.2032119632261, 'ACC-vase': 62.069436943554024, 'ACC-scissors': 59.15190663765162, 'ACC-teddy bear': 88.43450668035511, 'ACC-hair drier': 46.322545754703555, 'ACC-toothbrush': 80.82261987491314, 'ACC-banner': 67.11260924834973, 'ACC-blanket': 16.966528982738012, 'ACC-bridge': 51.5726740347206, 'ACC-cardboard': 58.289195870292275, 'ACC-counter': 51.697054373909836, 'ACC-curtain': 75.3184787548239, 'ACC-door-stuff': 60.407944502023646, 'ACC-floor-wood': 76.43797350240699, 'ACC-flower': 69.93997394100828, 'ACC-fruit': 54.067815522445706, 'ACC-gravel': 26.589221883516988, 'ACC-house': 25.077064986036486, 'ACC-light': 57.64962867970753, 'ACC-mirror-stuff': 68.25614323558966, 'ACC-net': 60.81031497407179, 'ACC-pillow': 27.178168551161185, 'ACC-platform': 46.73462811378955, 'ACC-playingfield': 91.47844411643095, 'ACC-railroad': 79.69832592375334, 'ACC-river': 60.21559973912661, 'ACC-road': 84.5605094031335, 'ACC-roof': 20.774446169329373, 'ACC-sand': 70.85554866036568, 'ACC-sea': 91.39139620193649, 'ACC-shelf': 59.51866304571888, 'ACC-snow': 95.04713256973825, 'ACC-stairs': 40.093910153527176, 'ACC-tent': 11.124017715621616, 'ACC-towel': 41.979296985714775, 'ACC-wall-brick': 61.039995761957414, 'ACC-wall-stone': 30.423012694448033, 'ACC-wall-tile': 71.54139201910115, 'ACC-wall-wood': 53.05180837577542, 'ACC-water-other': 42.395907750956134, 'ACC-window-blind': 55.95297850805271, 'ACC-window-other': 70.05829641333516, 'ACC-tree-merged': 89.09509307039023, 'ACC-fence-merged': 66.87147778270793, 'ACC-ceiling-merged': 82.33531325167776, 'ACC-sky-other-merged': 96.62036701351478, 'ACC-cabinet-merged': 75.3363180744218, 'ACC-table-merged': 57.200654092620574, 'ACC-floor-other-merged': 60.115593224516914, 'ACC-pavement-merged': 68.45656277474626, 'ACC-mountain-merged': 65.63000392955455, 'ACC-grass-merged': 82.67874555692998, 'ACC-dirt-merged': 68.67313587889771, 'ACC-paper-merged': 55.419211676647485, 'ACC-food-other-merged': 53.70684124849322, 'ACC-building-other-merged': 74.62309857459411, 'ACC-rock-merged': 82.18258258548954, 'ACC-wall-other-merged': 81.87301083688045, 'ACC-rug-merged': 75.96075013274512})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4890254609306408, 'noc@0.8': 2.87123207491952, 'noc@0.85': 3.453028972783143, 'noc@0.9': 4.514779045946737, 'miou@iter1': 0.8300720563156757}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 72.56121063232422, 'precision@0.6': 69.10221862792969, 'precision@0.7': 63.661094665527344, 'precision@0.8': 52.93431854248047, 'precision@0.9': 26.816946029663086, 'cIoU': 58.29267501831055, 'mIoU': 63.463096618652344}}}
INFO:trainer.default_trainer:This epoch takes 1:27:51.307959
INFO:trainer.default_trainer:PROGRESS: 44.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 22 training.
INFO:trainer.default_trainer:epochs[    22] optim steps[40200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42788/0.90640, loss_mask_bce_0: 0.24954/0.33450, loss_mask_dice_0: 0.39499/1.16627, loss_spatial_bce_0: 0.20002/0.08888, loss_spatial_dice_0: 0.23111/0.21275, loss_spatial_ce_0: 0.07108/0.06803, loss_grounding_bce_0: 0.07189/0.08622, loss_grounding_dice_0: 0.18953/0.17903, loss_grounding_ce_0: 0.41222/0.27475, loss_mask_ce_1: 0.45617/0.90690, loss_mask_bce_1: 0.18213/0.33532, loss_mask_dice_1: 0.38031/1.17317, loss_spatial_bce_1: 0.19246/0.08953, loss_spatial_dice_1: 0.23808/0.21691, loss_spatial_ce_1: 0.07502/0.07369, loss_grounding_bce_1: 0.08374/0.08634, loss_grounding_dice_1: 0.18496/0.17983, loss_grounding_ce_1: 0.43283/0.27636, loss_mask_ce_2: 0.38063/0.91440, loss_mask_bce_2: 0.18650/0.33576, loss_mask_dice_2: 0.37372/1.17267, loss_spatial_bce_2: 0.19468/0.09010, loss_spatial_dice_2: 0.23588/0.21801, loss_spatial_ce_2: 0.07299/0.07724, loss_grounding_bce_2: 0.08472/0.08643, loss_grounding_dice_2: 0.18419/0.17965, loss_grounding_ce_2: 0.41477/0.27960, loss_mask_ce_3: 0.43078/0.92365, loss_mask_bce_3: 0.20846/0.33669, loss_mask_dice_3: 0.39309/1.17006, loss_spatial_bce_3: 0.21905/0.09098, loss_spatial_dice_3: 0.24827/0.21864, loss_spatial_ce_3: 0.07400/0.08096, loss_grounding_bce_3: 0.08021/0.08669, loss_grounding_dice_3: 0.19343/0.17940, loss_grounding_ce_3: 0.50729/0.28112, loss_mask_ce_4: 0.45771/0.92335, loss_mask_bce_4: 0.18435/0.33862, loss_mask_dice_4: 0.36952/1.19364, loss_spatial_bce_4: 0.21765/0.09516, loss_spatial_dice_4: 0.26151/0.23007, loss_spatial_ce_4: 0.11598/0.09692, loss_grounding_bce_4: 0.08839/0.08711, loss_grounding_dice_4: 0.19645/0.18222, loss_grounding_ce_4: 0.64343/0.28393, loss_mask_ce_5: 0.45966/0.93882, loss_mask_bce_5: 0.18871/0.34089, loss_mask_dice_5: 0.35676/1.20001, loss_spatial_bce_5: 0.21887/0.09687, loss_spatial_dice_5: 0.28185/0.23370, loss_spatial_ce_5: 0.08128/0.11197, loss_grounding_bce_5: 0.08094/0.08753, loss_grounding_dice_5: 0.18296/0.18343, loss_grounding_ce_5: 0.59303/0.29663, loss_mask_ce_6: 0.58401/0.97805, loss_mask_bce_6: 0.20124/0.34358, loss_mask_dice_6: 0.36447/1.20260, loss_spatial_bce_6: 0.25790/0.10257, loss_spatial_dice_6: 0.30298/0.23611, loss_spatial_ce_6: 0.15078/0.13767, loss_grounding_bce_6: 0.12018/0.08825, loss_grounding_dice_6: 0.19142/0.18358, loss_grounding_ce_6: 0.75296/0.31295, loss_mask_ce_7: 0.65901/1.02313, loss_mask_bce_7: 0.21357/0.35147, loss_mask_dice_7: 0.41109/1.25819, loss_spatial_bce_7: 0.27766/0.11102, loss_spatial_dice_7: 0.29434/0.26380, loss_spatial_ce_7: 0.17965/0.17437, loss_grounding_bce_7: 0.11120/0.09018, loss_grounding_dice_7: 0.24522/0.19087, loss_grounding_ce_7: 0.69980/0.34613, loss_mask_ce_8: 0.67289/1.13186, loss_mask_bce_8: 0.23280/0.36510, loss_mask_dice_8: 0.48962/1.33192, loss_spatial_bce_8: 0.31217/0.13194, loss_spatial_dice_8: 0.28568/0.30302, loss_spatial_ce_8: 0.32711/0.23174, loss_grounding_bce_8: 0.11272/0.09385, loss_grounding_dice_8: 0.25348/0.20195, loss_grounding_ce_8: 0.74587/0.41373, loss_mask_ce_9: 3.96551/3.68232, loss_mask_bce_9: 0.32304/0.39196, loss_mask_dice_9: 0.55248/1.90514, loss_spatial_bce_9: 0.37400/0.33379, loss_spatial_dice_9: 0.78112/0.82303, loss_spatial_ce_9: 1.14328/1.50542, loss_grounding_bce_9: 0.13480/0.10529, loss_grounding_dice_9: 0.31985/0.28133, loss_grounding_ce_9: 0.76852/0.67983] items per batch[64] items per second[0.14] total items[2572800] mini batches[ 40200] memory[7341] epoch remaining[1:41:35]
INFO:trainer.default_trainer:epochs[    22] optim steps[40300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54709/0.90632, loss_mask_bce_0: 0.29443/0.33446, loss_mask_dice_0: 0.50863/1.16615, loss_spatial_bce_0: 0.05438/0.08887, loss_spatial_dice_0: 0.08974/0.21270, loss_spatial_ce_0: 0.00455/0.06799, loss_grounding_bce_0: 0.05988/0.08620, loss_grounding_dice_0: 0.08917/0.17900, loss_grounding_ce_0: 0.33175/0.27472, loss_mask_ce_1: 0.58538/0.90683, loss_mask_bce_1: 0.31474/0.33526, loss_mask_dice_1: 0.50436/1.17305, loss_spatial_bce_1: 0.05266/0.08952, loss_spatial_dice_1: 0.09100/0.21687, loss_spatial_ce_1: 0.00551/0.07366, loss_grounding_bce_1: 0.06065/0.08632, loss_grounding_dice_1: 0.08984/0.17980, loss_grounding_ce_1: 0.32906/0.27630, loss_mask_ce_2: 0.57613/0.91430, loss_mask_bce_2: 0.29899/0.33570, loss_mask_dice_2: 0.51197/1.17254, loss_spatial_bce_2: 0.05512/0.09009, loss_spatial_dice_2: 0.09353/0.21797, loss_spatial_ce_2: 0.01841/0.07719, loss_grounding_bce_2: 0.05802/0.08641, loss_grounding_dice_2: 0.08810/0.17961, loss_grounding_ce_2: 0.34171/0.27955, loss_mask_ce_3: 0.61258/0.92358, loss_mask_bce_3: 0.28841/0.33664, loss_mask_dice_3: 0.52212/1.16994, loss_spatial_bce_3: 0.05593/0.09096, loss_spatial_dice_3: 0.09109/0.21859, loss_spatial_ce_3: 0.03141/0.08090, loss_grounding_bce_3: 0.05831/0.08667, loss_grounding_dice_3: 0.08839/0.17937, loss_grounding_ce_3: 0.34501/0.28105, loss_mask_ce_4: 0.50159/0.92329, loss_mask_bce_4: 0.29844/0.33858, loss_mask_dice_4: 0.50781/1.19349, loss_spatial_bce_4: 0.06943/0.09514, loss_spatial_dice_4: 0.11815/0.23004, loss_spatial_ce_4: 0.03358/0.09687, loss_grounding_bce_4: 0.05751/0.08708, loss_grounding_dice_4: 0.09279/0.18218, loss_grounding_ce_4: 0.35074/0.28388, loss_mask_ce_5: 0.54275/0.93876, loss_mask_bce_5: 0.28959/0.34084, loss_mask_dice_5: 0.52365/1.19990, loss_spatial_bce_5: 0.07880/0.09685, loss_spatial_dice_5: 0.12128/0.23366, loss_spatial_ce_5: 0.04139/0.11193, loss_grounding_bce_5: 0.06111/0.08751, loss_grounding_dice_5: 0.10493/0.18340, loss_grounding_ce_5: 0.35572/0.29655, loss_mask_ce_6: 0.55358/0.97798, loss_mask_bce_6: 0.29724/0.34354, loss_mask_dice_6: 0.53549/1.20246, loss_spatial_bce_6: 0.08000/0.10255, loss_spatial_dice_6: 0.12870/0.23608, loss_spatial_ce_6: 0.08621/0.13764, loss_grounding_bce_6: 0.06795/0.08823, loss_grounding_dice_6: 0.12152/0.18356, loss_grounding_ce_6: 0.36398/0.31285, loss_mask_ce_7: 0.64725/1.02309, loss_mask_bce_7: 0.30898/0.35143, loss_mask_dice_7: 0.61470/1.25808, loss_spatial_bce_7: 0.10992/0.11100, loss_spatial_dice_7: 0.17147/0.26376, loss_spatial_ce_7: 0.06266/0.17432, loss_grounding_bce_7: 0.06063/0.09016, loss_grounding_dice_7: 0.11994/0.19085, loss_grounding_ce_7: 0.37187/0.34602, loss_mask_ce_8: 0.77588/1.13176, loss_mask_bce_8: 0.34454/0.36503, loss_mask_dice_8: 0.67296/1.33175, loss_spatial_bce_8: 0.12108/0.13192, loss_spatial_dice_8: 0.18799/0.30300, loss_spatial_ce_8: 0.15749/0.23170, loss_grounding_bce_8: 0.07872/0.09383, loss_grounding_dice_8: 0.15192/0.20192, loss_grounding_ce_8: 0.37232/0.41359, loss_mask_ce_9: 4.12703/3.68199, loss_mask_bce_9: 0.38826/0.39190, loss_mask_dice_9: 1.36005/1.90492, loss_spatial_bce_9: 0.30230/0.33376, loss_spatial_dice_9: 0.85529/0.82301, loss_spatial_ce_9: 1.50376/1.50529, loss_grounding_bce_9: 0.08839/0.10528, loss_grounding_dice_9: 0.31000/0.28130, loss_grounding_ce_9: 0.42755/0.67960] items per batch[64] items per second[0.24] total items[2579200] mini batches[ 40300] memory[7341] epoch remaining[1:17:10]
INFO:trainer.default_trainer:epochs[    22] optim steps[40400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61825/0.90622, loss_mask_bce_0: 0.33386/0.33452, loss_mask_dice_0: 0.74853/1.16594, loss_spatial_bce_0: 0.07537/0.08887, loss_spatial_dice_0: 0.13515/0.21266, loss_spatial_ce_0: 0.02101/0.06792, loss_grounding_bce_0: 0.05345/0.08623, loss_grounding_dice_0: 0.07324/0.17902, loss_grounding_ce_0: 0.11228/0.27467, loss_mask_ce_1: 0.61979/0.90669, loss_mask_bce_1: 0.33863/0.33531, loss_mask_dice_1: 0.75700/1.17286, loss_spatial_bce_1: 0.08179/0.08952, loss_spatial_dice_1: 0.13828/0.21684, loss_spatial_ce_1: 0.03346/0.07360, loss_grounding_bce_1: 0.05286/0.08634, loss_grounding_dice_1: 0.07300/0.17980, loss_grounding_ce_1: 0.11632/0.27624, loss_mask_ce_2: 0.65061/0.91412, loss_mask_bce_2: 0.35124/0.33576, loss_mask_dice_2: 0.73781/1.17235, loss_spatial_bce_2: 0.07787/0.09009, loss_spatial_dice_2: 0.14412/0.21794, loss_spatial_ce_2: 0.04033/0.07713, loss_grounding_bce_2: 0.05336/0.08644, loss_grounding_dice_2: 0.07183/0.17963, loss_grounding_ce_2: 0.12903/0.27951, loss_mask_ce_3: 0.63372/0.92346, loss_mask_bce_3: 0.33513/0.33669, loss_mask_dice_3: 0.68650/1.16972, loss_spatial_bce_3: 0.08206/0.09097, loss_spatial_dice_3: 0.14803/0.21856, loss_spatial_ce_3: 0.04158/0.08083, loss_grounding_bce_3: 0.05529/0.08669, loss_grounding_dice_3: 0.07082/0.17936, loss_grounding_ce_3: 0.08583/0.28101, loss_mask_ce_4: 0.64508/0.92317, loss_mask_bce_4: 0.34036/0.33864, loss_mask_dice_4: 0.68682/1.19333, loss_spatial_bce_4: 0.08802/0.09515, loss_spatial_dice_4: 0.15170/0.23000, loss_spatial_ce_4: 0.01721/0.09682, loss_grounding_bce_4: 0.05651/0.08710, loss_grounding_dice_4: 0.08030/0.18218, loss_grounding_ce_4: 0.11221/0.28392, loss_mask_ce_5: 0.71144/0.93865, loss_mask_bce_5: 0.34371/0.34089, loss_mask_dice_5: 0.73184/1.19971, loss_spatial_bce_5: 0.09555/0.09686, loss_spatial_dice_5: 0.15921/0.23363, loss_spatial_ce_5: 0.01316/0.11189, loss_grounding_bce_5: 0.05460/0.08754, loss_grounding_dice_5: 0.06995/0.18342, loss_grounding_ce_5: 0.12020/0.29651, loss_mask_ce_6: 0.74989/0.97796, loss_mask_bce_6: 0.34265/0.34359, loss_mask_dice_6: 0.72715/1.20227, loss_spatial_bce_6: 0.10367/0.10256, loss_spatial_dice_6: 0.16050/0.23606, loss_spatial_ce_6: 0.10401/0.13760, loss_grounding_bce_6: 0.05341/0.08824, loss_grounding_dice_6: 0.06643/0.18357, loss_grounding_ce_6: 0.09365/0.31295, loss_mask_ce_7: 0.73969/1.02297, loss_mask_bce_7: 0.36682/0.35147, loss_mask_dice_7: 0.73663/1.25789, loss_spatial_bce_7: 0.12671/0.11101, loss_spatial_dice_7: 0.18124/0.26375, loss_spatial_ce_7: 0.09568/0.17425, loss_grounding_bce_7: 0.05484/0.09018, loss_grounding_dice_7: 0.07888/0.19086, loss_grounding_ce_7: 0.11737/0.34605, loss_mask_ce_8: 1.24263/1.13172, loss_mask_bce_8: 0.33180/0.36506, loss_mask_dice_8: 0.87346/1.33155, loss_spatial_bce_8: 0.12112/0.13194, loss_spatial_dice_8: 0.21370/0.30298, loss_spatial_ce_8: 0.21060/0.23162, loss_grounding_bce_8: 0.05900/0.09386, loss_grounding_dice_8: 0.09618/0.20192, loss_grounding_ce_8: 0.19725/0.41368, loss_mask_ce_9: 3.40296/3.68202, loss_mask_bce_9: 0.37109/0.39194, loss_mask_dice_9: 1.55885/1.90472, loss_spatial_bce_9: 0.37651/0.33382, loss_spatial_dice_9: 0.80992/0.82301, loss_spatial_ce_9: 1.49172/1.50537, loss_grounding_bce_9: 0.06710/0.10531, loss_grounding_dice_9: 0.23007/0.28131, loss_grounding_ce_9: 0.30394/0.67961] items per batch[64] items per second[0.23] total items[2585600] mini batches[ 40400] memory[7341] epoch remaining[1:14:09]
INFO:trainer.default_trainer:epochs[    22] optim steps[40500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31323/0.90639, loss_mask_bce_0: 0.36621/0.33453, loss_mask_dice_0: 0.29993/1.16594, loss_spatial_bce_0: 0.15894/0.08886, loss_spatial_dice_0: 0.14728/0.21262, loss_spatial_ce_0: 0.03128/0.06787, loss_grounding_bce_0: 0.09105/0.08621, loss_grounding_dice_0: 0.08508/0.17900, loss_grounding_ce_0: 0.00478/0.27470, loss_mask_ce_1: 0.41045/0.90688, loss_mask_bce_1: 0.32750/0.33533, loss_mask_dice_1: 0.27494/1.17285, loss_spatial_bce_1: 0.16166/0.08951, loss_spatial_dice_1: 0.14616/0.21680, loss_spatial_ce_1: 0.04517/0.07355, loss_grounding_bce_1: 0.09603/0.08632, loss_grounding_dice_1: 0.09003/0.17978, loss_grounding_ce_1: 0.00559/0.27624, loss_mask_ce_2: 0.43951/0.91432, loss_mask_bce_2: 0.33328/0.33578, loss_mask_dice_2: 0.28270/1.17241, loss_spatial_bce_2: 0.18430/0.09009, loss_spatial_dice_2: 0.15416/0.21790, loss_spatial_ce_2: 0.05821/0.07709, loss_grounding_bce_2: 0.08933/0.08642, loss_grounding_dice_2: 0.08691/0.17961, loss_grounding_ce_2: 0.01013/0.27953, loss_mask_ce_3: 0.38681/0.92364, loss_mask_bce_3: 0.32149/0.33673, loss_mask_dice_3: 0.27919/1.16973, loss_spatial_bce_3: 0.18923/0.09096, loss_spatial_dice_3: 0.16000/0.21852, loss_spatial_ce_3: 0.04855/0.08079, loss_grounding_bce_3: 0.09041/0.08667, loss_grounding_dice_3: 0.09360/0.17935, loss_grounding_ce_3: 0.00811/0.28102, loss_mask_ce_4: 0.38665/0.92331, loss_mask_bce_4: 0.31420/0.33867, loss_mask_dice_4: 0.28647/1.19334, loss_spatial_bce_4: 0.19807/0.09515, loss_spatial_dice_4: 0.16727/0.22997, loss_spatial_ce_4: 0.05868/0.09677, loss_grounding_bce_4: 0.09213/0.08708, loss_grounding_dice_4: 0.09149/0.18217, loss_grounding_ce_4: 0.01319/0.28392, loss_mask_ce_5: 0.33112/0.93882, loss_mask_bce_5: 0.34970/0.34093, loss_mask_dice_5: 0.31349/1.19974, loss_spatial_bce_5: 0.21967/0.09686, loss_spatial_dice_5: 0.18154/0.23360, loss_spatial_ce_5: 0.02398/0.11185, loss_grounding_bce_5: 0.09277/0.08752, loss_grounding_dice_5: 0.08955/0.18340, loss_grounding_ce_5: 0.03252/0.29656, loss_mask_ce_6: 0.35402/0.97810, loss_mask_bce_6: 0.35112/0.34364, loss_mask_dice_6: 0.31000/1.20232, loss_spatial_bce_6: 0.19571/0.10256, loss_spatial_dice_6: 0.16614/0.23603, loss_spatial_ce_6: 0.10151/0.13755, loss_grounding_bce_6: 0.09774/0.08822, loss_grounding_dice_6: 0.09330/0.18355, loss_grounding_ce_6: 0.11952/0.31296, loss_mask_ce_7: 0.34307/1.02313, loss_mask_bce_7: 0.36481/0.35154, loss_mask_dice_7: 0.35245/1.25799, loss_spatial_bce_7: 0.18219/0.11100, loss_spatial_dice_7: 0.16615/0.26371, loss_spatial_ce_7: 0.13808/0.17417, loss_grounding_bce_7: 0.10224/0.09016, loss_grounding_dice_7: 0.11344/0.19085, loss_grounding_ce_7: 0.23963/0.34607, loss_mask_ce_8: 0.38262/1.13187, loss_mask_bce_8: 0.39650/0.36512, loss_mask_dice_8: 0.34662/1.33164, loss_spatial_bce_8: 0.22587/0.13193, loss_spatial_dice_8: 0.17899/0.30294, loss_spatial_ce_8: 0.10483/0.23154, loss_grounding_bce_8: 0.11381/0.09384, loss_grounding_dice_8: 0.12811/0.20193, loss_grounding_ce_8: 0.08675/0.41369, loss_mask_ce_9: 2.92896/3.68205, loss_mask_bce_9: 0.32235/0.39201, loss_mask_dice_9: 0.40112/1.90505, loss_spatial_bce_9: 0.45631/0.33385, loss_spatial_dice_9: 0.73022/0.82301, loss_spatial_ce_9: 0.90139/1.50526, loss_grounding_bce_9: 0.12573/0.10529, loss_grounding_dice_9: 0.19384/0.28131, loss_grounding_ce_9: 0.56149/0.67956] items per batch[64] items per second[0.22] total items[2592000] mini batches[ 40500] memory[7341] epoch remaining[1:10:38]
INFO:trainer.default_trainer:epochs[    22] optim steps[40600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70839/0.90639, loss_mask_bce_0: 0.06190/0.33453, loss_mask_dice_0: 0.30068/1.16592, loss_spatial_bce_0: 0.01878/0.08886, loss_spatial_dice_0: 0.09258/0.21260, loss_spatial_ce_0: 0.00250/0.06783, loss_grounding_bce_0: 0.02001/0.08619, loss_grounding_dice_0: 0.08470/0.17900, loss_grounding_ce_0: 0.09707/0.27455, loss_mask_ce_1: 0.38853/0.90688, loss_mask_bce_1: 0.06583/0.33533, loss_mask_dice_1: 0.48650/1.17283, loss_spatial_bce_1: 0.01723/0.08950, loss_spatial_dice_1: 0.13387/0.21678, loss_spatial_ce_1: 0.00068/0.07353, loss_grounding_bce_1: 0.02486/0.08631, loss_grounding_dice_1: 0.09285/0.17976, loss_grounding_ce_1: 0.10122/0.27611, loss_mask_ce_2: 0.62149/0.91438, loss_mask_bce_2: 0.06382/0.33578, loss_mask_dice_2: 0.35352/1.17241, loss_spatial_bce_2: 0.01889/0.09009, loss_spatial_dice_2: 0.12884/0.21790, loss_spatial_ce_2: 0.00483/0.07706, loss_grounding_bce_2: 0.02341/0.08641, loss_grounding_dice_2: 0.09515/0.17960, loss_grounding_ce_2: 0.09811/0.27939, loss_mask_ce_3: 0.34858/0.92366, loss_mask_bce_3: 0.06718/0.33673, loss_mask_dice_3: 0.43137/1.16977, loss_spatial_bce_3: 0.01829/0.09095, loss_spatial_dice_3: 0.09733/0.21851, loss_spatial_ce_3: 0.00509/0.08078, loss_grounding_bce_3: 0.02310/0.08666, loss_grounding_dice_3: 0.08850/0.17932, loss_grounding_ce_3: 0.09970/0.28088, loss_mask_ce_4: 0.71895/0.92334, loss_mask_bce_4: 0.06670/0.33868, loss_mask_dice_4: 0.51178/1.19334, loss_spatial_bce_4: 0.01866/0.09514, loss_spatial_dice_4: 0.12628/0.22996, loss_spatial_ce_4: 0.02987/0.09680, loss_grounding_bce_4: 0.02085/0.08706, loss_grounding_dice_4: 0.08638/0.18216, loss_grounding_ce_4: 0.15757/0.28381, loss_mask_ce_5: 0.32552/0.93887, loss_mask_bce_5: 0.06925/0.34094, loss_mask_dice_5: 0.39891/1.19972, loss_spatial_bce_5: 0.02048/0.09686, loss_spatial_dice_5: 0.10142/0.23359, loss_spatial_ce_5: 0.04765/0.11187, loss_grounding_bce_5: 0.02351/0.08750, loss_grounding_dice_5: 0.09726/0.18338, loss_grounding_ce_5: 0.12375/0.29643, loss_mask_ce_6: 0.43440/0.97812, loss_mask_bce_6: 0.07224/0.34363, loss_mask_dice_6: 0.53384/1.20230, loss_spatial_bce_6: 0.02216/0.10256, loss_spatial_dice_6: 0.11828/0.23602, loss_spatial_ce_6: 0.04303/0.13757, loss_grounding_bce_6: 0.02192/0.08820, loss_grounding_dice_6: 0.09636/0.18353, loss_grounding_ce_6: 0.11675/0.31280, loss_mask_ce_7: 0.70897/1.02320, loss_mask_bce_7: 0.06599/0.35153, loss_mask_dice_7: 0.38926/1.25795, loss_spatial_bce_7: 0.02274/0.11099, loss_spatial_dice_7: 0.17450/0.26371, loss_spatial_ce_7: 0.06047/0.17414, loss_grounding_bce_7: 0.02372/0.09014, loss_grounding_dice_7: 0.11085/0.19082, loss_grounding_ce_7: 0.09867/0.34587, loss_mask_ce_8: 0.33808/1.13198, loss_mask_bce_8: 0.07518/0.36508, loss_mask_dice_8: 0.47079/1.33161, loss_spatial_bce_8: 0.06798/0.13193, loss_spatial_dice_8: 0.18106/0.30294, loss_spatial_ce_8: 0.29695/0.23155, loss_grounding_bce_8: 0.02547/0.09381, loss_grounding_dice_8: 0.11507/0.20190, loss_grounding_ce_8: 0.15380/0.41351, loss_mask_ce_9: 2.48201/3.68184, loss_mask_bce_9: 0.10709/0.39196, loss_mask_dice_9: 0.66031/1.90509, loss_spatial_bce_9: 0.23165/0.33382, loss_spatial_dice_9: 0.72116/0.82300, loss_spatial_ce_9: 1.10929/1.50516, loss_grounding_bce_9: 0.03175/0.10526, loss_grounding_dice_9: 0.23486/0.28128, loss_grounding_ce_9: 0.28507/0.67930] items per batch[64] items per second[0.23] total items[2598400] mini batches[ 40600] memory[7341] epoch remaining[1:05:57]
INFO:trainer.default_trainer:epochs[    22] optim steps[40700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.87090/0.90647, loss_mask_bce_0: 0.14418/0.33443, loss_mask_dice_0: 2.58848/1.16583, loss_spatial_bce_0: 0.03226/0.08885, loss_spatial_dice_0: 0.40548/0.21259, loss_spatial_ce_0: 0.02152/0.06782, loss_grounding_bce_0: 0.08891/0.08618, loss_grounding_dice_0: 0.43078/0.17898, loss_grounding_ce_0: 0.02130/0.27453, loss_mask_ce_1: 1.73791/0.90701, loss_mask_bce_1: 0.13525/0.33522, loss_mask_dice_1: 2.97611/1.17268, loss_spatial_bce_1: 0.02808/0.08950, loss_spatial_dice_1: 0.39901/0.21676, loss_spatial_ce_1: 0.04581/0.07351, loss_grounding_bce_1: 0.08797/0.08630, loss_grounding_dice_1: 0.41137/0.17975, loss_grounding_ce_1: 0.01951/0.27610, loss_mask_ce_2: 1.76782/0.91450, loss_mask_bce_2: 0.13108/0.33567, loss_mask_dice_2: 2.44050/1.17230, loss_spatial_bce_2: 0.02617/0.09008, loss_spatial_dice_2: 0.39888/0.21789, loss_spatial_ce_2: 0.05992/0.07702, loss_grounding_bce_2: 0.07651/0.08640, loss_grounding_dice_2: 0.41818/0.17958, loss_grounding_ce_2: 0.01599/0.27937, loss_mask_ce_3: 1.56007/0.92374, loss_mask_bce_3: 0.13625/0.33662, loss_mask_dice_3: 2.57915/1.16964, loss_spatial_bce_3: 0.02990/0.09095, loss_spatial_dice_3: 0.40474/0.21849, loss_spatial_ce_3: 0.15963/0.08076, loss_grounding_bce_3: 0.09789/0.08665, loss_grounding_dice_3: 0.48505/0.17931, loss_grounding_ce_3: 0.02136/0.28085, loss_mask_ce_4: 1.57470/0.92347, loss_mask_bce_4: 0.13647/0.33857, loss_mask_dice_4: 2.95779/1.19324, loss_spatial_bce_4: 0.02746/0.09514, loss_spatial_dice_4: 0.43133/0.22996, loss_spatial_ce_4: 0.04471/0.09677, loss_grounding_bce_4: 0.07866/0.08705, loss_grounding_dice_4: 0.40085/0.18214, loss_grounding_ce_4: 0.03061/0.28379, loss_mask_ce_5: 1.92802/0.93905, loss_mask_bce_5: 0.15002/0.34083, loss_mask_dice_5: 2.83385/1.19960, loss_spatial_bce_5: 0.02776/0.09686, loss_spatial_dice_5: 0.37630/0.23360, loss_spatial_ce_5: 0.17030/0.11186, loss_grounding_bce_5: 0.07370/0.08749, loss_grounding_dice_5: 0.39444/0.18336, loss_grounding_ce_5: 0.02871/0.29639, loss_mask_ce_6: 1.56263/0.97825, loss_mask_bce_6: 0.14141/0.34352, loss_mask_dice_6: 3.46572/1.20220, loss_spatial_bce_6: 0.04166/0.10257, loss_spatial_dice_6: 0.41194/0.23603, loss_spatial_ce_6: 0.11570/0.13754, loss_grounding_bce_6: 0.08881/0.08818, loss_grounding_dice_6: 0.40955/0.18351, loss_grounding_ce_6: 0.04502/0.31283, loss_mask_ce_7: 1.74737/1.02334, loss_mask_bce_7: 0.14062/0.35143, loss_mask_dice_7: 2.84785/1.25783, loss_spatial_bce_7: 0.04801/0.11099, loss_spatial_dice_7: 0.48700/0.26372, loss_spatial_ce_7: 0.60529/0.17414, loss_grounding_bce_7: 0.06833/0.09013, loss_grounding_dice_7: 0.39854/0.19080, loss_grounding_ce_7: 0.04568/0.34587, loss_mask_ce_8: 1.85535/1.13214, loss_mask_bce_8: 0.16026/0.36498, loss_mask_dice_8: 2.64286/1.33152, loss_spatial_bce_8: 0.07689/0.13192, loss_spatial_dice_8: 0.50294/0.30296, loss_spatial_ce_8: 0.28388/0.23152, loss_grounding_bce_8: 0.09631/0.09380, loss_grounding_dice_8: 0.40650/0.20187, loss_grounding_ce_8: 0.21012/0.41346, loss_mask_ce_9: 4.48458/3.68190, loss_mask_bce_9: 0.11288/0.39186, loss_mask_dice_9: 3.36274/1.90486, loss_spatial_bce_9: 0.13754/0.33379, loss_spatial_dice_9: 0.77887/0.82300, loss_spatial_ce_9: 2.75686/1.50506, loss_grounding_bce_9: 0.21255/0.10525, loss_grounding_dice_9: 0.56317/0.28126, loss_grounding_ce_9: 0.36697/0.67930] items per batch[64] items per second[0.23] total items[2604800] mini batches[ 40700] memory[7341] epoch remaining[1:01:34]
INFO:trainer.default_trainer:epochs[    22] optim steps[40800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88231/0.90648, loss_mask_bce_0: 0.13167/0.33448, loss_mask_dice_0: 0.55768/1.16614, loss_spatial_bce_0: 0.05071/0.08886, loss_spatial_dice_0: 0.28725/0.21256, loss_spatial_ce_0: 0.05714/0.06776, loss_grounding_bce_0: 0.08001/0.08618, loss_grounding_dice_0: 0.20954/0.17896, loss_grounding_ce_0: 0.05226/0.27448, loss_mask_ce_1: 0.66820/0.90701, loss_mask_bce_1: 0.14080/0.33527, loss_mask_dice_1: 0.69750/1.17300, loss_spatial_bce_1: 0.04926/0.08951, loss_spatial_dice_1: 0.27598/0.21673, loss_spatial_ce_1: 0.01849/0.07345, loss_grounding_bce_1: 0.07922/0.08630, loss_grounding_dice_1: 0.22907/0.17973, loss_grounding_ce_1: 0.06096/0.27605, loss_mask_ce_2: 0.64820/0.91448, loss_mask_bce_2: 0.14339/0.33574, loss_mask_dice_2: 0.66317/1.17264, loss_spatial_bce_2: 0.05022/0.09009, loss_spatial_dice_2: 0.30640/0.21786, loss_spatial_ce_2: 0.01911/0.07697, loss_grounding_bce_2: 0.07949/0.08640, loss_grounding_dice_2: 0.20475/0.17957, loss_grounding_ce_2: 0.06233/0.27933, loss_mask_ce_3: 0.71465/0.92368, loss_mask_bce_3: 0.14158/0.33668, loss_mask_dice_3: 0.76401/1.17000, loss_spatial_bce_3: 0.05034/0.09096, loss_spatial_dice_3: 0.25399/0.21846, loss_spatial_ce_3: 0.02713/0.08070, loss_grounding_bce_3: 0.08189/0.08665, loss_grounding_dice_3: 0.22178/0.17929, loss_grounding_ce_3: 0.06530/0.28082, loss_mask_ce_4: 0.68980/0.92347, loss_mask_bce_4: 0.13420/0.33866, loss_mask_dice_4: 0.55567/1.19361, loss_spatial_bce_4: 0.05279/0.09515, loss_spatial_dice_4: 0.27632/0.22994, loss_spatial_ce_4: 0.03250/0.09670, loss_grounding_bce_4: 0.07987/0.08705, loss_grounding_dice_4: 0.18570/0.18212, loss_grounding_ce_4: 0.03953/0.28377, loss_mask_ce_5: 0.75093/0.93905, loss_mask_bce_5: 0.13946/0.34090, loss_mask_dice_5: 0.71551/1.19996, loss_spatial_bce_5: 0.05478/0.09687, loss_spatial_dice_5: 0.30370/0.23357, loss_spatial_ce_5: 0.01446/0.11179, loss_grounding_bce_5: 0.07778/0.08749, loss_grounding_dice_5: 0.18877/0.18335, loss_grounding_ce_5: 0.04427/0.29639, loss_mask_ce_6: 0.68119/0.97826, loss_mask_bce_6: 0.14259/0.34360, loss_mask_dice_6: 0.72545/1.20260, loss_spatial_bce_6: 0.05251/0.10258, loss_spatial_dice_6: 0.30259/0.23601, loss_spatial_ce_6: 0.03092/0.13747, loss_grounding_bce_6: 0.07999/0.08819, loss_grounding_dice_6: 0.21480/0.18350, loss_grounding_ce_6: 0.06318/0.31279, loss_mask_ce_7: 0.61525/1.02336, loss_mask_bce_7: 0.13945/0.35151, loss_mask_dice_7: 0.81741/1.25823, loss_spatial_bce_7: 0.05722/0.11101, loss_spatial_dice_7: 0.32399/0.26371, loss_spatial_ce_7: 0.02407/0.17408, loss_grounding_bce_7: 0.08083/0.09013, loss_grounding_dice_7: 0.19897/0.19079, loss_grounding_ce_7: 0.05212/0.34584, loss_mask_ce_8: 0.74542/1.13216, loss_mask_bce_8: 0.14284/0.36508, loss_mask_dice_8: 0.77689/1.33198, loss_spatial_bce_8: 0.06115/0.13194, loss_spatial_dice_8: 0.32977/0.30295, loss_spatial_ce_8: 0.13965/0.23144, loss_grounding_bce_8: 0.07894/0.09381, loss_grounding_dice_8: 0.18239/0.20186, loss_grounding_ce_8: 0.04260/0.41342, loss_mask_ce_9: 2.09661/3.68201, loss_mask_bce_9: 0.12430/0.39194, loss_mask_dice_9: 0.78655/1.90546, loss_spatial_bce_9: 0.18356/0.33384, loss_spatial_dice_9: 0.76494/0.82296, loss_spatial_ce_9: 2.22123/1.50484, loss_grounding_bce_9: 0.07694/0.10527, loss_grounding_dice_9: 0.26448/0.28127, loss_grounding_ce_9: 0.08807/0.67914] items per batch[64] items per second[0.23] total items[2611200] mini batches[ 40800] memory[7341] epoch remaining[0:56:58]
INFO:trainer.default_trainer:epochs[    22] optim steps[40900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24587/0.90648, loss_mask_bce_0: 0.30108/0.33439, loss_mask_dice_0: 0.77835/1.16585, loss_spatial_bce_0: 0.05312/0.08883, loss_spatial_dice_0: 0.13503/0.21254, loss_spatial_ce_0: 0.00715/0.06771, loss_grounding_bce_0: 0.02411/0.08616, loss_grounding_dice_0: 0.05230/0.17896, loss_grounding_ce_0: 0.35509/0.27441, loss_mask_ce_1: 0.26510/0.90702, loss_mask_bce_1: 0.29178/0.33517, loss_mask_dice_1: 0.79145/1.17272, loss_spatial_bce_1: 0.04972/0.08948, loss_spatial_dice_1: 0.15164/0.21670, loss_spatial_ce_1: 0.03519/0.07342, loss_grounding_bce_1: 0.02668/0.08628, loss_grounding_dice_1: 0.05365/0.17970, loss_grounding_ce_1: 0.34939/0.27600, loss_mask_ce_2: 0.26504/0.91451, loss_mask_bce_2: 0.29466/0.33564, loss_mask_dice_2: 0.73167/1.17235, loss_spatial_bce_2: 0.05080/0.09006, loss_spatial_dice_2: 0.16289/0.21784, loss_spatial_ce_2: 0.02262/0.07693, loss_grounding_bce_2: 0.02535/0.08638, loss_grounding_dice_2: 0.05408/0.17953, loss_grounding_ce_2: 0.32629/0.27929, loss_mask_ce_3: 0.24091/0.92368, loss_mask_bce_3: 0.30155/0.33659, loss_mask_dice_3: 0.80687/1.16967, loss_spatial_bce_3: 0.05504/0.09093, loss_spatial_dice_3: 0.16733/0.21844, loss_spatial_ce_3: 0.02705/0.08068, loss_grounding_bce_3: 0.02597/0.08662, loss_grounding_dice_3: 0.05808/0.17927, loss_grounding_ce_3: 0.41808/0.28073, loss_mask_ce_4: 0.37743/0.92356, loss_mask_bce_4: 0.29511/0.33857, loss_mask_dice_4: 0.69914/1.19326, loss_spatial_bce_4: 0.05336/0.09512, loss_spatial_dice_4: 0.16254/0.22993, loss_spatial_ce_4: 0.10030/0.09666, loss_grounding_bce_4: 0.02749/0.08703, loss_grounding_dice_4: 0.05675/0.18211, loss_grounding_ce_4: 0.35412/0.28375, loss_mask_ce_5: 0.27113/0.93908, loss_mask_bce_5: 0.29422/0.34082, loss_mask_dice_5: 0.79054/1.19970, loss_spatial_bce_5: 0.05843/0.09684, loss_spatial_dice_5: 0.18607/0.23356, loss_spatial_ce_5: 0.01787/0.11175, loss_grounding_bce_5: 0.02529/0.08747, loss_grounding_dice_5: 0.05302/0.18334, loss_grounding_ce_5: 0.22176/0.29630, loss_mask_ce_6: 0.27307/0.97837, loss_mask_bce_6: 0.29881/0.34351, loss_mask_dice_6: 0.86651/1.20231, loss_spatial_bce_6: 0.06194/0.10254, loss_spatial_dice_6: 0.17336/0.23600, loss_spatial_ce_6: 0.05863/0.13744, loss_grounding_bce_6: 0.02576/0.08817, loss_grounding_dice_6: 0.05459/0.18349, loss_grounding_ce_6: 0.17168/0.31277, loss_mask_ce_7: 0.35441/1.02342, loss_mask_bce_7: 0.29082/0.35141, loss_mask_dice_7: 0.75901/1.25792, loss_spatial_bce_7: 0.07247/0.11097, loss_spatial_dice_7: 0.23892/0.26372, loss_spatial_ce_7: 0.12797/0.17405, loss_grounding_bce_7: 0.02357/0.09012, loss_grounding_dice_7: 0.05325/0.19077, loss_grounding_ce_7: 0.16823/0.34580, loss_mask_ce_8: 0.63294/1.13221, loss_mask_bce_8: 0.31117/0.36499, loss_mask_dice_8: 0.83807/1.33165, loss_spatial_bce_8: 0.06995/0.13192, loss_spatial_dice_8: 0.22915/0.30295, loss_spatial_ce_8: 0.18928/0.23139, loss_grounding_bce_8: 0.02487/0.09379, loss_grounding_dice_8: 0.05552/0.20185, loss_grounding_ce_8: 0.26108/0.41345, loss_mask_ce_9: 2.29029/3.68181, loss_mask_bce_9: 0.28815/0.39185, loss_mask_dice_9: 1.38788/1.90505, loss_spatial_bce_9: 0.32829/0.33377, loss_spatial_dice_9: 0.88847/0.82295, loss_spatial_ce_9: 1.72157/1.50479, loss_grounding_bce_9: 0.04888/0.10527, loss_grounding_dice_9: 0.12644/0.28126, loss_grounding_ce_9: 0.70557/0.67923] items per batch[64] items per second[0.23] total items[2617600] mini batches[ 40900] memory[7341] epoch remaining[0:52:16]
INFO:trainer.default_trainer:epochs[    22] optim steps[41000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34877/0.90645, loss_mask_bce_0: 0.03659/0.33438, loss_mask_dice_0: 0.42240/1.16625, loss_spatial_bce_0: 0.01359/0.08881, loss_spatial_dice_0: 0.14860/0.21251, loss_spatial_ce_0: 0.01555/0.06765, loss_grounding_bce_0: 0.00538/0.08613, loss_grounding_dice_0: 0.12057/0.17895, loss_grounding_ce_0: 0.09308/0.27435, loss_mask_ce_1: 0.44143/0.90700, loss_mask_bce_1: 0.03534/0.33516, loss_mask_dice_1: 0.32996/1.17312, loss_spatial_bce_1: 0.01416/0.08945, loss_spatial_dice_1: 0.14897/0.21667, loss_spatial_ce_1: 0.01526/0.07337, loss_grounding_bce_1: 0.00479/0.08625, loss_grounding_dice_1: 0.14785/0.17969, loss_grounding_ce_1: 0.09318/0.27598, loss_mask_ce_2: 0.20875/0.91449, loss_mask_bce_2: 0.03794/0.33564, loss_mask_dice_2: 0.57465/1.17273, loss_spatial_bce_2: 0.01572/0.09004, loss_spatial_dice_2: 0.16867/0.21780, loss_spatial_ce_2: 0.04595/0.07688, loss_grounding_bce_2: 0.00551/0.08635, loss_grounding_dice_2: 0.13278/0.17953, loss_grounding_ce_2: 0.09991/0.27923, loss_mask_ce_3: 0.15031/0.92365, loss_mask_bce_3: 0.03837/0.33659, loss_mask_dice_3: 0.41806/1.17005, loss_spatial_bce_3: 0.01350/0.09091, loss_spatial_dice_3: 0.18716/0.21842, loss_spatial_ce_3: 0.09023/0.08062, loss_grounding_bce_3: 0.00493/0.08660, loss_grounding_dice_3: 0.16509/0.17926, loss_grounding_ce_3: 0.11455/0.28066, loss_mask_ce_4: 0.16842/0.92357, loss_mask_bce_4: 0.03669/0.33856, loss_mask_dice_4: 0.30945/1.19364, loss_spatial_bce_4: 0.01475/0.09510, loss_spatial_dice_4: 0.14107/0.22991, loss_spatial_ce_4: 0.06125/0.09659, loss_grounding_bce_4: 0.00591/0.08700, loss_grounding_dice_4: 0.17504/0.18211, loss_grounding_ce_4: 0.12402/0.28369, loss_mask_ce_5: 0.25253/0.93905, loss_mask_bce_5: 0.04049/0.34081, loss_mask_dice_5: 0.48110/1.20011, loss_spatial_bce_5: 0.01490/0.09682, loss_spatial_dice_5: 0.12889/0.23354, loss_spatial_ce_5: 0.10274/0.11172, loss_grounding_bce_5: 0.00614/0.08744, loss_grounding_dice_5: 0.12753/0.18332, loss_grounding_ce_5: 0.11714/0.29625, loss_mask_ce_6: 0.24430/0.97832, loss_mask_bce_6: 0.03620/0.34350, loss_mask_dice_6: 0.48985/1.20274, loss_spatial_bce_6: 0.01555/0.10252, loss_spatial_dice_6: 0.13310/0.23599, loss_spatial_ce_6: 0.20870/0.13739, loss_grounding_bce_6: 0.00570/0.08815, loss_grounding_dice_6: 0.16149/0.18348, loss_grounding_ce_6: 0.14919/0.31268, loss_mask_ce_7: 0.87658/1.02345, loss_mask_bce_7: 0.03732/0.35140, loss_mask_dice_7: 0.41096/1.25832, loss_spatial_bce_7: 0.02484/0.11094, loss_spatial_dice_7: 0.25214/0.26370, loss_spatial_ce_7: 0.07177/0.17399, loss_grounding_bce_7: 0.00448/0.09009, loss_grounding_dice_7: 0.11878/0.19076, loss_grounding_ce_7: 0.18133/0.34572, loss_mask_ce_8: 0.46905/1.13222, loss_mask_bce_8: 0.03931/0.36498, loss_mask_dice_8: 0.49314/1.33209, loss_spatial_bce_8: 0.04656/0.13189, loss_spatial_dice_8: 0.27605/0.30293, loss_spatial_ce_8: 0.23731/0.23133, loss_grounding_bce_8: 0.00459/0.09376, loss_grounding_dice_8: 0.11999/0.20185, loss_grounding_ce_8: 0.12348/0.41340, loss_mask_ce_9: 2.17935/3.68228, loss_mask_bce_9: 0.04383/0.39182, loss_mask_dice_9: 0.54733/1.90548, loss_spatial_bce_9: 0.12538/0.33377, loss_spatial_dice_9: 0.78248/0.82292, loss_spatial_ce_9: 1.45798/1.50478, loss_grounding_bce_9: 0.00398/0.10524, loss_grounding_dice_9: 0.17534/0.28124, loss_grounding_ce_9: 0.27087/0.67937] items per batch[64] items per second[0.23] total items[2624000] mini batches[ 41000] memory[7341] epoch remaining[0:47:41]
INFO:trainer.default_trainer:epochs[    22] optim steps[41100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61989/0.90638, loss_mask_bce_0: 0.47968/0.33447, loss_mask_dice_0: 1.27558/1.16601, loss_spatial_bce_0: 0.05157/0.08882, loss_spatial_dice_0: 0.17312/0.21246, loss_spatial_ce_0: 0.01279/0.06760, loss_grounding_bce_0: 0.15860/0.08616, loss_grounding_dice_0: 0.33432/0.17893, loss_grounding_ce_0: 0.41896/0.27441, loss_mask_ce_1: 0.70429/0.90694, loss_mask_bce_1: 0.49019/0.33526, loss_mask_dice_1: 1.33772/1.17283, loss_spatial_bce_1: 0.07190/0.08946, loss_spatial_dice_1: 0.19204/0.21662, loss_spatial_ce_1: 0.01164/0.07331, loss_grounding_bce_1: 0.17217/0.08628, loss_grounding_dice_1: 0.36165/0.17967, loss_grounding_ce_1: 0.40571/0.27605, loss_mask_ce_2: 0.64628/0.91439, loss_mask_bce_2: 0.49545/0.33574, loss_mask_dice_2: 1.49940/1.17253, loss_spatial_bce_2: 0.06405/0.09006, loss_spatial_dice_2: 0.20016/0.21776, loss_spatial_ce_2: 0.06277/0.07683, loss_grounding_bce_2: 0.16323/0.08638, loss_grounding_dice_2: 0.35970/0.17952, loss_grounding_ce_2: 0.41583/0.27928, loss_mask_ce_3: 0.58957/0.92355, loss_mask_bce_3: 0.48756/0.33669, loss_mask_dice_3: 1.38850/1.16981, loss_spatial_bce_3: 0.06600/0.09093, loss_spatial_dice_3: 0.19229/0.21837, loss_spatial_ce_3: 0.02116/0.08059, loss_grounding_bce_3: 0.14643/0.08663, loss_grounding_dice_3: 0.36091/0.17925, loss_grounding_ce_3: 0.40256/0.28075, loss_mask_ce_4: 0.72425/0.92349, loss_mask_bce_4: 0.47954/0.33865, loss_mask_dice_4: 1.53139/1.19339, loss_spatial_bce_4: 0.07405/0.09511, loss_spatial_dice_4: 0.23611/0.22987, loss_spatial_ce_4: 0.04085/0.09656, loss_grounding_bce_4: 0.16060/0.08703, loss_grounding_dice_4: 0.36095/0.18210, loss_grounding_ce_4: 0.44922/0.28377, loss_mask_ce_5: 0.91053/0.93899, loss_mask_bce_5: 0.50325/0.34091, loss_mask_dice_5: 1.41718/1.19984, loss_spatial_bce_5: 0.06163/0.09683, loss_spatial_dice_5: 0.20731/0.23349, loss_spatial_ce_5: 0.08646/0.11166, loss_grounding_bce_5: 0.15722/0.08746, loss_grounding_dice_5: 0.34586/0.18330, loss_grounding_ce_5: 0.62066/0.29636, loss_mask_ce_6: 1.08349/0.97827, loss_mask_bce_6: 0.36834/0.34361, loss_mask_dice_6: 1.54596/1.20251, loss_spatial_bce_6: 0.07078/0.10253, loss_spatial_dice_6: 0.21692/0.23595, loss_spatial_ce_6: 0.12419/0.13738, loss_grounding_bce_6: 0.15113/0.08818, loss_grounding_dice_6: 0.33711/0.18347, loss_grounding_ce_6: 0.62554/0.31279, loss_mask_ce_7: 1.26016/1.02335, loss_mask_bce_7: 0.50329/0.35150, loss_mask_dice_7: 1.61991/1.25807, loss_spatial_bce_7: 0.06327/0.11095, loss_spatial_dice_7: 0.26588/0.26366, loss_spatial_ce_7: 0.29597/0.17393, loss_grounding_bce_7: 0.13809/0.09012, loss_grounding_dice_7: 0.36639/0.19075, loss_grounding_ce_7: 0.80558/0.34585, loss_mask_ce_8: 0.82502/1.13206, loss_mask_bce_8: 0.57013/0.36506, loss_mask_dice_8: 1.75018/1.33186, loss_spatial_bce_8: 0.09372/0.13190, loss_spatial_dice_8: 0.24859/0.30286, loss_spatial_ce_8: 0.24264/0.23130, loss_grounding_bce_8: 0.18994/0.09379, loss_grounding_dice_8: 0.55713/0.20185, loss_grounding_ce_8: 0.21416/0.41359, loss_mask_ce_9: 3.41203/3.68223, loss_mask_bce_9: 0.46104/0.39192, loss_mask_dice_9: 2.91065/1.90522, loss_spatial_bce_9: 0.25355/0.33381, loss_spatial_dice_9: 0.86654/0.82293, loss_spatial_ce_9: 1.50685/1.50464, loss_grounding_bce_9: 0.15246/0.10529, loss_grounding_dice_9: 0.63171/0.28125, loss_grounding_ce_9: 0.03443/0.67957] items per batch[64] items per second[0.23] total items[2630400] mini batches[ 41100] memory[7341] epoch remaining[0:43:04]
INFO:trainer.default_trainer:epochs[    22] optim steps[41200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85840/0.90633, loss_mask_bce_0: 0.36122/0.33446, loss_mask_dice_0: 0.39012/1.16576, loss_spatial_bce_0: 0.23213/0.08881, loss_spatial_dice_0: 0.30409/0.21240, loss_spatial_ce_0: 0.19049/0.06757, loss_grounding_bce_0: 0.13995/0.08614, loss_grounding_dice_0: 0.07650/0.17890, loss_grounding_ce_0: 0.08142/0.27450, loss_mask_ce_1: 0.84765/0.90691, loss_mask_bce_1: 0.35777/0.33524, loss_mask_dice_1: 0.44752/1.17260, loss_spatial_bce_1: 0.23564/0.08946, loss_spatial_dice_1: 0.30896/0.21658, loss_spatial_ce_1: 0.14529/0.07328, loss_grounding_bce_1: 0.13709/0.08627, loss_grounding_dice_1: 0.07373/0.17965, loss_grounding_ce_1: 0.06483/0.27613, loss_mask_ce_2: 0.84833/0.91434, loss_mask_bce_2: 0.34548/0.33573, loss_mask_dice_2: 0.44100/1.17233, loss_spatial_bce_2: 0.23351/0.09005, loss_spatial_dice_2: 0.32973/0.21771, loss_spatial_ce_2: 0.18996/0.07679, loss_grounding_bce_2: 0.14258/0.08636, loss_grounding_dice_2: 0.07877/0.17949, loss_grounding_ce_2: 0.08274/0.27935, loss_mask_ce_3: 0.86613/0.92353, loss_mask_bce_3: 0.34637/0.33668, loss_mask_dice_3: 0.46036/1.16955, loss_spatial_bce_3: 0.23504/0.09092, loss_spatial_dice_3: 0.33365/0.21832, loss_spatial_ce_3: 0.14556/0.08055, loss_grounding_bce_3: 0.14304/0.08661, loss_grounding_dice_3: 0.07844/0.17924, loss_grounding_ce_3: 0.07194/0.28080, loss_mask_ce_4: 1.00090/0.92352, loss_mask_bce_4: 0.34268/0.33864, loss_mask_dice_4: 0.47698/1.19318, loss_spatial_bce_4: 0.23574/0.09510, loss_spatial_dice_4: 0.35008/0.22982, loss_spatial_ce_4: 0.25639/0.09650, loss_grounding_bce_4: 0.13585/0.08701, loss_grounding_dice_4: 0.07745/0.18206, loss_grounding_ce_4: 0.07222/0.28384, loss_mask_ce_5: 0.94170/0.93903, loss_mask_bce_5: 0.35968/0.34089, loss_mask_dice_5: 0.50481/1.19960, loss_spatial_bce_5: 0.27293/0.09683, loss_spatial_dice_5: 0.33403/0.23344, loss_spatial_ce_5: 0.28156/0.11163, loss_grounding_bce_5: 0.14274/0.08744, loss_grounding_dice_5: 0.08974/0.18327, loss_grounding_ce_5: 0.08010/0.29645, loss_mask_ce_6: 1.20583/0.97831, loss_mask_bce_6: 0.34297/0.34361, loss_mask_dice_6: 0.46923/1.20231, loss_spatial_bce_6: 0.26968/0.10252, loss_spatial_dice_6: 0.34460/0.23590, loss_spatial_ce_6: 0.29964/0.13733, loss_grounding_bce_6: 0.11211/0.08816, loss_grounding_dice_6: 0.07113/0.18343, loss_grounding_ce_6: 0.20539/0.31295, loss_mask_ce_7: 1.01029/1.02338, loss_mask_bce_7: 0.44331/0.35149, loss_mask_dice_7: 0.53404/1.25780, loss_spatial_bce_7: 0.29029/0.11094, loss_spatial_dice_7: 0.36574/0.26359, loss_spatial_ce_7: 0.41657/0.17385, loss_grounding_bce_7: 0.15139/0.09010, loss_grounding_dice_7: 0.10712/0.19072, loss_grounding_ce_7: 0.08532/0.34596, loss_mask_ce_8: 1.24371/1.13210, loss_mask_bce_8: 0.38634/0.36505, loss_mask_dice_8: 0.48141/1.33157, loss_spatial_bce_8: 0.43069/0.13189, loss_spatial_dice_8: 0.42605/0.30281, loss_spatial_ce_8: 0.46508/0.23127, loss_grounding_bce_8: 0.16648/0.09379, loss_grounding_dice_8: 0.08573/0.20181, loss_grounding_ce_8: 0.11811/0.41366, loss_mask_ce_9: 4.15126/3.68238, loss_mask_bce_9: 0.48443/0.39190, loss_mask_dice_9: 0.75895/1.90483, loss_spatial_bce_9: 0.63667/0.33385, loss_spatial_dice_9: 0.75090/0.82291, loss_spatial_ce_9: 1.53720/1.50457, loss_grounding_bce_9: 0.15845/0.10527, loss_grounding_dice_9: 0.15540/0.28122, loss_grounding_ce_9: 0.59167/0.67960] items per batch[64] items per second[0.24] total items[2636800] mini batches[ 41200] memory[7341] epoch remaining[0:38:11]
INFO:trainer.default_trainer:epochs[    22] optim steps[41300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.36755/0.90639, loss_mask_bce_0: 0.45781/0.33451, loss_mask_dice_0: 2.71445/1.16560, loss_spatial_bce_0: 0.02990/0.08881, loss_spatial_dice_0: 0.24725/0.21236, loss_spatial_ce_0: 0.14187/0.06753, loss_grounding_bce_0: 0.04440/0.08612, loss_grounding_dice_0: 0.19978/0.17890, loss_grounding_ce_0: 1.05951/0.27450, loss_mask_ce_1: 1.26448/0.90694, loss_mask_bce_1: 0.42665/0.33531, loss_mask_dice_1: 2.91977/1.17247, loss_spatial_bce_1: 0.02891/0.08946, loss_spatial_dice_1: 0.21762/0.21654, loss_spatial_ce_1: 0.15102/0.07322, loss_grounding_bce_1: 0.05103/0.08626, loss_grounding_dice_1: 0.19904/0.17965, loss_grounding_ce_1: 1.11600/0.27616, loss_mask_ce_2: 1.27152/0.91438, loss_mask_bce_2: 0.42263/0.33579, loss_mask_dice_2: 2.95499/1.17217, loss_spatial_bce_2: 0.03198/0.09006, loss_spatial_dice_2: 0.24196/0.21767, loss_spatial_ce_2: 0.13000/0.07674, loss_grounding_bce_2: 0.04862/0.08635, loss_grounding_dice_2: 0.20401/0.17949, loss_grounding_ce_2: 1.12453/0.27938, loss_mask_ce_3: 1.19870/0.92358, loss_mask_bce_3: 0.42230/0.33674, loss_mask_dice_3: 3.09543/1.16940, loss_spatial_bce_3: 0.03437/0.09093, loss_spatial_dice_3: 0.24350/0.21828, loss_spatial_ce_3: 0.13863/0.08048, loss_grounding_bce_3: 0.05057/0.08660, loss_grounding_dice_3: 0.22877/0.17924, loss_grounding_ce_3: 1.02006/0.28083, loss_mask_ce_4: 1.32086/0.92355, loss_mask_bce_4: 0.39703/0.33870, loss_mask_dice_4: 2.89093/1.19307, loss_spatial_bce_4: 0.03388/0.09510, loss_spatial_dice_4: 0.26079/0.22978, loss_spatial_ce_4: 0.18233/0.09646, loss_grounding_bce_4: 0.04425/0.08701, loss_grounding_dice_4: 0.21379/0.18207, loss_grounding_ce_4: 0.94863/0.28387, loss_mask_ce_5: 1.20914/0.93906, loss_mask_bce_5: 0.42673/0.34095, loss_mask_dice_5: 2.95992/1.19948, loss_spatial_bce_5: 0.03285/0.09681, loss_spatial_dice_5: 0.27820/0.23341, loss_spatial_ce_5: 0.12565/0.11158, loss_grounding_bce_5: 0.04636/0.08744, loss_grounding_dice_5: 0.18730/0.18328, loss_grounding_ce_5: 0.83169/0.29645, loss_mask_ce_6: 1.41955/0.97836, loss_mask_bce_6: 0.46521/0.34367, loss_mask_dice_6: 3.15938/1.20218, loss_spatial_bce_6: 0.05058/0.10252, loss_spatial_dice_6: 0.34006/0.23586, loss_spatial_ce_6: 0.15684/0.13729, loss_grounding_bce_6: 0.04849/0.08815, loss_grounding_dice_6: 0.17611/0.18344, loss_grounding_ce_6: 0.67349/0.31294, loss_mask_ce_7: 1.58084/1.02340, loss_mask_bce_7: 0.40249/0.35156, loss_mask_dice_7: 3.44349/1.25767, loss_spatial_bce_7: 0.05129/0.11094, loss_spatial_dice_7: 0.35012/0.26355, loss_spatial_ce_7: 0.18431/0.17380, loss_grounding_bce_7: 0.04485/0.09010, loss_grounding_dice_7: 0.16696/0.19072, loss_grounding_ce_7: 0.77377/0.34596, loss_mask_ce_8: 1.34104/1.13207, loss_mask_bce_8: 0.51072/0.36515, loss_mask_dice_8: 3.75059/1.33145, loss_spatial_bce_8: 0.06697/0.13189, loss_spatial_dice_8: 0.43143/0.30279, loss_spatial_ce_8: 0.16812/0.23121, loss_grounding_bce_8: 0.06443/0.09379, loss_grounding_dice_8: 0.28990/0.20182, loss_grounding_ce_8: 1.69775/0.41372, loss_mask_ce_9: 3.41497/3.68249, loss_mask_bce_9: 0.57016/0.39196, loss_mask_dice_9: 4.89457/1.90463, loss_spatial_bce_9: 0.13839/0.33387, loss_spatial_dice_9: 0.89638/0.82289, loss_spatial_ce_9: 1.13249/1.50432, loss_grounding_bce_9: 0.07712/0.10527, loss_grounding_dice_9: 0.21686/0.28124, loss_grounding_ce_9: 1.46630/0.67949] items per batch[64] items per second[0.23] total items[2643200] mini batches[ 41300] memory[7341] epoch remaining[0:33:33]
INFO:trainer.default_trainer:epochs[    22] optim steps[41400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.37489/0.90631, loss_mask_bce_0: 0.05530/0.33454, loss_mask_dice_0: 1.52929/1.16550, loss_spatial_bce_0: 0.00775/0.08881, loss_spatial_dice_0: 0.22647/0.21234, loss_spatial_ce_0: 0.12728/0.06749, loss_grounding_bce_0: 0.00541/0.08613, loss_grounding_dice_0: 0.42712/0.17888, loss_grounding_ce_0: 0.10417/0.27451, loss_mask_ce_1: 1.43089/0.90688, loss_mask_bce_1: 0.05759/0.33533, loss_mask_dice_1: 1.35623/1.17232, loss_spatial_bce_1: 0.00757/0.08946, loss_spatial_dice_1: 0.19129/0.21650, loss_spatial_ce_1: 0.08613/0.07317, loss_grounding_bce_1: 0.00509/0.08626, loss_grounding_dice_1: 0.23867/0.17964, loss_grounding_ce_1: 0.28719/0.27616, loss_mask_ce_2: 1.19744/0.91428, loss_mask_bce_2: 0.05955/0.33581, loss_mask_dice_2: 1.30248/1.17205, loss_spatial_bce_2: 0.00916/0.09006, loss_spatial_dice_2: 0.23679/0.21764, loss_spatial_ce_2: 0.09550/0.07672, loss_grounding_bce_2: 0.00455/0.08635, loss_grounding_dice_2: 0.35452/0.17947, loss_grounding_ce_2: 0.11130/0.27935, loss_mask_ce_3: 1.61900/0.92351, loss_mask_bce_3: 0.05830/0.33677, loss_mask_dice_3: 1.24750/1.16930, loss_spatial_bce_3: 0.01021/0.09093, loss_spatial_dice_3: 0.21455/0.21825, loss_spatial_ce_3: 0.09975/0.08044, loss_grounding_bce_3: 0.00944/0.08660, loss_grounding_dice_3: 0.29231/0.17921, loss_grounding_ce_3: 0.30673/0.28083, loss_mask_ce_4: 1.07196/0.92353, loss_mask_bce_4: 0.05009/0.33872, loss_mask_dice_4: 1.76388/1.19294, loss_spatial_bce_4: 0.01081/0.09511, loss_spatial_dice_4: 0.26975/0.22976, loss_spatial_ce_4: 0.19283/0.09640, loss_grounding_bce_4: 0.00531/0.08700, loss_grounding_dice_4: 0.42377/0.18206, loss_grounding_ce_4: 0.05244/0.28384, loss_mask_ce_5: 1.61608/0.93906, loss_mask_bce_5: 0.05834/0.34096, loss_mask_dice_5: 1.50745/1.19935, loss_spatial_bce_5: 0.01115/0.09682, loss_spatial_dice_5: 0.23132/0.23339, loss_spatial_ce_5: 0.17534/0.11154, loss_grounding_bce_5: 0.00529/0.08744, loss_grounding_dice_5: 0.22086/0.18326, loss_grounding_ce_5: 0.40660/0.29644, loss_mask_ce_6: 1.76956/0.97831, loss_mask_bce_6: 0.06110/0.34368, loss_mask_dice_6: 1.40742/1.20208, loss_spatial_bce_6: 0.01073/0.10252, loss_spatial_dice_6: 0.23924/0.23584, loss_spatial_ce_6: 0.37477/0.13726, loss_grounding_bce_6: 0.00785/0.08815, loss_grounding_dice_6: 0.47429/0.18342, loss_grounding_ce_6: 0.06205/0.31292, loss_mask_ce_7: 1.39136/1.02338, loss_mask_bce_7: 0.05195/0.35158, loss_mask_dice_7: 1.12988/1.25749, loss_spatial_bce_7: 0.01718/0.11096, loss_spatial_dice_7: 0.31659/0.26353, loss_spatial_ce_7: 0.35201/0.17377, loss_grounding_bce_7: 0.00502/0.09010, loss_grounding_dice_7: 0.44599/0.19070, loss_grounding_ce_7: 0.03511/0.34592, loss_mask_ce_8: 1.52948/1.13203, loss_mask_bce_8: 0.07052/0.36517, loss_mask_dice_8: 1.56985/1.33133, loss_spatial_bce_8: 0.01705/0.13189, loss_spatial_dice_8: 0.51888/0.30275, loss_spatial_ce_8: 0.60388/0.23116, loss_grounding_bce_8: 0.00603/0.09379, loss_grounding_dice_8: 0.45046/0.20180, loss_grounding_ce_8: 0.14824/0.41367, loss_mask_ce_9: 3.30732/3.68235, loss_mask_bce_9: 0.05452/0.39196, loss_mask_dice_9: 2.09868/1.90437, loss_spatial_bce_9: 0.07098/0.33392, loss_spatial_dice_9: 0.84143/0.82288, loss_spatial_ce_9: 1.45713/1.50425, loss_grounding_bce_9: 0.00547/0.10528, loss_grounding_dice_9: 0.31122/0.28123, loss_grounding_ce_9: 0.59492/0.67954] items per batch[64] items per second[0.23] total items[2649600] mini batches[ 41400] memory[7341] epoch remaining[0:28:53]
INFO:trainer.default_trainer:epochs[    22] optim steps[41500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11744/0.90630, loss_mask_bce_0: 0.31406/0.33460, loss_mask_dice_0: 0.80910/1.16525, loss_spatial_bce_0: 0.06351/0.08882, loss_spatial_dice_0: 0.17042/0.21231, loss_spatial_ce_0: 0.00613/0.06745, loss_grounding_bce_0: 0.04847/0.08614, loss_grounding_dice_0: 0.07690/0.17886, loss_grounding_ce_0: 0.01096/0.27456, loss_mask_ce_1: 1.23424/0.90687, loss_mask_bce_1: 0.32568/0.33539, loss_mask_dice_1: 0.77245/1.17204, loss_spatial_bce_1: 0.06166/0.08947, loss_spatial_dice_1: 0.17461/0.21646, loss_spatial_ce_1: 0.00808/0.07313, loss_grounding_bce_1: 0.04813/0.08627, loss_grounding_dice_1: 0.07127/0.17962, loss_grounding_ce_1: 0.00883/0.27620, loss_mask_ce_2: 1.23993/0.91425, loss_mask_bce_2: 0.31798/0.33586, loss_mask_dice_2: 0.76327/1.17181, loss_spatial_bce_2: 0.06542/0.09007, loss_spatial_dice_2: 0.17939/0.21761, loss_spatial_ce_2: 0.01186/0.07667, loss_grounding_bce_2: 0.04883/0.08635, loss_grounding_dice_2: 0.07236/0.17944, loss_grounding_ce_2: 0.01384/0.27935, loss_mask_ce_3: 1.30985/0.92350, loss_mask_bce_3: 0.31978/0.33682, loss_mask_dice_3: 0.74834/1.16906, loss_spatial_bce_3: 0.06453/0.09095, loss_spatial_dice_3: 0.18099/0.21822, loss_spatial_ce_3: 0.03095/0.08041, loss_grounding_bce_3: 0.04664/0.08661, loss_grounding_dice_3: 0.07739/0.17920, loss_grounding_ce_3: 0.01165/0.28088, loss_mask_ce_4: 1.22128/0.92358, loss_mask_bce_4: 0.32475/0.33878, loss_mask_dice_4: 0.79108/1.19270, loss_spatial_bce_4: 0.08909/0.09512, loss_spatial_dice_4: 0.18959/0.22973, loss_spatial_ce_4: 0.07036/0.09635, loss_grounding_bce_4: 0.04995/0.08702, loss_grounding_dice_4: 0.07441/0.18205, loss_grounding_ce_4: 0.00900/0.28390, loss_mask_ce_5: 1.13867/0.93908, loss_mask_bce_5: 0.32641/0.34102, loss_mask_dice_5: 0.82544/1.19912, loss_spatial_bce_5: 0.07207/0.09684, loss_spatial_dice_5: 0.18702/0.23336, loss_spatial_ce_5: 0.09958/0.11151, loss_grounding_bce_5: 0.04977/0.08744, loss_grounding_dice_5: 0.07725/0.18324, loss_grounding_ce_5: 0.00957/0.29652, loss_mask_ce_6: 0.93947/0.97835, loss_mask_bce_6: 0.35148/0.34374, loss_mask_dice_6: 0.85381/1.20186, loss_spatial_bce_6: 0.09686/0.10254, loss_spatial_dice_6: 0.19465/0.23582, loss_spatial_ce_6: 0.18262/0.13722, loss_grounding_bce_6: 0.04941/0.08816, loss_grounding_dice_6: 0.07498/0.18340, loss_grounding_ce_6: 0.00804/0.31306, loss_mask_ce_7: 0.92264/1.02335, loss_mask_bce_7: 0.35156/0.35163, loss_mask_dice_7: 0.87487/1.25720, loss_spatial_bce_7: 0.13503/0.11098, loss_spatial_dice_7: 0.24041/0.26349, loss_spatial_ce_7: 0.19159/0.17374, loss_grounding_bce_7: 0.04806/0.09010, loss_grounding_dice_7: 0.07865/0.19068, loss_grounding_ce_7: 0.01999/0.34601, loss_mask_ce_8: 1.16541/1.13197, loss_mask_bce_8: 0.36980/0.36520, loss_mask_dice_8: 0.85746/1.33100, loss_spatial_bce_8: 0.08887/0.13190, loss_spatial_dice_8: 0.24066/0.30271, loss_spatial_ce_8: 0.22298/0.23109, loss_grounding_bce_8: 0.04979/0.09379, loss_grounding_dice_8: 0.08720/0.20177, loss_grounding_ce_8: 0.10903/0.41367, loss_mask_ce_9: 3.75328/3.68223, loss_mask_bce_9: 0.41101/0.39204, loss_mask_dice_9: 1.20096/1.90395, loss_spatial_bce_9: 0.36934/0.33395, loss_spatial_dice_9: 0.82964/0.82285, loss_spatial_ce_9: 1.38813/1.50424, loss_grounding_bce_9: 0.05514/0.10529, loss_grounding_dice_9: 0.22052/0.28120, loss_grounding_ce_9: 0.50832/0.67946] items per batch[64] items per second[0.23] total items[2656000] mini batches[ 41500] memory[7341] epoch remaining[0:24:11]
INFO:trainer.default_trainer:epochs[    22] optim steps[41600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71327/0.90628, loss_mask_bce_0: 0.28470/0.33452, loss_mask_dice_0: 0.86129/1.16538, loss_spatial_bce_0: 0.08117/0.08880, loss_spatial_dice_0: 0.21315/0.21229, loss_spatial_ce_0: 0.01172/0.06738, loss_grounding_bce_0: 0.09769/0.08612, loss_grounding_dice_0: 0.12780/0.17888, loss_grounding_ce_0: 0.11303/0.27445, loss_mask_ce_1: 0.76219/0.90685, loss_mask_bce_1: 0.28294/0.33531, loss_mask_dice_1: 0.81155/1.17216, loss_spatial_bce_1: 0.08586/0.08945, loss_spatial_dice_1: 0.23606/0.21645, loss_spatial_ce_1: 0.02032/0.07307, loss_grounding_bce_1: 0.09936/0.08625, loss_grounding_dice_1: 0.11949/0.17965, loss_grounding_ce_1: 0.10374/0.27613, loss_mask_ce_2: 0.73133/0.91426, loss_mask_bce_2: 0.29115/0.33578, loss_mask_dice_2: 0.92382/1.17194, loss_spatial_bce_2: 0.08507/0.09006, loss_spatial_dice_2: 0.24730/0.21759, loss_spatial_ce_2: 0.01525/0.07661, loss_grounding_bce_2: 0.09776/0.08634, loss_grounding_dice_2: 0.11305/0.17948, loss_grounding_ce_2: 0.11884/0.27925, loss_mask_ce_3: 0.71582/0.92344, loss_mask_bce_3: 0.28725/0.33674, loss_mask_dice_3: 0.80946/1.16918, loss_spatial_bce_3: 0.09364/0.09094, loss_spatial_dice_3: 0.22898/0.21821, loss_spatial_ce_3: 0.01779/0.08036, loss_grounding_bce_3: 0.09381/0.08660, loss_grounding_dice_3: 0.10167/0.17923, loss_grounding_ce_3: 0.15494/0.28086, loss_mask_ce_4: 0.76750/0.92358, loss_mask_bce_4: 0.28982/0.33870, loss_mask_dice_4: 0.86219/1.19280, loss_spatial_bce_4: 0.08888/0.09510, loss_spatial_dice_4: 0.23192/0.22972, loss_spatial_ce_4: 0.01460/0.09631, loss_grounding_bce_4: 0.09626/0.08700, loss_grounding_dice_4: 0.12524/0.18209, loss_grounding_ce_4: 0.12924/0.28380, loss_mask_ce_5: 0.89906/0.93910, loss_mask_bce_5: 0.28695/0.34094, loss_mask_dice_5: 0.88561/1.19917, loss_spatial_bce_5: 0.09038/0.09682, loss_spatial_dice_5: 0.26066/0.23336, loss_spatial_ce_5: 0.01604/0.11146, loss_grounding_bce_5: 0.09931/0.08742, loss_grounding_dice_5: 0.09281/0.18326, loss_grounding_ce_5: 0.17250/0.29644, loss_mask_ce_6: 0.95236/0.97837, loss_mask_bce_6: 0.28617/0.34365, loss_mask_dice_6: 0.74277/1.20193, loss_spatial_bce_6: 0.10346/0.10252, loss_spatial_dice_6: 0.26686/0.23580, loss_spatial_ce_6: 0.05530/0.13717, loss_grounding_bce_6: 0.09931/0.08814, loss_grounding_dice_6: 0.12320/0.18342, loss_grounding_ce_6: 0.25689/0.31298, loss_mask_ce_7: 0.78634/1.02340, loss_mask_bce_7: 0.44476/0.35154, loss_mask_dice_7: 0.86403/1.25727, loss_spatial_bce_7: 0.18311/0.11097, loss_spatial_dice_7: 0.29981/0.26348, loss_spatial_ce_7: 0.04746/0.17370, loss_grounding_bce_7: 0.20105/0.09009, loss_grounding_dice_7: 0.19324/0.19071, loss_grounding_ce_7: 0.13146/0.34589, loss_mask_ce_8: 1.22379/1.13202, loss_mask_bce_8: 0.43851/0.36511, loss_mask_dice_8: 1.08918/1.33105, loss_spatial_bce_8: 0.10765/0.13188, loss_spatial_dice_8: 0.35992/0.30271, loss_spatial_ce_8: 0.15234/0.23105, loss_grounding_bce_8: 0.19722/0.09378, loss_grounding_dice_8: 0.22189/0.20179, loss_grounding_ce_8: 0.11555/0.41354, loss_mask_ce_9: 3.46486/3.68236, loss_mask_bce_9: 0.40584/0.39195, loss_mask_dice_9: 1.55085/1.90394, loss_spatial_bce_9: 0.34228/0.33391, loss_spatial_dice_9: 0.93060/0.82285, loss_spatial_ce_9: 1.64493/1.50426, loss_grounding_bce_9: 0.18817/0.10527, loss_grounding_dice_9: 0.28971/0.28122, loss_grounding_ce_9: 0.04584/0.67922] items per batch[64] items per second[0.23] total items[2662400] mini batches[ 41600] memory[7341] epoch remaining[0:19:32]
INFO:trainer.default_trainer:epochs[    22] optim steps[41700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90000/0.90617, loss_mask_bce_0: 0.38306/0.33455, loss_mask_dice_0: 2.27158/1.16560, loss_spatial_bce_0: 0.05815/0.08879, loss_spatial_dice_0: 0.18650/0.21228, loss_spatial_ce_0: 0.24861/0.06734, loss_grounding_bce_0: 0.10860/0.08613, loss_grounding_dice_0: 0.24100/0.17889, loss_grounding_ce_0: 0.43468/0.27443, loss_mask_ce_1: 1.02614/0.90676, loss_mask_bce_1: 0.35233/0.33533, loss_mask_dice_1: 2.10254/1.17234, loss_spatial_bce_1: 0.05773/0.08944, loss_spatial_dice_1: 0.19716/0.21642, loss_spatial_ce_1: 0.02420/0.07299, loss_grounding_bce_1: 0.09944/0.08626, loss_grounding_dice_1: 0.28403/0.17965, loss_grounding_ce_1: 0.32543/0.27608, loss_mask_ce_2: 0.93263/0.91416, loss_mask_bce_2: 0.35884/0.33579, loss_mask_dice_2: 2.20119/1.17212, loss_spatial_bce_2: 0.06034/0.09005, loss_spatial_dice_2: 0.21577/0.21757, loss_spatial_ce_2: 0.02533/0.07656, loss_grounding_bce_2: 0.09657/0.08635, loss_grounding_dice_2: 0.25012/0.17949, loss_grounding_ce_2: 0.41933/0.27920, loss_mask_ce_3: 1.03023/0.92332, loss_mask_bce_3: 0.36962/0.33676, loss_mask_dice_3: 2.16548/1.16934, loss_spatial_bce_3: 0.05979/0.09093, loss_spatial_dice_3: 0.18471/0.21819, loss_spatial_ce_3: 0.30684/0.08032, loss_grounding_bce_3: 0.10516/0.08661, loss_grounding_dice_3: 0.25712/0.17924, loss_grounding_ce_3: 0.44639/0.28081, loss_mask_ce_4: 1.31766/0.92350, loss_mask_bce_4: 0.36081/0.33873, loss_mask_dice_4: 2.06791/1.19300, loss_spatial_bce_4: 0.09254/0.09510, loss_spatial_dice_4: 0.25473/0.22971, loss_spatial_ce_4: 0.19505/0.09626, loss_grounding_bce_4: 0.09928/0.08702, loss_grounding_dice_4: 0.24312/0.18210, loss_grounding_ce_4: 0.41618/0.28375, loss_mask_ce_5: 1.31472/0.93898, loss_mask_bce_5: 0.35649/0.34098, loss_mask_dice_5: 2.03953/1.19937, loss_spatial_bce_5: 0.09297/0.09682, loss_spatial_dice_5: 0.23705/0.23336, loss_spatial_ce_5: 0.03101/0.11139, loss_grounding_bce_5: 0.09389/0.08744, loss_grounding_dice_5: 0.24847/0.18327, loss_grounding_ce_5: 0.45424/0.29637, loss_mask_ce_6: 1.28877/0.97832, loss_mask_bce_6: 0.38705/0.34369, loss_mask_dice_6: 2.14020/1.20211, loss_spatial_bce_6: 0.12712/0.10252, loss_spatial_dice_6: 0.27171/0.23579, loss_spatial_ce_6: 0.07152/0.13712, loss_grounding_bce_6: 0.11519/0.08816, loss_grounding_dice_6: 0.27047/0.18344, loss_grounding_ce_6: 0.44716/0.31293, loss_mask_ce_7: 1.39657/1.02331, loss_mask_bce_7: 0.38427/0.35158, loss_mask_dice_7: 2.30627/1.25750, loss_spatial_bce_7: 0.20600/0.11097, loss_spatial_dice_7: 0.38835/0.26348, loss_spatial_ce_7: 0.09122/0.17364, loss_grounding_bce_7: 0.09382/0.09010, loss_grounding_dice_7: 0.27934/0.19072, loss_grounding_ce_7: 0.34482/0.34582, loss_mask_ce_8: 1.62275/1.13192, loss_mask_bce_8: 0.36562/0.36516, loss_mask_dice_8: 2.60729/1.33133, loss_spatial_bce_8: 0.12190/0.13187, loss_spatial_dice_8: 0.38172/0.30272, loss_spatial_ce_8: 0.29106/0.23103, loss_grounding_bce_8: 0.10302/0.09380, loss_grounding_dice_8: 0.33569/0.20182, loss_grounding_ce_8: 0.47210/0.41347, loss_mask_ce_9: 3.94172/3.68220, loss_mask_bce_9: 0.45407/0.39200, loss_mask_dice_9: 3.64126/1.90444, loss_spatial_bce_9: 0.24844/0.33389, loss_spatial_dice_9: 0.83202/0.82286, loss_spatial_ce_9: 1.36619/1.50412, loss_grounding_bce_9: 0.12890/0.10529, loss_grounding_dice_9: 0.46238/0.28125, loss_grounding_ce_9: 0.48818/0.67903] items per batch[64] items per second[0.23] total items[2668800] mini batches[ 41700] memory[7341] epoch remaining[0:14:52]
INFO:trainer.default_trainer:epochs[    22] optim steps[41800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92965/0.90607, loss_mask_bce_0: 0.32991/0.33450, loss_mask_dice_0: 0.90539/1.16575, loss_spatial_bce_0: 0.12555/0.08878, loss_spatial_dice_0: 0.32828/0.21225, loss_spatial_ce_0: 0.20021/0.06732, loss_grounding_bce_0: 0.19714/0.08612, loss_grounding_dice_0: 0.12923/0.17888, loss_grounding_ce_0: 0.05382/0.27441, loss_mask_ce_1: 0.91864/0.90662, loss_mask_bce_1: 0.35365/0.33528, loss_mask_dice_1: 0.90799/1.17250, loss_spatial_bce_1: 0.12757/0.08942, loss_spatial_dice_1: 0.32323/0.21639, loss_spatial_ce_1: 0.15532/0.07295, loss_grounding_bce_1: 0.20795/0.08626, loss_grounding_dice_1: 0.13778/0.17963, loss_grounding_ce_1: 0.06741/0.27605, loss_mask_ce_2: 0.93199/0.91407, loss_mask_bce_2: 0.38875/0.33574, loss_mask_dice_2: 0.84031/1.17221, loss_spatial_bce_2: 0.12897/0.09003, loss_spatial_dice_2: 0.32217/0.21754, loss_spatial_ce_2: 0.20621/0.07654, loss_grounding_bce_2: 0.20314/0.08634, loss_grounding_dice_2: 0.14565/0.17948, loss_grounding_ce_2: 0.04832/0.27913, loss_mask_ce_3: 0.97990/0.92321, loss_mask_bce_3: 0.36858/0.33671, loss_mask_dice_3: 0.83397/1.16944, loss_spatial_bce_3: 0.14154/0.09092, loss_spatial_dice_3: 0.34339/0.21816, loss_spatial_ce_3: 0.17710/0.08027, loss_grounding_bce_3: 0.18730/0.08660, loss_grounding_dice_3: 0.13166/0.17921, loss_grounding_ce_3: 0.06080/0.28076, loss_mask_ce_4: 0.95217/0.92338, loss_mask_bce_4: 0.36489/0.33868, loss_mask_dice_4: 0.79359/1.19317, loss_spatial_bce_4: 0.13891/0.09508, loss_spatial_dice_4: 0.32803/0.22969, loss_spatial_ce_4: 0.25952/0.09622, loss_grounding_bce_4: 0.19933/0.08701, loss_grounding_dice_4: 0.11359/0.18208, loss_grounding_ce_4: 0.07266/0.28370, loss_mask_ce_5: 0.89927/0.93885, loss_mask_bce_5: 0.35669/0.34094, loss_mask_dice_5: 0.87199/1.19956, loss_spatial_bce_5: 0.13577/0.09680, loss_spatial_dice_5: 0.35984/0.23334, loss_spatial_ce_5: 0.28672/0.11136, loss_grounding_bce_5: 0.19831/0.08743, loss_grounding_dice_5: 0.10522/0.18325, loss_grounding_ce_5: 0.07623/0.29632, loss_mask_ce_6: 1.06759/0.97820, loss_mask_bce_6: 0.40357/0.34365, loss_mask_dice_6: 0.84128/1.20230, loss_spatial_bce_6: 0.14806/0.10250, loss_spatial_dice_6: 0.35043/0.23576, loss_spatial_ce_6: 0.37595/0.13709, loss_grounding_bce_6: 0.20280/0.08815, loss_grounding_dice_6: 0.10430/0.18343, loss_grounding_ce_6: 0.25212/0.31288, loss_mask_ce_7: 0.87502/1.02321, loss_mask_bce_7: 0.35251/0.35153, loss_mask_dice_7: 0.79985/1.25773, loss_spatial_bce_7: 0.15851/0.11095, loss_spatial_dice_7: 0.34111/0.26345, loss_spatial_ce_7: 0.37876/0.17363, loss_grounding_bce_7: 0.20859/0.09010, loss_grounding_dice_7: 0.11071/0.19071, loss_grounding_ce_7: 0.08853/0.34582, loss_mask_ce_8: 1.19424/1.13184, loss_mask_bce_8: 0.38078/0.36512, loss_mask_dice_8: 0.79120/1.33153, loss_spatial_bce_8: 0.18331/0.13185, loss_spatial_dice_8: 0.35484/0.30271, loss_spatial_ce_8: 0.37147/0.23102, loss_grounding_bce_8: 0.24125/0.09380, loss_grounding_dice_8: 0.12195/0.20181, loss_grounding_ce_8: 0.23804/0.41348, loss_mask_ce_9: 2.40721/3.68218, loss_mask_bce_9: 0.45696/0.39193, loss_mask_dice_9: 1.17837/1.90470, loss_spatial_bce_9: 0.38834/0.33387, loss_spatial_dice_9: 0.71869/0.82286, loss_spatial_ce_9: 2.25548/1.50430, loss_grounding_bce_9: 0.26497/0.10529, loss_grounding_dice_9: 0.18076/0.28121, loss_grounding_ce_9: 0.60724/0.67902] items per batch[64] items per second[0.23] total items[2675200] mini batches[ 41800] memory[7341] epoch remaining[0:10:15]
INFO:trainer.default_trainer:epochs[    22] optim steps[41900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82015/0.90593, loss_mask_bce_0: 0.12921/0.33442, loss_mask_dice_0: 4.35264/1.16558, loss_spatial_bce_0: 0.01521/0.08874, loss_spatial_dice_0: 0.30999/0.21222, loss_spatial_ce_0: 0.10799/0.06730, loss_grounding_bce_0: 0.00411/0.08610, loss_grounding_dice_0: 0.53358/0.17887, loss_grounding_ce_0: 0.32018/0.27434, loss_mask_ce_1: 0.90790/0.90649, loss_mask_bce_1: 0.11681/0.33521, loss_mask_dice_1: 4.15347/1.17230, loss_spatial_bce_1: 0.01616/0.08938, loss_spatial_dice_1: 0.24006/0.21636, loss_spatial_ce_1: 0.14261/0.07293, loss_grounding_bce_1: 0.00464/0.08623, loss_grounding_dice_1: 0.50578/0.17962, loss_grounding_ce_1: 0.30210/0.27594, loss_mask_ce_2: 0.83476/0.91392, loss_mask_bce_2: 0.14335/0.33567, loss_mask_dice_2: 4.72994/1.17201, loss_spatial_bce_2: 0.01650/0.09000, loss_spatial_dice_2: 0.33900/0.21752, loss_spatial_ce_2: 0.12551/0.07652, loss_grounding_bce_2: 0.00631/0.08632, loss_grounding_dice_2: 0.51729/0.17947, loss_grounding_ce_2: 0.31151/0.27906, loss_mask_ce_3: 0.87905/0.92303, loss_mask_bce_3: 0.12063/0.33664, loss_mask_dice_3: 3.88603/1.16925, loss_spatial_bce_3: 0.01736/0.09089, loss_spatial_dice_3: 0.30595/0.21814, loss_spatial_ce_3: 0.03267/0.08025, loss_grounding_bce_3: 0.00425/0.08658, loss_grounding_dice_3: 0.52614/0.17920, loss_grounding_ce_3: 0.33973/0.28067, loss_mask_ce_4: 0.81266/0.92324, loss_mask_bce_4: 0.13596/0.33860, loss_mask_dice_4: 4.21907/1.19297, loss_spatial_bce_4: 0.01847/0.09505, loss_spatial_dice_4: 0.33004/0.22968, loss_spatial_ce_4: 0.12368/0.09618, loss_grounding_bce_4: 0.00405/0.08698, loss_grounding_dice_4: 0.48698/0.18206, loss_grounding_ce_4: 0.33478/0.28364, loss_mask_ce_5: 1.03400/0.93873, loss_mask_bce_5: 0.11265/0.34086, loss_mask_dice_5: 3.94922/1.19939, loss_spatial_bce_5: 0.01839/0.09678, loss_spatial_dice_5: 0.32611/0.23335, loss_spatial_ce_5: 0.06235/0.11130, loss_grounding_bce_5: 0.00511/0.08740, loss_grounding_dice_5: 0.59157/0.18324, loss_grounding_ce_5: 0.34971/0.29624, loss_mask_ce_6: 1.02641/0.97803, loss_mask_bce_6: 0.12308/0.34356, loss_mask_dice_6: 4.22360/1.20216, loss_spatial_bce_6: 0.01765/0.10247, loss_spatial_dice_6: 0.39216/0.23575, loss_spatial_ce_6: 0.10980/0.13707, loss_grounding_bce_6: 0.00388/0.08812, loss_grounding_dice_6: 0.57971/0.18342, loss_grounding_ce_6: 0.41397/0.31278, loss_mask_ce_7: 1.04258/1.02311, loss_mask_bce_7: 0.14637/0.35144, loss_mask_dice_7: 4.20211/1.25752, loss_spatial_bce_7: 0.01987/0.11092, loss_spatial_dice_7: 0.42299/0.26345, loss_spatial_ce_7: 0.23358/0.17358, loss_grounding_bce_7: 0.00698/0.09007, loss_grounding_dice_7: 0.56774/0.19070, loss_grounding_ce_7: 0.39718/0.34572, loss_mask_ce_8: 1.25590/1.13174, loss_mask_bce_8: 0.13808/0.36505, loss_mask_dice_8: 4.25086/1.33129, loss_spatial_bce_8: 0.03289/0.13181, loss_spatial_dice_8: 0.59917/0.30271, loss_spatial_ce_8: 0.43119/0.23098, loss_grounding_bce_8: 0.00953/0.09378, loss_grounding_dice_8: 0.57727/0.20179, loss_grounding_ce_8: 0.37423/0.41339, loss_mask_ce_9: 5.89812/3.68183, loss_mask_bce_9: 0.11798/0.39184, loss_mask_dice_9: 5.08856/1.90436, loss_spatial_bce_9: 0.06687/0.33380, loss_spatial_dice_9: 0.83689/0.82286, loss_spatial_ce_9: 2.31413/1.50421, loss_grounding_bce_9: 0.00420/0.10526, loss_grounding_dice_9: 0.65026/0.28117, loss_grounding_ce_9: 0.43407/0.67896] items per batch[64] items per second[0.24] total items[2681600] mini batches[ 41900] memory[7341] epoch remaining[0:05:36]
INFO:trainer.default_trainer:epochs[    22] optim steps[42000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71411/0.90603, loss_mask_bce_0: 0.35029/0.33439, loss_mask_dice_0: 1.25608/1.16555, loss_spatial_bce_0: 0.11008/0.08874, loss_spatial_dice_0: 0.26953/0.21222, loss_spatial_ce_0: 0.03518/0.06728, loss_grounding_bce_0: 0.03122/0.08611, loss_grounding_dice_0: 0.20710/0.17889, loss_grounding_ce_0: 0.32262/0.27438, loss_mask_ce_1: 0.71864/0.90655, loss_mask_bce_1: 0.34291/0.33518, loss_mask_dice_1: 1.24433/1.17228, loss_spatial_bce_1: 0.10148/0.08937, loss_spatial_dice_1: 0.24001/0.21636, loss_spatial_ce_1: 0.05162/0.07291, loss_grounding_bce_1: 0.03429/0.08625, loss_grounding_dice_1: 0.19967/0.17966, loss_grounding_ce_1: 0.32297/0.27594, loss_mask_ce_2: 0.76946/0.91400, loss_mask_bce_2: 0.33553/0.33564, loss_mask_dice_2: 1.17308/1.17200, loss_spatial_bce_2: 0.09696/0.08999, loss_spatial_dice_2: 0.25944/0.21752, loss_spatial_ce_2: 0.10141/0.07651, loss_grounding_bce_2: 0.03186/0.08634, loss_grounding_dice_2: 0.19195/0.17950, loss_grounding_ce_2: 0.32787/0.27907, loss_mask_ce_3: 0.72684/0.92309, loss_mask_bce_3: 0.33883/0.33661, loss_mask_dice_3: 1.27112/1.16922, loss_spatial_bce_3: 0.10529/0.09088, loss_spatial_dice_3: 0.27934/0.21814, loss_spatial_ce_3: 0.09976/0.08023, loss_grounding_bce_3: 0.03183/0.08660, loss_grounding_dice_3: 0.22032/0.17923, loss_grounding_ce_3: 0.33938/0.28069, loss_mask_ce_4: 0.65692/0.92326, loss_mask_bce_4: 0.33931/0.33857, loss_mask_dice_4: 1.19859/1.19292, loss_spatial_bce_4: 0.10452/0.09504, loss_spatial_dice_4: 0.26512/0.22969, loss_spatial_ce_4: 0.19180/0.09622, loss_grounding_bce_4: 0.03010/0.08700, loss_grounding_dice_4: 0.20246/0.18210, loss_grounding_ce_4: 0.33169/0.28369, loss_mask_ce_5: 0.73405/0.93883, loss_mask_bce_5: 0.33749/0.34083, loss_mask_dice_5: 1.12190/1.19938, loss_spatial_bce_5: 0.10755/0.09677, loss_spatial_dice_5: 0.25219/0.23336, loss_spatial_ce_5: 0.10809/0.11129, loss_grounding_bce_5: 0.03135/0.08742, loss_grounding_dice_5: 0.18488/0.18325, loss_grounding_ce_5: 0.32629/0.29625, loss_mask_ce_6: 0.94920/0.97811, loss_mask_bce_6: 0.35138/0.34353, loss_mask_dice_6: 1.40003/1.20212, loss_spatial_bce_6: 0.10937/0.10247, loss_spatial_dice_6: 0.25732/0.23577, loss_spatial_ce_6: 0.07390/0.13706, loss_grounding_bce_6: 0.03330/0.08814, loss_grounding_dice_6: 0.22284/0.18344, loss_grounding_ce_6: 0.30327/0.31273, loss_mask_ce_7: 0.88370/1.02319, loss_mask_bce_7: 0.36461/0.35142, loss_mask_dice_7: 1.44563/1.25748, loss_spatial_bce_7: 0.10406/0.11094, loss_spatial_dice_7: 0.25508/0.26346, loss_spatial_ce_7: 0.14652/0.17361, loss_grounding_bce_7: 0.03317/0.09008, loss_grounding_dice_7: 0.21049/0.19073, loss_grounding_ce_7: 0.34810/0.34567, loss_mask_ce_8: 0.96573/1.13184, loss_mask_bce_8: 0.36758/0.36502, loss_mask_dice_8: 1.48550/1.33126, loss_spatial_bce_8: 0.11136/0.13181, loss_spatial_dice_8: 0.31851/0.30272, loss_spatial_ce_8: 0.33766/0.23097, loss_grounding_bce_8: 0.03323/0.09379, loss_grounding_dice_8: 0.24538/0.20181, loss_grounding_ce_8: 0.37651/0.41337, loss_mask_ce_9: 4.65561/3.68208, loss_mask_bce_9: 0.35847/0.39181, loss_mask_dice_9: 1.71510/1.90436, loss_spatial_bce_9: 0.34336/0.33377, loss_spatial_dice_9: 0.79730/0.82285, loss_spatial_ce_9: 1.36241/1.50427, loss_grounding_bce_9: 0.02867/0.10526, loss_grounding_dice_9: 0.30392/0.28120, loss_grounding_ce_9: 0.41568/0.67884] items per batch[64] items per second[0.23] total items[2688000] mini batches[ 42000] memory[7341] epoch remaining[0:00:58]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00042021.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0023 s/iter. Inference: 0.2171 s/iter. Eval: 0.0946 s/iter. Total: 0.3140 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0025 s/iter. Inference: 0.2233 s/iter. Eval: 0.0836 s/iter. Total: 0.3095 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0029 s/iter. Inference: 0.2265 s/iter. Eval: 0.0783 s/iter. Total: 0.3078 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0030 s/iter. Inference: 0.2262 s/iter. Eval: 0.0763 s/iter. Total: 0.3056 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2250 s/iter. Eval: 0.0752 s/iter. Total: 0.3034 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2274 s/iter. Eval: 0.0752 s/iter. Total: 0.3059 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2291 s/iter. Eval: 0.0761 s/iter. Total: 0.3084 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2286 s/iter. Eval: 0.0755 s/iter. Total: 0.3074 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0034 s/iter. Inference: 0.2293 s/iter. Eval: 0.0753 s/iter. Total: 0.3081 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalcvx_vk19 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.726 | 82.126 | 59.741 |      133      |
| Things | 54.835 | 82.842 | 65.590 |      80       |
| Stuff  | 42.015 | 81.044 | 50.912 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.36s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.91 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.00 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.22s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.74 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.606
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.712
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.905 | 61.263 | 40.911 | 19.286 | 42.141 | 60.560 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.715 | bicycle      | 18.291 | car            | 37.345 |
| motorcycle    | 34.949 | airplane     | 55.905 | bus            | 65.177 |
| train         | 67.892 | truck        | 33.700 | boat           | 23.615 |
| traffic light | 25.208 | fire hydrant | 64.897 | stop sign      | 63.345 |
| parking meter | 42.409 | bench        | 20.796 | bird           | 29.322 |
| cat           | 73.157 | dog          | 66.409 | horse          | 46.308 |
| sheep         | 47.079 | cow          | 50.922 | elephant       | 61.175 |
| bear          | 78.001 | zebra        | 60.101 | giraffe        | 56.087 |
| backpack      | 15.852 | umbrella     | 48.949 | handbag        | 14.589 |
| tie           | 32.961 | suitcase     | 42.527 | frisbee        | 66.793 |
| skis          | 4.890  | snowboard    | 23.005 | sports ball    | 46.921 |
| kite          | 33.961 | baseball bat | 27.543 | baseball glove | 43.320 |
| skateboard    | 36.480 | surfboard    | 35.445 | tennis racket  | 55.999 |
| bottle        | 33.757 | wine glass   | 27.178 | cup            | 40.496 |
| fork          | 15.563 | knife        | 13.056 | spoon          | 13.537 |
| bowl          | 31.633 | banana       | 19.853 | apple          | 20.763 |
| sandwich      | 41.561 | orange       | 29.601 | broccoli       | 21.943 |
| carrot        | 20.687 | hot dog      | 24.023 | pizza          | 51.665 |
| donut         | 45.733 | cake         | 43.430 | chair          | 20.432 |
| couch         | 44.279 | potted plant | 17.645 | bed            | 42.583 |
| dining table  | 12.695 | toilet       | 67.373 | tv             | 61.386 |
| laptop        | 62.474 | mouse        | 60.078 | remote         | 31.309 |
| keyboard      | 47.627 | cell phone   | 38.089 | microwave      | 53.901 |
| oven          | 33.793 | toaster      | 33.292 | sink           | 37.640 |
| refrigerator  | 59.500 | book         | 9.025  | clock          | 50.627 |
| vase          | 33.493 | scissors     | 24.645 | teddy bear     | 51.170 |
| hair drier    | 10.594 | toothbrush   | 19.218 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.69808710680052, 'fwIoU': 69.0769707859037, 'IoU-person': 87.45181108852114, 'IoU-bicycle': 74.68626251373504, 'IoU-car': 69.55160920758823, 'IoU-motorcycle': 82.27159926494532, 'IoU-airplane': 84.17476962044509, 'IoU-bus': 84.87527283536386, 'IoU-train': 84.8595790228897, 'IoU-truck': 60.893045021615265, 'IoU-boat': 69.06688200471993, 'IoU-traffic light': 76.31397472125026, 'IoU-fire hydrant': 81.3346432276988, 'IoU-stop sign': 90.80575383949817, 'IoU-parking meter': 83.56962490640238, 'IoU-bench': 56.63778687729103, 'IoU-bird': 75.61168413954204, 'IoU-cat': 84.65492211336169, 'IoU-dog': 80.5379805196557, 'IoU-horse': 85.69817531741731, 'IoU-sheep': 87.90311335410142, 'IoU-cow': 81.76410724022638, 'IoU-elephant': 90.66472701482343, 'IoU-bear': 87.34894625819898, 'IoU-zebra': 89.21206929151921, 'IoU-giraffe': 86.36675965455217, 'IoU-backpack': 39.88379571905441, 'IoU-umbrella': 77.16486862426969, 'IoU-handbag': 37.47780406554491, 'IoU-tie': 70.34422652854711, 'IoU-suitcase': 81.50098823826377, 'IoU-frisbee': 83.7633950771771, 'IoU-skis': 52.02054629631976, 'IoU-snowboard': 69.38078291814946, 'IoU-sports ball': 64.60612863396565, 'IoU-kite': 66.26789839869674, 'IoU-baseball bat': 60.10062617921304, 'IoU-baseball glove': 76.56320603788332, 'IoU-skateboard': 63.781142702588866, 'IoU-surfboard': 81.88803264931744, 'IoU-tennis racket': 82.21023140555296, 'IoU-bottle': 67.66487757781057, 'IoU-wine glass': 73.2067655503205, 'IoU-cup': 66.15034346578994, 'IoU-fork': 55.630648051053875, 'IoU-knife': 50.44974073438427, 'IoU-spoon': 49.78147756687427, 'IoU-bowl': 55.93627113330209, 'IoU-banana': 83.65943549790641, 'IoU-apple': 58.15825937728349, 'IoU-sandwich': 65.60726996197137, 'IoU-orange': 76.73230888523858, 'IoU-broccoli': 68.2443361977068, 'IoU-carrot': 64.61111279663031, 'IoU-hot dog': 66.58084443959463, 'IoU-pizza': 84.10521438729421, 'IoU-donut': 64.11958695043197, 'IoU-cake': 69.8578123984361, 'IoU-chair': 53.128635588863936, 'IoU-couch': 66.9561210866398, 'IoU-potted plant': 33.2780174269017, 'IoU-bed': 69.63584031392683, 'IoU-dining table': 51.867417703541605, 'IoU-toilet': 82.02209349319682, 'IoU-tv': 75.15168176369653, 'IoU-laptop': 76.06714722268354, 'IoU-mouse': 72.87574761309733, 'IoU-remote': 47.370826239524575, 'IoU-keyboard': 63.70785560506684, 'IoU-cell phone': 70.28195126920669, 'IoU-microwave': 40.22596141393858, 'IoU-oven': 64.68545062549275, 'IoU-toaster': 65.08007963580567, 'IoU-sink': 71.99177511868778, 'IoU-refrigerator': 83.16390357599796, 'IoU-book': 51.42419322018414, 'IoU-clock': 72.59969799524775, 'IoU-vase': 61.70379273641512, 'IoU-scissors': 56.25294975758356, 'IoU-teddy bear': 82.21371904952602, 'IoU-hair drier': 32.173095014111006, 'IoU-toothbrush': 50.922465508513824, 'IoU-banner': 30.58115085805631, 'IoU-blanket': 10.007127663798888, 'IoU-bridge': 37.80713968539091, 'IoU-cardboard': 44.04379392577476, 'IoU-counter': 33.13047203324368, 'IoU-curtain': 65.12343950424854, 'IoU-door-stuff': 43.25538273271632, 'IoU-floor-wood': 62.060419738369355, 'IoU-flower': 46.78360064443881, 'IoU-fruit': 38.54379992676278, 'IoU-gravel': 34.29430469010437, 'IoU-house': 26.13877422855554, 'IoU-light': 39.833352306192246, 'IoU-mirror-stuff': 57.24554629229799, 'IoU-net': 34.107948739087604, 'IoU-pillow': 10.11464553863173, 'IoU-platform': 32.32036378190054, 'IoU-playingfield': 71.38933474109945, 'IoU-railroad': 60.83501141147482, 'IoU-river': 47.1466479291982, 'IoU-road': 65.71249213798193, 'IoU-roof': 14.85398247102794, 'IoU-sand': 57.33881578292321, 'IoU-sea': 84.2153154913488, 'IoU-shelf': 36.997239928357445, 'IoU-snow': 88.56084401669028, 'IoU-stairs': 24.669363379773884, 'IoU-tent': 8.450223385435184, 'IoU-towel': 36.60162501116079, 'IoU-wall-brick': 42.198473062038424, 'IoU-wall-stone': 25.50713046612747, 'IoU-wall-tile': 68.36368442048227, 'IoU-wall-wood': 40.111794606968914, 'IoU-water-other': 22.2905259054511, 'IoU-window-blind': 48.97422165921492, 'IoU-window-other': 48.197251313595885, 'IoU-tree-merged': 81.20400617854358, 'IoU-fence-merged': 50.308849869062136, 'IoU-ceiling-merged': 67.3129282808132, 'IoU-sky-other-merged': 93.34863341957991, 'IoU-cabinet-merged': 59.144694574296764, 'IoU-table-merged': 37.88489695803594, 'IoU-floor-other-merged': 49.285329995489015, 'IoU-pavement-merged': 53.53451523010266, 'IoU-mountain-merged': 55.03972865586544, 'IoU-grass-merged': 72.29219799595671, 'IoU-dirt-merged': 40.85436258642642, 'IoU-paper-merged': 32.988271697575414, 'IoU-food-other-merged': 38.592362130926574, 'IoU-building-other-merged': 57.432420385032444, 'IoU-rock-merged': 58.43258742271489, 'IoU-wall-other-merged': 65.12826902159522, 'IoU-rug-merged': 63.770794912745785, 'mACC': 73.18781697172079, 'pACC': 80.43013700632909, 'ACC-person': 92.38806066020196, 'ACC-bicycle': 84.92563013212657, 'ACC-car': 85.36392085859248, 'ACC-motorcycle': 89.7979325512988, 'ACC-airplane': 90.05574767161433, 'ACC-bus': 89.5247394661059, 'ACC-train': 92.88628253898213, 'ACC-truck': 74.0342223292072, 'ACC-boat': 83.20959409729821, 'ACC-traffic light': 89.34798732545931, 'ACC-fire hydrant': 95.27391571692706, 'ACC-stop sign': 93.44149820325333, 'ACC-parking meter': 87.79718435441417, 'ACC-bench': 72.87324622811244, 'ACC-bird': 80.79997175286323, 'ACC-cat': 93.71549521360474, 'ACC-dog': 83.83506502932815, 'ACC-horse': 91.68407264848707, 'ACC-sheep': 91.20212424508229, 'ACC-cow': 87.09673693126445, 'ACC-elephant': 93.30589586285578, 'ACC-bear': 89.60036838356152, 'ACC-zebra': 91.62000252900208, 'ACC-giraffe': 90.63906142018952, 'ACC-backpack': 60.80019018238227, 'ACC-umbrella': 85.23815310724025, 'ACC-handbag': 53.7671841110547, 'ACC-tie': 82.23274934254881, 'ACC-suitcase': 89.36041285239294, 'ACC-frisbee': 93.71418181818181, 'ACC-skis': 69.9441561583253, 'ACC-snowboard': 78.78425705958708, 'ACC-sports ball': 74.37074249801016, 'ACC-kite': 75.4085713782315, 'ACC-baseball bat': 82.33897009792535, 'ACC-baseball glove': 90.3457195766052, 'ACC-skateboard': 69.32943880138784, 'ACC-surfboard': 90.71679415409021, 'ACC-tennis racket': 89.52009998544389, 'ACC-bottle': 85.12591075616685, 'ACC-wine glass': 85.97006400609236, 'ACC-cup': 82.0171731240749, 'ACC-fork': 67.82523602443183, 'ACC-knife': 65.69649007107789, 'ACC-spoon': 70.27254754477038, 'ACC-bowl': 72.94871360073901, 'ACC-banana': 90.0495578527116, 'ACC-apple': 69.45624799711766, 'ACC-sandwich': 81.94400371904585, 'ACC-orange': 84.96228618922065, 'ACC-broccoli': 79.09796522858474, 'ACC-carrot': 74.389111491792, 'ACC-hot dog': 73.78376670632171, 'ACC-pizza': 95.24456481621641, 'ACC-donut': 81.83865401110005, 'ACC-cake': 80.14527186079856, 'ACC-chair': 68.92080924822979, 'ACC-couch': 77.2357854774509, 'ACC-potted plant': 46.56541580177661, 'ACC-bed': 86.17369842977904, 'ACC-dining table': 77.65885899153734, 'ACC-toilet': 91.21438370982918, 'ACC-tv': 87.87171478266804, 'ACC-laptop': 91.09409034307187, 'ACC-mouse': 86.67936466709234, 'ACC-remote': 70.59535246309024, 'ACC-keyboard': 69.41339098339506, 'ACC-cell phone': 79.64005729008109, 'ACC-microwave': 45.11317443745974, 'ACC-oven': 83.34920222280195, 'ACC-toaster': 70.23703622603293, 'ACC-sink': 84.24459659485797, 'ACC-refrigerator': 90.7768014678989, 'ACC-book': 69.39060267294276, 'ACC-clock': 78.56939624121752, 'ACC-vase': 70.67239113498117, 'ACC-scissors': 60.19188037719564, 'ACC-teddy bear': 87.58050502376068, 'ACC-hair drier': 40.884638374178124, 'ACC-toothbrush': 82.1421125781793, 'ACC-banner': 72.61203220624684, 'ACC-blanket': 12.6785757844325, 'ACC-bridge': 59.73158325122152, 'ACC-cardboard': 53.62631961611167, 'ACC-counter': 54.559594533152136, 'ACC-curtain': 76.87059498082125, 'ACC-door-stuff': 63.51398171520716, 'ACC-floor-wood': 78.58185740537354, 'ACC-flower': 67.8997908753361, 'ACC-fruit': 59.150366285224344, 'ACC-gravel': 43.013116447630374, 'ACC-house': 32.63416891116436, 'ACC-light': 57.765546644719215, 'ACC-mirror-stuff': 69.01571650166159, 'ACC-net': 63.747940929336465, 'ACC-pillow': 20.85492604139992, 'ACC-platform': 50.70200748716365, 'ACC-playingfield': 88.98454872839226, 'ACC-railroad': 78.03298066921748, 'ACC-river': 72.78146958977403, 'ACC-road': 87.97540828143983, 'ACC-roof': 20.619084289829562, 'ACC-sand': 72.76352350700186, 'ACC-sea': 90.39845145052128, 'ACC-shelf': 57.099178909521065, 'ACC-snow': 95.2155125896733, 'ACC-stairs': 43.579206625205494, 'ACC-tent': 10.312835879570576, 'ACC-towel': 48.10327677373815, 'ACC-wall-brick': 54.78440775171649, 'ACC-wall-stone': 33.85898898985114, 'ACC-wall-tile': 81.52999083034878, 'ACC-wall-wood': 55.378455425571445, 'ACC-water-other': 36.70171460169377, 'ACC-window-blind': 63.1482523168009, 'ACC-window-other': 68.59257745348619, 'ACC-tree-merged': 89.44248027675208, 'ACC-fence-merged': 67.72522154672289, 'ACC-ceiling-merged': 82.05144999998366, 'ACC-sky-other-merged': 96.6984211068009, 'ACC-cabinet-merged': 74.59350820706008, 'ACC-table-merged': 48.260894308961234, 'ACC-floor-other-merged': 60.009841363958174, 'ACC-pavement-merged': 65.33936572046696, 'ACC-mountain-merged': 65.52298583569521, 'ACC-grass-merged': 84.6959626715948, 'ACC-dirt-merged': 56.55022881762627, 'ACC-paper-merged': 46.46744275562018, 'ACC-food-other-merged': 50.35168518684073, 'ACC-building-other-merged': 71.69802767778319, 'ACC-rock-merged': 81.28775645652424, 'ACC-wall-other-merged': 81.16927117462998, 'ACC-rug-merged': 78.02392803927852})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1531 s/iter. Inference: 0.5623 s/iter. Eval: 0.0000 s/iter. Total: 0.7154 s/iter. ETA=0:00:27
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1543 s/iter. Inference: 0.5680 s/iter. Eval: 0.0000 s/iter. Total: 0.7224 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1663 s/iter. Inference: 0.5723 s/iter. Eval: 0.0000 s/iter. Total: 0.7388 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1697 s/iter. Inference: 0.7062 s/iter. Eval: 0.0000 s/iter. Total: 0.8760 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1676 s/iter. Inference: 0.6305 s/iter. Eval: 0.0000 s/iter. Total: 0.7983 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1671 s/iter. Inference: 0.6630 s/iter. Eval: 0.0000 s/iter. Total: 0.8302 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1691 s/iter. Inference: 0.7069 s/iter. Eval: 0.0000 s/iter. Total: 0.8762 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5458004097161253, 'noc@0.8': 2.916008194322505, 'noc@0.85': 3.518876207199298, 'noc@0.9': 4.618378694761486, 'miou@iter1': 0.8322148053730515}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1012 s/iter. Eval: 0.0008 s/iter. Total: 0.1037 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.7061767578125, 'precision@0.6': 68.24718475341797, 'precision@0.7': 62.92265701293945, 'precision@0.8': 51.76836395263672, 'precision@0.9': 27.399921417236328, 'cIoU': 57.66267013549805, 'mIoU': 63.103023529052734}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.72636715060825, 'SQ': 82.12583873200866, 'RQ': 59.740697725107964, 'PQ_th': 54.835408197204025, 'SQ_th': 82.84223871197206, 'RQ_th': 65.58972440415211, 'PQ_st': 42.01460708027502, 'SQ_st': 81.04448027168647, 'RQ_st': 50.91197820956962}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.904866087943866, 'AP50': 61.26339773587017, 'AP75': 40.91085708411511, 'APs': 19.286166208004442, 'APm': 42.141468443896, 'APl': 60.56040410524538, 'AP-person': 43.715129101558276, 'AP-bicycle': 18.291423642833358, 'AP-car': 37.344971809290016, 'AP-motorcycle': 34.94868372560004, 'AP-airplane': 55.905233223550056, 'AP-bus': 65.17703822820543, 'AP-train': 67.8922244721635, 'AP-truck': 33.70041794169252, 'AP-boat': 23.614824720015896, 'AP-traffic light': 25.208078558806406, 'AP-fire hydrant': 64.89704491491935, 'AP-stop sign': 63.34483531060473, 'AP-parking meter': 42.40933120773164, 'AP-bench': 20.795927416618344, 'AP-bird': 29.322251713644388, 'AP-cat': 73.1573595593506, 'AP-dog': 66.40944752841035, 'AP-horse': 46.30798588067482, 'AP-sheep': 47.07945284365432, 'AP-cow': 50.92210173043584, 'AP-elephant': 61.17500023631464, 'AP-bear': 78.0007828900088, 'AP-zebra': 60.10133909592823, 'AP-giraffe': 56.08737916351303, 'AP-backpack': 15.85164819267207, 'AP-umbrella': 48.94863776666104, 'AP-handbag': 14.589129546408019, 'AP-tie': 32.96127429811006, 'AP-suitcase': 42.52724303442918, 'AP-frisbee': 66.79287174425225, 'AP-skis': 4.890414727955975, 'AP-snowboard': 23.005052943463127, 'AP-sports ball': 46.92069544984972, 'AP-kite': 33.96093215664299, 'AP-baseball bat': 27.542686957038065, 'AP-baseball glove': 43.32004842909515, 'AP-skateboard': 36.48037265023965, 'AP-surfboard': 35.444847332367615, 'AP-tennis racket': 55.999174400053455, 'AP-bottle': 33.7574722346833, 'AP-wine glass': 27.178074133582587, 'AP-cup': 40.49632162281861, 'AP-fork': 15.562951162568014, 'AP-knife': 13.056431041991518, 'AP-spoon': 13.536817907154806, 'AP-bowl': 31.632776342450597, 'AP-banana': 19.853308358877246, 'AP-apple': 20.762855120495235, 'AP-sandwich': 41.560961912111864, 'AP-orange': 29.600929224786928, 'AP-broccoli': 21.943373564614696, 'AP-carrot': 20.687187238293173, 'AP-hot dog': 24.022715669507757, 'AP-pizza': 51.66452237460328, 'AP-donut': 45.73333338014123, 'AP-cake': 43.42988839640414, 'AP-chair': 20.431873516784947, 'AP-couch': 44.27881550609044, 'AP-potted plant': 17.645458397755792, 'AP-bed': 42.58315751312184, 'AP-dining table': 12.694886631509064, 'AP-toilet': 67.37311523821896, 'AP-tv': 61.38559204985173, 'AP-laptop': 62.47414887788073, 'AP-mouse': 60.07848327535259, 'AP-remote': 31.30871111209338, 'AP-keyboard': 47.62670955173889, 'AP-cell phone': 38.08862064250659, 'AP-microwave': 53.90076232923199, 'AP-oven': 33.79251018459113, 'AP-toaster': 33.29194451952935, 'AP-sink': 37.64038719854132, 'AP-refrigerator': 59.500416455006146, 'AP-book': 9.024755838818068, 'AP-clock': 50.627122625991596, 'AP-vase': 33.49265520105534, 'AP-scissors': 24.644601345586246, 'AP-teddy bear': 51.16962425907287, 'AP-hair drier': 10.594059405940595, 'AP-toothbrush': 19.217661129391832}), ('sem_seg', {'mIoU': 60.69808710680052, 'fwIoU': 69.0769707859037, 'IoU-person': 87.45181108852114, 'IoU-bicycle': 74.68626251373504, 'IoU-car': 69.55160920758823, 'IoU-motorcycle': 82.27159926494532, 'IoU-airplane': 84.17476962044509, 'IoU-bus': 84.87527283536386, 'IoU-train': 84.8595790228897, 'IoU-truck': 60.893045021615265, 'IoU-boat': 69.06688200471993, 'IoU-traffic light': 76.31397472125026, 'IoU-fire hydrant': 81.3346432276988, 'IoU-stop sign': 90.80575383949817, 'IoU-parking meter': 83.56962490640238, 'IoU-bench': 56.63778687729103, 'IoU-bird': 75.61168413954204, 'IoU-cat': 84.65492211336169, 'IoU-dog': 80.5379805196557, 'IoU-horse': 85.69817531741731, 'IoU-sheep': 87.90311335410142, 'IoU-cow': 81.76410724022638, 'IoU-elephant': 90.66472701482343, 'IoU-bear': 87.34894625819898, 'IoU-zebra': 89.21206929151921, 'IoU-giraffe': 86.36675965455217, 'IoU-backpack': 39.88379571905441, 'IoU-umbrella': 77.16486862426969, 'IoU-handbag': 37.47780406554491, 'IoU-tie': 70.34422652854711, 'IoU-suitcase': 81.50098823826377, 'IoU-frisbee': 83.7633950771771, 'IoU-skis': 52.02054629631976, 'IoU-snowboard': 69.38078291814946, 'IoU-sports ball': 64.60612863396565, 'IoU-kite': 66.26789839869674, 'IoU-baseball bat': 60.10062617921304, 'IoU-baseball glove': 76.56320603788332, 'IoU-skateboard': 63.781142702588866, 'IoU-surfboard': 81.88803264931744, 'IoU-tennis racket': 82.21023140555296, 'IoU-bottle': 67.66487757781057, 'IoU-wine glass': 73.2067655503205, 'IoU-cup': 66.15034346578994, 'IoU-fork': 55.630648051053875, 'IoU-knife': 50.44974073438427, 'IoU-spoon': 49.78147756687427, 'IoU-bowl': 55.93627113330209, 'IoU-banana': 83.65943549790641, 'IoU-apple': 58.15825937728349, 'IoU-sandwich': 65.60726996197137, 'IoU-orange': 76.73230888523858, 'IoU-broccoli': 68.2443361977068, 'IoU-carrot': 64.61111279663031, 'IoU-hot dog': 66.58084443959463, 'IoU-pizza': 84.10521438729421, 'IoU-donut': 64.11958695043197, 'IoU-cake': 69.8578123984361, 'IoU-chair': 53.128635588863936, 'IoU-couch': 66.9561210866398, 'IoU-potted plant': 33.2780174269017, 'IoU-bed': 69.63584031392683, 'IoU-dining table': 51.867417703541605, 'IoU-toilet': 82.02209349319682, 'IoU-tv': 75.15168176369653, 'IoU-laptop': 76.06714722268354, 'IoU-mouse': 72.87574761309733, 'IoU-remote': 47.370826239524575, 'IoU-keyboard': 63.70785560506684, 'IoU-cell phone': 70.28195126920669, 'IoU-microwave': 40.22596141393858, 'IoU-oven': 64.68545062549275, 'IoU-toaster': 65.08007963580567, 'IoU-sink': 71.99177511868778, 'IoU-refrigerator': 83.16390357599796, 'IoU-book': 51.42419322018414, 'IoU-clock': 72.59969799524775, 'IoU-vase': 61.70379273641512, 'IoU-scissors': 56.25294975758356, 'IoU-teddy bear': 82.21371904952602, 'IoU-hair drier': 32.173095014111006, 'IoU-toothbrush': 50.922465508513824, 'IoU-banner': 30.58115085805631, 'IoU-blanket': 10.007127663798888, 'IoU-bridge': 37.80713968539091, 'IoU-cardboard': 44.04379392577476, 'IoU-counter': 33.13047203324368, 'IoU-curtain': 65.12343950424854, 'IoU-door-stuff': 43.25538273271632, 'IoU-floor-wood': 62.060419738369355, 'IoU-flower': 46.78360064443881, 'IoU-fruit': 38.54379992676278, 'IoU-gravel': 34.29430469010437, 'IoU-house': 26.13877422855554, 'IoU-light': 39.833352306192246, 'IoU-mirror-stuff': 57.24554629229799, 'IoU-net': 34.107948739087604, 'IoU-pillow': 10.11464553863173, 'IoU-platform': 32.32036378190054, 'IoU-playingfield': 71.38933474109945, 'IoU-railroad': 60.83501141147482, 'IoU-river': 47.1466479291982, 'IoU-road': 65.71249213798193, 'IoU-roof': 14.85398247102794, 'IoU-sand': 57.33881578292321, 'IoU-sea': 84.2153154913488, 'IoU-shelf': 36.997239928357445, 'IoU-snow': 88.56084401669028, 'IoU-stairs': 24.669363379773884, 'IoU-tent': 8.450223385435184, 'IoU-towel': 36.60162501116079, 'IoU-wall-brick': 42.198473062038424, 'IoU-wall-stone': 25.50713046612747, 'IoU-wall-tile': 68.36368442048227, 'IoU-wall-wood': 40.111794606968914, 'IoU-water-other': 22.2905259054511, 'IoU-window-blind': 48.97422165921492, 'IoU-window-other': 48.197251313595885, 'IoU-tree-merged': 81.20400617854358, 'IoU-fence-merged': 50.308849869062136, 'IoU-ceiling-merged': 67.3129282808132, 'IoU-sky-other-merged': 93.34863341957991, 'IoU-cabinet-merged': 59.144694574296764, 'IoU-table-merged': 37.88489695803594, 'IoU-floor-other-merged': 49.285329995489015, 'IoU-pavement-merged': 53.53451523010266, 'IoU-mountain-merged': 55.03972865586544, 'IoU-grass-merged': 72.29219799595671, 'IoU-dirt-merged': 40.85436258642642, 'IoU-paper-merged': 32.988271697575414, 'IoU-food-other-merged': 38.592362130926574, 'IoU-building-other-merged': 57.432420385032444, 'IoU-rock-merged': 58.43258742271489, 'IoU-wall-other-merged': 65.12826902159522, 'IoU-rug-merged': 63.770794912745785, 'mACC': 73.18781697172079, 'pACC': 80.43013700632909, 'ACC-person': 92.38806066020196, 'ACC-bicycle': 84.92563013212657, 'ACC-car': 85.36392085859248, 'ACC-motorcycle': 89.7979325512988, 'ACC-airplane': 90.05574767161433, 'ACC-bus': 89.5247394661059, 'ACC-train': 92.88628253898213, 'ACC-truck': 74.0342223292072, 'ACC-boat': 83.20959409729821, 'ACC-traffic light': 89.34798732545931, 'ACC-fire hydrant': 95.27391571692706, 'ACC-stop sign': 93.44149820325333, 'ACC-parking meter': 87.79718435441417, 'ACC-bench': 72.87324622811244, 'ACC-bird': 80.79997175286323, 'ACC-cat': 93.71549521360474, 'ACC-dog': 83.83506502932815, 'ACC-horse': 91.68407264848707, 'ACC-sheep': 91.20212424508229, 'ACC-cow': 87.09673693126445, 'ACC-elephant': 93.30589586285578, 'ACC-bear': 89.60036838356152, 'ACC-zebra': 91.62000252900208, 'ACC-giraffe': 90.63906142018952, 'ACC-backpack': 60.80019018238227, 'ACC-umbrella': 85.23815310724025, 'ACC-handbag': 53.7671841110547, 'ACC-tie': 82.23274934254881, 'ACC-suitcase': 89.36041285239294, 'ACC-frisbee': 93.71418181818181, 'ACC-skis': 69.9441561583253, 'ACC-snowboard': 78.78425705958708, 'ACC-sports ball': 74.37074249801016, 'ACC-kite': 75.4085713782315, 'ACC-baseball bat': 82.33897009792535, 'ACC-baseball glove': 90.3457195766052, 'ACC-skateboard': 69.32943880138784, 'ACC-surfboard': 90.71679415409021, 'ACC-tennis racket': 89.52009998544389, 'ACC-bottle': 85.12591075616685, 'ACC-wine glass': 85.97006400609236, 'ACC-cup': 82.0171731240749, 'ACC-fork': 67.82523602443183, 'ACC-knife': 65.69649007107789, 'ACC-spoon': 70.27254754477038, 'ACC-bowl': 72.94871360073901, 'ACC-banana': 90.0495578527116, 'ACC-apple': 69.45624799711766, 'ACC-sandwich': 81.94400371904585, 'ACC-orange': 84.96228618922065, 'ACC-broccoli': 79.09796522858474, 'ACC-carrot': 74.389111491792, 'ACC-hot dog': 73.78376670632171, 'ACC-pizza': 95.24456481621641, 'ACC-donut': 81.83865401110005, 'ACC-cake': 80.14527186079856, 'ACC-chair': 68.92080924822979, 'ACC-couch': 77.2357854774509, 'ACC-potted plant': 46.56541580177661, 'ACC-bed': 86.17369842977904, 'ACC-dining table': 77.65885899153734, 'ACC-toilet': 91.21438370982918, 'ACC-tv': 87.87171478266804, 'ACC-laptop': 91.09409034307187, 'ACC-mouse': 86.67936466709234, 'ACC-remote': 70.59535246309024, 'ACC-keyboard': 69.41339098339506, 'ACC-cell phone': 79.64005729008109, 'ACC-microwave': 45.11317443745974, 'ACC-oven': 83.34920222280195, 'ACC-toaster': 70.23703622603293, 'ACC-sink': 84.24459659485797, 'ACC-refrigerator': 90.7768014678989, 'ACC-book': 69.39060267294276, 'ACC-clock': 78.56939624121752, 'ACC-vase': 70.67239113498117, 'ACC-scissors': 60.19188037719564, 'ACC-teddy bear': 87.58050502376068, 'ACC-hair drier': 40.884638374178124, 'ACC-toothbrush': 82.1421125781793, 'ACC-banner': 72.61203220624684, 'ACC-blanket': 12.6785757844325, 'ACC-bridge': 59.73158325122152, 'ACC-cardboard': 53.62631961611167, 'ACC-counter': 54.559594533152136, 'ACC-curtain': 76.87059498082125, 'ACC-door-stuff': 63.51398171520716, 'ACC-floor-wood': 78.58185740537354, 'ACC-flower': 67.8997908753361, 'ACC-fruit': 59.150366285224344, 'ACC-gravel': 43.013116447630374, 'ACC-house': 32.63416891116436, 'ACC-light': 57.765546644719215, 'ACC-mirror-stuff': 69.01571650166159, 'ACC-net': 63.747940929336465, 'ACC-pillow': 20.85492604139992, 'ACC-platform': 50.70200748716365, 'ACC-playingfield': 88.98454872839226, 'ACC-railroad': 78.03298066921748, 'ACC-river': 72.78146958977403, 'ACC-road': 87.97540828143983, 'ACC-roof': 20.619084289829562, 'ACC-sand': 72.76352350700186, 'ACC-sea': 90.39845145052128, 'ACC-shelf': 57.099178909521065, 'ACC-snow': 95.2155125896733, 'ACC-stairs': 43.579206625205494, 'ACC-tent': 10.312835879570576, 'ACC-towel': 48.10327677373815, 'ACC-wall-brick': 54.78440775171649, 'ACC-wall-stone': 33.85898898985114, 'ACC-wall-tile': 81.52999083034878, 'ACC-wall-wood': 55.378455425571445, 'ACC-water-other': 36.70171460169377, 'ACC-window-blind': 63.1482523168009, 'ACC-window-other': 68.59257745348619, 'ACC-tree-merged': 89.44248027675208, 'ACC-fence-merged': 67.72522154672289, 'ACC-ceiling-merged': 82.05144999998366, 'ACC-sky-other-merged': 96.6984211068009, 'ACC-cabinet-merged': 74.59350820706008, 'ACC-table-merged': 48.260894308961234, 'ACC-floor-other-merged': 60.009841363958174, 'ACC-pavement-merged': 65.33936572046696, 'ACC-mountain-merged': 65.52298583569521, 'ACC-grass-merged': 84.6959626715948, 'ACC-dirt-merged': 56.55022881762627, 'ACC-paper-merged': 46.46744275562018, 'ACC-food-other-merged': 50.35168518684073, 'ACC-building-other-merged': 71.69802767778319, 'ACC-rock-merged': 81.28775645652424, 'ACC-wall-other-merged': 81.16927117462998, 'ACC-rug-merged': 78.02392803927852})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5458004097161253, 'noc@0.8': 2.916008194322505, 'noc@0.85': 3.518876207199298, 'noc@0.9': 4.618378694761486, 'miou@iter1': 0.8322148053730515}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.7061767578125, 'precision@0.6': 68.24718475341797, 'precision@0.7': 62.92265701293945, 'precision@0.8': 51.76836395263672, 'precision@0.9': 27.399921417236328, 'cIoU': 57.66267013549805, 'mIoU': 63.103023529052734}}}
INFO:trainer.default_trainer:This epoch takes 1:27:57.421790
INFO:trainer.default_trainer:PROGRESS: 46.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 23 training.
INFO:trainer.default_trainer:epochs[    23] optim steps[42100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47714/0.90619, loss_mask_bce_0: 0.20018/0.33446, loss_mask_dice_0: 0.24397/1.16567, loss_spatial_bce_0: 0.06099/0.08872, loss_spatial_dice_0: 0.07294/0.21219, loss_spatial_ce_0: 0.00042/0.06725, loss_grounding_bce_0: 0.08724/0.08610, loss_grounding_dice_0: 0.06907/0.17889, loss_grounding_ce_0: 0.02919/0.27436, loss_mask_ce_1: 0.49320/0.90671, loss_mask_bce_1: 0.20130/0.33525, loss_mask_dice_1: 0.24535/1.17239, loss_spatial_bce_1: 0.06208/0.08936, loss_spatial_dice_1: 0.07688/0.21633, loss_spatial_ce_1: 0.00115/0.07287, loss_grounding_bce_1: 0.08497/0.08624, loss_grounding_dice_1: 0.06950/0.17967, loss_grounding_ce_1: 0.03389/0.27593, loss_mask_ce_2: 0.49657/0.91413, loss_mask_bce_2: 0.21178/0.33571, loss_mask_dice_2: 0.24721/1.17213, loss_spatial_bce_2: 0.06182/0.08998, loss_spatial_dice_2: 0.07373/0.21749, loss_spatial_ce_2: 0.00093/0.07646, loss_grounding_bce_2: 0.08719/0.08633, loss_grounding_dice_2: 0.06822/0.17950, loss_grounding_ce_2: 0.03486/0.27905, loss_mask_ce_3: 0.44568/0.92323, loss_mask_bce_3: 0.20643/0.33668, loss_mask_dice_3: 0.23134/1.16931, loss_spatial_bce_3: 0.06291/0.09088, loss_spatial_dice_3: 0.08043/0.21812, loss_spatial_ce_3: 0.00299/0.08017, loss_grounding_bce_3: 0.08755/0.08658, loss_grounding_dice_3: 0.06900/0.17922, loss_grounding_ce_3: 0.04341/0.28071, loss_mask_ce_4: 0.55452/0.92342, loss_mask_bce_4: 0.20778/0.33865, loss_mask_dice_4: 0.23680/1.19308, loss_spatial_bce_4: 0.07315/0.09503, loss_spatial_dice_4: 0.10257/0.22967, loss_spatial_ce_4: 0.01066/0.09619, loss_grounding_bce_4: 0.08522/0.08699, loss_grounding_dice_4: 0.06824/0.18209, loss_grounding_ce_4: 0.04089/0.28369, loss_mask_ce_5: 0.45317/0.93904, loss_mask_bce_5: 0.20391/0.34089, loss_mask_dice_5: 0.24749/1.19949, loss_spatial_bce_5: 0.06947/0.09676, loss_spatial_dice_5: 0.10625/0.23334, loss_spatial_ce_5: 0.01195/0.11123, loss_grounding_bce_5: 0.08589/0.08741, loss_grounding_dice_5: 0.07032/0.18326, loss_grounding_ce_5: 0.02960/0.29626, loss_mask_ce_6: 0.45048/0.97824, loss_mask_bce_6: 0.20512/0.34359, loss_mask_dice_6: 0.26837/1.20227, loss_spatial_bce_6: 0.07301/0.10247, loss_spatial_dice_6: 0.10203/0.23576, loss_spatial_ce_6: 0.03465/0.13703, loss_grounding_bce_6: 0.08109/0.08812, loss_grounding_dice_6: 0.07592/0.18344, loss_grounding_ce_6: 0.02775/0.31270, loss_mask_ce_7: 0.48047/1.02333, loss_mask_bce_7: 0.20201/0.35148, loss_mask_dice_7: 0.26004/1.25756, loss_spatial_bce_7: 0.15038/0.11094, loss_spatial_dice_7: 0.16434/0.26344, loss_spatial_ce_7: 0.03681/0.17361, loss_grounding_bce_7: 0.08743/0.09006, loss_grounding_dice_7: 0.07784/0.19073, loss_grounding_ce_7: 0.03376/0.34558, loss_mask_ce_8: 0.47970/1.13189, loss_mask_bce_8: 0.21459/0.36510, loss_mask_dice_8: 0.34792/1.33134, loss_spatial_bce_8: 0.06750/0.13181, loss_spatial_dice_8: 0.10563/0.30269, loss_spatial_ce_8: 0.13777/0.23096, loss_grounding_bce_8: 0.09050/0.09378, loss_grounding_dice_8: 0.08649/0.20182, loss_grounding_ce_8: 0.04424/0.41322, loss_mask_ce_9: 2.62857/3.68184, loss_mask_bce_9: 0.20089/0.39190, loss_mask_dice_9: 0.40505/1.90458, loss_spatial_bce_9: 0.42468/0.33381, loss_spatial_dice_9: 0.81361/0.82286, loss_spatial_ce_9: 1.12247/1.50414, loss_grounding_bce_9: 0.09432/0.10525, loss_grounding_dice_9: 0.09326/0.28119, loss_grounding_ce_9: 0.17580/0.67861] items per batch[64] items per second[0.13] total items[2694400] mini batches[ 42100] memory[7341] epoch remaining[1:21:09]
INFO:trainer.default_trainer:epochs[    23] optim steps[42200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.93112/0.90605, loss_mask_bce_0: 0.57981/0.33437, loss_mask_dice_0: 0.87592/1.16554, loss_spatial_bce_0: 0.15789/0.08869, loss_spatial_dice_0: 0.22294/0.21216, loss_spatial_ce_0: 0.01975/0.06737, loss_grounding_bce_0: 0.13765/0.08608, loss_grounding_dice_0: 0.22937/0.17883, loss_grounding_ce_0: 1.89044/0.27435, loss_mask_ce_1: 0.93699/0.90652, loss_mask_bce_1: 0.59309/0.33517, loss_mask_dice_1: 0.81646/1.17224, loss_spatial_bce_1: 0.16016/0.08933, loss_spatial_dice_1: 0.23755/0.21630, loss_spatial_ce_1: 0.03048/0.07291, loss_grounding_bce_1: 0.14047/0.08622, loss_grounding_dice_1: 0.23996/0.17962, loss_grounding_ce_1: 1.81723/0.27591, loss_mask_ce_2: 0.94165/0.91396, loss_mask_bce_2: 0.58908/0.33563, loss_mask_dice_2: 0.88688/1.17197, loss_spatial_bce_2: 0.15529/0.08995, loss_spatial_dice_2: 0.22035/0.21745, loss_spatial_ce_2: 0.03057/0.07653, loss_grounding_bce_2: 0.15328/0.08632, loss_grounding_dice_2: 0.24504/0.17944, loss_grounding_ce_2: 2.10420/0.27905, loss_mask_ce_3: 0.96585/0.92310, loss_mask_bce_3: 0.59218/0.33660, loss_mask_dice_3: 0.86045/1.16913, loss_spatial_bce_3: 0.13900/0.09085, loss_spatial_dice_3: 0.21011/0.21808, loss_spatial_ce_3: 0.07500/0.08025, loss_grounding_bce_3: 0.15806/0.08657, loss_grounding_dice_3: 0.27392/0.17917, loss_grounding_ce_3: 1.14772/0.28065, loss_mask_ce_4: 1.00929/0.92329, loss_mask_bce_4: 0.63973/0.33857, loss_mask_dice_4: 0.88931/1.19289, loss_spatial_bce_4: 0.22044/0.09500, loss_spatial_dice_4: 0.25551/0.22965, loss_spatial_ce_4: 0.07924/0.09623, loss_grounding_bce_4: 0.16149/0.08697, loss_grounding_dice_4: 0.26546/0.18205, loss_grounding_ce_4: 1.07571/0.28360, loss_mask_ce_5: 1.02668/0.93890, loss_mask_bce_5: 0.62746/0.34082, loss_mask_dice_5: 0.87119/1.19932, loss_spatial_bce_5: 0.20381/0.09673, loss_spatial_dice_5: 0.26556/0.23332, loss_spatial_ce_5: 0.08702/0.11138, loss_grounding_bce_5: 0.15635/0.08739, loss_grounding_dice_5: 0.26311/0.18322, loss_grounding_ce_5: 1.87675/0.29619, loss_mask_ce_6: 1.09234/0.97815, loss_mask_bce_6: 0.62783/0.34351, loss_mask_dice_6: 0.88314/1.20208, loss_spatial_bce_6: 0.17683/0.10244, loss_spatial_dice_6: 0.26088/0.23573, loss_spatial_ce_6: 0.13154/0.13717, loss_grounding_bce_6: 0.17478/0.08811, loss_grounding_dice_6: 0.28931/0.18339, loss_grounding_ce_6: 0.93405/0.31271, loss_mask_ce_7: 1.06717/1.02321, loss_mask_bce_7: 0.65097/0.35142, loss_mask_dice_7: 0.89648/1.25740, loss_spatial_bce_7: 0.18173/0.11093, loss_spatial_dice_7: 0.27507/0.26343, loss_spatial_ce_7: 0.18673/0.17374, loss_grounding_bce_7: 0.20869/0.09006, loss_grounding_dice_7: 0.30266/0.19070, loss_grounding_ce_7: 1.18263/0.34558, loss_mask_ce_8: 1.12887/1.13181, loss_mask_bce_8: 0.72392/0.36504, loss_mask_dice_8: 1.00696/1.33117, loss_spatial_bce_8: 0.24772/0.13178, loss_spatial_dice_8: 0.27735/0.30268, loss_spatial_ce_8: 0.13164/0.23102, loss_grounding_bce_8: 0.21901/0.09377, loss_grounding_dice_8: 0.30725/0.20178, loss_grounding_ce_8: 1.06801/0.41321, loss_mask_ce_9: 4.30517/3.68178, loss_mask_bce_9: 0.61117/0.39181, loss_mask_dice_9: 1.31420/1.90427, loss_spatial_bce_9: 0.37849/0.33381, loss_spatial_dice_9: 0.81923/0.82284, loss_spatial_ce_9: 1.22433/1.50416, loss_grounding_bce_9: 0.15790/0.10524, loss_grounding_dice_9: 0.31683/0.28113, loss_grounding_ce_9: 1.75319/0.67854] items per batch[64] items per second[0.22] total items[2700800] mini batches[ 42200] memory[7341] epoch remaining[1:17:53]
INFO:trainer.default_trainer:epochs[    23] optim steps[42300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08506/0.90617, loss_mask_bce_0: 0.11039/0.33441, loss_mask_dice_0: 0.89066/1.16587, loss_spatial_bce_0: 0.02327/0.08868, loss_spatial_dice_0: 0.18737/0.21217, loss_spatial_ce_0: 0.10697/0.06733, loss_grounding_bce_0: 0.01196/0.08607, loss_grounding_dice_0: 0.27316/0.17887, loss_grounding_ce_0: 0.04488/0.27435, loss_mask_ce_1: 0.74918/0.90666, loss_mask_bce_1: 0.11292/0.33520, loss_mask_dice_1: 1.01861/1.17257, loss_spatial_bce_1: 0.02412/0.08931, loss_spatial_dice_1: 0.18493/0.21630, loss_spatial_ce_1: 0.13144/0.07288, loss_grounding_bce_1: 0.01401/0.08621, loss_grounding_dice_1: 0.21632/0.17965, loss_grounding_ce_1: 0.03086/0.27590, loss_mask_ce_2: 0.75644/0.91414, loss_mask_bce_2: 0.11637/0.33567, loss_mask_dice_2: 0.97089/1.17230, loss_spatial_bce_2: 0.02724/0.08994, loss_spatial_dice_2: 0.17686/0.21745, loss_spatial_ce_2: 0.03839/0.07651, loss_grounding_bce_2: 0.00971/0.08631, loss_grounding_dice_2: 0.26156/0.17946, loss_grounding_ce_2: 0.03454/0.27903, loss_mask_ce_3: 0.74079/0.92328, loss_mask_bce_3: 0.11816/0.33665, loss_mask_dice_3: 0.99032/1.16946, loss_spatial_bce_3: 0.02615/0.09083, loss_spatial_dice_3: 0.18906/0.21808, loss_spatial_ce_3: 0.04969/0.08023, loss_grounding_bce_3: 0.01014/0.08656, loss_grounding_dice_3: 0.25859/0.17919, loss_grounding_ce_3: 0.03988/0.28061, loss_mask_ce_4: 1.00939/0.92344, loss_mask_bce_4: 0.10482/0.33861, loss_mask_dice_4: 0.86497/1.19323, loss_spatial_bce_4: 0.02702/0.09498, loss_spatial_dice_4: 0.19356/0.22964, loss_spatial_ce_4: 0.15939/0.09622, loss_grounding_bce_4: 0.01215/0.08696, loss_grounding_dice_4: 0.27369/0.18207, loss_grounding_ce_4: 0.03064/0.28355, loss_mask_ce_5: 0.63214/0.93908, loss_mask_bce_5: 0.10144/0.34086, loss_mask_dice_5: 1.04768/1.19967, loss_spatial_bce_5: 0.02682/0.09672, loss_spatial_dice_5: 0.26244/0.23332, loss_spatial_ce_5: 0.06539/0.11137, loss_grounding_bce_5: 0.01480/0.08739, loss_grounding_dice_5: 0.24484/0.18325, loss_grounding_ce_5: 0.13737/0.29617, loss_mask_ce_6: 0.91592/0.97833, loss_mask_bce_6: 0.10621/0.34355, loss_mask_dice_6: 0.89303/1.20240, loss_spatial_bce_6: 0.02775/0.10242, loss_spatial_dice_6: 0.23079/0.23574, loss_spatial_ce_6: 0.10080/0.13718, loss_grounding_bce_6: 0.01200/0.08811, loss_grounding_dice_6: 0.27558/0.18342, loss_grounding_ce_6: 0.03052/0.31271, loss_mask_ce_7: 0.65764/1.02334, loss_mask_bce_7: 0.11474/0.35146, loss_mask_dice_7: 1.07423/1.25773, loss_spatial_bce_7: 0.03359/0.11092, loss_spatial_dice_7: 0.28252/0.26345, loss_spatial_ce_7: 0.15679/0.17371, loss_grounding_bce_7: 0.01114/0.09005, loss_grounding_dice_7: 0.28215/0.19073, loss_grounding_ce_7: 0.04995/0.34555, loss_mask_ce_8: 0.76224/1.13204, loss_mask_bce_8: 0.12348/0.36508, loss_mask_dice_8: 1.10841/1.33153, loss_spatial_bce_8: 0.05113/0.13178, loss_spatial_dice_8: 0.31960/0.30269, loss_spatial_ce_8: 0.17802/0.23100, loss_grounding_bce_8: 0.00986/0.09376, loss_grounding_dice_8: 0.26058/0.20180, loss_grounding_ce_8: 0.23942/0.41321, loss_mask_ce_9: 2.68070/3.68210, loss_mask_bce_9: 0.13110/0.39182, loss_mask_dice_9: 1.35278/1.90464, loss_spatial_bce_9: 0.22933/0.33378, loss_spatial_dice_9: 0.85765/0.82286, loss_spatial_ce_9: 1.91140/1.50410, loss_grounding_bce_9: 0.02067/0.10522, loss_grounding_dice_9: 0.33298/0.28115, loss_grounding_ce_9: 0.51473/0.67867] items per batch[64] items per second[0.23] total items[2707200] mini batches[ 42300] memory[7341] epoch remaining[1:12:58]
INFO:trainer.default_trainer:epochs[    23] optim steps[42400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98870/0.90621, loss_mask_bce_0: 0.52603/0.33438, loss_mask_dice_0: 1.98345/1.16563, loss_spatial_bce_0: 0.04004/0.08867, loss_spatial_dice_0: 0.11713/0.21212, loss_spatial_ce_0: 0.00425/0.06726, loss_grounding_bce_0: 0.04249/0.08608, loss_grounding_dice_0: 0.05552/0.17885, loss_grounding_ce_0: 0.06771/0.27419, loss_mask_ce_1: 1.03304/0.90672, loss_mask_bce_1: 0.54038/0.33517, loss_mask_dice_1: 2.00815/1.17233, loss_spatial_bce_1: 0.03969/0.08931, loss_spatial_dice_1: 0.12450/0.21625, loss_spatial_ce_1: 0.00052/0.07282, loss_grounding_bce_1: 0.04479/0.08622, loss_grounding_dice_1: 0.05556/0.17963, loss_grounding_ce_1: 0.03411/0.27576, loss_mask_ce_2: 1.01631/0.91413, loss_mask_bce_2: 0.55630/0.33564, loss_mask_dice_2: 2.03163/1.17205, loss_spatial_bce_2: 0.03744/0.08994, loss_spatial_dice_2: 0.12373/0.21741, loss_spatial_ce_2: 0.00037/0.07644, loss_grounding_bce_2: 0.04334/0.08632, loss_grounding_dice_2: 0.05593/0.17945, loss_grounding_ce_2: 0.08815/0.27887, loss_mask_ce_3: 0.91897/0.92328, loss_mask_bce_3: 0.52935/0.33662, loss_mask_dice_3: 1.96173/1.16923, loss_spatial_bce_3: 0.03956/0.09083, loss_spatial_dice_3: 0.11878/0.21803, loss_spatial_ce_3: 0.00085/0.08018, loss_grounding_bce_3: 0.04374/0.08657, loss_grounding_dice_3: 0.05664/0.17919, loss_grounding_ce_3: 0.08785/0.28047, loss_mask_ce_4: 0.98112/0.92346, loss_mask_bce_4: 0.54825/0.33857, loss_mask_dice_4: 2.08070/1.19302, loss_spatial_bce_4: 0.04041/0.09498, loss_spatial_dice_4: 0.14535/0.22959, loss_spatial_ce_4: 0.00803/0.09617, loss_grounding_bce_4: 0.04441/0.08697, loss_grounding_dice_4: 0.05449/0.18206, loss_grounding_ce_4: 0.10303/0.28338, loss_mask_ce_5: 1.03908/0.93907, loss_mask_bce_5: 0.52992/0.34083, loss_mask_dice_5: 2.16297/1.19945, loss_spatial_bce_5: 0.03828/0.09672, loss_spatial_dice_5: 0.12418/0.23329, loss_spatial_ce_5: 0.03433/0.11130, loss_grounding_bce_5: 0.04485/0.08740, loss_grounding_dice_5: 0.05722/0.18324, loss_grounding_ce_5: 0.17325/0.29602, loss_mask_ce_6: 1.04629/0.97830, loss_mask_bce_6: 0.52788/0.34353, loss_mask_dice_6: 2.16463/1.20218, loss_spatial_bce_6: 0.04524/0.10243, loss_spatial_dice_6: 0.14059/0.23569, loss_spatial_ce_6: 0.03263/0.13710, loss_grounding_bce_6: 0.04476/0.08812, loss_grounding_dice_6: 0.05794/0.18342, loss_grounding_ce_6: 0.24742/0.31251, loss_mask_ce_7: 1.22194/1.02331, loss_mask_bce_7: 0.51500/0.35141, loss_mask_dice_7: 2.48031/1.25750, loss_spatial_bce_7: 0.05213/0.11092, loss_spatial_dice_7: 0.14804/0.26340, loss_spatial_ce_7: 0.06126/0.17367, loss_grounding_bce_7: 0.04509/0.09006, loss_grounding_dice_7: 0.05259/0.19071, loss_grounding_ce_7: 0.20378/0.34535, loss_mask_ce_8: 1.34064/1.13200, loss_mask_bce_8: 0.51130/0.36504, loss_mask_dice_8: 2.64833/1.33127, loss_spatial_bce_8: 0.06179/0.13177, loss_spatial_dice_8: 0.20779/0.30262, loss_spatial_ce_8: 0.13541/0.23097, loss_grounding_bce_8: 0.04488/0.09377, loss_grounding_dice_8: 0.05671/0.20178, loss_grounding_ce_8: 0.07019/0.41299, loss_mask_ce_9: 5.49844/3.68194, loss_mask_bce_9: 0.53626/0.39183, loss_mask_dice_9: 4.24741/1.90443, loss_spatial_bce_9: 0.26861/0.33381, loss_spatial_dice_9: 0.95233/0.82287, loss_spatial_ce_9: 1.60605/1.50401, loss_grounding_bce_9: 0.06962/0.10523, loss_grounding_dice_9: 0.10192/0.28114, loss_grounding_ce_9: 0.82997/0.67839] items per batch[64] items per second[0.23] total items[2713600] mini batches[ 42400] memory[7341] epoch remaining[1:07:52]
INFO:trainer.default_trainer:epochs[    23] optim steps[42500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.37866/0.90606, loss_mask_bce_0: 0.39306/0.33431, loss_mask_dice_0: 0.95700/1.16540, loss_spatial_bce_0: 0.10912/0.08866, loss_spatial_dice_0: 0.19940/0.21209, loss_spatial_ce_0: 0.11700/0.06722, loss_grounding_bce_0: 0.01056/0.08606, loss_grounding_dice_0: 0.05079/0.17881, loss_grounding_ce_0: 0.19144/0.27407, loss_mask_ce_1: 1.33134/0.90654, loss_mask_bce_1: 0.38170/0.33511, loss_mask_dice_1: 0.80186/1.17211, loss_spatial_bce_1: 0.10708/0.08930, loss_spatial_dice_1: 0.17038/0.21622, loss_spatial_ce_1: 0.08503/0.07276, loss_grounding_bce_1: 0.01174/0.08620, loss_grounding_dice_1: 0.05324/0.17960, loss_grounding_ce_1: 0.18196/0.27562, loss_mask_ce_2: 1.26077/0.91398, loss_mask_bce_2: 0.38641/0.33557, loss_mask_dice_2: 0.93220/1.17181, loss_spatial_bce_2: 0.10633/0.08993, loss_spatial_dice_2: 0.18431/0.21738, loss_spatial_ce_2: 0.10326/0.07638, loss_grounding_bce_2: 0.01251/0.08631, loss_grounding_dice_2: 0.05296/0.17941, loss_grounding_ce_2: 0.16447/0.27875, loss_mask_ce_3: 1.25110/0.92309, loss_mask_bce_3: 0.38724/0.33657, loss_mask_dice_3: 0.86816/1.16904, loss_spatial_bce_3: 0.12483/0.09083, loss_spatial_dice_3: 0.18249/0.21800, loss_spatial_ce_3: 0.09799/0.08013, loss_grounding_bce_3: 0.01093/0.08656, loss_grounding_dice_3: 0.05250/0.17915, loss_grounding_ce_3: 0.18130/0.28033, loss_mask_ce_4: 1.35711/0.92335, loss_mask_bce_4: 0.37259/0.33852, loss_mask_dice_4: 0.84364/1.19281, loss_spatial_bce_4: 0.10248/0.09498, loss_spatial_dice_4: 0.17137/0.22957, loss_spatial_ce_4: 0.26905/0.09612, loss_grounding_bce_4: 0.01293/0.08696, loss_grounding_dice_4: 0.04982/0.18203, loss_grounding_ce_4: 0.19705/0.28324, loss_mask_ce_5: 1.33391/0.93894, loss_mask_bce_5: 0.43280/0.34077, loss_mask_dice_5: 0.94453/1.19927, loss_spatial_bce_5: 0.15955/0.09672, loss_spatial_dice_5: 0.18477/0.23327, loss_spatial_ce_5: 0.06944/0.11122, loss_grounding_bce_5: 0.01092/0.08738, loss_grounding_dice_5: 0.04845/0.18321, loss_grounding_ce_5: 0.16085/0.29586, loss_mask_ce_6: 1.44437/0.97821, loss_mask_bce_6: 0.38747/0.34347, loss_mask_dice_6: 0.73330/1.20195, loss_spatial_bce_6: 0.17690/0.10243, loss_spatial_dice_6: 0.18525/0.23567, loss_spatial_ce_6: 0.09009/0.13704, loss_grounding_bce_6: 0.01285/0.08811, loss_grounding_dice_6: 0.05083/0.18338, loss_grounding_ce_6: 0.18129/0.31234, loss_mask_ce_7: 1.63951/1.02320, loss_mask_bce_7: 0.36965/0.35134, loss_mask_dice_7: 0.87948/1.25727, loss_spatial_bce_7: 0.15188/0.11093, loss_spatial_dice_7: 0.20100/0.26338, loss_spatial_ce_7: 0.22750/0.17362, loss_grounding_bce_7: 0.01089/0.09004, loss_grounding_dice_7: 0.05897/0.19067, loss_grounding_ce_7: 0.13640/0.34517, loss_mask_ce_8: 1.43573/1.13193, loss_mask_bce_8: 0.39668/0.36498, loss_mask_dice_8: 0.94759/1.33101, loss_spatial_bce_8: 0.19137/0.13177, loss_spatial_dice_8: 0.25802/0.30259, loss_spatial_ce_8: 0.24039/0.23093, loss_grounding_bce_8: 0.01009/0.09375, loss_grounding_dice_8: 0.04720/0.20174, loss_grounding_ce_8: 0.14053/0.41283, loss_mask_ce_9: 4.10701/3.68160, loss_mask_bce_9: 0.43542/0.39179, loss_mask_dice_9: 1.45172/1.90424, loss_spatial_bce_9: 0.39220/0.33382, loss_spatial_dice_9: 0.78240/0.82286, loss_spatial_ce_9: 1.39363/1.50395, loss_grounding_bce_9: 0.02984/0.10523, loss_grounding_dice_9: 0.22405/0.28111, loss_grounding_ce_9: 1.89773/0.67836] items per batch[64] items per second[0.23] total items[2720000] mini batches[ 42500] memory[7341] epoch remaining[1:03:09]
INFO:trainer.default_trainer:epochs[    23] optim steps[42600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49835/0.90615, loss_mask_bce_0: 0.69946/0.33432, loss_mask_dice_0: 1.07356/1.16597, loss_spatial_bce_0: 0.12777/0.08863, loss_spatial_dice_0: 0.18211/0.21210, loss_spatial_ce_0: 0.00527/0.06718, loss_grounding_bce_0: 0.04917/0.08607, loss_grounding_dice_0: 0.10306/0.17885, loss_grounding_ce_0: 0.01896/0.27402, loss_mask_ce_1: 1.52830/0.90668, loss_mask_bce_1: 0.70146/0.33512, loss_mask_dice_1: 1.07901/1.17269, loss_spatial_bce_1: 0.14141/0.08927, loss_spatial_dice_1: 0.17699/0.21623, loss_spatial_ce_1: 0.03068/0.07271, loss_grounding_bce_1: 0.05545/0.08621, loss_grounding_dice_1: 0.11658/0.17963, loss_grounding_ce_1: 0.02337/0.27554, loss_mask_ce_2: 1.57106/0.91411, loss_mask_bce_2: 0.67442/0.33558, loss_mask_dice_2: 1.03519/1.17242, loss_spatial_bce_2: 0.11206/0.08990, loss_spatial_dice_2: 0.17048/0.21738, loss_spatial_ce_2: 0.05783/0.07636, loss_grounding_bce_2: 0.05733/0.08631, loss_grounding_dice_2: 0.11899/0.17944, loss_grounding_ce_2: 0.03623/0.27868, loss_mask_ce_3: 1.25785/0.92318, loss_mask_bce_3: 0.69726/0.33658, loss_mask_dice_3: 1.11739/1.16960, loss_spatial_bce_3: 0.10430/0.09080, loss_spatial_dice_3: 0.16495/0.21801, loss_spatial_ce_3: 0.07457/0.08010, loss_grounding_bce_3: 0.05721/0.08657, loss_grounding_dice_3: 0.11102/0.17918, loss_grounding_ce_3: 0.03828/0.28025, loss_mask_ce_4: 1.60561/0.92347, loss_mask_bce_4: 0.70580/0.33854, loss_mask_dice_4: 1.06317/1.19341, loss_spatial_bce_4: 0.11632/0.09495, loss_spatial_dice_4: 0.21147/0.22959, loss_spatial_ce_4: 0.07280/0.09609, loss_grounding_bce_4: 0.05265/0.08697, loss_grounding_dice_4: 0.11263/0.18206, loss_grounding_ce_4: 0.03111/0.28317, loss_mask_ce_5: 1.32288/0.93904, loss_mask_bce_5: 0.69263/0.34078, loss_mask_dice_5: 1.08486/1.19983, loss_spatial_bce_5: 0.16378/0.09669, loss_spatial_dice_5: 0.22597/0.23329, loss_spatial_ce_5: 0.01959/0.11119, loss_grounding_bce_5: 0.05725/0.08739, loss_grounding_dice_5: 0.12263/0.18324, loss_grounding_ce_5: 0.04096/0.29577, loss_mask_ce_6: 1.20180/0.97830, loss_mask_bce_6: 0.65372/0.34348, loss_mask_dice_6: 1.05907/1.20255, loss_spatial_bce_6: 0.12906/0.10241, loss_spatial_dice_6: 0.21793/0.23569, loss_spatial_ce_6: 0.14411/0.13700, loss_grounding_bce_6: 0.05853/0.08812, loss_grounding_dice_6: 0.10275/0.18341, loss_grounding_ce_6: 0.03584/0.31226, loss_mask_ce_7: 1.25827/1.02330, loss_mask_bce_7: 0.58640/0.35135, loss_mask_dice_7: 1.06459/1.25789, loss_spatial_bce_7: 0.17573/0.11090, loss_spatial_dice_7: 0.26331/0.26341, loss_spatial_ce_7: 0.22659/0.17361, loss_grounding_bce_7: 0.06698/0.09004, loss_grounding_dice_7: 0.11068/0.19070, loss_grounding_ce_7: 0.03268/0.34509, loss_mask_ce_8: 1.31325/1.13212, loss_mask_bce_8: 0.67743/0.36499, loss_mask_dice_8: 1.19909/1.33164, loss_spatial_bce_8: 0.15004/0.13173, loss_spatial_dice_8: 0.28998/0.30263, loss_spatial_ce_8: 0.19961/0.23089, loss_grounding_bce_8: 0.05957/0.09376, loss_grounding_dice_8: 0.10688/0.20178, loss_grounding_ce_8: 0.02186/0.41268, loss_mask_ce_9: 3.36344/3.68183, loss_mask_bce_9: 0.73582/0.39180, loss_mask_dice_9: 1.51851/1.90515, loss_spatial_bce_9: 0.29557/0.33377, loss_spatial_dice_9: 0.82218/0.82287, loss_spatial_ce_9: 1.20178/1.50397, loss_grounding_bce_9: 0.06958/0.10522, loss_grounding_dice_9: 0.19555/0.28113, loss_grounding_ce_9: 1.40761/0.67820] items per batch[64] items per second[0.23] total items[2726400] mini batches[ 42600] memory[7341] epoch remaining[0:58:25]
INFO:trainer.default_trainer:epochs[    23] optim steps[42700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15685/0.90621, loss_mask_bce_0: 0.12296/0.33432, loss_mask_dice_0: 0.27104/1.16596, loss_spatial_bce_0: 0.04345/0.08861, loss_spatial_dice_0: 0.09870/0.21208, loss_spatial_ce_0: 0.00271/0.06713, loss_grounding_bce_0: 0.05828/0.08606, loss_grounding_dice_0: 0.13314/0.17882, loss_grounding_ce_0: 0.17087/0.27396, loss_mask_ce_1: 0.50390/0.90677, loss_mask_bce_1: 0.11315/0.33512, loss_mask_dice_1: 0.25148/1.17262, loss_spatial_bce_1: 0.04798/0.08925, loss_spatial_dice_1: 0.10561/0.21621, loss_spatial_ce_1: 0.00345/0.07269, loss_grounding_bce_1: 0.05318/0.08620, loss_grounding_dice_1: 0.14750/0.17961, loss_grounding_ce_1: 0.05243/0.27553, loss_mask_ce_2: 0.19353/0.91418, loss_mask_bce_2: 0.10654/0.33558, loss_mask_dice_2: 0.19315/1.17238, loss_spatial_bce_2: 0.04709/0.08988, loss_spatial_dice_2: 0.09844/0.21736, loss_spatial_ce_2: 0.00297/0.07633, loss_grounding_bce_2: 0.04166/0.08630, loss_grounding_dice_2: 0.18865/0.17940, loss_grounding_ce_2: 0.07446/0.27866, loss_mask_ce_3: 0.49970/0.92326, loss_mask_bce_3: 0.10064/0.33658, loss_mask_dice_3: 0.23509/1.16956, loss_spatial_bce_3: 0.04859/0.09078, loss_spatial_dice_3: 0.10670/0.21799, loss_spatial_ce_3: 0.00733/0.08008, loss_grounding_bce_3: 0.03905/0.08656, loss_grounding_dice_3: 0.11634/0.17915, loss_grounding_ce_3: 0.08920/0.28019, loss_mask_ce_4: 0.45104/0.92359, loss_mask_bce_4: 0.09350/0.33854, loss_mask_dice_4: 0.20799/1.19334, loss_spatial_bce_4: 0.05131/0.09493, loss_spatial_dice_4: 0.11002/0.22958, loss_spatial_ce_4: 0.00961/0.09606, loss_grounding_bce_4: 0.04253/0.08696, loss_grounding_dice_4: 0.11540/0.18203, loss_grounding_ce_4: 0.17671/0.28314, loss_mask_ce_5: 0.26624/0.93912, loss_mask_bce_5: 0.08809/0.34079, loss_mask_dice_5: 0.20657/1.19976, loss_spatial_bce_5: 0.05003/0.09668, loss_spatial_dice_5: 0.11697/0.23328, loss_spatial_ce_5: 0.00940/0.11117, loss_grounding_bce_5: 0.03840/0.08738, loss_grounding_dice_5: 0.09520/0.18320, loss_grounding_ce_5: 0.10162/0.29573, loss_mask_ce_6: 0.30510/0.97840, loss_mask_bce_6: 0.08996/0.34348, loss_mask_dice_6: 0.17591/1.20249, loss_spatial_bce_6: 0.05380/0.10239, loss_spatial_dice_6: 0.11709/0.23569, loss_spatial_ce_6: 0.02811/0.13698, loss_grounding_bce_6: 0.03984/0.08811, loss_grounding_dice_6: 0.13262/0.18338, loss_grounding_ce_6: 0.20509/0.31225, loss_mask_ce_7: 0.25227/1.02341, loss_mask_bce_7: 0.09051/0.35135, loss_mask_dice_7: 0.23802/1.25787, loss_spatial_bce_7: 0.05460/0.11089, loss_spatial_dice_7: 0.18627/0.26341, loss_spatial_ce_7: 0.08831/0.17361, loss_grounding_bce_7: 0.03830/0.09003, loss_grounding_dice_7: 0.11515/0.19067, loss_grounding_ce_7: 0.30239/0.34504, loss_mask_ce_8: 0.44835/1.13228, loss_mask_bce_8: 0.12114/0.36499, loss_mask_dice_8: 0.27245/1.33158, loss_spatial_bce_8: 0.08453/0.13171, loss_spatial_dice_8: 0.25459/0.30263, loss_spatial_ce_8: 0.14565/0.23090, loss_grounding_bce_8: 0.04711/0.09376, loss_grounding_dice_8: 0.15445/0.20175, loss_grounding_ce_8: 0.35085/0.41270, loss_mask_ce_9: 2.34876/3.68196, loss_mask_bce_9: 0.14174/0.39183, loss_mask_dice_9: 0.47010/1.90500, loss_spatial_bce_9: 0.34015/0.33377, loss_spatial_dice_9: 0.62636/0.82284, loss_spatial_ce_9: 1.10950/1.50397, loss_grounding_bce_9: 0.08260/0.10523, loss_grounding_dice_9: 0.25120/0.28110, loss_grounding_ce_9: 0.33232/0.67829] items per batch[64] items per second[0.23] total items[2732800] mini batches[ 42700] memory[7341] epoch remaining[0:53:38]
INFO:trainer.default_trainer:epochs[    23] optim steps[42800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19256/0.90606, loss_mask_bce_0: 0.12919/0.33432, loss_mask_dice_0: 0.18808/1.16614, loss_spatial_bce_0: 0.04460/0.08860, loss_spatial_dice_0: 0.07172/0.21203, loss_spatial_ce_0: 0.01888/0.06712, loss_grounding_bce_0: 0.05721/0.08608, loss_grounding_dice_0: 0.06382/0.17882, loss_grounding_ce_0: 0.17230/0.27392, loss_mask_ce_1: 0.18067/0.90666, loss_mask_bce_1: 0.13610/0.33511, loss_mask_dice_1: 0.20059/1.17279, loss_spatial_bce_1: 0.04710/0.08924, loss_spatial_dice_1: 0.07480/0.21616, loss_spatial_ce_1: 0.02847/0.07266, loss_grounding_bce_1: 0.06060/0.08621, loss_grounding_dice_1: 0.06477/0.17961, loss_grounding_ce_1: 0.14756/0.27552, loss_mask_ce_2: 0.17138/0.91404, loss_mask_bce_2: 0.14025/0.33558, loss_mask_dice_2: 0.20920/1.17257, loss_spatial_bce_2: 0.04610/0.08987, loss_spatial_dice_2: 0.07474/0.21732, loss_spatial_ce_2: 0.04131/0.07629, loss_grounding_bce_2: 0.06098/0.08632, loss_grounding_dice_2: 0.06475/0.17940, loss_grounding_ce_2: 0.05982/0.27858, loss_mask_ce_3: 0.20431/0.92313, loss_mask_bce_3: 0.13278/0.33658, loss_mask_dice_3: 0.19828/1.16977, loss_spatial_bce_3: 0.04698/0.09077, loss_spatial_dice_3: 0.07847/0.21795, loss_spatial_ce_3: 0.01197/0.08002, loss_grounding_bce_3: 0.05654/0.08658, loss_grounding_dice_3: 0.06276/0.17913, loss_grounding_ce_3: 0.05737/0.28012, loss_mask_ce_4: 0.23067/0.92352, loss_mask_bce_4: 0.13006/0.33853, loss_mask_dice_4: 0.18994/1.19354, loss_spatial_bce_4: 0.04629/0.09492, loss_spatial_dice_4: 0.08006/0.22954, loss_spatial_ce_4: 0.03047/0.09602, loss_grounding_bce_4: 0.05643/0.08698, loss_grounding_dice_4: 0.06465/0.18203, loss_grounding_ce_4: 0.05793/0.28312, loss_mask_ce_5: 0.25607/0.93905, loss_mask_bce_5: 0.13165/0.34078, loss_mask_dice_5: 0.20593/1.19995, loss_spatial_bce_5: 0.05220/0.09667, loss_spatial_dice_5: 0.08341/0.23325, loss_spatial_ce_5: 0.01095/0.11113, loss_grounding_bce_5: 0.06010/0.08739, loss_grounding_dice_5: 0.06852/0.18319, loss_grounding_ce_5: 0.04912/0.29569, loss_mask_ce_6: 0.23721/0.97831, loss_mask_bce_6: 0.13326/0.34348, loss_mask_dice_6: 0.18711/1.20266, loss_spatial_bce_6: 0.05220/0.10239, loss_spatial_dice_6: 0.11026/0.23567, loss_spatial_ce_6: 0.02410/0.13696, loss_grounding_bce_6: 0.05774/0.08812, loss_grounding_dice_6: 0.06586/0.18338, loss_grounding_ce_6: 0.04646/0.31219, loss_mask_ce_7: 0.30004/1.02338, loss_mask_bce_7: 0.18565/0.35134, loss_mask_dice_7: 0.20954/1.25808, loss_spatial_bce_7: 0.05581/0.11090, loss_spatial_dice_7: 0.09621/0.26340, loss_spatial_ce_7: 0.03934/0.17357, loss_grounding_bce_7: 0.09062/0.09004, loss_grounding_dice_7: 0.07432/0.19066, loss_grounding_ce_7: 0.05483/0.34502, loss_mask_ce_8: 0.32446/1.13221, loss_mask_bce_8: 0.19099/0.36498, loss_mask_dice_8: 0.25797/1.33181, loss_spatial_bce_8: 0.06240/0.13172, loss_spatial_dice_8: 0.09782/0.30261, loss_spatial_ce_8: 0.11125/0.23086, loss_grounding_bce_8: 0.09998/0.09377, loss_grounding_dice_8: 0.08232/0.20176, loss_grounding_ce_8: 0.11586/0.41280, loss_mask_ce_9: 2.36824/3.68205, loss_mask_bce_9: 0.23027/0.39184, loss_mask_dice_9: 0.44660/1.90522, loss_spatial_bce_9: 0.52504/0.33377, loss_spatial_dice_9: 0.84461/0.82284, loss_spatial_ce_9: 1.57533/1.50387, loss_grounding_bce_9: 0.10948/0.10524, loss_grounding_dice_9: 0.12220/0.28108, loss_grounding_ce_9: 0.79229/0.67828] items per batch[64] items per second[0.23] total items[2739200] mini batches[ 42800] memory[7341] epoch remaining[0:48:55]
INFO:trainer.default_trainer:epochs[    23] optim steps[42900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31240/0.90598, loss_mask_bce_0: 0.22005/0.33435, loss_mask_dice_0: 0.68933/1.16592, loss_spatial_bce_0: 0.05766/0.08860, loss_spatial_dice_0: 0.18949/0.21199, loss_spatial_ce_0: 0.15633/0.06706, loss_grounding_bce_0: 0.03106/0.08607, loss_grounding_dice_0: 0.13195/0.17883, loss_grounding_ce_0: 0.03611/0.27400, loss_mask_ce_1: 0.32917/0.90661, loss_mask_bce_1: 0.21553/0.33514, loss_mask_dice_1: 0.77791/1.17259, loss_spatial_bce_1: 0.05436/0.08924, loss_spatial_dice_1: 0.23949/0.21612, loss_spatial_ce_1: 0.14747/0.07264, loss_grounding_bce_1: 0.02607/0.08621, loss_grounding_dice_1: 0.13187/0.17963, loss_grounding_ce_1: 0.03045/0.27566, loss_mask_ce_2: 0.35225/0.91397, loss_mask_bce_2: 0.21598/0.33561, loss_mask_dice_2: 0.42370/1.17239, loss_spatial_bce_2: 0.04882/0.08988, loss_spatial_dice_2: 0.16364/0.21728, loss_spatial_ce_2: 0.13308/0.07625, loss_grounding_bce_2: 0.03095/0.08631, loss_grounding_dice_2: 0.13314/0.17941, loss_grounding_ce_2: 0.04063/0.27871, loss_mask_ce_3: 0.46456/0.92304, loss_mask_bce_3: 0.20533/0.33661, loss_mask_dice_3: 0.73066/1.16955, loss_spatial_bce_3: 0.05059/0.09077, loss_spatial_dice_3: 0.19141/0.21791, loss_spatial_ce_3: 0.13798/0.07999, loss_grounding_bce_3: 0.03206/0.08657, loss_grounding_dice_3: 0.15988/0.17915, loss_grounding_ce_3: 0.04733/0.28023, loss_mask_ce_4: 0.48666/0.92347, loss_mask_bce_4: 0.20564/0.33855, loss_mask_dice_4: 0.83946/1.19333, loss_spatial_bce_4: 0.06020/0.09493, loss_spatial_dice_4: 0.21206/0.22950, loss_spatial_ce_4: 0.12822/0.09597, loss_grounding_bce_4: 0.02720/0.08698, loss_grounding_dice_4: 0.13577/0.18204, loss_grounding_ce_4: 0.03973/0.28326, loss_mask_ce_5: 0.52808/0.93899, loss_mask_bce_5: 0.19610/0.34081, loss_mask_dice_5: 0.63604/1.19976, loss_spatial_bce_5: 0.07353/0.09668, loss_spatial_dice_5: 0.26968/0.23322, loss_spatial_ce_5: 0.08582/0.11106, loss_grounding_bce_5: 0.03123/0.08739, loss_grounding_dice_5: 0.14009/0.18321, loss_grounding_ce_5: 0.03891/0.29586, loss_mask_ce_6: 0.52811/0.97825, loss_mask_bce_6: 0.19660/0.34351, loss_mask_dice_6: 0.85764/1.20246, loss_spatial_bce_6: 0.07633/0.10239, loss_spatial_dice_6: 0.24659/0.23564, loss_spatial_ce_6: 0.22611/0.13690, loss_grounding_bce_6: 0.02897/0.08811, loss_grounding_dice_6: 0.14978/0.18339, loss_grounding_ce_6: 0.03451/0.31240, loss_mask_ce_7: 0.49285/1.02329, loss_mask_bce_7: 0.21097/0.35136, loss_mask_dice_7: 0.98477/1.25786, loss_spatial_bce_7: 0.05815/0.11089, loss_spatial_dice_7: 0.27895/0.26336, loss_spatial_ce_7: 0.29837/0.17352, loss_grounding_bce_7: 0.03180/0.09003, loss_grounding_dice_7: 0.13480/0.19067, loss_grounding_ce_7: 0.02295/0.34531, loss_mask_ce_8: 0.64784/1.13207, loss_mask_bce_8: 0.19998/0.36501, loss_mask_dice_8: 0.89263/1.33156, loss_spatial_bce_8: 0.10313/0.13171, loss_spatial_dice_8: 0.33113/0.30258, loss_spatial_ce_8: 0.24143/0.23084, loss_grounding_bce_8: 0.02603/0.09377, loss_grounding_dice_8: 0.14279/0.20176, loss_grounding_ce_8: 0.02647/0.41324, loss_mask_ce_9: 3.10104/3.68198, loss_mask_bce_9: 0.21302/0.39189, loss_mask_dice_9: 0.85190/1.90482, loss_spatial_bce_9: 0.36620/0.33376, loss_spatial_dice_9: 0.85994/0.82281, loss_spatial_ce_9: 2.43074/1.50374, loss_grounding_bce_9: 0.02854/0.10525, loss_grounding_dice_9: 0.14528/0.28112, loss_grounding_ce_9: 0.16500/0.67849] items per batch[64] items per second[0.24] total items[2745600] mini batches[ 42900] memory[7341] epoch remaining[0:44:05]
INFO:trainer.default_trainer:epochs[    23] optim steps[43000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.91389/0.90610, loss_mask_bce_0: 0.14424/0.33439, loss_mask_dice_0: 1.55645/1.16582, loss_spatial_bce_0: 0.04386/0.08859, loss_spatial_dice_0: 0.30902/0.21197, loss_spatial_ce_0: 0.04488/0.06702, loss_grounding_bce_0: 0.09564/0.08608, loss_grounding_dice_0: 0.25289/0.17886, loss_grounding_ce_0: 0.05299/0.27401, loss_mask_ce_1: 2.00515/0.90670, loss_mask_bce_1: 0.14889/0.33518, loss_mask_dice_1: 1.49638/1.17251, loss_spatial_bce_1: 0.04343/0.08923, loss_spatial_dice_1: 0.30804/0.21609, loss_spatial_ce_1: 0.19512/0.07261, loss_grounding_bce_1: 0.09465/0.08622, loss_grounding_dice_1: 0.24620/0.17964, loss_grounding_ce_1: 0.06346/0.27569, loss_mask_ce_2: 2.07150/0.91406, loss_mask_bce_2: 0.14899/0.33564, loss_mask_dice_2: 1.41220/1.17237, loss_spatial_bce_2: 0.04243/0.08987, loss_spatial_dice_2: 0.28564/0.21726, loss_spatial_ce_2: 0.06737/0.07622, loss_grounding_bce_2: 0.09559/0.08632, loss_grounding_dice_2: 0.27550/0.17943, loss_grounding_ce_2: 0.05242/0.27871, loss_mask_ce_3: 2.00704/0.92312, loss_mask_bce_3: 0.14589/0.33665, loss_mask_dice_3: 1.45334/1.16946, loss_spatial_bce_3: 0.04490/0.09077, loss_spatial_dice_3: 0.36996/0.21789, loss_spatial_ce_3: 0.07175/0.07996, loss_grounding_bce_3: 0.09406/0.08658, loss_grounding_dice_3: 0.27828/0.17917, loss_grounding_ce_3: 0.05290/0.28023, loss_mask_ce_4: 1.99323/0.92354, loss_mask_bce_4: 0.14973/0.33859, loss_mask_dice_4: 1.36936/1.19329, loss_spatial_bce_4: 0.04490/0.09492, loss_spatial_dice_4: 0.36613/0.22947, loss_spatial_ce_4: 0.05391/0.09595, loss_grounding_bce_4: 0.09774/0.08699, loss_grounding_dice_4: 0.29045/0.18206, loss_grounding_ce_4: 0.07584/0.28331, loss_mask_ce_5: 2.04241/0.93910, loss_mask_bce_5: 0.14837/0.34084, loss_mask_dice_5: 1.57663/1.19970, loss_spatial_bce_5: 0.04663/0.09667, loss_spatial_dice_5: 0.37448/0.23320, loss_spatial_ce_5: 0.05463/0.11105, loss_grounding_bce_5: 0.09557/0.08739, loss_grounding_dice_5: 0.26351/0.18323, loss_grounding_ce_5: 0.07300/0.29590, loss_mask_ce_6: 2.05065/0.97838, loss_mask_bce_6: 0.14412/0.34357, loss_mask_dice_6: 1.11553/1.20241, loss_spatial_bce_6: 0.04780/0.10238, loss_spatial_dice_6: 0.32679/0.23562, loss_spatial_ce_6: 0.19603/0.13685, loss_grounding_bce_6: 0.09341/0.08812, loss_grounding_dice_6: 0.17519/0.18341, loss_grounding_ce_6: 0.04823/0.31240, loss_mask_ce_7: 1.95007/1.02338, loss_mask_bce_7: 0.15534/0.35140, loss_mask_dice_7: 1.44132/1.25778, loss_spatial_bce_7: 0.05098/0.11089, loss_spatial_dice_7: 0.39600/0.26335, loss_spatial_ce_7: 0.22915/0.17350, loss_grounding_bce_7: 0.09730/0.09004, loss_grounding_dice_7: 0.11334/0.19069, loss_grounding_ce_7: 0.04336/0.34537, loss_mask_ce_8: 2.23216/1.13215, loss_mask_bce_8: 0.17533/0.36506, loss_mask_dice_8: 1.50209/1.33148, loss_spatial_bce_8: 0.05656/0.13171, loss_spatial_dice_8: 0.44532/0.30255, loss_spatial_ce_8: 0.15789/0.23079, loss_grounding_bce_8: 0.09874/0.09378, loss_grounding_dice_8: 0.22814/0.20177, loss_grounding_ce_8: 0.04514/0.41338, loss_mask_ce_9: 4.05077/3.68206, loss_mask_bce_9: 0.15152/0.39191, loss_mask_dice_9: 1.92883/1.90467, loss_spatial_bce_9: 0.14309/0.33376, loss_spatial_dice_9: 0.81389/0.82281, loss_spatial_ce_9: 1.75020/1.50371, loss_grounding_bce_9: 0.10514/0.10525, loss_grounding_dice_9: 0.28111/0.28116, loss_grounding_ce_9: 0.33825/0.67846] items per batch[64] items per second[0.24] total items[2752000] mini batches[ 43000] memory[7341] epoch remaining[0:39:18]
INFO:trainer.default_trainer:epochs[    23] optim steps[43100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89299/0.90601, loss_mask_bce_0: 0.14901/0.33441, loss_mask_dice_0: 0.54335/1.16547, loss_spatial_bce_0: 0.03858/0.08859, loss_spatial_dice_0: 0.14537/0.21194, loss_spatial_ce_0: 0.05873/0.06698, loss_grounding_bce_0: 0.03784/0.08609, loss_grounding_dice_0: 0.08797/0.17884, loss_grounding_ce_0: 0.06784/0.27395, loss_mask_ce_1: 0.86866/0.90661, loss_mask_bce_1: 0.15149/0.33520, loss_mask_dice_1: 0.55086/1.17219, loss_spatial_bce_1: 0.03758/0.08923, loss_spatial_dice_1: 0.12242/0.21606, loss_spatial_ce_1: 0.04862/0.07259, loss_grounding_bce_1: 0.03665/0.08624, loss_grounding_dice_1: 0.08916/0.17963, loss_grounding_ce_1: 0.04264/0.27567, loss_mask_ce_2: 0.88560/0.91398, loss_mask_bce_2: 0.15265/0.33566, loss_mask_dice_2: 0.55505/1.17201, loss_spatial_bce_2: 0.03779/0.08987, loss_spatial_dice_2: 0.13693/0.21723, loss_spatial_ce_2: 0.02975/0.07618, loss_grounding_bce_2: 0.03622/0.08635, loss_grounding_dice_2: 0.08087/0.17942, loss_grounding_ce_2: 0.14332/0.27868, loss_mask_ce_3: 0.94595/0.92305, loss_mask_bce_3: 0.15472/0.33667, loss_mask_dice_3: 0.57511/1.16914, loss_spatial_bce_3: 0.03680/0.09077, loss_spatial_dice_3: 0.13501/0.21786, loss_spatial_ce_3: 0.02988/0.07994, loss_grounding_bce_3: 0.03511/0.08660, loss_grounding_dice_3: 0.07974/0.17916, loss_grounding_ce_3: 0.07435/0.28018, loss_mask_ce_4: 0.95201/0.92350, loss_mask_bce_4: 0.14958/0.33861, loss_mask_dice_4: 0.55568/1.19290, loss_spatial_bce_4: 0.03933/0.09492, loss_spatial_dice_4: 0.14057/0.22945, loss_spatial_ce_4: 0.06146/0.09591, loss_grounding_bce_4: 0.03367/0.08701, loss_grounding_dice_4: 0.07906/0.18205, loss_grounding_ce_4: 0.12706/0.28323, loss_mask_ce_5: 0.85449/0.93907, loss_mask_bce_5: 0.14660/0.34087, loss_mask_dice_5: 0.53601/1.19932, loss_spatial_bce_5: 0.03632/0.09667, loss_spatial_dice_5: 0.14182/0.23318, loss_spatial_ce_5: 0.15278/0.11102, loss_grounding_bce_5: 0.02970/0.08741, loss_grounding_dice_5: 0.06924/0.18321, loss_grounding_ce_5: 0.20716/0.29581, loss_mask_ce_6: 1.00458/0.97834, loss_mask_bce_6: 0.14819/0.34358, loss_mask_dice_6: 0.49593/1.20207, loss_spatial_bce_6: 0.03854/0.10238, loss_spatial_dice_6: 0.15147/0.23561, loss_spatial_ce_6: 0.13924/0.13682, loss_grounding_bce_6: 0.03452/0.08814, loss_grounding_dice_6: 0.08075/0.18339, loss_grounding_ce_6: 0.16373/0.31229, loss_mask_ce_7: 1.01186/1.02333, loss_mask_bce_7: 0.16309/0.35145, loss_mask_dice_7: 0.66044/1.25746, loss_spatial_bce_7: 0.04316/0.11088, loss_spatial_dice_7: 0.22473/0.26334, loss_spatial_ce_7: 0.32313/0.17348, loss_grounding_bce_7: 0.03164/0.09006, loss_grounding_dice_7: 0.08186/0.19068, loss_grounding_ce_7: 0.07951/0.34522, loss_mask_ce_8: 1.02670/1.13213, loss_mask_bce_8: 0.15168/0.36509, loss_mask_dice_8: 0.71934/1.33114, loss_spatial_bce_8: 0.10823/0.13171, loss_spatial_dice_8: 0.29968/0.30254, loss_spatial_ce_8: 0.17094/0.23078, loss_grounding_bce_8: 0.03574/0.09379, loss_grounding_dice_8: 0.09283/0.20175, loss_grounding_ce_8: 0.10428/0.41314, loss_mask_ce_9: 3.54231/3.68182, loss_mask_bce_9: 0.16064/0.39194, loss_mask_dice_9: 1.15579/1.90429, loss_spatial_bce_9: 0.26311/0.33379, loss_spatial_dice_9: 0.79588/0.82278, loss_spatial_ce_9: 1.28780/1.50369, loss_grounding_bce_9: 0.03550/0.10526, loss_grounding_dice_9: 0.08312/0.28114, loss_grounding_ce_9: 0.68808/0.67824] items per batch[64] items per second[0.23] total items[2758400] mini batches[ 43100] memory[7341] epoch remaining[0:34:41]
INFO:trainer.default_trainer:epochs[    23] optim steps[43200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.58988/0.90615, loss_mask_bce_0: 0.51356/0.33441, loss_mask_dice_0: 2.45028/1.16550, loss_spatial_bce_0: 0.07558/0.08858, loss_spatial_dice_0: 0.32095/0.21191, loss_spatial_ce_0: 0.09193/0.06693, loss_grounding_bce_0: 0.22798/0.08609, loss_grounding_dice_0: 0.31355/0.17881, loss_grounding_ce_0: 0.28972/0.27412, loss_mask_ce_1: 1.61633/0.90673, loss_mask_bce_1: 0.51787/0.33520, loss_mask_dice_1: 2.24799/1.17221, loss_spatial_bce_1: 0.08612/0.08922, loss_spatial_dice_1: 0.34147/0.21603, loss_spatial_ce_1: 0.08208/0.07255, loss_grounding_bce_1: 0.23556/0.08624, loss_grounding_dice_1: 0.29319/0.17960, loss_grounding_ce_1: 0.29396/0.27579, loss_mask_ce_2: 1.65735/0.91409, loss_mask_bce_2: 0.46527/0.33567, loss_mask_dice_2: 2.24617/1.17206, loss_spatial_bce_2: 0.12482/0.08987, loss_spatial_dice_2: 0.35783/0.21720, loss_spatial_ce_2: 0.08805/0.07614, loss_grounding_bce_2: 0.25831/0.08634, loss_grounding_dice_2: 0.27901/0.17939, loss_grounding_ce_2: 0.29874/0.27886, loss_mask_ce_3: 1.60080/0.92320, loss_mask_bce_3: 0.48957/0.33668, loss_mask_dice_3: 2.29023/1.16913, loss_spatial_bce_3: 0.08426/0.09076, loss_spatial_dice_3: 0.33188/0.21783, loss_spatial_ce_3: 0.10150/0.07990, loss_grounding_bce_3: 0.27012/0.08659, loss_grounding_dice_3: 0.28832/0.17914, loss_grounding_ce_3: 0.29095/0.28034, loss_mask_ce_4: 1.53547/0.92362, loss_mask_bce_4: 0.47402/0.33861, loss_mask_dice_4: 2.49870/1.19294, loss_spatial_bce_4: 0.11087/0.09492, loss_spatial_dice_4: 0.35140/0.22943, loss_spatial_ce_4: 0.07702/0.09589, loss_grounding_bce_4: 0.27851/0.08701, loss_grounding_dice_4: 0.29244/0.18202, loss_grounding_ce_4: 0.28922/0.28334, loss_mask_ce_5: 1.67569/0.93923, loss_mask_bce_5: 0.44648/0.34088, loss_mask_dice_5: 2.35937/1.19940, loss_spatial_bce_5: 0.08592/0.09667, loss_spatial_dice_5: 0.34682/0.23317, loss_spatial_ce_5: 0.05709/0.11100, loss_grounding_bce_5: 0.20226/0.08741, loss_grounding_dice_5: 0.28617/0.18318, loss_grounding_ce_5: 0.29940/0.29593, loss_mask_ce_6: 1.48577/0.97849, loss_mask_bce_6: 0.43612/0.34359, loss_mask_dice_6: 2.25011/1.20209, loss_spatial_bce_6: 0.09892/0.10239, loss_spatial_dice_6: 0.33623/0.23559, loss_spatial_ce_6: 0.17161/0.13678, loss_grounding_bce_6: 0.20847/0.08815, loss_grounding_dice_6: 0.29774/0.18336, loss_grounding_ce_6: 0.25341/0.31242, loss_mask_ce_7: 1.39322/1.02351, loss_mask_bce_7: 0.43636/0.35148, loss_mask_dice_7: 2.54620/1.25751, loss_spatial_bce_7: 0.11551/0.11087, loss_spatial_dice_7: 0.34197/0.26331, loss_spatial_ce_7: 0.09902/0.17345, loss_grounding_bce_7: 0.20909/0.09006, loss_grounding_dice_7: 0.28841/0.19065, loss_grounding_ce_7: 0.28351/0.34532, loss_mask_ce_8: 1.50640/1.13228, loss_mask_bce_8: 0.55969/0.36511, loss_mask_dice_8: 2.86696/1.33116, loss_spatial_bce_8: 0.18030/0.13170, loss_spatial_dice_8: 0.44405/0.30252, loss_spatial_ce_8: 0.19197/0.23073, loss_grounding_bce_8: 0.28323/0.09379, loss_grounding_dice_8: 0.29564/0.20172, loss_grounding_ce_8: 2.04345/0.41344, loss_mask_ce_9: 4.15405/3.68222, loss_mask_bce_9: 0.53271/0.39199, loss_mask_dice_9: 4.07134/1.90437, loss_spatial_bce_9: 0.23822/0.33381, loss_spatial_dice_9: 0.87862/0.82279, loss_spatial_ce_9: 1.54954/1.50372, loss_grounding_bce_9: 0.25916/0.10528, loss_grounding_dice_9: 0.47254/0.28112, loss_grounding_ce_9: 1.32556/0.67836] items per batch[64] items per second[0.23] total items[2764800] mini batches[ 43200] memory[7345] epoch remaining[0:30:01]
INFO:trainer.default_trainer:epochs[    23] optim steps[43300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.32771/0.90594, loss_mask_bce_0: 0.14447/0.33437, loss_mask_dice_0: 1.41868/1.16565, loss_spatial_bce_0: 0.04218/0.08855, loss_spatial_dice_0: 0.26019/0.21189, loss_spatial_ce_0: 0.00850/0.06688, loss_grounding_bce_0: 0.01755/0.08607, loss_grounding_dice_0: 0.32191/0.17877, loss_grounding_ce_0: 0.25640/0.27400, loss_mask_ce_1: 1.42854/0.90650, loss_mask_bce_1: 0.14545/0.33516, loss_mask_dice_1: 1.32415/1.17234, loss_spatial_bce_1: 0.03618/0.08919, loss_spatial_dice_1: 0.19776/0.21601, loss_spatial_ce_1: 0.21799/0.07251, loss_grounding_bce_1: 0.01435/0.08622, loss_grounding_dice_1: 0.39209/0.17957, loss_grounding_ce_1: 0.23126/0.27565, loss_mask_ce_2: 1.39435/0.91389, loss_mask_bce_2: 0.13034/0.33563, loss_mask_dice_2: 1.28148/1.17218, loss_spatial_bce_2: 0.04351/0.08984, loss_spatial_dice_2: 0.27322/0.21718, loss_spatial_ce_2: 0.01642/0.07610, loss_grounding_bce_2: 0.01428/0.08632, loss_grounding_dice_2: 0.38786/0.17936, loss_grounding_ce_2: 0.15856/0.27872, loss_mask_ce_3: 1.40211/0.92299, loss_mask_bce_3: 0.13388/0.33664, loss_mask_dice_3: 1.60638/1.16926, loss_spatial_bce_3: 0.04044/0.09073, loss_spatial_dice_3: 0.26316/0.21780, loss_spatial_ce_3: 0.03276/0.07988, loss_grounding_bce_3: 0.01442/0.08657, loss_grounding_dice_3: 0.34795/0.17911, loss_grounding_ce_3: 0.15936/0.28027, loss_mask_ce_4: 1.41510/0.92341, loss_mask_bce_4: 0.14454/0.33858, loss_mask_dice_4: 1.50600/1.19309, loss_spatial_bce_4: 0.04212/0.09489, loss_spatial_dice_4: 0.27000/0.22941, loss_spatial_ce_4: 0.02889/0.09586, loss_grounding_bce_4: 0.01634/0.08699, loss_grounding_dice_4: 0.40271/0.18198, loss_grounding_ce_4: 0.22206/0.28324, loss_mask_ce_5: 1.40500/0.93904, loss_mask_bce_5: 0.14747/0.34084, loss_mask_dice_5: 1.61787/1.19955, loss_spatial_bce_5: 0.03923/0.09665, loss_spatial_dice_5: 0.31555/0.23315, loss_spatial_ce_5: 0.07715/0.11096, loss_grounding_bce_5: 0.01442/0.08739, loss_grounding_dice_5: 0.32290/0.18315, loss_grounding_ce_5: 0.25596/0.29583, loss_mask_ce_6: 1.35870/0.97827, loss_mask_bce_6: 0.16179/0.34355, loss_mask_dice_6: 1.67650/1.20225, loss_spatial_bce_6: 0.04186/0.10237, loss_spatial_dice_6: 0.29400/0.23558, loss_spatial_ce_6: 0.03862/0.13677, loss_grounding_bce_6: 0.01436/0.08812, loss_grounding_dice_6: 0.34366/0.18333, loss_grounding_ce_6: 0.16421/0.31230, loss_mask_ce_7: 1.60123/1.02328, loss_mask_bce_7: 0.16856/0.35144, loss_mask_dice_7: 1.49358/1.25762, loss_spatial_bce_7: 0.04446/0.11083, loss_spatial_dice_7: 0.34822/0.26331, loss_spatial_ce_7: 0.10481/0.17344, loss_grounding_bce_7: 0.01264/0.09004, loss_grounding_dice_7: 0.30668/0.19061, loss_grounding_ce_7: 0.24116/0.34517, loss_mask_ce_8: 1.53452/1.13208, loss_mask_bce_8: 0.15997/0.36507, loss_mask_dice_8: 1.93537/1.33131, loss_spatial_bce_8: 0.06152/0.13167, loss_spatial_dice_8: 0.43936/0.30252, loss_spatial_ce_8: 0.12714/0.23069, loss_grounding_bce_8: 0.01913/0.09377, loss_grounding_dice_8: 0.49760/0.20170, loss_grounding_ce_8: 0.15902/0.41316, loss_mask_ce_9: 4.41085/3.68190, loss_mask_bce_9: 0.18360/0.39193, loss_mask_dice_9: 2.33098/1.90440, loss_spatial_bce_9: 0.24899/0.33377, loss_spatial_dice_9: 0.85929/0.82279, loss_spatial_ce_9: 1.68385/1.50387, loss_grounding_bce_9: 0.02402/0.10527, loss_grounding_dice_9: 0.61846/0.28108, loss_grounding_ce_9: 0.54645/0.67815] items per batch[64] items per second[0.23] total items[2771200] mini batches[ 43300] memory[7345] epoch remaining[0:25:21]
INFO:trainer.default_trainer:epochs[    23] optim steps[43400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34518/0.90581, loss_mask_bce_0: 0.21902/0.33434, loss_mask_dice_0: 0.56722/1.16561, loss_spatial_bce_0: 0.07490/0.08853, loss_spatial_dice_0: 0.19982/0.21186, loss_spatial_ce_0: 0.05086/0.06684, loss_grounding_bce_0: 0.13167/0.08605, loss_grounding_dice_0: 0.14002/0.17874, loss_grounding_ce_0: 0.03898/0.27399, loss_mask_ce_1: 0.36151/0.90632, loss_mask_bce_1: 0.22068/0.33513, loss_mask_dice_1: 0.61339/1.17232, loss_spatial_bce_1: 0.07647/0.08917, loss_spatial_dice_1: 0.17072/0.21598, loss_spatial_ce_1: 0.06253/0.07249, loss_grounding_bce_1: 0.13036/0.08619, loss_grounding_dice_1: 0.40079/0.17955, loss_grounding_ce_1: 0.40747/0.27562, loss_mask_ce_2: 0.34884/0.91373, loss_mask_bce_2: 0.22061/0.33560, loss_mask_dice_2: 0.57907/1.17214, loss_spatial_bce_2: 0.07712/0.08982, loss_spatial_dice_2: 0.18597/0.21715, loss_spatial_ce_2: 0.05540/0.07607, loss_grounding_bce_2: 0.12914/0.08630, loss_grounding_dice_2: 0.11703/0.17933, loss_grounding_ce_2: 0.03141/0.27868, loss_mask_ce_3: 0.33175/0.92283, loss_mask_bce_3: 0.22707/0.33662, loss_mask_dice_3: 0.62259/1.16920, loss_spatial_bce_3: 0.08289/0.09072, loss_spatial_dice_3: 0.18260/0.21777, loss_spatial_ce_3: 0.05331/0.07984, loss_grounding_bce_3: 0.13233/0.08655, loss_grounding_dice_3: 0.16998/0.17907, loss_grounding_ce_3: 0.04210/0.28022, loss_mask_ce_4: 0.35725/0.92326, loss_mask_bce_4: 0.20431/0.33856, loss_mask_dice_4: 0.62104/1.19306, loss_spatial_bce_4: 0.08531/0.09487, loss_spatial_dice_4: 0.18890/0.22938, loss_spatial_ce_4: 0.07554/0.09583, loss_grounding_bce_4: 0.12707/0.08697, loss_grounding_dice_4: 0.11527/0.18195, loss_grounding_ce_4: 0.04495/0.28321, loss_mask_ce_5: 0.37033/0.93885, loss_mask_bce_5: 0.20197/0.34081, loss_mask_dice_5: 0.59813/1.19952, loss_spatial_bce_5: 0.08039/0.09663, loss_spatial_dice_5: 0.18486/0.23312, loss_spatial_ce_5: 0.08426/0.11093, loss_grounding_bce_5: 0.12742/0.08736, loss_grounding_dice_5: 0.22261/0.18312, loss_grounding_ce_5: 0.07200/0.29580, loss_mask_ce_6: 0.42628/0.97810, loss_mask_bce_6: 0.22435/0.34351, loss_mask_dice_6: 0.55745/1.20222, loss_spatial_bce_6: 0.08189/0.10235, loss_spatial_dice_6: 0.15785/0.23554, loss_spatial_ce_6: 0.07818/0.13674, loss_grounding_bce_6: 0.13589/0.08809, loss_grounding_dice_6: 0.20415/0.18331, loss_grounding_ce_6: 0.12542/0.31223, loss_mask_ce_7: 0.34567/1.02310, loss_mask_bce_7: 0.24487/0.35140, loss_mask_dice_7: 0.65463/1.25759, loss_spatial_bce_7: 0.08338/0.11082, loss_spatial_dice_7: 0.22246/0.26327, loss_spatial_ce_7: 0.06015/0.17340, loss_grounding_bce_7: 0.14848/0.09002, loss_grounding_dice_7: 0.18497/0.19057, loss_grounding_ce_7: 0.08058/0.34509, loss_mask_ce_8: 1.06159/1.13198, loss_mask_bce_8: 0.24472/0.36505, loss_mask_dice_8: 0.64652/1.33120, loss_spatial_bce_8: 0.11494/0.13165, loss_spatial_dice_8: 0.27877/0.30247, loss_spatial_ce_8: 0.12865/0.23063, loss_grounding_bce_8: 0.16031/0.09375, loss_grounding_dice_8: 0.23927/0.20166, loss_grounding_ce_8: 0.10174/0.41304, loss_mask_ce_9: 3.16129/3.68189, loss_mask_bce_9: 0.22364/0.39191, loss_mask_dice_9: 0.77942/1.90428, loss_spatial_bce_9: 0.32197/0.33377, loss_spatial_dice_9: 0.85204/0.82279, loss_spatial_ce_9: 1.72921/1.50394, loss_grounding_bce_9: 0.14904/0.10524, loss_grounding_dice_9: 0.21159/0.28103, loss_grounding_ce_9: 0.16462/0.67806] items per batch[64] items per second[0.23] total items[2777600] mini batches[ 43400] memory[7345] epoch remaining[0:20:44]
INFO:trainer.default_trainer:epochs[    23] optim steps[43500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21896/0.90582, loss_mask_bce_0: 0.19908/0.33440, loss_mask_dice_0: 2.13746/1.16580, loss_spatial_bce_0: 0.04281/0.08853, loss_spatial_dice_0: 0.27238/0.21185, loss_spatial_ce_0: 0.04731/0.06683, loss_grounding_bce_0: 0.06146/0.08605, loss_grounding_dice_0: 0.35871/0.17878, loss_grounding_ce_0: 0.23834/0.27399, loss_mask_ce_1: 1.06033/0.90627, loss_mask_bce_1: 0.23246/0.33518, loss_mask_dice_1: 2.39525/1.17248, loss_spatial_bce_1: 0.04527/0.08916, loss_spatial_dice_1: 0.30464/0.21597, loss_spatial_ce_1: 0.04777/0.07247, loss_grounding_bce_1: 0.06108/0.08620, loss_grounding_dice_1: 0.31676/0.17959, loss_grounding_ce_1: 0.23216/0.27565, loss_mask_ce_2: 1.15773/0.91371, loss_mask_bce_2: 0.22271/0.33565, loss_mask_dice_2: 2.38243/1.17233, loss_spatial_bce_2: 0.03865/0.08982, loss_spatial_dice_2: 0.27329/0.21713, loss_spatial_ce_2: 0.06753/0.07603, loss_grounding_bce_2: 0.06229/0.08630, loss_grounding_dice_2: 0.31910/0.17937, loss_grounding_ce_2: 0.29285/0.27869, loss_mask_ce_3: 1.06902/0.92281, loss_mask_bce_3: 0.24274/0.33668, loss_mask_dice_3: 2.29678/1.16940, loss_spatial_bce_3: 0.03826/0.09072, loss_spatial_dice_3: 0.32131/0.21776, loss_spatial_ce_3: 0.06314/0.07984, loss_grounding_bce_3: 0.07278/0.08655, loss_grounding_dice_3: 0.35387/0.17911, loss_grounding_ce_3: 0.24019/0.28025, loss_mask_ce_4: 1.14889/0.92323, loss_mask_bce_4: 0.23582/0.33862, loss_mask_dice_4: 2.36587/1.19326, loss_spatial_bce_4: 0.04026/0.09487, loss_spatial_dice_4: 0.32935/0.22938, loss_spatial_ce_4: 0.03324/0.09579, loss_grounding_bce_4: 0.07223/0.08697, loss_grounding_dice_4: 0.36245/0.18199, loss_grounding_ce_4: 0.25508/0.28327, loss_mask_ce_5: 1.11831/0.93882, loss_mask_bce_5: 0.23021/0.34087, loss_mask_dice_5: 2.45218/1.19972, loss_spatial_bce_5: 0.04471/0.09663, loss_spatial_dice_5: 0.36395/0.23312, loss_spatial_ce_5: 0.01750/0.11091, loss_grounding_bce_5: 0.07345/0.08737, loss_grounding_dice_5: 0.39486/0.18315, loss_grounding_ce_5: 0.30270/0.29579, loss_mask_ce_6: 1.22204/0.97804, loss_mask_bce_6: 0.21057/0.34360, loss_mask_dice_6: 2.34719/1.20247, loss_spatial_bce_6: 0.04613/0.10236, loss_spatial_dice_6: 0.33654/0.23554, loss_spatial_ce_6: 0.49086/0.13674, loss_grounding_bce_6: 0.06015/0.08809, loss_grounding_dice_6: 0.39406/0.18334, loss_grounding_ce_6: 0.23423/0.31219, loss_mask_ce_7: 1.14437/1.02307, loss_mask_bce_7: 0.24111/0.35149, loss_mask_dice_7: 2.61584/1.25782, loss_spatial_bce_7: 0.05868/0.11084, loss_spatial_dice_7: 0.45005/0.26329, loss_spatial_ce_7: 0.10025/0.17336, loss_grounding_bce_7: 0.07335/0.09002, loss_grounding_dice_7: 0.45547/0.19062, loss_grounding_ce_7: 0.22295/0.34499, loss_mask_ce_8: 1.32812/1.13201, loss_mask_bce_8: 0.27929/0.36511, loss_mask_dice_8: 2.87419/1.33142, loss_spatial_bce_8: 0.06601/0.13166, loss_spatial_dice_8: 0.56540/0.30248, loss_spatial_ce_8: 0.28378/0.23058, loss_grounding_bce_8: 0.07701/0.09375, loss_grounding_dice_8: 0.38358/0.20171, loss_grounding_ce_8: 0.32688/0.41304, loss_mask_ce_9: 3.19880/3.68182, loss_mask_bce_9: 0.23417/0.39201, loss_mask_dice_9: 3.84880/1.90467, loss_spatial_bce_9: 0.15646/0.33375, loss_spatial_dice_9: 0.87921/0.82281, loss_spatial_ce_9: 1.07953/1.50395, loss_grounding_bce_9: 0.06930/0.10526, loss_grounding_dice_9: 0.56323/0.28111, loss_grounding_ce_9: 0.35653/0.67796] items per batch[64] items per second[0.22] total items[2784000] mini batches[ 43500] memory[7345] epoch remaining[0:16:08]
INFO:trainer.default_trainer:epochs[    23] optim steps[43600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57697/0.90567, loss_mask_bce_0: 0.27163/0.33446, loss_mask_dice_0: 0.45294/1.16614, loss_spatial_bce_0: 0.23378/0.08853, loss_spatial_dice_0: 0.20453/0.21184, loss_spatial_ce_0: 0.04830/0.06682, loss_grounding_bce_0: 0.08573/0.08607, loss_grounding_dice_0: 0.08227/0.17877, loss_grounding_ce_0: 0.10819/0.27403, loss_mask_ce_1: 0.57045/0.90612, loss_mask_bce_1: 0.26701/0.33525, loss_mask_dice_1: 0.43598/1.17282, loss_spatial_bce_1: 0.20746/0.08916, loss_spatial_dice_1: 0.20150/0.21595, loss_spatial_ce_1: 0.03857/0.07245, loss_grounding_bce_1: 0.08495/0.08621, loss_grounding_dice_1: 0.07270/0.17957, loss_grounding_ce_1: 0.12661/0.27569, loss_mask_ce_2: 0.61075/0.91358, loss_mask_bce_2: 0.27090/0.33571, loss_mask_dice_2: 0.44742/1.17263, loss_spatial_bce_2: 0.20546/0.08982, loss_spatial_dice_2: 0.19306/0.21712, loss_spatial_ce_2: 0.04740/0.07602, loss_grounding_bce_2: 0.08939/0.08631, loss_grounding_dice_2: 0.08105/0.17936, loss_grounding_ce_2: 0.12363/0.27872, loss_mask_ce_3: 0.80383/0.92269, loss_mask_bce_3: 0.27284/0.33674, loss_mask_dice_3: 0.41604/1.16974, loss_spatial_bce_3: 0.24815/0.09073, loss_spatial_dice_3: 0.21184/0.21774, loss_spatial_ce_3: 0.05711/0.07982, loss_grounding_bce_3: 0.09066/0.08657, loss_grounding_dice_3: 0.08154/0.17910, loss_grounding_ce_3: 0.18473/0.28029, loss_mask_ce_4: 0.68713/0.92307, loss_mask_bce_4: 0.27002/0.33867, loss_mask_dice_4: 0.45717/1.19354, loss_spatial_bce_4: 0.24730/0.09487, loss_spatial_dice_4: 0.20781/0.22938, loss_spatial_ce_4: 0.07787/0.09576, loss_grounding_bce_4: 0.09286/0.08699, loss_grounding_dice_4: 0.08364/0.18198, loss_grounding_ce_4: 0.16678/0.28329, loss_mask_ce_5: 0.68839/0.93871, loss_mask_bce_5: 0.28743/0.34093, loss_mask_dice_5: 0.46235/1.20005, loss_spatial_bce_5: 0.28357/0.09664, loss_spatial_dice_5: 0.22072/0.23310, loss_spatial_ce_5: 0.12244/0.11087, loss_grounding_bce_5: 0.09472/0.08738, loss_grounding_dice_5: 0.08165/0.18314, loss_grounding_ce_5: 0.20237/0.29582, loss_mask_ce_6: 0.68939/0.97793, loss_mask_bce_6: 0.28542/0.34364, loss_mask_dice_6: 0.46242/1.20281, loss_spatial_bce_6: 0.23437/0.10237, loss_spatial_dice_6: 0.22286/0.23553, loss_spatial_ce_6: 0.16333/0.13673, loss_grounding_bce_6: 0.09805/0.08811, loss_grounding_dice_6: 0.08584/0.18333, loss_grounding_ce_6: 0.27634/0.31225, loss_mask_ce_7: 0.68002/1.02292, loss_mask_bce_7: 0.28928/0.35152, loss_mask_dice_7: 0.46789/1.25813, loss_spatial_bce_7: 0.22399/0.11084, loss_spatial_dice_7: 0.23110/0.26329, loss_spatial_ce_7: 0.19444/0.17335, loss_grounding_bce_7: 0.09774/0.09004, loss_grounding_dice_7: 0.08334/0.19061, loss_grounding_ce_7: 0.25358/0.34501, loss_mask_ce_8: 0.62174/1.13188, loss_mask_bce_8: 0.29167/0.36515, loss_mask_dice_8: 0.47216/1.33174, loss_spatial_bce_8: 0.27777/0.13166, loss_spatial_dice_8: 0.27515/0.30247, loss_spatial_ce_8: 0.26313/0.23052, loss_grounding_bce_8: 0.09871/0.09377, loss_grounding_dice_8: 0.09243/0.20169, loss_grounding_ce_8: 0.57942/0.41305, loss_mask_ce_9: 2.80147/3.68176, loss_mask_bce_9: 0.30751/0.39204, loss_mask_dice_9: 0.61955/1.90507, loss_spatial_bce_9: 0.38120/0.33375, loss_spatial_dice_9: 0.77103/0.82282, loss_spatial_ce_9: 1.33989/1.50382, loss_grounding_bce_9: 0.10157/0.10528, loss_grounding_dice_9: 0.12647/0.28106, loss_grounding_ce_9: 0.65322/0.67793] items per batch[64] items per second[0.23] total items[2790400] mini batches[ 43600] memory[7345] epoch remaining[0:11:29]
INFO:trainer.default_trainer:epochs[    23] optim steps[43700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92895/0.90556, loss_mask_bce_0: 0.46041/0.33438, loss_mask_dice_0: 3.01937/1.16604, loss_spatial_bce_0: 0.07311/0.08850, loss_spatial_dice_0: 0.30915/0.21181, loss_spatial_ce_0: 0.03020/0.06679, loss_grounding_bce_0: 0.12532/0.08607, loss_grounding_dice_0: 0.27685/0.17878, loss_grounding_ce_0: 0.01924/0.27399, loss_mask_ce_1: 0.68549/0.90603, loss_mask_bce_1: 0.49704/0.33517, loss_mask_dice_1: 3.13015/1.17273, loss_spatial_bce_1: 0.07327/0.08913, loss_spatial_dice_1: 0.30317/0.21593, loss_spatial_ce_1: 0.04653/0.07243, loss_grounding_bce_1: 0.13405/0.08622, loss_grounding_dice_1: 0.28024/0.17958, loss_grounding_ce_1: 0.02574/0.27563, loss_mask_ce_2: 0.93137/0.91346, loss_mask_bce_2: 0.46893/0.33564, loss_mask_dice_2: 3.13769/1.17256, loss_spatial_bce_2: 0.07368/0.08979, loss_spatial_dice_2: 0.30567/0.21709, loss_spatial_ce_2: 0.05800/0.07599, loss_grounding_bce_2: 0.13110/0.08631, loss_grounding_dice_2: 0.26913/0.17937, loss_grounding_ce_2: 0.02665/0.27868, loss_mask_ce_3: 0.86876/0.92260, loss_mask_bce_3: 0.45648/0.33667, loss_mask_dice_3: 2.89615/1.16965, loss_spatial_bce_3: 0.07411/0.09070, loss_spatial_dice_3: 0.30134/0.21772, loss_spatial_ce_3: 0.04953/0.07978, loss_grounding_bce_3: 0.12594/0.08657, loss_grounding_dice_3: 0.26435/0.17911, loss_grounding_ce_3: 0.02110/0.28026, loss_mask_ce_4: 0.88431/0.92301, loss_mask_bce_4: 0.46397/0.33859, loss_mask_dice_4: 2.93203/1.19343, loss_spatial_bce_4: 0.08071/0.09484, loss_spatial_dice_4: 0.32586/0.22935, loss_spatial_ce_4: 0.06079/0.09571, loss_grounding_bce_4: 0.11624/0.08699, loss_grounding_dice_4: 0.26256/0.18199, loss_grounding_ce_4: 0.02620/0.28329, loss_mask_ce_5: 0.95720/0.93864, loss_mask_bce_5: 0.46018/0.34085, loss_mask_dice_5: 2.99522/1.19996, loss_spatial_bce_5: 0.08619/0.09660, loss_spatial_dice_5: 0.34286/0.23308, loss_spatial_ce_5: 0.13234/0.11085, loss_grounding_bce_5: 0.11250/0.08738, loss_grounding_dice_5: 0.27232/0.18314, loss_grounding_ce_5: 0.02594/0.29577, loss_mask_ce_6: 0.83015/0.97784, loss_mask_bce_6: 0.51184/0.34356, loss_mask_dice_6: 3.08513/1.20275, loss_spatial_bce_6: 0.08221/0.10233, loss_spatial_dice_6: 0.35824/0.23551, loss_spatial_ce_6: 0.21858/0.13672, loss_grounding_bce_6: 0.12876/0.08811, loss_grounding_dice_6: 0.28055/0.18335, loss_grounding_ce_6: 0.03958/0.31219, loss_mask_ce_7: 0.95436/1.02290, loss_mask_bce_7: 0.50204/0.35143, loss_mask_dice_7: 3.33337/1.25802, loss_spatial_bce_7: 0.09478/0.11080, loss_spatial_dice_7: 0.36203/0.26328, loss_spatial_ce_7: 0.19744/0.17332, loss_grounding_bce_7: 0.13363/0.09003, loss_grounding_dice_7: 0.28459/0.19062, loss_grounding_ce_7: 0.04705/0.34497, loss_mask_ce_8: 1.08372/1.13179, loss_mask_bce_8: 0.52529/0.36507, loss_mask_dice_8: 3.49188/1.33164, loss_spatial_bce_8: 0.09361/0.13161, loss_spatial_dice_8: 0.41511/0.30247, loss_spatial_ce_8: 0.25076/0.23049, loss_grounding_bce_8: 0.10620/0.09377, loss_grounding_dice_8: 0.29369/0.20171, loss_grounding_ce_8: 0.04920/0.41306, loss_mask_ce_9: 4.69850/3.68159, loss_mask_bce_9: 0.60552/0.39195, loss_mask_dice_9: 5.76904/1.90487, loss_spatial_bce_9: 0.42738/0.33369, loss_spatial_dice_9: 0.94379/0.82282, loss_spatial_ce_9: 1.48534/1.50379, loss_grounding_bce_9: 0.09643/0.10527, loss_grounding_dice_9: 0.31518/0.28105, loss_grounding_ce_9: 0.13694/0.67798] items per batch[64] items per second[0.23] total items[2796800] mini batches[ 43700] memory[7345] epoch remaining[0:06:51]
INFO:trainer.default_trainer:epochs[    23] optim steps[43800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31993/0.90557, loss_mask_bce_0: 0.14814/0.33441, loss_mask_dice_0: 3.30916/1.16586, loss_spatial_bce_0: 0.06419/0.08850, loss_spatial_dice_0: 0.21378/0.21180, loss_spatial_ce_0: 0.04777/0.06677, loss_grounding_bce_0: 0.00568/0.08608, loss_grounding_dice_0: 0.36190/0.17880, loss_grounding_ce_0: 0.32813/0.27392, loss_mask_ce_1: 0.28309/0.90599, loss_mask_bce_1: 0.15607/0.33521, loss_mask_dice_1: 3.46623/1.17255, loss_spatial_bce_1: 0.07561/0.08912, loss_spatial_dice_1: 0.21990/0.21591, loss_spatial_ce_1: 0.01815/0.07240, loss_grounding_bce_1: 0.00593/0.08623, loss_grounding_dice_1: 0.34356/0.17960, loss_grounding_ce_1: 0.29008/0.27555, loss_mask_ce_2: 0.30861/0.91344, loss_mask_bce_2: 0.14955/0.33568, loss_mask_dice_2: 3.15812/1.17244, loss_spatial_bce_2: 0.08100/0.08979, loss_spatial_dice_2: 0.21297/0.21708, loss_spatial_ce_2: 0.01723/0.07597, loss_grounding_bce_2: 0.00573/0.08633, loss_grounding_dice_2: 0.34869/0.17938, loss_grounding_ce_2: 0.32458/0.27857, loss_mask_ce_3: 0.33961/0.92258, loss_mask_bce_3: 0.14551/0.33670, loss_mask_dice_3: 3.16397/1.16948, loss_spatial_bce_3: 0.05553/0.09070, loss_spatial_dice_3: 0.19891/0.21771, loss_spatial_ce_3: 0.18208/0.07977, loss_grounding_bce_3: 0.00591/0.08659, loss_grounding_dice_3: 0.35295/0.17912, loss_grounding_ce_3: 0.33397/0.28017, loss_mask_ce_4: 0.35000/0.92299, loss_mask_bce_4: 0.13200/0.33862, loss_mask_dice_4: 2.66862/1.19327, loss_spatial_bce_4: 0.10569/0.09484, loss_spatial_dice_4: 0.27322/0.22934, loss_spatial_ce_4: 0.02454/0.09569, loss_grounding_bce_4: 0.00582/0.08700, loss_grounding_dice_4: 0.31235/0.18199, loss_grounding_ce_4: 0.31307/0.28318, loss_mask_ce_5: 0.33287/0.93864, loss_mask_bce_5: 0.12056/0.34089, loss_mask_dice_5: 2.86813/1.19982, loss_spatial_bce_5: 0.10642/0.09660, loss_spatial_dice_5: 0.23414/0.23306, loss_spatial_ce_5: 0.07546/0.11081, loss_grounding_bce_5: 0.00562/0.08739, loss_grounding_dice_5: 0.33151/0.18316, loss_grounding_ce_5: 0.32121/0.29567, loss_mask_ce_6: 0.44802/0.97794, loss_mask_bce_6: 0.14512/0.34358, loss_mask_dice_6: 2.98984/1.20260, loss_spatial_bce_6: 0.10468/0.10233, loss_spatial_dice_6: 0.22479/0.23550, loss_spatial_ce_6: 0.15718/0.13669, loss_grounding_bce_6: 0.00560/0.08812, loss_grounding_dice_6: 0.35211/0.18336, loss_grounding_ce_6: 0.29491/0.31217, loss_mask_ce_7: 0.35853/1.02290, loss_mask_bce_7: 0.12739/0.35145, loss_mask_dice_7: 3.22208/1.25787, loss_spatial_bce_7: 0.09588/0.11079, loss_spatial_dice_7: 0.26200/0.26327, loss_spatial_ce_7: 0.06024/0.17326, loss_grounding_bce_7: 0.00626/0.09005, loss_grounding_dice_7: 0.33927/0.19062, loss_grounding_ce_7: 0.29995/0.34493, loss_mask_ce_8: 0.64736/1.13184, loss_mask_bce_8: 0.14682/0.36509, loss_mask_dice_8: 3.75370/1.33145, loss_spatial_bce_8: 0.11517/0.13161, loss_spatial_dice_8: 0.27398/0.30245, loss_spatial_ce_8: 0.09520/0.23047, loss_grounding_bce_8: 0.00585/0.09379, loss_grounding_dice_8: 0.35517/0.20172, loss_grounding_ce_8: 0.32450/0.41301, loss_mask_ce_9: 4.28419/3.68139, loss_mask_bce_9: 0.12641/0.39198, loss_mask_dice_9: 3.59391/1.90460, loss_spatial_bce_9: 0.23956/0.33368, loss_spatial_dice_9: 0.90645/0.82283, loss_spatial_ce_9: 1.53003/1.50369, loss_grounding_bce_9: 0.00661/0.10529, loss_grounding_dice_9: 0.51360/0.28108, loss_grounding_ce_9: 0.76236/0.67789] items per batch[64] items per second[0.23] total items[2803200] mini batches[ 43800] memory[7345] epoch remaining[0:02:13]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00043848.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0032 s/iter. Inference: 0.2160 s/iter. Eval: 0.1006 s/iter. Total: 0.3199 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2228 s/iter. Eval: 0.0819 s/iter. Total: 0.3079 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2265 s/iter. Eval: 0.0789 s/iter. Total: 0.3086 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2258 s/iter. Eval: 0.0772 s/iter. Total: 0.3063 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0032 s/iter. Inference: 0.2242 s/iter. Eval: 0.0750 s/iter. Total: 0.3024 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2260 s/iter. Eval: 0.0755 s/iter. Total: 0.3047 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0031 s/iter. Inference: 0.2268 s/iter. Eval: 0.0755 s/iter. Total: 0.3056 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 130/157. Dataloading: 0.0032 s/iter. Inference: 0.2262 s/iter. Eval: 0.0748 s/iter. Total: 0.3042 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2272 s/iter. Eval: 0.0750 s/iter. Total: 0.3055 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalrqxario3 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.640 | 81.776 | 59.785 |      133      |
| Things | 54.596 | 82.590 | 65.438 |      80       |
| Stuff  | 42.160 | 80.547 | 51.251 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
DONE (t=0.36s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.26 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.25s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.08 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.610
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.710
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.792 | 60.971 | 40.945 | 19.110 | 41.982 | 60.123 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.118 | bicycle      | 18.574 | car            | 36.500 |
| motorcycle    | 34.814 | airplane     | 55.067 | bus            | 64.733 |
| train         | 67.992 | truck        | 33.893 | boat           | 22.508 |
| traffic light | 24.487 | fire hydrant | 62.696 | stop sign      | 63.312 |
| parking meter | 42.519 | bench        | 20.591 | bird           | 29.337 |
| cat           | 72.887 | dog          | 66.190 | horse          | 45.375 |
| sheep         | 46.708 | cow          | 49.663 | elephant       | 60.127 |
| bear          | 77.774 | zebra        | 60.293 | giraffe        | 57.001 |
| backpack      | 16.154 | umbrella     | 47.862 | handbag        | 14.708 |
| tie           | 33.286 | suitcase     | 41.706 | frisbee        | 66.733 |
| skis          | 5.517  | snowboard    | 22.262 | sports ball    | 47.039 |
| kite          | 32.649 | baseball bat | 29.061 | baseball glove | 43.959 |
| skateboard    | 35.729 | surfboard    | 35.714 | tennis racket  | 55.655 |
| bottle        | 34.562 | wine glass   | 26.521 | cup            | 40.474 |
| fork          | 15.312 | knife        | 13.115 | spoon          | 14.575 |
| bowl          | 30.404 | banana       | 20.489 | apple          | 20.298 |
| sandwich      | 42.392 | orange       | 29.852 | broccoli       | 21.721 |
| carrot        | 20.103 | hot dog      | 24.296 | pizza          | 50.644 |
| donut         | 46.233 | cake         | 43.549 | chair          | 21.121 |
| couch         | 41.860 | potted plant | 17.792 | bed            | 40.398 |
| dining table  | 12.490 | toilet       | 66.056 | tv             | 61.037 |
| laptop        | 62.005 | mouse        | 59.341 | remote         | 32.761 |
| keyboard      | 48.180 | cell phone   | 37.224 | microwave      | 53.526 |
| oven          | 34.507 | toaster      | 41.485 | sink           | 38.029 |
| refrigerator  | 60.187 | book         | 9.222  | clock          | 52.022 |
| vase          | 32.117 | scissors     | 22.200 | teddy bear     | 50.282 |
| hair drier    | 11.315 | toothbrush   | 18.459 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.561400511901944, 'fwIoU': 69.02621564081386, 'IoU-person': 87.66491068573592, 'IoU-bicycle': 69.556374795714, 'IoU-car': 69.08287654339019, 'IoU-motorcycle': 83.20122279443399, 'IoU-airplane': 81.90107510013624, 'IoU-bus': 83.94475309307128, 'IoU-train': 86.30464306830532, 'IoU-truck': 64.32466055987501, 'IoU-boat': 67.69070503864555, 'IoU-traffic light': 75.00040125995626, 'IoU-fire hydrant': 90.32932128032543, 'IoU-stop sign': 92.42984118119394, 'IoU-parking meter': 65.6290319493099, 'IoU-bench': 51.91393115574201, 'IoU-bird': 74.35787135292462, 'IoU-cat': 80.44527492833151, 'IoU-dog': 74.08744474094784, 'IoU-horse': 85.32216520370946, 'IoU-sheep': 90.2463226304166, 'IoU-cow': 83.55497904121259, 'IoU-elephant': 90.78806380866486, 'IoU-bear': 81.622517861043, 'IoU-zebra': 86.29407728016865, 'IoU-giraffe': 86.59637570723545, 'IoU-backpack': 40.458991054411456, 'IoU-umbrella': 78.26262756821177, 'IoU-handbag': 38.6569584647449, 'IoU-tie': 70.79100102001755, 'IoU-suitcase': 80.71405032373124, 'IoU-frisbee': 80.17509818226975, 'IoU-skis': 49.681239798846846, 'IoU-snowboard': 69.13947743015744, 'IoU-sports ball': 67.56541909924938, 'IoU-kite': 65.09765534676511, 'IoU-baseball bat': 58.92144418126857, 'IoU-baseball glove': 75.60280268213667, 'IoU-skateboard': 81.98350849622851, 'IoU-surfboard': 75.5141207619775, 'IoU-tennis racket': 83.37006323034608, 'IoU-bottle': 68.2617802275059, 'IoU-wine glass': 72.53538206548392, 'IoU-cup': 64.93628268780728, 'IoU-fork': 54.73057085871515, 'IoU-knife': 49.50626084496548, 'IoU-spoon': 50.67392719599128, 'IoU-bowl': 57.13057271615589, 'IoU-banana': 83.15624854019264, 'IoU-apple': 59.15270305085932, 'IoU-sandwich': 62.75804514218672, 'IoU-orange': 73.18588729791624, 'IoU-broccoli': 68.78352717382235, 'IoU-carrot': 62.086177846611925, 'IoU-hot dog': 59.62989421312288, 'IoU-pizza': 84.54210943364603, 'IoU-donut': 63.96801133619191, 'IoU-cake': 67.3054730964291, 'IoU-chair': 54.20077987250902, 'IoU-couch': 66.80304329832663, 'IoU-potted plant': 33.98236131331033, 'IoU-bed': 64.39401540524814, 'IoU-dining table': 50.88492578844378, 'IoU-toilet': 88.29480948552775, 'IoU-tv': 75.34696119180643, 'IoU-laptop': 75.29268190621563, 'IoU-mouse': 71.79515527096598, 'IoU-remote': 49.134228832512385, 'IoU-keyboard': 63.060835764366, 'IoU-cell phone': 69.98482765698787, 'IoU-microwave': 61.02107181154809, 'IoU-oven': 68.35006803487309, 'IoU-toaster': 58.76653748496592, 'IoU-sink': 70.0246657277567, 'IoU-refrigerator': 82.5018396847848, 'IoU-book': 51.790925219950665, 'IoU-clock': 73.56013453259881, 'IoU-vase': 63.80903597681722, 'IoU-scissors': 55.56976787137151, 'IoU-teddy bear': 81.96074740216433, 'IoU-hair drier': 39.152884438969416, 'IoU-toothbrush': 50.91131906110756, 'IoU-banner': 38.439725650975404, 'IoU-blanket': 18.192298088431, 'IoU-bridge': 37.12290886352449, 'IoU-cardboard': 41.43640807603163, 'IoU-counter': 31.42408785561475, 'IoU-curtain': 66.0616270097646, 'IoU-door-stuff': 41.265620784544275, 'IoU-floor-wood': 62.504470021416495, 'IoU-flower': 46.42358667864628, 'IoU-fruit': 39.51796917917137, 'IoU-gravel': 31.419923696657186, 'IoU-house': 26.470376758410865, 'IoU-light': 38.684583815588105, 'IoU-mirror-stuff': 55.9844915022562, 'IoU-net': 34.38204323633022, 'IoU-pillow': 12.168658535221976, 'IoU-platform': 28.981213482648215, 'IoU-playingfield': 66.38903121989571, 'IoU-railroad': 60.88645906523823, 'IoU-river': 44.658695836334495, 'IoU-road': 66.33183167896446, 'IoU-roof': 11.693805891970625, 'IoU-sand': 62.89125818838358, 'IoU-sea': 83.42438085799071, 'IoU-shelf': 36.585067169571026, 'IoU-snow': 88.45767873010797, 'IoU-stairs': 27.01754786991131, 'IoU-tent': 8.801368121301683, 'IoU-towel': 36.017289182260846, 'IoU-wall-brick': 44.86011688014175, 'IoU-wall-stone': 21.2496411892746, 'IoU-wall-tile': 68.10312273677351, 'IoU-wall-wood': 39.999965791687444, 'IoU-water-other': 20.49328403013561, 'IoU-window-blind': 47.26970354413224, 'IoU-window-other': 46.82885407285028, 'IoU-tree-merged': 80.99912365436886, 'IoU-fence-merged': 49.87347417209005, 'IoU-ceiling-merged': 66.52014005275066, 'IoU-sky-other-merged': 93.57697624500135, 'IoU-cabinet-merged': 60.35726024694631, 'IoU-table-merged': 39.10270352407348, 'IoU-floor-other-merged': 47.901508074666616, 'IoU-pavement-merged': 54.130148389783585, 'IoU-mountain-merged': 55.21209467915068, 'IoU-grass-merged': 71.01291939901193, 'IoU-dirt-merged': 44.25181400897687, 'IoU-paper-merged': 31.217638424573074, 'IoU-food-other-merged': 39.73953561178944, 'IoU-building-other-merged': 58.58427101843561, 'IoU-rock-merged': 59.044424039480894, 'IoU-wall-other-merged': 66.56561979140243, 'IoU-rug-merged': 62.977745996712564, 'mACC': 73.31384638895234, 'pACC': 80.25751891570403, 'ACC-person': 92.36209052339393, 'ACC-bicycle': 81.34956001072933, 'ACC-car': 85.8850830651913, 'ACC-motorcycle': 87.78959849526358, 'ACC-airplane': 88.00820749488523, 'ACC-bus': 90.22481968954979, 'ACC-train': 92.30066995682674, 'ACC-truck': 78.45845280271877, 'ACC-boat': 79.30166565210914, 'ACC-traffic light': 89.29615213270765, 'ACC-fire hydrant': 95.38584549953669, 'ACC-stop sign': 95.78155697968488, 'ACC-parking meter': 88.86260929969143, 'ACC-bench': 74.63597700435334, 'ACC-bird': 79.43613793162481, 'ACC-cat': 86.69230349870568, 'ACC-dog': 80.92956021004206, 'ACC-horse': 91.00196857709089, 'ACC-sheep': 93.83538118689594, 'ACC-cow': 87.4114598517967, 'ACC-elephant': 93.38636417989017, 'ACC-bear': 83.56736716406901, 'ACC-zebra': 88.53207994959665, 'ACC-giraffe': 90.68041351017297, 'ACC-backpack': 55.86048763814515, 'ACC-umbrella': 86.20183158854516, 'ACC-handbag': 56.53314651994281, 'ACC-tie': 81.43327776508993, 'ACC-suitcase': 90.02826549128926, 'ACC-frisbee': 93.90836363636363, 'ACC-skis': 71.66342415765749, 'ACC-snowboard': 78.2738605471333, 'ACC-sports ball': 80.39042391540299, 'ACC-kite': 74.40403826840742, 'ACC-baseball bat': 84.3379361499674, 'ACC-baseball glove': 90.3817470439463, 'ACC-skateboard': 89.80086718045163, 'ACC-surfboard': 83.8969939451919, 'ACC-tennis racket': 89.39702553343137, 'ACC-bottle': 83.80624706129812, 'ACC-wine glass': 86.49407988975722, 'ACC-cup': 83.13504576354492, 'ACC-fork': 68.85003115043263, 'ACC-knife': 64.75690627976844, 'ACC-spoon': 70.22486919279729, 'ACC-bowl': 73.78090434732053, 'ACC-banana': 89.25369548849706, 'ACC-apple': 71.3602460369333, 'ACC-sandwich': 72.95291906658062, 'ACC-orange': 83.34516155712952, 'ACC-broccoli': 81.33340015540077, 'ACC-carrot': 73.01909695185228, 'ACC-hot dog': 74.47636820798418, 'ACC-pizza': 94.12928106780322, 'ACC-donut': 81.57256021729417, 'ACC-cake': 73.190128662199, 'ACC-chair': 67.83985798952358, 'ACC-couch': 84.93106134529087, 'ACC-potted plant': 48.87202164487137, 'ACC-bed': 72.55332253039886, 'ACC-dining table': 75.3540067919263, 'ACC-toilet': 92.61971829776712, 'ACC-tv': 86.95460096954987, 'ACC-laptop': 89.79760117287745, 'ACC-mouse': 86.6779217124985, 'ACC-remote': 72.64540397519885, 'ACC-keyboard': 71.82625990860436, 'ACC-cell phone': 78.71711285778207, 'ACC-microwave': 65.77761184593855, 'ACC-oven': 85.94671251283256, 'ACC-toaster': 72.21940905171824, 'ACC-sink': 84.95914670253633, 'ACC-refrigerator': 91.22144523394351, 'ACC-book': 65.64571600683645, 'ACC-clock': 79.58110491182381, 'ACC-vase': 74.89018771867957, 'ACC-scissors': 60.43382548733346, 'ACC-teddy bear': 88.28618952962026, 'ACC-hair drier': 42.518429966128714, 'ACC-toothbrush': 82.27936066712995, 'ACC-banner': 75.74351041603789, 'ACC-blanket': 35.06214071405921, 'ACC-bridge': 55.78342289446685, 'ACC-cardboard': 51.46814017740293, 'ACC-counter': 52.606246485968576, 'ACC-curtain': 78.17077744431694, 'ACC-door-stuff': 68.2953794068761, 'ACC-floor-wood': 76.32078653488765, 'ACC-flower': 68.43834337114984, 'ACC-fruit': 59.266227004913674, 'ACC-gravel': 39.02652943208374, 'ACC-house': 31.912953628443745, 'ACC-light': 59.554918079853, 'ACC-mirror-stuff': 63.25060463433259, 'ACC-net': 64.75807721043869, 'ACC-pillow': 22.98775320557666, 'ACC-platform': 45.41838893974284, 'ACC-playingfield': 82.22601449121917, 'ACC-railroad': 79.10176912381357, 'ACC-river': 64.03174171056983, 'ACC-road': 84.70850892676724, 'ACC-roof': 15.541240368826575, 'ACC-sand': 70.15865795778916, 'ACC-sea': 89.2181042625623, 'ACC-shelf': 58.363419325603715, 'ACC-snow': 94.86406459397531, 'ACC-stairs': 46.0484982252099, 'ACC-tent': 11.12772938091514, 'ACC-towel': 44.15954030209648, 'ACC-wall-brick': 64.0032213713625, 'ACC-wall-stone': 25.747347946542643, 'ACC-wall-tile': 79.82793478039703, 'ACC-wall-wood': 53.08501855674377, 'ACC-water-other': 39.011381975690426, 'ACC-window-blind': 56.520582111424844, 'ACC-window-other': 67.43619634019069, 'ACC-tree-merged': 89.91647557777478, 'ACC-fence-merged': 67.1558954782201, 'ACC-ceiling-merged': 80.0020842391988, 'ACC-sky-other-merged': 96.51696765944202, 'ACC-cabinet-merged': 74.1179641501731, 'ACC-table-merged': 51.52738929156556, 'ACC-floor-other-merged': 59.67800732079077, 'ACC-pavement-merged': 68.95109256475462, 'ACC-mountain-merged': 68.79354235654296, 'ACC-grass-merged': 81.40810304609393, 'ACC-dirt-merged': 71.14862640159784, 'ACC-paper-merged': 42.22904017657198, 'ACC-food-other-merged': 56.46558253814966, 'ACC-building-other-merged': 76.63575163395559, 'ACC-rock-merged': 82.19431979540954, 'ACC-wall-other-merged': 80.41393849028138, 'ACC-rug-merged': 80.48563370299244})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1553 s/iter. Inference: 0.3759 s/iter. Eval: 0.0000 s/iter. Total: 0.5312 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1574 s/iter. Inference: 0.4406 s/iter. Eval: 0.0000 s/iter. Total: 0.5981 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1694 s/iter. Inference: 0.5513 s/iter. Eval: 0.0000 s/iter. Total: 0.7208 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1719 s/iter. Inference: 0.6734 s/iter. Eval: 0.0000 s/iter. Total: 0.8454 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1698 s/iter. Inference: 0.5971 s/iter. Eval: 0.0000 s/iter. Total: 0.7670 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1694 s/iter. Inference: 0.6337 s/iter. Eval: 0.0000 s/iter. Total: 0.8033 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1712 s/iter. Inference: 0.6739 s/iter. Eval: 0.0000 s/iter. Total: 0.8453 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4972197834357623, 'noc@0.8': 2.8627450980392157, 'noc@0.85': 3.518876207199298, 'noc@0.9': 4.515364354697103, 'miou@iter1': 0.834491723038001}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0019 s/iter. Inference: 0.1007 s/iter. Eval: 0.0008 s/iter. Total: 0.1035 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.16207122802734, 'precision@0.6': 67.70307159423828, 'precision@0.7': 62.30081558227539, 'precision@0.8': 51.923824310302734, 'precision@0.9': 26.23396873474121, 'cIoU': 58.01319885253906, 'mIoU': 62.69722366333008}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.64024773136997, 'SQ': 81.77571446463733, 'RQ': 59.78488337255663, 'PQ_th': 54.59596859059308, 'SQ_th': 82.58995367139586, 'RQ_th': 65.43847016426491, 'PQ_st': 42.15991435895773, 'SQ_st': 80.546674152549, 'RQ_st': 51.251167460544046}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.791860177637325, 'AP50': 60.970724752533954, 'AP75': 40.945404355114995, 'APs': 19.1095791056098, 'APm': 41.98158413312895, 'APl': 60.1228516399129, 'AP-person': 44.11839571126142, 'AP-bicycle': 18.573520677315997, 'AP-car': 36.499894884112535, 'AP-motorcycle': 34.81381680452562, 'AP-airplane': 55.06715416932235, 'AP-bus': 64.73341346947149, 'AP-train': 67.99248894900711, 'AP-truck': 33.89261316159378, 'AP-boat': 22.507574335563678, 'AP-traffic light': 24.487159921051127, 'AP-fire hydrant': 62.6955141020897, 'AP-stop sign': 63.312042458144404, 'AP-parking meter': 42.51911644867331, 'AP-bench': 20.591353542007813, 'AP-bird': 29.336668244216362, 'AP-cat': 72.88672655697991, 'AP-dog': 66.18953931356411, 'AP-horse': 45.37470500938354, 'AP-sheep': 46.70805650788585, 'AP-cow': 49.66265484909994, 'AP-elephant': 60.12707015639116, 'AP-bear': 77.77432813106309, 'AP-zebra': 60.29338528951826, 'AP-giraffe': 57.0014805356128, 'AP-backpack': 16.154424950886657, 'AP-umbrella': 47.86151480757545, 'AP-handbag': 14.707565553125923, 'AP-tie': 33.28587462457519, 'AP-suitcase': 41.705525109663924, 'AP-frisbee': 66.73347882377051, 'AP-skis': 5.516618718940693, 'AP-snowboard': 22.26227584142344, 'AP-sports ball': 47.03871353369891, 'AP-kite': 32.649386010460006, 'AP-baseball bat': 29.060603887218196, 'AP-baseball glove': 43.958532827939294, 'AP-skateboard': 35.729382576631885, 'AP-surfboard': 35.71425901814624, 'AP-tennis racket': 55.65516301433806, 'AP-bottle': 34.56192033808405, 'AP-wine glass': 26.521032258167626, 'AP-cup': 40.47426208756356, 'AP-fork': 15.3124753802463, 'AP-knife': 13.115031422047979, 'AP-spoon': 14.57492781329122, 'AP-bowl': 30.403894765791545, 'AP-banana': 20.488674294535404, 'AP-apple': 20.297811659184934, 'AP-sandwich': 42.392453074962184, 'AP-orange': 29.851537726899313, 'AP-broccoli': 21.72093989162896, 'AP-carrot': 20.103058581669828, 'AP-hot dog': 24.296128135002913, 'AP-pizza': 50.64409344633922, 'AP-donut': 46.23296323487703, 'AP-cake': 43.549468483638684, 'AP-chair': 21.120731069531114, 'AP-couch': 41.85961502116851, 'AP-potted plant': 17.791598790907788, 'AP-bed': 40.39805365624895, 'AP-dining table': 12.49013983432924, 'AP-toilet': 66.05592459209632, 'AP-tv': 61.03688761896768, 'AP-laptop': 62.00463364894283, 'AP-mouse': 59.34094715868529, 'AP-remote': 32.760977792380565, 'AP-keyboard': 48.18018445277678, 'AP-cell phone': 37.22441425685611, 'AP-microwave': 53.52637696812715, 'AP-oven': 34.50736757108324, 'AP-toaster': 41.485394570699526, 'AP-sink': 38.02872868204041, 'AP-refrigerator': 60.18729785918345, 'AP-book': 9.221545882615494, 'AP-clock': 52.02193337702483, 'AP-vase': 32.11725874741567, 'AP-scissors': 22.19993855751612, 'AP-teddy bear': 50.28151572084947, 'AP-hair drier': 11.315417256011315, 'AP-toothbrush': 18.459266005347857}), ('sem_seg', {'mIoU': 60.561400511901944, 'fwIoU': 69.02621564081386, 'IoU-person': 87.66491068573592, 'IoU-bicycle': 69.556374795714, 'IoU-car': 69.08287654339019, 'IoU-motorcycle': 83.20122279443399, 'IoU-airplane': 81.90107510013624, 'IoU-bus': 83.94475309307128, 'IoU-train': 86.30464306830532, 'IoU-truck': 64.32466055987501, 'IoU-boat': 67.69070503864555, 'IoU-traffic light': 75.00040125995626, 'IoU-fire hydrant': 90.32932128032543, 'IoU-stop sign': 92.42984118119394, 'IoU-parking meter': 65.6290319493099, 'IoU-bench': 51.91393115574201, 'IoU-bird': 74.35787135292462, 'IoU-cat': 80.44527492833151, 'IoU-dog': 74.08744474094784, 'IoU-horse': 85.32216520370946, 'IoU-sheep': 90.2463226304166, 'IoU-cow': 83.55497904121259, 'IoU-elephant': 90.78806380866486, 'IoU-bear': 81.622517861043, 'IoU-zebra': 86.29407728016865, 'IoU-giraffe': 86.59637570723545, 'IoU-backpack': 40.458991054411456, 'IoU-umbrella': 78.26262756821177, 'IoU-handbag': 38.6569584647449, 'IoU-tie': 70.79100102001755, 'IoU-suitcase': 80.71405032373124, 'IoU-frisbee': 80.17509818226975, 'IoU-skis': 49.681239798846846, 'IoU-snowboard': 69.13947743015744, 'IoU-sports ball': 67.56541909924938, 'IoU-kite': 65.09765534676511, 'IoU-baseball bat': 58.92144418126857, 'IoU-baseball glove': 75.60280268213667, 'IoU-skateboard': 81.98350849622851, 'IoU-surfboard': 75.5141207619775, 'IoU-tennis racket': 83.37006323034608, 'IoU-bottle': 68.2617802275059, 'IoU-wine glass': 72.53538206548392, 'IoU-cup': 64.93628268780728, 'IoU-fork': 54.73057085871515, 'IoU-knife': 49.50626084496548, 'IoU-spoon': 50.67392719599128, 'IoU-bowl': 57.13057271615589, 'IoU-banana': 83.15624854019264, 'IoU-apple': 59.15270305085932, 'IoU-sandwich': 62.75804514218672, 'IoU-orange': 73.18588729791624, 'IoU-broccoli': 68.78352717382235, 'IoU-carrot': 62.086177846611925, 'IoU-hot dog': 59.62989421312288, 'IoU-pizza': 84.54210943364603, 'IoU-donut': 63.96801133619191, 'IoU-cake': 67.3054730964291, 'IoU-chair': 54.20077987250902, 'IoU-couch': 66.80304329832663, 'IoU-potted plant': 33.98236131331033, 'IoU-bed': 64.39401540524814, 'IoU-dining table': 50.88492578844378, 'IoU-toilet': 88.29480948552775, 'IoU-tv': 75.34696119180643, 'IoU-laptop': 75.29268190621563, 'IoU-mouse': 71.79515527096598, 'IoU-remote': 49.134228832512385, 'IoU-keyboard': 63.060835764366, 'IoU-cell phone': 69.98482765698787, 'IoU-microwave': 61.02107181154809, 'IoU-oven': 68.35006803487309, 'IoU-toaster': 58.76653748496592, 'IoU-sink': 70.0246657277567, 'IoU-refrigerator': 82.5018396847848, 'IoU-book': 51.790925219950665, 'IoU-clock': 73.56013453259881, 'IoU-vase': 63.80903597681722, 'IoU-scissors': 55.56976787137151, 'IoU-teddy bear': 81.96074740216433, 'IoU-hair drier': 39.152884438969416, 'IoU-toothbrush': 50.91131906110756, 'IoU-banner': 38.439725650975404, 'IoU-blanket': 18.192298088431, 'IoU-bridge': 37.12290886352449, 'IoU-cardboard': 41.43640807603163, 'IoU-counter': 31.42408785561475, 'IoU-curtain': 66.0616270097646, 'IoU-door-stuff': 41.265620784544275, 'IoU-floor-wood': 62.504470021416495, 'IoU-flower': 46.42358667864628, 'IoU-fruit': 39.51796917917137, 'IoU-gravel': 31.419923696657186, 'IoU-house': 26.470376758410865, 'IoU-light': 38.684583815588105, 'IoU-mirror-stuff': 55.9844915022562, 'IoU-net': 34.38204323633022, 'IoU-pillow': 12.168658535221976, 'IoU-platform': 28.981213482648215, 'IoU-playingfield': 66.38903121989571, 'IoU-railroad': 60.88645906523823, 'IoU-river': 44.658695836334495, 'IoU-road': 66.33183167896446, 'IoU-roof': 11.693805891970625, 'IoU-sand': 62.89125818838358, 'IoU-sea': 83.42438085799071, 'IoU-shelf': 36.585067169571026, 'IoU-snow': 88.45767873010797, 'IoU-stairs': 27.01754786991131, 'IoU-tent': 8.801368121301683, 'IoU-towel': 36.017289182260846, 'IoU-wall-brick': 44.86011688014175, 'IoU-wall-stone': 21.2496411892746, 'IoU-wall-tile': 68.10312273677351, 'IoU-wall-wood': 39.999965791687444, 'IoU-water-other': 20.49328403013561, 'IoU-window-blind': 47.26970354413224, 'IoU-window-other': 46.82885407285028, 'IoU-tree-merged': 80.99912365436886, 'IoU-fence-merged': 49.87347417209005, 'IoU-ceiling-merged': 66.52014005275066, 'IoU-sky-other-merged': 93.57697624500135, 'IoU-cabinet-merged': 60.35726024694631, 'IoU-table-merged': 39.10270352407348, 'IoU-floor-other-merged': 47.901508074666616, 'IoU-pavement-merged': 54.130148389783585, 'IoU-mountain-merged': 55.21209467915068, 'IoU-grass-merged': 71.01291939901193, 'IoU-dirt-merged': 44.25181400897687, 'IoU-paper-merged': 31.217638424573074, 'IoU-food-other-merged': 39.73953561178944, 'IoU-building-other-merged': 58.58427101843561, 'IoU-rock-merged': 59.044424039480894, 'IoU-wall-other-merged': 66.56561979140243, 'IoU-rug-merged': 62.977745996712564, 'mACC': 73.31384638895234, 'pACC': 80.25751891570403, 'ACC-person': 92.36209052339393, 'ACC-bicycle': 81.34956001072933, 'ACC-car': 85.8850830651913, 'ACC-motorcycle': 87.78959849526358, 'ACC-airplane': 88.00820749488523, 'ACC-bus': 90.22481968954979, 'ACC-train': 92.30066995682674, 'ACC-truck': 78.45845280271877, 'ACC-boat': 79.30166565210914, 'ACC-traffic light': 89.29615213270765, 'ACC-fire hydrant': 95.38584549953669, 'ACC-stop sign': 95.78155697968488, 'ACC-parking meter': 88.86260929969143, 'ACC-bench': 74.63597700435334, 'ACC-bird': 79.43613793162481, 'ACC-cat': 86.69230349870568, 'ACC-dog': 80.92956021004206, 'ACC-horse': 91.00196857709089, 'ACC-sheep': 93.83538118689594, 'ACC-cow': 87.4114598517967, 'ACC-elephant': 93.38636417989017, 'ACC-bear': 83.56736716406901, 'ACC-zebra': 88.53207994959665, 'ACC-giraffe': 90.68041351017297, 'ACC-backpack': 55.86048763814515, 'ACC-umbrella': 86.20183158854516, 'ACC-handbag': 56.53314651994281, 'ACC-tie': 81.43327776508993, 'ACC-suitcase': 90.02826549128926, 'ACC-frisbee': 93.90836363636363, 'ACC-skis': 71.66342415765749, 'ACC-snowboard': 78.2738605471333, 'ACC-sports ball': 80.39042391540299, 'ACC-kite': 74.40403826840742, 'ACC-baseball bat': 84.3379361499674, 'ACC-baseball glove': 90.3817470439463, 'ACC-skateboard': 89.80086718045163, 'ACC-surfboard': 83.8969939451919, 'ACC-tennis racket': 89.39702553343137, 'ACC-bottle': 83.80624706129812, 'ACC-wine glass': 86.49407988975722, 'ACC-cup': 83.13504576354492, 'ACC-fork': 68.85003115043263, 'ACC-knife': 64.75690627976844, 'ACC-spoon': 70.22486919279729, 'ACC-bowl': 73.78090434732053, 'ACC-banana': 89.25369548849706, 'ACC-apple': 71.3602460369333, 'ACC-sandwich': 72.95291906658062, 'ACC-orange': 83.34516155712952, 'ACC-broccoli': 81.33340015540077, 'ACC-carrot': 73.01909695185228, 'ACC-hot dog': 74.47636820798418, 'ACC-pizza': 94.12928106780322, 'ACC-donut': 81.57256021729417, 'ACC-cake': 73.190128662199, 'ACC-chair': 67.83985798952358, 'ACC-couch': 84.93106134529087, 'ACC-potted plant': 48.87202164487137, 'ACC-bed': 72.55332253039886, 'ACC-dining table': 75.3540067919263, 'ACC-toilet': 92.61971829776712, 'ACC-tv': 86.95460096954987, 'ACC-laptop': 89.79760117287745, 'ACC-mouse': 86.6779217124985, 'ACC-remote': 72.64540397519885, 'ACC-keyboard': 71.82625990860436, 'ACC-cell phone': 78.71711285778207, 'ACC-microwave': 65.77761184593855, 'ACC-oven': 85.94671251283256, 'ACC-toaster': 72.21940905171824, 'ACC-sink': 84.95914670253633, 'ACC-refrigerator': 91.22144523394351, 'ACC-book': 65.64571600683645, 'ACC-clock': 79.58110491182381, 'ACC-vase': 74.89018771867957, 'ACC-scissors': 60.43382548733346, 'ACC-teddy bear': 88.28618952962026, 'ACC-hair drier': 42.518429966128714, 'ACC-toothbrush': 82.27936066712995, 'ACC-banner': 75.74351041603789, 'ACC-blanket': 35.06214071405921, 'ACC-bridge': 55.78342289446685, 'ACC-cardboard': 51.46814017740293, 'ACC-counter': 52.606246485968576, 'ACC-curtain': 78.17077744431694, 'ACC-door-stuff': 68.2953794068761, 'ACC-floor-wood': 76.32078653488765, 'ACC-flower': 68.43834337114984, 'ACC-fruit': 59.266227004913674, 'ACC-gravel': 39.02652943208374, 'ACC-house': 31.912953628443745, 'ACC-light': 59.554918079853, 'ACC-mirror-stuff': 63.25060463433259, 'ACC-net': 64.75807721043869, 'ACC-pillow': 22.98775320557666, 'ACC-platform': 45.41838893974284, 'ACC-playingfield': 82.22601449121917, 'ACC-railroad': 79.10176912381357, 'ACC-river': 64.03174171056983, 'ACC-road': 84.70850892676724, 'ACC-roof': 15.541240368826575, 'ACC-sand': 70.15865795778916, 'ACC-sea': 89.2181042625623, 'ACC-shelf': 58.363419325603715, 'ACC-snow': 94.86406459397531, 'ACC-stairs': 46.0484982252099, 'ACC-tent': 11.12772938091514, 'ACC-towel': 44.15954030209648, 'ACC-wall-brick': 64.0032213713625, 'ACC-wall-stone': 25.747347946542643, 'ACC-wall-tile': 79.82793478039703, 'ACC-wall-wood': 53.08501855674377, 'ACC-water-other': 39.011381975690426, 'ACC-window-blind': 56.520582111424844, 'ACC-window-other': 67.43619634019069, 'ACC-tree-merged': 89.91647557777478, 'ACC-fence-merged': 67.1558954782201, 'ACC-ceiling-merged': 80.0020842391988, 'ACC-sky-other-merged': 96.51696765944202, 'ACC-cabinet-merged': 74.1179641501731, 'ACC-table-merged': 51.52738929156556, 'ACC-floor-other-merged': 59.67800732079077, 'ACC-pavement-merged': 68.95109256475462, 'ACC-mountain-merged': 68.79354235654296, 'ACC-grass-merged': 81.40810304609393, 'ACC-dirt-merged': 71.14862640159784, 'ACC-paper-merged': 42.22904017657198, 'ACC-food-other-merged': 56.46558253814966, 'ACC-building-other-merged': 76.63575163395559, 'ACC-rock-merged': 82.19431979540954, 'ACC-wall-other-merged': 80.41393849028138, 'ACC-rug-merged': 80.48563370299244})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4972197834357623, 'noc@0.8': 2.8627450980392157, 'noc@0.85': 3.518876207199298, 'noc@0.9': 4.515364354697103, 'miou@iter1': 0.834491723038001}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.16207122802734, 'precision@0.6': 67.70307159423828, 'precision@0.7': 62.30081558227539, 'precision@0.8': 51.923824310302734, 'precision@0.9': 26.23396873474121, 'cIoU': 58.01319885253906, 'mIoU': 62.69722366333008}}}
INFO:trainer.default_trainer:This epoch takes 1:28:02.311755
INFO:trainer.default_trainer:PROGRESS: 48.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 24 training.
INFO:trainer.default_trainer:epochs[    24] optim steps[43900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.31496/0.90560, loss_mask_bce_0: 0.32556/0.33451, loss_mask_dice_0: 1.17925/1.16615, loss_spatial_bce_0: 0.04989/0.08849, loss_spatial_dice_0: 0.25449/0.21181, loss_spatial_ce_0: 0.29911/0.06677, loss_grounding_bce_0: 0.00496/0.08609, loss_grounding_dice_0: 0.20722/0.17880, loss_grounding_ce_0: 0.25447/0.27396, loss_mask_ce_1: 1.39972/0.90605, loss_mask_bce_1: 0.30563/0.33531, loss_mask_dice_1: 1.15325/1.17287, loss_spatial_bce_1: 0.05368/0.08912, loss_spatial_dice_1: 0.27403/0.21592, loss_spatial_ce_1: 0.01415/0.07238, loss_grounding_bce_1: 0.00656/0.08624, loss_grounding_dice_1: 0.22799/0.17961, loss_grounding_ce_1: 0.23672/0.27561, loss_mask_ce_2: 1.28259/0.91350, loss_mask_bce_2: 0.31629/0.33579, loss_mask_dice_2: 1.21022/1.17276, loss_spatial_bce_2: 0.04873/0.08979, loss_spatial_dice_2: 0.29567/0.21708, loss_spatial_ce_2: 0.37386/0.07595, loss_grounding_bce_2: 0.00755/0.08634, loss_grounding_dice_2: 0.23572/0.17938, loss_grounding_ce_2: 0.24091/0.27863, loss_mask_ce_3: 1.43194/0.92266, loss_mask_bce_3: 0.31005/0.33680, loss_mask_dice_3: 1.19021/1.16976, loss_spatial_bce_3: 0.05739/0.09070, loss_spatial_dice_3: 0.26896/0.21771, loss_spatial_ce_3: 0.00978/0.07975, loss_grounding_bce_3: 0.00639/0.08659, loss_grounding_dice_3: 0.25378/0.17913, loss_grounding_ce_3: 0.25949/0.28022, loss_mask_ce_4: 1.33780/0.92308, loss_mask_bce_4: 0.33233/0.33873, loss_mask_dice_4: 1.15297/1.19360, loss_spatial_bce_4: 0.05632/0.09484, loss_spatial_dice_4: 0.29467/0.22934, loss_spatial_ce_4: 0.02400/0.09569, loss_grounding_bce_4: 0.00530/0.08701, loss_grounding_dice_4: 0.21482/0.18200, loss_grounding_ce_4: 0.23853/0.28321, loss_mask_ce_5: 1.32595/0.93875, loss_mask_bce_5: 0.35708/0.34100, loss_mask_dice_5: 1.34959/1.20018, loss_spatial_bce_5: 0.05338/0.09660, loss_spatial_dice_5: 0.29695/0.23307, loss_spatial_ce_5: 0.02543/0.11078, loss_grounding_bce_5: 0.00671/0.08740, loss_grounding_dice_5: 0.24035/0.18316, loss_grounding_ce_5: 0.22660/0.29567, loss_mask_ce_6: 1.65189/0.97797, loss_mask_bce_6: 0.35616/0.34369, loss_mask_dice_6: 1.22596/1.20296, loss_spatial_bce_6: 0.05625/0.10234, loss_spatial_dice_6: 0.32968/0.23550, loss_spatial_ce_6: 0.20935/0.13670, loss_grounding_bce_6: 0.00683/0.08813, loss_grounding_dice_6: 0.22448/0.18337, loss_grounding_ce_6: 0.18553/0.31215, loss_mask_ce_7: 1.59606/1.02299, loss_mask_bce_7: 0.32595/0.35156, loss_mask_dice_7: 1.00169/1.25824, loss_spatial_bce_7: 0.05877/0.11078, loss_spatial_dice_7: 0.33160/0.26328, loss_spatial_ce_7: 0.09005/0.17327, loss_grounding_bce_7: 0.00562/0.09005, loss_grounding_dice_7: 0.20335/0.19063, loss_grounding_ce_7: 0.17502/0.34498, loss_mask_ce_8: 1.56399/1.13195, loss_mask_bce_8: 0.32954/0.36519, loss_mask_dice_8: 1.14931/1.33180, loss_spatial_bce_8: 0.07792/0.13161, loss_spatial_dice_8: 0.45205/0.30245, loss_spatial_ce_8: 0.28039/0.23044, loss_grounding_bce_8: 0.00717/0.09379, loss_grounding_dice_8: 0.18205/0.20172, loss_grounding_ce_8: 0.23517/0.41309, loss_mask_ce_9: 3.39226/3.68145, loss_mask_bce_9: 0.37500/0.39208, loss_mask_dice_9: 1.57000/1.90517, loss_spatial_bce_9: 0.14903/0.33364, loss_spatial_dice_9: 0.82809/0.82284, loss_spatial_ce_9: 1.87903/1.50369, loss_grounding_bce_9: 0.00601/0.10530, loss_grounding_dice_9: 0.27102/0.28109, loss_grounding_ce_9: 0.27713/0.67783] items per batch[64] items per second[0.13] total items[2809600] mini batches[ 43900] memory[7345] epoch remaining[1:25:16]
INFO:trainer.default_trainer:epochs[    24] optim steps[44000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42243/0.90549, loss_mask_bce_0: 0.42926/0.33445, loss_mask_dice_0: 2.07057/1.16602, loss_spatial_bce_0: 0.05923/0.08849, loss_spatial_dice_0: 0.24134/0.21177, loss_spatial_ce_0: 0.07991/0.06674, loss_grounding_bce_0: 0.05318/0.08609, loss_grounding_dice_0: 0.08876/0.17878, loss_grounding_ce_0: 0.05630/0.27392, loss_mask_ce_1: 1.43897/0.90595, loss_mask_bce_1: 0.43285/0.33526, loss_mask_dice_1: 1.96324/1.17269, loss_spatial_bce_1: 0.05135/0.08911, loss_spatial_dice_1: 0.24381/0.21588, loss_spatial_ce_1: 0.08617/0.07236, loss_grounding_bce_1: 0.05371/0.08624, loss_grounding_dice_1: 0.09237/0.17959, loss_grounding_ce_1: 0.06460/0.27555, loss_mask_ce_2: 1.46512/0.91338, loss_mask_bce_2: 0.42630/0.33574, loss_mask_dice_2: 2.04986/1.17255, loss_spatial_bce_2: 0.04629/0.08978, loss_spatial_dice_2: 0.22357/0.21705, loss_spatial_ce_2: 0.09203/0.07593, loss_grounding_bce_2: 0.05539/0.08634, loss_grounding_dice_2: 0.09573/0.17936, loss_grounding_ce_2: 0.05072/0.27852, loss_mask_ce_3: 1.63560/0.92258, loss_mask_bce_3: 0.43583/0.33675, loss_mask_dice_3: 2.02285/1.16962, loss_spatial_bce_3: 0.05025/0.09069, loss_spatial_dice_3: 0.21215/0.21769, loss_spatial_ce_3: 0.09124/0.07972, loss_grounding_bce_3: 0.05481/0.08660, loss_grounding_dice_3: 0.09446/0.17911, loss_grounding_ce_3: 0.06860/0.28016, loss_mask_ce_4: 1.55326/0.92299, loss_mask_bce_4: 0.44057/0.33867, loss_mask_dice_4: 2.00317/1.19344, loss_spatial_bce_4: 0.05069/0.09483, loss_spatial_dice_4: 0.27980/0.22932, loss_spatial_ce_4: 0.10002/0.09563, loss_grounding_bce_4: 0.05113/0.08701, loss_grounding_dice_4: 0.08624/0.18197, loss_grounding_ce_4: 0.07436/0.28312, loss_mask_ce_5: 1.62886/0.93864, loss_mask_bce_5: 0.46365/0.34094, loss_mask_dice_5: 2.28874/1.20002, loss_spatial_bce_5: 0.05107/0.09660, loss_spatial_dice_5: 0.26230/0.23305, loss_spatial_ce_5: 0.22101/0.11074, loss_grounding_bce_5: 0.05343/0.08740, loss_grounding_dice_5: 0.09293/0.18314, loss_grounding_ce_5: 0.02286/0.29554, loss_mask_ce_6: 1.75373/0.97789, loss_mask_bce_6: 0.45960/0.34364, loss_mask_dice_6: 2.01237/1.20276, loss_spatial_bce_6: 0.04904/0.10234, loss_spatial_dice_6: 0.24957/0.23548, loss_spatial_ce_6: 0.12082/0.13665, loss_grounding_bce_6: 0.05349/0.08814, loss_grounding_dice_6: 0.09458/0.18335, loss_grounding_ce_6: 0.13930/0.31203, loss_mask_ce_7: 1.58445/1.02290, loss_mask_bce_7: 0.46998/0.35149, loss_mask_dice_7: 2.22484/1.25800, loss_spatial_bce_7: 0.05198/0.11078, loss_spatial_dice_7: 0.26221/0.26325, loss_spatial_ce_7: 0.25685/0.17322, loss_grounding_bce_7: 0.05641/0.09006, loss_grounding_dice_7: 0.09343/0.19060, loss_grounding_ce_7: 0.27883/0.34485, loss_mask_ce_8: 1.54379/1.13191, loss_mask_bce_8: 0.51425/0.36512, loss_mask_dice_8: 2.38016/1.33156, loss_spatial_bce_8: 0.07819/0.13159, loss_spatial_dice_8: 0.37221/0.30241, loss_spatial_ce_8: 0.30780/0.23036, loss_grounding_bce_8: 0.05452/0.09379, loss_grounding_dice_8: 0.09401/0.20168, loss_grounding_ce_8: 0.23773/0.41300, loss_mask_ce_9: 4.22320/3.68124, loss_mask_bce_9: 0.56581/0.39202, loss_mask_dice_9: 3.98071/1.90488, loss_spatial_bce_9: 0.37037/0.33368, loss_spatial_dice_9: 0.94031/0.82284, loss_spatial_ce_9: 1.96469/1.50365, loss_grounding_bce_9: 0.08915/0.10529, loss_grounding_dice_9: 0.25970/0.28105, loss_grounding_ce_9: 0.62941/0.67768] items per batch[64] items per second[0.23] total items[2816000] mini batches[ 44000] memory[7345] epoch remaining[1:19:26]
INFO:trainer.default_trainer:epochs[    24] optim steps[44100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65616/0.90539, loss_mask_bce_0: 0.22197/0.33449, loss_mask_dice_0: 0.57070/1.16614, loss_spatial_bce_0: 0.07558/0.08847, loss_spatial_dice_0: 0.16084/0.21176, loss_spatial_ce_0: 0.03456/0.06671, loss_grounding_bce_0: 0.08201/0.08611, loss_grounding_dice_0: 0.16236/0.17878, loss_grounding_ce_0: 0.20194/0.27401, loss_mask_ce_1: 0.70941/0.90590, loss_mask_bce_1: 0.21480/0.33530, loss_mask_dice_1: 0.55142/1.17279, loss_spatial_bce_1: 0.07348/0.08910, loss_spatial_dice_1: 0.16807/0.21587, loss_spatial_ce_1: 0.02958/0.07233, loss_grounding_bce_1: 0.08256/0.08626, loss_grounding_dice_1: 0.16303/0.17959, loss_grounding_ce_1: 0.21011/0.27565, loss_mask_ce_2: 0.74351/0.91333, loss_mask_bce_2: 0.21985/0.33578, loss_mask_dice_2: 0.55983/1.17270, loss_spatial_bce_2: 0.07763/0.08977, loss_spatial_dice_2: 0.18135/0.21704, loss_spatial_ce_2: 0.04803/0.07589, loss_grounding_bce_2: 0.07958/0.08636, loss_grounding_dice_2: 0.15921/0.17936, loss_grounding_ce_2: 0.20651/0.27866, loss_mask_ce_3: 0.74265/0.92257, loss_mask_bce_3: 0.22244/0.33679, loss_mask_dice_3: 0.58771/1.16973, loss_spatial_bce_3: 0.07076/0.09069, loss_spatial_dice_3: 0.17282/0.21768, loss_spatial_ce_3: 0.04166/0.07968, loss_grounding_bce_3: 0.08362/0.08662, loss_grounding_dice_3: 0.16237/0.17910, loss_grounding_ce_3: 0.20331/0.28027, loss_mask_ce_4: 0.81950/0.92294, loss_mask_bce_4: 0.23228/0.33873, loss_mask_dice_4: 0.58911/1.19358, loss_spatial_bce_4: 0.07530/0.09483, loss_spatial_dice_4: 0.16999/0.22931, loss_spatial_ce_4: 0.06206/0.09561, loss_grounding_bce_4: 0.11124/0.08703, loss_grounding_dice_4: 0.19224/0.18197, loss_grounding_ce_4: 0.21711/0.28325, loss_mask_ce_5: 0.74038/0.93861, loss_mask_bce_5: 0.24795/0.34098, loss_mask_dice_5: 0.62226/1.20021, loss_spatial_bce_5: 0.07551/0.09660, loss_spatial_dice_5: 0.16132/0.23305, loss_spatial_ce_5: 0.10905/0.11071, loss_grounding_bce_5: 0.08435/0.08742, loss_grounding_dice_5: 0.18603/0.18315, loss_grounding_ce_5: 0.22581/0.29572, loss_mask_ce_6: 0.87084/0.97786, loss_mask_bce_6: 0.27225/0.34369, loss_mask_dice_6: 0.62271/1.20295, loss_spatial_bce_6: 0.07901/0.10234, loss_spatial_dice_6: 0.18194/0.23548, loss_spatial_ce_6: 0.19551/0.13662, loss_grounding_bce_6: 0.10023/0.08816, loss_grounding_dice_6: 0.18375/0.18337, loss_grounding_ce_6: 0.20303/0.31215, loss_mask_ce_7: 0.94169/1.02293, loss_mask_bce_7: 0.25545/0.35153, loss_mask_dice_7: 0.63655/1.25817, loss_spatial_bce_7: 0.11795/0.11078, loss_spatial_dice_7: 0.22587/0.26327, loss_spatial_ce_7: 0.18357/0.17322, loss_grounding_bce_7: 0.07839/0.09008, loss_grounding_dice_7: 0.20150/0.19060, loss_grounding_ce_7: 0.21078/0.34495, loss_mask_ce_8: 0.98018/1.13188, loss_mask_bce_8: 0.26058/0.36516, loss_mask_dice_8: 0.66372/1.33172, loss_spatial_bce_8: 0.11584/0.13159, loss_spatial_dice_8: 0.22874/0.30242, loss_spatial_ce_8: 0.43115/0.23032, loss_grounding_bce_8: 0.08486/0.09381, loss_grounding_dice_8: 0.20823/0.20170, loss_grounding_ce_8: 0.19766/0.41309, loss_mask_ce_9: 3.48515/3.68155, loss_mask_bce_9: 0.26132/0.39208, loss_mask_dice_9: 0.91560/1.90503, loss_spatial_bce_9: 0.21834/0.33368, loss_spatial_dice_9: 0.75851/0.82283, loss_spatial_ce_9: 1.44362/1.50357, loss_grounding_bce_9: 0.05929/0.10532, loss_grounding_dice_9: 0.28320/0.28106, loss_grounding_ce_9: 0.26032/0.67775] items per batch[64] items per second[0.23] total items[2822400] mini batches[ 44100] memory[7345] epoch remaining[1:13:43]
INFO:trainer.default_trainer:epochs[    24] optim steps[44200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75038/0.90531, loss_mask_bce_0: 0.09959/0.33451, loss_mask_dice_0: 1.61094/1.16610, loss_spatial_bce_0: 0.01536/0.08847, loss_spatial_dice_0: 0.27337/0.21173, loss_spatial_ce_0: 0.02648/0.06667, loss_grounding_bce_0: 0.01033/0.08611, loss_grounding_dice_0: 0.31265/0.17879, loss_grounding_ce_0: 0.36047/0.27389, loss_mask_ce_1: 0.71862/0.90584, loss_mask_bce_1: 0.09846/0.33532, loss_mask_dice_1: 2.09083/1.17275, loss_spatial_bce_1: 0.01430/0.08910, loss_spatial_dice_1: 0.25038/0.21585, loss_spatial_ce_1: 0.06146/0.07228, loss_grounding_bce_1: 0.01025/0.08627, loss_grounding_dice_1: 0.34330/0.17959, loss_grounding_ce_1: 0.29941/0.27553, loss_mask_ce_2: 0.81292/0.91325, loss_mask_bce_2: 0.10443/0.33580, loss_mask_dice_2: 1.65273/1.17266, loss_spatial_bce_2: 0.01361/0.08977, loss_spatial_dice_2: 0.28012/0.21702, loss_spatial_ce_2: 0.05486/0.07584, loss_grounding_bce_2: 0.01023/0.08637, loss_grounding_dice_2: 0.28271/0.17936, loss_grounding_ce_2: 0.30475/0.27855, loss_mask_ce_3: 0.87726/0.92251, loss_mask_bce_3: 0.10806/0.33681, loss_mask_dice_3: 1.67806/1.16969, loss_spatial_bce_3: 0.01311/0.09069, loss_spatial_dice_3: 0.24551/0.21766, loss_spatial_ce_3: 0.05018/0.07963, loss_grounding_bce_3: 0.01162/0.08662, loss_grounding_dice_3: 0.34780/0.17910, loss_grounding_ce_3: 0.37538/0.28015, loss_mask_ce_4: 0.82020/0.92281, loss_mask_bce_4: 0.11698/0.33875, loss_mask_dice_4: 2.15062/1.19358, loss_spatial_bce_4: 0.01624/0.09484, loss_spatial_dice_4: 0.33166/0.22929, loss_spatial_ce_4: 0.04250/0.09556, loss_grounding_bce_4: 0.01054/0.08704, loss_grounding_dice_4: 0.31655/0.18197, loss_grounding_ce_4: 0.32816/0.28314, loss_mask_ce_5: 0.93258/0.93852, loss_mask_bce_5: 0.11501/0.34101, loss_mask_dice_5: 1.72232/1.20018, loss_spatial_bce_5: 0.01651/0.09661, loss_spatial_dice_5: 0.31412/0.23303, loss_spatial_ce_5: 0.15795/0.11066, loss_grounding_bce_5: 0.01166/0.08743, loss_grounding_dice_5: 0.35936/0.18317, loss_grounding_ce_5: 0.34318/0.29551, loss_mask_ce_6: 0.79080/0.97779, loss_mask_bce_6: 0.09975/0.34372, loss_mask_dice_6: 1.84460/1.20293, loss_spatial_bce_6: 0.02164/0.10235, loss_spatial_dice_6: 0.29595/0.23547, loss_spatial_ce_6: 0.14028/0.13657, loss_grounding_bce_6: 0.01100/0.08817, loss_grounding_dice_6: 0.37014/0.18338, loss_grounding_ce_6: 0.33493/0.31198, loss_mask_ce_7: 0.80919/1.02283, loss_mask_bce_7: 0.09969/0.35155, loss_mask_dice_7: 2.20684/1.25814, loss_spatial_bce_7: 0.02105/0.11078, loss_spatial_dice_7: 0.36312/0.26326, loss_spatial_ce_7: 0.29530/0.17316, loss_grounding_bce_7: 0.00989/0.09008, loss_grounding_dice_7: 0.38784/0.19062, loss_grounding_ce_7: 0.37320/0.34478, loss_mask_ce_8: 1.09849/1.13180, loss_mask_bce_8: 0.10210/0.36518, loss_mask_dice_8: 1.80859/1.33166, loss_spatial_bce_8: 0.03223/0.13160, loss_spatial_dice_8: 0.48292/0.30240, loss_spatial_ce_8: 0.32521/0.23025, loss_grounding_bce_8: 0.01171/0.09380, loss_grounding_dice_8: 0.35036/0.20170, loss_grounding_ce_8: 0.42691/0.41293, loss_mask_ce_9: 3.11161/3.68140, loss_mask_bce_9: 0.09138/0.39213, loss_mask_dice_9: 2.34137/1.90491, loss_spatial_bce_9: 0.11490/0.33366, loss_spatial_dice_9: 0.76519/0.82284, loss_spatial_ce_9: 1.80202/1.50344, loss_grounding_bce_9: 0.00773/0.10532, loss_grounding_dice_9: 0.42504/0.28106, loss_grounding_ce_9: 0.55838/0.67755] items per batch[64] items per second[0.24] total items[2828800] mini batches[ 44200] memory[7345] epoch remaining[1:08:16]
INFO:trainer.default_trainer:epochs[    24] optim steps[44300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.21998/0.90531, loss_mask_bce_0: 0.14367/0.33448, loss_mask_dice_0: 0.30223/1.16598, loss_spatial_bce_0: 0.06007/0.08847, loss_spatial_dice_0: 0.13471/0.21171, loss_spatial_ce_0: 0.00190/0.06665, loss_grounding_bce_0: 0.09299/0.08613, loss_grounding_dice_0: 0.11323/0.17877, loss_grounding_ce_0: 0.01762/0.27386, loss_mask_ce_1: 0.21331/0.90582, loss_mask_bce_1: 0.14537/0.33530, loss_mask_dice_1: 0.34803/1.17258, loss_spatial_bce_1: 0.05466/0.08909, loss_spatial_dice_1: 0.12422/0.21581, loss_spatial_ce_1: 0.00307/0.07225, loss_grounding_bce_1: 0.09398/0.08629, loss_grounding_dice_1: 0.11413/0.17957, loss_grounding_ce_1: 0.01683/0.27547, loss_mask_ce_2: 0.51616/0.91323, loss_mask_bce_2: 0.14837/0.33578, loss_mask_dice_2: 0.37314/1.17247, loss_spatial_bce_2: 0.05628/0.08977, loss_spatial_dice_2: 0.13082/0.21699, loss_spatial_ce_2: 0.01012/0.07581, loss_grounding_bce_2: 0.09738/0.08639, loss_grounding_dice_2: 0.12060/0.17934, loss_grounding_ce_2: 0.01867/0.27849, loss_mask_ce_3: 0.59646/0.92250, loss_mask_bce_3: 0.14395/0.33680, loss_mask_dice_3: 0.33417/1.16956, loss_spatial_bce_3: 0.05551/0.09069, loss_spatial_dice_3: 0.12482/0.21764, loss_spatial_ce_3: 0.02912/0.07960, loss_grounding_bce_3: 0.09549/0.08664, loss_grounding_dice_3: 0.11160/0.17909, loss_grounding_ce_3: 0.02365/0.28010, loss_mask_ce_4: 0.16803/0.92281, loss_mask_bce_4: 0.14836/0.33873, loss_mask_dice_4: 0.32517/1.19340, loss_spatial_bce_4: 0.06100/0.09484, loss_spatial_dice_4: 0.13697/0.22927, loss_spatial_ce_4: 0.02904/0.09554, loss_grounding_bce_4: 0.09886/0.08705, loss_grounding_dice_4: 0.11507/0.18193, loss_grounding_ce_4: 0.01182/0.28308, loss_mask_ce_5: 0.25349/0.93849, loss_mask_bce_5: 0.14090/0.34098, loss_mask_dice_5: 0.31059/1.19999, loss_spatial_bce_5: 0.07981/0.09661, loss_spatial_dice_5: 0.15531/0.23301, loss_spatial_ce_5: 0.04974/0.11062, loss_grounding_bce_5: 0.09757/0.08744, loss_grounding_dice_5: 0.11362/0.18314, loss_grounding_ce_5: 0.01166/0.29545, loss_mask_ce_6: 0.32716/0.97781, loss_mask_bce_6: 0.14704/0.34369, loss_mask_dice_6: 0.31587/1.20277, loss_spatial_bce_6: 0.07873/0.10236, loss_spatial_dice_6: 0.15434/0.23546, loss_spatial_ce_6: 0.04785/0.13657, loss_grounding_bce_6: 0.10705/0.08819, loss_grounding_dice_6: 0.12388/0.18336, loss_grounding_ce_6: 0.00893/0.31185, loss_mask_ce_7: 0.41687/1.02284, loss_mask_bce_7: 0.15311/0.35154, loss_mask_dice_7: 0.31926/1.25797, loss_spatial_bce_7: 0.07909/0.11079, loss_spatial_dice_7: 0.23070/0.26325, loss_spatial_ce_7: 0.04081/0.17314, loss_grounding_bce_7: 0.12214/0.09010, loss_grounding_dice_7: 0.11990/0.19059, loss_grounding_ce_7: 0.04426/0.34466, loss_mask_ce_8: 0.63789/1.13186, loss_mask_bce_8: 0.16029/0.36513, loss_mask_dice_8: 0.40919/1.33144, loss_spatial_bce_8: 0.10645/0.13162, loss_spatial_dice_8: 0.27959/0.30239, loss_spatial_ce_8: 0.16777/0.23020, loss_grounding_bce_8: 0.10548/0.09382, loss_grounding_dice_8: 0.15070/0.20166, loss_grounding_ce_8: 0.00886/0.41283, loss_mask_ce_9: 2.40596/3.68130, loss_mask_bce_9: 0.16773/0.39209, loss_mask_dice_9: 0.58074/1.90477, loss_spatial_bce_9: 0.29071/0.33367, loss_spatial_dice_9: 0.67013/0.82279, loss_spatial_ce_9: 1.08281/1.50335, loss_grounding_bce_9: 0.11057/0.10533, loss_grounding_dice_9: 0.18348/0.28102, loss_grounding_ce_9: 0.35591/0.67759] items per batch[64] items per second[0.23] total items[2835200] mini batches[ 44300] memory[7345] epoch remaining[1:03:27]
INFO:trainer.default_trainer:epochs[    24] optim steps[44400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11612/0.90530, loss_mask_bce_0: 0.09248/0.33444, loss_mask_dice_0: 0.14177/1.16601, loss_spatial_bce_0: 0.08228/0.08845, loss_spatial_dice_0: 0.09310/0.21167, loss_spatial_ce_0: 0.00602/0.06660, loss_grounding_bce_0: 0.05899/0.08612, loss_grounding_dice_0: 0.09386/0.17879, loss_grounding_ce_0: 0.04622/0.27387, loss_mask_ce_1: 0.11002/0.90578, loss_mask_bce_1: 0.09635/0.33527, loss_mask_dice_1: 0.13802/1.17262, loss_spatial_bce_1: 0.09029/0.08907, loss_spatial_dice_1: 0.10369/0.21578, loss_spatial_ce_1: 0.01276/0.07221, loss_grounding_bce_1: 0.05581/0.08627, loss_grounding_dice_1: 0.08685/0.17958, loss_grounding_ce_1: 0.03553/0.27548, loss_mask_ce_2: 0.09277/0.91317, loss_mask_bce_2: 0.09244/0.33575, loss_mask_dice_2: 0.13290/1.17252, loss_spatial_bce_2: 0.09411/0.08975, loss_spatial_dice_2: 0.11547/0.21696, loss_spatial_ce_2: 0.01196/0.07576, loss_grounding_bce_2: 0.05467/0.08637, loss_grounding_dice_2: 0.08140/0.17936, loss_grounding_ce_2: 0.03578/0.27848, loss_mask_ce_3: 0.10232/0.92250, loss_mask_bce_3: 0.09348/0.33677, loss_mask_dice_3: 0.14839/1.16962, loss_spatial_bce_3: 0.09459/0.09067, loss_spatial_dice_3: 0.11404/0.21761, loss_spatial_ce_3: 0.00677/0.07957, loss_grounding_bce_3: 0.05503/0.08662, loss_grounding_dice_3: 0.09677/0.17910, loss_grounding_ce_3: 0.03583/0.28010, loss_mask_ce_4: 0.09413/0.92276, loss_mask_bce_4: 0.09720/0.33870, loss_mask_dice_4: 0.13185/1.19343, loss_spatial_bce_4: 0.08094/0.09482, loss_spatial_dice_4: 0.11323/0.22924, loss_spatial_ce_4: 0.01539/0.09549, loss_grounding_bce_4: 0.05803/0.08703, loss_grounding_dice_4: 0.08312/0.18195, loss_grounding_ce_4: 0.03014/0.28306, loss_mask_ce_5: 0.11300/0.93846, loss_mask_bce_5: 0.09928/0.34095, loss_mask_dice_5: 0.13918/1.20005, loss_spatial_bce_5: 0.10180/0.09659, loss_spatial_dice_5: 0.13406/0.23298, loss_spatial_ce_5: 0.06737/0.11057, loss_grounding_bce_5: 0.05603/0.08743, loss_grounding_dice_5: 0.08810/0.18316, loss_grounding_ce_5: 0.04213/0.29542, loss_mask_ce_6: 0.11497/0.97778, loss_mask_bce_6: 0.10451/0.34365, loss_mask_dice_6: 0.13751/1.20283, loss_spatial_bce_6: 0.08316/0.10234, loss_spatial_dice_6: 0.10421/0.23544, loss_spatial_ce_6: 0.15644/0.13652, loss_grounding_bce_6: 0.05718/0.08817, loss_grounding_dice_6: 0.08967/0.18337, loss_grounding_ce_6: 0.04997/0.31182, loss_mask_ce_7: 0.16660/1.02287, loss_mask_bce_7: 0.15068/0.35150, loss_mask_dice_7: 0.18414/1.25801, loss_spatial_bce_7: 0.13942/0.11077, loss_spatial_dice_7: 0.15705/0.26324, loss_spatial_ce_7: 0.11689/0.17308, loss_grounding_bce_7: 0.05747/0.09008, loss_grounding_dice_7: 0.10347/0.19060, loss_grounding_ce_7: 0.08485/0.34464, loss_mask_ce_8: 0.52557/1.13184, loss_mask_bce_8: 0.11468/0.36509, loss_mask_dice_8: 0.16118/1.33149, loss_spatial_bce_8: 0.10006/0.13160, loss_spatial_dice_8: 0.12847/0.30237, loss_spatial_ce_8: 0.16453/0.23015, loss_grounding_bce_8: 0.06412/0.09380, loss_grounding_dice_8: 0.10323/0.20168, loss_grounding_ce_8: 0.09836/0.41283, loss_mask_ce_9: 2.95099/3.68120, loss_mask_bce_9: 0.19660/0.39206, loss_mask_dice_9: 0.25885/1.90471, loss_spatial_bce_9: 0.71045/0.33368, loss_spatial_dice_9: 0.70840/0.82279, loss_spatial_ce_9: 1.09053/1.50341, loss_grounding_bce_9: 0.06019/0.10533, loss_grounding_dice_9: 0.15022/0.28104, loss_grounding_ce_9: 0.21528/0.67754] items per batch[64] items per second[0.24] total items[2841600] mini batches[ 44400] memory[7345] epoch remaining[0:58:34]
INFO:trainer.default_trainer:epochs[    24] optim steps[44500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12289/0.90536, loss_mask_bce_0: 0.08462/0.33450, loss_mask_dice_0: 0.44678/1.16599, loss_spatial_bce_0: 0.02471/0.08845, loss_spatial_dice_0: 0.12592/0.21162, loss_spatial_ce_0: 0.02736/0.06655, loss_grounding_bce_0: 0.01962/0.08614, loss_grounding_dice_0: 0.07057/0.17875, loss_grounding_ce_0: 0.14355/0.27386, loss_mask_ce_1: 1.03676/0.90580, loss_mask_bce_1: 0.08664/0.33533, loss_mask_dice_1: 0.43610/1.17261, loss_spatial_bce_1: 0.02564/0.08907, loss_spatial_dice_1: 0.12401/0.21573, loss_spatial_ce_1: 0.03749/0.07217, loss_grounding_bce_1: 0.08748/0.08630, loss_grounding_dice_1: 0.12653/0.17955, loss_grounding_ce_1: 0.08504/0.27547, loss_mask_ce_2: 1.09653/0.91321, loss_mask_bce_2: 0.08736/0.33581, loss_mask_dice_2: 0.38391/1.17250, loss_spatial_bce_2: 0.02525/0.08976, loss_spatial_dice_2: 0.11864/0.21691, loss_spatial_ce_2: 0.04052/0.07572, loss_grounding_bce_2: 0.09087/0.08640, loss_grounding_dice_2: 0.11870/0.17933, loss_grounding_ce_2: 0.09138/0.27845, loss_mask_ce_3: 1.11288/0.92257, loss_mask_bce_3: 0.08163/0.33684, loss_mask_dice_3: 0.39441/1.16963, loss_spatial_bce_3: 0.02358/0.09067, loss_spatial_dice_3: 0.12837/0.21756, loss_spatial_ce_3: 0.05586/0.07951, loss_grounding_bce_3: 0.08538/0.08665, loss_grounding_dice_3: 0.11698/0.17907, loss_grounding_ce_3: 0.12527/0.28009, loss_mask_ce_4: 1.12842/0.92281, loss_mask_bce_4: 0.08363/0.33877, loss_mask_dice_4: 0.43152/1.19347, loss_spatial_bce_4: 0.02814/0.09482, loss_spatial_dice_4: 0.13752/0.22920, loss_spatial_ce_4: 0.03058/0.09546, loss_grounding_bce_4: 0.10056/0.08706, loss_grounding_dice_4: 0.14346/0.18192, loss_grounding_ce_4: 0.06068/0.28304, loss_mask_ce_5: 1.17287/0.93858, loss_mask_bce_5: 0.08136/0.34101, loss_mask_dice_5: 0.43670/1.20006, loss_spatial_bce_5: 0.03145/0.09660, loss_spatial_dice_5: 0.13793/0.23295, loss_spatial_ce_5: 0.03876/0.11056, loss_grounding_bce_5: 0.02913/0.08745, loss_grounding_dice_5: 0.10047/0.18313, loss_grounding_ce_5: 0.12182/0.29543, loss_mask_ce_6: 1.24407/0.97785, loss_mask_bce_6: 0.08258/0.34371, loss_mask_dice_6: 0.41152/1.20286, loss_spatial_bce_6: 0.03988/0.10235, loss_spatial_dice_6: 0.14022/0.23539, loss_spatial_ce_6: 0.04330/0.13652, loss_grounding_bce_6: 0.03231/0.08819, loss_grounding_dice_6: 0.10944/0.18333, loss_grounding_ce_6: 0.15336/0.31185, loss_mask_ce_7: 1.24295/1.02292, loss_mask_bce_7: 0.07711/0.35157, loss_mask_dice_7: 0.42595/1.25808, loss_spatial_bce_7: 0.03185/0.11078, loss_spatial_dice_7: 0.14979/0.26321, loss_spatial_ce_7: 0.19401/0.17303, loss_grounding_bce_7: 0.02909/0.09010, loss_grounding_dice_7: 0.10593/0.19057, loss_grounding_ce_7: 0.13090/0.34468, loss_mask_ce_8: 2.15073/1.13194, loss_mask_bce_8: 0.08224/0.36518, loss_mask_dice_8: 0.45533/1.33159, loss_spatial_bce_8: 0.04936/0.13161, loss_spatial_dice_8: 0.23274/0.30232, loss_spatial_ce_8: 0.16127/0.23013, loss_grounding_bce_8: 0.03124/0.09382, loss_grounding_dice_8: 0.11403/0.20165, loss_grounding_ce_8: 0.16435/0.41284, loss_mask_ce_9: 3.22686/3.68155, loss_mask_bce_9: 0.10681/0.39215, loss_mask_dice_9: 0.86025/1.90509, loss_spatial_bce_9: 0.29238/0.33373, loss_spatial_dice_9: 0.84513/0.82278, loss_spatial_ce_9: 1.59364/1.50322, loss_grounding_bce_9: 0.04455/0.10534, loss_grounding_dice_9: 0.25588/0.28099, loss_grounding_ce_9: 0.63672/0.67769] items per batch[64] items per second[0.23] total items[2848000] mini batches[ 44500] memory[7345] epoch remaining[0:53:59]
INFO:trainer.default_trainer:epochs[    24] optim steps[44600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.55220/0.90567, loss_mask_bce_0: 0.36355/0.33444, loss_mask_dice_0: 1.88832/1.16585, loss_spatial_bce_0: 0.07144/0.08842, loss_spatial_dice_0: 0.31559/0.21159, loss_spatial_ce_0: 0.02689/0.06651, loss_grounding_bce_0: 0.04748/0.08610, loss_grounding_dice_0: 0.21109/0.17874, loss_grounding_ce_0: 0.23723/0.27388, loss_mask_ce_1: 2.51029/0.90610, loss_mask_bce_1: 0.36131/0.33527, loss_mask_dice_1: 1.88123/1.17248, loss_spatial_bce_1: 0.07211/0.08904, loss_spatial_dice_1: 0.32070/0.21570, loss_spatial_ce_1: 0.03013/0.07211, loss_grounding_bce_1: 0.04566/0.08626, loss_grounding_dice_1: 0.19354/0.17954, loss_grounding_ce_1: 0.24885/0.27549, loss_mask_ce_2: 2.59385/0.91354, loss_mask_bce_2: 0.36484/0.33575, loss_mask_dice_2: 1.85120/1.17236, loss_spatial_bce_2: 0.06732/0.08973, loss_spatial_dice_2: 0.30427/0.21689, loss_spatial_ce_2: 0.02586/0.07567, loss_grounding_bce_2: 0.04881/0.08635, loss_grounding_dice_2: 0.22332/0.17932, loss_grounding_ce_2: 0.21028/0.27849, loss_mask_ce_3: 2.46864/0.92287, loss_mask_bce_3: 0.36496/0.33678, loss_mask_dice_3: 1.89497/1.16952, loss_spatial_bce_3: 0.06268/0.09065, loss_spatial_dice_3: 0.28895/0.21754, loss_spatial_ce_3: 0.03784/0.07947, loss_grounding_bce_3: 0.05276/0.08660, loss_grounding_dice_3: 0.22185/0.17906, loss_grounding_ce_3: 0.22265/0.28010, loss_mask_ce_4: 2.66883/0.92313, loss_mask_bce_4: 0.39830/0.33871, loss_mask_dice_4: 1.79380/1.19332, loss_spatial_bce_4: 0.05804/0.09479, loss_spatial_dice_4: 0.33262/0.22918, loss_spatial_ce_4: 0.03068/0.09544, loss_grounding_bce_4: 0.05870/0.08701, loss_grounding_dice_4: 0.23788/0.18191, loss_grounding_ce_4: 0.19461/0.28305, loss_mask_ce_5: 2.65724/0.93889, loss_mask_bce_5: 0.41685/0.34095, loss_mask_dice_5: 2.00034/1.19992, loss_spatial_bce_5: 0.06912/0.09658, loss_spatial_dice_5: 0.33220/0.23293, loss_spatial_ce_5: 0.05646/0.11053, loss_grounding_bce_5: 0.05628/0.08740, loss_grounding_dice_5: 0.21169/0.18313, loss_grounding_ce_5: 0.18154/0.29544, loss_mask_ce_6: 2.78222/0.97814, loss_mask_bce_6: 0.37508/0.34366, loss_mask_dice_6: 1.97439/1.20272, loss_spatial_bce_6: 0.07386/0.10233, loss_spatial_dice_6: 0.30228/0.23538, loss_spatial_ce_6: 0.06749/0.13650, loss_grounding_bce_6: 0.04132/0.08815, loss_grounding_dice_6: 0.19278/0.18333, loss_grounding_ce_6: 0.25973/0.31189, loss_mask_ce_7: 2.93837/1.02328, loss_mask_bce_7: 0.36695/0.35152, loss_mask_dice_7: 2.07502/1.25791, loss_spatial_bce_7: 0.06329/0.11075, loss_spatial_dice_7: 0.34520/0.26321, loss_spatial_ce_7: 0.16492/0.17299, loss_grounding_bce_7: 0.05184/0.09006, loss_grounding_dice_7: 0.21264/0.19056, loss_grounding_ce_7: 0.25807/0.34469, loss_mask_ce_8: 3.15976/1.13226, loss_mask_bce_8: 0.39413/0.36512, loss_mask_dice_8: 2.04329/1.33145, loss_spatial_bce_8: 0.09917/0.13158, loss_spatial_dice_8: 0.35360/0.30233, loss_spatial_ce_8: 0.17858/0.23011, loss_grounding_bce_8: 0.05379/0.09378, loss_grounding_dice_8: 0.22697/0.20164, loss_grounding_ce_8: 0.25171/0.41285, loss_mask_ce_9: 5.18591/3.68150, loss_mask_bce_9: 0.39414/0.39212, loss_mask_dice_9: 2.76608/1.90507, loss_spatial_bce_9: 0.16464/0.33369, loss_spatial_dice_9: 0.91647/0.82279, loss_spatial_ce_9: 1.16840/1.50318, loss_grounding_bce_9: 0.07034/0.10530, loss_grounding_dice_9: 0.37885/0.28098, loss_grounding_ce_9: 0.59855/0.67788] items per batch[64] items per second[0.23] total items[2854400] mini batches[ 44600] memory[7345] epoch remaining[0:49:29]
INFO:trainer.default_trainer:epochs[    24] optim steps[44700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.29325/0.90554, loss_mask_bce_0: 0.55285/0.33444, loss_mask_dice_0: 1.35974/1.16576, loss_spatial_bce_0: 0.06700/0.08842, loss_spatial_dice_0: 0.12403/0.21155, loss_spatial_ce_0: 0.00019/0.06649, loss_grounding_bce_0: 0.06379/0.08611, loss_grounding_dice_0: 0.10386/0.17872, loss_grounding_ce_0: 0.26676/0.27396, loss_mask_ce_1: 1.26353/0.90600, loss_mask_bce_1: 0.53887/0.33527, loss_mask_dice_1: 1.23553/1.17240, loss_spatial_bce_1: 0.06901/0.08905, loss_spatial_dice_1: 0.12687/0.21566, loss_spatial_ce_1: 0.00012/0.07210, loss_grounding_bce_1: 0.06126/0.08627, loss_grounding_dice_1: 0.10440/0.17951, loss_grounding_ce_1: 0.25745/0.27557, loss_mask_ce_2: 1.23470/0.91339, loss_mask_bce_2: 0.53914/0.33575, loss_mask_dice_2: 1.39864/1.17230, loss_spatial_bce_2: 0.07029/0.08974, loss_spatial_dice_2: 0.12281/0.21685, loss_spatial_ce_2: 0.00030/0.07565, loss_grounding_bce_2: 0.05925/0.08637, loss_grounding_dice_2: 0.10241/0.17929, loss_grounding_ce_2: 0.26363/0.27859, loss_mask_ce_3: 1.29132/0.92277, loss_mask_bce_3: 0.52794/0.33678, loss_mask_dice_3: 1.30180/1.16944, loss_spatial_bce_3: 0.06392/0.09065, loss_spatial_dice_3: 0.12121/0.21751, loss_spatial_ce_3: 0.00200/0.07946, loss_grounding_bce_3: 0.05868/0.08661, loss_grounding_dice_3: 0.10162/0.17904, loss_grounding_ce_3: 0.25915/0.28021, loss_mask_ce_4: 1.26750/0.92299, loss_mask_bce_4: 0.49144/0.33870, loss_mask_dice_4: 1.20255/1.19324, loss_spatial_bce_4: 0.07031/0.09479, loss_spatial_dice_4: 0.12883/0.22915, loss_spatial_ce_4: 0.02529/0.09540, loss_grounding_bce_4: 0.05930/0.08703, loss_grounding_dice_4: 0.10174/0.18188, loss_grounding_ce_4: 0.28762/0.28310, loss_mask_ce_5: 1.33513/0.93879, loss_mask_bce_5: 0.50740/0.34094, loss_mask_dice_5: 1.34111/1.19982, loss_spatial_bce_5: 0.07657/0.09658, loss_spatial_dice_5: 0.13892/0.23290, loss_spatial_ce_5: 0.02523/0.11051, loss_grounding_bce_5: 0.05573/0.08742, loss_grounding_dice_5: 0.10427/0.18310, loss_grounding_ce_5: 0.27977/0.29549, loss_mask_ce_6: 1.41729/0.97803, loss_mask_bce_6: 0.50336/0.34367, loss_mask_dice_6: 1.23498/1.20265, loss_spatial_bce_6: 0.06758/0.10233, loss_spatial_dice_6: 0.14565/0.23535, loss_spatial_ce_6: 0.03913/0.13643, loss_grounding_bce_6: 0.06087/0.08817, loss_grounding_dice_6: 0.10566/0.18331, loss_grounding_ce_6: 0.25091/0.31190, loss_mask_ce_7: 1.52966/1.02317, loss_mask_bce_7: 0.49117/0.35152, loss_mask_dice_7: 1.19936/1.25781, loss_spatial_bce_7: 0.08514/0.11075, loss_spatial_dice_7: 0.15876/0.26318, loss_spatial_ce_7: 0.03347/0.17296, loss_grounding_bce_7: 0.08035/0.09008, loss_grounding_dice_7: 0.11558/0.19053, loss_grounding_ce_7: 0.30716/0.34472, loss_mask_ce_8: 1.70442/1.13216, loss_mask_bce_8: 0.45367/0.36511, loss_mask_dice_8: 1.18842/1.33132, loss_spatial_bce_8: 0.13008/0.13158, loss_spatial_dice_8: 0.18299/0.30229, loss_spatial_ce_8: 0.16527/0.23009, loss_grounding_bce_8: 0.05532/0.09379, loss_grounding_dice_8: 0.10383/0.20161, loss_grounding_ce_8: 0.47232/0.41286, loss_mask_ce_9: 4.83031/3.68152, loss_mask_bce_9: 0.67056/0.39209, loss_mask_dice_9: 4.65636/1.90488, loss_spatial_bce_9: 0.34651/0.33369, loss_spatial_dice_9: 0.87805/0.82276, loss_spatial_ce_9: 1.38404/1.50307, loss_grounding_bce_9: 0.06479/0.10531, loss_grounding_dice_9: 0.12801/0.28095, loss_grounding_ce_9: 1.34334/0.67807] items per batch[64] items per second[0.23] total items[2860800] mini batches[ 44700] memory[7345] epoch remaining[0:44:53]
INFO:trainer.default_trainer:epochs[    24] optim steps[44800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43701/0.90553, loss_mask_bce_0: 0.45090/0.33447, loss_mask_dice_0: 0.83372/1.16560, loss_spatial_bce_0: 0.09313/0.08843, loss_spatial_dice_0: 0.23774/0.21151, loss_spatial_ce_0: 0.08194/0.06646, loss_grounding_bce_0: 0.05776/0.08614, loss_grounding_dice_0: 0.13212/0.17871, loss_grounding_ce_0: 0.46874/0.27410, loss_mask_ce_1: 1.44020/0.90600, loss_mask_bce_1: 0.42625/0.33530, loss_mask_dice_1: 0.67247/1.17223, loss_spatial_bce_1: 0.09400/0.08905, loss_spatial_dice_1: 0.23051/0.21562, loss_spatial_ce_1: 0.08592/0.07209, loss_grounding_bce_1: 0.06084/0.08630, loss_grounding_dice_1: 0.12956/0.17950, loss_grounding_ce_1: 0.44003/0.27561, loss_mask_ce_2: 1.49782/0.91335, loss_mask_bce_2: 0.45966/0.33579, loss_mask_dice_2: 0.88030/1.17212, loss_spatial_bce_2: 0.08152/0.08974, loss_spatial_dice_2: 0.20833/0.21680, loss_spatial_ce_2: 0.09040/0.07561, loss_grounding_bce_2: 0.05783/0.08639, loss_grounding_dice_2: 0.13439/0.17928, loss_grounding_ce_2: 0.58279/0.27861, loss_mask_ce_3: 1.21393/0.92276, loss_mask_bce_3: 0.55869/0.33682, loss_mask_dice_3: 0.96198/1.16927, loss_spatial_bce_3: 0.08840/0.09065, loss_spatial_dice_3: 0.21574/0.21746, loss_spatial_ce_3: 0.09781/0.07942, loss_grounding_bce_3: 0.06315/0.08664, loss_grounding_dice_3: 0.13693/0.17903, loss_grounding_ce_3: 0.48799/0.28028, loss_mask_ce_4: 1.50702/0.92300, loss_mask_bce_4: 0.46709/0.33874, loss_mask_dice_4: 0.83175/1.19307, loss_spatial_bce_4: 0.10835/0.09479, loss_spatial_dice_4: 0.22638/0.22911, loss_spatial_ce_4: 0.08596/0.09536, loss_grounding_bce_4: 0.05412/0.08705, loss_grounding_dice_4: 0.14078/0.18188, loss_grounding_ce_4: 0.51791/0.28325, loss_mask_ce_5: 1.51102/0.93874, loss_mask_bce_5: 0.46331/0.34098, loss_mask_dice_5: 0.90263/1.19965, loss_spatial_bce_5: 0.12990/0.09658, loss_spatial_dice_5: 0.23933/0.23287, loss_spatial_ce_5: 0.13563/0.11047, loss_grounding_bce_5: 0.06515/0.08744, loss_grounding_dice_5: 0.14475/0.18309, loss_grounding_ce_5: 0.50485/0.29562, loss_mask_ce_6: 1.55589/0.97802, loss_mask_bce_6: 0.45665/0.34369, loss_mask_dice_6: 0.91082/1.20248, loss_spatial_bce_6: 0.11795/0.10233, loss_spatial_dice_6: 0.22070/0.23533, loss_spatial_ce_6: 0.25072/0.13640, loss_grounding_bce_6: 0.05228/0.08819, loss_grounding_dice_6: 0.13308/0.18330, loss_grounding_ce_6: 0.48463/0.31204, loss_mask_ce_7: 1.34223/1.02311, loss_mask_bce_7: 0.57580/0.35155, loss_mask_dice_7: 0.95631/1.25760, loss_spatial_bce_7: 0.11507/0.11076, loss_spatial_dice_7: 0.23643/0.26315, loss_spatial_ce_7: 0.32405/0.17291, loss_grounding_bce_7: 0.08930/0.09010, loss_grounding_dice_7: 0.18340/0.19052, loss_grounding_ce_7: 0.31420/0.34478, loss_mask_ce_8: 1.63101/1.13221, loss_mask_bce_8: 0.66016/0.36514, loss_mask_dice_8: 1.01444/1.33112, loss_spatial_bce_8: 0.14724/0.13158, loss_spatial_dice_8: 0.24813/0.30228, loss_spatial_ce_8: 0.26821/0.23006, loss_grounding_bce_8: 0.08542/0.09382, loss_grounding_dice_8: 0.17284/0.20160, loss_grounding_ce_8: 0.34508/0.41295, loss_mask_ce_9: 4.55063/3.68158, loss_mask_bce_9: 0.51315/0.39215, loss_mask_dice_9: 1.33581/1.90462, loss_spatial_bce_9: 0.36329/0.33370, loss_spatial_dice_9: 0.82236/0.82274, loss_spatial_ce_9: 1.43717/1.50303, loss_grounding_bce_9: 0.08245/0.10535, loss_grounding_dice_9: 0.26417/0.28096, loss_grounding_ce_9: 0.46419/0.67810] items per batch[64] items per second[0.23] total items[2867200] mini batches[ 44800] memory[7345] epoch remaining[0:40:18]
INFO:trainer.default_trainer:epochs[    24] optim steps[44900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97821/0.90552, loss_mask_bce_0: 0.45416/0.33444, loss_mask_dice_0: 0.71801/1.16506, loss_spatial_bce_0: 0.15087/0.08844, loss_spatial_dice_0: 0.23279/0.21145, loss_spatial_ce_0: 0.00994/0.06643, loss_grounding_bce_0: 0.17393/0.08616, loss_grounding_dice_0: 0.21301/0.17867, loss_grounding_ce_0: 0.39376/0.27416, loss_mask_ce_1: 0.97422/0.90596, loss_mask_bce_1: 0.44042/0.33528, loss_mask_dice_1: 0.69689/1.17172, loss_spatial_bce_1: 0.14952/0.08906, loss_spatial_dice_1: 0.23272/0.21556, loss_spatial_ce_1: 0.00710/0.07206, loss_grounding_bce_1: 0.15671/0.08632, loss_grounding_dice_1: 0.19965/0.17946, loss_grounding_ce_1: 0.41280/0.27570, loss_mask_ce_2: 0.94040/0.91328, loss_mask_bce_2: 0.44876/0.33575, loss_mask_dice_2: 0.69949/1.17157, loss_spatial_bce_2: 0.15427/0.08975, loss_spatial_dice_2: 0.24152/0.21674, loss_spatial_ce_2: 0.00908/0.07556, loss_grounding_bce_2: 0.14372/0.08641, loss_grounding_dice_2: 0.19113/0.17924, loss_grounding_ce_2: 0.40043/0.27866, loss_mask_ce_3: 1.04200/0.92271, loss_mask_bce_3: 0.44778/0.33680, loss_mask_dice_3: 0.69003/1.16876, loss_spatial_bce_3: 0.14910/0.09066, loss_spatial_dice_3: 0.23917/0.21741, loss_spatial_ce_3: 0.00967/0.07938, loss_grounding_bce_3: 0.13356/0.08665, loss_grounding_dice_3: 0.16784/0.17899, loss_grounding_ce_3: 0.40386/0.28033, loss_mask_ce_4: 1.01146/0.92295, loss_mask_bce_4: 0.45709/0.33872, loss_mask_dice_4: 0.68762/1.19252, loss_spatial_bce_4: 0.15856/0.09481, loss_spatial_dice_4: 0.27214/0.22906, loss_spatial_ce_4: 0.02642/0.09531, loss_grounding_bce_4: 0.13503/0.08707, loss_grounding_dice_4: 0.18474/0.18184, loss_grounding_ce_4: 0.38627/0.28328, loss_mask_ce_5: 1.09443/0.93874, loss_mask_bce_5: 0.39639/0.34096, loss_mask_dice_5: 0.64259/1.19913, loss_spatial_bce_5: 0.15950/0.09659, loss_spatial_dice_5: 0.27846/0.23281, loss_spatial_ce_5: 0.09391/0.11044, loss_grounding_bce_5: 0.17723/0.08746, loss_grounding_dice_5: 0.17094/0.18304, loss_grounding_ce_5: 0.35416/0.29571, loss_mask_ce_6: 1.06222/0.97795, loss_mask_bce_6: 0.44194/0.34366, loss_mask_dice_6: 0.67202/1.20197, loss_spatial_bce_6: 0.16804/0.10235, loss_spatial_dice_6: 0.24083/0.23528, loss_spatial_ce_6: 0.12321/0.13637, loss_grounding_bce_6: 0.18582/0.08821, loss_grounding_dice_6: 0.17331/0.18326, loss_grounding_ce_6: 0.34928/0.31212, loss_mask_ce_7: 1.11322/1.02303, loss_mask_bce_7: 0.44237/0.35152, loss_mask_dice_7: 0.70003/1.25704, loss_spatial_bce_7: 0.18270/0.11079, loss_spatial_dice_7: 0.31277/0.26309, loss_spatial_ce_7: 0.11655/0.17285, loss_grounding_bce_7: 0.19150/0.09013, loss_grounding_dice_7: 0.17497/0.19050, loss_grounding_ce_7: 0.39991/0.34477, loss_mask_ce_8: 1.01852/1.13209, loss_mask_bce_8: 0.48569/0.36511, loss_mask_dice_8: 0.79082/1.33052, loss_spatial_bce_8: 0.22682/0.13161, loss_spatial_dice_8: 0.37777/0.30221, loss_spatial_ce_8: 0.34774/0.23000, loss_grounding_bce_8: 0.17452/0.09384, loss_grounding_dice_8: 0.18960/0.20156, loss_grounding_ce_8: 0.40007/0.41290, loss_mask_ce_9: 2.86115/3.68126, loss_mask_bce_9: 0.53792/0.39210, loss_mask_dice_9: 1.15628/1.90392, loss_spatial_bce_9: 0.40469/0.33383, loss_spatial_dice_9: 0.86020/0.82271, loss_spatial_ce_9: 1.37389/1.50290, loss_grounding_bce_9: 0.17516/0.10537, loss_grounding_dice_9: 0.23880/0.28092, loss_grounding_ce_9: 0.49912/0.67806] items per batch[64] items per second[0.23] total items[2873600] mini batches[ 44900] memory[7345] epoch remaining[0:35:44]
INFO:trainer.default_trainer:epochs[    24] optim steps[45000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02676/0.90552, loss_mask_bce_0: 0.20314/0.33450, loss_mask_dice_0: 0.50066/1.16507, loss_spatial_bce_0: 0.42080/0.08843, loss_spatial_dice_0: 0.33477/0.21144, loss_spatial_ce_0: 0.06147/0.06638, loss_grounding_bce_0: 0.02489/0.08617, loss_grounding_dice_0: 0.38769/0.17871, loss_grounding_ce_0: 0.15862/0.27414, loss_mask_ce_1: 1.19342/0.90598, loss_mask_bce_1: 0.21230/0.33533, loss_mask_dice_1: 0.62194/1.17174, loss_spatial_bce_1: 0.50160/0.08905, loss_spatial_dice_1: 0.31747/0.21555, loss_spatial_ce_1: 0.06030/0.07203, loss_grounding_bce_1: 0.02655/0.08633, loss_grounding_dice_1: 0.42455/0.17950, loss_grounding_ce_1: 0.10559/0.27569, loss_mask_ce_2: 1.22708/0.91330, loss_mask_bce_2: 0.21024/0.33581, loss_mask_dice_2: 0.50924/1.17159, loss_spatial_bce_2: 0.48120/0.08974, loss_spatial_dice_2: 0.30535/0.21673, loss_spatial_ce_2: 0.06915/0.07554, loss_grounding_bce_2: 0.03240/0.08642, loss_grounding_dice_2: 0.38635/0.17927, loss_grounding_ce_2: 0.57777/0.27869, loss_mask_ce_3: 1.03141/0.92271, loss_mask_bce_3: 0.24346/0.33685, loss_mask_dice_3: 0.47435/1.16877, loss_spatial_bce_3: 0.37156/0.09066, loss_spatial_dice_3: 0.33850/0.21740, loss_spatial_ce_3: 0.32832/0.07934, loss_grounding_bce_3: 0.03486/0.08666, loss_grounding_dice_3: 0.44865/0.17903, loss_grounding_ce_3: 0.56706/0.28034, loss_mask_ce_4: 1.09427/0.92297, loss_mask_bce_4: 0.21350/0.33878, loss_mask_dice_4: 0.49737/1.19258, loss_spatial_bce_4: 0.34518/0.09480, loss_spatial_dice_4: 0.34914/0.22906, loss_spatial_ce_4: 0.27941/0.09527, loss_grounding_bce_4: 0.02668/0.08707, loss_grounding_dice_4: 0.46760/0.18187, loss_grounding_ce_4: 0.08621/0.28330, loss_mask_ce_5: 1.22248/0.93875, loss_mask_bce_5: 0.20767/0.34102, loss_mask_dice_5: 0.53643/1.19917, loss_spatial_bce_5: 0.31789/0.09658, loss_spatial_dice_5: 0.34939/0.23281, loss_spatial_ce_5: 0.23770/0.11042, loss_grounding_bce_5: 0.02313/0.08746, loss_grounding_dice_5: 0.43651/0.18307, loss_grounding_ce_5: 0.13592/0.29573, loss_mask_ce_6: 1.07773/0.97802, loss_mask_bce_6: 0.23384/0.34373, loss_mask_dice_6: 0.54815/1.20201, loss_spatial_bce_6: 0.31941/0.10234, loss_spatial_dice_6: 0.35371/0.23529, loss_spatial_ce_6: 0.19417/0.13636, loss_grounding_bce_6: 0.02960/0.08822, loss_grounding_dice_6: 0.47503/0.18330, loss_grounding_ce_6: 0.09863/0.31212, loss_mask_ce_7: 0.97099/1.02307, loss_mask_bce_7: 0.22935/0.35159, loss_mask_dice_7: 0.55784/1.25706, loss_spatial_bce_7: 0.17687/0.11079, loss_spatial_dice_7: 0.33321/0.26310, loss_spatial_ce_7: 0.40790/0.17281, loss_grounding_bce_7: 0.02627/0.09014, loss_grounding_dice_7: 0.46208/0.19055, loss_grounding_ce_7: 0.04254/0.34482, loss_mask_ce_8: 1.09674/1.13212, loss_mask_bce_8: 0.23302/0.36518, loss_mask_dice_8: 0.59422/1.33057, loss_spatial_bce_8: 0.17604/0.13160, loss_spatial_dice_8: 0.30551/0.30220, loss_spatial_ce_8: 0.15498/0.22994, loss_grounding_bce_8: 0.04251/0.09385, loss_grounding_dice_8: 0.44713/0.20161, loss_grounding_ce_8: 0.61400/0.41297, loss_mask_ce_9: 3.95967/3.68154, loss_mask_bce_9: 0.20799/0.39220, loss_mask_dice_9: 1.20076/1.90403, loss_spatial_bce_9: 0.52306/0.33382, loss_spatial_dice_9: 0.78142/0.82272, loss_spatial_ce_9: 1.65030/1.50280, loss_grounding_bce_9: 0.03434/0.10539, loss_grounding_dice_9: 0.47301/0.28098, loss_grounding_ce_9: 0.31322/0.67800] items per batch[64] items per second[0.22] total items[2880000] mini batches[ 45000] memory[7345] epoch remaining[0:31:14]
INFO:trainer.default_trainer:epochs[    24] optim steps[45100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59063/0.90524, loss_mask_bce_0: 0.18891/0.33454, loss_mask_dice_0: 0.20693/1.16488, loss_spatial_bce_0: 0.18363/0.08844, loss_spatial_dice_0: 0.14361/0.21140, loss_spatial_ce_0: 0.02165/0.06635, loss_grounding_bce_0: 0.15531/0.08619, loss_grounding_dice_0: 0.19515/0.17868, loss_grounding_ce_0: 0.27547/0.27409, loss_mask_ce_1: 0.57817/0.90568, loss_mask_bce_1: 0.19389/0.33538, loss_mask_dice_1: 0.20379/1.17151, loss_spatial_bce_1: 0.19616/0.08906, loss_spatial_dice_1: 0.14075/0.21551, loss_spatial_ce_1: 0.01638/0.07199, loss_grounding_bce_1: 0.16014/0.08636, loss_grounding_dice_1: 0.19126/0.17947, loss_grounding_ce_1: 0.27923/0.27563, loss_mask_ce_2: 0.14410/0.91300, loss_mask_bce_2: 0.31942/0.33586, loss_mask_dice_2: 0.21390/1.17142, loss_spatial_bce_2: 0.16450/0.08975, loss_spatial_dice_2: 0.13580/0.21668, loss_spatial_ce_2: 0.01131/0.07550, loss_grounding_bce_2: 0.16031/0.08645, loss_grounding_dice_2: 0.20408/0.17924, loss_grounding_ce_2: 0.27697/0.27865, loss_mask_ce_3: 0.16586/0.92244, loss_mask_bce_3: 0.30244/0.33690, loss_mask_dice_3: 0.21181/1.16862, loss_spatial_bce_3: 0.14625/0.09066, loss_spatial_dice_3: 0.13453/0.21736, loss_spatial_ce_3: 0.02575/0.07932, loss_grounding_bce_3: 0.29515/0.08669, loss_grounding_dice_3: 0.19406/0.17900, loss_grounding_ce_3: 0.01043/0.28031, loss_mask_ce_4: 0.53163/0.92267, loss_mask_bce_4: 0.19601/0.33883, loss_mask_dice_4: 0.19643/1.19239, loss_spatial_bce_4: 0.13596/0.09480, loss_spatial_dice_4: 0.13493/0.22902, loss_spatial_ce_4: 0.06437/0.09524, loss_grounding_bce_4: 0.15091/0.08710, loss_grounding_dice_4: 0.17567/0.18184, loss_grounding_ce_4: 0.26969/0.28327, loss_mask_ce_5: 0.62187/0.93846, loss_mask_bce_5: 0.19789/0.34106, loss_mask_dice_5: 0.19661/1.19899, loss_spatial_bce_5: 0.17553/0.09659, loss_spatial_dice_5: 0.14478/0.23278, loss_spatial_ce_5: 0.01917/0.11041, loss_grounding_bce_5: 0.34044/0.08750, loss_grounding_dice_5: 0.19994/0.18303, loss_grounding_ce_5: 0.01223/0.29574, loss_mask_ce_6: 0.20026/0.97773, loss_mask_bce_6: 0.32172/0.34376, loss_mask_dice_6: 0.19963/1.20182, loss_spatial_bce_6: 0.13687/0.10235, loss_spatial_dice_6: 0.12960/0.23526, loss_spatial_ce_6: 0.16871/0.13633, loss_grounding_bce_6: 0.15717/0.08825, loss_grounding_dice_6: 0.18466/0.18326, loss_grounding_ce_6: 0.25697/0.31209, loss_mask_ce_7: 0.25173/1.02275, loss_mask_bce_7: 0.33900/0.35163, loss_mask_dice_7: 0.20268/1.25683, loss_spatial_bce_7: 0.17059/0.11080, loss_spatial_dice_7: 0.15672/0.26304, loss_spatial_ce_7: 0.17414/0.17276, loss_grounding_bce_7: 0.19859/0.09017, loss_grounding_dice_7: 0.19916/0.19050, loss_grounding_ce_7: 0.20200/0.34473, loss_mask_ce_8: 0.69167/1.13176, loss_mask_bce_8: 0.20077/0.36521, loss_mask_dice_8: 0.20796/1.33032, loss_spatial_bce_8: 0.19402/0.13161, loss_spatial_dice_8: 0.16443/0.30214, loss_spatial_ce_8: 0.07752/0.22988, loss_grounding_bce_8: 0.16737/0.09388, loss_grounding_dice_8: 0.20373/0.20157, loss_grounding_ce_8: 0.29102/0.41283, loss_mask_ce_9: 2.20162/3.68115, loss_mask_bce_9: 0.26490/0.39224, loss_mask_dice_9: 0.28346/1.90368, loss_spatial_bce_9: 0.53698/0.33387, loss_spatial_dice_9: 0.76961/0.82270, loss_spatial_ce_9: 0.96380/1.50268, loss_grounding_bce_9: 0.19301/0.10541, loss_grounding_dice_9: 0.24529/0.28091, loss_grounding_ce_9: 0.25923/0.67786] items per batch[64] items per second[0.23] total items[2886400] mini batches[ 45100] memory[7345] epoch remaining[0:26:38]
INFO:trainer.default_trainer:epochs[    24] optim steps[45200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63120/0.90528, loss_mask_bce_0: 0.40610/0.33455, loss_mask_dice_0: 3.40643/1.16503, loss_spatial_bce_0: 0.01874/0.08845, loss_spatial_dice_0: 0.21976/0.21140, loss_spatial_ce_0: 0.04704/0.06635, loss_grounding_bce_0: 0.15367/0.08618, loss_grounding_dice_0: 0.16761/0.17868, loss_grounding_ce_0: 0.14905/0.27409, loss_mask_ce_1: 1.54859/0.90568, loss_mask_bce_1: 0.40106/0.33538, loss_mask_dice_1: 3.46002/1.17172, loss_spatial_bce_1: 0.01983/0.08906, loss_spatial_dice_1: 0.24379/0.21550, loss_spatial_ce_1: 0.05649/0.07198, loss_grounding_bce_1: 0.15635/0.08635, loss_grounding_dice_1: 0.16337/0.17948, loss_grounding_ce_1: 0.15972/0.27564, loss_mask_ce_2: 1.73654/0.91309, loss_mask_bce_2: 0.41530/0.33586, loss_mask_dice_2: 3.45686/1.17159, loss_spatial_bce_2: 0.01952/0.08976, loss_spatial_dice_2: 0.23819/0.21669, loss_spatial_ce_2: 0.24229/0.07549, loss_grounding_bce_2: 0.15254/0.08644, loss_grounding_dice_2: 0.17939/0.17923, loss_grounding_ce_2: 0.17087/0.27866, loss_mask_ce_3: 1.63865/0.92249, loss_mask_bce_3: 0.39984/0.33691, loss_mask_dice_3: 3.26988/1.16885, loss_spatial_bce_3: 0.02181/0.09067, loss_spatial_dice_3: 0.27176/0.21737, loss_spatial_ce_3: 0.06073/0.07929, loss_grounding_bce_3: 0.15552/0.08669, loss_grounding_dice_3: 0.17493/0.17900, loss_grounding_ce_3: 0.13642/0.28034, loss_mask_ce_4: 1.69123/0.92271, loss_mask_bce_4: 0.46167/0.33883, loss_mask_dice_4: 3.62544/1.19264, loss_spatial_bce_4: 0.02209/0.09481, loss_spatial_dice_4: 0.29515/0.22904, loss_spatial_ce_4: 0.12889/0.09524, loss_grounding_bce_4: 0.16671/0.08710, loss_grounding_dice_4: 0.20592/0.18183, loss_grounding_ce_4: 0.02182/0.28330, loss_mask_ce_5: 1.53309/0.93854, loss_mask_bce_5: 0.50073/0.34106, loss_mask_dice_5: 3.82524/1.19922, loss_spatial_bce_5: 0.01987/0.09660, loss_spatial_dice_5: 0.27491/0.23279, loss_spatial_ce_5: 0.04457/0.11039, loss_grounding_bce_5: 0.16712/0.08750, loss_grounding_dice_5: 0.21717/0.18303, loss_grounding_ce_5: 0.02506/0.29579, loss_mask_ce_6: 1.64456/0.97777, loss_mask_bce_6: 0.48669/0.34377, loss_mask_dice_6: 3.85868/1.20209, loss_spatial_bce_6: 0.02342/0.10237, loss_spatial_dice_6: 0.32363/0.23528, loss_spatial_ce_6: 0.12442/0.13632, loss_grounding_bce_6: 0.15946/0.08825, loss_grounding_dice_6: 0.21038/0.18326, loss_grounding_ce_6: 0.02581/0.31216, loss_mask_ce_7: 1.41541/1.02273, loss_mask_bce_7: 0.48361/0.35165, loss_mask_dice_7: 4.17741/1.25710, loss_spatial_bce_7: 0.03446/0.11081, loss_spatial_dice_7: 0.37119/0.26306, loss_spatial_ce_7: 0.27632/0.17272, loss_grounding_bce_7: 0.16824/0.09018, loss_grounding_dice_7: 0.25792/0.19049, loss_grounding_ce_7: 0.01257/0.34472, loss_mask_ce_8: 1.89156/1.13181, loss_mask_bce_8: 0.49653/0.36521, loss_mask_dice_8: 4.31620/1.33055, loss_spatial_bce_8: 0.04312/0.13162, loss_spatial_dice_8: 0.45362/0.30214, loss_spatial_ce_8: 0.23473/0.22989, loss_grounding_bce_8: 0.17543/0.09388, loss_grounding_dice_8: 0.23430/0.20156, loss_grounding_ce_8: 0.02322/0.41274, loss_mask_ce_9: 4.91483/3.68132, loss_mask_bce_9: 0.44271/0.39224, loss_mask_dice_9: 5.25053/1.90395, loss_spatial_bce_9: 0.17263/0.33389, loss_spatial_dice_9: 0.95416/0.82269, loss_spatial_ce_9: 1.42586/1.50262, loss_grounding_bce_9: 0.16792/0.10542, loss_grounding_dice_9: 0.31756/0.28091, loss_grounding_ce_9: 0.07313/0.67786] items per batch[64] items per second[0.23] total items[2892800] mini batches[ 45200] memory[7345] epoch remaining[0:22:00]
INFO:trainer.default_trainer:epochs[    24] optim steps[45300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97253/0.90520, loss_mask_bce_0: 0.17751/0.33458, loss_mask_dice_0: 0.89354/1.16506, loss_spatial_bce_0: 0.07461/0.08845, loss_spatial_dice_0: 0.21511/0.21138, loss_spatial_ce_0: 0.12787/0.06632, loss_grounding_bce_0: 0.04109/0.08616, loss_grounding_dice_0: 0.11116/0.17866, loss_grounding_ce_0: 0.01517/0.27410, loss_mask_ce_1: 0.97294/0.90562, loss_mask_bce_1: 0.16430/0.33541, loss_mask_dice_1: 1.10103/1.17174, loss_spatial_bce_1: 0.07473/0.08906, loss_spatial_dice_1: 0.22350/0.21548, loss_spatial_ce_1: 0.08823/0.07194, loss_grounding_bce_1: 0.03888/0.08633, loss_grounding_dice_1: 0.10817/0.17945, loss_grounding_ce_1: 0.01495/0.27565, loss_mask_ce_2: 0.93513/0.91303, loss_mask_bce_2: 0.17821/0.33589, loss_mask_dice_2: 1.04323/1.17164, loss_spatial_bce_2: 0.08172/0.08976, loss_spatial_dice_2: 0.24124/0.21667, loss_spatial_ce_2: 0.08319/0.07548, loss_grounding_bce_2: 0.04071/0.08641, loss_grounding_dice_2: 0.11197/0.17922, loss_grounding_ce_2: 0.01509/0.27868, loss_mask_ce_3: 1.09576/0.92242, loss_mask_bce_3: 0.18687/0.33694, loss_mask_dice_3: 0.89929/1.16893, loss_spatial_bce_3: 0.07863/0.09068, loss_spatial_dice_3: 0.24053/0.21734, loss_spatial_ce_3: 0.08657/0.07928, loss_grounding_bce_3: 0.04613/0.08667, loss_grounding_dice_3: 0.11994/0.17898, loss_grounding_ce_3: 0.01935/0.28037, loss_mask_ce_4: 0.86948/0.92267, loss_mask_bce_4: 0.16907/0.33886, loss_mask_dice_4: 0.85490/1.19267, loss_spatial_bce_4: 0.08626/0.09481, loss_spatial_dice_4: 0.27574/0.22903, loss_spatial_ce_4: 0.04895/0.09522, loss_grounding_bce_4: 0.03683/0.08708, loss_grounding_dice_4: 0.10579/0.18182, loss_grounding_ce_4: 0.02036/0.28333, loss_mask_ce_5: 0.87363/0.93852, loss_mask_bce_5: 0.16146/0.34109, loss_mask_dice_5: 0.88547/1.19926, loss_spatial_bce_5: 0.06558/0.09661, loss_spatial_dice_5: 0.26727/0.23279, loss_spatial_ce_5: 0.12709/0.11033, loss_grounding_bce_5: 0.03783/0.08747, loss_grounding_dice_5: 0.10897/0.18301, loss_grounding_ce_5: 0.03028/0.29583, loss_mask_ce_6: 0.89204/0.97772, loss_mask_bce_6: 0.16183/0.34380, loss_mask_dice_6: 0.80164/1.20213, loss_spatial_bce_6: 0.10864/0.10238, loss_spatial_dice_6: 0.27933/0.23528, loss_spatial_ce_6: 0.10831/0.13630, loss_grounding_bce_6: 0.03783/0.08823, loss_grounding_dice_6: 0.10455/0.18325, loss_grounding_ce_6: 0.06205/0.31219, loss_mask_ce_7: 0.97373/1.02267, loss_mask_bce_7: 0.15414/0.35169, loss_mask_dice_7: 0.86738/1.25716, loss_spatial_bce_7: 0.16215/0.11082, loss_spatial_dice_7: 0.34174/0.26305, loss_spatial_ce_7: 0.06438/0.17267, loss_grounding_bce_7: 0.04050/0.09015, loss_grounding_dice_7: 0.11473/0.19048, loss_grounding_ce_7: 0.06544/0.34469, loss_mask_ce_8: 0.89787/1.13177, loss_mask_bce_8: 0.19027/0.36523, loss_mask_dice_8: 0.95608/1.33059, loss_spatial_bce_8: 0.10870/0.13161, loss_spatial_dice_8: 0.33480/0.30211, loss_spatial_ce_8: 0.20460/0.22987, loss_grounding_bce_8: 0.05260/0.09385, loss_grounding_dice_8: 0.11929/0.20154, loss_grounding_ce_8: 0.34039/0.41275, loss_mask_ce_9: 3.81657/3.68131, loss_mask_bce_9: 0.19886/0.39224, loss_mask_dice_9: 1.64327/1.90391, loss_spatial_bce_9: 0.23754/0.33385, loss_spatial_dice_9: 0.88133/0.82270, loss_spatial_ce_9: 1.77288/1.50264, loss_grounding_bce_9: 0.06209/0.10539, loss_grounding_dice_9: 0.18571/0.28091, loss_grounding_ce_9: 1.03716/0.67778] items per batch[64] items per second[0.23] total items[2899200] mini batches[ 45300] memory[7345] epoch remaining[0:17:22]
INFO:trainer.default_trainer:epochs[    24] optim steps[45400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28512/0.90519, loss_mask_bce_0: 0.47099/0.33466, loss_mask_dice_0: 0.31536/1.16549, loss_spatial_bce_0: 0.22216/0.08846, loss_spatial_dice_0: 0.16052/0.21139, loss_spatial_ce_0: 0.04756/0.06632, loss_grounding_bce_0: 0.30750/0.08619, loss_grounding_dice_0: 0.21659/0.17868, loss_grounding_ce_0: 0.16762/0.27398, loss_mask_ce_1: 0.25355/0.90560, loss_mask_bce_1: 0.45984/0.33549, loss_mask_dice_1: 0.30776/1.17216, loss_spatial_bce_1: 0.22054/0.08907, loss_spatial_dice_1: 0.15530/0.21549, loss_spatial_ce_1: 0.05379/0.07195, loss_grounding_bce_1: 0.29550/0.08636, loss_grounding_dice_1: 0.20270/0.17948, loss_grounding_ce_1: 0.13344/0.27556, loss_mask_ce_2: 0.24487/0.91300, loss_mask_bce_2: 0.46185/0.33598, loss_mask_dice_2: 0.31977/1.17207, loss_spatial_bce_2: 0.21461/0.08977, loss_spatial_dice_2: 0.15084/0.21669, loss_spatial_ce_2: 0.05229/0.07547, loss_grounding_bce_2: 0.29218/0.08645, loss_grounding_dice_2: 0.19928/0.17925, loss_grounding_ce_2: 0.16053/0.27859, loss_mask_ce_3: 0.26283/0.92242, loss_mask_bce_3: 0.46520/0.33702, loss_mask_dice_3: 0.32069/1.16935, loss_spatial_bce_3: 0.21374/0.09068, loss_spatial_dice_3: 0.15487/0.21735, loss_spatial_ce_3: 0.05552/0.07928, loss_grounding_bce_3: 0.30195/0.08670, loss_grounding_dice_3: 0.20563/0.17901, loss_grounding_ce_3: 0.13570/0.28027, loss_mask_ce_4: 0.27775/0.92265, loss_mask_bce_4: 0.49202/0.33894, loss_mask_dice_4: 0.31620/1.19306, loss_spatial_bce_4: 0.22044/0.09482, loss_spatial_dice_4: 0.15406/0.22904, loss_spatial_ce_4: 0.06303/0.09523, loss_grounding_bce_4: 0.30178/0.08711, loss_grounding_dice_4: 0.20197/0.18186, loss_grounding_ce_4: 0.23309/0.28325, loss_mask_ce_5: 0.33817/0.93853, loss_mask_bce_5: 0.47746/0.34117, loss_mask_dice_5: 0.31085/1.19966, loss_spatial_bce_5: 0.23486/0.09662, loss_spatial_dice_5: 0.14252/0.23281, loss_spatial_ce_5: 0.05459/0.11032, loss_grounding_bce_5: 0.30866/0.08750, loss_grounding_dice_5: 0.19050/0.18305, loss_grounding_ce_5: 0.20245/0.29585, loss_mask_ce_6: 0.47247/0.97770, loss_mask_bce_6: 0.47720/0.34389, loss_mask_dice_6: 0.30525/1.20254, loss_spatial_bce_6: 0.23828/0.10238, loss_spatial_dice_6: 0.15020/0.23530, loss_spatial_ce_6: 0.06853/0.13630, loss_grounding_bce_6: 0.33594/0.08826, loss_grounding_dice_6: 0.20173/0.18329, loss_grounding_ce_6: 0.21446/0.31209, loss_mask_ce_7: 0.57203/1.02265, loss_mask_bce_7: 0.44301/0.35176, loss_mask_dice_7: 0.30397/1.25756, loss_spatial_bce_7: 0.22907/0.11082, loss_spatial_dice_7: 0.15083/0.26307, loss_spatial_ce_7: 0.09215/0.17264, loss_grounding_bce_7: 0.29764/0.09018, loss_grounding_dice_7: 0.19324/0.19051, loss_grounding_ce_7: 0.32533/0.34462, loss_mask_ce_8: 0.60887/1.13173, loss_mask_bce_8: 0.47966/0.36533, loss_mask_dice_8: 0.35780/1.33095, loss_spatial_bce_8: 0.23349/0.13161, loss_spatial_dice_8: 0.17319/0.30214, loss_spatial_ce_8: 0.25781/0.22981, loss_grounding_bce_8: 0.30490/0.09388, loss_grounding_dice_8: 0.17585/0.20157, loss_grounding_ce_8: 0.47301/0.41272, loss_mask_ce_9: 3.50542/3.68138, loss_mask_bce_9: 0.46093/0.39234, loss_mask_dice_9: 0.43066/1.90453, loss_spatial_bce_9: 0.56108/0.33383, loss_spatial_dice_9: 0.69266/0.82270, loss_spatial_ce_9: 1.09758/1.50281, loss_grounding_bce_9: 0.28501/0.10542, loss_grounding_dice_9: 0.21112/0.28094, loss_grounding_ce_9: 1.06363/0.67767] items per batch[64] items per second[0.22] total items[2905600] mini batches[ 45400] memory[7345] epoch remaining[0:12:45]
INFO:trainer.default_trainer:epochs[    24] optim steps[45500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27852/0.90507, loss_mask_bce_0: 0.19455/0.33453, loss_mask_dice_0: 0.24901/1.16534, loss_spatial_bce_0: 0.11008/0.08844, loss_spatial_dice_0: 0.11562/0.21137, loss_spatial_ce_0: 0.12919/0.06631, loss_grounding_bce_0: 0.10991/0.08618, loss_grounding_dice_0: 0.14845/0.17869, loss_grounding_ce_0: 0.07117/0.27395, loss_mask_ce_1: 0.23902/0.90549, loss_mask_bce_1: 0.19128/0.33537, loss_mask_dice_1: 0.25245/1.17200, loss_spatial_bce_1: 0.10891/0.08906, loss_spatial_dice_1: 0.12430/0.21547, loss_spatial_ce_1: 0.12970/0.07192, loss_grounding_bce_1: 0.10006/0.08635, loss_grounding_dice_1: 0.14820/0.17948, loss_grounding_ce_1: 0.07507/0.27554, loss_mask_ce_2: 0.23549/0.91286, loss_mask_bce_2: 0.19019/0.33586, loss_mask_dice_2: 0.24963/1.17192, loss_spatial_bce_2: 0.11516/0.08976, loss_spatial_dice_2: 0.12426/0.21668, loss_spatial_ce_2: 0.13184/0.07547, loss_grounding_bce_2: 0.10337/0.08644, loss_grounding_dice_2: 0.13904/0.17927, loss_grounding_ce_2: 0.06921/0.27857, loss_mask_ce_3: 0.23393/0.92229, loss_mask_bce_3: 0.18672/0.33690, loss_mask_dice_3: 0.25205/1.16919, loss_spatial_bce_3: 0.11114/0.09067, loss_spatial_dice_3: 0.13017/0.21734, loss_spatial_ce_3: 0.13160/0.07927, loss_grounding_bce_3: 0.10316/0.08669, loss_grounding_dice_3: 0.15637/0.17901, loss_grounding_ce_3: 0.07928/0.28026, loss_mask_ce_4: 0.24131/0.92250, loss_mask_bce_4: 0.17934/0.33882, loss_mask_dice_4: 0.25854/1.19288, loss_spatial_bce_4: 0.11136/0.09480, loss_spatial_dice_4: 0.13345/0.22903, loss_spatial_ce_4: 0.13637/0.09522, loss_grounding_bce_4: 0.09628/0.08710, loss_grounding_dice_4: 0.14825/0.18186, loss_grounding_ce_4: 0.08989/0.28324, loss_mask_ce_5: 0.28225/0.93841, loss_mask_bce_5: 0.18553/0.34106, loss_mask_dice_5: 0.25638/1.19946, loss_spatial_bce_5: 0.10651/0.09660, loss_spatial_dice_5: 0.15988/0.23279, loss_spatial_ce_5: 0.16511/0.11029, loss_grounding_bce_5: 0.09793/0.08749, loss_grounding_dice_5: 0.16520/0.18304, loss_grounding_ce_5: 0.13959/0.29588, loss_mask_ce_6: 0.29312/0.97756, loss_mask_bce_6: 0.18882/0.34378, loss_mask_dice_6: 0.26485/1.20235, loss_spatial_bce_6: 0.10670/0.10236, loss_spatial_dice_6: 0.12064/0.23529, loss_spatial_ce_6: 0.16349/0.13629, loss_grounding_bce_6: 0.10275/0.08824, loss_grounding_dice_6: 0.15254/0.18330, loss_grounding_ce_6: 0.16328/0.31207, loss_mask_ce_7: 0.25867/1.02254, loss_mask_bce_7: 0.19437/0.35163, loss_mask_dice_7: 0.28717/1.25735, loss_spatial_bce_7: 0.11824/0.11080, loss_spatial_dice_7: 0.14264/0.26306, loss_spatial_ce_7: 0.16059/0.17260, loss_grounding_bce_7: 0.10059/0.09017, loss_grounding_dice_7: 0.15928/0.19052, loss_grounding_ce_7: 0.12159/0.34458, loss_mask_ce_8: 0.31927/1.13154, loss_mask_bce_8: 0.20745/0.36520, loss_mask_dice_8: 0.31326/1.33070, loss_spatial_bce_8: 0.12155/0.13159, loss_spatial_dice_8: 0.16286/0.30213, loss_spatial_ce_8: 0.40463/0.22980, loss_grounding_bce_8: 0.10696/0.09387, loss_grounding_dice_8: 0.14855/0.20157, loss_grounding_ce_8: 0.12338/0.41270, loss_mask_ce_9: 2.87530/3.68104, loss_mask_bce_9: 0.25469/0.39219, loss_mask_dice_9: 0.46478/1.90399, loss_spatial_bce_9: 0.59644/0.33382, loss_spatial_dice_9: 0.67934/0.82267, loss_spatial_ce_9: 0.99256/1.50288, loss_grounding_bce_9: 0.14832/0.10541, loss_grounding_dice_9: 0.28382/0.28093, loss_grounding_ce_9: 0.19708/0.67779] items per batch[64] items per second[0.23] total items[2912000] mini batches[ 45500] memory[7345] epoch remaining[0:08:07]
INFO:trainer.default_trainer:epochs[    24] optim steps[45600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45832/0.90495, loss_mask_bce_0: 0.30926/0.33453, loss_mask_dice_0: 0.51912/1.16524, loss_spatial_bce_0: 0.11667/0.08842, loss_spatial_dice_0: 0.14096/0.21137, loss_spatial_ce_0: 0.04252/0.06628, loss_grounding_bce_0: 0.08548/0.08618, loss_grounding_dice_0: 0.09396/0.17867, loss_grounding_ce_0: 0.30528/0.27389, loss_mask_ce_1: 0.54907/0.90542, loss_mask_bce_1: 0.31501/0.33537, loss_mask_dice_1: 0.54276/1.17186, loss_spatial_bce_1: 0.12387/0.08904, loss_spatial_dice_1: 0.15457/0.21547, loss_spatial_ce_1: 0.04049/0.07190, loss_grounding_bce_1: 0.08133/0.08635, loss_grounding_dice_1: 0.08804/0.17946, loss_grounding_ce_1: 0.34626/0.27549, loss_mask_ce_2: 0.30265/0.91276, loss_mask_bce_2: 0.32222/0.33586, loss_mask_dice_2: 0.53964/1.17183, loss_spatial_bce_2: 0.11305/0.08974, loss_spatial_dice_2: 0.16731/0.21668, loss_spatial_ce_2: 0.04255/0.07544, loss_grounding_bce_2: 0.08185/0.08644, loss_grounding_dice_2: 0.08876/0.17925, loss_grounding_ce_2: 0.33822/0.27852, loss_mask_ce_3: 0.33349/0.92219, loss_mask_bce_3: 0.31790/0.33690, loss_mask_dice_3: 0.50961/1.16911, loss_spatial_bce_3: 0.13068/0.09065, loss_spatial_dice_3: 0.16848/0.21734, loss_spatial_ce_3: 0.04214/0.07926, loss_grounding_bce_3: 0.08293/0.08669, loss_grounding_dice_3: 0.09666/0.17899, loss_grounding_ce_3: 0.32841/0.28020, loss_mask_ce_4: 0.30299/0.92238, loss_mask_bce_4: 0.33296/0.33883, loss_mask_dice_4: 0.54858/1.19278, loss_spatial_bce_4: 0.13292/0.09477, loss_spatial_dice_4: 0.20598/0.22903, loss_spatial_ce_4: 0.05217/0.09520, loss_grounding_bce_4: 0.08536/0.08711, loss_grounding_dice_4: 0.09467/0.18184, loss_grounding_ce_4: 0.36320/0.28314, loss_mask_ce_5: 0.43996/0.93830, loss_mask_bce_5: 0.32462/0.34106, loss_mask_dice_5: 0.54136/1.19937, loss_spatial_bce_5: 0.12305/0.09658, loss_spatial_dice_5: 0.19708/0.23279, loss_spatial_ce_5: 0.06519/0.11027, loss_grounding_bce_5: 0.08169/0.08749, loss_grounding_dice_5: 0.10059/0.18303, loss_grounding_ce_5: 0.33711/0.29579, loss_mask_ce_6: 0.54074/0.97747, loss_mask_bce_6: 0.32582/0.34378, loss_mask_dice_6: 0.59155/1.20226, loss_spatial_bce_6: 0.13234/0.10234, loss_spatial_dice_6: 0.17682/0.23531, loss_spatial_ce_6: 0.10189/0.13628, loss_grounding_bce_6: 0.08236/0.08824, loss_grounding_dice_6: 0.08764/0.18329, loss_grounding_ce_6: 0.21775/0.31203, loss_mask_ce_7: 0.22999/1.02248, loss_mask_bce_7: 0.37120/0.35164, loss_mask_dice_7: 0.75923/1.25726, loss_spatial_bce_7: 0.13257/0.11078, loss_spatial_dice_7: 0.17198/0.26308, loss_spatial_ce_7: 0.11903/0.17260, loss_grounding_bce_7: 0.08829/0.09017, loss_grounding_dice_7: 0.09639/0.19052, loss_grounding_ce_7: 0.25572/0.34450, loss_mask_ce_8: 0.46624/1.13145, loss_mask_bce_8: 0.35417/0.36522, loss_mask_dice_8: 0.72413/1.33065, loss_spatial_bce_8: 0.19496/0.13157, loss_spatial_dice_8: 0.30189/0.30214, loss_spatial_ce_8: 0.16307/0.22980, loss_grounding_bce_8: 0.08090/0.09387, loss_grounding_dice_8: 0.10399/0.20157, loss_grounding_ce_8: 0.40331/0.41254, loss_mask_ce_9: 3.12656/3.68079, loss_mask_bce_9: 0.45217/0.39220, loss_mask_dice_9: 1.26080/1.90400, loss_spatial_bce_9: 0.74172/0.33379, loss_spatial_dice_9: 0.88720/0.82269, loss_spatial_ce_9: 1.77217/1.50292, loss_grounding_bce_9: 0.08917/0.10541, loss_grounding_dice_9: 0.14147/0.28091, loss_grounding_ce_9: 0.98361/0.67774] items per batch[64] items per second[0.24] total items[2918400] mini batches[ 45600] memory[7345] epoch remaining[0:03:28]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00045675.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0034 s/iter. Inference: 0.2189 s/iter. Eval: 0.0979 s/iter. Total: 0.3201 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2249 s/iter. Eval: 0.0816 s/iter. Total: 0.3096 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2273 s/iter. Eval: 0.0818 s/iter. Total: 0.3123 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2271 s/iter. Eval: 0.0791 s/iter. Total: 0.3095 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2253 s/iter. Eval: 0.0771 s/iter. Total: 0.3057 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2276 s/iter. Eval: 0.0764 s/iter. Total: 0.3073 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2291 s/iter. Eval: 0.0764 s/iter. Total: 0.3087 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2286 s/iter. Eval: 0.0756 s/iter. Total: 0.3075 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2289 s/iter. Eval: 0.0759 s/iter. Total: 0.3081 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval04pg1gfy ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.144 | 82.062 | 60.183 |      133      |
| Things | 54.993 | 82.907 | 65.672 |      80       |
| Stuff  | 42.825 | 80.786 | 51.897 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.26 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.21s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.06 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.491
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.508
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.297
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.718
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.874 | 61.085 | 40.874 | 19.697 | 41.788 | 60.189 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.752 | bicycle      | 18.059 | car            | 37.365 |
| motorcycle    | 34.901 | airplane     | 55.116 | bus            | 65.497 |
| train         | 68.254 | truck        | 36.007 | boat           | 23.129 |
| traffic light | 25.690 | fire hydrant | 64.956 | stop sign      | 63.525 |
| parking meter | 43.236 | bench        | 20.446 | bird           | 29.783 |
| cat           | 73.044 | dog          | 66.312 | horse          | 45.949 |
| sheep         | 46.880 | cow          | 50.092 | elephant       | 60.945 |
| bear          | 77.769 | zebra        | 60.790 | giraffe        | 56.317 |
| backpack      | 15.717 | umbrella     | 48.932 | handbag        | 13.955 |
| tie           | 32.883 | suitcase     | 40.874 | frisbee        | 67.130 |
| skis          | 4.995  | snowboard    | 22.111 | sports ball    | 46.736 |
| kite          | 34.419 | baseball bat | 27.959 | baseball glove | 42.149 |
| skateboard    | 35.868 | surfboard    | 35.683 | tennis racket  | 56.272 |
| bottle        | 35.054 | wine glass   | 27.190 | cup            | 40.989 |
| fork          | 15.661 | knife        | 13.079 | spoon          | 14.550 |
| bowl          | 32.842 | banana       | 20.289 | apple          | 20.564 |
| sandwich      | 42.470 | orange       | 29.983 | broccoli       | 22.098 |
| carrot        | 20.536 | hot dog      | 26.089 | pizza          | 51.153 |
| donut         | 45.802 | cake         | 42.628 | chair          | 21.359 |
| couch         | 40.822 | potted plant | 18.266 | bed            | 40.160 |
| dining table  | 12.713 | toilet       | 66.758 | tv             | 63.249 |
| laptop        | 63.445 | mouse        | 60.168 | remote         | 30.790 |
| keyboard      | 47.412 | cell phone   | 39.943 | microwave      | 55.515 |
| oven          | 33.389 | toaster      | 27.092 | sink           | 38.138 |
| refrigerator  | 59.246 | book         | 8.243  | clock          | 51.301 |
| vase          | 33.427 | scissors     | 23.620 | teddy bear     | 50.775 |
| hair drier    | 11.333 | toothbrush   | 17.290 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.56312879257353, 'fwIoU': 68.95133884549303, 'IoU-person': 87.4932563638428, 'IoU-bicycle': 68.91678046136965, 'IoU-car': 68.97546090402906, 'IoU-motorcycle': 82.7905740041128, 'IoU-airplane': 81.25794964049959, 'IoU-bus': 87.10500645082668, 'IoU-train': 85.40637044563738, 'IoU-truck': 64.94881844450234, 'IoU-boat': 67.4234847532429, 'IoU-traffic light': 75.67895453984895, 'IoU-fire hydrant': 85.5103934927531, 'IoU-stop sign': 82.11724954606457, 'IoU-parking meter': 82.57083953924985, 'IoU-bench': 54.97368974128355, 'IoU-bird': 75.70292886184198, 'IoU-cat': 80.89960486491447, 'IoU-dog': 79.06733781160996, 'IoU-horse': 85.5107001095423, 'IoU-sheep': 88.42093173016244, 'IoU-cow': 79.6306394899089, 'IoU-elephant': 90.82537425407143, 'IoU-bear': 84.3073773692944, 'IoU-zebra': 89.98773818626921, 'IoU-giraffe': 85.07775769459876, 'IoU-backpack': 38.978222644344974, 'IoU-umbrella': 74.40838525170716, 'IoU-handbag': 38.63943176792794, 'IoU-tie': 69.48394556613003, 'IoU-suitcase': 80.65366668208294, 'IoU-frisbee': 81.99590888076858, 'IoU-skis': 51.02131614332042, 'IoU-snowboard': 69.22775652143973, 'IoU-sports ball': 63.28081263742059, 'IoU-kite': 65.00735622376115, 'IoU-baseball bat': 58.83853224619282, 'IoU-baseball glove': 77.4340765959609, 'IoU-skateboard': 81.99355313996898, 'IoU-surfboard': 75.20752423270946, 'IoU-tennis racket': 82.38135193739983, 'IoU-bottle': 67.33214626416232, 'IoU-wine glass': 72.75013446135507, 'IoU-cup': 60.268825573687344, 'IoU-fork': 55.18070122479914, 'IoU-knife': 51.48326281657204, 'IoU-spoon': 49.75565271269122, 'IoU-bowl': 54.829955223992535, 'IoU-banana': 83.87676112748895, 'IoU-apple': 56.076507386292526, 'IoU-sandwich': 63.3907823718326, 'IoU-orange': 75.01800340242298, 'IoU-broccoli': 68.91576754979211, 'IoU-carrot': 63.470178878883544, 'IoU-hot dog': 64.91154054542172, 'IoU-pizza': 83.5266206291525, 'IoU-donut': 64.31328362646458, 'IoU-cake': 68.9118994597863, 'IoU-chair': 53.891555072208355, 'IoU-couch': 66.96263182229133, 'IoU-potted plant': 32.483497173538936, 'IoU-bed': 67.72559940626807, 'IoU-dining table': 51.61370629318404, 'IoU-toilet': 82.30952851930228, 'IoU-tv': 72.26799177660727, 'IoU-laptop': 72.97220437605209, 'IoU-mouse': 71.95900026220062, 'IoU-remote': 45.10111649773739, 'IoU-keyboard': 62.6643756072804, 'IoU-cell phone': 73.53078543792702, 'IoU-microwave': 63.05333238115782, 'IoU-oven': 67.83186031862695, 'IoU-toaster': 66.38647191973925, 'IoU-sink': 68.36851777135425, 'IoU-refrigerator': 79.05994628868147, 'IoU-book': 52.249753941256714, 'IoU-clock': 70.43038487465701, 'IoU-vase': 53.364720790437914, 'IoU-scissors': 52.96084095805838, 'IoU-teddy bear': 79.18823697935555, 'IoU-hair drier': 32.299670411526414, 'IoU-toothbrush': 60.40178484693448, 'IoU-banner': 33.487706097156135, 'IoU-blanket': 10.133802258898415, 'IoU-bridge': 39.75197652684642, 'IoU-cardboard': 49.74517612823138, 'IoU-counter': 31.213809763579203, 'IoU-curtain': 65.56533765582095, 'IoU-door-stuff': 42.45434894128101, 'IoU-floor-wood': 63.39513924114375, 'IoU-flower': 40.66038257656165, 'IoU-fruit': 38.93285131966267, 'IoU-gravel': 29.22356272265566, 'IoU-house': 26.96148292004506, 'IoU-light': 40.70382185671087, 'IoU-mirror-stuff': 55.78742188743089, 'IoU-net': 43.27664064307303, 'IoU-pillow': 12.270658692475509, 'IoU-platform': 28.66771023372071, 'IoU-playingfield': 69.6940005043374, 'IoU-railroad': 61.80456310200192, 'IoU-river': 48.891534758404816, 'IoU-road': 65.07634231189557, 'IoU-roof': 10.7052362451244, 'IoU-sand': 64.08752583722801, 'IoU-sea': 84.99122892312522, 'IoU-shelf': 37.19900934525914, 'IoU-snow': 88.6157134849565, 'IoU-stairs': 26.848072132824413, 'IoU-tent': 9.557505811512428, 'IoU-towel': 33.71301659100631, 'IoU-wall-brick': 47.67198045488529, 'IoU-wall-stone': 25.816175932409653, 'IoU-wall-tile': 68.00079760869104, 'IoU-wall-wood': 36.41737203308184, 'IoU-water-other': 21.566236704487892, 'IoU-window-blind': 47.308681192855246, 'IoU-window-other': 47.38869417626783, 'IoU-tree-merged': 80.90668422012732, 'IoU-fence-merged': 51.96334738350882, 'IoU-ceiling-merged': 66.81064502569642, 'IoU-sky-other-merged': 93.34497240061752, 'IoU-cabinet-merged': 59.72452391373637, 'IoU-table-merged': 37.54359910851145, 'IoU-floor-other-merged': 48.590834372997946, 'IoU-pavement-merged': 52.77031017562655, 'IoU-mountain-merged': 55.27675934518028, 'IoU-grass-merged': 71.57749684338319, 'IoU-dirt-merged': 46.42572828957971, 'IoU-paper-merged': 34.36640015186699, 'IoU-food-other-merged': 35.9281711397484, 'IoU-building-other-merged': 58.41567502670677, 'IoU-rock-merged': 61.47152030240094, 'IoU-wall-other-merged': 64.40016960121082, 'IoU-rug-merged': 61.55317934393667, 'mACC': 72.64345691125914, 'pACC': 80.39685519656513, 'ACC-person': 92.60863324159399, 'ACC-bicycle': 77.7962464632131, 'ACC-car': 85.35877656623528, 'ACC-motorcycle': 87.69444353613059, 'ACC-airplane': 87.77864890416981, 'ACC-bus': 92.71602946223021, 'ACC-train': 94.8957993555523, 'ACC-truck': 77.78431682374162, 'ACC-boat': 79.29850243953574, 'ACC-traffic light': 90.15095506939824, 'ACC-fire hydrant': 95.2564083403434, 'ACC-stop sign': 85.12199481107154, 'ACC-parking meter': 87.15023126758481, 'ACC-bench': 69.30563367011622, 'ACC-bird': 80.20699587420609, 'ACC-cat': 88.38157536919084, 'ACC-dog': 82.7155417143485, 'ACC-horse': 91.26060203740347, 'ACC-sheep': 91.78194606012121, 'ACC-cow': 84.63193891467425, 'ACC-elephant': 93.45159867363442, 'ACC-bear': 86.46677401581883, 'ACC-zebra': 92.44875687768005, 'ACC-giraffe': 89.24287863107956, 'ACC-backpack': 61.99912796557436, 'ACC-umbrella': 81.39842494905425, 'ACC-handbag': 53.6071608610399, 'ACC-tie': 80.12576018861293, 'ACC-suitcase': 89.55393065467938, 'ACC-frisbee': 94.16290909090908, 'ACC-skis': 70.33121292551542, 'ACC-snowboard': 78.78817565085352, 'ACC-sports ball': 80.62300369026576, 'ACC-kite': 74.4022763711965, 'ACC-baseball bat': 77.26213577684706, 'ACC-baseball glove': 87.8418105963987, 'ACC-skateboard': 88.79813367685409, 'ACC-surfboard': 83.50986997228652, 'ACC-tennis racket': 87.72006083451707, 'ACC-bottle': 83.74771005112419, 'ACC-wine glass': 86.20813765842867, 'ACC-cup': 82.33153959578235, 'ACC-fork': 66.40978510066304, 'ACC-knife': 62.59251117461713, 'ACC-spoon': 70.30357957558047, 'ACC-bowl': 71.53391998311318, 'ACC-banana': 90.57860838552257, 'ACC-apple': 73.82870233743536, 'ACC-sandwich': 76.51392457471292, 'ACC-orange': 85.9506523006534, 'ACC-broccoli': 80.52462952227877, 'ACC-carrot': 76.26831501165088, 'ACC-hot dog': 73.41668518687985, 'ACC-pizza': 92.50768721702568, 'ACC-donut': 81.76113002652615, 'ACC-cake': 76.34613583750432, 'ACC-chair': 68.45430303330284, 'ACC-couch': 82.85605638047922, 'ACC-potted plant': 44.50289611825213, 'ACC-bed': 80.09506984727635, 'ACC-dining table': 76.5359025388484, 'ACC-toilet': 88.68439081849215, 'ACC-tv': 82.37536634004088, 'ACC-laptop': 89.97665008516694, 'ACC-mouse': 86.13176339873525, 'ACC-remote': 66.417148098399, 'ACC-keyboard': 67.76020672849538, 'ACC-cell phone': 79.29123956202918, 'ACC-microwave': 73.16987528342734, 'ACC-oven': 82.13831610370944, 'ACC-toaster': 78.46966565848719, 'ACC-sink': 84.16048626463207, 'ACC-refrigerator': 88.85502889801495, 'ACC-book': 68.88504805132946, 'ACC-clock': 76.2876525900457, 'ACC-vase': 60.62412035869673, 'ACC-scissors': 56.62238657962152, 'ACC-teddy bear': 83.9137690265509, 'ACC-hair drier': 45.04881450488145, 'ACC-toothbrush': 81.01719944405838, 'ACC-banner': 68.16133034106296, 'ACC-blanket': 12.478707099124298, 'ACC-bridge': 54.4558780207836, 'ACC-cardboard': 64.59915660898648, 'ACC-counter': 47.144683344726246, 'ACC-curtain': 76.27824090399706, 'ACC-door-stuff': 67.52150318857443, 'ACC-floor-wood': 78.239648008166, 'ACC-flower': 54.62702754269733, 'ACC-fruit': 58.428144673268726, 'ACC-gravel': 36.4483636763619, 'ACC-house': 32.26567697655457, 'ACC-light': 54.482998698464954, 'ACC-mirror-stuff': 70.50812342965799, 'ACC-net': 63.76761710929758, 'ACC-pillow': 23.65381620040497, 'ACC-platform': 46.19216860476261, 'ACC-playingfield': 90.31606820479908, 'ACC-railroad': 78.19568035471877, 'ACC-river': 74.56670286886701, 'ACC-road': 86.86783607046361, 'ACC-roof': 13.737119387842414, 'ACC-sand': 70.89263106366064, 'ACC-sea': 90.34083717774422, 'ACC-shelf': 59.21516091496147, 'ACC-snow': 95.00713160869616, 'ACC-stairs': 48.017155880860805, 'ACC-tent': 12.197525417065714, 'ACC-towel': 41.11853685219586, 'ACC-wall-brick': 61.021291765326424, 'ACC-wall-stone': 28.269490260252933, 'ACC-wall-tile': 80.73606881808233, 'ACC-wall-wood': 45.96068960718721, 'ACC-water-other': 33.714017266559964, 'ACC-window-blind': 57.53443198804871, 'ACC-window-other': 66.73801981290435, 'ACC-tree-merged': 89.0937623014409, 'ACC-fence-merged': 69.49039288545696, 'ACC-ceiling-merged': 79.6778429365689, 'ACC-sky-other-merged': 96.58073638421526, 'ACC-cabinet-merged': 76.30634315525644, 'ACC-table-merged': 48.788132062076755, 'ACC-floor-other-merged': 59.45076484322791, 'ACC-pavement-merged': 66.28265147365494, 'ACC-mountain-merged': 68.87493292071295, 'ACC-grass-merged': 82.19796751579163, 'ACC-dirt-merged': 67.40055560100029, 'ACC-paper-merged': 49.17186643832231, 'ACC-food-other-merged': 46.236414418326376, 'ACC-building-other-merged': 75.96292205217982, 'ACC-rock-merged': 82.7840087169959, 'ACC-wall-other-merged': 82.81979238463218, 'ACC-rug-merged': 80.03497278432602})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1545 s/iter. Inference: 0.5643 s/iter. Eval: 0.0000 s/iter. Total: 0.7189 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1575 s/iter. Inference: 0.5213 s/iter. Eval: 0.0000 s/iter. Total: 0.6789 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1726 s/iter. Inference: 0.5974 s/iter. Eval: 0.0000 s/iter. Total: 0.7702 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1731 s/iter. Inference: 0.6681 s/iter. Eval: 0.0000 s/iter. Total: 0.8414 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1715 s/iter. Inference: 0.6169 s/iter. Eval: 0.0000 s/iter. Total: 0.7885 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1698 s/iter. Inference: 0.6574 s/iter. Eval: 0.0000 s/iter. Total: 0.8274 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5030728709394205, 'noc@0.8': 2.8826455955516535, 'noc@0.85': 3.511267193444542, 'noc@0.9': 4.517705589698566, 'miou@iter1': 0.8374173591665124}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1005 s/iter. Eval: 0.0008 s/iter. Total: 0.1029 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.62844848632812, 'precision@0.6': 68.16944885253906, 'precision@0.7': 63.07811737060547, 'precision@0.8': 52.50680160522461, 'precision@0.9': 27.477651596069336, 'cIoU': 58.11716842651367, 'mIoU': 63.04206085205078}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.144332856066086, 'SQ': 82.06215607476001, 'RQ': 60.182579277093375, 'PQ_th': 54.99348310474828, 'SQ_th': 82.90742785461141, 'RQ_th': 65.67157252099888, 'PQ_st': 42.82486078258358, 'SQ_st': 80.78627414290884, 'RQ_st': 51.89730645610401}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.87414436698754, 'AP50': 61.08472479119701, 'AP75': 40.873815484339296, 'APs': 19.69666083549651, 'APm': 41.78771870797854, 'APl': 60.18875926020829, 'AP-person': 44.7524218312073, 'AP-bicycle': 18.0588160582638, 'AP-car': 37.36481857622522, 'AP-motorcycle': 34.90144112147209, 'AP-airplane': 55.116184151976455, 'AP-bus': 65.49718910019624, 'AP-train': 68.25430722236709, 'AP-truck': 36.007185356018866, 'AP-boat': 23.12859130859296, 'AP-traffic light': 25.68976591556379, 'AP-fire hydrant': 64.95589934572162, 'AP-stop sign': 63.52482551046293, 'AP-parking meter': 43.23636510257149, 'AP-bench': 20.445516654819766, 'AP-bird': 29.782972286213184, 'AP-cat': 73.04376169072651, 'AP-dog': 66.31181934649713, 'AP-horse': 45.94865939731116, 'AP-sheep': 46.880475424592454, 'AP-cow': 50.0921100221768, 'AP-elephant': 60.94514421853183, 'AP-bear': 77.76901960398784, 'AP-zebra': 60.790369111497085, 'AP-giraffe': 56.317101889593545, 'AP-backpack': 15.717119631275143, 'AP-umbrella': 48.932284348791214, 'AP-handbag': 13.95529200481446, 'AP-tie': 32.88265095728704, 'AP-suitcase': 40.873727902740185, 'AP-frisbee': 67.13004131748698, 'AP-skis': 4.995271300124996, 'AP-snowboard': 22.110955954771033, 'AP-sports ball': 46.73555284724028, 'AP-kite': 34.419351163065684, 'AP-baseball bat': 27.958879035733545, 'AP-baseball glove': 42.14875396466735, 'AP-skateboard': 35.868426346742055, 'AP-surfboard': 35.682504884951456, 'AP-tennis racket': 56.272220993992505, 'AP-bottle': 35.05414992786809, 'AP-wine glass': 27.19033657102658, 'AP-cup': 40.9887676762501, 'AP-fork': 15.661114322265268, 'AP-knife': 13.079395670414895, 'AP-spoon': 14.550498076384036, 'AP-bowl': 32.841609088889456, 'AP-banana': 20.289123436901193, 'AP-apple': 20.564175571207937, 'AP-sandwich': 42.46979853890258, 'AP-orange': 29.982614903844336, 'AP-broccoli': 22.098476541370474, 'AP-carrot': 20.53590409151812, 'AP-hot dog': 26.089366774427475, 'AP-pizza': 51.15296433446069, 'AP-donut': 45.80238464255942, 'AP-cake': 42.62781637890154, 'AP-chair': 21.359462071464907, 'AP-couch': 40.822156549239644, 'AP-potted plant': 18.266395021722538, 'AP-bed': 40.1600396413358, 'AP-dining table': 12.713294030978956, 'AP-toilet': 66.75771553514996, 'AP-tv': 63.24901997003977, 'AP-laptop': 63.44458900136186, 'AP-mouse': 60.167961539247095, 'AP-remote': 30.789535158079406, 'AP-keyboard': 47.411858488069555, 'AP-cell phone': 39.94347601238049, 'AP-microwave': 55.51522012832065, 'AP-oven': 33.389315495390036, 'AP-toaster': 27.0918385818515, 'AP-sink': 38.138378446746756, 'AP-refrigerator': 59.24642995663332, 'AP-book': 8.242910906257904, 'AP-clock': 51.3005751773021, 'AP-vase': 33.42668657819809, 'AP-scissors': 23.61960322422639, 'AP-teddy bear': 50.77497716338587, 'AP-hair drier': 11.333452658356926, 'AP-toothbrush': 17.29036857580025}), ('sem_seg', {'mIoU': 60.56312879257353, 'fwIoU': 68.95133884549303, 'IoU-person': 87.4932563638428, 'IoU-bicycle': 68.91678046136965, 'IoU-car': 68.97546090402906, 'IoU-motorcycle': 82.7905740041128, 'IoU-airplane': 81.25794964049959, 'IoU-bus': 87.10500645082668, 'IoU-train': 85.40637044563738, 'IoU-truck': 64.94881844450234, 'IoU-boat': 67.4234847532429, 'IoU-traffic light': 75.67895453984895, 'IoU-fire hydrant': 85.5103934927531, 'IoU-stop sign': 82.11724954606457, 'IoU-parking meter': 82.57083953924985, 'IoU-bench': 54.97368974128355, 'IoU-bird': 75.70292886184198, 'IoU-cat': 80.89960486491447, 'IoU-dog': 79.06733781160996, 'IoU-horse': 85.5107001095423, 'IoU-sheep': 88.42093173016244, 'IoU-cow': 79.6306394899089, 'IoU-elephant': 90.82537425407143, 'IoU-bear': 84.3073773692944, 'IoU-zebra': 89.98773818626921, 'IoU-giraffe': 85.07775769459876, 'IoU-backpack': 38.978222644344974, 'IoU-umbrella': 74.40838525170716, 'IoU-handbag': 38.63943176792794, 'IoU-tie': 69.48394556613003, 'IoU-suitcase': 80.65366668208294, 'IoU-frisbee': 81.99590888076858, 'IoU-skis': 51.02131614332042, 'IoU-snowboard': 69.22775652143973, 'IoU-sports ball': 63.28081263742059, 'IoU-kite': 65.00735622376115, 'IoU-baseball bat': 58.83853224619282, 'IoU-baseball glove': 77.4340765959609, 'IoU-skateboard': 81.99355313996898, 'IoU-surfboard': 75.20752423270946, 'IoU-tennis racket': 82.38135193739983, 'IoU-bottle': 67.33214626416232, 'IoU-wine glass': 72.75013446135507, 'IoU-cup': 60.268825573687344, 'IoU-fork': 55.18070122479914, 'IoU-knife': 51.48326281657204, 'IoU-spoon': 49.75565271269122, 'IoU-bowl': 54.829955223992535, 'IoU-banana': 83.87676112748895, 'IoU-apple': 56.076507386292526, 'IoU-sandwich': 63.3907823718326, 'IoU-orange': 75.01800340242298, 'IoU-broccoli': 68.91576754979211, 'IoU-carrot': 63.470178878883544, 'IoU-hot dog': 64.91154054542172, 'IoU-pizza': 83.5266206291525, 'IoU-donut': 64.31328362646458, 'IoU-cake': 68.9118994597863, 'IoU-chair': 53.891555072208355, 'IoU-couch': 66.96263182229133, 'IoU-potted plant': 32.483497173538936, 'IoU-bed': 67.72559940626807, 'IoU-dining table': 51.61370629318404, 'IoU-toilet': 82.30952851930228, 'IoU-tv': 72.26799177660727, 'IoU-laptop': 72.97220437605209, 'IoU-mouse': 71.95900026220062, 'IoU-remote': 45.10111649773739, 'IoU-keyboard': 62.6643756072804, 'IoU-cell phone': 73.53078543792702, 'IoU-microwave': 63.05333238115782, 'IoU-oven': 67.83186031862695, 'IoU-toaster': 66.38647191973925, 'IoU-sink': 68.36851777135425, 'IoU-refrigerator': 79.05994628868147, 'IoU-book': 52.249753941256714, 'IoU-clock': 70.43038487465701, 'IoU-vase': 53.364720790437914, 'IoU-scissors': 52.96084095805838, 'IoU-teddy bear': 79.18823697935555, 'IoU-hair drier': 32.299670411526414, 'IoU-toothbrush': 60.40178484693448, 'IoU-banner': 33.487706097156135, 'IoU-blanket': 10.133802258898415, 'IoU-bridge': 39.75197652684642, 'IoU-cardboard': 49.74517612823138, 'IoU-counter': 31.213809763579203, 'IoU-curtain': 65.56533765582095, 'IoU-door-stuff': 42.45434894128101, 'IoU-floor-wood': 63.39513924114375, 'IoU-flower': 40.66038257656165, 'IoU-fruit': 38.93285131966267, 'IoU-gravel': 29.22356272265566, 'IoU-house': 26.96148292004506, 'IoU-light': 40.70382185671087, 'IoU-mirror-stuff': 55.78742188743089, 'IoU-net': 43.27664064307303, 'IoU-pillow': 12.270658692475509, 'IoU-platform': 28.66771023372071, 'IoU-playingfield': 69.6940005043374, 'IoU-railroad': 61.80456310200192, 'IoU-river': 48.891534758404816, 'IoU-road': 65.07634231189557, 'IoU-roof': 10.7052362451244, 'IoU-sand': 64.08752583722801, 'IoU-sea': 84.99122892312522, 'IoU-shelf': 37.19900934525914, 'IoU-snow': 88.6157134849565, 'IoU-stairs': 26.848072132824413, 'IoU-tent': 9.557505811512428, 'IoU-towel': 33.71301659100631, 'IoU-wall-brick': 47.67198045488529, 'IoU-wall-stone': 25.816175932409653, 'IoU-wall-tile': 68.00079760869104, 'IoU-wall-wood': 36.41737203308184, 'IoU-water-other': 21.566236704487892, 'IoU-window-blind': 47.308681192855246, 'IoU-window-other': 47.38869417626783, 'IoU-tree-merged': 80.90668422012732, 'IoU-fence-merged': 51.96334738350882, 'IoU-ceiling-merged': 66.81064502569642, 'IoU-sky-other-merged': 93.34497240061752, 'IoU-cabinet-merged': 59.72452391373637, 'IoU-table-merged': 37.54359910851145, 'IoU-floor-other-merged': 48.590834372997946, 'IoU-pavement-merged': 52.77031017562655, 'IoU-mountain-merged': 55.27675934518028, 'IoU-grass-merged': 71.57749684338319, 'IoU-dirt-merged': 46.42572828957971, 'IoU-paper-merged': 34.36640015186699, 'IoU-food-other-merged': 35.9281711397484, 'IoU-building-other-merged': 58.41567502670677, 'IoU-rock-merged': 61.47152030240094, 'IoU-wall-other-merged': 64.40016960121082, 'IoU-rug-merged': 61.55317934393667, 'mACC': 72.64345691125914, 'pACC': 80.39685519656513, 'ACC-person': 92.60863324159399, 'ACC-bicycle': 77.7962464632131, 'ACC-car': 85.35877656623528, 'ACC-motorcycle': 87.69444353613059, 'ACC-airplane': 87.77864890416981, 'ACC-bus': 92.71602946223021, 'ACC-train': 94.8957993555523, 'ACC-truck': 77.78431682374162, 'ACC-boat': 79.29850243953574, 'ACC-traffic light': 90.15095506939824, 'ACC-fire hydrant': 95.2564083403434, 'ACC-stop sign': 85.12199481107154, 'ACC-parking meter': 87.15023126758481, 'ACC-bench': 69.30563367011622, 'ACC-bird': 80.20699587420609, 'ACC-cat': 88.38157536919084, 'ACC-dog': 82.7155417143485, 'ACC-horse': 91.26060203740347, 'ACC-sheep': 91.78194606012121, 'ACC-cow': 84.63193891467425, 'ACC-elephant': 93.45159867363442, 'ACC-bear': 86.46677401581883, 'ACC-zebra': 92.44875687768005, 'ACC-giraffe': 89.24287863107956, 'ACC-backpack': 61.99912796557436, 'ACC-umbrella': 81.39842494905425, 'ACC-handbag': 53.6071608610399, 'ACC-tie': 80.12576018861293, 'ACC-suitcase': 89.55393065467938, 'ACC-frisbee': 94.16290909090908, 'ACC-skis': 70.33121292551542, 'ACC-snowboard': 78.78817565085352, 'ACC-sports ball': 80.62300369026576, 'ACC-kite': 74.4022763711965, 'ACC-baseball bat': 77.26213577684706, 'ACC-baseball glove': 87.8418105963987, 'ACC-skateboard': 88.79813367685409, 'ACC-surfboard': 83.50986997228652, 'ACC-tennis racket': 87.72006083451707, 'ACC-bottle': 83.74771005112419, 'ACC-wine glass': 86.20813765842867, 'ACC-cup': 82.33153959578235, 'ACC-fork': 66.40978510066304, 'ACC-knife': 62.59251117461713, 'ACC-spoon': 70.30357957558047, 'ACC-bowl': 71.53391998311318, 'ACC-banana': 90.57860838552257, 'ACC-apple': 73.82870233743536, 'ACC-sandwich': 76.51392457471292, 'ACC-orange': 85.9506523006534, 'ACC-broccoli': 80.52462952227877, 'ACC-carrot': 76.26831501165088, 'ACC-hot dog': 73.41668518687985, 'ACC-pizza': 92.50768721702568, 'ACC-donut': 81.76113002652615, 'ACC-cake': 76.34613583750432, 'ACC-chair': 68.45430303330284, 'ACC-couch': 82.85605638047922, 'ACC-potted plant': 44.50289611825213, 'ACC-bed': 80.09506984727635, 'ACC-dining table': 76.5359025388484, 'ACC-toilet': 88.68439081849215, 'ACC-tv': 82.37536634004088, 'ACC-laptop': 89.97665008516694, 'ACC-mouse': 86.13176339873525, 'ACC-remote': 66.417148098399, 'ACC-keyboard': 67.76020672849538, 'ACC-cell phone': 79.29123956202918, 'ACC-microwave': 73.16987528342734, 'ACC-oven': 82.13831610370944, 'ACC-toaster': 78.46966565848719, 'ACC-sink': 84.16048626463207, 'ACC-refrigerator': 88.85502889801495, 'ACC-book': 68.88504805132946, 'ACC-clock': 76.2876525900457, 'ACC-vase': 60.62412035869673, 'ACC-scissors': 56.62238657962152, 'ACC-teddy bear': 83.9137690265509, 'ACC-hair drier': 45.04881450488145, 'ACC-toothbrush': 81.01719944405838, 'ACC-banner': 68.16133034106296, 'ACC-blanket': 12.478707099124298, 'ACC-bridge': 54.4558780207836, 'ACC-cardboard': 64.59915660898648, 'ACC-counter': 47.144683344726246, 'ACC-curtain': 76.27824090399706, 'ACC-door-stuff': 67.52150318857443, 'ACC-floor-wood': 78.239648008166, 'ACC-flower': 54.62702754269733, 'ACC-fruit': 58.428144673268726, 'ACC-gravel': 36.4483636763619, 'ACC-house': 32.26567697655457, 'ACC-light': 54.482998698464954, 'ACC-mirror-stuff': 70.50812342965799, 'ACC-net': 63.76761710929758, 'ACC-pillow': 23.65381620040497, 'ACC-platform': 46.19216860476261, 'ACC-playingfield': 90.31606820479908, 'ACC-railroad': 78.19568035471877, 'ACC-river': 74.56670286886701, 'ACC-road': 86.86783607046361, 'ACC-roof': 13.737119387842414, 'ACC-sand': 70.89263106366064, 'ACC-sea': 90.34083717774422, 'ACC-shelf': 59.21516091496147, 'ACC-snow': 95.00713160869616, 'ACC-stairs': 48.017155880860805, 'ACC-tent': 12.197525417065714, 'ACC-towel': 41.11853685219586, 'ACC-wall-brick': 61.021291765326424, 'ACC-wall-stone': 28.269490260252933, 'ACC-wall-tile': 80.73606881808233, 'ACC-wall-wood': 45.96068960718721, 'ACC-water-other': 33.714017266559964, 'ACC-window-blind': 57.53443198804871, 'ACC-window-other': 66.73801981290435, 'ACC-tree-merged': 89.0937623014409, 'ACC-fence-merged': 69.49039288545696, 'ACC-ceiling-merged': 79.6778429365689, 'ACC-sky-other-merged': 96.58073638421526, 'ACC-cabinet-merged': 76.30634315525644, 'ACC-table-merged': 48.788132062076755, 'ACC-floor-other-merged': 59.45076484322791, 'ACC-pavement-merged': 66.28265147365494, 'ACC-mountain-merged': 68.87493292071295, 'ACC-grass-merged': 82.19796751579163, 'ACC-dirt-merged': 67.40055560100029, 'ACC-paper-merged': 49.17186643832231, 'ACC-food-other-merged': 46.236414418326376, 'ACC-building-other-merged': 75.96292205217982, 'ACC-rock-merged': 82.7840087169959, 'ACC-wall-other-merged': 82.81979238463218, 'ACC-rug-merged': 80.03497278432602})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5030728709394205, 'noc@0.8': 2.8826455955516535, 'noc@0.85': 3.511267193444542, 'noc@0.9': 4.517705589698566, 'miou@iter1': 0.8374173591665124}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.62844848632812, 'precision@0.6': 68.16944885253906, 'precision@0.7': 63.07811737060547, 'precision@0.8': 52.50680160522461, 'precision@0.9': 27.477651596069336, 'cIoU': 58.11716842651367, 'mIoU': 63.04206085205078}}}
INFO:trainer.default_trainer:This epoch takes 1:27:51.768063
INFO:trainer.default_trainer:PROGRESS: 50.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 25 training.
INFO:trainer.default_trainer:epochs[    25] optim steps[45700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18238/0.90492, loss_mask_bce_0: 0.47372/0.33457, loss_mask_dice_0: 2.28123/1.16544, loss_spatial_bce_0: 0.04567/0.08840, loss_spatial_dice_0: 0.23068/0.21135, loss_spatial_ce_0: 0.00592/0.06625, loss_grounding_bce_0: 0.01435/0.08617, loss_grounding_dice_0: 0.27330/0.17869, loss_grounding_ce_0: 0.21644/0.27391, loss_mask_ce_1: 1.07101/0.90537, loss_mask_bce_1: 0.47732/0.33541, loss_mask_dice_1: 2.37678/1.17211, loss_spatial_bce_1: 0.04626/0.08901, loss_spatial_dice_1: 0.24777/0.21546, loss_spatial_ce_1: 0.02029/0.07187, loss_grounding_bce_1: 0.01650/0.08634, loss_grounding_dice_1: 0.35209/0.17948, loss_grounding_ce_1: 0.22314/0.27550, loss_mask_ce_2: 1.06394/0.91273, loss_mask_bce_2: 0.52784/0.33590, loss_mask_dice_2: 2.33855/1.17201, loss_spatial_bce_2: 0.04653/0.08972, loss_spatial_dice_2: 0.23546/0.21666, loss_spatial_ce_2: 0.02835/0.07541, loss_grounding_bce_2: 0.01351/0.08643, loss_grounding_dice_2: 0.23601/0.17926, loss_grounding_ce_2: 0.28392/0.27857, loss_mask_ce_3: 1.14046/0.92216, loss_mask_bce_3: 0.50044/0.33694, loss_mask_dice_3: 2.35441/1.16933, loss_spatial_bce_3: 0.05236/0.09063, loss_spatial_dice_3: 0.24504/0.21732, loss_spatial_ce_3: 0.03297/0.07922, loss_grounding_bce_3: 0.01525/0.08668, loss_grounding_dice_3: 0.33616/0.17900, loss_grounding_ce_3: 0.23528/0.28024, loss_mask_ce_4: 1.16176/0.92235, loss_mask_bce_4: 0.50165/0.33886, loss_mask_dice_4: 2.26257/1.19304, loss_spatial_bce_4: 0.05200/0.09475, loss_spatial_dice_4: 0.27943/0.22903, loss_spatial_ce_4: 0.00742/0.09516, loss_grounding_bce_4: 0.01339/0.08710, loss_grounding_dice_4: 0.26906/0.18187, loss_grounding_ce_4: 0.33751/0.28316, loss_mask_ce_5: 1.07836/0.93828, loss_mask_bce_5: 0.48924/0.34110, loss_mask_dice_5: 2.13227/1.19964, loss_spatial_bce_5: 0.04860/0.09656, loss_spatial_dice_5: 0.25392/0.23278, loss_spatial_ce_5: 0.05718/0.11028, loss_grounding_bce_5: 0.01624/0.08748, loss_grounding_dice_5: 0.35292/0.18305, loss_grounding_ce_5: 0.21457/0.29579, loss_mask_ce_6: 1.12513/0.97747, loss_mask_bce_6: 0.50663/0.34382, loss_mask_dice_6: 2.41629/1.20249, loss_spatial_bce_6: 0.05605/0.10232, loss_spatial_dice_6: 0.25573/0.23530, loss_spatial_ce_6: 0.06196/0.13626, loss_grounding_bce_6: 0.01456/0.08824, loss_grounding_dice_6: 0.28704/0.18332, loss_grounding_ce_6: 0.24273/0.31202, loss_mask_ce_7: 1.33503/1.02246, loss_mask_bce_7: 0.56703/0.35169, loss_mask_dice_7: 2.41519/1.25751, loss_spatial_bce_7: 0.05790/0.11075, loss_spatial_dice_7: 0.29366/0.26307, loss_spatial_ce_7: 0.10974/0.17259, loss_grounding_bce_7: 0.01705/0.09016, loss_grounding_dice_7: 0.29744/0.19053, loss_grounding_ce_7: 0.39177/0.34452, loss_mask_ce_8: 1.76040/1.13150, loss_mask_bce_8: 0.51902/0.36526, loss_mask_dice_8: 2.56022/1.33098, loss_spatial_bce_8: 0.07630/0.13155, loss_spatial_dice_8: 0.37593/0.30213, loss_spatial_ce_8: 0.10634/0.22974, loss_grounding_bce_8: 0.01969/0.09386, loss_grounding_dice_8: 0.27676/0.20159, loss_grounding_ce_8: 0.76256/0.41253, loss_mask_ce_9: 4.26533/3.68121, loss_mask_bce_9: 0.54873/0.39227, loss_mask_dice_9: 3.73656/1.90460, loss_spatial_bce_9: 0.19420/0.33373, loss_spatial_dice_9: 0.90515/0.82272, loss_spatial_ce_9: 1.88060/1.50285, loss_grounding_bce_9: 0.01846/0.10541, loss_grounding_dice_9: 0.43409/0.28095, loss_grounding_ce_9: 0.74453/0.67778] items per batch[64] items per second[0.14] total items[2924800] mini batches[ 45700] memory[7345] epoch remaining[1:22:16]
INFO:trainer.default_trainer:epochs[    25] optim steps[45800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.41647/0.90482, loss_mask_bce_0: 0.66668/0.33460, loss_mask_dice_0: 1.59617/1.16513, loss_spatial_bce_0: 0.13615/0.08841, loss_spatial_dice_0: 0.30731/0.21132, loss_spatial_ce_0: 0.11943/0.06620, loss_grounding_bce_0: 0.02474/0.08618, loss_grounding_dice_0: 0.12440/0.17867, loss_grounding_ce_0: 0.34998/0.27385, loss_mask_ce_1: 1.45794/0.90527, loss_mask_bce_1: 0.67599/0.33544, loss_mask_dice_1: 1.61014/1.17182, loss_spatial_bce_1: 0.14317/0.08902, loss_spatial_dice_1: 0.32001/0.21543, loss_spatial_ce_1: 0.15621/0.07184, loss_grounding_bce_1: 0.02368/0.08635, loss_grounding_dice_1: 0.14210/0.17946, loss_grounding_ce_1: 0.38807/0.27544, loss_mask_ce_2: 1.50189/0.91264, loss_mask_bce_2: 0.68438/0.33594, loss_mask_dice_2: 1.59656/1.17171, loss_spatial_bce_2: 0.14009/0.08973, loss_spatial_dice_2: 0.30836/0.21664, loss_spatial_ce_2: 0.21240/0.07538, loss_grounding_bce_2: 0.02665/0.08645, loss_grounding_dice_2: 0.18744/0.17925, loss_grounding_ce_2: 0.34947/0.27849, loss_mask_ce_3: 1.47001/0.92207, loss_mask_bce_3: 0.67819/0.33696, loss_mask_dice_3: 1.53865/1.16907, loss_spatial_bce_3: 0.12587/0.09065, loss_spatial_dice_3: 0.29074/0.21730, loss_spatial_ce_3: 0.21673/0.07919, loss_grounding_bce_3: 0.02924/0.08670, loss_grounding_dice_3: 0.16486/0.17899, loss_grounding_ce_3: 0.35289/0.28018, loss_mask_ce_4: 1.68660/0.92226, loss_mask_bce_4: 0.67681/0.33890, loss_mask_dice_4: 1.57182/1.19274, loss_spatial_bce_4: 0.13069/0.09477, loss_spatial_dice_4: 0.31972/0.22901, loss_spatial_ce_4: 0.20470/0.09512, loss_grounding_bce_4: 0.02475/0.08712, loss_grounding_dice_4: 0.12188/0.18185, loss_grounding_ce_4: 0.40525/0.28305, loss_mask_ce_5: 1.61373/0.93818, loss_mask_bce_5: 0.66638/0.34114, loss_mask_dice_5: 1.63780/1.19935, loss_spatial_bce_5: 0.15555/0.09658, loss_spatial_dice_5: 0.35013/0.23276, loss_spatial_ce_5: 0.17659/0.11029, loss_grounding_bce_5: 0.02448/0.08750, loss_grounding_dice_5: 0.13216/0.18304, loss_grounding_ce_5: 0.39632/0.29571, loss_mask_ce_6: 1.54088/0.97733, loss_mask_bce_6: 0.72772/0.34385, loss_mask_dice_6: 1.72451/1.20217, loss_spatial_bce_6: 0.14648/0.10235, loss_spatial_dice_6: 0.33451/0.23529, loss_spatial_ce_6: 0.22551/0.13626, loss_grounding_bce_6: 0.02514/0.08826, loss_grounding_dice_6: 0.13774/0.18330, loss_grounding_ce_6: 0.40858/0.31189, loss_mask_ce_7: 1.38007/1.02233, loss_mask_bce_7: 0.70100/0.35173, loss_mask_dice_7: 1.76554/1.25726, loss_spatial_bce_7: 0.17514/0.11077, loss_spatial_dice_7: 0.42386/0.26307, loss_spatial_ce_7: 0.25123/0.17255, loss_grounding_bce_7: 0.02752/0.09018, loss_grounding_dice_7: 0.11702/0.19051, loss_grounding_ce_7: 0.50913/0.34437, loss_mask_ce_8: 1.59162/1.13134, loss_mask_bce_8: 0.68592/0.36529, loss_mask_dice_8: 1.71494/1.33067, loss_spatial_bce_8: 0.20982/0.13157, loss_spatial_dice_8: 0.45439/0.30210, loss_spatial_ce_8: 0.22649/0.22972, loss_grounding_bce_8: 0.03903/0.09388, loss_grounding_dice_8: 0.22385/0.20156, loss_grounding_ce_8: 0.76548/0.41238, loss_mask_ce_9: 5.62378/3.68073, loss_mask_bce_9: 0.83091/0.39231, loss_mask_dice_9: 3.40684/1.90419, loss_spatial_bce_9: 0.25568/0.33377, loss_spatial_dice_9: 0.93633/0.82269, loss_spatial_ce_9: 1.38677/1.50276, loss_grounding_bce_9: 0.24523/0.10543, loss_grounding_dice_9: 0.78965/0.28093, loss_grounding_ce_9: 0.33550/0.67757] items per batch[64] items per second[0.23] total items[2931200] mini batches[ 45800] memory[7345] epoch remaining[1:18:39]
INFO:trainer.default_trainer:epochs[    25] optim steps[45900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99315/0.90486, loss_mask_bce_0: 0.40495/0.33457, loss_mask_dice_0: 2.49127/1.16519, loss_spatial_bce_0: 0.02403/0.08839, loss_spatial_dice_0: 0.26217/0.21130, loss_spatial_ce_0: 0.11363/0.06617, loss_grounding_bce_0: 0.02440/0.08620, loss_grounding_dice_0: 0.21099/0.17868, loss_grounding_ce_0: 0.42176/0.27385, loss_mask_ce_1: 0.91325/0.90538, loss_mask_bce_1: 0.43252/0.33541, loss_mask_dice_1: 2.46714/1.17186, loss_spatial_bce_1: 0.03595/0.08900, loss_spatial_dice_1: 0.27343/0.21540, loss_spatial_ce_1: 0.07078/0.07182, loss_grounding_bce_1: 0.02117/0.08636, loss_grounding_dice_1: 0.25648/0.17945, loss_grounding_ce_1: 0.45768/0.27542, loss_mask_ce_2: 0.86611/0.91270, loss_mask_bce_2: 0.43355/0.33592, loss_mask_dice_2: 2.86201/1.17175, loss_spatial_bce_2: 0.03201/0.08971, loss_spatial_dice_2: 0.28099/0.21662, loss_spatial_ce_2: 0.07631/0.07537, loss_grounding_bce_2: 0.02070/0.08647, loss_grounding_dice_2: 0.16602/0.17925, loss_grounding_ce_2: 0.39939/0.27846, loss_mask_ce_3: 0.84319/0.92211, loss_mask_bce_3: 0.42314/0.33694, loss_mask_dice_3: 2.96118/1.16912, loss_spatial_bce_3: 0.03543/0.09063, loss_spatial_dice_3: 0.25619/0.21728, loss_spatial_ce_3: 0.08416/0.07916, loss_grounding_bce_3: 0.02403/0.08671, loss_grounding_dice_3: 0.21432/0.17899, loss_grounding_ce_3: 0.41112/0.28019, loss_mask_ce_4: 0.90689/0.92233, loss_mask_bce_4: 0.42884/0.33888, loss_mask_dice_4: 2.65973/1.19280, loss_spatial_bce_4: 0.03412/0.09475, loss_spatial_dice_4: 0.33845/0.22900, loss_spatial_ce_4: 0.12809/0.09510, loss_grounding_bce_4: 0.04232/0.08713, loss_grounding_dice_4: 0.28484/0.18185, loss_grounding_ce_4: 0.44004/0.28304, loss_mask_ce_5: 0.89279/0.93828, loss_mask_bce_5: 0.44036/0.34111, loss_mask_dice_5: 2.54154/1.19944, loss_spatial_bce_5: 0.03278/0.09656, loss_spatial_dice_5: 0.31001/0.23275, loss_spatial_ce_5: 0.15383/0.11026, loss_grounding_bce_5: 0.08716/0.08751, loss_grounding_dice_5: 0.22484/0.18304, loss_grounding_ce_5: 0.47290/0.29577, loss_mask_ce_6: 0.93154/0.97742, loss_mask_bce_6: 0.38017/0.34382, loss_mask_dice_6: 2.40499/1.20222, loss_spatial_bce_6: 0.06378/0.10234, loss_spatial_dice_6: 0.29657/0.23529, loss_spatial_ce_6: 0.13555/0.13625, loss_grounding_bce_6: 0.07855/0.08827, loss_grounding_dice_6: 0.26323/0.18331, loss_grounding_ce_6: 0.39973/0.31193, loss_mask_ce_7: 1.31455/1.02242, loss_mask_bce_7: 0.45398/0.35170, loss_mask_dice_7: 2.74718/1.25732, loss_spatial_bce_7: 0.07002/0.11076, loss_spatial_dice_7: 0.28191/0.26308, loss_spatial_ce_7: 0.15655/0.17253, loss_grounding_bce_7: 0.17611/0.09019, loss_grounding_dice_7: 0.22619/0.19050, loss_grounding_ce_7: 0.38811/0.34444, loss_mask_ce_8: 0.89564/1.13142, loss_mask_bce_8: 0.43604/0.36526, loss_mask_dice_8: 2.83356/1.33071, loss_spatial_bce_8: 0.06325/0.13156, loss_spatial_dice_8: 0.36853/0.30212, loss_spatial_ce_8: 0.15382/0.22971, loss_grounding_bce_8: 0.17449/0.09390, loss_grounding_dice_8: 0.28350/0.20157, loss_grounding_ce_8: 0.62766/0.41245, loss_mask_ce_9: 4.82329/3.68107, loss_mask_bce_9: 0.42658/0.39227, loss_mask_dice_9: 3.71143/1.90418, loss_spatial_bce_9: 0.14613/0.33373, loss_spatial_dice_9: 0.82919/0.82268, loss_spatial_ce_9: 1.31386/1.50269, loss_grounding_bce_9: 0.06564/0.10545, loss_grounding_dice_9: 0.38835/0.28092, loss_grounding_ce_9: 1.77685/0.67764] items per batch[64] items per second[0.23] total items[2937600] mini batches[ 45900] memory[7345] epoch remaining[1:13:28]
INFO:trainer.default_trainer:epochs[    25] optim steps[46000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37718/0.90481, loss_mask_bce_0: 0.12697/0.33457, loss_mask_dice_0: 0.22338/1.16497, loss_spatial_bce_0: 0.03422/0.08837, loss_spatial_dice_0: 0.06218/0.21128, loss_spatial_ce_0: 0.02235/0.06615, loss_grounding_bce_0: 0.05800/0.08619, loss_grounding_dice_0: 0.08234/0.17866, loss_grounding_ce_0: 0.07213/0.27370, loss_mask_ce_1: 0.36995/0.90537, loss_mask_bce_1: 0.12492/0.33541, loss_mask_dice_1: 0.22282/1.17165, loss_spatial_bce_1: 0.03504/0.08898, loss_spatial_dice_1: 0.07186/0.21537, loss_spatial_ce_1: 0.02411/0.07180, loss_grounding_bce_1: 0.06076/0.08635, loss_grounding_dice_1: 0.08672/0.17941, loss_grounding_ce_1: 0.07943/0.27529, loss_mask_ce_2: 0.39125/0.91265, loss_mask_bce_2: 0.13183/0.33592, loss_mask_dice_2: 0.24108/1.17153, loss_spatial_bce_2: 0.03652/0.08970, loss_spatial_dice_2: 0.07025/0.21660, loss_spatial_ce_2: 0.02379/0.07533, loss_grounding_bce_2: 0.05905/0.08646, loss_grounding_dice_2: 0.08188/0.17921, loss_grounding_ce_2: 0.07648/0.27834, loss_mask_ce_3: 0.43624/0.92204, loss_mask_bce_3: 0.13193/0.33695, loss_mask_dice_3: 0.22979/1.16891, loss_spatial_bce_3: 0.03584/0.09061, loss_spatial_dice_3: 0.06659/0.21726, loss_spatial_ce_3: 0.02355/0.07915, loss_grounding_bce_3: 0.06130/0.08671, loss_grounding_dice_3: 0.08686/0.17896, loss_grounding_ce_3: 0.08119/0.28005, loss_mask_ce_4: 0.47671/0.92231, loss_mask_bce_4: 0.12776/0.33888, loss_mask_dice_4: 0.21881/1.19259, loss_spatial_bce_4: 0.04073/0.09473, loss_spatial_dice_4: 0.07416/0.22897, loss_spatial_ce_4: 0.02425/0.09507, loss_grounding_bce_4: 0.05770/0.08712, loss_grounding_dice_4: 0.08077/0.18182, loss_grounding_ce_4: 0.09048/0.28291, loss_mask_ce_5: 0.48555/0.93820, loss_mask_bce_5: 0.13149/0.34112, loss_mask_dice_5: 0.26163/1.19923, loss_spatial_bce_5: 0.04368/0.09655, loss_spatial_dice_5: 0.08269/0.23273, loss_spatial_ce_5: 0.02768/0.11022, loss_grounding_bce_5: 0.06049/0.08750, loss_grounding_dice_5: 0.08670/0.18301, loss_grounding_ce_5: 0.08035/0.29560, loss_mask_ce_6: 0.57688/0.97733, loss_mask_bce_6: 0.12454/0.34383, loss_mask_dice_6: 0.23185/1.20200, loss_spatial_bce_6: 0.04574/0.10233, loss_spatial_dice_6: 0.08335/0.23528, loss_spatial_ce_6: 0.04856/0.13623, loss_grounding_bce_6: 0.05606/0.08826, loss_grounding_dice_6: 0.08162/0.18328, loss_grounding_ce_6: 0.07928/0.31178, loss_mask_ce_7: 0.53525/1.02239, loss_mask_bce_7: 0.12495/0.35171, loss_mask_dice_7: 0.22750/1.25710, loss_spatial_bce_7: 0.05609/0.11075, loss_spatial_dice_7: 0.10674/0.26307, loss_spatial_ce_7: 0.07327/0.17249, loss_grounding_bce_7: 0.05519/0.09018, loss_grounding_dice_7: 0.07840/0.19049, loss_grounding_ce_7: 0.10718/0.34427, loss_mask_ce_8: 0.52529/1.13144, loss_mask_bce_8: 0.12556/0.36527, loss_mask_dice_8: 0.24255/1.33050, loss_spatial_bce_8: 0.06399/0.13156, loss_spatial_dice_8: 0.11816/0.30210, loss_spatial_ce_8: 0.07394/0.22969, loss_grounding_bce_8: 0.05621/0.09389, loss_grounding_dice_8: 0.08179/0.20155, loss_grounding_ce_8: 0.15124/0.41228, loss_mask_ce_9: 3.91896/3.68104, loss_mask_bce_9: 0.17432/0.39232, loss_mask_dice_9: 0.42431/1.90399, loss_spatial_bce_9: 0.30306/0.33371, loss_spatial_dice_9: 0.75578/0.82267, loss_spatial_ce_9: 1.11504/1.50260, loss_grounding_bce_9: 0.10264/0.10546, loss_grounding_dice_9: 0.22287/0.28090, loss_grounding_ce_9: 0.05468/0.67733] items per batch[64] items per second[0.23] total items[2944000] mini batches[ 46000] memory[7345] epoch remaining[1:09:05]
INFO:trainer.default_trainer:epochs[    25] optim steps[46100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42233/0.90479, loss_mask_bce_0: 0.07538/0.33454, loss_mask_dice_0: 1.14647/1.16475, loss_spatial_bce_0: 0.01400/0.08837, loss_spatial_dice_0: 0.27683/0.21124, loss_spatial_ce_0: 0.05621/0.06610, loss_grounding_bce_0: 0.01514/0.08619, loss_grounding_dice_0: 0.21377/0.17864, loss_grounding_ce_0: 0.14978/0.27362, loss_mask_ce_1: 1.59721/0.90538, loss_mask_bce_1: 0.07874/0.33538, loss_mask_dice_1: 0.86628/1.17146, loss_spatial_bce_1: 0.01544/0.08897, loss_spatial_dice_1: 0.26894/0.21533, loss_spatial_ce_1: 0.17907/0.07175, loss_grounding_bce_1: 0.01690/0.08635, loss_grounding_dice_1: 0.28091/0.17939, loss_grounding_ce_1: 0.24597/0.27517, loss_mask_ce_2: 1.90206/0.91268, loss_mask_bce_2: 0.08068/0.33590, loss_mask_dice_2: 1.29184/1.17128, loss_spatial_bce_2: 0.01527/0.08969, loss_spatial_dice_2: 0.24440/0.21655, loss_spatial_ce_2: 0.28648/0.07529, loss_grounding_bce_2: 0.01780/0.08646, loss_grounding_dice_2: 0.21209/0.17918, loss_grounding_ce_2: 0.18292/0.27823, loss_mask_ce_3: 1.35770/0.92203, loss_mask_bce_3: 0.08019/0.33692, loss_mask_dice_3: 1.18685/1.16870, loss_spatial_bce_3: 0.01924/0.09060, loss_spatial_dice_3: 0.24810/0.21722, loss_spatial_ce_3: 0.06193/0.07909, loss_grounding_bce_3: 0.01652/0.08670, loss_grounding_dice_3: 0.22217/0.17893, loss_grounding_ce_3: 0.15818/0.27992, loss_mask_ce_4: 1.74519/0.92231, loss_mask_bce_4: 0.06701/0.33886, loss_mask_dice_4: 0.89751/1.19236, loss_spatial_bce_4: 0.01719/0.09472, loss_spatial_dice_4: 0.31216/0.22893, loss_spatial_ce_4: 0.11199/0.09502, loss_grounding_bce_4: 0.01566/0.08712, loss_grounding_dice_4: 0.21292/0.18179, loss_grounding_ce_4: 0.23140/0.28281, loss_mask_ce_5: 1.73006/0.93826, loss_mask_bce_5: 0.06845/0.34110, loss_mask_dice_5: 0.87243/1.19899, loss_spatial_bce_5: 0.02255/0.09654, loss_spatial_dice_5: 0.26961/0.23269, loss_spatial_ce_5: 0.07652/0.11018, loss_grounding_bce_5: 0.01460/0.08750, loss_grounding_dice_5: 0.20982/0.18299, loss_grounding_ce_5: 0.22644/0.29551, loss_mask_ce_6: 1.61168/0.97736, loss_mask_bce_6: 0.08117/0.34382, loss_mask_dice_6: 1.31565/1.20184, loss_spatial_bce_6: 0.02844/0.10232, loss_spatial_dice_6: 0.28343/0.23524, loss_spatial_ce_6: 0.15341/0.13619, loss_grounding_bce_6: 0.01348/0.08826, loss_grounding_dice_6: 0.20246/0.18326, loss_grounding_ce_6: 0.27683/0.31164, loss_mask_ce_7: 1.85935/1.02241, loss_mask_bce_7: 0.07357/0.35169, loss_mask_dice_7: 1.13297/1.25690, loss_spatial_bce_7: 0.03106/0.11075, loss_spatial_dice_7: 0.35122/0.26302, loss_spatial_ce_7: 0.09151/0.17241, loss_grounding_bce_7: 0.01243/0.09018, loss_grounding_dice_7: 0.23141/0.19047, loss_grounding_ce_7: 0.35043/0.34409, loss_mask_ce_8: 2.52735/1.13140, loss_mask_bce_8: 0.07965/0.36526, loss_mask_dice_8: 1.21147/1.33028, loss_spatial_bce_8: 0.03753/0.13154, loss_spatial_dice_8: 0.40922/0.30204, loss_spatial_ce_8: 0.26422/0.22962, loss_grounding_bce_8: 0.02417/0.09388, loss_grounding_dice_8: 0.33937/0.20153, loss_grounding_ce_8: 0.53287/0.41218, loss_mask_ce_9: 4.85890/3.68074, loss_mask_bce_9: 0.06815/0.39230, loss_mask_dice_9: 1.93303/1.90401, loss_spatial_bce_9: 0.09496/0.33371, loss_spatial_dice_9: 0.85423/0.82265, loss_spatial_ce_9: 1.49528/1.50246, loss_grounding_bce_9: 0.00958/0.10546, loss_grounding_dice_9: 0.40482/0.28089, loss_grounding_ce_9: 2.33079/0.67732] items per batch[64] items per second[0.23] total items[2950400] mini batches[ 46100] memory[7345] epoch remaining[1:04:28]
INFO:trainer.default_trainer:epochs[    25] optim steps[46200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.09176/0.90480, loss_mask_bce_0: 0.65725/0.33457, loss_mask_dice_0: 0.90612/1.16474, loss_spatial_bce_0: 0.23221/0.08838, loss_spatial_dice_0: 0.25788/0.21123, loss_spatial_ce_0: 0.02022/0.06606, loss_grounding_bce_0: 0.19819/0.08620, loss_grounding_dice_0: 0.19193/0.17868, loss_grounding_ce_0: 0.44204/0.27369, loss_mask_ce_1: 1.01113/0.90540, loss_mask_bce_1: 0.70551/0.33541, loss_mask_dice_1: 1.00075/1.17145, loss_spatial_bce_1: 0.21770/0.08898, loss_spatial_dice_1: 0.23756/0.21531, loss_spatial_ce_1: 0.02833/0.07171, loss_grounding_bce_1: 0.17682/0.08637, loss_grounding_dice_1: 0.17979/0.17943, loss_grounding_ce_1: 0.44174/0.27523, loss_mask_ce_2: 1.00063/0.91269, loss_mask_bce_2: 0.64933/0.33593, loss_mask_dice_2: 0.99115/1.17129, loss_spatial_bce_2: 0.26457/0.08970, loss_spatial_dice_2: 0.26395/0.21653, loss_spatial_ce_2: 0.04003/0.07524, loss_grounding_bce_2: 0.16799/0.08647, loss_grounding_dice_2: 0.17578/0.17922, loss_grounding_ce_2: 0.38101/0.27829, loss_mask_ce_3: 1.02805/0.92202, loss_mask_bce_3: 0.59383/0.33695, loss_mask_dice_3: 0.85400/1.16869, loss_spatial_bce_3: 0.27851/0.09062, loss_spatial_dice_3: 0.25816/0.21721, loss_spatial_ce_3: 0.05262/0.07905, loss_grounding_bce_3: 0.15957/0.08672, loss_grounding_dice_3: 0.20785/0.17897, loss_grounding_ce_3: 0.38411/0.27996, loss_mask_ce_4: 1.08787/0.92229, loss_mask_bce_4: 0.57021/0.33890, loss_mask_dice_4: 0.82913/1.19235, loss_spatial_bce_4: 0.31049/0.09474, loss_spatial_dice_4: 0.26410/0.22891, loss_spatial_ce_4: 0.09852/0.09497, loss_grounding_bce_4: 0.13687/0.08714, loss_grounding_dice_4: 0.18543/0.18182, loss_grounding_ce_4: 0.40683/0.28282, loss_mask_ce_5: 1.05690/0.93826, loss_mask_bce_5: 0.60702/0.34115, loss_mask_dice_5: 0.89130/1.19901, loss_spatial_bce_5: 0.26742/0.09655, loss_spatial_dice_5: 0.26020/0.23268, loss_spatial_ce_5: 0.17939/0.11013, loss_grounding_bce_5: 0.13316/0.08752, loss_grounding_dice_5: 0.17786/0.18303, loss_grounding_ce_5: 0.32434/0.29554, loss_mask_ce_6: 1.11193/0.97737, loss_mask_bce_6: 0.69601/0.34386, loss_mask_dice_6: 0.90236/1.20187, loss_spatial_bce_6: 0.31459/0.10235, loss_spatial_dice_6: 0.26844/0.23523, loss_spatial_ce_6: 0.10466/0.13615, loss_grounding_bce_6: 0.16807/0.08827, loss_grounding_dice_6: 0.19046/0.18331, loss_grounding_ce_6: 0.30729/0.31169, loss_mask_ce_7: 1.03907/1.02243, loss_mask_bce_7: 0.64419/0.35175, loss_mask_dice_7: 0.88078/1.25689, loss_spatial_bce_7: 0.27837/0.11077, loss_spatial_dice_7: 0.28942/0.26301, loss_spatial_ce_7: 0.06820/0.17235, loss_grounding_bce_7: 0.19358/0.09020, loss_grounding_dice_7: 0.20122/0.19052, loss_grounding_ce_7: 0.32967/0.34409, loss_mask_ce_8: 1.12422/1.13143, loss_mask_bce_8: 0.67976/0.36531, loss_mask_dice_8: 0.92984/1.33030, loss_spatial_bce_8: 0.26170/0.13155, loss_spatial_dice_8: 0.28037/0.30204, loss_spatial_ce_8: 0.60424/0.22962, loss_grounding_bce_8: 0.21657/0.09390, loss_grounding_dice_8: 0.21209/0.20158, loss_grounding_ce_8: 0.58473/0.41217, loss_mask_ce_9: 3.17914/3.68086, loss_mask_bce_9: 0.60642/0.39236, loss_mask_dice_9: 1.98654/1.90410, loss_spatial_bce_9: 0.42489/0.33374, loss_spatial_dice_9: 0.80383/0.82264, loss_spatial_ce_9: 1.50007/1.50236, loss_grounding_bce_9: 0.22946/0.10547, loss_grounding_dice_9: 0.27807/0.28096, loss_grounding_ce_9: 0.81502/0.67726] items per batch[64] items per second[0.22] total items[2956800] mini batches[ 46200] memory[7345] epoch remaining[1:00:15]
INFO:trainer.default_trainer:epochs[    25] optim steps[46300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47857/0.90477, loss_mask_bce_0: 0.53122/0.33467, loss_mask_dice_0: 0.87852/1.16500, loss_spatial_bce_0: 0.09939/0.08837, loss_spatial_dice_0: 0.19825/0.21119, loss_spatial_ce_0: 0.00447/0.06600, loss_grounding_bce_0: 0.10124/0.08623, loss_grounding_dice_0: 0.18392/0.17868, loss_grounding_ce_0: 0.43941/0.27364, loss_mask_ce_1: 0.44571/0.90536, loss_mask_bce_1: 0.53970/0.33552, loss_mask_dice_1: 0.89680/1.17171, loss_spatial_bce_1: 0.09866/0.08897, loss_spatial_dice_1: 0.19312/0.21527, loss_spatial_ce_1: 0.00638/0.07165, loss_grounding_bce_1: 0.10202/0.08640, loss_grounding_dice_1: 0.17937/0.17943, loss_grounding_ce_1: 0.40302/0.27515, loss_mask_ce_2: 0.48976/0.91267, loss_mask_bce_2: 0.52726/0.33605, loss_mask_dice_2: 0.88022/1.17153, loss_spatial_bce_2: 0.10344/0.08970, loss_spatial_dice_2: 0.19893/0.21650, loss_spatial_ce_2: 0.01061/0.07517, loss_grounding_bce_2: 0.10213/0.08650, loss_grounding_dice_2: 0.18703/0.17922, loss_grounding_ce_2: 0.41948/0.27823, loss_mask_ce_3: 0.49614/0.92200, loss_mask_bce_3: 0.53433/0.33707, loss_mask_dice_3: 0.89764/1.16895, loss_spatial_bce_3: 0.12002/0.09061, loss_spatial_dice_3: 0.20336/0.21717, loss_spatial_ce_3: 0.01251/0.07899, loss_grounding_bce_3: 0.09751/0.08675, loss_grounding_dice_3: 0.17280/0.17896, loss_grounding_ce_3: 0.44959/0.27989, loss_mask_ce_4: 0.45988/0.92229, loss_mask_bce_4: 0.54846/0.33901, loss_mask_dice_4: 0.93855/1.19259, loss_spatial_bce_4: 0.12605/0.09473, loss_spatial_dice_4: 0.21842/0.22888, loss_spatial_ce_4: 0.02255/0.09491, loss_grounding_bce_4: 0.10233/0.08717, loss_grounding_dice_4: 0.18615/0.18182, loss_grounding_ce_4: 0.44171/0.28275, loss_mask_ce_5: 0.56624/0.93830, loss_mask_bce_5: 0.56018/0.34125, loss_mask_dice_5: 0.91376/1.19925, loss_spatial_bce_5: 0.11493/0.09655, loss_spatial_dice_5: 0.23039/0.23265, loss_spatial_ce_5: 0.06150/0.11006, loss_grounding_bce_5: 0.10969/0.08755, loss_grounding_dice_5: 0.21660/0.18304, loss_grounding_ce_5: 0.36699/0.29548, loss_mask_ce_6: 0.50825/0.97737, loss_mask_bce_6: 0.55137/0.34396, loss_mask_dice_6: 0.85348/1.20210, loss_spatial_bce_6: 0.13995/0.10235, loss_spatial_dice_6: 0.23682/0.23520, loss_spatial_ce_6: 0.05342/0.13610, loss_grounding_bce_6: 0.12658/0.08831, loss_grounding_dice_6: 0.20765/0.18331, loss_grounding_ce_6: 0.51388/0.31160, loss_mask_ce_7: 0.71754/1.02242, loss_mask_bce_7: 0.57368/0.35185, loss_mask_dice_7: 0.99629/1.25715, loss_spatial_bce_7: 0.16432/0.11076, loss_spatial_dice_7: 0.25673/0.26297, loss_spatial_ce_7: 0.10812/0.17228, loss_grounding_bce_7: 0.14394/0.09023, loss_grounding_dice_7: 0.23329/0.19051, loss_grounding_ce_7: 0.40741/0.34396, loss_mask_ce_8: 0.71212/1.13140, loss_mask_bce_8: 0.62244/0.36542, loss_mask_dice_8: 1.11374/1.33063, loss_spatial_bce_8: 0.15420/0.13154, loss_spatial_dice_8: 0.26829/0.30200, loss_spatial_ce_8: 0.23654/0.22957, loss_grounding_bce_8: 0.16204/0.09394, loss_grounding_dice_8: 0.27344/0.20158, loss_grounding_ce_8: 0.41976/0.41212, loss_mask_ce_9: 4.59399/3.68101, loss_mask_bce_9: 0.89499/0.39249, loss_mask_dice_9: 1.88249/1.90470, loss_spatial_bce_9: 0.33566/0.33376, loss_spatial_dice_9: 0.81130/0.82262, loss_spatial_ce_9: 1.15372/1.50226, loss_grounding_bce_9: 0.26486/0.10551, loss_grounding_dice_9: 0.52103/0.28096, loss_grounding_ce_9: 0.35708/0.67716] items per batch[64] items per second[0.23] total items[2963200] mini batches[ 46300] memory[7345] epoch remaining[0:55:40]
INFO:trainer.default_trainer:epochs[    25] optim steps[46400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89753/0.90460, loss_mask_bce_0: 0.54156/0.33462, loss_mask_dice_0: 2.05736/1.16448, loss_spatial_bce_0: 0.06625/0.08836, loss_spatial_dice_0: 0.24948/0.21113, loss_spatial_ce_0: 0.28924/0.06596, loss_grounding_bce_0: 0.18640/0.08622, loss_grounding_dice_0: 0.17773/0.17865, loss_grounding_ce_0: 0.09894/0.27363, loss_mask_ce_1: 0.90002/0.90522, loss_mask_bce_1: 0.55927/0.33547, loss_mask_dice_1: 1.90103/1.17119, loss_spatial_bce_1: 0.07538/0.08895, loss_spatial_dice_1: 0.28263/0.21522, loss_spatial_ce_1: 0.03006/0.07159, loss_grounding_bce_1: 0.18340/0.08639, loss_grounding_dice_1: 0.16853/0.17939, loss_grounding_ce_1: 0.08527/0.27509, loss_mask_ce_2: 0.84288/0.91250, loss_mask_bce_2: 0.54120/0.33599, loss_mask_dice_2: 1.98826/1.17101, loss_spatial_bce_2: 0.07079/0.08968, loss_spatial_dice_2: 0.27570/0.21645, loss_spatial_ce_2: 0.01718/0.07511, loss_grounding_bce_2: 0.17838/0.08649, loss_grounding_dice_2: 0.16194/0.17920, loss_grounding_ce_2: 0.11411/0.27823, loss_mask_ce_3: 1.01637/0.92184, loss_mask_bce_3: 0.50948/0.33701, loss_mask_dice_3: 1.85238/1.16842, loss_spatial_bce_3: 0.06716/0.09060, loss_spatial_dice_3: 0.26055/0.21712, loss_spatial_ce_3: 0.01529/0.07893, loss_grounding_bce_3: 0.18515/0.08674, loss_grounding_dice_3: 0.16478/0.17894, loss_grounding_ce_3: 0.12340/0.27989, loss_mask_ce_4: 0.86881/0.92215, loss_mask_bce_4: 0.55666/0.33896, loss_mask_dice_4: 1.93446/1.19208, loss_spatial_bce_4: 0.08204/0.09472, loss_spatial_dice_4: 0.29027/0.22882, loss_spatial_ce_4: 0.01799/0.09485, loss_grounding_bce_4: 0.18510/0.08716, loss_grounding_dice_4: 0.16878/0.18179, loss_grounding_ce_4: 0.12461/0.28274, loss_mask_ce_5: 0.93333/0.93810, loss_mask_bce_5: 0.54312/0.34120, loss_mask_dice_5: 1.79576/1.19874, loss_spatial_bce_5: 0.08056/0.09654, loss_spatial_dice_5: 0.30622/0.23261, loss_spatial_ce_5: 0.04624/0.10999, loss_grounding_bce_5: 0.18850/0.08754, loss_grounding_dice_5: 0.18282/0.18300, loss_grounding_ce_5: 0.25984/0.29545, loss_mask_ce_6: 0.92972/0.97718, loss_mask_bce_6: 0.54758/0.34393, loss_mask_dice_6: 1.87159/1.20160, loss_spatial_bce_6: 0.07702/0.10234, loss_spatial_dice_6: 0.31045/0.23516, loss_spatial_ce_6: 0.05681/0.13605, loss_grounding_bce_6: 0.18555/0.08830, loss_grounding_dice_6: 0.17047/0.18328, loss_grounding_ce_6: 0.25548/0.31154, loss_mask_ce_7: 0.98959/1.02229, loss_mask_bce_7: 0.58615/0.35180, loss_mask_dice_7: 2.00668/1.25662, loss_spatial_bce_7: 0.08939/0.11077, loss_spatial_dice_7: 0.33434/0.26294, loss_spatial_ce_7: 0.06757/0.17225, loss_grounding_bce_7: 0.18906/0.09022, loss_grounding_dice_7: 0.18772/0.19049, loss_grounding_ce_7: 0.11907/0.34390, loss_mask_ce_8: 1.51634/1.13125, loss_mask_bce_8: 0.61325/0.36538, loss_mask_dice_8: 2.14936/1.33015, loss_spatial_bce_8: 0.10901/0.13155, loss_spatial_dice_8: 0.35564/0.30196, loss_spatial_ce_8: 0.15691/0.22949, loss_grounding_bce_8: 0.19121/0.09393, loss_grounding_dice_8: 0.16165/0.20155, loss_grounding_ce_8: 0.54246/0.41205, loss_mask_ce_9: 3.31451/3.68067, loss_mask_bce_9: 0.64234/0.39244, loss_mask_dice_9: 3.29444/1.90394, loss_spatial_bce_9: 0.25409/0.33379, loss_spatial_dice_9: 0.92700/0.82262, loss_spatial_ce_9: 1.28487/1.50216, loss_grounding_bce_9: 0.21779/0.10550, loss_grounding_dice_9: 0.28362/0.28094, loss_grounding_ce_9: 1.24517/0.67724] items per batch[64] items per second[0.23] total items[2969600] mini batches[ 46400] memory[7345] epoch remaining[0:51:10]
INFO:trainer.default_trainer:epochs[    25] optim steps[46500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.41448/0.90448, loss_mask_bce_0: 0.02986/0.33465, loss_mask_dice_0: 0.08760/1.16475, loss_spatial_bce_0: 0.05137/0.08834, loss_spatial_dice_0: 0.11675/0.21111, loss_spatial_ce_0: 0.31487/0.06592, loss_grounding_bce_0: 0.01583/0.08621, loss_grounding_dice_0: 0.04096/0.17863, loss_grounding_ce_0: 0.33520/0.27355, loss_mask_ce_1: 1.41718/0.90510, loss_mask_bce_1: 0.03048/0.33549, loss_mask_dice_1: 0.10605/1.17145, loss_spatial_bce_1: 0.04659/0.08894, loss_spatial_dice_1: 0.14263/0.21520, loss_spatial_ce_1: 0.09348/0.07156, loss_grounding_bce_1: 0.01628/0.08638, loss_grounding_dice_1: 0.03622/0.17937, loss_grounding_ce_1: 0.23252/0.27501, loss_mask_ce_2: 1.35486/0.91238, loss_mask_bce_2: 0.02960/0.33602, loss_mask_dice_2: 0.10130/1.17129, loss_spatial_bce_2: 0.04397/0.08967, loss_spatial_dice_2: 0.13730/0.21643, loss_spatial_ce_2: 0.11159/0.07507, loss_grounding_bce_2: 0.01543/0.08648, loss_grounding_dice_2: 0.03513/0.17917, loss_grounding_ce_2: 0.36496/0.27810, loss_mask_ce_3: 1.24076/0.92172, loss_mask_bce_3: 0.03167/0.33703, loss_mask_dice_3: 0.10750/1.16872, loss_spatial_bce_3: 0.04364/0.09059, loss_spatial_dice_3: 0.14659/0.21710, loss_spatial_ce_3: 0.26396/0.07888, loss_grounding_bce_3: 0.01676/0.08674, loss_grounding_dice_3: 0.03729/0.17892, loss_grounding_ce_3: 0.34214/0.27976, loss_mask_ce_4: 1.21052/0.92203, loss_mask_bce_4: 0.03316/0.33899, loss_mask_dice_4: 0.09568/1.19239, loss_spatial_bce_4: 0.04168/0.09471, loss_spatial_dice_4: 0.08569/0.22880, loss_spatial_ce_4: 0.59846/0.09485, loss_grounding_bce_4: 0.01653/0.08716, loss_grounding_dice_4: 0.04615/0.18177, loss_grounding_ce_4: 0.33400/0.28260, loss_mask_ce_5: 1.25112/0.93798, loss_mask_bce_5: 0.03368/0.34124, loss_mask_dice_5: 0.10524/1.19905, loss_spatial_bce_5: 0.04004/0.09653, loss_spatial_dice_5: 0.13188/0.23259, loss_spatial_ce_5: 0.31777/0.10993, loss_grounding_bce_5: 0.01601/0.08753, loss_grounding_dice_5: 0.04015/0.18299, loss_grounding_ce_5: 0.52434/0.29530, loss_mask_ce_6: 1.24105/0.97710, loss_mask_bce_6: 0.03452/0.34395, loss_mask_dice_6: 0.12006/1.20192, loss_spatial_bce_6: 0.04443/0.10232, loss_spatial_dice_6: 0.12490/0.23515, loss_spatial_ce_6: 0.85422/0.13601, loss_grounding_bce_6: 0.01723/0.08829, loss_grounding_dice_6: 0.03476/0.18328, loss_grounding_ce_6: 0.49887/0.31142, loss_mask_ce_7: 1.18380/1.02218, loss_mask_bce_7: 0.04241/0.35182, loss_mask_dice_7: 0.11638/1.25692, loss_spatial_bce_7: 0.06636/0.11076, loss_spatial_dice_7: 0.20881/0.26293, loss_spatial_ce_7: 0.11357/0.17219, loss_grounding_bce_7: 0.02845/0.09022, loss_grounding_dice_7: 0.05062/0.19049, loss_grounding_ce_7: 0.24327/0.34376, loss_mask_ce_8: 1.54543/1.13119, loss_mask_bce_8: 0.03877/0.36541, loss_mask_dice_8: 0.16576/1.33049, loss_spatial_bce_8: 0.17219/0.13153, loss_spatial_dice_8: 0.24054/0.30194, loss_spatial_ce_8: 0.27320/0.22945, loss_grounding_bce_8: 0.02017/0.09393, loss_grounding_dice_8: 0.05834/0.20154, loss_grounding_ce_8: 0.43996/0.41192, loss_mask_ce_9: 3.38743/3.68095, loss_mask_bce_9: 0.09531/0.39246, loss_mask_dice_9: 0.14350/1.90436, loss_spatial_bce_9: 0.20852/0.33375, loss_spatial_dice_9: 0.63238/0.82261, loss_spatial_ce_9: 1.04057/1.50211, loss_grounding_bce_9: 0.02303/0.10551, loss_grounding_dice_9: 0.03758/0.28091, loss_grounding_ce_9: 1.76491/0.67727] items per batch[64] items per second[0.23] total items[2976000] mini batches[ 46500] memory[7345] epoch remaining[0:46:27]
INFO:trainer.default_trainer:epochs[    25] optim steps[46600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33321/0.90440, loss_mask_bce_0: 0.10647/0.33468, loss_mask_dice_0: 0.46930/1.16458, loss_spatial_bce_0: 0.02761/0.08833, loss_spatial_dice_0: 0.11225/0.21106, loss_spatial_ce_0: 0.00074/0.06587, loss_grounding_bce_0: 0.03092/0.08622, loss_grounding_dice_0: 0.06514/0.17859, loss_grounding_ce_0: 0.18741/0.27362, loss_mask_ce_1: 0.38339/0.90500, loss_mask_bce_1: 0.10533/0.33552, loss_mask_dice_1: 0.50300/1.17128, loss_spatial_bce_1: 0.02696/0.08893, loss_spatial_dice_1: 0.11554/0.21514, loss_spatial_ce_1: 0.02082/0.07153, loss_grounding_bce_1: 0.02908/0.08638, loss_grounding_dice_1: 0.06138/0.17932, loss_grounding_ce_1: 0.18529/0.27512, loss_mask_ce_2: 0.36074/0.91227, loss_mask_bce_2: 0.10969/0.33605, loss_mask_dice_2: 0.52527/1.17115, loss_spatial_bce_2: 0.02604/0.08966, loss_spatial_dice_2: 0.11420/0.21638, loss_spatial_ce_2: 0.00719/0.07503, loss_grounding_bce_2: 0.03200/0.08648, loss_grounding_dice_2: 0.06517/0.17915, loss_grounding_ce_2: 0.19118/0.27815, loss_mask_ce_3: 0.39038/0.92162, loss_mask_bce_3: 0.11095/0.33707, loss_mask_dice_3: 0.52357/1.16855, loss_spatial_bce_3: 0.02817/0.09058, loss_spatial_dice_3: 0.12094/0.21705, loss_spatial_ce_3: 0.00914/0.07883, loss_grounding_bce_3: 0.02907/0.08674, loss_grounding_dice_3: 0.06672/0.17889, loss_grounding_ce_3: 0.21924/0.27981, loss_mask_ce_4: 0.39388/0.92196, loss_mask_bce_4: 0.10485/0.33902, loss_mask_dice_4: 0.48779/1.19223, loss_spatial_bce_4: 0.02714/0.09471, loss_spatial_dice_4: 0.12083/0.22876, loss_spatial_ce_4: 0.00153/0.09482, loss_grounding_bce_4: 0.02954/0.08716, loss_grounding_dice_4: 0.06866/0.18173, loss_grounding_ce_4: 0.21122/0.28270, loss_mask_ce_5: 0.34110/0.93789, loss_mask_bce_5: 0.10405/0.34126, loss_mask_dice_5: 0.46592/1.19887, loss_spatial_bce_5: 0.02951/0.09652, loss_spatial_dice_5: 0.11501/0.23255, loss_spatial_ce_5: 0.00151/0.10988, loss_grounding_bce_5: 0.02700/0.08753, loss_grounding_dice_5: 0.05486/0.18296, loss_grounding_ce_5: 0.24017/0.29540, loss_mask_ce_6: 0.32993/0.97705, loss_mask_bce_6: 0.11055/0.34397, loss_mask_dice_6: 0.48134/1.20177, loss_spatial_bce_6: 0.03875/0.10232, loss_spatial_dice_6: 0.12337/0.23511, loss_spatial_ce_6: 0.03542/0.13597, loss_grounding_bce_6: 0.03099/0.08830, loss_grounding_dice_6: 0.07124/0.18324, loss_grounding_ce_6: 0.23583/0.31151, loss_mask_ce_7: 0.40479/1.02211, loss_mask_bce_7: 0.10598/0.35185, loss_mask_dice_7: 0.50841/1.25677, loss_spatial_bce_7: 0.03276/0.11075, loss_spatial_dice_7: 0.11873/0.26290, loss_spatial_ce_7: 0.05651/0.17213, loss_grounding_bce_7: 0.03063/0.09022, loss_grounding_dice_7: 0.07968/0.19047, loss_grounding_ce_7: 0.21898/0.34377, loss_mask_ce_8: 0.54653/1.13106, loss_mask_bce_8: 0.11979/0.36543, loss_mask_dice_8: 0.55074/1.33035, loss_spatial_bce_8: 0.05780/0.13152, loss_spatial_dice_8: 0.14095/0.30190, loss_spatial_ce_8: 0.10437/0.22940, loss_grounding_bce_8: 0.03369/0.09393, loss_grounding_dice_8: 0.07240/0.20151, loss_grounding_ce_8: 0.24182/0.41206, loss_mask_ce_9: 2.72084/3.68097, loss_mask_bce_9: 0.13405/0.39248, loss_mask_dice_9: 0.69787/1.90425, loss_spatial_bce_9: 0.14102/0.33374, loss_spatial_dice_9: 0.73326/0.82260, loss_spatial_ce_9: 1.38726/1.50203, loss_grounding_bce_9: 0.03394/0.10551, loss_grounding_dice_9: 0.11065/0.28089, loss_grounding_ce_9: 0.35717/0.67733] items per batch[64] items per second[0.23] total items[2982400] mini batches[ 46600] memory[7345] epoch remaining[0:41:46]
INFO:trainer.default_trainer:epochs[    25] optim steps[46700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99453/0.90443, loss_mask_bce_0: 0.02640/0.33464, loss_mask_dice_0: 0.87985/1.16442, loss_spatial_bce_0: 0.00817/0.08833, loss_spatial_dice_0: 0.36319/0.21104, loss_spatial_ce_0: 0.06560/0.06585, loss_grounding_bce_0: 0.00805/0.08624, loss_grounding_dice_0: 0.11858/0.17859, loss_grounding_ce_0: 0.37026/0.27357, loss_mask_ce_1: 0.57818/0.90504, loss_mask_bce_1: 0.03376/0.33548, loss_mask_dice_1: 1.09335/1.17112, loss_spatial_bce_1: 0.00791/0.08893, loss_spatial_dice_1: 0.31836/0.21512, loss_spatial_ce_1: 0.15363/0.07153, loss_grounding_bce_1: 0.00534/0.08640, loss_grounding_dice_1: 0.11869/0.17932, loss_grounding_ce_1: 1.27474/0.27509, loss_mask_ce_2: 0.56306/0.91230, loss_mask_bce_2: 0.03435/0.33601, loss_mask_dice_2: 1.07417/1.17099, loss_spatial_bce_2: 0.00666/0.08966, loss_spatial_dice_2: 0.33118/0.21636, loss_spatial_ce_2: 0.19576/0.07500, loss_grounding_bce_2: 0.00695/0.08650, loss_grounding_dice_2: 0.11049/0.17914, loss_grounding_ce_2: 0.22782/0.27810, loss_mask_ce_3: 0.84955/0.92167, loss_mask_bce_3: 0.02864/0.33703, loss_mask_dice_3: 1.01991/1.16836, loss_spatial_bce_3: 0.00779/0.09058, loss_spatial_dice_3: 0.34756/0.21703, loss_spatial_ce_3: 0.04881/0.07879, loss_grounding_bce_3: 0.00692/0.08676, loss_grounding_dice_3: 0.10609/0.17889, loss_grounding_ce_3: 0.25374/0.27976, loss_mask_ce_4: 0.68114/0.92198, loss_mask_bce_4: 0.03103/0.33898, loss_mask_dice_4: 1.06405/1.19211, loss_spatial_bce_4: 0.00830/0.09470, loss_spatial_dice_4: 0.34100/0.22874, loss_spatial_ce_4: 0.05199/0.09477, loss_grounding_bce_4: 0.00501/0.08718, loss_grounding_dice_4: 0.05814/0.18173, loss_grounding_ce_4: 0.68533/0.28266, loss_mask_ce_5: 0.95829/0.93793, loss_mask_bce_5: 0.02533/0.34122, loss_mask_dice_5: 0.81278/1.19871, loss_spatial_bce_5: 0.00851/0.09652, loss_spatial_dice_5: 0.36894/0.23252, loss_spatial_ce_5: 0.05901/0.10985, loss_grounding_bce_5: 0.00790/0.08755, loss_grounding_dice_5: 0.10666/0.18295, loss_grounding_ce_5: 0.55156/0.29538, loss_mask_ce_6: 0.96220/0.97712, loss_mask_bce_6: 0.03180/0.34393, loss_mask_dice_6: 1.15742/1.20158, loss_spatial_bce_6: 0.00983/0.10232, loss_spatial_dice_6: 0.37333/0.23508, loss_spatial_ce_6: 0.19220/0.13592, loss_grounding_bce_6: 0.00772/0.08831, loss_grounding_dice_6: 0.09226/0.18324, loss_grounding_ce_6: 0.37691/0.31147, loss_mask_ce_7: 0.99774/1.02216, loss_mask_bce_7: 0.02957/0.35182, loss_mask_dice_7: 1.27310/1.25658, loss_spatial_bce_7: 0.01387/0.11075, loss_spatial_dice_7: 0.40289/0.26287, loss_spatial_ce_7: 0.15520/0.17209, loss_grounding_bce_7: 0.00524/0.09024, loss_grounding_dice_7: 0.09805/0.19047, loss_grounding_ce_7: 0.44344/0.34370, loss_mask_ce_8: 1.35765/1.13115, loss_mask_bce_8: 0.02832/0.36540, loss_mask_dice_8: 1.06295/1.33018, loss_spatial_bce_8: 0.01786/0.13152, loss_spatial_dice_8: 0.47347/0.30186, loss_spatial_ce_8: 0.28804/0.22939, loss_grounding_bce_8: 0.00768/0.09395, loss_grounding_dice_8: 0.10648/0.20150, loss_grounding_ce_8: 0.74528/0.41198, loss_mask_ce_9: 4.17217/3.68075, loss_mask_bce_9: 0.01645/0.39242, loss_mask_dice_9: 1.20287/1.90393, loss_spatial_bce_9: 0.02110/0.33374, loss_spatial_dice_9: 0.73046/0.82257, loss_spatial_ce_9: 2.12190/1.50195, loss_grounding_bce_9: 0.00538/0.10552, loss_grounding_dice_9: 0.15664/0.28086, loss_grounding_ce_9: 0.97316/0.67722] items per batch[64] items per second[0.23] total items[2988800] mini batches[ 46700] memory[7345] epoch remaining[0:37:09]
INFO:trainer.default_trainer:epochs[    25] optim steps[46800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12696/0.90436, loss_mask_bce_0: 0.25914/0.33464, loss_mask_dice_0: 1.34127/1.16450, loss_spatial_bce_0: 0.21173/0.08830, loss_spatial_dice_0: 0.27239/0.21102, loss_spatial_ce_0: 0.00323/0.06583, loss_grounding_bce_0: 0.02736/0.08623, loss_grounding_dice_0: 0.15291/0.17859, loss_grounding_ce_0: 0.18565/0.27364, loss_mask_ce_1: 1.12784/0.90498, loss_mask_bce_1: 0.26843/0.33547, loss_mask_dice_1: 1.32999/1.17118, loss_spatial_bce_1: 0.26022/0.08891, loss_spatial_dice_1: 0.29356/0.21509, loss_spatial_ce_1: 0.00646/0.07150, loss_grounding_bce_1: 0.02723/0.08639, loss_grounding_dice_1: 0.16858/0.17931, loss_grounding_ce_1: 0.18923/0.27519, loss_mask_ce_2: 1.05700/0.91224, loss_mask_bce_2: 0.27589/0.33599, loss_mask_dice_2: 1.39102/1.17107, loss_spatial_bce_2: 0.22205/0.08964, loss_spatial_dice_2: 0.27886/0.21634, loss_spatial_ce_2: 0.00778/0.07497, loss_grounding_bce_2: 0.02756/0.08649, loss_grounding_dice_2: 0.15802/0.17914, loss_grounding_ce_2: 0.18781/0.27821, loss_mask_ce_3: 1.12241/0.92160, loss_mask_bce_3: 0.26890/0.33702, loss_mask_dice_3: 1.33592/1.16843, loss_spatial_bce_3: 0.22075/0.09056, loss_spatial_dice_3: 0.29416/0.21701, loss_spatial_ce_3: 0.02240/0.07877, loss_grounding_bce_3: 0.02688/0.08675, loss_grounding_dice_3: 0.15011/0.17889, loss_grounding_ce_3: 0.15968/0.27989, loss_mask_ce_4: 1.04145/0.92192, loss_mask_bce_4: 0.26628/0.33898, loss_mask_dice_4: 1.44363/1.19220, loss_spatial_bce_4: 0.30994/0.09468, loss_spatial_dice_4: 0.32713/0.22873, loss_spatial_ce_4: 0.02503/0.09476, loss_grounding_bce_4: 0.02992/0.08717, loss_grounding_dice_4: 0.16080/0.18173, loss_grounding_ce_4: 0.17345/0.28271, loss_mask_ce_5: 1.02271/0.93791, loss_mask_bce_5: 0.27140/0.34121, loss_mask_dice_5: 1.50414/1.19884, loss_spatial_bce_5: 0.19945/0.09650, loss_spatial_dice_5: 0.33426/0.23251, loss_spatial_ce_5: 0.04124/0.10983, loss_grounding_bce_5: 0.03106/0.08754, loss_grounding_dice_5: 0.18608/0.18295, loss_grounding_ce_5: 0.22316/0.29544, loss_mask_ce_6: 1.03575/0.97713, loss_mask_bce_6: 0.28308/0.34392, loss_mask_dice_6: 1.57232/1.20167, loss_spatial_bce_6: 0.19500/0.10230, loss_spatial_dice_6: 0.34222/0.23507, loss_spatial_ce_6: 0.11473/0.13592, loss_grounding_bce_6: 0.03229/0.08831, loss_grounding_dice_6: 0.19461/0.18323, loss_grounding_ce_6: 0.21331/0.31155, loss_mask_ce_7: 0.96628/1.02214, loss_mask_bce_7: 0.27242/0.35182, loss_mask_dice_7: 1.54105/1.25666, loss_spatial_bce_7: 0.40757/0.11074, loss_spatial_dice_7: 0.39822/0.26288, loss_spatial_ce_7: 0.10626/0.17207, loss_grounding_bce_7: 0.03191/0.09023, loss_grounding_dice_7: 0.19030/0.19047, loss_grounding_ce_7: 0.19657/0.34377, loss_mask_ce_8: 1.08211/1.13119, loss_mask_bce_8: 0.29716/0.36538, loss_mask_dice_8: 1.59564/1.33023, loss_spatial_bce_8: 0.27639/0.13152, loss_spatial_dice_8: 0.43124/0.30187, loss_spatial_ce_8: 0.12931/0.22939, loss_grounding_bce_8: 0.03229/0.09394, loss_grounding_dice_8: 0.23308/0.20150, loss_grounding_ce_8: 0.26168/0.41198, loss_mask_ce_9: 4.55319/3.68084, loss_mask_bce_9: 0.29866/0.39240, loss_mask_dice_9: 2.34977/1.90400, loss_spatial_bce_9: 0.30858/0.33369, loss_spatial_dice_9: 0.89236/0.82256, loss_spatial_ce_9: 1.28369/1.50188, loss_grounding_bce_9: 0.03882/0.10550, loss_grounding_dice_9: 0.31709/0.28086, loss_grounding_ce_9: 0.37443/0.67716] items per batch[64] items per second[0.23] total items[2995200] mini batches[ 46800] memory[7345] epoch remaining[0:32:34]
INFO:trainer.default_trainer:epochs[    25] optim steps[46900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20217/0.90429, loss_mask_bce_0: 0.97101/0.33467, loss_mask_dice_0: 3.19568/1.16427, loss_spatial_bce_0: 0.09613/0.08831, loss_spatial_dice_0: 0.35216/0.21098, loss_spatial_ce_0: 0.17723/0.06580, loss_grounding_bce_0: 0.17891/0.08624, loss_grounding_dice_0: 0.36235/0.17857, loss_grounding_ce_0: 0.08516/0.27362, loss_mask_ce_1: 1.19328/0.90489, loss_mask_bce_1: 0.96779/0.33550, loss_mask_dice_1: 3.30671/1.17098, loss_spatial_bce_1: 0.10071/0.08891, loss_spatial_dice_1: 0.36560/0.21505, loss_spatial_ce_1: 0.08454/0.07148, loss_grounding_bce_1: 0.16322/0.08640, loss_grounding_dice_1: 0.36003/0.17930, loss_grounding_ce_1: 0.09426/0.27518, loss_mask_ce_2: 1.04653/0.91215, loss_mask_bce_2: 0.96596/0.33601, loss_mask_dice_2: 3.48068/1.17084, loss_spatial_bce_2: 0.11123/0.08965, loss_spatial_dice_2: 0.37122/0.21630, loss_spatial_ce_2: 0.14263/0.07496, loss_grounding_bce_2: 0.16773/0.08650, loss_grounding_dice_2: 0.36255/0.17913, loss_grounding_ce_2: 0.07635/0.27819, loss_mask_ce_3: 1.12586/0.92148, loss_mask_bce_3: 0.96767/0.33704, loss_mask_dice_3: 3.41746/1.16820, loss_spatial_bce_3: 0.11235/0.09057, loss_spatial_dice_3: 0.36468/0.21698, loss_spatial_ce_3: 0.23170/0.07876, loss_grounding_bce_3: 0.17017/0.08677, loss_grounding_dice_3: 0.36434/0.17888, loss_grounding_ce_3: 0.08661/0.27988, loss_mask_ce_4: 1.18851/0.92182, loss_mask_bce_4: 0.91626/0.33901, loss_mask_dice_4: 3.46131/1.19196, loss_spatial_bce_4: 0.09138/0.09469, loss_spatial_dice_4: 0.38741/0.22870, loss_spatial_ce_4: 0.20543/0.09476, loss_grounding_bce_4: 0.14779/0.08719, loss_grounding_dice_4: 0.35490/0.18173, loss_grounding_ce_4: 0.11032/0.28267, loss_mask_ce_5: 1.05348/0.93787, loss_mask_bce_5: 0.93203/0.34123, loss_mask_dice_5: 3.63463/1.19862, loss_spatial_bce_5: 0.08831/0.09652, loss_spatial_dice_5: 0.39376/0.23248, loss_spatial_ce_5: 0.18830/0.10979, loss_grounding_bce_5: 0.12162/0.08756, loss_grounding_dice_5: 0.36883/0.18295, loss_grounding_ce_5: 0.12937/0.29543, loss_mask_ce_6: 1.08772/0.97711, loss_mask_bce_6: 0.96702/0.34395, loss_mask_dice_6: 3.57294/1.20142, loss_spatial_bce_6: 0.12953/0.10232, loss_spatial_dice_6: 0.39358/0.23505, loss_spatial_ce_6: 0.21212/0.13588, loss_grounding_bce_6: 0.13821/0.08833, loss_grounding_dice_6: 0.36338/0.18324, loss_grounding_ce_6: 0.14687/0.31154, loss_mask_ce_7: 0.99420/1.02209, loss_mask_bce_7: 0.95705/0.35186, loss_mask_dice_7: 3.38853/1.25641, loss_spatial_bce_7: 0.11839/0.11074, loss_spatial_dice_7: 0.39687/0.26283, loss_spatial_ce_7: 0.20145/0.17203, loss_grounding_bce_7: 0.12694/0.09025, loss_grounding_dice_7: 0.36112/0.19046, loss_grounding_ce_7: 0.12729/0.34377, loss_mask_ce_8: 1.08249/1.13113, loss_mask_bce_8: 0.96236/0.36541, loss_mask_dice_8: 3.71870/1.32998, loss_spatial_bce_8: 0.13382/0.13153, loss_spatial_dice_8: 0.48348/0.30182, loss_spatial_ce_8: 0.27354/0.22938, loss_grounding_bce_8: 0.13998/0.09396, loss_grounding_dice_8: 0.35171/0.20150, loss_grounding_ce_8: 0.15217/0.41199, loss_mask_ce_9: 4.14168/3.68085, loss_mask_bce_9: 0.93288/0.39244, loss_mask_dice_9: 4.94640/1.90364, loss_spatial_bce_9: 0.23571/0.33372, loss_spatial_dice_9: 0.90914/0.82253, loss_spatial_ce_9: 1.50283/1.50177, loss_grounding_bce_9: 0.22427/0.10552, loss_grounding_dice_9: 0.46094/0.28084, loss_grounding_ce_9: 1.56463/0.67720] items per batch[64] items per second[0.23] total items[3001600] mini batches[ 46900] memory[7345] epoch remaining[0:27:57]
INFO:trainer.default_trainer:epochs[    25] optim steps[47000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54305/0.90422, loss_mask_bce_0: 0.18308/0.33463, loss_mask_dice_0: 0.47646/1.16412, loss_spatial_bce_0: 0.03552/0.08830, loss_spatial_dice_0: 0.08360/0.21096, loss_spatial_ce_0: 0.09076/0.06580, loss_grounding_bce_0: 0.02900/0.08622, loss_grounding_dice_0: 0.07900/0.17855, loss_grounding_ce_0: 0.10924/0.27361, loss_mask_ce_1: 0.45484/0.90484, loss_mask_bce_1: 0.17215/0.33546, loss_mask_dice_1: 0.45913/1.17081, loss_spatial_bce_1: 0.03661/0.08890, loss_spatial_dice_1: 0.07855/0.21502, loss_spatial_ce_1: 0.07611/0.07148, loss_grounding_bce_1: 0.02849/0.08637, loss_grounding_dice_1: 0.07751/0.17929, loss_grounding_ce_1: 0.09444/0.27515, loss_mask_ce_2: 0.45268/0.91210, loss_mask_bce_2: 0.17777/0.33596, loss_mask_dice_2: 0.47149/1.17066, loss_spatial_bce_2: 0.03670/0.08964, loss_spatial_dice_2: 0.08033/0.21629, loss_spatial_ce_2: 0.09425/0.07496, loss_grounding_bce_2: 0.03254/0.08648, loss_grounding_dice_2: 0.08528/0.17911, loss_grounding_ce_2: 0.07473/0.27819, loss_mask_ce_3: 0.44480/0.92139, loss_mask_bce_3: 0.18022/0.33700, loss_mask_dice_3: 0.46846/1.16804, loss_spatial_bce_3: 0.03924/0.09056, loss_spatial_dice_3: 0.07942/0.21697, loss_spatial_ce_3: 0.09091/0.07875, loss_grounding_bce_3: 0.03093/0.08674, loss_grounding_dice_3: 0.08258/0.17886, loss_grounding_ce_3: 0.07044/0.27985, loss_mask_ce_4: 0.45315/0.92175, loss_mask_bce_4: 0.18245/0.33896, loss_mask_dice_4: 0.45546/1.19182, loss_spatial_bce_4: 0.03871/0.09467, loss_spatial_dice_4: 0.09985/0.22868, loss_spatial_ce_4: 0.08466/0.09474, loss_grounding_bce_4: 0.03199/0.08717, loss_grounding_dice_4: 0.08055/0.18172, loss_grounding_ce_4: 0.07035/0.28263, loss_mask_ce_5: 0.51193/0.93782, loss_mask_bce_5: 0.19089/0.34119, loss_mask_dice_5: 0.49511/1.19849, loss_spatial_bce_5: 0.03911/0.09650, loss_spatial_dice_5: 0.09975/0.23246, loss_spatial_ce_5: 0.09177/0.10977, loss_grounding_bce_5: 0.02819/0.08754, loss_grounding_dice_5: 0.07524/0.18294, loss_grounding_ce_5: 0.16446/0.29542, loss_mask_ce_6: 0.64819/0.97702, loss_mask_bce_6: 0.18359/0.34391, loss_mask_dice_6: 0.46892/1.20128, loss_spatial_bce_6: 0.04047/0.10231, loss_spatial_dice_6: 0.10738/0.23503, loss_spatial_ce_6: 0.16574/0.13584, loss_grounding_bce_6: 0.02677/0.08831, loss_grounding_dice_6: 0.08006/0.18323, loss_grounding_ce_6: 0.22177/0.31155, loss_mask_ce_7: 0.90516/1.02197, loss_mask_bce_7: 0.18584/0.35181, loss_mask_dice_7: 0.55561/1.25626, loss_spatial_bce_7: 0.04418/0.11072, loss_spatial_dice_7: 0.13940/0.26281, loss_spatial_ce_7: 0.11960/0.17201, loss_grounding_bce_7: 0.02386/0.09023, loss_grounding_dice_7: 0.07218/0.19045, loss_grounding_ce_7: 0.10030/0.34379, loss_mask_ce_8: 0.89289/1.13104, loss_mask_bce_8: 0.23147/0.36537, loss_mask_dice_8: 0.66774/1.32984, loss_spatial_bce_8: 0.06127/0.13152, loss_spatial_dice_8: 0.18496/0.30179, loss_spatial_ce_8: 0.20621/0.22936, loss_grounding_bce_8: 0.03501/0.09394, loss_grounding_dice_8: 0.08934/0.20149, loss_grounding_ce_8: 0.10744/0.41202, loss_mask_ce_9: 4.33733/3.68071, loss_mask_bce_9: 0.22099/0.39242, loss_mask_dice_9: 1.28056/1.90342, loss_spatial_bce_9: 0.38920/0.33371, loss_spatial_dice_9: 0.88751/0.82253, loss_spatial_ce_9: 1.32969/1.50175, loss_grounding_bce_9: 0.03332/0.10551, loss_grounding_dice_9: 0.14386/0.28083, loss_grounding_ce_9: 1.47914/0.67713] items per batch[64] items per second[0.23] total items[3008000] mini batches[ 47000] memory[7345] epoch remaining[0:23:17]
INFO:trainer.default_trainer:epochs[    25] optim steps[47100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90217/0.90422, loss_mask_bce_0: 0.19711/0.33468, loss_mask_dice_0: 2.66672/1.16423, loss_spatial_bce_0: 0.01485/0.08830, loss_spatial_dice_0: 0.24114/0.21096, loss_spatial_ce_0: 0.17887/0.06578, loss_grounding_bce_0: 0.02671/0.08623, loss_grounding_dice_0: 0.47893/0.17859, loss_grounding_ce_0: 0.18418/0.27352, loss_mask_ce_1: 0.89690/0.90484, loss_mask_bce_1: 0.18725/0.33552, loss_mask_dice_1: 2.51377/1.17094, loss_spatial_bce_1: 0.01333/0.08889, loss_spatial_dice_1: 0.21540/0.21502, loss_spatial_ce_1: 0.22066/0.07149, loss_grounding_bce_1: 0.02378/0.08639, loss_grounding_dice_1: 0.46523/0.17932, loss_grounding_ce_1: 0.23719/0.27507, loss_mask_ce_2: 0.91515/0.91212, loss_mask_bce_2: 0.19477/0.33601, loss_mask_dice_2: 2.54756/1.17080, loss_spatial_bce_2: 0.01371/0.08963, loss_spatial_dice_2: 0.20506/0.21629, loss_spatial_ce_2: 0.18711/0.07496, loss_grounding_bce_2: 0.02535/0.08648, loss_grounding_dice_2: 0.46117/0.17915, loss_grounding_ce_2: 0.34714/0.27811, loss_mask_ce_3: 0.76260/0.92142, loss_mask_bce_3: 0.19520/0.33704, loss_mask_dice_3: 2.42370/1.16816, loss_spatial_bce_3: 0.01460/0.09057, loss_spatial_dice_3: 0.21038/0.21697, loss_spatial_ce_3: 0.16013/0.07875, loss_grounding_bce_3: 0.02703/0.08675, loss_grounding_dice_3: 0.42328/0.17889, loss_grounding_ce_3: 0.26757/0.27979, loss_mask_ce_4: 1.11287/0.92182, loss_mask_bce_4: 0.21144/0.33902, loss_mask_dice_4: 2.48771/1.19198, loss_spatial_bce_4: 0.01472/0.09467, loss_spatial_dice_4: 0.24249/0.22870, loss_spatial_ce_4: 0.31970/0.09475, loss_grounding_bce_4: 0.02577/0.08719, loss_grounding_dice_4: 0.49108/0.18175, loss_grounding_ce_4: 0.19956/0.28256, loss_mask_ce_5: 0.93175/0.93782, loss_mask_bce_5: 0.19560/0.34124, loss_mask_dice_5: 2.43893/1.19869, loss_spatial_bce_5: 0.01714/0.09650, loss_spatial_dice_5: 0.26565/0.23247, loss_spatial_ce_5: 0.37560/0.10977, loss_grounding_bce_5: 0.02496/0.08756, loss_grounding_dice_5: 0.44538/0.18298, loss_grounding_ce_5: 0.26311/0.29533, loss_mask_ce_6: 0.81469/0.97704, loss_mask_bce_6: 0.20388/0.34396, loss_mask_dice_6: 2.58623/1.20150, loss_spatial_bce_6: 0.01713/0.10232, loss_spatial_dice_6: 0.21843/0.23505, loss_spatial_ce_6: 0.28545/0.13583, loss_grounding_bce_6: 0.02418/0.08833, loss_grounding_dice_6: 0.45613/0.18327, loss_grounding_ce_6: 0.25546/0.31142, loss_mask_ce_7: 1.00599/1.02193, loss_mask_bce_7: 0.20432/0.35186, loss_mask_dice_7: 2.78528/1.25649, loss_spatial_bce_7: 0.01762/0.11073, loss_spatial_dice_7: 0.25772/0.26283, loss_spatial_ce_7: 0.76122/0.17201, loss_grounding_bce_7: 0.02992/0.09026, loss_grounding_dice_7: 0.42907/0.19050, loss_grounding_ce_7: 0.28995/0.34364, loss_mask_ce_8: 0.95675/1.13094, loss_mask_bce_8: 0.21145/0.36544, loss_mask_dice_8: 2.89358/1.33005, loss_spatial_bce_8: 0.02250/0.13153, loss_spatial_dice_8: 0.33144/0.30179, loss_spatial_ce_8: 0.82498/0.22933, loss_grounding_bce_8: 0.02662/0.09396, loss_grounding_dice_8: 0.44639/0.20154, loss_grounding_ce_8: 0.37359/0.41190, loss_mask_ce_9: 3.76767/3.68085, loss_mask_bce_9: 0.18827/0.39248, loss_mask_dice_9: 3.60743/1.90355, loss_spatial_bce_9: 0.15002/0.33369, loss_spatial_dice_9: 0.84211/0.82251, loss_spatial_ce_9: 1.38520/1.50184, loss_grounding_bce_9: 0.01892/0.10553, loss_grounding_dice_9: 0.51855/0.28087, loss_grounding_ce_9: 1.24555/0.67688] items per batch[64] items per second[0.23] total items[3014400] mini batches[ 47100] memory[7345] epoch remaining[0:18:38]
INFO:trainer.default_trainer:epochs[    25] optim steps[47200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58589/0.90422, loss_mask_bce_0: 0.24112/0.33473, loss_mask_dice_0: 0.15660/1.16467, loss_spatial_bce_0: 0.13600/0.08829, loss_spatial_dice_0: 0.10362/0.21098, loss_spatial_ce_0: 0.00413/0.06579, loss_grounding_bce_0: 0.14717/0.08626, loss_grounding_dice_0: 0.08453/0.17864, loss_grounding_ce_0: 0.00874/0.27349, loss_mask_ce_1: 0.61146/0.90483, loss_mask_bce_1: 0.23073/0.33557, loss_mask_dice_1: 0.15832/1.17139, loss_spatial_bce_1: 0.14007/0.08889, loss_spatial_dice_1: 0.10160/0.21504, loss_spatial_ce_1: 0.00300/0.07151, loss_grounding_bce_1: 0.14817/0.08642, loss_grounding_dice_1: 0.08178/0.17938, loss_grounding_ce_1: 0.00567/0.27504, loss_mask_ce_2: 0.67272/0.91216, loss_mask_bce_2: 0.22746/0.33607, loss_mask_dice_2: 0.15813/1.17126, loss_spatial_bce_2: 0.14290/0.08963, loss_spatial_dice_2: 0.10816/0.21632, loss_spatial_ce_2: 0.00441/0.07495, loss_grounding_bce_2: 0.14496/0.08652, loss_grounding_dice_2: 0.08714/0.17920, loss_grounding_ce_2: 0.01121/0.27811, loss_mask_ce_3: 0.65063/0.92146, loss_mask_bce_3: 0.22304/0.33711, loss_mask_dice_3: 0.15720/1.16861, loss_spatial_bce_3: 0.14258/0.09056, loss_spatial_dice_3: 0.09929/0.21699, loss_spatial_ce_3: 0.00340/0.07873, loss_grounding_bce_3: 0.14500/0.08678, loss_grounding_dice_3: 0.08671/0.17896, loss_grounding_ce_3: 0.01560/0.27975, loss_mask_ce_4: 0.63979/0.92185, loss_mask_bce_4: 0.23766/0.33909, loss_mask_dice_4: 0.15828/1.19244, loss_spatial_bce_4: 0.13799/0.09467, loss_spatial_dice_4: 0.10270/0.22871, loss_spatial_ce_4: 0.00447/0.09475, loss_grounding_bce_4: 0.14467/0.08722, loss_grounding_dice_4: 0.08888/0.18181, loss_grounding_ce_4: 0.01336/0.28256, loss_mask_ce_5: 0.59183/0.93784, loss_mask_bce_5: 0.24299/0.34130, loss_mask_dice_5: 0.16381/1.19915, loss_spatial_bce_5: 0.14529/0.09650, loss_spatial_dice_5: 0.11008/0.23250, loss_spatial_ce_5: 0.00679/0.10976, loss_grounding_bce_5: 0.14609/0.08759, loss_grounding_dice_5: 0.09171/0.18301, loss_grounding_ce_5: 0.03644/0.29537, loss_mask_ce_6: 0.65947/0.97708, loss_mask_bce_6: 0.22281/0.34400, loss_mask_dice_6: 0.15421/1.20193, loss_spatial_bce_6: 0.15072/0.10232, loss_spatial_dice_6: 0.10738/0.23508, loss_spatial_ce_6: 0.05012/0.13580, loss_grounding_bce_6: 0.14211/0.08836, loss_grounding_dice_6: 0.09462/0.18332, loss_grounding_ce_6: 0.04633/0.31141, loss_mask_ce_7: 0.75777/1.02196, loss_mask_bce_7: 0.22371/0.35190, loss_mask_dice_7: 0.16036/1.25693, loss_spatial_bce_7: 0.13621/0.11073, loss_spatial_dice_7: 0.10934/0.26285, loss_spatial_ce_7: 0.08015/0.17200, loss_grounding_bce_7: 0.13919/0.09028, loss_grounding_dice_7: 0.09964/0.19054, loss_grounding_ce_7: 0.00665/0.34363, loss_mask_ce_8: 0.85882/1.13096, loss_mask_bce_8: 0.24829/0.36549, loss_mask_dice_8: 0.18050/1.33048, loss_spatial_bce_8: 0.18733/0.13153, loss_spatial_dice_8: 0.13084/0.30182, loss_spatial_ce_8: 0.12197/0.22932, loss_grounding_bce_8: 0.13331/0.09399, loss_grounding_dice_8: 0.10030/0.20157, loss_grounding_ce_8: 0.00549/0.41194, loss_mask_ce_9: 2.93923/3.68086, loss_mask_bce_9: 0.26960/0.39252, loss_mask_dice_9: 0.32557/1.90409, loss_spatial_bce_9: 0.54301/0.33365, loss_spatial_dice_9: 0.70061/0.82250, loss_spatial_ce_9: 1.48576/1.50182, loss_grounding_bce_9: 0.13270/0.10555, loss_grounding_dice_9: 0.08706/0.28089, loss_grounding_ce_9: 0.37261/0.67679] items per batch[64] items per second[0.23] total items[3020800] mini batches[ 47200] memory[7345] epoch remaining[0:14:01]
INFO:trainer.default_trainer:epochs[    25] optim steps[47300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28630/0.90402, loss_mask_bce_0: 0.35683/0.33473, loss_mask_dice_0: 0.38461/1.16455, loss_spatial_bce_0: 0.07216/0.08829, loss_spatial_dice_0: 0.08856/0.21097, loss_spatial_ce_0: 0.00030/0.06577, loss_grounding_bce_0: 0.18213/0.08628, loss_grounding_dice_0: 0.19275/0.17863, loss_grounding_ce_0: 0.25674/0.27343, loss_mask_ce_1: 0.25135/0.90462, loss_mask_bce_1: 0.33564/0.33558, loss_mask_dice_1: 0.36873/1.17125, loss_spatial_bce_1: 0.06740/0.08888, loss_spatial_dice_1: 0.08998/0.21502, loss_spatial_ce_1: 0.00019/0.07149, loss_grounding_bce_1: 0.18241/0.08644, loss_grounding_dice_1: 0.19575/0.17937, loss_grounding_ce_1: 0.25386/0.27495, loss_mask_ce_2: 0.26738/0.91193, loss_mask_bce_2: 0.32833/0.33608, loss_mask_dice_2: 0.36980/1.17112, loss_spatial_bce_2: 0.08115/0.08962, loss_spatial_dice_2: 0.09828/0.21630, loss_spatial_ce_2: 0.00021/0.07496, loss_grounding_bce_2: 0.17104/0.08654, loss_grounding_dice_2: 0.19018/0.17919, loss_grounding_ce_2: 0.26515/0.27799, loss_mask_ce_3: 0.27281/0.92122, loss_mask_bce_3: 0.32412/0.33711, loss_mask_dice_3: 0.34612/1.16851, loss_spatial_bce_3: 0.11952/0.09056, loss_spatial_dice_3: 0.12246/0.21697, loss_spatial_ce_3: 0.00034/0.07871, loss_grounding_bce_3: 0.17736/0.08680, loss_grounding_dice_3: 0.18492/0.17894, loss_grounding_ce_3: 0.19814/0.27968, loss_mask_ce_4: 0.12057/0.92167, loss_mask_bce_4: 0.32763/0.33910, loss_mask_dice_4: 0.45746/1.19228, loss_spatial_bce_4: 0.12442/0.09466, loss_spatial_dice_4: 0.13078/0.22869, loss_spatial_ce_4: 0.00193/0.09473, loss_grounding_bce_4: 0.09260/0.08724, loss_grounding_dice_4: 0.21477/0.18180, loss_grounding_ce_4: 0.23345/0.28251, loss_mask_ce_5: 0.30679/0.93767, loss_mask_bce_5: 0.33616/0.34130, loss_mask_dice_5: 0.35490/1.19901, loss_spatial_bce_5: 0.11784/0.09650, loss_spatial_dice_5: 0.12273/0.23248, loss_spatial_ce_5: 0.00842/0.10974, loss_grounding_bce_5: 0.09730/0.08761, loss_grounding_dice_5: 0.21381/0.18301, loss_grounding_ce_5: 0.29288/0.29535, loss_mask_ce_6: 0.32651/0.97692, loss_mask_bce_6: 0.32571/0.34401, loss_mask_dice_6: 0.34035/1.20178, loss_spatial_bce_6: 0.09682/0.10230, loss_spatial_dice_6: 0.11182/0.23506, loss_spatial_ce_6: 0.05687/0.13578, loss_grounding_bce_6: 0.17298/0.08838, loss_grounding_dice_6: 0.23107/0.18331, loss_grounding_ce_6: 0.16150/0.31136, loss_mask_ce_7: 0.47067/1.02179, loss_mask_bce_7: 0.22078/0.35191, loss_mask_dice_7: 0.38359/1.25677, loss_spatial_bce_7: 0.12440/0.11072, loss_spatial_dice_7: 0.16112/0.26283, loss_spatial_ce_7: 0.03888/0.17199, loss_grounding_bce_7: 0.10954/0.09030, loss_grounding_dice_7: 0.20550/0.19054, loss_grounding_ce_7: 0.18654/0.34360, loss_mask_ce_8: 0.43967/1.13077, loss_mask_bce_8: 0.20274/0.36549, loss_mask_dice_8: 0.39927/1.33034, loss_spatial_bce_8: 0.18288/0.13152, loss_spatial_dice_8: 0.16523/0.30179, loss_spatial_ce_8: 0.03778/0.22929, loss_grounding_bce_8: 0.10042/0.09401, loss_grounding_dice_8: 0.22627/0.20157, loss_grounding_ce_8: 0.34100/0.41186, loss_mask_ce_9: 3.53227/3.68069, loss_mask_bce_9: 0.37219/0.39251, loss_mask_dice_9: 0.48939/1.90381, loss_spatial_bce_9: 0.45088/0.33365, loss_spatial_dice_9: 0.73991/0.82250, loss_spatial_ce_9: 0.92920/1.50190, loss_grounding_bce_9: 0.21273/0.10557, loss_grounding_dice_9: 0.30411/0.28086, loss_grounding_ce_9: 0.59718/0.67676] items per batch[64] items per second[0.23] total items[3027200] mini batches[ 47300] memory[7345] epoch remaining[0:09:22]
INFO:trainer.default_trainer:epochs[    25] optim steps[47400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01999/0.90417, loss_mask_bce_0: 0.21054/0.33468, loss_mask_dice_0: 1.57717/1.16453, loss_spatial_bce_0: 0.04969/0.08827, loss_spatial_dice_0: 0.23678/0.21096, loss_spatial_ce_0: 0.00404/0.06572, loss_grounding_bce_0: 0.03410/0.08629, loss_grounding_dice_0: 0.14506/0.17863, loss_grounding_ce_0: 0.00351/0.27342, loss_mask_ce_1: 1.03283/0.90481, loss_mask_bce_1: 0.21299/0.33552, loss_mask_dice_1: 1.52772/1.17119, loss_spatial_bce_1: 0.04926/0.08886, loss_spatial_dice_1: 0.21910/0.21502, loss_spatial_ce_1: 0.00399/0.07144, loss_grounding_bce_1: 0.04108/0.08645, loss_grounding_dice_1: 0.13900/0.17937, loss_grounding_ce_1: 0.00328/0.27494, loss_mask_ce_2: 1.04388/0.91210, loss_mask_bce_2: 0.21906/0.33602, loss_mask_dice_2: 1.52270/1.17108, loss_spatial_bce_2: 0.05127/0.08960, loss_spatial_dice_2: 0.23525/0.21630, loss_spatial_ce_2: 0.00353/0.07492, loss_grounding_bce_2: 0.03838/0.08654, loss_grounding_dice_2: 0.13145/0.17919, loss_grounding_ce_2: 0.00537/0.27798, loss_mask_ce_3: 1.23363/0.92139, loss_mask_bce_3: 0.21339/0.33705, loss_mask_dice_3: 1.38248/1.16845, loss_spatial_bce_3: 0.05000/0.09054, loss_spatial_dice_3: 0.22711/0.21696, loss_spatial_ce_3: 0.00286/0.07867, loss_grounding_bce_3: 0.03611/0.08680, loss_grounding_dice_3: 0.13905/0.17894, loss_grounding_ce_3: 0.00786/0.27967, loss_mask_ce_4: 0.95068/0.92186, loss_mask_bce_4: 0.23276/0.33904, loss_mask_dice_4: 1.61087/1.19223, loss_spatial_bce_4: 0.05164/0.09463, loss_spatial_dice_4: 0.25347/0.22869, loss_spatial_ce_4: 0.00573/0.09466, loss_grounding_bce_4: 0.04152/0.08724, loss_grounding_dice_4: 0.15733/0.18180, loss_grounding_ce_4: 0.00878/0.28254, loss_mask_ce_5: 1.25189/0.93784, loss_mask_bce_5: 0.21345/0.34125, loss_mask_dice_5: 1.43174/1.19900, loss_spatial_bce_5: 0.05380/0.09648, loss_spatial_dice_5: 0.26568/0.23248, loss_spatial_ce_5: 0.02568/0.10969, loss_grounding_bce_5: 0.03997/0.08761, loss_grounding_dice_5: 0.14476/0.18301, loss_grounding_ce_5: 0.00692/0.29540, loss_mask_ce_6: 1.08094/0.97707, loss_mask_bce_6: 0.22547/0.34396, loss_mask_dice_6: 1.54601/1.20177, loss_spatial_bce_6: 0.05512/0.10228, loss_spatial_dice_6: 0.26335/0.23507, loss_spatial_ce_6: 0.02580/0.13572, loss_grounding_bce_6: 0.03717/0.08838, loss_grounding_dice_6: 0.12888/0.18330, loss_grounding_ce_6: 0.00711/0.31144, loss_mask_ce_7: 1.08007/1.02196, loss_mask_bce_7: 0.21536/0.35187, loss_mask_dice_7: 1.45540/1.25675, loss_spatial_bce_7: 0.06182/0.11070, loss_spatial_dice_7: 0.30736/0.26284, loss_spatial_ce_7: 0.05954/0.17196, loss_grounding_bce_7: 0.03890/0.09030, loss_grounding_dice_7: 0.13236/0.19054, loss_grounding_ce_7: 0.00475/0.34362, loss_mask_ce_8: 1.28016/1.13091, loss_mask_bce_8: 0.22032/0.36545, loss_mask_dice_8: 1.56797/1.33034, loss_spatial_bce_8: 0.07975/0.13149, loss_spatial_dice_8: 0.35663/0.30180, loss_spatial_ce_8: 0.24850/0.22928, loss_grounding_bce_8: 0.04816/0.09402, loss_grounding_dice_8: 0.17836/0.20157, loss_grounding_ce_8: 0.00445/0.41190, loss_mask_ce_9: 4.10777/3.68057, loss_mask_bce_9: 0.19434/0.39244, loss_mask_dice_9: 1.80150/1.90376, loss_spatial_bce_9: 0.33788/0.33361, loss_spatial_dice_9: 0.92918/0.82252, loss_spatial_ce_9: 1.94529/1.50193, loss_grounding_bce_9: 0.03622/0.10558, loss_grounding_dice_9: 0.25106/0.28087, loss_grounding_ce_9: 0.19308/0.67679] items per batch[64] items per second[0.24] total items[3033600] mini batches[ 47400] memory[7345] epoch remaining[0:04:43]
INFO:trainer.default_trainer:epochs[    25] optim steps[47500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.47070/0.90415, loss_mask_bce_0: 0.54879/0.33468, loss_mask_dice_0: 1.85611/1.16458, loss_spatial_bce_0: 0.05439/0.08826, loss_spatial_dice_0: 0.21396/0.21094, loss_spatial_ce_0: 0.00106/0.06569, loss_grounding_bce_0: 0.05147/0.08627, loss_grounding_dice_0: 0.04461/0.17864, loss_grounding_ce_0: 0.19001/0.27347, loss_mask_ce_1: 1.45953/0.90482, loss_mask_bce_1: 0.59012/0.33553, loss_mask_dice_1: 1.87885/1.17120, loss_spatial_bce_1: 0.05460/0.08885, loss_spatial_dice_1: 0.21272/0.21501, loss_spatial_ce_1: 0.00201/0.07140, loss_grounding_bce_1: 0.04716/0.08643, loss_grounding_dice_1: 0.04347/0.17937, loss_grounding_ce_1: 0.16901/0.27501, loss_mask_ce_2: 1.38439/0.91211, loss_mask_bce_2: 0.61484/0.33603, loss_mask_dice_2: 1.95103/1.17111, loss_spatial_bce_2: 0.05414/0.08960, loss_spatial_dice_2: 0.21352/0.21629, loss_spatial_ce_2: 0.00464/0.07486, loss_grounding_bce_2: 0.04668/0.08653, loss_grounding_dice_2: 0.04146/0.17920, loss_grounding_ce_2: 0.21522/0.27808, loss_mask_ce_3: 1.43757/0.92141, loss_mask_bce_3: 0.56954/0.33706, loss_mask_dice_3: 1.83242/1.16847, loss_spatial_bce_3: 0.05733/0.09054, loss_spatial_dice_3: 0.20914/0.21695, loss_spatial_ce_3: 0.01392/0.07862, loss_grounding_bce_3: 0.04657/0.08679, loss_grounding_dice_3: 0.04100/0.17893, loss_grounding_ce_3: 0.23699/0.27977, loss_mask_ce_4: 1.49588/0.92188, loss_mask_bce_4: 0.57232/0.33905, loss_mask_dice_4: 1.85685/1.19226, loss_spatial_bce_4: 0.05531/0.09463, loss_spatial_dice_4: 0.21723/0.22868, loss_spatial_ce_4: 0.01744/0.09463, loss_grounding_bce_4: 0.04811/0.08723, loss_grounding_dice_4: 0.04205/0.18180, loss_grounding_ce_4: 0.25132/0.28263, loss_mask_ce_5: 1.52622/0.93789, loss_mask_bce_5: 0.60872/0.34125, loss_mask_dice_5: 1.91596/1.19904, loss_spatial_bce_5: 0.05858/0.09648, loss_spatial_dice_5: 0.21625/0.23247, loss_spatial_ce_5: 0.02751/0.10966, loss_grounding_bce_5: 0.05195/0.08760, loss_grounding_dice_5: 0.04807/0.18301, loss_grounding_ce_5: 0.31911/0.29553, loss_mask_ce_6: 1.51320/0.97715, loss_mask_bce_6: 0.62179/0.34397, loss_mask_dice_6: 1.98888/1.20180, loss_spatial_bce_6: 0.07580/0.10229, loss_spatial_dice_6: 0.27330/0.23506, loss_spatial_ce_6: 0.08277/0.13570, loss_grounding_bce_6: 0.04816/0.08836, loss_grounding_dice_6: 0.04752/0.18330, loss_grounding_ce_6: 0.34994/0.31154, loss_mask_ce_7: 1.51156/1.02202, loss_mask_bce_7: 0.56143/0.35188, loss_mask_dice_7: 1.94830/1.25681, loss_spatial_bce_7: 0.07031/0.11070, loss_spatial_dice_7: 0.27682/0.26284, loss_spatial_ce_7: 0.09055/0.17194, loss_grounding_bce_7: 0.05227/0.09029, loss_grounding_dice_7: 0.04992/0.19054, loss_grounding_ce_7: 0.20588/0.34368, loss_mask_ce_8: 1.56051/1.13098, loss_mask_bce_8: 0.59559/0.36545, loss_mask_dice_8: 1.92111/1.33045, loss_spatial_bce_8: 0.11736/0.13150, loss_spatial_dice_8: 0.37018/0.30180, loss_spatial_ce_8: 0.15790/0.22927, loss_grounding_bce_8: 0.05525/0.09401, loss_grounding_dice_8: 0.05500/0.20158, loss_grounding_ce_8: 0.49984/0.41194, loss_mask_ce_9: 3.72504/3.68079, loss_mask_bce_9: 0.69195/0.39249, loss_mask_dice_9: 2.94003/1.90395, loss_spatial_bce_9: 0.25113/0.33361, loss_spatial_dice_9: 0.91337/0.82252, loss_spatial_ce_9: 1.24800/1.50190, loss_grounding_bce_9: 0.11348/0.10558, loss_grounding_dice_9: 0.10535/0.28090, loss_grounding_ce_9: 1.38057/0.67686] items per batch[64] items per second[0.23] total items[3040000] mini batches[ 47500] memory[7345] epoch remaining[0:00:05]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00047502.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0029 s/iter. Inference: 0.2132 s/iter. Eval: 0.0931 s/iter. Total: 0.3092 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0031 s/iter. Inference: 0.2262 s/iter. Eval: 0.0864 s/iter. Total: 0.3157 s/iter. ETA=0:00:41
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2274 s/iter. Eval: 0.0826 s/iter. Total: 0.3133 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0032 s/iter. Inference: 0.2276 s/iter. Eval: 0.0803 s/iter. Total: 0.3112 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0033 s/iter. Inference: 0.2255 s/iter. Eval: 0.0802 s/iter. Total: 0.3091 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 94/157. Dataloading: 0.0033 s/iter. Inference: 0.2272 s/iter. Eval: 0.0807 s/iter. Total: 0.3114 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 110/157. Dataloading: 0.0033 s/iter. Inference: 0.2292 s/iter. Eval: 0.0813 s/iter. Total: 0.3140 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 127/157. Dataloading: 0.0033 s/iter. Inference: 0.2289 s/iter. Eval: 0.0811 s/iter. Total: 0.3135 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 143/157. Dataloading: 0.0034 s/iter. Inference: 0.2291 s/iter. Eval: 0.0810 s/iter. Total: 0.3136 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalwdjvq4zr ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.011 | 81.876 | 60.114 |      133      |
| Things | 55.100 | 82.619 | 65.947 |      80       |
| Stuff  | 42.330 | 80.755 | 51.310 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.45s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.82 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.32s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.36 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.486
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.501
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.287
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.534
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.703
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.866 | 61.311 | 40.820 | 19.404 | 41.818 | 60.297 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.469 | bicycle      | 17.910 | car            | 36.852 |
| motorcycle    | 33.714 | airplane     | 55.559 | bus            | 64.922 |
| train         | 68.599 | truck        | 36.155 | boat           | 22.608 |
| traffic light | 25.505 | fire hydrant | 64.763 | stop sign      | 63.538 |
| parking meter | 42.094 | bench        | 19.959 | bird           | 30.247 |
| cat           | 74.098 | dog          | 66.402 | horse          | 45.750 |
| sheep         | 46.967 | cow          | 50.310 | elephant       | 61.033 |
| bear          | 77.008 | zebra        | 59.593 | giraffe        | 56.197 |
| backpack      | 16.451 | umbrella     | 48.816 | handbag        | 15.541 |
| tie           | 34.031 | suitcase     | 40.607 | frisbee        | 67.196 |
| skis          | 4.235  | snowboard    | 23.026 | sports ball    | 47.331 |
| kite          | 34.467 | baseball bat | 29.012 | baseball glove | 43.435 |
| skateboard    | 35.140 | surfboard    | 34.962 | tennis racket  | 56.625 |
| bottle        | 34.709 | wine glass   | 25.957 | cup            | 40.381 |
| fork          | 15.873 | knife        | 12.844 | spoon          | 15.106 |
| bowl          | 32.691 | banana       | 20.150 | apple          | 19.381 |
| sandwich      | 42.086 | orange       | 29.132 | broccoli       | 21.546 |
| carrot        | 20.330 | hot dog      | 22.611 | pizza          | 50.927 |
| donut         | 44.525 | cake         | 43.089 | chair          | 20.816 |
| couch         | 42.863 | potted plant | 16.886 | bed            | 42.217 |
| dining table  | 12.857 | toilet       | 66.342 | tv             | 62.294 |
| laptop        | 63.384 | mouse        | 59.285 | remote         | 31.583 |
| keyboard      | 47.436 | cell phone   | 36.666 | microwave      | 55.268 |
| oven          | 32.288 | toaster      | 38.538 | sink           | 36.309 |
| refrigerator  | 58.346 | book         | 8.914  | clock          | 51.173 |
| vase          | 33.142 | scissors     | 24.002 | teddy bear     | 51.882 |
| hair drier    | 10.461 | toothbrush   | 19.843 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.52111613323581, 'fwIoU': 68.99284936470458, 'IoU-person': 87.46793296798144, 'IoU-bicycle': 66.53694655079802, 'IoU-car': 69.68746769941868, 'IoU-motorcycle': 85.1224291188347, 'IoU-airplane': 81.13337196268714, 'IoU-bus': 84.32861455919613, 'IoU-train': 84.1586251382794, 'IoU-truck': 64.72077930434995, 'IoU-boat': 67.9900267355583, 'IoU-traffic light': 76.0238085597441, 'IoU-fire hydrant': 89.8925926800959, 'IoU-stop sign': 92.13283273713105, 'IoU-parking meter': 82.72522218771536, 'IoU-bench': 54.35047822681107, 'IoU-bird': 75.85874693044825, 'IoU-cat': 87.81932007742805, 'IoU-dog': 78.4940032906833, 'IoU-horse': 85.94742564843753, 'IoU-sheep': 86.1210609956201, 'IoU-cow': 82.80077761510215, 'IoU-elephant': 90.84442147431098, 'IoU-bear': 85.55316061710828, 'IoU-zebra': 88.97549973910012, 'IoU-giraffe': 88.19073921625001, 'IoU-backpack': 42.104720650651075, 'IoU-umbrella': 77.00729152789457, 'IoU-handbag': 38.98836267302347, 'IoU-tie': 69.50926070057405, 'IoU-suitcase': 81.46739102594282, 'IoU-frisbee': 87.7798624564451, 'IoU-skis': 48.638755608612584, 'IoU-snowboard': 65.67258706328943, 'IoU-sports ball': 57.48362994269458, 'IoU-kite': 66.37478070556357, 'IoU-baseball bat': 59.25760945879771, 'IoU-baseball glove': 77.37797009033629, 'IoU-skateboard': 77.07550589467265, 'IoU-surfboard': 81.84317642713305, 'IoU-tennis racket': 73.83464364664538, 'IoU-bottle': 68.71678847625809, 'IoU-wine glass': 72.04847220432642, 'IoU-cup': 61.152282568877446, 'IoU-fork': 55.039138070344016, 'IoU-knife': 50.50572900806659, 'IoU-spoon': 48.50942288421044, 'IoU-bowl': 51.68216613097745, 'IoU-banana': 83.58515751448871, 'IoU-apple': 56.76576527079247, 'IoU-sandwich': 66.06091090392462, 'IoU-orange': 71.77949345191368, 'IoU-broccoli': 65.48232821344241, 'IoU-carrot': 63.815719853541495, 'IoU-hot dog': 65.40252691751142, 'IoU-pizza': 83.19490686611142, 'IoU-donut': 62.676176682178095, 'IoU-cake': 66.60190180557082, 'IoU-chair': 54.51191747783254, 'IoU-couch': 69.72104494393628, 'IoU-potted plant': 33.87778179134013, 'IoU-bed': 68.83328252471169, 'IoU-dining table': 48.86417240866898, 'IoU-toilet': 87.80054610930425, 'IoU-tv': 74.75320280243233, 'IoU-laptop': 73.98928643395932, 'IoU-mouse': 71.82564825994955, 'IoU-remote': 49.493324406285694, 'IoU-keyboard': 61.60153887243619, 'IoU-cell phone': 68.32788127907449, 'IoU-microwave': 55.40401129981214, 'IoU-oven': 68.87065266997826, 'IoU-toaster': 68.95789829945656, 'IoU-sink': 68.42595608191463, 'IoU-refrigerator': 83.28909357527975, 'IoU-book': 49.68427423508808, 'IoU-clock': 65.46219043955406, 'IoU-vase': 61.73676163312234, 'IoU-scissors': 55.76658095166036, 'IoU-teddy bear': 82.86296375608877, 'IoU-hair drier': 38.47822593604371, 'IoU-toothbrush': 62.05761150614092, 'IoU-banner': 32.47595119005065, 'IoU-blanket': 9.565459085549822, 'IoU-bridge': 36.75803935156151, 'IoU-cardboard': 45.48912500746609, 'IoU-counter': 30.898118582007754, 'IoU-curtain': 65.08462149447058, 'IoU-door-stuff': 42.93782748736214, 'IoU-floor-wood': 62.14420218284993, 'IoU-flower': 43.75963668168889, 'IoU-fruit': 36.92135917110877, 'IoU-gravel': 22.286971313290728, 'IoU-house': 23.39308472856067, 'IoU-light': 39.85347203418127, 'IoU-mirror-stuff': 51.111602694818515, 'IoU-net': 34.25906340183772, 'IoU-pillow': 13.538770248764123, 'IoU-platform': 30.401649496303286, 'IoU-playingfield': 70.84532545166468, 'IoU-railroad': 59.83532062048587, 'IoU-river': 52.88552871067256, 'IoU-road': 66.55852942671359, 'IoU-roof': 12.978456190188165, 'IoU-sand': 63.860981031995365, 'IoU-sea': 84.99453298470384, 'IoU-shelf': 35.79251274302418, 'IoU-snow': 88.47427980036382, 'IoU-stairs': 25.010185738737338, 'IoU-tent': 8.2215585144005, 'IoU-towel': 25.67311519026078, 'IoU-wall-brick': 47.50471162224665, 'IoU-wall-stone': 27.55065905860588, 'IoU-wall-tile': 66.17768892153538, 'IoU-wall-wood': 38.055414306206245, 'IoU-water-other': 23.256497613294897, 'IoU-window-blind': 48.01474657880797, 'IoU-window-other': 46.884658692570206, 'IoU-tree-merged': 80.87794404933555, 'IoU-fence-merged': 49.328814835307895, 'IoU-ceiling-merged': 67.02531020743409, 'IoU-sky-other-merged': 93.26074131438544, 'IoU-cabinet-merged': 59.192423525756055, 'IoU-table-merged': 37.87009574399583, 'IoU-floor-other-merged': 49.88872865845811, 'IoU-pavement-merged': 54.66246835322818, 'IoU-mountain-merged': 54.81550832246204, 'IoU-grass-merged': 71.91153196209363, 'IoU-dirt-merged': 45.98378527573283, 'IoU-paper-merged': 27.241269659983345, 'IoU-food-other-merged': 38.825859329066354, 'IoU-building-other-merged': 57.51298541476581, 'IoU-rock-merged': 59.69595558920795, 'IoU-wall-other-merged': 64.81400872397293, 'IoU-rug-merged': 64.04079098485369, 'mACC': 72.60715591102303, 'pACC': 80.34827724184397, 'ACC-person': 92.45552515541077, 'ACC-bicycle': 82.04748596101098, 'ACC-car': 83.81789709397917, 'ACC-motorcycle': 90.07021818962465, 'ACC-airplane': 87.45482577958082, 'ACC-bus': 88.72892080500354, 'ACC-train': 95.38355592832308, 'ACC-truck': 78.07493195305294, 'ACC-boat': 79.08597869245297, 'ACC-traffic light': 89.6992483897051, 'ACC-fire hydrant': 95.4510711525405, 'ACC-stop sign': 95.95812195618544, 'ACC-parking meter': 86.64868710772313, 'ACC-bench': 72.97797573961746, 'ACC-bird': 80.88075428414908, 'ACC-cat': 94.28301644490958, 'ACC-dog': 82.10130235025414, 'ACC-horse': 91.96759967480553, 'ACC-sheep': 89.29186293678866, 'ACC-cow': 88.46287999722769, 'ACC-elephant': 93.50064550215838, 'ACC-bear': 87.84031679142068, 'ACC-zebra': 91.55149491815992, 'ACC-giraffe': 92.70784587523629, 'ACC-backpack': 55.93739318673806, 'ACC-umbrella': 84.999645258796, 'ACC-handbag': 60.09151105362393, 'ACC-tie': 79.9973469468692, 'ACC-suitcase': 90.26432477692748, 'ACC-frisbee': 94.08181818181818, 'ACC-skis': 64.9679553794417, 'ACC-snowboard': 80.7587372339644, 'ACC-sports ball': 72.68686492800363, 'ACC-kite': 76.9448198963501, 'ACC-baseball bat': 75.25155654121932, 'ACC-baseball glove': 88.13471390588184, 'ACC-skateboard': 90.19080663042942, 'ACC-surfboard': 90.11847479910764, 'ACC-tennis racket': 80.50414347308875, 'ACC-bottle': 84.26685291759766, 'ACC-wine glass': 84.65920835524288, 'ACC-cup': 82.942076970635, 'ACC-fork': 66.763802424323, 'ACC-knife': 61.982486993478425, 'ACC-spoon': 67.74744448143628, 'ACC-bowl': 66.75621164877049, 'ACC-banana': 88.78996357749898, 'ACC-apple': 69.21267336436556, 'ACC-sandwich': 78.64788710871643, 'ACC-orange': 79.7220132053814, 'ACC-broccoli': 73.12557195020072, 'ACC-carrot': 72.41028245906912, 'ACC-hot dog': 71.4959211954035, 'ACC-pizza': 92.56171081891277, 'ACC-donut': 79.37419340436988, 'ACC-cake': 75.07232842343645, 'ACC-chair': 69.87944745955474, 'ACC-couch': 85.20880449157335, 'ACC-potted plant': 52.32703697553994, 'ACC-bed': 80.77540676376148, 'ACC-dining table': 81.32258900604457, 'ACC-toilet': 91.94905286510024, 'ACC-tv': 85.63591294923903, 'ACC-laptop': 92.23793635840025, 'ACC-mouse': 86.69271199708524, 'ACC-remote': 72.83768686723786, 'ACC-keyboard': 68.4463559302155, 'ACC-cell phone': 77.35100799078128, 'ACC-microwave': 63.8221981488158, 'ACC-oven': 81.04576133890785, 'ACC-toaster': 73.89316946995305, 'ACC-sink': 84.88483087946919, 'ACC-refrigerator': 91.02445770503306, 'ACC-book': 67.563986719341, 'ACC-clock': 70.66921450682202, 'ACC-vase': 72.00050520666167, 'ACC-scissors': 60.06195539395275, 'ACC-teddy bear': 89.5991632440746, 'ACC-hair drier': 40.89317735462386, 'ACC-toothbrush': 80.0582001389854, 'ACC-banner': 73.70769146220937, 'ACC-blanket': 10.843117565271127, 'ACC-bridge': 56.37577075058663, 'ACC-cardboard': 56.03713828704377, 'ACC-counter': 46.70120397986925, 'ACC-curtain': 76.73071003574837, 'ACC-door-stuff': 64.17440145267672, 'ACC-floor-wood': 78.4746522170763, 'ACC-flower': 60.59122278931631, 'ACC-fruit': 60.35551434007665, 'ACC-gravel': 28.88648171602836, 'ACC-house': 27.923630978278624, 'ACC-light': 57.3978749569345, 'ACC-mirror-stuff': 62.70448258644253, 'ACC-net': 66.15940920468326, 'ACC-pillow': 27.893598878543163, 'ACC-platform': 60.109316495232, 'ACC-playingfield': 91.67068545991845, 'ACC-railroad': 79.9585826828875, 'ACC-river': 80.99283262905476, 'ACC-road': 85.33250943290638, 'ACC-roof': 17.170808427760598, 'ACC-sand': 71.32188731815742, 'ACC-sea': 90.78944310414818, 'ACC-shelf': 60.807244709932704, 'ACC-snow': 95.53594633414494, 'ACC-stairs': 41.38441250678621, 'ACC-tent': 10.069016063669174, 'ACC-towel': 29.230641472102022, 'ACC-wall-brick': 61.77608791566907, 'ACC-wall-stone': 32.270926938738405, 'ACC-wall-tile': 77.44350023833087, 'ACC-wall-wood': 55.68361405723303, 'ACC-water-other': 33.400231765113745, 'ACC-window-blind': 55.645065826601716, 'ACC-window-other': 67.07566324796846, 'ACC-tree-merged': 89.50547628597434, 'ACC-fence-merged': 63.69025213111743, 'ACC-ceiling-merged': 79.23583231870957, 'ACC-sky-other-merged': 96.51625638721806, 'ACC-cabinet-merged': 74.70661896683465, 'ACC-table-merged': 46.88499213629266, 'ACC-floor-other-merged': 60.771010324510776, 'ACC-pavement-merged': 66.80933791594879, 'ACC-mountain-merged': 64.33308625238445, 'ACC-grass-merged': 82.84375871874997, 'ACC-dirt-merged': 66.84172050375852, 'ACC-paper-merged': 34.46033875405314, 'ACC-food-other-merged': 50.56335648232291, 'ACC-building-other-merged': 75.1561280465381, 'ACC-rock-merged': 83.6044790161072, 'ACC-wall-other-merged': 80.81784830007159, 'ACC-rug-merged': 76.3024518667356})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1540 s/iter. Inference: 0.5734 s/iter. Eval: 0.0000 s/iter. Total: 0.7275 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1589 s/iter. Inference: 0.5269 s/iter. Eval: 0.0000 s/iter. Total: 0.6859 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1734 s/iter. Inference: 0.6148 s/iter. Eval: 0.0000 s/iter. Total: 0.7884 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1724 s/iter. Inference: 0.6839 s/iter. Eval: 0.0000 s/iter. Total: 0.8565 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1709 s/iter. Inference: 0.6307 s/iter. Eval: 0.0000 s/iter. Total: 0.8018 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1693 s/iter. Inference: 0.6754 s/iter. Eval: 0.0000 s/iter. Total: 0.8449 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4942932396839332, 'noc@0.8': 2.855428738659643, 'noc@0.85': 3.4269827333918643, 'noc@0.9': 4.56745683347966, 'miou@iter1': 0.8398078358030333}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1004 s/iter. Eval: 0.0008 s/iter. Total: 0.1029 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.35639190673828, 'precision@0.6': 68.28604888916016, 'precision@0.7': 63.42790603637695, 'precision@0.8': 52.662261962890625, 'precision@0.9': 27.555383682250977, 'cIoU': 57.7298698425293, 'mIoU': 63.10740280151367}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.01126326169584, 'SQ': 81.87604898173043, 'RQ': 60.11391300649829, 'PQ_th': 55.09992599582743, 'SQ_th': 82.61891212229949, 'RQ_th': 65.94660474720853, 'PQ_st': 42.330262908289704, 'SQ_st': 80.75474612804125, 'RQ_st': 51.30985000165259}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.8657659804127, 'AP50': 61.31116898648887, 'AP75': 40.8201979673881, 'APs': 19.404467572570773, 'APm': 41.81793031954883, 'APl': 60.29711676561651, 'AP-person': 44.46934918278559, 'AP-bicycle': 17.910073886786186, 'AP-car': 36.851531756264684, 'AP-motorcycle': 33.71398690711569, 'AP-airplane': 55.55900527322821, 'AP-bus': 64.92174832029666, 'AP-train': 68.59915042114282, 'AP-truck': 36.15468878918986, 'AP-boat': 22.60785283028514, 'AP-traffic light': 25.504894516327646, 'AP-fire hydrant': 64.76346466061716, 'AP-stop sign': 63.53821141849875, 'AP-parking meter': 42.09411647261271, 'AP-bench': 19.959153019523942, 'AP-bird': 30.246620871779083, 'AP-cat': 74.09758665213725, 'AP-dog': 66.40174196702534, 'AP-horse': 45.750416877294356, 'AP-sheep': 46.96700922719344, 'AP-cow': 50.31031761375908, 'AP-elephant': 61.0334119633131, 'AP-bear': 77.00760835182484, 'AP-zebra': 59.59288709797958, 'AP-giraffe': 56.196586022080396, 'AP-backpack': 16.450922299141123, 'AP-umbrella': 48.81586795402913, 'AP-handbag': 15.540931836521969, 'AP-tie': 34.031106479313486, 'AP-suitcase': 40.60706805749659, 'AP-frisbee': 67.19585292161348, 'AP-skis': 4.235395815845747, 'AP-snowboard': 23.02587571021004, 'AP-sports ball': 47.331478737658614, 'AP-kite': 34.467069641053286, 'AP-baseball bat': 29.011684881111165, 'AP-baseball glove': 43.435499099501534, 'AP-skateboard': 35.14028641384451, 'AP-surfboard': 34.96189841774579, 'AP-tennis racket': 56.62515028109802, 'AP-bottle': 34.70860851850552, 'AP-wine glass': 25.95713628467121, 'AP-cup': 40.38121142045933, 'AP-fork': 15.873431622959913, 'AP-knife': 12.843755983934143, 'AP-spoon': 15.106202977841654, 'AP-bowl': 32.69146765710392, 'AP-banana': 20.149607492189066, 'AP-apple': 19.380913448180568, 'AP-sandwich': 42.08572047564883, 'AP-orange': 29.132271444830142, 'AP-broccoli': 21.54610737785873, 'AP-carrot': 20.329713988240037, 'AP-hot dog': 22.611275289000048, 'AP-pizza': 50.92731625964131, 'AP-donut': 44.52451842833721, 'AP-cake': 43.089021642175474, 'AP-chair': 20.81637837902704, 'AP-couch': 42.862735011651196, 'AP-potted plant': 16.88562249384431, 'AP-bed': 42.21748436360266, 'AP-dining table': 12.856508116163038, 'AP-toilet': 66.34244456800616, 'AP-tv': 62.29383282743789, 'AP-laptop': 63.38394171547552, 'AP-mouse': 59.28514650501133, 'AP-remote': 31.583260669673436, 'AP-keyboard': 47.43640570310238, 'AP-cell phone': 36.66620855622218, 'AP-microwave': 55.26772572193245, 'AP-oven': 32.287640852847396, 'AP-toaster': 38.53775965831878, 'AP-sink': 36.308910218256834, 'AP-refrigerator': 58.34577405928657, 'AP-book': 8.9141864446446, 'AP-clock': 51.17260059292925, 'AP-vase': 33.142378663814775, 'AP-scissors': 24.00238580988541, 'AP-teddy bear': 51.882155689137164, 'AP-hair drier': 10.461075519316637, 'AP-toothbrush': 19.842933336605736}), ('sem_seg', {'mIoU': 60.52111613323581, 'fwIoU': 68.99284936470458, 'IoU-person': 87.46793296798144, 'IoU-bicycle': 66.53694655079802, 'IoU-car': 69.68746769941868, 'IoU-motorcycle': 85.1224291188347, 'IoU-airplane': 81.13337196268714, 'IoU-bus': 84.32861455919613, 'IoU-train': 84.1586251382794, 'IoU-truck': 64.72077930434995, 'IoU-boat': 67.9900267355583, 'IoU-traffic light': 76.0238085597441, 'IoU-fire hydrant': 89.8925926800959, 'IoU-stop sign': 92.13283273713105, 'IoU-parking meter': 82.72522218771536, 'IoU-bench': 54.35047822681107, 'IoU-bird': 75.85874693044825, 'IoU-cat': 87.81932007742805, 'IoU-dog': 78.4940032906833, 'IoU-horse': 85.94742564843753, 'IoU-sheep': 86.1210609956201, 'IoU-cow': 82.80077761510215, 'IoU-elephant': 90.84442147431098, 'IoU-bear': 85.55316061710828, 'IoU-zebra': 88.97549973910012, 'IoU-giraffe': 88.19073921625001, 'IoU-backpack': 42.104720650651075, 'IoU-umbrella': 77.00729152789457, 'IoU-handbag': 38.98836267302347, 'IoU-tie': 69.50926070057405, 'IoU-suitcase': 81.46739102594282, 'IoU-frisbee': 87.7798624564451, 'IoU-skis': 48.638755608612584, 'IoU-snowboard': 65.67258706328943, 'IoU-sports ball': 57.48362994269458, 'IoU-kite': 66.37478070556357, 'IoU-baseball bat': 59.25760945879771, 'IoU-baseball glove': 77.37797009033629, 'IoU-skateboard': 77.07550589467265, 'IoU-surfboard': 81.84317642713305, 'IoU-tennis racket': 73.83464364664538, 'IoU-bottle': 68.71678847625809, 'IoU-wine glass': 72.04847220432642, 'IoU-cup': 61.152282568877446, 'IoU-fork': 55.039138070344016, 'IoU-knife': 50.50572900806659, 'IoU-spoon': 48.50942288421044, 'IoU-bowl': 51.68216613097745, 'IoU-banana': 83.58515751448871, 'IoU-apple': 56.76576527079247, 'IoU-sandwich': 66.06091090392462, 'IoU-orange': 71.77949345191368, 'IoU-broccoli': 65.48232821344241, 'IoU-carrot': 63.815719853541495, 'IoU-hot dog': 65.40252691751142, 'IoU-pizza': 83.19490686611142, 'IoU-donut': 62.676176682178095, 'IoU-cake': 66.60190180557082, 'IoU-chair': 54.51191747783254, 'IoU-couch': 69.72104494393628, 'IoU-potted plant': 33.87778179134013, 'IoU-bed': 68.83328252471169, 'IoU-dining table': 48.86417240866898, 'IoU-toilet': 87.80054610930425, 'IoU-tv': 74.75320280243233, 'IoU-laptop': 73.98928643395932, 'IoU-mouse': 71.82564825994955, 'IoU-remote': 49.493324406285694, 'IoU-keyboard': 61.60153887243619, 'IoU-cell phone': 68.32788127907449, 'IoU-microwave': 55.40401129981214, 'IoU-oven': 68.87065266997826, 'IoU-toaster': 68.95789829945656, 'IoU-sink': 68.42595608191463, 'IoU-refrigerator': 83.28909357527975, 'IoU-book': 49.68427423508808, 'IoU-clock': 65.46219043955406, 'IoU-vase': 61.73676163312234, 'IoU-scissors': 55.76658095166036, 'IoU-teddy bear': 82.86296375608877, 'IoU-hair drier': 38.47822593604371, 'IoU-toothbrush': 62.05761150614092, 'IoU-banner': 32.47595119005065, 'IoU-blanket': 9.565459085549822, 'IoU-bridge': 36.75803935156151, 'IoU-cardboard': 45.48912500746609, 'IoU-counter': 30.898118582007754, 'IoU-curtain': 65.08462149447058, 'IoU-door-stuff': 42.93782748736214, 'IoU-floor-wood': 62.14420218284993, 'IoU-flower': 43.75963668168889, 'IoU-fruit': 36.92135917110877, 'IoU-gravel': 22.286971313290728, 'IoU-house': 23.39308472856067, 'IoU-light': 39.85347203418127, 'IoU-mirror-stuff': 51.111602694818515, 'IoU-net': 34.25906340183772, 'IoU-pillow': 13.538770248764123, 'IoU-platform': 30.401649496303286, 'IoU-playingfield': 70.84532545166468, 'IoU-railroad': 59.83532062048587, 'IoU-river': 52.88552871067256, 'IoU-road': 66.55852942671359, 'IoU-roof': 12.978456190188165, 'IoU-sand': 63.860981031995365, 'IoU-sea': 84.99453298470384, 'IoU-shelf': 35.79251274302418, 'IoU-snow': 88.47427980036382, 'IoU-stairs': 25.010185738737338, 'IoU-tent': 8.2215585144005, 'IoU-towel': 25.67311519026078, 'IoU-wall-brick': 47.50471162224665, 'IoU-wall-stone': 27.55065905860588, 'IoU-wall-tile': 66.17768892153538, 'IoU-wall-wood': 38.055414306206245, 'IoU-water-other': 23.256497613294897, 'IoU-window-blind': 48.01474657880797, 'IoU-window-other': 46.884658692570206, 'IoU-tree-merged': 80.87794404933555, 'IoU-fence-merged': 49.328814835307895, 'IoU-ceiling-merged': 67.02531020743409, 'IoU-sky-other-merged': 93.26074131438544, 'IoU-cabinet-merged': 59.192423525756055, 'IoU-table-merged': 37.87009574399583, 'IoU-floor-other-merged': 49.88872865845811, 'IoU-pavement-merged': 54.66246835322818, 'IoU-mountain-merged': 54.81550832246204, 'IoU-grass-merged': 71.91153196209363, 'IoU-dirt-merged': 45.98378527573283, 'IoU-paper-merged': 27.241269659983345, 'IoU-food-other-merged': 38.825859329066354, 'IoU-building-other-merged': 57.51298541476581, 'IoU-rock-merged': 59.69595558920795, 'IoU-wall-other-merged': 64.81400872397293, 'IoU-rug-merged': 64.04079098485369, 'mACC': 72.60715591102303, 'pACC': 80.34827724184397, 'ACC-person': 92.45552515541077, 'ACC-bicycle': 82.04748596101098, 'ACC-car': 83.81789709397917, 'ACC-motorcycle': 90.07021818962465, 'ACC-airplane': 87.45482577958082, 'ACC-bus': 88.72892080500354, 'ACC-train': 95.38355592832308, 'ACC-truck': 78.07493195305294, 'ACC-boat': 79.08597869245297, 'ACC-traffic light': 89.6992483897051, 'ACC-fire hydrant': 95.4510711525405, 'ACC-stop sign': 95.95812195618544, 'ACC-parking meter': 86.64868710772313, 'ACC-bench': 72.97797573961746, 'ACC-bird': 80.88075428414908, 'ACC-cat': 94.28301644490958, 'ACC-dog': 82.10130235025414, 'ACC-horse': 91.96759967480553, 'ACC-sheep': 89.29186293678866, 'ACC-cow': 88.46287999722769, 'ACC-elephant': 93.50064550215838, 'ACC-bear': 87.84031679142068, 'ACC-zebra': 91.55149491815992, 'ACC-giraffe': 92.70784587523629, 'ACC-backpack': 55.93739318673806, 'ACC-umbrella': 84.999645258796, 'ACC-handbag': 60.09151105362393, 'ACC-tie': 79.9973469468692, 'ACC-suitcase': 90.26432477692748, 'ACC-frisbee': 94.08181818181818, 'ACC-skis': 64.9679553794417, 'ACC-snowboard': 80.7587372339644, 'ACC-sports ball': 72.68686492800363, 'ACC-kite': 76.9448198963501, 'ACC-baseball bat': 75.25155654121932, 'ACC-baseball glove': 88.13471390588184, 'ACC-skateboard': 90.19080663042942, 'ACC-surfboard': 90.11847479910764, 'ACC-tennis racket': 80.50414347308875, 'ACC-bottle': 84.26685291759766, 'ACC-wine glass': 84.65920835524288, 'ACC-cup': 82.942076970635, 'ACC-fork': 66.763802424323, 'ACC-knife': 61.982486993478425, 'ACC-spoon': 67.74744448143628, 'ACC-bowl': 66.75621164877049, 'ACC-banana': 88.78996357749898, 'ACC-apple': 69.21267336436556, 'ACC-sandwich': 78.64788710871643, 'ACC-orange': 79.7220132053814, 'ACC-broccoli': 73.12557195020072, 'ACC-carrot': 72.41028245906912, 'ACC-hot dog': 71.4959211954035, 'ACC-pizza': 92.56171081891277, 'ACC-donut': 79.37419340436988, 'ACC-cake': 75.07232842343645, 'ACC-chair': 69.87944745955474, 'ACC-couch': 85.20880449157335, 'ACC-potted plant': 52.32703697553994, 'ACC-bed': 80.77540676376148, 'ACC-dining table': 81.32258900604457, 'ACC-toilet': 91.94905286510024, 'ACC-tv': 85.63591294923903, 'ACC-laptop': 92.23793635840025, 'ACC-mouse': 86.69271199708524, 'ACC-remote': 72.83768686723786, 'ACC-keyboard': 68.4463559302155, 'ACC-cell phone': 77.35100799078128, 'ACC-microwave': 63.8221981488158, 'ACC-oven': 81.04576133890785, 'ACC-toaster': 73.89316946995305, 'ACC-sink': 84.88483087946919, 'ACC-refrigerator': 91.02445770503306, 'ACC-book': 67.563986719341, 'ACC-clock': 70.66921450682202, 'ACC-vase': 72.00050520666167, 'ACC-scissors': 60.06195539395275, 'ACC-teddy bear': 89.5991632440746, 'ACC-hair drier': 40.89317735462386, 'ACC-toothbrush': 80.0582001389854, 'ACC-banner': 73.70769146220937, 'ACC-blanket': 10.843117565271127, 'ACC-bridge': 56.37577075058663, 'ACC-cardboard': 56.03713828704377, 'ACC-counter': 46.70120397986925, 'ACC-curtain': 76.73071003574837, 'ACC-door-stuff': 64.17440145267672, 'ACC-floor-wood': 78.4746522170763, 'ACC-flower': 60.59122278931631, 'ACC-fruit': 60.35551434007665, 'ACC-gravel': 28.88648171602836, 'ACC-house': 27.923630978278624, 'ACC-light': 57.3978749569345, 'ACC-mirror-stuff': 62.70448258644253, 'ACC-net': 66.15940920468326, 'ACC-pillow': 27.893598878543163, 'ACC-platform': 60.109316495232, 'ACC-playingfield': 91.67068545991845, 'ACC-railroad': 79.9585826828875, 'ACC-river': 80.99283262905476, 'ACC-road': 85.33250943290638, 'ACC-roof': 17.170808427760598, 'ACC-sand': 71.32188731815742, 'ACC-sea': 90.78944310414818, 'ACC-shelf': 60.807244709932704, 'ACC-snow': 95.53594633414494, 'ACC-stairs': 41.38441250678621, 'ACC-tent': 10.069016063669174, 'ACC-towel': 29.230641472102022, 'ACC-wall-brick': 61.77608791566907, 'ACC-wall-stone': 32.270926938738405, 'ACC-wall-tile': 77.44350023833087, 'ACC-wall-wood': 55.68361405723303, 'ACC-water-other': 33.400231765113745, 'ACC-window-blind': 55.645065826601716, 'ACC-window-other': 67.07566324796846, 'ACC-tree-merged': 89.50547628597434, 'ACC-fence-merged': 63.69025213111743, 'ACC-ceiling-merged': 79.23583231870957, 'ACC-sky-other-merged': 96.51625638721806, 'ACC-cabinet-merged': 74.70661896683465, 'ACC-table-merged': 46.88499213629266, 'ACC-floor-other-merged': 60.771010324510776, 'ACC-pavement-merged': 66.80933791594879, 'ACC-mountain-merged': 64.33308625238445, 'ACC-grass-merged': 82.84375871874997, 'ACC-dirt-merged': 66.84172050375852, 'ACC-paper-merged': 34.46033875405314, 'ACC-food-other-merged': 50.56335648232291, 'ACC-building-other-merged': 75.1561280465381, 'ACC-rock-merged': 83.6044790161072, 'ACC-wall-other-merged': 80.81784830007159, 'ACC-rug-merged': 76.3024518667356})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4942932396839332, 'noc@0.8': 2.855428738659643, 'noc@0.85': 3.4269827333918643, 'noc@0.9': 4.56745683347966, 'miou@iter1': 0.8398078358030333}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.35639190673828, 'precision@0.6': 68.28604888916016, 'precision@0.7': 63.42790603637695, 'precision@0.8': 52.662261962890625, 'precision@0.9': 27.555383682250977, 'cIoU': 57.7298698425293, 'mIoU': 63.10740280151367}}}
INFO:trainer.default_trainer:This epoch takes 1:27:53.237048
INFO:trainer.default_trainer:PROGRESS: 52.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 26 training.
INFO:trainer.default_trainer:epochs[    26] optim steps[47600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.40502/0.90414, loss_mask_bce_0: 0.40268/0.33472, loss_mask_dice_0: 1.77480/1.16448, loss_spatial_bce_0: 0.04728/0.08827, loss_spatial_dice_0: 0.23821/0.21091, loss_spatial_ce_0: 0.03768/0.06565, loss_grounding_bce_0: 0.07967/0.08628, loss_grounding_dice_0: 0.09623/0.17864, loss_grounding_ce_0: 0.02866/0.27349, loss_mask_ce_1: 1.38200/0.90478, loss_mask_bce_1: 0.42131/0.33558, loss_mask_dice_1: 1.85528/1.17111, loss_spatial_bce_1: 0.04449/0.08886, loss_spatial_dice_1: 0.20374/0.21497, loss_spatial_ce_1: 0.06722/0.07137, loss_grounding_bce_1: 0.07822/0.08644, loss_grounding_dice_1: 0.10209/0.17937, loss_grounding_ce_1: 0.02684/0.27502, loss_mask_ce_2: 1.37983/0.91209, loss_mask_bce_2: 0.40359/0.33608, loss_mask_dice_2: 1.79385/1.17102, loss_spatial_bce_2: 0.04952/0.08961, loss_spatial_dice_2: 0.22831/0.21625, loss_spatial_ce_2: 0.03218/0.07483, loss_grounding_bce_2: 0.07384/0.08653, loss_grounding_dice_2: 0.09761/0.17920, loss_grounding_ce_2: 0.02875/0.27810, loss_mask_ce_3: 1.18018/0.92140, loss_mask_bce_3: 0.40913/0.33712, loss_mask_dice_3: 1.95365/1.16836, loss_spatial_bce_3: 0.05105/0.09055, loss_spatial_dice_3: 0.20249/0.21692, loss_spatial_ce_3: 0.11734/0.07858, loss_grounding_bce_3: 0.08521/0.08679, loss_grounding_dice_3: 0.10064/0.17893, loss_grounding_ce_3: 0.04385/0.27980, loss_mask_ce_4: 1.07503/0.92187, loss_mask_bce_4: 0.41004/0.33909, loss_mask_dice_4: 2.01343/1.19218, loss_spatial_bce_4: 0.05457/0.09463, loss_spatial_dice_4: 0.25320/0.22864, loss_spatial_ce_4: 0.02034/0.09458, loss_grounding_bce_4: 0.07348/0.08723, loss_grounding_dice_4: 0.10210/0.18180, loss_grounding_ce_4: 0.05244/0.28264, loss_mask_ce_5: 1.18283/0.93787, loss_mask_bce_5: 0.39916/0.34129, loss_mask_dice_5: 1.84420/1.19895, loss_spatial_bce_5: 0.04909/0.09649, loss_spatial_dice_5: 0.25324/0.23243, loss_spatial_ce_5: 0.04218/0.10961, loss_grounding_bce_5: 0.07830/0.08759, loss_grounding_dice_5: 0.12142/0.18301, loss_grounding_ce_5: 0.05068/0.29559, loss_mask_ce_6: 1.45446/0.97716, loss_mask_bce_6: 0.39660/0.34401, loss_mask_dice_6: 1.76852/1.20175, loss_spatial_bce_6: 0.05945/0.10228, loss_spatial_dice_6: 0.27921/0.23503, loss_spatial_ce_6: 0.08164/0.13566, loss_grounding_bce_6: 0.08106/0.08836, loss_grounding_dice_6: 0.10921/0.18330, loss_grounding_ce_6: 0.04958/0.31157, loss_mask_ce_7: 1.45856/1.02195, loss_mask_bce_7: 0.35149/0.35193, loss_mask_dice_7: 1.84125/1.25675, loss_spatial_bce_7: 0.05961/0.11069, loss_spatial_dice_7: 0.27751/0.26281, loss_spatial_ce_7: 0.17670/0.17188, loss_grounding_bce_7: 0.06566/0.09029, loss_grounding_dice_7: 0.10368/0.19055, loss_grounding_ce_7: 0.08460/0.34367, loss_mask_ce_8: 1.70920/1.13093, loss_mask_bce_8: 0.43654/0.36549, loss_mask_dice_8: 1.78559/1.33037, loss_spatial_bce_8: 0.10207/0.13150, loss_spatial_dice_8: 0.37679/0.30175, loss_spatial_ce_8: 0.28986/0.22922, loss_grounding_bce_8: 0.07373/0.09401, loss_grounding_dice_8: 0.10261/0.20159, loss_grounding_ce_8: 0.30301/0.41192, loss_mask_ce_9: 5.22826/3.68043, loss_mask_bce_9: 0.46517/0.39251, loss_mask_dice_9: 3.05970/1.90384, loss_spatial_bce_9: 0.17584/0.33363, loss_spatial_dice_9: 0.93261/0.82252, loss_spatial_ce_9: 1.55269/1.50176, loss_grounding_bce_9: 0.07517/0.10557, loss_grounding_dice_9: 0.15360/0.28089, loss_grounding_ce_9: 0.77614/0.67667] items per batch[64] items per second[0.13] total items[3046400] mini batches[ 47600] memory[7345] epoch remaining[1:21:32]
INFO:trainer.default_trainer:epochs[    26] optim steps[47700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87230/0.90422, loss_mask_bce_0: 0.19817/0.33472, loss_mask_dice_0: 1.43198/1.16437, loss_spatial_bce_0: 0.01927/0.08827, loss_spatial_dice_0: 0.14872/0.21091, loss_spatial_ce_0: 0.01352/0.06562, loss_grounding_bce_0: 0.07349/0.08629, loss_grounding_dice_0: 0.12803/0.17868, loss_grounding_ce_0: 0.02236/0.27355, loss_mask_ce_1: 0.93194/0.90486, loss_mask_bce_1: 0.18328/0.33559, loss_mask_dice_1: 1.18157/1.17101, loss_spatial_bce_1: 0.02058/0.08885, loss_spatial_dice_1: 0.16978/0.21496, loss_spatial_ce_1: 0.00822/0.07134, loss_grounding_bce_1: 0.07501/0.08645, loss_grounding_dice_1: 0.13470/0.17941, loss_grounding_ce_1: 0.02160/0.27510, loss_mask_ce_2: 0.84089/0.91218, loss_mask_bce_2: 0.19067/0.33609, loss_mask_dice_2: 1.16523/1.17090, loss_spatial_bce_2: 0.02123/0.08961, loss_spatial_dice_2: 0.18174/0.21625, loss_spatial_ce_2: 0.01092/0.07481, loss_grounding_bce_2: 0.08151/0.08654, loss_grounding_dice_2: 0.14516/0.17923, loss_grounding_ce_2: 0.01737/0.27817, loss_mask_ce_3: 0.89663/0.92146, loss_mask_bce_3: 0.18908/0.33712, loss_mask_dice_3: 1.16384/1.16824, loss_spatial_bce_3: 0.02102/0.09055, loss_spatial_dice_3: 0.17192/0.21691, loss_spatial_ce_3: 0.01822/0.07856, loss_grounding_bce_3: 0.08448/0.08680, loss_grounding_dice_3: 0.16323/0.17898, loss_grounding_ce_3: 0.02208/0.27987, loss_mask_ce_4: 0.76920/0.92187, loss_mask_bce_4: 0.17573/0.33911, loss_mask_dice_4: 1.16373/1.19204, loss_spatial_bce_4: 0.02402/0.09463, loss_spatial_dice_4: 0.20243/0.22864, loss_spatial_ce_4: 0.04810/0.09458, loss_grounding_bce_4: 0.08352/0.08724, loss_grounding_dice_4: 0.16799/0.18184, loss_grounding_ce_4: 0.00445/0.28280, loss_mask_ce_5: 0.92862/0.93792, loss_mask_bce_5: 0.19749/0.34130, loss_mask_dice_5: 1.23075/1.19883, loss_spatial_bce_5: 0.02808/0.09650, loss_spatial_dice_5: 0.21740/0.23243, loss_spatial_ce_5: 0.01888/0.10958, loss_grounding_bce_5: 0.09835/0.08761, loss_grounding_dice_5: 0.18766/0.18305, loss_grounding_ce_5: 0.03334/0.29567, loss_mask_ce_6: 1.03203/0.97716, loss_mask_bce_6: 0.19693/0.34403, loss_mask_dice_6: 1.27017/1.20166, loss_spatial_bce_6: 0.03604/0.10229, loss_spatial_dice_6: 0.21633/0.23503, loss_spatial_ce_6: 0.03125/0.13566, loss_grounding_bce_6: 0.07263/0.08838, loss_grounding_dice_6: 0.12743/0.18334, loss_grounding_ce_6: 0.17917/0.31165, loss_mask_ce_7: 1.13958/1.02197, loss_mask_bce_7: 0.18663/0.35193, loss_mask_dice_7: 1.19643/1.25661, loss_spatial_bce_7: 0.02987/0.11070, loss_spatial_dice_7: 0.20444/0.26281, loss_spatial_ce_7: 0.12563/0.17189, loss_grounding_bce_7: 0.06730/0.09030, loss_grounding_dice_7: 0.10342/0.19059, loss_grounding_ce_7: 0.21061/0.34377, loss_mask_ce_8: 1.11215/1.13105, loss_mask_bce_8: 0.19588/0.36549, loss_mask_dice_8: 1.32577/1.33020, loss_spatial_bce_8: 0.03608/0.13150, loss_spatial_dice_8: 0.26020/0.30175, loss_spatial_ce_8: 0.11917/0.22923, loss_grounding_bce_8: 0.07892/0.09402, loss_grounding_dice_8: 0.12736/0.20161, loss_grounding_ce_8: 0.22345/0.41198, loss_mask_ce_9: 4.01613/3.68041, loss_mask_bce_9: 0.25247/0.39250, loss_mask_dice_9: 2.47131/1.90356, loss_spatial_bce_9: 0.17162/0.33362, loss_spatial_dice_9: 0.93448/0.82250, loss_spatial_ce_9: 1.39093/1.50173, loss_grounding_bce_9: 0.11740/0.10558, loss_grounding_dice_9: 0.23784/0.28092, loss_grounding_ce_9: 0.45969/0.67682] items per batch[64] items per second[0.23] total items[3052800] mini batches[ 47700] memory[7345] epoch remaining[1:16:44]
INFO:trainer.default_trainer:epochs[    26] optim steps[47800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05322/0.90431, loss_mask_bce_0: 0.38592/0.33474, loss_mask_dice_0: 3.51133/1.16465, loss_spatial_bce_0: 0.03951/0.08824, loss_spatial_dice_0: 0.32813/0.21089, loss_spatial_ce_0: 0.09795/0.06572, loss_grounding_bce_0: 0.01422/0.08626, loss_grounding_dice_0: 0.32549/0.17870, loss_grounding_ce_0: 0.36738/0.27362, loss_mask_ce_1: 1.07964/0.90500, loss_mask_bce_1: 0.39073/0.33560, loss_mask_dice_1: 3.89641/1.17123, loss_spatial_bce_1: 0.03967/0.08883, loss_spatial_dice_1: 0.33411/0.21495, loss_spatial_ce_1: 0.10493/0.07146, loss_grounding_bce_1: 0.01150/0.08642, loss_grounding_dice_1: 0.30432/0.17942, loss_grounding_ce_1: 0.36728/0.27516, loss_mask_ce_2: 1.08042/0.91229, loss_mask_bce_2: 0.39018/0.33610, loss_mask_dice_2: 3.73232/1.17115, loss_spatial_bce_2: 0.04594/0.08959, loss_spatial_dice_2: 0.36902/0.21624, loss_spatial_ce_2: 0.09868/0.07495, loss_grounding_bce_2: 0.01217/0.08652, loss_grounding_dice_2: 0.36210/0.17925, loss_grounding_ce_2: 0.38338/0.27824, loss_mask_ce_3: 1.04621/0.92157, loss_mask_bce_3: 0.40350/0.33713, loss_mask_dice_3: 3.71170/1.16849, loss_spatial_bce_3: 0.04307/0.09053, loss_spatial_dice_3: 0.34881/0.21690, loss_spatial_ce_3: 0.11081/0.07871, loss_grounding_bce_3: 0.01688/0.08678, loss_grounding_dice_3: 0.35015/0.17900, loss_grounding_ce_3: 0.37564/0.27994, loss_mask_ce_4: 1.17077/0.92198, loss_mask_bce_4: 0.42560/0.33914, loss_mask_dice_4: 4.28147/1.19230, loss_spatial_bce_4: 0.04019/0.09461, loss_spatial_dice_4: 0.36435/0.22863, loss_spatial_ce_4: 0.29530/0.09473, loss_grounding_bce_4: 0.01470/0.08721, loss_grounding_dice_4: 0.40337/0.18187, loss_grounding_ce_4: 0.41205/0.28286, loss_mask_ce_5: 0.99352/0.93807, loss_mask_bce_5: 0.45376/0.34132, loss_mask_dice_5: 3.91270/1.19912, loss_spatial_bce_5: 0.04985/0.09648, loss_spatial_dice_5: 0.33986/0.23242, loss_spatial_ce_5: 0.29365/0.10971, loss_grounding_bce_5: 0.01245/0.08759, loss_grounding_dice_5: 0.37321/0.18308, loss_grounding_ce_5: 0.34186/0.29573, loss_mask_ce_6: 1.06641/0.97728, loss_mask_bce_6: 0.41179/0.34405, loss_mask_dice_6: 3.76649/1.20194, loss_spatial_bce_6: 0.05233/0.10227, loss_spatial_dice_6: 0.38164/0.23502, loss_spatial_ce_6: 0.13261/0.13577, loss_grounding_bce_6: 0.01333/0.08836, loss_grounding_dice_6: 0.40563/0.18337, loss_grounding_ce_6: 0.33636/0.31172, loss_mask_ce_7: 1.15927/1.02211, loss_mask_bce_7: 0.44323/0.35194, loss_mask_dice_7: 4.15743/1.25695, loss_spatial_bce_7: 0.06253/0.11068, loss_spatial_dice_7: 0.46073/0.26282, loss_spatial_ce_7: 0.22641/0.17201, loss_grounding_bce_7: 0.01331/0.09027, loss_grounding_dice_7: 0.36488/0.19062, loss_grounding_ce_7: 0.35072/0.34386, loss_mask_ce_8: 1.35419/1.13122, loss_mask_bce_8: 0.53910/0.36552, loss_mask_dice_8: 4.56612/1.33050, loss_spatial_bce_8: 0.16600/0.13148, loss_spatial_dice_8: 0.51797/0.30176, loss_spatial_ce_8: 0.21158/0.22945, loss_grounding_bce_8: 0.02144/0.09400, loss_grounding_dice_8: 0.43322/0.20164, loss_grounding_ce_8: 0.75721/0.41199, loss_mask_ce_9: 6.35858/3.68072, loss_mask_bce_9: 0.67312/0.39253, loss_mask_dice_9: 6.52911/1.90394, loss_spatial_bce_9: 0.10258/0.33358, loss_spatial_dice_9: 0.95431/0.82253, loss_spatial_ce_9: 1.47364/1.50183, loss_grounding_bce_9: 0.03432/0.10556, loss_grounding_dice_9: 0.54019/0.28095, loss_grounding_ce_9: 1.07633/0.67679] items per batch[64] items per second[0.23] total items[3059200] mini batches[ 47800] memory[7345] epoch remaining[1:11:21]
INFO:trainer.default_trainer:epochs[    26] optim steps[47900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78895/0.90433, loss_mask_bce_0: 0.32455/0.33470, loss_mask_dice_0: 0.72474/1.16430, loss_spatial_bce_0: 0.11452/0.08825, loss_spatial_dice_0: 0.20359/0.21086, loss_spatial_ce_0: 0.02232/0.06569, loss_grounding_bce_0: 0.15287/0.08627, loss_grounding_dice_0: 0.18294/0.17872, loss_grounding_ce_0: 0.10974/0.27357, loss_mask_ce_1: 0.79654/0.90507, loss_mask_bce_1: 0.32628/0.33556, loss_mask_dice_1: 0.73274/1.17087, loss_spatial_bce_1: 0.11231/0.08883, loss_spatial_dice_1: 0.20424/0.21491, loss_spatial_ce_1: 0.02246/0.07144, loss_grounding_bce_1: 0.14883/0.08643, loss_grounding_dice_1: 0.18325/0.17945, loss_grounding_ce_1: 0.10651/0.27511, loss_mask_ce_2: 0.81084/0.91236, loss_mask_bce_2: 0.33477/0.33606, loss_mask_dice_2: 0.75880/1.17080, loss_spatial_bce_2: 0.11225/0.08959, loss_spatial_dice_2: 0.20755/0.21621, loss_spatial_ce_2: 0.02264/0.07493, loss_grounding_bce_2: 0.15412/0.08653, loss_grounding_dice_2: 0.18265/0.17928, loss_grounding_ce_2: 0.11326/0.27818, loss_mask_ce_3: 0.76685/0.92161, loss_mask_bce_3: 0.33787/0.33709, loss_mask_dice_3: 0.73451/1.16815, loss_spatial_bce_3: 0.11861/0.09053, loss_spatial_dice_3: 0.19922/0.21687, loss_spatial_ce_3: 0.02295/0.07870, loss_grounding_bce_3: 0.15476/0.08678, loss_grounding_dice_3: 0.18093/0.17904, loss_grounding_ce_3: 0.10988/0.27987, loss_mask_ce_4: 0.76277/0.92205, loss_mask_bce_4: 0.35228/0.33910, loss_mask_dice_4: 0.74273/1.19195, loss_spatial_bce_4: 0.11092/0.09462, loss_spatial_dice_4: 0.20514/0.22860, loss_spatial_ce_4: 0.04213/0.09472, loss_grounding_bce_4: 0.15514/0.08722, loss_grounding_dice_4: 0.17873/0.18189, loss_grounding_ce_4: 0.11357/0.28281, loss_mask_ce_5: 0.85793/0.93819, loss_mask_bce_5: 0.31785/0.34128, loss_mask_dice_5: 0.73443/1.19878, loss_spatial_bce_5: 0.11203/0.09650, loss_spatial_dice_5: 0.19147/0.23240, loss_spatial_ce_5: 0.13589/0.10967, loss_grounding_bce_5: 0.15274/0.08759, loss_grounding_dice_5: 0.18100/0.18309, loss_grounding_ce_5: 0.12928/0.29566, loss_mask_ce_6: 0.87148/0.97735, loss_mask_bce_6: 0.32584/0.34403, loss_mask_dice_6: 0.73443/1.20163, loss_spatial_bce_6: 0.12278/0.10228, loss_spatial_dice_6: 0.19251/0.23500, loss_spatial_ce_6: 0.13785/0.13574, loss_grounding_bce_6: 0.15662/0.08837, loss_grounding_dice_6: 0.18256/0.18340, loss_grounding_ce_6: 0.14367/0.31162, loss_mask_ce_7: 1.01725/1.02221, loss_mask_bce_7: 0.31625/0.35193, loss_mask_dice_7: 0.75026/1.25661, loss_spatial_bce_7: 0.11027/0.11070, loss_spatial_dice_7: 0.16951/0.26280, loss_spatial_ce_7: 0.17817/0.17198, loss_grounding_bce_7: 0.14553/0.09029, loss_grounding_dice_7: 0.18513/0.19063, loss_grounding_ce_7: 0.18833/0.34375, loss_mask_ce_8: 1.04133/1.13132, loss_mask_bce_8: 0.31154/0.36550, loss_mask_dice_8: 0.79554/1.33013, loss_spatial_bce_8: 0.17675/0.13150, loss_spatial_dice_8: 0.25922/0.30174, loss_spatial_ce_8: 0.15476/0.22944, loss_grounding_bce_8: 0.14313/0.09402, loss_grounding_dice_8: 0.20643/0.20167, loss_grounding_ce_8: 0.13952/0.41183, loss_mask_ce_9: 3.35128/3.68038, loss_mask_bce_9: 0.36514/0.39249, loss_mask_dice_9: 1.09522/1.90356, loss_spatial_bce_9: 0.54638/0.33362, loss_spatial_dice_9: 0.91493/0.82252, loss_spatial_ce_9: 1.92074/1.50184, loss_grounding_bce_9: 0.16695/0.10555, loss_grounding_dice_9: 0.26825/0.28096, loss_grounding_ce_9: 0.15596/0.67659] items per batch[64] items per second[0.24] total items[3065600] mini batches[ 47900] memory[7345] epoch remaining[1:06:10]
INFO:trainer.default_trainer:epochs[    26] optim steps[48000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.51366/0.90430, loss_mask_bce_0: 0.18906/0.33470, loss_mask_dice_0: 1.04335/1.16413, loss_spatial_bce_0: 0.05007/0.08825, loss_spatial_dice_0: 0.15699/0.21082, loss_spatial_ce_0: 0.00634/0.06566, loss_grounding_bce_0: 0.15018/0.08627, loss_grounding_dice_0: 0.10592/0.17871, loss_grounding_ce_0: 0.02932/0.27355, loss_mask_ce_1: 0.50017/0.90504, loss_mask_bce_1: 0.18798/0.33557, loss_mask_dice_1: 1.02019/1.17068, loss_spatial_bce_1: 0.05533/0.08884, loss_spatial_dice_1: 0.17628/0.21488, loss_spatial_ce_1: 0.00309/0.07140, loss_grounding_bce_1: 0.15345/0.08643, loss_grounding_dice_1: 0.10901/0.17943, loss_grounding_ce_1: 0.02980/0.27508, loss_mask_ce_2: 0.46945/0.91235, loss_mask_bce_2: 0.20274/0.33607, loss_mask_dice_2: 1.21985/1.17064, loss_spatial_bce_2: 0.05506/0.08960, loss_spatial_dice_2: 0.19425/0.21617, loss_spatial_ce_2: 0.00423/0.07490, loss_grounding_bce_2: 0.15188/0.08653, loss_grounding_dice_2: 0.10801/0.17926, loss_grounding_ce_2: 0.02496/0.27814, loss_mask_ce_3: 0.51793/0.92158, loss_mask_bce_3: 0.20025/0.33710, loss_mask_dice_3: 1.17460/1.16798, loss_spatial_bce_3: 0.05333/0.09054, loss_spatial_dice_3: 0.15817/0.21683, loss_spatial_ce_3: 0.00708/0.07867, loss_grounding_bce_3: 0.15543/0.08678, loss_grounding_dice_3: 0.10760/0.17901, loss_grounding_ce_3: 0.03920/0.27983, loss_mask_ce_4: 0.50134/0.92203, loss_mask_bce_4: 0.19228/0.33910, loss_mask_dice_4: 1.08966/1.19179, loss_spatial_bce_4: 0.05844/0.09462, loss_spatial_dice_4: 0.17094/0.22857, loss_spatial_ce_4: 0.00300/0.09467, loss_grounding_bce_4: 0.15175/0.08722, loss_grounding_dice_4: 0.10692/0.18189, loss_grounding_ce_4: 0.03346/0.28275, loss_mask_ce_5: 0.59253/0.93818, loss_mask_bce_5: 0.19283/0.34129, loss_mask_dice_5: 1.04474/1.19862, loss_spatial_bce_5: 0.06157/0.09650, loss_spatial_dice_5: 0.18567/0.23237, loss_spatial_ce_5: 0.01124/0.10962, loss_grounding_bce_5: 0.15964/0.08759, loss_grounding_dice_5: 0.11790/0.18308, loss_grounding_ce_5: 0.04114/0.29565, loss_mask_ce_6: 0.50503/0.97731, loss_mask_bce_6: 0.19629/0.34404, loss_mask_dice_6: 1.17268/1.20150, loss_spatial_bce_6: 0.04262/0.10229, loss_spatial_dice_6: 0.20576/0.23497, loss_spatial_ce_6: 0.07681/0.13570, loss_grounding_bce_6: 0.15195/0.08837, loss_grounding_dice_6: 0.10799/0.18339, loss_grounding_ce_6: 0.06742/0.31158, loss_mask_ce_7: 0.59784/1.02217, loss_mask_bce_7: 0.19864/0.35193, loss_mask_dice_7: 1.17839/1.25644, loss_spatial_bce_7: 0.04453/0.11071, loss_spatial_dice_7: 0.19762/0.26278, loss_spatial_ce_7: 0.03152/0.17195, loss_grounding_bce_7: 0.18977/0.09029, loss_grounding_dice_7: 0.13861/0.19062, loss_grounding_ce_7: 0.02906/0.34366, loss_mask_ce_8: 0.75207/1.13127, loss_mask_bce_8: 0.20538/0.36551, loss_mask_dice_8: 1.46033/1.32998, loss_spatial_bce_8: 0.05411/0.13151, loss_spatial_dice_8: 0.16286/0.30171, loss_spatial_ce_8: 0.12098/0.22940, loss_grounding_bce_8: 0.14874/0.09402, loss_grounding_dice_8: 0.10977/0.20164, loss_grounding_ce_8: 0.15190/0.41182, loss_mask_ce_9: 3.97575/3.68039, loss_mask_bce_9: 0.19408/0.39253, loss_mask_dice_9: 2.42919/1.90346, loss_spatial_bce_9: 0.34979/0.33366, loss_spatial_dice_9: 0.83192/0.82249, loss_spatial_ce_9: 1.72035/1.50184, loss_grounding_bce_9: 0.16865/0.10554, loss_grounding_dice_9: 0.12193/0.28094, loss_grounding_ce_9: 0.21108/0.67668] items per batch[64] items per second[0.23] total items[3072000] mini batches[ 48000] memory[7345] epoch remaining[1:01:30]
INFO:trainer.default_trainer:epochs[    26] optim steps[48100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39846/0.90436, loss_mask_bce_0: 0.72525/0.33480, loss_mask_dice_0: 0.88631/1.16449, loss_spatial_bce_0: 0.14609/0.08826, loss_spatial_dice_0: 0.24291/0.21083, loss_spatial_ce_0: 0.00418/0.06566, loss_grounding_bce_0: 0.09330/0.08629, loss_grounding_dice_0: 0.07480/0.17872, loss_grounding_ce_0: 0.00176/0.27359, loss_mask_ce_1: 0.33229/0.90506, loss_mask_bce_1: 0.77134/0.33567, loss_mask_dice_1: 1.01377/1.17105, loss_spatial_bce_1: 0.14802/0.08886, loss_spatial_dice_1: 0.25079/0.21489, loss_spatial_ce_1: 0.01116/0.07138, loss_grounding_bce_1: 0.10164/0.08645, loss_grounding_dice_1: 0.08367/0.17944, loss_grounding_ce_1: 0.00130/0.27513, loss_mask_ce_2: 0.32319/0.91238, loss_mask_bce_2: 0.78004/0.33617, loss_mask_dice_2: 0.92536/1.17099, loss_spatial_bce_2: 0.14860/0.08962, loss_spatial_dice_2: 0.24247/0.21617, loss_spatial_ce_2: 0.01389/0.07489, loss_grounding_bce_2: 0.10775/0.08655, loss_grounding_dice_2: 0.08490/0.17926, loss_grounding_ce_2: 0.00164/0.27820, loss_mask_ce_3: 0.32975/0.92162, loss_mask_bce_3: 0.78239/0.33720, loss_mask_dice_3: 0.91326/1.16831, loss_spatial_bce_3: 0.15618/0.09056, loss_spatial_dice_3: 0.24749/0.21684, loss_spatial_ce_3: 0.01499/0.07866, loss_grounding_bce_3: 0.10250/0.08681, loss_grounding_dice_3: 0.08217/0.17902, loss_grounding_ce_3: 0.00167/0.27992, loss_mask_ce_4: 0.33937/0.92210, loss_mask_bce_4: 0.78095/0.33920, loss_mask_dice_4: 0.93640/1.19218, loss_spatial_bce_4: 0.17625/0.09464, loss_spatial_dice_4: 0.24283/0.22859, loss_spatial_ce_4: 0.01258/0.09463, loss_grounding_bce_4: 0.09780/0.08724, loss_grounding_dice_4: 0.07362/0.18190, loss_grounding_ce_4: 0.00320/0.28283, loss_mask_ce_5: 0.34644/0.93824, loss_mask_bce_5: 0.80123/0.34138, loss_mask_dice_5: 1.00647/1.19896, loss_spatial_bce_5: 0.16486/0.09653, loss_spatial_dice_5: 0.23907/0.23239, loss_spatial_ce_5: 0.03612/0.10960, loss_grounding_bce_5: 0.10030/0.08761, loss_grounding_dice_5: 0.07930/0.18309, loss_grounding_ce_5: 0.00220/0.29576, loss_mask_ce_6: 0.39366/0.97736, loss_mask_bce_6: 0.79205/0.34414, loss_mask_dice_6: 0.95233/1.20187, loss_spatial_bce_6: 0.16670/0.10230, loss_spatial_dice_6: 0.26502/0.23499, loss_spatial_ce_6: 0.12271/0.13569, loss_grounding_bce_6: 0.10076/0.08839, loss_grounding_dice_6: 0.07121/0.18339, loss_grounding_ce_6: 0.00151/0.31170, loss_mask_ce_7: 0.40846/1.02222, loss_mask_bce_7: 0.77165/0.35203, loss_mask_dice_7: 0.97951/1.25681, loss_spatial_bce_7: 0.17330/0.11072, loss_spatial_dice_7: 0.28022/0.26280, loss_spatial_ce_7: 0.09689/0.17191, loss_grounding_bce_7: 0.10478/0.09031, loss_grounding_dice_7: 0.07991/0.19063, loss_grounding_ce_7: 0.00324/0.34377, loss_mask_ce_8: 0.43922/1.13127, loss_mask_bce_8: 0.79762/0.36562, loss_mask_dice_8: 1.02205/1.33044, loss_spatial_bce_8: 0.24216/0.13154, loss_spatial_dice_8: 0.30707/0.30172, loss_spatial_ce_8: 0.13755/0.22936, loss_grounding_bce_8: 0.10330/0.09405, loss_grounding_dice_8: 0.06527/0.20166, loss_grounding_ce_8: 0.00927/0.41178, loss_mask_ce_9: 3.04436/3.68060, loss_mask_bce_9: 0.83305/0.39263, loss_mask_dice_9: 1.15887/1.90426, loss_spatial_bce_9: 0.39542/0.33366, loss_spatial_dice_9: 0.81077/0.82250, loss_spatial_ce_9: 1.76711/1.50173, loss_grounding_bce_9: 0.11166/0.10557, loss_grounding_dice_9: 0.12613/0.28097, loss_grounding_ce_9: 0.70124/0.67658] items per batch[64] items per second[0.23] total items[3078400] mini batches[ 48100] memory[7345] epoch remaining[0:57:00]
INFO:trainer.default_trainer:epochs[    26] optim steps[48200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63164/0.90435, loss_mask_bce_0: 0.14823/0.33478, loss_mask_dice_0: 0.90151/1.16416, loss_spatial_bce_0: 0.04331/0.08826, loss_spatial_dice_0: 0.32675/0.21080, loss_spatial_ce_0: 0.16730/0.06564, loss_grounding_bce_0: 0.07277/0.08631, loss_grounding_dice_0: 0.10924/0.17871, loss_grounding_ce_0: 0.08684/0.27363, loss_mask_ce_1: 0.71149/0.90502, loss_mask_bce_1: 0.14674/0.33565, loss_mask_dice_1: 1.09971/1.17073, loss_spatial_bce_1: 0.04458/0.08886, loss_spatial_dice_1: 0.32792/0.21486, loss_spatial_ce_1: 0.04350/0.07136, loss_grounding_bce_1: 0.06469/0.08647, loss_grounding_dice_1: 0.10616/0.17943, loss_grounding_ce_1: 0.11022/0.27519, loss_mask_ce_2: 0.57488/0.91233, loss_mask_bce_2: 0.15155/0.33614, loss_mask_dice_2: 1.27464/1.17064, loss_spatial_bce_2: 0.04451/0.08962, loss_spatial_dice_2: 0.38461/0.21615, loss_spatial_ce_2: 0.08162/0.07486, loss_grounding_bce_2: 0.07374/0.08658, loss_grounding_dice_2: 0.10846/0.17927, loss_grounding_ce_2: 0.20377/0.27823, loss_mask_ce_3: 0.65162/0.92161, loss_mask_bce_3: 0.14746/0.33718, loss_mask_dice_3: 0.90826/1.16797, loss_spatial_bce_3: 0.04377/0.09056, loss_spatial_dice_3: 0.33178/0.21681, loss_spatial_ce_3: 0.09195/0.07862, loss_grounding_bce_3: 0.06475/0.08683, loss_grounding_dice_3: 0.10150/0.17901, loss_grounding_ce_3: 0.30373/0.27999, loss_mask_ce_4: 0.70701/0.92208, loss_mask_bce_4: 0.15279/0.33919, loss_mask_dice_4: 1.29883/1.19185, loss_spatial_bce_4: 0.04531/0.09464, loss_spatial_dice_4: 0.35154/0.22857, loss_spatial_ce_4: 0.04710/0.09464, loss_grounding_bce_4: 0.11211/0.08726, loss_grounding_dice_4: 0.12314/0.18189, loss_grounding_ce_4: 0.10898/0.28289, loss_mask_ce_5: 0.72238/0.93823, loss_mask_bce_5: 0.15394/0.34137, loss_mask_dice_5: 1.33210/1.19867, loss_spatial_bce_5: 0.04427/0.09654, loss_spatial_dice_5: 0.36970/0.23237, loss_spatial_ce_5: 0.11724/0.10957, loss_grounding_bce_5: 0.11842/0.08763, loss_grounding_dice_5: 0.12332/0.18308, loss_grounding_ce_5: 0.13799/0.29581, loss_mask_ce_6: 0.61729/0.97734, loss_mask_bce_6: 0.17185/0.34413, loss_mask_dice_6: 1.32569/1.20156, loss_spatial_bce_6: 0.04989/0.10231, loss_spatial_dice_6: 0.34129/0.23497, loss_spatial_ce_6: 0.18604/0.13569, loss_grounding_bce_6: 0.11091/0.08841, loss_grounding_dice_6: 0.11094/0.18338, loss_grounding_ce_6: 0.29816/0.31177, loss_mask_ce_7: 0.57352/1.02218, loss_mask_bce_7: 0.17100/0.35201, loss_mask_dice_7: 1.28549/1.25645, loss_spatial_bce_7: 0.05459/0.11073, loss_spatial_dice_7: 0.36637/0.26278, loss_spatial_ce_7: 0.21265/0.17189, loss_grounding_bce_7: 0.10592/0.09034, loss_grounding_dice_7: 0.12987/0.19061, loss_grounding_ce_7: 0.29969/0.34380, loss_mask_ce_8: 0.85688/1.13120, loss_mask_bce_8: 0.17303/0.36560, loss_mask_dice_8: 0.99442/1.33010, loss_spatial_bce_8: 0.07825/0.13154, loss_spatial_dice_8: 0.40430/0.30168, loss_spatial_ce_8: 0.84065/0.22935, loss_grounding_bce_8: 0.10676/0.09408, loss_grounding_dice_8: 0.12127/0.20166, loss_grounding_ce_8: 0.62126/0.41182, loss_mask_ce_9: 3.21780/3.68037, loss_mask_bce_9: 0.19206/0.39263, loss_mask_dice_9: 1.32589/1.90375, loss_spatial_bce_9: 0.17223/0.33370, loss_spatial_dice_9: 0.68644/0.82248, loss_spatial_ce_9: 2.97658/1.50163, loss_grounding_bce_9: 0.09478/0.10560, loss_grounding_dice_9: 0.14052/0.28095, loss_grounding_ce_9: 0.56756/0.67673] items per batch[64] items per second[0.23] total items[3084800] mini batches[ 48200] memory[7345] epoch remaining[0:52:32]
INFO:trainer.default_trainer:epochs[    26] optim steps[48300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97573/0.90424, loss_mask_bce_0: 0.25539/0.33481, loss_mask_dice_0: 1.49698/1.16437, loss_spatial_bce_0: 0.05441/0.08825, loss_spatial_dice_0: 0.38253/0.21078, loss_spatial_ce_0: 0.06382/0.06560, loss_grounding_bce_0: 0.02116/0.08630, loss_grounding_dice_0: 0.32557/0.17869, loss_grounding_ce_0: 0.49012/0.27358, loss_mask_ce_1: 0.77862/0.90491, loss_mask_bce_1: 0.27312/0.33568, loss_mask_dice_1: 1.77275/1.17094, loss_spatial_bce_1: 0.05290/0.08885, loss_spatial_dice_1: 0.39599/0.21483, loss_spatial_ce_1: 0.12185/0.07131, loss_grounding_bce_1: 0.01965/0.08646, loss_grounding_dice_1: 0.38002/0.17941, loss_grounding_ce_1: 0.45985/0.27511, loss_mask_ce_2: 0.78256/0.91220, loss_mask_bce_2: 0.25896/0.33618, loss_mask_dice_2: 2.08791/1.17090, loss_spatial_bce_2: 0.05586/0.08961, loss_spatial_dice_2: 0.34677/0.21613, loss_spatial_ce_2: 0.06375/0.07481, loss_grounding_bce_2: 0.02105/0.08657, loss_grounding_dice_2: 0.31896/0.17925, loss_grounding_ce_2: 0.46167/0.27817, loss_mask_ce_3: 0.78659/0.92150, loss_mask_bce_3: 0.25667/0.33721, loss_mask_dice_3: 1.80223/1.16817, loss_spatial_bce_3: 0.05965/0.09055, loss_spatial_dice_3: 0.41167/0.21680, loss_spatial_ce_3: 0.06634/0.07857, loss_grounding_bce_3: 0.02879/0.08682, loss_grounding_dice_3: 0.49079/0.17898, loss_grounding_ce_3: 0.49896/0.27993, loss_mask_ce_4: 0.81087/0.92194, loss_mask_bce_4: 0.28116/0.33923, loss_mask_dice_4: 2.13333/1.19203, loss_spatial_bce_4: 0.05595/0.09464, loss_spatial_dice_4: 0.39748/0.22857, loss_spatial_ce_4: 0.08538/0.09461, loss_grounding_bce_4: 0.02025/0.08726, loss_grounding_dice_4: 0.24493/0.18187, loss_grounding_ce_4: 0.43495/0.28285, loss_mask_ce_5: 0.88316/0.93808, loss_mask_bce_5: 0.28006/0.34140, loss_mask_dice_5: 1.76706/1.19885, loss_spatial_bce_5: 0.07308/0.09654, loss_spatial_dice_5: 0.43051/0.23237, loss_spatial_ce_5: 0.09293/0.10954, loss_grounding_bce_5: 0.02176/0.08762, loss_grounding_dice_5: 0.43501/0.18306, loss_grounding_ce_5: 0.44173/0.29575, loss_mask_ce_6: 0.88995/0.97721, loss_mask_bce_6: 0.27704/0.34416, loss_mask_dice_6: 2.04553/1.20181, loss_spatial_bce_6: 0.06685/0.10231, loss_spatial_dice_6: 0.45043/0.23497, loss_spatial_ce_6: 0.10353/0.13566, loss_grounding_bce_6: 0.02779/0.08839, loss_grounding_dice_6: 0.31747/0.18337, loss_grounding_ce_6: 0.45697/0.31170, loss_mask_ce_7: 0.87196/1.02204, loss_mask_bce_7: 0.27384/0.35204, loss_mask_dice_7: 1.98362/1.25672, loss_spatial_bce_7: 0.07390/0.11072, loss_spatial_dice_7: 0.46498/0.26276, loss_spatial_ce_7: 0.22005/0.17184, loss_grounding_bce_7: 0.01886/0.09033, loss_grounding_dice_7: 0.43397/0.19060, loss_grounding_ce_7: 0.52017/0.34371, loss_mask_ce_8: 1.00728/1.13104, loss_mask_bce_8: 0.25434/0.36564, loss_mask_dice_8: 1.74353/1.33031, loss_spatial_bce_8: 0.06685/0.13154, loss_spatial_dice_8: 0.51322/0.30167, loss_spatial_ce_8: 0.28781/0.22929, loss_grounding_bce_8: 0.01572/0.09407, loss_grounding_dice_8: 0.49516/0.20164, loss_grounding_ce_8: 0.56951/0.41170, loss_mask_ce_9: 3.04507/3.68056, loss_mask_bce_9: 0.24424/0.39267, loss_mask_dice_9: 2.27933/1.90405, loss_spatial_bce_9: 0.21628/0.33370, loss_spatial_dice_9: 0.85261/0.82249, loss_spatial_ce_9: 1.68966/1.50150, loss_grounding_bce_9: 0.02061/0.10559, loss_grounding_dice_9: 0.61991/0.28094, loss_grounding_ce_9: 0.48486/0.67672] items per batch[64] items per second[0.23] total items[3091200] mini batches[ 48300] memory[7345] epoch remaining[0:47:53]
INFO:trainer.default_trainer:epochs[    26] optim steps[48400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56364/0.90412, loss_mask_bce_0: 0.39407/0.33484, loss_mask_dice_0: 0.45311/1.16413, loss_spatial_bce_0: 0.31784/0.08826, loss_spatial_dice_0: 0.28017/0.21075, loss_spatial_ce_0: 0.03707/0.06555, loss_grounding_bce_0: 0.23783/0.08632, loss_grounding_dice_0: 0.14687/0.17867, loss_grounding_ce_0: 0.12082/0.27353, loss_mask_ce_1: 0.58132/0.90479, loss_mask_bce_1: 0.39523/0.33572, loss_mask_dice_1: 0.45360/1.17069, loss_spatial_bce_1: 0.29457/0.08885, loss_spatial_dice_1: 0.29163/0.21481, loss_spatial_ce_1: 0.04600/0.07126, loss_grounding_bce_1: 0.23698/0.08649, loss_grounding_dice_1: 0.14964/0.17939, loss_grounding_ce_1: 0.14258/0.27508, loss_mask_ce_2: 0.57144/0.91207, loss_mask_bce_2: 0.42127/0.33621, loss_mask_dice_2: 0.47002/1.17066, loss_spatial_bce_2: 0.29317/0.08961, loss_spatial_dice_2: 0.30712/0.21611, loss_spatial_ce_2: 0.04541/0.07479, loss_grounding_bce_2: 0.24539/0.08660, loss_grounding_dice_2: 0.14311/0.17923, loss_grounding_ce_2: 0.15161/0.27813, loss_mask_ce_3: 0.61792/0.92138, loss_mask_bce_3: 0.34523/0.33724, loss_mask_dice_3: 0.43805/1.16793, loss_spatial_bce_3: 0.28984/0.09055, loss_spatial_dice_3: 0.31852/0.21678, loss_spatial_ce_3: 0.04064/0.07854, loss_grounding_bce_3: 0.22457/0.08684, loss_grounding_dice_3: 0.14284/0.17896, loss_grounding_ce_3: 0.13738/0.27992, loss_mask_ce_4: 0.57715/0.92184, loss_mask_bce_4: 0.36475/0.33926, loss_mask_dice_4: 0.45607/1.19178, loss_spatial_bce_4: 0.31040/0.09464, loss_spatial_dice_4: 0.31094/0.22854, loss_spatial_ce_4: 0.12001/0.09456, loss_grounding_bce_4: 0.22818/0.08728, loss_grounding_dice_4: 0.15496/0.18185, loss_grounding_ce_4: 0.10816/0.28282, loss_mask_ce_5: 0.44798/0.93793, loss_mask_bce_5: 0.40071/0.34144, loss_mask_dice_5: 0.46884/1.19858, loss_spatial_bce_5: 0.32408/0.09655, loss_spatial_dice_5: 0.32542/0.23235, loss_spatial_ce_5: 0.17470/0.10949, loss_grounding_bce_5: 0.20766/0.08765, loss_grounding_dice_5: 0.15579/0.18304, loss_grounding_ce_5: 0.09377/0.29573, loss_mask_ce_6: 0.53686/0.97710, loss_mask_bce_6: 0.38426/0.34419, loss_mask_dice_6: 0.46363/1.20157, loss_spatial_bce_6: 0.40246/0.10232, loss_spatial_dice_6: 0.32850/0.23496, loss_spatial_ce_6: 0.17973/0.13563, loss_grounding_bce_6: 0.21178/0.08843, loss_grounding_dice_6: 0.14832/0.18336, loss_grounding_ce_6: 0.09372/0.31167, loss_mask_ce_7: 0.45039/1.02190, loss_mask_bce_7: 0.43877/0.35208, loss_mask_dice_7: 0.47768/1.25645, loss_spatial_bce_7: 0.38622/0.11072, loss_spatial_dice_7: 0.33300/0.26274, loss_spatial_ce_7: 0.19430/0.17179, loss_grounding_bce_7: 0.24131/0.09036, loss_grounding_dice_7: 0.14500/0.19058, loss_grounding_ce_7: 0.08100/0.34363, loss_mask_ce_8: 0.39943/1.13089, loss_mask_bce_8: 0.44490/0.36567, loss_mask_dice_8: 0.47816/1.33000, loss_spatial_bce_8: 0.35521/0.13154, loss_spatial_dice_8: 0.39023/0.30164, loss_spatial_ce_8: 0.62066/0.22926, loss_grounding_bce_8: 0.25131/0.09410, loss_grounding_dice_8: 0.15225/0.20161, loss_grounding_ce_8: 0.04644/0.41164, loss_mask_ce_9: 2.58945/3.68050, loss_mask_bce_9: 0.42224/0.39268, loss_mask_dice_9: 0.50553/1.90367, loss_spatial_bce_9: 0.51815/0.33375, loss_spatial_dice_9: 0.80831/0.82248, loss_spatial_ce_9: 1.13410/1.50140, loss_grounding_bce_9: 0.27932/0.10561, loss_grounding_dice_9: 0.13471/0.28089, loss_grounding_ce_9: 0.08757/0.67683] items per batch[64] items per second[0.23] total items[3097600] mini batches[ 48400] memory[7345] epoch remaining[0:43:07]
INFO:trainer.default_trainer:epochs[    26] optim steps[48500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74423/0.90395, loss_mask_bce_0: 0.24033/0.33484, loss_mask_dice_0: 0.88304/1.16402, loss_spatial_bce_0: 0.07752/0.08824, loss_spatial_dice_0: 0.25546/0.21073, loss_spatial_ce_0: 0.01462/0.06553, loss_grounding_bce_0: 0.08840/0.08633, loss_grounding_dice_0: 0.29913/0.17865, loss_grounding_ce_0: 0.05753/0.27341, loss_mask_ce_1: 0.81909/0.90464, loss_mask_bce_1: 0.23603/0.33571, loss_mask_dice_1: 0.83499/1.17059, loss_spatial_bce_1: 0.08163/0.08883, loss_spatial_dice_1: 0.24708/0.21478, loss_spatial_ce_1: 0.01072/0.07124, loss_grounding_bce_1: 0.08915/0.08650, loss_grounding_dice_1: 0.27761/0.17936, loss_grounding_ce_1: 0.05694/0.27497, loss_mask_ce_2: 0.78464/0.91188, loss_mask_bce_2: 0.23504/0.33620, loss_mask_dice_2: 0.79605/1.17053, loss_spatial_bce_2: 0.08299/0.08960, loss_spatial_dice_2: 0.26254/0.21609, loss_spatial_ce_2: 0.00849/0.07476, loss_grounding_bce_2: 0.08980/0.08660, loss_grounding_dice_2: 0.29249/0.17921, loss_grounding_ce_2: 0.05950/0.27801, loss_mask_ce_3: 0.98806/0.92121, loss_mask_bce_3: 0.24347/0.33724, loss_mask_dice_3: 0.82057/1.16784, loss_spatial_bce_3: 0.08473/0.09055, loss_spatial_dice_3: 0.24834/0.21676, loss_spatial_ce_3: 0.01279/0.07854, loss_grounding_bce_3: 0.08826/0.08685, loss_grounding_dice_3: 0.27772/0.17894, loss_grounding_ce_3: 0.07181/0.27981, loss_mask_ce_4: 0.69238/0.92165, loss_mask_bce_4: 0.24191/0.33926, loss_mask_dice_4: 0.93412/1.19167, loss_spatial_bce_4: 0.08616/0.09463, loss_spatial_dice_4: 0.29022/0.22853, loss_spatial_ce_4: 0.04917/0.09456, loss_grounding_bce_4: 0.09061/0.08729, loss_grounding_dice_4: 0.29312/0.18183, loss_grounding_ce_4: 0.05854/0.28270, loss_mask_ce_5: 0.92948/0.93772, loss_mask_bce_5: 0.22327/0.34143, loss_mask_dice_5: 0.84147/1.19851, loss_spatial_bce_5: 0.08655/0.09653, loss_spatial_dice_5: 0.28841/0.23234, loss_spatial_ce_5: 0.07628/0.10949, loss_grounding_bce_5: 0.08792/0.08766, loss_grounding_dice_5: 0.33314/0.18303, loss_grounding_ce_5: 0.07325/0.29561, loss_mask_ce_6: 0.88174/0.97699, loss_mask_bce_6: 0.23239/0.34419, loss_mask_dice_6: 0.81483/1.20148, loss_spatial_bce_6: 0.08024/0.10231, loss_spatial_dice_6: 0.28467/0.23494, loss_spatial_ce_6: 0.12590/0.13563, loss_grounding_bce_6: 0.08806/0.08844, loss_grounding_dice_6: 0.32181/0.18334, loss_grounding_ce_6: 0.07921/0.31153, loss_mask_ce_7: 0.89238/1.02173, loss_mask_bce_7: 0.23500/0.35206, loss_mask_dice_7: 0.84839/1.25636, loss_spatial_bce_7: 0.12117/0.11072, loss_spatial_dice_7: 0.30949/0.26272, loss_spatial_ce_7: 0.12031/0.17174, loss_grounding_bce_7: 0.09030/0.09037, loss_grounding_dice_7: 0.31339/0.19057, loss_grounding_ce_7: 0.08528/0.34348, loss_mask_ce_8: 0.92095/1.13069, loss_mask_bce_8: 0.21242/0.36566, loss_mask_dice_8: 0.82581/1.32987, loss_spatial_bce_8: 0.10659/0.13153, loss_spatial_dice_8: 0.31311/0.30161, loss_spatial_ce_8: 0.43650/0.22921, loss_grounding_bce_8: 0.08550/0.09412, loss_grounding_dice_8: 0.29905/0.20158, loss_grounding_ce_8: 0.07930/0.41145, loss_mask_ce_9: 3.59652/3.68038, loss_mask_bce_9: 0.21253/0.39267, loss_mask_dice_9: 1.08253/1.90344, loss_spatial_bce_9: 0.38956/0.33376, loss_spatial_dice_9: 0.90943/0.82248, loss_spatial_ce_9: 1.61883/1.50145, loss_grounding_bce_9: 0.09412/0.10562, loss_grounding_dice_9: 0.47696/0.28085, loss_grounding_ce_9: 0.30812/0.67667] items per batch[64] items per second[0.23] total items[3104000] mini batches[ 48500] memory[7345] epoch remaining[0:38:26]
INFO:trainer.default_trainer:epochs[    26] optim steps[48600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16616/0.90388, loss_mask_bce_0: 0.20383/0.33486, loss_mask_dice_0: 0.22536/1.16376, loss_spatial_bce_0: 0.11082/0.08825, loss_spatial_dice_0: 0.12969/0.21070, loss_spatial_ce_0: 0.00093/0.06551, loss_grounding_bce_0: 0.11100/0.08633, loss_grounding_dice_0: 0.10684/0.17863, loss_grounding_ce_0: 0.04093/0.27337, loss_mask_ce_1: 0.16176/0.90458, loss_mask_bce_1: 0.21529/0.33573, loss_mask_dice_1: 0.22953/1.17034, loss_spatial_bce_1: 0.10817/0.08884, loss_spatial_dice_1: 0.12050/0.21476, loss_spatial_ce_1: 0.00075/0.07122, loss_grounding_bce_1: 0.10796/0.08650, loss_grounding_dice_1: 0.10578/0.17935, loss_grounding_ce_1: 0.04087/0.27492, loss_mask_ce_2: 0.15518/0.91182, loss_mask_bce_2: 0.21642/0.33621, loss_mask_dice_2: 0.23142/1.17026, loss_spatial_bce_2: 0.11726/0.08961, loss_spatial_dice_2: 0.11890/0.21607, loss_spatial_ce_2: 0.00236/0.07474, loss_grounding_bce_2: 0.10892/0.08661, loss_grounding_dice_2: 0.10630/0.17920, loss_grounding_ce_2: 0.04348/0.27797, loss_mask_ce_3: 0.17041/0.92118, loss_mask_bce_3: 0.20758/0.33726, loss_mask_dice_3: 0.22624/1.16757, loss_spatial_bce_3: 0.12134/0.09056, loss_spatial_dice_3: 0.12449/0.21673, loss_spatial_ce_3: 0.01549/0.07850, loss_grounding_bce_3: 0.10595/0.08686, loss_grounding_dice_3: 0.10562/0.17892, loss_grounding_ce_3: 0.04103/0.27980, loss_mask_ce_4: 0.18182/0.92163, loss_mask_bce_4: 0.20700/0.33928, loss_mask_dice_4: 0.22406/1.19143, loss_spatial_bce_4: 0.11283/0.09464, loss_spatial_dice_4: 0.11488/0.22851, loss_spatial_ce_4: 0.04589/0.09453, loss_grounding_bce_4: 0.10751/0.08730, loss_grounding_dice_4: 0.10387/0.18181, loss_grounding_ce_4: 0.04527/0.28266, loss_mask_ce_5: 0.21670/0.93765, loss_mask_bce_5: 0.21600/0.34146, loss_mask_dice_5: 0.23839/1.19824, loss_spatial_bce_5: 0.12135/0.09655, loss_spatial_dice_5: 0.12109/0.23232, loss_spatial_ce_5: 0.02126/0.10948, loss_grounding_bce_5: 0.10876/0.08767, loss_grounding_dice_5: 0.11052/0.18301, loss_grounding_ce_5: 0.05632/0.29556, loss_mask_ce_6: 0.53257/0.97694, loss_mask_bce_6: 0.17454/0.34421, loss_mask_dice_6: 0.18554/1.20119, loss_spatial_bce_6: 0.12009/0.10233, loss_spatial_dice_6: 0.11915/0.23493, loss_spatial_ce_6: 0.03269/0.13559, loss_grounding_bce_6: 0.11148/0.08844, loss_grounding_dice_6: 0.10897/0.18333, loss_grounding_ce_6: 0.15840/0.31147, loss_mask_ce_7: 0.34484/1.02167, loss_mask_bce_7: 0.20128/0.35209, loss_mask_dice_7: 0.22683/1.25610, loss_spatial_bce_7: 0.12309/0.11072, loss_spatial_dice_7: 0.13393/0.26271, loss_spatial_ce_7: 0.04108/0.17172, loss_grounding_bce_7: 0.11483/0.09037, loss_grounding_dice_7: 0.11714/0.19055, loss_grounding_ce_7: 0.17480/0.34344, loss_mask_ce_8: 0.40197/1.13058, loss_mask_bce_8: 0.19078/0.36569, loss_mask_dice_8: 0.22006/1.32966, loss_spatial_bce_8: 0.14806/0.13154, loss_spatial_dice_8: 0.15072/0.30157, loss_spatial_ce_8: 0.13447/0.22917, loss_grounding_bce_8: 0.12463/0.09412, loss_grounding_dice_8: 0.13040/0.20158, loss_grounding_ce_8: 0.12437/0.41140, loss_mask_ce_9: 2.60702/3.68037, loss_mask_bce_9: 0.34814/0.39270, loss_mask_dice_9: 0.45677/1.90310, loss_spatial_bce_9: 0.44803/0.33377, loss_spatial_dice_9: 0.71277/0.82248, loss_spatial_ce_9: 1.04125/1.50130, loss_grounding_bce_9: 0.23725/0.10562, loss_grounding_dice_9: 0.24358/0.28086, loss_grounding_ce_9: 0.36505/0.67662] items per batch[64] items per second[0.24] total items[3110400] mini batches[ 48600] memory[7345] epoch remaining[0:33:43]
INFO:trainer.default_trainer:epochs[    26] optim steps[48700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86921/0.90383, loss_mask_bce_0: 0.19215/0.33480, loss_mask_dice_0: 0.19226/1.16366, loss_spatial_bce_0: 0.13153/0.08824, loss_spatial_dice_0: 0.12346/0.21069, loss_spatial_ce_0: 0.00437/0.06551, loss_grounding_bce_0: 0.13679/0.08632, loss_grounding_dice_0: 0.13986/0.17863, loss_grounding_ce_0: 0.01422/0.27336, loss_mask_ce_1: 0.84969/0.90454, loss_mask_bce_1: 0.19606/0.33567, loss_mask_dice_1: 0.19335/1.17028, loss_spatial_bce_1: 0.13590/0.08883, loss_spatial_dice_1: 0.13061/0.21474, loss_spatial_ce_1: 0.00747/0.07124, loss_grounding_bce_1: 0.13931/0.08650, loss_grounding_dice_1: 0.13930/0.17936, loss_grounding_ce_1: 0.01209/0.27490, loss_mask_ce_2: 0.74579/0.91174, loss_mask_bce_2: 0.19324/0.33616, loss_mask_dice_2: 0.21387/1.17017, loss_spatial_bce_2: 0.11715/0.08960, loss_spatial_dice_2: 0.12343/0.21605, loss_spatial_ce_2: 0.01043/0.07475, loss_grounding_bce_2: 0.14040/0.08660, loss_grounding_dice_2: 0.13650/0.17920, loss_grounding_ce_2: 0.01055/0.27805, loss_mask_ce_3: 0.82065/0.92114, loss_mask_bce_3: 0.18969/0.33720, loss_mask_dice_3: 0.19039/1.16752, loss_spatial_bce_3: 0.12100/0.09054, loss_spatial_dice_3: 0.12875/0.21672, loss_spatial_ce_3: 0.02271/0.07850, loss_grounding_bce_3: 0.13886/0.08685, loss_grounding_dice_3: 0.13407/0.17893, loss_grounding_ce_3: 0.01870/0.27981, loss_mask_ce_4: 0.69106/0.92157, loss_mask_bce_4: 0.21841/0.33922, loss_mask_dice_4: 0.22012/1.19135, loss_spatial_bce_4: 0.11859/0.09462, loss_spatial_dice_4: 0.12446/0.22849, loss_spatial_ce_4: 0.02677/0.09456, loss_grounding_bce_4: 0.14041/0.08729, loss_grounding_dice_4: 0.13489/0.18182, loss_grounding_ce_4: 0.02083/0.28270, loss_mask_ce_5: 0.82248/0.93763, loss_mask_bce_5: 0.19966/0.34140, loss_mask_dice_5: 0.21677/1.19816, loss_spatial_bce_5: 0.12734/0.09654, loss_spatial_dice_5: 0.13323/0.23231, loss_spatial_ce_5: 0.09187/0.10948, loss_grounding_bce_5: 0.14105/0.08766, loss_grounding_dice_5: 0.14053/0.18301, loss_grounding_ce_5: 0.02076/0.29559, loss_mask_ce_6: 0.94244/0.97693, loss_mask_bce_6: 0.20332/0.34414, loss_mask_dice_6: 0.21763/1.20110, loss_spatial_bce_6: 0.13912/0.10231, loss_spatial_dice_6: 0.14447/0.23492, loss_spatial_ce_6: 0.08482/0.13560, loss_grounding_bce_6: 0.14305/0.08844, loss_grounding_dice_6: 0.13831/0.18334, loss_grounding_ce_6: 0.01440/0.31149, loss_mask_ce_7: 0.75233/1.02165, loss_mask_bce_7: 0.20005/0.35202, loss_mask_dice_7: 0.25305/1.25600, loss_spatial_bce_7: 0.14885/0.11071, loss_spatial_dice_7: 0.13865/0.26269, loss_spatial_ce_7: 0.12669/0.17171, loss_grounding_bce_7: 0.14347/0.09036, loss_grounding_dice_7: 0.13858/0.19056, loss_grounding_ce_7: 0.01790/0.34353, loss_mask_ce_8: 0.99033/1.13053, loss_mask_bce_8: 0.25331/0.36564, loss_mask_dice_8: 0.25442/1.32956, loss_spatial_bce_8: 0.29381/0.13153, loss_spatial_dice_8: 0.20649/0.30156, loss_spatial_ce_8: 0.21106/0.22913, loss_grounding_bce_8: 0.14009/0.09412, loss_grounding_dice_8: 0.14400/0.20158, loss_grounding_ce_8: 0.00989/0.41140, loss_mask_ce_9: 3.16111/3.68041, loss_mask_bce_9: 0.24457/0.39266, loss_mask_dice_9: 0.32272/1.90293, loss_spatial_bce_9: 0.56122/0.33377, loss_spatial_dice_9: 0.81929/0.82247, loss_spatial_ce_9: 1.04622/1.50141, loss_grounding_bce_9: 0.14265/0.10561, loss_grounding_dice_9: 0.12342/0.28086, loss_grounding_ce_9: 0.33282/0.67667] items per batch[64] items per second[0.23] total items[3116800] mini batches[ 48700] memory[7345] epoch remaining[0:29:07]
INFO:trainer.default_trainer:epochs[    26] optim steps[48800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44401/0.90371, loss_mask_bce_0: 0.66093/0.33477, loss_mask_dice_0: 0.58478/1.16359, loss_spatial_bce_0: 0.19279/0.08823, loss_spatial_dice_0: 0.21397/0.21066, loss_spatial_ce_0: 0.14262/0.06551, loss_grounding_bce_0: 0.15653/0.08632, loss_grounding_dice_0: 0.15113/0.17865, loss_grounding_ce_0: 0.22063/0.27326, loss_mask_ce_1: 1.46111/0.90439, loss_mask_bce_1: 0.66641/0.33565, loss_mask_dice_1: 0.58190/1.17022, loss_spatial_bce_1: 0.19933/0.08882, loss_spatial_dice_1: 0.21840/0.21472, loss_spatial_ce_1: 0.13668/0.07122, loss_grounding_bce_1: 0.16631/0.08649, loss_grounding_dice_1: 0.15578/0.17937, loss_grounding_ce_1: 0.21742/0.27479, loss_mask_ce_2: 1.18241/0.91165, loss_mask_bce_2: 0.65348/0.33614, loss_mask_dice_2: 0.60465/1.17008, loss_spatial_bce_2: 0.19702/0.08959, loss_spatial_dice_2: 0.22290/0.21604, loss_spatial_ce_2: 0.13679/0.07473, loss_grounding_bce_2: 0.16642/0.08659, loss_grounding_dice_2: 0.15109/0.17922, loss_grounding_ce_2: 0.21466/0.27793, loss_mask_ce_3: 1.05494/0.92106, loss_mask_bce_3: 0.66117/0.33718, loss_mask_dice_3: 0.61139/1.16746, loss_spatial_bce_3: 0.20985/0.09054, loss_spatial_dice_3: 0.22738/0.21669, loss_spatial_ce_3: 0.13457/0.07847, loss_grounding_bce_3: 0.17323/0.08684, loss_grounding_dice_3: 0.15679/0.17894, loss_grounding_ce_3: 0.22333/0.27973, loss_mask_ce_4: 1.02767/0.92148, loss_mask_bce_4: 0.68426/0.33920, loss_mask_dice_4: 0.62163/1.19130, loss_spatial_bce_4: 0.27312/0.09461, loss_spatial_dice_4: 0.27781/0.22848, loss_spatial_ce_4: 0.30514/0.09453, loss_grounding_bce_4: 0.19500/0.08729, loss_grounding_dice_4: 0.17762/0.18184, loss_grounding_ce_4: 0.25014/0.28260, loss_mask_ce_5: 1.09755/0.93748, loss_mask_bce_5: 0.71170/0.34139, loss_mask_dice_5: 0.66222/1.19812, loss_spatial_bce_5: 0.33780/0.09654, loss_spatial_dice_5: 0.29752/0.23232, loss_spatial_ce_5: 0.19362/0.10946, loss_grounding_bce_5: 0.18790/0.08766, loss_grounding_dice_5: 0.18934/0.18302, loss_grounding_ce_5: 0.15246/0.29550, loss_mask_ce_6: 1.11061/0.97680, loss_mask_bce_6: 0.69219/0.34413, loss_mask_dice_6: 0.66174/1.20105, loss_spatial_bce_6: 0.35877/0.10230, loss_spatial_dice_6: 0.29025/0.23492, loss_spatial_ce_6: 0.15346/0.13559, loss_grounding_bce_6: 0.18794/0.08843, loss_grounding_dice_6: 0.18298/0.18334, loss_grounding_ce_6: 0.21769/0.31140, loss_mask_ce_7: 0.88201/1.02149, loss_mask_bce_7: 0.76163/0.35200, loss_mask_dice_7: 0.65360/1.25594, loss_spatial_bce_7: 0.37800/0.11070, loss_spatial_dice_7: 0.35704/0.26268, loss_spatial_ce_7: 0.21982/0.17170, loss_grounding_bce_7: 0.17268/0.09036, loss_grounding_dice_7: 0.16444/0.19059, loss_grounding_ce_7: 0.20699/0.34337, loss_mask_ce_8: 0.82237/1.13045, loss_mask_bce_8: 0.65600/0.36561, loss_mask_dice_8: 0.70786/1.32954, loss_spatial_bce_8: 0.38187/0.13153, loss_spatial_dice_8: 0.34438/0.30154, loss_spatial_ce_8: 0.31625/0.22909, loss_grounding_bce_8: 0.19564/0.09411, loss_grounding_dice_8: 0.19593/0.20161, loss_grounding_ce_8: 0.51612/0.41128, loss_mask_ce_9: 3.47215/3.68028, loss_mask_bce_9: 0.64517/0.39264, loss_mask_dice_9: 0.75836/1.90283, loss_spatial_bce_9: 0.56020/0.33376, loss_spatial_dice_9: 0.75165/0.82248, loss_spatial_ce_9: 1.03223/1.50139, loss_grounding_bce_9: 0.23725/0.10561, loss_grounding_dice_9: 0.30235/0.28088, loss_grounding_ce_9: 1.25751/0.67660] items per batch[64] items per second[0.23] total items[3123200] mini batches[ 48800] memory[7345] epoch remaining[0:24:28]
INFO:trainer.default_trainer:epochs[    26] optim steps[48900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.17989/0.90353, loss_mask_bce_0: 0.19755/0.33474, loss_mask_dice_0: 2.57103/1.16363, loss_spatial_bce_0: 0.04719/0.08822, loss_spatial_dice_0: 0.35494/0.21063, loss_spatial_ce_0: 0.02985/0.06549, loss_grounding_bce_0: 0.05211/0.08632, loss_grounding_dice_0: 0.31647/0.17868, loss_grounding_ce_0: 0.92680/0.27315, loss_mask_ce_1: 1.50262/0.90425, loss_mask_bce_1: 0.19847/0.33563, loss_mask_dice_1: 2.45496/1.17022, loss_spatial_bce_1: 0.04705/0.08881, loss_spatial_dice_1: 0.36448/0.21470, loss_spatial_ce_1: 0.05296/0.07122, loss_grounding_bce_1: 0.05505/0.08650, loss_grounding_dice_1: 0.31643/0.17941, loss_grounding_ce_1: 0.94268/0.27472, loss_mask_ce_2: 1.26317/0.91151, loss_mask_bce_2: 0.25020/0.33612, loss_mask_dice_2: 2.36698/1.17009, loss_spatial_bce_2: 0.04614/0.08958, loss_spatial_dice_2: 0.34679/0.21601, loss_spatial_ce_2: 0.04833/0.07472, loss_grounding_bce_2: 0.06311/0.08660, loss_grounding_dice_2: 0.28946/0.17925, loss_grounding_ce_2: 0.93873/0.27783, loss_mask_ce_3: 1.28998/0.92095, loss_mask_bce_3: 0.21898/0.33717, loss_mask_dice_3: 2.44539/1.16746, loss_spatial_bce_3: 0.04410/0.09053, loss_spatial_dice_3: 0.28313/0.21667, loss_spatial_ce_3: 0.07260/0.07846, loss_grounding_bce_3: 0.05360/0.08685, loss_grounding_dice_3: 0.28620/0.17898, loss_grounding_ce_3: 0.89975/0.27962, loss_mask_ce_4: 1.04145/0.92138, loss_mask_bce_4: 0.21040/0.33918, loss_mask_dice_4: 2.70547/1.19134, loss_spatial_bce_4: 0.04963/0.09460, loss_spatial_dice_4: 0.33580/0.22847, loss_spatial_ce_4: 0.33028/0.09453, loss_grounding_bce_4: 0.05836/0.08729, loss_grounding_dice_4: 0.30440/0.18187, loss_grounding_ce_4: 0.48873/0.28250, loss_mask_ce_5: 1.33732/0.93738, loss_mask_bce_5: 0.21757/0.34136, loss_mask_dice_5: 2.53009/1.19815, loss_spatial_bce_5: 0.05188/0.09654, loss_spatial_dice_5: 0.35534/0.23231, loss_spatial_ce_5: 0.22643/0.10943, loss_grounding_bce_5: 0.05540/0.08766, loss_grounding_dice_5: 0.31082/0.18305, loss_grounding_ce_5: 0.84133/0.29541, loss_mask_ce_6: 1.45269/0.97673, loss_mask_bce_6: 0.22280/0.34411, loss_mask_dice_6: 2.84980/1.20108, loss_spatial_bce_6: 0.05791/0.10231, loss_spatial_dice_6: 0.33861/0.23492, loss_spatial_ce_6: 0.13922/0.13556, loss_grounding_bce_6: 0.05123/0.08843, loss_grounding_dice_6: 0.29975/0.18337, loss_grounding_ce_6: 0.84722/0.31127, loss_mask_ce_7: 1.52347/1.02137, loss_mask_bce_7: 0.20432/0.35199, loss_mask_dice_7: 2.85020/1.25600, loss_spatial_bce_7: 0.07990/0.11071, loss_spatial_dice_7: 0.40610/0.26268, loss_spatial_ce_7: 0.12828/0.17166, loss_grounding_bce_7: 0.04772/0.09036, loss_grounding_dice_7: 0.24533/0.19063, loss_grounding_ce_7: 0.68001/0.34321, loss_mask_ce_8: 1.65767/1.13036, loss_mask_bce_8: 0.23795/0.36560, loss_mask_dice_8: 3.20503/1.32959, loss_spatial_bce_8: 0.06118/0.13153, loss_spatial_dice_8: 0.46486/0.30153, loss_spatial_ce_8: 0.12811/0.22906, loss_grounding_bce_8: 0.05837/0.09411, loss_grounding_dice_8: 0.26205/0.20164, loss_grounding_ce_8: 0.76332/0.41109, loss_mask_ce_9: 5.07820/3.68030, loss_mask_bce_9: 0.26205/0.39260, loss_mask_dice_9: 2.69455/1.90269, loss_spatial_bce_9: 0.13821/0.33372, loss_spatial_dice_9: 0.76238/0.82245, loss_spatial_ce_9: 2.49782/1.50141, loss_grounding_bce_9: 0.11879/0.10560, loss_grounding_dice_9: 0.38089/0.28092, loss_grounding_ce_9: 0.24549/0.67636] items per batch[64] items per second[0.23] total items[3129600] mini batches[ 48900] memory[7345] epoch remaining[0:19:50]
INFO:trainer.default_trainer:epochs[    26] optim steps[49000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47551/0.90341, loss_mask_bce_0: 0.29368/0.33478, loss_mask_dice_0: 1.81533/1.16377, loss_spatial_bce_0: 0.04292/0.08821, loss_spatial_dice_0: 0.17557/0.21062, loss_spatial_ce_0: 0.06418/0.06546, loss_grounding_bce_0: 0.03267/0.08633, loss_grounding_dice_0: 0.10786/0.17872, loss_grounding_ce_0: 0.02571/0.27315, loss_mask_ce_1: 0.46862/0.90412, loss_mask_bce_1: 0.28608/0.33567, loss_mask_dice_1: 2.15547/1.17039, loss_spatial_bce_1: 0.04255/0.08879, loss_spatial_dice_1: 0.22105/0.21469, loss_spatial_ce_1: 0.09229/0.07120, loss_grounding_bce_1: 0.03455/0.08651, loss_grounding_dice_1: 0.11231/0.17945, loss_grounding_ce_1: 0.02350/0.27469, loss_mask_ce_2: 0.47978/0.91137, loss_mask_bce_2: 0.29049/0.33616, loss_mask_dice_2: 1.86971/1.17026, loss_spatial_bce_2: 0.04475/0.08957, loss_spatial_dice_2: 0.20891/0.21601, loss_spatial_ce_2: 0.10406/0.07471, loss_grounding_bce_2: 0.03767/0.08662, loss_grounding_dice_2: 0.12424/0.17929, loss_grounding_ce_2: 0.01091/0.27778, loss_mask_ce_3: 0.50407/0.92083, loss_mask_bce_3: 0.31785/0.33722, loss_mask_dice_3: 1.87000/1.16765, loss_spatial_bce_3: 0.05135/0.09052, loss_spatial_dice_3: 0.20185/0.21666, loss_spatial_ce_3: 0.06538/0.07845, loss_grounding_bce_3: 0.03377/0.08686, loss_grounding_dice_3: 0.11076/0.17903, loss_grounding_ce_3: 0.00216/0.27962, loss_mask_ce_4: 0.57094/0.92127, loss_mask_bce_4: 0.26656/0.33921, loss_mask_dice_4: 2.14685/1.19148, loss_spatial_bce_4: 0.05212/0.09459, loss_spatial_dice_4: 0.22528/0.22846, loss_spatial_ce_4: 0.06123/0.09451, loss_grounding_bce_4: 0.03397/0.08731, loss_grounding_dice_4: 0.11210/0.18191, loss_grounding_ce_4: 0.00514/0.28246, loss_mask_ce_5: 0.68536/0.93728, loss_mask_bce_5: 0.25913/0.34139, loss_mask_dice_5: 1.80035/1.19833, loss_spatial_bce_5: 0.04762/0.09653, loss_spatial_dice_5: 0.23932/0.23230, loss_spatial_ce_5: 0.19339/0.10942, loss_grounding_bce_5: 0.03389/0.08767, loss_grounding_dice_5: 0.12055/0.18308, loss_grounding_ce_5: 0.01386/0.29539, loss_mask_ce_6: 0.67522/0.97662, loss_mask_bce_6: 0.26633/0.34414, loss_mask_dice_6: 2.06211/1.20125, loss_spatial_bce_6: 0.05683/0.10230, loss_spatial_dice_6: 0.27250/0.23492, loss_spatial_ce_6: 0.09408/0.13554, loss_grounding_bce_6: 0.03293/0.08844, loss_grounding_dice_6: 0.11184/0.18342, loss_grounding_ce_6: 0.03392/0.31128, loss_mask_ce_7: 0.73642/1.02127, loss_mask_bce_7: 0.27918/0.35201, loss_mask_dice_7: 1.66264/1.25623, loss_spatial_bce_7: 0.05993/0.11069, loss_spatial_dice_7: 0.28613/0.26267, loss_spatial_ce_7: 0.08557/0.17164, loss_grounding_bce_7: 0.03767/0.09037, loss_grounding_dice_7: 0.12689/0.19067, loss_grounding_ce_7: 0.15706/0.34317, loss_mask_ce_8: 0.83822/1.13028, loss_mask_bce_8: 0.29290/0.36562, loss_mask_dice_8: 2.01800/1.32983, loss_spatial_bce_8: 0.06562/0.13151, loss_spatial_dice_8: 0.31133/0.30151, loss_spatial_ce_8: 0.16150/0.22904, loss_grounding_bce_8: 0.03609/0.09412, loss_grounding_dice_8: 0.11005/0.20169, loss_grounding_ce_8: 0.04704/0.41100, loss_mask_ce_9: 3.34865/3.68022, loss_mask_bce_9: 0.29874/0.39263, loss_mask_dice_9: 2.43904/1.90306, loss_spatial_bce_9: 0.22699/0.33368, loss_spatial_dice_9: 0.85636/0.82247, loss_spatial_ce_9: 1.92067/1.50144, loss_grounding_bce_9: 0.04614/0.10561, loss_grounding_dice_9: 0.14270/0.28096, loss_grounding_ce_9: 0.35599/0.67608] items per batch[64] items per second[0.23] total items[3136000] mini batches[ 49000] memory[7345] epoch remaining[0:15:12]
INFO:trainer.default_trainer:epochs[    26] optim steps[49100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.54013/0.90327, loss_mask_bce_0: 0.16196/0.33483, loss_mask_dice_0: 0.49772/1.16401, loss_spatial_bce_0: 0.03851/0.08820, loss_spatial_dice_0: 0.16814/0.21061, loss_spatial_ce_0: 0.04058/0.06544, loss_grounding_bce_0: 0.04777/0.08634, loss_grounding_dice_0: 0.06915/0.17871, loss_grounding_ce_0: 1.43163/0.27313, loss_mask_ce_1: 1.47783/0.90397, loss_mask_bce_1: 0.16305/0.33572, loss_mask_dice_1: 0.49850/1.17067, loss_spatial_bce_1: 0.03899/0.08878, loss_spatial_dice_1: 0.17395/0.21468, loss_spatial_ce_1: 0.00196/0.07117, loss_grounding_bce_1: 0.04629/0.08652, loss_grounding_dice_1: 0.05508/0.17944, loss_grounding_ce_1: 1.54185/0.27467, loss_mask_ce_2: 1.52436/0.91121, loss_mask_bce_2: 0.17527/0.33621, loss_mask_dice_2: 0.55672/1.17054, loss_spatial_bce_2: 0.04244/0.08957, loss_spatial_dice_2: 0.18631/0.21600, loss_spatial_ce_2: 0.00266/0.07467, loss_grounding_bce_2: 0.04792/0.08662, loss_grounding_dice_2: 0.06071/0.17928, loss_grounding_ce_2: 1.42200/0.27775, loss_mask_ce_3: 1.76746/0.92067, loss_mask_bce_3: 0.17650/0.33727, loss_mask_dice_3: 0.57734/1.16792, loss_spatial_bce_3: 0.04210/0.09051, loss_spatial_dice_3: 0.18084/0.21665, loss_spatial_ce_3: 0.00647/0.07843, loss_grounding_bce_3: 0.04737/0.08687, loss_grounding_dice_3: 0.05126/0.17902, loss_grounding_ce_3: 1.38901/0.27958, loss_mask_ce_4: 1.65645/0.92114, loss_mask_bce_4: 0.17673/0.33926, loss_mask_dice_4: 0.55096/1.19177, loss_spatial_bce_4: 0.04708/0.09458, loss_spatial_dice_4: 0.21035/0.22845, loss_spatial_ce_4: 0.00777/0.09450, loss_grounding_bce_4: 0.04559/0.08732, loss_grounding_dice_4: 0.05540/0.18190, loss_grounding_ce_4: 1.44153/0.28242, loss_mask_ce_5: 1.67130/0.93712, loss_mask_bce_5: 0.18658/0.34144, loss_mask_dice_5: 0.66799/1.19859, loss_spatial_bce_5: 0.05041/0.09652, loss_spatial_dice_5: 0.20927/0.23230, loss_spatial_ce_5: 0.05354/0.10937, loss_grounding_bce_5: 0.04816/0.08768, loss_grounding_dice_5: 0.07041/0.18307, loss_grounding_ce_5: 1.39097/0.29540, loss_mask_ce_6: 1.75558/0.97645, loss_mask_bce_6: 0.17495/0.34421, loss_mask_dice_6: 0.64006/1.20157, loss_spatial_bce_6: 0.04834/0.10229, loss_spatial_dice_6: 0.21280/0.23492, loss_spatial_ce_6: 0.12767/0.13550, loss_grounding_bce_6: 0.04574/0.08845, loss_grounding_dice_6: 0.06839/0.18342, loss_grounding_ce_6: 1.45444/0.31130, loss_mask_ce_7: 2.02565/1.02111, loss_mask_bce_7: 0.17208/0.35207, loss_mask_dice_7: 0.58037/1.25654, loss_spatial_bce_7: 0.05693/0.11067, loss_spatial_dice_7: 0.22885/0.26266, loss_spatial_ce_7: 0.21845/0.17162, loss_grounding_bce_7: 0.05201/0.09037, loss_grounding_dice_7: 0.07077/0.19067, loss_grounding_ce_7: 1.53858/0.34324, loss_mask_ce_8: 1.81881/1.13011, loss_mask_bce_8: 0.18763/0.36569, loss_mask_dice_8: 0.68722/1.33016, loss_spatial_bce_8: 0.06001/0.13150, loss_spatial_dice_8: 0.25537/0.30151, loss_spatial_ce_8: 0.45518/0.22903, loss_grounding_bce_8: 0.04799/0.09413, loss_grounding_dice_8: 0.05906/0.20170, loss_grounding_ce_8: 1.34838/0.41095, loss_mask_ce_9: 4.23873/3.68028, loss_mask_bce_9: 0.24101/0.39269, loss_mask_dice_9: 1.07529/1.90345, loss_spatial_bce_9: 0.27425/0.33366, loss_spatial_dice_9: 0.79916/0.82248, loss_spatial_ce_9: 1.28983/1.50143, loss_grounding_bce_9: 0.06559/0.10561, loss_grounding_dice_9: 0.12729/0.28096, loss_grounding_ce_9: 0.89590/0.67613] items per batch[64] items per second[0.23] total items[3142400] mini batches[ 49100] memory[7345] epoch remaining[0:10:35]
INFO:trainer.default_trainer:epochs[    26] optim steps[49200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20399/0.90306, loss_mask_bce_0: 0.48741/0.33483, loss_mask_dice_0: 0.58637/1.16377, loss_spatial_bce_0: 0.21073/0.08819, loss_spatial_dice_0: 0.22929/0.21059, loss_spatial_ce_0: 0.09414/0.06541, loss_grounding_bce_0: 0.22572/0.08634, loss_grounding_dice_0: 0.12450/0.17869, loss_grounding_ce_0: 0.23482/0.27308, loss_mask_ce_1: 1.24428/0.90379, loss_mask_bce_1: 0.46272/0.33571, loss_mask_dice_1: 0.58405/1.17042, loss_spatial_bce_1: 0.23183/0.08878, loss_spatial_dice_1: 0.24849/0.21465, loss_spatial_ce_1: 0.11533/0.07113, loss_grounding_bce_1: 0.21210/0.08652, loss_grounding_dice_1: 0.11609/0.17943, loss_grounding_ce_1: 0.24157/0.27458, loss_mask_ce_2: 1.13716/0.91103, loss_mask_bce_2: 0.50836/0.33621, loss_mask_dice_2: 0.60512/1.17027, loss_spatial_bce_2: 0.22321/0.08956, loss_spatial_dice_2: 0.22477/0.21597, loss_spatial_ce_2: 0.13288/0.07463, loss_grounding_bce_2: 0.27045/0.08662, loss_grounding_dice_2: 0.14182/0.17927, loss_grounding_ce_2: 0.11140/0.27767, loss_mask_ce_3: 1.27782/0.92048, loss_mask_bce_3: 0.49249/0.33726, loss_mask_dice_3: 0.59095/1.16771, loss_spatial_bce_3: 0.22908/0.09050, loss_spatial_dice_3: 0.23345/0.21663, loss_spatial_ce_3: 0.14823/0.07838, loss_grounding_bce_3: 0.23825/0.08687, loss_grounding_dice_3: 0.13039/0.17901, loss_grounding_ce_3: 0.22855/0.27950, loss_mask_ce_4: 1.26659/0.92096, loss_mask_bce_4: 0.53787/0.33925, loss_mask_dice_4: 0.68040/1.19152, loss_spatial_bce_4: 0.22044/0.09458, loss_spatial_dice_4: 0.23592/0.22843, loss_spatial_ce_4: 0.11251/0.09444, loss_grounding_bce_4: 0.23042/0.08732, loss_grounding_dice_4: 0.11905/0.18188, loss_grounding_ce_4: 0.22851/0.28232, loss_mask_ce_5: 1.42588/0.93692, loss_mask_bce_5: 0.53961/0.34144, loss_mask_dice_5: 0.69230/1.19834, loss_spatial_bce_5: 0.21074/0.09652, loss_spatial_dice_5: 0.19467/0.23228, loss_spatial_ce_5: 0.15039/0.10932, loss_grounding_bce_5: 0.22765/0.08768, loss_grounding_dice_5: 0.10922/0.18305, loss_grounding_ce_5: 0.26188/0.29532, loss_mask_ce_6: 1.42700/0.97629, loss_mask_bce_6: 0.60104/0.34420, loss_mask_dice_6: 0.75565/1.20134, loss_spatial_bce_6: 0.21949/0.10228, loss_spatial_dice_6: 0.20673/0.23490, loss_spatial_ce_6: 0.22938/0.13548, loss_grounding_bce_6: 0.21038/0.08844, loss_grounding_dice_6: 0.12143/0.18341, loss_grounding_ce_6: 0.28135/0.31123, loss_mask_ce_7: 1.31087/1.02099, loss_mask_bce_7: 0.60975/0.35206, loss_mask_dice_7: 0.77608/1.25626, loss_spatial_bce_7: 0.22546/0.11067, loss_spatial_dice_7: 0.21867/0.26264, loss_spatial_ce_7: 0.22662/0.17158, loss_grounding_bce_7: 0.32927/0.09037, loss_grounding_dice_7: 0.17218/0.19064, loss_grounding_ce_7: 0.01730/0.34322, loss_mask_ce_8: 1.24925/1.12985, loss_mask_bce_8: 0.64787/0.36568, loss_mask_dice_8: 0.79258/1.32988, loss_spatial_bce_8: 0.22798/0.13148, loss_spatial_dice_8: 0.28533/0.30147, loss_spatial_ce_8: 0.46078/0.22897, loss_grounding_bce_8: 0.28875/0.09413, loss_grounding_dice_8: 0.14478/0.20167, loss_grounding_ce_8: 0.03880/0.41076, loss_mask_ce_9: 3.86508/3.67988, loss_mask_bce_9: 0.65356/0.39267, loss_mask_dice_9: 1.02729/1.90307, loss_spatial_bce_9: 0.53417/0.33364, loss_spatial_dice_9: 0.83425/0.82247, loss_spatial_ce_9: 1.39688/1.50139, loss_grounding_bce_9: 0.34285/0.10560, loss_grounding_dice_9: 0.18401/0.28093, loss_grounding_ce_9: 0.11148/0.67602] items per batch[64] items per second[0.23] total items[3148800] mini batches[ 49200] memory[7345] epoch remaining[0:05:58]
INFO:trainer.default_trainer:epochs[    26] optim steps[49300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48396/0.90305, loss_mask_bce_0: 0.02376/0.33480, loss_mask_dice_0: 0.71476/1.16367, loss_spatial_bce_0: 0.02079/0.08818, loss_spatial_dice_0: 0.47475/0.21058, loss_spatial_ce_0: 0.96668/0.06543, loss_grounding_bce_0: 0.01087/0.08633, loss_grounding_dice_0: 0.17786/0.17871, loss_grounding_ce_0: 0.55867/0.27309, loss_mask_ce_1: 0.53154/0.90379, loss_mask_bce_1: 0.02261/0.33569, loss_mask_dice_1: 0.56416/1.17031, loss_spatial_bce_1: 0.03343/0.08876, loss_spatial_dice_1: 0.51999/0.21464, loss_spatial_ce_1: 0.27003/0.07113, loss_grounding_bce_1: 0.01128/0.08650, loss_grounding_dice_1: 0.23812/0.17947, loss_grounding_ce_1: 0.56325/0.27458, loss_mask_ce_2: 0.54193/0.91105, loss_mask_bce_2: 0.02080/0.33619, loss_mask_dice_2: 0.52548/1.17019, loss_spatial_bce_2: 0.03672/0.08954, loss_spatial_dice_2: 0.46992/0.21596, loss_spatial_ce_2: 0.29657/0.07462, loss_grounding_bce_2: 0.01122/0.08661, loss_grounding_dice_2: 0.31523/0.17931, loss_grounding_ce_2: 0.51979/0.27774, loss_mask_ce_3: 0.12741/0.92044, loss_mask_bce_3: 0.03422/0.33724, loss_mask_dice_3: 0.38639/1.16762, loss_spatial_bce_3: 0.03884/0.09049, loss_spatial_dice_3: 0.49245/0.21662, loss_spatial_ce_3: 0.37743/0.07841, loss_grounding_bce_3: 0.01356/0.08685, loss_grounding_dice_3: 0.28993/0.17904, loss_grounding_ce_3: 0.56065/0.27955, loss_mask_ce_4: 0.17673/0.92096, loss_mask_bce_4: 0.02690/0.33924, loss_mask_dice_4: 0.71249/1.19145, loss_spatial_bce_4: 0.03074/0.09456, loss_spatial_dice_4: 0.49434/0.22843, loss_spatial_ce_4: 0.36657/0.09443, loss_grounding_bce_4: 0.01064/0.08730, loss_grounding_dice_4: 0.27709/0.18191, loss_grounding_ce_4: 0.56716/0.28242, loss_mask_ce_5: 0.57640/0.93692, loss_mask_bce_5: 0.02556/0.34143, loss_mask_dice_5: 0.46445/1.19826, loss_spatial_bce_5: 0.08403/0.09651, loss_spatial_dice_5: 0.56664/0.23228, loss_spatial_ce_5: 0.34110/0.10931, loss_grounding_bce_5: 0.01087/0.08767, loss_grounding_dice_5: 0.32425/0.18308, loss_grounding_ce_5: 0.55408/0.29536, loss_mask_ce_6: 0.62010/0.97629, loss_mask_bce_6: 0.02774/0.34418, loss_mask_dice_6: 0.62226/1.20127, loss_spatial_bce_6: 0.11312/0.10227, loss_spatial_dice_6: 0.53098/0.23491, loss_spatial_ce_6: 0.73110/0.13546, loss_grounding_bce_6: 0.01229/0.08843, loss_grounding_dice_6: 0.20874/0.18344, loss_grounding_ce_6: 0.55026/0.31123, loss_mask_ce_7: 0.77861/1.02106, loss_mask_bce_7: 0.02165/0.35205, loss_mask_dice_7: 0.67797/1.25619, loss_spatial_bce_7: 0.11792/0.11065, loss_spatial_dice_7: 0.56473/0.26266, loss_spatial_ce_7: 0.47241/0.17159, loss_grounding_bce_7: 0.01014/0.09036, loss_grounding_dice_7: 0.31713/0.19068, loss_grounding_ce_7: 0.75153/0.34329, loss_mask_ce_8: 0.56465/1.12991, loss_mask_bce_8: 0.02476/0.36568, loss_mask_dice_8: 0.66798/1.32983, loss_spatial_bce_8: 0.05918/0.13147, loss_spatial_dice_8: 0.54217/0.30147, loss_spatial_ce_8: 0.40161/0.22896, loss_grounding_bce_8: 0.01144/0.09412, loss_grounding_dice_8: 0.26660/0.20171, loss_grounding_ce_8: 0.63193/0.41076, loss_mask_ce_9: 3.36284/3.67988, loss_mask_bce_9: 0.01359/0.39265, loss_mask_dice_9: 0.47878/1.90295, loss_spatial_bce_9: 0.05139/0.33363, loss_spatial_dice_9: 0.59823/0.82246, loss_spatial_ce_9: 3.25292/1.50149, loss_grounding_bce_9: 0.00632/0.10558, loss_grounding_dice_9: 0.26648/0.28095, loss_grounding_ce_9: 0.61665/0.67594] items per batch[64] items per second[0.23] total items[3155200] mini batches[ 49300] memory[7345] epoch remaining[0:01:20]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00049329.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0030 s/iter. Inference: 0.2236 s/iter. Eval: 0.0887 s/iter. Total: 0.3154 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0032 s/iter. Inference: 0.2268 s/iter. Eval: 0.0848 s/iter. Total: 0.3149 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0032 s/iter. Inference: 0.2276 s/iter. Eval: 0.0816 s/iter. Total: 0.3125 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0032 s/iter. Inference: 0.2283 s/iter. Eval: 0.0781 s/iter. Total: 0.3098 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0032 s/iter. Inference: 0.2270 s/iter. Eval: 0.0760 s/iter. Total: 0.3063 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2285 s/iter. Eval: 0.0754 s/iter. Total: 0.3072 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2308 s/iter. Eval: 0.0757 s/iter. Total: 0.3098 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2304 s/iter. Eval: 0.0756 s/iter. Total: 0.3093 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2312 s/iter. Eval: 0.0754 s/iter. Total: 0.3100 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalzucsxy8d ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.507 | 82.041 | 60.714 |      133      |
| Things | 55.593 | 82.840 | 66.460 |      80       |
| Stuff  | 42.830 | 80.835 | 52.042 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.15 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.17s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.46 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.11 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.392
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.616
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.413
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.492
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.507
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.233 | 61.613 | 41.320 | 19.787 | 42.133 | 61.006 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.510 | bicycle      | 19.182 | car            | 37.467 |
| motorcycle    | 35.676 | airplane     | 56.787 | bus            | 65.660 |
| train         | 69.654 | truck        | 35.763 | boat           | 23.596 |
| traffic light | 25.570 | fire hydrant | 63.717 | stop sign      | 63.918 |
| parking meter | 39.814 | bench        | 20.509 | bird           | 29.360 |
| cat           | 73.893 | dog          | 65.786 | horse          | 45.424 |
| sheep         | 48.514 | cow          | 51.142 | elephant       | 60.976 |
| bear          | 78.554 | zebra        | 60.622 | giraffe        | 56.847 |
| backpack      | 15.208 | umbrella     | 48.725 | handbag        | 15.178 |
| tie           | 33.577 | suitcase     | 41.223 | frisbee        | 67.508 |
| skis          | 5.607  | snowboard    | 23.543 | sports ball    | 46.945 |
| kite          | 34.680 | baseball bat | 28.776 | baseball glove | 43.742 |
| skateboard    | 36.284 | surfboard    | 36.558 | tennis racket  | 57.244 |
| bottle        | 35.197 | wine glass   | 28.208 | cup            | 40.632 |
| fork          | 16.411 | knife        | 14.607 | spoon          | 14.580 |
| bowl          | 33.451 | banana       | 20.709 | apple          | 19.945 |
| sandwich      | 43.109 | orange       | 29.573 | broccoli       | 22.473 |
| carrot        | 22.364 | hot dog      | 25.935 | pizza          | 51.344 |
| donut         | 44.897 | cake         | 41.762 | chair          | 20.708 |
| couch         | 42.144 | potted plant | 17.344 | bed            | 41.317 |
| dining table  | 12.502 | toilet       | 66.269 | tv             | 63.162 |
| laptop        | 61.726 | mouse        | 60.463 | remote         | 30.246 |
| keyboard      | 48.713 | cell phone   | 36.027 | microwave      | 56.260 |
| oven          | 33.122 | toaster      | 42.251 | sink           | 38.917 |
| refrigerator  | 59.346 | book         | 8.872  | clock          | 51.879 |
| vase          | 32.335 | scissors     | 23.295 | teddy bear     | 50.465 |
| hair drier    | 11.967 | toothbrush   | 17.413 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.77351531876787, 'fwIoU': 68.98392836534629, 'IoU-person': 87.34739307183973, 'IoU-bicycle': 69.67645404952856, 'IoU-car': 70.3294748236737, 'IoU-motorcycle': 83.83120425815036, 'IoU-airplane': 82.06138878058158, 'IoU-bus': 85.36079388583939, 'IoU-train': 88.48556283555783, 'IoU-truck': 65.52219115309771, 'IoU-boat': 68.48924547246172, 'IoU-traffic light': 76.67313297941635, 'IoU-fire hydrant': 89.77181015887643, 'IoU-stop sign': 92.05472663232977, 'IoU-parking meter': 87.74528293773122, 'IoU-bench': 54.184915823426024, 'IoU-bird': 75.33990756123364, 'IoU-cat': 84.40446780148154, 'IoU-dog': 79.21106701391578, 'IoU-horse': 85.97666183754399, 'IoU-sheep': 80.87593671767817, 'IoU-cow': 79.45684184672355, 'IoU-elephant': 89.60928141256011, 'IoU-bear': 79.34922640574044, 'IoU-zebra': 91.04391206485012, 'IoU-giraffe': 85.44165743956677, 'IoU-backpack': 40.004554527688505, 'IoU-umbrella': 77.49128387693669, 'IoU-handbag': 36.948815706150285, 'IoU-tie': 69.05740056188083, 'IoU-suitcase': 81.60291394005357, 'IoU-frisbee': 83.52126569057889, 'IoU-skis': 51.920724247614395, 'IoU-snowboard': 69.7250186387398, 'IoU-sports ball': 66.73919854421226, 'IoU-kite': 66.71205163116639, 'IoU-baseball bat': 58.027099713743034, 'IoU-baseball glove': 78.90867083012883, 'IoU-skateboard': 60.88024196876732, 'IoU-surfboard': 75.2907775326478, 'IoU-tennis racket': 83.16245406659122, 'IoU-bottle': 69.47894145217349, 'IoU-wine glass': 73.58345312151478, 'IoU-cup': 58.51854893077449, 'IoU-fork': 55.67970763859431, 'IoU-knife': 50.928908017482385, 'IoU-spoon': 50.20754531975648, 'IoU-bowl': 52.65226218523008, 'IoU-banana': 84.91371092489976, 'IoU-apple': 60.94173732332941, 'IoU-sandwich': 64.16224402476072, 'IoU-orange': 76.15638469322069, 'IoU-broccoli': 68.89751285282944, 'IoU-carrot': 64.3384098882967, 'IoU-hot dog': 66.96831316348566, 'IoU-pizza': 79.93868572128711, 'IoU-donut': 64.72343106210697, 'IoU-cake': 67.67886136265123, 'IoU-chair': 54.17779262142043, 'IoU-couch': 66.60794465481678, 'IoU-potted plant': 33.533472022247444, 'IoU-bed': 64.72356488980621, 'IoU-dining table': 52.01315413453778, 'IoU-toilet': 85.35240077344376, 'IoU-tv': 74.69048636783823, 'IoU-laptop': 73.12649821321274, 'IoU-mouse': 65.38708342578877, 'IoU-remote': 44.27423386085101, 'IoU-keyboard': 57.3410812874849, 'IoU-cell phone': 65.3479998912997, 'IoU-microwave': 62.56481050001928, 'IoU-oven': 68.63689257469765, 'IoU-toaster': 69.7837055935852, 'IoU-sink': 67.14047972781476, 'IoU-refrigerator': 82.33304879364644, 'IoU-book': 50.89001308718463, 'IoU-clock': 69.29628849810298, 'IoU-vase': 61.750217236372606, 'IoU-scissors': 54.792241502218275, 'IoU-teddy bear': 80.15296571234668, 'IoU-hair drier': 57.72028225076895, 'IoU-toothbrush': 56.9014524441668, 'IoU-banner': 31.74683551025649, 'IoU-blanket': 12.146407655233759, 'IoU-bridge': 39.438373409265274, 'IoU-cardboard': 41.76041738276342, 'IoU-counter': 30.2406496673708, 'IoU-curtain': 65.12901240320372, 'IoU-door-stuff': 41.9761065173379, 'IoU-floor-wood': 62.60863167975692, 'IoU-flower': 45.195908654675804, 'IoU-fruit': 44.59923989721377, 'IoU-gravel': 31.116989296134506, 'IoU-house': 23.963992040278796, 'IoU-light': 39.985347037245006, 'IoU-mirror-stuff': 54.403669614080705, 'IoU-net': 42.107068657293325, 'IoU-pillow': 12.533174148118295, 'IoU-platform': 32.293260384286654, 'IoU-playingfield': 70.28191057803915, 'IoU-railroad': 60.89563971132775, 'IoU-river': 49.60767749178147, 'IoU-road': 66.28718656123279, 'IoU-roof': 14.20165887896, 'IoU-sand': 64.63722188767466, 'IoU-sea': 85.1172516656336, 'IoU-shelf': 37.90744735741858, 'IoU-snow': 88.03815451967937, 'IoU-stairs': 24.75467919195616, 'IoU-tent': 6.484284983752573, 'IoU-towel': 32.997613264224604, 'IoU-wall-brick': 46.47777250855756, 'IoU-wall-stone': 27.07198333119206, 'IoU-wall-tile': 65.6197061160355, 'IoU-wall-wood': 39.19902552447832, 'IoU-water-other': 20.769841911018023, 'IoU-window-blind': 44.490549265995014, 'IoU-window-other': 46.9330799958757, 'IoU-tree-merged': 80.87113049347502, 'IoU-fence-merged': 50.99988603618404, 'IoU-ceiling-merged': 66.88743350393777, 'IoU-sky-other-merged': 93.36045012513345, 'IoU-cabinet-merged': 58.887795651003415, 'IoU-table-merged': 36.4313017980854, 'IoU-floor-other-merged': 49.252248630281144, 'IoU-pavement-merged': 55.18304477432968, 'IoU-mountain-merged': 55.23968995363704, 'IoU-grass-merged': 71.41802651361817, 'IoU-dirt-merged': 46.837182843446676, 'IoU-paper-merged': 27.994901300013254, 'IoU-food-other-merged': 39.967479633962064, 'IoU-building-other-merged': 58.266156600548236, 'IoU-rock-merged': 58.632912050339556, 'IoU-wall-other-merged': 64.31547395380977, 'IoU-rug-merged': 64.77287864820383, 'mACC': 73.01947157677233, 'pACC': 80.34177937737735, 'ACC-person': 92.49621617538524, 'ACC-bicycle': 79.50013411669016, 'ACC-car': 84.38365577623337, 'ACC-motorcycle': 89.5664357337936, 'ACC-airplane': 90.45260493737828, 'ACC-bus': 91.43734923140906, 'ACC-train': 94.87262985282061, 'ACC-truck': 76.03366138353347, 'ACC-boat': 79.00796504282292, 'ACC-traffic light': 89.97526481931135, 'ACC-fire hydrant': 95.05448637200186, 'ACC-stop sign': 95.33854059259579, 'ACC-parking meter': 92.55433719060103, 'ACC-bench': 68.52198291166557, 'ACC-bird': 80.15884734562512, 'ACC-cat': 92.3865699244701, 'ACC-dog': 82.41906523011761, 'ACC-horse': 91.75170271482409, 'ACC-sheep': 83.80040755241892, 'ACC-cow': 84.48649287974862, 'ACC-elephant': 92.09487603027024, 'ACC-bear': 81.35908291396468, 'ACC-zebra': 93.58432298485562, 'ACC-giraffe': 89.56194942655486, 'ACC-backpack': 56.58013514342556, 'ACC-umbrella': 84.58669371597615, 'ACC-handbag': 58.66969012538144, 'ACC-tie': 79.83693296117653, 'ACC-suitcase': 89.32516882806591, 'ACC-frisbee': 94.21745454545454, 'ACC-skis': 67.23952885378726, 'ACC-snowboard': 78.33361906394651, 'ACC-sports ball': 80.37078384552568, 'ACC-kite': 76.40026780837606, 'ACC-baseball bat': 80.34337561531142, 'ACC-baseball glove': 89.08475821966667, 'ACC-skateboard': 69.52132267461432, 'ACC-surfboard': 84.06495034949255, 'ACC-tennis racket': 89.62580748786573, 'ACC-bottle': 84.99450974112736, 'ACC-wine glass': 86.13497488712807, 'ACC-cup': 83.10259793849278, 'ACC-fork': 65.94987176534062, 'ACC-knife': 67.17740162673115, 'ACC-spoon': 69.45215107461252, 'ACC-bowl': 66.47175955889834, 'ACC-banana': 90.79638303985868, 'ACC-apple': 74.17064829114531, 'ACC-sandwich': 75.81751079409126, 'ACC-orange': 85.96602428450677, 'ACC-broccoli': 80.95973588972466, 'ACC-carrot': 75.2251944116564, 'ACC-hot dog': 73.19558652425712, 'ACC-pizza': 93.87960521925669, 'ACC-donut': 82.05995112470313, 'ACC-cake': 75.01440568332869, 'ACC-chair': 67.44560567906915, 'ACC-couch': 85.40567911986318, 'ACC-potted plant': 52.289102406189315, 'ACC-bed': 75.59487576475811, 'ACC-dining table': 73.07251719108355, 'ACC-toilet': 89.23790926324031, 'ACC-tv': 87.56483041226288, 'ACC-laptop': 86.11975215831686, 'ACC-mouse': 78.66339115973868, 'ACC-remote': 66.99312737183918, 'ACC-keyboard': 65.37117682388964, 'ACC-cell phone': 72.46944971692079, 'ACC-microwave': 76.27787925440029, 'ACC-oven': 85.93784908320613, 'ACC-toaster': 83.84951210499665, 'ACC-sink': 82.27390094406806, 'ACC-refrigerator': 90.44617186994343, 'ACC-book': 69.57825502227453, 'ACC-clock': 74.30179662887838, 'ACC-vase': 70.48443252752267, 'ACC-scissors': 59.98827002359768, 'ACC-teddy bear': 85.25814426276237, 'ACC-hair drier': 68.10263854495773, 'ACC-toothbrush': 79.12178596247395, 'ACC-banner': 75.40512319691234, 'ACC-blanket': 18.382278208265916, 'ACC-bridge': 56.42745040277115, 'ACC-cardboard': 52.82312054675003, 'ACC-counter': 55.169964983204565, 'ACC-curtain': 74.72926204488772, 'ACC-door-stuff': 60.396259975562806, 'ACC-floor-wood': 77.46331183122263, 'ACC-flower': 59.96490320766606, 'ACC-fruit': 59.24304329802973, 'ACC-gravel': 41.61431172902838, 'ACC-house': 28.440255632728984, 'ACC-light': 56.18019800558894, 'ACC-mirror-stuff': 69.98408483593944, 'ACC-net': 62.79400646930626, 'ACC-pillow': 26.10634304224207, 'ACC-platform': 48.35940266162845, 'ACC-playingfield': 89.67844134613043, 'ACC-railroad': 77.68246009034748, 'ACC-river': 73.42768566418549, 'ACC-road': 84.38131381701967, 'ACC-roof': 19.30023998989516, 'ACC-sand': 72.01966609230817, 'ACC-sea': 92.09044271208387, 'ACC-shelf': 64.70209141620705, 'ACC-snow': 95.70520042800634, 'ACC-stairs': 41.24483464512244, 'ACC-tent': 8.378796876764348, 'ACC-towel': 45.075632436648014, 'ACC-wall-brick': 60.16681746524891, 'ACC-wall-stone': 34.72204278694094, 'ACC-wall-tile': 76.50380278678676, 'ACC-wall-wood': 54.22702440232777, 'ACC-water-other': 30.78816460160529, 'ACC-window-blind': 55.6196473158695, 'ACC-window-other': 67.38369119484598, 'ACC-tree-merged': 89.38592425848191, 'ACC-fence-merged': 67.12960334333614, 'ACC-ceiling-merged': 81.52714786893075, 'ACC-sky-other-merged': 96.66570692681965, 'ACC-cabinet-merged': 75.24766164892245, 'ACC-table-merged': 50.82167539928373, 'ACC-floor-other-merged': 61.65230333245477, 'ACC-pavement-merged': 68.98739213253438, 'ACC-mountain-merged': 64.27760110677994, 'ACC-grass-merged': 83.16626162949106, 'ACC-dirt-merged': 69.63971475264927, 'ACC-paper-merged': 38.3939712710113, 'ACC-food-other-merged': 56.285987116599976, 'ACC-building-other-merged': 75.0350292260532, 'ACC-rock-merged': 81.89853000520915, 'ACC-wall-other-merged': 81.19885457501155, 'ACC-rug-merged': 78.47960344880015})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1536 s/iter. Inference: 0.5649 s/iter. Eval: 0.0000 s/iter. Total: 0.7186 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1566 s/iter. Inference: 0.5255 s/iter. Eval: 0.0000 s/iter. Total: 0.6822 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1716 s/iter. Inference: 0.6088 s/iter. Eval: 0.0000 s/iter. Total: 0.7805 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1711 s/iter. Inference: 0.6869 s/iter. Eval: 0.0000 s/iter. Total: 0.8581 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1701 s/iter. Inference: 0.6302 s/iter. Eval: 0.0000 s/iter. Total: 0.8004 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1686 s/iter. Inference: 0.6700 s/iter. Eval: 0.0000 s/iter. Total: 0.8388 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5294117647058822, 'noc@0.8': 2.8829382499268363, 'noc@0.85': 3.5062920690664328, 'noc@0.9': 4.570676031606673, 'miou@iter1': 0.8373717201150297}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0018 s/iter. Inference: 0.1008 s/iter. Eval: 0.0008 s/iter. Total: 0.1034 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.85114288330078, 'precision@0.6': 68.01399230957031, 'precision@0.7': 63.000389099121094, 'precision@0.8': 52.662261962890625, 'precision@0.9': 27.01127052307129, 'cIoU': 57.562400817871094, 'mIoU': 62.63715744018555}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.50678219051655, 'SQ': 82.04108272700181, 'RQ': 60.71441653664023, 'PQ_th': 55.59284227769613, 'SQ_th': 82.84010129687817, 'RQ_th': 66.46020520440854, 'PQ_st': 42.82971036081156, 'SQ_st': 80.83501696115059, 'RQ_st': 52.04152798151823}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.23345507119025, 'AP50': 61.6126196951164, 'AP75': 41.32006795950286, 'APs': 19.786969398198085, 'APm': 42.13347655053776, 'APl': 61.00630933034296, 'AP-person': 43.50960673251347, 'AP-bicycle': 19.18152017638633, 'AP-car': 37.467008376107685, 'AP-motorcycle': 35.67617518128077, 'AP-airplane': 56.786622842611955, 'AP-bus': 65.65964972453202, 'AP-train': 69.6542972931678, 'AP-truck': 35.76263997839999, 'AP-boat': 23.595558916384864, 'AP-traffic light': 25.569534777304924, 'AP-fire hydrant': 63.71728855940532, 'AP-stop sign': 63.917993125964166, 'AP-parking meter': 39.814231975855066, 'AP-bench': 20.50927529564245, 'AP-bird': 29.360299183849314, 'AP-cat': 73.8934352972304, 'AP-dog': 65.7862160388077, 'AP-horse': 45.42419335697734, 'AP-sheep': 48.514159980706324, 'AP-cow': 51.14159884032783, 'AP-elephant': 60.97649134998664, 'AP-bear': 78.55354009407894, 'AP-zebra': 60.62152923994154, 'AP-giraffe': 56.84653089596383, 'AP-backpack': 15.20848163911773, 'AP-umbrella': 48.72529585493549, 'AP-handbag': 15.177918019124176, 'AP-tie': 33.57675522205388, 'AP-suitcase': 41.223367137934495, 'AP-frisbee': 67.50822450456049, 'AP-skis': 5.606975834116382, 'AP-snowboard': 23.542747670708184, 'AP-sports ball': 46.9451326150734, 'AP-kite': 34.67996283794429, 'AP-baseball bat': 28.776320446640035, 'AP-baseball glove': 43.74239949628984, 'AP-skateboard': 36.284069108974585, 'AP-surfboard': 36.557521608383055, 'AP-tennis racket': 57.244064361147515, 'AP-bottle': 35.19658121591146, 'AP-wine glass': 28.20786808566314, 'AP-cup': 40.632184693320625, 'AP-fork': 16.410722129677442, 'AP-knife': 14.606947619908214, 'AP-spoon': 14.58024754745314, 'AP-bowl': 33.45055033001005, 'AP-banana': 20.70949357573751, 'AP-apple': 19.945263614523913, 'AP-sandwich': 43.108610169145514, 'AP-orange': 29.57292652461736, 'AP-broccoli': 22.472850232816835, 'AP-carrot': 22.36364241522204, 'AP-hot dog': 25.934565127381763, 'AP-pizza': 51.344423971308686, 'AP-donut': 44.896761642734454, 'AP-cake': 41.76199439278307, 'AP-chair': 20.708042669670583, 'AP-couch': 42.14431539401933, 'AP-potted plant': 17.34428142403951, 'AP-bed': 41.31667581803378, 'AP-dining table': 12.502483461571194, 'AP-toilet': 66.26910910003109, 'AP-tv': 63.161789357493994, 'AP-laptop': 61.72611213871442, 'AP-mouse': 60.46255700618992, 'AP-remote': 30.246050443970972, 'AP-keyboard': 48.713411711308815, 'AP-cell phone': 36.02662331139011, 'AP-microwave': 56.259951540607354, 'AP-oven': 33.12240873141888, 'AP-toaster': 42.25063121066206, 'AP-sink': 38.91651007518713, 'AP-refrigerator': 59.34575259888377, 'AP-book': 8.871614644041625, 'AP-clock': 51.87918520168975, 'AP-vase': 32.334853830293056, 'AP-scissors': 23.295054929600276, 'AP-teddy bear': 50.46541249241011, 'AP-hair drier': 11.966548985326725, 'AP-toothbrush': 17.41276274002001}), ('sem_seg', {'mIoU': 60.77351531876787, 'fwIoU': 68.98392836534629, 'IoU-person': 87.34739307183973, 'IoU-bicycle': 69.67645404952856, 'IoU-car': 70.3294748236737, 'IoU-motorcycle': 83.83120425815036, 'IoU-airplane': 82.06138878058158, 'IoU-bus': 85.36079388583939, 'IoU-train': 88.48556283555783, 'IoU-truck': 65.52219115309771, 'IoU-boat': 68.48924547246172, 'IoU-traffic light': 76.67313297941635, 'IoU-fire hydrant': 89.77181015887643, 'IoU-stop sign': 92.05472663232977, 'IoU-parking meter': 87.74528293773122, 'IoU-bench': 54.184915823426024, 'IoU-bird': 75.33990756123364, 'IoU-cat': 84.40446780148154, 'IoU-dog': 79.21106701391578, 'IoU-horse': 85.97666183754399, 'IoU-sheep': 80.87593671767817, 'IoU-cow': 79.45684184672355, 'IoU-elephant': 89.60928141256011, 'IoU-bear': 79.34922640574044, 'IoU-zebra': 91.04391206485012, 'IoU-giraffe': 85.44165743956677, 'IoU-backpack': 40.004554527688505, 'IoU-umbrella': 77.49128387693669, 'IoU-handbag': 36.948815706150285, 'IoU-tie': 69.05740056188083, 'IoU-suitcase': 81.60291394005357, 'IoU-frisbee': 83.52126569057889, 'IoU-skis': 51.920724247614395, 'IoU-snowboard': 69.7250186387398, 'IoU-sports ball': 66.73919854421226, 'IoU-kite': 66.71205163116639, 'IoU-baseball bat': 58.027099713743034, 'IoU-baseball glove': 78.90867083012883, 'IoU-skateboard': 60.88024196876732, 'IoU-surfboard': 75.2907775326478, 'IoU-tennis racket': 83.16245406659122, 'IoU-bottle': 69.47894145217349, 'IoU-wine glass': 73.58345312151478, 'IoU-cup': 58.51854893077449, 'IoU-fork': 55.67970763859431, 'IoU-knife': 50.928908017482385, 'IoU-spoon': 50.20754531975648, 'IoU-bowl': 52.65226218523008, 'IoU-banana': 84.91371092489976, 'IoU-apple': 60.94173732332941, 'IoU-sandwich': 64.16224402476072, 'IoU-orange': 76.15638469322069, 'IoU-broccoli': 68.89751285282944, 'IoU-carrot': 64.3384098882967, 'IoU-hot dog': 66.96831316348566, 'IoU-pizza': 79.93868572128711, 'IoU-donut': 64.72343106210697, 'IoU-cake': 67.67886136265123, 'IoU-chair': 54.17779262142043, 'IoU-couch': 66.60794465481678, 'IoU-potted plant': 33.533472022247444, 'IoU-bed': 64.72356488980621, 'IoU-dining table': 52.01315413453778, 'IoU-toilet': 85.35240077344376, 'IoU-tv': 74.69048636783823, 'IoU-laptop': 73.12649821321274, 'IoU-mouse': 65.38708342578877, 'IoU-remote': 44.27423386085101, 'IoU-keyboard': 57.3410812874849, 'IoU-cell phone': 65.3479998912997, 'IoU-microwave': 62.56481050001928, 'IoU-oven': 68.63689257469765, 'IoU-toaster': 69.7837055935852, 'IoU-sink': 67.14047972781476, 'IoU-refrigerator': 82.33304879364644, 'IoU-book': 50.89001308718463, 'IoU-clock': 69.29628849810298, 'IoU-vase': 61.750217236372606, 'IoU-scissors': 54.792241502218275, 'IoU-teddy bear': 80.15296571234668, 'IoU-hair drier': 57.72028225076895, 'IoU-toothbrush': 56.9014524441668, 'IoU-banner': 31.74683551025649, 'IoU-blanket': 12.146407655233759, 'IoU-bridge': 39.438373409265274, 'IoU-cardboard': 41.76041738276342, 'IoU-counter': 30.2406496673708, 'IoU-curtain': 65.12901240320372, 'IoU-door-stuff': 41.9761065173379, 'IoU-floor-wood': 62.60863167975692, 'IoU-flower': 45.195908654675804, 'IoU-fruit': 44.59923989721377, 'IoU-gravel': 31.116989296134506, 'IoU-house': 23.963992040278796, 'IoU-light': 39.985347037245006, 'IoU-mirror-stuff': 54.403669614080705, 'IoU-net': 42.107068657293325, 'IoU-pillow': 12.533174148118295, 'IoU-platform': 32.293260384286654, 'IoU-playingfield': 70.28191057803915, 'IoU-railroad': 60.89563971132775, 'IoU-river': 49.60767749178147, 'IoU-road': 66.28718656123279, 'IoU-roof': 14.20165887896, 'IoU-sand': 64.63722188767466, 'IoU-sea': 85.1172516656336, 'IoU-shelf': 37.90744735741858, 'IoU-snow': 88.03815451967937, 'IoU-stairs': 24.75467919195616, 'IoU-tent': 6.484284983752573, 'IoU-towel': 32.997613264224604, 'IoU-wall-brick': 46.47777250855756, 'IoU-wall-stone': 27.07198333119206, 'IoU-wall-tile': 65.6197061160355, 'IoU-wall-wood': 39.19902552447832, 'IoU-water-other': 20.769841911018023, 'IoU-window-blind': 44.490549265995014, 'IoU-window-other': 46.9330799958757, 'IoU-tree-merged': 80.87113049347502, 'IoU-fence-merged': 50.99988603618404, 'IoU-ceiling-merged': 66.88743350393777, 'IoU-sky-other-merged': 93.36045012513345, 'IoU-cabinet-merged': 58.887795651003415, 'IoU-table-merged': 36.4313017980854, 'IoU-floor-other-merged': 49.252248630281144, 'IoU-pavement-merged': 55.18304477432968, 'IoU-mountain-merged': 55.23968995363704, 'IoU-grass-merged': 71.41802651361817, 'IoU-dirt-merged': 46.837182843446676, 'IoU-paper-merged': 27.994901300013254, 'IoU-food-other-merged': 39.967479633962064, 'IoU-building-other-merged': 58.266156600548236, 'IoU-rock-merged': 58.632912050339556, 'IoU-wall-other-merged': 64.31547395380977, 'IoU-rug-merged': 64.77287864820383, 'mACC': 73.01947157677233, 'pACC': 80.34177937737735, 'ACC-person': 92.49621617538524, 'ACC-bicycle': 79.50013411669016, 'ACC-car': 84.38365577623337, 'ACC-motorcycle': 89.5664357337936, 'ACC-airplane': 90.45260493737828, 'ACC-bus': 91.43734923140906, 'ACC-train': 94.87262985282061, 'ACC-truck': 76.03366138353347, 'ACC-boat': 79.00796504282292, 'ACC-traffic light': 89.97526481931135, 'ACC-fire hydrant': 95.05448637200186, 'ACC-stop sign': 95.33854059259579, 'ACC-parking meter': 92.55433719060103, 'ACC-bench': 68.52198291166557, 'ACC-bird': 80.15884734562512, 'ACC-cat': 92.3865699244701, 'ACC-dog': 82.41906523011761, 'ACC-horse': 91.75170271482409, 'ACC-sheep': 83.80040755241892, 'ACC-cow': 84.48649287974862, 'ACC-elephant': 92.09487603027024, 'ACC-bear': 81.35908291396468, 'ACC-zebra': 93.58432298485562, 'ACC-giraffe': 89.56194942655486, 'ACC-backpack': 56.58013514342556, 'ACC-umbrella': 84.58669371597615, 'ACC-handbag': 58.66969012538144, 'ACC-tie': 79.83693296117653, 'ACC-suitcase': 89.32516882806591, 'ACC-frisbee': 94.21745454545454, 'ACC-skis': 67.23952885378726, 'ACC-snowboard': 78.33361906394651, 'ACC-sports ball': 80.37078384552568, 'ACC-kite': 76.40026780837606, 'ACC-baseball bat': 80.34337561531142, 'ACC-baseball glove': 89.08475821966667, 'ACC-skateboard': 69.52132267461432, 'ACC-surfboard': 84.06495034949255, 'ACC-tennis racket': 89.62580748786573, 'ACC-bottle': 84.99450974112736, 'ACC-wine glass': 86.13497488712807, 'ACC-cup': 83.10259793849278, 'ACC-fork': 65.94987176534062, 'ACC-knife': 67.17740162673115, 'ACC-spoon': 69.45215107461252, 'ACC-bowl': 66.47175955889834, 'ACC-banana': 90.79638303985868, 'ACC-apple': 74.17064829114531, 'ACC-sandwich': 75.81751079409126, 'ACC-orange': 85.96602428450677, 'ACC-broccoli': 80.95973588972466, 'ACC-carrot': 75.2251944116564, 'ACC-hot dog': 73.19558652425712, 'ACC-pizza': 93.87960521925669, 'ACC-donut': 82.05995112470313, 'ACC-cake': 75.01440568332869, 'ACC-chair': 67.44560567906915, 'ACC-couch': 85.40567911986318, 'ACC-potted plant': 52.289102406189315, 'ACC-bed': 75.59487576475811, 'ACC-dining table': 73.07251719108355, 'ACC-toilet': 89.23790926324031, 'ACC-tv': 87.56483041226288, 'ACC-laptop': 86.11975215831686, 'ACC-mouse': 78.66339115973868, 'ACC-remote': 66.99312737183918, 'ACC-keyboard': 65.37117682388964, 'ACC-cell phone': 72.46944971692079, 'ACC-microwave': 76.27787925440029, 'ACC-oven': 85.93784908320613, 'ACC-toaster': 83.84951210499665, 'ACC-sink': 82.27390094406806, 'ACC-refrigerator': 90.44617186994343, 'ACC-book': 69.57825502227453, 'ACC-clock': 74.30179662887838, 'ACC-vase': 70.48443252752267, 'ACC-scissors': 59.98827002359768, 'ACC-teddy bear': 85.25814426276237, 'ACC-hair drier': 68.10263854495773, 'ACC-toothbrush': 79.12178596247395, 'ACC-banner': 75.40512319691234, 'ACC-blanket': 18.382278208265916, 'ACC-bridge': 56.42745040277115, 'ACC-cardboard': 52.82312054675003, 'ACC-counter': 55.169964983204565, 'ACC-curtain': 74.72926204488772, 'ACC-door-stuff': 60.396259975562806, 'ACC-floor-wood': 77.46331183122263, 'ACC-flower': 59.96490320766606, 'ACC-fruit': 59.24304329802973, 'ACC-gravel': 41.61431172902838, 'ACC-house': 28.440255632728984, 'ACC-light': 56.18019800558894, 'ACC-mirror-stuff': 69.98408483593944, 'ACC-net': 62.79400646930626, 'ACC-pillow': 26.10634304224207, 'ACC-platform': 48.35940266162845, 'ACC-playingfield': 89.67844134613043, 'ACC-railroad': 77.68246009034748, 'ACC-river': 73.42768566418549, 'ACC-road': 84.38131381701967, 'ACC-roof': 19.30023998989516, 'ACC-sand': 72.01966609230817, 'ACC-sea': 92.09044271208387, 'ACC-shelf': 64.70209141620705, 'ACC-snow': 95.70520042800634, 'ACC-stairs': 41.24483464512244, 'ACC-tent': 8.378796876764348, 'ACC-towel': 45.075632436648014, 'ACC-wall-brick': 60.16681746524891, 'ACC-wall-stone': 34.72204278694094, 'ACC-wall-tile': 76.50380278678676, 'ACC-wall-wood': 54.22702440232777, 'ACC-water-other': 30.78816460160529, 'ACC-window-blind': 55.6196473158695, 'ACC-window-other': 67.38369119484598, 'ACC-tree-merged': 89.38592425848191, 'ACC-fence-merged': 67.12960334333614, 'ACC-ceiling-merged': 81.52714786893075, 'ACC-sky-other-merged': 96.66570692681965, 'ACC-cabinet-merged': 75.24766164892245, 'ACC-table-merged': 50.82167539928373, 'ACC-floor-other-merged': 61.65230333245477, 'ACC-pavement-merged': 68.98739213253438, 'ACC-mountain-merged': 64.27760110677994, 'ACC-grass-merged': 83.16626162949106, 'ACC-dirt-merged': 69.63971475264927, 'ACC-paper-merged': 38.3939712710113, 'ACC-food-other-merged': 56.285987116599976, 'ACC-building-other-merged': 75.0350292260532, 'ACC-rock-merged': 81.89853000520915, 'ACC-wall-other-merged': 81.19885457501155, 'ACC-rug-merged': 78.47960344880015})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5294117647058822, 'noc@0.8': 2.8829382499268363, 'noc@0.85': 3.5062920690664328, 'noc@0.9': 4.570676031606673, 'miou@iter1': 0.8373717201150297}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.85114288330078, 'precision@0.6': 68.01399230957031, 'precision@0.7': 63.000389099121094, 'precision@0.8': 52.662261962890625, 'precision@0.9': 27.01127052307129, 'cIoU': 57.562400817871094, 'mIoU': 62.63715744018555}}}
INFO:trainer.default_trainer:This epoch takes 1:27:48.744679
INFO:trainer.default_trainer:PROGRESS: 54.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 27 training.
INFO:trainer.default_trainer:epochs[    27] optim steps[49400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71759/0.90312, loss_mask_bce_0: 0.52675/0.33485, loss_mask_dice_0: 0.78432/1.16372, loss_spatial_bce_0: 0.12985/0.08820, loss_spatial_dice_0: 0.20612/0.21058, loss_spatial_ce_0: 0.06938/0.06541, loss_grounding_bce_0: 0.09341/0.08632, loss_grounding_dice_0: 0.09813/0.17871, loss_grounding_ce_0: 0.36316/0.27310, loss_mask_ce_1: 0.68444/0.90387, loss_mask_bce_1: 0.52430/0.33575, loss_mask_dice_1: 0.79777/1.17034, loss_spatial_bce_1: 0.13299/0.08878, loss_spatial_dice_1: 0.22360/0.21465, loss_spatial_ce_1: 0.06845/0.07112, loss_grounding_bce_1: 0.09381/0.08650, loss_grounding_dice_1: 0.10442/0.17948, loss_grounding_ce_1: 0.35672/0.27458, loss_mask_ce_2: 0.72013/0.91115, loss_mask_bce_2: 0.51193/0.33625, loss_mask_dice_2: 0.79658/1.17021, loss_spatial_bce_2: 0.13242/0.08956, loss_spatial_dice_2: 0.20803/0.21596, loss_spatial_ce_2: 0.07366/0.07460, loss_grounding_bce_2: 0.09361/0.08660, loss_grounding_dice_2: 0.10076/0.17930, loss_grounding_ce_2: 0.32463/0.27774, loss_mask_ce_3: 0.72878/0.92050, loss_mask_bce_3: 0.52826/0.33731, loss_mask_dice_3: 0.80106/1.16768, loss_spatial_bce_3: 0.13097/0.09050, loss_spatial_dice_3: 0.20889/0.21662, loss_spatial_ce_3: 0.09316/0.07839, loss_grounding_bce_3: 0.09012/0.08684, loss_grounding_dice_3: 0.10017/0.17904, loss_grounding_ce_3: 0.34520/0.27957, loss_mask_ce_4: 0.73517/0.92108, loss_mask_bce_4: 0.54172/0.33931, loss_mask_dice_4: 0.87452/1.19148, loss_spatial_bce_4: 0.12230/0.09458, loss_spatial_dice_4: 0.20101/0.22843, loss_spatial_ce_4: 0.10265/0.09442, loss_grounding_bce_4: 0.09175/0.08729, loss_grounding_dice_4: 0.10435/0.18191, loss_grounding_ce_4: 0.33940/0.28242, loss_mask_ce_5: 0.76221/0.93700, loss_mask_bce_5: 0.54738/0.34150, loss_mask_dice_5: 1.02322/1.19833, loss_spatial_bce_5: 0.15064/0.09651, loss_spatial_dice_5: 0.22438/0.23229, loss_spatial_ce_5: 0.12423/0.10929, loss_grounding_bce_5: 0.09622/0.08766, loss_grounding_dice_5: 0.11351/0.18308, loss_grounding_ce_5: 0.33879/0.29539, loss_mask_ce_6: 0.81342/0.97636, loss_mask_bce_6: 0.56588/0.34427, loss_mask_dice_6: 0.82753/1.20133, loss_spatial_bce_6: 0.13799/0.10228, loss_spatial_dice_6: 0.20311/0.23492, loss_spatial_ce_6: 0.16623/0.13547, loss_grounding_bce_6: 0.10089/0.08842, loss_grounding_dice_6: 0.10966/0.18343, loss_grounding_ce_6: 0.32423/0.31128, loss_mask_ce_7: 0.94516/1.02116, loss_mask_bce_7: 0.67914/0.35213, loss_mask_dice_7: 0.84129/1.25624, loss_spatial_bce_7: 0.16127/0.11066, loss_spatial_dice_7: 0.24937/0.26267, loss_spatial_ce_7: 0.20432/0.17155, loss_grounding_bce_7: 0.09468/0.09035, loss_grounding_dice_7: 0.11291/0.19065, loss_grounding_ce_7: 0.49286/0.34327, loss_mask_ce_8: 1.25452/1.13005, loss_mask_bce_8: 0.58095/0.36576, loss_mask_dice_8: 0.94185/1.32990, loss_spatial_bce_8: 0.21180/0.13149, loss_spatial_dice_8: 0.29885/0.30147, loss_spatial_ce_8: 0.23149/0.22895, loss_grounding_bce_8: 0.10598/0.09411, loss_grounding_dice_8: 0.14550/0.20170, loss_grounding_ce_8: 0.67372/0.41074, loss_mask_ce_9: 3.62647/3.67981, loss_mask_bce_9: 0.51959/0.39271, loss_mask_dice_9: 1.19381/1.90323, loss_spatial_bce_9: 0.39502/0.33360, loss_spatial_dice_9: 0.86394/0.82247, loss_spatial_ce_9: 1.63914/1.50133, loss_grounding_bce_9: 0.10223/0.10557, loss_grounding_dice_9: 0.19172/0.28092, loss_grounding_ce_9: 0.57593/0.67586] items per batch[64] items per second[0.13] total items[3161600] mini batches[ 49400] memory[7345] epoch remaining[1:23:56]
INFO:trainer.default_trainer:epochs[    27] optim steps[49500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23350/0.90304, loss_mask_bce_0: 0.53482/0.33480, loss_mask_dice_0: 1.99607/1.16362, loss_spatial_bce_0: 0.07925/0.08818, loss_spatial_dice_0: 0.16013/0.21056, loss_spatial_ce_0: 0.01390/0.06541, loss_grounding_bce_0: 0.10717/0.08630, loss_grounding_dice_0: 0.16002/0.17872, loss_grounding_ce_0: 0.39771/0.27300, loss_mask_ce_1: 1.27426/0.90376, loss_mask_bce_1: 0.55326/0.33570, loss_mask_dice_1: 2.09650/1.17021, loss_spatial_bce_1: 0.08025/0.08876, loss_spatial_dice_1: 0.18527/0.21463, loss_spatial_ce_1: 0.01988/0.07112, loss_grounding_bce_1: 0.11005/0.08648, loss_grounding_dice_1: 0.16455/0.17948, loss_grounding_ce_1: 0.42566/0.27449, loss_mask_ce_2: 1.38187/0.91108, loss_mask_bce_2: 0.54130/0.33619, loss_mask_dice_2: 2.24355/1.17009, loss_spatial_bce_2: 0.07697/0.08955, loss_spatial_dice_2: 0.18361/0.21595, loss_spatial_ce_2: 0.01798/0.07457, loss_grounding_bce_2: 0.11314/0.08658, loss_grounding_dice_2: 0.17566/0.17930, loss_grounding_ce_2: 0.49642/0.27773, loss_mask_ce_3: 1.29528/0.92043, loss_mask_bce_3: 0.53872/0.33725, loss_mask_dice_3: 2.18434/1.16755, loss_spatial_bce_3: 0.08121/0.09049, loss_spatial_dice_3: 0.16797/0.21661, loss_spatial_ce_3: 0.03984/0.07836, loss_grounding_bce_3: 0.10429/0.08682, loss_grounding_dice_3: 0.17226/0.17903, loss_grounding_ce_3: 0.55790/0.27954, loss_mask_ce_4: 1.37076/0.92101, loss_mask_bce_4: 0.55782/0.33924, loss_mask_dice_4: 2.26624/1.19133, loss_spatial_bce_4: 0.09184/0.09457, loss_spatial_dice_4: 0.19643/0.22842, loss_spatial_ce_4: 0.03616/0.09440, loss_grounding_bce_4: 0.10636/0.08727, loss_grounding_dice_4: 0.16184/0.18190, loss_grounding_ce_4: 0.43081/0.28237, loss_mask_ce_5: 1.64907/0.93694, loss_mask_bce_5: 0.55304/0.34143, loss_mask_dice_5: 2.11293/1.19817, loss_spatial_bce_5: 0.08842/0.09650, loss_spatial_dice_5: 0.19298/0.23228, loss_spatial_ce_5: 0.03807/0.10926, loss_grounding_bce_5: 0.10912/0.08764, loss_grounding_dice_5: 0.16161/0.18307, loss_grounding_ce_5: 0.61591/0.29533, loss_mask_ce_6: 1.75511/0.97630, loss_mask_bce_6: 0.63091/0.34419, loss_mask_dice_6: 2.13972/1.20117, loss_spatial_bce_6: 0.10599/0.10227, loss_spatial_dice_6: 0.20353/0.23491, loss_spatial_ce_6: 0.09531/0.13548, loss_grounding_bce_6: 0.10997/0.08840, loss_grounding_dice_6: 0.16949/0.18342, loss_grounding_ce_6: 0.46088/0.31115, loss_mask_ce_7: 1.77834/1.02110, loss_mask_bce_7: 0.61880/0.35206, loss_mask_dice_7: 2.29681/1.25613, loss_spatial_bce_7: 0.11491/0.11063, loss_spatial_dice_7: 0.25063/0.26267, loss_spatial_ce_7: 0.10302/0.17154, loss_grounding_bce_7: 0.10779/0.09033, loss_grounding_dice_7: 0.16468/0.19065, loss_grounding_ce_7: 0.71859/0.34318, loss_mask_ce_8: 1.98331/1.13001, loss_mask_bce_8: 0.76936/0.36569, loss_mask_dice_8: 2.67138/1.32970, loss_spatial_bce_8: 0.12509/0.13147, loss_spatial_dice_8: 0.25065/0.30143, loss_spatial_ce_8: 0.08869/0.22895, loss_grounding_bce_8: 0.12351/0.09408, loss_grounding_dice_8: 0.20453/0.20169, loss_grounding_ce_8: 0.97083/0.41061, loss_mask_ce_9: 4.85497/3.67967, loss_mask_bce_9: 0.78514/0.39263, loss_mask_dice_9: 3.96512/1.90290, loss_spatial_bce_9: 0.36631/0.33360, loss_spatial_dice_9: 0.90162/0.82244, loss_spatial_ce_9: 1.83826/1.50138, loss_grounding_bce_9: 0.17471/0.10554, loss_grounding_dice_9: 0.32649/0.28087, loss_grounding_ce_9: 0.67927/0.67581] items per batch[64] items per second[0.23] total items[3168000] mini batches[ 49500] memory[7345] epoch remaining[1:17:29]
INFO:trainer.default_trainer:epochs[    27] optim steps[49600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.76021/0.90318, loss_mask_bce_0: 0.63749/0.33487, loss_mask_dice_0: 2.34431/1.16387, loss_spatial_bce_0: 0.06973/0.08818, loss_spatial_dice_0: 0.28853/0.21056, loss_spatial_ce_0: 0.01171/0.06539, loss_grounding_bce_0: 0.22223/0.08630, loss_grounding_dice_0: 0.21545/0.17874, loss_grounding_ce_0: 0.04350/0.27311, loss_mask_ce_1: 1.57449/0.90389, loss_mask_bce_1: 0.60561/0.33577, loss_mask_dice_1: 1.98809/1.17044, loss_spatial_bce_1: 0.07176/0.08876, loss_spatial_dice_1: 0.27988/0.21463, loss_spatial_ce_1: 0.02623/0.07111, loss_grounding_bce_1: 0.22844/0.08648, loss_grounding_dice_1: 0.21806/0.17951, loss_grounding_ce_1: 0.04302/0.27456, loss_mask_ce_2: 1.53930/0.91122, loss_mask_bce_2: 0.61349/0.33626, loss_mask_dice_2: 2.45279/1.17035, loss_spatial_bce_2: 0.07715/0.08955, loss_spatial_dice_2: 0.29988/0.21595, loss_spatial_ce_2: 0.01215/0.07455, loss_grounding_bce_2: 0.24827/0.08658, loss_grounding_dice_2: 0.21605/0.17933, loss_grounding_ce_2: 0.04225/0.27782, loss_mask_ce_3: 1.75464/0.92059, loss_mask_bce_3: 0.65447/0.33732, loss_mask_dice_3: 2.56732/1.16784, loss_spatial_bce_3: 0.08714/0.09049, loss_spatial_dice_3: 0.28070/0.21661, loss_spatial_ce_3: 0.02441/0.07833, loss_grounding_bce_3: 0.26180/0.08682, loss_grounding_dice_3: 0.22155/0.17907, loss_grounding_ce_3: 0.04035/0.27961, loss_mask_ce_4: 1.59397/0.92117, loss_mask_bce_4: 0.55202/0.33930, loss_mask_dice_4: 2.41063/1.19160, loss_spatial_bce_4: 0.09514/0.09456, loss_spatial_dice_4: 0.30770/0.22843, loss_spatial_ce_4: 0.05421/0.09439, loss_grounding_bce_4: 0.24609/0.08727, loss_grounding_dice_4: 0.21366/0.18193, loss_grounding_ce_4: 0.04290/0.28245, loss_mask_ce_5: 1.86836/0.93708, loss_mask_bce_5: 0.60736/0.34150, loss_mask_dice_5: 2.27809/1.19843, loss_spatial_bce_5: 0.09336/0.09650, loss_spatial_dice_5: 0.32502/0.23229, loss_spatial_ce_5: 0.09319/0.10925, loss_grounding_bce_5: 0.17756/0.08763, loss_grounding_dice_5: 0.20709/0.18309, loss_grounding_ce_5: 0.17367/0.29541, loss_mask_ce_6: 1.83842/0.97650, loss_mask_bce_6: 0.64790/0.34426, loss_mask_dice_6: 2.27984/1.20144, loss_spatial_bce_6: 0.11320/0.10226, loss_spatial_dice_6: 0.32131/0.23492, loss_spatial_ce_6: 0.68392/0.13548, loss_grounding_bce_6: 0.17911/0.08840, loss_grounding_dice_6: 0.21205/0.18345, loss_grounding_ce_6: 0.16000/0.31120, loss_mask_ce_7: 2.09317/1.02125, loss_mask_bce_7: 0.65696/0.35215, loss_mask_dice_7: 2.64471/1.25638, loss_spatial_bce_7: 0.12341/0.11063, loss_spatial_dice_7: 0.37895/0.26268, loss_spatial_ce_7: 0.21656/0.17152, loss_grounding_bce_7: 0.18209/0.09032, loss_grounding_dice_7: 0.23505/0.19069, loss_grounding_ce_7: 0.30045/0.34328, loss_mask_ce_8: 2.41388/1.13024, loss_mask_bce_8: 0.71805/0.36577, loss_mask_dice_8: 2.61752/1.32997, loss_spatial_bce_8: 0.11018/0.13148, loss_spatial_dice_8: 0.36846/0.30144, loss_spatial_ce_8: 0.23301/0.22889, loss_grounding_bce_8: 0.21488/0.09409, loss_grounding_dice_8: 0.21599/0.20173, loss_grounding_ce_8: 0.07780/0.41076, loss_mask_ce_9: 4.87931/3.68003, loss_mask_bce_9: 0.56243/0.39270, loss_mask_dice_9: 3.20807/1.90325, loss_spatial_bce_9: 0.16837/0.33362, loss_spatial_dice_9: 0.88706/0.82247, loss_spatial_ce_9: 1.60745/1.50143, loss_grounding_bce_9: 0.19374/0.10553, loss_grounding_dice_9: 0.24742/0.28093, loss_grounding_ce_9: 0.54997/0.67577] items per batch[64] items per second[0.23] total items[3174400] mini batches[ 49600] memory[7345] epoch remaining[1:12:18]
INFO:trainer.default_trainer:epochs[    27] optim steps[49700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.25931/0.90323, loss_mask_bce_0: 0.26348/0.33483, loss_mask_dice_0: 2.08267/1.16374, loss_spatial_bce_0: 0.06013/0.08818, loss_spatial_dice_0: 0.27879/0.21055, loss_spatial_ce_0: 0.01782/0.06535, loss_grounding_bce_0: 0.09206/0.08629, loss_grounding_dice_0: 0.20625/0.17872, loss_grounding_ce_0: 0.04583/0.27305, loss_mask_ce_1: 2.38447/0.90397, loss_mask_bce_1: 0.26192/0.33573, loss_mask_dice_1: 1.71996/1.17029, loss_spatial_bce_1: 0.06240/0.08876, loss_spatial_dice_1: 0.22076/0.21461, loss_spatial_ce_1: 0.01638/0.07108, loss_grounding_bce_1: 0.09332/0.08647, loss_grounding_dice_1: 0.21916/0.17948, loss_grounding_ce_1: 0.07520/0.27451, loss_mask_ce_2: 2.16110/0.91125, loss_mask_bce_2: 0.28618/0.33623, loss_mask_dice_2: 2.23669/1.17022, loss_spatial_bce_2: 0.06000/0.08955, loss_spatial_dice_2: 0.24770/0.21594, loss_spatial_ce_2: 0.02513/0.07451, loss_grounding_bce_2: 0.09304/0.08657, loss_grounding_dice_2: 0.17114/0.17931, loss_grounding_ce_2: 0.23861/0.27775, loss_mask_ce_3: 2.09646/0.92065, loss_mask_bce_3: 0.28185/0.33728, loss_mask_dice_3: 2.02045/1.16769, loss_spatial_bce_3: 0.06049/0.09049, loss_spatial_dice_3: 0.22842/0.21660, loss_spatial_ce_3: 0.02103/0.07831, loss_grounding_bce_3: 0.09391/0.08682, loss_grounding_dice_3: 0.18391/0.17905, loss_grounding_ce_3: 0.26816/0.27956, loss_mask_ce_4: 2.43111/0.92123, loss_mask_bce_4: 0.29495/0.33929, loss_mask_dice_4: 2.38243/1.19149, loss_spatial_bce_4: 0.06289/0.09456, loss_spatial_dice_4: 0.26792/0.22841, loss_spatial_ce_4: 0.03568/0.09436, loss_grounding_bce_4: 0.09425/0.08726, loss_grounding_dice_4: 0.16851/0.18190, loss_grounding_ce_4: 0.12033/0.28239, loss_mask_ce_5: 2.26173/0.93711, loss_mask_bce_5: 0.27070/0.34148, loss_mask_dice_5: 1.99505/1.19832, loss_spatial_bce_5: 0.06670/0.09651, loss_spatial_dice_5: 0.23443/0.23228, loss_spatial_ce_5: 0.03269/0.10920, loss_grounding_bce_5: 0.09384/0.08763, loss_grounding_dice_5: 0.17474/0.18307, loss_grounding_ce_5: 0.18295/0.29535, loss_mask_ce_6: 2.19597/0.97654, loss_mask_bce_6: 0.27942/0.34424, loss_mask_dice_6: 1.73396/1.20129, loss_spatial_bce_6: 0.06968/0.10227, loss_spatial_dice_6: 0.29229/0.23491, loss_spatial_ce_6: 0.02007/0.13546, loss_grounding_bce_6: 0.09561/0.08840, loss_grounding_dice_6: 0.19640/0.18342, loss_grounding_ce_6: 0.14800/0.31108, loss_mask_ce_7: 2.34684/1.02124, loss_mask_bce_7: 0.28160/0.35213, loss_mask_dice_7: 2.14046/1.25631, loss_spatial_bce_7: 0.07994/0.11063, loss_spatial_dice_7: 0.33393/0.26267, loss_spatial_ce_7: 0.08443/0.17148, loss_grounding_bce_7: 0.09546/0.09033, loss_grounding_dice_7: 0.17760/0.19065, loss_grounding_ce_7: 0.18357/0.34316, loss_mask_ce_8: 2.71754/1.13025, loss_mask_bce_8: 0.26430/0.36576, loss_mask_dice_8: 2.37796/1.32990, loss_spatial_bce_8: 0.07465/0.13149, loss_spatial_dice_8: 0.35633/0.30140, loss_spatial_ce_8: 0.26366/0.22884, loss_grounding_bce_8: 0.09231/0.09409, loss_grounding_dice_8: 0.20359/0.20171, loss_grounding_ce_8: 0.22631/0.41052, loss_mask_ce_9: 4.72642/3.67983, loss_mask_bce_9: 0.23775/0.39266, loss_mask_dice_9: 2.38728/1.90296, loss_spatial_bce_9: 0.18257/0.33362, loss_spatial_dice_9: 0.82475/0.82246, loss_spatial_ce_9: 1.30839/1.50131, loss_grounding_bce_9: 0.08972/0.10554, loss_grounding_dice_9: 0.25691/0.28089, loss_grounding_ce_9: 0.38935/0.67547] items per batch[64] items per second[0.23] total items[3180800] mini batches[ 49700] memory[7345] epoch remaining[1:07:46]
INFO:trainer.default_trainer:epochs[    27] optim steps[49800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30860/0.90312, loss_mask_bce_0: 0.11430/0.33485, loss_mask_dice_0: 0.24700/1.16358, loss_spatial_bce_0: 0.07657/0.08818, loss_spatial_dice_0: 0.16531/0.21054, loss_spatial_ce_0: 0.00989/0.06533, loss_grounding_bce_0: 0.06878/0.08631, loss_grounding_dice_0: 0.20579/0.17874, loss_grounding_ce_0: 0.42566/0.27302, loss_mask_ce_1: 0.35304/0.90387, loss_mask_bce_1: 0.11379/0.33575, loss_mask_dice_1: 0.24653/1.17013, loss_spatial_bce_1: 0.07424/0.08876, loss_spatial_dice_1: 0.16823/0.21459, loss_spatial_ce_1: 0.01219/0.07104, loss_grounding_bce_1: 0.07066/0.08648, loss_grounding_dice_1: 0.19964/0.17950, loss_grounding_ce_1: 0.38969/0.27448, loss_mask_ce_2: 0.29917/0.91113, loss_mask_bce_2: 0.10609/0.33624, loss_mask_dice_2: 0.22858/1.17005, loss_spatial_bce_2: 0.07595/0.08956, loss_spatial_dice_2: 0.15241/0.21592, loss_spatial_ce_2: 0.01957/0.07448, loss_grounding_bce_2: 0.07227/0.08659, loss_grounding_dice_2: 0.22890/0.17932, loss_grounding_ce_2: 0.42031/0.27771, loss_mask_ce_3: 0.29110/0.92057, loss_mask_bce_3: 0.11666/0.33730, loss_mask_dice_3: 0.26994/1.16753, loss_spatial_bce_3: 0.07690/0.09050, loss_spatial_dice_3: 0.15705/0.21659, loss_spatial_ce_3: 0.02495/0.07826, loss_grounding_bce_3: 0.06798/0.08683, loss_grounding_dice_3: 0.21103/0.17907, loss_grounding_ce_3: 0.33449/0.27951, loss_mask_ce_4: 0.42629/0.92114, loss_mask_bce_4: 0.11655/0.33931, loss_mask_dice_4: 0.26425/1.19131, loss_spatial_bce_4: 0.07519/0.09457, loss_spatial_dice_4: 0.15748/0.22840, loss_spatial_ce_4: 0.10146/0.09432, loss_grounding_bce_4: 0.06771/0.08728, loss_grounding_dice_4: 0.21669/0.18192, loss_grounding_ce_4: 0.30187/0.28236, loss_mask_ce_5: 0.33550/0.93703, loss_mask_bce_5: 0.11427/0.34150, loss_mask_dice_5: 0.24505/1.19817, loss_spatial_bce_5: 0.07216/0.09653, loss_spatial_dice_5: 0.15927/0.23227, loss_spatial_ce_5: 0.09759/0.10916, loss_grounding_bce_5: 0.08557/0.08765, loss_grounding_dice_5: 0.29875/0.18309, loss_grounding_ce_5: 0.02938/0.29532, loss_mask_ce_6: 0.36654/0.97644, loss_mask_bce_6: 0.11323/0.34427, loss_mask_dice_6: 0.23466/1.20113, loss_spatial_bce_6: 0.06728/0.10229, loss_spatial_dice_6: 0.17553/0.23491, loss_spatial_ce_6: 0.24243/0.13546, loss_grounding_bce_6: 0.06540/0.08841, loss_grounding_dice_6: 0.21364/0.18344, loss_grounding_ce_6: 0.44798/0.31109, loss_mask_ce_7: 0.43898/1.02115, loss_mask_bce_7: 0.10858/0.35215, loss_mask_dice_7: 0.24508/1.25614, loss_spatial_bce_7: 0.13196/0.11065, loss_spatial_dice_7: 0.20788/0.26268, loss_spatial_ce_7: 0.07557/0.17149, loss_grounding_bce_7: 0.06259/0.09035, loss_grounding_dice_7: 0.22747/0.19069, loss_grounding_ce_7: 0.58848/0.34318, loss_mask_ce_8: 0.46289/1.13017, loss_mask_bce_8: 0.10628/0.36577, loss_mask_dice_8: 0.25839/1.32976, loss_spatial_bce_8: 0.15364/0.13150, loss_spatial_dice_8: 0.22173/0.30138, loss_spatial_ce_8: 0.14602/0.22882, loss_grounding_bce_8: 0.05963/0.09411, loss_grounding_dice_8: 0.23104/0.20173, loss_grounding_ce_8: 0.64161/0.41053, loss_mask_ce_9: 2.40439/3.67971, loss_mask_bce_9: 0.12487/0.39268, loss_mask_dice_9: 0.41332/1.90275, loss_spatial_bce_9: 0.35146/0.33362, loss_spatial_dice_9: 0.64621/0.82243, loss_spatial_ce_9: 0.76413/1.50101, loss_grounding_bce_9: 0.07100/0.10555, loss_grounding_dice_9: 0.35469/0.28091, loss_grounding_ce_9: 0.20815/0.67541] items per batch[64] items per second[0.23] total items[3187200] mini batches[ 49800] memory[7345] epoch remaining[1:02:50]
INFO:trainer.default_trainer:epochs[    27] optim steps[49900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92328/0.90303, loss_mask_bce_0: 0.50843/0.33493, loss_mask_dice_0: 0.94139/1.16397, loss_spatial_bce_0: 0.08720/0.08817, loss_spatial_dice_0: 0.22868/0.21053, loss_spatial_ce_0: 0.06421/0.06530, loss_grounding_bce_0: 0.03685/0.08630, loss_grounding_dice_0: 0.03271/0.17875, loss_grounding_ce_0: 0.04923/0.27305, loss_mask_ce_1: 0.92139/0.90376, loss_mask_bce_1: 0.49738/0.33584, loss_mask_dice_1: 1.04921/1.17050, loss_spatial_bce_1: 0.09536/0.08875, loss_spatial_dice_1: 0.22606/0.21459, loss_spatial_ce_1: 0.05272/0.07103, loss_grounding_bce_1: 0.03741/0.08648, loss_grounding_dice_1: 0.28348/0.17951, loss_grounding_ce_1: 0.05205/0.27451, loss_mask_ce_2: 0.96786/0.91102, loss_mask_bce_2: 0.50400/0.33633, loss_mask_dice_2: 1.00541/1.17044, loss_spatial_bce_2: 0.09059/0.08955, loss_spatial_dice_2: 0.20788/0.21592, loss_spatial_ce_2: 0.05062/0.07448, loss_grounding_bce_2: 0.03859/0.08659, loss_grounding_dice_2: 0.03416/0.17934, loss_grounding_ce_2: 0.05086/0.27773, loss_mask_ce_3: 1.02664/0.92051, loss_mask_bce_3: 0.51677/0.33739, loss_mask_dice_3: 0.97478/1.16791, loss_spatial_bce_3: 0.09532/0.09049, loss_spatial_dice_3: 0.23615/0.21659, loss_spatial_ce_3: 0.05327/0.07823, loss_grounding_bce_3: 0.03680/0.08683, loss_grounding_dice_3: 0.03344/0.17908, loss_grounding_ce_3: 0.04588/0.27952, loss_mask_ce_4: 0.95936/0.92112, loss_mask_bce_4: 0.50650/0.33940, loss_mask_dice_4: 0.94737/1.19171, loss_spatial_bce_4: 0.10811/0.09456, loss_spatial_dice_4: 0.27074/0.22840, loss_spatial_ce_4: 0.07437/0.09429, loss_grounding_bce_4: 0.03751/0.08728, loss_grounding_dice_4: 0.03309/0.18193, loss_grounding_ce_4: 0.04727/0.28235, loss_mask_ce_5: 0.93644/0.93700, loss_mask_bce_5: 0.50936/0.34160, loss_mask_dice_5: 1.03402/1.19856, loss_spatial_bce_5: 0.11646/0.09652, loss_spatial_dice_5: 0.24749/0.23228, loss_spatial_ce_5: 0.15093/0.10914, loss_grounding_bce_5: 0.03787/0.08765, loss_grounding_dice_5: 0.13274/0.18311, loss_grounding_ce_5: 0.04368/0.29535, loss_mask_ce_6: 0.85729/0.97641, loss_mask_bce_6: 0.52134/0.34437, loss_mask_dice_6: 0.99532/1.20158, loss_spatial_bce_6: 0.11430/0.10228, loss_spatial_dice_6: 0.27851/0.23492, loss_spatial_ce_6: 0.14889/0.13543, loss_grounding_bce_6: 0.04009/0.08842, loss_grounding_dice_6: 0.03769/0.18346, loss_grounding_ce_6: 0.04176/0.31106, loss_mask_ce_7: 1.21014/1.02113, loss_mask_bce_7: 0.52679/0.35225, loss_mask_dice_7: 0.95936/1.25659, loss_spatial_bce_7: 0.11556/0.11063, loss_spatial_dice_7: 0.25071/0.26270, loss_spatial_ce_7: 0.10866/0.17144, loss_grounding_bce_7: 0.03801/0.09035, loss_grounding_dice_7: 0.02950/0.19070, loss_grounding_ce_7: 0.02700/0.34318, loss_mask_ce_8: 1.38753/1.13014, loss_mask_bce_8: 0.50900/0.36587, loss_mask_dice_8: 0.99158/1.33020, loss_spatial_bce_8: 0.11103/0.13149, loss_spatial_dice_8: 0.27388/0.30139, loss_spatial_ce_8: 0.10953/0.22878, loss_grounding_bce_8: 0.04642/0.09411, loss_grounding_dice_8: 0.02851/0.20176, loss_grounding_ce_8: 0.09485/0.41047, loss_mask_ce_9: 5.47267/3.67993, loss_mask_bce_9: 0.48155/0.39278, loss_mask_dice_9: 1.63677/1.90341, loss_spatial_bce_9: 0.30114/0.33360, loss_spatial_dice_9: 0.83655/0.82244, loss_spatial_ce_9: 1.05506/1.50102, loss_grounding_bce_9: 0.04166/0.10554, loss_grounding_dice_9: 0.03370/0.28093, loss_grounding_ce_9: 0.91308/0.67540] items per batch[64] items per second[0.24] total items[3193600] mini batches[ 49900] memory[7345] epoch remaining[0:57:48]
INFO:trainer.default_trainer:epochs[    27] optim steps[50000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73134/0.90297, loss_mask_bce_0: 0.08043/0.33497, loss_mask_dice_0: 0.14683/1.16390, loss_spatial_bce_0: 0.03625/0.08817, loss_spatial_dice_0: 0.08075/0.21049, loss_spatial_ce_0: 0.01891/0.06525, loss_grounding_bce_0: 0.03832/0.08629, loss_grounding_dice_0: 0.09586/0.17875, loss_grounding_ce_0: 0.01464/0.27316, loss_mask_ce_1: 0.67504/0.90374, loss_mask_bce_1: 0.08366/0.33587, loss_mask_dice_1: 0.14875/1.17042, loss_spatial_bce_1: 0.03705/0.08875, loss_spatial_dice_1: 0.07854/0.21455, loss_spatial_ce_1: 0.02010/0.07098, loss_grounding_bce_1: 0.03864/0.08647, loss_grounding_dice_1: 0.09280/0.17951, loss_grounding_ce_1: 0.01505/0.27459, loss_mask_ce_2: 0.68785/0.91099, loss_mask_bce_2: 0.08244/0.33636, loss_mask_dice_2: 0.15407/1.17043, loss_spatial_bce_2: 0.03798/0.08955, loss_spatial_dice_2: 0.09130/0.21588, loss_spatial_ce_2: 0.01420/0.07445, loss_grounding_bce_2: 0.03568/0.08658, loss_grounding_dice_2: 0.08727/0.17933, loss_grounding_ce_2: 0.01032/0.27783, loss_mask_ce_3: 0.76565/0.92046, loss_mask_bce_3: 0.08609/0.33743, loss_mask_dice_3: 0.15006/1.16785, loss_spatial_bce_3: 0.03926/0.09049, loss_spatial_dice_3: 0.09757/0.21655, loss_spatial_ce_3: 0.01053/0.07819, loss_grounding_bce_3: 0.03883/0.08682, loss_grounding_dice_3: 0.09041/0.17907, loss_grounding_ce_3: 0.01236/0.27964, loss_mask_ce_4: 0.88184/0.92109, loss_mask_bce_4: 0.09174/0.33943, loss_mask_dice_4: 0.15937/1.19165, loss_spatial_bce_4: 0.04406/0.09456, loss_spatial_dice_4: 0.09900/0.22836, loss_spatial_ce_4: 0.01882/0.09426, loss_grounding_bce_4: 0.03610/0.08727, loss_grounding_dice_4: 0.09766/0.18193, loss_grounding_ce_4: 0.01552/0.28248, loss_mask_ce_5: 0.89152/0.93698, loss_mask_bce_5: 0.09183/0.34163, loss_mask_dice_5: 0.17091/1.19849, loss_spatial_bce_5: 0.04616/0.09652, loss_spatial_dice_5: 0.09979/0.23224, loss_spatial_ce_5: 0.06553/0.10909, loss_grounding_bce_5: 0.03648/0.08764, loss_grounding_dice_5: 0.10284/0.18310, loss_grounding_ce_5: 0.01352/0.29540, loss_mask_ce_6: 1.17726/0.97637, loss_mask_bce_6: 0.10273/0.34442, loss_mask_dice_6: 0.18714/1.20155, loss_spatial_bce_6: 0.05920/0.10228, loss_spatial_dice_6: 0.11871/0.23490, loss_spatial_ce_6: 0.07541/0.13541, loss_grounding_bce_6: 0.03576/0.08841, loss_grounding_dice_6: 0.08647/0.18345, loss_grounding_ce_6: 0.02234/0.31114, loss_mask_ce_7: 1.07330/1.02111, loss_mask_bce_7: 0.11930/0.35229, loss_mask_dice_7: 0.25338/1.25653, loss_spatial_bce_7: 0.05288/0.11065, loss_spatial_dice_7: 0.10637/0.26269, loss_spatial_ce_7: 0.09574/0.17141, loss_grounding_bce_7: 0.03646/0.09034, loss_grounding_dice_7: 0.09201/0.19070, loss_grounding_ce_7: 0.03657/0.34321, loss_mask_ce_8: 0.86338/1.13014, loss_mask_bce_8: 0.10285/0.36592, loss_mask_dice_8: 0.22173/1.33009, loss_spatial_bce_8: 0.04544/0.13149, loss_spatial_dice_8: 0.09262/0.30134, loss_spatial_ce_8: 0.12913/0.22873, loss_grounding_bce_8: 0.04113/0.09410, loss_grounding_dice_8: 0.10681/0.20176, loss_grounding_ce_8: 0.04958/0.41061, loss_mask_ce_9: 2.94491/3.67992, loss_mask_bce_9: 0.13312/0.39287, loss_mask_dice_9: 0.38301/1.90333, loss_spatial_bce_9: 0.74890/0.33364, loss_spatial_dice_9: 0.86584/0.82244, loss_spatial_ce_9: 1.66986/1.50093, loss_grounding_bce_9: 0.08121/0.10556, loss_grounding_dice_9: 0.20612/0.28093, loss_grounding_ce_9: 0.61249/0.67552] items per batch[64] items per second[0.23] total items[3200000] mini batches[ 50000] memory[7345] epoch remaining[0:53:20]
INFO:trainer.default_trainer:epochs[    27] optim steps[50100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90675/0.90297, loss_mask_bce_0: 0.42546/0.33500, loss_mask_dice_0: 0.86161/1.16377, loss_spatial_bce_0: 0.09568/0.08816, loss_spatial_dice_0: 0.16901/0.21046, loss_spatial_ce_0: 0.04012/0.06523, loss_grounding_bce_0: 0.09873/0.08629, loss_grounding_dice_0: 0.13480/0.17873, loss_grounding_ce_0: 0.21084/0.27321, loss_mask_ce_1: 0.91294/0.90373, loss_mask_bce_1: 0.43470/0.33589, loss_mask_dice_1: 0.83336/1.17033, loss_spatial_bce_1: 0.09866/0.08874, loss_spatial_dice_1: 0.18317/0.21452, loss_spatial_ce_1: 0.04170/0.07095, loss_grounding_bce_1: 0.09729/0.08646, loss_grounding_dice_1: 0.12755/0.17948, loss_grounding_ce_1: 0.19738/0.27464, loss_mask_ce_2: 0.94474/0.91099, loss_mask_bce_2: 0.44141/0.33639, loss_mask_dice_2: 0.81827/1.17034, loss_spatial_bce_2: 0.10153/0.08954, loss_spatial_dice_2: 0.18629/0.21586, loss_spatial_ce_2: 0.05260/0.07441, loss_grounding_bce_2: 0.09521/0.08657, loss_grounding_dice_2: 0.11816/0.17932, loss_grounding_ce_2: 0.24913/0.27786, loss_mask_ce_3: 0.96959/0.92045, loss_mask_bce_3: 0.42116/0.33746, loss_mask_dice_3: 0.83971/1.16776, loss_spatial_bce_3: 0.09869/0.09049, loss_spatial_dice_3: 0.19104/0.21652, loss_spatial_ce_3: 0.05853/0.07814, loss_grounding_bce_3: 0.09151/0.08682, loss_grounding_dice_3: 0.12521/0.17905, loss_grounding_ce_3: 0.23110/0.27968, loss_mask_ce_4: 1.00459/0.92107, loss_mask_bce_4: 0.44589/0.33947, loss_mask_dice_4: 0.85667/1.19156, loss_spatial_bce_4: 0.11698/0.09455, loss_spatial_dice_4: 0.26049/0.22834, loss_spatial_ce_4: 0.17819/0.09422, loss_grounding_bce_4: 0.09768/0.08727, loss_grounding_dice_4: 0.11360/0.18192, loss_grounding_ce_4: 0.19663/0.28250, loss_mask_ce_5: 0.96718/0.93696, loss_mask_bce_5: 0.44652/0.34166, loss_mask_dice_5: 0.89899/1.19842, loss_spatial_bce_5: 0.13931/0.09652, loss_spatial_dice_5: 0.25720/0.23223, loss_spatial_ce_5: 0.12601/0.10906, loss_grounding_bce_5: 0.09946/0.08764, loss_grounding_dice_5: 0.12500/0.18308, loss_grounding_ce_5: 0.22492/0.29539, loss_mask_ce_6: 1.00863/0.97635, loss_mask_bce_6: 0.42326/0.34446, loss_mask_dice_6: 0.88433/1.20146, loss_spatial_bce_6: 0.15989/0.10228, loss_spatial_dice_6: 0.29757/0.23489, loss_spatial_ce_6: 0.12871/0.13538, loss_grounding_bce_6: 0.09944/0.08841, loss_grounding_dice_6: 0.12554/0.18344, loss_grounding_ce_6: 0.27194/0.31108, loss_mask_ce_7: 1.10639/1.02109, loss_mask_bce_7: 0.41848/0.35232, loss_mask_dice_7: 0.91801/1.25644, loss_spatial_bce_7: 0.16632/0.11065, loss_spatial_dice_7: 0.30917/0.26268, loss_spatial_ce_7: 0.19923/0.17139, loss_grounding_bce_7: 0.09077/0.09033, loss_grounding_dice_7: 0.12934/0.19069, loss_grounding_ce_7: 0.50697/0.34318, loss_mask_ce_8: 1.44816/1.13017, loss_mask_bce_8: 0.42934/0.36594, loss_mask_dice_8: 0.90528/1.33002, loss_spatial_bce_8: 0.18297/0.13149, loss_spatial_dice_8: 0.29587/0.30132, loss_spatial_ce_8: 0.11671/0.22868, loss_grounding_bce_8: 0.10168/0.09409, loss_grounding_dice_8: 0.11662/0.20174, loss_grounding_ce_8: 0.47601/0.41059, loss_mask_ce_9: 4.07166/3.67993, loss_mask_bce_9: 0.41302/0.39291, loss_mask_dice_9: 1.16884/1.90322, loss_spatial_bce_9: 0.41472/0.33364, loss_spatial_dice_9: 0.82284/0.82243, loss_spatial_ce_9: 1.43211/1.50092, loss_grounding_bce_9: 0.12408/0.10556, loss_grounding_dice_9: 0.14713/0.28092, loss_grounding_ce_9: 0.89297/0.67556] items per batch[64] items per second[0.23] total items[3206400] mini batches[ 50100] memory[7345] epoch remaining[0:48:41]
INFO:trainer.default_trainer:epochs[    27] optim steps[50200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83043/0.90288, loss_mask_bce_0: 0.76139/0.33496, loss_mask_dice_0: 1.11151/1.16362, loss_spatial_bce_0: 0.13871/0.08815, loss_spatial_dice_0: 0.19192/0.21042, loss_spatial_ce_0: 0.08205/0.06520, loss_grounding_bce_0: 0.40374/0.08629, loss_grounding_dice_0: 0.28030/0.17870, loss_grounding_ce_0: 1.56098/0.27325, loss_mask_ce_1: 0.81197/0.90364, loss_mask_bce_1: 0.75846/0.33586, loss_mask_dice_1: 1.03844/1.17017, loss_spatial_bce_1: 0.14077/0.08873, loss_spatial_dice_1: 0.19525/0.21449, loss_spatial_ce_1: 0.12666/0.07093, loss_grounding_bce_1: 0.40880/0.08647, loss_grounding_dice_1: 0.27607/0.17946, loss_grounding_ce_1: 1.48941/0.27466, loss_mask_ce_2: 0.80603/0.91089, loss_mask_bce_2: 0.77210/0.33635, loss_mask_dice_2: 1.10249/1.17021, loss_spatial_bce_2: 0.15741/0.08953, loss_spatial_dice_2: 0.22245/0.21582, loss_spatial_ce_2: 0.11825/0.07438, loss_grounding_bce_2: 0.39254/0.08658, loss_grounding_dice_2: 0.26497/0.17928, loss_grounding_ce_2: 1.50706/0.27790, loss_mask_ce_3: 0.87693/0.92035, loss_mask_bce_3: 0.79597/0.33743, loss_mask_dice_3: 1.07403/1.16763, loss_spatial_bce_3: 0.16130/0.09047, loss_spatial_dice_3: 0.22932/0.21649, loss_spatial_ce_3: 0.16797/0.07811, loss_grounding_bce_3: 0.40464/0.08682, loss_grounding_dice_3: 0.27533/0.17902, loss_grounding_ce_3: 1.46320/0.27970, loss_mask_ce_4: 0.83068/0.92098, loss_mask_bce_4: 0.75377/0.33944, loss_mask_dice_4: 1.14331/1.19138, loss_spatial_bce_4: 0.16784/0.09454, loss_spatial_dice_4: 0.25343/0.22831, loss_spatial_ce_4: 0.12131/0.09418, loss_grounding_bce_4: 0.33801/0.08727, loss_grounding_dice_4: 0.25921/0.18189, loss_grounding_ce_4: 1.50957/0.28249, loss_mask_ce_5: 0.85199/0.93684, loss_mask_bce_5: 0.80356/0.34164, loss_mask_dice_5: 1.16745/1.19827, loss_spatial_bce_5: 0.18610/0.09652, loss_spatial_dice_5: 0.27000/0.23219, loss_spatial_ce_5: 0.11775/0.10905, loss_grounding_bce_5: 0.36229/0.08765, loss_grounding_dice_5: 0.25138/0.18305, loss_grounding_ce_5: 1.50230/0.29541, loss_mask_ce_6: 0.88997/0.97625, loss_mask_bce_6: 0.78108/0.34443, loss_mask_dice_6: 1.12338/1.20129, loss_spatial_bce_6: 0.22232/0.10227, loss_spatial_dice_6: 0.23696/0.23486, loss_spatial_ce_6: 0.08156/0.13534, loss_grounding_bce_6: 0.72186/0.08843, loss_grounding_dice_6: 0.30004/0.18341, loss_grounding_ce_6: 1.11112/0.31104, loss_mask_ce_7: 0.94052/1.02099, loss_mask_bce_7: 0.91776/0.35228, loss_mask_dice_7: 1.25466/1.25625, loss_spatial_bce_7: 0.20606/0.11063, loss_spatial_dice_7: 0.28089/0.26264, loss_spatial_ce_7: 0.48292/0.17134, loss_grounding_bce_7: 0.72924/0.09034, loss_grounding_dice_7: 0.32962/0.19065, loss_grounding_ce_7: 0.95477/0.34317, loss_mask_ce_8: 1.43873/1.13013, loss_mask_bce_8: 0.89701/0.36591, loss_mask_dice_8: 1.24432/1.32980, loss_spatial_bce_8: 0.28811/0.13147, loss_spatial_dice_8: 0.32340/0.30128, loss_spatial_ce_8: 0.20791/0.22861, loss_grounding_bce_8: 0.65267/0.09411, loss_grounding_dice_8: 0.36999/0.20171, loss_grounding_ce_8: 1.05140/0.41062, loss_mask_ce_9: 5.14677/3.67990, loss_mask_bce_9: 0.98036/0.39284, loss_mask_dice_9: 1.96601/1.90296, loss_spatial_bce_9: 0.42201/0.33363, loss_spatial_dice_9: 0.78640/0.82242, loss_spatial_ce_9: 1.17969/1.50084, loss_grounding_bce_9: 0.53727/0.10557, loss_grounding_dice_9: 0.38657/0.28088, loss_grounding_ce_9: 1.61184/0.67572] items per batch[64] items per second[0.23] total items[3212800] mini batches[ 50200] memory[7345] epoch remaining[0:44:06]
INFO:trainer.default_trainer:epochs[    27] optim steps[50300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52225/0.90291, loss_mask_bce_0: 0.06272/0.33497, loss_mask_dice_0: 1.02243/1.16341, loss_spatial_bce_0: 0.01018/0.08815, loss_spatial_dice_0: 0.19504/0.21038, loss_spatial_ce_0: 0.00253/0.06517, loss_grounding_bce_0: 0.01747/0.08630, loss_grounding_dice_0: 0.16104/0.17871, loss_grounding_ce_0: 0.04212/0.27323, loss_mask_ce_1: 0.58430/0.90365, loss_mask_bce_1: 0.06336/0.33586, loss_mask_dice_1: 0.99218/1.16997, loss_spatial_bce_1: 0.00978/0.08872, loss_spatial_dice_1: 0.17756/0.21444, loss_spatial_ce_1: 0.01491/0.07090, loss_grounding_bce_1: 0.01725/0.08647, loss_grounding_dice_1: 0.13960/0.17946, loss_grounding_ce_1: 0.03902/0.27465, loss_mask_ce_2: 0.45522/0.91089, loss_mask_bce_2: 0.06789/0.33636, loss_mask_dice_2: 1.17947/1.17004, loss_spatial_bce_2: 0.01004/0.08952, loss_spatial_dice_2: 0.18918/0.21578, loss_spatial_ce_2: 0.01502/0.07436, loss_grounding_bce_2: 0.01683/0.08659, loss_grounding_dice_2: 0.13083/0.17929, loss_grounding_ce_2: 0.06441/0.27788, loss_mask_ce_3: 0.61913/0.92037, loss_mask_bce_3: 0.06761/0.33743, loss_mask_dice_3: 1.05221/1.16745, loss_spatial_bce_3: 0.00999/0.09047, loss_spatial_dice_3: 0.17051/0.21645, loss_spatial_ce_3: 0.02088/0.07808, loss_grounding_bce_3: 0.01815/0.08683, loss_grounding_dice_3: 0.14853/0.17903, loss_grounding_ce_3: 0.07178/0.27970, loss_mask_ce_4: 0.47053/0.92101, loss_mask_bce_4: 0.06265/0.33945, loss_mask_dice_4: 1.06380/1.19121, loss_spatial_bce_4: 0.01110/0.09454, loss_spatial_dice_4: 0.19481/0.22828, loss_spatial_ce_4: 0.02660/0.09416, loss_grounding_bce_4: 0.01578/0.08729, loss_grounding_dice_4: 0.15451/0.18191, loss_grounding_ce_4: 0.07088/0.28247, loss_mask_ce_5: 0.47587/0.93685, loss_mask_bce_5: 0.07030/0.34165, loss_mask_dice_5: 1.12605/1.19813, loss_spatial_bce_5: 0.01278/0.09651, loss_spatial_dice_5: 0.20232/0.23216, loss_spatial_ce_5: 0.03741/0.10900, loss_grounding_bce_5: 0.01651/0.08766, loss_grounding_dice_5: 0.14902/0.18307, loss_grounding_ce_5: 0.04411/0.29538, loss_mask_ce_6: 0.65017/0.97626, loss_mask_bce_6: 0.06847/0.34443, loss_mask_dice_6: 1.10986/1.20111, loss_spatial_bce_6: 0.01844/0.10226, loss_spatial_dice_6: 0.24505/0.23483, loss_spatial_ce_6: 0.06576/0.13532, loss_grounding_bce_6: 0.01390/0.08843, loss_grounding_dice_6: 0.15075/0.18343, loss_grounding_ce_6: 0.05304/0.31102, loss_mask_ce_7: 0.48799/1.02099, loss_mask_bce_7: 0.06281/0.35229, loss_mask_dice_7: 1.09684/1.25605, loss_spatial_bce_7: 0.01508/0.11062, loss_spatial_dice_7: 0.21828/0.26262, loss_spatial_ce_7: 0.04362/0.17130, loss_grounding_bce_7: 0.01454/0.09036, loss_grounding_dice_7: 0.13837/0.19067, loss_grounding_ce_7: 0.10855/0.34307, loss_mask_ce_8: 0.62437/1.13011, loss_mask_bce_8: 0.06582/0.36591, loss_mask_dice_8: 1.19317/1.32965, loss_spatial_bce_8: 0.02749/0.13146, loss_spatial_dice_8: 0.26273/0.30125, loss_spatial_ce_8: 0.04281/0.22857, loss_grounding_bce_8: 0.01816/0.09412, loss_grounding_dice_8: 0.12063/0.20173, loss_grounding_ce_8: 1.95373/0.41066, loss_mask_ce_9: 3.37789/3.67992, loss_mask_bce_9: 0.10058/0.39286, loss_mask_dice_9: 1.57903/1.90271, loss_spatial_bce_9: 0.08512/0.33364, loss_spatial_dice_9: 0.75240/0.82240, loss_spatial_ce_9: 1.32711/1.50068, loss_grounding_bce_9: 0.04964/0.10558, loss_grounding_dice_9: 0.33276/0.28090, loss_grounding_ce_9: 1.57495/0.67564] items per batch[64] items per second[0.23] total items[3219200] mini batches[ 50300] memory[7345] epoch remaining[0:39:29]
INFO:trainer.default_trainer:epochs[    27] optim steps[50400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18790/0.90297, loss_mask_bce_0: 0.38746/0.33493, loss_mask_dice_0: 2.03627/1.16312, loss_spatial_bce_0: 0.03119/0.08814, loss_spatial_dice_0: 0.17546/0.21036, loss_spatial_ce_0: 0.00398/0.06516, loss_grounding_bce_0: 0.06099/0.08630, loss_grounding_dice_0: 0.37688/0.17871, loss_grounding_ce_0: 0.17843/0.27329, loss_mask_ce_1: 1.15369/0.90369, loss_mask_bce_1: 0.38066/0.33582, loss_mask_dice_1: 2.10336/1.16966, loss_spatial_bce_1: 0.03081/0.08871, loss_spatial_dice_1: 0.19206/0.21442, loss_spatial_ce_1: 0.00227/0.07088, loss_grounding_bce_1: 0.06825/0.08647, loss_grounding_dice_1: 0.46405/0.17946, loss_grounding_ce_1: 0.30860/0.27473, loss_mask_ce_2: 1.07272/0.91093, loss_mask_bce_2: 0.38865/0.33632, loss_mask_dice_2: 1.98454/1.16976, loss_spatial_bce_2: 0.03261/0.08952, loss_spatial_dice_2: 0.17477/0.21576, loss_spatial_ce_2: 0.00897/0.07433, loss_grounding_bce_2: 0.06352/0.08658, loss_grounding_dice_2: 0.36709/0.17929, loss_grounding_ce_2: 0.17214/0.27798, loss_mask_ce_3: 1.22816/0.92041, loss_mask_bce_3: 0.37496/0.33740, loss_mask_dice_3: 1.98368/1.16714, loss_spatial_bce_3: 0.03508/0.09047, loss_spatial_dice_3: 0.15998/0.21643, loss_spatial_ce_3: 0.05366/0.07807, loss_grounding_bce_3: 0.06586/0.08682, loss_grounding_dice_3: 0.36181/0.17903, loss_grounding_ce_3: 0.29250/0.27976, loss_mask_ce_4: 1.19151/0.92108, loss_mask_bce_4: 0.38085/0.33942, loss_mask_dice_4: 2.16762/1.19089, loss_spatial_bce_4: 0.03470/0.09453, loss_spatial_dice_4: 0.18619/0.22826, loss_spatial_ce_4: 0.05394/0.09412, loss_grounding_bce_4: 0.06359/0.08729, loss_grounding_dice_4: 0.39600/0.18191, loss_grounding_ce_4: 0.27659/0.28254, loss_mask_ce_5: 1.29857/0.93691, loss_mask_bce_5: 0.40986/0.34161, loss_mask_dice_5: 2.15239/1.19782, loss_spatial_bce_5: 0.03947/0.09650, loss_spatial_dice_5: 0.20293/0.23213, loss_spatial_ce_5: 0.02633/0.10896, loss_grounding_bce_5: 0.06357/0.08766, loss_grounding_dice_5: 0.39369/0.18309, loss_grounding_ce_5: 0.18852/0.29543, loss_mask_ce_6: 1.48086/0.97634, loss_mask_bce_6: 0.41319/0.34439, loss_mask_dice_6: 2.12967/1.20077, loss_spatial_bce_6: 0.03796/0.10226, loss_spatial_dice_6: 0.17839/0.23482, loss_spatial_ce_6: 0.03392/0.13531, loss_grounding_bce_6: 0.05853/0.08843, loss_grounding_dice_6: 0.35454/0.18343, loss_grounding_ce_6: 0.18445/0.31106, loss_mask_ce_7: 1.60395/1.02105, loss_mask_bce_7: 0.42712/0.35224, loss_mask_dice_7: 2.06752/1.25567, loss_spatial_bce_7: 0.03803/0.11061, loss_spatial_dice_7: 0.21886/0.26261, loss_spatial_ce_7: 0.07147/0.17129, loss_grounding_bce_7: 0.06190/0.09035, loss_grounding_dice_7: 0.38887/0.19067, loss_grounding_ce_7: 0.20148/0.34313, loss_mask_ce_8: 1.47784/1.13014, loss_mask_bce_8: 0.43063/0.36585, loss_mask_dice_8: 2.69095/1.32933, loss_spatial_bce_8: 0.05003/0.13144, loss_spatial_dice_8: 0.27065/0.30123, loss_spatial_ce_8: 0.09930/0.22856, loss_grounding_bce_8: 0.06149/0.09412, loss_grounding_dice_8: 0.39883/0.20174, loss_grounding_ce_8: 0.25812/0.41070, loss_mask_ce_9: 6.38111/3.67971, loss_mask_bce_9: 0.53220/0.39279, loss_mask_dice_9: 4.90459/1.90230, loss_spatial_bce_9: 0.18497/0.33362, loss_spatial_dice_9: 0.92166/0.82239, loss_spatial_ce_9: 1.67590/1.50064, loss_grounding_bce_9: 0.06151/0.10556, loss_grounding_dice_9: 0.51163/0.28090, loss_grounding_ce_9: 0.86686/0.67547] items per batch[64] items per second[0.23] total items[3225600] mini batches[ 50400] memory[7345] epoch remaining[0:34:53]
INFO:trainer.default_trainer:epochs[    27] optim steps[50500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24121/0.90306, loss_mask_bce_0: 0.49363/0.33502, loss_mask_dice_0: 1.76952/1.16311, loss_spatial_bce_0: 0.09534/0.08815, loss_spatial_dice_0: 0.32704/0.21036, loss_spatial_ce_0: 0.02527/0.06515, loss_grounding_bce_0: 0.04520/0.08632, loss_grounding_dice_0: 0.12804/0.17869, loss_grounding_ce_0: 0.21424/0.27333, loss_mask_ce_1: 1.44396/0.90379, loss_mask_bce_1: 0.47615/0.33590, loss_mask_dice_1: 1.64060/1.16963, loss_spatial_bce_1: 0.09233/0.08872, loss_spatial_dice_1: 0.31561/0.21441, loss_spatial_ce_1: 0.03144/0.07084, loss_grounding_bce_1: 0.04263/0.08649, loss_grounding_dice_1: 0.14125/0.17945, loss_grounding_ce_1: 0.21828/0.27476, loss_mask_ce_2: 1.41924/0.91099, loss_mask_bce_2: 0.47946/0.33640, loss_mask_dice_2: 1.83272/1.16976, loss_spatial_bce_2: 0.09943/0.08953, loss_spatial_dice_2: 0.33418/0.21576, loss_spatial_ce_2: 0.05839/0.07432, loss_grounding_bce_2: 0.04866/0.08660, loss_grounding_dice_2: 0.15999/0.17927, loss_grounding_ce_2: 0.21837/0.27800, loss_mask_ce_3: 1.20625/0.92047, loss_mask_bce_3: 0.52485/0.33748, loss_mask_dice_3: 1.87581/1.16715, loss_spatial_bce_3: 0.10128/0.09048, loss_spatial_dice_3: 0.36606/0.21643, loss_spatial_ce_3: 0.05866/0.07806, loss_grounding_bce_3: 0.05549/0.08684, loss_grounding_dice_3: 0.18065/0.17901, loss_grounding_ce_3: 0.21038/0.27977, loss_mask_ce_4: 1.45258/0.92113, loss_mask_bce_4: 0.50588/0.33951, loss_mask_dice_4: 1.80756/1.19090, loss_spatial_bce_4: 0.11170/0.09454, loss_spatial_dice_4: 0.38615/0.22825, loss_spatial_ce_4: 0.10208/0.09410, loss_grounding_bce_4: 0.09139/0.08731, loss_grounding_dice_4: 0.19388/0.18189, loss_grounding_ce_4: 0.17127/0.28256, loss_mask_ce_5: 1.35022/0.93700, loss_mask_bce_5: 0.50633/0.34170, loss_mask_dice_5: 1.94910/1.19783, loss_spatial_bce_5: 0.11115/0.09651, loss_spatial_dice_5: 0.36651/0.23213, loss_spatial_ce_5: 0.06454/0.10895, loss_grounding_bce_5: 0.10275/0.08769, loss_grounding_dice_5: 0.20568/0.18308, loss_grounding_ce_5: 0.21955/0.29548, loss_mask_ce_6: 1.20422/0.97645, loss_mask_bce_6: 0.60420/0.34448, loss_mask_dice_6: 2.17099/1.20079, loss_spatial_bce_6: 0.13400/0.10227, loss_spatial_dice_6: 0.36675/0.23482, loss_spatial_ce_6: 0.10319/0.13526, loss_grounding_bce_6: 0.10752/0.08846, loss_grounding_dice_6: 0.22441/0.18341, loss_grounding_ce_6: 0.13050/0.31110, loss_mask_ce_7: 1.26362/1.02117, loss_mask_bce_7: 0.61439/0.35233, loss_mask_dice_7: 2.36112/1.25570, loss_spatial_bce_7: 0.10646/0.11061, loss_spatial_dice_7: 0.36445/0.26261, loss_spatial_ce_7: 0.11263/0.17126, loss_grounding_bce_7: 0.08927/0.09037, loss_grounding_dice_7: 0.21927/0.19066, loss_grounding_ce_7: 0.17511/0.34320, loss_mask_ce_8: 1.96945/1.13023, loss_mask_bce_8: 0.53491/0.36596, loss_mask_dice_8: 2.21958/1.32939, loss_spatial_bce_8: 0.12954/0.13144, loss_spatial_dice_8: 0.35623/0.30121, loss_spatial_ce_8: 0.09216/0.22852, loss_grounding_bce_8: 0.06266/0.09414, loss_grounding_dice_8: 0.20033/0.20174, loss_grounding_ce_8: 0.18405/0.41078, loss_mask_ce_9: 4.51244/3.68010, loss_mask_bce_9: 0.60884/0.39291, loss_mask_dice_9: 7.22147/1.90258, loss_spatial_bce_9: 0.34226/0.33361, loss_spatial_dice_9: 0.85810/0.82240, loss_spatial_ce_9: 1.41150/1.50045, loss_grounding_bce_9: 0.08193/0.10558, loss_grounding_dice_9: 0.28749/0.28088, loss_grounding_ce_9: 0.30821/0.67558] items per batch[64] items per second[0.22] total items[3232000] mini batches[ 50500] memory[7345] epoch remaining[0:30:22]
INFO:trainer.default_trainer:epochs[    27] optim steps[50600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.59786/0.90294, loss_mask_bce_0: 0.34143/0.33507, loss_mask_dice_0: 1.59789/1.16298, loss_spatial_bce_0: 0.06146/0.08815, loss_spatial_dice_0: 0.25754/0.21033, loss_spatial_ce_0: 0.00496/0.06513, loss_grounding_bce_0: 0.05604/0.08631, loss_grounding_dice_0: 0.07989/0.17868, loss_grounding_ce_0: 0.13612/0.27331, loss_mask_ce_1: 1.44193/0.90364, loss_mask_bce_1: 0.33845/0.33595, loss_mask_dice_1: 1.59640/1.16950, loss_spatial_bce_1: 0.06268/0.08872, loss_spatial_dice_1: 0.26024/0.21438, loss_spatial_ce_1: 0.02249/0.07081, loss_grounding_bce_1: 0.05787/0.08649, loss_grounding_dice_1: 0.07451/0.17943, loss_grounding_ce_1: 0.08120/0.27471, loss_mask_ce_2: 1.34973/0.91089, loss_mask_bce_2: 0.33113/0.33646, loss_mask_dice_2: 1.52447/1.16967, loss_spatial_bce_2: 0.06413/0.08953, loss_spatial_dice_2: 0.22064/0.21572, loss_spatial_ce_2: 0.15640/0.07429, loss_grounding_bce_2: 0.05803/0.08660, loss_grounding_dice_2: 0.08317/0.17924, loss_grounding_ce_2: 0.05592/0.27795, loss_mask_ce_3: 1.49695/0.92036, loss_mask_bce_3: 0.34075/0.33753, loss_mask_dice_3: 1.59899/1.16704, loss_spatial_bce_3: 0.06380/0.09048, loss_spatial_dice_3: 0.21497/0.21639, loss_spatial_ce_3: 0.03357/0.07803, loss_grounding_bce_3: 0.05807/0.08684, loss_grounding_dice_3: 0.07520/0.17899, loss_grounding_ce_3: 0.04684/0.27974, loss_mask_ce_4: 1.44991/0.92106, loss_mask_bce_4: 0.34327/0.33956, loss_mask_dice_4: 1.87507/1.19081, loss_spatial_bce_4: 0.06319/0.09453, loss_spatial_dice_4: 0.27814/0.22822, loss_spatial_ce_4: 0.01700/0.09407, loss_grounding_bce_4: 0.05724/0.08730, loss_grounding_dice_4: 0.07594/0.18189, loss_grounding_ce_4: 0.05967/0.28253, loss_mask_ce_5: 1.39422/0.93692, loss_mask_bce_5: 0.35507/0.34175, loss_mask_dice_5: 1.69120/1.19772, loss_spatial_bce_5: 0.06630/0.09651, loss_spatial_dice_5: 0.26033/0.23210, loss_spatial_ce_5: 0.05225/0.10894, loss_grounding_bce_5: 0.05892/0.08768, loss_grounding_dice_5: 0.08229/0.18307, loss_grounding_ce_5: 0.12546/0.29545, loss_mask_ce_6: 1.43190/0.97638, loss_mask_bce_6: 0.35682/0.34453, loss_mask_dice_6: 1.58487/1.20069, loss_spatial_bce_6: 0.07422/0.10227, loss_spatial_dice_6: 0.29274/0.23479, loss_spatial_ce_6: 0.10538/0.13524, loss_grounding_bce_6: 0.05951/0.08846, loss_grounding_dice_6: 0.07613/0.18340, loss_grounding_ce_6: 0.14487/0.31106, loss_mask_ce_7: 1.52397/1.02106, loss_mask_bce_7: 0.36685/0.35239, loss_mask_dice_7: 1.58398/1.25562, loss_spatial_bce_7: 0.07687/0.11061, loss_spatial_dice_7: 0.35203/0.26260, loss_spatial_ce_7: 0.16015/0.17123, loss_grounding_bce_7: 0.06023/0.09037, loss_grounding_dice_7: 0.08088/0.19064, loss_grounding_ce_7: 0.20296/0.34317, loss_mask_ce_8: 1.87068/1.13012, loss_mask_bce_8: 0.35014/0.36603, loss_mask_dice_8: 1.75363/1.32928, loss_spatial_bce_8: 0.10121/0.13143, loss_spatial_dice_8: 0.41075/0.30117, loss_spatial_ce_8: 0.16476/0.22851, loss_grounding_bce_8: 0.06128/0.09414, loss_grounding_dice_8: 0.07497/0.20171, loss_grounding_ce_8: 0.25055/0.41071, loss_mask_ce_9: 3.46952/3.67989, loss_mask_bce_9: 0.42956/0.39298, loss_mask_dice_9: 2.62373/1.90253, loss_spatial_bce_9: 0.26092/0.33364, loss_spatial_dice_9: 0.87385/0.82238, loss_spatial_ce_9: 1.46636/1.50029, loss_grounding_bce_9: 0.12287/0.10557, loss_grounding_dice_9: 0.13066/0.28086, loss_grounding_ce_9: 0.57550/0.67548] items per batch[64] items per second[0.23] total items[3238400] mini batches[ 50600] memory[7345] epoch remaining[0:25:44]
INFO:trainer.default_trainer:epochs[    27] optim steps[50700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02979/0.90293, loss_mask_bce_0: 0.18461/0.33502, loss_mask_dice_0: 1.94468/1.16296, loss_spatial_bce_0: 0.02856/0.08812, loss_spatial_dice_0: 0.18450/0.21031, loss_spatial_ce_0: 0.00087/0.06509, loss_grounding_bce_0: 0.05882/0.08630, loss_grounding_dice_0: 0.18965/0.17870, loss_grounding_ce_0: 0.19239/0.27324, loss_mask_ce_1: 1.23286/0.90362, loss_mask_bce_1: 0.19193/0.33590, loss_mask_dice_1: 1.90674/1.16950, loss_spatial_bce_1: 0.03216/0.08869, loss_spatial_dice_1: 0.22261/0.21436, loss_spatial_ce_1: 0.00043/0.07077, loss_grounding_bce_1: 0.06259/0.08648, loss_grounding_dice_1: 0.20914/0.17947, loss_grounding_ce_1: 0.18917/0.27462, loss_mask_ce_2: 1.12329/0.91086, loss_mask_bce_2: 0.22160/0.33640, loss_mask_dice_2: 2.12708/1.16968, loss_spatial_bce_2: 0.03182/0.08950, loss_spatial_dice_2: 0.23797/0.21571, loss_spatial_ce_2: 0.00065/0.07426, loss_grounding_bce_2: 0.06911/0.08659, loss_grounding_dice_2: 0.26384/0.17927, loss_grounding_ce_2: 0.30437/0.27787, loss_mask_ce_3: 1.03108/0.92034, loss_mask_bce_3: 0.18709/0.33748, loss_mask_dice_3: 1.95112/1.16705, loss_spatial_bce_3: 0.03172/0.09046, loss_spatial_dice_3: 0.20270/0.21638, loss_spatial_ce_3: 0.00223/0.07800, loss_grounding_bce_3: 0.05957/0.08683, loss_grounding_dice_3: 0.16340/0.17902, loss_grounding_ce_3: 0.19700/0.27966, loss_mask_ce_4: 1.14726/0.92102, loss_mask_bce_4: 0.19862/0.33951, loss_mask_dice_4: 2.05543/1.19082, loss_spatial_bce_4: 0.03362/0.09450, loss_spatial_dice_4: 0.25060/0.22821, loss_spatial_ce_4: 0.00687/0.09404, loss_grounding_bce_4: 0.06442/0.08730, loss_grounding_dice_4: 0.19712/0.18192, loss_grounding_ce_4: 0.25413/0.28244, loss_mask_ce_5: 1.14008/0.93690, loss_mask_bce_5: 0.18632/0.34170, loss_mask_dice_5: 1.93273/1.19771, loss_spatial_bce_5: 0.03555/0.09648, loss_spatial_dice_5: 0.26967/0.23209, loss_spatial_ce_5: 0.01057/0.10889, loss_grounding_bce_5: 0.06279/0.08768, loss_grounding_dice_5: 0.21253/0.18310, loss_grounding_ce_5: 0.18699/0.29534, loss_mask_ce_6: 0.89242/0.97635, loss_mask_bce_6: 0.18757/0.34447, loss_mask_dice_6: 2.00138/1.20069, loss_spatial_bce_6: 0.04001/0.10224, loss_spatial_dice_6: 0.23174/0.23479, loss_spatial_ce_6: 0.04226/0.13521, loss_grounding_bce_6: 0.05885/0.08845, loss_grounding_dice_6: 0.18035/0.18342, loss_grounding_ce_6: 0.28033/0.31094, loss_mask_ce_7: 1.19784/1.02107, loss_mask_bce_7: 0.20004/0.35232, loss_mask_dice_7: 2.34343/1.25560, loss_spatial_bce_7: 0.03589/0.11058, loss_spatial_dice_7: 0.30920/0.26259, loss_spatial_ce_7: 0.05122/0.17119, loss_grounding_bce_7: 0.07245/0.09036, loss_grounding_dice_7: 0.33996/0.19066, loss_grounding_ce_7: 0.17105/0.34305, loss_mask_ce_8: 1.40987/1.13009, loss_mask_bce_8: 0.25389/0.36596, loss_mask_dice_8: 2.46762/1.32929, loss_spatial_bce_8: 0.07469/0.13139, loss_spatial_dice_8: 0.38753/0.30115, loss_spatial_ce_8: 0.11327/0.22848, loss_grounding_bce_8: 0.07184/0.09413, loss_grounding_dice_8: 0.27014/0.20174, loss_grounding_ce_8: 0.24700/0.41053, loss_mask_ce_9: 5.09568/3.67991, loss_mask_bce_9: 0.18641/0.39294, loss_mask_dice_9: 2.73823/1.90250, loss_spatial_bce_9: 0.17371/0.33362, loss_spatial_dice_9: 0.89416/0.82239, loss_spatial_ce_9: 1.79965/1.50033, loss_grounding_bce_9: 0.06287/0.10556, loss_grounding_dice_9: 0.23381/0.28090, loss_grounding_ce_9: 0.76528/0.67525] items per batch[64] items per second[0.23] total items[3244800] mini batches[ 50700] memory[7345] epoch remaining[0:21:06]
INFO:trainer.default_trainer:epochs[    27] optim steps[50800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75950/0.90290, loss_mask_bce_0: 0.27681/0.33510, loss_mask_dice_0: 1.11314/1.16305, loss_spatial_bce_0: 0.11317/0.08813, loss_spatial_dice_0: 0.26228/0.21030, loss_spatial_ce_0: 0.07843/0.06506, loss_grounding_bce_0: 0.16582/0.08633, loss_grounding_dice_0: 0.11851/0.17872, loss_grounding_ce_0: 0.11338/0.27323, loss_mask_ce_1: 0.69449/0.90354, loss_mask_bce_1: 0.28443/0.33599, loss_mask_dice_1: 1.17419/1.16955, loss_spatial_bce_1: 0.09908/0.08870, loss_spatial_dice_1: 0.23935/0.21435, loss_spatial_ce_1: 0.07469/0.07075, loss_grounding_bce_1: 0.16335/0.08651, loss_grounding_dice_1: 0.12427/0.17949, loss_grounding_ce_1: 0.11303/0.27461, loss_mask_ce_2: 0.66504/0.91081, loss_mask_bce_2: 0.29643/0.33649, loss_mask_dice_2: 1.15773/1.16974, loss_spatial_bce_2: 0.09930/0.08951, loss_spatial_dice_2: 0.23840/0.21571, loss_spatial_ce_2: 0.11341/0.07424, loss_grounding_bce_2: 0.16991/0.08662, loss_grounding_dice_2: 0.12310/0.17930, loss_grounding_ce_2: 0.10963/0.27786, loss_mask_ce_3: 0.73935/0.92031, loss_mask_bce_3: 0.27857/0.33756, loss_mask_dice_3: 1.06540/1.16711, loss_spatial_bce_3: 0.09698/0.09047, loss_spatial_dice_3: 0.19703/0.21637, loss_spatial_ce_3: 0.10921/0.07798, loss_grounding_bce_3: 0.16700/0.08686, loss_grounding_dice_3: 0.14511/0.17905, loss_grounding_ce_3: 0.12683/0.27965, loss_mask_ce_4: 0.80141/0.92098, loss_mask_bce_4: 0.32949/0.33961, loss_mask_dice_4: 1.18404/1.19091, loss_spatial_bce_4: 0.10060/0.09452, loss_spatial_dice_4: 0.29706/0.22821, loss_spatial_ce_4: 0.09109/0.09401, loss_grounding_bce_4: 0.21946/0.08733, loss_grounding_dice_4: 0.20422/0.18193, loss_grounding_ce_4: 0.12244/0.28246, loss_mask_ce_5: 0.70084/0.93686, loss_mask_bce_5: 0.35435/0.34179, loss_mask_dice_5: 1.31050/1.19781, loss_spatial_bce_5: 0.10857/0.09650, loss_spatial_dice_5: 0.30970/0.23209, loss_spatial_ce_5: 0.31528/0.10885, loss_grounding_bce_5: 0.21315/0.08772, loss_grounding_dice_5: 0.20922/0.18312, loss_grounding_ce_5: 0.13724/0.29534, loss_mask_ce_6: 0.65016/0.97633, loss_mask_bce_6: 0.32205/0.34454, loss_mask_dice_6: 0.89895/1.20078, loss_spatial_bce_6: 0.13306/0.10226, loss_spatial_dice_6: 0.32451/0.23480, loss_spatial_ce_6: 0.28300/0.13518, loss_grounding_bce_6: 0.19783/0.08848, loss_grounding_dice_6: 0.21476/0.18345, loss_grounding_ce_6: 0.11000/0.31094, loss_mask_ce_7: 0.85430/1.02103, loss_mask_bce_7: 0.32369/0.35241, loss_mask_dice_7: 1.02529/1.25566, loss_spatial_bce_7: 0.16822/0.11060, loss_spatial_dice_7: 0.38260/0.26258, loss_spatial_ce_7: 0.62525/0.17118, loss_grounding_bce_7: 0.20079/0.09039, loss_grounding_dice_7: 0.20868/0.19068, loss_grounding_ce_7: 0.11630/0.34300, loss_mask_ce_8: 0.79590/1.13007, loss_mask_bce_8: 0.36435/0.36606, loss_mask_dice_8: 1.25269/1.32933, loss_spatial_bce_8: 0.13709/0.13141, loss_spatial_dice_8: 0.42004/0.30114, loss_spatial_ce_8: 0.52379/0.22846, loss_grounding_bce_8: 0.24768/0.09416, loss_grounding_dice_8: 0.25122/0.20176, loss_grounding_ce_8: 0.14370/0.41051, loss_mask_ce_9: 3.34428/3.67992, loss_mask_bce_9: 0.58760/0.39303, loss_mask_dice_9: 1.79922/1.90266, loss_spatial_bce_9: 0.22134/0.33366, loss_spatial_dice_9: 0.83092/0.82240, loss_spatial_ce_9: 1.36152/1.50030, loss_grounding_bce_9: 0.39764/0.10561, loss_grounding_dice_9: 0.40509/0.28095, loss_grounding_ce_9: 0.09666/0.67516] items per batch[64] items per second[0.24] total items[3251200] mini batches[ 50800] memory[7345] epoch remaining[0:16:27]
INFO:trainer.default_trainer:epochs[    27] optim steps[50900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33893/0.90295, loss_mask_bce_0: 0.42993/0.33513, loss_mask_dice_0: 0.43721/1.16330, loss_spatial_bce_0: 0.16776/0.08814, loss_spatial_dice_0: 0.19195/0.21029, loss_spatial_ce_0: 0.00838/0.06505, loss_grounding_bce_0: 0.10387/0.08633, loss_grounding_dice_0: 0.05367/0.17872, loss_grounding_ce_0: 0.00394/0.27313, loss_mask_ce_1: 0.36252/0.90359, loss_mask_bce_1: 0.41437/0.33601, loss_mask_dice_1: 0.43807/1.16977, loss_spatial_bce_1: 0.16521/0.08871, loss_spatial_dice_1: 0.17977/0.21434, loss_spatial_ce_1: 0.01088/0.07076, loss_grounding_bce_1: 0.11897/0.08651, loss_grounding_dice_1: 0.05900/0.17949, loss_grounding_ce_1: 0.00466/0.27453, loss_mask_ce_2: 0.34609/0.91086, loss_mask_bce_2: 0.42389/0.33651, loss_mask_dice_2: 0.45031/1.16996, loss_spatial_bce_2: 0.15738/0.08952, loss_spatial_dice_2: 0.18931/0.21570, loss_spatial_ce_2: 0.01591/0.07423, loss_grounding_bce_2: 0.11841/0.08663, loss_grounding_dice_2: 0.05449/0.17929, loss_grounding_ce_2: 0.00471/0.27782, loss_mask_ce_3: 0.36255/0.92038, loss_mask_bce_3: 0.42780/0.33758, loss_mask_dice_3: 0.44295/1.16738, loss_spatial_bce_3: 0.16983/0.09048, loss_spatial_dice_3: 0.16727/0.21637, loss_spatial_ce_3: 0.00884/0.07797, loss_grounding_bce_3: 0.10266/0.08686, loss_grounding_dice_3: 0.05759/0.17903, loss_grounding_ce_3: 0.00264/0.27959, loss_mask_ce_4: 0.39514/0.92101, loss_mask_bce_4: 0.43197/0.33964, loss_mask_dice_4: 0.42613/1.19116, loss_spatial_bce_4: 0.18180/0.09452, loss_spatial_dice_4: 0.16669/0.22820, loss_spatial_ce_4: 0.02386/0.09401, loss_grounding_bce_4: 0.11082/0.08734, loss_grounding_dice_4: 0.05352/0.18193, loss_grounding_ce_4: 0.00117/0.28241, loss_mask_ce_5: 0.39067/0.93690, loss_mask_bce_5: 0.43914/0.34184, loss_mask_dice_5: 0.43985/1.19807, loss_spatial_bce_5: 0.17901/0.09651, loss_spatial_dice_5: 0.17173/0.23209, loss_spatial_ce_5: 0.06782/0.10887, loss_grounding_bce_5: 0.10571/0.08771, loss_grounding_dice_5: 0.04982/0.18311, loss_grounding_ce_5: 0.00291/0.29531, loss_mask_ce_6: 0.39153/0.97637, loss_mask_bce_6: 0.44381/0.34459, loss_mask_dice_6: 0.44526/1.20105, loss_spatial_bce_6: 0.18883/0.10228, loss_spatial_dice_6: 0.16411/0.23482, loss_spatial_ce_6: 0.08810/0.13518, loss_grounding_bce_6: 0.11299/0.08848, loss_grounding_dice_6: 0.05353/0.18343, loss_grounding_ce_6: 0.00280/0.31086, loss_mask_ce_7: 0.58485/1.02108, loss_mask_bce_7: 0.46138/0.35246, loss_mask_dice_7: 0.43100/1.25592, loss_spatial_bce_7: 0.19237/0.11061, loss_spatial_dice_7: 0.21954/0.26259, loss_spatial_ce_7: 0.05485/0.17116, loss_grounding_bce_7: 0.10015/0.09039, loss_grounding_dice_7: 0.04791/0.19068, loss_grounding_ce_7: 0.29054/0.34294, loss_mask_ce_8: 0.49987/1.13012, loss_mask_bce_8: 0.43868/0.36609, loss_mask_dice_8: 0.59942/1.32961, loss_spatial_bce_8: 0.23115/0.13141, loss_spatial_dice_8: 0.21672/0.30115, loss_spatial_ce_8: 0.19088/0.22845, loss_grounding_bce_8: 0.12315/0.09416, loss_grounding_dice_8: 0.04919/0.20175, loss_grounding_ce_8: 0.90106/0.41044, loss_mask_ce_9: 2.19620/3.68005, loss_mask_bce_9: 0.45868/0.39305, loss_mask_dice_9: 0.68211/1.90310, loss_spatial_bce_9: 0.51011/0.33362, loss_spatial_dice_9: 0.70470/0.82241, loss_spatial_ce_9: 1.05319/1.50041, loss_grounding_bce_9: 0.11649/0.10559, loss_grounding_dice_9: 0.04684/0.28093, loss_grounding_ce_9: 0.22276/0.67514] items per batch[64] items per second[0.23] total items[3257600] mini batches[ 50900] memory[7345] epoch remaining[0:11:49]
INFO:trainer.default_trainer:epochs[    27] optim steps[51000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52856/0.90275, loss_mask_bce_0: 0.14191/0.33509, loss_mask_dice_0: 1.05166/1.16305, loss_spatial_bce_0: 0.04085/0.08812, loss_spatial_dice_0: 0.17035/0.21027, loss_spatial_ce_0: 0.03767/0.06505, loss_grounding_bce_0: 0.02366/0.08631, loss_grounding_dice_0: 0.15746/0.17873, loss_grounding_ce_0: 0.05387/0.27314, loss_mask_ce_1: 0.45411/0.90342, loss_mask_bce_1: 0.14844/0.33596, loss_mask_dice_1: 1.06465/1.16954, loss_spatial_bce_1: 0.04108/0.08869, loss_spatial_dice_1: 0.17248/0.21432, loss_spatial_ce_1: 0.03921/0.07073, loss_grounding_bce_1: 0.02299/0.08649, loss_grounding_dice_1: 0.09421/0.17949, loss_grounding_ce_1: 0.05369/0.27450, loss_mask_ce_2: 0.43826/0.91069, loss_mask_bce_2: 0.14469/0.33646, loss_mask_dice_2: 1.01361/1.16973, loss_spatial_bce_2: 0.03701/0.08950, loss_spatial_dice_2: 0.15968/0.21567, loss_spatial_ce_2: 0.03888/0.07421, loss_grounding_bce_2: 0.02308/0.08661, loss_grounding_dice_2: 0.21966/0.17930, loss_grounding_ce_2: 0.05097/0.27784, loss_mask_ce_3: 0.43149/0.92023, loss_mask_bce_3: 0.14388/0.33753, loss_mask_dice_3: 1.06957/1.16716, loss_spatial_bce_3: 0.03922/0.09047, loss_spatial_dice_3: 0.15785/0.21634, loss_spatial_ce_3: 0.03871/0.07794, loss_grounding_bce_3: 0.02354/0.08684, loss_grounding_dice_3: 0.24259/0.17905, loss_grounding_ce_3: 0.05281/0.27960, loss_mask_ce_4: 0.43160/0.92087, loss_mask_bce_4: 0.14350/0.33959, loss_mask_dice_4: 1.18885/1.19092, loss_spatial_bce_4: 0.04210/0.09451, loss_spatial_dice_4: 0.17601/0.22818, loss_spatial_ce_4: 0.05871/0.09399, loss_grounding_bce_4: 0.02611/0.08731, loss_grounding_dice_4: 0.22560/0.18194, loss_grounding_ce_4: 0.04112/0.28240, loss_mask_ce_5: 0.50066/0.93672, loss_mask_bce_5: 0.14419/0.34179, loss_mask_dice_5: 1.36970/1.19784, loss_spatial_bce_5: 0.04141/0.09650, loss_spatial_dice_5: 0.19323/0.23206, loss_spatial_ce_5: 0.07110/0.10884, loss_grounding_bce_5: 0.02055/0.08769, loss_grounding_dice_5: 0.26437/0.18313, loss_grounding_ce_5: 0.04987/0.29527, loss_mask_ce_6: 0.48528/0.97616, loss_mask_bce_6: 0.16366/0.34454, loss_mask_dice_6: 1.30742/1.20082, loss_spatial_bce_6: 0.04196/0.10227, loss_spatial_dice_6: 0.17926/0.23480, loss_spatial_ce_6: 0.08067/0.13518, loss_grounding_bce_6: 0.02518/0.08845, loss_grounding_dice_6: 0.09740/0.18345, loss_grounding_ce_6: 0.05257/0.31084, loss_mask_ce_7: 0.56741/1.02093, loss_mask_bce_7: 0.16266/0.35239, loss_mask_dice_7: 1.28418/1.25565, loss_spatial_bce_7: 0.05095/0.11061, loss_spatial_dice_7: 0.20562/0.26258, loss_spatial_ce_7: 0.10908/0.17114, loss_grounding_bce_7: 0.02540/0.09037, loss_grounding_dice_7: 0.18795/0.19069, loss_grounding_ce_7: 0.05972/0.34293, loss_mask_ce_8: 0.86482/1.12991, loss_mask_bce_8: 0.15950/0.36604, loss_mask_dice_8: 1.41098/1.32934, loss_spatial_bce_8: 0.07804/0.13140, loss_spatial_dice_8: 0.25907/0.30114, loss_spatial_ce_8: 0.14268/0.22848, loss_grounding_bce_8: 0.02568/0.09414, loss_grounding_dice_8: 0.17564/0.20176, loss_grounding_ce_8: 0.08219/0.41038, loss_mask_ce_9: 2.63678/3.67967, loss_mask_bce_9: 0.17676/0.39300, loss_mask_dice_9: 1.88825/1.90261, loss_spatial_bce_9: 0.25646/0.33363, loss_spatial_dice_9: 0.85943/0.82241, loss_spatial_ce_9: 1.73246/1.50047, loss_grounding_bce_9: 0.01880/0.10558, loss_grounding_dice_9: 0.20161/0.28093, loss_grounding_ce_9: 0.33263/0.67518] items per batch[64] items per second[0.24] total items[3264000] mini batches[ 51000] memory[7345] epoch remaining[0:07:11]
INFO:trainer.default_trainer:epochs[    27] optim steps[51100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86839/0.90267, loss_mask_bce_0: 0.34177/0.33509, loss_mask_dice_0: 0.88942/1.16286, loss_spatial_bce_0: 0.11801/0.08813, loss_spatial_dice_0: 0.17760/0.21025, loss_spatial_ce_0: 0.05425/0.06506, loss_grounding_bce_0: 0.12167/0.08633, loss_grounding_dice_0: 0.16669/0.17874, loss_grounding_ce_0: 1.25478/0.27327, loss_mask_ce_1: 0.80461/0.90333, loss_mask_bce_1: 0.36097/0.33598, loss_mask_dice_1: 1.12570/1.16934, loss_spatial_bce_1: 0.12276/0.08870, loss_spatial_dice_1: 0.18201/0.21431, loss_spatial_ce_1: 0.04187/0.07074, loss_grounding_bce_1: 0.12947/0.08651, loss_grounding_dice_1: 0.16584/0.17951, loss_grounding_ce_1: 1.16846/0.27460, loss_mask_ce_2: 0.79383/0.91058, loss_mask_bce_2: 0.35539/0.33648, loss_mask_dice_2: 0.97992/1.16955, loss_spatial_bce_2: 0.11216/0.08951, loss_spatial_dice_2: 0.16467/0.21565, loss_spatial_ce_2: 0.06960/0.07423, loss_grounding_bce_2: 0.13224/0.08662, loss_grounding_dice_2: 0.17490/0.17931, loss_grounding_ce_2: 1.24044/0.27795, loss_mask_ce_3: 0.83701/0.92012, loss_mask_bce_3: 0.35518/0.33755, loss_mask_dice_3: 1.03311/1.16697, loss_spatial_bce_3: 0.11765/0.09048, loss_spatial_dice_3: 0.17481/0.21633, loss_spatial_ce_3: 0.07711/0.07796, loss_grounding_bce_3: 0.13250/0.08686, loss_grounding_dice_3: 0.16926/0.17907, loss_grounding_ce_3: 1.27592/0.27966, loss_mask_ce_4: 0.95935/0.92082, loss_mask_bce_4: 0.34053/0.33960, loss_mask_dice_4: 1.10860/1.19072, loss_spatial_bce_4: 0.14299/0.09452, loss_spatial_dice_4: 0.20148/0.22816, loss_spatial_ce_4: 0.09539/0.09399, loss_grounding_bce_4: 0.11316/0.08733, loss_grounding_dice_4: 0.17776/0.18195, loss_grounding_ce_4: 1.37333/0.28251, loss_mask_ce_5: 0.91448/0.93664, loss_mask_bce_5: 0.33849/0.34181, loss_mask_dice_5: 0.87681/1.19766, loss_spatial_bce_5: 0.12484/0.09651, loss_spatial_dice_5: 0.19747/0.23205, loss_spatial_ce_5: 0.06358/0.10885, loss_grounding_bce_5: 0.14284/0.08771, loss_grounding_dice_5: 0.19177/0.18315, loss_grounding_ce_5: 1.16011/0.29541, loss_mask_ce_6: 0.96150/0.97611, loss_mask_bce_6: 0.34339/0.34456, loss_mask_dice_6: 0.90588/1.20062, loss_spatial_bce_6: 0.10198/0.10228, loss_spatial_dice_6: 0.17981/0.23480, loss_spatial_ce_6: 0.14080/0.13522, loss_grounding_bce_6: 0.13453/0.08847, loss_grounding_dice_6: 0.17131/0.18346, loss_grounding_ce_6: 0.76812/0.31097, loss_mask_ce_7: 1.13354/1.02084, loss_mask_bce_7: 0.33493/0.35242, loss_mask_dice_7: 1.09430/1.25543, loss_spatial_bce_7: 0.15535/0.11061, loss_spatial_dice_7: 0.21319/0.26257, loss_spatial_ce_7: 0.11684/0.17118, loss_grounding_bce_7: 0.12097/0.09038, loss_grounding_dice_7: 0.15924/0.19070, loss_grounding_ce_7: 1.31735/0.34302, loss_mask_ce_8: 1.01962/1.12981, loss_mask_bce_8: 0.33481/0.36606, loss_mask_dice_8: 0.96312/1.32908, loss_spatial_bce_8: 0.13506/0.13141, loss_spatial_dice_8: 0.27733/0.30112, loss_spatial_ce_8: 0.21123/0.22848, loss_grounding_bce_8: 0.11309/0.09415, loss_grounding_dice_8: 0.16490/0.20176, loss_grounding_ce_8: 0.77291/0.41043, loss_mask_ce_9: 4.07150/3.67954, loss_mask_bce_9: 0.60767/0.39301, loss_mask_dice_9: 2.85167/1.90230, loss_spatial_bce_9: 0.28056/0.33362, loss_spatial_dice_9: 0.84329/0.82240, loss_spatial_ce_9: 1.80653/1.50039, loss_grounding_bce_9: 0.18104/0.10559, loss_grounding_dice_9: 0.29752/0.28096, loss_grounding_ce_9: 1.95440/0.67525] items per batch[64] items per second[0.23] total items[3270400] mini batches[ 51100] memory[7345] epoch remaining[0:02:35]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00051156.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2195 s/iter. Eval: 0.0938 s/iter. Total: 0.3165 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0027 s/iter. Inference: 0.2213 s/iter. Eval: 0.0783 s/iter. Total: 0.3025 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2239 s/iter. Eval: 0.0778 s/iter. Total: 0.3048 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0030 s/iter. Inference: 0.2333 s/iter. Eval: 0.0753 s/iter. Total: 0.3117 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2290 s/iter. Eval: 0.0746 s/iter. Total: 0.3068 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2303 s/iter. Eval: 0.0746 s/iter. Total: 0.3082 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2316 s/iter. Eval: 0.0746 s/iter. Total: 0.3095 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2307 s/iter. Eval: 0.0743 s/iter. Total: 0.3083 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2313 s/iter. Eval: 0.0739 s/iter. Total: 0.3086 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaltwa5hjwa ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.091 | 82.124 | 60.204 |      133      |
| Things | 54.861 | 82.765 | 65.660 |      80       |
| Stuff  | 42.891 | 81.157 | 51.968 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.44 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.27s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.36 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.475 | 60.789 | 40.504 | 19.402 | 41.772 | 60.276 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.710 | bicycle      | 17.446 | car            | 37.213 |
| motorcycle    | 34.537 | airplane     | 55.996 | bus            | 65.675 |
| train         | 67.640 | truck        | 34.922 | boat           | 22.185 |
| traffic light | 25.665 | fire hydrant | 61.876 | stop sign      | 63.835 |
| parking meter | 42.427 | bench        | 20.422 | bird           | 29.181 |
| cat           | 73.088 | dog          | 65.155 | horse          | 44.996 |
| sheep         | 46.363 | cow          | 49.603 | elephant       | 60.154 |
| bear          | 78.126 | zebra        | 59.602 | giraffe        | 55.734 |
| backpack      | 17.241 | umbrella     | 48.235 | handbag        | 15.426 |
| tie           | 34.142 | suitcase     | 41.316 | frisbee        | 66.509 |
| skis          | 4.778  | snowboard    | 23.174 | sports ball    | 46.626 |
| kite          | 33.558 | baseball bat | 28.645 | baseball glove | 43.623 |
| skateboard    | 35.374 | surfboard    | 35.062 | tennis racket  | 55.859 |
| bottle        | 33.586 | wine glass   | 26.861 | cup            | 40.752 |
| fork          | 16.818 | knife        | 13.070 | spoon          | 15.085 |
| bowl          | 32.316 | banana       | 21.111 | apple          | 20.754 |
| sandwich      | 41.375 | orange       | 28.320 | broccoli       | 22.458 |
| carrot        | 19.813 | hot dog      | 21.367 | pizza          | 49.037 |
| donut         | 46.333 | cake         | 43.867 | chair          | 20.530 |
| couch         | 40.801 | potted plant | 16.995 | bed            | 40.087 |
| dining table  | 12.660 | toilet       | 65.028 | tv             | 62.441 |
| laptop        | 61.286 | mouse        | 57.957 | remote         | 31.053 |
| keyboard      | 46.640 | cell phone   | 37.136 | microwave      | 54.474 |
| oven          | 33.017 | toaster      | 29.244 | sink           | 37.845 |
| refrigerator  | 59.261 | book         | 8.733  | clock          | 51.376 |
| vase          | 33.696 | scissors     | 23.464 | teddy bear     | 50.604 |
| hair drier    | 5.988  | toothbrush   | 19.642 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.385
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.608
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.405
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.708
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.46814236010637, 'fwIoU': 69.21410821981885, 'IoU-person': 87.49894295868104, 'IoU-bicycle': 75.0591574156462, 'IoU-car': 69.44210162178514, 'IoU-motorcycle': 85.47112522868942, 'IoU-airplane': 78.91916988499507, 'IoU-bus': 85.71133775266888, 'IoU-train': 87.57273722754198, 'IoU-truck': 65.98925342146504, 'IoU-boat': 66.76349689404823, 'IoU-traffic light': 75.61366672249477, 'IoU-fire hydrant': 85.0217753673388, 'IoU-stop sign': 92.26548180214236, 'IoU-parking meter': 83.4724947527094, 'IoU-bench': 52.30444110018892, 'IoU-bird': 76.03240883688095, 'IoU-cat': 81.02553575066834, 'IoU-dog': 78.40443151524202, 'IoU-horse': 85.93073973633828, 'IoU-sheep': 86.32352688764836, 'IoU-cow': 82.8584764585552, 'IoU-elephant': 90.62319162963284, 'IoU-bear': 87.50242985945945, 'IoU-zebra': 92.49091946221264, 'IoU-giraffe': 88.2265743525379, 'IoU-backpack': 39.806629627130334, 'IoU-umbrella': 77.18169612061453, 'IoU-handbag': 37.61982752210338, 'IoU-tie': 69.8230276587893, 'IoU-suitcase': 81.9135713132411, 'IoU-frisbee': 82.91713415611476, 'IoU-skis': 51.45040894138005, 'IoU-snowboard': 70.62438925171452, 'IoU-sports ball': 67.4717324437898, 'IoU-kite': 66.05226618353079, 'IoU-baseball bat': 59.03674915908954, 'IoU-baseball glove': 75.63474589051425, 'IoU-skateboard': 60.413747164898, 'IoU-surfboard': 75.07011219773128, 'IoU-tennis racket': 83.12194537456811, 'IoU-bottle': 67.4775530749617, 'IoU-wine glass': 75.17414947283619, 'IoU-cup': 65.81565807335322, 'IoU-fork': 54.48716997189565, 'IoU-knife': 52.27478069143129, 'IoU-spoon': 50.229097081377475, 'IoU-bowl': 55.501621260239865, 'IoU-banana': 82.7032541549701, 'IoU-apple': 59.774426646389834, 'IoU-sandwich': 62.77352359790732, 'IoU-orange': 78.06472775906187, 'IoU-broccoli': 67.64041234853654, 'IoU-carrot': 62.83968593655976, 'IoU-hot dog': 60.074845674566255, 'IoU-pizza': 82.88576849123885, 'IoU-donut': 64.84807953824122, 'IoU-cake': 68.02087155199888, 'IoU-chair': 56.17658403107948, 'IoU-couch': 64.60924782836496, 'IoU-potted plant': 33.864793092856736, 'IoU-bed': 65.71163104811075, 'IoU-dining table': 51.46946083008898, 'IoU-toilet': 83.00261629837706, 'IoU-tv': 76.03151366588433, 'IoU-laptop': 74.8828052996119, 'IoU-mouse': 70.59270219042769, 'IoU-remote': 50.06248782225511, 'IoU-keyboard': 60.827415687342246, 'IoU-cell phone': 69.62421766149116, 'IoU-microwave': 41.319099087151606, 'IoU-oven': 58.41368561356287, 'IoU-toaster': 44.65480834615798, 'IoU-sink': 72.43064631435003, 'IoU-refrigerator': 79.12076102261213, 'IoU-book': 50.29204168039957, 'IoU-clock': 73.11103467954564, 'IoU-vase': 59.7011978616761, 'IoU-scissors': 58.54404417918724, 'IoU-teddy bear': 81.08516946801775, 'IoU-hair drier': 28.12456614372524, 'IoU-toothbrush': 53.605645439677666, 'IoU-banner': 28.961170639199104, 'IoU-blanket': 15.840507600043077, 'IoU-bridge': 36.22182172481389, 'IoU-cardboard': 41.56350197883996, 'IoU-counter': 29.630453804839778, 'IoU-curtain': 63.492692485385895, 'IoU-door-stuff': 41.584585270651004, 'IoU-floor-wood': 61.38520292482487, 'IoU-flower': 45.19185979048561, 'IoU-fruit': 37.622463099631, 'IoU-gravel': 29.47769667516027, 'IoU-house': 26.66379844638963, 'IoU-light': 38.73576282967216, 'IoU-mirror-stuff': 59.10330196856063, 'IoU-net': 39.7039967411482, 'IoU-pillow': 11.950074916931959, 'IoU-platform': 32.92873141291446, 'IoU-playingfield': 70.38313939891106, 'IoU-railroad': 61.3376353565113, 'IoU-river': 50.47258684182962, 'IoU-road': 65.32423662919447, 'IoU-roof': 15.595275469655606, 'IoU-sand': 63.250744790597366, 'IoU-sea': 84.75862146181306, 'IoU-shelf': 38.18951197332525, 'IoU-snow': 88.64069539477303, 'IoU-stairs': 29.55237026511676, 'IoU-tent': 9.10484447932874, 'IoU-towel': 35.41510014966951, 'IoU-wall-brick': 47.0329635391175, 'IoU-wall-stone': 25.563157329697695, 'IoU-wall-tile': 66.93320991827004, 'IoU-wall-wood': 39.69950801941404, 'IoU-water-other': 22.862305580717294, 'IoU-window-blind': 46.19258041704096, 'IoU-window-other': 47.646909988872466, 'IoU-tree-merged': 81.00947801226545, 'IoU-fence-merged': 50.748994804156936, 'IoU-ceiling-merged': 66.1049786181589, 'IoU-sky-other-merged': 93.61467587220271, 'IoU-cabinet-merged': 58.17427714627993, 'IoU-table-merged': 40.44309843330433, 'IoU-floor-other-merged': 49.5752331600927, 'IoU-pavement-merged': 52.7770206268176, 'IoU-mountain-merged': 56.088229268649684, 'IoU-grass-merged': 72.00722362084522, 'IoU-dirt-merged': 45.77061558133362, 'IoU-paper-merged': 31.20964071437882, 'IoU-food-other-merged': 38.72136129705804, 'IoU-building-other-merged': 59.359064717573304, 'IoU-rock-merged': 61.92837069457303, 'IoU-wall-other-merged': 65.16933309479813, 'IoU-rug-merged': 65.04311865601156, 'mACC': 73.31823631989064, 'pACC': 80.50342486920289, 'ACC-person': 92.65833121150244, 'ACC-bicycle': 85.28973531422243, 'ACC-car': 85.67581879986686, 'ACC-motorcycle': 91.02758256928341, 'ACC-airplane': 90.2857038352267, 'ACC-bus': 91.41708796066202, 'ACC-train': 95.11240473412337, 'ACC-truck': 75.7803652473116, 'ACC-boat': 79.699641931693, 'ACC-traffic light': 88.43107770620911, 'ACC-fire hydrant': 95.31485522253583, 'ACC-stop sign': 95.1070486545513, 'ACC-parking meter': 87.05147761130198, 'ACC-bench': 70.56831240045966, 'ACC-bird': 80.49160717648469, 'ACC-cat': 95.58186643977385, 'ACC-dog': 83.18902495690827, 'ACC-horse': 92.05834155907029, 'ACC-sheep': 90.27819230194244, 'ACC-cow': 87.50572856987525, 'ACC-elephant': 93.26206060545739, 'ACC-bear': 89.89089115164816, 'ACC-zebra': 95.23406429592049, 'ACC-giraffe': 92.55756240424591, 'ACC-backpack': 58.16173828450233, 'ACC-umbrella': 85.53789186317339, 'ACC-handbag': 54.00228553386231, 'ACC-tie': 81.76449914049284, 'ACC-suitcase': 89.94893940892744, 'ACC-frisbee': 94.05818181818182, 'ACC-skis': 69.70190495984627, 'ACC-snowboard': 78.23565428228551, 'ACC-sports ball': 80.49586008000745, 'ACC-kite': 75.61307730449862, 'ACC-baseball bat': 84.62152260075374, 'ACC-baseball glove': 90.34499902725838, 'ACC-skateboard': 69.61277609957314, 'ACC-surfboard': 83.53277311832753, 'ACC-tennis racket': 89.13923173835134, 'ACC-bottle': 83.41313258685477, 'ACC-wine glass': 85.27470036808036, 'ACC-cup': 84.64065864578212, 'ACC-fork': 68.09546409506834, 'ACC-knife': 63.06404337949733, 'ACC-spoon': 73.64681849755648, 'ACC-bowl': 69.88971603524915, 'ACC-banana': 89.01961184043964, 'ACC-apple': 74.67403184213273, 'ACC-sandwich': 73.8226065745644, 'ACC-orange': 89.5844117593659, 'ACC-broccoli': 81.83164548420282, 'ACC-carrot': 76.22261389983306, 'ACC-hot dog': 73.82777944822838, 'ACC-pizza': 93.10150394070139, 'ACC-donut': 82.2903664814894, 'ACC-cake': 76.1986182115695, 'ACC-chair': 71.6313037256939, 'ACC-couch': 76.90225164294272, 'ACC-potted plant': 47.6342756191135, 'ACC-bed': 75.07739035316493, 'ACC-dining table': 72.62993807736275, 'ACC-toilet': 93.07624384430999, 'ACC-tv': 88.79082512291801, 'ACC-laptop': 88.35913915741052, 'ACC-mouse': 84.50807874203218, 'ACC-remote': 73.71578357089761, 'ACC-keyboard': 67.6386113579568, 'ACC-cell phone': 75.2721993956346, 'ACC-microwave': 49.51020680157267, 'ACC-oven': 81.58238717744997, 'ACC-toaster': 51.22692249791293, 'ACC-sink': 83.48078827826032, 'ACC-refrigerator': 91.49560392466103, 'ACC-book': 69.47371186013281, 'ACC-clock': 79.42703959028223, 'ACC-vase': 69.6765545629985, 'ACC-scissors': 64.95009595166607, 'ACC-teddy bear': 88.91201838205687, 'ACC-hair drier': 37.47900833973757, 'ACC-toothbrush': 82.28457261987492, 'ACC-banner': 80.4045330914051, 'ACC-blanket': 24.74214754404937, 'ACC-bridge': 57.33101896528956, 'ACC-cardboard': 55.34273665842664, 'ACC-counter': 54.04625669268588, 'ACC-curtain': 77.30180685120894, 'ACC-door-stuff': 60.745467366147786, 'ACC-floor-wood': 75.08071536377575, 'ACC-flower': 67.85555586239447, 'ACC-fruit': 56.588248693531625, 'ACC-gravel': 40.120628092007465, 'ACC-house': 31.240223505855663, 'ACC-light': 58.0339284347127, 'ACC-mirror-stuff': 71.43828916199057, 'ACC-net': 64.39442814924189, 'ACC-pillow': 25.834690714981534, 'ACC-platform': 52.713689950701614, 'ACC-playingfield': 88.73749842180436, 'ACC-railroad': 79.44693086021447, 'ACC-river': 79.63779570145074, 'ACC-road': 86.36799265381488, 'ACC-roof': 21.272557624059292, 'ACC-sand': 71.84710584333047, 'ACC-sea': 90.60735545851905, 'ACC-shelf': 60.11722421747603, 'ACC-snow': 95.68972624487783, 'ACC-stairs': 48.4234392561042, 'ACC-tent': 10.963997892192328, 'ACC-towel': 44.639294927689285, 'ACC-wall-brick': 66.76737755241183, 'ACC-wall-stone': 30.548946356028306, 'ACC-wall-tile': 82.95116168373342, 'ACC-wall-wood': 52.24448022612531, 'ACC-water-other': 32.57819909500304, 'ACC-window-blind': 55.44451652983188, 'ACC-window-other': 71.01306213483922, 'ACC-tree-merged': 89.62611974345434, 'ACC-fence-merged': 67.97960861227764, 'ACC-ceiling-merged': 77.83720327767848, 'ACC-sky-other-merged': 96.55682408944757, 'ACC-cabinet-merged': 76.3014030768928, 'ACC-table-merged': 55.40279656509871, 'ACC-floor-other-merged': 61.591689524047524, 'ACC-pavement-merged': 64.33407442417412, 'ACC-mountain-merged': 66.02094211739384, 'ACC-grass-merged': 82.55891620389492, 'ACC-dirt-merged': 68.46798141310758, 'ACC-paper-merged': 41.911647402329486, 'ACC-food-other-merged': 54.233376788856255, 'ACC-building-other-merged': 75.28401293613182, 'ACC-rock-merged': 80.12689194446243, 'ACC-wall-other-merged': 79.55335749844342, 'ACC-rug-merged': 79.45276554287491})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1547 s/iter. Inference: 0.3623 s/iter. Eval: 0.0000 s/iter. Total: 0.5170 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1579 s/iter. Inference: 0.4387 s/iter. Eval: 0.0000 s/iter. Total: 0.5967 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1727 s/iter. Inference: 0.5552 s/iter. Eval: 0.0000 s/iter. Total: 0.7281 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1722 s/iter. Inference: 0.6451 s/iter. Eval: 0.0000 s/iter. Total: 0.8175 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1709 s/iter. Inference: 0.6030 s/iter. Eval: 0.0000 s/iter. Total: 0.7740 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1694 s/iter. Inference: 0.6454 s/iter. Eval: 0.0000 s/iter. Total: 0.8150 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5089259584430788, 'noc@0.8': 2.841381328650863, 'noc@0.85': 3.5133157740708225, 'noc@0.9': 4.557799239098625, 'miou@iter1': 0.8320575431665204}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0986 s/iter. Eval: 0.0008 s/iter. Total: 0.1010 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.7734146118164, 'precision@0.6': 67.547607421875, 'precision@0.7': 62.41741180419922, 'precision@0.8': 51.6129035949707, 'precision@0.9': 26.661483764648438, 'cIoU': 56.73154067993164, 'mIoU': 62.193092346191406}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.090724261907006, 'SQ': 82.12444200909104, 'RQ': 60.20370545977879, 'PQ_th': 54.860541441658114, 'SQ_th': 82.76543029804932, 'RQ_th': 65.6597802937433, 'PQ_st': 42.89100021699962, 'SQ_st': 81.15691251632377, 'RQ_st': 51.968120804738035}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.475084080258085, 'AP50': 60.789499648503195, 'AP75': 40.50374934302097, 'APs': 19.40158528352275, 'APm': 41.77156042160739, 'APl': 60.275694858394566, 'AP-person': 43.70972525562264, 'AP-bicycle': 17.44618965093239, 'AP-car': 37.21344895238635, 'AP-motorcycle': 34.53728976748536, 'AP-airplane': 55.99621685437947, 'AP-bus': 65.67519751190017, 'AP-train': 67.6400055578793, 'AP-truck': 34.92213653048299, 'AP-boat': 22.184865816266765, 'AP-traffic light': 25.664567444426694, 'AP-fire hydrant': 61.87617550682395, 'AP-stop sign': 63.835075302475765, 'AP-parking meter': 42.426963275683306, 'AP-bench': 20.422367420521688, 'AP-bird': 29.180946735326845, 'AP-cat': 73.0875093124462, 'AP-dog': 65.15478976425318, 'AP-horse': 44.99613918039686, 'AP-sheep': 46.36270169114643, 'AP-cow': 49.603043559645016, 'AP-elephant': 60.15367943420644, 'AP-bear': 78.12630313463612, 'AP-zebra': 59.602129789324174, 'AP-giraffe': 55.734281796660454, 'AP-backpack': 17.241170768664087, 'AP-umbrella': 48.235151151760526, 'AP-handbag': 15.425935637288807, 'AP-tie': 34.14177329365089, 'AP-suitcase': 41.316061113798014, 'AP-frisbee': 66.50917387653959, 'AP-skis': 4.778011988232998, 'AP-snowboard': 23.174295061926273, 'AP-sports ball': 46.62611772937656, 'AP-kite': 33.557838106805264, 'AP-baseball bat': 28.644863819389364, 'AP-baseball glove': 43.62309886097601, 'AP-skateboard': 35.37445196108506, 'AP-surfboard': 35.06239718559016, 'AP-tennis racket': 55.859473129519735, 'AP-bottle': 33.58608834992501, 'AP-wine glass': 26.860662919074368, 'AP-cup': 40.752165651049445, 'AP-fork': 16.817976264047758, 'AP-knife': 13.070405258773125, 'AP-spoon': 15.085445284513884, 'AP-bowl': 32.316043383191925, 'AP-banana': 21.11107038794015, 'AP-apple': 20.754157656655934, 'AP-sandwich': 41.37499606103422, 'AP-orange': 28.319750402836487, 'AP-broccoli': 22.45822724199982, 'AP-carrot': 19.81273746540606, 'AP-hot dog': 21.367441380897805, 'AP-pizza': 49.036725389841045, 'AP-donut': 46.33341785947388, 'AP-cake': 43.867213137783914, 'AP-chair': 20.529550917141705, 'AP-couch': 40.80108740016424, 'AP-potted plant': 16.99488521175711, 'AP-bed': 40.08664699753099, 'AP-dining table': 12.66024695311235, 'AP-toilet': 65.02760393187111, 'AP-tv': 62.44146238011855, 'AP-laptop': 61.286407956539215, 'AP-mouse': 57.95731564728275, 'AP-remote': 31.053227119432016, 'AP-keyboard': 46.64034395261671, 'AP-cell phone': 37.136468300354096, 'AP-microwave': 54.4743680995878, 'AP-oven': 33.01727485755146, 'AP-toaster': 29.243970279380875, 'AP-sink': 37.84498685901323, 'AP-refrigerator': 59.26069649770004, 'AP-book': 8.7333709129849, 'AP-clock': 51.37642752778798, 'AP-vase': 33.69633047590907, 'AP-scissors': 23.46350530583015, 'AP-teddy bear': 50.60410768557201, 'AP-hair drier': 5.988391942642539, 'AP-toothbrush': 19.641962184408865}), ('sem_seg', {'mIoU': 60.46814236010637, 'fwIoU': 69.21410821981885, 'IoU-person': 87.49894295868104, 'IoU-bicycle': 75.0591574156462, 'IoU-car': 69.44210162178514, 'IoU-motorcycle': 85.47112522868942, 'IoU-airplane': 78.91916988499507, 'IoU-bus': 85.71133775266888, 'IoU-train': 87.57273722754198, 'IoU-truck': 65.98925342146504, 'IoU-boat': 66.76349689404823, 'IoU-traffic light': 75.61366672249477, 'IoU-fire hydrant': 85.0217753673388, 'IoU-stop sign': 92.26548180214236, 'IoU-parking meter': 83.4724947527094, 'IoU-bench': 52.30444110018892, 'IoU-bird': 76.03240883688095, 'IoU-cat': 81.02553575066834, 'IoU-dog': 78.40443151524202, 'IoU-horse': 85.93073973633828, 'IoU-sheep': 86.32352688764836, 'IoU-cow': 82.8584764585552, 'IoU-elephant': 90.62319162963284, 'IoU-bear': 87.50242985945945, 'IoU-zebra': 92.49091946221264, 'IoU-giraffe': 88.2265743525379, 'IoU-backpack': 39.806629627130334, 'IoU-umbrella': 77.18169612061453, 'IoU-handbag': 37.61982752210338, 'IoU-tie': 69.8230276587893, 'IoU-suitcase': 81.9135713132411, 'IoU-frisbee': 82.91713415611476, 'IoU-skis': 51.45040894138005, 'IoU-snowboard': 70.62438925171452, 'IoU-sports ball': 67.4717324437898, 'IoU-kite': 66.05226618353079, 'IoU-baseball bat': 59.03674915908954, 'IoU-baseball glove': 75.63474589051425, 'IoU-skateboard': 60.413747164898, 'IoU-surfboard': 75.07011219773128, 'IoU-tennis racket': 83.12194537456811, 'IoU-bottle': 67.4775530749617, 'IoU-wine glass': 75.17414947283619, 'IoU-cup': 65.81565807335322, 'IoU-fork': 54.48716997189565, 'IoU-knife': 52.27478069143129, 'IoU-spoon': 50.229097081377475, 'IoU-bowl': 55.501621260239865, 'IoU-banana': 82.7032541549701, 'IoU-apple': 59.774426646389834, 'IoU-sandwich': 62.77352359790732, 'IoU-orange': 78.06472775906187, 'IoU-broccoli': 67.64041234853654, 'IoU-carrot': 62.83968593655976, 'IoU-hot dog': 60.074845674566255, 'IoU-pizza': 82.88576849123885, 'IoU-donut': 64.84807953824122, 'IoU-cake': 68.02087155199888, 'IoU-chair': 56.17658403107948, 'IoU-couch': 64.60924782836496, 'IoU-potted plant': 33.864793092856736, 'IoU-bed': 65.71163104811075, 'IoU-dining table': 51.46946083008898, 'IoU-toilet': 83.00261629837706, 'IoU-tv': 76.03151366588433, 'IoU-laptop': 74.8828052996119, 'IoU-mouse': 70.59270219042769, 'IoU-remote': 50.06248782225511, 'IoU-keyboard': 60.827415687342246, 'IoU-cell phone': 69.62421766149116, 'IoU-microwave': 41.319099087151606, 'IoU-oven': 58.41368561356287, 'IoU-toaster': 44.65480834615798, 'IoU-sink': 72.43064631435003, 'IoU-refrigerator': 79.12076102261213, 'IoU-book': 50.29204168039957, 'IoU-clock': 73.11103467954564, 'IoU-vase': 59.7011978616761, 'IoU-scissors': 58.54404417918724, 'IoU-teddy bear': 81.08516946801775, 'IoU-hair drier': 28.12456614372524, 'IoU-toothbrush': 53.605645439677666, 'IoU-banner': 28.961170639199104, 'IoU-blanket': 15.840507600043077, 'IoU-bridge': 36.22182172481389, 'IoU-cardboard': 41.56350197883996, 'IoU-counter': 29.630453804839778, 'IoU-curtain': 63.492692485385895, 'IoU-door-stuff': 41.584585270651004, 'IoU-floor-wood': 61.38520292482487, 'IoU-flower': 45.19185979048561, 'IoU-fruit': 37.622463099631, 'IoU-gravel': 29.47769667516027, 'IoU-house': 26.66379844638963, 'IoU-light': 38.73576282967216, 'IoU-mirror-stuff': 59.10330196856063, 'IoU-net': 39.7039967411482, 'IoU-pillow': 11.950074916931959, 'IoU-platform': 32.92873141291446, 'IoU-playingfield': 70.38313939891106, 'IoU-railroad': 61.3376353565113, 'IoU-river': 50.47258684182962, 'IoU-road': 65.32423662919447, 'IoU-roof': 15.595275469655606, 'IoU-sand': 63.250744790597366, 'IoU-sea': 84.75862146181306, 'IoU-shelf': 38.18951197332525, 'IoU-snow': 88.64069539477303, 'IoU-stairs': 29.55237026511676, 'IoU-tent': 9.10484447932874, 'IoU-towel': 35.41510014966951, 'IoU-wall-brick': 47.0329635391175, 'IoU-wall-stone': 25.563157329697695, 'IoU-wall-tile': 66.93320991827004, 'IoU-wall-wood': 39.69950801941404, 'IoU-water-other': 22.862305580717294, 'IoU-window-blind': 46.19258041704096, 'IoU-window-other': 47.646909988872466, 'IoU-tree-merged': 81.00947801226545, 'IoU-fence-merged': 50.748994804156936, 'IoU-ceiling-merged': 66.1049786181589, 'IoU-sky-other-merged': 93.61467587220271, 'IoU-cabinet-merged': 58.17427714627993, 'IoU-table-merged': 40.44309843330433, 'IoU-floor-other-merged': 49.5752331600927, 'IoU-pavement-merged': 52.7770206268176, 'IoU-mountain-merged': 56.088229268649684, 'IoU-grass-merged': 72.00722362084522, 'IoU-dirt-merged': 45.77061558133362, 'IoU-paper-merged': 31.20964071437882, 'IoU-food-other-merged': 38.72136129705804, 'IoU-building-other-merged': 59.359064717573304, 'IoU-rock-merged': 61.92837069457303, 'IoU-wall-other-merged': 65.16933309479813, 'IoU-rug-merged': 65.04311865601156, 'mACC': 73.31823631989064, 'pACC': 80.50342486920289, 'ACC-person': 92.65833121150244, 'ACC-bicycle': 85.28973531422243, 'ACC-car': 85.67581879986686, 'ACC-motorcycle': 91.02758256928341, 'ACC-airplane': 90.2857038352267, 'ACC-bus': 91.41708796066202, 'ACC-train': 95.11240473412337, 'ACC-truck': 75.7803652473116, 'ACC-boat': 79.699641931693, 'ACC-traffic light': 88.43107770620911, 'ACC-fire hydrant': 95.31485522253583, 'ACC-stop sign': 95.1070486545513, 'ACC-parking meter': 87.05147761130198, 'ACC-bench': 70.56831240045966, 'ACC-bird': 80.49160717648469, 'ACC-cat': 95.58186643977385, 'ACC-dog': 83.18902495690827, 'ACC-horse': 92.05834155907029, 'ACC-sheep': 90.27819230194244, 'ACC-cow': 87.50572856987525, 'ACC-elephant': 93.26206060545739, 'ACC-bear': 89.89089115164816, 'ACC-zebra': 95.23406429592049, 'ACC-giraffe': 92.55756240424591, 'ACC-backpack': 58.16173828450233, 'ACC-umbrella': 85.53789186317339, 'ACC-handbag': 54.00228553386231, 'ACC-tie': 81.76449914049284, 'ACC-suitcase': 89.94893940892744, 'ACC-frisbee': 94.05818181818182, 'ACC-skis': 69.70190495984627, 'ACC-snowboard': 78.23565428228551, 'ACC-sports ball': 80.49586008000745, 'ACC-kite': 75.61307730449862, 'ACC-baseball bat': 84.62152260075374, 'ACC-baseball glove': 90.34499902725838, 'ACC-skateboard': 69.61277609957314, 'ACC-surfboard': 83.53277311832753, 'ACC-tennis racket': 89.13923173835134, 'ACC-bottle': 83.41313258685477, 'ACC-wine glass': 85.27470036808036, 'ACC-cup': 84.64065864578212, 'ACC-fork': 68.09546409506834, 'ACC-knife': 63.06404337949733, 'ACC-spoon': 73.64681849755648, 'ACC-bowl': 69.88971603524915, 'ACC-banana': 89.01961184043964, 'ACC-apple': 74.67403184213273, 'ACC-sandwich': 73.8226065745644, 'ACC-orange': 89.5844117593659, 'ACC-broccoli': 81.83164548420282, 'ACC-carrot': 76.22261389983306, 'ACC-hot dog': 73.82777944822838, 'ACC-pizza': 93.10150394070139, 'ACC-donut': 82.2903664814894, 'ACC-cake': 76.1986182115695, 'ACC-chair': 71.6313037256939, 'ACC-couch': 76.90225164294272, 'ACC-potted plant': 47.6342756191135, 'ACC-bed': 75.07739035316493, 'ACC-dining table': 72.62993807736275, 'ACC-toilet': 93.07624384430999, 'ACC-tv': 88.79082512291801, 'ACC-laptop': 88.35913915741052, 'ACC-mouse': 84.50807874203218, 'ACC-remote': 73.71578357089761, 'ACC-keyboard': 67.6386113579568, 'ACC-cell phone': 75.2721993956346, 'ACC-microwave': 49.51020680157267, 'ACC-oven': 81.58238717744997, 'ACC-toaster': 51.22692249791293, 'ACC-sink': 83.48078827826032, 'ACC-refrigerator': 91.49560392466103, 'ACC-book': 69.47371186013281, 'ACC-clock': 79.42703959028223, 'ACC-vase': 69.6765545629985, 'ACC-scissors': 64.95009595166607, 'ACC-teddy bear': 88.91201838205687, 'ACC-hair drier': 37.47900833973757, 'ACC-toothbrush': 82.28457261987492, 'ACC-banner': 80.4045330914051, 'ACC-blanket': 24.74214754404937, 'ACC-bridge': 57.33101896528956, 'ACC-cardboard': 55.34273665842664, 'ACC-counter': 54.04625669268588, 'ACC-curtain': 77.30180685120894, 'ACC-door-stuff': 60.745467366147786, 'ACC-floor-wood': 75.08071536377575, 'ACC-flower': 67.85555586239447, 'ACC-fruit': 56.588248693531625, 'ACC-gravel': 40.120628092007465, 'ACC-house': 31.240223505855663, 'ACC-light': 58.0339284347127, 'ACC-mirror-stuff': 71.43828916199057, 'ACC-net': 64.39442814924189, 'ACC-pillow': 25.834690714981534, 'ACC-platform': 52.713689950701614, 'ACC-playingfield': 88.73749842180436, 'ACC-railroad': 79.44693086021447, 'ACC-river': 79.63779570145074, 'ACC-road': 86.36799265381488, 'ACC-roof': 21.272557624059292, 'ACC-sand': 71.84710584333047, 'ACC-sea': 90.60735545851905, 'ACC-shelf': 60.11722421747603, 'ACC-snow': 95.68972624487783, 'ACC-stairs': 48.4234392561042, 'ACC-tent': 10.963997892192328, 'ACC-towel': 44.639294927689285, 'ACC-wall-brick': 66.76737755241183, 'ACC-wall-stone': 30.548946356028306, 'ACC-wall-tile': 82.95116168373342, 'ACC-wall-wood': 52.24448022612531, 'ACC-water-other': 32.57819909500304, 'ACC-window-blind': 55.44451652983188, 'ACC-window-other': 71.01306213483922, 'ACC-tree-merged': 89.62611974345434, 'ACC-fence-merged': 67.97960861227764, 'ACC-ceiling-merged': 77.83720327767848, 'ACC-sky-other-merged': 96.55682408944757, 'ACC-cabinet-merged': 76.3014030768928, 'ACC-table-merged': 55.40279656509871, 'ACC-floor-other-merged': 61.591689524047524, 'ACC-pavement-merged': 64.33407442417412, 'ACC-mountain-merged': 66.02094211739384, 'ACC-grass-merged': 82.55891620389492, 'ACC-dirt-merged': 68.46798141310758, 'ACC-paper-merged': 41.911647402329486, 'ACC-food-other-merged': 54.233376788856255, 'ACC-building-other-merged': 75.28401293613182, 'ACC-rock-merged': 80.12689194446243, 'ACC-wall-other-merged': 79.55335749844342, 'ACC-rug-merged': 79.45276554287491})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5089259584430788, 'noc@0.8': 2.841381328650863, 'noc@0.85': 3.5133157740708225, 'noc@0.9': 4.557799239098625, 'miou@iter1': 0.8320575431665204}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.7734146118164, 'precision@0.6': 67.547607421875, 'precision@0.7': 62.41741180419922, 'precision@0.8': 51.6129035949707, 'precision@0.9': 26.661483764648438, 'cIoU': 56.73154067993164, 'mIoU': 62.193092346191406}}}
INFO:trainer.default_trainer:This epoch takes 1:27:38.797680
INFO:trainer.default_trainer:PROGRESS: 56.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 28 training.
INFO:trainer.default_trainer:epochs[    28] optim steps[51200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28027/0.90283, loss_mask_bce_0: 0.15025/0.33511, loss_mask_dice_0: 0.42834/1.16315, loss_spatial_bce_0: 0.04729/0.08812, loss_spatial_dice_0: 0.15042/0.21025, loss_spatial_ce_0: 0.03594/0.06503, loss_grounding_bce_0: 0.07400/0.08632, loss_grounding_dice_0: 0.14402/0.17876, loss_grounding_ce_0: 0.14502/0.27336, loss_mask_ce_1: 0.99647/0.90348, loss_mask_bce_1: 0.13841/0.33600, loss_mask_dice_1: 0.45195/1.16963, loss_spatial_bce_1: 0.05036/0.08868, loss_spatial_dice_1: 0.16602/0.21430, loss_spatial_ce_1: 0.05006/0.07071, loss_grounding_bce_1: 0.07175/0.08649, loss_grounding_dice_1: 0.18382/0.17954, loss_grounding_ce_1: 0.18390/0.27468, loss_mask_ce_2: 0.97965/0.91071, loss_mask_bce_2: 0.14851/0.33650, loss_mask_dice_2: 0.47533/1.16984, loss_spatial_bce_2: 0.04838/0.08950, loss_spatial_dice_2: 0.17456/0.21564, loss_spatial_ce_2: 0.02492/0.07420, loss_grounding_bce_2: 0.07413/0.08661, loss_grounding_dice_2: 0.25549/0.17934, loss_grounding_ce_2: 0.12380/0.27804, loss_mask_ce_3: 1.23081/0.92028, loss_mask_bce_3: 0.14317/0.33756, loss_mask_dice_3: 0.48064/1.16727, loss_spatial_bce_3: 0.05342/0.09047, loss_spatial_dice_3: 0.16615/0.21632, loss_spatial_ce_3: 0.03897/0.07792, loss_grounding_bce_3: 0.07029/0.08685, loss_grounding_dice_3: 0.15349/0.17910, loss_grounding_ce_3: 0.14286/0.27974, loss_mask_ce_4: 1.53262/0.92098, loss_mask_bce_4: 0.14742/0.33962, loss_mask_dice_4: 0.47299/1.19108, loss_spatial_bce_4: 0.05560/0.09451, loss_spatial_dice_4: 0.20664/0.22816, loss_spatial_ce_4: 0.03563/0.09396, loss_grounding_bce_4: 0.07729/0.08733, loss_grounding_dice_4: 0.18490/0.18198, loss_grounding_ce_4: 0.17311/0.28260, loss_mask_ce_5: 1.13070/0.93682, loss_mask_bce_5: 0.15239/0.34183, loss_mask_dice_5: 0.46252/1.19798, loss_spatial_bce_5: 0.05059/0.09650, loss_spatial_dice_5: 0.19149/0.23205, loss_spatial_ce_5: 0.06058/0.10880, loss_grounding_bce_5: 0.07951/0.08770, loss_grounding_dice_5: 0.14276/0.18317, loss_grounding_ce_5: 0.27010/0.29550, loss_mask_ce_6: 1.21668/0.97624, loss_mask_bce_6: 0.14437/0.34458, loss_mask_dice_6: 0.50722/1.20092, loss_spatial_bce_6: 0.05753/0.10227, loss_spatial_dice_6: 0.20499/0.23480, loss_spatial_ce_6: 0.10506/0.13520, loss_grounding_bce_6: 0.06949/0.08845, loss_grounding_dice_6: 0.14870/0.18348, loss_grounding_ce_6: 0.12747/0.31105, loss_mask_ce_7: 1.29364/1.02095, loss_mask_bce_7: 0.13158/0.35243, loss_mask_dice_7: 0.41281/1.25581, loss_spatial_bce_7: 0.06385/0.11060, loss_spatial_dice_7: 0.26399/0.26258, loss_spatial_ce_7: 0.09971/0.17118, loss_grounding_bce_7: 0.06878/0.09037, loss_grounding_dice_7: 0.20207/0.19073, loss_grounding_ce_7: 0.19307/0.34307, loss_mask_ce_8: 1.16793/1.12987, loss_mask_bce_8: 0.19300/0.36607, loss_mask_dice_8: 0.68692/1.32945, loss_spatial_bce_8: 0.10382/0.13141, loss_spatial_dice_8: 0.31312/0.30112, loss_spatial_ce_8: 0.17448/0.22844, loss_grounding_bce_8: 0.09942/0.09414, loss_grounding_dice_8: 0.20242/0.20178, loss_grounding_ce_8: 0.19711/0.41043, loss_mask_ce_9: 3.09053/3.67975, loss_mask_bce_9: 0.18543/0.39303, loss_mask_dice_9: 0.73590/1.90272, loss_spatial_bce_9: 0.16186/0.33359, loss_spatial_dice_9: 0.76073/0.82241, loss_spatial_ce_9: 1.11992/1.50055, loss_grounding_bce_9: 0.09184/0.10559, loss_grounding_dice_9: 0.29433/0.28098, loss_grounding_ce_9: 0.37627/0.67523] items per batch[64] items per second[0.13] total items[3276800] mini batches[ 51200] memory[7345] epoch remaining[1:29:08]
INFO:trainer.default_trainer:epochs[    28] optim steps[51300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66022/0.90268, loss_mask_bce_0: 0.17169/0.33510, loss_mask_dice_0: 0.87820/1.16299, loss_spatial_bce_0: 0.06078/0.08812, loss_spatial_dice_0: 0.26983/0.21023, loss_spatial_ce_0: 0.10055/0.06507, loss_grounding_bce_0: 0.03739/0.08632, loss_grounding_dice_0: 0.10447/0.17875, loss_grounding_ce_0: 0.04443/0.27338, loss_mask_ce_1: 0.44095/0.90331, loss_mask_bce_1: 0.18168/0.33599, loss_mask_dice_1: 0.91253/1.16945, loss_spatial_bce_1: 0.06009/0.08868, loss_spatial_dice_1: 0.27334/0.21427, loss_spatial_ce_1: 0.11817/0.07074, loss_grounding_bce_1: 0.03909/0.08649, loss_grounding_dice_1: 0.09367/0.17952, loss_grounding_ce_1: 0.04325/0.27465, loss_mask_ce_2: 0.78146/0.91057, loss_mask_bce_2: 0.17554/0.33650, loss_mask_dice_2: 0.86108/1.16965, loss_spatial_bce_2: 0.06108/0.08950, loss_spatial_dice_2: 0.30139/0.21562, loss_spatial_ce_2: 0.11070/0.07424, loss_grounding_bce_2: 0.04065/0.08661, loss_grounding_dice_2: 0.11888/0.17933, loss_grounding_ce_2: 0.04672/0.27799, loss_mask_ce_3: 0.83826/0.92011, loss_mask_bce_3: 0.16260/0.33756, loss_mask_dice_3: 0.78549/1.16707, loss_spatial_bce_3: 0.06066/0.09047, loss_spatial_dice_3: 0.28183/0.21630, loss_spatial_ce_3: 0.09943/0.07801, loss_grounding_bce_3: 0.03860/0.08685, loss_grounding_dice_3: 0.09706/0.17908, loss_grounding_ce_3: 0.04765/0.27969, loss_mask_ce_4: 0.56954/0.92087, loss_mask_bce_4: 0.16854/0.33961, loss_mask_dice_4: 0.90276/1.19088, loss_spatial_bce_4: 0.06155/0.09451, loss_spatial_dice_4: 0.27955/0.22813, loss_spatial_ce_4: 0.13574/0.09397, loss_grounding_bce_4: 0.04142/0.08733, loss_grounding_dice_4: 0.11798/0.18198, loss_grounding_ce_4: 0.06579/0.28256, loss_mask_ce_5: 0.84106/0.93669, loss_mask_bce_5: 0.17508/0.34183, loss_mask_dice_5: 0.75814/1.19779, loss_spatial_bce_5: 0.06395/0.09651, loss_spatial_dice_5: 0.31535/0.23202, loss_spatial_ce_5: 0.18974/0.10888, loss_grounding_bce_5: 0.04121/0.08770, loss_grounding_dice_5: 0.10177/0.18316, loss_grounding_ce_5: 0.06150/0.29542, loss_mask_ce_6: 0.87428/0.97613, loss_mask_bce_6: 0.19218/0.34458, loss_mask_dice_6: 0.84027/1.20073, loss_spatial_bce_6: 0.07436/0.10227, loss_spatial_dice_6: 0.33108/0.23476, loss_spatial_ce_6: 0.15086/0.13537, loss_grounding_bce_6: 0.03925/0.08845, loss_grounding_dice_6: 0.11227/0.18346, loss_grounding_ce_6: 0.08640/0.31097, loss_mask_ce_7: 1.03492/1.02080, loss_mask_bce_7: 0.17623/0.35242, loss_mask_dice_7: 0.86827/1.25560, loss_spatial_bce_7: 0.08556/0.11060, loss_spatial_dice_7: 0.35130/0.26255, loss_spatial_ce_7: 0.38271/0.17130, loss_grounding_bce_7: 0.04076/0.09037, loss_grounding_dice_7: 0.12325/0.19071, loss_grounding_ce_7: 0.17184/0.34298, loss_mask_ce_8: 0.70568/1.12969, loss_mask_bce_8: 0.19073/0.36606, loss_mask_dice_8: 1.01715/1.32921, loss_spatial_bce_8: 0.13956/0.13141, loss_spatial_dice_8: 0.47849/0.30109, loss_spatial_ce_8: 0.23777/0.22851, loss_grounding_bce_8: 0.04825/0.09414, loss_grounding_dice_8: 0.19259/0.20177, loss_grounding_ce_8: 0.02320/0.41038, loss_mask_ce_9: 2.29587/3.67942, loss_mask_bce_9: 0.21099/0.39302, loss_mask_dice_9: 1.32409/1.90232, loss_spatial_bce_9: 0.31873/0.33363, loss_spatial_dice_9: 0.90244/0.82240, loss_spatial_ce_9: 1.37093/1.50057, loss_grounding_bce_9: 0.06481/0.10559, loss_grounding_dice_9: 0.32502/0.28094, loss_grounding_ce_9: 0.12477/0.67514] items per batch[64] items per second[0.24] total items[3283200] mini batches[ 51300] memory[7345] epoch remaining[1:18:31]
INFO:trainer.default_trainer:epochs[    28] optim steps[51400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64769/0.90268, loss_mask_bce_0: 0.63762/0.33511, loss_mask_dice_0: 1.03137/1.16318, loss_spatial_bce_0: 0.11127/0.08811, loss_spatial_dice_0: 0.16566/0.21022, loss_spatial_ce_0: 0.00089/0.06502, loss_grounding_bce_0: 0.29461/0.08631, loss_grounding_dice_0: 0.27493/0.17873, loss_grounding_ce_0: 0.22724/0.27336, loss_mask_ce_1: 0.63478/0.90329, loss_mask_bce_1: 0.64504/0.33601, loss_mask_dice_1: 1.04284/1.16967, loss_spatial_bce_1: 0.11504/0.08867, loss_spatial_dice_1: 0.16681/0.21427, loss_spatial_ce_1: 0.00117/0.07070, loss_grounding_bce_1: 0.29260/0.08649, loss_grounding_dice_1: 0.28519/0.17951, loss_grounding_ce_1: 0.23300/0.27462, loss_mask_ce_2: 0.63549/0.91060, loss_mask_bce_2: 0.65174/0.33651, loss_mask_dice_2: 1.01501/1.16986, loss_spatial_bce_2: 0.11216/0.08949, loss_spatial_dice_2: 0.17455/0.21561, loss_spatial_ce_2: 0.00146/0.07422, loss_grounding_bce_2: 0.29812/0.08661, loss_grounding_dice_2: 0.27895/0.17932, loss_grounding_ce_2: 0.22886/0.27796, loss_mask_ce_3: 0.64041/0.92014, loss_mask_bce_3: 0.65397/0.33757, loss_mask_dice_3: 1.02915/1.16726, loss_spatial_bce_3: 0.11136/0.09047, loss_spatial_dice_3: 0.17524/0.21629, loss_spatial_ce_3: 0.00091/0.07799, loss_grounding_bce_3: 0.28878/0.08685, loss_grounding_dice_3: 0.27443/0.17907, loss_grounding_ce_3: 0.20651/0.27970, loss_mask_ce_4: 0.59931/0.92090, loss_mask_bce_4: 0.65720/0.33962, loss_mask_dice_4: 1.02743/1.19107, loss_spatial_bce_4: 0.11863/0.09451, loss_spatial_dice_4: 0.19176/0.22813, loss_spatial_ce_4: 0.00150/0.09394, loss_grounding_bce_4: 0.30221/0.08732, loss_grounding_dice_4: 0.27940/0.18197, loss_grounding_ce_4: 0.28955/0.28255, loss_mask_ce_5: 0.66051/0.93672, loss_mask_bce_5: 0.65192/0.34184, loss_mask_dice_5: 1.03194/1.19796, loss_spatial_bce_5: 0.11393/0.09650, loss_spatial_dice_5: 0.18329/0.23202, loss_spatial_ce_5: 0.00199/0.10883, loss_grounding_bce_5: 0.30260/0.08770, loss_grounding_dice_5: 0.28429/0.18314, loss_grounding_ce_5: 0.29840/0.29543, loss_mask_ce_6: 0.70631/0.97616, loss_mask_bce_6: 0.64031/0.34458, loss_mask_dice_6: 1.04946/1.20095, loss_spatial_bce_6: 0.12177/0.10227, loss_spatial_dice_6: 0.18807/0.23477, loss_spatial_ce_6: 0.01625/0.13533, loss_grounding_bce_6: 0.29672/0.08844, loss_grounding_dice_6: 0.27977/0.18345, loss_grounding_ce_6: 0.27338/0.31098, loss_mask_ce_7: 0.72487/1.02081, loss_mask_bce_7: 0.63804/0.35243, loss_mask_dice_7: 0.97348/1.25580, loss_spatial_bce_7: 0.12103/0.11060, loss_spatial_dice_7: 0.21173/0.26256, loss_spatial_ce_7: 0.12568/0.17128, loss_grounding_bce_7: 0.29456/0.09036, loss_grounding_dice_7: 0.28695/0.19069, loss_grounding_ce_7: 0.41703/0.34301, loss_mask_ce_8: 0.82887/1.12970, loss_mask_bce_8: 0.69363/0.36608, loss_mask_dice_8: 1.08456/1.32942, loss_spatial_bce_8: 0.18521/0.13140, loss_spatial_dice_8: 0.24314/0.30109, loss_spatial_ce_8: 0.16567/0.22850, loss_grounding_bce_8: 0.31189/0.09413, loss_grounding_dice_8: 0.28650/0.20176, loss_grounding_ce_8: 0.22939/0.41042, loss_mask_ce_9: 3.02074/3.67985, loss_mask_bce_9: 0.78167/0.39303, loss_mask_dice_9: 1.83656/1.90261, loss_spatial_bce_9: 0.34640/0.33361, loss_spatial_dice_9: 0.88361/0.82240, loss_spatial_ce_9: 1.42378/1.50049, loss_grounding_bce_9: 0.32508/0.10558, loss_grounding_dice_9: 0.33876/0.28093, loss_grounding_ce_9: 0.48074/0.67527] items per batch[64] items per second[0.23] total items[3289600] mini batches[ 51400] memory[7345] epoch remaining[1:14:07]
INFO:trainer.default_trainer:epochs[    28] optim steps[51500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.92800/0.90263, loss_mask_bce_0: 0.58275/0.33522, loss_mask_dice_0: 4.46945/1.16307, loss_spatial_bce_0: 0.03855/0.08814, loss_spatial_dice_0: 0.24590/0.21020, loss_spatial_ce_0: 0.04223/0.06499, loss_grounding_bce_0: 0.03321/0.08632, loss_grounding_dice_0: 0.18421/0.17871, loss_grounding_ce_0: 0.07781/0.27323, loss_mask_ce_1: 1.98761/0.90326, loss_mask_bce_1: 0.58887/0.33612, loss_mask_dice_1: 4.46678/1.16954, loss_spatial_bce_1: 0.03907/0.08869, loss_spatial_dice_1: 0.26933/0.21424, loss_spatial_ce_1: 0.02826/0.07067, loss_grounding_bce_1: 0.03002/0.08649, loss_grounding_dice_1: 0.16290/0.17949, loss_grounding_ce_1: 0.07556/0.27453, loss_mask_ce_2: 1.92334/0.91056, loss_mask_bce_2: 0.56866/0.33662, loss_mask_dice_2: 4.51788/1.16973, loss_spatial_bce_2: 0.04122/0.08952, loss_spatial_dice_2: 0.27186/0.21559, loss_spatial_ce_2: 0.02987/0.07417, loss_grounding_bce_2: 0.02784/0.08661, loss_grounding_dice_2: 0.17667/0.17929, loss_grounding_ce_2: 0.15719/0.27787, loss_mask_ce_3: 1.95190/0.92014, loss_mask_bce_3: 0.59231/0.33768, loss_mask_dice_3: 4.50618/1.16714, loss_spatial_bce_3: 0.04013/0.09049, loss_spatial_dice_3: 0.26519/0.21627, loss_spatial_ce_3: 0.03202/0.07794, loss_grounding_bce_3: 0.02698/0.08686, loss_grounding_dice_3: 0.18814/0.17905, loss_grounding_ce_3: 0.07634/0.27960, loss_mask_ce_4: 1.68968/0.92085, loss_mask_bce_4: 0.61498/0.33974, loss_mask_dice_4: 4.88182/1.19094, loss_spatial_bce_4: 0.04716/0.09453, loss_spatial_dice_4: 0.28853/0.22811, loss_spatial_ce_4: 0.04345/0.09388, loss_grounding_bce_4: 0.02848/0.08733, loss_grounding_dice_4: 0.16115/0.18195, loss_grounding_ce_4: 0.08370/0.28246, loss_mask_ce_5: 1.42313/0.93668, loss_mask_bce_5: 0.65531/0.34195, loss_mask_dice_5: 4.70452/1.19786, loss_spatial_bce_5: 0.04848/0.09653, loss_spatial_dice_5: 0.29980/0.23201, loss_spatial_ce_5: 0.09878/0.10877, loss_grounding_bce_5: 0.02925/0.08770, loss_grounding_dice_5: 0.16769/0.18312, loss_grounding_ce_5: 0.09526/0.29537, loss_mask_ce_6: 1.73103/0.97618, loss_mask_bce_6: 0.82316/0.34470, loss_mask_dice_6: 4.93671/1.20086, loss_spatial_bce_6: 0.05733/0.10230, loss_spatial_dice_6: 0.30447/0.23476, loss_spatial_ce_6: 0.10794/0.13527, loss_grounding_bce_6: 0.02677/0.08845, loss_grounding_dice_6: 0.16058/0.18344, loss_grounding_ce_6: 0.09859/0.31086, loss_mask_ce_7: 1.61947/1.02082, loss_mask_bce_7: 0.69858/0.35256, loss_mask_dice_7: 5.09937/1.25568, loss_spatial_bce_7: 0.06235/0.11062, loss_spatial_dice_7: 0.36417/0.26254, loss_spatial_ce_7: 0.25193/0.17124, loss_grounding_bce_7: 0.02485/0.09037, loss_grounding_dice_7: 0.14168/0.19067, loss_grounding_ce_7: 0.12315/0.34290, loss_mask_ce_8: 1.50928/1.12968, loss_mask_bce_8: 0.72007/0.36620, loss_mask_dice_8: 5.24222/1.32928, loss_spatial_bce_8: 0.07978/0.13142, loss_spatial_dice_8: 0.42139/0.30106, loss_spatial_ce_8: 0.21617/0.22843, loss_grounding_bce_8: 0.02749/0.09414, loss_grounding_dice_8: 0.22132/0.20174, loss_grounding_ce_8: 0.27092/0.41028, loss_mask_ce_9: 4.43471/3.67982, loss_mask_bce_9: 0.65503/0.39316, loss_mask_dice_9: 7.87427/1.90248, loss_spatial_bce_9: 0.16499/0.33365, loss_spatial_dice_9: 0.94474/0.82240, loss_spatial_ce_9: 1.13215/1.50034, loss_grounding_bce_9: 0.04342/0.10560, loss_grounding_dice_9: 0.52301/0.28093, loss_grounding_ce_9: 0.62816/0.67505] items per batch[64] items per second[0.23] total items[3296000] mini batches[ 51500] memory[7345] epoch remaining[1:09:32]
INFO:trainer.default_trainer:epochs[    28] optim steps[51600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24537/0.90257, loss_mask_bce_0: 0.14128/0.33519, loss_mask_dice_0: 0.73774/1.16289, loss_spatial_bce_0: 0.03586/0.08812, loss_spatial_dice_0: 0.13065/0.21017, loss_spatial_ce_0: 0.01583/0.06495, loss_grounding_bce_0: 0.02962/0.08632, loss_grounding_dice_0: 0.17653/0.17872, loss_grounding_ce_0: 0.23989/0.27313, loss_mask_ce_1: 0.29423/0.90320, loss_mask_bce_1: 0.13554/0.33609, loss_mask_dice_1: 0.71105/1.16938, loss_spatial_bce_1: 0.03564/0.08868, loss_spatial_dice_1: 0.12981/0.21421, loss_spatial_ce_1: 0.02923/0.07062, loss_grounding_bce_1: 0.02718/0.08649, loss_grounding_dice_1: 0.15863/0.17950, loss_grounding_ce_1: 0.24436/0.27442, loss_mask_ce_2: 0.23120/0.91049, loss_mask_bce_2: 0.14113/0.33658, loss_mask_dice_2: 0.73461/1.16956, loss_spatial_bce_2: 0.03601/0.08950, loss_spatial_dice_2: 0.14832/0.21557, loss_spatial_ce_2: 0.02381/0.07413, loss_grounding_bce_2: 0.02903/0.08661, loss_grounding_dice_2: 0.17353/0.17931, loss_grounding_ce_2: 0.20860/0.27775, loss_mask_ce_3: 0.34487/0.92005, loss_mask_bce_3: 0.14900/0.33764, loss_mask_dice_3: 0.69854/1.16696, loss_spatial_bce_3: 0.03470/0.09047, loss_spatial_dice_3: 0.13975/0.21625, loss_spatial_ce_3: 0.03675/0.07791, loss_grounding_bce_3: 0.02877/0.08685, loss_grounding_dice_3: 0.15466/0.17906, loss_grounding_ce_3: 0.25070/0.27949, loss_mask_ce_4: 0.32945/0.92083, loss_mask_bce_4: 0.14993/0.33970, loss_mask_dice_4: 0.83697/1.19075, loss_spatial_bce_4: 0.03029/0.09451, loss_spatial_dice_4: 0.12077/0.22809, loss_spatial_ce_4: 0.01892/0.09382, loss_grounding_bce_4: 0.02787/0.08733, loss_grounding_dice_4: 0.17635/0.18196, loss_grounding_ce_4: 0.20654/0.28236, loss_mask_ce_5: 0.29389/0.93661, loss_mask_bce_5: 0.15251/0.34191, loss_mask_dice_5: 0.77635/1.19769, loss_spatial_bce_5: 0.03651/0.09652, loss_spatial_dice_5: 0.13744/0.23199, loss_spatial_ce_5: 0.02997/0.10873, loss_grounding_bce_5: 0.02761/0.08770, loss_grounding_dice_5: 0.16331/0.18313, loss_grounding_ce_5: 0.20775/0.29527, loss_mask_ce_6: 0.27883/0.97611, loss_mask_bce_6: 0.14269/0.34467, loss_mask_dice_6: 0.77981/1.20071, loss_spatial_bce_6: 0.03672/0.10228, loss_spatial_dice_6: 0.16197/0.23475, loss_spatial_ce_6: 0.06773/0.13525, loss_grounding_bce_6: 0.02738/0.08844, loss_grounding_dice_6: 0.16468/0.18344, loss_grounding_ce_6: 0.22909/0.31075, loss_mask_ce_7: 0.28091/1.02074, loss_mask_bce_7: 0.14313/0.35253, loss_mask_dice_7: 0.80078/1.25551, loss_spatial_bce_7: 0.04490/0.11062, loss_spatial_dice_7: 0.16971/0.26254, loss_spatial_ce_7: 0.07917/0.17121, loss_grounding_bce_7: 0.03202/0.09037, loss_grounding_dice_7: 0.18790/0.19069, loss_grounding_ce_7: 0.21729/0.34270, loss_mask_ce_8: 0.43215/1.12959, loss_mask_bce_8: 0.13981/0.36617, loss_mask_dice_8: 0.77597/1.32910, loss_spatial_bce_8: 0.07372/0.13141, loss_spatial_dice_8: 0.20171/0.30105, loss_spatial_ce_8: 0.06822/0.22839, loss_grounding_bce_8: 0.03054/0.09413, loss_grounding_dice_8: 0.18350/0.20176, loss_grounding_ce_8: 0.25445/0.41012, loss_mask_ce_9: 3.61262/3.67944, loss_mask_bce_9: 0.15455/0.39313, loss_mask_dice_9: 1.17964/1.90234, loss_spatial_bce_9: 0.36822/0.33363, loss_spatial_dice_9: 0.77886/0.82238, loss_spatial_ce_9: 1.51939/1.50025, loss_grounding_bce_9: 0.02683/0.10560, loss_grounding_dice_9: 0.30176/0.28094, loss_grounding_ce_9: 0.35801/0.67483] items per batch[64] items per second[0.23] total items[3302400] mini batches[ 51600] memory[7345] epoch remaining[1:04:55]
INFO:trainer.default_trainer:epochs[    28] optim steps[51700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38739/0.90261, loss_mask_bce_0: 0.14606/0.33523, loss_mask_dice_0: 2.19085/1.16299, loss_spatial_bce_0: 0.03845/0.08812, loss_spatial_dice_0: 0.32594/0.21016, loss_spatial_ce_0: 0.09094/0.06493, loss_grounding_bce_0: 0.00277/0.08631, loss_grounding_dice_0: 0.43166/0.17873, loss_grounding_ce_0: 0.32562/0.27319, loss_mask_ce_1: 0.43241/0.90327, loss_mask_bce_1: 0.14891/0.33613, loss_mask_dice_1: 2.23883/1.16953, loss_spatial_bce_1: 0.03973/0.08868, loss_spatial_dice_1: 0.27876/0.21419, loss_spatial_ce_1: 0.17814/0.07060, loss_grounding_bce_1: 0.00293/0.08648, loss_grounding_dice_1: 0.42954/0.17952, loss_grounding_ce_1: 0.32220/0.27448, loss_mask_ce_2: 0.29309/0.91052, loss_mask_bce_2: 0.15266/0.33663, loss_mask_dice_2: 2.10797/1.16963, loss_spatial_bce_2: 0.04626/0.08951, loss_spatial_dice_2: 0.31726/0.21555, loss_spatial_ce_2: 0.09931/0.07411, loss_grounding_bce_2: 0.00368/0.08660, loss_grounding_dice_2: 0.50566/0.17933, loss_grounding_ce_2: 0.34746/0.27777, loss_mask_ce_3: 0.29638/0.92010, loss_mask_bce_3: 0.15767/0.33769, loss_mask_dice_3: 2.20172/1.16707, loss_spatial_bce_3: 0.05130/0.09048, loss_spatial_dice_3: 0.29914/0.21623, loss_spatial_ce_3: 0.09020/0.07789, loss_grounding_bce_3: 0.00304/0.08684, loss_grounding_dice_3: 0.47183/0.17907, loss_grounding_ce_3: 0.30518/0.27957, loss_mask_ce_4: 0.48631/0.92086, loss_mask_bce_4: 0.16174/0.33974, loss_mask_dice_4: 1.94485/1.19088, loss_spatial_bce_4: 0.04422/0.09452, loss_spatial_dice_4: 0.30304/0.22808, loss_spatial_ce_4: 0.11453/0.09379, loss_grounding_bce_4: 0.00376/0.08732, loss_grounding_dice_4: 0.43255/0.18197, loss_grounding_ce_4: 0.36275/0.28239, loss_mask_ce_5: 0.29210/0.93659, loss_mask_bce_5: 0.17002/0.34195, loss_mask_dice_5: 2.22584/1.19784, loss_spatial_bce_5: 0.04410/0.09652, loss_spatial_dice_5: 0.34252/0.23198, loss_spatial_ce_5: 0.36402/0.10871, loss_grounding_bce_5: 0.00283/0.08769, loss_grounding_dice_5: 0.42567/0.18314, loss_grounding_ce_5: 0.33797/0.29528, loss_mask_ce_6: 0.39600/0.97608, loss_mask_bce_6: 0.16923/0.34471, loss_mask_dice_6: 1.95990/1.20087, loss_spatial_bce_6: 0.04466/0.10229, loss_spatial_dice_6: 0.32781/0.23474, loss_spatial_ce_6: 0.35347/0.13520, loss_grounding_bce_6: 0.00224/0.08843, loss_grounding_dice_6: 0.40260/0.18345, loss_grounding_ce_6: 0.36865/0.31075, loss_mask_ce_7: 0.44338/1.02070, loss_mask_bce_7: 0.18024/0.35258, loss_mask_dice_7: 2.60301/1.25565, loss_spatial_bce_7: 0.04409/0.11062, loss_spatial_dice_7: 0.35720/0.26253, loss_spatial_ce_7: 0.39014/0.17122, loss_grounding_bce_7: 0.00565/0.09036, loss_grounding_dice_7: 0.51935/0.19071, loss_grounding_ce_7: 0.28928/0.34267, loss_mask_ce_8: 0.45021/1.12959, loss_mask_bce_8: 0.15591/0.36621, loss_mask_dice_8: 2.25998/1.32924, loss_spatial_bce_8: 0.05045/0.13141, loss_spatial_dice_8: 0.44822/0.30103, loss_spatial_ce_8: 0.19498/0.22834, loss_grounding_bce_8: 0.00388/0.09413, loss_grounding_dice_8: 0.41410/0.20177, loss_grounding_ce_8: 0.31911/0.41005, loss_mask_ce_9: 2.44285/3.67947, loss_mask_bce_9: 0.13710/0.39317, loss_mask_dice_9: 2.68812/1.90249, loss_spatial_bce_9: 0.20377/0.33370, loss_spatial_dice_9: 0.88527/0.82238, loss_spatial_ce_9: 2.39067/1.50020, loss_grounding_bce_9: 0.00351/0.10558, loss_grounding_dice_9: 0.56681/0.28096, loss_grounding_ce_9: 0.45102/0.67480] items per batch[64] items per second[0.23] total items[3308800] mini batches[ 51700] memory[7345] epoch remaining[1:00:08]
INFO:trainer.default_trainer:epochs[    28] optim steps[51800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22388/0.90271, loss_mask_bce_0: 0.20208/0.33526, loss_mask_dice_0: 0.77027/1.16311, loss_spatial_bce_0: 0.03463/0.08812, loss_spatial_dice_0: 0.17129/0.21014, loss_spatial_ce_0: 0.00198/0.06488, loss_grounding_bce_0: 0.12822/0.08633, loss_grounding_dice_0: 0.25040/0.17872, loss_grounding_ce_0: 0.01130/0.27309, loss_mask_ce_1: 0.46127/0.90336, loss_mask_bce_1: 0.18853/0.33615, loss_mask_dice_1: 0.81643/1.16964, loss_spatial_bce_1: 0.04020/0.08867, loss_spatial_dice_1: 0.19084/0.21418, loss_spatial_ce_1: 0.00357/0.07056, loss_grounding_bce_1: 0.11509/0.08649, loss_grounding_dice_1: 0.24527/0.17951, loss_grounding_ce_1: 0.01024/0.27438, loss_mask_ce_2: 0.23609/0.91060, loss_mask_bce_2: 0.19608/0.33665, loss_mask_dice_2: 0.65234/1.16975, loss_spatial_bce_2: 0.04142/0.08951, loss_spatial_dice_2: 0.20265/0.21554, loss_spatial_ce_2: 0.00352/0.07406, loss_grounding_bce_2: 0.12276/0.08661, loss_grounding_dice_2: 0.25441/0.17932, loss_grounding_ce_2: 0.01290/0.27767, loss_mask_ce_3: 0.51603/0.92022, loss_mask_bce_3: 0.20653/0.33771, loss_mask_dice_3: 0.78196/1.16715, loss_spatial_bce_3: 0.04661/0.09048, loss_spatial_dice_3: 0.19308/0.21622, loss_spatial_ce_3: 0.00702/0.07785, loss_grounding_bce_3: 0.12463/0.08685, loss_grounding_dice_3: 0.25112/0.17906, loss_grounding_ce_3: 0.02209/0.27947, loss_mask_ce_4: 0.25294/0.92094, loss_mask_bce_4: 0.17426/0.33976, loss_mask_dice_4: 0.71455/1.19099, loss_spatial_bce_4: 0.05337/0.09451, loss_spatial_dice_4: 0.19448/0.22806, loss_spatial_ce_4: 0.04314/0.09374, loss_grounding_bce_4: 0.10314/0.08733, loss_grounding_dice_4: 0.24842/0.18196, loss_grounding_ce_4: 0.01966/0.28226, loss_mask_ce_5: 0.28259/0.93668, loss_mask_bce_5: 0.17065/0.34197, loss_mask_dice_5: 0.76625/1.19798, loss_spatial_bce_5: 0.05841/0.09652, loss_spatial_dice_5: 0.20608/0.23198, loss_spatial_ce_5: 0.07677/0.10865, loss_grounding_bce_5: 0.09787/0.08770, loss_grounding_dice_5: 0.24506/0.18313, loss_grounding_ce_5: 0.01974/0.29514, loss_mask_ce_6: 0.29838/0.97620, loss_mask_bce_6: 0.15388/0.34473, loss_mask_dice_6: 0.71104/1.20099, loss_spatial_bce_6: 0.06354/0.10230, loss_spatial_dice_6: 0.22997/0.23475, loss_spatial_ce_6: 0.10954/0.13516, loss_grounding_bce_6: 0.08717/0.08844, loss_grounding_dice_6: 0.23747/0.18344, loss_grounding_ce_6: 0.03318/0.31059, loss_mask_ce_7: 0.52588/1.02078, loss_mask_bce_7: 0.13742/0.35260, loss_mask_dice_7: 0.65879/1.25578, loss_spatial_bce_7: 0.04233/0.11062, loss_spatial_dice_7: 0.20307/0.26251, loss_spatial_ce_7: 0.07875/0.17116, loss_grounding_bce_7: 0.08076/0.09037, loss_grounding_dice_7: 0.22742/0.19070, loss_grounding_ce_7: 0.03757/0.34250, loss_mask_ce_8: 0.54354/1.12973, loss_mask_bce_8: 0.13453/0.36624, loss_mask_dice_8: 0.75452/1.32940, loss_spatial_bce_8: 0.05106/0.13140, loss_spatial_dice_8: 0.23990/0.30102, loss_spatial_ce_8: 0.15544/0.22832, loss_grounding_bce_8: 0.07605/0.09414, loss_grounding_dice_8: 0.22811/0.20175, loss_grounding_ce_8: 0.01515/0.40994, loss_mask_ce_9: 3.30848/3.67977, loss_mask_bce_9: 0.15798/0.39322, loss_mask_dice_9: 0.99078/1.90289, loss_spatial_bce_9: 0.33223/0.33371, loss_spatial_dice_9: 0.85406/0.82236, loss_spatial_ce_9: 1.73289/1.50013, loss_grounding_bce_9: 0.07646/0.10560, loss_grounding_dice_9: 0.26019/0.28093, loss_grounding_ce_9: 0.11944/0.67481] items per batch[64] items per second[0.23] total items[3315200] mini batches[ 51800] memory[7345] epoch remaining[0:55:11]
INFO:trainer.default_trainer:epochs[    28] optim steps[51900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19739/0.90266, loss_mask_bce_0: 0.29708/0.33518, loss_mask_dice_0: 2.24607/1.16274, loss_spatial_bce_0: 0.07820/0.08811, loss_spatial_dice_0: 0.33260/0.21012, loss_spatial_ce_0: 0.01594/0.06485, loss_grounding_bce_0: 0.07827/0.08632, loss_grounding_dice_0: 0.13940/0.17870, loss_grounding_ce_0: 0.46738/0.27302, loss_mask_ce_1: 1.30619/0.90330, loss_mask_bce_1: 0.29797/0.33608, loss_mask_dice_1: 2.01437/1.16926, loss_spatial_bce_1: 0.07459/0.08866, loss_spatial_dice_1: 0.32044/0.21415, loss_spatial_ce_1: 0.02434/0.07054, loss_grounding_bce_1: 0.05830/0.08649, loss_grounding_dice_1: 0.11588/0.17948, loss_grounding_ce_1: 0.99877/0.27430, loss_mask_ce_2: 1.30712/0.91053, loss_mask_bce_2: 0.29903/0.33658, loss_mask_dice_2: 2.33084/1.16936, loss_spatial_bce_2: 0.09247/0.08950, loss_spatial_dice_2: 0.35004/0.21551, loss_spatial_ce_2: 0.03031/0.07403, loss_grounding_bce_2: 0.06221/0.08661, loss_grounding_dice_2: 0.12842/0.17931, loss_grounding_ce_2: 0.63946/0.27758, loss_mask_ce_3: 1.22573/0.92017, loss_mask_bce_3: 0.30108/0.33765, loss_mask_dice_3: 2.34308/1.16675, loss_spatial_bce_3: 0.08160/0.09047, loss_spatial_dice_3: 0.32509/0.21619, loss_spatial_ce_3: 0.05341/0.07783, loss_grounding_bce_3: 0.06838/0.08685, loss_grounding_dice_3: 0.13044/0.17903, loss_grounding_ce_3: 0.43684/0.27938, loss_mask_ce_4: 1.36507/0.92089, loss_mask_bce_4: 0.31511/0.33970, loss_mask_dice_4: 2.44810/1.19060, loss_spatial_bce_4: 0.08343/0.09450, loss_spatial_dice_4: 0.34667/0.22804, loss_spatial_ce_4: 0.04741/0.09371, loss_grounding_bce_4: 0.08737/0.08733, loss_grounding_dice_4: 0.14099/0.18193, loss_grounding_ce_4: 0.40813/0.28220, loss_mask_ce_5: 1.42389/0.93660, loss_mask_bce_5: 0.31129/0.34191, loss_mask_dice_5: 2.21384/1.19762, loss_spatial_bce_5: 0.08452/0.09651, loss_spatial_dice_5: 0.35705/0.23195, loss_spatial_ce_5: 0.01873/0.10861, loss_grounding_bce_5: 0.08105/0.08770, loss_grounding_dice_5: 0.13481/0.18312, loss_grounding_ce_5: 0.47611/0.29506, loss_mask_ce_6: 1.05500/0.97614, loss_mask_bce_6: 0.30044/0.34466, loss_mask_dice_6: 2.27417/1.20062, loss_spatial_bce_6: 0.10815/0.10229, loss_spatial_dice_6: 0.34876/0.23473, loss_spatial_ce_6: 0.13927/0.13511, loss_grounding_bce_6: 0.06482/0.08844, loss_grounding_dice_6: 0.12224/0.18342, loss_grounding_ce_6: 0.62184/0.31051, loss_mask_ce_7: 1.29279/1.02072, loss_mask_bce_7: 0.29754/0.35251, loss_mask_dice_7: 2.24489/1.25539, loss_spatial_bce_7: 0.08811/0.11061, loss_spatial_dice_7: 0.34774/0.26249, loss_spatial_ce_7: 0.18703/0.17114, loss_grounding_bce_7: 0.07191/0.09036, loss_grounding_dice_7: 0.12517/0.19067, loss_grounding_ce_7: 1.02958/0.34240, loss_mask_ce_8: 1.60748/1.12968, loss_mask_bce_8: 0.32280/0.36616, loss_mask_dice_8: 2.68317/1.32902, loss_spatial_bce_8: 0.10923/0.13139, loss_spatial_dice_8: 0.40054/0.30099, loss_spatial_ce_8: 0.15297/0.22830, loss_grounding_bce_8: 0.07745/0.09414, loss_grounding_dice_8: 0.14363/0.20174, loss_grounding_ce_8: 0.92759/0.40985, loss_mask_ce_9: 4.70485/3.67965, loss_mask_bce_9: 0.49119/0.39315, loss_mask_dice_9: 3.82316/1.90227, loss_spatial_bce_9: 0.24547/0.33372, loss_spatial_dice_9: 0.91876/0.82235, loss_spatial_ce_9: 1.55981/1.50009, loss_grounding_bce_9: 0.23124/0.10560, loss_grounding_dice_9: 0.38347/0.28091, loss_grounding_ce_9: 1.67586/0.67487] items per batch[64] items per second[0.23] total items[3321600] mini batches[ 51900] memory[7345] epoch remaining[0:50:31]
INFO:trainer.default_trainer:epochs[    28] optim steps[52000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.29341/0.90264, loss_mask_bce_0: 0.44781/0.33519, loss_mask_dice_0: 0.56761/1.16269, loss_spatial_bce_0: 0.22885/0.08811, loss_spatial_dice_0: 0.26501/0.21009, loss_spatial_ce_0: 0.08320/0.06481, loss_grounding_bce_0: 0.14589/0.08633, loss_grounding_dice_0: 0.20367/0.17868, loss_grounding_ce_0: 0.16255/0.27302, loss_mask_ce_1: 1.24981/0.90326, loss_mask_bce_1: 0.48381/0.33609, loss_mask_dice_1: 0.59153/1.16922, loss_spatial_bce_1: 0.22302/0.08866, loss_spatial_dice_1: 0.26069/0.21412, loss_spatial_ce_1: 0.12946/0.07050, loss_grounding_bce_1: 0.16709/0.08649, loss_grounding_dice_1: 0.21755/0.17945, loss_grounding_ce_1: 0.15491/0.27433, loss_mask_ce_2: 1.31470/0.91047, loss_mask_bce_2: 0.43863/0.33659, loss_mask_dice_2: 0.57615/1.16933, loss_spatial_bce_2: 0.22445/0.08949, loss_spatial_dice_2: 0.27318/0.21548, loss_spatial_ce_2: 0.13028/0.07400, loss_grounding_bce_2: 0.16461/0.08661, loss_grounding_dice_2: 0.22719/0.17928, loss_grounding_ce_2: 0.16077/0.27760, loss_mask_ce_3: 1.26746/0.92014, loss_mask_bce_3: 0.49046/0.33766, loss_mask_dice_3: 0.59439/1.16673, loss_spatial_bce_3: 0.24118/0.09047, loss_spatial_dice_3: 0.28828/0.21617, loss_spatial_ce_3: 0.11518/0.07780, loss_grounding_bce_3: 0.17081/0.08686, loss_grounding_dice_3: 0.22986/0.17901, loss_grounding_ce_3: 0.17449/0.27942, loss_mask_ce_4: 1.23496/0.92087, loss_mask_bce_4: 0.47379/0.33972, loss_mask_dice_4: 0.58362/1.19061, loss_spatial_bce_4: 0.26162/0.09450, loss_spatial_dice_4: 0.26905/0.22801, loss_spatial_ce_4: 0.12464/0.09366, loss_grounding_bce_4: 0.15976/0.08734, loss_grounding_dice_4: 0.21692/0.18192, loss_grounding_ce_4: 0.17750/0.28226, loss_mask_ce_5: 1.19932/0.93657, loss_mask_bce_5: 0.47792/0.34193, loss_mask_dice_5: 0.59333/1.19763, loss_spatial_bce_5: 0.27619/0.09650, loss_spatial_dice_5: 0.29670/0.23193, loss_spatial_ce_5: 0.15560/0.10857, loss_grounding_bce_5: 0.17978/0.08772, loss_grounding_dice_5: 0.25038/0.18309, loss_grounding_ce_5: 0.19022/0.29511, loss_mask_ce_6: 1.33893/0.97611, loss_mask_bce_6: 0.48268/0.34467, loss_mask_dice_6: 0.58938/1.20058, loss_spatial_bce_6: 0.29907/0.10228, loss_spatial_dice_6: 0.30612/0.23470, loss_spatial_ce_6: 0.17991/0.13508, loss_grounding_bce_6: 0.14548/0.08846, loss_grounding_dice_6: 0.21656/0.18340, loss_grounding_ce_6: 0.22037/0.31055, loss_mask_ce_7: 1.29521/1.02072, loss_mask_bce_7: 0.59505/0.35252, loss_mask_dice_7: 0.58368/1.25537, loss_spatial_bce_7: 0.25180/0.11060, loss_spatial_dice_7: 0.33013/0.26247, loss_spatial_ce_7: 0.22595/0.17112, loss_grounding_bce_7: 0.18839/0.09038, loss_grounding_dice_7: 0.24064/0.19065, loss_grounding_ce_7: 0.16322/0.34242, loss_mask_ce_8: 1.22298/1.12970, loss_mask_bce_8: 0.57640/0.36618, loss_mask_dice_8: 0.59102/1.32901, loss_spatial_bce_8: 0.22202/0.13138, loss_spatial_dice_8: 0.26025/0.30096, loss_spatial_ce_8: 0.46179/0.22827, loss_grounding_bce_8: 0.15447/0.09414, loss_grounding_dice_8: 0.21112/0.20171, loss_grounding_ce_8: 0.18598/0.40990, loss_mask_ce_9: 3.08340/3.67972, loss_mask_bce_9: 0.49806/0.39315, loss_mask_dice_9: 0.81690/1.90232, loss_spatial_bce_9: 0.51491/0.33372, loss_spatial_dice_9: 0.79420/0.82234, loss_spatial_ce_9: 1.25117/1.50010, loss_grounding_bce_9: 0.15821/0.10560, loss_grounding_dice_9: 0.35557/0.28087, loss_grounding_ce_9: 0.32045/0.67495] items per batch[64] items per second[0.23] total items[3328000] mini batches[ 52000] memory[7345] epoch remaining[0:45:48]
INFO:trainer.default_trainer:epochs[    28] optim steps[52100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.27246/0.90257, loss_mask_bce_0: 0.15117/0.33519, loss_mask_dice_0: 0.34852/1.16264, loss_spatial_bce_0: 0.05723/0.08808, loss_spatial_dice_0: 0.22500/0.21006, loss_spatial_ce_0: 0.15052/0.06478, loss_grounding_bce_0: 0.02115/0.08633, loss_grounding_dice_0: 0.03343/0.17868, loss_grounding_ce_0: 0.00511/0.27300, loss_mask_ce_1: 0.85837/0.90316, loss_mask_bce_1: 0.14702/0.33610, loss_mask_dice_1: 0.31855/1.16921, loss_spatial_bce_1: 0.05863/0.08863, loss_spatial_dice_1: 0.15802/0.21409, loss_spatial_ce_1: 0.39692/0.07048, loss_grounding_bce_1: 0.02203/0.08650, loss_grounding_dice_1: 0.04066/0.17945, loss_grounding_ce_1: 0.00463/0.27432, loss_mask_ce_2: 0.71278/0.91036, loss_mask_bce_2: 0.16644/0.33661, loss_mask_dice_2: 0.58783/1.16932, loss_spatial_bce_2: 0.05059/0.08947, loss_spatial_dice_2: 0.22548/0.21545, loss_spatial_ce_2: 0.09753/0.07398, loss_grounding_bce_2: 0.02211/0.08662, loss_grounding_dice_2: 0.03065/0.17928, loss_grounding_ce_2: 0.00400/0.27759, loss_mask_ce_3: 0.89535/0.92005, loss_mask_bce_3: 0.15048/0.33767, loss_mask_dice_3: 0.38723/1.16671, loss_spatial_bce_3: 0.05478/0.09044, loss_spatial_dice_3: 0.23364/0.21614, loss_spatial_ce_3: 0.04771/0.07777, loss_grounding_bce_3: 0.02042/0.08686, loss_grounding_dice_3: 0.03289/0.17901, loss_grounding_ce_3: 0.00368/0.27941, loss_mask_ce_4: 0.84177/0.92079, loss_mask_bce_4: 0.16052/0.33972, loss_mask_dice_4: 0.60223/1.19061, loss_spatial_bce_4: 0.05177/0.09448, loss_spatial_dice_4: 0.22704/0.22798, loss_spatial_ce_4: 0.19387/0.09364, loss_grounding_bce_4: 0.02071/0.08734, loss_grounding_dice_4: 0.03144/0.18191, loss_grounding_ce_4: 0.00667/0.28229, loss_mask_ce_5: 0.88613/0.93654, loss_mask_bce_5: 0.15655/0.34192, loss_mask_dice_5: 0.50317/1.19760, loss_spatial_bce_5: 0.05284/0.09648, loss_spatial_dice_5: 0.23428/0.23190, loss_spatial_ce_5: 0.19814/0.10854, loss_grounding_bce_5: 0.02254/0.08771, loss_grounding_dice_5: 0.03913/0.18308, loss_grounding_ce_5: 0.00246/0.29512, loss_mask_ce_6: 1.01928/0.97605, loss_mask_bce_6: 0.13251/0.34466, loss_mask_dice_6: 0.55079/1.20062, loss_spatial_bce_6: 0.06066/0.10225, loss_spatial_dice_6: 0.28118/0.23468, loss_spatial_ce_6: 0.06861/0.13504, loss_grounding_bce_6: 0.02293/0.08845, loss_grounding_dice_6: 0.03793/0.18340, loss_grounding_ce_6: 0.00783/0.31058, loss_mask_ce_7: 1.03760/1.02067, loss_mask_bce_7: 0.14270/0.35254, loss_mask_dice_7: 0.54488/1.25539, loss_spatial_bce_7: 0.05105/0.11057, loss_spatial_dice_7: 0.25977/0.26246, loss_spatial_ce_7: 0.23356/0.17109, loss_grounding_bce_7: 0.02297/0.09038, loss_grounding_dice_7: 0.03619/0.19066, loss_grounding_ce_7: 0.04469/0.34247, loss_mask_ce_8: 1.04312/1.12969, loss_mask_bce_8: 0.18069/0.36618, loss_mask_dice_8: 0.60863/1.32900, loss_spatial_bce_8: 0.08328/0.13135, loss_spatial_dice_8: 0.31822/0.30094, loss_spatial_ce_8: 0.22040/0.22822, loss_grounding_bce_8: 0.01964/0.09415, loss_grounding_dice_8: 0.03246/0.20171, loss_grounding_ce_8: 0.03232/0.41000, loss_mask_ce_9: 2.73359/3.67987, loss_mask_bce_9: 0.17027/0.39319, loss_mask_dice_9: 0.46489/1.90230, loss_spatial_bce_9: 0.26451/0.33368, loss_spatial_dice_9: 0.62639/0.82234, loss_spatial_ce_9: 1.72953/1.50004, loss_grounding_bce_9: 0.03202/0.10560, loss_grounding_dice_9: 0.05739/0.28088, loss_grounding_ce_9: 0.22107/0.67506] items per batch[64] items per second[0.23] total items[3334400] mini batches[ 52100] memory[7345] epoch remaining[0:41:05]
INFO:trainer.default_trainer:epochs[    28] optim steps[52200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.09478/0.90252, loss_mask_bce_0: 0.10174/0.33520, loss_mask_dice_0: 1.35362/1.16239, loss_spatial_bce_0: 0.01685/0.08809, loss_spatial_dice_0: 0.25734/0.21003, loss_spatial_ce_0: 0.14266/0.06475, loss_grounding_bce_0: 0.02266/0.08634, loss_grounding_dice_0: 0.22424/0.17868, loss_grounding_ce_0: 0.24112/0.27301, loss_mask_ce_1: 0.99924/0.90311, loss_mask_bce_1: 0.09065/0.33612, loss_mask_dice_1: 1.40183/1.16896, loss_spatial_bce_1: 0.01797/0.08864, loss_spatial_dice_1: 0.29845/0.21407, loss_spatial_ce_1: 0.00918/0.07045, loss_grounding_bce_1: 0.01975/0.08651, loss_grounding_dice_1: 0.36736/0.17944, loss_grounding_ce_1: 0.21690/0.27435, loss_mask_ce_2: 0.91674/0.91027, loss_mask_bce_2: 0.10519/0.33662, loss_mask_dice_2: 1.92869/1.16909, loss_spatial_bce_2: 0.02040/0.08948, loss_spatial_dice_2: 0.27537/0.21544, loss_spatial_ce_2: 0.00823/0.07394, loss_grounding_bce_2: 0.01769/0.08663, loss_grounding_dice_2: 0.22605/0.17927, loss_grounding_ce_2: 0.25664/0.27753, loss_mask_ce_3: 0.73413/0.91994, loss_mask_bce_3: 0.09702/0.33769, loss_mask_dice_3: 1.35742/1.16648, loss_spatial_bce_3: 0.01950/0.09046, loss_spatial_dice_3: 0.24887/0.21613, loss_spatial_ce_3: 0.00714/0.07773, loss_grounding_bce_3: 0.01550/0.08688, loss_grounding_dice_3: 0.22536/0.17900, loss_grounding_ce_3: 0.20045/0.27937, loss_mask_ce_4: 0.91838/0.92071, loss_mask_bce_4: 0.08809/0.33974, loss_mask_dice_4: 1.52383/1.19037, loss_spatial_bce_4: 0.01990/0.09449, loss_spatial_dice_4: 0.27510/0.22796, loss_spatial_ce_4: 0.01972/0.09359, loss_grounding_bce_4: 0.01358/0.08735, loss_grounding_dice_4: 0.23535/0.18191, loss_grounding_ce_4: 0.22997/0.28226, loss_mask_ce_5: 1.07066/0.93649, loss_mask_bce_5: 0.09594/0.34193, loss_mask_dice_5: 1.54799/1.19735, loss_spatial_bce_5: 0.01908/0.09649, loss_spatial_dice_5: 0.28447/0.23188, loss_spatial_ce_5: 0.05107/0.10849, loss_grounding_bce_5: 0.01531/0.08773, loss_grounding_dice_5: 0.24338/0.18308, loss_grounding_ce_5: 0.20905/0.29507, loss_mask_ce_6: 1.42430/0.97600, loss_mask_bce_6: 0.10300/0.34467, loss_mask_dice_6: 1.49892/1.20036, loss_spatial_bce_6: 0.02089/0.10227, loss_spatial_dice_6: 0.28398/0.23466, loss_spatial_ce_6: 0.36709/0.13499, loss_grounding_bce_6: 0.01815/0.08847, loss_grounding_dice_6: 0.23220/0.18340, loss_grounding_ce_6: 0.35623/0.31060, loss_mask_ce_7: 1.32096/1.02065, loss_mask_bce_7: 0.09696/0.35255, loss_mask_dice_7: 1.38254/1.25511, loss_spatial_bce_7: 0.01881/0.11059, loss_spatial_dice_7: 0.34789/0.26244, loss_spatial_ce_7: 0.15867/0.17108, loss_grounding_bce_7: 0.01436/0.09040, loss_grounding_dice_7: 0.31504/0.19067, loss_grounding_ce_7: 0.41554/0.34241, loss_mask_ce_8: 1.58276/1.12963, loss_mask_bce_8: 0.11011/0.36620, loss_mask_dice_8: 1.48190/1.32874, loss_spatial_bce_8: 0.02463/0.13138, loss_spatial_dice_8: 0.36417/0.30091, loss_spatial_ce_8: 0.23731/0.22817, loss_grounding_bce_8: 0.02384/0.09416, loss_grounding_dice_8: 0.31037/0.20170, loss_grounding_ce_8: 0.32429/0.41005, loss_mask_ce_9: 3.61470/3.67962, loss_mask_bce_9: 0.11145/0.39318, loss_mask_dice_9: 1.85995/1.90194, loss_spatial_bce_9: 0.16871/0.33369, loss_spatial_dice_9: 0.78527/0.82231, loss_spatial_ce_9: 1.59963/1.49991, loss_grounding_bce_9: 0.02555/0.10562, loss_grounding_dice_9: 0.45585/0.28087, loss_grounding_ce_9: 0.44433/0.67496] items per batch[64] items per second[0.23] total items[3340800] mini batches[ 52200] memory[7345] epoch remaining[0:36:21]
INFO:trainer.default_trainer:epochs[    28] optim steps[52300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.57946/0.90252, loss_mask_bce_0: 0.07183/0.33517, loss_mask_dice_0: 1.23429/1.16247, loss_spatial_bce_0: 0.02334/0.08807, loss_spatial_dice_0: 0.33955/0.21002, loss_spatial_ce_0: 0.03365/0.06473, loss_grounding_bce_0: 0.01348/0.08633, loss_grounding_dice_0: 0.09184/0.17868, loss_grounding_ce_0: 4.04414/0.27304, loss_mask_ce_1: 1.51615/0.90310, loss_mask_bce_1: 0.09853/0.33610, loss_mask_dice_1: 1.29350/1.16902, loss_spatial_bce_1: 0.01697/0.08861, loss_spatial_dice_1: 0.28544/0.21406, loss_spatial_ce_1: 0.09576/0.07043, loss_grounding_bce_1: 0.03369/0.08650, loss_grounding_dice_1: 0.15817/0.17945, loss_grounding_ce_1: 4.81202/0.27437, loss_mask_ce_2: 1.36583/0.91027, loss_mask_bce_2: 0.08621/0.33660, loss_mask_dice_2: 1.21484/1.16917, loss_spatial_bce_2: 0.01575/0.08946, loss_spatial_dice_2: 0.28986/0.21543, loss_spatial_ce_2: 0.35295/0.07393, loss_grounding_bce_2: 0.01369/0.08663, loss_grounding_dice_2: 0.10866/0.17928, loss_grounding_ce_2: 4.35884/0.27756, loss_mask_ce_3: 1.84737/0.91993, loss_mask_bce_3: 0.08054/0.33767, loss_mask_dice_3: 1.43001/1.16657, loss_spatial_bce_3: 0.01733/0.09044, loss_spatial_dice_3: 0.29092/0.21612, loss_spatial_ce_3: 0.10918/0.07770, loss_grounding_bce_3: 0.01358/0.08687, loss_grounding_dice_3: 0.08025/0.17902, loss_grounding_ce_3: 4.29209/0.27939, loss_mask_ce_4: 1.42949/0.92072, loss_mask_bce_4: 0.08623/0.33973, loss_mask_dice_4: 1.48910/1.19046, loss_spatial_bce_4: 0.01793/0.09447, loss_spatial_dice_4: 0.33512/0.22796, loss_spatial_ce_4: 0.35240/0.09358, loss_grounding_bce_4: 0.02186/0.08735, loss_grounding_dice_4: 0.18426/0.18193, loss_grounding_ce_4: 3.45997/0.28227, loss_mask_ce_5: 1.57471/0.93651, loss_mask_bce_5: 0.07605/0.34192, loss_mask_dice_5: 1.36002/1.19742, loss_spatial_bce_5: 0.02360/0.09647, loss_spatial_dice_5: 0.30865/0.23187, loss_spatial_ce_5: 0.04207/0.10848, loss_grounding_bce_5: 0.02200/0.08773, loss_grounding_dice_5: 0.15030/0.18310, loss_grounding_ce_5: 4.57296/0.29509, loss_mask_ce_6: 1.91921/0.97607, loss_mask_bce_6: 0.08443/0.34466, loss_mask_dice_6: 1.49652/1.20045, loss_spatial_bce_6: 0.02034/0.10225, loss_spatial_dice_6: 0.36597/0.23466, loss_spatial_ce_6: 0.05872/0.13496, loss_grounding_bce_6: 0.02393/0.08846, loss_grounding_dice_6: 0.15152/0.18342, loss_grounding_ce_6: 2.71531/0.31057, loss_mask_ce_7: 1.63885/1.02067, loss_mask_bce_7: 0.08227/0.35255, loss_mask_dice_7: 1.56752/1.25525, loss_spatial_bce_7: 0.02580/0.11056, loss_spatial_dice_7: 0.36350/0.26245, loss_spatial_ce_7: 0.11911/0.17103, loss_grounding_bce_7: 0.02698/0.09039, loss_grounding_dice_7: 0.17341/0.19069, loss_grounding_ce_7: 3.58359/0.34237, loss_mask_ce_8: 1.86105/1.12958, loss_mask_bce_8: 0.09478/0.36621, loss_mask_dice_8: 1.72522/1.32890, loss_spatial_bce_8: 0.02743/0.13136, loss_spatial_dice_8: 0.43937/0.30091, loss_spatial_ce_8: 0.16480/0.22811, loss_grounding_bce_8: 0.03291/0.09416, loss_grounding_dice_8: 0.20455/0.20172, loss_grounding_ce_8: 4.10784/0.41007, loss_mask_ce_9: 4.76927/3.67983, loss_mask_bce_9: 0.06229/0.39319, loss_mask_dice_9: 1.98415/1.90205, loss_spatial_bce_9: 0.04902/0.33364, loss_spatial_dice_9: 0.84267/0.82233, loss_spatial_ce_9: 1.33441/1.49992, loss_grounding_bce_9: 0.01224/0.10560, loss_grounding_dice_9: 0.15089/0.28088, loss_grounding_ce_9: 3.03167/0.67492] items per batch[64] items per second[0.23] total items[3347200] mini batches[ 52300] memory[7345] epoch remaining[0:31:42]
INFO:trainer.default_trainer:epochs[    28] optim steps[52400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49709/0.90239, loss_mask_bce_0: 0.55681/0.33515, loss_mask_dice_0: 1.52481/1.16224, loss_spatial_bce_0: 0.06706/0.08807, loss_spatial_dice_0: 0.19281/0.20999, loss_spatial_ce_0: 0.04012/0.06472, loss_grounding_bce_0: 0.01627/0.08632, loss_grounding_dice_0: 0.12304/0.17865, loss_grounding_ce_0: 0.53978/0.27305, loss_mask_ce_1: 1.45970/0.90296, loss_mask_bce_1: 0.55444/0.33607, loss_mask_dice_1: 1.33015/1.16878, loss_spatial_bce_1: 0.09475/0.08861, loss_spatial_dice_1: 0.18469/0.21403, loss_spatial_ce_1: 0.04448/0.07043, loss_grounding_bce_1: 0.01808/0.08650, loss_grounding_dice_1: 0.11063/0.17944, loss_grounding_ce_1: 0.60264/0.27433, loss_mask_ce_2: 1.55826/0.91015, loss_mask_bce_2: 0.56337/0.33657, loss_mask_dice_2: 1.50946/1.16894, loss_spatial_bce_2: 0.10930/0.08946, loss_spatial_dice_2: 0.21645/0.21539, loss_spatial_ce_2: 0.05507/0.07393, loss_grounding_bce_2: 0.01753/0.08662, loss_grounding_dice_2: 0.10493/0.17926, loss_grounding_ce_2: 0.74645/0.27758, loss_mask_ce_3: 1.55940/0.91985, loss_mask_bce_3: 0.61390/0.33765, loss_mask_dice_3: 1.50254/1.16631, loss_spatial_bce_3: 0.12783/0.09044, loss_spatial_dice_3: 0.20296/0.21609, loss_spatial_ce_3: 0.07801/0.07773, loss_grounding_bce_3: 0.01752/0.08686, loss_grounding_dice_3: 0.12920/0.17899, loss_grounding_ce_3: 0.65121/0.27942, loss_mask_ce_4: 1.66290/0.92059, loss_mask_bce_4: 0.57203/0.33971, loss_mask_dice_4: 1.59350/1.19020, loss_spatial_bce_4: 0.09142/0.09447, loss_spatial_dice_4: 0.18886/0.22792, loss_spatial_ce_4: 0.06876/0.09357, loss_grounding_bce_4: 0.01603/0.08734, loss_grounding_dice_4: 0.11116/0.18191, loss_grounding_ce_4: 0.69101/0.28232, loss_mask_ce_5: 1.71399/0.93639, loss_mask_bce_5: 0.60326/0.34190, loss_mask_dice_5: 1.62204/1.19717, loss_spatial_bce_5: 0.10215/0.09647, loss_spatial_dice_5: 0.20910/0.23183, loss_spatial_ce_5: 0.09640/0.10848, loss_grounding_bce_5: 0.01589/0.08772, loss_grounding_dice_5: 0.10937/0.18308, loss_grounding_ce_5: 0.60476/0.29510, loss_mask_ce_6: 1.49040/0.97598, loss_mask_bce_6: 0.67418/0.34464, loss_mask_dice_6: 1.63105/1.20019, loss_spatial_bce_6: 0.09923/0.10226, loss_spatial_dice_6: 0.17979/0.23463, loss_spatial_ce_6: 0.14014/0.13498, loss_grounding_bce_6: 0.01733/0.08846, loss_grounding_dice_6: 0.10906/0.18340, loss_grounding_ce_6: 0.83925/0.31057, loss_mask_ce_7: 1.58540/1.02057, loss_mask_bce_7: 0.77330/0.35253, loss_mask_dice_7: 1.61444/1.25499, loss_spatial_bce_7: 0.10932/0.11058, loss_spatial_dice_7: 0.22477/0.26242, loss_spatial_ce_7: 0.16043/0.17100, loss_grounding_bce_7: 0.02318/0.09039, loss_grounding_dice_7: 0.11295/0.19066, loss_grounding_ce_7: 0.79672/0.34241, loss_mask_ce_8: 1.98693/1.12947, loss_mask_bce_8: 0.64708/0.36618, loss_mask_dice_8: 1.91711/1.32864, loss_spatial_bce_8: 0.11422/0.13137, loss_spatial_dice_8: 0.33613/0.30087, loss_spatial_ce_8: 0.20992/0.22808, loss_grounding_bce_8: 0.03193/0.09416, loss_grounding_dice_8: 0.13786/0.20170, loss_grounding_ce_8: 0.72659/0.40998, loss_mask_ce_9: 5.86194/3.67963, loss_mask_bce_9: 0.84850/0.39317, loss_mask_dice_9: 2.95107/1.90164, loss_spatial_bce_9: 0.18388/0.33366, loss_spatial_dice_9: 0.89107/0.82231, loss_spatial_ce_9: 1.45764/1.49984, loss_grounding_bce_9: 0.06078/0.10560, loss_grounding_dice_9: 0.28185/0.28085, loss_grounding_ce_9: 1.92127/0.67494] items per batch[64] items per second[0.23] total items[3353600] mini batches[ 52400] memory[7345] epoch remaining[0:27:03]
INFO:trainer.default_trainer:epochs[    28] optim steps[52500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25212/0.90251, loss_mask_bce_0: 0.09612/0.33510, loss_mask_dice_0: 0.89908/1.16232, loss_spatial_bce_0: 0.02265/0.08806, loss_spatial_dice_0: 0.18464/0.20999, loss_spatial_ce_0: 0.13593/0.06471, loss_grounding_bce_0: 0.01979/0.08631, loss_grounding_dice_0: 0.26301/0.17866, loss_grounding_ce_0: 0.43992/0.27300, loss_mask_ce_1: 1.28993/0.90303, loss_mask_bce_1: 0.13966/0.33603, loss_mask_dice_1: 1.13443/1.16892, loss_spatial_bce_1: 0.02047/0.08860, loss_spatial_dice_1: 0.19465/0.21403, loss_spatial_ce_1: 0.13287/0.07042, loss_grounding_bce_1: 0.02052/0.08648, loss_grounding_dice_1: 0.28087/0.17944, loss_grounding_ce_1: 0.27766/0.27429, loss_mask_ce_2: 1.45119/0.91023, loss_mask_bce_2: 0.10345/0.33653, loss_mask_dice_2: 1.00771/1.16908, loss_spatial_bce_2: 0.02283/0.08945, loss_spatial_dice_2: 0.25161/0.21539, loss_spatial_ce_2: 0.07163/0.07390, loss_grounding_bce_2: 0.01976/0.08661, loss_grounding_dice_2: 0.28308/0.17926, loss_grounding_ce_2: 0.25255/0.27753, loss_mask_ce_3: 1.11719/0.91993, loss_mask_bce_3: 0.11370/0.33761, loss_mask_dice_3: 0.97503/1.16641, loss_spatial_bce_3: 0.02334/0.09043, loss_spatial_dice_3: 0.21536/0.21609, loss_spatial_ce_3: 0.08684/0.07773, loss_grounding_bce_3: 0.01923/0.08685, loss_grounding_dice_3: 0.26810/0.17900, loss_grounding_ce_3: 0.46523/0.27937, loss_mask_ce_4: 1.82899/0.92073, loss_mask_bce_4: 0.11259/0.33967, loss_mask_dice_4: 0.99412/1.19031, loss_spatial_bce_4: 0.02381/0.09446, loss_spatial_dice_4: 0.23735/0.22793, loss_spatial_ce_4: 0.07996/0.09354, loss_grounding_bce_4: 0.02211/0.08733, loss_grounding_dice_4: 0.27469/0.18192, loss_grounding_ce_4: 0.39847/0.28225, loss_mask_ce_5: 1.75242/0.93648, loss_mask_bce_5: 0.10539/0.34186, loss_mask_dice_5: 0.98298/1.19728, loss_spatial_bce_5: 0.02572/0.09647, loss_spatial_dice_5: 0.23762/0.23184, loss_spatial_ce_5: 0.07243/0.10848, loss_grounding_bce_5: 0.02589/0.08771, loss_grounding_dice_5: 0.33918/0.18308, loss_grounding_ce_5: 0.37020/0.29503, loss_mask_ce_6: 1.28729/0.97602, loss_mask_bce_6: 0.09528/0.34461, loss_mask_dice_6: 0.85860/1.20031, loss_spatial_bce_6: 0.03233/0.10225, loss_spatial_dice_6: 0.23002/0.23464, loss_spatial_ce_6: 0.30170/0.13495, loss_grounding_bce_6: 0.02693/0.08845, loss_grounding_dice_6: 0.30937/0.18341, loss_grounding_ce_6: 0.55419/0.31050, loss_mask_ce_7: 1.58723/1.02067, loss_mask_bce_7: 0.11110/0.35249, loss_mask_dice_7: 0.95057/1.25511, loss_spatial_bce_7: 0.03476/0.11057, loss_spatial_dice_7: 0.31247/0.26244, loss_spatial_ce_7: 0.30065/0.17099, loss_grounding_bce_7: 0.03125/0.09038, loss_grounding_dice_7: 0.33702/0.19068, loss_grounding_ce_7: 0.52461/0.34231, loss_mask_ce_8: 1.67317/1.12960, loss_mask_bce_8: 0.10820/0.36614, loss_mask_dice_8: 1.14571/1.32877, loss_spatial_bce_8: 0.04405/0.13136, loss_spatial_dice_8: 0.41846/0.30088, loss_spatial_ce_8: 0.36466/0.22804, loss_grounding_bce_8: 0.02717/0.09415, loss_grounding_dice_8: 0.32925/0.20172, loss_grounding_ce_8: 0.41569/0.40984, loss_mask_ce_9: 2.60017/3.67978, loss_mask_bce_9: 0.09954/0.39313, loss_mask_dice_9: 1.15087/1.90183, loss_spatial_bce_9: 0.28547/0.33365, loss_spatial_dice_9: 0.81072/0.82232, loss_spatial_ce_9: 1.54710/1.49992, loss_grounding_bce_9: 0.02484/0.10560, loss_grounding_dice_9: 0.38502/0.28086, loss_grounding_ce_9: 0.30951/0.67487] items per batch[64] items per second[0.23] total items[3360000] mini batches[ 52500] memory[7345] epoch remaining[0:22:24]
INFO:trainer.default_trainer:epochs[    28] optim steps[52600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.12630/0.90237, loss_mask_bce_0: 0.25109/0.33511, loss_mask_dice_0: 0.12695/1.16233, loss_spatial_bce_0: 0.18554/0.08805, loss_spatial_dice_0: 0.08719/0.20997, loss_spatial_ce_0: 0.00004/0.06469, loss_grounding_bce_0: 0.32432/0.08630, loss_grounding_dice_0: 0.16669/0.17865, loss_grounding_ce_0: 0.00302/0.27293, loss_mask_ce_1: 0.13134/0.90293, loss_mask_bce_1: 0.25768/0.33604, loss_mask_dice_1: 0.13108/1.16888, loss_spatial_bce_1: 0.17608/0.08859, loss_spatial_dice_1: 0.08299/0.21400, loss_spatial_ce_1: 0.00006/0.07040, loss_grounding_bce_1: 0.34496/0.08648, loss_grounding_dice_1: 0.17673/0.17944, loss_grounding_ce_1: 0.00329/0.27424, loss_mask_ce_2: 0.11658/0.91008, loss_mask_bce_2: 0.24612/0.33654, loss_mask_dice_2: 0.13016/1.16904, loss_spatial_bce_2: 0.17499/0.08945, loss_spatial_dice_2: 0.08332/0.21538, loss_spatial_ce_2: 0.00008/0.07387, loss_grounding_bce_2: 0.34709/0.08661, loss_grounding_dice_2: 0.16563/0.17925, loss_grounding_ce_2: 0.00410/0.27748, loss_mask_ce_3: 0.12034/0.91984, loss_mask_bce_3: 0.24123/0.33761, loss_mask_dice_3: 0.12779/1.16636, loss_spatial_bce_3: 0.18152/0.09044, loss_spatial_dice_3: 0.08683/0.21607, loss_spatial_ce_3: 0.00007/0.07772, loss_grounding_bce_3: 0.35156/0.08685, loss_grounding_dice_3: 0.15777/0.17899, loss_grounding_ce_3: 0.00343/0.27931, loss_mask_ce_4: 0.13107/0.92061, loss_mask_bce_4: 0.25730/0.33969, loss_mask_dice_4: 0.12531/1.19031, loss_spatial_bce_4: 0.18971/0.09446, loss_spatial_dice_4: 0.08869/0.22791, loss_spatial_ce_4: 0.00010/0.09353, loss_grounding_bce_4: 0.36265/0.08733, loss_grounding_dice_4: 0.14711/0.18191, loss_grounding_ce_4: 0.00316/0.28218, loss_mask_ce_5: 0.11425/0.93641, loss_mask_bce_5: 0.25405/0.34188, loss_mask_dice_5: 0.12783/1.19728, loss_spatial_bce_5: 0.19291/0.09647, loss_spatial_dice_5: 0.09385/0.23182, loss_spatial_ce_5: 0.00071/0.10843, loss_grounding_bce_5: 0.35290/0.08770, loss_grounding_dice_5: 0.15461/0.18307, loss_grounding_ce_5: 0.00460/0.29496, loss_mask_ce_6: 0.10521/0.97590, loss_mask_bce_6: 0.25633/0.34462, loss_mask_dice_6: 0.13107/1.20027, loss_spatial_bce_6: 0.19017/0.10226, loss_spatial_dice_6: 0.09865/0.23463, loss_spatial_ce_6: 0.00069/0.13493, loss_grounding_bce_6: 0.36160/0.08845, loss_grounding_dice_6: 0.16399/0.18341, loss_grounding_ce_6: 0.00307/0.31050, loss_mask_ce_7: 0.11302/1.02054, loss_mask_bce_7: 0.25213/0.35250, loss_mask_dice_7: 0.14157/1.25510, loss_spatial_bce_7: 0.17705/0.11058, loss_spatial_dice_7: 0.11053/0.26243, loss_spatial_ce_7: 0.00618/0.17097, loss_grounding_bce_7: 0.37040/0.09038, loss_grounding_dice_7: 0.16902/0.19067, loss_grounding_ce_7: 0.00349/0.34223, loss_mask_ce_8: 0.15502/1.12953, loss_mask_bce_8: 0.24476/0.36616, loss_mask_dice_8: 0.14686/1.32877, loss_spatial_bce_8: 0.23723/0.13137, loss_spatial_dice_8: 0.10760/0.30087, loss_spatial_ce_8: 0.03176/0.22800, loss_grounding_bce_8: 0.35857/0.09416, loss_grounding_dice_8: 0.20743/0.20172, loss_grounding_ce_8: 0.04110/0.40970, loss_mask_ce_9: 2.56667/3.67972, loss_mask_bce_9: 0.26301/0.39314, loss_mask_dice_9: 0.24481/1.90171, loss_spatial_bce_9: 0.66180/0.33366, loss_spatial_dice_9: 0.60565/0.82232, loss_spatial_ce_9: 0.83498/1.49990, loss_grounding_bce_9: 0.32741/0.10560, loss_grounding_dice_9: 0.21833/0.28084, loss_grounding_ce_9: 1.32925/0.67482] items per batch[64] items per second[0.23] total items[3366400] mini batches[ 52600] memory[7345] epoch remaining[0:17:45]
INFO:trainer.default_trainer:epochs[    28] optim steps[52700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69300/0.90231, loss_mask_bce_0: 0.18875/0.33515, loss_mask_dice_0: 1.33547/1.16277, loss_spatial_bce_0: 0.03465/0.08805, loss_spatial_dice_0: 0.18600/0.20998, loss_spatial_ce_0: 0.05819/0.06469, loss_grounding_bce_0: 0.05442/0.08631, loss_grounding_dice_0: 0.22695/0.17866, loss_grounding_ce_0: 0.06880/0.27299, loss_mask_ce_1: 0.62465/0.90284, loss_mask_bce_1: 0.20464/0.33607, loss_mask_dice_1: 1.45066/1.16928, loss_spatial_bce_1: 0.03738/0.08859, loss_spatial_dice_1: 0.19541/0.21401, loss_spatial_ce_1: 0.02505/0.07039, loss_grounding_bce_1: 0.05607/0.08649, loss_grounding_dice_1: 0.23669/0.17946, loss_grounding_ce_1: 0.14075/0.27428, loss_mask_ce_2: 0.57812/0.90997, loss_mask_bce_2: 0.19232/0.33657, loss_mask_dice_2: 1.27890/1.16945, loss_spatial_bce_2: 0.03681/0.08944, loss_spatial_dice_2: 0.19453/0.21538, loss_spatial_ce_2: 0.04954/0.07387, loss_grounding_bce_2: 0.05580/0.08661, loss_grounding_dice_2: 0.24892/0.17927, loss_grounding_ce_2: 0.07476/0.27754, loss_mask_ce_3: 0.62938/0.91977, loss_mask_bce_3: 0.18915/0.33763, loss_mask_dice_3: 1.57889/1.16681, loss_spatial_bce_3: 0.03684/0.09043, loss_spatial_dice_3: 0.18240/0.21608, loss_spatial_ce_3: 0.02617/0.07771, loss_grounding_bce_3: 0.04546/0.08686, loss_grounding_dice_3: 0.23690/0.17900, loss_grounding_ce_3: 0.12851/0.27938, loss_mask_ce_4: 0.71065/0.92054, loss_mask_bce_4: 0.19012/0.33971, loss_mask_dice_4: 1.54741/1.19073, loss_spatial_bce_4: 0.03900/0.09446, loss_spatial_dice_4: 0.20755/0.22793, loss_spatial_ce_4: 0.03348/0.09352, loss_grounding_bce_4: 0.04711/0.08734, loss_grounding_dice_4: 0.25943/0.18193, loss_grounding_ce_4: 0.15503/0.28225, loss_mask_ce_5: 0.72053/0.93632, loss_mask_bce_5: 0.20114/0.34190, loss_mask_dice_5: 1.57646/1.19770, loss_spatial_bce_5: 0.04021/0.09647, loss_spatial_dice_5: 0.23204/0.23184, loss_spatial_ce_5: 0.04177/0.10842, loss_grounding_bce_5: 0.04784/0.08771, loss_grounding_dice_5: 0.23848/0.18308, loss_grounding_ce_5: 0.08253/0.29501, loss_mask_ce_6: 0.71437/0.97583, loss_mask_bce_6: 0.19228/0.34465, loss_mask_dice_6: 1.44028/1.20072, loss_spatial_bce_6: 0.03750/0.10226, loss_spatial_dice_6: 0.22096/0.23464, loss_spatial_ce_6: 0.05344/0.13492, loss_grounding_bce_6: 0.04794/0.08846, loss_grounding_dice_6: 0.23430/0.18341, loss_grounding_ce_6: 0.08798/0.31053, loss_mask_ce_7: 0.78920/1.02048, loss_mask_bce_7: 0.20420/0.35253, loss_mask_dice_7: 1.49039/1.25557, loss_spatial_bce_7: 0.04437/0.11057, loss_spatial_dice_7: 0.23389/0.26246, loss_spatial_ce_7: 0.04994/0.17092, loss_grounding_bce_7: 0.04851/0.09039, loss_grounding_dice_7: 0.23859/0.19068, loss_grounding_ce_7: 0.18580/0.34224, loss_mask_ce_8: 1.18005/1.12954, loss_mask_bce_8: 0.21485/0.36620, loss_mask_dice_8: 1.62566/1.32920, loss_spatial_bce_8: 0.06167/0.13136, loss_spatial_dice_8: 0.26710/0.30090, loss_spatial_ce_8: 0.16154/0.22798, loss_grounding_bce_8: 0.05082/0.09416, loss_grounding_dice_8: 0.23783/0.20173, loss_grounding_ce_8: 0.17051/0.40974, loss_mask_ce_9: 2.91748/3.67971, loss_mask_bce_9: 0.27498/0.39318, loss_mask_dice_9: 2.73445/1.90234, loss_spatial_bce_9: 0.13525/0.33362, loss_spatial_dice_9: 0.80280/0.82234, loss_spatial_ce_9: 1.07519/1.49998, loss_grounding_bce_9: 0.05163/0.10561, loss_grounding_dice_9: 0.26493/0.28085, loss_grounding_ce_9: 0.13496/0.67488] items per batch[64] items per second[0.23] total items[3372800] mini batches[ 52700] memory[7345] epoch remaining[0:13:06]
INFO:trainer.default_trainer:epochs[    28] optim steps[52800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85204/0.90223, loss_mask_bce_0: 0.28109/0.33507, loss_mask_dice_0: 0.25284/1.16263, loss_spatial_bce_0: 0.18609/0.08804, loss_spatial_dice_0: 0.15935/0.20997, loss_spatial_ce_0: 0.23591/0.06467, loss_grounding_bce_0: 0.10286/0.08631, loss_grounding_dice_0: 0.09651/0.17866, loss_grounding_ce_0: 0.13365/0.27304, loss_mask_ce_1: 0.84092/0.90273, loss_mask_bce_1: 0.29173/0.33599, loss_mask_dice_1: 0.26252/1.16917, loss_spatial_bce_1: 0.16228/0.08857, loss_spatial_dice_1: 0.16017/0.21400, loss_spatial_ce_1: 0.33135/0.07038, loss_grounding_bce_1: 0.10469/0.08649, loss_grounding_dice_1: 0.09366/0.17945, loss_grounding_ce_1: 0.13196/0.27426, loss_mask_ce_2: 0.84861/0.90988, loss_mask_bce_2: 0.29455/0.33649, loss_mask_dice_2: 0.26519/1.16935, loss_spatial_bce_2: 0.19145/0.08943, loss_spatial_dice_2: 0.16578/0.21537, loss_spatial_ce_2: 0.15434/0.07386, loss_grounding_bce_2: 0.10505/0.08661, loss_grounding_dice_2: 0.09511/0.17926, loss_grounding_ce_2: 0.14128/0.27751, loss_mask_ce_3: 0.83307/0.91970, loss_mask_bce_3: 0.28473/0.33756, loss_mask_dice_3: 0.25664/1.16668, loss_spatial_bce_3: 0.17883/0.09042, loss_spatial_dice_3: 0.16204/0.21608, loss_spatial_ce_3: 0.16099/0.07771, loss_grounding_bce_3: 0.10787/0.08686, loss_grounding_dice_3: 0.09407/0.17899, loss_grounding_ce_3: 0.14120/0.27935, loss_mask_ce_4: 0.85361/0.92044, loss_mask_bce_4: 0.32950/0.33964, loss_mask_dice_4: 0.28852/1.19060, loss_spatial_bce_4: 0.16108/0.09444, loss_spatial_dice_4: 0.15548/0.22792, loss_spatial_ce_4: 0.27037/0.09350, loss_grounding_bce_4: 0.11077/0.08734, loss_grounding_dice_4: 0.09965/0.18191, loss_grounding_ce_4: 0.11548/0.28223, loss_mask_ce_5: 0.82120/0.93622, loss_mask_bce_5: 0.34614/0.34183, loss_mask_dice_5: 0.29223/1.19757, loss_spatial_bce_5: 0.16500/0.09645, loss_spatial_dice_5: 0.15295/0.23184, loss_spatial_ce_5: 0.28889/0.10840, loss_grounding_bce_5: 0.11682/0.08772, loss_grounding_dice_5: 0.10947/0.18307, loss_grounding_ce_5: 0.13615/0.29502, loss_mask_ce_6: 0.82951/0.97572, loss_mask_bce_6: 0.37088/0.34457, loss_mask_dice_6: 0.30250/1.20056, loss_spatial_bce_6: 0.18213/0.10225, loss_spatial_dice_6: 0.17310/0.23464, loss_spatial_ce_6: 0.36088/0.13493, loss_grounding_bce_6: 0.11345/0.08846, loss_grounding_dice_6: 0.09986/0.18340, loss_grounding_ce_6: 0.12434/0.31049, loss_mask_ce_7: 0.91715/1.02038, loss_mask_bce_7: 0.34797/0.35246, loss_mask_dice_7: 0.30096/1.25541, loss_spatial_bce_7: 0.17117/0.11056, loss_spatial_dice_7: 0.16114/0.26246, loss_spatial_ce_7: 0.32903/0.17097, loss_grounding_bce_7: 0.13041/0.09039, loss_grounding_dice_7: 0.10704/0.19066, loss_grounding_ce_7: 0.10521/0.34219, loss_mask_ce_8: 0.84997/1.12946, loss_mask_bce_8: 0.36857/0.36611, loss_mask_dice_8: 0.34424/1.32899, loss_spatial_bce_8: 0.18388/0.13134, loss_spatial_dice_8: 0.17069/0.30090, loss_spatial_ce_8: 0.38722/0.22800, loss_grounding_bce_8: 0.13998/0.09417, loss_grounding_dice_8: 0.10117/0.20172, loss_grounding_ce_8: 0.14888/0.40971, loss_mask_ce_9: 2.95801/3.67943, loss_mask_bce_9: 0.35858/0.39308, loss_mask_dice_9: 0.39806/1.90194, loss_spatial_bce_9: 0.52117/0.33362, loss_spatial_dice_9: 0.66937/0.82233, loss_spatial_ce_9: 1.21506/1.50002, loss_grounding_bce_9: 0.14345/0.10560, loss_grounding_dice_9: 0.14948/0.28081, loss_grounding_ce_9: 0.31277/0.67494] items per batch[64] items per second[0.23] total items[3379200] mini batches[ 52800] memory[7345] epoch remaining[0:08:28]
INFO:trainer.default_trainer:epochs[    28] optim steps[52900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06798/0.90218, loss_mask_bce_0: 0.17713/0.33510, loss_mask_dice_0: 0.24980/1.16249, loss_spatial_bce_0: 0.13788/0.08803, loss_spatial_dice_0: 0.18403/0.20996, loss_spatial_ce_0: 0.00106/0.06465, loss_grounding_bce_0: 0.14534/0.08632, loss_grounding_dice_0: 0.19395/0.17867, loss_grounding_ce_0: 0.05565/0.27303, loss_mask_ce_1: 0.06641/0.90271, loss_mask_bce_1: 0.17999/0.33602, loss_mask_dice_1: 0.24305/1.16905, loss_spatial_bce_1: 0.15400/0.08857, loss_spatial_dice_1: 0.19561/0.21399, loss_spatial_ce_1: 0.00050/0.07037, loss_grounding_bce_1: 0.15324/0.08650, loss_grounding_dice_1: 0.19944/0.17945, loss_grounding_ce_1: 0.05159/0.27423, loss_mask_ce_2: 0.06191/0.90987, loss_mask_bce_2: 0.17113/0.33652, loss_mask_dice_2: 0.23583/1.16922, loss_spatial_bce_2: 0.15646/0.08942, loss_spatial_dice_2: 0.19405/0.21537, loss_spatial_ce_2: 0.00132/0.07384, loss_grounding_bce_2: 0.14796/0.08663, loss_grounding_dice_2: 0.19883/0.17926, loss_grounding_ce_2: 0.05806/0.27749, loss_mask_ce_3: 0.06286/0.91967, loss_mask_bce_3: 0.18165/0.33757, loss_mask_dice_3: 0.24538/1.16654, loss_spatial_bce_3: 0.15157/0.09042, loss_spatial_dice_3: 0.21357/0.21607, loss_spatial_ce_3: 0.00133/0.07772, loss_grounding_bce_3: 0.15731/0.08687, loss_grounding_dice_3: 0.20483/0.17900, loss_grounding_ce_3: 0.05743/0.27932, loss_mask_ce_4: 0.07397/0.92040, loss_mask_bce_4: 0.18924/0.33966, loss_mask_dice_4: 0.23519/1.19050, loss_spatial_bce_4: 0.15702/0.09444, loss_spatial_dice_4: 0.20714/0.22792, loss_spatial_ce_4: 0.00226/0.09345, loss_grounding_bce_4: 0.16448/0.08735, loss_grounding_dice_4: 0.20696/0.18192, loss_grounding_ce_4: 0.06322/0.28221, loss_mask_ce_5: 0.07227/0.93619, loss_mask_bce_5: 0.18293/0.34185, loss_mask_dice_5: 0.24544/1.19747, loss_spatial_bce_5: 0.14703/0.09645, loss_spatial_dice_5: 0.19700/0.23184, loss_spatial_ce_5: 0.01204/0.10836, loss_grounding_bce_5: 0.14756/0.08773, loss_grounding_dice_5: 0.20249/0.18308, loss_grounding_ce_5: 0.05553/0.29499, loss_mask_ce_6: 0.09003/0.97569, loss_mask_bce_6: 0.17776/0.34459, loss_mask_dice_6: 0.24613/1.20042, loss_spatial_bce_6: 0.13103/0.10225, loss_spatial_dice_6: 0.20433/0.23464, loss_spatial_ce_6: 0.00985/0.13487, loss_grounding_bce_6: 0.15494/0.08848, loss_grounding_dice_6: 0.20308/0.18340, loss_grounding_ce_6: 0.07342/0.31045, loss_mask_ce_7: 0.10025/1.02040, loss_mask_bce_7: 0.17124/0.35248, loss_mask_dice_7: 0.26138/1.25528, loss_spatial_bce_7: 0.18233/0.11055, loss_spatial_dice_7: 0.20946/0.26245, loss_spatial_ce_7: 0.00047/0.17095, loss_grounding_bce_7: 0.14274/0.09040, loss_grounding_dice_7: 0.21367/0.19066, loss_grounding_ce_7: 0.06384/0.34218, loss_mask_ce_8: 0.14970/1.12948, loss_mask_bce_8: 0.19000/0.36612, loss_mask_dice_8: 0.25544/1.32887, loss_spatial_bce_8: 0.21719/0.13135, loss_spatial_dice_8: 0.21181/0.30090, loss_spatial_ce_8: 0.02289/0.22799, loss_grounding_bce_8: 0.16161/0.09417, loss_grounding_dice_8: 0.22419/0.20173, loss_grounding_ce_8: 0.07858/0.40972, loss_mask_ce_9: 2.40925/3.67924, loss_mask_bce_9: 0.13176/0.39308, loss_mask_dice_9: 0.30013/1.90183, loss_spatial_bce_9: 0.48515/0.33364, loss_spatial_dice_9: 0.63419/0.82236, loss_spatial_ce_9: 0.88969/1.50008, loss_grounding_bce_9: 0.11246/0.10561, loss_grounding_dice_9: 0.26160/0.28080, loss_grounding_ce_9: 0.25342/0.67486] items per batch[64] items per second[0.23] total items[3385600] mini batches[ 52900] memory[7345] epoch remaining[0:03:50]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00052983.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0027 s/iter. Inference: 0.2238 s/iter. Eval: 0.0947 s/iter. Total: 0.3213 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2247 s/iter. Eval: 0.0816 s/iter. Total: 0.3094 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2268 s/iter. Eval: 0.0799 s/iter. Total: 0.3099 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2268 s/iter. Eval: 0.0767 s/iter. Total: 0.3068 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2258 s/iter. Eval: 0.0748 s/iter. Total: 0.3038 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2277 s/iter. Eval: 0.0753 s/iter. Total: 0.3062 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0031 s/iter. Inference: 0.2289 s/iter. Eval: 0.0758 s/iter. Total: 0.3079 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0031 s/iter. Inference: 0.2289 s/iter. Eval: 0.0749 s/iter. Total: 0.3070 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0031 s/iter. Inference: 0.2294 s/iter. Eval: 0.0748 s/iter. Total: 0.3076 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evallx9vzxp0 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.100 | 81.981 | 60.218 |      133      |
| Things | 55.245 | 82.689 | 66.114 |      80       |
| Stuff  | 42.333 | 80.912 | 51.318 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.36 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.14s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.17 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.809 | 61.241 | 40.752 | 18.716 | 42.015 | 60.487 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.451 | bicycle      | 18.365 | car            | 36.793 |
| motorcycle    | 35.023 | airplane     | 57.193 | bus            | 65.033 |
| train         | 67.826 | truck        | 32.726 | boat           | 22.732 |
| traffic light | 24.481 | fire hydrant | 64.504 | stop sign      | 62.266 |
| parking meter | 44.271 | bench        | 20.307 | bird           | 29.435 |
| cat           | 73.765 | dog          | 65.619 | horse          | 45.782 |
| sheep         | 46.365 | cow          | 50.655 | elephant       | 60.503 |
| bear          | 77.269 | zebra        | 60.029 | giraffe        | 56.318 |
| backpack      | 16.382 | umbrella     | 48.445 | handbag        | 14.979 |
| tie           | 34.251 | suitcase     | 40.386 | frisbee        | 66.895 |
| skis          | 5.033  | snowboard    | 22.636 | sports ball    | 47.066 |
| kite          | 34.815 | baseball bat | 29.035 | baseball glove | 43.984 |
| skateboard    | 35.968 | surfboard    | 35.409 | tennis racket  | 56.285 |
| bottle        | 33.880 | wine glass   | 26.913 | cup            | 41.101 |
| fork          | 16.644 | knife        | 13.445 | spoon          | 14.434 |
| bowl          | 33.023 | banana       | 20.107 | apple          | 19.909 |
| sandwich      | 43.389 | orange       | 28.942 | broccoli       | 21.885 |
| carrot        | 21.388 | hot dog      | 25.790 | pizza          | 51.981 |
| donut         | 46.898 | cake         | 42.969 | chair          | 21.074 |
| couch         | 39.311 | potted plant | 17.472 | bed            | 39.299 |
| dining table  | 12.576 | toilet       | 66.706 | tv             | 62.496 |
| laptop        | 63.821 | mouse        | 59.749 | remote         | 31.524 |
| keyboard      | 46.422 | cell phone   | 36.971 | microwave      | 54.457 |
| oven          | 33.893 | toaster      | 29.103 | sink           | 37.441 |
| refrigerator  | 60.044 | book         | 9.283  | clock          | 51.092 |
| vase          | 32.768 | scissors     | 24.095 | teddy bear     | 51.202 |
| hair drier    | 8.762  | toothbrush   | 19.188 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.491
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.715
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.88492438798777, 'fwIoU': 69.107864065029, 'IoU-person': 86.92124324751475, 'IoU-bicycle': 70.72476479030458, 'IoU-car': 68.8314883570178, 'IoU-motorcycle': 84.85026706067508, 'IoU-airplane': 82.60639823059985, 'IoU-bus': 84.29647687982744, 'IoU-train': 85.60362930337155, 'IoU-truck': 66.34213528414075, 'IoU-boat': 67.33925720409547, 'IoU-traffic light': 76.06104314725174, 'IoU-fire hydrant': 90.33723352599345, 'IoU-stop sign': 92.28375627225627, 'IoU-parking meter': 87.95153945691793, 'IoU-bench': 53.53777036364774, 'IoU-bird': 76.16613227992431, 'IoU-cat': 83.68803649713395, 'IoU-dog': 79.02484287125947, 'IoU-horse': 85.44554672272956, 'IoU-sheep': 85.6612861851201, 'IoU-cow': 82.95433548599055, 'IoU-elephant': 90.57172137010669, 'IoU-bear': 84.93162515871128, 'IoU-zebra': 91.10098059655405, 'IoU-giraffe': 88.22638690458562, 'IoU-backpack': 40.263466162165614, 'IoU-umbrella': 73.07416575187227, 'IoU-handbag': 38.209023285746376, 'IoU-tie': 69.34589881227076, 'IoU-suitcase': 78.89539385380905, 'IoU-frisbee': 82.36749319374398, 'IoU-skis': 51.56779228029599, 'IoU-snowboard': 70.0017928434935, 'IoU-sports ball': 68.23053882997672, 'IoU-kite': 65.81482469645545, 'IoU-baseball bat': 61.31274473423316, 'IoU-baseball glove': 77.12397822276682, 'IoU-skateboard': 77.86206374000419, 'IoU-surfboard': 75.61911060285546, 'IoU-tennis racket': 81.88905191567893, 'IoU-bottle': 67.92687505043523, 'IoU-wine glass': 73.02892955066869, 'IoU-cup': 64.22518887074571, 'IoU-fork': 55.31194256482833, 'IoU-knife': 47.77955948454208, 'IoU-spoon': 50.455693918065336, 'IoU-bowl': 54.95852391569611, 'IoU-banana': 85.12207798359148, 'IoU-apple': 56.49646458104215, 'IoU-sandwich': 67.41817378757752, 'IoU-orange': 81.14352431529485, 'IoU-broccoli': 67.6034206073596, 'IoU-carrot': 63.81920182302247, 'IoU-hot dog': 64.51784817571303, 'IoU-pizza': 82.79117287971748, 'IoU-donut': 65.52585575207219, 'IoU-cake': 67.88644160019102, 'IoU-chair': 54.6991888409914, 'IoU-couch': 68.47162164608075, 'IoU-potted plant': 33.58451591530109, 'IoU-bed': 68.74266683944712, 'IoU-dining table': 52.397124836733376, 'IoU-toilet': 83.55299203437166, 'IoU-tv': 75.16569076611005, 'IoU-laptop': 75.4279451907432, 'IoU-mouse': 69.81763472234887, 'IoU-remote': 48.572891090489414, 'IoU-keyboard': 62.48320517180975, 'IoU-cell phone': 65.80461498868647, 'IoU-microwave': 51.24842175783968, 'IoU-oven': 67.19851047589154, 'IoU-toaster': 64.2148082026827, 'IoU-sink': 69.28307899096579, 'IoU-refrigerator': 80.00216780730095, 'IoU-book': 50.31600551181867, 'IoU-clock': 67.81633030987265, 'IoU-vase': 54.93247925680358, 'IoU-scissors': 54.96924848304147, 'IoU-teddy bear': 80.80760197688596, 'IoU-hair drier': 39.75895816072908, 'IoU-toothbrush': 53.68078175895765, 'IoU-banner': 29.375174074977572, 'IoU-blanket': 10.920806105195814, 'IoU-bridge': 39.99635896093848, 'IoU-cardboard': 40.551507885271604, 'IoU-counter': 30.625394038614495, 'IoU-curtain': 63.08656315717365, 'IoU-door-stuff': 42.783218961485616, 'IoU-floor-wood': 64.99709907716455, 'IoU-flower': 43.738749535391854, 'IoU-fruit': 42.047275056921514, 'IoU-gravel': 32.77762565611908, 'IoU-house': 25.462630722161467, 'IoU-light': 39.875897193405144, 'IoU-mirror-stuff': 53.369890031452, 'IoU-net': 46.91998348521141, 'IoU-pillow': 13.237583345317885, 'IoU-platform': 28.789103725117478, 'IoU-playingfield': 69.79540768912244, 'IoU-railroad': 60.61417244535237, 'IoU-river': 51.84517537193837, 'IoU-road': 66.96038432149975, 'IoU-roof': 16.228419136419532, 'IoU-sand': 64.02519158861996, 'IoU-sea': 84.49133391744219, 'IoU-shelf': 35.705448602055874, 'IoU-snow': 88.7540659895882, 'IoU-stairs': 27.214123823176166, 'IoU-tent': 8.694177364260355, 'IoU-towel': 31.5348814875005, 'IoU-wall-brick': 43.88251881846638, 'IoU-wall-stone': 26.23608937943928, 'IoU-wall-tile': 66.34666017450083, 'IoU-wall-wood': 38.808294799749326, 'IoU-water-other': 21.27806173189609, 'IoU-window-blind': 46.55094549890992, 'IoU-window-other': 47.24402994212048, 'IoU-tree-merged': 81.27576871727598, 'IoU-fence-merged': 51.95509623927127, 'IoU-ceiling-merged': 67.34017727852107, 'IoU-sky-other-merged': 93.49102370060541, 'IoU-cabinet-merged': 59.84724561408108, 'IoU-table-merged': 38.84679342928433, 'IoU-floor-other-merged': 49.11112971610268, 'IoU-pavement-merged': 55.18062704203736, 'IoU-mountain-merged': 56.40594164651981, 'IoU-grass-merged': 71.22682755239703, 'IoU-dirt-merged': 44.51191292385715, 'IoU-paper-merged': 29.02422914613049, 'IoU-food-other-merged': 40.51798014259148, 'IoU-building-other-merged': 58.198796072449085, 'IoU-rock-merged': 62.220814287074354, 'IoU-wall-other-merged': 63.5327528914579, 'IoU-rug-merged': 64.2489683911742, 'mACC': 72.83441876751964, 'pACC': 80.50641289292034, 'ACC-person': 92.33880209461019, 'ACC-bicycle': 80.58955101193206, 'ACC-car': 86.1393472800815, 'ACC-motorcycle': 90.39273279412664, 'ACC-airplane': 90.37042661957283, 'ACC-bus': 91.02235155559883, 'ACC-train': 95.40100333468541, 'ACC-truck': 74.4738522559636, 'ACC-boat': 79.2740427144042, 'ACC-traffic light': 89.87517751164799, 'ACC-fire hydrant': 95.43089496855077, 'ACC-stop sign': 95.46705266114255, 'ACC-parking meter': 92.3147105364449, 'ACC-bench': 74.65679536334825, 'ACC-bird': 80.62294566278054, 'ACC-cat': 90.34315289549146, 'ACC-dog': 82.17503820765563, 'ACC-horse': 91.36712511893168, 'ACC-sheep': 89.5550895023906, 'ACC-cow': 87.37298897377426, 'ACC-elephant': 93.20740131572126, 'ACC-bear': 87.27935599729821, 'ACC-zebra': 93.61887128833514, 'ACC-giraffe': 92.67204494130121, 'ACC-backpack': 58.10137509750134, 'ACC-umbrella': 79.07204852391365, 'ACC-handbag': 50.806843684912074, 'ACC-tie': 78.08837949053175, 'ACC-suitcase': 86.28172300034758, 'ACC-frisbee': 93.95345454545453, 'ACC-skis': 70.49714307130908, 'ACC-snowboard': 78.41346036100022, 'ACC-sports ball': 80.17851789830578, 'ACC-kite': 75.74836584033687, 'ACC-baseball bat': 82.94210640673116, 'ACC-baseball glove': 89.56896738073107, 'ACC-skateboard': 89.94270414873132, 'ACC-surfboard': 83.58648403603428, 'ACC-tennis racket': 88.01931445723264, 'ACC-bottle': 81.5814895228584, 'ACC-wine glass': 85.19083969465649, 'ACC-cup': 83.56545827623746, 'ACC-fork': 69.60336146405487, 'ACC-knife': 63.20839744998901, 'ACC-spoon': 68.94659613558737, 'ACC-bowl': 68.48098328104176, 'ACC-banana': 91.09696991569187, 'ACC-apple': 66.06684301083835, 'ACC-sandwich': 80.6170282923023, 'ACC-orange': 89.05599477110471, 'ACC-broccoli': 78.26524125199825, 'ACC-carrot': 73.73692460626454, 'ACC-hot dog': 71.9445461240462, 'ACC-pizza': 95.55373650615617, 'ACC-donut': 81.84292936993697, 'ACC-cake': 76.27794857926425, 'ACC-chair': 68.93394836708762, 'ACC-couch': 84.18645672258923, 'ACC-potted plant': 46.13136402589923, 'ACC-bed': 82.85905123134121, 'ACC-dining table': 73.70028119786615, 'ACC-toilet': 93.51358115208419, 'ACC-tv': 86.95581703607562, 'ACC-laptop': 91.92160730243958, 'ACC-mouse': 82.86419272101556, 'ACC-remote': 73.69854041780594, 'ACC-keyboard': 66.64457329516908, 'ACC-cell phone': 80.4540504536827, 'ACC-microwave': 57.772701098852274, 'ACC-oven': 86.0741357356578, 'ACC-toaster': 73.34711026563933, 'ACC-sink': 85.57342554317684, 'ACC-refrigerator': 88.61936004234265, 'ACC-book': 67.12728699111821, 'ACC-clock': 73.35012740016974, 'ACC-vase': 62.40221240680726, 'ACC-scissors': 59.671721345343364, 'ACC-teddy bear': 85.92217250636479, 'ACC-hair drier': 43.709617738308715, 'ACC-toothbrush': 80.8825573314802, 'ACC-banner': 68.88909710253691, 'ACC-blanket': 15.180720320033824, 'ACC-bridge': 55.79307314892882, 'ACC-cardboard': 46.93031845281373, 'ACC-counter': 55.747438783149164, 'ACC-curtain': 74.29548905867308, 'ACC-door-stuff': 64.86462893224302, 'ACC-floor-wood': 79.39353289172143, 'ACC-flower': 63.221323184217994, 'ACC-fruit': 58.359229514651126, 'ACC-gravel': 39.539583163140925, 'ACC-house': 31.10441552265788, 'ACC-light': 57.19893628220343, 'ACC-mirror-stuff': 66.21304679829825, 'ACC-net': 61.602184222253264, 'ACC-pillow': 22.11286903401066, 'ACC-platform': 44.109889370665236, 'ACC-playingfield': 91.61811343489086, 'ACC-railroad': 77.44000514448877, 'ACC-river': 79.00422446290796, 'ACC-road': 86.57997632503836, 'ACC-roof': 22.36432829313808, 'ACC-sand': 71.43983477536663, 'ACC-sea': 91.12622631452369, 'ACC-shelf': 57.11927615774746, 'ACC-snow': 95.99863011971782, 'ACC-stairs': 47.59359466351826, 'ACC-tent': 9.741134870415832, 'ACC-towel': 37.9381653605426, 'ACC-wall-brick': 56.933282767518314, 'ACC-wall-stone': 33.843049155697194, 'ACC-wall-tile': 77.26720377006424, 'ACC-wall-wood': 50.207684529927896, 'ACC-water-other': 30.734205372679526, 'ACC-window-blind': 57.03638517577772, 'ACC-window-other': 68.8155290957687, 'ACC-tree-merged': 89.85312388426216, 'ACC-fence-merged': 70.05324084296521, 'ACC-ceiling-merged': 81.18618038338894, 'ACC-sky-other-merged': 96.60333642949524, 'ACC-cabinet-merged': 73.45183906246776, 'ACC-table-merged': 54.26212938080586, 'ACC-floor-other-merged': 60.257783503503916, 'ACC-pavement-merged': 68.15332291809722, 'ACC-mountain-merged': 67.33923665366626, 'ACC-grass-merged': 82.41418574438015, 'ACC-dirt-merged': 66.08563709629291, 'ACC-paper-merged': 39.16819976856333, 'ACC-food-other-merged': 53.0544680759093, 'ACC-building-other-merged': 74.46918235724573, 'ACC-rock-merged': 80.76124578988096, 'ACC-wall-other-merged': 81.19748466736849, 'ACC-rug-merged': 78.79765082068064})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1536 s/iter. Inference: 0.5416 s/iter. Eval: 0.0000 s/iter. Total: 0.6952 s/iter. ETA=0:00:27
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1584 s/iter. Inference: 0.5117 s/iter. Eval: 0.0000 s/iter. Total: 0.6702 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1707 s/iter. Inference: 0.5900 s/iter. Eval: 0.0000 s/iter. Total: 0.7608 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1729 s/iter. Inference: 0.7053 s/iter. Eval: 0.0000 s/iter. Total: 0.8783 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1693 s/iter. Inference: 0.6220 s/iter. Eval: 0.0000 s/iter. Total: 0.7914 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1684 s/iter. Inference: 0.6604 s/iter. Eval: 0.0000 s/iter. Total: 0.8290 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1702 s/iter. Inference: 0.7166 s/iter. Eval: 0.0000 s/iter. Total: 0.8869 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5068773778167983, 'noc@0.8': 2.829089844893181, 'noc@0.85': 3.4705882352941178, 'noc@0.9': 4.5440444834650275, 'miou@iter1': 0.8405157127748779}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.1008 s/iter. Eval: 0.0008 s/iter. Total: 0.1030 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.16207122802734, 'precision@0.6': 67.93626403808594, 'precision@0.7': 62.76719665527344, 'precision@0.8': 52.39020538330078, 'precision@0.9': 27.283327102661133, 'cIoU': 57.78112030029297, 'mIoU': 62.860748291015625}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.099589603495055, 'SQ': 81.98073158058496, 'RQ': 60.21812448018829, 'PQ_th': 55.24464784983436, 'SQ_th': 82.68884370930662, 'RQ_th': 66.11446157472966, 'PQ_st': 42.333463948643285, 'SQ_st': 80.9118830844013, 'RQ_st': 51.31799301672964}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.809062341065626, 'AP50': 61.241458485643804, 'AP75': 40.75237824599183, 'APs': 18.71640330299685, 'APm': 42.01542483245271, 'APl': 60.4872356966103, 'AP-person': 44.45123921041229, 'AP-bicycle': 18.36533047938659, 'AP-car': 36.792846474917994, 'AP-motorcycle': 35.02329608805275, 'AP-airplane': 57.193492686949845, 'AP-bus': 65.03285491268802, 'AP-train': 67.82648981741062, 'AP-truck': 32.72619112502373, 'AP-boat': 22.731844466546622, 'AP-traffic light': 24.48074819998044, 'AP-fire hydrant': 64.50399097755377, 'AP-stop sign': 62.26625576541045, 'AP-parking meter': 44.27063135783375, 'AP-bench': 20.307253866623633, 'AP-bird': 29.434978381221093, 'AP-cat': 73.76484198873197, 'AP-dog': 65.61936662800912, 'AP-horse': 45.78230067276257, 'AP-sheep': 46.365074742084445, 'AP-cow': 50.65537185592216, 'AP-elephant': 60.50270258064394, 'AP-bear': 77.2691648522647, 'AP-zebra': 60.02910266701183, 'AP-giraffe': 56.317812068561516, 'AP-backpack': 16.381693902681814, 'AP-umbrella': 48.4445449872673, 'AP-handbag': 14.979450841550962, 'AP-tie': 34.250565390035845, 'AP-suitcase': 40.38593181027616, 'AP-frisbee': 66.89459135100543, 'AP-skis': 5.032640302718881, 'AP-snowboard': 22.635589273870835, 'AP-sports ball': 47.065955871333855, 'AP-kite': 34.81504996308959, 'AP-baseball bat': 29.034890009984753, 'AP-baseball glove': 43.98369162939225, 'AP-skateboard': 35.96761133896757, 'AP-surfboard': 35.40852790118573, 'AP-tennis racket': 56.28516413699276, 'AP-bottle': 33.8796124143675, 'AP-wine glass': 26.913192138315573, 'AP-cup': 41.100720050287606, 'AP-fork': 16.643514361083252, 'AP-knife': 13.444812205053056, 'AP-spoon': 14.434320824442437, 'AP-bowl': 33.02325791943657, 'AP-banana': 20.106757713434316, 'AP-apple': 19.909487787135483, 'AP-sandwich': 43.38852553825785, 'AP-orange': 28.942263851357442, 'AP-broccoli': 21.884800683836207, 'AP-carrot': 21.38770320775179, 'AP-hot dog': 25.789975535281574, 'AP-pizza': 51.98060237402734, 'AP-donut': 46.897641767671935, 'AP-cake': 42.969452092378326, 'AP-chair': 21.073815610464607, 'AP-couch': 39.31078140733016, 'AP-potted plant': 17.47197173657785, 'AP-bed': 39.29878505480397, 'AP-dining table': 12.576273991266548, 'AP-toilet': 66.7058464871598, 'AP-tv': 62.495762156096376, 'AP-laptop': 63.8212829807279, 'AP-mouse': 59.74895419674251, 'AP-remote': 31.524328299412574, 'AP-keyboard': 46.422420693676564, 'AP-cell phone': 36.97097168643187, 'AP-microwave': 54.456971456342984, 'AP-oven': 33.89337701574514, 'AP-toaster': 29.10307160860451, 'AP-sink': 37.441207468581375, 'AP-refrigerator': 60.04401735766691, 'AP-book': 9.282823761766624, 'AP-clock': 51.09207008404376, 'AP-vase': 32.767682421599794, 'AP-scissors': 24.095320552738606, 'AP-teddy bear': 51.20190153050223, 'AP-hair drier': 8.761940532288524, 'AP-toothbrush': 19.1876881542027}), ('sem_seg', {'mIoU': 60.88492438798777, 'fwIoU': 69.107864065029, 'IoU-person': 86.92124324751475, 'IoU-bicycle': 70.72476479030458, 'IoU-car': 68.8314883570178, 'IoU-motorcycle': 84.85026706067508, 'IoU-airplane': 82.60639823059985, 'IoU-bus': 84.29647687982744, 'IoU-train': 85.60362930337155, 'IoU-truck': 66.34213528414075, 'IoU-boat': 67.33925720409547, 'IoU-traffic light': 76.06104314725174, 'IoU-fire hydrant': 90.33723352599345, 'IoU-stop sign': 92.28375627225627, 'IoU-parking meter': 87.95153945691793, 'IoU-bench': 53.53777036364774, 'IoU-bird': 76.16613227992431, 'IoU-cat': 83.68803649713395, 'IoU-dog': 79.02484287125947, 'IoU-horse': 85.44554672272956, 'IoU-sheep': 85.6612861851201, 'IoU-cow': 82.95433548599055, 'IoU-elephant': 90.57172137010669, 'IoU-bear': 84.93162515871128, 'IoU-zebra': 91.10098059655405, 'IoU-giraffe': 88.22638690458562, 'IoU-backpack': 40.263466162165614, 'IoU-umbrella': 73.07416575187227, 'IoU-handbag': 38.209023285746376, 'IoU-tie': 69.34589881227076, 'IoU-suitcase': 78.89539385380905, 'IoU-frisbee': 82.36749319374398, 'IoU-skis': 51.56779228029599, 'IoU-snowboard': 70.0017928434935, 'IoU-sports ball': 68.23053882997672, 'IoU-kite': 65.81482469645545, 'IoU-baseball bat': 61.31274473423316, 'IoU-baseball glove': 77.12397822276682, 'IoU-skateboard': 77.86206374000419, 'IoU-surfboard': 75.61911060285546, 'IoU-tennis racket': 81.88905191567893, 'IoU-bottle': 67.92687505043523, 'IoU-wine glass': 73.02892955066869, 'IoU-cup': 64.22518887074571, 'IoU-fork': 55.31194256482833, 'IoU-knife': 47.77955948454208, 'IoU-spoon': 50.455693918065336, 'IoU-bowl': 54.95852391569611, 'IoU-banana': 85.12207798359148, 'IoU-apple': 56.49646458104215, 'IoU-sandwich': 67.41817378757752, 'IoU-orange': 81.14352431529485, 'IoU-broccoli': 67.6034206073596, 'IoU-carrot': 63.81920182302247, 'IoU-hot dog': 64.51784817571303, 'IoU-pizza': 82.79117287971748, 'IoU-donut': 65.52585575207219, 'IoU-cake': 67.88644160019102, 'IoU-chair': 54.6991888409914, 'IoU-couch': 68.47162164608075, 'IoU-potted plant': 33.58451591530109, 'IoU-bed': 68.74266683944712, 'IoU-dining table': 52.397124836733376, 'IoU-toilet': 83.55299203437166, 'IoU-tv': 75.16569076611005, 'IoU-laptop': 75.4279451907432, 'IoU-mouse': 69.81763472234887, 'IoU-remote': 48.572891090489414, 'IoU-keyboard': 62.48320517180975, 'IoU-cell phone': 65.80461498868647, 'IoU-microwave': 51.24842175783968, 'IoU-oven': 67.19851047589154, 'IoU-toaster': 64.2148082026827, 'IoU-sink': 69.28307899096579, 'IoU-refrigerator': 80.00216780730095, 'IoU-book': 50.31600551181867, 'IoU-clock': 67.81633030987265, 'IoU-vase': 54.93247925680358, 'IoU-scissors': 54.96924848304147, 'IoU-teddy bear': 80.80760197688596, 'IoU-hair drier': 39.75895816072908, 'IoU-toothbrush': 53.68078175895765, 'IoU-banner': 29.375174074977572, 'IoU-blanket': 10.920806105195814, 'IoU-bridge': 39.99635896093848, 'IoU-cardboard': 40.551507885271604, 'IoU-counter': 30.625394038614495, 'IoU-curtain': 63.08656315717365, 'IoU-door-stuff': 42.783218961485616, 'IoU-floor-wood': 64.99709907716455, 'IoU-flower': 43.738749535391854, 'IoU-fruit': 42.047275056921514, 'IoU-gravel': 32.77762565611908, 'IoU-house': 25.462630722161467, 'IoU-light': 39.875897193405144, 'IoU-mirror-stuff': 53.369890031452, 'IoU-net': 46.91998348521141, 'IoU-pillow': 13.237583345317885, 'IoU-platform': 28.789103725117478, 'IoU-playingfield': 69.79540768912244, 'IoU-railroad': 60.61417244535237, 'IoU-river': 51.84517537193837, 'IoU-road': 66.96038432149975, 'IoU-roof': 16.228419136419532, 'IoU-sand': 64.02519158861996, 'IoU-sea': 84.49133391744219, 'IoU-shelf': 35.705448602055874, 'IoU-snow': 88.7540659895882, 'IoU-stairs': 27.214123823176166, 'IoU-tent': 8.694177364260355, 'IoU-towel': 31.5348814875005, 'IoU-wall-brick': 43.88251881846638, 'IoU-wall-stone': 26.23608937943928, 'IoU-wall-tile': 66.34666017450083, 'IoU-wall-wood': 38.808294799749326, 'IoU-water-other': 21.27806173189609, 'IoU-window-blind': 46.55094549890992, 'IoU-window-other': 47.24402994212048, 'IoU-tree-merged': 81.27576871727598, 'IoU-fence-merged': 51.95509623927127, 'IoU-ceiling-merged': 67.34017727852107, 'IoU-sky-other-merged': 93.49102370060541, 'IoU-cabinet-merged': 59.84724561408108, 'IoU-table-merged': 38.84679342928433, 'IoU-floor-other-merged': 49.11112971610268, 'IoU-pavement-merged': 55.18062704203736, 'IoU-mountain-merged': 56.40594164651981, 'IoU-grass-merged': 71.22682755239703, 'IoU-dirt-merged': 44.51191292385715, 'IoU-paper-merged': 29.02422914613049, 'IoU-food-other-merged': 40.51798014259148, 'IoU-building-other-merged': 58.198796072449085, 'IoU-rock-merged': 62.220814287074354, 'IoU-wall-other-merged': 63.5327528914579, 'IoU-rug-merged': 64.2489683911742, 'mACC': 72.83441876751964, 'pACC': 80.50641289292034, 'ACC-person': 92.33880209461019, 'ACC-bicycle': 80.58955101193206, 'ACC-car': 86.1393472800815, 'ACC-motorcycle': 90.39273279412664, 'ACC-airplane': 90.37042661957283, 'ACC-bus': 91.02235155559883, 'ACC-train': 95.40100333468541, 'ACC-truck': 74.4738522559636, 'ACC-boat': 79.2740427144042, 'ACC-traffic light': 89.87517751164799, 'ACC-fire hydrant': 95.43089496855077, 'ACC-stop sign': 95.46705266114255, 'ACC-parking meter': 92.3147105364449, 'ACC-bench': 74.65679536334825, 'ACC-bird': 80.62294566278054, 'ACC-cat': 90.34315289549146, 'ACC-dog': 82.17503820765563, 'ACC-horse': 91.36712511893168, 'ACC-sheep': 89.5550895023906, 'ACC-cow': 87.37298897377426, 'ACC-elephant': 93.20740131572126, 'ACC-bear': 87.27935599729821, 'ACC-zebra': 93.61887128833514, 'ACC-giraffe': 92.67204494130121, 'ACC-backpack': 58.10137509750134, 'ACC-umbrella': 79.07204852391365, 'ACC-handbag': 50.806843684912074, 'ACC-tie': 78.08837949053175, 'ACC-suitcase': 86.28172300034758, 'ACC-frisbee': 93.95345454545453, 'ACC-skis': 70.49714307130908, 'ACC-snowboard': 78.41346036100022, 'ACC-sports ball': 80.17851789830578, 'ACC-kite': 75.74836584033687, 'ACC-baseball bat': 82.94210640673116, 'ACC-baseball glove': 89.56896738073107, 'ACC-skateboard': 89.94270414873132, 'ACC-surfboard': 83.58648403603428, 'ACC-tennis racket': 88.01931445723264, 'ACC-bottle': 81.5814895228584, 'ACC-wine glass': 85.19083969465649, 'ACC-cup': 83.56545827623746, 'ACC-fork': 69.60336146405487, 'ACC-knife': 63.20839744998901, 'ACC-spoon': 68.94659613558737, 'ACC-bowl': 68.48098328104176, 'ACC-banana': 91.09696991569187, 'ACC-apple': 66.06684301083835, 'ACC-sandwich': 80.6170282923023, 'ACC-orange': 89.05599477110471, 'ACC-broccoli': 78.26524125199825, 'ACC-carrot': 73.73692460626454, 'ACC-hot dog': 71.9445461240462, 'ACC-pizza': 95.55373650615617, 'ACC-donut': 81.84292936993697, 'ACC-cake': 76.27794857926425, 'ACC-chair': 68.93394836708762, 'ACC-couch': 84.18645672258923, 'ACC-potted plant': 46.13136402589923, 'ACC-bed': 82.85905123134121, 'ACC-dining table': 73.70028119786615, 'ACC-toilet': 93.51358115208419, 'ACC-tv': 86.95581703607562, 'ACC-laptop': 91.92160730243958, 'ACC-mouse': 82.86419272101556, 'ACC-remote': 73.69854041780594, 'ACC-keyboard': 66.64457329516908, 'ACC-cell phone': 80.4540504536827, 'ACC-microwave': 57.772701098852274, 'ACC-oven': 86.0741357356578, 'ACC-toaster': 73.34711026563933, 'ACC-sink': 85.57342554317684, 'ACC-refrigerator': 88.61936004234265, 'ACC-book': 67.12728699111821, 'ACC-clock': 73.35012740016974, 'ACC-vase': 62.40221240680726, 'ACC-scissors': 59.671721345343364, 'ACC-teddy bear': 85.92217250636479, 'ACC-hair drier': 43.709617738308715, 'ACC-toothbrush': 80.8825573314802, 'ACC-banner': 68.88909710253691, 'ACC-blanket': 15.180720320033824, 'ACC-bridge': 55.79307314892882, 'ACC-cardboard': 46.93031845281373, 'ACC-counter': 55.747438783149164, 'ACC-curtain': 74.29548905867308, 'ACC-door-stuff': 64.86462893224302, 'ACC-floor-wood': 79.39353289172143, 'ACC-flower': 63.221323184217994, 'ACC-fruit': 58.359229514651126, 'ACC-gravel': 39.539583163140925, 'ACC-house': 31.10441552265788, 'ACC-light': 57.19893628220343, 'ACC-mirror-stuff': 66.21304679829825, 'ACC-net': 61.602184222253264, 'ACC-pillow': 22.11286903401066, 'ACC-platform': 44.109889370665236, 'ACC-playingfield': 91.61811343489086, 'ACC-railroad': 77.44000514448877, 'ACC-river': 79.00422446290796, 'ACC-road': 86.57997632503836, 'ACC-roof': 22.36432829313808, 'ACC-sand': 71.43983477536663, 'ACC-sea': 91.12622631452369, 'ACC-shelf': 57.11927615774746, 'ACC-snow': 95.99863011971782, 'ACC-stairs': 47.59359466351826, 'ACC-tent': 9.741134870415832, 'ACC-towel': 37.9381653605426, 'ACC-wall-brick': 56.933282767518314, 'ACC-wall-stone': 33.843049155697194, 'ACC-wall-tile': 77.26720377006424, 'ACC-wall-wood': 50.207684529927896, 'ACC-water-other': 30.734205372679526, 'ACC-window-blind': 57.03638517577772, 'ACC-window-other': 68.8155290957687, 'ACC-tree-merged': 89.85312388426216, 'ACC-fence-merged': 70.05324084296521, 'ACC-ceiling-merged': 81.18618038338894, 'ACC-sky-other-merged': 96.60333642949524, 'ACC-cabinet-merged': 73.45183906246776, 'ACC-table-merged': 54.26212938080586, 'ACC-floor-other-merged': 60.257783503503916, 'ACC-pavement-merged': 68.15332291809722, 'ACC-mountain-merged': 67.33923665366626, 'ACC-grass-merged': 82.41418574438015, 'ACC-dirt-merged': 66.08563709629291, 'ACC-paper-merged': 39.16819976856333, 'ACC-food-other-merged': 53.0544680759093, 'ACC-building-other-merged': 74.46918235724573, 'ACC-rock-merged': 80.76124578988096, 'ACC-wall-other-merged': 81.19748466736849, 'ACC-rug-merged': 78.79765082068064})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5068773778167983, 'noc@0.8': 2.829089844893181, 'noc@0.85': 3.4705882352941178, 'noc@0.9': 4.5440444834650275, 'miou@iter1': 0.8405157127748779}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.16207122802734, 'precision@0.6': 67.93626403808594, 'precision@0.7': 62.76719665527344, 'precision@0.8': 52.39020538330078, 'precision@0.9': 27.283327102661133, 'cIoU': 57.78112030029297, 'mIoU': 62.860748291015625}}}
INFO:trainer.default_trainer:This epoch takes 1:27:50.991883
INFO:trainer.default_trainer:PROGRESS: 58.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 29 training.
INFO:trainer.default_trainer:epochs[    29] optim steps[53000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01685/0.90223, loss_mask_bce_0: 0.10593/0.33510, loss_mask_dice_0: 1.79302/1.16278, loss_spatial_bce_0: 0.00800/0.08801, loss_spatial_dice_0: 0.15804/0.20995, loss_spatial_ce_0: 0.02625/0.06464, loss_grounding_bce_0: 0.01569/0.08630, loss_grounding_dice_0: 0.31614/0.17866, loss_grounding_ce_0: 0.20986/0.27299, loss_mask_ce_1: 1.10916/0.90281, loss_mask_bce_1: 0.11518/0.33601, loss_mask_dice_1: 2.17380/1.16933, loss_spatial_bce_1: 0.00738/0.08855, loss_spatial_dice_1: 0.14461/0.21397, loss_spatial_ce_1: 0.07959/0.07036, loss_grounding_bce_1: 0.01772/0.08648, loss_grounding_dice_1: 0.32902/0.17943, loss_grounding_ce_1: 0.23127/0.27419, loss_mask_ce_2: 1.29669/0.90991, loss_mask_bce_2: 0.10908/0.33651, loss_mask_dice_2: 2.20425/1.16952, loss_spatial_bce_2: 0.00703/0.08940, loss_spatial_dice_2: 0.14873/0.21535, loss_spatial_ce_2: 0.03322/0.07384, loss_grounding_bce_2: 0.01824/0.08661, loss_grounding_dice_2: 0.33717/0.17925, loss_grounding_ce_2: 0.21317/0.27744, loss_mask_ce_3: 1.08214/0.91975, loss_mask_bce_3: 0.12475/0.33757, loss_mask_dice_3: 2.29681/1.16689, loss_spatial_bce_3: 0.00745/0.09040, loss_spatial_dice_3: 0.16680/0.21605, loss_spatial_ce_3: 0.04134/0.07771, loss_grounding_bce_3: 0.01948/0.08685, loss_grounding_dice_3: 0.36303/0.17898, loss_grounding_ce_3: 0.21820/0.27928, loss_mask_ce_4: 1.02097/0.92044, loss_mask_bce_4: 0.11320/0.33966, loss_mask_dice_4: 2.18685/1.19083, loss_spatial_bce_4: 0.00772/0.09442, loss_spatial_dice_4: 0.18963/0.22791, loss_spatial_ce_4: 0.02675/0.09344, loss_grounding_bce_4: 0.01149/0.08733, loss_grounding_dice_4: 0.27825/0.18190, loss_grounding_ce_4: 0.35534/0.28218, loss_mask_ce_5: 1.13657/0.93626, loss_mask_bce_5: 0.11754/0.34185, loss_mask_dice_5: 2.06238/1.19780, loss_spatial_bce_5: 0.00886/0.09643, loss_spatial_dice_5: 0.17013/0.23183, loss_spatial_ce_5: 0.01486/0.10838, loss_grounding_bce_5: 0.01367/0.08771, loss_grounding_dice_5: 0.28791/0.18306, loss_grounding_ce_5: 0.38511/0.29494, loss_mask_ce_6: 1.06587/0.97577, loss_mask_bce_6: 0.12029/0.34460, loss_mask_dice_6: 2.13440/1.20078, loss_spatial_bce_6: 0.01065/0.10223, loss_spatial_dice_6: 0.18891/0.23463, loss_spatial_ce_6: 0.04710/0.13486, loss_grounding_bce_6: 0.00997/0.08846, loss_grounding_dice_6: 0.34124/0.18338, loss_grounding_ce_6: 0.37325/0.31039, loss_mask_ce_7: 1.26146/1.02050, loss_mask_bce_7: 0.14018/0.35248, loss_mask_dice_7: 2.70940/1.25565, loss_spatial_bce_7: 0.02216/0.11054, loss_spatial_dice_7: 0.30982/0.26245, loss_spatial_ce_7: 0.06250/0.17095, loss_grounding_bce_7: 0.01259/0.09038, loss_grounding_dice_7: 0.33624/0.19065, loss_grounding_ce_7: 0.44525/0.34216, loss_mask_ce_8: 1.21781/1.12954, loss_mask_bce_8: 0.14735/0.36613, loss_mask_dice_8: 2.50024/1.32926, loss_spatial_bce_8: 0.02475/0.13132, loss_spatial_dice_8: 0.37462/0.30091, loss_spatial_ce_8: 0.17573/0.22798, loss_grounding_bce_8: 0.01437/0.09415, loss_grounding_dice_8: 0.39969/0.20171, loss_grounding_ce_8: 0.34000/0.40965, loss_mask_ce_9: 4.18899/3.67971, loss_mask_bce_9: 0.11700/0.39310, loss_mask_dice_9: 3.93887/1.90244, loss_spatial_bce_9: 0.07958/0.33361, loss_spatial_dice_9: 0.95218/0.82236, loss_spatial_ce_9: 1.63634/1.50008, loss_grounding_bce_9: 0.01216/0.10560, loss_grounding_dice_9: 0.55600/0.28078, loss_grounding_ce_9: 0.43668/0.67481] items per batch[64] items per second[0.13] total items[3392000] mini batches[ 53000] memory[7345] epoch remaining[1:29:12]
INFO:trainer.default_trainer:epochs[    29] optim steps[53100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.01686/0.90214, loss_mask_bce_0: 0.77500/0.33510, loss_mask_dice_0: 1.46313/1.16255, loss_spatial_bce_0: 0.09542/0.08802, loss_spatial_dice_0: 0.23244/0.20992, loss_spatial_ce_0: 0.00520/0.06460, loss_grounding_bce_0: 0.07838/0.08632, loss_grounding_dice_0: 0.09583/0.17865, loss_grounding_ce_0: 0.12837/0.27291, loss_mask_ce_1: 1.78554/0.90273, loss_mask_bce_1: 0.78243/0.33600, loss_mask_dice_1: 1.57789/1.16910, loss_spatial_bce_1: 0.10052/0.08855, loss_spatial_dice_1: 0.24595/0.21394, loss_spatial_ce_1: 0.00681/0.07036, loss_grounding_bce_1: 0.07950/0.08650, loss_grounding_dice_1: 0.09588/0.17943, loss_grounding_ce_1: 0.13076/0.27411, loss_mask_ce_2: 2.05083/0.90982, loss_mask_bce_2: 0.73383/0.33651, loss_mask_dice_2: 1.49233/1.16929, loss_spatial_bce_2: 0.10128/0.08940, loss_spatial_dice_2: 0.24866/0.21532, loss_spatial_ce_2: 0.00676/0.07380, loss_grounding_bce_2: 0.08247/0.08662, loss_grounding_dice_2: 0.09673/0.17925, loss_grounding_ce_2: 0.13765/0.27736, loss_mask_ce_3: 1.99296/0.91963, loss_mask_bce_3: 0.73383/0.33758, loss_mask_dice_3: 1.57225/1.16668, loss_spatial_bce_3: 0.11154/0.09040, loss_spatial_dice_3: 0.26891/0.21603, loss_spatial_ce_3: 0.01102/0.07769, loss_grounding_bce_3: 0.07339/0.08687, loss_grounding_dice_3: 0.09261/0.17897, loss_grounding_ce_3: 0.14463/0.27921, loss_mask_ce_4: 2.26051/0.92036, loss_mask_bce_4: 0.71440/0.33965, loss_mask_dice_4: 1.47870/1.19059, loss_spatial_bce_4: 0.11231/0.09443, loss_spatial_dice_4: 0.27910/0.22789, loss_spatial_ce_4: 0.01955/0.09339, loss_grounding_bce_4: 0.07923/0.08736, loss_grounding_dice_4: 0.10247/0.18189, loss_grounding_ce_4: 0.12894/0.28207, loss_mask_ce_5: 2.04629/0.93616, loss_mask_bce_5: 0.83263/0.34185, loss_mask_dice_5: 1.47844/1.19758, loss_spatial_bce_5: 0.10406/0.09644, loss_spatial_dice_5: 0.23270/0.23181, loss_spatial_ce_5: 0.07325/0.10834, loss_grounding_bce_5: 0.07763/0.08773, loss_grounding_dice_5: 0.10365/0.18304, loss_grounding_ce_5: 0.14317/0.29484, loss_mask_ce_6: 2.01037/0.97567, loss_mask_bce_6: 0.73546/0.34460, loss_mask_dice_6: 1.30655/1.20054, loss_spatial_bce_6: 0.09663/0.10224, loss_spatial_dice_6: 0.22742/0.23461, loss_spatial_ce_6: 0.11873/0.13482, loss_grounding_bce_6: 0.07193/0.08847, loss_grounding_dice_6: 0.08849/0.18338, loss_grounding_ce_6: 0.12173/0.31033, loss_mask_ce_7: 1.93561/1.02039, loss_mask_bce_7: 0.86868/0.35249, loss_mask_dice_7: 1.36005/1.25538, loss_spatial_bce_7: 0.10423/0.11054, loss_spatial_dice_7: 0.26332/0.26243, loss_spatial_ce_7: 0.27968/0.17092, loss_grounding_bce_7: 0.07524/0.09040, loss_grounding_dice_7: 0.09156/0.19064, loss_grounding_ce_7: 0.14714/0.34204, loss_mask_ce_8: 1.83563/1.12943, loss_mask_bce_8: 0.89808/0.36612, loss_mask_dice_8: 1.56735/1.32899, loss_spatial_bce_8: 0.15822/0.13134, loss_spatial_dice_8: 0.35402/0.30089, loss_spatial_ce_8: 0.40929/0.22795, loss_grounding_bce_8: 0.07978/0.09415, loss_grounding_dice_8: 0.09695/0.20170, loss_grounding_ce_8: 0.14827/0.40954, loss_mask_ce_9: 4.95765/3.67931, loss_mask_bce_9: 0.80665/0.39309, loss_mask_dice_9: 2.08139/1.90203, loss_spatial_bce_9: 0.33597/0.33364, loss_spatial_dice_9: 0.81918/0.82234, loss_spatial_ce_9: 1.35185/1.50007, loss_grounding_bce_9: 0.14022/0.10560, loss_grounding_dice_9: 0.22200/0.28077, loss_grounding_ce_9: 0.29644/0.67459] items per batch[64] items per second[0.23] total items[3398400] mini batches[ 53100] memory[7345] epoch remaining[1:20:04]
INFO:trainer.default_trainer:epochs[    29] optim steps[53200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83327/0.90207, loss_mask_bce_0: 0.17735/0.33511, loss_mask_dice_0: 0.96879/1.16262, loss_spatial_bce_0: 0.02428/0.08801, loss_spatial_dice_0: 0.17930/0.20991, loss_spatial_ce_0: 0.03017/0.06457, loss_grounding_bce_0: 0.02008/0.08632, loss_grounding_dice_0: 0.10476/0.17864, loss_grounding_ce_0: 0.26150/0.27297, loss_mask_ce_1: 0.94166/0.90262, loss_mask_bce_1: 0.18814/0.33602, loss_mask_dice_1: 0.94823/1.16915, loss_spatial_bce_1: 0.02521/0.08854, loss_spatial_dice_1: 0.20156/0.21393, loss_spatial_ce_1: 0.03310/0.07032, loss_grounding_bce_1: 0.02092/0.08650, loss_grounding_dice_1: 0.10681/0.17942, loss_grounding_ce_1: 0.28069/0.27412, loss_mask_ce_2: 0.86970/0.90976, loss_mask_bce_2: 0.19271/0.33653, loss_mask_dice_2: 1.04498/1.16935, loss_spatial_bce_2: 0.02897/0.08940, loss_spatial_dice_2: 0.17975/0.21532, loss_spatial_ce_2: 0.04973/0.07376, loss_grounding_bce_2: 0.01796/0.08663, loss_grounding_dice_2: 0.10163/0.17923, loss_grounding_ce_2: 0.28136/0.27741, loss_mask_ce_3: 0.86354/0.91952, loss_mask_bce_3: 0.19512/0.33759, loss_mask_dice_3: 0.97414/1.16672, loss_spatial_bce_3: 0.02958/0.09039, loss_spatial_dice_3: 0.18164/0.21602, loss_spatial_ce_3: 0.03097/0.07766, loss_grounding_bce_3: 0.01928/0.08687, loss_grounding_dice_3: 0.10523/0.17896, loss_grounding_ce_3: 0.28729/0.27926, loss_mask_ce_4: 0.98965/0.92028, loss_mask_bce_4: 0.20508/0.33968, loss_mask_dice_4: 1.08540/1.19064, loss_spatial_bce_4: 0.03681/0.09442, loss_spatial_dice_4: 0.21371/0.22788, loss_spatial_ce_4: 0.08446/0.09336, loss_grounding_bce_4: 0.01942/0.08736, loss_grounding_dice_4: 0.11349/0.18187, loss_grounding_ce_4: 0.28410/0.28210, loss_mask_ce_5: 1.02193/0.93607, loss_mask_bce_5: 0.20897/0.34187, loss_mask_dice_5: 0.98758/1.19766, loss_spatial_bce_5: 0.03454/0.09643, loss_spatial_dice_5: 0.23752/0.23181, loss_spatial_ce_5: 0.19658/0.10833, loss_grounding_bce_5: 0.01912/0.08774, loss_grounding_dice_5: 0.09862/0.18304, loss_grounding_ce_5: 0.28561/0.29487, loss_mask_ce_6: 0.98253/0.97555, loss_mask_bce_6: 0.20335/0.34462, loss_mask_dice_6: 0.87806/1.20060, loss_spatial_bce_6: 0.03872/0.10223, loss_spatial_dice_6: 0.21200/0.23461, loss_spatial_ce_6: 0.18740/0.13481, loss_grounding_bce_6: 0.01703/0.08848, loss_grounding_dice_6: 0.08972/0.18336, loss_grounding_ce_6: 0.31427/0.31040, loss_mask_ce_7: 1.00573/1.02030, loss_mask_bce_7: 0.20228/0.35251, loss_mask_dice_7: 0.97337/1.25544, loss_spatial_bce_7: 0.08351/0.11053, loss_spatial_dice_7: 0.33079/0.26244, loss_spatial_ce_7: 0.15361/0.17090, loss_grounding_bce_7: 0.01978/0.09040, loss_grounding_dice_7: 0.10953/0.19062, loss_grounding_ce_7: 0.31337/0.34210, loss_mask_ce_8: 1.18591/1.12938, loss_mask_bce_8: 0.20143/0.36615, loss_mask_dice_8: 1.07034/1.32905, loss_spatial_bce_8: 0.08647/0.13134, loss_spatial_dice_8: 0.42955/0.30090, loss_spatial_ce_8: 0.12157/0.22790, loss_grounding_bce_8: 0.02305/0.09416, loss_grounding_dice_8: 0.12664/0.20168, loss_grounding_ce_8: 0.30654/0.40965, loss_mask_ce_9: 3.36062/3.67946, loss_mask_bce_9: 0.25615/0.39310, loss_mask_dice_9: 1.99639/1.90204, loss_spatial_bce_9: 0.16433/0.33368, loss_spatial_dice_9: 0.90722/0.82233, loss_spatial_ce_9: 2.07361/1.50001, loss_grounding_bce_9: 0.03484/0.10561, loss_grounding_dice_9: 0.29608/0.28076, loss_grounding_ce_9: 0.35487/0.67471] items per batch[64] items per second[0.24] total items[3404800] mini batches[ 53200] memory[7345] epoch remaining[1:14:12]
INFO:trainer.default_trainer:epochs[    29] optim steps[53300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90415/0.90215, loss_mask_bce_0: 0.47734/0.33515, loss_mask_dice_0: 1.15314/1.16264, loss_spatial_bce_0: 0.10875/0.08801, loss_spatial_dice_0: 0.22367/0.20988, loss_spatial_ce_0: 0.00624/0.06453, loss_grounding_bce_0: 0.16015/0.08631, loss_grounding_dice_0: 0.15419/0.17864, loss_grounding_ce_0: 0.00826/0.27297, loss_mask_ce_1: 0.77016/0.90269, loss_mask_bce_1: 0.47988/0.33606, loss_mask_dice_1: 1.22416/1.16921, loss_spatial_bce_1: 0.11186/0.08855, loss_spatial_dice_1: 0.23547/0.21390, loss_spatial_ce_1: 0.01501/0.07029, loss_grounding_bce_1: 0.15991/0.08649, loss_grounding_dice_1: 0.15971/0.17942, loss_grounding_ce_1: 0.00808/0.27412, loss_mask_ce_2: 0.76738/0.90982, loss_mask_bce_2: 0.47102/0.33656, loss_mask_dice_2: 1.28690/1.16940, loss_spatial_bce_2: 0.11084/0.08940, loss_spatial_dice_2: 0.24935/0.21529, loss_spatial_ce_2: 0.01496/0.07373, loss_grounding_bce_2: 0.15572/0.08661, loss_grounding_dice_2: 0.16107/0.17923, loss_grounding_ce_2: 0.00816/0.27742, loss_mask_ce_3: 0.78809/0.91957, loss_mask_bce_3: 0.46650/0.33763, loss_mask_dice_3: 1.24179/1.16679, loss_spatial_bce_3: 0.11622/0.09039, loss_spatial_dice_3: 0.24610/0.21599, loss_spatial_ce_3: 0.01642/0.07763, loss_grounding_bce_3: 0.15968/0.08685, loss_grounding_dice_3: 0.15217/0.17897, loss_grounding_ce_3: 0.00708/0.27926, loss_mask_ce_4: 0.83100/0.92035, loss_mask_bce_4: 0.47311/0.33971, loss_mask_dice_4: 1.29862/1.19070, loss_spatial_bce_4: 0.11294/0.09442, loss_spatial_dice_4: 0.26896/0.22786, loss_spatial_ce_4: 0.03090/0.09334, loss_grounding_bce_4: 0.15831/0.08735, loss_grounding_dice_4: 0.15533/0.18187, loss_grounding_ce_4: 0.00811/0.28210, loss_mask_ce_5: 0.91290/0.93615, loss_mask_bce_5: 0.46266/0.34191, loss_mask_dice_5: 1.39950/1.19771, loss_spatial_bce_5: 0.11209/0.09643, loss_spatial_dice_5: 0.25893/0.23179, loss_spatial_ce_5: 0.04566/0.10828, loss_grounding_bce_5: 0.16098/0.08773, loss_grounding_dice_5: 0.14792/0.18304, loss_grounding_ce_5: 0.00698/0.29485, loss_mask_ce_6: 0.84354/0.97567, loss_mask_bce_6: 0.48807/0.34465, loss_mask_dice_6: 1.41615/1.20063, loss_spatial_bce_6: 0.12874/0.10223, loss_spatial_dice_6: 0.29766/0.23459, loss_spatial_ce_6: 0.03587/0.13475, loss_grounding_bce_6: 0.16556/0.08846, loss_grounding_dice_6: 0.15133/0.18337, loss_grounding_ce_6: 0.01399/0.31034, loss_mask_ce_7: 1.02170/1.02039, loss_mask_bce_7: 0.48518/0.35254, loss_mask_dice_7: 1.42334/1.25546, loss_spatial_bce_7: 0.13217/0.11053, loss_spatial_dice_7: 0.30773/0.26243, loss_spatial_ce_7: 0.11347/0.17084, loss_grounding_bce_7: 0.17153/0.09038, loss_grounding_dice_7: 0.15297/0.19062, loss_grounding_ce_7: 0.00575/0.34207, loss_mask_ce_8: 0.97943/1.12939, loss_mask_bce_8: 0.54313/0.36619, loss_mask_dice_8: 1.66416/1.32910, loss_spatial_bce_8: 0.13769/0.13133, loss_spatial_dice_8: 0.31747/0.30087, loss_spatial_ce_8: 0.17158/0.22784, loss_grounding_bce_8: 0.17343/0.09415, loss_grounding_dice_8: 0.14237/0.20168, loss_grounding_ce_8: 0.00317/0.40960, loss_mask_ce_9: 2.88259/3.67944, loss_mask_bce_9: 0.44010/0.39316, loss_mask_dice_9: 1.77579/1.90198, loss_spatial_bce_9: 0.25984/0.33369, loss_spatial_dice_9: 0.85037/0.82235, loss_spatial_ce_9: 1.37469/1.49992, loss_grounding_bce_9: 0.17291/0.10560, loss_grounding_dice_9: 0.18216/0.28076, loss_grounding_ce_9: 0.34225/0.67462] items per batch[64] items per second[0.23] total items[3411200] mini batches[ 53300] memory[7345] epoch remaining[1:09:45]
INFO:trainer.default_trainer:epochs[    29] optim steps[53400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35768/0.90205, loss_mask_bce_0: 0.02059/0.33512, loss_mask_dice_0: 0.05084/1.16259, loss_spatial_bce_0: 0.01588/0.08800, loss_spatial_dice_0: 0.03425/0.20985, loss_spatial_ce_0: 0.02362/0.06451, loss_grounding_bce_0: 0.02047/0.08632, loss_grounding_dice_0: 0.03600/0.17864, loss_grounding_ce_0: 0.02214/0.27293, loss_mask_ce_1: 0.40443/0.90263, loss_mask_bce_1: 0.02085/0.33603, loss_mask_dice_1: 0.05047/1.16914, loss_spatial_bce_1: 0.01764/0.08854, loss_spatial_dice_1: 0.03488/0.21387, loss_spatial_ce_1: 0.02272/0.07027, loss_grounding_bce_1: 0.01978/0.08649, loss_grounding_dice_1: 0.03437/0.17942, loss_grounding_ce_1: 0.02348/0.27408, loss_mask_ce_2: 0.41132/0.90973, loss_mask_bce_2: 0.01969/0.33654, loss_mask_dice_2: 0.04977/1.16934, loss_spatial_bce_2: 0.01667/0.08939, loss_spatial_dice_2: 0.04050/0.21526, loss_spatial_ce_2: 0.03681/0.07371, loss_grounding_bce_2: 0.02082/0.08662, loss_grounding_dice_2: 0.03928/0.17924, loss_grounding_ce_2: 0.01734/0.27739, loss_mask_ce_3: 0.36964/0.91947, loss_mask_bce_3: 0.02007/0.33762, loss_mask_dice_3: 0.05383/1.16676, loss_spatial_bce_3: 0.01881/0.09039, loss_spatial_dice_3: 0.04539/0.21597, loss_spatial_ce_3: 0.03337/0.07760, loss_grounding_bce_3: 0.02632/0.08686, loss_grounding_dice_3: 0.04505/0.17897, loss_grounding_ce_3: 0.03530/0.27922, loss_mask_ce_4: 0.35856/0.92024, loss_mask_bce_4: 0.02361/0.33969, loss_mask_dice_4: 0.05497/1.19063, loss_spatial_bce_4: 0.01609/0.09442, loss_spatial_dice_4: 0.03956/0.22784, loss_spatial_ce_4: 0.01566/0.09333, loss_grounding_bce_4: 0.02176/0.08735, loss_grounding_dice_4: 0.03917/0.18188, loss_grounding_ce_4: 0.03334/0.28206, loss_mask_ce_5: 0.35215/0.93601, loss_mask_bce_5: 0.02262/0.34189, loss_mask_dice_5: 0.05517/1.19770, loss_spatial_bce_5: 0.02054/0.09643, loss_spatial_dice_5: 0.04536/0.23177, loss_spatial_ce_5: 0.01975/0.10826, loss_grounding_bce_5: 0.02126/0.08773, loss_grounding_dice_5: 0.03907/0.18304, loss_grounding_ce_5: 0.01734/0.29480, loss_mask_ce_6: 0.32791/0.97553, loss_mask_bce_6: 0.02390/0.34464, loss_mask_dice_6: 0.05683/1.20057, loss_spatial_bce_6: 0.02268/0.10224, loss_spatial_dice_6: 0.03986/0.23457, loss_spatial_ce_6: 0.02968/0.13473, loss_grounding_bce_6: 0.02473/0.08847, loss_grounding_dice_6: 0.04360/0.18338, loss_grounding_ce_6: 0.02445/0.31025, loss_mask_ce_7: 0.37614/1.02029, loss_mask_bce_7: 0.02443/0.35251, loss_mask_dice_7: 0.05415/1.25540, loss_spatial_bce_7: 0.03367/0.11053, loss_spatial_dice_7: 0.05327/0.26240, loss_spatial_ce_7: 0.02298/0.17082, loss_grounding_bce_7: 0.02350/0.09038, loss_grounding_dice_7: 0.04107/0.19063, loss_grounding_ce_7: 0.02184/0.34197, loss_mask_ce_8: 0.30885/1.12927, loss_mask_bce_8: 0.02388/0.36617, loss_mask_dice_8: 0.05490/1.32904, loss_spatial_bce_8: 0.02401/0.13132, loss_spatial_dice_8: 0.04880/0.30084, loss_spatial_ce_8: 0.08124/0.22779, loss_grounding_bce_8: 0.02251/0.09415, loss_grounding_dice_8: 0.03899/0.20168, loss_grounding_ce_8: 0.01507/0.40946, loss_mask_ce_9: 2.35639/3.67910, loss_mask_bce_9: 0.02620/0.39314, loss_mask_dice_9: 0.08448/1.90196, loss_spatial_bce_9: 0.43981/0.33371, loss_spatial_dice_9: 0.70773/0.82234, loss_spatial_ce_9: 0.89083/1.49982, loss_grounding_bce_9: 0.03284/0.10560, loss_grounding_dice_9: 0.09983/0.28077, loss_grounding_ce_9: 0.30134/0.67447] items per batch[64] items per second[0.23] total items[3417600] mini batches[ 53400] memory[7345] epoch remaining[1:05:02]
INFO:trainer.default_trainer:epochs[    29] optim steps[53500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31608/0.90193, loss_mask_bce_0: 0.29048/0.33514, loss_mask_dice_0: 0.33195/1.16251, loss_spatial_bce_0: 0.15026/0.08799, loss_spatial_dice_0: 0.17397/0.20983, loss_spatial_ce_0: 0.01735/0.06447, loss_grounding_bce_0: 0.23689/0.08632, loss_grounding_dice_0: 0.34578/0.17863, loss_grounding_ce_0: 0.16843/0.27291, loss_mask_ce_1: 0.31930/0.90248, loss_mask_bce_1: 0.28889/0.33605, loss_mask_dice_1: 0.33690/1.16906, loss_spatial_bce_1: 0.16067/0.08853, loss_spatial_dice_1: 0.18014/0.21385, loss_spatial_ce_1: 0.03420/0.07025, loss_grounding_bce_1: 0.27155/0.08650, loss_grounding_dice_1: 0.33180/0.17942, loss_grounding_ce_1: 0.36909/0.27406, loss_mask_ce_2: 0.31507/0.90959, loss_mask_bce_2: 0.29191/0.33656, loss_mask_dice_2: 0.34053/1.16924, loss_spatial_bce_2: 0.15987/0.08939, loss_spatial_dice_2: 0.17921/0.21524, loss_spatial_ce_2: 0.03438/0.07366, loss_grounding_bce_2: 0.24388/0.08662, loss_grounding_dice_2: 0.35118/0.17924, loss_grounding_ce_2: 0.21315/0.27736, loss_mask_ce_3: 0.31166/0.91936, loss_mask_bce_3: 0.29246/0.33764, loss_mask_dice_3: 0.33681/1.16669, loss_spatial_bce_3: 0.15325/0.09039, loss_spatial_dice_3: 0.17377/0.21595, loss_spatial_ce_3: 0.02697/0.07757, loss_grounding_bce_3: 0.26306/0.08686, loss_grounding_dice_3: 0.33524/0.17896, loss_grounding_ce_3: 0.42625/0.27923, loss_mask_ce_4: 0.35641/0.92011, loss_mask_bce_4: 0.29345/0.33972, loss_mask_dice_4: 0.32678/1.19054, loss_spatial_bce_4: 0.17031/0.09442, loss_spatial_dice_4: 0.17980/0.22783, loss_spatial_ce_4: 0.02557/0.09329, loss_grounding_bce_4: 0.23604/0.08736, loss_grounding_dice_4: 0.34859/0.18187, loss_grounding_ce_4: 0.21941/0.28203, loss_mask_ce_5: 0.32515/0.93586, loss_mask_bce_5: 0.27476/0.34193, loss_mask_dice_5: 0.33577/1.19761, loss_spatial_bce_5: 0.16228/0.09644, loss_spatial_dice_5: 0.19144/0.23175, loss_spatial_ce_5: 0.07229/0.10821, loss_grounding_bce_5: 0.25650/0.08774, loss_grounding_dice_5: 0.32861/0.18304, loss_grounding_ce_5: 0.40200/0.29482, loss_mask_ce_6: 0.29255/0.97540, loss_mask_bce_6: 0.28520/0.34468, loss_mask_dice_6: 0.33915/1.20049, loss_spatial_bce_6: 0.15480/0.10224, loss_spatial_dice_6: 0.19100/0.23456, loss_spatial_ce_6: 0.08486/0.13467, loss_grounding_bce_6: 0.25436/0.08848, loss_grounding_dice_6: 0.32137/0.18337, loss_grounding_ce_6: 0.45591/0.31027, loss_mask_ce_7: 0.30318/1.02022, loss_mask_bce_7: 0.31192/0.35254, loss_mask_dice_7: 0.34208/1.25532, loss_spatial_bce_7: 0.18762/0.11052, loss_spatial_dice_7: 0.22803/0.26239, loss_spatial_ce_7: 0.27180/0.17079, loss_grounding_bce_7: 0.24236/0.09039, loss_grounding_dice_7: 0.34104/0.19062, loss_grounding_ce_7: 0.16110/0.34202, loss_mask_ce_8: 0.55417/1.12915, loss_mask_bce_8: 0.30257/0.36620, loss_mask_dice_8: 0.33556/1.32895, loss_spatial_bce_8: 0.22069/0.13132, loss_spatial_dice_8: 0.21699/0.30083, loss_spatial_ce_8: 0.18718/0.22774, loss_grounding_bce_8: 0.23776/0.09415, loss_grounding_dice_8: 0.32025/0.20167, loss_grounding_ce_8: 0.42405/0.40952, loss_mask_ce_9: 3.21281/3.67918, loss_mask_bce_9: 0.30961/0.39318, loss_mask_dice_9: 0.47496/1.90194, loss_spatial_bce_9: 0.49894/0.33369, loss_spatial_dice_9: 0.71903/0.82233, loss_spatial_ce_9: 1.10388/1.49976, loss_grounding_bce_9: 0.27482/0.10561, loss_grounding_dice_9: 0.42601/0.28076, loss_grounding_ce_9: 0.12790/0.67442] items per batch[64] items per second[0.24] total items[3424000] mini batches[ 53500] memory[7345] epoch remaining[1:00:12]
INFO:trainer.default_trainer:epochs[    29] optim steps[53600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47975/0.90197, loss_mask_bce_0: 0.40755/0.33514, loss_mask_dice_0: 1.12143/1.16293, loss_spatial_bce_0: 0.04646/0.08798, loss_spatial_dice_0: 0.22340/0.20981, loss_spatial_ce_0: 0.00247/0.06443, loss_grounding_bce_0: 0.21428/0.08632, loss_grounding_dice_0: 0.14656/0.17864, loss_grounding_ce_0: 0.00690/0.27288, loss_mask_ce_1: 0.44246/0.90255, loss_mask_bce_1: 0.40145/0.33605, loss_mask_dice_1: 1.10327/1.16949, loss_spatial_bce_1: 0.04548/0.08852, loss_spatial_dice_1: 0.20856/0.21384, loss_spatial_ce_1: 0.00964/0.07020, loss_grounding_bce_1: 0.21020/0.08650, loss_grounding_dice_1: 0.14564/0.17943, loss_grounding_ce_1: 0.00709/0.27404, loss_mask_ce_2: 0.50651/0.90966, loss_mask_bce_2: 0.39564/0.33655, loss_mask_dice_2: 1.12490/1.16966, loss_spatial_bce_2: 0.04955/0.08938, loss_spatial_dice_2: 0.21322/0.21523, loss_spatial_ce_2: 0.00381/0.07360, loss_grounding_bce_2: 0.20866/0.08662, loss_grounding_dice_2: 0.14499/0.17926, loss_grounding_ce_2: 0.00503/0.27733, loss_mask_ce_3: 0.45643/0.91943, loss_mask_bce_3: 0.39756/0.33764, loss_mask_dice_3: 1.15665/1.16707, loss_spatial_bce_3: 0.05210/0.09038, loss_spatial_dice_3: 0.22057/0.21593, loss_spatial_ce_3: 0.00604/0.07753, loss_grounding_bce_3: 0.20314/0.08686, loss_grounding_dice_3: 0.14226/0.17897, loss_grounding_ce_3: 0.00677/0.27920, loss_mask_ce_4: 0.49231/0.92018, loss_mask_bce_4: 0.39708/0.33972, loss_mask_dice_4: 1.16657/1.19094, loss_spatial_bce_4: 0.05908/0.09441, loss_spatial_dice_4: 0.23808/0.22782, loss_spatial_ce_4: 0.01614/0.09325, loss_grounding_bce_4: 0.20025/0.08735, loss_grounding_dice_4: 0.13638/0.18188, loss_grounding_ce_4: 0.00662/0.28201, loss_mask_ce_5: 0.61562/0.93595, loss_mask_bce_5: 0.39456/0.34192, loss_mask_dice_5: 1.11094/1.19803, loss_spatial_bce_5: 0.05824/0.09643, loss_spatial_dice_5: 0.22433/0.23175, loss_spatial_ce_5: 0.03182/0.10817, loss_grounding_bce_5: 0.19705/0.08773, loss_grounding_dice_5: 0.13781/0.18304, loss_grounding_ce_5: 0.00513/0.29477, loss_mask_ce_6: 0.70123/0.97546, loss_mask_bce_6: 0.41639/0.34467, loss_mask_dice_6: 1.15427/1.20094, loss_spatial_bce_6: 0.06027/0.10223, loss_spatial_dice_6: 0.27488/0.23456, loss_spatial_ce_6: 0.06975/0.13460, loss_grounding_bce_6: 0.20432/0.08848, loss_grounding_dice_6: 0.14342/0.18338, loss_grounding_ce_6: 0.01215/0.31027, loss_mask_ce_7: 0.90180/1.02029, loss_mask_bce_7: 0.40598/0.35252, loss_mask_dice_7: 1.14780/1.25578, loss_spatial_bce_7: 0.07520/0.11050, loss_spatial_dice_7: 0.29919/0.26239, loss_spatial_ce_7: 0.10023/0.17073, loss_grounding_bce_7: 0.21463/0.09039, loss_grounding_dice_7: 0.14988/0.19063, loss_grounding_ce_7: 0.00366/0.34201, loss_mask_ce_8: 0.55299/1.12921, loss_mask_bce_8: 0.41685/0.36619, loss_mask_dice_8: 1.26709/1.32946, loss_spatial_bce_8: 0.09175/0.13131, loss_spatial_dice_8: 0.27616/0.30084, loss_spatial_ce_8: 0.11718/0.22767, loss_grounding_bce_8: 0.20485/0.09415, loss_grounding_dice_8: 0.14762/0.20169, loss_grounding_ce_8: 0.00397/0.40954, loss_mask_ce_9: 3.65754/3.67923, loss_mask_bce_9: 0.44305/0.39315, loss_mask_dice_9: 1.74490/1.90265, loss_spatial_bce_9: 0.24176/0.33367, loss_spatial_dice_9: 0.86435/0.82234, loss_spatial_ce_9: 1.09375/1.49962, loss_grounding_bce_9: 0.19459/0.10560, loss_grounding_dice_9: 0.17301/0.28077, loss_grounding_ce_9: 0.05171/0.67429] items per batch[64] items per second[0.24] total items[3430400] mini batches[ 53600] memory[7345] epoch remaining[0:55:14]
INFO:trainer.default_trainer:epochs[    29] optim steps[53700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80987/0.90190, loss_mask_bce_0: 0.33689/0.33509, loss_mask_dice_0: 0.99063/1.16249, loss_spatial_bce_0: 0.09623/0.08796, loss_spatial_dice_0: 0.22570/0.20979, loss_spatial_ce_0: 0.00735/0.06439, loss_grounding_bce_0: 0.03985/0.08631, loss_grounding_dice_0: 0.19311/0.17864, loss_grounding_ce_0: 0.37323/0.27286, loss_mask_ce_1: 0.47976/0.90245, loss_mask_bce_1: 0.33416/0.33601, loss_mask_dice_1: 1.06011/1.16908, loss_spatial_bce_1: 0.09824/0.08850, loss_spatial_dice_1: 0.22647/0.21381, loss_spatial_ce_1: 0.00724/0.07017, loss_grounding_bce_1: 0.04109/0.08649, loss_grounding_dice_1: 0.27362/0.17942, loss_grounding_ce_1: 0.35548/0.27398, loss_mask_ce_2: 0.67504/0.90956, loss_mask_bce_2: 0.32838/0.33651, loss_mask_dice_2: 1.05856/1.16923, loss_spatial_bce_2: 0.09337/0.08937, loss_spatial_dice_2: 0.23783/0.21521, loss_spatial_ce_2: 0.01201/0.07357, loss_grounding_bce_2: 0.04084/0.08662, loss_grounding_dice_2: 0.26571/0.17925, loss_grounding_ce_2: 0.35967/0.27731, loss_mask_ce_3: 0.51906/0.91933, loss_mask_bce_3: 0.34381/0.33760, loss_mask_dice_3: 1.25315/1.16669, loss_spatial_bce_3: 0.09320/0.09037, loss_spatial_dice_3: 0.27015/0.21591, loss_spatial_ce_3: 0.02209/0.07750, loss_grounding_bce_3: 0.04161/0.08684, loss_grounding_dice_3: 0.24392/0.17896, loss_grounding_ce_3: 0.36457/0.27916, loss_mask_ce_4: 0.57415/0.92009, loss_mask_bce_4: 0.33760/0.33967, loss_mask_dice_4: 1.42081/1.19055, loss_spatial_bce_4: 0.08984/0.09440, loss_spatial_dice_4: 0.25305/0.22781, loss_spatial_ce_4: 0.06703/0.09322, loss_grounding_bce_4: 0.04021/0.08734, loss_grounding_dice_4: 0.22347/0.18188, loss_grounding_ce_4: 0.39727/0.28200, loss_mask_ce_5: 0.51833/0.93589, loss_mask_bce_5: 0.33117/0.34187, loss_mask_dice_5: 1.14044/1.19762, loss_spatial_bce_5: 0.11968/0.09642, loss_spatial_dice_5: 0.22238/0.23174, loss_spatial_ce_5: 0.04103/0.10811, loss_grounding_bce_5: 0.04021/0.08773, loss_grounding_dice_5: 0.21566/0.18304, loss_grounding_ce_5: 0.37882/0.29472, loss_mask_ce_6: 0.60019/0.97540, loss_mask_bce_6: 0.32970/0.34463, loss_mask_dice_6: 1.41032/1.20056, loss_spatial_bce_6: 0.09955/0.10221, loss_spatial_dice_6: 0.28640/0.23455, loss_spatial_ce_6: 0.10657/0.13456, loss_grounding_bce_6: 0.03839/0.08847, loss_grounding_dice_6: 0.20089/0.18338, loss_grounding_ce_6: 0.42470/0.31027, loss_mask_ce_7: 0.70232/1.02021, loss_mask_bce_7: 0.32757/0.35247, loss_mask_dice_7: 1.14542/1.25536, loss_spatial_bce_7: 0.10379/0.11049, loss_spatial_dice_7: 0.35079/0.26238, loss_spatial_ce_7: 0.34136/0.17070, loss_grounding_bce_7: 0.03960/0.09038, loss_grounding_dice_7: 0.19706/0.19063, loss_grounding_ce_7: 0.37519/0.34202, loss_mask_ce_8: 1.21645/1.12912, loss_mask_bce_8: 0.34198/0.36614, loss_mask_dice_8: 1.19444/1.32907, loss_spatial_bce_8: 0.13488/0.13128, loss_spatial_dice_8: 0.39327/0.30082, loss_spatial_ce_8: 0.40032/0.22763, loss_grounding_bce_8: 0.04415/0.09415, loss_grounding_dice_8: 0.28160/0.20169, loss_grounding_ce_8: 0.50006/0.40960, loss_mask_ce_9: 4.26226/3.67898, loss_mask_bce_9: 0.38328/0.39310, loss_mask_dice_9: 1.99650/1.90207, loss_spatial_bce_9: 0.33929/0.33365, loss_spatial_dice_9: 0.87107/0.82235, loss_spatial_ce_9: 1.61282/1.49951, loss_grounding_bce_9: 0.04981/0.10559, loss_grounding_dice_9: 0.46681/0.28078, loss_grounding_ce_9: 0.46178/0.67444] items per batch[64] items per second[0.23] total items[3436800] mini batches[ 53700] memory[7345] epoch remaining[0:50:45]
INFO:trainer.default_trainer:epochs[    29] optim steps[53800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.51250/0.90175, loss_mask_bce_0: 0.18215/0.33507, loss_mask_dice_0: 0.22634/1.16284, loss_spatial_bce_0: 0.07917/0.08794, loss_spatial_dice_0: 0.09471/0.20977, loss_spatial_ce_0: 0.03715/0.06437, loss_grounding_bce_0: 0.07377/0.08630, loss_grounding_dice_0: 0.06900/0.17864, loss_grounding_ce_0: 0.23667/0.27286, loss_mask_ce_1: 0.53352/0.90233, loss_mask_bce_1: 0.19173/0.33599, loss_mask_dice_1: 0.21536/1.16940, loss_spatial_bce_1: 0.07940/0.08848, loss_spatial_dice_1: 0.09494/0.21380, loss_spatial_ce_1: 0.03199/0.07013, loss_grounding_bce_1: 0.07607/0.08647, loss_grounding_dice_1: 0.06563/0.17943, loss_grounding_ce_1: 0.27773/0.27402, loss_mask_ce_2: 0.52033/0.90946, loss_mask_bce_2: 0.17591/0.33649, loss_mask_dice_2: 0.21581/1.16954, loss_spatial_bce_2: 0.07722/0.08935, loss_spatial_dice_2: 0.09931/0.21520, loss_spatial_ce_2: 0.03024/0.07353, loss_grounding_bce_2: 0.07180/0.08660, loss_grounding_dice_2: 0.06253/0.17926, loss_grounding_ce_2: 0.35243/0.27730, loss_mask_ce_3: 0.52787/0.91921, loss_mask_bce_3: 0.19030/0.33758, loss_mask_dice_3: 0.22744/1.16703, loss_spatial_bce_3: 0.07600/0.09036, loss_spatial_dice_3: 0.09697/0.21590, loss_spatial_ce_3: 0.03731/0.07747, loss_grounding_bce_3: 0.07582/0.08683, loss_grounding_dice_3: 0.07244/0.17896, loss_grounding_ce_3: 0.33066/0.27921, loss_mask_ce_4: 0.52566/0.92001, loss_mask_bce_4: 0.19362/0.33965, loss_mask_dice_4: 0.22444/1.19086, loss_spatial_bce_4: 0.09094/0.09438, loss_spatial_dice_4: 0.11241/0.22780, loss_spatial_ce_4: 0.03356/0.09319, loss_grounding_bce_4: 0.07102/0.08733, loss_grounding_dice_4: 0.06447/0.18189, loss_grounding_ce_4: 0.35381/0.28195, loss_mask_ce_5: 0.48378/0.93575, loss_mask_bce_5: 0.19357/0.34186, loss_mask_dice_5: 0.22121/1.19794, loss_spatial_bce_5: 0.08774/0.09641, loss_spatial_dice_5: 0.10129/0.23173, loss_spatial_ce_5: 0.04755/0.10805, loss_grounding_bce_5: 0.07560/0.08772, loss_grounding_dice_5: 0.06649/0.18304, loss_grounding_ce_5: 0.28552/0.29470, loss_mask_ce_6: 0.43258/0.97527, loss_mask_bce_6: 0.19749/0.34462, loss_mask_dice_6: 0.22675/1.20089, loss_spatial_bce_6: 0.10115/0.10219, loss_spatial_dice_6: 0.12645/0.23455, loss_spatial_ce_6: 0.09279/0.13451, loss_grounding_bce_6: 0.07978/0.08846, loss_grounding_dice_6: 0.07161/0.18338, loss_grounding_ce_6: 0.22216/0.31026, loss_mask_ce_7: 0.42847/1.02007, loss_mask_bce_7: 0.19750/0.35246, loss_mask_dice_7: 0.24198/1.25571, loss_spatial_bce_7: 0.09192/0.11047, loss_spatial_dice_7: 0.11736/0.26238, loss_spatial_ce_7: 0.09599/0.17066, loss_grounding_bce_7: 0.07857/0.09037, loss_grounding_dice_7: 0.07133/0.19063, loss_grounding_ce_7: 0.17901/0.34195, loss_mask_ce_8: 0.59158/1.12905, loss_mask_bce_8: 0.19261/0.36612, loss_mask_dice_8: 0.24593/1.32935, loss_spatial_bce_8: 0.10468/0.13127, loss_spatial_dice_8: 0.14310/0.30080, loss_spatial_ce_8: 0.19780/0.22762, loss_grounding_bce_8: 0.08102/0.09413, loss_grounding_dice_8: 0.07304/0.20168, loss_grounding_ce_8: 0.94294/0.40956, loss_mask_ce_9: 2.96775/3.67914, loss_mask_bce_9: 0.22674/0.39309, loss_mask_dice_9: 0.39713/1.90232, loss_spatial_bce_9: 0.60986/0.33367, loss_spatial_dice_9: 0.74740/0.82233, loss_spatial_ce_9: 2.52915/1.49956, loss_grounding_bce_9: 0.08525/0.10558, loss_grounding_dice_9: 0.09610/0.28079, loss_grounding_ce_9: 1.16606/0.67454] items per batch[64] items per second[0.23] total items[3443200] mini batches[ 53800] memory[7345] epoch remaining[0:46:12]
INFO:trainer.default_trainer:epochs[    29] optim steps[53900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.16465/0.90169, loss_mask_bce_0: 0.21946/0.33515, loss_mask_dice_0: 3.70762/1.16290, loss_spatial_bce_0: 0.03741/0.08795, loss_spatial_dice_0: 0.31760/0.20976, loss_spatial_ce_0: 0.02213/0.06432, loss_grounding_bce_0: 0.00904/0.08631, loss_grounding_dice_0: 0.45412/0.17863, loss_grounding_ce_0: 0.41216/0.27299, loss_mask_ce_1: 1.12416/0.90228, loss_mask_bce_1: 0.22053/0.33607, loss_mask_dice_1: 3.86844/1.16947, loss_spatial_bce_1: 0.03349/0.08849, loss_spatial_dice_1: 0.25974/0.21378, loss_spatial_ce_1: 0.11541/0.07010, loss_grounding_bce_1: 0.01021/0.08649, loss_grounding_dice_1: 0.46126/0.17942, loss_grounding_ce_1: 0.52017/0.27412, loss_mask_ce_2: 1.33260/0.90942, loss_mask_bce_2: 0.22504/0.33657, loss_mask_dice_2: 3.62556/1.16965, loss_spatial_bce_2: 0.03618/0.08936, loss_spatial_dice_2: 0.30764/0.21518, loss_spatial_ce_2: 0.07621/0.07349, loss_grounding_bce_2: 0.00986/0.08661, loss_grounding_dice_2: 0.48195/0.17925, loss_grounding_ce_2: 0.46055/0.27746, loss_mask_ce_3: 1.49785/0.91915, loss_mask_bce_3: 0.22268/0.33765, loss_mask_dice_3: 3.59173/1.16712, loss_spatial_bce_3: 0.04035/0.09036, loss_spatial_dice_3: 0.25254/0.21589, loss_spatial_ce_3: 0.11495/0.07742, loss_grounding_bce_3: 0.01071/0.08684, loss_grounding_dice_3: 0.43414/0.17895, loss_grounding_ce_3: 0.57641/0.27939, loss_mask_ce_4: 1.31737/0.91993, loss_mask_bce_4: 0.23010/0.33973, loss_mask_dice_4: 4.08058/1.19096, loss_spatial_bce_4: 0.03838/0.09438, loss_spatial_dice_4: 0.31628/0.22779, loss_spatial_ce_4: 0.10657/0.09314, loss_grounding_bce_4: 0.00981/0.08735, loss_grounding_dice_4: 0.54440/0.18188, loss_grounding_ce_4: 0.59023/0.28212, loss_mask_ce_5: 1.33424/0.93570, loss_mask_bce_5: 0.21640/0.34195, loss_mask_dice_5: 4.08818/1.19805, loss_spatial_bce_5: 0.05136/0.09642, loss_spatial_dice_5: 0.38411/0.23172, loss_spatial_ce_5: 0.13078/0.10801, loss_grounding_bce_5: 0.01203/0.08774, loss_grounding_dice_5: 0.63285/0.18303, loss_grounding_ce_5: 0.51550/0.29485, loss_mask_ce_6: 1.31336/0.97522, loss_mask_bce_6: 0.22218/0.34470, loss_mask_dice_6: 3.96720/1.20100, loss_spatial_bce_6: 0.04637/0.10220, loss_spatial_dice_6: 0.32051/0.23454, loss_spatial_ce_6: 0.15460/0.13447, loss_grounding_bce_6: 0.00988/0.08847, loss_grounding_dice_6: 0.53515/0.18338, loss_grounding_ce_6: 0.58123/0.31048, loss_mask_ce_7: 1.41236/1.01998, loss_mask_bce_7: 0.25717/0.35253, loss_mask_dice_7: 4.33793/1.25580, loss_spatial_bce_7: 0.06579/0.11048, loss_spatial_dice_7: 0.38216/0.26237, loss_spatial_ce_7: 0.19749/0.17064, loss_grounding_bce_7: 0.01470/0.09038, loss_grounding_dice_7: 0.53244/0.19063, loss_grounding_ce_7: 0.56919/0.34214, loss_mask_ce_8: 1.27805/1.12898, loss_mask_bce_8: 0.31185/0.36620, loss_mask_dice_8: 4.97883/1.32947, loss_spatial_bce_8: 0.06260/0.13127, loss_spatial_dice_8: 0.47160/0.30078, loss_spatial_ce_8: 0.20631/0.22758, loss_grounding_bce_8: 0.01757/0.09414, loss_grounding_dice_8: 0.62954/0.20169, loss_grounding_ce_8: 0.43087/0.40971, loss_mask_ce_9: 5.04717/3.67917, loss_mask_bce_9: 0.26728/0.39317, loss_mask_dice_9: 6.07357/1.90250, loss_spatial_bce_9: 0.33711/0.33368, loss_spatial_dice_9: 0.94916/0.82234, loss_spatial_ce_9: 1.69770/1.49952, loss_grounding_bce_9: 0.01088/0.10560, loss_grounding_dice_9: 0.67946/0.28083, loss_grounding_ce_9: 0.61200/0.67470] items per batch[64] items per second[0.23] total items[3449600] mini batches[ 53900] memory[7345] epoch remaining[0:41:39]
INFO:trainer.default_trainer:epochs[    29] optim steps[54000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35085/0.90167, loss_mask_bce_0: 0.13961/0.33514, loss_mask_dice_0: 0.12726/1.16269, loss_spatial_bce_0: 0.07094/0.08795, loss_spatial_dice_0: 0.05794/0.20972, loss_spatial_ce_0: 0.10109/0.06429, loss_grounding_bce_0: 0.05762/0.08632, loss_grounding_dice_0: 0.05806/0.17862, loss_grounding_ce_0: 0.06330/0.27294, loss_mask_ce_1: 0.35823/0.90226, loss_mask_bce_1: 0.12927/0.33607, loss_mask_dice_1: 0.13300/1.16924, loss_spatial_bce_1: 0.07070/0.08849, loss_spatial_dice_1: 0.05903/0.21375, loss_spatial_ce_1: 0.09942/0.07007, loss_grounding_bce_1: 0.05501/0.08650, loss_grounding_dice_1: 0.05609/0.17940, loss_grounding_ce_1: 0.04946/0.27408, loss_mask_ce_2: 0.32291/0.90938, loss_mask_bce_2: 0.12996/0.33657, loss_mask_dice_2: 0.12467/1.16940, loss_spatial_bce_2: 0.07177/0.08936, loss_spatial_dice_2: 0.06164/0.21514, loss_spatial_ce_2: 0.10582/0.07346, loss_grounding_bce_2: 0.05467/0.08663, loss_grounding_dice_2: 0.05410/0.17925, loss_grounding_ce_2: 0.03999/0.27743, loss_mask_ce_3: 0.32501/0.91916, loss_mask_bce_3: 0.13123/0.33766, loss_mask_dice_3: 0.12240/1.16691, loss_spatial_bce_3: 0.07093/0.09037, loss_spatial_dice_3: 0.05821/0.21585, loss_spatial_ce_3: 0.15144/0.07741, loss_grounding_bce_3: 0.05403/0.08686, loss_grounding_dice_3: 0.05344/0.17894, loss_grounding_ce_3: 0.05219/0.27936, loss_mask_ce_4: 0.33200/0.91991, loss_mask_bce_4: 0.13164/0.33973, loss_mask_dice_4: 0.12699/1.19070, loss_spatial_bce_4: 0.07185/0.09439, loss_spatial_dice_4: 0.06844/0.22775, loss_spatial_ce_4: 0.09854/0.09313, loss_grounding_bce_4: 0.05723/0.08736, loss_grounding_dice_4: 0.05716/0.18187, loss_grounding_ce_4: 0.04071/0.28206, loss_mask_ce_5: 0.36455/0.93570, loss_mask_bce_5: 0.14106/0.34195, loss_mask_dice_5: 0.13233/1.19784, loss_spatial_bce_5: 0.09430/0.09642, loss_spatial_dice_5: 0.07275/0.23169, loss_spatial_ce_5: 0.09537/0.10798, loss_grounding_bce_5: 0.05886/0.08775, loss_grounding_dice_5: 0.05926/0.18303, loss_grounding_ce_5: 0.03916/0.29482, loss_mask_ce_6: 0.49405/0.97523, loss_mask_bce_6: 0.15333/0.34470, loss_mask_dice_6: 0.13642/1.20078, loss_spatial_bce_6: 0.07714/0.10220, loss_spatial_dice_6: 0.06770/0.23451, loss_spatial_ce_6: 0.18130/0.13445, loss_grounding_bce_6: 0.06602/0.08849, loss_grounding_dice_6: 0.06095/0.18337, loss_grounding_ce_6: 0.05242/0.31047, loss_mask_ce_7: 0.57829/1.02002, loss_mask_bce_7: 0.13886/0.35253, loss_mask_dice_7: 0.12560/1.25556, loss_spatial_bce_7: 0.07203/0.11048, loss_spatial_dice_7: 0.07786/0.26234, loss_spatial_ce_7: 0.16210/0.17063, loss_grounding_bce_7: 0.05841/0.09039, loss_grounding_dice_7: 0.05696/0.19061, loss_grounding_ce_7: 0.07759/0.34211, loss_mask_ce_8: 0.56356/1.12908, loss_mask_bce_8: 0.15444/0.36619, loss_mask_dice_8: 0.13369/1.32920, loss_spatial_bce_8: 0.09049/0.13128, loss_spatial_dice_8: 0.08422/0.30074, loss_spatial_ce_8: 0.14433/0.22754, loss_grounding_bce_8: 0.07332/0.09415, loss_grounding_dice_8: 0.06111/0.20167, loss_grounding_ce_8: 0.14433/0.40970, loss_mask_ce_9: 3.58700/3.67917, loss_mask_bce_9: 0.21719/0.39316, loss_mask_dice_9: 0.31559/1.90213, loss_spatial_bce_9: 0.48894/0.33369, loss_spatial_dice_9: 0.61670/0.82233, loss_spatial_ce_9: 1.04497/1.49952, loss_grounding_bce_9: 0.07248/0.10561, loss_grounding_dice_9: 0.10587/0.28080, loss_grounding_ce_9: 0.24349/0.67461] items per batch[64] items per second[0.23] total items[3456000] mini batches[ 54000] memory[7345] epoch remaining[0:37:07]
INFO:trainer.default_trainer:epochs[    29] optim steps[54100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52091/0.90163, loss_mask_bce_0: 0.41312/0.33516, loss_mask_dice_0: 0.87154/1.16269, loss_spatial_bce_0: 0.11618/0.08795, loss_spatial_dice_0: 0.18326/0.20971, loss_spatial_ce_0: 0.00209/0.06429, loss_grounding_bce_0: 0.18774/0.08634, loss_grounding_dice_0: 0.13983/0.17864, loss_grounding_ce_0: 0.00248/0.27294, loss_mask_ce_1: 0.61711/0.90223, loss_mask_bce_1: 0.41703/0.33609, loss_mask_dice_1: 0.66304/1.16924, loss_spatial_bce_1: 0.12011/0.08849, loss_spatial_dice_1: 0.18386/0.21374, loss_spatial_ce_1: 0.00557/0.07007, loss_grounding_bce_1: 0.19318/0.08651, loss_grounding_dice_1: 0.13325/0.17943, loss_grounding_ce_1: 0.00196/0.27405, loss_mask_ce_2: 0.51431/0.90934, loss_mask_bce_2: 0.40078/0.33660, loss_mask_dice_2: 0.78307/1.16943, loss_spatial_bce_2: 0.12468/0.08936, loss_spatial_dice_2: 0.14861/0.21513, loss_spatial_ce_2: 0.00956/0.07345, loss_grounding_bce_2: 0.19150/0.08664, loss_grounding_dice_2: 0.13605/0.17927, loss_grounding_ce_2: 0.00191/0.27745, loss_mask_ce_3: 0.46656/0.91915, loss_mask_bce_3: 0.40676/0.33767, loss_mask_dice_3: 0.64678/1.16689, loss_spatial_bce_3: 0.11614/0.09037, loss_spatial_dice_3: 0.19352/0.21584, loss_spatial_ce_3: 0.01839/0.07740, loss_grounding_bce_3: 0.19430/0.08687, loss_grounding_dice_3: 0.13665/0.17896, loss_grounding_ce_3: 0.00201/0.27940, loss_mask_ce_4: 0.47336/0.91987, loss_mask_bce_4: 0.39869/0.33975, loss_mask_dice_4: 0.78733/1.19067, loss_spatial_bce_4: 0.12439/0.09440, loss_spatial_dice_4: 0.20479/0.22775, loss_spatial_ce_4: 0.01481/0.09314, loss_grounding_bce_4: 0.19389/0.08737, loss_grounding_dice_4: 0.13161/0.18189, loss_grounding_ce_4: 0.00112/0.28211, loss_mask_ce_5: 0.41442/0.93563, loss_mask_bce_5: 0.40157/0.34198, loss_mask_dice_5: 0.80245/1.19787, loss_spatial_bce_5: 0.11972/0.09643, loss_spatial_dice_5: 0.23361/0.23170, loss_spatial_ce_5: 0.03063/0.10798, loss_grounding_bce_5: 0.19030/0.08776, loss_grounding_dice_5: 0.13712/0.18306, loss_grounding_ce_5: 0.00279/0.29484, loss_mask_ce_6: 0.45321/0.97523, loss_mask_bce_6: 0.40420/0.34471, loss_mask_dice_6: 0.80586/1.20078, loss_spatial_bce_6: 0.11878/0.10222, loss_spatial_dice_6: 0.18094/0.23452, loss_spatial_ce_6: 0.13680/0.13443, loss_grounding_bce_6: 0.19859/0.08849, loss_grounding_dice_6: 0.14414/0.18339, loss_grounding_ce_6: 0.00208/0.31049, loss_mask_ce_7: 0.55958/1.01991, loss_mask_bce_7: 0.40400/0.35257, loss_mask_dice_7: 0.78557/1.25557, loss_spatial_bce_7: 0.11557/0.11048, loss_spatial_dice_7: 0.23327/0.26235, loss_spatial_ce_7: 0.05810/0.17064, loss_grounding_bce_7: 0.19955/0.09040, loss_grounding_dice_7: 0.14541/0.19062, loss_grounding_ce_7: 0.00496/0.34213, loss_mask_ce_8: 0.57652/1.12902, loss_mask_bce_8: 0.39928/0.36622, loss_mask_dice_8: 0.79583/1.32922, loss_spatial_bce_8: 0.12313/0.13130, loss_spatial_dice_8: 0.31925/0.30075, loss_spatial_ce_8: 0.17096/0.22755, loss_grounding_bce_8: 0.19249/0.09416, loss_grounding_dice_8: 0.12924/0.20170, loss_grounding_ce_8: 0.59517/0.40972, loss_mask_ce_9: 2.83034/3.67925, loss_mask_bce_9: 0.38083/0.39319, loss_mask_dice_9: 0.85410/1.90213, loss_spatial_bce_9: 0.24407/0.33370, loss_spatial_dice_9: 0.76293/0.82234, loss_spatial_ce_9: 1.69355/1.49953, loss_grounding_bce_9: 0.17892/0.10561, loss_grounding_dice_9: 0.12473/0.28084, loss_grounding_ce_9: 0.55758/0.67458] items per batch[64] items per second[0.24] total items[3462400] mini batches[ 54100] memory[7345] epoch remaining[0:32:26]
INFO:trainer.default_trainer:epochs[    29] optim steps[54200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.79662/0.90165, loss_mask_bce_0: 0.55617/0.33513, loss_mask_dice_0: 2.51867/1.16254, loss_spatial_bce_0: 0.04889/0.08794, loss_spatial_dice_0: 0.31245/0.20971, loss_spatial_ce_0: 0.01250/0.06427, loss_grounding_bce_0: 0.02784/0.08635, loss_grounding_dice_0: 0.41437/0.17866, loss_grounding_ce_0: 0.14802/0.27306, loss_mask_ce_1: 1.70456/0.90224, loss_mask_bce_1: 0.53363/0.33606, loss_mask_dice_1: 2.43028/1.16906, loss_spatial_bce_1: 0.04962/0.08848, loss_spatial_dice_1: 0.31564/0.21373, loss_spatial_ce_1: 0.02973/0.07007, loss_grounding_bce_1: 0.03108/0.08652, loss_grounding_dice_1: 0.52008/0.17946, loss_grounding_ce_1: 0.15178/0.27412, loss_mask_ce_2: 1.71940/0.90934, loss_mask_bce_2: 0.52824/0.33657, loss_mask_dice_2: 1.95101/1.16925, loss_spatial_bce_2: 0.04929/0.08936, loss_spatial_dice_2: 0.32906/0.21513, loss_spatial_ce_2: 0.04668/0.07345, loss_grounding_bce_2: 0.03816/0.08665, loss_grounding_dice_2: 0.37855/0.17929, loss_grounding_ce_2: 0.22976/0.27753, loss_mask_ce_3: 1.65606/0.91914, loss_mask_bce_3: 0.54343/0.33765, loss_mask_dice_3: 2.46561/1.16674, loss_spatial_bce_3: 0.04967/0.09036, loss_spatial_dice_3: 0.33444/0.21585, loss_spatial_ce_3: 0.06125/0.07738, loss_grounding_bce_3: 0.02667/0.08688, loss_grounding_dice_3: 0.41742/0.17900, loss_grounding_ce_3: 0.21539/0.27948, loss_mask_ce_4: 1.78712/0.91988, loss_mask_bce_4: 0.55237/0.33973, loss_mask_dice_4: 2.09406/1.19050, loss_spatial_bce_4: 0.04734/0.09439, loss_spatial_dice_4: 0.34029/0.22776, loss_spatial_ce_4: 0.17989/0.09315, loss_grounding_bce_4: 0.02958/0.08739, loss_grounding_dice_4: 0.42068/0.18192, loss_grounding_ce_4: 0.14501/0.28218, loss_mask_ce_5: 1.83340/0.93561, loss_mask_bce_5: 0.60016/0.34196, loss_mask_dice_5: 2.51805/1.19771, loss_spatial_bce_5: 0.05331/0.09643, loss_spatial_dice_5: 0.31787/0.23171, loss_spatial_ce_5: 0.22911/0.10799, loss_grounding_bce_5: 0.02796/0.08778, loss_grounding_dice_5: 0.39489/0.18309, loss_grounding_ce_5: 0.15558/0.29493, loss_mask_ce_6: 1.83874/0.97525, loss_mask_bce_6: 0.57935/0.34468, loss_mask_dice_6: 2.41641/1.20063, loss_spatial_bce_6: 0.10157/0.10222, loss_spatial_dice_6: 0.36994/0.23454, loss_spatial_ce_6: 0.16993/0.13443, loss_grounding_bce_6: 0.03077/0.08851, loss_grounding_dice_6: 0.39586/0.18342, loss_grounding_ce_6: 0.19872/0.31060, loss_mask_ce_7: 1.97931/1.01989, loss_mask_bce_7: 0.59984/0.35255, loss_mask_dice_7: 2.49708/1.25542, loss_spatial_bce_7: 0.06047/0.11048, loss_spatial_dice_7: 0.40500/0.26236, loss_spatial_ce_7: 0.17590/0.17060, loss_grounding_bce_7: 0.03744/0.09042, loss_grounding_dice_7: 0.41774/0.19065, loss_grounding_ce_7: 0.16200/0.34224, loss_mask_ce_8: 2.29840/1.12898, loss_mask_bce_8: 0.64614/0.36621, loss_mask_dice_8: 2.50650/1.32905, loss_spatial_bce_8: 0.09062/0.13130, loss_spatial_dice_8: 0.47610/0.30076, loss_spatial_ce_8: 0.27465/0.22752, loss_grounding_bce_8: 0.03081/0.09419, loss_grounding_dice_8: 0.38395/0.20173, loss_grounding_ce_8: 0.39355/0.40983, loss_mask_ce_9: 4.27469/3.67921, loss_mask_bce_9: 0.72223/0.39315, loss_mask_dice_9: 3.76309/1.90180, loss_spatial_bce_9: 0.33186/0.33366, loss_spatial_dice_9: 0.94521/0.82231, loss_spatial_ce_9: 1.83983/1.49939, loss_grounding_bce_9: 0.07326/0.10562, loss_grounding_dice_9: 0.54500/0.28085, loss_grounding_ce_9: 0.54602/0.67455] items per batch[64] items per second[0.23] total items[3468800] mini batches[ 54200] memory[7345] epoch remaining[0:27:54]
INFO:trainer.default_trainer:epochs[    29] optim steps[54300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37923/0.90164, loss_mask_bce_0: 0.19279/0.33509, loss_mask_dice_0: 0.38472/1.16251, loss_spatial_bce_0: 0.06482/0.08793, loss_spatial_dice_0: 0.12432/0.20970, loss_spatial_ce_0: 0.01527/0.06425, loss_grounding_bce_0: 0.05426/0.08634, loss_grounding_dice_0: 0.09584/0.17862, loss_grounding_ce_0: 0.26325/0.27294, loss_mask_ce_1: 0.35845/0.90223, loss_mask_bce_1: 0.19881/0.33602, loss_mask_dice_1: 0.38718/1.16901, loss_spatial_bce_1: 0.06416/0.08847, loss_spatial_dice_1: 0.14264/0.21371, loss_spatial_ce_1: 0.01130/0.07005, loss_grounding_bce_1: 0.05548/0.08651, loss_grounding_dice_1: 0.09820/0.17942, loss_grounding_ce_1: 0.22391/0.27399, loss_mask_ce_2: 0.42831/0.90933, loss_mask_bce_2: 0.19858/0.33654, loss_mask_dice_2: 0.36651/1.16923, loss_spatial_bce_2: 0.06852/0.08935, loss_spatial_dice_2: 0.14383/0.21511, loss_spatial_ce_2: 0.01650/0.07345, loss_grounding_bce_2: 0.05839/0.08664, loss_grounding_dice_2: 0.09985/0.17926, loss_grounding_ce_2: 0.30925/0.27741, loss_mask_ce_3: 0.41044/0.91916, loss_mask_bce_3: 0.19261/0.33761, loss_mask_dice_3: 0.37196/1.16670, loss_spatial_bce_3: 0.06877/0.09036, loss_spatial_dice_3: 0.14792/0.21583, loss_spatial_ce_3: 0.01252/0.07739, loss_grounding_bce_3: 0.05665/0.08687, loss_grounding_dice_3: 0.10074/0.17896, loss_grounding_ce_3: 0.26514/0.27936, loss_mask_ce_4: 0.41893/0.91989, loss_mask_bce_4: 0.21638/0.33969, loss_mask_dice_4: 0.37375/1.19045, loss_spatial_bce_4: 0.06838/0.09438, loss_spatial_dice_4: 0.15890/0.22775, loss_spatial_ce_4: 0.04209/0.09314, loss_grounding_bce_4: 0.06294/0.08738, loss_grounding_dice_4: 0.10740/0.18188, loss_grounding_ce_4: 0.25502/0.28207, loss_mask_ce_5: 0.24207/0.93557, loss_mask_bce_5: 0.24984/0.34192, loss_mask_dice_5: 0.44249/1.19770, loss_spatial_bce_5: 0.06847/0.09642, loss_spatial_dice_5: 0.20756/0.23170, loss_spatial_ce_5: 0.03103/0.10797, loss_grounding_bce_5: 0.09041/0.08777, loss_grounding_dice_5: 0.16132/0.18306, loss_grounding_ce_5: 0.14015/0.29482, loss_mask_ce_6: 0.25634/0.97522, loss_mask_bce_6: 0.23988/0.34465, loss_mask_dice_6: 0.46964/1.20061, loss_spatial_bce_6: 0.06686/0.10221, loss_spatial_dice_6: 0.15389/0.23453, loss_spatial_ce_6: 0.08318/0.13440, loss_grounding_bce_6: 0.08455/0.08850, loss_grounding_dice_6: 0.16207/0.18339, loss_grounding_ce_6: 0.14668/0.31049, loss_mask_ce_7: 0.27877/1.01989, loss_mask_bce_7: 0.25885/0.35251, loss_mask_dice_7: 0.53288/1.25535, loss_spatial_bce_7: 0.07457/0.11048, loss_spatial_dice_7: 0.17499/0.26234, loss_spatial_ce_7: 0.07064/0.17054, loss_grounding_bce_7: 0.09294/0.09040, loss_grounding_dice_7: 0.16125/0.19061, loss_grounding_ce_7: 0.17454/0.34211, loss_mask_ce_8: 0.39182/1.12894, loss_mask_bce_8: 0.22161/0.36618, loss_mask_dice_8: 0.47940/1.32898, loss_spatial_bce_8: 0.09283/0.13128, loss_spatial_dice_8: 0.23444/0.30074, loss_spatial_ce_8: 0.18354/0.22748, loss_grounding_bce_8: 0.08942/0.09418, loss_grounding_dice_8: 0.16376/0.20169, loss_grounding_ce_8: 0.17658/0.40968, loss_mask_ce_9: 2.56114/3.67907, loss_mask_bce_9: 0.21839/0.39312, loss_mask_dice_9: 0.63060/1.90172, loss_spatial_bce_9: 0.39505/0.33365, loss_spatial_dice_9: 0.85426/0.82231, loss_spatial_ce_9: 1.51698/1.49938, loss_grounding_bce_9: 0.07512/0.10561, loss_grounding_dice_9: 0.17374/0.28081, loss_grounding_ce_9: 0.39576/0.67441] items per batch[64] items per second[0.23] total items[3475200] mini batches[ 54300] memory[7345] epoch remaining[0:23:21]
INFO:trainer.default_trainer:epochs[    29] optim steps[54400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18072/0.90152, loss_mask_bce_0: 0.01146/0.33505, loss_mask_dice_0: 0.10862/1.16254, loss_spatial_bce_0: 0.01176/0.08792, loss_spatial_dice_0: 0.10896/0.20968, loss_spatial_ce_0: 0.00000/0.06423, loss_grounding_bce_0: 0.00776/0.08633, loss_grounding_dice_0: 0.11685/0.17859, loss_grounding_ce_0: 0.02269/0.27288, loss_mask_ce_1: 0.16883/0.90213, loss_mask_bce_1: 0.01084/0.33598, loss_mask_dice_1: 0.09142/1.16904, loss_spatial_bce_1: 0.01053/0.08846, loss_spatial_dice_1: 0.10994/0.21368, loss_spatial_ce_1: 0.00001/0.07004, loss_grounding_bce_1: 0.00639/0.08651, loss_grounding_dice_1: 0.13180/0.17940, loss_grounding_ce_1: 0.02956/0.27395, loss_mask_ce_2: 0.17653/0.90925, loss_mask_bce_2: 0.01026/0.33650, loss_mask_dice_2: 0.10378/1.16926, loss_spatial_bce_2: 0.01127/0.08935, loss_spatial_dice_2: 0.12075/0.21509, loss_spatial_ce_2: 0.00001/0.07343, loss_grounding_bce_2: 0.00571/0.08663, loss_grounding_dice_2: 0.12479/0.17924, loss_grounding_ce_2: 0.03023/0.27736, loss_mask_ce_3: 0.27112/0.91906, loss_mask_bce_3: 0.01085/0.33757, loss_mask_dice_3: 0.11272/1.16674, loss_spatial_bce_3: 0.01161/0.09035, loss_spatial_dice_3: 0.10665/0.21581, loss_spatial_ce_3: 0.00001/0.07740, loss_grounding_bce_3: 0.00760/0.08687, loss_grounding_dice_3: 0.13548/0.17895, loss_grounding_ce_3: 0.02781/0.27931, loss_mask_ce_4: 0.29104/0.91977, loss_mask_bce_4: 0.01414/0.33966, loss_mask_dice_4: 0.25060/1.19050, loss_spatial_bce_4: 0.01338/0.09438, loss_spatial_dice_4: 0.14347/0.22773, loss_spatial_ce_4: 0.00460/0.09313, loss_grounding_bce_4: 0.00628/0.08737, loss_grounding_dice_4: 0.10414/0.18187, loss_grounding_ce_4: 0.00588/0.28203, loss_mask_ce_5: 0.18407/0.93546, loss_mask_bce_5: 0.00990/0.34188, loss_mask_dice_5: 0.08916/1.19773, loss_spatial_bce_5: 0.01273/0.09643, loss_spatial_dice_5: 0.07705/0.23169, loss_spatial_ce_5: 0.00129/0.10794, loss_grounding_bce_5: 0.00711/0.08777, loss_grounding_dice_5: 0.13974/0.18304, loss_grounding_ce_5: 0.00291/0.29478, loss_mask_ce_6: 0.25962/0.97515, loss_mask_bce_6: 0.01060/0.34460, loss_mask_dice_6: 0.11621/1.20064, loss_spatial_bce_6: 0.01259/0.10221, loss_spatial_dice_6: 0.08967/0.23452, loss_spatial_ce_6: 0.02249/0.13436, loss_grounding_bce_6: 0.00863/0.08849, loss_grounding_dice_6: 0.11691/0.18337, loss_grounding_ce_6: 0.00946/0.31047, loss_mask_ce_7: 0.61049/1.01979, loss_mask_bce_7: 0.01051/0.35245, loss_mask_dice_7: 0.11501/1.25538, loss_spatial_bce_7: 0.01445/0.11048, loss_spatial_dice_7: 0.13908/0.26234, loss_spatial_ce_7: 0.00803/0.17048, loss_grounding_bce_7: 0.00838/0.09039, loss_grounding_dice_7: 0.13677/0.19059, loss_grounding_ce_7: 0.00389/0.34205, loss_mask_ce_8: 0.21615/1.12885, loss_mask_bce_8: 0.01129/0.36614, loss_mask_dice_8: 0.11421/1.32898, loss_spatial_bce_8: 0.01751/0.13128, loss_spatial_dice_8: 0.16109/0.30072, loss_spatial_ce_8: 0.03989/0.22743, loss_grounding_bce_8: 0.00610/0.09417, loss_grounding_dice_8: 0.08730/0.20166, loss_grounding_ce_8: 0.00491/0.40958, loss_mask_ce_9: 1.48263/3.67921, loss_mask_bce_9: 0.01298/0.39309, loss_mask_dice_9: 0.14653/1.90179, loss_spatial_bce_9: 0.09182/0.33364, loss_spatial_dice_9: 0.73684/0.82229, loss_spatial_ce_9: 0.57931/1.49930, loss_grounding_bce_9: 0.00945/0.10561, loss_grounding_dice_9: 0.14973/0.28079, loss_grounding_ce_9: 0.16242/0.67428] items per batch[64] items per second[0.23] total items[3481600] mini batches[ 54400] memory[7345] epoch remaining[0:18:48]
INFO:trainer.default_trainer:epochs[    29] optim steps[54500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75410/0.90150, loss_mask_bce_0: 0.36524/0.33511, loss_mask_dice_0: 0.68963/1.16304, loss_spatial_bce_0: 0.11115/0.08792, loss_spatial_dice_0: 0.20807/0.20970, loss_spatial_ce_0: 0.00384/0.06423, loss_grounding_bce_0: 0.09957/0.08635, loss_grounding_dice_0: 0.09580/0.17864, loss_grounding_ce_0: 0.24681/0.27283, loss_mask_ce_1: 0.92092/0.90211, loss_mask_bce_1: 0.32665/0.33603, loss_mask_dice_1: 0.68485/1.16951, loss_spatial_bce_1: 0.12505/0.08845, loss_spatial_dice_1: 0.21443/0.21370, loss_spatial_ce_1: 0.00696/0.07003, loss_grounding_bce_1: 0.10923/0.08652, loss_grounding_dice_1: 0.11106/0.17944, loss_grounding_ce_1: 0.21593/0.27389, loss_mask_ce_2: 0.91216/0.90922, loss_mask_bce_2: 0.31740/0.33655, loss_mask_dice_2: 0.68630/1.16974, loss_spatial_bce_2: 0.12535/0.08934, loss_spatial_dice_2: 0.21057/0.21511, loss_spatial_ce_2: 0.01011/0.07340, loss_grounding_bce_2: 0.09910/0.08665, loss_grounding_dice_2: 0.09793/0.17928, loss_grounding_ce_2: 0.30935/0.27732, loss_mask_ce_3: 0.92152/0.91899, loss_mask_bce_3: 0.31771/0.33763, loss_mask_dice_3: 0.66212/1.16726, loss_spatial_bce_3: 0.12321/0.09035, loss_spatial_dice_3: 0.20835/0.21583, loss_spatial_ce_3: 0.00570/0.07739, loss_grounding_bce_3: 0.10946/0.08688, loss_grounding_dice_3: 0.09868/0.17898, loss_grounding_ce_3: 0.29284/0.27926, loss_mask_ce_4: 0.97797/0.91974, loss_mask_bce_4: 0.33060/0.33971, loss_mask_dice_4: 0.69420/1.19099, loss_spatial_bce_4: 0.14174/0.09437, loss_spatial_dice_4: 0.21876/0.22776, loss_spatial_ce_4: 0.01094/0.09314, loss_grounding_bce_4: 0.11043/0.08739, loss_grounding_dice_4: 0.10435/0.18190, loss_grounding_ce_4: 0.23230/0.28200, loss_mask_ce_5: 0.63765/0.93544, loss_mask_bce_5: 0.40431/0.34193, loss_mask_dice_5: 0.72114/1.19825, loss_spatial_bce_5: 0.13158/0.09643, loss_spatial_dice_5: 0.22087/0.23172, loss_spatial_ce_5: 0.02469/0.10791, loss_grounding_bce_5: 0.10709/0.08778, loss_grounding_dice_5: 0.10248/0.18308, loss_grounding_ce_5: 0.28322/0.29475, loss_mask_ce_6: 0.99667/0.97517, loss_mask_bce_6: 0.39500/0.34466, loss_mask_dice_6: 0.70709/1.20117, loss_spatial_bce_6: 0.15464/0.10221, loss_spatial_dice_6: 0.22562/0.23455, loss_spatial_ce_6: 0.06219/0.13432, loss_grounding_bce_6: 0.11439/0.08851, loss_grounding_dice_6: 0.11200/0.18340, loss_grounding_ce_6: 0.26761/0.31040, loss_mask_ce_7: 1.29041/1.01983, loss_mask_bce_7: 0.49823/0.35251, loss_mask_dice_7: 0.79654/1.25589, loss_spatial_bce_7: 0.20745/0.11046, loss_spatial_dice_7: 0.26452/0.26237, loss_spatial_ce_7: 0.08713/0.17044, loss_grounding_bce_7: 0.12038/0.09041, loss_grounding_dice_7: 0.10830/0.19062, loss_grounding_ce_7: 0.30519/0.34195, loss_mask_ce_8: 1.08825/1.12885, loss_mask_bce_8: 0.45098/0.36619, loss_mask_dice_8: 0.77920/1.32951, loss_spatial_bce_8: 0.20909/0.13126, loss_spatial_dice_8: 0.30631/0.30075, loss_spatial_ce_8: 0.19627/0.22741, loss_grounding_bce_8: 0.15069/0.09419, loss_grounding_dice_8: 0.12022/0.20170, loss_grounding_ce_8: 0.39833/0.40946, loss_mask_ce_9: 3.70240/3.67921, loss_mask_bce_9: 0.51933/0.39315, loss_mask_dice_9: 1.00278/1.90253, loss_spatial_bce_9: 0.55157/0.33361, loss_spatial_dice_9: 0.83254/0.82232, loss_spatial_ce_9: 1.66527/1.49941, loss_grounding_bce_9: 0.14964/0.10563, loss_grounding_dice_9: 0.20482/0.28083, loss_grounding_ce_9: 1.10151/0.67415] items per batch[64] items per second[0.23] total items[3488000] mini batches[ 54500] memory[7345] epoch remaining[0:14:12]
INFO:trainer.default_trainer:epochs[    29] optim steps[54600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39087/0.90121, loss_mask_bce_0: 0.31872/0.33509, loss_mask_dice_0: 0.31368/1.16302, loss_spatial_bce_0: 0.15363/0.08790, loss_spatial_dice_0: 0.13834/0.20967, loss_spatial_ce_0: 0.01115/0.06419, loss_grounding_bce_0: 0.15354/0.08634, loss_grounding_dice_0: 0.14431/0.17863, loss_grounding_ce_0: 0.01343/0.27279, loss_mask_ce_1: 0.44384/0.90184, loss_mask_bce_1: 0.31774/0.33602, loss_mask_dice_1: 0.32531/1.16947, loss_spatial_bce_1: 0.15779/0.08843, loss_spatial_dice_1: 0.13473/0.21367, loss_spatial_ce_1: 0.01820/0.07000, loss_grounding_bce_1: 0.14827/0.08652, loss_grounding_dice_1: 0.14623/0.17944, loss_grounding_ce_1: 0.00945/0.27384, loss_mask_ce_2: 0.39166/0.90894, loss_mask_bce_2: 0.31832/0.33653, loss_mask_dice_2: 0.34364/1.16973, loss_spatial_bce_2: 0.15774/0.08932, loss_spatial_dice_2: 0.14410/0.21509, loss_spatial_ce_2: 0.01039/0.07338, loss_grounding_bce_2: 0.15271/0.08665, loss_grounding_dice_2: 0.15067/0.17927, loss_grounding_ce_2: 0.00880/0.27726, loss_mask_ce_3: 0.39921/0.91870, loss_mask_bce_3: 0.33238/0.33761, loss_mask_dice_3: 0.31660/1.16721, loss_spatial_bce_3: 0.15833/0.09033, loss_spatial_dice_3: 0.15033/0.21581, loss_spatial_ce_3: 0.02434/0.07737, loss_grounding_bce_3: 0.15861/0.08688, loss_grounding_dice_3: 0.14542/0.17897, loss_grounding_ce_3: 0.01098/0.27921, loss_mask_ce_4: 0.43491/0.91948, loss_mask_bce_4: 0.30450/0.33970, loss_mask_dice_4: 0.32153/1.19099, loss_spatial_bce_4: 0.17586/0.09436, loss_spatial_dice_4: 0.14871/0.22774, loss_spatial_ce_4: 0.03423/0.09311, loss_grounding_bce_4: 0.14393/0.08738, loss_grounding_dice_4: 0.14200/0.18190, loss_grounding_ce_4: 0.01061/0.28194, loss_mask_ce_5: 0.41784/0.93518, loss_mask_bce_5: 0.31641/0.34191, loss_mask_dice_5: 0.33756/1.19822, loss_spatial_bce_5: 0.17939/0.09641, loss_spatial_dice_5: 0.16246/0.23171, loss_spatial_ce_5: 0.01699/0.10788, loss_grounding_bce_5: 0.15338/0.08778, loss_grounding_dice_5: 0.15689/0.18307, loss_grounding_ce_5: 0.01050/0.29470, loss_mask_ce_6: 0.52374/0.97492, loss_mask_bce_6: 0.32466/0.34464, loss_mask_dice_6: 0.34372/1.20116, loss_spatial_bce_6: 0.18960/0.10219, loss_spatial_dice_6: 0.16346/0.23454, loss_spatial_ce_6: 0.03008/0.13427, loss_grounding_bce_6: 0.15715/0.08850, loss_grounding_dice_6: 0.15411/0.18340, loss_grounding_ce_6: 0.01453/0.31035, loss_mask_ce_7: 0.54840/1.01959, loss_mask_bce_7: 0.32408/0.35249, loss_mask_dice_7: 0.35471/1.25587, loss_spatial_bce_7: 0.20036/0.11044, loss_spatial_dice_7: 0.18459/0.26235, loss_spatial_ce_7: 0.13693/0.17041, loss_grounding_bce_7: 0.14456/0.09041, loss_grounding_dice_7: 0.14941/0.19062, loss_grounding_ce_7: 0.01244/0.34194, loss_mask_ce_8: 0.50574/1.12862, loss_mask_bce_8: 0.34628/0.36616, loss_mask_dice_8: 0.36302/1.32946, loss_spatial_bce_8: 0.18808/0.13124, loss_spatial_dice_8: 0.19381/0.30072, loss_spatial_ce_8: 0.13052/0.22736, loss_grounding_bce_8: 0.17635/0.09418, loss_grounding_dice_8: 0.16635/0.20169, loss_grounding_ce_8: 0.04326/0.40944, loss_mask_ce_9: 2.37066/3.67900, loss_mask_bce_9: 0.30949/0.39311, loss_mask_dice_9: 0.32239/1.90234, loss_spatial_bce_9: 0.51887/0.33359, loss_spatial_dice_9: 0.70319/0.82231, loss_spatial_ce_9: 1.17750/1.49943, loss_grounding_bce_9: 0.14962/0.10563, loss_grounding_dice_9: 0.16443/0.28081, loss_grounding_ce_9: 0.05193/0.67414] items per batch[64] items per second[0.23] total items[3494400] mini batches[ 54600] memory[7345] epoch remaining[0:09:38]
INFO:trainer.default_trainer:epochs[    29] optim steps[54700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52175/0.90113, loss_mask_bce_0: 0.28664/0.33506, loss_mask_dice_0: 0.21655/1.16296, loss_spatial_bce_0: 0.10328/0.08788, loss_spatial_dice_0: 0.10095/0.20967, loss_spatial_ce_0: 0.01289/0.06418, loss_grounding_bce_0: 0.03411/0.08634, loss_grounding_dice_0: 0.05015/0.17863, loss_grounding_ce_0: 0.12331/0.27279, loss_mask_ce_1: 0.51283/0.90175, loss_mask_bce_1: 0.27230/0.33599, loss_mask_dice_1: 0.21297/1.16940, loss_spatial_bce_1: 0.10766/0.08841, loss_spatial_dice_1: 0.11786/0.21366, loss_spatial_ce_1: 0.01701/0.06998, loss_grounding_bce_1: 0.03167/0.08652, loss_grounding_dice_1: 0.04799/0.17944, loss_grounding_ce_1: 0.07986/0.27380, loss_mask_ce_2: 0.53899/0.90883, loss_mask_bce_2: 0.27620/0.33651, loss_mask_dice_2: 0.21642/1.16966, loss_spatial_bce_2: 0.10112/0.08930, loss_spatial_dice_2: 0.09873/0.21509, loss_spatial_ce_2: 0.01682/0.07336, loss_grounding_bce_2: 0.03380/0.08665, loss_grounding_dice_2: 0.05056/0.17926, loss_grounding_ce_2: 0.06452/0.27723, loss_mask_ce_3: 0.57271/0.91859, loss_mask_bce_3: 0.28748/0.33758, loss_mask_dice_3: 0.21094/1.16713, loss_spatial_bce_3: 0.11363/0.09031, loss_spatial_dice_3: 0.10202/0.21581, loss_spatial_ce_3: 0.01950/0.07736, loss_grounding_bce_3: 0.03328/0.08688, loss_grounding_dice_3: 0.04740/0.17897, loss_grounding_ce_3: 0.16624/0.27918, loss_mask_ce_4: 0.54992/0.91938, loss_mask_bce_4: 0.27879/0.33967, loss_mask_dice_4: 0.21193/1.19092, loss_spatial_bce_4: 0.11986/0.09434, loss_spatial_dice_4: 0.12401/0.22773, loss_spatial_ce_4: 0.03374/0.09311, loss_grounding_bce_4: 0.03289/0.08738, loss_grounding_dice_4: 0.05019/0.18190, loss_grounding_ce_4: 0.11361/0.28193, loss_mask_ce_5: 0.60850/0.93512, loss_mask_bce_5: 0.28326/0.34188, loss_mask_dice_5: 0.21293/1.19814, loss_spatial_bce_5: 0.11758/0.09640, loss_spatial_dice_5: 0.09483/0.23171, loss_spatial_ce_5: 0.06129/0.10786, loss_grounding_bce_5: 0.03116/0.08778, loss_grounding_dice_5: 0.04785/0.18307, loss_grounding_ce_5: 0.23724/0.29465, loss_mask_ce_6: 0.66326/0.97485, loss_mask_bce_6: 0.27340/0.34460, loss_mask_dice_6: 0.22383/1.20109, loss_spatial_bce_6: 0.13192/0.10217, loss_spatial_dice_6: 0.10259/0.23454, loss_spatial_ce_6: 0.12093/0.13421, loss_grounding_bce_6: 0.03250/0.08850, loss_grounding_dice_6: 0.04382/0.18340, loss_grounding_ce_6: 0.05377/0.31029, loss_mask_ce_7: 0.65718/1.01953, loss_mask_bce_7: 0.27643/0.35246, loss_mask_dice_7: 0.21365/1.25580, loss_spatial_bce_7: 0.14533/0.11042, loss_spatial_dice_7: 0.12483/0.26236, loss_spatial_ce_7: 0.22234/0.17036, loss_grounding_bce_7: 0.03588/0.09040, loss_grounding_dice_7: 0.04992/0.19062, loss_grounding_ce_7: 0.04343/0.34186, loss_mask_ce_8: 0.67106/1.12862, loss_mask_bce_8: 0.29360/0.36612, loss_mask_dice_8: 0.22781/1.32938, loss_spatial_bce_8: 0.12821/0.13122, loss_spatial_dice_8: 0.10796/0.30072, loss_spatial_ce_8: 0.14243/0.22733, loss_grounding_bce_8: 0.03750/0.09417, loss_grounding_dice_8: 0.05132/0.20170, loss_grounding_ce_8: 0.06175/0.40933, loss_mask_ce_9: 2.80868/3.67881, loss_mask_bce_9: 0.30169/0.39307, loss_mask_dice_9: 0.30212/1.90216, loss_spatial_bce_9: 0.50609/0.33356, loss_spatial_dice_9: 0.64720/0.82230, loss_spatial_ce_9: 1.01012/1.49941, loss_grounding_bce_9: 0.03633/0.10562, loss_grounding_dice_9: 0.06648/0.28081, loss_grounding_ce_9: 1.21141/0.67406] items per batch[64] items per second[0.23] total items[3500800] mini batches[ 54700] memory[7345] epoch remaining[0:05:03]
INFO:trainer.default_trainer:epochs[    29] optim steps[54800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.60968/0.90114, loss_mask_bce_0: 0.35930/0.33502, loss_mask_dice_0: 2.76573/1.16315, loss_spatial_bce_0: 0.08850/0.08787, loss_spatial_dice_0: 0.34818/0.20968, loss_spatial_ce_0: 0.05670/0.06416, loss_grounding_bce_0: 0.11266/0.08634, loss_grounding_dice_0: 0.36983/0.17868, loss_grounding_ce_0: 0.18773/0.27279, loss_mask_ce_1: 1.56522/0.90172, loss_mask_bce_1: 0.36305/0.33595, loss_mask_dice_1: 2.68809/1.16960, loss_spatial_bce_1: 0.08626/0.08840, loss_spatial_dice_1: 0.36774/0.21368, loss_spatial_ce_1: 0.02627/0.06997, loss_grounding_bce_1: 0.10618/0.08652, loss_grounding_dice_1: 0.34991/0.17950, loss_grounding_ce_1: 0.24355/0.27377, loss_mask_ce_2: 1.90769/0.90886, loss_mask_bce_2: 0.34648/0.33647, loss_mask_dice_2: 2.76809/1.16983, loss_spatial_bce_2: 0.11169/0.08929, loss_spatial_dice_2: 0.37201/0.21510, loss_spatial_ce_2: 0.18260/0.07336, loss_grounding_bce_2: 0.11510/0.08664, loss_grounding_dice_2: 0.35571/0.17932, loss_grounding_ce_2: 0.16549/0.27720, loss_mask_ce_3: 1.56867/0.91863, loss_mask_bce_3: 0.37826/0.33754, loss_mask_dice_3: 2.64590/1.16730, loss_spatial_bce_3: 0.09446/0.09031, loss_spatial_dice_3: 0.32976/0.21583, loss_spatial_ce_3: 0.15520/0.07736, loss_grounding_bce_3: 0.11574/0.08688, loss_grounding_dice_3: 0.33722/0.17902, loss_grounding_ce_3: 0.18371/0.27915, loss_mask_ce_4: 1.55053/0.91940, loss_mask_bce_4: 0.38407/0.33964, loss_mask_dice_4: 2.78618/1.19114, loss_spatial_bce_4: 0.13127/0.09434, loss_spatial_dice_4: 0.39373/0.22776, loss_spatial_ce_4: 0.09407/0.09308, loss_grounding_bce_4: 0.11330/0.08738, loss_grounding_dice_4: 0.35107/0.18197, loss_grounding_ce_4: 0.27789/0.28191, loss_mask_ce_5: 1.74133/0.93511, loss_mask_bce_5: 0.38268/0.34185, loss_mask_dice_5: 2.66131/1.19836, loss_spatial_bce_5: 0.22811/0.09640, loss_spatial_dice_5: 0.45619/0.23175, loss_spatial_ce_5: 0.11531/0.10786, loss_grounding_bce_5: 0.11697/0.08777, loss_grounding_dice_5: 0.38715/0.18314, loss_grounding_ce_5: 0.12642/0.29463, loss_mask_ce_6: 1.90157/0.97486, loss_mask_bce_6: 0.38956/0.34456, loss_mask_dice_6: 3.08513/1.20130, loss_spatial_bce_6: 0.11118/0.10217, loss_spatial_dice_6: 0.39917/0.23456, loss_spatial_ce_6: 0.23410/0.13421, loss_grounding_bce_6: 0.11849/0.08849, loss_grounding_dice_6: 0.37273/0.18347, loss_grounding_ce_6: 0.20195/0.31025, loss_mask_ce_7: 2.00429/1.01958, loss_mask_bce_7: 0.34637/0.35242, loss_mask_dice_7: 3.08030/1.25600, loss_spatial_bce_7: 0.09752/0.11042, loss_spatial_dice_7: 0.45549/0.26240, loss_spatial_ce_7: 0.28796/0.17037, loss_grounding_bce_7: 0.10702/0.09041, loss_grounding_dice_7: 0.36919/0.19069, loss_grounding_ce_7: 0.16098/0.34182, loss_mask_ce_8: 1.83318/1.12873, loss_mask_bce_8: 0.36105/0.36607, loss_mask_dice_8: 2.57697/1.32956, loss_spatial_bce_8: 0.12148/0.13121, loss_spatial_dice_8: 0.52629/0.30075, loss_spatial_ce_8: 0.24472/0.22729, loss_grounding_bce_8: 0.10520/0.09417, loss_grounding_dice_8: 0.35514/0.20176, loss_grounding_ce_8: 0.15047/0.40926, loss_mask_ce_9: 4.46531/3.67892, loss_mask_bce_9: 0.39009/0.39302, loss_mask_dice_9: 2.84529/1.90223, loss_spatial_bce_9: 0.22256/0.33354, loss_spatial_dice_9: 0.90921/0.82230, loss_spatial_ce_9: 1.40227/1.49942, loss_grounding_bce_9: 0.11879/0.10562, loss_grounding_dice_9: 0.45048/0.28087, loss_grounding_ce_9: 0.28877/0.67404] items per batch[64] items per second[0.22] total items[3507200] mini batches[ 54800] memory[7345] epoch remaining[0:00:27]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00054810.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0016 s/iter. Inference: 0.2260 s/iter. Eval: 0.0966 s/iter. Total: 0.3241 s/iter. ETA=0:00:47
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0026 s/iter. Inference: 0.2265 s/iter. Eval: 0.0813 s/iter. Total: 0.3106 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0029 s/iter. Inference: 0.2289 s/iter. Eval: 0.0797 s/iter. Total: 0.3117 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 59/157. Dataloading: 0.0030 s/iter. Inference: 0.2374 s/iter. Eval: 0.0776 s/iter. Total: 0.3182 s/iter. ETA=0:00:31
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 76/157. Dataloading: 0.0031 s/iter. Inference: 0.2333 s/iter. Eval: 0.0766 s/iter. Total: 0.3131 s/iter. ETA=0:00:25
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 93/157. Dataloading: 0.0031 s/iter. Inference: 0.2335 s/iter. Eval: 0.0748 s/iter. Total: 0.3115 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 109/157. Dataloading: 0.0031 s/iter. Inference: 0.2348 s/iter. Eval: 0.0755 s/iter. Total: 0.3136 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 126/157. Dataloading: 0.0032 s/iter. Inference: 0.2335 s/iter. Eval: 0.0750 s/iter. Total: 0.3119 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 142/157. Dataloading: 0.0032 s/iter. Inference: 0.2339 s/iter. Eval: 0.0749 s/iter. Total: 0.3121 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval0gdb7z1p ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.161 | 82.195 | 60.248 |      133      |
| Things | 55.343 | 82.669 | 66.238 |      80       |
| Stuff  | 42.340 | 81.479 | 51.207 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.35s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.27 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.00 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.12s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.84 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.867 | 61.274 | 40.717 | 19.565 | 42.029 | 60.384 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 45.156 | bicycle      | 18.849 | car            | 37.012 |
| motorcycle    | 34.649 | airplane     | 57.208 | bus            | 66.075 |
| train         | 68.258 | truck        | 36.014 | boat           | 23.083 |
| traffic light | 25.824 | fire hydrant | 64.522 | stop sign      | 64.341 |
| parking meter | 44.790 | bench        | 19.998 | bird           | 29.898 |
| cat           | 73.338 | dog          | 66.159 | horse          | 46.261 |
| sheep         | 47.834 | cow          | 50.342 | elephant       | 60.013 |
| bear          | 76.882 | zebra        | 61.308 | giraffe        | 56.802 |
| backpack      | 17.976 | umbrella     | 47.924 | handbag        | 14.783 |
| tie           | 33.473 | suitcase     | 39.460 | frisbee        | 68.064 |
| skis          | 5.759  | snowboard    | 23.225 | sports ball    | 46.679 |
| kite          | 34.128 | baseball bat | 28.926 | baseball glove | 43.383 |
| skateboard    | 36.256 | surfboard    | 35.726 | tennis racket  | 56.365 |
| bottle        | 34.204 | wine glass   | 28.141 | cup            | 40.280 |
| fork          | 16.918 | knife        | 13.543 | spoon          | 14.433 |
| bowl          | 33.173 | banana       | 19.652 | apple          | 21.376 |
| sandwich      | 41.456 | orange       | 29.802 | broccoli       | 21.602 |
| carrot        | 20.433 | hot dog      | 21.660 | pizza          | 49.511 |
| donut         | 45.709 | cake         | 43.391 | chair          | 21.368 |
| couch         | 41.877 | potted plant | 17.112 | bed            | 41.857 |
| dining table  | 12.605 | toilet       | 66.111 | tv             | 62.764 |
| laptop        | 63.169 | mouse        | 58.107 | remote         | 31.806 |
| keyboard      | 48.952 | cell phone   | 37.317 | microwave      | 56.061 |
| oven          | 34.108 | toaster      | 18.185 | sink           | 37.927 |
| refrigerator  | 60.100 | book         | 8.736  | clock          | 51.510 |
| vase          | 32.731 | scissors     | 24.661 | teddy bear     | 50.541 |
| hair drier    | 9.633  | toothbrush   | 20.074 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.297
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.8201685037474, 'fwIoU': 69.12877718377248, 'IoU-person': 87.65211647270492, 'IoU-bicycle': 68.40143428373693, 'IoU-car': 68.17626063052916, 'IoU-motorcycle': 83.1650149676939, 'IoU-airplane': 80.70011120242971, 'IoU-bus': 83.79372088942128, 'IoU-train': 84.30719937519923, 'IoU-truck': 61.50268817393545, 'IoU-boat': 67.51997198964989, 'IoU-traffic light': 75.58926560301505, 'IoU-fire hydrant': 90.09185108002559, 'IoU-stop sign': 88.53777317520615, 'IoU-parking meter': 87.67445202930044, 'IoU-bench': 57.88550094827182, 'IoU-bird': 75.38399845613648, 'IoU-cat': 84.17180912704123, 'IoU-dog': 75.7885502984301, 'IoU-horse': 85.35505073858546, 'IoU-sheep': 86.85264984807723, 'IoU-cow': 80.7496574998369, 'IoU-elephant': 90.77217901692185, 'IoU-bear': 84.31635893121694, 'IoU-zebra': 84.96907359340838, 'IoU-giraffe': 87.4307430313907, 'IoU-backpack': 39.28031346529198, 'IoU-umbrella': 71.3565648578719, 'IoU-handbag': 36.125875971880085, 'IoU-tie': 69.43273669850572, 'IoU-suitcase': 82.58381368380383, 'IoU-frisbee': 82.4194467344217, 'IoU-skis': 51.594243318691134, 'IoU-snowboard': 69.89202386703398, 'IoU-sports ball': 62.77127647626962, 'IoU-kite': 65.92560152791464, 'IoU-baseball bat': 60.81583185388348, 'IoU-baseball glove': 76.51910983595407, 'IoU-skateboard': 77.83650191588713, 'IoU-surfboard': 76.82583490640503, 'IoU-tennis racket': 82.11084083765283, 'IoU-bottle': 65.7464268982666, 'IoU-wine glass': 74.804072219785, 'IoU-cup': 60.933728669799116, 'IoU-fork': 55.160483792140184, 'IoU-knife': 47.181981467803666, 'IoU-spoon': 49.35471661164477, 'IoU-bowl': 58.575564768483666, 'IoU-banana': 84.50609048932075, 'IoU-apple': 55.85065128968709, 'IoU-sandwich': 66.29791193206535, 'IoU-orange': 77.44020207479635, 'IoU-broccoli': 68.20703323065366, 'IoU-carrot': 64.03059951947515, 'IoU-hot dog': 61.43481558538113, 'IoU-pizza': 82.51243372387194, 'IoU-donut': 63.302982816045706, 'IoU-cake': 67.41560138733338, 'IoU-chair': 55.10220943176299, 'IoU-couch': 67.00385970303925, 'IoU-potted plant': 33.4206802770131, 'IoU-bed': 70.62387745306715, 'IoU-dining table': 50.85348259796005, 'IoU-toilet': 83.92363473349577, 'IoU-tv': 75.02348407386523, 'IoU-laptop': 76.16115710828618, 'IoU-mouse': 70.08526750354032, 'IoU-remote': 50.3957126256839, 'IoU-keyboard': 62.638218416977864, 'IoU-cell phone': 69.63289426370673, 'IoU-microwave': 65.50573520154805, 'IoU-oven': 66.31031933484022, 'IoU-toaster': 61.96794389306073, 'IoU-sink': 71.62064360680756, 'IoU-refrigerator': 77.43658940806093, 'IoU-book': 50.121872697408506, 'IoU-clock': 74.05020197982209, 'IoU-vase': 59.982982212605776, 'IoU-scissors': 52.89020399742157, 'IoU-teddy bear': 81.19146321085691, 'IoU-hair drier': 48.64072140484101, 'IoU-toothbrush': 56.138874148352826, 'IoU-banner': 34.20148184261553, 'IoU-blanket': 11.311141368351672, 'IoU-bridge': 39.57296232414163, 'IoU-cardboard': 44.700437643309066, 'IoU-counter': 30.559128438224615, 'IoU-curtain': 64.93536173841953, 'IoU-door-stuff': 41.95697365276658, 'IoU-floor-wood': 60.19119459818544, 'IoU-flower': 40.10946026950265, 'IoU-fruit': 39.84375555940349, 'IoU-gravel': 31.5648657715709, 'IoU-house': 23.04551490953959, 'IoU-light': 38.56934129583682, 'IoU-mirror-stuff': 54.649541209887055, 'IoU-net': 47.11016403248441, 'IoU-pillow': 13.002671375187417, 'IoU-platform': 36.11994927618963, 'IoU-playingfield': 66.08013311714693, 'IoU-railroad': 61.41724903963854, 'IoU-river': 50.28024511231766, 'IoU-road': 65.43188995268011, 'IoU-roof': 13.815736178647436, 'IoU-sand': 61.34185055064193, 'IoU-sea': 85.06163805215805, 'IoU-shelf': 34.97150894379329, 'IoU-snow': 89.06265925195753, 'IoU-stairs': 26.813244856433016, 'IoU-tent': 9.622613431290219, 'IoU-towel': 26.795701055358418, 'IoU-wall-brick': 44.99745516637656, 'IoU-wall-stone': 26.98517349948662, 'IoU-wall-tile': 64.21516870930873, 'IoU-wall-wood': 38.747100806949064, 'IoU-water-other': 26.802392239489354, 'IoU-window-blind': 46.184060635890575, 'IoU-window-other': 45.9389436550688, 'IoU-tree-merged': 80.92522101189769, 'IoU-fence-merged': 51.35936269292338, 'IoU-ceiling-merged': 67.75083431459598, 'IoU-sky-other-merged': 93.80997524338075, 'IoU-cabinet-merged': 58.40132550271625, 'IoU-table-merged': 38.83757340743687, 'IoU-floor-other-merged': 50.756630155218254, 'IoU-pavement-merged': 53.876344162819336, 'IoU-mountain-merged': 54.41132452384452, 'IoU-grass-merged': 71.68664341256232, 'IoU-dirt-merged': 46.65555850650659, 'IoU-paper-merged': 33.16303541209062, 'IoU-food-other-merged': 40.59660540097247, 'IoU-building-other-merged': 58.923060784718906, 'IoU-rock-merged': 62.41108276627519, 'IoU-wall-other-merged': 65.50093040130052, 'IoU-rug-merged': 64.2534206646837, 'mACC': 72.91736628004148, 'pACC': 80.41534154110272, 'ACC-person': 92.41917283378451, 'ACC-bicycle': 80.986579678293, 'ACC-car': 84.9999747828806, 'ACC-motorcycle': 88.50139114190216, 'ACC-airplane': 87.79677822827205, 'ACC-bus': 88.43963391257725, 'ACC-train': 95.30583154166425, 'ACC-truck': 76.0570945770947, 'ACC-boat': 78.1655206151345, 'ACC-traffic light': 90.02172539530306, 'ACC-fire hydrant': 94.88608462468028, 'ACC-stop sign': 91.12206158759214, 'ACC-parking meter': 92.49575890629677, 'ACC-bench': 70.83053732349138, 'ACC-bird': 80.45656574735077, 'ACC-cat': 91.59693279124971, 'ACC-dog': 78.64996225022571, 'ACC-horse': 91.01019007179426, 'ACC-sheep': 91.15569456180613, 'ACC-cow': 85.16538334145692, 'ACC-elephant': 93.39940001960214, 'ACC-bear': 86.52705705795701, 'ACC-zebra': 87.36238887990056, 'ACC-giraffe': 91.79169058982508, 'ACC-backpack': 58.733709608154186, 'ACC-umbrella': 77.61369923893378, 'ACC-handbag': 52.53893390788617, 'ACC-tie': 80.80981027935007, 'ACC-suitcase': 91.21858916873293, 'ACC-frisbee': 94.15927272727272, 'ACC-skis': 68.07292749159276, 'ACC-snowboard': 78.66229090641914, 'ACC-sports ball': 72.21963800250153, 'ACC-kite': 75.02221248983763, 'ACC-baseball bat': 82.90052371711784, 'ACC-baseball glove': 89.1672611198778, 'ACC-skateboard': 90.08297013793197, 'ACC-surfboard': 85.15578868571073, 'ACC-tennis racket': 89.29202073995252, 'ACC-bottle': 80.08006964043106, 'ACC-wine glass': 85.4867545466084, 'ACC-cup': 82.8617460704191, 'ACC-fork': 66.30265234725853, 'ACC-knife': 61.787755550670475, 'ACC-spoon': 69.40508925305285, 'ACC-bowl': 72.38264035719955, 'ACC-banana': 90.81365624773582, 'ACC-apple': 66.14541936659732, 'ACC-sandwich': 79.14870238106325, 'ACC-orange': 84.97790025155989, 'ACC-broccoli': 78.40406804328019, 'ACC-carrot': 73.79441485839205, 'ACC-hot dog': 66.97589975553319, 'ACC-pizza': 89.46430515254656, 'ACC-donut': 76.54099255671377, 'ACC-cake': 75.14970874697663, 'ACC-chair': 70.89748168763433, 'ACC-couch': 77.79997808455354, 'ACC-potted plant': 53.51329738402038, 'ACC-bed': 84.91095898623267, 'ACC-dining table': 73.18616559424171, 'ACC-toilet': 87.8765316249648, 'ACC-tv': 88.12080876773075, 'ACC-laptop': 91.25043924245644, 'ACC-mouse': 84.26746606351165, 'ACC-remote': 72.39385680068509, 'ACC-keyboard': 67.52765353665782, 'ACC-cell phone': 75.17448548235838, 'ACC-microwave': 76.81508833022637, 'ACC-oven': 79.93049974666125, 'ACC-toaster': 72.67445838864634, 'ACC-sink': 84.77052081883713, 'ACC-refrigerator': 92.49756518282288, 'ACC-book': 64.96760387772828, 'ACC-clock': 79.464992744826, 'ACC-vase': 68.4840676560448, 'ACC-scissors': 56.78628488003746, 'ACC-teddy bear': 87.36162135365278, 'ACC-hair drier': 72.92716249679788, 'ACC-toothbrush': 79.94961779013204, 'ACC-banner': 63.707655425231835, 'ACC-blanket': 15.25293323684617, 'ACC-bridge': 51.43376116128115, 'ACC-cardboard': 58.65156318161989, 'ACC-counter': 60.56473416567278, 'ACC-curtain': 76.8175798832035, 'ACC-door-stuff': 60.853854226450466, 'ACC-floor-wood': 73.11105088494116, 'ACC-flower': 57.48402694268779, 'ACC-fruit': 58.26840257321801, 'ACC-gravel': 43.7876822077263, 'ACC-house': 26.6496070296052, 'ACC-light': 55.18341117023313, 'ACC-mirror-stuff': 70.41360358514679, 'ACC-net': 63.558661620696455, 'ACC-pillow': 22.551367105516736, 'ACC-platform': 61.901686040392775, 'ACC-playingfield': 79.27753109542937, 'ACC-railroad': 75.71798686325515, 'ACC-river': 69.52395138883958, 'ACC-road': 86.48992711430942, 'ACC-roof': 18.692768114347647, 'ACC-sand': 70.05210380906894, 'ACC-sea': 90.63972295940285, 'ACC-shelf': 60.41021739608824, 'ACC-snow': 94.64966172680201, 'ACC-stairs': 42.22086333311595, 'ACC-tent': 11.459322239462006, 'ACC-towel': 33.20163775501915, 'ACC-wall-brick': 56.692770025564045, 'ACC-wall-stone': 32.87771724827622, 'ACC-wall-tile': 77.94312781506459, 'ACC-wall-wood': 51.23861144927157, 'ACC-water-other': 45.43667941512443, 'ACC-window-blind': 54.762437864588165, 'ACC-window-other': 67.29180509129455, 'ACC-tree-merged': 89.43661203998636, 'ACC-fence-merged': 74.15962719727128, 'ACC-ceiling-merged': 80.24189425328211, 'ACC-sky-other-merged': 96.83459282795992, 'ACC-cabinet-merged': 73.38015114368493, 'ACC-table-merged': 53.862567793370296, 'ACC-floor-other-merged': 61.62631788234722, 'ACC-pavement-merged': 64.83821666254079, 'ACC-mountain-merged': 62.63843754818027, 'ACC-grass-merged': 83.34720810516572, 'ACC-dirt-merged': 74.72739696754425, 'ACC-paper-merged': 46.092624245127595, 'ACC-food-other-merged': 59.58398623046099, 'ACC-building-other-merged': 78.53071141242796, 'ACC-rock-merged': 81.22373118643227, 'ACC-wall-other-merged': 81.3083207860004, 'ACC-rug-merged': 79.34345549003417})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1528 s/iter. Inference: 0.3551 s/iter. Eval: 0.0000 s/iter. Total: 0.5079 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1560 s/iter. Inference: 0.4328 s/iter. Eval: 0.0000 s/iter. Total: 0.5890 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1718 s/iter. Inference: 0.5522 s/iter. Eval: 0.0000 s/iter. Total: 0.7241 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1720 s/iter. Inference: 0.6342 s/iter. Eval: 0.0000 s/iter. Total: 0.8064 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1702 s/iter. Inference: 0.5941 s/iter. Eval: 0.0000 s/iter. Total: 0.7645 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1690 s/iter. Inference: 0.6389 s/iter. Eval: 0.0000 s/iter. Total: 0.8080 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5098039215686274, 'noc@0.8': 2.8803043605501903, 'noc@0.85': 3.462101258413813, 'noc@0.9': 4.498683055311677, 'miou@iter1': 0.836781728117655}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0020 s/iter. Inference: 0.0992 s/iter. Eval: 0.0008 s/iter. Total: 0.1019 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.384765625, 'precision@0.6': 67.27555084228516, 'precision@0.7': 61.95103073120117, 'precision@0.8': 51.574039459228516, 'precision@0.9': 26.894676208496094, 'cIoU': 57.46813201904297, 'mIoU': 62.17697525024414}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.16127328996583, 'SQ': 82.1950077523108, 'RQ': 60.24833685251405, 'PQ_th': 55.34268619380474, 'SQ_th': 82.6692899499123, 'RQ_th': 66.23847325970115, 'PQ_st': 42.340272680397625, 'SQ_st': 81.47911009555392, 'RQ_st': 51.2066215209109}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.86679691964204, 'AP50': 61.274369557843976, 'AP75': 40.71695992506623, 'APs': 19.564693486323854, 'APm': 42.02935766414484, 'APl': 60.38405226630511, 'AP-person': 45.15612822012473, 'AP-bicycle': 18.848760901810344, 'AP-car': 37.011592785048066, 'AP-motorcycle': 34.64853362168914, 'AP-airplane': 57.208162330271925, 'AP-bus': 66.07516036142947, 'AP-train': 68.2584383011982, 'AP-truck': 36.014035399499974, 'AP-boat': 23.08292970894746, 'AP-traffic light': 25.824317164929113, 'AP-fire hydrant': 64.52237590159375, 'AP-stop sign': 64.34057901208806, 'AP-parking meter': 44.79024012285224, 'AP-bench': 19.998347060157897, 'AP-bird': 29.89750452103575, 'AP-cat': 73.33762480228513, 'AP-dog': 66.15900652191381, 'AP-horse': 46.26137351848766, 'AP-sheep': 47.83393720162593, 'AP-cow': 50.34156007807668, 'AP-elephant': 60.013287357917754, 'AP-bear': 76.88224270830982, 'AP-zebra': 61.30820045002616, 'AP-giraffe': 56.80168902732413, 'AP-backpack': 17.97590135172364, 'AP-umbrella': 47.9237318174839, 'AP-handbag': 14.783483225356738, 'AP-tie': 33.47330181910516, 'AP-suitcase': 39.460173107536264, 'AP-frisbee': 68.06449843787561, 'AP-skis': 5.759477042979904, 'AP-snowboard': 23.225455807035527, 'AP-sports ball': 46.679386662977215, 'AP-kite': 34.12818593928621, 'AP-baseball bat': 28.926041418500763, 'AP-baseball glove': 43.38279901221054, 'AP-skateboard': 36.256095305088785, 'AP-surfboard': 35.72600292835097, 'AP-tennis racket': 56.36509824230737, 'AP-bottle': 34.2036463207543, 'AP-wine glass': 28.141206573463617, 'AP-cup': 40.28009697667852, 'AP-fork': 16.918094300385018, 'AP-knife': 13.543483405414422, 'AP-spoon': 14.433242120429837, 'AP-bowl': 33.172691686463395, 'AP-banana': 19.651674826309318, 'AP-apple': 21.375886408206398, 'AP-sandwich': 41.45644375906962, 'AP-orange': 29.802015873786043, 'AP-broccoli': 21.60202755108526, 'AP-carrot': 20.433155982466143, 'AP-hot dog': 21.660174948910253, 'AP-pizza': 49.51119577387928, 'AP-donut': 45.70850376994863, 'AP-cake': 43.391169044238715, 'AP-chair': 21.36836401336901, 'AP-couch': 41.87661884591274, 'AP-potted plant': 17.112217837495777, 'AP-bed': 41.85668790022133, 'AP-dining table': 12.605228180438385, 'AP-toilet': 66.11139413450731, 'AP-tv': 62.764481290964724, 'AP-laptop': 63.1688986394948, 'AP-mouse': 58.10708133015912, 'AP-remote': 31.806260668113666, 'AP-keyboard': 48.95211389122689, 'AP-cell phone': 37.31699176481449, 'AP-microwave': 56.060511734097716, 'AP-oven': 34.10769709191672, 'AP-toaster': 18.185145279233804, 'AP-sink': 37.92653334361609, 'AP-refrigerator': 60.099829420079075, 'AP-book': 8.736379133073187, 'AP-clock': 51.51012952029953, 'AP-vase': 32.731323975565054, 'AP-scissors': 24.66131522498208, 'AP-teddy bear': 50.54136385546496, 'AP-hair drier': 9.632599444154941, 'AP-toothbrush': 20.07421853421135}), ('sem_seg', {'mIoU': 60.8201685037474, 'fwIoU': 69.12877718377248, 'IoU-person': 87.65211647270492, 'IoU-bicycle': 68.40143428373693, 'IoU-car': 68.17626063052916, 'IoU-motorcycle': 83.1650149676939, 'IoU-airplane': 80.70011120242971, 'IoU-bus': 83.79372088942128, 'IoU-train': 84.30719937519923, 'IoU-truck': 61.50268817393545, 'IoU-boat': 67.51997198964989, 'IoU-traffic light': 75.58926560301505, 'IoU-fire hydrant': 90.09185108002559, 'IoU-stop sign': 88.53777317520615, 'IoU-parking meter': 87.67445202930044, 'IoU-bench': 57.88550094827182, 'IoU-bird': 75.38399845613648, 'IoU-cat': 84.17180912704123, 'IoU-dog': 75.7885502984301, 'IoU-horse': 85.35505073858546, 'IoU-sheep': 86.85264984807723, 'IoU-cow': 80.7496574998369, 'IoU-elephant': 90.77217901692185, 'IoU-bear': 84.31635893121694, 'IoU-zebra': 84.96907359340838, 'IoU-giraffe': 87.4307430313907, 'IoU-backpack': 39.28031346529198, 'IoU-umbrella': 71.3565648578719, 'IoU-handbag': 36.125875971880085, 'IoU-tie': 69.43273669850572, 'IoU-suitcase': 82.58381368380383, 'IoU-frisbee': 82.4194467344217, 'IoU-skis': 51.594243318691134, 'IoU-snowboard': 69.89202386703398, 'IoU-sports ball': 62.77127647626962, 'IoU-kite': 65.92560152791464, 'IoU-baseball bat': 60.81583185388348, 'IoU-baseball glove': 76.51910983595407, 'IoU-skateboard': 77.83650191588713, 'IoU-surfboard': 76.82583490640503, 'IoU-tennis racket': 82.11084083765283, 'IoU-bottle': 65.7464268982666, 'IoU-wine glass': 74.804072219785, 'IoU-cup': 60.933728669799116, 'IoU-fork': 55.160483792140184, 'IoU-knife': 47.181981467803666, 'IoU-spoon': 49.35471661164477, 'IoU-bowl': 58.575564768483666, 'IoU-banana': 84.50609048932075, 'IoU-apple': 55.85065128968709, 'IoU-sandwich': 66.29791193206535, 'IoU-orange': 77.44020207479635, 'IoU-broccoli': 68.20703323065366, 'IoU-carrot': 64.03059951947515, 'IoU-hot dog': 61.43481558538113, 'IoU-pizza': 82.51243372387194, 'IoU-donut': 63.302982816045706, 'IoU-cake': 67.41560138733338, 'IoU-chair': 55.10220943176299, 'IoU-couch': 67.00385970303925, 'IoU-potted plant': 33.4206802770131, 'IoU-bed': 70.62387745306715, 'IoU-dining table': 50.85348259796005, 'IoU-toilet': 83.92363473349577, 'IoU-tv': 75.02348407386523, 'IoU-laptop': 76.16115710828618, 'IoU-mouse': 70.08526750354032, 'IoU-remote': 50.3957126256839, 'IoU-keyboard': 62.638218416977864, 'IoU-cell phone': 69.63289426370673, 'IoU-microwave': 65.50573520154805, 'IoU-oven': 66.31031933484022, 'IoU-toaster': 61.96794389306073, 'IoU-sink': 71.62064360680756, 'IoU-refrigerator': 77.43658940806093, 'IoU-book': 50.121872697408506, 'IoU-clock': 74.05020197982209, 'IoU-vase': 59.982982212605776, 'IoU-scissors': 52.89020399742157, 'IoU-teddy bear': 81.19146321085691, 'IoU-hair drier': 48.64072140484101, 'IoU-toothbrush': 56.138874148352826, 'IoU-banner': 34.20148184261553, 'IoU-blanket': 11.311141368351672, 'IoU-bridge': 39.57296232414163, 'IoU-cardboard': 44.700437643309066, 'IoU-counter': 30.559128438224615, 'IoU-curtain': 64.93536173841953, 'IoU-door-stuff': 41.95697365276658, 'IoU-floor-wood': 60.19119459818544, 'IoU-flower': 40.10946026950265, 'IoU-fruit': 39.84375555940349, 'IoU-gravel': 31.5648657715709, 'IoU-house': 23.04551490953959, 'IoU-light': 38.56934129583682, 'IoU-mirror-stuff': 54.649541209887055, 'IoU-net': 47.11016403248441, 'IoU-pillow': 13.002671375187417, 'IoU-platform': 36.11994927618963, 'IoU-playingfield': 66.08013311714693, 'IoU-railroad': 61.41724903963854, 'IoU-river': 50.28024511231766, 'IoU-road': 65.43188995268011, 'IoU-roof': 13.815736178647436, 'IoU-sand': 61.34185055064193, 'IoU-sea': 85.06163805215805, 'IoU-shelf': 34.97150894379329, 'IoU-snow': 89.06265925195753, 'IoU-stairs': 26.813244856433016, 'IoU-tent': 9.622613431290219, 'IoU-towel': 26.795701055358418, 'IoU-wall-brick': 44.99745516637656, 'IoU-wall-stone': 26.98517349948662, 'IoU-wall-tile': 64.21516870930873, 'IoU-wall-wood': 38.747100806949064, 'IoU-water-other': 26.802392239489354, 'IoU-window-blind': 46.184060635890575, 'IoU-window-other': 45.9389436550688, 'IoU-tree-merged': 80.92522101189769, 'IoU-fence-merged': 51.35936269292338, 'IoU-ceiling-merged': 67.75083431459598, 'IoU-sky-other-merged': 93.80997524338075, 'IoU-cabinet-merged': 58.40132550271625, 'IoU-table-merged': 38.83757340743687, 'IoU-floor-other-merged': 50.756630155218254, 'IoU-pavement-merged': 53.876344162819336, 'IoU-mountain-merged': 54.41132452384452, 'IoU-grass-merged': 71.68664341256232, 'IoU-dirt-merged': 46.65555850650659, 'IoU-paper-merged': 33.16303541209062, 'IoU-food-other-merged': 40.59660540097247, 'IoU-building-other-merged': 58.923060784718906, 'IoU-rock-merged': 62.41108276627519, 'IoU-wall-other-merged': 65.50093040130052, 'IoU-rug-merged': 64.2534206646837, 'mACC': 72.91736628004148, 'pACC': 80.41534154110272, 'ACC-person': 92.41917283378451, 'ACC-bicycle': 80.986579678293, 'ACC-car': 84.9999747828806, 'ACC-motorcycle': 88.50139114190216, 'ACC-airplane': 87.79677822827205, 'ACC-bus': 88.43963391257725, 'ACC-train': 95.30583154166425, 'ACC-truck': 76.0570945770947, 'ACC-boat': 78.1655206151345, 'ACC-traffic light': 90.02172539530306, 'ACC-fire hydrant': 94.88608462468028, 'ACC-stop sign': 91.12206158759214, 'ACC-parking meter': 92.49575890629677, 'ACC-bench': 70.83053732349138, 'ACC-bird': 80.45656574735077, 'ACC-cat': 91.59693279124971, 'ACC-dog': 78.64996225022571, 'ACC-horse': 91.01019007179426, 'ACC-sheep': 91.15569456180613, 'ACC-cow': 85.16538334145692, 'ACC-elephant': 93.39940001960214, 'ACC-bear': 86.52705705795701, 'ACC-zebra': 87.36238887990056, 'ACC-giraffe': 91.79169058982508, 'ACC-backpack': 58.733709608154186, 'ACC-umbrella': 77.61369923893378, 'ACC-handbag': 52.53893390788617, 'ACC-tie': 80.80981027935007, 'ACC-suitcase': 91.21858916873293, 'ACC-frisbee': 94.15927272727272, 'ACC-skis': 68.07292749159276, 'ACC-snowboard': 78.66229090641914, 'ACC-sports ball': 72.21963800250153, 'ACC-kite': 75.02221248983763, 'ACC-baseball bat': 82.90052371711784, 'ACC-baseball glove': 89.1672611198778, 'ACC-skateboard': 90.08297013793197, 'ACC-surfboard': 85.15578868571073, 'ACC-tennis racket': 89.29202073995252, 'ACC-bottle': 80.08006964043106, 'ACC-wine glass': 85.4867545466084, 'ACC-cup': 82.8617460704191, 'ACC-fork': 66.30265234725853, 'ACC-knife': 61.787755550670475, 'ACC-spoon': 69.40508925305285, 'ACC-bowl': 72.38264035719955, 'ACC-banana': 90.81365624773582, 'ACC-apple': 66.14541936659732, 'ACC-sandwich': 79.14870238106325, 'ACC-orange': 84.97790025155989, 'ACC-broccoli': 78.40406804328019, 'ACC-carrot': 73.79441485839205, 'ACC-hot dog': 66.97589975553319, 'ACC-pizza': 89.46430515254656, 'ACC-donut': 76.54099255671377, 'ACC-cake': 75.14970874697663, 'ACC-chair': 70.89748168763433, 'ACC-couch': 77.79997808455354, 'ACC-potted plant': 53.51329738402038, 'ACC-bed': 84.91095898623267, 'ACC-dining table': 73.18616559424171, 'ACC-toilet': 87.8765316249648, 'ACC-tv': 88.12080876773075, 'ACC-laptop': 91.25043924245644, 'ACC-mouse': 84.26746606351165, 'ACC-remote': 72.39385680068509, 'ACC-keyboard': 67.52765353665782, 'ACC-cell phone': 75.17448548235838, 'ACC-microwave': 76.81508833022637, 'ACC-oven': 79.93049974666125, 'ACC-toaster': 72.67445838864634, 'ACC-sink': 84.77052081883713, 'ACC-refrigerator': 92.49756518282288, 'ACC-book': 64.96760387772828, 'ACC-clock': 79.464992744826, 'ACC-vase': 68.4840676560448, 'ACC-scissors': 56.78628488003746, 'ACC-teddy bear': 87.36162135365278, 'ACC-hair drier': 72.92716249679788, 'ACC-toothbrush': 79.94961779013204, 'ACC-banner': 63.707655425231835, 'ACC-blanket': 15.25293323684617, 'ACC-bridge': 51.43376116128115, 'ACC-cardboard': 58.65156318161989, 'ACC-counter': 60.56473416567278, 'ACC-curtain': 76.8175798832035, 'ACC-door-stuff': 60.853854226450466, 'ACC-floor-wood': 73.11105088494116, 'ACC-flower': 57.48402694268779, 'ACC-fruit': 58.26840257321801, 'ACC-gravel': 43.7876822077263, 'ACC-house': 26.6496070296052, 'ACC-light': 55.18341117023313, 'ACC-mirror-stuff': 70.41360358514679, 'ACC-net': 63.558661620696455, 'ACC-pillow': 22.551367105516736, 'ACC-platform': 61.901686040392775, 'ACC-playingfield': 79.27753109542937, 'ACC-railroad': 75.71798686325515, 'ACC-river': 69.52395138883958, 'ACC-road': 86.48992711430942, 'ACC-roof': 18.692768114347647, 'ACC-sand': 70.05210380906894, 'ACC-sea': 90.63972295940285, 'ACC-shelf': 60.41021739608824, 'ACC-snow': 94.64966172680201, 'ACC-stairs': 42.22086333311595, 'ACC-tent': 11.459322239462006, 'ACC-towel': 33.20163775501915, 'ACC-wall-brick': 56.692770025564045, 'ACC-wall-stone': 32.87771724827622, 'ACC-wall-tile': 77.94312781506459, 'ACC-wall-wood': 51.23861144927157, 'ACC-water-other': 45.43667941512443, 'ACC-window-blind': 54.762437864588165, 'ACC-window-other': 67.29180509129455, 'ACC-tree-merged': 89.43661203998636, 'ACC-fence-merged': 74.15962719727128, 'ACC-ceiling-merged': 80.24189425328211, 'ACC-sky-other-merged': 96.83459282795992, 'ACC-cabinet-merged': 73.38015114368493, 'ACC-table-merged': 53.862567793370296, 'ACC-floor-other-merged': 61.62631788234722, 'ACC-pavement-merged': 64.83821666254079, 'ACC-mountain-merged': 62.63843754818027, 'ACC-grass-merged': 83.34720810516572, 'ACC-dirt-merged': 74.72739696754425, 'ACC-paper-merged': 46.092624245127595, 'ACC-food-other-merged': 59.58398623046099, 'ACC-building-other-merged': 78.53071141242796, 'ACC-rock-merged': 81.22373118643227, 'ACC-wall-other-merged': 81.3083207860004, 'ACC-rug-merged': 79.34345549003417})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5098039215686274, 'noc@0.8': 2.8803043605501903, 'noc@0.85': 3.462101258413813, 'noc@0.9': 4.498683055311677, 'miou@iter1': 0.836781728117655}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.384765625, 'precision@0.6': 67.27555084228516, 'precision@0.7': 61.95103073120117, 'precision@0.8': 51.574039459228516, 'precision@0.9': 26.894676208496094, 'cIoU': 57.46813201904297, 'mIoU': 62.17697525024414}}}
INFO:trainer.default_trainer:This epoch takes 1:27:21.529276
INFO:trainer.default_trainer:PROGRESS: 60.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 30 training.
INFO:trainer.default_trainer:epochs[    30] optim steps[54900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26363/0.90110, loss_mask_bce_0: 0.24421/0.33509, loss_mask_dice_0: 1.12356/1.16306, loss_spatial_bce_0: 0.04227/0.08787, loss_spatial_dice_0: 0.17720/0.20966, loss_spatial_ce_0: 0.07397/0.06414, loss_grounding_bce_0: 0.12521/0.08634, loss_grounding_dice_0: 0.29446/0.17867, loss_grounding_ce_0: 0.06036/0.27274, loss_mask_ce_1: 1.12693/0.90169, loss_mask_bce_1: 0.23825/0.33602, loss_mask_dice_1: 0.99812/1.16951, loss_spatial_bce_1: 0.04155/0.08840, loss_spatial_dice_1: 0.15319/0.21365, loss_spatial_ce_1: 0.07630/0.06995, loss_grounding_bce_1: 0.12546/0.08653, loss_grounding_dice_1: 0.25565/0.17948, loss_grounding_ce_1: 0.05373/0.27371, loss_mask_ce_2: 1.11528/0.90884, loss_mask_bce_2: 0.23287/0.33653, loss_mask_dice_2: 1.02835/1.16973, loss_spatial_bce_2: 0.04061/0.08929, loss_spatial_dice_2: 0.16532/0.21508, loss_spatial_ce_2: 0.32888/0.07335, loss_grounding_bce_2: 0.12521/0.08665, loss_grounding_dice_2: 0.20072/0.17930, loss_grounding_ce_2: 0.05502/0.27714, loss_mask_ce_3: 1.19654/0.91857, loss_mask_bce_3: 0.25150/0.33761, loss_mask_dice_3: 0.92495/1.16723, loss_spatial_bce_3: 0.04558/0.09032, loss_spatial_dice_3: 0.18296/0.21580, loss_spatial_ce_3: 0.09283/0.07736, loss_grounding_bce_3: 0.12954/0.08689, loss_grounding_dice_3: 0.31391/0.17901, loss_grounding_ce_3: 0.05627/0.27909, loss_mask_ce_4: 1.28181/0.91940, loss_mask_bce_4: 0.23258/0.33970, loss_mask_dice_4: 0.82580/1.19105, loss_spatial_bce_4: 0.05131/0.09434, loss_spatial_dice_4: 0.20863/0.22775, loss_spatial_ce_4: 0.04819/0.09307, loss_grounding_bce_4: 0.12301/0.08739, loss_grounding_dice_4: 0.25645/0.18195, loss_grounding_ce_4: 0.07504/0.28184, loss_mask_ce_5: 1.28190/0.93509, loss_mask_bce_5: 0.23754/0.34191, loss_mask_dice_5: 0.85135/1.19824, loss_spatial_bce_5: 0.05344/0.09640, loss_spatial_dice_5: 0.18468/0.23173, loss_spatial_ce_5: 0.07149/0.10786, loss_grounding_bce_5: 0.12557/0.08778, loss_grounding_dice_5: 0.15285/0.18312, loss_grounding_ce_5: 0.08314/0.29457, loss_mask_ce_6: 1.36701/0.97482, loss_mask_bce_6: 0.24781/0.34462, loss_mask_dice_6: 0.72868/1.20117, loss_spatial_bce_6: 0.05013/0.10218, loss_spatial_dice_6: 0.18123/0.23455, loss_spatial_ce_6: 0.06281/0.13421, loss_grounding_bce_6: 0.12931/0.08851, loss_grounding_dice_6: 0.23181/0.18345, loss_grounding_ce_6: 0.11343/0.31019, loss_mask_ce_7: 1.56991/1.01956, loss_mask_bce_7: 0.29679/0.35249, loss_mask_dice_7: 1.13662/1.25591, loss_spatial_bce_7: 0.07617/0.11042, loss_spatial_dice_7: 0.26888/0.26237, loss_spatial_ce_7: 0.09450/0.17035, loss_grounding_bce_7: 0.13805/0.09041, loss_grounding_dice_7: 0.20336/0.19068, loss_grounding_ce_7: 0.24869/0.34172, loss_mask_ce_8: 1.58306/1.12864, loss_mask_bce_8: 0.29142/0.36614, loss_mask_dice_8: 1.26721/1.32943, loss_spatial_bce_8: 0.06463/0.13122, loss_spatial_dice_8: 0.28752/0.30072, loss_spatial_ce_8: 0.14751/0.22726, loss_grounding_bce_8: 0.13471/0.09418, loss_grounding_dice_8: 0.30674/0.20176, loss_grounding_ce_8: 0.19840/0.40915, loss_mask_ce_9: 3.94780/3.67867, loss_mask_bce_9: 0.30674/0.39308, loss_mask_dice_9: 1.28914/1.90205, loss_spatial_bce_9: 0.21717/0.33355, loss_spatial_dice_9: 0.85507/0.82228, loss_spatial_ce_9: 1.48341/1.49940, loss_grounding_bce_9: 0.15117/0.10563, loss_grounding_dice_9: 0.34621/0.28084, loss_grounding_ce_9: 0.49236/0.67395] items per batch[64] items per second[0.13] total items[3513600] mini batches[ 54900] memory[7345] epoch remaining[1:21:41]
INFO:trainer.default_trainer:epochs[    30] optim steps[55000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97829/0.90101, loss_mask_bce_0: 0.83114/0.33509, loss_mask_dice_0: 2.29769/1.16301, loss_spatial_bce_0: 0.24678/0.08787, loss_spatial_dice_0: 0.31590/0.20964, loss_spatial_ce_0: 0.01269/0.06411, loss_grounding_bce_0: 0.11337/0.08635, loss_grounding_dice_0: 0.11622/0.17866, loss_grounding_ce_0: 1.40803/0.27276, loss_mask_ce_1: 1.03841/0.90163, loss_mask_bce_1: 0.81861/0.33602, loss_mask_dice_1: 2.05507/1.16942, loss_spatial_bce_1: 0.25000/0.08840, loss_spatial_dice_1: 0.31884/0.21363, loss_spatial_ce_1: 0.02462/0.06993, loss_grounding_bce_1: 0.11484/0.08654, loss_grounding_dice_1: 0.11668/0.17947, loss_grounding_ce_1: 1.37031/0.27372, loss_mask_ce_2: 1.04186/0.90879, loss_mask_bce_2: 0.85284/0.33654, loss_mask_dice_2: 2.31613/1.16965, loss_spatial_bce_2: 0.22851/0.08929, loss_spatial_dice_2: 0.33728/0.21507, loss_spatial_ce_2: 0.03145/0.07334, loss_grounding_bce_2: 0.16134/0.08666, loss_grounding_dice_2: 0.13575/0.17930, loss_grounding_ce_2: 1.36596/0.27720, loss_mask_ce_3: 0.99951/0.91851, loss_mask_bce_3: 0.82851/0.33762, loss_mask_dice_3: 2.22166/1.16716, loss_spatial_bce_3: 0.20329/0.09032, loss_spatial_dice_3: 0.30048/0.21580, loss_spatial_ce_3: 0.04926/0.07735, loss_grounding_bce_3: 0.14102/0.08690, loss_grounding_dice_3: 0.12992/0.17901, loss_grounding_ce_3: 1.37011/0.27914, loss_mask_ce_4: 0.96677/0.91933, loss_mask_bce_4: 0.90119/0.33972, loss_mask_dice_4: 2.61383/1.19099, loss_spatial_bce_4: 0.24108/0.09435, loss_spatial_dice_4: 0.34805/0.22775, loss_spatial_ce_4: 0.15329/0.09306, loss_grounding_bce_4: 0.12967/0.08740, loss_grounding_dice_4: 0.14759/0.18194, loss_grounding_ce_4: 1.03379/0.28191, loss_mask_ce_5: 1.07257/0.93502, loss_mask_bce_5: 0.88098/0.34192, loss_mask_dice_5: 2.65502/1.19817, loss_spatial_bce_5: 0.21957/0.09641, loss_spatial_dice_5: 0.34510/0.23173, loss_spatial_ce_5: 0.11734/0.10782, loss_grounding_bce_5: 0.12783/0.08779, loss_grounding_dice_5: 0.13594/0.18311, loss_grounding_ce_5: 1.03892/0.29462, loss_mask_ce_6: 1.12699/0.97480, loss_mask_bce_6: 0.88673/0.34462, loss_mask_dice_6: 2.33463/1.20112, loss_spatial_bce_6: 0.21345/0.10218, loss_spatial_dice_6: 0.35158/0.23454, loss_spatial_ce_6: 0.13879/0.13421, loss_grounding_bce_6: 0.14682/0.08851, loss_grounding_dice_6: 0.14383/0.18345, loss_grounding_ce_6: 1.19546/0.31026, loss_mask_ce_7: 0.91187/1.01956, loss_mask_bce_7: 0.87742/0.35249, loss_mask_dice_7: 2.34026/1.25581, loss_spatial_bce_7: 0.19570/0.11041, loss_spatial_dice_7: 0.35872/0.26236, loss_spatial_ce_7: 0.18041/0.17031, loss_grounding_bce_7: 0.08262/0.09043, loss_grounding_dice_7: 0.12065/0.19068, loss_grounding_ce_7: 0.89974/0.34173, loss_mask_ce_8: 0.87915/1.12862, loss_mask_bce_8: 1.04720/0.36614, loss_mask_dice_8: 2.46553/1.32933, loss_spatial_bce_8: 0.25687/0.13121, loss_spatial_dice_8: 0.44149/0.30070, loss_spatial_ce_8: 0.19399/0.22721, loss_grounding_bce_8: 0.14753/0.09419, loss_grounding_dice_8: 0.13470/0.20176, loss_grounding_ce_8: 1.13997/0.40921, loss_mask_ce_9: 4.72769/3.67874, loss_mask_bce_9: 0.89899/0.39306, loss_mask_dice_9: 3.92536/1.90194, loss_spatial_bce_9: 0.28376/0.33354, loss_spatial_dice_9: 0.89126/0.82226, loss_spatial_ce_9: 1.16897/1.49936, loss_grounding_bce_9: 0.20092/0.10563, loss_grounding_dice_9: 0.26723/0.28082, loss_grounding_ce_9: 1.65152/0.67392] items per batch[64] items per second[0.23] total items[3520000] mini batches[ 55000] memory[7345] epoch remaining[1:15:49]
INFO:trainer.default_trainer:epochs[    30] optim steps[55100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25852/0.90107, loss_mask_bce_0: 0.85376/0.33511, loss_mask_dice_0: 2.20334/1.16313, loss_spatial_bce_0: 0.11176/0.08786, loss_spatial_dice_0: 0.27530/0.20963, loss_spatial_ce_0: 0.05903/0.06409, loss_grounding_bce_0: 0.10009/0.08635, loss_grounding_dice_0: 0.21122/0.17867, loss_grounding_ce_0: 0.38456/0.27276, loss_mask_ce_1: 1.26281/0.90168, loss_mask_bce_1: 0.85094/0.33605, loss_mask_dice_1: 2.19287/1.16952, loss_spatial_bce_1: 0.11498/0.08839, loss_spatial_dice_1: 0.27260/0.21362, loss_spatial_ce_1: 0.06161/0.06990, loss_grounding_bce_1: 0.09938/0.08653, loss_grounding_dice_1: 0.20906/0.17947, loss_grounding_ce_1: 0.39063/0.27370, loss_mask_ce_2: 1.16237/0.90886, loss_mask_bce_2: 0.88788/0.33657, loss_mask_dice_2: 2.21806/1.16975, loss_spatial_bce_2: 0.10929/0.08928, loss_spatial_dice_2: 0.25048/0.21506, loss_spatial_ce_2: 0.09954/0.07331, loss_grounding_bce_2: 0.10255/0.08665, loss_grounding_dice_2: 0.21779/0.17929, loss_grounding_ce_2: 0.39825/0.27717, loss_mask_ce_3: 1.28789/0.91853, loss_mask_bce_3: 0.83238/0.33765, loss_mask_dice_3: 2.27143/1.16730, loss_spatial_bce_3: 0.10284/0.09031, loss_spatial_dice_3: 0.24423/0.21578, loss_spatial_ce_3: 0.16917/0.07735, loss_grounding_bce_3: 0.10689/0.08689, loss_grounding_dice_3: 0.21445/0.17901, loss_grounding_ce_3: 0.49128/0.27915, loss_mask_ce_4: 1.29301/0.91937, loss_mask_bce_4: 0.83267/0.33974, loss_mask_dice_4: 2.22435/1.19110, loss_spatial_bce_4: 0.10426/0.09434, loss_spatial_dice_4: 0.29057/0.22774, loss_spatial_ce_4: 0.11510/0.09304, loss_grounding_bce_4: 0.10356/0.08739, loss_grounding_dice_4: 0.21674/0.18194, loss_grounding_ce_4: 0.40620/0.28187, loss_mask_ce_5: 1.33353/0.93504, loss_mask_bce_5: 0.83451/0.34195, loss_mask_dice_5: 2.22426/1.19829, loss_spatial_bce_5: 0.11048/0.09640, loss_spatial_dice_5: 0.28462/0.23172, loss_spatial_ce_5: 0.12534/0.10780, loss_grounding_bce_5: 0.10471/0.08778, loss_grounding_dice_5: 0.21511/0.18311, loss_grounding_ce_5: 0.37795/0.29455, loss_mask_ce_6: 1.42781/0.97485, loss_mask_bce_6: 0.83279/0.34465, loss_mask_dice_6: 2.19514/1.20124, loss_spatial_bce_6: 0.10714/0.10217, loss_spatial_dice_6: 0.27646/0.23453, loss_spatial_ce_6: 0.17377/0.13418, loss_grounding_bce_6: 0.10264/0.08851, loss_grounding_dice_6: 0.20866/0.18346, loss_grounding_ce_6: 0.38174/0.31019, loss_mask_ce_7: 1.68020/1.01965, loss_mask_bce_7: 0.87456/0.35253, loss_mask_dice_7: 2.11772/1.25589, loss_spatial_bce_7: 0.12629/0.11040, loss_spatial_dice_7: 0.30341/0.26235, loss_spatial_ce_7: 0.20167/0.17026, loss_grounding_bce_7: 0.11398/0.09042, loss_grounding_dice_7: 0.21337/0.19068, loss_grounding_ce_7: 0.33483/0.34165, loss_mask_ce_8: 2.10939/1.12869, loss_mask_bce_8: 0.97531/0.36616, loss_mask_dice_8: 2.48623/1.32947, loss_spatial_bce_8: 0.20794/0.13119, loss_spatial_dice_8: 0.31071/0.30069, loss_spatial_ce_8: 0.39070/0.22718, loss_grounding_bce_8: 0.11479/0.09418, loss_grounding_dice_8: 0.24901/0.20176, loss_grounding_ce_8: 0.43538/0.40916, loss_mask_ce_9: 4.69605/3.67880, loss_mask_bce_9: 1.14584/0.39313, loss_mask_dice_9: 3.24325/1.90209, loss_spatial_bce_9: 0.25379/0.33350, loss_spatial_dice_9: 0.86598/0.82228, loss_spatial_ce_9: 1.28557/1.49934, loss_grounding_bce_9: 0.19069/0.10563, loss_grounding_dice_9: 0.34373/0.28082, loss_grounding_ce_9: 0.47364/0.67383] items per batch[64] items per second[0.23] total items[3526400] mini batches[ 55100] memory[7345] epoch remaining[1:11:01]
INFO:trainer.default_trainer:epochs[    30] optim steps[55200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12030/0.90105, loss_mask_bce_0: 0.61732/0.33515, loss_mask_dice_0: 2.87408/1.16312, loss_spatial_bce_0: 0.07254/0.08786, loss_spatial_dice_0: 0.34164/0.20961, loss_spatial_ce_0: 0.09582/0.06406, loss_grounding_bce_0: 0.29821/0.08636, loss_grounding_dice_0: 0.17905/0.17865, loss_grounding_ce_0: 0.00397/0.27266, loss_mask_ce_1: 1.15405/0.90163, loss_mask_bce_1: 0.60686/0.33608, loss_mask_dice_1: 2.87215/1.16948, loss_spatial_bce_1: 0.07747/0.08840, loss_spatial_dice_1: 0.34575/0.21360, loss_spatial_ce_1: 0.08481/0.06988, loss_grounding_bce_1: 0.28309/0.08654, loss_grounding_dice_1: 0.17618/0.17945, loss_grounding_ce_1: 0.00363/0.27362, loss_mask_ce_2: 1.07569/0.90882, loss_mask_bce_2: 0.69353/0.33660, loss_mask_dice_2: 2.98654/1.16970, loss_spatial_bce_2: 0.07441/0.08929, loss_spatial_dice_2: 0.32596/0.21505, loss_spatial_ce_2: 0.15311/0.07329, loss_grounding_bce_2: 0.29783/0.08666, loss_grounding_dice_2: 0.19005/0.17928, loss_grounding_ce_2: 0.00641/0.27710, loss_mask_ce_3: 1.11570/0.91853, loss_mask_bce_3: 0.71425/0.33769, loss_mask_dice_3: 2.87148/1.16726, loss_spatial_bce_3: 0.07801/0.09032, loss_spatial_dice_3: 0.32554/0.21578, loss_spatial_ce_3: 0.06560/0.07733, loss_grounding_bce_3: 0.34354/0.08690, loss_grounding_dice_3: 0.18411/0.17899, loss_grounding_ce_3: 0.00778/0.27905, loss_mask_ce_4: 1.08296/0.91932, loss_mask_bce_4: 0.61265/0.33977, loss_mask_dice_4: 2.95997/1.19108, loss_spatial_bce_4: 0.09060/0.09435, loss_spatial_dice_4: 0.39504/0.22774, loss_spatial_ce_4: 0.05669/0.09301, loss_grounding_bce_4: 0.31459/0.08740, loss_grounding_dice_4: 0.16996/0.18193, loss_grounding_ce_4: 0.00980/0.28177, loss_mask_ce_5: 1.11193/0.93497, loss_mask_bce_5: 0.60814/0.34198, loss_mask_dice_5: 2.90142/1.19829, loss_spatial_bce_5: 0.09642/0.09642, loss_spatial_dice_5: 0.38344/0.23172, loss_spatial_ce_5: 0.06057/0.10778, loss_grounding_bce_5: 0.34778/0.08780, loss_grounding_dice_5: 0.16279/0.18310, loss_grounding_ce_5: 0.01614/0.29443, loss_mask_ce_6: 1.39751/0.97477, loss_mask_bce_6: 0.58318/0.34467, loss_mask_dice_6: 2.83086/1.20123, loss_spatial_bce_6: 0.09665/0.10219, loss_spatial_dice_6: 0.37664/0.23453, loss_spatial_ce_6: 0.17601/0.13418, loss_grounding_bce_6: 0.34401/0.08852, loss_grounding_dice_6: 0.16640/0.18345, loss_grounding_ce_6: 0.01489/0.31005, loss_mask_ce_7: 1.26896/1.01954, loss_mask_bce_7: 0.63598/0.35256, loss_mask_dice_7: 3.04449/1.25587, loss_spatial_bce_7: 0.10340/0.11041, loss_spatial_dice_7: 0.38510/0.26234, loss_spatial_ce_7: 0.18107/0.17024, loss_grounding_bce_7: 0.36830/0.09043, loss_grounding_dice_7: 0.17679/0.19066, loss_grounding_ce_7: 0.05136/0.34147, loss_mask_ce_8: 1.36562/1.12860, loss_mask_bce_8: 0.61988/0.36618, loss_mask_dice_8: 2.99262/1.32944, loss_spatial_bce_8: 0.13411/0.13121, loss_spatial_dice_8: 0.38424/0.30067, loss_spatial_ce_8: 0.18671/0.22713, loss_grounding_bce_8: 0.30924/0.09420, loss_grounding_dice_8: 0.18121/0.20174, loss_grounding_ce_8: 0.05278/0.40894, loss_mask_ce_9: 4.62903/3.67850, loss_mask_bce_9: 0.52947/0.39315, loss_mask_dice_9: 3.82389/1.90208, loss_spatial_bce_9: 0.26987/0.33354, loss_spatial_dice_9: 0.91126/0.82229, loss_spatial_ce_9: 1.61773/1.49938, loss_grounding_bce_9: 0.24492/0.10564, loss_grounding_dice_9: 0.26980/0.28079, loss_grounding_ce_9: 0.10887/0.67361] items per batch[64] items per second[0.23] total items[3532800] mini batches[ 55200] memory[7345] epoch remaining[1:06:10]
INFO:trainer.default_trainer:epochs[    30] optim steps[55300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91648/0.90107, loss_mask_bce_0: 0.08610/0.33506, loss_mask_dice_0: 0.39082/1.16311, loss_spatial_bce_0: 0.03852/0.08786, loss_spatial_dice_0: 0.15196/0.20960, loss_spatial_ce_0: 0.00263/0.06406, loss_grounding_bce_0: 0.06851/0.08637, loss_grounding_dice_0: 0.18645/0.17866, loss_grounding_ce_0: 0.07066/0.27265, loss_mask_ce_1: 0.81491/0.90165, loss_mask_bce_1: 0.11457/0.33599, loss_mask_dice_1: 0.43864/1.16945, loss_spatial_bce_1: 0.03686/0.08839, loss_spatial_dice_1: 0.16373/0.21359, loss_spatial_ce_1: 0.00576/0.06989, loss_grounding_bce_1: 0.06967/0.08655, loss_grounding_dice_1: 0.19431/0.17946, loss_grounding_ce_1: 0.07050/0.27357, loss_mask_ce_2: 0.93826/0.90881, loss_mask_bce_2: 0.10561/0.33651, loss_mask_dice_2: 0.41445/1.16969, loss_spatial_bce_2: 0.04053/0.08928, loss_spatial_dice_2: 0.16287/0.21504, loss_spatial_ce_2: 0.00987/0.07326, loss_grounding_bce_2: 0.06386/0.08666, loss_grounding_dice_2: 0.19471/0.17928, loss_grounding_ce_2: 0.08408/0.27708, loss_mask_ce_3: 0.88468/0.91855, loss_mask_bce_3: 0.10777/0.33760, loss_mask_dice_3: 0.52583/1.16723, loss_spatial_bce_3: 0.03590/0.09032, loss_spatial_dice_3: 0.15889/0.21577, loss_spatial_ce_3: 0.00538/0.07734, loss_grounding_bce_3: 0.06107/0.08691, loss_grounding_dice_3: 0.18794/0.17899, loss_grounding_ce_3: 0.08595/0.27903, loss_mask_ce_4: 0.77660/0.91938, loss_mask_bce_4: 0.10389/0.33968, loss_mask_dice_4: 0.41065/1.19104, loss_spatial_bce_4: 0.03617/0.09434, loss_spatial_dice_4: 0.18720/0.22774, loss_spatial_ce_4: 0.01517/0.09299, loss_grounding_bce_4: 0.05867/0.08741, loss_grounding_dice_4: 0.19254/0.18194, loss_grounding_ce_4: 0.08880/0.28175, loss_mask_ce_5: 0.92128/0.93503, loss_mask_bce_5: 0.10754/0.34189, loss_mask_dice_5: 0.40949/1.19826, loss_spatial_bce_5: 0.04574/0.09642, loss_spatial_dice_5: 0.22594/0.23173, loss_spatial_ce_5: 0.01408/0.10775, loss_grounding_bce_5: 0.05824/0.08781, loss_grounding_dice_5: 0.17285/0.18310, loss_grounding_ce_5: 0.12007/0.29438, loss_mask_ce_6: 0.90994/0.97483, loss_mask_bce_6: 0.11051/0.34458, loss_mask_dice_6: 0.46729/1.20117, loss_spatial_bce_6: 0.04400/0.10218, loss_spatial_dice_6: 0.19994/0.23455, loss_spatial_ce_6: 0.04779/0.13416, loss_grounding_bce_6: 0.05895/0.08853, loss_grounding_dice_6: 0.19559/0.18345, loss_grounding_ce_6: 0.11174/0.31003, loss_mask_ce_7: 0.96687/1.01961, loss_mask_bce_7: 0.10048/0.35246, loss_mask_dice_7: 0.43064/1.25586, loss_spatial_bce_7: 0.07106/0.11041, loss_spatial_dice_7: 0.26344/0.26235, loss_spatial_ce_7: 0.04284/0.17022, loss_grounding_bce_7: 0.05594/0.09043, loss_grounding_dice_7: 0.19404/0.19066, loss_grounding_ce_7: 0.09787/0.34140, loss_mask_ce_8: 0.97555/1.12865, loss_mask_bce_8: 0.10149/0.36609, loss_mask_dice_8: 0.54342/1.32942, loss_spatial_bce_8: 0.06293/0.13121, loss_spatial_dice_8: 0.26744/0.30069, loss_spatial_ce_8: 0.12434/0.22710, loss_grounding_bce_8: 0.06014/0.09419, loss_grounding_dice_8: 0.21906/0.20176, loss_grounding_ce_8: 0.08433/0.40889, loss_mask_ce_9: 2.46670/3.67847, loss_mask_bce_9: 0.08773/0.39305, loss_mask_dice_9: 0.68467/1.90195, loss_spatial_bce_9: 0.40831/0.33353, loss_spatial_dice_9: 0.83801/0.82227, loss_spatial_ce_9: 1.64125/1.49928, loss_grounding_bce_9: 0.05626/0.10564, loss_grounding_dice_9: 0.24266/0.28081, loss_grounding_ce_9: 0.23707/0.67371] items per batch[64] items per second[0.23] total items[3539200] mini batches[ 55300] memory[7345] epoch remaining[1:01:37]
INFO:trainer.default_trainer:epochs[    30] optim steps[55400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75950/0.90112, loss_mask_bce_0: 0.21612/0.33515, loss_mask_dice_0: 1.38097/1.16346, loss_spatial_bce_0: 0.06802/0.08785, loss_spatial_dice_0: 0.18360/0.20960, loss_spatial_ce_0: 0.02830/0.06403, loss_grounding_bce_0: 0.01417/0.08639, loss_grounding_dice_0: 0.24394/0.17869, loss_grounding_ce_0: 0.31209/0.27266, loss_mask_ce_1: 0.51715/0.90167, loss_mask_bce_1: 0.22220/0.33609, loss_mask_dice_1: 1.57489/1.16976, loss_spatial_bce_1: 0.06996/0.08839, loss_spatial_dice_1: 0.20054/0.21359, loss_spatial_ce_1: 0.17591/0.06987, loss_grounding_bce_1: 0.01395/0.08657, loss_grounding_dice_1: 0.28423/0.17950, loss_grounding_ce_1: 0.31346/0.27360, loss_mask_ce_2: 0.74700/0.90881, loss_mask_bce_2: 0.23676/0.33661, loss_mask_dice_2: 1.31473/1.17004, loss_spatial_bce_2: 0.06494/0.08928, loss_spatial_dice_2: 0.18037/0.21505, loss_spatial_ce_2: 0.01831/0.07324, loss_grounding_bce_2: 0.01193/0.08669, loss_grounding_dice_2: 0.22003/0.17931, loss_grounding_ce_2: 0.30269/0.27710, loss_mask_ce_3: 0.51427/0.91857, loss_mask_bce_3: 0.22653/0.33769, loss_mask_dice_3: 1.35003/1.16759, loss_spatial_bce_3: 0.06248/0.09032, loss_spatial_dice_3: 0.18942/0.21578, loss_spatial_ce_3: 0.01450/0.07732, loss_grounding_bce_3: 0.01296/0.08693, loss_grounding_dice_3: 0.24878/0.17902, loss_grounding_ce_3: 0.32102/0.27905, loss_mask_ce_4: 0.66220/0.91940, loss_mask_bce_4: 0.21920/0.33977, loss_mask_dice_4: 1.41417/1.19140, loss_spatial_bce_4: 0.07089/0.09435, loss_spatial_dice_4: 0.26197/0.22776, loss_spatial_ce_4: 0.02753/0.09298, loss_grounding_bce_4: 0.01489/0.08743, loss_grounding_dice_4: 0.30704/0.18197, loss_grounding_ce_4: 0.34865/0.28180, loss_mask_ce_5: 0.76443/0.93507, loss_mask_bce_5: 0.23056/0.34200, loss_mask_dice_5: 1.30999/1.19862, loss_spatial_bce_5: 0.07151/0.09642, loss_spatial_dice_5: 0.23220/0.23174, loss_spatial_ce_5: 0.01219/0.10774, loss_grounding_bce_5: 0.01446/0.08782, loss_grounding_dice_5: 0.26670/0.18313, loss_grounding_ce_5: 0.31680/0.29441, loss_mask_ce_6: 0.74177/0.97486, loss_mask_bce_6: 0.24047/0.34468, loss_mask_dice_6: 1.58015/1.20155, loss_spatial_bce_6: 0.07475/0.10219, loss_spatial_dice_6: 0.22217/0.23455, loss_spatial_ce_6: 0.05213/0.13414, loss_grounding_bce_6: 0.01280/0.08854, loss_grounding_dice_6: 0.21652/0.18349, loss_grounding_ce_6: 0.34835/0.31006, loss_mask_ce_7: 0.58499/1.01964, loss_mask_bce_7: 0.28387/0.35254, loss_mask_dice_7: 1.98597/1.25623, loss_spatial_bce_7: 0.07087/0.11041, loss_spatial_dice_7: 0.25972/0.26235, loss_spatial_ce_7: 0.09311/0.17021, loss_grounding_bce_7: 0.01376/0.09045, loss_grounding_dice_7: 0.32996/0.19069, loss_grounding_ce_7: 0.36732/0.34138, loss_mask_ce_8: 0.50433/1.12864, loss_mask_bce_8: 0.31911/0.36619, loss_mask_dice_8: 1.75159/1.32978, loss_spatial_bce_8: 0.09048/0.13121, loss_spatial_dice_8: 0.24974/0.30069, loss_spatial_ce_8: 0.09890/0.22704, loss_grounding_bce_8: 0.01362/0.09422, loss_grounding_dice_8: 0.26670/0.20179, loss_grounding_ce_8: 0.43662/0.40884, loss_mask_ce_9: 4.06418/3.67856, loss_mask_bce_9: 0.28060/0.39317, loss_mask_dice_9: 2.89899/1.90267, loss_spatial_bce_9: 0.41267/0.33353, loss_spatial_dice_9: 0.87787/0.82227, loss_spatial_ce_9: 1.88191/1.49916, loss_grounding_bce_9: 0.01525/0.10566, loss_grounding_dice_9: 0.48813/0.28084, loss_grounding_ce_9: 0.40421/0.67367] items per batch[64] items per second[0.23] total items[3545600] mini batches[ 55400] memory[7345] epoch remaining[0:57:01]
INFO:trainer.default_trainer:epochs[    30] optim steps[55500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98203/0.90121, loss_mask_bce_0: 0.35975/0.33512, loss_mask_dice_0: 0.72574/1.16334, loss_spatial_bce_0: 0.06457/0.08785, loss_spatial_dice_0: 0.12910/0.20958, loss_spatial_ce_0: 0.00833/0.06402, loss_grounding_bce_0: 0.04733/0.08637, loss_grounding_dice_0: 0.08910/0.17868, loss_grounding_ce_0: 0.06970/0.27265, loss_mask_ce_1: 0.94258/0.90176, loss_mask_bce_1: 0.39809/0.33606, loss_mask_dice_1: 0.72994/1.16964, loss_spatial_bce_1: 0.06406/0.08839, loss_spatial_dice_1: 0.12710/0.21358, loss_spatial_ce_1: 0.00467/0.06985, loss_grounding_bce_1: 0.04610/0.08655, loss_grounding_dice_1: 0.08536/0.17948, loss_grounding_ce_1: 0.07450/0.27361, loss_mask_ce_2: 0.95546/0.90894, loss_mask_bce_2: 0.48531/0.33659, loss_mask_dice_2: 0.73655/1.16992, loss_spatial_bce_2: 0.06653/0.08929, loss_spatial_dice_2: 0.13679/0.21504, loss_spatial_ce_2: 0.01295/0.07323, loss_grounding_bce_2: 0.04472/0.08667, loss_grounding_dice_2: 0.09293/0.17929, loss_grounding_ce_2: 0.08724/0.27711, loss_mask_ce_3: 1.05727/0.91868, loss_mask_bce_3: 0.36695/0.33767, loss_mask_dice_3: 0.69218/1.16746, loss_spatial_bce_3: 0.06530/0.09032, loss_spatial_dice_3: 0.13337/0.21576, loss_spatial_ce_3: 0.01064/0.07732, loss_grounding_bce_3: 0.04712/0.08691, loss_grounding_dice_3: 0.08721/0.17900, loss_grounding_ce_3: 0.08621/0.27907, loss_mask_ce_4: 1.08286/0.91950, loss_mask_bce_4: 0.54708/0.33975, loss_mask_dice_4: 0.72393/1.19132, loss_spatial_bce_4: 0.07653/0.09434, loss_spatial_dice_4: 0.13389/0.22775, loss_spatial_ce_4: 0.01773/0.09297, loss_grounding_bce_4: 0.04235/0.08742, loss_grounding_dice_4: 0.08753/0.18196, loss_grounding_ce_4: 0.09130/0.28180, loss_mask_ce_5: 1.18727/0.93521, loss_mask_bce_5: 0.52986/0.34197, loss_mask_dice_5: 0.71310/1.19849, loss_spatial_bce_5: 0.09206/0.09642, loss_spatial_dice_5: 0.15030/0.23174, loss_spatial_ce_5: 0.05737/0.10774, loss_grounding_bce_5: 0.04379/0.08781, loss_grounding_dice_5: 0.09504/0.18313, loss_grounding_ce_5: 0.12198/0.29439, loss_mask_ce_6: 1.15644/0.97501, loss_mask_bce_6: 0.46037/0.34465, loss_mask_dice_6: 0.71530/1.20142, loss_spatial_bce_6: 0.11210/0.10220, loss_spatial_dice_6: 0.16822/0.23455, loss_spatial_ce_6: 0.07801/0.13412, loss_grounding_bce_6: 0.04700/0.08853, loss_grounding_dice_6: 0.09616/0.18348, loss_grounding_ce_6: 0.08178/0.31005, loss_mask_ce_7: 0.88267/1.01978, loss_mask_bce_7: 0.46702/0.35251, loss_mask_dice_7: 0.60007/1.25608, loss_spatial_bce_7: 0.12916/0.11041, loss_spatial_dice_7: 0.17949/0.26236, loss_spatial_ce_7: 0.08037/0.17016, loss_grounding_bce_7: 0.04797/0.09043, loss_grounding_dice_7: 0.10103/0.19068, loss_grounding_ce_7: 0.06944/0.34137, loss_mask_ce_8: 1.38098/1.12875, loss_mask_bce_8: 0.44344/0.36617, loss_mask_dice_8: 0.76261/1.32966, loss_spatial_bce_8: 0.10990/0.13121, loss_spatial_dice_8: 0.18715/0.30069, loss_spatial_ce_8: 0.16830/0.22699, loss_grounding_bce_8: 0.04753/0.09420, loss_grounding_dice_8: 0.10192/0.20178, loss_grounding_ce_8: 0.08853/0.40890, loss_mask_ce_9: 3.72599/3.67864, loss_mask_bce_9: 0.41805/0.39315, loss_mask_dice_9: 1.26339/1.90247, loss_spatial_bce_9: 0.29262/0.33353, loss_spatial_dice_9: 0.85317/0.82227, loss_spatial_ce_9: 1.33881/1.49904, loss_grounding_bce_9: 0.08782/0.10566, loss_grounding_dice_9: 0.30489/0.28083, loss_grounding_ce_9: 0.20173/0.67382] items per batch[64] items per second[0.23] total items[3552000] mini batches[ 55500] memory[7345] epoch remaining[0:52:25]
INFO:trainer.default_trainer:epochs[    30] optim steps[55600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52381/0.90127, loss_mask_bce_0: 0.14040/0.33508, loss_mask_dice_0: 0.08204/1.16346, loss_spatial_bce_0: 0.12994/0.08784, loss_spatial_dice_0: 0.11832/0.20957, loss_spatial_ce_0: 0.00113/0.06399, loss_grounding_bce_0: 0.14233/0.08635, loss_grounding_dice_0: 0.07653/0.17867, loss_grounding_ce_0: 0.00820/0.27264, loss_mask_ce_1: 0.51599/0.90186, loss_mask_bce_1: 0.14180/0.33602, loss_mask_dice_1: 0.08183/1.16973, loss_spatial_bce_1: 0.13058/0.08837, loss_spatial_dice_1: 0.11058/0.21356, loss_spatial_ce_1: 0.00110/0.06980, loss_grounding_bce_1: 0.14091/0.08653, loss_grounding_dice_1: 0.07583/0.17946, loss_grounding_ce_1: 0.01122/0.27360, loss_mask_ce_2: 0.55195/0.90899, loss_mask_bce_2: 0.13459/0.33656, loss_mask_dice_2: 0.08109/1.17005, loss_spatial_bce_2: 0.15172/0.08927, loss_spatial_dice_2: 0.13365/0.21502, loss_spatial_ce_2: 0.00258/0.07318, loss_grounding_bce_2: 0.14828/0.08665, loss_grounding_dice_2: 0.07944/0.17929, loss_grounding_ce_2: 0.01530/0.27711, loss_mask_ce_3: 0.53007/0.91875, loss_mask_bce_3: 0.13739/0.33763, loss_mask_dice_3: 0.08718/1.16759, loss_spatial_bce_3: 0.16785/0.09032, loss_spatial_dice_3: 0.14679/0.21575, loss_spatial_ce_3: 0.00381/0.07728, loss_grounding_bce_3: 0.14209/0.08689, loss_grounding_dice_3: 0.08021/0.17901, loss_grounding_ce_3: 0.00738/0.27904, loss_mask_ce_4: 0.55839/0.91954, loss_mask_bce_4: 0.14788/0.33971, loss_mask_dice_4: 0.08537/1.19146, loss_spatial_bce_4: 0.18104/0.09434, loss_spatial_dice_4: 0.13810/0.22774, loss_spatial_ce_4: 0.00270/0.09294, loss_grounding_bce_4: 0.14612/0.08740, loss_grounding_dice_4: 0.08033/0.18196, loss_grounding_ce_4: 0.01146/0.28178, loss_mask_ce_5: 0.52447/0.93528, loss_mask_bce_5: 0.14316/0.34194, loss_mask_dice_5: 0.08119/1.19864, loss_spatial_bce_5: 0.14118/0.09641, loss_spatial_dice_5: 0.09800/0.23173, loss_spatial_ce_5: 0.01246/0.10773, loss_grounding_bce_5: 0.15137/0.08780, loss_grounding_dice_5: 0.07821/0.18312, loss_grounding_ce_5: 0.01195/0.29434, loss_mask_ce_6: 0.53319/0.97506, loss_mask_bce_6: 0.13907/0.34462, loss_mask_dice_6: 0.08352/1.20154, loss_spatial_bce_6: 0.14987/0.10219, loss_spatial_dice_6: 0.10854/0.23455, loss_spatial_ce_6: 0.03793/0.13409, loss_grounding_bce_6: 0.14611/0.08851, loss_grounding_dice_6: 0.07811/0.18348, loss_grounding_ce_6: 0.01860/0.31002, loss_mask_ce_7: 0.57879/1.01985, loss_mask_bce_7: 0.13524/0.35248, loss_mask_dice_7: 0.08800/1.25621, loss_spatial_bce_7: 0.13591/0.11039, loss_spatial_dice_7: 0.09522/0.26235, loss_spatial_ce_7: 0.15605/0.17012, loss_grounding_bce_7: 0.14884/0.09041, loss_grounding_dice_7: 0.08142/0.19066, loss_grounding_ce_7: 0.01641/0.34130, loss_mask_ce_8: 0.46761/1.12884, loss_mask_bce_8: 0.13173/0.36611, loss_mask_dice_8: 0.08150/1.32980, loss_spatial_bce_8: 0.24139/0.13119, loss_spatial_dice_8: 0.15341/0.30068, loss_spatial_ce_8: 0.43352/0.22696, loss_grounding_bce_8: 0.14452/0.09418, loss_grounding_dice_8: 0.07427/0.20177, loss_grounding_ce_8: 0.02563/0.40882, loss_mask_ce_9: 2.06391/3.67875, loss_mask_bce_9: 0.16394/0.39309, loss_mask_dice_9: 0.14649/1.90262, loss_spatial_bce_9: 0.45175/0.33354, loss_spatial_dice_9: 0.54969/0.82227, loss_spatial_ce_9: 0.44422/1.49914, loss_grounding_bce_9: 0.17267/0.10562, loss_grounding_dice_9: 0.10096/0.28081, loss_grounding_ce_9: 0.10469/0.67387] items per batch[64] items per second[0.24] total items[3558400] mini batches[ 55600] memory[7345] epoch remaining[0:47:39]
INFO:trainer.default_trainer:epochs[    30] optim steps[55700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85747/0.90129, loss_mask_bce_0: 0.45725/0.33508, loss_mask_dice_0: 0.53297/1.16341, loss_spatial_bce_0: 0.20422/0.08785, loss_spatial_dice_0: 0.17276/0.20955, loss_spatial_ce_0: 0.02040/0.06398, loss_grounding_bce_0: 0.33225/0.08636, loss_grounding_dice_0: 0.19155/0.17866, loss_grounding_ce_0: 1.55856/0.27262, loss_mask_ce_1: 0.76987/0.90185, loss_mask_bce_1: 0.45759/0.33602, loss_mask_dice_1: 0.37802/1.16970, loss_spatial_bce_1: 0.20066/0.08838, loss_spatial_dice_1: 0.18093/0.21354, loss_spatial_ce_1: 0.03017/0.06978, loss_grounding_bce_1: 0.33962/0.08654, loss_grounding_dice_1: 0.19021/0.17944, loss_grounding_ce_1: 1.63229/0.27358, loss_mask_ce_2: 0.91765/0.90897, loss_mask_bce_2: 0.44513/0.33655, loss_mask_dice_2: 0.42363/1.16999, loss_spatial_bce_2: 0.19400/0.08929, loss_spatial_dice_2: 0.16907/0.21501, loss_spatial_ce_2: 0.01231/0.07317, loss_grounding_bce_2: 0.41585/0.08666, loss_grounding_dice_2: 0.20097/0.17927, loss_grounding_ce_2: 1.25532/0.27710, loss_mask_ce_3: 0.99743/0.91878, loss_mask_bce_3: 0.43920/0.33763, loss_mask_dice_3: 0.41750/1.16756, loss_spatial_bce_3: 0.18067/0.09033, loss_spatial_dice_3: 0.19889/0.21573, loss_spatial_ce_3: 0.01232/0.07729, loss_grounding_bce_3: 0.39931/0.08690, loss_grounding_dice_3: 0.22449/0.17899, loss_grounding_ce_3: 1.66972/0.27908, loss_mask_ce_4: 1.20529/0.91957, loss_mask_bce_4: 0.39641/0.33971, loss_mask_dice_4: 0.35147/1.19140, loss_spatial_bce_4: 0.17069/0.09435, loss_spatial_dice_4: 0.21826/0.22773, loss_spatial_ce_4: 0.01128/0.09293, loss_grounding_bce_4: 0.43320/0.08741, loss_grounding_dice_4: 0.22163/0.18195, loss_grounding_ce_4: 1.44312/0.28178, loss_mask_ce_5: 0.83368/0.93534, loss_mask_bce_5: 0.47140/0.34194, loss_mask_dice_5: 0.55675/1.19861, loss_spatial_bce_5: 0.17622/0.09643, loss_spatial_dice_5: 0.20632/0.23172, loss_spatial_ce_5: 0.02892/0.10775, loss_grounding_bce_5: 0.46332/0.08781, loss_grounding_dice_5: 0.23048/0.18311, loss_grounding_ce_5: 1.72462/0.29433, loss_mask_ce_6: 0.98784/0.97508, loss_mask_bce_6: 0.49784/0.34462, loss_mask_dice_6: 0.48731/1.20149, loss_spatial_bce_6: 0.19370/0.10220, loss_spatial_dice_6: 0.17249/0.23454, loss_spatial_ce_6: 0.10667/0.13406, loss_grounding_bce_6: 0.50452/0.08852, loss_grounding_dice_6: 0.21851/0.18346, loss_grounding_ce_6: 2.07752/0.31003, loss_mask_ce_7: 0.77817/1.01991, loss_mask_bce_7: 0.52430/0.35248, loss_mask_dice_7: 0.61204/1.25618, loss_spatial_bce_7: 0.20030/0.11039, loss_spatial_dice_7: 0.25702/0.26234, loss_spatial_ce_7: 0.12854/0.17010, loss_grounding_bce_7: 0.45669/0.09042, loss_grounding_dice_7: 0.27290/0.19065, loss_grounding_ce_7: 1.41946/0.34125, loss_mask_ce_8: 1.15292/1.12889, loss_mask_bce_8: 0.42344/0.36611, loss_mask_dice_8: 0.56769/1.32976, loss_spatial_bce_8: 0.27799/0.13119, loss_spatial_dice_8: 0.28028/0.30066, loss_spatial_ce_8: 0.15069/0.22697, loss_grounding_bce_8: 0.45886/0.09419, loss_grounding_dice_8: 0.28446/0.20175, loss_grounding_ce_8: 1.79923/0.40885, loss_mask_ce_9: 3.47029/3.67874, loss_mask_bce_9: 0.43976/0.39309, loss_mask_dice_9: 0.69226/1.90250, loss_spatial_bce_9: 0.41969/0.33353, loss_spatial_dice_9: 0.74250/0.82227, loss_spatial_ce_9: 1.33213/1.49913, loss_grounding_bce_9: 0.44701/0.10563, loss_grounding_dice_9: 0.28185/0.28078, loss_grounding_ce_9: 1.74654/0.67402] items per batch[64] items per second[0.23] total items[3564800] mini batches[ 55700] memory[7345] epoch remaining[0:43:04]
INFO:trainer.default_trainer:epochs[    30] optim steps[55800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69473/0.90132, loss_mask_bce_0: 0.27617/0.33510, loss_mask_dice_0: 2.36268/1.16316, loss_spatial_bce_0: 0.04736/0.08785, loss_spatial_dice_0: 0.17377/0.20951, loss_spatial_ce_0: 0.03863/0.06395, loss_grounding_bce_0: 0.18952/0.08637, loss_grounding_dice_0: 0.15115/0.17864, loss_grounding_ce_0: 0.30810/0.27262, loss_mask_ce_1: 0.71513/0.90186, loss_mask_bce_1: 0.28940/0.33604, loss_mask_dice_1: 2.07056/1.16947, loss_spatial_bce_1: 0.04586/0.08838, loss_spatial_dice_1: 0.20288/0.21351, loss_spatial_ce_1: 0.02760/0.06976, loss_grounding_bce_1: 0.20042/0.08654, loss_grounding_dice_1: 0.15315/0.17942, loss_grounding_ce_1: 0.28741/0.27359, loss_mask_ce_2: 0.61249/0.90898, loss_mask_bce_2: 0.28053/0.33658, loss_mask_dice_2: 2.47815/1.16974, loss_spatial_bce_2: 0.04710/0.08929, loss_spatial_dice_2: 0.13385/0.21498, loss_spatial_ce_2: 0.03703/0.07314, loss_grounding_bce_2: 0.19508/0.08667, loss_grounding_dice_2: 0.14638/0.17925, loss_grounding_ce_2: 0.29112/0.27711, loss_mask_ce_3: 0.71843/0.91884, loss_mask_bce_3: 0.28496/0.33765, loss_mask_dice_3: 2.33668/1.16731, loss_spatial_bce_3: 0.04647/0.09033, loss_spatial_dice_3: 0.13803/0.21570, loss_spatial_ce_3: 0.04504/0.07729, loss_grounding_bce_3: 0.19259/0.08690, loss_grounding_dice_3: 0.14860/0.17897, loss_grounding_ce_3: 0.30049/0.27910, loss_mask_ce_4: 0.51656/0.91960, loss_mask_bce_4: 0.29324/0.33974, loss_mask_dice_4: 2.31984/1.19116, loss_spatial_bce_4: 0.04931/0.09435, loss_spatial_dice_4: 0.20251/0.22770, loss_spatial_ce_4: 0.37572/0.09292, loss_grounding_bce_4: 0.19888/0.08742, loss_grounding_dice_4: 0.15093/0.18193, loss_grounding_ce_4: 0.29842/0.28174, loss_mask_ce_5: 0.64822/0.93538, loss_mask_bce_5: 0.30342/0.34197, loss_mask_dice_5: 2.38995/1.19838, loss_spatial_bce_5: 0.05381/0.09644, loss_spatial_dice_5: 0.19229/0.23169, loss_spatial_ce_5: 0.09616/0.10774, loss_grounding_bce_5: 0.20103/0.08781, loss_grounding_dice_5: 0.15507/0.18310, loss_grounding_ce_5: 0.30104/0.29435, loss_mask_ce_6: 0.80651/0.97513, loss_mask_bce_6: 0.28387/0.34464, loss_mask_dice_6: 2.09553/1.20124, loss_spatial_bce_6: 0.06388/0.10221, loss_spatial_dice_6: 0.21543/0.23452, loss_spatial_ce_6: 0.08247/0.13406, loss_grounding_bce_6: 0.18799/0.08852, loss_grounding_dice_6: 0.14105/0.18344, loss_grounding_ce_6: 0.29307/0.31004, loss_mask_ce_7: 0.78418/1.01997, loss_mask_bce_7: 0.31408/0.35252, loss_mask_dice_7: 2.45331/1.25594, loss_spatial_bce_7: 0.05245/0.11039, loss_spatial_dice_7: 0.26583/0.26230, loss_spatial_ce_7: 0.11342/0.17009, loss_grounding_bce_7: 0.20423/0.09043, loss_grounding_dice_7: 0.13913/0.19064, loss_grounding_ce_7: 0.37771/0.34118, loss_mask_ce_8: 0.75068/1.12892, loss_mask_bce_8: 0.31450/0.36615, loss_mask_dice_8: 2.21276/1.32949, loss_spatial_bce_8: 0.05757/0.13119, loss_spatial_dice_8: 0.31416/0.30063, loss_spatial_ce_8: 0.20609/0.22696, loss_grounding_bce_8: 0.19259/0.09420, loss_grounding_dice_8: 0.14350/0.20172, loss_grounding_ce_8: 0.33726/0.40883, loss_mask_ce_9: 7.01751/3.67878, loss_mask_bce_9: 0.31394/0.39314, loss_mask_dice_9: 3.64915/1.90226, loss_spatial_bce_9: 0.34760/0.33356, loss_spatial_dice_9: 0.82979/0.82225, loss_spatial_ce_9: 2.14456/1.49904, loss_grounding_bce_9: 0.20893/0.10564, loss_grounding_dice_9: 0.13704/0.28077, loss_grounding_ce_9: 0.88061/0.67396] items per batch[64] items per second[0.23] total items[3571200] mini batches[ 55800] memory[7345] epoch remaining[0:38:32]
INFO:trainer.default_trainer:epochs[    30] optim steps[55900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38050/0.90141, loss_mask_bce_0: 0.30227/0.33506, loss_mask_dice_0: 0.47003/1.16313, loss_spatial_bce_0: 0.09686/0.08785, loss_spatial_dice_0: 0.21165/0.20951, loss_spatial_ce_0: 0.01085/0.06394, loss_grounding_bce_0: 0.16212/0.08638, loss_grounding_dice_0: 0.16637/0.17866, loss_grounding_ce_0: 0.11263/0.27266, loss_mask_ce_1: 1.15735/0.90196, loss_mask_bce_1: 0.29204/0.33599, loss_mask_dice_1: 0.48320/1.16941, loss_spatial_bce_1: 0.09344/0.08838, loss_spatial_dice_1: 0.18255/0.21350, loss_spatial_ce_1: 0.03798/0.06973, loss_grounding_bce_1: 0.15303/0.08655, loss_grounding_dice_1: 0.15028/0.17945, loss_grounding_ce_1: 0.13206/0.27362, loss_mask_ce_2: 1.46150/0.90906, loss_mask_bce_2: 0.28243/0.33653, loss_mask_dice_2: 0.50888/1.16969, loss_spatial_bce_2: 0.09060/0.08929, loss_spatial_dice_2: 0.19531/0.21497, loss_spatial_ce_2: 0.06800/0.07313, loss_grounding_bce_2: 0.15833/0.08668, loss_grounding_dice_2: 0.15532/0.17930, loss_grounding_ce_2: 0.15469/0.27713, loss_mask_ce_3: 1.46088/0.91891, loss_mask_bce_3: 0.28979/0.33760, loss_mask_dice_3: 0.46441/1.16728, loss_spatial_bce_3: 0.09669/0.09034, loss_spatial_dice_3: 0.19429/0.21570, loss_spatial_ce_3: 0.03110/0.07728, loss_grounding_bce_3: 0.16742/0.08692, loss_grounding_dice_3: 0.16015/0.17900, loss_grounding_ce_3: 0.11350/0.27910, loss_mask_ce_4: 1.33892/0.91969, loss_mask_bce_4: 0.27623/0.33969, loss_mask_dice_4: 0.47048/1.19114, loss_spatial_bce_4: 0.09603/0.09436, loss_spatial_dice_4: 0.20724/0.22770, loss_spatial_ce_4: 0.07994/0.09290, loss_grounding_bce_4: 0.15534/0.08743, loss_grounding_dice_4: 0.17361/0.18195, loss_grounding_ce_4: 0.19450/0.28177, loss_mask_ce_5: 1.19996/0.93552, loss_mask_bce_5: 0.28813/0.34193, loss_mask_dice_5: 0.52676/1.19835, loss_spatial_bce_5: 0.11327/0.09644, loss_spatial_dice_5: 0.25734/0.23170, loss_spatial_ce_5: 0.04514/0.10772, loss_grounding_bce_5: 0.15634/0.08782, loss_grounding_dice_5: 0.16990/0.18314, loss_grounding_ce_5: 0.12289/0.29439, loss_mask_ce_6: 1.13690/0.97524, loss_mask_bce_6: 0.31639/0.34461, loss_mask_dice_6: 0.52891/1.20123, loss_spatial_bce_6: 0.12096/0.10221, loss_spatial_dice_6: 0.24077/0.23451, loss_spatial_ce_6: 0.09051/0.13404, loss_grounding_bce_6: 0.16414/0.08853, loss_grounding_dice_6: 0.17656/0.18347, loss_grounding_ce_6: 0.06928/0.31007, loss_mask_ce_7: 1.17448/1.02009, loss_mask_bce_7: 0.34729/0.35247, loss_mask_dice_7: 0.52363/1.25588, loss_spatial_bce_7: 0.12081/0.11039, loss_spatial_dice_7: 0.24912/0.26230, loss_spatial_ce_7: 0.10778/0.17007, loss_grounding_bce_7: 0.17272/0.09044, loss_grounding_dice_7: 0.18386/0.19067, loss_grounding_ce_7: 0.12902/0.34120, loss_mask_ce_8: 1.42614/1.12899, loss_mask_bce_8: 0.31885/0.36610, loss_mask_dice_8: 0.61612/1.32945, loss_spatial_bce_8: 0.11804/0.13119, loss_spatial_dice_8: 0.23867/0.30063, loss_spatial_ce_8: 0.13082/0.22692, loss_grounding_bce_8: 0.19438/0.09421, loss_grounding_dice_8: 0.20770/0.20175, loss_grounding_ce_8: 0.21948/0.40881, loss_mask_ce_9: 3.01605/3.67870, loss_mask_bce_9: 0.30540/0.39311, loss_mask_dice_9: 0.70200/1.90210, loss_spatial_bce_9: 0.34069/0.33355, loss_spatial_dice_9: 0.87401/0.82221, loss_spatial_ce_9: 1.70274/1.49911, loss_grounding_bce_9: 0.17241/0.10564, loss_grounding_dice_9: 0.29378/0.28080, loss_grounding_ce_9: 0.26152/0.67383] items per batch[64] items per second[0.23] total items[3577600] mini batches[ 55900] memory[7345] epoch remaining[0:33:54]
INFO:trainer.default_trainer:epochs[    30] optim steps[56000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35188/0.90141, loss_mask_bce_0: 0.11702/0.33504, loss_mask_dice_0: 0.61384/1.16324, loss_spatial_bce_0: 0.12580/0.08785, loss_spatial_dice_0: 0.23599/0.20951, loss_spatial_ce_0: 0.02733/0.06392, loss_grounding_bce_0: 0.02987/0.08636, loss_grounding_dice_0: 0.13099/0.17867, loss_grounding_ce_0: 0.00438/0.27265, loss_mask_ce_1: 0.26278/0.90195, loss_mask_bce_1: 0.12421/0.33598, loss_mask_dice_1: 0.70860/1.16951, loss_spatial_bce_1: 0.18163/0.08839, loss_spatial_dice_1: 0.25118/0.21351, loss_spatial_ce_1: 0.29421/0.06973, loss_grounding_bce_1: 0.03310/0.08654, loss_grounding_dice_1: 0.16319/0.17946, loss_grounding_ce_1: 0.00339/0.27360, loss_mask_ce_2: 0.29727/0.90904, loss_mask_bce_2: 0.12365/0.33652, loss_mask_dice_2: 0.67227/1.16979, loss_spatial_bce_2: 0.23512/0.08929, loss_spatial_dice_2: 0.25582/0.21498, loss_spatial_ce_2: 0.30110/0.07311, loss_grounding_bce_2: 0.03326/0.08667, loss_grounding_dice_2: 0.16337/0.17930, loss_grounding_ce_2: 0.00840/0.27713, loss_mask_ce_3: 0.36000/0.91895, loss_mask_bce_3: 0.13889/0.33758, loss_mask_dice_3: 0.63115/1.16740, loss_spatial_bce_3: 0.29560/0.09034, loss_spatial_dice_3: 0.23680/0.21570, loss_spatial_ce_3: 0.30734/0.07728, loss_grounding_bce_3: 0.03283/0.08690, loss_grounding_dice_3: 0.14122/0.17901, loss_grounding_ce_3: 0.01169/0.27909, loss_mask_ce_4: 0.39376/0.91970, loss_mask_bce_4: 0.11491/0.33968, loss_mask_dice_4: 0.67985/1.19126, loss_spatial_bce_4: 0.22563/0.09435, loss_spatial_dice_4: 0.25480/0.22771, loss_spatial_ce_4: 0.16176/0.09292, loss_grounding_bce_4: 0.03721/0.08741, loss_grounding_dice_4: 0.17784/0.18196, loss_grounding_ce_4: 0.04732/0.28178, loss_mask_ce_5: 0.43088/0.93555, loss_mask_bce_5: 0.11497/0.34192, loss_mask_dice_5: 0.66968/1.19850, loss_spatial_bce_5: 0.15030/0.09644, loss_spatial_dice_5: 0.28120/0.23171, loss_spatial_ce_5: 0.16371/0.10772, loss_grounding_bce_5: 0.03364/0.08781, loss_grounding_dice_5: 0.15519/0.18314, loss_grounding_ce_5: 0.05286/0.29442, loss_mask_ce_6: 0.42651/0.97525, loss_mask_bce_6: 0.11431/0.34459, loss_mask_dice_6: 0.69716/1.20135, loss_spatial_bce_6: 0.19686/0.10221, loss_spatial_dice_6: 0.27195/0.23453, loss_spatial_ce_6: 0.10505/0.13400, loss_grounding_bce_6: 0.03879/0.08851, loss_grounding_dice_6: 0.18680/0.18347, loss_grounding_ce_6: 0.06235/0.31005, loss_mask_ce_7: 0.37898/1.02009, loss_mask_bce_7: 0.11560/0.35247, loss_mask_dice_7: 0.64788/1.25601, loss_spatial_bce_7: 0.14994/0.11038, loss_spatial_dice_7: 0.28277/0.26232, loss_spatial_ce_7: 0.19131/0.17006, loss_grounding_bce_7: 0.03820/0.09042, loss_grounding_dice_7: 0.18788/0.19067, loss_grounding_ce_7: 0.06138/0.34122, loss_mask_ce_8: 0.65915/1.12894, loss_mask_bce_8: 0.11359/0.36610, loss_mask_dice_8: 0.62429/1.32956, loss_spatial_bce_8: 0.08288/0.13117, loss_spatial_dice_8: 0.31553/0.30065, loss_spatial_ce_8: 0.19614/0.22689, loss_grounding_bce_8: 0.03713/0.09420, loss_grounding_dice_8: 0.17237/0.20175, loss_grounding_ce_8: 0.55702/0.40887, loss_mask_ce_9: 2.50087/3.67876, loss_mask_bce_9: 0.11659/0.39310, loss_mask_dice_9: 1.00672/1.90219, loss_spatial_bce_9: 0.29332/0.33351, loss_spatial_dice_9: 0.85263/0.82222, loss_spatial_ce_9: 1.29432/1.49893, loss_grounding_bce_9: 0.04228/0.10563, loss_grounding_dice_9: 0.19861/0.28082, loss_grounding_ce_9: 2.10941/0.67401] items per batch[64] items per second[0.23] total items[3584000] mini batches[ 56000] memory[7345] epoch remaining[0:29:22]
INFO:trainer.default_trainer:epochs[    30] optim steps[56100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24057/0.90122, loss_mask_bce_0: 0.38643/0.33502, loss_mask_dice_0: 2.58700/1.16313, loss_spatial_bce_0: 0.06842/0.08784, loss_spatial_dice_0: 0.21451/0.20949, loss_spatial_ce_0: 0.02307/0.06389, loss_grounding_bce_0: 0.01667/0.08636, loss_grounding_dice_0: 0.27280/0.17865, loss_grounding_ce_0: 0.35589/0.27256, loss_mask_ce_1: 0.90340/0.90175, loss_mask_bce_1: 0.38746/0.33596, loss_mask_dice_1: 2.66796/1.16941, loss_spatial_bce_1: 0.07145/0.08837, loss_spatial_dice_1: 0.22309/0.21348, loss_spatial_ce_1: 0.01825/0.06970, loss_grounding_bce_1: 0.02677/0.08654, loss_grounding_dice_1: 0.29893/0.17943, loss_grounding_ce_1: 0.33940/0.27350, loss_mask_ce_2: 1.08527/0.90887, loss_mask_bce_2: 0.36791/0.33651, loss_mask_dice_2: 2.32855/1.16971, loss_spatial_bce_2: 0.06992/0.08928, loss_spatial_dice_2: 0.22480/0.21495, loss_spatial_ce_2: 0.01248/0.07309, loss_grounding_bce_2: 0.02938/0.08667, loss_grounding_dice_2: 0.32463/0.17928, loss_grounding_ce_2: 0.35860/0.27702, loss_mask_ce_3: 1.26861/0.91877, loss_mask_bce_3: 0.38146/0.33757, loss_mask_dice_3: 2.35315/1.16732, loss_spatial_bce_3: 0.06504/0.09033, loss_spatial_dice_3: 0.22258/0.21567, loss_spatial_ce_3: 0.00651/0.07726, loss_grounding_bce_3: 0.03374/0.08690, loss_grounding_dice_3: 0.28548/0.17899, loss_grounding_ce_3: 0.37177/0.27899, loss_mask_ce_4: 1.10046/0.91953, loss_mask_bce_4: 0.42670/0.33967, loss_mask_dice_4: 2.64907/1.19118, loss_spatial_bce_4: 0.07697/0.09435, loss_spatial_dice_4: 0.24776/0.22770, loss_spatial_ce_4: 0.02912/0.09291, loss_grounding_bce_4: 0.03323/0.08741, loss_grounding_dice_4: 0.29754/0.18194, loss_grounding_ce_4: 0.38030/0.28168, loss_mask_ce_5: 1.02391/0.93541, loss_mask_bce_5: 0.43337/0.34191, loss_mask_dice_5: 3.04221/1.19843, loss_spatial_bce_5: 0.07739/0.09643, loss_spatial_dice_5: 0.25854/0.23170, loss_spatial_ce_5: 0.05712/0.10772, loss_grounding_bce_5: 0.02872/0.08781, loss_grounding_dice_5: 0.34375/0.18312, loss_grounding_ce_5: 0.35232/0.29432, loss_mask_ce_6: 1.08452/0.97514, loss_mask_bce_6: 0.42692/0.34459, loss_mask_dice_6: 2.58985/1.20129, loss_spatial_bce_6: 0.08121/0.10220, loss_spatial_dice_6: 0.26411/0.23453, loss_spatial_ce_6: 0.03438/0.13400, loss_grounding_bce_6: 0.02797/0.08852, loss_grounding_dice_6: 0.32476/0.18345, loss_grounding_ce_6: 0.36954/0.30992, loss_mask_ce_7: 1.24826/1.02002, loss_mask_bce_7: 0.42718/0.35246, loss_mask_dice_7: 2.79481/1.25593, loss_spatial_bce_7: 0.07217/0.11037, loss_spatial_dice_7: 0.27659/0.26230, loss_spatial_ce_7: 0.07136/0.17001, loss_grounding_bce_7: 0.03029/0.09042, loss_grounding_dice_7: 0.36264/0.19065, loss_grounding_ce_7: 0.36899/0.34113, loss_mask_ce_8: 1.09311/1.12877, loss_mask_bce_8: 0.42714/0.36612, loss_mask_dice_8: 3.12393/1.32948, loss_spatial_bce_8: 0.08797/0.13116, loss_spatial_dice_8: 0.34106/0.30063, loss_spatial_ce_8: 0.15572/0.22685, loss_grounding_bce_8: 0.02761/0.09419, loss_grounding_dice_8: 0.36718/0.20173, loss_grounding_ce_8: 0.43209/0.40872, loss_mask_ce_9: 3.32603/3.67862, loss_mask_bce_9: 0.47296/0.39306, loss_mask_dice_9: 4.09594/1.90202, loss_spatial_bce_9: 0.22808/0.33349, loss_spatial_dice_9: 0.84450/0.82222, loss_spatial_ce_9: 1.83530/1.49896, loss_grounding_bce_9: 0.03148/0.10562, loss_grounding_dice_9: 0.57971/0.28079, loss_grounding_ce_9: 0.50546/0.67386] items per batch[64] items per second[0.23] total items[3590400] mini batches[ 56100] memory[7345] epoch remaining[0:24:45]
INFO:trainer.default_trainer:epochs[    30] optim steps[56200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82918/0.90111, loss_mask_bce_0: 0.24721/0.33500, loss_mask_dice_0: 0.51636/1.16311, loss_spatial_bce_0: 0.09130/0.08783, loss_spatial_dice_0: 0.11391/0.20947, loss_spatial_ce_0: 0.01045/0.06387, loss_grounding_bce_0: 0.03163/0.08634, loss_grounding_dice_0: 0.13356/0.17861, loss_grounding_ce_0: 0.38965/0.27253, loss_mask_ce_1: 0.80046/0.90166, loss_mask_bce_1: 0.23908/0.33594, loss_mask_dice_1: 0.66004/1.16938, loss_spatial_bce_1: 0.09418/0.08836, loss_spatial_dice_1: 0.11739/0.21346, loss_spatial_ce_1: 0.01039/0.06970, loss_grounding_bce_1: 0.03258/0.08652, loss_grounding_dice_1: 0.13245/0.17940, loss_grounding_ce_1: 0.38292/0.27344, loss_mask_ce_2: 0.87834/0.90878, loss_mask_bce_2: 0.23596/0.33649, loss_mask_dice_2: 0.58765/1.16970, loss_spatial_bce_2: 0.09613/0.08927, loss_spatial_dice_2: 0.11871/0.21494, loss_spatial_ce_2: 0.01771/0.07306, loss_grounding_bce_2: 0.03151/0.08665, loss_grounding_dice_2: 0.13094/0.17924, loss_grounding_ce_2: 0.38569/0.27697, loss_mask_ce_3: 0.90469/0.91867, loss_mask_bce_3: 0.25037/0.33755, loss_mask_dice_3: 0.61468/1.16730, loss_spatial_bce_3: 0.09539/0.09032, loss_spatial_dice_3: 0.11558/0.21566, loss_spatial_ce_3: 0.01760/0.07726, loss_grounding_bce_3: 0.03451/0.08689, loss_grounding_dice_3: 0.14699/0.17895, loss_grounding_ce_3: 0.39352/0.27892, loss_mask_ce_4: 0.94838/0.91942, loss_mask_bce_4: 0.24284/0.33965, loss_mask_dice_4: 0.65044/1.19117, loss_spatial_bce_4: 0.10742/0.09434, loss_spatial_dice_4: 0.11494/0.22768, loss_spatial_ce_4: 0.02076/0.09288, loss_grounding_bce_4: 0.03332/0.08739, loss_grounding_dice_4: 0.13339/0.18190, loss_grounding_ce_4: 0.43664/0.28162, loss_mask_ce_5: 0.93694/0.93534, loss_mask_bce_5: 0.25357/0.34190, loss_mask_dice_5: 0.72632/1.19841, loss_spatial_bce_5: 0.10243/0.09643, loss_spatial_dice_5: 0.11542/0.23168, loss_spatial_ce_5: 0.09517/0.10770, loss_grounding_bce_5: 0.03148/0.08779, loss_grounding_dice_5: 0.15323/0.18309, loss_grounding_ce_5: 0.38197/0.29426, loss_mask_ce_6: 1.02872/0.97509, loss_mask_bce_6: 0.25773/0.34457, loss_mask_dice_6: 0.73661/1.20127, loss_spatial_bce_6: 0.10870/0.10220, loss_spatial_dice_6: 0.12352/0.23452, loss_spatial_ce_6: 0.05509/0.13397, loss_grounding_bce_6: 0.03308/0.08850, loss_grounding_dice_6: 0.14378/0.18342, loss_grounding_ce_6: 0.41199/0.30986, loss_mask_ce_7: 1.04912/1.01994, loss_mask_bce_7: 0.25704/0.35245, loss_mask_dice_7: 0.69805/1.25592, loss_spatial_bce_7: 0.11799/0.11035, loss_spatial_dice_7: 0.12649/0.26228, loss_spatial_ce_7: 0.07539/0.16997, loss_grounding_bce_7: 0.03240/0.09041, loss_grounding_dice_7: 0.14179/0.19062, loss_grounding_ce_7: 0.42850/0.34105, loss_mask_ce_8: 1.06006/1.12868, loss_mask_bce_8: 0.29343/0.36611, loss_mask_dice_8: 0.70674/1.32951, loss_spatial_bce_8: 0.15201/0.13114, loss_spatial_dice_8: 0.18225/0.30061, loss_spatial_ce_8: 0.10645/0.22681, loss_grounding_bce_8: 0.04335/0.09419, loss_grounding_dice_8: 0.10870/0.20171, loss_grounding_ce_8: 0.42472/0.40870, loss_mask_ce_9: 3.40092/3.67864, loss_mask_bce_9: 0.43222/0.39306, loss_mask_dice_9: 1.12364/1.90195, loss_spatial_bce_9: 0.49168/0.33348, loss_spatial_dice_9: 0.82990/0.82221, loss_spatial_ce_9: 1.44869/1.49880, loss_grounding_bce_9: 0.05375/0.10562, loss_grounding_dice_9: 0.20024/0.28078, loss_grounding_ce_9: 0.57484/0.67383] items per batch[64] items per second[0.23] total items[3596800] mini batches[ 56200] memory[7345] epoch remaining[0:20:10]
INFO:trainer.default_trainer:epochs[    30] optim steps[56300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87172/0.90109, loss_mask_bce_0: 0.44421/0.33502, loss_mask_dice_0: 0.83704/1.16316, loss_spatial_bce_0: 0.10526/0.08782, loss_spatial_dice_0: 0.16262/0.20946, loss_spatial_ce_0: 0.04805/0.06386, loss_grounding_bce_0: 0.17895/0.08637, loss_grounding_dice_0: 0.14719/0.17867, loss_grounding_ce_0: 0.40632/0.27253, loss_mask_ce_1: 0.82966/0.90167, loss_mask_bce_1: 0.47842/0.33595, loss_mask_dice_1: 0.86359/1.16942, loss_spatial_bce_1: 0.10198/0.08835, loss_spatial_dice_1: 0.17487/0.21346, loss_spatial_ce_1: 0.06423/0.06968, loss_grounding_bce_1: 0.18236/0.08654, loss_grounding_dice_1: 0.14153/0.17945, loss_grounding_ce_1: 0.40702/0.27344, loss_mask_ce_2: 0.96355/0.90878, loss_mask_bce_2: 0.42600/0.33650, loss_mask_dice_2: 0.82401/1.16978, loss_spatial_bce_2: 0.10761/0.08927, loss_spatial_dice_2: 0.18281/0.21494, loss_spatial_ce_2: 0.05391/0.07304, loss_grounding_bce_2: 0.18012/0.08667, loss_grounding_dice_2: 0.14193/0.17930, loss_grounding_ce_2: 0.41654/0.27695, loss_mask_ce_3: 0.79124/0.91870, loss_mask_bce_3: 0.45247/0.33756, loss_mask_dice_3: 0.90817/1.16735, loss_spatial_bce_3: 0.11966/0.09031, loss_spatial_dice_3: 0.18660/0.21566, loss_spatial_ce_3: 0.03021/0.07725, loss_grounding_bce_3: 0.18160/0.08691, loss_grounding_dice_3: 0.14511/0.17901, loss_grounding_ce_3: 0.41161/0.27889, loss_mask_ce_4: 0.91874/0.91944, loss_mask_bce_4: 0.44569/0.33966, loss_mask_dice_4: 0.81402/1.19125, loss_spatial_bce_4: 0.11236/0.09433, loss_spatial_dice_4: 0.19520/0.22769, loss_spatial_ce_4: 0.05434/0.09287, loss_grounding_bce_4: 0.18141/0.08742, loss_grounding_dice_4: 0.16093/0.18196, loss_grounding_ce_4: 0.42491/0.28162, loss_mask_ce_5: 0.86844/0.93536, loss_mask_bce_5: 0.49727/0.34191, loss_mask_dice_5: 0.91437/1.19848, loss_spatial_bce_5: 0.12244/0.09642, loss_spatial_dice_5: 0.20038/0.23168, loss_spatial_ce_5: 0.13191/0.10765, loss_grounding_bce_5: 0.18468/0.08782, loss_grounding_dice_5: 0.15327/0.18313, loss_grounding_ce_5: 0.48811/0.29425, loss_mask_ce_6: 1.07223/0.97515, loss_mask_bce_6: 0.46499/0.34459, loss_mask_dice_6: 0.86362/1.20132, loss_spatial_bce_6: 0.11405/0.10219, loss_spatial_dice_6: 0.19078/0.23452, loss_spatial_ce_6: 0.07836/0.13394, loss_grounding_bce_6: 0.18133/0.08853, loss_grounding_dice_6: 0.15441/0.18348, loss_grounding_ce_6: 0.56951/0.30986, loss_mask_ce_7: 0.89222/1.02000, loss_mask_bce_7: 0.43730/0.35246, loss_mask_dice_7: 0.83563/1.25603, loss_spatial_bce_7: 0.13520/0.11034, loss_spatial_dice_7: 0.22216/0.26228, loss_spatial_ce_7: 0.21006/0.16995, loss_grounding_bce_7: 0.19302/0.09043, loss_grounding_dice_7: 0.16376/0.19069, loss_grounding_ce_7: 0.61343/0.34104, loss_mask_ce_8: 0.85979/1.12867, loss_mask_bce_8: 0.50205/0.36613, loss_mask_dice_8: 0.98593/1.32961, loss_spatial_bce_8: 0.16589/0.13113, loss_spatial_dice_8: 0.22397/0.30060, loss_spatial_ce_8: 0.27321/0.22678, loss_grounding_bce_8: 0.19734/0.09421, loss_grounding_dice_8: 0.18096/0.20178, loss_grounding_ce_8: 0.61470/0.40859, loss_mask_ce_9: 3.13332/3.67856, loss_mask_bce_9: 0.49876/0.39306, loss_mask_dice_9: 1.58754/1.90225, loss_spatial_bce_9: 0.36728/0.33343, loss_spatial_dice_9: 0.82007/0.82221, loss_spatial_ce_9: 1.34253/1.49874, loss_grounding_bce_9: 0.20180/0.10563, loss_grounding_dice_9: 0.25701/0.28083, loss_grounding_ce_9: 1.30564/0.67366] items per batch[64] items per second[0.23] total items[3603200] mini batches[ 56300] memory[7345] epoch remaining[0:15:32]
INFO:trainer.default_trainer:epochs[    30] optim steps[56400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67076/0.90102, loss_mask_bce_0: 0.15045/0.33502, loss_mask_dice_0: 0.88324/1.16336, loss_spatial_bce_0: 0.03340/0.08781, loss_spatial_dice_0: 0.15242/0.20945, loss_spatial_ce_0: 0.00399/0.06385, loss_grounding_bce_0: 0.03016/0.08637, loss_grounding_dice_0: 0.23383/0.17864, loss_grounding_ce_0: 0.15295/0.27254, loss_mask_ce_1: 0.50240/0.90159, loss_mask_bce_1: 0.15295/0.33596, loss_mask_dice_1: 0.99163/1.16965, loss_spatial_bce_1: 0.03353/0.08834, loss_spatial_dice_1: 0.16349/0.21345, loss_spatial_ce_1: 0.00754/0.06967, loss_grounding_bce_1: 0.03029/0.08655, loss_grounding_dice_1: 0.22514/0.17943, loss_grounding_ce_1: 0.14839/0.27347, loss_mask_ce_2: 0.51181/0.90867, loss_mask_bce_2: 0.14852/0.33651, loss_mask_dice_2: 0.85275/1.17003, loss_spatial_bce_2: 0.03346/0.08925, loss_spatial_dice_2: 0.16484/0.21493, loss_spatial_ce_2: 0.00535/0.07302, loss_grounding_bce_2: 0.02911/0.08668, loss_grounding_dice_2: 0.21618/0.17928, loss_grounding_ce_2: 0.14933/0.27695, loss_mask_ce_3: 0.50264/0.91859, loss_mask_bce_3: 0.15164/0.33758, loss_mask_dice_3: 0.89886/1.16756, loss_spatial_bce_3: 0.03507/0.09030, loss_spatial_dice_3: 0.18163/0.21564, loss_spatial_ce_3: 0.01492/0.07722, loss_grounding_bce_3: 0.02961/0.08692, loss_grounding_dice_3: 0.18988/0.17898, loss_grounding_ce_3: 0.13484/0.27890, loss_mask_ce_4: 0.46043/0.91936, loss_mask_bce_4: 0.15337/0.33967, loss_mask_dice_4: 0.89394/1.19146, loss_spatial_bce_4: 0.03393/0.09432, loss_spatial_dice_4: 0.15771/0.22768, loss_spatial_ce_4: 0.02668/0.09285, loss_grounding_bce_4: 0.03191/0.08743, loss_grounding_dice_4: 0.22346/0.18193, loss_grounding_ce_4: 0.15738/0.28161, loss_mask_ce_5: 0.52557/0.93527, loss_mask_bce_5: 0.15480/0.34192, loss_mask_dice_5: 0.89721/1.19869, loss_spatial_bce_5: 0.03502/0.09641, loss_spatial_dice_5: 0.16783/0.23167, loss_spatial_ce_5: 0.11588/0.10762, loss_grounding_bce_5: 0.03118/0.08784, loss_grounding_dice_5: 0.19090/0.18311, loss_grounding_ce_5: 0.33408/0.29426, loss_mask_ce_6: 0.70680/0.97510, loss_mask_bce_6: 0.15795/0.34459, loss_mask_dice_6: 0.93238/1.20157, loss_spatial_bce_6: 0.03626/0.10218, loss_spatial_dice_6: 0.16706/0.23451, loss_spatial_ce_6: 0.12525/0.13390, loss_grounding_bce_6: 0.03076/0.08854, loss_grounding_dice_6: 0.21997/0.18346, loss_grounding_ce_6: 0.27578/0.30989, loss_mask_ce_7: 0.57576/1.01994, loss_mask_bce_7: 0.16293/0.35247, loss_mask_dice_7: 0.90062/1.25625, loss_spatial_bce_7: 0.03897/0.11033, loss_spatial_dice_7: 0.21929/0.26228, loss_spatial_ce_7: 0.05868/0.16993, loss_grounding_bce_7: 0.03232/0.09045, loss_grounding_dice_7: 0.22917/0.19067, loss_grounding_ce_7: 0.20170/0.34103, loss_mask_ce_8: 0.88740/1.12863, loss_mask_bce_8: 0.15264/0.36614, loss_mask_dice_8: 1.03308/1.32980, loss_spatial_bce_8: 0.03996/0.13112, loss_spatial_dice_8: 0.23799/0.30060, loss_spatial_ce_8: 0.06667/0.22674, loss_grounding_bce_8: 0.03079/0.09423, loss_grounding_dice_8: 0.21290/0.20176, loss_grounding_ce_8: 0.23829/0.40860, loss_mask_ce_9: 3.27448/3.67868, loss_mask_bce_9: 0.16853/0.39305, loss_mask_dice_9: 1.26924/1.90254, loss_spatial_bce_9: 0.24460/0.33343, loss_spatial_dice_9: 0.77207/0.82221, loss_spatial_ce_9: 1.40846/1.49876, loss_grounding_bce_9: 0.02945/0.10565, loss_grounding_dice_9: 0.31764/0.28080, loss_grounding_ce_9: 0.37142/0.67372] items per batch[64] items per second[0.23] total items[3609600] mini batches[ 56400] memory[7345] epoch remaining[0:10:55]
INFO:trainer.default_trainer:epochs[    30] optim steps[56500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28037/0.90095, loss_mask_bce_0: 0.21364/0.33501, loss_mask_dice_0: 3.68877/1.16318, loss_spatial_bce_0: 0.03135/0.08781, loss_spatial_dice_0: 0.27449/0.20942, loss_spatial_ce_0: 0.09157/0.06384, loss_grounding_bce_0: 0.04322/0.08639, loss_grounding_dice_0: 0.25966/0.17862, loss_grounding_ce_0: 0.07272/0.27247, loss_mask_ce_1: 1.15721/0.90150, loss_mask_bce_1: 0.21126/0.33595, loss_mask_dice_1: 2.93405/1.16944, loss_spatial_bce_1: 0.03266/0.08834, loss_spatial_dice_1: 0.27480/0.21343, loss_spatial_ce_1: 0.07649/0.06965, loss_grounding_bce_1: 0.04040/0.08657, loss_grounding_dice_1: 0.13586/0.17941, loss_grounding_ce_1: 0.06960/0.27340, loss_mask_ce_2: 1.23189/0.90860, loss_mask_bce_2: 0.21073/0.33650, loss_mask_dice_2: 2.74218/1.16984, loss_spatial_bce_2: 0.03403/0.08926, loss_spatial_dice_2: 0.23658/0.21490, loss_spatial_ce_2: 0.15697/0.07301, loss_grounding_bce_2: 0.04328/0.08670, loss_grounding_dice_2: 0.25343/0.17926, loss_grounding_ce_2: 0.07270/0.27689, loss_mask_ce_3: 1.46336/0.91852, loss_mask_bce_3: 0.21822/0.33756, loss_mask_dice_3: 3.55242/1.16739, loss_spatial_bce_3: 0.03486/0.09031, loss_spatial_dice_3: 0.26986/0.21563, loss_spatial_ce_3: 0.20061/0.07721, loss_grounding_bce_3: 0.04016/0.08694, loss_grounding_dice_3: 0.16696/0.17897, loss_grounding_ce_3: 0.19044/0.27882, loss_mask_ce_4: 1.53548/0.91928, loss_mask_bce_4: 0.20567/0.33965, loss_mask_dice_4: 3.38129/1.19130, loss_spatial_bce_4: 0.03505/0.09432, loss_spatial_dice_4: 0.32796/0.22766, loss_spatial_ce_4: 0.09914/0.09284, loss_grounding_bce_4: 0.04156/0.08745, loss_grounding_dice_4: 0.20169/0.18191, loss_grounding_ce_4: 0.21203/0.28155, loss_mask_ce_5: 1.56215/0.93522, loss_mask_bce_5: 0.20865/0.34191, loss_mask_dice_5: 3.10213/1.19849, loss_spatial_bce_5: 0.03126/0.09641, loss_spatial_dice_5: 0.34927/0.23166, loss_spatial_ce_5: 0.13456/0.10760, loss_grounding_bce_5: 0.03993/0.08786, loss_grounding_dice_5: 0.19688/0.18309, loss_grounding_ce_5: 0.05448/0.29420, loss_mask_ce_6: 1.50741/0.97505, loss_mask_bce_6: 0.23472/0.34457, loss_mask_dice_6: 3.13718/1.20136, loss_spatial_bce_6: 0.03183/0.10218, loss_spatial_dice_6: 0.31006/0.23450, loss_spatial_ce_6: 0.14719/0.13391, loss_grounding_bce_6: 0.04497/0.08856, loss_grounding_dice_6: 0.22587/0.18344, loss_grounding_ce_6: 0.08633/0.30988, loss_mask_ce_7: 1.81421/1.01986, loss_mask_bce_7: 0.23949/0.35245, loss_mask_dice_7: 3.23895/1.25603, loss_spatial_bce_7: 0.04361/0.11032, loss_spatial_dice_7: 0.40227/0.26227, loss_spatial_ce_7: 0.16360/0.16990, loss_grounding_bce_7: 0.04172/0.09046, loss_grounding_dice_7: 0.21714/0.19064, loss_grounding_ce_7: 0.18550/0.34100, loss_mask_ce_8: 1.84425/1.12858, loss_mask_bce_8: 0.26606/0.36612, loss_mask_dice_8: 3.75000/1.32956, loss_spatial_bce_8: 0.04298/0.13111, loss_spatial_dice_8: 0.43704/0.30058, loss_spatial_ce_8: 0.35986/0.22669, loss_grounding_bce_8: 0.04102/0.09424, loss_grounding_dice_8: 0.20682/0.20173, loss_grounding_ce_8: 0.20970/0.40854, loss_mask_ce_9: 4.31882/3.67843, loss_mask_bce_9: 0.19741/0.39301, loss_mask_dice_9: 4.39653/1.90221, loss_spatial_bce_9: 0.16364/0.33345, loss_spatial_dice_9: 0.83153/0.82220, loss_spatial_ce_9: 2.66651/1.49879, loss_grounding_bce_9: 0.03369/0.10566, loss_grounding_dice_9: 0.26571/0.28077, loss_grounding_ce_9: 1.25798/0.67376] items per batch[64] items per second[0.23] total items[3616000] mini batches[ 56500] memory[7345] epoch remaining[0:06:18]
INFO:trainer.default_trainer:epochs[    30] optim steps[56600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87290/0.90093, loss_mask_bce_0: 0.63534/0.33497, loss_mask_dice_0: 2.66878/1.16294, loss_spatial_bce_0: 0.09438/0.08779, loss_spatial_dice_0: 0.26504/0.20941, loss_spatial_ce_0: 0.03281/0.06383, loss_grounding_bce_0: 0.05514/0.08639, loss_grounding_dice_0: 0.15522/0.17862, loss_grounding_ce_0: 0.21297/0.27247, loss_mask_ce_1: 0.83795/0.90148, loss_mask_bce_1: 0.62625/0.33590, loss_mask_dice_1: 2.63244/1.16916, loss_spatial_bce_1: 0.09441/0.08832, loss_spatial_dice_1: 0.26630/0.21341, loss_spatial_ce_1: 0.01540/0.06963, loss_grounding_bce_1: 0.05530/0.08656, loss_grounding_dice_1: 0.18347/0.17940, loss_grounding_ce_1: 0.18508/0.27343, loss_mask_ce_2: 0.78251/0.90856, loss_mask_bce_2: 0.63651/0.33646, loss_mask_dice_2: 2.60020/1.16961, loss_spatial_bce_2: 0.10568/0.08924, loss_spatial_dice_2: 0.28678/0.21488, loss_spatial_ce_2: 0.01759/0.07299, loss_grounding_bce_2: 0.05520/0.08669, loss_grounding_dice_2: 0.16444/0.17926, loss_grounding_ce_2: 0.28337/0.27691, loss_mask_ce_3: 0.78846/0.91851, loss_mask_bce_3: 0.63968/0.33752, loss_mask_dice_3: 2.65524/1.16714, loss_spatial_bce_3: 0.10908/0.09030, loss_spatial_dice_3: 0.28103/0.21562, loss_spatial_ce_3: 0.02604/0.07718, loss_grounding_bce_3: 0.05578/0.08694, loss_grounding_dice_3: 0.17424/0.17897, loss_grounding_ce_3: 0.23664/0.27883, loss_mask_ce_4: 0.80271/0.91924, loss_mask_bce_4: 0.63575/0.33962, loss_mask_dice_4: 2.64339/1.19107, loss_spatial_bce_4: 0.11019/0.09431, loss_spatial_dice_4: 0.28567/0.22764, loss_spatial_ce_4: 0.08086/0.09281, loss_grounding_bce_4: 0.05623/0.08745, loss_grounding_dice_4: 0.18043/0.18191, loss_grounding_ce_4: 0.31008/0.28154, loss_mask_ce_5: 0.86223/0.93519, loss_mask_bce_5: 0.64097/0.34188, loss_mask_dice_5: 2.80281/1.19827, loss_spatial_bce_5: 0.09518/0.09640, loss_spatial_dice_5: 0.30737/0.23165, loss_spatial_ce_5: 0.12175/0.10758, loss_grounding_bce_5: 0.05708/0.08785, loss_grounding_dice_5: 0.17082/0.18309, loss_grounding_ce_5: 0.54909/0.29418, loss_mask_ce_6: 0.94685/0.97503, loss_mask_bce_6: 0.68513/0.34454, loss_mask_dice_6: 2.75711/1.20113, loss_spatial_bce_6: 0.10281/0.10217, loss_spatial_dice_6: 0.30843/0.23448, loss_spatial_ce_6: 0.08458/0.13390, loss_grounding_bce_6: 0.05985/0.08855, loss_grounding_dice_6: 0.17528/0.18344, loss_grounding_ce_6: 0.39298/0.30988, loss_mask_ce_7: 0.92537/1.01986, loss_mask_bce_7: 0.71820/0.35242, loss_mask_dice_7: 2.89298/1.25580, loss_spatial_bce_7: 0.09558/0.11031, loss_spatial_dice_7: 0.29897/0.26225, loss_spatial_ce_7: 0.23729/0.16987, loss_grounding_bce_7: 0.05703/0.09046, loss_grounding_dice_7: 0.17113/0.19065, loss_grounding_ce_7: 0.10749/0.34099, loss_mask_ce_8: 1.12923/1.12855, loss_mask_bce_8: 0.79991/0.36608, loss_mask_dice_8: 2.98665/1.32935, loss_spatial_bce_8: 0.13936/0.13110, loss_spatial_dice_8: 0.36740/0.30056, loss_spatial_ce_8: 0.32146/0.22666, loss_grounding_bce_8: 0.05871/0.09424, loss_grounding_dice_8: 0.17579/0.20174, loss_grounding_ce_8: 0.13188/0.40844, loss_mask_ce_9: 5.52577/3.67838, loss_mask_bce_9: 0.82439/0.39299, loss_mask_dice_9: 4.31942/1.90193, loss_spatial_bce_9: 0.29915/0.33342, loss_spatial_dice_9: 0.89800/0.82219, loss_spatial_ce_9: 1.28277/1.49878, loss_grounding_bce_9: 0.08051/0.10566, loss_grounding_dice_9: 0.29691/0.28078, loss_grounding_ce_9: 1.27986/0.67372] items per batch[64] items per second[0.23] total items[3622400] mini batches[ 56600] memory[7345] epoch remaining[0:01:42]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00056637.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2188 s/iter. Eval: 0.0905 s/iter. Total: 0.3125 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2197 s/iter. Eval: 0.0831 s/iter. Total: 0.3058 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2213 s/iter. Eval: 0.0795 s/iter. Total: 0.3039 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2218 s/iter. Eval: 0.0778 s/iter. Total: 0.3028 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2206 s/iter. Eval: 0.0771 s/iter. Total: 0.3010 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2232 s/iter. Eval: 0.0767 s/iter. Total: 0.3032 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2252 s/iter. Eval: 0.0767 s/iter. Total: 0.3052 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2251 s/iter. Eval: 0.0767 s/iter. Total: 0.3052 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0032 s/iter. Inference: 0.2257 s/iter. Eval: 0.0771 s/iter. Total: 0.3062 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaljn2_6bv2 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.215 | 82.197 | 60.251 |      133      |
| Things | 55.241 | 82.956 | 66.010 |      80       |
| Stuff  | 42.630 | 81.052 | 51.557 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.42s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.49 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.36s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.54 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.10 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.887 | 61.460 | 41.115 | 19.083 | 42.309 | 60.483 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.238 | bicycle      | 18.623 | car            | 36.337 |
| motorcycle    | 34.808 | airplane     | 55.985 | bus            | 65.357 |
| train         | 68.036 | truck        | 34.905 | boat           | 22.672 |
| traffic light | 25.523 | fire hydrant | 63.424 | stop sign      | 62.373 |
| parking meter | 44.126 | bench        | 20.194 | bird           | 29.148 |
| cat           | 74.077 | dog          | 65.568 | horse          | 45.227 |
| sheep         | 46.112 | cow          | 49.643 | elephant       | 61.322 |
| bear          | 77.812 | zebra        | 60.325 | giraffe        | 56.950 |
| backpack      | 16.160 | umbrella     | 48.931 | handbag        | 15.176 |
| tie           | 33.322 | suitcase     | 41.414 | frisbee        | 68.476 |
| skis          | 4.921  | snowboard    | 24.513 | sports ball    | 46.146 |
| kite          | 33.766 | baseball bat | 29.382 | baseball glove | 42.682 |
| skateboard    | 35.421 | surfboard    | 34.691 | tennis racket  | 56.680 |
| bottle        | 33.693 | wine glass   | 26.406 | cup            | 40.158 |
| fork          | 16.724 | knife        | 13.327 | spoon          | 14.834 |
| bowl          | 31.658 | banana       | 20.380 | apple          | 21.043 |
| sandwich      | 41.782 | orange       | 29.645 | broccoli       | 20.876 |
| carrot        | 20.971 | hot dog      | 22.616 | pizza          | 49.827 |
| donut         | 44.710 | cake         | 43.653 | chair          | 19.901 |
| couch         | 41.995 | potted plant | 16.727 | bed            | 40.032 |
| dining table  | 12.352 | toilet       | 66.627 | tv             | 63.075 |
| laptop        | 64.054 | mouse        | 59.319 | remote         | 31.520 |
| keyboard      | 49.369 | cell phone   | 37.454 | microwave      | 56.541 |
| oven          | 33.557 | toaster      | 33.984 | sink           | 36.691 |
| refrigerator  | 58.460 | book         | 9.143  | clock          | 52.496 |
| vase          | 33.673 | scissors     | 23.514 | teddy bear     | 50.758 |
| hair drier    | 13.453 | toothbrush   | 19.467 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.615
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.411
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.605
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.492
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.508
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.720
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 61.149242747847154, 'fwIoU': 69.28403510271693, 'IoU-person': 87.57979015338115, 'IoU-bicycle': 75.40824721694553, 'IoU-car': 67.82576190420107, 'IoU-motorcycle': 83.95269918737391, 'IoU-airplane': 82.61249765962444, 'IoU-bus': 84.8379270029616, 'IoU-train': 82.52363671932312, 'IoU-truck': 65.38441225064922, 'IoU-boat': 67.21400428239367, 'IoU-traffic light': 76.49893494785671, 'IoU-fire hydrant': 89.99422425248498, 'IoU-stop sign': 92.00356742723115, 'IoU-parking meter': 83.94236286840446, 'IoU-bench': 51.767812190725216, 'IoU-bird': 72.3059781672888, 'IoU-cat': 85.67805606733633, 'IoU-dog': 76.73238293113694, 'IoU-horse': 85.09467230135385, 'IoU-sheep': 86.43142802703582, 'IoU-cow': 81.71746671314763, 'IoU-elephant': 90.76675432355981, 'IoU-bear': 90.53352828107133, 'IoU-zebra': 92.57442725222366, 'IoU-giraffe': 87.23980878149585, 'IoU-backpack': 38.59147420032351, 'IoU-umbrella': 74.54757728693198, 'IoU-handbag': 38.48896995767258, 'IoU-tie': 71.04793517148718, 'IoU-suitcase': 81.27781315756975, 'IoU-frisbee': 80.83672312512849, 'IoU-skis': 51.35078890132452, 'IoU-snowboard': 69.79425963235072, 'IoU-sports ball': 62.488607895883874, 'IoU-kite': 65.98216420852174, 'IoU-baseball bat': 59.92544959243483, 'IoU-baseball glove': 77.35690000031107, 'IoU-skateboard': 61.24288378819269, 'IoU-surfboard': 82.0479164474409, 'IoU-tennis racket': 81.77739413130983, 'IoU-bottle': 66.47074802410091, 'IoU-wine glass': 72.37268750333492, 'IoU-cup': 60.34846490158792, 'IoU-fork': 54.36758034541338, 'IoU-knife': 49.99214776694572, 'IoU-spoon': 48.8922917153923, 'IoU-bowl': 58.447860753858194, 'IoU-banana': 83.53400720155484, 'IoU-apple': 58.13587738907551, 'IoU-sandwich': 63.97224022051174, 'IoU-orange': 78.81898684167852, 'IoU-broccoli': 67.00729504798866, 'IoU-carrot': 63.45510211548634, 'IoU-hot dog': 66.90625711919706, 'IoU-pizza': 83.31502911816986, 'IoU-donut': 63.93823330309857, 'IoU-cake': 67.83355701807761, 'IoU-chair': 54.76215327103191, 'IoU-couch': 67.79552657296033, 'IoU-potted plant': 34.47178722346508, 'IoU-bed': 67.78779075296752, 'IoU-dining table': 51.844182284800056, 'IoU-toilet': 82.56845262818203, 'IoU-tv': 75.03679849205244, 'IoU-laptop': 76.02410483946662, 'IoU-mouse': 70.6669835234474, 'IoU-remote': 49.76320069262557, 'IoU-keyboard': 63.44858906160319, 'IoU-cell phone': 69.45873282726416, 'IoU-microwave': 59.294003282463194, 'IoU-oven': 68.55648287743101, 'IoU-toaster': 70.80025709259844, 'IoU-sink': 70.29092167023904, 'IoU-refrigerator': 81.07501509786252, 'IoU-book': 50.039262020005616, 'IoU-clock': 75.38773566201856, 'IoU-vase': 67.90795797947669, 'IoU-scissors': 55.63422364026336, 'IoU-teddy bear': 82.07676941792245, 'IoU-hair drier': 56.41999933142417, 'IoU-toothbrush': 57.47245325723882, 'IoU-banner': 38.095413791006116, 'IoU-blanket': 11.621032385888551, 'IoU-bridge': 38.42205985481528, 'IoU-cardboard': 47.59284655909056, 'IoU-counter': 30.900929124749215, 'IoU-curtain': 63.028162231861415, 'IoU-door-stuff': 42.521451422490145, 'IoU-floor-wood': 63.60571558141008, 'IoU-flower': 40.66986447960221, 'IoU-fruit': 40.598257779515386, 'IoU-gravel': 30.663593401923794, 'IoU-house': 25.09497804317695, 'IoU-light': 39.94687267993342, 'IoU-mirror-stuff': 58.940471490991385, 'IoU-net': 36.71474028169302, 'IoU-pillow': 12.585784126543997, 'IoU-platform': 29.73546329198748, 'IoU-playingfield': 69.78091203991356, 'IoU-railroad': 61.524409478285115, 'IoU-river': 50.39253135134844, 'IoU-road': 65.6103336756535, 'IoU-roof': 15.557532355353251, 'IoU-sand': 64.27206274877723, 'IoU-sea': 84.7082410826337, 'IoU-shelf': 35.8969378787275, 'IoU-snow': 88.53396183060408, 'IoU-stairs': 22.128310854969996, 'IoU-tent': 8.405578625264031, 'IoU-towel': 33.940948264061284, 'IoU-wall-brick': 42.849774247925346, 'IoU-wall-stone': 27.8208086226151, 'IoU-wall-tile': 68.23720644242248, 'IoU-wall-wood': 41.155526494120636, 'IoU-water-other': 23.650857537696197, 'IoU-window-blind': 45.4752140457673, 'IoU-window-other': 47.29223829812281, 'IoU-tree-merged': 80.99036138734915, 'IoU-fence-merged': 50.39517942730514, 'IoU-ceiling-merged': 67.31961027998814, 'IoU-sky-other-merged': 92.52262956903355, 'IoU-cabinet-merged': 59.53989209608276, 'IoU-table-merged': 38.732647226829265, 'IoU-floor-other-merged': 49.47325691229156, 'IoU-pavement-merged': 54.60035289545798, 'IoU-mountain-merged': 54.578884397532114, 'IoU-grass-merged': 71.94317397093198, 'IoU-dirt-merged': 45.95166441170746, 'IoU-paper-merged': 30.135737994615475, 'IoU-food-other-merged': 36.525272449719346, 'IoU-building-other-merged': 57.38114970758814, 'IoU-rock-merged': 60.86533613749582, 'IoU-wall-other-merged': 67.00377956058266, 'IoU-rug-merged': 63.150346347848775, 'mACC': 73.91305403217275, 'pACC': 80.64046783359149, 'ACC-person': 92.81859806610446, 'ACC-bicycle': 86.64353514289917, 'ACC-car': 86.40841394405834, 'ACC-motorcycle': 88.89745073424142, 'ACC-airplane': 91.04463073826902, 'ACC-bus': 91.84275820402277, 'ACC-train': 95.6343215874383, 'ACC-truck': 76.1628413973264, 'ACC-boat': 76.9081803987487, 'ACC-traffic light': 89.77568738362461, 'ACC-fire hydrant': 95.14362453936384, 'ACC-stop sign': 94.8876026770838, 'ACC-parking meter': 92.51558739633782, 'ACC-bench': 75.81828288035574, 'ACC-bird': 76.83098154520398, 'ACC-cat': 93.08050807111437, 'ACC-dog': 80.72829591481643, 'ACC-horse': 91.11251331856688, 'ACC-sheep': 90.41935675732734, 'ACC-cow': 87.94324691699356, 'ACC-elephant': 94.81747267000954, 'ACC-bear': 93.33578461318574, 'ACC-zebra': 95.2676186684528, 'ACC-giraffe': 91.74653046042683, 'ACC-backpack': 58.67750939956705, 'ACC-umbrella': 81.19934840011716, 'ACC-handbag': 55.154112077270426, 'ACC-tie': 80.84864878910005, 'ACC-suitcase': 90.72101057374785, 'ACC-frisbee': 94.37672727272727, 'ACC-skis': 68.9400573089333, 'ACC-snowboard': 79.24420170948544, 'ACC-sports ball': 73.35617783566431, 'ACC-kite': 76.04650401838414, 'ACC-baseball bat': 84.79759344866599, 'ACC-baseball glove': 89.59310578384961, 'ACC-skateboard': 70.52136307121921, 'ACC-surfboard': 90.07955971910879, 'ACC-tennis racket': 89.49249356268415, 'ACC-bottle': 80.77148759503486, 'ACC-wine glass': 84.84687494333738, 'ACC-cup': 83.66716756118822, 'ACC-fork': 66.34547453007679, 'ACC-knife': 60.64776141276471, 'ACC-spoon': 68.30807613739586, 'ACC-bowl': 72.94676614462493, 'ACC-banana': 90.81687947791384, 'ACC-apple': 71.94534416986977, 'ACC-sandwich': 83.31054295538553, 'ACC-orange': 86.28835178845567, 'ACC-broccoli': 76.80002273002609, 'ACC-carrot': 74.46348495720251, 'ACC-hot dog': 74.0685422195495, 'ACC-pizza': 94.93528486254542, 'ACC-donut': 81.58425124278628, 'ACC-cake': 75.60972630594564, 'ACC-chair': 71.35732834916759, 'ACC-couch': 85.01006887228371, 'ACC-potted plant': 50.46159302606193, 'ACC-bed': 79.06633870160385, 'ACC-dining table': 77.05018876638557, 'ACC-toilet': 92.58947643151988, 'ACC-tv': 86.99355139886673, 'ACC-laptop': 93.21358998935018, 'ACC-mouse': 86.89003603779099, 'ACC-remote': 72.6249730122919, 'ACC-keyboard': 70.19391433990734, 'ACC-cell phone': 79.24373619593045, 'ACC-microwave': 77.74990730409553, 'ACC-oven': 89.10686783301482, 'ACC-toaster': 85.93042192995661, 'ACC-sink': 84.58250709183534, 'ACC-refrigerator': 91.93038586729854, 'ACC-book': 74.43126418424812, 'ACC-clock': 81.00285456693982, 'ACC-vase': 79.79530981707987, 'ACC-scissors': 59.8395219862454, 'ACC-teddy bear': 88.27730595027938, 'ACC-hair drier': 72.05903281814818, 'ACC-toothbrush': 82.05350938151494, 'ACC-banner': 76.7422302024107, 'ACC-blanket': 15.291097839609064, 'ACC-bridge': 53.637955466615196, 'ACC-cardboard': 57.71949978188163, 'ACC-counter': 56.19745369921579, 'ACC-curtain': 74.37754080697879, 'ACC-door-stuff': 62.32729741240603, 'ACC-floor-wood': 76.35329694063597, 'ACC-flower': 60.91126135059888, 'ACC-fruit': 53.79232833216644, 'ACC-gravel': 38.29416066892532, 'ACC-house': 29.50493596857039, 'ACC-light': 57.57575938827853, 'ACC-mirror-stuff': 71.44028465483738, 'ACC-net': 63.6837577845619, 'ACC-pillow': 28.279297663895587, 'ACC-platform': 48.01701555769098, 'ACC-playingfield': 90.60793687918003, 'ACC-railroad': 79.07774582513495, 'ACC-river': 73.51113855945256, 'ACC-road': 85.60672671601431, 'ACC-roof': 20.900430677961268, 'ACC-sand': 70.75060459262966, 'ACC-sea': 90.65377352124503, 'ACC-shelf': 57.645172865526284, 'ACC-snow': 95.3307102623702, 'ACC-stairs': 39.43006835758019, 'ACC-tent': 9.467621732166233, 'ACC-towel': 41.40404275638214, 'ACC-wall-brick': 59.08628872736132, 'ACC-wall-stone': 37.57132552430989, 'ACC-wall-tile': 82.5402368898838, 'ACC-wall-wood': 52.78754810664958, 'ACC-water-other': 36.91875104836056, 'ACC-window-blind': 56.59596588964328, 'ACC-window-other': 69.3540490048583, 'ACC-tree-merged': 88.9269131277354, 'ACC-fence-merged': 69.01899786368372, 'ACC-ceiling-merged': 79.3743541880304, 'ACC-sky-other-merged': 96.42788146473826, 'ACC-cabinet-merged': 75.01147479888108, 'ACC-table-merged': 50.181281497512344, 'ACC-floor-other-merged': 60.348069921539825, 'ACC-pavement-merged': 67.1365480901975, 'ACC-mountain-merged': 64.00268667082334, 'ACC-grass-merged': 83.66534718365904, 'ACC-dirt-merged': 66.51460792605526, 'ACC-paper-merged': 42.04540252688447, 'ACC-food-other-merged': 45.66049283868046, 'ACC-building-other-merged': 75.07382820103163, 'ACC-rock-merged': 83.92206432112039, 'ACC-wall-other-merged': 79.93139962575671, 'ACC-rug-merged': 78.16756469424179})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1547 s/iter. Inference: 0.5896 s/iter. Eval: 0.0000 s/iter. Total: 0.7443 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1555 s/iter. Inference: 0.5729 s/iter. Eval: 0.0000 s/iter. Total: 0.7285 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1673 s/iter. Inference: 0.5757 s/iter. Eval: 0.0000 s/iter. Total: 0.7431 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1707 s/iter. Inference: 0.7091 s/iter. Eval: 0.0000 s/iter. Total: 0.8799 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1672 s/iter. Inference: 0.6195 s/iter. Eval: 0.0000 s/iter. Total: 0.7869 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1663 s/iter. Inference: 0.6539 s/iter. Eval: 0.0000 s/iter. Total: 0.8203 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1681 s/iter. Inference: 0.6939 s/iter. Eval: 0.0000 s/iter. Total: 0.8621 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.482294410301434, 'noc@0.8': 2.834942932396839, 'noc@0.85': 3.4416154521510096, 'noc@0.9': 4.504536142815335, 'miou@iter1': 0.8431558494579035}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0018 s/iter. Inference: 0.1001 s/iter. Eval: 0.0008 s/iter. Total: 0.1027 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.58958435058594, 'precision@0.6': 69.02448272705078, 'precision@0.7': 63.311309814453125, 'precision@0.8': 53.012046813964844, 'precision@0.9': 27.710844039916992, 'cIoU': 58.329280853271484, 'mIoU': 63.35831069946289}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.21520817628365, 'SQ': 82.19686204323642, 'RQ': 60.25063217822867, 'PQ_th': 55.24070977736297, 'SQ_th': 82.95555772499308, 'RQ_th': 66.01016188184433, 'PQ_st': 42.62954538220159, 'SQ_st': 81.05166101416981, 'RQ_st': 51.557002436922076}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.88661680201479, 'AP50': 61.4596303812449, 'AP75': 41.11451728608636, 'APs': 19.083390636650694, 'APm': 42.30939845478594, 'APl': 60.483496036589536, 'AP-person': 44.23756133994964, 'AP-bicycle': 18.623127725673747, 'AP-car': 36.337372367485784, 'AP-motorcycle': 34.808499736598364, 'AP-airplane': 55.98549362316957, 'AP-bus': 65.35726284002126, 'AP-train': 68.03616003378183, 'AP-truck': 34.905105843794104, 'AP-boat': 22.672200647827033, 'AP-traffic light': 25.522866217247252, 'AP-fire hydrant': 63.42415109907882, 'AP-stop sign': 62.37339987658236, 'AP-parking meter': 44.12630243352342, 'AP-bench': 20.193797676968487, 'AP-bird': 29.147976159088827, 'AP-cat': 74.07685004037033, 'AP-dog': 65.56831339294052, 'AP-horse': 45.22703451237485, 'AP-sheep': 46.111520605805225, 'AP-cow': 49.643141044801624, 'AP-elephant': 61.32190363737894, 'AP-bear': 77.8120430064975, 'AP-zebra': 60.324926220075646, 'AP-giraffe': 56.94965064725247, 'AP-backpack': 16.159935196517317, 'AP-umbrella': 48.93079818104712, 'AP-handbag': 15.176324586684494, 'AP-tie': 33.321635878490206, 'AP-suitcase': 41.41400169990682, 'AP-frisbee': 68.47572867013321, 'AP-skis': 4.920681928307637, 'AP-snowboard': 24.512859317377828, 'AP-sports ball': 46.14647393048922, 'AP-kite': 33.766233145596466, 'AP-baseball bat': 29.38178408182231, 'AP-baseball glove': 42.68171055417934, 'AP-skateboard': 35.42067145697887, 'AP-surfboard': 34.690971980908024, 'AP-tennis racket': 56.67987624150014, 'AP-bottle': 33.69285436781643, 'AP-wine glass': 26.405919340048783, 'AP-cup': 40.15757274237387, 'AP-fork': 16.723573945667564, 'AP-knife': 13.3265836367717, 'AP-spoon': 14.833982115688904, 'AP-bowl': 31.658030799583692, 'AP-banana': 20.379690951162043, 'AP-apple': 21.04334192008791, 'AP-sandwich': 41.7817443539405, 'AP-orange': 29.645315992911343, 'AP-broccoli': 20.876498139603523, 'AP-carrot': 20.971135032699458, 'AP-hot dog': 22.61555016506068, 'AP-pizza': 49.82716913279499, 'AP-donut': 44.70951420178614, 'AP-cake': 43.65270832580946, 'AP-chair': 19.90145330197172, 'AP-couch': 41.99494884845884, 'AP-potted plant': 16.726745082526868, 'AP-bed': 40.031535439479605, 'AP-dining table': 12.352017217769298, 'AP-toilet': 66.62695539066802, 'AP-tv': 63.07464932377642, 'AP-laptop': 64.05370278185782, 'AP-mouse': 59.318696311200135, 'AP-remote': 31.520475243024777, 'AP-keyboard': 49.36884554183782, 'AP-cell phone': 37.45404090322757, 'AP-microwave': 56.54104771379921, 'AP-oven': 33.55660720774081, 'AP-toaster': 33.98428453691045, 'AP-sink': 36.69132480834991, 'AP-refrigerator': 58.460094015955455, 'AP-book': 9.143352060737005, 'AP-clock': 52.495690666927366, 'AP-vase': 33.672971257017615, 'AP-scissors': 23.513935719739933, 'AP-teddy bear': 50.75849534741561, 'AP-hair drier': 13.452821409960544, 'AP-toothbrush': 19.467121288797596}), ('sem_seg', {'mIoU': 61.149242747847154, 'fwIoU': 69.28403510271693, 'IoU-person': 87.57979015338115, 'IoU-bicycle': 75.40824721694553, 'IoU-car': 67.82576190420107, 'IoU-motorcycle': 83.95269918737391, 'IoU-airplane': 82.61249765962444, 'IoU-bus': 84.8379270029616, 'IoU-train': 82.52363671932312, 'IoU-truck': 65.38441225064922, 'IoU-boat': 67.21400428239367, 'IoU-traffic light': 76.49893494785671, 'IoU-fire hydrant': 89.99422425248498, 'IoU-stop sign': 92.00356742723115, 'IoU-parking meter': 83.94236286840446, 'IoU-bench': 51.767812190725216, 'IoU-bird': 72.3059781672888, 'IoU-cat': 85.67805606733633, 'IoU-dog': 76.73238293113694, 'IoU-horse': 85.09467230135385, 'IoU-sheep': 86.43142802703582, 'IoU-cow': 81.71746671314763, 'IoU-elephant': 90.76675432355981, 'IoU-bear': 90.53352828107133, 'IoU-zebra': 92.57442725222366, 'IoU-giraffe': 87.23980878149585, 'IoU-backpack': 38.59147420032351, 'IoU-umbrella': 74.54757728693198, 'IoU-handbag': 38.48896995767258, 'IoU-tie': 71.04793517148718, 'IoU-suitcase': 81.27781315756975, 'IoU-frisbee': 80.83672312512849, 'IoU-skis': 51.35078890132452, 'IoU-snowboard': 69.79425963235072, 'IoU-sports ball': 62.488607895883874, 'IoU-kite': 65.98216420852174, 'IoU-baseball bat': 59.92544959243483, 'IoU-baseball glove': 77.35690000031107, 'IoU-skateboard': 61.24288378819269, 'IoU-surfboard': 82.0479164474409, 'IoU-tennis racket': 81.77739413130983, 'IoU-bottle': 66.47074802410091, 'IoU-wine glass': 72.37268750333492, 'IoU-cup': 60.34846490158792, 'IoU-fork': 54.36758034541338, 'IoU-knife': 49.99214776694572, 'IoU-spoon': 48.8922917153923, 'IoU-bowl': 58.447860753858194, 'IoU-banana': 83.53400720155484, 'IoU-apple': 58.13587738907551, 'IoU-sandwich': 63.97224022051174, 'IoU-orange': 78.81898684167852, 'IoU-broccoli': 67.00729504798866, 'IoU-carrot': 63.45510211548634, 'IoU-hot dog': 66.90625711919706, 'IoU-pizza': 83.31502911816986, 'IoU-donut': 63.93823330309857, 'IoU-cake': 67.83355701807761, 'IoU-chair': 54.76215327103191, 'IoU-couch': 67.79552657296033, 'IoU-potted plant': 34.47178722346508, 'IoU-bed': 67.78779075296752, 'IoU-dining table': 51.844182284800056, 'IoU-toilet': 82.56845262818203, 'IoU-tv': 75.03679849205244, 'IoU-laptop': 76.02410483946662, 'IoU-mouse': 70.6669835234474, 'IoU-remote': 49.76320069262557, 'IoU-keyboard': 63.44858906160319, 'IoU-cell phone': 69.45873282726416, 'IoU-microwave': 59.294003282463194, 'IoU-oven': 68.55648287743101, 'IoU-toaster': 70.80025709259844, 'IoU-sink': 70.29092167023904, 'IoU-refrigerator': 81.07501509786252, 'IoU-book': 50.039262020005616, 'IoU-clock': 75.38773566201856, 'IoU-vase': 67.90795797947669, 'IoU-scissors': 55.63422364026336, 'IoU-teddy bear': 82.07676941792245, 'IoU-hair drier': 56.41999933142417, 'IoU-toothbrush': 57.47245325723882, 'IoU-banner': 38.095413791006116, 'IoU-blanket': 11.621032385888551, 'IoU-bridge': 38.42205985481528, 'IoU-cardboard': 47.59284655909056, 'IoU-counter': 30.900929124749215, 'IoU-curtain': 63.028162231861415, 'IoU-door-stuff': 42.521451422490145, 'IoU-floor-wood': 63.60571558141008, 'IoU-flower': 40.66986447960221, 'IoU-fruit': 40.598257779515386, 'IoU-gravel': 30.663593401923794, 'IoU-house': 25.09497804317695, 'IoU-light': 39.94687267993342, 'IoU-mirror-stuff': 58.940471490991385, 'IoU-net': 36.71474028169302, 'IoU-pillow': 12.585784126543997, 'IoU-platform': 29.73546329198748, 'IoU-playingfield': 69.78091203991356, 'IoU-railroad': 61.524409478285115, 'IoU-river': 50.39253135134844, 'IoU-road': 65.6103336756535, 'IoU-roof': 15.557532355353251, 'IoU-sand': 64.27206274877723, 'IoU-sea': 84.7082410826337, 'IoU-shelf': 35.8969378787275, 'IoU-snow': 88.53396183060408, 'IoU-stairs': 22.128310854969996, 'IoU-tent': 8.405578625264031, 'IoU-towel': 33.940948264061284, 'IoU-wall-brick': 42.849774247925346, 'IoU-wall-stone': 27.8208086226151, 'IoU-wall-tile': 68.23720644242248, 'IoU-wall-wood': 41.155526494120636, 'IoU-water-other': 23.650857537696197, 'IoU-window-blind': 45.4752140457673, 'IoU-window-other': 47.29223829812281, 'IoU-tree-merged': 80.99036138734915, 'IoU-fence-merged': 50.39517942730514, 'IoU-ceiling-merged': 67.31961027998814, 'IoU-sky-other-merged': 92.52262956903355, 'IoU-cabinet-merged': 59.53989209608276, 'IoU-table-merged': 38.732647226829265, 'IoU-floor-other-merged': 49.47325691229156, 'IoU-pavement-merged': 54.60035289545798, 'IoU-mountain-merged': 54.578884397532114, 'IoU-grass-merged': 71.94317397093198, 'IoU-dirt-merged': 45.95166441170746, 'IoU-paper-merged': 30.135737994615475, 'IoU-food-other-merged': 36.525272449719346, 'IoU-building-other-merged': 57.38114970758814, 'IoU-rock-merged': 60.86533613749582, 'IoU-wall-other-merged': 67.00377956058266, 'IoU-rug-merged': 63.150346347848775, 'mACC': 73.91305403217275, 'pACC': 80.64046783359149, 'ACC-person': 92.81859806610446, 'ACC-bicycle': 86.64353514289917, 'ACC-car': 86.40841394405834, 'ACC-motorcycle': 88.89745073424142, 'ACC-airplane': 91.04463073826902, 'ACC-bus': 91.84275820402277, 'ACC-train': 95.6343215874383, 'ACC-truck': 76.1628413973264, 'ACC-boat': 76.9081803987487, 'ACC-traffic light': 89.77568738362461, 'ACC-fire hydrant': 95.14362453936384, 'ACC-stop sign': 94.8876026770838, 'ACC-parking meter': 92.51558739633782, 'ACC-bench': 75.81828288035574, 'ACC-bird': 76.83098154520398, 'ACC-cat': 93.08050807111437, 'ACC-dog': 80.72829591481643, 'ACC-horse': 91.11251331856688, 'ACC-sheep': 90.41935675732734, 'ACC-cow': 87.94324691699356, 'ACC-elephant': 94.81747267000954, 'ACC-bear': 93.33578461318574, 'ACC-zebra': 95.2676186684528, 'ACC-giraffe': 91.74653046042683, 'ACC-backpack': 58.67750939956705, 'ACC-umbrella': 81.19934840011716, 'ACC-handbag': 55.154112077270426, 'ACC-tie': 80.84864878910005, 'ACC-suitcase': 90.72101057374785, 'ACC-frisbee': 94.37672727272727, 'ACC-skis': 68.9400573089333, 'ACC-snowboard': 79.24420170948544, 'ACC-sports ball': 73.35617783566431, 'ACC-kite': 76.04650401838414, 'ACC-baseball bat': 84.79759344866599, 'ACC-baseball glove': 89.59310578384961, 'ACC-skateboard': 70.52136307121921, 'ACC-surfboard': 90.07955971910879, 'ACC-tennis racket': 89.49249356268415, 'ACC-bottle': 80.77148759503486, 'ACC-wine glass': 84.84687494333738, 'ACC-cup': 83.66716756118822, 'ACC-fork': 66.34547453007679, 'ACC-knife': 60.64776141276471, 'ACC-spoon': 68.30807613739586, 'ACC-bowl': 72.94676614462493, 'ACC-banana': 90.81687947791384, 'ACC-apple': 71.94534416986977, 'ACC-sandwich': 83.31054295538553, 'ACC-orange': 86.28835178845567, 'ACC-broccoli': 76.80002273002609, 'ACC-carrot': 74.46348495720251, 'ACC-hot dog': 74.0685422195495, 'ACC-pizza': 94.93528486254542, 'ACC-donut': 81.58425124278628, 'ACC-cake': 75.60972630594564, 'ACC-chair': 71.35732834916759, 'ACC-couch': 85.01006887228371, 'ACC-potted plant': 50.46159302606193, 'ACC-bed': 79.06633870160385, 'ACC-dining table': 77.05018876638557, 'ACC-toilet': 92.58947643151988, 'ACC-tv': 86.99355139886673, 'ACC-laptop': 93.21358998935018, 'ACC-mouse': 86.89003603779099, 'ACC-remote': 72.6249730122919, 'ACC-keyboard': 70.19391433990734, 'ACC-cell phone': 79.24373619593045, 'ACC-microwave': 77.74990730409553, 'ACC-oven': 89.10686783301482, 'ACC-toaster': 85.93042192995661, 'ACC-sink': 84.58250709183534, 'ACC-refrigerator': 91.93038586729854, 'ACC-book': 74.43126418424812, 'ACC-clock': 81.00285456693982, 'ACC-vase': 79.79530981707987, 'ACC-scissors': 59.8395219862454, 'ACC-teddy bear': 88.27730595027938, 'ACC-hair drier': 72.05903281814818, 'ACC-toothbrush': 82.05350938151494, 'ACC-banner': 76.7422302024107, 'ACC-blanket': 15.291097839609064, 'ACC-bridge': 53.637955466615196, 'ACC-cardboard': 57.71949978188163, 'ACC-counter': 56.19745369921579, 'ACC-curtain': 74.37754080697879, 'ACC-door-stuff': 62.32729741240603, 'ACC-floor-wood': 76.35329694063597, 'ACC-flower': 60.91126135059888, 'ACC-fruit': 53.79232833216644, 'ACC-gravel': 38.29416066892532, 'ACC-house': 29.50493596857039, 'ACC-light': 57.57575938827853, 'ACC-mirror-stuff': 71.44028465483738, 'ACC-net': 63.6837577845619, 'ACC-pillow': 28.279297663895587, 'ACC-platform': 48.01701555769098, 'ACC-playingfield': 90.60793687918003, 'ACC-railroad': 79.07774582513495, 'ACC-river': 73.51113855945256, 'ACC-road': 85.60672671601431, 'ACC-roof': 20.900430677961268, 'ACC-sand': 70.75060459262966, 'ACC-sea': 90.65377352124503, 'ACC-shelf': 57.645172865526284, 'ACC-snow': 95.3307102623702, 'ACC-stairs': 39.43006835758019, 'ACC-tent': 9.467621732166233, 'ACC-towel': 41.40404275638214, 'ACC-wall-brick': 59.08628872736132, 'ACC-wall-stone': 37.57132552430989, 'ACC-wall-tile': 82.5402368898838, 'ACC-wall-wood': 52.78754810664958, 'ACC-water-other': 36.91875104836056, 'ACC-window-blind': 56.59596588964328, 'ACC-window-other': 69.3540490048583, 'ACC-tree-merged': 88.9269131277354, 'ACC-fence-merged': 69.01899786368372, 'ACC-ceiling-merged': 79.3743541880304, 'ACC-sky-other-merged': 96.42788146473826, 'ACC-cabinet-merged': 75.01147479888108, 'ACC-table-merged': 50.181281497512344, 'ACC-floor-other-merged': 60.348069921539825, 'ACC-pavement-merged': 67.1365480901975, 'ACC-mountain-merged': 64.00268667082334, 'ACC-grass-merged': 83.66534718365904, 'ACC-dirt-merged': 66.51460792605526, 'ACC-paper-merged': 42.04540252688447, 'ACC-food-other-merged': 45.66049283868046, 'ACC-building-other-merged': 75.07382820103163, 'ACC-rock-merged': 83.92206432112039, 'ACC-wall-other-merged': 79.93139962575671, 'ACC-rug-merged': 78.16756469424179})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.482294410301434, 'noc@0.8': 2.834942932396839, 'noc@0.85': 3.4416154521510096, 'noc@0.9': 4.504536142815335, 'miou@iter1': 0.8431558494579035}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.58958435058594, 'precision@0.6': 69.02448272705078, 'precision@0.7': 63.311309814453125, 'precision@0.8': 53.012046813964844, 'precision@0.9': 27.710844039916992, 'cIoU': 58.329280853271484, 'mIoU': 63.35831069946289}}}
INFO:trainer.default_trainer:This epoch takes 1:27:24.861405
INFO:trainer.default_trainer:PROGRESS: 62.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 31 training.
INFO:trainer.default_trainer:epochs[    31] optim steps[56700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59666/0.90110, loss_mask_bce_0: 0.46122/0.33499, loss_mask_dice_0: 1.49762/1.16319, loss_spatial_bce_0: 0.04829/0.08779, loss_spatial_dice_0: 0.26590/0.20941, loss_spatial_ce_0: 0.13280/0.06381, loss_grounding_bce_0: 0.08756/0.08639, loss_grounding_dice_0: 0.28001/0.17863, loss_grounding_ce_0: 0.33862/0.27252, loss_mask_ce_1: 0.56140/0.90166, loss_mask_bce_1: 0.46184/0.33592, loss_mask_dice_1: 1.47084/1.16944, loss_spatial_bce_1: 0.04955/0.08832, loss_spatial_dice_1: 0.26767/0.21341, loss_spatial_ce_1: 0.06933/0.06962, loss_grounding_bce_1: 0.07835/0.08656, loss_grounding_dice_1: 0.30088/0.17941, loss_grounding_ce_1: 0.29038/0.27348, loss_mask_ce_2: 0.57053/0.90873, loss_mask_bce_2: 0.46189/0.33647, loss_mask_dice_2: 1.31825/1.16988, loss_spatial_bce_2: 0.04994/0.08924, loss_spatial_dice_2: 0.27042/0.21488, loss_spatial_ce_2: 0.06913/0.07297, loss_grounding_bce_2: 0.07794/0.08668, loss_grounding_dice_2: 0.31871/0.17928, loss_grounding_ce_2: 0.33594/0.27695, loss_mask_ce_3: 0.59806/0.91868, loss_mask_bce_3: 0.46777/0.33753, loss_mask_dice_3: 1.33002/1.16743, loss_spatial_bce_3: 0.05784/0.09031, loss_spatial_dice_3: 0.28151/0.21562, loss_spatial_ce_3: 0.11203/0.07716, loss_grounding_bce_3: 0.08944/0.08693, loss_grounding_dice_3: 0.29931/0.17899, loss_grounding_ce_3: 0.30993/0.27886, loss_mask_ce_4: 0.66984/0.91947, loss_mask_bce_4: 0.44736/0.33964, loss_mask_dice_4: 1.26031/1.19134, loss_spatial_bce_4: 0.06499/0.09431, loss_spatial_dice_4: 0.28979/0.22765, loss_spatial_ce_4: 0.14106/0.09279, loss_grounding_bce_4: 0.08199/0.08744, loss_grounding_dice_4: 0.27466/0.18193, loss_grounding_ce_4: 0.30402/0.28157, loss_mask_ce_5: 0.65926/0.93539, loss_mask_bce_5: 0.48873/0.34190, loss_mask_dice_5: 1.39043/1.19851, loss_spatial_bce_5: 0.08739/0.09641, loss_spatial_dice_5: 0.32506/0.23166, loss_spatial_ce_5: 0.07390/0.10754, loss_grounding_bce_5: 0.08551/0.08785, loss_grounding_dice_5: 0.28198/0.18312, loss_grounding_ce_5: 0.34138/0.29422, loss_mask_ce_6: 1.07487/0.97525, loss_mask_bce_6: 0.44477/0.34456, loss_mask_dice_6: 1.23254/1.20140, loss_spatial_bce_6: 0.12619/0.10217, loss_spatial_dice_6: 0.35175/0.23449, loss_spatial_ce_6: 0.12479/0.13386, loss_grounding_bce_6: 0.07669/0.08854, loss_grounding_dice_6: 0.30547/0.18346, loss_grounding_ce_6: 0.32547/0.30992, loss_mask_ce_7: 0.72515/1.02009, loss_mask_bce_7: 0.58263/0.35244, loss_mask_dice_7: 1.33077/1.25610, loss_spatial_bce_7: 0.11438/0.11031, loss_spatial_dice_7: 0.37262/0.26225, loss_spatial_ce_7: 0.14127/0.16982, loss_grounding_bce_7: 0.06152/0.09045, loss_grounding_dice_7: 0.30006/0.19066, loss_grounding_ce_7: 0.39932/0.34103, loss_mask_ce_8: 0.79342/1.12875, loss_mask_bce_8: 0.49968/0.36610, loss_mask_dice_8: 1.32471/1.32964, loss_spatial_bce_8: 0.14011/0.13111, loss_spatial_dice_8: 0.38034/0.30056, loss_spatial_ce_8: 0.16570/0.22663, loss_grounding_bce_8: 0.08316/0.09423, loss_grounding_dice_8: 0.29663/0.20176, loss_grounding_ce_8: 0.54634/0.40850, loss_mask_ce_9: 3.95538/3.67845, loss_mask_bce_9: 0.43602/0.39302, loss_mask_dice_9: 2.09028/1.90240, loss_spatial_bce_9: 0.27290/0.33342, loss_spatial_dice_9: 0.91164/0.82219, loss_spatial_ce_9: 1.18313/1.49869, loss_grounding_bce_9: 0.06590/0.10566, loss_grounding_dice_9: 0.38379/0.28082, loss_grounding_ce_9: 0.56757/0.67372] items per batch[64] items per second[0.13] total items[3628800] mini batches[ 56700] memory[7345] epoch remaining[1:23:36]
INFO:trainer.default_trainer:epochs[    31] optim steps[56800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.17543/0.90097, loss_mask_bce_0: 0.12316/0.33491, loss_mask_dice_0: 0.78555/1.16297, loss_spatial_bce_0: 0.05076/0.08777, loss_spatial_dice_0: 0.27972/0.20938, loss_spatial_ce_0: 0.09423/0.06379, loss_grounding_bce_0: 0.04139/0.08637, loss_grounding_dice_0: 0.15825/0.17862, loss_grounding_ce_0: 0.18645/0.27250, loss_mask_ce_1: 1.51465/0.90154, loss_mask_bce_1: 0.11749/0.33584, loss_mask_dice_1: 1.07792/1.16923, loss_spatial_bce_1: 0.05450/0.08830, loss_spatial_dice_1: 0.28867/0.21338, loss_spatial_ce_1: 0.07971/0.06959, loss_grounding_bce_1: 0.03848/0.08654, loss_grounding_dice_1: 0.24210/0.17940, loss_grounding_ce_1: 0.34260/0.27342, loss_mask_ce_2: 1.41695/0.90863, loss_mask_bce_2: 0.11710/0.33639, loss_mask_dice_2: 0.98619/1.16967, loss_spatial_bce_2: 0.04807/0.08922, loss_spatial_dice_2: 0.28788/0.21485, loss_spatial_ce_2: 0.15554/0.07295, loss_grounding_bce_2: 0.04124/0.08666, loss_grounding_dice_2: 0.21654/0.17927, loss_grounding_ce_2: 0.30663/0.27698, loss_mask_ce_3: 1.49029/0.91857, loss_mask_bce_3: 0.11937/0.33746, loss_mask_dice_3: 0.85752/1.16722, loss_spatial_bce_3: 0.04984/0.09028, loss_spatial_dice_3: 0.28768/0.21560, loss_spatial_ce_3: 0.10654/0.07713, loss_grounding_bce_3: 0.04184/0.08691, loss_grounding_dice_3: 0.23281/0.17898, loss_grounding_ce_3: 0.37276/0.27883, loss_mask_ce_4: 1.27635/0.91933, loss_mask_bce_4: 0.11424/0.33956, loss_mask_dice_4: 0.72315/1.19112, loss_spatial_bce_4: 0.06516/0.09428, loss_spatial_dice_4: 0.34855/0.22763, loss_spatial_ce_4: 0.15456/0.09276, loss_grounding_bce_4: 0.03717/0.08742, loss_grounding_dice_4: 0.23145/0.18193, loss_grounding_ce_4: 0.25226/0.28152, loss_mask_ce_5: 1.36820/0.93529, loss_mask_bce_5: 0.12933/0.34182, loss_mask_dice_5: 1.16977/1.19830, loss_spatial_bce_5: 0.06155/0.09638, loss_spatial_dice_5: 0.36078/0.23163, loss_spatial_ce_5: 0.17887/0.10751, loss_grounding_bce_5: 0.04563/0.08783, loss_grounding_dice_5: 0.25955/0.18312, loss_grounding_ce_5: 0.43793/0.29416, loss_mask_ce_6: 1.32062/0.97512, loss_mask_bce_6: 0.13677/0.34449, loss_mask_dice_6: 0.97419/1.20119, loss_spatial_bce_6: 0.05451/0.10215, loss_spatial_dice_6: 0.27831/0.23448, loss_spatial_ce_6: 0.29271/0.13384, loss_grounding_bce_6: 0.04620/0.08853, loss_grounding_dice_6: 0.17084/0.18346, loss_grounding_ce_6: 0.40604/0.30986, loss_mask_ce_7: 1.25087/1.01996, loss_mask_bce_7: 0.13474/0.35235, loss_mask_dice_7: 1.30456/1.25588, loss_spatial_bce_7: 0.08920/0.11028, loss_spatial_dice_7: 0.35966/0.26223, loss_spatial_ce_7: 0.28213/0.16979, loss_grounding_bce_7: 0.05069/0.09043, loss_grounding_dice_7: 0.15944/0.19066, loss_grounding_ce_7: 0.23424/0.34093, loss_mask_ce_8: 1.41083/1.12865, loss_mask_bce_8: 0.13596/0.36600, loss_mask_dice_8: 1.07901/1.32940, loss_spatial_bce_8: 0.18836/0.13108, loss_spatial_dice_8: 0.46572/0.30053, loss_spatial_ce_8: 0.34268/0.22659, loss_grounding_bce_8: 0.05128/0.09421, loss_grounding_dice_8: 0.31186/0.20175, loss_grounding_ce_8: 0.48492/0.40836, loss_mask_ce_9: 2.95517/3.67825, loss_mask_bce_9: 0.24301/0.39292, loss_mask_dice_9: 1.36378/1.90203, loss_spatial_bce_9: 0.22404/0.33344, loss_spatial_dice_9: 0.76807/0.82219, loss_spatial_ce_9: 2.70923/1.49865, loss_grounding_bce_9: 0.10005/0.10564, loss_grounding_dice_9: 0.41750/0.28082, loss_grounding_ce_9: 0.37276/0.67357] items per batch[64] items per second[0.23] total items[3635200] mini batches[ 56800] memory[7345] epoch remaining[1:17:56]
INFO:trainer.default_trainer:epochs[    31] optim steps[56900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39672/0.90095, loss_mask_bce_0: 0.27101/0.33493, loss_mask_dice_0: 2.60502/1.16319, loss_spatial_bce_0: 0.07865/0.08776, loss_spatial_dice_0: 0.20446/0.20936, loss_spatial_ce_0: 0.03897/0.06377, loss_grounding_bce_0: 0.13433/0.08637, loss_grounding_dice_0: 0.18373/0.17863, loss_grounding_ce_0: 0.24302/0.27243, loss_mask_ce_1: 1.35449/0.90154, loss_mask_bce_1: 0.27521/0.33585, loss_mask_dice_1: 2.66741/1.16945, loss_spatial_bce_1: 0.07963/0.08829, loss_spatial_dice_1: 0.21916/0.21337, loss_spatial_ce_1: 0.10188/0.06958, loss_grounding_bce_1: 0.13418/0.08653, loss_grounding_dice_1: 0.21280/0.17941, loss_grounding_ce_1: 0.24896/0.27339, loss_mask_ce_2: 1.47520/0.90861, loss_mask_bce_2: 0.27882/0.33640, loss_mask_dice_2: 2.56555/1.16990, loss_spatial_bce_2: 0.08139/0.08921, loss_spatial_dice_2: 0.23133/0.21484, loss_spatial_ce_2: 0.05403/0.07292, loss_grounding_bce_2: 0.13304/0.08665, loss_grounding_dice_2: 0.19077/0.17928, loss_grounding_ce_2: 0.28008/0.27695, loss_mask_ce_3: 1.27390/0.91856, loss_mask_bce_3: 0.26921/0.33747, loss_mask_dice_3: 2.34467/1.16744, loss_spatial_bce_3: 0.08112/0.09028, loss_spatial_dice_3: 0.22143/0.21560, loss_spatial_ce_3: 0.05030/0.07710, loss_grounding_bce_3: 0.13929/0.08690, loss_grounding_dice_3: 0.20411/0.17898, loss_grounding_ce_3: 0.25493/0.27879, loss_mask_ce_4: 1.41521/0.91933, loss_mask_bce_4: 0.29362/0.33956, loss_mask_dice_4: 2.89005/1.19135, loss_spatial_bce_4: 0.08952/0.09428, loss_spatial_dice_4: 0.28220/0.22762, loss_spatial_ce_4: 0.32135/0.09274, loss_grounding_bce_4: 0.13281/0.08742, loss_grounding_dice_4: 0.19973/0.18194, loss_grounding_ce_4: 0.31198/0.28147, loss_mask_ce_5: 1.55978/0.93527, loss_mask_bce_5: 0.28486/0.34184, loss_mask_dice_5: 2.57549/1.19854, loss_spatial_bce_5: 0.09676/0.09637, loss_spatial_dice_5: 0.29984/0.23162, loss_spatial_ce_5: 0.04415/0.10749, loss_grounding_bce_5: 0.13122/0.08782, loss_grounding_dice_5: 0.18711/0.18313, loss_grounding_ce_5: 0.31566/0.29416, loss_mask_ce_6: 1.48721/0.97515, loss_mask_bce_6: 0.30170/0.34450, loss_mask_dice_6: 2.76947/1.20142, loss_spatial_bce_6: 0.09551/0.10214, loss_spatial_dice_6: 0.27181/0.23447, loss_spatial_ce_6: 0.07011/0.13383, loss_grounding_bce_6: 0.15676/0.08851, loss_grounding_dice_6: 0.23209/0.18347, loss_grounding_ce_6: 0.31845/0.30990, loss_mask_ce_7: 1.61020/1.01998, loss_mask_bce_7: 0.34258/0.35236, loss_mask_dice_7: 2.95635/1.25611, loss_spatial_bce_7: 0.10001/0.11029, loss_spatial_dice_7: 0.33368/0.26222, loss_spatial_ce_7: 0.19621/0.16976, loss_grounding_bce_7: 0.16632/0.09043, loss_grounding_dice_7: 0.18226/0.19067, loss_grounding_ce_7: 0.27567/0.34091, loss_mask_ce_8: 1.54946/1.12867, loss_mask_bce_8: 0.37798/0.36601, loss_mask_dice_8: 3.12310/1.32963, loss_spatial_bce_8: 0.10428/0.13107, loss_spatial_dice_8: 0.38579/0.30052, loss_spatial_ce_8: 0.22679/0.22656, loss_grounding_bce_8: 0.19203/0.09420, loss_grounding_dice_8: 0.22426/0.20176, loss_grounding_ce_8: 0.26236/0.40834, loss_mask_ce_9: 4.50129/3.67849, loss_mask_bce_9: 0.33839/0.39293, loss_mask_dice_9: 3.92053/1.90232, loss_spatial_bce_9: 0.20500/0.33343, loss_spatial_dice_9: 0.81747/0.82218, loss_spatial_ce_9: 1.24348/1.49865, loss_grounding_bce_9: 0.16734/0.10563, loss_grounding_dice_9: 0.32053/0.28082, loss_grounding_ce_9: 0.39885/0.67362] items per batch[64] items per second[0.23] total items[3641600] mini batches[ 56900] memory[7345] epoch remaining[1:12:27]
INFO:trainer.default_trainer:epochs[    31] optim steps[57000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07585/0.90105, loss_mask_bce_0: 0.06227/0.33489, loss_mask_dice_0: 0.30160/1.16310, loss_spatial_bce_0: 0.03847/0.08775, loss_spatial_dice_0: 0.16803/0.20936, loss_spatial_ce_0: 0.05167/0.06372, loss_grounding_bce_0: 0.06741/0.08636, loss_grounding_dice_0: 0.28454/0.17863, loss_grounding_ce_0: 0.98048/0.27250, loss_mask_ce_1: 1.08848/0.90162, loss_mask_bce_1: 0.06699/0.33581, loss_mask_dice_1: 0.30905/1.16936, loss_spatial_bce_1: 0.03591/0.08828, loss_spatial_dice_1: 0.16527/0.21335, loss_spatial_ce_1: 0.07351/0.06954, loss_grounding_bce_1: 0.06909/0.08652, loss_grounding_dice_1: 0.30055/0.17942, loss_grounding_ce_1: 0.96383/0.27342, loss_mask_ce_2: 1.07495/0.90868, loss_mask_bce_2: 0.06209/0.33637, loss_mask_dice_2: 0.30339/1.16982, loss_spatial_bce_2: 0.03826/0.08921, loss_spatial_dice_2: 0.17243/0.21483, loss_spatial_ce_2: 0.08805/0.07289, loss_grounding_bce_2: 0.06393/0.08665, loss_grounding_dice_2: 0.29373/0.17929, loss_grounding_ce_2: 0.92508/0.27700, loss_mask_ce_3: 1.13486/0.91867, loss_mask_bce_3: 0.06337/0.33744, loss_mask_dice_3: 0.28701/1.16737, loss_spatial_bce_3: 0.05047/0.09027, loss_spatial_dice_3: 0.18485/0.21559, loss_spatial_ce_3: 0.06914/0.07708, loss_grounding_bce_3: 0.07301/0.08689, loss_grounding_dice_3: 0.29342/0.17900, loss_grounding_ce_3: 1.01061/0.27884, loss_mask_ce_4: 1.21434/0.91941, loss_mask_bce_4: 0.07209/0.33953, loss_mask_dice_4: 0.32572/1.19129, loss_spatial_bce_4: 0.03742/0.09427, loss_spatial_dice_4: 0.19703/0.22761, loss_spatial_ce_4: 0.07063/0.09271, loss_grounding_bce_4: 0.07869/0.08741, loss_grounding_dice_4: 0.27391/0.18195, loss_grounding_ce_4: 0.95396/0.28150, loss_mask_ce_5: 0.93839/0.93533, loss_mask_bce_5: 0.04902/0.34181, loss_mask_dice_5: 0.30864/1.19850, loss_spatial_bce_5: 0.05949/0.09637, loss_spatial_dice_5: 0.21360/0.23162, loss_spatial_ce_5: 0.07509/0.10745, loss_grounding_bce_5: 0.04917/0.08781, loss_grounding_dice_5: 0.28657/0.18313, loss_grounding_ce_5: 1.00198/0.29415, loss_mask_ce_6: 1.01523/0.97524, loss_mask_bce_6: 0.05282/0.34447, loss_mask_dice_6: 0.26515/1.20137, loss_spatial_bce_6: 0.07929/0.10214, loss_spatial_dice_6: 0.19804/0.23447, loss_spatial_ce_6: 0.10473/0.13380, loss_grounding_bce_6: 0.04640/0.08851, loss_grounding_dice_6: 0.28347/0.18347, loss_grounding_ce_6: 1.01575/0.30991, loss_mask_ce_7: 1.11104/1.02007, loss_mask_bce_7: 0.06125/0.35234, loss_mask_dice_7: 0.33596/1.25608, loss_spatial_bce_7: 0.04317/0.11028, loss_spatial_dice_7: 0.23254/0.26222, loss_spatial_ce_7: 0.13685/0.16973, loss_grounding_bce_7: 0.05139/0.09042, loss_grounding_dice_7: 0.29444/0.19068, loss_grounding_ce_7: 1.18160/0.34091, loss_mask_ce_8: 1.16959/1.12869, loss_mask_bce_8: 0.07341/0.36598, loss_mask_dice_8: 0.37662/1.32955, loss_spatial_bce_8: 0.05953/0.13106, loss_spatial_dice_8: 0.24595/0.30052, loss_spatial_ce_8: 0.17082/0.22648, loss_grounding_bce_8: 0.06151/0.09419, loss_grounding_dice_8: 0.29614/0.20177, loss_grounding_ce_8: 0.69189/0.40831, loss_mask_ce_9: 3.14117/3.67833, loss_mask_bce_9: 0.05778/0.39289, loss_mask_dice_9: 0.48488/1.90220, loss_spatial_bce_9: 0.71647/0.33343, loss_spatial_dice_9: 0.84742/0.82220, loss_spatial_ce_9: 1.52765/1.49858, loss_grounding_bce_9: 0.04409/0.10561, loss_grounding_dice_9: 0.30831/0.28084, loss_grounding_ce_9: 0.66154/0.67349] items per batch[64] items per second[0.23] total items[3648000] mini batches[ 57000] memory[7345] epoch remaining[1:07:41]
INFO:trainer.default_trainer:epochs[    31] optim steps[57100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84333/0.90111, loss_mask_bce_0: 0.44172/0.33488, loss_mask_dice_0: 0.96541/1.16310, loss_spatial_bce_0: 0.10191/0.08774, loss_spatial_dice_0: 0.16788/0.20935, loss_spatial_ce_0: 0.03223/0.06371, loss_grounding_bce_0: 0.04248/0.08634, loss_grounding_dice_0: 0.26753/0.17863, loss_grounding_ce_0: 0.36808/0.27246, loss_mask_ce_1: 0.75571/0.90167, loss_mask_bce_1: 0.46673/0.33580, loss_mask_dice_1: 0.97403/1.16935, loss_spatial_bce_1: 0.09842/0.08827, loss_spatial_dice_1: 0.16290/0.21334, loss_spatial_ce_1: 0.03754/0.06951, loss_grounding_bce_1: 0.03939/0.08651, loss_grounding_dice_1: 0.24496/0.17940, loss_grounding_ce_1: 0.54349/0.27337, loss_mask_ce_2: 0.71720/0.90878, loss_mask_bce_2: 0.46239/0.33636, loss_mask_dice_2: 0.97916/1.16982, loss_spatial_bce_2: 0.09874/0.08920, loss_spatial_dice_2: 0.16336/0.21482, loss_spatial_ce_2: 0.03147/0.07286, loss_grounding_bce_2: 0.04773/0.08663, loss_grounding_dice_2: 0.25875/0.17928, loss_grounding_ce_2: 0.26054/0.27695, loss_mask_ce_3: 0.69978/0.91877, loss_mask_bce_3: 0.42945/0.33743, loss_mask_dice_3: 0.94104/1.16737, loss_spatial_bce_3: 0.09678/0.09027, loss_spatial_dice_3: 0.16873/0.21558, loss_spatial_ce_3: 0.03896/0.07705, loss_grounding_bce_3: 0.04453/0.08688, loss_grounding_dice_3: 0.28097/0.17899, loss_grounding_ce_3: 0.76768/0.27880, loss_mask_ce_4: 0.80893/0.91952, loss_mask_bce_4: 0.42274/0.33952, loss_mask_dice_4: 0.97015/1.19130, loss_spatial_bce_4: 0.09743/0.09427, loss_spatial_dice_4: 0.18778/0.22761, loss_spatial_ce_4: 0.13704/0.09268, loss_grounding_bce_4: 0.04443/0.08740, loss_grounding_dice_4: 0.28790/0.18194, loss_grounding_ce_4: 0.26399/0.28145, loss_mask_ce_5: 0.78947/0.93544, loss_mask_bce_5: 0.42368/0.34180, loss_mask_dice_5: 0.95755/1.19852, loss_spatial_bce_5: 0.10338/0.09636, loss_spatial_dice_5: 0.19689/0.23162, loss_spatial_ce_5: 0.09749/0.10741, loss_grounding_bce_5: 0.04172/0.08779, loss_grounding_dice_5: 0.30473/0.18313, loss_grounding_ce_5: 0.30137/0.29411, loss_mask_ce_6: 0.86885/0.97536, loss_mask_bce_6: 0.43272/0.34446, loss_mask_dice_6: 0.97699/1.20140, loss_spatial_bce_6: 0.11867/0.10214, loss_spatial_dice_6: 0.19528/0.23447, loss_spatial_ce_6: 0.15133/0.13376, loss_grounding_bce_6: 0.03894/0.08849, loss_grounding_dice_6: 0.24051/0.18347, loss_grounding_ce_6: 0.27000/0.30987, loss_mask_ce_7: 0.71834/1.02018, loss_mask_bce_7: 0.39385/0.35233, loss_mask_dice_7: 1.05012/1.25608, loss_spatial_bce_7: 0.10629/0.11028, loss_spatial_dice_7: 0.20829/0.26222, loss_spatial_ce_7: 0.19774/0.16968, loss_grounding_bce_7: 0.04422/0.09040, loss_grounding_dice_7: 0.26285/0.19067, loss_grounding_ce_7: 0.89554/0.34085, loss_mask_ce_8: 0.98973/1.12872, loss_mask_bce_8: 0.48473/0.36597, loss_mask_dice_8: 1.10649/1.32956, loss_spatial_bce_8: 0.13109/0.13104, loss_spatial_dice_8: 0.24388/0.30051, loss_spatial_ce_8: 0.20440/0.22644, loss_grounding_bce_8: 0.04443/0.09418, loss_grounding_dice_8: 0.21868/0.20177, loss_grounding_ce_8: 0.87105/0.40823, loss_mask_ce_9: 3.30757/3.67831, loss_mask_bce_9: 0.55099/0.39288, loss_mask_dice_9: 1.74049/1.90219, loss_spatial_bce_9: 0.27890/0.33343, loss_spatial_dice_9: 0.89242/0.82218, loss_spatial_ce_9: 1.28591/1.49847, loss_grounding_bce_9: 0.07493/0.10561, loss_grounding_dice_9: 0.55428/0.28086, loss_grounding_ce_9: 1.13397/0.67348] items per batch[64] items per second[0.23] total items[3654400] mini batches[ 57100] memory[7345] epoch remaining[1:03:04]
INFO:trainer.default_trainer:epochs[    31] optim steps[57200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43861/0.90121, loss_mask_bce_0: 0.55269/0.33488, loss_mask_dice_0: 1.37510/1.16338, loss_spatial_bce_0: 0.07556/0.08772, loss_spatial_dice_0: 0.19678/0.20935, loss_spatial_ce_0: 0.00169/0.06367, loss_grounding_bce_0: 0.06984/0.08633, loss_grounding_dice_0: 0.22443/0.17864, loss_grounding_ce_0: 0.61644/0.27247, loss_mask_ce_1: 1.40275/0.90176, loss_mask_bce_1: 0.54444/0.33581, loss_mask_dice_1: 1.34464/1.16968, loss_spatial_bce_1: 0.08056/0.08825, loss_spatial_dice_1: 0.21701/0.21334, loss_spatial_ce_1: 0.00439/0.06948, loss_grounding_bce_1: 0.07101/0.08650, loss_grounding_dice_1: 0.23319/0.17941, loss_grounding_ce_1: 0.60321/0.27339, loss_mask_ce_2: 1.40784/0.90888, loss_mask_bce_2: 0.52621/0.33636, loss_mask_dice_2: 1.19361/1.17013, loss_spatial_bce_2: 0.08655/0.08919, loss_spatial_dice_2: 0.20726/0.21482, loss_spatial_ce_2: 0.00377/0.07283, loss_grounding_bce_2: 0.06644/0.08662, loss_grounding_dice_2: 0.21660/0.17928, loss_grounding_ce_2: 0.56166/0.27698, loss_mask_ce_3: 1.44643/0.91885, loss_mask_bce_3: 0.55029/0.33744, loss_mask_dice_3: 1.22462/1.16771, loss_spatial_bce_3: 0.08331/0.09026, loss_spatial_dice_3: 0.20625/0.21559, loss_spatial_ce_3: 0.02806/0.07701, loss_grounding_bce_3: 0.07175/0.08687, loss_grounding_dice_3: 0.24041/0.17899, loss_grounding_ce_3: 0.58842/0.27887, loss_mask_ce_4: 1.37825/0.91961, loss_mask_bce_4: 0.51800/0.33952, loss_mask_dice_4: 1.31536/1.19163, loss_spatial_bce_4: 0.09486/0.09426, loss_spatial_dice_4: 0.23188/0.22762, loss_spatial_ce_4: 0.01064/0.09267, loss_grounding_bce_4: 0.07360/0.08738, loss_grounding_dice_4: 0.24909/0.18194, loss_grounding_ce_4: 0.57175/0.28151, loss_mask_ce_5: 1.39019/0.93555, loss_mask_bce_5: 0.50115/0.34180, loss_mask_dice_5: 1.23650/1.19886, loss_spatial_bce_5: 0.09187/0.09636, loss_spatial_dice_5: 0.22050/0.23163, loss_spatial_ce_5: 0.10371/0.10739, loss_grounding_bce_5: 0.07530/0.08778, loss_grounding_dice_5: 0.27620/0.18313, loss_grounding_ce_5: 0.55305/0.29412, loss_mask_ce_6: 1.45984/0.97547, loss_mask_bce_6: 0.50360/0.34447, loss_mask_dice_6: 1.39638/1.20174, loss_spatial_bce_6: 0.10788/0.10213, loss_spatial_dice_6: 0.27262/0.23449, loss_spatial_ce_6: 0.08114/0.13375, loss_grounding_bce_6: 0.07095/0.08848, loss_grounding_dice_6: 0.24459/0.18346, loss_grounding_ce_6: 0.59570/0.30992, loss_mask_ce_7: 1.48512/1.02027, loss_mask_bce_7: 0.48935/0.35233, loss_mask_dice_7: 1.20186/1.25641, loss_spatial_bce_7: 0.11490/0.11026, loss_spatial_dice_7: 0.28415/0.26224, loss_spatial_ce_7: 0.26270/0.16965, loss_grounding_bce_7: 0.06986/0.09039, loss_grounding_dice_7: 0.29453/0.19068, loss_grounding_ce_7: 0.55921/0.34090, loss_mask_ce_8: 1.68733/1.12886, loss_mask_bce_8: 0.54787/0.36597, loss_mask_dice_8: 1.39385/1.32989, loss_spatial_bce_8: 0.16532/0.13102, loss_spatial_dice_8: 0.31189/0.30053, loss_spatial_ce_8: 0.30336/0.22639, loss_grounding_bce_8: 0.06111/0.09416, loss_grounding_dice_8: 0.29106/0.20177, loss_grounding_ce_8: 0.53338/0.40830, loss_mask_ce_9: 3.18082/3.67838, loss_mask_bce_9: 0.50174/0.39288, loss_mask_dice_9: 1.86872/1.90264, loss_spatial_bce_9: 0.36641/0.33343, loss_spatial_dice_9: 0.87906/0.82219, loss_spatial_ce_9: 1.33598/1.49850, loss_grounding_bce_9: 0.05838/0.10559, loss_grounding_dice_9: 0.43887/0.28088, loss_grounding_ce_9: 0.54338/0.67344] items per batch[64] items per second[0.24] total items[3660800] mini batches[ 57200] memory[7345] epoch remaining[0:58:01]
INFO:trainer.default_trainer:epochs[    31] optim steps[57300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11848/0.90117, loss_mask_bce_0: 0.80088/0.33487, loss_mask_dice_0: 1.51909/1.16340, loss_spatial_bce_0: 0.09174/0.08771, loss_spatial_dice_0: 0.15918/0.20932, loss_spatial_ce_0: 0.01068/0.06365, loss_grounding_bce_0: 0.02125/0.08634, loss_grounding_dice_0: 0.09043/0.17862, loss_grounding_ce_0: 0.10886/0.27238, loss_mask_ce_1: 1.11998/0.90173, loss_mask_bce_1: 0.78477/0.33581, loss_mask_dice_1: 1.42017/1.16973, loss_spatial_bce_1: 0.10770/0.08824, loss_spatial_dice_1: 0.17254/0.21332, loss_spatial_ce_1: 0.01001/0.06945, loss_grounding_bce_1: 0.02183/0.08651, loss_grounding_dice_1: 0.08658/0.17940, loss_grounding_ce_1: 0.10659/0.27329, loss_mask_ce_2: 1.10852/0.90884, loss_mask_bce_2: 0.81222/0.33635, loss_mask_dice_2: 1.46456/1.17019, loss_spatial_bce_2: 0.13759/0.08918, loss_spatial_dice_2: 0.17950/0.21480, loss_spatial_ce_2: 0.00881/0.07280, loss_grounding_bce_2: 0.02283/0.08663, loss_grounding_dice_2: 0.07482/0.17926, loss_grounding_ce_2: 0.11093/0.27689, loss_mask_ce_3: 1.10423/0.91879, loss_mask_bce_3: 0.81072/0.33743, loss_mask_dice_3: 1.41250/1.16778, loss_spatial_bce_3: 0.11534/0.09025, loss_spatial_dice_3: 0.17941/0.21556, loss_spatial_ce_3: 0.01367/0.07699, loss_grounding_bce_3: 0.02040/0.08687, loss_grounding_dice_3: 0.07637/0.17897, loss_grounding_ce_3: 0.11555/0.27881, loss_mask_ce_4: 1.14342/0.91958, loss_mask_bce_4: 0.81519/0.33952, loss_mask_dice_4: 1.41288/1.19166, loss_spatial_bce_4: 0.14884/0.09425, loss_spatial_dice_4: 0.20764/0.22760, loss_spatial_ce_4: 0.01386/0.09265, loss_grounding_bce_4: 0.02228/0.08739, loss_grounding_dice_4: 0.07538/0.18193, loss_grounding_ce_4: 0.12606/0.28143, loss_mask_ce_5: 1.16667/0.93553, loss_mask_bce_5: 0.84010/0.34180, loss_mask_dice_5: 1.46997/1.19890, loss_spatial_bce_5: 0.17726/0.09635, loss_spatial_dice_5: 0.20676/0.23161, loss_spatial_ce_5: 0.01756/0.10737, loss_grounding_bce_5: 0.02177/0.08779, loss_grounding_dice_5: 0.08480/0.18311, loss_grounding_ce_5: 0.11378/0.29404, loss_mask_ce_6: 1.22613/0.97546, loss_mask_bce_6: 0.84316/0.34447, loss_mask_dice_6: 1.39883/1.20179, loss_spatial_bce_6: 0.11668/0.10213, loss_spatial_dice_6: 0.19684/0.23448, loss_spatial_ce_6: 0.10523/0.13372, loss_grounding_bce_6: 0.02425/0.08849, loss_grounding_dice_6: 0.10064/0.18345, loss_grounding_ce_6: 0.11715/0.30986, loss_mask_ce_7: 1.43496/1.02028, loss_mask_bce_7: 0.85426/0.35233, loss_mask_dice_7: 1.44044/1.25645, loss_spatial_bce_7: 0.19593/0.11025, loss_spatial_dice_7: 0.24070/0.26223, loss_spatial_ce_7: 0.16630/0.16962, loss_grounding_bce_7: 0.02586/0.09040, loss_grounding_dice_7: 0.09053/0.19067, loss_grounding_ce_7: 0.15989/0.34079, loss_mask_ce_8: 1.65467/1.12878, loss_mask_bce_8: 0.73597/0.36597, loss_mask_dice_8: 1.52854/1.32995, loss_spatial_bce_8: 0.23616/0.13102, loss_spatial_dice_8: 0.25880/0.30050, loss_spatial_ce_8: 0.26686/0.22634, loss_grounding_bce_8: 0.02957/0.09417, loss_grounding_dice_8: 0.12069/0.20175, loss_grounding_ce_8: 0.21133/0.40818, loss_mask_ce_9: 4.90605/3.67849, loss_mask_bce_9: 0.98737/0.39289, loss_mask_dice_9: 2.66628/1.90272, loss_spatial_bce_9: 0.31576/0.33344, loss_spatial_dice_9: 0.94065/0.82218, loss_spatial_ce_9: 1.40166/1.49827, loss_grounding_bce_9: 0.08170/0.10560, loss_grounding_dice_9: 0.21125/0.28086, loss_grounding_ce_9: 1.09263/0.67347] items per batch[64] items per second[0.23] total items[3667200] mini batches[ 57300] memory[7345] epoch remaining[0:53:22]
INFO:trainer.default_trainer:epochs[    31] optim steps[57400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18619/0.90108, loss_mask_bce_0: 0.13453/0.33489, loss_mask_dice_0: 0.24802/1.16333, loss_spatial_bce_0: 0.06194/0.08770, loss_spatial_dice_0: 0.10464/0.20929, loss_spatial_ce_0: 0.00502/0.06359, loss_grounding_bce_0: 0.05543/0.08635, loss_grounding_dice_0: 0.05784/0.17862, loss_grounding_ce_0: 0.01176/0.27247, loss_mask_ce_1: 0.18693/0.90163, loss_mask_bce_1: 0.13286/0.33583, loss_mask_dice_1: 0.24607/1.16963, loss_spatial_bce_1: 0.05838/0.08823, loss_spatial_dice_1: 0.10087/0.21328, loss_spatial_ce_1: 0.00443/0.06939, loss_grounding_bce_1: 0.05263/0.08652, loss_grounding_dice_1: 0.05770/0.17941, loss_grounding_ce_1: 0.01028/0.27337, loss_mask_ce_2: 0.18400/0.90878, loss_mask_bce_2: 0.13592/0.33638, loss_mask_dice_2: 0.25587/1.17010, loss_spatial_bce_2: 0.05812/0.08917, loss_spatial_dice_2: 0.10082/0.21476, loss_spatial_ce_2: 0.00251/0.07275, loss_grounding_bce_2: 0.05479/0.08664, loss_grounding_dice_2: 0.06352/0.17927, loss_grounding_ce_2: 0.01231/0.27696, loss_mask_ce_3: 0.17588/0.91870, loss_mask_bce_3: 0.13449/0.33746, loss_mask_dice_3: 0.24526/1.16770, loss_spatial_bce_3: 0.05865/0.09024, loss_spatial_dice_3: 0.09777/0.21554, loss_spatial_ce_3: 0.00332/0.07693, loss_grounding_bce_3: 0.05549/0.08688, loss_grounding_dice_3: 0.06213/0.17898, loss_grounding_ce_3: 0.03202/0.27888, loss_mask_ce_4: 0.16471/0.91950, loss_mask_bce_4: 0.14203/0.33954, loss_mask_dice_4: 0.24724/1.19159, loss_spatial_bce_4: 0.06306/0.09424, loss_spatial_dice_4: 0.09747/0.22757, loss_spatial_ce_4: 0.00177/0.09260, loss_grounding_bce_4: 0.05458/0.08740, loss_grounding_dice_4: 0.05987/0.18194, loss_grounding_ce_4: 0.01669/0.28146, loss_mask_ce_5: 0.13218/0.93546, loss_mask_bce_5: 0.13567/0.34183, loss_mask_dice_5: 0.25309/1.19880, loss_spatial_bce_5: 0.06454/0.09634, loss_spatial_dice_5: 0.10122/0.23158, loss_spatial_ce_5: 0.00531/0.10733, loss_grounding_bce_5: 0.05387/0.08780, loss_grounding_dice_5: 0.05672/0.18311, loss_grounding_ce_5: 0.01315/0.29409, loss_mask_ce_6: 0.19149/0.97540, loss_mask_bce_6: 0.13631/0.34449, loss_mask_dice_6: 0.25346/1.20171, loss_spatial_bce_6: 0.06655/0.10213, loss_spatial_dice_6: 0.10218/0.23446, loss_spatial_ce_6: 0.03915/0.13369, loss_grounding_bce_6: 0.05523/0.08850, loss_grounding_dice_6: 0.05609/0.18346, loss_grounding_ce_6: 0.04111/0.30990, loss_mask_ce_7: 0.21736/1.02024, loss_mask_bce_7: 0.14585/0.35236, loss_mask_dice_7: 0.25956/1.25634, loss_spatial_bce_7: 0.06841/0.11024, loss_spatial_dice_7: 0.10676/0.26221, loss_spatial_ce_7: 0.04545/0.16956, loss_grounding_bce_7: 0.05326/0.09041, loss_grounding_dice_7: 0.05925/0.19067, loss_grounding_ce_7: 0.05779/0.34087, loss_mask_ce_8: 0.18811/1.12871, loss_mask_bce_8: 0.15554/0.36599, loss_mask_dice_8: 0.26491/1.32983, loss_spatial_bce_8: 0.07567/0.13100, loss_spatial_dice_8: 0.12737/0.30048, loss_spatial_ce_8: 0.09615/0.22628, loss_grounding_bce_8: 0.04915/0.09418, loss_grounding_dice_8: 0.05527/0.20175, loss_grounding_ce_8: 0.03734/0.40820, loss_mask_ce_9: 2.40779/3.67834, loss_mask_bce_9: 0.15269/0.39291, loss_mask_dice_9: 0.32051/1.90265, loss_spatial_bce_9: 0.50624/0.33344, loss_spatial_dice_9: 0.75251/0.82218, loss_spatial_ce_9: 1.26145/1.49815, loss_grounding_bce_9: 0.07050/0.10562, loss_grounding_dice_9: 0.07609/0.28087, loss_grounding_ce_9: 0.77149/0.67347] items per batch[64] items per second[0.23] total items[3673600] mini batches[ 57400] memory[7345] epoch remaining[0:48:46]
INFO:trainer.default_trainer:epochs[    31] optim steps[57500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04422/0.90093, loss_mask_bce_0: 0.44197/0.33490, loss_mask_dice_0: 1.13986/1.16327, loss_spatial_bce_0: 0.11602/0.08771, loss_spatial_dice_0: 0.26140/0.20927, loss_spatial_ce_0: 0.00259/0.06359, loss_grounding_bce_0: 0.04063/0.08636, loss_grounding_dice_0: 0.13844/0.17860, loss_grounding_ce_0: 3.50815/0.27257, loss_mask_ce_1: 1.03374/0.90149, loss_mask_bce_1: 0.44296/0.33584, loss_mask_dice_1: 1.12495/1.16960, loss_spatial_bce_1: 0.10759/0.08824, loss_spatial_dice_1: 0.24660/0.21326, loss_spatial_ce_1: 0.00312/0.06936, loss_grounding_bce_1: 0.03965/0.08654, loss_grounding_dice_1: 0.12712/0.17939, loss_grounding_ce_1: 3.65521/0.27347, loss_mask_ce_2: 0.94325/0.90865, loss_mask_bce_2: 0.44546/0.33639, loss_mask_dice_2: 1.25358/1.17006, loss_spatial_bce_2: 0.11268/0.08917, loss_spatial_dice_2: 0.27602/0.21475, loss_spatial_ce_2: 0.00414/0.07274, loss_grounding_bce_2: 0.03870/0.08665, loss_grounding_dice_2: 0.14230/0.17925, loss_grounding_ce_2: 3.92304/0.27707, loss_mask_ce_3: 1.23116/0.91859, loss_mask_bce_3: 0.44638/0.33746, loss_mask_dice_3: 1.20071/1.16766, loss_spatial_bce_3: 0.11513/0.09025, loss_spatial_dice_3: 0.27158/0.21552, loss_spatial_ce_3: 0.00733/0.07694, loss_grounding_bce_3: 0.04367/0.08689, loss_grounding_dice_3: 0.13705/0.17895, loss_grounding_ce_3: 3.66485/0.27898, loss_mask_ce_4: 1.21902/0.91936, loss_mask_bce_4: 0.44951/0.33955, loss_mask_dice_4: 1.08724/1.19152, loss_spatial_bce_4: 0.13250/0.09425, loss_spatial_dice_4: 0.28552/0.22755, loss_spatial_ce_4: 0.05053/0.09259, loss_grounding_bce_4: 0.03835/0.08741, loss_grounding_dice_4: 0.09213/0.18192, loss_grounding_ce_4: 3.17137/0.28156, loss_mask_ce_5: 1.10273/0.93530, loss_mask_bce_5: 0.45267/0.34185, loss_mask_dice_5: 1.20845/1.19875, loss_spatial_bce_5: 0.15084/0.09635, loss_spatial_dice_5: 0.28310/0.23157, loss_spatial_ce_5: 0.05163/0.10731, loss_grounding_bce_5: 0.03960/0.08781, loss_grounding_dice_5: 0.09777/0.18309, loss_grounding_ce_5: 2.43874/0.29415, loss_mask_ce_6: 1.23899/0.97527, loss_mask_bce_6: 0.48050/0.34451, loss_mask_dice_6: 1.24002/1.20167, loss_spatial_bce_6: 0.16410/0.10213, loss_spatial_dice_6: 0.31021/0.23444, loss_spatial_ce_6: 0.06532/0.13370, loss_grounding_bce_6: 0.04616/0.08852, loss_grounding_dice_6: 0.11714/0.18343, loss_grounding_ce_6: 1.87684/0.30995, loss_mask_ce_7: 1.26865/1.02013, loss_mask_bce_7: 0.49778/0.35236, loss_mask_dice_7: 1.25931/1.25627, loss_spatial_bce_7: 0.12681/0.11025, loss_spatial_dice_7: 0.31657/0.26219, loss_spatial_ce_7: 0.11256/0.16953, loss_grounding_bce_7: 0.04808/0.09043, loss_grounding_dice_7: 0.09000/0.19065, loss_grounding_ce_7: 0.37289/0.34092, loss_mask_ce_8: 1.31802/1.12855, loss_mask_bce_8: 0.58692/0.36600, loss_mask_dice_8: 1.42491/1.32976, loss_spatial_bce_8: 0.15742/0.13101, loss_spatial_dice_8: 0.36854/0.30045, loss_spatial_ce_8: 0.15659/0.22625, loss_grounding_bce_8: 0.07873/0.09420, loss_grounding_dice_8: 0.12595/0.20173, loss_grounding_ce_8: 2.02753/0.40824, loss_mask_ce_9: 4.40215/3.67826, loss_mask_bce_9: 0.50253/0.39291, loss_mask_dice_9: 1.81447/1.90249, loss_spatial_bce_9: 0.33480/0.33346, loss_spatial_dice_9: 0.93018/0.82218, loss_spatial_ce_9: 1.70291/1.49813, loss_grounding_bce_9: 0.07412/0.10564, loss_grounding_dice_9: 0.29538/0.28084, loss_grounding_ce_9: 0.66588/0.67355] items per batch[64] items per second[0.23] total items[3680000] mini batches[ 57500] memory[7345] epoch remaining[0:44:15]
INFO:trainer.default_trainer:epochs[    31] optim steps[57600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94749/0.90089, loss_mask_bce_0: 0.46732/0.33493, loss_mask_dice_0: 0.40803/1.16299, loss_spatial_bce_0: 0.14486/0.08772, loss_spatial_dice_0: 0.11929/0.20924, loss_spatial_ce_0: 0.20023/0.06357, loss_grounding_bce_0: 0.24002/0.08636, loss_grounding_dice_0: 0.11247/0.17856, loss_grounding_ce_0: 1.13826/0.27255, loss_mask_ce_1: 0.93017/0.90148, loss_mask_bce_1: 0.45117/0.33588, loss_mask_dice_1: 0.43818/1.16931, loss_spatial_bce_1: 0.19909/0.08825, loss_spatial_dice_1: 0.11360/0.21323, loss_spatial_ce_1: 0.01490/0.06933, loss_grounding_bce_1: 0.29903/0.08654, loss_grounding_dice_1: 0.12838/0.17935, loss_grounding_ce_1: 1.09572/0.27344, loss_mask_ce_2: 0.91158/0.90860, loss_mask_bce_2: 0.67666/0.33643, loss_mask_dice_2: 0.42684/1.16977, loss_spatial_bce_2: 0.15022/0.08919, loss_spatial_dice_2: 0.13157/0.21472, loss_spatial_ce_2: 0.20478/0.07270, loss_grounding_bce_2: 0.31306/0.08665, loss_grounding_dice_2: 0.13031/0.17922, loss_grounding_ce_2: 0.97148/0.27704, loss_mask_ce_3: 0.90245/0.91857, loss_mask_bce_3: 0.51980/0.33751, loss_mask_dice_3: 0.42223/1.16739, loss_spatial_bce_3: 0.13792/0.09026, loss_spatial_dice_3: 0.11440/0.21548, loss_spatial_ce_3: 0.18497/0.07691, loss_grounding_bce_3: 0.23258/0.08689, loss_grounding_dice_3: 0.09413/0.17892, loss_grounding_ce_3: 1.27375/0.27897, loss_mask_ce_4: 0.93125/0.91936, loss_mask_bce_4: 0.57683/0.33959, loss_mask_dice_4: 0.45083/1.19126, loss_spatial_bce_4: 0.11596/0.09426, loss_spatial_dice_4: 0.12037/0.22752, loss_spatial_ce_4: 0.19461/0.09256, loss_grounding_bce_4: 0.26060/0.08741, loss_grounding_dice_4: 0.12688/0.18188, loss_grounding_ce_4: 1.58137/0.28157, loss_mask_ce_5: 0.96191/0.93532, loss_mask_bce_5: 0.57900/0.34189, loss_mask_dice_5: 0.46081/1.19847, loss_spatial_bce_5: 0.13590/0.09635, loss_spatial_dice_5: 0.15208/0.23154, loss_spatial_ce_5: 0.20908/0.10729, loss_grounding_bce_5: 0.30866/0.08782, loss_grounding_dice_5: 0.13241/0.18306, loss_grounding_ce_5: 1.23041/0.29416, loss_mask_ce_6: 0.97276/0.97528, loss_mask_bce_6: 0.56749/0.34455, loss_mask_dice_6: 0.42658/1.20139, loss_spatial_bce_6: 0.16996/0.10214, loss_spatial_dice_6: 0.14789/0.23440, loss_spatial_ce_6: 0.23114/0.13367, loss_grounding_bce_6: 0.35371/0.08853, loss_grounding_dice_6: 0.15715/0.18340, loss_grounding_ce_6: 1.33230/0.30998, loss_mask_ce_7: 0.95316/1.02010, loss_mask_bce_7: 0.54272/0.35241, loss_mask_dice_7: 0.55340/1.25600, loss_spatial_bce_7: 0.17294/0.11026, loss_spatial_dice_7: 0.15307/0.26216, loss_spatial_ce_7: 0.12716/0.16948, loss_grounding_bce_7: 0.38742/0.09044, loss_grounding_dice_7: 0.16966/0.19062, loss_grounding_ce_7: 1.22292/0.34093, loss_mask_ce_8: 1.27358/1.12853, loss_mask_bce_8: 0.46183/0.36604, loss_mask_dice_8: 0.49052/1.32947, loss_spatial_bce_8: 0.18614/0.13101, loss_spatial_dice_8: 0.22238/0.30042, loss_spatial_ce_8: 0.21963/0.22621, loss_grounding_bce_8: 0.42527/0.09421, loss_grounding_dice_8: 0.22640/0.20169, loss_grounding_ce_8: 1.54826/0.40832, loss_mask_ce_9: 3.14469/3.67825, loss_mask_bce_9: 0.47858/0.39296, loss_mask_dice_9: 0.79671/1.90219, loss_spatial_bce_9: 0.55920/0.33350, loss_spatial_dice_9: 0.85660/0.82218, loss_spatial_ce_9: 1.82994/1.49809, loss_grounding_bce_9: 0.35883/0.10564, loss_grounding_dice_9: 0.15291/0.28080, loss_grounding_ce_9: 0.81333/0.67359] items per batch[64] items per second[0.22] total items[3686400] mini batches[ 57600] memory[7345] epoch remaining[0:39:48]
INFO:trainer.default_trainer:epochs[    31] optim steps[57700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73304/0.90075, loss_mask_bce_0: 0.15823/0.33491, loss_mask_dice_0: 0.50469/1.16284, loss_spatial_bce_0: 0.04928/0.08772, loss_spatial_dice_0: 0.14170/0.20922, loss_spatial_ce_0: 0.07172/0.06355, loss_grounding_bce_0: 0.06699/0.08636, loss_grounding_dice_0: 0.29941/0.17857, loss_grounding_ce_0: 0.18658/0.27267, loss_mask_ce_1: 0.60783/0.90138, loss_mask_bce_1: 0.17538/0.33586, loss_mask_dice_1: 0.55282/1.16912, loss_spatial_bce_1: 0.05211/0.08825, loss_spatial_dice_1: 0.15517/0.21322, loss_spatial_ce_1: 0.07306/0.06932, loss_grounding_bce_1: 0.06666/0.08653, loss_grounding_dice_1: 0.28197/0.17936, loss_grounding_ce_1: 0.21003/0.27355, loss_mask_ce_2: 0.72925/0.90849, loss_mask_bce_2: 0.16191/0.33641, loss_mask_dice_2: 0.52701/1.16960, loss_spatial_bce_2: 0.05664/0.08919, loss_spatial_dice_2: 0.14704/0.21471, loss_spatial_ce_2: 0.07156/0.07269, loss_grounding_bce_2: 0.06126/0.08665, loss_grounding_dice_2: 0.16697/0.17922, loss_grounding_ce_2: 0.18897/0.27709, loss_mask_ce_3: 0.69837/0.91847, loss_mask_bce_3: 0.17970/0.33749, loss_mask_dice_3: 0.52648/1.16721, loss_spatial_bce_3: 0.05979/0.09027, loss_spatial_dice_3: 0.15395/0.21547, loss_spatial_ce_3: 0.08895/0.07689, loss_grounding_bce_3: 0.06648/0.08689, loss_grounding_dice_3: 0.20140/0.17892, loss_grounding_ce_3: 0.19716/0.27908, loss_mask_ce_4: 0.81684/0.91928, loss_mask_bce_4: 0.21066/0.33958, loss_mask_dice_4: 0.52327/1.19109, loss_spatial_bce_4: 0.06137/0.09427, loss_spatial_dice_4: 0.15496/0.22752, loss_spatial_ce_4: 0.06710/0.09257, loss_grounding_bce_4: 0.06796/0.08741, loss_grounding_dice_4: 0.17818/0.18188, loss_grounding_ce_4: 0.20957/0.28162, loss_mask_ce_5: 1.23647/0.93523, loss_mask_bce_5: 0.19934/0.34188, loss_mask_dice_5: 0.50492/1.19832, loss_spatial_bce_5: 0.08787/0.09636, loss_spatial_dice_5: 0.19235/0.23154, loss_spatial_ce_5: 0.11270/0.10729, loss_grounding_bce_5: 0.06652/0.08781, loss_grounding_dice_5: 0.16551/0.18306, loss_grounding_ce_5: 0.23717/0.29422, loss_mask_ce_6: 0.63311/0.97519, loss_mask_bce_6: 0.19770/0.34454, loss_mask_dice_6: 0.53402/1.20121, loss_spatial_bce_6: 0.08406/0.10215, loss_spatial_dice_6: 0.19938/0.23440, loss_spatial_ce_6: 0.10486/0.13369, loss_grounding_bce_6: 0.06911/0.08853, loss_grounding_dice_6: 0.22994/0.18340, loss_grounding_ce_6: 0.27307/0.30993, loss_mask_ce_7: 1.01393/1.02000, loss_mask_bce_7: 0.26076/0.35240, loss_mask_dice_7: 0.67554/1.25582, loss_spatial_bce_7: 0.10310/0.11026, loss_spatial_dice_7: 0.20037/0.26215, loss_spatial_ce_7: 0.10847/0.16945, loss_grounding_bce_7: 0.06509/0.09044, loss_grounding_dice_7: 0.29174/0.19063, loss_grounding_ce_7: 0.47034/0.34090, loss_mask_ce_8: 1.26500/1.12841, loss_mask_bce_8: 0.21545/0.36602, loss_mask_dice_8: 0.70399/1.32925, loss_spatial_bce_8: 0.13091/0.13100, loss_spatial_dice_8: 0.26941/0.30040, loss_spatial_ce_8: 0.15355/0.22619, loss_grounding_bce_8: 0.06689/0.09421, loss_grounding_dice_8: 0.35422/0.20169, loss_grounding_ce_8: 0.44357/0.40830, loss_mask_ce_9: 3.38883/3.67798, loss_mask_bce_9: 0.25813/0.39292, loss_mask_dice_9: 0.79530/1.90191, loss_spatial_bce_9: 0.26841/0.33347, loss_spatial_dice_9: 0.79083/0.82217, loss_spatial_ce_9: 1.18006/1.49794, loss_grounding_bce_9: 0.08561/0.10565, loss_grounding_dice_9: 0.40442/0.28081, loss_grounding_ce_9: 0.12441/0.67346] items per batch[64] items per second[0.23] total items[3692800] mini batches[ 57700] memory[7345] epoch remaining[0:35:10]
INFO:trainer.default_trainer:epochs[    31] optim steps[57800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35594/0.90093, loss_mask_bce_0: 0.37956/0.33488, loss_mask_dice_0: 0.52247/1.16283, loss_spatial_bce_0: 0.11705/0.08771, loss_spatial_dice_0: 0.21525/0.20922, loss_spatial_ce_0: 0.17706/0.06354, loss_grounding_bce_0: 0.14502/0.08637, loss_grounding_dice_0: 0.17510/0.17858, loss_grounding_ce_0: 0.01186/0.27271, loss_mask_ce_1: 0.36010/0.90156, loss_mask_bce_1: 0.37395/0.33583, loss_mask_dice_1: 0.52031/1.16915, loss_spatial_bce_1: 0.10441/0.08824, loss_spatial_dice_1: 0.22965/0.21322, loss_spatial_ce_1: 0.21085/0.06928, loss_grounding_bce_1: 0.13495/0.08655, loss_grounding_dice_1: 0.16157/0.17938, loss_grounding_ce_1: 0.01403/0.27359, loss_mask_ce_2: 0.51149/0.90868, loss_mask_bce_2: 0.38510/0.33637, loss_mask_dice_2: 0.48000/1.16964, loss_spatial_bce_2: 0.10414/0.08918, loss_spatial_dice_2: 0.21085/0.21471, loss_spatial_ce_2: 0.18253/0.07266, loss_grounding_bce_2: 0.14738/0.08666, loss_grounding_dice_2: 0.16897/0.17923, loss_grounding_ce_2: 0.02442/0.27714, loss_mask_ce_3: 0.29626/0.91866, loss_mask_bce_3: 0.39408/0.33747, loss_mask_dice_3: 0.48052/1.16723, loss_spatial_bce_3: 0.11634/0.09025, loss_spatial_dice_3: 0.21482/0.21547, loss_spatial_ce_3: 0.19313/0.07686, loss_grounding_bce_3: 0.15312/0.08690, loss_grounding_dice_3: 0.17308/0.17893, loss_grounding_ce_3: 0.01686/0.27912, loss_mask_ce_4: 0.22624/0.91950, loss_mask_bce_4: 0.39136/0.33954, loss_mask_dice_4: 0.54906/1.19115, loss_spatial_bce_4: 0.12051/0.09426, loss_spatial_dice_4: 0.25526/0.22752, loss_spatial_ce_4: 0.16579/0.09254, loss_grounding_bce_4: 0.13230/0.08742, loss_grounding_dice_4: 0.16555/0.18190, loss_grounding_ce_4: 0.01070/0.28167, loss_mask_ce_5: 0.37334/0.93545, loss_mask_bce_5: 0.38540/0.34184, loss_mask_dice_5: 0.50466/1.19836, loss_spatial_bce_5: 0.13145/0.09636, loss_spatial_dice_5: 0.25783/0.23155, loss_spatial_ce_5: 0.18668/0.10727, loss_grounding_bce_5: 0.13505/0.08782, loss_grounding_dice_5: 0.16632/0.18308, loss_grounding_ce_5: 0.01061/0.29430, loss_mask_ce_6: 0.34583/0.97538, loss_mask_bce_6: 0.37591/0.34450, loss_mask_dice_6: 0.45195/1.20124, loss_spatial_bce_6: 0.13819/0.10214, loss_spatial_dice_6: 0.25875/0.23440, loss_spatial_ce_6: 0.22304/0.13367, loss_grounding_bce_6: 0.13290/0.08854, loss_grounding_dice_6: 0.17264/0.18341, loss_grounding_ce_6: 0.01258/0.31002, loss_mask_ce_7: 0.27809/1.02021, loss_mask_bce_7: 0.35495/0.35237, loss_mask_dice_7: 0.50363/1.25586, loss_spatial_bce_7: 0.13722/0.11025, loss_spatial_dice_7: 0.27139/0.26216, loss_spatial_ce_7: 0.18341/0.16942, loss_grounding_bce_7: 0.14417/0.09045, loss_grounding_dice_7: 0.16814/0.19065, loss_grounding_ce_7: 0.01756/0.34097, loss_mask_ce_8: 0.63842/1.12870, loss_mask_bce_8: 0.34878/0.36599, loss_mask_dice_8: 0.49291/1.32929, loss_spatial_bce_8: 0.14220/0.13098, loss_spatial_dice_8: 0.24788/0.30040, loss_spatial_ce_8: 0.27291/0.22617, loss_grounding_bce_8: 0.17512/0.09422, loss_grounding_dice_8: 0.18903/0.20172, loss_grounding_ce_8: 0.33695/0.40838, loss_mask_ce_9: 2.29430/3.67825, loss_mask_bce_9: 0.30027/0.39290, loss_mask_dice_9: 0.60735/1.90199, loss_spatial_bce_9: 0.50446/0.33346, loss_spatial_dice_9: 0.77224/0.82220, loss_spatial_ce_9: 1.10602/1.49803, loss_grounding_bce_9: 0.16525/0.10566, loss_grounding_dice_9: 0.22339/0.28083, loss_grounding_ce_9: 1.76230/0.67350] items per batch[64] items per second[0.23] total items[3699200] mini batches[ 57800] memory[7345] epoch remaining[0:30:36]
INFO:trainer.default_trainer:epochs[    31] optim steps[57900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.15924/0.90086, loss_mask_bce_0: 0.25707/0.33488, loss_mask_dice_0: 1.69663/1.16286, loss_spatial_bce_0: 0.02677/0.08770, loss_spatial_dice_0: 0.18034/0.20920, loss_spatial_ce_0: 0.03976/0.06351, loss_grounding_bce_0: 0.00590/0.08638, loss_grounding_dice_0: 0.17602/0.17858, loss_grounding_ce_0: 0.03022/0.27260, loss_mask_ce_1: 1.33036/0.90152, loss_mask_bce_1: 0.24132/0.33583, loss_mask_dice_1: 1.79342/1.16916, loss_spatial_bce_1: 0.02875/0.08823, loss_spatial_dice_1: 0.21271/0.21320, loss_spatial_ce_1: 0.02923/0.06927, loss_grounding_bce_1: 0.00513/0.08655, loss_grounding_dice_1: 0.16200/0.17937, loss_grounding_ce_1: 0.03937/0.27347, loss_mask_ce_2: 1.39028/0.90862, loss_mask_bce_2: 0.23717/0.33637, loss_mask_dice_2: 1.85902/1.16965, loss_spatial_bce_2: 0.03213/0.08917, loss_spatial_dice_2: 0.24540/0.21469, loss_spatial_ce_2: 0.02320/0.07264, loss_grounding_bce_2: 0.00500/0.08667, loss_grounding_dice_2: 0.18185/0.17922, loss_grounding_ce_2: 0.03384/0.27700, loss_mask_ce_3: 1.57587/0.91861, loss_mask_bce_3: 0.23356/0.33746, loss_mask_dice_3: 1.62278/1.16722, loss_spatial_bce_3: 0.03081/0.09025, loss_spatial_dice_3: 0.22827/0.21546, loss_spatial_ce_3: 0.12962/0.07685, loss_grounding_bce_3: 0.00549/0.08690, loss_grounding_dice_3: 0.12875/0.17892, loss_grounding_ce_3: 0.13068/0.27899, loss_mask_ce_4: 1.40011/0.91946, loss_mask_bce_4: 0.22313/0.33954, loss_mask_dice_4: 1.59324/1.19115, loss_spatial_bce_4: 0.03832/0.09426, loss_spatial_dice_4: 0.25937/0.22751, loss_spatial_ce_4: 0.02837/0.09254, loss_grounding_bce_4: 0.00602/0.08742, loss_grounding_dice_4: 0.18355/0.18188, loss_grounding_ce_4: 0.04319/0.28156, loss_mask_ce_5: 1.46486/0.93542, loss_mask_bce_5: 0.20839/0.34184, loss_mask_dice_5: 1.56560/1.19838, loss_spatial_bce_5: 0.03756/0.09636, loss_spatial_dice_5: 0.28040/0.23153, loss_spatial_ce_5: 0.03496/0.10726, loss_grounding_bce_5: 0.00625/0.08783, loss_grounding_dice_5: 0.10249/0.18306, loss_grounding_ce_5: 0.23654/0.29421, loss_mask_ce_6: 1.53063/0.97540, loss_mask_bce_6: 0.19554/0.34450, loss_mask_dice_6: 1.73879/1.20124, loss_spatial_bce_6: 0.04084/0.10214, loss_spatial_dice_6: 0.30008/0.23440, loss_spatial_ce_6: 0.03954/0.13365, loss_grounding_bce_6: 0.00542/0.08854, loss_grounding_dice_6: 0.24491/0.18340, loss_grounding_ce_6: 0.16807/0.30993, loss_mask_ce_7: 1.52473/1.02022, loss_mask_bce_7: 0.24685/0.35236, loss_mask_dice_7: 1.68646/1.25584, loss_spatial_bce_7: 0.03883/0.11024, loss_spatial_dice_7: 0.34897/0.26214, loss_spatial_ce_7: 0.03673/0.16940, loss_grounding_bce_7: 0.00593/0.09045, loss_grounding_dice_7: 0.20210/0.19064, loss_grounding_ce_7: 0.04786/0.34084, loss_mask_ce_8: 1.71132/1.12863, loss_mask_bce_8: 0.25027/0.36598, loss_mask_dice_8: 1.58855/1.32926, loss_spatial_bce_8: 0.07341/0.13098, loss_spatial_dice_8: 0.45365/0.30038, loss_spatial_ce_8: 0.17429/0.22612, loss_grounding_bce_8: 0.00528/0.09422, loss_grounding_dice_8: 0.16223/0.20170, loss_grounding_ce_8: 0.09446/0.40822, loss_mask_ce_9: 4.93586/3.67804, loss_mask_bce_9: 0.29918/0.39290, loss_mask_dice_9: 2.34916/1.90202, loss_spatial_bce_9: 0.16096/0.33345, loss_spatial_dice_9: 0.86269/0.82218, loss_spatial_ce_9: 1.35438/1.49789, loss_grounding_bce_9: 0.00998/0.10566, loss_grounding_dice_9: 0.21955/0.28081, loss_grounding_ce_9: 0.18892/0.67339] items per batch[64] items per second[0.23] total items[3705600] mini batches[ 57900] memory[7345] epoch remaining[0:25:58]
INFO:trainer.default_trainer:epochs[    31] optim steps[58000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04033/0.90082, loss_mask_bce_0: 0.15529/0.33482, loss_mask_dice_0: 0.92412/1.16290, loss_spatial_bce_0: 0.04194/0.08768, loss_spatial_dice_0: 0.21837/0.20918, loss_spatial_ce_0: 0.02973/0.06350, loss_grounding_bce_0: 0.01731/0.08634, loss_grounding_dice_0: 0.08636/0.17859, loss_grounding_ce_0: 0.33065/0.27263, loss_mask_ce_1: 1.10941/0.90147, loss_mask_bce_1: 0.15538/0.33578, loss_mask_dice_1: 0.89132/1.16920, loss_spatial_bce_1: 0.04034/0.08821, loss_spatial_dice_1: 0.20875/0.21319, loss_spatial_ce_1: 0.04362/0.06927, loss_grounding_bce_1: 0.01720/0.08651, loss_grounding_dice_1: 0.08548/0.17937, loss_grounding_ce_1: 0.41869/0.27351, loss_mask_ce_2: 1.12585/0.90857, loss_mask_bce_2: 0.16680/0.33632, loss_mask_dice_2: 0.77366/1.16968, loss_spatial_bce_2: 0.04260/0.08916, loss_spatial_dice_2: 0.20919/0.21467, loss_spatial_ce_2: 0.06019/0.07263, loss_grounding_bce_2: 0.01919/0.08663, loss_grounding_dice_2: 0.09094/0.17923, loss_grounding_ce_2: 0.25986/0.27705, loss_mask_ce_3: 1.28242/0.91860, loss_mask_bce_3: 0.13847/0.33740, loss_mask_dice_3: 0.80364/1.16722, loss_spatial_bce_3: 0.04451/0.09024, loss_spatial_dice_3: 0.21115/0.21544, loss_spatial_ce_3: 0.04168/0.07685, loss_grounding_bce_3: 0.01739/0.08687, loss_grounding_dice_3: 0.07940/0.17892, loss_grounding_ce_3: 0.36100/0.27903, loss_mask_ce_4: 1.42882/0.91944, loss_mask_bce_4: 0.13397/0.33949, loss_mask_dice_4: 0.83070/1.19115, loss_spatial_bce_4: 0.05126/0.09425, loss_spatial_dice_4: 0.24625/0.22749, loss_spatial_ce_4: 0.02671/0.09255, loss_grounding_bce_4: 0.01851/0.08738, loss_grounding_dice_4: 0.10912/0.18189, loss_grounding_ce_4: 0.30783/0.28157, loss_mask_ce_5: 1.45392/0.93542, loss_mask_bce_5: 0.14521/0.34179, loss_mask_dice_5: 0.80455/1.19843, loss_spatial_bce_5: 0.04677/0.09635, loss_spatial_dice_5: 0.25454/0.23153, loss_spatial_ce_5: 0.44997/0.10727, loss_grounding_bce_5: 0.01847/0.08779, loss_grounding_dice_5: 0.10038/0.18308, loss_grounding_ce_5: 0.26350/0.29422, loss_mask_ce_6: 1.38599/0.97538, loss_mask_bce_6: 0.15218/0.34445, loss_mask_dice_6: 0.83736/1.20127, loss_spatial_bce_6: 0.04906/0.10213, loss_spatial_dice_6: 0.25259/0.23440, loss_spatial_ce_6: 0.04542/0.13365, loss_grounding_bce_6: 0.01690/0.08850, loss_grounding_dice_6: 0.09335/0.18341, loss_grounding_ce_6: 0.26582/0.30995, loss_mask_ce_7: 1.57673/1.02025, loss_mask_bce_7: 0.15706/0.35231, loss_mask_dice_7: 0.90670/1.25589, loss_spatial_bce_7: 0.06230/0.11023, loss_spatial_dice_7: 0.29739/0.26213, loss_spatial_ce_7: 0.08501/0.16937, loss_grounding_bce_7: 0.02023/0.09042, loss_grounding_dice_7: 0.12881/0.19064, loss_grounding_ce_7: 0.36475/0.34086, loss_mask_ce_8: 1.82230/1.12863, loss_mask_bce_8: 0.14806/0.36593, loss_mask_dice_8: 0.96302/1.32925, loss_spatial_bce_8: 0.06751/0.13095, loss_spatial_dice_8: 0.32279/0.30035, loss_spatial_ce_8: 0.29498/0.22608, loss_grounding_bce_8: 0.02362/0.09418, loss_grounding_dice_8: 0.13444/0.20171, loss_grounding_ce_8: 0.28023/0.40823, loss_mask_ce_9: 2.95156/3.67814, loss_mask_bce_9: 0.17029/0.39286, loss_mask_dice_9: 1.20854/1.90215, loss_spatial_bce_9: 0.37759/0.33347, loss_spatial_dice_9: 0.87707/0.82218, loss_spatial_ce_9: 1.59721/1.49784, loss_grounding_bce_9: 0.04036/0.10563, loss_grounding_dice_9: 0.21639/0.28085, loss_grounding_ce_9: 0.33814/0.67339] items per batch[64] items per second[0.23] total items[3712000] mini batches[ 58000] memory[7345] epoch remaining[0:21:21]
INFO:trainer.default_trainer:epochs[    31] optim steps[58100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54270/0.90075, loss_mask_bce_0: 0.32620/0.33482, loss_mask_dice_0: 0.52090/1.16290, loss_spatial_bce_0: 0.21117/0.08768, loss_spatial_dice_0: 0.26067/0.20917, loss_spatial_ce_0: 0.06474/0.06349, loss_grounding_bce_0: 0.10296/0.08635, loss_grounding_dice_0: 0.15816/0.17858, loss_grounding_ce_0: 0.00326/0.27253, loss_mask_ce_1: 0.53767/0.90139, loss_mask_bce_1: 0.33809/0.33578, loss_mask_dice_1: 0.53289/1.16923, loss_spatial_bce_1: 0.20169/0.08821, loss_spatial_dice_1: 0.25717/0.21318, loss_spatial_ce_1: 0.06398/0.06927, loss_grounding_bce_1: 0.10283/0.08652, loss_grounding_dice_1: 0.16001/0.17936, loss_grounding_ce_1: 0.00301/0.27342, loss_mask_ce_2: 0.57350/0.90850, loss_mask_bce_2: 0.32861/0.33632, loss_mask_dice_2: 0.51850/1.16972, loss_spatial_bce_2: 0.19251/0.08915, loss_spatial_dice_2: 0.25298/0.21466, loss_spatial_ce_2: 0.06772/0.07262, loss_grounding_bce_2: 0.10926/0.08664, loss_grounding_dice_2: 0.16577/0.17922, loss_grounding_ce_2: 0.00305/0.27697, loss_mask_ce_3: 0.52251/0.91852, loss_mask_bce_3: 0.33767/0.33741, loss_mask_dice_3: 0.51834/1.16722, loss_spatial_bce_3: 0.22683/0.09024, loss_spatial_dice_3: 0.26956/0.21544, loss_spatial_ce_3: 0.07130/0.07684, loss_grounding_bce_3: 0.10768/0.08687, loss_grounding_dice_3: 0.16102/0.17891, loss_grounding_ce_3: 0.00263/0.27895, loss_mask_ce_4: 0.61333/0.91935, loss_mask_bce_4: 0.32484/0.33950, loss_mask_dice_4: 0.52502/1.19117, loss_spatial_bce_4: 0.22949/0.09425, loss_spatial_dice_4: 0.26740/0.22749, loss_spatial_ce_4: 0.08700/0.09259, loss_grounding_bce_4: 0.10834/0.08739, loss_grounding_dice_4: 0.17414/0.18187, loss_grounding_ce_4: 0.00275/0.28149, loss_mask_ce_5: 0.71705/0.93541, loss_mask_bce_5: 0.31610/0.34180, loss_mask_dice_5: 0.49533/1.19846, loss_spatial_bce_5: 0.22426/0.09636, loss_spatial_dice_5: 0.25656/0.23153, loss_spatial_ce_5: 0.10214/0.10731, loss_grounding_bce_5: 0.10916/0.08780, loss_grounding_dice_5: 0.17360/0.18307, loss_grounding_ce_5: 0.00291/0.29416, loss_mask_ce_6: 0.75002/0.97536, loss_mask_bce_6: 0.33232/0.34445, loss_mask_dice_6: 0.50637/1.20128, loss_spatial_bce_6: 0.27960/0.10214, loss_spatial_dice_6: 0.25725/0.23440, loss_spatial_ce_6: 0.16167/0.13368, loss_grounding_bce_6: 0.10497/0.08851, loss_grounding_dice_6: 0.16838/0.18339, loss_grounding_ce_6: 0.03654/0.30992, loss_mask_ce_7: 0.67987/1.02030, loss_mask_bce_7: 0.38988/0.35231, loss_mask_dice_7: 0.61031/1.25590, loss_spatial_bce_7: 0.30556/0.11025, loss_spatial_dice_7: 0.28168/0.26214, loss_spatial_ce_7: 0.14177/0.16936, loss_grounding_bce_7: 0.11124/0.09043, loss_grounding_dice_7: 0.17539/0.19064, loss_grounding_ce_7: 0.01179/0.34079, loss_mask_ce_8: 0.53805/1.12861, loss_mask_bce_8: 0.40831/0.36595, loss_mask_dice_8: 0.63061/1.32924, loss_spatial_bce_8: 0.33959/0.13096, loss_spatial_dice_8: 0.29038/0.30034, loss_spatial_ce_8: 0.19866/0.22608, loss_grounding_bce_8: 0.11200/0.09419, loss_grounding_dice_8: 0.18746/0.20169, loss_grounding_ce_8: 0.01592/0.40815, loss_mask_ce_9: 2.16014/3.67817, loss_mask_bce_9: 0.42348/0.39286, loss_mask_dice_9: 0.70110/1.90233, loss_spatial_bce_9: 0.43837/0.33345, loss_spatial_dice_9: 0.85552/0.82219, loss_spatial_ce_9: 1.29441/1.49789, loss_grounding_bce_9: 0.13444/0.10564, loss_grounding_dice_9: 0.25210/0.28083, loss_grounding_ce_9: 0.05685/0.67326] items per batch[64] items per second[0.22] total items[3718400] mini batches[ 58100] memory[7345] epoch remaining[0:16:47]
INFO:trainer.default_trainer:epochs[    31] optim steps[58200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11082/0.90071, loss_mask_bce_0: 0.15558/0.33481, loss_mask_dice_0: 0.22530/1.16330, loss_spatial_bce_0: 0.04734/0.08766, loss_spatial_dice_0: 0.07032/0.20918, loss_spatial_ce_0: 0.00044/0.06346, loss_grounding_bce_0: 0.05202/0.08634, loss_grounding_dice_0: 0.06965/0.17858, loss_grounding_ce_0: 0.18075/0.27252, loss_mask_ce_1: 0.13319/0.90137, loss_mask_bce_1: 0.16140/0.33576, loss_mask_dice_1: 0.22764/1.16966, loss_spatial_bce_1: 0.04999/0.08819, loss_spatial_dice_1: 0.07050/0.21318, loss_spatial_ce_1: 0.00060/0.06925, loss_grounding_bce_1: 0.05132/0.08651, loss_grounding_dice_1: 0.06978/0.17937, loss_grounding_ce_1: 0.18191/0.27339, loss_mask_ce_2: 0.15734/0.90848, loss_mask_bce_2: 0.15638/0.33631, loss_mask_dice_2: 0.22544/1.17014, loss_spatial_bce_2: 0.04866/0.08914, loss_spatial_dice_2: 0.06940/0.21468, loss_spatial_ce_2: 0.00045/0.07260, loss_grounding_bce_2: 0.05160/0.08662, loss_grounding_dice_2: 0.06896/0.17922, loss_grounding_ce_2: 0.18216/0.27696, loss_mask_ce_3: 0.13867/0.91850, loss_mask_bce_3: 0.16412/0.33739, loss_mask_dice_3: 0.24330/1.16761, loss_spatial_bce_3: 0.04417/0.09023, loss_spatial_dice_3: 0.06699/0.21546, loss_spatial_ce_3: 0.00093/0.07682, loss_grounding_bce_3: 0.05249/0.08686, loss_grounding_dice_3: 0.06622/0.17891, loss_grounding_ce_3: 0.18722/0.27894, loss_mask_ce_4: 0.18151/0.91934, loss_mask_bce_4: 0.15813/0.33948, loss_mask_dice_4: 0.22210/1.19157, loss_spatial_bce_4: 0.04618/0.09423, loss_spatial_dice_4: 0.06637/0.22751, loss_spatial_ce_4: 0.00311/0.09256, loss_grounding_bce_4: 0.05221/0.08738, loss_grounding_dice_4: 0.07805/0.18187, loss_grounding_ce_4: 0.19169/0.28148, loss_mask_ce_5: 0.18310/0.93540, loss_mask_bce_5: 0.15810/0.34179, loss_mask_dice_5: 0.22709/1.19890, loss_spatial_bce_5: 0.04687/0.09634, loss_spatial_dice_5: 0.06850/0.23155, loss_spatial_ce_5: 0.01139/0.10731, loss_grounding_bce_5: 0.05380/0.08779, loss_grounding_dice_5: 0.07008/0.18307, loss_grounding_ce_5: 0.18506/0.29413, loss_mask_ce_6: 0.21943/0.97536, loss_mask_bce_6: 0.16425/0.34443, loss_mask_dice_6: 0.22518/1.20173, loss_spatial_bce_6: 0.06668/0.10213, loss_spatial_dice_6: 0.08735/0.23442, loss_spatial_ce_6: 0.01897/0.13366, loss_grounding_bce_6: 0.05780/0.08850, loss_grounding_dice_6: 0.06603/0.18340, loss_grounding_ce_6: 0.20598/0.30991, loss_mask_ce_7: 0.37650/1.02029, loss_mask_bce_7: 0.17185/0.35230, loss_mask_dice_7: 0.23828/1.25632, loss_spatial_bce_7: 0.16999/0.11023, loss_spatial_dice_7: 0.12016/0.26216, loss_spatial_ce_7: 0.10273/0.16933, loss_grounding_bce_7: 0.06395/0.09041, loss_grounding_dice_7: 0.08434/0.19064, loss_grounding_ce_7: 0.27752/0.34079, loss_mask_ce_8: 0.49958/1.12864, loss_mask_bce_8: 0.17594/0.36594, loss_mask_dice_8: 0.28967/1.32966, loss_spatial_bce_8: 0.18674/0.13094, loss_spatial_dice_8: 0.18156/0.30037, loss_spatial_ce_8: 0.12653/0.22607, loss_grounding_bce_8: 0.05554/0.09418, loss_grounding_dice_8: 0.09197/0.20168, loss_grounding_ce_8: 0.29380/0.40824, loss_mask_ce_9: 3.69590/3.67820, loss_mask_bce_9: 0.21719/0.39283, loss_mask_dice_9: 0.45858/1.90283, loss_spatial_bce_9: 0.31292/0.33340, loss_spatial_dice_9: 0.70866/0.82221, loss_spatial_ce_9: 1.60111/1.49794, loss_grounding_bce_9: 0.06407/0.10562, loss_grounding_dice_9: 0.14858/0.28084, loss_grounding_ce_9: 0.53994/0.67322] items per batch[64] items per second[0.23] total items[3724800] mini batches[ 58200] memory[7345] epoch remaining[0:12:12]
INFO:trainer.default_trainer:epochs[    31] optim steps[58300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71479/0.90056, loss_mask_bce_0: 0.46081/0.33476, loss_mask_dice_0: 0.68331/1.16314, loss_spatial_bce_0: 0.13793/0.08765, loss_spatial_dice_0: 0.21059/0.20916, loss_spatial_ce_0: 0.03944/0.06343, loss_grounding_bce_0: 0.12367/0.08633, loss_grounding_dice_0: 0.15252/0.17855, loss_grounding_ce_0: 0.79544/0.27240, loss_mask_ce_1: 0.73514/0.90124, loss_mask_bce_1: 0.48792/0.33572, loss_mask_dice_1: 0.69998/1.16946, loss_spatial_bce_1: 0.13486/0.08818, loss_spatial_dice_1: 0.19776/0.21316, loss_spatial_ce_1: 0.04892/0.06923, loss_grounding_bce_1: 0.13307/0.08650, loss_grounding_dice_1: 0.15859/0.17935, loss_grounding_ce_1: 0.65036/0.27327, loss_mask_ce_2: 0.73569/0.90832, loss_mask_bce_2: 0.48024/0.33626, loss_mask_dice_2: 0.71394/1.16999, loss_spatial_bce_2: 0.12008/0.08913, loss_spatial_dice_2: 0.19472/0.21466, loss_spatial_ce_2: 0.05312/0.07259, loss_grounding_bce_2: 0.12680/0.08662, loss_grounding_dice_2: 0.15961/0.17919, loss_grounding_ce_2: 0.56317/0.27684, loss_mask_ce_3: 0.70733/0.91836, loss_mask_bce_3: 0.48026/0.33734, loss_mask_dice_3: 0.70436/1.16742, loss_spatial_bce_3: 0.11716/0.09022, loss_spatial_dice_3: 0.20406/0.21544, loss_spatial_ce_3: 0.05531/0.07680, loss_grounding_bce_3: 0.12762/0.08685, loss_grounding_dice_3: 0.15725/0.17890, loss_grounding_ce_3: 0.55918/0.27882, loss_mask_ce_4: 0.59522/0.91924, loss_mask_bce_4: 0.46122/0.33942, loss_mask_dice_4: 0.79579/1.19140, loss_spatial_bce_4: 0.11441/0.09422, loss_spatial_dice_4: 0.20210/0.22749, loss_spatial_ce_4: 0.06435/0.09255, loss_grounding_bce_4: 0.12618/0.08737, loss_grounding_dice_4: 0.16041/0.18185, loss_grounding_ce_4: 0.43479/0.28139, loss_mask_ce_5: 0.69427/0.93527, loss_mask_bce_5: 0.48112/0.34173, loss_mask_dice_5: 0.80522/1.19873, loss_spatial_bce_5: 0.11567/0.09633, loss_spatial_dice_5: 0.20211/0.23153, loss_spatial_ce_5: 0.15588/0.10729, loss_grounding_bce_5: 0.11687/0.08778, loss_grounding_dice_5: 0.15358/0.18304, loss_grounding_ce_5: 0.62289/0.29403, loss_mask_ce_6: 0.60151/0.97520, loss_mask_bce_6: 0.47451/0.34438, loss_mask_dice_6: 0.78996/1.20158, loss_spatial_bce_6: 0.14324/0.10212, loss_spatial_dice_6: 0.22146/0.23440, loss_spatial_ce_6: 0.06946/0.13362, loss_grounding_bce_6: 0.13614/0.08849, loss_grounding_dice_6: 0.19901/0.18338, loss_grounding_ce_6: 0.13809/0.30978, loss_mask_ce_7: 0.63277/1.02014, loss_mask_bce_7: 0.44986/0.35225, loss_mask_dice_7: 0.74871/1.25615, loss_spatial_bce_7: 0.18784/0.11021, loss_spatial_dice_7: 0.27315/0.26215, loss_spatial_ce_7: 0.06191/0.16928, loss_grounding_bce_7: 0.13193/0.09040, loss_grounding_dice_7: 0.19700/0.19061, loss_grounding_ce_7: 0.25728/0.34066, loss_mask_ce_8: 1.49161/1.12851, loss_mask_bce_8: 0.44824/0.36587, loss_mask_dice_8: 0.96854/1.32944, loss_spatial_bce_8: 0.11795/0.13091, loss_spatial_dice_8: 0.22667/0.30034, loss_spatial_ce_8: 0.18292/0.22606, loss_grounding_bce_8: 0.10341/0.09417, loss_grounding_dice_8: 0.15285/0.20165, loss_grounding_ce_8: 0.34685/0.40806, loss_mask_ce_9: 3.75505/3.67783, loss_mask_bce_9: 0.55853/0.39276, loss_mask_dice_9: 1.27426/1.90250, loss_spatial_bce_9: 0.35720/0.33337, loss_spatial_dice_9: 0.81977/0.82220, loss_spatial_ce_9: 1.21342/1.49788, loss_grounding_bce_9: 0.13984/0.10561, loss_grounding_dice_9: 0.22930/0.28078, loss_grounding_ce_9: 1.63259/0.67313] items per batch[64] items per second[0.23] total items[3731200] mini batches[ 58300] memory[7345] epoch remaining[0:07:34]
INFO:trainer.default_trainer:epochs[    31] optim steps[58400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92668/0.90045, loss_mask_bce_0: 0.16210/0.33479, loss_mask_dice_0: 0.34694/1.16296, loss_spatial_bce_0: 0.09766/0.08766, loss_spatial_dice_0: 0.23558/0.20915, loss_spatial_ce_0: 0.06026/0.06342, loss_grounding_bce_0: 0.05758/0.08634, loss_grounding_dice_0: 0.08109/0.17856, loss_grounding_ce_0: 0.10121/0.27235, loss_mask_ce_1: 0.88366/0.90113, loss_mask_bce_1: 0.16301/0.33574, loss_mask_dice_1: 0.40109/1.16926, loss_spatial_bce_1: 0.09890/0.08819, loss_spatial_dice_1: 0.27020/0.21315, loss_spatial_ce_1: 0.09724/0.06920, loss_grounding_bce_1: 0.05566/0.08651, loss_grounding_dice_1: 0.22777/0.17935, loss_grounding_ce_1: 0.11313/0.27325, loss_mask_ce_2: 1.34236/0.90820, loss_mask_bce_2: 0.16408/0.33629, loss_mask_dice_2: 0.64153/1.16980, loss_spatial_bce_2: 0.09647/0.08914, loss_spatial_dice_2: 0.29504/0.21465, loss_spatial_ce_2: 0.07042/0.07257, loss_grounding_bce_2: 0.05330/0.08663, loss_grounding_dice_2: 0.33667/0.17919, loss_grounding_ce_2: 0.20431/0.27677, loss_mask_ce_3: 0.89727/0.91822, loss_mask_bce_3: 0.17137/0.33737, loss_mask_dice_3: 0.37398/1.16724, loss_spatial_bce_3: 0.10124/0.09023, loss_spatial_dice_3: 0.29196/0.21544, loss_spatial_ce_3: 0.09878/0.07678, loss_grounding_bce_3: 0.05188/0.08686, loss_grounding_dice_3: 0.24395/0.17891, loss_grounding_ce_3: 0.29652/0.27878, loss_mask_ce_4: 0.82079/0.91914, loss_mask_bce_4: 0.17632/0.33945, loss_mask_dice_4: 0.56489/1.19123, loss_spatial_bce_4: 0.10070/0.09423, loss_spatial_dice_4: 0.29794/0.22749, loss_spatial_ce_4: 0.03730/0.09253, loss_grounding_bce_4: 0.05454/0.08738, loss_grounding_dice_4: 0.12183/0.18185, loss_grounding_ce_4: 0.12084/0.28135, loss_mask_ce_5: 0.85022/0.93519, loss_mask_bce_5: 0.18567/0.34176, loss_mask_dice_5: 0.46967/1.19854, loss_spatial_bce_5: 0.10135/0.09635, loss_spatial_dice_5: 0.27958/0.23152, loss_spatial_ce_5: 0.06864/0.10726, loss_grounding_bce_5: 0.05447/0.08779, loss_grounding_dice_5: 0.24307/0.18305, loss_grounding_ce_5: 0.12287/0.29398, loss_mask_ce_6: 1.01653/0.97516, loss_mask_bce_6: 0.17653/0.34441, loss_mask_dice_6: 0.19845/1.20140, loss_spatial_bce_6: 0.09204/0.10213, loss_spatial_dice_6: 0.30073/0.23440, loss_spatial_ce_6: 0.09247/0.13359, loss_grounding_bce_6: 0.05074/0.08849, loss_grounding_dice_6: 0.44109/0.18339, loss_grounding_ce_6: 0.11252/0.30975, loss_mask_ce_7: 0.94005/1.02009, loss_mask_bce_7: 0.17747/0.35228, loss_mask_dice_7: 0.48139/1.25593, loss_spatial_bce_7: 0.10013/0.11022, loss_spatial_dice_7: 0.33103/0.26214, loss_spatial_ce_7: 0.09185/0.16921, loss_grounding_bce_7: 0.05489/0.09041, loss_grounding_dice_7: 0.32454/0.19062, loss_grounding_ce_7: 0.22710/0.34058, loss_mask_ce_8: 0.98994/1.12848, loss_mask_bce_8: 0.18019/0.36590, loss_mask_dice_8: 0.40672/1.32925, loss_spatial_bce_8: 0.09859/0.13092, loss_spatial_dice_8: 0.45310/0.30032, loss_spatial_ce_8: 0.22951/0.22601, loss_grounding_bce_8: 0.05550/0.09417, loss_grounding_dice_8: 0.21244/0.20165, loss_grounding_ce_8: 0.25475/0.40803, loss_mask_ce_9: 2.82844/3.67764, loss_mask_bce_9: 0.17552/0.39279, loss_mask_dice_9: 0.56933/1.90235, loss_spatial_bce_9: 0.39086/0.33341, loss_spatial_dice_9: 0.70539/0.82220, loss_spatial_ce_9: 1.98416/1.49787, loss_grounding_bce_9: 0.06073/0.10562, loss_grounding_dice_9: 0.33410/0.28080, loss_grounding_ce_9: 0.44883/0.67309] items per batch[64] items per second[0.23] total items[3737600] mini batches[ 58400] memory[7345] epoch remaining[0:02:57]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00058464.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0033 s/iter. Inference: 0.2197 s/iter. Eval: 0.0895 s/iter. Total: 0.3125 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2227 s/iter. Eval: 0.0813 s/iter. Total: 0.3072 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2262 s/iter. Eval: 0.0780 s/iter. Total: 0.3075 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2264 s/iter. Eval: 0.0752 s/iter. Total: 0.3049 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0032 s/iter. Inference: 0.2249 s/iter. Eval: 0.0739 s/iter. Total: 0.3020 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0032 s/iter. Inference: 0.2271 s/iter. Eval: 0.0739 s/iter. Total: 0.3043 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0032 s/iter. Inference: 0.2281 s/iter. Eval: 0.0744 s/iter. Total: 0.3058 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0032 s/iter. Inference: 0.2279 s/iter. Eval: 0.0736 s/iter. Total: 0.3048 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2286 s/iter. Eval: 0.0736 s/iter. Total: 0.3056 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval1nlbg9_3 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.768 | 82.046 | 59.801 |      133      |
| Things | 54.678 | 82.887 | 65.373 |      80       |
| Stuff  | 42.357 | 80.776 | 51.389 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.33s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.30 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.08s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.71 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.411
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.894 | 61.201 | 41.126 | 19.441 | 41.907 | 60.439 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.431 | bicycle      | 18.495 | car            | 36.071 |
| motorcycle    | 34.018 | airplane     | 56.409 | bus            | 64.885 |
| train         | 68.790 | truck        | 36.943 | boat           | 23.964 |
| traffic light | 24.779 | fire hydrant | 62.301 | stop sign      | 64.089 |
| parking meter | 42.752 | bench        | 20.182 | bird           | 29.217 |
| cat           | 72.755 | dog          | 65.903 | horse          | 46.366 |
| sheep         | 46.738 | cow          | 50.576 | elephant       | 60.974 |
| bear          | 77.391 | zebra        | 59.659 | giraffe        | 57.185 |
| backpack      | 17.512 | umbrella     | 49.231 | handbag        | 15.119 |
| tie           | 34.674 | suitcase     | 40.784 | frisbee        | 66.707 |
| skis          | 4.706  | snowboard    | 23.278 | sports ball    | 45.906 |
| kite          | 33.285 | baseball bat | 28.247 | baseball glove | 43.225 |
| skateboard    | 35.698 | surfboard    | 35.155 | tennis racket  | 56.475 |
| bottle        | 33.461 | wine glass   | 27.377 | cup            | 39.880 |
| fork          | 16.678 | knife        | 13.276 | spoon          | 14.683 |
| bowl          | 33.319 | banana       | 21.107 | apple          | 19.885 |
| sandwich      | 42.838 | orange       | 29.669 | broccoli       | 21.166 |
| carrot        | 21.261 | hot dog      | 25.202 | pizza          | 51.218 |
| donut         | 45.416 | cake         | 43.707 | chair          | 20.927 |
| couch         | 42.008 | potted plant | 18.031 | bed            | 40.387 |
| dining table  | 12.895 | toilet       | 66.422 | tv             | 61.658 |
| laptop        | 61.992 | mouse        | 59.777 | remote         | 29.931 |
| keyboard      | 47.786 | cell phone   | 38.953 | microwave      | 55.069 |
| oven          | 31.960 | toaster      | 35.241 | sink           | 37.509 |
| refrigerator  | 57.722 | book         | 8.605  | clock          | 51.463 |
| vase          | 32.409 | scissors     | 24.240 | teddy bear     | 50.386 |
| hair drier    | 11.636 | toothbrush   | 19.463 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.74514031856664, 'fwIoU': 68.85833675611075, 'IoU-person': 87.34168158818659, 'IoU-bicycle': 71.15660108361257, 'IoU-car': 70.12828474660206, 'IoU-motorcycle': 83.92800281891843, 'IoU-airplane': 77.575535302364, 'IoU-bus': 82.02371448061841, 'IoU-train': 81.98951536161904, 'IoU-truck': 62.36822981918315, 'IoU-boat': 67.36236164801419, 'IoU-traffic light': 76.66859781289972, 'IoU-fire hydrant': 90.19045358791243, 'IoU-stop sign': 92.83562215275359, 'IoU-parking meter': 88.09019974180504, 'IoU-bench': 53.13618704747278, 'IoU-bird': 75.75494473875564, 'IoU-cat': 82.83849576899036, 'IoU-dog': 76.9118388040164, 'IoU-horse': 85.482818785383, 'IoU-sheep': 86.80015860736312, 'IoU-cow': 80.51898954147767, 'IoU-elephant': 89.42989426033085, 'IoU-bear': 83.77256284724905, 'IoU-zebra': 87.60351800386736, 'IoU-giraffe': 88.15018435649243, 'IoU-backpack': 38.62218204841961, 'IoU-umbrella': 78.75064048282967, 'IoU-handbag': 35.14703133537188, 'IoU-tie': 70.91057205567947, 'IoU-suitcase': 78.49358551702052, 'IoU-frisbee': 83.34654728984975, 'IoU-skis': 51.55881453372939, 'IoU-snowboard': 70.89975318923749, 'IoU-sports ball': 71.07581506012475, 'IoU-kite': 66.15124109636193, 'IoU-baseball bat': 59.622368826024704, 'IoU-baseball glove': 75.9760025916712, 'IoU-skateboard': 64.0987184254438, 'IoU-surfboard': 81.1926962446549, 'IoU-tennis racket': 74.70199418479633, 'IoU-bottle': 69.21401898672072, 'IoU-wine glass': 71.69719778622785, 'IoU-cup': 60.206385721953104, 'IoU-fork': 54.676882661996494, 'IoU-knife': 50.83145821546364, 'IoU-spoon': 49.56825350817132, 'IoU-bowl': 54.8158157393104, 'IoU-banana': 84.11781237907728, 'IoU-apple': 59.4862881854125, 'IoU-sandwich': 65.60294382818942, 'IoU-orange': 75.33247810364004, 'IoU-broccoli': 66.69976179790797, 'IoU-carrot': 64.33534069715598, 'IoU-hot dog': 63.230292776780374, 'IoU-pizza': 83.75939082690905, 'IoU-donut': 61.342855824063314, 'IoU-cake': 73.44235953957146, 'IoU-chair': 53.30502864510558, 'IoU-couch': 70.38893711135661, 'IoU-potted plant': 33.22218759642935, 'IoU-bed': 65.19193802734209, 'IoU-dining table': 51.52809296794776, 'IoU-toilet': 81.2380972412369, 'IoU-tv': 70.28545962497785, 'IoU-laptop': 74.38296966723222, 'IoU-mouse': 72.92336565147754, 'IoU-remote': 51.680122725088914, 'IoU-keyboard': 61.060922510035255, 'IoU-cell phone': 71.97914444949322, 'IoU-microwave': 52.63811574579055, 'IoU-oven': 69.44618582461261, 'IoU-toaster': 74.23975045670896, 'IoU-sink': 68.7669680393484, 'IoU-refrigerator': 78.91746267344867, 'IoU-book': 52.12579637057328, 'IoU-clock': 73.91633176769389, 'IoU-vase': 53.150293679863125, 'IoU-scissors': 61.11546111258479, 'IoU-teddy bear': 77.81580261056693, 'IoU-hair drier': 35.881666708934596, 'IoU-toothbrush': 58.924228367976326, 'IoU-banner': 37.493513918276925, 'IoU-blanket': 11.13552613763369, 'IoU-bridge': 37.07019025297383, 'IoU-cardboard': 48.81146839031242, 'IoU-counter': 31.93464076392198, 'IoU-curtain': 65.55446423754691, 'IoU-door-stuff': 42.73031861542073, 'IoU-floor-wood': 62.846425034536836, 'IoU-flower': 44.06827835588751, 'IoU-fruit': 41.03276000856664, 'IoU-gravel': 30.49379946290224, 'IoU-house': 23.671666256830378, 'IoU-light': 39.24594999995692, 'IoU-mirror-stuff': 56.508908912430044, 'IoU-net': 43.75468398700974, 'IoU-pillow': 11.330014841928392, 'IoU-platform': 31.57176798047575, 'IoU-playingfield': 70.66741628799198, 'IoU-railroad': 61.275371373936814, 'IoU-river': 53.63696308275468, 'IoU-road': 66.54447472736953, 'IoU-roof': 15.488439088700678, 'IoU-sand': 64.21691240211801, 'IoU-sea': 85.02612368351572, 'IoU-shelf': 36.16319170820358, 'IoU-snow': 87.5350984008685, 'IoU-stairs': 23.815617816401886, 'IoU-tent': 9.548871971516144, 'IoU-towel': 28.32885516056942, 'IoU-wall-brick': 43.531858586480524, 'IoU-wall-stone': 27.554315674203846, 'IoU-wall-tile': 64.58623204822072, 'IoU-wall-wood': 36.63352016643102, 'IoU-water-other': 26.163198375462603, 'IoU-window-blind': 49.1405921288741, 'IoU-window-other': 46.334262970007664, 'IoU-tree-merged': 80.69087265016186, 'IoU-fence-merged': 51.199859076431466, 'IoU-ceiling-merged': 65.27226052153675, 'IoU-sky-other-merged': 93.63194976397266, 'IoU-cabinet-merged': 60.33793383937691, 'IoU-table-merged': 39.36942713034955, 'IoU-floor-other-merged': 48.6258552184336, 'IoU-pavement-merged': 54.81891041778507, 'IoU-mountain-merged': 51.18179822760285, 'IoU-grass-merged': 70.18880916067769, 'IoU-dirt-merged': 45.2717351870724, 'IoU-paper-merged': 31.14994703000643, 'IoU-food-other-merged': 38.02123813241926, 'IoU-building-other-merged': 58.0835032267853, 'IoU-rock-merged': 61.711714322722344, 'IoU-wall-other-merged': 64.22563022379023, 'IoU-rug-merged': 64.78630348852045, 'mACC': 72.49604055474506, 'pACC': 80.34640699409665, 'ACC-person': 92.4402315372285, 'ACC-bicycle': 80.56341988907252, 'ACC-car': 83.8479054660628, 'ACC-motorcycle': 88.58551364200522, 'ACC-airplane': 89.98164008361753, 'ACC-bus': 85.88789381206107, 'ACC-train': 93.36083566910033, 'ACC-truck': 76.8476275088571, 'ACC-boat': 77.94497849199358, 'ACC-traffic light': 88.9865742073336, 'ACC-fire hydrant': 95.13353644736898, 'ACC-stop sign': 95.57599004375172, 'ACC-parking meter': 92.14584437616071, 'ACC-bench': 70.0064635656089, 'ACC-bird': 80.42644616780511, 'ACC-cat': 88.74136500064745, 'ACC-dog': 80.35037759949422, 'ACC-horse': 91.22244717498752, 'ACC-sheep': 89.79031894218554, 'ACC-cow': 85.51993316312732, 'ACC-elephant': 91.89090591897624, 'ACC-bear': 85.919299103016, 'ACC-zebra': 89.98312710186485, 'ACC-giraffe': 92.23975201618256, 'ACC-backpack': 60.94567532274038, 'ACC-umbrella': 85.47108812356889, 'ACC-handbag': 52.62316139205876, 'ACC-tie': 81.78077302309934, 'ACC-suitcase': 85.58765356421412, 'ACC-frisbee': 94.03854545454546, 'ACC-skis': 67.7747983795405, 'ACC-snowboard': 80.06318728417135, 'ACC-sports ball': 79.97746560403552, 'ACC-kite': 74.97728411095925, 'ACC-baseball bat': 83.45608343510477, 'ACC-baseball glove': 89.56248243660967, 'ACC-skateboard': 69.4421004439138, 'ACC-surfboard': 90.23359857743763, 'ACC-tennis racket': 80.21121423085997, 'ACC-bottle': 81.73603144353953, 'ACC-wine glass': 86.54747874018604, 'ACC-cup': 82.24396990126228, 'ACC-fork': 67.57046722557088, 'ACC-knife': 64.6821645782956, 'ACC-spoon': 68.88371003341595, 'ACC-bowl': 69.15571507596077, 'ACC-banana': 89.71133907582552, 'ACC-apple': 70.49765628819056, 'ACC-sandwich': 78.26855684736846, 'ACC-orange': 83.75342692356573, 'ACC-broccoli': 76.24392632788141, 'ACC-carrot': 74.55692481002747, 'ACC-hot dog': 73.87920027540646, 'ACC-pizza': 91.97227385527343, 'ACC-donut': 77.4343912134944, 'ACC-cake': 82.86209052019248, 'ACC-chair': 66.49620309837971, 'ACC-couch': 83.19212792998718, 'ACC-potted plant': 45.43473978432922, 'ACC-bed': 76.0399646043078, 'ACC-dining table': 76.83504810902186, 'ACC-toilet': 91.1903876796912, 'ACC-tv': 79.86420711464258, 'ACC-laptop': 89.03678761484316, 'ACC-mouse': 86.06466601012232, 'ACC-remote': 71.85511694190507, 'ACC-keyboard': 71.30611102655622, 'ACC-cell phone': 79.29501939976177, 'ACC-microwave': 57.39072562524883, 'ACC-oven': 86.09078893204354, 'ACC-toaster': 81.75628515512734, 'ACC-sink': 82.01471379837926, 'ACC-refrigerator': 85.2797828024353, 'ACC-book': 65.78304054243367, 'ACC-clock': 79.64197687453078, 'ACC-vase': 60.55354534207154, 'ACC-scissors': 65.84499444490355, 'ACC-teddy bear': 84.74233660924978, 'ACC-hair drier': 40.27125494549284, 'ACC-toothbrush': 81.65653231410703, 'ACC-banner': 65.22886633975122, 'ACC-blanket': 16.258019140234335, 'ACC-bridge': 54.15449803439554, 'ACC-cardboard': 61.74640104696816, 'ACC-counter': 51.44698354604624, 'ACC-curtain': 73.52454517384133, 'ACC-door-stuff': 61.339670268627536, 'ACC-floor-wood': 76.82261645445736, 'ACC-flower': 64.4579955664567, 'ACC-fruit': 64.24609880653051, 'ACC-gravel': 36.50217127992626, 'ACC-house': 27.06666668239201, 'ACC-light': 54.49215011675535, 'ACC-mirror-stuff': 68.38760186990983, 'ACC-net': 61.93601417128364, 'ACC-pillow': 26.541409160148156, 'ACC-platform': 50.85213412373748, 'ACC-playingfield': 91.72066629867311, 'ACC-railroad': 80.4990154107239, 'ACC-river': 80.54068532593234, 'ACC-road': 85.16609137906651, 'ACC-roof': 21.200968108643302, 'ACC-sand': 73.83622010745667, 'ACC-sea': 90.95380467973703, 'ACC-shelf': 54.35706096045535, 'ACC-snow': 96.00877640499368, 'ACC-stairs': 43.013272601153716, 'ACC-tent': 12.574703798654182, 'ACC-towel': 33.7086521918874, 'ACC-wall-brick': 57.33042713770139, 'ACC-wall-stone': 32.914381857418334, 'ACC-wall-tile': 74.4605199649225, 'ACC-wall-wood': 48.170642101331026, 'ACC-water-other': 36.6050048757984, 'ACC-window-blind': 60.49926726784044, 'ACC-window-other': 65.91062504130173, 'ACC-tree-merged': 88.51197582357368, 'ACC-fence-merged': 74.89089435783987, 'ACC-ceiling-merged': 75.5872948306621, 'ACC-sky-other-merged': 96.73691752178479, 'ACC-cabinet-merged': 74.03329196412027, 'ACC-table-merged': 52.04044179595604, 'ACC-floor-other-merged': 57.612842165180524, 'ACC-pavement-merged': 70.06816519337977, 'ACC-mountain-merged': 65.80562969928101, 'ACC-grass-merged': 81.94028114526385, 'ACC-dirt-merged': 63.88862655013453, 'ACC-paper-merged': 42.74491369334626, 'ACC-food-other-merged': 47.8578228291805, 'ACC-building-other-merged': 78.05711840204579, 'ACC-rock-merged': 83.73605374432121, 'ACC-wall-other-merged': 83.75295912292387, 'ACC-rug-merged': 79.06031950055088})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1543 s/iter. Inference: 0.4027 s/iter. Eval: 0.0000 s/iter. Total: 0.5571 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1542 s/iter. Inference: 0.4883 s/iter. Eval: 0.0000 s/iter. Total: 0.6425 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1675 s/iter. Inference: 0.5521 s/iter. Eval: 0.0000 s/iter. Total: 0.7197 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1700 s/iter. Inference: 0.6721 s/iter. Eval: 0.0000 s/iter. Total: 0.8423 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1671 s/iter. Inference: 0.5953 s/iter. Eval: 0.0000 s/iter. Total: 0.7626 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1666 s/iter. Inference: 0.6337 s/iter. Eval: 0.0000 s/iter. Total: 0.8005 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1684 s/iter. Inference: 0.6940 s/iter. Eval: 0.0000 s/iter. Total: 0.8626 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5411179397131987, 'noc@0.8': 2.942054433713784, 'noc@0.85': 3.5332162715832602, 'noc@0.9': 4.597307579748318, 'miou@iter1': 0.8267127637944097}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0018 s/iter. Inference: 0.1020 s/iter. Eval: 0.0008 s/iter. Total: 0.1046 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.8900146484375, 'precision@0.6': 67.6253433227539, 'precision@0.7': 62.68946838378906, 'precision@0.8': 52.27360916137695, 'precision@0.9': 26.544889450073242, 'cIoU': 57.032386779785156, 'mIoU': 62.50901794433594}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.76822824184481, 'SQ': 82.04576486553165, 'RQ': 59.80053606847431, 'PQ_th': 54.67804400484262, 'SQ_th': 82.8872785614773, 'RQ_th': 65.37329181765865, 'PQ_st': 42.35718558071606, 'SQ_st': 80.77555551316091, 'RQ_st': 51.388829277252576}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.89357842518022, 'AP50': 61.20057749702629, 'AP75': 41.126060719465436, 'APs': 19.4406765662836, 'APm': 41.907155463605086, 'APl': 60.43874941962394, 'AP-person': 44.4313740903123, 'AP-bicycle': 18.494540170042214, 'AP-car': 36.07090812659712, 'AP-motorcycle': 34.01837225794861, 'AP-airplane': 56.408908418587714, 'AP-bus': 64.88504849459518, 'AP-train': 68.79004811222124, 'AP-truck': 36.9425303693363, 'AP-boat': 23.964162630433872, 'AP-traffic light': 24.77875049353546, 'AP-fire hydrant': 62.30078932521963, 'AP-stop sign': 64.08862190046902, 'AP-parking meter': 42.752346974924045, 'AP-bench': 20.181647000168166, 'AP-bird': 29.217228494479514, 'AP-cat': 72.75466597440492, 'AP-dog': 65.9030685174933, 'AP-horse': 46.365894883968316, 'AP-sheep': 46.73764784078413, 'AP-cow': 50.57607642042353, 'AP-elephant': 60.973815312427185, 'AP-bear': 77.39078638053289, 'AP-zebra': 59.65891960730796, 'AP-giraffe': 57.1852236744734, 'AP-backpack': 17.512207302512802, 'AP-umbrella': 49.23140181273546, 'AP-handbag': 15.118907438255277, 'AP-tie': 34.67438112480119, 'AP-suitcase': 40.78441440172551, 'AP-frisbee': 66.7069653142706, 'AP-skis': 4.706036271834228, 'AP-snowboard': 23.277607796532383, 'AP-sports ball': 45.90614840634697, 'AP-kite': 33.285380551846885, 'AP-baseball bat': 28.246771987751668, 'AP-baseball glove': 43.22478679225059, 'AP-skateboard': 35.69778278352298, 'AP-surfboard': 35.15464798334523, 'AP-tennis racket': 56.47462676843988, 'AP-bottle': 33.460915556889326, 'AP-wine glass': 27.376691400258373, 'AP-cup': 39.880136640755296, 'AP-fork': 16.677956786182836, 'AP-knife': 13.275515478967481, 'AP-spoon': 14.682882715108736, 'AP-bowl': 33.318564840660116, 'AP-banana': 21.106545735014556, 'AP-apple': 19.88529219072763, 'AP-sandwich': 42.83826544439178, 'AP-orange': 29.668996795011683, 'AP-broccoli': 21.165626009628465, 'AP-carrot': 21.26104121706975, 'AP-hot dog': 25.201956664405305, 'AP-pizza': 51.21806705705123, 'AP-donut': 45.415885560872105, 'AP-cake': 43.70678592668808, 'AP-chair': 20.92718524494792, 'AP-couch': 42.00816684747154, 'AP-potted plant': 18.030576916080364, 'AP-bed': 40.38684798813883, 'AP-dining table': 12.894881617067533, 'AP-toilet': 66.42186539976022, 'AP-tv': 61.65821608921834, 'AP-laptop': 61.99237311774163, 'AP-mouse': 59.77731957588269, 'AP-remote': 29.931171328789446, 'AP-keyboard': 47.78611570691479, 'AP-cell phone': 38.95341521368663, 'AP-microwave': 55.06899974250861, 'AP-oven': 31.960159872864498, 'AP-toaster': 35.24092409240924, 'AP-sink': 37.50946298249037, 'AP-refrigerator': 57.72220116506903, 'AP-book': 8.60489485089103, 'AP-clock': 51.46251104123489, 'AP-vase': 32.408702101310396, 'AP-scissors': 24.239883672091022, 'AP-teddy bear': 50.38639231907149, 'AP-hair drier': 11.635948210205637, 'AP-toothbrush': 19.46349069203096}), ('sem_seg', {'mIoU': 60.74514031856664, 'fwIoU': 68.85833675611075, 'IoU-person': 87.34168158818659, 'IoU-bicycle': 71.15660108361257, 'IoU-car': 70.12828474660206, 'IoU-motorcycle': 83.92800281891843, 'IoU-airplane': 77.575535302364, 'IoU-bus': 82.02371448061841, 'IoU-train': 81.98951536161904, 'IoU-truck': 62.36822981918315, 'IoU-boat': 67.36236164801419, 'IoU-traffic light': 76.66859781289972, 'IoU-fire hydrant': 90.19045358791243, 'IoU-stop sign': 92.83562215275359, 'IoU-parking meter': 88.09019974180504, 'IoU-bench': 53.13618704747278, 'IoU-bird': 75.75494473875564, 'IoU-cat': 82.83849576899036, 'IoU-dog': 76.9118388040164, 'IoU-horse': 85.482818785383, 'IoU-sheep': 86.80015860736312, 'IoU-cow': 80.51898954147767, 'IoU-elephant': 89.42989426033085, 'IoU-bear': 83.77256284724905, 'IoU-zebra': 87.60351800386736, 'IoU-giraffe': 88.15018435649243, 'IoU-backpack': 38.62218204841961, 'IoU-umbrella': 78.75064048282967, 'IoU-handbag': 35.14703133537188, 'IoU-tie': 70.91057205567947, 'IoU-suitcase': 78.49358551702052, 'IoU-frisbee': 83.34654728984975, 'IoU-skis': 51.55881453372939, 'IoU-snowboard': 70.89975318923749, 'IoU-sports ball': 71.07581506012475, 'IoU-kite': 66.15124109636193, 'IoU-baseball bat': 59.622368826024704, 'IoU-baseball glove': 75.9760025916712, 'IoU-skateboard': 64.0987184254438, 'IoU-surfboard': 81.1926962446549, 'IoU-tennis racket': 74.70199418479633, 'IoU-bottle': 69.21401898672072, 'IoU-wine glass': 71.69719778622785, 'IoU-cup': 60.206385721953104, 'IoU-fork': 54.676882661996494, 'IoU-knife': 50.83145821546364, 'IoU-spoon': 49.56825350817132, 'IoU-bowl': 54.8158157393104, 'IoU-banana': 84.11781237907728, 'IoU-apple': 59.4862881854125, 'IoU-sandwich': 65.60294382818942, 'IoU-orange': 75.33247810364004, 'IoU-broccoli': 66.69976179790797, 'IoU-carrot': 64.33534069715598, 'IoU-hot dog': 63.230292776780374, 'IoU-pizza': 83.75939082690905, 'IoU-donut': 61.342855824063314, 'IoU-cake': 73.44235953957146, 'IoU-chair': 53.30502864510558, 'IoU-couch': 70.38893711135661, 'IoU-potted plant': 33.22218759642935, 'IoU-bed': 65.19193802734209, 'IoU-dining table': 51.52809296794776, 'IoU-toilet': 81.2380972412369, 'IoU-tv': 70.28545962497785, 'IoU-laptop': 74.38296966723222, 'IoU-mouse': 72.92336565147754, 'IoU-remote': 51.680122725088914, 'IoU-keyboard': 61.060922510035255, 'IoU-cell phone': 71.97914444949322, 'IoU-microwave': 52.63811574579055, 'IoU-oven': 69.44618582461261, 'IoU-toaster': 74.23975045670896, 'IoU-sink': 68.7669680393484, 'IoU-refrigerator': 78.91746267344867, 'IoU-book': 52.12579637057328, 'IoU-clock': 73.91633176769389, 'IoU-vase': 53.150293679863125, 'IoU-scissors': 61.11546111258479, 'IoU-teddy bear': 77.81580261056693, 'IoU-hair drier': 35.881666708934596, 'IoU-toothbrush': 58.924228367976326, 'IoU-banner': 37.493513918276925, 'IoU-blanket': 11.13552613763369, 'IoU-bridge': 37.07019025297383, 'IoU-cardboard': 48.81146839031242, 'IoU-counter': 31.93464076392198, 'IoU-curtain': 65.55446423754691, 'IoU-door-stuff': 42.73031861542073, 'IoU-floor-wood': 62.846425034536836, 'IoU-flower': 44.06827835588751, 'IoU-fruit': 41.03276000856664, 'IoU-gravel': 30.49379946290224, 'IoU-house': 23.671666256830378, 'IoU-light': 39.24594999995692, 'IoU-mirror-stuff': 56.508908912430044, 'IoU-net': 43.75468398700974, 'IoU-pillow': 11.330014841928392, 'IoU-platform': 31.57176798047575, 'IoU-playingfield': 70.66741628799198, 'IoU-railroad': 61.275371373936814, 'IoU-river': 53.63696308275468, 'IoU-road': 66.54447472736953, 'IoU-roof': 15.488439088700678, 'IoU-sand': 64.21691240211801, 'IoU-sea': 85.02612368351572, 'IoU-shelf': 36.16319170820358, 'IoU-snow': 87.5350984008685, 'IoU-stairs': 23.815617816401886, 'IoU-tent': 9.548871971516144, 'IoU-towel': 28.32885516056942, 'IoU-wall-brick': 43.531858586480524, 'IoU-wall-stone': 27.554315674203846, 'IoU-wall-tile': 64.58623204822072, 'IoU-wall-wood': 36.63352016643102, 'IoU-water-other': 26.163198375462603, 'IoU-window-blind': 49.1405921288741, 'IoU-window-other': 46.334262970007664, 'IoU-tree-merged': 80.69087265016186, 'IoU-fence-merged': 51.199859076431466, 'IoU-ceiling-merged': 65.27226052153675, 'IoU-sky-other-merged': 93.63194976397266, 'IoU-cabinet-merged': 60.33793383937691, 'IoU-table-merged': 39.36942713034955, 'IoU-floor-other-merged': 48.6258552184336, 'IoU-pavement-merged': 54.81891041778507, 'IoU-mountain-merged': 51.18179822760285, 'IoU-grass-merged': 70.18880916067769, 'IoU-dirt-merged': 45.2717351870724, 'IoU-paper-merged': 31.14994703000643, 'IoU-food-other-merged': 38.02123813241926, 'IoU-building-other-merged': 58.0835032267853, 'IoU-rock-merged': 61.711714322722344, 'IoU-wall-other-merged': 64.22563022379023, 'IoU-rug-merged': 64.78630348852045, 'mACC': 72.49604055474506, 'pACC': 80.34640699409665, 'ACC-person': 92.4402315372285, 'ACC-bicycle': 80.56341988907252, 'ACC-car': 83.8479054660628, 'ACC-motorcycle': 88.58551364200522, 'ACC-airplane': 89.98164008361753, 'ACC-bus': 85.88789381206107, 'ACC-train': 93.36083566910033, 'ACC-truck': 76.8476275088571, 'ACC-boat': 77.94497849199358, 'ACC-traffic light': 88.9865742073336, 'ACC-fire hydrant': 95.13353644736898, 'ACC-stop sign': 95.57599004375172, 'ACC-parking meter': 92.14584437616071, 'ACC-bench': 70.0064635656089, 'ACC-bird': 80.42644616780511, 'ACC-cat': 88.74136500064745, 'ACC-dog': 80.35037759949422, 'ACC-horse': 91.22244717498752, 'ACC-sheep': 89.79031894218554, 'ACC-cow': 85.51993316312732, 'ACC-elephant': 91.89090591897624, 'ACC-bear': 85.919299103016, 'ACC-zebra': 89.98312710186485, 'ACC-giraffe': 92.23975201618256, 'ACC-backpack': 60.94567532274038, 'ACC-umbrella': 85.47108812356889, 'ACC-handbag': 52.62316139205876, 'ACC-tie': 81.78077302309934, 'ACC-suitcase': 85.58765356421412, 'ACC-frisbee': 94.03854545454546, 'ACC-skis': 67.7747983795405, 'ACC-snowboard': 80.06318728417135, 'ACC-sports ball': 79.97746560403552, 'ACC-kite': 74.97728411095925, 'ACC-baseball bat': 83.45608343510477, 'ACC-baseball glove': 89.56248243660967, 'ACC-skateboard': 69.4421004439138, 'ACC-surfboard': 90.23359857743763, 'ACC-tennis racket': 80.21121423085997, 'ACC-bottle': 81.73603144353953, 'ACC-wine glass': 86.54747874018604, 'ACC-cup': 82.24396990126228, 'ACC-fork': 67.57046722557088, 'ACC-knife': 64.6821645782956, 'ACC-spoon': 68.88371003341595, 'ACC-bowl': 69.15571507596077, 'ACC-banana': 89.71133907582552, 'ACC-apple': 70.49765628819056, 'ACC-sandwich': 78.26855684736846, 'ACC-orange': 83.75342692356573, 'ACC-broccoli': 76.24392632788141, 'ACC-carrot': 74.55692481002747, 'ACC-hot dog': 73.87920027540646, 'ACC-pizza': 91.97227385527343, 'ACC-donut': 77.4343912134944, 'ACC-cake': 82.86209052019248, 'ACC-chair': 66.49620309837971, 'ACC-couch': 83.19212792998718, 'ACC-potted plant': 45.43473978432922, 'ACC-bed': 76.0399646043078, 'ACC-dining table': 76.83504810902186, 'ACC-toilet': 91.1903876796912, 'ACC-tv': 79.86420711464258, 'ACC-laptop': 89.03678761484316, 'ACC-mouse': 86.06466601012232, 'ACC-remote': 71.85511694190507, 'ACC-keyboard': 71.30611102655622, 'ACC-cell phone': 79.29501939976177, 'ACC-microwave': 57.39072562524883, 'ACC-oven': 86.09078893204354, 'ACC-toaster': 81.75628515512734, 'ACC-sink': 82.01471379837926, 'ACC-refrigerator': 85.2797828024353, 'ACC-book': 65.78304054243367, 'ACC-clock': 79.64197687453078, 'ACC-vase': 60.55354534207154, 'ACC-scissors': 65.84499444490355, 'ACC-teddy bear': 84.74233660924978, 'ACC-hair drier': 40.27125494549284, 'ACC-toothbrush': 81.65653231410703, 'ACC-banner': 65.22886633975122, 'ACC-blanket': 16.258019140234335, 'ACC-bridge': 54.15449803439554, 'ACC-cardboard': 61.74640104696816, 'ACC-counter': 51.44698354604624, 'ACC-curtain': 73.52454517384133, 'ACC-door-stuff': 61.339670268627536, 'ACC-floor-wood': 76.82261645445736, 'ACC-flower': 64.4579955664567, 'ACC-fruit': 64.24609880653051, 'ACC-gravel': 36.50217127992626, 'ACC-house': 27.06666668239201, 'ACC-light': 54.49215011675535, 'ACC-mirror-stuff': 68.38760186990983, 'ACC-net': 61.93601417128364, 'ACC-pillow': 26.541409160148156, 'ACC-platform': 50.85213412373748, 'ACC-playingfield': 91.72066629867311, 'ACC-railroad': 80.4990154107239, 'ACC-river': 80.54068532593234, 'ACC-road': 85.16609137906651, 'ACC-roof': 21.200968108643302, 'ACC-sand': 73.83622010745667, 'ACC-sea': 90.95380467973703, 'ACC-shelf': 54.35706096045535, 'ACC-snow': 96.00877640499368, 'ACC-stairs': 43.013272601153716, 'ACC-tent': 12.574703798654182, 'ACC-towel': 33.7086521918874, 'ACC-wall-brick': 57.33042713770139, 'ACC-wall-stone': 32.914381857418334, 'ACC-wall-tile': 74.4605199649225, 'ACC-wall-wood': 48.170642101331026, 'ACC-water-other': 36.6050048757984, 'ACC-window-blind': 60.49926726784044, 'ACC-window-other': 65.91062504130173, 'ACC-tree-merged': 88.51197582357368, 'ACC-fence-merged': 74.89089435783987, 'ACC-ceiling-merged': 75.5872948306621, 'ACC-sky-other-merged': 96.73691752178479, 'ACC-cabinet-merged': 74.03329196412027, 'ACC-table-merged': 52.04044179595604, 'ACC-floor-other-merged': 57.612842165180524, 'ACC-pavement-merged': 70.06816519337977, 'ACC-mountain-merged': 65.80562969928101, 'ACC-grass-merged': 81.94028114526385, 'ACC-dirt-merged': 63.88862655013453, 'ACC-paper-merged': 42.74491369334626, 'ACC-food-other-merged': 47.8578228291805, 'ACC-building-other-merged': 78.05711840204579, 'ACC-rock-merged': 83.73605374432121, 'ACC-wall-other-merged': 83.75295912292387, 'ACC-rug-merged': 79.06031950055088})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5411179397131987, 'noc@0.8': 2.942054433713784, 'noc@0.85': 3.5332162715832602, 'noc@0.9': 4.597307579748318, 'miou@iter1': 0.8267127637944097}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.8900146484375, 'precision@0.6': 67.6253433227539, 'precision@0.7': 62.68946838378906, 'precision@0.8': 52.27360916137695, 'precision@0.9': 26.544889450073242, 'cIoU': 57.032386779785156, 'mIoU': 62.50901794433594}}}
INFO:trainer.default_trainer:This epoch takes 1:27:40.789795
INFO:trainer.default_trainer:PROGRESS: 64.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 32 training.
INFO:trainer.default_trainer:epochs[    32] optim steps[58500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25782/0.90053, loss_mask_bce_0: 0.04651/0.33481, loss_mask_dice_0: 0.32433/1.16316, loss_spatial_bce_0: 0.01634/0.08766, loss_spatial_dice_0: 0.09878/0.20915, loss_spatial_ce_0: 0.02692/0.06340, loss_grounding_bce_0: 0.00308/0.08634, loss_grounding_dice_0: 0.07973/0.17857, loss_grounding_ce_0: 0.02672/0.27240, loss_mask_ce_1: 0.25263/0.90120, loss_mask_bce_1: 0.04722/0.33576, loss_mask_dice_1: 0.32110/1.16945, loss_spatial_bce_1: 0.01662/0.08818, loss_spatial_dice_1: 0.09701/0.21314, loss_spatial_ce_1: 0.02843/0.06917, loss_grounding_bce_1: 0.00210/0.08651, loss_grounding_dice_1: 0.07148/0.17936, loss_grounding_ce_1: 0.02503/0.27332, loss_mask_ce_2: 0.24488/0.90826, loss_mask_bce_2: 0.05167/0.33631, loss_mask_dice_2: 0.35147/1.16999, loss_spatial_bce_2: 0.01717/0.08914, loss_spatial_dice_2: 0.10117/0.21464, loss_spatial_ce_2: 0.03772/0.07254, loss_grounding_bce_2: 0.00375/0.08663, loss_grounding_dice_2: 0.09293/0.17920, loss_grounding_ce_2: 0.03060/0.27681, loss_mask_ce_3: 0.23704/0.91829, loss_mask_bce_3: 0.05223/0.33738, loss_mask_dice_3: 0.33778/1.16742, loss_spatial_bce_3: 0.01791/0.09023, loss_spatial_dice_3: 0.11615/0.21544, loss_spatial_ce_3: 0.04112/0.07676, loss_grounding_bce_3: 0.00238/0.08687, loss_grounding_dice_3: 0.06981/0.17893, loss_grounding_ce_3: 0.01502/0.27880, loss_mask_ce_4: 0.20692/0.91919, loss_mask_bce_4: 0.05099/0.33947, loss_mask_dice_4: 0.37463/1.19144, loss_spatial_bce_4: 0.01966/0.09423, loss_spatial_dice_4: 0.12006/0.22749, loss_spatial_ce_4: 0.03988/0.09252, loss_grounding_bce_4: 0.00281/0.08738, loss_grounding_dice_4: 0.07355/0.18187, loss_grounding_ce_4: 0.01067/0.28141, loss_mask_ce_5: 0.28221/0.93524, loss_mask_bce_5: 0.04934/0.34178, loss_mask_dice_5: 0.34300/1.19876, loss_spatial_bce_5: 0.03142/0.09634, loss_spatial_dice_5: 0.18138/0.23153, loss_spatial_ce_5: 0.06688/0.10727, loss_grounding_bce_5: 0.00395/0.08779, loss_grounding_dice_5: 0.10110/0.18306, loss_grounding_ce_5: 0.07863/0.29404, loss_mask_ce_6: 0.31564/0.97521, loss_mask_bce_6: 0.05109/0.34443, loss_mask_dice_6: 0.33213/1.20164, loss_spatial_bce_6: 0.02670/0.10213, loss_spatial_dice_6: 0.12717/0.23440, loss_spatial_ce_6: 0.43382/0.13358, loss_grounding_bce_6: 0.00410/0.08850, loss_grounding_dice_6: 0.12066/0.18341, loss_grounding_ce_6: 0.11031/0.30976, loss_mask_ce_7: 0.33163/1.02017, loss_mask_bce_7: 0.05626/0.35231, loss_mask_dice_7: 0.36959/1.25616, loss_spatial_bce_7: 0.01625/0.11020, loss_spatial_dice_7: 0.10198/0.26213, loss_spatial_ce_7: 0.16003/0.16919, loss_grounding_bce_7: 0.00662/0.09041, loss_grounding_dice_7: 0.14108/0.19064, loss_grounding_ce_7: 0.04223/0.34059, loss_mask_ce_8: 0.30282/1.12858, loss_mask_bce_8: 0.04972/0.36591, loss_mask_dice_8: 0.36692/1.32951, loss_spatial_bce_8: 0.02155/0.13091, loss_spatial_dice_8: 0.12293/0.30031, loss_spatial_ce_8: 0.19340/0.22596, loss_grounding_bce_8: 0.00397/0.09417, loss_grounding_dice_8: 0.14152/0.20167, loss_grounding_ce_8: 0.02487/0.40805, loss_mask_ce_9: 3.32355/3.67784, loss_mask_bce_9: 0.06271/0.39284, loss_mask_dice_9: 0.63347/1.90280, loss_spatial_bce_9: 0.18632/0.33337, loss_spatial_dice_9: 0.78664/0.82222, loss_spatial_ce_9: 1.66614/1.49786, loss_grounding_bce_9: 0.01294/0.10562, loss_grounding_dice_9: 0.31417/0.28083, loss_grounding_ce_9: 0.28079/0.67314] items per batch[64] items per second[0.13] total items[3744000] mini batches[ 58500] memory[7345] epoch remaining[1:30:07]
INFO:trainer.default_trainer:epochs[    32] optim steps[58600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81347/0.90040, loss_mask_bce_0: 0.15648/0.33479, loss_mask_dice_0: 0.18640/1.16301, loss_spatial_bce_0: 0.06544/0.08766, loss_spatial_dice_0: 0.08055/0.20911, loss_spatial_ce_0: 0.05501/0.06342, loss_grounding_bce_0: 0.02889/0.08633, loss_grounding_dice_0: 0.12103/0.17856, loss_grounding_ce_0: 0.25868/0.27236, loss_mask_ce_1: 0.82825/0.90107, loss_mask_bce_1: 0.14874/0.33573, loss_mask_dice_1: 0.20919/1.16931, loss_spatial_bce_1: 0.06740/0.08818, loss_spatial_dice_1: 0.11469/0.21311, loss_spatial_ce_1: 0.07328/0.06919, loss_grounding_bce_1: 0.02808/0.08649, loss_grounding_dice_1: 0.11068/0.17935, loss_grounding_ce_1: 0.26766/0.27326, loss_mask_ce_2: 0.83911/0.90815, loss_mask_bce_2: 0.14277/0.33629, loss_mask_dice_2: 0.18390/1.16985, loss_spatial_bce_2: 0.07616/0.08914, loss_spatial_dice_2: 0.10026/0.21461, loss_spatial_ce_2: 0.08335/0.07256, loss_grounding_bce_2: 0.02900/0.08662, loss_grounding_dice_2: 0.07113/0.17918, loss_grounding_ce_2: 0.26668/0.27677, loss_mask_ce_3: 0.86959/0.91818, loss_mask_bce_3: 0.14414/0.33737, loss_mask_dice_3: 0.20483/1.16726, loss_spatial_bce_3: 0.06993/0.09023, loss_spatial_dice_3: 0.08909/0.21541, loss_spatial_ce_3: 0.07261/0.07681, loss_grounding_bce_3: 0.02852/0.08686, loss_grounding_dice_3: 0.09412/0.17891, loss_grounding_ce_3: 0.26161/0.27877, loss_mask_ce_4: 0.84233/0.91904, loss_mask_bce_4: 0.14334/0.33946, loss_mask_dice_4: 0.17635/1.19130, loss_spatial_bce_4: 0.06899/0.09423, loss_spatial_dice_4: 0.08907/0.22746, loss_spatial_ce_4: 0.10927/0.09257, loss_grounding_bce_4: 0.02938/0.08737, loss_grounding_dice_4: 0.13887/0.18186, loss_grounding_ce_4: 0.25982/0.28138, loss_mask_ce_5: 0.84558/0.93510, loss_mask_bce_5: 0.14691/0.34177, loss_mask_dice_5: 0.18300/1.19861, loss_spatial_bce_5: 0.11671/0.09635, loss_spatial_dice_5: 0.14868/0.23151, loss_spatial_ce_5: 0.08713/0.10731, loss_grounding_bce_5: 0.02933/0.08778, loss_grounding_dice_5: 0.15698/0.18304, loss_grounding_ce_5: 0.26427/0.29401, loss_mask_ce_6: 0.93000/0.97509, loss_mask_bce_6: 0.14767/0.34442, loss_mask_dice_6: 0.23911/1.20150, loss_spatial_bce_6: 0.06955/0.10213, loss_spatial_dice_6: 0.11993/0.23438, loss_spatial_ce_6: 0.16449/0.13364, loss_grounding_bce_6: 0.03935/0.08849, loss_grounding_dice_6: 0.27291/0.18341, loss_grounding_ce_6: 0.25001/0.30973, loss_mask_ce_7: 1.02036/1.02006, loss_mask_bce_7: 0.15484/0.35230, loss_mask_dice_7: 0.26281/1.25598, loss_spatial_bce_7: 0.07563/0.11019, loss_spatial_dice_7: 0.14892/0.26208, loss_spatial_ce_7: 0.04073/0.16921, loss_grounding_bce_7: 0.03604/0.09040, loss_grounding_dice_7: 0.26630/0.19063, loss_grounding_ce_7: 0.48003/0.34051, loss_mask_ce_8: 0.81297/1.12847, loss_mask_bce_8: 0.25908/0.36591, loss_mask_dice_8: 0.41874/1.32933, loss_spatial_bce_8: 0.08865/0.13090, loss_spatial_dice_8: 0.19715/0.30025, loss_spatial_ce_8: 0.15350/0.22598, loss_grounding_bce_8: 0.03484/0.09416, loss_grounding_dice_8: 0.19645/0.20166, loss_grounding_ce_8: 0.60937/0.40797, loss_mask_ce_9: 2.98427/3.67760, loss_mask_bce_9: 0.26996/0.39282, loss_mask_dice_9: 0.53410/1.90249, loss_spatial_bce_9: 0.55350/0.33342, loss_spatial_dice_9: 0.67298/0.82220, loss_spatial_ce_9: 1.65529/1.49781, loss_grounding_bce_9: 0.04841/0.10561, loss_grounding_dice_9: 0.29659/0.28080, loss_grounding_ce_9: 0.84676/0.67311] items per batch[64] items per second[0.23] total items[3750400] mini batches[ 58600] memory[7345] epoch remaining[1:20:52]
INFO:trainer.default_trainer:epochs[    32] optim steps[58700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01382/0.90038, loss_mask_bce_0: 0.07191/0.33476, loss_mask_dice_0: 0.19496/1.16308, loss_spatial_bce_0: 0.05037/0.08765, loss_spatial_dice_0: 0.11109/0.20910, loss_spatial_ce_0: 0.06709/0.06341, loss_grounding_bce_0: 0.06069/0.08631, loss_grounding_dice_0: 0.15738/0.17855, loss_grounding_ce_0: 0.17955/0.27239, loss_mask_ce_1: 0.64974/0.90102, loss_mask_bce_1: 0.07597/0.33570, loss_mask_dice_1: 0.32615/1.16939, loss_spatial_bce_1: 0.05822/0.08817, loss_spatial_dice_1: 0.14994/0.21310, loss_spatial_ce_1: 0.18403/0.06918, loss_grounding_bce_1: 0.05583/0.08649, loss_grounding_dice_1: 0.15805/0.17933, loss_grounding_ce_1: 0.17025/0.27328, loss_mask_ce_2: 0.92565/0.90812, loss_mask_bce_2: 0.06852/0.33626, loss_mask_dice_2: 0.19048/1.16990, loss_spatial_bce_2: 0.05925/0.08913, loss_spatial_dice_2: 0.15019/0.21459, loss_spatial_ce_2: 0.16297/0.07256, loss_grounding_bce_2: 0.06442/0.08661, loss_grounding_dice_2: 0.16848/0.17918, loss_grounding_ce_2: 0.15926/0.27680, loss_mask_ce_3: 0.92729/0.91817, loss_mask_bce_3: 0.07378/0.33734, loss_mask_dice_3: 0.20581/1.16731, loss_spatial_bce_3: 0.06715/0.09023, loss_spatial_dice_3: 0.17807/0.21540, loss_spatial_ce_3: 0.16315/0.07681, loss_grounding_bce_3: 0.05993/0.08684, loss_grounding_dice_3: 0.17240/0.17890, loss_grounding_ce_3: 0.16008/0.27882, loss_mask_ce_4: 0.91402/0.91902, loss_mask_bce_4: 0.07761/0.33943, loss_mask_dice_4: 0.16756/1.19138, loss_spatial_bce_4: 0.05546/0.09423, loss_spatial_dice_4: 0.11189/0.22745, loss_spatial_ce_4: 0.29631/0.09259, loss_grounding_bce_4: 0.06380/0.08735, loss_grounding_dice_4: 0.16272/0.18184, loss_grounding_ce_4: 0.16089/0.28143, loss_mask_ce_5: 0.88734/0.93513, loss_mask_bce_5: 0.06864/0.34174, loss_mask_dice_5: 0.16532/1.19869, loss_spatial_bce_5: 0.05804/0.09635, loss_spatial_dice_5: 0.13248/0.23151, loss_spatial_ce_5: 0.13453/0.10732, loss_grounding_bce_5: 0.05988/0.08777, loss_grounding_dice_5: 0.19346/0.18303, loss_grounding_ce_5: 0.17731/0.29407, loss_mask_ce_6: 0.78485/0.97511, loss_mask_bce_6: 0.07923/0.34438, loss_mask_dice_6: 0.22693/1.20155, loss_spatial_bce_6: 0.05512/0.10213, loss_spatial_dice_6: 0.15107/0.23438, loss_spatial_ce_6: 0.09630/0.13363, loss_grounding_bce_6: 0.06180/0.08848, loss_grounding_dice_6: 0.31679/0.18340, loss_grounding_ce_6: 0.11055/0.30975, loss_mask_ce_7: 0.86774/1.02010, loss_mask_bce_7: 0.07180/0.35225, loss_mask_dice_7: 0.17672/1.25605, loss_spatial_bce_7: 0.05147/0.11019, loss_spatial_dice_7: 0.14951/0.26208, loss_spatial_ce_7: 0.51688/0.16920, loss_grounding_bce_7: 0.05868/0.09039, loss_grounding_dice_7: 0.11598/0.19061, loss_grounding_ce_7: 0.22413/0.34052, loss_mask_ce_8: 0.57668/1.12851, loss_mask_bce_8: 0.08408/0.36587, loss_mask_dice_8: 0.37816/1.32935, loss_spatial_bce_8: 0.07027/0.13089, loss_spatial_dice_8: 0.23904/0.30025, loss_spatial_ce_8: 0.66602/0.22598, loss_grounding_bce_8: 0.05881/0.09415, loss_grounding_dice_8: 0.19040/0.20164, loss_grounding_ce_8: 0.22859/0.40798, loss_mask_ce_9: 2.50914/3.67787, loss_mask_bce_9: 0.08000/0.39277, loss_mask_dice_9: 0.43871/1.90246, loss_spatial_bce_9: 0.40769/0.33340, loss_spatial_dice_9: 0.61351/0.82218, loss_spatial_ce_9: 1.22760/1.49786, loss_grounding_bce_9: 0.06135/0.10560, loss_grounding_dice_9: 0.38781/0.28077, loss_grounding_ce_9: 0.38775/0.67316] items per batch[64] items per second[0.23] total items[3756800] mini batches[ 58700] memory[7345] epoch remaining[1:14:55]
INFO:trainer.default_trainer:epochs[    32] optim steps[58800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.67690/0.90035, loss_mask_bce_0: 0.63823/0.33476, loss_mask_dice_0: 3.16527/1.16302, loss_spatial_bce_0: 0.05278/0.08765, loss_spatial_dice_0: 0.29383/0.20910, loss_spatial_ce_0: 0.01006/0.06339, loss_grounding_bce_0: 0.02003/0.08631, loss_grounding_dice_0: 0.32399/0.17855, loss_grounding_ce_0: 0.39414/0.27229, loss_mask_ce_1: 1.63499/0.90103, loss_mask_bce_1: 0.61563/0.33571, loss_mask_dice_1: 3.01398/1.16936, loss_spatial_bce_1: 0.05231/0.08817, loss_spatial_dice_1: 0.30575/0.21310, loss_spatial_ce_1: 0.00915/0.06916, loss_grounding_bce_1: 0.01872/0.08648, loss_grounding_dice_1: 0.28172/0.17934, loss_grounding_ce_1: 0.38433/0.27318, loss_mask_ce_2: 1.75916/0.90810, loss_mask_bce_2: 0.65047/0.33627, loss_mask_dice_2: 3.18989/1.16983, loss_spatial_bce_2: 0.05346/0.08914, loss_spatial_dice_2: 0.28187/0.21459, loss_spatial_ce_2: 0.00727/0.07253, loss_grounding_bce_2: 0.01772/0.08660, loss_grounding_dice_2: 0.31346/0.17918, loss_grounding_ce_2: 0.40455/0.27669, loss_mask_ce_3: 1.55034/0.91815, loss_mask_bce_3: 0.67768/0.33734, loss_mask_dice_3: 3.28699/1.16725, loss_spatial_bce_3: 0.06041/0.09023, loss_spatial_dice_3: 0.33426/0.21540, loss_spatial_ce_3: 0.01074/0.07679, loss_grounding_bce_3: 0.01825/0.08684, loss_grounding_dice_3: 0.29665/0.17890, loss_grounding_ce_3: 0.41729/0.27872, loss_mask_ce_4: 1.51425/0.91904, loss_mask_bce_4: 0.65065/0.33943, loss_mask_dice_4: 3.25695/1.19132, loss_spatial_bce_4: 0.06061/0.09423, loss_spatial_dice_4: 0.28842/0.22745, loss_spatial_ce_4: 0.07853/0.09257, loss_grounding_bce_4: 0.01872/0.08734, loss_grounding_dice_4: 0.32047/0.18184, loss_grounding_ce_4: 0.40567/0.28134, loss_mask_ce_5: 1.63761/0.93515, loss_mask_bce_5: 0.67672/0.34174, loss_mask_dice_5: 3.41591/1.19865, loss_spatial_bce_5: 0.06563/0.09635, loss_spatial_dice_5: 0.32918/0.23151, loss_spatial_ce_5: 0.07154/0.10730, loss_grounding_bce_5: 0.02935/0.08776, loss_grounding_dice_5: 0.30274/0.18303, loss_grounding_ce_5: 0.40458/0.29398, loss_mask_ce_6: 1.57201/0.97514, loss_mask_bce_6: 0.73368/0.34439, loss_mask_dice_6: 3.45970/1.20153, loss_spatial_bce_6: 0.06943/0.10214, loss_spatial_dice_6: 0.33110/0.23439, loss_spatial_ce_6: 0.05498/0.13362, loss_grounding_bce_6: 0.01805/0.08848, loss_grounding_dice_6: 0.30467/0.18340, loss_grounding_ce_6: 0.37220/0.30965, loss_mask_ce_7: 1.65313/1.02013, loss_mask_bce_7: 0.73532/0.35225, loss_mask_dice_7: 3.62836/1.25600, loss_spatial_bce_7: 0.06230/0.11019, loss_spatial_dice_7: 0.32400/0.26207, loss_spatial_ce_7: 0.09546/0.16919, loss_grounding_bce_7: 0.02323/0.09038, loss_grounding_dice_7: 0.30597/0.19063, loss_grounding_ce_7: 0.34581/0.34039, loss_mask_ce_8: 1.92225/1.12848, loss_mask_bce_8: 0.74322/0.36587, loss_mask_dice_8: 3.68572/1.32932, loss_spatial_bce_8: 0.10439/0.13089, loss_spatial_dice_8: 0.39860/0.30024, loss_spatial_ce_8: 0.11448/0.22597, loss_grounding_bce_8: 0.02048/0.09415, loss_grounding_dice_8: 0.30208/0.20163, loss_grounding_ce_8: 0.43846/0.40784, loss_mask_ce_9: 5.06773/3.67775, loss_mask_bce_9: 0.79130/0.39278, loss_mask_dice_9: 6.68791/1.90237, loss_spatial_bce_9: 0.20460/0.33340, loss_spatial_dice_9: 0.97118/0.82218, loss_spatial_ce_9: 1.33382/1.49777, loss_grounding_bce_9: 0.02083/0.10559, loss_grounding_dice_9: 0.43197/0.28077, loss_grounding_ce_9: 0.61073/0.67295] items per batch[64] items per second[0.23] total items[3763200] mini batches[ 58800] memory[7345] epoch remaining[1:10:16]
INFO:trainer.default_trainer:epochs[    32] optim steps[58900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19963/0.90038, loss_mask_bce_0: 0.43845/0.33475, loss_mask_dice_0: 2.34804/1.16304, loss_spatial_bce_0: 0.04914/0.08764, loss_spatial_dice_0: 0.16930/0.20908, loss_spatial_ce_0: 0.00913/0.06336, loss_grounding_bce_0: 0.12640/0.08631, loss_grounding_dice_0: 0.20917/0.17855, loss_grounding_ce_0: 0.99168/0.27225, loss_mask_ce_1: 1.20561/0.90101, loss_mask_bce_1: 0.41408/0.33570, loss_mask_dice_1: 2.34905/1.16937, loss_spatial_bce_1: 0.05294/0.08817, loss_spatial_dice_1: 0.19373/0.21308, loss_spatial_ce_1: 0.00844/0.06912, loss_grounding_bce_1: 0.11508/0.08648, loss_grounding_dice_1: 0.17166/0.17935, loss_grounding_ce_1: 1.12375/0.27314, loss_mask_ce_2: 1.26280/0.90810, loss_mask_bce_2: 0.43835/0.33625, loss_mask_dice_2: 2.42566/1.16985, loss_spatial_bce_2: 0.05179/0.08913, loss_spatial_dice_2: 0.17415/0.21458, loss_spatial_ce_2: 0.00735/0.07249, loss_grounding_bce_2: 0.11331/0.08660, loss_grounding_dice_2: 0.17551/0.17919, loss_grounding_ce_2: 1.08277/0.27661, loss_mask_ce_3: 1.19419/0.91818, loss_mask_bce_3: 0.41709/0.33733, loss_mask_dice_3: 2.37433/1.16726, loss_spatial_bce_3: 0.05051/0.09023, loss_spatial_dice_3: 0.19076/0.21539, loss_spatial_ce_3: 0.01356/0.07678, loss_grounding_bce_3: 0.11223/0.08684, loss_grounding_dice_3: 0.14916/0.17890, loss_grounding_ce_3: 0.81470/0.27868, loss_mask_ce_4: 1.37232/0.91909, loss_mask_bce_4: 0.42062/0.33943, loss_mask_dice_4: 2.53786/1.19133, loss_spatial_bce_4: 0.05194/0.09423, loss_spatial_dice_4: 0.20283/0.22744, loss_spatial_ce_4: 0.08236/0.09256, loss_grounding_bce_4: 0.09658/0.08734, loss_grounding_dice_4: 0.16820/0.18184, loss_grounding_ce_4: 0.66118/0.28127, loss_mask_ce_5: 1.26595/0.93521, loss_mask_bce_5: 0.42646/0.34173, loss_mask_dice_5: 2.37536/1.19865, loss_spatial_bce_5: 0.05930/0.09636, loss_spatial_dice_5: 0.22922/0.23150, loss_spatial_ce_5: 0.11868/0.10727, loss_grounding_bce_5: 0.09196/0.08776, loss_grounding_dice_5: 0.14404/0.18303, loss_grounding_ce_5: 0.58132/0.29394, loss_mask_ce_6: 1.22818/0.97518, loss_mask_bce_6: 0.42240/0.34438, loss_mask_dice_6: 2.45606/1.20161, loss_spatial_bce_6: 0.06224/0.10214, loss_spatial_dice_6: 0.23745/0.23438, loss_spatial_ce_6: 0.17376/0.13357, loss_grounding_bce_6: 0.09590/0.08848, loss_grounding_dice_6: 0.14329/0.18340, loss_grounding_ce_6: 0.41841/0.30959, loss_mask_ce_7: 1.55718/1.02017, loss_mask_bce_7: 0.41382/0.35224, loss_mask_dice_7: 2.61227/1.25601, loss_spatial_bce_7: 0.08591/0.11018, loss_spatial_dice_7: 0.26794/0.26206, loss_spatial_ce_7: 0.11161/0.16915, loss_grounding_bce_7: 0.10126/0.09038, loss_grounding_dice_7: 0.14645/0.19063, loss_grounding_ce_7: 0.34880/0.34032, loss_mask_ce_8: 1.74464/1.12849, loss_mask_bce_8: 0.43934/0.36587, loss_mask_dice_8: 2.66159/1.32931, loss_spatial_bce_8: 0.09163/0.13089, loss_spatial_dice_8: 0.28558/0.30022, loss_spatial_ce_8: 0.12657/0.22593, loss_grounding_bce_8: 0.11198/0.09415, loss_grounding_dice_8: 0.15661/0.20162, loss_grounding_ce_8: 0.78683/0.40776, loss_mask_ce_9: 4.27154/3.67754, loss_mask_bce_9: 0.41219/0.39276, loss_mask_dice_9: 3.49079/1.90235, loss_spatial_bce_9: 0.26332/0.33340, loss_spatial_dice_9: 0.85472/0.82217, loss_spatial_ce_9: 1.27476/1.49763, loss_grounding_bce_9: 0.21319/0.10558, loss_grounding_dice_9: 0.42701/0.28077, loss_grounding_ce_9: 2.05534/0.67285] items per batch[64] items per second[0.23] total items[3769600] mini batches[ 58900] memory[7345] epoch remaining[1:05:08]
INFO:trainer.default_trainer:epochs[    32] optim steps[59000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81883/0.90032, loss_mask_bce_0: 0.06172/0.33472, loss_mask_dice_0: 0.46705/1.16311, loss_spatial_bce_0: 0.02274/0.08763, loss_spatial_dice_0: 0.20520/0.20907, loss_spatial_ce_0: 0.03873/0.06334, loss_grounding_bce_0: 0.06604/0.08629, loss_grounding_dice_0: 0.11754/0.17854, loss_grounding_ce_0: 0.01511/0.27225, loss_mask_ce_1: 0.37176/0.90095, loss_mask_bce_1: 0.06542/0.33566, loss_mask_dice_1: 0.55448/1.16944, loss_spatial_bce_1: 0.02612/0.08815, loss_spatial_dice_1: 0.21840/0.21306, loss_spatial_ce_1: 0.01589/0.06910, loss_grounding_bce_1: 0.05583/0.08646, loss_grounding_dice_1: 0.10412/0.17934, loss_grounding_ce_1: 0.01241/0.27313, loss_mask_ce_2: 0.66397/0.90805, loss_mask_bce_2: 0.05621/0.33622, loss_mask_dice_2: 0.49976/1.16991, loss_spatial_bce_2: 0.02849/0.08912, loss_spatial_dice_2: 0.20804/0.21456, loss_spatial_ce_2: 0.02070/0.07246, loss_grounding_bce_2: 0.05402/0.08658, loss_grounding_dice_2: 0.11521/0.17917, loss_grounding_ce_2: 0.00587/0.27659, loss_mask_ce_3: 0.37101/0.91814, loss_mask_bce_3: 0.05807/0.33730, loss_mask_dice_3: 0.51000/1.16733, loss_spatial_bce_3: 0.02980/0.09023, loss_spatial_dice_3: 0.18735/0.21537, loss_spatial_ce_3: 0.01649/0.07677, loss_grounding_bce_3: 0.05319/0.08681, loss_grounding_dice_3: 0.10499/0.17889, loss_grounding_ce_3: 0.00535/0.27867, loss_mask_ce_4: 0.76749/0.91903, loss_mask_bce_4: 0.07099/0.33939, loss_mask_dice_4: 0.52034/1.19139, loss_spatial_bce_4: 0.03036/0.09423, loss_spatial_dice_4: 0.21059/0.22743, loss_spatial_ce_4: 0.01144/0.09255, loss_grounding_bce_4: 0.06240/0.08732, loss_grounding_dice_4: 0.11003/0.18182, loss_grounding_ce_4: 0.00851/0.28127, loss_mask_ce_5: 0.33451/0.93512, loss_mask_bce_5: 0.06207/0.34170, loss_mask_dice_5: 0.51140/1.19874, loss_spatial_bce_5: 0.02953/0.09635, loss_spatial_dice_5: 0.21327/0.23150, loss_spatial_ce_5: 0.04974/0.10725, loss_grounding_bce_5: 0.06490/0.08774, loss_grounding_dice_5: 0.11241/0.18303, loss_grounding_ce_5: 0.00904/0.29396, loss_mask_ce_6: 0.44375/0.97512, loss_mask_bce_6: 0.07472/0.34436, loss_mask_dice_6: 0.41831/1.20166, loss_spatial_bce_6: 0.03532/0.10213, loss_spatial_dice_6: 0.21822/0.23437, loss_spatial_ce_6: 0.04486/0.13355, loss_grounding_bce_6: 0.07205/0.08847, loss_grounding_dice_6: 0.11781/0.18340, loss_grounding_ce_6: 0.00611/0.30963, loss_mask_ce_7: 0.45931/1.02011, loss_mask_bce_7: 0.05783/0.35222, loss_mask_dice_7: 0.55048/1.25610, loss_spatial_bce_7: 0.03121/0.11017, loss_spatial_dice_7: 0.21132/0.26205, loss_spatial_ce_7: 0.09468/0.16909, loss_grounding_bce_7: 0.05782/0.09037, loss_grounding_dice_7: 0.09901/0.19062, loss_grounding_ce_7: 0.01587/0.34029, loss_mask_ce_8: 0.54781/1.12836, loss_mask_bce_8: 0.05788/0.36584, loss_mask_dice_8: 0.49982/1.32941, loss_spatial_bce_8: 0.03628/0.13087, loss_spatial_dice_8: 0.28230/0.30021, loss_spatial_ce_8: 0.07898/0.22588, loss_grounding_bce_8: 0.05753/0.09413, loss_grounding_dice_8: 0.11234/0.20161, loss_grounding_ce_8: 0.03253/0.40771, loss_mask_ce_9: 2.24222/3.67751, loss_mask_bce_9: 0.06749/0.39274, loss_mask_dice_9: 0.68804/1.90244, loss_spatial_bce_9: 0.09691/0.33338, loss_spatial_dice_9: 0.72948/0.82217, loss_spatial_ce_9: 1.94487/1.49756, loss_grounding_bce_9: 0.06374/0.10556, loss_grounding_dice_9: 0.11289/0.28077, loss_grounding_ce_9: 0.59125/0.67285] items per batch[64] items per second[0.23] total items[3776000] mini batches[ 59000] memory[7345] epoch remaining[1:00:17]
INFO:trainer.default_trainer:epochs[    32] optim steps[59100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33114/0.90045, loss_mask_bce_0: 0.21947/0.33471, loss_mask_dice_0: 1.99136/1.16338, loss_spatial_bce_0: 0.02696/0.08761, loss_spatial_dice_0: 0.17381/0.20905, loss_spatial_ce_0: 0.00101/0.06333, loss_grounding_bce_0: 0.07780/0.08628, loss_grounding_dice_0: 0.10896/0.17855, loss_grounding_ce_0: 0.18804/0.27229, loss_mask_ce_1: 0.39861/0.90107, loss_mask_bce_1: 0.21831/0.33566, loss_mask_dice_1: 1.76907/1.16971, loss_spatial_bce_1: 0.02833/0.08813, loss_spatial_dice_1: 0.15530/0.21304, loss_spatial_ce_1: 0.01533/0.06909, loss_grounding_bce_1: 0.08010/0.08645, loss_grounding_dice_1: 0.10560/0.17935, loss_grounding_ce_1: 0.16892/0.27316, loss_mask_ce_2: 0.36022/0.90820, loss_mask_bce_2: 0.21633/0.33621, loss_mask_dice_2: 1.97645/1.17019, loss_spatial_bce_2: 0.02911/0.08910, loss_spatial_dice_2: 0.16102/0.21455, loss_spatial_ce_2: 0.00120/0.07246, loss_grounding_bce_2: 0.08327/0.08658, loss_grounding_dice_2: 0.10692/0.17918, loss_grounding_ce_2: 0.21366/0.27663, loss_mask_ce_3: 0.38880/0.91830, loss_mask_bce_3: 0.21508/0.33729, loss_mask_dice_3: 1.61889/1.16761, loss_spatial_bce_3: 0.03400/0.09021, loss_spatial_dice_3: 0.15241/0.21536, loss_spatial_ce_3: 0.00654/0.07678, loss_grounding_bce_3: 0.07995/0.08681, loss_grounding_dice_3: 0.10602/0.17890, loss_grounding_ce_3: 0.22255/0.27872, loss_mask_ce_4: 0.45545/0.91919, loss_mask_bce_4: 0.22489/0.33938, loss_mask_dice_4: 1.92900/1.19169, loss_spatial_bce_4: 0.03136/0.09421, loss_spatial_dice_4: 0.20015/0.22742, loss_spatial_ce_4: 0.02722/0.09257, loss_grounding_bce_4: 0.07000/0.08732, loss_grounding_dice_4: 0.09967/0.18183, loss_grounding_ce_4: 0.40769/0.28130, loss_mask_ce_5: 0.48130/0.93529, loss_mask_bce_5: 0.21758/0.34169, loss_mask_dice_5: 1.71623/1.19903, loss_spatial_bce_5: 0.04026/0.09634, loss_spatial_dice_5: 0.17718/0.23149, loss_spatial_ce_5: 0.04076/0.10723, loss_grounding_bce_5: 0.07211/0.08774, loss_grounding_dice_5: 0.10596/0.18305, loss_grounding_ce_5: 0.37633/0.29399, loss_mask_ce_6: 0.51548/0.97530, loss_mask_bce_6: 0.23927/0.34435, loss_mask_dice_6: 1.87708/1.20196, loss_spatial_bce_6: 0.04040/0.10212, loss_spatial_dice_6: 0.17323/0.23437, loss_spatial_ce_6: 0.03657/0.13354, loss_grounding_bce_6: 0.06916/0.08846, loss_grounding_dice_6: 0.10341/0.18341, loss_grounding_ce_6: 0.29041/0.30965, loss_mask_ce_7: 0.67071/1.02028, loss_mask_bce_7: 0.24865/0.35221, loss_mask_dice_7: 2.24570/1.25644, loss_spatial_bce_7: 0.03760/0.11015, loss_spatial_dice_7: 0.26541/0.26205, loss_spatial_ce_7: 0.06194/0.16906, loss_grounding_bce_7: 0.08913/0.09036, loss_grounding_dice_7: 0.13744/0.19065, loss_grounding_ce_7: 0.46590/0.34025, loss_mask_ce_8: 0.73514/1.12853, loss_mask_bce_8: 0.25970/0.36584, loss_mask_dice_8: 1.93504/1.32975, loss_spatial_bce_8: 0.04497/0.13085, loss_spatial_dice_8: 0.28814/0.30020, loss_spatial_ce_8: 0.07737/0.22587, loss_grounding_bce_8: 0.10182/0.09412, loss_grounding_dice_8: 0.13601/0.20162, loss_grounding_ce_8: 0.73182/0.40766, loss_mask_ce_9: 4.70721/3.67777, loss_mask_bce_9: 0.35571/0.39276, loss_mask_dice_9: 3.27233/1.90297, loss_spatial_bce_9: 0.24090/0.33334, loss_spatial_dice_9: 0.87254/0.82214, loss_spatial_ce_9: 1.48680/1.49755, loss_grounding_bce_9: 0.11300/0.10557, loss_grounding_dice_9: 0.17127/0.28079, loss_grounding_ce_9: 0.79927/0.67285] items per batch[64] items per second[0.23] total items[3782400] mini batches[ 59100] memory[7345] epoch remaining[0:55:27]
INFO:trainer.default_trainer:epochs[    32] optim steps[59200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71566/0.90042, loss_mask_bce_0: 0.12746/0.33463, loss_mask_dice_0: 0.36941/1.16295, loss_spatial_bce_0: 0.03716/0.08759, loss_spatial_dice_0: 0.09381/0.20901, loss_spatial_ce_0: 0.03776/0.06332, loss_grounding_bce_0: 0.07324/0.08627, loss_grounding_dice_0: 0.09602/0.17853, loss_grounding_ce_0: 0.02183/0.27218, loss_mask_ce_1: 0.65217/0.90101, loss_mask_bce_1: 0.12872/0.33558, loss_mask_dice_1: 0.35714/1.16929, loss_spatial_bce_1: 0.03995/0.08812, loss_spatial_dice_1: 0.09630/0.21299, loss_spatial_ce_1: 0.03724/0.06907, loss_grounding_bce_1: 0.07267/0.08644, loss_grounding_dice_1: 0.09073/0.17932, loss_grounding_ce_1: 0.01854/0.27307, loss_mask_ce_2: 0.73828/0.90815, loss_mask_bce_2: 0.13651/0.33614, loss_mask_dice_2: 0.36609/1.16975, loss_spatial_bce_2: 0.04008/0.08909, loss_spatial_dice_2: 0.10800/0.21451, loss_spatial_ce_2: 0.07432/0.07241, loss_grounding_bce_2: 0.07270/0.08656, loss_grounding_dice_2: 0.09017/0.17915, loss_grounding_ce_2: 0.03027/0.27652, loss_mask_ce_3: 0.76806/0.91826, loss_mask_bce_3: 0.14119/0.33722, loss_mask_dice_3: 0.36364/1.16720, loss_spatial_bce_3: 0.04246/0.09019, loss_spatial_dice_3: 0.11095/0.21531, loss_spatial_ce_3: 0.07378/0.07676, loss_grounding_bce_3: 0.07776/0.08679, loss_grounding_dice_3: 0.09029/0.17887, loss_grounding_ce_3: 0.04542/0.27864, loss_mask_ce_4: 0.74509/0.91916, loss_mask_bce_4: 0.13242/0.33931, loss_mask_dice_4: 0.36560/1.19127, loss_spatial_bce_4: 0.04421/0.09420, loss_spatial_dice_4: 0.11355/0.22738, loss_spatial_ce_4: 0.04407/0.09253, loss_grounding_bce_4: 0.07576/0.08730, loss_grounding_dice_4: 0.09922/0.18179, loss_grounding_ce_4: 0.03049/0.28121, loss_mask_ce_5: 0.71243/0.93529, loss_mask_bce_5: 0.16345/0.34162, loss_mask_dice_5: 0.37528/1.19862, loss_spatial_bce_5: 0.05060/0.09633, loss_spatial_dice_5: 0.13299/0.23145, loss_spatial_ce_5: 0.04971/0.10721, loss_grounding_bce_5: 0.07102/0.08772, loss_grounding_dice_5: 0.08941/0.18301, loss_grounding_ce_5: 0.02665/0.29392, loss_mask_ce_6: 0.65954/0.97527, loss_mask_bce_6: 0.14594/0.34429, loss_mask_dice_6: 0.38905/1.20153, loss_spatial_bce_6: 0.05332/0.10211, loss_spatial_dice_6: 0.12613/0.23432, loss_spatial_ce_6: 0.04429/0.13350, loss_grounding_bce_6: 0.07938/0.08845, loss_grounding_dice_6: 0.09265/0.18338, loss_grounding_ce_6: 0.02736/0.30958, loss_mask_ce_7: 0.94895/1.02025, loss_mask_bce_7: 0.13133/0.35214, loss_mask_dice_7: 0.34338/1.25600, loss_spatial_bce_7: 0.04869/0.11013, loss_spatial_dice_7: 0.11677/0.26200, loss_spatial_ce_7: 0.20861/0.16900, loss_grounding_bce_7: 0.07367/0.09035, loss_grounding_dice_7: 0.09464/0.19061, loss_grounding_ce_7: 0.02919/0.34016, loss_mask_ce_8: 1.10308/1.12849, loss_mask_bce_8: 0.15476/0.36576, loss_mask_dice_8: 0.37813/1.32930, loss_spatial_bce_8: 0.08251/0.13083, loss_spatial_dice_8: 0.16998/0.30014, loss_spatial_ce_8: 0.15483/0.22580, loss_grounding_bce_8: 0.07642/0.09411, loss_grounding_dice_8: 0.09909/0.20159, loss_grounding_ce_8: 0.11154/0.40765, loss_mask_ce_9: 3.02117/3.67752, loss_mask_bce_9: 0.30344/0.39270, loss_mask_dice_9: 0.89654/1.90236, loss_spatial_bce_9: 0.71610/0.33336, loss_spatial_dice_9: 0.88418/0.82214, loss_spatial_ce_9: 1.62601/1.49754, loss_grounding_bce_9: 0.09470/0.10556, loss_grounding_dice_9: 0.28699/0.28074, loss_grounding_ce_9: 0.19482/0.67285] items per batch[64] items per second[0.23] total items[3788800] mini batches[ 59200] memory[7345] epoch remaining[0:50:49]
INFO:trainer.default_trainer:epochs[    32] optim steps[59300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38569/0.90037, loss_mask_bce_0: 0.17005/0.33463, loss_mask_dice_0: 0.79598/1.16316, loss_spatial_bce_0: 0.05447/0.08759, loss_spatial_dice_0: 0.17440/0.20901, loss_spatial_ce_0: 0.04214/0.06329, loss_grounding_bce_0: 0.07534/0.08627, loss_grounding_dice_0: 0.11740/0.17854, loss_grounding_ce_0: 0.05893/0.27228, loss_mask_ce_1: 0.43789/0.90094, loss_mask_bce_1: 0.17832/0.33558, loss_mask_dice_1: 1.03722/1.16951, loss_spatial_bce_1: 0.05467/0.08812, loss_spatial_dice_1: 0.12946/0.21299, loss_spatial_ce_1: 0.01918/0.06906, loss_grounding_bce_1: 0.07739/0.08644, loss_grounding_dice_1: 0.11667/0.17934, loss_grounding_ce_1: 0.06309/0.27314, loss_mask_ce_2: 0.37958/0.90808, loss_mask_bce_2: 0.18268/0.33614, loss_mask_dice_2: 0.96220/1.16996, loss_spatial_bce_2: 0.05630/0.08909, loss_spatial_dice_2: 0.17710/0.21451, loss_spatial_ce_2: 0.06283/0.07239, loss_grounding_bce_2: 0.07835/0.08657, loss_grounding_dice_2: 0.11589/0.17917, loss_grounding_ce_2: 0.07932/0.27660, loss_mask_ce_3: 0.41265/0.91822, loss_mask_bce_3: 0.17900/0.33722, loss_mask_dice_3: 0.95459/1.16739, loss_spatial_bce_3: 0.04753/0.09019, loss_spatial_dice_3: 0.11980/0.21531, loss_spatial_ce_3: 0.01776/0.07674, loss_grounding_bce_3: 0.07466/0.08680, loss_grounding_dice_3: 0.11785/0.17888, loss_grounding_ce_3: 0.07734/0.27875, loss_mask_ce_4: 0.42065/0.91908, loss_mask_bce_4: 0.18068/0.33931, loss_mask_dice_4: 0.81636/1.19148, loss_spatial_bce_4: 0.05336/0.09420, loss_spatial_dice_4: 0.16811/0.22738, loss_spatial_ce_4: 0.07586/0.09252, loss_grounding_bce_4: 0.07903/0.08731, loss_grounding_dice_4: 0.12082/0.18180, loss_grounding_ce_4: 0.06767/0.28129, loss_mask_ce_5: 0.43244/0.93521, loss_mask_bce_5: 0.17978/0.34162, loss_mask_dice_5: 0.97320/1.19881, loss_spatial_bce_5: 0.05072/0.09632, loss_spatial_dice_5: 0.13317/0.23145, loss_spatial_ce_5: 0.01640/0.10720, loss_grounding_bce_5: 0.08091/0.08772, loss_grounding_dice_5: 0.12488/0.18303, loss_grounding_ce_5: 0.07432/0.29403, loss_mask_ce_6: 0.40699/0.97520, loss_mask_bce_6: 0.18369/0.34429, loss_mask_dice_6: 1.17507/1.20177, loss_spatial_bce_6: 0.05714/0.10210, loss_spatial_dice_6: 0.21387/0.23432, loss_spatial_ce_6: 0.04034/0.13347, loss_grounding_bce_6: 0.08371/0.08845, loss_grounding_dice_6: 0.12410/0.18339, loss_grounding_ce_6: 0.07571/0.30965, loss_mask_ce_7: 0.55504/1.02021, loss_mask_bce_7: 0.18855/0.35213, loss_mask_dice_7: 0.88658/1.25621, loss_spatial_bce_7: 0.07318/0.11013, loss_spatial_dice_7: 0.15043/0.26200, loss_spatial_ce_7: 0.03696/0.16895, loss_grounding_bce_7: 0.08396/0.09035, loss_grounding_dice_7: 0.12831/0.19063, loss_grounding_ce_7: 0.07432/0.34024, loss_mask_ce_8: 0.56970/1.12840, loss_mask_bce_8: 0.21612/0.36575, loss_mask_dice_8: 1.34445/1.32955, loss_spatial_bce_8: 0.09114/0.13081, loss_spatial_dice_8: 0.19260/0.30015, loss_spatial_ce_8: 0.11942/0.22577, loss_grounding_bce_8: 0.09909/0.09411, loss_grounding_dice_8: 0.13921/0.20160, loss_grounding_ce_8: 0.08832/0.40769, loss_mask_ce_9: 4.30320/3.67761, loss_mask_bce_9: 0.24113/0.39268, loss_mask_dice_9: 2.73070/1.90258, loss_spatial_bce_9: 0.31327/0.33333, loss_spatial_dice_9: 0.87000/0.82214, loss_spatial_ce_9: 1.46597/1.49752, loss_grounding_bce_9: 0.11016/0.10555, loss_grounding_dice_9: 0.23076/0.28075, loss_grounding_ce_9: 0.21535/0.67298] items per batch[64] items per second[0.23] total items[3795200] mini batches[ 59300] memory[7345] epoch remaining[0:46:05]
INFO:trainer.default_trainer:epochs[    32] optim steps[59400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39541/0.90042, loss_mask_bce_0: 0.05159/0.33463, loss_mask_dice_0: 1.69115/1.16314, loss_spatial_bce_0: 0.01005/0.08759, loss_spatial_dice_0: 0.24754/0.20899, loss_spatial_ce_0: 0.26246/0.06325, loss_grounding_bce_0: 0.01407/0.08626, loss_grounding_dice_0: 0.25604/0.17854, loss_grounding_ce_0: 0.30469/0.27230, loss_mask_ce_1: 1.54211/0.90099, loss_mask_bce_1: 0.05445/0.33558, loss_mask_dice_1: 1.65576/1.16950, loss_spatial_bce_1: 0.00874/0.08811, loss_spatial_dice_1: 0.26266/0.21297, loss_spatial_ce_1: 0.02384/0.06902, loss_grounding_bce_1: 0.02054/0.08643, loss_grounding_dice_1: 0.35114/0.17932, loss_grounding_ce_1: 0.20707/0.27316, loss_mask_ce_2: 1.68608/0.90814, loss_mask_bce_2: 0.04426/0.33614, loss_mask_dice_2: 1.59014/1.16995, loss_spatial_bce_2: 0.00823/0.08908, loss_spatial_dice_2: 0.28173/0.21450, loss_spatial_ce_2: 0.01543/0.07237, loss_grounding_bce_2: 0.02238/0.08656, loss_grounding_dice_2: 0.37158/0.17915, loss_grounding_ce_2: 0.24741/0.27660, loss_mask_ce_3: 1.61166/0.91831, loss_mask_bce_3: 0.06361/0.33722, loss_mask_dice_3: 1.59490/1.16737, loss_spatial_bce_3: 0.01124/0.09019, loss_spatial_dice_3: 0.22835/0.21530, loss_spatial_ce_3: 0.03174/0.07671, loss_grounding_bce_3: 0.02360/0.08679, loss_grounding_dice_3: 0.29266/0.17887, loss_grounding_ce_3: 0.21648/0.27875, loss_mask_ce_4: 1.82570/0.91919, loss_mask_bce_4: 0.05732/0.33931, loss_mask_dice_4: 1.69021/1.19145, loss_spatial_bce_4: 0.01131/0.09420, loss_spatial_dice_4: 0.29080/0.22737, loss_spatial_ce_4: 0.05038/0.09251, loss_grounding_bce_4: 0.02209/0.08729, loss_grounding_dice_4: 0.29161/0.18179, loss_grounding_ce_4: 0.33272/0.28130, loss_mask_ce_5: 1.73966/0.93531, loss_mask_bce_5: 0.05575/0.34162, loss_mask_dice_5: 1.60884/1.19881, loss_spatial_bce_5: 0.01041/0.09632, loss_spatial_dice_5: 0.28469/0.23144, loss_spatial_ce_5: 0.17970/0.10721, loss_grounding_bce_5: 0.01799/0.08771, loss_grounding_dice_5: 0.28212/0.18302, loss_grounding_ce_5: 0.64623/0.29408, loss_mask_ce_6: 1.57716/0.97528, loss_mask_bce_6: 0.05268/0.34429, loss_mask_dice_6: 1.69547/1.20178, loss_spatial_bce_6: 0.01433/0.10209, loss_spatial_dice_6: 0.25792/0.23431, loss_spatial_ce_6: 0.07392/0.13346, loss_grounding_bce_6: 0.01583/0.08844, loss_grounding_dice_6: 0.26905/0.18338, loss_grounding_ce_6: 0.48737/0.30965, loss_mask_ce_7: 1.43632/1.02029, loss_mask_bce_7: 0.05464/0.35214, loss_mask_dice_7: 1.84804/1.25623, loss_spatial_bce_7: 0.01050/0.11012, loss_spatial_dice_7: 0.33435/0.26199, loss_spatial_ce_7: 0.23835/0.16891, loss_grounding_bce_7: 0.01398/0.09034, loss_grounding_dice_7: 0.33811/0.19062, loss_grounding_ce_7: 1.04693/0.34032, loss_mask_ce_8: 2.23880/1.12855, loss_mask_bce_8: 0.05698/0.36575, loss_mask_dice_8: 1.80393/1.32955, loss_spatial_bce_8: 0.01492/0.13080, loss_spatial_dice_8: 0.44317/0.30013, loss_spatial_ce_8: 0.30135/0.22573, loss_grounding_bce_8: 0.01038/0.09409, loss_grounding_dice_8: 0.32580/0.20159, loss_grounding_ce_8: 1.77353/0.40779, loss_mask_ce_9: 4.00857/3.67778, loss_mask_bce_9: 0.03283/0.39268, loss_mask_dice_9: 2.13057/1.90256, loss_spatial_bce_9: 0.03318/0.33332, loss_spatial_dice_9: 0.86880/0.82213, loss_spatial_ce_9: 1.34345/1.49745, loss_grounding_bce_9: 0.00750/0.10554, loss_grounding_dice_9: 0.39355/0.28075, loss_grounding_ce_9: 1.79090/0.67309] items per batch[64] items per second[0.23] total items[3801600] mini batches[ 59400] memory[7345] epoch remaining[0:41:26]
INFO:trainer.default_trainer:epochs[    32] optim steps[59500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30489/0.90038, loss_mask_bce_0: 0.51366/0.33463, loss_mask_dice_0: 0.47804/1.16292, loss_spatial_bce_0: 0.09972/0.08759, loss_spatial_dice_0: 0.13208/0.20897, loss_spatial_ce_0: 0.00767/0.06324, loss_grounding_bce_0: 0.18337/0.08627, loss_grounding_dice_0: 0.15706/0.17851, loss_grounding_ce_0: 0.89226/0.27236, loss_mask_ce_1: 0.33207/0.90096, loss_mask_bce_1: 0.49978/0.33557, loss_mask_dice_1: 0.45699/1.16924, loss_spatial_bce_1: 0.10083/0.08812, loss_spatial_dice_1: 0.13589/0.21295, loss_spatial_ce_1: 0.01178/0.06901, loss_grounding_bce_1: 0.17335/0.08644, loss_grounding_dice_1: 0.15246/0.17930, loss_grounding_ce_1: 0.96614/0.27321, loss_mask_ce_2: 0.37139/0.90807, loss_mask_bce_2: 0.50536/0.33614, loss_mask_dice_2: 0.47663/1.16969, loss_spatial_bce_2: 0.10506/0.08909, loss_spatial_dice_2: 0.14387/0.21448, loss_spatial_ce_2: 0.02073/0.07236, loss_grounding_bce_2: 0.19073/0.08656, loss_grounding_dice_2: 0.15620/0.17913, loss_grounding_ce_2: 1.20313/0.27665, loss_mask_ce_3: 0.33109/0.91826, loss_mask_bce_3: 0.49983/0.33721, loss_mask_dice_3: 0.47410/1.16715, loss_spatial_bce_3: 0.12177/0.09020, loss_spatial_dice_3: 0.14490/0.21528, loss_spatial_ce_3: 0.01962/0.07671, loss_grounding_bce_3: 0.18222/0.08679, loss_grounding_dice_3: 0.14560/0.17885, loss_grounding_ce_3: 1.39542/0.27879, loss_mask_ce_4: 0.36180/0.91916, loss_mask_bce_4: 0.49262/0.33931, loss_mask_dice_4: 0.46805/1.19120, loss_spatial_bce_4: 0.11435/0.09421, loss_spatial_dice_4: 0.14663/0.22735, loss_spatial_ce_4: 0.02817/0.09249, loss_grounding_bce_4: 0.17154/0.08730, loss_grounding_dice_4: 0.14856/0.18176, loss_grounding_ce_4: 1.20617/0.28134, loss_mask_ce_5: 0.36522/0.93531, loss_mask_bce_5: 0.48609/0.34162, loss_mask_dice_5: 0.48228/1.19857, loss_spatial_bce_5: 0.11550/0.09633, loss_spatial_dice_5: 0.14787/0.23142, loss_spatial_ce_5: 0.04945/0.10720, loss_grounding_bce_5: 0.18291/0.08772, loss_grounding_dice_5: 0.15660/0.18299, loss_grounding_ce_5: 1.55390/0.29413, loss_mask_ce_6: 0.44498/0.97524, loss_mask_bce_6: 0.49208/0.34428, loss_mask_dice_6: 0.50008/1.20152, loss_spatial_bce_6: 0.12679/0.10210, loss_spatial_dice_6: 0.14929/0.23430, loss_spatial_ce_6: 0.05690/0.13345, loss_grounding_bce_6: 0.18879/0.08845, loss_grounding_dice_6: 0.14540/0.18336, loss_grounding_ce_6: 1.58520/0.30968, loss_mask_ce_7: 0.43389/1.02024, loss_mask_bce_7: 0.47967/0.35214, loss_mask_dice_7: 0.55488/1.25600, loss_spatial_bce_7: 0.11765/0.11012, loss_spatial_dice_7: 0.18396/0.26196, loss_spatial_ce_7: 0.15682/0.16888, loss_grounding_bce_7: 0.21199/0.09035, loss_grounding_dice_7: 0.18995/0.19061, loss_grounding_ce_7: 1.07050/0.34038, loss_mask_ce_8: 0.88949/1.12852, loss_mask_bce_8: 0.47386/0.36574, loss_mask_dice_8: 0.67325/1.32934, loss_spatial_bce_8: 0.21833/0.13080, loss_spatial_dice_8: 0.27355/0.30010, loss_spatial_ce_8: 0.30105/0.22569, loss_grounding_bce_8: 0.18997/0.09410, loss_grounding_dice_8: 0.20654/0.20157, loss_grounding_ce_8: 0.94725/0.40782, loss_mask_ce_9: 4.14745/3.67763, loss_mask_bce_9: 0.48525/0.39268, loss_mask_dice_9: 0.95879/1.90220, loss_spatial_bce_9: 0.59612/0.33336, loss_spatial_dice_9: 0.71907/0.82211, loss_spatial_ce_9: 1.19563/1.49739, loss_grounding_bce_9: 0.20325/0.10555, loss_grounding_dice_9: 0.28386/0.28073, loss_grounding_ce_9: 1.61960/0.67300] items per batch[64] items per second[0.23] total items[3808000] mini batches[ 59500] memory[7345] epoch remaining[0:36:43]
INFO:trainer.default_trainer:epochs[    32] optim steps[59600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47237/0.90039, loss_mask_bce_0: 0.30713/0.33468, loss_mask_dice_0: 0.46408/1.16286, loss_spatial_bce_0: 0.06425/0.08760, loss_spatial_dice_0: 0.11049/0.20896, loss_spatial_ce_0: 0.08181/0.06321, loss_grounding_bce_0: 0.06781/0.08627, loss_grounding_dice_0: 0.11011/0.17854, loss_grounding_ce_0: 0.12296/0.27252, loss_mask_ce_1: 0.43571/0.90098, loss_mask_bce_1: 0.29968/0.33562, loss_mask_dice_1: 0.47631/1.16921, loss_spatial_bce_1: 0.06345/0.08812, loss_spatial_dice_1: 0.12707/0.21293, loss_spatial_ce_1: 0.05082/0.06899, loss_grounding_bce_1: 0.06848/0.08644, loss_grounding_dice_1: 0.09493/0.17934, loss_grounding_ce_1: 0.17067/0.27335, loss_mask_ce_2: 0.44931/0.90810, loss_mask_bce_2: 0.30559/0.33619, loss_mask_dice_2: 0.46579/1.16966, loss_spatial_bce_2: 0.06185/0.08910, loss_spatial_dice_2: 0.11724/0.21446, loss_spatial_ce_2: 0.04008/0.07234, loss_grounding_bce_2: 0.06548/0.08657, loss_grounding_dice_2: 0.11174/0.17916, loss_grounding_ce_2: 0.18325/0.27675, loss_mask_ce_3: 0.43180/0.91829, loss_mask_bce_3: 0.30072/0.33727, loss_mask_dice_3: 0.44107/1.16712, loss_spatial_bce_3: 0.06706/0.09021, loss_spatial_dice_3: 0.13914/0.21527, loss_spatial_ce_3: 0.03081/0.07670, loss_grounding_bce_3: 0.06577/0.08680, loss_grounding_dice_3: 0.08303/0.17888, loss_grounding_ce_3: 0.20186/0.27895, loss_mask_ce_4: 0.46322/0.91921, loss_mask_bce_4: 0.30303/0.33936, loss_mask_dice_4: 0.49631/1.19116, loss_spatial_bce_4: 0.06848/0.09422, loss_spatial_dice_4: 0.13536/0.22734, loss_spatial_ce_4: 0.08585/0.09247, loss_grounding_bce_4: 0.06392/0.08731, loss_grounding_dice_4: 0.10157/0.18180, loss_grounding_ce_4: 0.19289/0.28146, loss_mask_ce_5: 0.52412/0.93533, loss_mask_bce_5: 0.30035/0.34168, loss_mask_dice_5: 0.47861/1.19856, loss_spatial_bce_5: 0.06822/0.09634, loss_spatial_dice_5: 0.14008/0.23141, loss_spatial_ce_5: 0.15981/0.10719, loss_grounding_bce_5: 0.06971/0.08773, loss_grounding_dice_5: 0.10286/0.18303, loss_grounding_ce_5: 0.18690/0.29426, loss_mask_ce_6: 0.59768/0.97529, loss_mask_bce_6: 0.30231/0.34434, loss_mask_dice_6: 0.49448/1.20145, loss_spatial_bce_6: 0.06981/0.10210, loss_spatial_dice_6: 0.15415/0.23430, loss_spatial_ce_6: 0.19831/0.13344, loss_grounding_bce_6: 0.06869/0.08846, loss_grounding_dice_6: 0.10441/0.18340, loss_grounding_ce_6: 0.18719/0.30983, loss_mask_ce_7: 0.66844/1.02027, loss_mask_bce_7: 0.30981/0.35220, loss_mask_dice_7: 0.54590/1.25596, loss_spatial_bce_7: 0.08401/0.11013, loss_spatial_dice_7: 0.18037/0.26195, loss_spatial_ce_7: 0.17003/0.16886, loss_grounding_bce_7: 0.06832/0.09036, loss_grounding_dice_7: 0.10570/0.19065, loss_grounding_ce_7: 0.21083/0.34046, loss_mask_ce_8: 0.79603/1.12855, loss_mask_bce_8: 0.32394/0.36581, loss_mask_dice_8: 0.67497/1.32932, loss_spatial_bce_8: 0.10149/0.13081, loss_spatial_dice_8: 0.24347/0.30009, loss_spatial_ce_8: 0.18411/0.22564, loss_grounding_bce_8: 0.07342/0.09411, loss_grounding_dice_8: 0.14468/0.20161, loss_grounding_ce_8: 0.22414/0.40794, loss_mask_ce_9: 3.66100/3.67775, loss_mask_bce_9: 0.40361/0.39274, loss_mask_dice_9: 1.14864/1.90210, loss_spatial_bce_9: 0.39452/0.33339, loss_spatial_dice_9: 0.87561/0.82213, loss_spatial_ce_9: 1.58722/1.49740, loss_grounding_bce_9: 0.10296/0.10556, loss_grounding_dice_9: 0.24611/0.28078, loss_grounding_ce_9: 0.69972/0.67298] items per batch[64] items per second[0.23] total items[3814400] mini batches[ 59600] memory[7345] epoch remaining[0:32:07]
INFO:trainer.default_trainer:epochs[    32] optim steps[59700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.29701/0.90026, loss_mask_bce_0: 0.10684/0.33469, loss_mask_dice_0: 2.35284/1.16277, loss_spatial_bce_0: 0.02910/0.08759, loss_spatial_dice_0: 0.27276/0.20893, loss_spatial_ce_0: 0.02766/0.06319, loss_grounding_bce_0: 0.04349/0.08627, loss_grounding_dice_0: 0.13429/0.17852, loss_grounding_ce_0: 0.10078/0.27240, loss_mask_ce_1: 1.34577/0.90085, loss_mask_bce_1: 0.10176/0.33563, loss_mask_dice_1: 2.56249/1.16912, loss_spatial_bce_1: 0.02789/0.08812, loss_spatial_dice_1: 0.29031/0.21291, loss_spatial_ce_1: 0.06140/0.06896, loss_grounding_bce_1: 0.04382/0.08644, loss_grounding_dice_1: 0.19511/0.17932, loss_grounding_ce_1: 0.21668/0.27323, loss_mask_ce_2: 1.28861/0.90799, loss_mask_bce_2: 0.10755/0.33619, loss_mask_dice_2: 2.42263/1.16955, loss_spatial_bce_2: 0.02810/0.08909, loss_spatial_dice_2: 0.29811/0.21444, loss_spatial_ce_2: 0.11001/0.07233, loss_grounding_bce_2: 0.04296/0.08657, loss_grounding_dice_2: 0.16689/0.17914, loss_grounding_ce_2: 0.10800/0.27664, loss_mask_ce_3: 1.28665/0.91818, loss_mask_bce_3: 0.11045/0.33727, loss_mask_dice_3: 2.68331/1.16704, loss_spatial_bce_3: 0.03024/0.09021, loss_spatial_dice_3: 0.31099/0.21525, loss_spatial_ce_3: 0.09566/0.07668, loss_grounding_bce_3: 0.04567/0.08680, loss_grounding_dice_3: 0.18097/0.17886, loss_grounding_ce_3: 0.09508/0.27882, loss_mask_ce_4: 1.58299/0.91910, loss_mask_bce_4: 0.12162/0.33937, loss_mask_dice_4: 2.90509/1.19109, loss_spatial_bce_4: 0.03315/0.09421, loss_spatial_dice_4: 0.33250/0.22732, loss_spatial_ce_4: 0.18702/0.09246, loss_grounding_bce_4: 0.04978/0.08731, loss_grounding_dice_4: 0.20294/0.18177, loss_grounding_ce_4: 0.10332/0.28135, loss_mask_ce_5: 1.78958/0.93525, loss_mask_bce_5: 0.12158/0.34168, loss_mask_dice_5: 2.45164/1.19846, loss_spatial_bce_5: 0.03543/0.09633, loss_spatial_dice_5: 0.30941/0.23138, loss_spatial_ce_5: 0.11660/0.10718, loss_grounding_bce_5: 0.05118/0.08773, loss_grounding_dice_5: 0.08895/0.18301, loss_grounding_ce_5: 0.13614/0.29413, loss_mask_ce_6: 1.91862/0.97520, loss_mask_bce_6: 0.11378/0.34434, loss_mask_dice_6: 2.78281/1.20140, loss_spatial_bce_6: 0.03837/0.10209, loss_spatial_dice_6: 0.27815/0.23427, loss_spatial_ce_6: 0.09276/0.13341, loss_grounding_bce_6: 0.04492/0.08846, loss_grounding_dice_6: 0.17303/0.18338, loss_grounding_ce_6: 0.11256/0.30971, loss_mask_ce_7: 1.72988/1.02017, loss_mask_bce_7: 0.11496/0.35220, loss_mask_dice_7: 2.94027/1.25587, loss_spatial_bce_7: 0.04456/0.11013, loss_spatial_dice_7: 0.35112/0.26193, loss_spatial_ce_7: 0.19846/0.16882, loss_grounding_bce_7: 0.05026/0.09036, loss_grounding_dice_7: 0.21694/0.19063, loss_grounding_ce_7: 0.09963/0.34041, loss_mask_ce_8: 1.87981/1.12840, loss_mask_bce_8: 0.15462/0.36580, loss_mask_dice_8: 2.45682/1.32916, loss_spatial_bce_8: 0.05363/0.13080, loss_spatial_dice_8: 0.45201/0.30005, loss_spatial_ce_8: 0.44727/0.22557, loss_grounding_bce_8: 0.06710/0.09411, loss_grounding_dice_8: 0.09883/0.20159, loss_grounding_ce_8: 0.12467/0.40786, loss_mask_ce_9: 3.33731/3.67758, loss_mask_bce_9: 0.23649/0.39274, loss_mask_dice_9: 3.33602/1.90189, loss_spatial_bce_9: 0.20111/0.33339, loss_spatial_dice_9: 0.69468/0.82212, loss_spatial_ce_9: 2.83120/1.49729, loss_grounding_bce_9: 0.10859/0.10558, loss_grounding_dice_9: 0.25629/0.28075, loss_grounding_ce_9: 0.15270/0.67287] items per batch[64] items per second[0.23] total items[3820800] mini batches[ 59700] memory[7345] epoch remaining[0:27:30]
INFO:trainer.default_trainer:epochs[    32] optim steps[59800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19684/0.90019, loss_mask_bce_0: 0.42908/0.33471, loss_mask_dice_0: 1.18544/1.16262, loss_spatial_bce_0: 0.05514/0.08759, loss_spatial_dice_0: 0.18972/0.20890, loss_spatial_ce_0: 0.00054/0.06317, loss_grounding_bce_0: 0.05409/0.08626, loss_grounding_dice_0: 0.14199/0.17850, loss_grounding_ce_0: 0.26176/0.27249, loss_mask_ce_1: 1.14996/0.90078, loss_mask_bce_1: 0.42946/0.33565, loss_mask_dice_1: 1.20524/1.16899, loss_spatial_bce_1: 0.05802/0.08811, loss_spatial_dice_1: 0.20939/0.21288, loss_spatial_ce_1: 0.00084/0.06894, loss_grounding_bce_1: 0.05244/0.08643, loss_grounding_dice_1: 0.13285/0.17930, loss_grounding_ce_1: 0.25332/0.27328, loss_mask_ce_2: 1.16021/0.90792, loss_mask_bce_2: 0.41630/0.33621, loss_mask_dice_2: 1.04514/1.16939, loss_spatial_bce_2: 0.05481/0.08909, loss_spatial_dice_2: 0.18924/0.21441, loss_spatial_ce_2: 0.00550/0.07231, loss_grounding_bce_2: 0.05104/0.08656, loss_grounding_dice_2: 0.12918/0.17912, loss_grounding_ce_2: 0.25482/0.27667, loss_mask_ce_3: 1.16718/0.91809, loss_mask_bce_3: 0.43548/0.33730, loss_mask_dice_3: 1.20122/1.16692, loss_spatial_bce_3: 0.05701/0.09021, loss_spatial_dice_3: 0.19106/0.21522, loss_spatial_ce_3: 0.01060/0.07667, loss_grounding_bce_3: 0.04702/0.08679, loss_grounding_dice_3: 0.12158/0.17884, loss_grounding_ce_3: 0.26900/0.27887, loss_mask_ce_4: 1.22536/0.91901, loss_mask_bce_4: 0.43302/0.33940, loss_mask_dice_4: 1.12727/1.19098, loss_spatial_bce_4: 0.05766/0.09421, loss_spatial_dice_4: 0.21903/0.22730, loss_spatial_ce_4: 0.01699/0.09246, loss_grounding_bce_4: 0.05287/0.08730, loss_grounding_dice_4: 0.12453/0.18175, loss_grounding_ce_4: 0.26289/0.28141, loss_mask_ce_5: 1.15208/0.93517, loss_mask_bce_5: 0.44888/0.34171, loss_mask_dice_5: 1.38006/1.19835, loss_spatial_bce_5: 0.08718/0.09633, loss_spatial_dice_5: 0.25304/0.23136, loss_spatial_ce_5: 0.02119/0.10715, loss_grounding_bce_5: 0.06065/0.08772, loss_grounding_dice_5: 0.12302/0.18299, loss_grounding_ce_5: 0.26262/0.29420, loss_mask_ce_6: 1.28632/0.97510, loss_mask_bce_6: 0.41498/0.34437, loss_mask_dice_6: 1.07577/1.20132, loss_spatial_bce_6: 0.06561/0.10209, loss_spatial_dice_6: 0.23342/0.23425, loss_spatial_ce_6: 0.05308/0.13337, loss_grounding_bce_6: 0.05528/0.08846, loss_grounding_dice_6: 0.15162/0.18336, loss_grounding_ce_6: 0.26582/0.30978, loss_mask_ce_7: 1.18793/1.02007, loss_mask_bce_7: 0.44451/0.35223, loss_mask_dice_7: 1.35646/1.25576, loss_spatial_bce_7: 0.10774/0.11012, loss_spatial_dice_7: 0.32215/0.26191, loss_spatial_ce_7: 0.10006/0.16880, loss_grounding_bce_7: 0.05669/0.09036, loss_grounding_dice_7: 0.15112/0.19062, loss_grounding_ce_7: 0.28402/0.34046, loss_mask_ce_8: 1.30021/1.12831, loss_mask_bce_8: 0.45512/0.36583, loss_mask_dice_8: 1.29410/1.32902, loss_spatial_bce_8: 0.13430/0.13081, loss_spatial_dice_8: 0.33937/0.30004, loss_spatial_ce_8: 0.10828/0.22550, loss_grounding_bce_8: 0.06161/0.09410, loss_grounding_dice_8: 0.15948/0.20156, loss_grounding_ce_8: 0.27926/0.40788, loss_mask_ce_9: 4.84055/3.67751, loss_mask_bce_9: 0.58295/0.39280, loss_mask_dice_9: 2.87673/1.90178, loss_spatial_bce_9: 0.38589/0.33339, loss_spatial_dice_9: 0.91290/0.82212, loss_spatial_ce_9: 1.52700/1.49721, loss_grounding_bce_9: 0.09082/0.10557, loss_grounding_dice_9: 0.36380/0.28073, loss_grounding_ce_9: 0.42092/0.67288] items per batch[64] items per second[0.22] total items[3827200] mini batches[ 59800] memory[7345] epoch remaining[0:22:53]
INFO:trainer.default_trainer:epochs[    32] optim steps[59900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77849/0.90025, loss_mask_bce_0: 0.59506/0.33470, loss_mask_dice_0: 1.14524/1.16281, loss_spatial_bce_0: 0.07054/0.08758, loss_spatial_dice_0: 0.20352/0.20890, loss_spatial_ce_0: 0.00276/0.06316, loss_grounding_bce_0: 0.06379/0.08626, loss_grounding_dice_0: 0.06468/0.17850, loss_grounding_ce_0: 0.19271/0.27246, loss_mask_ce_1: 0.99829/0.90084, loss_mask_bce_1: 0.46784/0.33564, loss_mask_dice_1: 0.96482/1.16921, loss_spatial_bce_1: 0.07235/0.08810, loss_spatial_dice_1: 0.23085/0.21288, loss_spatial_ce_1: 0.00502/0.06893, loss_grounding_bce_1: 0.05764/0.08643, loss_grounding_dice_1: 0.04669/0.17930, loss_grounding_ce_1: 0.22449/0.27323, loss_mask_ce_2: 1.27307/0.90796, loss_mask_bce_2: 0.37132/0.33620, loss_mask_dice_2: 0.87708/1.16960, loss_spatial_bce_2: 0.07164/0.08908, loss_spatial_dice_2: 0.21680/0.21441, loss_spatial_ce_2: 0.01342/0.07230, loss_grounding_bce_2: 0.06346/0.08656, loss_grounding_dice_2: 0.04778/0.17912, loss_grounding_ce_2: 0.25741/0.27661, loss_mask_ce_3: 1.05944/0.91818, loss_mask_bce_3: 0.43634/0.33729, loss_mask_dice_3: 1.00546/1.16712, loss_spatial_bce_3: 0.08518/0.09020, loss_spatial_dice_3: 0.25583/0.21523, loss_spatial_ce_3: 0.03661/0.07665, loss_grounding_bce_3: 0.06749/0.08680, loss_grounding_dice_3: 0.05064/0.17884, loss_grounding_ce_3: 0.31194/0.27881, loss_mask_ce_4: 1.11672/0.91910, loss_mask_bce_4: 0.46151/0.33939, loss_mask_dice_4: 0.94217/1.19119, loss_spatial_bce_4: 0.08296/0.09420, loss_spatial_dice_4: 0.24285/0.22730, loss_spatial_ce_4: 0.02865/0.09245, loss_grounding_bce_4: 0.05596/0.08730, loss_grounding_dice_4: 0.06499/0.18175, loss_grounding_ce_4: 0.11268/0.28138, loss_mask_ce_5: 1.36908/0.93526, loss_mask_bce_5: 0.37249/0.34169, loss_mask_dice_5: 0.82076/1.19858, loss_spatial_bce_5: 0.07550/0.09632, loss_spatial_dice_5: 0.23952/0.23137, loss_spatial_ce_5: 0.06873/0.10716, loss_grounding_bce_5: 0.05500/0.08772, loss_grounding_dice_5: 0.06265/0.18298, loss_grounding_ce_5: 0.17644/0.29414, loss_mask_ce_6: 1.09558/0.97519, loss_mask_bce_6: 0.52149/0.34436, loss_mask_dice_6: 1.03111/1.20153, loss_spatial_bce_6: 0.09948/0.10208, loss_spatial_dice_6: 0.26764/0.23425, loss_spatial_ce_6: 0.07511/0.13334, loss_grounding_bce_6: 0.06501/0.08846, loss_grounding_dice_6: 0.05713/0.18337, loss_grounding_ce_6: 0.25903/0.30973, loss_mask_ce_7: 1.14022/1.02017, loss_mask_bce_7: 0.62934/0.35222, loss_mask_dice_7: 1.13370/1.25600, loss_spatial_bce_7: 0.08475/0.11011, loss_spatial_dice_7: 0.26494/0.26192, loss_spatial_ce_7: 0.07704/0.16875, loss_grounding_bce_7: 0.05823/0.09036, loss_grounding_dice_7: 0.05687/0.19062, loss_grounding_ce_7: 0.09183/0.34040, loss_mask_ce_8: 1.33575/1.12846, loss_mask_bce_8: 0.52590/0.36582, loss_mask_dice_8: 1.10788/1.32927, loss_spatial_bce_8: 0.10056/0.13079, loss_spatial_dice_8: 0.32963/0.30005, loss_spatial_ce_8: 0.15031/0.22544, loss_grounding_bce_8: 0.05907/0.09410, loss_grounding_dice_8: 0.06703/0.20155, loss_grounding_ce_8: 0.28429/0.40780, loss_mask_ce_9: 4.26497/3.67770, loss_mask_bce_9: 0.53522/0.39277, loss_mask_dice_9: 1.73693/1.90208, loss_spatial_bce_9: 0.24633/0.33334, loss_spatial_dice_9: 0.85750/0.82211, loss_spatial_ce_9: 1.47489/1.49725, loss_grounding_bce_9: 0.07205/0.10557, loss_grounding_dice_9: 0.36725/0.28074, loss_grounding_ce_9: 1.64648/0.67276] items per batch[64] items per second[0.23] total items[3833600] mini batches[ 59900] memory[7345] epoch remaining[0:18:13]
INFO:trainer.default_trainer:epochs[    32] optim steps[60000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85321/0.90030, loss_mask_bce_0: 0.65837/0.33469, loss_mask_dice_0: 1.24794/1.16301, loss_spatial_bce_0: 0.13682/0.08758, loss_spatial_dice_0: 0.22702/0.20891, loss_spatial_ce_0: 0.03753/0.06315, loss_grounding_bce_0: 0.25018/0.08627, loss_grounding_dice_0: 0.63265/0.17851, loss_grounding_ce_0: 1.25033/0.27247, loss_mask_ce_1: 0.92975/0.90089, loss_mask_bce_1: 0.64081/0.33563, loss_mask_dice_1: 1.30684/1.16941, loss_spatial_bce_1: 0.14278/0.08810, loss_spatial_dice_1: 0.23133/0.21288, loss_spatial_ce_1: 0.04653/0.06894, loss_grounding_bce_1: 0.23341/0.08643, loss_grounding_dice_1: 0.63579/0.17931, loss_grounding_ce_1: 1.52548/0.27322, loss_mask_ce_2: 0.91579/0.90802, loss_mask_bce_2: 0.63783/0.33619, loss_mask_dice_2: 1.55962/1.16980, loss_spatial_bce_2: 0.13772/0.08907, loss_spatial_dice_2: 0.24073/0.21441, loss_spatial_ce_2: 0.04271/0.07231, loss_grounding_bce_2: 0.18266/0.08657, loss_grounding_dice_2: 0.58315/0.17911, loss_grounding_ce_2: 1.68883/0.27662, loss_mask_ce_3: 1.17788/0.91827, loss_mask_bce_3: 0.63905/0.33728, loss_mask_dice_3: 1.28301/1.16732, loss_spatial_bce_3: 0.13754/0.09020, loss_spatial_dice_3: 0.22263/0.21524, loss_spatial_ce_3: 0.05335/0.07664, loss_grounding_bce_3: 0.20863/0.08680, loss_grounding_dice_3: 0.57847/0.17884, loss_grounding_ce_3: 1.63098/0.27878, loss_mask_ce_4: 0.99635/0.91917, loss_mask_bce_4: 0.61526/0.33937, loss_mask_dice_4: 1.27793/1.19137, loss_spatial_bce_4: 0.15461/0.09419, loss_spatial_dice_4: 0.25057/0.22730, loss_spatial_ce_4: 0.07918/0.09248, loss_grounding_bce_4: 0.24815/0.08730, loss_grounding_dice_4: 0.64145/0.18175, loss_grounding_ce_4: 1.09190/0.28136, loss_mask_ce_5: 0.94061/0.93538, loss_mask_bce_5: 0.64548/0.34167, loss_mask_dice_5: 1.43600/1.19876, loss_spatial_bce_5: 0.14289/0.09632, loss_spatial_dice_5: 0.24031/0.23137, loss_spatial_ce_5: 0.07460/0.10716, loss_grounding_bce_5: 0.21024/0.08772, loss_grounding_dice_5: 0.59312/0.18298, loss_grounding_ce_5: 1.36028/0.29412, loss_mask_ce_6: 0.86138/0.97529, loss_mask_bce_6: 0.66473/0.34434, loss_mask_dice_6: 1.37224/1.20170, loss_spatial_bce_6: 0.15230/0.10208, loss_spatial_dice_6: 0.24674/0.23427, loss_spatial_ce_6: 0.08170/0.13333, loss_grounding_bce_6: 0.27144/0.08846, loss_grounding_dice_6: 0.62026/0.18336, loss_grounding_ce_6: 1.40943/0.30973, loss_mask_ce_7: 1.10901/1.02024, loss_mask_bce_7: 0.63812/0.35221, loss_mask_dice_7: 1.35318/1.25617, loss_spatial_bce_7: 0.14987/0.11010, loss_spatial_dice_7: 0.30077/0.26192, loss_spatial_ce_7: 0.16607/0.16872, loss_grounding_bce_7: 0.38567/0.09036, loss_grounding_dice_7: 0.74915/0.19062, loss_grounding_ce_7: 0.15998/0.34033, loss_mask_ce_8: 0.99278/1.12854, loss_mask_bce_8: 0.71086/0.36580, loss_mask_dice_8: 1.50931/1.32948, loss_spatial_bce_8: 0.16772/0.13078, loss_spatial_dice_8: 0.30272/0.30006, loss_spatial_ce_8: 0.18923/0.22542, loss_grounding_bce_8: 0.23745/0.09409, loss_grounding_dice_8: 0.61327/0.20154, loss_grounding_ce_8: 0.97498/0.40772, loss_mask_ce_9: 3.53507/3.67774, loss_mask_bce_9: 0.58473/0.39274, loss_mask_dice_9: 2.06864/1.90241, loss_spatial_bce_9: 0.31624/0.33329, loss_spatial_dice_9: 0.83775/0.82212, loss_spatial_ce_9: 1.07861/1.49720, loss_grounding_bce_9: 0.15741/0.10557, loss_grounding_dice_9: 0.64448/0.28073, loss_grounding_ce_9: 1.48649/0.67272] items per batch[64] items per second[0.23] total items[3840000] mini batches[ 60000] memory[7345] epoch remaining[0:13:32]
INFO:trainer.default_trainer:epochs[    32] optim steps[60100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02520/0.90013, loss_mask_bce_0: 0.24209/0.33469, loss_mask_dice_0: 2.16566/1.16291, loss_spatial_bce_0: 0.01693/0.08758, loss_spatial_dice_0: 0.20935/0.20888, loss_spatial_ce_0: 0.00969/0.06314, loss_grounding_bce_0: 0.02675/0.08628, loss_grounding_dice_0: 0.28237/0.17849, loss_grounding_ce_0: 0.89315/0.27244, loss_mask_ce_1: 1.03999/0.90070, loss_mask_bce_1: 0.25822/0.33563, loss_mask_dice_1: 2.31610/1.16927, loss_spatial_bce_1: 0.01688/0.08810, loss_spatial_dice_1: 0.21216/0.21285, loss_spatial_ce_1: 0.01345/0.06891, loss_grounding_bce_1: 0.02735/0.08645, loss_grounding_dice_1: 0.28480/0.17929, loss_grounding_ce_1: 0.45836/0.27319, loss_mask_ce_2: 1.40335/0.90785, loss_mask_bce_2: 0.25540/0.33618, loss_mask_dice_2: 2.13582/1.16965, loss_spatial_bce_2: 0.01612/0.08908, loss_spatial_dice_2: 0.19049/0.21438, loss_spatial_ce_2: 0.02455/0.07229, loss_grounding_bce_2: 0.02905/0.08658, loss_grounding_dice_2: 0.28373/0.17910, loss_grounding_ce_2: 0.42840/0.27661, loss_mask_ce_3: 1.13033/0.91809, loss_mask_bce_3: 0.24001/0.33727, loss_mask_dice_3: 2.21733/1.16721, loss_spatial_bce_3: 0.01672/0.09020, loss_spatial_dice_3: 0.21686/0.21522, loss_spatial_ce_3: 0.02733/0.07662, loss_grounding_bce_3: 0.02829/0.08681, loss_grounding_dice_3: 0.29724/0.17883, loss_grounding_ce_3: 0.46329/0.27874, loss_mask_ce_4: 0.98539/0.91899, loss_mask_bce_4: 0.24887/0.33937, loss_mask_dice_4: 2.25816/1.19126, loss_spatial_bce_4: 0.01959/0.09420, loss_spatial_dice_4: 0.23330/0.22727, loss_spatial_ce_4: 0.16958/0.09246, loss_grounding_bce_4: 0.02832/0.08731, loss_grounding_dice_4: 0.28769/0.18174, loss_grounding_ce_4: 0.82121/0.28138, loss_mask_ce_5: 1.15645/0.93524, loss_mask_bce_5: 0.25171/0.34166, loss_mask_dice_5: 2.15748/1.19862, loss_spatial_bce_5: 0.01756/0.09632, loss_spatial_dice_5: 0.19842/0.23135, loss_spatial_ce_5: 0.08982/0.10717, loss_grounding_bce_5: 0.04061/0.08774, loss_grounding_dice_5: 0.30499/0.18296, loss_grounding_ce_5: 0.51734/0.29409, loss_mask_ce_6: 1.42443/0.97511, loss_mask_bce_6: 0.28097/0.34434, loss_mask_dice_6: 2.14475/1.20158, loss_spatial_bce_6: 0.02222/0.10208, loss_spatial_dice_6: 0.20623/0.23425, loss_spatial_ce_6: 0.12516/0.13331, loss_grounding_bce_6: 0.04783/0.08848, loss_grounding_dice_6: 0.33207/0.18334, loss_grounding_ce_6: 0.34714/0.30970, loss_mask_ce_7: 0.99905/1.02008, loss_mask_bce_7: 0.35471/0.35221, loss_mask_dice_7: 2.63234/1.25605, loss_spatial_bce_7: 0.03278/0.11011, loss_spatial_dice_7: 0.35770/0.26191, loss_spatial_ce_7: 0.10178/0.16873, loss_grounding_bce_7: 0.05555/0.09038, loss_grounding_dice_7: 0.34395/0.19060, loss_grounding_ce_7: 0.37126/0.34029, loss_mask_ce_8: 1.04006/1.12837, loss_mask_bce_8: 0.31368/0.36581, loss_mask_dice_8: 2.80046/1.32932, loss_spatial_bce_8: 0.04338/0.13078, loss_spatial_dice_8: 0.38118/0.30004, loss_spatial_ce_8: 0.15940/0.22540, loss_grounding_bce_8: 0.04802/0.09411, loss_grounding_dice_8: 0.40018/0.20152, loss_grounding_ce_8: 0.35553/0.40774, loss_mask_ce_9: 3.46670/3.67755, loss_mask_bce_9: 0.39911/0.39274, loss_mask_dice_9: 3.70355/1.90216, loss_spatial_bce_9: 0.10593/0.33331, loss_spatial_dice_9: 0.89031/0.82210, loss_spatial_ce_9: 1.24939/1.49716, loss_grounding_bce_9: 0.06313/0.10559, loss_grounding_dice_9: 0.58031/0.28070, loss_grounding_ce_9: 0.57132/0.67270] items per batch[64] items per second[0.24] total items[3846400] mini batches[ 60100] memory[7345] epoch remaining[0:08:52]
INFO:trainer.default_trainer:epochs[    32] optim steps[60200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86795/0.90025, loss_mask_bce_0: 0.22179/0.33465, loss_mask_dice_0: 1.15038/1.16296, loss_spatial_bce_0: 0.05999/0.08757, loss_spatial_dice_0: 0.19619/0.20890, loss_spatial_ce_0: 0.04565/0.06311, loss_grounding_bce_0: 0.04241/0.08628, loss_grounding_dice_0: 0.36505/0.17851, loss_grounding_ce_0: 0.01511/0.27245, loss_mask_ce_1: 0.94046/0.90083, loss_mask_bce_1: 0.22316/0.33559, loss_mask_dice_1: 1.07149/1.16930, loss_spatial_bce_1: 0.05835/0.08809, loss_spatial_dice_1: 0.20762/0.21287, loss_spatial_ce_1: 0.04366/0.06889, loss_grounding_bce_1: 0.04461/0.08644, loss_grounding_dice_1: 0.37622/0.17931, loss_grounding_ce_1: 0.01385/0.27320, loss_mask_ce_2: 0.91978/0.90795, loss_mask_bce_2: 0.22327/0.33614, loss_mask_dice_2: 1.09175/1.16968, loss_spatial_bce_2: 0.06088/0.08907, loss_spatial_dice_2: 0.18783/0.21440, loss_spatial_ce_2: 0.06276/0.07227, loss_grounding_bce_2: 0.04399/0.08658, loss_grounding_dice_2: 0.35723/0.17911, loss_grounding_ce_2: 0.01348/0.27662, loss_mask_ce_3: 0.84546/0.91819, loss_mask_bce_3: 0.21843/0.33723, loss_mask_dice_3: 1.05625/1.16726, loss_spatial_bce_3: 0.05962/0.09020, loss_spatial_dice_3: 0.20637/0.21524, loss_spatial_ce_3: 0.05502/0.07662, loss_grounding_bce_3: 0.04617/0.08680, loss_grounding_dice_3: 0.37145/0.17885, loss_grounding_ce_3: 0.02012/0.27877, loss_mask_ce_4: 0.78267/0.91910, loss_mask_bce_4: 0.22248/0.33933, loss_mask_dice_4: 1.23981/1.19128, loss_spatial_bce_4: 0.05448/0.09419, loss_spatial_dice_4: 0.18730/0.22730, loss_spatial_ce_4: 0.09851/0.09245, loss_grounding_bce_4: 0.04657/0.08731, loss_grounding_dice_4: 0.36033/0.18175, loss_grounding_ce_4: 0.01513/0.28144, loss_mask_ce_5: 1.00627/0.93532, loss_mask_bce_5: 0.23261/0.34163, loss_mask_dice_5: 1.30351/1.19867, loss_spatial_bce_5: 0.05528/0.09632, loss_spatial_dice_5: 0.21486/0.23138, loss_spatial_ce_5: 0.09186/0.10717, loss_grounding_bce_5: 0.05221/0.08773, loss_grounding_dice_5: 0.39968/0.18297, loss_grounding_ce_5: 0.01506/0.29415, loss_mask_ce_6: 0.82320/0.97523, loss_mask_bce_6: 0.23204/0.34430, loss_mask_dice_6: 1.05959/1.20163, loss_spatial_bce_6: 0.06099/0.10208, loss_spatial_dice_6: 0.24347/0.23427, loss_spatial_ce_6: 0.07451/0.13329, loss_grounding_bce_6: 0.04630/0.08847, loss_grounding_dice_6: 0.35470/0.18336, loss_grounding_ce_6: 0.02405/0.30973, loss_mask_ce_7: 0.83684/1.02023, loss_mask_bce_7: 0.28016/0.35218, loss_mask_dice_7: 1.51410/1.25610, loss_spatial_bce_7: 0.05822/0.11010, loss_spatial_dice_7: 0.23813/0.26193, loss_spatial_ce_7: 0.19033/0.16872, loss_grounding_bce_7: 0.04250/0.09037, loss_grounding_dice_7: 0.35148/0.19061, loss_grounding_ce_7: 0.02320/0.34031, loss_mask_ce_8: 1.07885/1.12850, loss_mask_bce_8: 0.27819/0.36577, loss_mask_dice_8: 1.52736/1.32931, loss_spatial_bce_8: 0.06038/0.13078, loss_spatial_dice_8: 0.25078/0.30007, loss_spatial_ce_8: 0.23028/0.22538, loss_grounding_bce_8: 0.04383/0.09411, loss_grounding_dice_8: 0.38988/0.20152, loss_grounding_ce_8: 0.01346/0.40775, loss_mask_ce_9: 4.38916/3.67729, loss_mask_bce_9: 0.40449/0.39268, loss_mask_dice_9: 2.24743/1.90207, loss_spatial_bce_9: 0.28802/0.33329, loss_spatial_dice_9: 0.88530/0.82210, loss_spatial_ce_9: 1.30468/1.49725, loss_grounding_bce_9: 0.03647/0.10558, loss_grounding_dice_9: 0.43432/0.28069, loss_grounding_ce_9: 0.11717/0.67274] items per batch[64] items per second[0.23] total items[3852800] mini batches[ 60200] memory[7345] epoch remaining[0:04:13]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00060291.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2257 s/iter. Eval: 0.0852 s/iter. Total: 0.3140 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 27/157. Dataloading: 0.0029 s/iter. Inference: 0.2278 s/iter. Eval: 0.0822 s/iter. Total: 0.3130 s/iter. ETA=0:00:40
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2289 s/iter. Eval: 0.0793 s/iter. Total: 0.3114 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2292 s/iter. Eval: 0.0765 s/iter. Total: 0.3089 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 77/157. Dataloading: 0.0031 s/iter. Inference: 0.2324 s/iter. Eval: 0.0750 s/iter. Total: 0.3107 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 94/157. Dataloading: 0.0032 s/iter. Inference: 0.2334 s/iter. Eval: 0.0744 s/iter. Total: 0.3112 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 110/157. Dataloading: 0.0032 s/iter. Inference: 0.2346 s/iter. Eval: 0.0751 s/iter. Total: 0.3130 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 127/157. Dataloading: 0.0032 s/iter. Inference: 0.2334 s/iter. Eval: 0.0747 s/iter. Total: 0.3113 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0032 s/iter. Inference: 0.2338 s/iter. Eval: 0.0743 s/iter. Total: 0.3114 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaloha2n91a ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.788 | 81.274 | 59.937 |      133      |
| Things | 54.836 | 82.402 | 65.819 |      80       |
| Stuff  | 42.168 | 79.570 | 51.058 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.21 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.98 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.14s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.95 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.610
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.406
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.303
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.658 | 60.994 | 40.633 | 19.750 | 41.845 | 60.301 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.777 | bicycle      | 18.303 | car            | 37.031 |
| motorcycle    | 35.222 | airplane     | 56.720 | bus            | 64.124 |
| train         | 68.473 | truck        | 33.954 | boat           | 23.053 |
| traffic light | 23.715 | fire hydrant | 64.539 | stop sign      | 63.891 |
| parking meter | 45.030 | bench        | 20.348 | bird           | 29.477 |
| cat           | 74.249 | dog          | 65.543 | horse          | 45.549 |
| sheep         | 46.806 | cow          | 50.629 | elephant       | 59.914 |
| bear          | 77.816 | zebra        | 60.630 | giraffe        | 56.405 |
| backpack      | 18.022 | umbrella     | 48.666 | handbag        | 14.857 |
| tie           | 32.695 | suitcase     | 40.829 | frisbee        | 65.695 |
| skis          | 5.069  | snowboard    | 20.669 | sports ball    | 47.005 |
| kite          | 32.628 | baseball bat | 27.729 | baseball glove | 43.440 |
| skateboard    | 35.011 | surfboard    | 34.284 | tennis racket  | 55.699 |
| bottle        | 34.227 | wine glass   | 27.913 | cup            | 39.911 |
| fork          | 15.996 | knife        | 12.634 | spoon          | 14.451 |
| bowl          | 32.730 | banana       | 19.998 | apple          | 20.719 |
| sandwich      | 43.429 | orange       | 28.988 | broccoli       | 21.220 |
| carrot        | 20.136 | hot dog      | 19.765 | pizza          | 51.167 |
| donut         | 45.457 | cake         | 43.497 | chair          | 20.595 |
| couch         | 44.062 | potted plant | 16.464 | bed            | 41.293 |
| dining table  | 13.537 | toilet       | 66.503 | tv             | 61.344 |
| laptop        | 62.637 | mouse        | 58.101 | remote         | 30.330 |
| keyboard      | 47.872 | cell phone   | 35.329 | microwave      | 54.871 |
| oven          | 33.584 | toaster      | 34.880 | sink           | 37.659 |
| refrigerator  | 58.701 | book         | 8.880  | clock          | 51.880 |
| vase          | 35.092 | scissors     | 23.720 | teddy bear     | 50.175 |
| hair drier    | 7.999  | toothbrush   | 16.401 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 61.38136269449078, 'fwIoU': 69.64810275905201, 'IoU-person': 87.70729623681306, 'IoU-bicycle': 75.07492964233585, 'IoU-car': 68.07881738035942, 'IoU-motorcycle': 85.43505817360007, 'IoU-airplane': 83.76452537170482, 'IoU-bus': 83.88982066328381, 'IoU-train': 84.15385371866202, 'IoU-truck': 60.97174055533298, 'IoU-boat': 68.03547004196427, 'IoU-traffic light': 77.42502179101972, 'IoU-fire hydrant': 90.44870468575394, 'IoU-stop sign': 92.88691770040552, 'IoU-parking meter': 83.46338054913973, 'IoU-bench': 55.56849150745583, 'IoU-bird': 75.65403867062827, 'IoU-cat': 87.68157637497576, 'IoU-dog': 79.41786447931564, 'IoU-horse': 86.55349640655989, 'IoU-sheep': 89.45853832383652, 'IoU-cow': 81.15709142000715, 'IoU-elephant': 92.48815431659726, 'IoU-bear': 92.29393850634234, 'IoU-zebra': 92.52431799203914, 'IoU-giraffe': 88.26537227732072, 'IoU-backpack': 38.96086387472709, 'IoU-umbrella': 78.43708901621403, 'IoU-handbag': 36.929800776436025, 'IoU-tie': 69.69256595340535, 'IoU-suitcase': 79.78695139912351, 'IoU-frisbee': 83.41262639357014, 'IoU-skis': 51.48150825699444, 'IoU-snowboard': 68.95223949792663, 'IoU-sports ball': 65.97609595476514, 'IoU-kite': 66.60166425350278, 'IoU-baseball bat': 61.62444241599642, 'IoU-baseball glove': 72.84341961996418, 'IoU-skateboard': 77.26615902146008, 'IoU-surfboard': 81.21945709645513, 'IoU-tennis racket': 82.96983503915098, 'IoU-bottle': 69.43134602965725, 'IoU-wine glass': 72.59555696233652, 'IoU-cup': 62.689563403659335, 'IoU-fork': 54.57494684389715, 'IoU-knife': 46.93190242105247, 'IoU-spoon': 48.29570939481857, 'IoU-bowl': 55.898729497233056, 'IoU-banana': 83.49986896485323, 'IoU-apple': 55.99832259451628, 'IoU-sandwich': 66.89461279647124, 'IoU-orange': 76.11638208801509, 'IoU-broccoli': 67.22848893888525, 'IoU-carrot': 62.665782535894834, 'IoU-hot dog': 66.50524516294887, 'IoU-pizza': 82.13724934601795, 'IoU-donut': 64.30511669653572, 'IoU-cake': 68.12638483138792, 'IoU-chair': 55.53656157364877, 'IoU-couch': 70.11225259929596, 'IoU-potted plant': 34.60915191741021, 'IoU-bed': 68.46321341777508, 'IoU-dining table': 52.15927442952996, 'IoU-toilet': 83.10095954321805, 'IoU-tv': 75.54076156725762, 'IoU-laptop': 75.73595767436254, 'IoU-mouse': 70.77362955172866, 'IoU-remote': 49.77060129858097, 'IoU-keyboard': 62.67531167197822, 'IoU-cell phone': 68.10589232713993, 'IoU-microwave': 65.59379819188142, 'IoU-oven': 67.92280115213107, 'IoU-toaster': 70.18868842390744, 'IoU-sink': 67.59984448482345, 'IoU-refrigerator': 82.8696785817925, 'IoU-book': 50.94975546362102, 'IoU-clock': 74.96068648154147, 'IoU-vase': 63.452200902913205, 'IoU-scissors': 55.72839955825205, 'IoU-teddy bear': 80.0533640085748, 'IoU-hair drier': 41.816392151715995, 'IoU-toothbrush': 58.370181997608796, 'IoU-banner': 35.86789196142248, 'IoU-blanket': 9.768210347674978, 'IoU-bridge': 33.463753384386386, 'IoU-cardboard': 44.240830704556956, 'IoU-counter': 30.524123799696273, 'IoU-curtain': 62.97904451493564, 'IoU-door-stuff': 41.869318013271766, 'IoU-floor-wood': 61.18021361399728, 'IoU-flower': 41.50333432611249, 'IoU-fruit': 37.82876242609217, 'IoU-gravel': 30.394007508082844, 'IoU-house': 24.586316131098048, 'IoU-light': 40.81013632998978, 'IoU-mirror-stuff': 51.668315608251426, 'IoU-net': 46.41577450379752, 'IoU-pillow': 14.270701606948935, 'IoU-platform': 32.134631804010944, 'IoU-playingfield': 70.75742207200052, 'IoU-railroad': 61.52670093081929, 'IoU-river': 44.76970714971552, 'IoU-road': 66.96390566717865, 'IoU-roof': 15.045949818563745, 'IoU-sand': 63.0712539233545, 'IoU-sea': 85.25341865053022, 'IoU-shelf': 36.07503421809831, 'IoU-snow': 88.47851977679446, 'IoU-stairs': 21.4775366492633, 'IoU-tent': 7.939767315714394, 'IoU-towel': 34.00914227069708, 'IoU-wall-brick': 46.14278953825637, 'IoU-wall-stone': 27.80703628785785, 'IoU-wall-tile': 64.67161985463098, 'IoU-wall-wood': 38.77210276559603, 'IoU-water-other': 32.76339100073843, 'IoU-window-blind': 48.69037864820861, 'IoU-window-other': 47.96566423047839, 'IoU-tree-merged': 81.11262212771474, 'IoU-fence-merged': 52.38719152873471, 'IoU-ceiling-merged': 65.69903223544043, 'IoU-sky-other-merged': 93.699328357863, 'IoU-cabinet-merged': 59.08136118685706, 'IoU-table-merged': 39.68854449207674, 'IoU-floor-other-merged': 50.1122682699206, 'IoU-pavement-merged': 54.62505146061609, 'IoU-mountain-merged': 56.674804944057264, 'IoU-grass-merged': 72.86910782931702, 'IoU-dirt-merged': 46.18679580361358, 'IoU-paper-merged': 32.27240477590156, 'IoU-food-other-merged': 39.404148097372826, 'IoU-building-other-merged': 58.86570870362247, 'IoU-rock-merged': 60.37085092346026, 'IoU-wall-other-merged': 66.45147600151999, 'IoU-rug-merged': 63.99613144234883, 'mACC': 73.62000840031601, 'pACC': 80.85765134153966, 'ACC-person': 92.53174503319667, 'ACC-bicycle': 86.3153818864594, 'ACC-car': 86.0922290924863, 'ACC-motorcycle': 90.69547143147577, 'ACC-airplane': 90.68252134370097, 'ACC-bus': 88.6246250834532, 'ACC-train': 95.13680752545449, 'ACC-truck': 71.52202437255266, 'ACC-boat': 77.92224749931495, 'ACC-traffic light': 89.22257960106012, 'ACC-fire hydrant': 95.43500493195609, 'ACC-stop sign': 95.53788814670932, 'ACC-parking meter': 87.15360081491205, 'ACC-bench': 74.7595294392579, 'ACC-bird': 80.77857240682724, 'ACC-cat': 92.99843293578893, 'ACC-dog': 83.07172592782027, 'ACC-horse': 92.97472004919662, 'ACC-sheep': 93.61317241996439, 'ACC-cow': 86.10161685667312, 'ACC-elephant': 95.18320884313766, 'ACC-bear': 94.88403871024258, 'ACC-zebra': 95.16169139806144, 'ACC-giraffe': 92.7361916334156, 'ACC-backpack': 58.57036748144188, 'ACC-umbrella': 85.87174665436898, 'ACC-handbag': 51.55493443890502, 'ACC-tie': 81.58589669776939, 'ACC-suitcase': 88.71420934193677, 'ACC-frisbee': 93.592, 'ACC-skis': 67.93663989751172, 'ACC-snowboard': 78.67894491930151, 'ACC-sports ball': 80.56925192007526, 'ACC-kite': 75.77731129451622, 'ACC-baseball bat': 80.42541713806202, 'ACC-baseball glove': 89.6619182464711, 'ACC-skateboard': 89.4616479121688, 'ACC-surfboard': 89.87363408744812, 'ACC-tennis racket': 89.50092606999985, 'ACC-bottle': 84.04067602345835, 'ACC-wine glass': 85.66725897989157, 'ACC-cup': 82.07392865140135, 'ACC-fork': 64.67803747636664, 'ACC-knife': 57.87279255514033, 'ACC-spoon': 69.92502990172505, 'ACC-bowl': 72.79156814201193, 'ACC-banana': 89.35548895437586, 'ACC-apple': 69.7904087358561, 'ACC-sandwich': 81.02472415763349, 'ACC-orange': 83.25442247133891, 'ACC-broccoli': 76.38725177015996, 'ACC-carrot': 71.11158494075593, 'ACC-hot dog': 72.8542154184043, 'ACC-pizza': 92.95089924839381, 'ACC-donut': 82.18817405477681, 'ACC-cake': 77.7865113088632, 'ACC-chair': 72.56275251040977, 'ACC-couch': 84.46965270264045, 'ACC-potted plant': 52.67451261188503, 'ACC-bed': 82.52978385675527, 'ACC-dining table': 75.53255666830606, 'ACC-toilet': 92.40231598178535, 'ACC-tv': 86.51015587976333, 'ACC-laptop': 91.84663580780854, 'ACC-mouse': 85.45357473963688, 'ACC-remote': 72.38545257480847, 'ACC-keyboard': 70.00216569592212, 'ACC-cell phone': 75.37691111660493, 'ACC-microwave': 75.51187789087342, 'ACC-oven': 86.97247174096489, 'ACC-toaster': 78.9842477658104, 'ACC-sink': 84.79537438075249, 'ACC-refrigerator': 91.59623282175683, 'ACC-book': 70.08128799977585, 'ACC-clock': 82.3814600554067, 'ACC-vase': 75.988695321904, 'ACC-scissors': 60.06023377315006, 'ACC-teddy bear': 87.27759428036538, 'ACC-hair drier': 45.34483249366693, 'ACC-toothbrush': 80.15201528839472, 'ACC-banner': 70.29012469695154, 'ACC-blanket': 13.063220096432957, 'ACC-bridge': 45.73192101012769, 'ACC-cardboard': 57.325810673258694, 'ACC-counter': 51.80965973231133, 'ACC-curtain': 72.82137481658009, 'ACC-door-stuff': 60.649442789061226, 'ACC-floor-wood': 74.7092348753692, 'ACC-flower': 61.9916299924434, 'ACC-fruit': 63.71836594758632, 'ACC-gravel': 43.65041331829851, 'ACC-house': 29.359586634230006, 'ACC-light': 58.07119205298014, 'ACC-mirror-stuff': 68.8378515725814, 'ACC-net': 61.55989429180163, 'ACC-pillow': 23.62504058945123, 'ACC-platform': 60.014741415819685, 'ACC-playingfield': 89.21756443774761, 'ACC-railroad': 79.7506334543909, 'ACC-river': 56.43695416269659, 'ACC-road': 84.96048958210716, 'ACC-roof': 19.950616680316347, 'ACC-sand': 71.29982271058938, 'ACC-sea': 91.50755195730811, 'ACC-shelf': 59.17218199529073, 'ACC-snow': 95.59916076604542, 'ACC-stairs': 39.16116076608597, 'ACC-tent': 8.600869470664252, 'ACC-towel': 41.03928481809041, 'ACC-wall-brick': 59.95233349570238, 'ACC-wall-stone': 32.37930584863318, 'ACC-wall-tile': 77.42001739198308, 'ACC-wall-wood': 49.43466205309977, 'ACC-water-other': 58.31757719915812, 'ACC-window-blind': 57.23000632251043, 'ACC-window-other': 68.62223492563673, 'ACC-tree-merged': 88.75436524902875, 'ACC-fence-merged': 73.57121727969808, 'ACC-ceiling-merged': 79.7187845161282, 'ACC-sky-other-merged': 96.54439549750961, 'ACC-cabinet-merged': 76.0250142098231, 'ACC-table-merged': 52.399469637742754, 'ACC-floor-other-merged': 61.90191629673455, 'ACC-pavement-merged': 67.16426333808052, 'ACC-mountain-merged': 67.83040009617014, 'ACC-grass-merged': 83.79592633280636, 'ACC-dirt-merged': 66.67308394372525, 'ACC-paper-merged': 41.159942081521734, 'ACC-food-other-merged': 52.972888272805065, 'ACC-building-other-merged': 78.5997350723622, 'ACC-rock-merged': 82.6708263177287, 'ACC-wall-other-merged': 81.1316345848455, 'ACC-rug-merged': 77.70968173918332})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1536 s/iter. Inference: 0.5968 s/iter. Eval: 0.0000 s/iter. Total: 0.7504 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1541 s/iter. Inference: 0.5802 s/iter. Eval: 0.0000 s/iter. Total: 0.7344 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1687 s/iter. Inference: 0.6044 s/iter. Eval: 0.0000 s/iter. Total: 0.7732 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1711 s/iter. Inference: 0.7239 s/iter. Eval: 0.0000 s/iter. Total: 0.8951 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1708 s/iter. Inference: 0.6356 s/iter. Eval: 0.0000 s/iter. Total: 0.8066 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1711 s/iter. Inference: 0.6653 s/iter. Eval: 0.0000 s/iter. Total: 0.8366 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1732 s/iter. Inference: 0.7175 s/iter. Eval: 0.0000 s/iter. Total: 0.8908 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4647351477904595, 'noc@0.8': 2.8507462686567164, 'noc@0.85': 3.4451273046532047, 'noc@0.9': 4.48697688030436, 'miou@iter1': 0.8321468821037992}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.0998 s/iter. Eval: 0.0008 s/iter. Total: 0.1024 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.2786636352539, 'precision@0.6': 67.89739227294922, 'precision@0.7': 62.53400802612305, 'precision@0.8': 52.27360916137695, 'precision@0.9': 27.05013656616211, 'cIoU': 57.79022979736328, 'mIoU': 62.6456413269043}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.787823895520965, 'SQ': 81.27361315217311, 'RQ': 59.93687083710103, 'PQ_th': 54.83613437990429, 'SQ_th': 82.40202412554352, 'RQ_th': 65.81939992178259, 'PQ_st': 42.1677325983386, 'SQ_st': 79.57035130557625, 'RQ_st': 51.05758165267606}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.658107776787865, 'AP50': 60.99357890847459, 'AP75': 40.63285196050696, 'APs': 19.74967829071954, 'APm': 41.84468842435315, 'APl': 60.30066375164183, 'AP-person': 44.77664140638, 'AP-bicycle': 18.3026089285031, 'AP-car': 37.03118252747701, 'AP-motorcycle': 35.22242873693826, 'AP-airplane': 56.72012342658766, 'AP-bus': 64.12358139748568, 'AP-train': 68.47346348828798, 'AP-truck': 33.953815466355515, 'AP-boat': 23.05339343332635, 'AP-traffic light': 23.71529552584804, 'AP-fire hydrant': 64.5394638694968, 'AP-stop sign': 63.89098470651842, 'AP-parking meter': 45.0298042060005, 'AP-bench': 20.348040482386185, 'AP-bird': 29.477026755507836, 'AP-cat': 74.24865790365197, 'AP-dog': 65.54345066222308, 'AP-horse': 45.548883927829635, 'AP-sheep': 46.80561483552367, 'AP-cow': 50.629288623866664, 'AP-elephant': 59.91407858340542, 'AP-bear': 77.81639259089344, 'AP-zebra': 60.630022747273415, 'AP-giraffe': 56.404563089798586, 'AP-backpack': 18.021531148175296, 'AP-umbrella': 48.66605412481983, 'AP-handbag': 14.85742753943554, 'AP-tie': 32.695437429964855, 'AP-suitcase': 40.82880831612176, 'AP-frisbee': 65.69531353217751, 'AP-skis': 5.069369927847391, 'AP-snowboard': 20.669070705800802, 'AP-sports ball': 47.004724946648984, 'AP-kite': 32.62822184780447, 'AP-baseball bat': 27.729055551481263, 'AP-baseball glove': 43.44035304806179, 'AP-skateboard': 35.011466959005716, 'AP-surfboard': 34.284115133478736, 'AP-tennis racket': 55.699255731652165, 'AP-bottle': 34.22693673863955, 'AP-wine glass': 27.913266398874708, 'AP-cup': 39.91064936670456, 'AP-fork': 15.996271051465172, 'AP-knife': 12.63369894768208, 'AP-spoon': 14.451391539159323, 'AP-bowl': 32.72961504343603, 'AP-banana': 19.998281216681697, 'AP-apple': 20.71884420619532, 'AP-sandwich': 43.42941663712602, 'AP-orange': 28.98830945890145, 'AP-broccoli': 21.219777907433205, 'AP-carrot': 20.13614470293865, 'AP-hot dog': 19.76467369669019, 'AP-pizza': 51.16692798340352, 'AP-donut': 45.45716188482445, 'AP-cake': 43.4974353618493, 'AP-chair': 20.595424620714322, 'AP-couch': 44.06189295062211, 'AP-potted plant': 16.463835988944968, 'AP-bed': 41.29250643284211, 'AP-dining table': 13.537220933577087, 'AP-toilet': 66.50330469493262, 'AP-tv': 61.344492286310484, 'AP-laptop': 62.63697790762751, 'AP-mouse': 58.10077506278638, 'AP-remote': 30.330370295528624, 'AP-keyboard': 47.87202305931681, 'AP-cell phone': 35.329400498894515, 'AP-microwave': 54.87114062210565, 'AP-oven': 33.58443312219384, 'AP-toaster': 34.87958026571888, 'AP-sink': 37.65921833018368, 'AP-refrigerator': 58.700718432959555, 'AP-book': 8.880109050889883, 'AP-clock': 51.87957031603757, 'AP-vase': 35.09219593532503, 'AP-scissors': 23.720211239164783, 'AP-teddy bear': 50.17546839666581, 'AP-hair drier': 7.998689574839836, 'AP-toothbrush': 16.401246718800415}), ('sem_seg', {'mIoU': 61.38136269449078, 'fwIoU': 69.64810275905201, 'IoU-person': 87.70729623681306, 'IoU-bicycle': 75.07492964233585, 'IoU-car': 68.07881738035942, 'IoU-motorcycle': 85.43505817360007, 'IoU-airplane': 83.76452537170482, 'IoU-bus': 83.88982066328381, 'IoU-train': 84.15385371866202, 'IoU-truck': 60.97174055533298, 'IoU-boat': 68.03547004196427, 'IoU-traffic light': 77.42502179101972, 'IoU-fire hydrant': 90.44870468575394, 'IoU-stop sign': 92.88691770040552, 'IoU-parking meter': 83.46338054913973, 'IoU-bench': 55.56849150745583, 'IoU-bird': 75.65403867062827, 'IoU-cat': 87.68157637497576, 'IoU-dog': 79.41786447931564, 'IoU-horse': 86.55349640655989, 'IoU-sheep': 89.45853832383652, 'IoU-cow': 81.15709142000715, 'IoU-elephant': 92.48815431659726, 'IoU-bear': 92.29393850634234, 'IoU-zebra': 92.52431799203914, 'IoU-giraffe': 88.26537227732072, 'IoU-backpack': 38.96086387472709, 'IoU-umbrella': 78.43708901621403, 'IoU-handbag': 36.929800776436025, 'IoU-tie': 69.69256595340535, 'IoU-suitcase': 79.78695139912351, 'IoU-frisbee': 83.41262639357014, 'IoU-skis': 51.48150825699444, 'IoU-snowboard': 68.95223949792663, 'IoU-sports ball': 65.97609595476514, 'IoU-kite': 66.60166425350278, 'IoU-baseball bat': 61.62444241599642, 'IoU-baseball glove': 72.84341961996418, 'IoU-skateboard': 77.26615902146008, 'IoU-surfboard': 81.21945709645513, 'IoU-tennis racket': 82.96983503915098, 'IoU-bottle': 69.43134602965725, 'IoU-wine glass': 72.59555696233652, 'IoU-cup': 62.689563403659335, 'IoU-fork': 54.57494684389715, 'IoU-knife': 46.93190242105247, 'IoU-spoon': 48.29570939481857, 'IoU-bowl': 55.898729497233056, 'IoU-banana': 83.49986896485323, 'IoU-apple': 55.99832259451628, 'IoU-sandwich': 66.89461279647124, 'IoU-orange': 76.11638208801509, 'IoU-broccoli': 67.22848893888525, 'IoU-carrot': 62.665782535894834, 'IoU-hot dog': 66.50524516294887, 'IoU-pizza': 82.13724934601795, 'IoU-donut': 64.30511669653572, 'IoU-cake': 68.12638483138792, 'IoU-chair': 55.53656157364877, 'IoU-couch': 70.11225259929596, 'IoU-potted plant': 34.60915191741021, 'IoU-bed': 68.46321341777508, 'IoU-dining table': 52.15927442952996, 'IoU-toilet': 83.10095954321805, 'IoU-tv': 75.54076156725762, 'IoU-laptop': 75.73595767436254, 'IoU-mouse': 70.77362955172866, 'IoU-remote': 49.77060129858097, 'IoU-keyboard': 62.67531167197822, 'IoU-cell phone': 68.10589232713993, 'IoU-microwave': 65.59379819188142, 'IoU-oven': 67.92280115213107, 'IoU-toaster': 70.18868842390744, 'IoU-sink': 67.59984448482345, 'IoU-refrigerator': 82.8696785817925, 'IoU-book': 50.94975546362102, 'IoU-clock': 74.96068648154147, 'IoU-vase': 63.452200902913205, 'IoU-scissors': 55.72839955825205, 'IoU-teddy bear': 80.0533640085748, 'IoU-hair drier': 41.816392151715995, 'IoU-toothbrush': 58.370181997608796, 'IoU-banner': 35.86789196142248, 'IoU-blanket': 9.768210347674978, 'IoU-bridge': 33.463753384386386, 'IoU-cardboard': 44.240830704556956, 'IoU-counter': 30.524123799696273, 'IoU-curtain': 62.97904451493564, 'IoU-door-stuff': 41.869318013271766, 'IoU-floor-wood': 61.18021361399728, 'IoU-flower': 41.50333432611249, 'IoU-fruit': 37.82876242609217, 'IoU-gravel': 30.394007508082844, 'IoU-house': 24.586316131098048, 'IoU-light': 40.81013632998978, 'IoU-mirror-stuff': 51.668315608251426, 'IoU-net': 46.41577450379752, 'IoU-pillow': 14.270701606948935, 'IoU-platform': 32.134631804010944, 'IoU-playingfield': 70.75742207200052, 'IoU-railroad': 61.52670093081929, 'IoU-river': 44.76970714971552, 'IoU-road': 66.96390566717865, 'IoU-roof': 15.045949818563745, 'IoU-sand': 63.0712539233545, 'IoU-sea': 85.25341865053022, 'IoU-shelf': 36.07503421809831, 'IoU-snow': 88.47851977679446, 'IoU-stairs': 21.4775366492633, 'IoU-tent': 7.939767315714394, 'IoU-towel': 34.00914227069708, 'IoU-wall-brick': 46.14278953825637, 'IoU-wall-stone': 27.80703628785785, 'IoU-wall-tile': 64.67161985463098, 'IoU-wall-wood': 38.77210276559603, 'IoU-water-other': 32.76339100073843, 'IoU-window-blind': 48.69037864820861, 'IoU-window-other': 47.96566423047839, 'IoU-tree-merged': 81.11262212771474, 'IoU-fence-merged': 52.38719152873471, 'IoU-ceiling-merged': 65.69903223544043, 'IoU-sky-other-merged': 93.699328357863, 'IoU-cabinet-merged': 59.08136118685706, 'IoU-table-merged': 39.68854449207674, 'IoU-floor-other-merged': 50.1122682699206, 'IoU-pavement-merged': 54.62505146061609, 'IoU-mountain-merged': 56.674804944057264, 'IoU-grass-merged': 72.86910782931702, 'IoU-dirt-merged': 46.18679580361358, 'IoU-paper-merged': 32.27240477590156, 'IoU-food-other-merged': 39.404148097372826, 'IoU-building-other-merged': 58.86570870362247, 'IoU-rock-merged': 60.37085092346026, 'IoU-wall-other-merged': 66.45147600151999, 'IoU-rug-merged': 63.99613144234883, 'mACC': 73.62000840031601, 'pACC': 80.85765134153966, 'ACC-person': 92.53174503319667, 'ACC-bicycle': 86.3153818864594, 'ACC-car': 86.0922290924863, 'ACC-motorcycle': 90.69547143147577, 'ACC-airplane': 90.68252134370097, 'ACC-bus': 88.6246250834532, 'ACC-train': 95.13680752545449, 'ACC-truck': 71.52202437255266, 'ACC-boat': 77.92224749931495, 'ACC-traffic light': 89.22257960106012, 'ACC-fire hydrant': 95.43500493195609, 'ACC-stop sign': 95.53788814670932, 'ACC-parking meter': 87.15360081491205, 'ACC-bench': 74.7595294392579, 'ACC-bird': 80.77857240682724, 'ACC-cat': 92.99843293578893, 'ACC-dog': 83.07172592782027, 'ACC-horse': 92.97472004919662, 'ACC-sheep': 93.61317241996439, 'ACC-cow': 86.10161685667312, 'ACC-elephant': 95.18320884313766, 'ACC-bear': 94.88403871024258, 'ACC-zebra': 95.16169139806144, 'ACC-giraffe': 92.7361916334156, 'ACC-backpack': 58.57036748144188, 'ACC-umbrella': 85.87174665436898, 'ACC-handbag': 51.55493443890502, 'ACC-tie': 81.58589669776939, 'ACC-suitcase': 88.71420934193677, 'ACC-frisbee': 93.592, 'ACC-skis': 67.93663989751172, 'ACC-snowboard': 78.67894491930151, 'ACC-sports ball': 80.56925192007526, 'ACC-kite': 75.77731129451622, 'ACC-baseball bat': 80.42541713806202, 'ACC-baseball glove': 89.6619182464711, 'ACC-skateboard': 89.4616479121688, 'ACC-surfboard': 89.87363408744812, 'ACC-tennis racket': 89.50092606999985, 'ACC-bottle': 84.04067602345835, 'ACC-wine glass': 85.66725897989157, 'ACC-cup': 82.07392865140135, 'ACC-fork': 64.67803747636664, 'ACC-knife': 57.87279255514033, 'ACC-spoon': 69.92502990172505, 'ACC-bowl': 72.79156814201193, 'ACC-banana': 89.35548895437586, 'ACC-apple': 69.7904087358561, 'ACC-sandwich': 81.02472415763349, 'ACC-orange': 83.25442247133891, 'ACC-broccoli': 76.38725177015996, 'ACC-carrot': 71.11158494075593, 'ACC-hot dog': 72.8542154184043, 'ACC-pizza': 92.95089924839381, 'ACC-donut': 82.18817405477681, 'ACC-cake': 77.7865113088632, 'ACC-chair': 72.56275251040977, 'ACC-couch': 84.46965270264045, 'ACC-potted plant': 52.67451261188503, 'ACC-bed': 82.52978385675527, 'ACC-dining table': 75.53255666830606, 'ACC-toilet': 92.40231598178535, 'ACC-tv': 86.51015587976333, 'ACC-laptop': 91.84663580780854, 'ACC-mouse': 85.45357473963688, 'ACC-remote': 72.38545257480847, 'ACC-keyboard': 70.00216569592212, 'ACC-cell phone': 75.37691111660493, 'ACC-microwave': 75.51187789087342, 'ACC-oven': 86.97247174096489, 'ACC-toaster': 78.9842477658104, 'ACC-sink': 84.79537438075249, 'ACC-refrigerator': 91.59623282175683, 'ACC-book': 70.08128799977585, 'ACC-clock': 82.3814600554067, 'ACC-vase': 75.988695321904, 'ACC-scissors': 60.06023377315006, 'ACC-teddy bear': 87.27759428036538, 'ACC-hair drier': 45.34483249366693, 'ACC-toothbrush': 80.15201528839472, 'ACC-banner': 70.29012469695154, 'ACC-blanket': 13.063220096432957, 'ACC-bridge': 45.73192101012769, 'ACC-cardboard': 57.325810673258694, 'ACC-counter': 51.80965973231133, 'ACC-curtain': 72.82137481658009, 'ACC-door-stuff': 60.649442789061226, 'ACC-floor-wood': 74.7092348753692, 'ACC-flower': 61.9916299924434, 'ACC-fruit': 63.71836594758632, 'ACC-gravel': 43.65041331829851, 'ACC-house': 29.359586634230006, 'ACC-light': 58.07119205298014, 'ACC-mirror-stuff': 68.8378515725814, 'ACC-net': 61.55989429180163, 'ACC-pillow': 23.62504058945123, 'ACC-platform': 60.014741415819685, 'ACC-playingfield': 89.21756443774761, 'ACC-railroad': 79.7506334543909, 'ACC-river': 56.43695416269659, 'ACC-road': 84.96048958210716, 'ACC-roof': 19.950616680316347, 'ACC-sand': 71.29982271058938, 'ACC-sea': 91.50755195730811, 'ACC-shelf': 59.17218199529073, 'ACC-snow': 95.59916076604542, 'ACC-stairs': 39.16116076608597, 'ACC-tent': 8.600869470664252, 'ACC-towel': 41.03928481809041, 'ACC-wall-brick': 59.95233349570238, 'ACC-wall-stone': 32.37930584863318, 'ACC-wall-tile': 77.42001739198308, 'ACC-wall-wood': 49.43466205309977, 'ACC-water-other': 58.31757719915812, 'ACC-window-blind': 57.23000632251043, 'ACC-window-other': 68.62223492563673, 'ACC-tree-merged': 88.75436524902875, 'ACC-fence-merged': 73.57121727969808, 'ACC-ceiling-merged': 79.7187845161282, 'ACC-sky-other-merged': 96.54439549750961, 'ACC-cabinet-merged': 76.0250142098231, 'ACC-table-merged': 52.399469637742754, 'ACC-floor-other-merged': 61.90191629673455, 'ACC-pavement-merged': 67.16426333808052, 'ACC-mountain-merged': 67.83040009617014, 'ACC-grass-merged': 83.79592633280636, 'ACC-dirt-merged': 66.67308394372525, 'ACC-paper-merged': 41.159942081521734, 'ACC-food-other-merged': 52.972888272805065, 'ACC-building-other-merged': 78.5997350723622, 'ACC-rock-merged': 82.6708263177287, 'ACC-wall-other-merged': 81.1316345848455, 'ACC-rug-merged': 77.70968173918332})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4647351477904595, 'noc@0.8': 2.8507462686567164, 'noc@0.85': 3.4451273046532047, 'noc@0.9': 4.48697688030436, 'miou@iter1': 0.8321468821037992}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.2786636352539, 'precision@0.6': 67.89739227294922, 'precision@0.7': 62.53400802612305, 'precision@0.8': 52.27360916137695, 'precision@0.9': 27.05013656616211, 'cIoU': 57.79022979736328, 'mIoU': 62.6456413269043}}}
INFO:trainer.default_trainer:This epoch takes 1:28:17.598708
INFO:trainer.default_trainer:PROGRESS: 66.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 33 training.
INFO:trainer.default_trainer:epochs[    33] optim steps[60300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13685/0.90041, loss_mask_bce_0: 0.20730/0.33468, loss_mask_dice_0: 0.11725/1.16298, loss_spatial_bce_0: 0.12570/0.08758, loss_spatial_dice_0: 0.06879/0.20890, loss_spatial_ce_0: 0.00002/0.06311, loss_grounding_bce_0: 0.13005/0.08629, loss_grounding_dice_0: 0.10918/0.17849, loss_grounding_ce_0: 0.00181/0.27255, loss_mask_ce_1: 0.13608/0.90101, loss_mask_bce_1: 0.21699/0.33562, loss_mask_dice_1: 0.12108/1.16933, loss_spatial_bce_1: 0.12878/0.08810, loss_spatial_dice_1: 0.07259/0.21287, loss_spatial_ce_1: 0.00002/0.06889, loss_grounding_bce_1: 0.12897/0.08645, loss_grounding_dice_1: 0.11352/0.17930, loss_grounding_ce_1: 0.00103/0.27326, loss_mask_ce_2: 0.13837/0.90811, loss_mask_bce_2: 0.21659/0.33618, loss_mask_dice_2: 0.12402/1.16969, loss_spatial_bce_2: 0.12521/0.08909, loss_spatial_dice_2: 0.07094/0.21441, loss_spatial_ce_2: 0.00002/0.07227, loss_grounding_bce_2: 0.13384/0.08659, loss_grounding_dice_2: 0.11773/0.17911, loss_grounding_ce_2: 0.00153/0.27667, loss_mask_ce_3: 0.11330/0.91838, loss_mask_bce_3: 0.21776/0.33726, loss_mask_dice_3: 0.12262/1.16728, loss_spatial_bce_3: 0.12469/0.09022, loss_spatial_dice_3: 0.07092/0.21524, loss_spatial_ce_3: 0.00009/0.07661, loss_grounding_bce_3: 0.13377/0.08682, loss_grounding_dice_3: 0.11762/0.17884, loss_grounding_ce_3: 0.00074/0.27882, loss_mask_ce_4: 0.12373/0.91930, loss_mask_bce_4: 0.21319/0.33936, loss_mask_dice_4: 0.12530/1.19130, loss_spatial_bce_4: 0.12907/0.09421, loss_spatial_dice_4: 0.07143/0.22730, loss_spatial_ce_4: 0.00028/0.09244, loss_grounding_bce_4: 0.13405/0.08732, loss_grounding_dice_4: 0.12267/0.18174, loss_grounding_ce_4: 0.00048/0.28150, loss_mask_ce_5: 0.13194/0.93551, loss_mask_bce_5: 0.21713/0.34167, loss_mask_dice_5: 0.12788/1.19873, loss_spatial_bce_5: 0.12807/0.09634, loss_spatial_dice_5: 0.07024/0.23140, loss_spatial_ce_5: 0.00210/0.10716, loss_grounding_bce_5: 0.13701/0.08775, loss_grounding_dice_5: 0.11280/0.18296, loss_grounding_ce_5: 0.00049/0.29422, loss_mask_ce_6: 0.14318/0.97546, loss_mask_bce_6: 0.21558/0.34435, loss_mask_dice_6: 0.12312/1.20166, loss_spatial_bce_6: 0.13325/0.10210, loss_spatial_dice_6: 0.06637/0.23427, loss_spatial_ce_6: 0.01775/0.13329, loss_grounding_bce_6: 0.12913/0.08849, loss_grounding_dice_6: 0.11481/0.18335, loss_grounding_ce_6: 0.00097/0.30980, loss_mask_ce_7: 0.12651/1.02045, loss_mask_bce_7: 0.22365/0.35222, loss_mask_dice_7: 0.12510/1.25614, loss_spatial_bce_7: 0.14471/0.11012, loss_spatial_dice_7: 0.07421/0.26194, loss_spatial_ce_7: 0.05902/0.16870, loss_grounding_bce_7: 0.13680/0.09038, loss_grounding_dice_7: 0.13149/0.19059, loss_grounding_ce_7: 0.00179/0.34044, loss_mask_ce_8: 0.17953/1.12875, loss_mask_bce_8: 0.23052/0.36582, loss_mask_dice_8: 0.13020/1.32933, loss_spatial_bce_8: 0.14486/0.13081, loss_spatial_dice_8: 0.07769/0.30007, loss_spatial_ce_8: 0.06835/0.22537, loss_grounding_bce_8: 0.13596/0.09413, loss_grounding_dice_8: 0.11660/0.20151, loss_grounding_ce_8: 0.00198/0.40779, loss_mask_ce_9: 1.42510/3.67752, loss_mask_bce_9: 0.21994/0.39275, loss_mask_dice_9: 0.12532/1.90235, loss_spatial_bce_9: 0.45107/0.33329, loss_spatial_dice_9: 0.53392/0.82209, loss_spatial_ce_9: 0.83260/1.49723, loss_grounding_bce_9: 0.12851/0.10560, loss_grounding_dice_9: 0.10343/0.28069, loss_grounding_ce_9: 0.13917/0.67277] items per batch[64] items per second[0.13] total items[3859200] mini batches[ 60300] memory[7345] epoch remaining[1:35:00]
INFO:trainer.default_trainer:epochs[    33] optim steps[60400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.41887/0.90035, loss_mask_bce_0: 0.32447/0.33466, loss_mask_dice_0: 0.64085/1.16286, loss_spatial_bce_0: 0.11412/0.08757, loss_spatial_dice_0: 0.12068/0.20889, loss_spatial_ce_0: 0.01539/0.06309, loss_grounding_bce_0: 0.02840/0.08628, loss_grounding_dice_0: 0.07516/0.17850, loss_grounding_ce_0: 0.05337/0.27248, loss_mask_ce_1: 0.65041/0.90094, loss_mask_bce_1: 0.31455/0.33560, loss_mask_dice_1: 0.67191/1.16925, loss_spatial_bce_1: 0.12402/0.08809, loss_spatial_dice_1: 0.12743/0.21285, loss_spatial_ce_1: 0.02038/0.06885, loss_grounding_bce_1: 0.02913/0.08645, loss_grounding_dice_1: 0.06390/0.17930, loss_grounding_ce_1: 0.05139/0.27320, loss_mask_ce_2: 0.62711/0.90805, loss_mask_bce_2: 0.30542/0.33615, loss_mask_dice_2: 0.66875/1.16959, loss_spatial_bce_2: 0.14393/0.08907, loss_spatial_dice_2: 0.14048/0.21439, loss_spatial_ce_2: 0.01133/0.07224, loss_grounding_bce_2: 0.02730/0.08659, loss_grounding_dice_2: 0.07324/0.17912, loss_grounding_ce_2: 0.04935/0.27661, loss_mask_ce_3: 0.62744/0.91832, loss_mask_bce_3: 0.29801/0.33724, loss_mask_dice_3: 0.64707/1.16720, loss_spatial_bce_3: 0.09127/0.09020, loss_spatial_dice_3: 0.13765/0.21523, loss_spatial_ce_3: 0.07542/0.07659, loss_grounding_bce_3: 0.02669/0.08682, loss_grounding_dice_3: 0.05932/0.17886, loss_grounding_ce_3: 0.05509/0.27877, loss_mask_ce_4: 0.57777/0.91921, loss_mask_bce_4: 0.32531/0.33934, loss_mask_dice_4: 0.61872/1.19124, loss_spatial_bce_4: 0.12316/0.09419, loss_spatial_dice_4: 0.14829/0.22730, loss_spatial_ce_4: 0.14567/0.09244, loss_grounding_bce_4: 0.02533/0.08731, loss_grounding_dice_4: 0.06697/0.18175, loss_grounding_ce_4: 0.05674/0.28146, loss_mask_ce_5: 0.42283/0.93542, loss_mask_bce_5: 0.34253/0.34164, loss_mask_dice_5: 0.68906/1.19866, loss_spatial_bce_5: 0.12184/0.09632, loss_spatial_dice_5: 0.15496/0.23139, loss_spatial_ce_5: 0.08310/0.10713, loss_grounding_bce_5: 0.02582/0.08774, loss_grounding_dice_5: 0.06663/0.18297, loss_grounding_ce_5: 0.07784/0.29419, loss_mask_ce_6: 0.59576/0.97536, loss_mask_bce_6: 0.33915/0.34432, loss_mask_dice_6: 0.66817/1.20157, loss_spatial_bce_6: 0.10949/0.10208, loss_spatial_dice_6: 0.15254/0.23426, loss_spatial_ce_6: 0.07700/0.13328, loss_grounding_bce_6: 0.03035/0.08848, loss_grounding_dice_6: 0.09400/0.18336, loss_grounding_ce_6: 0.09012/0.30973, loss_mask_ce_7: 0.55630/1.02039, loss_mask_bce_7: 0.32406/0.35218, loss_mask_dice_7: 0.61424/1.25606, loss_spatial_bce_7: 0.09144/0.11010, loss_spatial_dice_7: 0.14524/0.26193, loss_spatial_ce_7: 0.03911/0.16869, loss_grounding_bce_7: 0.03027/0.09038, loss_grounding_dice_7: 0.08150/0.19061, loss_grounding_ce_7: 0.09749/0.34037, loss_mask_ce_8: 0.63786/1.12871, loss_mask_bce_8: 0.34027/0.36578, loss_mask_dice_8: 0.65759/1.32925, loss_spatial_bce_8: 0.09736/0.13078, loss_spatial_dice_8: 0.16174/0.30005, loss_spatial_ce_8: 0.10188/0.22534, loss_grounding_bce_8: 0.03224/0.09413, loss_grounding_dice_8: 0.09566/0.20153, loss_grounding_ce_8: 0.11468/0.40773, loss_mask_ce_9: 3.33591/3.67725, loss_mask_bce_9: 0.44777/0.39270, loss_mask_dice_9: 1.39609/1.90217, loss_spatial_bce_9: 0.38279/0.33327, loss_spatial_dice_9: 0.74427/0.82210, loss_spatial_ce_9: 1.28610/1.49730, loss_grounding_bce_9: 0.05435/0.10560, loss_grounding_dice_9: 0.29812/0.28069, loss_grounding_ce_9: 0.25329/0.67262] items per batch[64] items per second[0.23] total items[3865600] mini batches[ 60400] memory[7345] epoch remaining[1:20:08]
INFO:trainer.default_trainer:epochs[    33] optim steps[60500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48106/0.90020, loss_mask_bce_0: 0.43376/0.33465, loss_mask_dice_0: 0.83922/1.16286, loss_spatial_bce_0: 0.13031/0.08756, loss_spatial_dice_0: 0.30646/0.20887, loss_spatial_ce_0: 0.08568/0.06306, loss_grounding_bce_0: 0.32627/0.08630, loss_grounding_dice_0: 0.39603/0.17853, loss_grounding_ce_0: 0.07353/0.27250, loss_mask_ce_1: 0.47959/0.90077, loss_mask_bce_1: 0.43658/0.33559, loss_mask_dice_1: 0.81769/1.16925, loss_spatial_bce_1: 0.15505/0.08808, loss_spatial_dice_1: 0.30075/0.21283, loss_spatial_ce_1: 0.08516/0.06882, loss_grounding_bce_1: 0.31604/0.08647, loss_grounding_dice_1: 0.39418/0.17933, loss_grounding_ce_1: 0.07615/0.27322, loss_mask_ce_2: 0.56448/0.90791, loss_mask_bce_2: 0.42959/0.33614, loss_mask_dice_2: 0.83620/1.16958, loss_spatial_bce_2: 0.14246/0.08907, loss_spatial_dice_2: 0.29615/0.21437, loss_spatial_ce_2: 0.15530/0.07221, loss_grounding_bce_2: 0.30810/0.08660, loss_grounding_dice_2: 0.39584/0.17914, loss_grounding_ce_2: 0.07495/0.27663, loss_mask_ce_3: 0.50251/0.91820, loss_mask_bce_3: 0.41736/0.33723, loss_mask_dice_3: 0.78960/1.16719, loss_spatial_bce_3: 0.13914/0.09020, loss_spatial_dice_3: 0.30802/0.21521, loss_spatial_ce_3: 0.14654/0.07657, loss_grounding_bce_3: 0.31146/0.08683, loss_grounding_dice_3: 0.38118/0.17889, loss_grounding_ce_3: 0.07529/0.27880, loss_mask_ce_4: 0.40273/0.91907, loss_mask_bce_4: 0.42695/0.33932, loss_mask_dice_4: 0.76786/1.19126, loss_spatial_bce_4: 0.15763/0.09419, loss_spatial_dice_4: 0.33543/0.22728, loss_spatial_ce_4: 0.19884/0.09241, loss_grounding_bce_4: 0.31612/0.08733, loss_grounding_dice_4: 0.39262/0.18177, loss_grounding_ce_4: 0.07454/0.28150, loss_mask_ce_5: 0.77159/0.93530, loss_mask_bce_5: 0.23754/0.34162, loss_mask_dice_5: 0.77537/1.19868, loss_spatial_bce_5: 0.16846/0.09632, loss_spatial_dice_5: 0.34199/0.23137, loss_spatial_ce_5: 0.11840/0.10712, loss_grounding_bce_5: 0.32330/0.08775, loss_grounding_dice_5: 0.38108/0.18300, loss_grounding_ce_5: 0.07610/0.29423, loss_mask_ce_6: 0.74379/0.97525, loss_mask_bce_6: 0.24494/0.34430, loss_mask_dice_6: 0.77211/1.20158, loss_spatial_bce_6: 0.12852/0.10208, loss_spatial_dice_6: 0.32204/0.23424, loss_spatial_ce_6: 0.23835/0.13323, loss_grounding_bce_6: 0.09114/0.08850, loss_grounding_dice_6: 0.33988/0.18338, loss_grounding_ce_6: 0.43497/0.30980, loss_mask_ce_7: 0.79671/1.02033, loss_mask_bce_7: 0.26399/0.35215, loss_mask_dice_7: 0.82573/1.25607, loss_spatial_bce_7: 0.14071/0.11010, loss_spatial_dice_7: 0.32591/0.26192, loss_spatial_ce_7: 0.26919/0.16863, loss_grounding_bce_7: 0.31800/0.09040, loss_grounding_dice_7: 0.37440/0.19064, loss_grounding_ce_7: 0.08129/0.34044, loss_mask_ce_8: 0.70388/1.12862, loss_mask_bce_8: 0.42709/0.36576, loss_mask_dice_8: 0.83091/1.32925, loss_spatial_bce_8: 0.15361/0.13078, loss_spatial_dice_8: 0.30427/0.30004, loss_spatial_ce_8: 0.28497/0.22528, loss_grounding_bce_8: 0.31241/0.09414, loss_grounding_dice_8: 0.38506/0.20154, loss_grounding_ce_8: 0.09344/0.40778, loss_mask_ce_9: 3.02730/3.67729, loss_mask_bce_9: 0.37379/0.39268, loss_mask_dice_9: 1.00924/1.90215, loss_spatial_bce_9: 0.36053/0.33327, loss_spatial_dice_9: 0.85054/0.82210, loss_spatial_ce_9: 1.57965/1.49722, loss_grounding_bce_9: 0.23827/0.10561, loss_grounding_dice_9: 0.41967/0.28072, loss_grounding_ce_9: 0.31482/0.67263] items per batch[64] items per second[0.22] total items[3872000] mini batches[ 60500] memory[7345] epoch remaining[1:16:37]
INFO:trainer.default_trainer:epochs[    33] optim steps[60600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58026/0.90027, loss_mask_bce_0: 0.31941/0.33465, loss_mask_dice_0: 0.62258/1.16294, loss_spatial_bce_0: 0.10513/0.08756, loss_spatial_dice_0: 0.20131/0.20888, loss_spatial_ce_0: 0.24096/0.06305, loss_grounding_bce_0: 0.07533/0.08629, loss_grounding_dice_0: 0.19856/0.17853, loss_grounding_ce_0: 0.05190/0.27251, loss_mask_ce_1: 0.64854/0.90085, loss_mask_bce_1: 0.34476/0.33559, loss_mask_dice_1: 0.61643/1.16933, loss_spatial_bce_1: 0.10083/0.08808, loss_spatial_dice_1: 0.19377/0.21285, loss_spatial_ce_1: 0.21540/0.06881, loss_grounding_bce_1: 0.07217/0.08646, loss_grounding_dice_1: 0.16623/0.17932, loss_grounding_ce_1: 0.06694/0.27327, loss_mask_ce_2: 0.61848/0.90796, loss_mask_bce_2: 0.31784/0.33615, loss_mask_dice_2: 0.62383/1.16967, loss_spatial_bce_2: 0.09376/0.08907, loss_spatial_dice_2: 0.19172/0.21439, loss_spatial_ce_2: 0.23208/0.07222, loss_grounding_bce_2: 0.07720/0.08660, loss_grounding_dice_2: 0.18590/0.17914, loss_grounding_ce_2: 0.08815/0.27666, loss_mask_ce_3: 0.63730/0.91827, loss_mask_bce_3: 0.35173/0.33723, loss_mask_dice_3: 0.63290/1.16729, loss_spatial_bce_3: 0.10435/0.09020, loss_spatial_dice_3: 0.20917/0.21523, loss_spatial_ce_3: 0.23823/0.07657, loss_grounding_bce_3: 0.08160/0.08682, loss_grounding_dice_3: 0.19626/0.17889, loss_grounding_ce_3: 0.10293/0.27885, loss_mask_ce_4: 0.65805/0.91919, loss_mask_bce_4: 0.34794/0.33933, loss_mask_dice_4: 0.63901/1.19135, loss_spatial_bce_4: 0.08195/0.09418, loss_spatial_dice_4: 0.17512/0.22731, loss_spatial_ce_4: 0.36507/0.09243, loss_grounding_bce_4: 0.07987/0.08732, loss_grounding_dice_4: 0.19524/0.18177, loss_grounding_ce_4: 0.11895/0.28153, loss_mask_ce_5: 0.67703/0.93541, loss_mask_bce_5: 0.33136/0.34163, loss_mask_dice_5: 0.62560/1.19878, loss_spatial_bce_5: 0.11745/0.09632, loss_spatial_dice_5: 0.21850/0.23140, loss_spatial_ce_5: 0.36906/0.10713, loss_grounding_bce_5: 0.07669/0.08774, loss_grounding_dice_5: 0.18390/0.18300, loss_grounding_ce_5: 0.07811/0.29429, loss_mask_ce_6: 0.46536/0.97533, loss_mask_bce_6: 0.38082/0.34430, loss_mask_dice_6: 0.71348/1.20169, loss_spatial_bce_6: 0.10838/0.10207, loss_spatial_dice_6: 0.22300/0.23426, loss_spatial_ce_6: 0.42778/0.13326, loss_grounding_bce_6: 0.08526/0.08849, loss_grounding_dice_6: 0.18774/0.18339, loss_grounding_ce_6: 0.10224/0.30987, loss_mask_ce_7: 0.57494/1.02041, loss_mask_bce_7: 0.34030/0.35215, loss_mask_dice_7: 0.69465/1.25615, loss_spatial_bce_7: 0.11192/0.11010, loss_spatial_dice_7: 0.27409/0.26195, loss_spatial_ce_7: 0.37508/0.16864, loss_grounding_bce_7: 0.08711/0.09039, loss_grounding_dice_7: 0.22167/0.19064, loss_grounding_ce_7: 0.05670/0.34048, loss_mask_ce_8: 0.68451/1.12868, loss_mask_bce_8: 0.37306/0.36577, loss_mask_dice_8: 0.67428/1.32932, loss_spatial_bce_8: 0.13557/0.13078, loss_spatial_dice_8: 0.34904/0.30005, loss_spatial_ce_8: 0.46540/0.22525, loss_grounding_bce_8: 0.11468/0.09413, loss_grounding_dice_8: 0.24869/0.20155, loss_grounding_ce_8: 0.05151/0.40782, loss_mask_ce_9: 2.00565/3.67728, loss_mask_bce_9: 0.43026/0.39268, loss_mask_dice_9: 1.02059/1.90231, loss_spatial_bce_9: 0.38647/0.33328, loss_spatial_dice_9: 0.83410/0.82210, loss_spatial_ce_9: 1.32256/1.49732, loss_grounding_bce_9: 0.15479/0.10561, loss_grounding_dice_9: 0.40433/0.28073, loss_grounding_ce_9: 0.03874/0.67262] items per batch[64] items per second[0.23] total items[3878400] mini batches[ 60600] memory[7345] epoch remaining[1:11:37]
INFO:trainer.default_trainer:epochs[    33] optim steps[60700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66873/0.90023, loss_mask_bce_0: 0.50185/0.33465, loss_mask_dice_0: 0.56394/1.16290, loss_spatial_bce_0: 0.23077/0.08756, loss_spatial_dice_0: 0.19615/0.20886, loss_spatial_ce_0: 0.00873/0.06300, loss_grounding_bce_0: 0.16992/0.08630, loss_grounding_dice_0: 0.15118/0.17852, loss_grounding_ce_0: 0.02624/0.27243, loss_mask_ce_1: 0.72282/0.90080, loss_mask_bce_1: 0.50885/0.33559, loss_mask_dice_1: 0.58882/1.16932, loss_spatial_bce_1: 0.23944/0.08807, loss_spatial_dice_1: 0.19545/0.21283, loss_spatial_ce_1: 0.00785/0.06877, loss_grounding_bce_1: 0.17923/0.08647, loss_grounding_dice_1: 0.15904/0.17932, loss_grounding_ce_1: 0.02876/0.27319, loss_mask_ce_2: 0.75821/0.90790, loss_mask_bce_2: 0.50863/0.33615, loss_mask_dice_2: 0.56570/1.16966, loss_spatial_bce_2: 0.24020/0.08907, loss_spatial_dice_2: 0.20191/0.21438, loss_spatial_ce_2: 0.00943/0.07219, loss_grounding_bce_2: 0.17151/0.08661, loss_grounding_dice_2: 0.15060/0.17913, loss_grounding_ce_2: 0.03216/0.27657, loss_mask_ce_3: 0.80478/0.91824, loss_mask_bce_3: 0.49757/0.33722, loss_mask_dice_3: 0.54945/1.16728, loss_spatial_bce_3: 0.22567/0.09020, loss_spatial_dice_3: 0.20211/0.21521, loss_spatial_ce_3: 0.01005/0.07654, loss_grounding_bce_3: 0.16642/0.08683, loss_grounding_dice_3: 0.14833/0.17888, loss_grounding_ce_3: 0.04656/0.27876, loss_mask_ce_4: 0.83759/0.91918, loss_mask_bce_4: 0.49982/0.33933, loss_mask_dice_4: 0.60377/1.19131, loss_spatial_bce_4: 0.21740/0.09419, loss_spatial_dice_4: 0.21317/0.22730, loss_spatial_ce_4: 0.02280/0.09240, loss_grounding_bce_4: 0.17137/0.08733, loss_grounding_dice_4: 0.15257/0.18176, loss_grounding_ce_4: 0.03454/0.28147, loss_mask_ce_5: 0.80809/0.93542, loss_mask_bce_5: 0.49720/0.34162, loss_mask_dice_5: 0.57153/1.19877, loss_spatial_bce_5: 0.19420/0.09632, loss_spatial_dice_5: 0.20154/0.23139, loss_spatial_ce_5: 0.04454/0.10710, loss_grounding_bce_5: 0.17419/0.08775, loss_grounding_dice_5: 0.15111/0.18300, loss_grounding_ce_5: 0.02956/0.29421, loss_mask_ce_6: 0.83079/0.97534, loss_mask_bce_6: 0.49874/0.34429, loss_mask_dice_6: 0.61911/1.20169, loss_spatial_bce_6: 0.27339/0.10208, loss_spatial_dice_6: 0.20295/0.23424, loss_spatial_ce_6: 0.04646/0.13323, loss_grounding_bce_6: 0.17727/0.08850, loss_grounding_dice_6: 0.16768/0.18338, loss_grounding_ce_6: 0.03510/0.30979, loss_mask_ce_7: 0.87215/1.02046, loss_mask_bce_7: 0.50993/0.35215, loss_mask_dice_7: 0.73052/1.25612, loss_spatial_bce_7: 0.19043/0.11010, loss_spatial_dice_7: 0.16760/0.26193, loss_spatial_ce_7: 0.08285/0.16859, loss_grounding_bce_7: 0.19108/0.09040, loss_grounding_dice_7: 0.15066/0.19064, loss_grounding_ce_7: 0.05534/0.34035, loss_mask_ce_8: 0.85235/1.12865, loss_mask_bce_8: 0.51456/0.36577, loss_mask_dice_8: 0.65670/1.32930, loss_spatial_bce_8: 0.18398/0.13078, loss_spatial_dice_8: 0.17169/0.30003, loss_spatial_ce_8: 0.12206/0.22519, loss_grounding_bce_8: 0.17806/0.09414, loss_grounding_dice_8: 0.14744/0.20154, loss_grounding_ce_8: 0.05851/0.40764, loss_mask_ce_9: 3.44122/3.67712, loss_mask_bce_9: 0.50810/0.39269, loss_mask_dice_9: 0.96502/1.90231, loss_spatial_bce_9: 0.47186/0.33329, loss_spatial_dice_9: 0.71472/0.82211, loss_spatial_ce_9: 1.79838/1.49727, loss_grounding_bce_9: 0.18263/0.10561, loss_grounding_dice_9: 0.21399/0.28072, loss_grounding_ce_9: 0.20162/0.67242] items per batch[64] items per second[0.23] total items[3884800] mini batches[ 60700] memory[7345] epoch remaining[1:06:48]
INFO:trainer.default_trainer:epochs[    33] optim steps[60800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04027/0.90020, loss_mask_bce_0: 0.47365/0.33463, loss_mask_dice_0: 2.30122/1.16284, loss_spatial_bce_0: 0.06558/0.08755, loss_spatial_dice_0: 0.18991/0.20884, loss_spatial_ce_0: 0.06149/0.06295, loss_grounding_bce_0: 0.17602/0.08630, loss_grounding_dice_0: 0.15535/0.17851, loss_grounding_ce_0: 0.01338/0.27239, loss_mask_ce_1: 1.05590/0.90077, loss_mask_bce_1: 0.51873/0.33557, loss_mask_dice_1: 2.52433/1.16930, loss_spatial_bce_1: 0.06371/0.08807, loss_spatial_dice_1: 0.19791/0.21281, loss_spatial_ce_1: 0.04050/0.06874, loss_grounding_bce_1: 0.16717/0.08646, loss_grounding_dice_1: 0.15116/0.17930, loss_grounding_ce_1: 0.01177/0.27317, loss_mask_ce_2: 1.07390/0.90785, loss_mask_bce_2: 0.53613/0.33613, loss_mask_dice_2: 2.44509/1.16962, loss_spatial_bce_2: 0.07237/0.08907, loss_spatial_dice_2: 0.21447/0.21435, loss_spatial_ce_2: 0.03087/0.07213, loss_grounding_bce_2: 0.18041/0.08660, loss_grounding_dice_2: 0.16299/0.17912, loss_grounding_ce_2: 0.01491/0.27654, loss_mask_ce_3: 1.10793/0.91822, loss_mask_bce_3: 0.55056/0.33720, loss_mask_dice_3: 2.40134/1.16724, loss_spatial_bce_3: 0.07167/0.09020, loss_spatial_dice_3: 0.20236/0.21519, loss_spatial_ce_3: 0.08690/0.07651, loss_grounding_bce_3: 0.18217/0.08683, loss_grounding_dice_3: 0.16314/0.17887, loss_grounding_ce_3: 0.03260/0.27872, loss_mask_ce_4: 1.28783/0.91913, loss_mask_bce_4: 0.55202/0.33931, loss_mask_dice_4: 2.66219/1.19128, loss_spatial_bce_4: 0.08064/0.09418, loss_spatial_dice_4: 0.24306/0.22729, loss_spatial_ce_4: 0.07502/0.09236, loss_grounding_bce_4: 0.20104/0.08733, loss_grounding_dice_4: 0.16520/0.18175, loss_grounding_ce_4: 0.03570/0.28145, loss_mask_ce_5: 1.16085/0.93539, loss_mask_bce_5: 0.57761/0.34160, loss_mask_dice_5: 2.54820/1.19871, loss_spatial_bce_5: 0.07350/0.09632, loss_spatial_dice_5: 0.24496/0.23137, loss_spatial_ce_5: 0.06151/0.10707, loss_grounding_bce_5: 0.19972/0.08775, loss_grounding_dice_5: 0.16919/0.18299, loss_grounding_ce_5: 0.07729/0.29420, loss_mask_ce_6: 1.17766/0.97533, loss_mask_bce_6: 0.57242/0.34427, loss_mask_dice_6: 2.55411/1.20166, loss_spatial_bce_6: 0.07248/0.10207, loss_spatial_dice_6: 0.24928/0.23423, loss_spatial_ce_6: 0.10628/0.13320, loss_grounding_bce_6: 0.16561/0.08849, loss_grounding_dice_6: 0.15874/0.18337, loss_grounding_ce_6: 0.08595/0.30978, loss_mask_ce_7: 1.08296/1.02043, loss_mask_bce_7: 0.54856/0.35212, loss_mask_dice_7: 2.58283/1.25611, loss_spatial_bce_7: 0.09106/0.11008, loss_spatial_dice_7: 0.26139/0.26191, loss_spatial_ce_7: 0.13088/0.16855, loss_grounding_bce_7: 0.17815/0.09039, loss_grounding_dice_7: 0.15442/0.19063, loss_grounding_ce_7: 0.17884/0.34033, loss_mask_ce_8: 1.44018/1.12860, loss_mask_bce_8: 0.44972/0.36573, loss_mask_dice_8: 2.69029/1.32925, loss_spatial_bce_8: 0.20721/0.13077, loss_spatial_dice_8: 0.37082/0.30001, loss_spatial_ce_8: 0.06202/0.22512, loss_grounding_bce_8: 0.12975/0.09413, loss_grounding_dice_8: 0.14283/0.20153, loss_grounding_ce_8: 0.36008/0.40760, loss_mask_ce_9: 4.56919/3.67712, loss_mask_bce_9: 0.50751/0.39268, loss_mask_dice_9: 3.99956/1.90225, loss_spatial_bce_9: 0.16961/0.33329, loss_spatial_dice_9: 0.89937/0.82211, loss_spatial_ce_9: 1.36100/1.49719, loss_grounding_bce_9: 0.15302/0.10561, loss_grounding_dice_9: 0.26298/0.28074, loss_grounding_ce_9: 1.62302/0.67248] items per batch[64] items per second[0.23] total items[3891200] mini batches[ 60800] memory[7345] epoch remaining[1:01:56]
INFO:trainer.default_trainer:epochs[    33] optim steps[60900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83293/0.90018, loss_mask_bce_0: 0.11713/0.33465, loss_mask_dice_0: 2.07732/1.16313, loss_spatial_bce_0: 0.03317/0.08755, loss_spatial_dice_0: 0.41704/0.20885, loss_spatial_ce_0: 0.24146/0.06294, loss_grounding_bce_0: 0.06440/0.08630, loss_grounding_dice_0: 0.23985/0.17852, loss_grounding_ce_0: 0.56789/0.27242, loss_mask_ce_1: 0.80053/0.90074, loss_mask_bce_1: 0.11918/0.33559, loss_mask_dice_1: 1.79099/1.16963, loss_spatial_bce_1: 0.03415/0.08807, loss_spatial_dice_1: 0.38124/0.21281, loss_spatial_ce_1: 0.15623/0.06874, loss_grounding_bce_1: 0.06248/0.08647, loss_grounding_dice_1: 0.21725/0.17932, loss_grounding_ce_1: 0.57935/0.27313, loss_mask_ce_2: 0.59232/0.90781, loss_mask_bce_2: 0.12467/0.33614, loss_mask_dice_2: 2.08912/1.16993, loss_spatial_bce_2: 0.03090/0.08906, loss_spatial_dice_2: 0.40707/0.21436, loss_spatial_ce_2: 0.07168/0.07212, loss_grounding_bce_2: 0.06599/0.08661, loss_grounding_dice_2: 0.31630/0.17914, loss_grounding_ce_2: 0.45143/0.27649, loss_mask_ce_3: 0.75504/0.91817, loss_mask_bce_3: 0.12552/0.33722, loss_mask_dice_3: 2.00872/1.16755, loss_spatial_bce_3: 0.03253/0.09020, loss_spatial_dice_3: 0.38971/0.21520, loss_spatial_ce_3: 0.07516/0.07648, loss_grounding_bce_3: 0.06734/0.08683, loss_grounding_dice_3: 0.32343/0.17889, loss_grounding_ce_3: 0.46275/0.27867, loss_mask_ce_4: 0.90572/0.91909, loss_mask_bce_4: 0.12604/0.33934, loss_mask_dice_4: 1.99973/1.19160, loss_spatial_bce_4: 0.03521/0.09418, loss_spatial_dice_4: 0.41987/0.22730, loss_spatial_ce_4: 0.29229/0.09237, loss_grounding_bce_4: 0.06814/0.08733, loss_grounding_dice_4: 0.29423/0.18177, loss_grounding_ce_4: 0.44373/0.28148, loss_mask_ce_5: 0.82490/0.93538, loss_mask_bce_5: 0.12744/0.34162, loss_mask_dice_5: 2.04294/1.19902, loss_spatial_bce_5: 0.03440/0.09632, loss_spatial_dice_5: 0.41002/0.23139, loss_spatial_ce_5: 0.20165/0.10709, loss_grounding_bce_5: 0.06300/0.08775, loss_grounding_dice_5: 0.30423/0.18301, loss_grounding_ce_5: 0.72948/0.29415, loss_mask_ce_6: 0.91140/0.97532, loss_mask_bce_6: 0.12719/0.34429, loss_mask_dice_6: 2.15368/1.20200, loss_spatial_bce_6: 0.03326/0.10207, loss_spatial_dice_6: 0.46260/0.23425, loss_spatial_ce_6: 0.20579/0.13319, loss_grounding_bce_6: 0.06830/0.08850, loss_grounding_dice_6: 0.24625/0.18339, loss_grounding_ce_6: 0.86336/0.30975, loss_mask_ce_7: 0.93416/1.02041, loss_mask_bce_7: 0.13788/0.35215, loss_mask_dice_7: 2.00308/1.25646, loss_spatial_bce_7: 0.03170/0.11008, loss_spatial_dice_7: 0.45565/0.26192, loss_spatial_ce_7: 0.69298/0.16856, loss_grounding_bce_7: 0.07199/0.09040, loss_grounding_dice_7: 0.25604/0.19066, loss_grounding_ce_7: 0.54806/0.34038, loss_mask_ce_8: 1.07203/1.12860, loss_mask_bce_8: 0.12964/0.36577, loss_mask_dice_8: 1.90532/1.32962, loss_spatial_bce_8: 0.04404/0.13076, loss_spatial_dice_8: 0.56736/0.30001, loss_spatial_ce_8: 0.42316/0.22510, loss_grounding_bce_8: 0.06921/0.09414, loss_grounding_dice_8: 0.33104/0.20155, loss_grounding_ce_8: 0.83488/0.40762, loss_mask_ce_9: 3.37681/3.67720, loss_mask_bce_9: 0.12699/0.39270, loss_mask_dice_9: 2.61259/1.90263, loss_spatial_bce_9: 0.14885/0.33329, loss_spatial_dice_9: 0.83676/0.82209, loss_spatial_ce_9: 2.05542/1.49707, loss_grounding_bce_9: 0.07834/0.10562, loss_grounding_dice_9: 0.38234/0.28075, loss_grounding_ce_9: 1.17827/0.67235] items per batch[64] items per second[0.23] total items[3897600] mini batches[ 60900] memory[7345] epoch remaining[0:57:09]
INFO:trainer.default_trainer:epochs[    33] optim steps[61000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.22151/0.90018, loss_mask_bce_0: 0.39778/0.33463, loss_mask_dice_0: 1.26661/1.16286, loss_spatial_bce_0: 0.09973/0.08755, loss_spatial_dice_0: 0.28935/0.20882, loss_spatial_ce_0: 0.00511/0.06292, loss_grounding_bce_0: 0.11225/0.08631, loss_grounding_dice_0: 0.35921/0.17850, loss_grounding_ce_0: 0.40511/0.27239, loss_mask_ce_1: 0.97212/0.90074, loss_mask_bce_1: 0.42486/0.33558, loss_mask_dice_1: 1.29374/1.16935, loss_spatial_bce_1: 0.09662/0.08806, loss_spatial_dice_1: 0.26427/0.21279, loss_spatial_ce_1: 0.00781/0.06872, loss_grounding_bce_1: 0.11616/0.08648, loss_grounding_dice_1: 0.33482/0.17929, loss_grounding_ce_1: 0.35329/0.27310, loss_mask_ce_2: 1.20647/0.90778, loss_mask_bce_2: 0.39992/0.33613, loss_mask_dice_2: 1.21078/1.16966, loss_spatial_bce_2: 0.09884/0.08906, loss_spatial_dice_2: 0.28225/0.21433, loss_spatial_ce_2: 0.31152/0.07211, loss_grounding_bce_2: 0.11075/0.08662, loss_grounding_dice_2: 0.33233/0.17911, loss_grounding_ce_2: 0.34812/0.27648, loss_mask_ce_3: 0.98821/0.91816, loss_mask_bce_3: 0.38221/0.33721, loss_mask_dice_3: 1.15178/1.16728, loss_spatial_bce_3: 0.09704/0.09020, loss_spatial_dice_3: 0.25092/0.21518, loss_spatial_ce_3: 0.02022/0.07646, loss_grounding_bce_3: 0.10669/0.08685, loss_grounding_dice_3: 0.31469/0.17885, loss_grounding_ce_3: 0.40565/0.27865, loss_mask_ce_4: 1.15693/0.91906, loss_mask_bce_4: 0.40087/0.33933, loss_mask_dice_4: 1.21591/1.19135, loss_spatial_bce_4: 0.10189/0.09418, loss_spatial_dice_4: 0.30987/0.22728, loss_spatial_ce_4: 0.03663/0.09236, loss_grounding_bce_4: 0.11118/0.08735, loss_grounding_dice_4: 0.32275/0.18174, loss_grounding_ce_4: 0.34346/0.28146, loss_mask_ce_5: 1.31978/0.93538, loss_mask_bce_5: 0.41215/0.34161, loss_mask_dice_5: 1.39641/1.19874, loss_spatial_bce_5: 0.10564/0.09632, loss_spatial_dice_5: 0.31409/0.23137, loss_spatial_ce_5: 0.03510/0.10709, loss_grounding_bce_5: 0.11299/0.08776, loss_grounding_dice_5: 0.33905/0.18298, loss_grounding_ce_5: 0.42390/0.29413, loss_mask_ce_6: 1.39976/0.97535, loss_mask_bce_6: 0.39239/0.34428, loss_mask_dice_6: 1.13440/1.20170, loss_spatial_bce_6: 0.10684/0.10207, loss_spatial_dice_6: 0.31304/0.23422, loss_spatial_ce_6: 0.04928/0.13316, loss_grounding_bce_6: 0.10148/0.08851, loss_grounding_dice_6: 0.32571/0.18336, loss_grounding_ce_6: 0.38638/0.30972, loss_mask_ce_7: 1.27419/1.02038, loss_mask_bce_7: 0.40176/0.35214, loss_mask_dice_7: 1.13104/1.25617, loss_spatial_bce_7: 0.11850/0.11007, loss_spatial_dice_7: 0.30559/0.26189, loss_spatial_ce_7: 0.10379/0.16853, loss_grounding_bce_7: 0.10810/0.09041, loss_grounding_dice_7: 0.34513/0.19062, loss_grounding_ce_7: 0.38748/0.34034, loss_mask_ce_8: 1.25468/1.12856, loss_mask_bce_8: 0.44949/0.36576, loss_mask_dice_8: 1.26741/1.32931, loss_spatial_bce_8: 0.14217/0.13076, loss_spatial_dice_8: 0.32518/0.29998, loss_spatial_ce_8: 0.10689/0.22503, loss_grounding_bce_8: 0.11952/0.09415, loss_grounding_dice_8: 0.34069/0.20152, loss_grounding_ce_8: 0.40221/0.40758, loss_mask_ce_9: 2.79869/3.67710, loss_mask_bce_9: 0.45963/0.39270, loss_mask_dice_9: 1.63632/1.90224, loss_spatial_bce_9: 0.30470/0.33333, loss_spatial_dice_9: 0.85498/0.82210, loss_spatial_ce_9: 1.55508/1.49702, loss_grounding_bce_9: 0.13165/0.10564, loss_grounding_dice_9: 0.42090/0.28072, loss_grounding_ce_9: 0.43337/0.67241] items per batch[64] items per second[0.23] total items[3904000] mini batches[ 61000] memory[7345] epoch remaining[0:52:19]
INFO:trainer.default_trainer:epochs[    33] optim steps[61100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84429/0.90021, loss_mask_bce_0: 0.29401/0.33462, loss_mask_dice_0: 1.36147/1.16314, loss_spatial_bce_0: 0.10266/0.08754, loss_spatial_dice_0: 0.24591/0.20880, loss_spatial_ce_0: 0.02224/0.06290, loss_grounding_bce_0: 0.23790/0.08629, loss_grounding_dice_0: 0.16306/0.17848, loss_grounding_ce_0: 0.03852/0.27234, loss_mask_ce_1: 0.82213/0.90079, loss_mask_bce_1: 0.27763/0.33556, loss_mask_dice_1: 1.20227/1.16965, loss_spatial_bce_1: 0.10570/0.08806, loss_spatial_dice_1: 0.22059/0.21277, loss_spatial_ce_1: 0.42463/0.06871, loss_grounding_bce_1: 0.22309/0.08646, loss_grounding_dice_1: 0.15788/0.17927, loss_grounding_ce_1: 0.04840/0.27307, loss_mask_ce_2: 0.75032/0.90783, loss_mask_bce_2: 0.27499/0.33612, loss_mask_dice_2: 1.41712/1.16994, loss_spatial_bce_2: 0.13628/0.08906, loss_spatial_dice_2: 0.25160/0.21432, loss_spatial_ce_2: 0.03972/0.07207, loss_grounding_bce_2: 0.22124/0.08660, loss_grounding_dice_2: 0.15578/0.17910, loss_grounding_ce_2: 0.05532/0.27646, loss_mask_ce_3: 0.77676/0.91817, loss_mask_bce_3: 0.28315/0.33721, loss_mask_dice_3: 1.38862/1.16757, loss_spatial_bce_3: 0.10647/0.09020, loss_spatial_dice_3: 0.24284/0.21516, loss_spatial_ce_3: 0.31993/0.07644, loss_grounding_bce_3: 0.22049/0.08683, loss_grounding_dice_3: 0.15628/0.17883, loss_grounding_ce_3: 0.05255/0.27861, loss_mask_ce_4: 0.73418/0.91908, loss_mask_bce_4: 0.26852/0.33932, loss_mask_dice_4: 1.58984/1.19162, loss_spatial_bce_4: 0.11768/0.09417, loss_spatial_dice_4: 0.27988/0.22728, loss_spatial_ce_4: 0.18968/0.09235, loss_grounding_bce_4: 0.21478/0.08733, loss_grounding_dice_4: 0.16146/0.18173, loss_grounding_ce_4: 0.03548/0.28143, loss_mask_ce_5: 0.77553/0.93541, loss_mask_bce_5: 0.30465/0.34161, loss_mask_dice_5: 1.44530/1.19905, loss_spatial_bce_5: 0.10238/0.09631, loss_spatial_dice_5: 0.27438/0.23136, loss_spatial_ce_5: 0.11480/0.10708, loss_grounding_bce_5: 0.24504/0.08775, loss_grounding_dice_5: 0.15643/0.18296, loss_grounding_ce_5: 0.04123/0.29411, loss_mask_ce_6: 0.68703/0.97538, loss_mask_bce_6: 0.31032/0.34428, loss_mask_dice_6: 1.53133/1.20203, loss_spatial_bce_6: 0.14788/0.10206, loss_spatial_dice_6: 0.27638/0.23422, loss_spatial_ce_6: 0.19280/0.13313, loss_grounding_bce_6: 0.22628/0.08849, loss_grounding_dice_6: 0.15194/0.18335, loss_grounding_ce_6: 0.02151/0.30970, loss_mask_ce_7: 0.75746/1.02040, loss_mask_bce_7: 0.30075/0.35213, loss_mask_dice_7: 1.57651/1.25645, loss_spatial_bce_7: 0.16646/0.11006, loss_spatial_dice_7: 0.28121/0.26187, loss_spatial_ce_7: 0.16650/0.16851, loss_grounding_bce_7: 0.22760/0.09040, loss_grounding_dice_7: 0.15222/0.19061, loss_grounding_ce_7: 0.04236/0.34029, loss_mask_ce_8: 0.95607/1.12859, loss_mask_bce_8: 0.27037/0.36575, loss_mask_dice_8: 1.49536/1.32963, loss_spatial_bce_8: 0.16819/0.13075, loss_spatial_dice_8: 0.32683/0.29998, loss_spatial_ce_8: 0.20593/0.22498, loss_grounding_bce_8: 0.19671/0.09413, loss_grounding_dice_8: 0.15425/0.20151, loss_grounding_ce_8: 0.11828/0.40764, loss_mask_ce_9: 4.07671/3.67737, loss_mask_bce_9: 0.32666/0.39270, loss_mask_dice_9: 1.97024/1.90265, loss_spatial_bce_9: 0.27063/0.33329, loss_spatial_dice_9: 0.81930/0.82208, loss_spatial_ce_9: 1.50321/1.49702, loss_grounding_bce_9: 0.26697/0.10561, loss_grounding_dice_9: 0.32721/0.28069, loss_grounding_ce_9: 1.39851/0.67251] items per batch[64] items per second[0.23] total items[3910400] mini batches[ 61100] memory[7345] epoch remaining[0:47:29]
INFO:trainer.default_trainer:epochs[    33] optim steps[61200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45169/0.90015, loss_mask_bce_0: 0.28870/0.33470, loss_mask_dice_0: 1.06344/1.16305, loss_spatial_bce_0: 0.10714/0.08756, loss_spatial_dice_0: 0.20143/0.20878, loss_spatial_ce_0: 0.01918/0.06286, loss_grounding_bce_0: 0.03594/0.08631, loss_grounding_dice_0: 0.09332/0.17847, loss_grounding_ce_0: 0.01360/0.27232, loss_mask_ce_1: 0.48394/0.90073, loss_mask_bce_1: 0.29593/0.33565, loss_mask_dice_1: 1.32958/1.16954, loss_spatial_bce_1: 0.11469/0.08807, loss_spatial_dice_1: 0.22066/0.21274, loss_spatial_ce_1: 0.10506/0.06866, loss_grounding_bce_1: 0.03766/0.08648, loss_grounding_dice_1: 0.09541/0.17926, loss_grounding_ce_1: 0.01575/0.27305, loss_mask_ce_2: 0.47141/0.90778, loss_mask_bce_2: 0.30500/0.33621, loss_mask_dice_2: 1.06080/1.16985, loss_spatial_bce_2: 0.11471/0.08908, loss_spatial_dice_2: 0.23941/0.21429, loss_spatial_ce_2: 0.16236/0.07203, loss_grounding_bce_2: 0.03666/0.08661, loss_grounding_dice_2: 0.09223/0.17908, loss_grounding_ce_2: 0.01960/0.27640, loss_mask_ce_3: 0.50015/0.91815, loss_mask_bce_3: 0.28625/0.33729, loss_mask_dice_3: 1.02421/1.16747, loss_spatial_bce_3: 0.12596/0.09022, loss_spatial_dice_3: 0.29963/0.21514, loss_spatial_ce_3: 0.02638/0.07639, loss_grounding_bce_3: 0.03906/0.08684, loss_grounding_dice_3: 0.09414/0.17882, loss_grounding_ce_3: 0.02241/0.27861, loss_mask_ce_4: 0.51276/0.91907, loss_mask_bce_4: 0.28101/0.33940, loss_mask_dice_4: 1.24307/1.19150, loss_spatial_bce_4: 0.10125/0.09418, loss_spatial_dice_4: 0.21534/0.22726, loss_spatial_ce_4: 0.07371/0.09231, loss_grounding_bce_4: 0.03526/0.08734, loss_grounding_dice_4: 0.09015/0.18171, loss_grounding_ce_4: 0.02203/0.28145, loss_mask_ce_5: 0.56559/0.93540, loss_mask_bce_5: 0.25595/0.34169, loss_mask_dice_5: 0.93879/1.19895, loss_spatial_bce_5: 0.09222/0.09632, loss_spatial_dice_5: 0.24693/0.23135, loss_spatial_ce_5: 0.28169/0.10703, loss_grounding_bce_5: 0.03795/0.08776, loss_grounding_dice_5: 0.10277/0.18295, loss_grounding_ce_5: 0.02520/0.29409, loss_mask_ce_6: 0.74453/0.97540, loss_mask_bce_6: 0.28763/0.34436, loss_mask_dice_6: 1.00625/1.20193, loss_spatial_bce_6: 0.14370/0.10207, loss_spatial_dice_6: 0.31291/0.23420, loss_spatial_ce_6: 0.36660/0.13308, loss_grounding_bce_6: 0.04072/0.08850, loss_grounding_dice_6: 0.09888/0.18333, loss_grounding_ce_6: 0.03029/0.30969, loss_mask_ce_7: 0.73731/1.02037, loss_mask_bce_7: 0.27949/0.35222, loss_mask_dice_7: 1.27433/1.25632, loss_spatial_bce_7: 0.10848/0.11008, loss_spatial_dice_7: 0.24929/0.26185, loss_spatial_ce_7: 0.32683/0.16847, loss_grounding_bce_7: 0.03800/0.09041, loss_grounding_dice_7: 0.10721/0.19059, loss_grounding_ce_7: 0.04088/0.34031, loss_mask_ce_8: 0.78741/1.12856, loss_mask_bce_8: 0.30074/0.36584, loss_mask_dice_8: 1.24543/1.32951, loss_spatial_bce_8: 0.15090/0.13077, loss_spatial_dice_8: 0.35769/0.29995, loss_spatial_ce_8: 0.33888/0.22491, loss_grounding_bce_8: 0.03829/0.09415, loss_grounding_dice_8: 0.14038/0.20149, loss_grounding_ce_8: 0.04424/0.40771, loss_mask_ce_9: 5.04177/3.67742, loss_mask_bce_9: 0.29691/0.39279, loss_mask_dice_9: 1.75727/1.90253, loss_spatial_bce_9: 0.44510/0.33334, loss_spatial_dice_9: 0.79599/0.82208, loss_spatial_ce_9: 1.64118/1.49698, loss_grounding_bce_9: 0.04917/0.10563, loss_grounding_dice_9: 0.22191/0.28068, loss_grounding_ce_9: 0.19737/0.67271] items per batch[64] items per second[0.23] total items[3916800] mini batches[ 61200] memory[7345] epoch remaining[0:42:45]
INFO:trainer.default_trainer:epochs[    33] optim steps[61300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.77280/0.90017, loss_mask_bce_0: 0.27707/0.33469, loss_mask_dice_0: 1.34341/1.16288, loss_spatial_bce_0: 0.06750/0.08754, loss_spatial_dice_0: 0.26079/0.20875, loss_spatial_ce_0: 0.01931/0.06284, loss_grounding_bce_0: 0.04822/0.08631, loss_grounding_dice_0: 0.34515/0.17846, loss_grounding_ce_0: 0.26427/0.27230, loss_mask_ce_1: 1.61919/0.90074, loss_mask_bce_1: 0.29754/0.33564, loss_mask_dice_1: 1.28915/1.16935, loss_spatial_bce_1: 0.06878/0.08806, loss_spatial_dice_1: 0.27083/0.21272, loss_spatial_ce_1: 0.02293/0.06864, loss_grounding_bce_1: 0.05094/0.08648, loss_grounding_dice_1: 0.31074/0.17925, loss_grounding_ce_1: 0.99443/0.27303, loss_mask_ce_2: 1.67181/0.90779, loss_mask_bce_2: 0.30044/0.33619, loss_mask_dice_2: 1.40937/1.16969, loss_spatial_bce_2: 0.07574/0.08906, loss_spatial_dice_2: 0.28971/0.21426, loss_spatial_ce_2: 0.03446/0.07200, loss_grounding_bce_2: 0.04474/0.08661, loss_grounding_dice_2: 0.32580/0.17907, loss_grounding_ce_2: 0.33995/0.27641, loss_mask_ce_3: 1.56397/0.91815, loss_mask_bce_3: 0.30499/0.33728, loss_mask_dice_3: 1.40738/1.16730, loss_spatial_bce_3: 0.06926/0.09020, loss_spatial_dice_3: 0.28991/0.21512, loss_spatial_ce_3: 0.05812/0.07636, loss_grounding_bce_3: 0.05689/0.08684, loss_grounding_dice_3: 0.33011/0.17882, loss_grounding_ce_3: 0.69613/0.27860, loss_mask_ce_4: 1.53150/0.91907, loss_mask_bce_4: 0.30146/0.33939, loss_mask_dice_4: 1.39352/1.19136, loss_spatial_bce_4: 0.07833/0.09417, loss_spatial_dice_4: 0.30754/0.22723, loss_spatial_ce_4: 0.07132/0.09230, loss_grounding_bce_4: 0.05725/0.08734, loss_grounding_dice_4: 0.30067/0.18170, loss_grounding_ce_4: 0.81558/0.28146, loss_mask_ce_5: 1.38747/0.93544, loss_mask_bce_5: 0.32312/0.34167, loss_mask_dice_5: 1.31574/1.19878, loss_spatial_bce_5: 0.06763/0.09631, loss_spatial_dice_5: 0.31003/0.23133, loss_spatial_ce_5: 0.15370/0.10701, loss_grounding_bce_5: 0.05422/0.08776, loss_grounding_dice_5: 0.28843/0.18294, loss_grounding_ce_5: 0.49640/0.29419, loss_mask_ce_6: 1.55014/0.97542, loss_mask_bce_6: 0.28222/0.34435, loss_mask_dice_6: 1.32741/1.20176, loss_spatial_bce_6: 0.11872/0.10205, loss_spatial_dice_6: 0.34183/0.23418, loss_spatial_ce_6: 0.07249/0.13307, loss_grounding_bce_6: 0.03555/0.08850, loss_grounding_dice_6: 0.29097/0.18332, loss_grounding_ce_6: 0.67779/0.30970, loss_mask_ce_7: 1.46878/1.02035, loss_mask_bce_7: 0.28751/0.35221, loss_mask_dice_7: 1.34123/1.25615, loss_spatial_bce_7: 0.13078/0.11006, loss_spatial_dice_7: 0.32837/0.26182, loss_spatial_ce_7: 0.09991/0.16843, loss_grounding_bce_7: 0.03961/0.09041, loss_grounding_dice_7: 0.31672/0.19057, loss_grounding_ce_7: 0.39680/0.34034, loss_mask_ce_8: 1.46794/1.12852, loss_mask_bce_8: 0.56511/0.36583, loss_mask_dice_8: 1.68300/1.32932, loss_spatial_bce_8: 0.16743/0.13075, loss_spatial_dice_8: 0.34253/0.29994, loss_spatial_ce_8: 0.20593/0.22484, loss_grounding_bce_8: 0.12438/0.09415, loss_grounding_dice_8: 0.35913/0.20148, loss_grounding_ce_8: 1.77433/0.40778, loss_mask_ce_9: 4.04503/3.67729, loss_mask_bce_9: 0.46676/0.39277, loss_mask_dice_9: 2.24019/1.90227, loss_spatial_bce_9: 0.23723/0.33335, loss_spatial_dice_9: 0.88597/0.82209, loss_spatial_ce_9: 1.16037/1.49692, loss_grounding_bce_9: 0.10312/0.10563, loss_grounding_dice_9: 0.52480/0.28067, loss_grounding_ce_9: 1.58196/0.67262] items per batch[64] items per second[0.23] total items[3923200] mini batches[ 61300] memory[7345] epoch remaining[0:38:03]
INFO:trainer.default_trainer:epochs[    33] optim steps[61400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89694/0.90021, loss_mask_bce_0: 0.66597/0.33472, loss_mask_dice_0: 0.72615/1.16285, loss_spatial_bce_0: 0.12024/0.08754, loss_spatial_dice_0: 0.13783/0.20873, loss_spatial_ce_0: 0.00594/0.06283, loss_grounding_bce_0: 0.06264/0.08630, loss_grounding_dice_0: 0.10157/0.17845, loss_grounding_ce_0: 0.56103/0.27237, loss_mask_ce_1: 0.91213/0.90079, loss_mask_bce_1: 0.66967/0.33567, loss_mask_dice_1: 0.70588/1.16930, loss_spatial_bce_1: 0.11547/0.08806, loss_spatial_dice_1: 0.13195/0.21270, loss_spatial_ce_1: 0.00989/0.06863, loss_grounding_bce_1: 0.06028/0.08647, loss_grounding_dice_1: 0.10787/0.17924, loss_grounding_ce_1: 0.60496/0.27309, loss_mask_ce_2: 0.89711/0.90779, loss_mask_bce_2: 0.65160/0.33623, loss_mask_dice_2: 0.70768/1.16965, loss_spatial_bce_2: 0.11734/0.08906, loss_spatial_dice_2: 0.14312/0.21425, loss_spatial_ce_2: 0.00855/0.07199, loss_grounding_bce_2: 0.06468/0.08660, loss_grounding_dice_2: 0.11037/0.17906, loss_grounding_ce_2: 0.59960/0.27647, loss_mask_ce_3: 0.96024/0.91818, loss_mask_bce_3: 0.69225/0.33731, loss_mask_dice_3: 0.71455/1.16726, loss_spatial_bce_3: 0.12288/0.09021, loss_spatial_dice_3: 0.14453/0.21511, loss_spatial_ce_3: 0.01010/0.07635, loss_grounding_bce_3: 0.06666/0.08683, loss_grounding_dice_3: 0.11126/0.17881, loss_grounding_ce_3: 0.71546/0.27866, loss_mask_ce_4: 1.02088/0.91915, loss_mask_bce_4: 0.61537/0.33943, loss_mask_dice_4: 0.70616/1.19134, loss_spatial_bce_4: 0.11925/0.09417, loss_spatial_dice_4: 0.13365/0.22722, loss_spatial_ce_4: 0.00707/0.09232, loss_grounding_bce_4: 0.07121/0.08733, loss_grounding_dice_4: 0.11521/0.18169, loss_grounding_ce_4: 0.65405/0.28152, loss_mask_ce_5: 0.93561/0.93547, loss_mask_bce_5: 0.62885/0.34172, loss_mask_dice_5: 0.70612/1.19879, loss_spatial_bce_5: 0.12785/0.09631, loss_spatial_dice_5: 0.14523/0.23133, loss_spatial_ce_5: 0.00721/0.10700, loss_grounding_bce_5: 0.07700/0.08775, loss_grounding_dice_5: 0.11509/0.18293, loss_grounding_ce_5: 0.70593/0.29424, loss_mask_ce_6: 1.23692/0.97541, loss_mask_bce_6: 0.63829/0.34440, loss_mask_dice_6: 0.71031/1.20177, loss_spatial_bce_6: 0.13986/0.10205, loss_spatial_dice_6: 0.13618/0.23417, loss_spatial_ce_6: 0.02499/0.13307, loss_grounding_bce_6: 0.06149/0.08849, loss_grounding_dice_6: 0.11210/0.18332, loss_grounding_ce_6: 0.90613/0.30976, loss_mask_ce_7: 1.25922/1.02036, loss_mask_bce_7: 0.63289/0.35225, loss_mask_dice_7: 0.68262/1.25615, loss_spatial_bce_7: 0.16248/0.11006, loss_spatial_dice_7: 0.17293/0.26182, loss_spatial_ce_7: 0.03493/0.16840, loss_grounding_bce_7: 0.07751/0.09040, loss_grounding_dice_7: 0.11494/0.19058, loss_grounding_ce_7: 1.71013/0.34042, loss_mask_ce_8: 1.69771/1.12849, loss_mask_bce_8: 0.63505/0.36588, loss_mask_dice_8: 0.65068/1.32928, loss_spatial_bce_8: 0.15551/0.13075, loss_spatial_dice_8: 0.17654/0.29993, loss_spatial_ce_8: 0.09110/0.22479, loss_grounding_bce_8: 0.04695/0.09415, loss_grounding_dice_8: 0.09235/0.20148, loss_grounding_ce_8: 1.52247/0.40783, loss_mask_ce_9: 3.41108/3.67731, loss_mask_bce_9: 0.70423/0.39281, loss_mask_dice_9: 1.57872/1.90223, loss_spatial_bce_9: 0.38576/0.33336, loss_spatial_dice_9: 0.71385/0.82209, loss_spatial_ce_9: 1.00133/1.49689, loss_grounding_bce_9: 0.09492/0.10562, loss_grounding_dice_9: 0.18833/0.28070, loss_grounding_ce_9: 1.51583/0.67260] items per batch[64] items per second[0.23] total items[3929600] mini batches[ 61400] memory[7345] epoch remaining[0:33:21]
INFO:trainer.default_trainer:epochs[    33] optim steps[61500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73662/0.90014, loss_mask_bce_0: 0.18530/0.33468, loss_mask_dice_0: 1.00157/1.16276, loss_spatial_bce_0: 0.04042/0.08753, loss_spatial_dice_0: 0.17129/0.20872, loss_spatial_ce_0: 0.00047/0.06280, loss_grounding_bce_0: 0.02347/0.08629, loss_grounding_dice_0: 0.16519/0.17844, loss_grounding_ce_0: 0.28751/0.27234, loss_mask_ce_1: 0.82689/0.90073, loss_mask_bce_1: 0.15315/0.33563, loss_mask_dice_1: 0.90091/1.16927, loss_spatial_bce_1: 0.04026/0.08805, loss_spatial_dice_1: 0.15509/0.21269, loss_spatial_ce_1: 0.00127/0.06861, loss_grounding_bce_1: 0.02399/0.08645, loss_grounding_dice_1: 0.17715/0.17922, loss_grounding_ce_1: 0.29782/0.27308, loss_mask_ce_2: 0.82637/0.90772, loss_mask_bce_2: 0.15267/0.33619, loss_mask_dice_2: 0.96730/1.16960, loss_spatial_bce_2: 0.04364/0.08905, loss_spatial_dice_2: 0.16280/0.21424, loss_spatial_ce_2: 0.00072/0.07198, loss_grounding_bce_2: 0.02354/0.08658, loss_grounding_dice_2: 0.17923/0.17905, loss_grounding_ce_2: 0.30514/0.27649, loss_mask_ce_3: 0.75057/0.91814, loss_mask_bce_3: 0.15182/0.33728, loss_mask_dice_3: 0.86224/1.16720, loss_spatial_bce_3: 0.04167/0.09020, loss_spatial_dice_3: 0.16040/0.21510, loss_spatial_ce_3: 0.00956/0.07634, loss_grounding_bce_3: 0.02263/0.08682, loss_grounding_dice_3: 0.17277/0.17880, loss_grounding_ce_3: 0.26423/0.27863, loss_mask_ce_4: 0.68161/0.91911, loss_mask_bce_4: 0.19744/0.33938, loss_mask_dice_4: 0.96634/1.19131, loss_spatial_bce_4: 0.04022/0.09416, loss_spatial_dice_4: 0.16596/0.22721, loss_spatial_ce_4: 0.02942/0.09232, loss_grounding_bce_4: 0.02609/0.08731, loss_grounding_dice_4: 0.18112/0.18168, loss_grounding_ce_4: 0.25215/0.28156, loss_mask_ce_5: 0.65810/0.93542, loss_mask_bce_5: 0.18583/0.34168, loss_mask_dice_5: 0.98035/1.19875, loss_spatial_bce_5: 0.04171/0.09630, loss_spatial_dice_5: 0.18861/0.23132, loss_spatial_ce_5: 0.04548/0.10699, loss_grounding_bce_5: 0.02527/0.08773, loss_grounding_dice_5: 0.15617/0.18293, loss_grounding_ce_5: 0.27895/0.29432, loss_mask_ce_6: 0.73476/0.97538, loss_mask_bce_6: 0.19401/0.34436, loss_mask_dice_6: 0.97676/1.20170, loss_spatial_bce_6: 0.04419/0.10204, loss_spatial_dice_6: 0.16840/0.23416, loss_spatial_ce_6: 0.04640/0.13305, loss_grounding_bce_6: 0.02749/0.08848, loss_grounding_dice_6: 0.17413/0.18330, loss_grounding_ce_6: 0.28013/0.30983, loss_mask_ce_7: 0.70693/1.02034, loss_mask_bce_7: 0.16644/0.35221, loss_mask_dice_7: 0.91960/1.25611, loss_spatial_bce_7: 0.04179/0.11004, loss_spatial_dice_7: 0.16533/0.26180, loss_spatial_ce_7: 0.09140/0.16836, loss_grounding_bce_7: 0.02549/0.09039, loss_grounding_dice_7: 0.17452/0.19057, loss_grounding_ce_7: 0.29294/0.34052, loss_mask_ce_8: 0.93148/1.12846, loss_mask_bce_8: 0.15506/0.36584, loss_mask_dice_8: 0.93266/1.32921, loss_spatial_bce_8: 0.05922/0.13074, loss_spatial_dice_8: 0.17466/0.29991, loss_spatial_ce_8: 0.06705/0.22474, loss_grounding_bce_8: 0.02849/0.09414, loss_grounding_dice_8: 0.19851/0.20147, loss_grounding_ce_8: 0.28815/0.40781, loss_mask_ce_9: 3.03710/3.67724, loss_mask_bce_9: 0.18293/0.39278, loss_mask_dice_9: 1.28528/1.90209, loss_spatial_bce_9: 0.32935/0.33334, loss_spatial_dice_9: 0.79749/0.82207, loss_spatial_ce_9: 1.24979/1.49681, loss_grounding_bce_9: 0.03643/0.10560, loss_grounding_dice_9: 0.30334/0.28070, loss_grounding_ce_9: 0.29868/0.67258] items per batch[64] items per second[0.23] total items[3936000] mini batches[ 61500] memory[7345] epoch remaining[0:28:42]
INFO:trainer.default_trainer:epochs[    33] optim steps[61600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12703/0.90011, loss_mask_bce_0: 0.42287/0.33467, loss_mask_dice_0: 1.12667/1.16278, loss_spatial_bce_0: 0.10237/0.08752, loss_spatial_dice_0: 0.28190/0.20873, loss_spatial_ce_0: 0.03982/0.06279, loss_grounding_bce_0: 0.16108/0.08628, loss_grounding_dice_0: 0.28672/0.17846, loss_grounding_ce_0: 0.45607/0.27237, loss_mask_ce_1: 1.03944/0.90069, loss_mask_bce_1: 0.41397/0.33561, loss_mask_dice_1: 1.08440/1.16930, loss_spatial_bce_1: 0.11058/0.08804, loss_spatial_dice_1: 0.27391/0.21269, loss_spatial_ce_1: 0.02633/0.06860, loss_grounding_bce_1: 0.16833/0.08644, loss_grounding_dice_1: 0.30482/0.17924, loss_grounding_ce_1: 0.33548/0.27311, loss_mask_ce_2: 1.09431/0.90769, loss_mask_bce_2: 0.41688/0.33617, loss_mask_dice_2: 1.09320/1.16964, loss_spatial_bce_2: 0.10652/0.08905, loss_spatial_dice_2: 0.27796/0.21425, loss_spatial_ce_2: 0.04022/0.07198, loss_grounding_bce_2: 0.16309/0.08657, loss_grounding_dice_2: 0.29371/0.17908, loss_grounding_ce_2: 0.32845/0.27652, loss_mask_ce_3: 1.06471/0.91810, loss_mask_bce_3: 0.43769/0.33727, loss_mask_dice_3: 1.08460/1.16722, loss_spatial_bce_3: 0.10899/0.09020, loss_spatial_dice_3: 0.28779/0.21511, loss_spatial_ce_3: 0.06038/0.07633, loss_grounding_bce_3: 0.13258/0.08681, loss_grounding_dice_3: 0.30591/0.17882, loss_grounding_ce_3: 0.31539/0.27865, loss_mask_ce_4: 1.07768/0.91910, loss_mask_bce_4: 0.45370/0.33937, loss_mask_dice_4: 1.01399/1.19131, loss_spatial_bce_4: 0.10568/0.09415, loss_spatial_dice_4: 0.30939/0.22722, loss_spatial_ce_4: 0.06265/0.09234, loss_grounding_bce_4: 0.13372/0.08730, loss_grounding_dice_4: 0.31370/0.18170, loss_grounding_ce_4: 0.31227/0.28158, loss_mask_ce_5: 1.08823/0.93541, loss_mask_bce_5: 0.45027/0.34167, loss_mask_dice_5: 1.08245/1.19877, loss_spatial_bce_5: 0.11356/0.09630, loss_spatial_dice_5: 0.31893/0.23134, loss_spatial_ce_5: 0.10331/0.10701, loss_grounding_bce_5: 0.12633/0.08772, loss_grounding_dice_5: 0.31250/0.18295, loss_grounding_ce_5: 0.31323/0.29433, loss_mask_ce_6: 0.93274/0.97537, loss_mask_bce_6: 0.42926/0.34435, loss_mask_dice_6: 1.07276/1.20171, loss_spatial_bce_6: 0.11609/0.10203, loss_spatial_dice_6: 0.31054/0.23418, loss_spatial_ce_6: 0.15245/0.13305, loss_grounding_bce_6: 0.16295/0.08847, loss_grounding_dice_6: 0.30174/0.18333, loss_grounding_ce_6: 0.35153/0.30984, loss_mask_ce_7: 1.01214/1.02033, loss_mask_bce_7: 0.39896/0.35218, loss_mask_dice_7: 1.06697/1.25611, loss_spatial_bce_7: 0.10463/0.11003, loss_spatial_dice_7: 0.29012/0.26181, loss_spatial_ce_7: 0.23124/0.16835, loss_grounding_bce_7: 0.14540/0.09037, loss_grounding_dice_7: 0.30557/0.19059, loss_grounding_ce_7: 0.36842/0.34053, loss_mask_ce_8: 1.16169/1.12845, loss_mask_bce_8: 0.41546/0.36582, loss_mask_dice_8: 1.21547/1.32920, loss_spatial_bce_8: 0.15271/0.13072, loss_spatial_dice_8: 0.31462/0.29991, loss_spatial_ce_8: 0.15990/0.22472, loss_grounding_bce_8: 0.11828/0.09413, loss_grounding_dice_8: 0.32621/0.20148, loss_grounding_ce_8: 0.38260/0.40779, loss_mask_ce_9: 3.73081/3.67718, loss_mask_bce_9: 0.40537/0.39275, loss_mask_dice_9: 1.56648/1.90196, loss_spatial_bce_9: 0.20989/0.33331, loss_spatial_dice_9: 0.80292/0.82208, loss_spatial_ce_9: 1.45138/1.49682, loss_grounding_bce_9: 0.13578/0.10558, loss_grounding_dice_9: 0.44527/0.28071, loss_grounding_ce_9: 0.34287/0.67252] items per batch[64] items per second[0.23] total items[3942400] mini batches[ 61600] memory[7345] epoch remaining[0:24:05]
INFO:trainer.default_trainer:epochs[    33] optim steps[61700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98305/0.90008, loss_mask_bce_0: 0.62225/0.33460, loss_mask_dice_0: 2.92605/1.16281, loss_spatial_bce_0: 0.04739/0.08751, loss_spatial_dice_0: 0.25856/0.20873, loss_spatial_ce_0: 0.00616/0.06278, loss_grounding_bce_0: 0.09226/0.08625, loss_grounding_dice_0: 0.20998/0.17845, loss_grounding_ce_0: 0.05207/0.27234, loss_mask_ce_1: 1.00448/0.90066, loss_mask_bce_1: 0.59587/0.33555, loss_mask_dice_1: 3.00428/1.16932, loss_spatial_bce_1: 0.04548/0.08803, loss_spatial_dice_1: 0.27494/0.21268, loss_spatial_ce_1: 0.02748/0.06859, loss_grounding_bce_1: 0.09766/0.08642, loss_grounding_dice_1: 0.22110/0.17922, loss_grounding_ce_1: 0.04167/0.27309, loss_mask_ce_2: 1.01538/0.90766, loss_mask_bce_2: 0.57271/0.33611, loss_mask_dice_2: 3.01552/1.16970, loss_spatial_bce_2: 0.04823/0.08904, loss_spatial_dice_2: 0.27706/0.21425, loss_spatial_ce_2: 0.01589/0.07196, loss_grounding_bce_2: 0.10127/0.08655, loss_grounding_dice_2: 0.22785/0.17906, loss_grounding_ce_2: 0.04997/0.27649, loss_mask_ce_3: 1.01470/0.91808, loss_mask_bce_3: 0.59169/0.33721, loss_mask_dice_3: 2.96762/1.16726, loss_spatial_bce_3: 0.04849/0.09019, loss_spatial_dice_3: 0.25526/0.21511, loss_spatial_ce_3: 0.01738/0.07633, loss_grounding_bce_3: 0.10133/0.08679, loss_grounding_dice_3: 0.22554/0.17880, loss_grounding_ce_3: 0.05146/0.27860, loss_mask_ce_4: 1.25187/0.91913, loss_mask_bce_4: 0.59588/0.33932, loss_mask_dice_4: 3.10001/1.19135, loss_spatial_bce_4: 0.04442/0.09414, loss_spatial_dice_4: 0.24084/0.22722, loss_spatial_ce_4: 0.03073/0.09234, loss_grounding_bce_4: 0.09803/0.08728, loss_grounding_dice_4: 0.22174/0.18169, loss_grounding_ce_4: 0.04525/0.28156, loss_mask_ce_5: 1.15054/0.93544, loss_mask_bce_5: 0.62054/0.34161, loss_mask_dice_5: 3.47745/1.19882, loss_spatial_bce_5: 0.05108/0.09629, loss_spatial_dice_5: 0.29260/0.23133, loss_spatial_ce_5: 0.05027/0.10699, loss_grounding_bce_5: 0.09994/0.08771, loss_grounding_dice_5: 0.22488/0.18293, loss_grounding_ce_5: 0.05558/0.29430, loss_mask_ce_6: 1.03801/0.97540, loss_mask_bce_6: 0.64664/0.34429, loss_mask_dice_6: 3.29814/1.20177, loss_spatial_bce_6: 0.05719/0.10202, loss_spatial_dice_6: 0.28479/0.23418, loss_spatial_ce_6: 0.08720/0.13302, loss_grounding_bce_6: 0.10325/0.08845, loss_grounding_dice_6: 0.23187/0.18331, loss_grounding_ce_6: 0.04500/0.30982, loss_mask_ce_7: 1.08334/1.02037, loss_mask_bce_7: 0.59391/0.35212, loss_mask_dice_7: 3.57726/1.25615, loss_spatial_bce_7: 0.05270/0.11001, loss_spatial_dice_7: 0.32396/0.26180, loss_spatial_ce_7: 0.07929/0.16832, loss_grounding_bce_7: 0.10057/0.09036, loss_grounding_dice_7: 0.23002/0.19058, loss_grounding_ce_7: 0.05167/0.34052, loss_mask_ce_8: 1.02759/1.12844, loss_mask_bce_8: 0.66054/0.36576, loss_mask_dice_8: 3.84166/1.32925, loss_spatial_bce_8: 0.05364/0.13069, loss_spatial_dice_8: 0.36390/0.29991, loss_spatial_ce_8: 0.25839/0.22468, loss_grounding_bce_8: 0.10338/0.09411, loss_grounding_dice_8: 0.23530/0.20147, loss_grounding_ce_8: 0.06151/0.40774, loss_mask_ce_9: 4.46794/3.67736, loss_mask_bce_9: 0.63739/0.39268, loss_mask_dice_9: 5.15167/1.90199, loss_spatial_bce_9: 0.22153/0.33327, loss_spatial_dice_9: 0.86867/0.82205, loss_spatial_ce_9: 1.63795/1.49682, loss_grounding_bce_9: 0.10710/0.10556, loss_grounding_dice_9: 0.30212/0.28069, loss_grounding_ce_9: 0.24801/0.67241] items per batch[64] items per second[0.23] total items[3948800] mini batches[ 61700] memory[7345] epoch remaining[0:19:26]
INFO:trainer.default_trainer:epochs[    33] optim steps[61800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.50037/0.90003, loss_mask_bce_0: 0.74599/0.33464, loss_mask_dice_0: 6.51792/1.16303, loss_spatial_bce_0: 0.02895/0.08751, loss_spatial_dice_0: 0.31355/0.20873, loss_spatial_ce_0: 0.02788/0.06280, loss_grounding_bce_0: 0.05625/0.08628, loss_grounding_dice_0: 0.08589/0.17847, loss_grounding_ce_0: 0.58000/0.27231, loss_mask_ce_1: 1.35437/0.90064, loss_mask_bce_1: 0.69648/0.33559, loss_mask_dice_1: 6.44712/1.16956, loss_spatial_bce_1: 0.02793/0.08803, loss_spatial_dice_1: 0.33849/0.21269, loss_spatial_ce_1: 0.03629/0.06858, loss_grounding_bce_1: 0.06253/0.08644, loss_grounding_dice_1: 0.09071/0.17924, loss_grounding_ce_1: 0.58026/0.27307, loss_mask_ce_2: 1.55282/0.90762, loss_mask_bce_2: 0.77692/0.33615, loss_mask_dice_2: 6.66368/1.16993, loss_spatial_bce_2: 0.02867/0.08904, loss_spatial_dice_2: 0.32713/0.21425, loss_spatial_ce_2: 0.02961/0.07197, loss_grounding_bce_2: 0.06224/0.08657, loss_grounding_dice_2: 0.09058/0.17908, loss_grounding_ce_2: 0.54276/0.27649, loss_mask_ce_3: 1.65733/0.91805, loss_mask_bce_3: 0.72856/0.33725, loss_mask_dice_3: 6.50387/1.16749, loss_spatial_bce_3: 0.03330/0.09019, loss_spatial_dice_3: 0.35376/0.21511, loss_spatial_ce_3: 0.22824/0.07634, loss_grounding_bce_3: 0.06482/0.08681, loss_grounding_dice_3: 0.08997/0.17882, loss_grounding_ce_3: 0.47884/0.27856, loss_mask_ce_4: 1.62326/0.91909, loss_mask_bce_4: 0.76232/0.33937, loss_mask_dice_4: 6.59316/1.19159, loss_spatial_bce_4: 0.03045/0.09415, loss_spatial_dice_4: 0.34994/0.22722, loss_spatial_ce_4: 0.09372/0.09234, loss_grounding_bce_4: 0.06651/0.08731, loss_grounding_dice_4: 0.09355/0.18171, loss_grounding_ce_4: 0.49627/0.28154, loss_mask_ce_5: 1.80674/0.93541, loss_mask_bce_5: 0.74887/0.34166, loss_mask_dice_5: 6.81407/1.19906, loss_spatial_bce_5: 0.02522/0.09630, loss_spatial_dice_5: 0.36856/0.23135, loss_spatial_ce_5: 0.08902/0.10700, loss_grounding_bce_5: 0.06457/0.08773, loss_grounding_dice_5: 0.09545/0.18296, loss_grounding_ce_5: 0.34664/0.29425, loss_mask_ce_6: 1.54588/0.97538, loss_mask_bce_6: 0.76644/0.34433, loss_mask_dice_6: 6.71019/1.20203, loss_spatial_bce_6: 0.03650/0.10202, loss_spatial_dice_6: 0.39457/0.23420, loss_spatial_ce_6: 0.12682/0.13301, loss_grounding_bce_6: 0.06492/0.08847, loss_grounding_dice_6: 0.08812/0.18333, loss_grounding_ce_6: 0.48237/0.30982, loss_mask_ce_7: 1.46268/1.02037, loss_mask_bce_7: 0.76342/0.35215, loss_mask_dice_7: 7.05696/1.25642, loss_spatial_bce_7: 0.04342/0.11000, loss_spatial_dice_7: 0.39862/0.26181, loss_spatial_ce_7: 0.23110/0.16830, loss_grounding_bce_7: 0.06597/0.09037, loss_grounding_dice_7: 0.09386/0.19060, loss_grounding_ce_7: 0.58449/0.34050, loss_mask_ce_8: 1.94412/1.12839, loss_mask_bce_8: 0.80951/0.36581, loss_mask_dice_8: 7.58583/1.32952, loss_spatial_bce_8: 0.06598/0.13069, loss_spatial_dice_8: 0.45795/0.29992, loss_spatial_ce_8: 0.14693/0.22465, loss_grounding_bce_8: 0.05892/0.09413, loss_grounding_dice_8: 0.09223/0.20149, loss_grounding_ce_8: 0.40914/0.40770, loss_mask_ce_9: 8.54106/3.67733, loss_mask_bce_9: 0.89522/0.39272, loss_mask_dice_9: 9.95612/1.90230, loss_spatial_bce_9: 0.18190/0.33325, loss_spatial_dice_9: 0.96377/0.82204, loss_spatial_ce_9: 1.36999/1.49678, loss_grounding_bce_9: 0.06792/0.10558, loss_grounding_dice_9: 0.11946/0.28070, loss_grounding_ce_9: 1.98653/0.67236] items per batch[64] items per second[0.23] total items[3955200] mini batches[ 61800] memory[7345] epoch remaining[0:14:47]
INFO:trainer.default_trainer:epochs[    33] optim steps[61900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09884/0.89987, loss_mask_bce_0: 0.27806/0.33460, loss_mask_dice_0: 0.27203/1.16314, loss_spatial_bce_0: 0.20971/0.08749, loss_spatial_dice_0: 0.20767/0.20870, loss_spatial_ce_0: 0.56854/0.06278, loss_grounding_bce_0: 0.15433/0.08628, loss_grounding_dice_0: 0.17267/0.17847, loss_grounding_ce_0: 0.00717/0.27228, loss_mask_ce_1: 0.22641/0.90051, loss_mask_bce_1: 0.26440/0.33555, loss_mask_dice_1: 0.30545/1.16966, loss_spatial_bce_1: 0.25163/0.08801, loss_spatial_dice_1: 0.20772/0.21266, loss_spatial_ce_1: 0.48849/0.06856, loss_grounding_bce_1: 0.15098/0.08644, loss_grounding_dice_1: 0.13849/0.17925, loss_grounding_ce_1: 0.00790/0.27301, loss_mask_ce_2: 0.23979/0.90745, loss_mask_bce_2: 0.14620/0.33611, loss_mask_dice_2: 0.18559/1.17003, loss_spatial_bce_2: 0.26318/0.08902, loss_spatial_dice_2: 0.19896/0.21422, loss_spatial_ce_2: 0.62494/0.07195, loss_grounding_bce_2: 0.14796/0.08657, loss_grounding_dice_2: 0.16055/0.17908, loss_grounding_ce_2: 0.00969/0.27643, loss_mask_ce_3: 0.09663/0.91791, loss_mask_bce_3: 0.26924/0.33721, loss_mask_dice_3: 0.22287/1.16760, loss_spatial_bce_3: 0.22952/0.09018, loss_spatial_dice_3: 0.18254/0.21509, loss_spatial_ce_3: 0.44712/0.07633, loss_grounding_bce_3: 0.15038/0.08681, loss_grounding_dice_3: 0.12363/0.17882, loss_grounding_ce_3: 0.01357/0.27854, loss_mask_ce_4: 0.09681/0.91897, loss_mask_bce_4: 0.26432/0.33933, loss_mask_dice_4: 0.24786/1.19168, loss_spatial_bce_4: 0.20393/0.09412, loss_spatial_dice_4: 0.22674/0.22720, loss_spatial_ce_4: 0.53868/0.09236, loss_grounding_bce_4: 0.14731/0.08731, loss_grounding_dice_4: 0.11847/0.18172, loss_grounding_ce_4: 0.00619/0.28153, loss_mask_ce_5: 0.08298/0.93527, loss_mask_bce_5: 0.27101/0.34161, loss_mask_dice_5: 0.26603/1.19917, loss_spatial_bce_5: 0.21021/0.09627, loss_spatial_dice_5: 0.24819/0.23133, loss_spatial_ce_5: 0.32159/0.10697, loss_grounding_bce_5: 0.15414/0.08773, loss_grounding_dice_5: 0.14983/0.18297, loss_grounding_ce_5: 0.00644/0.29426, loss_mask_ce_6: 0.11036/0.97525, loss_mask_bce_6: 0.26275/0.34429, loss_mask_dice_6: 0.24340/1.20216, loss_spatial_bce_6: 0.19927/0.10200, loss_spatial_dice_6: 0.25842/0.23417, loss_spatial_ce_6: 0.29424/0.13298, loss_grounding_bce_6: 0.14993/0.08848, loss_grounding_dice_6: 0.11810/0.18335, loss_grounding_ce_6: 0.01194/0.30986, loss_mask_ce_7: 0.14613/1.02027, loss_mask_bce_7: 0.27354/0.35211, loss_mask_dice_7: 0.26079/1.25653, loss_spatial_bce_7: 0.20955/0.10998, loss_spatial_dice_7: 0.15449/0.26180, loss_spatial_ce_7: 0.67171/0.16825, loss_grounding_bce_7: 0.14867/0.09038, loss_grounding_dice_7: 0.14962/0.19061, loss_grounding_ce_7: 0.11013/0.34048, loss_mask_ce_8: 0.21832/1.12827, loss_mask_bce_8: 0.26539/0.36576, loss_mask_dice_8: 0.26523/1.32964, loss_spatial_bce_8: 0.10998/0.13066, loss_spatial_dice_8: 0.14071/0.29989, loss_spatial_ce_8: 0.79438/0.22462, loss_grounding_bce_8: 0.14750/0.09413, loss_grounding_dice_8: 0.14114/0.20150, loss_grounding_ce_8: 0.01036/0.40767, loss_mask_ce_9: 2.14200/3.67719, loss_mask_bce_9: 0.24119/0.39267, loss_mask_dice_9: 0.28984/1.90239, loss_spatial_bce_9: 0.30899/0.33324, loss_spatial_dice_9: 0.66150/0.82203, loss_spatial_ce_9: 1.96600/1.49682, loss_grounding_bce_9: 0.13418/0.10559, loss_grounding_dice_9: 0.23723/0.28071, loss_grounding_ce_9: 0.08714/0.67236] items per batch[64] items per second[0.23] total items[3961600] mini batches[ 61900] memory[7345] epoch remaining[0:10:08]
INFO:trainer.default_trainer:epochs[    33] optim steps[62000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.93496/0.89975, loss_mask_bce_0: 0.21684/0.33456, loss_mask_dice_0: 1.14167/1.16307, loss_spatial_bce_0: 0.05185/0.08748, loss_spatial_dice_0: 0.21373/0.20871, loss_spatial_ce_0: 0.00543/0.06275, loss_grounding_bce_0: 0.08392/0.08628, loss_grounding_dice_0: 0.16351/0.17848, loss_grounding_ce_0: 0.02168/0.27225, loss_mask_ce_1: 1.03190/0.90037, loss_mask_bce_1: 0.21782/0.33550, loss_mask_dice_1: 1.08936/1.16958, loss_spatial_bce_1: 0.05251/0.08799, loss_spatial_dice_1: 0.22035/0.21266, loss_spatial_ce_1: 0.02515/0.06853, loss_grounding_bce_1: 0.08684/0.08644, loss_grounding_dice_1: 0.15447/0.17925, loss_grounding_ce_1: 0.03028/0.27297, loss_mask_ce_2: 1.11902/0.90730, loss_mask_bce_2: 0.21574/0.33606, loss_mask_dice_2: 1.01958/1.16997, loss_spatial_bce_2: 0.05361/0.08901, loss_spatial_dice_2: 0.21330/0.21423, loss_spatial_ce_2: 0.01736/0.07192, loss_grounding_bce_2: 0.08527/0.08657, loss_grounding_dice_2: 0.16429/0.17908, loss_grounding_ce_2: 0.03925/0.27641, loss_mask_ce_3: 1.18004/0.91777, loss_mask_bce_3: 0.20554/0.33717, loss_mask_dice_3: 0.90650/1.16753, loss_spatial_bce_3: 0.05635/0.09017, loss_spatial_dice_3: 0.21420/0.21510, loss_spatial_ce_3: 0.01030/0.07630, loss_grounding_bce_3: 0.08361/0.08681, loss_grounding_dice_3: 0.15754/0.17883, loss_grounding_ce_3: 0.04292/0.27854, loss_mask_ce_4: 1.03145/0.91886, loss_mask_bce_4: 0.22782/0.33928, loss_mask_dice_4: 1.19108/1.19161, loss_spatial_bce_4: 0.05399/0.09412, loss_spatial_dice_4: 0.25701/0.22722, loss_spatial_ce_4: 0.04345/0.09233, loss_grounding_bce_4: 0.08871/0.08730, loss_grounding_dice_4: 0.17244/0.18173, loss_grounding_ce_4: 0.03099/0.28152, loss_mask_ce_5: 1.27670/0.93516, loss_mask_bce_5: 0.22692/0.34157, loss_mask_dice_5: 1.05164/1.19909, loss_spatial_bce_5: 0.05773/0.09627, loss_spatial_dice_5: 0.31569/0.23135, loss_spatial_ce_5: 0.02645/0.10693, loss_grounding_bce_5: 0.08548/0.08774, loss_grounding_dice_5: 0.15541/0.18298, loss_grounding_ce_5: 0.02492/0.29427, loss_mask_ce_6: 1.04269/0.97515, loss_mask_bce_6: 0.22543/0.34424, loss_mask_dice_6: 1.18693/1.20207, loss_spatial_bce_6: 0.06445/0.10200, loss_spatial_dice_6: 0.28043/0.23419, loss_spatial_ce_6: 0.05588/0.13293, loss_grounding_bce_6: 0.08859/0.08847, loss_grounding_dice_6: 0.15672/0.18335, loss_grounding_ce_6: 0.01470/0.30988, loss_mask_ce_7: 1.21806/1.02014, loss_mask_bce_7: 0.21499/0.35208, loss_mask_dice_7: 1.11642/1.25646, loss_spatial_bce_7: 0.06326/0.10997, loss_spatial_dice_7: 0.26500/0.26182, loss_spatial_ce_7: 0.07312/0.16822, loss_grounding_bce_7: 0.08507/0.09037, loss_grounding_dice_7: 0.16450/0.19063, loss_grounding_ce_7: 0.01665/0.34044, loss_mask_ce_8: 1.07619/1.12819, loss_mask_bce_8: 0.24399/0.36570, loss_mask_dice_8: 1.29030/1.32953, loss_spatial_bce_8: 0.07874/0.13064, loss_spatial_dice_8: 0.34570/0.29991, loss_spatial_ce_8: 0.14902/0.22458, loss_grounding_bce_8: 0.09364/0.09413, loss_grounding_dice_8: 0.17898/0.20150, loss_grounding_ce_8: 0.01541/0.40762, loss_mask_ce_9: 3.23749/3.67694, loss_mask_bce_9: 0.34219/0.39260, loss_mask_dice_9: 2.27362/1.90218, loss_spatial_bce_9: 0.23492/0.33321, loss_spatial_dice_9: 0.90115/0.82203, loss_spatial_ce_9: 2.01230/1.49689, loss_grounding_bce_9: 0.10368/0.10559, loss_grounding_dice_9: 0.21946/0.28072, loss_grounding_ce_9: 0.39062/0.67238] items per batch[64] items per second[0.23] total items[3968000] mini batches[ 62000] memory[7345] epoch remaining[0:05:29]
INFO:trainer.default_trainer:epochs[    33] optim steps[62100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19182/0.89978, loss_mask_bce_0: 0.25111/0.33456, loss_mask_dice_0: 0.35908/1.16304, loss_spatial_bce_0: 0.12349/0.08748, loss_spatial_dice_0: 0.16984/0.20871, loss_spatial_ce_0: 0.24649/0.06274, loss_grounding_bce_0: 0.11958/0.08628, loss_grounding_dice_0: 0.16136/0.17851, loss_grounding_ce_0: 0.03000/0.27233, loss_mask_ce_1: 0.16276/0.90042, loss_mask_bce_1: 0.26074/0.33551, loss_mask_dice_1: 0.34451/1.16956, loss_spatial_bce_1: 0.12155/0.08799, loss_spatial_dice_1: 0.17908/0.21265, loss_spatial_ce_1: 0.22931/0.06853, loss_grounding_bce_1: 0.12893/0.08643, loss_grounding_dice_1: 0.19543/0.17928, loss_grounding_ce_1: 0.02368/0.27305, loss_mask_ce_2: 0.17982/0.90735, loss_mask_bce_2: 0.25529/0.33607, loss_mask_dice_2: 0.36666/1.16998, loss_spatial_bce_2: 0.11798/0.08901, loss_spatial_dice_2: 0.16880/0.21422, loss_spatial_ce_2: 0.23326/0.07189, loss_grounding_bce_2: 0.12807/0.08657, loss_grounding_dice_2: 0.17951/0.17912, loss_grounding_ce_2: 0.02845/0.27648, loss_mask_ce_3: 0.17342/0.91780, loss_mask_bce_3: 0.25785/0.33717, loss_mask_dice_3: 0.36710/1.16752, loss_spatial_bce_3: 0.11323/0.09017, loss_spatial_dice_3: 0.17584/0.21510, loss_spatial_ce_3: 0.27689/0.07629, loss_grounding_bce_3: 0.13555/0.08681, loss_grounding_dice_3: 0.20446/0.17886, loss_grounding_ce_3: 0.04376/0.27864, loss_mask_ce_4: 0.18864/0.91891, loss_mask_bce_4: 0.25554/0.33929, loss_mask_dice_4: 0.31708/1.19162, loss_spatial_bce_4: 0.11177/0.09412, loss_spatial_dice_4: 0.16281/0.22721, loss_spatial_ce_4: 0.23269/0.09233, loss_grounding_bce_4: 0.13395/0.08730, loss_grounding_dice_4: 0.16724/0.18176, loss_grounding_ce_4: 0.04537/0.28157, loss_mask_ce_5: 0.15745/0.93517, loss_mask_bce_5: 0.25883/0.34158, loss_mask_dice_5: 0.34000/1.19910, loss_spatial_bce_5: 0.11559/0.09627, loss_spatial_dice_5: 0.16286/0.23136, loss_spatial_ce_5: 0.21619/0.10694, loss_grounding_bce_5: 0.14466/0.08773, loss_grounding_dice_5: 0.16856/0.18301, loss_grounding_ce_5: 0.04131/0.29433, loss_mask_ce_6: 0.21913/0.97520, loss_mask_bce_6: 0.25638/0.34424, loss_mask_dice_6: 0.39365/1.20207, loss_spatial_bce_6: 0.10829/0.10200, loss_spatial_dice_6: 0.13983/0.23420, loss_spatial_ce_6: 0.36986/0.13292, loss_grounding_bce_6: 0.13554/0.08846, loss_grounding_dice_6: 0.17650/0.18338, loss_grounding_ce_6: 0.15756/0.30992, loss_mask_ce_7: 0.21891/1.02017, loss_mask_bce_7: 0.25972/0.35209, loss_mask_dice_7: 0.31981/1.25647, loss_spatial_bce_7: 0.18720/0.10998, loss_spatial_dice_7: 0.26511/0.26182, loss_spatial_ce_7: 0.25849/0.16821, loss_grounding_bce_7: 0.13241/0.09036, loss_grounding_dice_7: 0.17062/0.19066, loss_grounding_ce_7: 0.04492/0.34046, loss_mask_ce_8: 0.31031/1.12828, loss_mask_bce_8: 0.25768/0.36571, loss_mask_dice_8: 0.48368/1.32956, loss_spatial_bce_8: 0.13765/0.13064, loss_spatial_dice_8: 0.22101/0.29992, loss_spatial_ce_8: 0.24466/0.22455, loss_grounding_bce_8: 0.12609/0.09412, loss_grounding_dice_8: 0.17666/0.20153, loss_grounding_ce_8: 0.03981/0.40765, loss_mask_ce_9: 2.20903/3.67701, loss_mask_bce_9: 0.23071/0.39262, loss_mask_dice_9: 0.52600/1.90230, loss_spatial_bce_9: 0.62891/0.33320, loss_spatial_dice_9: 0.61755/0.82202, loss_spatial_ce_9: 1.63767/1.49695, loss_grounding_bce_9: 0.11770/0.10558, loss_grounding_dice_9: 0.27204/0.28078, loss_grounding_ce_9: 0.19689/0.67237] items per batch[64] items per second[0.24] total items[3974400] mini batches[ 62100] memory[7345] epoch remaining[0:00:50]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00062118.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2209 s/iter. Eval: 0.0900 s/iter. Total: 0.3140 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0028 s/iter. Inference: 0.2217 s/iter. Eval: 0.0834 s/iter. Total: 0.3081 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2242 s/iter. Eval: 0.0791 s/iter. Total: 0.3064 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2240 s/iter. Eval: 0.0769 s/iter. Total: 0.3041 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2224 s/iter. Eval: 0.0762 s/iter. Total: 0.3018 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2249 s/iter. Eval: 0.0761 s/iter. Total: 0.3042 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0031 s/iter. Inference: 0.2259 s/iter. Eval: 0.0766 s/iter. Total: 0.3057 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0032 s/iter. Inference: 0.2262 s/iter. Eval: 0.0757 s/iter. Total: 0.3052 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2272 s/iter. Eval: 0.0759 s/iter. Total: 0.3064 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval0se6iyft ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.978 | 81.886 | 60.148 |      133      |
| Things | 54.994 | 82.600 | 65.873 |      80       |
| Stuff  | 42.406 | 80.809 | 51.506 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.36s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.40 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.28s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.17 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.389
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.607
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.873 | 61.157 | 41.001 | 18.684 | 42.073 | 60.661 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.046 | bicycle      | 18.141 | car            | 37.819 |
| motorcycle    | 34.862 | airplane     | 57.424 | bus            | 66.090 |
| train         | 69.415 | truck        | 34.200 | boat           | 23.029 |
| traffic light | 25.277 | fire hydrant | 63.295 | stop sign      | 63.604 |
| parking meter | 43.411 | bench        | 20.111 | bird           | 30.081 |
| cat           | 73.658 | dog          | 65.382 | horse          | 45.345 |
| sheep         | 46.870 | cow          | 51.055 | elephant       | 60.735 |
| bear          | 77.574 | zebra        | 60.741 | giraffe        | 56.804 |
| backpack      | 15.185 | umbrella     | 47.957 | handbag        | 15.812 |
| tie           | 33.275 | suitcase     | 40.425 | frisbee        | 68.092 |
| skis          | 5.056  | snowboard    | 24.086 | sports ball    | 46.798 |
| kite          | 33.190 | baseball bat | 30.145 | baseball glove | 43.221 |
| skateboard    | 36.234 | surfboard    | 35.470 | tennis racket  | 56.266 |
| bottle        | 33.734 | wine glass   | 26.871 | cup            | 40.181 |
| fork          | 15.526 | knife        | 12.542 | spoon          | 14.113 |
| bowl          | 31.347 | banana       | 20.330 | apple          | 21.111 |
| sandwich      | 42.744 | orange       | 28.720 | broccoli       | 21.525 |
| carrot        | 19.873 | hot dog      | 23.088 | pizza          | 50.296 |
| donut         | 46.514 | cake         | 43.087 | chair          | 20.583 |
| couch         | 40.551 | potted plant | 17.279 | bed            | 41.299 |
| dining table  | 12.411 | toilet       | 67.800 | tv             | 62.525 |
| laptop        | 62.893 | mouse        | 58.928 | remote         | 31.234 |
| keyboard      | 47.142 | cell phone   | 37.929 | microwave      | 54.823 |
| oven          | 33.356 | toaster      | 34.372 | sink           | 37.288 |
| refrigerator  | 57.973 | book         | 9.204  | clock          | 52.083 |
| vase          | 33.898 | scissors     | 25.174 | teddy bear     | 52.446 |
| hair drier    | 8.447  | toothbrush   | 18.418 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.56565444942947, 'fwIoU': 69.15649864120466, 'IoU-person': 87.57512352590093, 'IoU-bicycle': 75.55893137055484, 'IoU-car': 66.70180132263151, 'IoU-motorcycle': 82.56003116031802, 'IoU-airplane': 82.23101801185014, 'IoU-bus': 85.00095255442271, 'IoU-train': 85.54637258639318, 'IoU-truck': 59.91038918422261, 'IoU-boat': 68.42013994949804, 'IoU-traffic light': 76.61336033220107, 'IoU-fire hydrant': 90.52956379555884, 'IoU-stop sign': 92.4045806143177, 'IoU-parking meter': 86.96178918228435, 'IoU-bench': 56.785379809039505, 'IoU-bird': 76.64043631092223, 'IoU-cat': 84.81105519855369, 'IoU-dog': 79.5147867831819, 'IoU-horse': 86.18481512714003, 'IoU-sheep': 89.19948724177947, 'IoU-cow': 82.82389254455548, 'IoU-elephant': 90.73149011872837, 'IoU-bear': 92.06067610808847, 'IoU-zebra': 90.01150886418841, 'IoU-giraffe': 88.21959879564072, 'IoU-backpack': 38.218520251224206, 'IoU-umbrella': 78.11738864861053, 'IoU-handbag': 38.341551244878886, 'IoU-tie': 69.77014168365227, 'IoU-suitcase': 80.80207830884643, 'IoU-frisbee': 83.28794624787501, 'IoU-skis': 50.06620509592745, 'IoU-snowboard': 69.1583976789201, 'IoU-sports ball': 66.58581643520027, 'IoU-kite': 66.81797748363151, 'IoU-baseball bat': 60.73390725038934, 'IoU-baseball glove': 77.57052446582762, 'IoU-skateboard': 60.13878859567778, 'IoU-surfboard': 80.91860808998157, 'IoU-tennis racket': 82.37372607197814, 'IoU-bottle': 69.25797792555277, 'IoU-wine glass': 68.80553960130875, 'IoU-cup': 59.09570795620465, 'IoU-fork': 53.675272289254195, 'IoU-knife': 50.69656276743222, 'IoU-spoon': 46.92210848822703, 'IoU-bowl': 51.136812072641646, 'IoU-banana': 83.04010309062892, 'IoU-apple': 59.03492232397264, 'IoU-sandwich': 64.87008789169467, 'IoU-orange': 76.53787290522162, 'IoU-broccoli': 66.7563549251345, 'IoU-carrot': 62.13585290254036, 'IoU-hot dog': 64.4103109415546, 'IoU-pizza': 82.20278605837665, 'IoU-donut': 62.20457524231813, 'IoU-cake': 68.43307247557806, 'IoU-chair': 53.534477611465725, 'IoU-couch': 66.52133105408518, 'IoU-potted plant': 33.88384291024492, 'IoU-bed': 71.10293891237423, 'IoU-dining table': 50.74639453627171, 'IoU-toilet': 86.81862208703734, 'IoU-tv': 76.35531030320239, 'IoU-laptop': 73.45620982837416, 'IoU-mouse': 72.23892333282927, 'IoU-remote': 48.9129808813869, 'IoU-keyboard': 57.34704964352761, 'IoU-cell phone': 66.83900350351269, 'IoU-microwave': 55.10560248182372, 'IoU-oven': 65.5684930549416, 'IoU-toaster': 46.735017107760754, 'IoU-sink': 68.60713119975573, 'IoU-refrigerator': 79.65239193935422, 'IoU-book': 50.75646770992995, 'IoU-clock': 74.40398214978623, 'IoU-vase': 62.98447195104283, 'IoU-scissors': 55.24978359346373, 'IoU-teddy bear': 81.45521957796326, 'IoU-hair drier': 41.81789258574907, 'IoU-toothbrush': 53.01285242340589, 'IoU-banner': 39.33408870744534, 'IoU-blanket': 10.99450357990239, 'IoU-bridge': 36.77684372246839, 'IoU-cardboard': 34.37509160938129, 'IoU-counter': 30.85062681947236, 'IoU-curtain': 64.8063624579804, 'IoU-door-stuff': 40.47114507879485, 'IoU-floor-wood': 61.69559048561275, 'IoU-flower': 43.033293052545964, 'IoU-fruit': 37.19431380178726, 'IoU-gravel': 30.424856181542275, 'IoU-house': 23.581952990901684, 'IoU-light': 38.62745105995017, 'IoU-mirror-stuff': 54.186021705255115, 'IoU-net': 45.50518789928521, 'IoU-pillow': 11.335271297969465, 'IoU-platform': 29.004598506530893, 'IoU-playingfield': 68.66732445667539, 'IoU-railroad': 60.73655054906533, 'IoU-river': 50.510182130169625, 'IoU-road': 67.37831975521169, 'IoU-roof': 14.771474468553444, 'IoU-sand': 64.216293382106, 'IoU-sea': 85.36904306132324, 'IoU-shelf': 35.01557709179026, 'IoU-snow': 88.47955226824281, 'IoU-stairs': 29.29477649891971, 'IoU-tent': 8.714237347385605, 'IoU-towel': 35.64143072936155, 'IoU-wall-brick': 43.502325299431014, 'IoU-wall-stone': 28.687543689809164, 'IoU-wall-tile': 67.74798545005135, 'IoU-wall-wood': 37.66158079472795, 'IoU-water-other': 23.758980753511914, 'IoU-window-blind': 47.06004711272476, 'IoU-window-other': 46.28418322516353, 'IoU-tree-merged': 80.9709738549381, 'IoU-fence-merged': 51.84949616763933, 'IoU-ceiling-merged': 66.3861778001818, 'IoU-sky-other-merged': 93.72692781672698, 'IoU-cabinet-merged': 59.0751781822727, 'IoU-table-merged': 34.04793370756924, 'IoU-floor-other-merged': 49.02231893147578, 'IoU-pavement-merged': 55.9986069755577, 'IoU-mountain-merged': 56.003802434544404, 'IoU-grass-merged': 72.61132484160102, 'IoU-dirt-merged': 45.56062099396321, 'IoU-paper-merged': 26.785663461171538, 'IoU-food-other-merged': 37.45622688167744, 'IoU-building-other-merged': 58.508945781112274, 'IoU-rock-merged': 61.13280329950903, 'IoU-wall-other-merged': 65.58399405353195, 'IoU-rug-merged': 62.59537128807228, 'mACC': 72.87319948962028, 'pACC': 80.39544867861038, 'ACC-person': 92.38018492481608, 'ACC-bicycle': 86.46208824012945, 'ACC-car': 83.07689204046844, 'ACC-motorcycle': 90.1031023104187, 'ACC-airplane': 90.6338584211108, 'ACC-bus': 92.35043502294383, 'ACC-train': 93.06016716320136, 'ACC-truck': 66.2263112540415, 'ACC-boat': 79.09285684072304, 'ACC-traffic light': 89.11269854730081, 'ACC-fire hydrant': 95.27663790048125, 'ACC-stop sign': 95.02992831998785, 'ACC-parking meter': 92.37367761467152, 'ACC-bench': 70.66080186887972, 'ACC-bird': 80.89182844572271, 'ACC-cat': 90.2051685592788, 'ACC-dog': 82.53221280154878, 'ACC-horse': 92.71412666599808, 'ACC-sheep': 92.46737999949633, 'ACC-cow': 88.1627468809585, 'ACC-elephant': 93.3810005470076, 'ACC-bear': 94.61045974176267, 'ACC-zebra': 92.54894143593204, 'ACC-giraffe': 92.57657578414101, 'ACC-backpack': 53.07140165292154, 'ACC-umbrella': 86.12369486262149, 'ACC-handbag': 63.18666927392142, 'ACC-tie': 82.43487369962789, 'ACC-suitcase': 90.46132914297019, 'ACC-frisbee': 93.53236363636364, 'ACC-skis': 73.17587573297172, 'ACC-snowboard': 80.32916166638093, 'ACC-sports ball': 79.17222273906617, 'ACC-kite': 76.5076176884412, 'ACC-baseball bat': 84.1498774996441, 'ACC-baseball glove': 89.68569637491623, 'ACC-skateboard': 69.71589965483346, 'ACC-surfboard': 90.7648299559638, 'ACC-tennis racket': 89.79686692198425, 'ACC-bottle': 84.06855747881413, 'ACC-wine glass': 86.99488676542583, 'ACC-cup': 81.921322474203, 'ACC-fork': 65.58054976262288, 'ACC-knife': 66.52396131017807, 'ACC-spoon': 67.4796647718632, 'ACC-bowl': 62.37563236512466, 'ACC-banana': 89.75759380632906, 'ACC-apple': 71.78457042813251, 'ACC-sandwich': 76.50186680534296, 'ACC-orange': 86.17727810985119, 'ACC-broccoli': 78.03178494238372, 'ACC-carrot': 75.2466622906154, 'ACC-hot dog': 72.98391138186936, 'ACC-pizza': 92.67554521122943, 'ACC-donut': 77.7703284799813, 'ACC-cake': 75.83445282296749, 'ACC-chair': 68.18336495308783, 'ACC-couch': 82.58800860688817, 'ACC-potted plant': 48.22826321990465, 'ACC-bed': 83.43690246421586, 'ACC-dining table': 78.85701229995594, 'ACC-toilet': 91.05553342445243, 'ACC-tv': 88.53897230036407, 'ACC-laptop': 88.81811144458673, 'ACC-mouse': 86.1653120930417, 'ACC-remote': 72.6114972708001, 'ACC-keyboard': 62.423048241558554, 'ACC-cell phone': 78.17184572541215, 'ACC-microwave': 64.04099465695681, 'ACC-oven': 82.50772466167055, 'ACC-toaster': 52.90205148558213, 'ACC-sink': 85.09284172525346, 'ACC-refrigerator': 90.50792794856073, 'ACC-book': 66.91855089518366, 'ACC-clock': 81.41372807225889, 'ACC-vase': 73.60616605635951, 'ACC-scissors': 59.704087816433905, 'ACC-teddy bear': 88.67337840398, 'ACC-hair drier': 48.63518629209006, 'ACC-toothbrush': 81.40548992355802, 'ACC-banner': 65.55342436874477, 'ACC-blanket': 13.09991096619968, 'ACC-bridge': 53.80023211401521, 'ACC-cardboard': 40.58185255198487, 'ACC-counter': 54.155140852073266, 'ACC-curtain': 78.09234327211679, 'ACC-door-stuff': 61.681625661931136, 'ACC-floor-wood': 74.36708410828926, 'ACC-flower': 64.92349250741225, 'ACC-fruit': 57.9712348590448, 'ACC-gravel': 39.21291022267278, 'ACC-house': 26.841035546781338, 'ACC-light': 56.942816196455226, 'ACC-mirror-stuff': 73.0984061776911, 'ACC-net': 61.181169396944476, 'ACC-pillow': 26.281108679410654, 'ACC-platform': 41.88887590917754, 'ACC-playingfield': 84.16551542635482, 'ACC-railroad': 77.90060418988287, 'ACC-river': 72.36145557923545, 'ACC-road': 84.7616418205461, 'ACC-roof': 19.276729944138175, 'ACC-sand': 71.14009256882645, 'ACC-sea': 91.09278615914515, 'ACC-shelf': 59.97607192434632, 'ACC-snow': 95.72590148536784, 'ACC-stairs': 48.3002075881911, 'ACC-tent': 10.347129575803708, 'ACC-towel': 41.786311964168696, 'ACC-wall-brick': 57.64009494286388, 'ACC-wall-stone': 41.580836790453084, 'ACC-wall-tile': 81.58844777384951, 'ACC-wall-wood': 52.999905748223405, 'ACC-water-other': 38.067941040496756, 'ACC-window-blind': 55.9498585464466, 'ACC-window-other': 69.98048333450748, 'ACC-tree-merged': 89.1101082660874, 'ACC-fence-merged': 70.78568796604138, 'ACC-ceiling-merged': 80.3446034702256, 'ACC-sky-other-merged': 96.53425009809521, 'ACC-cabinet-merged': 74.33788281112037, 'ACC-table-merged': 47.77282444683444, 'ACC-floor-other-merged': 61.678489900354386, 'ACC-pavement-merged': 71.06989949684997, 'ACC-mountain-merged': 66.38922721573665, 'ACC-grass-merged': 82.97355248642117, 'ACC-dirt-merged': 70.17735966745056, 'ACC-paper-merged': 38.64374889076016, 'ACC-food-other-merged': 49.51600306120745, 'ACC-building-other-merged': 74.88644640884287, 'ACC-rock-merged': 81.0674568914665, 'ACC-wall-other-merged': 81.48053172777273, 'ACC-rug-merged': 76.81974849769946})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1538 s/iter. Inference: 0.5831 s/iter. Eval: 0.0000 s/iter. Total: 0.7369 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1556 s/iter. Inference: 0.5713 s/iter. Eval: 0.0000 s/iter. Total: 0.7270 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/50. Dataloading: 0.1690 s/iter. Inference: 0.7099 s/iter. Eval: 0.0000 s/iter. Total: 0.8791 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1728 s/iter. Inference: 0.7553 s/iter. Eval: 0.0000 s/iter. Total: 0.9283 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1699 s/iter. Inference: 0.6519 s/iter. Eval: 0.0000 s/iter. Total: 0.8219 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1689 s/iter. Inference: 0.6775 s/iter. Eval: 0.0000 s/iter. Total: 0.8465 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1707 s/iter. Inference: 0.7302 s/iter. Eval: 0.0000 s/iter. Total: 0.9011 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.495756511559848, 'noc@0.8': 2.8206028680128767, 'noc@0.85': 3.4263974246414985, 'noc@0.9': 4.530875036581797, 'miou@iter1': 0.8271308901158557}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0015 s/iter. Inference: 0.1033 s/iter. Eval: 0.0008 s/iter. Total: 0.1057 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.61795806884766, 'precision@0.6': 67.39215087890625, 'precision@0.7': 61.87329864501953, 'precision@0.8': 51.496307373046875, 'precision@0.9': 26.855810165405273, 'cIoU': 56.77001953125, 'mIoU': 62.03797912597656}
INFO:trainer.default_trainer:This epoch takes 1:28:02.321953
INFO:trainer.default_trainer:PROGRESS: 68.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 34 training.
INFO:trainer.default_trainer:epochs[    34] optim steps[62200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20548/0.89983, loss_mask_bce_0: 0.43059/0.33455, loss_mask_dice_0: 0.61702/1.16314, loss_spatial_bce_0: 0.09882/0.08747, loss_spatial_dice_0: 0.14874/0.20870, loss_spatial_ce_0: 0.04888/0.06272, loss_grounding_bce_0: 0.04477/0.08627, loss_grounding_dice_0: 0.17594/0.17855, loss_grounding_ce_0: 0.11057/0.27234, loss_mask_ce_1: 1.25179/0.90047, loss_mask_bce_1: 0.44116/0.33550, loss_mask_dice_1: 0.56237/1.16966, loss_spatial_bce_1: 0.09887/0.08798, loss_spatial_dice_1: 0.17796/0.21265, loss_spatial_ce_1: 0.02933/0.06851, loss_grounding_bce_1: 0.04431/0.08643, loss_grounding_dice_1: 0.12446/0.17932, loss_grounding_ce_1: 0.12254/0.27305, loss_mask_ce_2: 1.33139/0.90738, loss_mask_bce_2: 0.45761/0.33606, loss_mask_dice_2: 0.64942/1.17007, loss_spatial_bce_2: 0.09419/0.08901, loss_spatial_dice_2: 0.15721/0.21422, loss_spatial_ce_2: 0.02802/0.07188, loss_grounding_bce_2: 0.04551/0.08657, loss_grounding_dice_2: 0.09168/0.17915, loss_grounding_ce_2: 0.13448/0.27650, loss_mask_ce_3: 1.52784/0.91784, loss_mask_bce_3: 0.50414/0.33717, loss_mask_dice_3: 0.68323/1.16762, loss_spatial_bce_3: 0.09442/0.09017, loss_spatial_dice_3: 0.16854/0.21509, loss_spatial_ce_3: 0.02671/0.07628, loss_grounding_bce_3: 0.04319/0.08680, loss_grounding_dice_3: 0.11913/0.17889, loss_grounding_ce_3: 0.16668/0.27866, loss_mask_ce_4: 1.44815/0.91893, loss_mask_bce_4: 0.50084/0.33928, loss_mask_dice_4: 0.69607/1.19175, loss_spatial_bce_4: 0.10153/0.09411, loss_spatial_dice_4: 0.17615/0.22721, loss_spatial_ce_4: 0.01848/0.09233, loss_grounding_bce_4: 0.04423/0.08729, loss_grounding_dice_4: 0.11411/0.18179, loss_grounding_ce_4: 0.16189/0.28159, loss_mask_ce_5: 1.11154/0.93518, loss_mask_bce_5: 0.70410/0.34158, loss_mask_dice_5: 0.74639/1.19922, loss_spatial_bce_5: 0.09592/0.09626, loss_spatial_dice_5: 0.20299/0.23136, loss_spatial_ce_5: 0.03739/0.10694, loss_grounding_bce_5: 0.04591/0.08772, loss_grounding_dice_5: 0.18338/0.18305, loss_grounding_ce_5: 0.13930/0.29433, loss_mask_ce_6: 1.21511/0.97521, loss_mask_bce_6: 0.67494/0.34425, loss_mask_dice_6: 0.83964/1.20218, loss_spatial_bce_6: 0.10955/0.10199, loss_spatial_dice_6: 0.22015/0.23420, loss_spatial_ce_6: 0.07511/0.13290, loss_grounding_bce_6: 0.04515/0.08846, loss_grounding_dice_6: 0.09978/0.18341, loss_grounding_ce_6: 0.13884/0.30991, loss_mask_ce_7: 1.04569/1.02019, loss_mask_bce_7: 0.61029/0.35208, loss_mask_dice_7: 0.77536/1.25660, loss_spatial_bce_7: 0.11795/0.10996, loss_spatial_dice_7: 0.22101/0.26182, loss_spatial_ce_7: 0.25874/0.16819, loss_grounding_bce_7: 0.04700/0.09035, loss_grounding_dice_7: 0.11099/0.19069, loss_grounding_ce_7: 0.20126/0.34045, loss_mask_ce_8: 1.32434/1.12826, loss_mask_bce_8: 0.57228/0.36571, loss_mask_dice_8: 0.72107/1.32967, loss_spatial_bce_8: 0.18059/0.13063, loss_spatial_dice_8: 0.27767/0.29991, loss_spatial_ce_8: 0.08599/0.22452, loss_grounding_bce_8: 0.04563/0.09411, loss_grounding_dice_8: 0.11072/0.20155, loss_grounding_ce_8: 0.20651/0.40768, loss_mask_ce_9: 3.35055/3.67681, loss_mask_bce_9: 0.59075/0.39261, loss_mask_dice_9: 1.35612/1.90243, loss_spatial_bce_9: 0.51419/0.33319, loss_spatial_dice_9: 0.85012/0.82201, loss_spatial_ce_9: 1.96662/1.49700, loss_grounding_bce_9: 0.05301/0.10557, loss_grounding_dice_9: 0.16812/0.28082, loss_grounding_ce_9: 0.93952/0.67229] items per batch[64] items per second[0.14] total items[3980800] mini batches[ 62200] memory[7345] epoch remaining[1:21:23]
INFO:trainer.default_trainer:epochs[    34] optim steps[62300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.32075/0.89969, loss_mask_bce_0: 0.52857/0.33449, loss_mask_dice_0: 0.88018/1.16296, loss_spatial_bce_0: 0.14743/0.08746, loss_spatial_dice_0: 0.22407/0.20867, loss_spatial_ce_0: 0.09241/0.06269, loss_grounding_bce_0: 0.08189/0.08625, loss_grounding_dice_0: 0.21600/0.17854, loss_grounding_ce_0: 0.65729/0.27230, loss_mask_ce_1: 1.33562/0.90033, loss_mask_bce_1: 0.50873/0.33544, loss_mask_dice_1: 0.87714/1.16950, loss_spatial_bce_1: 0.14228/0.08797, loss_spatial_dice_1: 0.21974/0.21261, loss_spatial_ce_1: 0.08916/0.06848, loss_grounding_bce_1: 0.07795/0.08641, loss_grounding_dice_1: 0.21075/0.17932, loss_grounding_ce_1: 0.61608/0.27300, loss_mask_ce_2: 1.36525/0.90724, loss_mask_bce_2: 0.51682/0.33600, loss_mask_dice_2: 0.88667/1.16991, loss_spatial_bce_2: 0.15707/0.08900, loss_spatial_dice_2: 0.22882/0.21419, loss_spatial_ce_2: 0.10401/0.07186, loss_grounding_bce_2: 0.08330/0.08655, loss_grounding_dice_2: 0.22599/0.17915, loss_grounding_ce_2: 0.64636/0.27643, loss_mask_ce_3: 1.21565/0.91772, loss_mask_bce_3: 0.53138/0.33710, loss_mask_dice_3: 0.88611/1.16745, loss_spatial_bce_3: 0.14925/0.09016, loss_spatial_dice_3: 0.22785/0.21506, loss_spatial_ce_3: 0.09462/0.07626, loss_grounding_bce_3: 0.08446/0.08678, loss_grounding_dice_3: 0.21915/0.17889, loss_grounding_ce_3: 0.63090/0.27859, loss_mask_ce_4: 0.83667/0.91881, loss_mask_bce_4: 0.63077/0.33922, loss_mask_dice_4: 0.93880/1.19159, loss_spatial_bce_4: 0.17028/0.09410, loss_spatial_dice_4: 0.22912/0.22718, loss_spatial_ce_4: 0.06362/0.09232, loss_grounding_bce_4: 0.08010/0.08727, loss_grounding_dice_4: 0.19820/0.18179, loss_grounding_ce_4: 0.70975/0.28155, loss_mask_ce_5: 0.94259/0.93508, loss_mask_bce_5: 0.60082/0.34152, loss_mask_dice_5: 0.94453/1.19905, loss_spatial_bce_5: 0.18530/0.09625, loss_spatial_dice_5: 0.23035/0.23133, loss_spatial_ce_5: 0.09344/0.10694, loss_grounding_bce_5: 0.08208/0.08771, loss_grounding_dice_5: 0.21048/0.18305, loss_grounding_ce_5: 0.82388/0.29429, loss_mask_ce_6: 0.89808/0.97509, loss_mask_bce_6: 0.59935/0.34419, loss_mask_dice_6: 0.98049/1.20200, loss_spatial_bce_6: 0.15719/0.10198, loss_spatial_dice_6: 0.20520/0.23417, loss_spatial_ce_6: 0.10612/0.13288, loss_grounding_bce_6: 0.08340/0.08844, loss_grounding_dice_6: 0.22167/0.18341, loss_grounding_ce_6: 1.16489/0.30986, loss_mask_ce_7: 1.01195/1.02008, loss_mask_bce_7: 0.60227/0.35202, loss_mask_dice_7: 1.05018/1.25644, loss_spatial_bce_7: 0.27332/0.10995, loss_spatial_dice_7: 0.27150/0.26178, loss_spatial_ce_7: 0.10545/0.16817, loss_grounding_bce_7: 0.11022/0.09033, loss_grounding_dice_7: 0.28563/0.19070, loss_grounding_ce_7: 0.79585/0.34039, loss_mask_ce_8: 1.05129/1.12818, loss_mask_bce_8: 0.57171/0.36564, loss_mask_dice_8: 1.06227/1.32949, loss_spatial_bce_8: 0.38432/0.13061, loss_spatial_dice_8: 0.35444/0.29988, loss_spatial_ce_8: 0.24548/0.22448, loss_grounding_bce_8: 0.09839/0.09409, loss_grounding_dice_8: 0.28329/0.20155, loss_grounding_ce_8: 0.50044/0.40768, loss_mask_ce_9: 3.64954/3.67680, loss_mask_bce_9: 0.69632/0.39254, loss_mask_dice_9: 1.37442/1.90212, loss_spatial_bce_9: 0.43913/0.33319, loss_spatial_dice_9: 0.80309/0.82200, loss_spatial_ce_9: 1.66504/1.49702, loss_grounding_bce_9: 0.11338/0.10556, loss_grounding_dice_9: 0.34787/0.28083, loss_grounding_ce_9: 0.95510/0.67236] items per batch[64] items per second[0.23] total items[3987200] mini batches[ 62300] memory[7345] epoch remaining[1:17:17]
INFO:trainer.default_trainer:epochs[    34] optim steps[62400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90215/0.89968, loss_mask_bce_0: 0.32196/0.33454, loss_mask_dice_0: 1.00636/1.16312, loss_spatial_bce_0: 0.08285/0.08746, loss_spatial_dice_0: 0.07981/0.20867, loss_spatial_ce_0: 0.02634/0.06267, loss_grounding_bce_0: 0.08318/0.08624, loss_grounding_dice_0: 0.06485/0.17854, loss_grounding_ce_0: 0.10653/0.27232, loss_mask_ce_1: 0.86925/0.90036, loss_mask_bce_1: 0.31678/0.33549, loss_mask_dice_1: 1.00781/1.16966, loss_spatial_bce_1: 0.07442/0.08797, loss_spatial_dice_1: 0.07190/0.21262, loss_spatial_ce_1: 0.02731/0.06847, loss_grounding_bce_1: 0.08393/0.08641, loss_grounding_dice_1: 0.06240/0.17933, loss_grounding_ce_1: 0.11518/0.27302, loss_mask_ce_2: 0.86600/0.90725, loss_mask_bce_2: 0.31190/0.33605, loss_mask_dice_2: 1.02352/1.17005, loss_spatial_bce_2: 0.07164/0.08900, loss_spatial_dice_2: 0.07185/0.21419, loss_spatial_ce_2: 0.02653/0.07186, loss_grounding_bce_2: 0.08204/0.08655, loss_grounding_dice_2: 0.07273/0.17915, loss_grounding_ce_2: 0.11080/0.27645, loss_mask_ce_3: 0.86394/0.91771, loss_mask_bce_3: 0.30926/0.33715, loss_mask_dice_3: 0.99980/1.16757, loss_spatial_bce_3: 0.07700/0.09016, loss_spatial_dice_3: 0.07361/0.21507, loss_spatial_ce_3: 0.02776/0.07625, loss_grounding_bce_3: 0.08105/0.08678, loss_grounding_dice_3: 0.07124/0.17889, loss_grounding_ce_3: 0.12491/0.27864, loss_mask_ce_4: 0.86273/0.91878, loss_mask_bce_4: 0.30943/0.33927, loss_mask_dice_4: 0.96733/1.19173, loss_spatial_bce_4: 0.09551/0.09410, loss_spatial_dice_4: 0.09199/0.22719, loss_spatial_ce_4: 0.02967/0.09232, loss_grounding_bce_4: 0.08348/0.08727, loss_grounding_dice_4: 0.06778/0.18180, loss_grounding_ce_4: 0.14344/0.28155, loss_mask_ce_5: 0.91311/0.93508, loss_mask_bce_5: 0.31544/0.34156, loss_mask_dice_5: 0.99878/1.19921, loss_spatial_bce_5: 0.11430/0.09625, loss_spatial_dice_5: 0.10645/0.23134, loss_spatial_ce_5: 0.03755/0.10693, loss_grounding_bce_5: 0.08083/0.08770, loss_grounding_dice_5: 0.06347/0.18305, loss_grounding_ce_5: 0.11667/0.29429, loss_mask_ce_6: 1.00690/0.97508, loss_mask_bce_6: 0.31619/0.34424, loss_mask_dice_6: 1.02480/1.20216, loss_spatial_bce_6: 0.09876/0.10197, loss_spatial_dice_6: 0.09906/0.23418, loss_spatial_ce_6: 0.03065/0.13284, loss_grounding_bce_6: 0.07764/0.08844, loss_grounding_dice_6: 0.06924/0.18342, loss_grounding_ce_6: 0.09829/0.30986, loss_mask_ce_7: 0.98639/1.02008, loss_mask_bce_7: 0.33295/0.35207, loss_mask_dice_7: 1.06815/1.25655, loss_spatial_bce_7: 0.12398/0.10994, loss_spatial_dice_7: 0.10709/0.26179, loss_spatial_ce_7: 0.09330/0.16812, loss_grounding_bce_7: 0.08410/0.09033, loss_grounding_dice_7: 0.07535/0.19071, loss_grounding_ce_7: 0.29512/0.34035, loss_mask_ce_8: 1.22110/1.12818, loss_mask_bce_8: 0.37197/0.36571, loss_mask_dice_8: 1.27029/1.32965, loss_spatial_bce_8: 0.10131/0.13060, loss_spatial_dice_8: 0.10553/0.29988, loss_spatial_ce_8: 0.13053/0.22444, loss_grounding_bce_8: 0.09415/0.09409, loss_grounding_dice_8: 0.07170/0.20156, loss_grounding_ce_8: 0.51211/0.40765, loss_mask_ce_9: 5.18418/3.67702, loss_mask_bce_9: 0.60637/0.39261, loss_mask_dice_9: 2.56279/1.90232, loss_spatial_bce_9: 0.45534/0.33320, loss_spatial_dice_9: 0.77185/0.82202, loss_spatial_ce_9: 1.13397/1.49708, loss_grounding_bce_9: 0.12794/0.10555, loss_grounding_dice_9: 0.11435/0.28083, loss_grounding_ce_9: 1.48661/0.67241] items per batch[64] items per second[0.23] total items[3993600] mini batches[ 62400] memory[7345] epoch remaining[1:11:45]
INFO:trainer.default_trainer:epochs[    34] optim steps[62500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.46250/0.89961, loss_mask_bce_0: 0.06216/0.33455, loss_mask_dice_0: 0.31255/1.16308, loss_spatial_bce_0: 0.05497/0.08747, loss_spatial_dice_0: 0.25710/0.20866, loss_spatial_ce_0: 0.00532/0.06266, loss_grounding_bce_0: 0.07357/0.08624, loss_grounding_dice_0: 0.22141/0.17853, loss_grounding_ce_0: 0.14670/0.27220, loss_mask_ce_1: 0.47973/0.90027, loss_mask_bce_1: 0.06283/0.33549, loss_mask_dice_1: 0.33826/1.16962, loss_spatial_bce_1: 0.05209/0.08798, loss_spatial_dice_1: 0.25304/0.21261, loss_spatial_ce_1: 0.01221/0.06845, loss_grounding_bce_1: 0.06849/0.08640, loss_grounding_dice_1: 0.21418/0.17932, loss_grounding_ce_1: 0.13178/0.27292, loss_mask_ce_2: 0.55749/0.90716, loss_mask_bce_2: 0.07040/0.33605, loss_mask_dice_2: 0.34849/1.17004, loss_spatial_bce_2: 0.05342/0.08901, loss_spatial_dice_2: 0.24336/0.21419, loss_spatial_ce_2: 0.02039/0.07183, loss_grounding_bce_2: 0.07631/0.08654, loss_grounding_dice_2: 0.22381/0.17914, loss_grounding_ce_2: 0.13473/0.27634, loss_mask_ce_3: 0.44573/0.91763, loss_mask_bce_3: 0.07963/0.33715, loss_mask_dice_3: 0.36358/1.16756, loss_spatial_bce_3: 0.05268/0.09018, loss_spatial_dice_3: 0.27787/0.21506, loss_spatial_ce_3: 0.04687/0.07625, loss_grounding_bce_3: 0.08744/0.08677, loss_grounding_dice_3: 0.24723/0.17888, loss_grounding_ce_3: 0.16425/0.27852, loss_mask_ce_4: 0.41786/0.91870, loss_mask_bce_4: 0.06395/0.33927, loss_mask_dice_4: 0.30975/1.19172, loss_spatial_bce_4: 0.04548/0.09411, loss_spatial_dice_4: 0.21718/0.22718, loss_spatial_ce_4: 0.07101/0.09233, loss_grounding_bce_4: 0.07625/0.08726, loss_grounding_dice_4: 0.21467/0.18179, loss_grounding_ce_4: 0.13904/0.28144, loss_mask_ce_5: 0.38291/0.93499, loss_mask_bce_5: 0.05877/0.34156, loss_mask_dice_5: 0.27966/1.19918, loss_spatial_bce_5: 0.04246/0.09627, loss_spatial_dice_5: 0.24400/0.23134, loss_spatial_ce_5: 0.06345/0.10689, loss_grounding_bce_5: 0.06887/0.08770, loss_grounding_dice_5: 0.22149/0.18304, loss_grounding_ce_5: 0.12740/0.29419, loss_mask_ce_6: 0.48837/0.97501, loss_mask_bce_6: 0.06636/0.34423, loss_mask_dice_6: 0.26809/1.20215, loss_spatial_bce_6: 0.05064/0.10199, loss_spatial_dice_6: 0.26564/0.23418, loss_spatial_ce_6: 0.04713/0.13280, loss_grounding_bce_6: 0.06745/0.08843, loss_grounding_dice_6: 0.19532/0.18342, loss_grounding_ce_6: 0.11237/0.30975, loss_mask_ce_7: 0.44121/1.01999, loss_mask_bce_7: 0.05793/0.35207, loss_mask_dice_7: 0.25337/1.25653, loss_spatial_bce_7: 0.05180/0.10995, loss_spatial_dice_7: 0.24142/0.26178, loss_spatial_ce_7: 0.06529/0.16808, loss_grounding_bce_7: 0.05898/0.09032, loss_grounding_dice_7: 0.18297/0.19070, loss_grounding_ce_7: 0.06768/0.34023, loss_mask_ce_8: 0.55882/1.12808, loss_mask_bce_8: 0.06156/0.36571, loss_mask_dice_8: 0.28758/1.32961, loss_spatial_bce_8: 0.07295/0.13062, loss_spatial_dice_8: 0.25624/0.29987, loss_spatial_ce_8: 0.12993/0.22437, loss_grounding_bce_8: 0.05976/0.09408, loss_grounding_dice_8: 0.19633/0.20155, loss_grounding_ce_8: 0.12305/0.40750, loss_mask_ce_9: 1.70738/3.67681, loss_mask_bce_9: 0.07468/0.39263, loss_mask_dice_9: 0.41044/1.90222, loss_spatial_bce_9: 0.27942/0.33325, loss_spatial_dice_9: 0.80623/0.82199, loss_spatial_ce_9: 0.97719/1.49707, loss_grounding_bce_9: 0.07382/0.10556, loss_grounding_dice_9: 0.28787/0.28083, loss_grounding_ce_9: 0.17289/0.67218] items per batch[64] items per second[0.23] total items[4000000] mini batches[ 62500] memory[7345] epoch remaining[1:07:06]
INFO:trainer.default_trainer:epochs[    34] optim steps[62600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.10236/0.89954, loss_mask_bce_0: 0.29738/0.33454, loss_mask_dice_0: 3.21846/1.16284, loss_spatial_bce_0: 0.05885/0.08747, loss_spatial_dice_0: 0.29479/0.20863, loss_spatial_ce_0: 0.03042/0.06262, loss_grounding_bce_0: 0.03608/0.08622, loss_grounding_dice_0: 0.26630/0.17850, loss_grounding_ce_0: 0.78428/0.27220, loss_mask_ce_1: 1.38522/0.90021, loss_mask_bce_1: 0.30296/0.33548, loss_mask_dice_1: 3.26537/1.16937, loss_spatial_bce_1: 0.06199/0.08799, loss_spatial_dice_1: 0.31055/0.21258, loss_spatial_ce_1: 0.03700/0.06841, loss_grounding_bce_1: 0.03962/0.08639, loss_grounding_dice_1: 0.29314/0.17929, loss_grounding_ce_1: 0.75013/0.27291, loss_mask_ce_2: 1.05816/0.90710, loss_mask_bce_2: 0.29863/0.33604, loss_mask_dice_2: 3.29097/1.16980, loss_spatial_bce_2: 0.06244/0.08902, loss_spatial_dice_2: 0.29645/0.21415, loss_spatial_ce_2: 0.12854/0.07181, loss_grounding_bce_2: 0.03816/0.08653, loss_grounding_dice_2: 0.26428/0.17910, loss_grounding_ce_2: 0.72386/0.27632, loss_mask_ce_3: 1.10671/0.91759, loss_mask_bce_3: 0.29705/0.33715, loss_mask_dice_3: 3.28699/1.16732, loss_spatial_bce_3: 0.06923/0.09018, loss_spatial_dice_3: 0.29206/0.21503, loss_spatial_ce_3: 0.04513/0.07622, loss_grounding_bce_3: 0.04063/0.08676, loss_grounding_dice_3: 0.25210/0.17886, loss_grounding_ce_3: 0.43404/0.27850, loss_mask_ce_4: 1.10751/0.91865, loss_mask_bce_4: 0.30089/0.33927, loss_mask_dice_4: 3.04883/1.19149, loss_spatial_bce_4: 0.06673/0.09412, loss_spatial_dice_4: 0.33357/0.22715, loss_spatial_ce_4: 0.17554/0.09231, loss_grounding_bce_4: 0.04243/0.08725, loss_grounding_dice_4: 0.26542/0.18177, loss_grounding_ce_4: 0.47802/0.28148, loss_mask_ce_5: 1.10237/0.93492, loss_mask_bce_5: 0.32530/0.34157, loss_mask_dice_5: 3.22684/1.19895, loss_spatial_bce_5: 0.06378/0.09627, loss_spatial_dice_5: 0.37318/0.23130, loss_spatial_ce_5: 0.03939/0.10686, loss_grounding_bce_5: 0.03550/0.08769, loss_grounding_dice_5: 0.24447/0.18302, loss_grounding_ce_5: 1.14237/0.29422, loss_mask_ce_6: 1.14264/0.97497, loss_mask_bce_6: 0.31370/0.34423, loss_mask_dice_6: 3.27545/1.20192, loss_spatial_bce_6: 0.08545/0.10200, loss_spatial_dice_6: 0.33663/0.23415, loss_spatial_ce_6: 0.05315/0.13275, loss_grounding_bce_6: 0.03893/0.08842, loss_grounding_dice_6: 0.26216/0.18338, loss_grounding_ce_6: 0.61170/0.30972, loss_mask_ce_7: 1.29313/1.01992, loss_mask_bce_7: 0.31868/0.35207, loss_mask_dice_7: 3.47858/1.25629, loss_spatial_bce_7: 0.07190/0.10996, loss_spatial_dice_7: 0.39567/0.26174, loss_spatial_ce_7: 0.04593/0.16803, loss_grounding_bce_7: 0.03435/0.09031, loss_grounding_dice_7: 0.26950/0.19067, loss_grounding_ce_7: 0.86508/0.34022, loss_mask_ce_8: 1.59406/1.12803, loss_mask_bce_8: 0.31622/0.36572, loss_mask_dice_8: 3.29660/1.32938, loss_spatial_bce_8: 0.07329/0.13061, loss_spatial_dice_8: 0.44044/0.29982, loss_spatial_ce_8: 0.13075/0.22427, loss_grounding_bce_8: 0.03665/0.09407, loss_grounding_dice_8: 0.24479/0.20152, loss_grounding_ce_8: 0.77724/0.40749, loss_mask_ce_9: 4.35311/3.67671, loss_mask_bce_9: 0.30845/0.39265, loss_mask_dice_9: 4.29919/1.90204, loss_spatial_bce_9: 0.14801/0.33334, loss_spatial_dice_9: 0.87304/0.82197, loss_spatial_ce_9: 1.53779/1.49695, loss_grounding_bce_9: 0.03522/0.10555, loss_grounding_dice_9: 0.39095/0.28080, loss_grounding_ce_9: 1.10871/0.67223] items per batch[64] items per second[0.23] total items[4006400] mini batches[ 62600] memory[7345] epoch remaining[1:02:43]
INFO:trainer.default_trainer:epochs[    34] optim steps[62700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20403/0.89950, loss_mask_bce_0: 0.48870/0.33457, loss_mask_dice_0: 0.55803/1.16321, loss_spatial_bce_0: 0.16215/0.08746, loss_spatial_dice_0: 0.19511/0.20863, loss_spatial_ce_0: 0.11736/0.06260, loss_grounding_bce_0: 0.41679/0.08621, loss_grounding_dice_0: 0.39675/0.17850, loss_grounding_ce_0: 0.43319/0.27216, loss_mask_ce_1: 1.30618/0.90014, loss_mask_bce_1: 0.45753/0.33551, loss_mask_dice_1: 0.45889/1.16971, loss_spatial_bce_1: 0.17629/0.08797, loss_spatial_dice_1: 0.21835/0.21258, loss_spatial_ce_1: 0.09911/0.06840, loss_grounding_bce_1: 0.37414/0.08638, loss_grounding_dice_1: 0.41258/0.17929, loss_grounding_ce_1: 0.47606/0.27289, loss_mask_ce_2: 1.30231/0.90704, loss_mask_bce_2: 0.45963/0.33607, loss_mask_dice_2: 0.49061/1.17020, loss_spatial_bce_2: 0.17627/0.08900, loss_spatial_dice_2: 0.22897/0.21415, loss_spatial_ce_2: 0.08822/0.07177, loss_grounding_bce_2: 0.38717/0.08652, loss_grounding_dice_2: 0.37980/0.17910, loss_grounding_ce_2: 0.47477/0.27629, loss_mask_ce_3: 1.26731/0.91753, loss_mask_bce_3: 0.46790/0.33718, loss_mask_dice_3: 0.50245/1.16765, loss_spatial_bce_3: 0.21465/0.09018, loss_spatial_dice_3: 0.26966/0.21503, loss_spatial_ce_3: 0.11564/0.07620, loss_grounding_bce_3: 0.44811/0.08675, loss_grounding_dice_3: 0.39337/0.17886, loss_grounding_ce_3: 0.47316/0.27847, loss_mask_ce_4: 1.26929/0.91863, loss_mask_bce_4: 0.45546/0.33929, loss_mask_dice_4: 0.50242/1.19184, loss_spatial_bce_4: 0.22632/0.09411, loss_spatial_dice_4: 0.31344/0.22716, loss_spatial_ce_4: 0.23120/0.09228, loss_grounding_bce_4: 0.38412/0.08723, loss_grounding_dice_4: 0.39304/0.18176, loss_grounding_ce_4: 0.46843/0.28144, loss_mask_ce_5: 1.04828/0.93490, loss_mask_bce_5: 0.45157/0.34158, loss_mask_dice_5: 0.57683/1.19934, loss_spatial_bce_5: 0.23102/0.09627, loss_spatial_dice_5: 0.31880/0.23131, loss_spatial_ce_5: 0.30183/0.10684, loss_grounding_bce_5: 0.30235/0.08767, loss_grounding_dice_5: 0.37153/0.18302, loss_grounding_ce_5: 0.41459/0.29419, loss_mask_ce_6: 0.85413/0.97497, loss_mask_bce_6: 0.55725/0.34425, loss_mask_dice_6: 0.50543/1.20228, loss_spatial_bce_6: 0.24680/0.10199, loss_spatial_dice_6: 0.32050/0.23416, loss_spatial_ce_6: 0.40780/0.13270, loss_grounding_bce_6: 0.34049/0.08841, loss_grounding_dice_6: 0.39222/0.18338, loss_grounding_ce_6: 0.37109/0.30969, loss_mask_ce_7: 1.18367/1.01989, loss_mask_bce_7: 0.52637/0.35209, loss_mask_dice_7: 0.57899/1.25669, loss_spatial_bce_7: 0.26983/0.10995, loss_spatial_dice_7: 0.31537/0.26176, loss_spatial_ce_7: 0.32922/0.16799, loss_grounding_bce_7: 0.37521/0.09030, loss_grounding_dice_7: 0.44410/0.19067, loss_grounding_ce_7: 0.42052/0.34016, loss_mask_ce_8: 1.16109/1.12805, loss_mask_bce_8: 0.50284/0.36575, loss_mask_dice_8: 0.58954/1.32981, loss_spatial_bce_8: 0.42517/0.13060, loss_spatial_dice_8: 0.34212/0.29984, loss_spatial_ce_8: 0.44332/0.22423, loss_grounding_bce_8: 0.47468/0.09406, loss_grounding_dice_8: 0.55012/0.20152, loss_grounding_ce_8: 0.41232/0.40747, loss_mask_ce_9: 2.73783/3.67671, loss_mask_bce_9: 0.50543/0.39268, loss_mask_dice_9: 0.75157/1.90266, loss_spatial_bce_9: 0.58655/0.33330, loss_spatial_dice_9: 0.65294/0.82198, loss_spatial_ce_9: 1.75618/1.49693, loss_grounding_bce_9: 0.46327/0.10555, loss_grounding_dice_9: 0.58318/0.28082, loss_grounding_ce_9: 0.40011/0.67209] items per batch[64] items per second[0.24] total items[4012800] mini batches[ 62700] memory[7345] epoch remaining[0:57:46]
INFO:trainer.default_trainer:epochs[    34] optim steps[62800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48673/0.89959, loss_mask_bce_0: 0.16461/0.33454, loss_mask_dice_0: 0.56415/1.16306, loss_spatial_bce_0: 0.03947/0.08746, loss_spatial_dice_0: 0.10937/0.20862, loss_spatial_ce_0: 0.05982/0.06255, loss_grounding_bce_0: 0.07283/0.08622, loss_grounding_dice_0: 0.10894/0.17848, loss_grounding_ce_0: 0.25625/0.27219, loss_mask_ce_1: 0.47282/0.90024, loss_mask_bce_1: 0.16339/0.33549, loss_mask_dice_1: 0.55750/1.16956, loss_spatial_bce_1: 0.03978/0.08797, loss_spatial_dice_1: 0.10950/0.21256, loss_spatial_ce_1: 0.04841/0.06836, loss_grounding_bce_1: 0.07331/0.08639, loss_grounding_dice_1: 0.11110/0.17928, loss_grounding_ce_1: 0.25445/0.27296, loss_mask_ce_2: 0.48067/0.90717, loss_mask_bce_2: 0.16244/0.33605, loss_mask_dice_2: 0.60050/1.17006, loss_spatial_bce_2: 0.04039/0.08900, loss_spatial_dice_2: 0.11854/0.21413, loss_spatial_ce_2: 0.06173/0.07173, loss_grounding_bce_2: 0.07422/0.08653, loss_grounding_dice_2: 0.11155/0.17909, loss_grounding_ce_2: 0.26280/0.27630, loss_mask_ce_3: 0.54042/0.91764, loss_mask_bce_3: 0.16456/0.33716, loss_mask_dice_3: 0.57091/1.16751, loss_spatial_bce_3: 0.03818/0.09018, loss_spatial_dice_3: 0.11503/0.21501, loss_spatial_ce_3: 0.09661/0.07617, loss_grounding_bce_3: 0.07446/0.08676, loss_grounding_dice_3: 0.10679/0.17884, loss_grounding_ce_3: 0.24628/0.27848, loss_mask_ce_4: 0.52967/0.91872, loss_mask_bce_4: 0.16058/0.33928, loss_mask_dice_4: 0.54040/1.19169, loss_spatial_bce_4: 0.04515/0.09412, loss_spatial_dice_4: 0.12865/0.22715, loss_spatial_ce_4: 0.04994/0.09225, loss_grounding_bce_4: 0.07147/0.08725, loss_grounding_dice_4: 0.10123/0.18175, loss_grounding_ce_4: 0.27843/0.28144, loss_mask_ce_5: 0.52548/0.93499, loss_mask_bce_5: 0.15466/0.34157, loss_mask_dice_5: 0.51895/1.19918, loss_spatial_bce_5: 0.04150/0.09627, loss_spatial_dice_5: 0.12297/0.23130, loss_spatial_ce_5: 0.08032/0.10679, loss_grounding_bce_5: 0.07189/0.08768, loss_grounding_dice_5: 0.09953/0.18300, loss_grounding_ce_5: 0.24470/0.29421, loss_mask_ce_6: 0.65105/0.97505, loss_mask_bce_6: 0.16045/0.34423, loss_mask_dice_6: 0.55955/1.20211, loss_spatial_bce_6: 0.05382/0.10199, loss_spatial_dice_6: 0.13660/0.23414, loss_spatial_ce_6: 0.10431/0.13265, loss_grounding_bce_6: 0.06921/0.08842, loss_grounding_dice_6: 0.09043/0.18336, loss_grounding_ce_6: 0.25542/0.30972, loss_mask_ce_7: 0.65240/1.01999, loss_mask_bce_7: 0.14833/0.35208, loss_mask_dice_7: 0.55988/1.25653, loss_spatial_bce_7: 0.05315/0.10994, loss_spatial_dice_7: 0.14398/0.26175, loss_spatial_ce_7: 0.17304/0.16794, loss_grounding_bce_7: 0.06894/0.09031, loss_grounding_dice_7: 0.09519/0.19065, loss_grounding_ce_7: 0.30196/0.34017, loss_mask_ce_8: 0.87170/1.12820, loss_mask_bce_8: 0.17640/0.36572, loss_mask_dice_8: 0.61808/1.32964, loss_spatial_bce_8: 0.07547/0.13061, loss_spatial_dice_8: 0.15802/0.29983, loss_spatial_ce_8: 0.12417/0.22416, loss_grounding_bce_8: 0.07050/0.09408, loss_grounding_dice_8: 0.08689/0.20150, loss_grounding_ce_8: 0.45470/0.40748, loss_mask_ce_9: 4.19208/3.67678, loss_mask_bce_9: 0.29434/0.39268, loss_mask_dice_9: 1.04019/1.90253, loss_spatial_bce_9: 0.29337/0.33329, loss_spatial_dice_9: 0.86238/0.82196, loss_spatial_ce_9: 1.30693/1.49679, loss_grounding_bce_9: 0.14823/0.10557, loss_grounding_dice_9: 0.24411/0.28080, loss_grounding_ce_9: 1.17628/0.67217] items per batch[64] items per second[0.22] total items[4019200] mini batches[ 62800] memory[7345] epoch remaining[0:53:21]
INFO:trainer.default_trainer:epochs[    34] optim steps[62900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.31637/0.89957, loss_mask_bce_0: 0.74877/0.33457, loss_mask_dice_0: 1.90707/1.16309, loss_spatial_bce_0: 0.07142/0.08746, loss_spatial_dice_0: 0.20921/0.20860, loss_spatial_ce_0: 0.10555/0.06251, loss_grounding_bce_0: 0.05244/0.08623, loss_grounding_dice_0: 0.12864/0.17846, loss_grounding_ce_0: 0.19982/0.27226, loss_mask_ce_1: 1.33897/0.90024, loss_mask_bce_1: 0.74747/0.33551, loss_mask_dice_1: 1.94358/1.16957, loss_spatial_bce_1: 0.07180/0.08797, loss_spatial_dice_1: 0.21991/0.21254, loss_spatial_ce_1: 0.10137/0.06833, loss_grounding_bce_1: 0.05110/0.08640, loss_grounding_dice_1: 0.12639/0.17926, loss_grounding_ce_1: 0.16884/0.27303, loss_mask_ce_2: 1.29498/0.90717, loss_mask_bce_2: 0.74026/0.33608, loss_mask_dice_2: 1.92596/1.17012, loss_spatial_bce_2: 0.06951/0.08900, loss_spatial_dice_2: 0.21374/0.21412, loss_spatial_ce_2: 0.08334/0.07170, loss_grounding_bce_2: 0.05582/0.08654, loss_grounding_dice_2: 0.13021/0.17907, loss_grounding_ce_2: 0.16021/0.27636, loss_mask_ce_3: 1.41212/0.91764, loss_mask_bce_3: 0.71621/0.33719, loss_mask_dice_3: 1.85420/1.16753, loss_spatial_bce_3: 0.07049/0.09018, loss_spatial_dice_3: 0.20439/0.21500, loss_spatial_ce_3: 0.08010/0.07615, loss_grounding_bce_3: 0.05234/0.08678, loss_grounding_dice_3: 0.12687/0.17882, loss_grounding_ce_3: 0.14887/0.27856, loss_mask_ce_4: 1.44213/0.91874, loss_mask_bce_4: 0.74167/0.33931, loss_mask_dice_4: 1.88470/1.19169, loss_spatial_bce_4: 0.06977/0.09411, loss_spatial_dice_4: 0.23403/0.22714, loss_spatial_ce_4: 0.03143/0.09222, loss_grounding_bce_4: 0.05955/0.08726, loss_grounding_dice_4: 0.13833/0.18173, loss_grounding_ce_4: 0.13142/0.28154, loss_mask_ce_5: 1.47895/0.93501, loss_mask_bce_5: 0.73783/0.34160, loss_mask_dice_5: 1.88160/1.19918, loss_spatial_bce_5: 0.07929/0.09627, loss_spatial_dice_5: 0.24490/0.23129, loss_spatial_ce_5: 0.01466/0.10675, loss_grounding_bce_5: 0.04693/0.08770, loss_grounding_dice_5: 0.12559/0.18298, loss_grounding_ce_5: 0.13873/0.29426, loss_mask_ce_6: 1.64505/0.97507, loss_mask_bce_6: 0.72627/0.34426, loss_mask_dice_6: 1.98786/1.20214, loss_spatial_bce_6: 0.07139/0.10199, loss_spatial_dice_6: 0.23047/0.23414, loss_spatial_ce_6: 0.01427/0.13260, loss_grounding_bce_6: 0.04346/0.08844, loss_grounding_dice_6: 0.10508/0.18334, loss_grounding_ce_6: 0.17618/0.30975, loss_mask_ce_7: 1.64700/1.02000, loss_mask_bce_7: 0.81757/0.35212, loss_mask_dice_7: 2.12606/1.25653, loss_spatial_bce_7: 0.08703/0.10994, loss_spatial_dice_7: 0.25270/0.26174, loss_spatial_ce_7: 0.08985/0.16791, loss_grounding_bce_7: 0.07695/0.09033, loss_grounding_dice_7: 0.13131/0.19064, loss_grounding_ce_7: 0.14212/0.34023, loss_mask_ce_8: 1.76989/1.12825, loss_mask_bce_8: 0.91346/0.36575, loss_mask_dice_8: 2.35220/1.32968, loss_spatial_bce_8: 0.10707/0.13060, loss_spatial_dice_8: 0.29738/0.29981, loss_spatial_ce_8: 0.12714/0.22411, loss_grounding_bce_8: 0.07669/0.09408, loss_grounding_dice_8: 0.14444/0.20148, loss_grounding_ce_8: 0.13835/0.40750, loss_mask_ce_9: 3.72416/3.67689, loss_mask_bce_9: 0.80334/0.39271, loss_mask_dice_9: 2.62735/1.90260, loss_spatial_bce_9: 0.32168/0.33330, loss_spatial_dice_9: 0.91635/0.82197, loss_spatial_ce_9: 1.50798/1.49678, loss_grounding_bce_9: 0.07785/0.10557, loss_grounding_dice_9: 0.17898/0.28078, loss_grounding_ce_9: 0.39823/0.67220] items per batch[64] items per second[0.23] total items[4025600] mini batches[ 62900] memory[7345] epoch remaining[0:48:35]
INFO:trainer.default_trainer:epochs[    34] optim steps[63000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83969/0.89953, loss_mask_bce_0: 0.16865/0.33459, loss_mask_dice_0: 0.76992/1.16290, loss_spatial_bce_0: 0.04778/0.08747, loss_spatial_dice_0: 0.19518/0.20858, loss_spatial_ce_0: 0.00936/0.06249, loss_grounding_bce_0: 0.02661/0.08625, loss_grounding_dice_0: 0.31491/0.17844, loss_grounding_ce_0: 0.23828/0.27226, loss_mask_ce_1: 0.90293/0.90022, loss_mask_bce_1: 0.18467/0.33553, loss_mask_dice_1: 0.70595/1.16940, loss_spatial_bce_1: 0.04770/0.08797, loss_spatial_dice_1: 0.22316/0.21252, loss_spatial_ce_1: 0.01272/0.06833, loss_grounding_bce_1: 0.02816/0.08642, loss_grounding_dice_1: 0.16165/0.17925, loss_grounding_ce_1: 0.13488/0.27305, loss_mask_ce_2: 0.91243/0.90713, loss_mask_bce_2: 0.18286/0.33610, loss_mask_dice_2: 0.65051/1.16994, loss_spatial_bce_2: 0.05004/0.08901, loss_spatial_dice_2: 0.17273/0.21410, loss_spatial_ce_2: 0.02453/0.07171, loss_grounding_bce_2: 0.02766/0.08656, loss_grounding_dice_2: 0.20696/0.17906, loss_grounding_ce_2: 0.12901/0.27637, loss_mask_ce_3: 0.92258/0.91761, loss_mask_bce_3: 0.17982/0.33721, loss_mask_dice_3: 0.83365/1.16737, loss_spatial_bce_3: 0.04791/0.09020, loss_spatial_dice_3: 0.16315/0.21497, loss_spatial_ce_3: 0.03501/0.07615, loss_grounding_bce_3: 0.02579/0.08680, loss_grounding_dice_3: 0.24651/0.17880, loss_grounding_ce_3: 0.14410/0.27860, loss_mask_ce_4: 0.84888/0.91869, loss_mask_bce_4: 0.16295/0.33933, loss_mask_dice_4: 0.86739/1.19150, loss_spatial_bce_4: 0.05296/0.09412, loss_spatial_dice_4: 0.21363/0.22711, loss_spatial_ce_4: 0.27811/0.09222, loss_grounding_bce_4: 0.02402/0.08728, loss_grounding_dice_4: 0.26783/0.18172, loss_grounding_ce_4: 0.14565/0.28154, loss_mask_ce_5: 0.83039/0.93496, loss_mask_bce_5: 0.17183/0.34162, loss_mask_dice_5: 0.80366/1.19898, loss_spatial_bce_5: 0.05453/0.09628, loss_spatial_dice_5: 0.21267/0.23127, loss_spatial_ce_5: 0.02693/0.10675, loss_grounding_bce_5: 0.02638/0.08772, loss_grounding_dice_5: 0.32606/0.18297, loss_grounding_ce_5: 0.14457/0.29422, loss_mask_ce_6: 0.90127/0.97503, loss_mask_bce_6: 0.18788/0.34428, loss_mask_dice_6: 0.82582/1.20195, loss_spatial_bce_6: 0.05901/0.10200, loss_spatial_dice_6: 0.20710/0.23412, loss_spatial_ce_6: 0.05478/0.13261, loss_grounding_bce_6: 0.02422/0.08846, loss_grounding_dice_6: 0.25863/0.18333, loss_grounding_ce_6: 0.15506/0.30976, loss_mask_ce_7: 0.96754/1.01995, loss_mask_bce_7: 0.18487/0.35214, loss_mask_dice_7: 0.85600/1.25635, loss_spatial_bce_7: 0.06292/0.10996, loss_spatial_dice_7: 0.23550/0.26171, loss_spatial_ce_7: 0.09718/0.16792, loss_grounding_bce_7: 0.02461/0.09036, loss_grounding_dice_7: 0.20805/0.19062, loss_grounding_ce_7: 0.15726/0.34026, loss_mask_ce_8: 1.45143/1.12822, loss_mask_bce_8: 0.16645/0.36578, loss_mask_dice_8: 0.79093/1.32947, loss_spatial_bce_8: 0.08202/0.13061, loss_spatial_dice_8: 0.31310/0.29977, loss_spatial_ce_8: 0.09868/0.22408, loss_grounding_bce_8: 0.02731/0.09412, loss_grounding_dice_8: 0.30558/0.20147, loss_grounding_ce_8: 0.21361/0.40750, loss_mask_ce_9: 3.36029/3.67685, loss_mask_bce_9: 0.20952/0.39272, loss_mask_dice_9: 1.10948/1.90238, loss_spatial_bce_9: 0.26627/0.33334, loss_spatial_dice_9: 0.75850/0.82194, loss_spatial_ce_9: 1.48542/1.49668, loss_grounding_bce_9: 0.04706/0.10560, loss_grounding_dice_9: 0.28798/0.28075, loss_grounding_ce_9: 0.41924/0.67240] items per batch[64] items per second[0.23] total items[4032000] mini batches[ 63000] memory[7345] epoch remaining[0:43:59]
INFO:trainer.default_trainer:epochs[    34] optim steps[63100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42089/0.89955, loss_mask_bce_0: 0.41921/0.33462, loss_mask_dice_0: 0.51687/1.16284, loss_spatial_bce_0: 0.21876/0.08747, loss_spatial_dice_0: 0.26519/0.20856, loss_spatial_ce_0: 0.04831/0.06249, loss_grounding_bce_0: 0.21354/0.08626, loss_grounding_dice_0: 0.20816/0.17845, loss_grounding_ce_0: 0.14661/0.27226, loss_mask_ce_1: 0.43401/0.90021, loss_mask_bce_1: 0.43328/0.33556, loss_mask_dice_1: 0.49875/1.16934, loss_spatial_bce_1: 0.21904/0.08798, loss_spatial_dice_1: 0.26154/0.21250, loss_spatial_ce_1: 0.04041/0.06833, loss_grounding_bce_1: 0.22192/0.08644, loss_grounding_dice_1: 0.19492/0.17926, loss_grounding_ce_1: 0.14813/0.27304, loss_mask_ce_2: 0.42977/0.90711, loss_mask_bce_2: 0.40563/0.33613, loss_mask_dice_2: 0.49912/1.16988, loss_spatial_bce_2: 0.21247/0.08902, loss_spatial_dice_2: 0.25231/0.21408, loss_spatial_ce_2: 0.05113/0.07170, loss_grounding_bce_2: 0.21329/0.08658, loss_grounding_dice_2: 0.19773/0.17906, loss_grounding_ce_2: 0.13965/0.27635, loss_mask_ce_3: 0.41084/0.91762, loss_mask_bce_3: 0.42759/0.33724, loss_mask_dice_3: 0.54795/1.16732, loss_spatial_bce_3: 0.21411/0.09020, loss_spatial_dice_3: 0.27816/0.21496, loss_spatial_ce_3: 0.03833/0.07615, loss_grounding_bce_3: 0.22142/0.08681, loss_grounding_dice_3: 0.24461/0.17882, loss_grounding_ce_3: 0.15540/0.27859, loss_mask_ce_4: 0.37487/0.91870, loss_mask_bce_4: 0.43583/0.33936, loss_mask_dice_4: 0.47840/1.19147, loss_spatial_bce_4: 0.24457/0.09414, loss_spatial_dice_4: 0.25201/0.22709, loss_spatial_ce_4: 0.06336/0.09223, loss_grounding_bce_4: 0.21663/0.08730, loss_grounding_dice_4: 0.21963/0.18173, loss_grounding_ce_4: 0.15061/0.28153, loss_mask_ce_5: 0.39991/0.93492, loss_mask_bce_5: 0.41881/0.34165, loss_mask_dice_5: 0.46020/1.19893, loss_spatial_bce_5: 0.21182/0.09629, loss_spatial_dice_5: 0.24975/0.23127, loss_spatial_ce_5: 0.11527/0.10675, loss_grounding_bce_5: 0.22919/0.08774, loss_grounding_dice_5: 0.24516/0.18297, loss_grounding_ce_5: 0.17125/0.29421, loss_mask_ce_6: 0.40412/0.97504, loss_mask_bce_6: 0.43588/0.34431, loss_mask_dice_6: 0.46316/1.20190, loss_spatial_bce_6: 0.23411/0.10201, loss_spatial_dice_6: 0.31669/0.23411, loss_spatial_ce_6: 0.12827/0.13264, loss_grounding_bce_6: 0.23268/0.08848, loss_grounding_dice_6: 0.20696/0.18334, loss_grounding_ce_6: 0.17699/0.30974, loss_mask_ce_7: 0.45311/1.01994, loss_mask_bce_7: 0.43498/0.35217, loss_mask_dice_7: 0.50749/1.25626, loss_spatial_bce_7: 0.22411/0.10996, loss_spatial_dice_7: 0.28279/0.26169, loss_spatial_ce_7: 0.15542/0.16795, loss_grounding_bce_7: 0.23152/0.09038, loss_grounding_dice_7: 0.22425/0.19064, loss_grounding_ce_7: 0.18635/0.34024, loss_mask_ce_8: 0.39928/1.12818, loss_mask_bce_8: 0.46145/0.36581, loss_mask_dice_8: 0.59355/1.32941, loss_spatial_bce_8: 0.19898/0.13064, loss_spatial_dice_8: 0.25247/0.29977, loss_spatial_ce_8: 0.40293/0.22405, loss_grounding_bce_8: 0.23126/0.09414, loss_grounding_dice_8: 0.26086/0.20148, loss_grounding_ce_8: 0.15768/0.40751, loss_mask_ce_9: 1.78918/3.67688, loss_mask_bce_9: 0.46806/0.39275, loss_mask_dice_9: 0.64691/1.90222, loss_spatial_bce_9: 0.29030/0.33333, loss_spatial_dice_9: 0.61201/0.82194, loss_spatial_ce_9: 1.24824/1.49664, loss_grounding_bce_9: 0.25206/0.10561, loss_grounding_dice_9: 0.27482/0.28077, loss_grounding_ce_9: 0.19099/0.67236] items per batch[64] items per second[0.23] total items[4038400] mini batches[ 63100] memory[7345] epoch remaining[0:39:17]
INFO:trainer.default_trainer:epochs[    34] optim steps[63200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35127/0.89952, loss_mask_bce_0: 0.14380/0.33461, loss_mask_dice_0: 0.18281/1.16270, loss_spatial_bce_0: 0.06922/0.08747, loss_spatial_dice_0: 0.07745/0.20855, loss_spatial_ce_0: 0.00259/0.06246, loss_grounding_bce_0: 0.07417/0.08627, loss_grounding_dice_0: 0.05641/0.17844, loss_grounding_ce_0: 0.03933/0.27226, loss_mask_ce_1: 0.37309/0.90018, loss_mask_bce_1: 0.15615/0.33555, loss_mask_dice_1: 0.22894/1.16919, loss_spatial_bce_1: 0.08618/0.08799, loss_spatial_dice_1: 0.10843/0.21249, loss_spatial_ce_1: 0.01018/0.06831, loss_grounding_bce_1: 0.07008/0.08644, loss_grounding_dice_1: 0.06341/0.17925, loss_grounding_ce_1: 0.02539/0.27305, loss_mask_ce_2: 0.32713/0.90708, loss_mask_bce_2: 0.15211/0.33612, loss_mask_dice_2: 0.18444/1.16976, loss_spatial_bce_2: 0.08163/0.08903, loss_spatial_dice_2: 0.10046/0.21408, loss_spatial_ce_2: 0.01294/0.07169, loss_grounding_bce_2: 0.07079/0.08658, loss_grounding_dice_2: 0.05896/0.17906, loss_grounding_ce_2: 0.02641/0.27635, loss_mask_ce_3: 0.34636/0.91760, loss_mask_bce_3: 0.15718/0.33723, loss_mask_dice_3: 0.16474/1.16719, loss_spatial_bce_3: 0.06856/0.09021, loss_spatial_dice_3: 0.08644/0.21496, loss_spatial_ce_3: 0.01912/0.07615, loss_grounding_bce_3: 0.06987/0.08681, loss_grounding_dice_3: 0.05943/0.17880, loss_grounding_ce_3: 0.02761/0.27862, loss_mask_ce_4: 0.36746/0.91868, loss_mask_bce_4: 0.17360/0.33935, loss_mask_dice_4: 0.23308/1.19131, loss_spatial_bce_4: 0.06226/0.09415, loss_spatial_dice_4: 0.06702/0.22709, loss_spatial_ce_4: 0.02838/0.09223, loss_grounding_bce_4: 0.06976/0.08730, loss_grounding_dice_4: 0.06137/0.18173, loss_grounding_ce_4: 0.02646/0.28154, loss_mask_ce_5: 0.31255/0.93490, loss_mask_bce_5: 0.15023/0.34165, loss_mask_dice_5: 0.26448/1.19875, loss_spatial_bce_5: 0.05757/0.09630, loss_spatial_dice_5: 0.07279/0.23126, loss_spatial_ce_5: 0.03245/0.10673, loss_grounding_bce_5: 0.07228/0.08775, loss_grounding_dice_5: 0.05381/0.18297, loss_grounding_ce_5: 0.02748/0.29424, loss_mask_ce_6: 0.46593/0.97504, loss_mask_bce_6: 0.13887/0.34429, loss_mask_dice_6: 0.20985/1.20176, loss_spatial_bce_6: 0.06115/0.10202, loss_spatial_dice_6: 0.08505/0.23411, loss_spatial_ce_6: 0.06259/0.13261, loss_grounding_bce_6: 0.06974/0.08848, loss_grounding_dice_6: 0.05662/0.18333, loss_grounding_ce_6: 0.05266/0.30974, loss_mask_ce_7: 0.50666/1.01996, loss_mask_bce_7: 0.14249/0.35216, loss_mask_dice_7: 0.29693/1.25607, loss_spatial_bce_7: 0.05714/0.10998, loss_spatial_dice_7: 0.10444/0.26168, loss_spatial_ce_7: 0.07753/0.16794, loss_grounding_bce_7: 0.07404/0.09038, loss_grounding_dice_7: 0.06909/0.19063, loss_grounding_ce_7: 0.07791/0.34026, loss_mask_ce_8: 0.85004/1.12827, loss_mask_bce_8: 0.16900/0.36580, loss_mask_dice_8: 0.42485/1.32922, loss_spatial_bce_8: 0.09282/0.13065, loss_spatial_dice_8: 0.14524/0.29976, loss_spatial_ce_8: 0.13380/0.22403, loss_grounding_bce_8: 0.06201/0.09414, loss_grounding_dice_8: 0.10184/0.20148, loss_grounding_ce_8: 0.15233/0.40747, loss_mask_ce_9: 3.75297/3.67683, loss_mask_bce_9: 0.35726/0.39275, loss_mask_dice_9: 0.81294/1.90203, loss_spatial_bce_9: 0.35493/0.33334, loss_spatial_dice_9: 0.77723/0.82191, loss_spatial_ce_9: 1.38807/1.49661, loss_grounding_bce_9: 0.11905/0.10562, loss_grounding_dice_9: 0.41589/0.28078, loss_grounding_ce_9: 2.48037/0.67231] items per batch[64] items per second[0.22] total items[4044800] mini batches[ 63200] memory[7345] epoch remaining[0:34:42]
INFO:trainer.default_trainer:epochs[    34] optim steps[63300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11282/0.89956, loss_mask_bce_0: 0.08918/0.33461, loss_mask_dice_0: 0.25190/1.16268, loss_spatial_bce_0: 0.05272/0.08747, loss_spatial_dice_0: 0.10772/0.20854, loss_spatial_ce_0: 0.00036/0.06244, loss_grounding_bce_0: 0.03417/0.08627, loss_grounding_dice_0: 0.19825/0.17845, loss_grounding_ce_0: 0.01529/0.27228, loss_mask_ce_1: 0.04096/0.90025, loss_mask_bce_1: 0.08799/0.33555, loss_mask_dice_1: 0.09790/1.16916, loss_spatial_bce_1: 0.05274/0.08798, loss_spatial_dice_1: 0.11225/0.21248, loss_spatial_ce_1: 0.00073/0.06829, loss_grounding_bce_1: 0.03378/0.08645, loss_grounding_dice_1: 0.03404/0.17926, loss_grounding_ce_1: 0.01749/0.27310, loss_mask_ce_2: 0.09922/0.90715, loss_mask_bce_2: 0.08665/0.33612, loss_mask_dice_2: 0.24105/1.16974, loss_spatial_bce_2: 0.05165/0.08902, loss_spatial_dice_2: 0.13385/0.21407, loss_spatial_ce_2: 0.00011/0.07167, loss_grounding_bce_2: 0.03526/0.08659, loss_grounding_dice_2: 0.14597/0.17907, loss_grounding_ce_2: 0.02198/0.27638, loss_mask_ce_3: 0.05397/0.91764, loss_mask_bce_3: 0.08776/0.33724, loss_mask_dice_3: 0.19873/1.16719, loss_spatial_bce_3: 0.05415/0.09020, loss_spatial_dice_3: 0.06275/0.21495, loss_spatial_ce_3: 0.00025/0.07614, loss_grounding_bce_3: 0.03619/0.08682, loss_grounding_dice_3: 0.26446/0.17880, loss_grounding_ce_3: 0.30034/0.27867, loss_mask_ce_4: 0.16194/0.91873, loss_mask_bce_4: 0.09101/0.33936, loss_mask_dice_4: 0.21404/1.19132, loss_spatial_bce_4: 0.05588/0.09414, loss_spatial_dice_4: 0.14628/0.22708, loss_spatial_ce_4: 0.00017/0.09222, loss_grounding_bce_4: 0.03720/0.08731, loss_grounding_dice_4: 0.17636/0.18174, loss_grounding_ce_4: 0.01092/0.28156, loss_mask_ce_5: 0.53271/0.93497, loss_mask_bce_5: 0.08763/0.34165, loss_mask_dice_5: 0.19500/1.19873, loss_spatial_bce_5: 0.05230/0.09629, loss_spatial_dice_5: 0.08071/0.23125, loss_spatial_ce_5: 0.02974/0.10671, loss_grounding_bce_5: 0.03378/0.08775, loss_grounding_dice_5: 0.03807/0.18297, loss_grounding_ce_5: 0.00710/0.29427, loss_mask_ce_6: 0.05699/0.97511, loss_mask_bce_6: 0.09238/0.34431, loss_mask_dice_6: 0.17262/1.20176, loss_spatial_bce_6: 0.05661/0.10201, loss_spatial_dice_6: 0.07675/0.23411, loss_spatial_ce_6: 0.00154/0.13259, loss_grounding_bce_6: 0.03724/0.08848, loss_grounding_dice_6: 0.07076/0.18333, loss_grounding_ce_6: 0.01661/0.30976, loss_mask_ce_7: 0.05438/1.02003, loss_mask_bce_7: 0.08969/0.35218, loss_mask_dice_7: 0.20880/1.25606, loss_spatial_bce_7: 0.06124/0.10997, loss_spatial_dice_7: 0.19228/0.26168, loss_spatial_ce_7: 0.03536/0.16793, loss_grounding_bce_7: 0.03035/0.09039, loss_grounding_dice_7: 0.08464/0.19063, loss_grounding_ce_7: 0.01939/0.34031, loss_mask_ce_8: 0.06699/1.12836, loss_mask_bce_8: 0.08526/0.36582, loss_mask_dice_8: 0.18712/1.32920, loss_spatial_bce_8: 0.06654/0.13065, loss_spatial_dice_8: 0.19871/0.29976, loss_spatial_ce_8: 0.07771/0.22398, loss_grounding_bce_8: 0.04052/0.09415, loss_grounding_dice_8: 0.17576/0.20148, loss_grounding_ce_8: 0.09747/0.40760, loss_mask_ce_9: 2.21824/3.67694, loss_mask_bce_9: 0.09369/0.39277, loss_mask_dice_9: 0.21974/1.90191, loss_spatial_bce_9: 0.34263/0.33334, loss_spatial_dice_9: 0.56945/0.82192, loss_spatial_ce_9: 1.33608/1.49655, loss_grounding_bce_9: 0.05342/0.10564, loss_grounding_dice_9: 0.18944/0.28078, loss_grounding_ce_9: 0.91070/0.67236] items per batch[64] items per second[0.23] total items[4051200] mini batches[ 63300] memory[7345] epoch remaining[0:30:04]
INFO:trainer.default_trainer:epochs[    34] optim steps[63400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43568/0.89950, loss_mask_bce_0: 0.21347/0.33458, loss_mask_dice_0: 0.33803/1.16284, loss_spatial_bce_0: 0.08509/0.08746, loss_spatial_dice_0: 0.14656/0.20854, loss_spatial_ce_0: 0.03513/0.06244, loss_grounding_bce_0: 0.02464/0.08626, loss_grounding_dice_0: 0.08976/0.17846, loss_grounding_ce_0: 0.29368/0.27227, loss_mask_ce_1: 0.44767/0.90018, loss_mask_bce_1: 0.22268/0.33552, loss_mask_dice_1: 0.33927/1.16932, loss_spatial_bce_1: 0.08916/0.08798, loss_spatial_dice_1: 0.13674/0.21248, loss_spatial_ce_1: 0.04757/0.06830, loss_grounding_bce_1: 0.02947/0.08644, loss_grounding_dice_1: 0.09716/0.17927, loss_grounding_ce_1: 0.26566/0.27306, loss_mask_ce_2: 0.45195/0.90712, loss_mask_bce_2: 0.20937/0.33610, loss_mask_dice_2: 0.32131/1.16990, loss_spatial_bce_2: 0.09302/0.08903, loss_spatial_dice_2: 0.14796/0.21407, loss_spatial_ce_2: 0.06846/0.07168, loss_grounding_bce_2: 0.02636/0.08658, loss_grounding_dice_2: 0.09076/0.17908, loss_grounding_ce_2: 0.27547/0.27637, loss_mask_ce_3: 0.52968/0.91756, loss_mask_bce_3: 0.20171/0.33722, loss_mask_dice_3: 0.30210/1.16736, loss_spatial_bce_3: 0.08865/0.09021, loss_spatial_dice_3: 0.14080/0.21495, loss_spatial_ce_3: 0.06546/0.07616, loss_grounding_bce_3: 0.03058/0.08681, loss_grounding_dice_3: 0.10522/0.17881, loss_grounding_ce_3: 0.25101/0.27865, loss_mask_ce_4: 0.26853/0.91869, loss_mask_bce_4: 0.20852/0.33933, loss_mask_dice_4: 0.33522/1.19150, loss_spatial_bce_4: 0.08891/0.09414, loss_spatial_dice_4: 0.16023/0.22709, loss_spatial_ce_4: 0.04434/0.09223, loss_grounding_bce_4: 0.03154/0.08730, loss_grounding_dice_4: 0.09395/0.18175, loss_grounding_ce_4: 0.51227/0.28150, loss_mask_ce_5: 0.24424/0.93493, loss_mask_bce_5: 0.20347/0.34162, loss_mask_dice_5: 0.32208/1.19890, loss_spatial_bce_5: 0.09973/0.09630, loss_spatial_dice_5: 0.18095/0.23126, loss_spatial_ce_5: 0.08170/0.10673, loss_grounding_bce_5: 0.03098/0.08775, loss_grounding_dice_5: 0.10101/0.18297, loss_grounding_ce_5: 0.48120/0.29420, loss_mask_ce_6: 0.27135/0.97506, loss_mask_bce_6: 0.21356/0.34428, loss_mask_dice_6: 0.32252/1.20191, loss_spatial_bce_6: 0.10690/0.10202, loss_spatial_dice_6: 0.17958/0.23412, loss_spatial_ce_6: 0.05701/0.13259, loss_grounding_bce_6: 0.02776/0.08848, loss_grounding_dice_6: 0.08950/0.18335, loss_grounding_ce_6: 0.56755/0.30968, loss_mask_ce_7: 0.47291/1.02004, loss_mask_bce_7: 0.20983/0.35215, loss_mask_dice_7: 0.36059/1.25622, loss_spatial_bce_7: 0.11177/0.10997, loss_spatial_dice_7: 0.19048/0.26170, loss_spatial_ce_7: 0.04299/0.16794, loss_grounding_bce_7: 0.03035/0.09038, loss_grounding_dice_7: 0.11054/0.19063, loss_grounding_ce_7: 0.45300/0.34022, loss_mask_ce_8: 0.35731/1.12830, loss_mask_bce_8: 0.21561/0.36578, loss_mask_dice_8: 0.35184/1.32937, loss_spatial_bce_8: 0.12666/0.13064, loss_spatial_dice_8: 0.25187/0.29977, loss_spatial_ce_8: 0.04571/0.22398, loss_grounding_bce_8: 0.03314/0.09414, loss_grounding_dice_8: 0.12933/0.20148, loss_grounding_ce_8: 0.18988/0.40746, loss_mask_ce_9: 2.29850/3.67687, loss_mask_bce_9: 0.21143/0.39271, loss_mask_dice_9: 0.58310/1.90192, loss_spatial_bce_9: 0.47918/0.33332, loss_spatial_dice_9: 0.82836/0.82190, loss_spatial_ce_9: 2.33037/1.49664, loss_grounding_bce_9: 0.04074/0.10562, loss_grounding_dice_9: 0.18755/0.28076, loss_grounding_ce_9: 0.49365/0.67222] items per batch[64] items per second[0.23] total items[4057600] mini batches[ 63400] memory[7345] epoch remaining[0:25:26]
INFO:trainer.default_trainer:epochs[    34] optim steps[63500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89927/0.89952, loss_mask_bce_0: 0.51601/0.33461, loss_mask_dice_0: 1.04016/1.16277, loss_spatial_bce_0: 0.07333/0.08747, loss_spatial_dice_0: 0.17216/0.20853, loss_spatial_ce_0: 0.01419/0.06245, loss_grounding_bce_0: 0.08600/0.08626, loss_grounding_dice_0: 0.09356/0.17846, loss_grounding_ce_0: 0.02755/0.27222, loss_mask_ce_1: 0.96941/0.90020, loss_mask_bce_1: 0.51922/0.33555, loss_mask_dice_1: 1.08389/1.16924, loss_spatial_bce_1: 0.07663/0.08799, loss_spatial_dice_1: 0.17070/0.21247, loss_spatial_ce_1: 0.01260/0.06829, loss_grounding_bce_1: 0.08277/0.08644, loss_grounding_dice_1: 0.09001/0.17928, loss_grounding_ce_1: 0.02735/0.27302, loss_mask_ce_2: 0.84620/0.90718, loss_mask_bce_2: 0.52455/0.33611, loss_mask_dice_2: 1.15987/1.16983, loss_spatial_bce_2: 0.07542/0.08904, loss_spatial_dice_2: 0.18024/0.21406, loss_spatial_ce_2: 0.01641/0.07167, loss_grounding_bce_2: 0.09150/0.08658, loss_grounding_dice_2: 0.09981/0.17909, loss_grounding_ce_2: 0.02829/0.27635, loss_mask_ce_3: 0.89918/0.91759, loss_mask_bce_3: 0.51433/0.33724, loss_mask_dice_3: 1.20787/1.16728, loss_spatial_bce_3: 0.07623/0.09022, loss_spatial_dice_3: 0.18159/0.21495, loss_spatial_ce_3: 0.02402/0.07615, loss_grounding_bce_3: 0.08967/0.08681, loss_grounding_dice_3: 0.09352/0.17881, loss_grounding_ce_3: 0.03027/0.27862, loss_mask_ce_4: 0.90374/0.91873, loss_mask_bce_4: 0.52328/0.33936, loss_mask_dice_4: 1.14539/1.19141, loss_spatial_bce_4: 0.08433/0.09416, loss_spatial_dice_4: 0.20275/0.22708, loss_spatial_ce_4: 0.04846/0.09223, loss_grounding_bce_4: 0.09004/0.08730, loss_grounding_dice_4: 0.09768/0.18175, loss_grounding_ce_4: 0.02337/0.28146, loss_mask_ce_5: 0.89105/0.93496, loss_mask_bce_5: 0.51121/0.34166, loss_mask_dice_5: 1.23400/1.19883, loss_spatial_bce_5: 0.08970/0.09632, loss_spatial_dice_5: 0.23847/0.23126, loss_spatial_ce_5: 0.06853/0.10670, loss_grounding_bce_5: 0.08631/0.08774, loss_grounding_dice_5: 0.09437/0.18297, loss_grounding_ce_5: 0.01821/0.29417, loss_mask_ce_6: 0.97259/0.97509, loss_mask_bce_6: 0.52809/0.34431, loss_mask_dice_6: 1.29185/1.20184, loss_spatial_bce_6: 0.09931/0.10204, loss_spatial_dice_6: 0.25209/0.23412, loss_spatial_ce_6: 0.07956/0.13258, loss_grounding_bce_6: 0.08437/0.08848, loss_grounding_dice_6: 0.09659/0.18335, loss_grounding_ce_6: 0.02404/0.30965, loss_mask_ce_7: 1.05168/1.02006, loss_mask_bce_7: 0.52483/0.35218, loss_mask_dice_7: 1.34596/1.25618, loss_spatial_bce_7: 0.10278/0.10998, loss_spatial_dice_7: 0.23093/0.26170, loss_spatial_ce_7: 0.12689/0.16793, loss_grounding_bce_7: 0.09134/0.09038, loss_grounding_dice_7: 0.11090/0.19062, loss_grounding_ce_7: 0.02193/0.34017, loss_mask_ce_8: 1.26556/1.12837, loss_mask_bce_8: 0.54246/0.36582, loss_mask_dice_8: 1.35159/1.32930, loss_spatial_bce_8: 0.11254/0.13067, loss_spatial_dice_8: 0.24690/0.29977, loss_spatial_ce_8: 0.21826/0.22396, loss_grounding_bce_8: 0.10338/0.09413, loss_grounding_dice_8: 0.13516/0.20148, loss_grounding_ce_8: 0.07169/0.40740, loss_mask_ce_9: 3.96878/3.67690, loss_mask_bce_9: 0.54093/0.39275, loss_mask_dice_9: 1.79427/1.90187, loss_spatial_bce_9: 0.34440/0.33331, loss_spatial_dice_9: 0.88335/0.82189, loss_spatial_ce_9: 1.37742/1.49665, loss_grounding_bce_9: 0.08651/0.10562, loss_grounding_dice_9: 0.17406/0.28078, loss_grounding_ce_9: 0.21415/0.67211] items per batch[64] items per second[0.22] total items[4064000] mini batches[ 63500] memory[7345] epoch remaining[0:20:48]
INFO:trainer.default_trainer:epochs[    34] optim steps[63600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08178/0.89949, loss_mask_bce_0: 0.23720/0.33461, loss_mask_dice_0: 3.95456/1.16287, loss_spatial_bce_0: 0.01273/0.08746, loss_spatial_dice_0: 0.28631/0.20852, loss_spatial_ce_0: 0.18546/0.06243, loss_grounding_bce_0: 0.01023/0.08627, loss_grounding_dice_0: 0.22904/0.17848, loss_grounding_ce_0: 0.53688/0.27216, loss_mask_ce_1: 1.04150/0.90011, loss_mask_bce_1: 0.23831/0.33556, loss_mask_dice_1: 4.54729/1.16935, loss_spatial_bce_1: 0.01391/0.08798, loss_spatial_dice_1: 0.33857/0.21246, loss_spatial_ce_1: 0.05003/0.06825, loss_grounding_bce_1: 0.00796/0.08644, loss_grounding_dice_1: 0.13646/0.17929, loss_grounding_ce_1: 0.54827/0.27297, loss_mask_ce_2: 1.10848/0.90710, loss_mask_bce_2: 0.23353/0.33612, loss_mask_dice_2: 3.74154/1.16991, loss_spatial_bce_2: 0.01387/0.08903, loss_spatial_dice_2: 0.27383/0.21405, loss_spatial_ce_2: 0.04360/0.07164, loss_grounding_bce_2: 0.00821/0.08659, loss_grounding_dice_2: 0.16664/0.17910, loss_grounding_ce_2: 0.47491/0.27628, loss_mask_ce_3: 1.18158/0.91749, loss_mask_bce_3: 0.23812/0.33725, loss_mask_dice_3: 4.19226/1.16738, loss_spatial_bce_3: 0.01416/0.09021, loss_spatial_dice_3: 0.33267/0.21494, loss_spatial_ce_3: 0.21086/0.07613, loss_grounding_bce_3: 0.00862/0.08682, loss_grounding_dice_3: 0.14904/0.17883, loss_grounding_ce_3: 0.54050/0.27855, loss_mask_ce_4: 0.97557/0.91868, loss_mask_bce_4: 0.24107/0.33937, loss_mask_dice_4: 3.96011/1.19146, loss_spatial_bce_4: 0.01359/0.09415, loss_spatial_dice_4: 0.39538/0.22708, loss_spatial_ce_4: 0.24050/0.09221, loss_grounding_bce_4: 0.01337/0.08731, loss_grounding_dice_4: 0.28198/0.18177, loss_grounding_ce_4: 0.48351/0.28139, loss_mask_ce_5: 1.21555/0.93489, loss_mask_bce_5: 0.26362/0.34166, loss_mask_dice_5: 4.34179/1.19889, loss_spatial_bce_5: 0.01675/0.09630, loss_spatial_dice_5: 0.37371/0.23125, loss_spatial_ce_5: 0.18091/0.10667, loss_grounding_bce_5: 0.01475/0.08775, loss_grounding_dice_5: 0.20210/0.18299, loss_grounding_ce_5: 1.60834/0.29412, loss_mask_ce_6: 1.34809/0.97501, loss_mask_bce_6: 0.25251/0.34432, loss_mask_dice_6: 3.83196/1.20192, loss_spatial_bce_6: 0.01583/0.10203, loss_spatial_dice_6: 0.36723/0.23411, loss_spatial_ce_6: 0.26307/0.13254, loss_grounding_bce_6: 0.01256/0.08849, loss_grounding_dice_6: 0.20318/0.18336, loss_grounding_ce_6: 1.29209/0.30960, loss_mask_ce_7: 1.23716/1.01998, loss_mask_bce_7: 0.25141/0.35218, loss_mask_dice_7: 3.81760/1.25625, loss_spatial_bce_7: 0.03059/0.10996, loss_spatial_dice_7: 0.41697/0.26169, loss_spatial_ce_7: 0.38482/0.16789, loss_grounding_bce_7: 0.01742/0.09038, loss_grounding_dice_7: 0.19433/0.19064, loss_grounding_ce_7: 1.06108/0.34012, loss_mask_ce_8: 1.19508/1.12831, loss_mask_bce_8: 0.28001/0.36584, loss_mask_dice_8: 4.15362/1.32938, loss_spatial_bce_8: 0.02709/0.13065, loss_spatial_dice_8: 0.41768/0.29975, loss_spatial_ce_8: 0.17262/0.22391, loss_grounding_bce_8: 0.01963/0.09414, loss_grounding_dice_8: 0.29730/0.20149, loss_grounding_ce_8: 2.14669/0.40737, loss_mask_ce_9: 4.94887/3.67683, loss_mask_bce_9: 0.30903/0.39277, loss_mask_dice_9: 4.32721/1.90209, loss_spatial_bce_9: 0.10931/0.33328, loss_spatial_dice_9: 0.76405/0.82191, loss_spatial_ce_9: 2.00404/1.49655, loss_grounding_bce_9: 0.05450/0.10564, loss_grounding_dice_9: 0.32906/0.28081, loss_grounding_ce_9: 1.20629/0.67199] items per batch[64] items per second[0.23] total items[4070400] mini batches[ 63600] memory[7345] epoch remaining[0:16:06]
INFO:trainer.default_trainer:epochs[    34] optim steps[63700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79876/0.89938, loss_mask_bce_0: 0.40339/0.33462, loss_mask_dice_0: 1.38988/1.16306, loss_spatial_bce_0: 0.09327/0.08745, loss_spatial_dice_0: 0.18523/0.20851, loss_spatial_ce_0: 0.00417/0.06241, loss_grounding_bce_0: 0.04149/0.08626, loss_grounding_dice_0: 0.24316/0.17850, loss_grounding_ce_0: 0.33253/0.27218, loss_mask_ce_1: 0.77921/0.90001, loss_mask_bce_1: 0.44017/0.33556, loss_mask_dice_1: 1.49958/1.16955, loss_spatial_bce_1: 0.08990/0.08797, loss_spatial_dice_1: 0.17610/0.21245, loss_spatial_ce_1: 0.00614/0.06823, loss_grounding_bce_1: 0.04158/0.08644, loss_grounding_dice_1: 0.26902/0.17931, loss_grounding_ce_1: 0.33037/0.27298, loss_mask_ce_2: 0.80432/0.90700, loss_mask_bce_2: 0.43138/0.33613, loss_mask_dice_2: 1.41854/1.17015, loss_spatial_bce_2: 0.08516/0.08902, loss_spatial_dice_2: 0.18897/0.21405, loss_spatial_ce_2: 0.00759/0.07163, loss_grounding_bce_2: 0.04020/0.08658, loss_grounding_dice_2: 0.24208/0.17911, loss_grounding_ce_2: 0.36424/0.27627, loss_mask_ce_3: 0.73461/0.91741, loss_mask_bce_3: 0.43248/0.33725, loss_mask_dice_3: 1.47084/1.16755, loss_spatial_bce_3: 0.08990/0.09021, loss_spatial_dice_3: 0.19070/0.21494, loss_spatial_ce_3: 0.01352/0.07610, loss_grounding_bce_3: 0.04241/0.08681, loss_grounding_dice_3: 0.25027/0.17884, loss_grounding_ce_3: 0.36919/0.27855, loss_mask_ce_4: 0.80746/0.91860, loss_mask_bce_4: 0.44082/0.33937, loss_mask_dice_4: 1.53213/1.19166, loss_spatial_bce_4: 0.09905/0.09414, loss_spatial_dice_4: 0.21667/0.22707, loss_spatial_ce_4: 0.01715/0.09220, loss_grounding_bce_4: 0.04136/0.08730, loss_grounding_dice_4: 0.27249/0.18178, loss_grounding_ce_4: 0.34078/0.28141, loss_mask_ce_5: 0.77214/0.93480, loss_mask_bce_5: 0.49654/0.34167, loss_mask_dice_5: 1.41808/1.19906, loss_spatial_bce_5: 0.09254/0.09629, loss_spatial_dice_5: 0.20407/0.23124, loss_spatial_ce_5: 0.03288/0.10667, loss_grounding_bce_5: 0.04235/0.08775, loss_grounding_dice_5: 0.30175/0.18300, loss_grounding_ce_5: 0.32489/0.29413, loss_mask_ce_6: 0.79642/0.97492, loss_mask_bce_6: 0.50621/0.34432, loss_mask_dice_6: 1.42900/1.20210, loss_spatial_bce_6: 0.09622/0.10202, loss_spatial_dice_6: 0.18316/0.23410, loss_spatial_ce_6: 0.05995/0.13252, loss_grounding_bce_6: 0.04227/0.08848, loss_grounding_dice_6: 0.30203/0.18337, loss_grounding_ce_6: 0.35185/0.30964, loss_mask_ce_7: 0.90564/1.01987, loss_mask_bce_7: 0.46532/0.35219, loss_mask_dice_7: 1.54941/1.25644, loss_spatial_bce_7: 0.10012/0.10995, loss_spatial_dice_7: 0.20422/0.26168, loss_spatial_ce_7: 0.13870/0.16788, loss_grounding_bce_7: 0.04264/0.09038, loss_grounding_dice_7: 0.31863/0.19066, loss_grounding_ce_7: 0.30691/0.34012, loss_mask_ce_8: 0.99753/1.12820, loss_mask_bce_8: 0.46367/0.36583, loss_mask_dice_8: 1.52963/1.32955, loss_spatial_bce_8: 0.11850/0.13063, loss_spatial_dice_8: 0.23857/0.29975, loss_spatial_ce_8: 0.21684/0.22386, loss_grounding_bce_8: 0.04174/0.09414, loss_grounding_dice_8: 0.31562/0.20150, loss_grounding_ce_8: 0.36839/0.40728, loss_mask_ce_9: 2.49989/3.67678, loss_mask_bce_9: 0.69573/0.39279, loss_mask_dice_9: 2.20621/1.90236, loss_spatial_bce_9: 0.34440/0.33326, loss_spatial_dice_9: 0.81404/0.82192, loss_spatial_ce_9: 1.84843/1.49657, loss_grounding_bce_9: 0.04720/0.10564, loss_grounding_dice_9: 0.44408/0.28080, loss_grounding_ce_9: 0.40264/0.67202] items per batch[64] items per second[0.24] total items[4076800] mini batches[ 63700] memory[7345] epoch remaining[0:11:24]
INFO:trainer.default_trainer:epochs[    34] optim steps[63800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30675/0.89923, loss_mask_bce_0: 0.06247/0.33457, loss_mask_dice_0: 0.65870/1.16295, loss_spatial_bce_0: 0.02084/0.08744, loss_spatial_dice_0: 0.20844/0.20850, loss_spatial_ce_0: 0.00724/0.06238, loss_grounding_bce_0: 0.03500/0.08626, loss_grounding_dice_0: 0.20773/0.17848, loss_grounding_ce_0: 0.03983/0.27207, loss_mask_ce_1: 0.50573/0.89986, loss_mask_bce_1: 0.06152/0.33551, loss_mask_dice_1: 0.66161/1.16946, loss_spatial_bce_1: 0.02079/0.08796, loss_spatial_dice_1: 0.23848/0.21244, loss_spatial_ce_1: 0.01844/0.06822, loss_grounding_bce_1: 0.03242/0.08643, loss_grounding_dice_1: 0.19457/0.17930, loss_grounding_ce_1: 0.03980/0.27287, loss_mask_ce_2: 0.35689/0.90685, loss_mask_bce_2: 0.05752/0.33607, loss_mask_dice_2: 0.61941/1.17005, loss_spatial_bce_2: 0.02148/0.08900, loss_spatial_dice_2: 0.22712/0.21403, loss_spatial_ce_2: 0.04214/0.07162, loss_grounding_bce_2: 0.03039/0.08657, loss_grounding_dice_2: 0.20413/0.17909, loss_grounding_ce_2: 0.05550/0.27618, loss_mask_ce_3: 0.37735/0.91727, loss_mask_bce_3: 0.05965/0.33720, loss_mask_dice_3: 0.63558/1.16748, loss_spatial_bce_3: 0.02132/0.09019, loss_spatial_dice_3: 0.23874/0.21492, loss_spatial_ce_3: 0.06045/0.07609, loss_grounding_bce_3: 0.03166/0.08680, loss_grounding_dice_3: 0.17792/0.17883, loss_grounding_ce_3: 0.05695/0.27845, loss_mask_ce_4: 0.41332/0.91848, loss_mask_bce_4: 0.05572/0.33931, loss_mask_dice_4: 0.63185/1.19157, loss_spatial_bce_4: 0.02228/0.09412, loss_spatial_dice_4: 0.23797/0.22706, loss_spatial_ce_4: 0.09123/0.09221, loss_grounding_bce_4: 0.03040/0.08729, loss_grounding_dice_4: 0.21368/0.18177, loss_grounding_ce_4: 0.10057/0.28133, loss_mask_ce_5: 0.46372/0.93467, loss_mask_bce_5: 0.05877/0.34162, loss_mask_dice_5: 0.62796/1.19899, loss_spatial_bce_5: 0.02453/0.09627, loss_spatial_dice_5: 0.21578/0.23123, loss_spatial_ce_5: 0.10331/0.10666, loss_grounding_bce_5: 0.03206/0.08774, loss_grounding_dice_5: 0.19591/0.18298, loss_grounding_ce_5: 0.10136/0.29405, loss_mask_ce_6: 0.49160/0.97483, loss_mask_bce_6: 0.06335/0.34426, loss_mask_dice_6: 0.64361/1.20200, loss_spatial_bce_6: 0.02815/0.10200, loss_spatial_dice_6: 0.22818/0.23409, loss_spatial_ce_6: 0.13586/0.13252, loss_grounding_bce_6: 0.03202/0.08847, loss_grounding_dice_6: 0.19835/0.18335, loss_grounding_ce_6: 0.09337/0.30957, loss_mask_ce_7: 0.74154/1.01977, loss_mask_bce_7: 0.06543/0.35214, loss_mask_dice_7: 0.67223/1.25636, loss_spatial_bce_7: 0.02708/0.10993, loss_spatial_dice_7: 0.25718/0.26168, loss_spatial_ce_7: 0.14210/0.16786, loss_grounding_bce_7: 0.03263/0.09037, loss_grounding_dice_7: 0.20019/0.19065, loss_grounding_ce_7: 0.16271/0.34004, loss_mask_ce_8: 1.11331/1.12810, loss_mask_bce_8: 0.06691/0.36577, loss_mask_dice_8: 0.75024/1.32945, loss_spatial_bce_8: 0.03951/0.13061, loss_spatial_dice_8: 0.32296/0.29975, loss_spatial_ce_8: 0.28279/0.22383, loss_grounding_bce_8: 0.03256/0.09413, loss_grounding_dice_8: 0.19670/0.20147, loss_grounding_ce_8: 0.24267/0.40716, loss_mask_ce_9: 1.91163/3.67655, loss_mask_bce_9: 0.07083/0.39272, loss_mask_dice_9: 1.08089/1.90218, loss_spatial_bce_9: 0.16517/0.33324, loss_spatial_dice_9: 0.85930/0.82191, loss_spatial_ce_9: 1.21600/1.49664, loss_grounding_bce_9: 0.04010/0.10563, loss_grounding_dice_9: 0.38634/0.28079, loss_grounding_ce_9: 0.02986/0.67203] items per batch[64] items per second[0.23] total items[4083200] mini batches[ 63800] memory[7345] epoch remaining[0:06:45]
INFO:trainer.default_trainer:epochs[    34] optim steps[63900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91020/0.89909, loss_mask_bce_0: 0.30869/0.33459, loss_mask_dice_0: 2.55326/1.16274, loss_spatial_bce_0: 0.04101/0.08743, loss_spatial_dice_0: 0.19785/0.20849, loss_spatial_ce_0: 0.12050/0.06235, loss_grounding_bce_0: 0.03198/0.08626, loss_grounding_dice_0: 0.18525/0.17849, loss_grounding_ce_0: 0.33989/0.27200, loss_mask_ce_1: 0.92559/0.89972, loss_mask_bce_1: 0.31358/0.33552, loss_mask_dice_1: 2.55147/1.16924, loss_spatial_bce_1: 0.04704/0.08795, loss_spatial_dice_1: 0.19677/0.21243, loss_spatial_ce_1: 0.17280/0.06821, loss_grounding_bce_1: 0.03342/0.08643, loss_grounding_dice_1: 0.18312/0.17931, loss_grounding_ce_1: 0.34530/0.27279, loss_mask_ce_2: 0.92527/0.90674, loss_mask_bce_2: 0.32297/0.33608, loss_mask_dice_2: 2.68534/1.16986, loss_spatial_bce_2: 0.05634/0.08899, loss_spatial_dice_2: 0.21897/0.21402, loss_spatial_ce_2: 0.11983/0.07163, loss_grounding_bce_2: 0.03571/0.08657, loss_grounding_dice_2: 0.19316/0.17910, loss_grounding_ce_2: 0.35068/0.27610, loss_mask_ce_3: 1.14997/0.91715, loss_mask_bce_3: 0.30110/0.33721, loss_mask_dice_3: 2.48544/1.16727, loss_spatial_bce_3: 0.04530/0.09019, loss_spatial_dice_3: 0.20995/0.21492, loss_spatial_ce_3: 0.19581/0.07609, loss_grounding_bce_3: 0.03563/0.08680, loss_grounding_dice_3: 0.19372/0.17883, loss_grounding_ce_3: 0.36143/0.27837, loss_mask_ce_4: 0.86439/0.91837, loss_mask_bce_4: 0.32203/0.33933, loss_mask_dice_4: 2.84052/1.19138, loss_spatial_bce_4: 0.08025/0.09413, loss_spatial_dice_4: 0.26763/0.22706, loss_spatial_ce_4: 0.16795/0.09220, loss_grounding_bce_4: 0.03321/0.08729, loss_grounding_dice_4: 0.19884/0.18178, loss_grounding_ce_4: 0.33781/0.28125, loss_mask_ce_5: 0.93916/0.93459, loss_mask_bce_5: 0.32647/0.34164, loss_mask_dice_5: 2.73072/1.19880, loss_spatial_bce_5: 0.05200/0.09628, loss_spatial_dice_5: 0.26757/0.23123, loss_spatial_ce_5: 0.16385/0.10664, loss_grounding_bce_5: 0.03291/0.08774, loss_grounding_dice_5: 0.19554/0.18300, loss_grounding_ce_5: 0.35353/0.29396, loss_mask_ce_6: 1.19278/0.97470, loss_mask_bce_6: 0.32444/0.34428, loss_mask_dice_6: 2.92317/1.20182, loss_spatial_bce_6: 0.06992/0.10200, loss_spatial_dice_6: 0.29193/0.23409, loss_spatial_ce_6: 0.17948/0.13250, loss_grounding_bce_6: 0.03393/0.08847, loss_grounding_dice_6: 0.22877/0.18336, loss_grounding_ce_6: 0.35331/0.30948, loss_mask_ce_7: 1.05022/1.01967, loss_mask_bce_7: 0.35820/0.35215, loss_mask_dice_7: 3.27905/1.25616, loss_spatial_bce_7: 0.10417/0.10992, loss_spatial_dice_7: 0.36328/0.26168, loss_spatial_ce_7: 0.23523/0.16785, loss_grounding_bce_7: 0.03522/0.09037, loss_grounding_dice_7: 0.26650/0.19066, loss_grounding_ce_7: 0.34190/0.33995, loss_mask_ce_8: 0.87624/1.12798, loss_mask_bce_8: 0.36801/0.36577, loss_mask_dice_8: 3.44570/1.32924, loss_spatial_bce_8: 0.09678/0.13061, loss_spatial_dice_8: 0.44109/0.29974, loss_spatial_ce_8: 0.24811/0.22379, loss_grounding_bce_8: 0.03286/0.09412, loss_grounding_dice_8: 0.31305/0.20147, loss_grounding_ce_8: 0.41568/0.40708, loss_mask_ce_9: 5.82215/3.67646, loss_mask_bce_9: 0.42995/0.39271, loss_mask_dice_9: 5.02723/1.90190, loss_spatial_bce_9: 0.29165/0.33322, loss_spatial_dice_9: 0.95151/0.82190, loss_spatial_ce_9: 1.51290/1.49661, loss_grounding_bce_9: 0.05368/0.10563, loss_grounding_dice_9: 0.49789/0.28081, loss_grounding_ce_9: 0.49559/0.67201] items per batch[64] items per second[0.24] total items[4089600] mini batches[ 63900] memory[7345] epoch remaining[0:02:05]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00063945.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2207 s/iter. Eval: 0.0842 s/iter. Total: 0.3080 s/iter. ETA=0:00:44
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2231 s/iter. Eval: 0.0742 s/iter. Total: 0.3003 s/iter. ETA=0:00:38
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/157. Dataloading: 0.0031 s/iter. Inference: 0.2268 s/iter. Eval: 0.0759 s/iter. Total: 0.3059 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 61/157. Dataloading: 0.0031 s/iter. Inference: 0.2273 s/iter. Eval: 0.0736 s/iter. Total: 0.3042 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2249 s/iter. Eval: 0.0726 s/iter. Total: 0.3008 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2273 s/iter. Eval: 0.0731 s/iter. Total: 0.3036 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2291 s/iter. Eval: 0.0733 s/iter. Total: 0.3057 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2288 s/iter. Eval: 0.0728 s/iter. Total: 0.3049 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2296 s/iter. Eval: 0.0727 s/iter. Total: 0.3056 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval2q89r8fd ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.396 | 82.047 | 60.513 |      133      |
| Things | 55.235 | 82.882 | 65.953 |      80       |
| Stuff  | 43.092 | 80.788 | 52.302 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.39 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.98 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.04s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.96 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.392
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.615
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.422
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.491
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.286
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.717
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.190 | 61.507 | 41.411 | 19.113 | 42.181 | 60.822 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.510 | bicycle      | 18.150 | car            | 37.158 |
| motorcycle    | 33.566 | airplane     | 56.469 | bus            | 65.542 |
| train         | 69.276 | truck        | 35.074 | boat           | 23.233 |
| traffic light | 25.093 | fire hydrant | 63.510 | stop sign      | 62.374 |
| parking meter | 42.901 | bench        | 19.989 | bird           | 29.853 |
| cat           | 73.541 | dog          | 65.662 | horse          | 45.860 |
| sheep         | 47.191 | cow          | 51.033 | elephant       | 60.716 |
| bear          | 78.275 | zebra        | 60.712 | giraffe        | 57.062 |
| backpack      | 17.444 | umbrella     | 48.156 | handbag        | 15.689 |
| tie           | 34.288 | suitcase     | 41.534 | frisbee        | 67.127 |
| skis          | 4.867  | snowboard    | 23.626 | sports ball    | 46.846 |
| kite          | 34.633 | baseball bat | 28.166 | baseball glove | 43.411 |
| skateboard    | 36.530 | surfboard    | 35.997 | tennis racket  | 56.608 |
| bottle        | 34.247 | wine glass   | 27.620 | cup            | 41.034 |
| fork          | 16.784 | knife        | 12.817 | spoon          | 14.446 |
| bowl          | 32.272 | banana       | 20.369 | apple          | 20.725 |
| sandwich      | 44.209 | orange       | 30.201 | broccoli       | 22.864 |
| carrot        | 20.583 | hot dog      | 24.876 | pizza          | 51.794 |
| donut         | 45.872 | cake         | 43.847 | chair          | 21.480 |
| couch         | 43.733 | potted plant | 17.376 | bed            | 40.798 |
| dining table  | 13.576 | toilet       | 67.815 | tv             | 61.787 |
| laptop        | 64.526 | mouse        | 58.952 | remote         | 32.292 |
| keyboard      | 47.935 | cell phone   | 39.093 | microwave      | 53.244 |
| oven          | 32.855 | toaster      | 36.340 | sink           | 37.884 |
| refrigerator  | 58.970 | book         | 9.472  | clock          | 51.723 |
| vase          | 33.042 | scissors     | 24.295 | teddy bear     | 50.749 |
| hair drier    | 11.049 | toothbrush   | 19.974 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.83109868208416, 'fwIoU': 69.28573876313233, 'IoU-person': 87.63118064870228, 'IoU-bicycle': 75.24623223539558, 'IoU-car': 68.69667974186198, 'IoU-motorcycle': 84.65494702629239, 'IoU-airplane': 74.16126395237043, 'IoU-bus': 85.09691571015262, 'IoU-train': 84.65673475903746, 'IoU-truck': 64.79607099266809, 'IoU-boat': 67.51156628351066, 'IoU-traffic light': 76.69885139766617, 'IoU-fire hydrant': 90.35044428753287, 'IoU-stop sign': 81.56862745098039, 'IoU-parking meter': 87.95347008031185, 'IoU-bench': 55.455391708208325, 'IoU-bird': 75.37557201348748, 'IoU-cat': 87.57680844013336, 'IoU-dog': 82.6715914852411, 'IoU-horse': 85.78216404683249, 'IoU-sheep': 86.52216947319282, 'IoU-cow': 81.70101128408574, 'IoU-elephant': 86.48913853791329, 'IoU-bear': 79.3933852790088, 'IoU-zebra': 89.48265463187556, 'IoU-giraffe': 84.97787776949816, 'IoU-backpack': 38.124612594252824, 'IoU-umbrella': 75.2387249600997, 'IoU-handbag': 35.26488561120887, 'IoU-tie': 70.44716691451696, 'IoU-suitcase': 80.34355892871213, 'IoU-frisbee': 81.59465266173311, 'IoU-skis': 51.268853847394745, 'IoU-snowboard': 70.35600566071113, 'IoU-sports ball': 65.18171407399923, 'IoU-kite': 64.0886340496222, 'IoU-baseball bat': 60.4403115474294, 'IoU-baseball glove': 76.33074983775148, 'IoU-skateboard': 60.61787358715254, 'IoU-surfboard': 82.32323516345087, 'IoU-tennis racket': 83.03245431381043, 'IoU-bottle': 68.31834010446894, 'IoU-wine glass': 72.56808038595463, 'IoU-cup': 58.18810900736751, 'IoU-fork': 54.73714832103636, 'IoU-knife': 50.86835009636371, 'IoU-spoon': 50.24958627545794, 'IoU-bowl': 54.52506850363048, 'IoU-banana': 84.40521625535553, 'IoU-apple': 58.74155769929141, 'IoU-sandwich': 67.48501683632033, 'IoU-orange': 78.27620913989547, 'IoU-broccoli': 66.4979539316785, 'IoU-carrot': 63.380121227569134, 'IoU-hot dog': 65.74822636528626, 'IoU-pizza': 84.28693860573647, 'IoU-donut': 63.83976336018018, 'IoU-cake': 69.10656082701455, 'IoU-chair': 50.12978550375073, 'IoU-couch': 66.50682968236046, 'IoU-potted plant': 33.85132470649677, 'IoU-bed': 67.41624383688466, 'IoU-dining table': 53.085655154943225, 'IoU-toilet': 84.3684223546967, 'IoU-tv': 75.30399865816946, 'IoU-laptop': 77.15867359497125, 'IoU-mouse': 66.43872111224915, 'IoU-remote': 50.53403295360567, 'IoU-keyboard': 64.84719546149262, 'IoU-cell phone': 70.30520069637964, 'IoU-microwave': 48.83052225992228, 'IoU-oven': 62.1500342266312, 'IoU-toaster': 62.07788284341863, 'IoU-sink': 67.51133071005782, 'IoU-refrigerator': 81.29052125597767, 'IoU-book': 53.76498540816138, 'IoU-clock': 76.28435524239944, 'IoU-vase': 59.41506767944785, 'IoU-scissors': 55.49917318550632, 'IoU-teddy bear': 82.23384458053896, 'IoU-hair drier': 32.98100903663237, 'IoU-toothbrush': 55.716188500436125, 'IoU-banner': 37.75715324598708, 'IoU-blanket': 12.565978625654603, 'IoU-bridge': 35.46420248980774, 'IoU-cardboard': 49.75426461352241, 'IoU-counter': 31.54454830717434, 'IoU-curtain': 64.77319691691791, 'IoU-door-stuff': 44.465875499157384, 'IoU-floor-wood': 64.29244689615511, 'IoU-flower': 47.31952230698113, 'IoU-fruit': 40.15741752355879, 'IoU-gravel': 31.072071810886882, 'IoU-house': 27.47280219650633, 'IoU-light': 39.47721427532247, 'IoU-mirror-stuff': 56.34657452712782, 'IoU-net': 45.444159815984264, 'IoU-pillow': 10.379462365591397, 'IoU-platform': 34.86707622336323, 'IoU-playingfield': 69.47053803601906, 'IoU-railroad': 61.76077287941294, 'IoU-river': 50.89648375162218, 'IoU-road': 66.21331113345327, 'IoU-roof': 16.024581377726975, 'IoU-sand': 65.40931350833246, 'IoU-sea': 84.60622733490851, 'IoU-shelf': 35.675745167979606, 'IoU-snow': 88.37929675309603, 'IoU-stairs': 27.645068703731784, 'IoU-tent': 8.590451873660514, 'IoU-towel': 28.276349159059144, 'IoU-wall-brick': 40.991149326865425, 'IoU-wall-stone': 30.480963246635444, 'IoU-wall-tile': 65.89620960570241, 'IoU-wall-wood': 39.103989008131656, 'IoU-water-other': 20.84627326701568, 'IoU-window-blind': 48.78236527432511, 'IoU-window-other': 46.76819333491648, 'IoU-tree-merged': 80.61291960345574, 'IoU-fence-merged': 51.09999243527351, 'IoU-ceiling-merged': 66.89191025402194, 'IoU-sky-other-merged': 93.70204130081238, 'IoU-cabinet-merged': 59.78493515362868, 'IoU-table-merged': 39.30410655513257, 'IoU-floor-other-merged': 48.83064460807642, 'IoU-pavement-merged': 54.765293346928686, 'IoU-mountain-merged': 55.0284976245058, 'IoU-grass-merged': 71.48601312865361, 'IoU-dirt-merged': 46.34064730920721, 'IoU-paper-merged': 36.541993800728804, 'IoU-food-other-merged': 44.26803289852466, 'IoU-building-other-merged': 57.51930858977723, 'IoU-rock-merged': 59.840291753274954, 'IoU-wall-other-merged': 64.76274931528859, 'IoU-rug-merged': 62.75806808206532, 'mACC': 73.25795687839098, 'pACC': 80.54519395069778, 'ACC-person': 92.80303694796605, 'ACC-bicycle': 85.45989045694854, 'ACC-car': 85.64152351748555, 'ACC-motorcycle': 90.11541430722271, 'ACC-airplane': 86.02149597139383, 'ACC-bus': 90.89579291058311, 'ACC-train': 95.40747809983236, 'ACC-truck': 74.20291230341329, 'ACC-boat': 77.5088965353627, 'ACC-traffic light': 90.07451607547873, 'ACC-fire hydrant': 95.27615751514816, 'ACC-stop sign': 84.01795633732094, 'ACC-parking meter': 92.16878321758074, 'ACC-bench': 73.01582730142638, 'ACC-bird': 80.36551152996765, 'ACC-cat': 93.66584098844896, 'ACC-dog': 86.91588075005716, 'ACC-horse': 91.83753206510202, 'ACC-sheep': 90.86862355412929, 'ACC-cow': 85.06124578848099, 'ACC-elephant': 88.946340585435, 'ACC-bear': 81.34876679107488, 'ACC-zebra': 92.0545896326008, 'ACC-giraffe': 89.11614064323149, 'ACC-backpack': 60.171580441889205, 'ACC-umbrella': 80.03909880280112, 'ACC-handbag': 50.80191023269749, 'ACC-tie': 79.94332859704089, 'ACC-suitcase': 87.3708011907291, 'ACC-frisbee': 94.4610909090909, 'ACC-skis': 69.30292302817406, 'ACC-snowboard': 77.92559574832848, 'ACC-sports ball': 80.01622889984597, 'ACC-kite': 75.25553802048331, 'ACC-baseball bat': 82.15315916055414, 'ACC-baseball glove': 88.1365152792489, 'ACC-skateboard': 69.74350400150814, 'ACC-surfboard': 89.01094891990384, 'ACC-tennis racket': 89.46819991065557, 'ACC-bottle': 82.66991973722607, 'ACC-wine glass': 86.64892748998206, 'ACC-cup': 82.75417815038946, 'ACC-fork': 68.93954033401303, 'ACC-knife': 60.58401113797905, 'ACC-spoon': 68.76636374472352, 'ACC-bowl': 68.72276233255083, 'ACC-banana': 89.85401522191113, 'ACC-apple': 69.40736408915701, 'ACC-sandwich': 79.08125050290448, 'ACC-orange': 85.88024780767671, 'ACC-broccoli': 75.13027343511071, 'ACC-carrot': 71.65745124608303, 'ACC-hot dog': 71.84943938225284, 'ACC-pizza': 92.42085970457387, 'ACC-donut': 80.77401399063886, 'ACC-cake': 76.24417638599388, 'ACC-chair': 61.236719562494336, 'ACC-couch': 81.28613478416018, 'ACC-potted plant': 48.03796300982158, 'ACC-bed': 78.49625240074651, 'ACC-dining table': 73.93953455535038, 'ACC-toilet': 90.03188166651785, 'ACC-tv': 86.94754052360194, 'ACC-laptop': 92.93120853267229, 'ACC-mouse': 86.81355944431819, 'ACC-remote': 72.6916272175202, 'ACC-keyboard': 69.96916201751213, 'ACC-cell phone': 79.60082870388344, 'ACC-microwave': 55.85735595398182, 'ACC-oven': 81.15721668207107, 'ACC-toaster': 70.30478041303425, 'ACC-sink': 82.64984677717864, 'ACC-refrigerator': 89.5226264697468, 'ACC-book': 71.1682567033706, 'ACC-clock': 81.88380849866626, 'ACC-vase': 67.78515317132617, 'ACC-scissors': 59.476030447437765, 'ACC-teddy bear': 88.64164857602982, 'ACC-hair drier': 43.474795776051, 'ACC-toothbrush': 82.12039610840863, 'ACC-banner': 76.57218071463129, 'ACC-blanket': 18.48376251148495, 'ACC-bridge': 56.51423920441271, 'ACC-cardboard': 64.6481896175658, 'ACC-counter': 53.1828134390946, 'ACC-curtain': 76.21504537096723, 'ACC-door-stuff': 62.064856441630646, 'ACC-floor-wood': 79.70334914343582, 'ACC-flower': 66.97020287351849, 'ACC-fruit': 63.17716225646267, 'ACC-gravel': 41.70919064744731, 'ACC-house': 35.719516356104926, 'ACC-light': 57.45493674156873, 'ACC-mirror-stuff': 72.73072553459254, 'ACC-net': 62.32903893777449, 'ACC-pillow': 25.483311465628983, 'ACC-platform': 59.99900904123293, 'ACC-playingfield': 89.46842788788307, 'ACC-railroad': 78.07815179012786, 'ACC-river': 78.82564070567551, 'ACC-road': 85.45957144680536, 'ACC-roof': 20.74054606868845, 'ACC-sand': 70.3592032914965, 'ACC-sea': 89.9310255889002, 'ACC-shelf': 59.94503907848166, 'ACC-snow': 94.95983937771558, 'ACC-stairs': 51.57291884580627, 'ACC-tent': 11.235576782331218, 'ACC-towel': 34.43934150405558, 'ACC-wall-brick': 61.68868784961222, 'ACC-wall-stone': 39.08237993387212, 'ACC-wall-tile': 78.61229720610834, 'ACC-wall-wood': 54.73851053572019, 'ACC-water-other': 31.94215271759639, 'ACC-window-blind': 58.41527056031758, 'ACC-window-other': 69.02456725239054, 'ACC-tree-merged': 89.23926266136904, 'ACC-fence-merged': 69.84216774767589, 'ACC-ceiling-merged': 77.59665822626017, 'ACC-sky-other-merged': 96.61782675557204, 'ACC-cabinet-merged': 75.42308174959027, 'ACC-table-merged': 50.57582156046217, 'ACC-floor-other-merged': 58.5496436837333, 'ACC-pavement-merged': 68.32210457674978, 'ACC-mountain-merged': 68.18786811270432, 'ACC-grass-merged': 81.37528493323995, 'ACC-dirt-merged': 71.06647296497741, 'ACC-paper-merged': 50.63330276000924, 'ACC-food-other-merged': 66.60739425031363, 'ACC-building-other-merged': 72.44001222242741, 'ACC-rock-merged': 82.12357890520872, 'ACC-wall-other-merged': 81.9188771212089, 'ACC-rug-merged': 77.31252821564826})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1558 s/iter. Inference: 0.3530 s/iter. Eval: 0.0000 s/iter. Total: 0.5089 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1567 s/iter. Inference: 0.4732 s/iter. Eval: 0.0000 s/iter. Total: 0.6301 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1703 s/iter. Inference: 0.5242 s/iter. Eval: 0.0000 s/iter. Total: 0.6946 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1735 s/iter. Inference: 0.6528 s/iter. Eval: 0.0000 s/iter. Total: 0.8265 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1698 s/iter. Inference: 0.5811 s/iter. Eval: 0.0000 s/iter. Total: 0.7512 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1687 s/iter. Inference: 0.6235 s/iter. Eval: 0.0000 s/iter. Total: 0.7924 s/iter. ETA=0:00:03
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1706 s/iter. Inference: 0.6823 s/iter. Eval: 0.0000 s/iter. Total: 0.8531 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5109745390693592, 'noc@0.8': 2.8247000292654376, 'noc@0.85': 3.4544922446590576, 'noc@0.9': 4.50395083406497, 'miou@iter1': 0.8388631752020859}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1009 s/iter. Eval: 0.0008 s/iter. Total: 0.1033 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.55072021484375, 'precision@0.6': 68.32491302490234, 'precision@0.7': 63.15584945678711, 'precision@0.8': 53.0509147644043, 'precision@0.9': 27.36105728149414, 'cIoU': 58.31878662109375, 'mIoU': 63.04774475097656}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.39614791554597, 'SQ': 82.04744393459448, 'RQ': 60.51310286480882, 'PQ_th': 55.234863743548225, 'SQ_th': 82.88181123680249, 'RQ_th': 65.95284276161544, 'PQ_st': 43.09242591101422, 'SQ_st': 80.78802159163905, 'RQ_st': 52.30217471868559}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.18986566274643, 'AP50': 61.506889005611484, 'AP75': 41.41062650578512, 'APs': 19.113497214580185, 'APm': 42.1812798258966, 'APl': 60.82190098083486, 'AP-person': 44.50992550486365, 'AP-bicycle': 18.1495655428463, 'AP-car': 37.15821551468484, 'AP-motorcycle': 33.56617486109329, 'AP-airplane': 56.46947596143374, 'AP-bus': 65.54210642478955, 'AP-train': 69.27624104479602, 'AP-truck': 35.073725597935336, 'AP-boat': 23.233037012727962, 'AP-traffic light': 25.09338876916334, 'AP-fire hydrant': 63.51018543657085, 'AP-stop sign': 62.3739826071547, 'AP-parking meter': 42.90078816789059, 'AP-bench': 19.989462114209395, 'AP-bird': 29.852650996641074, 'AP-cat': 73.54122739477921, 'AP-dog': 65.66240341710821, 'AP-horse': 45.860251472406034, 'AP-sheep': 47.19095763593918, 'AP-cow': 51.03307524987043, 'AP-elephant': 60.715622889450685, 'AP-bear': 78.2753304034365, 'AP-zebra': 60.711837991752326, 'AP-giraffe': 57.06179643759333, 'AP-backpack': 17.444159543795575, 'AP-umbrella': 48.15576133414906, 'AP-handbag': 15.689298712948672, 'AP-tie': 34.2884788433798, 'AP-suitcase': 41.53372354321464, 'AP-frisbee': 67.12735999186063, 'AP-skis': 4.866796958218049, 'AP-snowboard': 23.62609326024248, 'AP-sports ball': 46.84567430661858, 'AP-kite': 34.63345952154672, 'AP-baseball bat': 28.166115944805657, 'AP-baseball glove': 43.4109532239464, 'AP-skateboard': 36.52993629004791, 'AP-surfboard': 35.996570518024264, 'AP-tennis racket': 56.607767580222436, 'AP-bottle': 34.2466463025979, 'AP-wine glass': 27.619646002141145, 'AP-cup': 41.03408212545767, 'AP-fork': 16.78392010181356, 'AP-knife': 12.816614386871688, 'AP-spoon': 14.44600536685311, 'AP-bowl': 32.271878000211046, 'AP-banana': 20.368504531096633, 'AP-apple': 20.72457260255472, 'AP-sandwich': 44.208808751989245, 'AP-orange': 30.20081060884615, 'AP-broccoli': 22.86404486382341, 'AP-carrot': 20.58288491155692, 'AP-hot dog': 24.875757275273624, 'AP-pizza': 51.79434069884108, 'AP-donut': 45.8722609314328, 'AP-cake': 43.846517378331285, 'AP-chair': 21.480370083493902, 'AP-couch': 43.73287466309868, 'AP-potted plant': 17.37639801109023, 'AP-bed': 40.797632480515276, 'AP-dining table': 13.57626333694027, 'AP-toilet': 67.81463702923102, 'AP-tv': 61.78707353638549, 'AP-laptop': 64.5262561212371, 'AP-mouse': 58.951603736446835, 'AP-remote': 32.291667351195386, 'AP-keyboard': 47.934519723607174, 'AP-cell phone': 39.09328950347071, 'AP-microwave': 53.24355251515884, 'AP-oven': 32.85543101053609, 'AP-toaster': 36.33989062545023, 'AP-sink': 37.88432539227781, 'AP-refrigerator': 58.96966207394979, 'AP-book': 9.471792889419453, 'AP-clock': 51.72278992375057, 'AP-vase': 33.04180268334972, 'AP-scissors': 24.29456009619515, 'AP-teddy bear': 50.749357903551626, 'AP-hair drier': 11.04886494897237, 'AP-toothbrush': 19.97376451854088}), ('sem_seg', {'mIoU': 60.83109868208416, 'fwIoU': 69.28573876313233, 'IoU-person': 87.63118064870228, 'IoU-bicycle': 75.24623223539558, 'IoU-car': 68.69667974186198, 'IoU-motorcycle': 84.65494702629239, 'IoU-airplane': 74.16126395237043, 'IoU-bus': 85.09691571015262, 'IoU-train': 84.65673475903746, 'IoU-truck': 64.79607099266809, 'IoU-boat': 67.51156628351066, 'IoU-traffic light': 76.69885139766617, 'IoU-fire hydrant': 90.35044428753287, 'IoU-stop sign': 81.56862745098039, 'IoU-parking meter': 87.95347008031185, 'IoU-bench': 55.455391708208325, 'IoU-bird': 75.37557201348748, 'IoU-cat': 87.57680844013336, 'IoU-dog': 82.6715914852411, 'IoU-horse': 85.78216404683249, 'IoU-sheep': 86.52216947319282, 'IoU-cow': 81.70101128408574, 'IoU-elephant': 86.48913853791329, 'IoU-bear': 79.3933852790088, 'IoU-zebra': 89.48265463187556, 'IoU-giraffe': 84.97787776949816, 'IoU-backpack': 38.124612594252824, 'IoU-umbrella': 75.2387249600997, 'IoU-handbag': 35.26488561120887, 'IoU-tie': 70.44716691451696, 'IoU-suitcase': 80.34355892871213, 'IoU-frisbee': 81.59465266173311, 'IoU-skis': 51.268853847394745, 'IoU-snowboard': 70.35600566071113, 'IoU-sports ball': 65.18171407399923, 'IoU-kite': 64.0886340496222, 'IoU-baseball bat': 60.4403115474294, 'IoU-baseball glove': 76.33074983775148, 'IoU-skateboard': 60.61787358715254, 'IoU-surfboard': 82.32323516345087, 'IoU-tennis racket': 83.03245431381043, 'IoU-bottle': 68.31834010446894, 'IoU-wine glass': 72.56808038595463, 'IoU-cup': 58.18810900736751, 'IoU-fork': 54.73714832103636, 'IoU-knife': 50.86835009636371, 'IoU-spoon': 50.24958627545794, 'IoU-bowl': 54.52506850363048, 'IoU-banana': 84.40521625535553, 'IoU-apple': 58.74155769929141, 'IoU-sandwich': 67.48501683632033, 'IoU-orange': 78.27620913989547, 'IoU-broccoli': 66.4979539316785, 'IoU-carrot': 63.380121227569134, 'IoU-hot dog': 65.74822636528626, 'IoU-pizza': 84.28693860573647, 'IoU-donut': 63.83976336018018, 'IoU-cake': 69.10656082701455, 'IoU-chair': 50.12978550375073, 'IoU-couch': 66.50682968236046, 'IoU-potted plant': 33.85132470649677, 'IoU-bed': 67.41624383688466, 'IoU-dining table': 53.085655154943225, 'IoU-toilet': 84.3684223546967, 'IoU-tv': 75.30399865816946, 'IoU-laptop': 77.15867359497125, 'IoU-mouse': 66.43872111224915, 'IoU-remote': 50.53403295360567, 'IoU-keyboard': 64.84719546149262, 'IoU-cell phone': 70.30520069637964, 'IoU-microwave': 48.83052225992228, 'IoU-oven': 62.1500342266312, 'IoU-toaster': 62.07788284341863, 'IoU-sink': 67.51133071005782, 'IoU-refrigerator': 81.29052125597767, 'IoU-book': 53.76498540816138, 'IoU-clock': 76.28435524239944, 'IoU-vase': 59.41506767944785, 'IoU-scissors': 55.49917318550632, 'IoU-teddy bear': 82.23384458053896, 'IoU-hair drier': 32.98100903663237, 'IoU-toothbrush': 55.716188500436125, 'IoU-banner': 37.75715324598708, 'IoU-blanket': 12.565978625654603, 'IoU-bridge': 35.46420248980774, 'IoU-cardboard': 49.75426461352241, 'IoU-counter': 31.54454830717434, 'IoU-curtain': 64.77319691691791, 'IoU-door-stuff': 44.465875499157384, 'IoU-floor-wood': 64.29244689615511, 'IoU-flower': 47.31952230698113, 'IoU-fruit': 40.15741752355879, 'IoU-gravel': 31.072071810886882, 'IoU-house': 27.47280219650633, 'IoU-light': 39.47721427532247, 'IoU-mirror-stuff': 56.34657452712782, 'IoU-net': 45.444159815984264, 'IoU-pillow': 10.379462365591397, 'IoU-platform': 34.86707622336323, 'IoU-playingfield': 69.47053803601906, 'IoU-railroad': 61.76077287941294, 'IoU-river': 50.89648375162218, 'IoU-road': 66.21331113345327, 'IoU-roof': 16.024581377726975, 'IoU-sand': 65.40931350833246, 'IoU-sea': 84.60622733490851, 'IoU-shelf': 35.675745167979606, 'IoU-snow': 88.37929675309603, 'IoU-stairs': 27.645068703731784, 'IoU-tent': 8.590451873660514, 'IoU-towel': 28.276349159059144, 'IoU-wall-brick': 40.991149326865425, 'IoU-wall-stone': 30.480963246635444, 'IoU-wall-tile': 65.89620960570241, 'IoU-wall-wood': 39.103989008131656, 'IoU-water-other': 20.84627326701568, 'IoU-window-blind': 48.78236527432511, 'IoU-window-other': 46.76819333491648, 'IoU-tree-merged': 80.61291960345574, 'IoU-fence-merged': 51.09999243527351, 'IoU-ceiling-merged': 66.89191025402194, 'IoU-sky-other-merged': 93.70204130081238, 'IoU-cabinet-merged': 59.78493515362868, 'IoU-table-merged': 39.30410655513257, 'IoU-floor-other-merged': 48.83064460807642, 'IoU-pavement-merged': 54.765293346928686, 'IoU-mountain-merged': 55.0284976245058, 'IoU-grass-merged': 71.48601312865361, 'IoU-dirt-merged': 46.34064730920721, 'IoU-paper-merged': 36.541993800728804, 'IoU-food-other-merged': 44.26803289852466, 'IoU-building-other-merged': 57.51930858977723, 'IoU-rock-merged': 59.840291753274954, 'IoU-wall-other-merged': 64.76274931528859, 'IoU-rug-merged': 62.75806808206532, 'mACC': 73.25795687839098, 'pACC': 80.54519395069778, 'ACC-person': 92.80303694796605, 'ACC-bicycle': 85.45989045694854, 'ACC-car': 85.64152351748555, 'ACC-motorcycle': 90.11541430722271, 'ACC-airplane': 86.02149597139383, 'ACC-bus': 90.89579291058311, 'ACC-train': 95.40747809983236, 'ACC-truck': 74.20291230341329, 'ACC-boat': 77.5088965353627, 'ACC-traffic light': 90.07451607547873, 'ACC-fire hydrant': 95.27615751514816, 'ACC-stop sign': 84.01795633732094, 'ACC-parking meter': 92.16878321758074, 'ACC-bench': 73.01582730142638, 'ACC-bird': 80.36551152996765, 'ACC-cat': 93.66584098844896, 'ACC-dog': 86.91588075005716, 'ACC-horse': 91.83753206510202, 'ACC-sheep': 90.86862355412929, 'ACC-cow': 85.06124578848099, 'ACC-elephant': 88.946340585435, 'ACC-bear': 81.34876679107488, 'ACC-zebra': 92.0545896326008, 'ACC-giraffe': 89.11614064323149, 'ACC-backpack': 60.171580441889205, 'ACC-umbrella': 80.03909880280112, 'ACC-handbag': 50.80191023269749, 'ACC-tie': 79.94332859704089, 'ACC-suitcase': 87.3708011907291, 'ACC-frisbee': 94.4610909090909, 'ACC-skis': 69.30292302817406, 'ACC-snowboard': 77.92559574832848, 'ACC-sports ball': 80.01622889984597, 'ACC-kite': 75.25553802048331, 'ACC-baseball bat': 82.15315916055414, 'ACC-baseball glove': 88.1365152792489, 'ACC-skateboard': 69.74350400150814, 'ACC-surfboard': 89.01094891990384, 'ACC-tennis racket': 89.46819991065557, 'ACC-bottle': 82.66991973722607, 'ACC-wine glass': 86.64892748998206, 'ACC-cup': 82.75417815038946, 'ACC-fork': 68.93954033401303, 'ACC-knife': 60.58401113797905, 'ACC-spoon': 68.76636374472352, 'ACC-bowl': 68.72276233255083, 'ACC-banana': 89.85401522191113, 'ACC-apple': 69.40736408915701, 'ACC-sandwich': 79.08125050290448, 'ACC-orange': 85.88024780767671, 'ACC-broccoli': 75.13027343511071, 'ACC-carrot': 71.65745124608303, 'ACC-hot dog': 71.84943938225284, 'ACC-pizza': 92.42085970457387, 'ACC-donut': 80.77401399063886, 'ACC-cake': 76.24417638599388, 'ACC-chair': 61.236719562494336, 'ACC-couch': 81.28613478416018, 'ACC-potted plant': 48.03796300982158, 'ACC-bed': 78.49625240074651, 'ACC-dining table': 73.93953455535038, 'ACC-toilet': 90.03188166651785, 'ACC-tv': 86.94754052360194, 'ACC-laptop': 92.93120853267229, 'ACC-mouse': 86.81355944431819, 'ACC-remote': 72.6916272175202, 'ACC-keyboard': 69.96916201751213, 'ACC-cell phone': 79.60082870388344, 'ACC-microwave': 55.85735595398182, 'ACC-oven': 81.15721668207107, 'ACC-toaster': 70.30478041303425, 'ACC-sink': 82.64984677717864, 'ACC-refrigerator': 89.5226264697468, 'ACC-book': 71.1682567033706, 'ACC-clock': 81.88380849866626, 'ACC-vase': 67.78515317132617, 'ACC-scissors': 59.476030447437765, 'ACC-teddy bear': 88.64164857602982, 'ACC-hair drier': 43.474795776051, 'ACC-toothbrush': 82.12039610840863, 'ACC-banner': 76.57218071463129, 'ACC-blanket': 18.48376251148495, 'ACC-bridge': 56.51423920441271, 'ACC-cardboard': 64.6481896175658, 'ACC-counter': 53.1828134390946, 'ACC-curtain': 76.21504537096723, 'ACC-door-stuff': 62.064856441630646, 'ACC-floor-wood': 79.70334914343582, 'ACC-flower': 66.97020287351849, 'ACC-fruit': 63.17716225646267, 'ACC-gravel': 41.70919064744731, 'ACC-house': 35.719516356104926, 'ACC-light': 57.45493674156873, 'ACC-mirror-stuff': 72.73072553459254, 'ACC-net': 62.32903893777449, 'ACC-pillow': 25.483311465628983, 'ACC-platform': 59.99900904123293, 'ACC-playingfield': 89.46842788788307, 'ACC-railroad': 78.07815179012786, 'ACC-river': 78.82564070567551, 'ACC-road': 85.45957144680536, 'ACC-roof': 20.74054606868845, 'ACC-sand': 70.3592032914965, 'ACC-sea': 89.9310255889002, 'ACC-shelf': 59.94503907848166, 'ACC-snow': 94.95983937771558, 'ACC-stairs': 51.57291884580627, 'ACC-tent': 11.235576782331218, 'ACC-towel': 34.43934150405558, 'ACC-wall-brick': 61.68868784961222, 'ACC-wall-stone': 39.08237993387212, 'ACC-wall-tile': 78.61229720610834, 'ACC-wall-wood': 54.73851053572019, 'ACC-water-other': 31.94215271759639, 'ACC-window-blind': 58.41527056031758, 'ACC-window-other': 69.02456725239054, 'ACC-tree-merged': 89.23926266136904, 'ACC-fence-merged': 69.84216774767589, 'ACC-ceiling-merged': 77.59665822626017, 'ACC-sky-other-merged': 96.61782675557204, 'ACC-cabinet-merged': 75.42308174959027, 'ACC-table-merged': 50.57582156046217, 'ACC-floor-other-merged': 58.5496436837333, 'ACC-pavement-merged': 68.32210457674978, 'ACC-mountain-merged': 68.18786811270432, 'ACC-grass-merged': 81.37528493323995, 'ACC-dirt-merged': 71.06647296497741, 'ACC-paper-merged': 50.63330276000924, 'ACC-food-other-merged': 66.60739425031363, 'ACC-building-other-merged': 72.44001222242741, 'ACC-rock-merged': 82.12357890520872, 'ACC-wall-other-merged': 81.9188771212089, 'ACC-rug-merged': 77.31252821564826})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5109745390693592, 'noc@0.8': 2.8247000292654376, 'noc@0.85': 3.4544922446590576, 'noc@0.9': 4.50395083406497, 'miou@iter1': 0.8388631752020859}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.55072021484375, 'precision@0.6': 68.32491302490234, 'precision@0.7': 63.15584945678711, 'precision@0.8': 53.0509147644043, 'precision@0.9': 27.36105728149414, 'cIoU': 58.31878662109375, 'mIoU': 63.04774475097656}}}
INFO:trainer.default_trainer:This epoch takes 1:28:09.675960
INFO:trainer.default_trainer:PROGRESS: 70.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 35 training.
INFO:trainer.default_trainer:epochs[    35] optim steps[64000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34648/0.89917, loss_mask_bce_0: 0.22880/0.33456, loss_mask_dice_0: 0.68144/1.16300, loss_spatial_bce_0: 0.07885/0.08742, loss_spatial_dice_0: 0.23361/0.20849, loss_spatial_ce_0: 0.12391/0.06233, loss_grounding_bce_0: 0.06010/0.08624, loss_grounding_dice_0: 0.09843/0.17853, loss_grounding_ce_0: 0.00492/0.27198, loss_mask_ce_1: 0.41258/0.89980, loss_mask_bce_1: 0.22220/0.33549, loss_mask_dice_1: 0.71730/1.16953, loss_spatial_bce_1: 0.08797/0.08793, loss_spatial_dice_1: 0.26139/0.21244, loss_spatial_ce_1: 0.05587/0.06818, loss_grounding_bce_1: 0.06793/0.08641, loss_grounding_dice_1: 0.11255/0.17934, loss_grounding_ce_1: 0.00294/0.27278, loss_mask_ce_2: 0.43083/0.90684, loss_mask_bce_2: 0.23461/0.33606, loss_mask_dice_2: 0.71004/1.17016, loss_spatial_bce_2: 0.07400/0.08898, loss_spatial_dice_2: 0.26286/0.21403, loss_spatial_ce_2: 0.05067/0.07160, loss_grounding_bce_2: 0.06259/0.08655, loss_grounding_dice_2: 0.11098/0.17913, loss_grounding_ce_2: 0.00343/0.27607, loss_mask_ce_3: 0.54542/0.91725, loss_mask_bce_3: 0.21581/0.33718, loss_mask_dice_3: 0.60324/1.16753, loss_spatial_bce_3: 0.08256/0.09018, loss_spatial_dice_3: 0.24656/0.21493, loss_spatial_ce_3: 0.17553/0.07607, loss_grounding_bce_3: 0.06012/0.08678, loss_grounding_dice_3: 0.11415/0.17887, loss_grounding_ce_3: 0.00321/0.27838, loss_mask_ce_4: 0.64355/0.91847, loss_mask_bce_4: 0.22287/0.33930, loss_mask_dice_4: 0.68049/1.19164, loss_spatial_bce_4: 0.06885/0.09412, loss_spatial_dice_4: 0.26644/0.22707, loss_spatial_ce_4: 0.14552/0.09219, loss_grounding_bce_4: 0.06999/0.08728, loss_grounding_dice_4: 0.13499/0.18182, loss_grounding_ce_4: 0.00255/0.28124, loss_mask_ce_5: 0.63023/0.93471, loss_mask_bce_5: 0.22481/0.34161, loss_mask_dice_5: 0.77640/1.19910, loss_spatial_bce_5: 0.07935/0.09627, loss_spatial_dice_5: 0.29164/0.23125, loss_spatial_ce_5: 0.02257/0.10662, loss_grounding_bce_5: 0.06354/0.08772, loss_grounding_dice_5: 0.10448/0.18303, loss_grounding_ce_5: 0.00652/0.29394, loss_mask_ce_6: 0.59685/0.97477, loss_mask_bce_6: 0.21966/0.34425, loss_mask_dice_6: 0.69079/1.20209, loss_spatial_bce_6: 0.08815/0.10199, loss_spatial_dice_6: 0.24916/0.23410, loss_spatial_ce_6: 0.36991/0.13250, loss_grounding_bce_6: 0.06836/0.08846, loss_grounding_dice_6: 0.10705/0.18339, loss_grounding_ce_6: 0.01168/0.30944, loss_mask_ce_7: 0.51164/1.01979, loss_mask_bce_7: 0.23724/0.35212, loss_mask_dice_7: 0.90252/1.25645, loss_spatial_bce_7: 0.09355/0.10991, loss_spatial_dice_7: 0.34289/0.26171, loss_spatial_ce_7: 0.16661/0.16784, loss_grounding_bce_7: 0.06593/0.09035, loss_grounding_dice_7: 0.13183/0.19068, loss_grounding_ce_7: 0.01969/0.33991, loss_mask_ce_8: 0.91448/1.12811, loss_mask_bce_8: 0.30840/0.36576, loss_mask_dice_8: 0.85103/1.32952, loss_spatial_bce_8: 0.09622/0.13059, loss_spatial_dice_8: 0.34558/0.29976, loss_spatial_ce_8: 0.29390/0.22375, loss_grounding_bce_8: 0.06919/0.09411, loss_grounding_dice_8: 0.14185/0.20151, loss_grounding_ce_8: 0.98417/0.40705, loss_mask_ce_9: 2.52598/3.67647, loss_mask_bce_9: 0.21600/0.39270, loss_mask_dice_9: 0.91053/1.90231, loss_spatial_bce_9: 0.27516/0.33317, loss_spatial_dice_9: 0.79969/0.82191, loss_spatial_ce_9: 1.37359/1.49657, loss_grounding_bce_9: 0.13105/0.10562, loss_grounding_dice_9: 0.30096/0.28085, loss_grounding_ce_9: 1.38668/0.67196] items per batch[64] items per second[0.13] total items[4096000] mini batches[ 64000] memory[7345] epoch remaining[1:24:15]
INFO:trainer.default_trainer:epochs[    35] optim steps[64100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08367/0.89909, loss_mask_bce_0: 0.38361/0.33451, loss_mask_dice_0: 2.58312/1.16284, loss_spatial_bce_0: 0.06050/0.08740, loss_spatial_dice_0: 0.25676/0.20846, loss_spatial_ce_0: 0.04355/0.06231, loss_grounding_bce_0: 0.06310/0.08622, loss_grounding_dice_0: 0.16101/0.17850, loss_grounding_ce_0: 0.60389/0.27193, loss_mask_ce_1: 0.97009/0.89968, loss_mask_bce_1: 0.37117/0.33544, loss_mask_dice_1: 2.30725/1.16936, loss_spatial_bce_1: 0.05658/0.08792, loss_spatial_dice_1: 0.27400/0.21242, loss_spatial_ce_1: 0.04807/0.06815, loss_grounding_bce_1: 0.06022/0.08639, loss_grounding_dice_1: 0.15193/0.17931, loss_grounding_ce_1: 0.65097/0.27272, loss_mask_ce_2: 1.07498/0.90674, loss_mask_bce_2: 0.36978/0.33601, loss_mask_dice_2: 2.46644/1.16998, loss_spatial_bce_2: 0.06014/0.08897, loss_spatial_dice_2: 0.25552/0.21401, loss_spatial_ce_2: 0.01760/0.07158, loss_grounding_bce_2: 0.05837/0.08653, loss_grounding_dice_2: 0.15797/0.17911, loss_grounding_ce_2: 0.59824/0.27597, loss_mask_ce_3: 1.05785/0.91714, loss_mask_bce_3: 0.36809/0.33714, loss_mask_dice_3: 2.25723/1.16738, loss_spatial_bce_3: 0.06494/0.09017, loss_spatial_dice_3: 0.28969/0.21491, loss_spatial_ce_3: 0.01776/0.07606, loss_grounding_bce_3: 0.05525/0.08676, loss_grounding_dice_3: 0.15086/0.17885, loss_grounding_ce_3: 0.68701/0.27828, loss_mask_ce_4: 0.85325/0.91833, loss_mask_bce_4: 0.39364/0.33925, loss_mask_dice_4: 2.58782/1.19150, loss_spatial_bce_4: 0.07138/0.09410, loss_spatial_dice_4: 0.29368/0.22705, loss_spatial_ce_4: 0.04418/0.09218, loss_grounding_bce_4: 0.05924/0.08726, loss_grounding_dice_4: 0.14889/0.18179, loss_grounding_ce_4: 0.55735/0.28115, loss_mask_ce_5: 1.06708/0.93457, loss_mask_bce_5: 0.38773/0.34156, loss_mask_dice_5: 2.55800/1.19895, loss_spatial_bce_5: 0.07544/0.09626, loss_spatial_dice_5: 0.28287/0.23123, loss_spatial_ce_5: 0.03631/0.10660, loss_grounding_bce_5: 0.06615/0.08770, loss_grounding_dice_5: 0.15976/0.18300, loss_grounding_ce_5: 0.74099/0.29386, loss_mask_ce_6: 0.91858/0.97462, loss_mask_bce_6: 0.37072/0.34421, loss_mask_dice_6: 2.50618/1.20196, loss_spatial_bce_6: 0.06381/0.10197, loss_spatial_dice_6: 0.24495/0.23408, loss_spatial_ce_6: 0.03337/0.13248, loss_grounding_bce_6: 0.06559/0.08843, loss_grounding_dice_6: 0.15633/0.18336, loss_grounding_ce_6: 0.51736/0.30936, loss_mask_ce_7: 1.03000/1.01961, loss_mask_bce_7: 0.36323/0.35207, loss_mask_dice_7: 2.49439/1.25626, loss_spatial_bce_7: 0.06542/0.10989, loss_spatial_dice_7: 0.31201/0.26170, loss_spatial_ce_7: 0.07056/0.16781, loss_grounding_bce_7: 0.05638/0.09032, loss_grounding_dice_7: 0.15010/0.19065, loss_grounding_ce_7: 0.32403/0.33980, loss_mask_ce_8: 1.28392/1.12795, loss_mask_bce_8: 0.41324/0.36569, loss_mask_dice_8: 2.78598/1.32930, loss_spatial_bce_8: 0.08031/0.13057, loss_spatial_dice_8: 0.35401/0.29974, loss_spatial_ce_8: 0.10918/0.22366, loss_grounding_bce_8: 0.06184/0.09408, loss_grounding_dice_8: 0.14792/0.20149, loss_grounding_ce_8: 0.23072/0.40687, loss_mask_ce_9: 5.57143/3.67632, loss_mask_bce_9: 0.42671/0.39264, loss_mask_dice_9: 4.06682/1.90197, loss_spatial_bce_9: 0.23260/0.33315, loss_spatial_dice_9: 0.90442/0.82190, loss_spatial_ce_9: 1.31539/1.49663, loss_grounding_bce_9: 0.07934/0.10559, loss_grounding_dice_9: 0.21888/0.28083, loss_grounding_ce_9: 0.61610/0.67186] items per batch[64] items per second[0.23] total items[4102400] mini batches[ 64100] memory[7345] epoch remaining[1:17:55]
INFO:trainer.default_trainer:epochs[    35] optim steps[64200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.44031/0.89904, loss_mask_bce_0: 0.32734/0.33453, loss_mask_dice_0: 0.97240/1.16291, loss_spatial_bce_0: 0.13247/0.08739, loss_spatial_dice_0: 0.32614/0.20845, loss_spatial_ce_0: 0.28728/0.06230, loss_grounding_bce_0: 0.07113/0.08621, loss_grounding_dice_0: 0.10470/0.17849, loss_grounding_ce_0: 0.23773/0.27192, loss_mask_ce_1: 2.41350/0.89961, loss_mask_bce_1: 0.34751/0.33547, loss_mask_dice_1: 1.20406/1.16944, loss_spatial_bce_1: 0.13431/0.08790, loss_spatial_dice_1: 0.34131/0.21241, loss_spatial_ce_1: 0.14389/0.06813, loss_grounding_bce_1: 0.06968/0.08638, loss_grounding_dice_1: 0.12618/0.17930, loss_grounding_ce_1: 0.23130/0.27273, loss_mask_ce_2: 2.54546/0.90667, loss_mask_bce_2: 0.33850/0.33604, loss_mask_dice_2: 0.83032/1.17005, loss_spatial_bce_2: 0.12534/0.08896, loss_spatial_dice_2: 0.32378/0.21401, loss_spatial_ce_2: 0.17885/0.07156, loss_grounding_bce_2: 0.07309/0.08652, loss_grounding_dice_2: 0.10919/0.17910, loss_grounding_ce_2: 0.20506/0.27599, loss_mask_ce_3: 2.28380/0.91711, loss_mask_bce_3: 0.33782/0.33717, loss_mask_dice_3: 1.03741/1.16747, loss_spatial_bce_3: 0.11461/0.09016, loss_spatial_dice_3: 0.31812/0.21491, loss_spatial_ce_3: 0.14021/0.07605, loss_grounding_bce_3: 0.07378/0.08675, loss_grounding_dice_3: 0.16691/0.17884, loss_grounding_ce_3: 0.21836/0.27829, loss_mask_ce_4: 2.15945/0.91829, loss_mask_bce_4: 0.34583/0.33928, loss_mask_dice_4: 1.38982/1.19158, loss_spatial_bce_4: 0.12546/0.09410, loss_spatial_dice_4: 0.33759/0.22705, loss_spatial_ce_4: 0.31572/0.09220, loss_grounding_bce_4: 0.07208/0.08724, loss_grounding_dice_4: 0.13494/0.18178, loss_grounding_ce_4: 0.20958/0.28114, loss_mask_ce_5: 2.48918/0.93453, loss_mask_bce_5: 0.34276/0.34160, loss_mask_dice_5: 1.15956/1.19902, loss_spatial_bce_5: 0.10817/0.09625, loss_spatial_dice_5: 0.33180/0.23122, loss_spatial_ce_5: 0.12843/0.10661, loss_grounding_bce_5: 0.07239/0.08769, loss_grounding_dice_5: 0.10837/0.18299, loss_grounding_ce_5: 0.21492/0.29386, loss_mask_ce_6: 2.86276/0.97459, loss_mask_bce_6: 0.33771/0.34424, loss_mask_dice_6: 0.86198/1.20200, loss_spatial_bce_6: 0.10427/0.10196, loss_spatial_dice_6: 0.31444/0.23409, loss_spatial_ce_6: 0.31471/0.13245, loss_grounding_bce_6: 0.07374/0.08843, loss_grounding_dice_6: 0.10316/0.18335, loss_grounding_ce_6: 0.31772/0.30935, loss_mask_ce_7: 2.67001/1.01961, loss_mask_bce_7: 0.34568/0.35210, loss_mask_dice_7: 1.06014/1.25635, loss_spatial_bce_7: 0.10068/0.10989, loss_spatial_dice_7: 0.35068/0.26170, loss_spatial_ce_7: 0.32839/0.16779, loss_grounding_bce_7: 0.07627/0.09032, loss_grounding_dice_7: 0.10233/0.19064, loss_grounding_ce_7: 0.37214/0.33983, loss_mask_ce_8: 3.07033/1.12797, loss_mask_bce_8: 0.35251/0.36572, loss_mask_dice_8: 1.06825/1.32939, loss_spatial_bce_8: 0.15073/0.13056, loss_spatial_dice_8: 0.40153/0.29973, loss_spatial_ce_8: 0.52005/0.22363, loss_grounding_bce_8: 0.07528/0.09407, loss_grounding_dice_8: 0.11273/0.20147, loss_grounding_ce_8: 0.32524/0.40694, loss_mask_ce_9: 4.67173/3.67655, loss_mask_bce_9: 0.34602/0.39268, loss_mask_dice_9: 1.48701/1.90210, loss_spatial_bce_9: 0.26250/0.33315, loss_spatial_dice_9: 0.81076/0.82189, loss_spatial_ce_9: 1.83722/1.49659, loss_grounding_bce_9: 0.08282/0.10559, loss_grounding_dice_9: 0.15266/0.28081, loss_grounding_ce_9: 0.41286/0.67193] items per batch[64] items per second[0.24] total items[4108800] mini batches[ 64200] memory[7345] epoch remaining[1:12:23]
INFO:trainer.default_trainer:epochs[    35] optim steps[64300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44548/0.89902, loss_mask_bce_0: 0.20170/0.33451, loss_mask_dice_0: 0.39822/1.16294, loss_spatial_bce_0: 0.07358/0.08738, loss_spatial_dice_0: 0.20659/0.20845, loss_spatial_ce_0: 0.04405/0.06228, loss_grounding_bce_0: 0.12014/0.08620, loss_grounding_dice_0: 0.09869/0.17853, loss_grounding_ce_0: 0.00828/0.27185, loss_mask_ce_1: 0.41286/0.89958, loss_mask_bce_1: 0.20325/0.33545, loss_mask_dice_1: 0.45870/1.16950, loss_spatial_bce_1: 0.08438/0.08790, loss_spatial_dice_1: 0.22205/0.21240, loss_spatial_ce_1: 0.04786/0.06811, loss_grounding_bce_1: 0.11746/0.08638, loss_grounding_dice_1: 0.10149/0.17934, loss_grounding_ce_1: 0.00591/0.27269, loss_mask_ce_2: 0.58867/0.90668, loss_mask_bce_2: 0.19377/0.33601, loss_mask_dice_2: 0.35014/1.17009, loss_spatial_bce_2: 0.08279/0.08895, loss_spatial_dice_2: 0.20167/0.21401, loss_spatial_ce_2: 0.04580/0.07155, loss_grounding_bce_2: 0.12188/0.08652, loss_grounding_dice_2: 0.09957/0.17913, loss_grounding_ce_2: 0.00633/0.27593, loss_mask_ce_3: 0.42077/0.91710, loss_mask_bce_3: 0.22146/0.33715, loss_mask_dice_3: 0.47338/1.16753, loss_spatial_bce_3: 0.08820/0.09015, loss_spatial_dice_3: 0.20409/0.21491, loss_spatial_ce_3: 0.05082/0.07605, loss_grounding_bce_3: 0.12662/0.08675, loss_grounding_dice_3: 0.10255/0.17888, loss_grounding_ce_3: 0.01000/0.27824, loss_mask_ce_4: 0.50831/0.91826, loss_mask_bce_4: 0.19869/0.33927, loss_mask_dice_4: 0.41982/1.19159, loss_spatial_bce_4: 0.08590/0.09409, loss_spatial_dice_4: 0.21611/0.22705, loss_spatial_ce_4: 0.03511/0.09220, loss_grounding_bce_4: 0.11370/0.08724, loss_grounding_dice_4: 0.09408/0.18181, loss_grounding_ce_4: 0.00950/0.28109, loss_mask_ce_5: 0.59613/0.93455, loss_mask_bce_5: 0.15107/0.34158, loss_mask_dice_5: 0.44716/1.19907, loss_spatial_bce_5: 0.09786/0.09625, loss_spatial_dice_5: 0.22711/0.23123, loss_spatial_ce_5: 0.04775/0.10658, loss_grounding_bce_5: 0.11313/0.08768, loss_grounding_dice_5: 0.10049/0.18303, loss_grounding_ce_5: 0.01133/0.29379, loss_mask_ce_6: 0.66791/0.97463, loss_mask_bce_6: 0.15758/0.34423, loss_mask_dice_6: 0.42508/1.20205, loss_spatial_bce_6: 0.09097/0.10196, loss_spatial_dice_6: 0.21923/0.23410, loss_spatial_ce_6: 0.04845/0.13241, loss_grounding_bce_6: 0.11338/0.08842, loss_grounding_dice_6: 0.10174/0.18338, loss_grounding_ce_6: 0.02059/0.30932, loss_mask_ce_7: 0.51451/1.01958, loss_mask_bce_7: 0.19765/0.35209, loss_mask_dice_7: 0.54225/1.25644, loss_spatial_bce_7: 0.08253/0.10987, loss_spatial_dice_7: 0.23162/0.26170, loss_spatial_ce_7: 0.10074/0.16775, loss_grounding_bce_7: 0.08650/0.09031, loss_grounding_dice_7: 0.09681/0.19067, loss_grounding_ce_7: 0.01683/0.33979, loss_mask_ce_8: 0.68633/1.12795, loss_mask_bce_8: 0.17589/0.36570, loss_mask_dice_8: 0.50307/1.32944, loss_spatial_bce_8: 0.10735/0.13055, loss_spatial_dice_8: 0.25384/0.29974, loss_spatial_ce_8: 0.22603/0.22359, loss_grounding_bce_8: 0.05225/0.09407, loss_grounding_dice_8: 0.08075/0.20150, loss_grounding_ce_8: 0.02115/0.40687, loss_mask_ce_9: 2.31615/3.67637, loss_mask_bce_9: 0.24132/0.39266, loss_mask_dice_9: 1.23024/1.90210, loss_spatial_bce_9: 0.39365/0.33312, loss_spatial_dice_9: 0.73784/0.82191, loss_spatial_ce_9: 1.51541/1.49657, loss_grounding_bce_9: 0.11156/0.10557, loss_grounding_dice_9: 0.15582/0.28084, loss_grounding_ce_9: 0.26809/0.67172] items per batch[64] items per second[0.23] total items[4115200] mini batches[ 64300] memory[7345] epoch remaining[1:07:44]
INFO:trainer.default_trainer:epochs[    35] optim steps[64400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02481/0.89901, loss_mask_bce_0: 0.04506/0.33447, loss_mask_dice_0: 0.08290/1.16286, loss_spatial_bce_0: 0.05498/0.08738, loss_spatial_dice_0: 0.08365/0.20843, loss_spatial_ce_0: 0.06944/0.06228, loss_grounding_bce_0: 0.04477/0.08620, loss_grounding_dice_0: 0.08916/0.17851, loss_grounding_ce_0: 0.01318/0.27186, loss_mask_ce_1: 0.02417/0.89958, loss_mask_bce_1: 0.04666/0.33540, loss_mask_dice_1: 0.09616/1.16943, loss_spatial_bce_1: 0.05161/0.08790, loss_spatial_dice_1: 0.09620/0.21239, loss_spatial_ce_1: 0.06937/0.06811, loss_grounding_bce_1: 0.04611/0.08638, loss_grounding_dice_1: 0.08317/0.17932, loss_grounding_ce_1: 0.00873/0.27269, loss_mask_ce_2: 0.02926/0.90665, loss_mask_bce_2: 0.04585/0.33596, loss_mask_dice_2: 0.08712/1.17000, loss_spatial_bce_2: 0.05018/0.08896, loss_spatial_dice_2: 0.08769/0.21399, loss_spatial_ce_2: 0.06943/0.07154, loss_grounding_bce_2: 0.04504/0.08652, loss_grounding_dice_2: 0.07804/0.17910, loss_grounding_ce_2: 0.00813/0.27592, loss_mask_ce_3: 0.02763/0.91710, loss_mask_bce_3: 0.04346/0.33711, loss_mask_dice_3: 0.08443/1.16743, loss_spatial_bce_3: 0.05180/0.09016, loss_spatial_dice_3: 0.09908/0.21490, loss_spatial_ce_3: 0.06975/0.07605, loss_grounding_bce_3: 0.04530/0.08675, loss_grounding_dice_3: 0.08173/0.17887, loss_grounding_ce_3: 0.01254/0.27822, loss_mask_ce_4: 0.02570/0.91828, loss_mask_bce_4: 0.04462/0.33922, loss_mask_dice_4: 0.09408/1.19151, loss_spatial_bce_4: 0.05658/0.09409, loss_spatial_dice_4: 0.09829/0.22705, loss_spatial_ce_4: 0.06968/0.09221, loss_grounding_bce_4: 0.04661/0.08724, loss_grounding_dice_4: 0.07957/0.18180, loss_grounding_ce_4: 0.01106/0.28109, loss_mask_ce_5: 0.02684/0.93457, loss_mask_bce_5: 0.04929/0.34154, loss_mask_dice_5: 0.08075/1.19898, loss_spatial_bce_5: 0.04765/0.09625, loss_spatial_dice_5: 0.07865/0.23122, loss_spatial_ce_5: 0.07076/0.10659, loss_grounding_bce_5: 0.05278/0.08768, loss_grounding_dice_5: 0.09302/0.18301, loss_grounding_ce_5: 0.01497/0.29380, loss_mask_ce_6: 0.02559/0.97460, loss_mask_bce_6: 0.05009/0.34419, loss_mask_dice_6: 0.09168/1.20197, loss_spatial_bce_6: 0.05909/0.10197, loss_spatial_dice_6: 0.09938/0.23409, loss_spatial_ce_6: 0.07442/0.13241, loss_grounding_bce_6: 0.05385/0.08843, loss_grounding_dice_6: 0.09008/0.18337, loss_grounding_ce_6: 0.01781/0.30932, loss_mask_ce_7: 0.02553/1.01953, loss_mask_bce_7: 0.05608/0.35206, loss_mask_dice_7: 0.10818/1.25636, loss_spatial_bce_7: 0.06217/0.10988, loss_spatial_dice_7: 0.11637/0.26170, loss_spatial_ce_7: 0.12066/0.16776, loss_grounding_bce_7: 0.06067/0.09031, loss_grounding_dice_7: 0.09945/0.19066, loss_grounding_ce_7: 0.00710/0.33977, loss_mask_ce_8: 0.04460/1.12795, loss_mask_bce_8: 0.05065/0.36566, loss_mask_dice_8: 0.10108/1.32935, loss_spatial_bce_8: 0.07183/0.13055, loss_spatial_dice_8: 0.10379/0.29972, loss_spatial_ce_8: 0.07366/0.22355, loss_grounding_bce_8: 0.04891/0.09407, loss_grounding_dice_8: 0.10035/0.20148, loss_grounding_ce_8: 0.00912/0.40693, loss_mask_ce_9: 1.86655/3.67628, loss_mask_bce_9: 0.06261/0.39263, loss_mask_dice_9: 0.12641/1.90199, loss_spatial_bce_9: 0.24685/0.33312, loss_spatial_dice_9: 0.39450/0.82189, loss_spatial_ce_9: 0.57450/1.49652, loss_grounding_bce_9: 0.05889/0.10558, loss_grounding_dice_9: 0.14363/0.28082, loss_grounding_ce_9: 0.22195/0.67176] items per batch[64] items per second[0.23] total items[4121600] mini batches[ 64400] memory[7345] epoch remaining[1:03:00]
INFO:trainer.default_trainer:epochs[    35] optim steps[64500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25154/0.89899, loss_mask_bce_0: 0.82075/0.33449, loss_mask_dice_0: 2.02730/1.16305, loss_spatial_bce_0: 0.08699/0.08736, loss_spatial_dice_0: 0.23585/0.20842, loss_spatial_ce_0: 0.00680/0.06225, loss_grounding_bce_0: 0.14338/0.08620, loss_grounding_dice_0: 0.18014/0.17852, loss_grounding_ce_0: 0.06026/0.27187, loss_mask_ce_1: 1.33187/0.89957, loss_mask_bce_1: 0.70564/0.33542, loss_mask_dice_1: 1.90991/1.16965, loss_spatial_bce_1: 0.08192/0.08789, loss_spatial_dice_1: 0.23889/0.21238, loss_spatial_ce_1: 0.00514/0.06808, loss_grounding_bce_1: 0.13599/0.08638, loss_grounding_dice_1: 0.18846/0.17933, loss_grounding_ce_1: 0.07006/0.27269, loss_mask_ce_2: 1.52120/0.90662, loss_mask_bce_2: 0.76553/0.33598, loss_mask_dice_2: 1.95498/1.17019, loss_spatial_bce_2: 0.09087/0.08895, loss_spatial_dice_2: 0.24672/0.21399, loss_spatial_ce_2: 0.00701/0.07150, loss_grounding_bce_2: 0.13520/0.08652, loss_grounding_dice_2: 0.18139/0.17911, loss_grounding_ce_2: 0.08206/0.27593, loss_mask_ce_3: 1.60514/0.91710, loss_mask_bce_3: 0.76293/0.33712, loss_mask_dice_3: 1.96152/1.16763, loss_spatial_bce_3: 0.09008/0.09015, loss_spatial_dice_3: 0.24016/0.21489, loss_spatial_ce_3: 0.01653/0.07601, loss_grounding_bce_3: 0.13554/0.08675, loss_grounding_dice_3: 0.18816/0.17887, loss_grounding_ce_3: 0.07928/0.27824, loss_mask_ce_4: 1.53063/0.91827, loss_mask_bce_4: 0.69717/0.33924, loss_mask_dice_4: 1.83094/1.19169, loss_spatial_bce_4: 0.07918/0.09408, loss_spatial_dice_4: 0.24657/0.22704, loss_spatial_ce_4: 0.02010/0.09219, loss_grounding_bce_4: 0.12942/0.08725, loss_grounding_dice_4: 0.18036/0.18180, loss_grounding_ce_4: 0.08777/0.28114, loss_mask_ce_5: 1.34244/0.93457, loss_mask_bce_5: 0.69345/0.34156, loss_mask_dice_5: 1.94410/1.19920, loss_spatial_bce_5: 0.08012/0.09625, loss_spatial_dice_5: 0.23164/0.23122, loss_spatial_ce_5: 0.00963/0.10655, loss_grounding_bce_5: 0.13347/0.08769, loss_grounding_dice_5: 0.19055/0.18302, loss_grounding_ce_5: 0.07699/0.29382, loss_mask_ce_6: 1.46144/0.97461, loss_mask_bce_6: 0.79128/0.34421, loss_mask_dice_6: 1.91528/1.20215, loss_spatial_bce_6: 0.09479/0.10196, loss_spatial_dice_6: 0.22039/0.23409, loss_spatial_ce_6: 0.04224/0.13236, loss_grounding_bce_6: 0.13781/0.08843, loss_grounding_dice_6: 0.19490/0.18338, loss_grounding_ce_6: 0.06303/0.30935, loss_mask_ce_7: 1.72052/1.01953, loss_mask_bce_7: 0.75753/0.35209, loss_mask_dice_7: 1.94935/1.25659, loss_spatial_bce_7: 0.08141/0.10987, loss_spatial_dice_7: 0.19232/0.26171, loss_spatial_ce_7: 0.12648/0.16772, loss_grounding_bce_7: 0.12668/0.09032, loss_grounding_dice_7: 0.19185/0.19067, loss_grounding_ce_7: 0.05699/0.33977, loss_mask_ce_8: 1.51567/1.12795, loss_mask_bce_8: 0.95023/0.36570, loss_mask_dice_8: 2.15301/1.32960, loss_spatial_bce_8: 0.08768/0.13055, loss_spatial_dice_8: 0.22482/0.29971, loss_spatial_ce_8: 0.07540/0.22350, loss_grounding_bce_8: 0.11361/0.09407, loss_grounding_dice_8: 0.19234/0.20149, loss_grounding_ce_8: 0.08189/0.40687, loss_mask_ce_9: 4.82064/3.67630, loss_mask_bce_9: 0.73370/0.39266, loss_mask_dice_9: 4.94832/1.90227, loss_spatial_bce_9: 0.46892/0.33313, loss_spatial_dice_9: 0.72335/0.82190, loss_spatial_ce_9: 1.88352/1.49660, loss_grounding_bce_9: 0.14030/0.10559, loss_grounding_dice_9: 0.28919/0.28084, loss_grounding_ce_9: 0.23113/0.67174] items per batch[64] items per second[0.23] total items[4128000] mini batches[ 64500] memory[7345] epoch remaining[0:58:19]
INFO:trainer.default_trainer:epochs[    35] optim steps[64600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03862/0.89907, loss_mask_bce_0: 0.09655/0.33447, loss_mask_dice_0: 0.86091/1.16308, loss_spatial_bce_0: 0.02465/0.08735, loss_spatial_dice_0: 0.28026/0.20839, loss_spatial_ce_0: 0.06012/0.06222, loss_grounding_bce_0: 0.03147/0.08620, loss_grounding_dice_0: 0.13604/0.17851, loss_grounding_ce_0: 0.10698/0.27183, loss_mask_ce_1: 1.06699/0.89966, loss_mask_bce_1: 0.08939/0.33541, loss_mask_dice_1: 0.83290/1.16970, loss_spatial_bce_1: 0.02689/0.08787, loss_spatial_dice_1: 0.31845/0.21236, loss_spatial_ce_1: 0.03517/0.06805, loss_grounding_bce_1: 0.03231/0.08638, loss_grounding_dice_1: 0.12866/0.17933, loss_grounding_ce_1: 0.06504/0.27266, loss_mask_ce_2: 1.21249/0.90672, loss_mask_bce_2: 0.09397/0.33597, loss_mask_dice_2: 0.91230/1.17023, loss_spatial_bce_2: 0.01280/0.08894, loss_spatial_dice_2: 0.28273/0.21396, loss_spatial_ce_2: 0.17291/0.07147, loss_grounding_bce_2: 0.03198/0.08652, loss_grounding_dice_2: 0.15344/0.17911, loss_grounding_ce_2: 0.05192/0.27589, loss_mask_ce_3: 1.23144/0.91721, loss_mask_bce_3: 0.09984/0.33711, loss_mask_dice_3: 1.16579/1.16769, loss_spatial_bce_3: 0.01629/0.09014, loss_spatial_dice_3: 0.29058/0.21487, loss_spatial_ce_3: 0.03740/0.07598, loss_grounding_bce_3: 0.03591/0.08674, loss_grounding_dice_3: 0.15439/0.17887, loss_grounding_ce_3: 0.05916/0.27821, loss_mask_ce_4: 1.33690/0.91836, loss_mask_bce_4: 0.10443/0.33923, loss_mask_dice_4: 0.93660/1.19176, loss_spatial_bce_4: 0.02126/0.09407, loss_spatial_dice_4: 0.35249/0.22702, loss_spatial_ce_4: 0.05945/0.09215, loss_grounding_bce_4: 0.03140/0.08724, loss_grounding_dice_4: 0.13029/0.18180, loss_grounding_ce_4: 0.09905/0.28113, loss_mask_ce_5: 1.21125/0.93468, loss_mask_bce_5: 0.10569/0.34154, loss_mask_dice_5: 0.89075/1.19925, loss_spatial_bce_5: 0.02057/0.09624, loss_spatial_dice_5: 0.37497/0.23121, loss_spatial_ce_5: 0.07923/0.10653, loss_grounding_bce_5: 0.03211/0.08768, loss_grounding_dice_5: 0.12818/0.18302, loss_grounding_ce_5: 0.06251/0.29379, loss_mask_ce_6: 1.07472/0.97469, loss_mask_bce_6: 0.10973/0.34420, loss_mask_dice_6: 0.86680/1.20220, loss_spatial_bce_6: 0.02882/0.10195, loss_spatial_dice_6: 0.37381/0.23408, loss_spatial_ce_6: 0.12869/0.13235, loss_grounding_bce_6: 0.03654/0.08843, loss_grounding_dice_6: 0.07916/0.18338, loss_grounding_ce_6: 0.26320/0.30930, loss_mask_ce_7: 1.87204/1.01960, loss_mask_bce_7: 0.07331/0.35208, loss_mask_dice_7: 1.06188/1.25666, loss_spatial_bce_7: 0.03234/0.10986, loss_spatial_dice_7: 0.31423/0.26169, loss_spatial_ce_7: 0.15959/0.16770, loss_grounding_bce_7: 0.03602/0.09031, loss_grounding_dice_7: 0.13655/0.19068, loss_grounding_ce_7: 0.11525/0.33974, loss_mask_ce_8: 1.79384/1.12803, loss_mask_bce_8: 0.07325/0.36571, loss_mask_dice_8: 1.30442/1.32970, loss_spatial_bce_8: 0.04651/0.13055, loss_spatial_dice_8: 0.41486/0.29970, loss_spatial_ce_8: 0.11266/0.22344, loss_grounding_bce_8: 0.03995/0.09407, loss_grounding_dice_8: 0.15639/0.20149, loss_grounding_ce_8: 0.06996/0.40686, loss_mask_ce_9: 3.59119/3.67649, loss_mask_bce_9: 0.09536/0.39267, loss_mask_dice_9: 1.52920/1.90250, loss_spatial_bce_9: 0.16385/0.33312, loss_spatial_dice_9: 0.86460/0.82190, loss_spatial_ce_9: 1.64004/1.49653, loss_grounding_bce_9: 0.10819/0.10560, loss_grounding_dice_9: 0.17887/0.28084, loss_grounding_ce_9: 0.06017/0.67179] items per batch[64] items per second[0.23] total items[4134400] mini batches[ 64600] memory[7345] epoch remaining[0:53:45]
INFO:trainer.default_trainer:epochs[    35] optim steps[64700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48542/0.89907, loss_mask_bce_0: 0.27017/0.33446, loss_mask_dice_0: 0.26271/1.16299, loss_spatial_bce_0: 0.15265/0.08734, loss_spatial_dice_0: 0.13775/0.20836, loss_spatial_ce_0: 0.00389/0.06218, loss_grounding_bce_0: 0.13065/0.08619, loss_grounding_dice_0: 0.12188/0.17850, loss_grounding_ce_0: 0.03331/0.27181, loss_mask_ce_1: 0.47599/0.89966, loss_mask_bce_1: 0.26683/0.33539, loss_mask_dice_1: 0.27411/1.16961, loss_spatial_bce_1: 0.14160/0.08787, loss_spatial_dice_1: 0.12834/0.21232, loss_spatial_ce_1: 0.00638/0.06803, loss_grounding_bce_1: 0.13649/0.08637, loss_grounding_dice_1: 0.11925/0.17932, loss_grounding_ce_1: 0.03103/0.27262, loss_mask_ce_2: 0.46330/0.90671, loss_mask_bce_2: 0.27171/0.33595, loss_mask_dice_2: 0.27789/1.17014, loss_spatial_bce_2: 0.15331/0.08893, loss_spatial_dice_2: 0.13712/0.21392, loss_spatial_ce_2: 0.01291/0.07144, loss_grounding_bce_2: 0.13740/0.08651, loss_grounding_dice_2: 0.12223/0.17909, loss_grounding_ce_2: 0.02847/0.27586, loss_mask_ce_3: 0.48711/0.91720, loss_mask_bce_3: 0.26438/0.33709, loss_mask_dice_3: 0.28677/1.16762, loss_spatial_bce_3: 0.14502/0.09013, loss_spatial_dice_3: 0.13842/0.21484, loss_spatial_ce_3: 0.03925/0.07596, loss_grounding_bce_3: 0.12111/0.08674, loss_grounding_dice_3: 0.11143/0.17886, loss_grounding_ce_3: 0.04006/0.27817, loss_mask_ce_4: 0.45850/0.91835, loss_mask_bce_4: 0.29545/0.33921, loss_mask_dice_4: 0.30124/1.19168, loss_spatial_bce_4: 0.14837/0.09407, loss_spatial_dice_4: 0.14156/0.22699, loss_spatial_ce_4: 0.06178/0.09214, loss_grounding_bce_4: 0.14830/0.08724, loss_grounding_dice_4: 0.12473/0.18179, loss_grounding_ce_4: 0.04006/0.28110, loss_mask_ce_5: 0.52202/0.93465, loss_mask_bce_5: 0.29485/0.34152, loss_mask_dice_5: 0.30887/1.19919, loss_spatial_bce_5: 0.14660/0.09623, loss_spatial_dice_5: 0.14025/0.23118, loss_spatial_ce_5: 0.05805/0.10650, loss_grounding_bce_5: 0.14341/0.08767, loss_grounding_dice_5: 0.12400/0.18301, loss_grounding_ce_5: 0.03958/0.29381, loss_mask_ce_6: 0.51458/0.97468, loss_mask_bce_6: 0.30047/0.34419, loss_mask_dice_6: 0.29645/1.20214, loss_spatial_bce_6: 0.14655/0.10194, loss_spatial_dice_6: 0.13071/0.23405, loss_spatial_ce_6: 0.04928/0.13231, loss_grounding_bce_6: 0.15072/0.08842, loss_grounding_dice_6: 0.12910/0.18337, loss_grounding_ce_6: 0.06171/0.30933, loss_mask_ce_7: 0.52866/1.01955, loss_mask_bce_7: 0.33911/0.35207, loss_mask_dice_7: 0.34586/1.25660, loss_spatial_bce_7: 0.17937/0.10985, loss_spatial_dice_7: 0.15001/0.26165, loss_spatial_ce_7: 0.03621/0.16764, loss_grounding_bce_7: 0.17437/0.09031, loss_grounding_dice_7: 0.14059/0.19066, loss_grounding_ce_7: 0.08705/0.33975, loss_mask_ce_8: 0.54424/1.12800, loss_mask_bce_8: 0.36759/0.36570, loss_mask_dice_8: 0.34006/1.32964, loss_spatial_bce_8: 0.19344/0.13053, loss_spatial_dice_8: 0.17847/0.29966, loss_spatial_ce_8: 0.19264/0.22338, loss_grounding_bce_8: 0.21798/0.09406, loss_grounding_dice_8: 0.17714/0.20148, loss_grounding_ce_8: 0.05891/0.40685, loss_mask_ce_9: 2.39350/3.67646, loss_mask_bce_9: 0.37027/0.39268, loss_mask_dice_9: 0.50143/1.90241, loss_spatial_bce_9: 0.43357/0.33313, loss_spatial_dice_9: 0.61844/0.82191, loss_spatial_ce_9: 1.17211/1.49652, loss_grounding_bce_9: 0.18868/0.10559, loss_grounding_dice_9: 0.23837/0.28084, loss_grounding_ce_9: 0.18039/0.67186] items per batch[64] items per second[0.24] total items[4140800] mini batches[ 64700] memory[7345] epoch remaining[0:49:05]
INFO:trainer.default_trainer:epochs[    35] optim steps[64800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28154/0.89896, loss_mask_bce_0: 0.41028/0.33450, loss_mask_dice_0: 0.82040/1.16297, loss_spatial_bce_0: 0.09981/0.08735, loss_spatial_dice_0: 0.23127/0.20834, loss_spatial_ce_0: 0.11974/0.06218, loss_grounding_bce_0: 0.05943/0.08620, loss_grounding_dice_0: 0.12242/0.17848, loss_grounding_ce_0: 0.20397/0.27187, loss_mask_ce_1: 1.34305/0.89959, loss_mask_bce_1: 0.40755/0.33544, loss_mask_dice_1: 0.81555/1.16957, loss_spatial_bce_1: 0.10115/0.08788, loss_spatial_dice_1: 0.23307/0.21231, loss_spatial_ce_1: 0.05797/0.06802, loss_grounding_bce_1: 0.06257/0.08639, loss_grounding_dice_1: 0.13844/0.17931, loss_grounding_ce_1: 0.23242/0.27269, loss_mask_ce_2: 1.29454/0.90664, loss_mask_bce_2: 0.41495/0.33599, loss_mask_dice_2: 0.88239/1.17010, loss_spatial_bce_2: 0.10364/0.08894, loss_spatial_dice_2: 0.24335/0.21391, loss_spatial_ce_2: 0.05431/0.07142, loss_grounding_bce_2: 0.05829/0.08652, loss_grounding_dice_2: 0.13903/0.17908, loss_grounding_ce_2: 0.22663/0.27595, loss_mask_ce_3: 1.39994/0.91712, loss_mask_bce_3: 0.42881/0.33713, loss_mask_dice_3: 0.85859/1.16758, loss_spatial_bce_3: 0.11095/0.09015, loss_spatial_dice_3: 0.24652/0.21484, loss_spatial_ce_3: 0.05496/0.07597, loss_grounding_bce_3: 0.06285/0.08675, loss_grounding_dice_3: 0.16147/0.17885, loss_grounding_ce_3: 0.21665/0.27825, loss_mask_ce_4: 1.42631/0.91829, loss_mask_bce_4: 0.44571/0.33925, loss_mask_dice_4: 0.88518/1.19165, loss_spatial_bce_4: 0.12005/0.09408, loss_spatial_dice_4: 0.25234/0.22698, loss_spatial_ce_4: 0.04393/0.09215, loss_grounding_bce_4: 0.05997/0.08725, loss_grounding_dice_4: 0.15547/0.18178, loss_grounding_ce_4: 0.21512/0.28119, loss_mask_ce_5: 1.46607/0.93459, loss_mask_bce_5: 0.42742/0.34157, loss_mask_dice_5: 0.90686/1.19915, loss_spatial_bce_5: 0.12240/0.09625, loss_spatial_dice_5: 0.24685/0.23117, loss_spatial_ce_5: 0.08646/0.10649, loss_grounding_bce_5: 0.06138/0.08768, loss_grounding_dice_5: 0.15018/0.18300, loss_grounding_ce_5: 0.20203/0.29389, loss_mask_ce_6: 1.49147/0.97462, loss_mask_bce_6: 0.43462/0.34422, loss_mask_dice_6: 0.92628/1.20210, loss_spatial_bce_6: 0.13321/0.10195, loss_spatial_dice_6: 0.26182/0.23404, loss_spatial_ce_6: 0.12084/0.13229, loss_grounding_bce_6: 0.06209/0.08843, loss_grounding_dice_6: 0.15137/0.18336, loss_grounding_ce_6: 0.24694/0.30943, loss_mask_ce_7: 1.57040/1.01947, loss_mask_bce_7: 0.41717/0.35211, loss_mask_dice_7: 0.91231/1.25655, loss_spatial_bce_7: 0.12593/0.10986, loss_spatial_dice_7: 0.26148/0.26163, loss_spatial_ce_7: 0.12017/0.16764, loss_grounding_bce_7: 0.05624/0.09032, loss_grounding_dice_7: 0.14329/0.19065, loss_grounding_ce_7: 0.35523/0.33981, loss_mask_ce_8: 1.61733/1.12795, loss_mask_bce_8: 0.47772/0.36574, loss_mask_dice_8: 1.03892/1.32962, loss_spatial_bce_8: 0.15184/0.13055, loss_spatial_dice_8: 0.29239/0.29965, loss_spatial_ce_8: 0.13081/0.22334, loss_grounding_bce_8: 0.05224/0.09408, loss_grounding_dice_8: 0.14546/0.20148, loss_grounding_ce_8: 0.36266/0.40697, loss_mask_ce_9: 3.87436/3.67642, loss_mask_bce_9: 0.66054/0.39273, loss_mask_dice_9: 1.89921/1.90233, loss_spatial_bce_9: 0.35932/0.33316, loss_spatial_dice_9: 0.87560/0.82189, loss_spatial_ce_9: 1.32299/1.49642, loss_grounding_bce_9: 0.10105/0.10561, loss_grounding_dice_9: 0.35449/0.28085, loss_grounding_ce_9: 0.99986/0.67210] items per batch[64] items per second[0.24] total items[4147200] mini batches[ 64800] memory[7345] epoch remaining[0:44:21]
INFO:trainer.default_trainer:epochs[    35] optim steps[64900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77858/0.89894, loss_mask_bce_0: 0.35722/0.33453, loss_mask_dice_0: 0.25104/1.16289, loss_spatial_bce_0: 0.16532/0.08735, loss_spatial_dice_0: 0.15418/0.20831, loss_spatial_ce_0: 0.04000/0.06215, loss_grounding_bce_0: 0.15867/0.08621, loss_grounding_dice_0: 0.13419/0.17848, loss_grounding_ce_0: 0.22107/0.27193, loss_mask_ce_1: 0.80804/0.89957, loss_mask_bce_1: 0.36639/0.33547, loss_mask_dice_1: 0.25288/1.16950, loss_spatial_bce_1: 0.15952/0.08787, loss_spatial_dice_1: 0.16031/0.21228, loss_spatial_ce_1: 0.04067/0.06801, loss_grounding_bce_1: 0.16056/0.08639, loss_grounding_dice_1: 0.13074/0.17931, loss_grounding_ce_1: 0.23227/0.27274, loss_mask_ce_2: 0.81852/0.90659, loss_mask_bce_2: 0.34938/0.33602, loss_mask_dice_2: 0.24166/1.17002, loss_spatial_bce_2: 0.19491/0.08894, loss_spatial_dice_2: 0.16226/0.21388, loss_spatial_ce_2: 0.04138/0.07140, loss_grounding_bce_2: 0.15422/0.08652, loss_grounding_dice_2: 0.12710/0.17908, loss_grounding_ce_2: 0.24033/0.27601, loss_mask_ce_3: 0.84547/0.91710, loss_mask_bce_3: 0.33960/0.33717, loss_mask_dice_3: 0.24528/1.16751, loss_spatial_bce_3: 0.18248/0.09015, loss_spatial_dice_3: 0.15182/0.21481, loss_spatial_ce_3: 0.04294/0.07595, loss_grounding_bce_3: 0.14955/0.08675, loss_grounding_dice_3: 0.12902/0.17884, loss_grounding_ce_3: 0.24479/0.27831, loss_mask_ce_4: 0.83532/0.91828, loss_mask_bce_4: 0.35785/0.33929, loss_mask_dice_4: 0.23821/1.19157, loss_spatial_bce_4: 0.20737/0.09408, loss_spatial_dice_4: 0.16328/0.22696, loss_spatial_ce_4: 0.07527/0.09213, loss_grounding_bce_4: 0.14580/0.08726, loss_grounding_dice_4: 0.14832/0.18177, loss_grounding_ce_4: 0.42830/0.28121, loss_mask_ce_5: 1.00266/0.93454, loss_mask_bce_5: 0.35296/0.34161, loss_mask_dice_5: 0.31197/1.19907, loss_spatial_bce_5: 0.20016/0.09625, loss_spatial_dice_5: 0.14120/0.23115, loss_spatial_ce_5: 0.06904/0.10646, loss_grounding_bce_5: 0.14225/0.08769, loss_grounding_dice_5: 0.11442/0.18299, loss_grounding_ce_5: 0.28944/0.29392, loss_mask_ce_6: 0.78655/0.97465, loss_mask_bce_6: 0.34867/0.34425, loss_mask_dice_6: 0.22270/1.20203, loss_spatial_bce_6: 0.25197/0.10195, loss_spatial_dice_6: 0.20911/0.23402, loss_spatial_ce_6: 0.17931/0.13228, loss_grounding_bce_6: 0.14647/0.08844, loss_grounding_dice_6: 0.10981/0.18336, loss_grounding_ce_6: 0.25838/0.30949, loss_mask_ce_7: 0.86433/1.01942, loss_mask_bce_7: 0.34298/0.35215, loss_mask_dice_7: 0.28167/1.25646, loss_spatial_bce_7: 0.19504/0.10987, loss_spatial_dice_7: 0.15263/0.26161, loss_spatial_ce_7: 0.26334/0.16761, loss_grounding_bce_7: 0.13620/0.09033, loss_grounding_dice_7: 0.16181/0.19065, loss_grounding_ce_7: 0.30156/0.33988, loss_mask_ce_8: 1.22134/1.12792, loss_mask_bce_8: 0.35193/0.36578, loss_mask_dice_8: 0.39578/1.32954, loss_spatial_bce_8: 0.33214/0.13056, loss_spatial_dice_8: 0.25936/0.29962, loss_spatial_ce_8: 0.22346/0.22328, loss_grounding_bce_8: 0.14493/0.09409, loss_grounding_dice_8: 0.21905/0.20147, loss_grounding_ce_8: 0.46635/0.40698, loss_mask_ce_9: 2.53613/3.67648, loss_mask_bce_9: 0.35349/0.39277, loss_mask_dice_9: 0.63131/1.90221, loss_spatial_bce_9: 0.51274/0.33315, loss_spatial_dice_9: 0.69388/0.82187, loss_spatial_ce_9: 1.14132/1.49640, loss_grounding_bce_9: 0.16049/0.10561, loss_grounding_dice_9: 0.30550/0.28084, loss_grounding_ce_9: 0.47891/0.67209] items per batch[64] items per second[0.23] total items[4153600] mini batches[ 64900] memory[7345] epoch remaining[0:39:51]
INFO:trainer.default_trainer:epochs[    35] optim steps[65000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44349/0.89890, loss_mask_bce_0: 0.25551/0.33452, loss_mask_dice_0: 0.61600/1.16266, loss_spatial_bce_0: 0.06294/0.08736, loss_spatial_dice_0: 0.16499/0.20829, loss_spatial_ce_0: 0.11872/0.06213, loss_grounding_bce_0: 0.06555/0.08622, loss_grounding_dice_0: 0.23955/0.17847, loss_grounding_ce_0: 0.15468/0.27197, loss_mask_ce_1: 1.40737/0.89952, loss_mask_bce_1: 0.24259/0.33545, loss_mask_dice_1: 0.63080/1.16929, loss_spatial_bce_1: 0.07262/0.08788, loss_spatial_dice_1: 0.16838/0.21225, loss_spatial_ce_1: 0.08966/0.06800, loss_grounding_bce_1: 0.05348/0.08640, loss_grounding_dice_1: 0.19338/0.17930, loss_grounding_ce_1: 0.18033/0.27282, loss_mask_ce_2: 1.35780/0.90655, loss_mask_bce_2: 0.23827/0.33600, loss_mask_dice_2: 0.60887/1.16978, loss_spatial_bce_2: 0.07244/0.08895, loss_spatial_dice_2: 0.15655/0.21385, loss_spatial_ce_2: 0.11588/0.07138, loss_grounding_bce_2: 0.06843/0.08654, loss_grounding_dice_2: 0.23795/0.17907, loss_grounding_ce_2: 0.16522/0.27607, loss_mask_ce_3: 1.63347/0.91708, loss_mask_bce_3: 0.20473/0.33715, loss_mask_dice_3: 0.53171/1.16727, loss_spatial_bce_3: 0.07629/0.09016, loss_spatial_dice_3: 0.16735/0.21479, loss_spatial_ce_3: 0.08409/0.07594, loss_grounding_bce_3: 0.05796/0.08677, loss_grounding_dice_3: 0.23399/0.17884, loss_grounding_ce_3: 0.18370/0.27837, loss_mask_ce_4: 1.37274/0.91825, loss_mask_bce_4: 0.25880/0.33926, loss_mask_dice_4: 0.65240/1.19136, loss_spatial_bce_4: 0.12241/0.09409, loss_spatial_dice_4: 0.19200/0.22694, loss_spatial_ce_4: 0.07322/0.09211, loss_grounding_bce_4: 0.06210/0.08727, loss_grounding_dice_4: 0.23800/0.18178, loss_grounding_ce_4: 0.15507/0.28128, loss_mask_ce_5: 1.37638/0.93450, loss_mask_bce_5: 0.39270/0.34159, loss_mask_dice_5: 0.71530/1.19887, loss_spatial_bce_5: 0.07250/0.09626, loss_spatial_dice_5: 0.19130/0.23112, loss_spatial_ce_5: 0.19711/0.10648, loss_grounding_bce_5: 0.06446/0.08771, loss_grounding_dice_5: 0.24307/0.18298, loss_grounding_ce_5: 0.15845/0.29399, loss_mask_ce_6: 1.56497/0.97463, loss_mask_bce_6: 0.27835/0.34424, loss_mask_dice_6: 0.65394/1.20183, loss_spatial_bce_6: 0.11987/0.10196, loss_spatial_dice_6: 0.20730/0.23400, loss_spatial_ce_6: 0.14881/0.13228, loss_grounding_bce_6: 0.06996/0.08845, loss_grounding_dice_6: 0.25646/0.18336, loss_grounding_ce_6: 0.29085/0.30956, loss_mask_ce_7: 1.37870/1.01940, loss_mask_bce_7: 0.39697/0.35213, loss_mask_dice_7: 0.77171/1.25623, loss_spatial_bce_7: 0.23930/0.10988, loss_spatial_dice_7: 0.19672/0.26158, loss_spatial_ce_7: 0.26816/0.16760, loss_grounding_bce_7: 0.20094/0.09035, loss_grounding_dice_7: 0.31823/0.19064, loss_grounding_ce_7: 0.23192/0.33990, loss_mask_ce_8: 1.51194/1.12789, loss_mask_bce_8: 0.40088/0.36577, loss_mask_dice_8: 0.81394/1.32932, loss_spatial_bce_8: 0.18982/0.13058, loss_spatial_dice_8: 0.27403/0.29959, loss_spatial_ce_8: 0.42595/0.22324, loss_grounding_bce_8: 0.17800/0.09411, loss_grounding_dice_8: 0.30823/0.20147, loss_grounding_ce_8: 0.22459/0.40700, loss_mask_ce_9: 5.35443/3.67634, loss_mask_bce_9: 0.64212/0.39275, loss_mask_dice_9: 1.61786/1.90193, loss_spatial_bce_9: 0.44460/0.33319, loss_spatial_dice_9: 0.84480/0.82186, loss_spatial_ce_9: 1.24443/1.49629, loss_grounding_bce_9: 0.22276/0.10563, loss_grounding_dice_9: 0.33311/0.28083, loss_grounding_ce_9: 1.10442/0.67208] items per batch[64] items per second[0.23] total items[4160000] mini batches[ 65000] memory[7345] epoch remaining[0:35:20]
INFO:trainer.default_trainer:epochs[    35] optim steps[65100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91882/0.89888, loss_mask_bce_0: 0.39211/0.33455, loss_mask_dice_0: 2.19643/1.16271, loss_spatial_bce_0: 0.11079/0.08736, loss_spatial_dice_0: 0.32805/0.20828, loss_spatial_ce_0: 0.01126/0.06211, loss_grounding_bce_0: 0.05410/0.08623, loss_grounding_dice_0: 0.17911/0.17849, loss_grounding_ce_0: 0.22649/0.27197, loss_mask_ce_1: 0.79324/0.89950, loss_mask_bce_1: 0.39403/0.33548, loss_mask_dice_1: 2.42162/1.16932, loss_spatial_bce_1: 0.09124/0.08788, loss_spatial_dice_1: 0.33852/0.21225, loss_spatial_ce_1: 0.01466/0.06799, loss_grounding_bce_1: 0.05376/0.08641, loss_grounding_dice_1: 0.17138/0.17932, loss_grounding_ce_1: 0.22876/0.27283, loss_mask_ce_2: 0.85516/0.90657, loss_mask_bce_2: 0.40029/0.33603, loss_mask_dice_2: 2.42603/1.16981, loss_spatial_bce_2: 0.06832/0.08895, loss_spatial_dice_2: 0.36465/0.21385, loss_spatial_ce_2: 0.03074/0.07137, loss_grounding_bce_2: 0.05362/0.08654, loss_grounding_dice_2: 0.15718/0.17909, loss_grounding_ce_2: 0.22434/0.27610, loss_mask_ce_3: 1.06491/0.91711, loss_mask_bce_3: 0.38528/0.33718, loss_mask_dice_3: 2.43656/1.16732, loss_spatial_bce_3: 0.05480/0.09016, loss_spatial_dice_3: 0.33238/0.21479, loss_spatial_ce_3: 0.03276/0.07594, loss_grounding_bce_3: 0.05284/0.08677, loss_grounding_dice_3: 0.17746/0.17886, loss_grounding_ce_3: 0.21057/0.27840, loss_mask_ce_4: 1.02778/0.91825, loss_mask_bce_4: 0.37835/0.33930, loss_mask_dice_4: 2.54035/1.19142, loss_spatial_bce_4: 0.10575/0.09410, loss_spatial_dice_4: 0.31628/0.22695, loss_spatial_ce_4: 0.01348/0.09211, loss_grounding_bce_4: 0.05351/0.08728, loss_grounding_dice_4: 0.24057/0.18180, loss_grounding_ce_4: 0.18833/0.28131, loss_mask_ce_5: 0.85006/0.93454, loss_mask_bce_5: 0.39256/0.34162, loss_mask_dice_5: 2.58516/1.19894, loss_spatial_bce_5: 0.13620/0.09626, loss_spatial_dice_5: 0.30427/0.23112, loss_spatial_ce_5: 0.10012/0.10648, loss_grounding_bce_5: 0.05125/0.08771, loss_grounding_dice_5: 0.14241/0.18299, loss_grounding_ce_5: 0.22650/0.29401, loss_mask_ce_6: 0.94975/0.97466, loss_mask_bce_6: 0.38762/0.34428, loss_mask_dice_6: 2.45290/1.20189, loss_spatial_bce_6: 0.14805/0.10196, loss_spatial_dice_6: 0.32150/0.23399, loss_spatial_ce_6: 0.12804/0.13225, loss_grounding_bce_6: 0.05004/0.08846, loss_grounding_dice_6: 0.14555/0.18338, loss_grounding_ce_6: 0.25111/0.30962, loss_mask_ce_7: 0.82497/1.01943, loss_mask_bce_7: 0.39705/0.35216, loss_mask_dice_7: 2.52335/1.25628, loss_spatial_bce_7: 0.08133/0.10989, loss_spatial_dice_7: 0.37950/0.26158, loss_spatial_ce_7: 0.31629/0.16758, loss_grounding_bce_7: 0.04832/0.09035, loss_grounding_dice_7: 0.15848/0.19066, loss_grounding_ce_7: 0.22046/0.33991, loss_mask_ce_8: 0.99667/1.12795, loss_mask_bce_8: 0.41348/0.36579, loss_mask_dice_8: 2.47621/1.32934, loss_spatial_bce_8: 0.12614/0.13058, loss_spatial_dice_8: 0.44017/0.29958, loss_spatial_ce_8: 0.37207/0.22324, loss_grounding_bce_8: 0.05120/0.09411, loss_grounding_dice_8: 0.16482/0.20149, loss_grounding_ce_8: 0.20218/0.40705, loss_mask_ce_9: 4.26127/3.67657, loss_mask_bce_9: 0.34636/0.39281, loss_mask_dice_9: 4.01837/1.90199, loss_spatial_bce_9: 0.19167/0.33323, loss_spatial_dice_9: 0.90343/0.82186, loss_spatial_ce_9: 1.46278/1.49634, loss_grounding_bce_9: 0.04821/0.10565, loss_grounding_dice_9: 0.32745/0.28084, loss_grounding_ce_9: 0.28539/0.67214] items per batch[64] items per second[0.23] total items[4166400] mini batches[ 65100] memory[7345] epoch remaining[0:30:47]
INFO:trainer.default_trainer:epochs[    35] optim steps[65200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.14241/0.89873, loss_mask_bce_0: 0.45422/0.33458, loss_mask_dice_0: 1.62860/1.16264, loss_spatial_bce_0: 0.03701/0.08736, loss_spatial_dice_0: 0.14818/0.20826, loss_spatial_ce_0: 0.01633/0.06208, loss_grounding_bce_0: 0.03997/0.08624, loss_grounding_dice_0: 0.17617/0.17848, loss_grounding_ce_0: 0.47301/0.27193, loss_mask_ce_1: 1.02533/0.89937, loss_mask_bce_1: 0.47004/0.33551, loss_mask_dice_1: 1.67105/1.16925, loss_spatial_bce_1: 0.03626/0.08788, loss_spatial_dice_1: 0.14868/0.21223, loss_spatial_ce_1: 0.01677/0.06798, loss_grounding_bce_1: 0.04103/0.08642, loss_grounding_dice_1: 0.17637/0.17931, loss_grounding_ce_1: 0.42241/0.27278, loss_mask_ce_2: 1.15412/0.90644, loss_mask_bce_2: 0.44878/0.33606, loss_mask_dice_2: 1.63091/1.16973, loss_spatial_bce_2: 0.03958/0.08895, loss_spatial_dice_2: 0.16478/0.21384, loss_spatial_ce_2: 0.02137/0.07134, loss_grounding_bce_2: 0.04417/0.08656, loss_grounding_dice_2: 0.17723/0.17908, loss_grounding_ce_2: 0.43552/0.27607, loss_mask_ce_3: 1.02085/0.91700, loss_mask_bce_3: 0.44192/0.33722, loss_mask_dice_3: 1.59324/1.16724, loss_spatial_bce_3: 0.04454/0.09016, loss_spatial_dice_3: 0.19284/0.21477, loss_spatial_ce_3: 0.02837/0.07594, loss_grounding_bce_3: 0.04300/0.08679, loss_grounding_dice_3: 0.17687/0.17885, loss_grounding_ce_3: 0.43239/0.27837, loss_mask_ce_4: 0.98591/0.91811, loss_mask_bce_4: 0.45650/0.33934, loss_mask_dice_4: 1.80409/1.19134, loss_spatial_bce_4: 0.05657/0.09409, loss_spatial_dice_4: 0.22806/0.22693, loss_spatial_ce_4: 0.05090/0.09210, loss_grounding_bce_4: 0.04400/0.08730, loss_grounding_dice_4: 0.19940/0.18178, loss_grounding_ce_4: 0.39747/0.28129, loss_mask_ce_5: 1.09411/0.93439, loss_mask_bce_5: 0.44610/0.34166, loss_mask_dice_5: 1.67952/1.19887, loss_spatial_bce_5: 0.04483/0.09626, loss_spatial_dice_5: 0.20817/0.23111, loss_spatial_ce_5: 0.09710/0.10647, loss_grounding_bce_5: 0.04439/0.08773, loss_grounding_dice_5: 0.19316/0.18299, loss_grounding_ce_5: 0.42624/0.29402, loss_mask_ce_6: 0.99328/0.97451, loss_mask_bce_6: 0.43280/0.34432, loss_mask_dice_6: 1.61548/1.20180, loss_spatial_bce_6: 0.05122/0.10196, loss_spatial_dice_6: 0.22754/0.23398, loss_spatial_ce_6: 0.08343/0.13223, loss_grounding_bce_6: 0.03998/0.08847, loss_grounding_dice_6: 0.19979/0.18337, loss_grounding_ce_6: 0.43645/0.30959, loss_mask_ce_7: 1.15648/1.01927, loss_mask_bce_7: 0.41937/0.35221, loss_mask_dice_7: 1.76245/1.25620, loss_spatial_bce_7: 0.04274/0.10989, loss_spatial_dice_7: 0.17613/0.26156, loss_spatial_ce_7: 0.25411/0.16756, loss_grounding_bce_7: 0.04462/0.09037, loss_grounding_dice_7: 0.20420/0.19064, loss_grounding_ce_7: 0.48846/0.33988, loss_mask_ce_8: 1.32909/1.12779, loss_mask_bce_8: 0.44706/0.36584, loss_mask_dice_8: 1.96288/1.32923, loss_spatial_bce_8: 0.05891/0.13059, loss_spatial_dice_8: 0.22339/0.29957, loss_spatial_ce_8: 0.15322/0.22319, loss_grounding_bce_8: 0.04890/0.09413, loss_grounding_dice_8: 0.21214/0.20147, loss_grounding_ce_8: 0.48632/0.40710, loss_mask_ce_9: 3.76099/3.67639, loss_mask_bce_9: 0.57731/0.39284, loss_mask_dice_9: 3.74896/1.90185, loss_spatial_bce_9: 0.18886/0.33324, loss_spatial_dice_9: 0.91409/0.82185, loss_spatial_ce_9: 1.18202/1.49635, loss_grounding_bce_9: 0.05683/0.10565, loss_grounding_dice_9: 0.44679/0.28082, loss_grounding_ce_9: 0.59620/0.67203] items per batch[64] items per second[0.23] total items[4172800] mini batches[ 65200] memory[7345] epoch remaining[0:26:13]
INFO:trainer.default_trainer:epochs[    35] optim steps[65300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56823/0.89879, loss_mask_bce_0: 0.54016/0.33461, loss_mask_dice_0: 0.73156/1.16266, loss_spatial_bce_0: 0.16030/0.08735, loss_spatial_dice_0: 0.16503/0.20825, loss_spatial_ce_0: 0.01330/0.06205, loss_grounding_bce_0: 0.25113/0.08624, loss_grounding_dice_0: 0.26415/0.17850, loss_grounding_ce_0: 0.01110/0.27197, loss_mask_ce_1: 0.52853/0.89943, loss_mask_bce_1: 0.55059/0.33554, loss_mask_dice_1: 0.67774/1.16929, loss_spatial_bce_1: 0.11972/0.08787, loss_spatial_dice_1: 0.16212/0.21222, loss_spatial_ce_1: 0.01752/0.06794, loss_grounding_bce_1: 0.25120/0.08642, loss_grounding_dice_1: 0.25881/0.17933, loss_grounding_ce_1: 0.00600/0.27277, loss_mask_ce_2: 0.36874/0.90648, loss_mask_bce_2: 0.59236/0.33611, loss_mask_dice_2: 0.72067/1.16979, loss_spatial_bce_2: 0.21142/0.08895, loss_spatial_dice_2: 0.19295/0.21381, loss_spatial_ce_2: 0.02523/0.07130, loss_grounding_bce_2: 0.26504/0.08655, loss_grounding_dice_2: 0.25872/0.17909, loss_grounding_ce_2: 0.00647/0.27610, loss_mask_ce_3: 0.36506/0.91703, loss_mask_bce_3: 0.64561/0.33726, loss_mask_dice_3: 0.74425/1.16731, loss_spatial_bce_3: 0.20785/0.09016, loss_spatial_dice_3: 0.19159/0.21476, loss_spatial_ce_3: 0.02724/0.07592, loss_grounding_bce_3: 0.25585/0.08679, loss_grounding_dice_3: 0.25875/0.17886, loss_grounding_ce_3: 0.00585/0.27841, loss_mask_ce_4: 0.36274/0.91817, loss_mask_bce_4: 0.59381/0.33938, loss_mask_dice_4: 0.73472/1.19141, loss_spatial_bce_4: 0.24403/0.09409, loss_spatial_dice_4: 0.20948/0.22692, loss_spatial_ce_4: 0.05325/0.09208, loss_grounding_bce_4: 0.24068/0.08730, loss_grounding_dice_4: 0.25636/0.18179, loss_grounding_ce_4: 0.00827/0.28135, loss_mask_ce_5: 0.59033/0.93443, loss_mask_bce_5: 0.66289/0.34170, loss_mask_dice_5: 0.72041/1.19893, loss_spatial_bce_5: 0.18644/0.09626, loss_spatial_dice_5: 0.20698/0.23110, loss_spatial_ce_5: 0.09297/0.10644, loss_grounding_bce_5: 0.23529/0.08773, loss_grounding_dice_5: 0.26416/0.18300, loss_grounding_ce_5: 0.00556/0.29402, loss_mask_ce_6: 0.63763/0.97455, loss_mask_bce_6: 0.63599/0.34435, loss_mask_dice_6: 0.70554/1.20187, loss_spatial_bce_6: 0.13702/0.10196, loss_spatial_dice_6: 0.19526/0.23397, loss_spatial_ce_6: 0.20353/0.13224, loss_grounding_bce_6: 0.24027/0.08847, loss_grounding_dice_6: 0.26167/0.18338, loss_grounding_ce_6: 0.00368/0.30966, loss_mask_ce_7: 0.72091/1.01932, loss_mask_bce_7: 0.61379/0.35224, loss_mask_dice_7: 0.77089/1.25627, loss_spatial_bce_7: 0.11083/0.10988, loss_spatial_dice_7: 0.16335/0.26154, loss_spatial_ce_7: 0.13400/0.16754, loss_grounding_bce_7: 0.22546/0.09036, loss_grounding_dice_7: 0.27862/0.19066, loss_grounding_ce_7: 0.00218/0.33989, loss_mask_ce_8: 0.84071/1.12789, loss_mask_bce_8: 0.51177/0.36587, loss_mask_dice_8: 0.77590/1.32926, loss_spatial_bce_8: 0.12780/0.13058, loss_spatial_dice_8: 0.19250/0.29955, loss_spatial_ce_8: 0.16207/0.22316, loss_grounding_bce_8: 0.24507/0.09412, loss_grounding_dice_8: 0.29434/0.20148, loss_grounding_ce_8: 0.00487/0.40708, loss_mask_ce_9: 2.92745/3.67650, loss_mask_bce_9: 0.52029/0.39289, loss_mask_dice_9: 0.99274/1.90196, loss_spatial_bce_9: 0.39949/0.33326, loss_spatial_dice_9: 0.82696/0.82185, loss_spatial_ce_9: 1.32214/1.49644, loss_grounding_bce_9: 0.24236/0.10565, loss_grounding_dice_9: 0.26737/0.28083, loss_grounding_ce_9: 0.06113/0.67199] items per batch[64] items per second[0.23] total items[4179200] mini batches[ 65300] memory[7345] epoch remaining[0:21:40]
INFO:trainer.default_trainer:epochs[    35] optim steps[65400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66875/0.89869, loss_mask_bce_0: 0.15058/0.33460, loss_mask_dice_0: 0.39857/1.16278, loss_spatial_bce_0: 0.05588/0.08735, loss_spatial_dice_0: 0.13743/0.20825, loss_spatial_ce_0: 0.14275/0.06204, loss_grounding_bce_0: 0.07879/0.08624, loss_grounding_dice_0: 0.10798/0.17851, loss_grounding_ce_0: 0.07766/0.27192, loss_mask_ce_1: 0.76632/0.89933, loss_mask_bce_1: 0.14694/0.33553, loss_mask_dice_1: 0.37395/1.16942, loss_spatial_bce_1: 0.05521/0.08786, loss_spatial_dice_1: 0.14987/0.21222, loss_spatial_ce_1: 0.16870/0.06794, loss_grounding_bce_1: 0.08134/0.08643, loss_grounding_dice_1: 0.11499/0.17935, loss_grounding_ce_1: 0.18157/0.27270, loss_mask_ce_2: 0.67448/0.90640, loss_mask_bce_2: 0.15258/0.33610, loss_mask_dice_2: 0.39098/1.16993, loss_spatial_bce_2: 0.05479/0.08894, loss_spatial_dice_2: 0.14851/0.21382, loss_spatial_ce_2: 0.15942/0.07131, loss_grounding_bce_2: 0.08407/0.08656, loss_grounding_dice_2: 0.10538/0.17911, loss_grounding_ce_2: 0.18703/0.27604, loss_mask_ce_3: 0.70587/0.91696, loss_mask_bce_3: 0.16322/0.33726, loss_mask_dice_3: 0.39923/1.16744, loss_spatial_bce_3: 0.06826/0.09015, loss_spatial_dice_3: 0.18075/0.21476, loss_spatial_ce_3: 0.04775/0.07593, loss_grounding_bce_3: 0.07997/0.08679, loss_grounding_dice_3: 0.10980/0.17887, loss_grounding_ce_3: 0.21480/0.27833, loss_mask_ce_4: 0.52914/0.91805, loss_mask_bce_4: 0.16610/0.33937, loss_mask_dice_4: 0.39794/1.19157, loss_spatial_bce_4: 0.06624/0.09408, loss_spatial_dice_4: 0.20898/0.22693, loss_spatial_ce_4: 0.05911/0.09208, loss_grounding_bce_4: 0.07629/0.08730, loss_grounding_dice_4: 0.10259/0.18181, loss_grounding_ce_4: 0.11413/0.28128, loss_mask_ce_5: 0.50414/0.93438, loss_mask_bce_5: 0.16630/0.34170, loss_mask_dice_5: 0.39767/1.19910, loss_spatial_bce_5: 0.06762/0.09625, loss_spatial_dice_5: 0.19338/0.23111, loss_spatial_ce_5: 0.12433/0.10645, loss_grounding_bce_5: 0.07989/0.08773, loss_grounding_dice_5: 0.11003/0.18303, loss_grounding_ce_5: 0.11253/0.29394, loss_mask_ce_6: 0.60932/0.97450, loss_mask_bce_6: 0.17114/0.34434, loss_mask_dice_6: 0.39809/1.20200, loss_spatial_bce_6: 0.06604/0.10195, loss_spatial_dice_6: 0.16725/0.23398, loss_spatial_ce_6: 0.07426/0.13226, loss_grounding_bce_6: 0.07881/0.08847, loss_grounding_dice_6: 0.09609/0.18340, loss_grounding_ce_6: 0.14641/0.30961, loss_mask_ce_7: 0.66789/1.01929, loss_mask_bce_7: 0.15233/0.35223, loss_mask_dice_7: 0.40440/1.25640, loss_spatial_bce_7: 0.07105/0.10987, loss_spatial_dice_7: 0.22286/0.26155, loss_spatial_ce_7: 0.13065/0.16753, loss_grounding_bce_7: 0.08167/0.09036, loss_grounding_dice_7: 0.12836/0.19068, loss_grounding_ce_7: 0.14852/0.33986, loss_mask_ce_8: 0.90976/1.12787, loss_mask_bce_8: 0.17148/0.36585, loss_mask_dice_8: 0.42001/1.32943, loss_spatial_bce_8: 0.09389/0.13057, loss_spatial_dice_8: 0.24147/0.29956, loss_spatial_ce_8: 0.19741/0.22311, loss_grounding_bce_8: 0.08902/0.09412, loss_grounding_dice_8: 0.13322/0.20149, loss_grounding_ce_8: 0.19938/0.40704, loss_mask_ce_9: 2.94071/3.67646, loss_mask_bce_9: 0.19296/0.39286, loss_mask_dice_9: 0.69049/1.90210, loss_spatial_bce_9: 0.36042/0.33324, loss_spatial_dice_9: 0.82837/0.82186, loss_spatial_ce_9: 1.32951/1.49651, loss_grounding_bce_9: 0.08368/0.10566, loss_grounding_dice_9: 0.22889/0.28086, loss_grounding_ce_9: 0.40104/0.67196] items per batch[64] items per second[0.23] total items[4185600] mini batches[ 65400] memory[7345] epoch remaining[0:17:05]
INFO:trainer.default_trainer:epochs[    35] optim steps[65500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07172/0.89871, loss_mask_bce_0: 0.53747/0.33460, loss_mask_dice_0: 0.56379/1.16299, loss_spatial_bce_0: 0.15860/0.08734, loss_spatial_dice_0: 0.16810/0.20825, loss_spatial_ce_0: 0.04905/0.06201, loss_grounding_bce_0: 0.13733/0.08625, loss_grounding_dice_0: 0.09425/0.17851, loss_grounding_ce_0: 0.43724/0.27202, loss_mask_ce_1: 1.02163/0.89931, loss_mask_bce_1: 0.58227/0.33553, loss_mask_dice_1: 0.61150/1.16962, loss_spatial_bce_1: 0.16266/0.08785, loss_spatial_dice_1: 0.17714/0.21223, loss_spatial_ce_1: 0.04902/0.06791, loss_grounding_bce_1: 0.14080/0.08643, loss_grounding_dice_1: 0.09604/0.17936, loss_grounding_ce_1: 0.46111/0.27280, loss_mask_ce_2: 1.02377/0.90640, loss_mask_bce_2: 0.62293/0.33610, loss_mask_dice_2: 0.66845/1.17011, loss_spatial_bce_2: 0.15594/0.08893, loss_spatial_dice_2: 0.17006/0.21383, loss_spatial_ce_2: 0.05196/0.07129, loss_grounding_bce_2: 0.13734/0.08656, loss_grounding_dice_2: 0.09681/0.17911, loss_grounding_ce_2: 0.45012/0.27618, loss_mask_ce_3: 1.15379/0.91697, loss_mask_bce_3: 0.67467/0.33726, loss_mask_dice_3: 0.67108/1.16764, loss_spatial_bce_3: 0.15629/0.09014, loss_spatial_dice_3: 0.17697/0.21477, loss_spatial_ce_3: 0.20847/0.07591, loss_grounding_bce_3: 0.14104/0.08680, loss_grounding_dice_3: 0.09772/0.17888, loss_grounding_ce_3: 0.41232/0.27842, loss_mask_ce_4: 0.96240/0.91801, loss_mask_bce_4: 0.76982/0.33938, loss_mask_dice_4: 0.71416/1.19177, loss_spatial_bce_4: 0.16671/0.09407, loss_spatial_dice_4: 0.18278/0.22694, loss_spatial_ce_4: 0.20382/0.09208, loss_grounding_bce_4: 0.14478/0.08731, loss_grounding_dice_4: 0.10641/0.18181, loss_grounding_ce_4: 0.38084/0.28138, loss_mask_ce_5: 1.08080/0.93437, loss_mask_bce_5: 0.70320/0.34171, loss_mask_dice_5: 0.71026/1.19929, loss_spatial_bce_5: 0.22560/0.09624, loss_spatial_dice_5: 0.20156/0.23113, loss_spatial_ce_5: 0.14128/0.10643, loss_grounding_bce_5: 0.13898/0.08775, loss_grounding_dice_5: 0.10714/0.18303, loss_grounding_ce_5: 0.46867/0.29403, loss_mask_ce_6: 1.00092/0.97448, loss_mask_bce_6: 0.74341/0.34435, loss_mask_dice_6: 0.70367/1.20220, loss_spatial_bce_6: 0.16725/0.10194, loss_spatial_dice_6: 0.17945/0.23399, loss_spatial_ce_6: 0.14017/0.13226, loss_grounding_bce_6: 0.13293/0.08848, loss_grounding_dice_6: 0.09747/0.18340, loss_grounding_ce_6: 0.44046/0.30972, loss_mask_ce_7: 0.68796/1.01928, loss_mask_bce_7: 0.84572/0.35224, loss_mask_dice_7: 0.78772/1.25661, loss_spatial_bce_7: 0.23417/0.10987, loss_spatial_dice_7: 0.23306/0.26158, loss_spatial_ce_7: 0.13334/0.16752, loss_grounding_bce_7: 0.13673/0.09037, loss_grounding_dice_7: 0.10403/0.19068, loss_grounding_ce_7: 0.49788/0.33991, loss_mask_ce_8: 0.64844/1.12782, loss_mask_bce_8: 0.80938/0.36586, loss_mask_dice_8: 0.82456/1.32963, loss_spatial_bce_8: 0.17419/0.13055, loss_spatial_dice_8: 0.19002/0.29958, loss_spatial_ce_8: 0.18662/0.22306, loss_grounding_bce_8: 0.13807/0.09413, loss_grounding_dice_8: 0.08747/0.20149, loss_grounding_ce_8: 0.50854/0.40707, loss_mask_ce_9: 3.65431/3.67644, loss_mask_bce_9: 0.65143/0.39285, loss_mask_dice_9: 0.87716/1.90246, loss_spatial_bce_9: 0.50332/0.33322, loss_spatial_dice_9: 0.77153/0.82189, loss_spatial_ce_9: 1.46120/1.49668, loss_grounding_bce_9: 0.16135/0.10566, loss_grounding_dice_9: 0.14968/0.28086, loss_grounding_ce_9: 0.51136/0.67187] items per batch[64] items per second[0.23] total items[4192000] mini batches[ 65500] memory[7345] epoch remaining[0:12:31]
INFO:trainer.default_trainer:epochs[    35] optim steps[65600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42133/0.89856, loss_mask_bce_0: 0.54709/0.33454, loss_mask_dice_0: 2.45839/1.16288, loss_spatial_bce_0: 0.07870/0.08732, loss_spatial_dice_0: 0.20398/0.20824, loss_spatial_ce_0: 0.00762/0.06198, loss_grounding_bce_0: 0.02742/0.08624, loss_grounding_dice_0: 0.25509/0.17848, loss_grounding_ce_0: 0.53999/0.27202, loss_mask_ce_1: 1.39620/0.89916, loss_mask_bce_1: 0.52189/0.33547, loss_mask_dice_1: 2.55816/1.16954, loss_spatial_bce_1: 0.07894/0.08784, loss_spatial_dice_1: 0.20079/0.21221, loss_spatial_ce_1: 0.00481/0.06789, loss_grounding_bce_1: 0.02702/0.08642, loss_grounding_dice_1: 0.25448/0.17932, loss_grounding_ce_1: 0.55525/0.27283, loss_mask_ce_2: 1.29882/0.90624, loss_mask_bce_2: 0.52596/0.33604, loss_mask_dice_2: 2.63366/1.17002, loss_spatial_bce_2: 0.08016/0.08892, loss_spatial_dice_2: 0.22764/0.21381, loss_spatial_ce_2: 0.01260/0.07126, loss_grounding_bce_2: 0.02725/0.08655, loss_grounding_dice_2: 0.33646/0.17908, loss_grounding_ce_2: 0.60467/0.27621, loss_mask_ce_3: 1.33211/0.91684, loss_mask_bce_3: 0.60539/0.33721, loss_mask_dice_3: 2.69702/1.16756, loss_spatial_bce_3: 0.07689/0.09013, loss_spatial_dice_3: 0.21936/0.21475, loss_spatial_ce_3: 0.03864/0.07591, loss_grounding_bce_3: 0.02706/0.08680, loss_grounding_dice_3: 0.25998/0.17885, loss_grounding_ce_3: 0.56704/0.27841, loss_mask_ce_4: 1.29796/0.91785, loss_mask_bce_4: 0.58772/0.33932, loss_mask_dice_4: 2.94407/1.19166, loss_spatial_bce_4: 0.08499/0.09406, loss_spatial_dice_4: 0.24592/0.22693, loss_spatial_ce_4: 0.02160/0.09206, loss_grounding_bce_4: 0.05102/0.08730, loss_grounding_dice_4: 0.35873/0.18178, loss_grounding_ce_4: 0.48750/0.28138, loss_mask_ce_5: 1.31807/0.93422, loss_mask_bce_5: 0.61023/0.34165, loss_mask_dice_5: 2.80584/1.19918, loss_spatial_bce_5: 0.08461/0.09623, loss_spatial_dice_5: 0.25816/0.23112, loss_spatial_ce_5: 0.04122/0.10640, loss_grounding_bce_5: 0.05111/0.08774, loss_grounding_dice_5: 0.36949/0.18300, loss_grounding_ce_5: 0.42337/0.29406, loss_mask_ce_6: 1.07784/0.97431, loss_mask_bce_6: 0.60143/0.34429, loss_mask_dice_6: 2.87500/1.20210, loss_spatial_bce_6: 0.09053/0.10193, loss_spatial_dice_6: 0.29841/0.23398, loss_spatial_ce_6: 0.05373/0.13223, loss_grounding_bce_6: 0.04003/0.08847, loss_grounding_dice_6: 0.45481/0.18336, loss_grounding_ce_6: 0.46978/0.30978, loss_mask_ce_7: 1.36576/1.01915, loss_mask_bce_7: 0.61210/0.35217, loss_mask_dice_7: 2.67430/1.25648, loss_spatial_bce_7: 0.10699/0.10987, loss_spatial_dice_7: 0.36001/0.26158, loss_spatial_ce_7: 0.09658/0.16751, loss_grounding_bce_7: 0.03985/0.09035, loss_grounding_dice_7: 0.34458/0.19065, loss_grounding_ce_7: 0.58018/0.33997, loss_mask_ce_8: 1.64696/1.12765, loss_mask_bce_8: 0.65521/0.36579, loss_mask_dice_8: 3.13606/1.32948, loss_spatial_bce_8: 0.16823/0.13054, loss_spatial_dice_8: 0.36749/0.29957, loss_spatial_ce_8: 0.13184/0.22303, loss_grounding_bce_8: 0.04395/0.09411, loss_grounding_dice_8: 0.51086/0.20144, loss_grounding_ce_8: 0.42619/0.40705, loss_mask_ce_9: 3.42758/3.67621, loss_mask_bce_9: 0.65004/0.39278, loss_mask_dice_9: 4.12908/1.90215, loss_spatial_bce_9: 0.23087/0.33321, loss_spatial_dice_9: 0.91859/0.82189, loss_spatial_ce_9: 1.52827/1.49668, loss_grounding_bce_9: 0.03483/0.10566, loss_grounding_dice_9: 0.49792/0.28082, loss_grounding_ce_9: 0.68922/0.67193] items per batch[64] items per second[0.23] total items[4198400] mini batches[ 65600] memory[7345] epoch remaining[0:07:54]
INFO:trainer.default_trainer:epochs[    35] optim steps[65700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52864/0.89861, loss_mask_bce_0: 0.44225/0.33460, loss_mask_dice_0: 0.59526/1.16293, loss_spatial_bce_0: 0.07456/0.08733, loss_spatial_dice_0: 0.19299/0.20824, loss_spatial_ce_0: 0.01431/0.06197, loss_grounding_bce_0: 0.05252/0.08624, loss_grounding_dice_0: 0.10313/0.17848, loss_grounding_ce_0: 0.00092/0.27205, loss_mask_ce_1: 0.50592/0.89922, loss_mask_bce_1: 0.44245/0.33553, loss_mask_dice_1: 0.75695/1.16959, loss_spatial_bce_1: 0.06715/0.08784, loss_spatial_dice_1: 0.20274/0.21222, loss_spatial_ce_1: 0.02192/0.06788, loss_grounding_bce_1: 0.05362/0.08642, loss_grounding_dice_1: 0.11008/0.17933, loss_grounding_ce_1: 0.00068/0.27284, loss_mask_ce_2: 0.45244/0.90632, loss_mask_bce_2: 0.44535/0.33609, loss_mask_dice_2: 0.66327/1.17007, loss_spatial_bce_2: 0.08220/0.08893, loss_spatial_dice_2: 0.21995/0.21382, loss_spatial_ce_2: 0.03049/0.07127, loss_grounding_bce_2: 0.05038/0.08655, loss_grounding_dice_2: 0.10940/0.17908, loss_grounding_ce_2: 0.00074/0.27627, loss_mask_ce_3: 0.49913/0.91688, loss_mask_bce_3: 0.43810/0.33726, loss_mask_dice_3: 0.54081/1.16759, loss_spatial_bce_3: 0.08642/0.09015, loss_spatial_dice_3: 0.21885/0.21477, loss_spatial_ce_3: 0.05914/0.07591, loss_grounding_bce_3: 0.04918/0.08680, loss_grounding_dice_3: 0.10816/0.17885, loss_grounding_ce_3: 0.00067/0.27841, loss_mask_ce_4: 0.50411/0.91790, loss_mask_bce_4: 0.45128/0.33937, loss_mask_dice_4: 0.73020/1.19170, loss_spatial_bce_4: 0.08229/0.09407, loss_spatial_dice_4: 0.23340/0.22694, loss_spatial_ce_4: 0.02054/0.09207, loss_grounding_bce_4: 0.05370/0.08730, loss_grounding_dice_4: 0.11061/0.18178, loss_grounding_ce_4: 0.00067/0.28139, loss_mask_ce_5: 0.84529/0.93431, loss_mask_bce_5: 0.36662/0.34170, loss_mask_dice_5: 0.59427/1.19921, loss_spatial_bce_5: 0.10883/0.09625, loss_spatial_dice_5: 0.22166/0.23113, loss_spatial_ce_5: 0.06478/0.10640, loss_grounding_bce_5: 0.05374/0.08774, loss_grounding_dice_5: 0.11084/0.18300, loss_grounding_ce_5: 0.00099/0.29410, loss_mask_ce_6: 0.62040/0.97434, loss_mask_bce_6: 0.36294/0.34434, loss_mask_dice_6: 0.55676/1.20216, loss_spatial_bce_6: 0.12805/0.10195, loss_spatial_dice_6: 0.23135/0.23400, loss_spatial_ce_6: 0.14946/0.13222, loss_grounding_bce_6: 0.05394/0.08847, loss_grounding_dice_6: 0.10832/0.18336, loss_grounding_ce_6: 0.00060/0.30982, loss_mask_ce_7: 0.52812/1.01923, loss_mask_bce_7: 0.44643/0.35222, loss_mask_dice_7: 0.54033/1.25649, loss_spatial_bce_7: 0.11392/0.10988, loss_spatial_dice_7: 0.22983/0.26160, loss_spatial_ce_7: 0.08172/0.16750, loss_grounding_bce_7: 0.05408/0.09036, loss_grounding_dice_7: 0.11269/0.19064, loss_grounding_ce_7: 0.00052/0.33997, loss_mask_ce_8: 0.56327/1.12771, loss_mask_bce_8: 0.47514/0.36585, loss_mask_dice_8: 0.51457/1.32945, loss_spatial_bce_8: 0.17735/0.13055, loss_spatial_dice_8: 0.29911/0.29958, loss_spatial_ce_8: 0.21510/0.22300, loss_grounding_bce_8: 0.05593/0.09412, loss_grounding_dice_8: 0.11318/0.20144, loss_grounding_ce_8: 0.01632/0.40712, loss_mask_ce_9: 2.02499/3.67615, loss_mask_bce_9: 0.47240/0.39284, loss_mask_dice_9: 0.75783/1.90217, loss_spatial_bce_9: 0.34310/0.33320, loss_spatial_dice_9: 0.81554/0.82190, loss_spatial_ce_9: 1.28606/1.49664, loss_grounding_bce_9: 0.05748/0.10566, loss_grounding_dice_9: 0.14893/0.28082, loss_grounding_ce_9: 0.65721/0.67205] items per batch[64] items per second[0.23] total items[4204800] mini batches[ 65700] memory[7345] epoch remaining[0:03:18]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00065772.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2166 s/iter. Eval: 0.0906 s/iter. Total: 0.3103 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2234 s/iter. Eval: 0.0784 s/iter. Total: 0.3049 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2261 s/iter. Eval: 0.0772 s/iter. Total: 0.3064 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2262 s/iter. Eval: 0.0758 s/iter. Total: 0.3052 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2256 s/iter. Eval: 0.0739 s/iter. Total: 0.3028 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2274 s/iter. Eval: 0.0746 s/iter. Total: 0.3053 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0031 s/iter. Inference: 0.2283 s/iter. Eval: 0.0747 s/iter. Total: 0.3063 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 130/157. Dataloading: 0.0032 s/iter. Inference: 0.2284 s/iter. Eval: 0.0743 s/iter. Total: 0.3060 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2296 s/iter. Eval: 0.0745 s/iter. Total: 0.3074 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval9vxg_jmx ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.517 | 81.924 | 60.780 |      133      |
| Things | 55.388 | 82.684 | 66.315 |      80       |
| Stuff  | 43.164 | 80.777 | 52.423 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.40 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.09s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.02 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.298
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.970 | 61.326 | 41.011 | 19.463 | 42.102 | 60.801 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.384 | bicycle      | 17.765 | car            | 36.547 |
| motorcycle    | 34.375 | airplane     | 55.650 | bus            | 65.151 |
| train         | 68.635 | truck        | 34.562 | boat           | 22.871 |
| traffic light | 24.372 | fire hydrant | 64.000 | stop sign      | 64.327 |
| parking meter | 44.681 | bench        | 20.391 | bird           | 30.100 |
| cat           | 74.058 | dog          | 65.616 | horse          | 45.143 |
| sheep         | 46.747 | cow          | 50.314 | elephant       | 59.729 |
| bear          | 78.215 | zebra        | 60.038 | giraffe        | 56.402 |
| backpack      | 16.925 | umbrella     | 48.415 | handbag        | 16.280 |
| tie           | 34.360 | suitcase     | 39.769 | frisbee        | 67.534 |
| skis          | 5.501  | snowboard    | 23.438 | sports ball    | 47.470 |
| kite          | 34.665 | baseball bat | 28.898 | baseball glove | 43.015 |
| skateboard    | 35.186 | surfboard    | 35.669 | tennis racket  | 56.493 |
| bottle        | 34.638 | wine glass   | 26.728 | cup            | 40.503 |
| fork          | 15.219 | knife        | 12.985 | spoon          | 14.226 |
| bowl          | 31.639 | banana       | 20.989 | apple          | 19.307 |
| sandwich      | 44.664 | orange       | 29.068 | broccoli       | 21.634 |
| carrot        | 20.839 | hot dog      | 21.776 | pizza          | 52.191 |
| donut         | 45.734 | cake         | 42.979 | chair          | 20.602 |
| couch         | 41.155 | potted plant | 17.652 | bed            | 40.927 |
| dining table  | 13.165 | toilet       | 67.016 | tv             | 63.073 |
| laptop        | 63.166 | mouse        | 59.342 | remote         | 30.750 |
| keyboard      | 48.065 | cell phone   | 38.558 | microwave      | 56.359 |
| oven          | 32.916 | toaster      | 35.030 | sink           | 36.831 |
| refrigerator  | 58.916 | book         | 9.823  | clock          | 52.948 |
| vase          | 33.161 | scissors     | 25.192 | teddy bear     | 50.912 |
| hair drier    | 10.364 | toothbrush   | 18.889 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 59.94927859156919, 'fwIoU': 68.38291772205028, 'IoU-person': 87.1270185720052, 'IoU-bicycle': 66.79122169846598, 'IoU-car': 68.69321462413987, 'IoU-motorcycle': 73.63412231586075, 'IoU-airplane': 79.97146005637127, 'IoU-bus': 83.97913588205957, 'IoU-train': 84.9860659320413, 'IoU-truck': 63.61422307594265, 'IoU-boat': 69.45947542976648, 'IoU-traffic light': 75.97205640347198, 'IoU-fire hydrant': 88.31919289430317, 'IoU-stop sign': 87.7626387567396, 'IoU-parking meter': 85.48524610361609, 'IoU-bench': 50.21168251675589, 'IoU-bird': 74.54819292149767, 'IoU-cat': 80.6125074523832, 'IoU-dog': 78.68995972310961, 'IoU-horse': 82.89098631851289, 'IoU-sheep': 80.1804641429245, 'IoU-cow': 80.6508018317427, 'IoU-elephant': 77.28774596694753, 'IoU-bear': 65.30252528371886, 'IoU-zebra': 85.37479147375281, 'IoU-giraffe': 85.12789152856266, 'IoU-backpack': 39.27841866543372, 'IoU-umbrella': 71.4048464881148, 'IoU-handbag': 38.27975475840035, 'IoU-tie': 67.22729569949344, 'IoU-suitcase': 79.33285864637025, 'IoU-frisbee': 83.79465899117146, 'IoU-skis': 52.226003700609425, 'IoU-snowboard': 69.93807834736833, 'IoU-sports ball': 60.433255957453056, 'IoU-kite': 66.82105952234504, 'IoU-baseball bat': 61.83591930495563, 'IoU-baseball glove': 52.6973813120619, 'IoU-skateboard': 80.75050510078144, 'IoU-surfboard': 75.88762824558572, 'IoU-tennis racket': 82.33960914108252, 'IoU-bottle': 68.66095402121991, 'IoU-wine glass': 74.93630850196953, 'IoU-cup': 62.91279036656371, 'IoU-fork': 54.59384931582609, 'IoU-knife': 50.14096929038227, 'IoU-spoon': 45.62990619061369, 'IoU-bowl': 50.391491479999054, 'IoU-banana': 84.12750244907187, 'IoU-apple': 57.26432225121211, 'IoU-sandwich': 66.05769870266096, 'IoU-orange': 73.78412901165575, 'IoU-broccoli': 66.99009280143201, 'IoU-carrot': 63.21937253392359, 'IoU-hot dog': 62.206753791919965, 'IoU-pizza': 84.99865108709113, 'IoU-donut': 61.82472423636865, 'IoU-cake': 67.45722873532004, 'IoU-chair': 56.151306435758606, 'IoU-couch': 65.52757429593555, 'IoU-potted plant': 32.582319090204834, 'IoU-bed': 67.52834563582415, 'IoU-dining table': 50.91319488324723, 'IoU-toilet': 79.75515020138315, 'IoU-tv': 77.11736099438755, 'IoU-laptop': 70.41139773675731, 'IoU-mouse': 65.41936067278564, 'IoU-remote': 65.44152001245912, 'IoU-keyboard': 51.13604444540132, 'IoU-cell phone': 69.5614563678285, 'IoU-microwave': 58.11981565498277, 'IoU-oven': 65.98627902238906, 'IoU-toaster': 73.77396956714433, 'IoU-sink': 70.42621562469935, 'IoU-refrigerator': 78.31644750386204, 'IoU-book': 52.730005328376336, 'IoU-clock': 75.05294471648864, 'IoU-vase': 60.43787400228739, 'IoU-scissors': 55.16300396431585, 'IoU-teddy bear': 79.20278296902458, 'IoU-hair drier': 56.71803486220664, 'IoU-toothbrush': 50.882401909095954, 'IoU-banner': 32.8501543982073, 'IoU-blanket': 11.54257505887752, 'IoU-bridge': 37.567762570457866, 'IoU-cardboard': 44.92711178379907, 'IoU-counter': 31.6239052017337, 'IoU-curtain': 64.7625611890737, 'IoU-door-stuff': 42.26265137797541, 'IoU-floor-wood': 58.530121392768905, 'IoU-flower': 43.564954396792864, 'IoU-fruit': 38.70169245529016, 'IoU-gravel': 31.3072441272227, 'IoU-house': 25.003200690550813, 'IoU-light': 39.69402555561803, 'IoU-mirror-stuff': 51.14471221016158, 'IoU-net': 41.31596045610477, 'IoU-pillow': 10.863446788996807, 'IoU-platform': 29.901550679895365, 'IoU-playingfield': 70.56604893007807, 'IoU-railroad': 60.673476207255774, 'IoU-river': 49.623547738386506, 'IoU-road': 66.54434182778606, 'IoU-roof': 14.242401998536716, 'IoU-sand': 63.33368566444214, 'IoU-sea': 85.23949868829952, 'IoU-shelf': 36.04640228925574, 'IoU-snow': 88.24431334118485, 'IoU-stairs': 28.814501260564708, 'IoU-tent': 10.0014214913084, 'IoU-towel': 29.984960123416826, 'IoU-wall-brick': 45.28650334800938, 'IoU-wall-stone': 27.54055480347842, 'IoU-wall-tile': 67.48127861377021, 'IoU-wall-wood': 37.81227355799856, 'IoU-water-other': 26.32101383772327, 'IoU-window-blind': 46.13471455737647, 'IoU-window-other': 45.750344540234536, 'IoU-tree-merged': 80.13842418011517, 'IoU-fence-merged': 51.76462076118866, 'IoU-ceiling-merged': 65.87766171329793, 'IoU-sky-other-merged': 93.50372254654678, 'IoU-cabinet-merged': 59.06451723103903, 'IoU-table-merged': 37.7522870811292, 'IoU-floor-other-merged': 46.51700446992541, 'IoU-pavement-merged': 53.93969277234875, 'IoU-mountain-merged': 55.714597898292695, 'IoU-grass-merged': 70.2225972695456, 'IoU-dirt-merged': 45.10388492476301, 'IoU-paper-merged': 29.891903798804787, 'IoU-food-other-merged': 40.82652867474869, 'IoU-building-other-merged': 57.78086869169904, 'IoU-rock-merged': 60.99209876119652, 'IoU-wall-other-merged': 63.94462744153119, 'IoU-rug-merged': 62.542655859900634, 'mACC': 71.98971241011485, 'pACC': 79.8978014243793, 'ACC-person': 91.90555363115844, 'ACC-bicycle': 76.11766792707513, 'ACC-car': 85.53446423708127, 'ACC-motorcycle': 77.72961660783162, 'ACC-airplane': 87.94189233566921, 'ACC-bus': 89.23365196031729, 'ACC-train': 95.70665033647468, 'ACC-truck': 73.57781916916488, 'ACC-boat': 78.25732734284627, 'ACC-traffic light': 89.98589461690328, 'ACC-fire hydrant': 93.36860073360178, 'ACC-stop sign': 90.57894588679547, 'ACC-parking meter': 89.92038796449535, 'ACC-bench': 64.4768235298474, 'ACC-bird': 79.86439234416997, 'ACC-cat': 86.67507018371955, 'ACC-dog': 81.6567300758862, 'ACC-horse': 89.79386701948673, 'ACC-sheep': 83.14597138774117, 'ACC-cow': 85.4667218116067, 'ACC-elephant': 79.12237695197184, 'ACC-bear': 71.9025985103288, 'ACC-zebra': 87.65118103844802, 'ACC-giraffe': 89.14748992545628, 'ACC-backpack': 56.3058167763648, 'ACC-umbrella': 79.61413523109341, 'ACC-handbag': 54.68283284208108, 'ACC-tie': 78.22267321086439, 'ACC-suitcase': 86.66689369872522, 'ACC-frisbee': 94.43018181818181, 'ACC-skis': 69.43478127544745, 'ACC-snowboard': 79.00222869878279, 'ACC-sports ball': 85.04046888082613, 'ACC-kite': 76.56336915018663, 'ACC-baseball bat': 80.06878001633339, 'ACC-baseball glove': 60.073640143245214, 'ACC-skateboard': 89.20400734320508, 'ACC-surfboard': 83.83443254922149, 'ACC-tennis racket': 88.2584362718279, 'ACC-bottle': 83.51875905752253, 'ACC-wine glass': 84.37308480353937, 'ACC-cup': 84.63291285421153, 'ACC-fork': 65.62012549836828, 'ACC-knife': 63.529530299699566, 'ACC-spoon': 69.82371340378221, 'ACC-bowl': 65.54595792183822, 'ACC-banana': 89.49901911871378, 'ACC-apple': 69.44737647308035, 'ACC-sandwich': 78.97170387742048, 'ACC-orange': 80.17127052875993, 'ACC-broccoli': 76.55930543985559, 'ACC-carrot': 72.66382174819852, 'ACC-hot dog': 68.06363545248149, 'ACC-pizza': 90.79449240746453, 'ACC-donut': 78.44958084457633, 'ACC-cake': 77.98258717715251, 'ACC-chair': 70.01833064570107, 'ACC-couch': 76.79231002079885, 'ACC-potted plant': 44.10008703620598, 'ACC-bed': 79.4985648333896, 'ACC-dining table': 75.19876800520912, 'ACC-toilet': 82.31670724671525, 'ACC-tv': 86.85562767455228, 'ACC-laptop': 84.4389456244375, 'ACC-mouse': 78.29183035182841, 'ACC-remote': 71.84685761647461, 'ACC-keyboard': 55.71391866038887, 'ACC-cell phone': 76.35818763931766, 'ACC-microwave': 69.23478462939579, 'ACC-oven': 77.83783944308422, 'ACC-toaster': 85.42884123225993, 'ACC-sink': 83.34994998679782, 'ACC-refrigerator': 86.17753426840015, 'ACC-book': 68.58987559889049, 'ACC-clock': 81.65653031771443, 'ACC-vase': 70.26012258051958, 'ACC-scissors': 59.65083234627075, 'ACC-teddy bear': 85.57797243116336, 'ACC-hair drier': 66.31229897816867, 'ACC-toothbrush': 79.64298123697013, 'ACC-banner': 62.01423280428047, 'ACC-blanket': 18.405959572962995, 'ACC-bridge': 51.76777424499457, 'ACC-cardboard': 61.343492802093934, 'ACC-counter': 51.82169890559224, 'ACC-curtain': 77.32234379874625, 'ACC-door-stuff': 59.839997896243034, 'ACC-floor-wood': 81.02475495904612, 'ACC-flower': 69.32264194995568, 'ACC-fruit': 58.21295824677987, 'ACC-gravel': 40.93263082194733, 'ACC-house': 30.288588720943547, 'ACC-light': 57.42227873904222, 'ACC-mirror-stuff': 74.12003199883641, 'ACC-net': 63.47591081314169, 'ACC-pillow': 26.391723183902556, 'ACC-platform': 47.72388706108031, 'ACC-playingfield': 91.55875098930775, 'ACC-railroad': 78.36360817921432, 'ACC-river': 70.44593667943589, 'ACC-road': 84.99216118381425, 'ACC-roof': 19.289727699071417, 'ACC-sand': 70.560124070097, 'ACC-sea': 91.14465839726815, 'ACC-shelf': 58.96494456078643, 'ACC-snow': 95.06252329857227, 'ACC-stairs': 48.128953682678876, 'ACC-tent': 11.475789486891024, 'ACC-towel': 34.94816122643877, 'ACC-wall-brick': 60.00546202601002, 'ACC-wall-stone': 35.178107460201254, 'ACC-wall-tile': 81.99809668861846, 'ACC-wall-wood': 53.67388983990522, 'ACC-water-other': 43.362238096670794, 'ACC-window-blind': 55.244013114850375, 'ACC-window-other': 67.58184710800602, 'ACC-tree-merged': 89.59379679506073, 'ACC-fence-merged': 68.70608582603333, 'ACC-ceiling-merged': 77.67355946566379, 'ACC-sky-other-merged': 96.68259595116581, 'ACC-cabinet-merged': 73.63029702741667, 'ACC-table-merged': 54.53953624770712, 'ACC-floor-other-merged': 58.802118708900096, 'ACC-pavement-merged': 69.01592546112875, 'ACC-mountain-merged': 65.86298802175438, 'ACC-grass-merged': 83.17707100626703, 'ACC-dirt-merged': 64.85860807311714, 'ACC-paper-merged': 39.768478167802684, 'ACC-food-other-merged': 57.22421379724314, 'ACC-building-other-merged': 73.05030200821169, 'ACC-rock-merged': 82.81897834242766, 'ACC-wall-other-merged': 81.54559493898952, 'ACC-rug-merged': 79.31075165317384})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1526 s/iter. Inference: 0.5901 s/iter. Eval: 0.0000 s/iter. Total: 0.7427 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1556 s/iter. Inference: 0.5347 s/iter. Eval: 0.0000 s/iter. Total: 0.6904 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1667 s/iter. Inference: 0.5679 s/iter. Eval: 0.0000 s/iter. Total: 0.7347 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1714 s/iter. Inference: 0.6994 s/iter. Eval: 0.0000 s/iter. Total: 0.8709 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1688 s/iter. Inference: 0.6174 s/iter. Eval: 0.0000 s/iter. Total: 0.7863 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1681 s/iter. Inference: 0.6545 s/iter. Eval: 0.0000 s/iter. Total: 0.8227 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1694 s/iter. Inference: 0.6773 s/iter. Eval: 0.0000 s/iter. Total: 0.8469 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4942932396839332, 'noc@0.8': 2.820310213637694, 'noc@0.85': 3.476441322797776, 'noc@0.9': 4.472929470295581, 'miou@iter1': 0.8320807774303459}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1021 s/iter. Eval: 0.0008 s/iter. Total: 0.1046 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.55072021484375, 'precision@0.6': 68.32491302490234, 'precision@0.7': 63.42790603637695, 'precision@0.8': 53.478431701660156, 'precision@0.9': 27.244461059570312, 'cIoU': 58.01152038574219, 'mIoU': 62.986270904541016}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.51676505969096, 'SQ': 81.92393099430845, 'RQ': 60.779538452851575, 'PQ_th': 55.38824381408026, 'SQ_th': 82.68398842944131, 'RQ_th': 66.31544169887114, 'PQ_st': 43.16358958136751, 'SQ_st': 80.77667448844754, 'RQ_st': 52.42345808150137}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.97024731721061, 'AP50': 61.325872300675144, 'AP75': 41.01055603765922, 'APs': 19.463287216572958, 'APm': 42.102274346648905, 'APl': 60.801477822166646, 'AP-person': 44.38410469951155, 'AP-bicycle': 17.764553298040227, 'AP-car': 36.54650174643771, 'AP-motorcycle': 34.37487407862267, 'AP-airplane': 55.65041424577449, 'AP-bus': 65.15089411065769, 'AP-train': 68.63509639866113, 'AP-truck': 34.562381602170625, 'AP-boat': 22.870949272999425, 'AP-traffic light': 24.37237969789284, 'AP-fire hydrant': 64.000291681237, 'AP-stop sign': 64.32707992655095, 'AP-parking meter': 44.68084676007093, 'AP-bench': 20.391157997914704, 'AP-bird': 30.100182805729368, 'AP-cat': 74.05760109289487, 'AP-dog': 65.61571957007492, 'AP-horse': 45.143046510957284, 'AP-sheep': 46.746718594403326, 'AP-cow': 50.314392265453854, 'AP-elephant': 59.72922322204567, 'AP-bear': 78.21540333264467, 'AP-zebra': 60.037630031727275, 'AP-giraffe': 56.4024852740591, 'AP-backpack': 16.924576570838262, 'AP-umbrella': 48.41451915346515, 'AP-handbag': 16.27984358616105, 'AP-tie': 34.36020225759409, 'AP-suitcase': 39.76851961998578, 'AP-frisbee': 67.53360940015656, 'AP-skis': 5.501004627182595, 'AP-snowboard': 23.437532738153454, 'AP-sports ball': 47.46968558202415, 'AP-kite': 34.664760506390145, 'AP-baseball bat': 28.8980420806271, 'AP-baseball glove': 43.0154938625266, 'AP-skateboard': 35.18563734508911, 'AP-surfboard': 35.66924687451206, 'AP-tennis racket': 56.493488006444096, 'AP-bottle': 34.63775988910201, 'AP-wine glass': 26.728277237010506, 'AP-cup': 40.502941465613624, 'AP-fork': 15.218925229110711, 'AP-knife': 12.985463278775072, 'AP-spoon': 14.225680107057242, 'AP-bowl': 31.639004402323707, 'AP-banana': 20.989101762888453, 'AP-apple': 19.306556957743915, 'AP-sandwich': 44.66413509974736, 'AP-orange': 29.067725229890605, 'AP-broccoli': 21.63378238010072, 'AP-carrot': 20.839420175536738, 'AP-hot dog': 21.776235708952257, 'AP-pizza': 52.19112257554212, 'AP-donut': 45.73445037300644, 'AP-cake': 42.97855028091226, 'AP-chair': 20.602156748780374, 'AP-couch': 41.154652597689626, 'AP-potted plant': 17.652453204985775, 'AP-bed': 40.926760702132434, 'AP-dining table': 13.164740916194214, 'AP-toilet': 67.01598202952167, 'AP-tv': 63.073492953344825, 'AP-laptop': 63.165927689380695, 'AP-mouse': 59.342121869832184, 'AP-remote': 30.750321698675048, 'AP-keyboard': 48.06452179221173, 'AP-cell phone': 38.55760488624182, 'AP-microwave': 56.35887684258679, 'AP-oven': 32.91555942104378, 'AP-toaster': 35.02993870815652, 'AP-sink': 36.83112486905071, 'AP-refrigerator': 58.91610978537866, 'AP-book': 9.822900638893953, 'AP-clock': 52.948353785317856, 'AP-vase': 33.16054161890328, 'AP-scissors': 25.19225790442577, 'AP-teddy bear': 50.91179820338233, 'AP-hair drier': 10.363611942589609, 'AP-toothbrush': 18.888751987132434}), ('sem_seg', {'mIoU': 59.94927859156919, 'fwIoU': 68.38291772205028, 'IoU-person': 87.1270185720052, 'IoU-bicycle': 66.79122169846598, 'IoU-car': 68.69321462413987, 'IoU-motorcycle': 73.63412231586075, 'IoU-airplane': 79.97146005637127, 'IoU-bus': 83.97913588205957, 'IoU-train': 84.9860659320413, 'IoU-truck': 63.61422307594265, 'IoU-boat': 69.45947542976648, 'IoU-traffic light': 75.97205640347198, 'IoU-fire hydrant': 88.31919289430317, 'IoU-stop sign': 87.7626387567396, 'IoU-parking meter': 85.48524610361609, 'IoU-bench': 50.21168251675589, 'IoU-bird': 74.54819292149767, 'IoU-cat': 80.6125074523832, 'IoU-dog': 78.68995972310961, 'IoU-horse': 82.89098631851289, 'IoU-sheep': 80.1804641429245, 'IoU-cow': 80.6508018317427, 'IoU-elephant': 77.28774596694753, 'IoU-bear': 65.30252528371886, 'IoU-zebra': 85.37479147375281, 'IoU-giraffe': 85.12789152856266, 'IoU-backpack': 39.27841866543372, 'IoU-umbrella': 71.4048464881148, 'IoU-handbag': 38.27975475840035, 'IoU-tie': 67.22729569949344, 'IoU-suitcase': 79.33285864637025, 'IoU-frisbee': 83.79465899117146, 'IoU-skis': 52.226003700609425, 'IoU-snowboard': 69.93807834736833, 'IoU-sports ball': 60.433255957453056, 'IoU-kite': 66.82105952234504, 'IoU-baseball bat': 61.83591930495563, 'IoU-baseball glove': 52.6973813120619, 'IoU-skateboard': 80.75050510078144, 'IoU-surfboard': 75.88762824558572, 'IoU-tennis racket': 82.33960914108252, 'IoU-bottle': 68.66095402121991, 'IoU-wine glass': 74.93630850196953, 'IoU-cup': 62.91279036656371, 'IoU-fork': 54.59384931582609, 'IoU-knife': 50.14096929038227, 'IoU-spoon': 45.62990619061369, 'IoU-bowl': 50.391491479999054, 'IoU-banana': 84.12750244907187, 'IoU-apple': 57.26432225121211, 'IoU-sandwich': 66.05769870266096, 'IoU-orange': 73.78412901165575, 'IoU-broccoli': 66.99009280143201, 'IoU-carrot': 63.21937253392359, 'IoU-hot dog': 62.206753791919965, 'IoU-pizza': 84.99865108709113, 'IoU-donut': 61.82472423636865, 'IoU-cake': 67.45722873532004, 'IoU-chair': 56.151306435758606, 'IoU-couch': 65.52757429593555, 'IoU-potted plant': 32.582319090204834, 'IoU-bed': 67.52834563582415, 'IoU-dining table': 50.91319488324723, 'IoU-toilet': 79.75515020138315, 'IoU-tv': 77.11736099438755, 'IoU-laptop': 70.41139773675731, 'IoU-mouse': 65.41936067278564, 'IoU-remote': 65.44152001245912, 'IoU-keyboard': 51.13604444540132, 'IoU-cell phone': 69.5614563678285, 'IoU-microwave': 58.11981565498277, 'IoU-oven': 65.98627902238906, 'IoU-toaster': 73.77396956714433, 'IoU-sink': 70.42621562469935, 'IoU-refrigerator': 78.31644750386204, 'IoU-book': 52.730005328376336, 'IoU-clock': 75.05294471648864, 'IoU-vase': 60.43787400228739, 'IoU-scissors': 55.16300396431585, 'IoU-teddy bear': 79.20278296902458, 'IoU-hair drier': 56.71803486220664, 'IoU-toothbrush': 50.882401909095954, 'IoU-banner': 32.8501543982073, 'IoU-blanket': 11.54257505887752, 'IoU-bridge': 37.567762570457866, 'IoU-cardboard': 44.92711178379907, 'IoU-counter': 31.6239052017337, 'IoU-curtain': 64.7625611890737, 'IoU-door-stuff': 42.26265137797541, 'IoU-floor-wood': 58.530121392768905, 'IoU-flower': 43.564954396792864, 'IoU-fruit': 38.70169245529016, 'IoU-gravel': 31.3072441272227, 'IoU-house': 25.003200690550813, 'IoU-light': 39.69402555561803, 'IoU-mirror-stuff': 51.14471221016158, 'IoU-net': 41.31596045610477, 'IoU-pillow': 10.863446788996807, 'IoU-platform': 29.901550679895365, 'IoU-playingfield': 70.56604893007807, 'IoU-railroad': 60.673476207255774, 'IoU-river': 49.623547738386506, 'IoU-road': 66.54434182778606, 'IoU-roof': 14.242401998536716, 'IoU-sand': 63.33368566444214, 'IoU-sea': 85.23949868829952, 'IoU-shelf': 36.04640228925574, 'IoU-snow': 88.24431334118485, 'IoU-stairs': 28.814501260564708, 'IoU-tent': 10.0014214913084, 'IoU-towel': 29.984960123416826, 'IoU-wall-brick': 45.28650334800938, 'IoU-wall-stone': 27.54055480347842, 'IoU-wall-tile': 67.48127861377021, 'IoU-wall-wood': 37.81227355799856, 'IoU-water-other': 26.32101383772327, 'IoU-window-blind': 46.13471455737647, 'IoU-window-other': 45.750344540234536, 'IoU-tree-merged': 80.13842418011517, 'IoU-fence-merged': 51.76462076118866, 'IoU-ceiling-merged': 65.87766171329793, 'IoU-sky-other-merged': 93.50372254654678, 'IoU-cabinet-merged': 59.06451723103903, 'IoU-table-merged': 37.7522870811292, 'IoU-floor-other-merged': 46.51700446992541, 'IoU-pavement-merged': 53.93969277234875, 'IoU-mountain-merged': 55.714597898292695, 'IoU-grass-merged': 70.2225972695456, 'IoU-dirt-merged': 45.10388492476301, 'IoU-paper-merged': 29.891903798804787, 'IoU-food-other-merged': 40.82652867474869, 'IoU-building-other-merged': 57.78086869169904, 'IoU-rock-merged': 60.99209876119652, 'IoU-wall-other-merged': 63.94462744153119, 'IoU-rug-merged': 62.542655859900634, 'mACC': 71.98971241011485, 'pACC': 79.8978014243793, 'ACC-person': 91.90555363115844, 'ACC-bicycle': 76.11766792707513, 'ACC-car': 85.53446423708127, 'ACC-motorcycle': 77.72961660783162, 'ACC-airplane': 87.94189233566921, 'ACC-bus': 89.23365196031729, 'ACC-train': 95.70665033647468, 'ACC-truck': 73.57781916916488, 'ACC-boat': 78.25732734284627, 'ACC-traffic light': 89.98589461690328, 'ACC-fire hydrant': 93.36860073360178, 'ACC-stop sign': 90.57894588679547, 'ACC-parking meter': 89.92038796449535, 'ACC-bench': 64.4768235298474, 'ACC-bird': 79.86439234416997, 'ACC-cat': 86.67507018371955, 'ACC-dog': 81.6567300758862, 'ACC-horse': 89.79386701948673, 'ACC-sheep': 83.14597138774117, 'ACC-cow': 85.4667218116067, 'ACC-elephant': 79.12237695197184, 'ACC-bear': 71.9025985103288, 'ACC-zebra': 87.65118103844802, 'ACC-giraffe': 89.14748992545628, 'ACC-backpack': 56.3058167763648, 'ACC-umbrella': 79.61413523109341, 'ACC-handbag': 54.68283284208108, 'ACC-tie': 78.22267321086439, 'ACC-suitcase': 86.66689369872522, 'ACC-frisbee': 94.43018181818181, 'ACC-skis': 69.43478127544745, 'ACC-snowboard': 79.00222869878279, 'ACC-sports ball': 85.04046888082613, 'ACC-kite': 76.56336915018663, 'ACC-baseball bat': 80.06878001633339, 'ACC-baseball glove': 60.073640143245214, 'ACC-skateboard': 89.20400734320508, 'ACC-surfboard': 83.83443254922149, 'ACC-tennis racket': 88.2584362718279, 'ACC-bottle': 83.51875905752253, 'ACC-wine glass': 84.37308480353937, 'ACC-cup': 84.63291285421153, 'ACC-fork': 65.62012549836828, 'ACC-knife': 63.529530299699566, 'ACC-spoon': 69.82371340378221, 'ACC-bowl': 65.54595792183822, 'ACC-banana': 89.49901911871378, 'ACC-apple': 69.44737647308035, 'ACC-sandwich': 78.97170387742048, 'ACC-orange': 80.17127052875993, 'ACC-broccoli': 76.55930543985559, 'ACC-carrot': 72.66382174819852, 'ACC-hot dog': 68.06363545248149, 'ACC-pizza': 90.79449240746453, 'ACC-donut': 78.44958084457633, 'ACC-cake': 77.98258717715251, 'ACC-chair': 70.01833064570107, 'ACC-couch': 76.79231002079885, 'ACC-potted plant': 44.10008703620598, 'ACC-bed': 79.4985648333896, 'ACC-dining table': 75.19876800520912, 'ACC-toilet': 82.31670724671525, 'ACC-tv': 86.85562767455228, 'ACC-laptop': 84.4389456244375, 'ACC-mouse': 78.29183035182841, 'ACC-remote': 71.84685761647461, 'ACC-keyboard': 55.71391866038887, 'ACC-cell phone': 76.35818763931766, 'ACC-microwave': 69.23478462939579, 'ACC-oven': 77.83783944308422, 'ACC-toaster': 85.42884123225993, 'ACC-sink': 83.34994998679782, 'ACC-refrigerator': 86.17753426840015, 'ACC-book': 68.58987559889049, 'ACC-clock': 81.65653031771443, 'ACC-vase': 70.26012258051958, 'ACC-scissors': 59.65083234627075, 'ACC-teddy bear': 85.57797243116336, 'ACC-hair drier': 66.31229897816867, 'ACC-toothbrush': 79.64298123697013, 'ACC-banner': 62.01423280428047, 'ACC-blanket': 18.405959572962995, 'ACC-bridge': 51.76777424499457, 'ACC-cardboard': 61.343492802093934, 'ACC-counter': 51.82169890559224, 'ACC-curtain': 77.32234379874625, 'ACC-door-stuff': 59.839997896243034, 'ACC-floor-wood': 81.02475495904612, 'ACC-flower': 69.32264194995568, 'ACC-fruit': 58.21295824677987, 'ACC-gravel': 40.93263082194733, 'ACC-house': 30.288588720943547, 'ACC-light': 57.42227873904222, 'ACC-mirror-stuff': 74.12003199883641, 'ACC-net': 63.47591081314169, 'ACC-pillow': 26.391723183902556, 'ACC-platform': 47.72388706108031, 'ACC-playingfield': 91.55875098930775, 'ACC-railroad': 78.36360817921432, 'ACC-river': 70.44593667943589, 'ACC-road': 84.99216118381425, 'ACC-roof': 19.289727699071417, 'ACC-sand': 70.560124070097, 'ACC-sea': 91.14465839726815, 'ACC-shelf': 58.96494456078643, 'ACC-snow': 95.06252329857227, 'ACC-stairs': 48.128953682678876, 'ACC-tent': 11.475789486891024, 'ACC-towel': 34.94816122643877, 'ACC-wall-brick': 60.00546202601002, 'ACC-wall-stone': 35.178107460201254, 'ACC-wall-tile': 81.99809668861846, 'ACC-wall-wood': 53.67388983990522, 'ACC-water-other': 43.362238096670794, 'ACC-window-blind': 55.244013114850375, 'ACC-window-other': 67.58184710800602, 'ACC-tree-merged': 89.59379679506073, 'ACC-fence-merged': 68.70608582603333, 'ACC-ceiling-merged': 77.67355946566379, 'ACC-sky-other-merged': 96.68259595116581, 'ACC-cabinet-merged': 73.63029702741667, 'ACC-table-merged': 54.53953624770712, 'ACC-floor-other-merged': 58.802118708900096, 'ACC-pavement-merged': 69.01592546112875, 'ACC-mountain-merged': 65.86298802175438, 'ACC-grass-merged': 83.17707100626703, 'ACC-dirt-merged': 64.85860807311714, 'ACC-paper-merged': 39.768478167802684, 'ACC-food-other-merged': 57.22421379724314, 'ACC-building-other-merged': 73.05030200821169, 'ACC-rock-merged': 82.81897834242766, 'ACC-wall-other-merged': 81.54559493898952, 'ACC-rug-merged': 79.31075165317384})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4942932396839332, 'noc@0.8': 2.820310213637694, 'noc@0.85': 3.476441322797776, 'noc@0.9': 4.472929470295581, 'miou@iter1': 0.8320807774303459}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.55072021484375, 'precision@0.6': 68.32491302490234, 'precision@0.7': 63.42790603637695, 'precision@0.8': 53.478431701660156, 'precision@0.9': 27.244461059570312, 'cIoU': 58.01152038574219, 'mIoU': 62.986270904541016}}}
INFO:trainer.default_trainer:This epoch takes 1:27:16.976850
INFO:trainer.default_trainer:PROGRESS: 72.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 36 training.
INFO:trainer.default_trainer:epochs[    36] optim steps[65800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.65267/0.89871, loss_mask_bce_0: 0.33463/0.33457, loss_mask_dice_0: 1.87875/1.16313, loss_spatial_bce_0: 0.03856/0.08731, loss_spatial_dice_0: 0.20629/0.20824, loss_spatial_ce_0: 0.04954/0.06196, loss_grounding_bce_0: 0.07862/0.08622, loss_grounding_dice_0: 0.25340/0.17854, loss_grounding_ce_0: 0.13305/0.27208, loss_mask_ce_1: 1.62165/0.89930, loss_mask_bce_1: 0.29373/0.33550, loss_mask_dice_1: 1.98219/1.16985, loss_spatial_bce_1: 0.03699/0.08782, loss_spatial_dice_1: 0.22658/0.21222, loss_spatial_ce_1: 0.04648/0.06785, loss_grounding_bce_1: 0.05638/0.08640, loss_grounding_dice_1: 0.19258/0.17939, loss_grounding_ce_1: 0.22578/0.27287, loss_mask_ce_2: 1.50701/0.90641, loss_mask_bce_2: 0.36890/0.33607, loss_mask_dice_2: 2.00028/1.17029, loss_spatial_bce_2: 0.03834/0.08891, loss_spatial_dice_2: 0.23227/0.21382, loss_spatial_ce_2: 0.05654/0.07124, loss_grounding_bce_2: 0.05223/0.08653, loss_grounding_dice_2: 0.18060/0.17914, loss_grounding_ce_2: 0.20410/0.27631, loss_mask_ce_3: 1.46589/0.91699, loss_mask_bce_3: 0.34556/0.33723, loss_mask_dice_3: 2.02997/1.16784, loss_spatial_bce_3: 0.04291/0.09013, loss_spatial_dice_3: 0.23931/0.21477, loss_spatial_ce_3: 0.07091/0.07591, loss_grounding_bce_3: 0.05740/0.08678, loss_grounding_dice_3: 0.20091/0.17890, loss_grounding_ce_3: 0.27251/0.27845, loss_mask_ce_4: 1.68100/0.91802, loss_mask_bce_4: 0.41441/0.33935, loss_mask_dice_4: 1.99205/1.19193, loss_spatial_bce_4: 0.05704/0.09405, loss_spatial_dice_4: 0.28897/0.22695, loss_spatial_ce_4: 0.08312/0.09205, loss_grounding_bce_4: 0.05659/0.08728, loss_grounding_dice_4: 0.23108/0.18184, loss_grounding_ce_4: 0.19009/0.28144, loss_mask_ce_5: 1.76499/0.93443, loss_mask_bce_5: 0.41669/0.34168, loss_mask_dice_5: 1.95726/1.19945, loss_spatial_bce_5: 0.08640/0.09623, loss_spatial_dice_5: 0.29086/0.23114, loss_spatial_ce_5: 0.11538/0.10638, loss_grounding_bce_5: 0.05432/0.08772, loss_grounding_dice_5: 0.23160/0.18305, loss_grounding_ce_5: 0.28432/0.29410, loss_mask_ce_6: 1.83775/0.97447, loss_mask_bce_6: 0.36460/0.34431, loss_mask_dice_6: 1.84040/1.20241, loss_spatial_bce_6: 0.07259/0.10193, loss_spatial_dice_6: 0.28928/0.23401, loss_spatial_ce_6: 0.16597/0.13221, loss_grounding_bce_6: 0.05852/0.08845, loss_grounding_dice_6: 0.29999/0.18343, loss_grounding_ce_6: 0.24793/0.30983, loss_mask_ce_7: 1.68498/1.01937, loss_mask_bce_7: 0.37858/0.35219, loss_mask_dice_7: 2.13109/1.25675, loss_spatial_bce_7: 0.06937/0.10986, loss_spatial_dice_7: 0.32688/0.26161, loss_spatial_ce_7: 0.12626/0.16746, loss_grounding_bce_7: 0.05485/0.09034, loss_grounding_dice_7: 0.25287/0.19070, loss_grounding_ce_7: 0.20425/0.33998, loss_mask_ce_8: 1.87765/1.12786, loss_mask_bce_8: 0.41617/0.36583, loss_mask_dice_8: 2.15427/1.32973, loss_spatial_bce_8: 0.10795/0.13053, loss_spatial_dice_8: 0.31698/0.29958, loss_spatial_ce_8: 0.11015/0.22297, loss_grounding_bce_8: 0.05151/0.09410, loss_grounding_dice_8: 0.25665/0.20150, loss_grounding_ce_8: 0.25454/0.40714, loss_mask_ce_9: 3.92823/3.67640, loss_mask_bce_9: 0.33597/0.39285, loss_mask_dice_9: 2.90955/1.90264, loss_spatial_bce_9: 0.19477/0.33315, loss_spatial_dice_9: 0.90463/0.82190, loss_spatial_ce_9: 1.33297/1.49652, loss_grounding_bce_9: 0.06316/0.10565, loss_grounding_dice_9: 0.36780/0.28090, loss_grounding_ce_9: 0.35188/0.67202] items per batch[64] items per second[0.14] total items[4211200] mini batches[ 65800] memory[7345] epoch remaining[1:21:57]
INFO:trainer.default_trainer:epochs[    36] optim steps[65900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70211/0.89858, loss_mask_bce_0: 0.17827/0.33455, loss_mask_dice_0: 0.56713/1.16311, loss_spatial_bce_0: 0.03031/0.08730, loss_spatial_dice_0: 0.16917/0.20823, loss_spatial_ce_0: 0.02026/0.06194, loss_grounding_bce_0: 0.06342/0.08621, loss_grounding_dice_0: 0.10706/0.17855, loss_grounding_ce_0: 0.49297/0.27207, loss_mask_ce_1: 0.20811/0.89914, loss_mask_bce_1: 0.30577/0.33548, loss_mask_dice_1: 0.64008/1.16984, loss_spatial_bce_1: 0.03716/0.08782, loss_spatial_dice_1: 0.15283/0.21221, loss_spatial_ce_1: 0.02958/0.06783, loss_grounding_bce_1: 0.06614/0.08639, loss_grounding_dice_1: 0.09766/0.17939, loss_grounding_ce_1: 0.38951/0.27285, loss_mask_ce_2: 0.18247/0.90625, loss_mask_bce_2: 0.30259/0.33605, loss_mask_dice_2: 0.63313/1.17027, loss_spatial_bce_2: 0.04091/0.08890, loss_spatial_dice_2: 0.18698/0.21382, loss_spatial_ce_2: 0.02753/0.07121, loss_grounding_bce_2: 0.06454/0.08652, loss_grounding_dice_2: 0.10431/0.17915, loss_grounding_ce_2: 0.30599/0.27628, loss_mask_ce_3: 0.41377/0.91682, loss_mask_bce_3: 0.30043/0.33722, loss_mask_dice_3: 0.58434/1.16781, loss_spatial_bce_3: 0.03965/0.09013, loss_spatial_dice_3: 0.16961/0.21476, loss_spatial_ce_3: 0.03661/0.07588, loss_grounding_bce_3: 0.06672/0.08676, loss_grounding_dice_3: 0.11659/0.17891, loss_grounding_ce_3: 0.37595/0.27841, loss_mask_ce_4: 0.22550/0.91788, loss_mask_bce_4: 0.29950/0.33933, loss_mask_dice_4: 0.62538/1.19189, loss_spatial_bce_4: 0.03310/0.09405, loss_spatial_dice_4: 0.19166/0.22694, loss_spatial_ce_4: 0.02064/0.09203, loss_grounding_bce_4: 0.06799/0.08727, loss_grounding_dice_4: 0.10849/0.18185, loss_grounding_ce_4: 0.36932/0.28141, loss_mask_ce_5: 0.24440/0.93431, loss_mask_bce_5: 0.31385/0.34166, loss_mask_dice_5: 0.69935/1.19945, loss_spatial_bce_5: 0.03076/0.09623, loss_spatial_dice_5: 0.19902/0.23113, loss_spatial_ce_5: 0.02763/0.10634, loss_grounding_bce_5: 0.06376/0.08771, loss_grounding_dice_5: 0.10846/0.18306, loss_grounding_ce_5: 0.64351/0.29406, loss_mask_ce_6: 0.46803/0.97435, loss_mask_bce_6: 0.30674/0.34429, loss_mask_dice_6: 0.63471/1.20237, loss_spatial_bce_6: 0.04107/0.10192, loss_spatial_dice_6: 0.19816/0.23401, loss_spatial_ce_6: 0.02273/0.13217, loss_grounding_bce_6: 0.06189/0.08844, loss_grounding_dice_6: 0.10921/0.18345, loss_grounding_ce_6: 0.47805/0.30977, loss_mask_ce_7: 0.47104/1.01922, loss_mask_bce_7: 0.19802/0.35217, loss_mask_dice_7: 0.76337/1.25672, loss_spatial_bce_7: 0.06358/0.10986, loss_spatial_dice_7: 0.23830/0.26161, loss_spatial_ce_7: 0.07445/0.16742, loss_grounding_bce_7: 0.05889/0.09033, loss_grounding_dice_7: 0.11206/0.19072, loss_grounding_ce_7: 0.38030/0.33996, loss_mask_ce_8: 0.56074/1.12771, loss_mask_bce_8: 0.30437/0.36580, loss_mask_dice_8: 0.71956/1.32968, loss_spatial_bce_8: 0.08192/0.13052, loss_spatial_dice_8: 0.25425/0.29958, loss_spatial_ce_8: 0.14759/0.22293, loss_grounding_bce_8: 0.05847/0.09409, loss_grounding_dice_8: 0.11704/0.20152, loss_grounding_ce_8: 0.62120/0.40713, loss_mask_ce_9: 3.94518/3.67609, loss_mask_bce_9: 0.20764/0.39282, loss_mask_dice_9: 0.94131/1.90246, loss_spatial_bce_9: 0.37096/0.33313, loss_spatial_dice_9: 0.89407/0.82189, loss_spatial_ce_9: 1.74260/1.49647, loss_grounding_bce_9: 0.06796/0.10564, loss_grounding_dice_9: 0.19787/0.28092, loss_grounding_ce_9: 1.50300/0.67190] items per batch[64] items per second[0.23] total items[4217600] mini batches[ 65900] memory[7345] epoch remaining[1:18:31]
INFO:trainer.default_trainer:epochs[    36] optim steps[66000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83597/0.89852, loss_mask_bce_0: 0.44951/0.33454, loss_mask_dice_0: 0.92215/1.16307, loss_spatial_bce_0: 0.09425/0.08729, loss_spatial_dice_0: 0.19313/0.20822, loss_spatial_ce_0: 0.00430/0.06192, loss_grounding_bce_0: 0.06456/0.08620, loss_grounding_dice_0: 0.19155/0.17854, loss_grounding_ce_0: 0.15637/0.27221, loss_mask_ce_1: 0.83131/0.89907, loss_mask_bce_1: 0.45232/0.33547, loss_mask_dice_1: 0.90848/1.16982, loss_spatial_bce_1: 0.09702/0.08781, loss_spatial_dice_1: 0.19109/0.21220, loss_spatial_ce_1: 0.00542/0.06780, loss_grounding_bce_1: 0.03633/0.08639, loss_grounding_dice_1: 0.14050/0.17939, loss_grounding_ce_1: 0.21430/0.27302, loss_mask_ce_2: 0.93739/0.90621, loss_mask_bce_2: 0.44248/0.33604, loss_mask_dice_2: 0.95260/1.17022, loss_spatial_bce_2: 0.09967/0.08890, loss_spatial_dice_2: 0.18948/0.21381, loss_spatial_ce_2: 0.00806/0.07119, loss_grounding_bce_2: 0.06907/0.08652, loss_grounding_dice_2: 0.19948/0.17915, loss_grounding_ce_2: 0.16686/0.27646, loss_mask_ce_3: 0.92895/0.91677, loss_mask_bce_3: 0.44154/0.33720, loss_mask_dice_3: 0.96470/1.16779, loss_spatial_bce_3: 0.10209/0.09012, loss_spatial_dice_3: 0.18491/0.21475, loss_spatial_ce_3: 0.01448/0.07587, loss_grounding_bce_3: 0.04121/0.08676, loss_grounding_dice_3: 0.13731/0.17891, loss_grounding_ce_3: 0.24570/0.27858, loss_mask_ce_4: 0.93982/0.91785, loss_mask_bce_4: 0.44774/0.33931, loss_mask_dice_4: 0.97265/1.19186, loss_spatial_bce_4: 0.09721/0.09404, loss_spatial_dice_4: 0.20400/0.22693, loss_spatial_ce_4: 0.02101/0.09202, loss_grounding_bce_4: 0.04091/0.08727, loss_grounding_dice_4: 0.14974/0.18184, loss_grounding_ce_4: 0.20732/0.28153, loss_mask_ce_5: 0.92971/0.93430, loss_mask_bce_5: 0.43709/0.34165, loss_mask_dice_5: 0.93774/1.19942, loss_spatial_bce_5: 0.10346/0.09622, loss_spatial_dice_5: 0.21028/0.23113, loss_spatial_ce_5: 0.01522/0.10633, loss_grounding_bce_5: 0.06385/0.08770, loss_grounding_dice_5: 0.19069/0.18305, loss_grounding_ce_5: 0.12377/0.29420, loss_mask_ce_6: 1.02746/0.97435, loss_mask_bce_6: 0.42295/0.34428, loss_mask_dice_6: 0.94528/1.20233, loss_spatial_bce_6: 0.10819/0.10191, loss_spatial_dice_6: 0.22310/0.23401, loss_spatial_ce_6: 0.06909/0.13214, loss_grounding_bce_6: 0.03542/0.08843, loss_grounding_dice_6: 0.12806/0.18344, loss_grounding_ce_6: 0.29077/0.30984, loss_mask_ce_7: 0.98722/1.01922, loss_mask_bce_7: 0.43556/0.35215, loss_mask_dice_7: 0.92685/1.25668, loss_spatial_bce_7: 0.10871/0.10985, loss_spatial_dice_7: 0.22984/0.26162, loss_spatial_ce_7: 0.05439/0.16741, loss_grounding_bce_7: 0.03770/0.09032, loss_grounding_dice_7: 0.12159/0.19072, loss_grounding_ce_7: 0.23874/0.34000, loss_mask_ce_8: 0.95625/1.12773, loss_mask_bce_8: 0.50037/0.36578, loss_mask_dice_8: 1.04126/1.32961, loss_spatial_bce_8: 0.14163/0.13051, loss_spatial_dice_8: 0.25429/0.29958, loss_spatial_ce_8: 0.06870/0.22288, loss_grounding_bce_8: 0.05296/0.09408, loss_grounding_dice_8: 0.15981/0.20152, loss_grounding_ce_8: 0.19073/0.40722, loss_mask_ce_9: 2.72958/3.67634, loss_mask_bce_9: 0.53894/0.39281, loss_mask_dice_9: 1.47083/1.90243, loss_spatial_bce_9: 0.43121/0.33313, loss_spatial_dice_9: 0.87847/0.82190, loss_spatial_ce_9: 1.46423/1.49651, loss_grounding_bce_9: 0.06568/0.10563, loss_grounding_dice_9: 0.27228/0.28090, loss_grounding_ce_9: 0.76124/0.67196] items per batch[64] items per second[0.23] total items[4224000] mini batches[ 66000] memory[7345] epoch remaining[1:14:01]
INFO:trainer.default_trainer:epochs[    36] optim steps[66100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.64495/0.89851, loss_mask_bce_0: 0.09267/0.33451, loss_mask_dice_0: 0.44651/1.16301, loss_spatial_bce_0: 0.02421/0.08728, loss_spatial_dice_0: 0.12713/0.20820, loss_spatial_ce_0: 0.03832/0.06191, loss_grounding_bce_0: 0.01646/0.08620, loss_grounding_dice_0: 0.09745/0.17856, loss_grounding_ce_0: 0.48613/0.27210, loss_mask_ce_1: 1.81954/0.89907, loss_mask_bce_1: 0.09929/0.33544, loss_mask_dice_1: 0.56709/1.16978, loss_spatial_bce_1: 0.02529/0.08779, loss_spatial_dice_1: 0.14579/0.21218, loss_spatial_ce_1: 0.03859/0.06778, loss_grounding_bce_1: 0.01648/0.08638, loss_grounding_dice_1: 0.09671/0.17940, loss_grounding_ce_1: 0.48540/0.27292, loss_mask_ce_2: 1.56076/0.90621, loss_mask_bce_2: 0.09330/0.33600, loss_mask_dice_2: 0.40988/1.17015, loss_spatial_bce_2: 0.02403/0.08888, loss_spatial_dice_2: 0.12022/0.21379, loss_spatial_ce_2: 0.03951/0.07116, loss_grounding_bce_2: 0.01750/0.08651, loss_grounding_dice_2: 0.09011/0.17916, loss_grounding_ce_2: 0.52330/0.27638, loss_mask_ce_3: 1.89412/0.91677, loss_mask_bce_3: 0.09128/0.33717, loss_mask_dice_3: 0.37365/1.16770, loss_spatial_bce_3: 0.02331/0.09011, loss_spatial_dice_3: 0.12094/0.21473, loss_spatial_ce_3: 0.04223/0.07586, loss_grounding_bce_3: 0.01736/0.08676, loss_grounding_dice_3: 0.10081/0.17893, loss_grounding_ce_3: 0.46049/0.27847, loss_mask_ce_4: 1.43701/0.91784, loss_mask_bce_4: 0.09164/0.33928, loss_mask_dice_4: 0.41458/1.19180, loss_spatial_bce_4: 0.02400/0.09403, loss_spatial_dice_4: 0.13614/0.22692, loss_spatial_ce_4: 0.04624/0.09199, loss_grounding_bce_4: 0.01862/0.08726, loss_grounding_dice_4: 0.10581/0.18185, loss_grounding_ce_4: 0.46190/0.28147, loss_mask_ce_5: 1.89015/0.93431, loss_mask_bce_5: 0.09232/0.34161, loss_mask_dice_5: 0.45604/1.19938, loss_spatial_bce_5: 0.02466/0.09620, loss_spatial_dice_5: 0.14548/0.23112, loss_spatial_ce_5: 0.04840/0.10633, loss_grounding_bce_5: 0.01736/0.08769, loss_grounding_dice_5: 0.10231/0.18306, loss_grounding_ce_5: 0.49013/0.29411, loss_mask_ce_6: 1.44300/0.97438, loss_mask_bce_6: 0.09589/0.34425, loss_mask_dice_6: 0.43033/1.20226, loss_spatial_bce_6: 0.02878/0.10190, loss_spatial_dice_6: 0.15390/0.23400, loss_spatial_ce_6: 0.06405/0.13213, loss_grounding_bce_6: 0.01769/0.08843, loss_grounding_dice_6: 0.09579/0.18344, loss_grounding_ce_6: 0.43917/0.30975, loss_mask_ce_7: 1.52383/1.01924, loss_mask_bce_7: 0.10428/0.35212, loss_mask_dice_7: 0.46542/1.25663, loss_spatial_bce_7: 0.02531/0.10984, loss_spatial_dice_7: 0.13380/0.26161, loss_spatial_ce_7: 0.08330/0.16738, loss_grounding_bce_7: 0.02197/0.09031, loss_grounding_dice_7: 0.11296/0.19073, loss_grounding_ce_7: 0.51117/0.33990, loss_mask_ce_8: 1.63167/1.12774, loss_mask_bce_8: 0.09466/0.36574, loss_mask_dice_8: 0.52497/1.32956, loss_spatial_bce_8: 0.04713/0.13050, loss_spatial_dice_8: 0.21315/0.29957, loss_spatial_ce_8: 0.31481/0.22284, loss_grounding_bce_8: 0.01979/0.09407, loss_grounding_dice_8: 0.10859/0.20153, loss_grounding_ce_8: 0.37162/0.40708, loss_mask_ce_9: 4.35430/3.67629, loss_mask_bce_9: 0.15193/0.39279, loss_mask_dice_9: 0.93841/1.90232, loss_spatial_bce_9: 0.26361/0.33312, loss_spatial_dice_9: 0.80558/0.82190, loss_spatial_ce_9: 1.65209/1.49660, loss_grounding_bce_9: 0.05335/0.10561, loss_grounding_dice_9: 0.30073/0.28090, loss_grounding_ce_9: 0.33562/0.67183] items per batch[64] items per second[0.23] total items[4230400] mini batches[ 66100] memory[7345] epoch remaining[1:09:09]
INFO:trainer.default_trainer:epochs[    36] optim steps[66200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96843/0.89841, loss_mask_bce_0: 0.46293/0.33445, loss_mask_dice_0: 3.82585/1.16302, loss_spatial_bce_0: 0.05099/0.08726, loss_spatial_dice_0: 0.27563/0.20819, loss_spatial_ce_0: 0.01938/0.06188, loss_grounding_bce_0: 0.03365/0.08618, loss_grounding_dice_0: 0.07243/0.17855, loss_grounding_ce_0: 0.00525/0.27205, loss_mask_ce_1: 0.96206/0.89892, loss_mask_bce_1: 0.49295/0.33537, loss_mask_dice_1: 3.79284/1.16981, loss_spatial_bce_1: 0.05296/0.08778, loss_spatial_dice_1: 0.30639/0.21216, loss_spatial_ce_1: 0.02816/0.06775, loss_grounding_bce_1: 0.03683/0.08637, loss_grounding_dice_1: 0.07905/0.17939, loss_grounding_ce_1: 0.00477/0.27286, loss_mask_ce_2: 0.91308/0.90609, loss_mask_bce_2: 0.50338/0.33595, loss_mask_dice_2: 3.75715/1.17018, loss_spatial_bce_2: 0.05560/0.08887, loss_spatial_dice_2: 0.31676/0.21378, loss_spatial_ce_2: 0.02444/0.07113, loss_grounding_bce_2: 0.03734/0.08650, loss_grounding_dice_2: 0.07059/0.17915, loss_grounding_ce_2: 0.00458/0.27633, loss_mask_ce_3: 0.93137/0.91668, loss_mask_bce_3: 0.48144/0.33711, loss_mask_dice_3: 3.83089/1.16775, loss_spatial_bce_3: 0.05230/0.09009, loss_spatial_dice_3: 0.28923/0.21472, loss_spatial_ce_3: 0.18893/0.07583, loss_grounding_bce_3: 0.04135/0.08675, loss_grounding_dice_3: 0.08077/0.17893, loss_grounding_ce_3: 0.00654/0.27841, loss_mask_ce_4: 0.78118/0.91773, loss_mask_bce_4: 0.51275/0.33922, loss_mask_dice_4: 3.93893/1.19181, loss_spatial_bce_4: 0.05746/0.09401, loss_spatial_dice_4: 0.32714/0.22691, loss_spatial_ce_4: 0.05684/0.09197, loss_grounding_bce_4: 0.03743/0.08725, loss_grounding_dice_4: 0.08124/0.18185, loss_grounding_ce_4: 0.00493/0.28138, loss_mask_ce_5: 0.89251/0.93418, loss_mask_bce_5: 0.44194/0.34155, loss_mask_dice_5: 3.72551/1.19943, loss_spatial_bce_5: 0.04709/0.09619, loss_spatial_dice_5: 0.34572/0.23111, loss_spatial_ce_5: 0.22138/0.10630, loss_grounding_bce_5: 0.03657/0.08769, loss_grounding_dice_5: 0.08404/0.18306, loss_grounding_ce_5: 0.00777/0.29402, loss_mask_ce_6: 0.97383/0.97426, loss_mask_bce_6: 0.44278/0.34419, loss_mask_dice_6: 3.89118/1.20229, loss_spatial_bce_6: 0.04972/0.10189, loss_spatial_dice_6: 0.33828/0.23400, loss_spatial_ce_6: 0.13592/0.13209, loss_grounding_bce_6: 0.03835/0.08842, loss_grounding_dice_6: 0.07808/0.18344, loss_grounding_ce_6: 0.00862/0.30965, loss_mask_ce_7: 0.87908/1.01916, loss_mask_bce_7: 0.44223/0.35207, loss_mask_dice_7: 3.82512/1.25666, loss_spatial_bce_7: 0.05321/0.10984, loss_spatial_dice_7: 0.43716/0.26161, loss_spatial_ce_7: 0.18722/0.16734, loss_grounding_bce_7: 0.03569/0.09031, loss_grounding_dice_7: 0.10633/0.19073, loss_grounding_ce_7: 0.02703/0.33985, loss_mask_ce_8: 1.08792/1.12760, loss_mask_bce_8: 0.48109/0.36568, loss_mask_dice_8: 3.83869/1.32958, loss_spatial_bce_8: 0.07955/0.13048, loss_spatial_dice_8: 0.53822/0.29957, loss_spatial_ce_8: 0.26467/0.22281, loss_grounding_bce_8: 0.03860/0.09406, loss_grounding_dice_8: 0.09643/0.20153, loss_grounding_ce_8: 0.04844/0.40701, loss_mask_ce_9: 4.89229/3.67602, loss_mask_bce_9: 0.55548/0.39272, loss_mask_dice_9: 5.15255/1.90232, loss_spatial_bce_9: 0.16564/0.33309, loss_spatial_dice_9: 0.91467/0.82189, loss_spatial_ce_9: 1.77035/1.49650, loss_grounding_bce_9: 0.02970/0.10560, loss_grounding_dice_9: 0.11661/0.28089, loss_grounding_ce_9: 0.20325/0.67173] items per batch[64] items per second[0.24] total items[4236800] mini batches[ 66200] memory[7345] epoch remaining[1:03:57]
INFO:trainer.default_trainer:epochs[    36] optim steps[66300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02122/0.89836, loss_mask_bce_0: 0.27145/0.33448, loss_mask_dice_0: 0.94844/1.16305, loss_spatial_bce_0: 0.07908/0.08726, loss_spatial_dice_0: 0.27654/0.20818, loss_spatial_ce_0: 0.00419/0.06185, loss_grounding_bce_0: 0.10772/0.08619, loss_grounding_dice_0: 0.15440/0.17855, loss_grounding_ce_0: 0.19373/0.27203, loss_mask_ce_1: 1.01921/0.89886, loss_mask_bce_1: 0.26601/0.33541, loss_mask_dice_1: 0.93617/1.16982, loss_spatial_bce_1: 0.07790/0.08778, loss_spatial_dice_1: 0.28353/0.21215, loss_spatial_ce_1: 0.01002/0.06772, loss_grounding_bce_1: 0.11004/0.08638, loss_grounding_dice_1: 0.16085/0.17938, loss_grounding_ce_1: 0.19528/0.27287, loss_mask_ce_2: 1.04805/0.90603, loss_mask_bce_2: 0.27090/0.33598, loss_mask_dice_2: 0.91225/1.17018, loss_spatial_bce_2: 0.07609/0.08888, loss_spatial_dice_2: 0.28869/0.21377, loss_spatial_ce_2: 0.01719/0.07110, loss_grounding_bce_2: 0.11173/0.08652, loss_grounding_dice_2: 0.20450/0.17915, loss_grounding_ce_2: 0.18957/0.27635, loss_mask_ce_3: 1.09605/0.91662, loss_mask_bce_3: 0.27316/0.33714, loss_mask_dice_3: 1.01849/1.16776, loss_spatial_bce_3: 0.07129/0.09010, loss_spatial_dice_3: 0.26677/0.21471, loss_spatial_ce_3: 0.05285/0.07580, loss_grounding_bce_3: 0.11143/0.08676, loss_grounding_dice_3: 0.18999/0.17892, loss_grounding_ce_3: 0.18108/0.27844, loss_mask_ce_4: 1.12392/0.91766, loss_mask_bce_4: 0.27118/0.33926, loss_mask_dice_4: 1.01419/1.19182, loss_spatial_bce_4: 0.08709/0.09402, loss_spatial_dice_4: 0.29452/0.22690, loss_spatial_ce_4: 0.07168/0.09195, loss_grounding_bce_4: 0.11413/0.08727, loss_grounding_dice_4: 0.16554/0.18185, loss_grounding_ce_4: 0.22318/0.28137, loss_mask_ce_5: 1.16674/0.93413, loss_mask_bce_5: 0.28410/0.34158, loss_mask_dice_5: 0.96416/1.19943, loss_spatial_bce_5: 0.07945/0.09620, loss_spatial_dice_5: 0.28455/0.23110, loss_spatial_ce_5: 0.06731/0.10627, loss_grounding_bce_5: 0.11717/0.08770, loss_grounding_dice_5: 0.15744/0.18306, loss_grounding_ce_5: 0.22392/0.29401, loss_mask_ce_6: 0.99820/0.97423, loss_mask_bce_6: 0.27766/0.34423, loss_mask_dice_6: 1.10639/1.20230, loss_spatial_bce_6: 0.07984/0.10190, loss_spatial_dice_6: 0.28523/0.23399, loss_spatial_ce_6: 0.12115/0.13206, loss_grounding_bce_6: 0.11507/0.08843, loss_grounding_dice_6: 0.16454/0.18345, loss_grounding_ce_6: 0.19150/0.30963, loss_mask_ce_7: 1.22357/1.01913, loss_mask_bce_7: 0.29172/0.35211, loss_mask_dice_7: 1.00512/1.25667, loss_spatial_bce_7: 0.08994/0.10985, loss_spatial_dice_7: 0.31142/0.26160, loss_spatial_ce_7: 0.11692/0.16731, loss_grounding_bce_7: 0.12598/0.09031, loss_grounding_dice_7: 0.17642/0.19073, loss_grounding_ce_7: 0.18259/0.33983, loss_mask_ce_8: 1.27830/1.12756, loss_mask_bce_8: 0.30019/0.36573, loss_mask_dice_8: 1.18290/1.32961, loss_spatial_bce_8: 0.09256/0.13049, loss_spatial_dice_8: 0.31317/0.29957, loss_spatial_ce_8: 0.24643/0.22278, loss_grounding_bce_8: 0.12780/0.09407, loss_grounding_dice_8: 0.19445/0.20153, loss_grounding_ce_8: 0.23589/0.40696, loss_mask_ce_9: 2.97589/3.67611, loss_mask_bce_9: 0.29289/0.39276, loss_mask_dice_9: 1.41022/1.90241, loss_spatial_bce_9: 0.30529/0.33308, loss_spatial_dice_9: 0.80349/0.82188, loss_spatial_ce_9: 1.07623/1.49636, loss_grounding_bce_9: 0.12158/0.10561, loss_grounding_dice_9: 0.28597/0.28089, loss_grounding_ce_9: 0.31604/0.67169] items per batch[64] items per second[0.23] total items[4243200] mini batches[ 66300] memory[7345] epoch remaining[0:59:46]
INFO:trainer.default_trainer:epochs[    36] optim steps[66400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44860/0.89831, loss_mask_bce_0: 0.38223/0.33449, loss_mask_dice_0: 1.25577/1.16321, loss_spatial_bce_0: 0.08175/0.08726, loss_spatial_dice_0: 0.24884/0.20816, loss_spatial_ce_0: 0.05517/0.06183, loss_grounding_bce_0: 0.13250/0.08619, loss_grounding_dice_0: 0.11056/0.17855, loss_grounding_ce_0: 0.06687/0.27206, loss_mask_ce_1: 0.44684/0.89885, loss_mask_bce_1: 0.38388/0.33541, loss_mask_dice_1: 1.15430/1.17002, loss_spatial_bce_1: 0.07888/0.08778, loss_spatial_dice_1: 0.25691/0.21212, loss_spatial_ce_1: 0.04438/0.06770, loss_grounding_bce_1: 0.12942/0.08637, loss_grounding_dice_1: 0.10747/0.17939, loss_grounding_ce_1: 0.06345/0.27289, loss_mask_ce_2: 0.56405/0.90602, loss_mask_bce_2: 0.38323/0.33598, loss_mask_dice_2: 1.23785/1.17037, loss_spatial_bce_2: 0.08075/0.08888, loss_spatial_dice_2: 0.27164/0.21375, loss_spatial_ce_2: 0.04126/0.07107, loss_grounding_bce_2: 0.12947/0.08652, loss_grounding_dice_2: 0.10574/0.17916, loss_grounding_ce_2: 0.12845/0.27638, loss_mask_ce_3: 0.51535/0.91661, loss_mask_bce_3: 0.39763/0.33715, loss_mask_dice_3: 1.26409/1.16794, loss_spatial_bce_3: 0.08387/0.09010, loss_spatial_dice_3: 0.24622/0.21469, loss_spatial_ce_3: 0.04703/0.07578, loss_grounding_bce_3: 0.13265/0.08676, loss_grounding_dice_3: 0.10524/0.17894, loss_grounding_ce_3: 0.08433/0.27848, loss_mask_ce_4: 0.50071/0.91764, loss_mask_bce_4: 0.38393/0.33927, loss_mask_dice_4: 1.30457/1.19202, loss_spatial_bce_4: 0.07948/0.09402, loss_spatial_dice_4: 0.26147/0.22688, loss_spatial_ce_4: 0.05964/0.09193, loss_grounding_bce_4: 0.13500/0.08727, loss_grounding_dice_4: 0.09975/0.18186, loss_grounding_ce_4: 0.08972/0.28138, loss_mask_ce_5: 0.49204/0.93414, loss_mask_bce_5: 0.37542/0.34159, loss_mask_dice_5: 1.16478/1.19965, loss_spatial_bce_5: 0.07732/0.09621, loss_spatial_dice_5: 0.27747/0.23109, loss_spatial_ce_5: 0.05347/0.10623, loss_grounding_bce_5: 0.13523/0.08770, loss_grounding_dice_5: 0.10359/0.18307, loss_grounding_ce_5: 0.10396/0.29403, loss_mask_ce_6: 0.66679/0.97424, loss_mask_bce_6: 0.38643/0.34424, loss_mask_dice_6: 1.24536/1.20251, loss_spatial_bce_6: 0.08185/0.10190, loss_spatial_dice_6: 0.26413/0.23397, loss_spatial_ce_6: 0.07360/0.13202, loss_grounding_bce_6: 0.13273/0.08844, loss_grounding_dice_6: 0.10230/0.18346, loss_grounding_ce_6: 0.14874/0.30964, loss_mask_ce_7: 0.75712/1.01914, loss_mask_bce_7: 0.37802/0.35211, loss_mask_dice_7: 1.30718/1.25688, loss_spatial_bce_7: 0.08506/0.10985, loss_spatial_dice_7: 0.27280/0.26159, loss_spatial_ce_7: 0.06761/0.16726, loss_grounding_bce_7: 0.13158/0.09032, loss_grounding_dice_7: 0.12048/0.19074, loss_grounding_ce_7: 0.21354/0.33980, loss_mask_ce_8: 0.94218/1.12757, loss_mask_bce_8: 0.40299/0.36574, loss_mask_dice_8: 1.61185/1.32987, loss_spatial_bce_8: 0.11710/0.13048, loss_spatial_dice_8: 0.32879/0.29956, loss_spatial_ce_8: 0.10631/0.22272, loss_grounding_bce_8: 0.14412/0.09408, loss_grounding_dice_8: 0.13154/0.20153, loss_grounding_ce_8: 0.53701/0.40691, loss_mask_ce_9: 3.13780/3.67616, loss_mask_bce_9: 0.48478/0.39279, loss_mask_dice_9: 2.61865/1.90282, loss_spatial_bce_9: 0.28086/0.33308, loss_spatial_dice_9: 0.82656/0.82187, loss_spatial_ce_9: 1.47105/1.49622, loss_grounding_bce_9: 0.21078/0.10562, loss_grounding_dice_9: 0.19043/0.28089, loss_grounding_ce_9: 0.77542/0.67167] items per batch[64] items per second[0.23] total items[4249600] mini batches[ 66400] memory[7345] epoch remaining[0:55:11]
INFO:trainer.default_trainer:epochs[    36] optim steps[66500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34279/0.89832, loss_mask_bce_0: 0.29707/0.33445, loss_mask_dice_0: 0.43213/1.16293, loss_spatial_bce_0: 0.14069/0.08726, loss_spatial_dice_0: 0.15553/0.20813, loss_spatial_ce_0: 0.00608/0.06180, loss_grounding_bce_0: 0.14665/0.08619, loss_grounding_dice_0: 0.20395/0.17852, loss_grounding_ce_0: 0.09246/0.27200, loss_mask_ce_1: 0.22144/0.89886, loss_mask_bce_1: 0.35633/0.33536, loss_mask_dice_1: 0.45085/1.16972, loss_spatial_bce_1: 0.14552/0.08777, loss_spatial_dice_1: 0.16117/0.21210, loss_spatial_ce_1: 0.00442/0.06767, loss_grounding_bce_1: 0.14353/0.08637, loss_grounding_dice_1: 0.19792/0.17936, loss_grounding_ce_1: 0.07549/0.27286, loss_mask_ce_2: 0.20558/0.90601, loss_mask_bce_2: 0.31443/0.33594, loss_mask_dice_2: 0.42715/1.17007, loss_spatial_bce_2: 0.11698/0.08887, loss_spatial_dice_2: 0.15868/0.21372, loss_spatial_ce_2: 0.00807/0.07105, loss_grounding_bce_2: 0.15194/0.08652, loss_grounding_dice_2: 0.21656/0.17913, loss_grounding_ce_2: 0.05295/0.27633, loss_mask_ce_3: 0.19460/0.91663, loss_mask_bce_3: 0.28427/0.33710, loss_mask_dice_3: 0.39460/1.16764, loss_spatial_bce_3: 0.12332/0.09010, loss_spatial_dice_3: 0.15101/0.21466, loss_spatial_ce_3: 0.02093/0.07575, loss_grounding_bce_3: 0.13749/0.08676, loss_grounding_dice_3: 0.19072/0.17891, loss_grounding_ce_3: 0.04503/0.27841, loss_mask_ce_4: 0.20970/0.91764, loss_mask_bce_4: 0.26006/0.33923, loss_mask_dice_4: 0.41003/1.19169, loss_spatial_bce_4: 0.10470/0.09402, loss_spatial_dice_4: 0.16023/0.22686, loss_spatial_ce_4: 0.01385/0.09191, loss_grounding_bce_4: 0.13228/0.08728, loss_grounding_dice_4: 0.19732/0.18182, loss_grounding_ce_4: 0.04788/0.28133, loss_mask_ce_5: 0.19488/0.93414, loss_mask_bce_5: 0.26274/0.34155, loss_mask_dice_5: 0.37746/1.19936, loss_spatial_bce_5: 0.10960/0.09620, loss_spatial_dice_5: 0.14412/0.23106, loss_spatial_ce_5: 0.02497/0.10619, loss_grounding_bce_5: 0.12992/0.08770, loss_grounding_dice_5: 0.19013/0.18304, loss_grounding_ce_5: 0.03967/0.29398, loss_mask_ce_6: 0.21210/0.97424, loss_mask_bce_6: 0.25756/0.34419, loss_mask_dice_6: 0.38096/1.20221, loss_spatial_bce_6: 0.11573/0.10189, loss_spatial_dice_6: 0.14232/0.23395, loss_spatial_ce_6: 0.08931/0.13198, loss_grounding_bce_6: 0.12679/0.08844, loss_grounding_dice_6: 0.19457/0.18343, loss_grounding_ce_6: 0.04340/0.30957, loss_mask_ce_7: 0.22087/1.01913, loss_mask_bce_7: 0.24493/0.35206, loss_mask_dice_7: 0.37150/1.25654, loss_spatial_bce_7: 0.14179/0.10984, loss_spatial_dice_7: 0.21839/0.26158, loss_spatial_ce_7: 0.06162/0.16721, loss_grounding_bce_7: 0.11702/0.09032, loss_grounding_dice_7: 0.17781/0.19071, loss_grounding_ce_7: 0.04039/0.33974, loss_mask_ce_8: 0.43094/1.12754, loss_mask_bce_8: 0.25713/0.36568, loss_mask_dice_8: 0.39714/1.32951, loss_spatial_bce_8: 0.19257/0.13049, loss_spatial_dice_8: 0.27884/0.29954, loss_spatial_ce_8: 0.12355/0.22267, loss_grounding_bce_8: 0.12449/0.09408, loss_grounding_dice_8: 0.19119/0.20150, loss_grounding_ce_8: 0.11728/0.40685, loss_mask_ce_9: 2.70028/3.67595, loss_mask_bce_9: 0.42406/0.39274, loss_mask_dice_9: 0.59850/1.90236, loss_spatial_bce_9: 0.42297/0.33309, loss_spatial_dice_9: 0.71605/0.82187, loss_spatial_ce_9: 1.05988/1.49614, loss_grounding_bce_9: 0.21215/0.10562, loss_grounding_dice_9: 0.29778/0.28086, loss_grounding_ce_9: 0.37760/0.67167] items per batch[64] items per second[0.24] total items[4256000] mini batches[ 66500] memory[7345] epoch remaining[0:50:27]
INFO:trainer.default_trainer:epochs[    36] optim steps[66600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91354/0.89821, loss_mask_bce_0: 0.29820/0.33447, loss_mask_dice_0: 0.63243/1.16306, loss_spatial_bce_0: 0.07544/0.08725, loss_spatial_dice_0: 0.16226/0.20810, loss_spatial_ce_0: 0.02578/0.06177, loss_grounding_bce_0: 0.04975/0.08622, loss_grounding_dice_0: 0.10846/0.17853, loss_grounding_ce_0: 0.08125/0.27195, loss_mask_ce_1: 1.02480/0.89875, loss_mask_bce_1: 0.29789/0.33539, loss_mask_dice_1: 0.61624/1.16984, loss_spatial_bce_1: 0.07199/0.08776, loss_spatial_dice_1: 0.16077/0.21208, loss_spatial_ce_1: 0.02556/0.06764, loss_grounding_bce_1: 0.05095/0.08640, loss_grounding_dice_1: 0.11111/0.17936, loss_grounding_ce_1: 0.09238/0.27282, loss_mask_ce_2: 1.10071/0.90588, loss_mask_bce_2: 0.28567/0.33596, loss_mask_dice_2: 0.60648/1.17020, loss_spatial_bce_2: 0.07538/0.08886, loss_spatial_dice_2: 0.16364/0.21370, loss_spatial_ce_2: 0.02325/0.07102, loss_grounding_bce_2: 0.05347/0.08654, loss_grounding_dice_2: 0.11231/0.17914, loss_grounding_ce_2: 0.10662/0.27625, loss_mask_ce_3: 0.93596/0.91650, loss_mask_bce_3: 0.30050/0.33713, loss_mask_dice_3: 0.62182/1.16778, loss_spatial_bce_3: 0.07279/0.09009, loss_spatial_dice_3: 0.16465/0.21463, loss_spatial_ce_3: 0.03293/0.07573, loss_grounding_bce_3: 0.05181/0.08679, loss_grounding_dice_3: 0.10844/0.17892, loss_grounding_ce_3: 0.09457/0.27835, loss_mask_ce_4: 1.03868/0.91751, loss_mask_bce_4: 0.29178/0.33925, loss_mask_dice_4: 0.64725/1.19183, loss_spatial_bce_4: 0.07287/0.09401, loss_spatial_dice_4: 0.15899/0.22683, loss_spatial_ce_4: 0.02218/0.09187, loss_grounding_bce_4: 0.05086/0.08730, loss_grounding_dice_4: 0.10521/0.18183, loss_grounding_ce_4: 0.09235/0.28125, loss_mask_ce_5: 0.99520/0.93399, loss_mask_bce_5: 0.29959/0.34157, loss_mask_dice_5: 0.66973/1.19951, loss_spatial_bce_5: 0.07046/0.09619, loss_spatial_dice_5: 0.17060/0.23104, loss_spatial_ce_5: 0.03147/0.10618, loss_grounding_bce_5: 0.05033/0.08773, loss_grounding_dice_5: 0.11085/0.18305, loss_grounding_ce_5: 0.08253/0.29390, loss_mask_ce_6: 1.18981/0.97407, loss_mask_bce_6: 0.32523/0.34422, loss_mask_dice_6: 0.67973/1.20234, loss_spatial_bce_6: 0.07926/0.10188, loss_spatial_dice_6: 0.17307/0.23392, loss_spatial_ce_6: 0.03221/0.13195, loss_grounding_bce_6: 0.05100/0.08847, loss_grounding_dice_6: 0.10719/0.18344, loss_grounding_ce_6: 0.07289/0.30951, loss_mask_ce_7: 1.09673/1.01902, loss_mask_bce_7: 0.33014/0.35209, loss_mask_dice_7: 0.72405/1.25668, loss_spatial_bce_7: 0.08033/0.10983, loss_spatial_dice_7: 0.20915/0.26155, loss_spatial_ce_7: 0.04827/0.16716, loss_grounding_bce_7: 0.04955/0.09035, loss_grounding_dice_7: 0.09793/0.19072, loss_grounding_ce_7: 0.05849/0.33970, loss_mask_ce_8: 1.12916/1.12741, loss_mask_bce_8: 0.38380/0.36571, loss_mask_dice_8: 0.77306/1.32965, loss_spatial_bce_8: 0.08368/0.13047, loss_spatial_dice_8: 0.25421/0.29952, loss_spatial_ce_8: 0.08909/0.22261, loss_grounding_bce_8: 0.05375/0.09410, loss_grounding_dice_8: 0.11019/0.20151, loss_grounding_ce_8: 0.10826/0.40680, loss_mask_ce_9: 3.30565/3.67606, loss_mask_bce_9: 0.36055/0.39276, loss_mask_dice_9: 1.08198/1.90253, loss_spatial_bce_9: 0.26453/0.33310, loss_spatial_dice_9: 0.86466/0.82187, loss_spatial_ce_9: 1.87346/1.49615, loss_grounding_bce_9: 0.06798/0.10564, loss_grounding_dice_9: 0.17132/0.28086, loss_grounding_ce_9: 0.26206/0.67173] items per batch[64] items per second[0.22] total items[4262400] mini batches[ 66600] memory[7345] epoch remaining[0:46:02]
INFO:trainer.default_trainer:epochs[    36] optim steps[66700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25452/0.89811, loss_mask_bce_0: 0.28216/0.33444, loss_mask_dice_0: 0.56387/1.16308, loss_spatial_bce_0: 0.04256/0.08723, loss_spatial_dice_0: 0.09489/0.20809, loss_spatial_ce_0: 0.00011/0.06175, loss_grounding_bce_0: 0.03581/0.08623, loss_grounding_dice_0: 0.07626/0.17853, loss_grounding_ce_0: 0.15994/0.27194, loss_mask_ce_1: 0.25160/0.89863, loss_mask_bce_1: 0.28729/0.33536, loss_mask_dice_1: 0.60397/1.16987, loss_spatial_bce_1: 0.04478/0.08775, loss_spatial_dice_1: 0.08632/0.21206, loss_spatial_ce_1: 0.00015/0.06762, loss_grounding_bce_1: 0.03602/0.08641, loss_grounding_dice_1: 0.07322/0.17936, loss_grounding_ce_1: 0.16202/0.27279, loss_mask_ce_2: 0.25676/0.90577, loss_mask_bce_2: 0.30054/0.33593, loss_mask_dice_2: 0.62148/1.17024, loss_spatial_bce_2: 0.04683/0.08885, loss_spatial_dice_2: 0.09630/0.21369, loss_spatial_ce_2: 0.00018/0.07102, loss_grounding_bce_2: 0.03370/0.08655, loss_grounding_dice_2: 0.07283/0.17914, loss_grounding_ce_2: 0.15721/0.27625, loss_mask_ce_3: 0.26604/0.91643, loss_mask_bce_3: 0.30050/0.33710, loss_mask_dice_3: 0.59157/1.16781, loss_spatial_bce_3: 0.04618/0.09008, loss_spatial_dice_3: 0.09064/0.21462, loss_spatial_ce_3: 0.00049/0.07571, loss_grounding_bce_3: 0.03394/0.08680, loss_grounding_dice_3: 0.06766/0.17892, loss_grounding_ce_3: 0.14992/0.27834, loss_mask_ce_4: 0.29465/0.91740, loss_mask_bce_4: 0.29087/0.33921, loss_mask_dice_4: 0.57916/1.19190, loss_spatial_bce_4: 0.04682/0.09400, loss_spatial_dice_4: 0.08991/0.22682, loss_spatial_ce_4: 0.00526/0.09187, loss_grounding_bce_4: 0.03378/0.08731, loss_grounding_dice_4: 0.06419/0.18183, loss_grounding_ce_4: 0.15670/0.28126, loss_mask_ce_5: 0.29473/0.93389, loss_mask_bce_5: 0.29588/0.34154, loss_mask_dice_5: 0.62479/1.19956, loss_spatial_bce_5: 0.04914/0.09618, loss_spatial_dice_5: 0.10319/0.23103, loss_spatial_ce_5: 0.01060/0.10616, loss_grounding_bce_5: 0.03531/0.08773, loss_grounding_dice_5: 0.07305/0.18304, loss_grounding_ce_5: 0.15060/0.29392, loss_mask_ce_6: 0.29065/0.97399, loss_mask_bce_6: 0.29156/0.34419, loss_mask_dice_6: 0.62582/1.20241, loss_spatial_bce_6: 0.06765/0.10187, loss_spatial_dice_6: 0.12333/0.23391, loss_spatial_ce_6: 0.04608/0.13194, loss_grounding_bce_6: 0.03227/0.08848, loss_grounding_dice_6: 0.06515/0.18345, loss_grounding_ce_6: 0.14670/0.30951, loss_mask_ce_7: 0.31403/1.01894, loss_mask_bce_7: 0.29772/0.35206, loss_mask_dice_7: 0.63509/1.25674, loss_spatial_bce_7: 0.04957/0.10982, loss_spatial_dice_7: 0.11675/0.26155, loss_spatial_ce_7: 0.09307/0.16717, loss_grounding_bce_7: 0.03534/0.09036, loss_grounding_dice_7: 0.07723/0.19072, loss_grounding_ce_7: 0.16778/0.33976, loss_mask_ce_8: 0.53497/1.12732, loss_mask_bce_8: 0.29711/0.36568, loss_mask_dice_8: 0.62311/1.32973, loss_spatial_bce_8: 0.06668/0.13044, loss_spatial_dice_8: 0.14491/0.29950, loss_spatial_ce_8: 0.10619/0.22262, loss_grounding_bce_8: 0.03329/0.09411, loss_grounding_dice_8: 0.07569/0.20152, loss_grounding_ce_8: 0.22410/0.40682, loss_mask_ce_9: 4.46529/3.67615, loss_mask_bce_9: 0.29878/0.39273, loss_mask_dice_9: 1.11876/1.90255, loss_spatial_bce_9: 0.36378/0.33308, loss_spatial_dice_9: 0.89570/0.82188, loss_spatial_ce_9: 1.54188/1.49617, loss_grounding_bce_9: 0.03960/0.10566, loss_grounding_dice_9: 0.17248/0.28087, loss_grounding_ce_9: 0.30937/0.67179] items per batch[64] items per second[0.23] total items[4268800] mini batches[ 66700] memory[7345] epoch remaining[0:41:26]
INFO:trainer.default_trainer:epochs[    36] optim steps[66800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32583/0.89808, loss_mask_bce_0: 0.16908/0.33439, loss_mask_dice_0: 0.24675/1.16293, loss_spatial_bce_0: 0.10363/0.08723, loss_spatial_dice_0: 0.09397/0.20808, loss_spatial_ce_0: 0.00060/0.06175, loss_grounding_bce_0: 0.06332/0.08623, loss_grounding_dice_0: 0.11192/0.17852, loss_grounding_ce_0: 0.31852/0.27194, loss_mask_ce_1: 0.34692/0.89860, loss_mask_bce_1: 0.16355/0.33532, loss_mask_dice_1: 0.23913/1.16974, loss_spatial_bce_1: 0.09855/0.08775, loss_spatial_dice_1: 0.09699/0.21206, loss_spatial_ce_1: 0.00094/0.06762, loss_grounding_bce_1: 0.06445/0.08641, loss_grounding_dice_1: 0.11550/0.17937, loss_grounding_ce_1: 0.32310/0.27279, loss_mask_ce_2: 0.32586/0.90573, loss_mask_bce_2: 0.17376/0.33589, loss_mask_dice_2: 0.26099/1.17009, loss_spatial_bce_2: 0.09892/0.08885, loss_spatial_dice_2: 0.10566/0.21368, loss_spatial_ce_2: 0.00205/0.07101, loss_grounding_bce_2: 0.06396/0.08655, loss_grounding_dice_2: 0.10894/0.17915, loss_grounding_ce_2: 0.45015/0.27625, loss_mask_ce_3: 0.31834/0.91643, loss_mask_bce_3: 0.16830/0.33705, loss_mask_dice_3: 0.25015/1.16767, loss_spatial_bce_3: 0.10062/0.09008, loss_spatial_dice_3: 0.09466/0.21462, loss_spatial_ce_3: 0.01178/0.07571, loss_grounding_bce_3: 0.06517/0.08680, loss_grounding_dice_3: 0.11475/0.17892, loss_grounding_ce_3: 0.30793/0.27833, loss_mask_ce_4: 0.33291/0.91739, loss_mask_bce_4: 0.16731/0.33917, loss_mask_dice_4: 0.24050/1.19171, loss_spatial_bce_4: 0.09334/0.09400, loss_spatial_dice_4: 0.10987/0.22681, loss_spatial_ce_4: 0.01625/0.09188, loss_grounding_bce_4: 0.06088/0.08732, loss_grounding_dice_4: 0.11310/0.18182, loss_grounding_ce_4: 0.31644/0.28128, loss_mask_ce_5: 0.36032/0.93387, loss_mask_bce_5: 0.16769/0.34150, loss_mask_dice_5: 0.25675/1.19941, loss_spatial_bce_5: 0.08992/0.09617, loss_spatial_dice_5: 0.10207/0.23102, loss_spatial_ce_5: 0.02789/0.10615, loss_grounding_bce_5: 0.06319/0.08774, loss_grounding_dice_5: 0.10960/0.18304, loss_grounding_ce_5: 0.43715/0.29392, loss_mask_ce_6: 0.44591/0.97395, loss_mask_bce_6: 0.16790/0.34415, loss_mask_dice_6: 0.23900/1.20228, loss_spatial_bce_6: 0.10454/0.10186, loss_spatial_dice_6: 0.10360/0.23390, loss_spatial_ce_6: 0.04217/0.13193, loss_grounding_bce_6: 0.06225/0.08848, loss_grounding_dice_6: 0.11276/0.18345, loss_grounding_ce_6: 0.56274/0.30951, loss_mask_ce_7: 0.68906/1.01893, loss_mask_bce_7: 0.16310/0.35202, loss_mask_dice_7: 0.23829/1.25657, loss_spatial_bce_7: 0.11060/0.10982, loss_spatial_dice_7: 0.11704/0.26154, loss_spatial_ce_7: 0.03936/0.16716, loss_grounding_bce_7: 0.06320/0.09036, loss_grounding_dice_7: 0.10274/0.19072, loss_grounding_ce_7: 0.54218/0.33976, loss_mask_ce_8: 0.63155/1.12725, loss_mask_bce_8: 0.16914/0.36564, loss_mask_dice_8: 0.25964/1.32956, loss_spatial_bce_8: 0.10676/0.13043, loss_spatial_dice_8: 0.11705/0.29948, loss_spatial_ce_8: 0.09819/0.22260, loss_grounding_bce_8: 0.06853/0.09411, loss_grounding_dice_8: 0.12183/0.20151, loss_grounding_ce_8: 0.52972/0.40677, loss_mask_ce_9: 3.00407/3.67605, loss_mask_bce_9: 0.24444/0.39267, loss_mask_dice_9: 0.40506/1.90217, loss_spatial_bce_9: 0.34371/0.33306, loss_spatial_dice_9: 0.68289/0.82187, loss_spatial_ce_9: 1.49312/1.49608, loss_grounding_bce_9: 0.09761/0.10566, loss_grounding_dice_9: 0.18274/0.28086, loss_grounding_ce_9: 0.25130/0.67171] items per batch[64] items per second[0.23] total items[4275200] mini batches[ 66800] memory[7345] epoch remaining[0:36:47]
INFO:trainer.default_trainer:epochs[    36] optim steps[66900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.52745/0.89820, loss_mask_bce_0: 0.09433/0.33442, loss_mask_dice_0: 0.98577/1.16296, loss_spatial_bce_0: 0.02300/0.08723, loss_spatial_dice_0: 0.25164/0.20807, loss_spatial_ce_0: 0.14881/0.06174, loss_grounding_bce_0: 0.00984/0.08623, loss_grounding_dice_0: 0.18961/0.17853, loss_grounding_ce_0: 0.79987/0.27197, loss_mask_ce_1: 1.84594/0.89870, loss_mask_bce_1: 0.08498/0.33535, loss_mask_dice_1: 0.86497/1.16971, loss_spatial_bce_1: 0.02243/0.08775, loss_spatial_dice_1: 0.25373/0.21204, loss_spatial_ce_1: 0.17563/0.06760, loss_grounding_bce_1: 0.01455/0.08641, loss_grounding_dice_1: 0.25708/0.17938, loss_grounding_ce_1: 0.44735/0.27282, loss_mask_ce_2: 1.78080/0.90583, loss_mask_bce_2: 0.08739/0.33592, loss_mask_dice_2: 0.78046/1.17009, loss_spatial_bce_2: 0.02534/0.08885, loss_spatial_dice_2: 0.24056/0.21367, loss_spatial_ce_2: 0.15350/0.07100, loss_grounding_bce_2: 0.01432/0.08656, loss_grounding_dice_2: 0.24115/0.17916, loss_grounding_ce_2: 0.51199/0.27630, loss_mask_ce_3: 1.58202/0.91654, loss_mask_bce_3: 0.08769/0.33708, loss_mask_dice_3: 0.85580/1.16767, loss_spatial_bce_3: 0.02821/0.09008, loss_spatial_dice_3: 0.25342/0.21461, loss_spatial_ce_3: 0.14954/0.07569, loss_grounding_bce_3: 0.01267/0.08681, loss_grounding_dice_3: 0.20360/0.17892, loss_grounding_ce_3: 1.75033/0.27841, loss_mask_ce_4: 1.50899/0.91753, loss_mask_bce_4: 0.09127/0.33921, loss_mask_dice_4: 0.95108/1.19171, loss_spatial_bce_4: 0.04171/0.09400, loss_spatial_dice_4: 0.27416/0.22680, loss_spatial_ce_4: 0.18408/0.09186, loss_grounding_bce_4: 0.01880/0.08731, loss_grounding_dice_4: 0.21318/0.18183, loss_grounding_ce_4: 1.32214/0.28135, loss_mask_ce_5: 1.57717/0.93397, loss_mask_bce_5: 0.10422/0.34153, loss_mask_dice_5: 0.78981/1.19942, loss_spatial_bce_5: 0.02535/0.09617, loss_spatial_dice_5: 0.27010/0.23101, loss_spatial_ce_5: 0.27816/0.10612, loss_grounding_bce_5: 0.02932/0.08774, loss_grounding_dice_5: 0.29622/0.18305, loss_grounding_ce_5: 1.22291/0.29397, loss_mask_ce_6: 1.46261/0.97405, loss_mask_bce_6: 0.09777/0.34419, loss_mask_dice_6: 0.96931/1.20229, loss_spatial_bce_6: 0.02988/0.10186, loss_spatial_dice_6: 0.27199/0.23390, loss_spatial_ce_6: 0.40151/0.13191, loss_grounding_bce_6: 0.01520/0.08848, loss_grounding_dice_6: 0.23699/0.18345, loss_grounding_ce_6: 2.22456/0.30961, loss_mask_ce_7: 1.67166/1.01905, loss_mask_bce_7: 0.08327/0.35206, loss_mask_dice_7: 0.91662/1.25661, loss_spatial_bce_7: 0.03023/0.10981, loss_spatial_dice_7: 0.31982/0.26153, loss_spatial_ce_7: 0.23165/0.16714, loss_grounding_bce_7: 0.02509/0.09036, loss_grounding_dice_7: 0.21502/0.19072, loss_grounding_ce_7: 1.78397/0.33981, loss_mask_ce_8: 1.54585/1.12730, loss_mask_bce_8: 0.10015/0.36569, loss_mask_dice_8: 0.96558/1.32960, loss_spatial_bce_8: 0.05639/0.13043, loss_spatial_dice_8: 0.37715/0.29948, loss_spatial_ce_8: 0.41047/0.22258, loss_grounding_bce_8: 0.02585/0.09411, loss_grounding_dice_8: 0.30575/0.20153, loss_grounding_ce_8: 2.21690/0.40689, loss_mask_ce_9: 3.98030/3.67619, loss_mask_bce_9: 0.10164/0.39271, loss_mask_dice_9: 1.28953/1.90224, loss_spatial_bce_9: 0.08323/0.33304, loss_spatial_dice_9: 0.81243/0.82186, loss_spatial_ce_9: 1.43912/1.49599, loss_grounding_bce_9: 0.01511/0.10565, loss_grounding_dice_9: 0.35910/0.28086, loss_grounding_ce_9: 0.94430/0.67171] items per batch[64] items per second[0.23] total items[4281600] mini batches[ 66900] memory[7345] epoch remaining[0:32:14]
INFO:trainer.default_trainer:epochs[    36] optim steps[67000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43588/0.89817, loss_mask_bce_0: 0.41869/0.33443, loss_mask_dice_0: 0.59987/1.16291, loss_spatial_bce_0: 0.18889/0.08723, loss_spatial_dice_0: 0.17318/0.20805, loss_spatial_ce_0: 0.15210/0.06173, loss_grounding_bce_0: 0.16846/0.08624, loss_grounding_dice_0: 0.11836/0.17853, loss_grounding_ce_0: 0.00995/0.27189, loss_mask_ce_1: 1.44219/0.89869, loss_mask_bce_1: 0.41365/0.33535, loss_mask_dice_1: 0.57015/1.16963, loss_spatial_bce_1: 0.14256/0.08775, loss_spatial_dice_1: 0.16053/0.21203, loss_spatial_ce_1: 0.27958/0.06760, loss_grounding_bce_1: 0.16199/0.08642, loss_grounding_dice_1: 0.11057/0.17937, loss_grounding_ce_1: 0.00675/0.27275, loss_mask_ce_2: 1.48130/0.90581, loss_mask_bce_2: 0.41386/0.33592, loss_mask_dice_2: 0.57592/1.17002, loss_spatial_bce_2: 0.14476/0.08885, loss_spatial_dice_2: 0.19325/0.21365, loss_spatial_ce_2: 0.27523/0.07099, loss_grounding_bce_2: 0.16105/0.08657, loss_grounding_dice_2: 0.11303/0.17915, loss_grounding_ce_2: 0.00632/0.27622, loss_mask_ce_3: 1.56820/0.91654, loss_mask_bce_3: 0.43542/0.33709, loss_mask_dice_3: 0.60348/1.16761, loss_spatial_bce_3: 0.17303/0.09008, loss_spatial_dice_3: 0.18050/0.21460, loss_spatial_ce_3: 0.19320/0.07569, loss_grounding_bce_3: 0.16833/0.08681, loss_grounding_dice_3: 0.11476/0.17892, loss_grounding_ce_3: 0.00770/0.27834, loss_mask_ce_4: 1.46529/0.91756, loss_mask_bce_4: 0.43796/0.33921, loss_mask_dice_4: 0.61854/1.19166, loss_spatial_bce_4: 0.15911/0.09400, loss_spatial_dice_4: 0.18245/0.22679, loss_spatial_ce_4: 0.30033/0.09186, loss_grounding_bce_4: 0.17727/0.08732, loss_grounding_dice_4: 0.13396/0.18183, loss_grounding_ce_4: 0.00532/0.28128, loss_mask_ce_5: 1.45640/0.93401, loss_mask_bce_5: 0.44742/0.34155, loss_mask_dice_5: 0.63713/1.19937, loss_spatial_bce_5: 0.12829/0.09617, loss_spatial_dice_5: 0.18132/0.23100, loss_spatial_ce_5: 0.26829/0.10611, loss_grounding_bce_5: 0.16833/0.08775, loss_grounding_dice_5: 0.12527/0.18305, loss_grounding_ce_5: 0.00819/0.29390, loss_mask_ce_6: 1.43569/0.97406, loss_mask_bce_6: 0.46078/0.34420, loss_mask_dice_6: 0.61853/1.20221, loss_spatial_bce_6: 0.19666/0.10187, loss_spatial_dice_6: 0.23060/0.23389, loss_spatial_ce_6: 0.27074/0.13190, loss_grounding_bce_6: 0.16687/0.08849, loss_grounding_dice_6: 0.13142/0.18345, loss_grounding_ce_6: 0.01612/0.30954, loss_mask_ce_7: 1.57686/1.01907, loss_mask_bce_7: 0.52530/0.35208, loss_mask_dice_7: 0.73429/1.25653, loss_spatial_bce_7: 0.24767/0.10982, loss_spatial_dice_7: 0.24709/0.26152, loss_spatial_ce_7: 0.31761/0.16713, loss_grounding_bce_7: 0.21144/0.09037, loss_grounding_dice_7: 0.15060/0.19071, loss_grounding_ce_7: 0.11388/0.33979, loss_mask_ce_8: 1.78587/1.12730, loss_mask_bce_8: 0.50847/0.36570, loss_mask_dice_8: 0.72283/1.32952, loss_spatial_bce_8: 0.25078/0.13045, loss_spatial_dice_8: 0.26559/0.29946, loss_spatial_ce_8: 0.26596/0.22255, loss_grounding_bce_8: 0.18731/0.09412, loss_grounding_dice_8: 0.13068/0.20152, loss_grounding_ce_8: 0.11361/0.40693, loss_mask_ce_9: 4.15836/3.67609, loss_mask_bce_9: 0.58218/0.39272, loss_mask_dice_9: 1.28923/1.90202, loss_spatial_bce_9: 0.43482/0.33308, loss_spatial_dice_9: 0.73626/0.82184, loss_spatial_ce_9: 1.39980/1.49598, loss_grounding_bce_9: 0.20081/0.10566, loss_grounding_dice_9: 0.24651/0.28085, loss_grounding_ce_9: 1.59728/0.67177] items per batch[64] items per second[0.23] total items[4288000] mini batches[ 67000] memory[7345] epoch remaining[0:27:36]
INFO:trainer.default_trainer:epochs[    36] optim steps[67100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50300/0.89818, loss_mask_bce_0: 0.23154/0.33442, loss_mask_dice_0: 0.84709/1.16274, loss_spatial_bce_0: 0.03343/0.08722, loss_spatial_dice_0: 0.16679/0.20803, loss_spatial_ce_0: 0.00336/0.06170, loss_grounding_bce_0: 0.02500/0.08625, loss_grounding_dice_0: 0.07014/0.17851, loss_grounding_ce_0: 0.13169/0.27188, loss_mask_ce_1: 0.33748/0.89870, loss_mask_bce_1: 0.26304/0.33534, loss_mask_dice_1: 0.95900/1.16948, loss_spatial_bce_1: 0.03209/0.08774, loss_spatial_dice_1: 0.16110/0.21200, loss_spatial_ce_1: 0.00563/0.06757, loss_grounding_bce_1: 0.02948/0.08642, loss_grounding_dice_1: 0.06971/0.17936, loss_grounding_ce_1: 0.20849/0.27275, loss_mask_ce_2: 0.47321/0.90579, loss_mask_bce_2: 0.26411/0.33592, loss_mask_dice_2: 0.88164/1.16986, loss_spatial_bce_2: 0.03287/0.08885, loss_spatial_dice_2: 0.16309/0.21364, loss_spatial_ce_2: 0.00828/0.07095, loss_grounding_bce_2: 0.02604/0.08657, loss_grounding_dice_2: 0.06386/0.17913, loss_grounding_ce_2: 0.11762/0.27627, loss_mask_ce_3: 0.53735/0.91651, loss_mask_bce_3: 0.27429/0.33709, loss_mask_dice_3: 0.86382/1.16747, loss_spatial_bce_3: 0.03688/0.09008, loss_spatial_dice_3: 0.17344/0.21458, loss_spatial_ce_3: 0.01484/0.07566, loss_grounding_bce_3: 0.02466/0.08682, loss_grounding_dice_3: 0.06019/0.17890, loss_grounding_ce_3: 0.09557/0.27838, loss_mask_ce_4: 0.40071/0.91752, loss_mask_bce_4: 0.27755/0.33921, loss_mask_dice_4: 0.97811/1.19152, loss_spatial_bce_4: 0.05125/0.09400, loss_spatial_dice_4: 0.19014/0.22677, loss_spatial_ce_4: 0.03194/0.09184, loss_grounding_bce_4: 0.02667/0.08733, loss_grounding_dice_4: 0.07184/0.18180, loss_grounding_ce_4: 0.14971/0.28129, loss_mask_ce_5: 0.59233/0.93401, loss_mask_bce_5: 0.28101/0.34155, loss_mask_dice_5: 0.91284/1.19920, loss_spatial_bce_5: 0.05381/0.09617, loss_spatial_dice_5: 0.20489/0.23099, loss_spatial_ce_5: 0.04978/0.10611, loss_grounding_bce_5: 0.02641/0.08775, loss_grounding_dice_5: 0.06559/0.18303, loss_grounding_ce_5: 0.16629/0.29393, loss_mask_ce_6: 0.50272/0.97408, loss_mask_bce_6: 0.23678/0.34420, loss_mask_dice_6: 0.92761/1.20208, loss_spatial_bce_6: 0.05035/0.10187, loss_spatial_dice_6: 0.21272/0.23387, loss_spatial_ce_6: 0.08772/0.13187, loss_grounding_bce_6: 0.02509/0.08850, loss_grounding_dice_6: 0.06640/0.18343, loss_grounding_ce_6: 0.13799/0.30954, loss_mask_ce_7: 0.37409/1.01908, loss_mask_bce_7: 0.30605/0.35209, loss_mask_dice_7: 0.91855/1.25638, loss_spatial_bce_7: 0.04951/0.10982, loss_spatial_dice_7: 0.19585/0.26150, loss_spatial_ce_7: 0.08257/0.16711, loss_grounding_bce_7: 0.02894/0.09037, loss_grounding_dice_7: 0.07628/0.19069, loss_grounding_ce_7: 0.20295/0.33974, loss_mask_ce_8: 0.59013/1.12732, loss_mask_bce_8: 0.29113/0.36569, loss_mask_dice_8: 0.99515/1.32933, loss_spatial_bce_8: 0.06038/0.13044, loss_spatial_dice_8: 0.21981/0.29944, loss_spatial_ce_8: 0.08742/0.22250, loss_grounding_bce_8: 0.03215/0.09412, loss_grounding_dice_8: 0.09617/0.20150, loss_grounding_ce_8: 0.26656/0.40689, loss_mask_ce_9: 4.66928/3.67602, loss_mask_bce_9: 0.24224/0.39274, loss_mask_dice_9: 1.34362/1.90189, loss_spatial_bce_9: 0.23032/0.33307, loss_spatial_dice_9: 0.85303/0.82185, loss_spatial_ce_9: 1.29231/1.49595, loss_grounding_bce_9: 0.04128/0.10567, loss_grounding_dice_9: 0.23483/0.28083, loss_grounding_ce_9: 1.56944/0.67177] items per batch[64] items per second[0.23] total items[4294400] mini batches[ 67100] memory[7345] epoch remaining[0:22:58]
INFO:trainer.default_trainer:epochs[    36] optim steps[67200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.47398/0.89814, loss_mask_bce_0: 0.09187/0.33439, loss_mask_dice_0: 3.94936/1.16295, loss_spatial_bce_0: 0.00799/0.08721, loss_spatial_dice_0: 0.32065/0.20803, loss_spatial_ce_0: 0.01798/0.06169, loss_grounding_bce_0: 0.01135/0.08625, loss_grounding_dice_0: 0.10925/0.17853, loss_grounding_ce_0: 0.28574/0.27180, loss_mask_ce_1: 1.44632/0.89866, loss_mask_bce_1: 0.08541/0.33531, loss_mask_dice_1: 3.22073/1.16970, loss_spatial_bce_1: 0.00857/0.08773, loss_spatial_dice_1: 0.33214/0.21201, loss_spatial_ce_1: 0.03709/0.06757, loss_grounding_bce_1: 0.01383/0.08642, loss_grounding_dice_1: 0.09732/0.17938, loss_grounding_ce_1: 0.22619/0.27266, loss_mask_ce_2: 1.73096/0.90576, loss_mask_bce_2: 0.08902/0.33589, loss_mask_dice_2: 3.16283/1.17009, loss_spatial_bce_2: 0.00827/0.08884, loss_spatial_dice_2: 0.27788/0.21364, loss_spatial_ce_2: 0.04047/0.07095, loss_grounding_bce_2: 0.01384/0.08657, loss_grounding_dice_2: 0.10745/0.17915, loss_grounding_ce_2: 0.30939/0.27616, loss_mask_ce_3: 1.49978/0.91653, loss_mask_bce_3: 0.12009/0.33706, loss_mask_dice_3: 3.42075/1.16766, loss_spatial_bce_3: 0.00816/0.09007, loss_spatial_dice_3: 0.29815/0.21459, loss_spatial_ce_3: 0.03968/0.07567, loss_grounding_bce_3: 0.01330/0.08682, loss_grounding_dice_3: 0.10572/0.17891, loss_grounding_ce_3: 0.29876/0.27827, loss_mask_ce_4: 1.65704/0.91750, loss_mask_bce_4: 0.10676/0.33918, loss_mask_dice_4: 3.35895/1.19174, loss_spatial_bce_4: 0.00803/0.09398, loss_spatial_dice_4: 0.26044/0.22678, loss_spatial_ce_4: 0.16171/0.09184, loss_grounding_bce_4: 0.01419/0.08733, loss_grounding_dice_4: 0.10110/0.18182, loss_grounding_ce_4: 0.17374/0.28117, loss_mask_ce_5: 1.52764/0.93402, loss_mask_bce_5: 0.09372/0.34152, loss_mask_dice_5: 3.38101/1.19945, loss_spatial_bce_5: 0.00759/0.09616, loss_spatial_dice_5: 0.33508/0.23099, loss_spatial_ce_5: 0.13308/0.10610, loss_grounding_bce_5: 0.01453/0.08775, loss_grounding_dice_5: 0.11276/0.18305, loss_grounding_ce_5: 0.53460/0.29382, loss_mask_ce_6: 1.65794/0.97403, loss_mask_bce_6: 0.11174/0.34418, loss_mask_dice_6: 3.59131/1.20234, loss_spatial_bce_6: 0.00930/0.10186, loss_spatial_dice_6: 0.29731/0.23388, loss_spatial_ce_6: 0.12449/0.13184, loss_grounding_bce_6: 0.01376/0.08849, loss_grounding_dice_6: 0.09730/0.18345, loss_grounding_ce_6: 0.65188/0.30944, loss_mask_ce_7: 1.89010/1.01908, loss_mask_bce_7: 0.10049/0.35207, loss_mask_dice_7: 3.57402/1.25664, loss_spatial_bce_7: 0.01004/0.10981, loss_spatial_dice_7: 0.40710/0.26151, loss_spatial_ce_7: 0.09831/0.16709, loss_grounding_bce_7: 0.01487/0.09037, loss_grounding_dice_7: 0.11428/0.19072, loss_grounding_ce_7: 0.60756/0.33968, loss_mask_ce_8: 2.06292/1.12737, loss_mask_bce_8: 0.11328/0.36567, loss_mask_dice_8: 3.80896/1.32960, loss_spatial_bce_8: 0.01209/0.13043, loss_spatial_dice_8: 0.51417/0.29946, loss_spatial_ce_8: 0.18977/0.22247, loss_grounding_bce_8: 0.01298/0.09412, loss_grounding_dice_8: 0.10088/0.20152, loss_grounding_ce_8: 0.60359/0.40677, loss_mask_ce_9: 3.11595/3.67617, loss_mask_bce_9: 0.09636/0.39273, loss_mask_dice_9: 4.78020/1.90221, loss_spatial_bce_9: 0.09868/0.33304, loss_spatial_dice_9: 0.88827/0.82186, loss_spatial_ce_9: 1.78880/1.49599, loss_grounding_bce_9: 0.01574/0.10566, loss_grounding_dice_9: 0.16683/0.28085, loss_grounding_ce_9: 0.96037/0.67160] items per batch[64] items per second[0.23] total items[4300800] mini batches[ 67200] memory[7345] epoch remaining[0:18:23]
INFO:trainer.default_trainer:epochs[    36] optim steps[67300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44258/0.89810, loss_mask_bce_0: 0.17903/0.33439, loss_mask_dice_0: 2.11671/1.16318, loss_spatial_bce_0: 0.00474/0.08721, loss_spatial_dice_0: 0.21638/0.20804, loss_spatial_ce_0: 0.11751/0.06169, loss_grounding_bce_0: 0.00695/0.08626, loss_grounding_dice_0: 0.04349/0.17855, loss_grounding_ce_0: 0.37384/0.27177, loss_mask_ce_1: 1.50118/0.89864, loss_mask_bce_1: 0.17598/0.33532, loss_mask_dice_1: 2.08005/1.16993, loss_spatial_bce_1: 0.00417/0.08773, loss_spatial_dice_1: 0.24855/0.21202, loss_spatial_ce_1: 0.12822/0.06756, loss_grounding_bce_1: 0.00666/0.08643, loss_grounding_dice_1: 0.04665/0.17940, loss_grounding_ce_1: 0.37821/0.27263, loss_mask_ce_2: 1.56755/0.90571, loss_mask_bce_2: 0.16949/0.33589, loss_mask_dice_2: 1.93003/1.17031, loss_spatial_bce_2: 0.00366/0.08884, loss_spatial_dice_2: 0.22016/0.21365, loss_spatial_ce_2: 0.15634/0.07094, loss_grounding_bce_2: 0.00720/0.08658, loss_grounding_dice_2: 0.04690/0.17917, loss_grounding_ce_2: 0.40740/0.27613, loss_mask_ce_3: 1.48794/0.91649, loss_mask_bce_3: 0.17748/0.33706, loss_mask_dice_3: 2.14107/1.16792, loss_spatial_bce_3: 0.00523/0.09006, loss_spatial_dice_3: 0.23522/0.21461, loss_spatial_ce_3: 0.14421/0.07566, loss_grounding_bce_3: 0.00779/0.08683, loss_grounding_dice_3: 0.05008/0.17893, loss_grounding_ce_3: 0.44506/0.27825, loss_mask_ce_4: 1.55570/0.91745, loss_mask_bce_4: 0.16946/0.33918, loss_mask_dice_4: 2.10762/1.19196, loss_spatial_bce_4: 0.00674/0.09398, loss_spatial_dice_4: 0.29786/0.22679, loss_spatial_ce_4: 0.14032/0.09184, loss_grounding_bce_4: 0.00850/0.08734, loss_grounding_dice_4: 0.05229/0.18183, loss_grounding_ce_4: 0.41762/0.28115, loss_mask_ce_5: 1.35845/0.93398, loss_mask_bce_5: 0.16933/0.34151, loss_mask_dice_5: 2.23148/1.19969, loss_spatial_bce_5: 0.00850/0.09616, loss_spatial_dice_5: 0.29526/0.23102, loss_spatial_ce_5: 0.06068/0.10609, loss_grounding_bce_5: 0.00773/0.08775, loss_grounding_dice_5: 0.05465/0.18307, loss_grounding_ce_5: 0.38340/0.29380, loss_mask_ce_6: 1.26969/0.97399, loss_mask_bce_6: 0.17649/0.34418, loss_mask_dice_6: 2.32285/1.20259, loss_spatial_bce_6: 0.01022/0.10185, loss_spatial_dice_6: 0.24254/0.23390, loss_spatial_ce_6: 0.04783/0.13182, loss_grounding_bce_6: 0.00618/0.08850, loss_grounding_dice_6: 0.04215/0.18346, loss_grounding_ce_6: 0.45152/0.30940, loss_mask_ce_7: 1.53828/1.01904, loss_mask_bce_7: 0.18799/0.35207, loss_mask_dice_7: 2.69441/1.25689, loss_spatial_bce_7: 0.00939/0.10980, loss_spatial_dice_7: 0.28193/0.26152, loss_spatial_ce_7: 0.31220/0.16709, loss_grounding_bce_7: 0.00907/0.09037, loss_grounding_dice_7: 0.06133/0.19074, loss_grounding_ce_7: 0.34696/0.33962, loss_mask_ce_8: 1.60000/1.12732, loss_mask_bce_8: 0.20193/0.36566, loss_mask_dice_8: 2.98574/1.32986, loss_spatial_bce_8: 0.01339/0.13042, loss_spatial_dice_8: 0.41815/0.29948, loss_spatial_ce_8: 0.22766/0.22243, loss_grounding_bce_8: 0.00798/0.09412, loss_grounding_dice_8: 0.05082/0.20154, loss_grounding_ce_8: 0.41400/0.40672, loss_mask_ce_9: 4.89377/3.67611, loss_mask_bce_9: 0.17975/0.39274, loss_mask_dice_9: 5.04570/1.90258, loss_spatial_bce_9: 0.01879/0.33301, loss_spatial_dice_9: 0.94810/0.82187, loss_spatial_ce_9: 1.80281/1.49598, loss_grounding_bce_9: 0.00934/0.10567, loss_grounding_dice_9: 0.33029/0.28087, loss_grounding_ce_9: 1.03839/0.67149] items per batch[64] items per second[0.24] total items[4307200] mini batches[ 67300] memory[7345] epoch remaining[0:13:45]
INFO:trainer.default_trainer:epochs[    36] optim steps[67400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37130/0.89791, loss_mask_bce_0: 0.25697/0.33434, loss_mask_dice_0: 0.87999/1.16304, loss_spatial_bce_0: 0.09459/0.08719, loss_spatial_dice_0: 0.21684/0.20802, loss_spatial_ce_0: 0.09988/0.06168, loss_grounding_bce_0: 0.10035/0.08625, loss_grounding_dice_0: 0.09428/0.17851, loss_grounding_ce_0: 0.00414/0.27175, loss_mask_ce_1: 0.55604/0.89848, loss_mask_bce_1: 0.24948/0.33526, loss_mask_dice_1: 0.79946/1.16978, loss_spatial_bce_1: 0.09310/0.08771, loss_spatial_dice_1: 0.27568/0.21200, loss_spatial_ce_1: 0.02918/0.06755, loss_grounding_bce_1: 0.10319/0.08642, loss_grounding_dice_1: 0.08275/0.17937, loss_grounding_ce_1: 0.00255/0.27263, loss_mask_ce_2: 0.44240/0.90552, loss_mask_bce_2: 0.26846/0.33584, loss_mask_dice_2: 0.57680/1.17015, loss_spatial_bce_2: 0.09601/0.08882, loss_spatial_dice_2: 0.26032/0.21364, loss_spatial_ce_2: 0.21172/0.07092, loss_grounding_bce_2: 0.10613/0.08657, loss_grounding_dice_2: 0.10193/0.17914, loss_grounding_ce_2: 0.00171/0.27611, loss_mask_ce_3: 0.62234/0.91630, loss_mask_bce_3: 0.25809/0.33701, loss_mask_dice_3: 0.80498/1.16778, loss_spatial_bce_3: 0.09400/0.09004, loss_spatial_dice_3: 0.27723/0.21459, loss_spatial_ce_3: 0.25577/0.07564, loss_grounding_bce_3: 0.10301/0.08682, loss_grounding_dice_3: 0.08877/0.17890, loss_grounding_ce_3: 0.00090/0.27823, loss_mask_ce_4: 0.62859/0.91729, loss_mask_bce_4: 0.25632/0.33913, loss_mask_dice_4: 0.73158/1.19181, loss_spatial_bce_4: 0.09939/0.09396, loss_spatial_dice_4: 0.24935/0.22676, loss_spatial_ce_4: 0.09181/0.09181, loss_grounding_bce_4: 0.10849/0.08733, loss_grounding_dice_4: 0.09758/0.18180, loss_grounding_ce_4: 0.00101/0.28118, loss_mask_ce_5: 0.55008/0.93383, loss_mask_bce_5: 0.23946/0.34145, loss_mask_dice_5: 0.80322/1.19955, loss_spatial_bce_5: 0.10102/0.09614, loss_spatial_dice_5: 0.26222/0.23099, loss_spatial_ce_5: 0.13057/0.10606, loss_grounding_bce_5: 0.10767/0.08775, loss_grounding_dice_5: 0.11095/0.18305, loss_grounding_ce_5: 0.00267/0.29382, loss_mask_ce_6: 0.63986/0.97383, loss_mask_bce_6: 0.24042/0.34413, loss_mask_dice_6: 0.73179/1.20245, loss_spatial_bce_6: 0.09883/0.10183, loss_spatial_dice_6: 0.24130/0.23388, loss_spatial_ce_6: 0.23218/0.13179, loss_grounding_bce_6: 0.09737/0.08849, loss_grounding_dice_6: 0.10358/0.18343, loss_grounding_ce_6: 0.00143/0.30941, loss_mask_ce_7: 1.03382/1.01886, loss_mask_bce_7: 0.23063/0.35201, loss_mask_dice_7: 0.81289/1.25673, loss_spatial_bce_7: 0.09157/0.10979, loss_spatial_dice_7: 0.34962/0.26151, loss_spatial_ce_7: 0.13838/0.16706, loss_grounding_bce_7: 0.09038/0.09037, loss_grounding_dice_7: 0.11275/0.19071, loss_grounding_ce_7: 0.00323/0.33965, loss_mask_ce_8: 0.54567/1.12709, loss_mask_bce_8: 0.24199/0.36560, loss_mask_dice_8: 0.78812/1.32971, loss_spatial_bce_8: 0.12411/0.13041, loss_spatial_dice_8: 0.40411/0.29947, loss_spatial_ce_8: 0.39952/0.22239, loss_grounding_bce_8: 0.10895/0.09411, loss_grounding_dice_8: 0.11029/0.20151, loss_grounding_ce_8: 0.02106/0.40677, loss_mask_ce_9: 1.91287/3.67592, loss_mask_bce_9: 0.20907/0.39266, loss_mask_dice_9: 0.73733/1.90232, loss_spatial_bce_9: 0.21868/0.33299, loss_spatial_dice_9: 0.78144/0.82186, loss_spatial_ce_9: 1.64028/1.49595, loss_grounding_bce_9: 0.13244/0.10566, loss_grounding_dice_9: 0.12860/0.28082, loss_grounding_ce_9: 0.43642/0.67149] items per batch[64] items per second[0.23] total items[4313600] mini batches[ 67400] memory[7345] epoch remaining[0:09:09]
INFO:trainer.default_trainer:epochs[    36] optim steps[67500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15281/0.89780, loss_mask_bce_0: 0.24208/0.33433, loss_mask_dice_0: 0.11953/1.16300, loss_spatial_bce_0: 0.19064/0.08719, loss_spatial_dice_0: 0.13309/0.20803, loss_spatial_ce_0: 0.00580/0.06166, loss_grounding_bce_0: 0.28733/0.08624, loss_grounding_dice_0: 0.13693/0.17851, loss_grounding_ce_0: 0.10795/0.27168, loss_mask_ce_1: 0.15906/0.89840, loss_mask_bce_1: 0.25872/0.33524, loss_mask_dice_1: 0.11746/1.16975, loss_spatial_bce_1: 0.18935/0.08771, loss_spatial_dice_1: 0.14369/0.21201, loss_spatial_ce_1: 0.00694/0.06754, loss_grounding_bce_1: 0.31753/0.08642, loss_grounding_dice_1: 0.13693/0.17936, loss_grounding_ce_1: 0.11604/0.27254, loss_mask_ce_2: 0.20377/0.90544, loss_mask_bce_2: 0.25133/0.33582, loss_mask_dice_2: 0.12229/1.17013, loss_spatial_bce_2: 0.19285/0.08882, loss_spatial_dice_2: 0.15266/0.21364, loss_spatial_ce_2: 0.01474/0.07089, loss_grounding_bce_2: 0.29056/0.08657, loss_grounding_dice_2: 0.13977/0.17913, loss_grounding_ce_2: 0.13301/0.27602, loss_mask_ce_3: 0.22077/0.91623, loss_mask_bce_3: 0.23594/0.33699, loss_mask_dice_3: 0.11737/1.16775, loss_spatial_bce_3: 0.18300/0.09004, loss_spatial_dice_3: 0.14340/0.21460, loss_spatial_ce_3: 0.00885/0.07565, loss_grounding_bce_3: 0.27829/0.08682, loss_grounding_dice_3: 0.13526/0.17889, loss_grounding_ce_3: 0.14662/0.27816, loss_mask_ce_4: 0.20852/0.91724, loss_mask_bce_4: 0.21813/0.33912, loss_mask_dice_4: 0.11512/1.19178, loss_spatial_bce_4: 0.17044/0.09395, loss_spatial_dice_4: 0.13906/0.22678, loss_spatial_ce_4: 0.02595/0.09180, loss_grounding_bce_4: 0.25481/0.08733, loss_grounding_dice_4: 0.12903/0.18179, loss_grounding_ce_4: 0.15879/0.28113, loss_mask_ce_5: 0.17862/0.93376, loss_mask_bce_5: 0.21514/0.34144, loss_mask_dice_5: 0.12298/1.19951, loss_spatial_bce_5: 0.17457/0.09614, loss_spatial_dice_5: 0.12451/0.23101, loss_spatial_ce_5: 0.00929/0.10604, loss_grounding_bce_5: 0.23462/0.08775, loss_grounding_dice_5: 0.13475/0.18305, loss_grounding_ce_5: 0.11255/0.29373, loss_mask_ce_6: 0.29319/0.97373, loss_mask_bce_6: 0.19362/0.34411, loss_mask_dice_6: 0.09057/1.20243, loss_spatial_bce_6: 0.17240/0.10183, loss_spatial_dice_6: 0.13306/0.23389, loss_spatial_ce_6: 0.01264/0.13179, loss_grounding_bce_6: 0.24237/0.08849, loss_grounding_dice_6: 0.11898/0.18343, loss_grounding_ce_6: 0.26131/0.30932, loss_mask_ce_7: 0.31957/1.01879, loss_mask_bce_7: 0.20208/0.35201, loss_mask_dice_7: 0.10971/1.25672, loss_spatial_bce_7: 0.17637/0.10979, loss_spatial_dice_7: 0.11826/0.26152, loss_spatial_ce_7: 0.14513/0.16707, loss_grounding_bce_7: 0.22578/0.09037, loss_grounding_dice_7: 0.12267/0.19070, loss_grounding_ce_7: 0.20799/0.33954, loss_mask_ce_8: 0.27809/1.12708, loss_mask_bce_8: 0.23881/0.36557, loss_mask_dice_8: 0.11678/1.32967, loss_spatial_bce_8: 0.20347/0.13040, loss_spatial_dice_8: 0.22987/0.29948, loss_spatial_ce_8: 0.29886/0.22238, loss_grounding_bce_8: 0.20478/0.09411, loss_grounding_dice_8: 0.11825/0.20149, loss_grounding_ce_8: 0.25683/0.40670, loss_mask_ce_9: 2.06378/3.67566, loss_mask_bce_9: 0.19924/0.39264, loss_mask_dice_9: 0.17752/1.90223, loss_spatial_bce_9: 0.52530/0.33296, loss_spatial_dice_9: 0.52944/0.82186, loss_spatial_ce_9: 0.65165/1.49598, loss_grounding_bce_9: 0.19628/0.10565, loss_grounding_dice_9: 0.17055/0.28080, loss_grounding_ce_9: 0.09146/0.67145] items per batch[64] items per second[0.23] total items[4320000] mini batches[ 67500] memory[7345] epoch remaining[0:04:33]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00067599.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0030 s/iter. Inference: 0.2229 s/iter. Eval: 0.0824 s/iter. Total: 0.3083 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0028 s/iter. Inference: 0.2256 s/iter. Eval: 0.0732 s/iter. Total: 0.3018 s/iter. ETA=0:00:38
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2284 s/iter. Eval: 0.0735 s/iter. Total: 0.3050 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2286 s/iter. Eval: 0.0728 s/iter. Total: 0.3045 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2275 s/iter. Eval: 0.0717 s/iter. Total: 0.3025 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2298 s/iter. Eval: 0.0720 s/iter. Total: 0.3050 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0031 s/iter. Inference: 0.2307 s/iter. Eval: 0.0721 s/iter. Total: 0.3061 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 130/157. Dataloading: 0.0032 s/iter. Inference: 0.2310 s/iter. Eval: 0.0714 s/iter. Total: 0.3057 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2320 s/iter. Eval: 0.0715 s/iter. Total: 0.3068 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaludnhimn0 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.968 | 81.992 | 60.067 |      133      |
| Things | 55.042 | 82.753 | 65.853 |      80       |
| Stuff  | 42.310 | 80.844 | 51.335 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.34s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.16 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 0.98 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.09s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.37 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.406
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.715
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.711 | 61.112 | 40.582 | 18.914 | 41.868 | 60.417 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.397 | bicycle      | 18.551 | car            | 36.455 |
| motorcycle    | 34.243 | airplane     | 55.811 | bus            | 63.769 |
| train         | 68.655 | truck        | 34.744 | boat           | 23.520 |
| traffic light | 24.991 | fire hydrant | 64.232 | stop sign      | 63.920 |
| parking meter | 43.975 | bench        | 20.522 | bird           | 29.795 |
| cat           | 73.896 | dog          | 64.954 | horse          | 45.611 |
| sheep         | 46.095 | cow          | 51.187 | elephant       | 60.859 |
| bear          | 77.655 | zebra        | 60.261 | giraffe        | 57.098 |
| backpack      | 16.805 | umbrella     | 48.571 | handbag        | 15.226 |
| tie           | 32.879 | suitcase     | 40.704 | frisbee        | 66.132 |
| skis          | 5.169  | snowboard    | 22.593 | sports ball    | 47.674 |
| kite          | 34.467 | baseball bat | 28.912 | baseball glove | 43.258 |
| skateboard    | 34.759 | surfboard    | 35.427 | tennis racket  | 55.879 |
| bottle        | 33.522 | wine glass   | 27.234 | cup            | 40.364 |
| fork          | 16.338 | knife        | 12.796 | spoon          | 14.007 |
| bowl          | 29.731 | banana       | 20.299 | apple          | 20.970 |
| sandwich      | 43.159 | orange       | 30.065 | broccoli       | 21.512 |
| carrot        | 20.940 | hot dog      | 24.007 | pizza          | 51.586 |
| donut         | 47.220 | cake         | 43.767 | chair          | 20.706 |
| couch         | 40.068 | potted plant | 18.362 | bed            | 40.064 |
| dining table  | 12.417 | toilet       | 67.661 | tv             | 62.357 |
| laptop        | 61.643 | mouse        | 59.330 | remote         | 30.229 |
| keyboard      | 47.151 | cell phone   | 37.696 | microwave      | 55.733 |
| oven          | 32.085 | toaster      | 26.004 | sink           | 37.190 |
| refrigerator  | 58.232 | book         | 9.350  | clock          | 51.857 |
| vase          | 34.405 | scissors     | 24.297 | teddy bear     | 50.928 |
| hair drier    | 8.827  | toothbrush   | 19.130 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.21501247755149, 'fwIoU': 68.90838999891531, 'IoU-person': 87.69777194805359, 'IoU-bicycle': 73.0366239761876, 'IoU-car': 68.34209365463347, 'IoU-motorcycle': 84.04614954522839, 'IoU-airplane': 84.14574548375515, 'IoU-bus': 83.03883776097264, 'IoU-train': 82.747472347871, 'IoU-truck': 62.042352890738805, 'IoU-boat': 66.44909080481388, 'IoU-traffic light': 75.73577421939338, 'IoU-fire hydrant': 90.51942577184619, 'IoU-stop sign': 90.83242670609462, 'IoU-parking meter': 83.14746861022296, 'IoU-bench': 54.54607001153573, 'IoU-bird': 76.76014687151165, 'IoU-cat': 86.24767367815568, 'IoU-dog': 77.7163347262671, 'IoU-horse': 84.9871627714902, 'IoU-sheep': 89.33893448728298, 'IoU-cow': 79.41888983655866, 'IoU-elephant': 90.24612458492453, 'IoU-bear': 87.33780280763244, 'IoU-zebra': 92.30648025563059, 'IoU-giraffe': 88.33206237170873, 'IoU-backpack': 42.635357465831234, 'IoU-umbrella': 73.71278315837625, 'IoU-handbag': 38.78538022120833, 'IoU-tie': 70.52142020163177, 'IoU-suitcase': 81.07278068169602, 'IoU-frisbee': 83.30185273526644, 'IoU-skis': 52.40630140485979, 'IoU-snowboard': 69.19743710008477, 'IoU-sports ball': 72.11377751191964, 'IoU-kite': 64.88339928541242, 'IoU-baseball bat': 59.197552854957905, 'IoU-baseball glove': 52.615637127920486, 'IoU-skateboard': 60.23861916207629, 'IoU-surfboard': 74.70322718009551, 'IoU-tennis racket': 74.779828376631, 'IoU-bottle': 66.32591874958115, 'IoU-wine glass': 68.87145273095578, 'IoU-cup': 58.98360056214283, 'IoU-fork': 56.18867895283414, 'IoU-knife': 51.896270137213996, 'IoU-spoon': 49.90142627471472, 'IoU-bowl': 51.2594715228317, 'IoU-banana': 82.18771158595824, 'IoU-apple': 57.1201257667464, 'IoU-sandwich': 67.0426941858561, 'IoU-orange': 74.2850728754895, 'IoU-broccoli': 66.41965070120493, 'IoU-carrot': 63.78624381028709, 'IoU-hot dog': 66.72677684766694, 'IoU-pizza': 83.51478910287855, 'IoU-donut': 65.40253711054811, 'IoU-cake': 66.17878726361617, 'IoU-chair': 51.32639427180017, 'IoU-couch': 65.55177754883105, 'IoU-potted plant': 34.0795277843179, 'IoU-bed': 69.35066227341675, 'IoU-dining table': 51.054191865706876, 'IoU-toilet': 87.06319304073916, 'IoU-tv': 77.33324327905963, 'IoU-laptop': 75.09384024245065, 'IoU-mouse': 71.71412481503005, 'IoU-remote': 48.63959081458134, 'IoU-keyboard': 63.07793521816948, 'IoU-cell phone': 69.84817460436624, 'IoU-microwave': 55.14385504221051, 'IoU-oven': 63.673594295196565, 'IoU-toaster': 60.891111160604915, 'IoU-sink': 72.30611239367657, 'IoU-refrigerator': 77.28910188423616, 'IoU-book': 52.59027403617896, 'IoU-clock': 61.407573500760314, 'IoU-vase': 61.749659714446445, 'IoU-scissors': 54.0971620910372, 'IoU-teddy bear': 76.2018782203202, 'IoU-hair drier': 28.652352672453173, 'IoU-toothbrush': 58.40369384919275, 'IoU-banner': 38.16713217918411, 'IoU-blanket': 9.281505520282517, 'IoU-bridge': 37.32720852889183, 'IoU-cardboard': 44.45554912292262, 'IoU-counter': 29.78378087512679, 'IoU-curtain': 64.34171405490662, 'IoU-door-stuff': 40.848562995851914, 'IoU-floor-wood': 63.23624902637869, 'IoU-flower': 43.00495591461212, 'IoU-fruit': 41.72316001675502, 'IoU-gravel': 31.436713856678583, 'IoU-house': 21.660020775741152, 'IoU-light': 39.8319397049502, 'IoU-mirror-stuff': 59.09217133671702, 'IoU-net': 43.943988883887506, 'IoU-pillow': 12.501437055513273, 'IoU-platform': 29.683481702886795, 'IoU-playingfield': 71.05249618715071, 'IoU-railroad': 60.51660493889227, 'IoU-river': 51.85275351481362, 'IoU-road': 67.09618441119933, 'IoU-roof': 12.967789233826712, 'IoU-sand': 62.53067269188327, 'IoU-sea': 84.35940125165953, 'IoU-shelf': 35.38991322660765, 'IoU-snow': 88.47894005506505, 'IoU-stairs': 30.059035541515815, 'IoU-tent': 7.14322341947837, 'IoU-towel': 34.430155258976455, 'IoU-wall-brick': 41.69128879680359, 'IoU-wall-stone': 21.909768598010526, 'IoU-wall-tile': 66.12013308489459, 'IoU-wall-wood': 38.209866091347216, 'IoU-water-other': 23.139436760429362, 'IoU-window-blind': 46.48794613870726, 'IoU-window-other': 47.64832578961787, 'IoU-tree-merged': 81.04472792821326, 'IoU-fence-merged': 52.45217011845924, 'IoU-ceiling-merged': 67.80500922414991, 'IoU-sky-other-merged': 93.13992084290709, 'IoU-cabinet-merged': 58.93535803184514, 'IoU-table-merged': 36.966642201393476, 'IoU-floor-other-merged': 50.423654716457925, 'IoU-pavement-merged': 54.64533879588346, 'IoU-mountain-merged': 55.909025331501624, 'IoU-grass-merged': 72.05214102420679, 'IoU-dirt-merged': 43.06140037220038, 'IoU-paper-merged': 29.797991480518, 'IoU-food-other-merged': 34.30988611643156, 'IoU-building-other-merged': 57.10989092122455, 'IoU-rock-merged': 58.530526687796545, 'IoU-wall-other-merged': 65.31812630163309, 'IoU-rug-merged': 63.876839487570905, 'mACC': 72.38351552181791, 'pACC': 80.30195928410954, 'ACC-person': 92.69546590760856, 'ACC-bicycle': 85.54131226691817, 'ACC-car': 82.02363600601176, 'ACC-motorcycle': 89.67236724440167, 'ACC-airplane': 90.29182645784017, 'ACC-bus': 86.88476056999377, 'ACC-train': 95.1394826735194, 'ACC-truck': 77.2718190963261, 'ACC-boat': 76.28175396459042, 'ACC-traffic light': 89.07889817967703, 'ACC-fire hydrant': 95.47242161178887, 'ACC-stop sign': 93.62932353420628, 'ACC-parking meter': 87.1866482983138, 'ACC-bench': 71.93203834363212, 'ACC-bird': 81.37507917758033, 'ACC-cat': 91.81247240018735, 'ACC-dog': 81.02025463630314, 'ACC-horse': 90.93739293603066, 'ACC-sheep': 93.51056094451855, 'ACC-cow': 84.94992633529982, 'ACC-elephant': 92.83872067402618, 'ACC-bear': 89.64048023569171, 'ACC-zebra': 94.88830516919558, 'ACC-giraffe': 92.910369210875, 'ACC-backpack': 56.4527260935487, 'ACC-umbrella': 81.80142500595333, 'ACC-handbag': 57.181402140938864, 'ACC-tie': 79.75282844182361, 'ACC-suitcase': 90.49789752097209, 'ACC-frisbee': 93.97781818181818, 'ACC-skis': 68.49439687628835, 'ACC-snowboard': 78.76858269452131, 'ACC-sports ball': 84.73914886139279, 'ACC-kite': 76.51491697688643, 'ACC-baseball bat': 82.79225887659307, 'ACC-baseball glove': 60.517858815560984, 'ACC-skateboard': 69.8845554802483, 'ACC-surfboard': 82.96539665681628, 'ACC-tennis racket': 79.82673205206069, 'ACC-bottle': 79.23705944052384, 'ACC-wine glass': 83.9944878606009, 'ACC-cup': 82.95244224808222, 'ACC-fork': 67.25015111438161, 'ACC-knife': 68.86257052832124, 'ACC-spoon': 66.53123548584652, 'ACC-bowl': 66.80081314367581, 'ACC-banana': 88.58444821742975, 'ACC-apple': 70.34424530789923, 'ACC-sandwich': 80.06067565054053, 'ACC-orange': 81.17198264297257, 'ACC-broccoli': 75.09278467680546, 'ACC-carrot': 72.9780532781815, 'ACC-hot dog': 73.0845741876163, 'ACC-pizza': 92.4948136751659, 'ACC-donut': 82.19063333198389, 'ACC-cake': 74.81712978786734, 'ACC-chair': 62.822908188719595, 'ACC-couch': 81.05580135964263, 'ACC-potted plant': 48.97582936386613, 'ACC-bed': 85.38370631787332, 'ACC-dining table': 75.00611751113539, 'ACC-toilet': 93.77843894448374, 'ACC-tv': 87.6991966882334, 'ACC-laptop': 91.0236811972199, 'ACC-mouse': 86.36407908834128, 'ACC-remote': 70.30236376097801, 'ACC-keyboard': 69.61583172815443, 'ACC-cell phone': 76.03853595647261, 'ACC-microwave': 63.86944315818791, 'ACC-oven': 83.36900787611667, 'ACC-toaster': 74.83200810193105, 'ACC-sink': 84.56483616850306, 'ACC-refrigerator': 88.87739513051885, 'ACC-book': 69.05392816116108, 'ACC-clock': 65.67683207222754, 'ACC-vase': 72.24898902080899, 'ACC-scissors': 58.51249667153311, 'ACC-teddy bear': 84.77827648132237, 'ACC-hair drier': 40.1673640167364, 'ACC-toothbrush': 81.4176511466296, 'ACC-banner': 76.93178470428506, 'ACC-blanket': 10.612910307593486, 'ACC-bridge': 51.290975488353666, 'ACC-cardboard': 58.66590082884979, 'ACC-counter': 60.05940159367383, 'ACC-curtain': 74.81241827439379, 'ACC-door-stuff': 58.47842524031221, 'ACC-floor-wood': 78.12348706787947, 'ACC-flower': 62.87984495150969, 'ACC-fruit': 57.18472326690228, 'ACC-gravel': 39.34898910532718, 'ACC-house': 24.46165373434213, 'ACC-light': 54.876509684951955, 'ACC-mirror-stuff': 74.3107345985645, 'ACC-net': 62.22677822783575, 'ACC-pillow': 22.6788773815778, 'ACC-platform': 66.3276189249978, 'ACC-playingfield': 92.89829478449529, 'ACC-railroad': 77.0926475897103, 'ACC-river': 78.60950012410672, 'ACC-road': 84.18412010494393, 'ACC-roof': 17.1963557391811, 'ACC-sand': 71.82415316418819, 'ACC-sea': 90.66382132436529, 'ACC-shelf': 58.020373894146104, 'ACC-snow': 95.11429131513557, 'ACC-stairs': 50.20085491440269, 'ACC-tent': 7.719218271012207, 'ACC-towel': 39.919827270281935, 'ACC-wall-brick': 53.806827494263054, 'ACC-wall-stone': 27.830651373995764, 'ACC-wall-tile': 77.85008037204301, 'ACC-wall-wood': 51.75938459017995, 'ACC-water-other': 34.569223850372474, 'ACC-window-blind': 54.99125034296637, 'ACC-window-other': 65.70440820705886, 'ACC-tree-merged': 89.00874156748449, 'ACC-fence-merged': 69.98843636742585, 'ACC-ceiling-merged': 80.9928083947144, 'ACC-sky-other-merged': 96.39678349673359, 'ACC-cabinet-merged': 74.57749553926067, 'ACC-table-merged': 50.209772511550064, 'ACC-floor-other-merged': 63.71736535073577, 'ACC-pavement-merged': 68.00645808390739, 'ACC-mountain-merged': 68.12383016502277, 'ACC-grass-merged': 82.67805767108538, 'ACC-dirt-merged': 60.768465679620974, 'ACC-paper-merged': 40.749521439524514, 'ACC-food-other-merged': 46.274050765350985, 'ACC-building-other-merged': 79.70441375230251, 'ACC-rock-merged': 82.36497822263551, 'ACC-wall-other-merged': 80.67914372778334, 'ACC-rug-merged': 76.50484431979528})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1547 s/iter. Inference: 0.6026 s/iter. Eval: 0.0000 s/iter. Total: 0.7574 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1601 s/iter. Inference: 0.5236 s/iter. Eval: 0.0000 s/iter. Total: 0.6839 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1745 s/iter. Inference: 0.5831 s/iter. Eval: 0.0000 s/iter. Total: 0.7578 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1746 s/iter. Inference: 0.6630 s/iter. Eval: 0.0000 s/iter. Total: 0.8378 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1726 s/iter. Inference: 0.6139 s/iter. Eval: 0.0000 s/iter. Total: 0.7867 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1711 s/iter. Inference: 0.6550 s/iter. Eval: 0.0000 s/iter. Total: 0.8264 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5001463271875914, 'noc@0.8': 2.8270412642669007, 'noc@0.85': 3.4413227977758267, 'noc@0.9': 4.459174714661984, 'miou@iter1': 0.8289741781813589}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1008 s/iter. Eval: 0.0008 s/iter. Total: 0.1033 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.7734146118164, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.84492874145508, 'precision@0.8': 52.07928466796875, 'precision@0.9': 27.089000701904297, 'cIoU': 57.15098190307617, 'mIoU': 62.425140380859375}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.968179309447386, 'SQ': 81.9924664058628, 'RQ': 60.06732610273534, 'PQ_th': 55.04197245719109, 'SQ_th': 82.75319442071967, 'RQ_th': 65.85259682658611, 'PQ_st': 42.30962361473989, 'SQ_st': 80.84419770419201, 'RQ_st': 51.33484199126244}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.71115594379408, 'AP50': 61.1124812825375, 'AP75': 40.58194879135539, 'APs': 18.913959375660358, 'APm': 41.868260354648584, 'APl': 60.41738363086645, 'AP-person': 44.397492882594904, 'AP-bicycle': 18.55131313189044, 'AP-car': 36.454879037974116, 'AP-motorcycle': 34.24347557214144, 'AP-airplane': 55.811273676865724, 'AP-bus': 63.76888188668861, 'AP-train': 68.65490801987134, 'AP-truck': 34.74356686431314, 'AP-boat': 23.519717626146143, 'AP-traffic light': 24.991170318913483, 'AP-fire hydrant': 64.23168307648409, 'AP-stop sign': 63.919586472528735, 'AP-parking meter': 43.974541232969706, 'AP-bench': 20.52220060189973, 'AP-bird': 29.79475491039964, 'AP-cat': 73.8957854624176, 'AP-dog': 64.95425711766589, 'AP-horse': 45.611011600480836, 'AP-sheep': 46.09546353837209, 'AP-cow': 51.18744273885646, 'AP-elephant': 60.859434177387264, 'AP-bear': 77.6550530222123, 'AP-zebra': 60.260618288614296, 'AP-giraffe': 57.098308834648115, 'AP-backpack': 16.805299422531174, 'AP-umbrella': 48.57090720567823, 'AP-handbag': 15.22551386011742, 'AP-tie': 32.87929164281553, 'AP-suitcase': 40.70446007827416, 'AP-frisbee': 66.132404865744, 'AP-skis': 5.168583258275685, 'AP-snowboard': 22.59301457961772, 'AP-sports ball': 47.67409117192256, 'AP-kite': 34.46651269123989, 'AP-baseball bat': 28.912154031075165, 'AP-baseball glove': 43.25826599371806, 'AP-skateboard': 34.759195177072456, 'AP-surfboard': 35.42692666559753, 'AP-tennis racket': 55.878570950904496, 'AP-bottle': 33.52248309489199, 'AP-wine glass': 27.23424679299956, 'AP-cup': 40.363676942278246, 'AP-fork': 16.337793870953583, 'AP-knife': 12.796150603082173, 'AP-spoon': 14.006829734199197, 'AP-bowl': 29.730802651157695, 'AP-banana': 20.299450189721433, 'AP-apple': 20.970388942130477, 'AP-sandwich': 43.15933868276268, 'AP-orange': 30.065061457989366, 'AP-broccoli': 21.51168589454049, 'AP-carrot': 20.939992118071416, 'AP-hot dog': 24.007222590308945, 'AP-pizza': 51.58619887831188, 'AP-donut': 47.21965938443478, 'AP-cake': 43.767407907503994, 'AP-chair': 20.70572788851046, 'AP-couch': 40.067566111771484, 'AP-potted plant': 18.3615096951442, 'AP-bed': 40.06434302853998, 'AP-dining table': 12.417078707584011, 'AP-toilet': 67.66115239025724, 'AP-tv': 62.35665249105591, 'AP-laptop': 61.6425550402557, 'AP-mouse': 59.33045963138292, 'AP-remote': 30.22919379047732, 'AP-keyboard': 47.15051942340699, 'AP-cell phone': 37.6962784417506, 'AP-microwave': 55.732858835538735, 'AP-oven': 32.08478862040315, 'AP-toaster': 26.00434014650342, 'AP-sink': 37.19012830244697, 'AP-refrigerator': 58.231785105703224, 'AP-book': 9.35046580248276, 'AP-clock': 51.85711174298021, 'AP-vase': 34.4051025036683, 'AP-scissors': 24.297461369565728, 'AP-teddy bear': 50.927669020675424, 'AP-hair drier': 8.827113480578829, 'AP-toothbrush': 19.130212510584883}), ('sem_seg', {'mIoU': 60.21501247755149, 'fwIoU': 68.90838999891531, 'IoU-person': 87.69777194805359, 'IoU-bicycle': 73.0366239761876, 'IoU-car': 68.34209365463347, 'IoU-motorcycle': 84.04614954522839, 'IoU-airplane': 84.14574548375515, 'IoU-bus': 83.03883776097264, 'IoU-train': 82.747472347871, 'IoU-truck': 62.042352890738805, 'IoU-boat': 66.44909080481388, 'IoU-traffic light': 75.73577421939338, 'IoU-fire hydrant': 90.51942577184619, 'IoU-stop sign': 90.83242670609462, 'IoU-parking meter': 83.14746861022296, 'IoU-bench': 54.54607001153573, 'IoU-bird': 76.76014687151165, 'IoU-cat': 86.24767367815568, 'IoU-dog': 77.7163347262671, 'IoU-horse': 84.9871627714902, 'IoU-sheep': 89.33893448728298, 'IoU-cow': 79.41888983655866, 'IoU-elephant': 90.24612458492453, 'IoU-bear': 87.33780280763244, 'IoU-zebra': 92.30648025563059, 'IoU-giraffe': 88.33206237170873, 'IoU-backpack': 42.635357465831234, 'IoU-umbrella': 73.71278315837625, 'IoU-handbag': 38.78538022120833, 'IoU-tie': 70.52142020163177, 'IoU-suitcase': 81.07278068169602, 'IoU-frisbee': 83.30185273526644, 'IoU-skis': 52.40630140485979, 'IoU-snowboard': 69.19743710008477, 'IoU-sports ball': 72.11377751191964, 'IoU-kite': 64.88339928541242, 'IoU-baseball bat': 59.197552854957905, 'IoU-baseball glove': 52.615637127920486, 'IoU-skateboard': 60.23861916207629, 'IoU-surfboard': 74.70322718009551, 'IoU-tennis racket': 74.779828376631, 'IoU-bottle': 66.32591874958115, 'IoU-wine glass': 68.87145273095578, 'IoU-cup': 58.98360056214283, 'IoU-fork': 56.18867895283414, 'IoU-knife': 51.896270137213996, 'IoU-spoon': 49.90142627471472, 'IoU-bowl': 51.2594715228317, 'IoU-banana': 82.18771158595824, 'IoU-apple': 57.1201257667464, 'IoU-sandwich': 67.0426941858561, 'IoU-orange': 74.2850728754895, 'IoU-broccoli': 66.41965070120493, 'IoU-carrot': 63.78624381028709, 'IoU-hot dog': 66.72677684766694, 'IoU-pizza': 83.51478910287855, 'IoU-donut': 65.40253711054811, 'IoU-cake': 66.17878726361617, 'IoU-chair': 51.32639427180017, 'IoU-couch': 65.55177754883105, 'IoU-potted plant': 34.0795277843179, 'IoU-bed': 69.35066227341675, 'IoU-dining table': 51.054191865706876, 'IoU-toilet': 87.06319304073916, 'IoU-tv': 77.33324327905963, 'IoU-laptop': 75.09384024245065, 'IoU-mouse': 71.71412481503005, 'IoU-remote': 48.63959081458134, 'IoU-keyboard': 63.07793521816948, 'IoU-cell phone': 69.84817460436624, 'IoU-microwave': 55.14385504221051, 'IoU-oven': 63.673594295196565, 'IoU-toaster': 60.891111160604915, 'IoU-sink': 72.30611239367657, 'IoU-refrigerator': 77.28910188423616, 'IoU-book': 52.59027403617896, 'IoU-clock': 61.407573500760314, 'IoU-vase': 61.749659714446445, 'IoU-scissors': 54.0971620910372, 'IoU-teddy bear': 76.2018782203202, 'IoU-hair drier': 28.652352672453173, 'IoU-toothbrush': 58.40369384919275, 'IoU-banner': 38.16713217918411, 'IoU-blanket': 9.281505520282517, 'IoU-bridge': 37.32720852889183, 'IoU-cardboard': 44.45554912292262, 'IoU-counter': 29.78378087512679, 'IoU-curtain': 64.34171405490662, 'IoU-door-stuff': 40.848562995851914, 'IoU-floor-wood': 63.23624902637869, 'IoU-flower': 43.00495591461212, 'IoU-fruit': 41.72316001675502, 'IoU-gravel': 31.436713856678583, 'IoU-house': 21.660020775741152, 'IoU-light': 39.8319397049502, 'IoU-mirror-stuff': 59.09217133671702, 'IoU-net': 43.943988883887506, 'IoU-pillow': 12.501437055513273, 'IoU-platform': 29.683481702886795, 'IoU-playingfield': 71.05249618715071, 'IoU-railroad': 60.51660493889227, 'IoU-river': 51.85275351481362, 'IoU-road': 67.09618441119933, 'IoU-roof': 12.967789233826712, 'IoU-sand': 62.53067269188327, 'IoU-sea': 84.35940125165953, 'IoU-shelf': 35.38991322660765, 'IoU-snow': 88.47894005506505, 'IoU-stairs': 30.059035541515815, 'IoU-tent': 7.14322341947837, 'IoU-towel': 34.430155258976455, 'IoU-wall-brick': 41.69128879680359, 'IoU-wall-stone': 21.909768598010526, 'IoU-wall-tile': 66.12013308489459, 'IoU-wall-wood': 38.209866091347216, 'IoU-water-other': 23.139436760429362, 'IoU-window-blind': 46.48794613870726, 'IoU-window-other': 47.64832578961787, 'IoU-tree-merged': 81.04472792821326, 'IoU-fence-merged': 52.45217011845924, 'IoU-ceiling-merged': 67.80500922414991, 'IoU-sky-other-merged': 93.13992084290709, 'IoU-cabinet-merged': 58.93535803184514, 'IoU-table-merged': 36.966642201393476, 'IoU-floor-other-merged': 50.423654716457925, 'IoU-pavement-merged': 54.64533879588346, 'IoU-mountain-merged': 55.909025331501624, 'IoU-grass-merged': 72.05214102420679, 'IoU-dirt-merged': 43.06140037220038, 'IoU-paper-merged': 29.797991480518, 'IoU-food-other-merged': 34.30988611643156, 'IoU-building-other-merged': 57.10989092122455, 'IoU-rock-merged': 58.530526687796545, 'IoU-wall-other-merged': 65.31812630163309, 'IoU-rug-merged': 63.876839487570905, 'mACC': 72.38351552181791, 'pACC': 80.30195928410954, 'ACC-person': 92.69546590760856, 'ACC-bicycle': 85.54131226691817, 'ACC-car': 82.02363600601176, 'ACC-motorcycle': 89.67236724440167, 'ACC-airplane': 90.29182645784017, 'ACC-bus': 86.88476056999377, 'ACC-train': 95.1394826735194, 'ACC-truck': 77.2718190963261, 'ACC-boat': 76.28175396459042, 'ACC-traffic light': 89.07889817967703, 'ACC-fire hydrant': 95.47242161178887, 'ACC-stop sign': 93.62932353420628, 'ACC-parking meter': 87.1866482983138, 'ACC-bench': 71.93203834363212, 'ACC-bird': 81.37507917758033, 'ACC-cat': 91.81247240018735, 'ACC-dog': 81.02025463630314, 'ACC-horse': 90.93739293603066, 'ACC-sheep': 93.51056094451855, 'ACC-cow': 84.94992633529982, 'ACC-elephant': 92.83872067402618, 'ACC-bear': 89.64048023569171, 'ACC-zebra': 94.88830516919558, 'ACC-giraffe': 92.910369210875, 'ACC-backpack': 56.4527260935487, 'ACC-umbrella': 81.80142500595333, 'ACC-handbag': 57.181402140938864, 'ACC-tie': 79.75282844182361, 'ACC-suitcase': 90.49789752097209, 'ACC-frisbee': 93.97781818181818, 'ACC-skis': 68.49439687628835, 'ACC-snowboard': 78.76858269452131, 'ACC-sports ball': 84.73914886139279, 'ACC-kite': 76.51491697688643, 'ACC-baseball bat': 82.79225887659307, 'ACC-baseball glove': 60.517858815560984, 'ACC-skateboard': 69.8845554802483, 'ACC-surfboard': 82.96539665681628, 'ACC-tennis racket': 79.82673205206069, 'ACC-bottle': 79.23705944052384, 'ACC-wine glass': 83.9944878606009, 'ACC-cup': 82.95244224808222, 'ACC-fork': 67.25015111438161, 'ACC-knife': 68.86257052832124, 'ACC-spoon': 66.53123548584652, 'ACC-bowl': 66.80081314367581, 'ACC-banana': 88.58444821742975, 'ACC-apple': 70.34424530789923, 'ACC-sandwich': 80.06067565054053, 'ACC-orange': 81.17198264297257, 'ACC-broccoli': 75.09278467680546, 'ACC-carrot': 72.9780532781815, 'ACC-hot dog': 73.0845741876163, 'ACC-pizza': 92.4948136751659, 'ACC-donut': 82.19063333198389, 'ACC-cake': 74.81712978786734, 'ACC-chair': 62.822908188719595, 'ACC-couch': 81.05580135964263, 'ACC-potted plant': 48.97582936386613, 'ACC-bed': 85.38370631787332, 'ACC-dining table': 75.00611751113539, 'ACC-toilet': 93.77843894448374, 'ACC-tv': 87.6991966882334, 'ACC-laptop': 91.0236811972199, 'ACC-mouse': 86.36407908834128, 'ACC-remote': 70.30236376097801, 'ACC-keyboard': 69.61583172815443, 'ACC-cell phone': 76.03853595647261, 'ACC-microwave': 63.86944315818791, 'ACC-oven': 83.36900787611667, 'ACC-toaster': 74.83200810193105, 'ACC-sink': 84.56483616850306, 'ACC-refrigerator': 88.87739513051885, 'ACC-book': 69.05392816116108, 'ACC-clock': 65.67683207222754, 'ACC-vase': 72.24898902080899, 'ACC-scissors': 58.51249667153311, 'ACC-teddy bear': 84.77827648132237, 'ACC-hair drier': 40.1673640167364, 'ACC-toothbrush': 81.4176511466296, 'ACC-banner': 76.93178470428506, 'ACC-blanket': 10.612910307593486, 'ACC-bridge': 51.290975488353666, 'ACC-cardboard': 58.66590082884979, 'ACC-counter': 60.05940159367383, 'ACC-curtain': 74.81241827439379, 'ACC-door-stuff': 58.47842524031221, 'ACC-floor-wood': 78.12348706787947, 'ACC-flower': 62.87984495150969, 'ACC-fruit': 57.18472326690228, 'ACC-gravel': 39.34898910532718, 'ACC-house': 24.46165373434213, 'ACC-light': 54.876509684951955, 'ACC-mirror-stuff': 74.3107345985645, 'ACC-net': 62.22677822783575, 'ACC-pillow': 22.6788773815778, 'ACC-platform': 66.3276189249978, 'ACC-playingfield': 92.89829478449529, 'ACC-railroad': 77.0926475897103, 'ACC-river': 78.60950012410672, 'ACC-road': 84.18412010494393, 'ACC-roof': 17.1963557391811, 'ACC-sand': 71.82415316418819, 'ACC-sea': 90.66382132436529, 'ACC-shelf': 58.020373894146104, 'ACC-snow': 95.11429131513557, 'ACC-stairs': 50.20085491440269, 'ACC-tent': 7.719218271012207, 'ACC-towel': 39.919827270281935, 'ACC-wall-brick': 53.806827494263054, 'ACC-wall-stone': 27.830651373995764, 'ACC-wall-tile': 77.85008037204301, 'ACC-wall-wood': 51.75938459017995, 'ACC-water-other': 34.569223850372474, 'ACC-window-blind': 54.99125034296637, 'ACC-window-other': 65.70440820705886, 'ACC-tree-merged': 89.00874156748449, 'ACC-fence-merged': 69.98843636742585, 'ACC-ceiling-merged': 80.9928083947144, 'ACC-sky-other-merged': 96.39678349673359, 'ACC-cabinet-merged': 74.57749553926067, 'ACC-table-merged': 50.209772511550064, 'ACC-floor-other-merged': 63.71736535073577, 'ACC-pavement-merged': 68.00645808390739, 'ACC-mountain-merged': 68.12383016502277, 'ACC-grass-merged': 82.67805767108538, 'ACC-dirt-merged': 60.768465679620974, 'ACC-paper-merged': 40.749521439524514, 'ACC-food-other-merged': 46.274050765350985, 'ACC-building-other-merged': 79.70441375230251, 'ACC-rock-merged': 82.36497822263551, 'ACC-wall-other-merged': 80.67914372778334, 'ACC-rug-merged': 76.50484431979528})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5001463271875914, 'noc@0.8': 2.8270412642669007, 'noc@0.85': 3.4413227977758267, 'noc@0.9': 4.459174714661984, 'miou@iter1': 0.8289741781813589}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.7734146118164, 'precision@0.6': 67.6642074584961, 'precision@0.7': 62.84492874145508, 'precision@0.8': 52.07928466796875, 'precision@0.9': 27.089000701904297, 'cIoU': 57.15098190307617, 'mIoU': 62.425140380859375}}}
INFO:trainer.default_trainer:This epoch takes 1:27:15.866790
INFO:trainer.default_trainer:PROGRESS: 74.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 37 training.
INFO:trainer.default_trainer:epochs[    37] optim steps[67600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47720/0.89795, loss_mask_bce_0: 0.11057/0.33432, loss_mask_dice_0: 2.25220/1.16312, loss_spatial_bce_0: 0.03028/0.08719, loss_spatial_dice_0: 0.18345/0.20804, loss_spatial_ce_0: 0.02307/0.06167, loss_grounding_bce_0: 0.07577/0.08624, loss_grounding_dice_0: 0.17973/0.17854, loss_grounding_ce_0: 0.17442/0.27173, loss_mask_ce_1: 0.80953/0.89853, loss_mask_bce_1: 0.11471/0.33525, loss_mask_dice_1: 2.74823/1.16989, loss_spatial_bce_1: 0.03199/0.08771, loss_spatial_dice_1: 0.23109/0.21202, loss_spatial_ce_1: 0.02910/0.06755, loss_grounding_bce_1: 0.07718/0.08642, loss_grounding_dice_1: 0.20278/0.17939, loss_grounding_ce_1: 0.18175/0.27255, loss_mask_ce_2: 0.42246/0.90558, loss_mask_bce_2: 0.10941/0.33582, loss_mask_dice_2: 2.21712/1.17023, loss_spatial_bce_2: 0.03069/0.08883, loss_spatial_dice_2: 0.19859/0.21366, loss_spatial_ce_2: 0.05888/0.07090, loss_grounding_bce_2: 0.08610/0.08658, loss_grounding_dice_2: 0.13756/0.17917, loss_grounding_ce_2: 0.13322/0.27606, loss_mask_ce_3: 0.65897/0.91636, loss_mask_bce_3: 0.11740/0.33699, loss_mask_dice_3: 2.86852/1.16789, loss_spatial_bce_3: 0.03107/0.09005, loss_spatial_dice_3: 0.26455/0.21463, loss_spatial_ce_3: 0.04913/0.07567, loss_grounding_bce_3: 0.08030/0.08682, loss_grounding_dice_3: 0.09228/0.17892, loss_grounding_ce_3: 0.17316/0.27818, loss_mask_ce_4: 0.68179/0.91736, loss_mask_bce_4: 0.11148/0.33912, loss_mask_dice_4: 2.78202/1.19193, loss_spatial_bce_4: 0.03071/0.09396, loss_spatial_dice_4: 0.24721/0.22680, loss_spatial_ce_4: 0.22436/0.09179, loss_grounding_bce_4: 0.08024/0.08733, loss_grounding_dice_4: 0.08691/0.18182, loss_grounding_ce_4: 0.08105/0.28115, loss_mask_ce_5: 0.42841/0.93388, loss_mask_bce_5: 0.10769/0.34145, loss_mask_dice_5: 2.19867/1.19965, loss_spatial_bce_5: 0.02964/0.09615, loss_spatial_dice_5: 0.22531/0.23104, loss_spatial_ce_5: 0.03122/0.10604, loss_grounding_bce_5: 0.08016/0.08775, loss_grounding_dice_5: 0.15873/0.18308, loss_grounding_ce_5: 0.29384/0.29374, loss_mask_ce_6: 0.56857/0.97390, loss_mask_bce_6: 0.11313/0.34412, loss_mask_dice_6: 2.54709/1.20255, loss_spatial_bce_6: 0.02971/0.10184, loss_spatial_dice_6: 0.22397/0.23392, loss_spatial_ce_6: 0.31398/0.13179, loss_grounding_bce_6: 0.08486/0.08849, loss_grounding_dice_6: 0.18478/0.18345, loss_grounding_ce_6: 0.10487/0.30929, loss_mask_ce_7: 0.53025/1.01893, loss_mask_bce_7: 0.11017/0.35202, loss_mask_dice_7: 2.38443/1.25686, loss_spatial_bce_7: 0.02996/0.10980, loss_spatial_dice_7: 0.22669/0.26156, loss_spatial_ce_7: 0.12606/0.16709, loss_grounding_bce_7: 0.08059/0.09038, loss_grounding_dice_7: 0.13039/0.19073, loss_grounding_ce_7: 0.05060/0.33961, loss_mask_ce_8: 1.09992/1.12722, loss_mask_bce_8: 0.10711/0.36559, loss_mask_dice_8: 2.85664/1.32981, loss_spatial_bce_8: 0.03381/0.13041, loss_spatial_dice_8: 0.30389/0.29951, loss_spatial_ce_8: 0.06503/0.22239, loss_grounding_bce_8: 0.07456/0.09411, loss_grounding_dice_8: 0.12800/0.20152, loss_grounding_ce_8: 0.30914/0.40670, loss_mask_ce_9: 3.45220/3.67601, loss_mask_bce_9: 0.09737/0.39267, loss_mask_dice_9: 3.05286/1.90248, loss_spatial_bce_9: 0.17169/0.33293, loss_spatial_dice_9: 0.63363/0.82187, loss_spatial_ce_9: 1.89035/1.49600, loss_grounding_bce_9: 0.06775/0.10566, loss_grounding_dice_9: 0.20409/0.28084, loss_grounding_ce_9: 0.27391/0.67153] items per batch[64] items per second[0.14] total items[4326400] mini batches[ 67600] memory[7345] epoch remaining[3:01:21]
INFO:trainer.default_trainer:epochs[    37] optim steps[67700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75952/0.89795, loss_mask_bce_0: 0.09742/0.33429, loss_mask_dice_0: 0.22156/1.16314, loss_spatial_bce_0: 0.04666/0.08718, loss_spatial_dice_0: 0.14432/0.20802, loss_spatial_ce_0: 0.05986/0.06165, loss_grounding_bce_0: 0.04503/0.08622, loss_grounding_dice_0: 0.05666/0.17854, loss_grounding_ce_0: 0.11500/0.27168, loss_mask_ce_1: 0.64002/0.89855, loss_mask_bce_1: 0.09328/0.33521, loss_mask_dice_1: 0.22458/1.16989, loss_spatial_bce_1: 0.04062/0.08769, loss_spatial_dice_1: 0.12119/0.21200, loss_spatial_ce_1: 0.08717/0.06753, loss_grounding_bce_1: 0.04474/0.08640, loss_grounding_dice_1: 0.04991/0.17939, loss_grounding_ce_1: 0.12415/0.27251, loss_mask_ce_2: 0.82788/0.90558, loss_mask_bce_2: 0.09775/0.33579, loss_mask_dice_2: 0.27328/1.17023, loss_spatial_bce_2: 0.04097/0.08881, loss_spatial_dice_2: 0.12387/0.21365, loss_spatial_ce_2: 0.12104/0.07088, loss_grounding_bce_2: 0.04356/0.08656, loss_grounding_dice_2: 0.05547/0.17917, loss_grounding_ce_2: 0.12670/0.27601, loss_mask_ce_3: 0.72997/0.91636, loss_mask_bce_3: 0.09671/0.33695, loss_mask_dice_3: 0.22869/1.16786, loss_spatial_bce_3: 0.04156/0.09003, loss_spatial_dice_3: 0.13315/0.21461, loss_spatial_ce_3: 0.07630/0.07565, loss_grounding_bce_3: 0.04455/0.08680, loss_grounding_dice_3: 0.05617/0.17893, loss_grounding_ce_3: 0.13510/0.27813, loss_mask_ce_4: 0.95796/0.91737, loss_mask_bce_4: 0.09526/0.33908, loss_mask_dice_4: 0.21160/1.19195, loss_spatial_bce_4: 0.05178/0.09395, loss_spatial_dice_4: 0.18695/0.22679, loss_spatial_ce_4: 0.07332/0.09177, loss_grounding_bce_4: 0.04280/0.08731, loss_grounding_dice_4: 0.05268/0.18182, loss_grounding_ce_4: 0.13804/0.28111, loss_mask_ce_5: 0.62244/0.93386, loss_mask_bce_5: 0.09825/0.34141, loss_mask_dice_5: 0.23609/1.19966, loss_spatial_bce_5: 0.04820/0.09614, loss_spatial_dice_5: 0.15579/0.23102, loss_spatial_ce_5: 0.05926/0.10600, loss_grounding_bce_5: 0.04548/0.08773, loss_grounding_dice_5: 0.06039/0.18308, loss_grounding_ce_5: 0.08435/0.29369, loss_mask_ce_6: 0.57999/0.97385, loss_mask_bce_6: 0.10884/0.34409, loss_mask_dice_6: 0.23339/1.20257, loss_spatial_bce_6: 0.05382/0.10182, loss_spatial_dice_6: 0.15545/0.23391, loss_spatial_ce_6: 0.15590/0.13175, loss_grounding_bce_6: 0.05133/0.08847, loss_grounding_dice_6: 0.06005/0.18346, loss_grounding_ce_6: 0.11219/0.30924, loss_mask_ce_7: 0.96837/1.01892, loss_mask_bce_7: 0.10054/0.35198, loss_mask_dice_7: 0.26395/1.25690, loss_spatial_bce_7: 0.06037/0.10978, loss_spatial_dice_7: 0.23270/0.26155, loss_spatial_ce_7: 0.16440/0.16707, loss_grounding_bce_7: 0.05829/0.09035, loss_grounding_dice_7: 0.06899/0.19074, loss_grounding_ce_7: 0.04344/0.33958, loss_mask_ce_8: 0.91693/1.12723, loss_mask_bce_8: 0.09862/0.36555, loss_mask_dice_8: 0.33186/1.32984, loss_spatial_bce_8: 0.08404/0.13039, loss_spatial_dice_8: 0.26648/0.29949, loss_spatial_ce_8: 0.36883/0.22235, loss_grounding_bce_8: 0.05110/0.09408, loss_grounding_dice_8: 0.05551/0.20153, loss_grounding_ce_8: 0.06609/0.40664, loss_mask_ce_9: 2.58314/3.67582, loss_mask_bce_9: 0.12097/0.39261, loss_mask_dice_9: 0.35645/1.90244, loss_spatial_bce_9: 0.30807/0.33291, loss_spatial_dice_9: 0.64389/0.82186, loss_spatial_ce_9: 1.21265/1.49588, loss_grounding_bce_9: 0.07864/0.10564, loss_grounding_dice_9: 0.08945/0.28085, loss_grounding_ce_9: 0.29970/0.67142] items per batch[64] items per second[0.24] total items[4332800] mini batches[ 67700] memory[7345] epoch remaining[1:18:44]
INFO:trainer.default_trainer:epochs[    37] optim steps[67800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33929/0.89792, loss_mask_bce_0: 0.04048/0.33427, loss_mask_dice_0: 0.44862/1.16298, loss_spatial_bce_0: 0.01195/0.08716, loss_spatial_dice_0: 0.21134/0.20801, loss_spatial_ce_0: 0.00390/0.06163, loss_grounding_bce_0: 0.02897/0.08622, loss_grounding_dice_0: 0.22857/0.17853, loss_grounding_ce_0: 0.12445/0.27170, loss_mask_ce_1: 0.31831/0.89849, loss_mask_bce_1: 0.04115/0.33519, loss_mask_dice_1: 0.46282/1.16977, loss_spatial_bce_1: 0.01181/0.08768, loss_spatial_dice_1: 0.20924/0.21199, loss_spatial_ce_1: 0.00898/0.06750, loss_grounding_bce_1: 0.02617/0.08640, loss_grounding_dice_1: 0.24429/0.17939, loss_grounding_ce_1: 0.16511/0.27255, loss_mask_ce_2: 0.83261/0.90554, loss_mask_bce_2: 0.04052/0.33577, loss_mask_dice_2: 0.64236/1.17007, loss_spatial_bce_2: 0.01099/0.08879, loss_spatial_dice_2: 0.11301/0.21363, loss_spatial_ce_2: 0.01300/0.07085, loss_grounding_bce_2: 0.02590/0.08655, loss_grounding_dice_2: 0.19795/0.17917, loss_grounding_ce_2: 0.22502/0.27604, loss_mask_ce_3: 0.33775/0.91634, loss_mask_bce_3: 0.04340/0.33693, loss_mask_dice_3: 0.51971/1.16774, loss_spatial_bce_3: 0.01232/0.09002, loss_spatial_dice_3: 0.22791/0.21460, loss_spatial_ce_3: 0.00890/0.07562, loss_grounding_bce_3: 0.01082/0.08680, loss_grounding_dice_3: 0.12399/0.17892, loss_grounding_ce_3: 0.47971/0.27819, loss_mask_ce_4: 0.27841/0.91731, loss_mask_bce_4: 0.05355/0.33906, loss_mask_dice_4: 0.57210/1.19177, loss_spatial_bce_4: 0.01190/0.09394, loss_spatial_dice_4: 0.24815/0.22678, loss_spatial_ce_4: 0.03511/0.09174, loss_grounding_bce_4: 0.02760/0.08731, loss_grounding_dice_4: 0.20533/0.18181, loss_grounding_ce_4: 0.18716/0.28116, loss_mask_ce_5: 1.03572/0.93384, loss_mask_bce_5: 0.02148/0.34138, loss_mask_dice_5: 0.42707/1.19951, loss_spatial_bce_5: 0.01549/0.09613, loss_spatial_dice_5: 0.20115/0.23102, loss_spatial_ce_5: 0.04033/0.10597, loss_grounding_bce_5: 0.02938/0.08773, loss_grounding_dice_5: 0.18822/0.18308, loss_grounding_ce_5: 0.14306/0.29374, loss_mask_ce_6: 0.48566/0.97381, loss_mask_bce_6: 0.04459/0.34406, loss_mask_dice_6: 0.45817/1.20239, loss_spatial_bce_6: 0.01331/0.10181, loss_spatial_dice_6: 0.19081/0.23390, loss_spatial_ce_6: 0.04263/0.13177, loss_grounding_bce_6: 0.02986/0.08847, loss_grounding_dice_6: 0.21549/0.18344, loss_grounding_ce_6: 0.15317/0.30935, loss_mask_ce_7: 0.39243/1.01890, loss_mask_bce_7: 0.04718/0.35194, loss_mask_dice_7: 0.52969/1.25670, loss_spatial_bce_7: 0.01444/0.10977, loss_spatial_dice_7: 0.28236/0.26156, loss_spatial_ce_7: 0.27590/0.16704, loss_grounding_bce_7: 0.03182/0.09035, loss_grounding_dice_7: 0.22020/0.19073, loss_grounding_ce_7: 0.15400/0.33965, loss_mask_ce_8: 0.82988/1.12721, loss_mask_bce_8: 0.04879/0.36552, loss_mask_dice_8: 0.63385/1.32964, loss_spatial_bce_8: 0.01880/0.13037, loss_spatial_dice_8: 0.35370/0.29949, loss_spatial_ce_8: 0.31541/0.22231, loss_grounding_bce_8: 0.02586/0.09408, loss_grounding_dice_8: 0.22718/0.20151, loss_grounding_ce_8: 0.31352/0.40670, loss_mask_ce_9: 2.66853/3.67580, loss_mask_bce_9: 0.01919/0.39257, loss_mask_dice_9: 0.81828/1.90215, loss_spatial_bce_9: 0.04086/0.33289, loss_spatial_dice_9: 0.64914/0.82184, loss_spatial_ce_9: 3.04764/1.49597, loss_grounding_bce_9: 0.00646/0.10564, loss_grounding_dice_9: 0.27603/0.28082, loss_grounding_ce_9: 0.22974/0.67146] items per batch[64] items per second[0.22] total items[4339200] mini batches[ 67800] memory[7345] epoch remaining[1:15:38]
INFO:trainer.default_trainer:epochs[    37] optim steps[67900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35751/0.89794, loss_mask_bce_0: 0.09861/0.33431, loss_mask_dice_0: 0.39662/1.16311, loss_spatial_bce_0: 0.07131/0.08715, loss_spatial_dice_0: 0.17213/0.20801, loss_spatial_ce_0: 0.05596/0.06162, loss_grounding_bce_0: 0.04010/0.08621, loss_grounding_dice_0: 0.03790/0.17853, loss_grounding_ce_0: 0.11759/0.27168, loss_mask_ce_1: 0.34373/0.89851, loss_mask_bce_1: 0.09768/0.33523, loss_mask_dice_1: 0.40735/1.16989, loss_spatial_bce_1: 0.07370/0.08767, loss_spatial_dice_1: 0.17351/0.21198, loss_spatial_ce_1: 0.05162/0.06749, loss_grounding_bce_1: 0.03795/0.08639, loss_grounding_dice_1: 0.03790/0.17939, loss_grounding_ce_1: 0.08585/0.27255, loss_mask_ce_2: 0.31917/0.90557, loss_mask_bce_2: 0.09360/0.33580, loss_mask_dice_2: 0.39523/1.17021, loss_spatial_bce_2: 0.05901/0.08879, loss_spatial_dice_2: 0.16591/0.21363, loss_spatial_ce_2: 0.04896/0.07084, loss_grounding_bce_2: 0.03848/0.08654, loss_grounding_dice_2: 0.03841/0.17918, loss_grounding_ce_2: 0.08350/0.27601, loss_mask_ce_3: 0.31338/0.91638, loss_mask_bce_3: 0.09736/0.33696, loss_mask_dice_3: 0.40700/1.16787, loss_spatial_bce_3: 0.07373/0.09001, loss_spatial_dice_3: 0.17686/0.21460, loss_spatial_ce_3: 0.03562/0.07562, loss_grounding_bce_3: 0.03845/0.08678, loss_grounding_dice_3: 0.03707/0.17892, loss_grounding_ce_3: 0.08164/0.27819, loss_mask_ce_4: 0.34330/0.91736, loss_mask_bce_4: 0.09982/0.33909, loss_mask_dice_4: 0.40315/1.19191, loss_spatial_bce_4: 0.06194/0.09393, loss_spatial_dice_4: 0.18609/0.22678, loss_spatial_ce_4: 0.06210/0.09175, loss_grounding_bce_4: 0.03865/0.08730, loss_grounding_dice_4: 0.04338/0.18181, loss_grounding_ce_4: 0.07157/0.28116, loss_mask_ce_5: 0.35977/0.93389, loss_mask_bce_5: 0.09942/0.34142, loss_mask_dice_5: 0.38036/1.19963, loss_spatial_bce_5: 0.06663/0.09613, loss_spatial_dice_5: 0.19981/0.23102, loss_spatial_ce_5: 0.04956/0.10596, loss_grounding_bce_5: 0.03984/0.08771, loss_grounding_dice_5: 0.04112/0.18308, loss_grounding_ce_5: 0.10701/0.29374, loss_mask_ce_6: 0.41911/0.97383, loss_mask_bce_6: 0.09860/0.34409, loss_mask_dice_6: 0.41930/1.20254, loss_spatial_bce_6: 0.05844/0.10181, loss_spatial_dice_6: 0.19501/0.23391, loss_spatial_ce_6: 0.10869/0.13175, loss_grounding_bce_6: 0.03713/0.08845, loss_grounding_dice_6: 0.03534/0.18344, loss_grounding_ce_6: 0.11807/0.30934, loss_mask_ce_7: 0.46046/1.01897, loss_mask_bce_7: 0.10732/0.35198, loss_mask_dice_7: 0.42138/1.25685, loss_spatial_bce_7: 0.08242/0.10977, loss_spatial_dice_7: 0.22702/0.26156, loss_spatial_ce_7: 0.15794/0.16703, loss_grounding_bce_7: 0.04130/0.09033, loss_grounding_dice_7: 0.04159/0.19072, loss_grounding_ce_7: 0.09484/0.33970, loss_mask_ce_8: 0.28161/1.12725, loss_mask_bce_8: 0.14609/0.36555, loss_mask_dice_8: 0.49141/1.32978, loss_spatial_bce_8: 0.20041/0.13038, loss_spatial_dice_8: 0.28317/0.29948, loss_spatial_ce_8: 0.36977/0.22229, loss_grounding_bce_8: 0.03898/0.09406, loss_grounding_dice_8: 0.03752/0.20150, loss_grounding_ce_8: 0.24207/0.40672, loss_mask_ce_9: 2.89608/3.67592, loss_mask_bce_9: 0.13557/0.39262, loss_mask_dice_9: 0.58871/1.90238, loss_spatial_bce_9: 0.33784/0.33289, loss_spatial_dice_9: 0.74821/0.82186, loss_spatial_ce_9: 1.51347/1.49597, loss_grounding_bce_9: 0.04328/0.10562, loss_grounding_dice_9: 0.05700/0.28083, loss_grounding_ce_9: 1.23011/0.67149] items per batch[64] items per second[0.23] total items[4345600] mini batches[ 67900] memory[7345] epoch remaining[1:11:02]
INFO:trainer.default_trainer:epochs[    37] optim steps[68000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.82842/0.89792, loss_mask_bce_0: 0.80836/0.33431, loss_mask_dice_0: 5.98177/1.16307, loss_spatial_bce_0: 0.08870/0.08715, loss_spatial_dice_0: 0.18952/0.20798, loss_spatial_ce_0: 0.03026/0.06160, loss_grounding_bce_0: 0.10534/0.08621, loss_grounding_dice_0: 0.24739/0.17853, loss_grounding_ce_0: 0.28081/0.27162, loss_mask_ce_1: 1.94003/0.89850, loss_mask_bce_1: 0.77328/0.33524, loss_mask_dice_1: 5.66934/1.16985, loss_spatial_bce_1: 0.09276/0.08767, loss_spatial_dice_1: 0.19405/0.21196, loss_spatial_ce_1: 0.04624/0.06745, loss_grounding_bce_1: 0.11322/0.08639, loss_grounding_dice_1: 0.23868/0.17938, loss_grounding_ce_1: 0.24846/0.27251, loss_mask_ce_2: 1.99742/0.90556, loss_mask_bce_2: 0.74723/0.33581, loss_mask_dice_2: 5.64391/1.17018, loss_spatial_bce_2: 0.10847/0.08879, loss_spatial_dice_2: 0.18978/0.21361, loss_spatial_ce_2: 0.03500/0.07082, loss_grounding_bce_2: 0.12696/0.08655, loss_grounding_dice_2: 0.24672/0.17917, loss_grounding_ce_2: 0.28602/0.27593, loss_mask_ce_3: 1.99219/0.91637, loss_mask_bce_3: 0.82494/0.33697, loss_mask_dice_3: 5.97506/1.16785, loss_spatial_bce_3: 0.10805/0.09001, loss_spatial_dice_3: 0.20101/0.21458, loss_spatial_ce_3: 0.07565/0.07559, loss_grounding_bce_3: 0.11621/0.08678, loss_grounding_dice_3: 0.24274/0.17892, loss_grounding_ce_3: 0.31807/0.27813, loss_mask_ce_4: 1.99197/0.91736, loss_mask_bce_4: 0.78667/0.33910, loss_mask_dice_4: 5.84919/1.19187, loss_spatial_bce_4: 0.12688/0.09393, loss_spatial_dice_4: 0.24381/0.22676, loss_spatial_ce_4: 0.04347/0.09171, loss_grounding_bce_4: 0.11250/0.08730, loss_grounding_dice_4: 0.24725/0.18182, loss_grounding_ce_4: 0.47328/0.28110, loss_mask_ce_5: 2.17118/0.93387, loss_mask_bce_5: 0.77845/0.34143, loss_mask_dice_5: 5.89204/1.19958, loss_spatial_bce_5: 0.09288/0.09613, loss_spatial_dice_5: 0.23397/0.23100, loss_spatial_ce_5: 0.07767/0.10592, loss_grounding_bce_5: 0.10341/0.08772, loss_grounding_dice_5: 0.24250/0.18308, loss_grounding_ce_5: 0.64750/0.29366, loss_mask_ce_6: 2.01088/0.97382, loss_mask_bce_6: 0.87177/0.34411, loss_mask_dice_6: 5.99554/1.20251, loss_spatial_bce_6: 0.07890/0.10181, loss_spatial_dice_6: 0.21055/0.23389, loss_spatial_ce_6: 0.09692/0.13171, loss_grounding_bce_6: 0.09997/0.08845, loss_grounding_dice_6: 0.25231/0.18344, loss_grounding_ce_6: 0.87952/0.30926, loss_mask_ce_7: 2.04803/1.01894, loss_mask_bce_7: 0.89773/0.35198, loss_mask_dice_7: 6.25872/1.25682, loss_spatial_bce_7: 0.15075/0.10978, loss_spatial_dice_7: 0.29722/0.26154, loss_spatial_ce_7: 0.15277/0.16700, loss_grounding_bce_7: 0.10409/0.09034, loss_grounding_dice_7: 0.25943/0.19073, loss_grounding_ce_7: 1.00957/0.33961, loss_mask_ce_8: 2.45020/1.12728, loss_mask_bce_8: 0.88217/0.36555, loss_mask_dice_8: 6.83370/1.32974, loss_spatial_bce_8: 0.14832/0.13037, loss_spatial_dice_8: 0.30195/0.29945, loss_spatial_ce_8: 0.23667/0.22222, loss_grounding_bce_8: 0.09537/0.09407, loss_grounding_dice_8: 0.26884/0.20150, loss_grounding_ce_8: 1.28740/0.40663, loss_mask_ce_9: 7.80776/3.67584, loss_mask_bce_9: 1.32413/0.39263, loss_mask_dice_9: 10.62939/1.90239, loss_spatial_bce_9: 0.21178/0.33289, loss_spatial_dice_9: 0.89845/0.82185, loss_spatial_ce_9: 1.76455/1.49593, loss_grounding_bce_9: 0.11689/0.10562, loss_grounding_dice_9: 0.36094/0.28082, loss_grounding_ce_9: 2.30998/0.67135] items per batch[64] items per second[0.23] total items[4352000] mini batches[ 68000] memory[7345] epoch remaining[1:06:05]
INFO:trainer.default_trainer:epochs[    37] optim steps[68100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.64751/0.89789, loss_mask_bce_0: 0.93988/0.33427, loss_mask_dice_0: 2.44947/1.16291, loss_spatial_bce_0: 0.09739/0.08713, loss_spatial_dice_0: 0.25516/0.20797, loss_spatial_ce_0: 0.04309/0.06157, loss_grounding_bce_0: 0.05889/0.08619, loss_grounding_dice_0: 0.19766/0.17851, loss_grounding_ce_0: 0.32733/0.27155, loss_mask_ce_1: 1.64943/0.89851, loss_mask_bce_1: 0.97197/0.33519, loss_mask_dice_1: 2.41123/1.16970, loss_spatial_bce_1: 0.10610/0.08766, loss_spatial_dice_1: 0.23807/0.21194, loss_spatial_ce_1: 0.06496/0.06745, loss_grounding_bce_1: 0.06573/0.08637, loss_grounding_dice_1: 0.20220/0.17936, loss_grounding_ce_1: 0.32715/0.27244, loss_mask_ce_2: 1.77084/0.90554, loss_mask_bce_2: 0.89684/0.33577, loss_mask_dice_2: 2.37186/1.17003, loss_spatial_bce_2: 0.11151/0.08878, loss_spatial_dice_2: 0.26693/0.21360, loss_spatial_ce_2: 0.05675/0.07082, loss_grounding_bce_2: 0.06053/0.08653, loss_grounding_dice_2: 0.20541/0.17916, loss_grounding_ce_2: 0.33111/0.27585, loss_mask_ce_3: 1.90443/0.91635, loss_mask_bce_3: 0.99488/0.33693, loss_mask_dice_3: 2.51291/1.16768, loss_spatial_bce_3: 0.10793/0.09000, loss_spatial_dice_3: 0.27694/0.21456, loss_spatial_ce_3: 0.07300/0.07558, loss_grounding_bce_3: 0.06279/0.08677, loss_grounding_dice_3: 0.20612/0.17891, loss_grounding_ce_3: 0.34966/0.27805, loss_mask_ce_4: 1.84601/0.91734, loss_mask_bce_4: 0.96834/0.33907, loss_mask_dice_4: 2.70673/1.19171, loss_spatial_bce_4: 0.12615/0.09392, loss_spatial_dice_4: 0.29467/0.22674, loss_spatial_ce_4: 0.12727/0.09170, loss_grounding_bce_4: 0.08466/0.08729, loss_grounding_dice_4: 0.21792/0.18180, loss_grounding_ce_4: 0.35997/0.28104, loss_mask_ce_5: 1.89113/0.93385, loss_mask_bce_5: 1.03163/0.34139, loss_mask_dice_5: 2.64222/1.19943, loss_spatial_bce_5: 0.15241/0.09611, loss_spatial_dice_5: 0.31853/0.23099, loss_spatial_ce_5: 0.18729/0.10592, loss_grounding_bce_5: 0.06588/0.08770, loss_grounding_dice_5: 0.22095/0.18306, loss_grounding_ce_5: 0.34975/0.29356, loss_mask_ce_6: 1.83426/0.97380, loss_mask_bce_6: 1.16332/0.34407, loss_mask_dice_6: 2.71874/1.20235, loss_spatial_bce_6: 0.20386/0.10180, loss_spatial_dice_6: 0.32719/0.23388, loss_spatial_ce_6: 0.05974/0.13169, loss_grounding_bce_6: 0.07237/0.08844, loss_grounding_dice_6: 0.22459/0.18343, loss_grounding_ce_6: 0.45883/0.30916, loss_mask_ce_7: 2.11865/1.01894, loss_mask_bce_7: 1.38691/0.35194, loss_mask_dice_7: 2.78296/1.25662, loss_spatial_bce_7: 0.17127/0.10977, loss_spatial_dice_7: 0.35117/0.26153, loss_spatial_ce_7: 0.19474/0.16697, loss_grounding_bce_7: 0.10503/0.09032, loss_grounding_dice_7: 0.25040/0.19073, loss_grounding_ce_7: 0.38378/0.33949, loss_mask_ce_8: 2.63141/1.12726, loss_mask_bce_8: 1.18430/0.36551, loss_mask_dice_8: 2.97448/1.32956, loss_spatial_bce_8: 0.15943/0.13036, loss_spatial_dice_8: 0.35149/0.29945, loss_spatial_ce_8: 0.26872/0.22221, loss_grounding_bce_8: 0.08131/0.09405, loss_grounding_dice_8: 0.26432/0.20150, loss_grounding_ce_8: 0.38835/0.40649, loss_mask_ce_9: 6.36640/3.67577, loss_mask_bce_9: 1.17711/0.39258, loss_mask_dice_9: 4.46602/1.90212, loss_spatial_bce_9: 0.14399/0.33291, loss_spatial_dice_9: 0.88285/0.82184, loss_spatial_ce_9: 1.45727/1.49599, loss_grounding_bce_9: 0.08993/0.10560, loss_grounding_dice_9: 0.36781/0.28081, loss_grounding_ce_9: 0.34118/0.67127] items per batch[64] items per second[0.23] total items[4358400] mini batches[ 68100] memory[7345] epoch remaining[1:01:18]
INFO:trainer.default_trainer:epochs[    37] optim steps[68200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22100/0.89783, loss_mask_bce_0: 0.12790/0.33430, loss_mask_dice_0: 0.32255/1.16330, loss_spatial_bce_0: 0.04831/0.08713, loss_spatial_dice_0: 0.11553/0.20795, loss_spatial_ce_0: 0.02928/0.06154, loss_grounding_bce_0: 0.03776/0.08619, loss_grounding_dice_0: 0.12218/0.17855, loss_grounding_ce_0: 0.03657/0.27151, loss_mask_ce_1: 0.23020/0.89843, loss_mask_bce_1: 0.12535/0.33522, loss_mask_dice_1: 0.33622/1.17006, loss_spatial_bce_1: 0.04724/0.08765, loss_spatial_dice_1: 0.10833/0.21193, loss_spatial_ce_1: 0.03349/0.06742, loss_grounding_bce_1: 0.03684/0.08637, loss_grounding_dice_1: 0.12147/0.17940, loss_grounding_ce_1: 0.04232/0.27239, loss_mask_ce_2: 0.23659/0.90548, loss_mask_bce_2: 0.12481/0.33579, loss_mask_dice_2: 0.33316/1.17041, loss_spatial_bce_2: 0.05036/0.08878, loss_spatial_dice_2: 0.11902/0.21359, loss_spatial_ce_2: 0.03274/0.07079, loss_grounding_bce_2: 0.03722/0.08653, loss_grounding_dice_2: 0.12614/0.17920, loss_grounding_ce_2: 0.05912/0.27579, loss_mask_ce_3: 0.24201/0.91627, loss_mask_bce_3: 0.13561/0.33696, loss_mask_dice_3: 0.34621/1.16810, loss_spatial_bce_3: 0.05883/0.09000, loss_spatial_dice_3: 0.14367/0.21455, loss_spatial_ce_3: 0.06429/0.07556, loss_grounding_bce_3: 0.03821/0.08677, loss_grounding_dice_3: 0.12525/0.17895, loss_grounding_ce_3: 0.06442/0.27800, loss_mask_ce_4: 0.24677/0.91730, loss_mask_bce_4: 0.12841/0.33909, loss_mask_dice_4: 0.35046/1.19211, loss_spatial_bce_4: 0.09865/0.09392, loss_spatial_dice_4: 0.16268/0.22673, loss_spatial_ce_4: 0.08774/0.09167, loss_grounding_bce_4: 0.03787/0.08729, loss_grounding_dice_4: 0.13919/0.18184, loss_grounding_ce_4: 0.06794/0.28098, loss_mask_ce_5: 0.28971/0.93379, loss_mask_bce_5: 0.12475/0.34142, loss_mask_dice_5: 0.33600/1.19985, loss_spatial_bce_5: 0.10646/0.09611, loss_spatial_dice_5: 0.18686/0.23098, loss_spatial_ce_5: 0.14404/0.10592, loss_grounding_bce_5: 0.04008/0.08770, loss_grounding_dice_5: 0.13893/0.18310, loss_grounding_ce_5: 0.07321/0.29349, loss_mask_ce_6: 0.30996/0.97376, loss_mask_bce_6: 0.12832/0.34409, loss_mask_dice_6: 0.34045/1.20276, loss_spatial_bce_6: 0.15861/0.10180, loss_spatial_dice_6: 0.20262/0.23387, loss_spatial_ce_6: 0.20279/0.13167, loss_grounding_bce_6: 0.03700/0.08844, loss_grounding_dice_6: 0.12626/0.18347, loss_grounding_ce_6: 0.10074/0.30910, loss_mask_ce_7: 0.28801/1.01887, loss_mask_bce_7: 0.12942/0.35197, loss_mask_dice_7: 0.36506/1.25704, loss_spatial_bce_7: 0.04428/0.10977, loss_spatial_dice_7: 0.14552/0.26153, loss_spatial_ce_7: 0.21135/0.16696, loss_grounding_bce_7: 0.03502/0.09033, loss_grounding_dice_7: 0.12747/0.19077, loss_grounding_ce_7: 0.06494/0.33943, loss_mask_ce_8: 0.28920/1.12719, loss_mask_bce_8: 0.14895/0.36554, loss_mask_dice_8: 0.37803/1.33000, loss_spatial_bce_8: 0.06704/0.13036, loss_spatial_dice_8: 0.17723/0.29945, loss_spatial_ce_8: 0.17822/0.22217, loss_grounding_bce_8: 0.04081/0.09405, loss_grounding_dice_8: 0.15720/0.20154, loss_grounding_ce_8: 0.08863/0.40640, loss_mask_ce_9: 2.80211/3.67587, loss_mask_bce_9: 0.23656/0.39261, loss_mask_dice_9: 0.53671/1.90266, loss_spatial_bce_9: 0.47770/0.33290, loss_spatial_dice_9: 0.80025/0.82183, loss_spatial_ce_9: 1.24527/1.49583, loss_grounding_bce_9: 0.07066/0.10560, loss_grounding_dice_9: 0.24479/0.28086, loss_grounding_ce_9: 0.26591/0.67122] items per batch[64] items per second[0.23] total items[4364800] mini batches[ 68200] memory[7345] epoch remaining[0:56:52]
INFO:trainer.default_trainer:epochs[    37] optim steps[68300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81816/0.89788, loss_mask_bce_0: 0.19054/0.33425, loss_mask_dice_0: 0.78263/1.16305, loss_spatial_bce_0: 0.02696/0.08712, loss_spatial_dice_0: 0.13375/0.20792, loss_spatial_ce_0: 0.00957/0.06151, loss_grounding_bce_0: 0.03194/0.08620, loss_grounding_dice_0: 0.05059/0.17853, loss_grounding_ce_0: 0.04371/0.27155, loss_mask_ce_1: 0.61726/0.89846, loss_mask_bce_1: 0.19330/0.33518, loss_mask_dice_1: 0.80075/1.16981, loss_spatial_bce_1: 0.02752/0.08764, loss_spatial_dice_1: 0.13880/0.21189, loss_spatial_ce_1: 0.00984/0.06739, loss_grounding_bce_1: 0.03407/0.08638, loss_grounding_dice_1: 0.05759/0.17937, loss_grounding_ce_1: 0.06988/0.27243, loss_mask_ce_2: 0.61182/0.90552, loss_mask_bce_2: 0.19910/0.33575, loss_mask_dice_2: 0.89454/1.17016, loss_spatial_bce_2: 0.02797/0.08877, loss_spatial_dice_2: 0.13502/0.21355, loss_spatial_ce_2: 0.00248/0.07076, loss_grounding_bce_2: 0.03551/0.08654, loss_grounding_dice_2: 0.05877/0.17918, loss_grounding_ce_2: 0.07719/0.27584, loss_mask_ce_3: 0.68784/0.91631, loss_mask_bce_3: 0.18649/0.33692, loss_mask_dice_3: 0.70885/1.16788, loss_spatial_bce_3: 0.02865/0.08999, loss_spatial_dice_3: 0.13485/0.21452, loss_spatial_ce_3: 0.02647/0.07555, loss_grounding_bce_3: 0.03540/0.08678, loss_grounding_dice_3: 0.06059/0.17893, loss_grounding_ce_3: 0.05749/0.27803, loss_mask_ce_4: 0.65953/0.91733, loss_mask_bce_4: 0.19660/0.33905, loss_mask_dice_4: 0.78798/1.19184, loss_spatial_bce_4: 0.03061/0.09390, loss_spatial_dice_4: 0.14587/0.22669, loss_spatial_ce_4: 0.02247/0.09165, loss_grounding_bce_4: 0.03443/0.08730, loss_grounding_dice_4: 0.06031/0.18182, loss_grounding_ce_4: 0.12980/0.28099, loss_mask_ce_5: 0.72059/0.93385, loss_mask_bce_5: 0.18646/0.34138, loss_mask_dice_5: 0.82894/1.19958, loss_spatial_bce_5: 0.02957/0.09610, loss_spatial_dice_5: 0.15612/0.23095, loss_spatial_ce_5: 0.13785/0.10590, loss_grounding_bce_5: 0.03456/0.08772, loss_grounding_dice_5: 0.05757/0.18308, loss_grounding_ce_5: 0.04574/0.29353, loss_mask_ce_6: 0.76612/0.97384, loss_mask_bce_6: 0.19146/0.34405, loss_mask_dice_6: 0.71886/1.20250, loss_spatial_bce_6: 0.03410/0.10179, loss_spatial_dice_6: 0.16114/0.23384, loss_spatial_ce_6: 0.07715/0.13164, loss_grounding_bce_6: 0.03294/0.08845, loss_grounding_dice_6: 0.05239/0.18345, loss_grounding_ce_6: 0.08246/0.30915, loss_mask_ce_7: 0.64878/1.01895, loss_mask_bce_7: 0.25237/0.35192, loss_mask_dice_7: 0.91087/1.25680, loss_spatial_bce_7: 0.04361/0.10976, loss_spatial_dice_7: 0.21147/0.26150, loss_spatial_ce_7: 0.05386/0.16693, loss_grounding_bce_7: 0.03789/0.09034, loss_grounding_dice_7: 0.05835/0.19075, loss_grounding_ce_7: 0.12464/0.33943, loss_mask_ce_8: 0.66829/1.12722, loss_mask_bce_8: 0.24213/0.36550, loss_mask_dice_8: 1.04818/1.32976, loss_spatial_bce_8: 0.06021/0.13035, loss_spatial_dice_8: 0.28774/0.29942, loss_spatial_ce_8: 0.25451/0.22213, loss_grounding_bce_8: 0.03630/0.09406, loss_grounding_dice_8: 0.06814/0.20153, loss_grounding_ce_8: 0.15109/0.40645, loss_mask_ce_9: 3.37009/3.67583, loss_mask_bce_9: 0.22417/0.39259, loss_mask_dice_9: 1.32385/1.90226, loss_spatial_bce_9: 0.27791/0.33290, loss_spatial_dice_9: 0.85840/0.82182, loss_spatial_ce_9: 1.29571/1.49578, loss_grounding_bce_9: 0.05996/0.10562, loss_grounding_dice_9: 0.10581/0.28085, loss_grounding_ce_9: 0.94943/0.67127] items per batch[64] items per second[0.23] total items[4371200] mini batches[ 68300] memory[7345] epoch remaining[0:52:09]
INFO:trainer.default_trainer:epochs[    37] optim steps[68400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44230/0.89784, loss_mask_bce_0: 0.39674/0.33424, loss_mask_dice_0: 0.83728/1.16316, loss_spatial_bce_0: 0.06591/0.08711, loss_spatial_dice_0: 0.13632/0.20789, loss_spatial_ce_0: 0.00203/0.06150, loss_grounding_bce_0: 0.07436/0.08620, loss_grounding_dice_0: 0.09367/0.17851, loss_grounding_ce_0: 0.13221/0.27146, loss_mask_ce_1: 0.44982/0.89842, loss_mask_bce_1: 0.39871/0.33516, loss_mask_dice_1: 0.82991/1.16989, loss_spatial_bce_1: 0.06243/0.08763, loss_spatial_dice_1: 0.13660/0.21186, loss_spatial_ce_1: 0.00348/0.06737, loss_grounding_bce_1: 0.08573/0.08638, loss_grounding_dice_1: 0.10272/0.17936, loss_grounding_ce_1: 0.13302/0.27235, loss_mask_ce_2: 0.44138/0.90550, loss_mask_bce_2: 0.40188/0.33574, loss_mask_dice_2: 0.84141/1.17028, loss_spatial_bce_2: 0.06516/0.08876, loss_spatial_dice_2: 0.14272/0.21352, loss_spatial_ce_2: 0.00164/0.07074, loss_grounding_bce_2: 0.08780/0.08654, loss_grounding_dice_2: 0.10292/0.17916, loss_grounding_ce_2: 0.14945/0.27579, loss_mask_ce_3: 0.46983/0.91626, loss_mask_bce_3: 0.40517/0.33690, loss_mask_dice_3: 0.82443/1.16801, loss_spatial_bce_3: 0.06255/0.08997, loss_spatial_dice_3: 0.14117/0.21449, loss_spatial_ce_3: 0.00308/0.07554, loss_grounding_bce_3: 0.08963/0.08677, loss_grounding_dice_3: 0.10103/0.17892, loss_grounding_ce_3: 0.18840/0.27796, loss_mask_ce_4: 0.45065/0.91730, loss_mask_bce_4: 0.40518/0.33903, loss_mask_dice_4: 0.82547/1.19196, loss_spatial_bce_4: 0.06458/0.09389, loss_spatial_dice_4: 0.14955/0.22667, loss_spatial_ce_4: 0.03660/0.09163, loss_grounding_bce_4: 0.08964/0.08730, loss_grounding_dice_4: 0.10060/0.18180, loss_grounding_ce_4: 0.17687/0.28090, loss_mask_ce_5: 0.44704/0.93381, loss_mask_bce_5: 0.41140/0.34137, loss_mask_dice_5: 0.83884/1.19972, loss_spatial_bce_5: 0.06984/0.09609, loss_spatial_dice_5: 0.16425/0.23093, loss_spatial_ce_5: 0.01791/0.10587, loss_grounding_bce_5: 0.09998/0.08772, loss_grounding_dice_5: 0.10878/0.18308, loss_grounding_ce_5: 0.14669/0.29342, loss_mask_ce_6: 0.47047/0.97379, loss_mask_bce_6: 0.41840/0.34404, loss_mask_dice_6: 0.85279/1.20262, loss_spatial_bce_6: 0.07644/0.10177, loss_spatial_dice_6: 0.14540/0.23381, loss_spatial_ce_6: 0.06395/0.13161, loss_grounding_bce_6: 0.09447/0.08845, loss_grounding_dice_6: 0.11392/0.18344, loss_grounding_ce_6: 0.12714/0.30905, loss_mask_ce_7: 0.63161/1.01889, loss_mask_bce_7: 0.40899/0.35191, loss_mask_dice_7: 0.91501/1.25692, loss_spatial_bce_7: 0.08674/0.10974, loss_spatial_dice_7: 0.18745/0.26149, loss_spatial_ce_7: 0.06093/0.16687, loss_grounding_bce_7: 0.08085/0.09034, loss_grounding_dice_7: 0.11382/0.19074, loss_grounding_ce_7: 0.17639/0.33934, loss_mask_ce_8: 0.80219/1.12728, loss_mask_bce_8: 0.41050/0.36550, loss_mask_dice_8: 0.93497/1.32989, loss_spatial_bce_8: 0.09211/0.13034, loss_spatial_dice_8: 0.20213/0.29941, loss_spatial_ce_8: 0.15979/0.22208, loss_grounding_bce_8: 0.08089/0.09406, loss_grounding_dice_8: 0.11254/0.20151, loss_grounding_ce_8: 0.24184/0.40637, loss_mask_ce_9: 3.92360/3.67595, loss_mask_bce_9: 0.47060/0.39259, loss_mask_dice_9: 1.36141/1.90255, loss_spatial_bce_9: 0.33575/0.33289, loss_spatial_dice_9: 0.83258/0.82183, loss_spatial_ce_9: 1.46071/1.49584, loss_grounding_bce_9: 0.18281/0.10562, loss_grounding_dice_9: 0.29087/0.28084, loss_grounding_ce_9: 0.22599/0.67121] items per batch[64] items per second[0.24] total items[4377600] mini batches[ 68400] memory[7345] epoch remaining[0:47:20]
INFO:trainer.default_trainer:epochs[    37] optim steps[68500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69155/0.89773, loss_mask_bce_0: 0.29404/0.33421, loss_mask_dice_0: 0.38053/1.16298, loss_spatial_bce_0: 0.07832/0.08711, loss_spatial_dice_0: 0.17807/0.20787, loss_spatial_ce_0: 0.00332/0.06147, loss_grounding_bce_0: 0.16160/0.08620, loss_grounding_dice_0: 0.09938/0.17849, loss_grounding_ce_0: 0.08198/0.27160, loss_mask_ce_1: 0.66360/0.89831, loss_mask_bce_1: 0.27276/0.33514, loss_mask_dice_1: 0.38018/1.16974, loss_spatial_bce_1: 0.08173/0.08762, loss_spatial_dice_1: 0.17416/0.21183, loss_spatial_ce_1: 0.00611/0.06734, loss_grounding_bce_1: 0.15063/0.08638, loss_grounding_dice_1: 0.09369/0.17934, loss_grounding_ce_1: 0.07153/0.27247, loss_mask_ce_2: 0.71789/0.90538, loss_mask_bce_2: 0.27125/0.33572, loss_mask_dice_2: 0.35374/1.17011, loss_spatial_bce_2: 0.09672/0.08875, loss_spatial_dice_2: 0.18946/0.21350, loss_spatial_ce_2: 0.01746/0.07072, loss_grounding_bce_2: 0.14830/0.08654, loss_grounding_dice_2: 0.08977/0.17915, loss_grounding_ce_2: 0.07774/0.27590, loss_mask_ce_3: 0.75112/0.91616, loss_mask_bce_3: 0.26345/0.33688, loss_mask_dice_3: 0.38994/1.16786, loss_spatial_bce_3: 0.08660/0.08997, loss_spatial_dice_3: 0.17714/0.21446, loss_spatial_ce_3: 0.03010/0.07551, loss_grounding_bce_3: 0.14242/0.08678, loss_grounding_dice_3: 0.08711/0.17890, loss_grounding_ce_3: 0.07969/0.27805, loss_mask_ce_4: 0.79293/0.91718, loss_mask_bce_4: 0.30866/0.33902, loss_mask_dice_4: 0.39295/1.19180, loss_spatial_bce_4: 0.11428/0.09389, loss_spatial_dice_4: 0.19885/0.22665, loss_spatial_ce_4: 0.04136/0.09161, loss_grounding_bce_4: 0.16474/0.08731, loss_grounding_dice_4: 0.09750/0.18179, loss_grounding_ce_4: 0.07844/0.28097, loss_mask_ce_5: 0.76517/0.93371, loss_mask_bce_5: 0.28763/0.34135, loss_mask_dice_5: 0.43699/1.19957, loss_spatial_bce_5: 0.10184/0.09609, loss_spatial_dice_5: 0.19450/0.23091, loss_spatial_ce_5: 0.02061/0.10583, loss_grounding_bce_5: 0.15235/0.08773, loss_grounding_dice_5: 0.09413/0.18306, loss_grounding_ce_5: 0.12069/0.29350, loss_mask_ce_6: 0.89979/0.97369, loss_mask_bce_6: 0.28312/0.34403, loss_mask_dice_6: 0.41186/1.20247, loss_spatial_bce_6: 0.10581/0.10177, loss_spatial_dice_6: 0.19052/0.23379, loss_spatial_ce_6: 0.12002/0.13158, loss_grounding_bce_6: 0.14843/0.08846, loss_grounding_dice_6: 0.09741/0.18342, loss_grounding_ce_6: 0.15861/0.30915, loss_mask_ce_7: 0.94309/1.01877, loss_mask_bce_7: 0.33911/0.35191, loss_mask_dice_7: 0.47438/1.25678, loss_spatial_bce_7: 0.12891/0.10974, loss_spatial_dice_7: 0.20544/0.26147, loss_spatial_ce_7: 0.04180/0.16684, loss_grounding_bce_7: 0.18538/0.09035, loss_grounding_dice_7: 0.12510/0.19072, loss_grounding_ce_7: 0.16065/0.33941, loss_mask_ce_8: 1.28112/1.12717, loss_mask_bce_8: 0.40721/0.36550, loss_mask_dice_8: 0.72632/1.32974, loss_spatial_bce_8: 0.10081/0.13033, loss_spatial_dice_8: 0.18627/0.29939, loss_spatial_ce_8: 0.11514/0.22202, loss_grounding_bce_8: 0.20694/0.09407, loss_grounding_dice_8: 0.14713/0.20150, loss_grounding_ce_8: 0.25163/0.40649, loss_mask_ce_9: 3.54886/3.67589, loss_mask_bce_9: 0.48098/0.39257, loss_mask_dice_9: 0.87796/1.90235, loss_spatial_bce_9: 0.38249/0.33291, loss_spatial_dice_9: 0.85390/0.82182, loss_spatial_ce_9: 1.10832/1.49584, loss_grounding_bce_9: 0.30399/0.10563, loss_grounding_dice_9: 0.22584/0.28084, loss_grounding_ce_9: 0.19111/0.67132] items per batch[64] items per second[0.23] total items[4384000] mini batches[ 68500] memory[7345] epoch remaining[0:42:45]
INFO:trainer.default_trainer:epochs[    37] optim steps[68600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17807/0.89778, loss_mask_bce_0: 0.19087/0.33417, loss_mask_dice_0: 0.16316/1.16288, loss_spatial_bce_0: 0.12024/0.08710, loss_spatial_dice_0: 0.06996/0.20786, loss_spatial_ce_0: 0.00051/0.06146, loss_grounding_bce_0: 0.18370/0.08619, loss_grounding_dice_0: 0.19016/0.17846, loss_grounding_ce_0: 0.04173/0.27165, loss_mask_ce_1: 0.16932/0.89833, loss_mask_bce_1: 0.19559/0.33511, loss_mask_dice_1: 0.16794/1.16964, loss_spatial_bce_1: 0.11867/0.08762, loss_spatial_dice_1: 0.06404/0.21182, loss_spatial_ce_1: 0.00130/0.06732, loss_grounding_bce_1: 0.19911/0.08637, loss_grounding_dice_1: 0.19606/0.17932, loss_grounding_ce_1: 0.03558/0.27249, loss_mask_ce_2: 0.15013/0.90543, loss_mask_bce_2: 0.20098/0.33568, loss_mask_dice_2: 0.16813/1.17003, loss_spatial_bce_2: 0.13083/0.08875, loss_spatial_dice_2: 0.08224/0.21349, loss_spatial_ce_2: 0.00255/0.07071, loss_grounding_bce_2: 0.20417/0.08653, loss_grounding_dice_2: 0.19663/0.17912, loss_grounding_ce_2: 0.02311/0.27591, loss_mask_ce_3: 0.16071/0.91620, loss_mask_bce_3: 0.20563/0.33685, loss_mask_dice_3: 0.18602/1.16776, loss_spatial_bce_3: 0.13215/0.08996, loss_spatial_dice_3: 0.07826/0.21445, loss_spatial_ce_3: 0.02009/0.07550, loss_grounding_bce_3: 0.22734/0.08677, loss_grounding_dice_3: 0.22509/0.17887, loss_grounding_ce_3: 0.01747/0.27813, loss_mask_ce_4: 0.15571/0.91722, loss_mask_bce_4: 0.19883/0.33898, loss_mask_dice_4: 0.17320/1.19170, loss_spatial_bce_4: 0.11277/0.09388, loss_spatial_dice_4: 0.06433/0.22664, loss_spatial_ce_4: 0.03602/0.09161, loss_grounding_bce_4: 0.15676/0.08729, loss_grounding_dice_4: 0.17961/0.18176, loss_grounding_ce_4: 0.07132/0.28105, loss_mask_ce_5: 0.16864/0.93378, loss_mask_bce_5: 0.17450/0.34131, loss_mask_dice_5: 0.16216/1.19948, loss_spatial_bce_5: 0.15445/0.09608, loss_spatial_dice_5: 0.14087/0.23090, loss_spatial_ce_5: 0.02095/0.10583, loss_grounding_bce_5: 0.16145/0.08771, loss_grounding_dice_5: 0.18608/0.18302, loss_grounding_ce_5: 0.05565/0.29358, loss_mask_ce_6: 0.20008/0.97379, loss_mask_bce_6: 0.18869/0.34399, loss_mask_dice_6: 0.17918/1.20240, loss_spatial_bce_6: 0.18354/0.10176, loss_spatial_dice_6: 0.19235/0.23379, loss_spatial_ce_6: 0.05003/0.13155, loss_grounding_bce_6: 0.17532/0.08845, loss_grounding_dice_6: 0.20163/0.18339, loss_grounding_ce_6: 0.06868/0.30918, loss_mask_ce_7: 0.21012/1.01882, loss_mask_bce_7: 0.19651/0.35187, loss_mask_dice_7: 0.18651/1.25671, loss_spatial_bce_7: 0.18513/0.10973, loss_spatial_dice_7: 0.16578/0.26146, loss_spatial_ce_7: 0.01161/0.16681, loss_grounding_bce_7: 0.18023/0.09033, loss_grounding_dice_7: 0.21049/0.19069, loss_grounding_ce_7: 0.10279/0.33942, loss_mask_ce_8: 0.24441/1.12727, loss_mask_bce_8: 0.18150/0.36547, loss_mask_dice_8: 0.18084/1.32967, loss_spatial_bce_8: 0.17032/0.13032, loss_spatial_dice_8: 0.16593/0.29937, loss_spatial_ce_8: 0.07144/0.22197, loss_grounding_bce_8: 0.17806/0.09405, loss_grounding_dice_8: 0.21310/0.20147, loss_grounding_ce_8: 0.08055/0.40651, loss_mask_ce_9: 2.52381/3.67592, loss_mask_bce_9: 0.18380/0.39252, loss_mask_dice_9: 0.18010/1.90225, loss_spatial_bce_9: 1.05233/0.33290, loss_spatial_dice_9: 0.67378/0.82181, loss_spatial_ce_9: 1.34755/1.49572, loss_grounding_bce_9: 0.14331/0.10561, loss_grounding_dice_9: 0.18068/0.28081, loss_grounding_ce_9: 0.24924/0.67128] items per batch[64] items per second[0.24] total items[4390400] mini batches[ 68600] memory[7345] epoch remaining[0:38:01]
INFO:trainer.default_trainer:epochs[    37] optim steps[68700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21594/0.89772, loss_mask_bce_0: 0.23289/0.33415, loss_mask_dice_0: 0.71563/1.16283, loss_spatial_bce_0: 0.04404/0.08709, loss_spatial_dice_0: 0.19166/0.20784, loss_spatial_ce_0: 0.03359/0.06144, loss_grounding_bce_0: 0.06560/0.08619, loss_grounding_dice_0: 0.29961/0.17847, loss_grounding_ce_0: 0.27697/0.27173, loss_mask_ce_1: 1.09250/0.89831, loss_mask_bce_1: 0.22761/0.33508, loss_mask_dice_1: 0.79650/1.16957, loss_spatial_bce_1: 0.04831/0.08761, loss_spatial_dice_1: 0.23847/0.21181, loss_spatial_ce_1: 0.02278/0.06731, loss_grounding_bce_1: 0.08554/0.08638, loss_grounding_dice_1: 0.33254/0.17932, loss_grounding_ce_1: 0.24917/0.27252, loss_mask_ce_2: 1.12412/0.90537, loss_mask_bce_2: 0.22441/0.33565, loss_mask_dice_2: 0.80806/1.16997, loss_spatial_bce_2: 0.05205/0.08874, loss_spatial_dice_2: 0.22490/0.21348, loss_spatial_ce_2: 0.03213/0.07069, loss_grounding_bce_2: 0.06675/0.08654, loss_grounding_dice_2: 0.36571/0.17913, loss_grounding_ce_2: 0.29419/0.27596, loss_mask_ce_3: 1.12694/0.91613, loss_mask_bce_3: 0.22578/0.33683, loss_mask_dice_3: 0.87530/1.16772, loss_spatial_bce_3: 0.04775/0.08995, loss_spatial_dice_3: 0.19945/0.21444, loss_spatial_ce_3: 0.15853/0.07548, loss_grounding_bce_3: 0.06584/0.08677, loss_grounding_dice_3: 0.32963/0.17888, loss_grounding_ce_3: 0.30666/0.27818, loss_mask_ce_4: 1.56244/0.91719, loss_mask_bce_4: 0.20985/0.33896, loss_mask_dice_4: 0.78676/1.19162, loss_spatial_bce_4: 0.04882/0.09387, loss_spatial_dice_4: 0.24335/0.22663, loss_spatial_ce_4: 0.14712/0.09159, loss_grounding_bce_4: 0.06511/0.08730, loss_grounding_dice_4: 0.30551/0.18177, loss_grounding_ce_4: 0.40828/0.28111, loss_mask_ce_5: 1.25381/0.93377, loss_mask_bce_5: 0.22625/0.34128, loss_mask_dice_5: 0.84346/1.19943, loss_spatial_bce_5: 0.05091/0.09607, loss_spatial_dice_5: 0.22697/0.23089, loss_spatial_ce_5: 0.30646/0.10580, loss_grounding_bce_5: 0.11274/0.08772, loss_grounding_dice_5: 0.35203/0.18304, loss_grounding_ce_5: 0.27081/0.29363, loss_mask_ce_6: 1.25268/0.97375, loss_mask_bce_6: 0.21425/0.34396, loss_mask_dice_6: 0.87078/1.20232, loss_spatial_bce_6: 0.05138/0.10175, loss_spatial_dice_6: 0.21461/0.23378, loss_spatial_ce_6: 0.38122/0.13153, loss_grounding_bce_6: 0.07299/0.08845, loss_grounding_dice_6: 0.32213/0.18341, loss_grounding_ce_6: 0.26857/0.30921, loss_mask_ce_7: 1.01710/1.01883, loss_mask_bce_7: 0.24364/0.35184, loss_mask_dice_7: 0.84691/1.25663, loss_spatial_bce_7: 0.08604/0.10972, loss_spatial_dice_7: 0.26300/0.26145, loss_spatial_ce_7: 0.18166/0.16675, loss_grounding_bce_7: 0.09171/0.09035, loss_grounding_dice_7: 0.36377/0.19071, loss_grounding_ce_7: 0.24752/0.33945, loss_mask_ce_8: 1.58065/1.12732, loss_mask_bce_8: 0.26151/0.36544, loss_mask_dice_8: 1.01984/1.32961, loss_spatial_bce_8: 0.08664/0.13030, loss_spatial_dice_8: 0.31007/0.29936, loss_spatial_ce_8: 0.18024/0.22192, loss_grounding_bce_8: 0.06513/0.09406, loss_grounding_dice_8: 0.28170/0.20149, loss_grounding_ce_8: 0.17969/0.40653, loss_mask_ce_9: 4.95116/3.67590, loss_mask_bce_9: 0.23769/0.39249, loss_mask_dice_9: 1.25603/1.90212, loss_spatial_bce_9: 0.18289/0.33286, loss_spatial_dice_9: 0.74573/0.82179, loss_spatial_ce_9: 1.20479/1.49565, loss_grounding_bce_9: 0.06712/0.10562, loss_grounding_dice_9: 0.37951/0.28084, loss_grounding_ce_9: 0.18572/0.67121] items per batch[64] items per second[0.23] total items[4396800] mini batches[ 68700] memory[7345] epoch remaining[0:33:24]
INFO:trainer.default_trainer:epochs[    37] optim steps[68800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75821/0.89760, loss_mask_bce_0: 0.15051/0.33416, loss_mask_dice_0: 2.14173/1.16280, loss_spatial_bce_0: 0.02069/0.08709, loss_spatial_dice_0: 0.23946/0.20783, loss_spatial_ce_0: 0.01161/0.06142, loss_grounding_bce_0: 0.00845/0.08620, loss_grounding_dice_0: 0.36385/0.17846, loss_grounding_ce_0: 0.55158/0.27179, loss_mask_ce_1: 0.92256/0.89821, loss_mask_bce_1: 0.14358/0.33509, loss_mask_dice_1: 1.96119/1.16952, loss_spatial_bce_1: 0.01941/0.08761, loss_spatial_dice_1: 0.23315/0.21179, loss_spatial_ce_1: 0.02743/0.06728, loss_grounding_bce_1: 0.00897/0.08638, loss_grounding_dice_1: 0.35613/0.17931, loss_grounding_ce_1: 0.66153/0.27257, loss_mask_ce_2: 0.70517/0.90529, loss_mask_bce_2: 0.15163/0.33566, loss_mask_dice_2: 1.97904/1.16991, loss_spatial_bce_2: 0.02424/0.08874, loss_spatial_dice_2: 0.24077/0.21346, loss_spatial_ce_2: 0.01128/0.07066, loss_grounding_bce_2: 0.00925/0.08654, loss_grounding_dice_2: 0.37717/0.17912, loss_grounding_ce_2: 0.53623/0.27602, loss_mask_ce_3: 1.26128/0.91605, loss_mask_bce_3: 0.15918/0.33684, loss_mask_dice_3: 1.95977/1.16769, loss_spatial_bce_3: 0.02329/0.08995, loss_spatial_dice_3: 0.21803/0.21443, loss_spatial_ce_3: 0.02709/0.07546, loss_grounding_bce_3: 0.01212/0.08678, loss_grounding_dice_3: 0.36411/0.17887, loss_grounding_ce_3: 0.62290/0.27826, loss_mask_ce_4: 1.01952/0.91711, loss_mask_bce_4: 0.13892/0.33897, loss_mask_dice_4: 1.85078/1.19157, loss_spatial_bce_4: 0.02753/0.09388, loss_spatial_dice_4: 0.24401/0.22662, loss_spatial_ce_4: 0.10516/0.09157, loss_grounding_bce_4: 0.01171/0.08730, loss_grounding_dice_4: 0.33220/0.18175, loss_grounding_ce_4: 0.70286/0.28123, loss_mask_ce_5: 0.85868/0.93369, loss_mask_bce_5: 0.14271/0.34131, loss_mask_dice_5: 1.99630/1.19940, loss_spatial_bce_5: 0.02642/0.09608, loss_spatial_dice_5: 0.25477/0.23088, loss_spatial_ce_5: 0.01272/0.10577, loss_grounding_bce_5: 0.01011/0.08773, loss_grounding_dice_5: 0.33661/0.18302, loss_grounding_ce_5: 0.59434/0.29372, loss_mask_ce_6: 1.02713/0.97365, loss_mask_bce_6: 0.15337/0.34398, loss_mask_dice_6: 1.87387/1.20230, loss_spatial_bce_6: 0.02880/0.10176, loss_spatial_dice_6: 0.23723/0.23377, loss_spatial_ce_6: 0.07883/0.13149, loss_grounding_bce_6: 0.00809/0.08846, loss_grounding_dice_6: 0.32732/0.18340, loss_grounding_ce_6: 0.60532/0.30930, loss_mask_ce_7: 1.31749/1.01876, loss_mask_bce_7: 0.16071/0.35186, loss_mask_dice_7: 2.03429/1.25660, loss_spatial_bce_7: 0.02809/0.10972, loss_spatial_dice_7: 0.27631/0.26143, loss_spatial_ce_7: 0.14978/0.16672, loss_grounding_bce_7: 0.01129/0.09035, loss_grounding_dice_7: 0.44721/0.19070, loss_grounding_ce_7: 0.68920/0.33957, loss_mask_ce_8: 1.15176/1.12724, loss_mask_bce_8: 0.22042/0.36547, loss_mask_dice_8: 2.20416/1.32958, loss_spatial_bce_8: 0.04097/0.13030, loss_spatial_dice_8: 0.32109/0.29934, loss_spatial_ce_8: 0.11345/0.22187, loss_grounding_bce_8: 0.00893/0.09407, loss_grounding_dice_8: 0.39546/0.20148, loss_grounding_ce_8: 0.57228/0.40665, loss_mask_ce_9: 3.74260/3.67586, loss_mask_bce_9: 0.13553/0.39252, loss_mask_dice_9: 2.89565/1.90210, loss_spatial_bce_9: 0.13735/0.33285, loss_spatial_dice_9: 0.84044/0.82177, loss_spatial_ce_9: 1.13542/1.49558, loss_grounding_bce_9: 0.00831/0.10564, loss_grounding_dice_9: 0.56094/0.28081, loss_grounding_ce_9: 0.65403/0.67122] items per batch[64] items per second[0.23] total items[4403200] mini batches[ 68800] memory[7345] epoch remaining[0:28:46]
INFO:trainer.default_trainer:epochs[    37] optim steps[68900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98495/0.89759, loss_mask_bce_0: 0.29212/0.33419, loss_mask_dice_0: 0.84870/1.16276, loss_spatial_bce_0: 0.06965/0.08709, loss_spatial_dice_0: 0.22048/0.20781, loss_spatial_ce_0: 0.03077/0.06141, loss_grounding_bce_0: 0.08094/0.08618, loss_grounding_dice_0: 0.07519/0.17843, loss_grounding_ce_0: 0.16772/0.27176, loss_mask_ce_1: 1.23726/0.89821, loss_mask_bce_1: 0.30293/0.33512, loss_mask_dice_1: 0.82651/1.16951, loss_spatial_bce_1: 0.07945/0.08761, loss_spatial_dice_1: 0.23031/0.21178, loss_spatial_ce_1: 0.04019/0.06726, loss_grounding_bce_1: 0.08310/0.08637, loss_grounding_dice_1: 0.07934/0.17928, loss_grounding_ce_1: 0.18937/0.27253, loss_mask_ce_2: 1.18851/0.90528, loss_mask_bce_2: 0.30915/0.33569, loss_mask_dice_2: 0.81412/1.16988, loss_spatial_bce_2: 0.06639/0.08874, loss_spatial_dice_2: 0.22675/0.21345, loss_spatial_ce_2: 0.03345/0.07064, loss_grounding_bce_2: 0.06916/0.08653, loss_grounding_dice_2: 0.07829/0.17909, loss_grounding_ce_2: 0.16355/0.27598, loss_mask_ce_3: 0.76964/0.91603, loss_mask_bce_3: 0.39001/0.33687, loss_mask_dice_3: 1.00933/1.16769, loss_spatial_bce_3: 0.06758/0.08996, loss_spatial_dice_3: 0.21790/0.21442, loss_spatial_ce_3: 0.05771/0.07544, loss_grounding_bce_3: 0.06931/0.08676, loss_grounding_dice_3: 0.07567/0.17884, loss_grounding_ce_3: 0.21744/0.27822, loss_mask_ce_4: 1.13807/0.91708, loss_mask_bce_4: 0.28372/0.33901, loss_mask_dice_4: 0.84520/1.19155, loss_spatial_bce_4: 0.10768/0.09388, loss_spatial_dice_4: 0.26254/0.22662, loss_spatial_ce_4: 0.15983/0.09157, loss_grounding_bce_4: 0.06970/0.08729, loss_grounding_dice_4: 0.07255/0.18173, loss_grounding_ce_4: 0.16149/0.28118, loss_mask_ce_5: 1.20406/0.93365, loss_mask_bce_5: 0.33182/0.34135, loss_mask_dice_5: 0.82647/1.19937, loss_spatial_bce_5: 0.10821/0.09608, loss_spatial_dice_5: 0.29499/0.23088, loss_spatial_ce_5: 0.08355/0.10574, loss_grounding_bce_5: 0.07229/0.08772, loss_grounding_dice_5: 0.08233/0.18300, loss_grounding_ce_5: 0.12851/0.29364, loss_mask_ce_6: 1.37233/0.97363, loss_mask_bce_6: 0.31431/0.34402, loss_mask_dice_6: 0.75627/1.20229, loss_spatial_bce_6: 0.09983/0.10177, loss_spatial_dice_6: 0.28790/0.23376, loss_spatial_ce_6: 0.24558/0.13148, loss_grounding_bce_6: 0.07508/0.08845, loss_grounding_dice_6: 0.07902/0.18338, loss_grounding_ce_6: 0.30634/0.30922, loss_mask_ce_7: 1.52421/1.01876, loss_mask_bce_7: 0.40488/0.35190, loss_mask_dice_7: 0.93791/1.25658, loss_spatial_bce_7: 0.16196/0.10972, loss_spatial_dice_7: 0.31208/0.26143, loss_spatial_ce_7: 0.16794/0.16670, loss_grounding_bce_7: 0.11037/0.09034, loss_grounding_dice_7: 0.08771/0.19068, loss_grounding_ce_7: 0.26143/0.33948, loss_mask_ce_8: 1.40246/1.12724, loss_mask_bce_8: 0.39635/0.36550, loss_mask_dice_8: 1.05694/1.32957, loss_spatial_bce_8: 0.13153/0.13031, loss_spatial_dice_8: 0.35917/0.29934, loss_spatial_ce_8: 0.31568/0.22185, loss_grounding_bce_8: 0.10661/0.09405, loss_grounding_dice_8: 0.09237/0.20146, loss_grounding_ce_8: 0.20388/0.40659, loss_mask_ce_9: 4.31158/3.67582, loss_mask_bce_9: 0.29386/0.39254, loss_mask_dice_9: 1.56134/1.90206, loss_spatial_bce_9: 0.36644/0.33289, loss_spatial_dice_9: 0.89725/0.82177, loss_spatial_ce_9: 1.83629/1.49555, loss_grounding_bce_9: 0.06699/0.10562, loss_grounding_dice_9: 0.12614/0.28079, loss_grounding_ce_9: 0.59694/0.67115] items per batch[64] items per second[0.24] total items[4409600] mini batches[ 68900] memory[7345] epoch remaining[0:24:07]
INFO:trainer.default_trainer:epochs[    37] optim steps[69000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83771/0.89758, loss_mask_bce_0: 0.46792/0.33413, loss_mask_dice_0: 1.55125/1.16276, loss_spatial_bce_0: 0.10153/0.08707, loss_spatial_dice_0: 0.27306/0.20780, loss_spatial_ce_0: 0.08087/0.06138, loss_grounding_bce_0: 0.11194/0.08616, loss_grounding_dice_0: 0.12065/0.17844, loss_grounding_ce_0: 0.06089/0.27171, loss_mask_ce_1: 0.82244/0.89819, loss_mask_bce_1: 0.46285/0.33506, loss_mask_dice_1: 1.72238/1.16950, loss_spatial_bce_1: 0.11672/0.08759, loss_spatial_dice_1: 0.30182/0.21177, loss_spatial_ce_1: 0.06693/0.06724, loss_grounding_bce_1: 0.10978/0.08636, loss_grounding_dice_1: 0.11475/0.17929, loss_grounding_ce_1: 0.05876/0.27250, loss_mask_ce_2: 0.75531/0.90529, loss_mask_bce_2: 0.46440/0.33563, loss_mask_dice_2: 1.71144/1.16989, loss_spatial_bce_2: 0.11373/0.08873, loss_spatial_dice_2: 0.30424/0.21344, loss_spatial_ce_2: 0.09532/0.07062, loss_grounding_bce_2: 0.10875/0.08651, loss_grounding_dice_2: 0.11310/0.17909, loss_grounding_ce_2: 0.06619/0.27592, loss_mask_ce_3: 0.80557/0.91605, loss_mask_bce_3: 0.42578/0.33681, loss_mask_dice_3: 1.62901/1.16768, loss_spatial_bce_3: 0.11893/0.08994, loss_spatial_dice_3: 0.29486/0.21440, loss_spatial_ce_3: 0.12336/0.07543, loss_grounding_bce_3: 0.10843/0.08675, loss_grounding_dice_3: 0.11585/0.17884, loss_grounding_ce_3: 0.04312/0.27818, loss_mask_ce_4: 0.72471/0.91707, loss_mask_bce_4: 0.43924/0.33895, loss_mask_dice_4: 1.72069/1.19154, loss_spatial_bce_4: 0.14630/0.09386, loss_spatial_dice_4: 0.30630/0.22660, loss_spatial_ce_4: 0.24295/0.09155, loss_grounding_bce_4: 0.11321/0.08727, loss_grounding_dice_4: 0.10890/0.18173, loss_grounding_ce_4: 0.03722/0.28116, loss_mask_ce_5: 0.89247/0.93366, loss_mask_bce_5: 0.46061/0.34130, loss_mask_dice_5: 1.54190/1.19938, loss_spatial_bce_5: 0.10808/0.09606, loss_spatial_dice_5: 0.29710/0.23087, loss_spatial_ce_5: 0.20417/0.10572, loss_grounding_bce_5: 0.11040/0.08770, loss_grounding_dice_5: 0.12240/0.18300, loss_grounding_ce_5: 0.03083/0.29361, loss_mask_ce_6: 1.14363/0.97363, loss_mask_bce_6: 0.48304/0.34396, loss_mask_dice_6: 1.58431/1.20229, loss_spatial_bce_6: 0.11192/0.10175, loss_spatial_dice_6: 0.30255/0.23375, loss_spatial_ce_6: 0.18994/0.13147, loss_grounding_bce_6: 0.10960/0.08843, loss_grounding_dice_6: 0.11997/0.18339, loss_grounding_ce_6: 0.01628/0.30920, loss_mask_ce_7: 0.92930/1.01875, loss_mask_bce_7: 0.52589/0.35184, loss_mask_dice_7: 1.81430/1.25661, loss_spatial_bce_7: 0.12065/0.10970, loss_spatial_dice_7: 0.35652/0.26141, loss_spatial_ce_7: 0.14058/0.16668, loss_grounding_bce_7: 0.12406/0.09032, loss_grounding_dice_7: 0.12232/0.19069, loss_grounding_ce_7: 0.00690/0.33951, loss_mask_ce_8: 1.01293/1.12727, loss_mask_bce_8: 0.61195/0.36545, loss_mask_dice_8: 2.18155/1.32956, loss_spatial_bce_8: 0.15225/0.13028, loss_spatial_dice_8: 0.39988/0.29933, loss_spatial_ce_8: 0.10254/0.22182, loss_grounding_bce_8: 0.12687/0.09404, loss_grounding_dice_8: 0.13296/0.20148, loss_grounding_ce_8: 0.00546/0.40666, loss_mask_ce_9: 3.27254/3.67597, loss_mask_bce_9: 0.65529/0.39249, loss_mask_dice_9: 3.08248/1.90201, loss_spatial_bce_9: 0.27489/0.33286, loss_spatial_dice_9: 0.91451/0.82177, loss_spatial_ce_9: 1.68409/1.49562, loss_grounding_bce_9: 0.14001/0.10561, loss_grounding_dice_9: 0.16141/0.28080, loss_grounding_ce_9: 0.15728/0.67116] items per batch[64] items per second[0.24] total items[4416000] mini batches[ 69000] memory[7345] epoch remaining[0:19:31]
INFO:trainer.default_trainer:epochs[    37] optim steps[69100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84323/0.89758, loss_mask_bce_0: 0.34438/0.33413, loss_mask_dice_0: 2.68006/1.16291, loss_spatial_bce_0: 0.02740/0.08706, loss_spatial_dice_0: 0.15274/0.20781, loss_spatial_ce_0: 0.01194/0.06139, loss_grounding_bce_0: 0.10511/0.08616, loss_grounding_dice_0: 0.10119/0.17845, loss_grounding_ce_0: 0.00102/0.27173, loss_mask_ce_1: 0.76521/0.89820, loss_mask_bce_1: 0.35887/0.33506, loss_mask_dice_1: 2.58052/1.16964, loss_spatial_bce_1: 0.02651/0.08759, loss_spatial_dice_1: 0.18447/0.21177, loss_spatial_ce_1: 0.01043/0.06724, loss_grounding_bce_1: 0.10979/0.08635, loss_grounding_dice_1: 0.10354/0.17930, loss_grounding_ce_1: 0.00180/0.27253, loss_mask_ce_2: 0.75290/0.90530, loss_mask_bce_2: 0.35715/0.33563, loss_mask_dice_2: 2.76203/1.17004, loss_spatial_bce_2: 0.02698/0.08872, loss_spatial_dice_2: 0.18478/0.21345, loss_spatial_ce_2: 0.02988/0.07062, loss_grounding_bce_2: 0.10857/0.08650, loss_grounding_dice_2: 0.10558/0.17910, loss_grounding_ce_2: 0.00150/0.27592, loss_mask_ce_3: 0.83622/0.91608, loss_mask_bce_3: 0.37378/0.33681, loss_mask_dice_3: 2.59958/1.16783, loss_spatial_bce_3: 0.02730/0.08994, loss_spatial_dice_3: 0.16162/0.21441, loss_spatial_ce_3: 0.01885/0.07544, loss_grounding_bce_3: 0.10487/0.08674, loss_grounding_dice_3: 0.10337/0.17885, loss_grounding_ce_3: 0.00083/0.27818, loss_mask_ce_4: 0.73794/0.91709, loss_mask_bce_4: 0.38000/0.33896, loss_mask_dice_4: 2.87960/1.19172, loss_spatial_bce_4: 0.02739/0.09386, loss_spatial_dice_4: 0.23826/0.22661, loss_spatial_ce_4: 0.03069/0.09156, loss_grounding_bce_4: 0.10911/0.08726, loss_grounding_dice_4: 0.10560/0.18174, loss_grounding_ce_4: 0.00253/0.28118, loss_mask_ce_5: 0.79333/0.93366, loss_mask_bce_5: 0.36993/0.34130, loss_mask_dice_5: 2.69041/1.19953, loss_spatial_bce_5: 0.02807/0.09607, loss_spatial_dice_5: 0.19816/0.23088, loss_spatial_ce_5: 0.03632/0.10571, loss_grounding_bce_5: 0.10755/0.08769, loss_grounding_dice_5: 0.10804/0.18301, loss_grounding_ce_5: 0.03724/0.29363, loss_mask_ce_6: 0.84361/0.97365, loss_mask_bce_6: 0.36174/0.34397, loss_mask_dice_6: 2.96763/1.20247, loss_spatial_bce_6: 0.03559/0.10176, loss_spatial_dice_6: 0.22757/0.23376, loss_spatial_ce_6: 0.06125/0.13147, loss_grounding_bce_6: 0.10311/0.08842, loss_grounding_dice_6: 0.11020/0.18340, loss_grounding_ce_6: 0.00606/0.30924, loss_mask_ce_7: 1.00193/1.01877, loss_mask_bce_7: 0.39525/0.35185, loss_mask_dice_7: 3.08491/1.25680, loss_spatial_bce_7: 0.03706/0.10971, loss_spatial_dice_7: 0.28888/0.26142, loss_spatial_ce_7: 0.10787/0.16668, loss_grounding_bce_7: 0.10505/0.09031, loss_grounding_dice_7: 0.10817/0.19070, loss_grounding_ce_7: 0.05495/0.33951, loss_mask_ce_8: 0.90279/1.12731, loss_mask_bce_8: 0.44720/0.36546, loss_mask_dice_8: 3.36829/1.32975, loss_spatial_bce_8: 0.04899/0.13028, loss_spatial_dice_8: 0.31275/0.29933, loss_spatial_ce_8: 0.08282/0.22182, loss_grounding_bce_8: 0.10303/0.09403, loss_grounding_dice_8: 0.11377/0.20149, loss_grounding_ce_8: 0.34045/0.40660, loss_mask_ce_9: 3.94524/3.67592, loss_mask_bce_9: 0.35533/0.39249, loss_mask_dice_9: 4.29310/1.90224, loss_spatial_bce_9: 0.23054/0.33285, loss_spatial_dice_9: 0.92642/0.82179, loss_spatial_ce_9: 1.50280/1.49562, loss_grounding_bce_9: 0.10079/0.10561, loss_grounding_dice_9: 0.14358/0.28082, loss_grounding_ce_9: 0.42512/0.67110] items per batch[64] items per second[0.24] total items[4422400] mini batches[ 69100] memory[7345] epoch remaining[0:14:55]
INFO:trainer.default_trainer:epochs[    37] optim steps[69200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73837/0.89754, loss_mask_bce_0: 0.22578/0.33416, loss_mask_dice_0: 0.24926/1.16303, loss_spatial_bce_0: 0.12601/0.08707, loss_spatial_dice_0: 0.14311/0.20781, loss_spatial_ce_0: 0.00989/0.06137, loss_grounding_bce_0: 0.21899/0.08616, loss_grounding_dice_0: 0.22268/0.17843, loss_grounding_ce_0: 0.01649/0.27168, loss_mask_ce_1: 0.72230/0.89814, loss_mask_bce_1: 0.23081/0.33509, loss_mask_dice_1: 0.25790/1.16979, loss_spatial_bce_1: 0.13122/0.08759, loss_spatial_dice_1: 0.15866/0.21177, loss_spatial_ce_1: 0.00506/0.06722, loss_grounding_bce_1: 0.22621/0.08636, loss_grounding_dice_1: 0.19868/0.17928, loss_grounding_ce_1: 0.01740/0.27246, loss_mask_ce_2: 0.73878/0.90521, loss_mask_bce_2: 0.21104/0.33566, loss_mask_dice_2: 0.23998/1.17017, loss_spatial_bce_2: 0.11416/0.08872, loss_spatial_dice_2: 0.14154/0.21344, loss_spatial_ce_2: 0.00331/0.07060, loss_grounding_bce_2: 0.20340/0.08651, loss_grounding_dice_2: 0.23868/0.17909, loss_grounding_ce_2: 0.02302/0.27585, loss_mask_ce_3: 0.73309/0.91602, loss_mask_bce_3: 0.23141/0.33684, loss_mask_dice_3: 0.22498/1.16799, loss_spatial_bce_3: 0.11395/0.08994, loss_spatial_dice_3: 0.14005/0.21441, loss_spatial_ce_3: 0.02389/0.07542, loss_grounding_bce_3: 0.22778/0.08675, loss_grounding_dice_3: 0.21151/0.17883, loss_grounding_ce_3: 0.02083/0.27812, loss_mask_ce_4: 0.72481/0.91700, loss_mask_bce_4: 0.23091/0.33900, loss_mask_dice_4: 0.25524/1.19182, loss_spatial_bce_4: 0.12186/0.09386, loss_spatial_dice_4: 0.14966/0.22660, loss_spatial_ce_4: 0.02171/0.09155, loss_grounding_bce_4: 0.21451/0.08728, loss_grounding_dice_4: 0.25374/0.18172, loss_grounding_ce_4: 0.02855/0.28112, loss_mask_ce_5: 0.76452/0.93361, loss_mask_bce_5: 0.22541/0.34133, loss_mask_dice_5: 0.26879/1.19964, loss_spatial_bce_5: 0.12023/0.09608, loss_spatial_dice_5: 0.15461/0.23087, loss_spatial_ce_5: 0.07180/0.10570, loss_grounding_bce_5: 0.21088/0.08770, loss_grounding_dice_5: 0.22966/0.18299, loss_grounding_ce_5: 0.02724/0.29355, loss_mask_ce_6: 0.65564/0.97358, loss_mask_bce_6: 0.23020/0.34399, loss_mask_dice_6: 0.25173/1.20260, loss_spatial_bce_6: 0.12744/0.10176, loss_spatial_dice_6: 0.15693/0.23375, loss_spatial_ce_6: 0.12212/0.13146, loss_grounding_bce_6: 0.20091/0.08843, loss_grounding_dice_6: 0.20976/0.18338, loss_grounding_ce_6: 0.01908/0.30917, loss_mask_ce_7: 0.65895/1.01874, loss_mask_bce_7: 0.21161/0.35187, loss_mask_dice_7: 0.26920/1.25693, loss_spatial_bce_7: 0.12452/0.10971, loss_spatial_dice_7: 0.14028/0.26141, loss_spatial_ce_7: 0.08454/0.16667, loss_grounding_bce_7: 0.18903/0.09032, loss_grounding_dice_7: 0.19096/0.19068, loss_grounding_ce_7: 0.04268/0.33943, loss_mask_ce_8: 0.66197/1.12726, loss_mask_bce_8: 0.20810/0.36549, loss_mask_dice_8: 0.24859/1.32986, loss_spatial_bce_8: 0.15492/0.13027, loss_spatial_dice_8: 0.15495/0.29931, loss_spatial_ce_8: 0.34077/0.22180, loss_grounding_bce_8: 0.17747/0.09404, loss_grounding_dice_8: 0.24718/0.20147, loss_grounding_ce_8: 0.03065/0.40654, loss_mask_ce_9: 2.60921/3.67595, loss_mask_bce_9: 0.24305/0.39254, loss_mask_dice_9: 0.38996/1.90245, loss_spatial_bce_9: 0.42179/0.33290, loss_spatial_dice_9: 0.79826/0.82178, loss_spatial_ce_9: 1.50820/1.49562, loss_grounding_bce_9: 0.22592/0.10562, loss_grounding_dice_9: 0.33110/0.28078, loss_grounding_ce_9: 0.13944/0.67111] items per batch[64] items per second[0.23] total items[4428800] mini batches[ 69200] memory[7345] epoch remaining[0:10:21]
INFO:trainer.default_trainer:epochs[    37] optim steps[69300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02362/0.89745, loss_mask_bce_0: 0.14442/0.33414, loss_mask_dice_0: 0.32987/1.16288, loss_spatial_bce_0: 0.07838/0.08706, loss_spatial_dice_0: 0.14873/0.20780, loss_spatial_ce_0: 0.05270/0.06137, loss_grounding_bce_0: 0.10671/0.08617, loss_grounding_dice_0: 0.17991/0.17843, loss_grounding_ce_0: 0.02003/0.27166, loss_mask_ce_1: 0.03003/0.89804, loss_mask_bce_1: 0.14001/0.33507, loss_mask_dice_1: 0.29822/1.16962, loss_spatial_bce_1: 0.08411/0.08759, loss_spatial_dice_1: 0.16316/0.21177, loss_spatial_ce_1: 0.05251/0.06721, loss_grounding_bce_1: 0.10421/0.08636, loss_grounding_dice_1: 0.14652/0.17928, loss_grounding_ce_1: 0.01451/0.27240, loss_mask_ce_2: 0.02553/0.90511, loss_mask_bce_2: 0.14738/0.33564, loss_mask_dice_2: 0.34854/1.16999, loss_spatial_bce_2: 0.07946/0.08872, loss_spatial_dice_2: 0.14114/0.21344, loss_spatial_ce_2: 0.07173/0.07059, loss_grounding_bce_2: 0.10292/0.08652, loss_grounding_dice_2: 0.17263/0.17909, loss_grounding_ce_2: 0.01862/0.27579, loss_mask_ce_3: 0.04192/0.91592, loss_mask_bce_3: 0.14126/0.33683, loss_mask_dice_3: 0.32684/1.16783, loss_spatial_bce_3: 0.08321/0.08994, loss_spatial_dice_3: 0.14447/0.21440, loss_spatial_ce_3: 0.06679/0.07542, loss_grounding_bce_3: 0.10055/0.08676, loss_grounding_dice_3: 0.18116/0.17882, loss_grounding_ce_3: 0.02125/0.27807, loss_mask_ce_4: 0.02569/0.91689, loss_mask_bce_4: 0.14633/0.33898, loss_mask_dice_4: 0.30963/1.19163, loss_spatial_bce_4: 0.11226/0.09386, loss_spatial_dice_4: 0.15600/0.22660, loss_spatial_ce_4: 0.06458/0.09154, loss_grounding_bce_4: 0.10654/0.08729, loss_grounding_dice_4: 0.18137/0.18172, loss_grounding_ce_4: 0.00965/0.28105, loss_mask_ce_5: 0.02300/0.93353, loss_mask_bce_5: 0.13883/0.34130, loss_mask_dice_5: 0.29288/1.19945, loss_spatial_bce_5: 0.14822/0.09608, loss_spatial_dice_5: 0.18961/0.23087, loss_spatial_ce_5: 0.06747/0.10568, loss_grounding_bce_5: 0.10621/0.08771, loss_grounding_dice_5: 0.18702/0.18299, loss_grounding_ce_5: 0.00637/0.29349, loss_mask_ce_6: 0.03031/0.97353, loss_mask_bce_6: 0.13903/0.34397, loss_mask_dice_6: 0.32409/1.20241, loss_spatial_bce_6: 0.17652/0.10176, loss_spatial_dice_6: 0.18007/0.23375, loss_spatial_ce_6: 0.06386/0.13145, loss_grounding_bce_6: 0.10239/0.08844, loss_grounding_dice_6: 0.16539/0.18339, loss_grounding_ce_6: 0.00955/0.30908, loss_mask_ce_7: 0.10080/1.01870, loss_mask_bce_7: 0.14777/0.35185, loss_mask_dice_7: 0.31042/1.25673, loss_spatial_bce_7: 0.24592/0.10971, loss_spatial_dice_7: 0.19464/0.26141, loss_spatial_ce_7: 0.06447/0.16666, loss_grounding_bce_7: 0.10919/0.09033, loss_grounding_dice_7: 0.18214/0.19068, loss_grounding_ce_7: 0.02688/0.33940, loss_mask_ce_8: 0.09679/1.12719, loss_mask_bce_8: 0.16684/0.36546, loss_mask_dice_8: 0.30909/1.32966, loss_spatial_bce_8: 0.15589/0.13025, loss_spatial_dice_8: 0.19435/0.29930, loss_spatial_ce_8: 0.10334/0.22178, loss_grounding_bce_8: 0.12315/0.09404, loss_grounding_dice_8: 0.22405/0.20147, loss_grounding_ce_8: 0.00953/0.40651, loss_mask_ce_9: 2.49027/3.67575, loss_mask_bce_9: 0.16672/0.39251, loss_mask_dice_9: 0.40477/1.90218, loss_spatial_bce_9: 0.33391/0.33289, loss_spatial_dice_9: 0.82074/0.82179, loss_spatial_ce_9: 1.25907/1.49560, loss_grounding_bce_9: 0.12625/0.10563, loss_grounding_dice_9: 0.22806/0.28079, loss_grounding_ce_9: 0.15354/0.67111] items per batch[64] items per second[0.23] total items[4435200] mini batches[ 69300] memory[7345] epoch remaining[0:05:47]
INFO:trainer.default_trainer:epochs[    37] optim steps[69400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98943/0.89747, loss_mask_bce_0: 0.40827/0.33413, loss_mask_dice_0: 2.76383/1.16280, loss_spatial_bce_0: 0.03362/0.08706, loss_spatial_dice_0: 0.21967/0.20779, loss_spatial_ce_0: 0.14771/0.06134, loss_grounding_bce_0: 0.03044/0.08618, loss_grounding_dice_0: 0.32380/0.17845, loss_grounding_ce_0: 0.46705/0.27166, loss_mask_ce_1: 0.99104/0.89807, loss_mask_bce_1: 0.40332/0.33506, loss_mask_dice_1: 2.76466/1.16958, loss_spatial_bce_1: 0.03492/0.08758, loss_spatial_dice_1: 0.24367/0.21176, loss_spatial_ce_1: 0.01580/0.06719, loss_grounding_bce_1: 0.02988/0.08638, loss_grounding_dice_1: 0.35797/0.17931, loss_grounding_ce_1: 0.46763/0.27240, loss_mask_ce_2: 1.15517/0.90510, loss_mask_bce_2: 0.37424/0.33564, loss_mask_dice_2: 2.41463/1.16996, loss_spatial_bce_2: 0.04242/0.08871, loss_spatial_dice_2: 0.24469/0.21343, loss_spatial_ce_2: 0.18627/0.07058, loss_grounding_bce_2: 0.02949/0.08654, loss_grounding_dice_2: 0.34559/0.17911, loss_grounding_ce_2: 0.50116/0.27575, loss_mask_ce_3: 1.17081/0.91593, loss_mask_bce_3: 0.38530/0.33682, loss_mask_dice_3: 2.55718/1.16778, loss_spatial_bce_3: 0.04177/0.08994, loss_spatial_dice_3: 0.25667/0.21440, loss_spatial_ce_3: 0.02842/0.07541, loss_grounding_bce_3: 0.03201/0.08678, loss_grounding_dice_3: 0.36675/0.17885, loss_grounding_ce_3: 0.46062/0.27804, loss_mask_ce_4: 1.23445/0.91690, loss_mask_bce_4: 0.36871/0.33897, loss_mask_dice_4: 2.73049/1.19159, loss_spatial_bce_4: 0.04384/0.09386, loss_spatial_dice_4: 0.25144/0.22660, loss_spatial_ce_4: 0.13550/0.09155, loss_grounding_bce_4: 0.02757/0.08731, loss_grounding_dice_4: 0.37568/0.18175, loss_grounding_ce_4: 0.47866/0.28103, loss_mask_ce_5: 1.10234/0.93356, loss_mask_bce_5: 0.41496/0.34129, loss_mask_dice_5: 2.75432/1.19941, loss_spatial_bce_5: 0.04021/0.09608, loss_spatial_dice_5: 0.28444/0.23087, loss_spatial_ce_5: 0.27022/0.10568, loss_grounding_bce_5: 0.03195/0.08773, loss_grounding_dice_5: 0.36668/0.18301, loss_grounding_ce_5: 0.48360/0.29348, loss_mask_ce_6: 1.34524/0.97353, loss_mask_bce_6: 0.41494/0.34397, loss_mask_dice_6: 2.70853/1.20236, loss_spatial_bce_6: 0.04450/0.10177, loss_spatial_dice_6: 0.27240/0.23375, loss_spatial_ce_6: 0.08109/0.13142, loss_grounding_bce_6: 0.03211/0.08846, loss_grounding_dice_6: 0.30236/0.18341, loss_grounding_ce_6: 0.49435/0.30905, loss_mask_ce_7: 1.46292/1.01866, loss_mask_bce_7: 0.40261/0.35185, loss_mask_dice_7: 2.44453/1.25668, loss_spatial_bce_7: 0.04640/0.10972, loss_spatial_dice_7: 0.33755/0.26141, loss_spatial_ce_7: 0.37992/0.16666, loss_grounding_bce_7: 0.03677/0.09034, loss_grounding_dice_7: 0.45753/0.19070, loss_grounding_ce_7: 0.52815/0.33938, loss_mask_ce_8: 1.41446/1.12720, loss_mask_bce_8: 0.41355/0.36546, loss_mask_dice_8: 2.58152/1.32958, loss_spatial_bce_8: 0.04678/0.13025, loss_spatial_dice_8: 0.35495/0.29930, loss_spatial_ce_8: 0.48517/0.22175, loss_grounding_bce_8: 0.03694/0.09406, loss_grounding_dice_8: 0.43680/0.20150, loss_grounding_ce_8: 0.50777/0.40645, loss_mask_ce_9: 5.69138/3.67584, loss_mask_bce_9: 0.42593/0.39251, loss_mask_dice_9: 3.32797/1.90209, loss_spatial_bce_9: 0.18172/0.33288, loss_spatial_dice_9: 0.88396/0.82179, loss_spatial_ce_9: 1.94149/1.49563, loss_grounding_bce_9: 0.04514/0.10566, loss_grounding_dice_9: 0.46971/0.28083, loss_grounding_ce_9: 0.43549/0.67100] items per batch[64] items per second[0.23] total items[4441600] mini batches[ 69400] memory[7345] epoch remaining[0:01:11]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00069426.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2150 s/iter. Eval: 0.0947 s/iter. Total: 0.3128 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2425 s/iter. Eval: 0.0801 s/iter. Total: 0.3256 s/iter. ETA=0:00:42
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2364 s/iter. Eval: 0.0775 s/iter. Total: 0.3170 s/iter. ETA=0:00:35
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2319 s/iter. Eval: 0.0763 s/iter. Total: 0.3114 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2285 s/iter. Eval: 0.0755 s/iter. Total: 0.3072 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2298 s/iter. Eval: 0.0759 s/iter. Total: 0.3089 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0031 s/iter. Inference: 0.2302 s/iter. Eval: 0.0763 s/iter. Total: 0.3097 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0031 s/iter. Inference: 0.2296 s/iter. Eval: 0.0756 s/iter. Total: 0.3084 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2304 s/iter. Eval: 0.0755 s/iter. Total: 0.3092 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalgfqaq7cc ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.004 | 81.890 | 60.176 |      133      |
| Things | 54.944 | 82.511 | 65.853 |      80       |
| Stuff  | 42.548 | 80.951 | 51.608 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.43s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.73 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.26s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.45 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.12 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.507
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.717
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.989 | 61.340 | 40.932 | 19.185 | 42.118 | 60.776 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.267 | bicycle      | 19.191 | car            | 36.139 |
| motorcycle    | 35.382 | airplane     | 56.294 | bus            | 66.199 |
| train         | 67.818 | truck        | 36.369 | boat           | 21.993 |
| traffic light | 25.258 | fire hydrant | 64.544 | stop sign      | 64.667 |
| parking meter | 43.795 | bench        | 21.158 | bird           | 29.909 |
| cat           | 73.071 | dog          | 67.056 | horse          | 46.132 |
| sheep         | 45.599 | cow          | 49.622 | elephant       | 61.229 |
| bear          | 77.389 | zebra        | 60.170 | giraffe        | 57.058 |
| backpack      | 17.019 | umbrella     | 48.763 | handbag        | 15.571 |
| tie           | 33.504 | suitcase     | 41.095 | frisbee        | 67.554 |
| skis          | 5.706  | snowboard    | 24.857 | sports ball    | 47.740 |
| kite          | 34.032 | baseball bat | 28.113 | baseball glove | 43.443 |
| skateboard    | 36.339 | surfboard    | 35.941 | tennis racket  | 56.916 |
| bottle        | 34.220 | wine glass   | 25.728 | cup            | 41.074 |
| fork          | 16.542 | knife        | 13.082 | spoon          | 14.615 |
| bowl          | 30.743 | banana       | 20.049 | apple          | 20.353 |
| sandwich      | 43.745 | orange       | 29.288 | broccoli       | 22.361 |
| carrot        | 21.439 | hot dog      | 24.340 | pizza          | 51.347 |
| donut         | 47.183 | cake         | 43.186 | chair          | 21.457 |
| couch         | 40.241 | potted plant | 18.352 | bed            | 40.432 |
| dining table  | 12.975 | toilet       | 67.092 | tv             | 61.899 |
| laptop        | 62.900 | mouse        | 57.738 | remote         | 30.900 |
| keyboard      | 46.775 | cell phone   | 38.158 | microwave      | 55.365 |
| oven          | 33.832 | toaster      | 29.862 | sink           | 37.439 |
| refrigerator  | 60.004 | book         | 9.275  | clock          | 52.263 |
| vase          | 34.085 | scissors     | 24.949 | teddy bear     | 51.129 |
| hair drier    | 5.786  | toothbrush   | 20.027 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.14924377328798, 'fwIoU': 68.90016455223093, 'IoU-person': 87.36028680691092, 'IoU-bicycle': 72.52094629959937, 'IoU-car': 68.7471909851457, 'IoU-motorcycle': 75.61428103278315, 'IoU-airplane': 84.19266608355261, 'IoU-bus': 82.41717498451676, 'IoU-train': 82.57823555024488, 'IoU-truck': 64.50477813634429, 'IoU-boat': 68.01551348921323, 'IoU-traffic light': 74.37319078445519, 'IoU-fire hydrant': 81.07716307567091, 'IoU-stop sign': 93.84392130959546, 'IoU-parking meter': 87.33289052150268, 'IoU-bench': 52.516777774947485, 'IoU-bird': 75.5232970508698, 'IoU-cat': 76.24365097976937, 'IoU-dog': 77.34112984982727, 'IoU-horse': 85.95640156446281, 'IoU-sheep': 88.14769589187945, 'IoU-cow': 80.2517569176083, 'IoU-elephant': 90.97948906770637, 'IoU-bear': 78.97658168162724, 'IoU-zebra': 90.57167093367856, 'IoU-giraffe': 87.10548166920148, 'IoU-backpack': 40.06576052018553, 'IoU-umbrella': 78.09511514261345, 'IoU-handbag': 37.05512694576639, 'IoU-tie': 67.31140572865068, 'IoU-suitcase': 80.99842610915606, 'IoU-frisbee': 83.61895844768024, 'IoU-skis': 50.938732488457596, 'IoU-snowboard': 70.5798307247993, 'IoU-sports ball': 58.30613417556258, 'IoU-kite': 66.49165142299202, 'IoU-baseball bat': 60.16857039339448, 'IoU-baseball glove': 52.484027664461706, 'IoU-skateboard': 77.97015285897119, 'IoU-surfboard': 75.39023790451193, 'IoU-tennis racket': 76.92984060634028, 'IoU-bottle': 67.84551998750736, 'IoU-wine glass': 74.6789243283158, 'IoU-cup': 61.01868042906295, 'IoU-fork': 54.356881694002226, 'IoU-knife': 50.31618848874869, 'IoU-spoon': 48.898301984797605, 'IoU-bowl': 52.46780733380517, 'IoU-banana': 84.03299076594598, 'IoU-apple': 56.41070112509404, 'IoU-sandwich': 65.45640140034965, 'IoU-orange': 74.33588926848802, 'IoU-broccoli': 66.90141731930893, 'IoU-carrot': 63.51892724353184, 'IoU-hot dog': 64.78178388130209, 'IoU-pizza': 82.40743116381375, 'IoU-donut': 66.13092390714621, 'IoU-cake': 67.37503863444165, 'IoU-chair': 56.47637148927459, 'IoU-couch': 67.66654352834311, 'IoU-potted plant': 36.21600168273498, 'IoU-bed': 69.72893289076784, 'IoU-dining table': 50.86228287543558, 'IoU-toilet': 87.63666634533507, 'IoU-tv': 74.90857042633667, 'IoU-laptop': 74.34168155815338, 'IoU-mouse': 65.70312405874628, 'IoU-remote': 62.46038536003081, 'IoU-keyboard': 55.216884136214425, 'IoU-cell phone': 74.81491764236353, 'IoU-microwave': 51.71596905532514, 'IoU-oven': 61.82894424391464, 'IoU-toaster': 56.5930222061744, 'IoU-sink': 69.93158477312114, 'IoU-refrigerator': 80.2683269269987, 'IoU-book': 50.43662107478192, 'IoU-clock': 69.60240946149014, 'IoU-vase': 62.87098289419585, 'IoU-scissors': 55.31227708013227, 'IoU-teddy bear': 81.11725506050618, 'IoU-hair drier': 36.58504562463074, 'IoU-toothbrush': 52.48368372498123, 'IoU-banner': 36.323470264209554, 'IoU-blanket': 13.423629628320658, 'IoU-bridge': 35.81734711946228, 'IoU-cardboard': 44.701757457963154, 'IoU-counter': 30.373893091272606, 'IoU-curtain': 64.23677428107285, 'IoU-door-stuff': 45.244585520050215, 'IoU-floor-wood': 63.77105058850449, 'IoU-flower': 42.51086553669199, 'IoU-fruit': 42.38195648828611, 'IoU-gravel': 26.28226029677339, 'IoU-house': 26.41692400763917, 'IoU-light': 40.40582036051642, 'IoU-mirror-stuff': 56.70735557305079, 'IoU-net': 41.43678909800012, 'IoU-pillow': 10.417747519772995, 'IoU-platform': 29.18964271582128, 'IoU-playingfield': 70.54600694959143, 'IoU-railroad': 60.517148508601146, 'IoU-river': 50.715046394057836, 'IoU-road': 65.78035653156422, 'IoU-roof': 11.175321769766663, 'IoU-sand': 62.66477365003441, 'IoU-sea': 84.6940970929529, 'IoU-shelf': 35.30259933611785, 'IoU-snow': 88.56573654473998, 'IoU-stairs': 19.81647745955315, 'IoU-tent': 8.95401211599149, 'IoU-towel': 29.302838131613402, 'IoU-wall-brick': 43.161114692196826, 'IoU-wall-stone': 26.806372771104442, 'IoU-wall-tile': 67.39866976458876, 'IoU-wall-wood': 38.372268846255224, 'IoU-water-other': 19.785241041249037, 'IoU-window-blind': 48.85704535011385, 'IoU-window-other': 45.79299363839517, 'IoU-tree-merged': 81.04110996183672, 'IoU-fence-merged': 50.642993389214595, 'IoU-ceiling-merged': 66.23134299528853, 'IoU-sky-other-merged': 93.52187774989797, 'IoU-cabinet-merged': 59.78409269152778, 'IoU-table-merged': 38.09426814064065, 'IoU-floor-other-merged': 48.76108138056231, 'IoU-pavement-merged': 53.93920608058581, 'IoU-mountain-merged': 54.76041023923045, 'IoU-grass-merged': 70.4594000904935, 'IoU-dirt-merged': 44.3518296096559, 'IoU-paper-merged': 34.661218049297545, 'IoU-food-other-merged': 38.68353494737229, 'IoU-building-other-merged': 58.25033331007432, 'IoU-rock-merged': 60.08224700472231, 'IoU-wall-other-merged': 65.95137450651401, 'IoU-rug-merged': 64.47467491418361, 'mACC': 72.37334644354596, 'pACC': 80.30215395703742, 'ACC-person': 92.54595691174437, 'ACC-bicycle': 86.13938617819349, 'ACC-car': 83.99135304975842, 'ACC-motorcycle': 80.8626643672899, 'ACC-airplane': 90.96523543074241, 'ACC-bus': 93.32239912325862, 'ACC-train': 93.88578133412264, 'ACC-truck': 74.86179856942893, 'ACC-boat': 78.35332716629486, 'ACC-traffic light': 90.24769817107764, 'ACC-fire hydrant': 95.70674290203982, 'ACC-stop sign': 97.28992073888875, 'ACC-parking meter': 92.52634402819015, 'ACC-bench': 75.06941853006751, 'ACC-bird': 80.60523770393577, 'ACC-cat': 83.71256801348933, 'ACC-dog': 83.82525619309976, 'ACC-horse': 92.08257333293287, 'ACC-sheep': 91.55625439715624, 'ACC-cow': 86.5150783492578, 'ACC-elephant': 93.72768752912502, 'ACC-bear': 80.96537010099428, 'ACC-zebra': 93.07509902957368, 'ACC-giraffe': 91.40096429212167, 'ACC-backpack': 57.14838169691764, 'ACC-umbrella': 86.40235988366362, 'ACC-handbag': 54.91739607010167, 'ACC-tie': 76.75050223389809, 'ACC-suitcase': 91.67716689937453, 'ACC-frisbee': 94.17927272727272, 'ACC-skis': 70.14552107858002, 'ACC-snowboard': 79.36518821483676, 'ACC-sports ball': 77.09451008362535, 'ACC-kite': 76.53228424939404, 'ACC-baseball bat': 82.74243457282215, 'ACC-baseball glove': 60.25738022668482, 'ACC-skateboard': 89.82835931433496, 'ACC-surfboard': 83.67715076755941, 'ACC-tennis racket': 83.1755417133048, 'ACC-bottle': 82.23192805882215, 'ACC-wine glass': 83.16041413573643, 'ACC-cup': 84.92015497216444, 'ACC-fork': 66.32846933112369, 'ACC-knife': 61.131200996556025, 'ACC-spoon': 68.68004948684118, 'ACC-bowl': 63.89639609471401, 'ACC-banana': 90.38414016478144, 'ACC-apple': 70.86036281515787, 'ACC-sandwich': 77.79450743534588, 'ACC-orange': 82.5383946565209, 'ACC-broccoli': 75.4695269668477, 'ACC-carrot': 73.17592162695958, 'ACC-hot dog': 73.62269532288359, 'ACC-pizza': 91.67484385909417, 'ACC-donut': 81.8574580229757, 'ACC-cake': 73.29264526918216, 'ACC-chair': 70.91432180829374, 'ACC-couch': 83.52795221495668, 'ACC-potted plant': 55.37694289195257, 'ACC-bed': 82.38162210245486, 'ACC-dining table': 75.08218358302426, 'ACC-toilet': 93.65910269442365, 'ACC-tv': 87.75117899464695, 'ACC-laptop': 89.29231639013557, 'ACC-mouse': 78.69044655837293, 'ACC-remote': 71.19524031014491, 'ACC-keyboard': 62.51638591719404, 'ACC-cell phone': 88.09197877570034, 'ACC-microwave': 58.208841736907665, 'ACC-oven': 79.52401555394836, 'ACC-toaster': 68.64059998084002, 'ACC-sink': 83.40908432376885, 'ACC-refrigerator': 91.50131037781699, 'ACC-book': 65.66154632820599, 'ACC-clock': 74.21844660431903, 'ACC-vase': 71.92282289201842, 'ACC-scissors': 59.53812357105473, 'ACC-teddy bear': 86.05936587079451, 'ACC-hair drier': 39.65644835339994, 'ACC-toothbrush': 79.56393328700486, 'ACC-banner': 79.53288869709209, 'ACC-blanket': 14.669334970362721, 'ACC-bridge': 55.734727202543596, 'ACC-cardboard': 57.316155300276286, 'ACC-counter': 59.21137473599812, 'ACC-curtain': 74.35338628333592, 'ACC-door-stuff': 68.91129763579467, 'ACC-floor-wood': 77.74051507446539, 'ACC-flower': 58.80063365024214, 'ACC-fruit': 58.91529159148343, 'ACC-gravel': 34.66094134038504, 'ACC-house': 31.5184205277983, 'ACC-light': 55.778971117406115, 'ACC-mirror-stuff': 70.49317940544955, 'ACC-net': 60.631067745919, 'ACC-pillow': 20.780215051492505, 'ACC-platform': 54.531592958984795, 'ACC-playingfield': 93.2934128781082, 'ACC-railroad': 78.38580162924495, 'ACC-river': 73.87211312127576, 'ACC-road': 84.84317612236218, 'ACC-roof': 14.511321085292165, 'ACC-sand': 68.3657641910047, 'ACC-sea': 91.0938315427629, 'ACC-shelf': 54.73610179851285, 'ACC-snow': 95.0592246577748, 'ACC-stairs': 30.115363474133634, 'ACC-tent': 10.302903254137199, 'ACC-towel': 35.23520162331273, 'ACC-wall-brick': 58.227205364719325, 'ACC-wall-stone': 33.531639225030986, 'ACC-wall-tile': 81.78430772135334, 'ACC-wall-wood': 51.49352549510557, 'ACC-water-other': 31.08627754275492, 'ACC-window-blind': 56.882864712041304, 'ACC-window-other': 65.75348746094338, 'ACC-tree-merged': 89.63558138879846, 'ACC-fence-merged': 69.19176890866552, 'ACC-ceiling-merged': 77.01111115103907, 'ACC-sky-other-merged': 96.30697995055607, 'ACC-cabinet-merged': 74.84628501375178, 'ACC-table-merged': 52.79983020551873, 'ACC-floor-other-merged': 60.12681456594062, 'ACC-pavement-merged': 67.92837006898051, 'ACC-mountain-merged': 64.46837696634682, 'ACC-grass-merged': 82.73492831732993, 'ACC-dirt-merged': 65.31387832642541, 'ACC-paper-merged': 49.26811842050634, 'ACC-food-other-merged': 51.496621963475754, 'ACC-building-other-merged': 77.61613645534474, 'ACC-rock-merged': 82.56815598146973, 'ACC-wall-other-merged': 79.30221915131112, 'ACC-rug-merged': 78.35549845501052})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1521 s/iter. Inference: 0.5735 s/iter. Eval: 0.0000 s/iter. Total: 0.7256 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1552 s/iter. Inference: 0.5288 s/iter. Eval: 0.0000 s/iter. Total: 0.6841 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1663 s/iter. Inference: 0.5678 s/iter. Eval: 0.0000 s/iter. Total: 0.7342 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1703 s/iter. Inference: 0.7003 s/iter. Eval: 0.0000 s/iter. Total: 0.8707 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1674 s/iter. Inference: 0.6164 s/iter. Eval: 0.0000 s/iter. Total: 0.7839 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1665 s/iter. Inference: 0.6351 s/iter. Eval: 0.0000 s/iter. Total: 0.8018 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1683 s/iter. Inference: 0.6855 s/iter. Eval: 0.0000 s/iter. Total: 0.8539 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4667837284167398, 'noc@0.8': 2.758852794849283, 'noc@0.85': 3.362598770851624, 'noc@0.9': 4.386596429616623, 'miou@iter1': 0.8346167752224345}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.0999 s/iter. Eval: 0.0008 s/iter. Total: 0.1021 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.04547119140625, 'precision@0.6': 68.09172058105469, 'precision@0.7': 63.07811737060547, 'precision@0.8': 52.07928466796875, 'precision@0.9': 26.894676208496094, 'cIoU': 57.48524475097656, 'mIoU': 62.77397537231445}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.004037219520384, 'SQ': 81.88955663700345, 'RQ': 60.176109028339134, 'PQ_th': 54.94365090479405, 'SQ_th': 82.51124948088415, 'RQ_th': 65.85266372137988, 'PQ_st': 42.548016562503534, 'SQ_st': 80.9511523443533, 'RQ_st': 51.60772458601346}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.9891501274147, 'AP50': 61.340252123758276, 'AP75': 40.93161827177808, 'APs': 19.1851856827928, 'APm': 42.11838321819413, 'APl': 60.77554814439395, 'AP-person': 44.26685327036707, 'AP-bicycle': 19.190783240940352, 'AP-car': 36.13902668269801, 'AP-motorcycle': 35.381842824094235, 'AP-airplane': 56.29449345607973, 'AP-bus': 66.1992759036791, 'AP-train': 67.81798017483102, 'AP-truck': 36.368813309856755, 'AP-boat': 21.99333448121994, 'AP-traffic light': 25.257814169161218, 'AP-fire hydrant': 64.54373291140453, 'AP-stop sign': 64.66678161736428, 'AP-parking meter': 43.79522912852673, 'AP-bench': 21.158138848362164, 'AP-bird': 29.909307413609902, 'AP-cat': 73.07054691564173, 'AP-dog': 67.05592078805034, 'AP-horse': 46.13227499181473, 'AP-sheep': 45.599434610412416, 'AP-cow': 49.62162912685587, 'AP-elephant': 61.229069171245065, 'AP-bear': 77.38946511511413, 'AP-zebra': 60.170222200374404, 'AP-giraffe': 57.057881576468425, 'AP-backpack': 17.019324307341318, 'AP-umbrella': 48.76307459027491, 'AP-handbag': 15.571463641109156, 'AP-tie': 33.503817551390384, 'AP-suitcase': 41.094800432527045, 'AP-frisbee': 67.55382030418473, 'AP-skis': 5.706179173354797, 'AP-snowboard': 24.85701905980712, 'AP-sports ball': 47.739802878592066, 'AP-kite': 34.03241635536028, 'AP-baseball bat': 28.112983539952165, 'AP-baseball glove': 43.44348577555313, 'AP-skateboard': 36.33948118503334, 'AP-surfboard': 35.941150210582826, 'AP-tennis racket': 56.91647360315688, 'AP-bottle': 34.21975639827065, 'AP-wine glass': 25.7275329422202, 'AP-cup': 41.07431121148227, 'AP-fork': 16.542343103981192, 'AP-knife': 13.081538266642514, 'AP-spoon': 14.61516782016416, 'AP-bowl': 30.743276244444857, 'AP-banana': 20.049382039491263, 'AP-apple': 20.352696804810964, 'AP-sandwich': 43.74464205751721, 'AP-orange': 29.28751320157099, 'AP-broccoli': 22.360618821981266, 'AP-carrot': 21.438535353903088, 'AP-hot dog': 24.340467170080366, 'AP-pizza': 51.34698352820437, 'AP-donut': 47.18254458415562, 'AP-cake': 43.18567163085645, 'AP-chair': 21.456557959672143, 'AP-couch': 40.24056805805659, 'AP-potted plant': 18.352129968645222, 'AP-bed': 40.43152940805398, 'AP-dining table': 12.974510368696684, 'AP-toilet': 67.09232064689449, 'AP-tv': 61.898568652824146, 'AP-laptop': 62.89957512677141, 'AP-mouse': 57.737633877607166, 'AP-remote': 30.90020999877953, 'AP-keyboard': 46.774913220942295, 'AP-cell phone': 38.15782072747643, 'AP-microwave': 55.36468702971015, 'AP-oven': 33.83184021468568, 'AP-toaster': 29.862396870086094, 'AP-sink': 37.43935361667508, 'AP-refrigerator': 60.00436680291834, 'AP-book': 9.275448454646282, 'AP-clock': 52.26263693237274, 'AP-vase': 34.08542650353282, 'AP-scissors': 24.94880452117166, 'AP-teddy bear': 51.1288874404127, 'AP-hair drier': 5.786223783668689, 'AP-toothbrush': 20.0274742927067}), ('sem_seg', {'mIoU': 60.14924377328798, 'fwIoU': 68.90016455223093, 'IoU-person': 87.36028680691092, 'IoU-bicycle': 72.52094629959937, 'IoU-car': 68.7471909851457, 'IoU-motorcycle': 75.61428103278315, 'IoU-airplane': 84.19266608355261, 'IoU-bus': 82.41717498451676, 'IoU-train': 82.57823555024488, 'IoU-truck': 64.50477813634429, 'IoU-boat': 68.01551348921323, 'IoU-traffic light': 74.37319078445519, 'IoU-fire hydrant': 81.07716307567091, 'IoU-stop sign': 93.84392130959546, 'IoU-parking meter': 87.33289052150268, 'IoU-bench': 52.516777774947485, 'IoU-bird': 75.5232970508698, 'IoU-cat': 76.24365097976937, 'IoU-dog': 77.34112984982727, 'IoU-horse': 85.95640156446281, 'IoU-sheep': 88.14769589187945, 'IoU-cow': 80.2517569176083, 'IoU-elephant': 90.97948906770637, 'IoU-bear': 78.97658168162724, 'IoU-zebra': 90.57167093367856, 'IoU-giraffe': 87.10548166920148, 'IoU-backpack': 40.06576052018553, 'IoU-umbrella': 78.09511514261345, 'IoU-handbag': 37.05512694576639, 'IoU-tie': 67.31140572865068, 'IoU-suitcase': 80.99842610915606, 'IoU-frisbee': 83.61895844768024, 'IoU-skis': 50.938732488457596, 'IoU-snowboard': 70.5798307247993, 'IoU-sports ball': 58.30613417556258, 'IoU-kite': 66.49165142299202, 'IoU-baseball bat': 60.16857039339448, 'IoU-baseball glove': 52.484027664461706, 'IoU-skateboard': 77.97015285897119, 'IoU-surfboard': 75.39023790451193, 'IoU-tennis racket': 76.92984060634028, 'IoU-bottle': 67.84551998750736, 'IoU-wine glass': 74.6789243283158, 'IoU-cup': 61.01868042906295, 'IoU-fork': 54.356881694002226, 'IoU-knife': 50.31618848874869, 'IoU-spoon': 48.898301984797605, 'IoU-bowl': 52.46780733380517, 'IoU-banana': 84.03299076594598, 'IoU-apple': 56.41070112509404, 'IoU-sandwich': 65.45640140034965, 'IoU-orange': 74.33588926848802, 'IoU-broccoli': 66.90141731930893, 'IoU-carrot': 63.51892724353184, 'IoU-hot dog': 64.78178388130209, 'IoU-pizza': 82.40743116381375, 'IoU-donut': 66.13092390714621, 'IoU-cake': 67.37503863444165, 'IoU-chair': 56.47637148927459, 'IoU-couch': 67.66654352834311, 'IoU-potted plant': 36.21600168273498, 'IoU-bed': 69.72893289076784, 'IoU-dining table': 50.86228287543558, 'IoU-toilet': 87.63666634533507, 'IoU-tv': 74.90857042633667, 'IoU-laptop': 74.34168155815338, 'IoU-mouse': 65.70312405874628, 'IoU-remote': 62.46038536003081, 'IoU-keyboard': 55.216884136214425, 'IoU-cell phone': 74.81491764236353, 'IoU-microwave': 51.71596905532514, 'IoU-oven': 61.82894424391464, 'IoU-toaster': 56.5930222061744, 'IoU-sink': 69.93158477312114, 'IoU-refrigerator': 80.2683269269987, 'IoU-book': 50.43662107478192, 'IoU-clock': 69.60240946149014, 'IoU-vase': 62.87098289419585, 'IoU-scissors': 55.31227708013227, 'IoU-teddy bear': 81.11725506050618, 'IoU-hair drier': 36.58504562463074, 'IoU-toothbrush': 52.48368372498123, 'IoU-banner': 36.323470264209554, 'IoU-blanket': 13.423629628320658, 'IoU-bridge': 35.81734711946228, 'IoU-cardboard': 44.701757457963154, 'IoU-counter': 30.373893091272606, 'IoU-curtain': 64.23677428107285, 'IoU-door-stuff': 45.244585520050215, 'IoU-floor-wood': 63.77105058850449, 'IoU-flower': 42.51086553669199, 'IoU-fruit': 42.38195648828611, 'IoU-gravel': 26.28226029677339, 'IoU-house': 26.41692400763917, 'IoU-light': 40.40582036051642, 'IoU-mirror-stuff': 56.70735557305079, 'IoU-net': 41.43678909800012, 'IoU-pillow': 10.417747519772995, 'IoU-platform': 29.18964271582128, 'IoU-playingfield': 70.54600694959143, 'IoU-railroad': 60.517148508601146, 'IoU-river': 50.715046394057836, 'IoU-road': 65.78035653156422, 'IoU-roof': 11.175321769766663, 'IoU-sand': 62.66477365003441, 'IoU-sea': 84.6940970929529, 'IoU-shelf': 35.30259933611785, 'IoU-snow': 88.56573654473998, 'IoU-stairs': 19.81647745955315, 'IoU-tent': 8.95401211599149, 'IoU-towel': 29.302838131613402, 'IoU-wall-brick': 43.161114692196826, 'IoU-wall-stone': 26.806372771104442, 'IoU-wall-tile': 67.39866976458876, 'IoU-wall-wood': 38.372268846255224, 'IoU-water-other': 19.785241041249037, 'IoU-window-blind': 48.85704535011385, 'IoU-window-other': 45.79299363839517, 'IoU-tree-merged': 81.04110996183672, 'IoU-fence-merged': 50.642993389214595, 'IoU-ceiling-merged': 66.23134299528853, 'IoU-sky-other-merged': 93.52187774989797, 'IoU-cabinet-merged': 59.78409269152778, 'IoU-table-merged': 38.09426814064065, 'IoU-floor-other-merged': 48.76108138056231, 'IoU-pavement-merged': 53.93920608058581, 'IoU-mountain-merged': 54.76041023923045, 'IoU-grass-merged': 70.4594000904935, 'IoU-dirt-merged': 44.3518296096559, 'IoU-paper-merged': 34.661218049297545, 'IoU-food-other-merged': 38.68353494737229, 'IoU-building-other-merged': 58.25033331007432, 'IoU-rock-merged': 60.08224700472231, 'IoU-wall-other-merged': 65.95137450651401, 'IoU-rug-merged': 64.47467491418361, 'mACC': 72.37334644354596, 'pACC': 80.30215395703742, 'ACC-person': 92.54595691174437, 'ACC-bicycle': 86.13938617819349, 'ACC-car': 83.99135304975842, 'ACC-motorcycle': 80.8626643672899, 'ACC-airplane': 90.96523543074241, 'ACC-bus': 93.32239912325862, 'ACC-train': 93.88578133412264, 'ACC-truck': 74.86179856942893, 'ACC-boat': 78.35332716629486, 'ACC-traffic light': 90.24769817107764, 'ACC-fire hydrant': 95.70674290203982, 'ACC-stop sign': 97.28992073888875, 'ACC-parking meter': 92.52634402819015, 'ACC-bench': 75.06941853006751, 'ACC-bird': 80.60523770393577, 'ACC-cat': 83.71256801348933, 'ACC-dog': 83.82525619309976, 'ACC-horse': 92.08257333293287, 'ACC-sheep': 91.55625439715624, 'ACC-cow': 86.5150783492578, 'ACC-elephant': 93.72768752912502, 'ACC-bear': 80.96537010099428, 'ACC-zebra': 93.07509902957368, 'ACC-giraffe': 91.40096429212167, 'ACC-backpack': 57.14838169691764, 'ACC-umbrella': 86.40235988366362, 'ACC-handbag': 54.91739607010167, 'ACC-tie': 76.75050223389809, 'ACC-suitcase': 91.67716689937453, 'ACC-frisbee': 94.17927272727272, 'ACC-skis': 70.14552107858002, 'ACC-snowboard': 79.36518821483676, 'ACC-sports ball': 77.09451008362535, 'ACC-kite': 76.53228424939404, 'ACC-baseball bat': 82.74243457282215, 'ACC-baseball glove': 60.25738022668482, 'ACC-skateboard': 89.82835931433496, 'ACC-surfboard': 83.67715076755941, 'ACC-tennis racket': 83.1755417133048, 'ACC-bottle': 82.23192805882215, 'ACC-wine glass': 83.16041413573643, 'ACC-cup': 84.92015497216444, 'ACC-fork': 66.32846933112369, 'ACC-knife': 61.131200996556025, 'ACC-spoon': 68.68004948684118, 'ACC-bowl': 63.89639609471401, 'ACC-banana': 90.38414016478144, 'ACC-apple': 70.86036281515787, 'ACC-sandwich': 77.79450743534588, 'ACC-orange': 82.5383946565209, 'ACC-broccoli': 75.4695269668477, 'ACC-carrot': 73.17592162695958, 'ACC-hot dog': 73.62269532288359, 'ACC-pizza': 91.67484385909417, 'ACC-donut': 81.8574580229757, 'ACC-cake': 73.29264526918216, 'ACC-chair': 70.91432180829374, 'ACC-couch': 83.52795221495668, 'ACC-potted plant': 55.37694289195257, 'ACC-bed': 82.38162210245486, 'ACC-dining table': 75.08218358302426, 'ACC-toilet': 93.65910269442365, 'ACC-tv': 87.75117899464695, 'ACC-laptop': 89.29231639013557, 'ACC-mouse': 78.69044655837293, 'ACC-remote': 71.19524031014491, 'ACC-keyboard': 62.51638591719404, 'ACC-cell phone': 88.09197877570034, 'ACC-microwave': 58.208841736907665, 'ACC-oven': 79.52401555394836, 'ACC-toaster': 68.64059998084002, 'ACC-sink': 83.40908432376885, 'ACC-refrigerator': 91.50131037781699, 'ACC-book': 65.66154632820599, 'ACC-clock': 74.21844660431903, 'ACC-vase': 71.92282289201842, 'ACC-scissors': 59.53812357105473, 'ACC-teddy bear': 86.05936587079451, 'ACC-hair drier': 39.65644835339994, 'ACC-toothbrush': 79.56393328700486, 'ACC-banner': 79.53288869709209, 'ACC-blanket': 14.669334970362721, 'ACC-bridge': 55.734727202543596, 'ACC-cardboard': 57.316155300276286, 'ACC-counter': 59.21137473599812, 'ACC-curtain': 74.35338628333592, 'ACC-door-stuff': 68.91129763579467, 'ACC-floor-wood': 77.74051507446539, 'ACC-flower': 58.80063365024214, 'ACC-fruit': 58.91529159148343, 'ACC-gravel': 34.66094134038504, 'ACC-house': 31.5184205277983, 'ACC-light': 55.778971117406115, 'ACC-mirror-stuff': 70.49317940544955, 'ACC-net': 60.631067745919, 'ACC-pillow': 20.780215051492505, 'ACC-platform': 54.531592958984795, 'ACC-playingfield': 93.2934128781082, 'ACC-railroad': 78.38580162924495, 'ACC-river': 73.87211312127576, 'ACC-road': 84.84317612236218, 'ACC-roof': 14.511321085292165, 'ACC-sand': 68.3657641910047, 'ACC-sea': 91.0938315427629, 'ACC-shelf': 54.73610179851285, 'ACC-snow': 95.0592246577748, 'ACC-stairs': 30.115363474133634, 'ACC-tent': 10.302903254137199, 'ACC-towel': 35.23520162331273, 'ACC-wall-brick': 58.227205364719325, 'ACC-wall-stone': 33.531639225030986, 'ACC-wall-tile': 81.78430772135334, 'ACC-wall-wood': 51.49352549510557, 'ACC-water-other': 31.08627754275492, 'ACC-window-blind': 56.882864712041304, 'ACC-window-other': 65.75348746094338, 'ACC-tree-merged': 89.63558138879846, 'ACC-fence-merged': 69.19176890866552, 'ACC-ceiling-merged': 77.01111115103907, 'ACC-sky-other-merged': 96.30697995055607, 'ACC-cabinet-merged': 74.84628501375178, 'ACC-table-merged': 52.79983020551873, 'ACC-floor-other-merged': 60.12681456594062, 'ACC-pavement-merged': 67.92837006898051, 'ACC-mountain-merged': 64.46837696634682, 'ACC-grass-merged': 82.73492831732993, 'ACC-dirt-merged': 65.31387832642541, 'ACC-paper-merged': 49.26811842050634, 'ACC-food-other-merged': 51.496621963475754, 'ACC-building-other-merged': 77.61613645534474, 'ACC-rock-merged': 82.56815598146973, 'ACC-wall-other-merged': 79.30221915131112, 'ACC-rug-merged': 78.35549845501052})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4667837284167398, 'noc@0.8': 2.758852794849283, 'noc@0.85': 3.362598770851624, 'noc@0.9': 4.386596429616623, 'miou@iter1': 0.8346167752224345}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.04547119140625, 'precision@0.6': 68.09172058105469, 'precision@0.7': 63.07811737060547, 'precision@0.8': 52.07928466796875, 'precision@0.9': 26.894676208496094, 'cIoU': 57.48524475097656, 'mIoU': 62.77397537231445}}}
INFO:trainer.default_trainer:This epoch takes 1:27:07.685099
INFO:trainer.default_trainer:PROGRESS: 76.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 38 training.
INFO:trainer.default_trainer:epochs[    38] optim steps[69500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29751/0.89752, loss_mask_bce_0: 0.03414/0.33417, loss_mask_dice_0: 0.05175/1.16278, loss_spatial_bce_0: 0.02733/0.08706, loss_spatial_dice_0: 0.03817/0.20777, loss_spatial_ce_0: 0.00064/0.06132, loss_grounding_bce_0: 0.03208/0.08620, loss_grounding_dice_0: 0.03874/0.17845, loss_grounding_ce_0: 0.47927/0.27173, loss_mask_ce_1: 0.28041/0.89810, loss_mask_bce_1: 0.03349/0.33511, loss_mask_dice_1: 0.04904/1.16953, loss_spatial_bce_1: 0.02792/0.08759, loss_spatial_dice_1: 0.03657/0.21174, loss_spatial_ce_1: 0.00060/0.06717, loss_grounding_bce_1: 0.03185/0.08639, loss_grounding_dice_1: 0.03956/0.17930, loss_grounding_ce_1: 0.46443/0.27248, loss_mask_ce_2: 0.30119/0.90513, loss_mask_bce_2: 0.03473/0.33567, loss_mask_dice_2: 0.05007/1.16994, loss_spatial_bce_2: 0.02778/0.08872, loss_spatial_dice_2: 0.03328/0.21341, loss_spatial_ce_2: 0.00070/0.07056, loss_grounding_bce_2: 0.03142/0.08656, loss_grounding_dice_2: 0.03688/0.17911, loss_grounding_ce_2: 0.35626/0.27582, loss_mask_ce_3: 0.25330/0.91596, loss_mask_bce_3: 0.03523/0.33687, loss_mask_dice_3: 0.04700/1.16774, loss_spatial_bce_3: 0.02595/0.08995, loss_spatial_dice_3: 0.03556/0.21438, loss_spatial_ce_3: 0.00011/0.07539, loss_grounding_bce_3: 0.03107/0.08679, loss_grounding_dice_3: 0.03589/0.17885, loss_grounding_ce_3: 0.20060/0.27808, loss_mask_ce_4: 0.25657/0.91697, loss_mask_bce_4: 0.03252/0.33901, loss_mask_dice_4: 0.04684/1.19156, loss_spatial_bce_4: 0.02541/0.09387, loss_spatial_dice_4: 0.03335/0.22658, loss_spatial_ce_4: 0.00011/0.09154, loss_grounding_bce_4: 0.03050/0.08732, loss_grounding_dice_4: 0.03845/0.18175, loss_grounding_ce_4: 0.27752/0.28109, loss_mask_ce_5: 0.36340/0.93364, loss_mask_bce_5: 0.03085/0.34134, loss_mask_dice_5: 0.04447/1.19936, loss_spatial_bce_5: 0.02871/0.09610, loss_spatial_dice_5: 0.03754/0.23086, loss_spatial_ce_5: 0.00022/0.10567, loss_grounding_bce_5: 0.03014/0.08775, loss_grounding_dice_5: 0.03818/0.18301, loss_grounding_ce_5: 0.46726/0.29354, loss_mask_ce_6: 0.39589/0.97360, loss_mask_bce_6: 0.03039/0.34402, loss_mask_dice_6: 0.04618/1.20232, loss_spatial_bce_6: 0.02749/0.10178, loss_spatial_dice_6: 0.03762/0.23374, loss_spatial_ce_6: 0.03289/0.13140, loss_grounding_bce_6: 0.02855/0.08848, loss_grounding_dice_6: 0.03742/0.18341, loss_grounding_ce_6: 0.46874/0.30910, loss_mask_ce_7: 0.41255/1.01873, loss_mask_bce_7: 0.03195/0.35190, loss_mask_dice_7: 0.04789/1.25668, loss_spatial_bce_7: 0.03140/0.10973, loss_spatial_dice_7: 0.04168/0.26140, loss_spatial_ce_7: 0.06096/0.16664, loss_grounding_bce_7: 0.02963/0.09037, loss_grounding_dice_7: 0.03873/0.19071, loss_grounding_ce_7: 0.39272/0.33941, loss_mask_ce_8: 0.54959/1.12722, loss_mask_bce_8: 0.03055/0.36551, loss_mask_dice_8: 0.04616/1.32955, loss_spatial_bce_8: 0.03253/0.13026, loss_spatial_dice_8: 0.04782/0.29929, loss_spatial_ce_8: 0.11267/0.22170, loss_grounding_bce_8: 0.03017/0.09408, loss_grounding_dice_8: 0.03746/0.20149, loss_grounding_ce_8: 0.45850/0.40647, loss_mask_ce_9: 2.44584/3.67573, loss_mask_bce_9: 0.04061/0.39254, loss_mask_dice_9: 0.09771/1.90215, loss_spatial_bce_9: 0.34045/0.33290, loss_spatial_dice_9: 0.71894/0.82177, loss_spatial_ce_9: 1.01202/1.49557, loss_grounding_bce_9: 0.03783/0.10567, loss_grounding_dice_9: 0.07918/0.28082, loss_grounding_ce_9: 0.48287/0.67093] items per batch[64] items per second[0.13] total items[4448000] mini batches[ 69500] memory[7345] epoch remaining[1:22:15]
INFO:trainer.default_trainer:epochs[    38] optim steps[69600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26166/0.89745, loss_mask_bce_0: 0.17766/0.33412, loss_mask_dice_0: 0.17684/1.16276, loss_spatial_bce_0: 0.10200/0.08705, loss_spatial_dice_0: 0.13794/0.20775, loss_spatial_ce_0: 0.08189/0.06130, loss_grounding_bce_0: 0.17987/0.08617, loss_grounding_dice_0: 0.15302/0.17845, loss_grounding_ce_0: 0.04471/0.27170, loss_mask_ce_1: 0.28126/0.89801, loss_mask_bce_1: 0.19038/0.33506, loss_mask_dice_1: 0.17688/1.16950, loss_spatial_bce_1: 0.10528/0.08757, loss_spatial_dice_1: 0.13788/0.21172, loss_spatial_ce_1: 0.07190/0.06715, loss_grounding_bce_1: 0.20225/0.08637, loss_grounding_dice_1: 0.15600/0.17931, loss_grounding_ce_1: 0.02816/0.27245, loss_mask_ce_2: 0.28902/0.90504, loss_mask_bce_2: 0.19052/0.33562, loss_mask_dice_2: 0.17296/1.16992, loss_spatial_bce_2: 0.09412/0.08871, loss_spatial_dice_2: 0.11893/0.21339, loss_spatial_ce_2: 0.06827/0.07053, loss_grounding_bce_2: 0.22095/0.08653, loss_grounding_dice_2: 0.15302/0.17912, loss_grounding_ce_2: 0.02165/0.27579, loss_mask_ce_3: 0.28839/0.91591, loss_mask_bce_3: 0.19176/0.33682, loss_mask_dice_3: 0.18427/1.16770, loss_spatial_bce_3: 0.09386/0.08994, loss_spatial_dice_3: 0.13934/0.21437, loss_spatial_ce_3: 0.05850/0.07537, loss_grounding_bce_3: 0.21915/0.08677, loss_grounding_dice_3: 0.16273/0.17886, loss_grounding_ce_3: 0.03035/0.27806, loss_mask_ce_4: 0.25492/0.91690, loss_mask_bce_4: 0.17487/0.33896, loss_mask_dice_4: 0.17449/1.19151, loss_spatial_bce_4: 0.09852/0.09386, loss_spatial_dice_4: 0.13902/0.22657, loss_spatial_ce_4: 0.07489/0.09152, loss_grounding_bce_4: 0.21096/0.08729, loss_grounding_dice_4: 0.14279/0.18176, loss_grounding_ce_4: 0.03982/0.28105, loss_mask_ce_5: 0.31118/0.93355, loss_mask_bce_5: 0.17635/0.34129, loss_mask_dice_5: 0.16606/1.19936, loss_spatial_bce_5: 0.10594/0.09609, loss_spatial_dice_5: 0.13322/0.23084, loss_spatial_ce_5: 0.09529/0.10565, loss_grounding_bce_5: 0.19115/0.08772, loss_grounding_dice_5: 0.13493/0.18303, loss_grounding_ce_5: 0.04440/0.29350, loss_mask_ce_6: 0.33021/0.97353, loss_mask_bce_6: 0.18590/0.34397, loss_mask_dice_6: 0.17026/1.20229, loss_spatial_bce_6: 0.09974/0.10177, loss_spatial_dice_6: 0.12119/0.23372, loss_spatial_ce_6: 0.09694/0.13136, loss_grounding_bce_6: 0.20221/0.08845, loss_grounding_dice_6: 0.15325/0.18341, loss_grounding_ce_6: 0.08842/0.30909, loss_mask_ce_7: 0.36508/1.01870, loss_mask_bce_7: 0.17419/0.35184, loss_mask_dice_7: 0.19919/1.25665, loss_spatial_bce_7: 0.18292/0.10972, loss_spatial_dice_7: 0.21540/0.26139, loss_spatial_ce_7: 0.06682/0.16659, loss_grounding_bce_7: 0.18012/0.09034, loss_grounding_dice_7: 0.12865/0.19071, loss_grounding_ce_7: 0.06920/0.33940, loss_mask_ce_8: 0.40462/1.12721, loss_mask_bce_8: 0.21568/0.36545, loss_mask_dice_8: 0.20314/1.32950, loss_spatial_bce_8: 0.12032/0.13024, loss_spatial_dice_8: 0.17265/0.29929, loss_spatial_ce_8: 0.16064/0.22166, loss_grounding_bce_8: 0.19090/0.09405, loss_grounding_dice_8: 0.15980/0.20150, loss_grounding_ce_8: 0.35047/0.40641, loss_mask_ce_9: 2.47859/3.67574, loss_mask_bce_9: 0.23438/0.39246, loss_mask_dice_9: 0.28421/1.90205, loss_spatial_bce_9: 0.57668/0.33290, loss_spatial_dice_9: 0.68048/0.82175, loss_spatial_ce_9: 1.06102/1.49557, loss_grounding_bce_9: 0.23732/0.10563, loss_grounding_dice_9: 0.21752/0.28083, loss_grounding_ce_9: 0.38900/0.67097] items per batch[64] items per second[0.24] total items[4454400] mini batches[ 69600] memory[7345] epoch remaining[1:15:37]
INFO:trainer.default_trainer:epochs[    38] optim steps[69700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71654/0.89748, loss_mask_bce_0: 0.45061/0.33416, loss_mask_dice_0: 0.58893/1.16287, loss_spatial_bce_0: 0.11911/0.08704, loss_spatial_dice_0: 0.14892/0.20775, loss_spatial_ce_0: 0.00826/0.06127, loss_grounding_bce_0: 0.06612/0.08617, loss_grounding_dice_0: 0.11771/0.17844, loss_grounding_ce_0: 0.34756/0.27174, loss_mask_ce_1: 0.71924/0.89805, loss_mask_bce_1: 0.44793/0.33510, loss_mask_dice_1: 0.59973/1.16964, loss_spatial_bce_1: 0.12752/0.08757, loss_spatial_dice_1: 0.15905/0.21172, loss_spatial_ce_1: 0.01472/0.06712, loss_grounding_bce_1: 0.06954/0.08636, loss_grounding_dice_1: 0.11977/0.17930, loss_grounding_ce_1: 0.33310/0.27250, loss_mask_ce_2: 0.68760/0.90511, loss_mask_bce_2: 0.45313/0.33566, loss_mask_dice_2: 0.60079/1.17006, loss_spatial_bce_2: 0.13202/0.08871, loss_spatial_dice_2: 0.16754/0.21339, loss_spatial_ce_2: 0.01931/0.07050, loss_grounding_bce_2: 0.06858/0.08652, loss_grounding_dice_2: 0.11607/0.17912, loss_grounding_ce_2: 0.33533/0.27582, loss_mask_ce_3: 0.73937/0.91598, loss_mask_bce_3: 0.47381/0.33686, loss_mask_dice_3: 0.59065/1.16783, loss_spatial_bce_3: 0.13175/0.08994, loss_spatial_dice_3: 0.16258/0.21437, loss_spatial_ce_3: 0.02724/0.07534, loss_grounding_bce_3: 0.06866/0.08676, loss_grounding_dice_3: 0.11161/0.17885, loss_grounding_ce_3: 0.32555/0.27812, loss_mask_ce_4: 0.73483/0.91695, loss_mask_bce_4: 0.45740/0.33900, loss_mask_dice_4: 0.60307/1.19163, loss_spatial_bce_4: 0.13363/0.09386, loss_spatial_dice_4: 0.16000/0.22657, loss_spatial_ce_4: 0.03347/0.09151, loss_grounding_bce_4: 0.06870/0.08729, loss_grounding_dice_4: 0.11930/0.18174, loss_grounding_ce_4: 0.33455/0.28110, loss_mask_ce_5: 0.74414/0.93360, loss_mask_bce_5: 0.46201/0.34133, loss_mask_dice_5: 0.60522/1.19952, loss_spatial_bce_5: 0.14135/0.09609, loss_spatial_dice_5: 0.16190/0.23085, loss_spatial_ce_5: 0.10978/0.10563, loss_grounding_bce_5: 0.06989/0.08772, loss_grounding_dice_5: 0.11816/0.18302, loss_grounding_ce_5: 0.36086/0.29354, loss_mask_ce_6: 0.84016/0.97357, loss_mask_bce_6: 0.45900/0.34401, loss_mask_dice_6: 0.60834/1.20241, loss_spatial_bce_6: 0.14088/0.10177, loss_spatial_dice_6: 0.16951/0.23373, loss_spatial_ce_6: 0.14029/0.13134, loss_grounding_bce_6: 0.07251/0.08844, loss_grounding_dice_6: 0.11716/0.18340, loss_grounding_ce_6: 0.38017/0.30915, loss_mask_ce_7: 0.87592/1.01873, loss_mask_bce_7: 0.47018/0.35187, loss_mask_dice_7: 0.63218/1.25678, loss_spatial_bce_7: 0.15521/0.10973, loss_spatial_dice_7: 0.18689/0.26140, loss_spatial_ce_7: 0.17483/0.16657, loss_grounding_bce_7: 0.06626/0.09033, loss_grounding_dice_7: 0.11796/0.19070, loss_grounding_ce_7: 0.39735/0.33946, loss_mask_ce_8: 0.79629/1.12726, loss_mask_bce_8: 0.50284/0.36548, loss_mask_dice_8: 0.66069/1.32962, loss_spatial_bce_8: 0.20214/0.13025, loss_spatial_dice_8: 0.23882/0.29929, loss_spatial_ce_8: 0.20733/0.22161, loss_grounding_bce_8: 0.07048/0.09405, loss_grounding_dice_8: 0.12501/0.20150, loss_grounding_ce_8: 0.40224/0.40645, loss_mask_ce_9: 4.94231/3.67588, loss_mask_bce_9: 0.53922/0.39251, loss_mask_dice_9: 0.99769/1.90223, loss_spatial_bce_9: 0.45751/0.33289, loss_spatial_dice_9: 0.85483/0.82176, loss_spatial_ce_9: 1.49203/1.49560, loss_grounding_bce_9: 0.08693/0.10563, loss_grounding_dice_9: 0.22053/0.28084, loss_grounding_ce_9: 0.47817/0.67096] items per batch[64] items per second[0.23] total items[4460800] mini batches[ 69700] memory[7345] epoch remaining[1:11:43]
INFO:trainer.default_trainer:epochs[    38] optim steps[69800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57833/0.89755, loss_mask_bce_0: 0.41357/0.33417, loss_mask_dice_0: 0.34046/1.16275, loss_spatial_bce_0: 0.20470/0.08704, loss_spatial_dice_0: 0.18773/0.20772, loss_spatial_ce_0: 0.14011/0.06127, loss_grounding_bce_0: 0.26926/0.08617, loss_grounding_dice_0: 0.20510/0.17843, loss_grounding_ce_0: 0.12334/0.27167, loss_mask_ce_1: 0.57436/0.89815, loss_mask_bce_1: 0.42408/0.33511, loss_mask_dice_1: 0.30038/1.16954, loss_spatial_bce_1: 0.20471/0.08757, loss_spatial_dice_1: 0.19815/0.21170, loss_spatial_ce_1: 0.13528/0.06712, loss_grounding_bce_1: 0.26055/0.08637, loss_grounding_dice_1: 0.19358/0.17930, loss_grounding_ce_1: 0.12192/0.27243, loss_mask_ce_2: 0.52096/0.90520, loss_mask_bce_2: 0.41360/0.33566, loss_mask_dice_2: 0.30285/1.16994, loss_spatial_bce_2: 0.21206/0.08871, loss_spatial_dice_2: 0.19099/0.21338, loss_spatial_ce_2: 0.15551/0.07049, loss_grounding_bce_2: 0.26515/0.08653, loss_grounding_dice_2: 0.21245/0.17911, loss_grounding_ce_2: 0.10986/0.27574, loss_mask_ce_3: 0.52795/0.91609, loss_mask_bce_3: 0.40985/0.33687, loss_mask_dice_3: 0.29007/1.16772, loss_spatial_bce_3: 0.20690/0.08994, loss_spatial_dice_3: 0.20982/0.21435, loss_spatial_ce_3: 0.14753/0.07534, loss_grounding_bce_3: 0.25845/0.08677, loss_grounding_dice_3: 0.20265/0.17884, loss_grounding_ce_3: 0.11684/0.27807, loss_mask_ce_4: 0.59539/0.91701, loss_mask_bce_4: 0.40397/0.33902, loss_mask_dice_4: 0.27965/1.19154, loss_spatial_bce_4: 0.25953/0.09386, loss_spatial_dice_4: 0.26007/0.22656, loss_spatial_ce_4: 0.17684/0.09153, loss_grounding_bce_4: 0.25979/0.08729, loss_grounding_dice_4: 0.22345/0.18174, loss_grounding_ce_4: 0.09920/0.28102, loss_mask_ce_5: 0.60956/0.93368, loss_mask_bce_5: 0.41118/0.34134, loss_mask_dice_5: 0.31913/1.19943, loss_spatial_bce_5: 0.23577/0.09610, loss_spatial_dice_5: 0.24940/0.23083, loss_spatial_ce_5: 0.21198/0.10562, loss_grounding_bce_5: 0.26613/0.08773, loss_grounding_dice_5: 0.22204/0.18302, loss_grounding_ce_5: 0.09065/0.29347, loss_mask_ce_6: 0.45521/0.97364, loss_mask_bce_6: 0.42635/0.34402, loss_mask_dice_6: 0.34369/1.20230, loss_spatial_bce_6: 0.26704/0.10178, loss_spatial_dice_6: 0.27657/0.23371, loss_spatial_ce_6: 0.17026/0.13133, loss_grounding_bce_6: 0.28073/0.08846, loss_grounding_dice_6: 0.23490/0.18341, loss_grounding_ce_6: 0.10085/0.30905, loss_mask_ce_7: 0.48205/1.01879, loss_mask_bce_7: 0.43933/0.35190, loss_mask_dice_7: 0.37670/1.25667, loss_spatial_bce_7: 0.26109/0.10973, loss_spatial_dice_7: 0.25857/0.26138, loss_spatial_ce_7: 0.21650/0.16656, loss_grounding_bce_7: 0.26624/0.09034, loss_grounding_dice_7: 0.23109/0.19069, loss_grounding_ce_7: 0.20842/0.33935, loss_mask_ce_8: 0.72015/1.12732, loss_mask_bce_8: 0.43584/0.36550, loss_mask_dice_8: 0.40082/1.32949, loss_spatial_bce_8: 0.25118/0.13025, loss_spatial_dice_8: 0.23280/0.29926, loss_spatial_ce_8: 0.23644/0.22159, loss_grounding_bce_8: 0.28091/0.09406, loss_grounding_dice_8: 0.25636/0.20150, loss_grounding_ce_8: 0.17109/0.40630, loss_mask_ce_9: 1.92566/3.67568, loss_mask_bce_9: 0.42231/0.39252, loss_mask_dice_9: 0.46826/1.90208, loss_spatial_bce_9: 0.65475/0.33289, loss_spatial_dice_9: 0.71022/0.82175, loss_spatial_ce_9: 1.26535/1.49557, loss_grounding_bce_9: 0.23447/0.10564, loss_grounding_dice_9: 0.25461/0.28083, loss_grounding_ce_9: 0.07202/0.67078] items per batch[64] items per second[0.23] total items[4467200] mini batches[ 69800] memory[7345] epoch remaining[1:07:14]
INFO:trainer.default_trainer:epochs[    38] optim steps[69900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99316/0.89749, loss_mask_bce_0: 0.63406/0.33416, loss_mask_dice_0: 0.64096/1.16271, loss_spatial_bce_0: 0.16027/0.08704, loss_spatial_dice_0: 0.20648/0.20772, loss_spatial_ce_0: 0.05784/0.06127, loss_grounding_bce_0: 0.22001/0.08617, loss_grounding_dice_0: 0.21804/0.17843, loss_grounding_ce_0: 0.37618/0.27167, loss_mask_ce_1: 0.86205/0.89810, loss_mask_bce_1: 0.65398/0.33510, loss_mask_dice_1: 0.70958/1.16950, loss_spatial_bce_1: 0.15042/0.08757, loss_spatial_dice_1: 0.22537/0.21169, loss_spatial_ce_1: 0.03408/0.06709, loss_grounding_bce_1: 0.21288/0.08636, loss_grounding_dice_1: 0.23404/0.17930, loss_grounding_ce_1: 0.36952/0.27245, loss_mask_ce_2: 0.82855/0.90515, loss_mask_bce_2: 0.66157/0.33566, loss_mask_dice_2: 0.65633/1.16988, loss_spatial_bce_2: 0.20010/0.08871, loss_spatial_dice_2: 0.25485/0.21337, loss_spatial_ce_2: 0.04036/0.07047, loss_grounding_bce_2: 0.20520/0.08652, loss_grounding_dice_2: 0.23121/0.17910, loss_grounding_ce_2: 0.37459/0.27576, loss_mask_ce_3: 1.08592/0.91605, loss_mask_bce_3: 0.63533/0.33686, loss_mask_dice_3: 0.66282/1.16769, loss_spatial_bce_3: 0.17974/0.08994, loss_spatial_dice_3: 0.24780/0.21435, loss_spatial_ce_3: 0.04322/0.07532, loss_grounding_bce_3: 0.21081/0.08677, loss_grounding_dice_3: 0.23192/0.17885, loss_grounding_ce_3: 0.39521/0.27808, loss_mask_ce_4: 0.94980/0.91698, loss_mask_bce_4: 0.64882/0.33901, loss_mask_dice_4: 0.70146/1.19148, loss_spatial_bce_4: 0.21759/0.09387, loss_spatial_dice_4: 0.27149/0.22656, loss_spatial_ce_4: 0.07003/0.09152, loss_grounding_bce_4: 0.21323/0.08729, loss_grounding_dice_4: 0.21242/0.18174, loss_grounding_ce_4: 0.40227/0.28102, loss_mask_ce_5: 1.00223/0.93366, loss_mask_bce_5: 0.64850/0.34133, loss_mask_dice_5: 0.66964/1.19939, loss_spatial_bce_5: 0.23625/0.09610, loss_spatial_dice_5: 0.30920/0.23084, loss_spatial_ce_5: 0.17093/0.10560, loss_grounding_bce_5: 0.20848/0.08772, loss_grounding_dice_5: 0.22202/0.18302, loss_grounding_ce_5: 0.38310/0.29350, loss_mask_ce_6: 1.09895/0.97361, loss_mask_bce_6: 0.64233/0.34401, loss_mask_dice_6: 0.61704/1.20226, loss_spatial_bce_6: 0.26365/0.10179, loss_spatial_dice_6: 0.30756/0.23372, loss_spatial_ce_6: 0.15376/0.13130, loss_grounding_bce_6: 0.24085/0.08845, loss_grounding_dice_6: 0.23696/0.18340, loss_grounding_ce_6: 0.40070/0.30904, loss_mask_ce_7: 1.15799/1.01875, loss_mask_bce_7: 0.61534/0.35189, loss_mask_dice_7: 0.68564/1.25661, loss_spatial_bce_7: 0.31465/0.10974, loss_spatial_dice_7: 0.33887/0.26138, loss_spatial_ce_7: 0.33107/0.16653, loss_grounding_bce_7: 0.19267/0.09035, loss_grounding_dice_7: 0.22103/0.19069, loss_grounding_ce_7: 0.43250/0.33936, loss_mask_ce_8: 1.08381/1.12727, loss_mask_bce_8: 0.69813/0.36549, loss_mask_dice_8: 0.76390/1.32942, loss_spatial_bce_8: 0.30262/0.13025, loss_spatial_dice_8: 0.35655/0.29925, loss_spatial_ce_8: 0.23164/0.22155, loss_grounding_bce_8: 0.19772/0.09406, loss_grounding_dice_8: 0.26873/0.20149, loss_grounding_ce_8: 0.38795/0.40628, loss_mask_ce_9: 2.65716/3.67555, loss_mask_bce_9: 0.72926/0.39251, loss_mask_dice_9: 0.91481/1.90192, loss_spatial_bce_9: 0.54829/0.33287, loss_spatial_dice_9: 0.78229/0.82174, loss_spatial_ce_9: 1.40132/1.49543, loss_grounding_bce_9: 0.26830/0.10565, loss_grounding_dice_9: 0.33027/0.28083, loss_grounding_ce_9: 0.47065/0.67083] items per batch[64] items per second[0.23] total items[4473600] mini batches[ 69900] memory[7345] epoch remaining[1:02:30]
INFO:trainer.default_trainer:epochs[    38] optim steps[70000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.73007/0.89748, loss_mask_bce_0: 0.12233/0.33418, loss_mask_dice_0: 1.38963/1.16307, loss_spatial_bce_0: 0.05443/0.08703, loss_spatial_dice_0: 0.28908/0.20771, loss_spatial_ce_0: 0.16639/0.06123, loss_grounding_bce_0: 0.09641/0.08615, loss_grounding_dice_0: 0.19741/0.17844, loss_grounding_ce_0: 0.15304/0.27166, loss_mask_ce_1: 1.92702/0.89812, loss_mask_bce_1: 0.11924/0.33511, loss_mask_dice_1: 1.07598/1.16983, loss_spatial_bce_1: 0.05379/0.08756, loss_spatial_dice_1: 0.33408/0.21169, loss_spatial_ce_1: 0.02634/0.06706, loss_grounding_bce_1: 0.10086/0.08634, loss_grounding_dice_1: 0.15532/0.17929, loss_grounding_ce_1: 0.10705/0.27244, loss_mask_ce_2: 1.81131/0.90515, loss_mask_bce_2: 0.11538/0.33567, loss_mask_dice_2: 1.26480/1.17023, loss_spatial_bce_2: 0.05812/0.08870, loss_spatial_dice_2: 0.25110/0.21337, loss_spatial_ce_2: 0.22018/0.07044, loss_grounding_bce_2: 0.09814/0.08650, loss_grounding_dice_2: 0.20737/0.17911, loss_grounding_ce_2: 0.12572/0.27575, loss_mask_ce_3: 1.94723/0.91605, loss_mask_bce_3: 0.11361/0.33688, loss_mask_dice_3: 1.16945/1.16802, loss_spatial_bce_3: 0.05219/0.08993, loss_spatial_dice_3: 0.29151/0.21436, loss_spatial_ce_3: 0.35693/0.07529, loss_grounding_bce_3: 0.09920/0.08675, loss_grounding_dice_3: 0.22344/0.17886, loss_grounding_ce_3: 0.12941/0.27809, loss_mask_ce_4: 1.81952/0.91701, loss_mask_bce_4: 0.10970/0.33901, loss_mask_dice_4: 1.31321/1.19184, loss_spatial_bce_4: 0.06399/0.09386, loss_spatial_dice_4: 0.33414/0.22657, loss_spatial_ce_4: 0.28182/0.09150, loss_grounding_bce_4: 0.09194/0.08727, loss_grounding_dice_4: 0.23780/0.18175, loss_grounding_ce_4: 0.11014/0.28102, loss_mask_ce_5: 2.16493/0.93371, loss_mask_bce_5: 0.12644/0.34133, loss_mask_dice_5: 1.30083/1.19972, loss_spatial_bce_5: 0.05362/0.09609, loss_spatial_dice_5: 0.32371/0.23084, loss_spatial_ce_5: 0.13488/0.10557, loss_grounding_bce_5: 0.09895/0.08770, loss_grounding_dice_5: 0.21370/0.18303, loss_grounding_ce_5: 0.17041/0.29350, loss_mask_ce_6: 2.17365/0.97363, loss_mask_bce_6: 0.13977/0.34403, loss_mask_dice_6: 1.23673/1.20258, loss_spatial_bce_6: 0.05345/0.10178, loss_spatial_dice_6: 0.32131/0.23372, loss_spatial_ce_6: 0.32438/0.13128, loss_grounding_bce_6: 0.12695/0.08844, loss_grounding_dice_6: 0.23355/0.18341, loss_grounding_ce_6: 0.50732/0.30903, loss_mask_ce_7: 2.15603/1.01881, loss_mask_bce_7: 0.16973/0.35190, loss_mask_dice_7: 1.05559/1.25698, loss_spatial_bce_7: 0.07698/0.10972, loss_spatial_dice_7: 0.34008/0.26138, loss_spatial_ce_7: 0.18765/0.16651, loss_grounding_bce_7: 0.15205/0.09033, loss_grounding_dice_7: 0.30391/0.19070, loss_grounding_ce_7: 0.24389/0.33938, loss_mask_ce_8: 1.84780/1.12733, loss_mask_bce_8: 0.13013/0.36550, loss_mask_dice_8: 1.35889/1.32980, loss_spatial_bce_8: 0.05414/0.13023, loss_spatial_dice_8: 0.46088/0.29925, loss_spatial_ce_8: 0.40958/0.22150, loss_grounding_bce_8: 0.11163/0.09404, loss_grounding_dice_8: 0.29244/0.20149, loss_grounding_ce_8: 0.37758/0.40630, loss_mask_ce_9: 5.16218/3.67564, loss_mask_bce_9: 0.12659/0.39253, loss_mask_dice_9: 1.27784/1.90242, loss_spatial_bce_9: 0.19002/0.33285, loss_spatial_dice_9: 0.71263/0.82174, loss_spatial_ce_9: 1.98922/1.49541, loss_grounding_bce_9: 0.11400/0.10563, loss_grounding_dice_9: 0.41144/0.28085, loss_grounding_ce_9: 0.36587/0.67082] items per batch[64] items per second[0.23] total items[4480000] mini batches[ 70000] memory[7345] epoch remaining[0:57:43]
INFO:trainer.default_trainer:epochs[    38] optim steps[70100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94727/0.89750, loss_mask_bce_0: 0.25466/0.33413, loss_mask_dice_0: 0.35703/1.16306, loss_spatial_bce_0: 0.09295/0.08701, loss_spatial_dice_0: 0.10825/0.20770, loss_spatial_ce_0: 0.00166/0.06122, loss_grounding_bce_0: 0.12701/0.08614, loss_grounding_dice_0: 0.12293/0.17843, loss_grounding_ce_0: 0.20517/0.27167, loss_mask_ce_1: 0.89944/0.89815, loss_mask_bce_1: 0.25388/0.33506, loss_mask_dice_1: 0.40286/1.16981, loss_spatial_bce_1: 0.08820/0.08754, loss_spatial_dice_1: 0.12605/0.21168, loss_spatial_ce_1: 0.00211/0.06706, loss_grounding_bce_1: 0.13137/0.08634, loss_grounding_dice_1: 0.12513/0.17929, loss_grounding_ce_1: 0.18109/0.27244, loss_mask_ce_2: 0.85772/0.90519, loss_mask_bce_2: 0.25468/0.33562, loss_mask_dice_2: 0.33963/1.17019, loss_spatial_bce_2: 0.09147/0.08868, loss_spatial_dice_2: 0.12775/0.21337, loss_spatial_ce_2: 0.00288/0.07045, loss_grounding_bce_2: 0.12912/0.08649, loss_grounding_dice_2: 0.12455/0.17910, loss_grounding_ce_2: 0.18246/0.27577, loss_mask_ce_3: 0.90403/0.91612, loss_mask_bce_3: 0.26508/0.33683, loss_mask_dice_3: 0.32632/1.16796, loss_spatial_bce_3: 0.09336/0.08992, loss_spatial_dice_3: 0.12272/0.21435, loss_spatial_ce_3: 0.00799/0.07530, loss_grounding_bce_3: 0.12669/0.08674, loss_grounding_dice_3: 0.11860/0.17885, loss_grounding_ce_3: 0.20275/0.27811, loss_mask_ce_4: 0.85605/0.91706, loss_mask_bce_4: 0.27203/0.33896, loss_mask_dice_4: 0.34023/1.19180, loss_spatial_bce_4: 0.08990/0.09384, loss_spatial_dice_4: 0.11334/0.22656, loss_spatial_ce_4: 0.02116/0.09149, loss_grounding_bce_4: 0.12541/0.08726, loss_grounding_dice_4: 0.11813/0.18174, loss_grounding_ce_4: 0.23258/0.28106, loss_mask_ce_5: 0.77821/0.93376, loss_mask_bce_5: 0.26796/0.34128, loss_mask_dice_5: 0.37504/1.19969, loss_spatial_bce_5: 0.09215/0.09608, loss_spatial_dice_5: 0.11088/0.23084, loss_spatial_ce_5: 0.03980/0.10557, loss_grounding_bce_5: 0.12310/0.08770, loss_grounding_dice_5: 0.12279/0.18302, loss_grounding_ce_5: 0.20167/0.29355, loss_mask_ce_6: 0.80107/0.97371, loss_mask_bce_6: 0.25892/0.34397, loss_mask_dice_6: 0.35972/1.20256, loss_spatial_bce_6: 0.09776/0.10176, loss_spatial_dice_6: 0.13571/0.23372, loss_spatial_ce_6: 0.10622/0.13129, loss_grounding_bce_6: 0.10732/0.08843, loss_grounding_dice_6: 0.11311/0.18340, loss_grounding_ce_6: 0.18596/0.30906, loss_mask_ce_7: 0.85706/1.01893, loss_mask_bce_7: 0.26320/0.35184, loss_mask_dice_7: 0.39116/1.25696, loss_spatial_bce_7: 0.09670/0.10971, loss_spatial_dice_7: 0.13683/0.26139, loss_spatial_ce_7: 0.13635/0.16652, loss_grounding_bce_7: 0.10946/0.09032, loss_grounding_dice_7: 0.12154/0.19070, loss_grounding_ce_7: 0.18309/0.33946, loss_mask_ce_8: 0.95623/1.12741, loss_mask_bce_8: 0.29124/0.36546, loss_mask_dice_8: 0.42129/1.32982, loss_spatial_bce_8: 0.17457/0.13022, loss_spatial_dice_8: 0.20556/0.29925, loss_spatial_ce_8: 0.16889/0.22147, loss_grounding_bce_8: 0.12497/0.09403, loss_grounding_dice_8: 0.13481/0.20149, loss_grounding_ce_8: 0.17622/0.40637, loss_mask_ce_9: 2.84911/3.67564, loss_mask_bce_9: 0.28251/0.39249, loss_mask_dice_9: 0.65706/1.90239, loss_spatial_bce_9: 0.50910/0.33284, loss_spatial_dice_9: 0.79165/0.82172, loss_spatial_ce_9: 1.22674/1.49537, loss_grounding_bce_9: 0.10750/0.10564, loss_grounding_dice_9: 0.16679/0.28085, loss_grounding_ce_9: 0.35717/0.67097] items per batch[64] items per second[0.24] total items[4486400] mini batches[ 70100] memory[7345] epoch remaining[0:52:59]
INFO:trainer.default_trainer:epochs[    38] optim steps[70200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47334/0.89750, loss_mask_bce_0: 0.70983/0.33414, loss_mask_dice_0: 0.86429/1.16316, loss_spatial_bce_0: 0.14036/0.08700, loss_spatial_dice_0: 0.15442/0.20770, loss_spatial_ce_0: 0.04631/0.06120, loss_grounding_bce_0: 0.32081/0.08614, loss_grounding_dice_0: 0.16325/0.17847, loss_grounding_ce_0: 0.00164/0.27167, loss_mask_ce_1: 0.46724/0.89815, loss_mask_bce_1: 0.70277/0.33507, loss_mask_dice_1: 0.91575/1.16990, loss_spatial_bce_1: 0.14385/0.08753, loss_spatial_dice_1: 0.16432/0.21167, loss_spatial_ce_1: 0.06433/0.06705, loss_grounding_bce_1: 0.32038/0.08634, loss_grounding_dice_1: 0.17010/0.17932, loss_grounding_ce_1: 0.00150/0.27242, loss_mask_ce_2: 0.53997/0.90518, loss_mask_bce_2: 0.69784/0.33563, loss_mask_dice_2: 0.94326/1.17025, loss_spatial_bce_2: 0.14513/0.08867, loss_spatial_dice_2: 0.17615/0.21336, loss_spatial_ce_2: 0.08321/0.07042, loss_grounding_bce_2: 0.31163/0.08649, loss_grounding_dice_2: 0.16780/0.17914, loss_grounding_ce_2: 0.00199/0.27578, loss_mask_ce_3: 0.49322/0.91611, loss_mask_bce_3: 0.69508/0.33684, loss_mask_dice_3: 0.90390/1.16806, loss_spatial_bce_3: 0.14443/0.08990, loss_spatial_dice_3: 0.17846/0.21435, loss_spatial_ce_3: 0.05948/0.07529, loss_grounding_bce_3: 0.30883/0.08674, loss_grounding_dice_3: 0.16940/0.17888, loss_grounding_ce_3: 0.00301/0.27813, loss_mask_ce_4: 0.53700/0.91706, loss_mask_bce_4: 0.70773/0.33897, loss_mask_dice_4: 0.87407/1.19189, loss_spatial_bce_4: 0.15708/0.09383, loss_spatial_dice_4: 0.18578/0.22656, loss_spatial_ce_4: 0.03905/0.09148, loss_grounding_bce_4: 0.30734/0.08726, loss_grounding_dice_4: 0.17248/0.18176, loss_grounding_ce_4: 0.00248/0.28108, loss_mask_ce_5: 0.63743/0.93375, loss_mask_bce_5: 0.70132/0.34127, loss_mask_dice_5: 0.87596/1.19978, loss_spatial_bce_5: 0.18215/0.09607, loss_spatial_dice_5: 0.18129/0.23084, loss_spatial_ce_5: 0.03740/0.10555, loss_grounding_bce_5: 0.29955/0.08769, loss_grounding_dice_5: 0.16139/0.18305, loss_grounding_ce_5: 0.00186/0.29357, loss_mask_ce_6: 0.51882/0.97370, loss_mask_bce_6: 0.69548/0.34397, loss_mask_dice_6: 0.90390/1.20265, loss_spatial_bce_6: 0.17197/0.10175, loss_spatial_dice_6: 0.17354/0.23371, loss_spatial_ce_6: 0.08177/0.13126, loss_grounding_bce_6: 0.30455/0.08843, loss_grounding_dice_6: 0.15782/0.18343, loss_grounding_ce_6: 0.00278/0.30904, loss_mask_ce_7: 0.72395/1.01893, loss_mask_bce_7: 0.71009/0.35184, loss_mask_dice_7: 0.91800/1.25704, loss_spatial_bce_7: 0.17080/0.10970, loss_spatial_dice_7: 0.18506/0.26138, loss_spatial_ce_7: 0.18835/0.16649, loss_grounding_bce_7: 0.31593/0.09032, loss_grounding_dice_7: 0.16580/0.19072, loss_grounding_ce_7: 0.00148/0.33943, loss_mask_ce_8: 0.85616/1.12742, loss_mask_bce_8: 0.76710/0.36547, loss_mask_dice_8: 0.92373/1.32990, loss_spatial_bce_8: 0.24601/0.13021, loss_spatial_dice_8: 0.28391/0.29926, loss_spatial_ce_8: 0.12287/0.22143, loss_grounding_bce_8: 0.33176/0.09403, loss_grounding_dice_8: 0.16943/0.20151, loss_grounding_ce_8: 0.02016/0.40643, loss_mask_ce_9: 3.29333/3.67577, loss_mask_bce_9: 0.69630/0.39250, loss_mask_dice_9: 1.20158/1.90247, loss_spatial_bce_9: 0.48234/0.33282, loss_spatial_dice_9: 0.84685/0.82172, loss_spatial_ce_9: 1.72638/1.49537, loss_grounding_bce_9: 0.26616/0.10563, loss_grounding_dice_9: 0.15116/0.28087, loss_grounding_ce_9: 0.14510/0.67103] items per batch[64] items per second[0.23] total items[4492800] mini batches[ 70200] memory[7345] epoch remaining[0:48:22]
INFO:trainer.default_trainer:epochs[    38] optim steps[70300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19552/0.89741, loss_mask_bce_0: 0.61719/0.33411, loss_mask_dice_0: 0.98907/1.16300, loss_spatial_bce_0: 0.09456/0.08700, loss_spatial_dice_0: 0.16266/0.20767, loss_spatial_ce_0: 0.00597/0.06119, loss_grounding_bce_0: 0.06951/0.08615, loss_grounding_dice_0: 0.14323/0.17846, loss_grounding_ce_0: 0.32621/0.27166, loss_mask_ce_1: 0.21081/0.89806, loss_mask_bce_1: 0.63289/0.33503, loss_mask_dice_1: 1.00488/1.16973, loss_spatial_bce_1: 0.09335/0.08753, loss_spatial_dice_1: 0.16004/0.21164, loss_spatial_ce_1: 0.01024/0.06703, loss_grounding_bce_1: 0.07013/0.08634, loss_grounding_dice_1: 0.14703/0.17931, loss_grounding_ce_1: 0.32592/0.27241, loss_mask_ce_2: 0.22298/0.90508, loss_mask_bce_2: 0.64790/0.33560, loss_mask_dice_2: 1.02304/1.17012, loss_spatial_bce_2: 0.09479/0.08867, loss_spatial_dice_2: 0.16455/0.21334, loss_spatial_ce_2: 0.00696/0.07041, loss_grounding_bce_2: 0.07151/0.08649, loss_grounding_dice_2: 0.15104/0.17913, loss_grounding_ce_2: 0.32255/0.27579, loss_mask_ce_3: 0.22342/0.91601, loss_mask_bce_3: 0.65533/0.33681, loss_mask_dice_3: 1.04455/1.16793, loss_spatial_bce_3: 0.10364/0.08990, loss_spatial_dice_3: 0.18383/0.21433, loss_spatial_ce_3: 0.01150/0.07528, loss_grounding_bce_3: 0.06915/0.08674, loss_grounding_dice_3: 0.14985/0.17887, loss_grounding_ce_3: 0.31926/0.27815, loss_mask_ce_4: 0.24103/0.91697, loss_mask_bce_4: 0.65003/0.33894, loss_mask_dice_4: 1.05203/1.19176, loss_spatial_bce_4: 0.10746/0.09383, loss_spatial_dice_4: 0.18365/0.22654, loss_spatial_ce_4: 0.01944/0.09147, loss_grounding_bce_4: 0.07301/0.08727, loss_grounding_dice_4: 0.16354/0.18176, loss_grounding_ce_4: 0.32658/0.28113, loss_mask_ce_5: 0.26968/0.93369, loss_mask_bce_5: 0.63218/0.34124, loss_mask_dice_5: 0.98130/1.19964, loss_spatial_bce_5: 0.10865/0.09607, loss_spatial_dice_5: 0.18030/0.23082, loss_spatial_ce_5: 0.04431/0.10553, loss_grounding_bce_5: 0.07377/0.08770, loss_grounding_dice_5: 0.15881/0.18304, loss_grounding_ce_5: 0.31382/0.29359, loss_mask_ce_6: 0.27144/0.97365, loss_mask_bce_6: 0.63605/0.34394, loss_mask_dice_6: 1.09914/1.20250, loss_spatial_bce_6: 0.10449/0.10175, loss_spatial_dice_6: 0.18821/0.23369, loss_spatial_ce_6: 0.07407/0.13122, loss_grounding_bce_6: 0.06985/0.08843, loss_grounding_dice_6: 0.15285/0.18341, loss_grounding_ce_6: 0.33116/0.30909, loss_mask_ce_7: 0.32465/1.01886, loss_mask_bce_7: 0.64128/0.35181, loss_mask_dice_7: 1.14769/1.25693, loss_spatial_bce_7: 0.10082/0.10970, loss_spatial_dice_7: 0.18073/0.26136, loss_spatial_ce_7: 0.10982/0.16645, loss_grounding_bce_7: 0.07302/0.09032, loss_grounding_dice_7: 0.15922/0.19071, loss_grounding_ce_7: 0.35572/0.33948, loss_mask_ce_8: 0.42837/1.12735, loss_mask_bce_8: 0.64503/0.36544, loss_mask_dice_8: 1.11804/1.32975, loss_spatial_bce_8: 0.17407/0.13021, loss_spatial_dice_8: 0.24206/0.29923, loss_spatial_ce_8: 0.13485/0.22139, loss_grounding_bce_8: 0.07559/0.09404, loss_grounding_dice_8: 0.15800/0.20150, loss_grounding_ce_8: 0.35024/0.40647, loss_mask_ce_9: 3.75328/3.67566, loss_mask_bce_9: 0.70301/0.39247, loss_mask_dice_9: 1.66194/1.90225, loss_spatial_bce_9: 0.39239/0.33286, loss_spatial_dice_9: 0.85567/0.82170, loss_spatial_ce_9: 1.38493/1.49536, loss_grounding_bce_9: 0.08792/0.10564, loss_grounding_dice_9: 0.27728/0.28085, loss_grounding_ce_9: 0.33629/0.67115] items per batch[64] items per second[0.23] total items[4499200] mini batches[ 70300] memory[7345] epoch remaining[0:43:52]
INFO:trainer.default_trainer:epochs[    38] optim steps[70400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.95731/0.89751, loss_mask_bce_0: 0.14287/0.33409, loss_mask_dice_0: 2.02304/1.16291, loss_spatial_bce_0: 0.02012/0.08699, loss_spatial_dice_0: 0.23268/0.20764, loss_spatial_ce_0: 0.02910/0.06116, loss_grounding_bce_0: 0.00396/0.08615, loss_grounding_dice_0: 0.37703/0.17845, loss_grounding_ce_0: 0.17416/0.27165, loss_mask_ce_1: 1.83863/0.89816, loss_mask_bce_1: 0.15081/0.33501, loss_mask_dice_1: 2.18122/1.16963, loss_spatial_bce_1: 0.02121/0.08752, loss_spatial_dice_1: 0.29167/0.21162, loss_spatial_ce_1: 0.26479/0.06701, loss_grounding_bce_1: 0.00361/0.08634, loss_grounding_dice_1: 0.43192/0.17929, loss_grounding_ce_1: 0.13605/0.27240, loss_mask_ce_2: 1.69632/0.90515, loss_mask_bce_2: 0.14606/0.33559, loss_mask_dice_2: 2.22600/1.17002, loss_spatial_bce_2: 0.01982/0.08866, loss_spatial_dice_2: 0.26652/0.21331, loss_spatial_ce_2: 0.07194/0.07040, loss_grounding_bce_2: 0.00656/0.08649, loss_grounding_dice_2: 0.49896/0.17912, loss_grounding_ce_2: 0.36988/0.27579, loss_mask_ce_3: 1.89716/0.91610, loss_mask_bce_3: 0.15931/0.33679, loss_mask_dice_3: 2.26981/1.16784, loss_spatial_bce_3: 0.01941/0.08990, loss_spatial_dice_3: 0.28408/0.21431, loss_spatial_ce_3: 0.03720/0.07527, loss_grounding_bce_3: 0.00294/0.08674, loss_grounding_dice_3: 0.48870/0.17885, loss_grounding_ce_3: 0.16919/0.27817, loss_mask_ce_4: 1.64598/0.91703, loss_mask_bce_4: 0.16813/0.33892, loss_mask_dice_4: 2.22384/1.19166, loss_spatial_bce_4: 0.02096/0.09382, loss_spatial_dice_4: 0.28264/0.22652, loss_spatial_ce_4: 0.07635/0.09144, loss_grounding_bce_4: 0.00391/0.08726, loss_grounding_dice_4: 0.47908/0.18174, loss_grounding_ce_4: 0.18581/0.28112, loss_mask_ce_5: 1.70913/0.93378, loss_mask_bce_5: 0.16577/0.34123, loss_mask_dice_5: 2.20345/1.19956, loss_spatial_bce_5: 0.02072/0.09607, loss_spatial_dice_5: 0.27880/0.23079, loss_spatial_ce_5: 0.05877/0.10550, loss_grounding_bce_5: 0.00343/0.08770, loss_grounding_dice_5: 0.42882/0.18303, loss_grounding_ce_5: 0.20288/0.29360, loss_mask_ce_6: 1.94099/0.97375, loss_mask_bce_6: 0.15831/0.34392, loss_mask_dice_6: 2.26581/1.20241, loss_spatial_bce_6: 0.02434/0.10174, loss_spatial_dice_6: 0.33299/0.23367, loss_spatial_ce_6: 0.07546/0.13119, loss_grounding_bce_6: 0.01435/0.08843, loss_grounding_dice_6: 0.45392/0.18340, loss_grounding_ce_6: 0.34045/0.30910, loss_mask_ce_7: 1.94221/1.01895, loss_mask_bce_7: 0.16356/0.35179, loss_mask_dice_7: 2.53277/1.25683, loss_spatial_bce_7: 0.02375/0.10969, loss_spatial_dice_7: 0.37053/0.26133, loss_spatial_ce_7: 0.16266/0.16641, loss_grounding_bce_7: 0.00535/0.09032, loss_grounding_dice_7: 0.56888/0.19069, loss_grounding_ce_7: 0.31225/0.33951, loss_mask_ce_8: 1.86336/1.12746, loss_mask_bce_8: 0.18980/0.36542, loss_mask_dice_8: 3.11943/1.32965, loss_spatial_bce_8: 0.02928/0.13020, loss_spatial_dice_8: 0.44615/0.29921, loss_spatial_ce_8: 0.14565/0.22132, loss_grounding_bce_8: 0.00678/0.09404, loss_grounding_dice_8: 0.52051/0.20149, loss_grounding_ce_8: 0.43871/0.40651, loss_mask_ce_9: 4.63472/3.67570, loss_mask_bce_9: 0.15650/0.39245, loss_mask_dice_9: 3.50374/1.90216, loss_spatial_bce_9: 0.07698/0.33289, loss_spatial_dice_9: 0.92722/0.82171, loss_spatial_ce_9: 1.39913/1.49538, loss_grounding_bce_9: 0.00507/0.10565, loss_grounding_dice_9: 0.54562/0.28085, loss_grounding_ce_9: 1.65374/0.67113] items per batch[64] items per second[0.23] total items[4505600] mini batches[ 70400] memory[7345] epoch remaining[0:39:14]
INFO:trainer.default_trainer:epochs[    38] optim steps[70500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32411/0.89743, loss_mask_bce_0: 0.33485/0.33408, loss_mask_dice_0: 0.62779/1.16270, loss_spatial_bce_0: 0.09662/0.08699, loss_spatial_dice_0: 0.17300/0.20763, loss_spatial_ce_0: 0.03186/0.06116, loss_grounding_bce_0: 0.09922/0.08615, loss_grounding_dice_0: 0.17527/0.17846, loss_grounding_ce_0: 0.25158/0.27163, loss_mask_ce_1: 0.33791/0.89806, loss_mask_bce_1: 0.34009/0.33501, loss_mask_dice_1: 0.61715/1.16941, loss_spatial_bce_1: 0.09617/0.08752, loss_spatial_dice_1: 0.19308/0.21161, loss_spatial_ce_1: 0.05070/0.06700, loss_grounding_bce_1: 0.10164/0.08635, loss_grounding_dice_1: 0.17966/0.17930, loss_grounding_ce_1: 0.23959/0.27240, loss_mask_ce_2: 0.32879/0.90506, loss_mask_bce_2: 0.33418/0.33558, loss_mask_dice_2: 0.57642/1.16983, loss_spatial_bce_2: 0.09844/0.08866, loss_spatial_dice_2: 0.18728/0.21330, loss_spatial_ce_2: 0.06674/0.07040, loss_grounding_bce_2: 0.09798/0.08650, loss_grounding_dice_2: 0.17643/0.17913, loss_grounding_ce_2: 0.33257/0.27580, loss_mask_ce_3: 0.34282/0.91602, loss_mask_bce_3: 0.34397/0.33678, loss_mask_dice_3: 0.62530/1.16764, loss_spatial_bce_3: 0.10266/0.08990, loss_spatial_dice_3: 0.20545/0.21430, loss_spatial_ce_3: 0.07333/0.07526, loss_grounding_bce_3: 0.09463/0.08674, loss_grounding_dice_3: 0.16748/0.17886, loss_grounding_ce_3: 0.26172/0.27814, loss_mask_ce_4: 0.33412/0.91696, loss_mask_bce_4: 0.32180/0.33891, loss_mask_dice_4: 0.60350/1.19144, loss_spatial_bce_4: 0.09414/0.09382, loss_spatial_dice_4: 0.19095/0.22651, loss_spatial_ce_4: 0.15848/0.09143, loss_grounding_bce_4: 0.09184/0.08727, loss_grounding_dice_4: 0.17285/0.18175, loss_grounding_ce_4: 0.24397/0.28110, loss_mask_ce_5: 0.34427/0.93373, loss_mask_bce_5: 0.34631/0.34122, loss_mask_dice_5: 0.61358/1.19933, loss_spatial_bce_5: 0.09204/0.09607, loss_spatial_dice_5: 0.17183/0.23079, loss_spatial_ce_5: 0.18485/0.10549, loss_grounding_bce_5: 0.09420/0.08770, loss_grounding_dice_5: 0.17568/0.18304, loss_grounding_ce_5: 0.30662/0.29357, loss_mask_ce_6: 0.41287/0.97368, loss_mask_bce_6: 0.29943/0.34392, loss_mask_dice_6: 0.59867/1.20218, loss_spatial_bce_6: 0.10253/0.10174, loss_spatial_dice_6: 0.17095/0.23366, loss_spatial_ce_6: 0.28292/0.13118, loss_grounding_bce_6: 0.09478/0.08843, loss_grounding_dice_6: 0.19775/0.18341, loss_grounding_ce_6: 0.33365/0.30909, loss_mask_ce_7: 0.48589/1.01886, loss_mask_bce_7: 0.33141/0.35177, loss_mask_dice_7: 0.62232/1.25661, loss_spatial_bce_7: 0.11070/0.10969, loss_spatial_dice_7: 0.18171/0.26132, loss_spatial_ce_7: 0.35803/0.16641, loss_grounding_bce_7: 0.09341/0.09032, loss_grounding_dice_7: 0.16439/0.19069, loss_grounding_ce_7: 0.28899/0.33950, loss_mask_ce_8: 0.32730/1.12738, loss_mask_bce_8: 0.32021/0.36541, loss_mask_dice_8: 0.67501/1.32942, loss_spatial_bce_8: 0.12780/0.13020, loss_spatial_dice_8: 0.19144/0.29920, loss_spatial_ce_8: 0.41452/0.22128, loss_grounding_bce_8: 0.08565/0.09404, loss_grounding_dice_8: 0.15932/0.20150, loss_grounding_ce_8: 0.37281/0.40643, loss_mask_ce_9: 3.50429/3.67550, loss_mask_bce_9: 0.33049/0.39242, loss_mask_dice_9: 0.91150/1.90189, loss_spatial_bce_9: 0.42935/0.33289, loss_spatial_dice_9: 0.70854/0.82170, loss_spatial_ce_9: 1.52306/1.49530, loss_grounding_bce_9: 0.07893/0.10565, loss_grounding_dice_9: 0.25207/0.28085, loss_grounding_ce_9: 0.29616/0.67102] items per batch[64] items per second[0.23] total items[4512000] mini batches[ 70500] memory[7345] epoch remaining[0:34:38]
INFO:trainer.default_trainer:epochs[    38] optim steps[70600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54427/0.89749, loss_mask_bce_0: 0.50160/0.33413, loss_mask_dice_0: 0.91403/1.16270, loss_spatial_bce_0: 0.14508/0.08699, loss_spatial_dice_0: 0.22384/0.20762, loss_spatial_ce_0: 0.04643/0.06112, loss_grounding_bce_0: 0.14636/0.08616, loss_grounding_dice_0: 0.28549/0.17846, loss_grounding_ce_0: 0.38794/0.27167, loss_mask_ce_1: 0.58015/0.89813, loss_mask_bce_1: 0.50895/0.33506, loss_mask_dice_1: 0.89091/1.16940, loss_spatial_bce_1: 0.13565/0.08752, loss_spatial_dice_1: 0.20323/0.21160, loss_spatial_ce_1: 0.06630/0.06698, loss_grounding_bce_1: 0.15087/0.08636, loss_grounding_dice_1: 0.28094/0.17931, loss_grounding_ce_1: 0.34916/0.27243, loss_mask_ce_2: 0.52655/0.90512, loss_mask_bce_2: 0.55198/0.33564, loss_mask_dice_2: 0.92584/1.16984, loss_spatial_bce_2: 0.13943/0.08867, loss_spatial_dice_2: 0.21137/0.21330, loss_spatial_ce_2: 0.08241/0.07039, loss_grounding_bce_2: 0.15582/0.08651, loss_grounding_dice_2: 0.28809/0.17914, loss_grounding_ce_2: 0.34049/0.27583, loss_mask_ce_3: 0.50519/0.91612, loss_mask_bce_3: 0.54639/0.33684, loss_mask_dice_3: 0.93495/1.16764, loss_spatial_bce_3: 0.12871/0.08991, loss_spatial_dice_3: 0.21680/0.21429, loss_spatial_ce_3: 0.09369/0.07524, loss_grounding_bce_3: 0.14860/0.08676, loss_grounding_dice_3: 0.28919/0.17887, loss_grounding_ce_3: 0.37623/0.27818, loss_mask_ce_4: 0.50315/0.91704, loss_mask_bce_4: 0.53765/0.33897, loss_mask_dice_4: 0.90043/1.19144, loss_spatial_bce_4: 0.14517/0.09383, loss_spatial_dice_4: 0.24423/0.22651, loss_spatial_ce_4: 0.14464/0.09140, loss_grounding_bce_4: 0.15148/0.08728, loss_grounding_dice_4: 0.28600/0.18175, loss_grounding_ce_4: 0.33862/0.28114, loss_mask_ce_5: 0.50662/0.93381, loss_mask_bce_5: 0.54944/0.34128, loss_mask_dice_5: 0.93207/1.19932, loss_spatial_bce_5: 0.13200/0.09608, loss_spatial_dice_5: 0.21253/0.23079, loss_spatial_ce_5: 0.21379/0.10548, loss_grounding_bce_5: 0.15091/0.08771, loss_grounding_dice_5: 0.29090/0.18304, loss_grounding_ce_5: 0.34371/0.29360, loss_mask_ce_6: 0.53470/0.97377, loss_mask_bce_6: 0.49480/0.34398, loss_mask_dice_6: 0.92573/1.20217, loss_spatial_bce_6: 0.13413/0.10175, loss_spatial_dice_6: 0.20436/0.23366, loss_spatial_ce_6: 0.16920/0.13115, loss_grounding_bce_6: 0.14433/0.08845, loss_grounding_dice_6: 0.27237/0.18342, loss_grounding_ce_6: 0.37785/0.30914, loss_mask_ce_7: 0.53112/1.01895, loss_mask_bce_7: 0.53359/0.35183, loss_mask_dice_7: 1.03723/1.25665, loss_spatial_bce_7: 0.15299/0.10970, loss_spatial_dice_7: 0.21374/0.26132, loss_spatial_ce_7: 0.13441/0.16637, loss_grounding_bce_7: 0.14086/0.09033, loss_grounding_dice_7: 0.29165/0.19070, loss_grounding_ce_7: 0.36417/0.33962, loss_mask_ce_8: 0.67534/1.12743, loss_mask_bce_8: 0.54814/0.36548, loss_mask_dice_8: 1.06755/1.32947, loss_spatial_bce_8: 0.15479/0.13021, loss_spatial_dice_8: 0.25285/0.29920, loss_spatial_ce_8: 0.19807/0.22121, loss_grounding_bce_8: 0.16154/0.09406, loss_grounding_dice_8: 0.31264/0.20151, loss_grounding_ce_8: 0.36181/0.40655, loss_mask_ce_9: 4.14978/3.67578, loss_mask_bce_9: 0.80095/0.39251, loss_mask_dice_9: 1.74113/1.90194, loss_spatial_bce_9: 0.35245/0.33288, loss_spatial_dice_9: 0.87855/0.82171, loss_spatial_ce_9: 1.18241/1.49528, loss_grounding_bce_9: 0.25261/0.10566, loss_grounding_dice_9: 0.51385/0.28087, loss_grounding_ce_9: 0.77601/0.67113] items per batch[64] items per second[0.24] total items[4518400] mini batches[ 70600] memory[7345] epoch remaining[0:29:59]
INFO:trainer.default_trainer:epochs[    38] optim steps[70700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21239/0.89744, loss_mask_bce_0: 0.51803/0.33416, loss_mask_dice_0: 1.39075/1.16267, loss_spatial_bce_0: 0.06695/0.08700, loss_spatial_dice_0: 0.31272/0.20761, loss_spatial_ce_0: 0.22975/0.06111, loss_grounding_bce_0: 0.27840/0.08615, loss_grounding_dice_0: 0.52756/0.17845, loss_grounding_ce_0: 0.51181/0.27179, loss_mask_ce_1: 1.06577/0.89808, loss_mask_bce_1: 0.55210/0.33509, loss_mask_dice_1: 1.73830/1.16935, loss_spatial_bce_1: 0.06012/0.08753, loss_spatial_dice_1: 0.30932/0.21159, loss_spatial_ce_1: 0.20532/0.06696, loss_grounding_bce_1: 0.39746/0.08635, loss_grounding_dice_1: 0.47592/0.17930, loss_grounding_ce_1: 0.34977/0.27254, loss_mask_ce_2: 1.31712/0.90508, loss_mask_bce_2: 0.61485/0.33566, loss_mask_dice_2: 1.52455/1.16979, loss_spatial_bce_2: 0.06140/0.08868, loss_spatial_dice_2: 0.28629/0.21329, loss_spatial_ce_2: 0.21181/0.07040, loss_grounding_bce_2: 0.25536/0.08650, loss_grounding_dice_2: 0.51347/0.17913, loss_grounding_ce_2: 0.53793/0.27594, loss_mask_ce_3: 1.10359/0.91606, loss_mask_bce_3: 0.67136/0.33686, loss_mask_dice_3: 1.54375/1.16759, loss_spatial_bce_3: 0.06067/0.08991, loss_spatial_dice_3: 0.28260/0.21429, loss_spatial_ce_3: 0.22906/0.07523, loss_grounding_bce_3: 0.27814/0.08675, loss_grounding_dice_3: 0.54889/0.17886, loss_grounding_ce_3: 0.50942/0.27829, loss_mask_ce_4: 1.58513/0.91698, loss_mask_bce_4: 0.57954/0.33900, loss_mask_dice_4: 1.49457/1.19139, loss_spatial_bce_4: 0.06742/0.09384, loss_spatial_dice_4: 0.32153/0.22651, loss_spatial_ce_4: 0.25112/0.09139, loss_grounding_bce_4: 0.33418/0.08727, loss_grounding_dice_4: 0.51141/0.18175, loss_grounding_ce_4: 0.51071/0.28125, loss_mask_ce_5: 1.41678/0.93376, loss_mask_bce_5: 0.51673/0.34131, loss_mask_dice_5: 1.84538/1.19929, loss_spatial_bce_5: 0.03739/0.09609, loss_spatial_dice_5: 0.25243/0.23078, loss_spatial_ce_5: 0.22285/0.10548, loss_grounding_bce_5: 0.27584/0.08770, loss_grounding_dice_5: 0.53169/0.18304, loss_grounding_ce_5: 0.49356/0.29371, loss_mask_ce_6: 1.49627/0.97374, loss_mask_bce_6: 0.40655/0.34400, loss_mask_dice_6: 1.80147/1.20214, loss_spatial_bce_6: 0.07094/0.10176, loss_spatial_dice_6: 0.31668/0.23366, loss_spatial_ce_6: 0.32113/0.13115, loss_grounding_bce_6: 0.24702/0.08844, loss_grounding_dice_6: 0.47892/0.18341, loss_grounding_ce_6: 0.52894/0.30922, loss_mask_ce_7: 1.73762/1.01892, loss_mask_bce_7: 0.33019/0.35185, loss_mask_dice_7: 1.38694/1.25660, loss_spatial_bce_7: 0.07555/0.10970, loss_spatial_dice_7: 0.37201/0.26131, loss_spatial_ce_7: 0.37519/0.16635, loss_grounding_bce_7: 0.16965/0.09032, loss_grounding_dice_7: 0.50396/0.19070, loss_grounding_ce_7: 0.58722/0.33964, loss_mask_ce_8: 1.60892/1.12739, loss_mask_bce_8: 0.36079/0.36550, loss_mask_dice_8: 1.55362/1.32944, loss_spatial_bce_8: 0.09083/0.13022, loss_spatial_dice_8: 0.43547/0.29920, loss_spatial_ce_8: 0.23421/0.22119, loss_grounding_bce_8: 0.26429/0.09405, loss_grounding_dice_8: 0.52075/0.20150, loss_grounding_ce_8: 0.51669/0.40654, loss_mask_ce_9: 3.35395/3.67573, loss_mask_bce_9: 0.43356/0.39254, loss_mask_dice_9: 1.82495/1.90188, loss_spatial_bce_9: 0.22114/0.33288, loss_spatial_dice_9: 0.88107/0.82170, loss_spatial_ce_9: 2.78916/1.49526, loss_grounding_bce_9: 0.33025/0.10565, loss_grounding_dice_9: 0.58092/0.28086, loss_grounding_ce_9: 0.25018/0.67109] items per batch[64] items per second[0.23] total items[4524800] mini batches[ 70700] memory[7345] epoch remaining[0:25:25]
INFO:trainer.default_trainer:epochs[    38] optim steps[70800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.17841/0.89748, loss_mask_bce_0: 0.33092/0.33413, loss_mask_dice_0: 0.25401/1.16257, loss_spatial_bce_0: 0.22939/0.08701, loss_spatial_dice_0: 0.16264/0.20762, loss_spatial_ce_0: 0.15823/0.06110, loss_grounding_bce_0: 0.22689/0.08616, loss_grounding_dice_0: 0.13163/0.17844, loss_grounding_ce_0: 0.25835/0.27177, loss_mask_ce_1: 1.05478/0.89811, loss_mask_bce_1: 0.34596/0.33507, loss_mask_dice_1: 0.24924/1.16924, loss_spatial_bce_1: 0.23240/0.08754, loss_spatial_dice_1: 0.16857/0.21159, loss_spatial_ce_1: 0.16833/0.06696, loss_grounding_bce_1: 0.24054/0.08635, loss_grounding_dice_1: 0.13906/0.17929, loss_grounding_ce_1: 0.25071/0.27251, loss_mask_ce_2: 1.22688/0.90511, loss_mask_bce_2: 0.33021/0.33564, loss_mask_dice_2: 0.19903/1.16970, loss_spatial_bce_2: 0.22992/0.08869, loss_spatial_dice_2: 0.16057/0.21329, loss_spatial_ce_2: 0.16278/0.07039, loss_grounding_bce_2: 0.22766/0.08650, loss_grounding_dice_2: 0.12501/0.17913, loss_grounding_ce_2: 0.24559/0.27591, loss_mask_ce_3: 1.00417/0.91612, loss_mask_bce_3: 0.31840/0.33684, loss_mask_dice_3: 0.22886/1.16749, loss_spatial_bce_3: 0.21939/0.08993, loss_spatial_dice_3: 0.16040/0.21429, loss_spatial_ce_3: 0.11085/0.07522, loss_grounding_bce_3: 0.20005/0.08675, loss_grounding_dice_3: 0.11291/0.17886, loss_grounding_ce_3: 0.24954/0.27826, loss_mask_ce_4: 1.26749/0.91703, loss_mask_bce_4: 0.30171/0.33898, loss_mask_dice_4: 0.19165/1.19127, loss_spatial_bce_4: 0.16318/0.09385, loss_spatial_dice_4: 0.12808/0.22651, loss_spatial_ce_4: 0.18354/0.09138, loss_grounding_bce_4: 0.19236/0.08728, loss_grounding_dice_4: 0.10856/0.18174, loss_grounding_ce_4: 0.24250/0.28126, loss_mask_ce_5: 1.05556/0.93382, loss_mask_bce_5: 0.30575/0.34128, loss_mask_dice_5: 0.18962/1.19920, loss_spatial_bce_5: 0.24774/0.09610, loss_spatial_dice_5: 0.17259/0.23078, loss_spatial_ce_5: 0.10895/0.10547, loss_grounding_bce_5: 0.18636/0.08771, loss_grounding_dice_5: 0.10569/0.18304, loss_grounding_ce_5: 0.22973/0.29372, loss_mask_ce_6: 1.04370/0.97381, loss_mask_bce_6: 0.39207/0.34398, loss_mask_dice_6: 0.23314/1.20200, loss_spatial_bce_6: 0.18663/0.10177, loss_spatial_dice_6: 0.13456/0.23367, loss_spatial_ce_6: 0.24635/0.13112, loss_grounding_bce_6: 0.18855/0.08845, loss_grounding_dice_6: 0.10781/0.18340, loss_grounding_ce_6: 0.26952/0.30924, loss_mask_ce_7: 1.07170/1.01896, loss_mask_bce_7: 0.33527/0.35183, loss_mask_dice_7: 0.23104/1.25646, loss_spatial_bce_7: 0.28160/0.10972, loss_spatial_dice_7: 0.20014/0.26132, loss_spatial_ce_7: 0.12280/0.16632, loss_grounding_bce_7: 0.20678/0.09033, loss_grounding_dice_7: 0.11976/0.19069, loss_grounding_ce_7: 0.27510/0.33967, loss_mask_ce_8: 1.24048/1.12745, loss_mask_bce_8: 0.36570/0.36549, loss_mask_dice_8: 0.24095/1.32929, loss_spatial_bce_8: 0.23131/0.13023, loss_spatial_dice_8: 0.20080/0.29921, loss_spatial_ce_8: 0.11508/0.22113, loss_grounding_bce_8: 0.22268/0.09406, loss_grounding_dice_8: 0.12413/0.20149, loss_grounding_ce_8: 0.30385/0.40651, loss_mask_ce_9: 3.20457/3.67572, loss_mask_bce_9: 0.42730/0.39252, loss_mask_dice_9: 0.45386/1.90180, loss_spatial_bce_9: 0.54660/0.33289, loss_spatial_dice_9: 0.71123/0.82170, loss_spatial_ce_9: 0.90969/1.49528, loss_grounding_bce_9: 0.25586/0.10565, loss_grounding_dice_9: 0.21243/0.28084, loss_grounding_ce_9: 0.43760/0.67107] items per batch[64] items per second[0.23] total items[4531200] mini batches[ 70800] memory[7345] epoch remaining[0:20:50]
INFO:trainer.default_trainer:epochs[    38] optim steps[70900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.05312/0.89749, loss_mask_bce_0: 0.80604/0.33415, loss_mask_dice_0: 1.97154/1.16263, loss_spatial_bce_0: 0.11410/0.08700, loss_spatial_dice_0: 0.32760/0.20761, loss_spatial_ce_0: 0.04299/0.06109, loss_grounding_bce_0: 0.13990/0.08616, loss_grounding_dice_0: 0.21338/0.17846, loss_grounding_ce_0: 0.28784/0.27179, loss_mask_ce_1: 2.08311/0.89808, loss_mask_bce_1: 0.82890/0.33509, loss_mask_dice_1: 2.05964/1.16932, loss_spatial_bce_1: 0.11749/0.08754, loss_spatial_dice_1: 0.33741/0.21159, loss_spatial_ce_1: 0.03789/0.06693, loss_grounding_bce_1: 0.15029/0.08636, loss_grounding_dice_1: 0.21461/0.17930, loss_grounding_ce_1: 0.29003/0.27254, loss_mask_ce_2: 2.04518/0.90509, loss_mask_bce_2: 0.73866/0.33566, loss_mask_dice_2: 1.89930/1.16976, loss_spatial_bce_2: 0.11641/0.08869, loss_spatial_dice_2: 0.32836/0.21328, loss_spatial_ce_2: 0.03521/0.07036, loss_grounding_bce_2: 0.14666/0.08651, loss_grounding_dice_2: 0.25766/0.17915, loss_grounding_ce_2: 0.29804/0.27592, loss_mask_ce_3: 2.22260/0.91608, loss_mask_bce_3: 0.75788/0.33686, loss_mask_dice_3: 2.14992/1.16757, loss_spatial_bce_3: 0.13375/0.08993, loss_spatial_dice_3: 0.33498/0.21428, loss_spatial_ce_3: 0.05671/0.07520, loss_grounding_bce_3: 0.14832/0.08675, loss_grounding_dice_3: 0.21237/0.17888, loss_grounding_ce_3: 0.29404/0.27826, loss_mask_ce_4: 2.01125/0.91704, loss_mask_bce_4: 0.80588/0.33900, loss_mask_dice_4: 2.18478/1.19133, loss_spatial_bce_4: 0.14394/0.09385, loss_spatial_dice_4: 0.38260/0.22650, loss_spatial_ce_4: 0.05787/0.09136, loss_grounding_bce_4: 0.14146/0.08729, loss_grounding_dice_4: 0.21745/0.18176, loss_grounding_ce_4: 0.28254/0.28128, loss_mask_ce_5: 2.05151/0.93381, loss_mask_bce_5: 0.82737/0.34131, loss_mask_dice_5: 2.39422/1.19931, loss_spatial_bce_5: 0.13598/0.09611, loss_spatial_dice_5: 0.37619/0.23078, loss_spatial_ce_5: 0.11762/0.10544, loss_grounding_bce_5: 0.14512/0.08772, loss_grounding_dice_5: 0.22571/0.18306, loss_grounding_ce_5: 0.26689/0.29372, loss_mask_ce_6: 2.41251/0.97381, loss_mask_bce_6: 0.85165/0.34401, loss_mask_dice_6: 2.35972/1.20206, loss_spatial_bce_6: 0.14222/0.10178, loss_spatial_dice_6: 0.36318/0.23367, loss_spatial_ce_6: 0.13873/0.13110, loss_grounding_bce_6: 0.13275/0.08845, loss_grounding_dice_6: 0.22368/0.18343, loss_grounding_ce_6: 0.25900/0.30925, loss_mask_ce_7: 2.22863/1.01898, loss_mask_bce_7: 0.89087/0.35186, loss_mask_dice_7: 2.46062/1.25653, loss_spatial_bce_7: 0.13839/0.10973, loss_spatial_dice_7: 0.42049/0.26132, loss_spatial_ce_7: 0.55654/0.16630, loss_grounding_bce_7: 0.13801/0.09033, loss_grounding_dice_7: 0.21142/0.19071, loss_grounding_ce_7: 0.24186/0.33967, loss_mask_ce_8: 2.47229/1.12748, loss_mask_bce_8: 0.92995/0.36550, loss_mask_dice_8: 2.70525/1.32937, loss_spatial_bce_8: 0.20572/0.13023, loss_spatial_dice_8: 0.54187/0.29920, loss_spatial_ce_8: 0.21875/0.22108, loss_grounding_bce_8: 0.17854/0.09407, loss_grounding_dice_8: 0.25191/0.20152, loss_grounding_ce_8: 0.24823/0.40647, loss_mask_ce_9: 4.95405/3.67566, loss_mask_bce_9: 1.10248/0.39254, loss_mask_dice_9: 3.52856/1.90201, loss_spatial_bce_9: 0.30700/0.33287, loss_spatial_dice_9: 0.89701/0.82170, loss_spatial_ce_9: 1.22595/1.49521, loss_grounding_bce_9: 0.26238/0.10566, loss_grounding_dice_9: 0.27019/0.28087, loss_grounding_ce_9: 0.41180/0.67099] items per batch[64] items per second[0.23] total items[4537600] mini batches[ 70900] memory[7345] epoch remaining[0:16:16]
INFO:trainer.default_trainer:epochs[    38] optim steps[71000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90468/0.89736, loss_mask_bce_0: 0.11538/0.33413, loss_mask_dice_0: 0.91070/1.16290, loss_spatial_bce_0: 0.02877/0.08699, loss_spatial_dice_0: 0.19858/0.20761, loss_spatial_ce_0: 0.04392/0.06107, loss_grounding_bce_0: 0.04304/0.08616, loss_grounding_dice_0: 0.11435/0.17847, loss_grounding_ce_0: 0.44767/0.27188, loss_mask_ce_1: 0.90078/0.89794, loss_mask_bce_1: 0.11088/0.33506, loss_mask_dice_1: 0.96217/1.16960, loss_spatial_bce_1: 0.02885/0.08753, loss_spatial_dice_1: 0.20177/0.21159, loss_spatial_ce_1: 0.03543/0.06693, loss_grounding_bce_1: 0.04302/0.08636, loss_grounding_dice_1: 0.11552/0.17931, loss_grounding_ce_1: 0.42672/0.27263, loss_mask_ce_2: 1.01575/0.90495, loss_mask_bce_2: 0.09912/0.33563, loss_mask_dice_2: 0.87846/1.17004, loss_spatial_bce_2: 0.03210/0.08868, loss_spatial_dice_2: 0.21053/0.21329, loss_spatial_ce_2: 0.08269/0.07036, loss_grounding_bce_2: 0.05498/0.08651, loss_grounding_dice_2: 0.10585/0.17917, loss_grounding_ce_2: 0.36525/0.27601, loss_mask_ce_3: 1.13554/0.91594, loss_mask_bce_3: 0.12170/0.33684, loss_mask_dice_3: 0.92506/1.16785, loss_spatial_bce_3: 0.02860/0.08991, loss_spatial_dice_3: 0.21524/0.21428, loss_spatial_ce_3: 0.24997/0.07520, loss_grounding_bce_3: 0.04201/0.08676, loss_grounding_dice_3: 0.11269/0.17889, loss_grounding_ce_3: 0.48532/0.27836, loss_mask_ce_4: 0.88623/0.91690, loss_mask_bce_4: 0.11411/0.33898, loss_mask_dice_4: 0.91513/1.19163, loss_spatial_bce_4: 0.03567/0.09383, loss_spatial_dice_4: 0.22369/0.22651, loss_spatial_ce_4: 0.11147/0.09135, loss_grounding_bce_4: 0.03746/0.08729, loss_grounding_dice_4: 0.11037/0.18178, loss_grounding_ce_4: 0.45488/0.28138, loss_mask_ce_5: 0.91184/0.93368, loss_mask_bce_5: 0.14272/0.34128, loss_mask_dice_5: 0.86590/1.19959, loss_spatial_bce_5: 0.04465/0.09609, loss_spatial_dice_5: 0.26791/0.23079, loss_spatial_ce_5: 0.04360/0.10542, loss_grounding_bce_5: 0.05084/0.08772, loss_grounding_dice_5: 0.12109/0.18306, loss_grounding_ce_5: 0.43027/0.29381, loss_mask_ce_6: 0.86461/0.97370, loss_mask_bce_6: 0.13780/0.34398, loss_mask_dice_6: 0.93557/1.20236, loss_spatial_bce_6: 0.05299/0.10176, loss_spatial_dice_6: 0.26876/0.23368, loss_spatial_ce_6: 0.08746/0.13107, loss_grounding_bce_6: 0.05355/0.08845, loss_grounding_dice_6: 0.11782/0.18344, loss_grounding_ce_6: 0.45266/0.30930, loss_mask_ce_7: 0.80138/1.01885, loss_mask_bce_7: 0.12288/0.35184, loss_mask_dice_7: 0.87512/1.25686, loss_spatial_bce_7: 0.05291/0.10971, loss_spatial_dice_7: 0.24871/0.26133, loss_spatial_ce_7: 0.08984/0.16628, loss_grounding_bce_7: 0.04657/0.09033, loss_grounding_dice_7: 0.11224/0.19073, loss_grounding_ce_7: 0.41299/0.33976, loss_mask_ce_8: 1.08523/1.12737, loss_mask_bce_8: 0.12309/0.36548, loss_mask_dice_8: 1.05125/1.32970, loss_spatial_bce_8: 0.04432/0.13021, loss_spatial_dice_8: 0.27031/0.29922, loss_spatial_ce_8: 0.44506/0.22105, loss_grounding_bce_8: 0.04111/0.09407, loss_grounding_dice_8: 0.14136/0.20154, loss_grounding_ce_8: 0.46768/0.40653, loss_mask_ce_9: 3.71722/3.67559, loss_mask_bce_9: 0.14029/0.39252, loss_mask_dice_9: 1.05766/1.90236, loss_spatial_bce_9: 0.13548/0.33284, loss_spatial_dice_9: 0.79437/0.82171, loss_spatial_ce_9: 1.21891/1.49519, loss_grounding_bce_9: 0.05662/0.10567, loss_grounding_dice_9: 0.18785/0.28088, loss_grounding_ce_9: 0.48636/0.67095] items per batch[64] items per second[0.24] total items[4544000] mini batches[ 71000] memory[7345] epoch remaining[0:11:38]
INFO:trainer.default_trainer:epochs[    38] optim steps[71100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78263/0.89740, loss_mask_bce_0: 0.25643/0.33408, loss_mask_dice_0: 1.71684/1.16282, loss_spatial_bce_0: 0.02478/0.08697, loss_spatial_dice_0: 0.28107/0.20760, loss_spatial_ce_0: 0.01016/0.06105, loss_grounding_bce_0: 0.03112/0.08615, loss_grounding_dice_0: 0.11467/0.17845, loss_grounding_ce_0: 0.09042/0.27187, loss_mask_ce_1: 0.82887/0.89797, loss_mask_bce_1: 0.26061/0.33502, loss_mask_dice_1: 1.78997/1.16952, loss_spatial_bce_1: 0.02318/0.08751, loss_spatial_dice_1: 0.20252/0.21158, loss_spatial_ce_1: 0.00944/0.06690, loss_grounding_bce_1: 0.03207/0.08635, loss_grounding_dice_1: 0.12640/0.17929, loss_grounding_ce_1: 0.10548/0.27258, loss_mask_ce_2: 0.80529/0.90498, loss_mask_bce_2: 0.25742/0.33559, loss_mask_dice_2: 1.76376/1.16997, loss_spatial_bce_2: 0.02291/0.08866, loss_spatial_dice_2: 0.20496/0.21328, loss_spatial_ce_2: 0.01658/0.07035, loss_grounding_bce_2: 0.03659/0.08650, loss_grounding_dice_2: 0.14307/0.17914, loss_grounding_ce_2: 0.15714/0.27604, loss_mask_ce_3: 0.92377/0.91597, loss_mask_bce_3: 0.28585/0.33680, loss_mask_dice_3: 2.17583/1.16776, loss_spatial_bce_3: 0.02246/0.08990, loss_spatial_dice_3: 0.20775/0.21428, loss_spatial_ce_3: 0.03027/0.07519, loss_grounding_bce_3: 0.03646/0.08675, loss_grounding_dice_3: 0.12834/0.17887, loss_grounding_ce_3: 0.17346/0.27833, loss_mask_ce_4: 0.70619/0.91694, loss_mask_bce_4: 0.30219/0.33894, loss_mask_dice_4: 1.96738/1.19155, loss_spatial_bce_4: 0.02861/0.09382, loss_spatial_dice_4: 0.25711/0.22650, loss_spatial_ce_4: 0.06486/0.09133, loss_grounding_bce_4: 0.03403/0.08728, loss_grounding_dice_4: 0.13447/0.18175, loss_grounding_ce_4: 0.12656/0.28136, loss_mask_ce_5: 0.79352/0.93369, loss_mask_bce_5: 0.29329/0.34125, loss_mask_dice_5: 1.92114/1.19953, loss_spatial_bce_5: 0.02830/0.09608, loss_spatial_dice_5: 0.30211/0.23079, loss_spatial_ce_5: 0.18188/0.10540, loss_grounding_bce_5: 0.03477/0.08771, loss_grounding_dice_5: 0.21247/0.18304, loss_grounding_ce_5: 0.11788/0.29381, loss_mask_ce_6: 0.96382/0.97375, loss_mask_bce_6: 0.28662/0.34394, loss_mask_dice_6: 2.17780/1.20229, loss_spatial_bce_6: 0.04370/0.10175, loss_spatial_dice_6: 0.23276/0.23367, loss_spatial_ce_6: 0.05383/0.13103, loss_grounding_bce_6: 0.03724/0.08845, loss_grounding_dice_6: 0.10331/0.18342, loss_grounding_ce_6: 0.07444/0.30925, loss_mask_ce_7: 0.98826/1.01890, loss_mask_bce_7: 0.29174/0.35179, loss_mask_dice_7: 2.06590/1.25678, loss_spatial_bce_7: 0.03953/0.10970, loss_spatial_dice_7: 0.28972/0.26134, loss_spatial_ce_7: 0.08927/0.16625, loss_grounding_bce_7: 0.04338/0.09032, loss_grounding_dice_7: 0.19025/0.19070, loss_grounding_ce_7: 0.19852/0.33973, loss_mask_ce_8: 1.02572/1.12742, loss_mask_bce_8: 0.38580/0.36543, loss_mask_dice_8: 2.39133/1.32961, loss_spatial_bce_8: 0.10739/0.13019, loss_spatial_dice_8: 0.35249/0.29922, loss_spatial_ce_8: 0.10933/0.22102, loss_grounding_bce_8: 0.03731/0.09406, loss_grounding_dice_8: 0.15616/0.20151, loss_grounding_ce_8: 1.06094/0.40650, loss_mask_ce_9: 4.45736/3.67540, loss_mask_bce_9: 0.70729/0.39247, loss_mask_dice_9: 4.53646/1.90220, loss_spatial_bce_9: 0.14402/0.33281, loss_spatial_dice_9: 0.90134/0.82171, loss_spatial_ce_9: 1.45029/1.49513, loss_grounding_bce_9: 0.24720/0.10566, loss_grounding_dice_9: 0.31315/0.28084, loss_grounding_ce_9: 0.82141/0.67097] items per batch[64] items per second[0.23] total items[4550400] mini batches[ 71100] memory[7345] epoch remaining[0:07:02]
INFO:trainer.default_trainer:epochs[    38] optim steps[71200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04853/0.89740, loss_mask_bce_0: 0.28184/0.33407, loss_mask_dice_0: 1.12314/1.16266, loss_spatial_bce_0: 0.04872/0.08697, loss_spatial_dice_0: 0.16495/0.20759, loss_spatial_ce_0: 0.04455/0.06102, loss_grounding_bce_0: 0.01749/0.08615, loss_grounding_dice_0: 0.03737/0.17847, loss_grounding_ce_0: 0.10004/0.27190, loss_mask_ce_1: 0.91742/0.89797, loss_mask_bce_1: 0.28123/0.33501, loss_mask_dice_1: 1.00319/1.16934, loss_spatial_bce_1: 0.05178/0.08751, loss_spatial_dice_1: 0.17584/0.21157, loss_spatial_ce_1: 0.03409/0.06687, loss_grounding_bce_1: 0.02357/0.08635, loss_grounding_dice_1: 0.04292/0.17930, loss_grounding_ce_1: 0.10970/0.27263, loss_mask_ce_2: 0.96903/0.90498, loss_mask_bce_2: 0.27947/0.33558, loss_mask_dice_2: 1.17906/1.16979, loss_spatial_bce_2: 0.06342/0.08866, loss_spatial_dice_2: 0.18141/0.21327, loss_spatial_ce_2: 0.04094/0.07033, loss_grounding_bce_2: 0.02465/0.08651, loss_grounding_dice_2: 0.04352/0.17916, loss_grounding_ce_2: 0.09485/0.27607, loss_mask_ce_3: 0.89736/0.91598, loss_mask_bce_3: 0.28535/0.33679, loss_mask_dice_3: 1.07683/1.16758, loss_spatial_bce_3: 0.08104/0.08990, loss_spatial_dice_3: 0.18501/0.21427, loss_spatial_ce_3: 0.02212/0.07516, loss_grounding_bce_3: 0.02197/0.08676, loss_grounding_dice_3: 0.04287/0.17888, loss_grounding_ce_3: 0.09487/0.27835, loss_mask_ce_4: 0.95596/0.91690, loss_mask_bce_4: 0.30174/0.33892, loss_mask_dice_4: 1.10063/1.19135, loss_spatial_bce_4: 0.08421/0.09381, loss_spatial_dice_4: 0.19562/0.22650, loss_spatial_ce_4: 0.07495/0.09131, loss_grounding_bce_4: 0.02465/0.08729, loss_grounding_dice_4: 0.04514/0.18177, loss_grounding_ce_4: 0.10997/0.28140, loss_mask_ce_5: 1.02315/0.93370, loss_mask_bce_5: 0.32592/0.34123, loss_mask_dice_5: 1.09948/1.19934, loss_spatial_bce_5: 0.10059/0.09608, loss_spatial_dice_5: 0.18554/0.23078, loss_spatial_ce_5: 0.02742/0.10538, loss_grounding_bce_5: 0.01761/0.08771, loss_grounding_dice_5: 0.03800/0.18306, loss_grounding_ce_5: 0.14920/0.29383, loss_mask_ce_6: 1.14133/0.97376, loss_mask_bce_6: 0.31787/0.34392, loss_mask_dice_6: 1.04878/1.20210, loss_spatial_bce_6: 0.11530/0.10175, loss_spatial_dice_6: 0.20522/0.23367, loss_spatial_ce_6: 0.04722/0.13100, loss_grounding_bce_6: 0.02492/0.08845, loss_grounding_dice_6: 0.04552/0.18344, loss_grounding_ce_6: 0.18527/0.30927, loss_mask_ce_7: 1.27108/1.01891, loss_mask_bce_7: 0.30653/0.35178, loss_mask_dice_7: 1.24832/1.25660, loss_spatial_bce_7: 0.08402/0.10970, loss_spatial_dice_7: 0.18085/0.26133, loss_spatial_ce_7: 0.06554/0.16621, loss_grounding_bce_7: 0.02611/0.09032, loss_grounding_dice_7: 0.04366/0.19073, loss_grounding_ce_7: 0.16764/0.33974, loss_mask_ce_8: 1.24262/1.12742, loss_mask_bce_8: 0.29051/0.36543, loss_mask_dice_8: 1.23630/1.32944, loss_spatial_bce_8: 0.11442/0.13019, loss_spatial_dice_8: 0.20376/0.29921, loss_spatial_ce_8: 0.07358/0.22095, loss_grounding_bce_8: 0.02704/0.09407, loss_grounding_dice_8: 0.04102/0.20154, loss_grounding_ce_8: 0.13929/0.40648, loss_mask_ce_9: 5.22183/3.67528, loss_mask_bce_9: 0.58019/0.39245, loss_mask_dice_9: 3.54215/1.90194, loss_spatial_bce_9: 0.34125/0.33277, loss_spatial_dice_9: 0.91126/0.82170, loss_spatial_ce_9: 1.34149/1.49509, loss_grounding_bce_9: 0.01353/0.10566, loss_grounding_dice_9: 0.05355/0.28087, loss_grounding_ce_9: 0.79873/0.67092] items per batch[64] items per second[0.23] total items[4556800] mini batches[ 71200] memory[7345] epoch remaining[0:02:26]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00071253.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0013 s/iter. Inference: 0.2227 s/iter. Eval: 0.0884 s/iter. Total: 0.3124 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0025 s/iter. Inference: 0.2246 s/iter. Eval: 0.0752 s/iter. Total: 0.3025 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0028 s/iter. Inference: 0.2266 s/iter. Eval: 0.0736 s/iter. Total: 0.3032 s/iter. ETA=0:00:33
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0029 s/iter. Inference: 0.2265 s/iter. Eval: 0.0721 s/iter. Total: 0.3017 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0030 s/iter. Inference: 0.2253 s/iter. Eval: 0.0714 s/iter. Total: 0.2998 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0030 s/iter. Inference: 0.2276 s/iter. Eval: 0.0716 s/iter. Total: 0.3024 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0030 s/iter. Inference: 0.2290 s/iter. Eval: 0.0718 s/iter. Total: 0.3040 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0031 s/iter. Inference: 0.2290 s/iter. Eval: 0.0711 s/iter. Total: 0.3032 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0031 s/iter. Inference: 0.2297 s/iter. Eval: 0.0714 s/iter. Total: 0.3043 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalg_pe8val ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.019 | 82.047 | 60.134 |      133      |
| Things | 54.873 | 82.726 | 65.659 |      80       |
| Stuff  | 42.693 | 81.023 | 51.793 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.36s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.16 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.18s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.08 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.609
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.491
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.296
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.752 | 60.890 | 40.954 | 19.267 | 41.639 | 61.132 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.168 | bicycle      | 18.963 | car            | 37.077 |
| motorcycle    | 34.312 | airplane     | 56.235 | bus            | 64.272 |
| train         | 69.271 | truck        | 34.896 | boat           | 22.541 |
| traffic light | 25.226 | fire hydrant | 64.363 | stop sign      | 64.183 |
| parking meter | 45.157 | bench        | 20.378 | bird           | 29.562 |
| cat           | 73.186 | dog          | 66.058 | horse          | 46.268 |
| sheep         | 46.613 | cow          | 49.884 | elephant       | 60.685 |
| bear          | 77.966 | zebra        | 60.570 | giraffe        | 56.965 |
| backpack      | 17.765 | umbrella     | 48.782 | handbag        | 15.374 |
| tie           | 34.819 | suitcase     | 40.500 | frisbee        | 67.796 |
| skis          | 5.982  | snowboard    | 24.178 | sports ball    | 46.509 |
| kite          | 32.782 | baseball bat | 29.390 | baseball glove | 44.095 |
| skateboard    | 35.840 | surfboard    | 36.010 | tennis racket  | 56.098 |
| bottle        | 34.125 | wine glass   | 26.783 | cup            | 39.521 |
| fork          | 15.588 | knife        | 13.026 | spoon          | 16.485 |
| bowl          | 32.193 | banana       | 21.026 | apple          | 18.944 |
| sandwich      | 41.162 | orange       | 30.502 | broccoli       | 20.831 |
| carrot        | 20.652 | hot dog      | 26.568 | pizza          | 53.320 |
| donut         | 44.199 | cake         | 43.789 | chair          | 20.386 |
| couch         | 40.028 | potted plant | 17.243 | bed            | 38.305 |
| dining table  | 12.667 | toilet       | 66.932 | tv             | 61.810 |
| laptop        | 63.310 | mouse        | 58.901 | remote         | 30.494 |
| keyboard      | 47.316 | cell phone   | 37.249 | microwave      | 53.957 |
| oven          | 32.428 | toaster      | 19.554 | sink           | 37.204 |
| refrigerator  | 59.095 | book         | 8.907  | clock          | 51.669 |
| vase          | 34.086 | scissors     | 24.753 | teddy bear     | 50.750 |
| hair drier    | 11.971 | toothbrush   | 17.674 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.59777492372434, 'fwIoU': 68.82912080182042, 'IoU-person': 87.17859327300155, 'IoU-bicycle': 71.18331141656222, 'IoU-car': 70.71126391452825, 'IoU-motorcycle': 83.7102150107905, 'IoU-airplane': 83.95328023867928, 'IoU-bus': 83.13084703064445, 'IoU-train': 84.06909537328346, 'IoU-truck': 63.797014406529016, 'IoU-boat': 67.1150171033913, 'IoU-traffic light': 76.61384030913027, 'IoU-fire hydrant': 90.45567301165111, 'IoU-stop sign': 85.63867834614605, 'IoU-parking meter': 83.2847003641301, 'IoU-bench': 53.33729838535179, 'IoU-bird': 75.12964791508236, 'IoU-cat': 81.54324351870068, 'IoU-dog': 79.4278104681598, 'IoU-horse': 86.48291833251392, 'IoU-sheep': 85.48284015115534, 'IoU-cow': 79.05468210030604, 'IoU-elephant': 86.56731919293671, 'IoU-bear': 76.26028931173278, 'IoU-zebra': 86.10633565556175, 'IoU-giraffe': 82.36823137969309, 'IoU-backpack': 40.21689478403573, 'IoU-umbrella': 74.810014306393, 'IoU-handbag': 38.509277076779966, 'IoU-tie': 69.27258496070161, 'IoU-suitcase': 79.0513551050811, 'IoU-frisbee': 80.50641875836706, 'IoU-skis': 50.632336694697656, 'IoU-snowboard': 67.50819411363341, 'IoU-sports ball': 70.29631053857163, 'IoU-kite': 64.97540867403134, 'IoU-baseball bat': 60.2850417775477, 'IoU-baseball glove': 76.96205316895922, 'IoU-skateboard': 77.24879294998016, 'IoU-surfboard': 75.63861187702697, 'IoU-tennis racket': 82.75047118709466, 'IoU-bottle': 67.08677825794173, 'IoU-wine glass': 74.55227075406398, 'IoU-cup': 63.65375568650121, 'IoU-fork': 56.793627999398865, 'IoU-knife': 50.50152362538973, 'IoU-spoon': 47.0010542467931, 'IoU-bowl': 53.333776003928804, 'IoU-banana': 82.7646649837196, 'IoU-apple': 59.38089264597782, 'IoU-sandwich': 60.717785770107156, 'IoU-orange': 76.96597753755667, 'IoU-broccoli': 68.80384149228779, 'IoU-carrot': 63.81154681525617, 'IoU-hot dog': 57.206724894450964, 'IoU-pizza': 84.09960180843478, 'IoU-donut': 64.52362954073827, 'IoU-cake': 68.86725017534083, 'IoU-chair': 55.25171425150654, 'IoU-couch': 68.80443802017072, 'IoU-potted plant': 35.65221857981934, 'IoU-bed': 68.36599143338583, 'IoU-dining table': 49.97370973000812, 'IoU-toilet': 86.18316476175758, 'IoU-tv': 75.5895355924372, 'IoU-laptop': 75.77850603486233, 'IoU-mouse': 70.08952234251595, 'IoU-remote': 43.943073676550156, 'IoU-keyboard': 60.53445889435135, 'IoU-cell phone': 67.75579001250782, 'IoU-microwave': 65.23235275751638, 'IoU-oven': 65.56023752966615, 'IoU-toaster': 39.896998215567116, 'IoU-sink': 72.43912227143082, 'IoU-refrigerator': 81.75066131093261, 'IoU-book': 52.956427297348, 'IoU-clock': 71.9968828291428, 'IoU-vase': 60.51191015663482, 'IoU-scissors': 55.01877933778512, 'IoU-teddy bear': 77.98924436410448, 'IoU-hair drier': 70.37027595436875, 'IoU-toothbrush': 57.157126673406985, 'IoU-banner': 33.77136591201623, 'IoU-blanket': 12.053580689034355, 'IoU-bridge': 38.35898727160738, 'IoU-cardboard': 39.46926213872097, 'IoU-counter': 31.86209269085901, 'IoU-curtain': 64.48458435707671, 'IoU-door-stuff': 43.36397218394756, 'IoU-floor-wood': 58.6996769183183, 'IoU-flower': 44.2984751522517, 'IoU-fruit': 40.684923560723774, 'IoU-gravel': 28.545996360285177, 'IoU-house': 21.09840625699233, 'IoU-light': 39.83236354630965, 'IoU-mirror-stuff': 56.65656106961876, 'IoU-net': 43.31220619035126, 'IoU-pillow': 9.521618339520547, 'IoU-platform': 31.493272496863145, 'IoU-playingfield': 67.63608234943449, 'IoU-railroad': 59.78424349073618, 'IoU-river': 49.52184413929556, 'IoU-road': 66.95289510317598, 'IoU-roof': 13.501843704909392, 'IoU-sand': 64.42430618590612, 'IoU-sea': 85.35172585859681, 'IoU-shelf': 37.190108867728036, 'IoU-snow': 88.55369061487166, 'IoU-stairs': 25.86645358569931, 'IoU-tent': 7.825087646417077, 'IoU-towel': 33.479243173457945, 'IoU-wall-brick': 45.05164092699812, 'IoU-wall-stone': 31.757093754169592, 'IoU-wall-tile': 66.01271775299296, 'IoU-wall-wood': 37.39302477619517, 'IoU-water-other': 23.31137200821761, 'IoU-window-blind': 48.78599462184608, 'IoU-window-other': 47.57855497712268, 'IoU-tree-merged': 80.28311357688303, 'IoU-fence-merged': 50.171125965954744, 'IoU-ceiling-merged': 67.25706495241755, 'IoU-sky-other-merged': 93.4874770545692, 'IoU-cabinet-merged': 59.44930017642336, 'IoU-table-merged': 37.7708758926241, 'IoU-floor-other-merged': 50.39329689483537, 'IoU-pavement-merged': 54.81448087892511, 'IoU-mountain-merged': 55.052974793209884, 'IoU-grass-merged': 70.6307567237581, 'IoU-dirt-merged': 45.48783518628267, 'IoU-paper-merged': 30.22084568904768, 'IoU-food-other-merged': 38.73568050800142, 'IoU-building-other-merged': 59.19979602640591, 'IoU-rock-merged': 60.58343082680473, 'IoU-wall-other-merged': 64.65261713584206, 'IoU-rug-merged': 63.69536745285509, 'mACC': 72.698474681456, 'pACC': 80.233999231217, 'ACC-person': 92.62397717324762, 'ACC-bicycle': 80.86639381851849, 'ACC-car': 87.19425503585875, 'ACC-motorcycle': 89.33719943302259, 'ACC-airplane': 90.66280172801085, 'ACC-bus': 87.91604876200839, 'ACC-train': 95.4031253165403, 'ACC-truck': 74.77298458937427, 'ACC-boat': 78.0339530410059, 'ACC-traffic light': 90.00954293064713, 'ACC-fire hydrant': 95.66329471746937, 'ACC-stop sign': 88.17865730747904, 'ACC-parking meter': 87.44377067897675, 'ACC-bench': 67.18878507595204, 'ACC-bird': 80.7595804872203, 'ACC-cat': 87.32100353373997, 'ACC-dog': 86.81822652713609, 'ACC-horse': 93.0395247721526, 'ACC-sheep': 88.72656211905824, 'ACC-cow': 83.95578561073648, 'ACC-elephant': 88.83628934477004, 'ACC-bear': 78.02876181167255, 'ACC-zebra': 88.37260925095707, 'ACC-giraffe': 86.27048933038309, 'ACC-backpack': 59.895728345939126, 'ACC-umbrella': 83.0643036921886, 'ACC-handbag': 53.511182790683485, 'ACC-tie': 81.57577680438382, 'ACC-suitcase': 86.85470867192193, 'ACC-frisbee': 94.25018181818182, 'ACC-skis': 68.3959290895648, 'ACC-snowboard': 78.9943915162499, 'ACC-sports ball': 85.03374990955231, 'ACC-kite': 74.98357660099823, 'ACC-baseball bat': 83.76214701541181, 'ACC-baseball glove': 90.73013265313476, 'ACC-skateboard': 89.98287632803839, 'ACC-surfboard': 84.14169953504586, 'ACC-tennis racket': 89.56085710413645, 'ACC-bottle': 82.14013697028307, 'ACC-wine glass': 84.46592083552429, 'ACC-cup': 83.92266601695906, 'ACC-fork': 68.35409771187318, 'ACC-knife': 62.875174030922544, 'ACC-spoon': 69.99901355133848, 'ACC-bowl': 68.97832557343321, 'ACC-banana': 90.24873694832831, 'ACC-apple': 71.76278389631301, 'ACC-sandwich': 70.40025347580125, 'ACC-orange': 84.62200453090233, 'ACC-broccoli': 79.41488335274198, 'ACC-carrot': 74.78819572301633, 'ACC-hot dog': 76.65636071274496, 'ACC-pizza': 94.7202041972077, 'ACC-donut': 82.12555707358119, 'ACC-cake': 76.80077470325578, 'ACC-chair': 69.09630872879588, 'ACC-couch': 80.62981661560437, 'ACC-potted plant': 49.63957976694707, 'ACC-bed': 77.17508836892301, 'ACC-dining table': 71.92159451330157, 'ACC-toilet': 90.12887456472318, 'ACC-tv': 87.00072074629458, 'ACC-laptop': 91.46745806968698, 'ACC-mouse': 86.33954886024624, 'ACC-remote': 67.19671249867778, 'ACC-keyboard': 65.10840208190477, 'ACC-cell phone': 78.96969796031783, 'ACC-microwave': 74.96860300010081, 'ACC-oven': 85.19718161212992, 'ACC-toaster': 42.99155592658994, 'ACC-sink': 82.50484726078243, 'ACC-refrigerator': 90.85326411035439, 'ACC-book': 68.32450757894148, 'ACC-clock': 77.27644245248877, 'ACC-vase': 69.51756838058178, 'ACC-scissors': 60.03945954879762, 'ACC-teddy bear': 85.10333823668289, 'ACC-hair drier': 78.57142857142857, 'ACC-toothbrush': 81.51841556636553, 'ACC-banner': 64.33091495183207, 'ACC-blanket': 15.650637872295444, 'ACC-bridge': 55.71028412380768, 'ACC-cardboard': 47.38624400174495, 'ACC-counter': 54.27493844386336, 'ACC-curtain': 74.64930721016533, 'ACC-door-stuff': 60.80730589249856, 'ACC-floor-wood': 74.90977922678881, 'ACC-flower': 67.27753830395629, 'ACC-fruit': 57.175877613153546, 'ACC-gravel': 39.41103026926959, 'ACC-house': 23.487291899180587, 'ACC-light': 58.588815411706165, 'ACC-mirror-stuff': 70.20864429763529, 'ACC-net': 59.38531613801482, 'ACC-pillow': 21.79633731351952, 'ACC-platform': 58.26873716771688, 'ACC-playingfield': 84.5568163961033, 'ACC-railroad': 73.46358718823626, 'ACC-river': 73.59060311939187, 'ACC-road': 84.84008167145008, 'ACC-roof': 18.220978131990368, 'ACC-sand': 70.56379187789955, 'ACC-sea': 90.55595288549968, 'ACC-shelf': 58.95486225413431, 'ACC-snow': 95.4561552439626, 'ACC-stairs': 40.686861979684984, 'ACC-tent': 8.429139604618776, 'ACC-towel': 39.137416528111004, 'ACC-wall-brick': 58.20790850111946, 'ACC-wall-stone': 37.22680978424702, 'ACC-wall-tile': 80.58589034511445, 'ACC-wall-wood': 56.2118582551973, 'ACC-water-other': 38.18698957253632, 'ACC-window-blind': 56.51998564817661, 'ACC-window-other': 66.10803363990304, 'ACC-tree-merged': 89.16112957086723, 'ACC-fence-merged': 70.36198212204926, 'ACC-ceiling-merged': 80.63343230747168, 'ACC-sky-other-merged': 96.60678075872633, 'ACC-cabinet-merged': 75.4325171099898, 'ACC-table-merged': 53.76573494697605, 'ACC-floor-other-merged': 61.4640931771511, 'ACC-pavement-merged': 68.35577025629335, 'ACC-mountain-merged': 63.60340152451896, 'ACC-grass-merged': 81.13070991967678, 'ACC-dirt-merged': 74.4449507450722, 'ACC-paper-merged': 39.15188149128676, 'ACC-food-other-merged': 56.572263063427705, 'ACC-building-other-merged': 75.15218062181694, 'ACC-rock-merged': 81.33264825941411, 'ACC-wall-other-merged': 82.96146338203278, 'ACC-rug-merged': 77.96710201608848})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1521 s/iter. Inference: 0.5801 s/iter. Eval: 0.0000 s/iter. Total: 0.7322 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1542 s/iter. Inference: 0.5349 s/iter. Eval: 0.0000 s/iter. Total: 0.6893 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1701 s/iter. Inference: 0.6011 s/iter. Eval: 0.0000 s/iter. Total: 0.7713 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1702 s/iter. Inference: 0.6722 s/iter. Eval: 0.0000 s/iter. Total: 0.8424 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1689 s/iter. Inference: 0.6207 s/iter. Eval: 0.0000 s/iter. Total: 0.7898 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1671 s/iter. Inference: 0.6626 s/iter. Eval: 0.0000 s/iter. Total: 0.8298 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4746853965466784, 'noc@0.8': 2.8358208955223883, 'noc@0.85': 3.463271875914545, 'noc@0.9': 4.45771144278607, 'miou@iter1': 0.8351333278782158}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.1008 s/iter. Eval: 0.0008 s/iter. Total: 0.1030 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.16207122802734, 'precision@0.6': 68.36377716064453, 'precision@0.7': 62.572872161865234, 'precision@0.8': 52.584529876708984, 'precision@0.9': 26.9724063873291, 'cIoU': 57.06294250488281, 'mIoU': 62.838016510009766}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.01921157515879, 'SQ': 82.047478193674, 'RQ': 60.13365100379849, 'PQ_th': 54.87301772893745, 'SQ_th': 82.72595096160768, 'RQ_th': 65.65938203466459, 'PQ_st': 42.69271172039858, 'SQ_st': 81.02336835528357, 'RQ_st': 51.79292491947225}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.75150618944303, 'AP50': 60.889825824960894, 'AP75': 40.95421565327929, 'APs': 19.26732783202653, 'APm': 41.63900356635789, 'APl': 61.13176308377223, 'AP-person': 44.167575233769526, 'AP-bicycle': 18.962516575535197, 'AP-car': 37.07668481644559, 'AP-motorcycle': 34.31183412741255, 'AP-airplane': 56.235052434209976, 'AP-bus': 64.27179456556654, 'AP-train': 69.27134400799628, 'AP-truck': 34.896122323155254, 'AP-boat': 22.540832406638405, 'AP-traffic light': 25.225739671030773, 'AP-fire hydrant': 64.3626453462423, 'AP-stop sign': 64.18331620914695, 'AP-parking meter': 45.15745825131858, 'AP-bench': 20.378240405006938, 'AP-bird': 29.561738286081347, 'AP-cat': 73.18610235065408, 'AP-dog': 66.05781537652827, 'AP-horse': 46.267833975962326, 'AP-sheep': 46.612963628483456, 'AP-cow': 49.884245655374734, 'AP-elephant': 60.68487661644995, 'AP-bear': 77.96594840446895, 'AP-zebra': 60.56955742847487, 'AP-giraffe': 56.96452333412486, 'AP-backpack': 17.764700600194555, 'AP-umbrella': 48.78208073377975, 'AP-handbag': 15.373683118706023, 'AP-tie': 34.8189344085166, 'AP-suitcase': 40.499677394858786, 'AP-frisbee': 67.7959240812992, 'AP-skis': 5.982475098072759, 'AP-snowboard': 24.177750661910107, 'AP-sports ball': 46.50851191706442, 'AP-kite': 32.78186414385933, 'AP-baseball bat': 29.39049618350127, 'AP-baseball glove': 44.09521239259076, 'AP-skateboard': 35.8401028943234, 'AP-surfboard': 36.01017386185378, 'AP-tennis racket': 56.097951298596314, 'AP-bottle': 34.12489968135425, 'AP-wine glass': 26.782643333546424, 'AP-cup': 39.52110185711724, 'AP-fork': 15.588313588876993, 'AP-knife': 13.02648429325216, 'AP-spoon': 16.485265106526636, 'AP-bowl': 32.19306668883518, 'AP-banana': 21.026275652422672, 'AP-apple': 18.94385183566033, 'AP-sandwich': 41.16175037599064, 'AP-orange': 30.502059866541508, 'AP-broccoli': 20.83069273719229, 'AP-carrot': 20.651598559424393, 'AP-hot dog': 26.568406043185394, 'AP-pizza': 53.320105190352095, 'AP-donut': 44.19887334795835, 'AP-cake': 43.78942425331274, 'AP-chair': 20.386269468600588, 'AP-couch': 40.027908888177876, 'AP-potted plant': 17.242563918886724, 'AP-bed': 38.30503524097116, 'AP-dining table': 12.66702411582184, 'AP-toilet': 66.93176009194514, 'AP-tv': 61.809945642201555, 'AP-laptop': 63.31008344701423, 'AP-mouse': 58.901385263328464, 'AP-remote': 30.493896008313975, 'AP-keyboard': 47.316016584916035, 'AP-cell phone': 37.249017978529665, 'AP-microwave': 53.95684173105164, 'AP-oven': 32.427670604425195, 'AP-toaster': 19.554334554334552, 'AP-sink': 37.20405414595484, 'AP-refrigerator': 59.09512823751996, 'AP-book': 8.907321365677651, 'AP-clock': 51.66893754025871, 'AP-vase': 34.08556297712602, 'AP-scissors': 24.752873071011912, 'AP-teddy bear': 50.75027731077567, 'AP-hair drier': 11.971183015737472, 'AP-toothbrush': 17.67429132210755}), ('sem_seg', {'mIoU': 60.59777492372434, 'fwIoU': 68.82912080182042, 'IoU-person': 87.17859327300155, 'IoU-bicycle': 71.18331141656222, 'IoU-car': 70.71126391452825, 'IoU-motorcycle': 83.7102150107905, 'IoU-airplane': 83.95328023867928, 'IoU-bus': 83.13084703064445, 'IoU-train': 84.06909537328346, 'IoU-truck': 63.797014406529016, 'IoU-boat': 67.1150171033913, 'IoU-traffic light': 76.61384030913027, 'IoU-fire hydrant': 90.45567301165111, 'IoU-stop sign': 85.63867834614605, 'IoU-parking meter': 83.2847003641301, 'IoU-bench': 53.33729838535179, 'IoU-bird': 75.12964791508236, 'IoU-cat': 81.54324351870068, 'IoU-dog': 79.4278104681598, 'IoU-horse': 86.48291833251392, 'IoU-sheep': 85.48284015115534, 'IoU-cow': 79.05468210030604, 'IoU-elephant': 86.56731919293671, 'IoU-bear': 76.26028931173278, 'IoU-zebra': 86.10633565556175, 'IoU-giraffe': 82.36823137969309, 'IoU-backpack': 40.21689478403573, 'IoU-umbrella': 74.810014306393, 'IoU-handbag': 38.509277076779966, 'IoU-tie': 69.27258496070161, 'IoU-suitcase': 79.0513551050811, 'IoU-frisbee': 80.50641875836706, 'IoU-skis': 50.632336694697656, 'IoU-snowboard': 67.50819411363341, 'IoU-sports ball': 70.29631053857163, 'IoU-kite': 64.97540867403134, 'IoU-baseball bat': 60.2850417775477, 'IoU-baseball glove': 76.96205316895922, 'IoU-skateboard': 77.24879294998016, 'IoU-surfboard': 75.63861187702697, 'IoU-tennis racket': 82.75047118709466, 'IoU-bottle': 67.08677825794173, 'IoU-wine glass': 74.55227075406398, 'IoU-cup': 63.65375568650121, 'IoU-fork': 56.793627999398865, 'IoU-knife': 50.50152362538973, 'IoU-spoon': 47.0010542467931, 'IoU-bowl': 53.333776003928804, 'IoU-banana': 82.7646649837196, 'IoU-apple': 59.38089264597782, 'IoU-sandwich': 60.717785770107156, 'IoU-orange': 76.96597753755667, 'IoU-broccoli': 68.80384149228779, 'IoU-carrot': 63.81154681525617, 'IoU-hot dog': 57.206724894450964, 'IoU-pizza': 84.09960180843478, 'IoU-donut': 64.52362954073827, 'IoU-cake': 68.86725017534083, 'IoU-chair': 55.25171425150654, 'IoU-couch': 68.80443802017072, 'IoU-potted plant': 35.65221857981934, 'IoU-bed': 68.36599143338583, 'IoU-dining table': 49.97370973000812, 'IoU-toilet': 86.18316476175758, 'IoU-tv': 75.5895355924372, 'IoU-laptop': 75.77850603486233, 'IoU-mouse': 70.08952234251595, 'IoU-remote': 43.943073676550156, 'IoU-keyboard': 60.53445889435135, 'IoU-cell phone': 67.75579001250782, 'IoU-microwave': 65.23235275751638, 'IoU-oven': 65.56023752966615, 'IoU-toaster': 39.896998215567116, 'IoU-sink': 72.43912227143082, 'IoU-refrigerator': 81.75066131093261, 'IoU-book': 52.956427297348, 'IoU-clock': 71.9968828291428, 'IoU-vase': 60.51191015663482, 'IoU-scissors': 55.01877933778512, 'IoU-teddy bear': 77.98924436410448, 'IoU-hair drier': 70.37027595436875, 'IoU-toothbrush': 57.157126673406985, 'IoU-banner': 33.77136591201623, 'IoU-blanket': 12.053580689034355, 'IoU-bridge': 38.35898727160738, 'IoU-cardboard': 39.46926213872097, 'IoU-counter': 31.86209269085901, 'IoU-curtain': 64.48458435707671, 'IoU-door-stuff': 43.36397218394756, 'IoU-floor-wood': 58.6996769183183, 'IoU-flower': 44.2984751522517, 'IoU-fruit': 40.684923560723774, 'IoU-gravel': 28.545996360285177, 'IoU-house': 21.09840625699233, 'IoU-light': 39.83236354630965, 'IoU-mirror-stuff': 56.65656106961876, 'IoU-net': 43.31220619035126, 'IoU-pillow': 9.521618339520547, 'IoU-platform': 31.493272496863145, 'IoU-playingfield': 67.63608234943449, 'IoU-railroad': 59.78424349073618, 'IoU-river': 49.52184413929556, 'IoU-road': 66.95289510317598, 'IoU-roof': 13.501843704909392, 'IoU-sand': 64.42430618590612, 'IoU-sea': 85.35172585859681, 'IoU-shelf': 37.190108867728036, 'IoU-snow': 88.55369061487166, 'IoU-stairs': 25.86645358569931, 'IoU-tent': 7.825087646417077, 'IoU-towel': 33.479243173457945, 'IoU-wall-brick': 45.05164092699812, 'IoU-wall-stone': 31.757093754169592, 'IoU-wall-tile': 66.01271775299296, 'IoU-wall-wood': 37.39302477619517, 'IoU-water-other': 23.31137200821761, 'IoU-window-blind': 48.78599462184608, 'IoU-window-other': 47.57855497712268, 'IoU-tree-merged': 80.28311357688303, 'IoU-fence-merged': 50.171125965954744, 'IoU-ceiling-merged': 67.25706495241755, 'IoU-sky-other-merged': 93.4874770545692, 'IoU-cabinet-merged': 59.44930017642336, 'IoU-table-merged': 37.7708758926241, 'IoU-floor-other-merged': 50.39329689483537, 'IoU-pavement-merged': 54.81448087892511, 'IoU-mountain-merged': 55.052974793209884, 'IoU-grass-merged': 70.6307567237581, 'IoU-dirt-merged': 45.48783518628267, 'IoU-paper-merged': 30.22084568904768, 'IoU-food-other-merged': 38.73568050800142, 'IoU-building-other-merged': 59.19979602640591, 'IoU-rock-merged': 60.58343082680473, 'IoU-wall-other-merged': 64.65261713584206, 'IoU-rug-merged': 63.69536745285509, 'mACC': 72.698474681456, 'pACC': 80.233999231217, 'ACC-person': 92.62397717324762, 'ACC-bicycle': 80.86639381851849, 'ACC-car': 87.19425503585875, 'ACC-motorcycle': 89.33719943302259, 'ACC-airplane': 90.66280172801085, 'ACC-bus': 87.91604876200839, 'ACC-train': 95.4031253165403, 'ACC-truck': 74.77298458937427, 'ACC-boat': 78.0339530410059, 'ACC-traffic light': 90.00954293064713, 'ACC-fire hydrant': 95.66329471746937, 'ACC-stop sign': 88.17865730747904, 'ACC-parking meter': 87.44377067897675, 'ACC-bench': 67.18878507595204, 'ACC-bird': 80.7595804872203, 'ACC-cat': 87.32100353373997, 'ACC-dog': 86.81822652713609, 'ACC-horse': 93.0395247721526, 'ACC-sheep': 88.72656211905824, 'ACC-cow': 83.95578561073648, 'ACC-elephant': 88.83628934477004, 'ACC-bear': 78.02876181167255, 'ACC-zebra': 88.37260925095707, 'ACC-giraffe': 86.27048933038309, 'ACC-backpack': 59.895728345939126, 'ACC-umbrella': 83.0643036921886, 'ACC-handbag': 53.511182790683485, 'ACC-tie': 81.57577680438382, 'ACC-suitcase': 86.85470867192193, 'ACC-frisbee': 94.25018181818182, 'ACC-skis': 68.3959290895648, 'ACC-snowboard': 78.9943915162499, 'ACC-sports ball': 85.03374990955231, 'ACC-kite': 74.98357660099823, 'ACC-baseball bat': 83.76214701541181, 'ACC-baseball glove': 90.73013265313476, 'ACC-skateboard': 89.98287632803839, 'ACC-surfboard': 84.14169953504586, 'ACC-tennis racket': 89.56085710413645, 'ACC-bottle': 82.14013697028307, 'ACC-wine glass': 84.46592083552429, 'ACC-cup': 83.92266601695906, 'ACC-fork': 68.35409771187318, 'ACC-knife': 62.875174030922544, 'ACC-spoon': 69.99901355133848, 'ACC-bowl': 68.97832557343321, 'ACC-banana': 90.24873694832831, 'ACC-apple': 71.76278389631301, 'ACC-sandwich': 70.40025347580125, 'ACC-orange': 84.62200453090233, 'ACC-broccoli': 79.41488335274198, 'ACC-carrot': 74.78819572301633, 'ACC-hot dog': 76.65636071274496, 'ACC-pizza': 94.7202041972077, 'ACC-donut': 82.12555707358119, 'ACC-cake': 76.80077470325578, 'ACC-chair': 69.09630872879588, 'ACC-couch': 80.62981661560437, 'ACC-potted plant': 49.63957976694707, 'ACC-bed': 77.17508836892301, 'ACC-dining table': 71.92159451330157, 'ACC-toilet': 90.12887456472318, 'ACC-tv': 87.00072074629458, 'ACC-laptop': 91.46745806968698, 'ACC-mouse': 86.33954886024624, 'ACC-remote': 67.19671249867778, 'ACC-keyboard': 65.10840208190477, 'ACC-cell phone': 78.96969796031783, 'ACC-microwave': 74.96860300010081, 'ACC-oven': 85.19718161212992, 'ACC-toaster': 42.99155592658994, 'ACC-sink': 82.50484726078243, 'ACC-refrigerator': 90.85326411035439, 'ACC-book': 68.32450757894148, 'ACC-clock': 77.27644245248877, 'ACC-vase': 69.51756838058178, 'ACC-scissors': 60.03945954879762, 'ACC-teddy bear': 85.10333823668289, 'ACC-hair drier': 78.57142857142857, 'ACC-toothbrush': 81.51841556636553, 'ACC-banner': 64.33091495183207, 'ACC-blanket': 15.650637872295444, 'ACC-bridge': 55.71028412380768, 'ACC-cardboard': 47.38624400174495, 'ACC-counter': 54.27493844386336, 'ACC-curtain': 74.64930721016533, 'ACC-door-stuff': 60.80730589249856, 'ACC-floor-wood': 74.90977922678881, 'ACC-flower': 67.27753830395629, 'ACC-fruit': 57.175877613153546, 'ACC-gravel': 39.41103026926959, 'ACC-house': 23.487291899180587, 'ACC-light': 58.588815411706165, 'ACC-mirror-stuff': 70.20864429763529, 'ACC-net': 59.38531613801482, 'ACC-pillow': 21.79633731351952, 'ACC-platform': 58.26873716771688, 'ACC-playingfield': 84.5568163961033, 'ACC-railroad': 73.46358718823626, 'ACC-river': 73.59060311939187, 'ACC-road': 84.84008167145008, 'ACC-roof': 18.220978131990368, 'ACC-sand': 70.56379187789955, 'ACC-sea': 90.55595288549968, 'ACC-shelf': 58.95486225413431, 'ACC-snow': 95.4561552439626, 'ACC-stairs': 40.686861979684984, 'ACC-tent': 8.429139604618776, 'ACC-towel': 39.137416528111004, 'ACC-wall-brick': 58.20790850111946, 'ACC-wall-stone': 37.22680978424702, 'ACC-wall-tile': 80.58589034511445, 'ACC-wall-wood': 56.2118582551973, 'ACC-water-other': 38.18698957253632, 'ACC-window-blind': 56.51998564817661, 'ACC-window-other': 66.10803363990304, 'ACC-tree-merged': 89.16112957086723, 'ACC-fence-merged': 70.36198212204926, 'ACC-ceiling-merged': 80.63343230747168, 'ACC-sky-other-merged': 96.60678075872633, 'ACC-cabinet-merged': 75.4325171099898, 'ACC-table-merged': 53.76573494697605, 'ACC-floor-other-merged': 61.4640931771511, 'ACC-pavement-merged': 68.35577025629335, 'ACC-mountain-merged': 63.60340152451896, 'ACC-grass-merged': 81.13070991967678, 'ACC-dirt-merged': 74.4449507450722, 'ACC-paper-merged': 39.15188149128676, 'ACC-food-other-merged': 56.572263063427705, 'ACC-building-other-merged': 75.15218062181694, 'ACC-rock-merged': 81.33264825941411, 'ACC-wall-other-merged': 82.96146338203278, 'ACC-rug-merged': 77.96710201608848})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4746853965466784, 'noc@0.8': 2.8358208955223883, 'noc@0.85': 3.463271875914545, 'noc@0.9': 4.45771144278607, 'miou@iter1': 0.8351333278782158}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.16207122802734, 'precision@0.6': 68.36377716064453, 'precision@0.7': 62.572872161865234, 'precision@0.8': 52.584529876708984, 'precision@0.9': 26.9724063873291, 'cIoU': 57.06294250488281, 'mIoU': 62.838016510009766}}}
INFO:trainer.default_trainer:This epoch takes 1:27:22.903828
INFO:trainer.default_trainer:PROGRESS: 78.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 39 training.
INFO:trainer.default_trainer:epochs[    39] optim steps[71300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05848/0.89742, loss_mask_bce_0: 0.59994/0.33410, loss_mask_dice_0: 1.52163/1.16279, loss_spatial_bce_0: 0.07227/0.08696, loss_spatial_dice_0: 0.20801/0.20758, loss_spatial_ce_0: 0.03052/0.06100, loss_grounding_bce_0: 0.17369/0.08615, loss_grounding_dice_0: 0.21862/0.17847, loss_grounding_ce_0: 0.64503/0.27190, loss_mask_ce_1: 0.96749/0.89798, loss_mask_bce_1: 0.58100/0.33504, loss_mask_dice_1: 1.66209/1.16948, loss_spatial_bce_1: 0.07618/0.08750, loss_spatial_dice_1: 0.20145/0.21156, loss_spatial_ce_1: 0.04893/0.06684, loss_grounding_bce_1: 0.17744/0.08634, loss_grounding_dice_1: 0.21415/0.17932, loss_grounding_ce_1: 0.65099/0.27262, loss_mask_ce_2: 1.02836/0.90502, loss_mask_bce_2: 0.59134/0.33562, loss_mask_dice_2: 1.60859/1.16993, loss_spatial_bce_2: 0.07528/0.08866, loss_spatial_dice_2: 0.21797/0.21326, loss_spatial_ce_2: 0.07189/0.07030, loss_grounding_bce_2: 0.14873/0.08650, loss_grounding_dice_2: 0.23249/0.17917, loss_grounding_ce_2: 0.73828/0.27606, loss_mask_ce_3: 1.18781/0.91601, loss_mask_bce_3: 0.58020/0.33682, loss_mask_dice_3: 1.40723/1.16772, loss_spatial_bce_3: 0.07960/0.08990, loss_spatial_dice_3: 0.22804/0.21426, loss_spatial_ce_3: 0.05718/0.07513, loss_grounding_bce_3: 0.14666/0.08675, loss_grounding_dice_3: 0.20765/0.17889, loss_grounding_ce_3: 0.67956/0.27834, loss_mask_ce_4: 0.99947/0.91692, loss_mask_bce_4: 0.58230/0.33897, loss_mask_dice_4: 1.66577/1.19150, loss_spatial_bce_4: 0.07097/0.09381, loss_spatial_dice_4: 0.22437/0.22650, loss_spatial_ce_4: 0.13619/0.09131, loss_grounding_bce_4: 0.13771/0.08728, loss_grounding_dice_4: 0.18066/0.18178, loss_grounding_ce_4: 1.04919/0.28141, loss_mask_ce_5: 1.02864/0.93371, loss_mask_bce_5: 0.59091/0.34128, loss_mask_dice_5: 1.68893/1.19951, loss_spatial_bce_5: 0.07130/0.09607, loss_spatial_dice_5: 0.22799/0.23077, loss_spatial_ce_5: 0.11795/0.10536, loss_grounding_bce_5: 0.14308/0.08771, loss_grounding_dice_5: 0.19100/0.18308, loss_grounding_ce_5: 0.89527/0.29385, loss_mask_ce_6: 1.18700/0.97378, loss_mask_bce_6: 0.59961/0.34398, loss_mask_dice_6: 1.68197/1.20227, loss_spatial_bce_6: 0.07314/0.10174, loss_spatial_dice_6: 0.21545/0.23367, loss_spatial_ce_6: 0.09301/0.13098, loss_grounding_bce_6: 0.17957/0.08845, loss_grounding_dice_6: 0.16083/0.18345, loss_grounding_ce_6: 0.74945/0.30926, loss_mask_ce_7: 1.18342/1.01895, loss_mask_bce_7: 0.58738/0.35182, loss_mask_dice_7: 1.80822/1.25677, loss_spatial_bce_7: 0.08643/0.10970, loss_spatial_dice_7: 0.24908/0.26133, loss_spatial_ce_7: 0.11165/0.16618, loss_grounding_bce_7: 0.16377/0.09032, loss_grounding_dice_7: 0.23512/0.19075, loss_grounding_ce_7: 2.16917/0.33979, loss_mask_ce_8: 1.30786/1.12745, loss_mask_bce_8: 0.60146/0.36548, loss_mask_dice_8: 1.87333/1.32969, loss_spatial_bce_8: 0.10087/0.13020, loss_spatial_dice_8: 0.31569/0.29921, loss_spatial_ce_8: 0.12081/0.22091, loss_grounding_bce_8: 0.18968/0.09407, loss_grounding_dice_8: 0.24749/0.20155, loss_grounding_ce_8: 3.22083/0.40651, loss_mask_ce_9: 3.74776/3.67534, loss_mask_bce_9: 0.66590/0.39251, loss_mask_dice_9: 2.71622/1.90242, loss_spatial_bce_9: 0.18808/0.33275, loss_spatial_dice_9: 0.89299/0.82171, loss_spatial_ce_9: 1.25302/1.49503, loss_grounding_bce_9: 0.17673/0.10566, loss_grounding_dice_9: 0.46615/0.28090, loss_grounding_ce_9: 2.30053/0.67093] items per batch[64] items per second[0.13] total items[4563200] mini batches[ 71300] memory[7345] epoch remaining[1:26:58]
INFO:trainer.default_trainer:epochs[    39] optim steps[71400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64145/0.89739, loss_mask_bce_0: 0.44713/0.33407, loss_mask_dice_0: 0.83917/1.16266, loss_spatial_bce_0: 0.11962/0.08696, loss_spatial_dice_0: 0.20992/0.20757, loss_spatial_ce_0: 0.20156/0.06099, loss_grounding_bce_0: 0.10547/0.08614, loss_grounding_dice_0: 0.27395/0.17846, loss_grounding_ce_0: 0.54781/0.27194, loss_mask_ce_1: 0.70844/0.89795, loss_mask_bce_1: 0.46199/0.33501, loss_mask_dice_1: 0.81887/1.16933, loss_spatial_bce_1: 0.16334/0.08749, loss_spatial_dice_1: 0.25549/0.21156, loss_spatial_ce_1: 0.01308/0.06682, loss_grounding_bce_1: 0.10272/0.08634, loss_grounding_dice_1: 0.26967/0.17930, loss_grounding_ce_1: 0.55600/0.27265, loss_mask_ce_2: 0.77605/0.90499, loss_mask_bce_2: 0.47365/0.33558, loss_mask_dice_2: 0.83199/1.16979, loss_spatial_bce_2: 0.18295/0.08865, loss_spatial_dice_2: 0.24444/0.21327, loss_spatial_ce_2: 0.02819/0.07030, loss_grounding_bce_2: 0.10885/0.08649, loss_grounding_dice_2: 0.27580/0.17916, loss_grounding_ce_2: 0.55780/0.27609, loss_mask_ce_3: 0.61521/0.91595, loss_mask_bce_3: 0.49649/0.33679, loss_mask_dice_3: 0.96150/1.16761, loss_spatial_bce_3: 0.14820/0.08989, loss_spatial_dice_3: 0.24295/0.21426, loss_spatial_ce_3: 0.07586/0.07512, loss_grounding_bce_3: 0.10760/0.08674, loss_grounding_dice_3: 0.27589/0.17887, loss_grounding_ce_3: 0.53952/0.27834, loss_mask_ce_4: 0.70570/0.91690, loss_mask_bce_4: 0.44657/0.33893, loss_mask_dice_4: 0.80985/1.19136, loss_spatial_bce_4: 0.16059/0.09381, loss_spatial_dice_4: 0.21224/0.22650, loss_spatial_ce_4: 0.14203/0.09130, loss_grounding_bce_4: 0.10470/0.08727, loss_grounding_dice_4: 0.27722/0.18176, loss_grounding_ce_4: 0.55989/0.28140, loss_mask_ce_5: 0.96990/0.93366, loss_mask_bce_5: 0.50209/0.34124, loss_mask_dice_5: 0.76252/1.19937, loss_spatial_bce_5: 0.13569/0.09607, loss_spatial_dice_5: 0.22863/0.23077, loss_spatial_ce_5: 0.31016/0.10536, loss_grounding_bce_5: 0.09842/0.08770, loss_grounding_dice_5: 0.25836/0.18306, loss_grounding_ce_5: 0.57418/0.29383, loss_mask_ce_6: 1.06100/0.97376, loss_mask_bce_6: 0.38654/0.34395, loss_mask_dice_6: 0.79212/1.20212, loss_spatial_bce_6: 0.20716/0.10173, loss_spatial_dice_6: 0.28647/0.23367, loss_spatial_ce_6: 0.13331/0.13096, loss_grounding_bce_6: 0.10831/0.08844, loss_grounding_dice_6: 0.25409/0.18344, loss_grounding_ce_6: 0.54236/0.30925, loss_mask_ce_7: 1.31891/1.01893, loss_mask_bce_7: 0.49337/0.35178, loss_mask_dice_7: 0.90137/1.25664, loss_spatial_bce_7: 0.16569/0.10969, loss_spatial_dice_7: 0.30298/0.26132, loss_spatial_ce_7: 0.34452/0.16616, loss_grounding_bce_7: 0.10837/0.09031, loss_grounding_dice_7: 0.28833/0.19073, loss_grounding_ce_7: 0.57847/0.33977, loss_mask_ce_8: 1.04624/1.12747, loss_mask_bce_8: 0.54036/0.36544, loss_mask_dice_8: 0.92338/1.32953, loss_spatial_bce_8: 0.24881/0.13018, loss_spatial_dice_8: 0.29822/0.29921, loss_spatial_ce_8: 0.13258/0.22086, loss_grounding_bce_8: 0.10250/0.09405, loss_grounding_dice_8: 0.26599/0.20153, loss_grounding_ce_8: 0.60512/0.40649, loss_mask_ce_9: 5.40017/3.67516, loss_mask_bce_9: 0.67303/0.39249, loss_mask_dice_9: 1.48467/1.90218, loss_spatial_bce_9: 0.36194/0.33275, loss_spatial_dice_9: 0.86924/0.82170, loss_spatial_ce_9: 1.25604/1.49510, loss_grounding_bce_9: 0.18796/0.10565, loss_grounding_dice_9: 0.47206/0.28088, loss_grounding_ce_9: 0.74889/0.67087] items per batch[64] items per second[0.23] total items[4569600] mini batches[ 71400] memory[7345] epoch remaining[1:18:12]
INFO:trainer.default_trainer:epochs[    39] optim steps[71500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74503/0.89732, loss_mask_bce_0: 0.25955/0.33408, loss_mask_dice_0: 1.03680/1.16279, loss_spatial_bce_0: 0.05679/0.08695, loss_spatial_dice_0: 0.24931/0.20756, loss_spatial_ce_0: 0.01472/0.06098, loss_grounding_bce_0: 0.05527/0.08614, loss_grounding_dice_0: 0.21728/0.17847, loss_grounding_ce_0: 0.60386/0.27192, loss_mask_ce_1: 0.95495/0.89789, loss_mask_bce_1: 0.24846/0.33502, loss_mask_dice_1: 1.04611/1.16943, loss_spatial_bce_1: 0.05870/0.08748, loss_spatial_dice_1: 0.26257/0.21155, loss_spatial_ce_1: 0.01521/0.06682, loss_grounding_bce_1: 0.05292/0.08633, loss_grounding_dice_1: 0.21195/0.17929, loss_grounding_ce_1: 0.66339/0.27265, loss_mask_ce_2: 0.77299/0.90491, loss_mask_bce_2: 0.24675/0.33560, loss_mask_dice_2: 1.01416/1.16990, loss_spatial_bce_2: 0.05508/0.08864, loss_spatial_dice_2: 0.26143/0.21325, loss_spatial_ce_2: 0.04427/0.07028, loss_grounding_bce_2: 0.05535/0.08649, loss_grounding_dice_2: 0.21884/0.17916, loss_grounding_ce_2: 0.67163/0.27608, loss_mask_ce_3: 0.96535/0.91591, loss_mask_bce_3: 0.24929/0.33680, loss_mask_dice_3: 1.04084/1.16774, loss_spatial_bce_3: 0.05729/0.08989, loss_spatial_dice_3: 0.25516/0.21425, loss_spatial_ce_3: 0.04208/0.07511, loss_grounding_bce_3: 0.04607/0.08674, loss_grounding_dice_3: 0.20238/0.17887, loss_grounding_ce_3: 0.65605/0.27832, loss_mask_ce_4: 0.79469/0.91687, loss_mask_bce_4: 0.20918/0.33894, loss_mask_dice_4: 1.05141/1.19148, loss_spatial_bce_4: 0.06568/0.09381, loss_spatial_dice_4: 0.26355/0.22649, loss_spatial_ce_4: 0.06119/0.09128, loss_grounding_bce_4: 0.04753/0.08727, loss_grounding_dice_4: 0.23655/0.18176, loss_grounding_ce_4: 0.72009/0.28138, loss_mask_ce_5: 0.71773/0.93361, loss_mask_bce_5: 0.24104/0.34126, loss_mask_dice_5: 1.09574/1.19949, loss_spatial_bce_5: 0.08109/0.09607, loss_spatial_dice_5: 0.32928/0.23077, loss_spatial_ce_5: 0.04418/0.10535, loss_grounding_bce_5: 0.04953/0.08770, loss_grounding_dice_5: 0.23093/0.18306, loss_grounding_ce_5: 0.70879/0.29382, loss_mask_ce_6: 0.81518/0.97370, loss_mask_bce_6: 0.23856/0.34397, loss_mask_dice_6: 1.10564/1.20224, loss_spatial_bce_6: 0.08634/0.10173, loss_spatial_dice_6: 0.32276/0.23367, loss_spatial_ce_6: 0.03482/0.13093, loss_grounding_bce_6: 0.06578/0.08844, loss_grounding_dice_6: 0.24541/0.18343, loss_grounding_ce_6: 0.38461/0.30927, loss_mask_ce_7: 0.79789/1.01891, loss_mask_bce_7: 0.23250/0.35179, loss_mask_dice_7: 1.06753/1.25678, loss_spatial_bce_7: 0.06024/0.10969, loss_spatial_dice_7: 0.27352/0.26132, loss_spatial_ce_7: 0.08803/0.16615, loss_grounding_bce_7: 0.05006/0.09031, loss_grounding_dice_7: 0.23485/0.19073, loss_grounding_ce_7: 0.47455/0.33979, loss_mask_ce_8: 1.16913/1.12745, loss_mask_bce_8: 0.22551/0.36545, loss_mask_dice_8: 1.10513/1.32967, loss_spatial_bce_8: 0.11411/0.13018, loss_spatial_dice_8: 0.35306/0.29921, loss_spatial_ce_8: 0.10540/0.22082, loss_grounding_bce_8: 0.05928/0.09405, loss_grounding_dice_8: 0.23049/0.20152, loss_grounding_ce_8: 0.39490/0.40648, loss_mask_ce_9: 4.60036/3.67537, loss_mask_bce_9: 0.34420/0.39252, loss_mask_dice_9: 1.63952/1.90239, loss_spatial_bce_9: 0.45310/0.33276, loss_spatial_dice_9: 0.93265/0.82171, loss_spatial_ce_9: 1.45872/1.49504, loss_grounding_bce_9: 0.06363/0.10565, loss_grounding_dice_9: 0.40811/0.28088, loss_grounding_ce_9: 0.40146/0.67087] items per batch[64] items per second[0.24] total items[4576000] mini batches[ 71500] memory[7345] epoch remaining[1:12:15]
INFO:trainer.default_trainer:epochs[    39] optim steps[71600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95038/0.89731, loss_mask_bce_0: 0.18782/0.33412, loss_mask_dice_0: 1.51869/1.16267, loss_spatial_bce_0: 0.04174/0.08695, loss_spatial_dice_0: 0.21425/0.20755, loss_spatial_ce_0: 0.03008/0.06096, loss_grounding_bce_0: 0.04029/0.08615, loss_grounding_dice_0: 0.03377/0.17847, loss_grounding_ce_0: 0.00176/0.27185, loss_mask_ce_1: 1.04204/0.89785, loss_mask_bce_1: 0.19134/0.33506, loss_mask_dice_1: 1.68733/1.16934, loss_spatial_bce_1: 0.04497/0.08749, loss_spatial_dice_1: 0.18241/0.21153, loss_spatial_ce_1: 0.03627/0.06680, loss_grounding_bce_1: 0.03815/0.08634, loss_grounding_dice_1: 0.03460/0.17929, loss_grounding_ce_1: 0.00411/0.27259, loss_mask_ce_2: 1.04448/0.90488, loss_mask_bce_2: 0.18077/0.33563, loss_mask_dice_2: 1.50973/1.16978, loss_spatial_bce_2: 0.03863/0.08865, loss_spatial_dice_2: 0.17707/0.21324, loss_spatial_ce_2: 0.02981/0.07027, loss_grounding_bce_2: 0.03734/0.08650, loss_grounding_dice_2: 0.03283/0.17916, loss_grounding_ce_2: 0.00355/0.27601, loss_mask_ce_3: 1.02544/0.91590, loss_mask_bce_3: 0.18443/0.33683, loss_mask_dice_3: 1.50272/1.16764, loss_spatial_bce_3: 0.03789/0.08990, loss_spatial_dice_3: 0.19531/0.21424, loss_spatial_ce_3: 0.03768/0.07509, loss_grounding_bce_3: 0.03922/0.08675, loss_grounding_dice_3: 0.03286/0.17887, loss_grounding_ce_3: 0.00252/0.27824, loss_mask_ce_4: 1.02060/0.91686, loss_mask_bce_4: 0.21368/0.33897, loss_mask_dice_4: 1.66206/1.19135, loss_spatial_bce_4: 0.04201/0.09382, loss_spatial_dice_4: 0.22259/0.22648, loss_spatial_ce_4: 0.04957/0.09127, loss_grounding_bce_4: 0.03975/0.08728, loss_grounding_dice_4: 0.03434/0.18176, loss_grounding_ce_4: 0.00401/0.28132, loss_mask_ce_5: 1.05783/0.93360, loss_mask_bce_5: 0.21850/0.34129, loss_mask_dice_5: 1.66121/1.19937, loss_spatial_bce_5: 0.03905/0.09608, loss_spatial_dice_5: 0.21365/0.23076, loss_spatial_ce_5: 0.13736/0.10533, loss_grounding_bce_5: 0.03985/0.08770, loss_grounding_dice_5: 0.03501/0.18306, loss_grounding_ce_5: 0.00559/0.29376, loss_mask_ce_6: 0.87591/0.97370, loss_mask_bce_6: 0.20541/0.34400, loss_mask_dice_6: 1.72905/1.20211, loss_spatial_bce_6: 0.04010/0.10174, loss_spatial_dice_6: 0.28606/0.23366, loss_spatial_ce_6: 0.16018/0.13091, loss_grounding_bce_6: 0.04036/0.08844, loss_grounding_dice_6: 0.03401/0.18343, loss_grounding_ce_6: 0.04445/0.30919, loss_mask_ce_7: 1.15086/1.01891, loss_mask_bce_7: 0.21366/0.35182, loss_mask_dice_7: 1.88612/1.25665, loss_spatial_bce_7: 0.03682/0.10970, loss_spatial_dice_7: 0.26515/0.26131, loss_spatial_ce_7: 0.41148/0.16613, loss_grounding_bce_7: 0.03994/0.09031, loss_grounding_dice_7: 0.03654/0.19073, loss_grounding_ce_7: 0.10353/0.33972, loss_mask_ce_8: 0.91965/1.12744, loss_mask_bce_8: 0.21437/0.36548, loss_mask_dice_8: 1.85074/1.32954, loss_spatial_bce_8: 0.07773/0.13018, loss_spatial_dice_8: 0.36165/0.29918, loss_spatial_ce_8: 0.18591/0.22075, loss_grounding_bce_8: 0.05094/0.09405, loss_grounding_dice_8: 0.04630/0.20152, loss_grounding_ce_8: 0.24865/0.40636, loss_mask_ce_9: 3.17166/3.67536, loss_mask_bce_9: 0.25491/0.39255, loss_mask_dice_9: 2.94814/1.90223, loss_spatial_bce_9: 0.24953/0.33277, loss_spatial_dice_9: 0.85468/0.82171, loss_spatial_ce_9: 1.46833/1.49501, loss_grounding_bce_9: 0.07344/0.10566, loss_grounding_dice_9: 0.09843/0.28088, loss_grounding_ce_9: 0.68177/0.67078] items per batch[64] items per second[0.23] total items[4582400] mini batches[ 71600] memory[7345] epoch remaining[1:07:34]
INFO:trainer.default_trainer:epochs[    39] optim steps[71700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01471/0.89723, loss_mask_bce_0: 0.40419/0.33410, loss_mask_dice_0: 4.60479/1.16270, loss_spatial_bce_0: 0.03590/0.08694, loss_spatial_dice_0: 0.19366/0.20753, loss_spatial_ce_0: 0.03833/0.06094, loss_grounding_bce_0: 0.03368/0.08614, loss_grounding_dice_0: 0.13236/0.17846, loss_grounding_ce_0: 0.04078/0.27178, loss_mask_ce_1: 0.98491/0.89779, loss_mask_bce_1: 0.40101/0.33504, loss_mask_dice_1: 4.38993/1.16939, loss_spatial_bce_1: 0.03558/0.08748, loss_spatial_dice_1: 0.18687/0.21151, loss_spatial_ce_1: 0.03609/0.06679, loss_grounding_bce_1: 0.03368/0.08633, loss_grounding_dice_1: 0.13252/0.17928, loss_grounding_ce_1: 0.04335/0.27254, loss_mask_ce_2: 0.96801/0.90483, loss_mask_bce_2: 0.42883/0.33561, loss_mask_dice_2: 4.21585/1.16980, loss_spatial_bce_2: 0.03641/0.08864, loss_spatial_dice_2: 0.18612/0.21322, loss_spatial_ce_2: 0.02187/0.07025, loss_grounding_bce_2: 0.03237/0.08650, loss_grounding_dice_2: 0.14749/0.17914, loss_grounding_ce_2: 0.04146/0.27594, loss_mask_ce_3: 1.03731/0.91584, loss_mask_bce_3: 0.43117/0.33682, loss_mask_dice_3: 4.30775/1.16767, loss_spatial_bce_3: 0.03723/0.08989, loss_spatial_dice_3: 0.20661/0.21423, loss_spatial_ce_3: 0.02908/0.07508, loss_grounding_bce_3: 0.03463/0.08675, loss_grounding_dice_3: 0.15330/0.17887, loss_grounding_ce_3: 0.03717/0.27817, loss_mask_ce_4: 1.03211/0.91681, loss_mask_bce_4: 0.42362/0.33895, loss_mask_dice_4: 4.40400/1.19140, loss_spatial_bce_4: 0.03629/0.09381, loss_spatial_dice_4: 0.19377/0.22646, loss_spatial_ce_4: 0.04785/0.09127, loss_grounding_bce_4: 0.03606/0.08728, loss_grounding_dice_4: 0.15379/0.18175, loss_grounding_ce_4: 0.04714/0.28125, loss_mask_ce_5: 0.98738/0.93354, loss_mask_bce_5: 0.41585/0.34127, loss_mask_dice_5: 4.77813/1.19937, loss_spatial_bce_5: 0.03847/0.09607, loss_spatial_dice_5: 0.21300/0.23074, loss_spatial_ce_5: 0.07178/0.10532, loss_grounding_bce_5: 0.03767/0.08769, loss_grounding_dice_5: 0.14144/0.18304, loss_grounding_ce_5: 0.04971/0.29371, loss_mask_ce_6: 1.21366/0.97363, loss_mask_bce_6: 0.39916/0.34399, loss_mask_dice_6: 4.40813/1.20215, loss_spatial_bce_6: 0.03967/0.10172, loss_spatial_dice_6: 0.20089/0.23365, loss_spatial_ce_6: 0.05566/0.13090, loss_grounding_bce_6: 0.03584/0.08843, loss_grounding_dice_6: 0.16203/0.18342, loss_grounding_ce_6: 0.04887/0.30917, loss_mask_ce_7: 1.21696/1.01883, loss_mask_bce_7: 0.40984/0.35180, loss_mask_dice_7: 4.39360/1.25666, loss_spatial_bce_7: 0.04671/0.10969, loss_spatial_dice_7: 0.21780/0.26130, loss_spatial_ce_7: 0.11246/0.16610, loss_grounding_bce_7: 0.03556/0.09030, loss_grounding_dice_7: 0.15130/0.19072, loss_grounding_ce_7: 0.06744/0.33966, loss_mask_ce_8: 1.51042/1.12736, loss_mask_bce_8: 0.42849/0.36546, loss_mask_dice_8: 4.88117/1.32958, loss_spatial_bce_8: 0.07565/0.13016, loss_spatial_dice_8: 0.26289/0.29917, loss_spatial_ce_8: 0.06579/0.22069, loss_grounding_bce_8: 0.03836/0.09405, loss_grounding_dice_8: 0.17653/0.20150, loss_grounding_ce_8: 0.10328/0.40620, loss_mask_ce_9: 5.80559/3.67527, loss_mask_bce_9: 0.51696/0.39252, loss_mask_dice_9: 7.24661/1.90223, loss_spatial_bce_9: 0.19781/0.33276, loss_spatial_dice_9: 0.91375/0.82171, loss_spatial_ce_9: 1.78411/1.49501, loss_grounding_bce_9: 0.04299/0.10564, loss_grounding_dice_9: 0.30227/0.28085, loss_grounding_ce_9: 1.33719/0.67079] items per batch[64] items per second[0.23] total items[4588800] mini batches[ 71700] memory[7345] epoch remaining[1:03:08]
INFO:trainer.default_trainer:epochs[    39] optim steps[71800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64754/0.89717, loss_mask_bce_0: 0.55257/0.33410, loss_mask_dice_0: 0.88305/1.16268, loss_spatial_bce_0: 0.07533/0.08694, loss_spatial_dice_0: 0.16312/0.20752, loss_spatial_ce_0: 0.03016/0.06092, loss_grounding_bce_0: 0.16408/0.08614, loss_grounding_dice_0: 0.23640/0.17845, loss_grounding_ce_0: 0.01311/0.27178, loss_mask_ce_1: 0.67613/0.89773, loss_mask_bce_1: 0.55680/0.33504, loss_mask_dice_1: 1.02079/1.16937, loss_spatial_bce_1: 0.07626/0.08748, loss_spatial_dice_1: 0.17736/0.21151, loss_spatial_ce_1: 0.05808/0.06678, loss_grounding_bce_1: 0.17052/0.08633, loss_grounding_dice_1: 0.24986/0.17928, loss_grounding_ce_1: 0.01178/0.27257, loss_mask_ce_2: 0.60189/0.90477, loss_mask_bce_2: 0.55492/0.33561, loss_mask_dice_2: 0.94845/1.16979, loss_spatial_bce_2: 0.07800/0.08864, loss_spatial_dice_2: 0.18913/0.21321, loss_spatial_ce_2: 0.02651/0.07024, loss_grounding_bce_2: 0.17060/0.08649, loss_grounding_dice_2: 0.24820/0.17914, loss_grounding_ce_2: 0.00976/0.27597, loss_mask_ce_3: 0.69027/0.91578, loss_mask_bce_3: 0.54703/0.33682, loss_mask_dice_3: 1.00051/1.16765, loss_spatial_bce_3: 0.07588/0.08989, loss_spatial_dice_3: 0.18526/0.21422, loss_spatial_ce_3: 0.01879/0.07506, loss_grounding_bce_3: 0.16243/0.08675, loss_grounding_dice_3: 0.24499/0.17886, loss_grounding_ce_3: 0.01250/0.27821, loss_mask_ce_4: 0.65615/0.91675, loss_mask_bce_4: 0.55020/0.33896, loss_mask_dice_4: 0.91303/1.19140, loss_spatial_bce_4: 0.07765/0.09381, loss_spatial_dice_4: 0.20501/0.22646, loss_spatial_ce_4: 0.08664/0.09129, loss_grounding_bce_4: 0.16525/0.08727, loss_grounding_dice_4: 0.23983/0.18174, loss_grounding_ce_4: 0.00798/0.28130, loss_mask_ce_5: 0.61953/0.93348, loss_mask_bce_5: 0.54677/0.34128, loss_mask_dice_5: 0.91910/1.19937, loss_spatial_bce_5: 0.08010/0.09607, loss_spatial_dice_5: 0.21716/0.23074, loss_spatial_ce_5: 0.14210/0.10532, loss_grounding_bce_5: 0.16210/0.08769, loss_grounding_dice_5: 0.24080/0.18304, loss_grounding_ce_5: 0.00776/0.29376, loss_mask_ce_6: 0.91113/0.97360, loss_mask_bce_6: 0.53534/0.34400, loss_mask_dice_6: 0.87236/1.20214, loss_spatial_bce_6: 0.07749/0.10173, loss_spatial_dice_6: 0.19495/0.23366, loss_spatial_ce_6: 0.09094/0.13089, loss_grounding_bce_6: 0.17042/0.08843, loss_grounding_dice_6: 0.24407/0.18341, loss_grounding_ce_6: 0.01320/0.30922, loss_mask_ce_7: 0.64795/1.01879, loss_mask_bce_7: 0.56673/0.35181, loss_mask_dice_7: 1.03343/1.25669, loss_spatial_bce_7: 0.08366/0.10969, loss_spatial_dice_7: 0.22902/0.26129, loss_spatial_ce_7: 0.11763/0.16611, loss_grounding_bce_7: 0.15965/0.09031, loss_grounding_dice_7: 0.23517/0.19071, loss_grounding_ce_7: 0.00904/0.33973, loss_mask_ce_8: 0.66921/1.12730, loss_mask_bce_8: 0.57271/0.36547, loss_mask_dice_8: 1.11916/1.32959, loss_spatial_bce_8: 0.09532/0.13017, loss_spatial_dice_8: 0.27068/0.29917, loss_spatial_ce_8: 0.22670/0.22062, loss_grounding_bce_8: 0.16768/0.09405, loss_grounding_dice_8: 0.25053/0.20150, loss_grounding_ce_8: 0.01187/0.40623, loss_mask_ce_9: 2.45955/3.67513, loss_mask_bce_9: 0.53922/0.39253, loss_mask_dice_9: 1.23507/1.90223, loss_spatial_bce_9: 0.30277/0.33277, loss_spatial_dice_9: 0.86912/0.82172, loss_spatial_ce_9: 1.57415/1.49501, loss_grounding_bce_9: 0.15577/0.10564, loss_grounding_dice_9: 0.29226/0.28085, loss_grounding_ce_9: 0.02030/0.67072] items per batch[64] items per second[0.23] total items[4595200] mini batches[ 71800] memory[7345] epoch remaining[0:58:34]
INFO:trainer.default_trainer:epochs[    39] optim steps[71900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38623/0.89716, loss_mask_bce_0: 0.26287/0.33411, loss_mask_dice_0: 0.36912/1.16286, loss_spatial_bce_0: 0.06040/0.08693, loss_spatial_dice_0: 0.13046/0.20751, loss_spatial_ce_0: 0.00065/0.06090, loss_grounding_bce_0: 0.04459/0.08615, loss_grounding_dice_0: 0.09154/0.17844, loss_grounding_ce_0: 0.14794/0.27179, loss_mask_ce_1: 0.41651/0.89771, loss_mask_bce_1: 0.25855/0.33505, loss_mask_dice_1: 0.40828/1.16956, loss_spatial_bce_1: 0.05767/0.08747, loss_spatial_dice_1: 0.11746/0.21150, loss_spatial_ce_1: 0.00156/0.06676, loss_grounding_bce_1: 0.04873/0.08634, loss_grounding_dice_1: 0.09898/0.17927, loss_grounding_ce_1: 0.08993/0.27252, loss_mask_ce_2: 0.40225/0.90475, loss_mask_bce_2: 0.25885/0.33562, loss_mask_dice_2: 0.42420/1.16998, loss_spatial_bce_2: 0.06239/0.08863, loss_spatial_dice_2: 0.12766/0.21320, loss_spatial_ce_2: 0.00085/0.07022, loss_grounding_bce_2: 0.04623/0.08650, loss_grounding_dice_2: 0.09294/0.17913, loss_grounding_ce_2: 0.09115/0.27593, loss_mask_ce_3: 0.42245/0.91576, loss_mask_bce_3: 0.25407/0.33683, loss_mask_dice_3: 0.38924/1.16781, loss_spatial_bce_3: 0.05579/0.08988, loss_spatial_dice_3: 0.11947/0.21421, loss_spatial_ce_3: 0.00576/0.07503, loss_grounding_bce_3: 0.04601/0.08676, loss_grounding_dice_3: 0.09406/0.17886, loss_grounding_ce_3: 0.10280/0.27816, loss_mask_ce_4: 0.53200/0.91672, loss_mask_bce_4: 0.26707/0.33897, loss_mask_dice_4: 0.43924/1.19159, loss_spatial_bce_4: 0.06944/0.09379, loss_spatial_dice_4: 0.13074/0.22646, loss_spatial_ce_4: 0.01223/0.09129, loss_grounding_bce_4: 0.04671/0.08728, loss_grounding_dice_4: 0.09659/0.18174, loss_grounding_ce_4: 0.08818/0.28123, loss_mask_ce_5: 0.52506/0.93344, loss_mask_bce_5: 0.26773/0.34128, loss_mask_dice_5: 0.40740/1.19958, loss_spatial_bce_5: 0.06200/0.09607, loss_spatial_dice_5: 0.12240/0.23074, loss_spatial_ce_5: 0.01135/0.10530, loss_grounding_bce_5: 0.04473/0.08770, loss_grounding_dice_5: 0.09737/0.18303, loss_grounding_ce_5: 0.02837/0.29370, loss_mask_ce_6: 0.52895/0.97356, loss_mask_bce_6: 0.26520/0.34401, loss_mask_dice_6: 0.44573/1.20236, loss_spatial_bce_6: 0.06589/0.10172, loss_spatial_dice_6: 0.11459/0.23366, loss_spatial_ce_6: 0.02209/0.13086, loss_grounding_bce_6: 0.04364/0.08843, loss_grounding_dice_6: 0.09245/0.18341, loss_grounding_ce_6: 0.08449/0.30923, loss_mask_ce_7: 0.69535/1.01876, loss_mask_bce_7: 0.25791/0.35182, loss_mask_dice_7: 0.47594/1.25695, loss_spatial_bce_7: 0.08256/0.10968, loss_spatial_dice_7: 0.13886/0.26129, loss_spatial_ce_7: 0.02798/0.16607, loss_grounding_bce_7: 0.04568/0.09031, loss_grounding_dice_7: 0.10008/0.19071, loss_grounding_ce_7: 0.25421/0.33970, loss_mask_ce_8: 0.48732/1.12735, loss_mask_bce_8: 0.27680/0.36548, loss_mask_dice_8: 0.56226/1.32984, loss_spatial_bce_8: 0.13627/0.13017, loss_spatial_dice_8: 0.21834/0.29917, loss_spatial_ce_8: 0.30366/0.22059, loss_grounding_bce_8: 0.04375/0.09405, loss_grounding_dice_8: 0.10818/0.20150, loss_grounding_ce_8: 0.32848/0.40616, loss_mask_ce_9: 4.02978/3.67526, loss_mask_bce_9: 0.32547/0.39255, loss_mask_dice_9: 0.77808/1.90262, loss_spatial_bce_9: 0.31890/0.33277, loss_spatial_dice_9: 0.77762/0.82174, loss_spatial_ce_9: 1.17382/1.49495, loss_grounding_bce_9: 0.04736/0.10565, loss_grounding_dice_9: 0.15911/0.28085, loss_grounding_ce_9: 0.86651/0.67067] items per batch[64] items per second[0.24] total items[4601600] mini batches[ 71900] memory[7345] epoch remaining[0:53:55]
INFO:trainer.default_trainer:epochs[    39] optim steps[72000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.62135/0.89709, loss_mask_bce_0: 0.63180/0.33411, loss_mask_dice_0: 5.69510/1.16268, loss_spatial_bce_0: 0.06374/0.08693, loss_spatial_dice_0: 0.26877/0.20749, loss_spatial_ce_0: 0.14441/0.06089, loss_grounding_bce_0: 0.15664/0.08615, loss_grounding_dice_0: 0.49833/0.17845, loss_grounding_ce_0: 0.29019/0.27176, loss_mask_ce_1: 1.57531/0.89764, loss_mask_bce_1: 0.63274/0.33504, loss_mask_dice_1: 5.60043/1.16939, loss_spatial_bce_1: 0.06066/0.08746, loss_spatial_dice_1: 0.31001/0.21147, loss_spatial_ce_1: 0.23712/0.06674, loss_grounding_bce_1: 0.15636/0.08634, loss_grounding_dice_1: 0.52264/0.17928, loss_grounding_ce_1: 0.20983/0.27250, loss_mask_ce_2: 1.55075/0.90465, loss_mask_bce_2: 0.64599/0.33562, loss_mask_dice_2: 6.06992/1.16981, loss_spatial_bce_2: 0.05985/0.08862, loss_spatial_dice_2: 0.26382/0.21318, loss_spatial_ce_2: 0.02561/0.07019, loss_grounding_bce_2: 0.16757/0.08651, loss_grounding_dice_2: 0.51824/0.17914, loss_grounding_ce_2: 0.19693/0.27589, loss_mask_ce_3: 1.72581/0.91569, loss_mask_bce_3: 0.63384/0.33683, loss_mask_dice_3: 5.89208/1.16767, loss_spatial_bce_3: 0.07080/0.08987, loss_spatial_dice_3: 0.28487/0.21419, loss_spatial_ce_3: 0.31856/0.07500, loss_grounding_bce_3: 0.15103/0.08676, loss_grounding_dice_3: 0.47764/0.17887, loss_grounding_ce_3: 0.21353/0.27810, loss_mask_ce_4: 1.53942/0.91663, loss_mask_bce_4: 0.65776/0.33897, loss_mask_dice_4: 5.70862/1.19143, loss_spatial_bce_4: 0.06690/0.09379, loss_spatial_dice_4: 0.28500/0.22643, loss_spatial_ce_4: 0.12487/0.09127, loss_grounding_bce_4: 0.15755/0.08729, loss_grounding_dice_4: 0.54642/0.18176, loss_grounding_ce_4: 0.20345/0.28117, loss_mask_ce_5: 1.72728/0.93339, loss_mask_bce_5: 0.61910/0.34128, loss_mask_dice_5: 5.49599/1.19941, loss_spatial_bce_5: 0.06193/0.09606, loss_spatial_dice_5: 0.30496/0.23071, loss_spatial_ce_5: 0.22517/0.10529, loss_grounding_bce_5: 0.15169/0.08770, loss_grounding_dice_5: 0.44976/0.18304, loss_grounding_ce_5: 0.20127/0.29365, loss_mask_ce_6: 1.86579/0.97352, loss_mask_bce_6: 0.63060/0.34400, loss_mask_dice_6: 6.01424/1.20221, loss_spatial_bce_6: 0.08216/0.10171, loss_spatial_dice_6: 0.35698/0.23364, loss_spatial_ce_6: 0.07699/0.13083, loss_grounding_bce_6: 0.15002/0.08844, loss_grounding_dice_6: 0.50981/0.18341, loss_grounding_ce_6: 0.22021/0.30917, loss_mask_ce_7: 1.98320/1.01870, loss_mask_bce_7: 0.64752/0.35181, loss_mask_dice_7: 5.72580/1.25676, loss_spatial_bce_7: 0.11997/0.10968, loss_spatial_dice_7: 0.39532/0.26127, loss_spatial_ce_7: 0.23594/0.16606, loss_grounding_bce_7: 0.16712/0.09031, loss_grounding_dice_7: 0.36925/0.19071, loss_grounding_ce_7: 0.23876/0.33965, loss_mask_ce_8: 2.10417/1.12731, loss_mask_bce_8: 0.67354/0.36547, loss_mask_dice_8: 5.63573/1.32963, loss_spatial_bce_8: 0.15851/0.13017, loss_spatial_dice_8: 0.39279/0.29914, loss_spatial_ce_8: 0.14441/0.22053, loss_grounding_bce_8: 0.14659/0.09405, loss_grounding_dice_8: 0.40423/0.20150, loss_grounding_ce_8: 0.40728/0.40611, loss_mask_ce_9: 6.43748/3.67518, loss_mask_bce_9: 0.60074/0.39253, loss_mask_dice_9: 7.92228/1.90233, loss_spatial_bce_9: 0.14471/0.33279, loss_spatial_dice_9: 0.94797/0.82173, loss_spatial_ce_9: 1.35757/1.49492, loss_grounding_bce_9: 0.12170/0.10564, loss_grounding_dice_9: 0.57178/0.28086, loss_grounding_ce_9: 1.39516/0.67065] items per batch[64] items per second[0.23] total items[4608000] mini batches[ 72000] memory[7345] epoch remaining[0:49:35]
INFO:trainer.default_trainer:epochs[    39] optim steps[72100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75049/0.89697, loss_mask_bce_0: 0.36070/0.33409, loss_mask_dice_0: 1.01253/1.16277, loss_spatial_bce_0: 0.06492/0.08692, loss_spatial_dice_0: 0.17417/0.20747, loss_spatial_ce_0: 0.02086/0.06088, loss_grounding_bce_0: 0.02867/0.08614, loss_grounding_dice_0: 0.34162/0.17844, loss_grounding_ce_0: 0.03738/0.27170, loss_mask_ce_1: 0.87900/0.89751, loss_mask_bce_1: 0.33297/0.33503, loss_mask_dice_1: 0.94756/1.16948, loss_spatial_bce_1: 0.06525/0.08745, loss_spatial_dice_1: 0.17437/0.21147, loss_spatial_ce_1: 0.03834/0.06673, loss_grounding_bce_1: 0.03090/0.08633, loss_grounding_dice_1: 0.40930/0.17927, loss_grounding_ce_1: 0.03016/0.27246, loss_mask_ce_2: 0.88340/0.90452, loss_mask_bce_2: 0.35460/0.33560, loss_mask_dice_2: 0.96637/1.16990, loss_spatial_bce_2: 0.06779/0.08861, loss_spatial_dice_2: 0.20092/0.21317, loss_spatial_ce_2: 0.05127/0.07018, loss_grounding_bce_2: 0.02752/0.08650, loss_grounding_dice_2: 0.37912/0.17912, loss_grounding_ce_2: 0.02797/0.27583, loss_mask_ce_3: 0.93904/0.91556, loss_mask_bce_3: 0.35643/0.33681, loss_mask_dice_3: 0.97800/1.16775, loss_spatial_bce_3: 0.07281/0.08986, loss_spatial_dice_3: 0.20280/0.21418, loss_spatial_ce_3: 0.05744/0.07501, loss_grounding_bce_3: 0.02888/0.08675, loss_grounding_dice_3: 0.36670/0.17886, loss_grounding_ce_3: 0.04944/0.27807, loss_mask_ce_4: 0.94046/0.91653, loss_mask_bce_4: 0.36345/0.33895, loss_mask_dice_4: 0.94222/1.19152, loss_spatial_bce_4: 0.07596/0.09378, loss_spatial_dice_4: 0.19791/0.22643, loss_spatial_ce_4: 0.06198/0.09127, loss_grounding_bce_4: 0.02791/0.08728, loss_grounding_dice_4: 0.34300/0.18175, loss_grounding_ce_4: 0.04373/0.28109, loss_mask_ce_5: 0.95034/0.93326, loss_mask_bce_5: 0.35775/0.34126, loss_mask_dice_5: 0.97104/1.19950, loss_spatial_bce_5: 0.06763/0.09605, loss_spatial_dice_5: 0.21653/0.23071, loss_spatial_ce_5: 0.09989/0.10528, loss_grounding_bce_5: 0.02421/0.08769, loss_grounding_dice_5: 0.34605/0.18303, loss_grounding_ce_5: 0.04395/0.29361, loss_mask_ce_6: 1.03266/0.97339, loss_mask_bce_6: 0.33857/0.34398, loss_mask_dice_6: 0.79724/1.20229, loss_spatial_bce_6: 0.07304/0.10170, loss_spatial_dice_6: 0.20878/0.23363, loss_spatial_ce_6: 0.09738/0.13083, loss_grounding_bce_6: 0.03004/0.08842, loss_grounding_dice_6: 0.31010/0.18340, loss_grounding_ce_6: 0.08891/0.30912, loss_mask_ce_7: 1.14353/1.01863, loss_mask_bce_7: 0.33781/0.35180, loss_mask_dice_7: 0.99766/1.25684, loss_spatial_bce_7: 0.07609/0.10966, loss_spatial_dice_7: 0.23878/0.26126, loss_spatial_ce_7: 0.16311/0.16606, loss_grounding_bce_7: 0.02987/0.09030, loss_grounding_dice_7: 0.38748/0.19070, loss_grounding_ce_7: 0.05763/0.33958, loss_mask_ce_8: 1.70124/1.12723, loss_mask_bce_8: 0.35608/0.36546, loss_mask_dice_8: 0.95850/1.32972, loss_spatial_bce_8: 0.09256/0.13015, loss_spatial_dice_8: 0.30682/0.29914, loss_spatial_ce_8: 0.15570/0.22047, loss_grounding_bce_8: 0.02823/0.09404, loss_grounding_dice_8: 0.36785/0.20149, loss_grounding_ce_8: 0.21229/0.40603, loss_mask_ce_9: 2.92468/3.67513, loss_mask_bce_9: 0.38704/0.39249, loss_mask_dice_9: 1.31212/1.90232, loss_spatial_bce_9: 0.25196/0.33276, loss_spatial_dice_9: 0.76748/0.82172, loss_spatial_ce_9: 1.61427/1.49492, loss_grounding_bce_9: 0.04115/0.10563, loss_grounding_dice_9: 0.48220/0.28084, loss_grounding_ce_9: 1.94790/0.67070] items per batch[64] items per second[0.23] total items[4614400] mini batches[ 72100] memory[7345] epoch remaining[0:45:05]
INFO:trainer.default_trainer:epochs[    39] optim steps[72200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35395/0.89690, loss_mask_bce_0: 0.27029/0.33413, loss_mask_dice_0: 0.17859/1.16270, loss_spatial_bce_0: 0.17338/0.08692, loss_spatial_dice_0: 0.10434/0.20746, loss_spatial_ce_0: 0.01248/0.06085, loss_grounding_bce_0: 0.15912/0.08615, loss_grounding_dice_0: 0.10689/0.17843, loss_grounding_ce_0: 0.37740/0.27178, loss_mask_ce_1: 0.33514/0.89743, loss_mask_bce_1: 0.27281/0.33508, loss_mask_dice_1: 0.18032/1.16941, loss_spatial_bce_1: 0.17838/0.08745, loss_spatial_dice_1: 0.10692/0.21145, loss_spatial_ce_1: 0.01436/0.06670, loss_grounding_bce_1: 0.17628/0.08634, loss_grounding_dice_1: 0.11367/0.17925, loss_grounding_ce_1: 0.23546/0.27253, loss_mask_ce_2: 0.33953/0.90443, loss_mask_bce_2: 0.28682/0.33565, loss_mask_dice_2: 0.18821/1.16984, loss_spatial_bce_2: 0.17856/0.08862, loss_spatial_dice_2: 0.10504/0.21316, loss_spatial_ce_2: 0.01999/0.07015, loss_grounding_bce_2: 0.17400/0.08651, loss_grounding_dice_2: 0.11620/0.17911, loss_grounding_ce_2: 0.16145/0.27588, loss_mask_ce_3: 0.33020/0.91547, loss_mask_bce_3: 0.28283/0.33687, loss_mask_dice_3: 0.18850/1.16769, loss_spatial_bce_3: 0.17833/0.08987, loss_spatial_dice_3: 0.10694/0.21417, loss_spatial_ce_3: 0.01279/0.07499, loss_grounding_bce_3: 0.16939/0.08676, loss_grounding_dice_3: 0.12357/0.17885, loss_grounding_ce_3: 0.32722/0.27814, loss_mask_ce_4: 0.33607/0.91646, loss_mask_bce_4: 0.29502/0.33901, loss_mask_dice_4: 0.19486/1.19146, loss_spatial_bce_4: 0.17351/0.09378, loss_spatial_dice_4: 0.10670/0.22641, loss_spatial_ce_4: 0.02151/0.09125, loss_grounding_bce_4: 0.15941/0.08729, loss_grounding_dice_4: 0.11653/0.18174, loss_grounding_ce_4: 0.11254/0.28114, loss_mask_ce_5: 0.34188/0.93319, loss_mask_bce_5: 0.28985/0.34131, loss_mask_dice_5: 0.18806/1.19944, loss_spatial_bce_5: 0.15639/0.09606, loss_spatial_dice_5: 0.10584/0.23069, loss_spatial_ce_5: 0.02076/0.10526, loss_grounding_bce_5: 0.15638/0.08770, loss_grounding_dice_5: 0.10485/0.18302, loss_grounding_ce_5: 0.17017/0.29366, loss_mask_ce_6: 0.33683/0.97333, loss_mask_bce_6: 0.28600/0.34403, loss_mask_dice_6: 0.18858/1.20223, loss_spatial_bce_6: 0.16496/0.10170, loss_spatial_dice_6: 0.09988/0.23361, loss_spatial_ce_6: 0.02871/0.13080, loss_grounding_bce_6: 0.14963/0.08843, loss_grounding_dice_6: 0.09906/0.18339, loss_grounding_ce_6: 0.74337/0.30922, loss_mask_ce_7: 0.30900/1.01858, loss_mask_bce_7: 0.31702/0.35185, loss_mask_dice_7: 0.19984/1.25680, loss_spatial_bce_7: 0.16811/0.10967, loss_spatial_dice_7: 0.10154/0.26125, loss_spatial_ce_7: 0.06446/0.16603, loss_grounding_bce_7: 0.15934/0.09031, loss_grounding_dice_7: 0.11237/0.19069, loss_grounding_ce_7: 0.12818/0.33962, loss_mask_ce_8: 0.44807/1.12717, loss_mask_bce_8: 0.29705/0.36551, loss_mask_dice_8: 0.20992/1.32966, loss_spatial_bce_8: 0.25340/0.13015, loss_spatial_dice_8: 0.16212/0.29912, loss_spatial_ce_8: 0.17219/0.22041, loss_grounding_bce_8: 0.15092/0.09404, loss_grounding_dice_8: 0.11286/0.20147, loss_grounding_ce_8: 0.04332/0.40612, loss_mask_ce_9: 1.94922/3.67520, loss_mask_bce_9: 0.38923/0.39256, loss_mask_dice_9: 0.27537/1.90226, loss_spatial_bce_9: 0.91880/0.33277, loss_spatial_dice_9: 0.73491/0.82173, loss_spatial_ce_9: 1.32491/1.49491, loss_grounding_bce_9: 0.18596/0.10564, loss_grounding_dice_9: 0.12206/0.28082, loss_grounding_ce_9: 0.61564/0.67069] items per batch[64] items per second[0.23] total items[4620800] mini batches[ 72200] memory[7345] epoch remaining[0:40:34]
INFO:trainer.default_trainer:epochs[    39] optim steps[72300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43730/0.89689, loss_mask_bce_0: 0.61981/0.33411, loss_mask_dice_0: 0.61180/1.16254, loss_spatial_bce_0: 0.18724/0.08693, loss_spatial_dice_0: 0.19015/0.20744, loss_spatial_ce_0: 0.02625/0.06084, loss_grounding_bce_0: 0.31937/0.08616, loss_grounding_dice_0: 0.17211/0.17843, loss_grounding_ce_0: 0.00743/0.27173, loss_mask_ce_1: 0.83219/0.89743, loss_mask_bce_1: 0.55441/0.33505, loss_mask_dice_1: 0.64476/1.16928, loss_spatial_bce_1: 0.19181/0.08746, loss_spatial_dice_1: 0.19234/0.21143, loss_spatial_ce_1: 0.02962/0.06669, loss_grounding_bce_1: 0.31986/0.08634, loss_grounding_dice_1: 0.17237/0.17925, loss_grounding_ce_1: 0.01109/0.27249, loss_mask_ce_2: 0.86570/0.90444, loss_mask_bce_2: 0.55453/0.33564, loss_mask_dice_2: 0.65688/1.16969, loss_spatial_bce_2: 0.19433/0.08862, loss_spatial_dice_2: 0.18376/0.21314, loss_spatial_ce_2: 0.02802/0.07015, loss_grounding_bce_2: 0.31077/0.08652, loss_grounding_dice_2: 0.17397/0.17911, loss_grounding_ce_2: 0.01044/0.27585, loss_mask_ce_3: 0.48599/0.91547, loss_mask_bce_3: 0.60547/0.33685, loss_mask_dice_3: 0.63242/1.16755, loss_spatial_bce_3: 0.21040/0.08987, loss_spatial_dice_3: 0.19785/0.21415, loss_spatial_ce_3: 0.02797/0.07498, loss_grounding_bce_3: 0.32618/0.08677, loss_grounding_dice_3: 0.17465/0.17885, loss_grounding_ce_3: 0.00939/0.27811, loss_mask_ce_4: 0.89795/0.91647, loss_mask_bce_4: 0.55818/0.33898, loss_mask_dice_4: 0.69792/1.19132, loss_spatial_bce_4: 0.22073/0.09379, loss_spatial_dice_4: 0.21111/0.22639, loss_spatial_ce_4: 0.04406/0.09124, loss_grounding_bce_4: 0.33856/0.08729, loss_grounding_dice_4: 0.17617/0.18175, loss_grounding_ce_4: 0.01087/0.28111, loss_mask_ce_5: 1.24109/0.93323, loss_mask_bce_5: 0.55963/0.34129, loss_mask_dice_5: 0.61066/1.19930, loss_spatial_bce_5: 0.23237/0.09606, loss_spatial_dice_5: 0.21702/0.23067, loss_spatial_ce_5: 0.04298/0.10526, loss_grounding_bce_5: 0.32563/0.08771, loss_grounding_dice_5: 0.16902/0.18302, loss_grounding_ce_5: 0.01148/0.29364, loss_mask_ce_6: 0.95125/0.97335, loss_mask_bce_6: 0.60635/0.34401, loss_mask_dice_6: 0.61543/1.20208, loss_spatial_bce_6: 0.24017/0.10171, loss_spatial_dice_6: 0.21354/0.23360, loss_spatial_ce_6: 0.06781/0.13080, loss_grounding_bce_6: 0.32147/0.08844, loss_grounding_dice_6: 0.16683/0.18339, loss_grounding_ce_6: 0.01655/0.30918, loss_mask_ce_7: 0.91802/1.01861, loss_mask_bce_7: 0.61915/0.35185, loss_mask_dice_7: 0.58437/1.25665, loss_spatial_bce_7: 0.21978/0.10967, loss_spatial_dice_7: 0.23878/0.26124, loss_spatial_ce_7: 0.12725/0.16602, loss_grounding_bce_7: 0.32730/0.09032, loss_grounding_dice_7: 0.17146/0.19069, loss_grounding_ce_7: 0.02142/0.33957, loss_mask_ce_8: 0.66913/1.12720, loss_mask_bce_8: 0.58154/0.36550, loss_mask_dice_8: 0.58512/1.32952, loss_spatial_bce_8: 0.21814/0.13016, loss_spatial_dice_8: 0.23467/0.29909, loss_spatial_ce_8: 0.22437/0.22035, loss_grounding_bce_8: 0.31612/0.09404, loss_grounding_dice_8: 0.15795/0.20148, loss_grounding_ce_8: 0.02277/0.40606, loss_mask_ce_9: 2.38679/3.67503, loss_mask_bce_9: 0.52950/0.39253, loss_mask_dice_9: 0.66236/1.90204, loss_spatial_bce_9: 0.62911/0.33279, loss_spatial_dice_9: 0.74914/0.82172, loss_spatial_ce_9: 1.36400/1.49486, loss_grounding_bce_9: 0.29377/0.10564, loss_grounding_dice_9: 0.19197/0.28081, loss_grounding_ce_9: 0.28131/0.67059] items per batch[64] items per second[0.23] total items[4627200] mini batches[ 72300] memory[7345] epoch remaining[0:35:56]
INFO:trainer.default_trainer:epochs[    39] optim steps[72400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45218/0.89701, loss_mask_bce_0: 0.18304/0.33415, loss_mask_dice_0: 0.65665/1.16250, loss_spatial_bce_0: 0.04453/0.08693, loss_spatial_dice_0: 0.17375/0.20743, loss_spatial_ce_0: 0.06568/0.06083, loss_grounding_bce_0: 0.05458/0.08616, loss_grounding_dice_0: 0.24458/0.17842, loss_grounding_ce_0: 1.27674/0.27179, loss_mask_ce_1: 0.36632/0.89754, loss_mask_bce_1: 0.17619/0.33509, loss_mask_dice_1: 0.68579/1.16924, loss_spatial_bce_1: 0.05728/0.08746, loss_spatial_dice_1: 0.18468/0.21143, loss_spatial_ce_1: 0.05613/0.06668, loss_grounding_bce_1: 0.06170/0.08635, loss_grounding_dice_1: 0.24992/0.17924, loss_grounding_ce_1: 0.70974/0.27253, loss_mask_ce_2: 0.45161/0.90457, loss_mask_bce_2: 0.18230/0.33568, loss_mask_dice_2: 0.66865/1.16964, loss_spatial_bce_2: 0.06791/0.08863, loss_spatial_dice_2: 0.18580/0.21314, loss_spatial_ce_2: 0.06043/0.07013, loss_grounding_bce_2: 0.06103/0.08652, loss_grounding_dice_2: 0.25137/0.17911, loss_grounding_ce_2: 0.67203/0.27588, loss_mask_ce_3: 0.47713/0.91561, loss_mask_bce_3: 0.17958/0.33688, loss_mask_dice_3: 0.63533/1.16751, loss_spatial_bce_3: 0.07789/0.08987, loss_spatial_dice_3: 0.18234/0.21415, loss_spatial_ce_3: 0.07209/0.07497, loss_grounding_bce_3: 0.05507/0.08677, loss_grounding_dice_3: 0.24984/0.17884, loss_grounding_ce_3: 0.68310/0.27814, loss_mask_ce_4: 0.42380/0.91661, loss_mask_bce_4: 0.18134/0.33901, loss_mask_dice_4: 0.66485/1.19131, loss_spatial_bce_4: 0.08129/0.09379, loss_spatial_dice_4: 0.19107/0.22639, loss_spatial_ce_4: 0.10010/0.09124, loss_grounding_bce_4: 0.06273/0.08730, loss_grounding_dice_4: 0.25070/0.18174, loss_grounding_ce_4: 0.63051/0.28113, loss_mask_ce_5: 0.45594/0.93337, loss_mask_bce_5: 0.18975/0.34132, loss_mask_dice_5: 0.69598/1.19931, loss_spatial_bce_5: 0.09721/0.09607, loss_spatial_dice_5: 0.19201/0.23067, loss_spatial_ce_5: 0.10364/0.10526, loss_grounding_bce_5: 0.06320/0.08771, loss_grounding_dice_5: 0.25485/0.18301, loss_grounding_ce_5: 0.64408/0.29366, loss_mask_ce_6: 0.56483/0.97349, loss_mask_bce_6: 0.19534/0.34405, loss_mask_dice_6: 0.66207/1.20206, loss_spatial_bce_6: 0.09726/0.10172, loss_spatial_dice_6: 0.18994/0.23361, loss_spatial_ce_6: 0.12398/0.13079, loss_grounding_bce_6: 0.06734/0.08844, loss_grounding_dice_6: 0.24265/0.18337, loss_grounding_ce_6: 1.66033/0.30922, loss_mask_ce_7: 0.61789/1.01873, loss_mask_bce_7: 0.21030/0.35189, loss_mask_dice_7: 0.70352/1.25664, loss_spatial_bce_7: 0.12255/0.10968, loss_spatial_dice_7: 0.24073/0.26123, loss_spatial_ce_7: 0.21437/0.16601, loss_grounding_bce_7: 0.07311/0.09033, loss_grounding_dice_7: 0.24490/0.19069, loss_grounding_ce_7: 1.74362/0.33961, loss_mask_ce_8: 0.87676/1.12739, loss_mask_bce_8: 0.20853/0.36553, loss_mask_dice_8: 0.81891/1.32948, loss_spatial_bce_8: 0.06842/0.13016, loss_spatial_dice_8: 0.23990/0.29909, loss_spatial_ce_8: 0.27506/0.22032, loss_grounding_bce_8: 0.07182/0.09405, loss_grounding_dice_8: 0.24006/0.20147, loss_grounding_ce_8: 1.56099/0.40610, loss_mask_ce_9: 5.19608/3.67536, loss_mask_bce_9: 0.14473/0.39257, loss_mask_dice_9: 0.89269/1.90204, loss_spatial_bce_9: 0.24287/0.33279, loss_spatial_dice_9: 0.86267/0.82173, loss_spatial_ce_9: 2.02993/1.49487, loss_grounding_bce_9: 0.03648/0.10565, loss_grounding_dice_9: 0.22488/0.28081, loss_grounding_ce_9: 1.38933/0.67064] items per batch[64] items per second[0.23] total items[4633600] mini batches[ 72400] memory[7345] epoch remaining[0:31:20]
INFO:trainer.default_trainer:epochs[    39] optim steps[72500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47567/0.89689, loss_mask_bce_0: 0.19928/0.33415, loss_mask_dice_0: 5.04311/1.16252, loss_spatial_bce_0: 0.06289/0.08692, loss_spatial_dice_0: 0.40500/0.20741, loss_spatial_ce_0: 0.03089/0.06081, loss_grounding_bce_0: 0.05142/0.08617, loss_grounding_dice_0: 0.34468/0.17843, loss_grounding_ce_0: 0.60682/0.27179, loss_mask_ce_1: 0.46454/0.89743, loss_mask_bce_1: 0.20727/0.33510, loss_mask_dice_1: 5.30389/1.16924, loss_spatial_bce_1: 0.06073/0.08746, loss_spatial_dice_1: 0.44193/0.21141, loss_spatial_ce_1: 0.07351/0.06667, loss_grounding_bce_1: 0.05002/0.08635, loss_grounding_dice_1: 0.46419/0.17925, loss_grounding_ce_1: 0.60676/0.27254, loss_mask_ce_2: 0.47599/0.90448, loss_mask_bce_2: 0.19999/0.33568, loss_mask_dice_2: 4.85495/1.16961, loss_spatial_bce_2: 0.06035/0.08863, loss_spatial_dice_2: 0.39114/0.21312, loss_spatial_ce_2: 0.11407/0.07012, loss_grounding_bce_2: 0.04981/0.08652, loss_grounding_dice_2: 0.37184/0.17912, loss_grounding_ce_2: 0.60801/0.27588, loss_mask_ce_3: 0.58322/0.91550, loss_mask_bce_3: 0.20693/0.33689, loss_mask_dice_3: 5.02665/1.16750, loss_spatial_bce_3: 0.07802/0.08987, loss_spatial_dice_3: 0.41680/0.21413, loss_spatial_ce_3: 0.18946/0.07497, loss_grounding_bce_3: 0.05304/0.08677, loss_grounding_dice_3: 0.29975/0.17884, loss_grounding_ce_3: 0.59283/0.27815, loss_mask_ce_4: 0.72414/0.91650, loss_mask_bce_4: 0.19261/0.33902, loss_mask_dice_4: 4.75182/1.19129, loss_spatial_bce_4: 0.10648/0.09379, loss_spatial_dice_4: 0.40726/0.22637, loss_spatial_ce_4: 0.29467/0.09123, loss_grounding_bce_4: 0.04896/0.08730, loss_grounding_dice_4: 0.38230/0.18175, loss_grounding_ce_4: 0.61843/0.28113, loss_mask_ce_5: 0.67165/0.93328, loss_mask_bce_5: 0.21943/0.34132, loss_mask_dice_5: 4.82089/1.19927, loss_spatial_bce_5: 0.10463/0.09607, loss_spatial_dice_5: 0.49020/0.23065, loss_spatial_ce_5: 0.19399/0.10525, loss_grounding_bce_5: 0.05414/0.08772, loss_grounding_dice_5: 0.31723/0.18302, loss_grounding_ce_5: 0.56970/0.29367, loss_mask_ce_6: 0.86805/0.97342, loss_mask_bce_6: 0.19661/0.34405, loss_mask_dice_6: 4.86095/1.20202, loss_spatial_bce_6: 0.09918/0.10172, loss_spatial_dice_6: 0.45535/0.23359, loss_spatial_ce_6: 0.10627/0.13077, loss_grounding_bce_6: 0.05089/0.08845, loss_grounding_dice_6: 0.30810/0.18338, loss_grounding_ce_6: 0.61504/0.30923, loss_mask_ce_7: 0.64820/1.01865, loss_mask_bce_7: 0.39999/0.35190, loss_mask_dice_7: 4.88278/1.25663, loss_spatial_bce_7: 0.11011/0.10967, loss_spatial_dice_7: 0.42116/0.26121, loss_spatial_ce_7: 0.26138/0.16599, loss_grounding_bce_7: 0.04683/0.09033, loss_grounding_dice_7: 0.28543/0.19070, loss_grounding_ce_7: 0.51028/0.33956, loss_mask_ce_8: 0.65789/1.12725, loss_mask_bce_8: 0.32452/0.36554, loss_mask_dice_8: 4.52283/1.32944, loss_spatial_bce_8: 0.16534/0.13016, loss_spatial_dice_8: 0.54417/0.29906, loss_spatial_ce_8: 0.74620/0.22028, loss_grounding_bce_8: 0.09014/0.09405, loss_grounding_dice_8: 0.39598/0.20147, loss_grounding_ce_8: 0.39192/0.40610, loss_mask_ce_9: 4.91327/3.67514, loss_mask_bce_9: 0.34114/0.39257, loss_mask_dice_9: 6.40241/1.90191, loss_spatial_bce_9: 0.13199/0.33277, loss_spatial_dice_9: 0.78630/0.82171, loss_spatial_ce_9: 1.63606/1.49481, loss_grounding_bce_9: 0.09675/0.10565, loss_grounding_dice_9: 0.46978/0.28080, loss_grounding_ce_9: 0.43483/0.67065] items per batch[64] items per second[0.22] total items[4640000] mini batches[ 72500] memory[7345] epoch remaining[0:26:49]
INFO:trainer.default_trainer:epochs[    39] optim steps[72600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56988/0.89691, loss_mask_bce_0: 0.30328/0.33414, loss_mask_dice_0: 0.51953/1.16254, loss_spatial_bce_0: 0.10574/0.08691, loss_spatial_dice_0: 0.19436/0.20741, loss_spatial_ce_0: 0.09037/0.06080, loss_grounding_bce_0: 0.14758/0.08616, loss_grounding_dice_0: 0.19223/0.17843, loss_grounding_ce_0: 0.08672/0.27179, loss_mask_ce_1: 0.54043/0.89745, loss_mask_bce_1: 0.30629/0.33508, loss_mask_dice_1: 0.51778/1.16926, loss_spatial_bce_1: 0.10857/0.08745, loss_spatial_dice_1: 0.18330/0.21141, loss_spatial_ce_1: 0.06642/0.06664, loss_grounding_bce_1: 0.16029/0.08634, loss_grounding_dice_1: 0.20077/0.17925, loss_grounding_ce_1: 0.09028/0.27257, loss_mask_ce_2: 0.54375/0.90449, loss_mask_bce_2: 0.31473/0.33567, loss_mask_dice_2: 0.51547/1.16963, loss_spatial_bce_2: 0.11668/0.08862, loss_spatial_dice_2: 0.18152/0.21312, loss_spatial_ce_2: 0.03847/0.07011, loss_grounding_bce_2: 0.15451/0.08652, loss_grounding_dice_2: 0.19506/0.17913, loss_grounding_ce_2: 0.10108/0.27589, loss_mask_ce_3: 0.54836/0.91550, loss_mask_bce_3: 0.31135/0.33688, loss_mask_dice_3: 0.50772/1.16752, loss_spatial_bce_3: 0.12208/0.08986, loss_spatial_dice_3: 0.18080/0.21413, loss_spatial_ce_3: 0.04109/0.07497, loss_grounding_bce_3: 0.15244/0.08676, loss_grounding_dice_3: 0.19666/0.17885, loss_grounding_ce_3: 0.11560/0.27816, loss_mask_ce_4: 0.55985/0.91652, loss_mask_bce_4: 0.30213/0.33902, loss_mask_dice_4: 0.50246/1.19132, loss_spatial_bce_4: 0.12460/0.09378, loss_spatial_dice_4: 0.18373/0.22637, loss_spatial_ce_4: 0.01289/0.09122, loss_grounding_bce_4: 0.14823/0.08729, loss_grounding_dice_4: 0.19163/0.18176, loss_grounding_ce_4: 0.08919/0.28113, loss_mask_ce_5: 0.47750/0.93331, loss_mask_bce_5: 0.32213/0.34132, loss_mask_dice_5: 0.53300/1.19930, loss_spatial_bce_5: 0.12030/0.09606, loss_spatial_dice_5: 0.21062/0.23065, loss_spatial_ce_5: 0.07308/0.10524, loss_grounding_bce_5: 0.15172/0.08770, loss_grounding_dice_5: 0.20172/0.18303, loss_grounding_ce_5: 0.05160/0.29367, loss_mask_ce_6: 0.49432/0.97345, loss_mask_bce_6: 0.31213/0.34404, loss_mask_dice_6: 0.51590/1.20205, loss_spatial_bce_6: 0.14290/0.10172, loss_spatial_dice_6: 0.19167/0.23359, loss_spatial_ce_6: 0.04896/0.13076, loss_grounding_bce_6: 0.15485/0.08844, loss_grounding_dice_6: 0.19779/0.18339, loss_grounding_ce_6: 0.02790/0.30920, loss_mask_ce_7: 0.51015/1.01866, loss_mask_bce_7: 0.32164/0.35189, loss_mask_dice_7: 0.51839/1.25667, loss_spatial_bce_7: 0.13861/0.10966, loss_spatial_dice_7: 0.21178/0.26121, loss_spatial_ce_7: 0.11432/0.16596, loss_grounding_bce_7: 0.15926/0.09031, loss_grounding_dice_7: 0.21294/0.19071, loss_grounding_ce_7: 0.01841/0.33958, loss_mask_ce_8: 0.52639/1.12729, loss_mask_bce_8: 0.31763/0.36552, loss_mask_dice_8: 0.54763/1.32947, loss_spatial_bce_8: 0.17082/0.13014, loss_spatial_dice_8: 0.22560/0.29905, loss_spatial_ce_8: 0.05999/0.22021, loss_grounding_bce_8: 0.16564/0.09403, loss_grounding_dice_8: 0.24821/0.20149, loss_grounding_ce_8: 0.00482/0.40614, loss_mask_ce_9: 2.22134/3.67524, loss_mask_bce_9: 0.31500/0.39256, loss_mask_dice_9: 0.65598/1.90200, loss_spatial_bce_9: 0.33253/0.33276, loss_spatial_dice_9: 0.77909/0.82171, loss_spatial_ce_9: 1.36942/1.49468, loss_grounding_bce_9: 0.19860/0.10564, loss_grounding_dice_9: 0.29471/0.28083, loss_grounding_ce_9: 0.09266/0.67064] items per batch[64] items per second[0.23] total items[4646400] mini batches[ 72600] memory[7345] epoch remaining[0:22:10]
INFO:trainer.default_trainer:epochs[    39] optim steps[72700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.29282/0.89686, loss_mask_bce_0: 0.22904/0.33417, loss_mask_dice_0: 0.82923/1.16254, loss_spatial_bce_0: 0.05545/0.08692, loss_spatial_dice_0: 0.22012/0.20740, loss_spatial_ce_0: 0.00835/0.06078, loss_grounding_bce_0: 0.08644/0.08617, loss_grounding_dice_0: 0.13014/0.17843, loss_grounding_ce_0: 0.17481/0.27173, loss_mask_ce_1: 1.29039/0.89742, loss_mask_bce_1: 0.23687/0.33512, loss_mask_dice_1: 0.77747/1.16927, loss_spatial_bce_1: 0.05919/0.08746, loss_spatial_dice_1: 0.24264/0.21140, loss_spatial_ce_1: 0.02394/0.06662, loss_grounding_bce_1: 0.08672/0.08636, loss_grounding_dice_1: 0.12252/0.17924, loss_grounding_ce_1: 0.18929/0.27251, loss_mask_ce_2: 1.21875/0.90447, loss_mask_bce_2: 0.24784/0.33570, loss_mask_dice_2: 0.87099/1.16967, loss_spatial_bce_2: 0.05266/0.08863, loss_spatial_dice_2: 0.20034/0.21312, loss_spatial_ce_2: 0.00831/0.07008, loss_grounding_bce_2: 0.08686/0.08653, loss_grounding_dice_2: 0.12311/0.17911, loss_grounding_ce_2: 0.20551/0.27582, loss_mask_ce_3: 1.32575/0.91548, loss_mask_bce_3: 0.24966/0.33691, loss_mask_dice_3: 0.78794/1.16754, loss_spatial_bce_3: 0.06146/0.08988, loss_spatial_dice_3: 0.20031/0.21413, loss_spatial_ce_3: 0.00223/0.07494, loss_grounding_bce_3: 0.09082/0.08677, loss_grounding_dice_3: 0.12680/0.17884, loss_grounding_ce_3: 0.22729/0.27809, loss_mask_ce_4: 1.31706/0.91648, loss_mask_bce_4: 0.26112/0.33906, loss_mask_dice_4: 0.94518/1.19133, loss_spatial_bce_4: 0.10308/0.09380, loss_spatial_dice_4: 0.23855/0.22636, loss_spatial_ce_4: 0.00373/0.09121, loss_grounding_bce_4: 0.09483/0.08730, loss_grounding_dice_4: 0.13103/0.18174, loss_grounding_ce_4: 0.19786/0.28105, loss_mask_ce_5: 1.24512/0.93327, loss_mask_bce_5: 0.26572/0.34136, loss_mask_dice_5: 0.91800/1.19934, loss_spatial_bce_5: 0.12543/0.09608, loss_spatial_dice_5: 0.32174/0.23065, loss_spatial_ce_5: 0.00920/0.10524, loss_grounding_bce_5: 0.09206/0.08772, loss_grounding_dice_5: 0.12650/0.18303, loss_grounding_ce_5: 0.12192/0.29357, loss_mask_ce_6: 1.26558/0.97340, loss_mask_bce_6: 0.26287/0.34407, loss_mask_dice_6: 0.83925/1.20209, loss_spatial_bce_6: 0.27966/0.10174, loss_spatial_dice_6: 0.35287/0.23358, loss_spatial_ce_6: 0.11881/0.13073, loss_grounding_bce_6: 0.08383/0.08844, loss_grounding_dice_6: 0.12413/0.18339, loss_grounding_ce_6: 0.22599/0.30914, loss_mask_ce_7: 1.29202/1.01867, loss_mask_bce_7: 0.25364/0.35193, loss_mask_dice_7: 0.90904/1.25669, loss_spatial_bce_7: 0.30491/0.10968, loss_spatial_dice_7: 0.37881/0.26120, loss_spatial_ce_7: 0.08108/0.16594, loss_grounding_bce_7: 0.08752/0.09032, loss_grounding_dice_7: 0.13387/0.19070, loss_grounding_ce_7: 0.31612/0.33953, loss_mask_ce_8: 1.44444/1.12731, loss_mask_bce_8: 0.23705/0.36556, loss_mask_dice_8: 0.86912/1.32950, loss_spatial_bce_8: 0.18417/0.13016, loss_spatial_dice_8: 0.36351/0.29903, loss_spatial_ce_8: 0.17417/0.22015, loss_grounding_bce_8: 0.09336/0.09405, loss_grounding_dice_8: 0.15860/0.20149, loss_grounding_ce_8: 0.33024/0.40603, loss_mask_ce_9: 3.52143/3.67527, loss_mask_bce_9: 0.56556/0.39261, loss_mask_dice_9: 3.36783/1.90209, loss_spatial_bce_9: 0.47728/0.33277, loss_spatial_dice_9: 0.73441/0.82170, loss_spatial_ce_9: 1.33086/1.49475, loss_grounding_bce_9: 0.31433/0.10566, loss_grounding_dice_9: 0.28614/0.28083, loss_grounding_ce_9: 0.29894/0.67057] items per batch[64] items per second[0.23] total items[4652800] mini batches[ 72700] memory[7345] epoch remaining[0:17:33]
INFO:trainer.default_trainer:epochs[    39] optim steps[72800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92039/0.89685, loss_mask_bce_0: 0.28835/0.33418, loss_mask_dice_0: 2.48500/1.16276, loss_spatial_bce_0: 0.00854/0.08691, loss_spatial_dice_0: 0.23276/0.20739, loss_spatial_ce_0: 0.00081/0.06076, loss_grounding_bce_0: 0.05132/0.08617, loss_grounding_dice_0: 0.37799/0.17843, loss_grounding_ce_0: 0.41670/0.27177, loss_mask_ce_1: 0.89422/0.89740, loss_mask_bce_1: 0.28805/0.33512, loss_mask_dice_1: 2.23907/1.16948, loss_spatial_bce_1: 0.00853/0.08746, loss_spatial_dice_1: 0.20163/0.21139, loss_spatial_ce_1: 0.00228/0.06660, loss_grounding_bce_1: 0.05253/0.08635, loss_grounding_dice_1: 0.37800/0.17925, loss_grounding_ce_1: 0.46261/0.27255, loss_mask_ce_2: 0.97678/0.90444, loss_mask_bce_2: 0.28756/0.33570, loss_mask_dice_2: 2.40553/1.16988, loss_spatial_bce_2: 0.00979/0.08862, loss_spatial_dice_2: 0.22059/0.21312, loss_spatial_ce_2: 0.00243/0.07007, loss_grounding_bce_2: 0.05409/0.08652, loss_grounding_dice_2: 0.39324/0.17912, loss_grounding_ce_2: 0.42718/0.27587, loss_mask_ce_3: 0.93505/0.91549, loss_mask_bce_3: 0.28677/0.33691, loss_mask_dice_3: 2.30960/1.16775, loss_spatial_bce_3: 0.00964/0.08987, loss_spatial_dice_3: 0.19796/0.21413, loss_spatial_ce_3: 0.00809/0.07492, loss_grounding_bce_3: 0.05482/0.08676, loss_grounding_dice_3: 0.41210/0.17885, loss_grounding_ce_3: 0.45962/0.27814, loss_mask_ce_4: 0.88391/0.91647, loss_mask_bce_4: 0.27769/0.33907, loss_mask_dice_4: 2.23910/1.19156, loss_spatial_bce_4: 0.01112/0.09379, loss_spatial_dice_4: 0.24974/0.22636, loss_spatial_ce_4: 0.06099/0.09120, loss_grounding_bce_4: 0.05134/0.08730, loss_grounding_dice_4: 0.35801/0.18176, loss_grounding_ce_4: 0.42468/0.28110, loss_mask_ce_5: 1.12715/0.93329, loss_mask_bce_5: 0.28058/0.34136, loss_mask_dice_5: 2.21179/1.19957, loss_spatial_bce_5: 0.01193/0.09607, loss_spatial_dice_5: 0.25290/0.23066, loss_spatial_ce_5: 0.09692/0.10522, loss_grounding_bce_5: 0.05113/0.08772, loss_grounding_dice_5: 0.37572/0.18304, loss_grounding_ce_5: 0.43415/0.29361, loss_mask_ce_6: 1.02785/0.97341, loss_mask_bce_6: 0.28819/0.34408, loss_mask_dice_6: 2.46004/1.20233, loss_spatial_bce_6: 0.01237/0.10173, loss_spatial_dice_6: 0.27505/0.23359, loss_spatial_ce_6: 0.04674/0.13073, loss_grounding_bce_6: 0.04927/0.08844, loss_grounding_dice_6: 0.36809/0.18340, loss_grounding_ce_6: 0.45758/0.30920, loss_mask_ce_7: 1.02180/1.01869, loss_mask_bce_7: 0.30653/0.35193, loss_mask_dice_7: 2.34620/1.25693, loss_spatial_bce_7: 0.02299/0.10967, loss_spatial_dice_7: 0.29570/0.26121, loss_spatial_ce_7: 0.09242/0.16591, loss_grounding_bce_7: 0.05225/0.09032, loss_grounding_dice_7: 0.38432/0.19072, loss_grounding_ce_7: 0.50162/0.33959, loss_mask_ce_8: 0.93377/1.12734, loss_mask_bce_8: 0.35412/0.36557, loss_mask_dice_8: 2.64923/1.32974, loss_spatial_bce_8: 0.03702/0.13014, loss_spatial_dice_8: 0.36362/0.29904, loss_spatial_ce_8: 0.13412/0.22009, loss_grounding_bce_8: 0.05070/0.09405, loss_grounding_dice_8: 0.42831/0.20151, loss_grounding_ce_8: 0.46712/0.40609, loss_mask_ce_9: 3.69173/3.67534, loss_mask_bce_9: 0.33569/0.39263, loss_mask_dice_9: 3.80498/1.90248, loss_spatial_bce_9: 0.14925/0.33275, loss_spatial_dice_9: 0.92440/0.82171, loss_spatial_ce_9: 1.79651/1.49489, loss_grounding_bce_9: 0.07107/0.10566, loss_grounding_dice_9: 0.60396/0.28084, loss_grounding_ce_9: 0.48263/0.67055] items per batch[64] items per second[0.23] total items[4659200] mini batches[ 72800] memory[7345] epoch remaining[0:12:57]
INFO:trainer.default_trainer:epochs[    39] optim steps[72900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35989/0.89667, loss_mask_bce_0: 0.22957/0.33412, loss_mask_dice_0: 0.27880/1.16261, loss_spatial_bce_0: 0.12579/0.08690, loss_spatial_dice_0: 0.14658/0.20739, loss_spatial_ce_0: 0.03251/0.06075, loss_grounding_bce_0: 0.09218/0.08616, loss_grounding_dice_0: 0.12763/0.17842, loss_grounding_ce_0: 0.01691/0.27178, loss_mask_ce_1: 0.35412/0.89726, loss_mask_bce_1: 0.22944/0.33507, loss_mask_dice_1: 0.27724/1.16933, loss_spatial_bce_1: 0.13147/0.08745, loss_spatial_dice_1: 0.15297/0.21139, loss_spatial_ce_1: 0.03282/0.06659, loss_grounding_bce_1: 0.09032/0.08635, loss_grounding_dice_1: 0.12706/0.17924, loss_grounding_ce_1: 0.01712/0.27256, loss_mask_ce_2: 0.32885/0.90430, loss_mask_bce_2: 0.22605/0.33565, loss_mask_dice_2: 0.27477/1.16971, loss_spatial_bce_2: 0.13269/0.08861, loss_spatial_dice_2: 0.16340/0.21311, loss_spatial_ce_2: 0.03274/0.07007, loss_grounding_bce_2: 0.09181/0.08652, loss_grounding_dice_2: 0.12861/0.17910, loss_grounding_ce_2: 0.01674/0.27588, loss_mask_ce_3: 0.35865/0.91533, loss_mask_bce_3: 0.23198/0.33685, loss_mask_dice_3: 0.27466/1.16760, loss_spatial_bce_3: 0.12000/0.08986, loss_spatial_dice_3: 0.15293/0.21412, loss_spatial_ce_3: 0.03370/0.07491, loss_grounding_bce_3: 0.09463/0.08676, loss_grounding_dice_3: 0.13034/0.17883, loss_grounding_ce_3: 0.01733/0.27815, loss_mask_ce_4: 0.31997/0.91633, loss_mask_bce_4: 0.23560/0.33901, loss_mask_dice_4: 0.27360/1.19143, loss_spatial_bce_4: 0.15240/0.09378, loss_spatial_dice_4: 0.18959/0.22636, loss_spatial_ce_4: 0.04139/0.09119, loss_grounding_bce_4: 0.09531/0.08730, loss_grounding_dice_4: 0.13159/0.18175, loss_grounding_ce_4: 0.01766/0.28109, loss_mask_ce_5: 0.30266/0.93315, loss_mask_bce_5: 0.23722/0.34130, loss_mask_dice_5: 0.28451/1.19942, loss_spatial_bce_5: 0.15220/0.09606, loss_spatial_dice_5: 0.16378/0.23065, loss_spatial_ce_5: 0.06435/0.10521, loss_grounding_bce_5: 0.09528/0.08772, loss_grounding_dice_5: 0.13102/0.18303, loss_grounding_ce_5: 0.01522/0.29362, loss_mask_ce_6: 0.39095/0.97327, loss_mask_bce_6: 0.23825/0.34402, loss_mask_dice_6: 0.27962/1.20219, loss_spatial_bce_6: 0.15279/0.10172, loss_spatial_dice_6: 0.16611/0.23358, loss_spatial_ce_6: 0.06196/0.13069, loss_grounding_bce_6: 0.09398/0.08844, loss_grounding_dice_6: 0.12678/0.18339, loss_grounding_ce_6: 0.02602/0.30921, loss_mask_ce_7: 0.45938/1.01855, loss_mask_bce_7: 0.24226/0.35187, loss_mask_dice_7: 0.28072/1.25679, loss_spatial_bce_7: 0.12982/0.10966, loss_spatial_dice_7: 0.16788/0.26120, loss_spatial_ce_7: 0.10823/0.16588, loss_grounding_bce_7: 0.09516/0.09032, loss_grounding_dice_7: 0.13652/0.19070, loss_grounding_ce_7: 0.02694/0.33964, loss_mask_ce_8: 0.49070/1.12723, loss_mask_bce_8: 0.24173/0.36550, loss_mask_dice_8: 0.30517/1.32954, loss_spatial_bce_8: 0.17569/0.13013, loss_spatial_dice_8: 0.17651/0.29903, loss_spatial_ce_8: 0.14807/0.22003, loss_grounding_bce_8: 0.08787/0.09404, loss_grounding_dice_8: 0.13630/0.20149, loss_grounding_ce_8: 0.07813/0.40614, loss_mask_ce_9: 2.52050/3.67509, loss_mask_bce_9: 0.25960/0.39255, loss_mask_dice_9: 0.43195/1.90213, loss_spatial_bce_9: 0.52257/0.33276, loss_spatial_dice_9: 0.78743/0.82171, loss_spatial_ce_9: 1.50657/1.49491, loss_grounding_bce_9: 0.09996/0.10565, loss_grounding_dice_9: 0.17258/0.28082, loss_grounding_ce_9: 0.22770/0.67057] items per batch[64] items per second[0.23] total items[4665600] mini batches[ 72900] memory[7345] epoch remaining[0:08:20]
INFO:trainer.default_trainer:epochs[    39] optim steps[73000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03788/0.89664, loss_mask_bce_0: 0.52910/0.33413, loss_mask_dice_0: 1.08060/1.16251, loss_spatial_bce_0: 0.08740/0.08690, loss_spatial_dice_0: 0.19957/0.20740, loss_spatial_ce_0: 0.01800/0.06074, loss_grounding_bce_0: 0.15997/0.08616, loss_grounding_dice_0: 0.21851/0.17844, loss_grounding_ce_0: 0.50356/0.27186, loss_mask_ce_1: 1.00001/0.89724, loss_mask_bce_1: 0.52103/0.33507, loss_mask_dice_1: 1.04961/1.16924, loss_spatial_bce_1: 0.08852/0.08744, loss_spatial_dice_1: 0.19419/0.21140, loss_spatial_ce_1: 0.01453/0.06657, loss_grounding_bce_1: 0.15255/0.08635, loss_grounding_dice_1: 0.20594/0.17925, loss_grounding_ce_1: 0.44242/0.27263, loss_mask_ce_2: 1.04953/0.90427, loss_mask_bce_2: 0.51702/0.33566, loss_mask_dice_2: 1.05233/1.16962, loss_spatial_bce_2: 0.09900/0.08861, loss_spatial_dice_2: 0.20128/0.21312, loss_spatial_ce_2: 0.02034/0.07004, loss_grounding_bce_2: 0.14974/0.08651, loss_grounding_dice_2: 0.18979/0.17913, loss_grounding_ce_2: 0.45010/0.27600, loss_mask_ce_3: 0.90193/0.91530, loss_mask_bce_3: 0.52417/0.33687, loss_mask_dice_3: 1.05236/1.16750, loss_spatial_bce_3: 0.11504/0.08986, loss_spatial_dice_3: 0.20773/0.21413, loss_spatial_ce_3: 0.02428/0.07490, loss_grounding_bce_3: 0.15098/0.08676, loss_grounding_dice_3: 0.20192/0.17885, loss_grounding_ce_3: 0.38859/0.27823, loss_mask_ce_4: 0.99408/0.91631, loss_mask_bce_4: 0.52696/0.33901, loss_mask_dice_4: 1.04486/1.19133, loss_spatial_bce_4: 0.14325/0.09378, loss_spatial_dice_4: 0.21221/0.22637, loss_spatial_ce_4: 0.02914/0.09118, loss_grounding_bce_4: 0.14743/0.08730, loss_grounding_dice_4: 0.19191/0.18177, loss_grounding_ce_4: 0.39843/0.28121, loss_mask_ce_5: 0.85450/0.93313, loss_mask_bce_5: 0.52861/0.34131, loss_mask_dice_5: 1.20144/1.19933, loss_spatial_bce_5: 0.14967/0.09606, loss_spatial_dice_5: 0.23674/0.23066, loss_spatial_ce_5: 0.03781/0.10521, loss_grounding_bce_5: 0.13866/0.08772, loss_grounding_dice_5: 0.17603/0.18305, loss_grounding_ce_5: 0.46311/0.29369, loss_mask_ce_6: 0.98294/0.97326, loss_mask_bce_6: 0.51743/0.34403, loss_mask_dice_6: 1.18690/1.20210, loss_spatial_bce_6: 0.14341/0.10171, loss_spatial_dice_6: 0.23101/0.23360, loss_spatial_ce_6: 0.06537/0.13069, loss_grounding_bce_6: 0.16630/0.08844, loss_grounding_dice_6: 0.18155/0.18341, loss_grounding_ce_6: 0.78061/0.30932, loss_mask_ce_7: 1.31991/1.01856, loss_mask_bce_7: 0.49086/0.35189, loss_mask_dice_7: 1.23914/1.25668, loss_spatial_bce_7: 0.11880/0.10965, loss_spatial_dice_7: 0.27412/0.26121, loss_spatial_ce_7: 0.06976/0.16586, loss_grounding_bce_7: 0.15017/0.09032, loss_grounding_dice_7: 0.17847/0.19072, loss_grounding_ce_7: 1.33473/0.33975, loss_mask_ce_8: 1.92620/1.12727, loss_mask_bce_8: 0.51664/0.36551, loss_mask_dice_8: 1.23173/1.32941, loss_spatial_bce_8: 0.14418/0.13011, loss_spatial_dice_8: 0.27224/0.29904, loss_spatial_ce_8: 0.16475/0.21998, loss_grounding_bce_8: 0.21994/0.09404, loss_grounding_dice_8: 0.27475/0.20150, loss_grounding_ce_8: 1.88323/0.40633, loss_mask_ce_9: 4.19803/3.67505, loss_mask_bce_9: 0.56729/0.39255, loss_mask_dice_9: 1.97812/1.90205, loss_spatial_bce_9: 0.29732/0.33273, loss_spatial_dice_9: 0.84813/0.82171, loss_spatial_ce_9: 1.34684/1.49491, loss_grounding_bce_9: 0.25626/0.10565, loss_grounding_dice_9: 0.37888/0.28084, loss_grounding_ce_9: 1.60088/0.67063] items per batch[64] items per second[0.22] total items[4672000] mini batches[ 73000] memory[7345] epoch remaining[0:03:42]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00073080.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0030 s/iter. Inference: 0.2151 s/iter. Eval: 0.0900 s/iter. Total: 0.3081 s/iter. ETA=0:00:44
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2178 s/iter. Eval: 0.0780 s/iter. Total: 0.2990 s/iter. ETA=0:00:38
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2214 s/iter. Eval: 0.0751 s/iter. Total: 0.2996 s/iter. ETA=0:00:33
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2217 s/iter. Eval: 0.0739 s/iter. Total: 0.2988 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0032 s/iter. Inference: 0.2211 s/iter. Eval: 0.0735 s/iter. Total: 0.2979 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0032 s/iter. Inference: 0.2238 s/iter. Eval: 0.0742 s/iter. Total: 0.3013 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 113/157. Dataloading: 0.0032 s/iter. Inference: 0.2250 s/iter. Eval: 0.0743 s/iter. Total: 0.3027 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 130/157. Dataloading: 0.0032 s/iter. Inference: 0.2248 s/iter. Eval: 0.0740 s/iter. Total: 0.3021 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2257 s/iter. Eval: 0.0744 s/iter. Total: 0.3035 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval9m6w61fu ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.874 | 82.110 | 59.880 |      133      |
| Things | 54.769 | 82.865 | 65.428 |      80       |
| Stuff  | 42.487 | 80.969 | 51.505 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.38s)
creating index...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.67 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.30s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.05 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.806 | 61.272 | 40.686 | 19.057 | 41.864 | 60.386 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.330 | bicycle      | 17.093 | car            | 37.780 |
| motorcycle    | 34.454 | airplane     | 55.345 | bus            | 65.054 |
| train         | 68.784 | truck        | 33.885 | boat           | 23.009 |
| traffic light | 25.129 | fire hydrant | 65.060 | stop sign      | 63.996 |
| parking meter | 42.440 | bench        | 19.998 | bird           | 29.840 |
| cat           | 72.828 | dog          | 65.926 | horse          | 46.669 |
| sheep         | 48.260 | cow          | 51.042 | elephant       | 61.463 |
| bear          | 78.234 | zebra        | 60.210 | giraffe        | 57.340 |
| backpack      | 16.459 | umbrella     | 48.627 | handbag        | 15.516 |
| tie           | 33.869 | suitcase     | 41.597 | frisbee        | 67.448 |
| skis          | 5.624  | snowboard    | 22.052 | sports ball    | 47.267 |
| kite          | 35.298 | baseball bat | 28.143 | baseball glove | 43.824 |
| skateboard    | 35.865 | surfboard    | 36.090 | tennis racket  | 54.820 |
| bottle        | 34.361 | wine glass   | 27.078 | cup            | 39.812 |
| fork          | 13.779 | knife        | 13.102 | spoon          | 15.306 |
| bowl          | 30.487 | banana       | 19.206 | apple          | 21.635 |
| sandwich      | 42.006 | orange       | 30.367 | broccoli       | 22.015 |
| carrot        | 21.170 | hot dog      | 24.667 | pizza          | 50.230 |
| donut         | 46.544 | cake         | 42.324 | chair          | 21.143 |
| couch         | 40.972 | potted plant | 18.195 | bed            | 41.074 |
| dining table  | 12.697 | toilet       | 66.312 | tv             | 62.211 |
| laptop        | 61.515 | mouse        | 60.053 | remote         | 30.331 |
| keyboard      | 48.266 | cell phone   | 37.072 | microwave      | 53.792 |
| oven          | 33.769 | toaster      | 32.000 | sink           | 37.013 |
| refrigerator  | 57.991 | book         | 9.129  | clock          | 51.898 |
| vase          | 34.021 | scissors     | 25.531 | teddy bear     | 50.126 |
| hair drier    | 7.696  | toothbrush   | 16.956 |                |        |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.295
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.9666284308651, 'fwIoU': 69.10180205094743, 'IoU-person': 87.54854702194825, 'IoU-bicycle': 74.96967482590127, 'IoU-car': 69.3533238593557, 'IoU-motorcycle': 83.30517658292172, 'IoU-airplane': 83.91494498228205, 'IoU-bus': 84.01122505571682, 'IoU-train': 86.84328907869897, 'IoU-truck': 63.223585680709974, 'IoU-boat': 66.75185757144942, 'IoU-traffic light': 76.46955565296449, 'IoU-fire hydrant': 90.48191124898158, 'IoU-stop sign': 91.36770477275604, 'IoU-parking meter': 83.307921473841, 'IoU-bench': 56.432272082569234, 'IoU-bird': 75.8322903441338, 'IoU-cat': 85.08847948881909, 'IoU-dog': 83.89545788505787, 'IoU-horse': 86.7121166183414, 'IoU-sheep': 84.88072642122529, 'IoU-cow': 86.06844176796655, 'IoU-elephant': 92.48919756607975, 'IoU-bear': 76.04715687881097, 'IoU-zebra': 91.28865472872222, 'IoU-giraffe': 87.17996226363982, 'IoU-backpack': 39.897146810079434, 'IoU-umbrella': 77.03583321718074, 'IoU-handbag': 37.85456842644954, 'IoU-tie': 71.63151862741304, 'IoU-suitcase': 79.95139673957962, 'IoU-frisbee': 83.35513007176817, 'IoU-skis': 51.93574905744496, 'IoU-snowboard': 69.00814185857776, 'IoU-sports ball': 63.318168906763574, 'IoU-kite': 64.34056488619238, 'IoU-baseball bat': 60.990812435486575, 'IoU-baseball glove': 79.00921321558415, 'IoU-skateboard': 77.22359981902456, 'IoU-surfboard': 81.59956406605905, 'IoU-tennis racket': 81.92410885396704, 'IoU-bottle': 66.69029837565546, 'IoU-wine glass': 72.82526670624964, 'IoU-cup': 64.8493815902471, 'IoU-fork': 54.81246358258751, 'IoU-knife': 50.448421152861926, 'IoU-spoon': 48.27905034168401, 'IoU-bowl': 55.538531462602, 'IoU-banana': 83.21874709814912, 'IoU-apple': 54.6898850945442, 'IoU-sandwich': 66.54659605852957, 'IoU-orange': 78.0446579290066, 'IoU-broccoli': 67.71747544000928, 'IoU-carrot': 63.34262261347895, 'IoU-hot dog': 64.0687753832697, 'IoU-pizza': 83.38998067814421, 'IoU-donut': 65.68007244388676, 'IoU-cake': 67.98748341560393, 'IoU-chair': 56.70939125349116, 'IoU-couch': 67.13252701023347, 'IoU-potted plant': 32.875792318134096, 'IoU-bed': 69.06024534409899, 'IoU-dining table': 50.61879231863732, 'IoU-toilet': 84.16359887302772, 'IoU-tv': 74.72608627279978, 'IoU-laptop': 72.784929573969, 'IoU-mouse': 65.86318447214771, 'IoU-remote': 49.08657688411158, 'IoU-keyboard': 50.8219193914485, 'IoU-cell phone': 64.37120486973168, 'IoU-microwave': 49.91530061959196, 'IoU-oven': 63.197327542082014, 'IoU-toaster': 64.2899488008623, 'IoU-sink': 69.68880305847588, 'IoU-refrigerator': 80.56714470858518, 'IoU-book': 51.63121861570256, 'IoU-clock': 72.70302360372189, 'IoU-vase': 63.57716347525335, 'IoU-scissors': 55.34304151480948, 'IoU-teddy bear': 79.90168708330064, 'IoU-hair drier': 50.20027701868004, 'IoU-toothbrush': 58.84102291649119, 'IoU-banner': 30.60136943472641, 'IoU-blanket': 9.196148440494612, 'IoU-bridge': 38.48278616982259, 'IoU-cardboard': 45.21522686569143, 'IoU-counter': 31.13928896013559, 'IoU-curtain': 65.27448029933038, 'IoU-door-stuff': 42.22399742950314, 'IoU-floor-wood': 61.115474686262395, 'IoU-flower': 42.438041221248945, 'IoU-fruit': 41.738656406117045, 'IoU-gravel': 34.961264969546725, 'IoU-house': 22.317759595476364, 'IoU-light': 38.78674258922879, 'IoU-mirror-stuff': 55.486646466800046, 'IoU-net': 40.7987371578867, 'IoU-pillow': 12.90177648323203, 'IoU-platform': 30.597451637555128, 'IoU-playingfield': 71.23287300474419, 'IoU-railroad': 61.970678668036825, 'IoU-river': 49.42475716945024, 'IoU-road': 66.38355281391054, 'IoU-roof': 17.069113517090013, 'IoU-sand': 63.580978819387646, 'IoU-sea': 86.19708481057414, 'IoU-shelf': 37.39929269963685, 'IoU-snow': 88.74892210937271, 'IoU-stairs': 23.37070004902865, 'IoU-tent': 8.558141692263161, 'IoU-towel': 35.71804168537313, 'IoU-wall-brick': 45.21427623359865, 'IoU-wall-stone': 31.933604783847656, 'IoU-wall-tile': 68.54963116137218, 'IoU-wall-wood': 37.460896813322115, 'IoU-water-other': 23.545472518516227, 'IoU-window-blind': 48.573837583172704, 'IoU-window-other': 47.137602314236524, 'IoU-tree-merged': 80.73601049631445, 'IoU-fence-merged': 50.31093251384947, 'IoU-ceiling-merged': 66.1219391469902, 'IoU-sky-other-merged': 92.98031196410051, 'IoU-cabinet-merged': 58.43882679459973, 'IoU-table-merged': 36.77163604072627, 'IoU-floor-other-merged': 50.48448359889585, 'IoU-pavement-merged': 53.574999531014186, 'IoU-mountain-merged': 54.719540705065086, 'IoU-grass-merged': 71.29721353392728, 'IoU-dirt-merged': 47.06065629933984, 'IoU-paper-merged': 33.903067594621945, 'IoU-food-other-merged': 36.877057093093484, 'IoU-building-other-merged': 57.443742137112864, 'IoU-rock-merged': 62.946175089598455, 'IoU-wall-other-merged': 64.47005815094985, 'IoU-rug-merged': 62.34071560450576, 'mACC': 73.30154783902032, 'pACC': 80.43997287012907, 'ACC-person': 92.62346900718674, 'ACC-bicycle': 86.01902726462521, 'ACC-car': 86.12179616498048, 'ACC-motorcycle': 88.61268249407503, 'ACC-airplane': 90.21294799507965, 'ACC-bus': 89.44047775342192, 'ACC-train': 95.42685705378055, 'ACC-truck': 73.16049067801443, 'ACC-boat': 76.9024424782667, 'ACC-traffic light': 90.01109559771113, 'ACC-fire hydrant': 95.43676634484409, 'ACC-stop sign': 94.49709096598784, 'ACC-parking meter': 92.195091606328, 'ACC-bench': 71.92029201657076, 'ACC-bird': 80.93794403643024, 'ACC-cat': 93.15193689500649, 'ACC-dog': 87.03256927066622, 'ACC-horse': 93.0346631452537, 'ACC-sheep': 88.180738727607, 'ACC-cow': 91.60513711779295, 'ACC-elephant': 95.24840186549363, 'ACC-bear': 77.96156293296018, 'ACC-zebra': 93.87771667556602, 'ACC-giraffe': 91.48136219469647, 'ACC-backpack': 58.50803235729748, 'ACC-umbrella': 85.9309638494127, 'ACC-handbag': 55.63104235772373, 'ACC-tie': 81.02930256156382, 'ACC-suitcase': 89.41776547480241, 'ACC-frisbee': 94.09854545454546, 'ACC-skis': 68.66577852584524, 'ACC-snowboard': 77.46907986578825, 'ACC-sports ball': 72.58246245128745, 'ACC-kite': 76.35307413308364, 'ACC-baseball bat': 80.35086799181832, 'ACC-baseball glove': 89.41260817247068, 'ACC-skateboard': 89.25214663069873, 'ACC-surfboard': 89.33328198704722, 'ACC-tennis racket': 89.04536990096823, 'ACC-bottle': 85.52109702641118, 'ACC-wine glass': 86.17885441787094, 'ACC-cup': 82.52566321579916, 'ACC-fork': 67.91350846627019, 'ACC-knife': 62.11658239906207, 'ACC-spoon': 68.89275247947981, 'ACC-bowl': 67.69946439256991, 'ACC-banana': 89.37378147436058, 'ACC-apple': 65.08319015176342, 'ACC-sandwich': 77.7714902280733, 'ACC-orange': 85.8626164246218, 'ACC-broccoli': 80.40438926274608, 'ACC-carrot': 73.47479816555155, 'ACC-hot dog': 72.77430614566039, 'ACC-pizza': 92.89327341690374, 'ACC-donut': 81.96929838334684, 'ACC-cake': 74.46224318079202, 'ACC-chair': 71.59805095570778, 'ACC-couch': 82.03528126601526, 'ACC-potted plant': 52.2274535029668, 'ACC-bed': 80.13292389086062, 'ACC-dining table': 74.56884705462402, 'ACC-toilet': 93.25720138464392, 'ACC-tv': 86.59266690104685, 'ACC-laptop': 86.82732589937395, 'ACC-mouse': 78.29291256777378, 'ACC-remote': 71.82178983929091, 'ACC-keyboard': 55.1720582675851, 'ACC-cell phone': 70.34972693226327, 'ACC-microwave': 57.34664167436997, 'ACC-oven': 87.32291986957053, 'ACC-toaster': 73.46549152171235, 'ACC-sink': 84.00210092178555, 'ACC-refrigerator': 90.59086301103873, 'ACC-book': 66.78900142893166, 'ACC-clock': 78.2048010985357, 'ACC-vase': 73.14564030643773, 'ACC-scissors': 59.48383512840996, 'ACC-teddy bear': 86.94372619417999, 'ACC-hair drier': 76.33848518486893, 'ACC-toothbrush': 78.88898540653231, 'ACC-banner': 76.18771121047307, 'ACC-blanket': 13.167397773748446, 'ACC-bridge': 54.55701522708573, 'ACC-cardboard': 57.54136978333576, 'ACC-counter': 52.810099396665436, 'ACC-curtain': 76.79072018096844, 'ACC-door-stuff': 61.917851001412714, 'ACC-floor-wood': 77.90787477366938, 'ACC-flower': 63.97211336414554, 'ACC-fruit': 58.475957455296204, 'ACC-gravel': 41.00335581910096, 'ACC-house': 25.18811734989149, 'ACC-light': 55.34060023733874, 'ACC-mirror-stuff': 73.62918510280335, 'ACC-net': 60.423608755512106, 'ACC-pillow': 26.684759222055316, 'ACC-platform': 55.38721713823405, 'ACC-playingfield': 92.18639919361965, 'ACC-railroad': 79.57758205368096, 'ACC-river': 74.36610773067798, 'ACC-road': 87.04623909119906, 'ACC-roof': 23.8607814136179, 'ACC-sand': 71.06088091646099, 'ACC-sea': 92.04907279169757, 'ACC-shelf': 60.100495223142325, 'ACC-snow': 95.30724203629708, 'ACC-stairs': 35.08333594477189, 'ACC-tent': 10.03132436399831, 'ACC-towel': 44.45199655551524, 'ACC-wall-brick': 63.19388145988327, 'ACC-wall-stone': 38.289156367081524, 'ACC-wall-tile': 81.4231588898084, 'ACC-wall-wood': 48.341193405139045, 'ACC-water-other': 34.426401007920646, 'ACC-window-blind': 56.80312216393196, 'ACC-window-other': 71.21703702540593, 'ACC-tree-merged': 89.14327222499936, 'ACC-fence-merged': 73.55129811063469, 'ACC-ceiling-merged': 79.91682807542071, 'ACC-sky-other-merged': 96.58582471627902, 'ACC-cabinet-merged': 74.29510665364202, 'ACC-table-merged': 52.36714887436391, 'ACC-floor-other-merged': 60.96689905397801, 'ACC-pavement-merged': 65.3547655371008, 'ACC-mountain-merged': 63.45588370000599, 'ACC-grass-merged': 83.39196582193141, 'ACC-dirt-merged': 66.39144361186054, 'ACC-paper-merged': 47.40792605949799, 'ACC-food-other-merged': 51.087809301315744, 'ACC-building-other-merged': 72.56572123482532, 'ACC-rock-merged': 82.71585424805322, 'ACC-wall-other-merged': 79.64525404607005, 'ACC-rug-merged': 77.97357845763034})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1554 s/iter. Inference: 0.6115 s/iter. Eval: 0.0000 s/iter. Total: 0.7670 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1592 s/iter. Inference: 0.5434 s/iter. Eval: 0.0000 s/iter. Total: 0.7027 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1744 s/iter. Inference: 0.5867 s/iter. Eval: 0.0000 s/iter. Total: 0.7613 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1738 s/iter. Inference: 0.6657 s/iter. Eval: 0.0000 s/iter. Total: 0.8398 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1717 s/iter. Inference: 0.6185 s/iter. Eval: 0.0000 s/iter. Total: 0.7904 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1701 s/iter. Inference: 0.6607 s/iter. Eval: 0.0000 s/iter. Total: 0.8309 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4992683640620428, 'noc@0.8': 2.8741586186713493, 'noc@0.85': 3.4460052677787534, 'noc@0.9': 4.470002926543752, 'miou@iter1': 0.8220450435076885}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.0995 s/iter. Eval: 0.0008 s/iter. Total: 0.1018 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.16207122802734, 'precision@0.6': 67.78079986572266, 'precision@0.7': 62.53400802612305, 'precision@0.8': 52.39020538330078, 'precision@0.9': 27.283327102661133, 'cIoU': 57.304100036621094, 'mIoU': 62.80027389526367}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.874251297793734, 'SQ': 82.1095726481748, 'RQ': 59.87987637688694, 'PQ_th': 54.76860746214882, 'SQ_th': 82.86535531419868, 'RQ_th': 65.42841790251077, 'PQ_st': 42.48654387989925, 'SQ_st': 80.96876862398784, 'RQ_st': 51.504719357077356}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.80646860078957, 'AP50': 61.27178328022986, 'AP75': 40.68597682267471, 'APs': 19.057041755456435, 'APm': 41.86382600958345, 'APl': 60.38600463287843, 'AP-person': 44.330376462210864, 'AP-bicycle': 17.092623381526007, 'AP-car': 37.779593286682704, 'AP-motorcycle': 34.45446866238968, 'AP-airplane': 55.34522621399971, 'AP-bus': 65.0539420648367, 'AP-train': 68.78357636222742, 'AP-truck': 33.884862661594426, 'AP-boat': 23.00921970543108, 'AP-traffic light': 25.129318251910586, 'AP-fire hydrant': 65.06006729838518, 'AP-stop sign': 63.99620844381108, 'AP-parking meter': 42.439919626950925, 'AP-bench': 19.997581578258277, 'AP-bird': 29.839604933414105, 'AP-cat': 72.82803257224406, 'AP-dog': 65.92575954161161, 'AP-horse': 46.6693483968517, 'AP-sheep': 48.25962121377226, 'AP-cow': 51.041826504643936, 'AP-elephant': 61.46263444132073, 'AP-bear': 78.23362657882942, 'AP-zebra': 60.209877938669386, 'AP-giraffe': 57.340231533724726, 'AP-backpack': 16.459282352186875, 'AP-umbrella': 48.62671793916743, 'AP-handbag': 15.515545446678395, 'AP-tie': 33.869164747954756, 'AP-suitcase': 41.59737435370195, 'AP-frisbee': 67.44834115251619, 'AP-skis': 5.624097623219346, 'AP-snowboard': 22.05190585546398, 'AP-sports ball': 47.26692244456356, 'AP-kite': 35.29799247448364, 'AP-baseball bat': 28.142789348223246, 'AP-baseball glove': 43.82372294243462, 'AP-skateboard': 35.8652141147427, 'AP-surfboard': 36.089817070715505, 'AP-tennis racket': 54.81959631838139, 'AP-bottle': 34.360725193252975, 'AP-wine glass': 27.077537444907897, 'AP-cup': 39.81231221163928, 'AP-fork': 13.77903205557128, 'AP-knife': 13.10247358649043, 'AP-spoon': 15.305623419695733, 'AP-bowl': 30.486686809135126, 'AP-banana': 19.205972307835907, 'AP-apple': 21.634671543759946, 'AP-sandwich': 42.00557142160377, 'AP-orange': 30.367232087422703, 'AP-broccoli': 22.015001889945836, 'AP-carrot': 21.16966346656404, 'AP-hot dog': 24.66731084865281, 'AP-pizza': 50.230343990107016, 'AP-donut': 46.54390074429891, 'AP-cake': 42.32370172146514, 'AP-chair': 21.14321701682918, 'AP-couch': 40.97234868088705, 'AP-potted plant': 18.195352318114377, 'AP-bed': 41.07392785070778, 'AP-dining table': 12.696876768530924, 'AP-toilet': 66.31222462721632, 'AP-tv': 62.21052752083771, 'AP-laptop': 61.51458693159978, 'AP-mouse': 60.05258442447615, 'AP-remote': 30.331159103985488, 'AP-keyboard': 48.26634696197447, 'AP-cell phone': 37.07183909756067, 'AP-microwave': 53.79238602446094, 'AP-oven': 33.7694545347253, 'AP-toaster': 32.00019413706077, 'AP-sink': 37.01305708619015, 'AP-refrigerator': 57.99127075413377, 'AP-book': 9.12890024422769, 'AP-clock': 51.89759288850283, 'AP-vase': 34.02061866105491, 'AP-scissors': 25.531094744974638, 'AP-teddy bear': 50.126375417045445, 'AP-hair drier': 7.695689378857693, 'AP-toothbrush': 16.956070307132716}), ('sem_seg', {'mIoU': 60.9666284308651, 'fwIoU': 69.10180205094743, 'IoU-person': 87.54854702194825, 'IoU-bicycle': 74.96967482590127, 'IoU-car': 69.3533238593557, 'IoU-motorcycle': 83.30517658292172, 'IoU-airplane': 83.91494498228205, 'IoU-bus': 84.01122505571682, 'IoU-train': 86.84328907869897, 'IoU-truck': 63.223585680709974, 'IoU-boat': 66.75185757144942, 'IoU-traffic light': 76.46955565296449, 'IoU-fire hydrant': 90.48191124898158, 'IoU-stop sign': 91.36770477275604, 'IoU-parking meter': 83.307921473841, 'IoU-bench': 56.432272082569234, 'IoU-bird': 75.8322903441338, 'IoU-cat': 85.08847948881909, 'IoU-dog': 83.89545788505787, 'IoU-horse': 86.7121166183414, 'IoU-sheep': 84.88072642122529, 'IoU-cow': 86.06844176796655, 'IoU-elephant': 92.48919756607975, 'IoU-bear': 76.04715687881097, 'IoU-zebra': 91.28865472872222, 'IoU-giraffe': 87.17996226363982, 'IoU-backpack': 39.897146810079434, 'IoU-umbrella': 77.03583321718074, 'IoU-handbag': 37.85456842644954, 'IoU-tie': 71.63151862741304, 'IoU-suitcase': 79.95139673957962, 'IoU-frisbee': 83.35513007176817, 'IoU-skis': 51.93574905744496, 'IoU-snowboard': 69.00814185857776, 'IoU-sports ball': 63.318168906763574, 'IoU-kite': 64.34056488619238, 'IoU-baseball bat': 60.990812435486575, 'IoU-baseball glove': 79.00921321558415, 'IoU-skateboard': 77.22359981902456, 'IoU-surfboard': 81.59956406605905, 'IoU-tennis racket': 81.92410885396704, 'IoU-bottle': 66.69029837565546, 'IoU-wine glass': 72.82526670624964, 'IoU-cup': 64.8493815902471, 'IoU-fork': 54.81246358258751, 'IoU-knife': 50.448421152861926, 'IoU-spoon': 48.27905034168401, 'IoU-bowl': 55.538531462602, 'IoU-banana': 83.21874709814912, 'IoU-apple': 54.6898850945442, 'IoU-sandwich': 66.54659605852957, 'IoU-orange': 78.0446579290066, 'IoU-broccoli': 67.71747544000928, 'IoU-carrot': 63.34262261347895, 'IoU-hot dog': 64.0687753832697, 'IoU-pizza': 83.38998067814421, 'IoU-donut': 65.68007244388676, 'IoU-cake': 67.98748341560393, 'IoU-chair': 56.70939125349116, 'IoU-couch': 67.13252701023347, 'IoU-potted plant': 32.875792318134096, 'IoU-bed': 69.06024534409899, 'IoU-dining table': 50.61879231863732, 'IoU-toilet': 84.16359887302772, 'IoU-tv': 74.72608627279978, 'IoU-laptop': 72.784929573969, 'IoU-mouse': 65.86318447214771, 'IoU-remote': 49.08657688411158, 'IoU-keyboard': 50.8219193914485, 'IoU-cell phone': 64.37120486973168, 'IoU-microwave': 49.91530061959196, 'IoU-oven': 63.197327542082014, 'IoU-toaster': 64.2899488008623, 'IoU-sink': 69.68880305847588, 'IoU-refrigerator': 80.56714470858518, 'IoU-book': 51.63121861570256, 'IoU-clock': 72.70302360372189, 'IoU-vase': 63.57716347525335, 'IoU-scissors': 55.34304151480948, 'IoU-teddy bear': 79.90168708330064, 'IoU-hair drier': 50.20027701868004, 'IoU-toothbrush': 58.84102291649119, 'IoU-banner': 30.60136943472641, 'IoU-blanket': 9.196148440494612, 'IoU-bridge': 38.48278616982259, 'IoU-cardboard': 45.21522686569143, 'IoU-counter': 31.13928896013559, 'IoU-curtain': 65.27448029933038, 'IoU-door-stuff': 42.22399742950314, 'IoU-floor-wood': 61.115474686262395, 'IoU-flower': 42.438041221248945, 'IoU-fruit': 41.738656406117045, 'IoU-gravel': 34.961264969546725, 'IoU-house': 22.317759595476364, 'IoU-light': 38.78674258922879, 'IoU-mirror-stuff': 55.486646466800046, 'IoU-net': 40.7987371578867, 'IoU-pillow': 12.90177648323203, 'IoU-platform': 30.597451637555128, 'IoU-playingfield': 71.23287300474419, 'IoU-railroad': 61.970678668036825, 'IoU-river': 49.42475716945024, 'IoU-road': 66.38355281391054, 'IoU-roof': 17.069113517090013, 'IoU-sand': 63.580978819387646, 'IoU-sea': 86.19708481057414, 'IoU-shelf': 37.39929269963685, 'IoU-snow': 88.74892210937271, 'IoU-stairs': 23.37070004902865, 'IoU-tent': 8.558141692263161, 'IoU-towel': 35.71804168537313, 'IoU-wall-brick': 45.21427623359865, 'IoU-wall-stone': 31.933604783847656, 'IoU-wall-tile': 68.54963116137218, 'IoU-wall-wood': 37.460896813322115, 'IoU-water-other': 23.545472518516227, 'IoU-window-blind': 48.573837583172704, 'IoU-window-other': 47.137602314236524, 'IoU-tree-merged': 80.73601049631445, 'IoU-fence-merged': 50.31093251384947, 'IoU-ceiling-merged': 66.1219391469902, 'IoU-sky-other-merged': 92.98031196410051, 'IoU-cabinet-merged': 58.43882679459973, 'IoU-table-merged': 36.77163604072627, 'IoU-floor-other-merged': 50.48448359889585, 'IoU-pavement-merged': 53.574999531014186, 'IoU-mountain-merged': 54.719540705065086, 'IoU-grass-merged': 71.29721353392728, 'IoU-dirt-merged': 47.06065629933984, 'IoU-paper-merged': 33.903067594621945, 'IoU-food-other-merged': 36.877057093093484, 'IoU-building-other-merged': 57.443742137112864, 'IoU-rock-merged': 62.946175089598455, 'IoU-wall-other-merged': 64.47005815094985, 'IoU-rug-merged': 62.34071560450576, 'mACC': 73.30154783902032, 'pACC': 80.43997287012907, 'ACC-person': 92.62346900718674, 'ACC-bicycle': 86.01902726462521, 'ACC-car': 86.12179616498048, 'ACC-motorcycle': 88.61268249407503, 'ACC-airplane': 90.21294799507965, 'ACC-bus': 89.44047775342192, 'ACC-train': 95.42685705378055, 'ACC-truck': 73.16049067801443, 'ACC-boat': 76.9024424782667, 'ACC-traffic light': 90.01109559771113, 'ACC-fire hydrant': 95.43676634484409, 'ACC-stop sign': 94.49709096598784, 'ACC-parking meter': 92.195091606328, 'ACC-bench': 71.92029201657076, 'ACC-bird': 80.93794403643024, 'ACC-cat': 93.15193689500649, 'ACC-dog': 87.03256927066622, 'ACC-horse': 93.0346631452537, 'ACC-sheep': 88.180738727607, 'ACC-cow': 91.60513711779295, 'ACC-elephant': 95.24840186549363, 'ACC-bear': 77.96156293296018, 'ACC-zebra': 93.87771667556602, 'ACC-giraffe': 91.48136219469647, 'ACC-backpack': 58.50803235729748, 'ACC-umbrella': 85.9309638494127, 'ACC-handbag': 55.63104235772373, 'ACC-tie': 81.02930256156382, 'ACC-suitcase': 89.41776547480241, 'ACC-frisbee': 94.09854545454546, 'ACC-skis': 68.66577852584524, 'ACC-snowboard': 77.46907986578825, 'ACC-sports ball': 72.58246245128745, 'ACC-kite': 76.35307413308364, 'ACC-baseball bat': 80.35086799181832, 'ACC-baseball glove': 89.41260817247068, 'ACC-skateboard': 89.25214663069873, 'ACC-surfboard': 89.33328198704722, 'ACC-tennis racket': 89.04536990096823, 'ACC-bottle': 85.52109702641118, 'ACC-wine glass': 86.17885441787094, 'ACC-cup': 82.52566321579916, 'ACC-fork': 67.91350846627019, 'ACC-knife': 62.11658239906207, 'ACC-spoon': 68.89275247947981, 'ACC-bowl': 67.69946439256991, 'ACC-banana': 89.37378147436058, 'ACC-apple': 65.08319015176342, 'ACC-sandwich': 77.7714902280733, 'ACC-orange': 85.8626164246218, 'ACC-broccoli': 80.40438926274608, 'ACC-carrot': 73.47479816555155, 'ACC-hot dog': 72.77430614566039, 'ACC-pizza': 92.89327341690374, 'ACC-donut': 81.96929838334684, 'ACC-cake': 74.46224318079202, 'ACC-chair': 71.59805095570778, 'ACC-couch': 82.03528126601526, 'ACC-potted plant': 52.2274535029668, 'ACC-bed': 80.13292389086062, 'ACC-dining table': 74.56884705462402, 'ACC-toilet': 93.25720138464392, 'ACC-tv': 86.59266690104685, 'ACC-laptop': 86.82732589937395, 'ACC-mouse': 78.29291256777378, 'ACC-remote': 71.82178983929091, 'ACC-keyboard': 55.1720582675851, 'ACC-cell phone': 70.34972693226327, 'ACC-microwave': 57.34664167436997, 'ACC-oven': 87.32291986957053, 'ACC-toaster': 73.46549152171235, 'ACC-sink': 84.00210092178555, 'ACC-refrigerator': 90.59086301103873, 'ACC-book': 66.78900142893166, 'ACC-clock': 78.2048010985357, 'ACC-vase': 73.14564030643773, 'ACC-scissors': 59.48383512840996, 'ACC-teddy bear': 86.94372619417999, 'ACC-hair drier': 76.33848518486893, 'ACC-toothbrush': 78.88898540653231, 'ACC-banner': 76.18771121047307, 'ACC-blanket': 13.167397773748446, 'ACC-bridge': 54.55701522708573, 'ACC-cardboard': 57.54136978333576, 'ACC-counter': 52.810099396665436, 'ACC-curtain': 76.79072018096844, 'ACC-door-stuff': 61.917851001412714, 'ACC-floor-wood': 77.90787477366938, 'ACC-flower': 63.97211336414554, 'ACC-fruit': 58.475957455296204, 'ACC-gravel': 41.00335581910096, 'ACC-house': 25.18811734989149, 'ACC-light': 55.34060023733874, 'ACC-mirror-stuff': 73.62918510280335, 'ACC-net': 60.423608755512106, 'ACC-pillow': 26.684759222055316, 'ACC-platform': 55.38721713823405, 'ACC-playingfield': 92.18639919361965, 'ACC-railroad': 79.57758205368096, 'ACC-river': 74.36610773067798, 'ACC-road': 87.04623909119906, 'ACC-roof': 23.8607814136179, 'ACC-sand': 71.06088091646099, 'ACC-sea': 92.04907279169757, 'ACC-shelf': 60.100495223142325, 'ACC-snow': 95.30724203629708, 'ACC-stairs': 35.08333594477189, 'ACC-tent': 10.03132436399831, 'ACC-towel': 44.45199655551524, 'ACC-wall-brick': 63.19388145988327, 'ACC-wall-stone': 38.289156367081524, 'ACC-wall-tile': 81.4231588898084, 'ACC-wall-wood': 48.341193405139045, 'ACC-water-other': 34.426401007920646, 'ACC-window-blind': 56.80312216393196, 'ACC-window-other': 71.21703702540593, 'ACC-tree-merged': 89.14327222499936, 'ACC-fence-merged': 73.55129811063469, 'ACC-ceiling-merged': 79.91682807542071, 'ACC-sky-other-merged': 96.58582471627902, 'ACC-cabinet-merged': 74.29510665364202, 'ACC-table-merged': 52.36714887436391, 'ACC-floor-other-merged': 60.96689905397801, 'ACC-pavement-merged': 65.3547655371008, 'ACC-mountain-merged': 63.45588370000599, 'ACC-grass-merged': 83.39196582193141, 'ACC-dirt-merged': 66.39144361186054, 'ACC-paper-merged': 47.40792605949799, 'ACC-food-other-merged': 51.087809301315744, 'ACC-building-other-merged': 72.56572123482532, 'ACC-rock-merged': 82.71585424805322, 'ACC-wall-other-merged': 79.64525404607005, 'ACC-rug-merged': 77.97357845763034})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4992683640620428, 'noc@0.8': 2.8741586186713493, 'noc@0.85': 3.4460052677787534, 'noc@0.9': 4.470002926543752, 'miou@iter1': 0.8220450435076885}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.16207122802734, 'precision@0.6': 67.78079986572266, 'precision@0.7': 62.53400802612305, 'precision@0.8': 52.39020538330078, 'precision@0.9': 27.283327102661133, 'cIoU': 57.304100036621094, 'mIoU': 62.80027389526367}}}
INFO:trainer.default_trainer:This epoch takes 1:28:03.938298
INFO:trainer.default_trainer:PROGRESS: 80.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 40 training.
INFO:trainer.default_trainer:epochs[    40] optim steps[73100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23307/0.89659, loss_mask_bce_0: 0.21339/0.33415, loss_mask_dice_0: 0.48923/1.16265, loss_spatial_bce_0: 0.06596/0.08689, loss_spatial_dice_0: 0.14934/0.20738, loss_spatial_ce_0: 0.02141/0.06071, loss_grounding_bce_0: 0.12913/0.08616, loss_grounding_dice_0: 0.15674/0.17844, loss_grounding_ce_0: 0.14303/0.27190, loss_mask_ce_1: 1.17530/0.89722, loss_mask_bce_1: 0.20899/0.33508, loss_mask_dice_1: 0.47475/1.16936, loss_spatial_bce_1: 0.06067/0.08743, loss_spatial_dice_1: 0.15789/0.21138, loss_spatial_ce_1: 0.03907/0.06655, loss_grounding_bce_1: 0.13243/0.08634, loss_grounding_dice_1: 0.16024/0.17926, loss_grounding_ce_1: 0.14423/0.27265, loss_mask_ce_2: 1.21508/0.90423, loss_mask_bce_2: 0.20997/0.33567, loss_mask_dice_2: 0.45150/1.16977, loss_spatial_bce_2: 0.05227/0.08860, loss_spatial_dice_2: 0.13437/0.21311, loss_spatial_ce_2: 0.13260/0.07002, loss_grounding_bce_2: 0.12984/0.08651, loss_grounding_dice_2: 0.16483/0.17913, loss_grounding_ce_2: 0.13884/0.27603, loss_mask_ce_3: 1.21695/0.91526, loss_mask_bce_3: 0.21531/0.33687, loss_mask_dice_3: 0.49757/1.16765, loss_spatial_bce_3: 0.05477/0.08985, loss_spatial_dice_3: 0.13108/0.21412, loss_spatial_ce_3: 0.11420/0.07489, loss_grounding_bce_3: 0.14480/0.08675, loss_grounding_dice_3: 0.16787/0.17886, loss_grounding_ce_3: 0.12584/0.27827, loss_mask_ce_4: 1.19545/0.91627, loss_mask_bce_4: 0.20299/0.33903, loss_mask_dice_4: 0.58183/1.19145, loss_spatial_bce_4: 0.05203/0.09377, loss_spatial_dice_4: 0.13991/0.22636, loss_spatial_ce_4: 0.14878/0.09116, loss_grounding_bce_4: 0.13636/0.08730, loss_grounding_dice_4: 0.16536/0.18177, loss_grounding_ce_4: 0.11543/0.28120, loss_mask_ce_5: 1.17577/0.93310, loss_mask_bce_5: 0.20065/0.34132, loss_mask_dice_5: 0.58884/1.19948, loss_spatial_bce_5: 0.05596/0.09605, loss_spatial_dice_5: 0.12943/0.23065, loss_spatial_ce_5: 0.18735/0.10521, loss_grounding_bce_5: 0.12719/0.08771, loss_grounding_dice_5: 0.16514/0.18306, loss_grounding_ce_5: 0.13816/0.29372, loss_mask_ce_6: 1.16926/0.97323, loss_mask_bce_6: 0.21412/0.34406, loss_mask_dice_6: 0.62191/1.20225, loss_spatial_bce_6: 0.06269/0.10170, loss_spatial_dice_6: 0.17088/0.23359, loss_spatial_ce_6: 0.07344/0.13065, loss_grounding_bce_6: 0.14692/0.08843, loss_grounding_dice_6: 0.17126/0.18341, loss_grounding_ce_6: 0.15759/0.30936, loss_mask_ce_7: 1.34088/1.01856, loss_mask_bce_7: 0.21701/0.35192, loss_mask_dice_7: 0.62398/1.25684, loss_spatial_bce_7: 0.09723/0.10964, loss_spatial_dice_7: 0.17733/0.26120, loss_spatial_ce_7: 0.12621/0.16583, loss_grounding_bce_7: 0.14969/0.09031, loss_grounding_dice_7: 0.17046/0.19072, loss_grounding_ce_7: 0.18547/0.33979, loss_mask_ce_8: 1.15867/1.12725, loss_mask_bce_8: 0.22497/0.36554, loss_mask_dice_8: 0.62867/1.32957, loss_spatial_bce_8: 0.09068/0.13010, loss_spatial_dice_8: 0.20773/0.29902, loss_spatial_ce_8: 0.25434/0.21989, loss_grounding_bce_8: 0.17144/0.09404, loss_grounding_dice_8: 0.16565/0.20151, loss_grounding_ce_8: 0.20778/0.40628, loss_mask_ce_9: 4.47237/3.67531, loss_mask_bce_9: 0.28486/0.39257, loss_mask_dice_9: 1.07734/1.90238, loss_spatial_bce_9: 0.36499/0.33272, loss_spatial_dice_9: 0.83419/0.82173, loss_spatial_ce_9: 1.66998/1.49484, loss_grounding_bce_9: 0.13343/0.10565, loss_grounding_dice_9: 0.18188/0.28087, loss_grounding_ce_9: 0.64903/0.67058] items per batch[64] items per second[0.13] total items[4678400] mini batches[ 73100] memory[7345] epoch remaining[1:28:36]
INFO:trainer.default_trainer:epochs[    40] optim steps[73200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78697/0.89649, loss_mask_bce_0: 0.46433/0.33415, loss_mask_dice_0: 1.08202/1.16247, loss_spatial_bce_0: 0.13103/0.08688, loss_spatial_dice_0: 0.30994/0.20735, loss_spatial_ce_0: 0.00919/0.06069, loss_grounding_bce_0: 0.12302/0.08616, loss_grounding_dice_0: 0.14874/0.17843, loss_grounding_ce_0: 0.12219/0.27188, loss_mask_ce_1: 0.82483/0.89711, loss_mask_bce_1: 0.46856/0.33508, loss_mask_dice_1: 1.06327/1.16917, loss_spatial_bce_1: 0.13084/0.08742, loss_spatial_dice_1: 0.31061/0.21134, loss_spatial_ce_1: 0.00832/0.06651, loss_grounding_bce_1: 0.10727/0.08634, loss_grounding_dice_1: 0.13191/0.17925, loss_grounding_ce_1: 0.09975/0.27263, loss_mask_ce_2: 0.81572/0.90411, loss_mask_bce_2: 0.47786/0.33568, loss_mask_dice_2: 1.04828/1.16958, loss_spatial_bce_2: 0.13176/0.08859, loss_spatial_dice_2: 0.29097/0.21307, loss_spatial_ce_2: 0.01859/0.06999, loss_grounding_bce_2: 0.11294/0.08651, loss_grounding_dice_2: 0.13331/0.17913, loss_grounding_ce_2: 0.11589/0.27600, loss_mask_ce_3: 0.80563/0.91513, loss_mask_bce_3: 0.46721/0.33688, loss_mask_dice_3: 1.04007/1.16746, loss_spatial_bce_3: 0.13077/0.08985, loss_spatial_dice_3: 0.30641/0.21409, loss_spatial_ce_3: 0.02302/0.07485, loss_grounding_bce_3: 0.12438/0.08675, loss_grounding_dice_3: 0.13759/0.17885, loss_grounding_ce_3: 0.05459/0.27822, loss_mask_ce_4: 0.82193/0.91615, loss_mask_bce_4: 0.47230/0.33903, loss_mask_dice_4: 1.17316/1.19127, loss_spatial_bce_4: 0.14497/0.09377, loss_spatial_dice_4: 0.30024/0.22633, loss_spatial_ce_4: 0.04625/0.09112, loss_grounding_bce_4: 0.11713/0.08730, loss_grounding_dice_4: 0.16247/0.18177, loss_grounding_ce_4: 0.09237/0.28117, loss_mask_ce_5: 0.88243/0.93298, loss_mask_bce_5: 0.47361/0.34133, loss_mask_dice_5: 1.12303/1.19929, loss_spatial_bce_5: 0.13548/0.09604, loss_spatial_dice_5: 0.29122/0.23061, loss_spatial_ce_5: 0.01167/0.10518, loss_grounding_bce_5: 0.10977/0.08771, loss_grounding_dice_5: 0.17268/0.18305, loss_grounding_ce_5: 0.15672/0.29369, loss_mask_ce_6: 0.98904/0.97313, loss_mask_bce_6: 0.49695/0.34406, loss_mask_dice_6: 1.10135/1.20206, loss_spatial_bce_6: 0.13629/0.10169, loss_spatial_dice_6: 0.31161/0.23355, loss_spatial_ce_6: 0.01851/0.13061, loss_grounding_bce_6: 0.13190/0.08844, loss_grounding_dice_6: 0.15551/0.18340, loss_grounding_ce_6: 0.13636/0.30933, loss_mask_ce_7: 0.92527/1.01846, loss_mask_bce_7: 0.54960/0.35192, loss_mask_dice_7: 1.22469/1.25666, loss_spatial_bce_7: 0.13237/0.10962, loss_spatial_dice_7: 0.32345/0.26116, loss_spatial_ce_7: 0.12771/0.16577, loss_grounding_bce_7: 0.16036/0.09031, loss_grounding_dice_7: 0.21599/0.19073, loss_grounding_ce_7: 0.18273/0.33973, loss_mask_ce_8: 1.30072/1.12714, loss_mask_bce_8: 0.55828/0.36554, loss_mask_dice_8: 1.36520/1.32939, loss_spatial_bce_8: 0.15189/0.13008, loss_spatial_dice_8: 0.31930/0.29896, loss_spatial_ce_8: 0.08568/0.21981, loss_grounding_bce_8: 0.08575/0.09403, loss_grounding_dice_8: 0.15795/0.20150, loss_grounding_ce_8: 0.40773/0.40621, loss_mask_ce_9: 2.96947/3.67497, loss_mask_bce_9: 0.57270/0.39256, loss_mask_dice_9: 1.99679/1.90208, loss_spatial_bce_9: 0.19694/0.33275, loss_spatial_dice_9: 0.88714/0.82171, loss_spatial_ce_9: 1.35474/1.49474, loss_grounding_bce_9: 0.08864/0.10565, loss_grounding_dice_9: 0.14634/0.28086, loss_grounding_ce_9: 1.20786/0.67043] items per batch[64] items per second[0.24] total items[4684800] mini batches[ 73200] memory[7345] epoch remaining[1:18:06]
INFO:trainer.default_trainer:epochs[    40] optim steps[73300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.93281/0.89657, loss_mask_bce_0: 0.18121/0.33414, loss_mask_dice_0: 2.40252/1.16248, loss_spatial_bce_0: 0.05166/0.08688, loss_spatial_dice_0: 0.28975/0.20734, loss_spatial_ce_0: 0.01230/0.06069, loss_grounding_bce_0: 0.01211/0.08616, loss_grounding_dice_0: 0.26656/0.17846, loss_grounding_ce_0: 0.16234/0.27198, loss_mask_ce_1: 0.86585/0.89721, loss_mask_bce_1: 0.17457/0.33508, loss_mask_dice_1: 2.21395/1.16919, loss_spatial_bce_1: 0.04912/0.08742, loss_spatial_dice_1: 0.28621/0.21134, loss_spatial_ce_1: 0.02033/0.06651, loss_grounding_bce_1: 0.01325/0.08634, loss_grounding_dice_1: 0.31980/0.17928, loss_grounding_ce_1: 0.15015/0.27274, loss_mask_ce_2: 0.82833/0.90420, loss_mask_bce_2: 0.18673/0.33567, loss_mask_dice_2: 2.17627/1.16959, loss_spatial_bce_2: 0.05316/0.08859, loss_spatial_dice_2: 0.32276/0.21308, loss_spatial_ce_2: 0.02945/0.06998, loss_grounding_bce_2: 0.01534/0.08651, loss_grounding_dice_2: 0.30154/0.17915, loss_grounding_ce_2: 0.13918/0.27609, loss_mask_ce_3: 0.85058/0.91522, loss_mask_bce_3: 0.19478/0.33687, loss_mask_dice_3: 2.31982/1.16749, loss_spatial_bce_3: 0.04875/0.08985, loss_spatial_dice_3: 0.32202/0.21408, loss_spatial_ce_3: 0.01571/0.07485, loss_grounding_bce_3: 0.01374/0.08676, loss_grounding_dice_3: 0.30236/0.17887, loss_grounding_ce_3: 0.14481/0.27831, loss_mask_ce_4: 0.93521/0.91623, loss_mask_bce_4: 0.20566/0.33902, loss_mask_dice_4: 2.42391/1.19131, loss_spatial_bce_4: 0.05032/0.09377, loss_spatial_dice_4: 0.32762/0.22633, loss_spatial_ce_4: 0.07410/0.09111, loss_grounding_bce_4: 0.01408/0.08730, loss_grounding_dice_4: 0.36209/0.18180, loss_grounding_ce_4: 0.13099/0.28128, loss_mask_ce_5: 1.22000/0.93307, loss_mask_bce_5: 0.18129/0.34132, loss_mask_dice_5: 2.20806/1.19930, loss_spatial_bce_5: 0.05072/0.09604, loss_spatial_dice_5: 0.31614/0.23061, loss_spatial_ce_5: 0.10562/0.10518, loss_grounding_bce_5: 0.01270/0.08772, loss_grounding_dice_5: 0.33814/0.18308, loss_grounding_ce_5: 0.30943/0.29378, loss_mask_ce_6: 1.16580/0.97320, loss_mask_bce_6: 0.18276/0.34406, loss_mask_dice_6: 2.06175/1.20207, loss_spatial_bce_6: 0.03803/0.10169, loss_spatial_dice_6: 0.30701/0.23355, loss_spatial_ce_6: 0.18065/0.13059, loss_grounding_bce_6: 0.01414/0.08844, loss_grounding_dice_6: 0.23990/0.18343, loss_grounding_ce_6: 0.21298/0.30942, loss_mask_ce_7: 0.93693/1.01851, loss_mask_bce_7: 0.21015/0.35191, loss_mask_dice_7: 2.48198/1.25668, loss_spatial_bce_7: 0.05550/0.10962, loss_spatial_dice_7: 0.38278/0.26116, loss_spatial_ce_7: 0.51910/0.16577, loss_grounding_bce_7: 0.01221/0.09032, loss_grounding_dice_7: 0.28974/0.19075, loss_grounding_ce_7: 0.31444/0.33980, loss_mask_ce_8: 1.30212/1.12725, loss_mask_bce_8: 0.20461/0.36554, loss_mask_dice_8: 2.56721/1.32943, loss_spatial_bce_8: 0.13884/0.13006, loss_spatial_dice_8: 0.42344/0.29895, loss_spatial_ce_8: 0.23541/0.21979, loss_grounding_bce_8: 0.01803/0.09404, loss_grounding_dice_8: 0.37725/0.20153, loss_grounding_ce_8: 0.66788/0.40637, loss_mask_ce_9: 3.14705/3.67524, loss_mask_bce_9: 0.14646/0.39255, loss_mask_dice_9: 2.86582/1.90205, loss_spatial_bce_9: 0.30274/0.33276, loss_spatial_dice_9: 0.89890/0.82171, loss_spatial_ce_9: 1.81674/1.49479, loss_grounding_bce_9: 0.01243/0.10565, loss_grounding_dice_9: 0.39674/0.28088, loss_grounding_ce_9: 1.01013/0.67052] items per batch[64] items per second[0.23] total items[4691200] mini batches[ 73300] memory[7345] epoch remaining[1:14:18]
INFO:trainer.default_trainer:epochs[    40] optim steps[73400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42964/0.89664, loss_mask_bce_0: 0.26789/0.33413, loss_mask_dice_0: 0.67124/1.16251, loss_spatial_bce_0: 0.08589/0.08688, loss_spatial_dice_0: 0.17718/0.20735, loss_spatial_ce_0: 0.01263/0.06067, loss_grounding_bce_0: 0.03878/0.08615, loss_grounding_dice_0: 0.05560/0.17847, loss_grounding_ce_0: 0.19348/0.27195, loss_mask_ce_1: 0.52153/0.89727, loss_mask_bce_1: 0.26735/0.33507, loss_mask_dice_1: 0.61206/1.16919, loss_spatial_bce_1: 0.08501/0.08742, loss_spatial_dice_1: 0.16982/0.21134, loss_spatial_ce_1: 0.00876/0.06648, loss_grounding_bce_1: 0.03943/0.08633, loss_grounding_dice_1: 0.06349/0.17929, loss_grounding_ce_1: 0.18837/0.27272, loss_mask_ce_2: 0.35874/0.90428, loss_mask_bce_2: 0.30359/0.33566, loss_mask_dice_2: 0.63406/1.16963, loss_spatial_bce_2: 0.08912/0.08859, loss_spatial_dice_2: 0.16215/0.21308, loss_spatial_ce_2: 0.01094/0.06997, loss_grounding_bce_2: 0.04558/0.08650, loss_grounding_dice_2: 0.07287/0.17916, loss_grounding_ce_2: 0.15509/0.27609, loss_mask_ce_3: 0.50727/0.91529, loss_mask_bce_3: 0.27116/0.33686, loss_mask_dice_3: 0.56586/1.16752, loss_spatial_bce_3: 0.09295/0.08985, loss_spatial_dice_3: 0.16924/0.21408, loss_spatial_ce_3: 0.00935/0.07483, loss_grounding_bce_3: 0.04251/0.08675, loss_grounding_dice_3: 0.06709/0.17889, loss_grounding_ce_3: 0.20938/0.27831, loss_mask_ce_4: 0.36356/0.91629, loss_mask_bce_4: 0.30302/0.33901, loss_mask_dice_4: 0.64694/1.19132, loss_spatial_bce_4: 0.08542/0.09377, loss_spatial_dice_4: 0.17174/0.22634, loss_spatial_ce_4: 0.04320/0.09109, loss_grounding_bce_4: 0.04336/0.08729, loss_grounding_dice_4: 0.05885/0.18181, loss_grounding_ce_4: 0.22895/0.28123, loss_mask_ce_5: 0.49769/0.93312, loss_mask_bce_5: 0.30184/0.34132, loss_mask_dice_5: 0.57833/1.19933, loss_spatial_bce_5: 0.09432/0.09605, loss_spatial_dice_5: 0.18120/0.23062, loss_spatial_ce_5: 0.08190/0.10518, loss_grounding_bce_5: 0.04320/0.08771, loss_grounding_dice_5: 0.06424/0.18309, loss_grounding_ce_5: 0.26349/0.29374, loss_mask_ce_6: 0.40951/0.97328, loss_mask_bce_6: 0.31755/0.34405, loss_mask_dice_6: 0.63381/1.20206, loss_spatial_bce_6: 0.12456/0.10169, loss_spatial_dice_6: 0.19623/0.23356, loss_spatial_ce_6: 0.07735/0.13057, loss_grounding_bce_6: 0.04090/0.08843, loss_grounding_dice_6: 0.06194/0.18344, loss_grounding_ce_6: 0.27325/0.30940, loss_mask_ce_7: 0.55774/1.01858, loss_mask_bce_7: 0.29298/0.35191, loss_mask_dice_7: 0.65435/1.25672, loss_spatial_bce_7: 0.11329/0.10962, loss_spatial_dice_7: 0.20081/0.26117, loss_spatial_ce_7: 0.11512/0.16578, loss_grounding_bce_7: 0.03870/0.09031, loss_grounding_dice_7: 0.05715/0.19077, loss_grounding_ce_7: 0.48416/0.33975, loss_mask_ce_8: 0.57534/1.12733, loss_mask_bce_8: 0.29249/0.36554, loss_mask_dice_8: 0.68078/1.32946, loss_spatial_bce_8: 0.10155/0.13006, loss_spatial_dice_8: 0.20424/0.29896, loss_spatial_ce_8: 0.11262/0.21976, loss_grounding_bce_8: 0.04606/0.09403, loss_grounding_dice_8: 0.05964/0.20155, loss_grounding_ce_8: 0.60751/0.40631, loss_mask_ce_9: 3.03282/3.67520, loss_mask_bce_9: 0.32243/0.39255, loss_mask_dice_9: 0.86078/1.90210, loss_spatial_bce_9: 0.65087/0.33275, loss_spatial_dice_9: 0.83925/0.82172, loss_spatial_ce_9: 1.52785/1.49485, loss_grounding_bce_9: 0.05688/0.10563, loss_grounding_dice_9: 0.08185/0.28090, loss_grounding_ce_9: 0.95307/0.67045] items per batch[64] items per second[0.24] total items[4697600] mini batches[ 73400] memory[7345] epoch remaining[1:09:16]
INFO:trainer.default_trainer:epochs[    40] optim steps[73500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44704/0.89662, loss_mask_bce_0: 0.06974/0.33412, loss_mask_dice_0: 0.57228/1.16252, loss_spatial_bce_0: 0.03120/0.08688, loss_spatial_dice_0: 0.18142/0.20733, loss_spatial_ce_0: 0.03183/0.06065, loss_grounding_bce_0: 0.01678/0.08615, loss_grounding_dice_0: 0.15736/0.17849, loss_grounding_ce_0: 0.02760/0.27196, loss_mask_ce_1: 0.61437/0.89727, loss_mask_bce_1: 0.07509/0.33506, loss_mask_dice_1: 0.56110/1.16921, loss_spatial_bce_1: 0.03157/0.08742, loss_spatial_dice_1: 0.18978/0.21132, loss_spatial_ce_1: 0.05290/0.06645, loss_grounding_bce_1: 0.01894/0.08633, loss_grounding_dice_1: 0.16532/0.17930, loss_grounding_ce_1: 0.03083/0.27273, loss_mask_ce_2: 0.65277/0.90426, loss_mask_bce_2: 0.07020/0.33564, loss_mask_dice_2: 0.50361/1.16966, loss_spatial_bce_2: 0.03215/0.08859, loss_spatial_dice_2: 0.19307/0.21306, loss_spatial_ce_2: 0.07250/0.06995, loss_grounding_bce_2: 0.01775/0.08650, loss_grounding_dice_2: 0.06993/0.17916, loss_grounding_ce_2: 0.15172/0.27610, loss_mask_ce_3: 0.64994/0.91527, loss_mask_bce_3: 0.07034/0.33684, loss_mask_dice_3: 0.38414/1.16754, loss_spatial_bce_3: 0.03132/0.08985, loss_spatial_dice_3: 0.18874/0.21407, loss_spatial_ce_3: 0.14103/0.07482, loss_grounding_bce_3: 0.01886/0.08674, loss_grounding_dice_3: 0.09719/0.17889, loss_grounding_ce_3: 0.11986/0.27831, loss_mask_ce_4: 0.44006/0.91627, loss_mask_bce_4: 0.06662/0.33900, loss_mask_dice_4: 0.51300/1.19135, loss_spatial_bce_4: 0.03090/0.09377, loss_spatial_dice_4: 0.17309/0.22633, loss_spatial_ce_4: 0.09569/0.09108, loss_grounding_bce_4: 0.01727/0.08728, loss_grounding_dice_4: 0.06149/0.18182, loss_grounding_ce_4: 0.12388/0.28122, loss_mask_ce_5: 0.48259/0.93314, loss_mask_bce_5: 0.07145/0.34130, loss_mask_dice_5: 0.52922/1.19936, loss_spatial_bce_5: 0.03151/0.09605, loss_spatial_dice_5: 0.14649/0.23060, loss_spatial_ce_5: 0.12175/0.10517, loss_grounding_bce_5: 0.01914/0.08770, loss_grounding_dice_5: 0.17677/0.18311, loss_grounding_ce_5: 0.06455/0.29373, loss_mask_ce_6: 0.80155/0.97327, loss_mask_bce_6: 0.07627/0.34404, loss_mask_dice_6: 0.56850/1.20211, loss_spatial_bce_6: 0.03337/0.10169, loss_spatial_dice_6: 0.22257/0.23354, loss_spatial_ce_6: 0.15411/0.13053, loss_grounding_bce_6: 0.01954/0.08843, loss_grounding_dice_6: 0.15015/0.18345, loss_grounding_ce_6: 0.03556/0.30938, loss_mask_ce_7: 0.99004/1.01863, loss_mask_bce_7: 0.07346/0.35189, loss_mask_dice_7: 0.50263/1.25674, loss_spatial_bce_7: 0.03073/0.10961, loss_spatial_dice_7: 0.18767/0.26116, loss_spatial_ce_7: 0.08440/0.16576, loss_grounding_bce_7: 0.01785/0.09031, loss_grounding_dice_7: 0.17313/0.19079, loss_grounding_ce_7: 0.15458/0.33975, loss_mask_ce_8: 0.80898/1.12734, loss_mask_bce_8: 0.07521/0.36551, loss_mask_dice_8: 0.55402/1.32949, loss_spatial_bce_8: 0.03075/0.13005, loss_spatial_dice_8: 0.26038/0.29895, loss_spatial_ce_8: 0.36037/0.21972, loss_grounding_bce_8: 0.02047/0.09402, loss_grounding_dice_8: 0.19333/0.20155, loss_grounding_ce_8: 0.46216/0.40628, loss_mask_ce_9: 2.85280/3.67509, loss_mask_bce_9: 0.08737/0.39253, loss_mask_dice_9: 0.70804/1.90209, loss_spatial_bce_9: 0.25583/0.33275, loss_spatial_dice_9: 0.73532/0.82171, loss_spatial_ce_9: 1.31570/1.49475, loss_grounding_bce_9: 0.02307/0.10563, loss_grounding_dice_9: 0.16979/0.28091, loss_grounding_ce_9: 0.46850/0.67037] items per batch[64] items per second[0.22] total items[4704000] mini batches[ 73500] memory[7345] epoch remaining[1:05:16]
INFO:trainer.default_trainer:epochs[    40] optim steps[73600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.49166/0.89659, loss_mask_bce_0: 0.20355/0.33412, loss_mask_dice_0: 1.87273/1.16239, loss_spatial_bce_0: 0.13284/0.08688, loss_spatial_dice_0: 0.24273/0.20730, loss_spatial_ce_0: 0.20691/0.06062, loss_grounding_bce_0: 0.04318/0.08615, loss_grounding_dice_0: 0.12608/0.17846, loss_grounding_ce_0: 0.59863/0.27200, loss_mask_ce_1: 0.50945/0.89722, loss_mask_bce_1: 0.18781/0.33506, loss_mask_dice_1: 1.70315/1.16908, loss_spatial_bce_1: 0.13932/0.08742, loss_spatial_dice_1: 0.29980/0.21129, loss_spatial_ce_1: 0.21132/0.06642, loss_grounding_bce_1: 0.04512/0.08633, loss_grounding_dice_1: 0.11233/0.17927, loss_grounding_ce_1: 0.58803/0.27276, loss_mask_ce_2: 0.53381/0.90422, loss_mask_bce_2: 0.19428/0.33565, loss_mask_dice_2: 1.82436/1.16952, loss_spatial_bce_2: 0.16387/0.08860, loss_spatial_dice_2: 0.34283/0.21304, loss_spatial_ce_2: 0.15326/0.06992, loss_grounding_bce_2: 0.04448/0.08650, loss_grounding_dice_2: 0.10573/0.17914, loss_grounding_ce_2: 0.50753/0.27612, loss_mask_ce_3: 0.51441/0.91520, loss_mask_bce_3: 0.21103/0.33685, loss_mask_dice_3: 2.05603/1.16742, loss_spatial_bce_3: 0.14472/0.08985, loss_spatial_dice_3: 0.32997/0.21405, loss_spatial_ce_3: 0.23598/0.07480, loss_grounding_bce_3: 0.04736/0.08674, loss_grounding_dice_3: 0.13654/0.17886, loss_grounding_ce_3: 0.42331/0.27833, loss_mask_ce_4: 0.59599/0.91623, loss_mask_bce_4: 0.19985/0.33900, loss_mask_dice_4: 1.92188/1.19124, loss_spatial_bce_4: 0.11681/0.09377, loss_spatial_dice_4: 0.30606/0.22630, loss_spatial_ce_4: 0.12485/0.09105, loss_grounding_bce_4: 0.04369/0.08729, loss_grounding_dice_4: 0.11267/0.18180, loss_grounding_ce_4: 0.36381/0.28123, loss_mask_ce_5: 0.87721/0.93310, loss_mask_bce_5: 0.19364/0.34130, loss_mask_dice_5: 1.62640/1.19922, loss_spatial_bce_5: 0.07414/0.09605, loss_spatial_dice_5: 0.28855/0.23057, loss_spatial_ce_5: 0.16850/0.10514, loss_grounding_bce_5: 0.04401/0.08771, loss_grounding_dice_5: 0.11916/0.18308, loss_grounding_ce_5: 0.31581/0.29372, loss_mask_ce_6: 0.57574/0.97324, loss_mask_bce_6: 0.18988/0.34405, loss_mask_dice_6: 1.89552/1.20197, loss_spatial_bce_6: 0.07201/0.10169, loss_spatial_dice_6: 0.34168/0.23352, loss_spatial_ce_6: 0.19352/0.13050, loss_grounding_bce_6: 0.04800/0.08843, loss_grounding_dice_6: 0.11075/0.18343, loss_grounding_ce_6: 0.24570/0.30936, loss_mask_ce_7: 0.72218/1.01861, loss_mask_bce_7: 0.19106/0.35189, loss_mask_dice_7: 1.42407/1.25658, loss_spatial_bce_7: 0.09251/0.10960, loss_spatial_dice_7: 0.27769/0.26113, loss_spatial_ce_7: 0.10155/0.16573, loss_grounding_bce_7: 0.04835/0.09032, loss_grounding_dice_7: 0.10537/0.19077, loss_grounding_ce_7: 0.19555/0.33969, loss_mask_ce_8: 0.88139/1.12727, loss_mask_bce_8: 0.20696/0.36553, loss_mask_dice_8: 1.91123/1.32936, loss_spatial_bce_8: 0.09085/0.13004, loss_spatial_dice_8: 0.26501/0.29891, loss_spatial_ce_8: 0.21682/0.21964, loss_grounding_bce_8: 0.06379/0.09402, loss_grounding_dice_8: 0.15488/0.20151, loss_grounding_ce_8: 0.42872/0.40621, loss_mask_ce_9: 3.82157/3.67510, loss_mask_bce_9: 0.21772/0.39253, loss_mask_dice_9: 1.90949/1.90197, loss_spatial_bce_9: 0.19838/0.33278, loss_spatial_dice_9: 0.60163/0.82170, loss_spatial_ce_9: 2.60185/1.49464, loss_grounding_bce_9: 0.05830/0.10563, loss_grounding_dice_9: 0.12701/0.28088, loss_grounding_ce_9: 0.92083/0.67033] items per batch[64] items per second[0.23] total items[4710400] mini batches[ 73600] memory[7345] epoch remaining[1:00:35]
INFO:trainer.default_trainer:epochs[    40] optim steps[73700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85523/0.89661, loss_mask_bce_0: 0.58847/0.33413, loss_mask_dice_0: 1.35230/1.16259, loss_spatial_bce_0: 0.07701/0.08687, loss_spatial_dice_0: 0.23977/0.20728, loss_spatial_ce_0: 0.00633/0.06059, loss_grounding_bce_0: 0.07059/0.08614, loss_grounding_dice_0: 0.18494/0.17845, loss_grounding_ce_0: 0.25977/0.27202, loss_mask_ce_1: 0.89466/0.89724, loss_mask_bce_1: 0.61127/0.33507, loss_mask_dice_1: 1.40437/1.16929, loss_spatial_bce_1: 0.08305/0.08741, loss_spatial_dice_1: 0.22627/0.21127, loss_spatial_ce_1: 0.00451/0.06639, loss_grounding_bce_1: 0.07525/0.08632, loss_grounding_dice_1: 0.21064/0.17926, loss_grounding_ce_1: 0.26541/0.27276, loss_mask_ce_2: 0.94760/0.90422, loss_mask_bce_2: 0.63171/0.33566, loss_mask_dice_2: 1.38303/1.16973, loss_spatial_bce_2: 0.08938/0.08858, loss_spatial_dice_2: 0.24720/0.21302, loss_spatial_ce_2: 0.00408/0.06988, loss_grounding_bce_2: 0.07968/0.08649, loss_grounding_dice_2: 0.22075/0.17914, loss_grounding_ce_2: 0.26153/0.27612, loss_mask_ce_3: 0.92289/0.91521, loss_mask_bce_3: 0.60856/0.33685, loss_mask_dice_3: 1.36882/1.16764, loss_spatial_bce_3: 0.08764/0.08984, loss_spatial_dice_3: 0.24396/0.21403, loss_spatial_ce_3: 0.00754/0.07477, loss_grounding_bce_3: 0.07391/0.08674, loss_grounding_dice_3: 0.20326/0.17886, loss_grounding_ce_3: 0.28615/0.27832, loss_mask_ce_4: 1.02532/0.91624, loss_mask_bce_4: 0.64041/0.33900, loss_mask_dice_4: 1.38424/1.19145, loss_spatial_bce_4: 0.07976/0.09376, loss_spatial_dice_4: 0.23881/0.22629, loss_spatial_ce_4: 0.03473/0.09103, loss_grounding_bce_4: 0.07441/0.08728, loss_grounding_dice_4: 0.20747/0.18180, loss_grounding_ce_4: 0.26364/0.28126, loss_mask_ce_5: 1.27746/0.93313, loss_mask_bce_5: 0.65813/0.34130, loss_mask_dice_5: 1.63084/1.19947, loss_spatial_bce_5: 0.06776/0.09604, loss_spatial_dice_5: 0.23729/0.23056, loss_spatial_ce_5: 0.03493/0.10512, loss_grounding_bce_5: 0.07656/0.08770, loss_grounding_dice_5: 0.20859/0.18308, loss_grounding_ce_5: 0.28940/0.29372, loss_mask_ce_6: 1.22671/0.97326, loss_mask_bce_6: 0.64089/0.34405, loss_mask_dice_6: 1.54381/1.20220, loss_spatial_bce_6: 0.08130/0.10168, loss_spatial_dice_6: 0.24487/0.23350, loss_spatial_ce_6: 0.03014/0.13047, loss_grounding_bce_6: 0.07327/0.08843, loss_grounding_dice_6: 0.22467/0.18343, loss_grounding_ce_6: 0.28765/0.30936, loss_mask_ce_7: 1.13789/1.01861, loss_mask_bce_7: 0.67309/0.35190, loss_mask_dice_7: 1.73003/1.25682, loss_spatial_bce_7: 0.07565/0.10959, loss_spatial_dice_7: 0.25502/0.26112, loss_spatial_ce_7: 0.05014/0.16570, loss_grounding_bce_7: 0.07296/0.09031, loss_grounding_dice_7: 0.21638/0.19076, loss_grounding_ce_7: 0.31824/0.33969, loss_mask_ce_8: 1.40601/1.12727, loss_mask_bce_8: 0.71631/0.36553, loss_mask_dice_8: 1.77603/1.32963, loss_spatial_bce_8: 0.11731/0.13002, loss_spatial_dice_8: 0.31831/0.29889, loss_spatial_ce_8: 0.12706/0.21958, loss_grounding_bce_8: 0.09648/0.09402, loss_grounding_dice_8: 0.24681/0.20152, loss_grounding_ce_8: 0.31188/0.40618, loss_mask_ce_9: 4.85845/3.67526, loss_mask_bce_9: 0.68262/0.39255, loss_mask_dice_9: 3.29534/1.90239, loss_spatial_bce_9: 0.28879/0.33278, loss_spatial_dice_9: 0.94111/0.82170, loss_spatial_ce_9: 1.50844/1.49459, loss_grounding_bce_9: 0.08497/0.10562, loss_grounding_dice_9: 0.39105/0.28089, loss_grounding_ce_9: 0.50058/0.67031] items per batch[64] items per second[0.23] total items[4716800] mini batches[ 73700] memory[7345] epoch remaining[0:55:52]
INFO:trainer.default_trainer:epochs[    40] optim steps[73800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72233/0.89649, loss_mask_bce_0: 0.13921/0.33412, loss_mask_dice_0: 0.14166/1.16227, loss_spatial_bce_0: 0.08565/0.08686, loss_spatial_dice_0: 0.07827/0.20725, loss_spatial_ce_0: 0.04786/0.06058, loss_grounding_bce_0: 0.06143/0.08616, loss_grounding_dice_0: 0.05586/0.17842, loss_grounding_ce_0: 0.15475/0.27199, loss_mask_ce_1: 0.76113/0.89710, loss_mask_bce_1: 0.13256/0.33506, loss_mask_dice_1: 0.12986/1.16897, loss_spatial_bce_1: 0.08044/0.08741, loss_spatial_dice_1: 0.07801/0.21124, loss_spatial_ce_1: 0.04130/0.06638, loss_grounding_bce_1: 0.06231/0.08634, loss_grounding_dice_1: 0.05305/0.17924, loss_grounding_ce_1: 0.15455/0.27273, loss_mask_ce_2: 0.76061/0.90411, loss_mask_bce_2: 0.13649/0.33565, loss_mask_dice_2: 0.13577/1.16939, loss_spatial_bce_2: 0.08267/0.08858, loss_spatial_dice_2: 0.07516/0.21299, loss_spatial_ce_2: 0.04036/0.06987, loss_grounding_bce_2: 0.06134/0.08651, loss_grounding_dice_2: 0.05660/0.17911, loss_grounding_ce_2: 0.17440/0.27608, loss_mask_ce_3: 0.76929/0.91508, loss_mask_bce_3: 0.14324/0.33685, loss_mask_dice_3: 0.13957/1.16733, loss_spatial_bce_3: 0.08605/0.08984, loss_spatial_dice_3: 0.07550/0.21400, loss_spatial_ce_3: 0.04029/0.07475, loss_grounding_bce_3: 0.06112/0.08675, loss_grounding_dice_3: 0.05265/0.17884, loss_grounding_ce_3: 0.17137/0.27828, loss_mask_ce_4: 0.77978/0.91613, loss_mask_bce_4: 0.13219/0.33900, loss_mask_dice_4: 0.13114/1.19113, loss_spatial_bce_4: 0.09467/0.09376, loss_spatial_dice_4: 0.07757/0.22625, loss_spatial_ce_4: 0.04052/0.09101, loss_grounding_bce_4: 0.06231/0.08730, loss_grounding_dice_4: 0.05584/0.18177, loss_grounding_ce_4: 0.16021/0.28123, loss_mask_ce_5: 0.72336/0.93299, loss_mask_bce_5: 0.13264/0.34129, loss_mask_dice_5: 0.12407/1.19916, loss_spatial_bce_5: 0.08055/0.09604, loss_spatial_dice_5: 0.06236/0.23053, loss_spatial_ce_5: 0.04684/0.10511, loss_grounding_bce_5: 0.06349/0.08771, loss_grounding_dice_5: 0.05053/0.18306, loss_grounding_ce_5: 0.15637/0.29369, loss_mask_ce_6: 0.88255/0.97313, loss_mask_bce_6: 0.14529/0.34404, loss_mask_dice_6: 0.13523/1.20188, loss_spatial_bce_6: 0.10154/0.10169, loss_spatial_dice_6: 0.08446/0.23348, loss_spatial_ce_6: 0.05941/0.13044, loss_grounding_bce_6: 0.06606/0.08844, loss_grounding_dice_6: 0.05090/0.18341, loss_grounding_ce_6: 0.17053/0.30928, loss_mask_ce_7: 0.84850/1.01848, loss_mask_bce_7: 0.15317/0.35188, loss_mask_dice_7: 0.14690/1.25648, loss_spatial_bce_7: 0.08761/0.10960, loss_spatial_dice_7: 0.07557/0.26109, loss_spatial_ce_7: 0.08103/0.16564, loss_grounding_bce_7: 0.06911/0.09032, loss_grounding_dice_7: 0.05658/0.19074, loss_grounding_ce_7: 0.20301/0.33966, loss_mask_ce_8: 1.00627/1.12714, loss_mask_bce_8: 0.14828/0.36552, loss_mask_dice_8: 0.15979/1.32926, loss_spatial_bce_8: 0.11025/0.13002, loss_spatial_dice_8: 0.08281/0.29886, loss_spatial_ce_8: 0.08196/0.21951, loss_grounding_bce_8: 0.06412/0.09403, loss_grounding_dice_8: 0.06056/0.20149, loss_grounding_ce_8: 0.22496/0.40616, loss_mask_ce_9: 3.36639/3.67497, loss_mask_bce_9: 0.15014/0.39255, loss_mask_dice_9: 0.16259/1.90189, loss_spatial_bce_9: 0.48656/0.33282, loss_spatial_dice_9: 0.74622/0.82171, loss_spatial_ce_9: 1.12835/1.49462, loss_grounding_bce_9: 0.07006/0.10563, loss_grounding_dice_9: 0.06420/0.28085, loss_grounding_ce_9: 0.55239/0.67031] items per batch[64] items per second[0.23] total items[4723200] mini batches[ 73800] memory[7345] epoch remaining[0:51:24]
INFO:trainer.default_trainer:epochs[    40] optim steps[73900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66078/0.89636, loss_mask_bce_0: 0.18122/0.33413, loss_mask_dice_0: 1.28154/1.16249, loss_spatial_bce_0: 0.04584/0.08686, loss_spatial_dice_0: 0.18919/0.20724, loss_spatial_ce_0: 0.00883/0.06057, loss_grounding_bce_0: 0.04657/0.08616, loss_grounding_dice_0: 0.07165/0.17841, loss_grounding_ce_0: 0.04508/0.27196, loss_mask_ce_1: 0.70157/0.89701, loss_mask_bce_1: 0.17350/0.33507, loss_mask_dice_1: 1.10761/1.16918, loss_spatial_bce_1: 0.04495/0.08741, loss_spatial_dice_1: 0.18712/0.21123, loss_spatial_ce_1: 0.09957/0.06636, loss_grounding_bce_1: 0.04397/0.08634, loss_grounding_dice_1: 0.08830/0.17923, loss_grounding_ce_1: 0.04955/0.27270, loss_mask_ce_2: 0.94382/0.90399, loss_mask_bce_2: 0.17032/0.33566, loss_mask_dice_2: 1.01189/1.16961, loss_spatial_bce_2: 0.04087/0.08858, loss_spatial_dice_2: 0.19457/0.21298, loss_spatial_ce_2: 0.02835/0.06986, loss_grounding_bce_2: 0.04379/0.08651, loss_grounding_dice_2: 0.07614/0.17910, loss_grounding_ce_2: 0.03761/0.27607, loss_mask_ce_3: 0.70537/0.91494, loss_mask_bce_3: 0.17343/0.33686, loss_mask_dice_3: 1.15668/1.16756, loss_spatial_bce_3: 0.03581/0.08984, loss_spatial_dice_3: 0.16061/0.21400, loss_spatial_ce_3: 0.05599/0.07474, loss_grounding_bce_3: 0.04462/0.08676, loss_grounding_dice_3: 0.07543/0.17883, loss_grounding_ce_3: 0.09526/0.27827, loss_mask_ce_4: 0.82717/0.91601, loss_mask_bce_4: 0.18283/0.33901, loss_mask_dice_4: 1.03901/1.19134, loss_spatial_bce_4: 0.04210/0.09376, loss_spatial_dice_4: 0.19684/0.22625, loss_spatial_ce_4: 0.06923/0.09100, loss_grounding_bce_4: 0.04600/0.08731, loss_grounding_dice_4: 0.08855/0.18177, loss_grounding_ce_4: 0.05868/0.28120, loss_mask_ce_5: 0.79078/0.93286, loss_mask_bce_5: 0.17575/0.34131, loss_mask_dice_5: 0.92730/1.19940, loss_spatial_bce_5: 0.04454/0.09604, loss_spatial_dice_5: 0.21579/0.23052, loss_spatial_ce_5: 0.13704/0.10509, loss_grounding_bce_5: 0.04366/0.08772, loss_grounding_dice_5: 0.07281/0.18306, loss_grounding_ce_5: 0.03977/0.29364, loss_mask_ce_6: 0.73015/0.97302, loss_mask_bce_6: 0.17675/0.34406, loss_mask_dice_6: 1.01953/1.20209, loss_spatial_bce_6: 0.04331/0.10169, loss_spatial_dice_6: 0.18963/0.23348, loss_spatial_ce_6: 0.10999/0.13041, loss_grounding_bce_6: 0.04117/0.08845, loss_grounding_dice_6: 0.06975/0.18340, loss_grounding_ce_6: 0.02356/0.30925, loss_mask_ce_7: 0.71420/1.01835, loss_mask_bce_7: 0.17095/0.35190, loss_mask_dice_7: 1.09345/1.25670, loss_spatial_bce_7: 0.04289/0.10959, loss_spatial_dice_7: 0.19862/0.26109, loss_spatial_ce_7: 0.15344/0.16563, loss_grounding_bce_7: 0.04291/0.09033, loss_grounding_dice_7: 0.07347/0.19072, loss_grounding_ce_7: 0.04194/0.33961, loss_mask_ce_8: 1.06752/1.12707, loss_mask_bce_8: 0.17265/0.36554, loss_mask_dice_8: 1.02681/1.32947, loss_spatial_bce_8: 0.05232/0.13001, loss_spatial_dice_8: 0.25472/0.29885, loss_spatial_ce_8: 0.23316/0.21945, loss_grounding_bce_8: 0.04997/0.09404, loss_grounding_dice_8: 0.07796/0.20148, loss_grounding_ce_8: 0.12281/0.40612, loss_mask_ce_9: 4.60343/3.67511, loss_mask_bce_9: 0.19286/0.39255, loss_mask_dice_9: 1.69132/1.90208, loss_spatial_bce_9: 0.31811/0.33281, loss_spatial_dice_9: 0.81144/0.82169, loss_spatial_ce_9: 1.39581/1.49451, loss_grounding_bce_9: 0.05915/0.10563, loss_grounding_dice_9: 0.21096/0.28084, loss_grounding_ce_9: 0.17334/0.67036] items per batch[64] items per second[0.22] total items[4729600] mini batches[ 73900] memory[7345] epoch remaining[0:46:52]
INFO:trainer.default_trainer:epochs[    40] optim steps[74000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.84920/0.89633, loss_mask_bce_0: 0.37093/0.33415, loss_mask_dice_0: 1.17850/1.16243, loss_spatial_bce_0: 0.04756/0.08686, loss_spatial_dice_0: 0.20190/0.20723, loss_spatial_ce_0: 0.00184/0.06056, loss_grounding_bce_0: 0.00787/0.08617, loss_grounding_dice_0: 0.02218/0.17840, loss_grounding_ce_0: 0.15019/0.27204, loss_mask_ce_1: 1.87659/0.89699, loss_mask_bce_1: 0.39364/0.33509, loss_mask_dice_1: 1.28983/1.16912, loss_spatial_bce_1: 0.04615/0.08740, loss_spatial_dice_1: 0.19803/0.21122, loss_spatial_ce_1: 0.00430/0.06635, loss_grounding_bce_1: 0.00932/0.08635, loss_grounding_dice_1: 0.02485/0.17922, loss_grounding_ce_1: 0.15386/0.27281, loss_mask_ce_2: 1.68961/0.90400, loss_mask_bce_2: 0.40266/0.33569, loss_mask_dice_2: 1.28561/1.16956, loss_spatial_bce_2: 0.05183/0.08857, loss_spatial_dice_2: 0.20860/0.21297, loss_spatial_ce_2: 0.00905/0.06984, loss_grounding_bce_2: 0.00696/0.08652, loss_grounding_dice_2: 0.01993/0.17910, loss_grounding_ce_2: 0.26417/0.27612, loss_mask_ce_3: 1.81009/0.91492, loss_mask_bce_3: 0.38665/0.33689, loss_mask_dice_3: 1.20961/1.16752, loss_spatial_bce_3: 0.06028/0.08984, loss_spatial_dice_3: 0.22010/0.21398, loss_spatial_ce_3: 0.02573/0.07472, loss_grounding_bce_3: 0.00908/0.08677, loss_grounding_dice_3: 0.02354/0.17882, loss_grounding_ce_3: 0.15998/0.27835, loss_mask_ce_4: 1.92810/0.91599, loss_mask_bce_4: 0.41189/0.33904, loss_mask_dice_4: 1.27050/1.19127, loss_spatial_bce_4: 0.07180/0.09375, loss_spatial_dice_4: 0.25620/0.22623, loss_spatial_ce_4: 0.01478/0.09097, loss_grounding_bce_4: 0.00952/0.08732, loss_grounding_dice_4: 0.02308/0.18177, loss_grounding_ce_4: 0.15350/0.28128, loss_mask_ce_5: 1.68268/0.93282, loss_mask_bce_5: 0.39886/0.34134, loss_mask_dice_5: 1.33301/1.19936, loss_spatial_bce_5: 0.05898/0.09604, loss_spatial_dice_5: 0.24074/0.23051, loss_spatial_ce_5: 0.01476/0.10505, loss_grounding_bce_5: 0.00801/0.08774, loss_grounding_dice_5: 0.02195/0.18306, loss_grounding_ce_5: 0.18865/0.29366, loss_mask_ce_6: 1.62578/0.97301, loss_mask_bce_6: 0.39050/0.34409, loss_mask_dice_6: 1.38982/1.20206, loss_spatial_bce_6: 0.06013/0.10169, loss_spatial_dice_6: 0.28332/0.23346, loss_spatial_ce_6: 0.09212/0.13038, loss_grounding_bce_6: 0.00880/0.08846, loss_grounding_dice_6: 0.02227/0.18341, loss_grounding_ce_6: 0.17648/0.30931, loss_mask_ce_7: 1.63729/1.01832, loss_mask_bce_7: 0.42038/0.35194, loss_mask_dice_7: 1.53970/1.25664, loss_spatial_bce_7: 0.05247/0.10959, loss_spatial_dice_7: 0.26604/0.26108, loss_spatial_ce_7: 0.10145/0.16558, loss_grounding_bce_7: 0.00796/0.09035, loss_grounding_dice_7: 0.02810/0.19073, loss_grounding_ce_7: 0.29468/0.33969, loss_mask_ce_8: 1.70023/1.12709, loss_mask_bce_8: 0.40480/0.36557, loss_mask_dice_8: 1.56743/1.32942, loss_spatial_bce_8: 0.05654/0.12999, loss_spatial_dice_8: 0.33253/0.29883, loss_spatial_ce_8: 0.10415/0.21940, loss_grounding_bce_8: 0.01007/0.09405, loss_grounding_dice_8: 0.03292/0.20148, loss_grounding_ce_8: 0.72572/0.40615, loss_mask_ce_9: 4.56620/3.67515, loss_mask_bce_9: 0.42695/0.39259, loss_mask_dice_9: 2.25968/1.90216, loss_spatial_bce_9: 0.21748/0.33279, loss_spatial_dice_9: 0.95822/0.82170, loss_spatial_ce_9: 1.30523/1.49445, loss_grounding_bce_9: 0.01033/0.10565, loss_grounding_dice_9: 0.03857/0.28083, loss_grounding_ce_9: 0.96975/0.67044] items per batch[64] items per second[0.23] total items[4736000] mini batches[ 74000] memory[7345] epoch remaining[0:42:08]
INFO:trainer.default_trainer:epochs[    40] optim steps[74100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26670/0.89632, loss_mask_bce_0: 0.14538/0.33411, loss_mask_dice_0: 0.31378/1.16221, loss_spatial_bce_0: 0.16695/0.08685, loss_spatial_dice_0: 0.27758/0.20721, loss_spatial_ce_0: 0.14090/0.06055, loss_grounding_bce_0: 0.17904/0.08618, loss_grounding_dice_0: 0.13575/0.17841, loss_grounding_ce_0: 0.11318/0.27196, loss_mask_ce_1: 1.29193/0.89698, loss_mask_bce_1: 0.14697/0.33505, loss_mask_dice_1: 0.31193/1.16889, loss_spatial_bce_1: 0.17728/0.08739, loss_spatial_dice_1: 0.28843/0.21120, loss_spatial_ce_1: 0.15021/0.06633, loss_grounding_bce_1: 0.18981/0.08635, loss_grounding_dice_1: 0.13837/0.17923, loss_grounding_ce_1: 0.12015/0.27274, loss_mask_ce_2: 1.24616/0.90400, loss_mask_bce_2: 0.16346/0.33565, loss_mask_dice_2: 0.31309/1.16933, loss_spatial_bce_2: 0.17358/0.08857, loss_spatial_dice_2: 0.31618/0.21295, loss_spatial_ce_2: 0.18396/0.06982, loss_grounding_bce_2: 0.21512/0.08653, loss_grounding_dice_2: 0.14159/0.17912, loss_grounding_ce_2: 0.11837/0.27604, loss_mask_ce_3: 1.40458/0.91493, loss_mask_bce_3: 0.18199/0.33685, loss_mask_dice_3: 0.29076/1.16728, loss_spatial_bce_3: 0.17700/0.08984, loss_spatial_dice_3: 0.32742/0.21397, loss_spatial_ce_3: 0.14460/0.07470, loss_grounding_bce_3: 0.23399/0.08677, loss_grounding_dice_3: 0.14618/0.17883, loss_grounding_ce_3: 0.14586/0.27827, loss_mask_ce_4: 1.37988/0.91598, loss_mask_bce_4: 0.34485/0.33900, loss_mask_dice_4: 0.34733/1.19108, loss_spatial_bce_4: 0.18969/0.09375, loss_spatial_dice_4: 0.34888/0.22622, loss_spatial_ce_4: 0.15880/0.09097, loss_grounding_bce_4: 0.29985/0.08732, loss_grounding_dice_4: 0.16614/0.18178, loss_grounding_ce_4: 0.19667/0.28121, loss_mask_ce_5: 1.50439/0.93284, loss_mask_bce_5: 0.30077/0.34130, loss_mask_dice_5: 0.31537/1.19916, loss_spatial_bce_5: 0.29377/0.09604, loss_spatial_dice_5: 0.36882/0.23049, loss_spatial_ce_5: 0.22729/0.10505, loss_grounding_bce_5: 0.28175/0.08774, loss_grounding_dice_5: 0.15223/0.18308, loss_grounding_ce_5: 0.15371/0.29358, loss_mask_ce_6: 1.47460/0.97302, loss_mask_bce_6: 0.23603/0.34406, loss_mask_dice_6: 0.33540/1.20188, loss_spatial_bce_6: 0.42393/0.10169, loss_spatial_dice_6: 0.43513/0.23345, loss_spatial_ce_6: 0.18235/0.13037, loss_grounding_bce_6: 0.26519/0.08847, loss_grounding_dice_6: 0.15752/0.18342, loss_grounding_ce_6: 0.18187/0.30922, loss_mask_ce_7: 1.75100/1.01835, loss_mask_bce_7: 0.26493/0.35190, loss_mask_dice_7: 0.33867/1.25643, loss_spatial_bce_7: 0.52994/0.10959, loss_spatial_dice_7: 0.45291/0.26106, loss_spatial_ce_7: 0.31499/0.16556, loss_grounding_bce_7: 0.34217/0.09035, loss_grounding_dice_7: 0.25020/0.19074, loss_grounding_ce_7: 0.09745/0.33960, loss_mask_ce_8: 1.56530/1.12713, loss_mask_bce_8: 0.15081/0.36554, loss_mask_dice_8: 0.31915/1.32923, loss_spatial_bce_8: 0.75913/0.13000, loss_spatial_dice_8: 0.47937/0.29880, loss_spatial_ce_8: 0.40580/0.21935, loss_grounding_bce_8: 0.15058/0.09405, loss_grounding_dice_8: 0.13296/0.20149, loss_grounding_ce_8: 0.36997/0.40609, loss_mask_ce_9: 2.59180/3.67505, loss_mask_bce_9: 0.25447/0.39254, loss_mask_dice_9: 0.47555/1.90186, loss_spatial_bce_9: 0.67138/0.33280, loss_spatial_dice_9: 0.65796/0.82168, loss_spatial_ce_9: 2.37532/1.49441, loss_grounding_bce_9: 0.22755/0.10565, loss_grounding_dice_9: 0.24531/0.28083, loss_grounding_ce_9: 0.70767/0.67038] items per batch[64] items per second[0.22] total items[4742400] mini batches[ 74100] memory[7345] epoch remaining[0:37:35]
INFO:trainer.default_trainer:epochs[    40] optim steps[74200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03145/0.89625, loss_mask_bce_0: 0.30953/0.33415, loss_mask_dice_0: 1.50528/1.16213, loss_spatial_bce_0: 0.04512/0.08686, loss_spatial_dice_0: 0.19508/0.20719, loss_spatial_ce_0: 0.01326/0.06053, loss_grounding_bce_0: 0.07343/0.08619, loss_grounding_dice_0: 0.33176/0.17841, loss_grounding_ce_0: 0.13370/0.27193, loss_mask_ce_1: 1.03322/0.89691, loss_mask_bce_1: 0.31459/0.33509, loss_mask_dice_1: 1.50522/1.16881, loss_spatial_bce_1: 0.04918/0.08740, loss_spatial_dice_1: 0.20652/0.21118, loss_spatial_ce_1: 0.02376/0.06630, loss_grounding_bce_1: 0.07570/0.08637, loss_grounding_dice_1: 0.29226/0.17923, loss_grounding_ce_1: 0.09317/0.27270, loss_mask_ce_2: 1.21920/0.90393, loss_mask_bce_2: 0.30637/0.33569, loss_mask_dice_2: 1.62356/1.16926, loss_spatial_bce_2: 0.04956/0.08858, loss_spatial_dice_2: 0.21531/0.21293, loss_spatial_ce_2: 0.01498/0.06981, loss_grounding_bce_2: 0.07708/0.08654, loss_grounding_dice_2: 0.34505/0.17911, loss_grounding_ce_2: 0.07835/0.27600, loss_mask_ce_3: 1.52630/0.91485, loss_mask_bce_3: 0.28309/0.33689, loss_mask_dice_3: 1.54048/1.16721, loss_spatial_bce_3: 0.05084/0.08985, loss_spatial_dice_3: 0.20049/0.21395, loss_spatial_ce_3: 0.05408/0.07469, loss_grounding_bce_3: 0.07931/0.08679, loss_grounding_dice_3: 0.34238/0.17882, loss_grounding_ce_3: 0.07511/0.27822, loss_mask_ce_4: 1.01379/0.91592, loss_mask_bce_4: 0.31191/0.33903, loss_mask_dice_4: 1.58227/1.19100, loss_spatial_bce_4: 0.05560/0.09376, loss_spatial_dice_4: 0.27137/0.22621, loss_spatial_ce_4: 0.06001/0.09096, loss_grounding_bce_4: 0.07725/0.08734, loss_grounding_dice_4: 0.33724/0.18177, loss_grounding_ce_4: 0.08878/0.28117, loss_mask_ce_5: 1.04036/0.93280, loss_mask_bce_5: 0.29586/0.34134, loss_mask_dice_5: 1.48515/1.19907, loss_spatial_bce_5: 0.05388/0.09605, loss_spatial_dice_5: 0.26162/0.23048, loss_spatial_ce_5: 0.05871/0.10504, loss_grounding_bce_5: 0.08888/0.08776, loss_grounding_dice_5: 0.37076/0.18307, loss_grounding_ce_5: 0.21811/0.29356, loss_mask_ce_6: 1.44794/0.97296, loss_mask_bce_6: 0.29999/0.34410, loss_mask_dice_6: 1.53373/1.20182, loss_spatial_bce_6: 0.06044/0.10170, loss_spatial_dice_6: 0.22673/0.23344, loss_spatial_ce_6: 0.15401/0.13035, loss_grounding_bce_6: 0.08296/0.08849, loss_grounding_dice_6: 0.34723/0.18341, loss_grounding_ce_6: 0.06413/0.30918, loss_mask_ce_7: 1.32998/1.01827, loss_mask_bce_7: 0.30325/0.35193, loss_mask_dice_7: 1.43222/1.25634, loss_spatial_bce_7: 0.06953/0.10960, loss_spatial_dice_7: 0.26561/0.26104, loss_spatial_ce_7: 0.05899/0.16553, loss_grounding_bce_7: 0.08207/0.09037, loss_grounding_dice_7: 0.34507/0.19073, loss_grounding_ce_7: 0.24522/0.33957, loss_mask_ce_8: 1.41275/1.12706, loss_mask_bce_8: 0.33086/0.36558, loss_mask_dice_8: 1.63503/1.32914, loss_spatial_bce_8: 0.13194/0.13000, loss_spatial_dice_8: 0.33797/0.29877, loss_spatial_ce_8: 0.11427/0.21928, loss_grounding_bce_8: 0.08053/0.09407, loss_grounding_dice_8: 0.37945/0.20148, loss_grounding_ce_8: 0.02835/0.40602, loss_mask_ce_9: 4.50949/3.67512, loss_mask_bce_9: 0.31546/0.39258, loss_mask_dice_9: 2.46904/1.90180, loss_spatial_bce_9: 0.31101/0.33282, loss_spatial_dice_9: 0.82452/0.82168, loss_spatial_ce_9: 1.48395/1.49439, loss_grounding_bce_9: 0.08696/0.10567, loss_grounding_dice_9: 0.56582/0.28083, loss_grounding_ce_9: 0.03737/0.67031] items per batch[64] items per second[0.23] total items[4748800] mini batches[ 74200] memory[7345] epoch remaining[0:32:54]
INFO:trainer.default_trainer:epochs[    40] optim steps[74300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72375/0.89620, loss_mask_bce_0: 0.18103/0.33416, loss_mask_dice_0: 0.58348/1.16203, loss_spatial_bce_0: 0.09520/0.08686, loss_spatial_dice_0: 0.28939/0.20718, loss_spatial_ce_0: 0.00519/0.06050, loss_grounding_bce_0: 0.09556/0.08621, loss_grounding_dice_0: 0.24110/0.17841, loss_grounding_ce_0: 0.29025/0.27191, loss_mask_ce_1: 1.83171/0.89686, loss_mask_bce_1: 0.16037/0.33510, loss_mask_dice_1: 0.57452/1.16871, loss_spatial_bce_1: 0.08919/0.08740, loss_spatial_dice_1: 0.28513/0.21117, loss_spatial_ce_1: 0.00292/0.06628, loss_grounding_bce_1: 0.09801/0.08638, loss_grounding_dice_1: 0.24677/0.17923, loss_grounding_ce_1: 0.23828/0.27268, loss_mask_ce_2: 1.91905/0.90391, loss_mask_bce_2: 0.15591/0.33570, loss_mask_dice_2: 0.50997/1.16919, loss_spatial_bce_2: 0.09390/0.08858, loss_spatial_dice_2: 0.28867/0.21292, loss_spatial_ce_2: 0.00197/0.06980, loss_grounding_bce_2: 0.09525/0.08656, loss_grounding_dice_2: 0.25760/0.17912, loss_grounding_ce_2: 0.29056/0.27598, loss_mask_ce_3: 1.96144/0.91483, loss_mask_bce_3: 0.15919/0.33690, loss_mask_dice_3: 0.44707/1.16708, loss_spatial_bce_3: 0.09687/0.08985, loss_spatial_dice_3: 0.29762/0.21394, loss_spatial_ce_3: 0.02079/0.07468, loss_grounding_bce_3: 0.09500/0.08681, loss_grounding_dice_3: 0.26141/0.17883, loss_grounding_ce_3: 0.26171/0.27822, loss_mask_ce_4: 1.63230/0.91590, loss_mask_bce_4: 0.16228/0.33905, loss_mask_dice_4: 0.52416/1.19090, loss_spatial_bce_4: 0.09486/0.09376, loss_spatial_dice_4: 0.28808/0.22620, loss_spatial_ce_4: 0.03598/0.09095, loss_grounding_bce_4: 0.08819/0.08735, loss_grounding_dice_4: 0.27585/0.18177, loss_grounding_ce_4: 0.30158/0.28114, loss_mask_ce_5: 1.60502/0.93276, loss_mask_bce_5: 0.16965/0.34136, loss_mask_dice_5: 0.73024/1.19900, loss_spatial_bce_5: 0.10182/0.09605, loss_spatial_dice_5: 0.29329/0.23047, loss_spatial_ce_5: 0.03485/0.10505, loss_grounding_bce_5: 0.10013/0.08778, loss_grounding_dice_5: 0.31870/0.18308, loss_grounding_ce_5: 0.33743/0.29351, loss_mask_ce_6: 1.99533/0.97294, loss_mask_bce_6: 0.15946/0.34413, loss_mask_dice_6: 0.55735/1.20175, loss_spatial_bce_6: 0.11157/0.10170, loss_spatial_dice_6: 0.28771/0.23343, loss_spatial_ce_6: 0.09873/0.13035, loss_grounding_bce_6: 0.09549/0.08850, loss_grounding_dice_6: 0.34426/0.18342, loss_grounding_ce_6: 0.23075/0.30914, loss_mask_ce_7: 1.61823/1.01827, loss_mask_bce_7: 0.17352/0.35196, loss_mask_dice_7: 0.51265/1.25626, loss_spatial_bce_7: 0.09763/0.10960, loss_spatial_dice_7: 0.34045/0.26104, loss_spatial_ce_7: 0.18893/0.16553, loss_grounding_bce_7: 0.09336/0.09039, loss_grounding_dice_7: 0.26606/0.19075, loss_grounding_ce_7: 0.42876/0.33951, loss_mask_ce_8: 2.33273/1.12708, loss_mask_bce_8: 0.18938/0.36560, loss_mask_dice_8: 0.66044/1.32902, loss_spatial_bce_8: 0.12806/0.13000, loss_spatial_dice_8: 0.40015/0.29877, loss_spatial_ce_8: 0.09199/0.21924, loss_grounding_bce_8: 0.10979/0.09409, loss_grounding_dice_8: 0.30612/0.20148, loss_grounding_ce_8: 0.46674/0.40594, loss_mask_ce_9: 3.45031/3.67509, loss_mask_bce_9: 0.20037/0.39260, loss_mask_dice_9: 0.73999/1.90157, loss_spatial_bce_9: 0.19372/0.33281, loss_spatial_dice_9: 0.80004/0.82166, loss_spatial_ce_9: 2.22241/1.49436, loss_grounding_bce_9: 0.10853/0.10569, loss_grounding_dice_9: 0.38192/0.28085, loss_grounding_ce_9: 0.34484/0.67024] items per batch[64] items per second[0.23] total items[4755200] mini batches[ 74300] memory[7345] epoch remaining[0:28:16]
INFO:trainer.default_trainer:epochs[    40] optim steps[74400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75217/0.89613, loss_mask_bce_0: 0.35937/0.33413, loss_mask_dice_0: 0.95618/1.16196, loss_spatial_bce_0: 0.06533/0.08686, loss_spatial_dice_0: 0.23131/0.20717, loss_spatial_ce_0: 0.03283/0.06051, loss_grounding_bce_0: 0.02875/0.08618, loss_grounding_dice_0: 0.11952/0.17841, loss_grounding_ce_0: 0.34633/0.27195, loss_mask_ce_1: 0.39906/0.89680, loss_mask_bce_1: 0.37965/0.33507, loss_mask_dice_1: 1.01666/1.16865, loss_spatial_bce_1: 0.07091/0.08740, loss_spatial_dice_1: 0.23845/0.21116, loss_spatial_ce_1: 0.04474/0.06627, loss_grounding_bce_1: 0.02066/0.08635, loss_grounding_dice_1: 0.09858/0.17924, loss_grounding_ce_1: 0.31263/0.27274, loss_mask_ce_2: 0.33841/0.90384, loss_mask_bce_2: 0.40557/0.33568, loss_mask_dice_2: 1.02240/1.16914, loss_spatial_bce_2: 0.07191/0.08858, loss_spatial_dice_2: 0.24475/0.21292, loss_spatial_ce_2: 0.07540/0.06979, loss_grounding_bce_2: 0.02438/0.08653, loss_grounding_dice_2: 0.10142/0.17913, loss_grounding_ce_2: 0.44364/0.27604, loss_mask_ce_3: 0.90596/0.91477, loss_mask_bce_3: 0.37556/0.33686, loss_mask_dice_3: 0.99879/1.16702, loss_spatial_bce_3: 0.06973/0.08984, loss_spatial_dice_3: 0.24031/0.21394, loss_spatial_ce_3: 0.06248/0.07468, loss_grounding_bce_3: 0.02705/0.08677, loss_grounding_dice_3: 0.10624/0.17882, loss_grounding_ce_3: 0.27898/0.27828, loss_mask_ce_4: 0.33520/0.91584, loss_mask_bce_4: 0.39154/0.33902, loss_mask_dice_4: 1.04071/1.19084, loss_spatial_bce_4: 0.08431/0.09375, loss_spatial_dice_4: 0.25767/0.22619, loss_spatial_ce_4: 0.09294/0.09095, loss_grounding_bce_4: 0.02476/0.08732, loss_grounding_dice_4: 0.10965/0.18177, loss_grounding_ce_4: 0.43709/0.28119, loss_mask_ce_5: 0.31365/0.93272, loss_mask_bce_5: 0.40047/0.34133, loss_mask_dice_5: 1.01401/1.19895, loss_spatial_bce_5: 0.10091/0.09604, loss_spatial_dice_5: 0.28010/0.23047, loss_spatial_ce_5: 0.11771/0.10505, loss_grounding_bce_5: 0.03026/0.08775, loss_grounding_dice_5: 0.12329/0.18308, loss_grounding_ce_5: 0.41929/0.29353, loss_mask_ce_6: 0.35404/0.97288, loss_mask_bce_6: 0.40521/0.34411, loss_mask_dice_6: 1.01958/1.20170, loss_spatial_bce_6: 0.12657/0.10170, loss_spatial_dice_6: 0.28142/0.23342, loss_spatial_ce_6: 0.14657/0.13035, loss_grounding_bce_6: 0.03883/0.08847, loss_grounding_dice_6: 0.13823/0.18342, loss_grounding_ce_6: 0.15753/0.30909, loss_mask_ce_7: 0.33033/1.01819, loss_mask_bce_7: 0.40070/0.35193, loss_mask_dice_7: 1.04299/1.25620, loss_spatial_bce_7: 0.13435/0.10958, loss_spatial_dice_7: 0.30523/0.26103, loss_spatial_ce_7: 0.11399/0.16550, loss_grounding_bce_7: 0.03541/0.09036, loss_grounding_dice_7: 0.13492/0.19075, loss_grounding_ce_7: 0.40210/0.33946, loss_mask_ce_8: 0.44076/1.12700, loss_mask_bce_8: 0.42340/0.36556, loss_mask_dice_8: 1.10020/1.32900, loss_spatial_bce_8: 0.11299/0.12999, loss_spatial_dice_8: 0.29257/0.29877, loss_spatial_ce_8: 0.25284/0.21918, loss_grounding_bce_8: 0.03464/0.09406, loss_grounding_dice_8: 0.14458/0.20148, loss_grounding_ce_8: 0.57772/0.40590, loss_mask_ce_9: 4.73715/3.67501, loss_mask_bce_9: 0.30201/0.39257, loss_mask_dice_9: 1.33840/1.90151, loss_spatial_bce_9: 0.44065/0.33279, loss_spatial_dice_9: 0.89200/0.82166, loss_spatial_ce_9: 1.80642/1.49436, loss_grounding_bce_9: 0.06119/0.10566, loss_grounding_dice_9: 0.30751/0.28085, loss_grounding_ce_9: 1.69074/0.67027] items per batch[64] items per second[0.23] total items[4761600] mini batches[ 74400] memory[7345] epoch remaining[0:23:37]
INFO:trainer.default_trainer:epochs[    40] optim steps[74500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80091/0.89602, loss_mask_bce_0: 0.32121/0.33411, loss_mask_dice_0: 0.67113/1.16193, loss_spatial_bce_0: 0.07193/0.08685, loss_spatial_dice_0: 0.14816/0.20715, loss_spatial_ce_0: 0.00320/0.06050, loss_grounding_bce_0: 0.05720/0.08618, loss_grounding_dice_0: 0.23453/0.17841, loss_grounding_ce_0: 0.20708/0.27190, loss_mask_ce_1: 0.77581/0.89667, loss_mask_bce_1: 0.32004/0.33505, loss_mask_dice_1: 0.64566/1.16862, loss_spatial_bce_1: 0.07160/0.08739, loss_spatial_dice_1: 0.15437/0.21114, loss_spatial_ce_1: 0.00170/0.06626, loss_grounding_bce_1: 0.05212/0.08636, loss_grounding_dice_1: 0.23079/0.17923, loss_grounding_ce_1: 0.22405/0.27267, loss_mask_ce_2: 0.77037/0.90372, loss_mask_bce_2: 0.32776/0.33566, loss_mask_dice_2: 0.63850/1.16911, loss_spatial_bce_2: 0.07165/0.08857, loss_spatial_dice_2: 0.15498/0.21290, loss_spatial_ce_2: 0.00224/0.06978, loss_grounding_bce_2: 0.05203/0.08653, loss_grounding_dice_2: 0.22105/0.17912, loss_grounding_ce_2: 0.22004/0.27596, loss_mask_ce_3: 0.76718/0.91466, loss_mask_bce_3: 0.33175/0.33685, loss_mask_dice_3: 0.64646/1.16698, loss_spatial_bce_3: 0.07172/0.08983, loss_spatial_dice_3: 0.15280/0.21392, loss_spatial_ce_3: 0.00494/0.07468, loss_grounding_bce_3: 0.05570/0.08678, loss_grounding_dice_3: 0.22470/0.17882, loss_grounding_ce_3: 0.34020/0.27820, loss_mask_ce_4: 0.72422/0.91572, loss_mask_bce_4: 0.30911/0.33900, loss_mask_dice_4: 0.62000/1.19080, loss_spatial_bce_4: 0.08257/0.09375, loss_spatial_dice_4: 0.15861/0.22618, loss_spatial_ce_4: 0.01514/0.09093, loss_grounding_bce_4: 0.04882/0.08733, loss_grounding_dice_4: 0.23180/0.18177, loss_grounding_ce_4: 0.21018/0.28112, loss_mask_ce_5: 0.76686/0.93261, loss_mask_bce_5: 0.31361/0.34132, loss_mask_dice_5: 0.64800/1.19893, loss_spatial_bce_5: 0.08465/0.09604, loss_spatial_dice_5: 0.16310/0.23046, loss_spatial_ce_5: 0.02100/0.10502, loss_grounding_bce_5: 0.04882/0.08775, loss_grounding_dice_5: 0.22066/0.18307, loss_grounding_ce_5: 0.27958/0.29352, loss_mask_ce_6: 0.97480/0.97277, loss_mask_bce_6: 0.33382/0.34409, loss_mask_dice_6: 0.67476/1.20166, loss_spatial_bce_6: 0.08998/0.10170, loss_spatial_dice_6: 0.15798/0.23342, loss_spatial_ce_6: 0.07786/0.13033, loss_grounding_bce_6: 0.05188/0.08848, loss_grounding_dice_6: 0.22253/0.18342, loss_grounding_ce_6: 0.21146/0.30901, loss_mask_ce_7: 1.15163/1.01809, loss_mask_bce_7: 0.25307/0.35192, loss_mask_dice_7: 0.65319/1.25616, loss_spatial_bce_7: 0.08596/0.10958, loss_spatial_dice_7: 0.15899/0.26102, loss_spatial_ce_7: 0.06648/0.16545, loss_grounding_bce_7: 0.05047/0.09036, loss_grounding_dice_7: 0.22251/0.19074, loss_grounding_ce_7: 0.17487/0.33936, loss_mask_ce_8: 1.05864/1.12690, loss_mask_bce_8: 0.34335/0.36556, loss_mask_dice_8: 0.73889/1.32897, loss_spatial_bce_8: 0.11299/0.12999, loss_spatial_dice_8: 0.18676/0.29875, loss_spatial_ce_8: 0.08267/0.21912, loss_grounding_bce_8: 0.05122/0.09406, loss_grounding_dice_8: 0.22341/0.20147, loss_grounding_ce_8: 0.17914/0.40578, loss_mask_ce_9: 3.64773/3.67511, loss_mask_bce_9: 0.33641/0.39257, loss_mask_dice_9: 1.15735/1.90150, loss_spatial_bce_9: 0.37709/0.33282, loss_spatial_dice_9: 0.89082/0.82165, loss_spatial_ce_9: 1.36100/1.49434, loss_grounding_bce_9: 0.05713/0.10566, loss_grounding_dice_9: 0.43874/0.28086, loss_grounding_ce_9: 0.25665/0.67015] items per batch[64] items per second[0.24] total items[4768000] mini batches[ 74500] memory[7345] epoch remaining[0:18:55]
INFO:trainer.default_trainer:epochs[    40] optim steps[74600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32989/0.89601, loss_mask_bce_0: 0.24650/0.33411, loss_mask_dice_0: 0.51551/1.16218, loss_spatial_bce_0: 0.07782/0.08684, loss_spatial_dice_0: 0.18433/0.20716, loss_spatial_ce_0: 0.00189/0.06048, loss_grounding_bce_0: 0.30636/0.08618, loss_grounding_dice_0: 0.26394/0.17842, loss_grounding_ce_0: 0.00241/0.27190, loss_mask_ce_1: 0.23338/0.89665, loss_mask_bce_1: 0.24713/0.33506, loss_mask_dice_1: 0.49381/1.16885, loss_spatial_bce_1: 0.08064/0.08738, loss_spatial_dice_1: 0.20999/0.21115, loss_spatial_ce_1: 0.00098/0.06624, loss_grounding_bce_1: 0.31141/0.08636, loss_grounding_dice_1: 0.25092/0.17924, loss_grounding_ce_1: 0.00247/0.27267, loss_mask_ce_2: 0.24448/0.90368, loss_mask_bce_2: 0.26028/0.33566, loss_mask_dice_2: 0.49087/1.16935, loss_spatial_bce_2: 0.06514/0.08857, loss_spatial_dice_2: 0.19972/0.21291, loss_spatial_ce_2: 0.00295/0.06976, loss_grounding_bce_2: 0.32432/0.08653, loss_grounding_dice_2: 0.26323/0.17913, loss_grounding_ce_2: 0.00208/0.27597, loss_mask_ce_3: 0.31212/0.91464, loss_mask_bce_3: 0.25947/0.33685, loss_mask_dice_3: 0.60004/1.16725, loss_spatial_bce_3: 0.07055/0.08983, loss_spatial_dice_3: 0.20417/0.21393, loss_spatial_ce_3: 0.02129/0.07466, loss_grounding_bce_3: 0.31641/0.08678, loss_grounding_dice_3: 0.25451/0.17884, loss_grounding_ce_3: 0.00400/0.27819, loss_mask_ce_4: 0.24663/0.91569, loss_mask_bce_4: 0.26456/0.33901, loss_mask_dice_4: 0.44503/1.19104, loss_spatial_bce_4: 0.08825/0.09374, loss_spatial_dice_4: 0.21420/0.22619, loss_spatial_ce_4: 0.03288/0.09092, loss_grounding_bce_4: 0.31281/0.08733, loss_grounding_dice_4: 0.25261/0.18178, loss_grounding_ce_4: 0.00393/0.28112, loss_mask_ce_5: 0.31933/0.93262, loss_mask_bce_5: 0.27029/0.34132, loss_mask_dice_5: 0.49900/1.19917, loss_spatial_bce_5: 0.08028/0.09603, loss_spatial_dice_5: 0.22321/0.23046, loss_spatial_ce_5: 0.02657/0.10501, loss_grounding_bce_5: 0.31290/0.08775, loss_grounding_dice_5: 0.25043/0.18309, loss_grounding_ce_5: 0.00245/0.29351, loss_mask_ce_6: 0.34009/0.97276, loss_mask_bce_6: 0.25900/0.34409, loss_mask_dice_6: 0.47462/1.20193, loss_spatial_bce_6: 0.06768/0.10169, loss_spatial_dice_6: 0.22060/0.23342, loss_spatial_ce_6: 0.06638/0.13029, loss_grounding_bce_6: 0.30854/0.08848, loss_grounding_dice_6: 0.24207/0.18342, loss_grounding_ce_6: 0.00403/0.30900, loss_mask_ce_7: 0.30432/1.01804, loss_mask_bce_7: 0.25691/0.35192, loss_mask_dice_7: 0.58360/1.25645, loss_spatial_bce_7: 0.08507/0.10958, loss_spatial_dice_7: 0.22670/0.26102, loss_spatial_ce_7: 0.06037/0.16541, loss_grounding_bce_7: 0.29010/0.09036, loss_grounding_dice_7: 0.25812/0.19076, loss_grounding_ce_7: 0.00470/0.33936, loss_mask_ce_8: 0.54715/1.12691, loss_mask_bce_8: 0.27165/0.36556, loss_mask_dice_8: 0.57311/1.32924, loss_spatial_bce_8: 0.06771/0.12997, loss_spatial_dice_8: 0.25390/0.29875, loss_spatial_ce_8: 0.25319/0.21905, loss_grounding_bce_8: 0.31966/0.09407, loss_grounding_dice_8: 0.24817/0.20148, loss_grounding_ce_8: 0.00328/0.40586, loss_mask_ce_9: 1.84953/3.67520, loss_mask_bce_9: 0.21732/0.39257, loss_mask_dice_9: 0.63245/1.90183, loss_spatial_bce_9: 0.19592/0.33280, loss_spatial_dice_9: 0.77209/0.82166, loss_spatial_ce_9: 1.34775/1.49440, loss_grounding_bce_9: 0.27861/0.10566, loss_grounding_dice_9: 0.25603/0.28087, loss_grounding_ce_9: 0.15215/0.67013] items per batch[64] items per second[0.23] total items[4774400] mini batches[ 74600] memory[7345] epoch remaining[0:14:15]
INFO:trainer.default_trainer:epochs[    40] optim steps[74700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27650/0.89592, loss_mask_bce_0: 0.02275/0.33408, loss_mask_dice_0: 0.50696/1.16212, loss_spatial_bce_0: 0.00825/0.08682, loss_spatial_dice_0: 0.17578/0.20714, loss_spatial_ce_0: 0.01449/0.06045, loss_grounding_bce_0: 0.01244/0.08618, loss_grounding_dice_0: 0.35650/0.17842, loss_grounding_ce_0: 0.34314/0.27190, loss_mask_ce_1: 0.28198/0.89654, loss_mask_bce_1: 0.02335/0.33502, loss_mask_dice_1: 0.60611/1.16880, loss_spatial_bce_1: 0.00937/0.08736, loss_spatial_dice_1: 0.16407/0.21113, loss_spatial_ce_1: 0.03348/0.06620, loss_grounding_bce_1: 0.00892/0.08636, loss_grounding_dice_1: 0.35827/0.17923, loss_grounding_ce_1: 0.56425/0.27265, loss_mask_ce_2: 0.28883/0.90358, loss_mask_bce_2: 0.02187/0.33562, loss_mask_dice_2: 0.72802/1.16928, loss_spatial_bce_2: 0.01024/0.08855, loss_spatial_dice_2: 0.20290/0.21289, loss_spatial_ce_2: 0.02619/0.06973, loss_grounding_bce_2: 0.00974/0.08653, loss_grounding_dice_2: 0.35108/0.17913, loss_grounding_ce_2: 0.13888/0.27594, loss_mask_ce_3: 0.57886/0.91456, loss_mask_bce_3: 0.02637/0.33681, loss_mask_dice_3: 0.55007/1.16719, loss_spatial_bce_3: 0.00954/0.08981, loss_spatial_dice_3: 0.23924/0.21391, loss_spatial_ce_3: 0.02102/0.07464, loss_grounding_bce_3: 0.01110/0.08678, loss_grounding_dice_3: 0.31609/0.17884, loss_grounding_ce_3: 0.19132/0.27817, loss_mask_ce_4: 0.27465/0.91560, loss_mask_bce_4: 0.02689/0.33897, loss_mask_dice_4: 0.75215/1.19097, loss_spatial_bce_4: 0.00985/0.09372, loss_spatial_dice_4: 0.18940/0.22617, loss_spatial_ce_4: 0.05984/0.09090, loss_grounding_bce_4: 0.01236/0.08733, loss_grounding_dice_4: 0.40924/0.18179, loss_grounding_ce_4: 0.17897/0.28110, loss_mask_ce_5: 0.43010/0.93252, loss_mask_bce_5: 0.03105/0.34128, loss_mask_dice_5: 0.73536/1.19909, loss_spatial_bce_5: 0.01151/0.09601, loss_spatial_dice_5: 0.18839/0.23045, loss_spatial_ce_5: 0.10139/0.10498, loss_grounding_bce_5: 0.00962/0.08775, loss_grounding_dice_5: 0.36928/0.18309, loss_grounding_ce_5: 0.30853/0.29351, loss_mask_ce_6: 0.49781/0.97268, loss_mask_bce_6: 0.02588/0.34405, loss_mask_dice_6: 0.65815/1.20189, loss_spatial_bce_6: 0.01094/0.10167, loss_spatial_dice_6: 0.19558/0.23341, loss_spatial_ce_6: 0.04744/0.13024, loss_grounding_bce_6: 0.00949/0.08847, loss_grounding_dice_6: 0.32530/0.18343, loss_grounding_ce_6: 0.42788/0.30897, loss_mask_ce_7: 0.55592/1.01798, loss_mask_bce_7: 0.03913/0.35187, loss_mask_dice_7: 0.84693/1.25639, loss_spatial_bce_7: 0.01182/0.10955, loss_spatial_dice_7: 0.24734/0.26101, loss_spatial_ce_7: 0.07939/0.16538, loss_grounding_bce_7: 0.01483/0.09036, loss_grounding_dice_7: 0.41997/0.19076, loss_grounding_ce_7: 0.47478/0.33934, loss_mask_ce_8: 0.35433/1.12685, loss_mask_bce_8: 0.03183/0.36551, loss_mask_dice_8: 0.70942/1.32917, loss_spatial_bce_8: 0.01199/0.12995, loss_spatial_dice_8: 0.28170/0.29874, loss_spatial_ce_8: 0.11446/0.21899, loss_grounding_bce_8: 0.01490/0.09406, loss_grounding_dice_8: 0.37749/0.20148, loss_grounding_ce_8: 0.09588/0.40586, loss_mask_ce_9: 2.92449/3.67510, loss_mask_bce_9: 0.02372/0.39251, loss_mask_dice_9: 0.86109/1.90174, loss_spatial_bce_9: 0.09563/0.33278, loss_spatial_dice_9: 0.80034/0.82166, loss_spatial_ce_9: 1.37352/1.49438, loss_grounding_bce_9: 0.00829/0.10566, loss_grounding_dice_9: 0.38517/0.28085, loss_grounding_ce_9: 0.33861/0.67018] items per batch[64] items per second[0.23] total items[4780800] mini batches[ 74700] memory[7345] epoch remaining[0:09:36]
INFO:trainer.default_trainer:epochs[    40] optim steps[74800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.70358/0.89585, loss_mask_bce_0: 0.31993/0.33406, loss_mask_dice_0: 1.53715/1.16204, loss_spatial_bce_0: 0.05930/0.08681, loss_spatial_dice_0: 0.25714/0.20713, loss_spatial_ce_0: 0.03442/0.06043, loss_grounding_bce_0: 0.15094/0.08618, loss_grounding_dice_0: 0.26612/0.17844, loss_grounding_ce_0: 0.03378/0.27188, loss_mask_ce_1: 1.67245/0.89648, loss_mask_bce_1: 0.32772/0.33500, loss_mask_dice_1: 1.59527/1.16873, loss_spatial_bce_1: 0.05707/0.08735, loss_spatial_dice_1: 0.24868/0.21112, loss_spatial_ce_1: 0.02797/0.06618, loss_grounding_bce_1: 0.14220/0.08635, loss_grounding_dice_1: 0.26595/0.17925, loss_grounding_ce_1: 0.04478/0.27262, loss_mask_ce_2: 1.76330/0.90350, loss_mask_bce_2: 0.32791/0.33560, loss_mask_dice_2: 1.58776/1.16921, loss_spatial_bce_2: 0.06153/0.08853, loss_spatial_dice_2: 0.28720/0.21288, loss_spatial_ce_2: 0.03268/0.06970, loss_grounding_bce_2: 0.14635/0.08653, loss_grounding_dice_2: 0.24015/0.17914, loss_grounding_ce_2: 0.03180/0.27591, loss_mask_ce_3: 1.65697/0.91449, loss_mask_bce_3: 0.32158/0.33680, loss_mask_dice_3: 1.52519/1.16713, loss_spatial_bce_3: 0.06171/0.08979, loss_spatial_dice_3: 0.26530/0.21390, loss_spatial_ce_3: 0.05739/0.07462, loss_grounding_bce_3: 0.15818/0.08677, loss_grounding_dice_3: 0.25966/0.17886, loss_grounding_ce_3: 0.04054/0.27815, loss_mask_ce_4: 1.81451/0.91552, loss_mask_bce_4: 0.34457/0.33895, loss_mask_dice_4: 1.56895/1.19088, loss_spatial_bce_4: 0.06471/0.09371, loss_spatial_dice_4: 0.29324/0.22616, loss_spatial_ce_4: 0.10256/0.09089, loss_grounding_bce_4: 0.13250/0.08731, loss_grounding_dice_4: 0.22211/0.18180, loss_grounding_ce_4: 0.03601/0.28107, loss_mask_ce_5: 1.56978/0.93247, loss_mask_bce_5: 0.42942/0.34126, loss_mask_dice_5: 1.68031/1.19904, loss_spatial_bce_5: 0.07036/0.09600, loss_spatial_dice_5: 0.30041/0.23044, loss_spatial_ce_5: 0.12787/0.10495, loss_grounding_bce_5: 0.14290/0.08774, loss_grounding_dice_5: 0.22470/0.18311, loss_grounding_ce_5: 0.03230/0.29349, loss_mask_ce_6: 1.61100/0.97263, loss_mask_bce_6: 0.39706/0.34404, loss_mask_dice_6: 1.63263/1.20185, loss_spatial_bce_6: 0.06828/0.10166, loss_spatial_dice_6: 0.26707/0.23340, loss_spatial_ce_6: 0.15612/0.13021, loss_grounding_bce_6: 0.13600/0.08846, loss_grounding_dice_6: 0.23420/0.18345, loss_grounding_ce_6: 0.04225/0.30896, loss_mask_ce_7: 1.60852/1.01792, loss_mask_bce_7: 0.37991/0.35186, loss_mask_dice_7: 1.81562/1.25632, loss_spatial_bce_7: 0.07470/0.10954, loss_spatial_dice_7: 0.31630/0.26101, loss_spatial_ce_7: 0.18300/0.16532, loss_grounding_bce_7: 0.14574/0.09035, loss_grounding_dice_7: 0.26314/0.19078, loss_grounding_ce_7: 0.08686/0.33933, loss_mask_ce_8: 1.70405/1.12681, loss_mask_bce_8: 0.43569/0.36550, loss_mask_dice_8: 1.87194/1.32908, loss_spatial_bce_8: 0.09821/0.12993, loss_spatial_dice_8: 0.40555/0.29874, loss_spatial_ce_8: 0.13647/0.21892, loss_grounding_bce_8: 0.15159/0.09405, loss_grounding_dice_8: 0.22875/0.20150, loss_grounding_ce_8: 0.18936/0.40580, loss_mask_ce_9: 4.12047/3.67492, loss_mask_bce_9: 0.54934/0.39250, loss_mask_dice_9: 2.83629/1.90164, loss_spatial_bce_9: 0.25775/0.33276, loss_spatial_dice_9: 0.93185/0.82167, loss_spatial_ce_9: 1.14450/1.49436, loss_grounding_bce_9: 0.28209/0.10565, loss_grounding_dice_9: 0.39667/0.28087, loss_grounding_ce_9: 1.48060/0.67018] items per batch[64] items per second[0.23] total items[4787200] mini batches[ 74800] memory[7345] epoch remaining[0:04:58]
INFO:trainer.default_trainer:epochs[    40] optim steps[74900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.70765/0.89590, loss_mask_bce_0: 0.24393/0.33406, loss_mask_dice_0: 1.33007/1.16202, loss_spatial_bce_0: 0.07462/0.08681, loss_spatial_dice_0: 0.15520/0.20713, loss_spatial_ce_0: 0.04323/0.06042, loss_grounding_bce_0: 0.04540/0.08617, loss_grounding_dice_0: 0.28945/0.17846, loss_grounding_ce_0: 0.12847/0.27201, loss_mask_ce_1: 2.69165/0.89652, loss_mask_bce_1: 0.25541/0.33501, loss_mask_dice_1: 1.60225/1.16872, loss_spatial_bce_1: 0.07654/0.08735, loss_spatial_dice_1: 0.17388/0.21111, loss_spatial_ce_1: 0.03876/0.06618, loss_grounding_bce_1: 0.04308/0.08635, loss_grounding_dice_1: 0.14616/0.17928, loss_grounding_ce_1: 0.15327/0.27273, loss_mask_ce_2: 2.84538/0.90354, loss_mask_bce_2: 0.25093/0.33561, loss_mask_dice_2: 1.75962/1.16922, loss_spatial_bce_2: 0.07285/0.08853, loss_spatial_dice_2: 0.19228/0.21288, loss_spatial_ce_2: 0.05897/0.06969, loss_grounding_bce_2: 0.04505/0.08652, loss_grounding_dice_2: 0.17366/0.17916, loss_grounding_ce_2: 0.13007/0.27601, loss_mask_ce_3: 3.08695/0.91455, loss_mask_bce_3: 0.24740/0.33680, loss_mask_dice_3: 1.80968/1.16712, loss_spatial_bce_3: 0.07501/0.08979, loss_spatial_dice_3: 0.16521/0.21390, loss_spatial_ce_3: 0.09081/0.07460, loss_grounding_bce_3: 0.04512/0.08677, loss_grounding_dice_3: 0.23472/0.17887, loss_grounding_ce_3: 0.26680/0.27823, loss_mask_ce_4: 3.09734/0.91558, loss_mask_bce_4: 0.25635/0.33896, loss_mask_dice_4: 1.19397/1.19087, loss_spatial_bce_4: 0.07790/0.09370, loss_spatial_dice_4: 0.17468/0.22617, loss_spatial_ce_4: 0.10468/0.09089, loss_grounding_bce_4: 0.04421/0.08731, loss_grounding_dice_4: 0.27823/0.18183, loss_grounding_ce_4: 0.40523/0.28119, loss_mask_ce_5: 3.37129/0.93251, loss_mask_bce_5: 0.26466/0.34127, loss_mask_dice_5: 1.76039/1.19908, loss_spatial_bce_5: 0.07021/0.09600, loss_spatial_dice_5: 0.17216/0.23044, loss_spatial_ce_5: 0.11774/0.10495, loss_grounding_bce_5: 0.04560/0.08774, loss_grounding_dice_5: 0.32179/0.18313, loss_grounding_ce_5: 0.24736/0.29359, loss_mask_ce_6: 3.29393/0.97266, loss_mask_bce_6: 0.24672/0.34405, loss_mask_dice_6: 1.43298/1.20186, loss_spatial_bce_6: 0.07167/0.10166, loss_spatial_dice_6: 0.20286/0.23341, loss_spatial_ce_6: 0.16919/0.13022, loss_grounding_bce_6: 0.04529/0.08846, loss_grounding_dice_6: 0.27644/0.18347, loss_grounding_ce_6: 0.48779/0.30907, loss_mask_ce_7: 3.20867/1.01795, loss_mask_bce_7: 0.26611/0.35187, loss_mask_dice_7: 1.67470/1.25633, loss_spatial_bce_7: 0.07474/0.10954, loss_spatial_dice_7: 0.19024/0.26101, loss_spatial_ce_7: 0.18855/0.16530, loss_grounding_bce_7: 0.04480/0.09036, loss_grounding_dice_7: 0.19972/0.19081, loss_grounding_ce_7: 0.31755/0.33942, loss_mask_ce_8: 2.82921/1.12683, loss_mask_bce_8: 0.27275/0.36550, loss_mask_dice_8: 1.58699/1.32908, loss_spatial_bce_8: 0.08640/0.12993, loss_spatial_dice_8: 0.20424/0.29874, loss_spatial_ce_8: 0.24969/0.21888, loss_grounding_bce_8: 0.04455/0.09406, loss_grounding_dice_8: 0.18683/0.20152, loss_grounding_ce_8: 0.16415/0.40584, loss_mask_ce_9: 4.04574/3.67503, loss_mask_bce_9: 0.29830/0.39252, loss_mask_dice_9: 2.62629/1.90172, loss_spatial_bce_9: 0.30795/0.33275, loss_spatial_dice_9: 0.81677/0.82167, loss_spatial_ce_9: 1.41630/1.49431, loss_grounding_bce_9: 0.06817/0.10566, loss_grounding_dice_9: 0.24561/0.28089, loss_grounding_ce_9: 0.79443/0.67015] items per batch[64] items per second[0.23] total items[4793600] mini batches[ 74900] memory[7345] epoch remaining[0:00:19]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00074907.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0033 s/iter. Inference: 0.2194 s/iter. Eval: 0.0892 s/iter. Total: 0.3119 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2217 s/iter. Eval: 0.0783 s/iter. Total: 0.3032 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2256 s/iter. Eval: 0.0760 s/iter. Total: 0.3048 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2257 s/iter. Eval: 0.0761 s/iter. Total: 0.3050 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0032 s/iter. Inference: 0.2250 s/iter. Eval: 0.0749 s/iter. Total: 0.3031 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2273 s/iter. Eval: 0.0750 s/iter. Total: 0.3056 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2294 s/iter. Eval: 0.0750 s/iter. Total: 0.3077 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2290 s/iter. Eval: 0.0749 s/iter. Total: 0.3072 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 144/157. Dataloading: 0.0032 s/iter. Inference: 0.2296 s/iter. Eval: 0.0749 s/iter. Total: 0.3079 s/iter. ETA=0:00:04
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalbhsvpt4t ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.220 | 82.085 | 60.376 |      133      |
| Things | 55.099 | 82.768 | 65.953 |      80       |
| Stuff  | 42.855 | 81.055 | 51.956 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
DONE (t=0.44s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.42 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.03 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.13s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.15 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.614
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.813 | 61.385 | 40.670 | 19.362 | 41.934 | 60.248 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 43.207 | bicycle      | 18.982 | car            | 37.050 |
| motorcycle    | 34.832 | airplane     | 57.173 | bus            | 64.730 |
| train         | 68.655 | truck        | 36.633 | boat           | 22.223 |
| traffic light | 25.449 | fire hydrant | 65.064 | stop sign      | 64.774 |
| parking meter | 42.915 | bench        | 19.608 | bird           | 28.837 |
| cat           | 73.659 | dog          | 64.609 | horse          | 45.357 |
| sheep         | 46.478 | cow          | 51.088 | elephant       | 61.027 |
| bear          | 76.359 | zebra        | 59.958 | giraffe        | 56.478 |
| backpack      | 18.000 | umbrella     | 49.673 | handbag        | 15.386 |
| tie           | 34.568 | suitcase     | 40.137 | frisbee        | 67.684 |
| skis          | 5.113  | snowboard    | 22.366 | sports ball    | 46.348 |
| kite          | 33.632 | baseball bat | 30.146 | baseball glove | 43.833 |
| skateboard    | 35.634 | surfboard    | 36.150 | tennis racket  | 55.792 |
| bottle        | 33.628 | wine glass   | 26.993 | cup            | 40.732 |
| fork          | 15.663 | knife        | 14.014 | spoon          | 13.733 |
| bowl          | 30.923 | banana       | 19.710 | apple          | 20.071 |
| sandwich      | 41.799 | orange       | 30.155 | broccoli       | 21.907 |
| carrot        | 21.664 | hot dog      | 24.171 | pizza          | 52.086 |
| donut         | 47.325 | cake         | 43.750 | chair          | 22.104 |
| couch         | 36.973 | potted plant | 18.249 | bed            | 38.627 |
| dining table  | 13.040 | toilet       | 67.102 | tv             | 61.856 |
| laptop        | 63.241 | mouse        | 59.504 | remote         | 31.859 |
| keyboard      | 48.487 | cell phone   | 38.524 | microwave      | 53.891 |
| oven          | 32.132 | toaster      | 29.108 | sink           | 36.482 |
| refrigerator  | 57.390 | book         | 9.037  | clock          | 52.254 |
| vase          | 32.560 | scissors     | 23.788 | teddy bear     | 50.832 |
| hair drier    | 12.021 | toothbrush   | 18.032 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.8497581717945, 'fwIoU': 69.02071168555305, 'IoU-person': 87.5570756634669, 'IoU-bicycle': 76.62053854647488, 'IoU-car': 71.83869187540547, 'IoU-motorcycle': 84.96267733948457, 'IoU-airplane': 81.56335429384495, 'IoU-bus': 82.5870094243728, 'IoU-train': 82.09341194529712, 'IoU-truck': 65.91199329439283, 'IoU-boat': 67.18641174185527, 'IoU-traffic light': 76.72205949544728, 'IoU-fire hydrant': 90.57284947829535, 'IoU-stop sign': 91.39248198903705, 'IoU-parking meter': 83.28104244731259, 'IoU-bench': 54.451040147200345, 'IoU-bird': 75.53397695503753, 'IoU-cat': 83.72745907683087, 'IoU-dog': 76.96700770061273, 'IoU-horse': 85.06935175391051, 'IoU-sheep': 90.04296255604793, 'IoU-cow': 80.52172351059923, 'IoU-elephant': 88.93478686485986, 'IoU-bear': 84.79367963764484, 'IoU-zebra': 92.31329417694965, 'IoU-giraffe': 86.91242814779419, 'IoU-backpack': 40.89244410177332, 'IoU-umbrella': 75.60258975965354, 'IoU-handbag': 37.200401536815406, 'IoU-tie': 70.51940072671226, 'IoU-suitcase': 78.64712186403558, 'IoU-frisbee': 83.49014169586863, 'IoU-skis': 51.14527393916448, 'IoU-snowboard': 70.40822569086365, 'IoU-sports ball': 67.04538563495707, 'IoU-kite': 66.28601315825694, 'IoU-baseball bat': 62.44501216070518, 'IoU-baseball glove': 78.12921634115591, 'IoU-skateboard': 77.10441337874872, 'IoU-surfboard': 76.65872716361768, 'IoU-tennis racket': 83.2942474436517, 'IoU-bottle': 66.3185054676238, 'IoU-wine glass': 74.70389918277138, 'IoU-cup': 62.66030694700427, 'IoU-fork': 54.149606507722936, 'IoU-knife': 48.82504826289005, 'IoU-spoon': 49.34207318128263, 'IoU-bowl': 51.86441365329387, 'IoU-banana': 82.51407914264833, 'IoU-apple': 57.5504611159015, 'IoU-sandwich': 67.93702087606019, 'IoU-orange': 77.47831918389022, 'IoU-broccoli': 67.84106127727352, 'IoU-carrot': 61.62403867029438, 'IoU-hot dog': 60.62046553620463, 'IoU-pizza': 78.22453748707035, 'IoU-donut': 55.91764221452813, 'IoU-cake': 66.95231995614964, 'IoU-chair': 53.956299336634885, 'IoU-couch': 62.477643266057356, 'IoU-potted plant': 33.77768140342314, 'IoU-bed': 67.3631450746204, 'IoU-dining table': 52.10245474064988, 'IoU-toilet': 89.1830180096042, 'IoU-tv': 75.8932192223613, 'IoU-laptop': 72.25551772544515, 'IoU-mouse': 65.1698855235279, 'IoU-remote': 63.821857773377666, 'IoU-keyboard': 57.960140679953106, 'IoU-cell phone': 65.24866729297231, 'IoU-microwave': 48.34406163492646, 'IoU-oven': 66.62542530695463, 'IoU-toaster': 63.702923717332936, 'IoU-sink': 73.3041627374377, 'IoU-refrigerator': 81.88662163394058, 'IoU-book': 50.09883838260255, 'IoU-clock': 73.26801242137431, 'IoU-vase': 60.9723316826132, 'IoU-scissors': 53.98969933717659, 'IoU-teddy bear': 83.33242797906821, 'IoU-hair drier': 46.757500473479574, 'IoU-toothbrush': 55.83667782524798, 'IoU-banner': 36.62382809373113, 'IoU-blanket': 11.236897692328766, 'IoU-bridge': 39.45353614496607, 'IoU-cardboard': 48.69934705821068, 'IoU-counter': 32.10065952979387, 'IoU-curtain': 65.12363629037176, 'IoU-door-stuff': 40.7882617327438, 'IoU-floor-wood': 62.70227385571063, 'IoU-flower': 44.6141124299848, 'IoU-fruit': 40.52576418859884, 'IoU-gravel': 30.937632849498005, 'IoU-house': 23.383210919238405, 'IoU-light': 38.860760235828394, 'IoU-mirror-stuff': 54.81011045614809, 'IoU-net': 39.16420893916152, 'IoU-pillow': 11.20410776247405, 'IoU-platform': 27.89530341914167, 'IoU-playingfield': 68.5398401024309, 'IoU-railroad': 61.025142623305975, 'IoU-river': 43.12228798411656, 'IoU-road': 66.09501430608248, 'IoU-roof': 15.988045671694243, 'IoU-sand': 63.99844999091275, 'IoU-sea': 85.37083921246372, 'IoU-shelf': 37.5728905408558, 'IoU-snow': 88.37735609063239, 'IoU-stairs': 21.257054497004884, 'IoU-tent': 8.899806236171996, 'IoU-towel': 32.46369545832567, 'IoU-wall-brick': 47.4821997244654, 'IoU-wall-stone': 28.777969675154484, 'IoU-wall-tile': 65.84671789607545, 'IoU-wall-wood': 38.15453129719462, 'IoU-water-other': 24.930442331142977, 'IoU-window-blind': 47.93715423055933, 'IoU-window-other': 48.84395854219213, 'IoU-tree-merged': 80.68852871152744, 'IoU-fence-merged': 51.05740003421098, 'IoU-ceiling-merged': 67.467793890734, 'IoU-sky-other-merged': 93.5079983190603, 'IoU-cabinet-merged': 59.630867585087266, 'IoU-table-merged': 36.496763005545844, 'IoU-floor-other-merged': 48.79414087744406, 'IoU-pavement-merged': 54.913936342982375, 'IoU-mountain-merged': 56.02665098575423, 'IoU-grass-merged': 72.41067663723602, 'IoU-dirt-merged': 45.08419715599171, 'IoU-paper-merged': 35.19247355712472, 'IoU-food-other-merged': 39.25778184262612, 'IoU-building-other-merged': 58.696644234812936, 'IoU-rock-merged': 62.46589726816598, 'IoU-wall-other-merged': 64.36534158160666, 'IoU-rug-merged': 61.87576230849753, 'mACC': 72.8715358738593, 'pACC': 80.28111032179606, 'ACC-person': 92.2254584219703, 'ACC-bicycle': 86.37119173495081, 'ACC-car': 86.00512916208555, 'ACC-motorcycle': 89.77532738395658, 'ACC-airplane': 88.31191343088712, 'ACC-bus': 86.77612939102731, 'ACC-train': 93.86187729921042, 'ACC-truck': 78.45328734333314, 'ACC-boat': 79.26521514443192, 'ACC-traffic light': 89.84794612006417, 'ACC-fire hydrant': 95.45064414335552, 'ACC-stop sign': 94.3193475803004, 'ACC-parking meter': 87.10253921310702, 'ACC-bench': 74.02644343022374, 'ACC-bird': 80.03344717785424, 'ACC-cat': 90.91301974269558, 'ACC-dog': 81.03840708841045, 'ACC-horse': 90.68497014044756, 'ACC-sheep': 93.62603234378005, 'ACC-cow': 86.22652165255022, 'ACC-elephant': 91.42903906784484, 'ACC-bear': 86.94341923570785, 'ACC-zebra': 95.12421652012627, 'ACC-giraffe': 91.21176909443481, 'ACC-backpack': 56.24435806872859, 'ACC-umbrella': 85.75935340161692, 'ACC-handbag': 54.48154799172615, 'ACC-tie': 82.09189136704717, 'ACC-suitcase': 88.34303895383627, 'ACC-frisbee': 93.48254545454544, 'ACC-skis': 69.96187354555583, 'ACC-snowboard': 78.92287722563738, 'ACC-sports ball': 79.99968989363352, 'ACC-kite': 75.76069912081329, 'ACC-baseball bat': 83.96781275052633, 'ACC-baseball glove': 89.08187602227939, 'ACC-skateboard': 89.94629495805486, 'ACC-surfboard': 83.71498487311388, 'ACC-tennis racket': 89.28027546190565, 'ACC-bottle': 79.967033866136, 'ACC-wine glass': 84.33219705898351, 'ACC-cup': 83.89486566684928, 'ACC-fork': 66.38535945125768, 'ACC-knife': 63.149226936323, 'ACC-spoon': 64.46277594873754, 'ACC-bowl': 61.23158264628506, 'ACC-banana': 88.3614668066522, 'ACC-apple': 68.23384854558304, 'ACC-sandwich': 79.96337780859473, 'ACC-orange': 84.88324756358091, 'ACC-broccoli': 77.92081821780022, 'ACC-carrot': 74.15485690749017, 'ACC-hot dog': 66.19941258241495, 'ACC-pizza': 85.0231005275987, 'ACC-donut': 71.05843129138916, 'ACC-cake': 73.49919994617021, 'ACC-chair': 77.4862323295444, 'ACC-couch': 73.65970256939089, 'ACC-potted plant': 51.40326538430814, 'ACC-bed': 79.24880644756888, 'ACC-dining table': 74.03083444525933, 'ACC-toilet': 93.6654987740132, 'ACC-tv': 87.1266290073249, 'ACC-laptop': 87.20410142119958, 'ACC-mouse': 77.11401866461767, 'ACC-remote': 71.36520853347707, 'ACC-keyboard': 62.97560073513648, 'ACC-cell phone': 73.75474778266546, 'ACC-microwave': 53.24973899908927, 'ACC-oven': 87.54932567118777, 'ACC-toaster': 70.7899382775185, 'ACC-sink': 83.27424660854987, 'ACC-refrigerator': 90.13396377512898, 'ACC-book': 66.68631447703903, 'ACC-clock': 79.11361999146911, 'ACC-vase': 69.41476154109762, 'ACC-scissors': 58.280881286211425, 'ACC-teddy bear': 88.8782800926905, 'ACC-hair drier': 59.73016821791478, 'ACC-toothbrush': 78.67529534398888, 'ACC-banner': 75.63729142473092, 'ACC-blanket': 16.050070332636782, 'ACC-bridge': 56.42579970135002, 'ACC-cardboard': 66.20311182201542, 'ACC-counter': 52.79371361277141, 'ACC-curtain': 77.09163528366038, 'ACC-door-stuff': 58.20732253820447, 'ACC-floor-wood': 76.78078385842231, 'ACC-flower': 64.60812347644021, 'ACC-fruit': 57.78987004983052, 'ACC-gravel': 37.49621288384955, 'ACC-house': 26.964849025514326, 'ACC-light': 55.50257435975959, 'ACC-mirror-stuff': 70.58993863637775, 'ACC-net': 60.551975045061226, 'ACC-pillow': 31.23341772419237, 'ACC-platform': 42.444933976467986, 'ACC-playingfield': 88.3297706843932, 'ACC-railroad': 79.52524838234845, 'ACC-river': 58.202215550489235, 'ACC-road': 80.55268202745667, 'ACC-roof': 22.50379950046246, 'ACC-sand': 71.39163342715845, 'ACC-sea': 91.15157913982745, 'ACC-shelf': 61.51621724429227, 'ACC-snow': 95.21723877586957, 'ACC-stairs': 39.88920160277394, 'ACC-tent': 10.156841383290606, 'ACC-towel': 44.88950750005867, 'ACC-wall-brick': 65.98689687499821, 'ACC-wall-stone': 32.50517969845303, 'ACC-wall-tile': 78.28414611000312, 'ACC-wall-wood': 56.78690265469258, 'ACC-water-other': 45.20770665214644, 'ACC-window-blind': 57.55122472257577, 'ACC-window-other': 68.68392616229876, 'ACC-tree-merged': 89.12846312674148, 'ACC-fence-merged': 70.42289622615279, 'ACC-ceiling-merged': 80.6912715786544, 'ACC-sky-other-merged': 96.67860796331176, 'ACC-cabinet-merged': 75.23680672577474, 'ACC-table-merged': 55.41459826353452, 'ACC-floor-other-merged': 61.58466071505833, 'ACC-pavement-merged': 71.38944569200217, 'ACC-mountain-merged': 66.75060626089814, 'ACC-grass-merged': 82.91916436783102, 'ACC-dirt-merged': 67.54970330320866, 'ACC-paper-merged': 50.344060154675375, 'ACC-food-other-merged': 54.86619988820108, 'ACC-building-other-merged': 72.9155059990956, 'ACC-rock-merged': 84.14678046516862, 'ACC-wall-other-merged': 81.31060282666265, 'ACC-rug-merged': 78.26696272142705})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1540 s/iter. Inference: 0.5859 s/iter. Eval: 0.0000 s/iter. Total: 0.7399 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1569 s/iter. Inference: 0.5739 s/iter. Eval: 0.0000 s/iter. Total: 0.7309 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1704 s/iter. Inference: 0.6015 s/iter. Eval: 0.0000 s/iter. Total: 0.7720 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1727 s/iter. Inference: 0.6837 s/iter. Eval: 0.0000 s/iter. Total: 0.8566 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1694 s/iter. Inference: 0.6055 s/iter. Eval: 0.0000 s/iter. Total: 0.7751 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1685 s/iter. Inference: 0.6441 s/iter. Eval: 0.0000 s/iter. Total: 0.8128 s/iter. ETA=0:00:04
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5062920690664325, 'noc@0.8': 2.788410886742757, 'noc@0.85': 3.349721978343576, 'noc@0.9': 4.3769388352355865, 'miou@iter1': 0.8321052743288241}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0988 s/iter. Eval: 0.0008 s/iter. Total: 0.1012 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.57909393310547, 'precision@0.6': 67.74193572998047, 'precision@0.7': 62.30081558227539, 'precision@0.8': 52.42906951904297, 'precision@0.9': 27.01127052307129, 'cIoU': 57.35614776611328, 'mIoU': 62.47899627685547}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.21962424622036, 'SQ': 82.08529677514511, 'RQ': 60.375561761610896, 'PQ_th': 55.098982444478196, 'SQ_th': 82.7676473992739, 'RQ_th': 65.95323585215209, 'PQ_st': 42.85455526771801, 'SQ_st': 81.05533356891299, 'RQ_st': 51.956431058907185}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.81272453505181, 'AP50': 61.384684376447154, 'AP75': 40.670270693895475, 'APs': 19.361559136956533, 'APm': 41.934164356342905, 'APl': 60.247748072864695, 'AP-person': 43.20710546672315, 'AP-bicycle': 18.98177252578732, 'AP-car': 37.04964603945856, 'AP-motorcycle': 34.83226056584345, 'AP-airplane': 57.17341217790969, 'AP-bus': 64.73017120715538, 'AP-train': 68.6552183537192, 'AP-truck': 36.633172252308924, 'AP-boat': 22.22312549150236, 'AP-traffic light': 25.448517233422063, 'AP-fire hydrant': 65.06401953466153, 'AP-stop sign': 64.77409815480178, 'AP-parking meter': 42.91499832913288, 'AP-bench': 19.6075191029161, 'AP-bird': 28.83658583374334, 'AP-cat': 73.65946841471477, 'AP-dog': 64.6092919236003, 'AP-horse': 45.35669307605103, 'AP-sheep': 46.47750716503372, 'AP-cow': 51.088189678238734, 'AP-elephant': 61.02654423642315, 'AP-bear': 76.35905223100346, 'AP-zebra': 59.95756830052061, 'AP-giraffe': 56.47819206973119, 'AP-backpack': 17.999593499770107, 'AP-umbrella': 49.673363533642416, 'AP-handbag': 15.385613073270202, 'AP-tie': 34.56760274955019, 'AP-suitcase': 40.13676644897525, 'AP-frisbee': 67.68350685305454, 'AP-skis': 5.112718274492733, 'AP-snowboard': 22.366135698176308, 'AP-sports ball': 46.34826633259763, 'AP-kite': 33.63213149881506, 'AP-baseball bat': 30.145530424024724, 'AP-baseball glove': 43.83301079073305, 'AP-skateboard': 35.63419717470395, 'AP-surfboard': 36.15008787409297, 'AP-tennis racket': 55.79199122901175, 'AP-bottle': 33.628184443432275, 'AP-wine glass': 26.992519481467546, 'AP-cup': 40.73162908733332, 'AP-fork': 15.663166784207199, 'AP-knife': 14.013890621587649, 'AP-spoon': 13.73314120153517, 'AP-bowl': 30.92294253297887, 'AP-banana': 19.710392230832632, 'AP-apple': 20.07072992378728, 'AP-sandwich': 41.79879472061066, 'AP-orange': 30.15455670021377, 'AP-broccoli': 21.907374918389934, 'AP-carrot': 21.663604368088148, 'AP-hot dog': 24.171055572872746, 'AP-pizza': 52.085730006781446, 'AP-donut': 47.3248248065385, 'AP-cake': 43.74964787693343, 'AP-chair': 22.104192435010763, 'AP-couch': 36.97270254168554, 'AP-potted plant': 18.248730605079345, 'AP-bed': 38.62667329419691, 'AP-dining table': 13.040166504069402, 'AP-toilet': 67.10160472184906, 'AP-tv': 61.85604086066863, 'AP-laptop': 63.240637725352485, 'AP-mouse': 59.50412661860067, 'AP-remote': 31.859131683619896, 'AP-keyboard': 48.48691261799294, 'AP-cell phone': 38.52374830682738, 'AP-microwave': 53.89093266661864, 'AP-oven': 32.13236613570441, 'AP-toaster': 29.10762037742236, 'AP-sink': 36.48218897761701, 'AP-refrigerator': 57.38952574548095, 'AP-book': 9.036668765747468, 'AP-clock': 52.25375644370597, 'AP-vase': 32.55964535368157, 'AP-scissors': 23.78847589427818, 'AP-teddy bear': 50.83168355255716, 'AP-hair drier': 12.02132520944402, 'AP-toothbrush': 18.032275670029392}), ('sem_seg', {'mIoU': 60.8497581717945, 'fwIoU': 69.02071168555305, 'IoU-person': 87.5570756634669, 'IoU-bicycle': 76.62053854647488, 'IoU-car': 71.83869187540547, 'IoU-motorcycle': 84.96267733948457, 'IoU-airplane': 81.56335429384495, 'IoU-bus': 82.5870094243728, 'IoU-train': 82.09341194529712, 'IoU-truck': 65.91199329439283, 'IoU-boat': 67.18641174185527, 'IoU-traffic light': 76.72205949544728, 'IoU-fire hydrant': 90.57284947829535, 'IoU-stop sign': 91.39248198903705, 'IoU-parking meter': 83.28104244731259, 'IoU-bench': 54.451040147200345, 'IoU-bird': 75.53397695503753, 'IoU-cat': 83.72745907683087, 'IoU-dog': 76.96700770061273, 'IoU-horse': 85.06935175391051, 'IoU-sheep': 90.04296255604793, 'IoU-cow': 80.52172351059923, 'IoU-elephant': 88.93478686485986, 'IoU-bear': 84.79367963764484, 'IoU-zebra': 92.31329417694965, 'IoU-giraffe': 86.91242814779419, 'IoU-backpack': 40.89244410177332, 'IoU-umbrella': 75.60258975965354, 'IoU-handbag': 37.200401536815406, 'IoU-tie': 70.51940072671226, 'IoU-suitcase': 78.64712186403558, 'IoU-frisbee': 83.49014169586863, 'IoU-skis': 51.14527393916448, 'IoU-snowboard': 70.40822569086365, 'IoU-sports ball': 67.04538563495707, 'IoU-kite': 66.28601315825694, 'IoU-baseball bat': 62.44501216070518, 'IoU-baseball glove': 78.12921634115591, 'IoU-skateboard': 77.10441337874872, 'IoU-surfboard': 76.65872716361768, 'IoU-tennis racket': 83.2942474436517, 'IoU-bottle': 66.3185054676238, 'IoU-wine glass': 74.70389918277138, 'IoU-cup': 62.66030694700427, 'IoU-fork': 54.149606507722936, 'IoU-knife': 48.82504826289005, 'IoU-spoon': 49.34207318128263, 'IoU-bowl': 51.86441365329387, 'IoU-banana': 82.51407914264833, 'IoU-apple': 57.5504611159015, 'IoU-sandwich': 67.93702087606019, 'IoU-orange': 77.47831918389022, 'IoU-broccoli': 67.84106127727352, 'IoU-carrot': 61.62403867029438, 'IoU-hot dog': 60.62046553620463, 'IoU-pizza': 78.22453748707035, 'IoU-donut': 55.91764221452813, 'IoU-cake': 66.95231995614964, 'IoU-chair': 53.956299336634885, 'IoU-couch': 62.477643266057356, 'IoU-potted plant': 33.77768140342314, 'IoU-bed': 67.3631450746204, 'IoU-dining table': 52.10245474064988, 'IoU-toilet': 89.1830180096042, 'IoU-tv': 75.8932192223613, 'IoU-laptop': 72.25551772544515, 'IoU-mouse': 65.1698855235279, 'IoU-remote': 63.821857773377666, 'IoU-keyboard': 57.960140679953106, 'IoU-cell phone': 65.24866729297231, 'IoU-microwave': 48.34406163492646, 'IoU-oven': 66.62542530695463, 'IoU-toaster': 63.702923717332936, 'IoU-sink': 73.3041627374377, 'IoU-refrigerator': 81.88662163394058, 'IoU-book': 50.09883838260255, 'IoU-clock': 73.26801242137431, 'IoU-vase': 60.9723316826132, 'IoU-scissors': 53.98969933717659, 'IoU-teddy bear': 83.33242797906821, 'IoU-hair drier': 46.757500473479574, 'IoU-toothbrush': 55.83667782524798, 'IoU-banner': 36.62382809373113, 'IoU-blanket': 11.236897692328766, 'IoU-bridge': 39.45353614496607, 'IoU-cardboard': 48.69934705821068, 'IoU-counter': 32.10065952979387, 'IoU-curtain': 65.12363629037176, 'IoU-door-stuff': 40.7882617327438, 'IoU-floor-wood': 62.70227385571063, 'IoU-flower': 44.6141124299848, 'IoU-fruit': 40.52576418859884, 'IoU-gravel': 30.937632849498005, 'IoU-house': 23.383210919238405, 'IoU-light': 38.860760235828394, 'IoU-mirror-stuff': 54.81011045614809, 'IoU-net': 39.16420893916152, 'IoU-pillow': 11.20410776247405, 'IoU-platform': 27.89530341914167, 'IoU-playingfield': 68.5398401024309, 'IoU-railroad': 61.025142623305975, 'IoU-river': 43.12228798411656, 'IoU-road': 66.09501430608248, 'IoU-roof': 15.988045671694243, 'IoU-sand': 63.99844999091275, 'IoU-sea': 85.37083921246372, 'IoU-shelf': 37.5728905408558, 'IoU-snow': 88.37735609063239, 'IoU-stairs': 21.257054497004884, 'IoU-tent': 8.899806236171996, 'IoU-towel': 32.46369545832567, 'IoU-wall-brick': 47.4821997244654, 'IoU-wall-stone': 28.777969675154484, 'IoU-wall-tile': 65.84671789607545, 'IoU-wall-wood': 38.15453129719462, 'IoU-water-other': 24.930442331142977, 'IoU-window-blind': 47.93715423055933, 'IoU-window-other': 48.84395854219213, 'IoU-tree-merged': 80.68852871152744, 'IoU-fence-merged': 51.05740003421098, 'IoU-ceiling-merged': 67.467793890734, 'IoU-sky-other-merged': 93.5079983190603, 'IoU-cabinet-merged': 59.630867585087266, 'IoU-table-merged': 36.496763005545844, 'IoU-floor-other-merged': 48.79414087744406, 'IoU-pavement-merged': 54.913936342982375, 'IoU-mountain-merged': 56.02665098575423, 'IoU-grass-merged': 72.41067663723602, 'IoU-dirt-merged': 45.08419715599171, 'IoU-paper-merged': 35.19247355712472, 'IoU-food-other-merged': 39.25778184262612, 'IoU-building-other-merged': 58.696644234812936, 'IoU-rock-merged': 62.46589726816598, 'IoU-wall-other-merged': 64.36534158160666, 'IoU-rug-merged': 61.87576230849753, 'mACC': 72.8715358738593, 'pACC': 80.28111032179606, 'ACC-person': 92.2254584219703, 'ACC-bicycle': 86.37119173495081, 'ACC-car': 86.00512916208555, 'ACC-motorcycle': 89.77532738395658, 'ACC-airplane': 88.31191343088712, 'ACC-bus': 86.77612939102731, 'ACC-train': 93.86187729921042, 'ACC-truck': 78.45328734333314, 'ACC-boat': 79.26521514443192, 'ACC-traffic light': 89.84794612006417, 'ACC-fire hydrant': 95.45064414335552, 'ACC-stop sign': 94.3193475803004, 'ACC-parking meter': 87.10253921310702, 'ACC-bench': 74.02644343022374, 'ACC-bird': 80.03344717785424, 'ACC-cat': 90.91301974269558, 'ACC-dog': 81.03840708841045, 'ACC-horse': 90.68497014044756, 'ACC-sheep': 93.62603234378005, 'ACC-cow': 86.22652165255022, 'ACC-elephant': 91.42903906784484, 'ACC-bear': 86.94341923570785, 'ACC-zebra': 95.12421652012627, 'ACC-giraffe': 91.21176909443481, 'ACC-backpack': 56.24435806872859, 'ACC-umbrella': 85.75935340161692, 'ACC-handbag': 54.48154799172615, 'ACC-tie': 82.09189136704717, 'ACC-suitcase': 88.34303895383627, 'ACC-frisbee': 93.48254545454544, 'ACC-skis': 69.96187354555583, 'ACC-snowboard': 78.92287722563738, 'ACC-sports ball': 79.99968989363352, 'ACC-kite': 75.76069912081329, 'ACC-baseball bat': 83.96781275052633, 'ACC-baseball glove': 89.08187602227939, 'ACC-skateboard': 89.94629495805486, 'ACC-surfboard': 83.71498487311388, 'ACC-tennis racket': 89.28027546190565, 'ACC-bottle': 79.967033866136, 'ACC-wine glass': 84.33219705898351, 'ACC-cup': 83.89486566684928, 'ACC-fork': 66.38535945125768, 'ACC-knife': 63.149226936323, 'ACC-spoon': 64.46277594873754, 'ACC-bowl': 61.23158264628506, 'ACC-banana': 88.3614668066522, 'ACC-apple': 68.23384854558304, 'ACC-sandwich': 79.96337780859473, 'ACC-orange': 84.88324756358091, 'ACC-broccoli': 77.92081821780022, 'ACC-carrot': 74.15485690749017, 'ACC-hot dog': 66.19941258241495, 'ACC-pizza': 85.0231005275987, 'ACC-donut': 71.05843129138916, 'ACC-cake': 73.49919994617021, 'ACC-chair': 77.4862323295444, 'ACC-couch': 73.65970256939089, 'ACC-potted plant': 51.40326538430814, 'ACC-bed': 79.24880644756888, 'ACC-dining table': 74.03083444525933, 'ACC-toilet': 93.6654987740132, 'ACC-tv': 87.1266290073249, 'ACC-laptop': 87.20410142119958, 'ACC-mouse': 77.11401866461767, 'ACC-remote': 71.36520853347707, 'ACC-keyboard': 62.97560073513648, 'ACC-cell phone': 73.75474778266546, 'ACC-microwave': 53.24973899908927, 'ACC-oven': 87.54932567118777, 'ACC-toaster': 70.7899382775185, 'ACC-sink': 83.27424660854987, 'ACC-refrigerator': 90.13396377512898, 'ACC-book': 66.68631447703903, 'ACC-clock': 79.11361999146911, 'ACC-vase': 69.41476154109762, 'ACC-scissors': 58.280881286211425, 'ACC-teddy bear': 88.8782800926905, 'ACC-hair drier': 59.73016821791478, 'ACC-toothbrush': 78.67529534398888, 'ACC-banner': 75.63729142473092, 'ACC-blanket': 16.050070332636782, 'ACC-bridge': 56.42579970135002, 'ACC-cardboard': 66.20311182201542, 'ACC-counter': 52.79371361277141, 'ACC-curtain': 77.09163528366038, 'ACC-door-stuff': 58.20732253820447, 'ACC-floor-wood': 76.78078385842231, 'ACC-flower': 64.60812347644021, 'ACC-fruit': 57.78987004983052, 'ACC-gravel': 37.49621288384955, 'ACC-house': 26.964849025514326, 'ACC-light': 55.50257435975959, 'ACC-mirror-stuff': 70.58993863637775, 'ACC-net': 60.551975045061226, 'ACC-pillow': 31.23341772419237, 'ACC-platform': 42.444933976467986, 'ACC-playingfield': 88.3297706843932, 'ACC-railroad': 79.52524838234845, 'ACC-river': 58.202215550489235, 'ACC-road': 80.55268202745667, 'ACC-roof': 22.50379950046246, 'ACC-sand': 71.39163342715845, 'ACC-sea': 91.15157913982745, 'ACC-shelf': 61.51621724429227, 'ACC-snow': 95.21723877586957, 'ACC-stairs': 39.88920160277394, 'ACC-tent': 10.156841383290606, 'ACC-towel': 44.88950750005867, 'ACC-wall-brick': 65.98689687499821, 'ACC-wall-stone': 32.50517969845303, 'ACC-wall-tile': 78.28414611000312, 'ACC-wall-wood': 56.78690265469258, 'ACC-water-other': 45.20770665214644, 'ACC-window-blind': 57.55122472257577, 'ACC-window-other': 68.68392616229876, 'ACC-tree-merged': 89.12846312674148, 'ACC-fence-merged': 70.42289622615279, 'ACC-ceiling-merged': 80.6912715786544, 'ACC-sky-other-merged': 96.67860796331176, 'ACC-cabinet-merged': 75.23680672577474, 'ACC-table-merged': 55.41459826353452, 'ACC-floor-other-merged': 61.58466071505833, 'ACC-pavement-merged': 71.38944569200217, 'ACC-mountain-merged': 66.75060626089814, 'ACC-grass-merged': 82.91916436783102, 'ACC-dirt-merged': 67.54970330320866, 'ACC-paper-merged': 50.344060154675375, 'ACC-food-other-merged': 54.86619988820108, 'ACC-building-other-merged': 72.9155059990956, 'ACC-rock-merged': 84.14678046516862, 'ACC-wall-other-merged': 81.31060282666265, 'ACC-rug-merged': 78.26696272142705})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.5062920690664325, 'noc@0.8': 2.788410886742757, 'noc@0.85': 3.349721978343576, 'noc@0.9': 4.3769388352355865, 'miou@iter1': 0.8321052743288241}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.57909393310547, 'precision@0.6': 67.74193572998047, 'precision@0.7': 62.30081558227539, 'precision@0.8': 52.42906951904297, 'precision@0.9': 27.01127052307129, 'cIoU': 57.35614776611328, 'mIoU': 62.47899627685547}}}
INFO:trainer.default_trainer:This epoch takes 1:28:05.132979
INFO:trainer.default_trainer:PROGRESS: 82.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 41 training.
INFO:trainer.default_trainer:epochs[    41] optim steps[75000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99779/0.89590, loss_mask_bce_0: 0.12100/0.33408, loss_mask_dice_0: 1.22155/1.16206, loss_spatial_bce_0: 0.01776/0.08680, loss_spatial_dice_0: 0.24577/0.20712, loss_spatial_ce_0: 0.06081/0.06041, loss_grounding_bce_0: 0.00230/0.08617, loss_grounding_dice_0: 0.14222/0.17847, loss_grounding_ce_0: 0.43127/0.27200, loss_mask_ce_1: 1.17953/0.89653, loss_mask_bce_1: 0.07584/0.33502, loss_mask_dice_1: 1.61524/1.16879, loss_spatial_bce_1: 0.01733/0.08734, loss_spatial_dice_1: 0.23325/0.21110, loss_spatial_ce_1: 0.08488/0.06616, loss_grounding_bce_1: 0.00312/0.08635, loss_grounding_dice_1: 0.21579/0.17929, loss_grounding_ce_1: 0.45980/0.27274, loss_mask_ce_2: 1.07669/0.90357, loss_mask_bce_2: 0.07300/0.33563, loss_mask_dice_2: 1.30200/1.16927, loss_spatial_bce_2: 0.01471/0.08853, loss_spatial_dice_2: 0.20460/0.21287, loss_spatial_ce_2: 0.08037/0.06966, loss_grounding_bce_2: 0.00281/0.08652, loss_grounding_dice_2: 0.14368/0.17918, loss_grounding_ce_2: 0.47204/0.27600, loss_mask_ce_3: 1.16022/0.91459, loss_mask_bce_3: 0.07911/0.33682, loss_mask_dice_3: 1.27078/1.16718, loss_spatial_bce_3: 0.01433/0.08979, loss_spatial_dice_3: 0.22711/0.21390, loss_spatial_ce_3: 0.11490/0.07458, loss_grounding_bce_3: 0.00245/0.08676, loss_grounding_dice_3: 0.15185/0.17889, loss_grounding_ce_3: 0.44058/0.27822, loss_mask_ce_4: 1.00308/0.91564, loss_mask_bce_4: 0.14077/0.33897, loss_mask_dice_4: 1.44402/1.19093, loss_spatial_bce_4: 0.01517/0.09370, loss_spatial_dice_4: 0.20076/0.22616, loss_spatial_ce_4: 0.13391/0.09087, loss_grounding_bce_4: 0.00200/0.08731, loss_grounding_dice_4: 0.10172/0.18183, loss_grounding_ce_4: 0.41529/0.28120, loss_mask_ce_5: 1.21635/0.93252, loss_mask_bce_5: 0.09890/0.34129, loss_mask_dice_5: 1.31920/1.19914, loss_spatial_bce_5: 0.01654/0.09600, loss_spatial_dice_5: 0.23069/0.23045, loss_spatial_ce_5: 0.23450/0.10493, loss_grounding_bce_5: 0.00209/0.08773, loss_grounding_dice_5: 0.04127/0.18314, loss_grounding_ce_5: 0.44748/0.29359, loss_mask_ce_6: 1.55082/0.97267, loss_mask_bce_6: 0.10706/0.34406, loss_mask_dice_6: 1.33330/1.20193, loss_spatial_bce_6: 0.01864/0.10166, loss_spatial_dice_6: 0.24073/0.23341, loss_spatial_ce_6: 0.21290/0.13022, loss_grounding_bce_6: 0.00220/0.08846, loss_grounding_dice_6: 0.15375/0.18348, loss_grounding_ce_6: 0.40748/0.30905, loss_mask_ce_7: 1.62345/1.01797, loss_mask_bce_7: 0.12049/0.35188, loss_mask_dice_7: 1.72341/1.25640, loss_spatial_bce_7: 0.02433/0.10954, loss_spatial_dice_7: 0.32323/0.26101, loss_spatial_ce_7: 0.21868/0.16528, loss_grounding_bce_7: 0.00190/0.09035, loss_grounding_dice_7: 0.07320/0.19082, loss_grounding_ce_7: 0.40362/0.33938, loss_mask_ce_8: 1.47934/1.12690, loss_mask_bce_8: 0.14849/0.36551, loss_mask_dice_8: 1.56202/1.32913, loss_spatial_bce_8: 0.05045/0.12993, loss_spatial_dice_8: 0.33921/0.29874, loss_spatial_ce_8: 0.18452/0.21884, loss_grounding_bce_8: 0.00370/0.09405, loss_grounding_dice_8: 0.13095/0.20152, loss_grounding_ce_8: 0.78744/0.40580, loss_mask_ce_9: 3.51633/3.67489, loss_mask_bce_9: 0.10513/0.39251, loss_mask_dice_9: 1.85263/1.90174, loss_spatial_bce_9: 0.42463/0.33275, loss_spatial_dice_9: 0.85970/0.82166, loss_spatial_ce_9: 2.26426/1.49426, loss_grounding_bce_9: 0.00539/0.10565, loss_grounding_dice_9: 0.10405/0.28088, loss_grounding_ce_9: 1.17160/0.67007] items per batch[64] items per second[0.13] total items[4800000] mini batches[ 75000] memory[7345] epoch remaining[1:22:06]
INFO:trainer.default_trainer:epochs[    41] optim steps[75100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30496/0.89585, loss_mask_bce_0: 0.30972/0.33406, loss_mask_dice_0: 1.57992/1.16194, loss_spatial_bce_0: 0.03531/0.08679, loss_spatial_dice_0: 0.19569/0.20711, loss_spatial_ce_0: 0.03462/0.06039, loss_grounding_bce_0: 0.02162/0.08617, loss_grounding_dice_0: 0.08937/0.17846, loss_grounding_ce_0: 0.14770/0.27201, loss_mask_ce_1: 0.48450/0.89647, loss_mask_bce_1: 0.30343/0.33500, loss_mask_dice_1: 1.61257/1.16868, loss_spatial_bce_1: 0.03426/0.08734, loss_spatial_dice_1: 0.18611/0.21109, loss_spatial_ce_1: 0.03802/0.06615, loss_grounding_bce_1: 0.01952/0.08634, loss_grounding_dice_1: 0.08160/0.17928, loss_grounding_ce_1: 0.16231/0.27276, loss_mask_ce_2: 0.52709/0.90353, loss_mask_bce_2: 0.29211/0.33560, loss_mask_dice_2: 1.32113/1.16916, loss_spatial_bce_2: 0.03563/0.08852, loss_spatial_dice_2: 0.18396/0.21286, loss_spatial_ce_2: 0.03592/0.06964, loss_grounding_bce_2: 0.02261/0.08652, loss_grounding_dice_2: 0.08788/0.17917, loss_grounding_ce_2: 0.14722/0.27603, loss_mask_ce_3: 0.54679/0.91454, loss_mask_bce_3: 0.29416/0.33679, loss_mask_dice_3: 1.42758/1.16707, loss_spatial_bce_3: 0.04047/0.08978, loss_spatial_dice_3: 0.20182/0.21389, loss_spatial_ce_3: 0.04465/0.07458, loss_grounding_bce_3: 0.02122/0.08676, loss_grounding_dice_3: 0.08864/0.17887, loss_grounding_ce_3: 0.27203/0.27826, loss_mask_ce_4: 0.29416/0.91559, loss_mask_bce_4: 0.30876/0.33894, loss_mask_dice_4: 1.66159/1.19083, loss_spatial_bce_4: 0.03936/0.09370, loss_spatial_dice_4: 0.21144/0.22615, loss_spatial_ce_4: 0.08832/0.09086, loss_grounding_bce_4: 0.02228/0.08731, loss_grounding_dice_4: 0.09145/0.18183, loss_grounding_ce_4: 0.24661/0.28120, loss_mask_ce_5: 0.30995/0.93250, loss_mask_bce_5: 0.31112/0.34127, loss_mask_dice_5: 1.82054/1.19902, loss_spatial_bce_5: 0.04358/0.09600, loss_spatial_dice_5: 0.22363/0.23043, loss_spatial_ce_5: 0.07956/0.10491, loss_grounding_bce_5: 0.02103/0.08773, loss_grounding_dice_5: 0.09417/0.18313, loss_grounding_ce_5: 0.19273/0.29362, loss_mask_ce_6: 0.52351/0.97263, loss_mask_bce_6: 0.30469/0.34404, loss_mask_dice_6: 1.37499/1.20183, loss_spatial_bce_6: 0.04942/0.10165, loss_spatial_dice_6: 0.23407/0.23339, loss_spatial_ce_6: 0.10989/0.13021, loss_grounding_bce_6: 0.02235/0.08845, loss_grounding_dice_6: 0.09345/0.18346, loss_grounding_ce_6: 0.16938/0.30911, loss_mask_ce_7: 0.40612/1.01795, loss_mask_bce_7: 0.34136/0.35184, loss_mask_dice_7: 1.80498/1.25628, loss_spatial_bce_7: 0.05229/0.10954, loss_spatial_dice_7: 0.30852/0.26100, loss_spatial_ce_7: 0.08663/0.16526, loss_grounding_bce_7: 0.01911/0.09034, loss_grounding_dice_7: 0.09558/0.19081, loss_grounding_ce_7: 0.17610/0.33942, loss_mask_ce_8: 0.54575/1.12686, loss_mask_bce_8: 0.33545/0.36547, loss_mask_dice_8: 1.85731/1.32896, loss_spatial_bce_8: 0.05081/0.12992, loss_spatial_dice_8: 0.29736/0.29873, loss_spatial_ce_8: 0.11622/0.21876, loss_grounding_bce_8: 0.02056/0.09404, loss_grounding_dice_8: 0.10407/0.20151, loss_grounding_ce_8: 0.33087/0.40580, loss_mask_ce_9: 3.64827/3.67491, loss_mask_bce_9: 0.38820/0.39248, loss_mask_dice_9: 2.45288/1.90147, loss_spatial_bce_9: 0.20517/0.33275, loss_spatial_dice_9: 0.82231/0.82164, loss_spatial_ce_9: 1.16189/1.49419, loss_grounding_bce_9: 0.02971/0.10565, loss_grounding_dice_9: 0.15708/0.28087, loss_grounding_ce_9: 0.80058/0.67010] items per batch[64] items per second[0.23] total items[4806400] mini batches[ 75100] memory[7345] epoch remaining[1:15:58]
INFO:trainer.default_trainer:epochs[    41] optim steps[75200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.78982/0.89596, loss_mask_bce_0: 0.20382/0.33410, loss_mask_dice_0: 2.83654/1.16202, loss_spatial_bce_0: 0.05418/0.08679, loss_spatial_dice_0: 0.37692/0.20710, loss_spatial_ce_0: 0.06123/0.06036, loss_grounding_bce_0: 0.02486/0.08616, loss_grounding_dice_0: 0.37702/0.17846, loss_grounding_ce_0: 0.44914/0.27202, loss_mask_ce_1: 1.52628/0.89658, loss_mask_bce_1: 0.19891/0.33505, loss_mask_dice_1: 2.21339/1.16875, loss_spatial_bce_1: 0.05406/0.08733, loss_spatial_dice_1: 0.36163/0.21109, loss_spatial_ce_1: 0.08828/0.06613, loss_grounding_bce_1: 0.02500/0.08634, loss_grounding_dice_1: 0.41097/0.17927, loss_grounding_ce_1: 0.54388/0.27275, loss_mask_ce_2: 1.45521/0.90363, loss_mask_bce_2: 0.21104/0.33565, loss_mask_dice_2: 2.63142/1.16922, loss_spatial_bce_2: 0.05264/0.08852, loss_spatial_dice_2: 0.40016/0.21286, loss_spatial_ce_2: 0.07150/0.06961, loss_grounding_bce_2: 0.02545/0.08651, loss_grounding_dice_2: 0.40958/0.17917, loss_grounding_ce_2: 0.35621/0.27602, loss_mask_ce_3: 1.77801/0.91463, loss_mask_bce_3: 0.19891/0.33684, loss_mask_dice_3: 2.68669/1.16715, loss_spatial_bce_3: 0.05302/0.08978, loss_spatial_dice_3: 0.36438/0.21388, loss_spatial_ce_3: 0.08784/0.07455, loss_grounding_bce_3: 0.02398/0.08676, loss_grounding_dice_3: 0.53177/0.17886, loss_grounding_ce_3: 0.35890/0.27825, loss_mask_ce_4: 2.22507/0.91569, loss_mask_bce_4: 0.18950/0.33899, loss_mask_dice_4: 2.30362/1.19090, loss_spatial_bce_4: 0.05505/0.09369, loss_spatial_dice_4: 0.41324/0.22614, loss_spatial_ce_4: 0.16529/0.09084, loss_grounding_bce_4: 0.02520/0.08730, loss_grounding_dice_4: 0.49739/0.18182, loss_grounding_ce_4: 0.34806/0.28119, loss_mask_ce_5: 1.82917/0.93260, loss_mask_bce_5: 0.19427/0.34132, loss_mask_dice_5: 2.70097/1.19909, loss_spatial_bce_5: 0.05670/0.09599, loss_spatial_dice_5: 0.37769/0.23042, loss_spatial_ce_5: 0.15404/0.10489, loss_grounding_bce_5: 0.02453/0.08773, loss_grounding_dice_5: 0.37184/0.18313, loss_grounding_ce_5: 0.57679/0.29365, loss_mask_ce_6: 2.16479/0.97273, loss_mask_bce_6: 0.18786/0.34409, loss_mask_dice_6: 2.39649/1.20189, loss_spatial_bce_6: 0.06194/0.10164, loss_spatial_dice_6: 0.48060/0.23338, loss_spatial_ce_6: 0.22488/0.13018, loss_grounding_bce_6: 0.02693/0.08845, loss_grounding_dice_6: 0.57913/0.18346, loss_grounding_ce_6: 0.43249/0.30913, loss_mask_ce_7: 2.31912/1.01809, loss_mask_bce_7: 0.20339/0.35191, loss_mask_dice_7: 2.23953/1.25636, loss_spatial_bce_7: 0.06527/0.10953, loss_spatial_dice_7: 0.44723/0.26099, loss_spatial_ce_7: 0.12749/0.16521, loss_grounding_bce_7: 0.02583/0.09034, loss_grounding_dice_7: 0.45942/0.19081, loss_grounding_ce_7: 0.37739/0.33944, loss_mask_ce_8: 2.07135/1.12699, loss_mask_bce_8: 0.19520/0.36553, loss_mask_dice_8: 2.18146/1.32902, loss_spatial_bce_8: 0.06938/0.12991, loss_spatial_dice_8: 0.58829/0.29871, loss_spatial_ce_8: 0.60888/0.21872, loss_grounding_bce_8: 0.03103/0.09404, loss_grounding_dice_8: 0.46962/0.20151, loss_grounding_ce_8: 0.49945/0.40582, loss_mask_ce_9: 4.75909/3.67506, loss_mask_bce_9: 0.20622/0.39254, loss_mask_dice_9: 2.91474/1.90157, loss_spatial_bce_9: 0.21735/0.33277, loss_spatial_dice_9: 0.85421/0.82166, loss_spatial_ce_9: 2.47299/1.49421, loss_grounding_bce_9: 0.03056/0.10565, loss_grounding_dice_9: 0.42938/0.28086, loss_grounding_ce_9: 0.54748/0.67007] items per batch[64] items per second[0.23] total items[4812800] mini batches[ 75200] memory[7345] epoch remaining[1:10:56]
INFO:trainer.default_trainer:epochs[    41] optim steps[75300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54385/0.89606, loss_mask_bce_0: 0.43303/0.33410, loss_mask_dice_0: 0.77708/1.16190, loss_spatial_bce_0: 0.09094/0.08680, loss_spatial_dice_0: 0.15128/0.20710, loss_spatial_ce_0: 0.03436/0.06035, loss_grounding_bce_0: 0.04560/0.08617, loss_grounding_dice_0: 0.11713/0.17847, loss_grounding_ce_0: 0.38413/0.27194, loss_mask_ce_1: 0.51268/0.89666, loss_mask_bce_1: 0.41546/0.33504, loss_mask_dice_1: 0.76563/1.16863, loss_spatial_bce_1: 0.08579/0.08734, loss_spatial_dice_1: 0.15763/0.21108, loss_spatial_ce_1: 0.03992/0.06612, loss_grounding_bce_1: 0.04806/0.08635, loss_grounding_dice_1: 0.12688/0.17928, loss_grounding_ce_1: 0.38528/0.27267, loss_mask_ce_2: 0.58818/0.90371, loss_mask_bce_2: 0.45511/0.33565, loss_mask_dice_2: 0.76053/1.16908, loss_spatial_bce_2: 0.09692/0.08853, loss_spatial_dice_2: 0.17103/0.21285, loss_spatial_ce_2: 0.06938/0.06960, loss_grounding_bce_2: 0.04360/0.08652, loss_grounding_dice_2: 0.13335/0.17917, loss_grounding_ce_2: 0.38312/0.27594, loss_mask_ce_3: 0.60297/0.91470, loss_mask_bce_3: 0.42926/0.33684, loss_mask_dice_3: 0.73814/1.16703, loss_spatial_bce_3: 0.09620/0.08979, loss_spatial_dice_3: 0.17712/0.21388, loss_spatial_ce_3: 0.06480/0.07456, loss_grounding_bce_3: 0.04241/0.08676, loss_grounding_dice_3: 0.12241/0.17886, loss_grounding_ce_3: 0.40553/0.27817, loss_mask_ce_4: 0.63073/0.91575, loss_mask_bce_4: 0.45358/0.33898, loss_mask_dice_4: 0.75584/1.19078, loss_spatial_bce_4: 0.10445/0.09371, loss_spatial_dice_4: 0.17916/0.22614, loss_spatial_ce_4: 0.05623/0.09084, loss_grounding_bce_4: 0.05747/0.08731, loss_grounding_dice_4: 0.15198/0.18183, loss_grounding_ce_4: 0.29355/0.28111, loss_mask_ce_5: 0.76035/0.93272, loss_mask_bce_5: 0.45663/0.34131, loss_mask_dice_5: 0.79665/1.19897, loss_spatial_bce_5: 0.11757/0.09601, loss_spatial_dice_5: 0.18074/0.23042, loss_spatial_ce_5: 0.07115/0.10487, loss_grounding_bce_5: 0.05417/0.08773, loss_grounding_dice_5: 0.14298/0.18313, loss_grounding_ce_5: 0.35462/0.29357, loss_mask_ce_6: 0.98374/0.97284, loss_mask_bce_6: 0.45825/0.34409, loss_mask_dice_6: 0.84552/1.20177, loss_spatial_bce_6: 0.12450/0.10164, loss_spatial_dice_6: 0.16124/0.23338, loss_spatial_ce_6: 0.07907/0.13015, loss_grounding_bce_6: 0.05880/0.08846, loss_grounding_dice_6: 0.14996/0.18347, loss_grounding_ce_6: 0.34878/0.30905, loss_mask_ce_7: 1.06905/1.01819, loss_mask_bce_7: 0.43055/0.35190, loss_mask_dice_7: 0.79767/1.25623, loss_spatial_bce_7: 0.12034/0.10954, loss_spatial_dice_7: 0.21554/0.26098, loss_spatial_ce_7: 0.11789/0.16519, loss_grounding_bce_7: 0.05306/0.09035, loss_grounding_dice_7: 0.14317/0.19082, loss_grounding_ce_7: 0.36973/0.33934, loss_mask_ce_8: 1.23407/1.12710, loss_mask_bce_8: 0.48245/0.36553, loss_mask_dice_8: 0.85960/1.32890, loss_spatial_bce_8: 0.15422/0.12991, loss_spatial_dice_8: 0.24644/0.29871, loss_spatial_ce_8: 0.13454/0.21867, loss_grounding_bce_8: 0.06772/0.09405, loss_grounding_dice_8: 0.17676/0.20151, loss_grounding_ce_8: 0.37344/0.40567, loss_mask_ce_9: 4.42359/3.67499, loss_mask_bce_9: 0.54915/0.39255, loss_mask_dice_9: 1.58689/1.90140, loss_spatial_bce_9: 0.31694/0.33278, loss_spatial_dice_9: 0.79281/0.82166, loss_spatial_ce_9: 1.23509/1.49421, loss_grounding_bce_9: 0.06190/0.10567, loss_grounding_dice_9: 0.31576/0.28086, loss_grounding_ce_9: 0.40010/0.66989] items per batch[64] items per second[0.23] total items[4819200] mini batches[ 75300] memory[7345] epoch remaining[1:06:19]
INFO:trainer.default_trainer:epochs[    41] optim steps[75400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05718/0.89613, loss_mask_bce_0: 0.69100/0.33406, loss_mask_dice_0: 1.40076/1.16190, loss_spatial_bce_0: 0.11288/0.08678, loss_spatial_dice_0: 0.21454/0.20708, loss_spatial_ce_0: 0.15484/0.06033, loss_grounding_bce_0: 0.13961/0.08617, loss_grounding_dice_0: 0.12963/0.17848, loss_grounding_ce_0: 0.04378/0.27185, loss_mask_ce_1: 1.06641/0.89673, loss_mask_bce_1: 0.70806/0.33501, loss_mask_dice_1: 1.41859/1.16864, loss_spatial_bce_1: 0.11692/0.08733, loss_spatial_dice_1: 0.23080/0.21106, loss_spatial_ce_1: 0.16833/0.06611, loss_grounding_bce_1: 0.13531/0.08635, loss_grounding_dice_1: 0.12720/0.17929, loss_grounding_ce_1: 0.04709/0.27258, loss_mask_ce_2: 1.03817/0.90375, loss_mask_bce_2: 0.69500/0.33562, loss_mask_dice_2: 1.40436/1.16911, loss_spatial_bce_2: 0.17214/0.08852, loss_spatial_dice_2: 0.24037/0.21284, loss_spatial_ce_2: 0.09302/0.06959, loss_grounding_bce_2: 0.14436/0.08652, loss_grounding_dice_2: 0.13569/0.17917, loss_grounding_ce_2: 0.04067/0.27586, loss_mask_ce_3: 1.07600/0.91476, loss_mask_bce_3: 0.70188/0.33680, loss_mask_dice_3: 1.45501/1.16705, loss_spatial_bce_3: 0.10728/0.08978, loss_spatial_dice_3: 0.23615/0.21387, loss_spatial_ce_3: 0.07287/0.07455, loss_grounding_bce_3: 0.14548/0.08676, loss_grounding_dice_3: 0.13567/0.17887, loss_grounding_ce_3: 0.04858/0.27809, loss_mask_ce_4: 1.00035/0.91583, loss_mask_bce_4: 0.70110/0.33894, loss_mask_dice_4: 1.56018/1.19078, loss_spatial_bce_4: 0.09073/0.09370, loss_spatial_dice_4: 0.28100/0.22613, loss_spatial_ce_4: 0.08530/0.09083, loss_grounding_bce_4: 0.14524/0.08730, loss_grounding_dice_4: 0.14634/0.18184, loss_grounding_ce_4: 0.08235/0.28103, loss_mask_ce_5: 1.15069/0.93278, loss_mask_bce_5: 0.72599/0.34127, loss_mask_dice_5: 1.56504/1.19899, loss_spatial_bce_5: 0.16735/0.09600, loss_spatial_dice_5: 0.28171/0.23041, loss_spatial_ce_5: 0.09463/0.10485, loss_grounding_bce_5: 0.15405/0.08773, loss_grounding_dice_5: 0.13843/0.18314, loss_grounding_ce_5: 0.06489/0.29347, loss_mask_ce_6: 1.16433/0.97290, loss_mask_bce_6: 0.77615/0.34405, loss_mask_dice_6: 1.68787/1.20178, loss_spatial_bce_6: 0.11286/0.10163, loss_spatial_dice_6: 0.32194/0.23337, loss_spatial_ce_6: 0.13891/0.13013, loss_grounding_bce_6: 0.13718/0.08845, loss_grounding_dice_6: 0.11897/0.18348, loss_grounding_ce_6: 0.06770/0.30895, loss_mask_ce_7: 1.14450/1.01827, loss_mask_bce_7: 0.79917/0.35187, loss_mask_dice_7: 1.75681/1.25624, loss_spatial_bce_7: 0.10851/0.10952, loss_spatial_dice_7: 0.29437/0.26097, loss_spatial_ce_7: 0.15000/0.16514, loss_grounding_bce_7: 0.15040/0.09034, loss_grounding_dice_7: 0.13292/0.19082, loss_grounding_ce_7: 0.25294/0.33923, loss_mask_ce_8: 1.30968/1.12716, loss_mask_bce_8: 0.68498/0.36549, loss_mask_dice_8: 1.82185/1.32892, loss_spatial_bce_8: 0.11054/0.12989, loss_spatial_dice_8: 0.31696/0.29870, loss_spatial_ce_8: 0.12052/0.21861, loss_grounding_bce_8: 0.14117/0.09404, loss_grounding_dice_8: 0.13152/0.20152, loss_grounding_ce_8: 0.11659/0.40557, loss_mask_ce_9: 3.95894/3.67502, loss_mask_bce_9: 0.69428/0.39251, loss_mask_dice_9: 2.59616/1.90137, loss_spatial_bce_9: 0.27693/0.33278, loss_spatial_dice_9: 0.89620/0.82166, loss_spatial_ce_9: 1.65748/1.49406, loss_grounding_bce_9: 0.09751/0.10566, loss_grounding_dice_9: 0.14681/0.28087, loss_grounding_ce_9: 0.92905/0.66987] items per batch[64] items per second[0.22] total items[4825600] mini batches[ 75400] memory[7345] epoch remaining[1:02:05]
INFO:trainer.default_trainer:epochs[    41] optim steps[75500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77005/0.89614, loss_mask_bce_0: 0.33391/0.33407, loss_mask_dice_0: 1.25028/1.16218, loss_spatial_bce_0: 0.10080/0.08678, loss_spatial_dice_0: 0.23823/0.20708, loss_spatial_ce_0: 0.01015/0.06031, loss_grounding_bce_0: 0.07793/0.08616, loss_grounding_dice_0: 0.28431/0.17847, loss_grounding_ce_0: 0.29091/0.27181, loss_mask_ce_1: 0.99409/0.89674, loss_mask_bce_1: 0.31882/0.33502, loss_mask_dice_1: 1.28608/1.16889, loss_spatial_bce_1: 0.09962/0.08733, loss_spatial_dice_1: 0.23449/0.21106, loss_spatial_ce_1: 0.02802/0.06608, loss_grounding_bce_1: 0.08185/0.08633, loss_grounding_dice_1: 0.29727/0.17927, loss_grounding_ce_1: 0.27559/0.27253, loss_mask_ce_2: 0.80470/0.90375, loss_mask_bce_2: 0.33375/0.33563, loss_mask_dice_2: 1.28445/1.16939, loss_spatial_bce_2: 0.10063/0.08852, loss_spatial_dice_2: 0.23885/0.21285, loss_spatial_ce_2: 0.02950/0.06956, loss_grounding_bce_2: 0.07804/0.08651, loss_grounding_dice_2: 0.33505/0.17916, loss_grounding_ce_2: 0.27407/0.27580, loss_mask_ce_3: 0.88691/0.91477, loss_mask_bce_3: 0.31549/0.33681, loss_mask_dice_3: 1.21420/1.16732, loss_spatial_bce_3: 0.11703/0.08978, loss_spatial_dice_3: 0.24003/0.21388, loss_spatial_ce_3: 0.03701/0.07453, loss_grounding_bce_3: 0.08830/0.08676, loss_grounding_dice_3: 0.31557/0.17887, loss_grounding_ce_3: 0.27592/0.27804, loss_mask_ce_4: 0.79067/0.91583, loss_mask_bce_4: 0.30728/0.33895, loss_mask_dice_4: 1.22999/1.19103, loss_spatial_bce_4: 0.11257/0.09369, loss_spatial_dice_4: 0.28471/0.22614, loss_spatial_ce_4: 0.06070/0.09082, loss_grounding_bce_4: 0.09924/0.08730, loss_grounding_dice_4: 0.34749/0.18184, loss_grounding_ce_4: 0.29763/0.28098, loss_mask_ce_5: 0.84197/0.93278, loss_mask_bce_5: 0.32405/0.34129, loss_mask_dice_5: 1.38302/1.19924, loss_spatial_bce_5: 0.11400/0.09599, loss_spatial_dice_5: 0.26118/0.23042, loss_spatial_ce_5: 0.11319/0.10483, loss_grounding_bce_5: 0.07929/0.08772, loss_grounding_dice_5: 0.35747/0.18313, loss_grounding_ce_5: 0.26330/0.29340, loss_mask_ce_6: 0.72946/0.97286, loss_mask_bce_6: 0.32258/0.34407, loss_mask_dice_6: 1.34493/1.20207, loss_spatial_bce_6: 0.11405/0.10162, loss_spatial_dice_6: 0.27272/0.23339, loss_spatial_ce_6: 0.14426/0.13011, loss_grounding_bce_6: 0.09188/0.08845, loss_grounding_dice_6: 0.31468/0.18348, loss_grounding_ce_6: 0.21683/0.30887, loss_mask_ce_7: 0.95734/1.01828, loss_mask_bce_7: 0.34344/0.35188, loss_mask_dice_7: 1.33542/1.25652, loss_spatial_bce_7: 0.10958/0.10951, loss_spatial_dice_7: 0.29416/0.26098, loss_spatial_ce_7: 0.26582/0.16511, loss_grounding_bce_7: 0.09493/0.09034, loss_grounding_dice_7: 0.33181/0.19083, loss_grounding_ce_7: 0.26631/0.33916, loss_mask_ce_8: 0.95891/1.12716, loss_mask_bce_8: 0.38001/0.36551, loss_mask_dice_8: 1.44818/1.32923, loss_spatial_bce_8: 0.20036/0.12988, loss_spatial_dice_8: 0.38611/0.29871, loss_spatial_ce_8: 0.26251/0.21856, loss_grounding_bce_8: 0.09773/0.09404, loss_grounding_dice_8: 0.33163/0.20152, loss_grounding_ce_8: 0.20237/0.40547, loss_mask_ce_9: 4.00699/3.67509, loss_mask_bce_9: 0.36313/0.39254, loss_mask_dice_9: 2.44705/1.90182, loss_spatial_bce_9: 0.26080/0.33273, loss_spatial_dice_9: 0.92369/0.82165, loss_spatial_ce_9: 1.63626/1.49397, loss_grounding_bce_9: 0.11119/0.10565, loss_grounding_dice_9: 0.40351/0.28088, loss_grounding_ce_9: 0.16229/0.66974] items per batch[64] items per second[0.24] total items[4832000] mini batches[ 75500] memory[7345] epoch remaining[0:57:10]
INFO:trainer.default_trainer:epochs[    41] optim steps[75600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33514/0.89610, loss_mask_bce_0: 0.06510/0.33404, loss_mask_dice_0: 0.52964/1.16204, loss_spatial_bce_0: 0.02799/0.08677, loss_spatial_dice_0: 0.17794/0.20706, loss_spatial_ce_0: 0.00144/0.06029, loss_grounding_bce_0: 0.01743/0.08616, loss_grounding_dice_0: 0.05462/0.17845, loss_grounding_ce_0: 0.07640/0.27176, loss_mask_ce_1: 0.20239/0.89672, loss_mask_bce_1: 0.06811/0.33499, loss_mask_dice_1: 0.57629/1.16876, loss_spatial_bce_1: 0.02776/0.08732, loss_spatial_dice_1: 0.17191/0.21104, loss_spatial_ce_1: 0.00401/0.06607, loss_grounding_bce_1: 0.01543/0.08634, loss_grounding_dice_1: 0.21029/0.17926, loss_grounding_ce_1: 0.06806/0.27248, loss_mask_ce_2: 0.33491/0.90372, loss_mask_bce_2: 0.06489/0.33561, loss_mask_dice_2: 0.50303/1.16924, loss_spatial_bce_2: 0.02937/0.08851, loss_spatial_dice_2: 0.19110/0.21282, loss_spatial_ce_2: 0.00856/0.06954, loss_grounding_bce_2: 0.01614/0.08652, loss_grounding_dice_2: 0.21976/0.17915, loss_grounding_ce_2: 0.06934/0.27574, loss_mask_ce_3: 0.37264/0.91475, loss_mask_bce_3: 0.07031/0.33678, loss_mask_dice_3: 0.70261/1.16717, loss_spatial_bce_3: 0.02882/0.08977, loss_spatial_dice_3: 0.18836/0.21385, loss_spatial_ce_3: 0.01300/0.07453, loss_grounding_bce_3: 0.01529/0.08676, loss_grounding_dice_3: 0.17961/0.17886, loss_grounding_ce_3: 0.07374/0.27798, loss_mask_ce_4: 0.31765/0.91580, loss_mask_bce_4: 0.06626/0.33893, loss_mask_dice_4: 0.66447/1.19092, loss_spatial_bce_4: 0.02519/0.09368, loss_spatial_dice_4: 0.19785/0.22612, loss_spatial_ce_4: 0.01721/0.09081, loss_grounding_bce_4: 0.01420/0.08730, loss_grounding_dice_4: 0.17306/0.18183, loss_grounding_ce_4: 0.07337/0.28093, loss_mask_ce_5: 0.26260/0.93275, loss_mask_bce_5: 0.08156/0.34127, loss_mask_dice_5: 0.65213/1.19915, loss_spatial_bce_5: 0.02501/0.09599, loss_spatial_dice_5: 0.19477/0.23040, loss_spatial_ce_5: 0.03182/0.10483, loss_grounding_bce_5: 0.01397/0.08773, loss_grounding_dice_5: 0.17706/0.18312, loss_grounding_ce_5: 0.06960/0.29336, loss_mask_ce_6: 0.35837/0.97283, loss_mask_bce_6: 0.06864/0.34406, loss_mask_dice_6: 0.55207/1.20195, loss_spatial_bce_6: 0.02669/0.10162, loss_spatial_dice_6: 0.19422/0.23337, loss_spatial_ce_6: 0.03000/0.13010, loss_grounding_bce_6: 0.01295/0.08846, loss_grounding_dice_6: 0.15294/0.18347, loss_grounding_ce_6: 0.08605/0.30882, loss_mask_ce_7: 0.36851/1.01824, loss_mask_bce_7: 0.07177/0.35187, loss_mask_dice_7: 0.38654/1.25641, loss_spatial_bce_7: 0.02977/0.10950, loss_spatial_dice_7: 0.25074/0.26096, loss_spatial_ce_7: 0.36499/0.16511, loss_grounding_bce_7: 0.01764/0.09035, loss_grounding_dice_7: 0.18502/0.19081, loss_grounding_ce_7: 0.19878/0.33909, loss_mask_ce_8: 0.90430/1.12711, loss_mask_bce_8: 0.08312/0.36551, loss_mask_dice_8: 0.45193/1.32910, loss_spatial_bce_8: 0.03957/0.12987, loss_spatial_dice_8: 0.21605/0.29869, loss_spatial_ce_8: 0.33506/0.21850, loss_grounding_bce_8: 0.01250/0.09404, loss_grounding_dice_8: 0.08227/0.20151, loss_grounding_ce_8: 0.11154/0.40546, loss_mask_ce_9: 2.38359/3.67508, loss_mask_bce_9: 0.08798/0.39253, loss_mask_dice_9: 0.77292/1.90158, loss_spatial_bce_9: 0.35642/0.33275, loss_spatial_dice_9: 0.84724/0.82164, loss_spatial_ce_9: 1.51123/1.49395, loss_grounding_bce_9: 0.01656/0.10567, loss_grounding_dice_9: 0.19549/0.28086, loss_grounding_ce_9: 0.17403/0.66979] items per batch[64] items per second[0.23] total items[4838400] mini batches[ 75600] memory[7345] epoch remaining[0:52:33]
INFO:trainer.default_trainer:epochs[    41] optim steps[75700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.94839/0.89613, loss_mask_bce_0: 0.02074/0.33406, loss_mask_dice_0: 0.02575/1.16213, loss_spatial_bce_0: 0.03469/0.08677, loss_spatial_dice_0: 0.04142/0.20704, loss_spatial_ce_0: 0.00003/0.06026, loss_grounding_bce_0: 0.01958/0.08616, loss_grounding_dice_0: 0.02378/0.17842, loss_grounding_ce_0: 0.12966/0.27175, loss_mask_ce_1: 2.85586/0.89676, loss_mask_bce_1: 0.02108/0.33501, loss_mask_dice_1: 0.02395/1.16886, loss_spatial_bce_1: 0.03771/0.08731, loss_spatial_dice_1: 0.04584/0.21101, loss_spatial_ce_1: 0.00004/0.06604, loss_grounding_bce_1: 0.01885/0.08634, loss_grounding_dice_1: 0.02048/0.17924, loss_grounding_ce_1: 0.11074/0.27248, loss_mask_ce_2: 2.88756/0.90376, loss_mask_bce_2: 0.02275/0.33563, loss_mask_dice_2: 0.17681/1.16934, loss_spatial_bce_2: 0.03303/0.08851, loss_spatial_dice_2: 0.04152/0.21281, loss_spatial_ce_2: 0.00001/0.06952, loss_grounding_bce_2: 0.02230/0.08652, loss_grounding_dice_2: 0.02414/0.17912, loss_grounding_ce_2: 0.08872/0.27575, loss_mask_ce_3: 2.78016/0.91480, loss_mask_bce_3: 0.02186/0.33680, loss_mask_dice_3: 0.15881/1.16724, loss_spatial_bce_3: 0.03157/0.08977, loss_spatial_dice_3: 0.04158/0.21383, loss_spatial_ce_3: 0.00002/0.07450, loss_grounding_bce_3: 0.01859/0.08676, loss_grounding_dice_3: 0.02210/0.17883, loss_grounding_ce_3: 0.08323/0.27799, loss_mask_ce_4: 2.86351/0.91587, loss_mask_bce_4: 0.02230/0.33895, loss_mask_dice_4: 0.02378/1.19102, loss_spatial_bce_4: 0.03365/0.09368, loss_spatial_dice_4: 0.04376/0.22610, loss_spatial_ce_4: 0.00078/0.09080, loss_grounding_bce_4: 0.02129/0.08730, loss_grounding_dice_4: 0.02459/0.18180, loss_grounding_ce_4: 0.09164/0.28094, loss_mask_ce_5: 2.78214/0.93280, loss_mask_bce_5: 0.02303/0.34128, loss_mask_dice_5: 0.02977/1.19924, loss_spatial_bce_5: 0.03684/0.09599, loss_spatial_dice_5: 0.04282/0.23039, loss_spatial_ce_5: 0.00044/0.10481, loss_grounding_bce_5: 0.02024/0.08773, loss_grounding_dice_5: 0.02420/0.18308, loss_grounding_ce_5: 0.07819/0.29336, loss_mask_ce_6: 2.87810/0.97288, loss_mask_bce_6: 0.02371/0.34407, loss_mask_dice_6: 0.02668/1.20205, loss_spatial_bce_6: 0.04047/0.10162, loss_spatial_dice_6: 0.04884/0.23335, loss_spatial_ce_6: 0.00075/0.13005, loss_grounding_bce_6: 0.02234/0.08846, loss_grounding_dice_6: 0.02392/0.18344, loss_grounding_ce_6: 0.04860/0.30880, loss_mask_ce_7: 2.67396/1.01830, loss_mask_bce_7: 0.02311/0.35188, loss_mask_dice_7: 0.02518/1.25654, loss_spatial_bce_7: 0.03893/0.10949, loss_spatial_dice_7: 0.05243/0.26094, loss_spatial_ce_7: 0.01022/0.16506, loss_grounding_bce_7: 0.02277/0.09035, loss_grounding_dice_7: 0.02621/0.19078, loss_grounding_ce_7: 0.02163/0.33907, loss_mask_ce_8: 3.13956/1.12718, loss_mask_bce_8: 0.02264/0.36552, loss_mask_dice_8: 0.02744/1.32924, loss_spatial_bce_8: 0.04185/0.12986, loss_spatial_dice_8: 0.05561/0.29867, loss_spatial_ce_8: 0.08112/0.21842, loss_grounding_bce_8: 0.02138/0.09404, loss_grounding_dice_8: 0.02508/0.20148, loss_grounding_ce_8: 0.02070/0.40547, loss_mask_ce_9: 4.78477/3.67526, loss_mask_bce_9: 0.02811/0.39254, loss_mask_dice_9: 0.18201/1.90188, loss_spatial_bce_9: 0.34087/0.33275, loss_spatial_dice_9: 0.45970/0.82162, loss_spatial_ce_9: 1.77673/1.49387, loss_grounding_bce_9: 0.02975/0.10567, loss_grounding_dice_9: 0.04488/0.28083, loss_grounding_ce_9: 0.20370/0.66983] items per batch[64] items per second[0.23] total items[4844800] mini batches[ 75700] memory[7345] epoch remaining[0:47:48]
INFO:trainer.default_trainer:epochs[    41] optim steps[75800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.27683/0.89611, loss_mask_bce_0: 0.34452/0.33411, loss_mask_dice_0: 0.54442/1.16192, loss_spatial_bce_0: 0.14316/0.08678, loss_spatial_dice_0: 0.17890/0.20702, loss_spatial_ce_0: 0.06388/0.06024, loss_grounding_bce_0: 0.13260/0.08619, loss_grounding_dice_0: 0.10994/0.17841, loss_grounding_ce_0: 0.00346/0.27175, loss_mask_ce_1: 1.25831/0.89673, loss_mask_bce_1: 0.34367/0.33506, loss_mask_dice_1: 0.51245/1.16864, loss_spatial_bce_1: 0.13999/0.08733, loss_spatial_dice_1: 0.19303/0.21099, loss_spatial_ce_1: 0.06202/0.06602, loss_grounding_bce_1: 0.12901/0.08636, loss_grounding_dice_1: 0.10983/0.17922, loss_grounding_ce_1: 0.00244/0.27250, loss_mask_ce_2: 1.24292/0.90376, loss_mask_bce_2: 0.35034/0.33568, loss_mask_dice_2: 0.53415/1.16913, loss_spatial_bce_2: 0.14582/0.08852, loss_spatial_dice_2: 0.19737/0.21279, loss_spatial_ce_2: 0.06190/0.06951, loss_grounding_bce_2: 0.13380/0.08654, loss_grounding_dice_2: 0.11362/0.17911, loss_grounding_ce_2: 0.00258/0.27578, loss_mask_ce_3: 1.34567/0.91480, loss_mask_bce_3: 0.35931/0.33685, loss_mask_dice_3: 0.52464/1.16704, loss_spatial_bce_3: 0.14649/0.08979, loss_spatial_dice_3: 0.19591/0.21381, loss_spatial_ce_3: 0.06092/0.07451, loss_grounding_bce_3: 0.13867/0.08678, loss_grounding_dice_3: 0.10949/0.17881, loss_grounding_ce_3: 0.00340/0.27801, loss_mask_ce_4: 1.34519/0.91585, loss_mask_bce_4: 0.34684/0.33900, loss_mask_dice_4: 0.51207/1.19080, loss_spatial_bce_4: 0.13952/0.09370, loss_spatial_dice_4: 0.19976/0.22608, loss_spatial_ce_4: 0.07187/0.09080, loss_grounding_bce_4: 0.13546/0.08732, loss_grounding_dice_4: 0.10844/0.18179, loss_grounding_ce_4: 0.00264/0.28098, loss_mask_ce_5: 1.35119/0.93278, loss_mask_bce_5: 0.33418/0.34134, loss_mask_dice_5: 0.52872/1.19903, loss_spatial_bce_5: 0.13798/0.09601, loss_spatial_dice_5: 0.19900/0.23037, loss_spatial_ce_5: 0.06904/0.10481, loss_grounding_bce_5: 0.12664/0.08775, loss_grounding_dice_5: 0.10656/0.18307, loss_grounding_ce_5: 0.00221/0.29338, loss_mask_ce_6: 1.47458/0.97288, loss_mask_bce_6: 0.32275/0.34413, loss_mask_dice_6: 0.52064/1.20185, loss_spatial_bce_6: 0.14613/0.10164, loss_spatial_dice_6: 0.20940/0.23334, loss_spatial_ce_6: 0.07916/0.13003, loss_grounding_bce_6: 0.13457/0.08847, loss_grounding_dice_6: 0.10923/0.18343, loss_grounding_ce_6: 0.00465/0.30883, loss_mask_ce_7: 1.52503/1.01832, loss_mask_bce_7: 0.33570/0.35193, loss_mask_dice_7: 0.53520/1.25632, loss_spatial_bce_7: 0.17015/0.10951, loss_spatial_dice_7: 0.23279/0.26092, loss_spatial_ce_7: 0.14544/0.16505, loss_grounding_bce_7: 0.13067/0.09036, loss_grounding_dice_7: 0.11342/0.19077, loss_grounding_ce_7: 0.00228/0.33909, loss_mask_ce_8: 1.89434/1.12719, loss_mask_bce_8: 0.33968/0.36557, loss_mask_dice_8: 0.48560/1.32904, loss_spatial_bce_8: 0.17564/0.12987, loss_spatial_dice_8: 0.24907/0.29864, loss_spatial_ce_8: 0.13789/0.21838, loss_grounding_bce_8: 0.13316/0.09406, loss_grounding_dice_8: 0.12524/0.20145, loss_grounding_ce_8: 0.00313/0.40550, loss_mask_ce_9: 3.05658/3.67519, loss_mask_bce_9: 0.36728/0.39258, loss_mask_dice_9: 0.70875/1.90161, loss_spatial_bce_9: 0.36934/0.33279, loss_spatial_dice_9: 0.83661/0.82161, loss_spatial_ce_9: 1.21367/1.49382, loss_grounding_bce_9: 0.12512/0.10569, loss_grounding_dice_9: 0.10193/0.28080, loss_grounding_ce_9: 0.03149/0.66996] items per batch[64] items per second[0.23] total items[4851200] mini batches[ 75800] memory[7345] epoch remaining[0:43:05]
INFO:trainer.default_trainer:epochs[    41] optim steps[75900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89241/0.89614, loss_mask_bce_0: 0.50558/0.33411, loss_mask_dice_0: 2.23937/1.16172, loss_spatial_bce_0: 0.04765/0.08679, loss_spatial_dice_0: 0.22359/0.20699, loss_spatial_ce_0: 0.00703/0.06022, loss_grounding_bce_0: 0.04313/0.08619, loss_grounding_dice_0: 0.22247/0.17841, loss_grounding_ce_0: 0.65613/0.27172, loss_mask_ce_1: 0.90486/0.89674, loss_mask_bce_1: 0.51082/0.33506, loss_mask_dice_1: 2.18024/1.16842, loss_spatial_bce_1: 0.05449/0.08733, loss_spatial_dice_1: 0.22247/0.21096, loss_spatial_ce_1: 0.01420/0.06601, loss_grounding_bce_1: 0.03008/0.08637, loss_grounding_dice_1: 0.22962/0.17921, loss_grounding_ce_1: 0.76923/0.27247, loss_mask_ce_2: 0.87228/0.90376, loss_mask_bce_2: 0.52937/0.33568, loss_mask_dice_2: 2.22815/1.16892, loss_spatial_bce_2: 0.05174/0.08853, loss_spatial_dice_2: 0.23130/0.21276, loss_spatial_ce_2: 0.02214/0.06949, loss_grounding_bce_2: 0.03230/0.08654, loss_grounding_dice_2: 0.25812/0.17910, loss_grounding_ce_2: 0.64754/0.27575, loss_mask_ce_3: 0.86732/0.91482, loss_mask_bce_3: 0.49835/0.33685, loss_mask_dice_3: 2.27117/1.16684, loss_spatial_bce_3: 0.04753/0.08979, loss_spatial_dice_3: 0.21524/0.21379, loss_spatial_ce_3: 0.01149/0.07449, loss_grounding_bce_3: 0.04185/0.08678, loss_grounding_dice_3: 0.23645/0.17880, loss_grounding_ce_3: 0.63689/0.27798, loss_mask_ce_4: 0.80841/0.91588, loss_mask_bce_4: 0.53585/0.33900, loss_mask_dice_4: 2.35777/1.19059, loss_spatial_bce_4: 0.03576/0.09370, loss_spatial_dice_4: 0.22651/0.22605, loss_spatial_ce_4: 0.07523/0.09078, loss_grounding_bce_4: 0.04156/0.08732, loss_grounding_dice_4: 0.22743/0.18178, loss_grounding_ce_4: 0.64208/0.28097, loss_mask_ce_5: 0.88866/0.93282, loss_mask_bce_5: 0.52356/0.34134, loss_mask_dice_5: 2.32047/1.19882, loss_spatial_bce_5: 0.02779/0.09601, loss_spatial_dice_5: 0.22296/0.23034, loss_spatial_ce_5: 0.18157/0.10479, loss_grounding_bce_5: 0.04008/0.08775, loss_grounding_dice_5: 0.22922/0.18306, loss_grounding_ce_5: 0.64571/0.29336, loss_mask_ce_6: 0.90884/0.97290, loss_mask_bce_6: 0.55881/0.34413, loss_mask_dice_6: 2.39186/1.20163, loss_spatial_bce_6: 0.02880/0.10164, loss_spatial_dice_6: 0.24129/0.23331, loss_spatial_ce_6: 0.12575/0.13002, loss_grounding_bce_6: 0.04169/0.08848, loss_grounding_dice_6: 0.25957/0.18342, loss_grounding_ce_6: 0.62459/0.30882, loss_mask_ce_7: 1.04348/1.01838, loss_mask_bce_7: 0.57521/0.35193, loss_mask_dice_7: 2.70584/1.25610, loss_spatial_bce_7: 0.06024/0.10951, loss_spatial_dice_7: 0.27169/0.26090, loss_spatial_ce_7: 0.09720/0.16502, loss_grounding_bce_7: 0.03673/0.09037, loss_grounding_dice_7: 0.27183/0.19076, loss_grounding_ce_7: 0.73892/0.33907, loss_mask_ce_8: 1.14011/1.12724, loss_mask_bce_8: 0.57358/0.36556, loss_mask_dice_8: 2.85107/1.32883, loss_spatial_bce_8: 0.05079/0.12987, loss_spatial_dice_8: 0.25468/0.29861, loss_spatial_ce_8: 0.11591/0.21832, loss_grounding_bce_8: 0.05055/0.09407, loss_grounding_dice_8: 0.29089/0.20145, loss_grounding_ce_8: 0.70509/0.40552, loss_mask_ce_9: 5.47709/3.67516, loss_mask_bce_9: 0.54821/0.39259, loss_mask_dice_9: 4.16901/1.90143, loss_spatial_bce_9: 0.17003/0.33280, loss_spatial_dice_9: 0.93046/0.82162, loss_spatial_ce_9: 1.76261/1.49376, loss_grounding_bce_9: 0.04322/0.10569, loss_grounding_dice_9: 0.40154/0.28080, loss_grounding_ce_9: 0.61869/0.66992] items per batch[64] items per second[0.23] total items[4857600] mini batches[ 75900] memory[7345] epoch remaining[0:38:29]
INFO:trainer.default_trainer:epochs[    41] optim steps[76000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48660/0.89615, loss_mask_bce_0: 0.46603/0.33408, loss_mask_dice_0: 0.51068/1.16162, loss_spatial_bce_0: 0.12428/0.08678, loss_spatial_dice_0: 0.14362/0.20698, loss_spatial_ce_0: 0.01724/0.06020, loss_grounding_bce_0: 0.12810/0.08619, loss_grounding_dice_0: 0.09253/0.17841, loss_grounding_ce_0: 0.04906/0.27167, loss_mask_ce_1: 0.49492/0.89675, loss_mask_bce_1: 0.48972/0.33503, loss_mask_dice_1: 0.53055/1.16832, loss_spatial_bce_1: 0.12168/0.08732, loss_spatial_dice_1: 0.13935/0.21095, loss_spatial_ce_1: 0.01558/0.06599, loss_grounding_bce_1: 0.13983/0.08636, loss_grounding_dice_1: 0.10391/0.17922, loss_grounding_ce_1: 0.03308/0.27243, loss_mask_ce_2: 0.50085/0.90377, loss_mask_bce_2: 0.46764/0.33565, loss_mask_dice_2: 0.50280/1.16884, loss_spatial_bce_2: 0.12701/0.08852, loss_spatial_dice_2: 0.14261/0.21276, loss_spatial_ce_2: 0.02474/0.06948, loss_grounding_bce_2: 0.11853/0.08653, loss_grounding_dice_2: 0.09530/0.17911, loss_grounding_ce_2: 0.04074/0.27572, loss_mask_ce_3: 0.51310/0.91482, loss_mask_bce_3: 0.46926/0.33682, loss_mask_dice_3: 0.50611/1.16675, loss_spatial_bce_3: 0.12843/0.08978, loss_spatial_dice_3: 0.13662/0.21378, loss_spatial_ce_3: 0.03050/0.07448, loss_grounding_bce_3: 0.12525/0.08677, loss_grounding_dice_3: 0.09639/0.17881, loss_grounding_ce_3: 0.06639/0.27793, loss_mask_ce_4: 0.53748/0.91590, loss_mask_bce_4: 0.47858/0.33896, loss_mask_dice_4: 0.52365/1.19051, loss_spatial_bce_4: 0.13690/0.09369, loss_spatial_dice_4: 0.16296/0.22604, loss_spatial_ce_4: 0.03335/0.09077, loss_grounding_bce_4: 0.11928/0.08731, loss_grounding_dice_4: 0.09148/0.18179, loss_grounding_ce_4: 0.04450/0.28092, loss_mask_ce_5: 0.52633/0.93282, loss_mask_bce_5: 0.46496/0.34131, loss_mask_dice_5: 0.53120/1.19875, loss_spatial_bce_5: 0.13907/0.09600, loss_spatial_dice_5: 0.17566/0.23033, loss_spatial_ce_5: 0.09081/0.10478, loss_grounding_bce_5: 0.12228/0.08774, loss_grounding_dice_5: 0.09483/0.18306, loss_grounding_ce_5: 0.04030/0.29333, loss_mask_ce_6: 0.67852/0.97291, loss_mask_bce_6: 0.46295/0.34410, loss_mask_dice_6: 0.49290/1.20155, loss_spatial_bce_6: 0.15559/0.10163, loss_spatial_dice_6: 0.19591/0.23331, loss_spatial_ce_6: 0.14033/0.12999, loss_grounding_bce_6: 0.12001/0.08847, loss_grounding_dice_6: 0.09725/0.18343, loss_grounding_ce_6: 0.05457/0.30876, loss_mask_ce_7: 0.85115/1.01839, loss_mask_bce_7: 0.42316/0.35190, loss_mask_dice_7: 0.46932/1.25600, loss_spatial_bce_7: 0.16028/0.10950, loss_spatial_dice_7: 0.20611/0.26090, loss_spatial_ce_7: 0.14680/0.16502, loss_grounding_bce_7: 0.11177/0.09036, loss_grounding_dice_7: 0.07972/0.19077, loss_grounding_ce_7: 0.09850/0.33901, loss_mask_ce_8: 1.03806/1.12725, loss_mask_bce_8: 0.48973/0.36552, loss_mask_dice_8: 0.53821/1.32874, loss_spatial_bce_8: 0.18502/0.12985, loss_spatial_dice_8: 0.22685/0.29860, loss_spatial_ce_8: 0.15860/0.21827, loss_grounding_bce_8: 0.10775/0.09406, loss_grounding_dice_8: 0.07688/0.20145, loss_grounding_ce_8: 0.26903/0.40546, loss_mask_ce_9: 3.43585/3.67515, loss_mask_bce_9: 0.50833/0.39256, loss_mask_dice_9: 0.72852/1.90132, loss_spatial_bce_9: 0.58929/0.33279, loss_spatial_dice_9: 0.73201/0.82163, loss_spatial_ce_9: 1.31222/1.49365, loss_grounding_bce_9: 0.10365/0.10568, loss_grounding_dice_9: 0.07290/0.28082, loss_grounding_ce_9: 0.90513/0.66986] items per batch[64] items per second[0.23] total items[4864000] mini batches[ 76000] memory[7345] epoch remaining[0:33:51]
INFO:trainer.default_trainer:epochs[    41] optim steps[76100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17280/0.89608, loss_mask_bce_0: 0.23039/0.33408, loss_mask_dice_0: 0.40556/1.16146, loss_spatial_bce_0: 0.09023/0.08678, loss_spatial_dice_0: 0.15503/0.20696, loss_spatial_ce_0: 0.01788/0.06017, loss_grounding_bce_0: 0.07648/0.08619, loss_grounding_dice_0: 0.30778/0.17839, loss_grounding_ce_0: 0.01950/0.27167, loss_mask_ce_1: 0.17797/0.89672, loss_mask_bce_1: 0.23637/0.33503, loss_mask_dice_1: 0.40471/1.16815, loss_spatial_bce_1: 0.08976/0.08732, loss_spatial_dice_1: 0.15540/0.21093, loss_spatial_ce_1: 0.01393/0.06597, loss_grounding_bce_1: 0.07196/0.08636, loss_grounding_dice_1: 0.30101/0.17921, loss_grounding_ce_1: 0.01821/0.27242, loss_mask_ce_2: 0.16975/0.90371, loss_mask_bce_2: 0.23153/0.33565, loss_mask_dice_2: 0.41563/1.16869, loss_spatial_bce_2: 0.09248/0.08852, loss_spatial_dice_2: 0.15102/0.21273, loss_spatial_ce_2: 0.00837/0.06946, loss_grounding_bce_2: 0.06845/0.08653, loss_grounding_dice_2: 0.29067/0.17909, loss_grounding_ce_2: 0.01622/0.27572, loss_mask_ce_3: 0.17329/0.91479, loss_mask_bce_3: 0.23029/0.33682, loss_mask_dice_3: 0.40905/1.16658, loss_spatial_bce_3: 0.06428/0.08978, loss_spatial_dice_3: 0.12403/0.21376, loss_spatial_ce_3: 0.11071/0.07446, loss_grounding_bce_3: 0.06773/0.08678, loss_grounding_dice_3: 0.28559/0.17879, loss_grounding_ce_3: 0.01243/0.27794, loss_mask_ce_4: 0.15860/0.91585, loss_mask_bce_4: 0.23150/0.33897, loss_mask_dice_4: 0.41511/1.19034, loss_spatial_bce_4: 0.06492/0.09369, loss_spatial_dice_4: 0.12311/0.22602, loss_spatial_ce_4: 0.12375/0.09074, loss_grounding_bce_4: 0.07837/0.08731, loss_grounding_dice_4: 0.31969/0.18176, loss_grounding_ce_4: 0.01757/0.28095, loss_mask_ce_5: 0.20223/0.93279, loss_mask_bce_5: 0.23077/0.34131, loss_mask_dice_5: 0.41823/1.19859, loss_spatial_bce_5: 0.06947/0.09601, loss_spatial_dice_5: 0.13100/0.23031, loss_spatial_ce_5: 0.11338/0.10475, loss_grounding_bce_5: 0.07351/0.08774, loss_grounding_dice_5: 0.32467/0.18305, loss_grounding_ce_5: 0.01566/0.29335, loss_mask_ce_6: 0.17965/0.97284, loss_mask_bce_6: 0.24192/0.34411, loss_mask_dice_6: 0.44915/1.20140, loss_spatial_bce_6: 0.06737/0.10163, loss_spatial_dice_6: 0.14560/0.23328, loss_spatial_ce_6: 0.10900/0.12997, loss_grounding_bce_6: 0.07711/0.08847, loss_grounding_dice_6: 0.32601/0.18340, loss_grounding_ce_6: 0.01008/0.30881, loss_mask_ce_7: 0.21213/1.01834, loss_mask_bce_7: 0.24344/0.35190, loss_mask_dice_7: 0.42380/1.25584, loss_spatial_bce_7: 0.07740/0.10950, loss_spatial_dice_7: 0.14882/0.26088, loss_spatial_ce_7: 0.14539/0.16501, loss_grounding_bce_7: 0.07555/0.09036, loss_grounding_dice_7: 0.30761/0.19076, loss_grounding_ce_7: 0.10806/0.33905, loss_mask_ce_8: 0.27957/1.12720, loss_mask_bce_8: 0.25505/0.36552, loss_mask_dice_8: 0.43434/1.32855, loss_spatial_bce_8: 0.08472/0.12984, loss_spatial_dice_8: 0.14769/0.29858, loss_spatial_ce_8: 0.07084/0.21820, loss_grounding_bce_8: 0.09168/0.09406, loss_grounding_dice_8: 0.35674/0.20144, loss_grounding_ce_8: 0.03616/0.40554, loss_mask_ce_9: 3.48096/3.67516, loss_mask_bce_9: 0.28724/0.39257, loss_mask_dice_9: 0.62630/1.90121, loss_spatial_bce_9: 0.55380/0.33281, loss_spatial_dice_9: 0.84233/0.82162, loss_spatial_ce_9: 1.61345/1.49356, loss_grounding_bce_9: 0.09786/0.10568, loss_grounding_dice_9: 0.51909/0.28081, loss_grounding_ce_9: 0.65614/0.67001] items per batch[64] items per second[0.23] total items[4870400] mini batches[ 76100] memory[7345] epoch remaining[0:29:18]
INFO:trainer.default_trainer:epochs[    41] optim steps[76200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.43890/0.89607, loss_mask_bce_0: 0.32111/0.33410, loss_mask_dice_0: 1.36597/1.16148, loss_spatial_bce_0: 0.04995/0.08677, loss_spatial_dice_0: 0.21227/0.20695, loss_spatial_ce_0: 0.00032/0.06016, loss_grounding_bce_0: 0.02044/0.08618, loss_grounding_dice_0: 0.13244/0.17838, loss_grounding_ce_0: 1.33003/0.27167, loss_mask_ce_1: 2.55439/0.89668, loss_mask_bce_1: 0.31627/0.33504, loss_mask_dice_1: 1.32951/1.16820, loss_spatial_bce_1: 0.04970/0.08731, loss_spatial_dice_1: 0.20889/0.21092, loss_spatial_ce_1: 0.00049/0.06594, loss_grounding_bce_1: 0.02117/0.08636, loss_grounding_dice_1: 0.13613/0.17919, loss_grounding_ce_1: 1.51822/0.27243, loss_mask_ce_2: 2.54194/0.90371, loss_mask_bce_2: 0.33288/0.33566, loss_mask_dice_2: 1.34441/1.16874, loss_spatial_bce_2: 0.05632/0.08851, loss_spatial_dice_2: 0.22982/0.21272, loss_spatial_ce_2: 0.00094/0.06945, loss_grounding_bce_2: 0.02022/0.08654, loss_grounding_dice_2: 0.12897/0.17907, loss_grounding_ce_2: 1.29609/0.27570, loss_mask_ce_3: 2.55660/0.91477, loss_mask_bce_3: 0.33083/0.33683, loss_mask_dice_3: 1.40884/1.16660, loss_spatial_bce_3: 0.05644/0.08978, loss_spatial_dice_3: 0.20741/0.21376, loss_spatial_ce_3: 0.00398/0.07445, loss_grounding_bce_3: 0.02267/0.08678, loss_grounding_dice_3: 0.12384/0.17877, loss_grounding_ce_3: 1.12630/0.27793, loss_mask_ce_4: 2.90161/0.91585, loss_mask_bce_4: 0.34061/0.33898, loss_mask_dice_4: 1.28016/1.19039, loss_spatial_bce_4: 0.05859/0.09369, loss_spatial_dice_4: 0.24753/0.22601, loss_spatial_ce_4: 0.00989/0.09071, loss_grounding_bce_4: 0.02428/0.08731, loss_grounding_dice_4: 0.12550/0.18176, loss_grounding_ce_4: 1.62317/0.28096, loss_mask_ce_5: 2.72930/0.93276, loss_mask_bce_5: 0.34853/0.34133, loss_mask_dice_5: 1.33456/1.19864, loss_spatial_bce_5: 0.06073/0.09600, loss_spatial_dice_5: 0.25729/0.23030, loss_spatial_ce_5: 0.04748/0.10474, loss_grounding_bce_5: 0.01650/0.08775, loss_grounding_dice_5: 0.09643/0.18303, loss_grounding_ce_5: 1.54729/0.29336, loss_mask_ce_6: 2.94600/0.97282, loss_mask_bce_6: 0.34274/0.34412, loss_mask_dice_6: 1.38771/1.20144, loss_spatial_bce_6: 0.06492/0.10162, loss_spatial_dice_6: 0.26563/0.23328, loss_spatial_ce_6: 0.10445/0.12995, loss_grounding_bce_6: 0.01874/0.08848, loss_grounding_dice_6: 0.11408/0.18339, loss_grounding_ce_6: 1.22847/0.30880, loss_mask_ce_7: 2.97185/1.01832, loss_mask_bce_7: 0.32220/0.35192, loss_mask_dice_7: 1.44115/1.25586, loss_spatial_bce_7: 0.06564/0.10950, loss_spatial_dice_7: 0.29070/0.26087, loss_spatial_ce_7: 0.21000/0.16498, loss_grounding_bce_7: 0.02400/0.09037, loss_grounding_dice_7: 0.14021/0.19074, loss_grounding_ce_7: 2.03819/0.33904, loss_mask_ce_8: 2.64538/1.12715, loss_mask_bce_8: 0.38237/0.36553, loss_mask_dice_8: 1.52112/1.32859, loss_spatial_bce_8: 0.08317/0.12983, loss_spatial_dice_8: 0.40362/0.29858, loss_spatial_ce_8: 0.15962/0.21814, loss_grounding_bce_8: 0.02154/0.09406, loss_grounding_dice_8: 0.11822/0.20143, loss_grounding_ce_8: 2.02988/0.40554, loss_mask_ce_9: 4.86708/3.67505, loss_mask_bce_9: 0.35301/0.39257, loss_mask_dice_9: 2.61074/1.90123, loss_spatial_bce_9: 0.19188/0.33282, loss_spatial_dice_9: 0.88036/0.82164, loss_spatial_ce_9: 1.28572/1.49355, loss_grounding_bce_9: 0.02302/0.10568, loss_grounding_dice_9: 0.25823/0.28080, loss_grounding_ce_9: 1.90002/0.66993] items per batch[64] items per second[0.23] total items[4876800] mini batches[ 76200] memory[7345] epoch remaining[0:24:41]
INFO:trainer.default_trainer:epochs[    41] optim steps[76300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18290/0.89612, loss_mask_bce_0: 0.31289/0.33409, loss_mask_dice_0: 0.25759/1.16135, loss_spatial_bce_0: 0.09915/0.08676, loss_spatial_dice_0: 0.09742/0.20694, loss_spatial_ce_0: 0.00136/0.06014, loss_grounding_bce_0: 0.02274/0.08618, loss_grounding_dice_0: 0.07166/0.17837, loss_grounding_ce_0: 0.00302/0.27161, loss_mask_ce_1: 0.17327/0.89674, loss_mask_bce_1: 0.31049/0.33503, loss_mask_dice_1: 0.28631/1.16808, loss_spatial_bce_1: 0.10363/0.08731, loss_spatial_dice_1: 0.10680/0.21091, loss_spatial_ce_1: 0.00191/0.06593, loss_grounding_bce_1: 0.02256/0.08635, loss_grounding_dice_1: 0.06544/0.17918, loss_grounding_ce_1: 0.00317/0.27238, loss_mask_ce_2: 0.16769/0.90377, loss_mask_bce_2: 0.32217/0.33565, loss_mask_dice_2: 0.28206/1.16858, loss_spatial_bce_2: 0.09445/0.08851, loss_spatial_dice_2: 0.09857/0.21272, loss_spatial_ce_2: 0.00230/0.06943, loss_grounding_bce_2: 0.02144/0.08653, loss_grounding_dice_2: 0.05731/0.17907, loss_grounding_ce_2: 0.00228/0.27568, loss_mask_ce_3: 0.19578/0.91484, loss_mask_bce_3: 0.31046/0.33683, loss_mask_dice_3: 0.27676/1.16648, loss_spatial_bce_3: 0.10138/0.08977, loss_spatial_dice_3: 0.10455/0.21375, loss_spatial_ce_3: 0.00964/0.07442, loss_grounding_bce_3: 0.02125/0.08677, loss_grounding_dice_3: 0.07643/0.17876, loss_grounding_ce_3: 0.00366/0.27790, loss_mask_ce_4: 0.17955/0.91592, loss_mask_bce_4: 0.31404/0.33898, loss_mask_dice_4: 0.32936/1.19024, loss_spatial_bce_4: 0.10979/0.09369, loss_spatial_dice_4: 0.10846/0.22600, loss_spatial_ce_4: 0.02033/0.09070, loss_grounding_bce_4: 0.02207/0.08730, loss_grounding_dice_4: 0.08558/0.18174, loss_grounding_ce_4: 0.00091/0.28091, loss_mask_ce_5: 0.18166/0.93287, loss_mask_bce_5: 0.30752/0.34133, loss_mask_dice_5: 0.28817/1.19851, loss_spatial_bce_5: 0.11233/0.09600, loss_spatial_dice_5: 0.11722/0.23030, loss_spatial_ce_5: 0.00818/0.10471, loss_grounding_bce_5: 0.02222/0.08774, loss_grounding_dice_5: 0.06887/0.18302, loss_grounding_ce_5: 0.00179/0.29333, loss_mask_ce_6: 0.17867/0.97290, loss_mask_bce_6: 0.32052/0.34413, loss_mask_dice_6: 0.27753/1.20132, loss_spatial_bce_6: 0.12492/0.10162, loss_spatial_dice_6: 0.11028/0.23327, loss_spatial_ce_6: 0.00856/0.12992, loss_grounding_bce_6: 0.02303/0.08847, loss_grounding_dice_6: 0.06927/0.18338, loss_grounding_ce_6: 0.00126/0.30876, loss_mask_ce_7: 0.18221/1.01838, loss_mask_bce_7: 0.30915/0.35192, loss_mask_dice_7: 0.30326/1.25573, loss_spatial_bce_7: 0.12207/0.10950, loss_spatial_dice_7: 0.13033/0.26087, loss_spatial_ce_7: 0.02636/0.16495, loss_grounding_bce_7: 0.02260/0.09036, loss_grounding_dice_7: 0.07493/0.19074, loss_grounding_ce_7: 0.00937/0.33898, loss_mask_ce_8: 0.26084/1.12726, loss_mask_bce_8: 0.32249/0.36553, loss_mask_dice_8: 0.30915/1.32846, loss_spatial_bce_8: 0.13500/0.12982, loss_spatial_dice_8: 0.12314/0.29857, loss_spatial_ce_8: 0.09353/0.21809, loss_grounding_bce_8: 0.02379/0.09405, loss_grounding_dice_8: 0.06874/0.20141, loss_grounding_ce_8: 0.00528/0.40548, loss_mask_ce_9: 2.52772/3.67515, loss_mask_bce_9: 0.28797/0.39257, loss_mask_dice_9: 0.32602/1.90117, loss_spatial_bce_9: 0.45134/0.33280, loss_spatial_dice_9: 0.76991/0.82163, loss_spatial_ce_9: 1.31447/1.49351, loss_grounding_bce_9: 0.03110/0.10566, loss_grounding_dice_9: 0.11454/0.28079, loss_grounding_ce_9: 0.06072/0.66991] items per batch[64] items per second[0.24] total items[4883200] mini batches[ 76300] memory[7345] epoch remaining[0:20:01]
INFO:trainer.default_trainer:epochs[    41] optim steps[76400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69691/0.89611, loss_mask_bce_0: 0.51001/0.33410, loss_mask_dice_0: 0.93128/1.16154, loss_spatial_bce_0: 0.13088/0.08677, loss_spatial_dice_0: 0.26380/0.20694, loss_spatial_ce_0: 0.08931/0.06012, loss_grounding_bce_0: 0.01526/0.08618, loss_grounding_dice_0: 0.13366/0.17839, loss_grounding_ce_0: 0.19573/0.27162, loss_mask_ce_1: 0.69535/0.89674, loss_mask_bce_1: 0.50698/0.33504, loss_mask_dice_1: 0.95146/1.16827, loss_spatial_bce_1: 0.13882/0.08731, loss_spatial_dice_1: 0.27133/0.21092, loss_spatial_ce_1: 0.07757/0.06590, loss_grounding_bce_1: 0.01547/0.08636, loss_grounding_dice_1: 0.19666/0.17920, loss_grounding_ce_1: 0.24650/0.27237, loss_mask_ce_2: 0.84716/0.90374, loss_mask_bce_2: 0.48997/0.33566, loss_mask_dice_2: 0.93851/1.16875, loss_spatial_bce_2: 0.13935/0.08851, loss_spatial_dice_2: 0.28009/0.21272, loss_spatial_ce_2: 0.10238/0.06941, loss_grounding_bce_2: 0.01953/0.08654, loss_grounding_dice_2: 0.15954/0.17908, loss_grounding_ce_2: 0.21046/0.27567, loss_mask_ce_3: 0.66254/0.91481, loss_mask_bce_3: 0.50497/0.33684, loss_mask_dice_3: 0.95163/1.16666, loss_spatial_bce_3: 0.14143/0.08978, loss_spatial_dice_3: 0.27924/0.21375, loss_spatial_ce_3: 0.07442/0.07440, loss_grounding_bce_3: 0.02083/0.08678, loss_grounding_dice_3: 0.17551/0.17878, loss_grounding_ce_3: 0.22120/0.27793, loss_mask_ce_4: 0.84783/0.91590, loss_mask_bce_4: 0.50697/0.33899, loss_mask_dice_4: 0.90220/1.19040, loss_spatial_bce_4: 0.13891/0.09369, loss_spatial_dice_4: 0.26966/0.22601, loss_spatial_ce_4: 0.08033/0.09069, loss_grounding_bce_4: 0.01772/0.08731, loss_grounding_dice_4: 0.19278/0.18176, loss_grounding_ce_4: 0.26137/0.28090, loss_mask_ce_5: 0.89755/0.93286, loss_mask_bce_5: 0.48863/0.34135, loss_mask_dice_5: 0.89989/1.19871, loss_spatial_bce_5: 0.13577/0.09601, loss_spatial_dice_5: 0.28812/0.23031, loss_spatial_ce_5: 0.11859/0.10468, loss_grounding_bce_5: 0.02219/0.08774, loss_grounding_dice_5: 0.19777/0.18303, loss_grounding_ce_5: 0.43931/0.29331, loss_mask_ce_6: 0.85840/0.97287, loss_mask_bce_6: 0.49391/0.34414, loss_mask_dice_6: 0.90412/1.20149, loss_spatial_bce_6: 0.14636/0.10162, loss_spatial_dice_6: 0.26899/0.23328, loss_spatial_ce_6: 0.22593/0.12989, loss_grounding_bce_6: 0.01801/0.08847, loss_grounding_dice_6: 0.21273/0.18340, loss_grounding_ce_6: 0.28417/0.30874, loss_mask_ce_7: 1.00352/1.01839, loss_mask_bce_7: 0.50060/0.35194, loss_mask_dice_7: 0.94649/1.25591, loss_spatial_bce_7: 0.15063/0.10949, loss_spatial_dice_7: 0.29350/0.26087, loss_spatial_ce_7: 0.10772/0.16493, loss_grounding_bce_7: 0.01621/0.09036, loss_grounding_dice_7: 0.24351/0.19075, loss_grounding_ce_7: 0.39657/0.33894, loss_mask_ce_8: 1.18066/1.12728, loss_mask_bce_8: 0.54945/0.36553, loss_mask_dice_8: 1.19079/1.32868, loss_spatial_bce_8: 0.18865/0.12981, loss_spatial_dice_8: 0.35293/0.29857, loss_spatial_ce_8: 0.10382/0.21802, loss_grounding_bce_8: 0.02008/0.09406, loss_grounding_dice_8: 0.27955/0.20143, loss_grounding_ce_8: 0.77933/0.40544, loss_mask_ce_9: 3.03073/3.67511, loss_mask_bce_9: 0.63346/0.39259, loss_mask_dice_9: 1.58188/1.90144, loss_spatial_bce_9: 0.33153/0.33279, loss_spatial_dice_9: 0.82878/0.82165, loss_spatial_ce_9: 1.53631/1.49347, loss_grounding_bce_9: 0.02873/0.10567, loss_grounding_dice_9: 0.52834/0.28081, loss_grounding_ce_9: 1.17477/0.66992] items per batch[64] items per second[0.24] total items[4889600] mini batches[ 76400] memory[7345] epoch remaining[0:15:23]
INFO:trainer.default_trainer:epochs[    41] optim steps[76500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24351/0.89613, loss_mask_bce_0: 0.74288/0.33408, loss_mask_dice_0: 0.76219/1.16171, loss_spatial_bce_0: 0.13581/0.08675, loss_spatial_dice_0: 0.18250/0.20694, loss_spatial_ce_0: 0.12138/0.06010, loss_grounding_bce_0: 0.58917/0.08618, loss_grounding_dice_0: 0.42347/0.17841, loss_grounding_ce_0: 0.03629/0.27163, loss_mask_ce_1: 1.25152/0.89674, loss_mask_bce_1: 0.73229/0.33502, loss_mask_dice_1: 0.74921/1.16842, loss_spatial_bce_1: 0.12543/0.08729, loss_spatial_dice_1: 0.19648/0.21092, loss_spatial_ce_1: 0.11418/0.06587, loss_grounding_bce_1: 0.55868/0.08635, loss_grounding_dice_1: 0.38563/0.17921, loss_grounding_ce_1: 0.04103/0.27238, loss_mask_ce_2: 1.20315/0.90376, loss_mask_bce_2: 0.70273/0.33563, loss_mask_dice_2: 0.73039/1.16891, loss_spatial_bce_2: 0.15636/0.08850, loss_spatial_dice_2: 0.19552/0.21273, loss_spatial_ce_2: 0.10865/0.06939, loss_grounding_bce_2: 0.57618/0.08653, loss_grounding_dice_2: 0.39716/0.17909, loss_grounding_ce_2: 0.04897/0.27566, loss_mask_ce_3: 1.54506/0.91484, loss_mask_bce_3: 0.69478/0.33681, loss_mask_dice_3: 0.76013/1.16680, loss_spatial_bce_3: 0.16295/0.08976, loss_spatial_dice_3: 0.20843/0.21376, loss_spatial_ce_3: 0.13628/0.07439, loss_grounding_bce_3: 0.57829/0.08677, loss_grounding_dice_3: 0.41077/0.17879, loss_grounding_ce_3: 0.03233/0.27790, loss_mask_ce_4: 1.76000/0.91591, loss_mask_bce_4: 0.67502/0.33896, loss_mask_dice_4: 0.75798/1.19056, loss_spatial_bce_4: 0.13873/0.09367, loss_spatial_dice_4: 0.22678/0.22601, loss_spatial_ce_4: 0.13385/0.09068, loss_grounding_bce_4: 0.29052/0.08730, loss_grounding_dice_4: 0.39818/0.18178, loss_grounding_ce_4: 0.28621/0.28089, loss_mask_ce_5: 1.46736/0.93287, loss_mask_bce_5: 0.64152/0.34132, loss_mask_dice_5: 0.74508/1.19886, loss_spatial_bce_5: 0.17929/0.09599, loss_spatial_dice_5: 0.26443/0.23031, loss_spatial_ce_5: 0.21357/0.10469, loss_grounding_bce_5: 0.57945/0.08774, loss_grounding_dice_5: 0.40177/0.18304, loss_grounding_ce_5: 0.03040/0.29331, loss_mask_ce_6: 1.78638/0.97292, loss_mask_bce_6: 0.46652/0.34411, loss_mask_dice_6: 0.81330/1.20161, loss_spatial_bce_6: 0.24447/0.10161, loss_spatial_dice_6: 0.28540/0.23328, loss_spatial_ce_6: 0.19370/0.12987, loss_grounding_bce_6: 0.26989/0.08846, loss_grounding_dice_6: 0.32917/0.18342, loss_grounding_ce_6: 0.28960/0.30879, loss_mask_ce_7: 1.73615/1.01840, loss_mask_bce_7: 0.52925/0.35191, loss_mask_dice_7: 0.80614/1.25609, loss_spatial_bce_7: 0.24060/0.10948, loss_spatial_dice_7: 0.32509/0.26087, loss_spatial_ce_7: 0.24475/0.16492, loss_grounding_bce_7: 0.30690/0.09035, loss_grounding_dice_7: 0.35008/0.19076, loss_grounding_ce_7: 0.17348/0.33897, loss_mask_ce_8: 1.71169/1.12733, loss_mask_bce_8: 0.60924/0.36551, loss_mask_dice_8: 0.96870/1.32882, loss_spatial_bce_8: 0.26457/0.12979, loss_spatial_dice_8: 0.35771/0.29857, loss_spatial_ce_8: 0.28957/0.21795, loss_grounding_bce_8: 0.27727/0.09405, loss_grounding_dice_8: 0.29868/0.20145, loss_grounding_ce_8: 0.20770/0.40551, loss_mask_ce_9: 4.30022/3.67513, loss_mask_bce_9: 0.49364/0.39257, loss_mask_dice_9: 1.14689/1.90172, loss_spatial_bce_9: 0.34866/0.33275, loss_spatial_dice_9: 0.77963/0.82164, loss_spatial_ce_9: 1.23912/1.49345, loss_grounding_bce_9: 0.26145/0.10566, loss_grounding_dice_9: 0.45290/0.28081, loss_grounding_ce_9: 2.16075/0.67001] items per batch[64] items per second[0.23] total items[4896000] mini batches[ 76500] memory[7345] epoch remaining[0:10:47]
INFO:trainer.default_trainer:epochs[    41] optim steps[76600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.00816/0.89602, loss_mask_bce_0: 0.18264/0.33406, loss_mask_dice_0: 0.89198/1.16164, loss_spatial_bce_0: 0.04578/0.08674, loss_spatial_dice_0: 0.23091/0.20693, loss_spatial_ce_0: 0.10053/0.06009, loss_grounding_bce_0: 0.07877/0.08618, loss_grounding_dice_0: 0.10362/0.17840, loss_grounding_ce_0: 0.32462/0.27157, loss_mask_ce_1: 1.35078/0.89666, loss_mask_bce_1: 0.18771/0.33500, loss_mask_dice_1: 0.82147/1.16831, loss_spatial_bce_1: 0.04690/0.08728, loss_spatial_dice_1: 0.21400/0.21091, loss_spatial_ce_1: 0.00890/0.06586, loss_grounding_bce_1: 0.08620/0.08636, loss_grounding_dice_1: 0.14030/0.17920, loss_grounding_ce_1: 0.44405/0.27231, loss_mask_ce_2: 1.35918/0.90367, loss_mask_bce_2: 0.19604/0.33562, loss_mask_dice_2: 0.83698/1.16882, loss_spatial_bce_2: 0.04997/0.08849, loss_spatial_dice_2: 0.24369/0.21272, loss_spatial_ce_2: 0.01115/0.06937, loss_grounding_bce_2: 0.08734/0.08654, loss_grounding_dice_2: 0.11717/0.17909, loss_grounding_ce_2: 0.34487/0.27558, loss_mask_ce_3: 1.32389/0.91475, loss_mask_bce_3: 0.20437/0.33680, loss_mask_dice_3: 0.86687/1.16670, loss_spatial_bce_3: 0.04831/0.08976, loss_spatial_dice_3: 0.26960/0.21375, loss_spatial_ce_3: 0.02586/0.07437, loss_grounding_bce_3: 0.09740/0.08678, loss_grounding_dice_3: 0.11071/0.17878, loss_grounding_ce_3: 0.34220/0.27785, loss_mask_ce_4: 1.12584/0.91583, loss_mask_bce_4: 0.21295/0.33894, loss_mask_dice_4: 0.89634/1.19046, loss_spatial_bce_4: 0.05099/0.09366, loss_spatial_dice_4: 0.27110/0.22600, loss_spatial_ce_4: 0.10479/0.09066, loss_grounding_bce_4: 0.09807/0.08730, loss_grounding_dice_4: 0.11971/0.18176, loss_grounding_ce_4: 0.39512/0.28083, loss_mask_ce_5: 1.54280/0.93278, loss_mask_bce_5: 0.24515/0.34130, loss_mask_dice_5: 0.84139/1.19876, loss_spatial_bce_5: 0.05211/0.09599, loss_spatial_dice_5: 0.29932/0.23031, loss_spatial_ce_5: 0.09698/0.10468, loss_grounding_bce_5: 0.11729/0.08774, loss_grounding_dice_5: 0.12393/0.18303, loss_grounding_ce_5: 0.46056/0.29326, loss_mask_ce_6: 1.50974/0.97281, loss_mask_bce_6: 0.32810/0.34410, loss_mask_dice_6: 0.98368/1.20151, loss_spatial_bce_6: 0.05333/0.10160, loss_spatial_dice_6: 0.25047/0.23327, loss_spatial_ce_6: 0.05649/0.12985, loss_grounding_bce_6: 0.11765/0.08847, loss_grounding_dice_6: 0.15359/0.18340, loss_grounding_ce_6: 0.41293/0.30876, loss_mask_ce_7: 1.19777/1.01833, loss_mask_bce_7: 0.42095/0.35189, loss_mask_dice_7: 1.08913/1.25600, loss_spatial_bce_7: 0.04765/0.10947, loss_spatial_dice_7: 0.26077/0.26086, loss_spatial_ce_7: 0.15255/0.16489, loss_grounding_bce_7: 0.17001/0.09036, loss_grounding_dice_7: 0.16853/0.19075, loss_grounding_ce_7: 0.44826/0.33892, loss_mask_ce_8: 1.40224/1.12727, loss_mask_bce_8: 0.34916/0.36549, loss_mask_dice_8: 1.03676/1.32874, loss_spatial_bce_8: 0.06389/0.12978, loss_spatial_dice_8: 0.33222/0.29856, loss_spatial_ce_8: 0.24701/0.21790, loss_grounding_bce_8: 0.14471/0.09406, loss_grounding_dice_8: 0.17224/0.20144, loss_grounding_ce_8: 0.63165/0.40549, loss_mask_ce_9: 3.84454/3.67494, loss_mask_bce_9: 0.28652/0.39254, loss_mask_dice_9: 1.20427/1.90153, loss_spatial_bce_9: 0.22021/0.33275, loss_spatial_dice_9: 0.86839/0.82164, loss_spatial_ce_9: 1.61854/1.49349, loss_grounding_bce_9: 0.18217/0.10567, loss_grounding_dice_9: 0.40570/0.28080, loss_grounding_ce_9: 0.85075/0.67005] items per batch[64] items per second[0.23] total items[4902400] mini batches[ 76600] memory[7345] epoch remaining[0:06:11]
INFO:trainer.default_trainer:epochs[    41] optim steps[76700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43804/0.89604, loss_mask_bce_0: 0.21015/0.33404, loss_mask_dice_0: 1.17204/1.16150, loss_spatial_bce_0: 0.04505/0.08675, loss_spatial_dice_0: 0.26713/0.20692, loss_spatial_ce_0: 0.02788/0.06006, loss_grounding_bce_0: 0.08507/0.08619, loss_grounding_dice_0: 0.39471/0.17840, loss_grounding_ce_0: 1.06645/0.27160, loss_mask_ce_1: 1.38351/0.89668, loss_mask_bce_1: 0.21871/0.33499, loss_mask_dice_1: 1.29932/1.16818, loss_spatial_bce_1: 0.04425/0.08729, loss_spatial_dice_1: 0.26769/0.21090, loss_spatial_ce_1: 0.03589/0.06584, loss_grounding_bce_1: 0.08622/0.08637, loss_grounding_dice_1: 0.39773/0.17920, loss_grounding_ce_1: 0.73999/0.27232, loss_mask_ce_2: 1.55676/0.90370, loss_mask_bce_2: 0.21456/0.33561, loss_mask_dice_2: 1.27013/1.16867, loss_spatial_bce_2: 0.04473/0.08849, loss_spatial_dice_2: 0.23424/0.21271, loss_spatial_ce_2: 0.03736/0.06935, loss_grounding_bce_2: 0.08358/0.08655, loss_grounding_dice_2: 0.43451/0.17908, loss_grounding_ce_2: 1.05619/0.27558, loss_mask_ce_3: 1.48355/0.91479, loss_mask_bce_3: 0.20972/0.33679, loss_mask_dice_3: 1.23715/1.16654, loss_spatial_bce_3: 0.04558/0.08977, loss_spatial_dice_3: 0.26724/0.21375, loss_spatial_ce_3: 0.06089/0.07435, loss_grounding_bce_3: 0.08193/0.08679, loss_grounding_dice_3: 0.38786/0.17878, loss_grounding_ce_3: 1.13810/0.27788, loss_mask_ce_4: 1.63281/0.91590, loss_mask_bce_4: 0.21269/0.33893, loss_mask_dice_4: 1.21581/1.19032, loss_spatial_bce_4: 0.05622/0.09367, loss_spatial_dice_4: 0.28170/0.22599, loss_spatial_ce_4: 0.11277/0.09065, loss_grounding_bce_4: 0.09066/0.08731, loss_grounding_dice_4: 0.44911/0.18177, loss_grounding_ce_4: 1.28279/0.28084, loss_mask_ce_5: 1.41793/0.93284, loss_mask_bce_5: 0.21823/0.34129, loss_mask_dice_5: 1.33507/1.19862, loss_spatial_bce_5: 0.06142/0.09599, loss_spatial_dice_5: 0.31083/0.23030, loss_spatial_ce_5: 0.08676/0.10466, loss_grounding_bce_5: 0.15251/0.08775, loss_grounding_dice_5: 0.39628/0.18304, loss_grounding_ce_5: 1.26593/0.29328, loss_mask_ce_6: 1.54230/0.97287, loss_mask_bce_6: 0.26878/0.34409, loss_mask_dice_6: 1.32160/1.20137, loss_spatial_bce_6: 0.08956/0.10161, loss_spatial_dice_6: 0.37020/0.23327, loss_spatial_ce_6: 0.06911/0.12981, loss_grounding_bce_6: 0.18960/0.08848, loss_grounding_dice_6: 0.48083/0.18341, loss_grounding_ce_6: 0.73234/0.30878, loss_mask_ce_7: 1.80591/1.01839, loss_mask_bce_7: 0.30939/0.35189, loss_mask_dice_7: 1.44705/1.25588, loss_spatial_bce_7: 0.07861/0.10948, loss_spatial_dice_7: 0.36166/0.26085, loss_spatial_ce_7: 0.12909/0.16485, loss_grounding_bce_7: 0.15891/0.09037, loss_grounding_dice_7: 0.44765/0.19075, loss_grounding_ce_7: 0.79885/0.33893, loss_mask_ce_8: 2.10497/1.12734, loss_mask_bce_8: 0.34918/0.36549, loss_mask_dice_8: 1.62679/1.32861, loss_spatial_bce_8: 0.11225/0.12979, loss_spatial_dice_8: 0.42435/0.29854, loss_spatial_ce_8: 0.17325/0.21784, loss_grounding_bce_8: 0.22235/0.09407, loss_grounding_dice_8: 0.49488/0.20144, loss_grounding_ce_8: 1.68834/0.40551, loss_mask_ce_9: 3.99235/3.67489, loss_mask_bce_9: 0.29428/0.39254, loss_mask_dice_9: 2.12337/1.90138, loss_spatial_bce_9: 0.21875/0.33277, loss_spatial_dice_9: 0.88241/0.82165, loss_spatial_ce_9: 1.39969/1.49347, loss_grounding_bce_9: 0.16618/0.10568, loss_grounding_dice_9: 0.53968/0.28082, loss_grounding_ce_9: 1.41643/0.67003] items per batch[64] items per second[0.24] total items[4908800] mini batches[ 76700] memory[7345] epoch remaining[0:01:34]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00076734.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0040 s/iter. Inference: 0.2230 s/iter. Eval: 0.0940 s/iter. Total: 0.3210 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0031 s/iter. Inference: 0.2227 s/iter. Eval: 0.0826 s/iter. Total: 0.3085 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2250 s/iter. Eval: 0.0792 s/iter. Total: 0.3075 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2244 s/iter. Eval: 0.0768 s/iter. Total: 0.3045 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0032 s/iter. Inference: 0.2230 s/iter. Eval: 0.0759 s/iter. Total: 0.3021 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2258 s/iter. Eval: 0.0758 s/iter. Total: 0.3049 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2276 s/iter. Eval: 0.0760 s/iter. Total: 0.3069 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2271 s/iter. Eval: 0.0756 s/iter. Total: 0.3060 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2278 s/iter. Eval: 0.0752 s/iter. Total: 0.3063 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval6rveegeq ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 49.706 | 82.000 | 59.717 |      133      |
| Things | 54.692 | 82.869 | 65.398 |      80       |
| Stuff  | 42.180 | 80.689 | 51.142 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.44 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.26s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.42 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.610
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.408
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.607
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.491
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.507
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.11 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.754 | 60.980 | 40.833 | 19.340 | 41.447 | 60.674 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 45.052 | bicycle      | 18.740 | car            | 37.078 |
| motorcycle    | 35.399 | airplane     | 57.099 | bus            | 63.638 |
| train         | 69.601 | truck        | 35.276 | boat           | 23.611 |
| traffic light | 26.305 | fire hydrant | 64.793 | stop sign      | 65.076 |
| parking meter | 43.051 | bench        | 20.565 | bird           | 29.639 |
| cat           | 73.823 | dog          | 65.849 | horse          | 45.710 |
| sheep         | 47.094 | cow          | 50.531 | elephant       | 61.207 |
| bear          | 77.133 | zebra        | 59.294 | giraffe        | 56.711 |
| backpack      | 17.080 | umbrella     | 48.270 | handbag        | 15.652 |
| tie           | 34.231 | suitcase     | 41.464 | frisbee        | 67.553 |
| skis          | 5.383  | snowboard    | 22.958 | sports ball    | 46.646 |
| kite          | 34.967 | baseball bat | 29.071 | baseball glove | 43.239 |
| skateboard    | 36.036 | surfboard    | 35.445 | tennis racket  | 56.277 |
| bottle        | 34.714 | wine glass   | 26.779 | cup            | 39.626 |
| fork          | 15.841 | knife        | 13.731 | spoon          | 13.961 |
| bowl          | 31.980 | banana       | 19.633 | apple          | 18.374 |
| sandwich      | 41.272 | orange       | 28.860 | broccoli       | 21.470 |
| carrot        | 20.149 | hot dog      | 23.191 | pizza          | 49.070 |
| donut         | 45.826 | cake         | 43.330 | chair          | 20.637 |
| couch         | 43.160 | potted plant | 17.023 | bed            | 39.968 |
| dining table  | 12.618 | toilet       | 67.239 | tv             | 62.587 |
| laptop        | 61.536 | mouse        | 59.489 | remote         | 31.424 |
| keyboard      | 47.342 | cell phone   | 37.371 | microwave      | 53.439 |
| oven          | 32.686 | toaster      | 22.473 | sink           | 37.222 |
| refrigerator  | 59.051 | book         | 8.959  | clock          | 52.106 |
| vase          | 34.565 | scissors     | 23.688 | teddy bear     | 51.251 |
| hair drier    | 11.275 | toothbrush   | 18.890 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.04054415808447, 'fwIoU': 68.7150729452486, 'IoU-person': 87.96332528663392, 'IoU-bicycle': 75.89274752274845, 'IoU-car': 70.11705977770643, 'IoU-motorcycle': 82.86871644416158, 'IoU-airplane': 76.69427838332213, 'IoU-bus': 81.10335263893207, 'IoU-train': 81.90187832503707, 'IoU-truck': 62.422665220704886, 'IoU-boat': 64.50470245022821, 'IoU-traffic light': 75.44114152921043, 'IoU-fire hydrant': 90.08375721802658, 'IoU-stop sign': 83.3319940682733, 'IoU-parking meter': 88.08537340011682, 'IoU-bench': 54.43421243362464, 'IoU-bird': 74.39959770531914, 'IoU-cat': 80.92738704418406, 'IoU-dog': 78.13350143637958, 'IoU-horse': 85.31096184119612, 'IoU-sheep': 89.92505068098585, 'IoU-cow': 79.96072897855629, 'IoU-elephant': 85.05152554736809, 'IoU-bear': 82.89609654309473, 'IoU-zebra': 89.26155073325212, 'IoU-giraffe': 88.21360786324716, 'IoU-backpack': 40.22377886772501, 'IoU-umbrella': 73.82226130569889, 'IoU-handbag': 37.34807397963934, 'IoU-tie': 69.14148048271217, 'IoU-suitcase': 79.6493538407878, 'IoU-frisbee': 83.15008325660037, 'IoU-skis': 49.925230419716165, 'IoU-snowboard': 69.23631250549256, 'IoU-sports ball': 59.64234755342145, 'IoU-kite': 66.49750602994501, 'IoU-baseball bat': 60.06059089722391, 'IoU-baseball glove': 73.97890301618622, 'IoU-skateboard': 64.05710028299374, 'IoU-surfboard': 76.3825386084944, 'IoU-tennis racket': 81.48018527673176, 'IoU-bottle': 67.70526139459258, 'IoU-wine glass': 72.0603659247174, 'IoU-cup': 64.56707675940218, 'IoU-fork': 54.49525103900555, 'IoU-knife': 48.001494102604624, 'IoU-spoon': 51.454776433481676, 'IoU-bowl': 52.39828251943373, 'IoU-banana': 83.08256478953064, 'IoU-apple': 59.4178268621828, 'IoU-sandwich': 64.78426823031711, 'IoU-orange': 75.11735246817024, 'IoU-broccoli': 66.68472198368653, 'IoU-carrot': 63.687735467101824, 'IoU-hot dog': 61.43943993899028, 'IoU-pizza': 81.20017310069284, 'IoU-donut': 64.78868403782116, 'IoU-cake': 66.97327677972955, 'IoU-chair': 53.93449183474477, 'IoU-couch': 67.53761527755829, 'IoU-potted plant': 33.93690607348535, 'IoU-bed': 66.68826898373052, 'IoU-dining table': 51.07712937863808, 'IoU-toilet': 85.55238444360259, 'IoU-tv': 76.47994292289167, 'IoU-laptop': 70.58744603313012, 'IoU-mouse': 68.76940252173083, 'IoU-remote': 48.483264506248446, 'IoU-keyboard': 52.95680421387866, 'IoU-cell phone': 69.52645537819889, 'IoU-microwave': 50.60712983757364, 'IoU-oven': 66.92476513480592, 'IoU-toaster': 65.43137001715519, 'IoU-sink': 71.7791937241229, 'IoU-refrigerator': 82.1618298594251, 'IoU-book': 49.56431520197559, 'IoU-clock': 68.28346947763079, 'IoU-vase': 60.16936536430252, 'IoU-scissors': 55.56512871384499, 'IoU-teddy bear': 81.61632176356271, 'IoU-hair drier': 41.07878408676156, 'IoU-toothbrush': 59.58490325052686, 'IoU-banner': 36.47947851409051, 'IoU-blanket': 11.882916065248487, 'IoU-bridge': 36.852788756433625, 'IoU-cardboard': 39.51785897350854, 'IoU-counter': 31.168457678667867, 'IoU-curtain': 64.04816874430134, 'IoU-door-stuff': 42.163131743579726, 'IoU-floor-wood': 63.01586205293095, 'IoU-flower': 40.967939498107455, 'IoU-fruit': 42.9470673660167, 'IoU-gravel': 27.121605236363084, 'IoU-house': 26.4857424887267, 'IoU-light': 40.63295042123776, 'IoU-mirror-stuff': 54.40467794745426, 'IoU-net': 30.131149092102106, 'IoU-pillow': 12.911350480248752, 'IoU-platform': 31.194889596562764, 'IoU-playingfield': 70.3297273167872, 'IoU-railroad': 61.024901107112406, 'IoU-river': 45.60248747280842, 'IoU-road': 66.77829930290498, 'IoU-roof': 16.206153632588908, 'IoU-sand': 62.09233938354135, 'IoU-sea': 84.32019591355655, 'IoU-shelf': 36.76329851075341, 'IoU-snow': 88.97799022765498, 'IoU-stairs': 24.100157840174596, 'IoU-tent': 8.842004485537245, 'IoU-towel': 34.00889191356447, 'IoU-wall-brick': 46.39233024624677, 'IoU-wall-stone': 25.938146065900614, 'IoU-wall-tile': 67.54572667141719, 'IoU-wall-wood': 38.721367744864104, 'IoU-water-other': 20.405666725935042, 'IoU-window-blind': 48.04520065137151, 'IoU-window-other': 47.77457078569754, 'IoU-tree-merged': 80.93530250372936, 'IoU-fence-merged': 48.05428387529576, 'IoU-ceiling-merged': 66.16516178385116, 'IoU-sky-other-merged': 93.08842317656001, 'IoU-cabinet-merged': 59.025293716969706, 'IoU-table-merged': 35.599775761616314, 'IoU-floor-other-merged': 47.93147114729327, 'IoU-pavement-merged': 54.56303566957246, 'IoU-mountain-merged': 55.19368861674492, 'IoU-grass-merged': 71.50305289420832, 'IoU-dirt-merged': 44.30731562378778, 'IoU-paper-merged': 28.38575647927648, 'IoU-food-other-merged': 38.75253342167463, 'IoU-building-other-merged': 58.81518204393609, 'IoU-rock-merged': 60.849964595329155, 'IoU-wall-other-merged': 65.02427901084496, 'IoU-rug-merged': 61.726162823880635, 'mACC': 72.50589084952065, 'pACC': 80.1273557393795, 'ACC-person': 92.51452273194502, 'ACC-bicycle': 86.38049337636605, 'ACC-car': 85.6714688467707, 'ACC-motorcycle': 87.67013231842292, 'ACC-airplane': 88.24814273821177, 'ACC-bus': 84.87090563684635, 'ACC-train': 95.68436045869994, 'ACC-truck': 78.67451700415545, 'ACC-boat': 75.28519487780252, 'ACC-traffic light': 89.87577469128797, 'ACC-fire hydrant': 94.93540418554403, 'ACC-stop sign': 97.08533581061131, 'ACC-parking meter': 92.05460740237709, 'ACC-bench': 73.07931095346028, 'ACC-bird': 79.54682604899594, 'ACC-cat': 87.66178189477995, 'ACC-dog': 82.54725844384102, 'ACC-horse': 91.45369262198484, 'ACC-sheep': 93.42230971733352, 'ACC-cow': 85.94994316003107, 'ACC-elephant': 88.96927426314673, 'ACC-bear': 85.18204210822363, 'ACC-zebra': 91.73995526942616, 'ACC-giraffe': 92.63222143051951, 'ACC-backpack': 59.67388541730572, 'ACC-umbrella': 80.21899825298738, 'ACC-handbag': 53.97483759972302, 'ACC-tie': 81.55745159203701, 'ACC-suitcase': 86.90100699529016, 'ACC-frisbee': 94.2429090909091, 'ACC-skis': 70.98164546826715, 'ACC-snowboard': 77.18057358379663, 'ACC-sports ball': 72.15761672920479, 'ACC-kite': 77.06072756286827, 'ACC-baseball bat': 82.7345675774899, 'ACC-baseball glove': 89.82368157483265, 'ACC-skateboard': 69.36725451207634, 'ACC-surfboard': 82.90026794654041, 'ACC-tennis racket': 89.51397637894082, 'ACC-bottle': 81.18261615839195, 'ACC-wine glass': 86.33197947453355, 'ACC-cup': 82.25737716230813, 'ACC-fork': 69.54894482740495, 'ACC-knife': 61.20832417381109, 'ACC-spoon': 65.17569061681813, 'ACC-bowl': 66.05044405324327, 'ACC-banana': 88.84817456353468, 'ACC-apple': 70.81208241223376, 'ACC-sandwich': 79.97350155951129, 'ACC-orange': 82.63595228633044, 'ACC-broccoli': 77.6829816427416, 'ACC-carrot': 74.98016950163957, 'ACC-hot dog': 71.48687897367515, 'ACC-pizza': 89.89985998546905, 'ACC-donut': 80.67855619997026, 'ACC-cake': 73.39773336296258, 'ACC-chair': 68.39029482585323, 'ACC-couch': 86.20407028663686, 'ACC-potted plant': 50.34778931801005, 'ACC-bed': 80.6168700162615, 'ACC-dining table': 77.40006114774715, 'ACC-toilet': 89.91402638790635, 'ACC-tv': 87.00814419717557, 'ACC-laptop': 84.05578281432116, 'ACC-mouse': 83.1080520473722, 'ACC-remote': 72.84464208865299, 'ACC-keyboard': 58.92542205158502, 'ACC-cell phone': 76.12337798990274, 'ACC-microwave': 57.640022076149044, 'ACC-oven': 86.62981337911859, 'ACC-toaster': 72.81678960982086, 'ACC-sink': 84.01715997654216, 'ACC-refrigerator': 91.33999775571708, 'ACC-book': 64.5151788966406, 'ACC-clock': 73.35007842835742, 'ACC-vase': 69.16152443846687, 'ACC-scissors': 59.660358648045616, 'ACC-teddy bear': 86.74956204919457, 'ACC-hair drier': 67.27435744172145, 'ACC-toothbrush': 81.04933981931896, 'ACC-banner': 73.53948886952902, 'ACC-blanket': 16.726259685983074, 'ACC-bridge': 55.546356774986535, 'ACC-cardboard': 47.64525229024284, 'ACC-counter': 46.63369079775627, 'ACC-curtain': 74.97541728980482, 'ACC-door-stuff': 61.80062619302132, 'ACC-floor-wood': 81.04303290132212, 'ACC-flower': 60.7248316082013, 'ACC-fruit': 61.48347972986645, 'ACC-gravel': 36.63249310278507, 'ACC-house': 32.74661296776946, 'ACC-light': 56.43853117942044, 'ACC-mirror-stuff': 65.38986165469815, 'ACC-net': 64.95328708599655, 'ACC-pillow': 27.580763108082778, 'ACC-platform': 50.03806511322427, 'ACC-playingfield': 93.49457013135205, 'ACC-railroad': 79.1181331988656, 'ACC-river': 64.7688888089879, 'ACC-road': 84.36068988749983, 'ACC-roof': 23.593695885130813, 'ACC-sand': 70.53109207172297, 'ACC-sea': 92.01583868294512, 'ACC-shelf': 54.85284547843352, 'ACC-snow': 94.96866357127368, 'ACC-stairs': 37.980931699165666, 'ACC-tent': 10.428629381333355, 'ACC-towel': 45.28775348465236, 'ACC-wall-brick': 64.27026012898874, 'ACC-wall-stone': 35.37808808119088, 'ACC-wall-tile': 80.9042346843642, 'ACC-wall-wood': 52.027158328674204, 'ACC-water-other': 31.978492606464766, 'ACC-window-blind': 59.549147195200405, 'ACC-window-other': 66.63248052426887, 'ACC-tree-merged': 89.34299726442215, 'ACC-fence-merged': 65.02482477556762, 'ACC-ceiling-merged': 79.25836529656797, 'ACC-sky-other-merged': 96.75960354502593, 'ACC-cabinet-merged': 76.63675111677001, 'ACC-table-merged': 51.971024241465244, 'ACC-floor-other-merged': 56.76166525738101, 'ACC-pavement-merged': 69.0723481561359, 'ACC-mountain-merged': 65.04781644184085, 'ACC-grass-merged': 82.56634392663692, 'ACC-dirt-merged': 64.47041109427988, 'ACC-paper-merged': 39.86827363424156, 'ACC-food-other-merged': 50.588339314607836, 'ACC-building-other-merged': 74.90469960087451, 'ACC-rock-merged': 83.10852139590517, 'ACC-wall-other-merged': 80.66516793684131, 'ACC-rug-merged': 80.47782688632573})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1549 s/iter. Inference: 0.4856 s/iter. Eval: 0.0000 s/iter. Total: 0.6405 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1544 s/iter. Inference: 0.5284 s/iter. Eval: 0.0000 s/iter. Total: 0.6829 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/50. Dataloading: 0.1666 s/iter. Inference: 0.6546 s/iter. Eval: 0.0000 s/iter. Total: 0.8214 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1711 s/iter. Inference: 0.6687 s/iter. Eval: 0.0000 s/iter. Total: 0.8400 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1683 s/iter. Inference: 0.6098 s/iter. Eval: 0.0000 s/iter. Total: 0.7783 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1675 s/iter. Inference: 0.6452 s/iter. Eval: 0.0000 s/iter. Total: 0.8128 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1693 s/iter. Inference: 0.7032 s/iter. Eval: 0.0000 s/iter. Total: 0.8726 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4705882352941178, 'noc@0.8': 2.796605209247878, 'noc@0.85': 3.4041556921275973, 'noc@0.9': 4.484342990927714, 'miou@iter1': 0.8314117808979716}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0018 s/iter. Inference: 0.1044 s/iter. Eval: 0.0008 s/iter. Total: 0.1070 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.8900146484375, 'precision@0.6': 67.58647155761719, 'precision@0.7': 62.41741180419922, 'precision@0.8': 52.39020538330078, 'precision@0.9': 27.283327102661133, 'cIoU': 56.909767150878906, 'mIoU': 62.63808059692383}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 49.70612262766172, 'SQ': 81.99998888023893, 'RQ': 59.71744638444216, 'PQ_th': 54.69212588573491, 'SQ_th': 82.86850929780012, 'RQ_th': 65.39837453838209, 'PQ_st': 42.180079973966336, 'SQ_st': 80.68901466505226, 'RQ_st': 51.142460491702565}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.75446978652448, 'AP50': 60.98042433577192, 'AP75': 40.83346280144434, 'APs': 19.34007404513352, 'APm': 41.446944224645534, 'APl': 60.67364516111351, 'AP-person': 45.05213874807167, 'AP-bicycle': 18.73993732046923, 'AP-car': 37.07844978595075, 'AP-motorcycle': 35.39897528673894, 'AP-airplane': 57.098601281279336, 'AP-bus': 63.63781840935327, 'AP-train': 69.60128996804268, 'AP-truck': 35.276304604833946, 'AP-boat': 23.611236668101782, 'AP-traffic light': 26.305369124561302, 'AP-fire hydrant': 64.79310034669321, 'AP-stop sign': 65.07584124150586, 'AP-parking meter': 43.051020013493954, 'AP-bench': 20.56474030796093, 'AP-bird': 29.639182692378174, 'AP-cat': 73.8231908991488, 'AP-dog': 65.84922666678735, 'AP-horse': 45.70983582588037, 'AP-sheep': 47.09396555096782, 'AP-cow': 50.531251160891024, 'AP-elephant': 61.2068335503026, 'AP-bear': 77.13284522648172, 'AP-zebra': 59.293916326403085, 'AP-giraffe': 56.710796507945794, 'AP-backpack': 17.080434326879544, 'AP-umbrella': 48.27042259894469, 'AP-handbag': 15.651974627595663, 'AP-tie': 34.23149470002623, 'AP-suitcase': 41.46378974691633, 'AP-frisbee': 67.55302348626734, 'AP-skis': 5.383423130906833, 'AP-snowboard': 22.957899581452924, 'AP-sports ball': 46.64612649018621, 'AP-kite': 34.96673056188418, 'AP-baseball bat': 29.07071320559551, 'AP-baseball glove': 43.2393067645333, 'AP-skateboard': 36.03608621155402, 'AP-surfboard': 35.44471090078673, 'AP-tennis racket': 56.276621773478986, 'AP-bottle': 34.71442910184032, 'AP-wine glass': 26.77936964828512, 'AP-cup': 39.62602426969421, 'AP-fork': 15.841067361255732, 'AP-knife': 13.7311558803319, 'AP-spoon': 13.960911188565275, 'AP-bowl': 31.980229418017853, 'AP-banana': 19.632727808173815, 'AP-apple': 18.374482965350538, 'AP-sandwich': 41.27213277615592, 'AP-orange': 28.859943432304348, 'AP-broccoli': 21.47020062724519, 'AP-carrot': 20.148870475396183, 'AP-hot dog': 23.191184948001965, 'AP-pizza': 49.06987696957027, 'AP-donut': 45.826371926412385, 'AP-cake': 43.33031743682278, 'AP-chair': 20.636717055077177, 'AP-couch': 43.159618242448126, 'AP-potted plant': 17.023176917800544, 'AP-bed': 39.967827785652844, 'AP-dining table': 12.617771551614695, 'AP-toilet': 67.23937836698775, 'AP-tv': 62.586651172474376, 'AP-laptop': 61.53551613627708, 'AP-mouse': 59.48901855281693, 'AP-remote': 31.42443462426643, 'AP-keyboard': 47.34209968008823, 'AP-cell phone': 37.37118015370102, 'AP-microwave': 53.43917250007175, 'AP-oven': 32.68598179359232, 'AP-toaster': 22.473151126364908, 'AP-sink': 37.222482333689605, 'AP-refrigerator': 59.05098693113682, 'AP-book': 8.959143346192107, 'AP-clock': 52.1064728261165, 'AP-vase': 34.564816826932734, 'AP-scissors': 23.688392505091155, 'AP-teddy bear': 51.25054093879119, 'AP-hair drier': 11.274827482748275, 'AP-toothbrush': 18.890302217349888}), ('sem_seg', {'mIoU': 60.04054415808447, 'fwIoU': 68.7150729452486, 'IoU-person': 87.96332528663392, 'IoU-bicycle': 75.89274752274845, 'IoU-car': 70.11705977770643, 'IoU-motorcycle': 82.86871644416158, 'IoU-airplane': 76.69427838332213, 'IoU-bus': 81.10335263893207, 'IoU-train': 81.90187832503707, 'IoU-truck': 62.422665220704886, 'IoU-boat': 64.50470245022821, 'IoU-traffic light': 75.44114152921043, 'IoU-fire hydrant': 90.08375721802658, 'IoU-stop sign': 83.3319940682733, 'IoU-parking meter': 88.08537340011682, 'IoU-bench': 54.43421243362464, 'IoU-bird': 74.39959770531914, 'IoU-cat': 80.92738704418406, 'IoU-dog': 78.13350143637958, 'IoU-horse': 85.31096184119612, 'IoU-sheep': 89.92505068098585, 'IoU-cow': 79.96072897855629, 'IoU-elephant': 85.05152554736809, 'IoU-bear': 82.89609654309473, 'IoU-zebra': 89.26155073325212, 'IoU-giraffe': 88.21360786324716, 'IoU-backpack': 40.22377886772501, 'IoU-umbrella': 73.82226130569889, 'IoU-handbag': 37.34807397963934, 'IoU-tie': 69.14148048271217, 'IoU-suitcase': 79.6493538407878, 'IoU-frisbee': 83.15008325660037, 'IoU-skis': 49.925230419716165, 'IoU-snowboard': 69.23631250549256, 'IoU-sports ball': 59.64234755342145, 'IoU-kite': 66.49750602994501, 'IoU-baseball bat': 60.06059089722391, 'IoU-baseball glove': 73.97890301618622, 'IoU-skateboard': 64.05710028299374, 'IoU-surfboard': 76.3825386084944, 'IoU-tennis racket': 81.48018527673176, 'IoU-bottle': 67.70526139459258, 'IoU-wine glass': 72.0603659247174, 'IoU-cup': 64.56707675940218, 'IoU-fork': 54.49525103900555, 'IoU-knife': 48.001494102604624, 'IoU-spoon': 51.454776433481676, 'IoU-bowl': 52.39828251943373, 'IoU-banana': 83.08256478953064, 'IoU-apple': 59.4178268621828, 'IoU-sandwich': 64.78426823031711, 'IoU-orange': 75.11735246817024, 'IoU-broccoli': 66.68472198368653, 'IoU-carrot': 63.687735467101824, 'IoU-hot dog': 61.43943993899028, 'IoU-pizza': 81.20017310069284, 'IoU-donut': 64.78868403782116, 'IoU-cake': 66.97327677972955, 'IoU-chair': 53.93449183474477, 'IoU-couch': 67.53761527755829, 'IoU-potted plant': 33.93690607348535, 'IoU-bed': 66.68826898373052, 'IoU-dining table': 51.07712937863808, 'IoU-toilet': 85.55238444360259, 'IoU-tv': 76.47994292289167, 'IoU-laptop': 70.58744603313012, 'IoU-mouse': 68.76940252173083, 'IoU-remote': 48.483264506248446, 'IoU-keyboard': 52.95680421387866, 'IoU-cell phone': 69.52645537819889, 'IoU-microwave': 50.60712983757364, 'IoU-oven': 66.92476513480592, 'IoU-toaster': 65.43137001715519, 'IoU-sink': 71.7791937241229, 'IoU-refrigerator': 82.1618298594251, 'IoU-book': 49.56431520197559, 'IoU-clock': 68.28346947763079, 'IoU-vase': 60.16936536430252, 'IoU-scissors': 55.56512871384499, 'IoU-teddy bear': 81.61632176356271, 'IoU-hair drier': 41.07878408676156, 'IoU-toothbrush': 59.58490325052686, 'IoU-banner': 36.47947851409051, 'IoU-blanket': 11.882916065248487, 'IoU-bridge': 36.852788756433625, 'IoU-cardboard': 39.51785897350854, 'IoU-counter': 31.168457678667867, 'IoU-curtain': 64.04816874430134, 'IoU-door-stuff': 42.163131743579726, 'IoU-floor-wood': 63.01586205293095, 'IoU-flower': 40.967939498107455, 'IoU-fruit': 42.9470673660167, 'IoU-gravel': 27.121605236363084, 'IoU-house': 26.4857424887267, 'IoU-light': 40.63295042123776, 'IoU-mirror-stuff': 54.40467794745426, 'IoU-net': 30.131149092102106, 'IoU-pillow': 12.911350480248752, 'IoU-platform': 31.194889596562764, 'IoU-playingfield': 70.3297273167872, 'IoU-railroad': 61.024901107112406, 'IoU-river': 45.60248747280842, 'IoU-road': 66.77829930290498, 'IoU-roof': 16.206153632588908, 'IoU-sand': 62.09233938354135, 'IoU-sea': 84.32019591355655, 'IoU-shelf': 36.76329851075341, 'IoU-snow': 88.97799022765498, 'IoU-stairs': 24.100157840174596, 'IoU-tent': 8.842004485537245, 'IoU-towel': 34.00889191356447, 'IoU-wall-brick': 46.39233024624677, 'IoU-wall-stone': 25.938146065900614, 'IoU-wall-tile': 67.54572667141719, 'IoU-wall-wood': 38.721367744864104, 'IoU-water-other': 20.405666725935042, 'IoU-window-blind': 48.04520065137151, 'IoU-window-other': 47.77457078569754, 'IoU-tree-merged': 80.93530250372936, 'IoU-fence-merged': 48.05428387529576, 'IoU-ceiling-merged': 66.16516178385116, 'IoU-sky-other-merged': 93.08842317656001, 'IoU-cabinet-merged': 59.025293716969706, 'IoU-table-merged': 35.599775761616314, 'IoU-floor-other-merged': 47.93147114729327, 'IoU-pavement-merged': 54.56303566957246, 'IoU-mountain-merged': 55.19368861674492, 'IoU-grass-merged': 71.50305289420832, 'IoU-dirt-merged': 44.30731562378778, 'IoU-paper-merged': 28.38575647927648, 'IoU-food-other-merged': 38.75253342167463, 'IoU-building-other-merged': 58.81518204393609, 'IoU-rock-merged': 60.849964595329155, 'IoU-wall-other-merged': 65.02427901084496, 'IoU-rug-merged': 61.726162823880635, 'mACC': 72.50589084952065, 'pACC': 80.1273557393795, 'ACC-person': 92.51452273194502, 'ACC-bicycle': 86.38049337636605, 'ACC-car': 85.6714688467707, 'ACC-motorcycle': 87.67013231842292, 'ACC-airplane': 88.24814273821177, 'ACC-bus': 84.87090563684635, 'ACC-train': 95.68436045869994, 'ACC-truck': 78.67451700415545, 'ACC-boat': 75.28519487780252, 'ACC-traffic light': 89.87577469128797, 'ACC-fire hydrant': 94.93540418554403, 'ACC-stop sign': 97.08533581061131, 'ACC-parking meter': 92.05460740237709, 'ACC-bench': 73.07931095346028, 'ACC-bird': 79.54682604899594, 'ACC-cat': 87.66178189477995, 'ACC-dog': 82.54725844384102, 'ACC-horse': 91.45369262198484, 'ACC-sheep': 93.42230971733352, 'ACC-cow': 85.94994316003107, 'ACC-elephant': 88.96927426314673, 'ACC-bear': 85.18204210822363, 'ACC-zebra': 91.73995526942616, 'ACC-giraffe': 92.63222143051951, 'ACC-backpack': 59.67388541730572, 'ACC-umbrella': 80.21899825298738, 'ACC-handbag': 53.97483759972302, 'ACC-tie': 81.55745159203701, 'ACC-suitcase': 86.90100699529016, 'ACC-frisbee': 94.2429090909091, 'ACC-skis': 70.98164546826715, 'ACC-snowboard': 77.18057358379663, 'ACC-sports ball': 72.15761672920479, 'ACC-kite': 77.06072756286827, 'ACC-baseball bat': 82.7345675774899, 'ACC-baseball glove': 89.82368157483265, 'ACC-skateboard': 69.36725451207634, 'ACC-surfboard': 82.90026794654041, 'ACC-tennis racket': 89.51397637894082, 'ACC-bottle': 81.18261615839195, 'ACC-wine glass': 86.33197947453355, 'ACC-cup': 82.25737716230813, 'ACC-fork': 69.54894482740495, 'ACC-knife': 61.20832417381109, 'ACC-spoon': 65.17569061681813, 'ACC-bowl': 66.05044405324327, 'ACC-banana': 88.84817456353468, 'ACC-apple': 70.81208241223376, 'ACC-sandwich': 79.97350155951129, 'ACC-orange': 82.63595228633044, 'ACC-broccoli': 77.6829816427416, 'ACC-carrot': 74.98016950163957, 'ACC-hot dog': 71.48687897367515, 'ACC-pizza': 89.89985998546905, 'ACC-donut': 80.67855619997026, 'ACC-cake': 73.39773336296258, 'ACC-chair': 68.39029482585323, 'ACC-couch': 86.20407028663686, 'ACC-potted plant': 50.34778931801005, 'ACC-bed': 80.6168700162615, 'ACC-dining table': 77.40006114774715, 'ACC-toilet': 89.91402638790635, 'ACC-tv': 87.00814419717557, 'ACC-laptop': 84.05578281432116, 'ACC-mouse': 83.1080520473722, 'ACC-remote': 72.84464208865299, 'ACC-keyboard': 58.92542205158502, 'ACC-cell phone': 76.12337798990274, 'ACC-microwave': 57.640022076149044, 'ACC-oven': 86.62981337911859, 'ACC-toaster': 72.81678960982086, 'ACC-sink': 84.01715997654216, 'ACC-refrigerator': 91.33999775571708, 'ACC-book': 64.5151788966406, 'ACC-clock': 73.35007842835742, 'ACC-vase': 69.16152443846687, 'ACC-scissors': 59.660358648045616, 'ACC-teddy bear': 86.74956204919457, 'ACC-hair drier': 67.27435744172145, 'ACC-toothbrush': 81.04933981931896, 'ACC-banner': 73.53948886952902, 'ACC-blanket': 16.726259685983074, 'ACC-bridge': 55.546356774986535, 'ACC-cardboard': 47.64525229024284, 'ACC-counter': 46.63369079775627, 'ACC-curtain': 74.97541728980482, 'ACC-door-stuff': 61.80062619302132, 'ACC-floor-wood': 81.04303290132212, 'ACC-flower': 60.7248316082013, 'ACC-fruit': 61.48347972986645, 'ACC-gravel': 36.63249310278507, 'ACC-house': 32.74661296776946, 'ACC-light': 56.43853117942044, 'ACC-mirror-stuff': 65.38986165469815, 'ACC-net': 64.95328708599655, 'ACC-pillow': 27.580763108082778, 'ACC-platform': 50.03806511322427, 'ACC-playingfield': 93.49457013135205, 'ACC-railroad': 79.1181331988656, 'ACC-river': 64.7688888089879, 'ACC-road': 84.36068988749983, 'ACC-roof': 23.593695885130813, 'ACC-sand': 70.53109207172297, 'ACC-sea': 92.01583868294512, 'ACC-shelf': 54.85284547843352, 'ACC-snow': 94.96866357127368, 'ACC-stairs': 37.980931699165666, 'ACC-tent': 10.428629381333355, 'ACC-towel': 45.28775348465236, 'ACC-wall-brick': 64.27026012898874, 'ACC-wall-stone': 35.37808808119088, 'ACC-wall-tile': 80.9042346843642, 'ACC-wall-wood': 52.027158328674204, 'ACC-water-other': 31.978492606464766, 'ACC-window-blind': 59.549147195200405, 'ACC-window-other': 66.63248052426887, 'ACC-tree-merged': 89.34299726442215, 'ACC-fence-merged': 65.02482477556762, 'ACC-ceiling-merged': 79.25836529656797, 'ACC-sky-other-merged': 96.75960354502593, 'ACC-cabinet-merged': 76.63675111677001, 'ACC-table-merged': 51.971024241465244, 'ACC-floor-other-merged': 56.76166525738101, 'ACC-pavement-merged': 69.0723481561359, 'ACC-mountain-merged': 65.04781644184085, 'ACC-grass-merged': 82.56634392663692, 'ACC-dirt-merged': 64.47041109427988, 'ACC-paper-merged': 39.86827363424156, 'ACC-food-other-merged': 50.588339314607836, 'ACC-building-other-merged': 74.90469960087451, 'ACC-rock-merged': 83.10852139590517, 'ACC-wall-other-merged': 80.66516793684131, 'ACC-rug-merged': 80.47782688632573})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4705882352941178, 'noc@0.8': 2.796605209247878, 'noc@0.85': 3.4041556921275973, 'noc@0.9': 4.484342990927714, 'miou@iter1': 0.8314117808979716}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.8900146484375, 'precision@0.6': 67.58647155761719, 'precision@0.7': 62.41741180419922, 'precision@0.8': 52.39020538330078, 'precision@0.9': 27.283327102661133, 'cIoU': 56.909767150878906, 'mIoU': 62.63808059692383}}}
INFO:trainer.default_trainer:This epoch takes 1:27:30.764060
INFO:trainer.default_trainer:PROGRESS: 84.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 42 training.
INFO:trainer.default_trainer:epochs[    42] optim steps[76800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98938/0.89600, loss_mask_bce_0: 0.35912/0.33402, loss_mask_dice_0: 0.29484/1.16154, loss_spatial_bce_0: 0.34610/0.08674, loss_spatial_dice_0: 0.19978/0.20692, loss_spatial_ce_0: 0.01332/0.06006, loss_grounding_bce_0: 0.08195/0.08617, loss_grounding_dice_0: 0.09919/0.17839, loss_grounding_ce_0: 0.24366/0.27161, loss_mask_ce_1: 0.98597/0.89666, loss_mask_bce_1: 0.36617/0.33497, loss_mask_dice_1: 0.29428/1.16825, loss_spatial_bce_1: 0.31123/0.08728, loss_spatial_dice_1: 0.20793/0.21089, loss_spatial_ce_1: 0.01738/0.06584, loss_grounding_bce_1: 0.08063/0.08635, loss_grounding_dice_1: 0.10358/0.17918, loss_grounding_ce_1: 0.21849/0.27234, loss_mask_ce_2: 1.00522/0.90370, loss_mask_bce_2: 0.34546/0.33559, loss_mask_dice_2: 0.28287/1.16872, loss_spatial_bce_2: 0.32980/0.08849, loss_spatial_dice_2: 0.20538/0.21271, loss_spatial_ce_2: 0.01510/0.06933, loss_grounding_bce_2: 0.07685/0.08653, loss_grounding_dice_2: 0.10354/0.17907, loss_grounding_ce_2: 0.18228/0.27562, loss_mask_ce_3: 0.98375/0.91477, loss_mask_bce_3: 0.35467/0.33678, loss_mask_dice_3: 0.28817/1.16660, loss_spatial_bce_3: 0.34546/0.08976, loss_spatial_dice_3: 0.21306/0.21374, loss_spatial_ce_3: 0.02333/0.07434, loss_grounding_bce_3: 0.07774/0.08677, loss_grounding_dice_3: 0.09849/0.17877, loss_grounding_ce_3: 0.23970/0.27790, loss_mask_ce_4: 1.00771/0.91588, loss_mask_bce_4: 0.34128/0.33890, loss_mask_dice_4: 0.28515/1.19037, loss_spatial_bce_4: 0.33876/0.09366, loss_spatial_dice_4: 0.20591/0.22598, loss_spatial_ce_4: 0.02649/0.09064, loss_grounding_bce_4: 0.08505/0.08730, loss_grounding_dice_4: 0.11083/0.18176, loss_grounding_ce_4: 0.16106/0.28087, loss_mask_ce_5: 0.97931/0.93281, loss_mask_bce_5: 0.35059/0.34127, loss_mask_dice_5: 0.28512/1.19867, loss_spatial_bce_5: 0.31530/0.09598, loss_spatial_dice_5: 0.21228/0.23030, loss_spatial_ce_5: 0.01842/0.10466, loss_grounding_bce_5: 0.08464/0.08774, loss_grounding_dice_5: 0.09858/0.18303, loss_grounding_ce_5: 0.16615/0.29331, loss_mask_ce_6: 0.86889/0.97284, loss_mask_bce_6: 0.34630/0.34408, loss_mask_dice_6: 0.26305/1.20143, loss_spatial_bce_6: 0.39480/0.10160, loss_spatial_dice_6: 0.21117/0.23326, loss_spatial_ce_6: 0.02439/0.12980, loss_grounding_bce_6: 0.07963/0.08846, loss_grounding_dice_6: 0.08893/0.18340, loss_grounding_ce_6: 0.33053/0.30878, loss_mask_ce_7: 1.02121/1.01836, loss_mask_bce_7: 0.37230/0.35188, loss_mask_dice_7: 0.27999/1.25595, loss_spatial_bce_7: 0.35617/0.10947, loss_spatial_dice_7: 0.22018/0.26084, loss_spatial_ce_7: 0.07036/0.16483, loss_grounding_bce_7: 0.08273/0.09036, loss_grounding_dice_7: 0.09621/0.19074, loss_grounding_ce_7: 0.35337/0.33888, loss_mask_ce_8: 1.06287/1.12732, loss_mask_bce_8: 0.39557/0.36548, loss_mask_dice_8: 0.27512/1.32868, loss_spatial_bce_8: 0.30587/0.12978, loss_spatial_dice_8: 0.22704/0.29853, loss_spatial_ce_8: 0.15726/0.21780, loss_grounding_bce_8: 0.08397/0.09406, loss_grounding_dice_8: 0.10124/0.20144, loss_grounding_ce_8: 0.82861/0.40545, loss_mask_ce_9: 2.51393/3.67478, loss_mask_bce_9: 0.36475/0.39251, loss_mask_dice_9: 0.38562/1.90154, loss_spatial_bce_9: 0.60420/0.33273, loss_spatial_dice_9: 0.74255/0.82165, loss_spatial_ce_9: 1.29322/1.49343, loss_grounding_bce_9: 0.10889/0.10566, loss_grounding_dice_9: 0.15477/0.28081, loss_grounding_ce_9: 0.95617/0.66997] items per batch[64] items per second[0.13] total items[4915200] mini batches[ 76800] memory[7345] epoch remaining[1:22:34]
INFO:trainer.default_trainer:epochs[    42] optim steps[76900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45070/0.89593, loss_mask_bce_0: 0.07703/0.33400, loss_mask_dice_0: 2.66915/1.16142, loss_spatial_bce_0: 0.03425/0.08673, loss_spatial_dice_0: 0.37650/0.20689, loss_spatial_ce_0: 0.02204/0.06003, loss_grounding_bce_0: 0.07603/0.08618, loss_grounding_dice_0: 0.35539/0.17838, loss_grounding_ce_0: 0.04546/0.27158, loss_mask_ce_1: 0.50968/0.89660, loss_mask_bce_1: 0.07994/0.33494, loss_mask_dice_1: 2.88440/1.16813, loss_spatial_bce_1: 0.03491/0.08727, loss_spatial_dice_1: 0.35618/0.21086, loss_spatial_ce_1: 0.05475/0.06581, loss_grounding_bce_1: 0.08756/0.08635, loss_grounding_dice_1: 0.36256/0.17917, loss_grounding_ce_1: 0.05185/0.27231, loss_mask_ce_2: 0.50445/0.90362, loss_mask_bce_2: 0.08224/0.33556, loss_mask_dice_2: 3.06684/1.16858, loss_spatial_bce_2: 0.04292/0.08848, loss_spatial_dice_2: 0.32631/0.21268, loss_spatial_ce_2: 0.06948/0.06931, loss_grounding_bce_2: 0.07128/0.08653, loss_grounding_dice_2: 0.35111/0.17906, loss_grounding_ce_2: 0.07025/0.27560, loss_mask_ce_3: 0.59033/0.91471, loss_mask_bce_3: 0.07757/0.33675, loss_mask_dice_3: 2.71269/1.16644, loss_spatial_bce_3: 0.04771/0.08976, loss_spatial_dice_3: 0.43389/0.21372, loss_spatial_ce_3: 0.07947/0.07431, loss_grounding_bce_3: 0.07958/0.08677, loss_grounding_dice_3: 0.35664/0.17875, loss_grounding_ce_3: 0.04205/0.27788, loss_mask_ce_4: 0.58897/0.91582, loss_mask_bce_4: 0.08216/0.33888, loss_mask_dice_4: 2.33476/1.19024, loss_spatial_bce_4: 0.04951/0.09366, loss_spatial_dice_4: 0.36228/0.22596, loss_spatial_ce_4: 0.13883/0.09062, loss_grounding_bce_4: 0.08034/0.08730, loss_grounding_dice_4: 0.35928/0.18175, loss_grounding_ce_4: 0.07244/0.28083, loss_mask_ce_5: 0.58547/0.93276, loss_mask_bce_5: 0.07928/0.34125, loss_mask_dice_5: 2.73133/1.19855, loss_spatial_bce_5: 0.04605/0.09598, loss_spatial_dice_5: 0.39162/0.23028, loss_spatial_ce_5: 0.09092/0.10463, loss_grounding_bce_5: 0.08647/0.08775, loss_grounding_dice_5: 0.36147/0.18302, loss_grounding_ce_5: 0.17879/0.29327, loss_mask_ce_6: 0.56132/0.97276, loss_mask_bce_6: 0.07311/0.34405, loss_mask_dice_6: 2.60113/1.20128, loss_spatial_bce_6: 0.05957/0.10160, loss_spatial_dice_6: 0.33323/0.23323, loss_spatial_ce_6: 0.16151/0.12977, loss_grounding_bce_6: 0.07983/0.08847, loss_grounding_dice_6: 0.35751/0.18339, loss_grounding_ce_6: 0.29439/0.30873, loss_mask_ce_7: 0.53273/1.01829, loss_mask_bce_7: 0.12432/0.35184, loss_mask_dice_7: 2.63281/1.25579, loss_spatial_bce_7: 0.06529/0.10947, loss_spatial_dice_7: 0.44373/0.26082, loss_spatial_ce_7: 0.13536/0.16478, loss_grounding_bce_7: 0.06706/0.09036, loss_grounding_dice_7: 0.32417/0.19073, loss_grounding_ce_7: 0.32363/0.33880, loss_mask_ce_8: 0.79588/1.12723, loss_mask_bce_8: 0.07556/0.36544, loss_mask_dice_8: 2.52145/1.32851, loss_spatial_bce_8: 0.08383/0.12977, loss_spatial_dice_8: 0.55186/0.29850, loss_spatial_ce_8: 0.19160/0.21772, loss_grounding_bce_8: 0.07060/0.09406, loss_grounding_dice_8: 0.33956/0.20141, loss_grounding_ce_8: 0.28332/0.40538, loss_mask_ce_9: 4.48628/3.67471, loss_mask_bce_9: 0.22369/0.39246, loss_mask_dice_9: 3.35187/1.90129, loss_spatial_bce_9: 0.16882/0.33273, loss_spatial_dice_9: 0.74215/0.82164, loss_spatial_ce_9: 1.91288/1.49337, loss_grounding_bce_9: 0.30214/0.10566, loss_grounding_dice_9: 0.49168/0.28079, loss_grounding_ce_9: 0.04498/0.66986] items per batch[64] items per second[0.23] total items[4921600] mini batches[ 76900] memory[7345] epoch remaining[1:17:07]
INFO:trainer.default_trainer:epochs[    42] optim steps[77000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52024/0.89591, loss_mask_bce_0: 0.19233/0.33404, loss_mask_dice_0: 0.29562/1.16156, loss_spatial_bce_0: 0.09386/0.08672, loss_spatial_dice_0: 0.13055/0.20688, loss_spatial_ce_0: 0.00145/0.06001, loss_grounding_bce_0: 0.07276/0.08617, loss_grounding_dice_0: 0.13424/0.17837, loss_grounding_ce_0: 0.17085/0.27157, loss_mask_ce_1: 0.53254/0.89657, loss_mask_bce_1: 0.19740/0.33498, loss_mask_dice_1: 0.33645/1.16828, loss_spatial_bce_1: 0.07782/0.08726, loss_spatial_dice_1: 0.12891/0.21085, loss_spatial_ce_1: 0.00592/0.06579, loss_grounding_bce_1: 0.06914/0.08634, loss_grounding_dice_1: 0.13313/0.17916, loss_grounding_ce_1: 0.21273/0.27230, loss_mask_ce_2: 0.51267/0.90361, loss_mask_bce_2: 0.20004/0.33560, loss_mask_dice_2: 0.29506/1.16869, loss_spatial_bce_2: 0.08487/0.08847, loss_spatial_dice_2: 0.13672/0.21267, loss_spatial_ce_2: 0.00588/0.06929, loss_grounding_bce_2: 0.07126/0.08652, loss_grounding_dice_2: 0.13191/0.17905, loss_grounding_ce_2: 0.50823/0.27560, loss_mask_ce_3: 0.58035/0.91468, loss_mask_bce_3: 0.18850/0.33679, loss_mask_dice_3: 0.28863/1.16660, loss_spatial_bce_3: 0.08342/0.08975, loss_spatial_dice_3: 0.13308/0.21371, loss_spatial_ce_3: 0.03010/0.07429, loss_grounding_bce_3: 0.06440/0.08676, loss_grounding_dice_3: 0.12353/0.17875, loss_grounding_ce_3: 0.53497/0.27788, loss_mask_ce_4: 0.52752/0.91582, loss_mask_bce_4: 0.21287/0.33892, loss_mask_dice_4: 0.36597/1.19037, loss_spatial_bce_4: 0.08736/0.09365, loss_spatial_dice_4: 0.14470/0.22595, loss_spatial_ce_4: 0.08169/0.09059, loss_grounding_bce_4: 0.07513/0.08729, loss_grounding_dice_4: 0.12586/0.18175, loss_grounding_ce_4: 0.47541/0.28080, loss_mask_ce_5: 0.48982/0.93274, loss_mask_bce_5: 0.23242/0.34129, loss_mask_dice_5: 0.39824/1.19868, loss_spatial_bce_5: 0.09728/0.09597, loss_spatial_dice_5: 0.17101/0.23027, loss_spatial_ce_5: 0.10408/0.10461, loss_grounding_bce_5: 0.06807/0.08774, loss_grounding_dice_5: 0.13561/0.18301, loss_grounding_ce_5: 0.69454/0.29327, loss_mask_ce_6: 0.46596/0.97274, loss_mask_bce_6: 0.21697/0.34409, loss_mask_dice_6: 0.37821/1.20140, loss_spatial_bce_6: 0.09501/0.10159, loss_spatial_dice_6: 0.17052/0.23323, loss_spatial_ce_6: 0.11036/0.12973, loss_grounding_bce_6: 0.07444/0.08847, loss_grounding_dice_6: 0.13661/0.18338, loss_grounding_ce_6: 0.51989/0.30870, loss_mask_ce_7: 0.49611/1.01831, loss_mask_bce_7: 0.23119/0.35188, loss_mask_dice_7: 0.36604/1.25591, loss_spatial_bce_7: 0.10661/0.10945, loss_spatial_dice_7: 0.16297/0.26080, loss_spatial_ce_7: 0.10696/0.16475, loss_grounding_bce_7: 0.07327/0.09036, loss_grounding_dice_7: 0.14384/0.19071, loss_grounding_ce_7: 0.42712/0.33880, loss_mask_ce_8: 0.49474/1.12722, loss_mask_bce_8: 0.22969/0.36547, loss_mask_dice_8: 0.39262/1.32865, loss_spatial_bce_8: 0.11662/0.12976, loss_spatial_dice_8: 0.17972/0.29849, loss_spatial_ce_8: 0.15070/0.21768, loss_grounding_bce_8: 0.09264/0.09405, loss_grounding_dice_8: 0.16519/0.20141, loss_grounding_ce_8: 0.91700/0.40538, loss_mask_ce_9: 3.15092/3.67492, loss_mask_bce_9: 0.26771/0.39251, loss_mask_dice_9: 0.60172/1.90154, loss_spatial_bce_9: 0.44538/0.33274, loss_spatial_dice_9: 0.85954/0.82167, loss_spatial_ce_9: 1.30169/1.49337, loss_grounding_bce_9: 0.10455/0.10565, loss_grounding_dice_9: 0.28029/0.28079, loss_grounding_ce_9: 1.03670/0.66985] items per batch[64] items per second[0.23] total items[4928000] mini batches[ 77000] memory[7345] epoch remaining[1:12:37]
INFO:trainer.default_trainer:epochs[    42] optim steps[77100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.54282/0.89584, loss_mask_bce_0: 0.71489/0.33403, loss_mask_dice_0: 2.20315/1.16144, loss_spatial_bce_0: 0.10032/0.08672, loss_spatial_dice_0: 0.29852/0.20687, loss_spatial_ce_0: 0.01962/0.05999, loss_grounding_bce_0: 0.26357/0.08617, loss_grounding_dice_0: 0.33316/0.17837, loss_grounding_ce_0: 0.40839/0.27152, loss_mask_ce_1: 1.49683/0.89651, loss_mask_bce_1: 0.72004/0.33497, loss_mask_dice_1: 2.17274/1.16815, loss_spatial_bce_1: 0.09838/0.08725, loss_spatial_dice_1: 0.28990/0.21084, loss_spatial_ce_1: 0.04365/0.06578, loss_grounding_bce_1: 0.27711/0.08635, loss_grounding_dice_1: 0.33276/0.17916, loss_grounding_ce_1: 0.39909/0.27224, loss_mask_ce_2: 1.61450/0.90353, loss_mask_bce_2: 0.70144/0.33560, loss_mask_dice_2: 2.24851/1.16857, loss_spatial_bce_2: 0.10242/0.08847, loss_spatial_dice_2: 0.32043/0.21267, loss_spatial_ce_2: 0.06196/0.06927, loss_grounding_bce_2: 0.27289/0.08653, loss_grounding_dice_2: 0.34190/0.17905, loss_grounding_ce_2: 0.44488/0.27556, loss_mask_ce_3: 1.65630/0.91461, loss_mask_bce_3: 0.73331/0.33679, loss_mask_dice_3: 2.12345/1.16648, loss_spatial_bce_3: 0.10759/0.08974, loss_spatial_dice_3: 0.29615/0.21371, loss_spatial_ce_3: 0.10621/0.07430, loss_grounding_bce_3: 0.27236/0.08677, loss_grounding_dice_3: 0.34125/0.17876, loss_grounding_ce_3: 0.46966/0.27784, loss_mask_ce_4: 1.63386/0.91574, loss_mask_bce_4: 0.78404/0.33892, loss_mask_dice_4: 2.18913/1.19025, loss_spatial_bce_4: 0.08933/0.09365, loss_spatial_dice_4: 0.29972/0.22594, loss_spatial_ce_4: 0.13301/0.09058, loss_grounding_bce_4: 0.42124/0.08730, loss_grounding_dice_4: 0.41236/0.18175, loss_grounding_ce_4: 0.41287/0.28077, loss_mask_ce_5: 1.62690/0.93264, loss_mask_bce_5: 0.86486/0.34129, loss_mask_dice_5: 2.54771/1.19855, loss_spatial_bce_5: 0.10115/0.09597, loss_spatial_dice_5: 0.35170/0.23027, loss_spatial_ce_5: 0.29735/0.10460, loss_grounding_bce_5: 0.48388/0.08774, loss_grounding_dice_5: 0.42636/0.18301, loss_grounding_ce_5: 0.45311/0.29322, loss_mask_ce_6: 1.54545/0.97266, loss_mask_bce_6: 0.88786/0.34409, loss_mask_dice_6: 2.40645/1.20128, loss_spatial_bce_6: 0.12711/0.10159, loss_spatial_dice_6: 0.33072/0.23323, loss_spatial_ce_6: 0.27544/0.12973, loss_grounding_bce_6: 0.47824/0.08847, loss_grounding_dice_6: 0.42842/0.18338, loss_grounding_ce_6: 0.42141/0.30866, loss_mask_ce_7: 1.56269/1.01820, loss_mask_bce_7: 0.85273/0.35189, loss_mask_dice_7: 2.51752/1.25580, loss_spatial_bce_7: 0.11920/0.10945, loss_spatial_dice_7: 0.37301/0.26080, loss_spatial_ce_7: 0.37973/0.16473, loss_grounding_bce_7: 0.48172/0.09037, loss_grounding_dice_7: 0.39832/0.19072, loss_grounding_ce_7: 0.31941/0.33872, loss_mask_ce_8: 1.64583/1.12716, loss_mask_bce_8: 0.86979/0.36548, loss_mask_dice_8: 3.14529/1.32856, loss_spatial_bce_8: 0.13817/0.12976, loss_spatial_dice_8: 0.43853/0.29848, loss_spatial_ce_8: 0.17718/0.21762, loss_grounding_bce_8: 0.37810/0.09406, loss_grounding_dice_8: 0.41163/0.20141, loss_grounding_ce_8: 0.27571/0.40531, loss_mask_ce_9: 4.56373/3.67472, loss_mask_bce_9: 0.77364/0.39253, loss_mask_dice_9: 4.18629/1.90142, loss_spatial_bce_9: 0.25710/0.33275, loss_spatial_dice_9: 0.95587/0.82167, loss_spatial_ce_9: 1.52915/1.49334, loss_grounding_bce_9: 0.29017/0.10566, loss_grounding_dice_9: 0.43875/0.28081, loss_grounding_ce_9: 0.28972/0.66973] items per batch[64] items per second[0.23] total items[4934400] mini batches[ 77100] memory[7345] epoch remaining[1:08:09]
INFO:trainer.default_trainer:epochs[    42] optim steps[77200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.15182/0.89579, loss_mask_bce_0: 0.61046/0.33405, loss_mask_dice_0: 0.53787/1.16130, loss_spatial_bce_0: 0.17025/0.08671, loss_spatial_dice_0: 0.19225/0.20685, loss_spatial_ce_0: 0.02906/0.05997, loss_grounding_bce_0: 0.06241/0.08617, loss_grounding_dice_0: 0.11582/0.17836, loss_grounding_ce_0: 0.11650/0.27147, loss_mask_ce_1: 1.16719/0.89645, loss_mask_bce_1: 0.62993/0.33499, loss_mask_dice_1: 0.54139/1.16799, loss_spatial_bce_1: 0.19697/0.08725, loss_spatial_dice_1: 0.18325/0.21082, loss_spatial_ce_1: 0.04436/0.06576, loss_grounding_bce_1: 0.05296/0.08634, loss_grounding_dice_1: 0.10071/0.17915, loss_grounding_ce_1: 0.12106/0.27220, loss_mask_ce_2: 1.16532/0.90347, loss_mask_bce_2: 0.64568/0.33561, loss_mask_dice_2: 0.54580/1.16843, loss_spatial_bce_2: 0.21537/0.08847, loss_spatial_dice_2: 0.19577/0.21265, loss_spatial_ce_2: 0.01107/0.06924, loss_grounding_bce_2: 0.06553/0.08653, loss_grounding_dice_2: 0.11928/0.17904, loss_grounding_ce_2: 0.10989/0.27551, loss_mask_ce_3: 1.22756/0.91455, loss_mask_bce_3: 0.62411/0.33680, loss_mask_dice_3: 0.55231/1.16634, loss_spatial_bce_3: 0.23406/0.08975, loss_spatial_dice_3: 0.22690/0.21369, loss_spatial_ce_3: 0.01604/0.07427, loss_grounding_bce_3: 0.06118/0.08676, loss_grounding_dice_3: 0.12663/0.17874, loss_grounding_ce_3: 0.13147/0.27779, loss_mask_ce_4: 1.06789/0.91572, loss_mask_bce_4: 0.81503/0.33893, loss_mask_dice_4: 0.68492/1.19009, loss_spatial_bce_4: 0.28483/0.09366, loss_spatial_dice_4: 0.21075/0.22593, loss_spatial_ce_4: 0.03565/0.09054, loss_grounding_bce_4: 0.04890/0.08730, loss_grounding_dice_4: 0.11256/0.18174, loss_grounding_ce_4: 0.12263/0.28071, loss_mask_ce_5: 1.24370/0.93263, loss_mask_bce_5: 0.66002/0.34129, loss_mask_dice_5: 0.58477/1.19840, loss_spatial_bce_5: 0.23310/0.09598, loss_spatial_dice_5: 0.23983/0.23026, loss_spatial_ce_5: 0.17620/0.10458, loss_grounding_bce_5: 0.05472/0.08774, loss_grounding_dice_5: 0.12127/0.18300, loss_grounding_ce_5: 0.12793/0.29316, loss_mask_ce_6: 1.31611/0.97261, loss_mask_bce_6: 0.74021/0.34411, loss_mask_dice_6: 0.71234/1.20116, loss_spatial_bce_6: 0.21788/0.10159, loss_spatial_dice_6: 0.20449/0.23322, loss_spatial_ce_6: 0.18385/0.12970, loss_grounding_bce_6: 0.04557/0.08847, loss_grounding_dice_6: 0.09581/0.18337, loss_grounding_ce_6: 0.17312/0.30861, loss_mask_ce_7: 1.38272/1.01815, loss_mask_bce_7: 0.79318/0.35190, loss_mask_dice_7: 0.77851/1.25565, loss_spatial_bce_7: 0.26408/0.10946, loss_spatial_dice_7: 0.23959/0.26079, loss_spatial_ce_7: 0.20540/0.16468, loss_grounding_bce_7: 0.04366/0.09036, loss_grounding_dice_7: 0.10269/0.19071, loss_grounding_ce_7: 0.22811/0.33866, loss_mask_ce_8: 1.59751/1.12712, loss_mask_bce_8: 0.88530/0.36550, loss_mask_dice_8: 0.86043/1.32840, loss_spatial_bce_8: 0.28020/0.12976, loss_spatial_dice_8: 0.25083/0.29846, loss_spatial_ce_8: 0.32053/0.21754, loss_grounding_bce_8: 0.05962/0.09406, loss_grounding_dice_8: 0.22353/0.20139, loss_grounding_ce_8: 0.34047/0.40525, loss_mask_ce_9: 3.41593/3.67467, loss_mask_bce_9: 0.76331/0.39255, loss_mask_dice_9: 1.02737/1.90135, loss_spatial_bce_9: 0.51231/0.33277, loss_spatial_dice_9: 0.77713/0.82167, loss_spatial_ce_9: 1.47266/1.49323, loss_grounding_bce_9: 0.06132/0.10566, loss_grounding_dice_9: 0.29523/0.28080, loss_grounding_ce_9: 0.62345/0.66974] items per batch[64] items per second[0.23] total items[4940800] mini batches[ 77200] memory[7345] epoch remaining[1:03:34]
INFO:trainer.default_trainer:epochs[    42] optim steps[77300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07333/0.89574, loss_mask_bce_0: 0.02343/0.33401, loss_mask_dice_0: 0.04923/1.16171, loss_spatial_bce_0: 0.02501/0.08670, loss_spatial_dice_0: 0.05065/0.20686, loss_spatial_ce_0: 0.06933/0.05996, loss_grounding_bce_0: 0.02613/0.08616, loss_grounding_dice_0: 0.04609/0.17838, loss_grounding_ce_0: 0.00075/0.27142, loss_mask_ce_1: 0.07885/0.89642, loss_mask_bce_1: 0.02451/0.33495, loss_mask_dice_1: 0.04664/1.16842, loss_spatial_bce_1: 0.02429/0.08724, loss_spatial_dice_1: 0.04494/0.21082, loss_spatial_ce_1: 0.06933/0.06576, loss_grounding_bce_1: 0.02611/0.08634, loss_grounding_dice_1: 0.04888/0.17918, loss_grounding_ce_1: 0.00086/0.27215, loss_mask_ce_2: 0.09108/0.90343, loss_mask_bce_2: 0.02505/0.33557, loss_mask_dice_2: 0.06062/1.16883, loss_spatial_bce_2: 0.02365/0.08845, loss_spatial_dice_2: 0.04588/0.21266, loss_spatial_ce_2: 0.06932/0.06924, loss_grounding_bce_2: 0.02401/0.08652, loss_grounding_dice_2: 0.05658/0.17907, loss_grounding_ce_2: 0.00082/0.27546, loss_mask_ce_3: 0.08265/0.91450, loss_mask_bce_3: 0.02739/0.33677, loss_mask_dice_3: 0.05761/1.16676, loss_spatial_bce_3: 0.02646/0.08974, loss_spatial_dice_3: 0.04526/0.21370, loss_spatial_ce_3: 0.06933/0.07426, loss_grounding_bce_3: 0.02497/0.08676, loss_grounding_dice_3: 0.04252/0.17877, loss_grounding_ce_3: 0.00133/0.27774, loss_mask_ce_4: 0.08140/0.91570, loss_mask_bce_4: 0.02447/0.33890, loss_mask_dice_4: 0.04780/1.19050, loss_spatial_bce_4: 0.02638/0.09365, loss_spatial_dice_4: 0.05064/0.22594, loss_spatial_ce_4: 0.06937/0.09054, loss_grounding_bce_4: 0.02594/0.08730, loss_grounding_dice_4: 0.05245/0.18177, loss_grounding_ce_4: 0.00198/0.28068, loss_mask_ce_5: 0.06231/0.93259, loss_mask_bce_5: 0.02421/0.34126, loss_mask_dice_5: 0.04916/1.19882, loss_spatial_bce_5: 0.02291/0.09597, loss_spatial_dice_5: 0.05237/0.23028, loss_spatial_ce_5: 0.07122/0.10455, loss_grounding_bce_5: 0.02794/0.08774, loss_grounding_dice_5: 0.05315/0.18302, loss_grounding_ce_5: 0.00208/0.29311, loss_mask_ce_6: 0.05380/0.97259, loss_mask_bce_6: 0.02596/0.34407, loss_mask_dice_6: 0.05235/1.20158, loss_spatial_bce_6: 0.02561/0.10157, loss_spatial_dice_6: 0.04980/0.23323, loss_spatial_ce_6: 0.07195/0.12968, loss_grounding_bce_6: 0.03153/0.08847, loss_grounding_dice_6: 0.07526/0.18339, loss_grounding_ce_6: 0.00382/0.30859, loss_mask_ce_7: 0.05990/1.01815, loss_mask_bce_7: 0.02670/0.35186, loss_mask_dice_7: 0.04629/1.25610, loss_spatial_bce_7: 0.02492/0.10944, loss_spatial_dice_7: 0.04453/0.26081, loss_spatial_ce_7: 0.08262/0.16464, loss_grounding_bce_7: 0.02504/0.09036, loss_grounding_dice_7: 0.05128/0.19074, loss_grounding_ce_7: 0.00380/0.33863, loss_mask_ce_8: 0.08650/1.12709, loss_mask_bce_8: 0.02526/0.36547, loss_mask_dice_8: 0.05200/1.32887, loss_spatial_bce_8: 0.03015/0.12974, loss_spatial_dice_8: 0.06065/0.29848, loss_spatial_ce_8: 0.07046/0.21749, loss_grounding_bce_8: 0.02525/0.09406, loss_grounding_dice_8: 0.05736/0.20142, loss_grounding_ce_8: 0.00506/0.40519, loss_mask_ce_9: 1.65107/3.67467, loss_mask_bce_9: 0.02426/0.39251, loss_mask_dice_9: 0.05469/1.90186, loss_spatial_bce_9: 0.36570/0.33272, loss_spatial_dice_9: 0.73569/0.82167, loss_spatial_ce_9: 0.91569/1.49327, loss_grounding_bce_9: 0.02675/0.10566, loss_grounding_dice_9: 0.05738/0.28083, loss_grounding_ce_9: 0.13546/0.66961] items per batch[64] items per second[0.23] total items[4947200] mini batches[ 77300] memory[7345] epoch remaining[0:58:47]
INFO:trainer.default_trainer:epochs[    42] optim steps[77400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01682/0.89573, loss_mask_bce_0: 0.60770/0.33402, loss_mask_dice_0: 1.83931/1.16161, loss_spatial_bce_0: 0.09601/0.08670, loss_spatial_dice_0: 0.23257/0.20684, loss_spatial_ce_0: 0.00814/0.05994, loss_grounding_bce_0: 0.06743/0.08616, loss_grounding_dice_0: 0.26080/0.17835, loss_grounding_ce_0: 0.22416/0.27136, loss_mask_ce_1: 0.96231/0.89641, loss_mask_bce_1: 0.63682/0.33495, loss_mask_dice_1: 1.81388/1.16833, loss_spatial_bce_1: 0.10084/0.08723, loss_spatial_dice_1: 0.24822/0.21080, loss_spatial_ce_1: 0.01767/0.06573, loss_grounding_bce_1: 0.07388/0.08634, loss_grounding_dice_1: 0.25613/0.17915, loss_grounding_ce_1: 0.22403/0.27209, loss_mask_ce_2: 0.93338/0.90340, loss_mask_bce_2: 0.63922/0.33557, loss_mask_dice_2: 1.86495/1.16875, loss_spatial_bce_2: 0.11155/0.08845, loss_spatial_dice_2: 0.26964/0.21264, loss_spatial_ce_2: 0.00940/0.06923, loss_grounding_bce_2: 0.07047/0.08652, loss_grounding_dice_2: 0.24778/0.17903, loss_grounding_ce_2: 0.22212/0.27540, loss_mask_ce_3: 0.93069/0.91447, loss_mask_bce_3: 0.65892/0.33678, loss_mask_dice_3: 1.81688/1.16668, loss_spatial_bce_3: 0.09561/0.08973, loss_spatial_dice_3: 0.26260/0.21368, loss_spatial_ce_3: 0.11556/0.07425, loss_grounding_bce_3: 0.07290/0.08676, loss_grounding_dice_3: 0.24042/0.17874, loss_grounding_ce_3: 0.21515/0.27770, loss_mask_ce_4: 0.98853/0.91569, loss_mask_bce_4: 0.55979/0.33890, loss_mask_dice_4: 1.81818/1.19041, loss_spatial_bce_4: 0.11603/0.09365, loss_spatial_dice_4: 0.26735/0.22593, loss_spatial_ce_4: 0.15297/0.09054, loss_grounding_bce_4: 0.07203/0.08730, loss_grounding_dice_4: 0.26285/0.18174, loss_grounding_ce_4: 0.21479/0.28062, loss_mask_ce_5: 0.85500/0.93257, loss_mask_bce_5: 0.68547/0.34126, loss_mask_dice_5: 1.81175/1.19872, loss_spatial_bce_5: 0.10971/0.09597, loss_spatial_dice_5: 0.27414/0.23026, loss_spatial_ce_5: 0.05551/0.10453, loss_grounding_bce_5: 0.07715/0.08774, loss_grounding_dice_5: 0.25438/0.18299, loss_grounding_ce_5: 0.22938/0.29306, loss_mask_ce_6: 0.99229/0.97257, loss_mask_bce_6: 0.68148/0.34408, loss_mask_dice_6: 1.78519/1.20148, loss_spatial_bce_6: 0.11228/0.10158, loss_spatial_dice_6: 0.26383/0.23322, loss_spatial_ce_6: 0.09633/0.12967, loss_grounding_bce_6: 0.08167/0.08846, loss_grounding_dice_6: 0.23697/0.18337, loss_grounding_ce_6: 0.27965/0.30853, loss_mask_ce_7: 1.09173/1.01811, loss_mask_bce_7: 0.65346/0.35186, loss_mask_dice_7: 1.92960/1.25603, loss_spatial_bce_7: 0.13244/0.10945, loss_spatial_dice_7: 0.30473/0.26079, loss_spatial_ce_7: 0.05509/0.16460, loss_grounding_bce_7: 0.08977/0.09036, loss_grounding_dice_7: 0.25225/0.19071, loss_grounding_ce_7: 0.26751/0.33858, loss_mask_ce_8: 1.28452/1.12704, loss_mask_bce_8: 0.65180/0.36547, loss_mask_dice_8: 1.96125/1.32877, loss_spatial_bce_8: 0.11281/0.12974, loss_spatial_dice_8: 0.35805/0.29846, loss_spatial_ce_8: 0.08286/0.21741, loss_grounding_bce_8: 0.09432/0.09406, loss_grounding_dice_8: 0.28657/0.20140, loss_grounding_ce_8: 0.32700/0.40510, loss_mask_ce_9: 3.13114/3.67474, loss_mask_bce_9: 0.65050/0.39254, loss_mask_dice_9: 3.03657/1.90180, loss_spatial_bce_9: 0.30634/0.33271, loss_spatial_dice_9: 0.91146/0.82166, loss_spatial_ce_9: 1.33469/1.49323, loss_grounding_bce_9: 0.08626/0.10567, loss_grounding_dice_9: 0.42713/0.28080, loss_grounding_ce_9: 0.66164/0.66957] items per batch[64] items per second[0.24] total items[4953600] mini batches[ 77400] memory[7345] epoch remaining[0:53:53]
INFO:trainer.default_trainer:epochs[    42] optim steps[77500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.51544/0.89581, loss_mask_bce_0: 0.48262/0.33401, loss_mask_dice_0: 3.87509/1.16157, loss_spatial_bce_0: 0.02991/0.08669, loss_spatial_dice_0: 0.18482/0.20682, loss_spatial_ce_0: 0.00580/0.05992, loss_grounding_bce_0: 0.06910/0.08616, loss_grounding_dice_0: 0.12290/0.17835, loss_grounding_ce_0: 0.06706/0.27129, loss_mask_ce_1: 1.41784/0.89648, loss_mask_bce_1: 0.48165/0.33494, loss_mask_dice_1: 3.93100/1.16828, loss_spatial_bce_1: 0.02976/0.08723, loss_spatial_dice_1: 0.20168/0.21078, loss_spatial_ce_1: 0.00765/0.06571, loss_grounding_bce_1: 0.06451/0.08634, loss_grounding_dice_1: 0.12180/0.17914, loss_grounding_ce_1: 0.07305/0.27205, loss_mask_ce_2: 1.44304/0.90346, loss_mask_bce_2: 0.48580/0.33556, loss_mask_dice_2: 4.07288/1.16870, loss_spatial_bce_2: 0.03166/0.08844, loss_spatial_dice_2: 0.21829/0.21262, loss_spatial_ce_2: 0.02001/0.06921, loss_grounding_bce_2: 0.06321/0.08652, loss_grounding_dice_2: 0.11693/0.17903, loss_grounding_ce_2: 0.07592/0.27535, loss_mask_ce_3: 1.55451/0.91454, loss_mask_bce_3: 0.47405/0.33677, loss_mask_dice_3: 3.58011/1.16663, loss_spatial_bce_3: 0.03153/0.08973, loss_spatial_dice_3: 0.22436/0.21366, loss_spatial_ce_3: 0.01697/0.07423, loss_grounding_bce_3: 0.05589/0.08676, loss_grounding_dice_3: 0.11316/0.17874, loss_grounding_ce_3: 0.09790/0.27766, loss_mask_ce_4: 1.30651/0.91575, loss_mask_bce_4: 0.48952/0.33890, loss_mask_dice_4: 3.76311/1.19037, loss_spatial_bce_4: 0.03515/0.09365, loss_spatial_dice_4: 0.22331/0.22591, loss_spatial_ce_4: 0.04400/0.09052, loss_grounding_bce_4: 0.06291/0.08730, loss_grounding_dice_4: 0.12271/0.18173, loss_grounding_ce_4: 0.17895/0.28056, loss_mask_ce_5: 1.37400/0.93265, loss_mask_bce_5: 0.47384/0.34125, loss_mask_dice_5: 3.88825/1.19867, loss_spatial_bce_5: 0.03381/0.09597, loss_spatial_dice_5: 0.18608/0.23024, loss_spatial_ce_5: 0.02409/0.10450, loss_grounding_bce_5: 0.06697/0.08774, loss_grounding_dice_5: 0.11612/0.18299, loss_grounding_ce_5: 0.11381/0.29302, loss_mask_ce_6: 1.42999/0.97263, loss_mask_bce_6: 0.50480/0.34407, loss_mask_dice_6: 3.99855/1.20143, loss_spatial_bce_6: 0.05197/0.10157, loss_spatial_dice_6: 0.23178/0.23320, loss_spatial_ce_6: 0.03559/0.12963, loss_grounding_bce_6: 0.05507/0.08847, loss_grounding_dice_6: 0.11846/0.18337, loss_grounding_ce_6: 0.06154/0.30847, loss_mask_ce_7: 1.50551/1.01814, loss_mask_bce_7: 0.46136/0.35186, loss_mask_dice_7: 4.04671/1.25596, loss_spatial_bce_7: 0.04505/0.10944, loss_spatial_dice_7: 0.23886/0.26076, loss_spatial_ce_7: 0.21734/0.16458, loss_grounding_bce_7: 0.05885/0.09036, loss_grounding_dice_7: 0.12412/0.19070, loss_grounding_ce_7: 0.11894/0.33852, loss_mask_ce_8: 1.74341/1.12711, loss_mask_bce_8: 0.46379/0.36548, loss_mask_dice_8: 4.46447/1.32872, loss_spatial_bce_8: 0.03724/0.12974, loss_spatial_dice_8: 0.30662/0.29844, loss_spatial_ce_8: 0.18461/0.21736, loss_grounding_bce_8: 0.06832/0.09406, loss_grounding_dice_8: 0.12239/0.20138, loss_grounding_ce_8: 0.18484/0.40506, loss_mask_ce_9: 4.75445/3.67475, loss_mask_bce_9: 0.63673/0.39253, loss_mask_dice_9: 6.47844/1.90172, loss_spatial_bce_9: 0.15731/0.33273, loss_spatial_dice_9: 0.95197/0.82167, loss_spatial_ce_9: 1.71573/1.49324, loss_grounding_bce_9: 0.11877/0.10567, loss_grounding_dice_9: 0.19329/0.28078, loss_grounding_ce_9: 1.81234/0.66956] items per batch[64] items per second[0.23] total items[4960000] mini batches[ 77500] memory[7345] epoch remaining[0:49:08]
INFO:trainer.default_trainer:epochs[    42] optim steps[77600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01106/0.89569, loss_mask_bce_0: 0.18985/0.33402, loss_mask_dice_0: 0.41836/1.16147, loss_spatial_bce_0: 0.15805/0.08670, loss_spatial_dice_0: 0.29173/0.20680, loss_spatial_ce_0: 0.04400/0.05990, loss_grounding_bce_0: 0.12123/0.08616, loss_grounding_dice_0: 0.35883/0.17835, loss_grounding_ce_0: 0.13459/0.27133, loss_mask_ce_1: 0.89014/0.89637, loss_mask_bce_1: 0.19268/0.33495, loss_mask_dice_1: 0.42209/1.16817, loss_spatial_bce_1: 0.15957/0.08723, loss_spatial_dice_1: 0.36815/0.21076, loss_spatial_ce_1: 0.04018/0.06569, loss_grounding_bce_1: 0.12089/0.08634, loss_grounding_dice_1: 0.28606/0.17913, loss_grounding_ce_1: 0.35853/0.27209, loss_mask_ce_2: 0.91785/0.90335, loss_mask_bce_2: 0.18609/0.33557, loss_mask_dice_2: 0.43279/1.16858, loss_spatial_bce_2: 0.15502/0.08845, loss_spatial_dice_2: 0.32126/0.21260, loss_spatial_ce_2: 0.06624/0.06919, loss_grounding_bce_2: 0.11684/0.08652, loss_grounding_dice_2: 0.34507/0.17902, loss_grounding_ce_2: 0.31571/0.27541, loss_mask_ce_3: 1.05984/0.91444, loss_mask_bce_3: 0.18754/0.33677, loss_mask_dice_3: 0.47218/1.16651, loss_spatial_bce_3: 0.15247/0.08974, loss_spatial_dice_3: 0.32241/0.21365, loss_spatial_ce_3: 0.09812/0.07421, loss_grounding_bce_3: 0.11421/0.08676, loss_grounding_dice_3: 0.26983/0.17873, loss_grounding_ce_3: 0.06839/0.27772, loss_mask_ce_4: 1.20778/0.91564, loss_mask_bce_4: 0.19100/0.33890, loss_mask_dice_4: 0.46212/1.19024, loss_spatial_bce_4: 0.15329/0.09365, loss_spatial_dice_4: 0.34466/0.22589, loss_spatial_ce_4: 0.20730/0.09049, loss_grounding_bce_4: 0.11723/0.08730, loss_grounding_dice_4: 0.41489/0.18172, loss_grounding_ce_4: 0.28637/0.28061, loss_mask_ce_5: 1.05519/0.93255, loss_mask_bce_5: 0.19608/0.34126, loss_mask_dice_5: 0.51162/1.19856, loss_spatial_bce_5: 0.15659/0.09598, loss_spatial_dice_5: 0.36119/0.23022, loss_spatial_ce_5: 0.04633/0.10448, loss_grounding_bce_5: 0.13145/0.08774, loss_grounding_dice_5: 0.41974/0.18298, loss_grounding_ce_5: 0.04750/0.29308, loss_mask_ce_6: 1.01034/0.97257, loss_mask_bce_6: 0.18834/0.34409, loss_mask_dice_6: 0.46515/1.20133, loss_spatial_bce_6: 0.15260/0.10158, loss_spatial_dice_6: 0.33063/0.23318, loss_spatial_ce_6: 0.16092/0.12961, loss_grounding_bce_6: 0.12608/0.08847, loss_grounding_dice_6: 0.33949/0.18337, loss_grounding_ce_6: 0.36986/0.30853, loss_mask_ce_7: 1.13946/1.01805, loss_mask_bce_7: 0.18666/0.35188, loss_mask_dice_7: 0.39658/1.25584, loss_spatial_bce_7: 0.15744/0.10945, loss_spatial_dice_7: 0.33080/0.26075, loss_spatial_ce_7: 0.21822/0.16458, loss_grounding_bce_7: 0.11499/0.09036, loss_grounding_dice_7: 0.25952/0.19070, loss_grounding_ce_7: 0.09008/0.33855, loss_mask_ce_8: 1.39548/1.12699, loss_mask_bce_8: 0.18879/0.36550, loss_mask_dice_8: 0.32319/1.32861, loss_spatial_bce_8: 0.17569/0.12975, loss_spatial_dice_8: 0.36331/0.29842, loss_spatial_ce_8: 0.25931/0.21731, loss_grounding_bce_8: 0.11333/0.09406, loss_grounding_dice_8: 0.34879/0.20138, loss_grounding_ce_8: 0.08004/0.40510, loss_mask_ce_9: 2.92224/3.67470, loss_mask_bce_9: 0.17981/0.39255, loss_mask_dice_9: 0.42928/1.90155, loss_spatial_bce_9: 0.36484/0.33273, loss_spatial_dice_9: 0.72079/0.82165, loss_spatial_ce_9: 1.92595/1.49316, loss_grounding_bce_9: 0.11540/0.10567, loss_grounding_dice_9: 0.40786/0.28078, loss_grounding_ce_9: 0.45886/0.66964] items per batch[64] items per second[0.24] total items[4966400] mini batches[ 77600] memory[7345] epoch remaining[0:44:22]
INFO:trainer.default_trainer:epochs[    42] optim steps[77700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.45014/0.89568, loss_mask_bce_0: 0.49321/0.33402, loss_mask_dice_0: 0.65933/1.16130, loss_spatial_bce_0: 0.11764/0.08669, loss_spatial_dice_0: 0.20232/0.20677, loss_spatial_ce_0: 0.00301/0.05988, loss_grounding_bce_0: 0.09931/0.08616, loss_grounding_dice_0: 0.15102/0.17832, loss_grounding_ce_0: 1.21448/0.27134, loss_mask_ce_1: 1.43083/0.89633, loss_mask_bce_1: 0.47634/0.33495, loss_mask_dice_1: 0.75903/1.16800, loss_spatial_bce_1: 0.13274/0.08723, loss_spatial_dice_1: 0.21577/0.21074, loss_spatial_ce_1: 0.00756/0.06567, loss_grounding_bce_1: 0.09867/0.08634, loss_grounding_dice_1: 0.18123/0.17911, loss_grounding_ce_1: 1.10595/0.27208, loss_mask_ce_2: 1.54971/0.90334, loss_mask_bce_2: 0.50694/0.33557, loss_mask_dice_2: 0.73891/1.16841, loss_spatial_bce_2: 0.09638/0.08844, loss_spatial_dice_2: 0.22839/0.21258, loss_spatial_ce_2: 0.04169/0.06916, loss_grounding_bce_2: 0.10037/0.08653, loss_grounding_dice_2: 0.17860/0.17899, loss_grounding_ce_2: 1.21160/0.27540, loss_mask_ce_3: 1.57790/0.91442, loss_mask_bce_3: 0.45527/0.33678, loss_mask_dice_3: 0.65007/1.16634, loss_spatial_bce_3: 0.10054/0.08973, loss_spatial_dice_3: 0.22993/0.21362, loss_spatial_ce_3: 0.08466/0.07419, loss_grounding_bce_3: 0.10929/0.08676, loss_grounding_dice_3: 0.18715/0.17870, loss_grounding_ce_3: 1.23104/0.27772, loss_mask_ce_4: 1.65339/0.91564, loss_mask_bce_4: 0.47208/0.33890, loss_mask_dice_4: 0.72337/1.19006, loss_spatial_bce_4: 0.11594/0.09365, loss_spatial_dice_4: 0.25168/0.22588, loss_spatial_ce_4: 0.10771/0.09048, loss_grounding_bce_4: 0.11698/0.08730, loss_grounding_dice_4: 0.20200/0.18170, loss_grounding_ce_4: 1.30927/0.28063, loss_mask_ce_5: 1.84673/0.93253, loss_mask_bce_5: 0.36482/0.34126, loss_mask_dice_5: 0.67074/1.19839, loss_spatial_bce_5: 0.14187/0.09597, loss_spatial_dice_5: 0.26542/0.23020, loss_spatial_ce_5: 0.11088/0.10446, loss_grounding_bce_5: 0.11647/0.08774, loss_grounding_dice_5: 0.18731/0.18296, loss_grounding_ce_5: 1.35896/0.29311, loss_mask_ce_6: 1.45932/0.97257, loss_mask_bce_6: 0.36006/0.34409, loss_mask_dice_6: 0.54282/1.20115, loss_spatial_bce_6: 0.12120/0.10158, loss_spatial_dice_6: 0.23643/0.23316, loss_spatial_ce_6: 0.05204/0.12960, loss_grounding_bce_6: 0.11472/0.08847, loss_grounding_dice_6: 0.19210/0.18335, loss_grounding_ce_6: 1.24498/0.30853, loss_mask_ce_7: 1.55296/1.01810, loss_mask_bce_7: 0.40401/0.35188, loss_mask_dice_7: 0.74590/1.25566, loss_spatial_bce_7: 0.11232/0.10945, loss_spatial_dice_7: 0.24684/0.26072, loss_spatial_ce_7: 0.14065/0.16454, loss_grounding_bce_7: 0.10018/0.09037, loss_grounding_dice_7: 0.16407/0.19068, loss_grounding_ce_7: 1.31745/0.33852, loss_mask_ce_8: 1.30210/1.12700, loss_mask_bce_8: 0.39707/0.36550, loss_mask_dice_8: 0.88635/1.32845, loss_spatial_bce_8: 0.11864/0.12973, loss_spatial_dice_8: 0.25298/0.29839, loss_spatial_ce_8: 0.09369/0.21725, loss_grounding_bce_8: 0.11564/0.09407, loss_grounding_dice_8: 0.20253/0.20136, loss_grounding_ce_8: 0.89687/0.40504, loss_mask_ce_9: 3.82417/3.67468, loss_mask_bce_9: 0.39698/0.39254, loss_mask_dice_9: 1.16380/1.90127, loss_spatial_bce_9: 0.28930/0.33274, loss_spatial_dice_9: 0.75440/0.82165, loss_spatial_ce_9: 1.51308/1.49311, loss_grounding_bce_9: 0.10558/0.10567, loss_grounding_dice_9: 0.33056/0.28075, loss_grounding_ce_9: 0.98624/0.66956] items per batch[64] items per second[0.23] total items[4972800] mini batches[ 77700] memory[7345] epoch remaining[0:39:49]
INFO:trainer.default_trainer:epochs[    42] optim steps[77800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74627/0.89563, loss_mask_bce_0: 0.08110/0.33398, loss_mask_dice_0: 0.21235/1.16119, loss_spatial_bce_0: 0.05283/0.08668, loss_spatial_dice_0: 0.14009/0.20676, loss_spatial_ce_0: 0.02807/0.05986, loss_grounding_bce_0: 0.06412/0.08615, loss_grounding_dice_0: 0.20579/0.17835, loss_grounding_ce_0: 0.07646/0.27137, loss_mask_ce_1: 0.70639/0.89628, loss_mask_bce_1: 0.07622/0.33492, loss_mask_dice_1: 0.27947/1.16790, loss_spatial_bce_1: 0.05230/0.08722, loss_spatial_dice_1: 0.15485/0.21073, loss_spatial_ce_1: 0.16317/0.06565, loss_grounding_bce_1: 0.06112/0.08633, loss_grounding_dice_1: 0.16962/0.17913, loss_grounding_ce_1: 0.07137/0.27211, loss_mask_ce_2: 0.41493/0.90328, loss_mask_bce_2: 0.08516/0.33553, loss_mask_dice_2: 0.26775/1.16832, loss_spatial_bce_2: 0.05244/0.08844, loss_spatial_dice_2: 0.14297/0.21257, loss_spatial_ce_2: 0.10357/0.06915, loss_grounding_bce_2: 0.05775/0.08652, loss_grounding_dice_2: 0.13160/0.17901, loss_grounding_ce_2: 0.05517/0.27550, loss_mask_ce_3: 0.42417/0.91435, loss_mask_bce_3: 0.08743/0.33674, loss_mask_dice_3: 0.19003/1.16623, loss_spatial_bce_3: 0.05437/0.08973, loss_spatial_dice_3: 0.16043/0.21362, loss_spatial_ce_3: 0.19731/0.07417, loss_grounding_bce_3: 0.06299/0.08676, loss_grounding_dice_3: 0.14207/0.17872, loss_grounding_ce_3: 0.11282/0.27774, loss_mask_ce_4: 0.36609/0.91560, loss_mask_bce_4: 0.08033/0.33886, loss_mask_dice_4: 0.18005/1.18995, loss_spatial_bce_4: 0.05633/0.09364, loss_spatial_dice_4: 0.20029/0.22587, loss_spatial_ce_4: 0.35930/0.09048, loss_grounding_bce_4: 0.05634/0.08730, loss_grounding_dice_4: 0.16186/0.18171, loss_grounding_ce_4: 0.04385/0.28064, loss_mask_ce_5: 0.41695/0.93249, loss_mask_bce_5: 0.08482/0.34122, loss_mask_dice_5: 0.18864/1.19830, loss_spatial_bce_5: 0.05905/0.09597, loss_spatial_dice_5: 0.17668/0.23020, loss_spatial_ce_5: 0.01652/0.10445, loss_grounding_bce_5: 0.06403/0.08773, loss_grounding_dice_5: 0.13053/0.18297, loss_grounding_ce_5: 0.04613/0.29318, loss_mask_ce_6: 0.42287/0.97256, loss_mask_bce_6: 0.08635/0.34405, loss_mask_dice_6: 0.22415/1.20105, loss_spatial_bce_6: 0.05764/0.10157, loss_spatial_dice_6: 0.18164/0.23316, loss_spatial_ce_6: 0.01098/0.12958, loss_grounding_bce_6: 0.05861/0.08848, loss_grounding_dice_6: 0.10017/0.18336, loss_grounding_ce_6: 0.17103/0.30858, loss_mask_ce_7: 0.79317/1.01808, loss_mask_bce_7: 0.07006/0.35184, loss_mask_dice_7: 0.27198/1.25558, loss_spatial_bce_7: 0.07420/0.10945, loss_spatial_dice_7: 0.25758/0.26071, loss_spatial_ce_7: 0.42115/0.16452, loss_grounding_bce_7: 0.06623/0.09036, loss_grounding_dice_7: 0.12031/0.19069, loss_grounding_ce_7: 0.25344/0.33852, loss_mask_ce_8: 0.66722/1.12696, loss_mask_bce_8: 0.07057/0.36546, loss_mask_dice_8: 0.35122/1.32832, loss_spatial_bce_8: 0.07999/0.12972, loss_spatial_dice_8: 0.33268/0.29839, loss_spatial_ce_8: 0.11830/0.21720, loss_grounding_bce_8: 0.06043/0.09406, loss_grounding_dice_8: 0.20887/0.20138, loss_grounding_ce_8: 0.20245/0.40506, loss_mask_ce_9: 2.98478/3.67450, loss_mask_bce_9: 0.08528/0.39249, loss_mask_dice_9: 0.38578/1.90109, loss_spatial_bce_9: 0.19668/0.33273, loss_spatial_dice_9: 0.72557/0.82163, loss_spatial_ce_9: 0.84446/1.49306, loss_grounding_bce_9: 0.05247/0.10565, loss_grounding_dice_9: 0.24046/0.28075, loss_grounding_ce_9: 0.59693/0.66952] items per batch[64] items per second[0.23] total items[4979200] mini batches[ 77800] memory[7345] epoch remaining[0:35:11]
INFO:trainer.default_trainer:epochs[    42] optim steps[77900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.01856/0.89567, loss_mask_bce_0: 0.04066/0.33399, loss_mask_dice_0: 0.89330/1.16128, loss_spatial_bce_0: 0.01286/0.08668, loss_spatial_dice_0: 0.25319/0.20676, loss_spatial_ce_0: 0.07548/0.05984, loss_grounding_bce_0: 0.02282/0.08615, loss_grounding_dice_0: 0.24023/0.17836, loss_grounding_ce_0: 0.00957/0.27138, loss_mask_ce_1: 1.82440/0.89634, loss_mask_bce_1: 0.04413/0.33492, loss_mask_dice_1: 1.01008/1.16798, loss_spatial_bce_1: 0.01175/0.08721, loss_spatial_dice_1: 0.28752/0.21073, loss_spatial_ce_1: 0.18756/0.06563, loss_grounding_bce_1: 0.02472/0.08633, loss_grounding_dice_1: 0.28235/0.17914, loss_grounding_ce_1: 0.00765/0.27213, loss_mask_ce_2: 1.96180/0.90334, loss_mask_bce_2: 0.04546/0.33554, loss_mask_dice_2: 0.97677/1.16841, loss_spatial_bce_2: 0.01398/0.08843, loss_spatial_dice_2: 0.26955/0.21257, loss_spatial_ce_2: 0.22764/0.06913, loss_grounding_bce_2: 0.02518/0.08651, loss_grounding_dice_2: 0.21995/0.17901, loss_grounding_ce_2: 0.00549/0.27553, loss_mask_ce_3: 1.91467/0.91441, loss_mask_bce_3: 0.04215/0.33674, loss_mask_dice_3: 0.75875/1.16632, loss_spatial_bce_3: 0.01507/0.08972, loss_spatial_dice_3: 0.25540/0.21362, loss_spatial_ce_3: 0.27737/0.07416, loss_grounding_bce_3: 0.02601/0.08676, loss_grounding_dice_3: 0.22428/0.17873, loss_grounding_ce_3: 0.00630/0.27776, loss_mask_ce_4: 1.63808/0.91566, loss_mask_bce_4: 0.04903/0.33887, loss_mask_dice_4: 0.81340/1.19003, loss_spatial_bce_4: 0.01930/0.09363, loss_spatial_dice_4: 0.33717/0.22587, loss_spatial_ce_4: 0.18902/0.09048, loss_grounding_bce_4: 0.01718/0.08729, loss_grounding_dice_4: 0.19805/0.18172, loss_grounding_ce_4: 0.01198/0.28069, loss_mask_ce_5: 2.31460/0.93256, loss_mask_bce_5: 0.04499/0.34124, loss_mask_dice_5: 0.87835/1.19839, loss_spatial_bce_5: 0.01649/0.09596, loss_spatial_dice_5: 0.30245/0.23020, loss_spatial_ce_5: 0.15974/0.10444, loss_grounding_bce_5: 0.02488/0.08773, loss_grounding_dice_5: 0.19488/0.18297, loss_grounding_ce_5: 0.00667/0.29323, loss_mask_ce_6: 1.98399/0.97263, loss_mask_bce_6: 0.04346/0.34406, loss_mask_dice_6: 0.83117/1.20114, loss_spatial_bce_6: 0.01739/0.10156, loss_spatial_dice_6: 0.31787/0.23316, loss_spatial_ce_6: 0.15888/0.12955, loss_grounding_bce_6: 0.02231/0.08847, loss_grounding_dice_6: 0.19866/0.18336, loss_grounding_ce_6: 0.06806/0.30865, loss_mask_ce_7: 1.85093/1.01815, loss_mask_bce_7: 0.05272/0.35185, loss_mask_dice_7: 1.04260/1.25565, loss_spatial_bce_7: 0.01795/0.10944, loss_spatial_dice_7: 0.36246/0.26071, loss_spatial_ce_7: 0.19089/0.16450, loss_grounding_bce_7: 0.02201/0.09035, loss_grounding_dice_7: 0.22336/0.19069, loss_grounding_ce_7: 0.17483/0.33857, loss_mask_ce_8: 1.48584/1.12707, loss_mask_bce_8: 0.05741/0.36547, loss_mask_dice_8: 1.14900/1.32839, loss_spatial_bce_8: 0.02539/0.12972, loss_spatial_dice_8: 0.40075/0.29840, loss_spatial_ce_8: 0.29937/0.21713, loss_grounding_bce_8: 0.02379/0.09405, loss_grounding_dice_8: 0.20994/0.20138, loss_grounding_ce_8: 1.85944/0.40514, loss_mask_ce_9: 3.95341/3.67476, loss_mask_bce_9: 0.03940/0.39252, loss_mask_dice_9: 1.15234/1.90121, loss_spatial_bce_9: 0.07895/0.33269, loss_spatial_dice_9: 0.83292/0.82164, loss_spatial_ce_9: 1.72574/1.49305, loss_grounding_bce_9: 0.01408/0.10565, loss_grounding_dice_9: 0.22356/0.28077, loss_grounding_ce_9: 0.71933/0.66960] items per batch[64] items per second[0.23] total items[4985600] mini batches[ 77900] memory[7345] epoch remaining[0:30:36]
INFO:trainer.default_trainer:epochs[    42] optim steps[78000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38636/0.89561, loss_mask_bce_0: 0.21289/0.33400, loss_mask_dice_0: 0.81259/1.16129, loss_spatial_bce_0: 0.07899/0.08667, loss_spatial_dice_0: 0.28887/0.20674, loss_spatial_ce_0: 0.06477/0.05984, loss_grounding_bce_0: 0.06578/0.08613, loss_grounding_dice_0: 0.33174/0.17835, loss_grounding_ce_0: 0.36966/0.27136, loss_mask_ce_1: 0.39690/0.89628, loss_mask_bce_1: 0.22149/0.33493, loss_mask_dice_1: 0.80460/1.16799, loss_spatial_bce_1: 0.07102/0.08721, loss_spatial_dice_1: 0.28514/0.21072, loss_spatial_ce_1: 0.05984/0.06561, loss_grounding_bce_1: 0.06507/0.08632, loss_grounding_dice_1: 0.34356/0.17913, loss_grounding_ce_1: 0.33765/0.27212, loss_mask_ce_2: 0.57005/0.90330, loss_mask_bce_2: 0.22641/0.33555, loss_mask_dice_2: 0.83690/1.16840, loss_spatial_bce_2: 0.07483/0.08843, loss_spatial_dice_2: 0.29032/0.21256, loss_spatial_ce_2: 0.11466/0.06911, loss_grounding_bce_2: 0.06994/0.08650, loss_grounding_dice_2: 0.34048/0.17899, loss_grounding_ce_2: 0.32316/0.27547, loss_mask_ce_3: 0.41837/0.91435, loss_mask_bce_3: 0.22933/0.33675, loss_mask_dice_3: 0.88963/1.16631, loss_spatial_bce_3: 0.08304/0.08972, loss_spatial_dice_3: 0.29551/0.21361, loss_spatial_ce_3: 0.11301/0.07415, loss_grounding_bce_3: 0.07034/0.08675, loss_grounding_dice_3: 0.34045/0.17871, loss_grounding_ce_3: 0.32750/0.27776, loss_mask_ce_4: 0.46341/0.91561, loss_mask_bce_4: 0.20939/0.33887, loss_mask_dice_4: 0.80329/1.19001, loss_spatial_bce_4: 0.07450/0.09363, loss_spatial_dice_4: 0.28605/0.22586, loss_spatial_ce_4: 0.12492/0.09046, loss_grounding_bce_4: 0.05822/0.08728, loss_grounding_dice_4: 0.32322/0.18171, loss_grounding_ce_4: 0.37115/0.28068, loss_mask_ce_5: 0.32107/0.93249, loss_mask_bce_5: 0.22613/0.34124, loss_mask_dice_5: 0.86944/1.19840, loss_spatial_bce_5: 0.07161/0.09596, loss_spatial_dice_5: 0.31468/0.23019, loss_spatial_ce_5: 0.25424/0.10442, loss_grounding_bce_5: 0.05822/0.08772, loss_grounding_dice_5: 0.30549/0.18296, loss_grounding_ce_5: 0.38909/0.29315, loss_mask_ce_6: 0.51789/0.97257, loss_mask_bce_6: 0.24054/0.34407, loss_mask_dice_6: 0.89297/1.20116, loss_spatial_bce_6: 0.07240/0.10155, loss_spatial_dice_6: 0.31498/0.23314, loss_spatial_ce_6: 0.24195/0.12953, loss_grounding_bce_6: 0.06627/0.08845, loss_grounding_dice_6: 0.35409/0.18335, loss_grounding_ce_6: 0.44228/0.30856, loss_mask_ce_7: 0.54229/1.01810, loss_mask_bce_7: 0.23525/0.35185, loss_mask_dice_7: 0.96218/1.25567, loss_spatial_bce_7: 0.06918/0.10943, loss_spatial_dice_7: 0.27021/0.26069, loss_spatial_ce_7: 0.08413/0.16447, loss_grounding_bce_7: 0.05973/0.09034, loss_grounding_dice_7: 0.35476/0.19068, loss_grounding_ce_7: 0.49039/0.33849, loss_mask_ce_8: 0.50021/1.12700, loss_mask_bce_8: 0.24917/0.36548, loss_mask_dice_8: 0.92271/1.32840, loss_spatial_bce_8: 0.08913/0.12971, loss_spatial_dice_8: 0.31795/0.29839, loss_spatial_ce_8: 0.05301/0.21706, loss_grounding_bce_8: 0.06207/0.09403, loss_grounding_dice_8: 0.37652/0.20138, loss_grounding_ce_8: 0.37431/0.40509, loss_mask_ce_9: 3.19063/3.67467, loss_mask_bce_9: 0.20597/0.39254, loss_mask_dice_9: 1.15832/1.90123, loss_spatial_bce_9: 0.30625/0.33269, loss_spatial_dice_9: 0.78933/0.82163, loss_spatial_ce_9: 1.13429/1.49303, loss_grounding_bce_9: 0.05908/0.10564, loss_grounding_dice_9: 0.48891/0.28076, loss_grounding_ce_9: 0.51170/0.66961] items per batch[64] items per second[0.23] total items[4992000] mini batches[ 78000] memory[7345] epoch remaining[0:25:56]
INFO:trainer.default_trainer:epochs[    42] optim steps[78100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31789/0.89565, loss_mask_bce_0: 0.24455/0.33396, loss_mask_dice_0: 0.38861/1.16124, loss_spatial_bce_0: 0.12956/0.08666, loss_spatial_dice_0: 0.22427/0.20673, loss_spatial_ce_0: 0.05148/0.05983, loss_grounding_bce_0: 0.16308/0.08613, loss_grounding_dice_0: 0.16096/0.17835, loss_grounding_ce_0: 0.04329/0.27132, loss_mask_ce_1: 0.26992/0.89634, loss_mask_bce_1: 0.24811/0.33488, loss_mask_dice_1: 0.52722/1.16795, loss_spatial_bce_1: 0.12835/0.08720, loss_spatial_dice_1: 0.28146/0.21070, loss_spatial_ce_1: 0.06526/0.06560, loss_grounding_bce_1: 0.15611/0.08631, loss_grounding_dice_1: 0.16662/0.17913, loss_grounding_ce_1: 0.04044/0.27208, loss_mask_ce_2: 0.25179/0.90332, loss_mask_bce_2: 0.25206/0.33551, loss_mask_dice_2: 0.48419/1.16837, loss_spatial_bce_2: 0.12870/0.08843, loss_spatial_dice_2: 0.21114/0.21255, loss_spatial_ce_2: 0.05305/0.06911, loss_grounding_bce_2: 0.15740/0.08649, loss_grounding_dice_2: 0.12563/0.17900, loss_grounding_ce_2: 0.03614/0.27543, loss_mask_ce_3: 0.24978/0.91438, loss_mask_bce_3: 0.23566/0.33671, loss_mask_dice_3: 0.30330/1.16629, loss_spatial_bce_3: 0.14163/0.08972, loss_spatial_dice_3: 0.21741/0.21360, loss_spatial_ce_3: 0.08376/0.07416, loss_grounding_bce_3: 0.15143/0.08674, loss_grounding_dice_3: 0.12182/0.17871, loss_grounding_ce_3: 0.04063/0.27769, loss_mask_ce_4: 0.22919/0.91566, loss_mask_bce_4: 0.25188/0.33884, loss_mask_dice_4: 0.46534/1.18996, loss_spatial_bce_4: 0.14682/0.09363, loss_spatial_dice_4: 0.22649/0.22585, loss_spatial_ce_4: 0.14002/0.09046, loss_grounding_bce_4: 0.15153/0.08728, loss_grounding_dice_4: 0.11586/0.18170, loss_grounding_ce_4: 0.03609/0.28065, loss_mask_ce_5: 0.29728/0.93255, loss_mask_bce_5: 0.24178/0.34120, loss_mask_dice_5: 0.41937/1.19834, loss_spatial_bce_5: 0.13960/0.09596, loss_spatial_dice_5: 0.18064/0.23018, loss_spatial_ce_5: 0.15867/0.10441, loss_grounding_bce_5: 0.14948/0.08771, loss_grounding_dice_5: 0.20707/0.18296, loss_grounding_ce_5: 0.02755/0.29311, loss_mask_ce_6: 0.57210/0.97262, loss_mask_bce_6: 0.25152/0.34403, loss_mask_dice_6: 0.41470/1.20110, loss_spatial_bce_6: 0.14058/0.10155, loss_spatial_dice_6: 0.19780/0.23313, loss_spatial_ce_6: 0.12628/0.12951, loss_grounding_bce_6: 0.15145/0.08845, loss_grounding_dice_6: 0.14233/0.18335, loss_grounding_ce_6: 0.05104/0.30849, loss_mask_ce_7: 0.34216/1.01818, loss_mask_bce_7: 0.27214/0.35181, loss_mask_dice_7: 0.37272/1.25561, loss_spatial_bce_7: 0.14381/0.10943, loss_spatial_dice_7: 0.27412/0.26068, loss_spatial_ce_7: 0.09605/0.16444, loss_grounding_bce_7: 0.16537/0.09034, loss_grounding_dice_7: 0.13307/0.19069, loss_grounding_ce_7: 0.02969/0.33845, loss_mask_ce_8: 0.35803/1.12702, loss_mask_bce_8: 0.29307/0.36545, loss_mask_dice_8: 0.57250/1.32834, loss_spatial_bce_8: 0.15601/0.12970, loss_spatial_dice_8: 0.24607/0.29837, loss_spatial_ce_8: 0.14265/0.21699, loss_grounding_bce_8: 0.16207/0.09402, loss_grounding_dice_8: 0.21787/0.20138, loss_grounding_ce_8: 0.04802/0.40497, loss_mask_ce_9: 2.19958/3.67462, loss_mask_bce_9: 0.27360/0.39251, loss_mask_dice_9: 0.56658/1.90107, loss_spatial_bce_9: 0.29399/0.33269, loss_spatial_dice_9: 0.79408/0.82163, loss_spatial_ce_9: 0.79603/1.49299, loss_grounding_bce_9: 0.16224/0.10564, loss_grounding_dice_9: 0.23120/0.28075, loss_grounding_ce_9: 0.12614/0.66950] items per batch[64] items per second[0.23] total items[4998400] mini batches[ 78100] memory[7345] epoch remaining[0:21:21]
INFO:trainer.default_trainer:epochs[    42] optim steps[78200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27616/0.89564, loss_mask_bce_0: 0.20142/0.33397, loss_mask_dice_0: 0.37195/1.16130, loss_spatial_bce_0: 0.06783/0.08666, loss_spatial_dice_0: 0.12469/0.20673, loss_spatial_ce_0: 0.02195/0.05982, loss_grounding_bce_0: 0.09686/0.08614, loss_grounding_dice_0: 0.16622/0.17836, loss_grounding_ce_0: 0.11762/0.27135, loss_mask_ce_1: 0.43158/0.89632, loss_mask_bce_1: 0.17540/0.33490, loss_mask_dice_1: 0.31782/1.16800, loss_spatial_bce_1: 0.07299/0.08720, loss_spatial_dice_1: 0.12845/0.21070, loss_spatial_ce_1: 0.02270/0.06558, loss_grounding_bce_1: 0.10098/0.08633, loss_grounding_dice_1: 0.17628/0.17914, loss_grounding_ce_1: 0.13020/0.27212, loss_mask_ce_2: 0.43976/0.90327, loss_mask_bce_2: 0.17291/0.33553, loss_mask_dice_2: 0.30910/1.16844, loss_spatial_bce_2: 0.07311/0.08842, loss_spatial_dice_2: 0.13932/0.21254, loss_spatial_ce_2: 0.02430/0.06909, loss_grounding_bce_2: 0.09816/0.08651, loss_grounding_dice_2: 0.17351/0.17901, loss_grounding_ce_2: 0.11661/0.27546, loss_mask_ce_3: 0.48043/0.91436, loss_mask_bce_3: 0.17014/0.33672, loss_mask_dice_3: 0.30882/1.16634, loss_spatial_bce_3: 0.07359/0.08972, loss_spatial_dice_3: 0.13158/0.21359, loss_spatial_ce_3: 0.02853/0.07414, loss_grounding_bce_3: 0.10291/0.08675, loss_grounding_dice_3: 0.17168/0.17872, loss_grounding_ce_3: 0.10346/0.27773, loss_mask_ce_4: 0.33119/0.91564, loss_mask_bce_4: 0.18353/0.33885, loss_mask_dice_4: 0.37452/1.19001, loss_spatial_bce_4: 0.08142/0.09363, loss_spatial_dice_4: 0.14088/0.22585, loss_spatial_ce_4: 0.03750/0.09046, loss_grounding_bce_4: 0.09870/0.08729, loss_grounding_dice_4: 0.16396/0.18171, loss_grounding_ce_4: 0.22422/0.28068, loss_mask_ce_5: 0.38281/0.93252, loss_mask_bce_5: 0.20916/0.34122, loss_mask_dice_5: 0.40359/1.19838, loss_spatial_bce_5: 0.07621/0.09596, loss_spatial_dice_5: 0.14068/0.23018, loss_spatial_ce_5: 0.01267/0.10438, loss_grounding_bce_5: 0.08869/0.08773, loss_grounding_dice_5: 0.18858/0.18297, loss_grounding_ce_5: 0.11253/0.29313, loss_mask_ce_6: 0.49565/0.97261, loss_mask_bce_6: 0.17904/0.34405, loss_mask_dice_6: 0.36943/1.20118, loss_spatial_bce_6: 0.08285/0.10155, loss_spatial_dice_6: 0.15087/0.23313, loss_spatial_ce_6: 0.04806/0.12949, loss_grounding_bce_6: 0.08826/0.08847, loss_grounding_dice_6: 0.17448/0.18337, loss_grounding_ce_6: 0.05944/0.30849, loss_mask_ce_7: 0.46667/1.01816, loss_mask_bce_7: 0.23169/0.35183, loss_mask_dice_7: 0.46030/1.25567, loss_spatial_bce_7: 0.08671/0.10943, loss_spatial_dice_7: 0.15427/0.26068, loss_spatial_ce_7: 0.03062/0.16439, loss_grounding_bce_7: 0.11339/0.09036, loss_grounding_dice_7: 0.23167/0.19070, loss_grounding_ce_7: 0.17293/0.33845, loss_mask_ce_8: 0.58490/1.12700, loss_mask_bce_8: 0.18978/0.36547, loss_mask_dice_8: 0.39563/1.32840, loss_spatial_bce_8: 0.12299/0.12970, loss_spatial_dice_8: 0.18826/0.29837, loss_spatial_ce_8: 0.05197/0.21693, loss_grounding_bce_8: 0.08555/0.09404, loss_grounding_dice_8: 0.17617/0.20139, loss_grounding_ce_8: 0.38095/0.40495, loss_mask_ce_9: 2.84003/3.67461, loss_mask_bce_9: 0.32259/0.39253, loss_mask_dice_9: 0.68574/1.90118, loss_spatial_bce_9: 0.47068/0.33269, loss_spatial_dice_9: 0.81047/0.82164, loss_spatial_ce_9: 1.23533/1.49298, loss_grounding_bce_9: 0.16050/0.10565, loss_grounding_dice_9: 0.34000/0.28077, loss_grounding_ce_9: 0.34109/0.66946] items per batch[64] items per second[0.23] total items[5004800] mini batches[ 78200] memory[7345] epoch remaining[0:16:44]
INFO:trainer.default_trainer:epochs[    42] optim steps[78300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.13820/0.89560, loss_mask_bce_0: 0.20762/0.33399, loss_mask_dice_0: 1.59074/1.16152, loss_spatial_bce_0: 0.01200/0.08665, loss_spatial_dice_0: 0.17926/0.20673, loss_spatial_ce_0: 0.00207/0.05981, loss_grounding_bce_0: 0.07152/0.08615, loss_grounding_dice_0: 0.18545/0.17837, loss_grounding_ce_0: 0.31106/0.27138, loss_mask_ce_1: 1.11492/0.89626, loss_mask_bce_1: 0.19437/0.33492, loss_mask_dice_1: 1.63426/1.16826, loss_spatial_bce_1: 0.01214/0.08719, loss_spatial_dice_1: 0.17711/0.21071, loss_spatial_ce_1: 0.00566/0.06558, loss_grounding_bce_1: 0.06684/0.08634, loss_grounding_dice_1: 0.19970/0.17915, loss_grounding_ce_1: 0.32090/0.27217, loss_mask_ce_2: 1.24333/0.90323, loss_mask_bce_2: 0.22494/0.33555, loss_mask_dice_2: 1.40112/1.16870, loss_spatial_bce_2: 0.01264/0.08841, loss_spatial_dice_2: 0.18415/0.21255, loss_spatial_ce_2: 0.01498/0.06908, loss_grounding_bce_2: 0.08536/0.08652, loss_grounding_dice_2: 0.23974/0.17902, loss_grounding_ce_2: 0.36111/0.27549, loss_mask_ce_3: 1.04523/0.91433, loss_mask_bce_3: 0.20416/0.33674, loss_mask_dice_3: 1.56003/1.16658, loss_spatial_bce_3: 0.01321/0.08971, loss_spatial_dice_3: 0.20220/0.21360, loss_spatial_ce_3: 0.01885/0.07413, loss_grounding_bce_3: 0.06747/0.08677, loss_grounding_dice_3: 0.21221/0.17872, loss_grounding_ce_3: 0.36506/0.27779, loss_mask_ce_4: 1.00288/0.91560, loss_mask_bce_4: 0.20265/0.33887, loss_mask_dice_4: 1.53189/1.19028, loss_spatial_bce_4: 0.02191/0.09362, loss_spatial_dice_4: 0.24683/0.22586, loss_spatial_ce_4: 0.02277/0.09046, loss_grounding_bce_4: 0.07450/0.08730, loss_grounding_dice_4: 0.23909/0.18172, loss_grounding_ce_4: 0.33455/0.28072, loss_mask_ce_5: 1.38261/0.93250, loss_mask_bce_5: 0.21401/0.34124, loss_mask_dice_5: 1.45803/1.19864, loss_spatial_bce_5: 0.01417/0.09595, loss_spatial_dice_5: 0.23173/0.23019, loss_spatial_ce_5: 0.03357/0.10437, loss_grounding_bce_5: 0.08073/0.08774, loss_grounding_dice_5: 0.33340/0.18299, loss_grounding_ce_5: 0.40188/0.29317, loss_mask_ce_6: 1.13918/0.97260, loss_mask_bce_6: 0.20990/0.34407, loss_mask_dice_6: 1.74170/1.20145, loss_spatial_bce_6: 0.03317/0.10154, loss_spatial_dice_6: 0.21498/0.23315, loss_spatial_ce_6: 0.02947/0.12945, loss_grounding_bce_6: 0.05838/0.08848, loss_grounding_dice_6: 0.23981/0.18337, loss_grounding_ce_6: 0.43123/0.30852, loss_mask_ce_7: 1.41572/1.01814, loss_mask_bce_7: 0.11273/0.35186, loss_mask_dice_7: 1.50851/1.25595, loss_spatial_bce_7: 0.01708/0.10941, loss_spatial_dice_7: 0.26895/0.26071, loss_spatial_ce_7: 0.05036/0.16437, loss_grounding_bce_7: 0.01553/0.09037, loss_grounding_dice_7: 0.19003/0.19071, loss_grounding_ce_7: 0.61662/0.33846, loss_mask_ce_8: 1.70598/1.12695, loss_mask_bce_8: 0.11789/0.36549, loss_mask_dice_8: 1.82400/1.32868, loss_spatial_bce_8: 0.02529/0.12968, loss_spatial_dice_8: 0.34383/0.29839, loss_spatial_ce_8: 0.13657/0.21687, loss_grounding_bce_8: 0.01365/0.09406, loss_grounding_dice_8: 0.19024/0.20139, loss_grounding_ce_8: 0.57323/0.40496, loss_mask_ce_9: 3.41729/3.67456, loss_mask_bce_9: 0.14361/0.39256, loss_mask_dice_9: 2.68572/1.90148, loss_spatial_bce_9: 0.16367/0.33267, loss_spatial_dice_9: 0.89744/0.82165, loss_spatial_ce_9: 1.55135/1.49300, loss_grounding_bce_9: 0.03371/0.10567, loss_grounding_dice_9: 0.38922/0.28077, loss_grounding_ce_9: 0.49939/0.66942] items per batch[64] items per second[0.23] total items[5011200] mini batches[ 78300] memory[7345] epoch remaining[0:12:06]
INFO:trainer.default_trainer:epochs[    42] optim steps[78400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96729/0.89556, loss_mask_bce_0: 0.59086/0.33396, loss_mask_dice_0: 4.14561/1.16145, loss_spatial_bce_0: 0.07952/0.08664, loss_spatial_dice_0: 0.25136/0.20672, loss_spatial_ce_0: 0.06484/0.05978, loss_grounding_bce_0: 0.05500/0.08614, loss_grounding_dice_0: 0.30715/0.17837, loss_grounding_ce_0: 0.41492/0.27130, loss_mask_ce_1: 0.99291/0.89621, loss_mask_bce_1: 0.53168/0.33488, loss_mask_dice_1: 3.84787/1.16818, loss_spatial_bce_1: 0.07725/0.08718, loss_spatial_dice_1: 0.23813/0.21070, loss_spatial_ce_1: 0.11057/0.06556, loss_grounding_bce_1: 0.05723/0.08632, loss_grounding_dice_1: 0.29446/0.17914, loss_grounding_ce_1: 0.41387/0.27210, loss_mask_ce_2: 0.95404/0.90320, loss_mask_bce_2: 0.55569/0.33551, loss_mask_dice_2: 3.97513/1.16861, loss_spatial_bce_2: 0.07941/0.08840, loss_spatial_dice_2: 0.23306/0.21254, loss_spatial_ce_2: 0.06577/0.06907, loss_grounding_bce_2: 0.05130/0.08651, loss_grounding_dice_2: 0.30848/0.17902, loss_grounding_ce_2: 0.42287/0.27542, loss_mask_ce_3: 0.97438/0.91431, loss_mask_bce_3: 0.53874/0.33669, loss_mask_dice_3: 3.97492/1.16650, loss_spatial_bce_3: 0.08213/0.08970, loss_spatial_dice_3: 0.26950/0.21359, loss_spatial_ce_3: 0.06307/0.07411, loss_grounding_bce_3: 0.04638/0.08675, loss_grounding_dice_3: 0.33074/0.17872, loss_grounding_ce_3: 0.40811/0.27773, loss_mask_ce_4: 0.94551/0.91558, loss_mask_bce_4: 0.58387/0.33883, loss_mask_dice_4: 3.64691/1.19021, loss_spatial_bce_4: 0.09766/0.09361, loss_spatial_dice_4: 0.30711/0.22585, loss_spatial_ce_4: 0.05225/0.09045, loss_grounding_bce_4: 0.05109/0.08729, loss_grounding_dice_4: 0.30702/0.18172, loss_grounding_ce_4: 0.40511/0.28066, loss_mask_ce_5: 1.03582/0.93249, loss_mask_bce_5: 0.59270/0.34119, loss_mask_dice_5: 4.05517/1.19857, loss_spatial_bce_5: 0.11778/0.09594, loss_spatial_dice_5: 0.28434/0.23019, loss_spatial_ce_5: 0.04330/0.10436, loss_grounding_bce_5: 0.05577/0.08772, loss_grounding_dice_5: 0.28828/0.18298, loss_grounding_ce_5: 0.40081/0.29311, loss_mask_ce_6: 0.93446/0.97257, loss_mask_bce_6: 0.59354/0.34402, loss_mask_dice_6: 3.99428/1.20137, loss_spatial_bce_6: 0.12185/0.10153, loss_spatial_dice_6: 0.30915/0.23315, loss_spatial_ce_6: 0.19999/0.12943, loss_grounding_bce_6: 0.04980/0.08846, loss_grounding_dice_6: 0.33053/0.18337, loss_grounding_ce_6: 0.49711/0.30847, loss_mask_ce_7: 1.03985/1.01814, loss_mask_bce_7: 0.57536/0.35181, loss_mask_dice_7: 4.00125/1.25586, loss_spatial_bce_7: 0.11831/0.10940, loss_spatial_dice_7: 0.33831/0.26070, loss_spatial_ce_7: 0.13964/0.16433, loss_grounding_bce_7: 0.03616/0.09035, loss_grounding_dice_7: 0.29208/0.19071, loss_grounding_ce_7: 0.58256/0.33840, loss_mask_ce_8: 0.92426/1.12690, loss_mask_bce_8: 0.63515/0.36544, loss_mask_dice_8: 4.18924/1.32856, loss_spatial_bce_8: 0.13202/0.12966, loss_spatial_dice_8: 0.44592/0.29839, loss_spatial_ce_8: 0.17247/0.21683, loss_grounding_bce_8: 0.03389/0.09404, loss_grounding_dice_8: 0.30852/0.20139, loss_grounding_ce_8: 0.47757/0.40487, loss_mask_ce_9: 5.54893/3.67428, loss_mask_bce_9: 0.59667/0.39249, loss_mask_dice_9: 5.13261/1.90120, loss_spatial_bce_9: 0.19682/0.33266, loss_spatial_dice_9: 0.93858/0.82165, loss_spatial_ce_9: 1.36627/1.49301, loss_grounding_bce_9: 0.04287/0.10565, loss_grounding_dice_9: 0.49133/0.28076, loss_grounding_ce_9: 0.57482/0.66941] items per batch[64] items per second[0.24] total items[5017600] mini batches[ 78400] memory[7345] epoch remaining[0:07:27]
INFO:trainer.default_trainer:epochs[    42] optim steps[78500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58194/0.89558, loss_mask_bce_0: 0.08910/0.33394, loss_mask_dice_0: 0.32372/1.16134, loss_spatial_bce_0: 0.03385/0.08663, loss_spatial_dice_0: 0.15926/0.20672, loss_spatial_ce_0: 0.00130/0.05977, loss_grounding_bce_0: 0.04695/0.08614, loss_grounding_dice_0: 0.12496/0.17838, loss_grounding_ce_0: 0.12482/0.27129, loss_mask_ce_1: 0.57995/0.89623, loss_mask_bce_1: 0.08612/0.33487, loss_mask_dice_1: 0.34639/1.16807, loss_spatial_bce_1: 0.03415/0.08717, loss_spatial_dice_1: 0.14977/0.21069, loss_spatial_ce_1: 0.00060/0.06555, loss_grounding_bce_1: 0.05084/0.08632, loss_grounding_dice_1: 0.15329/0.17915, loss_grounding_ce_1: 0.07747/0.27206, loss_mask_ce_2: 0.61576/0.90322, loss_mask_bce_2: 0.09297/0.33550, loss_mask_dice_2: 0.36423/1.16851, loss_spatial_bce_2: 0.03496/0.08839, loss_spatial_dice_2: 0.15785/0.21254, loss_spatial_ce_2: 0.00032/0.06905, loss_grounding_bce_2: 0.05742/0.08650, loss_grounding_dice_2: 0.16874/0.17903, loss_grounding_ce_2: 0.08671/0.27538, loss_mask_ce_3: 0.66740/0.91431, loss_mask_bce_3: 0.09301/0.33668, loss_mask_dice_3: 0.35418/1.16643, loss_spatial_bce_3: 0.03931/0.08970, loss_spatial_dice_3: 0.16220/0.21359, loss_spatial_ce_3: 0.00042/0.07410, loss_grounding_bce_3: 0.05478/0.08675, loss_grounding_dice_3: 0.14984/0.17872, loss_grounding_ce_3: 0.10815/0.27770, loss_mask_ce_4: 0.64515/0.91560, loss_mask_bce_4: 0.08744/0.33882, loss_mask_dice_4: 0.36065/1.19012, loss_spatial_bce_4: 0.04863/0.09361, loss_spatial_dice_4: 0.18447/0.22585, loss_spatial_ce_4: 0.01327/0.09044, loss_grounding_bce_4: 0.05295/0.08729, loss_grounding_dice_4: 0.15950/0.18172, loss_grounding_ce_4: 0.07017/0.28063, loss_mask_ce_5: 0.70025/0.93252, loss_mask_bce_5: 0.09260/0.34118, loss_mask_dice_5: 0.34343/1.19846, loss_spatial_bce_5: 0.05849/0.09594, loss_spatial_dice_5: 0.21064/0.23019, loss_spatial_ce_5: 0.00797/0.10435, loss_grounding_bce_5: 0.05679/0.08772, loss_grounding_dice_5: 0.16710/0.18299, loss_grounding_ce_5: 0.06986/0.29313, loss_mask_ce_6: 0.69168/0.97257, loss_mask_bce_6: 0.09366/0.34401, loss_mask_dice_6: 0.37178/1.20128, loss_spatial_bce_6: 0.05521/0.10154, loss_spatial_dice_6: 0.17543/0.23315, loss_spatial_ce_6: 0.03904/0.12940, loss_grounding_bce_6: 0.05667/0.08846, loss_grounding_dice_6: 0.16891/0.18337, loss_grounding_ce_6: 0.07719/0.30850, loss_mask_ce_7: 0.71559/1.01818, loss_mask_bce_7: 0.10458/0.35181, loss_mask_dice_7: 0.40081/1.25575, loss_spatial_bce_7: 0.04995/0.10939, loss_spatial_dice_7: 0.22605/0.26070, loss_spatial_ce_7: 0.06324/0.16433, loss_grounding_bce_7: 0.05490/0.09035, loss_grounding_dice_7: 0.16461/0.19071, loss_grounding_ce_7: 0.10088/0.33842, loss_mask_ce_8: 0.91983/1.12695, loss_mask_bce_8: 0.11019/0.36543, loss_mask_dice_8: 0.38551/1.32845, loss_spatial_bce_8: 0.06447/0.12965, loss_spatial_dice_8: 0.24742/0.29839, loss_spatial_ce_8: 0.09067/0.21677, loss_grounding_bce_8: 0.05890/0.09404, loss_grounding_dice_8: 0.16836/0.20139, loss_grounding_ce_8: 0.16937/0.40486, loss_mask_ce_9: 3.32126/3.67417, loss_mask_bce_9: 0.08712/0.39246, loss_mask_dice_9: 0.62937/1.90106, loss_spatial_bce_9: 0.99069/0.33266, loss_spatial_dice_9: 0.93946/0.82165, loss_spatial_ce_9: 1.98040/1.49302, loss_grounding_bce_9: 0.04930/0.10564, loss_grounding_dice_9: 0.30132/0.28078, loss_grounding_ce_9: 0.47791/0.66936] items per batch[64] items per second[0.23] total items[5024000] mini batches[ 78500] memory[7345] epoch remaining[0:02:49]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00078561.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0028 s/iter. Inference: 0.2212 s/iter. Eval: 0.0972 s/iter. Total: 0.3212 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2237 s/iter. Eval: 0.0818 s/iter. Total: 0.3085 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2268 s/iter. Eval: 0.0791 s/iter. Total: 0.3090 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0030 s/iter. Inference: 0.2266 s/iter. Eval: 0.0771 s/iter. Total: 0.3068 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0031 s/iter. Inference: 0.2257 s/iter. Eval: 0.0753 s/iter. Total: 0.3042 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0031 s/iter. Inference: 0.2275 s/iter. Eval: 0.0752 s/iter. Total: 0.3060 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0031 s/iter. Inference: 0.2289 s/iter. Eval: 0.0756 s/iter. Total: 0.3078 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0031 s/iter. Inference: 0.2284 s/iter. Eval: 0.0749 s/iter. Total: 0.3066 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2290 s/iter. Eval: 0.0753 s/iter. Total: 0.3076 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalbp4hp1da ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.044 | 81.884 | 60.193 |      133      |
| Things | 55.149 | 82.634 | 66.016 |      80       |
| Stuff  | 42.339 | 80.753 | 51.403 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
DONE (t=0.43s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.46 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.21s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.14 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.613
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.493
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.509
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.993 | 61.264 | 41.017 | 18.855 | 41.923 | 61.110 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.263 | bicycle      | 19.238 | car            | 36.757 |
| motorcycle    | 35.210 | airplane     | 57.241 | bus            | 66.062 |
| train         | 68.689 | truck        | 36.333 | boat           | 23.507 |
| traffic light | 24.513 | fire hydrant | 64.194 | stop sign      | 64.579 |
| parking meter | 42.387 | bench        | 20.277 | bird           | 30.287 |
| cat           | 73.087 | dog          | 64.991 | horse          | 46.119 |
| sheep         | 46.310 | cow          | 50.505 | elephant       | 61.057 |
| bear          | 76.263 | zebra        | 60.674 | giraffe        | 56.751 |
| backpack      | 17.341 | umbrella     | 48.203 | handbag        | 15.271 |
| tie           | 34.305 | suitcase     | 40.845 | frisbee        | 66.722 |
| skis          | 4.960  | snowboard    | 22.655 | sports ball    | 47.605 |
| kite          | 33.900 | baseball bat | 29.150 | baseball glove | 43.310 |
| skateboard    | 37.023 | surfboard    | 36.077 | tennis racket  | 55.823 |
| bottle        | 33.708 | wine glass   | 27.771 | cup            | 40.430 |
| fork          | 15.056 | knife        | 13.636 | spoon          | 12.941 |
| bowl          | 32.541 | banana       | 20.255 | apple          | 20.843 |
| sandwich      | 43.249 | orange       | 29.098 | broccoli       | 21.464 |
| carrot        | 19.983 | hot dog      | 25.875 | pizza          | 51.290 |
| donut         | 45.889 | cake         | 44.034 | chair          | 21.124 |
| couch         | 42.200 | potted plant | 17.548 | bed            | 39.310 |
| dining table  | 12.664 | toilet       | 66.452 | tv             | 62.563 |
| laptop        | 63.594 | mouse        | 59.525 | remote         | 32.320 |
| keyboard      | 48.166 | cell phone   | 37.508 | microwave      | 54.838 |
| oven          | 33.632 | toaster      | 29.525 | sink           | 37.376 |
| refrigerator  | 59.368 | book         | 9.194  | clock          | 52.550 |
| vase          | 33.387 | scissors     | 24.342 | teddy bear     | 50.155 |
| hair drier    | 12.242 | toothbrush   | 19.329 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.82120126159259, 'fwIoU': 68.7834771834944, 'IoU-person': 87.69051948136382, 'IoU-bicycle': 75.2291963165766, 'IoU-car': 69.33953229454582, 'IoU-motorcycle': 83.78343776560519, 'IoU-airplane': 79.26124818792125, 'IoU-bus': 86.58377126730267, 'IoU-train': 87.44945783674616, 'IoU-truck': 66.63769626392899, 'IoU-boat': 66.54342039200083, 'IoU-traffic light': 76.43810522137808, 'IoU-fire hydrant': 90.0483191013487, 'IoU-stop sign': 91.82009316162566, 'IoU-parking meter': 85.69224199358942, 'IoU-bench': 51.9366364759679, 'IoU-bird': 75.62660300793527, 'IoU-cat': 84.89537596326848, 'IoU-dog': 82.61304658207334, 'IoU-horse': 85.83877586351218, 'IoU-sheep': 90.21654923272685, 'IoU-cow': 80.99219161912443, 'IoU-elephant': 87.00525548390581, 'IoU-bear': 90.29550556366772, 'IoU-zebra': 90.9604214507632, 'IoU-giraffe': 86.90624629207598, 'IoU-backpack': 43.966508711088366, 'IoU-umbrella': 70.10138650545356, 'IoU-handbag': 38.18435402698381, 'IoU-tie': 68.90104136366281, 'IoU-suitcase': 81.87091824229648, 'IoU-frisbee': 83.70803250558487, 'IoU-skis': 49.601943184407546, 'IoU-snowboard': 69.66795698924732, 'IoU-sports ball': 60.71566602772561, 'IoU-kite': 66.47025667141098, 'IoU-baseball bat': 61.66797984730113, 'IoU-baseball glove': 76.16065194195153, 'IoU-skateboard': 77.76510045589767, 'IoU-surfboard': 82.17244629198495, 'IoU-tennis racket': 82.48302796466007, 'IoU-bottle': 67.86698186024671, 'IoU-wine glass': 71.74169853300074, 'IoU-cup': 60.570385663569525, 'IoU-fork': 53.64552223947756, 'IoU-knife': 51.55794498379566, 'IoU-spoon': 52.13237686503571, 'IoU-bowl': 52.747238214439285, 'IoU-banana': 82.62293587867595, 'IoU-apple': 56.75052449518551, 'IoU-sandwich': 64.30750086136678, 'IoU-orange': 78.58021723317428, 'IoU-broccoli': 66.8841104139499, 'IoU-carrot': 63.77239648247307, 'IoU-hot dog': 64.57697536628392, 'IoU-pizza': 82.19607098543989, 'IoU-donut': 66.28879617886429, 'IoU-cake': 70.29228723217442, 'IoU-chair': 54.282690687187326, 'IoU-couch': 67.41367157161986, 'IoU-potted plant': 34.32193114170299, 'IoU-bed': 69.49918253931116, 'IoU-dining table': 50.35674269213087, 'IoU-toilet': 88.50097445369965, 'IoU-tv': 75.69175431041596, 'IoU-laptop': 74.83143288141574, 'IoU-mouse': 70.84486320200925, 'IoU-remote': 49.206481096801, 'IoU-keyboard': 63.45394441265352, 'IoU-cell phone': 70.16855546733132, 'IoU-microwave': 63.50891197516657, 'IoU-oven': 67.42955487570832, 'IoU-toaster': 40.48488592522606, 'IoU-sink': 65.61411707755433, 'IoU-refrigerator': 81.79783668083103, 'IoU-book': 53.50524146432654, 'IoU-clock': 72.0315228011725, 'IoU-vase': 60.038815226604214, 'IoU-scissors': 62.33977688189335, 'IoU-teddy bear': 80.26066569080868, 'IoU-hair drier': 39.99463015169821, 'IoU-toothbrush': 58.02701657665299, 'IoU-banner': 39.82995868307576, 'IoU-blanket': 11.328266640850893, 'IoU-bridge': 39.04645281958592, 'IoU-cardboard': 40.23147443410542, 'IoU-counter': 31.783868108740542, 'IoU-curtain': 65.25033236675347, 'IoU-door-stuff': 40.6057941609296, 'IoU-floor-wood': 62.942497303288356, 'IoU-flower': 40.79939835957926, 'IoU-fruit': 41.99257903092043, 'IoU-gravel': 30.299238960939345, 'IoU-house': 25.21637180929251, 'IoU-light': 38.67901745324901, 'IoU-mirror-stuff': 54.07885337383863, 'IoU-net': 32.7941187447213, 'IoU-pillow': 12.16425384685423, 'IoU-platform': 28.469746041432536, 'IoU-playingfield': 66.68460096016894, 'IoU-railroad': 60.37579852860796, 'IoU-river': 48.90994911046706, 'IoU-road': 66.6158405129629, 'IoU-roof': 14.137686976057335, 'IoU-sand': 63.05881759580344, 'IoU-sea': 84.64379845855412, 'IoU-shelf': 35.740521490619706, 'IoU-snow': 88.24634221662124, 'IoU-stairs': 28.1766288915277, 'IoU-tent': 8.928468366557778, 'IoU-towel': 35.00306827786633, 'IoU-wall-brick': 43.28850160914058, 'IoU-wall-stone': 27.670394079943666, 'IoU-wall-tile': 67.78841314581862, 'IoU-wall-wood': 38.97392561252952, 'IoU-water-other': 23.545915467761382, 'IoU-window-blind': 47.51153697757011, 'IoU-window-other': 47.537715867762195, 'IoU-tree-merged': 80.56578315324087, 'IoU-fence-merged': 47.66558813776216, 'IoU-ceiling-merged': 66.81982425256363, 'IoU-sky-other-merged': 91.90826866361405, 'IoU-cabinet-merged': 57.18517022060061, 'IoU-table-merged': 37.29392912520009, 'IoU-floor-other-merged': 48.53026004239805, 'IoU-pavement-merged': 54.5926789775787, 'IoU-mountain-merged': 54.47200070318418, 'IoU-grass-merged': 71.83617212889848, 'IoU-dirt-merged': 44.42665694657176, 'IoU-paper-merged': 34.43408911409328, 'IoU-food-other-merged': 35.000674768396124, 'IoU-building-other-merged': 55.6201268262379, 'IoU-rock-merged': 63.32278825433837, 'IoU-wall-other-merged': 65.41352132609207, 'IoU-rug-merged': 60.40200804686056, 'mACC': 73.27285376828468, 'pACC': 80.11581324022248, 'ACC-person': 92.40464146469358, 'ACC-bicycle': 86.35250192522346, 'ACC-car': 85.31124229617002, 'ACC-motorcycle': 88.85288016844403, 'ACC-airplane': 85.29815184263111, 'ACC-bus': 92.4631569157626, 'ACC-train': 95.00037497414401, 'ACC-truck': 74.84953785824673, 'ACC-boat': 77.51036779702474, 'ACC-traffic light': 89.70557849388906, 'ACC-fire hydrant': 95.00639446254489, 'ACC-stop sign': 94.32183533302826, 'ACC-parking meter': 89.45979676445698, 'ACC-bench': 73.74685616036226, 'ACC-bird': 80.63995814287915, 'ACC-cat': 94.06000085470387, 'ACC-dog': 87.13597584014447, 'ACC-horse': 91.87841045672313, 'ACC-sheep': 93.8166539228394, 'ACC-cow': 85.9442428406374, 'ACC-elephant': 89.52572352711945, 'ACC-bear': 92.58907597507533, 'ACC-zebra': 93.5771077824241, 'ACC-giraffe': 91.13837047557317, 'ACC-backpack': 65.50008325956827, 'ACC-umbrella': 76.97996520023912, 'ACC-handbag': 51.98871943726353, 'ACC-tie': 80.9823954881686, 'ACC-suitcase': 88.65591075261669, 'ACC-frisbee': 94.01781818181819, 'ACC-skis': 71.943495163494, 'ACC-snowboard': 79.34069701942151, 'ACC-sports ball': 80.38215441229674, 'ACC-kite': 76.87648345452669, 'ACC-baseball bat': 83.2706471165589, 'ACC-baseball glove': 90.77480671263771, 'ACC-skateboard': 89.78942147573287, 'ACC-surfboard': 90.06638534306751, 'ACC-tennis racket': 89.51277173503857, 'ACC-bottle': 82.77723173668107, 'ACC-wine glass': 86.8811988903193, 'ACC-cup': 83.55999397236583, 'ACC-fork': 70.81088518244259, 'ACC-knife': 66.12588847365721, 'ACC-spoon': 69.14964015174868, 'ACC-bowl': 68.58056003707956, 'ACC-banana': 90.40766699018347, 'ACC-apple': 70.65396409265733, 'ACC-sandwich': 79.19113140318291, 'ACC-orange': 90.00486174292475, 'ACC-broccoli': 77.31614427726213, 'ACC-carrot': 74.91838567372025, 'ACC-hot dog': 73.0413785139381, 'ACC-pizza': 91.86092952877522, 'ACC-donut': 80.74272441755804, 'ACC-cake': 77.20021231909323, 'ACC-chair': 70.89295949090413, 'ACC-couch': 83.61384410725658, 'ACC-potted plant': 51.85396320054003, 'ACC-bed': 79.34771960993614, 'ACC-dining table': 77.484783968718, 'ACC-toilet': 92.58323059541061, 'ACC-tv': 88.24294177745729, 'ACC-laptop': 89.79938886872559, 'ACC-mouse': 85.9834998142196, 'ACC-remote': 72.9385375777572, 'ACC-keyboard': 73.19397598314139, 'ACC-cell phone': 75.96922190507908, 'ACC-microwave': 75.71939710925473, 'ACC-oven': 85.3577741643819, 'ACC-toaster': 45.74101192023977, 'ACC-sink': 83.1956048781543, 'ACC-refrigerator': 88.67784161260536, 'ACC-book': 72.0233041943347, 'ACC-clock': 77.07918399248577, 'ACC-vase': 68.17347420571419, 'ACC-scissors': 67.8502235811549, 'ACC-teddy bear': 84.4367028556652, 'ACC-hair drier': 42.398884239888424, 'ACC-toothbrush': 80.82348853370395, 'ACC-banner': 73.5924181802948, 'ACC-blanket': 16.590981160610472, 'ACC-bridge': 53.20217029143769, 'ACC-cardboard': 46.809393630943724, 'ACC-counter': 56.57542119907665, 'ACC-curtain': 77.76885833619993, 'ACC-door-stuff': 61.67292326519348, 'ACC-floor-wood': 78.37822031472359, 'ACC-flower': 60.77102481127316, 'ACC-fruit': 58.665011623651544, 'ACC-gravel': 49.03850668944618, 'ACC-house': 30.800035806603297, 'ACC-light': 57.766144776633624, 'ACC-mirror-stuff': 67.24030434370015, 'ACC-net': 63.314621563319605, 'ACC-pillow': 20.103856195864232, 'ACC-platform': 47.614375267405165, 'ACC-playingfield': 81.07387993485654, 'ACC-railroad': 76.60538503544547, 'ACC-river': 66.10801771727324, 'ACC-road': 82.38763628333979, 'ACC-roof': 19.297265582026427, 'ACC-sand': 69.68963342499242, 'ACC-sea': 89.71903997180313, 'ACC-shelf': 54.052660628659886, 'ACC-snow': 94.87639386790143, 'ACC-stairs': 44.373817971157855, 'ACC-tent': 10.10906022725847, 'ACC-towel': 40.93214112277928, 'ACC-wall-brick': 61.933905277835635, 'ACC-wall-stone': 37.27773949825111, 'ACC-wall-tile': 83.79144897501045, 'ACC-wall-wood': 53.82858118507545, 'ACC-water-other': 44.93148023497435, 'ACC-window-blind': 57.40307242807341, 'ACC-window-other': 72.21649887495572, 'ACC-tree-merged': 88.90789343663202, 'ACC-fence-merged': 65.19846618893372, 'ACC-ceiling-merged': 80.9321269791697, 'ACC-sky-other-merged': 96.70011808595306, 'ACC-cabinet-merged': 75.70428766625209, 'ACC-table-merged': 48.13375377255976, 'ACC-floor-other-merged': 63.12257237288208, 'ACC-pavement-merged': 69.51323285625502, 'ACC-mountain-merged': 65.31329294818896, 'ACC-grass-merged': 83.89228451529063, 'ACC-dirt-merged': 69.38600142732182, 'ACC-paper-merged': 49.38400404291783, 'ACC-food-other-merged': 44.74160009981344, 'ACC-building-other-merged': 69.45289131041142, 'ACC-rock-merged': 81.92867466934646, 'ACC-wall-other-merged': 80.326519479523, 'ACC-rug-merged': 74.86555901485575})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1534 s/iter. Inference: 0.3950 s/iter. Eval: 0.0000 s/iter. Total: 0.5485 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1542 s/iter. Inference: 0.4871 s/iter. Eval: 0.0000 s/iter. Total: 0.6414 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1700 s/iter. Inference: 0.5329 s/iter. Eval: 0.0000 s/iter. Total: 0.7031 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1722 s/iter. Inference: 0.6615 s/iter. Eval: 0.0000 s/iter. Total: 0.8339 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1691 s/iter. Inference: 0.5882 s/iter. Eval: 0.0000 s/iter. Total: 0.7575 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1679 s/iter. Inference: 0.6304 s/iter. Eval: 0.0000 s/iter. Total: 0.7984 s/iter. ETA=0:00:03
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1688 s/iter. Inference: 0.6565 s/iter. Eval: 0.0000 s/iter. Total: 0.8255 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4869768803043606, 'noc@0.8': 2.7553409423470883, 'noc@0.85': 3.3529411764705883, 'noc@0.9': 4.369622475856014, 'miou@iter1': 0.8336106597034074}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0996 s/iter. Eval: 0.0008 s/iter. Total: 0.1021 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.92887878417969, 'precision@0.6': 67.89739227294922, 'precision@0.7': 62.72833251953125, 'precision@0.8': 52.11814880371094, 'precision@0.9': 26.894676208496094, 'cIoU': 56.76154708862305, 'mIoU': 62.43230056762695}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.04421650616939, 'SQ': 81.88423801318324, 'RQ': 60.19296117267523, 'PQ_th': 55.14863358897053, 'SQ_th': 82.63385448049011, 'RQ_th': 66.01598981427956, 'PQ_st': 42.33943600382811, 'SQ_st': 80.75274145875778, 'RQ_st': 51.40348397780071}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.99317041800352, 'AP50': 61.2644362911072, 'AP75': 41.016575923219335, 'APs': 18.854718300748498, 'APm': 41.922674724785786, 'APl': 61.110261508011575, 'AP-person': 44.26259873655076, 'AP-bicycle': 19.237550288513148, 'AP-car': 36.756742908128345, 'AP-motorcycle': 35.21017490694628, 'AP-airplane': 57.24132108477205, 'AP-bus': 66.06219121998868, 'AP-train': 68.6894952379856, 'AP-truck': 36.33345972215361, 'AP-boat': 23.50744492344835, 'AP-traffic light': 24.513327287827398, 'AP-fire hydrant': 64.19441207230598, 'AP-stop sign': 64.57873433126159, 'AP-parking meter': 42.387061734238, 'AP-bench': 20.276607900728486, 'AP-bird': 30.286627327394594, 'AP-cat': 73.08684992077622, 'AP-dog': 64.99051323334118, 'AP-horse': 46.118967580419586, 'AP-sheep': 46.30958928438403, 'AP-cow': 50.505086490504816, 'AP-elephant': 61.056601252510355, 'AP-bear': 76.26271450771841, 'AP-zebra': 60.67377264930914, 'AP-giraffe': 56.75076081808421, 'AP-backpack': 17.34075394172759, 'AP-umbrella': 48.20322034054256, 'AP-handbag': 15.271125039408922, 'AP-tie': 34.30502091930015, 'AP-suitcase': 40.8446703260991, 'AP-frisbee': 66.72217667849787, 'AP-skis': 4.95957881692663, 'AP-snowboard': 22.655497774434917, 'AP-sports ball': 47.60493849691946, 'AP-kite': 33.89964003615744, 'AP-baseball bat': 29.149938543885085, 'AP-baseball glove': 43.3104886968117, 'AP-skateboard': 37.02260854623075, 'AP-surfboard': 36.0769022198733, 'AP-tennis racket': 55.82257731420553, 'AP-bottle': 33.707786201126524, 'AP-wine glass': 27.770617231407098, 'AP-cup': 40.43010942134139, 'AP-fork': 15.05613177178079, 'AP-knife': 13.635617530434333, 'AP-spoon': 12.940664730101053, 'AP-bowl': 32.54132482052453, 'AP-banana': 20.25493568471862, 'AP-apple': 20.842799645768746, 'AP-sandwich': 43.24909250185, 'AP-orange': 29.098350641383075, 'AP-broccoli': 21.463956067584093, 'AP-carrot': 19.982920645016332, 'AP-hot dog': 25.87480556214822, 'AP-pizza': 51.29006842594559, 'AP-donut': 45.888536513020036, 'AP-cake': 44.03435138664244, 'AP-chair': 21.124202259625445, 'AP-couch': 42.199530165839924, 'AP-potted plant': 17.54794168960889, 'AP-bed': 39.31001924096707, 'AP-dining table': 12.6644693916143, 'AP-toilet': 66.4523059193706, 'AP-tv': 62.56294244998234, 'AP-laptop': 63.593784538412265, 'AP-mouse': 59.52475274725022, 'AP-remote': 32.31982840635158, 'AP-keyboard': 48.165705047618786, 'AP-cell phone': 37.50783210108084, 'AP-microwave': 54.837607724824146, 'AP-oven': 33.63178736190939, 'AP-toaster': 29.524627581398466, 'AP-sink': 37.37627315412822, 'AP-refrigerator': 59.367527882380344, 'AP-book': 9.193539751347584, 'AP-clock': 52.54959401012024, 'AP-vase': 33.38738366934908, 'AP-scissors': 24.341503913900866, 'AP-teddy bear': 50.1554589936344, 'AP-hair drier': 12.242456388495992, 'AP-toothbrush': 19.328747159966188}), ('sem_seg', {'mIoU': 60.82120126159259, 'fwIoU': 68.7834771834944, 'IoU-person': 87.69051948136382, 'IoU-bicycle': 75.2291963165766, 'IoU-car': 69.33953229454582, 'IoU-motorcycle': 83.78343776560519, 'IoU-airplane': 79.26124818792125, 'IoU-bus': 86.58377126730267, 'IoU-train': 87.44945783674616, 'IoU-truck': 66.63769626392899, 'IoU-boat': 66.54342039200083, 'IoU-traffic light': 76.43810522137808, 'IoU-fire hydrant': 90.0483191013487, 'IoU-stop sign': 91.82009316162566, 'IoU-parking meter': 85.69224199358942, 'IoU-bench': 51.9366364759679, 'IoU-bird': 75.62660300793527, 'IoU-cat': 84.89537596326848, 'IoU-dog': 82.61304658207334, 'IoU-horse': 85.83877586351218, 'IoU-sheep': 90.21654923272685, 'IoU-cow': 80.99219161912443, 'IoU-elephant': 87.00525548390581, 'IoU-bear': 90.29550556366772, 'IoU-zebra': 90.9604214507632, 'IoU-giraffe': 86.90624629207598, 'IoU-backpack': 43.966508711088366, 'IoU-umbrella': 70.10138650545356, 'IoU-handbag': 38.18435402698381, 'IoU-tie': 68.90104136366281, 'IoU-suitcase': 81.87091824229648, 'IoU-frisbee': 83.70803250558487, 'IoU-skis': 49.601943184407546, 'IoU-snowboard': 69.66795698924732, 'IoU-sports ball': 60.71566602772561, 'IoU-kite': 66.47025667141098, 'IoU-baseball bat': 61.66797984730113, 'IoU-baseball glove': 76.16065194195153, 'IoU-skateboard': 77.76510045589767, 'IoU-surfboard': 82.17244629198495, 'IoU-tennis racket': 82.48302796466007, 'IoU-bottle': 67.86698186024671, 'IoU-wine glass': 71.74169853300074, 'IoU-cup': 60.570385663569525, 'IoU-fork': 53.64552223947756, 'IoU-knife': 51.55794498379566, 'IoU-spoon': 52.13237686503571, 'IoU-bowl': 52.747238214439285, 'IoU-banana': 82.62293587867595, 'IoU-apple': 56.75052449518551, 'IoU-sandwich': 64.30750086136678, 'IoU-orange': 78.58021723317428, 'IoU-broccoli': 66.8841104139499, 'IoU-carrot': 63.77239648247307, 'IoU-hot dog': 64.57697536628392, 'IoU-pizza': 82.19607098543989, 'IoU-donut': 66.28879617886429, 'IoU-cake': 70.29228723217442, 'IoU-chair': 54.282690687187326, 'IoU-couch': 67.41367157161986, 'IoU-potted plant': 34.32193114170299, 'IoU-bed': 69.49918253931116, 'IoU-dining table': 50.35674269213087, 'IoU-toilet': 88.50097445369965, 'IoU-tv': 75.69175431041596, 'IoU-laptop': 74.83143288141574, 'IoU-mouse': 70.84486320200925, 'IoU-remote': 49.206481096801, 'IoU-keyboard': 63.45394441265352, 'IoU-cell phone': 70.16855546733132, 'IoU-microwave': 63.50891197516657, 'IoU-oven': 67.42955487570832, 'IoU-toaster': 40.48488592522606, 'IoU-sink': 65.61411707755433, 'IoU-refrigerator': 81.79783668083103, 'IoU-book': 53.50524146432654, 'IoU-clock': 72.0315228011725, 'IoU-vase': 60.038815226604214, 'IoU-scissors': 62.33977688189335, 'IoU-teddy bear': 80.26066569080868, 'IoU-hair drier': 39.99463015169821, 'IoU-toothbrush': 58.02701657665299, 'IoU-banner': 39.82995868307576, 'IoU-blanket': 11.328266640850893, 'IoU-bridge': 39.04645281958592, 'IoU-cardboard': 40.23147443410542, 'IoU-counter': 31.783868108740542, 'IoU-curtain': 65.25033236675347, 'IoU-door-stuff': 40.6057941609296, 'IoU-floor-wood': 62.942497303288356, 'IoU-flower': 40.79939835957926, 'IoU-fruit': 41.99257903092043, 'IoU-gravel': 30.299238960939345, 'IoU-house': 25.21637180929251, 'IoU-light': 38.67901745324901, 'IoU-mirror-stuff': 54.07885337383863, 'IoU-net': 32.7941187447213, 'IoU-pillow': 12.16425384685423, 'IoU-platform': 28.469746041432536, 'IoU-playingfield': 66.68460096016894, 'IoU-railroad': 60.37579852860796, 'IoU-river': 48.90994911046706, 'IoU-road': 66.6158405129629, 'IoU-roof': 14.137686976057335, 'IoU-sand': 63.05881759580344, 'IoU-sea': 84.64379845855412, 'IoU-shelf': 35.740521490619706, 'IoU-snow': 88.24634221662124, 'IoU-stairs': 28.1766288915277, 'IoU-tent': 8.928468366557778, 'IoU-towel': 35.00306827786633, 'IoU-wall-brick': 43.28850160914058, 'IoU-wall-stone': 27.670394079943666, 'IoU-wall-tile': 67.78841314581862, 'IoU-wall-wood': 38.97392561252952, 'IoU-water-other': 23.545915467761382, 'IoU-window-blind': 47.51153697757011, 'IoU-window-other': 47.537715867762195, 'IoU-tree-merged': 80.56578315324087, 'IoU-fence-merged': 47.66558813776216, 'IoU-ceiling-merged': 66.81982425256363, 'IoU-sky-other-merged': 91.90826866361405, 'IoU-cabinet-merged': 57.18517022060061, 'IoU-table-merged': 37.29392912520009, 'IoU-floor-other-merged': 48.53026004239805, 'IoU-pavement-merged': 54.5926789775787, 'IoU-mountain-merged': 54.47200070318418, 'IoU-grass-merged': 71.83617212889848, 'IoU-dirt-merged': 44.42665694657176, 'IoU-paper-merged': 34.43408911409328, 'IoU-food-other-merged': 35.000674768396124, 'IoU-building-other-merged': 55.6201268262379, 'IoU-rock-merged': 63.32278825433837, 'IoU-wall-other-merged': 65.41352132609207, 'IoU-rug-merged': 60.40200804686056, 'mACC': 73.27285376828468, 'pACC': 80.11581324022248, 'ACC-person': 92.40464146469358, 'ACC-bicycle': 86.35250192522346, 'ACC-car': 85.31124229617002, 'ACC-motorcycle': 88.85288016844403, 'ACC-airplane': 85.29815184263111, 'ACC-bus': 92.4631569157626, 'ACC-train': 95.00037497414401, 'ACC-truck': 74.84953785824673, 'ACC-boat': 77.51036779702474, 'ACC-traffic light': 89.70557849388906, 'ACC-fire hydrant': 95.00639446254489, 'ACC-stop sign': 94.32183533302826, 'ACC-parking meter': 89.45979676445698, 'ACC-bench': 73.74685616036226, 'ACC-bird': 80.63995814287915, 'ACC-cat': 94.06000085470387, 'ACC-dog': 87.13597584014447, 'ACC-horse': 91.87841045672313, 'ACC-sheep': 93.8166539228394, 'ACC-cow': 85.9442428406374, 'ACC-elephant': 89.52572352711945, 'ACC-bear': 92.58907597507533, 'ACC-zebra': 93.5771077824241, 'ACC-giraffe': 91.13837047557317, 'ACC-backpack': 65.50008325956827, 'ACC-umbrella': 76.97996520023912, 'ACC-handbag': 51.98871943726353, 'ACC-tie': 80.9823954881686, 'ACC-suitcase': 88.65591075261669, 'ACC-frisbee': 94.01781818181819, 'ACC-skis': 71.943495163494, 'ACC-snowboard': 79.34069701942151, 'ACC-sports ball': 80.38215441229674, 'ACC-kite': 76.87648345452669, 'ACC-baseball bat': 83.2706471165589, 'ACC-baseball glove': 90.77480671263771, 'ACC-skateboard': 89.78942147573287, 'ACC-surfboard': 90.06638534306751, 'ACC-tennis racket': 89.51277173503857, 'ACC-bottle': 82.77723173668107, 'ACC-wine glass': 86.8811988903193, 'ACC-cup': 83.55999397236583, 'ACC-fork': 70.81088518244259, 'ACC-knife': 66.12588847365721, 'ACC-spoon': 69.14964015174868, 'ACC-bowl': 68.58056003707956, 'ACC-banana': 90.40766699018347, 'ACC-apple': 70.65396409265733, 'ACC-sandwich': 79.19113140318291, 'ACC-orange': 90.00486174292475, 'ACC-broccoli': 77.31614427726213, 'ACC-carrot': 74.91838567372025, 'ACC-hot dog': 73.0413785139381, 'ACC-pizza': 91.86092952877522, 'ACC-donut': 80.74272441755804, 'ACC-cake': 77.20021231909323, 'ACC-chair': 70.89295949090413, 'ACC-couch': 83.61384410725658, 'ACC-potted plant': 51.85396320054003, 'ACC-bed': 79.34771960993614, 'ACC-dining table': 77.484783968718, 'ACC-toilet': 92.58323059541061, 'ACC-tv': 88.24294177745729, 'ACC-laptop': 89.79938886872559, 'ACC-mouse': 85.9834998142196, 'ACC-remote': 72.9385375777572, 'ACC-keyboard': 73.19397598314139, 'ACC-cell phone': 75.96922190507908, 'ACC-microwave': 75.71939710925473, 'ACC-oven': 85.3577741643819, 'ACC-toaster': 45.74101192023977, 'ACC-sink': 83.1956048781543, 'ACC-refrigerator': 88.67784161260536, 'ACC-book': 72.0233041943347, 'ACC-clock': 77.07918399248577, 'ACC-vase': 68.17347420571419, 'ACC-scissors': 67.8502235811549, 'ACC-teddy bear': 84.4367028556652, 'ACC-hair drier': 42.398884239888424, 'ACC-toothbrush': 80.82348853370395, 'ACC-banner': 73.5924181802948, 'ACC-blanket': 16.590981160610472, 'ACC-bridge': 53.20217029143769, 'ACC-cardboard': 46.809393630943724, 'ACC-counter': 56.57542119907665, 'ACC-curtain': 77.76885833619993, 'ACC-door-stuff': 61.67292326519348, 'ACC-floor-wood': 78.37822031472359, 'ACC-flower': 60.77102481127316, 'ACC-fruit': 58.665011623651544, 'ACC-gravel': 49.03850668944618, 'ACC-house': 30.800035806603297, 'ACC-light': 57.766144776633624, 'ACC-mirror-stuff': 67.24030434370015, 'ACC-net': 63.314621563319605, 'ACC-pillow': 20.103856195864232, 'ACC-platform': 47.614375267405165, 'ACC-playingfield': 81.07387993485654, 'ACC-railroad': 76.60538503544547, 'ACC-river': 66.10801771727324, 'ACC-road': 82.38763628333979, 'ACC-roof': 19.297265582026427, 'ACC-sand': 69.68963342499242, 'ACC-sea': 89.71903997180313, 'ACC-shelf': 54.052660628659886, 'ACC-snow': 94.87639386790143, 'ACC-stairs': 44.373817971157855, 'ACC-tent': 10.10906022725847, 'ACC-towel': 40.93214112277928, 'ACC-wall-brick': 61.933905277835635, 'ACC-wall-stone': 37.27773949825111, 'ACC-wall-tile': 83.79144897501045, 'ACC-wall-wood': 53.82858118507545, 'ACC-water-other': 44.93148023497435, 'ACC-window-blind': 57.40307242807341, 'ACC-window-other': 72.21649887495572, 'ACC-tree-merged': 88.90789343663202, 'ACC-fence-merged': 65.19846618893372, 'ACC-ceiling-merged': 80.9321269791697, 'ACC-sky-other-merged': 96.70011808595306, 'ACC-cabinet-merged': 75.70428766625209, 'ACC-table-merged': 48.13375377255976, 'ACC-floor-other-merged': 63.12257237288208, 'ACC-pavement-merged': 69.51323285625502, 'ACC-mountain-merged': 65.31329294818896, 'ACC-grass-merged': 83.89228451529063, 'ACC-dirt-merged': 69.38600142732182, 'ACC-paper-merged': 49.38400404291783, 'ACC-food-other-merged': 44.74160009981344, 'ACC-building-other-merged': 69.45289131041142, 'ACC-rock-merged': 81.92867466934646, 'ACC-wall-other-merged': 80.326519479523, 'ACC-rug-merged': 74.86555901485575})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4869768803043606, 'noc@0.8': 2.7553409423470883, 'noc@0.85': 3.3529411764705883, 'noc@0.9': 4.369622475856014, 'miou@iter1': 0.8336106597034074}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.92887878417969, 'precision@0.6': 67.89739227294922, 'precision@0.7': 62.72833251953125, 'precision@0.8': 52.11814880371094, 'precision@0.9': 26.894676208496094, 'cIoU': 56.76154708862305, 'mIoU': 62.43230056762695}}}
INFO:trainer.default_trainer:This epoch takes 1:27:36.787118
INFO:trainer.default_trainer:PROGRESS: 86.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 43 training.
INFO:trainer.default_trainer:epochs[    43] optim steps[78600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.37060/0.89562, loss_mask_bce_0: 0.44372/0.33395, loss_mask_dice_0: 2.62392/1.16162, loss_spatial_bce_0: 0.06813/0.08663, loss_spatial_dice_0: 0.20844/0.20671, loss_spatial_ce_0: 0.25795/0.05976, loss_grounding_bce_0: 0.09224/0.08612, loss_grounding_dice_0: 0.29924/0.17839, loss_grounding_ce_0: 0.17480/0.27136, loss_mask_ce_1: 1.50810/0.89626, loss_mask_bce_1: 0.44135/0.33488, loss_mask_dice_1: 2.42948/1.16838, loss_spatial_bce_1: 0.06898/0.08717, loss_spatial_dice_1: 0.22093/0.21069, loss_spatial_ce_1: 0.03475/0.06554, loss_grounding_bce_1: 0.09140/0.08631, loss_grounding_dice_1: 0.34044/0.17916, loss_grounding_ce_1: 0.17174/0.27213, loss_mask_ce_2: 1.36193/0.90327, loss_mask_bce_2: 0.48169/0.33550, loss_mask_dice_2: 2.58058/1.16880, loss_spatial_bce_2: 0.07058/0.08839, loss_spatial_dice_2: 0.23125/0.21254, loss_spatial_ce_2: 0.03506/0.06903, loss_grounding_bce_2: 0.09718/0.08649, loss_grounding_dice_2: 0.34296/0.17905, loss_grounding_ce_2: 0.17543/0.27544, loss_mask_ce_3: 1.46968/0.91436, loss_mask_bce_3: 0.45828/0.33669, loss_mask_dice_3: 2.54924/1.16671, loss_spatial_bce_3: 0.06888/0.08969, loss_spatial_dice_3: 0.20544/0.21359, loss_spatial_ce_3: 0.31168/0.07409, loss_grounding_bce_3: 0.09836/0.08674, loss_grounding_dice_3: 0.33118/0.17874, loss_grounding_ce_3: 0.19960/0.27776, loss_mask_ce_4: 1.39489/0.91566, loss_mask_bce_4: 0.46415/0.33883, loss_mask_dice_4: 2.50561/1.19043, loss_spatial_bce_4: 0.07034/0.09361, loss_spatial_dice_4: 0.22996/0.22586, loss_spatial_ce_4: 0.05597/0.09044, loss_grounding_bce_4: 0.10289/0.08727, loss_grounding_dice_4: 0.33977/0.18174, loss_grounding_ce_4: 0.16754/0.28072, loss_mask_ce_5: 1.39861/0.93258, loss_mask_bce_5: 0.43938/0.34118, loss_mask_dice_5: 2.50558/1.19876, loss_spatial_bce_5: 0.08119/0.09594, loss_spatial_dice_5: 0.23749/0.23020, loss_spatial_ce_5: 0.04650/0.10434, loss_grounding_bce_5: 0.08645/0.08771, loss_grounding_dice_5: 0.29274/0.18301, loss_grounding_ce_5: 0.12741/0.29320, loss_mask_ce_6: 1.42356/0.97262, loss_mask_bce_6: 0.44000/0.34403, loss_mask_dice_6: 2.66723/1.20160, loss_spatial_bce_6: 0.07378/0.10154, loss_spatial_dice_6: 0.21571/0.23316, loss_spatial_ce_6: 0.16490/0.12939, loss_grounding_bce_6: 0.09227/0.08844, loss_grounding_dice_6: 0.31511/0.18339, loss_grounding_ce_6: 0.19626/0.30861, loss_mask_ce_7: 1.25012/1.01825, loss_mask_bce_7: 0.44928/0.35182, loss_mask_dice_7: 2.74604/1.25610, loss_spatial_bce_7: 0.07036/0.10939, loss_spatial_dice_7: 0.25486/0.26071, loss_spatial_ce_7: 0.08241/0.16433, loss_grounding_bce_7: 0.09363/0.09033, loss_grounding_dice_7: 0.35830/0.19073, loss_grounding_ce_7: 0.17214/0.33856, loss_mask_ce_8: 1.34738/1.12701, loss_mask_bce_8: 0.42858/0.36544, loss_mask_dice_8: 2.66620/1.32881, loss_spatial_bce_8: 0.10553/0.12966, loss_spatial_dice_8: 0.28534/0.29840, loss_spatial_ce_8: 0.04019/0.21672, loss_grounding_bce_8: 0.10351/0.09402, loss_grounding_dice_8: 0.35080/0.20142, loss_grounding_ce_8: 0.29688/0.40495, loss_mask_ce_9: 3.54010/3.67436, loss_mask_bce_9: 0.44051/0.39249, loss_mask_dice_9: 3.71547/1.90155, loss_spatial_bce_9: 0.35012/0.33266, loss_spatial_dice_9: 0.86286/0.82166, loss_spatial_ce_9: 1.88172/1.49303, loss_grounding_bce_9: 0.09278/0.10563, loss_grounding_dice_9: 0.44164/0.28081, loss_grounding_ce_9: 0.41059/0.66946] items per batch[64] items per second[0.14] total items[5030400] mini batches[ 78600] memory[7345] epoch remaining[1:26:28]
INFO:trainer.default_trainer:epochs[    43] optim steps[78700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73817/0.89555, loss_mask_bce_0: 0.23376/0.33392, loss_mask_dice_0: 0.84270/1.16147, loss_spatial_bce_0: 0.06053/0.08662, loss_spatial_dice_0: 0.16576/0.20668, loss_spatial_ce_0: 0.00467/0.05974, loss_grounding_bce_0: 0.04894/0.08612, loss_grounding_dice_0: 0.07278/0.17839, loss_grounding_ce_0: 0.03082/0.27137, loss_mask_ce_1: 0.76062/0.89618, loss_mask_bce_1: 0.22855/0.33485, loss_mask_dice_1: 0.79090/1.16821, loss_spatial_bce_1: 0.06033/0.08716, loss_spatial_dice_1: 0.16755/0.21067, loss_spatial_ce_1: 0.00086/0.06552, loss_grounding_bce_1: 0.04916/0.08630, loss_grounding_dice_1: 0.07643/0.17916, loss_grounding_ce_1: 0.03300/0.27214, loss_mask_ce_2: 0.79240/0.90317, loss_mask_bce_2: 0.22852/0.33548, loss_mask_dice_2: 0.86203/1.16865, loss_spatial_bce_2: 0.05754/0.08838, loss_spatial_dice_2: 0.17288/0.21251, loss_spatial_ce_2: 0.00100/0.06900, loss_grounding_bce_2: 0.05256/0.08649, loss_grounding_dice_2: 0.07235/0.17905, loss_grounding_ce_2: 0.02855/0.27544, loss_mask_ce_3: 0.85316/0.91428, loss_mask_bce_3: 0.22046/0.33666, loss_mask_dice_3: 0.77321/1.16653, loss_spatial_bce_3: 0.06030/0.08969, loss_spatial_dice_3: 0.16904/0.21357, loss_spatial_ce_3: 0.00117/0.07406, loss_grounding_bce_3: 0.05014/0.08673, loss_grounding_dice_3: 0.06414/0.17873, loss_grounding_ce_3: 0.03412/0.27775, loss_mask_ce_4: 0.83520/0.91557, loss_mask_bce_4: 0.22489/0.33880, loss_mask_dice_4: 0.84198/1.19027, loss_spatial_bce_4: 0.06528/0.09360, loss_spatial_dice_4: 0.22287/0.22583, loss_spatial_ce_4: 0.03244/0.09042, loss_grounding_bce_4: 0.04788/0.08727, loss_grounding_dice_4: 0.06280/0.18174, loss_grounding_ce_4: 0.02259/0.28071, loss_mask_ce_5: 0.88857/0.93248, loss_mask_bce_5: 0.23914/0.34116, loss_mask_dice_5: 0.99447/1.19861, loss_spatial_bce_5: 0.06530/0.09593, loss_spatial_dice_5: 0.21527/0.23018, loss_spatial_ce_5: 0.02761/0.10432, loss_grounding_bce_5: 0.04692/0.08770, loss_grounding_dice_5: 0.05257/0.18300, loss_grounding_ce_5: 0.02134/0.29321, loss_mask_ce_6: 1.13007/0.97255, loss_mask_bce_6: 0.23604/0.34400, loss_mask_dice_6: 0.86334/1.20147, loss_spatial_bce_6: 0.05878/0.10153, loss_spatial_dice_6: 0.21070/0.23314, loss_spatial_ce_6: 0.01572/0.12937, loss_grounding_bce_6: 0.05110/0.08843, loss_grounding_dice_6: 0.06387/0.18339, loss_grounding_ce_6: 0.07732/0.30863, loss_mask_ce_7: 0.66834/1.01818, loss_mask_bce_7: 0.28253/0.35179, loss_mask_dice_7: 0.98718/1.25594, loss_spatial_bce_7: 0.08483/0.10939, loss_spatial_dice_7: 0.19502/0.26070, loss_spatial_ce_7: 0.11446/0.16430, loss_grounding_bce_7: 0.04958/0.09032, loss_grounding_dice_7: 0.06348/0.19073, loss_grounding_ce_7: 0.12471/0.33858, loss_mask_ce_8: 1.10369/1.12697, loss_mask_bce_8: 0.28821/0.36540, loss_mask_dice_8: 1.00701/1.32863, loss_spatial_bce_8: 0.08881/0.12964, loss_spatial_dice_8: 0.24963/0.29837, loss_spatial_ce_8: 0.11777/0.21667, loss_grounding_bce_8: 0.05931/0.09401, loss_grounding_dice_8: 0.09690/0.20142, loss_grounding_ce_8: 0.03730/0.40502, loss_mask_ce_9: 4.04061/3.67414, loss_mask_bce_9: 0.25685/0.39246, loss_mask_dice_9: 1.62010/1.90131, loss_spatial_bce_9: 0.38041/0.33266, loss_spatial_dice_9: 0.84614/0.82165, loss_spatial_ce_9: 1.40258/1.49295, loss_grounding_bce_9: 0.05947/0.10561, loss_grounding_dice_9: 0.12602/0.28079, loss_grounding_ce_9: 0.30108/0.66945] items per batch[64] items per second[0.23] total items[5036800] mini batches[ 78700] memory[7345] epoch remaining[1:19:53]
INFO:trainer.default_trainer:epochs[    43] optim steps[78800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04434/0.89552, loss_mask_bce_0: 0.58537/0.33391, loss_mask_dice_0: 0.92523/1.16150, loss_spatial_bce_0: 0.14244/0.08660, loss_spatial_dice_0: 0.21887/0.20666, loss_spatial_ce_0: 0.06397/0.05973, loss_grounding_bce_0: 0.26717/0.08611, loss_grounding_dice_0: 0.17967/0.17839, loss_grounding_ce_0: 0.11819/0.27142, loss_mask_ce_1: 0.94891/0.89617, loss_mask_bce_1: 0.60373/0.33484, loss_mask_dice_1: 0.91704/1.16827, loss_spatial_bce_1: 0.14038/0.08713, loss_spatial_dice_1: 0.21242/0.21064, loss_spatial_ce_1: 0.06830/0.06550, loss_grounding_bce_1: 0.27409/0.08629, loss_grounding_dice_1: 0.17494/0.17916, loss_grounding_ce_1: 0.12065/0.27219, loss_mask_ce_2: 1.01217/0.90312, loss_mask_bce_2: 0.55686/0.33547, loss_mask_dice_2: 0.90507/1.16868, loss_spatial_bce_2: 0.13297/0.08836, loss_spatial_dice_2: 0.22463/0.21249, loss_spatial_ce_2: 0.07877/0.06898, loss_grounding_bce_2: 0.26904/0.08647, loss_grounding_dice_2: 0.17863/0.17905, loss_grounding_ce_2: 0.12500/0.27548, loss_mask_ce_3: 1.10305/0.91425, loss_mask_bce_3: 0.55203/0.33665, loss_mask_dice_3: 0.92487/1.16660, loss_spatial_bce_3: 0.14120/0.08966, loss_spatial_dice_3: 0.23589/0.21355, loss_spatial_ce_3: 0.07780/0.07404, loss_grounding_bce_3: 0.26847/0.08672, loss_grounding_dice_3: 0.17400/0.17874, loss_grounding_ce_3: 0.16657/0.27781, loss_mask_ce_4: 1.07985/0.91556, loss_mask_bce_4: 0.60229/0.33879, loss_mask_dice_4: 0.93634/1.19035, loss_spatial_bce_4: 0.14774/0.09358, loss_spatial_dice_4: 0.23725/0.22582, loss_spatial_ce_4: 0.07422/0.09039, loss_grounding_bce_4: 0.26574/0.08726, loss_grounding_dice_4: 0.16485/0.18173, loss_grounding_ce_4: 0.13496/0.28080, loss_mask_ce_5: 1.02151/0.93246, loss_mask_bce_5: 0.62971/0.34115, loss_mask_dice_5: 1.00413/1.19869, loss_spatial_bce_5: 0.16148/0.09591, loss_spatial_dice_5: 0.25486/0.23017, loss_spatial_ce_5: 0.08964/0.10428, loss_grounding_bce_5: 0.26853/0.08769, loss_grounding_dice_5: 0.17248/0.18299, loss_grounding_ce_5: 0.14368/0.29332, loss_mask_ce_6: 1.06069/0.97253, loss_mask_bce_6: 0.62600/0.34398, loss_mask_dice_6: 0.97971/1.20154, loss_spatial_bce_6: 0.18642/0.10151, loss_spatial_dice_6: 0.28505/0.23313, loss_spatial_ce_6: 0.09966/0.12933, loss_grounding_bce_6: 0.26974/0.08842, loss_grounding_dice_6: 0.18083/0.18339, loss_grounding_ce_6: 0.15357/0.30872, loss_mask_ce_7: 0.97873/1.01816, loss_mask_bce_7: 0.63373/0.35178, loss_mask_dice_7: 1.10264/1.25601, loss_spatial_bce_7: 0.23350/0.10937, loss_spatial_dice_7: 0.29684/0.26069, loss_spatial_ce_7: 0.10301/0.16427, loss_grounding_bce_7: 0.25554/0.09031, loss_grounding_dice_7: 0.15488/0.19073, loss_grounding_ce_7: 0.15841/0.33867, loss_mask_ce_8: 1.02465/1.12692, loss_mask_bce_8: 0.57392/0.36538, loss_mask_dice_8: 0.97794/1.32870, loss_spatial_bce_8: 0.21228/0.12963, loss_spatial_dice_8: 0.28841/0.29836, loss_spatial_ce_8: 0.27896/0.21660, loss_grounding_bce_8: 0.26291/0.09399, loss_grounding_dice_8: 0.16523/0.20141, loss_grounding_ce_8: 0.10763/0.40507, loss_mask_ce_9: 4.45617/3.67438, loss_mask_bce_9: 0.73331/0.39245, loss_mask_dice_9: 1.40912/1.90138, loss_spatial_bce_9: 0.45416/0.33263, loss_spatial_dice_9: 0.81167/0.82166, loss_spatial_ce_9: 1.30165/1.49296, loss_grounding_bce_9: 0.23671/0.10560, loss_grounding_dice_9: 0.15944/0.28078, loss_grounding_ce_9: 0.35449/0.66945] items per batch[64] items per second[0.23] total items[5043200] mini batches[ 78800] memory[7345] epoch remaining[1:15:02]
INFO:trainer.default_trainer:epochs[    43] optim steps[78900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36412/0.89551, loss_mask_bce_0: 0.22553/0.33392, loss_mask_dice_0: 0.70864/1.16138, loss_spatial_bce_0: 0.03555/0.08660, loss_spatial_dice_0: 0.13836/0.20664, loss_spatial_ce_0: 0.11255/0.05973, loss_grounding_bce_0: 0.07798/0.08612, loss_grounding_dice_0: 0.12682/0.17840, loss_grounding_ce_0: 0.01268/0.27138, loss_mask_ce_1: 0.38782/0.89615, loss_mask_bce_1: 0.22895/0.33485, loss_mask_dice_1: 0.76215/1.16815, loss_spatial_bce_1: 0.03733/0.08714, loss_spatial_dice_1: 0.13489/0.21062, loss_spatial_ce_1: 0.12006/0.06549, loss_grounding_bce_1: 0.07873/0.08630, loss_grounding_dice_1: 0.12508/0.17918, loss_grounding_ce_1: 0.01616/0.27215, loss_mask_ce_2: 0.43983/0.90312, loss_mask_bce_2: 0.21961/0.33548, loss_mask_dice_2: 0.70304/1.16855, loss_spatial_bce_2: 0.03550/0.08837, loss_spatial_dice_2: 0.14392/0.21247, loss_spatial_ce_2: 0.12412/0.06895, loss_grounding_bce_2: 0.08223/0.08649, loss_grounding_dice_2: 0.13173/0.17906, loss_grounding_ce_2: 0.01278/0.27545, loss_mask_ce_3: 0.74273/0.91423, loss_mask_bce_3: 0.21960/0.33666, loss_mask_dice_3: 0.78706/1.16648, loss_spatial_bce_3: 0.03625/0.08967, loss_spatial_dice_3: 0.15584/0.21352, loss_spatial_ce_3: 0.11744/0.07400, loss_grounding_bce_3: 0.08172/0.08673, loss_grounding_dice_3: 0.12862/0.17875, loss_grounding_ce_3: 0.01281/0.27778, loss_mask_ce_4: 0.87373/0.91553, loss_mask_bce_4: 0.22585/0.33880, loss_mask_dice_4: 0.82926/1.19023, loss_spatial_bce_4: 0.04791/0.09358, loss_spatial_dice_4: 0.16770/0.22580, loss_spatial_ce_4: 0.16488/0.09036, loss_grounding_bce_4: 0.07749/0.08727, loss_grounding_dice_4: 0.12584/0.18175, loss_grounding_ce_4: 0.01546/0.28076, loss_mask_ce_5: 0.91533/0.93244, loss_mask_bce_5: 0.22225/0.34117, loss_mask_dice_5: 0.86963/1.19858, loss_spatial_bce_5: 0.04714/0.09592, loss_spatial_dice_5: 0.17313/0.23014, loss_spatial_ce_5: 0.16142/0.10425, loss_grounding_bce_5: 0.07251/0.08770, loss_grounding_dice_5: 0.12458/0.18301, loss_grounding_ce_5: 0.02530/0.29328, loss_mask_ce_6: 0.64927/0.97251, loss_mask_bce_6: 0.26831/0.34400, loss_mask_dice_6: 0.84712/1.20143, loss_spatial_bce_6: 0.04281/0.10151, loss_spatial_dice_6: 0.14965/0.23311, loss_spatial_ce_6: 0.16500/0.12929, loss_grounding_bce_6: 0.07471/0.08844, loss_grounding_dice_6: 0.12416/0.18341, loss_grounding_ce_6: 0.09709/0.30867, loss_mask_ce_7: 0.64498/1.01816, loss_mask_bce_7: 0.28016/0.35179, loss_mask_dice_7: 0.95858/1.25589, loss_spatial_bce_7: 0.04980/0.10938, loss_spatial_dice_7: 0.17124/0.26067, loss_spatial_ce_7: 0.23536/0.16422, loss_grounding_bce_7: 0.08729/0.09032, loss_grounding_dice_7: 0.13669/0.19075, loss_grounding_ce_7: 0.07890/0.33861, loss_mask_ce_8: 0.69753/1.12686, loss_mask_bce_8: 0.27330/0.36539, loss_mask_dice_8: 0.97845/1.32858, loss_spatial_bce_8: 0.05931/0.12963, loss_spatial_dice_8: 0.17364/0.29833, loss_spatial_ce_8: 0.03043/0.21653, loss_grounding_bce_8: 0.06261/0.09400, loss_grounding_dice_8: 0.12817/0.20142, loss_grounding_ce_8: 0.43618/0.40500, loss_mask_ce_9: 3.06966/3.67430, loss_mask_bce_9: 0.30850/0.39247, loss_mask_dice_9: 1.56371/1.90129, loss_spatial_bce_9: 0.28586/0.33263, loss_spatial_dice_9: 0.87324/0.82165, loss_spatial_ce_9: 1.34113/1.49283, loss_grounding_bce_9: 0.05867/0.10561, loss_grounding_dice_9: 0.21511/0.28080, loss_grounding_ce_9: 1.58056/0.66935] items per batch[64] items per second[0.23] total items[5049600] mini batches[ 78900] memory[7345] epoch remaining[1:09:56]
INFO:trainer.default_trainer:epochs[    43] optim steps[79000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63847/0.89554, loss_mask_bce_0: 0.17451/0.33388, loss_mask_dice_0: 1.24521/1.16130, loss_spatial_bce_0: 0.03625/0.08659, loss_spatial_dice_0: 0.16677/0.20662, loss_spatial_ce_0: 0.00300/0.05971, loss_grounding_bce_0: 0.02474/0.08612, loss_grounding_dice_0: 0.13141/0.17838, loss_grounding_ce_0: 0.27622/0.27135, loss_mask_ce_1: 1.60402/0.89617, loss_mask_bce_1: 0.18729/0.33482, loss_mask_dice_1: 1.44611/1.16810, loss_spatial_bce_1: 0.03160/0.08713, loss_spatial_dice_1: 0.17696/0.21060, loss_spatial_ce_1: 0.03883/0.06549, loss_grounding_bce_1: 0.02055/0.08630, loss_grounding_dice_1: 0.11724/0.17916, loss_grounding_ce_1: 0.26604/0.27212, loss_mask_ce_2: 1.66219/0.90314, loss_mask_bce_2: 0.18584/0.33544, loss_mask_dice_2: 1.17066/1.16849, loss_spatial_bce_2: 0.03257/0.08836, loss_spatial_dice_2: 0.19863/0.21245, loss_spatial_ce_2: 0.01038/0.06895, loss_grounding_bce_2: 0.02271/0.08649, loss_grounding_dice_2: 0.12555/0.17905, loss_grounding_ce_2: 0.25166/0.27541, loss_mask_ce_3: 1.67917/0.91426, loss_mask_bce_3: 0.17820/0.33662, loss_mask_dice_3: 1.36494/1.16643, loss_spatial_bce_3: 0.03735/0.08966, loss_spatial_dice_3: 0.19648/0.21351, loss_spatial_ce_3: 0.02547/0.07399, loss_grounding_bce_3: 0.02629/0.08673, loss_grounding_dice_3: 0.13396/0.17873, loss_grounding_ce_3: 0.33302/0.27776, loss_mask_ce_4: 1.61754/0.91557, loss_mask_bce_4: 0.19335/0.33876, loss_mask_dice_4: 1.32458/1.19019, loss_spatial_bce_4: 0.03638/0.09358, loss_spatial_dice_4: 0.20464/0.22578, loss_spatial_ce_4: 0.05111/0.09034, loss_grounding_bce_4: 0.02412/0.08727, loss_grounding_dice_4: 0.13408/0.18173, loss_grounding_ce_4: 0.33034/0.28074, loss_mask_ce_5: 1.58215/0.93249, loss_mask_bce_5: 0.22078/0.34113, loss_mask_dice_5: 1.47877/1.19855, loss_spatial_bce_5: 0.04125/0.09591, loss_spatial_dice_5: 0.24286/0.23013, loss_spatial_ce_5: 0.12417/0.10423, loss_grounding_bce_5: 0.02666/0.08770, loss_grounding_dice_5: 0.14009/0.18299, loss_grounding_ce_5: 0.32505/0.29325, loss_mask_ce_6: 1.82630/0.97258, loss_mask_bce_6: 0.22047/0.34397, loss_mask_dice_6: 1.32369/1.20139, loss_spatial_bce_6: 0.04804/0.10150, loss_spatial_dice_6: 0.23265/0.23310, loss_spatial_ce_6: 0.12810/0.12926, loss_grounding_bce_6: 0.03674/0.08844, loss_grounding_dice_6: 0.16195/0.18339, loss_grounding_ce_6: 0.34221/0.30866, loss_mask_ce_7: 1.83005/1.01826, loss_mask_bce_7: 0.25421/0.35175, loss_mask_dice_7: 1.46720/1.25583, loss_spatial_bce_7: 0.04889/0.10937, loss_spatial_dice_7: 0.29052/0.26067, loss_spatial_ce_7: 0.13820/0.16420, loss_grounding_bce_7: 0.05850/0.09033, loss_grounding_dice_7: 0.20362/0.19073, loss_grounding_ce_7: 0.55532/0.33859, loss_mask_ce_8: 1.67150/1.12691, loss_mask_bce_8: 0.29190/0.36535, loss_mask_dice_8: 1.80395/1.32853, loss_spatial_bce_8: 0.08231/0.12962, loss_spatial_dice_8: 0.33324/0.29831, loss_spatial_ce_8: 0.25911/0.21649, loss_grounding_bce_8: 0.08604/0.09401, loss_grounding_dice_8: 0.24976/0.20141, loss_grounding_ce_8: 0.53923/0.40496, loss_mask_ce_9: 3.81997/3.67415, loss_mask_bce_9: 0.34700/0.39243, loss_mask_dice_9: 2.27605/1.90121, loss_spatial_bce_9: 0.18067/0.33264, loss_spatial_dice_9: 0.86577/0.82165, loss_spatial_ce_9: 1.27985/1.49288, loss_grounding_bce_9: 0.09989/0.10561, loss_grounding_dice_9: 0.25790/0.28078, loss_grounding_ce_9: 0.95764/0.66932] items per batch[64] items per second[0.23] total items[5056000] mini batches[ 79000] memory[7345] epoch remaining[1:05:03]
INFO:trainer.default_trainer:epochs[    43] optim steps[79100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73032/0.89552, loss_mask_bce_0: 0.54660/0.33386, loss_mask_dice_0: 2.70082/1.16145, loss_spatial_bce_0: 0.13611/0.08658, loss_spatial_dice_0: 0.42186/0.20663, loss_spatial_ce_0: 0.03957/0.05971, loss_grounding_bce_0: 0.03860/0.08612, loss_grounding_dice_0: 0.49902/0.17840, loss_grounding_ce_0: 0.47691/0.27137, loss_mask_ce_1: 0.83520/0.89618, loss_mask_bce_1: 0.51484/0.33479, loss_mask_dice_1: 2.52025/1.16824, loss_spatial_bce_1: 0.13241/0.08712, loss_spatial_dice_1: 0.43827/0.21061, loss_spatial_ce_1: 0.04432/0.06549, loss_grounding_bce_1: 0.03701/0.08630, loss_grounding_dice_1: 0.50820/0.17918, loss_grounding_ce_1: 0.43845/0.27214, loss_mask_ce_2: 0.83071/0.90313, loss_mask_bce_2: 0.52079/0.33542, loss_mask_dice_2: 2.35835/1.16862, loss_spatial_bce_2: 0.11944/0.08835, loss_spatial_dice_2: 0.46328/0.21246, loss_spatial_ce_2: 0.24860/0.06896, loss_grounding_bce_2: 0.03866/0.08649, loss_grounding_dice_2: 0.45801/0.17906, loss_grounding_ce_2: 0.46733/0.27543, loss_mask_ce_3: 0.75550/0.91426, loss_mask_bce_3: 0.50025/0.33660, loss_mask_dice_3: 2.52832/1.16658, loss_spatial_bce_3: 0.12403/0.08965, loss_spatial_dice_3: 0.45087/0.21351, loss_spatial_ce_3: 0.13491/0.07400, loss_grounding_bce_3: 0.04939/0.08673, loss_grounding_dice_3: 0.42139/0.17875, loss_grounding_ce_3: 0.46520/0.27778, loss_mask_ce_4: 0.74665/0.91556, loss_mask_bce_4: 0.52836/0.33873, loss_mask_dice_4: 2.57855/1.19033, loss_spatial_bce_4: 0.16559/0.09357, loss_spatial_dice_4: 0.48968/0.22579, loss_spatial_ce_4: 0.12419/0.09034, loss_grounding_bce_4: 0.05014/0.08727, loss_grounding_dice_4: 0.44211/0.18175, loss_grounding_ce_4: 0.51649/0.28077, loss_mask_ce_5: 0.71933/0.93248, loss_mask_bce_5: 0.48953/0.34111, loss_mask_dice_5: 2.32149/1.19868, loss_spatial_bce_5: 0.16134/0.09590, loss_spatial_dice_5: 0.48113/0.23014, loss_spatial_ce_5: 0.10344/0.10422, loss_grounding_bce_5: 0.05991/0.08771, loss_grounding_dice_5: 0.46993/0.18301, loss_grounding_ce_5: 0.50624/0.29326, loss_mask_ce_6: 0.90091/0.97258, loss_mask_bce_6: 0.53091/0.34395, loss_mask_dice_6: 2.83160/1.20155, loss_spatial_bce_6: 0.17746/0.10149, loss_spatial_dice_6: 0.44682/0.23311, loss_spatial_ce_6: 0.18271/0.12924, loss_grounding_bce_6: 0.05785/0.08844, loss_grounding_dice_6: 0.49996/0.18342, loss_grounding_ce_6: 0.52539/0.30870, loss_mask_ce_7: 1.15399/1.01829, loss_mask_bce_7: 0.55515/0.35173, loss_mask_dice_7: 2.41357/1.25599, loss_spatial_bce_7: 0.25278/0.10937, loss_spatial_dice_7: 0.56345/0.26068, loss_spatial_ce_7: 0.35470/0.16418, loss_grounding_bce_7: 0.10661/0.09033, loss_grounding_dice_7: 0.51206/0.19075, loss_grounding_ce_7: 0.46370/0.33863, loss_mask_ce_8: 0.89697/1.12691, loss_mask_bce_8: 0.79121/0.36534, loss_mask_dice_8: 2.80694/1.32867, loss_spatial_bce_8: 0.21123/0.12960, loss_spatial_dice_8: 0.64580/0.29832, loss_spatial_ce_8: 0.20873/0.21647, loss_grounding_bce_8: 0.10091/0.09401, loss_grounding_dice_8: 0.52175/0.20142, loss_grounding_ce_8: 0.57629/0.40498, loss_mask_ce_9: 2.59134/3.67418, loss_mask_bce_9: 0.55202/0.39241, loss_mask_dice_9: 2.83495/1.90132, loss_spatial_bce_9: 0.20343/0.33260, loss_spatial_dice_9: 0.83931/0.82165, loss_spatial_ce_9: 2.18351/1.49288, loss_grounding_bce_9: 0.06871/0.10560, loss_grounding_dice_9: 0.54312/0.28080, loss_grounding_ce_9: 0.45476/0.66924] items per batch[64] items per second[0.24] total items[5062400] mini batches[ 79100] memory[7345] epoch remaining[0:59:50]
INFO:trainer.default_trainer:epochs[    43] optim steps[79200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83217/0.89555, loss_mask_bce_0: 0.26435/0.33386, loss_mask_dice_0: 0.69362/1.16167, loss_spatial_bce_0: 0.06235/0.08657, loss_spatial_dice_0: 0.14447/0.20662, loss_spatial_ce_0: 0.01643/0.05970, loss_grounding_bce_0: 0.04106/0.08612, loss_grounding_dice_0: 0.06945/0.17839, loss_grounding_ce_0: 0.01790/0.27138, loss_mask_ce_1: 0.85659/0.89621, loss_mask_bce_1: 0.26374/0.33480, loss_mask_dice_1: 0.69108/1.16845, loss_spatial_bce_1: 0.06534/0.08711, loss_spatial_dice_1: 0.15794/0.21060, loss_spatial_ce_1: 0.01506/0.06549, loss_grounding_bce_1: 0.03869/0.08630, loss_grounding_dice_1: 0.06690/0.17917, loss_grounding_ce_1: 0.02564/0.27218, loss_mask_ce_2: 0.89218/0.90314, loss_mask_bce_2: 0.30225/0.33542, loss_mask_dice_2: 0.71148/1.16882, loss_spatial_bce_2: 0.08072/0.08834, loss_spatial_dice_2: 0.15414/0.21245, loss_spatial_ce_2: 0.01976/0.06895, loss_grounding_bce_2: 0.03895/0.08649, loss_grounding_dice_2: 0.06658/0.17905, loss_grounding_ce_2: 0.03392/0.27544, loss_mask_ce_3: 0.96366/0.91428, loss_mask_bce_3: 0.31239/0.33661, loss_mask_dice_3: 0.71490/1.16679, loss_spatial_bce_3: 0.07896/0.08964, loss_spatial_dice_3: 0.14904/0.21350, loss_spatial_ce_3: 0.01302/0.07400, loss_grounding_bce_3: 0.04005/0.08674, loss_grounding_dice_3: 0.06618/0.17874, loss_grounding_ce_3: 0.01571/0.27778, loss_mask_ce_4: 0.78398/0.91560, loss_mask_bce_4: 0.26180/0.33874, loss_mask_dice_4: 0.71207/1.19054, loss_spatial_bce_4: 0.09216/0.09356, loss_spatial_dice_4: 0.17679/0.22578, loss_spatial_ce_4: 0.02536/0.09033, loss_grounding_bce_4: 0.04104/0.08727, loss_grounding_dice_4: 0.06490/0.18174, loss_grounding_ce_4: 0.02623/0.28077, loss_mask_ce_5: 0.82332/0.93253, loss_mask_bce_5: 0.26105/0.34111, loss_mask_dice_5: 0.71484/1.19888, loss_spatial_bce_5: 0.09364/0.09590, loss_spatial_dice_5: 0.17560/0.23014, loss_spatial_ce_5: 0.02505/0.10420, loss_grounding_bce_5: 0.04266/0.08770, loss_grounding_dice_5: 0.07452/0.18301, loss_grounding_ce_5: 0.04384/0.29328, loss_mask_ce_6: 0.85786/0.97265, loss_mask_bce_6: 0.25677/0.34395, loss_mask_dice_6: 0.69018/1.20176, loss_spatial_bce_6: 0.09766/0.10150, loss_spatial_dice_6: 0.18260/0.23310, loss_spatial_ce_6: 0.02265/0.12922, loss_grounding_bce_6: 0.04217/0.08844, loss_grounding_dice_6: 0.06603/0.18341, loss_grounding_ce_6: 0.03264/0.30875, loss_mask_ce_7: 0.80859/1.01836, loss_mask_bce_7: 0.26209/0.35174, loss_mask_dice_7: 0.69526/1.25625, loss_spatial_bce_7: 0.11417/0.10937, loss_spatial_dice_7: 0.20067/0.26069, loss_spatial_ce_7: 0.05645/0.16416, loss_grounding_bce_7: 0.03989/0.09033, loss_grounding_dice_7: 0.07327/0.19074, loss_grounding_ce_7: 0.09212/0.33868, loss_mask_ce_8: 0.78975/1.12698, loss_mask_bce_8: 0.27686/0.36535, loss_mask_dice_8: 0.73833/1.32894, loss_spatial_bce_8: 0.09071/0.12960, loss_spatial_dice_8: 0.24259/0.29834, loss_spatial_ce_8: 0.08749/0.21643, loss_grounding_bce_8: 0.03985/0.09401, loss_grounding_dice_8: 0.06751/0.20142, loss_grounding_ce_8: 0.52400/0.40498, loss_mask_ce_9: 2.64435/3.67436, loss_mask_bce_9: 0.36018/0.39241, loss_mask_dice_9: 1.07009/1.90170, loss_spatial_bce_9: 0.31352/0.33261, loss_spatial_dice_9: 0.88545/0.82164, loss_spatial_ce_9: 1.34949/1.49292, loss_grounding_bce_9: 0.04444/0.10561, loss_grounding_dice_9: 0.10258/0.28080, loss_grounding_ce_9: 0.59452/0.66926] items per batch[64] items per second[0.24] total items[5068800] mini batches[ 79200] memory[7345] epoch remaining[0:54:54]
INFO:trainer.default_trainer:epochs[    43] optim steps[79300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.57375/0.89547, loss_mask_bce_0: 0.40308/0.33382, loss_mask_dice_0: 1.17218/1.16142, loss_spatial_bce_0: 0.17647/0.08657, loss_spatial_dice_0: 0.22788/0.20661, loss_spatial_ce_0: 0.08146/0.05968, loss_grounding_bce_0: 0.02952/0.08611, loss_grounding_dice_0: 0.09362/0.17836, loss_grounding_ce_0: 0.24435/0.27141, loss_mask_ce_1: 2.52731/0.89615, loss_mask_bce_1: 0.40305/0.33475, loss_mask_dice_1: 1.09450/1.16818, loss_spatial_bce_1: 0.17102/0.08711, loss_spatial_dice_1: 0.20674/0.21058, loss_spatial_ce_1: 0.10221/0.06546, loss_grounding_bce_1: 0.02993/0.08629, loss_grounding_dice_1: 0.12871/0.17914, loss_grounding_ce_1: 0.23882/0.27222, loss_mask_ce_2: 2.65715/0.90306, loss_mask_bce_2: 0.39688/0.33538, loss_mask_dice_2: 0.99102/1.16857, loss_spatial_bce_2: 0.17152/0.08834, loss_spatial_dice_2: 0.18777/0.21243, loss_spatial_ce_2: 0.65759/0.06893, loss_grounding_bce_2: 0.02898/0.08648, loss_grounding_dice_2: 0.11312/0.17903, loss_grounding_ce_2: 0.23561/0.27549, loss_mask_ce_3: 2.52626/0.91419, loss_mask_bce_3: 0.41195/0.33657, loss_mask_dice_3: 1.16214/1.16656, loss_spatial_bce_3: 0.17313/0.08964, loss_spatial_dice_3: 0.17282/0.21349, loss_spatial_ce_3: 0.51064/0.07398, loss_grounding_bce_3: 0.02971/0.08672, loss_grounding_dice_3: 0.10545/0.17872, loss_grounding_ce_3: 0.23588/0.27785, loss_mask_ce_4: 2.87894/0.91553, loss_mask_bce_4: 0.39441/0.33870, loss_mask_dice_4: 1.15175/1.19029, loss_spatial_bce_4: 0.16474/0.09356, loss_spatial_dice_4: 0.21707/0.22577, loss_spatial_ce_4: 0.11819/0.09031, loss_grounding_bce_4: 0.03039/0.08727, loss_grounding_dice_4: 0.10526/0.18172, loss_grounding_ce_4: 0.26364/0.28083, loss_mask_ce_5: 2.72716/0.93245, loss_mask_bce_5: 0.37082/0.34109, loss_mask_dice_5: 1.13934/1.19864, loss_spatial_bce_5: 0.18946/0.09590, loss_spatial_dice_5: 0.25963/0.23013, loss_spatial_ce_5: 0.10133/0.10416, loss_grounding_bce_5: 0.03060/0.08769, loss_grounding_dice_5: 0.10886/0.18298, loss_grounding_ce_5: 0.25440/0.29334, loss_mask_ce_6: 2.49774/0.97254, loss_mask_bce_6: 0.39141/0.34392, loss_mask_dice_6: 0.99634/1.20151, loss_spatial_bce_6: 0.20878/0.10149, loss_spatial_dice_6: 0.28042/0.23309, loss_spatial_ce_6: 0.12876/0.12918, loss_grounding_bce_6: 0.03867/0.08844, loss_grounding_dice_6: 0.09503/0.18339, loss_grounding_ce_6: 0.26272/0.30879, loss_mask_ce_7: 2.46405/1.01830, loss_mask_bce_7: 0.45628/0.35170, loss_mask_dice_7: 1.27585/1.25601, loss_spatial_bce_7: 0.19045/0.10937, loss_spatial_dice_7: 0.32553/0.26067, loss_spatial_ce_7: 0.14314/0.16412, loss_grounding_bce_7: 0.05616/0.09032, loss_grounding_dice_7: 0.06934/0.19072, loss_grounding_ce_7: 0.28364/0.33871, loss_mask_ce_8: 2.75413/1.12691, loss_mask_bce_8: 0.42507/0.36532, loss_mask_dice_8: 1.24669/1.32865, loss_spatial_bce_8: 0.26126/0.12960, loss_spatial_dice_8: 0.30801/0.29832, loss_spatial_ce_8: 0.14340/0.21636, loss_grounding_bce_8: 0.03417/0.09400, loss_grounding_dice_8: 0.12004/0.20140, loss_grounding_ce_8: 0.28875/0.40503, loss_mask_ce_9: 4.57634/3.67412, loss_mask_bce_9: 0.47814/0.39237, loss_mask_dice_9: 3.12695/1.90127, loss_spatial_bce_9: 0.41432/0.33262, loss_spatial_dice_9: 0.74390/0.82164, loss_spatial_ce_9: 1.43172/1.49285, loss_grounding_bce_9: 0.04543/0.10560, loss_grounding_dice_9: 0.16622/0.28078, loss_grounding_ce_9: 0.41755/0.66931] items per batch[64] items per second[0.24] total items[5075200] mini batches[ 79300] memory[7345] epoch remaining[0:50:03]
INFO:trainer.default_trainer:epochs[    43] optim steps[79400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55574/0.89543, loss_mask_bce_0: 0.28989/0.33382, loss_mask_dice_0: 0.29419/1.16161, loss_spatial_bce_0: 0.14034/0.08656, loss_spatial_dice_0: 0.13947/0.20659, loss_spatial_ce_0: 0.04286/0.05967, loss_grounding_bce_0: 0.11855/0.08611, loss_grounding_dice_0: 0.12646/0.17836, loss_grounding_ce_0: 0.52746/0.27138, loss_mask_ce_1: 0.56932/0.89610, loss_mask_bce_1: 0.29783/0.33475, loss_mask_dice_1: 0.28560/1.16838, loss_spatial_bce_1: 0.13479/0.08710, loss_spatial_dice_1: 0.13652/0.21057, loss_spatial_ce_1: 0.03012/0.06546, loss_grounding_bce_1: 0.11940/0.08629, loss_grounding_dice_1: 0.09827/0.17914, loss_grounding_ce_1: 0.52000/0.27219, loss_mask_ce_2: 0.53064/0.90302, loss_mask_bce_2: 0.30833/0.33539, loss_mask_dice_2: 0.28757/1.16875, loss_spatial_bce_2: 0.14631/0.08833, loss_spatial_dice_2: 0.14503/0.21242, loss_spatial_ce_2: 0.02459/0.06893, loss_grounding_bce_2: 0.11054/0.08648, loss_grounding_dice_2: 0.09424/0.17902, loss_grounding_ce_2: 0.46532/0.27547, loss_mask_ce_3: 0.56175/0.91416, loss_mask_bce_3: 0.30125/0.33657, loss_mask_dice_3: 0.27419/1.16674, loss_spatial_bce_3: 0.14864/0.08964, loss_spatial_dice_3: 0.15537/0.21347, loss_spatial_ce_3: 0.04136/0.07398, loss_grounding_bce_3: 0.10673/0.08672, loss_grounding_dice_3: 0.09518/0.17871, loss_grounding_ce_3: 0.52055/0.27783, loss_mask_ce_4: 0.58077/0.91552, loss_mask_bce_4: 0.30290/0.33871, loss_mask_dice_4: 0.26033/1.19049, loss_spatial_bce_4: 0.15102/0.09356, loss_spatial_dice_4: 0.14841/0.22575, loss_spatial_ce_4: 0.03302/0.09031, loss_grounding_bce_4: 0.12407/0.08727, loss_grounding_dice_4: 0.12400/0.18172, loss_grounding_ce_4: 0.56568/0.28082, loss_mask_ce_5: 0.62692/0.93242, loss_mask_bce_5: 0.28942/0.34109, loss_mask_dice_5: 0.22846/1.19883, loss_spatial_bce_5: 0.15814/0.09590, loss_spatial_dice_5: 0.15345/0.23012, loss_spatial_ce_5: 0.04385/0.10417, loss_grounding_bce_5: 0.10916/0.08770, loss_grounding_dice_5: 0.08492/0.18298, loss_grounding_ce_5: 0.83340/0.29332, loss_mask_ce_6: 0.67544/0.97254, loss_mask_bce_6: 0.31073/0.34393, loss_mask_dice_6: 0.26877/1.20169, loss_spatial_bce_6: 0.16150/0.10149, loss_spatial_dice_6: 0.16868/0.23309, loss_spatial_ce_6: 0.03950/0.12916, loss_grounding_bce_6: 0.12104/0.08844, loss_grounding_dice_6: 0.10537/0.18339, loss_grounding_ce_6: 0.96132/0.30876, loss_mask_ce_7: 0.76202/1.01826, loss_mask_bce_7: 0.31893/0.35171, loss_mask_dice_7: 0.30322/1.25620, loss_spatial_bce_7: 0.17131/0.10937, loss_spatial_dice_7: 0.18592/0.26066, loss_spatial_ce_7: 0.14186/0.16410, loss_grounding_bce_7: 0.11707/0.09032, loss_grounding_dice_7: 0.11089/0.19072, loss_grounding_ce_7: 0.69628/0.33871, loss_mask_ce_8: 0.97915/1.12688, loss_mask_bce_8: 0.31631/0.36532, loss_mask_dice_8: 0.30863/1.32884, loss_spatial_bce_8: 0.17901/0.12960, loss_spatial_dice_8: 0.22421/0.29832, loss_spatial_ce_8: 0.10139/0.21630, loss_grounding_bce_8: 0.10946/0.09400, loss_grounding_dice_8: 0.08980/0.20140, loss_grounding_ce_8: 0.74779/0.40503, loss_mask_ce_9: 3.31584/3.67425, loss_mask_bce_9: 0.35580/0.39235, loss_mask_dice_9: 0.51419/1.90146, loss_spatial_bce_9: 0.57124/0.33264, loss_spatial_dice_9: 0.67021/0.82163, loss_spatial_ce_9: 0.83139/1.49283, loss_grounding_bce_9: 0.16341/0.10560, loss_grounding_dice_9: 0.24210/0.28077, loss_grounding_ce_9: 2.02773/0.66941] items per batch[64] items per second[0.24] total items[5081600] mini batches[ 79400] memory[7345] epoch remaining[0:45:18]
INFO:trainer.default_trainer:epochs[    43] optim steps[79500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43354/0.89542, loss_mask_bce_0: 0.59923/0.33385, loss_mask_dice_0: 0.95200/1.16156, loss_spatial_bce_0: 0.09052/0.08657, loss_spatial_dice_0: 0.14938/0.20658, loss_spatial_ce_0: 0.06842/0.05965, loss_grounding_bce_0: 0.17642/0.08612, loss_grounding_dice_0: 0.16342/0.17837, loss_grounding_ce_0: 1.29390/0.27136, loss_mask_ce_1: 1.39437/0.89609, loss_mask_bce_1: 0.60233/0.33478, loss_mask_dice_1: 0.96752/1.16831, loss_spatial_bce_1: 0.09819/0.08710, loss_spatial_dice_1: 0.14687/0.21056, loss_spatial_ce_1: 0.06072/0.06544, loss_grounding_bce_1: 0.17613/0.08630, loss_grounding_dice_1: 0.16671/0.17915, loss_grounding_ce_1: 1.28241/0.27218, loss_mask_ce_2: 1.50758/0.90299, loss_mask_bce_2: 0.60646/0.33541, loss_mask_dice_2: 0.96186/1.16870, loss_spatial_bce_2: 0.10187/0.08834, loss_spatial_dice_2: 0.15305/0.21241, loss_spatial_ce_2: 0.04845/0.06892, loss_grounding_bce_2: 0.17820/0.08649, loss_grounding_dice_2: 0.16221/0.17904, loss_grounding_ce_2: 0.87568/0.27545, loss_mask_ce_3: 1.52425/0.91416, loss_mask_bce_3: 0.62664/0.33659, loss_mask_dice_3: 0.93973/1.16667, loss_spatial_bce_3: 0.09922/0.08964, loss_spatial_dice_3: 0.15779/0.21347, loss_spatial_ce_3: 0.04673/0.07397, loss_grounding_bce_3: 0.18242/0.08674, loss_grounding_dice_3: 0.16659/0.17872, loss_grounding_ce_3: 1.01618/0.27784, loss_mask_ce_4: 1.52012/0.91551, loss_mask_bce_4: 0.61591/0.33873, loss_mask_dice_4: 0.93995/1.19044, loss_spatial_bce_4: 0.10037/0.09356, loss_spatial_dice_4: 0.17359/0.22574, loss_spatial_ce_4: 0.06385/0.09029, loss_grounding_bce_4: 0.17707/0.08729, loss_grounding_dice_4: 0.15118/0.18173, loss_grounding_ce_4: 1.15197/0.28081, loss_mask_ce_5: 1.48379/0.93240, loss_mask_bce_5: 0.61629/0.34111, loss_mask_dice_5: 0.92621/1.19878, loss_spatial_bce_5: 0.11612/0.09590, loss_spatial_dice_5: 0.20794/0.23011, loss_spatial_ce_5: 0.07373/0.10414, loss_grounding_bce_5: 0.17703/0.08772, loss_grounding_dice_5: 0.16069/0.18299, loss_grounding_ce_5: 1.16912/0.29332, loss_mask_ce_6: 1.62408/0.97254, loss_mask_bce_6: 0.60406/0.34396, loss_mask_dice_6: 0.94693/1.20164, loss_spatial_bce_6: 0.15230/0.10149, loss_spatial_dice_6: 0.22270/0.23308, loss_spatial_ce_6: 0.08990/0.12913, loss_grounding_bce_6: 0.17396/0.08846, loss_grounding_dice_6: 0.16406/0.18340, loss_grounding_ce_6: 1.47799/0.30876, loss_mask_ce_7: 1.74920/1.01830, loss_mask_bce_7: 0.58777/0.35173, loss_mask_dice_7: 0.92001/1.25614, loss_spatial_bce_7: 0.14416/0.10938, loss_spatial_dice_7: 0.20122/0.26066, loss_spatial_ce_7: 0.08990/0.16406, loss_grounding_bce_7: 0.17904/0.09034, loss_grounding_dice_7: 0.16194/0.19074, loss_grounding_ce_7: 1.57568/0.33875, loss_mask_ce_8: 1.89986/1.12686, loss_mask_bce_8: 0.66083/0.36536, loss_mask_dice_8: 1.10455/1.32880, loss_spatial_bce_8: 0.20174/0.12960, loss_spatial_dice_8: 0.22527/0.29831, loss_spatial_ce_8: 0.10369/0.21624, loss_grounding_bce_8: 0.18257/0.09403, loss_grounding_dice_8: 0.17468/0.20142, loss_grounding_ce_8: 2.13585/0.40504, loss_mask_ce_9: 4.13223/3.67431, loss_mask_bce_9: 0.76739/0.39240, loss_mask_dice_9: 1.81506/1.90140, loss_spatial_bce_9: 0.34742/0.33266, loss_spatial_dice_9: 0.87043/0.82164, loss_spatial_ce_9: 1.20764/1.49284, loss_grounding_bce_9: 0.18190/0.10563, loss_grounding_dice_9: 0.24251/0.28078, loss_grounding_ce_9: 1.11542/0.66951] items per batch[64] items per second[0.23] total items[5088000] mini batches[ 79500] memory[7345] epoch remaining[0:40:45]
INFO:trainer.default_trainer:epochs[    43] optim steps[79600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75186/0.89538, loss_mask_bce_0: 0.13134/0.33385, loss_mask_dice_0: 0.43702/1.16141, loss_spatial_bce_0: 0.03204/0.08657, loss_spatial_dice_0: 0.13687/0.20656, loss_spatial_ce_0: 0.00073/0.05965, loss_grounding_bce_0: 0.01607/0.08613, loss_grounding_dice_0: 0.05683/0.17835, loss_grounding_ce_0: 0.21411/0.27141, loss_mask_ce_1: 0.67112/0.89602, loss_mask_bce_1: 0.12283/0.33479, loss_mask_dice_1: 0.46937/1.16819, loss_spatial_bce_1: 0.03190/0.08711, loss_spatial_dice_1: 0.13551/0.21053, loss_spatial_ce_1: 0.00087/0.06544, loss_grounding_bce_1: 0.01228/0.08631, loss_grounding_dice_1: 0.04233/0.17914, loss_grounding_ce_1: 0.21508/0.27222, loss_mask_ce_2: 0.74485/0.90290, loss_mask_bce_2: 0.13167/0.33541, loss_mask_dice_2: 0.47408/1.16856, loss_spatial_bce_2: 0.03456/0.08834, loss_spatial_dice_2: 0.15271/0.21239, loss_spatial_ce_2: 0.00094/0.06892, loss_grounding_bce_2: 0.01783/0.08650, loss_grounding_dice_2: 0.05522/0.17903, loss_grounding_ce_2: 0.18157/0.27549, loss_mask_ce_3: 0.69774/0.91410, loss_mask_bce_3: 0.12777/0.33660, loss_mask_dice_3: 0.45683/1.16652, loss_spatial_bce_3: 0.04138/0.08965, loss_spatial_dice_3: 0.17579/0.21344, loss_spatial_ce_3: 0.00650/0.07397, loss_grounding_bce_3: 0.01298/0.08674, loss_grounding_dice_3: 0.04664/0.17871, loss_grounding_ce_3: 0.35076/0.27789, loss_mask_ce_4: 0.75553/0.91546, loss_mask_bce_4: 0.13142/0.33873, loss_mask_dice_4: 0.48360/1.19029, loss_spatial_bce_4: 0.03794/0.09357, loss_spatial_dice_4: 0.15576/0.22572, loss_spatial_ce_4: 0.03700/0.09028, loss_grounding_bce_4: 0.01810/0.08730, loss_grounding_dice_4: 0.05778/0.18172, loss_grounding_ce_4: 0.11427/0.28084, loss_mask_ce_5: 0.74646/0.93232, loss_mask_bce_5: 0.12350/0.34112, loss_mask_dice_5: 0.47434/1.19862, loss_spatial_bce_5: 0.04379/0.09591, loss_spatial_dice_5: 0.19274/0.23009, loss_spatial_ce_5: 0.06868/0.10414, loss_grounding_bce_5: 0.01708/0.08772, loss_grounding_dice_5: 0.05450/0.18299, loss_grounding_ce_5: 0.06630/0.29335, loss_mask_ce_6: 0.82600/0.97249, loss_mask_bce_6: 0.13518/0.34397, loss_mask_dice_6: 0.45329/1.20150, loss_spatial_bce_6: 0.05401/0.10150, loss_spatial_dice_6: 0.21785/0.23306, loss_spatial_ce_6: 0.04823/0.12911, loss_grounding_bce_6: 0.01662/0.08846, loss_grounding_dice_6: 0.05552/0.18339, loss_grounding_ce_6: 0.05785/0.30877, loss_mask_ce_7: 0.97186/1.01825, loss_mask_bce_7: 0.12465/0.35174, loss_mask_dice_7: 0.41539/1.25600, loss_spatial_bce_7: 0.07038/0.10939, loss_spatial_dice_7: 0.27915/0.26063, loss_spatial_ce_7: 0.11915/0.16403, loss_grounding_bce_7: 0.02293/0.09034, loss_grounding_dice_7: 0.06485/0.19073, loss_grounding_ce_7: 0.26517/0.33875, loss_mask_ce_8: 0.74133/1.12678, loss_mask_bce_8: 0.12953/0.36535, loss_mask_dice_8: 0.48481/1.32863, loss_spatial_bce_8: 0.10751/0.12961, loss_spatial_dice_8: 0.25890/0.29828, loss_spatial_ce_8: 0.07356/0.21617, loss_grounding_bce_8: 0.01707/0.09402, loss_grounding_dice_8: 0.05902/0.20141, loss_grounding_ce_8: 0.16916/0.40505, loss_mask_ce_9: 2.79963/3.67418, loss_mask_bce_9: 0.14056/0.39240, loss_mask_dice_9: 0.61769/1.90116, loss_spatial_bce_9: 0.41378/0.33270, loss_spatial_dice_9: 0.86381/0.82163, loss_spatial_ce_9: 1.60588/1.49279, loss_grounding_bce_9: 0.02079/0.10563, loss_grounding_dice_9: 0.08591/0.28078, loss_grounding_ce_9: 1.21468/0.66940] items per batch[64] items per second[0.23] total items[5094400] mini batches[ 79600] memory[7345] epoch remaining[0:36:12]
INFO:trainer.default_trainer:epochs[    43] optim steps[79700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.17226/0.89541, loss_mask_bce_0: 0.46386/0.33388, loss_mask_dice_0: 0.95361/1.16146, loss_spatial_bce_0: 0.17806/0.08658, loss_spatial_dice_0: 0.30391/0.20656, loss_spatial_ce_0: 0.15453/0.05964, loss_grounding_bce_0: 0.12721/0.08614, loss_grounding_dice_0: 0.10378/0.17838, loss_grounding_ce_0: 0.00218/0.27141, loss_mask_ce_1: 1.33974/0.89605, loss_mask_bce_1: 0.50675/0.33482, loss_mask_dice_1: 0.92326/1.16825, loss_spatial_bce_1: 0.17347/0.08711, loss_spatial_dice_1: 0.28831/0.21053, loss_spatial_ce_1: 0.29816/0.06543, loss_grounding_bce_1: 0.11964/0.08633, loss_grounding_dice_1: 0.10219/0.17916, loss_grounding_ce_1: 0.00284/0.27221, loss_mask_ce_2: 1.22690/0.90289, loss_mask_bce_2: 0.50399/0.33545, loss_mask_dice_2: 0.95031/1.16863, loss_spatial_bce_2: 0.18918/0.08835, loss_spatial_dice_2: 0.31172/0.21239, loss_spatial_ce_2: 0.23197/0.06891, loss_grounding_bce_2: 0.12137/0.08652, loss_grounding_dice_2: 0.10357/0.17905, loss_grounding_ce_2: 0.00245/0.27547, loss_mask_ce_3: 1.13034/0.91409, loss_mask_bce_3: 0.52952/0.33664, loss_mask_dice_3: 1.02094/1.16658, loss_spatial_bce_3: 0.17948/0.08965, loss_spatial_dice_3: 0.29899/0.21345, loss_spatial_ce_3: 0.17883/0.07397, loss_grounding_bce_3: 0.11746/0.08676, loss_grounding_dice_3: 0.10485/0.17873, loss_grounding_ce_3: 0.00131/0.27790, loss_mask_ce_4: 1.02524/0.91547, loss_mask_bce_4: 0.52949/0.33877, loss_mask_dice_4: 1.08400/1.19036, loss_spatial_bce_4: 0.13444/0.09357, loss_spatial_dice_4: 0.27126/0.22572, loss_spatial_ce_4: 0.25726/0.09029, loss_grounding_bce_4: 0.13593/0.08732, loss_grounding_dice_4: 0.11436/0.18175, loss_grounding_ce_4: 0.00119/0.28081, loss_mask_ce_5: 1.13948/0.93233, loss_mask_bce_5: 0.53959/0.34116, loss_mask_dice_5: 1.10432/1.19871, loss_spatial_bce_5: 0.14175/0.09592, loss_spatial_dice_5: 0.28194/0.23009, loss_spatial_ce_5: 0.29258/0.10415, loss_grounding_bce_5: 0.12471/0.08774, loss_grounding_dice_5: 0.10949/0.18301, loss_grounding_ce_5: 0.00381/0.29337, loss_mask_ce_6: 1.28439/0.97250, loss_mask_bce_6: 0.53066/0.34401, loss_mask_dice_6: 1.10333/1.20156, loss_spatial_bce_6: 0.17180/0.10150, loss_spatial_dice_6: 0.28827/0.23306, loss_spatial_ce_6: 0.30451/0.12911, loss_grounding_bce_6: 0.12104/0.08848, loss_grounding_dice_6: 0.10637/0.18341, loss_grounding_ce_6: 0.00133/0.30879, loss_mask_ce_7: 1.40846/1.01829, loss_mask_bce_7: 0.51926/0.35177, loss_mask_dice_7: 1.20709/1.25608, loss_spatial_bce_7: 0.46154/0.10940, loss_spatial_dice_7: 0.34515/0.26064, loss_spatial_ce_7: 0.20062/0.16404, loss_grounding_bce_7: 0.12979/0.09036, loss_grounding_dice_7: 0.11837/0.19076, loss_grounding_ce_7: 0.10339/0.33872, loss_mask_ce_8: 1.61617/1.12680, loss_mask_bce_8: 0.49660/0.36538, loss_mask_dice_8: 1.15349/1.32869, loss_spatial_bce_8: 0.38645/0.12961, loss_spatial_dice_8: 0.40482/0.29829, loss_spatial_ce_8: 0.10470/0.21615, loss_grounding_bce_8: 0.11994/0.09404, loss_grounding_dice_8: 0.09897/0.20143, loss_grounding_ce_8: 0.01806/0.40502, loss_mask_ce_9: 3.21346/3.67428, loss_mask_bce_9: 0.59525/0.39244, loss_mask_dice_9: 1.50007/1.90116, loss_spatial_bce_9: 0.34470/0.33269, loss_spatial_dice_9: 0.82887/0.82163, loss_spatial_ce_9: 1.78170/1.49280, loss_grounding_bce_9: 0.13102/0.10564, loss_grounding_dice_9: 0.08555/0.28079, loss_grounding_ce_9: 0.38391/0.66938] items per batch[64] items per second[0.24] total items[5100800] mini batches[ 79700] memory[7345] epoch remaining[0:31:31]
INFO:trainer.default_trainer:epochs[    43] optim steps[79800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34896/0.89532, loss_mask_bce_0: 0.29998/0.33388, loss_mask_dice_0: 1.36656/1.16145, loss_spatial_bce_0: 0.05593/0.08658, loss_spatial_dice_0: 0.23116/0.20656, loss_spatial_ce_0: 0.03548/0.05965, loss_grounding_bce_0: 0.11081/0.08614, loss_grounding_dice_0: 0.10910/0.17837, loss_grounding_ce_0: 0.16456/0.27133, loss_mask_ce_1: 1.83007/0.89594, loss_mask_bce_1: 0.28975/0.33482, loss_mask_dice_1: 1.27082/1.16822, loss_spatial_bce_1: 0.06077/0.08712, loss_spatial_dice_1: 0.24866/0.21053, loss_spatial_ce_1: 0.05098/0.06544, loss_grounding_bce_1: 0.11629/0.08632, loss_grounding_dice_1: 0.11560/0.17916, loss_grounding_ce_1: 0.17195/0.27214, loss_mask_ce_2: 1.96671/0.90279, loss_mask_bce_2: 0.29021/0.33545, loss_mask_dice_2: 1.23484/1.16862, loss_spatial_bce_2: 0.05725/0.08835, loss_spatial_dice_2: 0.24494/0.21239, loss_spatial_ce_2: 0.08688/0.06892, loss_grounding_bce_2: 0.11597/0.08651, loss_grounding_dice_2: 0.11187/0.17905, loss_grounding_ce_2: 0.15669/0.27538, loss_mask_ce_3: 1.38052/0.91401, loss_mask_bce_3: 0.30297/0.33664, loss_mask_dice_3: 1.38382/1.16657, loss_spatial_bce_3: 0.05643/0.08966, loss_spatial_dice_3: 0.23502/0.21345, loss_spatial_ce_3: 0.10643/0.07398, loss_grounding_bce_3: 0.12208/0.08676, loss_grounding_dice_3: 0.11048/0.17872, loss_grounding_ce_3: 0.15673/0.27783, loss_mask_ce_4: 1.63897/0.91537, loss_mask_bce_4: 0.31282/0.33876, loss_mask_dice_4: 1.39218/1.19038, loss_spatial_bce_4: 0.06066/0.09357, loss_spatial_dice_4: 0.21942/0.22572, loss_spatial_ce_4: 0.15456/0.09029, loss_grounding_bce_4: 0.12255/0.08732, loss_grounding_dice_4: 0.11560/0.18174, loss_grounding_ce_4: 0.15223/0.28075, loss_mask_ce_5: 1.41617/0.93222, loss_mask_bce_5: 0.35632/0.34115, loss_mask_dice_5: 1.44578/1.19869, loss_spatial_bce_5: 0.06963/0.09592, loss_spatial_dice_5: 0.25142/0.23010, loss_spatial_ce_5: 0.08984/0.10413, loss_grounding_bce_5: 0.15919/0.08774, loss_grounding_dice_5: 0.12739/0.18301, loss_grounding_ce_5: 0.22707/0.29330, loss_mask_ce_6: 1.33508/0.97242, loss_mask_bce_6: 0.33927/0.34400, loss_mask_dice_6: 1.44244/1.20156, loss_spatial_bce_6: 0.07441/0.10151, loss_spatial_dice_6: 0.27466/0.23307, loss_spatial_ce_6: 0.09089/0.12909, loss_grounding_bce_6: 0.14555/0.08847, loss_grounding_dice_6: 0.12578/0.18341, loss_grounding_ce_6: 0.21142/0.30873, loss_mask_ce_7: 1.27910/1.01823, loss_mask_bce_7: 0.35753/0.35175, loss_mask_dice_7: 1.58590/1.25607, loss_spatial_bce_7: 0.11830/0.10941, loss_spatial_dice_7: 0.38032/0.26063, loss_spatial_ce_7: 0.16901/0.16401, loss_grounding_bce_7: 0.09605/0.09035, loss_grounding_dice_7: 0.11030/0.19076, loss_grounding_ce_7: 0.38135/0.33863, loss_mask_ce_8: 2.04191/1.12668, loss_mask_bce_8: 0.32960/0.36537, loss_mask_dice_8: 1.59446/1.32865, loss_spatial_bce_8: 0.12673/0.12961, loss_spatial_dice_8: 0.44279/0.29828, loss_spatial_ce_8: 0.17769/0.21608, loss_grounding_bce_8: 0.09443/0.09404, loss_grounding_dice_8: 0.10451/0.20143, loss_grounding_ce_8: 0.28438/0.40490, loss_mask_ce_9: 3.79370/3.67415, loss_mask_bce_9: 0.35250/0.39243, loss_mask_dice_9: 2.36849/1.90108, loss_spatial_bce_9: 0.39414/0.33269, loss_spatial_dice_9: 0.83807/0.82161, loss_spatial_ce_9: 1.38447/1.49270, loss_grounding_bce_9: 0.08470/0.10565, loss_grounding_dice_9: 0.14634/0.28079, loss_grounding_ce_9: 1.02972/0.66930] items per batch[64] items per second[0.23] total items[5107200] mini batches[ 79800] memory[7345] epoch remaining[0:27:00]
INFO:trainer.default_trainer:epochs[    43] optim steps[79900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07422/0.89527, loss_mask_bce_0: 0.50639/0.33389, loss_mask_dice_0: 1.47368/1.16148, loss_spatial_bce_0: 0.12982/0.08658, loss_spatial_dice_0: 0.31132/0.20655, loss_spatial_ce_0: 0.02150/0.05968, loss_grounding_bce_0: 0.21664/0.08614, loss_grounding_dice_0: 0.25218/0.17837, loss_grounding_ce_0: 0.60502/0.27131, loss_mask_ce_1: 1.08583/0.89590, loss_mask_bce_1: 0.49741/0.33483, loss_mask_dice_1: 1.41786/1.16823, loss_spatial_bce_1: 0.12755/0.08712, loss_spatial_dice_1: 0.31797/0.21052, loss_spatial_ce_1: 0.02408/0.06548, loss_grounding_bce_1: 0.21273/0.08633, loss_grounding_dice_1: 0.24968/0.17917, loss_grounding_ce_1: 0.63452/0.27212, loss_mask_ce_2: 1.33001/0.90275, loss_mask_bce_2: 0.48517/0.33547, loss_mask_dice_2: 1.32061/1.16865, loss_spatial_bce_2: 0.12693/0.08835, loss_spatial_dice_2: 0.33495/0.21238, loss_spatial_ce_2: 0.03394/0.06895, loss_grounding_bce_2: 0.21959/0.08652, loss_grounding_dice_2: 0.22813/0.17905, loss_grounding_ce_2: 0.63609/0.27537, loss_mask_ce_3: 1.07610/0.91397, loss_mask_bce_3: 0.48737/0.33665, loss_mask_dice_3: 1.36691/1.16655, loss_spatial_bce_3: 0.12707/0.08966, loss_spatial_dice_3: 0.31316/0.21344, loss_spatial_ce_3: 0.04195/0.07400, loss_grounding_bce_3: 0.21216/0.08676, loss_grounding_dice_3: 0.23908/0.17873, loss_grounding_ce_3: 0.52638/0.27781, loss_mask_ce_4: 1.70042/0.91536, loss_mask_bce_4: 0.43926/0.33877, loss_mask_dice_4: 1.24170/1.19036, loss_spatial_bce_4: 0.16919/0.09358, loss_spatial_dice_4: 0.34418/0.22570, loss_spatial_ce_4: 0.10503/0.09032, loss_grounding_bce_4: 0.20997/0.08731, loss_grounding_dice_4: 0.23717/0.18173, loss_grounding_ce_4: 0.61792/0.28075, loss_mask_ce_5: 1.63131/0.93219, loss_mask_bce_5: 0.46354/0.34117, loss_mask_dice_5: 1.42903/1.19870, loss_spatial_bce_5: 0.17259/0.09592, loss_spatial_dice_5: 0.37920/0.23009, loss_spatial_ce_5: 0.07258/0.10418, loss_grounding_bce_5: 0.20120/0.08774, loss_grounding_dice_5: 0.24656/0.18301, loss_grounding_ce_5: 0.69209/0.29329, loss_mask_ce_6: 1.44015/0.97242, loss_mask_bce_6: 0.50394/0.34402, loss_mask_dice_6: 1.45802/1.20157, loss_spatial_bce_6: 0.16993/0.10151, loss_spatial_dice_6: 0.35698/0.23306, loss_spatial_ce_6: 0.06174/0.12913, loss_grounding_bce_6: 0.20424/0.08848, loss_grounding_dice_6: 0.26744/0.18341, loss_grounding_ce_6: 0.58373/0.30870, loss_mask_ce_7: 1.66314/1.01821, loss_mask_bce_7: 0.50188/0.35178, loss_mask_dice_7: 1.59868/1.25606, loss_spatial_bce_7: 0.18435/0.10941, loss_spatial_dice_7: 0.39202/0.26062, loss_spatial_ce_7: 0.29884/0.16409, loss_grounding_bce_7: 0.20553/0.09035, loss_grounding_dice_7: 0.27656/0.19075, loss_grounding_ce_7: 0.79203/0.33864, loss_mask_ce_8: 1.69218/1.12673, loss_mask_bce_8: 0.50234/0.36539, loss_mask_dice_8: 1.89010/1.32866, loss_spatial_bce_8: 0.16552/0.12961, loss_spatial_dice_8: 0.43723/0.29827, loss_spatial_ce_8: 0.26176/0.21609, loss_grounding_bce_8: 0.24227/0.09404, loss_grounding_dice_8: 0.30752/0.20144, loss_grounding_ce_8: 0.58172/0.40490, loss_mask_ce_9: 6.14434/3.67419, loss_mask_bce_9: 0.57221/0.39245, loss_mask_dice_9: 2.64754/1.90116, loss_spatial_bce_9: 0.28160/0.33270, loss_spatial_dice_9: 0.87969/0.82160, loss_spatial_ce_9: 1.14192/1.49274, loss_grounding_bce_9: 0.22372/0.10565, loss_grounding_dice_9: 0.30152/0.28079, loss_grounding_ce_9: 0.78751/0.66930] items per batch[64] items per second[0.23] total items[5113600] mini batches[ 79900] memory[7345] epoch remaining[0:22:24]
INFO:trainer.default_trainer:epochs[    43] optim steps[80000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.64709/0.89527, loss_mask_bce_0: 0.22124/0.33389, loss_mask_dice_0: 1.62243/1.16153, loss_spatial_bce_0: 0.04812/0.08659, loss_spatial_dice_0: 0.27916/0.20654, loss_spatial_ce_0: 0.02239/0.05966, loss_grounding_bce_0: 0.01028/0.08614, loss_grounding_dice_0: 0.09337/0.17839, loss_grounding_ce_0: 0.01550/0.27131, loss_mask_ce_1: 1.54970/0.89588, loss_mask_bce_1: 0.19257/0.33483, loss_mask_dice_1: 1.54033/1.16828, loss_spatial_bce_1: 0.04151/0.08713, loss_spatial_dice_1: 0.28611/0.21051, loss_spatial_ce_1: 0.04346/0.06545, loss_grounding_bce_1: 0.00861/0.08632, loss_grounding_dice_1: 0.08277/0.17917, loss_grounding_ce_1: 0.01922/0.27212, loss_mask_ce_2: 1.51844/0.90274, loss_mask_bce_2: 0.18383/0.33547, loss_mask_dice_2: 1.63363/1.16869, loss_spatial_bce_2: 0.03895/0.08837, loss_spatial_dice_2: 0.26599/0.21237, loss_spatial_ce_2: 0.24420/0.06894, loss_grounding_bce_2: 0.01172/0.08651, loss_grounding_dice_2: 0.07761/0.17907, loss_grounding_ce_2: 0.02099/0.27536, loss_mask_ce_3: 1.57142/0.91396, loss_mask_bce_3: 0.18550/0.33665, loss_mask_dice_3: 1.77080/1.16659, loss_spatial_bce_3: 0.05276/0.08967, loss_spatial_dice_3: 0.26547/0.21343, loss_spatial_ce_3: 0.22300/0.07398, loss_grounding_bce_3: 0.01143/0.08675, loss_grounding_dice_3: 0.14005/0.17874, loss_grounding_ce_3: 0.01411/0.27781, loss_mask_ce_4: 1.52461/0.91534, loss_mask_bce_4: 0.20553/0.33878, loss_mask_dice_4: 1.55913/1.19039, loss_spatial_bce_4: 0.03894/0.09359, loss_spatial_dice_4: 0.32022/0.22570, loss_spatial_ce_4: 0.20533/0.09030, loss_grounding_bce_4: 0.01010/0.08731, loss_grounding_dice_4: 0.08506/0.18174, loss_grounding_ce_4: 0.02608/0.28075, loss_mask_ce_5: 1.62820/0.93216, loss_mask_bce_5: 0.23825/0.34118, loss_mask_dice_5: 1.64518/1.19874, loss_spatial_bce_5: 0.05277/0.09594, loss_spatial_dice_5: 0.38739/0.23008, loss_spatial_ce_5: 0.18002/0.10417, loss_grounding_bce_5: 0.01029/0.08774, loss_grounding_dice_5: 0.11106/0.18302, loss_grounding_ce_5: 0.02431/0.29329, loss_mask_ce_6: 1.73626/0.97240, loss_mask_bce_6: 0.22631/0.34402, loss_mask_dice_6: 1.63179/1.20165, loss_spatial_bce_6: 0.06996/0.10152, loss_spatial_dice_6: 0.37626/0.23305, loss_spatial_ce_6: 0.17043/0.12912, loss_grounding_bce_6: 0.01209/0.08847, loss_grounding_dice_6: 0.07861/0.18343, loss_grounding_ce_6: 0.01897/0.30869, loss_mask_ce_7: 1.75602/1.01822, loss_mask_bce_7: 0.23102/0.35178, loss_mask_dice_7: 1.94985/1.25611, loss_spatial_bce_7: 0.09858/0.10942, loss_spatial_dice_7: 0.46033/0.26061, loss_spatial_ce_7: 0.21614/0.16407, loss_grounding_bce_7: 0.01066/0.09035, loss_grounding_dice_7: 0.17505/0.19077, loss_grounding_ce_7: 0.12462/0.33862, loss_mask_ce_8: 1.52056/1.12673, loss_mask_bce_8: 0.27392/0.36539, loss_mask_dice_8: 2.08500/1.32870, loss_spatial_bce_8: 0.18222/0.12962, loss_spatial_dice_8: 0.46623/0.29827, loss_spatial_ce_8: 0.38231/0.21604, loss_grounding_bce_8: 0.01080/0.09404, loss_grounding_dice_8: 0.14575/0.20145, loss_grounding_ce_8: 0.16732/0.40486, loss_mask_ce_9: 4.93866/3.67424, loss_mask_bce_9: 0.22764/0.39245, loss_mask_dice_9: 2.80814/1.90122, loss_spatial_bce_9: 0.19262/0.33271, loss_spatial_dice_9: 0.88147/0.82160, loss_spatial_ce_9: 1.64989/1.49276, loss_grounding_bce_9: 0.02261/0.10565, loss_grounding_dice_9: 0.26986/0.28081, loss_grounding_ce_9: 0.30782/0.66925] items per batch[64] items per second[0.23] total items[5120000] mini batches[ 80000] memory[7345] epoch remaining[0:17:49]
INFO:trainer.default_trainer:epochs[    43] optim steps[80100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79147/0.89523, loss_mask_bce_0: 0.06321/0.33390, loss_mask_dice_0: 1.06413/1.16170, loss_spatial_bce_0: 0.00762/0.08659, loss_spatial_dice_0: 0.18049/0.20654, loss_spatial_ce_0: 0.00669/0.05964, loss_grounding_bce_0: 0.03628/0.08614, loss_grounding_dice_0: 0.32100/0.17840, loss_grounding_ce_0: 0.16989/0.27133, loss_mask_ce_1: 0.94508/0.89586, loss_mask_bce_1: 0.06247/0.33484, loss_mask_dice_1: 1.09426/1.16847, loss_spatial_bce_1: 0.00876/0.08713, loss_spatial_dice_1: 0.18305/0.21051, loss_spatial_ce_1: 0.00728/0.06543, loss_grounding_bce_1: 0.03064/0.08633, loss_grounding_dice_1: 0.21750/0.17919, loss_grounding_ce_1: 0.05539/0.27213, loss_mask_ce_2: 0.85398/0.90271, loss_mask_bce_2: 0.06843/0.33548, loss_mask_dice_2: 1.18482/1.16889, loss_spatial_bce_2: 0.00964/0.08836, loss_spatial_dice_2: 0.18547/0.21237, loss_spatial_ce_2: 0.01394/0.06892, loss_grounding_bce_2: 0.03438/0.08651, loss_grounding_dice_2: 0.35292/0.17908, loss_grounding_ce_2: 0.16025/0.27539, loss_mask_ce_3: 1.05310/0.91394, loss_mask_bce_3: 0.07471/0.33666, loss_mask_dice_3: 1.09167/1.16679, loss_spatial_bce_3: 0.01097/0.08967, loss_spatial_dice_3: 0.23143/0.21343, loss_spatial_ce_3: 0.00779/0.07396, loss_grounding_bce_3: 0.04108/0.08676, loss_grounding_dice_3: 0.35675/0.17876, loss_grounding_ce_3: 0.17568/0.27782, loss_mask_ce_4: 0.65190/0.91532, loss_mask_bce_4: 0.07406/0.33879, loss_mask_dice_4: 1.35537/1.19060, loss_spatial_bce_4: 0.00915/0.09359, loss_spatial_dice_4: 0.21065/0.22570, loss_spatial_ce_4: 0.02526/0.09029, loss_grounding_bce_4: 0.03881/0.08731, loss_grounding_dice_4: 0.27216/0.18177, loss_grounding_ce_4: 0.16882/0.28078, loss_mask_ce_5: 0.86296/0.93214, loss_mask_bce_5: 0.06905/0.34119, loss_mask_dice_5: 1.16121/1.19893, loss_spatial_bce_5: 0.00927/0.09594, loss_spatial_dice_5: 0.20036/0.23008, loss_spatial_ce_5: 0.02325/0.10416, loss_grounding_bce_5: 0.04144/0.08774, loss_grounding_dice_5: 0.23183/0.18305, loss_grounding_ce_5: 0.11128/0.29330, loss_mask_ce_6: 0.88287/0.97239, loss_mask_bce_6: 0.07239/0.34403, loss_mask_dice_6: 1.00429/1.20186, loss_spatial_bce_6: 0.01345/0.10152, loss_spatial_dice_6: 0.22158/0.23305, loss_spatial_ce_6: 0.04393/0.12909, loss_grounding_bce_6: 0.05175/0.08848, loss_grounding_dice_6: 0.42025/0.18345, loss_grounding_ce_6: 0.08863/0.30869, loss_mask_ce_7: 0.80592/1.01819, loss_mask_bce_7: 0.08566/0.35179, loss_mask_dice_7: 1.15352/1.25631, loss_spatial_bce_7: 0.02843/0.10942, loss_spatial_dice_7: 0.34273/0.26062, loss_spatial_ce_7: 0.19002/0.16405, loss_grounding_bce_7: 0.05376/0.09035, loss_grounding_dice_7: 0.46632/0.19079, loss_grounding_ce_7: 0.06486/0.33862, loss_mask_ce_8: 0.89426/1.12666, loss_mask_bce_8: 0.07147/0.36539, loss_mask_dice_8: 1.20586/1.32889, loss_spatial_bce_8: 0.01667/0.12961, loss_spatial_dice_8: 0.40880/0.29828, loss_spatial_ce_8: 0.19384/0.21599, loss_grounding_bce_8: 0.04115/0.09404, loss_grounding_dice_8: 0.36907/0.20148, loss_grounding_ce_8: 0.24547/0.40485, loss_mask_ce_9: 2.80220/3.67420, loss_mask_bce_9: 0.03031/0.39246, loss_mask_dice_9: 1.26406/1.90152, loss_spatial_bce_9: 0.09514/0.33269, loss_spatial_dice_9: 0.82599/0.82161, loss_spatial_ce_9: 1.78410/1.49276, loss_grounding_bce_9: 0.05082/0.10566, loss_grounding_dice_9: 0.34274/0.28084, loss_grounding_ce_9: 0.12591/0.66923] items per batch[64] items per second[0.24] total items[5126400] mini batches[ 80100] memory[7345] epoch remaining[0:13:12]
INFO:trainer.default_trainer:epochs[    43] optim steps[80200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62885/0.89511, loss_mask_bce_0: 0.40985/0.33386, loss_mask_dice_0: 0.38321/1.16162, loss_spatial_bce_0: 0.19123/0.08658, loss_spatial_dice_0: 0.16912/0.20652, loss_spatial_ce_0: 0.00235/0.05962, loss_grounding_bce_0: 0.26058/0.08614, loss_grounding_dice_0: 0.17610/0.17839, loss_grounding_ce_0: 0.05140/0.27131, loss_mask_ce_1: 0.65580/0.89575, loss_mask_bce_1: 0.41942/0.33479, loss_mask_dice_1: 0.41536/1.16835, loss_spatial_bce_1: 0.18273/0.08712, loss_spatial_dice_1: 0.15376/0.21048, loss_spatial_ce_1: 0.00490/0.06541, loss_grounding_bce_1: 0.25185/0.08633, loss_grounding_dice_1: 0.17528/0.17917, loss_grounding_ce_1: 0.05527/0.27210, loss_mask_ce_2: 0.66514/0.90260, loss_mask_bce_2: 0.41739/0.33543, loss_mask_dice_2: 0.42069/1.16878, loss_spatial_bce_2: 0.16289/0.08835, loss_spatial_dice_2: 0.15446/0.21235, loss_spatial_ce_2: 0.02048/0.06890, loss_grounding_bce_2: 0.24917/0.08651, loss_grounding_dice_2: 0.17851/0.17906, loss_grounding_ce_2: 0.05831/0.27539, loss_mask_ce_3: 0.78424/0.91384, loss_mask_bce_3: 0.41771/0.33662, loss_mask_dice_3: 0.43802/1.16670, loss_spatial_bce_3: 0.16513/0.08966, loss_spatial_dice_3: 0.16894/0.21341, loss_spatial_ce_3: 0.04767/0.07394, loss_grounding_bce_3: 0.26024/0.08676, loss_grounding_dice_3: 0.16496/0.17874, loss_grounding_ce_3: 0.06626/0.27778, loss_mask_ce_4: 0.78404/0.91520, loss_mask_bce_4: 0.46041/0.33875, loss_mask_dice_4: 0.46093/1.19051, loss_spatial_bce_4: 0.16192/0.09358, loss_spatial_dice_4: 0.15518/0.22568, loss_spatial_ce_4: 0.06746/0.09026, loss_grounding_bce_4: 0.27537/0.08732, loss_grounding_dice_4: 0.18133/0.18175, loss_grounding_ce_4: 0.07417/0.28072, loss_mask_ce_5: 0.45546/0.93200, loss_mask_bce_5: 0.50228/0.34116, loss_mask_dice_5: 0.58487/1.19882, loss_spatial_bce_5: 0.18685/0.09593, loss_spatial_dice_5: 0.20796/0.23006, loss_spatial_ce_5: 0.02877/0.10413, loss_grounding_bce_5: 0.24672/0.08775, loss_grounding_dice_5: 0.18536/0.18303, loss_grounding_ce_5: 0.11466/0.29324, loss_mask_ce_6: 0.65537/0.97228, loss_mask_bce_6: 0.48592/0.34399, loss_mask_dice_6: 0.51852/1.20173, loss_spatial_bce_6: 0.18259/0.10151, loss_spatial_dice_6: 0.20134/0.23303, loss_spatial_ce_6: 0.05972/0.12906, loss_grounding_bce_6: 0.24689/0.08848, loss_grounding_dice_6: 0.19094/0.18343, loss_grounding_ce_6: 0.14237/0.30865, loss_mask_ce_7: 0.92465/1.01806, loss_mask_bce_7: 0.50102/0.35174, loss_mask_dice_7: 0.53721/1.25621, loss_spatial_bce_7: 0.18934/0.10941, loss_spatial_dice_7: 0.21473/0.26061, loss_spatial_ce_7: 0.07826/0.16402, loss_grounding_bce_7: 0.24998/0.09035, loss_grounding_dice_7: 0.17364/0.19078, loss_grounding_ce_7: 0.71895/0.33857, loss_mask_ce_8: 0.80291/1.12652, loss_mask_bce_8: 0.53074/0.36535, loss_mask_dice_8: 0.58419/1.32874, loss_spatial_bce_8: 0.18109/0.12959, loss_spatial_dice_8: 0.23631/0.29825, loss_spatial_ce_8: 0.13295/0.21591, loss_grounding_bce_8: 0.25550/0.09404, loss_grounding_dice_8: 0.20455/0.20146, loss_grounding_ce_8: 1.54863/0.40482, loss_mask_ce_9: 2.50135/3.67405, loss_mask_bce_9: 0.52040/0.39241, loss_mask_dice_9: 0.73895/1.90128, loss_spatial_bce_9: 0.40094/0.33270, loss_spatial_dice_9: 0.81614/0.82160, loss_spatial_ce_9: 1.61084/1.49272, loss_grounding_bce_9: 0.26950/0.10566, loss_grounding_dice_9: 0.20280/0.28081, loss_grounding_ce_9: 0.64947/0.66926] items per batch[64] items per second[0.24] total items[5132800] mini batches[ 80200] memory[7345] epoch remaining[0:08:37]
INFO:trainer.default_trainer:epochs[    43] optim steps[80300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72531/0.89507, loss_mask_bce_0: 0.31285/0.33388, loss_mask_dice_0: 0.94778/1.16157, loss_spatial_bce_0: 0.05032/0.08658, loss_spatial_dice_0: 0.15917/0.20651, loss_spatial_ce_0: 0.09475/0.05962, loss_grounding_bce_0: 0.02999/0.08615, loss_grounding_dice_0: 0.08639/0.17840, loss_grounding_ce_0: 0.31277/0.27131, loss_mask_ce_1: 0.80874/0.89573, loss_mask_bce_1: 0.30183/0.33480, loss_mask_dice_1: 0.91822/1.16837, loss_spatial_bce_1: 0.05501/0.08711, loss_spatial_dice_1: 0.16465/0.21048, loss_spatial_ce_1: 0.07432/0.06541, loss_grounding_bce_1: 0.02940/0.08633, loss_grounding_dice_1: 0.10324/0.17918, loss_grounding_ce_1: 0.28332/0.27220, loss_mask_ce_2: 0.82591/0.90259, loss_mask_bce_2: 0.30519/0.33544, loss_mask_dice_2: 0.89546/1.16876, loss_spatial_bce_2: 0.05399/0.08835, loss_spatial_dice_2: 0.15867/0.21235, loss_spatial_ce_2: 0.11736/0.06890, loss_grounding_bce_2: 0.03044/0.08652, loss_grounding_dice_2: 0.10203/0.17908, loss_grounding_ce_2: 0.33368/0.27548, loss_mask_ce_3: 0.77577/0.91382, loss_mask_bce_3: 0.29386/0.33662, loss_mask_dice_3: 0.90706/1.16669, loss_spatial_bce_3: 0.04954/0.08966, loss_spatial_dice_3: 0.15211/0.21341, loss_spatial_ce_3: 0.12987/0.07393, loss_grounding_bce_3: 0.02833/0.08676, loss_grounding_dice_3: 0.09362/0.17874, loss_grounding_ce_3: 0.29650/0.27780, loss_mask_ce_4: 0.76219/0.91519, loss_mask_bce_4: 0.29584/0.33876, loss_mask_dice_4: 0.87950/1.19050, loss_spatial_bce_4: 0.09485/0.09358, loss_spatial_dice_4: 0.20859/0.22568, loss_spatial_ce_4: 0.13029/0.09024, loss_grounding_bce_4: 0.02900/0.08732, loss_grounding_dice_4: 0.09277/0.18176, loss_grounding_ce_4: 0.28847/0.28076, loss_mask_ce_5: 0.80079/0.93198, loss_mask_bce_5: 0.30419/0.34116, loss_mask_dice_5: 0.85942/1.19878, loss_spatial_bce_5: 0.05804/0.09593, loss_spatial_dice_5: 0.21730/0.23007, loss_spatial_ce_5: 0.22717/0.10412, loss_grounding_bce_5: 0.03011/0.08775, loss_grounding_dice_5: 0.10212/0.18304, loss_grounding_ce_5: 0.28287/0.29327, loss_mask_ce_6: 0.83951/0.97228, loss_mask_bce_6: 0.33115/0.34400, loss_mask_dice_6: 0.89698/1.20170, loss_spatial_bce_6: 0.06313/0.10151, loss_spatial_dice_6: 0.22360/0.23304, loss_spatial_ce_6: 0.17302/0.12905, loss_grounding_bce_6: 0.03038/0.08848, loss_grounding_dice_6: 0.10308/0.18344, loss_grounding_ce_6: 0.38582/0.30868, loss_mask_ce_7: 0.99372/1.01806, loss_mask_bce_7: 0.32932/0.35175, loss_mask_dice_7: 0.96050/1.25620, loss_spatial_bce_7: 0.07845/0.10941, loss_spatial_dice_7: 0.25760/0.26061, loss_spatial_ce_7: 0.27194/0.16401, loss_grounding_bce_7: 0.02894/0.09035, loss_grounding_dice_7: 0.09754/0.19079, loss_grounding_ce_7: 0.36486/0.33861, loss_mask_ce_8: 1.24773/1.12650, loss_mask_bce_8: 0.35807/0.36537, loss_mask_dice_8: 0.92296/1.32873, loss_spatial_bce_8: 0.13420/0.12959, loss_spatial_dice_8: 0.31371/0.29826, loss_spatial_ce_8: 0.22111/0.21586, loss_grounding_bce_8: 0.02811/0.09404, loss_grounding_dice_8: 0.09473/0.20146, loss_grounding_ce_8: 0.34596/0.40486, loss_mask_ce_9: 5.00714/3.67391, loss_mask_bce_9: 0.37049/0.39242, loss_mask_dice_9: 1.47643/1.90124, loss_spatial_bce_9: 0.31553/0.33269, loss_spatial_dice_9: 0.85559/0.82161, loss_spatial_ce_9: 1.14109/1.49272, loss_grounding_bce_9: 0.02940/0.10567, loss_grounding_dice_9: 0.15808/0.28081, loss_grounding_ce_9: 0.56482/0.66923] items per batch[64] items per second[0.23] total items[5139200] mini batches[ 80300] memory[7345] epoch remaining[0:04:02]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00080388.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0028 s/iter. Inference: 0.2182 s/iter. Eval: 0.0914 s/iter. Total: 0.3125 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2236 s/iter. Eval: 0.0779 s/iter. Total: 0.3045 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2264 s/iter. Eval: 0.0759 s/iter. Total: 0.3055 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2262 s/iter. Eval: 0.0740 s/iter. Total: 0.3034 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2249 s/iter. Eval: 0.0738 s/iter. Total: 0.3019 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2276 s/iter. Eval: 0.0734 s/iter. Total: 0.3042 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2296 s/iter. Eval: 0.0740 s/iter. Total: 0.3069 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2288 s/iter. Eval: 0.0737 s/iter. Total: 0.3058 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2292 s/iter. Eval: 0.0737 s/iter. Total: 0.3062 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalvgsskqct ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.038 | 81.940 | 60.157 |      133      |
| Things | 55.203 | 82.708 | 66.059 |      80       |
| Stuff  | 42.242 | 80.781 | 51.248 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.39s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.97 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.02 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.12s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.02 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.614
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.609
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.493
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.509
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.290
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.14 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 38.964 | 61.376 | 41.016 | 19.414 | 41.956 | 60.917 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.423 | bicycle      | 19.534 | car            | 36.800 |
| motorcycle    | 35.173 | airplane     | 57.101 | bus            | 64.674 |
| train         | 68.908 | truck        | 36.426 | boat           | 22.798 |
| traffic light | 25.108 | fire hydrant | 65.691 | stop sign      | 64.765 |
| parking meter | 42.665 | bench        | 19.702 | bird           | 28.833 |
| cat           | 73.554 | dog          | 65.594 | horse          | 45.396 |
| sheep         | 45.468 | cow          | 49.685 | elephant       | 60.470 |
| bear          | 77.974 | zebra        | 59.954 | giraffe        | 57.273 |
| backpack      | 18.131 | umbrella     | 48.448 | handbag        | 15.676 |
| tie           | 34.643 | suitcase     | 42.488 | frisbee        | 68.869 |
| skis          | 4.904  | snowboard    | 24.246 | sports ball    | 47.109 |
| kite          | 34.679 | baseball bat | 29.305 | baseball glove | 42.848 |
| skateboard    | 35.993 | surfboard    | 36.204 | tennis racket  | 56.781 |
| bottle        | 33.990 | wine glass   | 27.769 | cup            | 40.347 |
| fork          | 15.223 | knife        | 12.953 | spoon          | 13.599 |
| bowl          | 31.349 | banana       | 19.715 | apple          | 21.542 |
| sandwich      | 41.189 | orange       | 29.662 | broccoli       | 21.310 |
| carrot        | 20.155 | hot dog      | 23.873 | pizza          | 49.769 |
| donut         | 45.808 | cake         | 42.883 | chair          | 20.206 |
| couch         | 40.963 | potted plant | 16.731 | bed            | 40.294 |
| dining table  | 12.636 | toilet       | 68.227 | tv             | 63.490 |
| laptop        | 60.310 | mouse        | 59.390 | remote         | 31.887 |
| keyboard      | 47.471 | cell phone   | 37.732 | microwave      | 53.131 |
| oven          | 32.780 | toaster      | 36.141 | sink           | 35.749 |
| refrigerator  | 59.151 | book         | 9.727  | clock          | 52.895 |
| vase          | 32.779 | scissors     | 26.056 | teddy bear     | 51.082 |
| hair drier    | 11.302 | toothbrush   | 17.594 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.15889319653668, 'fwIoU': 68.72091237233829, 'IoU-person': 87.50347187572368, 'IoU-bicycle': 74.04343581695065, 'IoU-car': 70.998250412859, 'IoU-motorcycle': 80.15783956592111, 'IoU-airplane': 79.13087442588676, 'IoU-bus': 84.67788745837802, 'IoU-train': 86.92143231190208, 'IoU-truck': 64.94611581594306, 'IoU-boat': 66.90747428969078, 'IoU-traffic light': 75.730988601703, 'IoU-fire hydrant': 90.1414924647177, 'IoU-stop sign': 91.10788168484906, 'IoU-parking meter': 87.56610795017684, 'IoU-bench': 52.42109942034692, 'IoU-bird': 75.30277008849761, 'IoU-cat': 79.80401834721856, 'IoU-dog': 81.87704138500415, 'IoU-horse': 85.19309452888601, 'IoU-sheep': 87.67743776949645, 'IoU-cow': 81.06411012778067, 'IoU-elephant': 89.3145876854294, 'IoU-bear': 77.96275043318019, 'IoU-zebra': 89.03246571694014, 'IoU-giraffe': 88.20037415194825, 'IoU-backpack': 38.78660957526945, 'IoU-umbrella': 73.6431216393261, 'IoU-handbag': 37.51877358144987, 'IoU-tie': 70.63447985291864, 'IoU-suitcase': 81.01991633615482, 'IoU-frisbee': 84.17387353766773, 'IoU-skis': 51.26117124099997, 'IoU-snowboard': 68.96013243676828, 'IoU-sports ball': 68.78924921113246, 'IoU-kite': 66.86124752941434, 'IoU-baseball bat': 58.862604087812265, 'IoU-baseball glove': 78.81349451641888, 'IoU-skateboard': 59.91594514618298, 'IoU-surfboard': 77.37975874479046, 'IoU-tennis racket': 83.42356801216529, 'IoU-bottle': 66.64984756928277, 'IoU-wine glass': 72.49881719256105, 'IoU-cup': 64.81352699501448, 'IoU-fork': 57.31872721575119, 'IoU-knife': 52.3703362704328, 'IoU-spoon': 46.35011928344582, 'IoU-bowl': 52.640012786467295, 'IoU-banana': 81.49555270404362, 'IoU-apple': 56.676047302461605, 'IoU-sandwich': 63.73022304716932, 'IoU-orange': 76.05828292866842, 'IoU-broccoli': 67.35520749877637, 'IoU-carrot': 61.57676167728425, 'IoU-hot dog': 61.61713807340041, 'IoU-pizza': 79.57699934502345, 'IoU-donut': 58.45137522203972, 'IoU-cake': 64.03665434680951, 'IoU-chair': 53.540612595120976, 'IoU-couch': 65.60207848933976, 'IoU-potted plant': 31.586253215828837, 'IoU-bed': 63.65596169196962, 'IoU-dining table': 50.53801011304281, 'IoU-toilet': 80.96306002408373, 'IoU-tv': 74.6569631305435, 'IoU-laptop': 71.28173386823596, 'IoU-mouse': 65.93178401586364, 'IoU-remote': 49.74095761106664, 'IoU-keyboard': 57.91268619285906, 'IoU-cell phone': 70.21824025844985, 'IoU-microwave': 66.83431489690292, 'IoU-oven': 70.15328317629584, 'IoU-toaster': 41.77246063213718, 'IoU-sink': 70.70005524595703, 'IoU-refrigerator': 81.45139168469248, 'IoU-book': 50.114113633578874, 'IoU-clock': 71.77897140024663, 'IoU-vase': 56.160147120978564, 'IoU-scissors': 54.714750284443085, 'IoU-teddy bear': 75.76891240134643, 'IoU-hair drier': 50.30771762382754, 'IoU-toothbrush': 54.41830269938153, 'IoU-banner': 32.23016656773151, 'IoU-blanket': 10.46287687607916, 'IoU-bridge': 39.95518419752391, 'IoU-cardboard': 40.20576311984723, 'IoU-counter': 29.52392898343304, 'IoU-curtain': 64.1740464870226, 'IoU-door-stuff': 40.409630611702696, 'IoU-floor-wood': 63.27685506078594, 'IoU-flower': 43.078558971280216, 'IoU-fruit': 39.9324120534123, 'IoU-gravel': 30.51617710710632, 'IoU-house': 23.0694526252914, 'IoU-light': 38.98027956065171, 'IoU-mirror-stuff': 52.4691284319321, 'IoU-net': 44.569588175310706, 'IoU-pillow': 11.93595244704416, 'IoU-platform': 28.029264744223177, 'IoU-playingfield': 69.69348529569902, 'IoU-railroad': 61.395279723278584, 'IoU-river': 50.14235138707591, 'IoU-road': 66.13711523284606, 'IoU-roof': 12.669163139240384, 'IoU-sand': 62.44412404840308, 'IoU-sea': 84.64323193909027, 'IoU-shelf': 36.180840766341156, 'IoU-snow': 88.30946683783687, 'IoU-stairs': 20.359995784592684, 'IoU-tent': 8.105883811907418, 'IoU-towel': 33.53331068829613, 'IoU-wall-brick': 41.20962289451132, 'IoU-wall-stone': 30.824334664419915, 'IoU-wall-tile': 66.24378783300146, 'IoU-wall-wood': 41.35883930474916, 'IoU-water-other': 23.907026448710504, 'IoU-window-blind': 46.2102072411756, 'IoU-window-other': 46.42553138959001, 'IoU-tree-merged': 80.91147799239724, 'IoU-fence-merged': 52.650098403402204, 'IoU-ceiling-merged': 67.22349874924137, 'IoU-sky-other-merged': 93.1352403781629, 'IoU-cabinet-merged': 57.71583553654509, 'IoU-table-merged': 36.710468541754366, 'IoU-floor-other-merged': 50.03038009631594, 'IoU-pavement-merged': 54.432020266406475, 'IoU-mountain-merged': 57.96996164198821, 'IoU-grass-merged': 70.95455675812157, 'IoU-dirt-merged': 44.11538643644858, 'IoU-paper-merged': 32.00634089922526, 'IoU-food-other-merged': 40.35988824006721, 'IoU-building-other-merged': 57.98518971935712, 'IoU-rock-merged': 63.57366727836601, 'IoU-wall-other-merged': 64.66997097331428, 'IoU-rug-merged': 63.33060953439522, 'mACC': 72.53230205404238, 'pACC': 80.13081015522131, 'ACC-person': 92.2948222507235, 'ACC-bicycle': 86.82688563740038, 'ACC-car': 84.63803346816087, 'ACC-motorcycle': 85.12449191687554, 'ACC-airplane': 90.10170709850505, 'ACC-bus': 89.68096452739232, 'ACC-train': 95.50578752414322, 'ACC-truck': 76.82055237623466, 'ACC-boat': 78.36737771516742, 'ACC-traffic light': 90.53494157791582, 'ACC-fire hydrant': 95.35931755392059, 'ACC-stop sign': 94.23528772496978, 'ACC-parking meter': 92.24822677571905, 'ACC-bench': 71.42408684135711, 'ACC-bird': 80.66927524694846, 'ACC-cat': 90.39794819659089, 'ACC-dog': 85.01960071388791, 'ACC-horse': 91.81485295742182, 'ACC-sheep': 90.77477290178287, 'ACC-cow': 86.50249746812906, 'ACC-elephant': 91.73386759540459, 'ACC-bear': 80.02357723952078, 'ACC-zebra': 91.49349720677795, 'ACC-giraffe': 92.46153008632719, 'ACC-backpack': 60.01809800089395, 'ACC-umbrella': 81.57701314462507, 'ACC-handbag': 55.12325557614649, 'ACC-tie': 80.70204709032552, 'ACC-suitcase': 87.97438213495542, 'ACC-frisbee': 94.32218181818182, 'ACC-skis': 74.81098614295887, 'ACC-snowboard': 79.16827900369817, 'ACC-sports ball': 80.10977765373522, 'ACC-kite': 76.84476930473019, 'ACC-baseball bat': 81.56238527298474, 'ACC-baseball glove': 89.1150212922332, 'ACC-skateboard': 69.9570449434672, 'ACC-surfboard': 84.57118419399168, 'ACC-tennis racket': 89.43748149114838, 'ACC-bottle': 82.88858198218888, 'ACC-wine glass': 86.68782071041323, 'ACC-cup': 82.92768388157107, 'ACC-fork': 73.23690870217867, 'ACC-knife': 62.31204660364915, 'ACC-spoon': 69.19464687193019, 'ACC-bowl': 61.88644088905456, 'ACC-banana': 90.14206731371864, 'ACC-apple': 68.24591864631407, 'ACC-sandwich': 73.77456651125276, 'ACC-orange': 85.62384634471573, 'ACC-broccoli': 77.76407574623346, 'ACC-carrot': 76.09460712326056, 'ACC-hot dog': 72.80791488545792, 'ACC-pizza': 86.82575070563348, 'ACC-donut': 73.36765475380176, 'ACC-cake': 70.13530949236494, 'ACC-chair': 72.76242661976481, 'ACC-couch': 77.83885067278078, 'ACC-potted plant': 45.94825758199953, 'ACC-bed': 71.16430506427724, 'ACC-dining table': 73.64606464476472, 'ACC-toilet': 90.0482367425153, 'ACC-tv': 88.9570632320108, 'ACC-laptop': 83.46666254503458, 'ACC-mouse': 81.32131352156676, 'ACC-remote': 73.28731295163658, 'ACC-keyboard': 65.00246300178547, 'ACC-cell phone': 78.69167557141952, 'ACC-microwave': 78.0858338445135, 'ACC-oven': 87.30752308717823, 'ACC-toaster': 45.70679768438052, 'ACC-sink': 82.04777026004008, 'ACC-refrigerator': 91.43055418851746, 'ACC-book': 69.75879773612395, 'ACC-clock': 75.73730736325479, 'ACC-vase': 65.4450413468194, 'ACC-scissors': 59.16843419735742, 'ACC-teddy bear': 81.35143575057458, 'ACC-hair drier': 65.26342754675092, 'ACC-toothbrush': 81.55229325920779, 'ACC-banner': 63.90847148258828, 'ACC-blanket': 17.29390098140454, 'ACC-bridge': 57.655508771573395, 'ACC-cardboard': 49.907401483204886, 'ACC-counter': 55.27637876677588, 'ACC-curtain': 76.00149633192189, 'ACC-door-stuff': 58.94457278145136, 'ACC-floor-wood': 79.83687925902072, 'ACC-flower': 65.96745889683602, 'ACC-fruit': 57.25595101473524, 'ACC-gravel': 39.90899986041542, 'ACC-house': 26.2371234512848, 'ACC-light': 56.810389790605974, 'ACC-mirror-stuff': 68.20173279729961, 'ACC-net': 61.16914198553164, 'ACC-pillow': 28.265569849495638, 'ACC-platform': 47.241391407374834, 'ACC-playingfield': 90.5725717639188, 'ACC-railroad': 77.65634553607114, 'ACC-river': 73.6250478335861, 'ACC-road': 85.68641744184156, 'ACC-roof': 17.00244879332755, 'ACC-sand': 71.95966711034146, 'ACC-sea': 90.55391666001813, 'ACC-shelf': 59.93659598872399, 'ACC-snow': 95.27181788445735, 'ACC-stairs': 32.72558806067233, 'ACC-tent': 9.020914972544132, 'ACC-towel': 43.7531705326856, 'ACC-wall-brick': 52.28059666896737, 'ACC-wall-stone': 39.20230251352942, 'ACC-wall-tile': 80.46976033153508, 'ACC-wall-wood': 54.98837150201309, 'ACC-water-other': 37.96501735092042, 'ACC-window-blind': 56.95696380018664, 'ACC-window-other': 68.52543669830047, 'ACC-tree-merged': 89.12576613121888, 'ACC-fence-merged': 72.57027611356332, 'ACC-ceiling-merged': 81.04542890081864, 'ACC-sky-other-merged': 96.53498221346128, 'ACC-cabinet-merged': 75.21521990825082, 'ACC-table-merged': 52.77612860994606, 'ACC-floor-other-merged': 62.98389907682292, 'ACC-pavement-merged': 65.32993015442462, 'ACC-mountain-merged': 69.08659161471346, 'ACC-grass-merged': 83.34601107546763, 'ACC-dirt-merged': 64.14337225820081, 'ACC-paper-merged': 44.640023821947366, 'ACC-food-other-merged': 58.1981119934296, 'ACC-building-other-merged': 73.8872352476933, 'ACC-rock-merged': 80.7098349953989, 'ACC-wall-other-merged': 82.28068760800336, 'ACC-rug-merged': 78.63385386574848})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1626 s/iter. Inference: 0.6072 s/iter. Eval: 0.0000 s/iter. Total: 0.7698 s/iter. ETA=0:00:30
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1615 s/iter. Inference: 0.5383 s/iter. Eval: 0.0000 s/iter. Total: 0.7000 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/50. Dataloading: 0.1731 s/iter. Inference: 0.6145 s/iter. Eval: 0.0000 s/iter. Total: 0.7878 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1752 s/iter. Inference: 0.7305 s/iter. Eval: 0.0000 s/iter. Total: 0.9058 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1711 s/iter. Inference: 0.6392 s/iter. Eval: 0.0000 s/iter. Total: 0.8105 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1701 s/iter. Inference: 0.6748 s/iter. Eval: 0.0000 s/iter. Total: 0.8451 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 49/50. Dataloading: 0.1718 s/iter. Inference: 0.7298 s/iter. Eval: 0.0000 s/iter. Total: 0.9018 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4743927421714955, 'noc@0.8': 2.7825577992390986, 'noc@0.85': 3.4041556921275973, 'noc@0.9': 4.479367866549605, 'miou@iter1': 0.8317208204975525}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.0997 s/iter. Eval: 0.0008 s/iter. Total: 0.1022 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.43412017822266, 'precision@0.6': 68.59696960449219, 'precision@0.7': 63.6999626159668, 'precision@0.8': 53.750484466552734, 'precision@0.9': 27.555383682250977, 'cIoU': 57.432952880859375, 'mIoU': 63.20283889770508}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.038311978102165, 'SQ': 81.94002299454169, 'RQ': 60.15675443224681, 'PQ_th': 55.2031636281504, 'SQ_th': 82.70810142249786, 'RQ_th': 66.05863151024369, 'PQ_st': 42.24230948746338, 'SQ_st': 80.78065932970217, 'RQ_st': 51.248260729609996}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 38.96445403856587, 'AP50': 61.37634348180081, 'AP75': 41.01574579847982, 'APs': 19.413756025141502, 'APm': 41.955776120598145, 'APl': 60.91698579429339, 'AP-person': 44.42307017394655, 'AP-bicycle': 19.533990061792363, 'AP-car': 36.79992517241392, 'AP-motorcycle': 35.17322224411049, 'AP-airplane': 57.100677985027836, 'AP-bus': 64.67360167635951, 'AP-train': 68.90832980812817, 'AP-truck': 36.426376288656755, 'AP-boat': 22.79781071328659, 'AP-traffic light': 25.10763044988585, 'AP-fire hydrant': 65.69052903553192, 'AP-stop sign': 64.76547290518707, 'AP-parking meter': 42.665138623071364, 'AP-bench': 19.70200572663424, 'AP-bird': 28.832858542758306, 'AP-cat': 73.55422172088812, 'AP-dog': 65.59416257412417, 'AP-horse': 45.395772088508764, 'AP-sheep': 45.46770989669283, 'AP-cow': 49.685190350308595, 'AP-elephant': 60.46968815496495, 'AP-bear': 77.97390454461359, 'AP-zebra': 59.954144924736916, 'AP-giraffe': 57.273065444608505, 'AP-backpack': 18.131385226087115, 'AP-umbrella': 48.44794891471716, 'AP-handbag': 15.676459963125094, 'AP-tie': 34.64347832303052, 'AP-suitcase': 42.4881327838514, 'AP-frisbee': 68.86883928238278, 'AP-skis': 4.9035656701765555, 'AP-snowboard': 24.245622825829432, 'AP-sports ball': 47.10914422988006, 'AP-kite': 34.67943183171484, 'AP-baseball bat': 29.305021605370936, 'AP-baseball glove': 42.84828229747134, 'AP-skateboard': 35.99326887725389, 'AP-surfboard': 36.20358628910233, 'AP-tennis racket': 56.78074271588617, 'AP-bottle': 33.99034173197331, 'AP-wine glass': 27.769052702454317, 'AP-cup': 40.34653800027761, 'AP-fork': 15.223478124285256, 'AP-knife': 12.953324637259506, 'AP-spoon': 13.599495624528748, 'AP-bowl': 31.349144648035026, 'AP-banana': 19.71486103640539, 'AP-apple': 21.54181933965021, 'AP-sandwich': 41.18871193943514, 'AP-orange': 29.662250236937865, 'AP-broccoli': 21.31047095987161, 'AP-carrot': 20.15460866901451, 'AP-hot dog': 23.873404021870552, 'AP-pizza': 49.76918564222558, 'AP-donut': 45.8080384335258, 'AP-cake': 42.883398434427974, 'AP-chair': 20.206215278582945, 'AP-couch': 40.962965681549676, 'AP-potted plant': 16.73069175101654, 'AP-bed': 40.29380118212537, 'AP-dining table': 12.635644761942544, 'AP-toilet': 68.2274819403593, 'AP-tv': 63.48995626293846, 'AP-laptop': 60.30989196253316, 'AP-mouse': 59.390454429871596, 'AP-remote': 31.88691304052822, 'AP-keyboard': 47.47091761450942, 'AP-cell phone': 37.73216558724038, 'AP-microwave': 53.130520500317346, 'AP-oven': 32.77995390007145, 'AP-toaster': 36.1410891089109, 'AP-sink': 35.74913565789051, 'AP-refrigerator': 59.15145635629179, 'AP-book': 9.726679066398617, 'AP-clock': 52.89515971847097, 'AP-vase': 32.77920551042047, 'AP-scissors': 26.055933021474935, 'AP-teddy bear': 51.08210631246094, 'AP-hair drier': 11.302462389096052, 'AP-toothbrush': 17.59398792597301}), ('sem_seg', {'mIoU': 60.15889319653668, 'fwIoU': 68.72091237233829, 'IoU-person': 87.50347187572368, 'IoU-bicycle': 74.04343581695065, 'IoU-car': 70.998250412859, 'IoU-motorcycle': 80.15783956592111, 'IoU-airplane': 79.13087442588676, 'IoU-bus': 84.67788745837802, 'IoU-train': 86.92143231190208, 'IoU-truck': 64.94611581594306, 'IoU-boat': 66.90747428969078, 'IoU-traffic light': 75.730988601703, 'IoU-fire hydrant': 90.1414924647177, 'IoU-stop sign': 91.10788168484906, 'IoU-parking meter': 87.56610795017684, 'IoU-bench': 52.42109942034692, 'IoU-bird': 75.30277008849761, 'IoU-cat': 79.80401834721856, 'IoU-dog': 81.87704138500415, 'IoU-horse': 85.19309452888601, 'IoU-sheep': 87.67743776949645, 'IoU-cow': 81.06411012778067, 'IoU-elephant': 89.3145876854294, 'IoU-bear': 77.96275043318019, 'IoU-zebra': 89.03246571694014, 'IoU-giraffe': 88.20037415194825, 'IoU-backpack': 38.78660957526945, 'IoU-umbrella': 73.6431216393261, 'IoU-handbag': 37.51877358144987, 'IoU-tie': 70.63447985291864, 'IoU-suitcase': 81.01991633615482, 'IoU-frisbee': 84.17387353766773, 'IoU-skis': 51.26117124099997, 'IoU-snowboard': 68.96013243676828, 'IoU-sports ball': 68.78924921113246, 'IoU-kite': 66.86124752941434, 'IoU-baseball bat': 58.862604087812265, 'IoU-baseball glove': 78.81349451641888, 'IoU-skateboard': 59.91594514618298, 'IoU-surfboard': 77.37975874479046, 'IoU-tennis racket': 83.42356801216529, 'IoU-bottle': 66.64984756928277, 'IoU-wine glass': 72.49881719256105, 'IoU-cup': 64.81352699501448, 'IoU-fork': 57.31872721575119, 'IoU-knife': 52.3703362704328, 'IoU-spoon': 46.35011928344582, 'IoU-bowl': 52.640012786467295, 'IoU-banana': 81.49555270404362, 'IoU-apple': 56.676047302461605, 'IoU-sandwich': 63.73022304716932, 'IoU-orange': 76.05828292866842, 'IoU-broccoli': 67.35520749877637, 'IoU-carrot': 61.57676167728425, 'IoU-hot dog': 61.61713807340041, 'IoU-pizza': 79.57699934502345, 'IoU-donut': 58.45137522203972, 'IoU-cake': 64.03665434680951, 'IoU-chair': 53.540612595120976, 'IoU-couch': 65.60207848933976, 'IoU-potted plant': 31.586253215828837, 'IoU-bed': 63.65596169196962, 'IoU-dining table': 50.53801011304281, 'IoU-toilet': 80.96306002408373, 'IoU-tv': 74.6569631305435, 'IoU-laptop': 71.28173386823596, 'IoU-mouse': 65.93178401586364, 'IoU-remote': 49.74095761106664, 'IoU-keyboard': 57.91268619285906, 'IoU-cell phone': 70.21824025844985, 'IoU-microwave': 66.83431489690292, 'IoU-oven': 70.15328317629584, 'IoU-toaster': 41.77246063213718, 'IoU-sink': 70.70005524595703, 'IoU-refrigerator': 81.45139168469248, 'IoU-book': 50.114113633578874, 'IoU-clock': 71.77897140024663, 'IoU-vase': 56.160147120978564, 'IoU-scissors': 54.714750284443085, 'IoU-teddy bear': 75.76891240134643, 'IoU-hair drier': 50.30771762382754, 'IoU-toothbrush': 54.41830269938153, 'IoU-banner': 32.23016656773151, 'IoU-blanket': 10.46287687607916, 'IoU-bridge': 39.95518419752391, 'IoU-cardboard': 40.20576311984723, 'IoU-counter': 29.52392898343304, 'IoU-curtain': 64.1740464870226, 'IoU-door-stuff': 40.409630611702696, 'IoU-floor-wood': 63.27685506078594, 'IoU-flower': 43.078558971280216, 'IoU-fruit': 39.9324120534123, 'IoU-gravel': 30.51617710710632, 'IoU-house': 23.0694526252914, 'IoU-light': 38.98027956065171, 'IoU-mirror-stuff': 52.4691284319321, 'IoU-net': 44.569588175310706, 'IoU-pillow': 11.93595244704416, 'IoU-platform': 28.029264744223177, 'IoU-playingfield': 69.69348529569902, 'IoU-railroad': 61.395279723278584, 'IoU-river': 50.14235138707591, 'IoU-road': 66.13711523284606, 'IoU-roof': 12.669163139240384, 'IoU-sand': 62.44412404840308, 'IoU-sea': 84.64323193909027, 'IoU-shelf': 36.180840766341156, 'IoU-snow': 88.30946683783687, 'IoU-stairs': 20.359995784592684, 'IoU-tent': 8.105883811907418, 'IoU-towel': 33.53331068829613, 'IoU-wall-brick': 41.20962289451132, 'IoU-wall-stone': 30.824334664419915, 'IoU-wall-tile': 66.24378783300146, 'IoU-wall-wood': 41.35883930474916, 'IoU-water-other': 23.907026448710504, 'IoU-window-blind': 46.2102072411756, 'IoU-window-other': 46.42553138959001, 'IoU-tree-merged': 80.91147799239724, 'IoU-fence-merged': 52.650098403402204, 'IoU-ceiling-merged': 67.22349874924137, 'IoU-sky-other-merged': 93.1352403781629, 'IoU-cabinet-merged': 57.71583553654509, 'IoU-table-merged': 36.710468541754366, 'IoU-floor-other-merged': 50.03038009631594, 'IoU-pavement-merged': 54.432020266406475, 'IoU-mountain-merged': 57.96996164198821, 'IoU-grass-merged': 70.95455675812157, 'IoU-dirt-merged': 44.11538643644858, 'IoU-paper-merged': 32.00634089922526, 'IoU-food-other-merged': 40.35988824006721, 'IoU-building-other-merged': 57.98518971935712, 'IoU-rock-merged': 63.57366727836601, 'IoU-wall-other-merged': 64.66997097331428, 'IoU-rug-merged': 63.33060953439522, 'mACC': 72.53230205404238, 'pACC': 80.13081015522131, 'ACC-person': 92.2948222507235, 'ACC-bicycle': 86.82688563740038, 'ACC-car': 84.63803346816087, 'ACC-motorcycle': 85.12449191687554, 'ACC-airplane': 90.10170709850505, 'ACC-bus': 89.68096452739232, 'ACC-train': 95.50578752414322, 'ACC-truck': 76.82055237623466, 'ACC-boat': 78.36737771516742, 'ACC-traffic light': 90.53494157791582, 'ACC-fire hydrant': 95.35931755392059, 'ACC-stop sign': 94.23528772496978, 'ACC-parking meter': 92.24822677571905, 'ACC-bench': 71.42408684135711, 'ACC-bird': 80.66927524694846, 'ACC-cat': 90.39794819659089, 'ACC-dog': 85.01960071388791, 'ACC-horse': 91.81485295742182, 'ACC-sheep': 90.77477290178287, 'ACC-cow': 86.50249746812906, 'ACC-elephant': 91.73386759540459, 'ACC-bear': 80.02357723952078, 'ACC-zebra': 91.49349720677795, 'ACC-giraffe': 92.46153008632719, 'ACC-backpack': 60.01809800089395, 'ACC-umbrella': 81.57701314462507, 'ACC-handbag': 55.12325557614649, 'ACC-tie': 80.70204709032552, 'ACC-suitcase': 87.97438213495542, 'ACC-frisbee': 94.32218181818182, 'ACC-skis': 74.81098614295887, 'ACC-snowboard': 79.16827900369817, 'ACC-sports ball': 80.10977765373522, 'ACC-kite': 76.84476930473019, 'ACC-baseball bat': 81.56238527298474, 'ACC-baseball glove': 89.1150212922332, 'ACC-skateboard': 69.9570449434672, 'ACC-surfboard': 84.57118419399168, 'ACC-tennis racket': 89.43748149114838, 'ACC-bottle': 82.88858198218888, 'ACC-wine glass': 86.68782071041323, 'ACC-cup': 82.92768388157107, 'ACC-fork': 73.23690870217867, 'ACC-knife': 62.31204660364915, 'ACC-spoon': 69.19464687193019, 'ACC-bowl': 61.88644088905456, 'ACC-banana': 90.14206731371864, 'ACC-apple': 68.24591864631407, 'ACC-sandwich': 73.77456651125276, 'ACC-orange': 85.62384634471573, 'ACC-broccoli': 77.76407574623346, 'ACC-carrot': 76.09460712326056, 'ACC-hot dog': 72.80791488545792, 'ACC-pizza': 86.82575070563348, 'ACC-donut': 73.36765475380176, 'ACC-cake': 70.13530949236494, 'ACC-chair': 72.76242661976481, 'ACC-couch': 77.83885067278078, 'ACC-potted plant': 45.94825758199953, 'ACC-bed': 71.16430506427724, 'ACC-dining table': 73.64606464476472, 'ACC-toilet': 90.0482367425153, 'ACC-tv': 88.9570632320108, 'ACC-laptop': 83.46666254503458, 'ACC-mouse': 81.32131352156676, 'ACC-remote': 73.28731295163658, 'ACC-keyboard': 65.00246300178547, 'ACC-cell phone': 78.69167557141952, 'ACC-microwave': 78.0858338445135, 'ACC-oven': 87.30752308717823, 'ACC-toaster': 45.70679768438052, 'ACC-sink': 82.04777026004008, 'ACC-refrigerator': 91.43055418851746, 'ACC-book': 69.75879773612395, 'ACC-clock': 75.73730736325479, 'ACC-vase': 65.4450413468194, 'ACC-scissors': 59.16843419735742, 'ACC-teddy bear': 81.35143575057458, 'ACC-hair drier': 65.26342754675092, 'ACC-toothbrush': 81.55229325920779, 'ACC-banner': 63.90847148258828, 'ACC-blanket': 17.29390098140454, 'ACC-bridge': 57.655508771573395, 'ACC-cardboard': 49.907401483204886, 'ACC-counter': 55.27637876677588, 'ACC-curtain': 76.00149633192189, 'ACC-door-stuff': 58.94457278145136, 'ACC-floor-wood': 79.83687925902072, 'ACC-flower': 65.96745889683602, 'ACC-fruit': 57.25595101473524, 'ACC-gravel': 39.90899986041542, 'ACC-house': 26.2371234512848, 'ACC-light': 56.810389790605974, 'ACC-mirror-stuff': 68.20173279729961, 'ACC-net': 61.16914198553164, 'ACC-pillow': 28.265569849495638, 'ACC-platform': 47.241391407374834, 'ACC-playingfield': 90.5725717639188, 'ACC-railroad': 77.65634553607114, 'ACC-river': 73.6250478335861, 'ACC-road': 85.68641744184156, 'ACC-roof': 17.00244879332755, 'ACC-sand': 71.95966711034146, 'ACC-sea': 90.55391666001813, 'ACC-shelf': 59.93659598872399, 'ACC-snow': 95.27181788445735, 'ACC-stairs': 32.72558806067233, 'ACC-tent': 9.020914972544132, 'ACC-towel': 43.7531705326856, 'ACC-wall-brick': 52.28059666896737, 'ACC-wall-stone': 39.20230251352942, 'ACC-wall-tile': 80.46976033153508, 'ACC-wall-wood': 54.98837150201309, 'ACC-water-other': 37.96501735092042, 'ACC-window-blind': 56.95696380018664, 'ACC-window-other': 68.52543669830047, 'ACC-tree-merged': 89.12576613121888, 'ACC-fence-merged': 72.57027611356332, 'ACC-ceiling-merged': 81.04542890081864, 'ACC-sky-other-merged': 96.53498221346128, 'ACC-cabinet-merged': 75.21521990825082, 'ACC-table-merged': 52.77612860994606, 'ACC-floor-other-merged': 62.98389907682292, 'ACC-pavement-merged': 65.32993015442462, 'ACC-mountain-merged': 69.08659161471346, 'ACC-grass-merged': 83.34601107546763, 'ACC-dirt-merged': 64.14337225820081, 'ACC-paper-merged': 44.640023821947366, 'ACC-food-other-merged': 58.1981119934296, 'ACC-building-other-merged': 73.8872352476933, 'ACC-rock-merged': 80.7098349953989, 'ACC-wall-other-merged': 82.28068760800336, 'ACC-rug-merged': 78.63385386574848})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4743927421714955, 'noc@0.8': 2.7825577992390986, 'noc@0.85': 3.4041556921275973, 'noc@0.9': 4.479367866549605, 'miou@iter1': 0.8317208204975525}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.43412017822266, 'precision@0.6': 68.59696960449219, 'precision@0.7': 63.6999626159668, 'precision@0.8': 53.750484466552734, 'precision@0.9': 27.555383682250977, 'cIoU': 57.432952880859375, 'mIoU': 63.20283889770508}}}
INFO:trainer.default_trainer:This epoch takes 1:27:10.154903
INFO:trainer.default_trainer:PROGRESS: 88.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 44 training.
INFO:trainer.default_trainer:epochs[    44] optim steps[80400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42401/0.89512, loss_mask_bce_0: 0.27734/0.33388, loss_mask_dice_0: 0.77325/1.16174, loss_spatial_bce_0: 0.04256/0.08657, loss_spatial_dice_0: 0.15509/0.20652, loss_spatial_ce_0: 0.00052/0.05963, loss_grounding_bce_0: 0.12538/0.08614, loss_grounding_dice_0: 0.17177/0.17841, loss_grounding_ce_0: 0.07587/0.27136, loss_mask_ce_1: 1.42265/0.89579, loss_mask_bce_1: 0.26389/0.33480, loss_mask_dice_1: 0.74049/1.16855, loss_spatial_bce_1: 0.04541/0.08711, loss_spatial_dice_1: 0.14480/0.21049, loss_spatial_ce_1: 0.00066/0.06543, loss_grounding_bce_1: 0.11107/0.08633, loss_grounding_dice_1: 0.16224/0.17919, loss_grounding_ce_1: 0.07343/0.27225, loss_mask_ce_2: 1.38049/0.90265, loss_mask_bce_2: 0.24012/0.33544, loss_mask_dice_2: 0.74153/1.16892, loss_spatial_bce_2: 0.04360/0.08834, loss_spatial_dice_2: 0.15268/0.21236, loss_spatial_ce_2: 0.00100/0.06890, loss_grounding_bce_2: 0.10371/0.08651, loss_grounding_dice_2: 0.14809/0.17909, loss_grounding_ce_2: 0.08138/0.27553, loss_mask_ce_3: 1.44047/0.91388, loss_mask_bce_3: 0.24379/0.33662, loss_mask_dice_3: 0.74186/1.16685, loss_spatial_bce_3: 0.04429/0.08966, loss_spatial_dice_3: 0.16331/0.21341, loss_spatial_ce_3: 0.00166/0.07393, loss_grounding_bce_3: 0.10570/0.08676, loss_grounding_dice_3: 0.15199/0.17875, loss_grounding_ce_3: 0.10180/0.27786, loss_mask_ce_4: 1.45276/0.91525, loss_mask_bce_4: 0.29531/0.33876, loss_mask_dice_4: 0.79373/1.19069, loss_spatial_bce_4: 0.05160/0.09357, loss_spatial_dice_4: 0.15832/0.22569, loss_spatial_ce_4: 0.00769/0.09026, loss_grounding_bce_4: 0.13924/0.08732, loss_grounding_dice_4: 0.17451/0.18177, loss_grounding_ce_4: 0.07774/0.28081, loss_mask_ce_5: 1.49926/0.93202, loss_mask_bce_5: 0.30235/0.34117, loss_mask_dice_5: 0.83978/1.19895, loss_spatial_bce_5: 0.05161/0.09593, loss_spatial_dice_5: 0.17084/0.23008, loss_spatial_ce_5: 0.01603/0.10415, loss_grounding_bce_5: 0.18186/0.08775, loss_grounding_dice_5: 0.18379/0.18305, loss_grounding_ce_5: 0.08902/0.29336, loss_mask_ce_6: 1.50430/0.97231, loss_mask_bce_6: 0.29692/0.34401, loss_mask_dice_6: 0.81065/1.20188, loss_spatial_bce_6: 0.07235/0.10151, loss_spatial_dice_6: 0.17207/0.23305, loss_spatial_ce_6: 0.02436/0.12906, loss_grounding_bce_6: 0.13857/0.08849, loss_grounding_dice_6: 0.18800/0.18346, loss_grounding_ce_6: 0.05598/0.30871, loss_mask_ce_7: 1.47473/1.01810, loss_mask_bce_7: 0.29482/0.35174, loss_mask_dice_7: 0.87942/1.25637, loss_spatial_bce_7: 0.05936/0.10941, loss_spatial_dice_7: 0.20473/0.26062, loss_spatial_ce_7: 0.01264/0.16400, loss_grounding_bce_7: 0.13575/0.09035, loss_grounding_dice_7: 0.19468/0.19081, loss_grounding_ce_7: 0.04438/0.33863, loss_mask_ce_8: 1.54734/1.12657, loss_mask_bce_8: 0.29889/0.36536, loss_mask_dice_8: 0.91866/1.32892, loss_spatial_bce_8: 0.06453/0.12958, loss_spatial_dice_8: 0.25512/0.29827, loss_spatial_ce_8: 0.04617/0.21581, loss_grounding_bce_8: 0.12621/0.09404, loss_grounding_dice_8: 0.18264/0.20148, loss_grounding_ce_8: 0.03487/0.40487, loss_mask_ce_9: 3.88052/3.67412, loss_mask_bce_9: 0.52566/0.39244, loss_mask_dice_9: 1.68220/1.90145, loss_spatial_bce_9: 0.28028/0.33268, loss_spatial_dice_9: 0.87491/0.82161, loss_spatial_ce_9: 1.23759/1.49272, loss_grounding_bce_9: 0.18689/0.10568, loss_grounding_dice_9: 0.28203/0.28083, loss_grounding_ce_9: 0.19381/0.66924] items per batch[64] items per second[0.13] total items[5145600] mini batches[ 80400] memory[7345] epoch remaining[1:32:53]
INFO:trainer.default_trainer:epochs[    44] optim steps[80500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94003/0.89508, loss_mask_bce_0: 0.52944/0.33387, loss_mask_dice_0: 0.55994/1.16177, loss_spatial_bce_0: 0.26438/0.08658, loss_spatial_dice_0: 0.22045/0.20652, loss_spatial_ce_0: 0.14450/0.05962, loss_grounding_bce_0: 0.33664/0.08615, loss_grounding_dice_0: 0.26127/0.17841, loss_grounding_ce_0: 0.40281/0.27132, loss_mask_ce_1: 0.73034/0.89575, loss_mask_bce_1: 0.56562/0.33480, loss_mask_dice_1: 0.60670/1.16857, loss_spatial_bce_1: 0.29428/0.08711, loss_spatial_dice_1: 0.24796/0.21049, loss_spatial_ce_1: 0.11273/0.06543, loss_grounding_bce_1: 0.37447/0.08633, loss_grounding_dice_1: 0.28797/0.17919, loss_grounding_ce_1: 0.31639/0.27221, loss_mask_ce_2: 0.72926/0.90262, loss_mask_bce_2: 0.57936/0.33544, loss_mask_dice_2: 0.62883/1.16894, loss_spatial_bce_2: 0.28276/0.08835, loss_spatial_dice_2: 0.24737/0.21236, loss_spatial_ce_2: 0.10665/0.06890, loss_grounding_bce_2: 0.36699/0.08652, loss_grounding_dice_2: 0.27410/0.17909, loss_grounding_ce_2: 0.34609/0.27550, loss_mask_ce_3: 0.74389/0.91383, loss_mask_bce_3: 0.59422/0.33663, loss_mask_dice_3: 0.61542/1.16690, loss_spatial_bce_3: 0.30097/0.08966, loss_spatial_dice_3: 0.24653/0.21342, loss_spatial_ce_3: 0.11382/0.07393, loss_grounding_bce_3: 0.38455/0.08677, loss_grounding_dice_3: 0.29631/0.17876, loss_grounding_ce_3: 0.32877/0.27780, loss_mask_ce_4: 0.85578/0.91522, loss_mask_bce_4: 0.57102/0.33877, loss_mask_dice_4: 0.61236/1.19072, loss_spatial_bce_4: 0.30612/0.09358, loss_spatial_dice_4: 0.23667/0.22569, loss_spatial_ce_4: 0.14984/0.09027, loss_grounding_bce_4: 0.38571/0.08732, loss_grounding_dice_4: 0.31110/0.18177, loss_grounding_ce_4: 0.39122/0.28078, loss_mask_ce_5: 0.72604/0.93199, loss_mask_bce_5: 0.58272/0.34117, loss_mask_dice_5: 0.64129/1.19901, loss_spatial_bce_5: 0.25473/0.09594, loss_spatial_dice_5: 0.22757/0.23008, loss_spatial_ce_5: 0.21163/0.10416, loss_grounding_bce_5: 0.37627/0.08775, loss_grounding_dice_5: 0.32974/0.18305, loss_grounding_ce_5: 0.31804/0.29331, loss_mask_ce_6: 0.70649/0.97230, loss_mask_bce_6: 0.61186/0.34401, loss_mask_dice_6: 0.64813/1.20191, loss_spatial_bce_6: 0.22625/0.10151, loss_spatial_dice_6: 0.20814/0.23306, loss_spatial_ce_6: 0.28339/0.12906, loss_grounding_bce_6: 0.39049/0.08850, loss_grounding_dice_6: 0.32949/0.18346, loss_grounding_ce_6: 0.30927/0.30865, loss_mask_ce_7: 0.66585/1.01807, loss_mask_bce_7: 0.59723/0.35174, loss_mask_dice_7: 0.66113/1.25638, loss_spatial_bce_7: 0.25899/0.10942, loss_spatial_dice_7: 0.21990/0.26062, loss_spatial_ce_7: 0.25587/0.16402, loss_grounding_bce_7: 0.38952/0.09036, loss_grounding_dice_7: 0.33223/0.19081, loss_grounding_ce_7: 0.32403/0.33857, loss_mask_ce_8: 0.82837/1.12650, loss_mask_bce_8: 0.56707/0.36537, loss_mask_dice_8: 0.61526/1.32892, loss_spatial_bce_8: 0.33144/0.12959, loss_spatial_dice_8: 0.26430/0.29828, loss_spatial_ce_8: 0.20079/0.21578, loss_grounding_bce_8: 0.36768/0.09405, loss_grounding_dice_8: 0.26755/0.20148, loss_grounding_ce_8: 0.36392/0.40478, loss_mask_ce_9: 2.64002/3.67391, loss_mask_bce_9: 0.74849/0.39243, loss_mask_dice_9: 0.94738/1.90136, loss_spatial_bce_9: 0.48793/0.33268, loss_spatial_dice_9: 0.72239/0.82160, loss_spatial_ce_9: 1.21414/1.49268, loss_grounding_bce_9: 0.41369/0.10569, loss_grounding_dice_9: 0.32198/0.28082, loss_grounding_ce_9: 0.28886/0.66912] items per batch[64] items per second[0.24] total items[5152000] mini batches[ 80500] memory[7345] epoch remaining[1:18:34]
INFO:trainer.default_trainer:epochs[    44] optim steps[80600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63578/0.89506, loss_mask_bce_0: 0.23686/0.33383, loss_mask_dice_0: 1.15049/1.16170, loss_spatial_bce_0: 0.05109/0.08656, loss_spatial_dice_0: 0.25046/0.20651, loss_spatial_ce_0: 0.02127/0.05964, loss_grounding_bce_0: 0.04545/0.08613, loss_grounding_dice_0: 0.34943/0.17842, loss_grounding_ce_0: 0.70808/0.27136, loss_mask_ce_1: 1.55796/0.89573, loss_mask_bce_1: 0.28575/0.33475, loss_mask_dice_1: 1.21276/1.16849, loss_spatial_bce_1: 0.05600/0.08709, loss_spatial_dice_1: 0.25275/0.21048, loss_spatial_ce_1: 0.02516/0.06547, loss_grounding_bce_1: 0.03960/0.08631, loss_grounding_dice_1: 0.25087/0.17919, loss_grounding_ce_1: 0.86196/0.27226, loss_mask_ce_2: 1.63468/0.90261, loss_mask_bce_2: 0.22153/0.33540, loss_mask_dice_2: 1.18250/1.16884, loss_spatial_bce_2: 0.05156/0.08833, loss_spatial_dice_2: 0.25231/0.21235, loss_spatial_ce_2: 0.02155/0.06894, loss_grounding_bce_2: 0.07849/0.08650, loss_grounding_dice_2: 0.37643/0.17910, loss_grounding_ce_2: 0.64646/0.27553, loss_mask_ce_3: 1.71648/0.91382, loss_mask_bce_3: 0.22655/0.33659, loss_mask_dice_3: 1.15065/1.16681, loss_spatial_bce_3: 0.05106/0.08964, loss_spatial_dice_3: 0.27335/0.21342, loss_spatial_ce_3: 0.05709/0.07398, loss_grounding_bce_3: 0.04957/0.08675, loss_grounding_dice_3: 0.27411/0.17876, loss_grounding_ce_3: 0.68387/0.27782, loss_mask_ce_4: 1.81588/0.91521, loss_mask_bce_4: 0.33855/0.33872, loss_mask_dice_4: 1.16415/1.19064, loss_spatial_bce_4: 0.05473/0.09356, loss_spatial_dice_4: 0.26155/0.22568, loss_spatial_ce_4: 0.03719/0.09031, loss_grounding_bce_4: 0.03875/0.08730, loss_grounding_dice_4: 0.16210/0.18178, loss_grounding_ce_4: 0.52984/0.28078, loss_mask_ce_5: 1.69492/0.93198, loss_mask_bce_5: 0.35051/0.34112, loss_mask_dice_5: 1.21770/1.19896, loss_spatial_bce_5: 0.06390/0.09592, loss_spatial_dice_5: 0.24775/0.23007, loss_spatial_ce_5: 0.08893/0.10422, loss_grounding_bce_5: 0.04417/0.08773, loss_grounding_dice_5: 0.35618/0.18306, loss_grounding_ce_5: 0.57187/0.29332, loss_mask_ce_6: 1.64019/0.97230, loss_mask_bce_6: 0.39303/0.34397, loss_mask_dice_6: 1.20326/1.20185, loss_spatial_bce_6: 0.06211/0.10149, loss_spatial_dice_6: 0.25882/0.23305, loss_spatial_ce_6: 0.08825/0.12911, loss_grounding_bce_6: 0.08068/0.08848, loss_grounding_dice_6: 0.40651/0.18347, loss_grounding_ce_6: 0.37047/0.30865, loss_mask_ce_7: 1.84159/1.01803, loss_mask_bce_7: 0.50305/0.35171, loss_mask_dice_7: 1.16273/1.25632, loss_spatial_bce_7: 0.06836/0.10940, loss_spatial_dice_7: 0.30897/0.26061, loss_spatial_ce_7: 0.16247/0.16407, loss_grounding_bce_7: 0.03679/0.09034, loss_grounding_dice_7: 0.37292/0.19081, loss_grounding_ce_7: 0.88719/0.33860, loss_mask_ce_8: 1.55263/1.12647, loss_mask_bce_8: 0.53031/0.36532, loss_mask_dice_8: 1.44735/1.32882, loss_spatial_bce_8: 0.06830/0.12957, loss_spatial_dice_8: 0.31182/0.29827, loss_spatial_ce_8: 0.22724/0.21577, loss_grounding_bce_8: 0.03556/0.09403, loss_grounding_dice_8: 0.27881/0.20148, loss_grounding_ce_8: 1.29317/0.40484, loss_mask_ce_9: 5.65737/3.67400, loss_mask_bce_9: 0.43820/0.39238, loss_mask_dice_9: 1.87733/1.90117, loss_spatial_bce_9: 0.20759/0.33267, loss_spatial_dice_9: 0.80867/0.82159, loss_spatial_ce_9: 1.17732/1.49266, loss_grounding_bce_9: 0.04856/0.10567, loss_grounding_dice_9: 0.50290/0.28082, loss_grounding_ce_9: 1.08132/0.66920] items per batch[64] items per second[0.23] total items[5158400] mini batches[ 80600] memory[7345] epoch remaining[1:14:20]
INFO:trainer.default_trainer:epochs[    44] optim steps[80700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77074/0.89514, loss_mask_bce_0: 0.26024/0.33384, loss_mask_dice_0: 0.99539/1.16168, loss_spatial_bce_0: 0.07328/0.08655, loss_spatial_dice_0: 0.19441/0.20650, loss_spatial_ce_0: 0.01767/0.05962, loss_grounding_bce_0: 0.06220/0.08612, loss_grounding_dice_0: 0.19529/0.17843, loss_grounding_ce_0: 0.48140/0.27136, loss_mask_ce_1: 0.70519/0.89577, loss_mask_bce_1: 0.25774/0.33477, loss_mask_dice_1: 1.04991/1.16846, loss_spatial_bce_1: 0.07198/0.08708, loss_spatial_dice_1: 0.22254/0.21046, loss_spatial_ce_1: 0.01000/0.06546, loss_grounding_bce_1: 0.06289/0.08631, loss_grounding_dice_1: 0.18715/0.17920, loss_grounding_ce_1: 0.51530/0.27228, loss_mask_ce_2: 0.71622/0.90266, loss_mask_bce_2: 0.25287/0.33542, loss_mask_dice_2: 1.05550/1.16883, loss_spatial_bce_2: 0.07160/0.08832, loss_spatial_dice_2: 0.18914/0.21234, loss_spatial_ce_2: 0.23776/0.06893, loss_grounding_bce_2: 0.05910/0.08649, loss_grounding_dice_2: 0.17583/0.17910, loss_grounding_ce_2: 0.49063/0.27552, loss_mask_ce_3: 0.76442/0.91393, loss_mask_bce_3: 0.26748/0.33660, loss_mask_dice_3: 1.06590/1.16679, loss_spatial_bce_3: 0.06921/0.08964, loss_spatial_dice_3: 0.18191/0.21341, loss_spatial_ce_3: 0.29170/0.07396, loss_grounding_bce_3: 0.06401/0.08675, loss_grounding_dice_3: 0.19663/0.17878, loss_grounding_ce_3: 0.46263/0.27783, loss_mask_ce_4: 0.70057/0.91528, loss_mask_bce_4: 0.25283/0.33875, loss_mask_dice_4: 1.00493/1.19063, loss_spatial_bce_4: 0.06524/0.09356, loss_spatial_dice_4: 0.19621/0.22568, loss_spatial_ce_4: 0.20304/0.09030, loss_grounding_bce_4: 0.05855/0.08730, loss_grounding_dice_4: 0.19576/0.18180, loss_grounding_ce_4: 0.51250/0.28080, loss_mask_ce_5: 0.71536/0.93206, loss_mask_bce_5: 0.26622/0.34115, loss_mask_dice_5: 1.09993/1.19895, loss_spatial_bce_5: 0.08604/0.09592, loss_spatial_dice_5: 0.22968/0.23007, loss_spatial_ce_5: 0.03514/0.10418, loss_grounding_bce_5: 0.04940/0.08772, loss_grounding_dice_5: 0.22942/0.18308, loss_grounding_ce_5: 0.59735/0.29335, loss_mask_ce_6: 0.76739/0.97239, loss_mask_bce_6: 0.25464/0.34399, loss_mask_dice_6: 1.06481/1.20184, loss_spatial_bce_6: 0.07932/0.10149, loss_spatial_dice_6: 0.22012/0.23305, loss_spatial_ce_6: 0.08119/0.12908, loss_grounding_bce_6: 0.04151/0.08847, loss_grounding_dice_6: 0.18475/0.18348, loss_grounding_ce_6: 0.59759/0.30869, loss_mask_ce_7: 0.89980/1.01810, loss_mask_bce_7: 0.31630/0.35174, loss_mask_dice_7: 1.07116/1.25629, loss_spatial_bce_7: 0.10172/0.10939, loss_spatial_dice_7: 0.27731/0.26061, loss_spatial_ce_7: 0.22721/0.16405, loss_grounding_bce_7: 0.07949/0.09034, loss_grounding_dice_7: 0.20192/0.19083, loss_grounding_ce_7: 0.49204/0.33859, loss_mask_ce_8: 0.91963/1.12654, loss_mask_bce_8: 0.35945/0.36533, loss_mask_dice_8: 1.16597/1.32879, loss_spatial_bce_8: 0.11034/0.12955, loss_spatial_dice_8: 0.33041/0.29826, loss_spatial_ce_8: 0.09643/0.21571, loss_grounding_bce_8: 0.07404/0.09402, loss_grounding_dice_8: 0.19680/0.20150, loss_grounding_ce_8: 0.50042/0.40476, loss_mask_ce_9: 4.75037/3.67407, loss_mask_bce_9: 0.24568/0.39240, loss_mask_dice_9: 1.92352/1.90123, loss_spatial_bce_9: 0.30684/0.33265, loss_spatial_dice_9: 0.85054/0.82159, loss_spatial_ce_9: 1.33522/1.49267, loss_grounding_bce_9: 0.05157/0.10565, loss_grounding_dice_9: 0.42826/0.28084, loss_grounding_ce_9: 0.51101/0.66915] items per batch[64] items per second[0.23] total items[5164800] mini batches[ 80700] memory[7345] epoch remaining[1:09:59]
INFO:trainer.default_trainer:epochs[    44] optim steps[80800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43385/0.89511, loss_mask_bce_0: 0.08815/0.33382, loss_mask_dice_0: 0.49826/1.16167, loss_spatial_bce_0: 0.03467/0.08655, loss_spatial_dice_0: 0.25205/0.20650, loss_spatial_ce_0: 0.01079/0.05960, loss_grounding_bce_0: 0.03619/0.08612, loss_grounding_dice_0: 0.32575/0.17844, loss_grounding_ce_0: 0.33755/0.27136, loss_mask_ce_1: 0.55482/0.89573, loss_mask_bce_1: 0.09138/0.33474, loss_mask_dice_1: 0.50717/1.16847, loss_spatial_bce_1: 0.03570/0.08709, loss_spatial_dice_1: 0.24242/0.21046, loss_spatial_ce_1: 0.14265/0.06545, loss_grounding_bce_1: 0.03658/0.08631, loss_grounding_dice_1: 0.30796/0.17922, loss_grounding_ce_1: 0.26934/0.27227, loss_mask_ce_2: 0.55880/0.90265, loss_mask_bce_2: 0.08727/0.33539, loss_mask_dice_2: 0.50596/1.16883, loss_spatial_bce_2: 0.03600/0.08832, loss_spatial_dice_2: 0.26151/0.21234, loss_spatial_ce_2: 0.01202/0.06891, loss_grounding_bce_2: 0.03252/0.08649, loss_grounding_dice_2: 0.28162/0.17912, loss_grounding_ce_2: 0.35031/0.27552, loss_mask_ce_3: 0.55291/0.91393, loss_mask_bce_3: 0.08570/0.33658, loss_mask_dice_3: 0.50620/1.16680, loss_spatial_bce_3: 0.03632/0.08964, loss_spatial_dice_3: 0.30533/0.21340, loss_spatial_ce_3: 0.03045/0.07394, loss_grounding_bce_3: 0.03320/0.08675, loss_grounding_dice_3: 0.30513/0.17879, loss_grounding_ce_3: 0.31092/0.27781, loss_mask_ce_4: 0.60020/0.91525, loss_mask_bce_4: 0.07876/0.33873, loss_mask_dice_4: 0.57476/1.19065, loss_spatial_bce_4: 0.03459/0.09356, loss_spatial_dice_4: 0.30352/0.22567, loss_spatial_ce_4: 0.04271/0.09028, loss_grounding_bce_4: 0.03117/0.08730, loss_grounding_dice_4: 0.26733/0.18181, loss_grounding_ce_4: 0.30610/0.28077, loss_mask_ce_5: 0.71797/0.93202, loss_mask_bce_5: 0.08287/0.34114, loss_mask_dice_5: 0.68673/1.19898, loss_spatial_bce_5: 0.03753/0.09592, loss_spatial_dice_5: 0.29455/0.23007, loss_spatial_ce_5: 0.04800/0.10416, loss_grounding_bce_5: 0.03233/0.08772, loss_grounding_dice_5: 0.31525/0.18310, loss_grounding_ce_5: 0.40816/0.29333, loss_mask_ce_6: 0.77315/0.97238, loss_mask_bce_6: 0.07578/0.34397, loss_mask_dice_6: 0.52176/1.20186, loss_spatial_bce_6: 0.04226/0.10149, loss_spatial_dice_6: 0.35814/0.23305, loss_spatial_ce_6: 0.12295/0.12907, loss_grounding_bce_6: 0.02802/0.08847, loss_grounding_dice_6: 0.26578/0.18349, loss_grounding_ce_6: 0.36730/0.30867, loss_mask_ce_7: 0.59912/1.01807, loss_mask_bce_7: 0.07374/0.35172, loss_mask_dice_7: 0.56254/1.25631, loss_spatial_bce_7: 0.03835/0.10939, loss_spatial_dice_7: 0.33543/0.26061, loss_spatial_ce_7: 0.04625/0.16406, loss_grounding_bce_7: 0.03003/0.09034, loss_grounding_dice_7: 0.29042/0.19085, loss_grounding_ce_7: 0.41186/0.33853, loss_mask_ce_8: 0.67203/1.12654, loss_mask_bce_8: 0.07585/0.36529, loss_mask_dice_8: 0.55532/1.32880, loss_spatial_bce_8: 0.04064/0.12954, loss_spatial_dice_8: 0.33263/0.29826, loss_spatial_ce_8: 0.20926/0.21567, loss_grounding_bce_8: 0.02724/0.09401, loss_grounding_dice_8: 0.28647/0.20152, loss_grounding_ce_8: 0.56022/0.40472, loss_mask_ce_9: 2.87261/3.67390, loss_mask_bce_9: 0.06888/0.39236, loss_mask_dice_9: 0.87671/1.90120, loss_spatial_bce_9: 0.23769/0.33265, loss_spatial_dice_9: 0.79081/0.82159, loss_spatial_ce_9: 1.08708/1.49267, loss_grounding_bce_9: 0.02724/0.10565, loss_grounding_dice_9: 0.45973/0.28086, loss_grounding_ce_9: 0.27058/0.66908] items per batch[64] items per second[0.24] total items[5171200] mini batches[ 80800] memory[7345] epoch remaining[1:04:48]
INFO:trainer.default_trainer:epochs[    44] optim steps[80900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55426/0.89513, loss_mask_bce_0: 0.26969/0.33381, loss_mask_dice_0: 2.69579/1.16171, loss_spatial_bce_0: 0.03439/0.08655, loss_spatial_dice_0: 0.28773/0.20649, loss_spatial_ce_0: 0.06228/0.05956, loss_grounding_bce_0: 0.02528/0.08611, loss_grounding_dice_0: 0.10619/0.17844, loss_grounding_ce_0: 0.07972/0.27135, loss_mask_ce_1: 0.57840/0.89576, loss_mask_bce_1: 0.28317/0.33473, loss_mask_dice_1: 2.74758/1.16853, loss_spatial_bce_1: 0.03451/0.08708, loss_spatial_dice_1: 0.26197/0.21044, loss_spatial_ce_1: 0.18550/0.06542, loss_grounding_bce_1: 0.02600/0.08630, loss_grounding_dice_1: 0.10386/0.17922, loss_grounding_ce_1: 0.09701/0.27226, loss_mask_ce_2: 0.62790/0.90267, loss_mask_bce_2: 0.27916/0.33538, loss_mask_dice_2: 2.94211/1.16889, loss_spatial_bce_2: 0.03406/0.08832, loss_spatial_dice_2: 0.25792/0.21233, loss_spatial_ce_2: 0.06742/0.06887, loss_grounding_bce_2: 0.02663/0.08648, loss_grounding_dice_2: 0.10571/0.17911, loss_grounding_ce_2: 0.10371/0.27551, loss_mask_ce_3: 0.60217/0.91399, loss_mask_bce_3: 0.26948/0.33657, loss_mask_dice_3: 2.64378/1.16685, loss_spatial_bce_3: 0.03750/0.08964, loss_spatial_dice_3: 0.26883/0.21339, loss_spatial_ce_3: 0.07835/0.07392, loss_grounding_bce_3: 0.02678/0.08674, loss_grounding_dice_3: 0.11181/0.17879, loss_grounding_ce_3: 0.08531/0.27782, loss_mask_ce_4: 0.68267/0.91530, loss_mask_bce_4: 0.26299/0.33872, loss_mask_dice_4: 2.52065/1.19070, loss_spatial_bce_4: 0.03467/0.09356, loss_spatial_dice_4: 0.25407/0.22566, loss_spatial_ce_4: 0.06761/0.09025, loss_grounding_bce_4: 0.02728/0.08729, loss_grounding_dice_4: 0.11531/0.18181, loss_grounding_ce_4: 0.03554/0.28079, loss_mask_ce_5: 0.71824/0.93204, loss_mask_bce_5: 0.26695/0.34113, loss_mask_dice_5: 2.46805/1.19902, loss_spatial_bce_5: 0.03649/0.09592, loss_spatial_dice_5: 0.23677/0.23006, loss_spatial_ce_5: 0.07265/0.10413, loss_grounding_bce_5: 0.02536/0.08771, loss_grounding_dice_5: 0.11243/0.18309, loss_grounding_ce_5: 0.08899/0.29336, loss_mask_ce_6: 0.80310/0.97244, loss_mask_bce_6: 0.26090/0.34396, loss_mask_dice_6: 2.65362/1.20192, loss_spatial_bce_6: 0.04070/0.10148, loss_spatial_dice_6: 0.28765/0.23304, loss_spatial_ce_6: 0.10283/0.12904, loss_grounding_bce_6: 0.02571/0.08846, loss_grounding_dice_6: 0.10646/0.18349, loss_grounding_ce_6: 0.18516/0.30868, loss_mask_ce_7: 0.79904/1.01811, loss_mask_bce_7: 0.28035/0.35170, loss_mask_dice_7: 2.61358/1.25635, loss_spatial_bce_7: 0.05130/0.10938, loss_spatial_dice_7: 0.30872/0.26061, loss_spatial_ce_7: 0.34149/0.16402, loss_grounding_bce_7: 0.02653/0.09033, loss_grounding_dice_7: 0.11109/0.19084, loss_grounding_ce_7: 0.15102/0.33853, loss_mask_ce_8: 1.07903/1.12661, loss_mask_bce_8: 0.24873/0.36528, loss_mask_dice_8: 2.99458/1.32888, loss_spatial_bce_8: 0.07223/0.12954, loss_spatial_dice_8: 0.38669/0.29826, loss_spatial_ce_8: 0.17704/0.21558, loss_grounding_bce_8: 0.02632/0.09400, loss_grounding_dice_8: 0.10823/0.20151, loss_grounding_ce_8: 0.17585/0.40474, loss_mask_ce_9: 4.43061/3.67399, loss_mask_bce_9: 0.20673/0.39237, loss_mask_dice_9: 3.89497/1.90129, loss_spatial_bce_9: 0.13271/0.33267, loss_spatial_dice_9: 0.84132/0.82159, loss_spatial_ce_9: 1.34496/1.49268, loss_grounding_bce_9: 0.03457/0.10564, loss_grounding_dice_9: 0.17259/0.28085, loss_grounding_ce_9: 0.91247/0.66905] items per batch[64] items per second[0.23] total items[5177600] mini batches[ 80900] memory[7345] epoch remaining[1:00:36]
INFO:trainer.default_trainer:epochs[    44] optim steps[81000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73870/0.89514, loss_mask_bce_0: 0.07770/0.33385, loss_mask_dice_0: 0.47756/1.16189, loss_spatial_bce_0: 0.04417/0.08655, loss_spatial_dice_0: 0.21421/0.20649, loss_spatial_ce_0: 0.06268/0.05956, loss_grounding_bce_0: 0.02777/0.08613, loss_grounding_dice_0: 0.13313/0.17845, loss_grounding_ce_0: 0.03499/0.27133, loss_mask_ce_1: 0.48570/0.89577, loss_mask_bce_1: 0.07224/0.33478, loss_mask_dice_1: 0.54257/1.16874, loss_spatial_bce_1: 0.04955/0.08708, loss_spatial_dice_1: 0.22964/0.21044, loss_spatial_ce_1: 0.05898/0.06540, loss_grounding_bce_1: 0.02479/0.08631, loss_grounding_dice_1: 0.13655/0.17923, loss_grounding_ce_1: 0.03718/0.27223, loss_mask_ce_2: 0.50961/0.90267, loss_mask_bce_2: 0.07162/0.33543, loss_mask_dice_2: 0.50812/1.16910, loss_spatial_bce_2: 0.04623/0.08832, loss_spatial_dice_2: 0.21396/0.21233, loss_spatial_ce_2: 0.05989/0.06885, loss_grounding_bce_2: 0.02484/0.08650, loss_grounding_dice_2: 0.12274/0.17913, loss_grounding_ce_2: 0.03370/0.27549, loss_mask_ce_3: 0.72598/0.91400, loss_mask_bce_3: 0.07422/0.33662, loss_mask_dice_3: 0.52934/1.16705, loss_spatial_bce_3: 0.05015/0.08964, loss_spatial_dice_3: 0.22975/0.21339, loss_spatial_ce_3: 0.06243/0.07392, loss_grounding_bce_3: 0.02622/0.08675, loss_grounding_dice_3: 0.13941/0.17880, loss_grounding_ce_3: 0.03085/0.27778, loss_mask_ce_4: 0.52318/0.91530, loss_mask_bce_4: 0.08226/0.33877, loss_mask_dice_4: 0.46009/1.19087, loss_spatial_bce_4: 0.04890/0.09356, loss_spatial_dice_4: 0.23184/0.22565, loss_spatial_ce_4: 0.06196/0.09025, loss_grounding_bce_4: 0.02978/0.08730, loss_grounding_dice_4: 0.14227/0.18183, loss_grounding_ce_4: 0.03668/0.28074, loss_mask_ce_5: 0.52108/0.93206, loss_mask_bce_5: 0.08393/0.34118, loss_mask_dice_5: 0.55288/1.19921, loss_spatial_bce_5: 0.04749/0.09592, loss_spatial_dice_5: 0.22209/0.23005, loss_spatial_ce_5: 0.06071/0.10414, loss_grounding_bce_5: 0.03275/0.08773, loss_grounding_dice_5: 0.14267/0.18311, loss_grounding_ce_5: 0.03593/0.29332, loss_mask_ce_6: 0.89723/0.97244, loss_mask_bce_6: 0.08489/0.34402, loss_mask_dice_6: 0.55035/1.20214, loss_spatial_bce_6: 0.04846/0.10149, loss_spatial_dice_6: 0.25294/0.23304, loss_spatial_ce_6: 0.12465/0.12904, loss_grounding_bce_6: 0.03199/0.08848, loss_grounding_dice_6: 0.13498/0.18350, loss_grounding_ce_6: 0.05187/0.30867, loss_mask_ce_7: 1.00157/1.01813, loss_mask_bce_7: 0.07649/0.35175, loss_mask_dice_7: 0.44860/1.25655, loss_spatial_bce_7: 0.05634/0.10939, loss_spatial_dice_7: 0.21322/0.26061, loss_spatial_ce_7: 0.13916/0.16402, loss_grounding_bce_7: 0.02843/0.09035, loss_grounding_dice_7: 0.14406/0.19086, loss_grounding_ce_7: 0.10786/0.33856, loss_mask_ce_8: 1.28394/1.12664, loss_mask_bce_8: 0.08722/0.36533, loss_mask_dice_8: 0.52546/1.32911, loss_spatial_bce_8: 0.07010/0.12955, loss_spatial_dice_8: 0.26759/0.29827, loss_spatial_ce_8: 0.08380/0.21554, loss_grounding_bce_8: 0.03111/0.09402, loss_grounding_dice_8: 0.14034/0.20151, loss_grounding_ce_8: 0.11705/0.40478, loss_mask_ce_9: 2.91950/3.67404, loss_mask_bce_9: 0.10054/0.39242, loss_mask_dice_9: 0.59095/1.90158, loss_spatial_bce_9: 0.50513/0.33265, loss_spatial_dice_9: 0.89587/0.82158, loss_spatial_ce_9: 2.23028/1.49260, loss_grounding_bce_9: 0.04439/0.10566, loss_grounding_dice_9: 0.17265/0.28087, loss_grounding_ce_9: 0.44257/0.66902] items per batch[64] items per second[0.23] total items[5184000] mini batches[ 81000] memory[7345] epoch remaining[0:55:59]
INFO:trainer.default_trainer:epochs[    44] optim steps[81100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86333/0.89510, loss_mask_bce_0: 0.65935/0.33386, loss_mask_dice_0: 1.39490/1.16167, loss_spatial_bce_0: 0.11881/0.08655, loss_spatial_dice_0: 0.20707/0.20647, loss_spatial_ce_0: 0.11648/0.05954, loss_grounding_bce_0: 0.05970/0.08613, loss_grounding_dice_0: 0.10058/0.17842, loss_grounding_ce_0: 1.01658/0.27130, loss_mask_ce_1: 0.84436/0.89572, loss_mask_bce_1: 0.65768/0.33479, loss_mask_dice_1: 1.40056/1.16853, loss_spatial_bce_1: 0.12298/0.08708, loss_spatial_dice_1: 0.20519/0.21043, loss_spatial_ce_1: 0.16085/0.06538, loss_grounding_bce_1: 0.05934/0.08631, loss_grounding_dice_1: 0.10292/0.17920, loss_grounding_ce_1: 1.08960/0.27221, loss_mask_ce_2: 0.88606/0.90265, loss_mask_bce_2: 0.63970/0.33543, loss_mask_dice_2: 1.38600/1.16888, loss_spatial_bce_2: 0.11916/0.08832, loss_spatial_dice_2: 0.19834/0.21232, loss_spatial_ce_2: 0.23888/0.06883, loss_grounding_bce_2: 0.05455/0.08650, loss_grounding_dice_2: 0.09663/0.17910, loss_grounding_ce_2: 1.24801/0.27547, loss_mask_ce_3: 0.87049/0.91398, loss_mask_bce_3: 0.66285/0.33663, loss_mask_dice_3: 1.34496/1.16683, loss_spatial_bce_3: 0.12784/0.08964, loss_spatial_dice_3: 0.20656/0.21338, loss_spatial_ce_3: 0.21798/0.07390, loss_grounding_bce_3: 0.05510/0.08675, loss_grounding_dice_3: 0.10341/0.17877, loss_grounding_ce_3: 1.36138/0.27775, loss_mask_ce_4: 0.85159/0.91529, loss_mask_bce_4: 0.65462/0.33877, loss_mask_dice_4: 1.54407/1.19065, loss_spatial_bce_4: 0.13580/0.09356, loss_spatial_dice_4: 0.23025/0.22564, loss_spatial_ce_4: 0.16714/0.09024, loss_grounding_bce_4: 0.05758/0.08730, loss_grounding_dice_4: 0.10820/0.18179, loss_grounding_ce_4: 0.92910/0.28073, loss_mask_ce_5: 0.90892/0.93205, loss_mask_bce_5: 0.62671/0.34118, loss_mask_dice_5: 1.54468/1.19898, loss_spatial_bce_5: 0.14314/0.09592, loss_spatial_dice_5: 0.23496/0.23004, loss_spatial_ce_5: 0.22122/0.10412, loss_grounding_bce_5: 0.05865/0.08773, loss_grounding_dice_5: 0.10901/0.18308, loss_grounding_ce_5: 0.90091/0.29331, loss_mask_ce_6: 1.00774/0.97242, loss_mask_bce_6: 0.66063/0.34402, loss_mask_dice_6: 1.64776/1.20191, loss_spatial_bce_6: 0.14019/0.10148, loss_spatial_dice_6: 0.23557/0.23303, loss_spatial_ce_6: 0.22891/0.12903, loss_grounding_bce_6: 0.06174/0.08848, loss_grounding_dice_6: 0.10460/0.18347, loss_grounding_ce_6: 0.95695/0.30863, loss_mask_ce_7: 0.95371/1.01811, loss_mask_bce_7: 0.75459/0.35176, loss_mask_dice_7: 1.88547/1.25631, loss_spatial_bce_7: 0.18137/0.10938, loss_spatial_dice_7: 0.29493/0.26060, loss_spatial_ce_7: 0.19107/0.16400, loss_grounding_bce_7: 0.06281/0.09034, loss_grounding_dice_7: 0.11523/0.19083, loss_grounding_ce_7: 0.93130/0.33851, loss_mask_ce_8: 0.96476/1.12660, loss_mask_bce_8: 0.79609/0.36534, loss_mask_dice_8: 1.87532/1.32886, loss_spatial_bce_8: 0.21797/0.12954, loss_spatial_dice_8: 0.32493/0.29825, loss_spatial_ce_8: 0.29971/0.21548, loss_grounding_bce_8: 0.05925/0.09401, loss_grounding_dice_8: 0.09653/0.20148, loss_grounding_ce_8: 1.73513/0.40475, loss_mask_ce_9: 3.19320/3.67389, loss_mask_bce_9: 0.77481/0.39243, loss_mask_dice_9: 2.69011/1.90124, loss_spatial_bce_9: 0.38169/0.33265, loss_spatial_dice_9: 0.86448/0.82159, loss_spatial_ce_9: 1.20596/1.49264, loss_grounding_bce_9: 0.08079/0.10566, loss_grounding_dice_9: 0.14297/0.28083, loss_grounding_ce_9: 3.09393/0.66914] items per batch[64] items per second[0.23] total items[5190400] mini batches[ 81100] memory[7345] epoch remaining[0:51:21]
INFO:trainer.default_trainer:epochs[    44] optim steps[81200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.67623/0.89507, loss_mask_bce_0: 0.32423/0.33385, loss_mask_dice_0: 2.24766/1.16177, loss_spatial_bce_0: 0.06995/0.08653, loss_spatial_dice_0: 0.27456/0.20647, loss_spatial_ce_0: 0.03810/0.05952, loss_grounding_bce_0: 0.09182/0.08612, loss_grounding_dice_0: 0.22862/0.17843, loss_grounding_ce_0: 0.35192/0.27129, loss_mask_ce_1: 0.63906/0.89570, loss_mask_bce_1: 0.35184/0.33477, loss_mask_dice_1: 2.21418/1.16862, loss_spatial_bce_1: 0.07596/0.08707, loss_spatial_dice_1: 0.30607/0.21043, loss_spatial_ce_1: 0.02679/0.06536, loss_grounding_bce_1: 0.09934/0.08630, loss_grounding_dice_1: 0.23100/0.17920, loss_grounding_ce_1: 0.42119/0.27219, loss_mask_ce_2: 0.63132/0.90264, loss_mask_bce_2: 0.34516/0.33542, loss_mask_dice_2: 2.05211/1.16897, loss_spatial_bce_2: 0.08089/0.08831, loss_spatial_dice_2: 0.30416/0.21232, loss_spatial_ce_2: 0.04247/0.06880, loss_grounding_bce_2: 0.05999/0.08648, loss_grounding_dice_2: 0.21410/0.17910, loss_grounding_ce_2: 3.16433/0.27552, loss_mask_ce_3: 0.84540/0.91398, loss_mask_bce_3: 0.34386/0.33661, loss_mask_dice_3: 2.17726/1.16692, loss_spatial_bce_3: 0.09213/0.08963, loss_spatial_dice_3: 0.28953/0.21337, loss_spatial_ce_3: 0.04045/0.07388, loss_grounding_bce_3: 0.09824/0.08674, loss_grounding_dice_3: 0.22724/0.17878, loss_grounding_ce_3: 0.43848/0.27774, loss_mask_ce_4: 0.72217/0.91527, loss_mask_bce_4: 0.33986/0.33876, loss_mask_dice_4: 2.33823/1.19074, loss_spatial_bce_4: 0.07462/0.09355, loss_spatial_dice_4: 0.32716/0.22564, loss_spatial_ce_4: 0.02148/0.09020, loss_grounding_bce_4: 0.09544/0.08729, loss_grounding_dice_4: 0.25666/0.18180, loss_grounding_ce_4: 0.41372/0.28072, loss_mask_ce_5: 0.76230/0.93202, loss_mask_bce_5: 0.30445/0.34117, loss_mask_dice_5: 1.99246/1.19908, loss_spatial_bce_5: 0.06218/0.09591, loss_spatial_dice_5: 0.30287/0.23003, loss_spatial_ce_5: 0.04868/0.10409, loss_grounding_bce_5: 0.08045/0.08772, loss_grounding_dice_5: 0.23605/0.18309, loss_grounding_ce_5: 0.46406/0.29328, loss_mask_ce_6: 0.81397/0.97237, loss_mask_bce_6: 0.34339/0.34401, loss_mask_dice_6: 2.21490/1.20198, loss_spatial_bce_6: 0.09796/0.10146, loss_spatial_dice_6: 0.27636/0.23303, loss_spatial_ce_6: 0.06531/0.12900, loss_grounding_bce_6: 0.09300/0.08847, loss_grounding_dice_6: 0.22747/0.18348, loss_grounding_ce_6: 0.44335/0.30863, loss_mask_ce_7: 0.86394/1.01803, loss_mask_bce_7: 0.34133/0.35175, loss_mask_dice_7: 2.18072/1.25641, loss_spatial_bce_7: 0.06168/0.10937, loss_spatial_dice_7: 0.28647/0.26060, loss_spatial_ce_7: 0.21472/0.16397, loss_grounding_bce_7: 0.09311/0.09033, loss_grounding_dice_7: 0.27572/0.19083, loss_grounding_ce_7: 0.66910/0.33852, loss_mask_ce_8: 0.92232/1.12661, loss_mask_bce_8: 0.22754/0.36533, loss_mask_dice_8: 2.19972/1.32894, loss_spatial_bce_8: 0.06884/0.12952, loss_spatial_dice_8: 0.38876/0.29824, loss_spatial_ce_8: 0.19016/0.21540, loss_grounding_bce_8: 0.06013/0.09400, loss_grounding_dice_8: 0.27585/0.20148, loss_grounding_ce_8: 0.75458/0.40472, loss_mask_ce_9: 3.83634/3.67404, loss_mask_bce_9: 0.30086/0.39241, loss_mask_dice_9: 3.00820/1.90143, loss_spatial_bce_9: 0.19272/0.33264, loss_spatial_dice_9: 0.94631/0.82159, loss_spatial_ce_9: 1.56403/1.49260, loss_grounding_bce_9: 0.06789/0.10565, loss_grounding_dice_9: 0.41162/0.28085, loss_grounding_ce_9: 1.06257/0.66914] items per batch[64] items per second[0.24] total items[5196800] mini batches[ 81200] memory[7345] epoch remaining[0:46:36]
INFO:trainer.default_trainer:epochs[    44] optim steps[81300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.22860/0.89497, loss_mask_bce_0: 0.20891/0.33387, loss_mask_dice_0: 0.38173/1.16171, loss_spatial_bce_0: 0.06149/0.08653, loss_spatial_dice_0: 0.10427/0.20645, loss_spatial_ce_0: 0.00316/0.05950, loss_grounding_bce_0: 0.06974/0.08613, loss_grounding_dice_0: 0.09934/0.17842, loss_grounding_ce_0: 0.01693/0.27132, loss_mask_ce_1: 0.23619/0.89562, loss_mask_bce_1: 0.20983/0.33479, loss_mask_dice_1: 0.38668/1.16856, loss_spatial_bce_1: 0.05860/0.08707, loss_spatial_dice_1: 0.10074/0.21041, loss_spatial_ce_1: 0.00487/0.06533, loss_grounding_bce_1: 0.07186/0.08631, loss_grounding_dice_1: 0.10201/0.17920, loss_grounding_ce_1: 0.01736/0.27225, loss_mask_ce_2: 0.23417/0.90256, loss_mask_bce_2: 0.21634/0.33544, loss_mask_dice_2: 0.37939/1.16890, loss_spatial_bce_2: 0.05877/0.08831, loss_spatial_dice_2: 0.09597/0.21230, loss_spatial_ce_2: 0.01058/0.06877, loss_grounding_bce_2: 0.07349/0.08650, loss_grounding_dice_2: 0.10229/0.17910, loss_grounding_ce_2: 0.01586/0.27558, loss_mask_ce_3: 0.20812/0.91387, loss_mask_bce_3: 0.21284/0.33663, loss_mask_dice_3: 0.39263/1.16687, loss_spatial_bce_3: 0.05915/0.08963, loss_spatial_dice_3: 0.09837/0.21336, loss_spatial_ce_3: 0.01942/0.07385, loss_grounding_bce_3: 0.07405/0.08675, loss_grounding_dice_3: 0.10567/0.17877, loss_grounding_ce_3: 0.01650/0.27783, loss_mask_ce_4: 0.22133/0.91519, loss_mask_bce_4: 0.22042/0.33879, loss_mask_dice_4: 0.38569/1.19066, loss_spatial_bce_4: 0.06075/0.09355, loss_spatial_dice_4: 0.09670/0.22562, loss_spatial_ce_4: 0.01834/0.09017, loss_grounding_bce_4: 0.07456/0.08731, loss_grounding_dice_4: 0.10084/0.18180, loss_grounding_ce_4: 0.02055/0.28074, loss_mask_ce_5: 0.22927/0.93194, loss_mask_bce_5: 0.21210/0.34120, loss_mask_dice_5: 0.37667/1.19901, loss_spatial_bce_5: 0.05875/0.09591, loss_spatial_dice_5: 0.09986/0.23002, loss_spatial_ce_5: 0.02164/0.10405, loss_grounding_bce_5: 0.07177/0.08773, loss_grounding_dice_5: 0.09881/0.18308, loss_grounding_ce_5: 0.01498/0.29337, loss_mask_ce_6: 0.25572/0.97230, loss_mask_bce_6: 0.20744/0.34403, loss_mask_dice_6: 0.37851/1.20190, loss_spatial_bce_6: 0.07232/0.10146, loss_spatial_dice_6: 0.10749/0.23301, loss_spatial_ce_6: 0.02444/0.12896, loss_grounding_bce_6: 0.07365/0.08848, loss_grounding_dice_6: 0.10228/0.18347, loss_grounding_ce_6: 0.01305/0.30871, loss_mask_ce_7: 0.35014/1.01794, loss_mask_bce_7: 0.21222/0.35177, loss_mask_dice_7: 0.37871/1.25634, loss_spatial_bce_7: 0.06758/0.10937, loss_spatial_dice_7: 0.11281/0.26058, loss_spatial_ce_7: 0.05830/0.16394, loss_grounding_bce_7: 0.07373/0.09034, loss_grounding_dice_7: 0.10168/0.19082, loss_grounding_ce_7: 0.01413/0.33862, loss_mask_ce_8: 0.35378/1.12654, loss_mask_bce_8: 0.21418/0.36536, loss_mask_dice_8: 0.38189/1.32888, loss_spatial_bce_8: 0.07912/0.12952, loss_spatial_dice_8: 0.12502/0.29823, loss_spatial_ce_8: 0.02120/0.21531, loss_grounding_bce_8: 0.08385/0.09401, loss_grounding_dice_8: 0.10709/0.20147, loss_grounding_ce_8: 0.02631/0.40485, loss_mask_ce_9: 2.98650/3.67400, loss_mask_bce_9: 0.26937/0.39246, loss_mask_dice_9: 0.58677/1.90141, loss_spatial_bce_9: 0.38585/0.33265, loss_spatial_dice_9: 0.83984/0.82159, loss_spatial_ce_9: 1.24268/1.49252, loss_grounding_bce_9: 0.07274/0.10566, loss_grounding_dice_9: 0.12454/0.28084, loss_grounding_ce_9: 0.21367/0.66930] items per batch[64] items per second[0.23] total items[5203200] mini batches[ 81300] memory[7345] epoch remaining[0:42:03]
INFO:trainer.default_trainer:epochs[    44] optim steps[81400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.41729/0.89501, loss_mask_bce_0: 0.32140/0.33384, loss_mask_dice_0: 1.15672/1.16155, loss_spatial_bce_0: 0.05662/0.08653, loss_spatial_dice_0: 0.30847/0.20643, loss_spatial_ce_0: 0.06813/0.05949, loss_grounding_bce_0: 0.11179/0.08613, loss_grounding_dice_0: 0.28361/0.17841, loss_grounding_ce_0: 0.09055/0.27129, loss_mask_ce_1: 1.51674/0.89565, loss_mask_bce_1: 0.30791/0.33477, loss_mask_dice_1: 1.12845/1.16840, loss_spatial_bce_1: 0.05926/0.08706, loss_spatial_dice_1: 0.30591/0.21039, loss_spatial_ce_1: 0.03741/0.06532, loss_grounding_bce_1: 0.10711/0.08632, loss_grounding_dice_1: 0.26069/0.17920, loss_grounding_ce_1: 0.06304/0.27221, loss_mask_ce_2: 1.57271/0.90259, loss_mask_bce_2: 0.34232/0.33542, loss_mask_dice_2: 1.19420/1.16875, loss_spatial_bce_2: 0.05620/0.08830, loss_spatial_dice_2: 0.31654/0.21228, loss_spatial_ce_2: 0.01727/0.06876, loss_grounding_bce_2: 0.12174/0.08650, loss_grounding_dice_2: 0.29473/0.17910, loss_grounding_ce_2: 0.05340/0.27554, loss_mask_ce_3: 1.59280/0.91393, loss_mask_bce_3: 0.30990/0.33661, loss_mask_dice_3: 1.10985/1.16672, loss_spatial_bce_3: 0.06754/0.08963, loss_spatial_dice_3: 0.31949/0.21334, loss_spatial_ce_3: 0.01716/0.07384, loss_grounding_bce_3: 0.12025/0.08675, loss_grounding_dice_3: 0.29088/0.17877, loss_grounding_ce_3: 0.06318/0.27780, loss_mask_ce_4: 1.50555/0.91525, loss_mask_bce_4: 0.31785/0.33876, loss_mask_dice_4: 1.22327/1.19050, loss_spatial_bce_4: 0.06516/0.09355, loss_spatial_dice_4: 0.31332/0.22560, loss_spatial_ce_4: 0.11951/0.09014, loss_grounding_bce_4: 0.10049/0.08731, loss_grounding_dice_4: 0.29783/0.18179, loss_grounding_ce_4: 0.17042/0.28071, loss_mask_ce_5: 1.61010/0.93198, loss_mask_bce_5: 0.31538/0.34118, loss_mask_dice_5: 1.14280/1.19886, loss_spatial_bce_5: 0.06802/0.09590, loss_spatial_dice_5: 0.35272/0.23000, loss_spatial_ce_5: 0.07470/0.10403, loss_grounding_bce_5: 0.10340/0.08773, loss_grounding_dice_5: 0.28718/0.18307, loss_grounding_ce_5: 0.19998/0.29333, loss_mask_ce_6: 1.79681/0.97235, loss_mask_bce_6: 0.30530/0.34401, loss_mask_dice_6: 1.09768/1.20173, loss_spatial_bce_6: 0.08554/0.10145, loss_spatial_dice_6: 0.33081/0.23299, loss_spatial_ce_6: 0.18110/0.12893, loss_grounding_bce_6: 0.11155/0.08848, loss_grounding_dice_6: 0.27216/0.18347, loss_grounding_ce_6: 0.15020/0.30868, loss_mask_ce_7: 2.00785/1.01802, loss_mask_bce_7: 0.30486/0.35176, loss_mask_dice_7: 1.12319/1.25614, loss_spatial_bce_7: 0.07538/0.10936, loss_spatial_dice_7: 0.37936/0.26057, loss_spatial_ce_7: 0.17275/0.16392, loss_grounding_bce_7: 0.11250/0.09034, loss_grounding_dice_7: 0.34876/0.19080, loss_grounding_ce_7: 0.22150/0.33859, loss_mask_ce_8: 2.14516/1.12664, loss_mask_bce_8: 0.26669/0.36534, loss_mask_dice_8: 1.18285/1.32870, loss_spatial_bce_8: 0.10318/0.12951, loss_spatial_dice_8: 0.41657/0.29821, loss_spatial_ce_8: 0.21699/0.21527, loss_grounding_bce_8: 0.09943/0.09401, loss_grounding_dice_8: 0.25915/0.20146, loss_grounding_ce_8: 1.26867/0.40478, loss_mask_ce_9: 3.06958/3.67399, loss_mask_bce_9: 0.23212/0.39244, loss_mask_dice_9: 1.60868/1.90119, loss_spatial_bce_9: 0.21978/0.33266, loss_spatial_dice_9: 0.87757/0.82158, loss_spatial_ce_9: 1.15306/1.49254, loss_grounding_bce_9: 0.08380/0.10566, loss_grounding_dice_9: 0.28654/0.28084, loss_grounding_ce_9: 0.91191/0.66921] items per batch[64] items per second[0.23] total items[5209600] mini batches[ 81400] memory[7345] epoch remaining[0:37:30]
INFO:trainer.default_trainer:epochs[    44] optim steps[81500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.12743/0.89500, loss_mask_bce_0: 0.29694/0.33383, loss_mask_dice_0: 1.90987/1.16153, loss_spatial_bce_0: 0.05690/0.08652, loss_spatial_dice_0: 0.23177/0.20642, loss_spatial_ce_0: 0.03603/0.05947, loss_grounding_bce_0: 0.11039/0.08613, loss_grounding_dice_0: 0.28688/0.17841, loss_grounding_ce_0: 0.20058/0.27126, loss_mask_ce_1: 1.09723/0.89567, loss_mask_bce_1: 0.30113/0.33476, loss_mask_dice_1: 2.26002/1.16841, loss_spatial_bce_1: 0.06200/0.08706, loss_spatial_dice_1: 0.24150/0.21037, loss_spatial_ce_1: 0.01612/0.06531, loss_grounding_bce_1: 0.10906/0.08632, loss_grounding_dice_1: 0.29344/0.17919, loss_grounding_ce_1: 0.21962/0.27218, loss_mask_ce_2: 1.14162/0.90260, loss_mask_bce_2: 0.28336/0.33541, loss_mask_dice_2: 1.76248/1.16874, loss_spatial_bce_2: 0.05868/0.08830, loss_spatial_dice_2: 0.22199/0.21227, loss_spatial_ce_2: 0.03557/0.06874, loss_grounding_bce_2: 0.10313/0.08650, loss_grounding_dice_2: 0.28390/0.17909, loss_grounding_ce_2: 0.20380/0.27550, loss_mask_ce_3: 1.22164/0.91392, loss_mask_bce_3: 0.28444/0.33660, loss_mask_dice_3: 1.87342/1.16673, loss_spatial_bce_3: 0.06189/0.08962, loss_spatial_dice_3: 0.22731/0.21333, loss_spatial_ce_3: 0.06532/0.07383, loss_grounding_bce_3: 0.10743/0.08675, loss_grounding_dice_3: 0.30822/0.17877, loss_grounding_ce_3: 0.20275/0.27776, loss_mask_ce_4: 1.21647/0.91524, loss_mask_bce_4: 0.28636/0.33876, loss_mask_dice_4: 2.03383/1.19049, loss_spatial_bce_4: 0.05988/0.09354, loss_spatial_dice_4: 0.24566/0.22559, loss_spatial_ce_4: 0.29539/0.09011, loss_grounding_bce_4: 0.11018/0.08731, loss_grounding_dice_4: 0.30154/0.18178, loss_grounding_ce_4: 0.23081/0.28066, loss_mask_ce_5: 1.19542/0.93197, loss_mask_bce_5: 0.30001/0.34117, loss_mask_dice_5: 2.15304/1.19885, loss_spatial_bce_5: 0.05474/0.09590, loss_spatial_dice_5: 0.25559/0.22999, loss_spatial_ce_5: 0.03520/0.10401, loss_grounding_bce_5: 0.10167/0.08773, loss_grounding_dice_5: 0.30252/0.18307, loss_grounding_ce_5: 0.24297/0.29333, loss_mask_ce_6: 1.12876/0.97234, loss_mask_bce_6: 0.29085/0.34400, loss_mask_dice_6: 2.22324/1.20173, loss_spatial_bce_6: 0.06679/0.10145, loss_spatial_dice_6: 0.24974/0.23297, loss_spatial_ce_6: 0.10332/0.12893, loss_grounding_bce_6: 0.10160/0.08848, loss_grounding_dice_6: 0.30343/0.18346, loss_grounding_ce_6: 0.15086/0.30865, loss_mask_ce_7: 0.97357/1.01797, loss_mask_bce_7: 0.32495/0.35175, loss_mask_dice_7: 2.34718/1.25617, loss_spatial_bce_7: 0.07335/0.10935, loss_spatial_dice_7: 0.31915/0.26056, loss_spatial_ce_7: 0.09858/0.16389, loss_grounding_bce_7: 0.12183/0.09034, loss_grounding_dice_7: 0.30294/0.19080, loss_grounding_ce_7: 0.11929/0.33857, loss_mask_ce_8: 1.12982/1.12665, loss_mask_bce_8: 0.34315/0.36533, loss_mask_dice_8: 2.34894/1.32870, loss_spatial_bce_8: 0.08237/0.12949, loss_spatial_dice_8: 0.35081/0.29820, loss_spatial_ce_8: 0.10058/0.21520, loss_grounding_bce_8: 0.12582/0.09400, loss_grounding_dice_8: 0.34803/0.20146, loss_grounding_ce_8: 0.18237/0.40476, loss_mask_ce_9: 3.32656/3.67399, loss_mask_bce_9: 0.31985/0.39242, loss_mask_dice_9: 2.56516/1.90117, loss_spatial_bce_9: 0.23069/0.33264, loss_spatial_dice_9: 0.88976/0.82158, loss_spatial_ce_9: 1.48366/1.49251, loss_grounding_bce_9: 0.09903/0.10565, loss_grounding_dice_9: 0.37028/0.28084, loss_grounding_ce_9: 0.85102/0.66915] items per batch[64] items per second[0.24] total items[5216000] mini batches[ 81500] memory[7345] epoch remaining[0:32:51]
INFO:trainer.default_trainer:epochs[    44] optim steps[81600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.23965/0.89489, loss_mask_bce_0: 0.45490/0.33385, loss_mask_dice_0: 0.91253/1.16138, loss_spatial_bce_0: 0.10400/0.08652, loss_spatial_dice_0: 0.14629/0.20639, loss_spatial_ce_0: 0.00179/0.05944, loss_grounding_bce_0: 0.16481/0.08613, loss_grounding_dice_0: 0.17056/0.17841, loss_grounding_ce_0: 0.06655/0.27124, loss_mask_ce_1: 0.23244/0.89556, loss_mask_bce_1: 0.45017/0.33478, loss_mask_dice_1: 0.90010/1.16827, loss_spatial_bce_1: 0.10132/0.08705, loss_spatial_dice_1: 0.14537/0.21035, loss_spatial_ce_1: 0.00292/0.06527, loss_grounding_bce_1: 0.16184/0.08632, loss_grounding_dice_1: 0.16945/0.17920, loss_grounding_ce_1: 0.07031/0.27217, loss_mask_ce_2: 0.27450/0.90249, loss_mask_bce_2: 0.45063/0.33544, loss_mask_dice_2: 0.92466/1.16861, loss_spatial_bce_2: 0.10832/0.08829, loss_spatial_dice_2: 0.15741/0.21225, loss_spatial_ce_2: 0.00496/0.06871, loss_grounding_bce_2: 0.16151/0.08650, loss_grounding_dice_2: 0.17405/0.17909, loss_grounding_ce_2: 0.06815/0.27549, loss_mask_ce_3: 0.26006/0.91382, loss_mask_bce_3: 0.45332/0.33662, loss_mask_dice_3: 0.89111/1.16658, loss_spatial_bce_3: 0.12045/0.08962, loss_spatial_dice_3: 0.17192/0.21331, loss_spatial_ce_3: 0.01052/0.07381, loss_grounding_bce_3: 0.15813/0.08676, loss_grounding_dice_3: 0.16746/0.17877, loss_grounding_ce_3: 0.06626/0.27773, loss_mask_ce_4: 0.30832/0.91516, loss_mask_bce_4: 0.45027/0.33878, loss_mask_dice_4: 0.89932/1.19037, loss_spatial_bce_4: 0.10364/0.09354, loss_spatial_dice_4: 0.15308/0.22557, loss_spatial_ce_4: 0.00954/0.09008, loss_grounding_bce_4: 0.15774/0.08731, loss_grounding_dice_4: 0.16698/0.18178, loss_grounding_ce_4: 0.06735/0.28064, loss_mask_ce_5: 0.34981/0.93190, loss_mask_bce_5: 0.43631/0.34119, loss_mask_dice_5: 0.87136/1.19870, loss_spatial_bce_5: 0.11528/0.09590, loss_spatial_dice_5: 0.17301/0.22997, loss_spatial_ce_5: 0.03287/0.10397, loss_grounding_bce_5: 0.15632/0.08773, loss_grounding_dice_5: 0.16623/0.18307, loss_grounding_ce_5: 0.07271/0.29331, loss_mask_ce_6: 0.39834/0.97226, loss_mask_bce_6: 0.45645/0.34402, loss_mask_dice_6: 0.87295/1.20159, loss_spatial_bce_6: 0.11116/0.10145, loss_spatial_dice_6: 0.14992/0.23296, loss_spatial_ce_6: 0.04849/0.12889, loss_grounding_bce_6: 0.16554/0.08849, loss_grounding_dice_6: 0.17106/0.18346, loss_grounding_ce_6: 0.08124/0.30864, loss_mask_ce_7: 0.49259/1.01789, loss_mask_bce_7: 0.45190/0.35177, loss_mask_dice_7: 0.90750/1.25601, loss_spatial_bce_7: 0.12336/0.10935, loss_spatial_dice_7: 0.15422/0.26054, loss_spatial_ce_7: 0.06178/0.16385, loss_grounding_bce_7: 0.15656/0.09034, loss_grounding_dice_7: 0.16610/0.19079, loss_grounding_ce_7: 0.07317/0.33855, loss_mask_ce_8: 0.54051/1.12658, loss_mask_bce_8: 0.42807/0.36536, loss_mask_dice_8: 0.86909/1.32853, loss_spatial_bce_8: 0.13680/0.12949, loss_spatial_dice_8: 0.18038/0.29818, loss_spatial_ce_8: 0.10461/0.21512, loss_grounding_bce_8: 0.15547/0.09400, loss_grounding_dice_8: 0.16853/0.20146, loss_grounding_ce_8: 0.07127/0.40480, loss_mask_ce_9: 2.71185/3.67385, loss_mask_bce_9: 0.46198/0.39245, loss_mask_dice_9: 1.19470/1.90093, loss_spatial_bce_9: 0.35918/0.33265, loss_spatial_dice_9: 0.77684/0.82157, loss_spatial_ce_9: 1.28253/1.49237, loss_grounding_bce_9: 0.16598/0.10566, loss_grounding_dice_9: 0.24155/0.28085, loss_grounding_ce_9: 0.17903/0.66914] items per batch[64] items per second[0.23] total items[5222400] mini batches[ 81600] memory[7345] epoch remaining[0:28:16]
INFO:trainer.default_trainer:epochs[    44] optim steps[81700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.24596/0.89485, loss_mask_bce_0: 0.70795/0.33383, loss_mask_dice_0: 1.49665/1.16136, loss_spatial_bce_0: 0.11152/0.08651, loss_spatial_dice_0: 0.24056/0.20638, loss_spatial_ce_0: 0.04924/0.05944, loss_grounding_bce_0: 0.14856/0.08613, loss_grounding_dice_0: 0.18132/0.17841, loss_grounding_ce_0: 0.24243/0.27119, loss_mask_ce_1: 1.21748/0.89553, loss_mask_bce_1: 0.70530/0.33477, loss_mask_dice_1: 1.47485/1.16822, loss_spatial_bce_1: 0.10918/0.08704, loss_spatial_dice_1: 0.25386/0.21033, loss_spatial_ce_1: 0.06034/0.06526, loss_grounding_bce_1: 0.14769/0.08632, loss_grounding_dice_1: 0.18341/0.17920, loss_grounding_ce_1: 0.26117/0.27213, loss_mask_ce_2: 1.34734/0.90247, loss_mask_bce_2: 0.65804/0.33542, loss_mask_dice_2: 1.41236/1.16858, loss_spatial_bce_2: 0.10745/0.08829, loss_spatial_dice_2: 0.24978/0.21224, loss_spatial_ce_2: 0.06395/0.06870, loss_grounding_bce_2: 0.13133/0.08650, loss_grounding_dice_2: 0.17267/0.17910, loss_grounding_ce_2: 0.27604/0.27545, loss_mask_ce_3: 1.35149/0.91381, loss_mask_bce_3: 0.63533/0.33660, loss_mask_dice_3: 1.42960/1.16655, loss_spatial_bce_3: 0.11070/0.08962, loss_spatial_dice_3: 0.25019/0.21330, loss_spatial_ce_3: 0.06643/0.07380, loss_grounding_bce_3: 0.12635/0.08675, loss_grounding_dice_3: 0.16761/0.17877, loss_grounding_ce_3: 0.23823/0.27769, loss_mask_ce_4: 1.35972/0.91514, loss_mask_bce_4: 0.68373/0.33876, loss_mask_dice_4: 1.46151/1.19036, loss_spatial_bce_4: 0.11276/0.09354, loss_spatial_dice_4: 0.24596/0.22556, loss_spatial_ce_4: 0.06845/0.09007, loss_grounding_bce_4: 0.13283/0.08731, loss_grounding_dice_4: 0.17192/0.18178, loss_grounding_ce_4: 0.28752/0.28060, loss_mask_ce_5: 1.29934/0.93191, loss_mask_bce_5: 0.71951/0.34118, loss_mask_dice_5: 1.50844/1.19866, loss_spatial_bce_5: 0.10613/0.09589, loss_spatial_dice_5: 0.24416/0.22996, loss_spatial_ce_5: 0.10565/0.10395, loss_grounding_bce_5: 0.11576/0.08773, loss_grounding_dice_5: 0.16406/0.18307, loss_grounding_ce_5: 0.38196/0.29327, loss_mask_ce_6: 1.29126/0.97226, loss_mask_bce_6: 0.67803/0.34400, loss_mask_dice_6: 1.48981/1.20156, loss_spatial_bce_6: 0.12007/0.10144, loss_spatial_dice_6: 0.25327/0.23295, loss_spatial_ce_6: 0.08862/0.12886, loss_grounding_bce_6: 0.12510/0.08848, loss_grounding_dice_6: 0.17958/0.18347, loss_grounding_ce_6: 0.31815/0.30859, loss_mask_ce_7: 1.39979/1.01789, loss_mask_bce_7: 0.70104/0.35177, loss_mask_dice_7: 1.51922/1.25598, loss_spatial_bce_7: 0.12718/0.10935, loss_spatial_dice_7: 0.28460/0.26054, loss_spatial_ce_7: 0.09697/0.16384, loss_grounding_bce_7: 0.14592/0.09034, loss_grounding_dice_7: 0.21319/0.19080, loss_grounding_ce_7: 0.29727/0.33851, loss_mask_ce_8: 1.49217/1.12657, loss_mask_bce_8: 0.72532/0.36534, loss_mask_dice_8: 1.59920/1.32847, loss_spatial_bce_8: 0.15116/0.12948, loss_spatial_dice_8: 0.32819/0.29818, loss_spatial_ce_8: 0.12851/0.21506, loss_grounding_bce_8: 0.17024/0.09400, loss_grounding_dice_8: 0.27634/0.20146, loss_grounding_ce_8: 0.45278/0.40474, loss_mask_ce_9: 3.82532/3.67376, loss_mask_bce_9: 0.80285/0.39243, loss_mask_dice_9: 2.27635/1.90083, loss_spatial_bce_9: 0.26836/0.33265, loss_spatial_dice_9: 0.84774/0.82156, loss_spatial_ce_9: 1.07335/1.49239, loss_grounding_bce_9: 0.24766/0.10565, loss_grounding_dice_9: 0.41278/0.28086, loss_grounding_ce_9: 0.67927/0.66917] items per batch[64] items per second[0.24] total items[5228800] mini batches[ 81700] memory[7345] epoch remaining[0:23:38]
INFO:trainer.default_trainer:epochs[    44] optim steps[81800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.75481/0.89475, loss_mask_bce_0: 0.46818/0.33382, loss_mask_dice_0: 0.69370/1.16141, loss_spatial_bce_0: 0.08433/0.08650, loss_spatial_dice_0: 0.16413/0.20637, loss_spatial_ce_0: 0.03333/0.05943, loss_grounding_bce_0: 0.05487/0.08613, loss_grounding_dice_0: 0.11084/0.17841, loss_grounding_ce_0: 0.14762/0.27119, loss_mask_ce_1: 0.94417/0.89544, loss_mask_bce_1: 0.36054/0.33475, loss_mask_dice_1: 0.65368/1.16826, loss_spatial_bce_1: 0.08789/0.08704, loss_spatial_dice_1: 0.17755/0.21031, loss_spatial_ce_1: 0.03921/0.06525, loss_grounding_bce_1: 0.05847/0.08632, loss_grounding_dice_1: 0.11151/0.17920, loss_grounding_ce_1: 0.16067/0.27215, loss_mask_ce_2: 0.83550/0.90238, loss_mask_bce_2: 0.50188/0.33541, loss_mask_dice_2: 0.72527/1.16862, loss_spatial_bce_2: 0.09691/0.08829, loss_spatial_dice_2: 0.17271/0.21222, loss_spatial_ce_2: 0.03491/0.06869, loss_grounding_bce_2: 0.05708/0.08650, loss_grounding_dice_2: 0.10860/0.17908, loss_grounding_ce_2: 0.14457/0.27545, loss_mask_ce_3: 1.15112/0.91370, loss_mask_bce_3: 0.36351/0.33660, loss_mask_dice_3: 0.66436/1.16662, loss_spatial_bce_3: 0.10857/0.08961, loss_spatial_dice_3: 0.16979/0.21328, loss_spatial_ce_3: 0.05581/0.07379, loss_grounding_bce_3: 0.05356/0.08675, loss_grounding_dice_3: 0.10539/0.17876, loss_grounding_ce_3: 0.12822/0.27770, loss_mask_ce_4: 0.89843/0.91506, loss_mask_bce_4: 0.50706/0.33876, loss_mask_dice_4: 0.72707/1.19040, loss_spatial_bce_4: 0.10517/0.09353, loss_spatial_dice_4: 0.19398/0.22554, loss_spatial_ce_4: 0.04887/0.09005, loss_grounding_bce_4: 0.05006/0.08730, loss_grounding_dice_4: 0.10058/0.18178, loss_grounding_ce_4: 0.13023/0.28062, loss_mask_ce_5: 0.92000/0.93185, loss_mask_bce_5: 0.48590/0.34118, loss_mask_dice_5: 0.70455/1.19870, loss_spatial_bce_5: 0.09945/0.09588, loss_spatial_dice_5: 0.18759/0.22995, loss_spatial_ce_5: 0.05390/0.10394, loss_grounding_bce_5: 0.05460/0.08773, loss_grounding_dice_5: 0.10459/0.18308, loss_grounding_ce_5: 0.13272/0.29327, loss_mask_ce_6: 0.98169/0.97220, loss_mask_bce_6: 0.49233/0.34400, loss_mask_dice_6: 0.75241/1.20164, loss_spatial_bce_6: 0.11444/0.10144, loss_spatial_dice_6: 0.20361/0.23293, loss_spatial_ce_6: 0.11831/0.12885, loss_grounding_bce_6: 0.04856/0.08848, loss_grounding_dice_6: 0.10717/0.18347, loss_grounding_ce_6: 0.16650/0.30860, loss_mask_ce_7: 0.93857/1.01785, loss_mask_bce_7: 0.50739/0.35177, loss_mask_dice_7: 0.69530/1.25603, loss_spatial_bce_7: 0.13806/0.10935, loss_spatial_dice_7: 0.21326/0.26053, loss_spatial_ce_7: 0.17194/0.16380, loss_grounding_bce_7: 0.04551/0.09034, loss_grounding_dice_7: 0.09327/0.19080, loss_grounding_ce_7: 0.22661/0.33850, loss_mask_ce_8: 0.99769/1.12656, loss_mask_bce_8: 0.49767/0.36535, loss_mask_dice_8: 0.75757/1.32853, loss_spatial_bce_8: 0.12425/0.12948, loss_spatial_dice_8: 0.25259/0.29817, loss_spatial_ce_8: 0.12070/0.21497, loss_grounding_bce_8: 0.06921/0.09400, loss_grounding_dice_8: 0.11155/0.20146, loss_grounding_ce_8: 0.17086/0.40472, loss_mask_ce_9: 3.80420/3.67391, loss_mask_bce_9: 0.43956/0.39244, loss_mask_dice_9: 1.25551/1.90098, loss_spatial_bce_9: 0.32757/0.33265, loss_spatial_dice_9: 0.87754/0.82157, loss_spatial_ce_9: 1.41874/1.49231, loss_grounding_bce_9: 0.06081/0.10565, loss_grounding_dice_9: 0.18781/0.28087, loss_grounding_ce_9: 0.45548/0.66907] items per batch[64] items per second[0.24] total items[5235200] mini batches[ 81800] memory[7345] epoch remaining[0:19:00]
INFO:trainer.default_trainer:epochs[    44] optim steps[81900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.92256/0.89470, loss_mask_bce_0: 0.37284/0.33383, loss_mask_dice_0: 3.34293/1.16167, loss_spatial_bce_0: 0.05837/0.08650, loss_spatial_dice_0: 0.20160/0.20636, loss_spatial_ce_0: 0.02079/0.05940, loss_grounding_bce_0: 0.03016/0.08612, loss_grounding_dice_0: 0.39378/0.17842, loss_grounding_ce_0: 0.43245/0.27117, loss_mask_ce_1: 0.96484/0.89540, loss_mask_bce_1: 0.37663/0.33476, loss_mask_dice_1: 3.26280/1.16848, loss_spatial_bce_1: 0.05541/0.08704, loss_spatial_dice_1: 0.23966/0.21031, loss_spatial_ce_1: 0.17011/0.06523, loss_grounding_bce_1: 0.02920/0.08631, loss_grounding_dice_1: 0.43047/0.17922, loss_grounding_ce_1: 0.37816/0.27212, loss_mask_ce_2: 0.91622/0.90233, loss_mask_bce_2: 0.37926/0.33541, loss_mask_dice_2: 3.33360/1.16887, loss_spatial_bce_2: 0.05576/0.08828, loss_spatial_dice_2: 0.18989/0.21221, loss_spatial_ce_2: 0.05419/0.06867, loss_grounding_bce_2: 0.03004/0.08650, loss_grounding_dice_2: 0.42232/0.17910, loss_grounding_ce_2: 0.48711/0.27542, loss_mask_ce_3: 0.99330/0.91365, loss_mask_bce_3: 0.36591/0.33660, loss_mask_dice_3: 3.42377/1.16688, loss_spatial_bce_3: 0.05647/0.08961, loss_spatial_dice_3: 0.19855/0.21328, loss_spatial_ce_3: 0.08545/0.07377, loss_grounding_bce_3: 0.02972/0.08675, loss_grounding_dice_3: 0.41860/0.17879, loss_grounding_ce_3: 0.42616/0.27767, loss_mask_ce_4: 0.98319/0.91503, loss_mask_bce_4: 0.40527/0.33877, loss_mask_dice_4: 3.46419/1.19067, loss_spatial_bce_4: 0.06295/0.09353, loss_spatial_dice_4: 0.22110/0.22554, loss_spatial_ce_4: 0.04434/0.09003, loss_grounding_bce_4: 0.03026/0.08730, loss_grounding_dice_4: 0.40704/0.18180, loss_grounding_ce_4: 0.36597/0.28058, loss_mask_ce_5: 1.04916/0.93182, loss_mask_bce_5: 0.41357/0.34119, loss_mask_dice_5: 3.69913/1.19895, loss_spatial_bce_5: 0.06290/0.09588, loss_spatial_dice_5: 0.20715/0.22995, loss_spatial_ce_5: 0.07414/0.10391, loss_grounding_bce_5: 0.03366/0.08772, loss_grounding_dice_5: 0.43479/0.18309, loss_grounding_ce_5: 0.33193/0.29324, loss_mask_ce_6: 1.30954/0.97218, loss_mask_bce_6: 0.40929/0.34401, loss_mask_dice_6: 3.60728/1.20191, loss_spatial_bce_6: 0.06316/0.10144, loss_spatial_dice_6: 0.20176/0.23293, loss_spatial_ce_6: 0.10721/0.12882, loss_grounding_bce_6: 0.03082/0.08848, loss_grounding_dice_6: 0.41923/0.18349, loss_grounding_ce_6: 0.44268/0.30856, loss_mask_ce_7: 1.18095/1.01783, loss_mask_bce_7: 0.47534/0.35178, loss_mask_dice_7: 3.98067/1.25628, loss_spatial_bce_7: 0.08314/0.10934, loss_spatial_dice_7: 0.21502/0.26053, loss_spatial_ce_7: 0.10028/0.16376, loss_grounding_bce_7: 0.03207/0.09033, loss_grounding_dice_7: 0.44285/0.19082, loss_grounding_ce_7: 0.39582/0.33846, loss_mask_ce_8: 1.01592/1.12655, loss_mask_bce_8: 0.55745/0.36536, loss_mask_dice_8: 3.96717/1.32883, loss_spatial_bce_8: 0.12784/0.12947, loss_spatial_dice_8: 0.30882/0.29819, loss_spatial_ce_8: 0.21470/0.21490, loss_grounding_bce_8: 0.03258/0.09399, loss_grounding_dice_8: 0.41125/0.20149, loss_grounding_ce_8: 0.41680/0.40465, loss_mask_ce_9: 4.67648/3.67384, loss_mask_bce_9: 0.46125/0.39244, loss_mask_dice_9: 5.73341/1.90134, loss_spatial_bce_9: 0.44364/0.33262, loss_spatial_dice_9: 0.93620/0.82159, loss_spatial_ce_9: 1.52267/1.49228, loss_grounding_bce_9: 0.04046/0.10565, loss_grounding_dice_9: 0.67796/0.28089, loss_grounding_ce_9: 0.48414/0.66906] items per batch[64] items per second[0.24] total items[5241600] mini batches[ 81900] memory[7345] epoch remaining[0:14:24]
INFO:trainer.default_trainer:epochs[    44] optim steps[82000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.67253/0.89462, loss_mask_bce_0: 0.46634/0.33382, loss_mask_dice_0: 1.19546/1.16165, loss_spatial_bce_0: 0.06620/0.08650, loss_spatial_dice_0: 0.20669/0.20634, loss_spatial_ce_0: 0.00456/0.05938, loss_grounding_bce_0: 0.03507/0.08613, loss_grounding_dice_0: 0.26277/0.17840, loss_grounding_ce_0: 1.32727/0.27115, loss_mask_ce_1: 0.87687/0.89534, loss_mask_bce_1: 0.37166/0.33474, loss_mask_dice_1: 1.12184/1.16847, loss_spatial_bce_1: 0.07069/0.08703, loss_spatial_dice_1: 0.22431/0.21029, loss_spatial_ce_1: 0.00398/0.06521, loss_grounding_bce_1: 0.03894/0.08632, loss_grounding_dice_1: 0.25487/0.17921, loss_grounding_ce_1: 1.42047/0.27210, loss_mask_ce_2: 0.68581/0.90227, loss_mask_bce_2: 0.46550/0.33540, loss_mask_dice_2: 1.19219/1.16887, loss_spatial_bce_2: 0.06988/0.08828, loss_spatial_dice_2: 0.22345/0.21220, loss_spatial_ce_2: 0.00510/0.06865, loss_grounding_bce_2: 0.04020/0.08650, loss_grounding_dice_2: 0.26466/0.17909, loss_grounding_ce_2: 1.56879/0.27541, loss_mask_ce_3: 0.83845/0.91358, loss_mask_bce_3: 0.41019/0.33659, loss_mask_dice_3: 1.23939/1.16687, loss_spatial_bce_3: 0.06789/0.08960, loss_spatial_dice_3: 0.21900/0.21327, loss_spatial_ce_3: 0.00626/0.07376, loss_grounding_bce_3: 0.04140/0.08675, loss_grounding_dice_3: 0.26851/0.17877, loss_grounding_ce_3: 1.49368/0.27767, loss_mask_ce_4: 0.90207/0.91498, loss_mask_bce_4: 0.36444/0.33875, loss_mask_dice_4: 1.09470/1.19066, loss_spatial_bce_4: 0.07035/0.09352, loss_spatial_dice_4: 0.24290/0.22552, loss_spatial_ce_4: 0.01962/0.09000, loss_grounding_bce_4: 0.03716/0.08731, loss_grounding_dice_4: 0.25474/0.18178, loss_grounding_ce_4: 1.02123/0.28057, loss_mask_ce_5: 0.97538/0.93176, loss_mask_bce_5: 0.40161/0.34117, loss_mask_dice_5: 1.16011/1.19894, loss_spatial_bce_5: 0.07323/0.09587, loss_spatial_dice_5: 0.23605/0.22993, loss_spatial_ce_5: 0.05640/0.10388, loss_grounding_bce_5: 0.03562/0.08772, loss_grounding_dice_5: 0.27644/0.18307, loss_grounding_ce_5: 1.08958/0.29328, loss_mask_ce_6: 0.87542/0.97212, loss_mask_bce_6: 0.43345/0.34400, loss_mask_dice_6: 1.25666/1.20193, loss_spatial_bce_6: 0.08473/0.10144, loss_spatial_dice_6: 0.24843/0.23292, loss_spatial_ce_6: 0.18462/0.12879, loss_grounding_bce_6: 0.03293/0.08848, loss_grounding_dice_6: 0.23985/0.18347, loss_grounding_ce_6: 1.35553/0.30860, loss_mask_ce_7: 0.91182/1.01778, loss_mask_bce_7: 0.37407/0.35177, loss_mask_dice_7: 1.17666/1.25626, loss_spatial_bce_7: 0.08021/0.10933, loss_spatial_dice_7: 0.32213/0.26052, loss_spatial_ce_7: 0.17168/0.16372, loss_grounding_bce_7: 0.03114/0.09034, loss_grounding_dice_7: 0.25872/0.19080, loss_grounding_ce_7: 1.64871/0.33849, loss_mask_ce_8: 0.87331/1.12650, loss_mask_bce_8: 0.46778/0.36535, loss_mask_dice_8: 1.27451/1.32883, loss_spatial_bce_8: 0.13135/0.12946, loss_spatial_dice_8: 0.31186/0.29818, loss_spatial_ce_8: 0.03172/0.21482, loss_grounding_bce_8: 0.03983/0.09400, loss_grounding_dice_8: 0.26583/0.20147, loss_grounding_ce_8: 1.91435/0.40469, loss_mask_ce_9: 3.50416/3.67373, loss_mask_bce_9: 0.60615/0.39242, loss_mask_dice_9: 1.86764/1.90124, loss_spatial_bce_9: 0.19987/0.33263, loss_spatial_dice_9: 0.85123/0.82159, loss_spatial_ce_9: 1.49653/1.49223, loss_grounding_bce_9: 0.03352/0.10565, loss_grounding_dice_9: 0.37288/0.28087, loss_grounding_ce_9: 3.33894/0.66905] items per batch[64] items per second[0.23] total items[5248000] mini batches[ 82000] memory[7345] epoch remaining[0:09:50]
INFO:trainer.default_trainer:epochs[    44] optim steps[82100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.83534/0.89455, loss_mask_bce_0: 0.53852/0.33379, loss_mask_dice_0: 1.18769/1.16157, loss_spatial_bce_0: 0.11283/0.08649, loss_spatial_dice_0: 0.29671/0.20634, loss_spatial_ce_0: 0.10758/0.05936, loss_grounding_bce_0: 0.05448/0.08612, loss_grounding_dice_0: 0.08293/0.17839, loss_grounding_ce_0: 0.00688/0.27112, loss_mask_ce_1: 0.80274/0.89529, loss_mask_bce_1: 0.54192/0.33472, loss_mask_dice_1: 1.23826/1.16839, loss_spatial_bce_1: 0.10832/0.08703, loss_spatial_dice_1: 0.29111/0.21028, loss_spatial_ce_1: 0.10737/0.06519, loss_grounding_bce_1: 0.05839/0.08631, loss_grounding_dice_1: 0.09087/0.17920, loss_grounding_ce_1: 0.00722/0.27207, loss_mask_ce_2: 0.83427/0.90219, loss_mask_bce_2: 0.55552/0.33538, loss_mask_dice_2: 1.28745/1.16881, loss_spatial_bce_2: 0.10638/0.08827, loss_spatial_dice_2: 0.29564/0.21219, loss_spatial_ce_2: 0.16312/0.06863, loss_grounding_bce_2: 0.05556/0.08649, loss_grounding_dice_2: 0.09169/0.17908, loss_grounding_ce_2: 0.01072/0.27538, loss_mask_ce_3: 1.14335/0.91352, loss_mask_bce_3: 0.51764/0.33657, loss_mask_dice_3: 1.09424/1.16681, loss_spatial_bce_3: 0.10388/0.08960, loss_spatial_dice_3: 0.30178/0.21326, loss_spatial_ce_3: 0.21798/0.07374, loss_grounding_bce_3: 0.05927/0.08674, loss_grounding_dice_3: 0.09979/0.17876, loss_grounding_ce_3: 0.01181/0.27764, loss_mask_ce_4: 0.87520/0.91495, loss_mask_bce_4: 0.55905/0.33873, loss_mask_dice_4: 1.20964/1.19061, loss_spatial_bce_4: 0.11639/0.09351, loss_spatial_dice_4: 0.30396/0.22551, loss_spatial_ce_4: 0.14726/0.08998, loss_grounding_bce_4: 0.05726/0.08729, loss_grounding_dice_4: 0.09000/0.18177, loss_grounding_ce_4: 0.01337/0.28055, loss_mask_ce_5: 0.86490/0.93169, loss_mask_bce_5: 0.56386/0.34116, loss_mask_dice_5: 1.16070/1.19887, loss_spatial_bce_5: 0.13582/0.09586, loss_spatial_dice_5: 0.31957/0.22992, loss_spatial_ce_5: 0.12085/0.10386, loss_grounding_bce_5: 0.05514/0.08772, loss_grounding_dice_5: 0.08664/0.18306, loss_grounding_ce_5: 0.02041/0.29327, loss_mask_ce_6: 0.96118/0.97206, loss_mask_bce_6: 0.59659/0.34397, loss_mask_dice_6: 1.18609/1.20185, loss_spatial_bce_6: 0.14195/0.10143, loss_spatial_dice_6: 0.32417/0.23290, loss_spatial_ce_6: 0.16786/0.12877, loss_grounding_bce_6: 0.05366/0.08848, loss_grounding_dice_6: 0.07858/0.18346, loss_grounding_ce_6: 0.01881/0.30857, loss_mask_ce_7: 0.98695/1.01771, loss_mask_bce_7: 0.72523/0.35174, loss_mask_dice_7: 1.30271/1.25617, loss_spatial_bce_7: 0.12978/0.10932, loss_spatial_dice_7: 0.33150/0.26052, loss_spatial_ce_7: 0.10080/0.16368, loss_grounding_bce_7: 0.07501/0.09032, loss_grounding_dice_7: 0.09172/0.19079, loss_grounding_ce_7: 0.03949/0.33845, loss_mask_ce_8: 1.01150/1.12643, loss_mask_bce_8: 0.73820/0.36534, loss_mask_dice_8: 1.33867/1.32875, loss_spatial_bce_8: 0.15512/0.12944, loss_spatial_dice_8: 0.35046/0.29817, loss_spatial_ce_8: 0.05563/0.21475, loss_grounding_bce_8: 0.10285/0.09399, loss_grounding_dice_8: 0.09461/0.20146, loss_grounding_ce_8: 0.05266/0.40464, loss_mask_ce_9: 5.21343/3.67352, loss_mask_bce_9: 1.04991/0.39240, loss_mask_dice_9: 2.53982/1.90108, loss_spatial_bce_9: 0.40621/0.33261, loss_spatial_dice_9: 0.91849/0.82158, loss_spatial_ce_9: 1.64709/1.49225, loss_grounding_bce_9: 0.15541/0.10564, loss_grounding_dice_9: 0.18768/0.28085, loss_grounding_ce_9: 0.77900/0.66902] items per batch[64] items per second[0.24] total items[5254400] mini batches[ 82100] memory[7345] epoch remaining[0:05:15]
INFO:trainer.default_trainer:epochs[    44] optim steps[82200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.48130/0.89452, loss_mask_bce_0: 0.28872/0.33375, loss_mask_dice_0: 0.22829/1.16151, loss_spatial_bce_0: 0.11786/0.08647, loss_spatial_dice_0: 0.08722/0.20633, loss_spatial_ce_0: 0.02997/0.05934, loss_grounding_bce_0: 0.12759/0.08612, loss_grounding_dice_0: 0.10341/0.17841, loss_grounding_ce_0: 0.05441/0.27116, loss_mask_ce_1: 0.49479/0.89527, loss_mask_bce_1: 0.27594/0.33468, loss_mask_dice_1: 0.21779/1.16834, loss_spatial_bce_1: 0.10906/0.08701, loss_spatial_dice_1: 0.08807/0.21027, loss_spatial_ce_1: 0.04606/0.06517, loss_grounding_bce_1: 0.12372/0.08631, loss_grounding_dice_1: 0.10079/0.17922, loss_grounding_ce_1: 0.05061/0.27212, loss_mask_ce_2: 0.40574/0.90216, loss_mask_bce_2: 0.29791/0.33534, loss_mask_dice_2: 0.22605/1.16874, loss_spatial_bce_2: 0.12826/0.08826, loss_spatial_dice_2: 0.10178/0.21218, loss_spatial_ce_2: 0.01873/0.06861, loss_grounding_bce_2: 0.12646/0.08650, loss_grounding_dice_2: 0.09803/0.17910, loss_grounding_ce_2: 0.06360/0.27543, loss_mask_ce_3: 0.54674/0.91350, loss_mask_bce_3: 0.26551/0.33653, loss_mask_dice_3: 0.22514/1.16674, loss_spatial_bce_3: 0.14869/0.08958, loss_spatial_dice_3: 0.11531/0.21325, loss_spatial_ce_3: 0.01580/0.07372, loss_grounding_bce_3: 0.13284/0.08674, loss_grounding_dice_3: 0.09242/0.17877, loss_grounding_ce_3: 0.05735/0.27770, loss_mask_ce_4: 0.52383/0.91493, loss_mask_bce_4: 0.27519/0.33870, loss_mask_dice_4: 0.20760/1.19054, loss_spatial_bce_4: 0.17103/0.09350, loss_spatial_dice_4: 0.12467/0.22550, loss_spatial_ce_4: 0.00947/0.08996, loss_grounding_bce_4: 0.12894/0.08730, loss_grounding_dice_4: 0.09870/0.18179, loss_grounding_ce_4: 0.07161/0.28059, loss_mask_ce_5: 0.49419/0.93169, loss_mask_bce_5: 0.26063/0.34112, loss_mask_dice_5: 0.20939/1.19881, loss_spatial_bce_5: 0.16049/0.09585, loss_spatial_dice_5: 0.14264/0.22991, loss_spatial_ce_5: 0.03243/0.10382, loss_grounding_bce_5: 0.11058/0.08772, loss_grounding_dice_5: 0.09403/0.18308, loss_grounding_ce_5: 0.02388/0.29334, loss_mask_ce_6: 0.54002/0.97204, loss_mask_bce_6: 0.26763/0.34394, loss_mask_dice_6: 0.21771/1.20180, loss_spatial_bce_6: 0.18306/0.10141, loss_spatial_dice_6: 0.14838/0.23290, loss_spatial_ce_6: 0.02947/0.12873, loss_grounding_bce_6: 0.12134/0.08848, loss_grounding_dice_6: 0.10074/0.18348, loss_grounding_ce_6: 0.02920/0.30864, loss_mask_ce_7: 0.63643/1.01772, loss_mask_bce_7: 0.26674/0.35171, loss_mask_dice_7: 0.22234/1.25613, loss_spatial_bce_7: 0.16258/0.10931, loss_spatial_dice_7: 0.15188/0.26051, loss_spatial_ce_7: 0.10688/0.16364, loss_grounding_bce_7: 0.11253/0.09033, loss_grounding_dice_7: 0.09657/0.19082, loss_grounding_ce_7: 0.07198/0.33855, loss_mask_ce_8: 0.63634/1.12643, loss_mask_bce_8: 0.29971/0.36530, loss_mask_dice_8: 0.27172/1.32870, loss_spatial_bce_8: 0.22400/0.12943, loss_spatial_dice_8: 0.21506/0.29817, loss_spatial_ce_8: 0.23929/0.21468, loss_grounding_bce_8: 0.13745/0.09399, loss_grounding_dice_8: 0.12730/0.20148, loss_grounding_ce_8: 0.03377/0.40465, loss_mask_ce_9: 3.41359/3.67356, loss_mask_bce_9: 0.35814/0.39236, loss_mask_dice_9: 0.49270/1.90105, loss_spatial_bce_9: 0.55742/0.33257, loss_spatial_dice_9: 0.69797/0.82158, loss_spatial_ce_9: 1.10315/1.49218, loss_grounding_bce_9: 0.14552/0.10565, loss_grounding_dice_9: 0.17885/0.28087, loss_grounding_ce_9: 0.43494/0.66896] items per batch[64] items per second[0.23] total items[5260800] mini batches[ 82200] memory[7345] epoch remaining[0:00:41]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00082215.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0034 s/iter. Inference: 0.2153 s/iter. Eval: 0.0911 s/iter. Total: 0.3098 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2190 s/iter. Eval: 0.0772 s/iter. Total: 0.2993 s/iter. ETA=0:00:38
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2226 s/iter. Eval: 0.0765 s/iter. Total: 0.3024 s/iter. ETA=0:00:33
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2217 s/iter. Eval: 0.0759 s/iter. Total: 0.3009 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0032 s/iter. Inference: 0.2206 s/iter. Eval: 0.0749 s/iter. Total: 0.2989 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0032 s/iter. Inference: 0.2232 s/iter. Eval: 0.0752 s/iter. Total: 0.3017 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0032 s/iter. Inference: 0.2248 s/iter. Eval: 0.0757 s/iter. Total: 0.3039 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0032 s/iter. Inference: 0.2248 s/iter. Eval: 0.0757 s/iter. Total: 0.3038 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2256 s/iter. Eval: 0.0760 s/iter. Total: 0.3050 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval7meyfai1 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.168 | 82.062 | 60.315 |      133      |
| Things | 55.188 | 82.713 | 66.060 |      80       |
| Stuff  | 42.592 | 81.079 | 51.643 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.37s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 11.10 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.25s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.79 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.393
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.617
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.424
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.321
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.511
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.313 | 61.681 | 41.428 | 19.430 | 42.408 | 61.143 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.771 | bicycle      | 19.462 | car            | 36.812 |
| motorcycle    | 34.995 | airplane     | 57.299 | bus            | 65.232 |
| train         | 68.240 | truck        | 36.013 | boat           | 23.983 |
| traffic light | 25.891 | fire hydrant | 65.446 | stop sign      | 63.947 |
| parking meter | 42.537 | bench        | 20.398 | bird           | 29.469 |
| cat           | 73.505 | dog          | 65.780 | horse          | 45.211 |
| sheep         | 48.108 | cow          | 51.962 | elephant       | 61.819 |
| bear          | 77.242 | zebra        | 60.814 | giraffe        | 57.402 |
| backpack      | 16.969 | umbrella     | 49.433 | handbag        | 15.401 |
| tie           | 34.587 | suitcase     | 42.856 | frisbee        | 67.571 |
| skis          | 5.526  | snowboard    | 22.826 | sports ball    | 47.819 |
| kite          | 35.353 | baseball bat | 28.983 | baseball glove | 43.636 |
| skateboard    | 36.053 | surfboard    | 36.920 | tennis racket  | 56.516 |
| bottle        | 34.794 | wine glass   | 27.516 | cup            | 40.841 |
| fork          | 15.991 | knife        | 13.853 | spoon          | 14.488 |
| bowl          | 33.553 | banana       | 20.991 | apple          | 20.909 |
| sandwich      | 43.233 | orange       | 28.892 | broccoli       | 22.059 |
| carrot        | 21.254 | hot dog      | 22.313 | pizza          | 51.758 |
| donut         | 46.798 | cake         | 44.750 | chair          | 21.572 |
| couch         | 42.294 | potted plant | 18.334 | bed            | 42.341 |
| dining table  | 13.109 | toilet       | 67.880 | tv             | 62.575 |
| laptop        | 62.440 | mouse        | 59.902 | remote         | 32.785 |
| keyboard      | 48.659 | cell phone   | 37.918 | microwave      | 55.238 |
| oven          | 32.623 | toaster      | 28.151 | sink           | 37.951 |
| refrigerator  | 58.879 | book         | 9.608  | clock          | 52.106 |
| vase          | 34.871 | scissors     | 24.447 | teddy bear     | 51.443 |
| hair drier    | 9.696  | toothbrush   | 19.449 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.51103687067223, 'fwIoU': 69.02124675575517, 'IoU-person': 87.51065057832717, 'IoU-bicycle': 74.16362463158205, 'IoU-car': 70.76325779845794, 'IoU-motorcycle': 78.9803964914755, 'IoU-airplane': 83.95961644846027, 'IoU-bus': 85.80076945348128, 'IoU-train': 85.45074387620728, 'IoU-truck': 63.53105010469451, 'IoU-boat': 68.66581249027152, 'IoU-traffic light': 76.09309508052063, 'IoU-fire hydrant': 90.31746968236895, 'IoU-stop sign': 90.32674881986134, 'IoU-parking meter': 87.693497020562, 'IoU-bench': 53.043782027307884, 'IoU-bird': 75.39496655644737, 'IoU-cat': 76.05306792788407, 'IoU-dog': 80.24756491320582, 'IoU-horse': 84.43961283555048, 'IoU-sheep': 87.73922983035087, 'IoU-cow': 80.58382947378887, 'IoU-elephant': 89.1610233814037, 'IoU-bear': 74.42428321768197, 'IoU-zebra': 89.07635200399271, 'IoU-giraffe': 86.21033056201378, 'IoU-backpack': 40.223479074511125, 'IoU-umbrella': 73.79521890609915, 'IoU-handbag': 37.724308390133984, 'IoU-tie': 70.69903865323113, 'IoU-suitcase': 80.29251398433442, 'IoU-frisbee': 83.5317447522007, 'IoU-skis': 50.51134096858002, 'IoU-snowboard': 69.47693411393946, 'IoU-sports ball': 67.54519912044955, 'IoU-kite': 65.7341182209507, 'IoU-baseball bat': 61.87211078817276, 'IoU-baseball glove': 48.98243560736644, 'IoU-skateboard': 60.584656433713036, 'IoU-surfboard': 81.19978800394752, 'IoU-tennis racket': 82.38742652223742, 'IoU-bottle': 68.46533354478743, 'IoU-wine glass': 72.6215071545438, 'IoU-cup': 65.72056485446808, 'IoU-fork': 55.56727959241731, 'IoU-knife': 51.65610227980787, 'IoU-spoon': 48.45015407824953, 'IoU-bowl': 54.691980322388766, 'IoU-banana': 80.95804244349718, 'IoU-apple': 59.39479827030328, 'IoU-sandwich': 65.71132480100796, 'IoU-orange': 76.98609416720996, 'IoU-broccoli': 66.5777073857557, 'IoU-carrot': 63.94764000246268, 'IoU-hot dog': 63.74104476425527, 'IoU-pizza': 81.98302866868352, 'IoU-donut': 64.02951760251393, 'IoU-cake': 68.38223166025107, 'IoU-chair': 54.28009073632928, 'IoU-couch': 65.42111958018499, 'IoU-potted plant': 33.3279190406169, 'IoU-bed': 68.19498989767278, 'IoU-dining table': 50.98123893171186, 'IoU-toilet': 81.72701382298202, 'IoU-tv': 74.20595560454929, 'IoU-laptop': 70.58217603382701, 'IoU-mouse': 66.63386002366836, 'IoU-remote': 48.83153413484311, 'IoU-keyboard': 57.131825074889996, 'IoU-cell phone': 71.50247338405809, 'IoU-microwave': 68.31459099880601, 'IoU-oven': 67.72925079042325, 'IoU-toaster': 68.07236013866753, 'IoU-sink': 70.4805095122607, 'IoU-refrigerator': 79.49575335595382, 'IoU-book': 52.22248530509634, 'IoU-clock': 75.54776739821924, 'IoU-vase': 61.977359283319444, 'IoU-scissors': 52.98476897889173, 'IoU-teddy bear': 79.17522806360887, 'IoU-hair drier': 31.027677270804944, 'IoU-toothbrush': 58.30252800437384, 'IoU-banner': 37.0372764684007, 'IoU-blanket': 11.18121102203229, 'IoU-bridge': 36.055147819744995, 'IoU-cardboard': 44.48950491023432, 'IoU-counter': 30.405962545029, 'IoU-curtain': 65.37616850910732, 'IoU-door-stuff': 42.59326292670507, 'IoU-floor-wood': 63.71431418941565, 'IoU-flower': 42.262957172289845, 'IoU-fruit': 41.91365907298164, 'IoU-gravel': 29.201833281006646, 'IoU-house': 26.270671072037278, 'IoU-light': 39.39305444710291, 'IoU-mirror-stuff': 55.54278427784307, 'IoU-net': 37.48317031512864, 'IoU-pillow': 11.392505685365972, 'IoU-platform': 30.382028139515942, 'IoU-playingfield': 70.43568606596821, 'IoU-railroad': 61.15901673977292, 'IoU-river': 49.701969794616936, 'IoU-road': 66.24159221709581, 'IoU-roof': 15.57029242487572, 'IoU-sand': 63.95670979079499, 'IoU-sea': 86.36575768256085, 'IoU-shelf': 36.822001235121796, 'IoU-snow': 89.54287812560611, 'IoU-stairs': 27.31386664248095, 'IoU-tent': 9.356880813244683, 'IoU-towel': 35.76982551572113, 'IoU-wall-brick': 47.05491779730111, 'IoU-wall-stone': 27.145342525946916, 'IoU-wall-tile': 67.82600997657148, 'IoU-wall-wood': 39.26193237650833, 'IoU-water-other': 24.750293548138806, 'IoU-window-blind': 47.04840262998324, 'IoU-window-other': 47.5565443725881, 'IoU-tree-merged': 81.00866180272133, 'IoU-fence-merged': 49.26122984605502, 'IoU-ceiling-merged': 66.50385804700372, 'IoU-sky-other-merged': 93.54672794368474, 'IoU-cabinet-merged': 60.90192330546552, 'IoU-table-merged': 37.37863606153361, 'IoU-floor-other-merged': 48.31555858706179, 'IoU-pavement-merged': 54.173573299192725, 'IoU-mountain-merged': 55.04701643623221, 'IoU-grass-merged': 70.57024030581816, 'IoU-dirt-merged': 44.46625715194981, 'IoU-paper-merged': 29.61474463960562, 'IoU-food-other-merged': 40.077415577008466, 'IoU-building-other-merged': 57.860398158632485, 'IoU-rock-merged': 60.89026401442567, 'IoU-wall-other-merged': 65.34882135836554, 'IoU-rug-merged': 64.20122943168313, 'mACC': 73.04401781284515, 'pACC': 80.3523403635461, 'ACC-person': 92.54508397301602, 'ACC-bicycle': 84.88067940919434, 'ACC-car': 84.98386104358528, 'ACC-motorcycle': 86.14603222444799, 'ACC-airplane': 90.51005422099169, 'ACC-bus': 90.24757210014278, 'ACC-train': 93.93806007511996, 'ACC-truck': 78.24854072144927, 'ACC-boat': 78.53278430752312, 'ACC-traffic light': 90.6334762185152, 'ACC-fire hydrant': 95.60063111957538, 'ACC-stop sign': 93.58919215467537, 'ACC-parking meter': 92.17604070413171, 'ACC-bench': 73.43709049855856, 'ACC-bird': 79.5459700751545, 'ACC-cat': 86.64364535701398, 'ACC-dog': 84.89600666403922, 'ACC-horse': 90.15400921316477, 'ACC-sheep': 90.99958044498551, 'ACC-cow': 86.70193331228985, 'ACC-elephant': 91.74994467025616, 'ACC-bear': 76.34149939947486, 'ACC-zebra': 91.45683219850346, 'ACC-giraffe': 90.33042250733651, 'ACC-backpack': 55.99523667627804, 'ACC-umbrella': 82.32915815814621, 'ACC-handbag': 55.145590659808875, 'ACC-tie': 80.99073756244589, 'ACC-suitcase': 89.08994739883424, 'ACC-frisbee': 94.20254545454544, 'ACC-skis': 70.74689008746256, 'ACC-snowboard': 79.6938600573094, 'ACC-sports ball': 80.01726258773427, 'ACC-kite': 75.29694260493986, 'ACC-baseball bat': 82.38017816871334, 'ACC-baseball glove': 60.91560205500674, 'ACC-skateboard': 69.55857732134602, 'ACC-surfboard': 90.05375145424843, 'ACC-tennis racket': 89.48486415130328, 'ACC-bottle': 83.37791205823783, 'ACC-wine glass': 86.0744138818879, 'ACC-cup': 82.8400860205362, 'ACC-fork': 66.43220104473758, 'ACC-knife': 69.4046310544442, 'ACC-spoon': 68.11263599633371, 'ACC-bowl': 68.06984204610949, 'ACC-banana': 88.99385354799128, 'ACC-apple': 70.89862503447523, 'ACC-sandwich': 78.9998307136934, 'ACC-orange': 86.61749783642352, 'ACC-broccoli': 77.10214266011548, 'ACC-carrot': 76.34225184223511, 'ACC-hot dog': 72.07081040095173, 'ACC-pizza': 90.24381385960793, 'ACC-donut': 80.73882740906065, 'ACC-cake': 75.78163019834092, 'ACC-chair': 69.55072200470556, 'ACC-couch': 81.08760738763974, 'ACC-potted plant': 50.28873351655131, 'ACC-bed': 78.14860856414907, 'ACC-dining table': 74.39402514977158, 'ACC-toilet': 91.08371480868425, 'ACC-tv': 87.40719552008352, 'ACC-laptop': 83.18210233404177, 'ACC-mouse': 80.84117038047106, 'ACC-remote': 72.74842819241042, 'ACC-keyboard': 66.75220347285979, 'ACC-cell phone': 77.76658582581281, 'ACC-microwave': 76.64627071988409, 'ACC-oven': 85.49572842086123, 'ACC-toaster': 75.11119626654258, 'ACC-sink': 82.59275296253233, 'ACC-refrigerator': 88.9927880389678, 'ACC-book': 70.67972317951305, 'ACC-clock': 82.48861038075094, 'ACC-vase': 72.65288273365695, 'ACC-scissors': 56.86823403024543, 'ACC-teddy bear': 86.60479833019608, 'ACC-hair drier': 46.985739902655624, 'ACC-toothbrush': 81.51667824878388, 'ACC-banner': 76.13329537438366, 'ACC-blanket': 15.827790742110956, 'ACC-bridge': 55.74221884745487, 'ACC-cardboard': 57.019514323106, 'ACC-counter': 52.75456284667347, 'ACC-curtain': 77.52113440783882, 'ACC-door-stuff': 64.3102510167029, 'ACC-floor-wood': 78.99355816130644, 'ACC-flower': 60.99445932613155, 'ACC-fruit': 59.56628545560609, 'ACC-gravel': 42.718059092520136, 'ACC-house': 32.98923926654018, 'ACC-light': 57.68880632010106, 'ACC-mirror-stuff': 70.13494409515928, 'ACC-net': 64.80851474216999, 'ACC-pillow': 26.10634304224207, 'ACC-platform': 48.708408212805864, 'ACC-playingfield': 92.24990514904934, 'ACC-railroad': 78.47585632342104, 'ACC-river': 71.64104740332932, 'ACC-road': 85.49408939200174, 'ACC-roof': 22.08444873628411, 'ACC-sand': 70.60395726137499, 'ACC-sea': 92.2266062008702, 'ACC-shelf': 58.25460226984384, 'ACC-snow': 94.94136083998592, 'ACC-stairs': 43.871320730107406, 'ACC-tent': 11.37468581537445, 'ACC-towel': 44.160894266232674, 'ACC-wall-brick': 64.9344815062799, 'ACC-wall-stone': 34.049040858609786, 'ACC-wall-tile': 81.78032998069003, 'ACC-wall-wood': 53.805970627988245, 'ACC-water-other': 38.63537817966653, 'ACC-window-blind': 58.09882386623807, 'ACC-window-other': 69.29869734675623, 'ACC-tree-merged': 89.28560688393813, 'ACC-fence-merged': 67.49805539842193, 'ACC-ceiling-merged': 80.95440677750159, 'ACC-sky-other-merged': 96.79934360082146, 'ACC-cabinet-merged': 75.31744735362274, 'ACC-table-merged': 51.35864375095828, 'ACC-floor-other-merged': 61.39345957720606, 'ACC-pavement-merged': 66.94987091598425, 'ACC-mountain-merged': 65.01282302780722, 'ACC-grass-merged': 83.84846383987231, 'ACC-dirt-merged': 63.17503938724502, 'ACC-paper-merged': 41.331839088098775, 'ACC-food-other-merged': 56.668981893095484, 'ACC-building-other-merged': 72.04525932944343, 'ACC-rock-merged': 83.11370936369197, 'ACC-wall-other-merged': 80.54151868371257, 'ACC-rug-merged': 79.38342522180716})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1545 s/iter. Inference: 0.4781 s/iter. Eval: 0.0000 s/iter. Total: 0.6326 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 20/50. Dataloading: 0.1581 s/iter. Inference: 0.4394 s/iter. Eval: 0.0000 s/iter. Total: 0.5976 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1727 s/iter. Inference: 0.5431 s/iter. Eval: 0.0000 s/iter. Total: 0.7160 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1723 s/iter. Inference: 0.6246 s/iter. Eval: 0.0000 s/iter. Total: 0.7971 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1708 s/iter. Inference: 0.5847 s/iter. Eval: 0.0000 s/iter. Total: 0.7557 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1692 s/iter. Inference: 0.6290 s/iter. Eval: 0.0000 s/iter. Total: 0.7984 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4547848990342405, 'noc@0.8': 2.7319285923324554, 'noc@0.85': 3.286508633304068, 'noc@0.9': 4.326602282704126, 'miou@iter1': 0.8311092275117691}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0020 s/iter. Inference: 0.0983 s/iter. Eval: 0.0008 s/iter. Total: 0.1012 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 70.54022216796875, 'precision@0.6': 68.09172058105469, 'precision@0.7': 63.1947135925293, 'precision@0.8': 53.400699615478516, 'precision@0.9': 27.283327102661133, 'cIoU': 57.68621826171875, 'mIoU': 62.91379165649414}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.168172811789724, 'SQ': 82.06213651101918, 'RQ': 60.31477331640615, 'PQ_th': 55.18766769093346, 'SQ_th': 82.71323652630741, 'RQ_th': 66.05961680732456, 'PQ_st': 42.59157676779919, 'SQ_st': 81.07934403511241, 'RQ_st': 51.643311443321736}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.313122101836996, 'AP50': 61.680949771640556, 'AP75': 41.42826400710655, 'APs': 19.42950106615976, 'APm': 42.407859307873046, 'APl': 61.142734152645, 'AP-person': 44.77130721278844, 'AP-bicycle': 19.461778801728112, 'AP-car': 36.812400274278694, 'AP-motorcycle': 34.99533602377446, 'AP-airplane': 57.29855186010672, 'AP-bus': 65.23213814479519, 'AP-train': 68.23965080996982, 'AP-truck': 36.013413441188064, 'AP-boat': 23.98330844686905, 'AP-traffic light': 25.891211239952998, 'AP-fire hydrant': 65.44558759403375, 'AP-stop sign': 63.94724670365479, 'AP-parking meter': 42.53667882773272, 'AP-bench': 20.398136698162936, 'AP-bird': 29.46919597354643, 'AP-cat': 73.50470183477708, 'AP-dog': 65.77969337373042, 'AP-horse': 45.210529084141086, 'AP-sheep': 48.10847830754574, 'AP-cow': 51.9624453460917, 'AP-elephant': 61.81931945597107, 'AP-bear': 77.2417495559605, 'AP-zebra': 60.81415871879988, 'AP-giraffe': 57.40181227800216, 'AP-backpack': 16.968581551507704, 'AP-umbrella': 49.43337725713838, 'AP-handbag': 15.401194137118798, 'AP-tie': 34.586950084366386, 'AP-suitcase': 42.856462897472966, 'AP-frisbee': 67.57101572465695, 'AP-skis': 5.525612914712847, 'AP-snowboard': 22.825981671596416, 'AP-sports ball': 47.818660347960204, 'AP-kite': 35.35275561642236, 'AP-baseball bat': 28.983328387890445, 'AP-baseball glove': 43.635566498218346, 'AP-skateboard': 36.052826428613116, 'AP-surfboard': 36.91968312096287, 'AP-tennis racket': 56.51600845622309, 'AP-bottle': 34.79410226700823, 'AP-wine glass': 27.516237325584186, 'AP-cup': 40.84126388196217, 'AP-fork': 15.991066464722827, 'AP-knife': 13.853471469775592, 'AP-spoon': 14.488418091705372, 'AP-bowl': 33.55293849120662, 'AP-banana': 20.990520511277964, 'AP-apple': 20.90897152647845, 'AP-sandwich': 43.23289173597737, 'AP-orange': 28.89225141872021, 'AP-broccoli': 22.059497742371505, 'AP-carrot': 21.253501670427422, 'AP-hot dog': 22.312863186645266, 'AP-pizza': 51.75753544392718, 'AP-donut': 46.798023833832545, 'AP-cake': 44.749855658426526, 'AP-chair': 21.57231644280863, 'AP-couch': 42.29403589530068, 'AP-potted plant': 18.333929046575943, 'AP-bed': 42.34078651635226, 'AP-dining table': 13.10892989661783, 'AP-toilet': 67.88004569648231, 'AP-tv': 62.57451764231475, 'AP-laptop': 62.44013124564643, 'AP-mouse': 59.90205647297617, 'AP-remote': 32.78493106874601, 'AP-keyboard': 48.658722664250206, 'AP-cell phone': 37.91795919505861, 'AP-microwave': 55.23783416279156, 'AP-oven': 32.62332644899274, 'AP-toaster': 28.15108552540349, 'AP-sink': 37.951069281023344, 'AP-refrigerator': 58.87943320647199, 'AP-book': 9.607696551263881, 'AP-clock': 52.10572494717528, 'AP-vase': 34.87059610532267, 'AP-scissors': 24.446691203323546, 'AP-teddy bear': 51.4434246606784, 'AP-hair drier': 9.695514267778279, 'AP-toothbrush': 19.448764181091803}), ('sem_seg', {'mIoU': 60.51103687067223, 'fwIoU': 69.02124675575517, 'IoU-person': 87.51065057832717, 'IoU-bicycle': 74.16362463158205, 'IoU-car': 70.76325779845794, 'IoU-motorcycle': 78.9803964914755, 'IoU-airplane': 83.95961644846027, 'IoU-bus': 85.80076945348128, 'IoU-train': 85.45074387620728, 'IoU-truck': 63.53105010469451, 'IoU-boat': 68.66581249027152, 'IoU-traffic light': 76.09309508052063, 'IoU-fire hydrant': 90.31746968236895, 'IoU-stop sign': 90.32674881986134, 'IoU-parking meter': 87.693497020562, 'IoU-bench': 53.043782027307884, 'IoU-bird': 75.39496655644737, 'IoU-cat': 76.05306792788407, 'IoU-dog': 80.24756491320582, 'IoU-horse': 84.43961283555048, 'IoU-sheep': 87.73922983035087, 'IoU-cow': 80.58382947378887, 'IoU-elephant': 89.1610233814037, 'IoU-bear': 74.42428321768197, 'IoU-zebra': 89.07635200399271, 'IoU-giraffe': 86.21033056201378, 'IoU-backpack': 40.223479074511125, 'IoU-umbrella': 73.79521890609915, 'IoU-handbag': 37.724308390133984, 'IoU-tie': 70.69903865323113, 'IoU-suitcase': 80.29251398433442, 'IoU-frisbee': 83.5317447522007, 'IoU-skis': 50.51134096858002, 'IoU-snowboard': 69.47693411393946, 'IoU-sports ball': 67.54519912044955, 'IoU-kite': 65.7341182209507, 'IoU-baseball bat': 61.87211078817276, 'IoU-baseball glove': 48.98243560736644, 'IoU-skateboard': 60.584656433713036, 'IoU-surfboard': 81.19978800394752, 'IoU-tennis racket': 82.38742652223742, 'IoU-bottle': 68.46533354478743, 'IoU-wine glass': 72.6215071545438, 'IoU-cup': 65.72056485446808, 'IoU-fork': 55.56727959241731, 'IoU-knife': 51.65610227980787, 'IoU-spoon': 48.45015407824953, 'IoU-bowl': 54.691980322388766, 'IoU-banana': 80.95804244349718, 'IoU-apple': 59.39479827030328, 'IoU-sandwich': 65.71132480100796, 'IoU-orange': 76.98609416720996, 'IoU-broccoli': 66.5777073857557, 'IoU-carrot': 63.94764000246268, 'IoU-hot dog': 63.74104476425527, 'IoU-pizza': 81.98302866868352, 'IoU-donut': 64.02951760251393, 'IoU-cake': 68.38223166025107, 'IoU-chair': 54.28009073632928, 'IoU-couch': 65.42111958018499, 'IoU-potted plant': 33.3279190406169, 'IoU-bed': 68.19498989767278, 'IoU-dining table': 50.98123893171186, 'IoU-toilet': 81.72701382298202, 'IoU-tv': 74.20595560454929, 'IoU-laptop': 70.58217603382701, 'IoU-mouse': 66.63386002366836, 'IoU-remote': 48.83153413484311, 'IoU-keyboard': 57.131825074889996, 'IoU-cell phone': 71.50247338405809, 'IoU-microwave': 68.31459099880601, 'IoU-oven': 67.72925079042325, 'IoU-toaster': 68.07236013866753, 'IoU-sink': 70.4805095122607, 'IoU-refrigerator': 79.49575335595382, 'IoU-book': 52.22248530509634, 'IoU-clock': 75.54776739821924, 'IoU-vase': 61.977359283319444, 'IoU-scissors': 52.98476897889173, 'IoU-teddy bear': 79.17522806360887, 'IoU-hair drier': 31.027677270804944, 'IoU-toothbrush': 58.30252800437384, 'IoU-banner': 37.0372764684007, 'IoU-blanket': 11.18121102203229, 'IoU-bridge': 36.055147819744995, 'IoU-cardboard': 44.48950491023432, 'IoU-counter': 30.405962545029, 'IoU-curtain': 65.37616850910732, 'IoU-door-stuff': 42.59326292670507, 'IoU-floor-wood': 63.71431418941565, 'IoU-flower': 42.262957172289845, 'IoU-fruit': 41.91365907298164, 'IoU-gravel': 29.201833281006646, 'IoU-house': 26.270671072037278, 'IoU-light': 39.39305444710291, 'IoU-mirror-stuff': 55.54278427784307, 'IoU-net': 37.48317031512864, 'IoU-pillow': 11.392505685365972, 'IoU-platform': 30.382028139515942, 'IoU-playingfield': 70.43568606596821, 'IoU-railroad': 61.15901673977292, 'IoU-river': 49.701969794616936, 'IoU-road': 66.24159221709581, 'IoU-roof': 15.57029242487572, 'IoU-sand': 63.95670979079499, 'IoU-sea': 86.36575768256085, 'IoU-shelf': 36.822001235121796, 'IoU-snow': 89.54287812560611, 'IoU-stairs': 27.31386664248095, 'IoU-tent': 9.356880813244683, 'IoU-towel': 35.76982551572113, 'IoU-wall-brick': 47.05491779730111, 'IoU-wall-stone': 27.145342525946916, 'IoU-wall-tile': 67.82600997657148, 'IoU-wall-wood': 39.26193237650833, 'IoU-water-other': 24.750293548138806, 'IoU-window-blind': 47.04840262998324, 'IoU-window-other': 47.5565443725881, 'IoU-tree-merged': 81.00866180272133, 'IoU-fence-merged': 49.26122984605502, 'IoU-ceiling-merged': 66.50385804700372, 'IoU-sky-other-merged': 93.54672794368474, 'IoU-cabinet-merged': 60.90192330546552, 'IoU-table-merged': 37.37863606153361, 'IoU-floor-other-merged': 48.31555858706179, 'IoU-pavement-merged': 54.173573299192725, 'IoU-mountain-merged': 55.04701643623221, 'IoU-grass-merged': 70.57024030581816, 'IoU-dirt-merged': 44.46625715194981, 'IoU-paper-merged': 29.61474463960562, 'IoU-food-other-merged': 40.077415577008466, 'IoU-building-other-merged': 57.860398158632485, 'IoU-rock-merged': 60.89026401442567, 'IoU-wall-other-merged': 65.34882135836554, 'IoU-rug-merged': 64.20122943168313, 'mACC': 73.04401781284515, 'pACC': 80.3523403635461, 'ACC-person': 92.54508397301602, 'ACC-bicycle': 84.88067940919434, 'ACC-car': 84.98386104358528, 'ACC-motorcycle': 86.14603222444799, 'ACC-airplane': 90.51005422099169, 'ACC-bus': 90.24757210014278, 'ACC-train': 93.93806007511996, 'ACC-truck': 78.24854072144927, 'ACC-boat': 78.53278430752312, 'ACC-traffic light': 90.6334762185152, 'ACC-fire hydrant': 95.60063111957538, 'ACC-stop sign': 93.58919215467537, 'ACC-parking meter': 92.17604070413171, 'ACC-bench': 73.43709049855856, 'ACC-bird': 79.5459700751545, 'ACC-cat': 86.64364535701398, 'ACC-dog': 84.89600666403922, 'ACC-horse': 90.15400921316477, 'ACC-sheep': 90.99958044498551, 'ACC-cow': 86.70193331228985, 'ACC-elephant': 91.74994467025616, 'ACC-bear': 76.34149939947486, 'ACC-zebra': 91.45683219850346, 'ACC-giraffe': 90.33042250733651, 'ACC-backpack': 55.99523667627804, 'ACC-umbrella': 82.32915815814621, 'ACC-handbag': 55.145590659808875, 'ACC-tie': 80.99073756244589, 'ACC-suitcase': 89.08994739883424, 'ACC-frisbee': 94.20254545454544, 'ACC-skis': 70.74689008746256, 'ACC-snowboard': 79.6938600573094, 'ACC-sports ball': 80.01726258773427, 'ACC-kite': 75.29694260493986, 'ACC-baseball bat': 82.38017816871334, 'ACC-baseball glove': 60.91560205500674, 'ACC-skateboard': 69.55857732134602, 'ACC-surfboard': 90.05375145424843, 'ACC-tennis racket': 89.48486415130328, 'ACC-bottle': 83.37791205823783, 'ACC-wine glass': 86.0744138818879, 'ACC-cup': 82.8400860205362, 'ACC-fork': 66.43220104473758, 'ACC-knife': 69.4046310544442, 'ACC-spoon': 68.11263599633371, 'ACC-bowl': 68.06984204610949, 'ACC-banana': 88.99385354799128, 'ACC-apple': 70.89862503447523, 'ACC-sandwich': 78.9998307136934, 'ACC-orange': 86.61749783642352, 'ACC-broccoli': 77.10214266011548, 'ACC-carrot': 76.34225184223511, 'ACC-hot dog': 72.07081040095173, 'ACC-pizza': 90.24381385960793, 'ACC-donut': 80.73882740906065, 'ACC-cake': 75.78163019834092, 'ACC-chair': 69.55072200470556, 'ACC-couch': 81.08760738763974, 'ACC-potted plant': 50.28873351655131, 'ACC-bed': 78.14860856414907, 'ACC-dining table': 74.39402514977158, 'ACC-toilet': 91.08371480868425, 'ACC-tv': 87.40719552008352, 'ACC-laptop': 83.18210233404177, 'ACC-mouse': 80.84117038047106, 'ACC-remote': 72.74842819241042, 'ACC-keyboard': 66.75220347285979, 'ACC-cell phone': 77.76658582581281, 'ACC-microwave': 76.64627071988409, 'ACC-oven': 85.49572842086123, 'ACC-toaster': 75.11119626654258, 'ACC-sink': 82.59275296253233, 'ACC-refrigerator': 88.9927880389678, 'ACC-book': 70.67972317951305, 'ACC-clock': 82.48861038075094, 'ACC-vase': 72.65288273365695, 'ACC-scissors': 56.86823403024543, 'ACC-teddy bear': 86.60479833019608, 'ACC-hair drier': 46.985739902655624, 'ACC-toothbrush': 81.51667824878388, 'ACC-banner': 76.13329537438366, 'ACC-blanket': 15.827790742110956, 'ACC-bridge': 55.74221884745487, 'ACC-cardboard': 57.019514323106, 'ACC-counter': 52.75456284667347, 'ACC-curtain': 77.52113440783882, 'ACC-door-stuff': 64.3102510167029, 'ACC-floor-wood': 78.99355816130644, 'ACC-flower': 60.99445932613155, 'ACC-fruit': 59.56628545560609, 'ACC-gravel': 42.718059092520136, 'ACC-house': 32.98923926654018, 'ACC-light': 57.68880632010106, 'ACC-mirror-stuff': 70.13494409515928, 'ACC-net': 64.80851474216999, 'ACC-pillow': 26.10634304224207, 'ACC-platform': 48.708408212805864, 'ACC-playingfield': 92.24990514904934, 'ACC-railroad': 78.47585632342104, 'ACC-river': 71.64104740332932, 'ACC-road': 85.49408939200174, 'ACC-roof': 22.08444873628411, 'ACC-sand': 70.60395726137499, 'ACC-sea': 92.2266062008702, 'ACC-shelf': 58.25460226984384, 'ACC-snow': 94.94136083998592, 'ACC-stairs': 43.871320730107406, 'ACC-tent': 11.37468581537445, 'ACC-towel': 44.160894266232674, 'ACC-wall-brick': 64.9344815062799, 'ACC-wall-stone': 34.049040858609786, 'ACC-wall-tile': 81.78032998069003, 'ACC-wall-wood': 53.805970627988245, 'ACC-water-other': 38.63537817966653, 'ACC-window-blind': 58.09882386623807, 'ACC-window-other': 69.29869734675623, 'ACC-tree-merged': 89.28560688393813, 'ACC-fence-merged': 67.49805539842193, 'ACC-ceiling-merged': 80.95440677750159, 'ACC-sky-other-merged': 96.79934360082146, 'ACC-cabinet-merged': 75.31744735362274, 'ACC-table-merged': 51.35864375095828, 'ACC-floor-other-merged': 61.39345957720606, 'ACC-pavement-merged': 66.94987091598425, 'ACC-mountain-merged': 65.01282302780722, 'ACC-grass-merged': 83.84846383987231, 'ACC-dirt-merged': 63.17503938724502, 'ACC-paper-merged': 41.331839088098775, 'ACC-food-other-merged': 56.668981893095484, 'ACC-building-other-merged': 72.04525932944343, 'ACC-rock-merged': 83.11370936369197, 'ACC-wall-other-merged': 80.54151868371257, 'ACC-rug-merged': 79.38342522180716})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4547848990342405, 'noc@0.8': 2.7319285923324554, 'noc@0.85': 3.286508633304068, 'noc@0.9': 4.326602282704126, 'miou@iter1': 0.8311092275117691}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 70.54022216796875, 'precision@0.6': 68.09172058105469, 'precision@0.7': 63.1947135925293, 'precision@0.8': 53.400699615478516, 'precision@0.9': 27.283327102661133, 'cIoU': 57.68621826171875, 'mIoU': 62.91379165649414}}}
INFO:trainer.default_trainer:This epoch takes 1:26:43.041265
INFO:trainer.default_trainer:PROGRESS: 90.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 45 training.
INFO:trainer.default_trainer:epochs[    45] optim steps[82300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.80229/0.89449, loss_mask_bce_0: 0.10149/0.33379, loss_mask_dice_0: 0.43112/1.16152, loss_spatial_bce_0: 0.03521/0.08647, loss_spatial_dice_0: 0.12386/0.20631, loss_spatial_ce_0: 0.00584/0.05931, loss_grounding_bce_0: 0.04953/0.08612, loss_grounding_dice_0: 0.29170/0.17843, loss_grounding_ce_0: 0.13185/0.27119, loss_mask_ce_1: 0.68163/0.89524, loss_mask_bce_1: 0.11052/0.33471, loss_mask_dice_1: 0.46721/1.16836, loss_spatial_bce_1: 0.03238/0.08701, loss_spatial_dice_1: 0.11975/0.21025, loss_spatial_ce_1: 0.00589/0.06514, loss_grounding_bce_1: 0.04449/0.08631, loss_grounding_dice_1: 0.16684/0.17924, loss_grounding_ce_1: 0.21026/0.27216, loss_mask_ce_2: 0.71338/0.90214, loss_mask_bce_2: 0.10849/0.33538, loss_mask_dice_2: 0.48718/1.16874, loss_spatial_bce_2: 0.03431/0.08825, loss_spatial_dice_2: 0.12475/0.21216, loss_spatial_ce_2: 0.00680/0.06858, loss_grounding_bce_2: 0.05162/0.08650, loss_grounding_dice_2: 0.16346/0.17913, loss_grounding_ce_2: 0.19052/0.27546, loss_mask_ce_3: 0.72792/0.91348, loss_mask_bce_3: 0.11007/0.33657, loss_mask_dice_3: 0.52370/1.16676, loss_spatial_bce_3: 0.03591/0.08958, loss_spatial_dice_3: 0.13256/0.21323, loss_spatial_ce_3: 0.01313/0.07369, loss_grounding_bce_3: 0.04770/0.08674, loss_grounding_dice_3: 0.19797/0.17880, loss_grounding_ce_3: 0.17692/0.27771, loss_mask_ce_4: 0.76734/0.91492, loss_mask_bce_4: 0.10880/0.33873, loss_mask_dice_4: 0.49476/1.19055, loss_spatial_bce_4: 0.03868/0.09350, loss_spatial_dice_4: 0.14424/0.22548, loss_spatial_ce_4: 0.02909/0.08994, loss_grounding_bce_4: 0.04369/0.08730, loss_grounding_dice_4: 0.20862/0.18182, loss_grounding_ce_4: 0.25785/0.28063, loss_mask_ce_5: 0.83670/0.93167, loss_mask_bce_5: 0.10296/0.34115, loss_mask_dice_5: 0.44606/1.19882, loss_spatial_bce_5: 0.03546/0.09585, loss_spatial_dice_5: 0.13628/0.22989, loss_spatial_ce_5: 0.05657/0.10378, loss_grounding_bce_5: 0.04751/0.08772, loss_grounding_dice_5: 0.18857/0.18311, loss_grounding_ce_5: 0.23560/0.29338, loss_mask_ce_6: 0.74743/0.97202, loss_mask_bce_6: 0.10612/0.34397, loss_mask_dice_6: 0.44724/1.20180, loss_spatial_bce_6: 0.03724/0.10141, loss_spatial_dice_6: 0.13778/0.23287, loss_spatial_ce_6: 0.07630/0.12869, loss_grounding_bce_6: 0.05352/0.08848, loss_grounding_dice_6: 0.22995/0.18350, loss_grounding_ce_6: 0.19797/0.30867, loss_mask_ce_7: 0.70019/1.01771, loss_mask_bce_7: 0.11895/0.35175, loss_mask_dice_7: 0.43048/1.25618, loss_spatial_bce_7: 0.04499/0.10931, loss_spatial_dice_7: 0.15488/0.26049, loss_spatial_ce_7: 0.13504/0.16359, loss_grounding_bce_7: 0.05268/0.09033, loss_grounding_dice_7: 0.18894/0.19085, loss_grounding_ce_7: 0.12570/0.33861, loss_mask_ce_8: 0.95297/1.12647, loss_mask_bce_8: 0.11455/0.36535, loss_mask_dice_8: 0.44097/1.32877, loss_spatial_bce_8: 0.05748/0.12942, loss_spatial_dice_8: 0.19619/0.29815, loss_spatial_ce_8: 0.13437/0.21462, loss_grounding_bce_8: 0.04832/0.09399, loss_grounding_dice_8: 0.16395/0.20150, loss_grounding_ce_8: 0.21389/0.40466, loss_mask_ce_9: 2.48364/3.67336, loss_mask_bce_9: 0.09775/0.39241, loss_mask_dice_9: 0.69192/1.90113, loss_spatial_bce_9: 0.22046/0.33256, loss_spatial_dice_9: 0.72197/0.82158, loss_spatial_ce_9: 1.40778/1.49212, loss_grounding_bce_9: 0.04177/0.10564, loss_grounding_dice_9: 0.29230/0.28090, loss_grounding_ce_9: 0.16153/0.66889] items per batch[64] items per second[0.14] total items[5267200] mini batches[ 82300] memory[7345] epoch remaining[1:18:49]
INFO:trainer.default_trainer:epochs[    45] optim steps[82400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.34875/0.89437, loss_mask_bce_0: 0.11178/0.33377, loss_mask_dice_0: 0.93249/1.16135, loss_spatial_bce_0: 0.03084/0.08646, loss_spatial_dice_0: 0.29573/0.20628, loss_spatial_ce_0: 0.02153/0.05934, loss_grounding_bce_0: 0.10611/0.08611, loss_grounding_dice_0: 0.17155/0.17842, loss_grounding_ce_0: 0.02709/0.27115, loss_mask_ce_1: 1.29063/0.89514, loss_mask_bce_1: 0.10250/0.33469, loss_mask_dice_1: 0.64942/1.16819, loss_spatial_bce_1: 0.02956/0.08700, loss_spatial_dice_1: 0.29493/0.21022, loss_spatial_ce_1: 0.02145/0.06516, loss_grounding_bce_1: 0.09542/0.08630, loss_grounding_dice_1: 0.19579/0.17924, loss_grounding_ce_1: 0.02528/0.27212, loss_mask_ce_2: 2.03496/0.90202, loss_mask_bce_2: 0.09135/0.33536, loss_mask_dice_2: 0.74368/1.16858, loss_spatial_bce_2: 0.03326/0.08824, loss_spatial_dice_2: 0.29511/0.21213, loss_spatial_ce_2: 0.02171/0.06860, loss_grounding_bce_2: 0.08958/0.08649, loss_grounding_dice_2: 0.17973/0.17912, loss_grounding_ce_2: 0.02933/0.27542, loss_mask_ce_3: 1.47653/0.91338, loss_mask_bce_3: 0.09175/0.33655, loss_mask_dice_3: 0.85241/1.16658, loss_spatial_bce_3: 0.03212/0.08957, loss_spatial_dice_3: 0.32277/0.21320, loss_spatial_ce_3: 0.02569/0.07373, loss_grounding_bce_3: 0.08494/0.08673, loss_grounding_dice_3: 0.17958/0.17879, loss_grounding_ce_3: 0.03992/0.27767, loss_mask_ce_4: 1.27464/0.91481, loss_mask_bce_4: 0.09208/0.33871, loss_mask_dice_4: 0.72545/1.19036, loss_spatial_bce_4: 0.02811/0.09348, loss_spatial_dice_4: 0.27032/0.22544, loss_spatial_ce_4: 0.03059/0.08998, loss_grounding_bce_4: 0.08608/0.08729, loss_grounding_dice_4: 0.18912/0.18181, loss_grounding_ce_4: 0.05259/0.28058, loss_mask_ce_5: 1.59667/0.93158, loss_mask_bce_5: 0.08370/0.34113, loss_mask_dice_5: 0.66244/1.19865, loss_spatial_bce_5: 0.02936/0.09584, loss_spatial_dice_5: 0.30509/0.22986, loss_spatial_ce_5: 0.00590/0.10385, loss_grounding_bce_5: 0.08272/0.08771, loss_grounding_dice_5: 0.17226/0.18310, loss_grounding_ce_5: 0.06279/0.29334, loss_mask_ce_6: 1.31889/0.97192, loss_mask_bce_6: 0.09273/0.34394, loss_mask_dice_6: 0.64516/1.20162, loss_spatial_bce_6: 0.03321/0.10140, loss_spatial_dice_6: 0.36142/0.23284, loss_spatial_ce_6: 0.08975/0.12874, loss_grounding_bce_6: 0.07491/0.08847, loss_grounding_dice_6: 0.15871/0.18350, loss_grounding_ce_6: 0.09517/0.30862, loss_mask_ce_7: 1.54946/1.01762, loss_mask_bce_7: 0.09981/0.35173, loss_mask_dice_7: 0.62228/1.25601, loss_spatial_bce_7: 0.03424/0.10929, loss_spatial_dice_7: 0.30150/0.26046, loss_spatial_ce_7: 0.28880/0.16365, loss_grounding_bce_7: 0.08473/0.09032, loss_grounding_dice_7: 0.17410/0.19084, loss_grounding_ce_7: 0.07994/0.33858, loss_mask_ce_8: 1.31227/1.12640, loss_mask_bce_8: 0.13675/0.36532, loss_mask_dice_8: 0.90310/1.32859, loss_spatial_bce_8: 0.03600/0.12940, loss_spatial_dice_8: 0.33876/0.29811, loss_spatial_ce_8: 0.21862/0.21463, loss_grounding_bce_8: 0.13399/0.09399, loss_grounding_dice_8: 0.22411/0.20150, loss_grounding_ce_8: 0.02420/0.40460, loss_mask_ce_9: 3.33707/3.67330, loss_mask_bce_9: 0.09988/0.39238, loss_mask_dice_9: 0.85525/1.90089, loss_spatial_bce_9: 0.12790/0.33258, loss_spatial_dice_9: 0.77777/0.82157, loss_spatial_ce_9: 1.27511/1.49209, loss_grounding_bce_9: 0.08166/0.10564, loss_grounding_dice_9: 0.20881/0.28089, loss_grounding_ce_9: 0.22646/0.66890] items per batch[64] items per second[0.24] total items[5273600] mini batches[ 82400] memory[7345] epoch remaining[1:13:50]
INFO:trainer.default_trainer:epochs[    45] optim steps[82500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.71061/0.89435, loss_mask_bce_0: 0.21273/0.33377, loss_mask_dice_0: 0.68834/1.16145, loss_spatial_bce_0: 0.06093/0.08645, loss_spatial_dice_0: 0.12859/0.20626, loss_spatial_ce_0: 0.00020/0.05931, loss_grounding_bce_0: 0.09159/0.08610, loss_grounding_dice_0: 0.12531/0.17843, loss_grounding_ce_0: 0.04482/0.27119, loss_mask_ce_1: 0.74550/0.89510, loss_mask_bce_1: 0.22459/0.33470, loss_mask_dice_1: 0.70283/1.16829, loss_spatial_bce_1: 0.06574/0.08698, loss_spatial_dice_1: 0.13178/0.21020, loss_spatial_ce_1: 0.00031/0.06514, loss_grounding_bce_1: 0.09598/0.08629, loss_grounding_dice_1: 0.14638/0.17924, loss_grounding_ce_1: 0.04358/0.27214, loss_mask_ce_2: 0.69479/0.90199, loss_mask_bce_2: 0.22246/0.33536, loss_mask_dice_2: 0.72395/1.16869, loss_spatial_bce_2: 0.05687/0.08823, loss_spatial_dice_2: 0.13882/0.21211, loss_spatial_ce_2: 0.00019/0.06857, loss_grounding_bce_2: 0.09115/0.08647, loss_grounding_dice_2: 0.14043/0.17912, loss_grounding_ce_2: 0.03937/0.27547, loss_mask_ce_3: 0.77165/0.91336, loss_mask_bce_3: 0.22679/0.33655, loss_mask_dice_3: 0.68337/1.16666, loss_spatial_bce_3: 0.05862/0.08956, loss_spatial_dice_3: 0.12858/0.21318, loss_spatial_ce_3: 0.00078/0.07370, loss_grounding_bce_3: 0.08958/0.08672, loss_grounding_dice_3: 0.12601/0.17879, loss_grounding_ce_3: 0.03717/0.27772, loss_mask_ce_4: 0.80376/0.91478, loss_mask_bce_4: 0.21577/0.33871, loss_mask_dice_4: 0.73961/1.19046, loss_spatial_bce_4: 0.05719/0.09347, loss_spatial_dice_4: 0.13126/0.22542, loss_spatial_ce_4: 0.00034/0.08995, loss_grounding_bce_4: 0.09263/0.08728, loss_grounding_dice_4: 0.13697/0.18182, loss_grounding_ce_4: 0.03969/0.28059, loss_mask_ce_5: 0.80126/0.93155, loss_mask_bce_5: 0.22209/0.34113, loss_mask_dice_5: 0.86632/1.19874, loss_spatial_bce_5: 0.06124/0.09582, loss_spatial_dice_5: 0.14992/0.22985, loss_spatial_ce_5: 0.00339/0.10381, loss_grounding_bce_5: 0.09340/0.08769, loss_grounding_dice_5: 0.13575/0.18310, loss_grounding_ce_5: 0.08750/0.29338, loss_mask_ce_6: 0.83296/0.97189, loss_mask_bce_6: 0.22208/0.34395, loss_mask_dice_6: 0.94642/1.20172, loss_spatial_bce_6: 0.06079/0.10139, loss_spatial_dice_6: 0.13850/0.23282, loss_spatial_ce_6: 0.01713/0.12870, loss_grounding_bce_6: 0.09617/0.08846, loss_grounding_dice_6: 0.12883/0.18350, loss_grounding_ce_6: 0.08414/0.30863, loss_mask_ce_7: 0.93123/1.01764, loss_mask_bce_7: 0.23363/0.35173, loss_mask_dice_7: 1.09594/1.25607, loss_spatial_bce_7: 0.06498/0.10927, loss_spatial_dice_7: 0.15910/0.26045, loss_spatial_ce_7: 0.05596/0.16363, loss_grounding_bce_7: 0.09525/0.09031, loss_grounding_dice_7: 0.13182/0.19084, loss_grounding_ce_7: 0.09363/0.33860, loss_mask_ce_8: 0.91992/1.12643, loss_mask_bce_8: 0.25258/0.36532, loss_mask_dice_8: 1.27708/1.32869, loss_spatial_bce_8: 0.05746/0.12938, loss_spatial_dice_8: 0.15791/0.29810, loss_spatial_ce_8: 0.02460/0.21456, loss_grounding_bce_8: 0.09523/0.09397, loss_grounding_dice_8: 0.14536/0.20150, loss_grounding_ce_8: 0.03873/0.40461, loss_mask_ce_9: 4.97487/3.67346, loss_mask_bce_9: 0.27567/0.39240, loss_mask_dice_9: 2.89083/1.90107, loss_spatial_bce_9: 0.31075/0.33255, loss_spatial_dice_9: 0.88796/0.82159, loss_spatial_ce_9: 1.36602/1.49210, loss_grounding_bce_9: 0.09839/0.10563, loss_grounding_dice_9: 0.23847/0.28091, loss_grounding_ce_9: 0.44747/0.66893] items per batch[64] items per second[0.23] total items[5280000] mini batches[ 82500] memory[7345] epoch remaining[1:10:07]
INFO:trainer.default_trainer:epochs[    45] optim steps[82600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.64283/0.89425, loss_mask_bce_0: 0.38002/0.33379, loss_mask_dice_0: 0.55410/1.16127, loss_spatial_bce_0: 0.22348/0.08645, loss_spatial_dice_0: 0.27991/0.20624, loss_spatial_ce_0: 0.04564/0.05938, loss_grounding_bce_0: 0.19631/0.08610, loss_grounding_dice_0: 0.28964/0.17841, loss_grounding_ce_0: 0.50562/0.27116, loss_mask_ce_1: 0.68081/0.89501, loss_mask_bce_1: 0.38360/0.33471, loss_mask_dice_1: 0.55415/1.16810, loss_spatial_bce_1: 0.21607/0.08698, loss_spatial_dice_1: 0.26707/0.21018, loss_spatial_ce_1: 0.05326/0.06522, loss_grounding_bce_1: 0.20384/0.08630, loss_grounding_dice_1: 0.32765/0.17923, loss_grounding_ce_1: 0.47486/0.27209, loss_mask_ce_2: 0.73566/0.90192, loss_mask_bce_2: 0.44091/0.33537, loss_mask_dice_2: 0.56828/1.16848, loss_spatial_bce_2: 0.20980/0.08823, loss_spatial_dice_2: 0.25790/0.21209, loss_spatial_ce_2: 0.06531/0.06864, loss_grounding_bce_2: 0.19792/0.08648, loss_grounding_dice_2: 0.36101/0.17911, loss_grounding_ce_2: 0.49292/0.27543, loss_mask_ce_3: 0.79488/0.91328, loss_mask_bce_3: 0.38779/0.33655, loss_mask_dice_3: 0.56892/1.16649, loss_spatial_bce_3: 0.20195/0.08956, loss_spatial_dice_3: 0.25962/0.21316, loss_spatial_ce_3: 0.07627/0.07379, loss_grounding_bce_3: 0.16471/0.08672, loss_grounding_dice_3: 0.32154/0.17878, loss_grounding_ce_3: 0.49561/0.27767, loss_mask_ce_4: 0.68865/0.91471, loss_mask_bce_4: 0.38814/0.33872, loss_mask_dice_4: 0.62697/1.19027, loss_spatial_bce_4: 0.18064/0.09347, loss_spatial_dice_4: 0.22212/0.22539, loss_spatial_ce_4: 0.08635/0.09003, loss_grounding_bce_4: 0.16768/0.08729, loss_grounding_dice_4: 0.32532/0.18180, loss_grounding_ce_4: 0.53414/0.28055, loss_mask_ce_5: 0.74078/0.93146, loss_mask_bce_5: 0.48291/0.34115, loss_mask_dice_5: 0.65988/1.19855, loss_spatial_bce_5: 0.15844/0.09582, loss_spatial_dice_5: 0.20721/0.22982, loss_spatial_ce_5: 0.10932/0.10388, loss_grounding_bce_5: 0.19145/0.08770, loss_grounding_dice_5: 0.31485/0.18308, loss_grounding_ce_5: 0.65330/0.29332, loss_mask_ce_6: 0.80767/0.97180, loss_mask_bce_6: 0.48060/0.34396, loss_mask_dice_6: 0.69901/1.20154, loss_spatial_bce_6: 0.16495/0.10139, loss_spatial_dice_6: 0.22556/0.23280, loss_spatial_ce_6: 0.08448/0.12876, loss_grounding_bce_6: 0.24860/0.08847, loss_grounding_dice_6: 0.37752/0.18349, loss_grounding_ce_6: 0.50168/0.30854, loss_mask_ce_7: 0.59198/1.01759, loss_mask_bce_7: 0.54503/0.35175, loss_mask_dice_7: 0.78714/1.25588, loss_spatial_bce_7: 0.20333/0.10927, loss_spatial_dice_7: 0.24351/0.26042, loss_spatial_ce_7: 0.14688/0.16369, loss_grounding_bce_7: 0.29738/0.09031, loss_grounding_dice_7: 0.46436/0.19083, loss_grounding_ce_7: 0.25679/0.33853, loss_mask_ce_8: 0.79965/1.12637, loss_mask_bce_8: 0.47503/0.36534, loss_mask_dice_8: 0.88210/1.32849, loss_spatial_bce_8: 0.19462/0.12938, loss_spatial_dice_8: 0.25952/0.29808, loss_spatial_ce_8: 0.23477/0.21457, loss_grounding_bce_8: 0.29732/0.09398, loss_grounding_dice_8: 0.55580/0.20149, loss_grounding_ce_8: 0.36032/0.40447, loss_mask_ce_9: 2.21499/3.67329, loss_mask_bce_9: 0.45351/0.39242, loss_mask_dice_9: 1.02921/1.90089, loss_spatial_bce_9: 0.33300/0.33257, loss_spatial_dice_9: 0.86168/0.82158, loss_spatial_ce_9: 1.03233/1.49204, loss_grounding_bce_9: 0.27349/0.10564, loss_grounding_dice_9: 0.55150/0.28090, loss_grounding_ce_9: 0.35420/0.66876] items per batch[64] items per second[0.24] total items[5286400] mini batches[ 82600] memory[7345] epoch remaining[1:05:10]
INFO:trainer.default_trainer:epochs[    45] optim steps[82700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.97460/0.89417, loss_mask_bce_0: 0.31343/0.33376, loss_mask_dice_0: 0.67988/1.16114, loss_spatial_bce_0: 0.08911/0.08644, loss_spatial_dice_0: 0.17257/0.20621, loss_spatial_ce_0: 0.00053/0.05936, loss_grounding_bce_0: 0.05440/0.08610, loss_grounding_dice_0: 0.18693/0.17839, loss_grounding_ce_0: 0.07534/0.27109, loss_mask_ce_1: 0.95785/0.89494, loss_mask_bce_1: 0.31629/0.33468, loss_mask_dice_1: 0.65955/1.16796, loss_spatial_bce_1: 0.08784/0.08698, loss_spatial_dice_1: 0.16198/0.21014, loss_spatial_ce_1: 0.00109/0.06519, loss_grounding_bce_1: 0.05327/0.08630, loss_grounding_dice_1: 0.14974/0.17921, loss_grounding_ce_1: 0.07241/0.27202, loss_mask_ce_2: 0.97190/0.90186, loss_mask_bce_2: 0.30726/0.33535, loss_mask_dice_2: 0.66594/1.16834, loss_spatial_bce_2: 0.09225/0.08822, loss_spatial_dice_2: 0.15236/0.21206, loss_spatial_ce_2: 0.00044/0.06861, loss_grounding_bce_2: 0.05289/0.08648, loss_grounding_dice_2: 0.10861/0.17910, loss_grounding_ce_2: 0.06608/0.27538, loss_mask_ce_3: 0.97857/0.91322, loss_mask_bce_3: 0.31306/0.33652, loss_mask_dice_3: 0.66752/1.16637, loss_spatial_bce_3: 0.09840/0.08955, loss_spatial_dice_3: 0.16704/0.21313, loss_spatial_ce_3: 0.00197/0.07376, loss_grounding_bce_3: 0.05395/0.08672, loss_grounding_dice_3: 0.19041/0.17876, loss_grounding_ce_3: 0.08925/0.27761, loss_mask_ce_4: 0.96754/0.91464, loss_mask_bce_4: 0.30912/0.33869, loss_mask_dice_4: 0.65248/1.19016, loss_spatial_bce_4: 0.09321/0.09346, loss_spatial_dice_4: 0.17292/0.22536, loss_spatial_ce_4: 0.00362/0.08999, loss_grounding_bce_4: 0.05288/0.08728, loss_grounding_dice_4: 0.21920/0.18179, loss_grounding_ce_4: 0.06597/0.28047, loss_mask_ce_5: 1.04248/0.93139, loss_mask_bce_5: 0.32382/0.34112, loss_mask_dice_5: 0.64989/1.19842, loss_spatial_bce_5: 0.09118/0.09581, loss_spatial_dice_5: 0.17920/0.22979, loss_spatial_ce_5: 0.00803/0.10385, loss_grounding_bce_5: 0.05645/0.08770, loss_grounding_dice_5: 0.16605/0.18306, loss_grounding_ce_5: 0.07771/0.29325, loss_mask_ce_6: 0.93722/0.97175, loss_mask_bce_6: 0.31843/0.34394, loss_mask_dice_6: 0.66284/1.20141, loss_spatial_bce_6: 0.11263/0.10138, loss_spatial_dice_6: 0.19130/0.23277, loss_spatial_ce_6: 0.03022/0.12873, loss_grounding_bce_6: 0.05337/0.08846, loss_grounding_dice_6: 0.17247/0.18347, loss_grounding_ce_6: 0.08903/0.30850, loss_mask_ce_7: 0.97705/1.01756, loss_mask_bce_7: 0.32170/0.35174, loss_mask_dice_7: 0.68187/1.25576, loss_spatial_bce_7: 0.09368/0.10926, loss_spatial_dice_7: 0.19999/0.26039, loss_spatial_ce_7: 0.06240/0.16363, loss_grounding_bce_7: 0.05443/0.09031, loss_grounding_dice_7: 0.07758/0.19081, loss_grounding_ce_7: 0.30680/0.33844, loss_mask_ce_8: 0.97565/1.12631, loss_mask_bce_8: 0.31929/0.36531, loss_mask_dice_8: 0.68754/1.32836, loss_spatial_bce_8: 0.11258/0.12936, loss_spatial_dice_8: 0.24291/0.29804, loss_spatial_ce_8: 0.03239/0.21449, loss_grounding_bce_8: 0.05484/0.09398, loss_grounding_dice_8: 0.13492/0.20147, loss_grounding_ce_8: 0.24861/0.40440, loss_mask_ce_9: 3.31446/3.67328, loss_mask_bce_9: 0.40254/0.39241, loss_mask_dice_9: 1.02974/1.90075, loss_spatial_bce_9: 0.35320/0.33259, loss_spatial_dice_9: 0.79046/0.82157, loss_spatial_ce_9: 1.49784/1.49197, loss_grounding_bce_9: 0.07006/0.10563, loss_grounding_dice_9: 0.17212/0.28086, loss_grounding_ce_9: 0.30029/0.66876] items per batch[64] items per second[0.24] total items[5292800] mini batches[ 82700] memory[7345] epoch remaining[1:00:37]
INFO:trainer.default_trainer:epochs[    45] optim steps[82800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.22798/0.89413, loss_mask_bce_0: 0.34320/0.33374, loss_mask_dice_0: 0.81083/1.16131, loss_spatial_bce_0: 0.04885/0.08643, loss_spatial_dice_0: 0.14922/0.20619, loss_spatial_ce_0: 0.01061/0.05934, loss_grounding_bce_0: 0.06335/0.08608, loss_grounding_dice_0: 0.08989/0.17839, loss_grounding_ce_0: 0.04279/0.27113, loss_mask_ce_1: 0.28546/0.89491, loss_mask_bce_1: 0.32553/0.33466, loss_mask_dice_1: 0.83062/1.16813, loss_spatial_bce_1: 0.05221/0.08696, loss_spatial_dice_1: 0.15854/0.21013, loss_spatial_ce_1: 0.01793/0.06517, loss_grounding_bce_1: 0.06307/0.08628, loss_grounding_dice_1: 0.08842/0.17921, loss_grounding_ce_1: 0.04144/0.27204, loss_mask_ce_2: 0.24770/0.90180, loss_mask_bce_2: 0.33332/0.33533, loss_mask_dice_2: 0.79487/1.16854, loss_spatial_bce_2: 0.05999/0.08821, loss_spatial_dice_2: 0.16084/0.21204, loss_spatial_ce_2: 0.03132/0.06860, loss_grounding_bce_2: 0.06249/0.08645, loss_grounding_dice_2: 0.09164/0.17910, loss_grounding_ce_2: 0.03894/0.27538, loss_mask_ce_3: 0.25724/0.91317, loss_mask_bce_3: 0.33873/0.33651, loss_mask_dice_3: 0.78418/1.16655, loss_spatial_bce_3: 0.05990/0.08954, loss_spatial_dice_3: 0.16922/0.21312, loss_spatial_ce_3: 0.01166/0.07375, loss_grounding_bce_3: 0.06415/0.08670, loss_grounding_dice_3: 0.09128/0.17876, loss_grounding_ce_3: 0.02652/0.27762, loss_mask_ce_4: 0.25014/0.91459, loss_mask_bce_4: 0.31517/0.33868, loss_mask_dice_4: 0.79467/1.19034, loss_spatial_bce_4: 0.06420/0.09345, loss_spatial_dice_4: 0.17575/0.22535, loss_spatial_ce_4: 0.01715/0.08998, loss_grounding_bce_4: 0.06770/0.08726, loss_grounding_dice_4: 0.09388/0.18179, loss_grounding_ce_4: 0.02952/0.28050, loss_mask_ce_5: 0.28130/0.93134, loss_mask_bce_5: 0.30566/0.34111, loss_mask_dice_5: 0.84955/1.19859, loss_spatial_bce_5: 0.06066/0.09580, loss_spatial_dice_5: 0.18136/0.22977, loss_spatial_ce_5: 0.03302/0.10383, loss_grounding_bce_5: 0.06502/0.08768, loss_grounding_dice_5: 0.09519/0.18306, loss_grounding_ce_5: 0.08778/0.29324, loss_mask_ce_6: 0.29870/0.97169, loss_mask_bce_6: 0.33485/0.34393, loss_mask_dice_6: 0.80280/1.20161, loss_spatial_bce_6: 0.05973/0.10136, loss_spatial_dice_6: 0.17013/0.23276, loss_spatial_ce_6: 0.05090/0.12870, loss_grounding_bce_6: 0.06342/0.08845, loss_grounding_dice_6: 0.09477/0.18347, loss_grounding_ce_6: 0.05043/0.30850, loss_mask_ce_7: 0.34061/1.01751, loss_mask_bce_7: 0.38503/0.35174, loss_mask_dice_7: 1.02971/1.25596, loss_spatial_bce_7: 0.06939/0.10925, loss_spatial_dice_7: 0.20353/0.26038, loss_spatial_ce_7: 0.09025/0.16360, loss_grounding_bce_7: 0.06573/0.09029, loss_grounding_dice_7: 0.09685/0.19082, loss_grounding_ce_7: 0.06157/0.33845, loss_mask_ce_8: 0.55102/1.12629, loss_mask_bce_8: 0.33767/0.36531, loss_mask_dice_8: 0.88457/1.32854, loss_spatial_bce_8: 0.08345/0.12934, loss_spatial_dice_8: 0.20554/0.29804, loss_spatial_ce_8: 0.04950/0.21441, loss_grounding_bce_8: 0.06601/0.09396, loss_grounding_dice_8: 0.09439/0.20147, loss_grounding_ce_8: 0.19092/0.40440, loss_mask_ce_9: 3.24583/3.67333, loss_mask_bce_9: 0.26231/0.39240, loss_mask_dice_9: 1.25646/1.90108, loss_spatial_bce_9: 0.26533/0.33257, loss_spatial_dice_9: 0.81692/0.82157, loss_spatial_ce_9: 1.31460/1.49191, loss_grounding_bce_9: 0.08150/0.10561, loss_grounding_dice_9: 0.14158/0.28086, loss_grounding_ce_9: 0.66583/0.66873] items per batch[64] items per second[0.24] total items[5299200] mini batches[ 82800] memory[7345] epoch remaining[0:56:04]
INFO:trainer.default_trainer:epochs[    45] optim steps[82900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.72799/0.89406, loss_mask_bce_0: 0.19124/0.33374, loss_mask_dice_0: 0.71102/1.16122, loss_spatial_bce_0: 0.05684/0.08641, loss_spatial_dice_0: 0.20034/0.20616, loss_spatial_ce_0: 0.02003/0.05931, loss_grounding_bce_0: 0.07520/0.08606, loss_grounding_dice_0: 0.10792/0.17837, loss_grounding_ce_0: 0.23804/0.27114, loss_mask_ce_1: 0.70956/0.89484, loss_mask_bce_1: 0.19368/0.33466, loss_mask_dice_1: 0.68711/1.16805, loss_spatial_bce_1: 0.05827/0.08695, loss_spatial_dice_1: 0.19519/0.21009, loss_spatial_ce_1: 0.02508/0.06515, loss_grounding_bce_1: 0.07566/0.08626, loss_grounding_dice_1: 0.10241/0.17919, loss_grounding_ce_1: 0.21745/0.27204, loss_mask_ce_2: 0.75405/0.90174, loss_mask_bce_2: 0.19825/0.33532, loss_mask_dice_2: 0.71306/1.16846, loss_spatial_bce_2: 0.05906/0.08820, loss_spatial_dice_2: 0.20137/0.21201, loss_spatial_ce_2: 0.01380/0.06857, loss_grounding_bce_2: 0.07106/0.08644, loss_grounding_dice_2: 0.09933/0.17909, loss_grounding_ce_2: 0.28709/0.27537, loss_mask_ce_3: 0.75803/0.91310, loss_mask_bce_3: 0.20200/0.33650, loss_mask_dice_3: 0.77612/1.16647, loss_spatial_bce_3: 0.06041/0.08953, loss_spatial_dice_3: 0.22570/0.21309, loss_spatial_ce_3: 0.01046/0.07372, loss_grounding_bce_3: 0.07853/0.08669, loss_grounding_dice_3: 0.10506/0.17874, loss_grounding_ce_3: 0.36083/0.27761, loss_mask_ce_4: 1.04984/0.91457, loss_mask_bce_4: 0.19962/0.33867, loss_mask_dice_4: 0.72047/1.19024, loss_spatial_bce_4: 0.06168/0.09344, loss_spatial_dice_4: 0.22215/0.22531, loss_spatial_ce_4: 0.07633/0.08994, loss_grounding_bce_4: 0.07531/0.08725, loss_grounding_dice_4: 0.10157/0.18177, loss_grounding_ce_4: 0.09709/0.28049, loss_mask_ce_5: 0.73564/0.93132, loss_mask_bce_5: 0.19943/0.34110, loss_mask_dice_5: 0.56216/1.19850, loss_spatial_bce_5: 0.05925/0.09579, loss_spatial_dice_5: 0.23179/0.22974, loss_spatial_ce_5: 0.04824/0.10379, loss_grounding_bce_5: 0.07587/0.08767, loss_grounding_dice_5: 0.09936/0.18304, loss_grounding_ce_5: 0.29377/0.29325, loss_mask_ce_6: 0.77470/0.97165, loss_mask_bce_6: 0.19894/0.34392, loss_mask_dice_6: 0.66433/1.20155, loss_spatial_bce_6: 0.06531/0.10135, loss_spatial_dice_6: 0.19850/0.23273, loss_spatial_ce_6: 0.09778/0.12866, loss_grounding_bce_6: 0.07555/0.08843, loss_grounding_dice_6: 0.10550/0.18346, loss_grounding_ce_6: 0.34570/0.30853, loss_mask_ce_7: 0.79922/1.01749, loss_mask_bce_7: 0.19910/0.35174, loss_mask_dice_7: 0.66342/1.25590, loss_spatial_bce_7: 0.06118/0.10924, loss_spatial_dice_7: 0.23837/0.26034, loss_spatial_ce_7: 0.09917/0.16356, loss_grounding_bce_7: 0.07576/0.09029, loss_grounding_dice_7: 0.12788/0.19082, loss_grounding_ce_7: 0.13701/0.33846, loss_mask_ce_8: 0.95755/1.12627, loss_mask_bce_8: 0.20748/0.36531, loss_mask_dice_8: 0.73973/1.32848, loss_spatial_bce_8: 0.06800/0.12932, loss_spatial_dice_8: 0.26846/0.29801, loss_spatial_ce_8: 0.10039/0.21432, loss_grounding_bce_8: 0.07262/0.09396, loss_grounding_dice_8: 0.10690/0.20147, loss_grounding_ce_8: 0.32747/0.40441, loss_mask_ce_9: 3.51506/3.67329, loss_mask_bce_9: 0.23415/0.39241, loss_mask_dice_9: 1.13467/1.90100, loss_spatial_bce_9: 0.21855/0.33257, loss_spatial_dice_9: 0.79475/0.82156, loss_spatial_ce_9: 1.39218/1.49177, loss_grounding_bce_9: 0.11024/0.10561, loss_grounding_dice_9: 0.16997/0.28088, loss_grounding_ce_9: 1.39971/0.66878] items per batch[64] items per second[0.23] total items[5305600] mini batches[ 82900] memory[7345] epoch remaining[0:51:37]
INFO:trainer.default_trainer:epochs[    45] optim steps[83000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.14897/0.89410, loss_mask_bce_0: 0.48208/0.33374, loss_mask_dice_0: 4.55197/1.16135, loss_spatial_bce_0: 0.03893/0.08641, loss_spatial_dice_0: 0.31815/0.20615, loss_spatial_ce_0: 0.11187/0.05930, loss_grounding_bce_0: 0.02558/0.08607, loss_grounding_dice_0: 0.09628/0.17837, loss_grounding_ce_0: 0.01457/0.27107, loss_mask_ce_1: 0.97018/0.89486, loss_mask_bce_1: 0.47316/0.33465, loss_mask_dice_1: 4.69196/1.16821, loss_spatial_bce_1: 0.03059/0.08694, loss_spatial_dice_1: 0.32223/0.21008, loss_spatial_ce_1: 0.15402/0.06514, loss_grounding_bce_1: 0.02570/0.08626, loss_grounding_dice_1: 0.09609/0.17919, loss_grounding_ce_1: 0.00976/0.27197, loss_mask_ce_2: 1.13875/0.90176, loss_mask_bce_2: 0.51357/0.33532, loss_mask_dice_2: 4.54215/1.16864, loss_spatial_bce_2: 0.03203/0.08819, loss_spatial_dice_2: 0.31213/0.21200, loss_spatial_ce_2: 0.12321/0.06855, loss_grounding_bce_2: 0.02478/0.08644, loss_grounding_dice_2: 0.09276/0.17908, loss_grounding_ce_2: 0.01462/0.27530, loss_mask_ce_3: 0.88312/0.91307, loss_mask_bce_3: 0.50760/0.33650, loss_mask_dice_3: 4.70475/1.16663, loss_spatial_bce_3: 0.04269/0.08953, loss_spatial_dice_3: 0.36033/0.21308, loss_spatial_ce_3: 0.09549/0.07371, loss_grounding_bce_3: 0.02361/0.08669, loss_grounding_dice_3: 0.09581/0.17874, loss_grounding_ce_3: 0.04285/0.27755, loss_mask_ce_4: 1.18973/0.91458, loss_mask_bce_4: 0.51630/0.33867, loss_mask_dice_4: 4.60006/1.19040, loss_spatial_bce_4: 0.03647/0.09343, loss_spatial_dice_4: 0.35373/0.22530, loss_spatial_ce_4: 0.20792/0.08994, loss_grounding_bce_4: 0.02714/0.08725, loss_grounding_dice_4: 0.09772/0.18177, loss_grounding_ce_4: 0.00597/0.28042, loss_mask_ce_5: 1.09143/0.93133, loss_mask_bce_5: 0.56366/0.34110, loss_mask_dice_5: 4.61155/1.19867, loss_spatial_bce_5: 0.03872/0.09579, loss_spatial_dice_5: 0.39904/0.22973, loss_spatial_ce_5: 0.05429/0.10377, loss_grounding_bce_5: 0.02507/0.08767, loss_grounding_dice_5: 0.09402/0.18304, loss_grounding_ce_5: 0.01292/0.29318, loss_mask_ce_6: 0.93952/0.97164, loss_mask_bce_6: 0.50565/0.34392, loss_mask_dice_6: 4.72917/1.20170, loss_spatial_bce_6: 0.04008/0.10135, loss_spatial_dice_6: 0.37727/0.23272, loss_spatial_ce_6: 0.10107/0.12865, loss_grounding_bce_6: 0.01897/0.08844, loss_grounding_dice_6: 0.07974/0.18345, loss_grounding_ce_6: 0.07039/0.30845, loss_mask_ce_7: 1.27256/1.01750, loss_mask_bce_7: 0.51479/0.35174, loss_mask_dice_7: 4.76044/1.25605, loss_spatial_bce_7: 0.04250/0.10924, loss_spatial_dice_7: 0.42158/0.26034, loss_spatial_ce_7: 0.16898/0.16352, loss_grounding_bce_7: 0.02139/0.09029, loss_grounding_dice_7: 0.08981/0.19081, loss_grounding_ce_7: 0.21715/0.33838, loss_mask_ce_8: 1.28837/1.12629, loss_mask_bce_8: 0.50821/0.36532, loss_mask_dice_8: 4.58493/1.32863, loss_spatial_bce_8: 0.06242/0.12932, loss_spatial_dice_8: 0.48407/0.29800, loss_spatial_ce_8: 0.16835/0.21427, loss_grounding_bce_8: 0.02934/0.09396, loss_grounding_dice_8: 0.09724/0.20146, loss_grounding_ce_8: 0.16858/0.40433, loss_mask_ce_9: 5.64372/3.67338, loss_mask_bce_9: 0.50254/0.39240, loss_mask_dice_9: 6.23928/1.90117, loss_spatial_bce_9: 0.17247/0.33256, loss_spatial_dice_9: 0.94559/0.82157, loss_spatial_ce_9: 1.75399/1.49178, loss_grounding_bce_9: 0.02281/0.10562, loss_grounding_dice_9: 0.12898/0.28088, loss_grounding_ce_9: 1.75076/0.66881] items per batch[64] items per second[0.23] total items[5312000] mini batches[ 83000] memory[7345] epoch remaining[0:47:10]
INFO:trainer.default_trainer:epochs[    45] optim steps[83100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.78621/0.89401, loss_mask_bce_0: 0.35905/0.33371, loss_mask_dice_0: 1.10641/1.16126, loss_spatial_bce_0: 0.05527/0.08641, loss_spatial_dice_0: 0.18532/0.20613, loss_spatial_ce_0: 0.10246/0.05929, loss_grounding_bce_0: 0.06301/0.08606, loss_grounding_dice_0: 0.15549/0.17834, loss_grounding_ce_0: 0.37464/0.27107, loss_mask_ce_1: 0.87517/0.89478, loss_mask_bce_1: 0.36018/0.33463, loss_mask_dice_1: 1.39352/1.16813, loss_spatial_bce_1: 0.05303/0.08694, loss_spatial_dice_1: 0.17309/0.21006, loss_spatial_ce_1: 0.05960/0.06514, loss_grounding_bce_1: 0.06016/0.08626, loss_grounding_dice_1: 0.15236/0.17917, loss_grounding_ce_1: 0.37894/0.27198, loss_mask_ce_2: 0.80863/0.90167, loss_mask_bce_2: 0.36955/0.33530, loss_mask_dice_2: 1.11777/1.16855, loss_spatial_bce_2: 0.05850/0.08819, loss_spatial_dice_2: 0.14825/0.21198, loss_spatial_ce_2: 0.18687/0.06855, loss_grounding_bce_2: 0.06463/0.08644, loss_grounding_dice_2: 0.15965/0.17905, loss_grounding_ce_2: 0.37292/0.27533, loss_mask_ce_3: 0.65282/0.91302, loss_mask_bce_3: 0.37221/0.33648, loss_mask_dice_3: 1.13091/1.16653, loss_spatial_bce_3: 0.06228/0.08953, loss_spatial_dice_3: 0.16964/0.21306, loss_spatial_ce_3: 0.17802/0.07369, loss_grounding_bce_3: 0.06186/0.08669, loss_grounding_dice_3: 0.14921/0.17872, loss_grounding_ce_3: 0.36904/0.27753, loss_mask_ce_4: 0.62650/0.91451, loss_mask_bce_4: 0.37497/0.33864, loss_mask_dice_4: 1.22039/1.19031, loss_spatial_bce_4: 0.06396/0.09344, loss_spatial_dice_4: 0.18132/0.22528, loss_spatial_ce_4: 0.30247/0.08993, loss_grounding_bce_4: 0.06313/0.08725, loss_grounding_dice_4: 0.16201/0.18174, loss_grounding_ce_4: 0.37675/0.28043, loss_mask_ce_5: 0.75822/0.93126, loss_mask_bce_5: 0.38619/0.34107, loss_mask_dice_5: 1.17838/1.19857, loss_spatial_bce_5: 0.07547/0.09579, loss_spatial_dice_5: 0.24307/0.22970, loss_spatial_ce_5: 0.11163/0.10375, loss_grounding_bce_5: 0.06243/0.08767, loss_grounding_dice_5: 0.15697/0.18302, loss_grounding_ce_5: 0.37445/0.29319, loss_mask_ce_6: 0.59966/0.97156, loss_mask_bce_6: 0.39667/0.34389, loss_mask_dice_6: 1.13728/1.20162, loss_spatial_bce_6: 0.08227/0.10136, loss_spatial_dice_6: 0.24322/0.23270, loss_spatial_ce_6: 0.24452/0.12863, loss_grounding_bce_6: 0.06327/0.08844, loss_grounding_dice_6: 0.16236/0.18342, loss_grounding_ce_6: 0.37937/0.30842, loss_mask_ce_7: 0.76180/1.01741, loss_mask_bce_7: 0.38480/0.35172, loss_mask_dice_7: 1.30994/1.25598, loss_spatial_bce_7: 0.08633/0.10924, loss_spatial_dice_7: 0.19780/0.26031, loss_spatial_ce_7: 0.40508/0.16348, loss_grounding_bce_7: 0.06392/0.09030, loss_grounding_dice_7: 0.16596/0.19079, loss_grounding_ce_7: 0.41831/0.33841, loss_mask_ce_8: 0.99997/1.12620, loss_mask_bce_8: 0.41025/0.36529, loss_mask_dice_8: 1.72187/1.32853, loss_spatial_bce_8: 0.08227/0.12931, loss_spatial_dice_8: 0.25275/0.29797, loss_spatial_ce_8: 0.36865/0.21419, loss_grounding_bce_8: 0.06445/0.09396, loss_grounding_dice_8: 0.15920/0.20144, loss_grounding_ce_8: 0.44838/0.40431, loss_mask_ce_9: 4.14437/3.67333, loss_mask_bce_9: 0.43865/0.39237, loss_mask_dice_9: 2.66567/1.90094, loss_spatial_bce_9: 0.32705/0.33260, loss_spatial_dice_9: 0.84068/0.82156, loss_spatial_ce_9: 1.50702/1.49172, loss_grounding_bce_9: 0.10003/0.10562, loss_grounding_dice_9: 0.42944/0.28086, loss_grounding_ce_9: 1.11222/0.66888] items per batch[64] items per second[0.24] total items[5318400] mini batches[ 83100] memory[7345] epoch remaining[0:42:38]
INFO:trainer.default_trainer:epochs[    45] optim steps[83200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.11087/0.89397, loss_mask_bce_0: 0.06146/0.33369, loss_mask_dice_0: 0.18940/1.16114, loss_spatial_bce_0: 0.02831/0.08639, loss_spatial_dice_0: 0.11372/0.20610, loss_spatial_ce_0: 0.06939/0.05927, loss_grounding_bce_0: 0.04140/0.08606, loss_grounding_dice_0: 0.02126/0.17833, loss_grounding_ce_0: 0.00252/0.27106, loss_mask_ce_1: 0.10536/0.89473, loss_mask_bce_1: 0.06300/0.33461, loss_mask_dice_1: 0.27870/1.16803, loss_spatial_bce_1: 0.02816/0.08693, loss_spatial_dice_1: 0.09223/0.21003, loss_spatial_ce_1: 0.06938/0.06511, loss_grounding_bce_1: 0.04137/0.08626, loss_grounding_dice_1: 0.02105/0.17916, loss_grounding_ce_1: 0.00154/0.27195, loss_mask_ce_2: 0.16584/0.90163, loss_mask_bce_2: 0.06565/0.33528, loss_mask_dice_2: 0.37621/1.16845, loss_spatial_bce_2: 0.02920/0.08818, loss_spatial_dice_2: 0.09104/0.21195, loss_spatial_ce_2: 0.06941/0.06853, loss_grounding_bce_2: 0.04109/0.08644, loss_grounding_dice_2: 0.02169/0.17904, loss_grounding_ce_2: 0.00376/0.27531, loss_mask_ce_3: 0.11796/0.91298, loss_mask_bce_3: 0.06438/0.33646, loss_mask_dice_3: 0.24186/1.16642, loss_spatial_bce_3: 0.03041/0.08952, loss_spatial_dice_3: 0.09703/0.21303, loss_spatial_ce_3: 0.06957/0.07367, loss_grounding_bce_3: 0.04407/0.08669, loss_grounding_dice_3: 0.02259/0.17871, loss_grounding_ce_3: 0.00396/0.27751, loss_mask_ce_4: 0.11158/0.91448, loss_mask_bce_4: 0.06542/0.33861, loss_mask_dice_4: 0.21838/1.19019, loss_spatial_bce_4: 0.02770/0.09342, loss_spatial_dice_4: 0.08637/0.22525, loss_spatial_ce_4: 0.06981/0.08990, loss_grounding_bce_4: 0.04204/0.08725, loss_grounding_dice_4: 0.02104/0.18173, loss_grounding_ce_4: 0.00165/0.28039, loss_mask_ce_5: 0.08993/0.93121, loss_mask_bce_5: 0.06435/0.34104, loss_mask_dice_5: 0.32448/1.19845, loss_spatial_bce_5: 0.03078/0.09577, loss_spatial_dice_5: 0.07770/0.22967, loss_spatial_ce_5: 0.06965/0.10372, loss_grounding_bce_5: 0.04087/0.08767, loss_grounding_dice_5: 0.02071/0.18301, loss_grounding_ce_5: 0.00165/0.29316, loss_mask_ce_6: 0.11838/0.97152, loss_mask_bce_6: 0.06202/0.34387, loss_mask_dice_6: 0.26869/1.20150, loss_spatial_bce_6: 0.03558/0.10134, loss_spatial_dice_6: 0.09152/0.23267, loss_spatial_ce_6: 0.06943/0.12860, loss_grounding_bce_6: 0.04216/0.08844, loss_grounding_dice_6: 0.02149/0.18342, loss_grounding_ce_6: 0.00237/0.30838, loss_mask_ce_7: 0.09069/1.01736, loss_mask_bce_7: 0.06733/0.35170, loss_mask_dice_7: 0.22110/1.25586, loss_spatial_bce_7: 0.03182/0.10922, loss_spatial_dice_7: 0.13335/0.26028, loss_spatial_ce_7: 0.03554/0.16345, loss_grounding_bce_7: 0.03915/0.09030, loss_grounding_dice_7: 0.02059/0.19079, loss_grounding_ce_7: 0.00282/0.33839, loss_mask_ce_8: 0.12255/1.12616, loss_mask_bce_8: 0.07207/0.36526, loss_mask_dice_8: 0.26090/1.32842, loss_spatial_bce_8: 0.03751/0.12930, loss_spatial_dice_8: 0.18495/0.29795, loss_spatial_ce_8: 0.02506/0.21411, loss_grounding_bce_8: 0.04531/0.09396, loss_grounding_dice_8: 0.02472/0.20143, loss_grounding_ce_8: 0.00517/0.40430, loss_mask_ce_9: 2.04293/3.67322, loss_mask_bce_9: 0.07207/0.39234, loss_mask_dice_9: 0.47979/1.90079, loss_spatial_bce_9: 0.70885/0.33260, loss_spatial_dice_9: 0.75113/0.82156, loss_spatial_ce_9: 1.15462/1.49168, loss_grounding_bce_9: 0.05257/0.10562, loss_grounding_dice_9: 0.03796/0.28085, loss_grounding_ce_9: 0.16675/0.66885] items per batch[64] items per second[0.23] total items[5324800] mini batches[ 83200] memory[7345] epoch remaining[0:38:09]
INFO:trainer.default_trainer:epochs[    45] optim steps[83300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.84254/0.89399, loss_mask_bce_0: 0.52144/0.33368, loss_mask_dice_0: 1.21968/1.16094, loss_spatial_bce_0: 0.05884/0.08639, loss_spatial_dice_0: 0.12019/0.20609, loss_spatial_ce_0: 0.00038/0.05926, loss_grounding_bce_0: 0.14432/0.08607, loss_grounding_dice_0: 0.38369/0.17833, loss_grounding_ce_0: 0.34270/0.27102, loss_mask_ce_1: 0.85149/0.89474, loss_mask_bce_1: 0.51079/0.33460, loss_mask_dice_1: 1.19672/1.16786, loss_spatial_bce_1: 0.06165/0.08693, loss_spatial_dice_1: 0.13712/0.21001, loss_spatial_ce_1: 0.00041/0.06510, loss_grounding_bce_1: 0.13931/0.08626, loss_grounding_dice_1: 0.39534/0.17916, loss_grounding_ce_1: 0.34545/0.27192, loss_mask_ce_2: 0.88845/0.90164, loss_mask_bce_2: 0.52356/0.33527, loss_mask_dice_2: 1.21577/1.16829, loss_spatial_bce_2: 0.05998/0.08818, loss_spatial_dice_2: 0.13380/0.21194, loss_spatial_ce_2: 0.00042/0.06851, loss_grounding_bce_2: 0.14170/0.08645, loss_grounding_dice_2: 0.39757/0.17904, loss_grounding_ce_2: 0.39528/0.27527, loss_mask_ce_3: 0.95163/0.91300, loss_mask_bce_3: 0.52941/0.33644, loss_mask_dice_3: 1.17335/1.16623, loss_spatial_bce_3: 0.06428/0.08952, loss_spatial_dice_3: 0.13828/0.21302, loss_spatial_ce_3: 0.00074/0.07365, loss_grounding_bce_3: 0.14757/0.08670, loss_grounding_dice_3: 0.39361/0.17871, loss_grounding_ce_3: 0.42238/0.27747, loss_mask_ce_4: 0.98880/0.91452, loss_mask_bce_4: 0.54804/0.33860, loss_mask_dice_4: 1.27191/1.19001, loss_spatial_bce_4: 0.06547/0.09342, loss_spatial_dice_4: 0.15776/0.22524, loss_spatial_ce_4: 0.00036/0.08988, loss_grounding_bce_4: 0.14298/0.08726, loss_grounding_dice_4: 0.38170/0.18173, loss_grounding_ce_4: 0.63668/0.28036, loss_mask_ce_5: 0.98904/0.93123, loss_mask_bce_5: 0.53662/0.34103, loss_mask_dice_5: 1.22602/1.19827, loss_spatial_bce_5: 0.06247/0.09577, loss_spatial_dice_5: 0.16909/0.22966, loss_spatial_ce_5: 0.00093/0.10371, loss_grounding_bce_5: 0.13991/0.08767, loss_grounding_dice_5: 0.43428/0.18300, loss_grounding_ce_5: 0.35759/0.29316, loss_mask_ce_6: 1.27595/0.97151, loss_mask_bce_6: 0.51844/0.34385, loss_mask_dice_6: 1.26154/1.20133, loss_spatial_bce_6: 0.07055/0.10135, loss_spatial_dice_6: 0.16597/0.23265, loss_spatial_ce_6: 0.00197/0.12859, loss_grounding_bce_6: 0.13920/0.08844, loss_grounding_dice_6: 0.39358/0.18342, loss_grounding_ce_6: 0.27674/0.30835, loss_mask_ce_7: 1.21815/1.01735, loss_mask_bce_7: 0.54537/0.35168, loss_mask_dice_7: 1.24494/1.25564, loss_spatial_bce_7: 0.06901/0.10922, loss_spatial_dice_7: 0.19070/0.26027, loss_spatial_ce_7: 0.19204/0.16346, loss_grounding_bce_7: 0.13743/0.09030, loss_grounding_dice_7: 0.32015/0.19079, loss_grounding_ce_7: 0.49377/0.33836, loss_mask_ce_8: 1.29328/1.12619, loss_mask_bce_8: 0.52676/0.36524, loss_mask_dice_8: 1.37941/1.32819, loss_spatial_bce_8: 0.11323/0.12930, loss_spatial_dice_8: 0.29806/0.29795, loss_spatial_ce_8: 0.03590/0.21404, loss_grounding_bce_8: 0.12322/0.09396, loss_grounding_dice_8: 0.28894/0.20142, loss_grounding_ce_8: 0.56063/0.40427, loss_mask_ce_9: 5.17794/3.67317, loss_mask_bce_9: 0.73391/0.39233, loss_mask_dice_9: 2.73918/1.90065, loss_spatial_bce_9: 0.26722/0.33261, loss_spatial_dice_9: 0.88778/0.82154, loss_spatial_ce_9: 1.45012/1.49168, loss_grounding_bce_9: 0.17419/0.10562, loss_grounding_dice_9: 0.56408/0.28084, loss_grounding_ce_9: 0.11686/0.66878] items per batch[64] items per second[0.23] total items[5331200] mini batches[ 83300] memory[7345] epoch remaining[0:33:40]
INFO:trainer.default_trainer:epochs[    45] optim steps[83400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.02722/0.89398, loss_mask_bce_0: 0.04549/0.33369, loss_mask_dice_0: 1.28013/1.16093, loss_spatial_bce_0: 0.01757/0.08639, loss_spatial_dice_0: 0.30036/0.20608, loss_spatial_ce_0: 0.01195/0.05923, loss_grounding_bce_0: 0.02557/0.08607, loss_grounding_dice_0: 0.13542/0.17831, loss_grounding_ce_0: 0.03861/0.27106, loss_mask_ce_1: 1.19005/0.89470, loss_mask_bce_1: 0.04774/0.33461, loss_mask_dice_1: 1.58268/1.16784, loss_spatial_bce_1: 0.01646/0.08692, loss_spatial_dice_1: 0.30169/0.20999, loss_spatial_ce_1: 0.01520/0.06508, loss_grounding_bce_1: 0.02599/0.08627, loss_grounding_dice_1: 0.09246/0.17915, loss_grounding_ce_1: 0.03033/0.27194, loss_mask_ce_2: 1.01966/0.90161, loss_mask_bce_2: 0.04005/0.33527, loss_mask_dice_2: 1.24438/1.16828, loss_spatial_bce_2: 0.01614/0.08817, loss_spatial_dice_2: 0.31698/0.21192, loss_spatial_ce_2: 0.01782/0.06848, loss_grounding_bce_2: 0.02545/0.08645, loss_grounding_dice_2: 0.08850/0.17903, loss_grounding_ce_2: 0.03492/0.27531, loss_mask_ce_3: 1.05101/0.91298, loss_mask_bce_3: 0.03889/0.33645, loss_mask_dice_3: 1.27499/1.16622, loss_spatial_bce_3: 0.01790/0.08952, loss_spatial_dice_3: 0.28017/0.21301, loss_spatial_ce_3: 0.03824/0.07363, loss_grounding_bce_3: 0.02556/0.08670, loss_grounding_dice_3: 0.09265/0.17871, loss_grounding_ce_3: 0.03650/0.27750, loss_mask_ce_4: 0.76885/0.91449, loss_mask_bce_4: 0.03869/0.33861, loss_mask_dice_4: 1.31772/1.18998, loss_spatial_bce_4: 0.02112/0.09341, loss_spatial_dice_4: 0.34873/0.22522, loss_spatial_ce_4: 0.30952/0.08986, loss_grounding_bce_4: 0.02493/0.08726, loss_grounding_dice_4: 0.15297/0.18172, loss_grounding_ce_4: 0.02594/0.28039, loss_mask_ce_5: 0.82455/0.93123, loss_mask_bce_5: 0.04183/0.34103, loss_mask_dice_5: 1.56235/1.19824, loss_spatial_bce_5: 0.01942/0.09577, loss_spatial_dice_5: 0.33578/0.22964, loss_spatial_ce_5: 0.06763/0.10368, loss_grounding_bce_5: 0.02432/0.08767, loss_grounding_dice_5: 0.11051/0.18300, loss_grounding_ce_5: 0.02249/0.29319, loss_mask_ce_6: 0.95927/0.97149, loss_mask_bce_6: 0.03663/0.34386, loss_mask_dice_6: 1.34515/1.20131, loss_spatial_bce_6: 0.02631/0.10135, loss_spatial_dice_6: 0.33455/0.23264, loss_spatial_ce_6: 0.19579/0.12855, loss_grounding_bce_6: 0.02592/0.08844, loss_grounding_dice_6: 0.09325/0.18341, loss_grounding_ce_6: 0.02648/0.30841, loss_mask_ce_7: 1.52705/1.01742, loss_mask_bce_7: 0.04055/0.35168, loss_mask_dice_7: 1.31096/1.25561, loss_spatial_bce_7: 0.01893/0.10922, loss_spatial_dice_7: 0.35616/0.26026, loss_spatial_ce_7: 0.26568/0.16342, loss_grounding_bce_7: 0.02604/0.09030, loss_grounding_dice_7: 0.10018/0.19078, loss_grounding_ce_7: 0.02065/0.33840, loss_mask_ce_8: 1.01375/1.12622, loss_mask_bce_8: 0.04226/0.36524, loss_mask_dice_8: 1.07059/1.32814, loss_spatial_bce_8: 0.01925/0.12929, loss_spatial_dice_8: 0.40407/0.29794, loss_spatial_ce_8: 0.52190/0.21399, loss_grounding_bce_8: 0.02066/0.09396, loss_grounding_dice_8: 0.12338/0.20142, loss_grounding_ce_8: 0.04612/0.40433, loss_mask_ce_9: 2.53428/3.67327, loss_mask_bce_9: 0.04298/0.39233, loss_mask_dice_9: 1.52845/1.90062, loss_spatial_bce_9: 0.11782/0.33259, loss_spatial_dice_9: 0.68424/0.82154, loss_spatial_ce_9: 2.59356/1.49166, loss_grounding_bce_9: 0.02734/0.10562, loss_grounding_dice_9: 0.13621/0.28084, loss_grounding_ce_9: 0.09467/0.66894] items per batch[64] items per second[0.24] total items[5337600] mini batches[ 83400] memory[7345] epoch remaining[0:29:06]
INFO:trainer.default_trainer:epochs[    45] optim steps[83500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.80074/0.89385, loss_mask_bce_0: 0.08768/0.33364, loss_mask_dice_0: 0.66700/1.16095, loss_spatial_bce_0: 0.02774/0.08638, loss_spatial_dice_0: 0.27676/0.20606, loss_spatial_ce_0: 0.09263/0.05919, loss_grounding_bce_0: 0.03476/0.08605, loss_grounding_dice_0: 0.29913/0.17830, loss_grounding_ce_0: 0.63622/0.27100, loss_mask_ce_1: 0.77319/0.89458, loss_mask_bce_1: 0.10439/0.33456, loss_mask_dice_1: 0.62228/1.16785, loss_spatial_bce_1: 0.02628/0.08691, loss_spatial_dice_1: 0.23358/0.20998, loss_spatial_ce_1: 0.00623/0.06504, loss_grounding_bce_1: 0.02575/0.08625, loss_grounding_dice_1: 0.19663/0.17914, loss_grounding_ce_1: 0.45469/0.27186, loss_mask_ce_2: 0.65371/0.90149, loss_mask_bce_2: 0.10334/0.33522, loss_mask_dice_2: 0.71854/1.16830, loss_spatial_bce_2: 0.03159/0.08816, loss_spatial_dice_2: 0.26681/0.21190, loss_spatial_ce_2: 0.02910/0.06845, loss_grounding_bce_2: 0.02905/0.08643, loss_grounding_dice_2: 0.31176/0.17903, loss_grounding_ce_2: 0.28927/0.27526, loss_mask_ce_3: 0.76367/0.91290, loss_mask_bce_3: 0.10276/0.33640, loss_mask_dice_3: 0.67347/1.16625, loss_spatial_bce_3: 0.03399/0.08950, loss_spatial_dice_3: 0.28200/0.21299, loss_spatial_ce_3: 0.04142/0.07359, loss_grounding_bce_3: 0.02391/0.08668, loss_grounding_dice_3: 0.34598/0.17870, loss_grounding_ce_3: 0.14976/0.27744, loss_mask_ce_4: 0.79999/0.91440, loss_mask_bce_4: 0.10332/0.33856, loss_mask_dice_4: 0.67140/1.19000, loss_spatial_bce_4: 0.03580/0.09340, loss_spatial_dice_4: 0.26510/0.22520, loss_spatial_ce_4: 0.42017/0.08983, loss_grounding_bce_4: 0.02328/0.08724, loss_grounding_dice_4: 0.36074/0.18171, loss_grounding_ce_4: 0.15404/0.28032, loss_mask_ce_5: 0.81306/0.93114, loss_mask_bce_5: 0.08553/0.34098, loss_mask_dice_5: 0.70703/1.19825, loss_spatial_bce_5: 0.03647/0.09575, loss_spatial_dice_5: 0.28760/0.22963, loss_spatial_ce_5: 0.09854/0.10365, loss_grounding_bce_5: 0.02430/0.08766, loss_grounding_dice_5: 0.30401/0.18299, loss_grounding_ce_5: 0.16428/0.29312, loss_mask_ce_6: 0.90940/0.97139, loss_mask_bce_6: 0.08313/0.34381, loss_mask_dice_6: 0.64451/1.20135, loss_spatial_bce_6: 0.04681/0.10133, loss_spatial_dice_6: 0.32032/0.23262, loss_spatial_ce_6: 0.04691/0.12850, loss_grounding_bce_6: 0.02288/0.08843, loss_grounding_dice_6: 0.23575/0.18340, loss_grounding_ce_6: 0.41013/0.30833, loss_mask_ce_7: 1.04760/1.01733, loss_mask_bce_7: 0.09357/0.35163, loss_mask_dice_7: 0.64361/1.25563, loss_spatial_bce_7: 0.04741/0.10920, loss_spatial_dice_7: 0.32305/0.26025, loss_spatial_ce_7: 0.14800/0.16339, loss_grounding_bce_7: 0.02308/0.09028, loss_grounding_dice_7: 0.36591/0.19077, loss_grounding_ce_7: 0.18462/0.33833, loss_mask_ce_8: 0.85839/1.12610, loss_mask_bce_8: 0.12295/0.36520, loss_mask_dice_8: 0.80803/1.32816, loss_spatial_bce_8: 0.04672/0.12926, loss_spatial_dice_8: 0.33007/0.29793, loss_spatial_ce_8: 0.13238/0.21391, loss_grounding_bce_8: 0.03067/0.09394, loss_grounding_dice_8: 0.38345/0.20141, loss_grounding_ce_8: 0.15351/0.40426, loss_mask_ce_9: 2.11252/3.67315, loss_mask_bce_9: 0.12856/0.39228, loss_mask_dice_9: 0.97371/1.90057, loss_spatial_bce_9: 0.08565/0.33257, loss_spatial_dice_9: 0.80105/0.82154, loss_spatial_ce_9: 1.15074/1.49168, loss_grounding_bce_9: 0.05333/0.10560, loss_grounding_dice_9: 0.45520/0.28085, loss_grounding_ce_9: 0.58223/0.66885] items per batch[64] items per second[0.23] total items[5344000] mini batches[ 83500] memory[7345] epoch remaining[0:24:36]
INFO:trainer.default_trainer:epochs[    45] optim steps[83600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.15118/0.89380, loss_mask_bce_0: 0.78168/0.33362, loss_mask_dice_0: 1.07563/1.16091, loss_spatial_bce_0: 0.09123/0.08637, loss_spatial_dice_0: 0.16039/0.20605, loss_spatial_ce_0: 0.05338/0.05918, loss_grounding_bce_0: 0.13872/0.08604, loss_grounding_dice_0: 0.20839/0.17829, loss_grounding_ce_0: 0.14699/0.27102, loss_mask_ce_1: 1.17725/0.89451, loss_mask_bce_1: 0.73860/0.33454, loss_mask_dice_1: 1.04251/1.16785, loss_spatial_bce_1: 0.09514/0.08690, loss_spatial_dice_1: 0.17041/0.20996, loss_spatial_ce_1: 0.04541/0.06504, loss_grounding_bce_1: 0.13168/0.08624, loss_grounding_dice_1: 0.20004/0.17914, loss_grounding_ce_1: 0.14570/0.27189, loss_mask_ce_2: 1.13093/0.90144, loss_mask_bce_2: 0.75111/0.33521, loss_mask_dice_2: 1.05044/1.16830, loss_spatial_bce_2: 0.09981/0.08815, loss_spatial_dice_2: 0.17322/0.21189, loss_spatial_ce_2: 0.04748/0.06843, loss_grounding_bce_2: 0.13905/0.08642, loss_grounding_dice_2: 0.20287/0.17902, loss_grounding_ce_2: 0.14838/0.27527, loss_mask_ce_3: 1.17564/0.91285, loss_mask_bce_3: 0.76880/0.33638, loss_mask_dice_3: 1.07940/1.16625, loss_spatial_bce_3: 0.09688/0.08949, loss_spatial_dice_3: 0.16885/0.21298, loss_spatial_ce_3: 0.07681/0.07358, loss_grounding_bce_3: 0.14695/0.08667, loss_grounding_dice_3: 0.20035/0.17870, loss_grounding_ce_3: 0.16624/0.27746, loss_mask_ce_4: 1.17352/0.91435, loss_mask_bce_4: 0.76527/0.33855, loss_mask_dice_4: 1.04251/1.19004, loss_spatial_bce_4: 0.11516/0.09339, loss_spatial_dice_4: 0.19974/0.22519, loss_spatial_ce_4: 0.06610/0.08983, loss_grounding_bce_4: 0.14296/0.08723, loss_grounding_dice_4: 0.19782/0.18171, loss_grounding_ce_4: 0.16506/0.28036, loss_mask_ce_5: 1.15412/0.93112, loss_mask_bce_5: 0.84604/0.34098, loss_mask_dice_5: 1.11544/1.19827, loss_spatial_bce_5: 0.12426/0.09575, loss_spatial_dice_5: 0.18869/0.22962, loss_spatial_ce_5: 0.07679/0.10362, loss_grounding_bce_5: 0.14490/0.08765, loss_grounding_dice_5: 0.20603/0.18299, loss_grounding_ce_5: 0.15756/0.29318, loss_mask_ce_6: 1.20256/0.97137, loss_mask_bce_6: 0.84392/0.34380, loss_mask_dice_6: 1.15341/1.20134, loss_spatial_bce_6: 0.12663/0.10133, loss_spatial_dice_6: 0.19640/0.23261, loss_spatial_ce_6: 0.07657/0.12847, loss_grounding_bce_6: 0.14132/0.08841, loss_grounding_dice_6: 0.20975/0.18339, loss_grounding_ce_6: 0.18083/0.30837, loss_mask_ce_7: 1.29296/1.01731, loss_mask_bce_7: 0.81491/0.35162, loss_mask_dice_7: 1.03340/1.25563, loss_spatial_bce_7: 0.15246/0.10920, loss_spatial_dice_7: 0.21783/0.26024, loss_spatial_ce_7: 0.08273/0.16336, loss_grounding_bce_7: 0.13581/0.09027, loss_grounding_dice_7: 0.19684/0.19077, loss_grounding_ce_7: 0.22118/0.33837, loss_mask_ce_8: 1.17548/1.12612, loss_mask_bce_8: 0.79301/0.36519, loss_mask_dice_8: 1.02503/1.32818, loss_spatial_bce_8: 0.18910/0.12926, loss_spatial_dice_8: 0.25573/0.29792, loss_spatial_ce_8: 0.10240/0.21382, loss_grounding_bce_8: 0.14833/0.09393, loss_grounding_dice_8: 0.20084/0.20141, loss_grounding_ce_8: 0.17243/0.40428, loss_mask_ce_9: 5.28679/3.67325, loss_mask_bce_9: 0.99330/0.39227, loss_mask_dice_9: 2.24864/1.90060, loss_spatial_bce_9: 0.32252/0.33256, loss_spatial_dice_9: 0.90456/0.82154, loss_spatial_ce_9: 1.40949/1.49164, loss_grounding_bce_9: 0.26675/0.10560, loss_grounding_dice_9: 0.40655/0.28086, loss_grounding_ce_9: 0.40152/0.66880] items per batch[64] items per second[0.24] total items[5350400] mini batches[ 83600] memory[7345] epoch remaining[0:20:02]
INFO:trainer.default_trainer:epochs[    45] optim steps[83700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.46325/0.89373, loss_mask_bce_0: 0.09606/0.33364, loss_mask_dice_0: 0.60275/1.16102, loss_spatial_bce_0: 0.02777/0.08637, loss_spatial_dice_0: 0.18161/0.20604, loss_spatial_ce_0: 0.00357/0.05916, loss_grounding_bce_0: 0.03256/0.08605, loss_grounding_dice_0: 0.13562/0.17829, loss_grounding_ce_0: 0.04897/0.27103, loss_mask_ce_1: 0.40495/0.89442, loss_mask_bce_1: 0.09783/0.33456, loss_mask_dice_1: 0.60585/1.16797, loss_spatial_bce_1: 0.03090/0.08690, loss_spatial_dice_1: 0.19574/0.20995, loss_spatial_ce_1: 0.00285/0.06501, loss_grounding_bce_1: 0.03144/0.08625, loss_grounding_dice_1: 0.12820/0.17914, loss_grounding_ce_1: 0.05373/0.27189, loss_mask_ce_2: 0.39683/0.90136, loss_mask_bce_2: 0.10826/0.33523, loss_mask_dice_2: 0.63103/1.16841, loss_spatial_bce_2: 0.02942/0.08815, loss_spatial_dice_2: 0.18592/0.21188, loss_spatial_ce_2: 0.00202/0.06842, loss_grounding_bce_2: 0.03021/0.08643, loss_grounding_dice_2: 0.12385/0.17903, loss_grounding_ce_2: 0.04810/0.27525, loss_mask_ce_3: 0.77382/0.91280, loss_mask_bce_3: 0.14365/0.33640, loss_mask_dice_3: 0.58084/1.16637, loss_spatial_bce_3: 0.03248/0.08949, loss_spatial_dice_3: 0.18511/0.21297, loss_spatial_ce_3: 0.00256/0.07357, loss_grounding_bce_3: 0.03138/0.08668, loss_grounding_dice_3: 0.12748/0.17870, loss_grounding_ce_3: 0.05210/0.27745, loss_mask_ce_4: 0.39404/0.91429, loss_mask_bce_4: 0.12323/0.33857, loss_mask_dice_4: 0.59456/1.19016, loss_spatial_bce_4: 0.03567/0.09339, loss_spatial_dice_4: 0.21394/0.22518, loss_spatial_ce_4: 0.00634/0.08981, loss_grounding_bce_4: 0.02929/0.08724, loss_grounding_dice_4: 0.11602/0.18171, loss_grounding_ce_4: 0.17905/0.28034, loss_mask_ce_5: 0.73542/0.93106, loss_mask_bce_5: 0.12918/0.34100, loss_mask_dice_5: 0.57845/1.19839, loss_spatial_bce_5: 0.03425/0.09574, loss_spatial_dice_5: 0.19683/0.22960, loss_spatial_ce_5: 0.02620/0.10359, loss_grounding_bce_5: 0.03067/0.08766, loss_grounding_dice_5: 0.12550/0.18299, loss_grounding_ce_5: 0.15150/0.29316, loss_mask_ce_6: 0.76264/0.97131, loss_mask_bce_6: 0.15093/0.34382, loss_mask_dice_6: 0.65584/1.20146, loss_spatial_bce_6: 0.03395/0.10132, loss_spatial_dice_6: 0.19308/0.23260, loss_spatial_ce_6: 0.05202/0.12844, loss_grounding_bce_6: 0.03357/0.08842, loss_grounding_dice_6: 0.13849/0.18340, loss_grounding_ce_6: 0.10786/0.30836, loss_mask_ce_7: 0.98522/1.01726, loss_mask_bce_7: 0.12134/0.35165, loss_mask_dice_7: 0.65568/1.25577, loss_spatial_bce_7: 0.04364/0.10919, loss_spatial_dice_7: 0.23264/0.26023, loss_spatial_ce_7: 0.22136/0.16332, loss_grounding_bce_7: 0.03327/0.09028, loss_grounding_dice_7: 0.13544/0.19078, loss_grounding_ce_7: 0.16937/0.33833, loss_mask_ce_8: 0.82414/1.12609, loss_mask_bce_8: 0.13671/0.36522, loss_mask_dice_8: 0.68307/1.32829, loss_spatial_bce_8: 0.04537/0.12925, loss_spatial_dice_8: 0.25210/0.29791, loss_spatial_ce_8: 0.06016/0.21372, loss_grounding_bce_8: 0.03156/0.09395, loss_grounding_dice_8: 0.13114/0.20142, loss_grounding_ce_8: 0.30684/0.40424, loss_mask_ce_9: 3.14478/3.67320, loss_mask_bce_9: 0.17438/0.39229, loss_mask_dice_9: 1.02082/1.90084, loss_spatial_bce_9: 0.40728/0.33258, loss_spatial_dice_9: 0.86307/0.82156, loss_spatial_ce_9: 1.79246/1.49165, loss_grounding_bce_9: 0.04759/0.10562, loss_grounding_dice_9: 0.20825/0.28088, loss_grounding_ce_9: 0.58848/0.66871] items per batch[64] items per second[0.23] total items[5356800] mini batches[ 83700] memory[7345] epoch remaining[0:15:30]
INFO:trainer.default_trainer:epochs[    45] optim steps[83800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.90135/0.89355, loss_mask_bce_0: 0.52535/0.33365, loss_mask_dice_0: 0.87995/1.16112, loss_spatial_bce_0: 0.09447/0.08636, loss_spatial_dice_0: 0.13306/0.20602, loss_spatial_ce_0: 0.00015/0.05916, loss_grounding_bce_0: 0.05778/0.08605, loss_grounding_dice_0: 0.03264/0.17828, loss_grounding_ce_0: 0.08946/0.27101, loss_mask_ce_1: 1.00296/0.89424, loss_mask_bce_1: 0.51101/0.33457, loss_mask_dice_1: 0.83317/1.16802, loss_spatial_bce_1: 0.11036/0.08689, loss_spatial_dice_1: 0.14897/0.20993, loss_spatial_ce_1: 0.00024/0.06501, loss_grounding_bce_1: 0.05548/0.08625, loss_grounding_dice_1: 0.02773/0.17913, loss_grounding_ce_1: 0.08830/0.27188, loss_mask_ce_2: 0.87415/0.90120, loss_mask_bce_2: 0.52505/0.33524, loss_mask_dice_2: 0.87158/1.16849, loss_spatial_bce_2: 0.11807/0.08814, loss_spatial_dice_2: 0.16489/0.21186, loss_spatial_ce_2: 0.00046/0.06842, loss_grounding_bce_2: 0.05792/0.08643, loss_grounding_dice_2: 0.02944/0.17902, loss_grounding_ce_2: 0.06956/0.27523, loss_mask_ce_3: 0.91432/0.91262, loss_mask_bce_3: 0.55494/0.33642, loss_mask_dice_3: 0.87297/1.16644, loss_spatial_bce_3: 0.11535/0.08948, loss_spatial_dice_3: 0.14521/0.21295, loss_spatial_ce_3: 0.00174/0.07357, loss_grounding_bce_3: 0.05661/0.08668, loss_grounding_dice_3: 0.02877/0.17870, loss_grounding_ce_3: 0.06109/0.27744, loss_mask_ce_4: 0.82504/0.91412, loss_mask_bce_4: 0.52759/0.33858, loss_mask_dice_4: 0.90561/1.19023, loss_spatial_bce_4: 0.10574/0.09338, loss_spatial_dice_4: 0.14323/0.22516, loss_spatial_ce_4: 0.02772/0.08980, loss_grounding_bce_4: 0.05947/0.08724, loss_grounding_dice_4: 0.03116/0.18171, loss_grounding_ce_4: 0.06662/0.28033, loss_mask_ce_5: 0.87182/0.93090, loss_mask_bce_5: 0.50595/0.34101, loss_mask_dice_5: 0.90403/1.19848, loss_spatial_bce_5: 0.13795/0.09573, loss_spatial_dice_5: 0.16059/0.22958, loss_spatial_ce_5: 0.03333/0.10357, loss_grounding_bce_5: 0.05653/0.08766, loss_grounding_dice_5: 0.03216/0.18299, loss_grounding_ce_5: 0.08619/0.29315, loss_mask_ce_6: 0.88895/0.97117, loss_mask_bce_6: 0.54344/0.34383, loss_mask_dice_6: 0.90027/1.20152, loss_spatial_bce_6: 0.11878/0.10132, loss_spatial_dice_6: 0.15920/0.23258, loss_spatial_ce_6: 0.03045/0.12842, loss_grounding_bce_6: 0.05896/0.08842, loss_grounding_dice_6: 0.02971/0.18339, loss_grounding_ce_6: 0.15953/0.30834, loss_mask_ce_7: 0.99766/1.01712, loss_mask_bce_7: 0.48821/0.35166, loss_mask_dice_7: 0.88451/1.25585, loss_spatial_bce_7: 0.11991/0.10919, loss_spatial_dice_7: 0.19201/0.26021, loss_spatial_ce_7: 0.06328/0.16332, loss_grounding_bce_7: 0.06082/0.09028, loss_grounding_dice_7: 0.03813/0.19077, loss_grounding_ce_7: 0.34299/0.33831, loss_mask_ce_8: 1.27525/1.12598, loss_mask_bce_8: 0.53069/0.36523, loss_mask_dice_8: 0.96039/1.32838, loss_spatial_bce_8: 0.14704/0.12924, loss_spatial_dice_8: 0.19594/0.29790, loss_spatial_ce_8: 0.06310/0.21367, loss_grounding_bce_8: 0.07681/0.09395, loss_grounding_dice_8: 0.05022/0.20140, loss_grounding_ce_8: 0.85299/0.40423, loss_mask_ce_9: 4.90211/3.67318, loss_mask_bce_9: 0.70992/0.39231, loss_mask_dice_9: 1.61691/1.90091, loss_spatial_bce_9: 0.27977/0.33258, loss_spatial_dice_9: 0.89695/0.82155, loss_spatial_ce_9: 1.57883/1.49173, loss_grounding_bce_9: 0.20976/0.10562, loss_grounding_dice_9: 0.11695/0.28087, loss_grounding_ce_9: 1.16155/0.66868] items per batch[64] items per second[0.23] total items[5363200] mini batches[ 83800] memory[7345] epoch remaining[0:10:58]
INFO:trainer.default_trainer:epochs[    45] optim steps[83900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.80649/0.89343, loss_mask_bce_0: 0.58331/0.33362, loss_mask_dice_0: 1.65052/1.16106, loss_spatial_bce_0: 0.06915/0.08635, loss_spatial_dice_0: 0.20610/0.20601, loss_spatial_ce_0: 0.02050/0.05914, loss_grounding_bce_0: 0.17489/0.08605, loss_grounding_dice_0: 0.24373/0.17828, loss_grounding_ce_0: 0.31009/0.27097, loss_mask_ce_1: 0.60962/0.89410, loss_mask_bce_1: 0.58696/0.33454, loss_mask_dice_1: 1.83175/1.16797, loss_spatial_bce_1: 0.07073/0.08688, loss_spatial_dice_1: 0.21732/0.20992, loss_spatial_ce_1: 0.02243/0.06499, loss_grounding_bce_1: 0.19396/0.08625, loss_grounding_dice_1: 0.26839/0.17912, loss_grounding_ce_1: 0.28958/0.27183, loss_mask_ce_2: 0.76849/0.90108, loss_mask_bce_2: 0.59114/0.33520, loss_mask_dice_2: 1.70643/1.16843, loss_spatial_bce_2: 0.07066/0.08813, loss_spatial_dice_2: 0.20905/0.21185, loss_spatial_ce_2: 0.02803/0.06840, loss_grounding_bce_2: 0.18686/0.08643, loss_grounding_dice_2: 0.29161/0.17901, loss_grounding_ce_2: 0.30085/0.27519, loss_mask_ce_3: 0.71945/0.91248, loss_mask_bce_3: 0.58714/0.33638, loss_mask_dice_3: 1.85731/1.16639, loss_spatial_bce_3: 0.07587/0.08947, loss_spatial_dice_3: 0.22838/0.21295, loss_spatial_ce_3: 0.03464/0.07356, loss_grounding_bce_3: 0.18699/0.08668, loss_grounding_dice_3: 0.43312/0.17870, loss_grounding_ce_3: 0.31496/0.27740, loss_mask_ce_4: 0.83845/0.91399, loss_mask_bce_4: 0.56189/0.33855, loss_mask_dice_4: 1.79660/1.19016, loss_spatial_bce_4: 0.07891/0.09336, loss_spatial_dice_4: 0.25872/0.22515, loss_spatial_ce_4: 0.08029/0.08979, loss_grounding_bce_4: 0.18857/0.08724, loss_grounding_dice_4: 0.26414/0.18170, loss_grounding_ce_4: 0.38983/0.28029, loss_mask_ce_5: 0.73392/0.93077, loss_mask_bce_5: 0.57427/0.34097, loss_mask_dice_5: 1.82537/1.19842, loss_spatial_bce_5: 0.06741/0.09571, loss_spatial_dice_5: 0.21881/0.22957, loss_spatial_ce_5: 0.05386/0.10356, loss_grounding_bce_5: 0.20000/0.08766, loss_grounding_dice_5: 0.38712/0.18298, loss_grounding_ce_5: 0.22691/0.29311, loss_mask_ce_6: 0.64149/0.97104, loss_mask_bce_6: 0.59223/0.34379, loss_mask_dice_6: 1.99399/1.20147, loss_spatial_bce_6: 0.07369/0.10130, loss_spatial_dice_6: 0.21455/0.23257, loss_spatial_ce_6: 0.06340/0.12840, loss_grounding_bce_6: 0.18924/0.08842, loss_grounding_dice_6: 0.36738/0.18338, loss_grounding_ce_6: 0.53017/0.30829, loss_mask_ce_7: 0.74693/1.01699, loss_mask_bce_7: 0.54688/0.35162, loss_mask_dice_7: 1.87709/1.25577, loss_spatial_bce_7: 0.09036/0.10917, loss_spatial_dice_7: 0.24305/0.26020, loss_spatial_ce_7: 0.06980/0.16329, loss_grounding_bce_7: 0.18476/0.09028, loss_grounding_dice_7: 0.32741/0.19076, loss_grounding_ce_7: 0.45100/0.33826, loss_mask_ce_8: 0.84366/1.12583, loss_mask_bce_8: 0.57735/0.36518, loss_mask_dice_8: 1.81638/1.32831, loss_spatial_bce_8: 0.07916/0.12921, loss_spatial_dice_8: 0.26215/0.29789, loss_spatial_ce_8: 0.06786/0.21360, loss_grounding_bce_8: 0.19053/0.09394, loss_grounding_dice_8: 0.46324/0.20140, loss_grounding_ce_8: 0.87260/0.40414, loss_mask_ce_9: 4.85240/3.67295, loss_mask_bce_9: 0.61915/0.39227, loss_mask_dice_9: 3.22157/1.90080, loss_spatial_bce_9: 0.27012/0.33256, loss_spatial_dice_9: 0.89549/0.82156, loss_spatial_ce_9: 2.10093/1.49179, loss_grounding_bce_9: 0.16939/0.10562, loss_grounding_dice_9: 0.46212/0.28086, loss_grounding_ce_9: 0.86753/0.66869] items per batch[64] items per second[0.24] total items[5369600] mini batches[ 83900] memory[7345] epoch remaining[0:06:26]
INFO:trainer.default_trainer:epochs[    45] optim steps[84000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.32248/0.89339, loss_mask_bce_0: 0.63509/0.33359, loss_mask_dice_0: 2.51384/1.16094, loss_spatial_bce_0: 0.10271/0.08633, loss_spatial_dice_0: 0.24207/0.20600, loss_spatial_ce_0: 0.01581/0.05915, loss_grounding_bce_0: 0.14059/0.08604, loss_grounding_dice_0: 0.11138/0.17828, loss_grounding_ce_0: 0.10232/0.27098, loss_mask_ce_1: 1.35550/0.89406, loss_mask_bce_1: 0.61757/0.33451, loss_mask_dice_1: 2.50638/1.16786, loss_spatial_bce_1: 0.10317/0.08687, loss_spatial_dice_1: 0.24824/0.20990, loss_spatial_ce_1: 0.00422/0.06501, loss_grounding_bce_1: 0.13334/0.08624, loss_grounding_dice_1: 0.09906/0.17913, loss_grounding_ce_1: 0.14095/0.27186, loss_mask_ce_2: 1.27149/0.90104, loss_mask_bce_2: 0.61183/0.33517, loss_mask_dice_2: 2.52633/1.16832, loss_spatial_bce_2: 0.10217/0.08812, loss_spatial_dice_2: 0.25632/0.21184, loss_spatial_ce_2: 0.00547/0.06842, loss_grounding_bce_2: 0.13254/0.08642, loss_grounding_dice_2: 0.09545/0.17901, loss_grounding_ce_2: 0.16905/0.27519, loss_mask_ce_3: 1.35479/0.91244, loss_mask_bce_3: 0.61918/0.33635, loss_mask_dice_3: 2.48306/1.16628, loss_spatial_bce_3: 0.10532/0.08946, loss_spatial_dice_3: 0.25194/0.21294, loss_spatial_ce_3: 0.00980/0.07356, loss_grounding_bce_3: 0.14086/0.08667, loss_grounding_dice_3: 0.09828/0.17870, loss_grounding_ce_3: 0.18409/0.27740, loss_mask_ce_4: 1.32198/0.91395, loss_mask_bce_4: 0.61536/0.33852, loss_mask_dice_4: 2.45388/1.19002, loss_spatial_bce_4: 0.14814/0.09336, loss_spatial_dice_4: 0.29868/0.22514, loss_spatial_ce_4: 0.01802/0.08982, loss_grounding_bce_4: 0.13987/0.08723, loss_grounding_dice_4: 0.09863/0.18169, loss_grounding_ce_4: 0.35437/0.28032, loss_mask_ce_5: 1.43652/0.93075, loss_mask_bce_5: 0.59688/0.34095, loss_mask_dice_5: 2.50092/1.19830, loss_spatial_bce_5: 0.17397/0.09571, loss_spatial_dice_5: 0.30587/0.22956, loss_spatial_ce_5: 0.02414/0.10360, loss_grounding_bce_5: 0.14481/0.08765, loss_grounding_dice_5: 0.10870/0.18299, loss_grounding_ce_5: 0.60444/0.29313, loss_mask_ce_6: 1.49097/0.97099, loss_mask_bce_6: 0.63915/0.34376, loss_mask_dice_6: 2.46759/1.20133, loss_spatial_bce_6: 0.18502/0.10130, loss_spatial_dice_6: 0.31615/0.23256, loss_spatial_ce_6: 0.04160/0.12841, loss_grounding_bce_6: 0.31178/0.08841, loss_grounding_dice_6: 0.28822/0.18339, loss_grounding_ce_6: 0.09956/0.30830, loss_mask_ce_7: 1.46520/1.01694, loss_mask_bce_7: 0.67058/0.35159, loss_mask_dice_7: 2.68297/1.25565, loss_spatial_bce_7: 0.19869/0.10916, loss_spatial_dice_7: 0.36587/0.26020, loss_spatial_ce_7: 0.18670/0.16331, loss_grounding_bce_7: 0.33814/0.09027, loss_grounding_dice_7: 0.30771/0.19076, loss_grounding_ce_7: 0.07194/0.33826, loss_mask_ce_8: 1.64669/1.12581, loss_mask_bce_8: 0.74657/0.36515, loss_mask_dice_8: 2.73845/1.32816, loss_spatial_bce_8: 0.19198/0.12921, loss_spatial_dice_8: 0.42680/0.29789, loss_spatial_ce_8: 0.12442/0.21355, loss_grounding_bce_8: 0.36279/0.09393, loss_grounding_dice_8: 0.35675/0.20140, loss_grounding_ce_8: 0.02861/0.40414, loss_mask_ce_9: 5.08596/3.67291, loss_mask_bce_9: 0.73755/0.39225, loss_mask_dice_9: 4.21201/1.90066, loss_spatial_bce_9: 0.30292/0.33255, loss_spatial_dice_9: 0.89132/0.82155, loss_spatial_ce_9: 1.51886/1.49172, loss_grounding_bce_9: 0.27339/0.10561, loss_grounding_dice_9: 0.24649/0.28087, loss_grounding_ce_9: 0.84258/0.66867] items per batch[64] items per second[0.23] total items[5376000] mini batches[ 84000] memory[7345] epoch remaining[0:01:54]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00084042.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0024 s/iter. Inference: 0.2146 s/iter. Eval: 0.0988 s/iter. Total: 0.3158 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0028 s/iter. Inference: 0.2194 s/iter. Eval: 0.0815 s/iter. Total: 0.3039 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 43/157. Dataloading: 0.0030 s/iter. Inference: 0.2222 s/iter. Eval: 0.0980 s/iter. Total: 0.3233 s/iter. ETA=0:00:36
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 60/157. Dataloading: 0.0030 s/iter. Inference: 0.2229 s/iter. Eval: 0.0897 s/iter. Total: 0.3158 s/iter. ETA=0:00:30
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/157. Dataloading: 0.0031 s/iter. Inference: 0.2213 s/iter. Eval: 0.0855 s/iter. Total: 0.3100 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2233 s/iter. Eval: 0.0836 s/iter. Total: 0.3101 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2259 s/iter. Eval: 0.0826 s/iter. Total: 0.3118 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0031 s/iter. Inference: 0.2255 s/iter. Eval: 0.0816 s/iter. Total: 0.3103 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2259 s/iter. Eval: 0.0809 s/iter. Total: 0.3101 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval95jydl_2 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.461 | 81.974 | 60.691 |      133      |
| Things | 55.547 | 82.597 | 66.556 |      80       |
| Stuff  | 42.784 | 81.034 | 51.839 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.38s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.47 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.27s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.02 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.396
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.622
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.612
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.496
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.514
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.719
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.11 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.604 | 62.162 | 41.806 | 19.447 | 42.548 | 61.204 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.661 | bicycle      | 19.690 | car            | 37.617 |
| motorcycle    | 35.172 | airplane     | 56.893 | bus            | 65.208 |
| train         | 68.348 | truck        | 35.608 | boat           | 23.854 |
| traffic light | 26.352 | fire hydrant | 66.345 | stop sign      | 65.116 |
| parking meter | 43.556 | bench        | 20.442 | bird           | 29.564 |
| cat           | 74.320 | dog          | 66.142 | horse          | 45.828 |
| sheep         | 48.087 | cow          | 51.964 | elephant       | 60.874 |
| bear          | 77.579 | zebra        | 60.907 | giraffe        | 57.366 |
| backpack      | 17.089 | umbrella     | 49.473 | handbag        | 15.965 |
| tie           | 34.641 | suitcase     | 42.505 | frisbee        | 67.317 |
| skis          | 5.575  | snowboard    | 22.448 | sports ball    | 47.959 |
| kite          | 35.641 | baseball bat | 29.085 | baseball glove | 43.779 |
| skateboard    | 37.220 | surfboard    | 37.060 | tennis racket  | 56.785 |
| bottle        | 35.157 | wine glass   | 27.684 | cup            | 41.477 |
| fork          | 16.408 | knife        | 14.222 | spoon          | 14.282 |
| bowl          | 32.763 | banana       | 21.488 | apple          | 20.404 |
| sandwich      | 43.245 | orange       | 29.748 | broccoli       | 22.318 |
| carrot        | 21.394 | hot dog      | 23.166 | pizza          | 51.495 |
| donut         | 47.037 | cake         | 45.522 | chair          | 21.721 |
| couch         | 42.082 | potted plant | 18.644 | bed            | 40.767 |
| dining table  | 13.281 | toilet       | 67.348 | tv             | 62.475 |
| laptop        | 63.386 | mouse        | 59.094 | remote         | 31.833 |
| keyboard      | 48.480 | cell phone   | 38.494 | microwave      | 55.801 |
| oven          | 33.137 | toaster      | 35.721 | sink           | 37.557 |
| refrigerator  | 59.355 | book         | 9.432  | clock          | 52.577 |
| vase          | 35.447 | scissors     | 26.965 | teddy bear     | 51.668 |
| hair drier    | 11.143 | toothbrush   | 20.037 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.88806901636217, 'fwIoU': 69.10082091885037, 'IoU-person': 87.54637287834447, 'IoU-bicycle': 74.8058267912697, 'IoU-car': 71.09205807790867, 'IoU-motorcycle': 78.19909386755329, 'IoU-airplane': 84.28097524677995, 'IoU-bus': 83.61053623802333, 'IoU-train': 85.43963954472886, 'IoU-truck': 63.07362488511992, 'IoU-boat': 67.93794110867282, 'IoU-traffic light': 76.00417628001576, 'IoU-fire hydrant': 90.26916503721411, 'IoU-stop sign': 92.38668119892397, 'IoU-parking meter': 87.99722525022297, 'IoU-bench': 53.1319357542559, 'IoU-bird': 75.89379055026454, 'IoU-cat': 77.70387184609466, 'IoU-dog': 80.62889495378164, 'IoU-horse': 86.3944515992862, 'IoU-sheep': 85.32630067081904, 'IoU-cow': 81.44470702299412, 'IoU-elephant': 89.50576494454108, 'IoU-bear': 79.77333836938544, 'IoU-zebra': 89.960948870048, 'IoU-giraffe': 85.11419291405183, 'IoU-backpack': 40.8267121938407, 'IoU-umbrella': 77.13139002109412, 'IoU-handbag': 38.22014481359914, 'IoU-tie': 70.3962345524026, 'IoU-suitcase': 80.74593861253759, 'IoU-frisbee': 83.74012677847419, 'IoU-skis': 52.03906751122515, 'IoU-snowboard': 69.2930297561122, 'IoU-sports ball': 67.72834029227558, 'IoU-kite': 66.39627431774157, 'IoU-baseball bat': 60.38399149951111, 'IoU-baseball glove': 52.468488698111805, 'IoU-skateboard': 64.14173775188142, 'IoU-surfboard': 81.22874036814743, 'IoU-tennis racket': 83.02286794134456, 'IoU-bottle': 69.07533235545728, 'IoU-wine glass': 72.73845247970951, 'IoU-cup': 65.57787113099185, 'IoU-fork': 56.105040697718934, 'IoU-knife': 51.695027222830845, 'IoU-spoon': 50.53878463723446, 'IoU-bowl': 51.6500362931466, 'IoU-banana': 81.69407444922766, 'IoU-apple': 59.68090434789619, 'IoU-sandwich': 67.72934361309082, 'IoU-orange': 74.93866971910353, 'IoU-broccoli': 65.25906262691036, 'IoU-carrot': 64.23357889335163, 'IoU-hot dog': 64.10080676172832, 'IoU-pizza': 83.06129864983268, 'IoU-donut': 64.88801510375055, 'IoU-cake': 68.69275162810399, 'IoU-chair': 54.99115230316842, 'IoU-couch': 68.83209732912967, 'IoU-potted plant': 33.735604789190226, 'IoU-bed': 68.08018680678167, 'IoU-dining table': 51.22776493571084, 'IoU-toilet': 81.68476459611426, 'IoU-tv': 74.63991435251418, 'IoU-laptop': 70.64747445060131, 'IoU-mouse': 70.59551787812659, 'IoU-remote': 49.51760873662533, 'IoU-keyboard': 59.22189160607195, 'IoU-cell phone': 70.49774339318155, 'IoU-microwave': 66.91511048285098, 'IoU-oven': 66.8946551657153, 'IoU-toaster': 71.98205350269437, 'IoU-sink': 69.94961522664697, 'IoU-refrigerator': 79.41869395540793, 'IoU-book': 52.068320866121475, 'IoU-clock': 75.02423465822143, 'IoU-vase': 59.454515429772904, 'IoU-scissors': 52.924096646339194, 'IoU-teddy bear': 78.53131952339166, 'IoU-hair drier': 39.44774815985362, 'IoU-toothbrush': 59.23730201731286, 'IoU-banner': 37.77365716669368, 'IoU-blanket': 10.696679182857073, 'IoU-bridge': 37.28967236767124, 'IoU-cardboard': 44.04322968240885, 'IoU-counter': 28.96844228454257, 'IoU-curtain': 64.93111776747412, 'IoU-door-stuff': 42.417834984980516, 'IoU-floor-wood': 63.704125569077455, 'IoU-flower': 43.620798952193844, 'IoU-fruit': 41.39371044080566, 'IoU-gravel': 29.21887677109558, 'IoU-house': 25.87565981210903, 'IoU-light': 38.82600874303087, 'IoU-mirror-stuff': 55.96191806587313, 'IoU-net': 42.89410265432862, 'IoU-pillow': 11.229698861844623, 'IoU-platform': 30.654550431471232, 'IoU-playingfield': 70.85428546209545, 'IoU-railroad': 61.129460151141224, 'IoU-river': 47.06004772422089, 'IoU-road': 66.2839740291276, 'IoU-roof': 15.573063844751548, 'IoU-sand': 64.22621855898055, 'IoU-sea': 85.82100134723535, 'IoU-shelf': 36.306844092753465, 'IoU-snow': 88.39925925962278, 'IoU-stairs': 24.864134681347554, 'IoU-tent': 9.884116002572158, 'IoU-towel': 36.737544946864745, 'IoU-wall-brick': 46.565725215110604, 'IoU-wall-stone': 30.10005304855247, 'IoU-wall-tile': 68.17471022268006, 'IoU-wall-wood': 39.89898885797399, 'IoU-water-other': 25.185942132651068, 'IoU-window-blind': 48.099478987028355, 'IoU-window-other': 47.03092371493482, 'IoU-tree-merged': 80.91556712775306, 'IoU-fence-merged': 50.44312282321164, 'IoU-ceiling-merged': 66.77024429816535, 'IoU-sky-other-merged': 93.6860940675903, 'IoU-cabinet-merged': 60.495055324189984, 'IoU-table-merged': 37.68324633424264, 'IoU-floor-other-merged': 49.22801832721973, 'IoU-pavement-merged': 54.07018609583122, 'IoU-mountain-merged': 55.73333595476784, 'IoU-grass-merged': 70.9420457776173, 'IoU-dirt-merged': 44.727842528205706, 'IoU-paper-merged': 32.44933189264748, 'IoU-food-other-merged': 38.676703832224675, 'IoU-building-other-merged': 58.530347479398806, 'IoU-rock-merged': 61.901302653885004, 'IoU-wall-other-merged': 64.8243869148551, 'IoU-rug-merged': 62.87745529602984, 'mACC': 73.20968064470524, 'pACC': 80.39047794379648, 'ACC-person': 92.48681845749026, 'ACC-bicycle': 85.2202974794715, 'ACC-car': 86.22794762908643, 'ACC-motorcycle': 84.83992271591845, 'ACC-airplane': 90.42282672739461, 'ACC-bus': 90.402354922571, 'ACC-train': 93.98911459581642, 'ACC-truck': 75.820194421507, 'ACC-boat': 78.81019069390217, 'ACC-traffic light': 90.33548357815708, 'ACC-fire hydrant': 95.46041197846165, 'ACC-stop sign': 94.94861808609326, 'ACC-parking meter': 92.06432725043642, 'ACC-bench': 74.06717500217029, 'ACC-bird': 80.43361494872717, 'ACC-cat': 85.41702747915802, 'ACC-dog': 85.01897663855388, 'ACC-horse': 92.10273253975971, 'ACC-sheep': 88.65403750703948, 'ACC-cow': 86.6560043071312, 'ACC-elephant': 92.06937112648593, 'ACC-bear': 81.91903514858674, 'ACC-zebra': 92.38474036222894, 'ACC-giraffe': 89.0518866824012, 'ACC-backpack': 57.21575621598408, 'ACC-umbrella': 85.31905283396074, 'ACC-handbag': 56.03630303236883, 'ACC-tie': 80.13670061389462, 'ACC-suitcase': 88.8918349001248, 'ACC-frisbee': 94.10654545454545, 'ACC-skis': 70.7639260367227, 'ACC-snowboard': 79.17268741887291, 'ACC-sports ball': 80.48345582534809, 'ACC-kite': 76.09369769367656, 'ACC-baseball bat': 80.04705212446336, 'ACC-baseball glove': 60.94802677561373, 'ACC-skateboard': 69.64453231952817, 'ACC-surfboard': 89.94443791355718, 'ACC-tennis racket': 89.39802940334991, 'ACC-bottle': 83.55879036621975, 'ACC-wine glass': 86.01222099327302, 'ACC-cup': 84.12188777615506, 'ACC-fork': 67.96344191410519, 'ACC-knife': 68.80889572799883, 'ACC-spoon': 68.74231905859916, 'ACC-bowl': 64.63660991399558, 'ACC-banana': 88.87572353941529, 'ACC-apple': 71.47883477661563, 'ACC-sandwich': 79.90753004115639, 'ACC-orange': 83.94345853498135, 'ACC-broccoli': 77.4260060700219, 'ACC-carrot': 75.78794393026651, 'ACC-hot dog': 71.73194497099954, 'ACC-pizza': 90.93253850694866, 'ACC-donut': 81.15550463800764, 'ACC-cake': 76.17763916511538, 'ACC-chair': 70.08182138785318, 'ACC-couch': 82.15476209377364, 'ACC-potted plant': 51.14981059901069, 'ACC-bed': 78.37775562307095, 'ACC-dining table': 73.63134591780197, 'ACC-toilet': 91.14443463807633, 'ACC-tv': 87.97085142988551, 'ACC-laptop': 82.5864644236422, 'ACC-mouse': 84.95431245017298, 'ACC-remote': 72.71191327998099, 'ACC-keyboard': 68.85712898546693, 'ACC-cell phone': 76.49155461661208, 'ACC-microwave': 76.83986419409241, 'ACC-oven': 83.38582097973793, 'ACC-toaster': 81.89930066101904, 'ACC-sink': 84.44799586018831, 'ACC-refrigerator': 89.08778707959429, 'ACC-book': 69.12968255302458, 'ACC-clock': 81.4871857907307, 'ACC-vase': 69.43155649373901, 'ACC-scissors': 56.96590731711796, 'ACC-teddy bear': 84.92595633180015, 'ACC-hair drier': 54.000512338826745, 'ACC-toothbrush': 81.67564280750521, 'ACC-banner': 76.32028224160811, 'ACC-blanket': 16.784548366900566, 'ACC-bridge': 56.791176112064846, 'ACC-cardboard': 55.57001599534681, 'ACC-counter': 55.20404991535054, 'ACC-curtain': 76.61115394755187, 'ACC-door-stuff': 64.37351283223975, 'ACC-floor-wood': 79.43548274801167, 'ACC-flower': 62.708176950093765, 'ACC-fruit': 58.52215142487294, 'ACC-gravel': 43.180424967499945, 'ACC-house': 32.38579893129791, 'ACC-light': 58.26068024346361, 'ACC-mirror-stuff': 69.99321975874935, 'ACC-net': 62.49642503209157, 'ACC-pillow': 25.476975551290547, 'ACC-platform': 48.1973411201957, 'ACC-playingfield': 92.43487047332495, 'ACC-railroad': 78.43654071818422, 'ACC-river': 65.18069953890915, 'ACC-road': 85.3341290230274, 'ACC-roof': 21.778818141443278, 'ACC-sand': 70.48316508709058, 'ACC-sea': 91.72789155305493, 'ACC-shelf': 57.65487343673723, 'ACC-snow': 94.94307384918834, 'ACC-stairs': 39.75292685798207, 'ACC-tent': 11.329989000924257, 'ACC-towel': 45.954445425316244, 'ACC-wall-brick': 64.75784539903677, 'ACC-wall-stone': 37.263414581628695, 'ACC-wall-tile': 81.6224211815274, 'ACC-wall-wood': 53.49134734148306, 'ACC-water-other': 43.11566565807743, 'ACC-window-blind': 57.97072191323387, 'ACC-window-other': 68.29979808232368, 'ACC-tree-merged': 89.33471790922702, 'ACC-fence-merged': 68.99578414583682, 'ACC-ceiling-merged': 81.10812758549383, 'ACC-sky-other-merged': 96.51946536263632, 'ACC-cabinet-merged': 75.00432966638193, 'ACC-table-merged': 52.64715800684311, 'ACC-floor-other-merged': 62.039898572687655, 'ACC-pavement-merged': 66.71640007783928, 'ACC-mountain-merged': 66.52073040723894, 'ACC-grass-merged': 83.56680798778821, 'ACC-dirt-merged': 64.85246644927285, 'ACC-paper-merged': 45.30203184603819, 'ACC-food-other-merged': 54.51806122218412, 'ACC-building-other-merged': 72.82135345855785, 'ACC-rock-merged': 82.39603040300376, 'ACC-wall-other-merged': 81.18389377150822, 'ACC-rug-merged': 77.86040885497682})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1570 s/iter. Inference: 0.4670 s/iter. Eval: 0.0000 s/iter. Total: 0.6241 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1580 s/iter. Inference: 0.4774 s/iter. Eval: 0.0000 s/iter. Total: 0.6355 s/iter. ETA=0:00:19
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1724 s/iter. Inference: 0.5676 s/iter. Eval: 0.0000 s/iter. Total: 0.7401 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1720 s/iter. Inference: 0.6366 s/iter. Eval: 0.0000 s/iter. Total: 0.8088 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1699 s/iter. Inference: 0.5937 s/iter. Eval: 0.0000 s/iter. Total: 0.7637 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1686 s/iter. Inference: 0.6382 s/iter. Eval: 0.0000 s/iter. Total: 0.8070 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4457126134035705, 'noc@0.8': 2.684225929177641, 'noc@0.85': 3.269827333918642, 'noc@0.9': 4.275095112671934, 'miou@iter1': 0.835810301001741}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0014 s/iter. Inference: 0.1018 s/iter. Eval: 0.0008 s/iter. Total: 0.1040 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.00660705566406, 'precision@0.6': 68.63583374023438, 'precision@0.7': 63.46677017211914, 'precision@0.8': 53.71162033081055, 'precision@0.9': 27.399921417236328, 'cIoU': 57.722557067871094, 'mIoU': 63.19989013671875}
INFO:trainer.default_trainer:This epoch takes 1:26:11.038482
INFO:trainer.default_trainer:PROGRESS: 92.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 46 training.
INFO:trainer.default_trainer:epochs[    46] optim steps[84100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.59616/0.89343, loss_mask_bce_0: 0.33457/0.33362, loss_mask_dice_0: 2.73707/1.16109, loss_spatial_bce_0: 0.02170/0.08633, loss_spatial_dice_0: 0.24198/0.20600, loss_spatial_ce_0: 0.13906/0.05914, loss_grounding_bce_0: 0.02048/0.08604, loss_grounding_dice_0: 0.31433/0.17829, loss_grounding_ce_0: 0.35497/0.27102, loss_mask_ce_1: 0.81121/0.89408, loss_mask_bce_1: 0.33237/0.33453, loss_mask_dice_1: 2.50834/1.16800, loss_spatial_bce_1: 0.02381/0.08686, loss_spatial_dice_1: 0.25808/0.20990, loss_spatial_ce_1: 0.02159/0.06499, loss_grounding_bce_1: 0.02028/0.08625, loss_grounding_dice_1: 0.32876/0.17914, loss_grounding_ce_1: 0.37106/0.27190, loss_mask_ce_2: 0.79633/0.90108, loss_mask_bce_2: 0.33668/0.33519, loss_mask_dice_2: 2.46722/1.16845, loss_spatial_bce_2: 0.02426/0.08811, loss_spatial_dice_2: 0.27090/0.21183, loss_spatial_ce_2: 0.01938/0.06840, loss_grounding_bce_2: 0.02007/0.08643, loss_grounding_dice_2: 0.34076/0.17902, loss_grounding_ce_2: 0.35518/0.27525, loss_mask_ce_3: 0.69503/0.91248, loss_mask_bce_3: 0.32763/0.33638, loss_mask_dice_3: 2.51026/1.16640, loss_spatial_bce_3: 0.02236/0.08945, loss_spatial_dice_3: 0.25583/0.21293, loss_spatial_ce_3: 0.01847/0.07356, loss_grounding_bce_3: 0.01981/0.08668, loss_grounding_dice_3: 0.31537/0.17871, loss_grounding_ce_3: 0.36730/0.27744, loss_mask_ce_4: 0.82610/0.91399, loss_mask_bce_4: 0.31691/0.33855, loss_mask_dice_4: 2.68083/1.19018, loss_spatial_bce_4: 0.02398/0.09335, loss_spatial_dice_4: 0.26515/0.22514, loss_spatial_ce_4: 0.04806/0.08981, loss_grounding_bce_4: 0.01825/0.08724, loss_grounding_dice_4: 0.32438/0.18171, loss_grounding_ce_4: 0.32658/0.28036, loss_mask_ce_5: 0.83383/0.93082, loss_mask_bce_5: 0.31689/0.34097, loss_mask_dice_5: 2.42637/1.19844, loss_spatial_bce_5: 0.02617/0.09570, loss_spatial_dice_5: 0.26938/0.22956, loss_spatial_ce_5: 0.08595/0.10357, loss_grounding_bce_5: 0.02093/0.08765, loss_grounding_dice_5: 0.34349/0.18300, loss_grounding_ce_5: 0.35475/0.29315, loss_mask_ce_6: 0.97453/0.97104, loss_mask_bce_6: 0.31870/0.34378, loss_mask_dice_6: 2.62903/1.20148, loss_spatial_bce_6: 0.02839/0.10130, loss_spatial_dice_6: 0.25867/0.23256, loss_spatial_ce_6: 0.04679/0.12838, loss_grounding_bce_6: 0.02175/0.08841, loss_grounding_dice_6: 0.34150/0.18340, loss_grounding_ce_6: 0.36333/0.30834, loss_mask_ce_7: 0.93015/1.01698, loss_mask_bce_7: 0.32403/0.35161, loss_mask_dice_7: 2.76443/1.25580, loss_spatial_bce_7: 0.06706/0.10916, loss_spatial_dice_7: 0.32099/0.26019, loss_spatial_ce_7: 0.09524/0.16328, loss_grounding_bce_7: 0.02460/0.09027, loss_grounding_dice_7: 0.35639/0.19079, loss_grounding_ce_7: 0.38951/0.33829, loss_mask_ce_8: 0.85480/1.12587, loss_mask_bce_8: 0.35910/0.36517, loss_mask_dice_8: 3.14257/1.32831, loss_spatial_bce_8: 0.10549/0.12920, loss_spatial_dice_8: 0.40984/0.29789, loss_spatial_ce_8: 0.16865/0.21348, loss_grounding_bce_8: 0.02600/0.09393, loss_grounding_dice_8: 0.37023/0.20142, loss_grounding_ce_8: 0.50731/0.40417, loss_mask_ce_9: 4.00726/3.67287, loss_mask_bce_9: 0.31555/0.39226, loss_mask_dice_9: 3.87700/1.90087, loss_spatial_bce_9: 0.14083/0.33253, loss_spatial_dice_9: 0.94180/0.82157, loss_spatial_ce_9: 2.34098/1.49171, loss_grounding_bce_9: 0.02241/0.10562, loss_grounding_dice_9: 0.47799/0.28090, loss_grounding_ce_9: 1.05327/0.66864] items per batch[64] items per second[0.14] total items[5382400] mini batches[ 84100] memory[7345] epoch remaining[1:23:40]
INFO:trainer.default_trainer:epochs[    46] optim steps[84200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.50910/0.89332, loss_mask_bce_0: 0.29103/0.33360, loss_mask_dice_0: 0.76438/1.16099, loss_spatial_bce_0: 0.07715/0.08632, loss_spatial_dice_0: 0.17505/0.20598, loss_spatial_ce_0: 0.00625/0.05912, loss_grounding_bce_0: 0.05441/0.08604, loss_grounding_dice_0: 0.11962/0.17827, loss_grounding_ce_0: 0.07102/0.27100, loss_mask_ce_1: 0.60920/0.89400, loss_mask_bce_1: 0.28316/0.33451, loss_mask_dice_1: 0.79587/1.16790, loss_spatial_bce_1: 0.07703/0.08685, loss_spatial_dice_1: 0.17562/0.20988, loss_spatial_ce_1: 0.00340/0.06498, loss_grounding_bce_1: 0.05459/0.08624, loss_grounding_dice_1: 0.12421/0.17912, loss_grounding_ce_1: 0.07101/0.27189, loss_mask_ce_2: 0.57996/0.90099, loss_mask_bce_2: 0.29516/0.33517, loss_mask_dice_2: 0.81275/1.16835, loss_spatial_bce_2: 0.07871/0.08810, loss_spatial_dice_2: 0.18443/0.21181, loss_spatial_ce_2: 0.00476/0.06837, loss_grounding_bce_2: 0.05480/0.08642, loss_grounding_dice_2: 0.11977/0.17900, loss_grounding_ce_2: 0.07101/0.27523, loss_mask_ce_3: 0.60697/0.91239, loss_mask_bce_3: 0.29697/0.33636, loss_mask_dice_3: 0.71591/1.16632, loss_spatial_bce_3: 0.07348/0.08944, loss_spatial_dice_3: 0.17771/0.21291, loss_spatial_ce_3: 0.01434/0.07354, loss_grounding_bce_3: 0.05139/0.08668, loss_grounding_dice_3: 0.11217/0.17869, loss_grounding_ce_3: 0.07096/0.27742, loss_mask_ce_4: 0.54584/0.91392, loss_mask_bce_4: 0.30757/0.33853, loss_mask_dice_4: 0.79119/1.19010, loss_spatial_bce_4: 0.07756/0.09334, loss_spatial_dice_4: 0.19181/0.22512, loss_spatial_ce_4: 0.01295/0.08980, loss_grounding_bce_4: 0.04873/0.08723, loss_grounding_dice_4: 0.11530/0.18168, loss_grounding_ce_4: 0.07343/0.28032, loss_mask_ce_5: 0.49445/0.93073, loss_mask_bce_5: 0.31058/0.34095, loss_mask_dice_5: 0.79652/1.19833, loss_spatial_bce_5: 0.06772/0.09569, loss_spatial_dice_5: 0.19290/0.22954, loss_spatial_ce_5: 0.05257/0.10355, loss_grounding_bce_5: 0.05208/0.08765, loss_grounding_dice_5: 0.12066/0.18298, loss_grounding_ce_5: 0.07110/0.29311, loss_mask_ce_6: 0.59784/0.97095, loss_mask_bce_6: 0.30132/0.34375, loss_mask_dice_6: 0.83450/1.20139, loss_spatial_bce_6: 0.06627/0.10129, loss_spatial_dice_6: 0.19221/0.23254, loss_spatial_ce_6: 0.13285/0.12834, loss_grounding_bce_6: 0.05044/0.08841, loss_grounding_dice_6: 0.12610/0.18338, loss_grounding_ce_6: 0.07120/0.30830, loss_mask_ce_7: 0.67010/1.01689, loss_mask_bce_7: 0.26904/0.35158, loss_mask_dice_7: 0.81811/1.25568, loss_spatial_bce_7: 0.07632/0.10915, loss_spatial_dice_7: 0.21216/0.26018, loss_spatial_ce_7: 0.12044/0.16326, loss_grounding_bce_7: 0.05188/0.09027, loss_grounding_dice_7: 0.12622/0.19076, loss_grounding_ce_7: 0.07673/0.33822, loss_mask_ce_8: 0.75667/1.12580, loss_mask_bce_8: 0.27053/0.36513, loss_mask_dice_8: 0.89017/1.32818, loss_spatial_bce_8: 0.09915/0.12918, loss_spatial_dice_8: 0.23737/0.29787, loss_spatial_ce_8: 0.16891/0.21341, loss_grounding_bce_8: 0.05166/0.09392, loss_grounding_dice_8: 0.12185/0.20140, loss_grounding_ce_8: 0.07989/0.40412, loss_mask_ce_9: 2.86743/3.67278, loss_mask_bce_9: 0.31744/0.39223, loss_mask_dice_9: 1.12103/1.90066, loss_spatial_bce_9: 0.41648/0.33256, loss_spatial_dice_9: 0.79762/0.82156, loss_spatial_ce_9: 1.38274/1.49170, loss_grounding_bce_9: 0.07138/0.10561, loss_grounding_dice_9: 0.20780/0.28087, loss_grounding_ce_9: 1.63955/0.66860] items per batch[64] items per second[0.23] total items[5388800] mini batches[ 84200] memory[7345] epoch remaining[1:18:55]
INFO:trainer.default_trainer:epochs[    46] optim steps[84300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.74186/0.89328, loss_mask_bce_0: 0.28453/0.33362, loss_mask_dice_0: 0.47420/1.16107, loss_spatial_bce_0: 0.07177/0.08631, loss_spatial_dice_0: 0.12403/0.20596, loss_spatial_ce_0: 0.01400/0.05912, loss_grounding_bce_0: 0.03592/0.08604, loss_grounding_dice_0: 0.05664/0.17825, loss_grounding_ce_0: 0.16483/0.27105, loss_mask_ce_1: 0.74588/0.89395, loss_mask_bce_1: 0.29841/0.33454, loss_mask_dice_1: 0.41247/1.16800, loss_spatial_bce_1: 0.06976/0.08684, loss_spatial_dice_1: 0.11837/0.20986, loss_spatial_ce_1: 0.02358/0.06498, loss_grounding_bce_1: 0.03628/0.08625, loss_grounding_dice_1: 0.05653/0.17910, loss_grounding_ce_1: 0.16928/0.27192, loss_mask_ce_2: 0.77586/0.90096, loss_mask_bce_2: 0.28721/0.33519, loss_mask_dice_2: 0.41104/1.16843, loss_spatial_bce_2: 0.06858/0.08810, loss_spatial_dice_2: 0.11592/0.21179, loss_spatial_ce_2: 0.02802/0.06836, loss_grounding_bce_2: 0.03637/0.08643, loss_grounding_dice_2: 0.07172/0.17898, loss_grounding_ce_2: 0.17648/0.27529, loss_mask_ce_3: 0.71980/0.91236, loss_mask_bce_3: 0.29289/0.33638, loss_mask_dice_3: 0.47675/1.16639, loss_spatial_bce_3: 0.07370/0.08944, loss_spatial_dice_3: 0.12911/0.21290, loss_spatial_ce_3: 0.02013/0.07353, loss_grounding_bce_3: 0.03707/0.08668, loss_grounding_dice_3: 0.06325/0.17868, loss_grounding_ce_3: 0.16395/0.27747, loss_mask_ce_4: 0.69097/0.91389, loss_mask_bce_4: 0.28805/0.33856, loss_mask_dice_4: 0.42609/1.19020, loss_spatial_bce_4: 0.07340/0.09334, loss_spatial_dice_4: 0.14061/0.22511, loss_spatial_ce_4: 0.05856/0.08979, loss_grounding_bce_4: 0.03468/0.08723, loss_grounding_dice_4: 0.07425/0.18166, loss_grounding_ce_4: 0.15562/0.28037, loss_mask_ce_5: 0.58316/0.93071, loss_mask_bce_5: 0.29052/0.34098, loss_mask_dice_5: 0.46066/1.19840, loss_spatial_bce_5: 0.07164/0.09569, loss_spatial_dice_5: 0.15732/0.22953, loss_spatial_ce_5: 0.05619/0.10353, loss_grounding_bce_5: 0.03467/0.08765, loss_grounding_dice_5: 0.06942/0.18297, loss_grounding_ce_5: 0.14428/0.29316, loss_mask_ce_6: 0.65608/0.97092, loss_mask_bce_6: 0.29554/0.34378, loss_mask_dice_6: 0.48665/1.20149, loss_spatial_bce_6: 0.07724/0.10128, loss_spatial_dice_6: 0.13295/0.23253, loss_spatial_ce_6: 0.08079/0.12832, loss_grounding_bce_6: 0.03435/0.08842, loss_grounding_dice_6: 0.07046/0.18337, loss_grounding_ce_6: 0.15708/0.30835, loss_mask_ce_7: 0.78028/1.01690, loss_mask_bce_7: 0.30058/0.35161, loss_mask_dice_7: 0.51161/1.25576, loss_spatial_bce_7: 0.10739/0.10914, loss_spatial_dice_7: 0.14344/0.26016, loss_spatial_ce_7: 0.14512/0.16323, loss_grounding_bce_7: 0.03635/0.09028, loss_grounding_dice_7: 0.07871/0.19074, loss_grounding_ce_7: 0.17199/0.33828, loss_mask_ce_8: 0.92897/1.12581, loss_mask_bce_8: 0.30427/0.36516, loss_mask_dice_8: 0.50599/1.32825, loss_spatial_bce_8: 0.15634/0.12918, loss_spatial_dice_8: 0.19914/0.29785, loss_spatial_ce_8: 0.41956/0.21334, loss_grounding_bce_8: 0.03521/0.09393, loss_grounding_dice_8: 0.06399/0.20139, loss_grounding_ce_8: 0.18115/0.40415, loss_mask_ce_9: 2.39852/3.67297, loss_mask_bce_9: 0.27810/0.39226, loss_mask_dice_9: 0.78438/1.90075, loss_spatial_bce_9: 0.41906/0.33257, loss_spatial_dice_9: 0.81532/0.82156, loss_spatial_ce_9: 1.17931/1.49170, loss_grounding_bce_9: 0.03586/0.10562, loss_grounding_dice_9: 0.10884/0.28085, loss_grounding_ce_9: 0.36144/0.66872] items per batch[64] items per second[0.24] total items[5395200] mini batches[ 84300] memory[7345] epoch remaining[1:12:37]
INFO:trainer.default_trainer:epochs[    46] optim steps[84400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.24138/0.89330, loss_mask_bce_0: 0.02883/0.33361, loss_mask_dice_0: 1.44904/1.16108, loss_spatial_bce_0: 0.00571/0.08631, loss_spatial_dice_0: 0.36316/0.20594, loss_spatial_ce_0: 0.11263/0.05911, loss_grounding_bce_0: 0.00944/0.08603, loss_grounding_dice_0: 0.21089/0.17825, loss_grounding_ce_0: 0.06436/0.27104, loss_mask_ce_1: 0.78411/0.89397, loss_mask_bce_1: 0.02291/0.33452, loss_mask_dice_1: 1.02339/1.16800, loss_spatial_bce_1: 0.00577/0.08684, loss_spatial_dice_1: 0.29561/0.20985, loss_spatial_ce_1: 0.14000/0.06495, loss_grounding_bce_1: 0.00919/0.08624, loss_grounding_dice_1: 0.19611/0.17910, loss_grounding_ce_1: 0.05845/0.27192, loss_mask_ce_2: 1.05209/0.90100, loss_mask_bce_2: 0.02279/0.33518, loss_mask_dice_2: 1.28564/1.16845, loss_spatial_bce_2: 0.00700/0.08809, loss_spatial_dice_2: 0.44919/0.21178, loss_spatial_ce_2: 0.09758/0.06833, loss_grounding_bce_2: 0.00806/0.08642, loss_grounding_dice_2: 0.12025/0.17898, loss_grounding_ce_2: 0.07294/0.27528, loss_mask_ce_3: 1.08291/0.91239, loss_mask_bce_3: 0.02676/0.33637, loss_mask_dice_3: 1.22972/1.16640, loss_spatial_bce_3: 0.00611/0.08943, loss_spatial_dice_3: 0.37620/0.21289, loss_spatial_ce_3: 0.24972/0.07352, loss_grounding_bce_3: 0.01109/0.08667, loss_grounding_dice_3: 0.15588/0.17868, loss_grounding_ce_3: 0.04288/0.27747, loss_mask_ce_4: 0.93744/0.91390, loss_mask_bce_4: 0.02461/0.33855, loss_mask_dice_4: 1.51608/1.19024, loss_spatial_bce_4: 0.00567/0.09333, loss_spatial_dice_4: 0.32655/0.22509, loss_spatial_ce_4: 0.24621/0.08977, loss_grounding_bce_4: 0.00973/0.08723, loss_grounding_dice_4: 0.26388/0.18166, loss_grounding_ce_4: 0.03697/0.28036, loss_mask_ce_5: 1.10008/0.93073, loss_mask_bce_5: 0.03626/0.34097, loss_mask_dice_5: 1.17446/1.19842, loss_spatial_bce_5: 0.00521/0.09568, loss_spatial_dice_5: 0.34901/0.22952, loss_spatial_ce_5: 0.47846/0.10350, loss_grounding_bce_5: 0.01092/0.08765, loss_grounding_dice_5: 0.30586/0.18296, loss_grounding_ce_5: 0.04112/0.29315, loss_mask_ce_6: 0.95303/0.97097, loss_mask_bce_6: 0.02611/0.34377, loss_mask_dice_6: 1.38962/1.20150, loss_spatial_bce_6: 0.00802/0.10128, loss_spatial_dice_6: 0.32854/0.23252, loss_spatial_ce_6: 0.33424/0.12829, loss_grounding_bce_6: 0.01221/0.08841, loss_grounding_dice_6: 0.19966/0.18338, loss_grounding_ce_6: 0.05980/0.30834, loss_mask_ce_7: 0.85160/1.01694, loss_mask_bce_7: 0.02537/0.35160, loss_mask_dice_7: 1.33650/1.25578, loss_spatial_bce_7: 0.00835/0.10914, loss_spatial_dice_7: 0.42439/0.26016, loss_spatial_ce_7: 0.22730/0.16321, loss_grounding_bce_7: 0.00866/0.09027, loss_grounding_dice_7: 0.19404/0.19075, loss_grounding_ce_7: 0.04559/0.33825, loss_mask_ce_8: 0.99002/1.12586, loss_mask_bce_8: 0.02729/0.36515, loss_mask_dice_8: 1.40457/1.32829, loss_spatial_bce_8: 0.00750/0.12917, loss_spatial_dice_8: 0.43341/0.29785, loss_spatial_ce_8: 0.51965/0.21328, loss_grounding_bce_8: 0.01126/0.09392, loss_grounding_dice_8: 0.19706/0.20139, loss_grounding_ce_8: 0.08382/0.40410, loss_mask_ce_9: 3.36690/3.67287, loss_mask_bce_9: 0.02338/0.39224, loss_mask_dice_9: 1.51111/1.90075, loss_spatial_bce_9: 0.06220/0.33255, loss_spatial_dice_9: 0.77696/0.82155, loss_spatial_ce_9: 1.98040/1.49170, loss_grounding_bce_9: 0.00952/0.10561, loss_grounding_dice_9: 0.26452/0.28085, loss_grounding_ce_9: 0.25736/0.66865] items per batch[64] items per second[0.23] total items[5401600] mini batches[ 84400] memory[7345] epoch remaining[1:07:42]
INFO:trainer.default_trainer:epochs[    46] optim steps[84500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.62562/0.89320, loss_mask_bce_0: 0.29657/0.33356, loss_mask_dice_0: 0.49444/1.16103, loss_spatial_bce_0: 0.09532/0.08630, loss_spatial_dice_0: 0.15372/0.20592, loss_spatial_ce_0: 0.12119/0.05908, loss_grounding_bce_0: 0.10153/0.08602, loss_grounding_dice_0: 0.10788/0.17822, loss_grounding_ce_0: 0.19310/0.27098, loss_mask_ce_1: 0.59361/0.89386, loss_mask_bce_1: 0.29004/0.33448, loss_mask_dice_1: 0.48591/1.16795, loss_spatial_bce_1: 0.09631/0.08683, loss_spatial_dice_1: 0.15081/0.20982, loss_spatial_ce_1: 0.17762/0.06493, loss_grounding_bce_1: 0.09790/0.08623, loss_grounding_dice_1: 0.10355/0.17907, loss_grounding_ce_1: 0.23008/0.27186, loss_mask_ce_2: 0.59305/0.90088, loss_mask_bce_2: 0.29347/0.33514, loss_mask_dice_2: 0.46758/1.16842, loss_spatial_bce_2: 0.10086/0.08808, loss_spatial_dice_2: 0.14969/0.21176, loss_spatial_ce_2: 0.21384/0.06830, loss_grounding_bce_2: 0.09607/0.08641, loss_grounding_dice_2: 0.10098/0.17896, loss_grounding_ce_2: 0.20197/0.27522, loss_mask_ce_3: 0.60065/0.91230, loss_mask_bce_3: 0.31229/0.33633, loss_mask_dice_3: 0.51125/1.16633, loss_spatial_bce_3: 0.10913/0.08942, loss_spatial_dice_3: 0.17553/0.21287, loss_spatial_ce_3: 0.15293/0.07349, loss_grounding_bce_3: 0.09643/0.08666, loss_grounding_dice_3: 0.10097/0.17865, loss_grounding_ce_3: 0.15844/0.27740, loss_mask_ce_4: 0.58917/0.91383, loss_mask_bce_4: 0.29869/0.33851, loss_mask_dice_4: 0.48543/1.19019, loss_spatial_bce_4: 0.12676/0.09332, loss_spatial_dice_4: 0.18828/0.22507, loss_spatial_ce_4: 0.25605/0.08974, loss_grounding_bce_4: 0.10245/0.08722, loss_grounding_dice_4: 0.10454/0.18164, loss_grounding_ce_4: 0.12150/0.28029, loss_mask_ce_5: 0.64037/0.93064, loss_mask_bce_5: 0.28873/0.34093, loss_mask_dice_5: 0.46527/1.19838, loss_spatial_bce_5: 0.15456/0.09567, loss_spatial_dice_5: 0.20072/0.22950, loss_spatial_ce_5: 0.22908/0.10347, loss_grounding_bce_5: 0.09880/0.08764, loss_grounding_dice_5: 0.10212/0.18294, loss_grounding_ce_5: 0.19100/0.29307, loss_mask_ce_6: 0.70894/0.97087, loss_mask_bce_6: 0.29550/0.34373, loss_mask_dice_6: 0.45851/1.20145, loss_spatial_bce_6: 0.16487/0.10127, loss_spatial_dice_6: 0.20955/0.23250, loss_spatial_ce_6: 0.24260/0.12824, loss_grounding_bce_6: 0.10153/0.08840, loss_grounding_dice_6: 0.10608/0.18335, loss_grounding_ce_6: 0.30625/0.30825, loss_mask_ce_7: 0.82328/1.01686, loss_mask_bce_7: 0.29588/0.35156, loss_mask_dice_7: 0.62672/1.25573, loss_spatial_bce_7: 0.16044/0.10912, loss_spatial_dice_7: 0.21967/0.26014, loss_spatial_ce_7: 0.16971/0.16317, loss_grounding_bce_7: 0.10022/0.09027, loss_grounding_dice_7: 0.10104/0.19072, loss_grounding_ce_7: 0.68207/0.33817, loss_mask_ce_8: 0.86050/1.12577, loss_mask_bce_8: 0.32459/0.36510, loss_mask_dice_8: 0.50202/1.32823, loss_spatial_bce_8: 0.16106/0.12915, loss_spatial_dice_8: 0.26716/0.29784, loss_spatial_ce_8: 0.14456/0.21321, loss_grounding_bce_8: 0.09984/0.09392, loss_grounding_dice_8: 0.10795/0.20136, loss_grounding_ce_8: 0.53316/0.40400, loss_mask_ce_9: 2.86580/3.67279, loss_mask_bce_9: 0.40084/0.39221, loss_mask_dice_9: 0.75404/1.90067, loss_spatial_bce_9: 0.31749/0.33253, loss_spatial_dice_9: 0.78874/0.82155, loss_spatial_ce_9: 1.49344/1.49159, loss_grounding_bce_9: 0.09652/0.10561, loss_grounding_dice_9: 0.14803/0.28084, loss_grounding_ce_9: 1.28128/0.66861] items per batch[64] items per second[0.23] total items[5408000] mini batches[ 84500] memory[7345] epoch remaining[1:03:00]
INFO:trainer.default_trainer:epochs[    46] optim steps[84600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.60049/0.89316, loss_mask_bce_0: 1.17810/0.33357, loss_mask_dice_0: 2.55888/1.16115, loss_spatial_bce_0: 0.05581/0.08629, loss_spatial_dice_0: 0.18276/0.20591, loss_spatial_ce_0: 0.00662/0.05906, loss_grounding_bce_0: 0.06973/0.08602, loss_grounding_dice_0: 0.12195/0.17822, loss_grounding_ce_0: 0.91680/0.27096, loss_mask_ce_1: 1.56394/0.89384, loss_mask_bce_1: 1.13696/0.33449, loss_mask_dice_1: 2.54473/1.16807, loss_spatial_bce_1: 0.05165/0.08682, loss_spatial_dice_1: 0.20181/0.20981, loss_spatial_ce_1: 0.01477/0.06492, loss_grounding_bce_1: 0.07946/0.08622, loss_grounding_dice_1: 0.12748/0.17907, loss_grounding_ce_1: 0.89171/0.27185, loss_mask_ce_2: 1.53178/0.90083, loss_mask_bce_2: 1.15885/0.33515, loss_mask_dice_2: 2.63624/1.16853, loss_spatial_bce_2: 0.04608/0.08807, loss_spatial_dice_2: 0.20037/0.21174, loss_spatial_ce_2: 0.01214/0.06829, loss_grounding_bce_2: 0.07285/0.08641, loss_grounding_dice_2: 0.11941/0.17896, loss_grounding_ce_2: 0.96569/0.27521, loss_mask_ce_3: 1.60019/0.91226, loss_mask_bce_3: 1.04321/0.33634, loss_mask_dice_3: 2.56971/1.16641, loss_spatial_bce_3: 0.04439/0.08941, loss_spatial_dice_3: 0.20003/0.21286, loss_spatial_ce_3: 0.01345/0.07348, loss_grounding_bce_3: 0.06860/0.08666, loss_grounding_dice_3: 0.11480/0.17864, loss_grounding_ce_3: 0.99695/0.27739, loss_mask_ce_4: 1.57887/0.91379, loss_mask_bce_4: 1.04775/0.33853, loss_mask_dice_4: 2.64940/1.19033, loss_spatial_bce_4: 0.03972/0.09332, loss_spatial_dice_4: 0.19693/0.22506, loss_spatial_ce_4: 0.19666/0.08973, loss_grounding_bce_4: 0.06846/0.08722, loss_grounding_dice_4: 0.12117/0.18163, loss_grounding_ce_4: 0.97965/0.28030, loss_mask_ce_5: 1.85657/0.93061, loss_mask_bce_5: 1.04937/0.34096, loss_mask_dice_5: 2.63719/1.19851, loss_spatial_bce_5: 0.04735/0.09567, loss_spatial_dice_5: 0.23677/0.22949, loss_spatial_ce_5: 0.01658/0.10343, loss_grounding_bce_5: 0.06858/0.08764, loss_grounding_dice_5: 0.12353/0.18293, loss_grounding_ce_5: 0.93619/0.29308, loss_mask_ce_6: 1.77332/0.97082, loss_mask_bce_6: 1.16450/0.34375, loss_mask_dice_6: 2.67137/1.20156, loss_spatial_bce_6: 0.04928/0.10128, loss_spatial_dice_6: 0.23768/0.23249, loss_spatial_ce_6: 0.03045/0.12820, loss_grounding_bce_6: 0.08120/0.08840, loss_grounding_dice_6: 0.13530/0.18336, loss_grounding_ce_6: 0.97316/0.30823, loss_mask_ce_7: 1.75813/1.01682, loss_mask_bce_7: 1.20812/0.35158, loss_mask_dice_7: 2.70070/1.25587, loss_spatial_bce_7: 0.06098/0.10913, loss_spatial_dice_7: 0.27052/0.26013, loss_spatial_ce_7: 0.11945/0.16312, loss_grounding_bce_7: 0.08513/0.09026, loss_grounding_dice_7: 0.12299/0.19071, loss_grounding_ce_7: 0.90927/0.33814, loss_mask_ce_8: 1.89201/1.12571, loss_mask_bce_8: 1.21540/0.36512, loss_mask_dice_8: 2.83185/1.32835, loss_spatial_bce_8: 0.09111/0.12915, loss_spatial_dice_8: 0.32282/0.29783, loss_spatial_ce_8: 0.06943/0.21313, loss_grounding_bce_8: 0.07601/0.09392, loss_grounding_dice_8: 0.14301/0.20136, loss_grounding_ce_8: 0.79612/0.40393, loss_mask_ce_9: 6.11643/3.67276, loss_mask_bce_9: 1.13841/0.39222, loss_mask_dice_9: 5.33527/1.90091, loss_spatial_bce_9: 0.32117/0.33254, loss_spatial_dice_9: 0.94422/0.82155, loss_spatial_ce_9: 1.21273/1.49162, loss_grounding_bce_9: 0.10705/0.10560, loss_grounding_dice_9: 0.29784/0.28082, loss_grounding_ce_9: 1.28469/0.66855] items per batch[64] items per second[0.24] total items[5414400] mini batches[ 84600] memory[7345] epoch remaining[0:58:12]
INFO:trainer.default_trainer:epochs[    46] optim steps[84700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.03106/0.89310, loss_mask_bce_0: 0.09161/0.33355, loss_mask_dice_0: 0.08165/1.16118, loss_spatial_bce_0: 0.08520/0.08628, loss_spatial_dice_0: 0.06101/0.20588, loss_spatial_ce_0: 0.07616/0.05903, loss_grounding_bce_0: 0.07527/0.08601, loss_grounding_dice_0: 0.06706/0.17821, loss_grounding_ce_0: 0.00092/0.27088, loss_mask_ce_1: 0.03502/0.89380, loss_mask_bce_1: 0.09288/0.33447, loss_mask_dice_1: 0.08687/1.16810, loss_spatial_bce_1: 0.09291/0.08681, loss_spatial_dice_1: 0.07031/0.20978, loss_spatial_ce_1: 0.07181/0.06488, loss_grounding_bce_1: 0.07634/0.08622, loss_grounding_dice_1: 0.06673/0.17905, loss_grounding_ce_1: 0.00083/0.27177, loss_mask_ce_2: 0.03912/0.90079, loss_mask_bce_2: 0.09357/0.33513, loss_mask_dice_2: 0.08308/1.16855, loss_spatial_bce_2: 0.10542/0.08807, loss_spatial_dice_2: 0.08172/0.21172, loss_spatial_ce_2: 0.06270/0.06826, loss_grounding_bce_2: 0.07568/0.08641, loss_grounding_dice_2: 0.06580/0.17894, loss_grounding_ce_2: 0.00134/0.27511, loss_mask_ce_3: 0.03888/0.91222, loss_mask_bce_3: 0.09211/0.33631, loss_mask_dice_3: 0.07725/1.16644, loss_spatial_bce_3: 0.10113/0.08941, loss_spatial_dice_3: 0.08508/0.21283, loss_spatial_ce_3: 0.06300/0.07345, loss_grounding_bce_3: 0.07845/0.08666, loss_grounding_dice_3: 0.06553/0.17863, loss_grounding_ce_3: 0.00109/0.27731, loss_mask_ce_4: 0.04323/0.91373, loss_mask_bce_4: 0.09264/0.33851, loss_mask_dice_4: 0.08309/1.19037, loss_spatial_bce_4: 0.06924/0.09331, loss_spatial_dice_4: 0.05942/0.22504, loss_spatial_ce_4: 0.10126/0.08970, loss_grounding_bce_4: 0.07271/0.08722, loss_grounding_dice_4: 0.06033/0.18162, loss_grounding_ce_4: 0.00141/0.28020, loss_mask_ce_5: 0.04814/0.93056, loss_mask_bce_5: 0.09352/0.34094, loss_mask_dice_5: 0.10107/1.19853, loss_spatial_bce_5: 0.05815/0.09566, loss_spatial_dice_5: 0.06159/0.22947, loss_spatial_ce_5: 0.13846/0.10339, loss_grounding_bce_5: 0.07851/0.08764, loss_grounding_dice_5: 0.06449/0.18292, loss_grounding_ce_5: 0.00108/0.29299, loss_mask_ce_6: 0.03971/0.97079, loss_mask_bce_6: 0.09234/0.34373, loss_mask_dice_6: 0.08436/1.20160, loss_spatial_bce_6: 0.05889/0.10128, loss_spatial_dice_6: 0.04973/0.23246, loss_spatial_ce_6: 0.17923/0.12817, loss_grounding_bce_6: 0.07700/0.08840, loss_grounding_dice_6: 0.06960/0.18335, loss_grounding_ce_6: 0.00321/0.30812, loss_mask_ce_7: 0.06540/1.01680, loss_mask_bce_7: 0.09260/0.35156, loss_mask_dice_7: 0.09133/1.25589, loss_spatial_bce_7: 0.07176/0.10912, loss_spatial_dice_7: 0.05801/0.26010, loss_spatial_ce_7: 0.25096/0.16308, loss_grounding_bce_7: 0.07424/0.09026, loss_grounding_dice_7: 0.06408/0.19070, loss_grounding_ce_7: 0.00272/0.33804, loss_mask_ce_8: 0.06998/1.12570, loss_mask_bce_8: 0.09533/0.36511, loss_mask_dice_8: 0.07929/1.32839, loss_spatial_bce_8: 0.06435/0.12914, loss_spatial_dice_8: 0.05374/0.29781, loss_spatial_ce_8: 0.25823/0.21303, loss_grounding_bce_8: 0.07953/0.09392, loss_grounding_dice_8: 0.06282/0.20136, loss_grounding_ce_8: 0.00458/0.40385, loss_mask_ce_9: 2.23088/3.67280, loss_mask_bce_9: 0.10243/0.39222, loss_mask_dice_9: 0.10583/1.90103, loss_spatial_bce_9: 0.51523/0.33253, loss_spatial_dice_9: 0.73230/0.82155, loss_spatial_ce_9: 0.83376/1.49155, loss_grounding_bce_9: 0.07717/0.10561, loss_grounding_dice_9: 0.06438/0.28081, loss_grounding_ce_9: 0.11878/0.66855] items per batch[64] items per second[0.23] total items[5420800] mini batches[ 84700] memory[7345] epoch remaining[0:53:45]
INFO:trainer.default_trainer:epochs[    46] optim steps[84800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.27401/0.89308, loss_mask_bce_0: 0.15946/0.33357, loss_mask_dice_0: 0.28626/1.16112, loss_spatial_bce_0: 0.04666/0.08629, loss_spatial_dice_0: 0.09955/0.20586, loss_spatial_ce_0: 0.00057/0.05899, loss_grounding_bce_0: 0.07277/0.08601, loss_grounding_dice_0: 0.08458/0.17821, loss_grounding_ce_0: 0.10490/0.27084, loss_mask_ce_1: 0.26239/0.89379, loss_mask_bce_1: 0.15102/0.33450, loss_mask_dice_1: 0.28426/1.16802, loss_spatial_bce_1: 0.04650/0.08681, loss_spatial_dice_1: 0.09323/0.20976, loss_spatial_ce_1: 0.00123/0.06485, loss_grounding_bce_1: 0.07256/0.08622, loss_grounding_dice_1: 0.08000/0.17905, loss_grounding_ce_1: 0.09939/0.27172, loss_mask_ce_2: 0.26843/0.90077, loss_mask_bce_2: 0.15294/0.33516, loss_mask_dice_2: 0.28527/1.16850, loss_spatial_bce_2: 0.05078/0.08807, loss_spatial_dice_2: 0.11397/0.21170, loss_spatial_ce_2: 0.00208/0.06822, loss_grounding_bce_2: 0.07269/0.08640, loss_grounding_dice_2: 0.07983/0.17894, loss_grounding_ce_2: 0.11101/0.27505, loss_mask_ce_3: 0.30542/0.91219, loss_mask_bce_3: 0.14953/0.33634, loss_mask_dice_3: 0.27760/1.16636, loss_spatial_bce_3: 0.04605/0.08941, loss_spatial_dice_3: 0.09525/0.21281, loss_spatial_ce_3: 0.00613/0.07341, loss_grounding_bce_3: 0.07232/0.08666, loss_grounding_dice_3: 0.07903/0.17863, loss_grounding_ce_3: 0.10492/0.27727, loss_mask_ce_4: 0.27551/0.91370, loss_mask_bce_4: 0.14547/0.33854, loss_mask_dice_4: 0.28412/1.19029, loss_spatial_bce_4: 0.04467/0.09332, loss_spatial_dice_4: 0.09973/0.22501, loss_spatial_ce_4: 0.00903/0.08965, loss_grounding_bce_4: 0.06834/0.08721, loss_grounding_dice_4: 0.07804/0.18163, loss_grounding_ce_4: 0.09971/0.28014, loss_mask_ce_5: 0.27686/0.93052, loss_mask_bce_5: 0.14325/0.34097, loss_mask_dice_5: 0.28875/1.19846, loss_spatial_bce_5: 0.04552/0.09566, loss_spatial_dice_5: 0.10233/0.22944, loss_spatial_ce_5: 0.02392/0.10333, loss_grounding_bce_5: 0.07022/0.08764, loss_grounding_dice_5: 0.08344/0.18292, loss_grounding_ce_5: 0.10107/0.29294, loss_mask_ce_6: 0.29746/0.97076, loss_mask_bce_6: 0.14140/0.34376, loss_mask_dice_6: 0.26543/1.20154, loss_spatial_bce_6: 0.04885/0.10127, loss_spatial_dice_6: 0.10122/0.23243, loss_spatial_ce_6: 0.01669/0.12812, loss_grounding_bce_6: 0.06994/0.08840, loss_grounding_dice_6: 0.08309/0.18335, loss_grounding_ce_6: 0.12371/0.30807, loss_mask_ce_7: 0.32669/1.01677, loss_mask_bce_7: 0.15009/0.35160, loss_mask_dice_7: 0.25873/1.25581, loss_spatial_bce_7: 0.05455/0.10911, loss_spatial_dice_7: 0.11663/0.26007, loss_spatial_ce_7: 0.08625/0.16302, loss_grounding_bce_7: 0.06987/0.09026, loss_grounding_dice_7: 0.08356/0.19070, loss_grounding_ce_7: 0.25006/0.33800, loss_mask_ce_8: 0.30345/1.12570, loss_mask_bce_8: 0.16718/0.36515, loss_mask_dice_8: 0.30968/1.32831, loss_spatial_bce_8: 0.06526/0.12913, loss_spatial_dice_8: 0.16782/0.29777, loss_spatial_ce_8: 0.11342/0.21292, loss_grounding_bce_8: 0.08134/0.09392, loss_grounding_dice_8: 0.09344/0.20136, loss_grounding_ce_8: 0.33550/0.40383, loss_mask_ce_9: 2.80083/3.67280, loss_mask_bce_9: 0.17503/0.39226, loss_mask_dice_9: 0.49660/1.90096, loss_spatial_bce_9: 0.30694/0.33255, loss_spatial_dice_9: 0.74079/0.82155, loss_spatial_ce_9: 1.44953/1.49152, loss_grounding_bce_9: 0.12789/0.10561, loss_grounding_dice_9: 0.23765/0.28081, loss_grounding_ce_9: 0.72338/0.66857] items per batch[64] items per second[0.23] total items[5427200] mini batches[ 84800] memory[7345] epoch remaining[0:49:07]
INFO:trainer.default_trainer:epochs[    46] optim steps[84900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.91895/0.89298, loss_mask_bce_0: 0.25458/0.33357, loss_mask_dice_0: 1.33556/1.16106, loss_spatial_bce_0: 0.02556/0.08628, loss_spatial_dice_0: 0.22372/0.20583, loss_spatial_ce_0: 0.04189/0.05897, loss_grounding_bce_0: 0.06061/0.08601, loss_grounding_dice_0: 0.17063/0.17822, loss_grounding_ce_0: 0.35900/0.27099, loss_mask_ce_1: 1.07505/0.89367, loss_mask_bce_1: 0.24738/0.33449, loss_mask_dice_1: 1.17743/1.16795, loss_spatial_bce_1: 0.02926/0.08680, loss_spatial_dice_1: 0.23363/0.20974, loss_spatial_ce_1: 0.07366/0.06482, loss_grounding_bce_1: 0.06375/0.08622, loss_grounding_dice_1: 0.17247/0.17906, loss_grounding_ce_1: 0.37335/0.27187, loss_mask_ce_2: 0.89292/0.90066, loss_mask_bce_2: 0.25358/0.33515, loss_mask_dice_2: 1.30827/1.16842, loss_spatial_bce_2: 0.02834/0.08806, loss_spatial_dice_2: 0.24374/0.21168, loss_spatial_ce_2: 0.04600/0.06820, loss_grounding_bce_2: 0.06523/0.08640, loss_grounding_dice_2: 0.16952/0.17895, loss_grounding_ce_2: 0.36629/0.27521, loss_mask_ce_3: 0.95385/0.91208, loss_mask_bce_3: 0.26265/0.33633, loss_mask_dice_3: 1.22742/1.16629, loss_spatial_bce_3: 0.02818/0.08940, loss_spatial_dice_3: 0.25214/0.21279, loss_spatial_ce_3: 0.03320/0.07338, loss_grounding_bce_3: 0.06633/0.08666, loss_grounding_dice_3: 0.18483/0.17863, loss_grounding_ce_3: 0.37138/0.27739, loss_mask_ce_4: 0.82817/0.91361, loss_mask_bce_4: 0.26119/0.33853, loss_mask_dice_4: 1.41661/1.19021, loss_spatial_bce_4: 0.03134/0.09331, loss_spatial_dice_4: 0.27670/0.22500, loss_spatial_ce_4: 0.22370/0.08962, loss_grounding_bce_4: 0.06529/0.08721, loss_grounding_dice_4: 0.17467/0.18163, loss_grounding_ce_4: 0.37294/0.28030, loss_mask_ce_5: 0.91915/0.93042, loss_mask_bce_5: 0.17236/0.34096, loss_mask_dice_5: 1.44139/1.19838, loss_spatial_bce_5: 0.04253/0.09565, loss_spatial_dice_5: 0.31785/0.22942, loss_spatial_ce_5: 0.17059/0.10330, loss_grounding_bce_5: 0.06944/0.08764, loss_grounding_dice_5: 0.18772/0.18293, loss_grounding_ce_5: 0.35496/0.29311, loss_mask_ce_6: 0.96446/0.97064, loss_mask_bce_6: 0.26368/0.34375, loss_mask_dice_6: 1.42918/1.20147, loss_spatial_bce_6: 0.06119/0.10127, loss_spatial_dice_6: 0.33374/0.23242, loss_spatial_ce_6: 0.15288/0.12809, loss_grounding_bce_6: 0.06386/0.08840, loss_grounding_dice_6: 0.17663/0.18336, loss_grounding_ce_6: 0.37500/0.30821, loss_mask_ce_7: 0.99148/1.01667, loss_mask_bce_7: 0.20276/0.35159, loss_mask_dice_7: 1.43715/1.25574, loss_spatial_bce_7: 0.05718/0.10910, loss_spatial_dice_7: 0.34397/0.26005, loss_spatial_ce_7: 0.04573/0.16298, loss_grounding_bce_7: 0.04019/0.09026, loss_grounding_dice_7: 0.18885/0.19071, loss_grounding_ce_7: 0.46865/0.33815, loss_mask_ce_8: 1.16052/1.12562, loss_mask_bce_8: 0.16033/0.36513, loss_mask_dice_8: 1.34872/1.32822, loss_spatial_bce_8: 0.05855/0.12911, loss_spatial_dice_8: 0.37621/0.29775, loss_spatial_ce_8: 0.12089/0.21283, loss_grounding_bce_8: 0.03770/0.09392, loss_grounding_dice_8: 0.19772/0.20136, loss_grounding_ce_8: 0.50829/0.40404, loss_mask_ce_9: 5.49836/3.67272, loss_mask_bce_9: 0.18878/0.39224, loss_mask_dice_9: 2.43659/1.90085, loss_spatial_bce_9: 0.11897/0.33254, loss_spatial_dice_9: 0.91829/0.82155, loss_spatial_ce_9: 1.49227/1.49148, loss_grounding_bce_9: 0.04940/0.10561, loss_grounding_dice_9: 0.44845/0.28081, loss_grounding_ce_9: 1.58969/0.66876] items per batch[64] items per second[0.23] total items[5433600] mini batches[ 84900] memory[7345] epoch remaining[0:44:34]
INFO:trainer.default_trainer:epochs[    46] optim steps[85000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.46199/0.89292, loss_mask_bce_0: 0.37411/0.33354, loss_mask_dice_0: 1.32710/1.16099, loss_spatial_bce_0: 0.07382/0.08627, loss_spatial_dice_0: 0.20800/0.20581, loss_spatial_ce_0: 0.02501/0.05895, loss_grounding_bce_0: 0.05030/0.08600, loss_grounding_dice_0: 0.21877/0.17821, loss_grounding_ce_0: 0.37315/0.27105, loss_mask_ce_1: 1.59651/0.89363, loss_mask_bce_1: 0.39704/0.33446, loss_mask_dice_1: 1.32101/1.16785, loss_spatial_bce_1: 0.06549/0.08680, loss_spatial_dice_1: 0.22373/0.20971, loss_spatial_ce_1: 0.02008/0.06479, loss_grounding_bce_1: 0.04174/0.08621, loss_grounding_dice_1: 0.24115/0.17906, loss_grounding_ce_1: 0.24277/0.27190, loss_mask_ce_2: 1.86470/0.90060, loss_mask_bce_2: 0.41277/0.33513, loss_mask_dice_2: 1.34381/1.16836, loss_spatial_bce_2: 0.06351/0.08805, loss_spatial_dice_2: 0.20314/0.21166, loss_spatial_ce_2: 0.03164/0.06819, loss_grounding_bce_2: 0.04947/0.08639, loss_grounding_dice_2: 0.21772/0.17894, loss_grounding_ce_2: 0.28732/0.27530, loss_mask_ce_3: 1.56701/0.91204, loss_mask_bce_3: 0.40571/0.33630, loss_mask_dice_3: 1.44915/1.16621, loss_spatial_bce_3: 0.07030/0.08939, loss_spatial_dice_3: 0.22841/0.21277, loss_spatial_ce_3: 0.04847/0.07337, loss_grounding_bce_3: 0.04830/0.08664, loss_grounding_dice_3: 0.25453/0.17863, loss_grounding_ce_3: 0.38906/0.27745, loss_mask_ce_4: 1.58841/0.91357, loss_mask_bce_4: 0.42295/0.33851, loss_mask_dice_4: 1.47009/1.19014, loss_spatial_bce_4: 0.07234/0.09330, loss_spatial_dice_4: 0.21738/0.22498, loss_spatial_ce_4: 0.13454/0.08959, loss_grounding_bce_4: 0.04039/0.08721, loss_grounding_dice_4: 0.22312/0.18163, loss_grounding_ce_4: 0.27565/0.28034, loss_mask_ce_5: 1.26404/0.93036, loss_mask_bce_5: 0.55597/0.34093, loss_mask_dice_5: 1.42902/1.19830, loss_spatial_bce_5: 0.06838/0.09564, loss_spatial_dice_5: 0.22709/0.22940, loss_spatial_ce_5: 0.15942/0.10326, loss_grounding_bce_5: 0.05059/0.08763, loss_grounding_dice_5: 0.22056/0.18293, loss_grounding_ce_5: 0.26033/0.29314, loss_mask_ce_6: 1.37328/0.97060, loss_mask_bce_6: 0.51866/0.34372, loss_mask_dice_6: 1.33083/1.20140, loss_spatial_bce_6: 0.08245/0.10125, loss_spatial_dice_6: 0.24408/0.23239, loss_spatial_ce_6: 0.24170/0.12804, loss_grounding_bce_6: 0.04693/0.08840, loss_grounding_dice_6: 0.22340/0.18336, loss_grounding_ce_6: 0.27278/0.30827, loss_mask_ce_7: 1.53146/1.01663, loss_mask_bce_7: 0.47947/0.35157, loss_mask_dice_7: 1.30864/1.25567, loss_spatial_bce_7: 0.09010/0.10909, loss_spatial_dice_7: 0.27749/0.26003, loss_spatial_ce_7: 0.28516/0.16292, loss_grounding_bce_7: 0.05194/0.09026, loss_grounding_dice_7: 0.25734/0.19071, loss_grounding_ce_7: 0.38040/0.33820, loss_mask_ce_8: 1.69123/1.12561, loss_mask_bce_8: 0.47237/0.36512, loss_mask_dice_8: 1.47746/1.32815, loss_spatial_bce_8: 0.10989/0.12910, loss_spatial_dice_8: 0.33897/0.29773, loss_spatial_ce_8: 0.12602/0.21273, loss_grounding_bce_8: 0.03929/0.09391, loss_grounding_dice_8: 0.25483/0.20137, loss_grounding_ce_8: 0.49444/0.40409, loss_mask_ce_9: 3.46933/3.67270, loss_mask_bce_9: 0.44821/0.39222, loss_mask_dice_9: 1.96697/1.90073, loss_spatial_bce_9: 0.26906/0.33255, loss_spatial_dice_9: 0.87702/0.82155, loss_spatial_ce_9: 1.68870/1.49151, loss_grounding_bce_9: 0.03896/0.10560, loss_grounding_dice_9: 0.43046/0.28082, loss_grounding_ce_9: 0.33581/0.66880] items per batch[64] items per second[0.23] total items[5440000] mini batches[ 85000] memory[7345] epoch remaining[0:39:58]
INFO:trainer.default_trainer:epochs[    46] optim steps[85100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.88061/0.89286, loss_mask_bce_0: 0.31274/0.33349, loss_mask_dice_0: 0.51463/1.16077, loss_spatial_bce_0: 0.07603/0.08627, loss_spatial_dice_0: 0.12912/0.20579, loss_spatial_ce_0: 0.02992/0.05894, loss_grounding_bce_0: 0.13904/0.08598, loss_grounding_dice_0: 0.13143/0.17818, loss_grounding_ce_0: 0.32261/0.27107, loss_mask_ce_1: 1.01782/0.89357, loss_mask_bce_1: 0.29665/0.33442, loss_mask_dice_1: 0.45338/1.16763, loss_spatial_bce_1: 0.07509/0.08680, loss_spatial_dice_1: 0.12818/0.20969, loss_spatial_ce_1: 0.03201/0.06478, loss_grounding_bce_1: 0.11004/0.08620, loss_grounding_dice_1: 0.11463/0.17903, loss_grounding_ce_1: 0.66617/0.27191, loss_mask_ce_2: 1.07111/0.90055, loss_mask_bce_2: 0.28286/0.33509, loss_mask_dice_2: 0.43924/1.16814, loss_spatial_bce_2: 0.07891/0.08806, loss_spatial_dice_2: 0.14243/0.21163, loss_spatial_ce_2: 0.02671/0.06817, loss_grounding_bce_2: 0.10289/0.08638, loss_grounding_dice_2: 0.10447/0.17892, loss_grounding_ce_2: 0.60599/0.27531, loss_mask_ce_3: 0.98013/0.91201, loss_mask_bce_3: 0.30534/0.33626, loss_mask_dice_3: 0.50655/1.16598, loss_spatial_bce_3: 0.09175/0.08939, loss_spatial_dice_3: 0.14707/0.21274, loss_spatial_ce_3: 0.03299/0.07335, loss_grounding_bce_3: 0.12894/0.08663, loss_grounding_dice_3: 0.11470/0.17860, loss_grounding_ce_3: 0.36173/0.27745, loss_mask_ce_4: 0.91009/0.91354, loss_mask_bce_4: 0.31494/0.33847, loss_mask_dice_4: 0.52365/1.18990, loss_spatial_bce_4: 0.08744/0.09330, loss_spatial_dice_4: 0.13390/0.22495, loss_spatial_ce_4: 0.06311/0.08958, loss_grounding_bce_4: 0.14711/0.08720, loss_grounding_dice_4: 0.13667/0.18160, loss_grounding_ce_4: 0.30454/0.28036, loss_mask_ce_5: 0.91999/0.93034, loss_mask_bce_5: 0.32255/0.34089, loss_mask_dice_5: 0.52125/1.19808, loss_spatial_bce_5: 0.09605/0.09565, loss_spatial_dice_5: 0.14082/0.22937, loss_spatial_ce_5: 0.07108/0.10324, loss_grounding_bce_5: 0.14926/0.08762, loss_grounding_dice_5: 0.13593/0.18290, loss_grounding_ce_5: 0.37361/0.29316, loss_mask_ce_6: 0.89529/0.97056, loss_mask_bce_6: 0.34319/0.34369, loss_mask_dice_6: 0.53712/1.20117, loss_spatial_bce_6: 0.10061/0.10126, loss_spatial_dice_6: 0.14328/0.23237, loss_spatial_ce_6: 0.12061/0.12803, loss_grounding_bce_6: 0.13745/0.08839, loss_grounding_dice_6: 0.13587/0.18334, loss_grounding_ce_6: 0.32481/0.30829, loss_mask_ce_7: 1.58280/1.01660, loss_mask_bce_7: 0.40015/0.35154, loss_mask_dice_7: 0.58674/1.25545, loss_spatial_bce_7: 0.11025/0.10910, loss_spatial_dice_7: 0.15854/0.26000, loss_spatial_ce_7: 0.09273/0.16290, loss_grounding_bce_7: 0.13945/0.09025, loss_grounding_dice_7: 0.12729/0.19069, loss_grounding_ce_7: 0.63498/0.33820, loss_mask_ce_8: 1.40153/1.12557, loss_mask_bce_8: 0.46974/0.36508, loss_mask_dice_8: 0.66025/1.32791, loss_spatial_bce_8: 0.12515/0.12910, loss_spatial_dice_8: 0.19255/0.29771, loss_spatial_ce_8: 0.09465/0.21268, loss_grounding_bce_8: 0.17542/0.09391, loss_grounding_dice_8: 0.16062/0.20134, loss_grounding_ce_8: 0.76638/0.40407, loss_mask_ce_9: 3.67950/3.67255, loss_mask_bce_9: 0.51724/0.39219, loss_mask_dice_9: 0.87053/1.90047, loss_spatial_bce_9: 0.40178/0.33255, loss_spatial_dice_9: 0.82560/0.82154, loss_spatial_ce_9: 1.82706/1.49142, loss_grounding_bce_9: 0.23368/0.10559, loss_grounding_dice_9: 0.22778/0.28080, loss_grounding_ce_9: 1.36298/0.66877] items per batch[64] items per second[0.23] total items[5446400] mini batches[ 85100] memory[7345] epoch remaining[0:35:25]
INFO:trainer.default_trainer:epochs[    46] optim steps[85200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.93773/0.89285, loss_mask_bce_0: 0.46035/0.33349, loss_mask_dice_0: 1.33941/1.16070, loss_spatial_bce_0: 0.05492/0.08626, loss_spatial_dice_0: 0.27060/0.20578, loss_spatial_ce_0: 0.05054/0.05892, loss_grounding_bce_0: 0.05258/0.08599, loss_grounding_dice_0: 0.20894/0.17815, loss_grounding_ce_0: 0.42484/0.27106, loss_mask_ce_1: 0.93638/0.89357, loss_mask_bce_1: 0.46402/0.33441, loss_mask_dice_1: 1.58185/1.16756, loss_spatial_bce_1: 0.05776/0.08679, loss_spatial_dice_1: 0.26071/0.20967, loss_spatial_ce_1: 0.08092/0.06478, loss_grounding_bce_1: 0.05281/0.08620, loss_grounding_dice_1: 0.21677/0.17901, loss_grounding_ce_1: 0.41569/0.27189, loss_mask_ce_2: 0.93063/0.90054, loss_mask_bce_2: 0.46040/0.33508, loss_mask_dice_2: 1.47915/1.16808, loss_spatial_bce_2: 0.05279/0.08805, loss_spatial_dice_2: 0.26155/0.21162, loss_spatial_ce_2: 0.14156/0.06815, loss_grounding_bce_2: 0.05015/0.08638, loss_grounding_dice_2: 0.20339/0.17890, loss_grounding_ce_2: 0.44322/0.27530, loss_mask_ce_3: 1.05709/0.91201, loss_mask_bce_3: 0.44995/0.33625, loss_mask_dice_3: 1.48304/1.16592, loss_spatial_bce_3: 0.05785/0.08939, loss_spatial_dice_3: 0.25024/0.21273, loss_spatial_ce_3: 0.15174/0.07335, loss_grounding_bce_3: 0.04985/0.08663, loss_grounding_dice_3: 0.19208/0.17858, loss_grounding_ce_3: 0.42945/0.27745, loss_mask_ce_4: 1.10043/0.91357, loss_mask_bce_4: 0.46935/0.33845, loss_mask_dice_4: 1.52698/1.18983, loss_spatial_bce_4: 0.07397/0.09329, loss_spatial_dice_4: 0.28140/0.22494, loss_spatial_ce_4: 0.16451/0.08957, loss_grounding_bce_4: 0.05245/0.08720, loss_grounding_dice_4: 0.20252/0.18158, loss_grounding_ce_4: 0.47325/0.28034, loss_mask_ce_5: 1.06140/0.93037, loss_mask_bce_5: 0.48503/0.34088, loss_mask_dice_5: 1.47787/1.19803, loss_spatial_bce_5: 0.06918/0.09564, loss_spatial_dice_5: 0.28890/0.22936, loss_spatial_ce_5: 0.04355/0.10323, loss_grounding_bce_5: 0.05253/0.08762, loss_grounding_dice_5: 0.19488/0.18288, loss_grounding_ce_5: 0.40028/0.29316, loss_mask_ce_6: 1.12015/0.97058, loss_mask_bce_6: 0.46522/0.34368, loss_mask_dice_6: 1.55546/1.20114, loss_spatial_bce_6: 0.06612/0.10126, loss_spatial_dice_6: 0.29740/0.23236, loss_spatial_ce_6: 0.23200/0.12802, loss_grounding_bce_6: 0.05264/0.08839, loss_grounding_dice_6: 0.19019/0.18332, loss_grounding_ce_6: 0.42419/0.30830, loss_mask_ce_7: 1.35267/1.01664, loss_mask_bce_7: 0.51635/0.35154, loss_mask_dice_7: 1.59125/1.25539, loss_spatial_bce_7: 0.06823/0.10910, loss_spatial_dice_7: 0.36570/0.25999, loss_spatial_ce_7: 0.22874/0.16286, loss_grounding_bce_7: 0.05156/0.09025, loss_grounding_dice_7: 0.21402/0.19068, loss_grounding_ce_7: 0.40874/0.33821, loss_mask_ce_8: 1.45441/1.12564, loss_mask_bce_8: 0.52728/0.36508, loss_mask_dice_8: 1.67329/1.32786, loss_spatial_bce_8: 0.06167/0.12909, loss_spatial_dice_8: 0.36295/0.29770, loss_spatial_ce_8: 0.18670/0.21260, loss_grounding_bce_8: 0.05333/0.09391, loss_grounding_dice_8: 0.25991/0.20132, loss_grounding_ce_8: 0.40299/0.40408, loss_mask_ce_9: 5.79999/3.67276, loss_mask_bce_9: 0.41144/0.39219, loss_mask_dice_9: 2.14317/1.90050, loss_spatial_bce_9: 0.19794/0.33255, loss_spatial_dice_9: 0.88282/0.82155, loss_spatial_ce_9: 1.05017/1.49142, loss_grounding_bce_9: 0.05717/0.10560, loss_grounding_dice_9: 0.33272/0.28078, loss_grounding_ce_9: 0.45202/0.66877] items per batch[64] items per second[0.24] total items[5452800] mini batches[ 85200] memory[7345] epoch remaining[0:30:44]
INFO:trainer.default_trainer:epochs[    46] optim steps[85300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.03598/0.89277, loss_mask_bce_0: 0.34222/0.33349, loss_mask_dice_0: 1.00071/1.16061, loss_spatial_bce_0: 0.05605/0.08626, loss_spatial_dice_0: 0.30936/0.20575, loss_spatial_ce_0: 0.27179/0.05891, loss_grounding_bce_0: 0.26181/0.08599, loss_grounding_dice_0: 0.10480/0.17814, loss_grounding_ce_0: 0.04030/0.27108, loss_mask_ce_1: 0.86126/0.89349, loss_mask_bce_1: 0.32844/0.33442, loss_mask_dice_1: 1.44437/1.16747, loss_spatial_bce_1: 0.06016/0.08679, loss_spatial_dice_1: 0.31001/0.20965, loss_spatial_ce_1: 0.45193/0.06476, loss_grounding_bce_1: 0.26953/0.08619, loss_grounding_dice_1: 0.10348/0.17900, loss_grounding_ce_1: 0.04609/0.27188, loss_mask_ce_2: 1.03488/0.90045, loss_mask_bce_2: 0.32327/0.33509, loss_mask_dice_2: 1.26131/1.16800, loss_spatial_bce_2: 0.06019/0.08805, loss_spatial_dice_2: 0.32932/0.21160, loss_spatial_ce_2: 0.29580/0.06814, loss_grounding_bce_2: 0.25442/0.08638, loss_grounding_dice_2: 0.24919/0.17889, loss_grounding_ce_2: 0.04475/0.27529, loss_mask_ce_3: 0.82177/0.91193, loss_mask_bce_3: 0.31536/0.33626, loss_mask_dice_3: 1.43070/1.16585, loss_spatial_bce_3: 0.06633/0.08939, loss_spatial_dice_3: 0.34643/0.21271, loss_spatial_ce_3: 0.42644/0.07333, loss_grounding_bce_3: 0.23473/0.08664, loss_grounding_dice_3: 0.10307/0.17857, loss_grounding_ce_3: 0.04662/0.27742, loss_mask_ce_4: 0.80760/0.91350, loss_mask_bce_4: 0.32082/0.33846, loss_mask_dice_4: 1.37867/1.18975, loss_spatial_bce_4: 0.07085/0.09330, loss_spatial_dice_4: 0.34427/0.22491, loss_spatial_ce_4: 0.20195/0.08955, loss_grounding_bce_4: 0.24617/0.08720, loss_grounding_dice_4: 0.10459/0.18157, loss_grounding_ce_4: 0.05100/0.28034, loss_mask_ce_5: 0.58037/0.93030, loss_mask_bce_5: 0.34254/0.34089, loss_mask_dice_5: 1.28692/1.19795, loss_spatial_bce_5: 0.07749/0.09564, loss_spatial_dice_5: 0.34302/0.22934, loss_spatial_ce_5: 0.35417/0.10320, loss_grounding_bce_5: 0.24545/0.08762, loss_grounding_dice_5: 0.10458/0.18286, loss_grounding_ce_5: 0.05585/0.29315, loss_mask_ce_6: 0.61997/0.97052, loss_mask_bce_6: 0.34622/0.34369, loss_mask_dice_6: 1.44644/1.20105, loss_spatial_bce_6: 0.10043/0.10126, loss_spatial_dice_6: 0.34636/0.23233, loss_spatial_ce_6: 0.33814/0.12799, loss_grounding_bce_6: 0.23975/0.08839, loss_grounding_dice_6: 0.27144/0.18331, loss_grounding_ce_6: 0.05305/0.30829, loss_mask_ce_7: 0.59107/1.01655, loss_mask_bce_7: 0.32889/0.35155, loss_mask_dice_7: 1.19054/1.25530, loss_spatial_bce_7: 0.06635/0.10910, loss_spatial_dice_7: 0.39476/0.25996, loss_spatial_ce_7: 0.15385/0.16282, loss_grounding_bce_7: 0.24062/0.09025, loss_grounding_dice_7: 0.17002/0.19066, loss_grounding_ce_7: 0.04371/0.33822, loss_mask_ce_8: 0.82115/1.12555, loss_mask_bce_8: 0.32924/0.36510, loss_mask_dice_8: 1.39728/1.32776, loss_spatial_bce_8: 0.08607/0.12908, loss_spatial_dice_8: 0.45908/0.29767, loss_spatial_ce_8: 0.18042/0.21252, loss_grounding_bce_8: 0.24019/0.09390, loss_grounding_dice_8: 0.10652/0.20131, loss_grounding_ce_8: 0.02951/0.40409, loss_mask_ce_9: 3.68900/3.67266, loss_mask_bce_9: 0.28299/0.39221, loss_mask_dice_9: 1.46215/1.90041, loss_spatial_bce_9: 0.29883/0.33254, loss_spatial_dice_9: 0.71801/0.82154, loss_spatial_ce_9: 2.57199/1.49139, loss_grounding_bce_9: 0.21639/0.10560, loss_grounding_dice_9: 0.21776/0.28077, loss_grounding_ce_9: 0.05826/0.66883] items per batch[64] items per second[0.23] total items[5459200] mini batches[ 85300] memory[7345] epoch remaining[0:26:08]
INFO:trainer.default_trainer:epochs[    46] optim steps[85400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.81946/0.89275, loss_mask_bce_0: 0.09397/0.33347, loss_mask_dice_0: 1.03420/1.16062, loss_spatial_bce_0: 0.01715/0.08625, loss_spatial_dice_0: 0.19997/0.20574, loss_spatial_ce_0: 0.01348/0.05889, loss_grounding_bce_0: 0.02006/0.08597, loss_grounding_dice_0: 0.16532/0.17814, loss_grounding_ce_0: 0.21661/0.27101, loss_mask_ce_1: 0.80377/0.89347, loss_mask_bce_1: 0.10154/0.33439, loss_mask_dice_1: 1.09387/1.16749, loss_spatial_bce_1: 0.01662/0.08678, loss_spatial_dice_1: 0.19940/0.20963, loss_spatial_ce_1: 0.01699/0.06473, loss_grounding_bce_1: 0.01988/0.08618, loss_grounding_dice_1: 0.15127/0.17900, loss_grounding_ce_1: 0.20485/0.27181, loss_mask_ce_2: 1.13982/0.90041, loss_mask_bce_2: 0.09052/0.33507, loss_mask_dice_2: 1.33330/1.16805, loss_spatial_bce_2: 0.01630/0.08804, loss_spatial_dice_2: 0.21987/0.21159, loss_spatial_ce_2: 0.01359/0.06811, loss_grounding_bce_2: 0.02097/0.08637, loss_grounding_dice_2: 0.17434/0.17889, loss_grounding_ce_2: 0.21310/0.27523, loss_mask_ce_3: 1.01188/0.91191, loss_mask_bce_3: 0.10225/0.33623, loss_mask_dice_3: 1.09729/1.16588, loss_spatial_bce_3: 0.01921/0.08938, loss_spatial_dice_3: 0.18780/0.21270, loss_spatial_ce_3: 0.02203/0.07330, loss_grounding_bce_3: 0.02177/0.08662, loss_grounding_dice_3: 0.17568/0.17857, loss_grounding_ce_3: 0.24631/0.27736, loss_mask_ce_4: 0.86893/0.91347, loss_mask_bce_4: 0.09967/0.33844, loss_mask_dice_4: 1.22696/1.18977, loss_spatial_bce_4: 0.02033/0.09329, loss_spatial_dice_4: 0.21080/0.22490, loss_spatial_ce_4: 0.03091/0.08953, loss_grounding_bce_4: 0.01421/0.08718, loss_grounding_dice_4: 0.17643/0.18157, loss_grounding_ce_4: 0.21965/0.28029, loss_mask_ce_5: 0.78428/0.93028, loss_mask_bce_5: 0.16116/0.34086, loss_mask_dice_5: 1.38310/1.19800, loss_spatial_bce_5: 0.02168/0.09563, loss_spatial_dice_5: 0.24676/0.22933, loss_spatial_ce_5: 0.10178/0.10317, loss_grounding_bce_5: 0.01581/0.08761, loss_grounding_dice_5: 0.18829/0.18287, loss_grounding_ce_5: 0.18777/0.29310, loss_mask_ce_6: 1.44163/0.97050, loss_mask_bce_6: 0.09525/0.34366, loss_mask_dice_6: 1.07773/1.20107, loss_spatial_bce_6: 0.02264/0.10125, loss_spatial_dice_6: 0.20212/0.23232, loss_spatial_ce_6: 0.06223/0.12797, loss_grounding_bce_6: 0.01513/0.08837, loss_grounding_dice_6: 0.20501/0.18331, loss_grounding_ce_6: 0.24019/0.30822, loss_mask_ce_7: 1.38186/1.01654, loss_mask_bce_7: 0.09428/0.35152, loss_mask_dice_7: 1.40960/1.25533, loss_spatial_bce_7: 0.02822/0.10909, loss_spatial_dice_7: 0.27348/0.25995, loss_spatial_ce_7: 0.13248/0.16279, loss_grounding_bce_7: 0.01610/0.09024, loss_grounding_dice_7: 0.17438/0.19066, loss_grounding_ce_7: 0.21797/0.33812, loss_mask_ce_8: 1.56560/1.12559, loss_mask_bce_8: 0.09042/0.36507, loss_mask_dice_8: 1.37181/1.32777, loss_spatial_bce_8: 0.02755/0.12907, loss_spatial_dice_8: 0.29497/0.29767, loss_spatial_ce_8: 0.11366/0.21246, loss_grounding_bce_8: 0.01475/0.09390, loss_grounding_dice_8: 0.14689/0.20132, loss_grounding_ce_8: 0.31262/0.40403, loss_mask_ce_9: 3.49739/3.67269, loss_mask_bce_9: 0.12862/0.39219, loss_mask_dice_9: 1.85867/1.90047, loss_spatial_bce_9: 0.12217/0.33252, loss_spatial_dice_9: 0.87541/0.82154, loss_spatial_ce_9: 1.52427/1.49142, loss_grounding_bce_9: 0.01897/0.10560, loss_grounding_dice_9: 0.30527/0.28079, loss_grounding_ce_9: 0.45255/0.66878] items per batch[64] items per second[0.23] total items[5465600] mini batches[ 85400] memory[7345] epoch remaining[0:21:32]
INFO:trainer.default_trainer:epochs[    46] optim steps[85500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.99824/0.89267, loss_mask_bce_0: 0.48021/0.33348, loss_mask_dice_0: 0.95616/1.16052, loss_spatial_bce_0: 0.12463/0.08625, loss_spatial_dice_0: 0.26228/0.20571, loss_spatial_ce_0: 0.07315/0.05886, loss_grounding_bce_0: 0.15512/0.08599, loss_grounding_dice_0: 0.26372/0.17815, loss_grounding_ce_0: 0.21389/0.27102, loss_mask_ce_1: 1.00854/0.89342, loss_mask_bce_1: 0.47987/0.33440, loss_mask_dice_1: 0.95106/1.16740, loss_spatial_bce_1: 0.13013/0.08678, loss_spatial_dice_1: 0.26303/0.20960, loss_spatial_ce_1: 0.05959/0.06470, loss_grounding_bce_1: 0.15298/0.08619, loss_grounding_dice_1: 0.26918/0.17900, loss_grounding_ce_1: 0.21493/0.27182, loss_mask_ce_2: 0.96501/0.90034, loss_mask_bce_2: 0.47996/0.33508, loss_mask_dice_2: 0.94211/1.16797, loss_spatial_bce_2: 0.11151/0.08804, loss_spatial_dice_2: 0.26040/0.21156, loss_spatial_ce_2: 0.13001/0.06808, loss_grounding_bce_2: 0.15527/0.08638, loss_grounding_dice_2: 0.27015/0.17889, loss_grounding_ce_2: 0.21525/0.27524, loss_mask_ce_3: 1.03910/0.91184, loss_mask_bce_3: 0.48227/0.33624, loss_mask_dice_3: 0.96182/1.16581, loss_spatial_bce_3: 0.12721/0.08937, loss_spatial_dice_3: 0.25980/0.21267, loss_spatial_ce_3: 0.07289/0.07327, loss_grounding_bce_3: 0.15847/0.08663, loss_grounding_dice_3: 0.26984/0.17858, loss_grounding_ce_3: 0.20905/0.27737, loss_mask_ce_4: 0.98073/0.91340, loss_mask_bce_4: 0.48312/0.33845, loss_mask_dice_4: 0.97378/1.18969, loss_spatial_bce_4: 0.13105/0.09328, loss_spatial_dice_4: 0.30776/0.22487, loss_spatial_ce_4: 0.12221/0.08949, loss_grounding_bce_4: 0.15222/0.08719, loss_grounding_dice_4: 0.26883/0.18158, loss_grounding_ce_4: 0.19655/0.28029, loss_mask_ce_5: 0.98310/0.93024, loss_mask_bce_5: 0.49015/0.34087, loss_mask_dice_5: 1.02127/1.19793, loss_spatial_bce_5: 0.13970/0.09563, loss_spatial_dice_5: 0.30761/0.22930, loss_spatial_ce_5: 0.07230/0.10312, loss_grounding_bce_5: 0.15222/0.08762, loss_grounding_dice_5: 0.28888/0.18287, loss_grounding_ce_5: 0.23073/0.29309, loss_mask_ce_6: 1.05714/0.97046, loss_mask_bce_6: 0.46129/0.34367, loss_mask_dice_6: 1.01195/1.20100, loss_spatial_bce_6: 0.16091/0.10124, loss_spatial_dice_6: 0.28615/0.23229, loss_spatial_ce_6: 0.12472/0.12792, loss_grounding_bce_6: 0.15120/0.08838, loss_grounding_dice_6: 0.29630/0.18331, loss_grounding_ce_6: 0.25156/0.30823, loss_mask_ce_7: 1.22182/1.01649, loss_mask_bce_7: 0.47146/0.35154, loss_mask_dice_7: 1.00038/1.25526, loss_spatial_bce_7: 0.20055/0.10908, loss_spatial_dice_7: 0.35559/0.25993, loss_spatial_ce_7: 0.20387/0.16274, loss_grounding_bce_7: 0.14792/0.09025, loss_grounding_dice_7: 0.27432/0.19066, loss_grounding_ce_7: 0.27124/0.33808, loss_mask_ce_8: 1.04825/1.12550, loss_mask_bce_8: 0.50649/0.36509, loss_mask_dice_8: 1.13241/1.32770, loss_spatial_bce_8: 0.16176/0.12906, loss_spatial_dice_8: 0.36101/0.29763, loss_spatial_ce_8: 0.13430/0.21236, loss_grounding_bce_8: 0.14713/0.09390, loss_grounding_dice_8: 0.28023/0.20133, loss_grounding_ce_8: 0.27211/0.40397, loss_mask_ce_9: 3.72108/3.67267, loss_mask_bce_9: 0.52256/0.39221, loss_mask_dice_9: 1.56776/1.90049, loss_spatial_bce_9: 0.40952/0.33254, loss_spatial_dice_9: 0.87687/0.82154, loss_spatial_ce_9: 1.72488/1.49143, loss_grounding_bce_9: 0.16817/0.10560, loss_grounding_dice_9: 0.37232/0.28080, loss_grounding_ce_9: 0.42196/0.66872] items per batch[64] items per second[0.23] total items[5472000] mini batches[ 85500] memory[7345] epoch remaining[0:16:56]
INFO:trainer.default_trainer:epochs[    46] optim steps[85600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.19288/0.89266, loss_mask_bce_0: 0.42542/0.33348, loss_mask_dice_0: 1.07444/1.16066, loss_spatial_bce_0: 0.05713/0.08624, loss_spatial_dice_0: 0.13089/0.20571, loss_spatial_ce_0: 0.02456/0.05884, loss_grounding_bce_0: 0.13456/0.08599, loss_grounding_dice_0: 0.26401/0.17815, loss_grounding_ce_0: 0.34001/0.27104, loss_mask_ce_1: 1.16992/0.89342, loss_mask_bce_1: 0.43169/0.33441, loss_mask_dice_1: 1.54624/1.16756, loss_spatial_bce_1: 0.05734/0.08677, loss_spatial_dice_1: 0.12107/0.20960, loss_spatial_ce_1: 0.03466/0.06468, loss_grounding_bce_1: 0.13563/0.08620, loss_grounding_dice_1: 0.23030/0.17901, loss_grounding_ce_1: 0.36499/0.27186, loss_mask_ce_2: 1.39524/0.90033, loss_mask_bce_2: 0.43226/0.33509, loss_mask_dice_2: 1.01212/1.16811, loss_spatial_bce_2: 0.05824/0.08803, loss_spatial_dice_2: 0.16473/0.21156, loss_spatial_ce_2: 0.03439/0.06806, loss_grounding_bce_2: 0.15178/0.08638, loss_grounding_dice_2: 0.23940/0.17890, loss_grounding_ce_2: 0.36547/0.27526, loss_mask_ce_3: 1.16389/0.91183, loss_mask_bce_3: 0.44571/0.33625, loss_mask_dice_3: 1.05907/1.16596, loss_spatial_bce_3: 0.06009/0.08937, loss_spatial_dice_3: 0.15949/0.21268, loss_spatial_ce_3: 0.06064/0.07325, loss_grounding_bce_3: 0.19378/0.08664, loss_grounding_dice_3: 0.29093/0.17858, loss_grounding_ce_3: 0.35626/0.27740, loss_mask_ce_4: 1.16011/0.91341, loss_mask_bce_4: 0.42384/0.33846, loss_mask_dice_4: 1.51255/1.18984, loss_spatial_bce_4: 0.05542/0.09328, loss_spatial_dice_4: 0.11599/0.22487, loss_spatial_ce_4: 0.09645/0.08947, loss_grounding_bce_4: 0.15497/0.08720, loss_grounding_dice_4: 0.27677/0.18159, loss_grounding_ce_4: 0.50902/0.28030, loss_mask_ce_5: 1.26487/0.93022, loss_mask_bce_5: 0.48970/0.34088, loss_mask_dice_5: 1.48961/1.19809, loss_spatial_bce_5: 0.05929/0.09562, loss_spatial_dice_5: 0.17115/0.22931, loss_spatial_ce_5: 0.19992/0.10311, loss_grounding_bce_5: 0.12144/0.08763, loss_grounding_dice_5: 0.27069/0.18288, loss_grounding_ce_5: 1.23643/0.29310, loss_mask_ce_6: 1.30195/0.97044, loss_mask_bce_6: 0.42909/0.34367, loss_mask_dice_6: 1.18812/1.20117, loss_spatial_bce_6: 0.06870/0.10124, loss_spatial_dice_6: 0.19348/0.23229, loss_spatial_ce_6: 0.04523/0.12790, loss_grounding_bce_6: 0.07732/0.08839, loss_grounding_dice_6: 0.19472/0.18332, loss_grounding_ce_6: 1.05880/0.30824, loss_mask_ce_7: 1.60753/1.01647, loss_mask_bce_7: 0.37731/0.35155, loss_mask_dice_7: 1.42454/1.25543, loss_spatial_bce_7: 0.07358/0.10907, loss_spatial_dice_7: 0.18870/0.25993, loss_spatial_ce_7: 0.08911/0.16270, loss_grounding_bce_7: 0.08741/0.09025, loss_grounding_dice_7: 0.22215/0.19067, loss_grounding_ce_7: 1.27050/0.33811, loss_mask_ce_8: 1.70806/1.12548, loss_mask_bce_8: 0.39473/0.36511, loss_mask_dice_8: 1.54000/1.32790, loss_spatial_bce_8: 0.07419/0.12905, loss_spatial_dice_8: 0.24767/0.29764, loss_spatial_ce_8: 0.13726/0.21230, loss_grounding_bce_8: 0.08196/0.09390, loss_grounding_dice_8: 0.18277/0.20133, loss_grounding_ce_8: 1.01949/0.40396, loss_mask_ce_9: 4.34991/3.67265, loss_mask_bce_9: 0.61449/0.39221, loss_mask_dice_9: 2.28225/1.90080, loss_spatial_bce_9: 0.34952/0.33251, loss_spatial_dice_9: 0.87848/0.82156, loss_spatial_ce_9: 2.04804/1.49143, loss_grounding_bce_9: 0.10920/0.10561, loss_grounding_dice_9: 0.34559/0.28080, loss_grounding_ce_9: 1.48551/0.66871] items per batch[64] items per second[0.23] total items[5478400] mini batches[ 85600] memory[7345] epoch remaining[0:12:21]
INFO:trainer.default_trainer:epochs[    46] optim steps[85700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.09260/0.89253, loss_mask_bce_0: 0.31491/0.33346, loss_mask_dice_0: 1.48016/1.16044, loss_spatial_bce_0: 0.05495/0.08624, loss_spatial_dice_0: 0.18025/0.20568, loss_spatial_ce_0: 0.01395/0.05882, loss_grounding_bce_0: 0.02453/0.08599, loss_grounding_dice_0: 0.31390/0.17814, loss_grounding_ce_0: 0.13310/0.27101, loss_mask_ce_1: 1.09510/0.89330, loss_mask_bce_1: 0.33585/0.33439, loss_mask_dice_1: 1.57528/1.16736, loss_spatial_bce_1: 0.05404/0.08677, loss_spatial_dice_1: 0.19607/0.20957, loss_spatial_ce_1: 0.01348/0.06466, loss_grounding_bce_1: 0.02490/0.08620, loss_grounding_dice_1: 0.32063/0.17900, loss_grounding_ce_1: 0.13478/0.27182, loss_mask_ce_2: 1.12284/0.90020, loss_mask_bce_2: 0.33047/0.33507, loss_mask_dice_2: 1.49492/1.16788, loss_spatial_bce_2: 0.05696/0.08803, loss_spatial_dice_2: 0.21056/0.21153, loss_spatial_ce_2: 0.01834/0.06803, loss_grounding_bce_2: 0.02347/0.08638, loss_grounding_dice_2: 0.33994/0.17889, loss_grounding_ce_2: 0.12363/0.27521, loss_mask_ce_3: 1.09104/0.91171, loss_mask_bce_3: 0.31483/0.33623, loss_mask_dice_3: 1.40965/1.16574, loss_spatial_bce_3: 0.05904/0.08936, loss_spatial_dice_3: 0.19088/0.21265, loss_spatial_ce_3: 0.01337/0.07323, loss_grounding_bce_3: 0.02714/0.08664, loss_grounding_dice_3: 0.37519/0.17856, loss_grounding_ce_3: 0.13374/0.27736, loss_mask_ce_4: 1.11111/0.91330, loss_mask_bce_4: 0.32988/0.33844, loss_mask_dice_4: 1.60043/1.18962, loss_spatial_bce_4: 0.06447/0.09327, loss_spatial_dice_4: 0.22098/0.22484, loss_spatial_ce_4: 0.02172/0.08945, loss_grounding_bce_4: 0.02452/0.08720, loss_grounding_dice_4: 0.35400/0.18157, loss_grounding_ce_4: 0.14557/0.28026, loss_mask_ce_5: 1.10602/0.93009, loss_mask_bce_5: 0.33536/0.34085, loss_mask_dice_5: 1.62793/1.19787, loss_spatial_bce_5: 0.06635/0.09562, loss_spatial_dice_5: 0.22025/0.22928, loss_spatial_ce_5: 0.02050/0.10309, loss_grounding_bce_5: 0.02245/0.08762, loss_grounding_dice_5: 0.31211/0.18286, loss_grounding_ce_5: 0.11576/0.29305, loss_mask_ce_6: 1.08375/0.97032, loss_mask_bce_6: 0.32337/0.34365, loss_mask_dice_6: 1.63112/1.20094, loss_spatial_bce_6: 0.08059/0.10124, loss_spatial_dice_6: 0.22994/0.23226, loss_spatial_ce_6: 0.03789/0.12789, loss_grounding_bce_6: 0.02429/0.08839, loss_grounding_dice_6: 0.35139/0.18331, loss_grounding_ce_6: 0.12574/0.30818, loss_mask_ce_7: 1.29023/1.01633, loss_mask_bce_7: 0.34712/0.35152, loss_mask_dice_7: 1.62698/1.25517, loss_spatial_bce_7: 0.09493/0.10907, loss_spatial_dice_7: 0.25100/0.25990, loss_spatial_ce_7: 0.13708/0.16267, loss_grounding_bce_7: 0.02670/0.09025, loss_grounding_dice_7: 0.41164/0.19066, loss_grounding_ce_7: 0.13296/0.33807, loss_mask_ce_8: 1.64218/1.12536, loss_mask_bce_8: 0.39031/0.36508, loss_mask_dice_8: 1.74608/1.32765, loss_spatial_bce_8: 0.10602/0.12904, loss_spatial_dice_8: 0.29013/0.29761, loss_spatial_ce_8: 0.06045/0.21223, loss_grounding_bce_8: 0.02586/0.09390, loss_grounding_dice_8: 0.35521/0.20131, loss_grounding_ce_8: 0.16625/0.40388, loss_mask_ce_9: 3.98356/3.67235, loss_mask_bce_9: 0.41379/0.39220, loss_mask_dice_9: 2.37186/1.90048, loss_spatial_bce_9: 0.34885/0.33253, loss_spatial_dice_9: 0.81603/0.82156, loss_spatial_ce_9: 1.31500/1.49140, loss_grounding_bce_9: 0.02565/0.10561, loss_grounding_dice_9: 0.46306/0.28080, loss_grounding_ce_9: 0.23989/0.66866] items per batch[64] items per second[0.23] total items[5484800] mini batches[ 85700] memory[7345] epoch remaining[0:07:45]
INFO:trainer.default_trainer:epochs[    46] optim steps[85800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.38400/0.89249, loss_mask_bce_0: 0.26264/0.33350, loss_mask_dice_0: 0.42566/1.16037, loss_spatial_bce_0: 0.17280/0.08624, loss_spatial_dice_0: 0.13897/0.20567, loss_spatial_ce_0: 0.02420/0.05881, loss_grounding_bce_0: 0.15588/0.08599, loss_grounding_dice_0: 0.14899/0.17812, loss_grounding_ce_0: 0.00506/0.27106, loss_mask_ce_1: 0.51236/0.89327, loss_mask_bce_1: 0.23179/0.33442, loss_mask_dice_1: 0.40819/1.16727, loss_spatial_bce_1: 0.12288/0.08677, loss_spatial_dice_1: 0.15976/0.20956, loss_spatial_ce_1: 0.02327/0.06464, loss_grounding_bce_1: 0.16562/0.08620, loss_grounding_dice_1: 0.15482/0.17898, loss_grounding_ce_1: 0.00541/0.27184, loss_mask_ce_2: 0.51474/0.90018, loss_mask_bce_2: 0.22591/0.33510, loss_mask_dice_2: 0.39714/1.16780, loss_spatial_bce_2: 0.11392/0.08803, loss_spatial_dice_2: 0.16802/0.21152, loss_spatial_ce_2: 0.02553/0.06802, loss_grounding_bce_2: 0.17344/0.08639, loss_grounding_dice_2: 0.15490/0.17887, loss_grounding_ce_2: 0.00574/0.27523, loss_mask_ce_3: 0.48665/0.91168, loss_mask_bce_3: 0.21236/0.33627, loss_mask_dice_3: 0.41312/1.16565, loss_spatial_bce_3: 0.08698/0.08937, loss_spatial_dice_3: 0.14707/0.21264, loss_spatial_ce_3: 0.05494/0.07321, loss_grounding_bce_3: 0.16534/0.08664, loss_grounding_dice_3: 0.15792/0.17854, loss_grounding_ce_3: 0.00537/0.27737, loss_mask_ce_4: 0.47613/0.91328, loss_mask_bce_4: 0.23164/0.33847, loss_mask_dice_4: 0.43317/1.18955, loss_spatial_bce_4: 0.09940/0.09328, loss_spatial_dice_4: 0.16743/0.22483, loss_spatial_ce_4: 0.10634/0.08943, loss_grounding_bce_4: 0.16708/0.08720, loss_grounding_dice_4: 0.15599/0.18156, loss_grounding_ce_4: 0.00449/0.28028, loss_mask_ce_5: 0.45408/0.93007, loss_mask_bce_5: 0.22661/0.34088, loss_mask_dice_5: 0.43495/1.19777, loss_spatial_bce_5: 0.08699/0.09563, loss_spatial_dice_5: 0.15677/0.22928, loss_spatial_ce_5: 0.15518/0.10306, loss_grounding_bce_5: 0.18020/0.08763, loss_grounding_dice_5: 0.15488/0.18285, loss_grounding_ce_5: 0.00950/0.29307, loss_mask_ce_6: 0.38650/0.97032, loss_mask_bce_6: 0.28133/0.34368, loss_mask_dice_6: 0.49396/1.20086, loss_spatial_bce_6: 0.08022/0.10124, loss_spatial_dice_6: 0.16014/0.23226, loss_spatial_ce_6: 0.19567/0.12786, loss_grounding_bce_6: 0.15071/0.08839, loss_grounding_dice_6: 0.15659/0.18329, loss_grounding_ce_6: 0.00794/0.30818, loss_mask_ce_7: 0.44155/1.01631, loss_mask_bce_7: 0.30179/0.35156, loss_mask_dice_7: 0.48846/1.25510, loss_spatial_bce_7: 0.11605/0.10907, loss_spatial_dice_7: 0.23657/0.25989, loss_spatial_ce_7: 0.28205/0.16262, loss_grounding_bce_7: 0.02782/0.09026, loss_grounding_dice_7: 0.06647/0.19064, loss_grounding_ce_7: 0.61513/0.33807, loss_mask_ce_8: 0.60355/1.12535, loss_mask_bce_8: 0.31740/0.36511, loss_mask_dice_8: 0.49856/1.32758, loss_spatial_bce_8: 0.14908/0.12904, loss_spatial_dice_8: 0.25148/0.29761, loss_spatial_ce_8: 0.18913/0.21215, loss_grounding_bce_8: 0.21660/0.09391, loss_grounding_dice_8: 0.15566/0.20129, loss_grounding_ce_8: 0.00640/0.40390, loss_mask_ce_9: 2.71509/3.67227, loss_mask_bce_9: 0.24787/0.39223, loss_mask_dice_9: 0.73012/1.90038, loss_spatial_bce_9: 0.38918/0.33252, loss_spatial_dice_9: 0.83346/0.82156, loss_spatial_ce_9: 1.28600/1.49139, loss_grounding_bce_9: 0.07265/0.10562, loss_grounding_dice_9: 0.13477/0.28078, loss_grounding_ce_9: 0.36250/0.66865] items per batch[64] items per second[0.23] total items[5491200] mini batches[ 85800] memory[7345] epoch remaining[0:03:10]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00085869.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0032 s/iter. Inference: 0.2153 s/iter. Eval: 0.0912 s/iter. Total: 0.3097 s/iter. ETA=0:00:45
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0030 s/iter. Inference: 0.2193 s/iter. Eval: 0.0807 s/iter. Total: 0.3031 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2225 s/iter. Eval: 0.0789 s/iter. Total: 0.3047 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2221 s/iter. Eval: 0.0770 s/iter. Total: 0.3024 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 80/157. Dataloading: 0.0032 s/iter. Inference: 0.2216 s/iter. Eval: 0.0757 s/iter. Total: 0.3007 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 96/157. Dataloading: 0.0032 s/iter. Inference: 0.2239 s/iter. Eval: 0.0760 s/iter. Total: 0.3032 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 112/157. Dataloading: 0.0032 s/iter. Inference: 0.2253 s/iter. Eval: 0.0765 s/iter. Total: 0.3051 s/iter. ETA=0:00:13
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 129/157. Dataloading: 0.0032 s/iter. Inference: 0.2251 s/iter. Eval: 0.0758 s/iter. Total: 0.3042 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 146/157. Dataloading: 0.0032 s/iter. Inference: 0.2260 s/iter. Eval: 0.0759 s/iter. Total: 0.3053 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalu6dt73xe ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.301 | 82.142 | 60.424 |      133      |
| Things | 55.399 | 82.724 | 66.292 |      80       |
| Stuff  | 42.606 | 81.262 | 51.566 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.40s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.00 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.06 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.26s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.25 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.395
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.619
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.415
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.426
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.614
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.497
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.514
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.550
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.720
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.09 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.485 | 61.918 | 41.458 | 19.470 | 42.553 | 61.443 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.771 | bicycle      | 19.790 | car            | 37.535 |
| motorcycle    | 35.444 | airplane     | 56.973 | bus            | 65.604 |
| train         | 68.554 | truck        | 36.923 | boat           | 23.673 |
| traffic light | 25.807 | fire hydrant | 65.842 | stop sign      | 65.070 |
| parking meter | 43.105 | bench        | 20.303 | bird           | 29.882 |
| cat           | 73.816 | dog          | 66.380 | horse          | 46.230 |
| sheep         | 48.470 | cow          | 52.040 | elephant       | 61.642 |
| bear          | 76.876 | zebra        | 60.734 | giraffe        | 57.628 |
| backpack      | 17.142 | umbrella     | 49.447 | handbag        | 16.060 |
| tie           | 34.539 | suitcase     | 42.588 | frisbee        | 67.012 |
| skis          | 5.436  | snowboard    | 22.142 | sports ball    | 47.830 |
| kite          | 36.005 | baseball bat | 29.093 | baseball glove | 43.956 |
| skateboard    | 37.010 | surfboard    | 36.837 | tennis racket  | 56.397 |
| bottle        | 34.960 | wine glass   | 27.162 | cup            | 40.845 |
| fork          | 16.135 | knife        | 14.303 | spoon          | 14.926 |
| bowl          | 32.638 | banana       | 21.482 | apple          | 20.596 |
| sandwich      | 42.822 | orange       | 29.688 | broccoli       | 22.165 |
| carrot        | 20.747 | hot dog      | 22.562 | pizza          | 50.798 |
| donut         | 47.330 | cake         | 45.060 | chair          | 21.593 |
| couch         | 42.599 | potted plant | 18.135 | bed            | 41.230 |
| dining table  | 13.176 | toilet       | 68.426 | tv             | 62.530 |
| laptop        | 63.276 | mouse        | 59.568 | remote         | 31.621 |
| keyboard      | 48.348 | cell phone   | 38.183 | microwave      | 56.044 |
| oven          | 33.769 | toaster      | 32.824 | sink           | 37.233 |
| refrigerator  | 59.740 | book         | 9.780  | clock          | 51.929 |
| vase          | 34.978 | scissors     | 24.221 | teddy bear     | 51.488 |
| hair drier    | 9.671  | toothbrush   | 19.611 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.99585137672007, 'fwIoU': 69.22378135556716, 'IoU-person': 87.5091268766779, 'IoU-bicycle': 71.87860146793038, 'IoU-car': 70.70059707870791, 'IoU-motorcycle': 79.42274704772254, 'IoU-airplane': 83.93571310496763, 'IoU-bus': 86.127165564864, 'IoU-train': 86.03418235200874, 'IoU-truck': 63.06589731991673, 'IoU-boat': 68.54333289879688, 'IoU-traffic light': 76.07086779495515, 'IoU-fire hydrant': 90.16524847691456, 'IoU-stop sign': 91.93945284359052, 'IoU-parking meter': 87.8209580535443, 'IoU-bench': 54.58452623565175, 'IoU-bird': 75.51347458590759, 'IoU-cat': 81.77122955103229, 'IoU-dog': 79.95175205578113, 'IoU-horse': 86.78151037970487, 'IoU-sheep': 85.23346320571981, 'IoU-cow': 80.36401763020613, 'IoU-elephant': 90.34324479367956, 'IoU-bear': 74.39354430575057, 'IoU-zebra': 89.2171355033777, 'IoU-giraffe': 85.09732718223971, 'IoU-backpack': 41.34675043921809, 'IoU-umbrella': 77.5369278354725, 'IoU-handbag': 37.765490747609086, 'IoU-tie': 70.21128726704539, 'IoU-suitcase': 80.76708310413612, 'IoU-frisbee': 83.95611596440747, 'IoU-skis': 52.136200680852774, 'IoU-snowboard': 69.24670635517634, 'IoU-sports ball': 67.57026406479412, 'IoU-kite': 66.48655814142025, 'IoU-baseball bat': 61.02774700473035, 'IoU-baseball glove': 52.20602028438152, 'IoU-skateboard': 82.69651690740612, 'IoU-surfboard': 75.43851643360452, 'IoU-tennis racket': 82.85480390522481, 'IoU-bottle': 68.50372427702047, 'IoU-wine glass': 74.53734597780013, 'IoU-cup': 64.42071814897945, 'IoU-fork': 55.24389412232472, 'IoU-knife': 51.549510450473136, 'IoU-spoon': 49.65120414629503, 'IoU-bowl': 52.54561776632071, 'IoU-banana': 82.34534729018472, 'IoU-apple': 60.093457379083596, 'IoU-sandwich': 66.50994309102892, 'IoU-orange': 77.32936816704951, 'IoU-broccoli': 66.84256640640858, 'IoU-carrot': 64.59753202349549, 'IoU-hot dog': 59.90649667541024, 'IoU-pizza': 83.2452779990864, 'IoU-donut': 64.43407403897739, 'IoU-cake': 68.49007631918518, 'IoU-chair': 55.76252829972812, 'IoU-couch': 67.55368140395966, 'IoU-potted plant': 34.78498379418941, 'IoU-bed': 69.16490578266462, 'IoU-dining table': 51.61585459357981, 'IoU-toilet': 81.75371507110187, 'IoU-tv': 74.42983323777386, 'IoU-laptop': 71.74695923963267, 'IoU-mouse': 66.65252604314483, 'IoU-remote': 49.672257748095795, 'IoU-keyboard': 61.464566959607794, 'IoU-cell phone': 68.98429553645794, 'IoU-microwave': 68.57637148295423, 'IoU-oven': 65.71992883727061, 'IoU-toaster': 74.1977328850674, 'IoU-sink': 68.48458812592246, 'IoU-refrigerator': 78.96654399745161, 'IoU-book': 51.954469847965946, 'IoU-clock': 74.4319583402863, 'IoU-vase': 64.33084819324831, 'IoU-scissors': 54.07498480499656, 'IoU-teddy bear': 78.93727921767511, 'IoU-hair drier': 37.40392910359972, 'IoU-toothbrush': 54.76727472450422, 'IoU-banner': 34.48081071725768, 'IoU-blanket': 9.429966678339019, 'IoU-bridge': 37.2881384335399, 'IoU-cardboard': 43.79130781944848, 'IoU-counter': 28.080347020108803, 'IoU-curtain': 64.54849353035856, 'IoU-door-stuff': 42.97889424503167, 'IoU-floor-wood': 64.20119642132047, 'IoU-flower': 44.818089806516085, 'IoU-fruit': 41.36882912242377, 'IoU-gravel': 31.722091426900935, 'IoU-house': 25.713488700236297, 'IoU-light': 39.13122597926938, 'IoU-mirror-stuff': 56.53428634766382, 'IoU-net': 43.57164360900639, 'IoU-pillow': 11.623699188770129, 'IoU-platform': 30.614827041705695, 'IoU-playingfield': 71.11697634882604, 'IoU-railroad': 61.18193197418227, 'IoU-river': 46.99363971838969, 'IoU-road': 66.19210426811775, 'IoU-roof': 15.548219705613626, 'IoU-sand': 64.0024742455085, 'IoU-sea': 85.9211110325128, 'IoU-shelf': 36.556265756873465, 'IoU-snow': 88.38766702266972, 'IoU-stairs': 28.763144579373463, 'IoU-tent': 10.329787775688622, 'IoU-towel': 35.1326395992189, 'IoU-wall-brick': 46.780006586239686, 'IoU-wall-stone': 29.823170966595946, 'IoU-wall-tile': 68.40204062013655, 'IoU-wall-wood': 39.9869007789053, 'IoU-water-other': 23.533733278691386, 'IoU-window-blind': 47.077290153079005, 'IoU-window-other': 47.034938441594406, 'IoU-tree-merged': 80.9559973000523, 'IoU-fence-merged': 50.82929051745231, 'IoU-ceiling-merged': 66.84177184499977, 'IoU-sky-other-merged': 93.59288740293385, 'IoU-cabinet-merged': 60.58895289061792, 'IoU-table-merged': 36.20359563613315, 'IoU-floor-other-merged': 49.85969075213356, 'IoU-pavement-merged': 53.550896491531844, 'IoU-mountain-merged': 56.720095774107484, 'IoU-grass-merged': 70.93222071509038, 'IoU-dirt-merged': 45.36794605789996, 'IoU-paper-merged': 31.22463939074469, 'IoU-food-other-merged': 39.03801443084815, 'IoU-building-other-merged': 58.60045840438553, 'IoU-rock-merged': 62.98958066513656, 'IoU-wall-other-merged': 65.45273502693871, 'IoU-rug-merged': 63.6478038675175, 'mACC': 73.3365599095911, 'pACC': 80.50908885908076, 'ACC-person': 92.52505166438809, 'ACC-bicycle': 82.05548969897293, 'ACC-car': 85.31692875659428, 'ACC-motorcycle': 84.25163389865207, 'ACC-airplane': 90.64109424783581, 'ACC-bus': 90.60557948895097, 'ACC-train': 95.38492524140038, 'ACC-truck': 77.27990100890419, 'ACC-boat': 79.0989993581621, 'ACC-traffic light': 90.44082606665242, 'ACC-fire hydrant': 95.58451152284285, 'ACC-stop sign': 95.24708294625866, 'ACC-parking meter': 92.25561386024414, 'ACC-bench': 73.4963569928909, 'ACC-bird': 79.93747111088285, 'ACC-cat': 90.02952584177889, 'ACC-dog': 84.50406022055637, 'ACC-horse': 92.75503051116316, 'ACC-sheep': 88.48886786053198, 'ACC-cow': 85.40100491357488, 'ACC-elephant': 92.87272721240525, 'ACC-bear': 76.41526832293269, 'ACC-zebra': 91.50454088396906, 'ACC-giraffe': 89.1493939451637, 'ACC-backpack': 55.48351898756365, 'ACC-umbrella': 85.36965321296016, 'ACC-handbag': 57.952097075987716, 'ACC-tie': 80.13164066720184, 'ACC-suitcase': 89.21092305288929, 'ACC-frisbee': 94.11236363636364, 'ACC-skis': 70.75745237600385, 'ACC-snowboard': 78.91014180402145, 'ACC-sports ball': 80.15836098448435, 'ACC-kite': 76.37547539762245, 'ACC-baseball bat': 80.27744270205066, 'ACC-baseball glove': 60.15938551551703, 'ACC-skateboard': 89.49800485656961, 'ACC-surfboard': 83.76687164644568, 'ACC-tennis racket': 89.30888575458393, 'ACC-bottle': 83.87370332797126, 'ACC-wine glass': 86.27821798335479, 'ACC-cup': 83.1538891619476, 'ACC-fork': 66.37190988481296, 'ACC-knife': 69.0193815490584, 'ACC-spoon': 69.14285831720079, 'ACC-bowl': 65.24106229266124, 'ACC-banana': 89.63690174299616, 'ACC-apple': 72.00768624014552, 'ACC-sandwich': 79.25846389748652, 'ACC-orange': 87.17169618343094, 'ACC-broccoli': 77.97239485685805, 'ACC-carrot': 76.29356705910435, 'ACC-hot dog': 66.86663049777975, 'ACC-pizza': 91.40145825340682, 'ACC-donut': 81.29556993370923, 'ACC-cake': 76.51351819894779, 'ACC-chair': 71.495817811718, 'ACC-couch': 84.12068435536443, 'ACC-potted plant': 52.25275715727455, 'ACC-bed': 79.59130356977252, 'ACC-dining table': 76.66516142036231, 'ACC-toilet': 91.25728895512957, 'ACC-tv': 87.34281659520612, 'ACC-laptop': 84.43192519386723, 'ACC-mouse': 80.48367837985059, 'ACC-remote': 72.78957991911658, 'ACC-keyboard': 69.3089793462435, 'ACC-cell phone': 74.96715627483711, 'ACC-microwave': 77.72077430554963, 'ACC-oven': 83.93955689248158, 'ACC-toaster': 85.32551423996496, 'ACC-sink': 82.88524143256095, 'ACC-refrigerator': 90.19257938959335, 'ACC-book': 68.63354907399625, 'ACC-clock': 80.3845364646563, 'ACC-vase': 75.58063682295631, 'ACC-scissors': 58.10240659633272, 'ACC-teddy bear': 85.43334389706789, 'ACC-hair drier': 54.300799817835085, 'ACC-toothbrush': 81.42112578179291, 'ACC-banner': 71.07730742514894, 'ACC-blanket': 12.097721710424679, 'ACC-bridge': 56.507572910212, 'ACC-cardboard': 55.37812999854588, 'ACC-counter': 51.77538717668566, 'ACC-curtain': 76.10842291769679, 'ACC-door-stuff': 66.14149554890028, 'ACC-floor-wood': 79.30507468672091, 'ACC-flower': 61.43118593517386, 'ACC-fruit': 58.771159468636384, 'ACC-gravel': 43.76462180619871, 'ACC-house': 31.9295124133686, 'ACC-light': 57.87895245569039, 'ACC-mirror-stuff': 72.26355848700845, 'ACC-net': 62.89122342607188, 'ACC-pillow': 25.25310657799907, 'ACC-platform': 50.73860786206432, 'ACC-playingfield': 92.30590024657268, 'ACC-railroad': 78.72011497723275, 'ACC-river': 67.21775691488968, 'ACC-road': 85.07476372592289, 'ACC-roof': 21.626226943245854, 'ACC-sand': 70.50026949001227, 'ACC-sea': 91.71677109543992, 'ACC-shelf': 59.12583482172731, 'ACC-snow': 94.90419293262475, 'ACC-stairs': 44.29420438501469, 'ACC-tent': 11.984967232791464, 'ACC-towel': 43.652255072400976, 'ACC-wall-brick': 65.33763104511546, 'ACC-wall-stone': 37.50762599945452, 'ACC-wall-tile': 82.5356087010617, 'ACC-wall-wood': 54.48198779464166, 'ACC-water-other': 38.45051276015811, 'ACC-window-blind': 56.39885772699785, 'ACC-window-other': 68.05366289513522, 'ACC-tree-merged': 89.43132442181303, 'ACC-fence-merged': 70.38231018901129, 'ACC-ceiling-merged': 80.38231905259241, 'ACC-sky-other-merged': 96.40854163426752, 'ACC-cabinet-merged': 75.34615091239083, 'ACC-table-merged': 48.379723664391655, 'ACC-floor-other-merged': 63.3374538415329, 'ACC-pavement-merged': 65.80873400102907, 'ACC-mountain-merged': 67.97984255845495, 'ACC-grass-merged': 83.51577083016285, 'ACC-dirt-merged': 66.0757346496143, 'ACC-paper-merged': 43.63765881234878, 'ACC-food-other-merged': 53.31536822415583, 'ACC-building-other-merged': 73.65028763991917, 'ACC-rock-merged': 82.49817135479468, 'ACC-wall-other-merged': 80.78592250565696, 'ACC-rug-merged': 78.78474179611753})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1593 s/iter. Inference: 0.5057 s/iter. Eval: 0.0000 s/iter. Total: 0.6651 s/iter. ETA=0:00:25
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1592 s/iter. Inference: 0.5407 s/iter. Eval: 0.0000 s/iter. Total: 0.7001 s/iter. ETA=0:00:22
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/50. Dataloading: 0.1709 s/iter. Inference: 0.5466 s/iter. Eval: 0.0000 s/iter. Total: 0.7177 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 28/50. Dataloading: 0.1744 s/iter. Inference: 0.6841 s/iter. Eval: 0.0000 s/iter. Total: 0.8588 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 38/50. Dataloading: 0.1711 s/iter. Inference: 0.6045 s/iter. Eval: 0.0000 s/iter. Total: 0.7758 s/iter. ETA=0:00:09
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 45/50. Dataloading: 0.1702 s/iter. Inference: 0.6430 s/iter. Eval: 0.0000 s/iter. Total: 0.8135 s/iter. ETA=0:00:04
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 50/50. Dataloading: 0.1711 s/iter. Inference: 0.6686 s/iter. Eval: 0.0000 s/iter. Total: 0.8399 s/iter. ETA=0:00:00
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4492244659057654, 'noc@0.8': 2.71027216856892, 'noc@0.85': 3.263388937664618, 'noc@0.9': 4.239976587649985, 'miou@iter1': 0.8362504547717524}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0017 s/iter. Inference: 0.1015 s/iter. Eval: 0.0008 s/iter. Total: 0.1040 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.7061767578125, 'precision@0.6': 68.9467544555664, 'precision@0.7': 63.77769088745117, 'precision@0.8': 53.67275619506836, 'precision@0.9': 27.633113861083984, 'cIoU': 57.386287689208984, 'mIoU': 63.45586395263672}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.3006590458295, 'SQ': 82.14161043379734, 'RQ': 60.42367990912244, 'PQ_th': 55.39850549167598, 'SQ_th': 82.72445055502673, 'RQ_th': 66.2915830949604, 'PQ_st': 42.605796486061216, 'SQ_st': 81.26185176024364, 'RQ_st': 51.56646755314054}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.48475427253662, 'AP50': 61.91755430666215, 'AP75': 41.45781300399386, 'APs': 19.47032317253746, 'APm': 42.552985096503335, 'APl': 61.44328402356112, 'AP-person': 44.771279305405905, 'AP-bicycle': 19.790086310106563, 'AP-car': 37.534709308419274, 'AP-motorcycle': 35.44391691571551, 'AP-airplane': 56.97278440645939, 'AP-bus': 65.60432708817564, 'AP-train': 68.5536622329128, 'AP-truck': 36.92321530249202, 'AP-boat': 23.673210015210667, 'AP-traffic light': 25.806983456586924, 'AP-fire hydrant': 65.84195502057041, 'AP-stop sign': 65.06971968996244, 'AP-parking meter': 43.10542784915836, 'AP-bench': 20.302886399148562, 'AP-bird': 29.881523927121627, 'AP-cat': 73.81588545508143, 'AP-dog': 66.3803895215464, 'AP-horse': 46.23044996832214, 'AP-sheep': 48.470489837026356, 'AP-cow': 52.03966830521032, 'AP-elephant': 61.64184299821612, 'AP-bear': 76.8762156362595, 'AP-zebra': 60.73396202882475, 'AP-giraffe': 57.62799741217116, 'AP-backpack': 17.142391958949656, 'AP-umbrella': 49.447372777995604, 'AP-handbag': 16.060142288241376, 'AP-tie': 34.53870886926335, 'AP-suitcase': 42.588366359090806, 'AP-frisbee': 67.01204334911507, 'AP-skis': 5.436189208231647, 'AP-snowboard': 22.14176688218462, 'AP-sports ball': 47.829669215359985, 'AP-kite': 36.005120598789226, 'AP-baseball bat': 29.093252291499343, 'AP-baseball glove': 43.95636013803833, 'AP-skateboard': 37.00954893183372, 'AP-surfboard': 36.83695033896381, 'AP-tennis racket': 56.39665824446721, 'AP-bottle': 34.96025803736225, 'AP-wine glass': 27.162400566522, 'AP-cup': 40.84459511436819, 'AP-fork': 16.134843686132765, 'AP-knife': 14.303177649452184, 'AP-spoon': 14.926044590805434, 'AP-bowl': 32.637512776692155, 'AP-banana': 21.481597330367613, 'AP-apple': 20.596318313218344, 'AP-sandwich': 42.822336598083396, 'AP-orange': 29.688368460489983, 'AP-broccoli': 22.165069090921037, 'AP-carrot': 20.74697432604089, 'AP-hot dog': 22.561721270081787, 'AP-pizza': 50.79830835295609, 'AP-donut': 47.330249175005996, 'AP-cake': 45.05955437153189, 'AP-chair': 21.593257819537868, 'AP-couch': 42.59864304716445, 'AP-potted plant': 18.134646142743176, 'AP-bed': 41.230417062059765, 'AP-dining table': 13.175942261661975, 'AP-toilet': 68.42609015212479, 'AP-tv': 62.52965623442025, 'AP-laptop': 63.276482143911196, 'AP-mouse': 59.56844688482113, 'AP-remote': 31.620725183506714, 'AP-keyboard': 48.34827037604578, 'AP-cell phone': 38.183017046577625, 'AP-microwave': 56.04399209758867, 'AP-oven': 33.76917503815699, 'AP-toaster': 32.82393974691586, 'AP-sink': 37.233443092861066, 'AP-refrigerator': 59.740247190167636, 'AP-book': 9.780301056228824, 'AP-clock': 51.92859618503519, 'AP-vase': 34.97759793992484, 'AP-scissors': 24.220743154086133, 'AP-teddy bear': 51.488463523997254, 'AP-hair drier': 9.670610963535378, 'AP-toothbrush': 19.611177907697137}), ('sem_seg', {'mIoU': 60.99585137672007, 'fwIoU': 69.22378135556716, 'IoU-person': 87.5091268766779, 'IoU-bicycle': 71.87860146793038, 'IoU-car': 70.70059707870791, 'IoU-motorcycle': 79.42274704772254, 'IoU-airplane': 83.93571310496763, 'IoU-bus': 86.127165564864, 'IoU-train': 86.03418235200874, 'IoU-truck': 63.06589731991673, 'IoU-boat': 68.54333289879688, 'IoU-traffic light': 76.07086779495515, 'IoU-fire hydrant': 90.16524847691456, 'IoU-stop sign': 91.93945284359052, 'IoU-parking meter': 87.8209580535443, 'IoU-bench': 54.58452623565175, 'IoU-bird': 75.51347458590759, 'IoU-cat': 81.77122955103229, 'IoU-dog': 79.95175205578113, 'IoU-horse': 86.78151037970487, 'IoU-sheep': 85.23346320571981, 'IoU-cow': 80.36401763020613, 'IoU-elephant': 90.34324479367956, 'IoU-bear': 74.39354430575057, 'IoU-zebra': 89.2171355033777, 'IoU-giraffe': 85.09732718223971, 'IoU-backpack': 41.34675043921809, 'IoU-umbrella': 77.5369278354725, 'IoU-handbag': 37.765490747609086, 'IoU-tie': 70.21128726704539, 'IoU-suitcase': 80.76708310413612, 'IoU-frisbee': 83.95611596440747, 'IoU-skis': 52.136200680852774, 'IoU-snowboard': 69.24670635517634, 'IoU-sports ball': 67.57026406479412, 'IoU-kite': 66.48655814142025, 'IoU-baseball bat': 61.02774700473035, 'IoU-baseball glove': 52.20602028438152, 'IoU-skateboard': 82.69651690740612, 'IoU-surfboard': 75.43851643360452, 'IoU-tennis racket': 82.85480390522481, 'IoU-bottle': 68.50372427702047, 'IoU-wine glass': 74.53734597780013, 'IoU-cup': 64.42071814897945, 'IoU-fork': 55.24389412232472, 'IoU-knife': 51.549510450473136, 'IoU-spoon': 49.65120414629503, 'IoU-bowl': 52.54561776632071, 'IoU-banana': 82.34534729018472, 'IoU-apple': 60.093457379083596, 'IoU-sandwich': 66.50994309102892, 'IoU-orange': 77.32936816704951, 'IoU-broccoli': 66.84256640640858, 'IoU-carrot': 64.59753202349549, 'IoU-hot dog': 59.90649667541024, 'IoU-pizza': 83.2452779990864, 'IoU-donut': 64.43407403897739, 'IoU-cake': 68.49007631918518, 'IoU-chair': 55.76252829972812, 'IoU-couch': 67.55368140395966, 'IoU-potted plant': 34.78498379418941, 'IoU-bed': 69.16490578266462, 'IoU-dining table': 51.61585459357981, 'IoU-toilet': 81.75371507110187, 'IoU-tv': 74.42983323777386, 'IoU-laptop': 71.74695923963267, 'IoU-mouse': 66.65252604314483, 'IoU-remote': 49.672257748095795, 'IoU-keyboard': 61.464566959607794, 'IoU-cell phone': 68.98429553645794, 'IoU-microwave': 68.57637148295423, 'IoU-oven': 65.71992883727061, 'IoU-toaster': 74.1977328850674, 'IoU-sink': 68.48458812592246, 'IoU-refrigerator': 78.96654399745161, 'IoU-book': 51.954469847965946, 'IoU-clock': 74.4319583402863, 'IoU-vase': 64.33084819324831, 'IoU-scissors': 54.07498480499656, 'IoU-teddy bear': 78.93727921767511, 'IoU-hair drier': 37.40392910359972, 'IoU-toothbrush': 54.76727472450422, 'IoU-banner': 34.48081071725768, 'IoU-blanket': 9.429966678339019, 'IoU-bridge': 37.2881384335399, 'IoU-cardboard': 43.79130781944848, 'IoU-counter': 28.080347020108803, 'IoU-curtain': 64.54849353035856, 'IoU-door-stuff': 42.97889424503167, 'IoU-floor-wood': 64.20119642132047, 'IoU-flower': 44.818089806516085, 'IoU-fruit': 41.36882912242377, 'IoU-gravel': 31.722091426900935, 'IoU-house': 25.713488700236297, 'IoU-light': 39.13122597926938, 'IoU-mirror-stuff': 56.53428634766382, 'IoU-net': 43.57164360900639, 'IoU-pillow': 11.623699188770129, 'IoU-platform': 30.614827041705695, 'IoU-playingfield': 71.11697634882604, 'IoU-railroad': 61.18193197418227, 'IoU-river': 46.99363971838969, 'IoU-road': 66.19210426811775, 'IoU-roof': 15.548219705613626, 'IoU-sand': 64.0024742455085, 'IoU-sea': 85.9211110325128, 'IoU-shelf': 36.556265756873465, 'IoU-snow': 88.38766702266972, 'IoU-stairs': 28.763144579373463, 'IoU-tent': 10.329787775688622, 'IoU-towel': 35.1326395992189, 'IoU-wall-brick': 46.780006586239686, 'IoU-wall-stone': 29.823170966595946, 'IoU-wall-tile': 68.40204062013655, 'IoU-wall-wood': 39.9869007789053, 'IoU-water-other': 23.533733278691386, 'IoU-window-blind': 47.077290153079005, 'IoU-window-other': 47.034938441594406, 'IoU-tree-merged': 80.9559973000523, 'IoU-fence-merged': 50.82929051745231, 'IoU-ceiling-merged': 66.84177184499977, 'IoU-sky-other-merged': 93.59288740293385, 'IoU-cabinet-merged': 60.58895289061792, 'IoU-table-merged': 36.20359563613315, 'IoU-floor-other-merged': 49.85969075213356, 'IoU-pavement-merged': 53.550896491531844, 'IoU-mountain-merged': 56.720095774107484, 'IoU-grass-merged': 70.93222071509038, 'IoU-dirt-merged': 45.36794605789996, 'IoU-paper-merged': 31.22463939074469, 'IoU-food-other-merged': 39.03801443084815, 'IoU-building-other-merged': 58.60045840438553, 'IoU-rock-merged': 62.98958066513656, 'IoU-wall-other-merged': 65.45273502693871, 'IoU-rug-merged': 63.6478038675175, 'mACC': 73.3365599095911, 'pACC': 80.50908885908076, 'ACC-person': 92.52505166438809, 'ACC-bicycle': 82.05548969897293, 'ACC-car': 85.31692875659428, 'ACC-motorcycle': 84.25163389865207, 'ACC-airplane': 90.64109424783581, 'ACC-bus': 90.60557948895097, 'ACC-train': 95.38492524140038, 'ACC-truck': 77.27990100890419, 'ACC-boat': 79.0989993581621, 'ACC-traffic light': 90.44082606665242, 'ACC-fire hydrant': 95.58451152284285, 'ACC-stop sign': 95.24708294625866, 'ACC-parking meter': 92.25561386024414, 'ACC-bench': 73.4963569928909, 'ACC-bird': 79.93747111088285, 'ACC-cat': 90.02952584177889, 'ACC-dog': 84.50406022055637, 'ACC-horse': 92.75503051116316, 'ACC-sheep': 88.48886786053198, 'ACC-cow': 85.40100491357488, 'ACC-elephant': 92.87272721240525, 'ACC-bear': 76.41526832293269, 'ACC-zebra': 91.50454088396906, 'ACC-giraffe': 89.1493939451637, 'ACC-backpack': 55.48351898756365, 'ACC-umbrella': 85.36965321296016, 'ACC-handbag': 57.952097075987716, 'ACC-tie': 80.13164066720184, 'ACC-suitcase': 89.21092305288929, 'ACC-frisbee': 94.11236363636364, 'ACC-skis': 70.75745237600385, 'ACC-snowboard': 78.91014180402145, 'ACC-sports ball': 80.15836098448435, 'ACC-kite': 76.37547539762245, 'ACC-baseball bat': 80.27744270205066, 'ACC-baseball glove': 60.15938551551703, 'ACC-skateboard': 89.49800485656961, 'ACC-surfboard': 83.76687164644568, 'ACC-tennis racket': 89.30888575458393, 'ACC-bottle': 83.87370332797126, 'ACC-wine glass': 86.27821798335479, 'ACC-cup': 83.1538891619476, 'ACC-fork': 66.37190988481296, 'ACC-knife': 69.0193815490584, 'ACC-spoon': 69.14285831720079, 'ACC-bowl': 65.24106229266124, 'ACC-banana': 89.63690174299616, 'ACC-apple': 72.00768624014552, 'ACC-sandwich': 79.25846389748652, 'ACC-orange': 87.17169618343094, 'ACC-broccoli': 77.97239485685805, 'ACC-carrot': 76.29356705910435, 'ACC-hot dog': 66.86663049777975, 'ACC-pizza': 91.40145825340682, 'ACC-donut': 81.29556993370923, 'ACC-cake': 76.51351819894779, 'ACC-chair': 71.495817811718, 'ACC-couch': 84.12068435536443, 'ACC-potted plant': 52.25275715727455, 'ACC-bed': 79.59130356977252, 'ACC-dining table': 76.66516142036231, 'ACC-toilet': 91.25728895512957, 'ACC-tv': 87.34281659520612, 'ACC-laptop': 84.43192519386723, 'ACC-mouse': 80.48367837985059, 'ACC-remote': 72.78957991911658, 'ACC-keyboard': 69.3089793462435, 'ACC-cell phone': 74.96715627483711, 'ACC-microwave': 77.72077430554963, 'ACC-oven': 83.93955689248158, 'ACC-toaster': 85.32551423996496, 'ACC-sink': 82.88524143256095, 'ACC-refrigerator': 90.19257938959335, 'ACC-book': 68.63354907399625, 'ACC-clock': 80.3845364646563, 'ACC-vase': 75.58063682295631, 'ACC-scissors': 58.10240659633272, 'ACC-teddy bear': 85.43334389706789, 'ACC-hair drier': 54.300799817835085, 'ACC-toothbrush': 81.42112578179291, 'ACC-banner': 71.07730742514894, 'ACC-blanket': 12.097721710424679, 'ACC-bridge': 56.507572910212, 'ACC-cardboard': 55.37812999854588, 'ACC-counter': 51.77538717668566, 'ACC-curtain': 76.10842291769679, 'ACC-door-stuff': 66.14149554890028, 'ACC-floor-wood': 79.30507468672091, 'ACC-flower': 61.43118593517386, 'ACC-fruit': 58.771159468636384, 'ACC-gravel': 43.76462180619871, 'ACC-house': 31.9295124133686, 'ACC-light': 57.87895245569039, 'ACC-mirror-stuff': 72.26355848700845, 'ACC-net': 62.89122342607188, 'ACC-pillow': 25.25310657799907, 'ACC-platform': 50.73860786206432, 'ACC-playingfield': 92.30590024657268, 'ACC-railroad': 78.72011497723275, 'ACC-river': 67.21775691488968, 'ACC-road': 85.07476372592289, 'ACC-roof': 21.626226943245854, 'ACC-sand': 70.50026949001227, 'ACC-sea': 91.71677109543992, 'ACC-shelf': 59.12583482172731, 'ACC-snow': 94.90419293262475, 'ACC-stairs': 44.29420438501469, 'ACC-tent': 11.984967232791464, 'ACC-towel': 43.652255072400976, 'ACC-wall-brick': 65.33763104511546, 'ACC-wall-stone': 37.50762599945452, 'ACC-wall-tile': 82.5356087010617, 'ACC-wall-wood': 54.48198779464166, 'ACC-water-other': 38.45051276015811, 'ACC-window-blind': 56.39885772699785, 'ACC-window-other': 68.05366289513522, 'ACC-tree-merged': 89.43132442181303, 'ACC-fence-merged': 70.38231018901129, 'ACC-ceiling-merged': 80.38231905259241, 'ACC-sky-other-merged': 96.40854163426752, 'ACC-cabinet-merged': 75.34615091239083, 'ACC-table-merged': 48.379723664391655, 'ACC-floor-other-merged': 63.3374538415329, 'ACC-pavement-merged': 65.80873400102907, 'ACC-mountain-merged': 67.97984255845495, 'ACC-grass-merged': 83.51577083016285, 'ACC-dirt-merged': 66.0757346496143, 'ACC-paper-merged': 43.63765881234878, 'ACC-food-other-merged': 53.31536822415583, 'ACC-building-other-merged': 73.65028763991917, 'ACC-rock-merged': 82.49817135479468, 'ACC-wall-other-merged': 80.78592250565696, 'ACC-rug-merged': 78.78474179611753})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4492244659057654, 'noc@0.8': 2.71027216856892, 'noc@0.85': 3.263388937664618, 'noc@0.9': 4.239976587649985, 'miou@iter1': 0.8362504547717524}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.7061767578125, 'precision@0.6': 68.9467544555664, 'precision@0.7': 63.77769088745117, 'precision@0.8': 53.67275619506836, 'precision@0.9': 27.633113861083984, 'cIoU': 57.386287689208984, 'mIoU': 63.45586395263672}}}
INFO:trainer.default_trainer:epochs[    47] optim steps[85900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.05127/0.89251, loss_mask_bce_0: 0.32617/0.33352, loss_mask_dice_0: 0.71106/1.16042, loss_spatial_bce_0: 0.10961/0.08624, loss_spatial_dice_0: 0.25027/0.20565, loss_spatial_ce_0: 0.10494/0.05879, loss_grounding_bce_0: 0.04517/0.08598, loss_grounding_dice_0: 0.08485/0.17812, loss_grounding_ce_0: 0.55615/0.27108, loss_mask_ce_1: 1.02857/0.89329, loss_mask_bce_1: 0.32337/0.33445, loss_mask_dice_1: 0.71082/1.16733, loss_spatial_bce_1: 0.11536/0.08676, loss_spatial_dice_1: 0.24586/0.20954, loss_spatial_ce_1: 0.09032/0.06461, loss_grounding_bce_1: 0.04982/0.08619, loss_grounding_dice_1: 0.08586/0.17897, loss_grounding_ce_1: 0.46734/0.27186, loss_mask_ce_2: 1.15167/0.90019, loss_mask_bce_2: 0.32559/0.33513, loss_mask_dice_2: 0.69483/1.16786, loss_spatial_bce_2: 0.11623/0.08803, loss_spatial_dice_2: 0.26016/0.21150, loss_spatial_ce_2: 0.07920/0.06800, loss_grounding_bce_2: 0.04896/0.08638, loss_grounding_dice_2: 0.08369/0.17886, loss_grounding_ce_2: 0.59088/0.27526, loss_mask_ce_3: 1.14456/0.91168, loss_mask_bce_3: 0.31948/0.33629, loss_mask_dice_3: 0.67396/1.16572, loss_spatial_bce_3: 0.11360/0.08936, loss_spatial_dice_3: 0.26533/0.21262, loss_spatial_ce_3: 0.09627/0.07319, loss_grounding_bce_3: 0.04928/0.08663, loss_grounding_dice_3: 0.08941/0.17854, loss_grounding_ce_3: 0.74739/0.27740, loss_mask_ce_4: 1.09361/0.91331, loss_mask_bce_4: 0.44640/0.33849, loss_mask_dice_4: 0.78771/1.18961, loss_spatial_bce_4: 0.11411/0.09327, loss_spatial_dice_4: 0.26107/0.22482, loss_spatial_ce_4: 0.12407/0.08942, loss_grounding_bce_4: 0.05530/0.08719, loss_grounding_dice_4: 0.09045/0.18155, loss_grounding_ce_4: 0.59347/0.28030, loss_mask_ce_5: 1.09131/0.93009, loss_mask_bce_5: 0.42683/0.34091, loss_mask_dice_5: 0.77693/1.19783, loss_spatial_bce_5: 0.12979/0.09562, loss_spatial_dice_5: 0.26739/0.22926, loss_spatial_ce_5: 0.11707/0.10304, loss_grounding_bce_5: 0.09005/0.08762, loss_grounding_dice_5: 0.14837/0.18284, loss_grounding_ce_5: 0.19955/0.29306, loss_mask_ce_6: 1.16545/0.97037, loss_mask_bce_6: 0.45921/0.34370, loss_mask_dice_6: 0.82599/1.20094, loss_spatial_bce_6: 0.14429/0.10124, loss_spatial_dice_6: 0.27538/0.23224, loss_spatial_ce_6: 0.23862/0.12786, loss_grounding_bce_6: 0.06869/0.08838, loss_grounding_dice_6: 0.09615/0.18328, loss_grounding_ce_6: 0.69178/0.30821, loss_mask_ce_7: 1.34235/1.01636, loss_mask_bce_7: 0.46967/0.35158, loss_mask_dice_7: 0.87060/1.25519, loss_spatial_bce_7: 0.14837/0.10907, loss_spatial_dice_7: 0.26753/0.25988, loss_spatial_ce_7: 0.18828/0.16258, loss_grounding_bce_7: 0.07165/0.09025, loss_grounding_dice_7: 0.12553/0.19064, loss_grounding_ce_7: 0.08921/0.33809, loss_mask_ce_8: 1.34719/1.12537, loss_mask_bce_8: 0.49994/0.36514, loss_mask_dice_8: 0.86899/1.32765, loss_spatial_bce_8: 0.20089/0.12903, loss_spatial_dice_8: 0.30713/0.29759, loss_spatial_ce_8: 0.10112/0.21209, loss_grounding_bce_8: 0.08167/0.09390, loss_grounding_dice_8: 0.15214/0.20130, loss_grounding_ce_8: 0.24116/0.40389, loss_mask_ce_9: 3.76340/3.67246, loss_mask_bce_9: 0.43417/0.39228, loss_mask_dice_9: 1.14746/1.90071, loss_spatial_bce_9: 0.39098/0.33251, loss_spatial_dice_9: 0.86226/0.82157, loss_spatial_ce_9: 1.19174/1.49131, loss_grounding_bce_9: 0.04402/0.10561, loss_grounding_dice_9: 0.10848/0.28078, loss_grounding_ce_9: 1.03057/0.66863] items per batch[64] items per second[0.14] total items[5497600] mini batches[ 85900] memory[7345] epoch remaining[1:23:11]
INFO:trainer.default_trainer:epochs[    47] optim steps[86000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.52132/0.89239, loss_mask_bce_0: 0.17941/0.33348, loss_mask_dice_0: 1.15741/1.16030, loss_spatial_bce_0: 0.02038/0.08622, loss_spatial_dice_0: 0.23225/0.20563, loss_spatial_ce_0: 0.01677/0.05879, loss_grounding_bce_0: 0.03464/0.08597, loss_grounding_dice_0: 0.17224/0.17811, loss_grounding_ce_0: 0.00827/0.27101, loss_mask_ce_1: 0.52384/0.89317, loss_mask_bce_1: 0.17733/0.33441, loss_mask_dice_1: 1.15362/1.16721, loss_spatial_bce_1: 0.01884/0.08675, loss_spatial_dice_1: 0.20070/0.20952, loss_spatial_ce_1: 0.01730/0.06461, loss_grounding_bce_1: 0.03680/0.08618, loss_grounding_dice_1: 0.19168/0.17896, loss_grounding_ce_1: 0.00829/0.27179, loss_mask_ce_2: 0.46677/0.90006, loss_mask_bce_2: 0.17694/0.33508, loss_mask_dice_2: 1.02443/1.16775, loss_spatial_bce_2: 0.02051/0.08801, loss_spatial_dice_2: 0.20125/0.21148, loss_spatial_ce_2: 0.00965/0.06798, loss_grounding_bce_2: 0.04246/0.08636, loss_grounding_dice_2: 0.19981/0.17886, loss_grounding_ce_2: 0.01212/0.27518, loss_mask_ce_3: 0.49015/0.91158, loss_mask_bce_3: 0.17968/0.33625, loss_mask_dice_3: 1.07884/1.16561, loss_spatial_bce_3: 0.02100/0.08935, loss_spatial_dice_3: 0.19737/0.21261, loss_spatial_ce_3: 0.02970/0.07318, loss_grounding_bce_3: 0.04342/0.08662, loss_grounding_dice_3: 0.19167/0.17852, loss_grounding_ce_3: 0.00879/0.27732, loss_mask_ce_4: 0.49885/0.91320, loss_mask_bce_4: 0.17785/0.33845, loss_mask_dice_4: 1.32532/1.18950, loss_spatial_bce_4: 0.02124/0.09326, loss_spatial_dice_4: 0.26590/0.22480, loss_spatial_ce_4: 0.02659/0.08939, loss_grounding_bce_4: 0.04241/0.08718, loss_grounding_dice_4: 0.19513/0.18154, loss_grounding_ce_4: 0.00285/0.28021, loss_mask_ce_5: 0.48413/0.92999, loss_mask_bce_5: 0.18124/0.34087, loss_mask_dice_5: 1.14559/1.19772, loss_spatial_bce_5: 0.02257/0.09560, loss_spatial_dice_5: 0.25952/0.22924, loss_spatial_ce_5: 0.02518/0.10300, loss_grounding_bce_5: 0.03508/0.08761, loss_grounding_dice_5: 0.19379/0.18283, loss_grounding_ce_5: 0.00459/0.29298, loss_mask_ce_6: 0.42738/0.97024, loss_mask_bce_6: 0.18420/0.34366, loss_mask_dice_6: 1.30658/1.20082, loss_spatial_bce_6: 0.02749/0.10122, loss_spatial_dice_6: 0.26556/0.23222, loss_spatial_ce_6: 0.05212/0.12783, loss_grounding_bce_6: 0.04097/0.08837, loss_grounding_dice_6: 0.19603/0.18327, loss_grounding_ce_6: 0.00656/0.30812, loss_mask_ce_7: 0.49497/1.01627, loss_mask_bce_7: 0.18463/0.35154, loss_mask_dice_7: 1.55429/1.25506, loss_spatial_bce_7: 0.02871/0.10904, loss_spatial_dice_7: 0.30516/0.25985, loss_spatial_ce_7: 0.07224/0.16254, loss_grounding_bce_7: 0.03125/0.09023, loss_grounding_dice_7: 0.19092/0.19063, loss_grounding_ce_7: 0.01567/0.33797, loss_mask_ce_8: 0.64449/1.12527, loss_mask_bce_8: 0.20887/0.36510, loss_mask_dice_8: 1.51888/1.32755, loss_spatial_bce_8: 0.03229/0.12901, loss_spatial_dice_8: 0.38119/0.29757, loss_spatial_ce_8: 0.17033/0.21200, loss_grounding_bce_8: 0.05132/0.09388, loss_grounding_dice_8: 0.21877/0.20129, loss_grounding_ce_8: 0.02268/0.40373, loss_mask_ce_9: 4.21366/3.67222, loss_mask_bce_9: 0.32565/0.39223, loss_mask_dice_9: 2.29977/1.90056, loss_spatial_bce_9: 0.12217/0.33249, loss_spatial_dice_9: 0.90863/0.82156, loss_spatial_ce_9: 1.16825/1.49127, loss_grounding_bce_9: 0.07571/0.10559, loss_grounding_dice_9: 0.30725/0.28077, loss_grounding_ce_9: 0.70247/0.66853] items per batch[64] items per second[0.23] total items[5504000] mini batches[ 86000] memory[7345] epoch remaining[1:17:43]
INFO:trainer.default_trainer:epochs[    47] optim steps[86100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.86977/0.89235, loss_mask_bce_0: 0.25428/0.33348, loss_mask_dice_0: 1.89895/1.16027, loss_spatial_bce_0: 0.03593/0.08622, loss_spatial_dice_0: 0.28961/0.20561, loss_spatial_ce_0: 0.01227/0.05876, loss_grounding_bce_0: 0.05409/0.08597, loss_grounding_dice_0: 0.07172/0.17810, loss_grounding_ce_0: 0.01928/0.27102, loss_mask_ce_1: 0.82169/0.89313, loss_mask_bce_1: 0.24483/0.33441, loss_mask_dice_1: 2.02018/1.16718, loss_spatial_bce_1: 0.03738/0.08674, loss_spatial_dice_1: 0.27319/0.20950, loss_spatial_ce_1: 0.01315/0.06458, loss_grounding_bce_1: 0.05534/0.08618, loss_grounding_dice_1: 0.07386/0.17895, loss_grounding_ce_1: 0.01121/0.27179, loss_mask_ce_2: 0.87848/0.90003, loss_mask_bce_2: 0.24706/0.33508, loss_mask_dice_2: 2.12627/1.16771, loss_spatial_bce_2: 0.03919/0.08801, loss_spatial_dice_2: 0.27808/0.21146, loss_spatial_ce_2: 0.01090/0.06796, loss_grounding_bce_2: 0.05502/0.08637, loss_grounding_dice_2: 0.07107/0.17885, loss_grounding_ce_2: 0.01422/0.27519, loss_mask_ce_3: 0.65969/0.91153, loss_mask_bce_3: 0.28622/0.33625, loss_mask_dice_3: 2.11053/1.16558, loss_spatial_bce_3: 0.03937/0.08934, loss_spatial_dice_3: 0.29228/0.21259, loss_spatial_ce_3: 0.01654/0.07315, loss_grounding_bce_3: 0.05216/0.08663, loss_grounding_dice_3: 0.06681/0.17851, loss_grounding_ce_3: 0.01174/0.27733, loss_mask_ce_4: 0.61277/0.91318, loss_mask_bce_4: 0.27316/0.33845, loss_mask_dice_4: 2.00504/1.18946, loss_spatial_bce_4: 0.03803/0.09325, loss_spatial_dice_4: 0.26899/0.22478, loss_spatial_ce_4: 0.02048/0.08937, loss_grounding_bce_4: 0.05536/0.08719, loss_grounding_dice_4: 0.07216/0.18153, loss_grounding_ce_4: 0.01478/0.28021, loss_mask_ce_5: 0.62375/0.92997, loss_mask_bce_5: 0.26774/0.34086, loss_mask_dice_5: 2.00304/1.19770, loss_spatial_bce_5: 0.06680/0.09560, loss_spatial_dice_5: 0.33603/0.22922, loss_spatial_ce_5: 0.01703/0.10298, loss_grounding_bce_5: 0.05640/0.08761, loss_grounding_dice_5: 0.07078/0.18283, loss_grounding_ce_5: 0.02095/0.29300, loss_mask_ce_6: 0.80718/0.97024, loss_mask_bce_6: 0.26718/0.34366, loss_mask_dice_6: 2.13530/1.20080, loss_spatial_bce_6: 0.04482/0.10122, loss_spatial_dice_6: 0.29054/0.23220, loss_spatial_ce_6: 0.03400/0.12779, loss_grounding_bce_6: 0.05623/0.08838, loss_grounding_dice_6: 0.07267/0.18326, loss_grounding_ce_6: 0.02644/0.30814, loss_mask_ce_7: 0.79113/1.01626, loss_mask_bce_7: 0.28022/0.35154, loss_mask_dice_7: 2.06687/1.25504, loss_spatial_bce_7: 0.05161/0.10904, loss_spatial_dice_7: 0.32193/0.25984, loss_spatial_ce_7: 0.05940/0.16250, loss_grounding_bce_7: 0.06022/0.09024, loss_grounding_dice_7: 0.08277/0.19062, loss_grounding_ce_7: 0.02386/0.33799, loss_mask_ce_8: 1.09935/1.12526, loss_mask_bce_8: 0.30182/0.36510, loss_mask_dice_8: 2.37357/1.32754, loss_spatial_bce_8: 0.05078/0.12899, loss_spatial_dice_8: 0.33962/0.29755, loss_spatial_ce_8: 0.20784/0.21193, loss_grounding_bce_8: 0.05417/0.09389, loss_grounding_dice_8: 0.07239/0.20128, loss_grounding_ce_8: 0.24226/0.40377, loss_mask_ce_9: 3.98591/3.67233, loss_mask_bce_9: 0.29301/0.39223, loss_mask_dice_9: 3.10106/1.90055, loss_spatial_bce_9: 0.27369/0.33248, loss_spatial_dice_9: 0.92306/0.82156, loss_spatial_ce_9: 1.32735/1.49120, loss_grounding_bce_9: 0.06639/0.10560, loss_grounding_dice_9: 0.10170/0.28077, loss_grounding_ce_9: 1.02954/0.66856] items per batch[64] items per second[0.23] total items[5510400] mini batches[ 86100] memory[7345] epoch remaining[1:12:55]
INFO:trainer.default_trainer:epochs[    47] optim steps[86200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.73545/0.89229, loss_mask_bce_0: 0.45462/0.33346, loss_mask_dice_0: 0.59453/1.16014, loss_spatial_bce_0: 0.10876/0.08621, loss_spatial_dice_0: 0.16594/0.20559, loss_spatial_ce_0: 0.01622/0.05875, loss_grounding_bce_0: 0.03162/0.08597, loss_grounding_dice_0: 0.04745/0.17809, loss_grounding_ce_0: 0.18712/0.27099, loss_mask_ce_1: 0.77135/0.89310, loss_mask_bce_1: 0.45097/0.33438, loss_mask_dice_1: 0.63869/1.16703, loss_spatial_bce_1: 0.10946/0.08673, loss_spatial_dice_1: 0.15789/0.20948, loss_spatial_ce_1: 0.02516/0.06457, loss_grounding_bce_1: 0.03440/0.08618, loss_grounding_dice_1: 0.04571/0.17895, loss_grounding_ce_1: 0.20569/0.27178, loss_mask_ce_2: 0.76767/0.90001, loss_mask_bce_2: 0.43623/0.33506, loss_mask_dice_2: 0.58343/1.16760, loss_spatial_bce_2: 0.11361/0.08800, loss_spatial_dice_2: 0.17479/0.21145, loss_spatial_ce_2: 0.03613/0.06794, loss_grounding_bce_2: 0.03362/0.08636, loss_grounding_dice_2: 0.05182/0.17885, loss_grounding_ce_2: 0.29317/0.27517, loss_mask_ce_3: 0.78939/0.91148, loss_mask_bce_3: 0.39957/0.33623, loss_mask_dice_3: 0.63501/1.16548, loss_spatial_bce_3: 0.13271/0.08934, loss_spatial_dice_3: 0.17738/0.21257, loss_spatial_ce_3: 0.03702/0.07314, loss_grounding_bce_3: 0.03443/0.08662, loss_grounding_dice_3: 0.05508/0.17851, loss_grounding_ce_3: 0.28449/0.27733, loss_mask_ce_4: 0.94469/0.91316, loss_mask_bce_4: 0.35864/0.33843, loss_mask_dice_4: 0.68901/1.18933, loss_spatial_bce_4: 0.12849/0.09324, loss_spatial_dice_4: 0.18875/0.22476, loss_spatial_ce_4: 0.10221/0.08935, loss_grounding_bce_4: 0.03068/0.08718, loss_grounding_dice_4: 0.04504/0.18153, loss_grounding_ce_4: 0.15382/0.28018, loss_mask_ce_5: 0.95971/0.92995, loss_mask_bce_5: 0.37049/0.34084, loss_mask_dice_5: 0.74307/1.19758, loss_spatial_bce_5: 0.14544/0.09559, loss_spatial_dice_5: 0.21372/0.22920, loss_spatial_ce_5: 0.04349/0.10295, loss_grounding_bce_5: 0.03326/0.08761, loss_grounding_dice_5: 0.05044/0.18283, loss_grounding_ce_5: 0.11930/0.29297, loss_mask_ce_6: 0.89871/0.97020, loss_mask_bce_6: 0.42970/0.34364, loss_mask_dice_6: 0.74707/1.20066, loss_spatial_bce_6: 0.15541/0.10121, loss_spatial_dice_6: 0.19610/0.23218, loss_spatial_ce_6: 0.11654/0.12774, loss_grounding_bce_6: 0.03064/0.08837, loss_grounding_dice_6: 0.05072/0.18327, loss_grounding_ce_6: 0.14437/0.30807, loss_mask_ce_7: 0.75581/1.01624, loss_mask_bce_7: 0.45428/0.35151, loss_mask_dice_7: 0.90953/1.25490, loss_spatial_bce_7: 0.16463/0.10903, loss_spatial_dice_7: 0.26046/0.25982, loss_spatial_ce_7: 0.10728/0.16246, loss_grounding_bce_7: 0.03186/0.09023, loss_grounding_dice_7: 0.04812/0.19062, loss_grounding_ce_7: 0.11903/0.33794, loss_mask_ce_8: 0.99438/1.12524, loss_mask_bce_8: 0.44354/0.36507, loss_mask_dice_8: 0.99846/1.32740, loss_spatial_bce_8: 0.25472/0.12898, loss_spatial_dice_8: 0.30614/0.29753, loss_spatial_ce_8: 0.16878/0.21183, loss_grounding_bce_8: 0.03676/0.09388, loss_grounding_dice_8: 0.06441/0.20128, loss_grounding_ce_8: 0.37573/0.40366, loss_mask_ce_9: 3.69276/3.67226, loss_mask_bce_9: 0.59543/0.39224, loss_mask_dice_9: 1.46396/1.90047, loss_spatial_bce_9: 0.44427/0.33249, loss_spatial_dice_9: 0.89386/0.82155, loss_spatial_ce_9: 2.13716/1.49123, loss_grounding_bce_9: 0.05352/0.10559, loss_grounding_dice_9: 0.21688/0.28078, loss_grounding_ce_9: 1.41031/0.66844] items per batch[64] items per second[0.23] total items[5516800] mini batches[ 86200] memory[7345] epoch remaining[1:08:13]
INFO:trainer.default_trainer:epochs[    47] optim steps[86300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.25527/0.89225, loss_mask_bce_0: 0.26349/0.33343, loss_mask_dice_0: 1.13017/1.16016, loss_spatial_bce_0: 0.03501/0.08619, loss_spatial_dice_0: 0.15263/0.20558, loss_spatial_ce_0: 0.02033/0.05873, loss_grounding_bce_0: 0.02842/0.08597, loss_grounding_dice_0: 0.10976/0.17809, loss_grounding_ce_0: 0.17857/0.27094, loss_mask_ce_1: 1.48006/0.89310, loss_mask_bce_1: 0.23394/0.33435, loss_mask_dice_1: 1.05644/1.16704, loss_spatial_bce_1: 0.03740/0.08672, loss_spatial_dice_1: 0.15915/0.20946, loss_spatial_ce_1: 0.01168/0.06455, loss_grounding_bce_1: 0.03253/0.08618, loss_grounding_dice_1: 0.11173/0.17895, loss_grounding_ce_1: 0.17409/0.27173, loss_mask_ce_2: 1.34697/0.89999, loss_mask_bce_2: 0.25226/0.33503, loss_mask_dice_2: 1.08919/1.16762, loss_spatial_bce_2: 0.04024/0.08799, loss_spatial_dice_2: 0.16342/0.21143, loss_spatial_ce_2: 0.03598/0.06793, loss_grounding_bce_2: 0.03081/0.08636, loss_grounding_dice_2: 0.11477/0.17884, loss_grounding_ce_2: 0.16758/0.27512, loss_mask_ce_3: 1.28952/0.91147, loss_mask_bce_3: 0.26318/0.33620, loss_mask_dice_3: 1.03795/1.16549, loss_spatial_bce_3: 0.03812/0.08932, loss_spatial_dice_3: 0.15294/0.21255, loss_spatial_ce_3: 0.15345/0.07313, loss_grounding_bce_3: 0.03081/0.08662, loss_grounding_dice_3: 0.12301/0.17850, loss_grounding_ce_3: 0.15600/0.27728, loss_mask_ce_4: 1.25281/0.91315, loss_mask_bce_4: 0.23237/0.33840, loss_mask_dice_4: 1.02354/1.18934, loss_spatial_bce_4: 0.04603/0.09323, loss_spatial_dice_4: 0.20441/0.22474, loss_spatial_ce_4: 0.21702/0.08934, loss_grounding_bce_4: 0.03051/0.08718, loss_grounding_dice_4: 0.11961/0.18153, loss_grounding_ce_4: 0.11732/0.28014, loss_mask_ce_5: 1.69941/0.92997, loss_mask_bce_5: 0.24903/0.34081, loss_mask_dice_5: 1.15741/1.19757, loss_spatial_bce_5: 0.04730/0.09558, loss_spatial_dice_5: 0.23219/0.22919, loss_spatial_ce_5: 0.03953/0.10293, loss_grounding_bce_5: 0.03426/0.08761, loss_grounding_dice_5: 0.13314/0.18283, loss_grounding_ce_5: 0.14912/0.29292, loss_mask_ce_6: 1.98469/0.97021, loss_mask_bce_6: 0.23525/0.34361, loss_mask_dice_6: 1.10338/1.20068, loss_spatial_bce_6: 0.05321/0.10120, loss_spatial_dice_6: 0.21369/0.23217, loss_spatial_ce_6: 0.02244/0.12770, loss_grounding_bce_6: 0.03502/0.08837, loss_grounding_dice_6: 0.15477/0.18326, loss_grounding_ce_6: 0.05566/0.30802, loss_mask_ce_7: 1.78909/1.01624, loss_mask_bce_7: 0.26817/0.35149, loss_mask_dice_7: 1.15260/1.25491, loss_spatial_bce_7: 0.05401/0.10901, loss_spatial_dice_7: 0.24802/0.25980, loss_spatial_ce_7: 0.15580/0.16242, loss_grounding_bce_7: 0.03357/0.09023, loss_grounding_dice_7: 0.12645/0.19062, loss_grounding_ce_7: 0.26075/0.33789, loss_mask_ce_8: 2.13753/1.12524, loss_mask_bce_8: 0.27126/0.36505, loss_mask_dice_8: 1.37043/1.32742, loss_spatial_bce_8: 0.07006/0.12896, loss_spatial_dice_8: 0.31138/0.29752, loss_spatial_ce_8: 0.22064/0.21174, loss_grounding_bce_8: 0.04103/0.09388, loss_grounding_dice_8: 0.15152/0.20128, loss_grounding_ce_8: 0.18364/0.40361, loss_mask_ce_9: 4.00899/3.67214, loss_mask_bce_9: 0.28291/0.39221, loss_mask_dice_9: 2.03966/1.90051, loss_spatial_bce_9: 0.28927/0.33249, loss_spatial_dice_9: 0.93471/0.82155, loss_spatial_ce_9: 1.62387/1.49125, loss_grounding_bce_9: 0.03848/0.10559, loss_grounding_dice_9: 0.31652/0.28079, loss_grounding_ce_9: 0.10353/0.66836] items per batch[64] items per second[0.23] total items[5523200] mini batches[ 86300] memory[7345] epoch remaining[1:03:42]
INFO:trainer.default_trainer:epochs[    47] optim steps[86400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.32011/0.89216, loss_mask_bce_0: 0.09350/0.33342, loss_mask_dice_0: 0.27612/1.16018, loss_spatial_bce_0: 0.03635/0.08618, loss_spatial_dice_0: 0.07928/0.20556, loss_spatial_ce_0: 0.00587/0.05870, loss_grounding_bce_0: 0.02055/0.08596, loss_grounding_dice_0: 0.05222/0.17810, loss_grounding_ce_0: 0.03639/0.27096, loss_mask_ce_1: 0.30545/0.89302, loss_mask_bce_1: 0.09098/0.33434, loss_mask_dice_1: 0.26070/1.16707, loss_spatial_bce_1: 0.03606/0.08671, loss_spatial_dice_1: 0.07250/0.20944, loss_spatial_ce_1: 0.01752/0.06452, loss_grounding_bce_1: 0.02012/0.08617, loss_grounding_dice_1: 0.05196/0.17894, loss_grounding_ce_1: 0.03229/0.27176, loss_mask_ce_2: 0.33580/0.89986, loss_mask_bce_2: 0.09246/0.33502, loss_mask_dice_2: 0.26910/1.16766, loss_spatial_bce_2: 0.03741/0.08798, loss_spatial_dice_2: 0.08542/0.21141, loss_spatial_ce_2: 0.05517/0.06790, loss_grounding_bce_2: 0.02081/0.08636, loss_grounding_dice_2: 0.05260/0.17884, loss_grounding_ce_2: 0.03393/0.27513, loss_mask_ce_3: 0.28886/0.91137, loss_mask_bce_3: 0.08638/0.33619, loss_mask_dice_3: 0.25932/1.16551, loss_spatial_bce_3: 0.03575/0.08931, loss_spatial_dice_3: 0.09446/0.21254, loss_spatial_ce_3: 0.02280/0.07310, loss_grounding_bce_3: 0.02025/0.08662, loss_grounding_dice_3: 0.05311/0.17850, loss_grounding_ce_3: 0.02985/0.27728, loss_mask_ce_4: 0.30393/0.91304, loss_mask_bce_4: 0.08801/0.33839, loss_mask_dice_4: 0.25964/1.18937, loss_spatial_bce_4: 0.03767/0.09322, loss_spatial_dice_4: 0.09361/0.22473, loss_spatial_ce_4: 0.06284/0.08931, loss_grounding_bce_4: 0.02011/0.08718, loss_grounding_dice_4: 0.05069/0.18152, loss_grounding_ce_4: 0.03410/0.28015, loss_mask_ce_5: 0.31970/0.92987, loss_mask_bce_5: 0.08882/0.34080, loss_mask_dice_5: 0.25797/1.19761, loss_spatial_bce_5: 0.03722/0.09557, loss_spatial_dice_5: 0.09144/0.22918, loss_spatial_ce_5: 0.06164/0.10289, loss_grounding_bce_5: 0.01815/0.08760, loss_grounding_dice_5: 0.04852/0.18282, loss_grounding_ce_5: 0.03884/0.29293, loss_mask_ce_6: 0.38273/0.97012, loss_mask_bce_6: 0.09001/0.34361, loss_mask_dice_6: 0.26740/1.20071, loss_spatial_bce_6: 0.03419/0.10119, loss_spatial_dice_6: 0.08817/0.23216, loss_spatial_ce_6: 0.03918/0.12766, loss_grounding_bce_6: 0.01914/0.08836, loss_grounding_dice_6: 0.05101/0.18326, loss_grounding_ce_6: 0.04611/0.30804, loss_mask_ce_7: 0.46047/1.01615, loss_mask_bce_7: 0.08538/0.35148, loss_mask_dice_7: 0.27677/1.25496, loss_spatial_bce_7: 0.04465/0.10900, loss_spatial_dice_7: 0.13461/0.25979, loss_spatial_ce_7: 0.04110/0.16237, loss_grounding_bce_7: 0.01810/0.09023, loss_grounding_dice_7: 0.04672/0.19063, loss_grounding_ce_7: 0.07678/0.33791, loss_mask_ce_8: 0.50197/1.12516, loss_mask_bce_8: 0.08201/0.36505, loss_mask_dice_8: 0.24239/1.32748, loss_spatial_bce_8: 0.04761/0.12895, loss_spatial_dice_8: 0.13898/0.29751, loss_spatial_ce_8: 0.14882/0.21164, loss_grounding_bce_8: 0.01745/0.09387, loss_grounding_dice_8: 0.04627/0.20128, loss_grounding_ce_8: 0.07234/0.40364, loss_mask_ce_9: 1.81269/3.67220, loss_mask_bce_9: 0.09272/0.39222, loss_mask_dice_9: 0.35002/1.90056, loss_spatial_bce_9: 0.48507/0.33249, loss_spatial_dice_9: 0.75765/0.82155, loss_spatial_ce_9: 1.20912/1.49119, loss_grounding_bce_9: 0.02596/0.10558, loss_grounding_dice_9: 0.07603/0.28079, loss_grounding_ce_9: 0.33925/0.66834] items per batch[64] items per second[0.24] total items[5529600] mini batches[ 86400] memory[7345] epoch remaining[0:59:03]
INFO:trainer.default_trainer:epochs[    47] optim steps[86500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.37721/0.89215, loss_mask_bce_0: 0.10767/0.33344, loss_mask_dice_0: 0.24682/1.16040, loss_spatial_bce_0: 0.06423/0.08618, loss_spatial_dice_0: 0.10534/0.20554, loss_spatial_ce_0: 0.11276/0.05868, loss_grounding_bce_0: 0.02537/0.08597, loss_grounding_dice_0: 0.10184/0.17811, loss_grounding_ce_0: 0.35780/0.27096, loss_mask_ce_1: 0.37506/0.89305, loss_mask_bce_1: 0.09544/0.33436, loss_mask_dice_1: 0.20054/1.16730, loss_spatial_bce_1: 0.05829/0.08671, loss_spatial_dice_1: 0.09192/0.20942, loss_spatial_ce_1: 0.12839/0.06450, loss_grounding_bce_1: 0.02555/0.08618, loss_grounding_dice_1: 0.19138/0.17895, loss_grounding_ce_1: 0.14859/0.27179, loss_mask_ce_2: 0.40881/0.89986, loss_mask_bce_2: 0.09815/0.33505, loss_mask_dice_2: 0.22584/1.16788, loss_spatial_bce_2: 0.07170/0.08798, loss_spatial_dice_2: 0.10371/0.21140, loss_spatial_ce_2: 0.09170/0.06788, loss_grounding_bce_2: 0.02785/0.08636, loss_grounding_dice_2: 0.19587/0.17886, loss_grounding_ce_2: 0.14843/0.27512, loss_mask_ce_3: 0.41246/0.91137, loss_mask_bce_3: 0.09527/0.33621, loss_mask_dice_3: 0.21355/1.16574, loss_spatial_bce_3: 0.08479/0.08931, loss_spatial_dice_3: 0.11805/0.21252, loss_spatial_ce_3: 0.10050/0.07308, loss_grounding_bce_3: 0.02569/0.08662, loss_grounding_dice_3: 0.13363/0.17852, loss_grounding_ce_3: 0.41202/0.27729, loss_mask_ce_4: 0.38809/0.91306, loss_mask_bce_4: 0.11112/0.33841, loss_mask_dice_4: 0.22539/1.18960, loss_spatial_bce_4: 0.08160/0.09322, loss_spatial_dice_4: 0.11949/0.22472, loss_spatial_ce_4: 0.05316/0.08929, loss_grounding_bce_4: 0.02776/0.08718, loss_grounding_dice_4: 0.14895/0.18153, loss_grounding_ce_4: 0.28999/0.28018, loss_mask_ce_5: 0.47064/0.92988, loss_mask_bce_5: 0.08867/0.34083, loss_mask_dice_5: 0.20434/1.19786, loss_spatial_bce_5: 0.08893/0.09556, loss_spatial_dice_5: 0.15381/0.22917, loss_spatial_ce_5: 0.05291/0.10286, loss_grounding_bce_5: 0.02900/0.08761, loss_grounding_dice_5: 0.13410/0.18283, loss_grounding_ce_5: 0.36546/0.29295, loss_mask_ce_6: 0.76004/0.97014, loss_mask_bce_6: 0.09540/0.34363, loss_mask_dice_6: 0.22540/1.20098, loss_spatial_bce_6: 0.10024/0.10119, loss_spatial_dice_6: 0.15760/0.23215, loss_spatial_ce_6: 0.06366/0.12762, loss_grounding_bce_6: 0.03027/0.08837, loss_grounding_dice_6: 0.16271/0.18327, loss_grounding_ce_6: 0.69329/0.30807, loss_mask_ce_7: 0.78690/1.01619, loss_mask_bce_7: 0.10554/0.35151, loss_mask_dice_7: 0.24916/1.25520, loss_spatial_bce_7: 0.11831/0.10899, loss_spatial_dice_7: 0.17457/0.25978, loss_spatial_ce_7: 0.13116/0.16234, loss_grounding_bce_7: 0.02448/0.09023, loss_grounding_dice_7: 0.12211/0.19063, loss_grounding_ce_7: 0.67862/0.33791, loss_mask_ce_8: 0.67909/1.12520, loss_mask_bce_8: 0.09145/0.36507, loss_mask_dice_8: 0.22083/1.32772, loss_spatial_bce_8: 0.12141/0.12894, loss_spatial_dice_8: 0.20445/0.29750, loss_spatial_ce_8: 0.14959/0.21156, loss_grounding_bce_8: 0.02648/0.09388, loss_grounding_dice_8: 0.12357/0.20129, loss_grounding_ce_8: 0.34539/0.40366, loss_mask_ce_9: 3.05244/3.67232, loss_mask_bce_9: 0.26457/0.39225, loss_mask_dice_9: 0.44063/1.90098, loss_spatial_bce_9: 0.45552/0.33249, loss_spatial_dice_9: 0.80010/0.82155, loss_spatial_ce_9: 1.66260/1.49116, loss_grounding_bce_9: 0.03288/0.10559, loss_grounding_dice_9: 0.18869/0.28080, loss_grounding_ce_9: 0.31719/0.66834] items per batch[64] items per second[0.24] total items[5536000] mini batches[ 86500] memory[7345] epoch remaining[0:54:23]
INFO:trainer.default_trainer:epochs[    47] optim steps[86600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.06465/0.89207, loss_mask_bce_0: 0.04103/0.33343, loss_mask_dice_0: 0.08190/1.16015, loss_spatial_bce_0: 0.04176/0.08618, loss_spatial_dice_0: 0.08954/0.20551, loss_spatial_ce_0: 0.00607/0.05865, loss_grounding_bce_0: 0.03751/0.08597, loss_grounding_dice_0: 0.06598/0.17810, loss_grounding_ce_0: 0.05901/0.27092, loss_mask_ce_1: 0.06989/0.89297, loss_mask_bce_1: 0.03957/0.33435, loss_mask_dice_1: 0.07919/1.16705, loss_spatial_bce_1: 0.04503/0.08670, loss_spatial_dice_1: 0.10238/0.20939, loss_spatial_ce_1: 0.00717/0.06446, loss_grounding_bce_1: 0.03816/0.08618, loss_grounding_dice_1: 0.07176/0.17894, loss_grounding_ce_1: 0.08551/0.27175, loss_mask_ce_2: 0.06365/0.89979, loss_mask_bce_2: 0.04392/0.33503, loss_mask_dice_2: 0.08276/1.16762, loss_spatial_bce_2: 0.04589/0.08797, loss_spatial_dice_2: 0.11584/0.21137, loss_spatial_ce_2: 0.00516/0.06785, loss_grounding_bce_2: 0.04082/0.08636, loss_grounding_dice_2: 0.07949/0.17884, loss_grounding_ce_2: 0.07991/0.27510, loss_mask_ce_3: 0.06762/0.91130, loss_mask_bce_3: 0.04233/0.33620, loss_mask_dice_3: 0.08427/1.16550, loss_spatial_bce_3: 0.04885/0.08931, loss_spatial_dice_3: 0.11853/0.21249, loss_spatial_ce_3: 0.00778/0.07305, loss_grounding_bce_3: 0.04056/0.08663, loss_grounding_dice_3: 0.07297/0.17850, loss_grounding_ce_3: 0.05902/0.27727, loss_mask_ce_4: 0.07431/0.91299, loss_mask_bce_4: 0.04444/0.33841, loss_mask_dice_4: 0.08597/1.18936, loss_spatial_bce_4: 0.04358/0.09322, loss_spatial_dice_4: 0.08135/0.22469, loss_spatial_ce_4: 0.05475/0.08925, loss_grounding_bce_4: 0.04071/0.08718, loss_grounding_dice_4: 0.06696/0.18153, loss_grounding_ce_4: 0.07680/0.28016, loss_mask_ce_5: 0.07270/0.92981, loss_mask_bce_5: 0.04439/0.34082, loss_mask_dice_5: 0.08765/1.19761, loss_spatial_bce_5: 0.04330/0.09556, loss_spatial_dice_5: 0.08102/0.22914, loss_spatial_ce_5: 0.10665/0.10281, loss_grounding_bce_5: 0.04258/0.08761, loss_grounding_dice_5: 0.07570/0.18282, loss_grounding_ce_5: 0.10649/0.29294, loss_mask_ce_6: 0.08669/0.97004, loss_mask_bce_6: 0.04297/0.34362, loss_mask_dice_6: 0.08018/1.20073, loss_spatial_bce_6: 0.04350/0.10119, loss_spatial_dice_6: 0.12087/0.23212, loss_spatial_ce_6: 0.14215/0.12757, loss_grounding_bce_6: 0.04048/0.08837, loss_grounding_dice_6: 0.07115/0.18326, loss_grounding_ce_6: 0.12208/0.30805, loss_mask_ce_7: 0.10694/1.01611, loss_mask_bce_7: 0.04132/0.35150, loss_mask_dice_7: 0.07811/1.25493, loss_spatial_bce_7: 0.05516/0.10899, loss_spatial_dice_7: 0.19633/0.25975, loss_spatial_ce_7: 0.16549/0.16228, loss_grounding_bce_7: 0.03800/0.09024, loss_grounding_dice_7: 0.06568/0.19062, loss_grounding_ce_7: 0.24913/0.33782, loss_mask_ce_8: 0.12181/1.12512, loss_mask_bce_8: 0.04532/0.36505, loss_mask_dice_8: 0.08671/1.32745, loss_spatial_bce_8: 0.15613/0.12894, loss_spatial_dice_8: 0.26939/0.29747, loss_spatial_ce_8: 0.20105/0.21147, loss_grounding_bce_8: 0.04003/0.09389, loss_grounding_dice_8: 0.07102/0.20128, loss_grounding_ce_8: 0.26256/0.40365, loss_mask_ce_9: 2.78818/3.67220, loss_mask_bce_9: 0.07003/0.39225, loss_mask_dice_9: 0.19475/1.90063, loss_spatial_bce_9: 0.26506/0.33251, loss_spatial_dice_9: 0.60504/0.82154, loss_spatial_ce_9: 1.22982/1.49112, loss_grounding_bce_9: 0.06232/0.10560, loss_grounding_dice_9: 0.19012/0.28080, loss_grounding_ce_9: 0.84082/0.66840] items per batch[64] items per second[0.24] total items[5542400] mini batches[ 86600] memory[7345] epoch remaining[0:49:43]
INFO:trainer.default_trainer:epochs[    47] optim steps[86700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.68543/0.89199, loss_mask_bce_0: 0.52044/0.33348, loss_mask_dice_0: 1.03102/1.16022, loss_spatial_bce_0: 0.12974/0.08618, loss_spatial_dice_0: 0.21889/0.20549, loss_spatial_ce_0: 0.08823/0.05864, loss_grounding_bce_0: 0.07867/0.08599, loss_grounding_dice_0: 0.16724/0.17809, loss_grounding_ce_0: 0.50684/0.27091, loss_mask_ce_1: 0.71162/0.89291, loss_mask_bce_1: 0.53288/0.33441, loss_mask_dice_1: 1.05012/1.16711, loss_spatial_bce_1: 0.13272/0.08671, loss_spatial_dice_1: 0.22056/0.20937, loss_spatial_ce_1: 0.07287/0.06445, loss_grounding_bce_1: 0.08269/0.08619, loss_grounding_dice_1: 0.18032/0.17893, loss_grounding_ce_1: 0.61619/0.27173, loss_mask_ce_2: 0.71508/0.89972, loss_mask_bce_2: 0.54465/0.33508, loss_mask_dice_2: 1.05465/1.16767, loss_spatial_bce_2: 0.13594/0.08798, loss_spatial_dice_2: 0.22003/0.21134, loss_spatial_ce_2: 0.08355/0.06782, loss_grounding_bce_2: 0.08142/0.08638, loss_grounding_dice_2: 0.16169/0.17883, loss_grounding_ce_2: 0.68455/0.27509, loss_mask_ce_3: 0.73737/0.91125, loss_mask_bce_3: 0.54636/0.33625, loss_mask_dice_3: 1.03275/1.16555, loss_spatial_bce_3: 0.14412/0.08932, loss_spatial_dice_3: 0.22591/0.21247, loss_spatial_ce_3: 0.10405/0.07304, loss_grounding_bce_3: 0.08479/0.08664, loss_grounding_dice_3: 0.16786/0.17849, loss_grounding_ce_3: 0.54422/0.27724, loss_mask_ce_4: 0.88343/0.91291, loss_mask_bce_4: 0.49967/0.33846, loss_mask_dice_4: 1.02657/1.18941, loss_spatial_bce_4: 0.14521/0.09322, loss_spatial_dice_4: 0.24815/0.22466, loss_spatial_ce_4: 0.08549/0.08924, loss_grounding_bce_4: 0.07989/0.08720, loss_grounding_dice_4: 0.16723/0.18152, loss_grounding_ce_4: 0.58229/0.28014, loss_mask_ce_5: 0.92101/0.92973, loss_mask_bce_5: 0.41731/0.34086, loss_mask_dice_5: 1.02990/1.19766, loss_spatial_bce_5: 0.13047/0.09556, loss_spatial_dice_5: 0.23575/0.22912, loss_spatial_ce_5: 0.10065/0.10280, loss_grounding_bce_5: 0.07586/0.08762, loss_grounding_dice_5: 0.17161/0.18282, loss_grounding_ce_5: 0.70366/0.29289, loss_mask_ce_6: 1.00259/0.96996, loss_mask_bce_6: 0.53875/0.34367, loss_mask_dice_6: 1.06780/1.20078, loss_spatial_bce_6: 0.14743/0.10119, loss_spatial_dice_6: 0.24069/0.23210, loss_spatial_ce_6: 0.11975/0.12754, loss_grounding_bce_6: 0.07904/0.08838, loss_grounding_dice_6: 0.16344/0.18325, loss_grounding_ce_6: 0.74009/0.30799, loss_mask_ce_7: 1.33804/1.01604, loss_mask_bce_7: 0.54420/0.35155, loss_mask_dice_7: 1.23101/1.25498, loss_spatial_bce_7: 0.17284/0.10900, loss_spatial_dice_7: 0.27680/0.25974, loss_spatial_ce_7: 0.19547/0.16224, loss_grounding_bce_7: 0.07380/0.09025, loss_grounding_dice_7: 0.16462/0.19061, loss_grounding_ce_7: 1.17519/0.33783, loss_mask_ce_8: 1.24377/1.12504, loss_mask_bce_8: 0.53227/0.36511, loss_mask_dice_8: 1.25597/1.32751, loss_spatial_bce_8: 0.20719/0.12894, loss_spatial_dice_8: 0.33789/0.29746, loss_spatial_ce_8: 0.14964/0.21138, loss_grounding_bce_8: 0.08770/0.09390, loss_grounding_dice_8: 0.19181/0.20127, loss_grounding_ce_8: 1.01295/0.40361, loss_mask_ce_9: 2.64727/3.67226, loss_mask_bce_9: 0.71503/0.39230, loss_mask_dice_9: 1.84080/1.90075, loss_spatial_bce_9: 0.30766/0.33254, loss_spatial_dice_9: 0.82442/0.82154, loss_spatial_ce_9: 1.06868/1.49109, loss_grounding_bce_9: 0.10918/0.10561, loss_grounding_dice_9: 0.28535/0.28079, loss_grounding_ce_9: 1.25397/0.66847] items per batch[64] items per second[0.23] total items[5548800] mini batches[ 86700] memory[7345] epoch remaining[0:45:21]
INFO:trainer.default_trainer:epochs[    47] optim steps[86800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.75441/0.89201, loss_mask_bce_0: 0.72324/0.33348, loss_mask_dice_0: 1.27348/1.16014, loss_spatial_bce_0: 0.14651/0.08618, loss_spatial_dice_0: 0.17971/0.20548, loss_spatial_ce_0: 0.03859/0.05863, loss_grounding_bce_0: 0.15367/0.08598, loss_grounding_dice_0: 0.19271/0.17810, loss_grounding_ce_0: 0.27309/0.27089, loss_mask_ce_1: 0.72833/0.89292, loss_mask_bce_1: 0.72124/0.33440, loss_mask_dice_1: 1.30997/1.16705, loss_spatial_bce_1: 0.14118/0.08670, loss_spatial_dice_1: 0.18424/0.20935, loss_spatial_ce_1: 0.03686/0.06444, loss_grounding_bce_1: 0.15408/0.08619, loss_grounding_dice_1: 0.17938/0.17893, loss_grounding_ce_1: 0.23652/0.27172, loss_mask_ce_2: 0.83276/0.89975, loss_mask_bce_2: 0.70451/0.33507, loss_mask_dice_2: 1.12447/1.16757, loss_spatial_bce_2: 0.14085/0.08797, loss_spatial_dice_2: 0.19477/0.21132, loss_spatial_ce_2: 0.03532/0.06782, loss_grounding_bce_2: 0.15656/0.08637, loss_grounding_dice_2: 0.18871/0.17884, loss_grounding_ce_2: 0.22056/0.27509, loss_mask_ce_3: 0.83776/0.91130, loss_mask_bce_3: 0.71580/0.33624, loss_mask_dice_3: 1.11267/1.16546, loss_spatial_bce_3: 0.14924/0.08931, loss_spatial_dice_3: 0.19449/0.21246, loss_spatial_ce_3: 0.04334/0.07303, loss_grounding_bce_3: 0.16122/0.08663, loss_grounding_dice_3: 0.25506/0.17850, loss_grounding_ce_3: 0.22115/0.27723, loss_mask_ce_4: 0.82759/0.91295, loss_mask_bce_4: 0.70442/0.33845, loss_mask_dice_4: 1.10401/1.18934, loss_spatial_bce_4: 0.14946/0.09322, loss_spatial_dice_4: 0.19051/0.22465, loss_spatial_ce_4: 0.03467/0.08921, loss_grounding_bce_4: 0.16310/0.08719, loss_grounding_dice_4: 0.21025/0.18152, loss_grounding_ce_4: 0.21702/0.28014, loss_mask_ce_5: 0.77578/0.92976, loss_mask_bce_5: 0.73951/0.34085, loss_mask_dice_5: 1.11621/1.19756, loss_spatial_bce_5: 0.13676/0.09555, loss_spatial_dice_5: 0.21626/0.22910, loss_spatial_ce_5: 0.04215/0.10278, loss_grounding_bce_5: 0.17072/0.08761, loss_grounding_dice_5: 0.27283/0.18282, loss_grounding_ce_5: 0.16326/0.29288, loss_mask_ce_6: 0.69212/0.97001, loss_mask_bce_6: 0.76086/0.34366, loss_mask_dice_6: 1.30179/1.20071, loss_spatial_bce_6: 0.15180/0.10118, loss_spatial_dice_6: 0.20534/0.23208, loss_spatial_ce_6: 0.09090/0.12753, loss_grounding_bce_6: 0.16822/0.08837, loss_grounding_dice_6: 0.22145/0.18325, loss_grounding_ce_6: 0.22961/0.30800, loss_mask_ce_7: 0.77091/1.01610, loss_mask_bce_7: 0.68179/0.35154, loss_mask_dice_7: 1.32089/1.25490, loss_spatial_bce_7: 0.16684/0.10899, loss_spatial_dice_7: 0.24377/0.25972, loss_spatial_ce_7: 0.13481/0.16222, loss_grounding_bce_7: 0.16575/0.09024, loss_grounding_dice_7: 0.24109/0.19062, loss_grounding_ce_7: 0.20594/0.33785, loss_mask_ce_8: 0.89361/1.12511, loss_mask_bce_8: 0.74160/0.36510, loss_mask_dice_8: 1.37512/1.32742, loss_spatial_bce_8: 0.15830/0.12893, loss_spatial_dice_8: 0.27971/0.29744, loss_spatial_ce_8: 0.19635/0.21132, loss_grounding_bce_8: 0.15861/0.09389, loss_grounding_dice_8: 0.30277/0.20127, loss_grounding_ce_8: 0.31328/0.40364, loss_mask_ce_9: 3.53478/3.67235, loss_mask_bce_9: 0.72632/0.39231, loss_mask_dice_9: 1.83083/1.90072, loss_spatial_bce_9: 0.32898/0.33254, loss_spatial_dice_9: 0.85466/0.82154, loss_spatial_ce_9: 2.10777/1.49109, loss_grounding_bce_9: 0.14465/0.10561, loss_grounding_dice_9: 0.33667/0.28081, loss_grounding_ce_9: 0.22496/0.66848] items per batch[64] items per second[0.23] total items[5555200] mini batches[ 86800] memory[7345] epoch remaining[0:40:47]
INFO:trainer.default_trainer:epochs[    47] optim steps[86900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.76901/0.89199, loss_mask_bce_0: 0.21686/0.33343, loss_mask_dice_0: 1.05601/1.15996, loss_spatial_bce_0: 0.04008/0.08617, loss_spatial_dice_0: 0.19990/0.20546, loss_spatial_ce_0: 0.05777/0.05861, loss_grounding_bce_0: 0.03304/0.08598, loss_grounding_dice_0: 0.23712/0.17808, loss_grounding_ce_0: 0.41085/0.27089, loss_mask_ce_1: 0.79398/0.89290, loss_mask_bce_1: 0.20416/0.33435, loss_mask_dice_1: 1.09611/1.16689, loss_spatial_bce_1: 0.04072/0.08670, loss_spatial_dice_1: 0.22050/0.20933, loss_spatial_ce_1: 0.05883/0.06441, loss_grounding_bce_1: 0.02412/0.08619, loss_grounding_dice_1: 0.19722/0.17892, loss_grounding_ce_1: 0.56736/0.27174, loss_mask_ce_2: 0.97806/0.89974, loss_mask_bce_2: 0.20747/0.33503, loss_mask_dice_2: 1.11192/1.16741, loss_spatial_bce_2: 0.04188/0.08797, loss_spatial_dice_2: 0.20096/0.21130, loss_spatial_ce_2: 0.06486/0.06779, loss_grounding_bce_2: 0.02493/0.08637, loss_grounding_dice_2: 0.19392/0.17883, loss_grounding_ce_2: 0.61181/0.27512, loss_mask_ce_3: 0.99922/0.91128, loss_mask_bce_3: 0.20644/0.33620, loss_mask_dice_3: 1.12592/1.16529, loss_spatial_bce_3: 0.04027/0.08931, loss_spatial_dice_3: 0.17537/0.21244, loss_spatial_ce_3: 0.06594/0.07301, loss_grounding_bce_3: 0.02514/0.08663, loss_grounding_dice_3: 0.20004/0.17848, loss_grounding_ce_3: 0.63000/0.27724, loss_mask_ce_4: 0.81431/0.91293, loss_mask_bce_4: 0.21970/0.33840, loss_mask_dice_4: 1.16601/1.18917, loss_spatial_bce_4: 0.04217/0.09321, loss_spatial_dice_4: 0.20831/0.22463, loss_spatial_ce_4: 0.06508/0.08919, loss_grounding_bce_4: 0.02178/0.08719, loss_grounding_dice_4: 0.19485/0.18151, loss_grounding_ce_4: 0.54285/0.28018, loss_mask_ce_5: 0.82754/0.92974, loss_mask_bce_5: 0.21979/0.34080, loss_mask_dice_5: 1.08074/1.19740, loss_spatial_bce_5: 0.04342/0.09555, loss_spatial_dice_5: 0.20924/0.22908, loss_spatial_ce_5: 0.08203/0.10274, loss_grounding_bce_5: 0.02222/0.08761, loss_grounding_dice_5: 0.20510/0.18281, loss_grounding_ce_5: 0.51809/0.29292, loss_mask_ce_6: 0.91886/0.96997, loss_mask_bce_6: 0.23043/0.34361, loss_mask_dice_6: 1.10484/1.20055, loss_spatial_bce_6: 0.04636/0.10118, loss_spatial_dice_6: 0.20893/0.23206, loss_spatial_ce_6: 0.05598/0.12749, loss_grounding_bce_6: 0.02566/0.08837, loss_grounding_dice_6: 0.21788/0.18324, loss_grounding_ce_6: 0.48959/0.30802, loss_mask_ce_7: 0.81488/1.01605, loss_mask_bce_7: 0.20985/0.35149, loss_mask_dice_7: 1.12327/1.25472, loss_spatial_bce_7: 0.04215/0.10898, loss_spatial_dice_7: 0.24284/0.25969, loss_spatial_ce_7: 0.09039/0.16218, loss_grounding_bce_7: 0.02532/0.09024, loss_grounding_dice_7: 0.21823/0.19062, loss_grounding_ce_7: 0.53954/0.33787, loss_mask_ce_8: 1.00484/1.12510, loss_mask_bce_8: 0.22944/0.36505, loss_mask_dice_8: 1.15398/1.32724, loss_spatial_bce_8: 0.06458/0.12892, loss_spatial_dice_8: 0.28209/0.29742, loss_spatial_ce_8: 0.11843/0.21124, loss_grounding_bce_8: 0.02826/0.09389, loss_grounding_dice_8: 0.23130/0.20126, loss_grounding_ce_8: 1.04415/0.40365, loss_mask_ce_9: 3.29243/3.67221, loss_mask_bce_9: 0.31253/0.39226, loss_mask_dice_9: 2.02426/1.90044, loss_spatial_bce_9: 0.27036/0.33253, loss_spatial_dice_9: 0.85032/0.82152, loss_spatial_ce_9: 1.38234/1.49102, loss_grounding_bce_9: 0.03337/0.10562, loss_grounding_dice_9: 0.35019/0.28080, loss_grounding_ce_9: 0.87150/0.66841] items per batch[64] items per second[0.23] total items[5561600] mini batches[ 86900] memory[7345] epoch remaining[0:36:13]
INFO:trainer.default_trainer:epochs[    47] optim steps[87000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.09135/0.89192, loss_mask_bce_0: 0.26037/0.33347, loss_mask_dice_0: 0.67324/1.15991, loss_spatial_bce_0: 0.15023/0.08618, loss_spatial_dice_0: 0.16381/0.20544, loss_spatial_ce_0: 0.02734/0.05859, loss_grounding_bce_0: 0.03152/0.08600, loss_grounding_dice_0: 0.24342/0.17808, loss_grounding_ce_0: 0.11759/0.27091, loss_mask_ce_1: 1.08320/0.89281, loss_mask_bce_1: 0.26439/0.33440, loss_mask_dice_1: 0.66424/1.16686, loss_spatial_bce_1: 0.16014/0.08670, loss_spatial_dice_1: 0.17587/0.20931, loss_spatial_ce_1: 0.03211/0.06440, loss_grounding_bce_1: 0.03349/0.08620, loss_grounding_dice_1: 0.24052/0.17892, loss_grounding_ce_1: 0.12300/0.27174, loss_mask_ce_2: 1.24513/0.89965, loss_mask_bce_2: 0.29087/0.33507, loss_mask_dice_2: 0.70698/1.16738, loss_spatial_bce_2: 0.13711/0.08797, loss_spatial_dice_2: 0.17701/0.21128, loss_spatial_ce_2: 0.04117/0.06778, loss_grounding_bce_2: 0.03225/0.08638, loss_grounding_dice_2: 0.24699/0.17883, loss_grounding_ce_2: 0.12269/0.27515, loss_mask_ce_3: 1.28239/0.91119, loss_mask_bce_3: 0.27718/0.33624, loss_mask_dice_3: 0.65245/1.16525, loss_spatial_bce_3: 0.13058/0.08931, loss_spatial_dice_3: 0.16055/0.21242, loss_spatial_ce_3: 0.06746/0.07300, loss_grounding_bce_3: 0.03222/0.08665, loss_grounding_dice_3: 0.23898/0.17849, loss_grounding_ce_3: 0.15163/0.27727, loss_mask_ce_4: 1.24406/0.91283, loss_mask_bce_4: 0.30504/0.33844, loss_mask_dice_4: 0.70138/1.18914, loss_spatial_bce_4: 0.13296/0.09321, loss_spatial_dice_4: 0.16082/0.22461, loss_spatial_ce_4: 0.08444/0.08917, loss_grounding_bce_4: 0.03630/0.08720, loss_grounding_dice_4: 0.25288/0.18151, loss_grounding_ce_4: 0.12791/0.28019, loss_mask_ce_5: 1.34006/0.92967, loss_mask_bce_5: 0.33408/0.34085, loss_mask_dice_5: 0.73568/1.19735, loss_spatial_bce_5: 0.14310/0.09555, loss_spatial_dice_5: 0.16357/0.22906, loss_spatial_ce_5: 0.06679/0.10273, loss_grounding_bce_5: 0.03610/0.08763, loss_grounding_dice_5: 0.24893/0.18281, loss_grounding_ce_5: 0.14044/0.29293, loss_mask_ce_6: 1.00085/0.96986, loss_mask_bce_6: 0.35385/0.34366, loss_mask_dice_6: 0.74863/1.20053, loss_spatial_bce_6: 0.22537/0.10118, loss_spatial_dice_6: 0.18973/0.23204, loss_spatial_ce_6: 0.03435/0.12747, loss_grounding_bce_6: 0.03249/0.08839, loss_grounding_dice_6: 0.24072/0.18324, loss_grounding_ce_6: 0.14409/0.30802, loss_mask_ce_7: 1.29930/1.01594, loss_mask_bce_7: 0.32117/0.35154, loss_mask_dice_7: 0.73507/1.25471, loss_spatial_bce_7: 0.17895/0.10898, loss_spatial_dice_7: 0.21585/0.25966, loss_spatial_ce_7: 0.15445/0.16213, loss_grounding_bce_7: 0.03359/0.09026, loss_grounding_dice_7: 0.23944/0.19062, loss_grounding_ce_7: 0.15783/0.33787, loss_mask_ce_8: 1.24135/1.12495, loss_mask_bce_8: 0.39528/0.36510, loss_mask_dice_8: 0.75676/1.32724, loss_spatial_bce_8: 0.17941/0.12892, loss_spatial_dice_8: 0.23178/0.29739, loss_spatial_ce_8: 0.14957/0.21114, loss_grounding_bce_8: 0.03763/0.09391, loss_grounding_dice_8: 0.25231/0.20127, loss_grounding_ce_8: 0.14619/0.40359, loss_mask_ce_9: 3.63431/3.67225, loss_mask_bce_9: 0.47960/0.39231, loss_mask_dice_9: 1.07861/1.90041, loss_spatial_bce_9: 0.37420/0.33255, loss_spatial_dice_9: 0.84108/0.82151, loss_spatial_ce_9: 1.25749/1.49097, loss_grounding_bce_9: 0.04398/0.10564, loss_grounding_dice_9: 0.37833/0.28081, loss_grounding_ce_9: 0.34053/0.66837] items per batch[64] items per second[0.23] total items[5568000] mini batches[ 87000] memory[7345] epoch remaining[0:31:41]
INFO:trainer.default_trainer:epochs[    47] optim steps[87100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.02864/0.89184, loss_mask_bce_0: 0.65513/0.33346, loss_mask_dice_0: 0.99270/1.15984, loss_spatial_bce_0: 0.11133/0.08617, loss_spatial_dice_0: 0.27887/0.20542, loss_spatial_ce_0: 0.02967/0.05857, loss_grounding_bce_0: 0.12900/0.08599, loss_grounding_dice_0: 0.22396/0.17807, loss_grounding_ce_0: 0.46416/0.27086, loss_mask_ce_1: 1.02518/0.89273, loss_mask_bce_1: 0.65905/0.33438, loss_mask_dice_1: 1.15992/1.16678, loss_spatial_bce_1: 0.09496/0.08670, loss_spatial_dice_1: 0.24896/0.20928, loss_spatial_ce_1: 0.06773/0.06439, loss_grounding_bce_1: 0.12981/0.08620, loss_grounding_dice_1: 0.21066/0.17891, loss_grounding_ce_1: 0.46771/0.27169, loss_mask_ce_2: 1.10876/0.89957, loss_mask_bce_2: 0.64567/0.33506, loss_mask_dice_2: 1.11165/1.16731, loss_spatial_bce_2: 0.10165/0.08797, loss_spatial_dice_2: 0.25685/0.21126, loss_spatial_ce_2: 0.06716/0.06778, loss_grounding_bce_2: 0.12757/0.08638, loss_grounding_dice_2: 0.20968/0.17882, loss_grounding_ce_2: 0.47058/0.27510, loss_mask_ce_3: 1.01130/0.91111, loss_mask_bce_3: 0.63478/0.33623, loss_mask_dice_3: 1.12059/1.16518, loss_spatial_bce_3: 0.10057/0.08931, loss_spatial_dice_3: 0.25527/0.21240, loss_spatial_ce_3: 0.05408/0.07300, loss_grounding_bce_3: 0.13077/0.08664, loss_grounding_dice_3: 0.20167/0.17847, loss_grounding_ce_3: 0.45182/0.27722, loss_mask_ce_4: 1.20082/0.91277, loss_mask_bce_4: 0.66779/0.33843, loss_mask_dice_4: 1.19066/1.18906, loss_spatial_bce_4: 0.10590/0.09321, loss_spatial_dice_4: 0.26550/0.22459, loss_spatial_ce_4: 0.11630/0.08916, loss_grounding_bce_4: 0.13167/0.08720, loss_grounding_dice_4: 0.22428/0.18150, loss_grounding_ce_4: 0.44985/0.28015, loss_mask_ce_5: 1.17311/0.92961, loss_mask_bce_5: 0.67777/0.34083, loss_mask_dice_5: 1.21231/1.19726, loss_spatial_bce_5: 0.10288/0.09555, loss_spatial_dice_5: 0.26265/0.22904, loss_spatial_ce_5: 0.09141/0.10271, loss_grounding_bce_5: 0.12702/0.08763, loss_grounding_dice_5: 0.23561/0.18280, loss_grounding_ce_5: 0.51951/0.29288, loss_mask_ce_6: 1.17306/0.96980, loss_mask_bce_6: 0.69404/0.34364, loss_mask_dice_6: 1.09400/1.20045, loss_spatial_bce_6: 0.11434/0.10118, loss_spatial_dice_6: 0.28213/0.23202, loss_spatial_ce_6: 0.15140/0.12744, loss_grounding_bce_6: 0.12274/0.08839, loss_grounding_dice_6: 0.19981/0.18323, loss_grounding_ce_6: 0.60484/0.30799, loss_mask_ce_7: 0.99128/1.01588, loss_mask_bce_7: 0.70990/0.35152, loss_mask_dice_7: 1.27626/1.25464, loss_spatial_bce_7: 0.14751/0.10898, loss_spatial_dice_7: 0.30583/0.25965, loss_spatial_ce_7: 0.19794/0.16211, loss_grounding_bce_7: 0.10973/0.09026, loss_grounding_dice_7: 0.21462/0.19061, loss_grounding_ce_7: 0.50343/0.33784, loss_mask_ce_8: 1.39913/1.12492, loss_mask_bce_8: 0.70915/0.36508, loss_mask_dice_8: 1.31808/1.32713, loss_spatial_bce_8: 0.14206/0.12891, loss_spatial_dice_8: 0.33200/0.29737, loss_spatial_ce_8: 0.18956/0.21109, loss_grounding_bce_8: 0.08258/0.09390, loss_grounding_dice_8: 0.26780/0.20126, loss_grounding_ce_8: 0.61773/0.40357, loss_mask_ce_9: 4.49314/3.67218, loss_mask_bce_9: 0.74734/0.39229, loss_mask_dice_9: 1.92394/1.90025, loss_spatial_bce_9: 0.42529/0.33255, loss_spatial_dice_9: 0.90501/0.82151, loss_spatial_ce_9: 1.35541/1.49090, loss_grounding_bce_9: 0.12002/0.10564, loss_grounding_dice_9: 0.37688/0.28080, loss_grounding_ce_9: 0.63007/0.66837] items per batch[64] items per second[0.23] total items[5574400] mini batches[ 87100] memory[7345] epoch remaining[0:27:12]
INFO:trainer.default_trainer:epochs[    47] optim steps[87200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.35015/0.89173, loss_mask_bce_0: 0.66098/0.33343, loss_mask_dice_0: 0.71217/1.15973, loss_spatial_bce_0: 0.12204/0.08617, loss_spatial_dice_0: 0.13037/0.20539, loss_spatial_ce_0: 0.08020/0.05858, loss_grounding_bce_0: 0.17928/0.08598, loss_grounding_dice_0: 0.17382/0.17806, loss_grounding_ce_0: 0.26387/0.27085, loss_mask_ce_1: 1.33072/0.89263, loss_mask_bce_1: 0.62237/0.33435, loss_mask_dice_1: 0.67001/1.16668, loss_spatial_bce_1: 0.12924/0.08669, loss_spatial_dice_1: 0.13985/0.20925, loss_spatial_ce_1: 0.08786/0.06439, loss_grounding_bce_1: 0.17179/0.08619, loss_grounding_dice_1: 0.16617/0.17890, loss_grounding_ce_1: 0.27564/0.27168, loss_mask_ce_2: 1.26668/0.89947, loss_mask_bce_2: 0.62119/0.33502, loss_mask_dice_2: 0.66275/1.16718, loss_spatial_bce_2: 0.13812/0.08796, loss_spatial_dice_2: 0.15942/0.21123, loss_spatial_ce_2: 0.11218/0.06776, loss_grounding_bce_2: 0.17344/0.08637, loss_grounding_dice_2: 0.17213/0.17881, loss_grounding_ce_2: 0.25999/0.27507, loss_mask_ce_3: 1.44291/0.91101, loss_mask_bce_3: 0.60189/0.33620, loss_mask_dice_3: 0.65552/1.16508, loss_spatial_bce_3: 0.13452/0.08930, loss_spatial_dice_3: 0.16332/0.21237, loss_spatial_ce_3: 0.16205/0.07299, loss_grounding_bce_3: 0.17614/0.08663, loss_grounding_dice_3: 0.16407/0.17846, loss_grounding_ce_3: 0.31082/0.27719, loss_mask_ce_4: 1.45246/0.91269, loss_mask_bce_4: 0.60207/0.33839, loss_mask_dice_4: 0.65189/1.18894, loss_spatial_bce_4: 0.14124/0.09321, loss_spatial_dice_4: 0.15443/0.22456, loss_spatial_ce_4: 0.23879/0.08914, loss_grounding_bce_4: 0.19427/0.08719, loss_grounding_dice_4: 0.17587/0.18149, loss_grounding_ce_4: 0.32241/0.28015, loss_mask_ce_5: 1.19723/0.92952, loss_mask_bce_5: 0.70549/0.34080, loss_mask_dice_5: 0.78280/1.19715, loss_spatial_bce_5: 0.14108/0.09554, loss_spatial_dice_5: 0.18155/0.22901, loss_spatial_ce_5: 0.15456/0.10269, loss_grounding_bce_5: 0.21053/0.08761, loss_grounding_dice_5: 0.19930/0.18280, loss_grounding_ce_5: 0.34074/0.29288, loss_mask_ce_6: 1.15976/0.96971, loss_mask_bce_6: 0.71957/0.34361, loss_mask_dice_6: 0.80051/1.20033, loss_spatial_bce_6: 0.16249/0.10117, loss_spatial_dice_6: 0.19055/0.23199, loss_spatial_ce_6: 0.19030/0.12740, loss_grounding_bce_6: 0.20389/0.08838, loss_grounding_dice_6: 0.18554/0.18322, loss_grounding_ce_6: 0.40218/0.30798, loss_mask_ce_7: 1.19240/1.01579, loss_mask_bce_7: 0.70976/0.35149, loss_mask_dice_7: 0.79359/1.25453, loss_spatial_bce_7: 0.14814/0.10897, loss_spatial_dice_7: 0.17823/0.25962, loss_spatial_ce_7: 0.22339/0.16207, loss_grounding_bce_7: 0.21338/0.09025, loss_grounding_dice_7: 0.21960/0.19060, loss_grounding_ce_7: 0.42558/0.33782, loss_mask_ce_8: 1.08844/1.12479, loss_mask_bce_8: 0.71647/0.36506, loss_mask_dice_8: 0.80624/1.32703, loss_spatial_bce_8: 0.16109/0.12890, loss_spatial_dice_8: 0.20152/0.29735, loss_spatial_ce_8: 0.13415/0.21102, loss_grounding_bce_8: 0.22416/0.09389, loss_grounding_dice_8: 0.24731/0.20125, loss_grounding_ce_8: 0.37398/0.40354, loss_mask_ce_9: 4.04089/3.67206, loss_mask_bce_9: 0.70428/0.39226, loss_mask_dice_9: 1.17277/1.90017, loss_spatial_bce_9: 0.45451/0.33256, loss_spatial_dice_9: 0.78155/0.82151, loss_spatial_ce_9: 1.24021/1.49090, loss_grounding_bce_9: 0.24741/0.10562, loss_grounding_dice_9: 0.30072/0.28079, loss_grounding_ce_9: 1.07949/0.66834] items per batch[64] items per second[0.24] total items[5580800] mini batches[ 87200] memory[7345] epoch remaining[0:22:35]
INFO:trainer.default_trainer:epochs[    47] optim steps[87300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.28877/0.89170, loss_mask_bce_0: 0.08986/0.33339, loss_mask_dice_0: 0.18022/1.15984, loss_spatial_bce_0: 0.04309/0.08615, loss_spatial_dice_0: 0.07521/0.20537, loss_spatial_ce_0: 0.02797/0.05857, loss_grounding_bce_0: 0.02765/0.08597, loss_grounding_dice_0: 0.05326/0.17806, loss_grounding_ce_0: 0.32114/0.27081, loss_mask_ce_1: 0.26869/0.89260, loss_mask_bce_1: 0.09132/0.33431, loss_mask_dice_1: 0.18585/1.16681, loss_spatial_bce_1: 0.04246/0.08668, loss_spatial_dice_1: 0.06768/0.20923, loss_spatial_ce_1: 0.02746/0.06438, loss_grounding_bce_1: 0.02781/0.08618, loss_grounding_dice_1: 0.05678/0.17890, loss_grounding_ce_1: 0.31839/0.27165, loss_mask_ce_2: 0.27221/0.89943, loss_mask_bce_2: 0.09226/0.33499, loss_mask_dice_2: 0.19322/1.16732, loss_spatial_bce_2: 0.03949/0.08794, loss_spatial_dice_2: 0.06619/0.21121, loss_spatial_ce_2: 0.02826/0.06775, loss_grounding_bce_2: 0.02874/0.08636, loss_grounding_dice_2: 0.05392/0.17880, loss_grounding_ce_2: 0.32241/0.27501, loss_mask_ce_3: 0.25333/0.91097, loss_mask_bce_3: 0.09332/0.33616, loss_mask_dice_3: 0.19399/1.16522, loss_spatial_bce_3: 0.04102/0.08929, loss_spatial_dice_3: 0.08070/0.21236, loss_spatial_ce_3: 0.03695/0.07298, loss_grounding_bce_3: 0.03144/0.08662, loss_grounding_dice_3: 0.05888/0.17846, loss_grounding_ce_3: 0.31940/0.27716, loss_mask_ce_4: 0.28103/0.91266, loss_mask_bce_4: 0.09819/0.33835, loss_mask_dice_4: 0.19669/1.18907, loss_spatial_bce_4: 0.04085/0.09319, loss_spatial_dice_4: 0.07345/0.22453, loss_spatial_ce_4: 0.03323/0.08913, loss_grounding_bce_4: 0.02880/0.08717, loss_grounding_dice_4: 0.05851/0.18148, loss_grounding_ce_4: 0.29756/0.28011, loss_mask_ce_5: 0.30040/0.92949, loss_mask_bce_5: 0.09727/0.34076, loss_mask_dice_5: 0.20441/1.19726, loss_spatial_bce_5: 0.04680/0.09552, loss_spatial_dice_5: 0.07524/0.22899, loss_spatial_ce_5: 0.04190/0.10267, loss_grounding_bce_5: 0.03042/0.08760, loss_grounding_dice_5: 0.06008/0.18280, loss_grounding_ce_5: 0.30592/0.29282, loss_mask_ce_6: 0.31249/0.96971, loss_mask_bce_6: 0.10864/0.34357, loss_mask_dice_6: 0.21033/1.20045, loss_spatial_bce_6: 0.04467/0.10115, loss_spatial_dice_6: 0.07608/0.23197, loss_spatial_ce_6: 0.06430/0.12738, loss_grounding_bce_6: 0.03257/0.08837, loss_grounding_dice_6: 0.06321/0.18322, loss_grounding_ce_6: 0.31741/0.30793, loss_mask_ce_7: 0.30629/1.01579, loss_mask_bce_7: 0.13338/0.35145, loss_mask_dice_7: 0.24017/1.25467, loss_spatial_bce_7: 0.06336/0.10895, loss_spatial_dice_7: 0.13467/0.25961, loss_spatial_ce_7: 0.11819/0.16202, loss_grounding_bce_7: 0.03491/0.09023, loss_grounding_dice_7: 0.08304/0.19060, loss_grounding_ce_7: 0.33827/0.33777, loss_mask_ce_8: 0.38496/1.12481, loss_mask_bce_8: 0.14239/0.36501, loss_mask_dice_8: 0.23532/1.32716, loss_spatial_bce_8: 0.06319/0.12888, loss_spatial_dice_8: 0.15973/0.29734, loss_spatial_ce_8: 0.06070/0.21094, loss_grounding_bce_8: 0.04226/0.09388, loss_grounding_dice_8: 0.07531/0.20125, loss_grounding_ce_8: 0.34074/0.40347, loss_mask_ce_9: 4.43021/3.67218, loss_mask_bce_9: 0.22861/0.39223, loss_mask_dice_9: 0.70064/1.90040, loss_spatial_bce_9: 0.39260/0.33253, loss_spatial_dice_9: 0.80029/0.82152, loss_spatial_ce_9: 1.37079/1.49083, loss_grounding_bce_9: 0.08103/0.10562, loss_grounding_dice_9: 0.25601/0.28079, loss_grounding_ce_9: 0.49751/0.66824] items per batch[64] items per second[0.23] total items[5587200] mini batches[ 87300] memory[7345] epoch remaining[0:18:05]
INFO:trainer.default_trainer:epochs[    47] optim steps[87400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.01367/0.89156, loss_mask_bce_0: 0.03127/0.33339, loss_mask_dice_0: 0.10066/1.15991, loss_spatial_bce_0: 0.01801/0.08615, loss_spatial_dice_0: 0.06074/0.20536, loss_spatial_ce_0: 0.00285/0.05854, loss_grounding_bce_0: 0.01756/0.08596, loss_grounding_dice_0: 0.07488/0.17804, loss_grounding_ce_0: 0.00067/0.27078, loss_mask_ce_1: 0.01336/0.89247, loss_mask_bce_1: 0.03065/0.33431, loss_mask_dice_1: 0.12464/1.16688, loss_spatial_bce_1: 0.01749/0.08667, loss_spatial_dice_1: 0.07498/0.20922, loss_spatial_ce_1: 0.00208/0.06436, loss_grounding_bce_1: 0.01641/0.08616, loss_grounding_dice_1: 0.07282/0.17888, loss_grounding_ce_1: 0.00065/0.27160, loss_mask_ce_2: 0.01428/0.89929, loss_mask_bce_2: 0.03021/0.33499, loss_mask_dice_2: 0.10919/1.16739, loss_spatial_bce_2: 0.01768/0.08794, loss_spatial_dice_2: 0.07683/0.21120, loss_spatial_ce_2: 0.00093/0.06772, loss_grounding_bce_2: 0.01786/0.08634, loss_grounding_dice_2: 0.09036/0.17878, loss_grounding_ce_2: 0.00089/0.27498, loss_mask_ce_3: 0.01998/0.91086, loss_mask_bce_3: 0.03122/0.33616, loss_mask_dice_3: 0.11542/1.16531, loss_spatial_bce_3: 0.01910/0.08928, loss_spatial_dice_3: 0.09396/0.21235, loss_spatial_ce_3: 0.00064/0.07295, loss_grounding_bce_3: 0.01875/0.08661, loss_grounding_dice_3: 0.07995/0.17844, loss_grounding_ce_3: 0.00062/0.27712, loss_mask_ce_4: 0.01347/0.91253, loss_mask_bce_4: 0.02984/0.33835, loss_mask_dice_4: 0.13345/1.18916, loss_spatial_bce_4: 0.01702/0.09318, loss_spatial_dice_4: 0.06237/0.22452, loss_spatial_ce_4: 0.00041/0.08911, loss_grounding_bce_4: 0.01664/0.08716, loss_grounding_dice_4: 0.06746/0.18147, loss_grounding_ce_4: 0.00092/0.28006, loss_mask_ce_5: 0.01650/0.92935, loss_mask_bce_5: 0.02858/0.34077, loss_mask_dice_5: 0.12021/1.19734, loss_spatial_bce_5: 0.02016/0.09552, loss_spatial_dice_5: 0.07601/0.22899, loss_spatial_ce_5: 0.00190/0.10264, loss_grounding_bce_5: 0.01624/0.08759, loss_grounding_dice_5: 0.09209/0.18278, loss_grounding_ce_5: 0.00112/0.29278, loss_mask_ce_6: 0.03057/0.96961, loss_mask_bce_6: 0.02809/0.34358, loss_mask_dice_6: 0.12032/1.20055, loss_spatial_bce_6: 0.01873/0.10115, loss_spatial_dice_6: 0.07391/0.23197, loss_spatial_ce_6: 0.00491/0.12734, loss_grounding_bce_6: 0.01733/0.08836, loss_grounding_dice_6: 0.08518/0.18321, loss_grounding_ce_6: 0.00356/0.30787, loss_mask_ce_7: 0.01895/1.01568, loss_mask_bce_7: 0.02853/0.35146, loss_mask_dice_7: 0.10571/1.25476, loss_spatial_bce_7: 0.01858/0.10895, loss_spatial_dice_7: 0.07495/0.25961, loss_spatial_ce_7: 0.04040/0.16198, loss_grounding_bce_7: 0.01548/0.09022, loss_grounding_dice_7: 0.07414/0.19058, loss_grounding_ce_7: 0.00151/0.33772, loss_mask_ce_8: 0.03697/1.12470, loss_mask_bce_8: 0.02954/0.36502, loss_mask_dice_8: 0.13941/1.32725, loss_spatial_bce_8: 0.02015/0.12887, loss_spatial_dice_8: 0.08416/0.29734, loss_spatial_ce_8: 0.02325/0.21087, loss_grounding_bce_8: 0.01868/0.09386, loss_grounding_dice_8: 0.07658/0.20123, loss_grounding_ce_8: 0.00632/0.40341, loss_mask_ce_9: 1.80951/3.67211, loss_mask_bce_9: 0.03763/0.39225, loss_mask_dice_9: 0.16397/1.90061, loss_spatial_bce_9: 0.37490/0.33253, loss_spatial_dice_9: 0.70664/0.82152, loss_spatial_ce_9: 0.98048/1.49077, loss_grounding_bce_9: 0.02124/0.10560, loss_grounding_dice_9: 0.13811/0.28077, loss_grounding_ce_9: 0.14106/0.66820] items per batch[64] items per second[0.24] total items[5593600] mini batches[ 87400] memory[7345] epoch remaining[0:13:30]
INFO:trainer.default_trainer:epochs[    47] optim steps[87500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.51170/0.89141, loss_mask_bce_0: 0.17090/0.33336, loss_mask_dice_0: 1.08576/1.15978, loss_spatial_bce_0: 0.02815/0.08614, loss_spatial_dice_0: 0.16673/0.20534, loss_spatial_ce_0: 0.02551/0.05853, loss_grounding_bce_0: 0.04005/0.08594, loss_grounding_dice_0: 0.12846/0.17803, loss_grounding_ce_0: 0.00134/0.27080, loss_mask_ce_1: 0.51903/0.89231, loss_mask_bce_1: 0.18289/0.33428, loss_mask_dice_1: 1.14472/1.16676, loss_spatial_bce_1: 0.02686/0.08666, loss_spatial_dice_1: 0.16200/0.20919, loss_spatial_ce_1: 0.17082/0.06434, loss_grounding_bce_1: 0.04643/0.08615, loss_grounding_dice_1: 0.14968/0.17886, loss_grounding_ce_1: 0.00158/0.27162, loss_mask_ce_2: 0.53974/0.89913, loss_mask_bce_2: 0.18692/0.33496, loss_mask_dice_2: 1.15021/1.16727, loss_spatial_bce_2: 0.02776/0.08793, loss_spatial_dice_2: 0.17311/0.21118, loss_spatial_ce_2: 0.03288/0.06771, loss_grounding_bce_2: 0.04432/0.08633, loss_grounding_dice_2: 0.14312/0.17876, loss_grounding_ce_2: 0.00314/0.27501, loss_mask_ce_3: 0.46582/0.91070, loss_mask_bce_3: 0.19005/0.33613, loss_mask_dice_3: 1.27451/1.16520, loss_spatial_bce_3: 0.02870/0.08927, loss_spatial_dice_3: 0.17143/0.21233, loss_spatial_ce_3: 0.02997/0.07294, loss_grounding_bce_3: 0.04573/0.08659, loss_grounding_dice_3: 0.14919/0.17842, loss_grounding_ce_3: 0.00180/0.27714, loss_mask_ce_4: 0.43832/0.91237, loss_mask_bce_4: 0.18970/0.33833, loss_mask_dice_4: 1.22375/1.18905, loss_spatial_bce_4: 0.03012/0.09318, loss_spatial_dice_4: 0.17829/0.22450, loss_spatial_ce_4: 0.04084/0.08908, loss_grounding_bce_4: 0.04272/0.08715, loss_grounding_dice_4: 0.14162/0.18145, loss_grounding_ce_4: 0.00189/0.28007, loss_mask_ce_5: 0.42939/0.92918, loss_mask_bce_5: 0.18187/0.34074, loss_mask_dice_5: 1.23137/1.19723, loss_spatial_bce_5: 0.03068/0.09551, loss_spatial_dice_5: 0.18840/0.22896, loss_spatial_ce_5: 0.01386/0.10260, loss_grounding_bce_5: 0.04296/0.08758, loss_grounding_dice_5: 0.13535/0.18276, loss_grounding_ce_5: 0.00194/0.29281, loss_mask_ce_6: 0.43623/0.96944, loss_mask_bce_6: 0.18672/0.34355, loss_mask_dice_6: 1.23844/1.20043, loss_spatial_bce_6: 0.02787/0.10114, loss_spatial_dice_6: 0.17505/0.23194, loss_spatial_ce_6: 0.01575/0.12732, loss_grounding_bce_6: 0.03980/0.08834, loss_grounding_dice_6: 0.14835/0.18319, loss_grounding_ce_6: 0.00173/0.30786, loss_mask_ce_7: 0.49005/1.01552, loss_mask_bce_7: 0.19291/0.35143, loss_mask_dice_7: 1.22298/1.25463, loss_spatial_bce_7: 0.03387/0.10894, loss_spatial_dice_7: 0.20859/0.25958, loss_spatial_ce_7: 0.04113/0.16194, loss_grounding_bce_7: 0.03965/0.09021, loss_grounding_dice_7: 0.12784/0.19056, loss_grounding_ce_7: 0.00119/0.33774, loss_mask_ce_8: 0.48833/1.12456, loss_mask_bce_8: 0.22320/0.36499, loss_mask_dice_8: 1.37245/1.32713, loss_spatial_bce_8: 0.03420/0.12885, loss_spatial_dice_8: 0.22303/0.29731, loss_spatial_ce_8: 0.01551/0.21079, loss_grounding_bce_8: 0.03882/0.09385, loss_grounding_dice_8: 0.13054/0.20121, loss_grounding_ce_8: 0.00184/0.40341, loss_mask_ce_9: 2.35209/3.67195, loss_mask_bce_9: 0.30145/0.39221, loss_mask_dice_9: 2.27778/1.90046, loss_spatial_bce_9: 0.20362/0.33253, loss_spatial_dice_9: 0.85431/0.82151, loss_spatial_ce_9: 1.18211/1.49079, loss_grounding_bce_9: 0.04140/0.10558, loss_grounding_dice_9: 0.13864/0.28073, loss_grounding_ce_9: 0.10216/0.66825] items per batch[64] items per second[0.23] total items[5600000] mini batches[ 87500] memory[7345] epoch remaining[0:08:56]
INFO:trainer.default_trainer:epochs[    47] optim steps[87600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.90114/0.89138, loss_mask_bce_0: 0.45047/0.33336, loss_mask_dice_0: 0.57697/1.15975, loss_spatial_bce_0: 0.12913/0.08614, loss_spatial_dice_0: 0.23964/0.20533, loss_spatial_ce_0: 0.01049/0.05850, loss_grounding_bce_0: 0.32918/0.08595, loss_grounding_dice_0: 0.22254/0.17803, loss_grounding_ce_0: 0.06907/0.27083, loss_mask_ce_1: 0.94913/0.89229, loss_mask_bce_1: 0.43396/0.33428, loss_mask_dice_1: 0.60824/1.16673, loss_spatial_bce_1: 0.13492/0.08666, loss_spatial_dice_1: 0.23303/0.20919, loss_spatial_ce_1: 0.00808/0.06432, loss_grounding_bce_1: 0.29008/0.08615, loss_grounding_dice_1: 0.26330/0.17887, loss_grounding_ce_1: 0.09142/0.27165, loss_mask_ce_2: 0.96492/0.89912, loss_mask_bce_2: 0.44669/0.33496, loss_mask_dice_2: 0.59200/1.16721, loss_spatial_bce_2: 0.14092/0.08793, loss_spatial_dice_2: 0.25911/0.21117, loss_spatial_ce_2: 0.01141/0.06769, loss_grounding_bce_2: 0.29093/0.08633, loss_grounding_dice_2: 0.22481/0.17877, loss_grounding_ce_2: 0.13320/0.27501, loss_mask_ce_3: 0.99780/0.91069, loss_mask_bce_3: 0.25986/0.33613, loss_mask_dice_3: 0.55849/1.16515, loss_spatial_bce_3: 0.14093/0.08927, loss_spatial_dice_3: 0.25761/0.21232, loss_spatial_ce_3: 0.01554/0.07291, loss_grounding_bce_3: 0.33278/0.08659, loss_grounding_dice_3: 0.24153/0.17843, loss_grounding_ce_3: 0.11875/0.27716, loss_mask_ce_4: 1.33476/0.91235, loss_mask_bce_4: 0.28389/0.33833, loss_mask_dice_4: 0.57096/1.18902, loss_spatial_bce_4: 0.13721/0.09317, loss_spatial_dice_4: 0.26046/0.22450, loss_spatial_ce_4: 0.10782/0.08906, loss_grounding_bce_4: 0.34487/0.08715, loss_grounding_dice_4: 0.25637/0.18146, loss_grounding_ce_4: 0.17521/0.28010, loss_mask_ce_5: 1.32070/0.92917, loss_mask_bce_5: 0.28276/0.34074, loss_mask_dice_5: 0.56336/1.19720, loss_spatial_bce_5: 0.13512/0.09550, loss_spatial_dice_5: 0.25641/0.22895, loss_spatial_ce_5: 0.09038/0.10258, loss_grounding_bce_5: 0.35951/0.08758, loss_grounding_dice_5: 0.31031/0.18277, loss_grounding_ce_5: 0.20178/0.29281, loss_mask_ce_6: 1.32734/0.96942, loss_mask_bce_6: 0.28162/0.34355, loss_mask_dice_6: 0.55806/1.20039, loss_spatial_bce_6: 0.14761/0.10113, loss_spatial_dice_6: 0.26209/0.23193, loss_spatial_ce_6: 0.05974/0.12730, loss_grounding_bce_6: 0.15572/0.08835, loss_grounding_dice_6: 0.17781/0.18320, loss_grounding_ce_6: 0.57827/0.30787, loss_mask_ce_7: 1.44450/1.01550, loss_mask_bce_7: 0.30829/0.35143, loss_mask_dice_7: 0.54725/1.25461, loss_spatial_bce_7: 0.15040/0.10894, loss_spatial_dice_7: 0.31823/0.25958, loss_spatial_ce_7: 0.10045/0.16189, loss_grounding_bce_7: 0.18755/0.09021, loss_grounding_dice_7: 0.34754/0.19057, loss_grounding_ce_7: 0.43084/0.33776, loss_mask_ce_8: 1.25387/1.12457, loss_mask_bce_8: 0.50612/0.36499, loss_mask_dice_8: 0.73646/1.32709, loss_spatial_bce_8: 0.27447/0.12884, loss_spatial_dice_8: 0.40701/0.29731, loss_spatial_ce_8: 0.15945/0.21071, loss_grounding_bce_8: 0.36883/0.09385, loss_grounding_dice_8: 0.37119/0.20121, loss_grounding_ce_8: 0.00992/0.40338, loss_mask_ce_9: 3.17591/3.67177, loss_mask_bce_9: 0.32457/0.39222, loss_mask_dice_9: 0.84947/1.90039, loss_spatial_bce_9: 0.50100/0.33253, loss_spatial_dice_9: 0.88962/0.82151, loss_spatial_ce_9: 1.81503/1.49084, loss_grounding_bce_9: 0.17873/0.10559, loss_grounding_dice_9: 0.34285/0.28074, loss_grounding_ce_9: 0.15299/0.66819] items per batch[64] items per second[0.23] total items[5606400] mini batches[ 87600] memory[7345] epoch remaining[0:04:23]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00087696.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0036 s/iter. Inference: 0.2169 s/iter. Eval: 0.0953 s/iter. Total: 0.3158 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0031 s/iter. Inference: 0.2211 s/iter. Eval: 0.0798 s/iter. Total: 0.3042 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2239 s/iter. Eval: 0.0780 s/iter. Total: 0.3052 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2235 s/iter. Eval: 0.0768 s/iter. Total: 0.3036 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0032 s/iter. Inference: 0.2230 s/iter. Eval: 0.0757 s/iter. Total: 0.3019 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2252 s/iter. Eval: 0.0754 s/iter. Total: 0.3039 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2273 s/iter. Eval: 0.0754 s/iter. Total: 0.3060 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2266 s/iter. Eval: 0.0754 s/iter. Total: 0.3053 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2274 s/iter. Eval: 0.0752 s/iter. Total: 0.3059 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalk0phtq_1 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.571 | 82.023 | 60.793 |      133      |
| Things | 55.618 | 82.658 | 66.597 |      80       |
| Stuff  | 42.954 | 81.065 | 52.030 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
DONE (t=0.43s)
creating index...
index created!
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.61 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.05 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
Loading and preparing results...
DONE (t=2.23s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.76 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.396
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.622
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.417
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.427
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.616
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.321
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.497
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.514
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.550
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.718
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.08 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.641 | 62.190 | 41.653 | 19.583 | 42.673 | 61.642 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.733 | bicycle      | 19.834 | car            | 37.782 |
| motorcycle    | 34.953 | airplane     | 56.903 | bus            | 65.758 |
| train         | 68.676 | truck        | 36.471 | boat           | 24.116 |
| traffic light | 25.964 | fire hydrant | 66.130 | stop sign      | 64.854 |
| parking meter | 43.006 | bench        | 20.648 | bird           | 30.094 |
| cat           | 74.032 | dog          | 66.219 | horse          | 45.485 |
| sheep         | 48.013 | cow          | 52.193 | elephant       | 61.616 |
| bear          | 76.896 | zebra        | 61.356 | giraffe        | 57.386 |
| backpack      | 17.661 | umbrella     | 48.871 | handbag        | 15.749 |
| tie           | 34.424 | suitcase     | 43.204 | frisbee        | 67.304 |
| skis          | 5.283  | snowboard    | 22.192 | sports ball    | 47.403 |
| kite          | 35.123 | baseball bat | 30.248 | baseball glove | 44.582 |
| skateboard    | 36.362 | surfboard    | 36.529 | tennis racket  | 57.005 |
| bottle        | 35.307 | wine glass   | 27.668 | cup            | 41.367 |
| fork          | 16.041 | knife        | 14.430 | spoon          | 14.838 |
| bowl          | 32.987 | banana       | 21.480 | apple          | 20.231 |
| sandwich      | 43.148 | orange       | 30.136 | broccoli       | 22.506 |
| carrot        | 21.713 | hot dog      | 22.469 | pizza          | 51.622 |
| donut         | 47.235 | cake         | 45.190 | chair          | 21.385 |
| couch         | 42.368 | potted plant | 18.712 | bed            | 40.623 |
| dining table  | 13.180 | toilet       | 67.945 | tv             | 63.192 |
| laptop        | 63.022 | mouse        | 59.471 | remote         | 32.371 |
| keyboard      | 48.501 | cell phone   | 38.226 | microwave      | 55.864 |
| oven          | 33.415 | toaster      | 36.842 | sink           | 38.148 |
| refrigerator  | 59.824 | book         | 9.850  | clock          | 51.996 |
| vase          | 34.819 | scissors     | 25.052 | teddy bear     | 51.395 |
| hair drier    | 12.771 | toothbrush   | 18.880 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.790407778521306, 'fwIoU': 69.08384261817419, 'IoU-person': 87.4851419991802, 'IoU-bicycle': 72.17428329312222, 'IoU-car': 70.72379511037776, 'IoU-motorcycle': 77.80722341066273, 'IoU-airplane': 84.07581247002169, 'IoU-bus': 86.26978287688706, 'IoU-train': 85.2545375270413, 'IoU-truck': 63.59628167257474, 'IoU-boat': 68.04958745499343, 'IoU-traffic light': 76.30674099499208, 'IoU-fire hydrant': 90.45846756364095, 'IoU-stop sign': 91.97772340696972, 'IoU-parking meter': 83.23682196279768, 'IoU-bench': 55.519153013765866, 'IoU-bird': 75.6643072595022, 'IoU-cat': 80.25577380249374, 'IoU-dog': 79.37951845286443, 'IoU-horse': 84.33598216375196, 'IoU-sheep': 85.1705963838626, 'IoU-cow': 78.86499267046578, 'IoU-elephant': 88.89194830142551, 'IoU-bear': 78.3733029955735, 'IoU-zebra': 87.77254433385441, 'IoU-giraffe': 86.86753564042267, 'IoU-backpack': 40.31179479344709, 'IoU-umbrella': 77.78805425323094, 'IoU-handbag': 38.18124035455661, 'IoU-tie': 70.37218737186632, 'IoU-suitcase': 81.08991524789592, 'IoU-frisbee': 83.8639057470222, 'IoU-skis': 51.87586904310454, 'IoU-snowboard': 68.96411703105298, 'IoU-sports ball': 68.80499725988336, 'IoU-kite': 66.35823013430038, 'IoU-baseball bat': 61.03083628177172, 'IoU-baseball glove': 53.06939727277336, 'IoU-skateboard': 82.5097003971189, 'IoU-surfboard': 75.35155152256876, 'IoU-tennis racket': 82.646339412868, 'IoU-bottle': 68.77524200037234, 'IoU-wine glass': 75.13984367270209, 'IoU-cup': 64.65176859297563, 'IoU-fork': 55.07341412730784, 'IoU-knife': 50.4887642530516, 'IoU-spoon': 49.05204441957535, 'IoU-bowl': 53.59073248406651, 'IoU-banana': 79.54798021787069, 'IoU-apple': 59.16453864954024, 'IoU-sandwich': 66.39890616988549, 'IoU-orange': 75.94034516537974, 'IoU-broccoli': 67.11854862237179, 'IoU-carrot': 64.61458255753332, 'IoU-hot dog': 64.89603845095175, 'IoU-pizza': 82.55362584938032, 'IoU-donut': 67.08059058453799, 'IoU-cake': 69.242329584228, 'IoU-chair': 55.90130536643334, 'IoU-couch': 68.64103393962931, 'IoU-potted plant': 34.11810139507909, 'IoU-bed': 69.2713188430411, 'IoU-dining table': 50.58049208840212, 'IoU-toilet': 81.57651621901213, 'IoU-tv': 74.93097732637494, 'IoU-laptop': 72.59193742168083, 'IoU-mouse': 67.74513129809034, 'IoU-remote': 49.45893066282098, 'IoU-keyboard': 55.476467339732125, 'IoU-cell phone': 70.15284290835572, 'IoU-microwave': 60.569667226285674, 'IoU-oven': 66.18591516442541, 'IoU-toaster': 72.3708682550146, 'IoU-sink': 67.83435069364262, 'IoU-refrigerator': 77.91117495488179, 'IoU-book': 52.789702418648055, 'IoU-clock': 72.66440273402891, 'IoU-vase': 63.64468388385344, 'IoU-scissors': 52.87597494978948, 'IoU-teddy bear': 78.35906516123866, 'IoU-hair drier': 39.33958503786687, 'IoU-toothbrush': 55.234426680560354, 'IoU-banner': 35.045072465925, 'IoU-blanket': 11.082892747471522, 'IoU-bridge': 36.7855644394267, 'IoU-cardboard': 45.45085215783696, 'IoU-counter': 28.862847324282427, 'IoU-curtain': 64.79178690448948, 'IoU-door-stuff': 41.91552121569241, 'IoU-floor-wood': 63.382317754379066, 'IoU-flower': 43.29927101011438, 'IoU-fruit': 39.959487248753405, 'IoU-gravel': 31.94172824554147, 'IoU-house': 25.20818518451749, 'IoU-light': 38.94621626570143, 'IoU-mirror-stuff': 56.23702214650649, 'IoU-net': 46.361467605267684, 'IoU-pillow': 10.798721613743439, 'IoU-platform': 30.43965044120772, 'IoU-playingfield': 71.29251530351547, 'IoU-railroad': 61.24448603949163, 'IoU-river': 47.49304602887894, 'IoU-road': 66.30824160161504, 'IoU-roof': 16.56673887886955, 'IoU-sand': 63.646604280497655, 'IoU-sea': 86.05178513438614, 'IoU-shelf': 36.15352507984348, 'IoU-snow': 88.05686626065179, 'IoU-stairs': 21.28690807494787, 'IoU-tent': 9.853944302673916, 'IoU-towel': 35.46403602296819, 'IoU-wall-brick': 46.19337973636301, 'IoU-wall-stone': 31.21535538535975, 'IoU-wall-tile': 68.04843151008401, 'IoU-wall-wood': 39.18814871157213, 'IoU-water-other': 22.380939092282595, 'IoU-window-blind': 47.25690491606089, 'IoU-window-other': 47.32028997332883, 'IoU-tree-merged': 81.0161372112179, 'IoU-fence-merged': 51.29360449090954, 'IoU-ceiling-merged': 66.75927573985271, 'IoU-sky-other-merged': 93.5149382964752, 'IoU-cabinet-merged': 59.686364665407076, 'IoU-table-merged': 36.030052105046266, 'IoU-floor-other-merged': 49.18646392987802, 'IoU-pavement-merged': 53.900746618555615, 'IoU-mountain-merged': 56.548390187180864, 'IoU-grass-merged': 70.18163100550672, 'IoU-dirt-merged': 44.84751218944181, 'IoU-paper-merged': 31.842844978527385, 'IoU-food-other-merged': 39.936725722420675, 'IoU-building-other-merged': 58.07382508858754, 'IoU-rock-merged': 64.69005794911882, 'IoU-wall-other-merged': 65.53680265652348, 'IoU-rug-merged': 62.263952349112536, 'mACC': 72.90747507186455, 'pACC': 80.39087487326402, 'ACC-person': 92.56636573284976, 'ACC-bicycle': 82.42850715144803, 'ACC-car': 85.5659099849706, 'ACC-motorcycle': 82.55616104546768, 'ACC-airplane': 90.66463056333694, 'ACC-bus': 90.6830408268976, 'ACC-train': 94.10078162385477, 'ACC-truck': 76.8283586041826, 'ACC-boat': 78.6999196323317, 'ACC-traffic light': 90.70239074897076, 'ACC-fire hydrant': 95.54650770538075, 'ACC-stop sign': 95.34273049192691, 'ACC-parking meter': 87.2384874879636, 'ACC-bench': 73.301339163571, 'ACC-bird': 80.72132915618099, 'ACC-cat': 87.70148599586979, 'ACC-dog': 83.35810867186593, 'ACC-horse': 89.98568492686942, 'ACC-sheep': 88.30848063750929, 'ACC-cow': 84.63934681873211, 'ACC-elephant': 91.39851612608021, 'ACC-bear': 80.57603731785416, 'ACC-zebra': 90.12947423077567, 'ACC-giraffe': 91.07918496156964, 'ACC-backpack': 55.49995179709205, 'ACC-umbrella': 85.15514600703067, 'ACC-handbag': 55.39118688096387, 'ACC-tie': 80.03618545661915, 'ACC-suitcase': 89.33249331471671, 'ACC-frisbee': 94.16727272727273, 'ACC-skis': 69.91383216864226, 'ACC-snowboard': 79.03063848546448, 'ACC-sports ball': 79.81517660557572, 'ACC-kite': 76.15523824625787, 'ACC-baseball bat': 80.83937094006848, 'ACC-baseball glove': 60.34744889503758, 'ACC-skateboard': 89.50451319846852, 'ACC-surfboard': 83.69296001880896, 'ACC-tennis racket': 89.30918691555948, 'ACC-bottle': 83.41158751879979, 'ACC-wine glass': 86.06353465938967, 'ACC-cup': 83.92908798233395, 'ACC-fork': 66.08637713511867, 'ACC-knife': 67.41445006228474, 'ACC-spoon': 68.99961775114366, 'ACC-bowl': 65.36339103598301, 'ACC-banana': 86.59012275548163, 'ACC-apple': 71.67449110946555, 'ACC-sandwich': 79.40363466534676, 'ACC-orange': 85.07404575686847, 'ACC-broccoli': 78.07661360494662, 'ACC-carrot': 75.76312851763932, 'ACC-hot dog': 73.4592816771905, 'ACC-pizza': 90.81473772520579, 'ACC-donut': 81.38819009683121, 'ACC-cake': 77.18909063869518, 'ACC-chair': 70.71994609361509, 'ACC-couch': 82.80254312879177, 'ACC-potted plant': 51.9149847404326, 'ACC-bed': 79.51016329276837, 'ACC-dining table': 74.55604704184977, 'ACC-toilet': 91.14278195979313, 'ACC-tv': 87.83505128442943, 'ACC-laptop': 85.29713274069974, 'ACC-mouse': 81.40320119476641, 'ACC-remote': 73.00142437138564, 'ACC-keyboard': 62.25322931446723, 'ACC-cell phone': 75.7867681161493, 'ACC-microwave': 67.84101413591185, 'ACC-oven': 88.15320392421494, 'ACC-toaster': 84.14033310980032, 'ACC-sink': 82.95759835419632, 'ACC-refrigerator': 89.13273018549036, 'ACC-book': 69.65786192597574, 'ACC-clock': 78.11973706054532, 'ACC-vase': 74.26189352000026, 'ACC-scissors': 56.777562001303835, 'ACC-teddy bear': 84.84961548585551, 'ACC-hair drier': 53.374320439472854, 'ACC-toothbrush': 81.95969423210563, 'ACC-banner': 65.76581730513679, 'ACC-blanket': 14.993759503036907, 'ACC-bridge': 55.273610109403414, 'ACC-cardboard': 57.19854587756289, 'ACC-counter': 53.73586491609791, 'ACC-curtain': 76.82153360576136, 'ACC-door-stuff': 62.08684287077158, 'ACC-floor-wood': 79.0917782825506, 'ACC-flower': 59.606956093862586, 'ACC-fruit': 57.81212872102831, 'ACC-gravel': 43.09082067369821, 'ACC-house': 31.82873262905031, 'ACC-light': 57.88906088504383, 'ACC-mirror-stuff': 72.10105885284881, 'ACC-net': 61.288363008338266, 'ACC-pillow': 25.35976113602944, 'ACC-platform': 49.62660022661853, 'ACC-playingfield': 91.79972335205727, 'ACC-railroad': 78.17649308460985, 'ACC-river': 67.38520656933214, 'ACC-road': 85.1339821434331, 'ACC-roof': 23.437763571245217, 'ACC-sand': 70.23940749019782, 'ACC-sea': 91.75038396637271, 'ACC-shelf': 56.715691976325225, 'ACC-snow': 94.48838410656006, 'ACC-stairs': 32.968409498409, 'ACC-tent': 11.607266081443345, 'ACC-towel': 44.225704016218685, 'ACC-wall-brick': 64.85979939294248, 'ACC-wall-stone': 39.55288914727569, 'ACC-wall-tile': 81.81497885376358, 'ACC-wall-wood': 54.71218912334498, 'ACC-water-other': 38.1658109684718, 'ACC-window-blind': 56.52856554259345, 'ACC-window-other': 69.5856073597553, 'ACC-tree-merged': 89.47515004503008, 'ACC-fence-merged': 70.65747510921663, 'ACC-ceiling-merged': 80.01199090905824, 'ACC-sky-other-merged': 96.44176733969432, 'ACC-cabinet-merged': 75.05159164214452, 'ACC-table-merged': 49.65243239298406, 'ACC-floor-other-merged': 62.50127825827454, 'ACC-pavement-merged': 66.61429476094756, 'ACC-mountain-merged': 68.10138291913826, 'ACC-grass-merged': 83.73152144081499, 'ACC-dirt-merged': 65.32294327894344, 'ACC-paper-merged': 43.96791762813459, 'ACC-food-other-merged': 54.85757427799811, 'ACC-building-other-merged': 73.28690989569566, 'ACC-rock-merged': 81.77100884949327, 'ACC-wall-other-merged': 81.52909455515916, 'ACC-rug-merged': 76.53675870328482})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1541 s/iter. Inference: 0.4824 s/iter. Eval: 0.0000 s/iter. Total: 0.6365 s/iter. ETA=0:00:24
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/50. Dataloading: 0.1570 s/iter. Inference: 0.5254 s/iter. Eval: 0.0000 s/iter. Total: 0.6826 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1739 s/iter. Inference: 0.5803 s/iter. Eval: 0.0000 s/iter. Total: 0.7544 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1732 s/iter. Inference: 0.6553 s/iter. Eval: 0.0000 s/iter. Total: 0.8287 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1703 s/iter. Inference: 0.6109 s/iter. Eval: 0.0000 s/iter. Total: 0.7814 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1686 s/iter. Inference: 0.6529 s/iter. Eval: 0.0000 s/iter. Total: 0.8218 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4559555165349722, 'noc@0.8': 2.675738952297337, 'noc@0.85': 3.2405618964003513, 'noc@0.9': 4.268656716417911, 'miou@iter1': 0.830964012246471}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0988 s/iter. Eval: 0.0008 s/iter. Total: 0.1012 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.35639190673828, 'precision@0.6': 68.71356201171875, 'precision@0.7': 63.54450225830078, 'precision@0.8': 53.71162033081055, 'precision@0.9': 27.710844039916992, 'cIoU': 57.604007720947266, 'mIoU': 63.283042907714844}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.57133778685758, 'SQ': 82.02287638665881, 'RQ': 60.79257275256301, 'PQ_th': 55.61793365946437, 'SQ_th': 82.65765296421162, 'RQ_th': 66.59747547065122, 'PQ_st': 42.95383458292284, 'SQ_st': 81.0647230620509, 'RQ_st': 52.0304554422411}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.64096349977346, 'AP50': 62.18988331148941, 'AP75': 41.65274856542721, 'APs': 19.582796223875544, 'APm': 42.67346861033292, 'APl': 61.641714416060836, 'AP-person': 44.73330806929533, 'AP-bicycle': 19.834130295508807, 'AP-car': 37.78154000061685, 'AP-motorcycle': 34.95268311365303, 'AP-airplane': 56.902711474153165, 'AP-bus': 65.7582259790704, 'AP-train': 68.67560136014073, 'AP-truck': 36.470592842325736, 'AP-boat': 24.115816000871916, 'AP-traffic light': 25.96391521841344, 'AP-fire hydrant': 66.13027239772376, 'AP-stop sign': 64.85359435234972, 'AP-parking meter': 43.00617472807786, 'AP-bench': 20.647604371218158, 'AP-bird': 30.09423712861835, 'AP-cat': 74.03200938839024, 'AP-dog': 66.21887804953847, 'AP-horse': 45.48510207366393, 'AP-sheep': 48.01262796689462, 'AP-cow': 52.1931773109087, 'AP-elephant': 61.61644292531431, 'AP-bear': 76.89561350572272, 'AP-zebra': 61.35619851549906, 'AP-giraffe': 57.385661169574284, 'AP-backpack': 17.660864024319373, 'AP-umbrella': 48.8714919360809, 'AP-handbag': 15.749127337193123, 'AP-tie': 34.4240498859911, 'AP-suitcase': 43.204162850258605, 'AP-frisbee': 67.30399145886985, 'AP-skis': 5.283029154271699, 'AP-snowboard': 22.192194550118973, 'AP-sports ball': 47.4026093272415, 'AP-kite': 35.12337202034258, 'AP-baseball bat': 30.24842767006636, 'AP-baseball glove': 44.58155951137115, 'AP-skateboard': 36.361514446980244, 'AP-surfboard': 36.52905831036627, 'AP-tennis racket': 57.00548104038155, 'AP-bottle': 35.30711680839316, 'AP-wine glass': 27.66823215089631, 'AP-cup': 41.3671008886473, 'AP-fork': 16.040697833906588, 'AP-knife': 14.430457785744155, 'AP-spoon': 14.837969924755662, 'AP-bowl': 32.98740382764404, 'AP-banana': 21.480377601251195, 'AP-apple': 20.230687369998623, 'AP-sandwich': 43.147603882629866, 'AP-orange': 30.136233501819344, 'AP-broccoli': 22.506412167137565, 'AP-carrot': 21.712816630210995, 'AP-hot dog': 22.46858278383556, 'AP-pizza': 51.62208862283366, 'AP-donut': 47.23533924420573, 'AP-cake': 45.18964228456716, 'AP-chair': 21.385186801250704, 'AP-couch': 42.36790798898633, 'AP-potted plant': 18.71197629559555, 'AP-bed': 40.62310940711895, 'AP-dining table': 13.179706486029497, 'AP-toilet': 67.94525205683172, 'AP-tv': 63.192308304471744, 'AP-laptop': 63.02189522884283, 'AP-mouse': 59.471153292693614, 'AP-remote': 32.370591945228625, 'AP-keyboard': 48.50077764798005, 'AP-cell phone': 38.22601438070084, 'AP-microwave': 55.863758208946415, 'AP-oven': 33.41532940492085, 'AP-toaster': 36.84221279270784, 'AP-sink': 38.14798052062958, 'AP-refrigerator': 59.82352875517919, 'AP-book': 9.850331507169487, 'AP-clock': 51.996307350080485, 'AP-vase': 34.81878139046512, 'AP-scissors': 25.05162508129778, 'AP-teddy bear': 51.394833569632894, 'AP-hair drier': 12.77100424328147, 'AP-toothbrush': 18.87969225196192}), ('sem_seg', {'mIoU': 60.790407778521306, 'fwIoU': 69.08384261817419, 'IoU-person': 87.4851419991802, 'IoU-bicycle': 72.17428329312222, 'IoU-car': 70.72379511037776, 'IoU-motorcycle': 77.80722341066273, 'IoU-airplane': 84.07581247002169, 'IoU-bus': 86.26978287688706, 'IoU-train': 85.2545375270413, 'IoU-truck': 63.59628167257474, 'IoU-boat': 68.04958745499343, 'IoU-traffic light': 76.30674099499208, 'IoU-fire hydrant': 90.45846756364095, 'IoU-stop sign': 91.97772340696972, 'IoU-parking meter': 83.23682196279768, 'IoU-bench': 55.519153013765866, 'IoU-bird': 75.6643072595022, 'IoU-cat': 80.25577380249374, 'IoU-dog': 79.37951845286443, 'IoU-horse': 84.33598216375196, 'IoU-sheep': 85.1705963838626, 'IoU-cow': 78.86499267046578, 'IoU-elephant': 88.89194830142551, 'IoU-bear': 78.3733029955735, 'IoU-zebra': 87.77254433385441, 'IoU-giraffe': 86.86753564042267, 'IoU-backpack': 40.31179479344709, 'IoU-umbrella': 77.78805425323094, 'IoU-handbag': 38.18124035455661, 'IoU-tie': 70.37218737186632, 'IoU-suitcase': 81.08991524789592, 'IoU-frisbee': 83.8639057470222, 'IoU-skis': 51.87586904310454, 'IoU-snowboard': 68.96411703105298, 'IoU-sports ball': 68.80499725988336, 'IoU-kite': 66.35823013430038, 'IoU-baseball bat': 61.03083628177172, 'IoU-baseball glove': 53.06939727277336, 'IoU-skateboard': 82.5097003971189, 'IoU-surfboard': 75.35155152256876, 'IoU-tennis racket': 82.646339412868, 'IoU-bottle': 68.77524200037234, 'IoU-wine glass': 75.13984367270209, 'IoU-cup': 64.65176859297563, 'IoU-fork': 55.07341412730784, 'IoU-knife': 50.4887642530516, 'IoU-spoon': 49.05204441957535, 'IoU-bowl': 53.59073248406651, 'IoU-banana': 79.54798021787069, 'IoU-apple': 59.16453864954024, 'IoU-sandwich': 66.39890616988549, 'IoU-orange': 75.94034516537974, 'IoU-broccoli': 67.11854862237179, 'IoU-carrot': 64.61458255753332, 'IoU-hot dog': 64.89603845095175, 'IoU-pizza': 82.55362584938032, 'IoU-donut': 67.08059058453799, 'IoU-cake': 69.242329584228, 'IoU-chair': 55.90130536643334, 'IoU-couch': 68.64103393962931, 'IoU-potted plant': 34.11810139507909, 'IoU-bed': 69.2713188430411, 'IoU-dining table': 50.58049208840212, 'IoU-toilet': 81.57651621901213, 'IoU-tv': 74.93097732637494, 'IoU-laptop': 72.59193742168083, 'IoU-mouse': 67.74513129809034, 'IoU-remote': 49.45893066282098, 'IoU-keyboard': 55.476467339732125, 'IoU-cell phone': 70.15284290835572, 'IoU-microwave': 60.569667226285674, 'IoU-oven': 66.18591516442541, 'IoU-toaster': 72.3708682550146, 'IoU-sink': 67.83435069364262, 'IoU-refrigerator': 77.91117495488179, 'IoU-book': 52.789702418648055, 'IoU-clock': 72.66440273402891, 'IoU-vase': 63.64468388385344, 'IoU-scissors': 52.87597494978948, 'IoU-teddy bear': 78.35906516123866, 'IoU-hair drier': 39.33958503786687, 'IoU-toothbrush': 55.234426680560354, 'IoU-banner': 35.045072465925, 'IoU-blanket': 11.082892747471522, 'IoU-bridge': 36.7855644394267, 'IoU-cardboard': 45.45085215783696, 'IoU-counter': 28.862847324282427, 'IoU-curtain': 64.79178690448948, 'IoU-door-stuff': 41.91552121569241, 'IoU-floor-wood': 63.382317754379066, 'IoU-flower': 43.29927101011438, 'IoU-fruit': 39.959487248753405, 'IoU-gravel': 31.94172824554147, 'IoU-house': 25.20818518451749, 'IoU-light': 38.94621626570143, 'IoU-mirror-stuff': 56.23702214650649, 'IoU-net': 46.361467605267684, 'IoU-pillow': 10.798721613743439, 'IoU-platform': 30.43965044120772, 'IoU-playingfield': 71.29251530351547, 'IoU-railroad': 61.24448603949163, 'IoU-river': 47.49304602887894, 'IoU-road': 66.30824160161504, 'IoU-roof': 16.56673887886955, 'IoU-sand': 63.646604280497655, 'IoU-sea': 86.05178513438614, 'IoU-shelf': 36.15352507984348, 'IoU-snow': 88.05686626065179, 'IoU-stairs': 21.28690807494787, 'IoU-tent': 9.853944302673916, 'IoU-towel': 35.46403602296819, 'IoU-wall-brick': 46.19337973636301, 'IoU-wall-stone': 31.21535538535975, 'IoU-wall-tile': 68.04843151008401, 'IoU-wall-wood': 39.18814871157213, 'IoU-water-other': 22.380939092282595, 'IoU-window-blind': 47.25690491606089, 'IoU-window-other': 47.32028997332883, 'IoU-tree-merged': 81.0161372112179, 'IoU-fence-merged': 51.29360449090954, 'IoU-ceiling-merged': 66.75927573985271, 'IoU-sky-other-merged': 93.5149382964752, 'IoU-cabinet-merged': 59.686364665407076, 'IoU-table-merged': 36.030052105046266, 'IoU-floor-other-merged': 49.18646392987802, 'IoU-pavement-merged': 53.900746618555615, 'IoU-mountain-merged': 56.548390187180864, 'IoU-grass-merged': 70.18163100550672, 'IoU-dirt-merged': 44.84751218944181, 'IoU-paper-merged': 31.842844978527385, 'IoU-food-other-merged': 39.936725722420675, 'IoU-building-other-merged': 58.07382508858754, 'IoU-rock-merged': 64.69005794911882, 'IoU-wall-other-merged': 65.53680265652348, 'IoU-rug-merged': 62.263952349112536, 'mACC': 72.90747507186455, 'pACC': 80.39087487326402, 'ACC-person': 92.56636573284976, 'ACC-bicycle': 82.42850715144803, 'ACC-car': 85.5659099849706, 'ACC-motorcycle': 82.55616104546768, 'ACC-airplane': 90.66463056333694, 'ACC-bus': 90.6830408268976, 'ACC-train': 94.10078162385477, 'ACC-truck': 76.8283586041826, 'ACC-boat': 78.6999196323317, 'ACC-traffic light': 90.70239074897076, 'ACC-fire hydrant': 95.54650770538075, 'ACC-stop sign': 95.34273049192691, 'ACC-parking meter': 87.2384874879636, 'ACC-bench': 73.301339163571, 'ACC-bird': 80.72132915618099, 'ACC-cat': 87.70148599586979, 'ACC-dog': 83.35810867186593, 'ACC-horse': 89.98568492686942, 'ACC-sheep': 88.30848063750929, 'ACC-cow': 84.63934681873211, 'ACC-elephant': 91.39851612608021, 'ACC-bear': 80.57603731785416, 'ACC-zebra': 90.12947423077567, 'ACC-giraffe': 91.07918496156964, 'ACC-backpack': 55.49995179709205, 'ACC-umbrella': 85.15514600703067, 'ACC-handbag': 55.39118688096387, 'ACC-tie': 80.03618545661915, 'ACC-suitcase': 89.33249331471671, 'ACC-frisbee': 94.16727272727273, 'ACC-skis': 69.91383216864226, 'ACC-snowboard': 79.03063848546448, 'ACC-sports ball': 79.81517660557572, 'ACC-kite': 76.15523824625787, 'ACC-baseball bat': 80.83937094006848, 'ACC-baseball glove': 60.34744889503758, 'ACC-skateboard': 89.50451319846852, 'ACC-surfboard': 83.69296001880896, 'ACC-tennis racket': 89.30918691555948, 'ACC-bottle': 83.41158751879979, 'ACC-wine glass': 86.06353465938967, 'ACC-cup': 83.92908798233395, 'ACC-fork': 66.08637713511867, 'ACC-knife': 67.41445006228474, 'ACC-spoon': 68.99961775114366, 'ACC-bowl': 65.36339103598301, 'ACC-banana': 86.59012275548163, 'ACC-apple': 71.67449110946555, 'ACC-sandwich': 79.40363466534676, 'ACC-orange': 85.07404575686847, 'ACC-broccoli': 78.07661360494662, 'ACC-carrot': 75.76312851763932, 'ACC-hot dog': 73.4592816771905, 'ACC-pizza': 90.81473772520579, 'ACC-donut': 81.38819009683121, 'ACC-cake': 77.18909063869518, 'ACC-chair': 70.71994609361509, 'ACC-couch': 82.80254312879177, 'ACC-potted plant': 51.9149847404326, 'ACC-bed': 79.51016329276837, 'ACC-dining table': 74.55604704184977, 'ACC-toilet': 91.14278195979313, 'ACC-tv': 87.83505128442943, 'ACC-laptop': 85.29713274069974, 'ACC-mouse': 81.40320119476641, 'ACC-remote': 73.00142437138564, 'ACC-keyboard': 62.25322931446723, 'ACC-cell phone': 75.7867681161493, 'ACC-microwave': 67.84101413591185, 'ACC-oven': 88.15320392421494, 'ACC-toaster': 84.14033310980032, 'ACC-sink': 82.95759835419632, 'ACC-refrigerator': 89.13273018549036, 'ACC-book': 69.65786192597574, 'ACC-clock': 78.11973706054532, 'ACC-vase': 74.26189352000026, 'ACC-scissors': 56.777562001303835, 'ACC-teddy bear': 84.84961548585551, 'ACC-hair drier': 53.374320439472854, 'ACC-toothbrush': 81.95969423210563, 'ACC-banner': 65.76581730513679, 'ACC-blanket': 14.993759503036907, 'ACC-bridge': 55.273610109403414, 'ACC-cardboard': 57.19854587756289, 'ACC-counter': 53.73586491609791, 'ACC-curtain': 76.82153360576136, 'ACC-door-stuff': 62.08684287077158, 'ACC-floor-wood': 79.0917782825506, 'ACC-flower': 59.606956093862586, 'ACC-fruit': 57.81212872102831, 'ACC-gravel': 43.09082067369821, 'ACC-house': 31.82873262905031, 'ACC-light': 57.88906088504383, 'ACC-mirror-stuff': 72.10105885284881, 'ACC-net': 61.288363008338266, 'ACC-pillow': 25.35976113602944, 'ACC-platform': 49.62660022661853, 'ACC-playingfield': 91.79972335205727, 'ACC-railroad': 78.17649308460985, 'ACC-river': 67.38520656933214, 'ACC-road': 85.1339821434331, 'ACC-roof': 23.437763571245217, 'ACC-sand': 70.23940749019782, 'ACC-sea': 91.75038396637271, 'ACC-shelf': 56.715691976325225, 'ACC-snow': 94.48838410656006, 'ACC-stairs': 32.968409498409, 'ACC-tent': 11.607266081443345, 'ACC-towel': 44.225704016218685, 'ACC-wall-brick': 64.85979939294248, 'ACC-wall-stone': 39.55288914727569, 'ACC-wall-tile': 81.81497885376358, 'ACC-wall-wood': 54.71218912334498, 'ACC-water-other': 38.1658109684718, 'ACC-window-blind': 56.52856554259345, 'ACC-window-other': 69.5856073597553, 'ACC-tree-merged': 89.47515004503008, 'ACC-fence-merged': 70.65747510921663, 'ACC-ceiling-merged': 80.01199090905824, 'ACC-sky-other-merged': 96.44176733969432, 'ACC-cabinet-merged': 75.05159164214452, 'ACC-table-merged': 49.65243239298406, 'ACC-floor-other-merged': 62.50127825827454, 'ACC-pavement-merged': 66.61429476094756, 'ACC-mountain-merged': 68.10138291913826, 'ACC-grass-merged': 83.73152144081499, 'ACC-dirt-merged': 65.32294327894344, 'ACC-paper-merged': 43.96791762813459, 'ACC-food-other-merged': 54.85757427799811, 'ACC-building-other-merged': 73.28690989569566, 'ACC-rock-merged': 81.77100884949327, 'ACC-wall-other-merged': 81.52909455515916, 'ACC-rug-merged': 76.53675870328482})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4559555165349722, 'noc@0.8': 2.675738952297337, 'noc@0.85': 3.2405618964003513, 'noc@0.9': 4.268656716417911, 'miou@iter1': 0.830964012246471}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.35639190673828, 'precision@0.6': 68.71356201171875, 'precision@0.7': 63.54450225830078, 'precision@0.8': 53.71162033081055, 'precision@0.9': 27.710844039916992, 'cIoU': 57.604007720947266, 'mIoU': 63.283042907714844}}}
INFO:trainer.default_trainer:This epoch takes 1:26:38.621629
INFO:trainer.default_trainer:PROGRESS: 96.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 48 training.
INFO:trainer.default_trainer:epochs[    48] optim steps[87700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.66264/0.89138, loss_mask_bce_0: 0.18166/0.33336, loss_mask_dice_0: 0.37838/1.15979, loss_spatial_bce_0: 0.11406/0.08614, loss_spatial_dice_0: 0.18460/0.20533, loss_spatial_ce_0: 0.02690/0.05850, loss_grounding_bce_0: 0.03087/0.08596, loss_grounding_dice_0: 0.05404/0.17804, loss_grounding_ce_0: 0.10810/0.27086, loss_mask_ce_1: 1.66246/0.89229, loss_mask_bce_1: 0.18100/0.33429, loss_mask_dice_1: 0.37944/1.16679, loss_spatial_bce_1: 0.12505/0.08666, loss_spatial_dice_1: 0.19217/0.20918, loss_spatial_ce_1: 0.03923/0.06431, loss_grounding_bce_1: 0.02871/0.08616, loss_grounding_dice_1: 0.05288/0.17889, loss_grounding_ce_1: 0.09870/0.27167, loss_mask_ce_2: 1.91499/0.89913, loss_mask_bce_2: 0.17910/0.33496, loss_mask_dice_2: 0.48473/1.16728, loss_spatial_bce_2: 0.12240/0.08793, loss_spatial_dice_2: 0.19531/0.21117, loss_spatial_ce_2: 0.04876/0.06769, loss_grounding_bce_2: 0.02980/0.08634, loss_grounding_dice_2: 0.05546/0.17879, loss_grounding_ce_2: 0.11622/0.27504, loss_mask_ce_3: 1.85556/0.91071, loss_mask_bce_3: 0.17574/0.33612, loss_mask_dice_3: 0.37530/1.16521, loss_spatial_bce_3: 0.11440/0.08927, loss_spatial_dice_3: 0.18633/0.21232, loss_spatial_ce_3: 0.03749/0.07289, loss_grounding_bce_3: 0.03119/0.08660, loss_grounding_dice_3: 0.05301/0.17844, loss_grounding_ce_3: 0.11598/0.27720, loss_mask_ce_4: 1.65899/0.91235, loss_mask_bce_4: 0.17326/0.33833, loss_mask_dice_4: 0.50194/1.18906, loss_spatial_bce_4: 0.11330/0.09318, loss_spatial_dice_4: 0.18937/0.22450, loss_spatial_ce_4: 0.01763/0.08904, loss_grounding_bce_4: 0.02984/0.08716, loss_grounding_dice_4: 0.06025/0.18147, loss_grounding_ce_4: 0.12437/0.28013, loss_mask_ce_5: 1.67494/0.92918, loss_mask_bce_5: 0.18856/0.34074, loss_mask_dice_5: 0.38017/1.19726, loss_spatial_bce_5: 0.12034/0.09551, loss_spatial_dice_5: 0.19199/0.22895, loss_spatial_ce_5: 0.02647/0.10256, loss_grounding_bce_5: 0.02873/0.08759, loss_grounding_dice_5: 0.04786/0.18278, loss_grounding_ce_5: 0.13353/0.29282, loss_mask_ce_6: 1.57363/0.96943, loss_mask_bce_6: 0.18318/0.34356, loss_mask_dice_6: 0.48274/1.20047, loss_spatial_bce_6: 0.11377/0.10114, loss_spatial_dice_6: 0.19512/0.23193, loss_spatial_ce_6: 0.05299/0.12728, loss_grounding_bce_6: 0.03039/0.08835, loss_grounding_dice_6: 0.05017/0.18322, loss_grounding_ce_6: 0.10429/0.30789, loss_mask_ce_7: 1.67292/1.01553, loss_mask_bce_7: 0.18960/0.35143, loss_mask_dice_7: 0.47500/1.25467, loss_spatial_bce_7: 0.09917/0.10894, loss_spatial_dice_7: 0.18457/0.25958, loss_spatial_ce_7: 0.14032/0.16188, loss_grounding_bce_7: 0.02800/0.09022, loss_grounding_dice_7: 0.04902/0.19059, loss_grounding_ce_7: 0.19029/0.33779, loss_mask_ce_8: 1.79633/1.12459, loss_mask_bce_8: 0.16545/0.36499, loss_mask_dice_8: 0.46555/1.32713, loss_spatial_bce_8: 0.17961/0.12886, loss_spatial_dice_8: 0.20293/0.29732, loss_spatial_ce_8: 0.07472/0.21066, loss_grounding_bce_8: 0.02565/0.09386, loss_grounding_dice_8: 0.04830/0.20123, loss_grounding_ce_8: 0.17188/0.40341, loss_mask_ce_9: 3.31711/3.67194, loss_mask_bce_9: 0.20226/0.39223, loss_mask_dice_9: 1.52052/1.90062, loss_spatial_bce_9: 0.35270/0.33252, loss_spatial_dice_9: 0.75569/0.82150, loss_spatial_ce_9: 1.27356/1.49080, loss_grounding_bce_9: 0.03317/0.10560, loss_grounding_dice_9: 0.12476/0.28077, loss_grounding_ce_9: 0.34383/0.66818] items per batch[64] items per second[0.14] total items[5612800] mini batches[ 87700] memory[7345] epoch remaining[1:50:24]
INFO:trainer.default_trainer:epochs[    48] optim steps[87800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.23323/0.89126, loss_mask_bce_0: 0.09884/0.33338, loss_mask_dice_0: 0.06705/1.15963, loss_spatial_bce_0: 0.07114/0.08614, loss_spatial_dice_0: 0.04047/0.20531, loss_spatial_ce_0: 0.00009/0.05848, loss_grounding_bce_0: 0.05668/0.08595, loss_grounding_dice_0: 0.04576/0.17803, loss_grounding_ce_0: 0.03121/0.27085, loss_mask_ce_1: 0.22608/0.89217, loss_mask_bce_1: 0.10601/0.33430, loss_mask_dice_1: 0.06759/1.16661, loss_spatial_bce_1: 0.06730/0.08666, loss_spatial_dice_1: 0.04048/0.20916, loss_spatial_ce_1: 0.00018/0.06428, loss_grounding_bce_1: 0.05693/0.08616, loss_grounding_dice_1: 0.04846/0.17888, loss_grounding_ce_1: 0.02680/0.27165, loss_mask_ce_2: 0.23331/0.89902, loss_mask_bce_2: 0.09828/0.33497, loss_mask_dice_2: 0.06781/1.16712, loss_spatial_bce_2: 0.06777/0.08793, loss_spatial_dice_2: 0.03932/0.21115, loss_spatial_ce_2: 0.00013/0.06766, loss_grounding_bce_2: 0.05403/0.08634, loss_grounding_dice_2: 0.04598/0.17878, loss_grounding_ce_2: 0.04720/0.27501, loss_mask_ce_3: 0.23373/0.91061, loss_mask_bce_3: 0.09858/0.33613, loss_mask_dice_3: 0.06477/1.16505, loss_spatial_bce_3: 0.07077/0.08927, loss_spatial_dice_3: 0.04018/0.21230, loss_spatial_ce_3: 0.00031/0.07288, loss_grounding_bce_3: 0.05319/0.08659, loss_grounding_dice_3: 0.04457/0.17843, loss_grounding_ce_3: 0.04593/0.27718, loss_mask_ce_4: 0.24925/0.91225, loss_mask_bce_4: 0.09318/0.33834, loss_mask_dice_4: 0.06355/1.18889, loss_spatial_bce_4: 0.07356/0.09318, loss_spatial_dice_4: 0.04311/0.22447, loss_spatial_ce_4: 0.00055/0.08901, loss_grounding_bce_4: 0.05948/0.08715, loss_grounding_dice_4: 0.05069/0.18147, loss_grounding_ce_4: 0.02803/0.28014, loss_mask_ce_5: 0.29115/0.92908, loss_mask_bce_5: 0.10525/0.34075, loss_mask_dice_5: 0.07229/1.19709, loss_spatial_bce_5: 0.06836/0.09551, loss_spatial_dice_5: 0.04115/0.22893, loss_spatial_ce_5: 0.00125/0.10252, loss_grounding_bce_5: 0.05668/0.08758, loss_grounding_dice_5: 0.04834/0.18277, loss_grounding_ce_5: 0.05259/0.29282, loss_mask_ce_6: 0.24524/0.96933, loss_mask_bce_6: 0.09964/0.34356, loss_mask_dice_6: 0.07005/1.20029, loss_spatial_bce_6: 0.07267/0.10114, loss_spatial_dice_6: 0.04575/0.23190, loss_spatial_ce_6: 0.00150/0.12725, loss_grounding_bce_6: 0.05719/0.08835, loss_grounding_dice_6: 0.04479/0.18321, loss_grounding_ce_6: 0.03384/0.30784, loss_mask_ce_7: 0.28132/1.01542, loss_mask_bce_7: 0.09685/0.35144, loss_mask_dice_7: 0.06778/1.25452, loss_spatial_bce_7: 0.07851/0.10894, loss_spatial_dice_7: 0.05405/0.25956, loss_spatial_ce_7: 0.03432/0.16184, loss_grounding_bce_7: 0.05296/0.09022, loss_grounding_dice_7: 0.04707/0.19058, loss_grounding_ce_7: 0.03381/0.33771, loss_mask_ce_8: 0.24661/1.12446, loss_mask_bce_8: 0.09162/0.36501, loss_mask_dice_8: 0.06771/1.32698, loss_spatial_bce_8: 0.07741/0.12886, loss_spatial_dice_8: 0.06607/0.29729, loss_spatial_ce_8: 0.02224/0.21057, loss_grounding_bce_8: 0.05350/0.09386, loss_grounding_dice_8: 0.04946/0.20123, loss_grounding_ce_8: 0.03988/0.40334, loss_mask_ce_9: 1.74244/3.67169, loss_mask_bce_9: 0.09276/0.39224, loss_mask_dice_9: 0.07333/1.90048, loss_spatial_bce_9: 0.56609/0.33254, loss_spatial_dice_9: 0.56458/0.82149, loss_spatial_ce_9: 0.76205/1.49075, loss_grounding_bce_9: 0.05367/0.10560, loss_grounding_dice_9: 0.05651/0.28078, loss_grounding_ce_9: 0.14876/0.66810] items per batch[64] items per second[0.24] total items[5619200] mini batches[ 87800] memory[7345] epoch remaining[1:18:16]
INFO:trainer.default_trainer:epochs[    48] optim steps[87900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.12481/0.89123, loss_mask_bce_0: 0.11962/0.33336, loss_mask_dice_0: 0.21375/1.15948, loss_spatial_bce_0: 0.04658/0.08614, loss_spatial_dice_0: 0.07769/0.20529, loss_spatial_ce_0: 0.03224/0.05846, loss_grounding_bce_0: 0.03830/0.08594, loss_grounding_dice_0: 0.09103/0.17801, loss_grounding_ce_0: 0.01168/0.27085, loss_mask_ce_1: 0.11895/0.89215, loss_mask_bce_1: 0.12663/0.33428, loss_mask_dice_1: 0.21780/1.16647, loss_spatial_bce_1: 0.04828/0.08666, loss_spatial_dice_1: 0.08620/0.20914, loss_spatial_ce_1: 0.03799/0.06425, loss_grounding_bce_1: 0.04065/0.08615, loss_grounding_dice_1: 0.09848/0.17885, loss_grounding_ce_1: 0.00901/0.27165, loss_mask_ce_2: 0.13368/0.89900, loss_mask_bce_2: 0.12700/0.33496, loss_mask_dice_2: 0.22061/1.16699, loss_spatial_bce_2: 0.04943/0.08793, loss_spatial_dice_2: 0.08270/0.21112, loss_spatial_ce_2: 0.03782/0.06764, loss_grounding_bce_2: 0.04179/0.08633, loss_grounding_dice_2: 0.09484/0.17875, loss_grounding_ce_2: 0.01195/0.27500, loss_mask_ce_3: 0.14972/0.91059, loss_mask_bce_3: 0.12980/0.33612, loss_mask_dice_3: 0.21721/1.16491, loss_spatial_bce_3: 0.04775/0.08927, loss_spatial_dice_3: 0.08395/0.21228, loss_spatial_ce_3: 0.04188/0.07287, loss_grounding_bce_3: 0.04476/0.08658, loss_grounding_dice_3: 0.10051/0.17840, loss_grounding_ce_3: 0.01465/0.27716, loss_mask_ce_4: 0.15529/0.91223, loss_mask_bce_4: 0.13219/0.33832, loss_mask_dice_4: 0.22610/1.18874, loss_spatial_bce_4: 0.05186/0.09318, loss_spatial_dice_4: 0.10000/0.22445, loss_spatial_ce_4: 0.06044/0.08899, loss_grounding_bce_4: 0.04187/0.08714, loss_grounding_dice_4: 0.09716/0.18144, loss_grounding_ce_4: 0.00734/0.28012, loss_mask_ce_5: 0.16275/0.92906, loss_mask_bce_5: 0.13318/0.34073, loss_mask_dice_5: 0.22882/1.19695, loss_spatial_bce_5: 0.05783/0.09550, loss_spatial_dice_5: 0.10341/0.22890, loss_spatial_ce_5: 0.04399/0.10249, loss_grounding_bce_5: 0.04320/0.08757, loss_grounding_dice_5: 0.09990/0.18274, loss_grounding_ce_5: 0.00980/0.29281, loss_mask_ce_6: 0.21336/0.96931, loss_mask_bce_6: 0.13708/0.34354, loss_mask_dice_6: 0.22962/1.20013, loss_spatial_bce_6: 0.05310/0.10114, loss_spatial_dice_6: 0.08848/0.23188, loss_spatial_ce_6: 0.02645/0.12721, loss_grounding_bce_6: 0.04405/0.08834, loss_grounding_dice_6: 0.09989/0.18318, loss_grounding_ce_6: 0.01776/0.30785, loss_mask_ce_7: 0.30256/1.01538, loss_mask_bce_7: 0.14559/0.35143, loss_mask_dice_7: 0.24767/1.25439, loss_spatial_bce_7: 0.05922/0.10893, loss_spatial_dice_7: 0.12831/0.25953, loss_spatial_ce_7: 0.02017/0.16180, loss_grounding_bce_7: 0.05078/0.09022, loss_grounding_dice_7: 0.10196/0.19056, loss_grounding_ce_7: 0.06654/0.33774, loss_mask_ce_8: 0.46513/1.12440, loss_mask_bce_8: 0.15848/0.36500, loss_mask_dice_8: 0.28657/1.32683, loss_spatial_bce_8: 0.07837/0.12885, loss_spatial_dice_8: 0.18495/0.29727, loss_spatial_ce_8: 0.11102/0.21047, loss_grounding_bce_8: 0.05054/0.09385, loss_grounding_dice_8: 0.13189/0.20120, loss_grounding_ce_8: 0.10545/0.40335, loss_mask_ce_9: 2.79366/3.67169, loss_mask_bce_9: 0.23466/0.39223, loss_mask_dice_9: 0.63060/1.90032, loss_spatial_bce_9: 0.45420/0.33255, loss_spatial_dice_9: 0.70891/0.82149, loss_spatial_ce_9: 1.47917/1.49070, loss_grounding_bce_9: 0.11049/0.10560, loss_grounding_dice_9: 0.31664/0.28076, loss_grounding_ce_9: 0.18729/0.66811] items per batch[64] items per second[0.24] total items[5625600] mini batches[ 87900] memory[7345] epoch remaining[1:13:34]
INFO:trainer.default_trainer:epochs[    48] optim steps[88000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.23500/0.89124, loss_mask_bce_0: 0.21533/0.33337, loss_mask_dice_0: 0.17935/1.15953, loss_spatial_bce_0: 0.12911/0.08613, loss_spatial_dice_0: 0.13304/0.20528, loss_spatial_ce_0: 0.00041/0.05844, loss_grounding_bce_0: 0.13688/0.08594, loss_grounding_dice_0: 0.11371/0.17799, loss_grounding_ce_0: 0.04962/0.27081, loss_mask_ce_1: 0.23333/0.89216, loss_mask_bce_1: 0.22598/0.33430, loss_mask_dice_1: 0.18957/1.16652, loss_spatial_bce_1: 0.12663/0.08665, loss_spatial_dice_1: 0.13811/0.20913, loss_spatial_ce_1: 0.00081/0.06423, loss_grounding_bce_1: 0.14006/0.08614, loss_grounding_dice_1: 0.11614/0.17883, loss_grounding_ce_1: 0.04564/0.27160, loss_mask_ce_2: 0.24658/0.89900, loss_mask_bce_2: 0.22009/0.33497, loss_mask_dice_2: 0.19678/1.16703, loss_spatial_bce_2: 0.12691/0.08792, loss_spatial_dice_2: 0.13439/0.21111, loss_spatial_ce_2: 0.00154/0.06762, loss_grounding_bce_2: 0.14191/0.08632, loss_grounding_dice_2: 0.11783/0.17874, loss_grounding_ce_2: 0.04353/0.27497, loss_mask_ce_3: 0.24915/0.91061, loss_mask_bce_3: 0.21706/0.33613, loss_mask_dice_3: 0.18853/1.16495, loss_spatial_bce_3: 0.12723/0.08926, loss_spatial_dice_3: 0.12702/0.21227, loss_spatial_ce_3: 0.00319/0.07284, loss_grounding_bce_3: 0.13695/0.08658, loss_grounding_dice_3: 0.11300/0.17839, loss_grounding_ce_3: 0.04215/0.27714, loss_mask_ce_4: 0.23086/0.91224, loss_mask_bce_4: 0.22415/0.33834, loss_mask_dice_4: 0.20315/1.18880, loss_spatial_bce_4: 0.14503/0.09317, loss_spatial_dice_4: 0.17355/0.22444, loss_spatial_ce_4: 0.00387/0.08897, loss_grounding_bce_4: 0.13567/0.08714, loss_grounding_dice_4: 0.11513/0.18142, loss_grounding_ce_4: 0.05096/0.28008, loss_mask_ce_5: 0.26329/0.92907, loss_mask_bce_5: 0.23017/0.34075, loss_mask_dice_5: 0.19717/1.19700, loss_spatial_bce_5: 0.15600/0.09550, loss_spatial_dice_5: 0.13193/0.22889, loss_spatial_ce_5: 0.01838/0.10247, loss_grounding_bce_5: 0.15177/0.08757, loss_grounding_dice_5: 0.12329/0.18272, loss_grounding_ce_5: 0.05791/0.29280, loss_mask_ce_6: 0.27189/0.96935, loss_mask_bce_6: 0.23792/0.34356, loss_mask_dice_6: 0.19609/1.20018, loss_spatial_bce_6: 0.15181/0.10113, loss_spatial_dice_6: 0.15650/0.23187, loss_spatial_ce_6: 0.02642/0.12718, loss_grounding_bce_6: 0.14755/0.08833, loss_grounding_dice_6: 0.11773/0.18317, loss_grounding_ce_6: 0.03866/0.30785, loss_mask_ce_7: 0.25118/1.01540, loss_mask_bce_7: 0.22566/0.35145, loss_mask_dice_7: 0.19669/1.25445, loss_spatial_bce_7: 0.19710/0.10892, loss_spatial_dice_7: 0.20558/0.25952, loss_spatial_ce_7: 0.05003/0.16177, loss_grounding_bce_7: 0.14349/0.09021, loss_grounding_dice_7: 0.12140/0.19053, loss_grounding_ce_7: 0.04355/0.33772, loss_mask_ce_8: 0.29902/1.12441, loss_mask_bce_8: 0.25258/0.36501, loss_mask_dice_8: 0.22408/1.32688, loss_spatial_bce_8: 0.26251/0.12884, loss_spatial_dice_8: 0.25261/0.29727, loss_spatial_ce_8: 0.07695/0.21041, loss_grounding_bce_8: 0.15231/0.09384, loss_grounding_dice_8: 0.12828/0.20118, loss_grounding_ce_8: 0.06031/0.40331, loss_mask_ce_9: 2.11223/3.67173, loss_mask_bce_9: 0.30171/0.39225, loss_mask_dice_9: 0.37506/1.90043, loss_spatial_bce_9: 0.59245/0.33256, loss_spatial_dice_9: 0.72200/0.82150, loss_spatial_ce_9: 0.86039/1.49073, loss_grounding_bce_9: 0.17751/0.10559, loss_grounding_dice_9: 0.23153/0.28074, loss_grounding_ce_9: 0.13882/0.66803] items per batch[64] items per second[0.23] total items[5632000] mini batches[ 88000] memory[7345] epoch remaining[1:09:24]
INFO:trainer.default_trainer:epochs[    48] optim steps[88100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.35013/0.89119, loss_mask_bce_0: 0.09274/0.33337, loss_mask_dice_0: 0.53843/1.15950, loss_spatial_bce_0: 0.03050/0.08613, loss_spatial_dice_0: 0.18762/0.20526, loss_spatial_ce_0: 0.03105/0.05842, loss_grounding_bce_0: 0.02685/0.08594, loss_grounding_dice_0: 0.03241/0.17798, loss_grounding_ce_0: 0.02980/0.27075, loss_mask_ce_1: 1.31540/0.89213, loss_mask_bce_1: 0.09567/0.33429, loss_mask_dice_1: 0.52864/1.16649, loss_spatial_bce_1: 0.03183/0.08665, loss_spatial_dice_1: 0.16781/0.20911, loss_spatial_ce_1: 0.03063/0.06421, loss_grounding_bce_1: 0.02714/0.08615, loss_grounding_dice_1: 0.03148/0.17881, loss_grounding_ce_1: 0.02973/0.27155, loss_mask_ce_2: 1.41016/0.89897, loss_mask_bce_2: 0.09159/0.33496, loss_mask_dice_2: 0.48933/1.16700, loss_spatial_bce_2: 0.03111/0.08792, loss_spatial_dice_2: 0.17267/0.21109, loss_spatial_ce_2: 0.03013/0.06760, loss_grounding_bce_2: 0.02760/0.08632, loss_grounding_dice_2: 0.02925/0.17873, loss_grounding_ce_2: 0.02262/0.27491, loss_mask_ce_3: 1.25140/0.91056, loss_mask_bce_3: 0.09077/0.33612, loss_mask_dice_3: 0.59222/1.16492, loss_spatial_bce_3: 0.03223/0.08926, loss_spatial_dice_3: 0.18354/0.21226, loss_spatial_ce_3: 0.03066/0.07282, loss_grounding_bce_3: 0.02813/0.08658, loss_grounding_dice_3: 0.03160/0.17837, loss_grounding_ce_3: 0.02003/0.27709, loss_mask_ce_4: 1.42746/0.91221, loss_mask_bce_4: 0.08845/0.33833, loss_mask_dice_4: 0.57292/1.18878, loss_spatial_bce_4: 0.03261/0.09317, loss_spatial_dice_4: 0.16327/0.22442, loss_spatial_ce_4: 0.03534/0.08894, loss_grounding_bce_4: 0.02723/0.08714, loss_grounding_dice_4: 0.03069/0.18141, loss_grounding_ce_4: 0.02206/0.28003, loss_mask_ce_5: 1.46400/0.92904, loss_mask_bce_5: 0.09374/0.34074, loss_mask_dice_5: 0.53079/1.19697, loss_spatial_bce_5: 0.03393/0.09550, loss_spatial_dice_5: 0.19281/0.22887, loss_spatial_ce_5: 0.22547/0.10244, loss_grounding_bce_5: 0.02625/0.08757, loss_grounding_dice_5: 0.02961/0.18271, loss_grounding_ce_5: 0.01971/0.29274, loss_mask_ce_6: 1.52320/0.96930, loss_mask_bce_6: 0.09824/0.34355, loss_mask_dice_6: 0.58481/1.20017, loss_spatial_bce_6: 0.03774/0.10114, loss_spatial_dice_6: 0.18515/0.23186, loss_spatial_ce_6: 0.05459/0.12715, loss_grounding_bce_6: 0.02925/0.08834, loss_grounding_dice_6: 0.03480/0.18315, loss_grounding_ce_6: 0.03532/0.30778, loss_mask_ce_7: 1.67249/1.01538, loss_mask_bce_7: 0.09245/0.35144, loss_mask_dice_7: 0.52768/1.25444, loss_spatial_bce_7: 0.04175/0.10893, loss_spatial_dice_7: 0.18981/0.25951, loss_spatial_ce_7: 0.05922/0.16174, loss_grounding_bce_7: 0.02906/0.09021, loss_grounding_dice_7: 0.03223/0.19052, loss_grounding_ce_7: 0.04291/0.33765, loss_mask_ce_8: 1.61827/1.12439, loss_mask_bce_8: 0.09456/0.36500, loss_mask_dice_8: 0.64054/1.32688, loss_spatial_bce_8: 0.04746/0.12884, loss_spatial_dice_8: 0.23173/0.29725, loss_spatial_ce_8: 0.11614/0.21033, loss_grounding_bce_8: 0.02458/0.09384, loss_grounding_dice_8: 0.02772/0.20116, loss_grounding_ce_8: 0.03481/0.40319, loss_mask_ce_9: 3.51913/3.67166, loss_mask_bce_9: 0.13856/0.39227, loss_mask_dice_9: 1.11364/1.90046, loss_spatial_bce_9: 0.18525/0.33258, loss_spatial_dice_9: 0.79744/0.82150, loss_spatial_ce_9: 1.71999/1.49066, loss_grounding_bce_9: 0.03105/0.10560, loss_grounding_dice_9: 0.03626/0.28072, loss_grounding_ce_9: 0.33826/0.66797] items per batch[64] items per second[0.23] total items[5638400] mini batches[ 88100] memory[7345] epoch remaining[1:04:56]
INFO:trainer.default_trainer:epochs[    48] optim steps[88200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.65753/0.89116, loss_mask_bce_0: 0.44281/0.33332, loss_mask_dice_0: 1.59785/1.15931, loss_spatial_bce_0: 0.08141/0.08613, loss_spatial_dice_0: 0.14692/0.20523, loss_spatial_ce_0: 0.01058/0.05840, loss_grounding_bce_0: 0.05880/0.08593, loss_grounding_dice_0: 0.14295/0.17796, loss_grounding_ce_0: 0.00300/0.27070, loss_mask_ce_1: 0.67190/0.89210, loss_mask_bce_1: 0.44146/0.33425, loss_mask_dice_1: 1.68924/1.16631, loss_spatial_bce_1: 0.07882/0.08664, loss_spatial_dice_1: 0.15337/0.20908, loss_spatial_ce_1: 0.01726/0.06419, loss_grounding_bce_1: 0.05781/0.08613, loss_grounding_dice_1: 0.13126/0.17880, loss_grounding_ce_1: 0.00294/0.27150, loss_mask_ce_2: 0.70288/0.89896, loss_mask_bce_2: 0.45161/0.33492, loss_mask_dice_2: 1.55498/1.16684, loss_spatial_bce_2: 0.08090/0.08792, loss_spatial_dice_2: 0.15300/0.21107, loss_spatial_ce_2: 0.01899/0.06757, loss_grounding_bce_2: 0.05961/0.08631, loss_grounding_dice_2: 0.14097/0.17871, loss_grounding_ce_2: 0.00272/0.27485, loss_mask_ce_3: 0.69245/0.91056, loss_mask_bce_3: 0.44152/0.33608, loss_mask_dice_3: 1.62346/1.16474, loss_spatial_bce_3: 0.07985/0.08926, loss_spatial_dice_3: 0.15339/0.21223, loss_spatial_ce_3: 0.03425/0.07280, loss_grounding_bce_3: 0.05962/0.08657, loss_grounding_dice_3: 0.14819/0.17836, loss_grounding_ce_3: 0.00507/0.27702, loss_mask_ce_4: 0.68301/0.91220, loss_mask_bce_4: 0.42715/0.33829, loss_mask_dice_4: 1.60099/1.18860, loss_spatial_bce_4: 0.08544/0.09317, loss_spatial_dice_4: 0.17430/0.22439, loss_spatial_ce_4: 0.03689/0.08891, loss_grounding_bce_4: 0.05743/0.08713, loss_grounding_dice_4: 0.13089/0.18139, loss_grounding_ce_4: 0.00231/0.27998, loss_mask_ce_5: 0.77266/0.92902, loss_mask_bce_5: 0.44840/0.34070, loss_mask_dice_5: 1.55786/1.19679, loss_spatial_bce_5: 0.08813/0.09550, loss_spatial_dice_5: 0.18856/0.22884, loss_spatial_ce_5: 0.04730/0.10241, loss_grounding_bce_5: 0.06114/0.08756, loss_grounding_dice_5: 0.13764/0.18269, loss_grounding_ce_5: 0.00273/0.29265, loss_mask_ce_6: 0.73037/0.96930, loss_mask_bce_6: 0.45790/0.34351, loss_mask_dice_6: 1.68662/1.19999, loss_spatial_bce_6: 0.08575/0.10113, loss_spatial_dice_6: 0.16081/0.23183, loss_spatial_ce_6: 0.11863/0.12711, loss_grounding_bce_6: 0.06089/0.08832, loss_grounding_dice_6: 0.14589/0.18313, loss_grounding_ce_6: 0.00829/0.30771, loss_mask_ce_7: 0.81344/1.01539, loss_mask_bce_7: 0.47961/0.35140, loss_mask_dice_7: 1.80138/1.25424, loss_spatial_bce_7: 0.13166/0.10893, loss_spatial_dice_7: 0.20681/0.25948, loss_spatial_ce_7: 0.12369/0.16170, loss_grounding_bce_7: 0.05755/0.09019, loss_grounding_dice_7: 0.14070/0.19050, loss_grounding_ce_7: 0.01369/0.33760, loss_mask_ce_8: 0.88330/1.12437, loss_mask_bce_8: 0.48055/0.36496, loss_mask_dice_8: 1.72350/1.32668, loss_spatial_bce_8: 0.09866/0.12883, loss_spatial_dice_8: 0.22157/0.29722, loss_spatial_ce_8: 0.09479/0.21023, loss_grounding_bce_8: 0.07400/0.09382, loss_grounding_dice_8: 0.15403/0.20114, loss_grounding_ce_8: 0.15178/0.40311, loss_mask_ce_9: 3.94632/3.67161, loss_mask_bce_9: 0.64562/0.39224, loss_mask_dice_9: 3.40556/1.90031, loss_spatial_bce_9: 0.27302/0.33259, loss_spatial_dice_9: 0.89114/0.82149, loss_spatial_ce_9: 1.33164/1.49058, loss_grounding_bce_9: 0.11988/0.10559, loss_grounding_dice_9: 0.24270/0.28069, loss_grounding_ce_9: 0.15451/0.66795] items per batch[64] items per second[0.23] total items[5644800] mini batches[ 88200] memory[7345] epoch remaining[1:00:29]
INFO:trainer.default_trainer:epochs[    48] optim steps[88300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.58307/0.89107, loss_mask_bce_0: 0.36556/0.33333, loss_mask_dice_0: 0.53233/1.15946, loss_spatial_bce_0: 0.10788/0.08612, loss_spatial_dice_0: 0.13500/0.20521, loss_spatial_ce_0: 0.08634/0.05837, loss_grounding_bce_0: 0.14078/0.08594, loss_grounding_dice_0: 0.07732/0.17797, loss_grounding_ce_0: 0.17550/0.27070, loss_mask_ce_1: 1.60145/0.89206, loss_mask_bce_1: 0.38963/0.33426, loss_mask_dice_1: 0.56368/1.16645, loss_spatial_bce_1: 0.12095/0.08664, loss_spatial_dice_1: 0.13548/0.20906, loss_spatial_ce_1: 0.09743/0.06416, loss_grounding_bce_1: 0.14556/0.08614, loss_grounding_dice_1: 0.07502/0.17881, loss_grounding_ce_1: 0.19354/0.27150, loss_mask_ce_2: 1.55931/0.89891, loss_mask_bce_2: 0.38600/0.33493, loss_mask_dice_2: 0.61503/1.16698, loss_spatial_bce_2: 0.13097/0.08791, loss_spatial_dice_2: 0.15180/0.21105, loss_spatial_ce_2: 0.11040/0.06753, loss_grounding_bce_2: 0.14319/0.08632, loss_grounding_dice_2: 0.07408/0.17872, loss_grounding_ce_2: 0.22047/0.27485, loss_mask_ce_3: 1.58337/0.91050, loss_mask_bce_3: 0.37260/0.33609, loss_mask_dice_3: 0.58362/1.16488, loss_spatial_bce_3: 0.11919/0.08926, loss_spatial_dice_3: 0.14746/0.21221, loss_spatial_ce_3: 0.11904/0.07277, loss_grounding_bce_3: 0.13535/0.08658, loss_grounding_dice_3: 0.07719/0.17837, loss_grounding_ce_3: 0.30907/0.27701, loss_mask_ce_4: 1.65297/0.91215, loss_mask_bce_4: 0.36949/0.33830, loss_mask_dice_4: 0.55187/1.18874, loss_spatial_bce_4: 0.11384/0.09316, loss_spatial_dice_4: 0.12735/0.22438, loss_spatial_ce_4: 0.26310/0.08888, loss_grounding_bce_4: 0.13590/0.08713, loss_grounding_dice_4: 0.07797/0.18139, loss_grounding_ce_4: 0.22744/0.28003, loss_mask_ce_5: 1.62326/0.92899, loss_mask_bce_5: 0.38524/0.34071, loss_mask_dice_5: 0.62530/1.19694, loss_spatial_bce_5: 0.17920/0.09549, loss_spatial_dice_5: 0.15413/0.22882, loss_spatial_ce_5: 0.37092/0.10237, loss_grounding_bce_5: 0.14028/0.08756, loss_grounding_dice_5: 0.07997/0.18269, loss_grounding_ce_5: 0.31780/0.29267, loss_mask_ce_6: 1.75817/0.96925, loss_mask_bce_6: 0.39495/0.34352, loss_mask_dice_6: 0.57355/1.20015, loss_spatial_bce_6: 0.18007/0.10113, loss_spatial_dice_6: 0.19598/0.23182, loss_spatial_ce_6: 0.09082/0.12705, loss_grounding_bce_6: 0.14387/0.08833, loss_grounding_dice_6: 0.08192/0.18313, loss_grounding_ce_6: 0.48826/0.30773, loss_mask_ce_7: 1.88508/1.01536, loss_mask_bce_7: 0.38057/0.35141, loss_mask_dice_7: 0.66313/1.25441, loss_spatial_bce_7: 0.21246/0.10892, loss_spatial_dice_7: 0.28619/0.25946, loss_spatial_ce_7: 0.23332/0.16164, loss_grounding_bce_7: 0.13677/0.09020, loss_grounding_dice_7: 0.08597/0.19050, loss_grounding_ce_7: 0.22735/0.33762, loss_mask_ce_8: 1.79845/1.12434, loss_mask_bce_8: 0.37728/0.36497, loss_mask_dice_8: 0.76523/1.32685, loss_spatial_bce_8: 0.26604/0.12883, loss_spatial_dice_8: 0.32700/0.29721, loss_spatial_ce_8: 0.38480/0.21013, loss_grounding_bce_8: 0.13268/0.09383, loss_grounding_dice_8: 0.07682/0.20114, loss_grounding_ce_8: 0.18452/0.40306, loss_mask_ce_9: 4.12774/3.67167, loss_mask_bce_9: 0.45652/0.39226, loss_mask_dice_9: 1.22076/1.90059, loss_spatial_bce_9: 0.40840/0.33261, loss_spatial_dice_9: 0.76243/0.82149, loss_spatial_ce_9: 1.69013/1.49054, loss_grounding_bce_9: 0.15676/0.10559, loss_grounding_dice_9: 0.07867/0.28069, loss_grounding_ce_9: 1.47347/0.66788] items per batch[64] items per second[0.23] total items[5651200] mini batches[ 88300] memory[7345] epoch remaining[0:55:58]
INFO:trainer.default_trainer:epochs[    48] optim steps[88400] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.47013/0.89103, loss_mask_bce_0: 0.22585/0.33327, loss_mask_dice_0: 0.45863/1.15927, loss_spatial_bce_0: 0.03192/0.08611, loss_spatial_dice_0: 0.10065/0.20519, loss_spatial_ce_0: 0.04509/0.05835, loss_grounding_bce_0: 0.02432/0.08592, loss_grounding_dice_0: 0.08242/0.17794, loss_grounding_ce_0: 0.47649/0.27066, loss_mask_ce_1: 0.47481/0.89202, loss_mask_bce_1: 0.22885/0.33420, loss_mask_dice_1: 0.51639/1.16626, loss_spatial_bce_1: 0.03089/0.08662, loss_spatial_dice_1: 0.09472/0.20903, loss_spatial_ce_1: 0.06553/0.06414, loss_grounding_bce_1: 0.02673/0.08612, loss_grounding_dice_1: 0.08434/0.17878, loss_grounding_ce_1: 0.51568/0.27147, loss_mask_ce_2: 0.44108/0.89887, loss_mask_bce_2: 0.23309/0.33487, loss_mask_dice_2: 0.50025/1.16679, loss_spatial_bce_2: 0.03240/0.08790, loss_spatial_dice_2: 0.10528/0.21102, loss_spatial_ce_2: 0.04302/0.06751, loss_grounding_bce_2: 0.02585/0.08630, loss_grounding_dice_2: 0.07563/0.17869, loss_grounding_ce_2: 0.53245/0.27482, loss_mask_ce_3: 0.43106/0.91047, loss_mask_bce_3: 0.23788/0.33603, loss_mask_dice_3: 0.47931/1.16467, loss_spatial_bce_3: 0.03281/0.08925, loss_spatial_dice_3: 0.10427/0.21219, loss_spatial_ce_3: 0.06684/0.07275, loss_grounding_bce_3: 0.02601/0.08655, loss_grounding_dice_3: 0.14169/0.17834, loss_grounding_ce_3: 0.47239/0.27698, loss_mask_ce_4: 0.45119/0.91212, loss_mask_bce_4: 0.23068/0.33824, loss_mask_dice_4: 0.69504/1.18855, loss_spatial_bce_4: 0.03429/0.09315, loss_spatial_dice_4: 0.12017/0.22436, loss_spatial_ce_4: 0.08436/0.08887, loss_grounding_bce_4: 0.02649/0.08711, loss_grounding_dice_4: 0.08055/0.18137, loss_grounding_ce_4: 0.48940/0.27998, loss_mask_ce_5: 0.39744/0.92897, loss_mask_bce_5: 0.23781/0.34065, loss_mask_dice_5: 0.53902/1.19674, loss_spatial_bce_5: 0.03141/0.09548, loss_spatial_dice_5: 0.09854/0.22880, loss_spatial_ce_5: 0.03155/0.10234, loss_grounding_bce_5: 0.02450/0.08754, loss_grounding_dice_5: 0.08507/0.18267, loss_grounding_ce_5: 0.46846/0.29264, loss_mask_ce_6: 0.37359/0.96923, loss_mask_bce_6: 0.22197/0.34346, loss_mask_dice_6: 0.48316/1.19997, loss_spatial_bce_6: 0.03518/0.10112, loss_spatial_dice_6: 0.10633/0.23180, loss_spatial_ce_6: 0.05790/0.12700, loss_grounding_bce_6: 0.02504/0.08831, loss_grounding_dice_6: 0.08509/0.18311, loss_grounding_ce_6: 0.55578/0.30771, loss_mask_ce_7: 0.58952/1.01534, loss_mask_bce_7: 0.20455/0.35135, loss_mask_dice_7: 0.52212/1.25420, loss_spatial_bce_7: 0.05494/0.10891, loss_spatial_dice_7: 0.15754/0.25944, loss_spatial_ce_7: 0.21126/0.16159, loss_grounding_bce_7: 0.02396/0.09018, loss_grounding_dice_7: 0.08099/0.19048, loss_grounding_ce_7: 0.54165/0.33762, loss_mask_ce_8: 0.61196/1.12429, loss_mask_bce_8: 0.22626/0.36490, loss_mask_dice_8: 0.65430/1.32664, loss_spatial_bce_8: 0.04871/0.12881, loss_spatial_dice_8: 0.15872/0.29720, loss_spatial_ce_8: 0.49704/0.21006, loss_grounding_bce_8: 0.02470/0.09381, loss_grounding_dice_8: 0.10350/0.20112, loss_grounding_ce_8: 0.59737/0.40304, loss_mask_ce_9: 2.99086/3.67153, loss_mask_bce_9: 0.19507/0.39220, loss_mask_dice_9: 0.81786/1.90037, loss_spatial_bce_9: 0.28516/0.33259, loss_spatial_dice_9: 0.80377/0.82149, loss_spatial_ce_9: 1.53160/1.49053, loss_grounding_bce_9: 0.02598/0.10558, loss_grounding_dice_9: 0.15970/0.28066, loss_grounding_ce_9: 0.54228/0.66797] items per batch[64] items per second[0.23] total items[5657600] mini batches[ 88400] memory[7345] epoch remaining[0:51:34]
INFO:trainer.default_trainer:epochs[    48] optim steps[88500] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.19119/0.89085, loss_mask_bce_0: 0.16634/0.33325, loss_mask_dice_0: 0.24157/1.15938, loss_spatial_bce_0: 0.06067/0.08609, loss_spatial_dice_0: 0.09833/0.20517, loss_spatial_ce_0: 0.00371/0.05833, loss_grounding_bce_0: 0.01371/0.08590, loss_grounding_dice_0: 0.08489/0.17793, loss_grounding_ce_0: 0.13723/0.27059, loss_mask_ce_1: 0.22309/0.89186, loss_mask_bce_1: 0.15745/0.33418, loss_mask_dice_1: 0.21565/1.16636, loss_spatial_bce_1: 0.05457/0.08661, loss_spatial_dice_1: 0.09500/0.20900, loss_spatial_ce_1: 0.00404/0.06411, loss_grounding_bce_1: 0.01163/0.08611, loss_grounding_dice_1: 0.07450/0.17878, loss_grounding_ce_1: 0.14863/0.27140, loss_mask_ce_2: 0.19838/0.89871, loss_mask_bce_2: 0.16616/0.33485, loss_mask_dice_2: 0.23516/1.16691, loss_spatial_bce_2: 0.06001/0.08789, loss_spatial_dice_2: 0.10051/0.21100, loss_spatial_ce_2: 0.00554/0.06748, loss_grounding_bce_2: 0.01077/0.08629, loss_grounding_dice_2: 0.07258/0.17869, loss_grounding_ce_2: 0.13350/0.27477, loss_mask_ce_3: 0.47602/0.91030, loss_mask_bce_3: 0.12351/0.33600, loss_mask_dice_3: 0.24855/1.16480, loss_spatial_bce_3: 0.05994/0.08923, loss_spatial_dice_3: 0.10183/0.21216, loss_spatial_ce_3: 0.00700/0.07273, loss_grounding_bce_3: 0.01068/0.08654, loss_grounding_dice_3: 0.06460/0.17833, loss_grounding_ce_3: 0.12965/0.27691, loss_mask_ce_4: 0.50133/0.91194, loss_mask_bce_4: 0.11463/0.33821, loss_mask_dice_4: 0.22895/1.18865, loss_spatial_bce_4: 0.05608/0.09314, loss_spatial_dice_4: 0.10052/0.22433, loss_spatial_ce_4: 0.02108/0.08884, loss_grounding_bce_4: 0.00968/0.08710, loss_grounding_dice_4: 0.06750/0.18137, loss_grounding_ce_4: 0.12218/0.27993, loss_mask_ce_5: 0.40388/0.92882, loss_mask_bce_5: 0.11545/0.34063, loss_mask_dice_5: 0.24879/1.19685, loss_spatial_bce_5: 0.05970/0.09547, loss_spatial_dice_5: 0.09641/0.22878, loss_spatial_ce_5: 0.02262/0.10231, loss_grounding_bce_5: 0.00944/0.08753, loss_grounding_dice_5: 0.06895/0.18266, loss_grounding_ce_5: 0.12045/0.29257, loss_mask_ce_6: 0.40642/0.96908, loss_mask_bce_6: 0.12245/0.34343, loss_mask_dice_6: 0.23461/1.20009, loss_spatial_bce_6: 0.05744/0.10111, loss_spatial_dice_6: 0.09963/0.23178, loss_spatial_ce_6: 0.00891/0.12695, loss_grounding_bce_6: 0.01062/0.08830, loss_grounding_dice_6: 0.07227/0.18311, loss_grounding_ce_6: 0.16908/0.30763, loss_mask_ce_7: 0.40052/1.01519, loss_mask_bce_7: 0.12553/0.35132, loss_mask_dice_7: 0.24992/1.25432, loss_spatial_bce_7: 0.06023/0.10889, loss_spatial_dice_7: 0.11508/0.25942, loss_spatial_ce_7: 0.02062/0.16153, loss_grounding_bce_7: 0.01118/0.09017, loss_grounding_dice_7: 0.08024/0.19047, loss_grounding_ce_7: 0.14151/0.33756, loss_mask_ce_8: 0.28092/1.12416, loss_mask_bce_8: 0.15760/0.36488, loss_mask_dice_8: 0.26417/1.32673, loss_spatial_bce_8: 0.06104/0.12879, loss_spatial_dice_8: 0.11727/0.29718, loss_spatial_ce_8: 0.04930/0.20997, loss_grounding_bce_8: 0.01143/0.09379, loss_grounding_dice_8: 0.08743/0.20111, loss_grounding_ce_8: 0.26988/0.40296, loss_mask_ce_9: 2.97041/3.67163, loss_mask_bce_9: 0.14928/0.39217, loss_mask_dice_9: 0.40306/1.90052, loss_spatial_bce_9: 0.30790/0.33258, loss_spatial_dice_9: 0.75520/0.82149, loss_spatial_ce_9: 1.11544/1.49056, loss_grounding_bce_9: 0.01108/0.10557, loss_grounding_dice_9: 0.10351/0.28065, loss_grounding_ce_9: 0.44340/0.66798] items per batch[64] items per second[0.24] total items[5664000] mini batches[ 88500] memory[7345] epoch remaining[0:46:50]
INFO:trainer.default_trainer:epochs[    48] optim steps[88600] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.87356/0.89080, loss_mask_bce_0: 0.18391/0.33328, loss_mask_dice_0: 1.27987/1.15920, loss_spatial_bce_0: 0.04946/0.08610, loss_spatial_dice_0: 0.25534/0.20513, loss_spatial_ce_0: 0.00558/0.05831, loss_grounding_bce_0: 0.05485/0.08590, loss_grounding_dice_0: 0.40819/0.17792, loss_grounding_ce_0: 0.41430/0.27069, loss_mask_ce_1: 0.61241/0.89183, loss_mask_bce_1: 0.20311/0.33421, loss_mask_dice_1: 1.55121/1.16618, loss_spatial_bce_1: 0.05201/0.08662, loss_spatial_dice_1: 0.24110/0.20897, loss_spatial_ce_1: 0.00572/0.06408, loss_grounding_bce_1: 0.05587/0.08611, loss_grounding_dice_1: 0.40110/0.17876, loss_grounding_ce_1: 0.41861/0.27151, loss_mask_ce_2: 0.69622/0.89868, loss_mask_bce_2: 0.19828/0.33488, loss_mask_dice_2: 1.22806/1.16675, loss_spatial_bce_2: 0.06135/0.08789, loss_spatial_dice_2: 0.28512/0.21097, loss_spatial_ce_2: 0.00984/0.06746, loss_grounding_bce_2: 0.05311/0.08629, loss_grounding_dice_2: 0.38603/0.17868, loss_grounding_ce_2: 0.38936/0.27487, loss_mask_ce_3: 0.62155/0.91026, loss_mask_bce_3: 0.19259/0.33603, loss_mask_dice_3: 1.33922/1.16463, loss_spatial_bce_3: 0.06188/0.08924, loss_spatial_dice_3: 0.21912/0.21213, loss_spatial_ce_3: 0.04814/0.07271, loss_grounding_bce_3: 0.05395/0.08655, loss_grounding_dice_3: 0.41513/0.17832, loss_grounding_ce_3: 0.37186/0.27700, loss_mask_ce_4: 1.18068/0.91189, loss_mask_bce_4: 0.17614/0.33824, loss_mask_dice_4: 1.40487/1.18847, loss_spatial_bce_4: 0.07836/0.09314, loss_spatial_dice_4: 0.28769/0.22430, loss_spatial_ce_4: 0.05156/0.08883, loss_grounding_bce_4: 0.05316/0.08710, loss_grounding_dice_4: 0.41751/0.18136, loss_grounding_ce_4: 0.39979/0.28002, loss_mask_ce_5: 0.96311/0.92875, loss_mask_bce_5: 0.19591/0.34065, loss_mask_dice_5: 1.29191/1.19668, loss_spatial_bce_5: 0.09404/0.09547, loss_spatial_dice_5: 0.26481/0.22875, loss_spatial_ce_5: 0.06868/0.10228, loss_grounding_bce_5: 0.05327/0.08754, loss_grounding_dice_5: 0.40420/0.18265, loss_grounding_ce_5: 0.39790/0.29269, loss_mask_ce_6: 0.86411/0.96903, loss_mask_bce_6: 0.17894/0.34346, loss_mask_dice_6: 1.62524/1.19992, loss_spatial_bce_6: 0.08966/0.10111, loss_spatial_dice_6: 0.32448/0.23175, loss_spatial_ce_6: 0.03537/0.12692, loss_grounding_bce_6: 0.05917/0.08830, loss_grounding_dice_6: 0.41696/0.18310, loss_grounding_ce_6: 0.53276/0.30773, loss_mask_ce_7: 0.98066/1.01512, loss_mask_bce_7: 0.25737/0.35136, loss_mask_dice_7: 1.69545/1.25416, loss_spatial_bce_7: 0.11363/0.10890, loss_spatial_dice_7: 0.36610/0.25939, loss_spatial_ce_7: 0.13903/0.16149, loss_grounding_bce_7: 0.07860/0.09018, loss_grounding_dice_7: 0.53246/0.19047, loss_grounding_ce_7: 0.60575/0.33766, loss_mask_ce_8: 1.46947/1.12413, loss_mask_bce_8: 0.24312/0.36491, loss_mask_dice_8: 1.39491/1.32657, loss_spatial_bce_8: 0.12614/0.12879, loss_spatial_dice_8: 0.36464/0.29715, loss_spatial_ce_8: 0.79960/0.20991, loss_grounding_bce_8: 0.06791/0.09380, loss_grounding_dice_8: 0.34890/0.20111, loss_grounding_ce_8: 0.60151/0.40306, loss_mask_ce_9: 2.51536/3.67156, loss_mask_bce_9: 0.37630/0.39221, loss_mask_dice_9: 1.77558/1.90037, loss_spatial_bce_9: 0.12364/0.33259, loss_spatial_dice_9: 0.75982/0.82148, loss_spatial_ce_9: 3.34138/1.49051, loss_grounding_bce_9: 0.10272/0.10558, loss_grounding_dice_9: 0.51192/0.28066, loss_grounding_ce_9: 0.38055/0.66806] items per batch[64] items per second[0.23] total items[5670400] mini batches[ 88600] memory[7345] epoch remaining[0:42:22]
INFO:trainer.default_trainer:epochs[    48] optim steps[88700] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.38074/0.89077, loss_mask_bce_0: 0.43012/0.33326, loss_mask_dice_0: 1.47143/1.15911, loss_spatial_bce_0: 0.10274/0.08609, loss_spatial_dice_0: 0.16750/0.20511, loss_spatial_ce_0: 0.15389/0.05830, loss_grounding_bce_0: 0.09100/0.08591, loss_grounding_dice_0: 0.11334/0.17793, loss_grounding_ce_0: 0.01674/0.27066, loss_mask_ce_1: 0.31381/0.89178, loss_mask_bce_1: 0.41219/0.33418, loss_mask_dice_1: 1.38923/1.16606, loss_spatial_bce_1: 0.11330/0.08661, loss_spatial_dice_1: 0.18363/0.20895, loss_spatial_ce_1: 0.12706/0.06407, loss_grounding_bce_1: 0.09298/0.08611, loss_grounding_dice_1: 0.12295/0.17877, loss_grounding_ce_1: 0.01855/0.27148, loss_mask_ce_2: 0.28674/0.89863, loss_mask_bce_2: 0.42755/0.33486, loss_mask_dice_2: 1.38888/1.16665, loss_spatial_bce_2: 0.10745/0.08789, loss_spatial_dice_2: 0.15630/0.21094, loss_spatial_ce_2: 0.08934/0.06745, loss_grounding_bce_2: 0.09602/0.08629, loss_grounding_dice_2: 0.11066/0.17868, loss_grounding_ce_2: 0.01774/0.27485, loss_mask_ce_3: 0.31276/0.91024, loss_mask_bce_3: 0.41192/0.33601, loss_mask_dice_3: 1.38492/1.16452, loss_spatial_bce_3: 0.09288/0.08923, loss_spatial_dice_3: 0.15673/0.21211, loss_spatial_ce_3: 0.05845/0.07269, loss_grounding_bce_3: 0.09772/0.08655, loss_grounding_dice_3: 0.10420/0.17832, loss_grounding_ce_3: 0.01124/0.27699, loss_mask_ce_4: 0.32586/0.91187, loss_mask_bce_4: 0.41543/0.33822, loss_mask_dice_4: 1.44462/1.18836, loss_spatial_bce_4: 0.09245/0.09314, loss_spatial_dice_4: 0.16680/0.22427, loss_spatial_ce_4: 0.05195/0.08881, loss_grounding_bce_4: 0.09645/0.08710, loss_grounding_dice_4: 0.11213/0.18136, loss_grounding_ce_4: 0.01412/0.28001, loss_mask_ce_5: 0.32398/0.92874, loss_mask_bce_5: 0.43247/0.34063, loss_mask_dice_5: 1.39352/1.19657, loss_spatial_bce_5: 0.09435/0.09547, loss_spatial_dice_5: 0.17348/0.22872, loss_spatial_ce_5: 0.12240/0.10226, loss_grounding_bce_5: 0.09838/0.08754, loss_grounding_dice_5: 0.11381/0.18266, loss_grounding_ce_5: 0.01158/0.29264, loss_mask_ce_6: 0.41185/0.96900, loss_mask_bce_6: 0.42667/0.34344, loss_mask_dice_6: 1.34679/1.19981, loss_spatial_bce_6: 0.08942/0.10111, loss_spatial_dice_6: 0.15797/0.23172, loss_spatial_ce_6: 0.12041/0.12689, loss_grounding_bce_6: 0.09501/0.08831, loss_grounding_dice_6: 0.11782/0.18311, loss_grounding_ce_6: 0.01523/0.30770, loss_mask_ce_7: 0.41087/1.01508, loss_mask_bce_7: 0.49715/0.35135, loss_mask_dice_7: 1.51014/1.25405, loss_spatial_bce_7: 0.12034/0.10889, loss_spatial_dice_7: 0.19341/0.25936, loss_spatial_ce_7: 0.10884/0.16145, loss_grounding_bce_7: 0.09483/0.09018, loss_grounding_dice_7: 0.12763/0.19047, loss_grounding_ce_7: 0.02025/0.33765, loss_mask_ce_8: 0.52278/1.12411, loss_mask_bce_8: 0.42111/0.36490, loss_mask_dice_8: 1.46264/1.32644, loss_spatial_bce_8: 0.10781/0.12879, loss_spatial_dice_8: 0.23759/0.29712, loss_spatial_ce_8: 0.22849/0.20984, loss_grounding_bce_8: 0.09889/0.09381, loss_grounding_dice_8: 0.11940/0.20111, loss_grounding_ce_8: 0.14222/0.40301, loss_mask_ce_9: 3.18809/3.67156, loss_mask_bce_9: 0.45306/0.39221, loss_mask_dice_9: 2.07859/1.90025, loss_spatial_bce_9: 0.28278/0.33262, loss_spatial_dice_9: 0.88192/0.82146, loss_spatial_ce_9: 1.25285/1.49048, loss_grounding_bce_9: 0.15084/0.10559, loss_grounding_dice_9: 0.22405/0.28065, loss_grounding_ce_9: 0.70514/0.66799] items per batch[64] items per second[0.24] total items[5676800] mini batches[ 88700] memory[7345] epoch remaining[0:37:35]
INFO:trainer.default_trainer:epochs[    48] optim steps[88800] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.69539/0.89069, loss_mask_bce_0: 0.32337/0.33328, loss_mask_dice_0: 0.66009/1.15902, loss_spatial_bce_0: 0.08290/0.08610, loss_spatial_dice_0: 0.16149/0.20510, loss_spatial_ce_0: 0.03897/0.05829, loss_grounding_bce_0: 0.13076/0.08591, loss_grounding_dice_0: 0.07367/0.17794, loss_grounding_ce_0: 1.55603/0.27069, loss_mask_ce_1: 0.62413/0.89171, loss_mask_bce_1: 0.33024/0.33420, loss_mask_dice_1: 0.67408/1.16597, loss_spatial_bce_1: 0.08366/0.08662, loss_spatial_dice_1: 0.13862/0.20893, loss_spatial_ce_1: 0.04999/0.06405, loss_grounding_bce_1: 0.13405/0.08612, loss_grounding_dice_1: 0.07487/0.17878, loss_grounding_ce_1: 1.13297/0.27150, loss_mask_ce_2: 0.68552/0.89857, loss_mask_bce_2: 0.32727/0.33487, loss_mask_dice_2: 0.66558/1.16657, loss_spatial_bce_2: 0.08513/0.08790, loss_spatial_dice_2: 0.14179/0.21093, loss_spatial_ce_2: 0.04847/0.06743, loss_grounding_bce_2: 0.14898/0.08629, loss_grounding_dice_2: 0.07814/0.17869, loss_grounding_ce_2: 1.03070/0.27485, loss_mask_ce_3: 0.65414/0.91016, loss_mask_bce_3: 0.33153/0.33603, loss_mask_dice_3: 0.65184/1.16443, loss_spatial_bce_3: 0.07880/0.08925, loss_spatial_dice_3: 0.12679/0.21210, loss_spatial_ce_3: 0.05331/0.07268, loss_grounding_bce_3: 0.14613/0.08655, loss_grounding_dice_3: 0.07291/0.17833, loss_grounding_ce_3: 1.37540/0.27699, loss_mask_ce_4: 0.65170/0.91182, loss_mask_bce_4: 0.33055/0.33824, loss_mask_dice_4: 0.62350/1.18828, loss_spatial_bce_4: 0.10152/0.09315, loss_spatial_dice_4: 0.17501/0.22427, loss_spatial_ce_4: 0.12729/0.08878, loss_grounding_bce_4: 0.15126/0.08711, loss_grounding_dice_4: 0.08172/0.18137, loss_grounding_ce_4: 1.02494/0.28001, loss_mask_ce_5: 0.66485/0.92869, loss_mask_bce_5: 0.33874/0.34065, loss_mask_dice_5: 0.67741/1.19649, loss_spatial_bce_5: 0.09605/0.09548, loss_spatial_dice_5: 0.13869/0.22871, loss_spatial_ce_5: 0.05790/0.10224, loss_grounding_bce_5: 0.15659/0.08754, loss_grounding_dice_5: 0.09018/0.18267, loss_grounding_ce_5: 1.11973/0.29264, loss_mask_ce_6: 0.65330/0.96890, loss_mask_bce_6: 0.34710/0.34346, loss_mask_dice_6: 0.66159/1.19973, loss_spatial_bce_6: 0.10680/0.10112, loss_spatial_dice_6: 0.12505/0.23171, loss_spatial_ce_6: 0.04661/0.12685, loss_grounding_bce_6: 0.15137/0.08832, loss_grounding_dice_6: 0.07950/0.18312, loss_grounding_ce_6: 1.21113/0.30769, loss_mask_ce_7: 0.76738/1.01500, loss_mask_bce_7: 0.33452/0.35137, loss_mask_dice_7: 0.70573/1.25397, loss_spatial_bce_7: 0.10110/0.10890, loss_spatial_dice_7: 0.13491/0.25936, loss_spatial_ce_7: 0.21610/0.16142, loss_grounding_bce_7: 0.14020/0.09019, loss_grounding_dice_7: 0.08032/0.19048, loss_grounding_ce_7: 1.30178/0.33763, loss_mask_ce_8: 0.70117/1.12403, loss_mask_bce_8: 0.40664/0.36492, loss_mask_dice_8: 0.88125/1.32638, loss_spatial_bce_8: 0.14952/0.12879, loss_spatial_dice_8: 0.17463/0.29712, loss_spatial_ce_8: 0.38257/0.20977, loss_grounding_bce_8: 0.15890/0.09381, loss_grounding_dice_8: 0.08114/0.20112, loss_grounding_ce_8: 1.79495/0.40300, loss_mask_ce_9: 3.45816/3.67155, loss_mask_bce_9: 0.50863/0.39223, loss_mask_dice_9: 1.62176/1.90021, loss_spatial_bce_9: 0.35140/0.33264, loss_spatial_dice_9: 0.82123/0.82147, loss_spatial_ce_9: 1.76079/1.49050, loss_grounding_bce_9: 0.10836/0.10559, loss_grounding_dice_9: 0.08436/0.28066, loss_grounding_ce_9: 2.28826/0.66793] items per batch[64] items per second[0.24] total items[5683200] mini batches[ 88800] memory[7345] epoch remaining[0:32:56]
INFO:trainer.default_trainer:epochs[    48] optim steps[88900] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.50222/0.89058, loss_mask_bce_0: 0.36942/0.33327, loss_mask_dice_0: 1.03288/1.15895, loss_spatial_bce_0: 0.07023/0.08610, loss_spatial_dice_0: 0.19874/0.20508, loss_spatial_ce_0: 0.00190/0.05827, loss_grounding_bce_0: 0.09020/0.08591, loss_grounding_dice_0: 0.15765/0.17792, loss_grounding_ce_0: 0.07268/0.27065, loss_mask_ce_1: 0.49096/0.89159, loss_mask_bce_1: 0.38347/0.33419, loss_mask_dice_1: 1.01733/1.16589, loss_spatial_bce_1: 0.07000/0.08661, loss_spatial_dice_1: 0.18099/0.20891, loss_spatial_ce_1: 0.01010/0.06403, loss_grounding_bce_1: 0.08811/0.08611, loss_grounding_dice_1: 0.14075/0.17877, loss_grounding_ce_1: 0.07647/0.27147, loss_mask_ce_2: 0.48816/0.89846, loss_mask_bce_2: 0.38460/0.33487, loss_mask_dice_2: 1.13865/1.16648, loss_spatial_bce_2: 0.07297/0.08789, loss_spatial_dice_2: 0.17156/0.21091, loss_spatial_ce_2: 0.00161/0.06741, loss_grounding_bce_2: 0.09248/0.08629, loss_grounding_dice_2: 0.15120/0.17867, loss_grounding_ce_2: 0.06984/0.27480, loss_mask_ce_3: 0.54645/0.91006, loss_mask_bce_3: 0.35560/0.33602, loss_mask_dice_3: 1.13843/1.16434, loss_spatial_bce_3: 0.07502/0.08924, loss_spatial_dice_3: 0.20362/0.21208, loss_spatial_ce_3: 0.01754/0.07265, loss_grounding_bce_3: 0.09441/0.08654, loss_grounding_dice_3: 0.18737/0.17832, loss_grounding_ce_3: 0.04800/0.27696, loss_mask_ce_4: 0.62833/0.91172, loss_mask_bce_4: 0.39081/0.33823, loss_mask_dice_4: 1.06747/1.18820, loss_spatial_bce_4: 0.07407/0.09314, loss_spatial_dice_4: 0.21065/0.22425, loss_spatial_ce_4: 0.01927/0.08876, loss_grounding_bce_4: 0.09135/0.08711, loss_grounding_dice_4: 0.11134/0.18136, loss_grounding_ce_4: 0.03876/0.27996, loss_mask_ce_5: 0.64217/0.92859, loss_mask_bce_5: 0.38187/0.34064, loss_mask_dice_5: 1.15602/1.19641, loss_spatial_bce_5: 0.07540/0.09547, loss_spatial_dice_5: 0.25152/0.22869, loss_spatial_ce_5: 0.00926/0.10221, loss_grounding_bce_5: 0.09654/0.08754, loss_grounding_dice_5: 0.12945/0.18266, loss_grounding_ce_5: 0.05200/0.29259, loss_mask_ce_6: 0.59611/0.96882, loss_mask_bce_6: 0.44807/0.34346, loss_mask_dice_6: 1.25254/1.19964, loss_spatial_bce_6: 0.08784/0.10111, loss_spatial_dice_6: 0.26994/0.23169, loss_spatial_ce_6: 0.05726/0.12681, loss_grounding_bce_6: 0.09870/0.08831, loss_grounding_dice_6: 0.15954/0.18310, loss_grounding_ce_6: 0.00926/0.30764, loss_mask_ce_7: 0.89181/1.01491, loss_mask_bce_7: 0.47184/0.35137, loss_mask_dice_7: 1.29470/1.25390, loss_spatial_bce_7: 0.09646/0.10890, loss_spatial_dice_7: 0.30611/0.25933, loss_spatial_ce_7: 0.04764/0.16136, loss_grounding_bce_7: 0.10157/0.09018, loss_grounding_dice_7: 0.17136/0.19047, loss_grounding_ce_7: 0.01572/0.33757, loss_mask_ce_8: 1.50237/1.12395, loss_mask_bce_8: 0.44325/0.36492, loss_mask_dice_8: 1.54235/1.32631, loss_spatial_bce_8: 0.10028/0.12878, loss_spatial_dice_8: 0.35834/0.29710, loss_spatial_ce_8: 0.07309/0.20969, loss_grounding_bce_8: 0.09735/0.09380, loss_grounding_dice_8: 0.16516/0.20111, loss_grounding_ce_8: 0.00602/0.40294, loss_mask_ce_9: 3.94672/3.67153, loss_mask_bce_9: 0.47548/0.39222, loss_mask_dice_9: 1.88886/1.90010, loss_spatial_bce_9: 0.47966/0.33264, loss_spatial_dice_9: 0.90998/0.82147, loss_spatial_ce_9: 1.74769/1.49045, loss_grounding_bce_9: 0.12641/0.10558, loss_grounding_dice_9: 0.25235/0.28066, loss_grounding_ce_9: 0.57028/0.66795] items per batch[64] items per second[0.24] total items[5689600] mini batches[ 88900] memory[7345] epoch remaining[0:28:21]
INFO:trainer.default_trainer:epochs[    48] optim steps[89000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.64587/0.89056, loss_mask_bce_0: 0.49422/0.33330, loss_mask_dice_0: 0.73284/1.15891, loss_spatial_bce_0: 0.09766/0.08610, loss_spatial_dice_0: 0.15662/0.20506, loss_spatial_ce_0: 0.01446/0.05827, loss_grounding_bce_0: 0.03044/0.08590, loss_grounding_dice_0: 0.20185/0.17792, loss_grounding_ce_0: 0.28783/0.27061, loss_mask_ce_1: 1.43191/0.89156, loss_mask_bce_1: 0.51459/0.33422, loss_mask_dice_1: 0.85478/1.16585, loss_spatial_bce_1: 0.10258/0.08662, loss_spatial_dice_1: 0.15793/0.20889, loss_spatial_ce_1: 0.01140/0.06403, loss_grounding_bce_1: 0.03622/0.08611, loss_grounding_dice_1: 0.24947/0.17875, loss_grounding_ce_1: 0.30242/0.27144, loss_mask_ce_2: 1.39661/0.89843, loss_mask_bce_2: 0.47827/0.33489, loss_mask_dice_2: 0.89125/1.16644, loss_spatial_bce_2: 0.10351/0.08790, loss_spatial_dice_2: 0.17006/0.21089, loss_spatial_ce_2: 0.01088/0.06741, loss_grounding_bce_2: 0.03204/0.08628, loss_grounding_dice_2: 0.24161/0.17866, loss_grounding_ce_2: 0.30868/0.27476, loss_mask_ce_3: 1.67628/0.91004, loss_mask_bce_3: 0.46712/0.33604, loss_mask_dice_3: 0.81245/1.16431, loss_spatial_bce_3: 0.10077/0.08925, loss_spatial_dice_3: 0.15675/0.21207, loss_spatial_ce_3: 0.01448/0.07264, loss_grounding_bce_3: 0.03285/0.08654, loss_grounding_dice_3: 0.25811/0.17830, loss_grounding_ce_3: 0.29193/0.27693, loss_mask_ce_4: 1.63246/0.91173, loss_mask_bce_4: 0.47466/0.33827, loss_mask_dice_4: 0.76380/1.18817, loss_spatial_bce_4: 0.11559/0.09315, loss_spatial_dice_4: 0.17559/0.22422, loss_spatial_ce_4: 0.01988/0.08875, loss_grounding_bce_4: 0.03360/0.08710, loss_grounding_dice_4: 0.28299/0.18135, loss_grounding_ce_4: 0.15250/0.27994, loss_mask_ce_5: 1.56099/0.92857, loss_mask_bce_5: 0.46767/0.34067, loss_mask_dice_5: 0.83285/1.19639, loss_spatial_bce_5: 0.12268/0.09548, loss_spatial_dice_5: 0.17949/0.22867, loss_spatial_ce_5: 0.03626/0.10220, loss_grounding_bce_5: 0.02330/0.08753, loss_grounding_dice_5: 0.22228/0.18265, loss_grounding_ce_5: 0.31215/0.29258, loss_mask_ce_6: 1.74464/0.96882, loss_mask_bce_6: 0.46001/0.34348, loss_mask_dice_6: 0.73482/1.19958, loss_spatial_bce_6: 0.13394/0.10112, loss_spatial_dice_6: 0.18820/0.23167, loss_spatial_ce_6: 0.04511/0.12681, loss_grounding_bce_6: 0.03842/0.08830, loss_grounding_dice_6: 0.32765/0.18309, loss_grounding_ce_6: 0.14400/0.30766, loss_mask_ce_7: 1.32798/1.01488, loss_mask_bce_7: 0.42250/0.35139, loss_mask_dice_7: 0.83833/1.25384, loss_spatial_bce_7: 0.13690/0.10891, loss_spatial_dice_7: 0.23697/0.25931, loss_spatial_ce_7: 0.14441/0.16134, loss_grounding_bce_7: 0.04018/0.09018, loss_grounding_dice_7: 0.33974/0.19045, loss_grounding_ce_7: 0.17513/0.33756, loss_mask_ce_8: 1.97174/1.12391, loss_mask_bce_8: 0.43004/0.36495, loss_mask_dice_8: 0.89177/1.32625, loss_spatial_bce_8: 0.22132/0.12879, loss_spatial_dice_8: 0.26930/0.29708, loss_spatial_ce_8: 0.11713/0.20963, loss_grounding_bce_8: 0.03986/0.09380, loss_grounding_dice_8: 0.37209/0.20108, loss_grounding_ce_8: 0.16872/0.40287, loss_mask_ce_9: 4.47488/3.67145, loss_mask_bce_9: 0.53145/0.39227, loss_mask_dice_9: 1.39684/1.90002, loss_spatial_bce_9: 0.49519/0.33267, loss_spatial_dice_9: 0.86583/0.82148, loss_spatial_ce_9: 1.58756/1.49048, loss_grounding_bce_9: 0.04230/0.10558, loss_grounding_dice_9: 0.50810/0.28065, loss_grounding_ce_9: 0.36184/0.66789] items per batch[64] items per second[0.24] total items[5696000] mini batches[ 89000] memory[7345] epoch remaining[0:23:48]
INFO:trainer.default_trainer:epochs[    48] optim steps[89100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.13521/0.89049, loss_mask_bce_0: 0.09042/0.33328, loss_mask_dice_0: 0.22351/1.15889, loss_spatial_bce_0: 0.04198/0.08610, loss_spatial_dice_0: 0.10583/0.20505, loss_spatial_ce_0: 0.00853/0.05826, loss_grounding_bce_0: 0.04445/0.08590, loss_grounding_dice_0: 0.12844/0.17791, loss_grounding_ce_0: 0.01914/0.27057, loss_mask_ce_1: 0.13356/0.89149, loss_mask_bce_1: 0.09635/0.33420, loss_mask_dice_1: 0.24105/1.16582, loss_spatial_bce_1: 0.04291/0.08662, loss_spatial_dice_1: 0.10561/0.20888, loss_spatial_ce_1: 0.01154/0.06401, loss_grounding_bce_1: 0.04274/0.08610, loss_grounding_dice_1: 0.12256/0.17874, loss_grounding_ce_1: 0.01611/0.27140, loss_mask_ce_2: 0.13363/0.89836, loss_mask_bce_2: 0.09487/0.33488, loss_mask_dice_2: 0.24266/1.16642, loss_spatial_bce_2: 0.03837/0.08790, loss_spatial_dice_2: 0.10776/0.21088, loss_spatial_ce_2: 0.00896/0.06740, loss_grounding_bce_2: 0.04674/0.08628, loss_grounding_dice_2: 0.12443/0.17865, loss_grounding_ce_2: 0.01765/0.27471, loss_mask_ce_3: 0.14668/0.90997, loss_mask_bce_3: 0.09345/0.33602, loss_mask_dice_3: 0.23676/1.16429, loss_spatial_bce_3: 0.04190/0.08925, loss_spatial_dice_3: 0.11223/0.21206, loss_spatial_ce_3: 0.01538/0.07264, loss_grounding_bce_3: 0.04660/0.08654, loss_grounding_dice_3: 0.13214/0.17830, loss_grounding_ce_3: 0.02032/0.27689, loss_mask_ce_4: 0.14012/0.91166, loss_mask_bce_4: 0.09498/0.33825, loss_mask_dice_4: 0.23563/1.18814, loss_spatial_bce_4: 0.04072/0.09314, loss_spatial_dice_4: 0.11483/0.22422, loss_spatial_ce_4: 0.02721/0.08874, loss_grounding_bce_4: 0.04364/0.08710, loss_grounding_dice_4: 0.12342/0.18133, loss_grounding_ce_4: 0.01664/0.27989, loss_mask_ce_5: 0.14796/0.92851, loss_mask_bce_5: 0.09146/0.34066, loss_mask_dice_5: 0.25993/1.19636, loss_spatial_bce_5: 0.03664/0.09547, loss_spatial_dice_5: 0.11647/0.22866, loss_spatial_ce_5: 0.02211/0.10217, loss_grounding_bce_5: 0.04462/0.08753, loss_grounding_dice_5: 0.12341/0.18264, loss_grounding_ce_5: 0.01637/0.29252, loss_mask_ce_6: 0.17076/0.96875, loss_mask_bce_6: 0.08758/0.34347, loss_mask_dice_6: 0.24980/1.19957, loss_spatial_bce_6: 0.04519/0.10112, loss_spatial_dice_6: 0.12676/0.23166, loss_spatial_ce_6: 0.02757/0.12678, loss_grounding_bce_6: 0.04310/0.08830, loss_grounding_dice_6: 0.12592/0.18308, loss_grounding_ce_6: 0.01645/0.30760, loss_mask_ce_7: 0.17246/1.01483, loss_mask_bce_7: 0.08206/0.35138, loss_mask_dice_7: 0.25343/1.25384, loss_spatial_bce_7: 0.06412/0.10890, loss_spatial_dice_7: 0.14772/0.25930, loss_spatial_ce_7: 0.12434/0.16131, loss_grounding_bce_7: 0.04958/0.09018, loss_grounding_dice_7: 0.14269/0.19045, loss_grounding_ce_7: 0.02539/0.33750, loss_mask_ce_8: 0.17902/1.12388, loss_mask_bce_8: 0.09911/0.36494, loss_mask_dice_8: 0.28200/1.32624, loss_spatial_bce_8: 0.07267/0.12877, loss_spatial_dice_8: 0.18873/0.29706, loss_spatial_ce_8: 0.05607/0.20956, loss_grounding_bce_8: 0.05331/0.09380, loss_grounding_dice_8: 0.15433/0.20107, loss_grounding_ce_8: 0.00820/0.40288, loss_mask_ce_9: 1.76943/3.67149, loss_mask_bce_9: 0.07692/0.39226, loss_mask_dice_9: 0.30921/1.90002, loss_spatial_bce_9: 0.19708/0.33264, loss_spatial_dice_9: 0.68452/0.82146, loss_spatial_ce_9: 0.89798/1.49048, loss_grounding_bce_9: 0.04419/0.10559, loss_grounding_dice_9: 0.17488/0.28064, loss_grounding_ce_9: 0.09841/0.66788] items per batch[64] items per second[0.23] total items[5702400] mini batches[ 89100] memory[7345] epoch remaining[0:19:17]
INFO:trainer.default_trainer:epochs[    48] optim steps[89200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.86254/0.89046, loss_mask_bce_0: 0.54542/0.33327, loss_mask_dice_0: 2.97984/1.15896, loss_spatial_bce_0: 0.04208/0.08609, loss_spatial_dice_0: 0.13973/0.20505, loss_spatial_ce_0: 0.00019/0.05824, loss_grounding_bce_0: 0.05845/0.08591, loss_grounding_dice_0: 0.06455/0.17792, loss_grounding_ce_0: 0.20518/0.27053, loss_mask_ce_1: 0.95086/0.89145, loss_mask_bce_1: 0.52779/0.33420, loss_mask_dice_1: 2.87939/1.16590, loss_spatial_bce_1: 0.04458/0.08661, loss_spatial_dice_1: 0.14830/0.20888, loss_spatial_ce_1: 0.12400/0.06400, loss_grounding_bce_1: 0.06207/0.08611, loss_grounding_dice_1: 0.07311/0.17874, loss_grounding_ce_1: 0.22487/0.27137, loss_mask_ce_2: 0.97654/0.89832, loss_mask_bce_2: 0.54225/0.33487, loss_mask_dice_2: 2.87955/1.16650, loss_spatial_bce_2: 0.04418/0.08788, loss_spatial_dice_2: 0.14471/0.21087, loss_spatial_ce_2: 0.00072/0.06738, loss_grounding_bce_2: 0.06500/0.08629, loss_grounding_dice_2: 0.07698/0.17866, loss_grounding_ce_2: 0.17531/0.27468, loss_mask_ce_3: 0.93745/0.90993, loss_mask_bce_3: 0.55030/0.33602, loss_mask_dice_3: 2.91648/1.16437, loss_spatial_bce_3: 0.04586/0.08924, loss_spatial_dice_3: 0.14501/0.21206, loss_spatial_ce_3: 0.00058/0.07262, loss_grounding_bce_3: 0.06296/0.08655, loss_grounding_dice_3: 0.07254/0.17831, loss_grounding_ce_3: 0.24598/0.27684, loss_mask_ce_4: 0.83993/0.91162, loss_mask_bce_4: 0.54655/0.33825, loss_mask_dice_4: 3.01556/1.18820, loss_spatial_bce_4: 0.04398/0.09313, loss_spatial_dice_4: 0.15802/0.22421, loss_spatial_ce_4: 0.01384/0.08871, loss_grounding_bce_4: 0.06012/0.08711, loss_grounding_dice_4: 0.07177/0.18134, loss_grounding_ce_4: 0.26678/0.27984, loss_mask_ce_5: 0.86998/0.92847, loss_mask_bce_5: 0.56127/0.34065, loss_mask_dice_5: 3.15454/1.19642, loss_spatial_bce_5: 0.04386/0.09546, loss_spatial_dice_5: 0.14846/0.22866, loss_spatial_ce_5: 0.00675/0.10214, loss_grounding_bce_5: 0.06045/0.08754, loss_grounding_dice_5: 0.06631/0.18264, loss_grounding_ce_5: 0.39274/0.29250, loss_mask_ce_6: 0.72289/0.96873, loss_mask_bce_6: 0.56003/0.34346, loss_mask_dice_6: 3.10335/1.19964, loss_spatial_bce_6: 0.04873/0.10111, loss_spatial_dice_6: 0.16052/0.23166, loss_spatial_ce_6: 0.00486/0.12674, loss_grounding_bce_6: 0.05915/0.08831, loss_grounding_dice_6: 0.07480/0.18309, loss_grounding_ce_6: 0.41144/0.30762, loss_mask_ce_7: 0.88334/1.01476, loss_mask_bce_7: 0.56276/0.35138, loss_mask_dice_7: 3.18519/1.25392, loss_spatial_bce_7: 0.05016/0.10889, loss_spatial_dice_7: 0.18366/0.25930, loss_spatial_ce_7: 0.06026/0.16128, loss_grounding_bce_7: 0.06095/0.09019, loss_grounding_dice_7: 0.06541/0.19045, loss_grounding_ce_7: 0.50782/0.33751, loss_mask_ce_8: 0.89452/1.12382, loss_mask_bce_8: 0.63398/0.36494, loss_mask_dice_8: 3.29018/1.32631, loss_spatial_bce_8: 0.06547/0.12875, loss_spatial_dice_8: 0.24699/0.29707, loss_spatial_ce_8: 0.04426/0.20947, loss_grounding_bce_8: 0.06928/0.09380, loss_grounding_dice_8: 0.06140/0.20108, loss_grounding_ce_8: 1.11350/0.40289, loss_mask_ce_9: 5.92688/3.67146, loss_mask_bce_9: 0.87108/0.39227, loss_mask_dice_9: 5.86065/1.90020, loss_spatial_bce_9: 0.26678/0.33261, loss_spatial_dice_9: 0.92041/0.82147, loss_spatial_ce_9: 1.41902/1.49049, loss_grounding_bce_9: 0.13061/0.10560, loss_grounding_dice_9: 0.19412/0.28065, loss_grounding_ce_9: 1.52824/0.66779] items per batch[64] items per second[0.23] total items[5708800] mini batches[ 89200] memory[7345] epoch remaining[0:14:44]
INFO:trainer.default_trainer:epochs[    48] optim steps[89300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.21886/0.89032, loss_mask_bce_0: 0.31614/0.33327, loss_mask_dice_0: 0.49549/1.15903, loss_spatial_bce_0: 0.07656/0.08607, loss_spatial_dice_0: 0.10945/0.20503, loss_spatial_ce_0: 0.03526/0.05824, loss_grounding_bce_0: 0.00977/0.08590, loss_grounding_dice_0: 0.06858/0.17791, loss_grounding_ce_0: 0.09307/0.27054, loss_mask_ce_1: 1.34433/0.89130, loss_mask_bce_1: 0.32238/0.33419, loss_mask_dice_1: 0.48059/1.16601, loss_spatial_bce_1: 0.07742/0.08659, loss_spatial_dice_1: 0.10803/0.20886, loss_spatial_ce_1: 0.03912/0.06398, loss_grounding_bce_1: 0.00887/0.08610, loss_grounding_dice_1: 0.05632/0.17874, loss_grounding_ce_1: 0.09424/0.27140, loss_mask_ce_2: 1.21468/0.89817, loss_mask_bce_2: 0.31950/0.33487, loss_mask_dice_2: 0.50534/1.16658, loss_spatial_bce_2: 0.07789/0.08787, loss_spatial_dice_2: 0.11390/0.21086, loss_spatial_ce_2: 0.03562/0.06737, loss_grounding_bce_2: 0.00762/0.08628, loss_grounding_dice_2: 0.05439/0.17865, loss_grounding_ce_2: 0.08505/0.27469, loss_mask_ce_3: 1.27255/0.90979, loss_mask_bce_3: 0.32065/0.33602, loss_mask_dice_3: 0.55105/1.16446, loss_spatial_bce_3: 0.08125/0.08922, loss_spatial_dice_3: 0.11652/0.21204, loss_spatial_ce_3: 0.03564/0.07261, loss_grounding_bce_3: 0.00920/0.08654, loss_grounding_dice_3: 0.06239/0.17830, loss_grounding_ce_3: 0.08052/0.27683, loss_mask_ce_4: 1.32054/0.91147, loss_mask_bce_4: 0.31762/0.33825, loss_mask_dice_4: 0.50569/1.18832, loss_spatial_bce_4: 0.08731/0.09312, loss_spatial_dice_4: 0.12762/0.22420, loss_spatial_ce_4: 0.03041/0.08870, loss_grounding_bce_4: 0.00807/0.08710, loss_grounding_dice_4: 0.05977/0.18133, loss_grounding_ce_4: 0.07788/0.27985, loss_mask_ce_5: 1.42011/0.92832, loss_mask_bce_5: 0.31631/0.34065, loss_mask_dice_5: 0.54403/1.19654, loss_spatial_bce_5: 0.08041/0.09545, loss_spatial_dice_5: 0.11087/0.22864, loss_spatial_ce_5: 0.05933/0.10211, loss_grounding_bce_5: 0.00800/0.08753, loss_grounding_dice_5: 0.05894/0.18264, loss_grounding_ce_5: 0.09559/0.29251, loss_mask_ce_6: 1.54210/0.96857, loss_mask_bce_6: 0.32200/0.34345, loss_mask_dice_6: 0.58007/1.19973, loss_spatial_bce_6: 0.07501/0.10110, loss_spatial_dice_6: 0.10808/0.23165, loss_spatial_ce_6: 0.07054/0.12671, loss_grounding_bce_6: 0.01000/0.08830, loss_grounding_dice_6: 0.06779/0.18309, loss_grounding_ce_6: 0.10453/0.30764, loss_mask_ce_7: 1.21973/1.01462, loss_mask_bce_7: 0.32780/0.35138, loss_mask_dice_7: 0.60919/1.25402, loss_spatial_bce_7: 0.07607/0.10888, loss_spatial_dice_7: 0.13233/0.25929, loss_spatial_ce_7: 0.05313/0.16124, loss_grounding_bce_7: 0.01036/0.09018, loss_grounding_dice_7: 0.07388/0.19044, loss_grounding_ce_7: 0.10069/0.33751, loss_mask_ce_8: 1.39442/1.12370, loss_mask_bce_8: 0.34030/0.36493, loss_mask_dice_8: 0.65435/1.32640, loss_spatial_bce_8: 0.08923/0.12874, loss_spatial_dice_8: 0.19325/0.29706, loss_spatial_ce_8: 0.07811/0.20939, loss_grounding_bce_8: 0.00967/0.09379, loss_grounding_dice_8: 0.06655/0.20107, loss_grounding_ce_8: 0.14195/0.40286, loss_mask_ce_9: 3.98092/3.67146, loss_mask_bce_9: 0.33094/0.39226, loss_mask_dice_9: 0.97818/1.90028, loss_spatial_bce_9: 0.47213/0.33260, loss_spatial_dice_9: 0.86704/0.82147, loss_spatial_ce_9: 1.66872/1.49048, loss_grounding_bce_9: 0.00753/0.10559, loss_grounding_dice_9: 0.07222/0.28065, loss_grounding_ce_9: 0.48619/0.66787] items per batch[64] items per second[0.24] total items[5715200] mini batches[ 89300] memory[7345] epoch remaining[0:10:09]
INFO:trainer.default_trainer:epochs[    48] optim steps[89400] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.80329/0.89024, loss_mask_bce_0: 0.28373/0.33322, loss_mask_dice_0: 0.56861/1.15893, loss_spatial_bce_0: 0.07323/0.08605, loss_spatial_dice_0: 0.13503/0.20502, loss_spatial_ce_0: 0.06874/0.05821, loss_grounding_bce_0: 0.04883/0.08590, loss_grounding_dice_0: 0.16145/0.17790, loss_grounding_ce_0: 0.30247/0.27051, loss_mask_ce_1: 0.72102/0.89121, loss_mask_bce_1: 0.30367/0.33414, loss_mask_dice_1: 0.73995/1.16589, loss_spatial_bce_1: 0.07525/0.08657, loss_spatial_dice_1: 0.12863/0.20884, loss_spatial_ce_1: 0.03613/0.06397, loss_grounding_bce_1: 0.04751/0.08610, loss_grounding_dice_1: 0.16600/0.17872, loss_grounding_ce_1: 0.35276/0.27137, loss_mask_ce_2: 0.43982/0.89809, loss_mask_bce_2: 0.36034/0.33482, loss_mask_dice_2: 0.68467/1.16647, loss_spatial_bce_2: 0.07475/0.08785, loss_spatial_dice_2: 0.13665/0.21084, loss_spatial_ce_2: 0.02558/0.06735, loss_grounding_bce_2: 0.04322/0.08628, loss_grounding_dice_2: 0.14242/0.17864, loss_grounding_ce_2: 0.35000/0.27466, loss_mask_ce_3: 0.82245/0.90970, loss_mask_bce_3: 0.29637/0.33598, loss_mask_dice_3: 0.59817/1.16435, loss_spatial_bce_3: 0.07920/0.08920, loss_spatial_dice_3: 0.14439/0.21202, loss_spatial_ce_3: 0.08035/0.07258, loss_grounding_bce_3: 0.05001/0.08654, loss_grounding_dice_3: 0.14918/0.17829, loss_grounding_ce_3: 0.32922/0.27683, loss_mask_ce_4: 0.52816/0.91141, loss_mask_bce_4: 0.35565/0.33820, loss_mask_dice_4: 0.69637/1.18819, loss_spatial_bce_4: 0.08286/0.09309, loss_spatial_dice_4: 0.14635/0.22419, loss_spatial_ce_4: 0.03685/0.08867, loss_grounding_bce_4: 0.04367/0.08710, loss_grounding_dice_4: 0.14974/0.18132, loss_grounding_ce_4: 0.33190/0.27982, loss_mask_ce_5: 0.84421/0.92825, loss_mask_bce_5: 0.30862/0.34060, loss_mask_dice_5: 0.59575/1.19641, loss_spatial_bce_5: 0.08619/0.09542, loss_spatial_dice_5: 0.15074/0.22863, loss_spatial_ce_5: 0.01396/0.10207, loss_grounding_bce_5: 0.04498/0.08753, loss_grounding_dice_5: 0.17544/0.18263, loss_grounding_ce_5: 0.37374/0.29250, loss_mask_ce_6: 0.79991/0.96849, loss_mask_bce_6: 0.31385/0.34340, loss_mask_dice_6: 0.56372/1.19961, loss_spatial_bce_6: 0.09311/0.10107, loss_spatial_dice_6: 0.15392/0.23164, loss_spatial_ce_6: 0.05365/0.12666, loss_grounding_bce_6: 0.05248/0.08830, loss_grounding_dice_6: 0.14632/0.18308, loss_grounding_ce_6: 0.33377/0.30765, loss_mask_ce_7: 0.76631/1.01457, loss_mask_bce_7: 0.31760/0.35132, loss_mask_dice_7: 0.58307/1.25389, loss_spatial_bce_7: 0.09725/0.10886, loss_spatial_dice_7: 0.17760/0.25927, loss_spatial_ce_7: 0.15685/0.16119, loss_grounding_bce_7: 0.06351/0.09018, loss_grounding_dice_7: 0.15212/0.19044, loss_grounding_ce_7: 0.50847/0.33751, loss_mask_ce_8: 0.66232/1.12365, loss_mask_bce_8: 0.42555/0.36488, loss_mask_dice_8: 0.76210/1.32625, loss_spatial_bce_8: 0.09844/0.12870, loss_spatial_dice_8: 0.22781/0.29705, loss_spatial_ce_8: 0.03666/0.20933, loss_grounding_bce_8: 0.05364/0.09379, loss_grounding_dice_8: 0.18382/0.20106, loss_grounding_ce_8: 0.33351/0.40287, loss_mask_ce_9: 3.51692/3.67130, loss_mask_bce_9: 0.36553/0.39221, loss_mask_dice_9: 1.05659/1.90006, loss_spatial_bce_9: 0.29986/0.33257, loss_spatial_dice_9: 0.81536/0.82148, loss_spatial_ce_9: 1.13237/1.49053, loss_grounding_bce_9: 0.04298/0.10559, loss_grounding_dice_9: 0.29420/0.28064, loss_grounding_ce_9: 0.70656/0.66789] items per batch[64] items per second[0.23] total items[5721600] mini batches[ 89400] memory[7345] epoch remaining[0:05:36]
INFO:trainer.default_trainer:epochs[    48] optim steps[89500] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.25325/0.89016, loss_mask_bce_0: 0.27279/0.33322, loss_mask_dice_0: 4.09820/1.15890, loss_spatial_bce_0: 0.00918/0.08605, loss_spatial_dice_0: 0.37666/0.20501, loss_spatial_ce_0: 0.15583/0.05820, loss_grounding_bce_0: 0.01733/0.08592, loss_grounding_dice_0: 0.17902/0.17791, loss_grounding_ce_0: 0.31966/0.27049, loss_mask_ce_1: 1.41902/0.89112, loss_mask_bce_1: 0.27788/0.33413, loss_mask_dice_1: 4.86162/1.16586, loss_spatial_bce_1: 0.00865/0.08656, loss_spatial_dice_1: 0.33250/0.20883, loss_spatial_ce_1: 0.06645/0.06396, loss_grounding_bce_1: 0.01595/0.08611, loss_grounding_dice_1: 0.18128/0.17873, loss_grounding_ce_1: 0.32099/0.27134, loss_mask_ce_2: 1.38207/0.89800, loss_mask_bce_2: 0.26419/0.33482, loss_mask_dice_2: 3.85842/1.16642, loss_spatial_bce_2: 0.00855/0.08784, loss_spatial_dice_2: 0.38138/0.21084, loss_spatial_ce_2: 0.24168/0.06733, loss_grounding_bce_2: 0.01490/0.08629, loss_grounding_dice_2: 0.17280/0.17865, loss_grounding_ce_2: 0.33049/0.27463, loss_mask_ce_3: 1.54797/0.90963, loss_mask_bce_3: 0.27902/0.33597, loss_mask_dice_3: 4.62576/1.16431, loss_spatial_bce_3: 0.00962/0.08920, loss_spatial_dice_3: 0.36282/0.21202, loss_spatial_ce_3: 0.11752/0.07256, loss_grounding_bce_3: 0.01391/0.08655, loss_grounding_dice_3: 0.14032/0.17830, loss_grounding_ce_3: 0.30814/0.27680, loss_mask_ce_4: 1.26883/0.91134, loss_mask_bce_4: 0.24589/0.33819, loss_mask_dice_4: 4.64687/1.18817, loss_spatial_bce_4: 0.00778/0.09309, loss_spatial_dice_4: 0.35636/0.22419, loss_spatial_ce_4: 0.16908/0.08864, loss_grounding_bce_4: 0.01454/0.08712, loss_grounding_dice_4: 0.15653/0.18133, loss_grounding_ce_4: 0.31326/0.27978, loss_mask_ce_5: 1.57388/0.92818, loss_mask_bce_5: 0.25380/0.34059, loss_mask_dice_5: 4.89472/1.19637, loss_spatial_bce_5: 0.00959/0.09542, loss_spatial_dice_5: 0.38997/0.22863, loss_spatial_ce_5: 0.14911/0.10203, loss_grounding_bce_5: 0.01438/0.08755, loss_grounding_dice_5: 0.17072/0.18265, loss_grounding_ce_5: 0.30740/0.29246, loss_mask_ce_6: 1.50350/0.96839, loss_mask_bce_6: 0.25527/0.34340, loss_mask_dice_6: 4.12762/1.19960, loss_spatial_bce_6: 0.01117/0.10107, loss_spatial_dice_6: 0.37924/0.23163, loss_spatial_ce_6: 0.18973/0.12661, loss_grounding_bce_6: 0.01388/0.08832, loss_grounding_dice_6: 0.17564/0.18310, loss_grounding_ce_6: 0.57708/0.30760, loss_mask_ce_7: 1.88210/1.01449, loss_mask_bce_7: 0.27023/0.35132, loss_mask_dice_7: 4.39292/1.25386, loss_spatial_bce_7: 0.01132/0.10886, loss_spatial_dice_7: 0.42958/0.25927, loss_spatial_ce_7: 0.18545/0.16114, loss_grounding_bce_7: 0.01519/0.09020, loss_grounding_dice_7: 0.14826/0.19045, loss_grounding_ce_7: 0.32506/0.33745, loss_mask_ce_8: 2.00614/1.12362, loss_mask_bce_8: 0.28564/0.36487, loss_mask_dice_8: 4.83980/1.32623, loss_spatial_bce_8: 0.01141/0.12869, loss_spatial_dice_8: 0.47680/0.29705, loss_spatial_ce_8: 0.32197/0.20923, loss_grounding_bce_8: 0.01702/0.09381, loss_grounding_dice_8: 0.20473/0.20107, loss_grounding_ce_8: 0.40746/0.40282, loss_mask_ce_9: 5.53217/3.67132, loss_mask_bce_9: 0.32138/0.39222, loss_mask_dice_9: 6.91408/1.90007, loss_spatial_bce_9: 0.05511/0.33257, loss_spatial_dice_9: 0.90801/0.82148, loss_spatial_ce_9: 1.87923/1.49049, loss_grounding_bce_9: 0.02306/0.10561, loss_grounding_dice_9: 0.40216/0.28067, loss_grounding_ce_9: 0.73720/0.66783] items per batch[64] items per second[0.24] total items[5728000] mini batches[ 89500] memory[7345] epoch remaining[0:01:02]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00089523.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0031 s/iter. Inference: 0.2213 s/iter. Eval: 0.0989 s/iter. Total: 0.3233 s/iter. ETA=0:00:47
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2215 s/iter. Eval: 0.0840 s/iter. Total: 0.3085 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0031 s/iter. Inference: 0.2241 s/iter. Eval: 0.0816 s/iter. Total: 0.3090 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2232 s/iter. Eval: 0.0791 s/iter. Total: 0.3055 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0032 s/iter. Inference: 0.2218 s/iter. Eval: 0.0781 s/iter. Total: 0.3032 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0032 s/iter. Inference: 0.2241 s/iter. Eval: 0.0775 s/iter. Total: 0.3050 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0032 s/iter. Inference: 0.2261 s/iter. Eval: 0.0775 s/iter. Total: 0.3069 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2253 s/iter. Eval: 0.0776 s/iter. Total: 0.3062 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2260 s/iter. Eval: 0.0775 s/iter. Total: 0.3068 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval0buxvbg7 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.611 | 81.976 | 60.847 |      133      |
| Things | 55.579 | 82.660 | 66.544 |      80       |
| Stuff  | 43.113 | 80.944 | 52.247 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
Loading and preparing results...
DONE (t=0.41s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.45 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.04 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
DONE (t=2.27s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.96 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.10 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.396
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.621
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.416
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.427
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.614
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.321
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.497
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.515
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.720
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.631 | 62.142 | 41.648 | 19.624 | 42.676 | 61.361 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.807 | bicycle      | 19.561 | car            | 37.622 |
| motorcycle    | 35.535 | airplane     | 57.017 | bus            | 65.699 |
| train         | 68.617 | truck        | 36.237 | boat           | 23.892 |
| traffic light | 26.031 | fire hydrant | 66.297 | stop sign      | 64.787 |
| parking meter | 44.440 | bench        | 20.265 | bird           | 30.164 |
| cat           | 74.098 | dog          | 66.114 | horse          | 45.348 |
| sheep         | 48.385 | cow          | 51.880 | elephant       | 61.239 |
| bear          | 76.869 | zebra        | 60.960 | giraffe        | 57.390 |
| backpack      | 16.936 | umbrella     | 48.921 | handbag        | 15.887 |
| tie           | 34.876 | suitcase     | 43.268 | frisbee        | 67.021 |
| skis          | 5.189  | snowboard    | 21.967 | sports ball    | 48.351 |
| kite          | 35.593 | baseball bat | 29.740 | baseball glove | 44.207 |
| skateboard    | 36.438 | surfboard    | 36.821 | tennis racket  | 56.700 |
| bottle        | 35.364 | wine glass   | 27.453 | cup            | 41.452 |
| fork          | 15.738 | knife        | 14.154 | spoon          | 14.396 |
| bowl          | 33.089 | banana       | 21.589 | apple          | 20.116 |
| sandwich      | 42.939 | orange       | 29.888 | broccoli       | 22.437 |
| carrot        | 21.051 | hot dog      | 21.913 | pizza          | 51.141 |
| donut         | 47.289 | cake         | 45.577 | chair          | 21.719 |
| couch         | 41.934 | potted plant | 18.173 | bed            | 40.958 |
| dining table  | 13.156 | toilet       | 68.449 | tv             | 63.081 |
| laptop        | 63.614 | mouse        | 58.924 | remote         | 32.784 |
| keyboard      | 48.523 | cell phone   | 38.341 | microwave      | 56.005 |
| oven          | 34.090 | toaster      | 36.918 | sink           | 37.526 |
| refrigerator  | 60.419 | book         | 9.927  | clock          | 52.264 |
| vase          | 34.520 | scissors     | 24.831 | teddy bear     | 50.962 |
| hair drier    | 12.718 | toothbrush   | 19.909 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 61.045693811406146, 'fwIoU': 69.30011407664445, 'IoU-person': 87.44989829613836, 'IoU-bicycle': 75.59670138534868, 'IoU-car': 70.75222365709936, 'IoU-motorcycle': 81.90130487088868, 'IoU-airplane': 84.23708068836024, 'IoU-bus': 85.6758696994543, 'IoU-train': 84.89781682749324, 'IoU-truck': 62.15703636163564, 'IoU-boat': 68.09959554418339, 'IoU-traffic light': 75.42893475886372, 'IoU-fire hydrant': 90.38516220692121, 'IoU-stop sign': 92.54811428542428, 'IoU-parking meter': 83.38242328433402, 'IoU-bench': 54.80244140897451, 'IoU-bird': 75.76645981671362, 'IoU-cat': 83.15470343458588, 'IoU-dog': 79.66836374049579, 'IoU-horse': 86.37953808094832, 'IoU-sheep': 85.2768529096958, 'IoU-cow': 81.35790756659273, 'IoU-elephant': 90.5852448078245, 'IoU-bear': 77.80525107335463, 'IoU-zebra': 90.80005796288405, 'IoU-giraffe': 87.16033955239054, 'IoU-backpack': 40.49943640260709, 'IoU-umbrella': 77.3274650584504, 'IoU-handbag': 37.067919067484404, 'IoU-tie': 70.31919489937437, 'IoU-suitcase': 81.13731857936709, 'IoU-frisbee': 83.46945158263662, 'IoU-skis': 51.50177850425415, 'IoU-snowboard': 69.50068637871752, 'IoU-sports ball': 67.87085071330927, 'IoU-kite': 66.45128155604037, 'IoU-baseball bat': 60.290556900726386, 'IoU-baseball glove': 51.99762121921759, 'IoU-skateboard': 82.29545149049248, 'IoU-surfboard': 76.20915113044279, 'IoU-tennis racket': 82.72015195486702, 'IoU-bottle': 68.57666442957472, 'IoU-wine glass': 75.00755366672033, 'IoU-cup': 65.64204118102488, 'IoU-fork': 55.48147690342651, 'IoU-knife': 50.01100894201315, 'IoU-spoon': 49.327491488817024, 'IoU-bowl': 54.04485087657462, 'IoU-banana': 80.70322258685579, 'IoU-apple': 58.58875033744621, 'IoU-sandwich': 66.15299337682129, 'IoU-orange': 75.99035148619686, 'IoU-broccoli': 66.95770808660075, 'IoU-carrot': 64.38039144886532, 'IoU-hot dog': 64.93275973460146, 'IoU-pizza': 82.2282290407703, 'IoU-donut': 66.89989173624066, 'IoU-cake': 68.6443567386348, 'IoU-chair': 55.50567282374911, 'IoU-couch': 69.93548124874283, 'IoU-potted plant': 34.12187356742873, 'IoU-bed': 69.82098382451484, 'IoU-dining table': 49.95509330809472, 'IoU-toilet': 81.71407352138293, 'IoU-tv': 74.62269525030612, 'IoU-laptop': 71.36179521352275, 'IoU-mouse': 67.12988558242392, 'IoU-remote': 50.02125365717204, 'IoU-keyboard': 54.70437000760905, 'IoU-cell phone': 68.35877160287048, 'IoU-microwave': 65.59793684796382, 'IoU-oven': 67.82736647049248, 'IoU-toaster': 73.59588300923518, 'IoU-sink': 68.1396871599446, 'IoU-refrigerator': 77.9034266910401, 'IoU-book': 52.809219324728886, 'IoU-clock': 73.77587719258543, 'IoU-vase': 64.00469596369808, 'IoU-scissors': 52.76313380146742, 'IoU-teddy bear': 78.62440859581774, 'IoU-hair drier': 37.268076126771774, 'IoU-toothbrush': 57.678144781891184, 'IoU-banner': 35.55247068108191, 'IoU-blanket': 11.052841149928529, 'IoU-bridge': 38.24908645770302, 'IoU-cardboard': 45.35421083489841, 'IoU-counter': 28.935582759810792, 'IoU-curtain': 65.2347007274358, 'IoU-door-stuff': 41.93151506778211, 'IoU-floor-wood': 63.72516537606907, 'IoU-flower': 43.79614094853959, 'IoU-fruit': 40.5984492153691, 'IoU-gravel': 31.93653483597619, 'IoU-house': 24.932726099706635, 'IoU-light': 38.9077397131755, 'IoU-mirror-stuff': 55.84661411759134, 'IoU-net': 45.25834763502358, 'IoU-pillow': 10.73835139809936, 'IoU-platform': 31.69958922707414, 'IoU-playingfield': 70.68215768332578, 'IoU-railroad': 61.73124607207874, 'IoU-river': 49.2437305943318, 'IoU-road': 66.52825095351444, 'IoU-roof': 16.647474980452913, 'IoU-sand': 64.56694982700893, 'IoU-sea': 85.96576783616945, 'IoU-shelf': 36.338707868471964, 'IoU-snow': 88.97373500806222, 'IoU-stairs': 27.663958385149947, 'IoU-tent': 10.312891247344295, 'IoU-towel': 33.79064707842227, 'IoU-wall-brick': 46.07545538808234, 'IoU-wall-stone': 30.106961305599146, 'IoU-wall-tile': 68.23302258712582, 'IoU-wall-wood': 39.34099710952977, 'IoU-water-other': 23.148940668041554, 'IoU-window-blind': 47.505266418239216, 'IoU-window-other': 47.56140602762727, 'IoU-tree-merged': 81.16478626938748, 'IoU-fence-merged': 50.95845704947871, 'IoU-ceiling-merged': 66.30180341789857, 'IoU-sky-other-merged': 93.79715222053471, 'IoU-cabinet-merged': 59.343518160142196, 'IoU-table-merged': 36.89793990726149, 'IoU-floor-other-merged': 49.097889121692425, 'IoU-pavement-merged': 53.69125920879557, 'IoU-mountain-merged': 56.92281546460167, 'IoU-grass-merged': 71.33475093531413, 'IoU-dirt-merged': 45.17599654141119, 'IoU-paper-merged': 31.130025701479003, 'IoU-food-other-merged': 39.77409418486885, 'IoU-building-other-merged': 58.67861386444251, 'IoU-rock-merged': 64.06369791129461, 'IoU-wall-other-merged': 65.4367090832723, 'IoU-rug-merged': 62.402949298067114, 'mACC': 73.37263269603416, 'pACC': 80.531784511539, 'ACC-person': 92.5045162210464, 'ACC-bicycle': 86.60944354552612, 'ACC-car': 85.5824271981763, 'ACC-motorcycle': 86.96369951334914, 'ACC-airplane': 90.68029493547789, 'ACC-bus': 91.74680124532519, 'ACC-train': 93.94670216361442, 'ACC-truck': 75.6486895621301, 'ACC-boat': 79.1655371668282, 'ACC-traffic light': 90.39699308107669, 'ACC-fire hydrant': 95.5219546772451, 'ACC-stop sign': 95.50580922995542, 'ACC-parking meter': 87.42847811803006, 'ACC-bench': 74.35229600579622, 'ACC-bird': 80.82597195829695, 'ACC-cat': 90.59585819927175, 'ACC-dog': 83.64062486221164, 'ACC-horse': 92.44961343702768, 'ACC-sheep': 88.69039358332668, 'ACC-cow': 86.5265292111235, 'ACC-elephant': 93.18164758360736, 'ACC-bear': 80.01190676530186, 'ACC-zebra': 93.30547013578017, 'ACC-giraffe': 91.39144419358463, 'ACC-backpack': 56.7591432152216, 'ACC-umbrella': 85.33195464276623, 'ACC-handbag': 56.45313489493541, 'ACC-tie': 80.57281331668565, 'ACC-suitcase': 90.0665906649823, 'ACC-frisbee': 94.13745454545455, 'ACC-skis': 70.10156832948888, 'ACC-snowboard': 79.10901031079327, 'ACC-sports ball': 80.06067747904197, 'ACC-kite': 76.20557816656977, 'ACC-baseball bat': 79.75447482186875, 'ACC-baseball glove': 60.7967114127811, 'ACC-skateboard': 89.31060949499755, 'ACC-surfboard': 83.74842751998789, 'ACC-tennis racket': 89.36038428140482, 'ACC-bottle': 83.49540746078249, 'ACC-wine glass': 85.7489438088158, 'ACC-cup': 83.70161520879134, 'ACC-fork': 66.61369289538247, 'ACC-knife': 66.57525463471825, 'ACC-spoon': 68.90261696609494, 'ACC-bowl': 65.84031828842737, 'ACC-banana': 87.8557502564121, 'ACC-apple': 71.2523996869016, 'ACC-sandwich': 79.30297019868577, 'ACC-orange': 85.27480951449142, 'ACC-broccoli': 76.92677419494865, 'ACC-carrot': 75.77600924501472, 'ACC-hot dog': 73.46908649593209, 'ACC-pizza': 90.03008156983198, 'ACC-donut': 81.15047257849162, 'ACC-cake': 77.4856902116998, 'ACC-chair': 71.47932641767211, 'ACC-couch': 83.30040376997735, 'ACC-potted plant': 52.05120623147448, 'ACC-bed': 80.27026204769577, 'ACC-dining table': 72.63101332994489, 'ACC-toilet': 91.29718933082412, 'ACC-tv': 87.90070072657252, 'ACC-laptop': 83.82705085491898, 'ACC-mouse': 80.59586809952057, 'ACC-remote': 72.80957618068507, 'ACC-keyboard': 62.31929122282175, 'ACC-cell phone': 74.00671993854188, 'ACC-microwave': 75.31965136087841, 'ACC-oven': 86.32569266103458, 'ACC-toaster': 86.21371580287126, 'ACC-sink': 82.77187001219825, 'ACC-refrigerator': 89.01720323283976, 'ACC-book': 70.0336569443277, 'ACC-clock': 79.8234664109685, 'ACC-vase': 74.16456427962555, 'ACC-scissors': 56.88476158995124, 'ACC-teddy bear': 85.07910151478546, 'ACC-hair drier': 53.28466114479264, 'ACC-toothbrush': 81.36466296038915, 'ACC-banner': 74.43419692946934, 'ACC-blanket': 15.30522534902063, 'ACC-bridge': 56.62629643550075, 'ACC-cardboard': 57.99712083757452, 'ACC-counter': 53.483198630098435, 'ACC-curtain': 77.58986835384441, 'ACC-door-stuff': 63.314355240510125, 'ACC-floor-wood': 78.62802863085021, 'ACC-flower': 61.89040662571203, 'ACC-fruit': 57.92654407245804, 'ACC-gravel': 42.62163638153032, 'ACC-house': 31.16221794781223, 'ACC-light': 57.92016374459289, 'ACC-mirror-stuff': 72.69019485765928, 'ACC-net': 61.53534063624452, 'ACC-pillow': 25.469583651229033, 'ACC-platform': 52.04341846054918, 'ACC-playingfield': 92.35614776655665, 'ACC-railroad': 79.52077832350835, 'ACC-river': 70.3782407301796, 'ACC-road': 85.3293047120286, 'ACC-roof': 23.37990522640133, 'ACC-sand': 70.60018837225506, 'ACC-sea': 91.75541695822517, 'ACC-shelf': 58.422610774011275, 'ACC-snow': 94.39916268110186, 'ACC-stairs': 44.086870279824815, 'ACC-tent': 12.134740768931584, 'ACC-towel': 42.84312610460908, 'ACC-wall-brick': 63.486757742507926, 'ACC-wall-stone': 38.686366268081095, 'ACC-wall-tile': 82.58284124428398, 'ACC-wall-wood': 52.3619741947518, 'ACC-water-other': 37.89872851684289, 'ACC-window-blind': 56.64882170979402, 'ACC-window-other': 69.62270439032189, 'ACC-tree-merged': 89.33993793630468, 'ACC-fence-merged': 70.83946388111399, 'ACC-ceiling-merged': 79.43378604010559, 'ACC-sky-other-merged': 96.75102832898264, 'ACC-cabinet-merged': 75.17737436538448, 'ACC-table-merged': 51.92632597817624, 'ACC-floor-other-merged': 62.316827458622456, 'ACC-pavement-merged': 66.46086317077747, 'ACC-mountain-merged': 68.49763187216618, 'ACC-grass-merged': 83.90254502777064, 'ACC-dirt-merged': 64.33149767605904, 'ACC-paper-merged': 43.317102756024724, 'ACC-food-other-merged': 55.00551626438379, 'ACC-building-other-merged': 74.24318082900463, 'ACC-rock-merged': 82.01405682140626, 'ACC-wall-other-merged': 80.47770808962575, 'ACC-rug-merged': 76.77767355693771})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1536 s/iter. Inference: 0.5313 s/iter. Eval: 0.0000 s/iter. Total: 0.6849 s/iter. ETA=0:00:26
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1557 s/iter. Inference: 0.5056 s/iter. Eval: 0.0000 s/iter. Total: 0.6614 s/iter. ETA=0:00:20
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1722 s/iter. Inference: 0.5645 s/iter. Eval: 0.0000 s/iter. Total: 0.7368 s/iter. ETA=0:00:17
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1726 s/iter. Inference: 0.6440 s/iter. Eval: 0.0000 s/iter. Total: 0.8168 s/iter. ETA=0:00:15
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1708 s/iter. Inference: 0.6027 s/iter. Eval: 0.0000 s/iter. Total: 0.7737 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1698 s/iter. Inference: 0.6480 s/iter. Eval: 0.0000 s/iter. Total: 0.8180 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4495171202809483, 'noc@0.8': 2.667544629792215, 'noc@0.85': 3.251975417032485, 'noc@0.9': 4.221832016388645, 'miou@iter1': 0.8364697256247342}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.1027 s/iter. Eval: 0.0008 s/iter. Total: 0.1050 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.58958435058594, 'precision@0.6': 68.9467544555664, 'precision@0.7': 63.77769088745117, 'precision@0.8': 54.255733489990234, 'precision@0.9': 27.944034576416016, 'cIoU': 57.81306457519531, 'mIoU': 63.563289642333984}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.61145659159936, 'SQ': 81.97635029249963, 'RQ': 60.84699285279396, 'PQ_th': 55.579465513925456, 'SQ_th': 82.66004835974924, 'RQ_th': 66.54448640896133, 'PQ_st': 43.11257519940903, 'SQ_st': 80.94435320985876, 'RQ_st': 52.2470025793338}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.631279824256396, 'AP50': 62.14241879672867, 'AP75': 41.64769377087317, 'APs': 19.62393181902185, 'APm': 42.67555090512069, 'APl': 61.36083288531496, 'AP-person': 44.806527151233155, 'AP-bicycle': 19.56075935599283, 'AP-car': 37.62219969229913, 'AP-motorcycle': 35.53545571762234, 'AP-airplane': 57.01720804639421, 'AP-bus': 65.6989713660056, 'AP-train': 68.61653650518599, 'AP-truck': 36.23714480175218, 'AP-boat': 23.892244848138606, 'AP-traffic light': 26.031221634535108, 'AP-fire hydrant': 66.29741710016873, 'AP-stop sign': 64.78656283577247, 'AP-parking meter': 44.43961799626584, 'AP-bench': 20.26511814187518, 'AP-bird': 30.163893817735183, 'AP-cat': 74.0978687850469, 'AP-dog': 66.11394070812621, 'AP-horse': 45.34837773014108, 'AP-sheep': 48.384884242791564, 'AP-cow': 51.88002477022621, 'AP-elephant': 61.238588012197646, 'AP-bear': 76.86859733820569, 'AP-zebra': 60.959856276905334, 'AP-giraffe': 57.38978154239064, 'AP-backpack': 16.936225996783637, 'AP-umbrella': 48.92084750549613, 'AP-handbag': 15.88747888796963, 'AP-tie': 34.875905547236016, 'AP-suitcase': 43.268041118006614, 'AP-frisbee': 67.02055947270993, 'AP-skis': 5.18948499029727, 'AP-snowboard': 21.96745915916422, 'AP-sports ball': 48.35120141320722, 'AP-kite': 35.5930670513982, 'AP-baseball bat': 29.73984495701285, 'AP-baseball glove': 44.206632539937765, 'AP-skateboard': 36.43755401643498, 'AP-surfboard': 36.82080091816079, 'AP-tennis racket': 56.699853510460564, 'AP-bottle': 35.364487552922604, 'AP-wine glass': 27.452669141316278, 'AP-cup': 41.45185792669207, 'AP-fork': 15.738276143469228, 'AP-knife': 14.154366842841831, 'AP-spoon': 14.396236252553408, 'AP-bowl': 33.088830051325644, 'AP-banana': 21.589348717638252, 'AP-apple': 20.11555836245657, 'AP-sandwich': 42.938922849349304, 'AP-orange': 29.888280834757747, 'AP-broccoli': 22.43702298358865, 'AP-carrot': 21.051206113957097, 'AP-hot dog': 21.912632451970296, 'AP-pizza': 51.1408989540763, 'AP-donut': 47.28928119482134, 'AP-cake': 45.57695369491885, 'AP-chair': 21.718501492904103, 'AP-couch': 41.93416053359633, 'AP-potted plant': 18.173259404617177, 'AP-bed': 40.95792531962748, 'AP-dining table': 13.156407555411414, 'AP-toilet': 68.44905010396562, 'AP-tv': 63.0805563968142, 'AP-laptop': 63.61369767324966, 'AP-mouse': 58.9242791253675, 'AP-remote': 32.78419620755269, 'AP-keyboard': 48.52334558127793, 'AP-cell phone': 38.34108773426012, 'AP-microwave': 56.00523289268546, 'AP-oven': 34.090147708101846, 'AP-toaster': 36.918497732126156, 'AP-sink': 37.525954326529266, 'AP-refrigerator': 60.41901385976788, 'AP-book': 9.927341654467481, 'AP-clock': 52.26391915716783, 'AP-vase': 34.52046142814248, 'AP-scissors': 24.83098004903419, 'AP-teddy bear': 50.96249559332651, 'AP-hair drier': 12.717992488904063, 'AP-toothbrush': 19.909296351675163}), ('sem_seg', {'mIoU': 61.045693811406146, 'fwIoU': 69.30011407664445, 'IoU-person': 87.44989829613836, 'IoU-bicycle': 75.59670138534868, 'IoU-car': 70.75222365709936, 'IoU-motorcycle': 81.90130487088868, 'IoU-airplane': 84.23708068836024, 'IoU-bus': 85.6758696994543, 'IoU-train': 84.89781682749324, 'IoU-truck': 62.15703636163564, 'IoU-boat': 68.09959554418339, 'IoU-traffic light': 75.42893475886372, 'IoU-fire hydrant': 90.38516220692121, 'IoU-stop sign': 92.54811428542428, 'IoU-parking meter': 83.38242328433402, 'IoU-bench': 54.80244140897451, 'IoU-bird': 75.76645981671362, 'IoU-cat': 83.15470343458588, 'IoU-dog': 79.66836374049579, 'IoU-horse': 86.37953808094832, 'IoU-sheep': 85.2768529096958, 'IoU-cow': 81.35790756659273, 'IoU-elephant': 90.5852448078245, 'IoU-bear': 77.80525107335463, 'IoU-zebra': 90.80005796288405, 'IoU-giraffe': 87.16033955239054, 'IoU-backpack': 40.49943640260709, 'IoU-umbrella': 77.3274650584504, 'IoU-handbag': 37.067919067484404, 'IoU-tie': 70.31919489937437, 'IoU-suitcase': 81.13731857936709, 'IoU-frisbee': 83.46945158263662, 'IoU-skis': 51.50177850425415, 'IoU-snowboard': 69.50068637871752, 'IoU-sports ball': 67.87085071330927, 'IoU-kite': 66.45128155604037, 'IoU-baseball bat': 60.290556900726386, 'IoU-baseball glove': 51.99762121921759, 'IoU-skateboard': 82.29545149049248, 'IoU-surfboard': 76.20915113044279, 'IoU-tennis racket': 82.72015195486702, 'IoU-bottle': 68.57666442957472, 'IoU-wine glass': 75.00755366672033, 'IoU-cup': 65.64204118102488, 'IoU-fork': 55.48147690342651, 'IoU-knife': 50.01100894201315, 'IoU-spoon': 49.327491488817024, 'IoU-bowl': 54.04485087657462, 'IoU-banana': 80.70322258685579, 'IoU-apple': 58.58875033744621, 'IoU-sandwich': 66.15299337682129, 'IoU-orange': 75.99035148619686, 'IoU-broccoli': 66.95770808660075, 'IoU-carrot': 64.38039144886532, 'IoU-hot dog': 64.93275973460146, 'IoU-pizza': 82.2282290407703, 'IoU-donut': 66.89989173624066, 'IoU-cake': 68.6443567386348, 'IoU-chair': 55.50567282374911, 'IoU-couch': 69.93548124874283, 'IoU-potted plant': 34.12187356742873, 'IoU-bed': 69.82098382451484, 'IoU-dining table': 49.95509330809472, 'IoU-toilet': 81.71407352138293, 'IoU-tv': 74.62269525030612, 'IoU-laptop': 71.36179521352275, 'IoU-mouse': 67.12988558242392, 'IoU-remote': 50.02125365717204, 'IoU-keyboard': 54.70437000760905, 'IoU-cell phone': 68.35877160287048, 'IoU-microwave': 65.59793684796382, 'IoU-oven': 67.82736647049248, 'IoU-toaster': 73.59588300923518, 'IoU-sink': 68.1396871599446, 'IoU-refrigerator': 77.9034266910401, 'IoU-book': 52.809219324728886, 'IoU-clock': 73.77587719258543, 'IoU-vase': 64.00469596369808, 'IoU-scissors': 52.76313380146742, 'IoU-teddy bear': 78.62440859581774, 'IoU-hair drier': 37.268076126771774, 'IoU-toothbrush': 57.678144781891184, 'IoU-banner': 35.55247068108191, 'IoU-blanket': 11.052841149928529, 'IoU-bridge': 38.24908645770302, 'IoU-cardboard': 45.35421083489841, 'IoU-counter': 28.935582759810792, 'IoU-curtain': 65.2347007274358, 'IoU-door-stuff': 41.93151506778211, 'IoU-floor-wood': 63.72516537606907, 'IoU-flower': 43.79614094853959, 'IoU-fruit': 40.5984492153691, 'IoU-gravel': 31.93653483597619, 'IoU-house': 24.932726099706635, 'IoU-light': 38.9077397131755, 'IoU-mirror-stuff': 55.84661411759134, 'IoU-net': 45.25834763502358, 'IoU-pillow': 10.73835139809936, 'IoU-platform': 31.69958922707414, 'IoU-playingfield': 70.68215768332578, 'IoU-railroad': 61.73124607207874, 'IoU-river': 49.2437305943318, 'IoU-road': 66.52825095351444, 'IoU-roof': 16.647474980452913, 'IoU-sand': 64.56694982700893, 'IoU-sea': 85.96576783616945, 'IoU-shelf': 36.338707868471964, 'IoU-snow': 88.97373500806222, 'IoU-stairs': 27.663958385149947, 'IoU-tent': 10.312891247344295, 'IoU-towel': 33.79064707842227, 'IoU-wall-brick': 46.07545538808234, 'IoU-wall-stone': 30.106961305599146, 'IoU-wall-tile': 68.23302258712582, 'IoU-wall-wood': 39.34099710952977, 'IoU-water-other': 23.148940668041554, 'IoU-window-blind': 47.505266418239216, 'IoU-window-other': 47.56140602762727, 'IoU-tree-merged': 81.16478626938748, 'IoU-fence-merged': 50.95845704947871, 'IoU-ceiling-merged': 66.30180341789857, 'IoU-sky-other-merged': 93.79715222053471, 'IoU-cabinet-merged': 59.343518160142196, 'IoU-table-merged': 36.89793990726149, 'IoU-floor-other-merged': 49.097889121692425, 'IoU-pavement-merged': 53.69125920879557, 'IoU-mountain-merged': 56.92281546460167, 'IoU-grass-merged': 71.33475093531413, 'IoU-dirt-merged': 45.17599654141119, 'IoU-paper-merged': 31.130025701479003, 'IoU-food-other-merged': 39.77409418486885, 'IoU-building-other-merged': 58.67861386444251, 'IoU-rock-merged': 64.06369791129461, 'IoU-wall-other-merged': 65.4367090832723, 'IoU-rug-merged': 62.402949298067114, 'mACC': 73.37263269603416, 'pACC': 80.531784511539, 'ACC-person': 92.5045162210464, 'ACC-bicycle': 86.60944354552612, 'ACC-car': 85.5824271981763, 'ACC-motorcycle': 86.96369951334914, 'ACC-airplane': 90.68029493547789, 'ACC-bus': 91.74680124532519, 'ACC-train': 93.94670216361442, 'ACC-truck': 75.6486895621301, 'ACC-boat': 79.1655371668282, 'ACC-traffic light': 90.39699308107669, 'ACC-fire hydrant': 95.5219546772451, 'ACC-stop sign': 95.50580922995542, 'ACC-parking meter': 87.42847811803006, 'ACC-bench': 74.35229600579622, 'ACC-bird': 80.82597195829695, 'ACC-cat': 90.59585819927175, 'ACC-dog': 83.64062486221164, 'ACC-horse': 92.44961343702768, 'ACC-sheep': 88.69039358332668, 'ACC-cow': 86.5265292111235, 'ACC-elephant': 93.18164758360736, 'ACC-bear': 80.01190676530186, 'ACC-zebra': 93.30547013578017, 'ACC-giraffe': 91.39144419358463, 'ACC-backpack': 56.7591432152216, 'ACC-umbrella': 85.33195464276623, 'ACC-handbag': 56.45313489493541, 'ACC-tie': 80.57281331668565, 'ACC-suitcase': 90.0665906649823, 'ACC-frisbee': 94.13745454545455, 'ACC-skis': 70.10156832948888, 'ACC-snowboard': 79.10901031079327, 'ACC-sports ball': 80.06067747904197, 'ACC-kite': 76.20557816656977, 'ACC-baseball bat': 79.75447482186875, 'ACC-baseball glove': 60.7967114127811, 'ACC-skateboard': 89.31060949499755, 'ACC-surfboard': 83.74842751998789, 'ACC-tennis racket': 89.36038428140482, 'ACC-bottle': 83.49540746078249, 'ACC-wine glass': 85.7489438088158, 'ACC-cup': 83.70161520879134, 'ACC-fork': 66.61369289538247, 'ACC-knife': 66.57525463471825, 'ACC-spoon': 68.90261696609494, 'ACC-bowl': 65.84031828842737, 'ACC-banana': 87.8557502564121, 'ACC-apple': 71.2523996869016, 'ACC-sandwich': 79.30297019868577, 'ACC-orange': 85.27480951449142, 'ACC-broccoli': 76.92677419494865, 'ACC-carrot': 75.77600924501472, 'ACC-hot dog': 73.46908649593209, 'ACC-pizza': 90.03008156983198, 'ACC-donut': 81.15047257849162, 'ACC-cake': 77.4856902116998, 'ACC-chair': 71.47932641767211, 'ACC-couch': 83.30040376997735, 'ACC-potted plant': 52.05120623147448, 'ACC-bed': 80.27026204769577, 'ACC-dining table': 72.63101332994489, 'ACC-toilet': 91.29718933082412, 'ACC-tv': 87.90070072657252, 'ACC-laptop': 83.82705085491898, 'ACC-mouse': 80.59586809952057, 'ACC-remote': 72.80957618068507, 'ACC-keyboard': 62.31929122282175, 'ACC-cell phone': 74.00671993854188, 'ACC-microwave': 75.31965136087841, 'ACC-oven': 86.32569266103458, 'ACC-toaster': 86.21371580287126, 'ACC-sink': 82.77187001219825, 'ACC-refrigerator': 89.01720323283976, 'ACC-book': 70.0336569443277, 'ACC-clock': 79.8234664109685, 'ACC-vase': 74.16456427962555, 'ACC-scissors': 56.88476158995124, 'ACC-teddy bear': 85.07910151478546, 'ACC-hair drier': 53.28466114479264, 'ACC-toothbrush': 81.36466296038915, 'ACC-banner': 74.43419692946934, 'ACC-blanket': 15.30522534902063, 'ACC-bridge': 56.62629643550075, 'ACC-cardboard': 57.99712083757452, 'ACC-counter': 53.483198630098435, 'ACC-curtain': 77.58986835384441, 'ACC-door-stuff': 63.314355240510125, 'ACC-floor-wood': 78.62802863085021, 'ACC-flower': 61.89040662571203, 'ACC-fruit': 57.92654407245804, 'ACC-gravel': 42.62163638153032, 'ACC-house': 31.16221794781223, 'ACC-light': 57.92016374459289, 'ACC-mirror-stuff': 72.69019485765928, 'ACC-net': 61.53534063624452, 'ACC-pillow': 25.469583651229033, 'ACC-platform': 52.04341846054918, 'ACC-playingfield': 92.35614776655665, 'ACC-railroad': 79.52077832350835, 'ACC-river': 70.3782407301796, 'ACC-road': 85.3293047120286, 'ACC-roof': 23.37990522640133, 'ACC-sand': 70.60018837225506, 'ACC-sea': 91.75541695822517, 'ACC-shelf': 58.422610774011275, 'ACC-snow': 94.39916268110186, 'ACC-stairs': 44.086870279824815, 'ACC-tent': 12.134740768931584, 'ACC-towel': 42.84312610460908, 'ACC-wall-brick': 63.486757742507926, 'ACC-wall-stone': 38.686366268081095, 'ACC-wall-tile': 82.58284124428398, 'ACC-wall-wood': 52.3619741947518, 'ACC-water-other': 37.89872851684289, 'ACC-window-blind': 56.64882170979402, 'ACC-window-other': 69.62270439032189, 'ACC-tree-merged': 89.33993793630468, 'ACC-fence-merged': 70.83946388111399, 'ACC-ceiling-merged': 79.43378604010559, 'ACC-sky-other-merged': 96.75102832898264, 'ACC-cabinet-merged': 75.17737436538448, 'ACC-table-merged': 51.92632597817624, 'ACC-floor-other-merged': 62.316827458622456, 'ACC-pavement-merged': 66.46086317077747, 'ACC-mountain-merged': 68.49763187216618, 'ACC-grass-merged': 83.90254502777064, 'ACC-dirt-merged': 64.33149767605904, 'ACC-paper-merged': 43.317102756024724, 'ACC-food-other-merged': 55.00551626438379, 'ACC-building-other-merged': 74.24318082900463, 'ACC-rock-merged': 82.01405682140626, 'ACC-wall-other-merged': 80.47770808962575, 'ACC-rug-merged': 76.77767355693771})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4495171202809483, 'noc@0.8': 2.667544629792215, 'noc@0.85': 3.251975417032485, 'noc@0.9': 4.221832016388645, 'miou@iter1': 0.8364697256247342}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.58958435058594, 'precision@0.6': 68.9467544555664, 'precision@0.7': 63.77769088745117, 'precision@0.8': 54.255733489990234, 'precision@0.9': 27.944034576416016, 'cIoU': 57.81306457519531, 'mIoU': 63.563289642333984}}}
INFO:trainer.default_trainer:This epoch takes 1:26:15.285652
INFO:trainer.default_trainer:PROGRESS: 98.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']
INFO:trainer.default_trainer:Start epoch: 49 training.
INFO:trainer.default_trainer:epochs[    49] optim steps[89600] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.15824/0.89015, loss_mask_bce_0: 0.60468/0.33322, loss_mask_dice_0: 1.61162/1.15899, loss_spatial_bce_0: 0.11091/0.08604, loss_spatial_dice_0: 0.24238/0.20500, loss_spatial_ce_0: 0.02086/0.05819, loss_grounding_bce_0: 0.02018/0.08591, loss_grounding_dice_0: 0.12314/0.17792, loss_grounding_ce_0: 0.27969/0.27050, loss_mask_ce_1: 1.22524/0.89113, loss_mask_bce_1: 0.62314/0.33414, loss_mask_dice_1: 1.69934/1.16594, loss_spatial_bce_1: 0.09873/0.08656, loss_spatial_dice_1: 0.24382/0.20882, loss_spatial_ce_1: 0.02152/0.06394, loss_grounding_bce_1: 0.01985/0.08610, loss_grounding_dice_1: 0.12155/0.17874, loss_grounding_ce_1: 0.27492/0.27138, loss_mask_ce_2: 1.16074/0.89803, loss_mask_bce_2: 0.61928/0.33481, loss_mask_dice_2: 1.66918/1.16649, loss_spatial_bce_2: 0.13987/0.08784, loss_spatial_dice_2: 0.27868/0.21084, loss_spatial_ce_2: 0.04159/0.06732, loss_grounding_bce_2: 0.02218/0.08628, loss_grounding_dice_2: 0.13400/0.17865, loss_grounding_ce_2: 0.28545/0.27466, loss_mask_ce_3: 1.17633/0.90967, loss_mask_bce_3: 0.59660/0.33597, loss_mask_dice_3: 1.69541/1.16439, loss_spatial_bce_3: 0.16489/0.08920, loss_spatial_dice_3: 0.29633/0.21202, loss_spatial_ce_3: 0.03382/0.07255, loss_grounding_bce_3: 0.02284/0.08654, loss_grounding_dice_3: 0.14106/0.17830, loss_grounding_ce_3: 0.29673/0.27684, loss_mask_ce_4: 1.12588/0.91137, loss_mask_bce_4: 0.63901/0.33819, loss_mask_dice_4: 1.80467/1.18827, loss_spatial_bce_4: 0.11411/0.09309, loss_spatial_dice_4: 0.23821/0.22419, loss_spatial_ce_4: 0.19164/0.08862, loss_grounding_bce_4: 0.02700/0.08711, loss_grounding_dice_4: 0.17956/0.18133, loss_grounding_ce_4: 0.28880/0.27980, loss_mask_ce_5: 1.01512/0.92822, loss_mask_bce_5: 0.60486/0.34059, loss_mask_dice_5: 1.82593/1.19646, loss_spatial_bce_5: 0.13841/0.09542, loss_spatial_dice_5: 0.25606/0.22863, loss_spatial_ce_5: 0.07329/0.10201, loss_grounding_bce_5: 0.02684/0.08754, loss_grounding_dice_5: 0.16253/0.18265, loss_grounding_ce_5: 0.30078/0.29252, loss_mask_ce_6: 1.05476/0.96842, loss_mask_bce_6: 0.61288/0.34340, loss_mask_dice_6: 1.84557/1.19969, loss_spatial_bce_6: 0.16692/0.10107, loss_spatial_dice_6: 0.25894/0.23163, loss_spatial_ce_6: 0.21112/0.12657, loss_grounding_bce_6: 0.03079/0.08831, loss_grounding_dice_6: 0.18127/0.18310, loss_grounding_ce_6: 0.31222/0.30763, loss_mask_ce_7: 1.52585/1.01451, loss_mask_bce_7: 0.59958/0.35133, loss_mask_dice_7: 1.83982/1.25396, loss_spatial_bce_7: 0.14559/0.10886, loss_spatial_dice_7: 0.25006/0.25926, loss_spatial_ce_7: 0.20666/0.16110, loss_grounding_bce_7: 0.03225/0.09019, loss_grounding_dice_7: 0.22794/0.19045, loss_grounding_ce_7: 0.41731/0.33745, loss_mask_ce_8: 1.63285/1.12360, loss_mask_bce_8: 0.60531/0.36488, loss_mask_dice_8: 1.89172/1.32631, loss_spatial_bce_8: 0.19240/0.12869, loss_spatial_dice_8: 0.31255/0.29704, loss_spatial_ce_8: 0.30739/0.20917, loss_grounding_bce_8: 0.03510/0.09380, loss_grounding_dice_8: 0.19994/0.20107, loss_grounding_ce_8: 0.79017/0.40280, loss_mask_ce_9: 3.81099/3.67121, loss_mask_bce_9: 0.60064/0.39222, loss_mask_dice_9: 3.52480/1.90021, loss_spatial_bce_9: 0.32242/0.33256, loss_spatial_dice_9: 0.85959/0.82147, loss_spatial_ce_9: 1.61509/1.49046, loss_grounding_bce_9: 0.03006/0.10560, loss_grounding_dice_9: 0.36721/0.28066, loss_grounding_ce_9: 0.50820/0.66781] items per batch[64] items per second[0.14] total items[5734400] mini batches[ 89600] memory[7345] epoch remaining[1:21:10]
INFO:trainer.default_trainer:epochs[    49] optim steps[89700] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.21534/0.89007, loss_mask_bce_0: 0.32731/0.33317, loss_mask_dice_0: 0.37521/1.15882, loss_spatial_bce_0: 0.10213/0.08603, loss_spatial_dice_0: 0.12482/0.20497, loss_spatial_ce_0: 0.00616/0.05818, loss_grounding_bce_0: 0.20445/0.08590, loss_grounding_dice_0: 0.12638/0.17790, loss_grounding_ce_0: 0.05531/0.27042, loss_mask_ce_1: 0.20455/0.89106, loss_mask_bce_1: 0.32156/0.33409, loss_mask_dice_1: 0.32413/1.16578, loss_spatial_bce_1: 0.10565/0.08655, loss_spatial_dice_1: 0.11302/0.20879, loss_spatial_ce_1: 0.01301/0.06391, loss_grounding_bce_1: 0.20124/0.08609, loss_grounding_dice_1: 0.12472/0.17872, loss_grounding_ce_1: 0.05432/0.27131, loss_mask_ce_2: 0.21869/0.89797, loss_mask_bce_2: 0.30572/0.33477, loss_mask_dice_2: 0.36087/1.16633, loss_spatial_bce_2: 0.10680/0.08783, loss_spatial_dice_2: 0.10644/0.21080, loss_spatial_ce_2: 0.01240/0.06730, loss_grounding_bce_2: 0.18846/0.08627, loss_grounding_dice_2: 0.12379/0.17863, loss_grounding_ce_2: 0.05109/0.27458, loss_mask_ce_3: 0.21416/0.90959, loss_mask_bce_3: 0.31411/0.33593, loss_mask_dice_3: 0.33930/1.16423, loss_spatial_bce_3: 0.10634/0.08919, loss_spatial_dice_3: 0.13362/0.21199, loss_spatial_ce_3: 0.01819/0.07253, loss_grounding_bce_3: 0.19539/0.08653, loss_grounding_dice_3: 0.12608/0.17828, loss_grounding_ce_3: 0.05376/0.27677, loss_mask_ce_4: 0.22229/0.91130, loss_mask_bce_4: 0.32733/0.33815, loss_mask_dice_4: 0.37993/1.18811, loss_spatial_bce_4: 0.10604/0.09308, loss_spatial_dice_4: 0.13189/0.22416, loss_spatial_ce_4: 0.02237/0.08859, loss_grounding_bce_4: 0.17976/0.08710, loss_grounding_dice_4: 0.11972/0.18132, loss_grounding_ce_4: 0.04019/0.27972, loss_mask_ce_5: 0.22862/0.92815, loss_mask_bce_5: 0.31793/0.34055, loss_mask_dice_5: 0.35466/1.19631, loss_spatial_bce_5: 0.11534/0.09541, loss_spatial_dice_5: 0.12405/0.22860, loss_spatial_ce_5: 0.05736/0.10197, loss_grounding_bce_5: 0.17988/0.08753, loss_grounding_dice_5: 0.11391/0.18263, loss_grounding_ce_5: 0.03897/0.29245, loss_mask_ce_6: 0.26064/0.96835, loss_mask_bce_6: 0.33582/0.34335, loss_mask_dice_6: 0.35048/1.19953, loss_spatial_bce_6: 0.11352/0.10106, loss_spatial_dice_6: 0.13652/0.23160, loss_spatial_ce_6: 0.04885/0.12653, loss_grounding_bce_6: 0.18918/0.08830, loss_grounding_dice_6: 0.12062/0.18309, loss_grounding_ce_6: 0.07288/0.30754, loss_mask_ce_7: 0.35247/1.01443, loss_mask_bce_7: 0.35192/0.35128, loss_mask_dice_7: 0.35986/1.25379, loss_spatial_bce_7: 0.11831/0.10884, loss_spatial_dice_7: 0.13894/0.25923, loss_spatial_ce_7: 0.08858/0.16105, loss_grounding_bce_7: 0.26129/0.09017, loss_grounding_dice_7: 0.15373/0.19043, loss_grounding_ce_7: 0.03761/0.33737, loss_mask_ce_8: 0.41006/1.12353, loss_mask_bce_8: 0.32794/0.36482, loss_mask_dice_8: 0.37189/1.32615, loss_spatial_bce_8: 0.15775/0.12867, loss_spatial_dice_8: 0.17233/0.29700, loss_spatial_ce_8: 0.16066/0.20909, loss_grounding_bce_8: 0.18726/0.09378, loss_grounding_dice_8: 0.10869/0.20105, loss_grounding_ce_8: 0.05662/0.40271, loss_mask_ce_9: 2.31203/3.67116, loss_mask_bce_9: 0.32727/0.39217, loss_mask_dice_9: 0.47840/1.90005, loss_spatial_bce_9: 0.49506/0.33255, loss_spatial_dice_9: 0.83292/0.82146, loss_spatial_ce_9: 1.54750/1.49046, loss_grounding_bce_9: 0.22725/0.10559, loss_grounding_dice_9: 0.14340/0.28064, loss_grounding_ce_9: 0.14914/0.66771] items per batch[64] items per second[0.23] total items[5740800] mini batches[ 89700] memory[7345] epoch remaining[1:17:03]
INFO:trainer.default_trainer:epochs[    49] optim steps[89800] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.50202/0.89006, loss_mask_bce_0: 0.20493/0.33321, loss_mask_dice_0: 0.40564/1.15891, loss_spatial_bce_0: 0.06721/0.08603, loss_spatial_dice_0: 0.13084/0.20497, loss_spatial_ce_0: 0.00086/0.05817, loss_grounding_bce_0: 0.06187/0.08590, loss_grounding_dice_0: 0.13847/0.17791, loss_grounding_ce_0: 0.12506/0.27037, loss_mask_ce_1: 0.49106/0.89106, loss_mask_bce_1: 0.20051/0.33412, loss_mask_dice_1: 0.39637/1.16586, loss_spatial_bce_1: 0.06964/0.08655, loss_spatial_dice_1: 0.12407/0.20879, loss_spatial_ce_1: 0.00062/0.06391, loss_grounding_bce_1: 0.06226/0.08610, loss_grounding_dice_1: 0.13389/0.17873, loss_grounding_ce_1: 0.11721/0.27125, loss_mask_ce_2: 0.51073/0.89795, loss_mask_bce_2: 0.19817/0.33480, loss_mask_dice_2: 0.41151/1.16641, loss_spatial_bce_2: 0.06963/0.08783, loss_spatial_dice_2: 0.12648/0.21080, loss_spatial_ce_2: 0.00126/0.06729, loss_grounding_bce_2: 0.06430/0.08628, loss_grounding_dice_2: 0.14119/0.17863, loss_grounding_ce_2: 0.10499/0.27452, loss_mask_ce_3: 0.54294/0.90959, loss_mask_bce_3: 0.20097/0.33596, loss_mask_dice_3: 0.40431/1.16433, loss_spatial_bce_3: 0.07095/0.08918, loss_spatial_dice_3: 0.13934/0.21199, loss_spatial_ce_3: 0.01290/0.07252, loss_grounding_bce_3: 0.06232/0.08654, loss_grounding_dice_3: 0.13783/0.17829, loss_grounding_ce_3: 0.11052/0.27671, loss_mask_ce_4: 0.55959/0.91131, loss_mask_bce_4: 0.19873/0.33819, loss_mask_dice_4: 0.40225/1.18818, loss_spatial_bce_4: 0.08119/0.09308, loss_spatial_dice_4: 0.16555/0.22416, loss_spatial_ce_4: 0.00202/0.08857, loss_grounding_bce_4: 0.06327/0.08710, loss_grounding_dice_4: 0.13960/0.18132, loss_grounding_ce_4: 0.11233/0.27969, loss_mask_ce_5: 0.50981/0.92816, loss_mask_bce_5: 0.19561/0.34058, loss_mask_dice_5: 0.40206/1.19636, loss_spatial_bce_5: 0.07783/0.09540, loss_spatial_dice_5: 0.15169/0.22860, loss_spatial_ce_5: 0.00932/0.10195, loss_grounding_bce_5: 0.06345/0.08753, loss_grounding_dice_5: 0.14013/0.18264, loss_grounding_ce_5: 0.09103/0.29241, loss_mask_ce_6: 0.53092/0.96835, loss_mask_bce_6: 0.19430/0.34339, loss_mask_dice_6: 0.41183/1.19961, loss_spatial_bce_6: 0.08114/0.10106, loss_spatial_dice_6: 0.14375/0.23160, loss_spatial_ce_6: 0.05599/0.12650, loss_grounding_bce_6: 0.06566/0.08830, loss_grounding_dice_6: 0.13872/0.18310, loss_grounding_ce_6: 0.17986/0.30750, loss_mask_ce_7: 0.56341/1.01445, loss_mask_bce_7: 0.19687/0.35131, loss_mask_dice_7: 0.43028/1.25389, loss_spatial_bce_7: 0.08253/0.10883, loss_spatial_dice_7: 0.15800/0.25923, loss_spatial_ce_7: 0.07630/0.16101, loss_grounding_bce_7: 0.07403/0.09018, loss_grounding_dice_7: 0.14857/0.19044, loss_grounding_ce_7: 0.14688/0.33735, loss_mask_ce_8: 0.57187/1.12357, loss_mask_bce_8: 0.19358/0.36486, loss_mask_dice_8: 0.41683/1.32623, loss_spatial_bce_8: 0.10609/0.12866, loss_spatial_dice_8: 0.18800/0.29701, loss_spatial_ce_8: 0.14844/0.20903, loss_grounding_bce_8: 0.07128/0.09379, loss_grounding_dice_8: 0.13991/0.20105, loss_grounding_ce_8: 0.14919/0.40270, loss_mask_ce_9: 2.66406/3.67128, loss_mask_bce_9: 0.20367/0.39221, loss_mask_dice_9: 0.49999/1.90020, loss_spatial_bce_9: 0.41460/0.33255, loss_spatial_dice_9: 0.79930/0.82146, loss_spatial_ce_9: 1.11604/1.49046, loss_grounding_bce_9: 0.08640/0.10560, loss_grounding_dice_9: 0.18122/0.28065, loss_grounding_ce_9: 0.74730/0.66773] items per batch[64] items per second[0.23] total items[5747200] mini batches[ 89800] memory[7345] epoch remaining[1:12:35]
INFO:trainer.default_trainer:epochs[    49] optim steps[89900] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.13854/0.89000, loss_mask_bce_0: 0.07792/0.33319, loss_mask_dice_0: 0.12597/1.15882, loss_spatial_bce_0: 0.05784/0.08602, loss_spatial_dice_0: 0.09009/0.20495, loss_spatial_ce_0: 0.01726/0.05816, loss_grounding_bce_0: 0.01963/0.08590, loss_grounding_dice_0: 0.07375/0.17792, loss_grounding_ce_0: 0.01944/0.27031, loss_mask_ce_1: 0.14770/0.89101, loss_mask_bce_1: 0.08166/0.33411, loss_mask_dice_1: 0.13542/1.16575, loss_spatial_bce_1: 0.05761/0.08654, loss_spatial_dice_1: 0.08301/0.20877, loss_spatial_ce_1: 0.02361/0.06389, loss_grounding_bce_1: 0.01822/0.08610, loss_grounding_dice_1: 0.06181/0.17874, loss_grounding_ce_1: 0.01879/0.27119, loss_mask_ce_2: 0.16612/0.89790, loss_mask_bce_2: 0.08184/0.33479, loss_mask_dice_2: 0.13072/1.16633, loss_spatial_bce_2: 0.05663/0.08782, loss_spatial_dice_2: 0.08216/0.21078, loss_spatial_ce_2: 0.02393/0.06727, loss_grounding_bce_2: 0.01897/0.08627, loss_grounding_dice_2: 0.06618/0.17864, loss_grounding_ce_2: 0.01528/0.27445, loss_mask_ce_3: 0.19279/0.90953, loss_mask_bce_3: 0.07942/0.33594, loss_mask_dice_3: 0.12538/1.16421, loss_spatial_bce_3: 0.05836/0.08917, loss_spatial_dice_3: 0.08773/0.21197, loss_spatial_ce_3: 0.02099/0.07251, loss_grounding_bce_3: 0.01940/0.08653, loss_grounding_dice_3: 0.06747/0.17830, loss_grounding_ce_3: 0.02097/0.27665, loss_mask_ce_4: 0.22840/0.91126, loss_mask_bce_4: 0.07475/0.33817, loss_mask_dice_4: 0.12427/1.18807, loss_spatial_bce_4: 0.05470/0.09307, loss_spatial_dice_4: 0.08948/0.22413, loss_spatial_ce_4: 0.03285/0.08855, loss_grounding_bce_4: 0.01327/0.08710, loss_grounding_dice_4: 0.05141/0.18133, loss_grounding_ce_4: 0.01484/0.27961, loss_mask_ce_5: 0.17653/0.92811, loss_mask_bce_5: 0.07859/0.34056, loss_mask_dice_5: 0.13190/1.19627, loss_spatial_bce_5: 0.06196/0.09540, loss_spatial_dice_5: 0.08960/0.22858, loss_spatial_ce_5: 0.02873/0.10193, loss_grounding_bce_5: 0.01684/0.08753, loss_grounding_dice_5: 0.06389/0.18265, loss_grounding_ce_5: 0.00823/0.29232, loss_mask_ce_6: 0.15487/0.96827, loss_mask_bce_6: 0.08367/0.34337, loss_mask_dice_6: 0.12790/1.19951, loss_spatial_bce_6: 0.06692/0.10105, loss_spatial_dice_6: 0.09667/0.23159, loss_spatial_ce_6: 0.03550/0.12647, loss_grounding_bce_6: 0.02221/0.08830, loss_grounding_dice_6: 0.06901/0.18310, loss_grounding_ce_6: 0.00914/0.30742, loss_mask_ce_7: 0.30330/1.01440, loss_mask_bce_7: 0.08422/0.35131, loss_mask_dice_7: 0.13769/1.25378, loss_spatial_bce_7: 0.07477/0.10883, loss_spatial_dice_7: 0.09879/0.25921, loss_spatial_ce_7: 0.06828/0.16098, loss_grounding_bce_7: 0.02002/0.09018, loss_grounding_dice_7: 0.05982/0.19044, loss_grounding_ce_7: 0.03405/0.33725, loss_mask_ce_8: 0.29060/1.12348, loss_mask_bce_8: 0.10704/0.36485, loss_mask_dice_8: 0.14916/1.32610, loss_spatial_bce_8: 0.14785/0.12865, loss_spatial_dice_8: 0.13214/0.29699, loss_spatial_ce_8: 0.03014/0.20894, loss_grounding_bce_8: 0.01746/0.09379, loss_grounding_dice_8: 0.08849/0.20106, loss_grounding_ce_8: 0.05128/0.40257, loss_mask_ce_9: 2.08230/3.67106, loss_mask_bce_9: 0.10812/0.39221, loss_mask_dice_9: 0.21987/1.90007, loss_spatial_bce_9: 0.33813/0.33254, loss_spatial_dice_9: 0.73119/0.82145, loss_spatial_ce_9: 0.89493/1.49049, loss_grounding_bce_9: 0.01971/0.10560, loss_grounding_dice_9: 0.14377/0.28065, loss_grounding_ce_9: 0.23467/0.66761] items per batch[64] items per second[0.23] total items[5753600] mini batches[ 89900] memory[7345] epoch remaining[1:07:47]
INFO:trainer.default_trainer:epochs[    49] optim steps[90000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.35685/0.88987, loss_mask_bce_0: 0.36105/0.33318, loss_mask_dice_0: 0.72935/1.15873, loss_spatial_bce_0: 0.09594/0.08603, loss_spatial_dice_0: 0.16504/0.20493, loss_spatial_ce_0: 0.00054/0.05814, loss_grounding_bce_0: 0.11145/0.08589, loss_grounding_dice_0: 0.09221/0.17790, loss_grounding_ce_0: 0.01527/0.27028, loss_mask_ce_1: 0.36178/0.89088, loss_mask_bce_1: 0.35991/0.33410, loss_mask_dice_1: 0.73125/1.16566, loss_spatial_bce_1: 0.09175/0.08654, loss_spatial_dice_1: 0.17233/0.20874, loss_spatial_ce_1: 0.00050/0.06387, loss_grounding_bce_1: 0.11604/0.08609, loss_grounding_dice_1: 0.09359/0.17873, loss_grounding_ce_1: 0.01269/0.27117, loss_mask_ce_2: 0.36374/0.89779, loss_mask_bce_2: 0.35579/0.33478, loss_mask_dice_2: 0.70903/1.16622, loss_spatial_bce_2: 0.09476/0.08782, loss_spatial_dice_2: 0.18104/0.21076, loss_spatial_ce_2: 0.00085/0.06725, loss_grounding_bce_2: 0.10946/0.08627, loss_grounding_dice_2: 0.09304/0.17863, loss_grounding_ce_2: 0.01370/0.27443, loss_mask_ce_3: 0.36943/0.90941, loss_mask_bce_3: 0.36058/0.33593, loss_mask_dice_3: 0.73256/1.16410, loss_spatial_bce_3: 0.09414/0.08917, loss_spatial_dice_3: 0.17294/0.21195, loss_spatial_ce_3: 0.00105/0.07249, loss_grounding_bce_3: 0.10858/0.08653, loss_grounding_dice_3: 0.09423/0.17827, loss_grounding_ce_3: 0.01281/0.27661, loss_mask_ce_4: 0.35984/0.91115, loss_mask_bce_4: 0.35988/0.33816, loss_mask_dice_4: 0.71130/1.18796, loss_spatial_bce_4: 0.09615/0.09307, loss_spatial_dice_4: 0.16897/0.22411, loss_spatial_ce_4: 0.00378/0.08852, loss_grounding_bce_4: 0.11501/0.08709, loss_grounding_dice_4: 0.09125/0.18131, loss_grounding_ce_4: 0.01459/0.27957, loss_mask_ce_5: 0.39048/0.92800, loss_mask_bce_5: 0.35487/0.34056, loss_mask_dice_5: 0.71436/1.19615, loss_spatial_bce_5: 0.09842/0.09540, loss_spatial_dice_5: 0.17204/0.22856, loss_spatial_ce_5: 0.00935/0.10190, loss_grounding_bce_5: 0.11289/0.08752, loss_grounding_dice_5: 0.09077/0.18263, loss_grounding_ce_5: 0.01701/0.29228, loss_mask_ce_6: 0.37916/0.96817, loss_mask_bce_6: 0.36593/0.34336, loss_mask_dice_6: 0.71372/1.19940, loss_spatial_bce_6: 0.11322/0.10106, loss_spatial_dice_6: 0.18728/0.23156, loss_spatial_ce_6: 0.07848/0.12643, loss_grounding_bce_6: 0.11576/0.08829, loss_grounding_dice_6: 0.09782/0.18309, loss_grounding_ce_6: 0.02163/0.30740, loss_mask_ce_7: 0.44561/1.01434, loss_mask_bce_7: 0.36383/0.35130, loss_mask_dice_7: 0.67212/1.25366, loss_spatial_bce_7: 0.10352/0.10882, loss_spatial_dice_7: 0.19087/0.25919, loss_spatial_ce_7: 0.06949/0.16093, loss_grounding_bce_7: 0.11688/0.09017, loss_grounding_dice_7: 0.09423/0.19042, loss_grounding_ce_7: 0.02919/0.33725, loss_mask_ce_8: 0.39370/1.12337, loss_mask_bce_8: 0.37669/0.36485, loss_mask_dice_8: 0.72912/1.32601, loss_spatial_bce_8: 0.12438/0.12865, loss_spatial_dice_8: 0.23197/0.29696, loss_spatial_ce_8: 0.05226/0.20885, loss_grounding_bce_8: 0.12365/0.09378, loss_grounding_dice_8: 0.09657/0.20103, loss_grounding_ce_8: 0.01269/0.40256, loss_mask_ce_9: 2.87553/3.67102, loss_mask_bce_9: 0.47276/0.39222, loss_mask_dice_9: 1.37115/1.89995, loss_spatial_bce_9: 0.57674/0.33256, loss_spatial_dice_9: 0.84689/0.82144, loss_spatial_ce_9: 1.94855/1.49042, loss_grounding_bce_9: 0.12879/0.10560, loss_grounding_dice_9: 0.12842/0.28063, loss_grounding_ce_9: 0.10715/0.66761] items per batch[64] items per second[0.24] total items[5760000] mini batches[ 90000] memory[7345] epoch remaining[1:02:37]
INFO:trainer.default_trainer:epochs[    49] optim steps[90100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.07861/0.88983, loss_mask_bce_0: 0.34279/0.33318, loss_mask_dice_0: 1.20541/1.15899, loss_spatial_bce_0: 0.02997/0.08601, loss_spatial_dice_0: 0.20909/0.20493, loss_spatial_ce_0: 0.06840/0.05813, loss_grounding_bce_0: 0.02363/0.08588, loss_grounding_dice_0: 0.08536/0.17791, loss_grounding_ce_0: 0.13050/0.27027, loss_mask_ce_1: 1.09228/0.89086, loss_mask_bce_1: 0.33570/0.33409, loss_mask_dice_1: 1.24251/1.16593, loss_spatial_bce_1: 0.02854/0.08652, loss_spatial_dice_1: 0.19326/0.20874, loss_spatial_ce_1: 0.05669/0.06385, loss_grounding_bce_1: 0.02306/0.08608, loss_grounding_dice_1: 0.08909/0.17874, loss_grounding_ce_1: 0.11269/0.27115, loss_mask_ce_2: 1.07235/0.89778, loss_mask_bce_2: 0.32883/0.33477, loss_mask_dice_2: 1.15510/1.16648, loss_spatial_bce_2: 0.02994/0.08780, loss_spatial_dice_2: 0.21855/0.21076, loss_spatial_ce_2: 0.07993/0.06723, loss_grounding_bce_2: 0.02175/0.08626, loss_grounding_dice_2: 0.08270/0.17864, loss_grounding_ce_2: 0.11579/0.27442, loss_mask_ce_3: 1.16350/0.90938, loss_mask_bce_3: 0.33754/0.33593, loss_mask_dice_3: 1.19928/1.16436, loss_spatial_bce_3: 0.02903/0.08915, loss_spatial_dice_3: 0.20397/0.21195, loss_spatial_ce_3: 0.09776/0.07247, loss_grounding_bce_3: 0.02381/0.08652, loss_grounding_dice_3: 0.08511/0.17829, loss_grounding_ce_3: 0.13631/0.27663, loss_mask_ce_4: 1.23206/0.91114, loss_mask_bce_4: 0.34589/0.33815, loss_mask_dice_4: 1.37528/1.18823, loss_spatial_bce_4: 0.03166/0.09305, loss_spatial_dice_4: 0.21021/0.22411, loss_spatial_ce_4: 0.06405/0.08850, loss_grounding_bce_4: 0.02341/0.08708, loss_grounding_dice_4: 0.10242/0.18132, loss_grounding_ce_4: 0.10805/0.27955, loss_mask_ce_5: 1.30612/0.92801, loss_mask_bce_5: 0.33451/0.34056, loss_mask_dice_5: 1.27586/1.19643, loss_spatial_bce_5: 0.05113/0.09538, loss_spatial_dice_5: 0.21947/0.22856, loss_spatial_ce_5: 0.03332/0.10188, loss_grounding_bce_5: 0.02615/0.08751, loss_grounding_dice_5: 0.11228/0.18264, loss_grounding_ce_5: 0.09805/0.29226, loss_mask_ce_6: 1.42512/0.96815, loss_mask_bce_6: 0.35403/0.34336, loss_mask_dice_6: 1.33182/1.19969, loss_spatial_bce_6: 0.06910/0.10104, loss_spatial_dice_6: 0.25635/0.23157, loss_spatial_ce_6: 0.05659/0.12641, loss_grounding_bce_6: 0.02452/0.08828, loss_grounding_dice_6: 0.10099/0.18310, loss_grounding_ce_6: 0.73966/0.30738, loss_mask_ce_7: 1.26810/1.01436, loss_mask_bce_7: 0.34231/0.35130, loss_mask_dice_7: 1.19766/1.25396, loss_spatial_bce_7: 0.06090/0.10880, loss_spatial_dice_7: 0.27130/0.25919, loss_spatial_ce_7: 0.06915/0.16090, loss_grounding_bce_7: 0.03661/0.09016, loss_grounding_dice_7: 0.08661/0.19043, loss_grounding_ce_7: 1.12715/0.33724, loss_mask_ce_8: 1.31359/1.12334, loss_mask_bce_8: 0.33986/0.36485, loss_mask_dice_8: 1.33391/1.32631, loss_spatial_bce_8: 0.05909/0.12862, loss_spatial_dice_8: 0.30085/0.29697, loss_spatial_ce_8: 0.13439/0.20878, loss_grounding_bce_8: 0.02880/0.09377, loss_grounding_dice_8: 0.08294/0.20104, loss_grounding_ce_8: 0.33426/0.40254, loss_mask_ce_9: 3.75259/3.67108, loss_mask_bce_9: 0.43954/0.39223, loss_mask_dice_9: 2.90224/1.90041, loss_spatial_bce_9: 0.17981/0.33253, loss_spatial_dice_9: 0.84444/0.82144, loss_spatial_ce_9: 1.37256/1.49042, loss_grounding_bce_9: 0.05495/0.10559, loss_grounding_dice_9: 0.12932/0.28065, loss_grounding_ce_9: 0.78059/0.66754] items per batch[64] items per second[0.24] total items[5766400] mini batches[ 90100] memory[7345] epoch remaining[0:57:28]
INFO:trainer.default_trainer:epochs[    49] optim steps[90200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.50492/0.88984, loss_mask_bce_0: 0.10997/0.33316, loss_mask_dice_0: 1.05772/1.15889, loss_spatial_bce_0: 0.02055/0.08599, loss_spatial_dice_0: 0.15672/0.20490, loss_spatial_ce_0: 0.00152/0.05810, loss_grounding_bce_0: 0.01505/0.08588, loss_grounding_dice_0: 0.19965/0.17790, loss_grounding_ce_0: 0.34122/0.27023, loss_mask_ce_1: 0.66189/0.89086, loss_mask_bce_1: 0.11301/0.33407, loss_mask_dice_1: 0.96643/1.16583, loss_spatial_bce_1: 0.02122/0.08651, loss_spatial_dice_1: 0.16379/0.20871, loss_spatial_ce_1: 0.00305/0.06382, loss_grounding_bce_1: 0.01562/0.08608, loss_grounding_dice_1: 0.26706/0.17872, loss_grounding_ce_1: 0.32413/0.27112, loss_mask_ce_2: 0.64585/0.89778, loss_mask_bce_2: 0.10454/0.33475, loss_mask_dice_2: 0.87031/1.16636, loss_spatial_bce_2: 0.02191/0.08779, loss_spatial_dice_2: 0.11637/0.21073, loss_spatial_ce_2: 0.00176/0.06721, loss_grounding_bce_2: 0.01395/0.08626, loss_grounding_dice_2: 0.17589/0.17862, loss_grounding_ce_2: 0.31210/0.27439, loss_mask_ce_3: 0.58242/0.90939, loss_mask_bce_3: 0.11262/0.33591, loss_mask_dice_3: 0.97701/1.16425, loss_spatial_bce_3: 0.02214/0.08914, loss_spatial_dice_3: 0.16034/0.21192, loss_spatial_ce_3: 0.00431/0.07244, loss_grounding_bce_3: 0.01907/0.08652, loss_grounding_dice_3: 0.21999/0.17827, loss_grounding_ce_3: 0.29128/0.27658, loss_mask_ce_4: 0.53706/0.91115, loss_mask_bce_4: 0.10494/0.33813, loss_mask_dice_4: 0.93658/1.18811, loss_spatial_bce_4: 0.02325/0.09304, loss_spatial_dice_4: 0.15789/0.22409, loss_spatial_ce_4: 0.01992/0.08846, loss_grounding_bce_4: 0.01799/0.08708, loss_grounding_dice_4: 0.36007/0.18130, loss_grounding_ce_4: 0.26935/0.27953, loss_mask_ce_5: 0.48721/0.92803, loss_mask_bce_5: 0.11162/0.34053, loss_mask_dice_5: 0.85772/1.19631, loss_spatial_bce_5: 0.02248/0.09537, loss_spatial_dice_5: 0.16504/0.22854, loss_spatial_ce_5: 0.01150/0.10183, loss_grounding_bce_5: 0.01706/0.08751, loss_grounding_dice_5: 0.23475/0.18262, loss_grounding_ce_5: 0.34540/0.29222, loss_mask_ce_6: 0.50226/0.96816, loss_mask_bce_6: 0.10425/0.34333, loss_mask_dice_6: 0.97455/1.19958, loss_spatial_bce_6: 0.02689/0.10102, loss_spatial_dice_6: 0.16394/0.23154, loss_spatial_ce_6: 0.03160/0.12636, loss_grounding_bce_6: 0.02032/0.08828, loss_grounding_dice_6: 0.29426/0.18309, loss_grounding_ce_6: 0.33487/0.30735, loss_mask_ce_7: 0.64321/1.01437, loss_mask_bce_7: 0.10717/0.35128, loss_mask_dice_7: 1.17564/1.25386, loss_spatial_bce_7: 0.02515/0.10879, loss_spatial_dice_7: 0.18917/0.25917, loss_spatial_ce_7: 0.03451/0.16084, loss_grounding_bce_7: 0.01923/0.09016, loss_grounding_dice_7: 0.38437/0.19041, loss_grounding_ce_7: 0.32778/0.33725, loss_mask_ce_8: 0.81010/1.12334, loss_mask_bce_8: 0.10740/0.36483, loss_mask_dice_8: 1.12624/1.32621, loss_spatial_bce_8: 0.02480/0.12861, loss_spatial_dice_8: 0.27821/0.29695, loss_spatial_ce_8: 0.19091/0.20869, loss_grounding_bce_8: 0.01820/0.09377, loss_grounding_dice_8: 0.26935/0.20102, loss_grounding_ce_8: 0.30539/0.40252, loss_mask_ce_9: 2.75891/3.67111, loss_mask_bce_9: 0.11784/0.39222, loss_mask_dice_9: 1.44376/1.90031, loss_spatial_bce_9: 0.15701/0.33253, loss_spatial_dice_9: 0.77859/0.82145, loss_spatial_ce_9: 1.37678/1.49038, loss_grounding_bce_9: 0.01503/0.10560, loss_grounding_dice_9: 0.35255/0.28064, loss_grounding_ce_9: 0.31597/0.66757] items per batch[64] items per second[0.24] total items[5772800] mini batches[ 90200] memory[7345] epoch remaining[0:52:41]
INFO:trainer.default_trainer:epochs[    49] optim steps[90300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.73366/0.88990, loss_mask_bce_0: 0.57970/0.33319, loss_mask_dice_0: 1.93345/1.15887, loss_spatial_bce_0: 0.04301/0.08600, loss_spatial_dice_0: 0.18441/0.20489, loss_spatial_ce_0: 0.00809/0.05809, loss_grounding_bce_0: 0.11811/0.08589, loss_grounding_dice_0: 0.23731/0.17790, loss_grounding_ce_0: 0.17293/0.27027, loss_mask_ce_1: 0.73852/0.89091, loss_mask_bce_1: 0.56913/0.33411, loss_mask_dice_1: 1.77947/1.16582, loss_spatial_bce_1: 0.04009/0.08651, loss_spatial_dice_1: 0.19066/0.20870, loss_spatial_ce_1: 0.00387/0.06380, loss_grounding_bce_1: 0.11121/0.08609, loss_grounding_dice_1: 0.25630/0.17872, loss_grounding_ce_1: 0.19171/0.27116, loss_mask_ce_2: 0.79996/0.89784, loss_mask_bce_2: 0.57088/0.33479, loss_mask_dice_2: 1.61961/1.16634, loss_spatial_bce_2: 0.03950/0.08779, loss_spatial_dice_2: 0.19703/0.21072, loss_spatial_ce_2: 0.27710/0.06719, loss_grounding_bce_2: 0.11405/0.08626, loss_grounding_dice_2: 0.26979/0.17863, loss_grounding_ce_2: 0.17376/0.27443, loss_mask_ce_3: 0.79432/0.90945, loss_mask_bce_3: 0.57643/0.33594, loss_mask_dice_3: 1.81759/1.16425, loss_spatial_bce_3: 0.04213/0.08915, loss_spatial_dice_3: 0.18517/0.21191, loss_spatial_ce_3: 0.01432/0.07243, loss_grounding_bce_3: 0.12168/0.08652, loss_grounding_dice_3: 0.28558/0.17828, loss_grounding_ce_3: 0.18939/0.27660, loss_mask_ce_4: 0.88557/0.91119, loss_mask_bce_4: 0.57482/0.33816, loss_mask_dice_4: 1.90515/1.18811, loss_spatial_bce_4: 0.04889/0.09304, loss_spatial_dice_4: 0.20567/0.22408, loss_spatial_ce_4: 0.03814/0.08844, loss_grounding_bce_4: 0.11000/0.08709, loss_grounding_dice_4: 0.24220/0.18131, loss_grounding_ce_4: 0.19842/0.27955, loss_mask_ce_5: 0.76030/0.92808, loss_mask_bce_5: 0.56747/0.34057, loss_mask_dice_5: 1.69666/1.19629, loss_spatial_bce_5: 0.05127/0.09537, loss_spatial_dice_5: 0.22926/0.22853, loss_spatial_ce_5: 0.02968/0.10180, loss_grounding_bce_5: 0.11405/0.08752, loss_grounding_dice_5: 0.25703/0.18264, loss_grounding_ce_5: 0.20524/0.29225, loss_mask_ce_6: 0.86362/0.96823, loss_mask_bce_6: 0.58075/0.34337, loss_mask_dice_6: 1.77349/1.19958, loss_spatial_bce_6: 0.06218/0.10103, loss_spatial_dice_6: 0.24437/0.23154, loss_spatial_ce_6: 0.07306/0.12634, loss_grounding_bce_6: 0.09964/0.08829, loss_grounding_dice_6: 0.25983/0.18309, loss_grounding_ce_6: 0.19618/0.30740, loss_mask_ce_7: 0.91524/1.01441, loss_mask_bce_7: 0.59830/0.35132, loss_mask_dice_7: 1.86068/1.25384, loss_spatial_bce_7: 0.07595/0.10879, loss_spatial_dice_7: 0.27308/0.25916, loss_spatial_ce_7: 0.09332/0.16082, loss_grounding_bce_7: 0.09303/0.09017, loss_grounding_dice_7: 0.24776/0.19043, loss_grounding_ce_7: 0.23812/0.33727, loss_mask_ce_8: 1.21120/1.12336, loss_mask_bce_8: 0.64584/0.36487, loss_mask_dice_8: 2.20440/1.32619, loss_spatial_bce_8: 0.07935/0.12861, loss_spatial_dice_8: 0.33403/0.29695, loss_spatial_ce_8: 0.06084/0.20862, loss_grounding_bce_8: 0.09183/0.09377, loss_grounding_dice_8: 0.23640/0.20104, loss_grounding_ce_8: 0.61331/0.40255, loss_mask_ce_9: 4.49364/3.67120, loss_mask_bce_9: 0.69992/0.39226, loss_mask_dice_9: 3.99778/1.90040, loss_spatial_bce_9: 0.30362/0.33253, loss_spatial_dice_9: 0.92562/0.82145, loss_spatial_ce_9: 1.71471/1.49043, loss_grounding_bce_9: 0.07739/0.10561, loss_grounding_dice_9: 0.52792/0.28065, loss_grounding_ce_9: 0.31965/0.66761] items per batch[64] items per second[0.24] total items[5779200] mini batches[ 90300] memory[7345] epoch remaining[0:48:02]
INFO:trainer.default_trainer:epochs[    49] optim steps[90400] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.78189/0.88980, loss_mask_bce_0: 0.48220/0.33317, loss_mask_dice_0: 0.56155/1.15877, loss_spatial_bce_0: 0.13274/0.08599, loss_spatial_dice_0: 0.10939/0.20487, loss_spatial_ce_0: 0.01051/0.05807, loss_grounding_bce_0: 0.02772/0.08589, loss_grounding_dice_0: 0.12344/0.17790, loss_grounding_ce_0: 1.18163/0.27029, loss_mask_ce_1: 0.77763/0.89080, loss_mask_bce_1: 0.49045/0.33409, loss_mask_dice_1: 0.53731/1.16573, loss_spatial_bce_1: 0.14575/0.08650, loss_spatial_dice_1: 0.10760/0.20868, loss_spatial_ce_1: 0.01471/0.06378, loss_grounding_bce_1: 0.02994/0.08609, loss_grounding_dice_1: 0.12746/0.17872, loss_grounding_ce_1: 1.21717/0.27120, loss_mask_ce_2: 0.81539/0.89771, loss_mask_bce_2: 0.48923/0.33476, loss_mask_dice_2: 0.58658/1.16624, loss_spatial_bce_2: 0.13681/0.08778, loss_spatial_dice_2: 0.11468/0.21070, loss_spatial_ce_2: 0.03615/0.06717, loss_grounding_bce_2: 0.02861/0.08626, loss_grounding_dice_2: 0.13298/0.17863, loss_grounding_ce_2: 1.98347/0.27449, loss_mask_ce_3: 0.89210/0.90935, loss_mask_bce_3: 0.50127/0.33592, loss_mask_dice_3: 0.57457/1.16416, loss_spatial_bce_3: 0.14896/0.08914, loss_spatial_dice_3: 0.11776/0.21189, loss_spatial_ce_3: 0.04323/0.07241, loss_grounding_bce_3: 0.03195/0.08652, loss_grounding_dice_3: 0.14467/0.17828, loss_grounding_ce_3: 1.73879/0.27667, loss_mask_ce_4: 0.78841/0.91108, loss_mask_bce_4: 0.53377/0.33814, loss_mask_dice_4: 0.57719/1.18801, loss_spatial_bce_4: 0.13571/0.09303, loss_spatial_dice_4: 0.12429/0.22406, loss_spatial_ce_4: 0.04403/0.08841, loss_grounding_bce_4: 0.03000/0.08709, loss_grounding_dice_4: 0.10814/0.18131, loss_grounding_ce_4: 1.37013/0.27959, loss_mask_ce_5: 0.83911/0.92800, loss_mask_bce_5: 0.56232/0.34054, loss_mask_dice_5: 0.62235/1.19617, loss_spatial_bce_5: 0.17327/0.09536, loss_spatial_dice_5: 0.12685/0.22851, loss_spatial_ce_5: 0.12652/0.10178, loss_grounding_bce_5: 0.02906/0.08752, loss_grounding_dice_5: 0.10305/0.18264, loss_grounding_ce_5: 1.37890/0.29228, loss_mask_ce_6: 1.04713/0.96814, loss_mask_bce_6: 0.57987/0.34334, loss_mask_dice_6: 0.60787/1.19947, loss_spatial_bce_6: 0.15963/0.10102, loss_spatial_dice_6: 0.13102/0.23151, loss_spatial_ce_6: 0.12192/0.12631, loss_grounding_bce_6: 0.03329/0.08829, loss_grounding_dice_6: 0.10634/0.18310, loss_grounding_ce_6: 0.91169/0.30745, loss_mask_ce_7: 1.02242/1.01433, loss_mask_bce_7: 0.51606/0.35130, loss_mask_dice_7: 0.63116/1.25372, loss_spatial_bce_7: 0.22136/0.10878, loss_spatial_dice_7: 0.17084/0.25914, loss_spatial_ce_7: 0.23171/0.16077, loss_grounding_bce_7: 0.02407/0.09017, loss_grounding_dice_7: 0.09487/0.19043, loss_grounding_ce_7: 1.71207/0.33735, loss_mask_ce_8: 1.71049/1.12327, loss_mask_bce_8: 0.55171/0.36484, loss_mask_dice_8: 0.68264/1.32605, loss_spatial_bce_8: 0.16053/0.12859, loss_spatial_dice_8: 0.15598/0.29693, loss_spatial_ce_8: 0.17817/0.20854, loss_grounding_bce_8: 0.02279/0.09378, loss_grounding_dice_8: 0.12910/0.20104, loss_grounding_ce_8: 2.65461/0.40269, loss_mask_ce_9: 4.72398/3.67113, loss_mask_bce_9: 0.69377/0.39224, loss_mask_dice_9: 1.24301/1.90020, loss_spatial_bce_9: 0.48353/0.33252, loss_spatial_dice_9: 0.72045/0.82143, loss_spatial_ce_9: 1.31718/1.49042, loss_grounding_bce_9: 0.03293/0.10562, loss_grounding_dice_9: 0.25781/0.28065, loss_grounding_ce_9: 1.12766/0.66771] items per batch[64] items per second[0.23] total items[5785600] mini batches[ 90400] memory[7345] epoch remaining[0:43:27]
INFO:trainer.default_trainer:epochs[    49] optim steps[90500] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.43676/0.88978, loss_mask_bce_0: 0.22850/0.33316, loss_mask_dice_0: 1.07595/1.15872, loss_spatial_bce_0: 0.06687/0.08597, loss_spatial_dice_0: 0.19791/0.20485, loss_spatial_ce_0: 0.02598/0.05805, loss_grounding_bce_0: 0.14375/0.08589, loss_grounding_dice_0: 0.26276/0.17792, loss_grounding_ce_0: 0.32690/0.27031, loss_mask_ce_1: 0.67697/0.89077, loss_mask_bce_1: 0.23058/0.33407, loss_mask_dice_1: 1.02064/1.16569, loss_spatial_bce_1: 0.07227/0.08649, loss_spatial_dice_1: 0.13951/0.20866, loss_spatial_ce_1: 0.03660/0.06376, loss_grounding_bce_1: 0.14550/0.08609, loss_grounding_dice_1: 0.13683/0.17873, loss_grounding_ce_1: 0.48423/0.27122, loss_mask_ce_2: 0.53950/0.89769, loss_mask_bce_2: 0.23705/0.33475, loss_mask_dice_2: 1.31890/1.16619, loss_spatial_bce_2: 0.07022/0.08777, loss_spatial_dice_2: 0.23425/0.21068, loss_spatial_ce_2: 0.03340/0.06716, loss_grounding_bce_2: 0.14868/0.08626, loss_grounding_dice_2: 0.31698/0.17864, loss_grounding_ce_2: 0.05819/0.27451, loss_mask_ce_3: 0.52663/0.90934, loss_mask_bce_3: 0.23155/0.33590, loss_mask_dice_3: 1.03863/1.16411, loss_spatial_bce_3: 0.06803/0.08912, loss_spatial_dice_3: 0.22379/0.21188, loss_spatial_ce_3: 0.04927/0.07238, loss_grounding_bce_3: 0.14847/0.08652, loss_grounding_dice_3: 0.26629/0.17830, loss_grounding_ce_3: 0.55544/0.27670, loss_mask_ce_4: 0.52530/0.91107, loss_mask_bce_4: 0.22343/0.33812, loss_mask_dice_4: 0.98579/1.18795, loss_spatial_bce_4: 0.06737/0.09302, loss_spatial_dice_4: 0.22685/0.22404, loss_spatial_ce_4: 0.04925/0.08839, loss_grounding_bce_4: 0.14967/0.08709, loss_grounding_dice_4: 0.13371/0.18133, loss_grounding_ce_4: 0.04914/0.27961, loss_mask_ce_5: 0.52526/0.92798, loss_mask_bce_5: 0.21764/0.34053, loss_mask_dice_5: 0.75588/1.19612, loss_spatial_bce_5: 0.07847/0.09535, loss_spatial_dice_5: 0.22174/0.22850, loss_spatial_ce_5: 0.05533/0.10175, loss_grounding_bce_5: 0.14657/0.08752, loss_grounding_dice_5: 0.25320/0.18266, loss_grounding_ce_5: 0.27893/0.29231, loss_mask_ce_6: 0.61080/0.96812, loss_mask_bce_6: 0.21671/0.34333, loss_mask_dice_6: 0.85465/1.19942, loss_spatial_bce_6: 0.06210/0.10101, loss_spatial_dice_6: 0.19306/0.23150, loss_spatial_ce_6: 0.06000/0.12627, loss_grounding_bce_6: 0.15104/0.08829, loss_grounding_dice_6: 0.13043/0.18311, loss_grounding_ce_6: 0.06926/0.30747, loss_mask_ce_7: 0.60011/1.01432, loss_mask_bce_7: 0.22320/0.35129, loss_mask_dice_7: 0.91261/1.25367, loss_spatial_bce_7: 0.06872/0.10876, loss_spatial_dice_7: 0.31081/0.25912, loss_spatial_ce_7: 0.16657/0.16074, loss_grounding_bce_7: 0.14360/0.09017, loss_grounding_dice_7: 0.22042/0.19045, loss_grounding_ce_7: 0.07134/0.33740, loss_mask_ce_8: 0.98738/1.12326, loss_mask_bce_8: 0.22204/0.36483, loss_mask_dice_8: 0.88006/1.32601, loss_spatial_bce_8: 0.07154/0.12857, loss_spatial_dice_8: 0.38410/0.29692, loss_spatial_ce_8: 0.09857/0.20846, loss_grounding_bce_8: 0.14672/0.09378, loss_grounding_dice_8: 0.27989/0.20105, loss_grounding_ce_8: 0.16436/0.40278, loss_mask_ce_9: 2.80634/3.67116, loss_mask_bce_9: 0.25628/0.39224, loss_mask_dice_9: 1.12215/1.90012, loss_spatial_bce_9: 0.20376/0.33251, loss_spatial_dice_9: 0.69146/0.82143, loss_spatial_ce_9: 1.99783/1.49040, loss_grounding_bce_9: 0.15794/0.10562, loss_grounding_dice_9: 0.28365/0.28067, loss_grounding_ce_9: 0.45215/0.66770] items per batch[64] items per second[0.23] total items[5792000] mini batches[ 90500] memory[7345] epoch remaining[0:38:55]
INFO:trainer.default_trainer:epochs[    49] optim steps[90600] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.51651/0.88978, loss_mask_bce_0: 0.81784/0.33314, loss_mask_dice_0: 1.48470/1.15863, loss_spatial_bce_0: 0.10842/0.08596, loss_spatial_dice_0: 0.21381/0.20484, loss_spatial_ce_0: 0.04341/0.05803, loss_grounding_bce_0: 0.18524/0.08589, loss_grounding_dice_0: 0.18720/0.17794, loss_grounding_ce_0: 0.01264/0.27028, loss_mask_ce_1: 0.51546/0.89076, loss_mask_bce_1: 0.81815/0.33406, loss_mask_dice_1: 1.51214/1.16563, loss_spatial_bce_1: 0.11234/0.08648, loss_spatial_dice_1: 0.21553/0.20864, loss_spatial_ce_1: 0.05534/0.06375, loss_grounding_bce_1: 0.18459/0.08609, loss_grounding_dice_1: 0.19049/0.17875, loss_grounding_ce_1: 0.01286/0.27118, loss_mask_ce_2: 0.55532/0.89768, loss_mask_bce_2: 0.81353/0.33474, loss_mask_dice_2: 1.53439/1.16611, loss_spatial_bce_2: 0.11696/0.08776, loss_spatial_dice_2: 0.23026/0.21067, loss_spatial_ce_2: 0.08329/0.06715, loss_grounding_bce_2: 0.18104/0.08627, loss_grounding_dice_2: 0.18521/0.17866, loss_grounding_ce_2: 0.01617/0.27447, loss_mask_ce_3: 0.58153/0.90932, loss_mask_bce_3: 0.79296/0.33589, loss_mask_dice_3: 1.46428/1.16402, loss_spatial_bce_3: 0.13232/0.08911, loss_spatial_dice_3: 0.23985/0.21187, loss_spatial_ce_3: 0.06838/0.07238, loss_grounding_bce_3: 0.18258/0.08653, loss_grounding_dice_3: 0.18995/0.17831, loss_grounding_ce_3: 0.01615/0.27667, loss_mask_ce_4: 0.62157/0.91108, loss_mask_bce_4: 0.79204/0.33811, loss_mask_dice_4: 1.50944/1.18787, loss_spatial_bce_4: 0.13145/0.09301, loss_spatial_dice_4: 0.25385/0.22403, loss_spatial_ce_4: 0.11316/0.08838, loss_grounding_bce_4: 0.17665/0.08709, loss_grounding_dice_4: 0.18575/0.18135, loss_grounding_ce_4: 0.01153/0.27958, loss_mask_ce_5: 0.74345/0.92798, loss_mask_bce_5: 0.80449/0.34051, loss_mask_dice_5: 1.54446/1.19603, loss_spatial_bce_5: 0.12003/0.09534, loss_spatial_dice_5: 0.25334/0.22849, loss_spatial_ce_5: 0.12533/0.10171, loss_grounding_bce_5: 0.17496/0.08753, loss_grounding_dice_5: 0.19117/0.18268, loss_grounding_ce_5: 0.01461/0.29227, loss_mask_ce_6: 0.76141/0.96811, loss_mask_bce_6: 0.84194/0.34331, loss_mask_dice_6: 1.53317/1.19933, loss_spatial_bce_6: 0.14242/0.10100, loss_spatial_dice_6: 0.26092/0.23149, loss_spatial_ce_6: 0.11863/0.12623, loss_grounding_bce_6: 0.18396/0.08829, loss_grounding_dice_6: 0.19608/0.18313, loss_grounding_ce_6: 0.04526/0.30745, loss_mask_ce_7: 0.61870/1.01433, loss_mask_bce_7: 0.91010/0.35127, loss_mask_dice_7: 1.66864/1.25356, loss_spatial_bce_7: 0.14372/0.10875, loss_spatial_dice_7: 0.24523/0.25911, loss_spatial_ce_7: 0.17853/0.16072, loss_grounding_bce_7: 0.16537/0.09017, loss_grounding_dice_7: 0.17668/0.19047, loss_grounding_ce_7: 0.05371/0.33738, loss_mask_ce_8: 0.82005/1.12329, loss_mask_bce_8: 0.89668/0.36482, loss_mask_dice_8: 1.69246/1.32590, loss_spatial_bce_8: 0.14978/0.12855, loss_spatial_dice_8: 0.26741/0.29691, loss_spatial_ce_8: 0.25333/0.20840, loss_grounding_bce_8: 0.19176/0.09378, loss_grounding_dice_8: 0.18100/0.20107, loss_grounding_ce_8: 0.11405/0.40273, loss_mask_ce_9: 3.60714/3.67104, loss_mask_bce_9: 0.97048/0.39222, loss_mask_dice_9: 2.21715/1.89997, loss_spatial_bce_9: 0.33730/0.33249, loss_spatial_dice_9: 0.86620/0.82142, loss_spatial_ce_9: 1.39561/1.49037, loss_grounding_bce_9: 0.22135/0.10562, loss_grounding_dice_9: 0.26514/0.28067, loss_grounding_ce_9: 0.36012/0.66756] items per batch[64] items per second[0.24] total items[5798400] mini batches[ 90600] memory[7345] epoch remaining[0:34:17]
INFO:trainer.default_trainer:epochs[    49] optim steps[90700] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.19146/0.88978, loss_mask_bce_0: 0.08701/0.33311, loss_mask_dice_0: 0.05568/1.15856, loss_spatial_bce_0: 0.07480/0.08595, loss_spatial_dice_0: 0.05359/0.20483, loss_spatial_ce_0: 0.00004/0.05801, loss_grounding_bce_0: 0.07649/0.08588, loss_grounding_dice_0: 0.04707/0.17794, loss_grounding_ce_0: 0.00785/0.27028, loss_mask_ce_1: 0.18385/0.89075, loss_mask_bce_1: 0.08591/0.33403, loss_mask_dice_1: 0.05568/1.16557, loss_spatial_bce_1: 0.08131/0.08646, loss_spatial_dice_1: 0.06200/0.20863, loss_spatial_ce_1: 0.00010/0.06373, loss_grounding_bce_1: 0.07701/0.08608, loss_grounding_dice_1: 0.04860/0.17875, loss_grounding_ce_1: 0.00815/0.27118, loss_mask_ce_2: 0.18920/0.89765, loss_mask_bce_2: 0.08509/0.33471, loss_mask_dice_2: 0.05644/1.16606, loss_spatial_bce_2: 0.07944/0.08774, loss_spatial_dice_2: 0.06584/0.21065, loss_spatial_ce_2: 0.00007/0.06713, loss_grounding_bce_2: 0.07752/0.08626, loss_grounding_dice_2: 0.05157/0.17866, loss_grounding_ce_2: 0.01451/0.27445, loss_mask_ce_3: 0.19501/0.90931, loss_mask_bce_3: 0.08542/0.33586, loss_mask_dice_3: 0.05275/1.16396, loss_spatial_bce_3: 0.07880/0.08910, loss_spatial_dice_3: 0.06707/0.21185, loss_spatial_ce_3: 0.00036/0.07235, loss_grounding_bce_3: 0.07832/0.08652, loss_grounding_dice_3: 0.04648/0.17832, loss_grounding_ce_3: 0.02156/0.27667, loss_mask_ce_4: 0.20608/0.91108, loss_mask_bce_4: 0.08391/0.33807, loss_mask_dice_4: 0.05254/1.18781, loss_spatial_bce_4: 0.07223/0.09299, loss_spatial_dice_4: 0.05066/0.22401, loss_spatial_ce_4: 0.00039/0.08835, loss_grounding_bce_4: 0.07839/0.08708, loss_grounding_dice_4: 0.05013/0.18135, loss_grounding_ce_4: 0.02678/0.27959, loss_mask_ce_5: 0.22189/0.92798, loss_mask_bce_5: 0.08517/0.34048, loss_mask_dice_5: 0.05312/1.19597, loss_spatial_bce_5: 0.07258/0.09533, loss_spatial_dice_5: 0.05627/0.22847, loss_spatial_ce_5: 0.00041/0.10167, loss_grounding_bce_5: 0.07814/0.08751, loss_grounding_dice_5: 0.04586/0.18267, loss_grounding_ce_5: 0.08705/0.29230, loss_mask_ce_6: 0.21329/0.96814, loss_mask_bce_6: 0.08495/0.34327, loss_mask_dice_6: 0.05137/1.19925, loss_spatial_bce_6: 0.09754/0.10098, loss_spatial_dice_6: 0.07809/0.23148, loss_spatial_ce_6: 0.00676/0.12620, loss_grounding_bce_6: 0.07822/0.08828, loss_grounding_dice_6: 0.04350/0.18313, loss_grounding_ce_6: 0.13553/0.30749, loss_mask_ce_7: 0.31541/1.01434, loss_mask_bce_7: 0.09266/0.35123, loss_mask_dice_7: 0.06045/1.25350, loss_spatial_bce_7: 0.07490/0.10872, loss_spatial_dice_7: 0.05819/0.25909, loss_spatial_ce_7: 0.00434/0.16068, loss_grounding_bce_7: 0.07983/0.09016, loss_grounding_dice_7: 0.05033/0.19047, loss_grounding_ce_7: 0.19569/0.33739, loss_mask_ce_8: 0.27102/1.12330, loss_mask_bce_8: 0.09419/0.36478, loss_mask_dice_8: 0.06065/1.32584, loss_spatial_bce_8: 0.08151/0.12852, loss_spatial_dice_8: 0.05457/0.29689, loss_spatial_ce_8: 0.03569/0.20835, loss_grounding_bce_8: 0.18464/0.09377, loss_grounding_dice_8: 0.09537/0.20107, loss_grounding_ce_8: 0.01637/0.40278, loss_mask_ce_9: 1.81348/3.67117, loss_mask_bce_9: 0.12906/0.39220, loss_mask_dice_9: 0.10801/1.89993, loss_spatial_bce_9: 0.52262/0.33246, loss_spatial_dice_9: 0.61434/0.82143, loss_spatial_ce_9: 0.76104/1.49030, loss_grounding_bce_9: 0.11866/0.10561, loss_grounding_dice_9: 0.09271/0.28067, loss_grounding_ce_9: 0.07689/0.66765] items per batch[64] items per second[0.23] total items[5804800] mini batches[ 90700] memory[7345] epoch remaining[0:29:44]
INFO:trainer.default_trainer:epochs[    49] optim steps[90800] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.04765/0.88964, loss_mask_bce_0: 0.12921/0.33311, loss_mask_dice_0: 0.19819/1.15848, loss_spatial_bce_0: 0.07166/0.08594, loss_spatial_dice_0: 0.08863/0.20481, loss_spatial_ce_0: 0.02749/0.05799, loss_grounding_bce_0: 0.09630/0.08588, loss_grounding_dice_0: 0.11977/0.17793, loss_grounding_ce_0: 0.24641/0.27018, loss_mask_ce_1: 1.02652/0.89062, loss_mask_bce_1: 0.13551/0.33402, loss_mask_dice_1: 0.19834/1.16547, loss_spatial_bce_1: 0.08042/0.08645, loss_spatial_dice_1: 0.10621/0.20860, loss_spatial_ce_1: 0.04597/0.06370, loss_grounding_bce_1: 0.09896/0.08608, loss_grounding_dice_1: 0.11656/0.17874, loss_grounding_ce_1: 0.24743/0.27108, loss_mask_ce_2: 1.00484/0.89755, loss_mask_bce_2: 0.13321/0.33470, loss_mask_dice_2: 0.18643/1.16596, loss_spatial_bce_2: 0.09886/0.08773, loss_spatial_dice_2: 0.12435/0.21064, loss_spatial_ce_2: 0.02422/0.06710, loss_grounding_bce_2: 0.10165/0.08626, loss_grounding_dice_2: 0.12386/0.17864, loss_grounding_ce_2: 0.25758/0.27435, loss_mask_ce_3: 0.98899/0.90920, loss_mask_bce_3: 0.13414/0.33585, loss_mask_dice_3: 0.20941/1.16387, loss_spatial_bce_3: 0.08540/0.08909, loss_spatial_dice_3: 0.11767/0.21184, loss_spatial_ce_3: 0.02594/0.07233, loss_grounding_bce_3: 0.09449/0.08651, loss_grounding_dice_3: 0.11659/0.17830, loss_grounding_ce_3: 0.23784/0.27657, loss_mask_ce_4: 0.98906/0.91097, loss_mask_bce_4: 0.14362/0.33806, loss_mask_dice_4: 0.18641/1.18774, loss_spatial_bce_4: 0.08447/0.09299, loss_spatial_dice_4: 0.10571/0.22400, loss_spatial_ce_4: 0.03204/0.08833, loss_grounding_bce_4: 0.10911/0.08708, loss_grounding_dice_4: 0.12322/0.18133, loss_grounding_ce_4: 0.24095/0.27949, loss_mask_ce_5: 0.90241/0.92785, loss_mask_bce_5: 0.13327/0.34047, loss_mask_dice_5: 0.20234/1.19588, loss_spatial_bce_5: 0.08881/0.09532, loss_spatial_dice_5: 0.10852/0.22846, loss_spatial_ce_5: 0.02304/0.10164, loss_grounding_bce_5: 0.10195/0.08751, loss_grounding_dice_5: 0.12650/0.18266, loss_grounding_ce_5: 0.21448/0.29222, loss_mask_ce_6: 1.00653/0.96803, loss_mask_bce_6: 0.13704/0.34326, loss_mask_dice_6: 0.18886/1.19918, loss_spatial_bce_6: 0.09685/0.10097, loss_spatial_dice_6: 0.12159/0.23146, loss_spatial_ce_6: 0.02473/0.12617, loss_grounding_bce_6: 0.11116/0.08828, loss_grounding_dice_6: 0.13452/0.18312, loss_grounding_ce_6: 0.19768/0.30742, loss_mask_ce_7: 1.09674/1.01425, loss_mask_bce_7: 0.14922/0.35122, loss_mask_dice_7: 0.20938/1.25341, loss_spatial_bce_7: 0.08855/0.10872, loss_spatial_dice_7: 0.10237/0.25907, loss_spatial_ce_7: 0.11961/0.16064, loss_grounding_bce_7: 0.11031/0.09016, loss_grounding_dice_7: 0.11666/0.19045, loss_grounding_ce_7: 0.24249/0.33730, loss_mask_ce_8: 0.92546/1.12315, loss_mask_bce_8: 0.17248/0.36477, loss_mask_dice_8: 0.19661/1.32575, loss_spatial_bce_8: 0.10472/0.12851, loss_spatial_dice_8: 0.12107/0.29688, loss_spatial_ce_8: 0.10940/0.20826, loss_grounding_bce_8: 0.12031/0.09376, loss_grounding_dice_8: 0.14439/0.20106, loss_grounding_ce_8: 0.23078/0.40267, loss_mask_ce_9: 2.35135/3.67103, loss_mask_bce_9: 0.25947/0.39219, loss_mask_dice_9: 0.34777/1.89988, loss_spatial_bce_9: 0.37643/0.33245, loss_spatial_dice_9: 0.70220/0.82142, loss_spatial_ce_9: 1.18783/1.49034, loss_grounding_bce_9: 0.19647/0.10560, loss_grounding_dice_9: 0.25625/0.28066, loss_grounding_ce_9: 0.18946/0.66759] items per batch[64] items per second[0.24] total items[5811200] mini batches[ 90800] memory[7345] epoch remaining[0:25:08]
INFO:trainer.default_trainer:epochs[    49] optim steps[90900] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.43261/0.88962, loss_mask_bce_0: 0.16168/0.33308, loss_mask_dice_0: 2.79683/1.15853, loss_spatial_bce_0: 0.01736/0.08593, loss_spatial_dice_0: 0.23721/0.20479, loss_spatial_ce_0: 0.00147/0.05799, loss_grounding_bce_0: 0.03202/0.08587, loss_grounding_dice_0: 0.28911/0.17793, loss_grounding_ce_0: 0.17146/0.27012, loss_mask_ce_1: 1.46565/0.89057, loss_mask_bce_1: 0.14577/0.33400, loss_mask_dice_1: 2.28918/1.16549, loss_spatial_bce_1: 0.01866/0.08645, loss_spatial_dice_1: 0.26593/0.20858, loss_spatial_ce_1: 0.00745/0.06369, loss_grounding_bce_1: 0.03098/0.08607, loss_grounding_dice_1: 0.28457/0.17874, loss_grounding_ce_1: 0.15332/0.27103, loss_mask_ce_2: 1.63809/0.89752, loss_mask_bce_2: 0.13965/0.33468, loss_mask_dice_2: 2.25665/1.16597, loss_spatial_bce_2: 0.02063/0.08772, loss_spatial_dice_2: 0.28333/0.21061, loss_spatial_ce_2: 0.00395/0.06709, loss_grounding_bce_2: 0.03007/0.08625, loss_grounding_dice_2: 0.28240/0.17864, loss_grounding_ce_2: 0.15801/0.27430, loss_mask_ce_3: 1.45957/0.90918, loss_mask_bce_3: 0.14583/0.33583, loss_mask_dice_3: 2.44814/1.16389, loss_spatial_bce_3: 0.01954/0.08908, loss_spatial_dice_3: 0.26005/0.21182, loss_spatial_ce_3: 0.00937/0.07232, loss_grounding_bce_3: 0.03217/0.08650, loss_grounding_dice_3: 0.28719/0.17830, loss_grounding_ce_3: 0.16302/0.27652, loss_mask_ce_4: 1.48188/0.91095, loss_mask_bce_4: 0.13781/0.33804, loss_mask_dice_4: 2.42918/1.18776, loss_spatial_bce_4: 0.01909/0.09298, loss_spatial_dice_4: 0.25529/0.22398, loss_spatial_ce_4: 0.01882/0.08833, loss_grounding_bce_4: 0.03005/0.08707, loss_grounding_dice_4: 0.27416/0.18133, loss_grounding_ce_4: 0.15245/0.27944, loss_mask_ce_5: 1.59450/0.92784, loss_mask_bce_5: 0.14706/0.34045, loss_mask_dice_5: 2.43170/1.19591, loss_spatial_bce_5: 0.02194/0.09531, loss_spatial_dice_5: 0.27819/0.22844, loss_spatial_ce_5: 0.02528/0.10163, loss_grounding_bce_5: 0.08860/0.08750, loss_grounding_dice_5: 0.29933/0.18266, loss_grounding_ce_5: 0.14128/0.29219, loss_mask_ce_6: 1.55261/0.96805, loss_mask_bce_6: 0.15117/0.34324, loss_mask_dice_6: 2.51690/1.19920, loss_spatial_bce_6: 0.01984/0.10097, loss_spatial_dice_6: 0.29372/0.23144, loss_spatial_ce_6: 0.01506/0.12614, loss_grounding_bce_6: 0.08439/0.08827, loss_grounding_dice_6: 0.29368/0.18312, loss_grounding_ce_6: 0.11038/0.30737, loss_mask_ce_7: 1.89378/1.01425, loss_mask_bce_7: 0.14211/0.35120, loss_mask_dice_7: 2.49416/1.25343, loss_spatial_bce_7: 0.01900/0.10871, loss_spatial_dice_7: 0.27009/0.25906, loss_spatial_ce_7: 0.03192/0.16062, loss_grounding_bce_7: 0.02888/0.09015, loss_grounding_dice_7: 0.28931/0.19045, loss_grounding_ce_7: 0.15518/0.33724, loss_mask_ce_8: 1.72117/1.12314, loss_mask_bce_8: 0.14857/0.36475, loss_mask_dice_8: 2.83687/1.32581, loss_spatial_bce_8: 0.02515/0.12851, loss_spatial_dice_8: 0.37590/0.29687, loss_spatial_ce_8: 0.06822/0.20820, loss_grounding_bce_8: 0.03265/0.09375, loss_grounding_dice_8: 0.29848/0.20105, loss_grounding_ce_8: 0.12887/0.40260, loss_mask_ce_9: 4.37314/3.67107, loss_mask_bce_9: 0.12430/0.39218, loss_mask_dice_9: 3.46220/1.89993, loss_spatial_bce_9: 0.16493/0.33244, loss_spatial_dice_9: 0.88028/0.82141, loss_spatial_ce_9: 1.64972/1.49037, loss_grounding_bce_9: 0.06876/0.10560, loss_grounding_dice_9: 0.34865/0.28067, loss_grounding_ce_9: 0.18641/0.66750] items per batch[64] items per second[0.23] total items[5817600] mini batches[ 90900] memory[7345] epoch remaining[0:20:34]
INFO:trainer.default_trainer:epochs[    49] optim steps[91000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.91672/0.88955, loss_mask_bce_0: 0.09444/0.33311, loss_mask_dice_0: 0.51816/1.15861, loss_spatial_bce_0: 0.02749/0.08594, loss_spatial_dice_0: 0.22698/0.20478, loss_spatial_ce_0: 0.02452/0.05797, loss_grounding_bce_0: 0.11577/0.08587, loss_grounding_dice_0: 0.13065/0.17795, loss_grounding_ce_0: 0.02376/0.27015, loss_mask_ce_1: 0.78043/0.89049, loss_mask_bce_1: 0.09694/0.33402, loss_mask_dice_1: 0.94923/1.16561, loss_spatial_bce_1: 0.02980/0.08645, loss_spatial_dice_1: 0.23126/0.20857, loss_spatial_ce_1: 0.01709/0.06367, loss_grounding_bce_1: 0.11593/0.08607, loss_grounding_dice_1: 0.13366/0.17876, loss_grounding_ce_1: 0.03716/0.27106, loss_mask_ce_2: 1.15341/0.89744, loss_mask_bce_2: 0.09036/0.33469, loss_mask_dice_2: 0.48382/1.16608, loss_spatial_bce_2: 0.02831/0.08773, loss_spatial_dice_2: 0.24969/0.21061, loss_spatial_ce_2: 0.04101/0.06707, loss_grounding_bce_2: 0.11371/0.08625, loss_grounding_dice_2: 0.13016/0.17866, loss_grounding_ce_2: 0.03949/0.27434, loss_mask_ce_3: 1.04524/0.90910, loss_mask_bce_3: 0.08927/0.33585, loss_mask_dice_3: 0.50426/1.16397, loss_spatial_bce_3: 0.02881/0.08909, loss_spatial_dice_3: 0.19687/0.21182, loss_spatial_ce_3: 0.06739/0.07231, loss_grounding_bce_3: 0.11391/0.08650, loss_grounding_dice_3: 0.13338/0.17832, loss_grounding_ce_3: 0.04850/0.27652, loss_mask_ce_4: 0.80401/0.91087, loss_mask_bce_4: 0.09934/0.33806, loss_mask_dice_4: 0.59527/1.18786, loss_spatial_bce_4: 0.02845/0.09299, loss_spatial_dice_4: 0.23595/0.22397, loss_spatial_ce_4: 0.10423/0.08831, loss_grounding_bce_4: 0.12345/0.08707, loss_grounding_dice_4: 0.15371/0.18134, loss_grounding_ce_4: 0.03884/0.27947, loss_mask_ce_5: 1.67885/0.92776, loss_mask_bce_5: 0.07910/0.34047, loss_mask_dice_5: 0.55314/1.19602, loss_spatial_bce_5: 0.02586/0.09532, loss_spatial_dice_5: 0.18788/0.22843, loss_spatial_ce_5: 0.17240/0.10161, loss_grounding_bce_5: 0.12565/0.08750, loss_grounding_dice_5: 0.14899/0.18268, loss_grounding_ce_5: 0.06158/0.29221, loss_mask_ce_6: 1.02489/0.96798, loss_mask_bce_6: 0.09561/0.34326, loss_mask_dice_6: 0.79023/1.19931, loss_spatial_bce_6: 0.02701/0.10098, loss_spatial_dice_6: 0.21663/0.23144, loss_spatial_ce_6: 0.13306/0.12612, loss_grounding_bce_6: 0.12101/0.08827, loss_grounding_dice_6: 0.19468/0.18314, loss_grounding_ce_6: 0.03916/0.30735, loss_mask_ce_7: 1.20324/1.01418, loss_mask_bce_7: 0.09731/0.35122, loss_mask_dice_7: 0.52448/1.25352, loss_spatial_bce_7: 0.02878/0.10871, loss_spatial_dice_7: 0.24868/0.25906, loss_spatial_ce_7: 0.16023/0.16058, loss_grounding_bce_7: 0.12694/0.09015, loss_grounding_dice_7: 0.16488/0.19047, loss_grounding_ce_7: 0.02009/0.33727, loss_mask_ce_8: 1.40884/1.12305, loss_mask_bce_8: 0.09559/0.36477, loss_mask_dice_8: 0.67422/1.32591, loss_spatial_bce_8: 0.04393/0.12852, loss_spatial_dice_8: 0.35456/0.29687, loss_spatial_ce_8: 0.09397/0.20812, loss_grounding_bce_8: 0.12245/0.09375, loss_grounding_dice_8: 0.16674/0.20108, loss_grounding_ce_8: 0.02861/0.40256, loss_mask_ce_9: 2.96700/3.67103, loss_mask_bce_9: 0.08477/0.39220, loss_mask_dice_9: 0.77539/1.90007, loss_spatial_bce_9: 0.13492/0.33243, loss_spatial_dice_9: 0.76151/0.82141, loss_spatial_ce_9: 3.11719/1.49036, loss_grounding_bce_9: 0.11445/0.10560, loss_grounding_dice_9: 0.20054/0.28070, loss_grounding_ce_9: 0.02182/0.66740] items per batch[64] items per second[0.24] total items[5824000] mini batches[ 91000] memory[7345] epoch remaining[0:15:59]
INFO:trainer.default_trainer:epochs[    49] optim steps[91100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.79695/0.88946, loss_mask_bce_0: 0.39308/0.33309, loss_mask_dice_0: 1.14858/1.15868, loss_spatial_bce_0: 0.06601/0.08592, loss_spatial_dice_0: 0.23016/0.20477, loss_spatial_ce_0: 0.00594/0.05795, loss_grounding_bce_0: 0.09133/0.08587, loss_grounding_dice_0: 0.09670/0.17793, loss_grounding_ce_0: 0.08392/0.27012, loss_mask_ce_1: 1.02049/0.89042, loss_mask_bce_1: 0.41429/0.33401, loss_mask_dice_1: 1.15543/1.16568, loss_spatial_bce_1: 0.07032/0.08644, loss_spatial_dice_1: 0.23776/0.20856, loss_spatial_ce_1: 0.00776/0.06365, loss_grounding_bce_1: 0.09845/0.08607, loss_grounding_dice_1: 0.11271/0.17874, loss_grounding_ce_1: 0.06932/0.27101, loss_mask_ce_2: 0.86830/0.89737, loss_mask_bce_2: 0.39693/0.33468, loss_mask_dice_2: 1.09764/1.16615, loss_spatial_bce_2: 0.06921/0.08772, loss_spatial_dice_2: 0.23589/0.21059, loss_spatial_ce_2: 0.01248/0.06705, loss_grounding_bce_2: 0.09259/0.08625, loss_grounding_dice_2: 0.10211/0.17864, loss_grounding_ce_2: 0.07383/0.27431, loss_mask_ce_3: 1.15496/0.90904, loss_mask_bce_3: 0.40693/0.33583, loss_mask_dice_3: 1.14376/1.16404, loss_spatial_bce_3: 0.07253/0.08908, loss_spatial_dice_3: 0.23211/0.21180, loss_spatial_ce_3: 0.00711/0.07229, loss_grounding_bce_3: 0.09218/0.08651, loss_grounding_dice_3: 0.09811/0.17831, loss_grounding_ce_3: 0.09324/0.27650, loss_mask_ce_4: 1.07174/0.91081, loss_mask_bce_4: 0.39973/0.33804, loss_mask_dice_4: 1.19642/1.18793, loss_spatial_bce_4: 0.07782/0.09298, loss_spatial_dice_4: 0.25895/0.22396, loss_spatial_ce_4: 0.03645/0.08828, loss_grounding_bce_4: 0.09199/0.08707, loss_grounding_dice_4: 0.10320/0.18133, loss_grounding_ce_4: 0.09995/0.27945, loss_mask_ce_5: 0.85606/0.92770, loss_mask_bce_5: 0.40546/0.34045, loss_mask_dice_5: 1.09647/1.19611, loss_spatial_bce_5: 0.08314/0.09531, loss_spatial_dice_5: 0.25692/0.22842, loss_spatial_ce_5: 0.02300/0.10158, loss_grounding_bce_5: 0.09023/0.08751, loss_grounding_dice_5: 0.09846/0.18267, loss_grounding_ce_5: 0.11049/0.29220, loss_mask_ce_6: 0.88644/0.96791, loss_mask_bce_6: 0.48881/0.34324, loss_mask_dice_6: 1.21141/1.19939, loss_spatial_bce_6: 0.08834/0.10097, loss_spatial_dice_6: 0.25823/0.23142, loss_spatial_ce_6: 0.04235/0.12607, loss_grounding_bce_6: 0.09176/0.08828, loss_grounding_dice_6: 0.09798/0.18313, loss_grounding_ce_6: 0.18999/0.30736, loss_mask_ce_7: 0.99326/1.01414, loss_mask_bce_7: 0.42412/0.35120, loss_mask_dice_7: 1.08758/1.25359, loss_spatial_bce_7: 0.08956/0.10870, loss_spatial_dice_7: 0.27634/0.25904, loss_spatial_ce_7: 0.10151/0.16053, loss_grounding_bce_7: 0.10000/0.09015, loss_grounding_dice_7: 0.11150/0.19045, loss_grounding_ce_7: 0.25557/0.33725, loss_mask_ce_8: 0.80453/1.12300, loss_mask_bce_8: 0.42047/0.36474, loss_mask_dice_8: 1.24448/1.32599, loss_spatial_bce_8: 0.14273/0.12850, loss_spatial_dice_8: 0.33110/0.29686, loss_spatial_ce_8: 0.08398/0.20805, loss_grounding_bce_8: 0.10172/0.09376, loss_grounding_dice_8: 0.11126/0.20106, loss_grounding_ce_8: 0.16286/0.40251, loss_mask_ce_9: 3.05486/3.67100, loss_mask_bce_9: 0.50266/0.39218, loss_mask_dice_9: 1.78892/1.90029, loss_spatial_bce_9: 0.36528/0.33243, loss_spatial_dice_9: 0.85514/0.82140, loss_spatial_ce_9: 1.42964/1.49029, loss_grounding_bce_9: 0.13064/0.10560, loss_grounding_dice_9: 0.23706/0.28068, loss_grounding_ce_9: 1.41538/0.66738] items per batch[64] items per second[0.23] total items[5830400] mini batches[ 91100] memory[7345] epoch remaining[0:11:26]
INFO:trainer.default_trainer:epochs[    49] optim steps[91200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.01948/0.88943, loss_mask_bce_0: 0.35938/0.33305, loss_mask_dice_0: 1.97717/1.15860, loss_spatial_bce_0: 0.03111/0.08591, loss_spatial_dice_0: 0.21650/0.20476, loss_spatial_ce_0: 0.10085/0.05793, loss_grounding_bce_0: 0.10963/0.08587, loss_grounding_dice_0: 0.19031/0.17792, loss_grounding_ce_0: 0.00176/0.27008, loss_mask_ce_1: 0.67363/0.89037, loss_mask_bce_1: 0.36499/0.33397, loss_mask_dice_1: 2.01546/1.16558, loss_spatial_bce_1: 0.03225/0.08643, loss_spatial_dice_1: 0.22437/0.20854, loss_spatial_ce_1: 0.10994/0.06363, loss_grounding_bce_1: 0.10176/0.08607, loss_grounding_dice_1: 0.17994/0.17874, loss_grounding_ce_1: 0.00203/0.27097, loss_mask_ce_2: 0.64324/0.89734, loss_mask_bce_2: 0.35866/0.33464, loss_mask_dice_2: 1.96999/1.16606, loss_spatial_bce_2: 0.03299/0.08770, loss_spatial_dice_2: 0.24627/0.21058, loss_spatial_ce_2: 0.11028/0.06704, loss_grounding_bce_2: 0.10435/0.08625, loss_grounding_dice_2: 0.18620/0.17863, loss_grounding_ce_2: 0.00159/0.27427, loss_mask_ce_3: 0.72071/0.90901, loss_mask_bce_3: 0.37143/0.33579, loss_mask_dice_3: 2.05050/1.16394, loss_spatial_bce_3: 0.03223/0.08907, loss_spatial_dice_3: 0.23196/0.21179, loss_spatial_ce_3: 0.11794/0.07227, loss_grounding_bce_3: 0.10675/0.08650, loss_grounding_dice_3: 0.18951/0.17830, loss_grounding_ce_3: 0.00150/0.27647, loss_mask_ce_4: 0.64069/0.91078, loss_mask_bce_4: 0.36392/0.33800, loss_mask_dice_4: 2.07500/1.18785, loss_spatial_bce_4: 0.03590/0.09296, loss_spatial_dice_4: 0.24936/0.22395, loss_spatial_ce_4: 0.16250/0.08824, loss_grounding_bce_4: 0.10756/0.08707, loss_grounding_dice_4: 0.18706/0.18132, loss_grounding_ce_4: 0.00274/0.27943, loss_mask_ce_5: 0.59391/0.92767, loss_mask_bce_5: 0.37033/0.34042, loss_mask_dice_5: 2.05135/1.19601, loss_spatial_bce_5: 0.03338/0.09529, loss_spatial_dice_5: 0.25549/0.22840, loss_spatial_ce_5: 0.29437/0.10154, loss_grounding_bce_5: 0.11034/0.08750, loss_grounding_dice_5: 0.20185/0.18267, loss_grounding_ce_5: 0.00552/0.29217, loss_mask_ce_6: 0.67611/0.96788, loss_mask_bce_6: 0.37329/0.34320, loss_mask_dice_6: 1.97674/1.19928, loss_spatial_bce_6: 0.03827/0.10095, loss_spatial_dice_6: 0.26910/0.23141, loss_spatial_ce_6: 0.18037/0.12602, loss_grounding_bce_6: 0.10560/0.08827, loss_grounding_dice_6: 0.18711/0.18313, loss_grounding_ce_6: 0.00283/0.30733, loss_mask_ce_7: 0.75982/1.01411, loss_mask_bce_7: 0.34271/0.35115, loss_mask_dice_7: 2.06211/1.25349, loss_spatial_bce_7: 0.04066/0.10869, loss_spatial_dice_7: 0.29779/0.25902, loss_spatial_ce_7: 0.20636/0.16049, loss_grounding_bce_7: 0.10466/0.09014, loss_grounding_dice_7: 0.18883/0.19045, loss_grounding_ce_7: 0.00895/0.33721, loss_mask_ce_8: 1.09091/1.12298, loss_mask_bce_8: 0.35321/0.36471, loss_mask_dice_8: 2.14287/1.32587, loss_spatial_bce_8: 0.05956/0.12848, loss_spatial_dice_8: 0.37736/0.29686, loss_spatial_ce_8: 0.22097/0.20797, loss_grounding_bce_8: 0.11766/0.09375, loss_grounding_dice_8: 0.19319/0.20106, loss_grounding_ce_8: 0.01327/0.40245, loss_mask_ce_9: 3.28246/3.67091, loss_mask_bce_9: 0.36129/0.39214, loss_mask_dice_9: 2.74364/1.90014, loss_spatial_bce_9: 0.25678/0.33242, loss_spatial_dice_9: 0.91668/0.82140, loss_spatial_ce_9: 1.62901/1.49030, loss_grounding_bce_9: 0.09905/0.10560, loss_grounding_dice_9: 0.21335/0.28068, loss_grounding_ce_9: 0.37094/0.66738] items per batch[64] items per second[0.23] total items[5836800] mini batches[ 91200] memory[7345] epoch remaining[0:06:51]
INFO:trainer.default_trainer:epochs[    49] optim steps[91300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.28893/0.88937, loss_mask_bce_0: 0.12387/0.33305, loss_mask_dice_0: 2.32587/1.15846, loss_spatial_bce_0: 0.01338/0.08592, loss_spatial_dice_0: 0.25684/0.20475, loss_spatial_ce_0: 0.04735/0.05791, loss_grounding_bce_0: 0.02050/0.08587, loss_grounding_dice_0: 0.25612/0.17792, loss_grounding_ce_0: 0.09768/0.27005, loss_mask_ce_1: 0.95928/0.89030, loss_mask_bce_1: 0.11200/0.33398, loss_mask_dice_1: 2.39650/1.16545, loss_spatial_bce_1: 0.01505/0.08643, loss_spatial_dice_1: 0.25909/0.20853, loss_spatial_ce_1: 0.13402/0.06361, loss_grounding_bce_1: 0.02379/0.08608, loss_grounding_dice_1: 0.26069/0.17874, loss_grounding_ce_1: 0.10044/0.27097, loss_mask_ce_2: 1.41995/0.89727, loss_mask_bce_2: 0.14519/0.33465, loss_mask_dice_2: 2.42901/1.16593, loss_spatial_bce_2: 0.01380/0.08770, loss_spatial_dice_2: 0.24232/0.21057, loss_spatial_ce_2: 0.16910/0.06702, loss_grounding_bce_2: 0.02354/0.08625, loss_grounding_dice_2: 0.31731/0.17863, loss_grounding_ce_2: 0.10097/0.27423, loss_mask_ce_3: 1.18156/0.90895, loss_mask_bce_3: 0.13517/0.33580, loss_mask_dice_3: 2.62455/1.16381, loss_spatial_bce_3: 0.01284/0.08907, loss_spatial_dice_3: 0.24401/0.21178, loss_spatial_ce_3: 0.16574/0.07225, loss_grounding_bce_3: 0.01989/0.08651, loss_grounding_dice_3: 0.25965/0.17830, loss_grounding_ce_3: 0.10588/0.27645, loss_mask_ce_4: 1.26050/0.91072, loss_mask_bce_4: 0.13137/0.33801, loss_mask_dice_4: 2.28466/1.18771, loss_spatial_bce_4: 0.01516/0.09297, loss_spatial_dice_4: 0.28792/0.22394, loss_spatial_ce_4: 0.07325/0.08822, loss_grounding_bce_4: 0.01976/0.08707, loss_grounding_dice_4: 0.21570/0.18132, loss_grounding_ce_4: 0.12815/0.27940, loss_mask_ce_5: 1.35396/0.92763, loss_mask_bce_5: 0.11543/0.34042, loss_mask_dice_5: 2.49966/1.19587, loss_spatial_bce_5: 0.01368/0.09530, loss_spatial_dice_5: 0.26167/0.22839, loss_spatial_ce_5: 0.17667/0.10153, loss_grounding_bce_5: 0.01871/0.08751, loss_grounding_dice_5: 0.19774/0.18268, loss_grounding_ce_5: 0.12970/0.29215, loss_mask_ce_6: 1.65269/0.96782, loss_mask_bce_6: 0.12026/0.34320, loss_mask_dice_6: 2.50052/1.19915, loss_spatial_bce_6: 0.01527/0.10096, loss_spatial_dice_6: 0.27280/0.23141, loss_spatial_ce_6: 0.06572/0.12600, loss_grounding_bce_6: 0.02492/0.08828, loss_grounding_dice_6: 0.27740/0.18313, loss_grounding_ce_6: 0.13413/0.30732, loss_mask_ce_7: 1.72529/1.01406, loss_mask_bce_7: 0.12799/0.35115, loss_mask_dice_7: 2.41715/1.25335, loss_spatial_bce_7: 0.01763/0.10869, loss_spatial_dice_7: 0.32631/0.25902, loss_spatial_ce_7: 0.23067/0.16046, loss_grounding_bce_7: 0.02360/0.09015, loss_grounding_dice_7: 0.28416/0.19044, loss_grounding_ce_7: 0.20628/0.33720, loss_mask_ce_8: 1.56725/1.12291, loss_mask_bce_8: 0.12318/0.36471, loss_mask_dice_8: 3.11229/1.32575, loss_spatial_bce_8: 0.01729/0.12848, loss_spatial_dice_8: 0.34607/0.29685, loss_spatial_ce_8: 0.10656/0.20790, loss_grounding_bce_8: 0.02811/0.09376, loss_grounding_dice_8: 0.38377/0.20106, loss_grounding_ce_8: 0.13530/0.40242, loss_mask_ce_9: 4.49781/3.67078, loss_mask_bce_9: 0.13039/0.39216, loss_mask_dice_9: 3.83444/1.89998, loss_spatial_bce_9: 0.14791/0.33242, loss_spatial_dice_9: 0.89596/0.82139, loss_spatial_ce_9: 1.82434/1.49029, loss_grounding_bce_9: 0.02532/0.10560, loss_grounding_dice_9: 0.67266/0.28069, loss_grounding_ce_9: 0.21504/0.66739] items per batch[64] items per second[0.23] total items[5843200] mini batches[ 91300] memory[7345] epoch remaining[0:02:17]
WARNING:trainer.utils_trainer:Saving checkpoint...
WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focalt_unicl_lang_v1.yaml_conf~/run_1/00091350.
INFO:trainer.default_trainer:Evaluation start ...
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/157. Dataloading: 0.0026 s/iter. Inference: 0.2202 s/iter. Eval: 0.0948 s/iter. Total: 0.3176 s/iter. ETA=0:00:46
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 28/157. Dataloading: 0.0029 s/iter. Inference: 0.2209 s/iter. Eval: 0.0835 s/iter. Total: 0.3074 s/iter. ETA=0:00:39
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/157. Dataloading: 0.0030 s/iter. Inference: 0.2241 s/iter. Eval: 0.0813 s/iter. Total: 0.3086 s/iter. ETA=0:00:34
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 62/157. Dataloading: 0.0031 s/iter. Inference: 0.2232 s/iter. Eval: 0.0785 s/iter. Total: 0.3049 s/iter. ETA=0:00:28
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/157. Dataloading: 0.0031 s/iter. Inference: 0.2220 s/iter. Eval: 0.0773 s/iter. Total: 0.3026 s/iter. ETA=0:00:23
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 95/157. Dataloading: 0.0031 s/iter. Inference: 0.2240 s/iter. Eval: 0.0772 s/iter. Total: 0.3045 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 111/157. Dataloading: 0.0031 s/iter. Inference: 0.2260 s/iter. Eval: 0.0772 s/iter. Total: 0.3065 s/iter. ETA=0:00:14
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 128/157. Dataloading: 0.0032 s/iter. Inference: 0.2254 s/iter. Eval: 0.0773 s/iter. Total: 0.3061 s/iter. ETA=0:00:08
INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 145/157. Dataloading: 0.0032 s/iter. Inference: 0.2260 s/iter. Eval: 0.0771 s/iter. Total: 0.3064 s/iter. ETA=0:00:03
INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalxi64p633 ...
INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results:
|        |   PQ   |   SQ   |   RQ   |  #categories  |
|:------:|:------:|:------:|:------:|:-------------:|
|  All   | 50.544 | 82.034 | 60.746 |      133      |
| Things | 55.509 | 82.661 | 66.462 |      80       |
| Stuff  | 43.049 | 81.088 | 52.117 |      53       |
INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ...
INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/xueyanz/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json
Loading and preparing results...
INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API...
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox*
DONE (t=0.39s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 10.52 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.15 seconds.
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox:
|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl  |
|:-----:|:------:|:------:|:-----:|:-----:|:-----:|
| 0.000 | 0.000  | 0.000  | 0.000 | 0.000 | 0.000 |
INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP:
| category      | AP    | category     | AP    | category       | AP    |
|:--------------|:------|:-------------|:------|:---------------|:------|
| person        | 0.000 | bicycle      | 0.000 | car            | 0.000 |
| motorcycle    | 0.000 | airplane     | 0.000 | bus            | 0.000 |
| train         | 0.000 | truck        | 0.000 | boat           | 0.000 |
| traffic light | 0.000 | fire hydrant | 0.000 | stop sign      | 0.000 |
| parking meter | 0.000 | bench        | 0.000 | bird           | 0.000 |
| cat           | 0.000 | dog          | 0.000 | horse          | 0.000 |
| sheep         | 0.000 | cow          | 0.000 | elephant       | 0.000 |
| bear          | 0.000 | zebra        | 0.000 | giraffe        | 0.000 |
| backpack      | 0.000 | umbrella     | 0.000 | handbag        | 0.000 |
| tie           | 0.000 | suitcase     | 0.000 | frisbee        | 0.000 |
| skis          | 0.000 | snowboard    | 0.000 | sports ball    | 0.000 |
| kite          | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 |
| skateboard    | 0.000 | surfboard    | 0.000 | tennis racket  | 0.000 |
| bottle        | 0.000 | wine glass   | 0.000 | cup            | 0.000 |
| fork          | 0.000 | knife        | 0.000 | spoon          | 0.000 |
| bowl          | 0.000 | banana       | 0.000 | apple          | 0.000 |
| sandwich      | 0.000 | orange       | 0.000 | broccoli       | 0.000 |
| carrot        | 0.000 | hot dog      | 0.000 | pizza          | 0.000 |
| donut         | 0.000 | cake         | 0.000 | chair          | 0.000 |
| couch         | 0.000 | potted plant | 0.000 | bed            | 0.000 |
| dining table  | 0.000 | toilet       | 0.000 | tv             | 0.000 |
| laptop        | 0.000 | mouse        | 0.000 | remote         | 0.000 |
| keyboard      | 0.000 | cell phone   | 0.000 | microwave      | 0.000 |
| oven          | 0.000 | toaster      | 0.000 | sink           | 0.000 |
| refrigerator  | 0.000 | book         | 0.000 | clock          | 0.000 |
| vase          | 0.000 | scissors     | 0.000 | teddy bear     | 0.000 |
| hair drier    | 0.000 | toothbrush   | 0.000 |                |       |
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Loading and preparing results...
DONE (t=2.34s)
creating index...
index created!
INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm*
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.66 seconds.
INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results...
INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.11 seconds.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.395
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.620
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.415
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.426
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.614
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.497
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.514
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.719
INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.537 | 62.040 | 41.548 | 19.506 | 42.599 | 61.361 |
INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP:
| category      | AP     | category     | AP     | category       | AP     |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person        | 44.738 | bicycle      | 19.583 | car            | 37.714 |
| motorcycle    | 35.710 | airplane     | 56.774 | bus            | 65.778 |
| train         | 68.646 | truck        | 36.063 | boat           | 24.057 |
| traffic light | 26.013 | fire hydrant | 66.199 | stop sign      | 64.490 |
| parking meter | 43.756 | bench        | 20.300 | bird           | 30.045 |
| cat           | 74.045 | dog          | 66.033 | horse          | 45.392 |
| sheep         | 48.285 | cow          | 52.249 | elephant       | 61.355 |
| bear          | 76.682 | zebra        | 60.616 | giraffe        | 57.321 |
| backpack      | 17.241 | umbrella     | 48.964 | handbag        | 15.868 |
| tie           | 34.707 | suitcase     | 43.333 | frisbee        | 66.592 |
| skis          | 5.182  | snowboard    | 21.939 | sports ball    | 48.021 |
| kite          | 35.745 | baseball bat | 29.858 | baseball glove | 44.390 |
| skateboard    | 36.148 | surfboard    | 36.788 | tennis racket  | 56.492 |
| bottle        | 35.458 | wine glass   | 27.582 | cup            | 41.220 |
| fork          | 15.801 | knife        | 14.121 | spoon          | 14.327 |
| bowl          | 32.721 | banana       | 21.554 | apple          | 20.304 |
| sandwich      | 42.807 | orange       | 30.105 | broccoli       | 22.632 |
| carrot        | 20.902 | hot dog      | 22.232 | pizza          | 51.116 |
| donut         | 47.522 | cake         | 45.064 | chair          | 21.644 |
| couch         | 41.754 | potted plant | 18.302 | bed            | 40.770 |
| dining table  | 13.089 | toilet       | 67.974 | tv             | 62.858 |
| laptop        | 63.445 | mouse        | 59.240 | remote         | 32.508 |
| keyboard      | 48.546 | cell phone   | 38.355 | microwave      | 55.473 |
| oven          | 34.119 | toaster      | 34.208 | sink           | 37.520 |
| refrigerator  | 60.170 | book         | 10.001 | clock          | 52.475 |
| vase          | 34.681 | scissors     | 24.505 | teddy bear     | 51.190 |
| hair drier    | 12.715 | toothbrush   | 18.870 |                |        |
INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 60.99785355339221, 'fwIoU': 69.31511411098542, 'IoU-person': 87.40445614270267, 'IoU-bicycle': 75.58945374113894, 'IoU-car': 70.77308249611035, 'IoU-motorcycle': 82.02421363678313, 'IoU-airplane': 84.04597435243811, 'IoU-bus': 85.72193103736605, 'IoU-train': 85.17951364310245, 'IoU-truck': 62.276149173606335, 'IoU-boat': 67.64813295028425, 'IoU-traffic light': 75.50507317949179, 'IoU-fire hydrant': 90.29567693766307, 'IoU-stop sign': 92.56820780191379, 'IoU-parking meter': 83.37944522327797, 'IoU-bench': 54.043285170310554, 'IoU-bird': 75.74563442685192, 'IoU-cat': 83.0335064717743, 'IoU-dog': 80.65897488060294, 'IoU-horse': 86.65680476890934, 'IoU-sheep': 85.30342398030851, 'IoU-cow': 81.68751789870767, 'IoU-elephant': 90.51818437298276, 'IoU-bear': 77.75520867789677, 'IoU-zebra': 90.86327846203265, 'IoU-giraffe': 86.93988841770908, 'IoU-backpack': 39.84742318429776, 'IoU-umbrella': 77.05063065024676, 'IoU-handbag': 36.733892503327795, 'IoU-tie': 70.11491094342148, 'IoU-suitcase': 81.17707733728324, 'IoU-frisbee': 83.3651028753582, 'IoU-skis': 51.93398333682481, 'IoU-snowboard': 69.45987548156302, 'IoU-sports ball': 68.65954239091877, 'IoU-kite': 66.40169889593713, 'IoU-baseball bat': 60.400685756386466, 'IoU-baseball glove': 52.62528675198027, 'IoU-skateboard': 82.27774195415127, 'IoU-surfboard': 76.26930249725011, 'IoU-tennis racket': 82.7915514211367, 'IoU-bottle': 68.54732214917291, 'IoU-wine glass': 74.72564180115752, 'IoU-cup': 65.95846259784078, 'IoU-fork': 55.60498954071906, 'IoU-knife': 49.85849277582886, 'IoU-spoon': 49.60452245086513, 'IoU-bowl': 53.95809842787623, 'IoU-banana': 80.78855763789252, 'IoU-apple': 58.36016277596614, 'IoU-sandwich': 66.26678603044016, 'IoU-orange': 75.9659356831304, 'IoU-broccoli': 66.99847044377698, 'IoU-carrot': 63.53756404166264, 'IoU-hot dog': 64.41704976859873, 'IoU-pizza': 81.93252023984299, 'IoU-donut': 66.97947473679486, 'IoU-cake': 69.08503679147432, 'IoU-chair': 56.0928666707336, 'IoU-couch': 70.0553064861817, 'IoU-potted plant': 33.83562377922478, 'IoU-bed': 69.59758687510312, 'IoU-dining table': 50.175369969237096, 'IoU-toilet': 81.75577507990465, 'IoU-tv': 74.69920610027381, 'IoU-laptop': 71.50892050728945, 'IoU-mouse': 68.33763494491033, 'IoU-remote': 50.085803098074486, 'IoU-keyboard': 56.148726662903414, 'IoU-cell phone': 68.17942509649362, 'IoU-microwave': 59.28707081533035, 'IoU-oven': 66.900969466469, 'IoU-toaster': 71.7286690416399, 'IoU-sink': 68.4924926059582, 'IoU-refrigerator': 77.91976806383946, 'IoU-book': 52.47575323053552, 'IoU-clock': 73.92581030523961, 'IoU-vase': 63.98893904204008, 'IoU-scissors': 52.75631551373845, 'IoU-teddy bear': 78.5070274013033, 'IoU-hair drier': 37.01908753252428, 'IoU-toothbrush': 56.83359083886212, 'IoU-banner': 35.413863026568556, 'IoU-blanket': 11.12810385194576, 'IoU-bridge': 37.95334804953623, 'IoU-cardboard': 45.33377229726893, 'IoU-counter': 28.56210684364633, 'IoU-curtain': 64.62554260897, 'IoU-door-stuff': 42.84935476871246, 'IoU-floor-wood': 63.546603194566366, 'IoU-flower': 44.07710829733872, 'IoU-fruit': 40.82225947548066, 'IoU-gravel': 31.8839243854198, 'IoU-house': 24.915445150365958, 'IoU-light': 38.83553882227333, 'IoU-mirror-stuff': 55.87154709361597, 'IoU-net': 45.42897757109326, 'IoU-pillow': 10.715804959322215, 'IoU-platform': 31.30886546367293, 'IoU-playingfield': 70.72567259399749, 'IoU-railroad': 61.72047375845838, 'IoU-river': 49.13895429222417, 'IoU-road': 66.4683324439633, 'IoU-roof': 16.59096988014264, 'IoU-sand': 64.6247989762493, 'IoU-sea': 85.98319765047185, 'IoU-shelf': 35.88559499130959, 'IoU-snow': 88.98255962016023, 'IoU-stairs': 27.840336202894537, 'IoU-tent': 10.317666638972788, 'IoU-towel': 34.37946420188556, 'IoU-wall-brick': 45.87927377268665, 'IoU-wall-stone': 29.565881029413987, 'IoU-wall-tile': 68.34398996183911, 'IoU-wall-wood': 39.50274542335535, 'IoU-water-other': 23.03312109382886, 'IoU-window-blind': 47.58691313533567, 'IoU-window-other': 47.45552019234325, 'IoU-tree-merged': 81.11659321285137, 'IoU-fence-merged': 50.92656608730086, 'IoU-ceiling-merged': 66.03362363762626, 'IoU-sky-other-merged': 93.79941564910065, 'IoU-cabinet-merged': 59.84909857975267, 'IoU-table-merged': 36.51081189204728, 'IoU-floor-other-merged': 49.16420991766498, 'IoU-pavement-merged': 53.98854681746834, 'IoU-mountain-merged': 56.95276976191583, 'IoU-grass-merged': 71.48693164342055, 'IoU-dirt-merged': 45.947646483230265, 'IoU-paper-merged': 31.59177139952165, 'IoU-food-other-merged': 39.904258885340546, 'IoU-building-other-merged': 58.67399212052234, 'IoU-rock-merged': 64.03756552005366, 'IoU-wall-other-merged': 65.36378165812648, 'IoU-rug-merged': 62.44675677098983, 'mACC': 73.29653877715342, 'pACC': 80.54498193227487, 'ACC-person': 92.47825091187298, 'ACC-bicycle': 86.58132230403821, 'ACC-car': 85.66964060561433, 'ACC-motorcycle': 87.06199644857182, 'ACC-airplane': 90.70820455284579, 'ACC-bus': 91.76091770445221, 'ACC-train': 94.22646824141297, 'ACC-truck': 75.30335828859624, 'ACC-boat': 79.10127981373827, 'ACC-traffic light': 90.39030466910873, 'ACC-fire hydrant': 95.51837847532101, 'ACC-stop sign': 95.35916275336615, 'ACC-parking meter': 87.40839043204076, 'ACC-bench': 75.1400816359445, 'ACC-bird': 80.79569188365603, 'ACC-cat': 90.56103812032839, 'ACC-dog': 84.72009882097245, 'ACC-horse': 92.7447981864859, 'ACC-sheep': 88.68886646737357, 'ACC-cow': 86.8856744444637, 'ACC-elephant': 93.10380578782988, 'ACC-bear': 79.92903199035472, 'ACC-zebra': 93.4029305869917, 'ACC-giraffe': 91.13448198462142, 'ACC-backpack': 56.088903690589916, 'ACC-umbrella': 85.34464571422282, 'ACC-handbag': 56.388820617883496, 'ACC-tie': 80.25608800478096, 'ACC-suitcase': 89.76266501041374, 'ACC-frisbee': 94.148, 'ACC-skis': 70.10770127122254, 'ACC-snowboard': 79.12958291494208, 'ACC-sports ball': 80.02863315450533, 'ACC-kite': 76.14403761398846, 'ACC-baseball bat': 79.85037724115712, 'ACC-baseball glove': 60.58090688340791, 'ACC-skateboard': 89.34438554519707, 'ACC-surfboard': 84.60347830551851, 'ACC-tennis racket': 89.33528753344142, 'ACC-bottle': 83.53824798412512, 'ACC-wine glass': 85.78856231074687, 'ACC-cup': 83.66437907622282, 'ACC-fork': 66.73551195697375, 'ACC-knife': 66.2878288268484, 'ACC-spoon': 68.8504173910899, 'ACC-bowl': 66.05439596418695, 'ACC-banana': 88.01608529603716, 'ACC-apple': 71.44703006118938, 'ACC-sandwich': 79.14469108353026, 'ACC-orange': 85.26435979318428, 'ACC-broccoli': 77.12700362614947, 'ACC-carrot': 75.24105881034475, 'ACC-hot dog': 72.90656225624132, 'ACC-pizza': 90.0146884356816, 'ACC-donut': 81.29984529254615, 'ACC-cake': 77.20325524513663, 'ACC-chair': 71.68123912690105, 'ACC-couch': 83.14402847029923, 'ACC-potted plant': 51.75923133771846, 'ACC-bed': 79.91833607947115, 'ACC-dining table': 73.29620514067958, 'ACC-toilet': 91.23526755358971, 'ACC-tv': 88.05162002749036, 'ACC-laptop': 83.78846641953012, 'ACC-mouse': 82.0471196822614, 'ACC-remote': 72.8679710604829, 'ACC-keyboard': 63.3233122164343, 'ACC-cell phone': 73.88683778775291, 'ACC-microwave': 67.77010390484702, 'ACC-oven': 86.20135336346596, 'ACC-toaster': 84.0445332493944, 'ACC-sink': 82.85589872153113, 'ACC-refrigerator': 88.98445814929383, 'ACC-book': 69.87791039757923, 'ACC-clock': 80.06519127655312, 'ACC-vase': 74.13550131575552, 'ACC-scissors': 56.86433168975934, 'ACC-teddy bear': 84.91462011207601, 'ACC-hair drier': 53.04983918253494, 'ACC-toothbrush': 81.48106323835998, 'ACC-banner': 74.09990891653926, 'ACC-blanket': 15.434354850512655, 'ACC-bridge': 56.67518259297258, 'ACC-cardboard': 57.78685473316854, 'ACC-counter': 53.65593731374461, 'ACC-curtain': 76.85871780617379, 'ACC-door-stuff': 64.83056487772319, 'ACC-floor-wood': 78.56267069611991, 'ACC-flower': 60.99265176601134, 'ACC-fruit': 57.77848054794489, 'ACC-gravel': 42.49855108634753, 'ACC-house': 31.008306164856858, 'ACC-light': 57.78522518470314, 'ACC-mirror-stuff': 72.6890640783794, 'ACC-net': 61.8547598619231, 'ACC-pillow': 25.53690274107494, 'ACC-platform': 51.06641991552257, 'ACC-playingfield': 92.08180044697366, 'ACC-railroad': 79.64667191049007, 'ACC-river': 70.27200598008582, 'ACC-road': 85.18034721806795, 'ACC-roof': 23.277145546333532, 'ACC-sand': 70.56447778683899, 'ACC-sea': 91.75776831384077, 'ACC-shelf': 57.74404234478973, 'ACC-snow': 94.38467238018193, 'ACC-stairs': 43.42734794440262, 'ACC-tent': 12.075354124235188, 'ACC-towel': 42.968232390793766, 'ACC-wall-brick': 63.45814713007444, 'ACC-wall-stone': 38.000744058299716, 'ACC-wall-tile': 82.55220346818957, 'ACC-wall-wood': 52.24156878381159, 'ACC-water-other': 37.9145583926757, 'ACC-window-blind': 56.75659803057012, 'ACC-window-other': 69.49252051483424, 'ACC-tree-merged': 89.35862410413266, 'ACC-fence-merged': 70.93974901146012, 'ACC-ceiling-merged': 79.42279314840665, 'ACC-sky-other-merged': 96.75850406928045, 'ACC-cabinet-merged': 74.94161338416757, 'ACC-table-merged': 51.067091807527866, 'ACC-floor-other-merged': 62.263206361139176, 'ACC-pavement-merged': 66.80609046717908, 'ACC-mountain-merged': 68.4276862128213, 'ACC-grass-merged': 83.8492419994498, 'ACC-dirt-merged': 65.82044641388424, 'ACC-paper-merged': 43.85162138737598, 'ACC-food-other-merged': 55.08114262809964, 'ACC-building-other-merged': 74.31214821625277, 'ACC-rock-merged': 81.97010278528832, 'ACC-wall-other-merged': 80.41887835674488, 'ACC-rug-merged': 76.62390558371598})])
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/50. Dataloading: 0.1571 s/iter. Inference: 0.5920 s/iter. Eval: 0.0000 s/iter. Total: 0.7491 s/iter. ETA=0:00:29
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 19/50. Dataloading: 0.1595 s/iter. Inference: 0.5340 s/iter. Eval: 0.0000 s/iter. Total: 0.6937 s/iter. ETA=0:00:21
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 26/50. Dataloading: 0.1742 s/iter. Inference: 0.5978 s/iter. Eval: 0.0000 s/iter. Total: 0.7722 s/iter. ETA=0:00:18
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 31/50. Dataloading: 0.1738 s/iter. Inference: 0.6729 s/iter. Eval: 0.0000 s/iter. Total: 0.8469 s/iter. ETA=0:00:16
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 40/50. Dataloading: 0.1718 s/iter. Inference: 0.6197 s/iter. Eval: 0.0000 s/iter. Total: 0.7917 s/iter. ETA=0:00:07
INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 46/50. Dataloading: 0.1706 s/iter. Inference: 0.6602 s/iter. Eval: 0.0000 s/iter. Total: 0.8310 s/iter. ETA=0:00:03
INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4685396546678373, 'noc@0.8': 2.7023705004389815, 'noc@0.85': 3.260462393912789, 'noc@0.9': 4.248170910155107, 'miou@iter1': 0.8336845310870967}
INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/41. Dataloading: 0.0016 s/iter. Inference: 0.0991 s/iter. Eval: 0.0008 s/iter. Total: 0.1015 s/iter. ETA=0:00:03
INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 71.74504089355469, 'precision@0.6': 69.02448272705078, 'precision@0.7': 64.08861541748047, 'precision@0.8': 54.10027313232422, 'precision@0.9': 27.74970817565918, 'cIoU': 57.8400993347168, 'mIoU': 63.5446662902832}
INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 50.54390895439897, 'SQ': 82.03371980495469, 'RQ': 60.74554464850777, 'PQ_th': 55.50903587447045, 'SQ_th': 82.66051570356373, 'RQ_th': 66.46183529285454, 'PQ_st': 43.04937775429109, 'SQ_st': 81.0876127881864, 'RQ_st': 52.11718141175786}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 39.53740818216615, 'AP50': 62.03998029200839, 'AP75': 41.54823557400116, 'APs': 19.505981862119604, 'APm': 42.59897722044648, 'APl': 61.36135771841176, 'AP-person': 44.73768683784062, 'AP-bicycle': 19.58336037164826, 'AP-car': 37.71371322581255, 'AP-motorcycle': 35.70982486019041, 'AP-airplane': 56.773501832565124, 'AP-bus': 65.77812660572025, 'AP-train': 68.64575008393503, 'AP-truck': 36.0628299559, 'AP-boat': 24.056553082315876, 'AP-traffic light': 26.013472835550967, 'AP-fire hydrant': 66.19903204580886, 'AP-stop sign': 64.49022006643688, 'AP-parking meter': 43.7562228299815, 'AP-bench': 20.300013110198634, 'AP-bird': 30.04478684723778, 'AP-cat': 74.0445301055961, 'AP-dog': 66.03275036387373, 'AP-horse': 45.39240119767729, 'AP-sheep': 48.28495528849962, 'AP-cow': 52.24868455340352, 'AP-elephant': 61.35524575828518, 'AP-bear': 76.68169635939547, 'AP-zebra': 60.61636273525841, 'AP-giraffe': 57.32085207976709, 'AP-backpack': 17.24137869978149, 'AP-umbrella': 48.96442628449831, 'AP-handbag': 15.867878772431233, 'AP-tie': 34.706825766070416, 'AP-suitcase': 43.3329157821337, 'AP-frisbee': 66.59173669123064, 'AP-skis': 5.1823582411134765, 'AP-snowboard': 21.939008591286886, 'AP-sports ball': 48.02139536375753, 'AP-kite': 35.74464157654867, 'AP-baseball bat': 29.85845295458259, 'AP-baseball glove': 44.38999531320448, 'AP-skateboard': 36.14763104414474, 'AP-surfboard': 36.78789183350643, 'AP-tennis racket': 56.4923985452687, 'AP-bottle': 35.458077882690866, 'AP-wine glass': 27.581687925326747, 'AP-cup': 41.220395760712385, 'AP-fork': 15.801076345965399, 'AP-knife': 14.121162857356575, 'AP-spoon': 14.326792757182075, 'AP-bowl': 32.72125387722317, 'AP-banana': 21.55428676120534, 'AP-apple': 20.304283491960557, 'AP-sandwich': 42.80693266691753, 'AP-orange': 30.104714724495835, 'AP-broccoli': 22.63157822559721, 'AP-carrot': 20.90234951994648, 'AP-hot dog': 22.231876546828975, 'AP-pizza': 51.11577931855772, 'AP-donut': 47.522068795634, 'AP-cake': 45.063932631976975, 'AP-chair': 21.643560354431486, 'AP-couch': 41.75387329019819, 'AP-potted plant': 18.3023468398235, 'AP-bed': 40.769747362580404, 'AP-dining table': 13.088585382051331, 'AP-toilet': 67.97430201953385, 'AP-tv': 62.858328999535715, 'AP-laptop': 63.445224860957694, 'AP-mouse': 59.239558837095466, 'AP-remote': 32.507919287599435, 'AP-keyboard': 48.54617130447755, 'AP-cell phone': 38.35467504750997, 'AP-microwave': 55.47321714837848, 'AP-oven': 34.11858663643287, 'AP-toaster': 34.20845537239146, 'AP-sink': 37.520094129234906, 'AP-refrigerator': 60.17011599931973, 'AP-book': 10.001105361229943, 'AP-clock': 52.47531806897141, 'AP-vase': 34.681289327278094, 'AP-scissors': 24.505350259581423, 'AP-teddy bear': 51.18960290546983, 'AP-hair drier': 12.715388488001341, 'AP-toothbrush': 18.870078709171874}), ('sem_seg', {'mIoU': 60.99785355339221, 'fwIoU': 69.31511411098542, 'IoU-person': 87.40445614270267, 'IoU-bicycle': 75.58945374113894, 'IoU-car': 70.77308249611035, 'IoU-motorcycle': 82.02421363678313, 'IoU-airplane': 84.04597435243811, 'IoU-bus': 85.72193103736605, 'IoU-train': 85.17951364310245, 'IoU-truck': 62.276149173606335, 'IoU-boat': 67.64813295028425, 'IoU-traffic light': 75.50507317949179, 'IoU-fire hydrant': 90.29567693766307, 'IoU-stop sign': 92.56820780191379, 'IoU-parking meter': 83.37944522327797, 'IoU-bench': 54.043285170310554, 'IoU-bird': 75.74563442685192, 'IoU-cat': 83.0335064717743, 'IoU-dog': 80.65897488060294, 'IoU-horse': 86.65680476890934, 'IoU-sheep': 85.30342398030851, 'IoU-cow': 81.68751789870767, 'IoU-elephant': 90.51818437298276, 'IoU-bear': 77.75520867789677, 'IoU-zebra': 90.86327846203265, 'IoU-giraffe': 86.93988841770908, 'IoU-backpack': 39.84742318429776, 'IoU-umbrella': 77.05063065024676, 'IoU-handbag': 36.733892503327795, 'IoU-tie': 70.11491094342148, 'IoU-suitcase': 81.17707733728324, 'IoU-frisbee': 83.3651028753582, 'IoU-skis': 51.93398333682481, 'IoU-snowboard': 69.45987548156302, 'IoU-sports ball': 68.65954239091877, 'IoU-kite': 66.40169889593713, 'IoU-baseball bat': 60.400685756386466, 'IoU-baseball glove': 52.62528675198027, 'IoU-skateboard': 82.27774195415127, 'IoU-surfboard': 76.26930249725011, 'IoU-tennis racket': 82.7915514211367, 'IoU-bottle': 68.54732214917291, 'IoU-wine glass': 74.72564180115752, 'IoU-cup': 65.95846259784078, 'IoU-fork': 55.60498954071906, 'IoU-knife': 49.85849277582886, 'IoU-spoon': 49.60452245086513, 'IoU-bowl': 53.95809842787623, 'IoU-banana': 80.78855763789252, 'IoU-apple': 58.36016277596614, 'IoU-sandwich': 66.26678603044016, 'IoU-orange': 75.9659356831304, 'IoU-broccoli': 66.99847044377698, 'IoU-carrot': 63.53756404166264, 'IoU-hot dog': 64.41704976859873, 'IoU-pizza': 81.93252023984299, 'IoU-donut': 66.97947473679486, 'IoU-cake': 69.08503679147432, 'IoU-chair': 56.0928666707336, 'IoU-couch': 70.0553064861817, 'IoU-potted plant': 33.83562377922478, 'IoU-bed': 69.59758687510312, 'IoU-dining table': 50.175369969237096, 'IoU-toilet': 81.75577507990465, 'IoU-tv': 74.69920610027381, 'IoU-laptop': 71.50892050728945, 'IoU-mouse': 68.33763494491033, 'IoU-remote': 50.085803098074486, 'IoU-keyboard': 56.148726662903414, 'IoU-cell phone': 68.17942509649362, 'IoU-microwave': 59.28707081533035, 'IoU-oven': 66.900969466469, 'IoU-toaster': 71.7286690416399, 'IoU-sink': 68.4924926059582, 'IoU-refrigerator': 77.91976806383946, 'IoU-book': 52.47575323053552, 'IoU-clock': 73.92581030523961, 'IoU-vase': 63.98893904204008, 'IoU-scissors': 52.75631551373845, 'IoU-teddy bear': 78.5070274013033, 'IoU-hair drier': 37.01908753252428, 'IoU-toothbrush': 56.83359083886212, 'IoU-banner': 35.413863026568556, 'IoU-blanket': 11.12810385194576, 'IoU-bridge': 37.95334804953623, 'IoU-cardboard': 45.33377229726893, 'IoU-counter': 28.56210684364633, 'IoU-curtain': 64.62554260897, 'IoU-door-stuff': 42.84935476871246, 'IoU-floor-wood': 63.546603194566366, 'IoU-flower': 44.07710829733872, 'IoU-fruit': 40.82225947548066, 'IoU-gravel': 31.8839243854198, 'IoU-house': 24.915445150365958, 'IoU-light': 38.83553882227333, 'IoU-mirror-stuff': 55.87154709361597, 'IoU-net': 45.42897757109326, 'IoU-pillow': 10.715804959322215, 'IoU-platform': 31.30886546367293, 'IoU-playingfield': 70.72567259399749, 'IoU-railroad': 61.72047375845838, 'IoU-river': 49.13895429222417, 'IoU-road': 66.4683324439633, 'IoU-roof': 16.59096988014264, 'IoU-sand': 64.6247989762493, 'IoU-sea': 85.98319765047185, 'IoU-shelf': 35.88559499130959, 'IoU-snow': 88.98255962016023, 'IoU-stairs': 27.840336202894537, 'IoU-tent': 10.317666638972788, 'IoU-towel': 34.37946420188556, 'IoU-wall-brick': 45.87927377268665, 'IoU-wall-stone': 29.565881029413987, 'IoU-wall-tile': 68.34398996183911, 'IoU-wall-wood': 39.50274542335535, 'IoU-water-other': 23.03312109382886, 'IoU-window-blind': 47.58691313533567, 'IoU-window-other': 47.45552019234325, 'IoU-tree-merged': 81.11659321285137, 'IoU-fence-merged': 50.92656608730086, 'IoU-ceiling-merged': 66.03362363762626, 'IoU-sky-other-merged': 93.79941564910065, 'IoU-cabinet-merged': 59.84909857975267, 'IoU-table-merged': 36.51081189204728, 'IoU-floor-other-merged': 49.16420991766498, 'IoU-pavement-merged': 53.98854681746834, 'IoU-mountain-merged': 56.95276976191583, 'IoU-grass-merged': 71.48693164342055, 'IoU-dirt-merged': 45.947646483230265, 'IoU-paper-merged': 31.59177139952165, 'IoU-food-other-merged': 39.904258885340546, 'IoU-building-other-merged': 58.67399212052234, 'IoU-rock-merged': 64.03756552005366, 'IoU-wall-other-merged': 65.36378165812648, 'IoU-rug-merged': 62.44675677098983, 'mACC': 73.29653877715342, 'pACC': 80.54498193227487, 'ACC-person': 92.47825091187298, 'ACC-bicycle': 86.58132230403821, 'ACC-car': 85.66964060561433, 'ACC-motorcycle': 87.06199644857182, 'ACC-airplane': 90.70820455284579, 'ACC-bus': 91.76091770445221, 'ACC-train': 94.22646824141297, 'ACC-truck': 75.30335828859624, 'ACC-boat': 79.10127981373827, 'ACC-traffic light': 90.39030466910873, 'ACC-fire hydrant': 95.51837847532101, 'ACC-stop sign': 95.35916275336615, 'ACC-parking meter': 87.40839043204076, 'ACC-bench': 75.1400816359445, 'ACC-bird': 80.79569188365603, 'ACC-cat': 90.56103812032839, 'ACC-dog': 84.72009882097245, 'ACC-horse': 92.7447981864859, 'ACC-sheep': 88.68886646737357, 'ACC-cow': 86.8856744444637, 'ACC-elephant': 93.10380578782988, 'ACC-bear': 79.92903199035472, 'ACC-zebra': 93.4029305869917, 'ACC-giraffe': 91.13448198462142, 'ACC-backpack': 56.088903690589916, 'ACC-umbrella': 85.34464571422282, 'ACC-handbag': 56.388820617883496, 'ACC-tie': 80.25608800478096, 'ACC-suitcase': 89.76266501041374, 'ACC-frisbee': 94.148, 'ACC-skis': 70.10770127122254, 'ACC-snowboard': 79.12958291494208, 'ACC-sports ball': 80.02863315450533, 'ACC-kite': 76.14403761398846, 'ACC-baseball bat': 79.85037724115712, 'ACC-baseball glove': 60.58090688340791, 'ACC-skateboard': 89.34438554519707, 'ACC-surfboard': 84.60347830551851, 'ACC-tennis racket': 89.33528753344142, 'ACC-bottle': 83.53824798412512, 'ACC-wine glass': 85.78856231074687, 'ACC-cup': 83.66437907622282, 'ACC-fork': 66.73551195697375, 'ACC-knife': 66.2878288268484, 'ACC-spoon': 68.8504173910899, 'ACC-bowl': 66.05439596418695, 'ACC-banana': 88.01608529603716, 'ACC-apple': 71.44703006118938, 'ACC-sandwich': 79.14469108353026, 'ACC-orange': 85.26435979318428, 'ACC-broccoli': 77.12700362614947, 'ACC-carrot': 75.24105881034475, 'ACC-hot dog': 72.90656225624132, 'ACC-pizza': 90.0146884356816, 'ACC-donut': 81.29984529254615, 'ACC-cake': 77.20325524513663, 'ACC-chair': 71.68123912690105, 'ACC-couch': 83.14402847029923, 'ACC-potted plant': 51.75923133771846, 'ACC-bed': 79.91833607947115, 'ACC-dining table': 73.29620514067958, 'ACC-toilet': 91.23526755358971, 'ACC-tv': 88.05162002749036, 'ACC-laptop': 83.78846641953012, 'ACC-mouse': 82.0471196822614, 'ACC-remote': 72.8679710604829, 'ACC-keyboard': 63.3233122164343, 'ACC-cell phone': 73.88683778775291, 'ACC-microwave': 67.77010390484702, 'ACC-oven': 86.20135336346596, 'ACC-toaster': 84.0445332493944, 'ACC-sink': 82.85589872153113, 'ACC-refrigerator': 88.98445814929383, 'ACC-book': 69.87791039757923, 'ACC-clock': 80.06519127655312, 'ACC-vase': 74.13550131575552, 'ACC-scissors': 56.86433168975934, 'ACC-teddy bear': 84.91462011207601, 'ACC-hair drier': 53.04983918253494, 'ACC-toothbrush': 81.48106323835998, 'ACC-banner': 74.09990891653926, 'ACC-blanket': 15.434354850512655, 'ACC-bridge': 56.67518259297258, 'ACC-cardboard': 57.78685473316854, 'ACC-counter': 53.65593731374461, 'ACC-curtain': 76.85871780617379, 'ACC-door-stuff': 64.83056487772319, 'ACC-floor-wood': 78.56267069611991, 'ACC-flower': 60.99265176601134, 'ACC-fruit': 57.77848054794489, 'ACC-gravel': 42.49855108634753, 'ACC-house': 31.008306164856858, 'ACC-light': 57.78522518470314, 'ACC-mirror-stuff': 72.6890640783794, 'ACC-net': 61.8547598619231, 'ACC-pillow': 25.53690274107494, 'ACC-platform': 51.06641991552257, 'ACC-playingfield': 92.08180044697366, 'ACC-railroad': 79.64667191049007, 'ACC-river': 70.27200598008582, 'ACC-road': 85.18034721806795, 'ACC-roof': 23.277145546333532, 'ACC-sand': 70.56447778683899, 'ACC-sea': 91.75776831384077, 'ACC-shelf': 57.74404234478973, 'ACC-snow': 94.38467238018193, 'ACC-stairs': 43.42734794440262, 'ACC-tent': 12.075354124235188, 'ACC-towel': 42.968232390793766, 'ACC-wall-brick': 63.45814713007444, 'ACC-wall-stone': 38.000744058299716, 'ACC-wall-tile': 82.55220346818957, 'ACC-wall-wood': 52.24156878381159, 'ACC-water-other': 37.9145583926757, 'ACC-window-blind': 56.75659803057012, 'ACC-window-other': 69.49252051483424, 'ACC-tree-merged': 89.35862410413266, 'ACC-fence-merged': 70.93974901146012, 'ACC-ceiling-merged': 79.42279314840665, 'ACC-sky-other-merged': 96.75850406928045, 'ACC-cabinet-merged': 74.94161338416757, 'ACC-table-merged': 51.067091807527866, 'ACC-floor-other-merged': 62.263206361139176, 'ACC-pavement-merged': 66.80609046717908, 'ACC-mountain-merged': 68.4276862128213, 'ACC-grass-merged': 83.8492419994498, 'ACC-dirt-merged': 65.82044641388424, 'ACC-paper-merged': 43.85162138737598, 'ACC-food-other-merged': 55.08114262809964, 'ACC-building-other-merged': 74.31214821625277, 'ACC-rock-merged': 81.97010278528832, 'ACC-wall-other-merged': 80.41887835674488, 'ACC-rug-merged': 76.62390558371598})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4685396546678373, 'noc@0.8': 2.7023705004389815, 'noc@0.85': 3.260462393912789, 'noc@0.9': 4.248170910155107, 'miou@iter1': 0.8336845310870967}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 71.74504089355469, 'precision@0.6': 69.02448272705078, 'precision@0.7': 64.08861541748047, 'precision@0.8': 54.10027313232422, 'precision@0.9': 27.74970817565918, 'cIoU': 57.8400993347168, 'mIoU': 63.5446662902832}}}
INFO:trainer.default_trainer:This epoch takes 1:26:40.925974
INFO:trainer.default_trainer:PROGRESS: 100.00%
INFO:trainer.default_trainer:Config files are at ['configs/seem/focalt_unicl_lang_v1.yaml']