diff --git "a/ablation/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/20230304_211228.log" "b/ablation/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/20230304_211228.log"
new file mode 100644--- /dev/null
+++ "b/ablation/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/20230304_211228.log"
@@ -0,0 +1,4349 @@
+2023-03-04 21:12:28,359 - mmseg - INFO - Multi-processing start method is `None`
+2023-03-04 21:12:28,372 - mmseg - INFO - OpenCV num_threads is `128
+2023-03-04 21:12:28,372 - mmseg - INFO - OMP num threads is 1
+2023-03-04 21:12:28,450 - mmseg - INFO - Environment info:
+------------------------------------------------------------
+sys.platform: linux
+Python: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0]
+CUDA available: True
+GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB
+CUDA_HOME: /mnt/petrelfs/laizeqiang/miniconda3/envs/torch
+NVCC: Cuda compilation tools, release 11.6, V11.6.124
+GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
+PyTorch: 1.13.1
+PyTorch compiling details: PyTorch built with:
+  - GCC 9.3
+  - C++ Version: 201402
+  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
+  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
+  - OpenMP 201511 (a.k.a. OpenMP 4.5)
+  - LAPACK is enabled (usually provided by MKL)
+  - NNPACK is enabled
+  - CPU capability usage: AVX2
+  - CUDA Runtime 11.6
+  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
+  - CuDNN 8.3.2  (built against CUDA 11.5)
+  - Magma 2.6.1
+  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 
+
+TorchVision: 0.14.1
+OpenCV: 4.7.0
+MMCV: 1.7.1
+MMCV Compiler: GCC 9.3
+MMCV CUDA Compiler: 11.6
+MMSegmentation: 0.30.0+6749699
+------------------------------------------------------------
+
+2023-03-04 21:12:28,450 - mmseg - INFO - Distributed training: True
+2023-03-04 21:12:29,150 - mmseg - INFO - Config:
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+checkpoint = 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'
+model = dict(
+    type='EncoderDecoderFreeze',
+    freeze_parameters=['backbone', 'decode_head'],
+    pretrained=
+    'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth',
+    backbone=dict(
+        type='MixVisionTransformerCustomInitWeights',
+        in_channels=3,
+        embed_dims=64,
+        num_stages=4,
+        num_layers=[3, 4, 6, 3],
+        num_heads=[1, 2, 5, 8],
+        patch_sizes=[7, 3, 3, 3],
+        sr_ratios=[8, 4, 2, 1],
+        out_indices=(0, 1, 2, 3),
+        mlp_ratio=4,
+        qkv_bias=True,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.1),
+    decode_head=dict(
+        type='SegformerHeadUnetFCHeadSingleStepLogits',
+        pretrained=
+        'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth',
+        dim=128,
+        out_dim=256,
+        unet_channels=166,
+        dim_mults=[1, 1, 1],
+        cat_embedding_dim=16,
+        in_channels=[64, 128, 320, 512],
+        in_index=[0, 1, 2, 3],
+        channels=256,
+        dropout_ratio=0.1,
+        num_classes=151,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        align_corners=False,
+        ignore_index=0,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+dataset_type = 'ADE20K151Dataset'
+data_root = 'data/ade/ADEChallengeData2016'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=False),
+    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 512),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type='ADE20K151Dataset',
+        data_root='data/ade/ADEChallengeData2016',
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', reduce_zero_label=False),
+            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
+            dict(type='RandomFlip', prob=0.5),
+            dict(type='PhotoMetricDistortion'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=0),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+        ]),
+    val=dict(
+        type='ADE20K151Dataset',
+        data_root='data/ade/ADEChallengeData2016',
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(2048, 512),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(
+                        type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='ADE20K151Dataset',
+        data_root='data/ade/ADEChallengeData2016',
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(2048, 512),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(
+                        type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+log_config = dict(
+    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+cudnn_benchmark = True
+optimizer = dict(
+    type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045)
+optimizer_config = dict()
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1e-06,
+    step=10000,
+    gamma=0.5,
+    min_lr=1e-06,
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=80000)
+checkpoint_config = dict(by_epoch=False, interval=8000)
+evaluation = dict(
+    interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU')
+work_dir = './work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits'
+gpu_ids = range(0, 8)
+auto_resume = True
+
+2023-03-04 21:12:34,049 - mmseg - INFO - Set random seed to 1340171616, deterministic: False
+2023-03-04 21:12:34,298 - mmseg - INFO - Parameters in backbone freezed!
+2023-03-04 21:12:34,299 - mmseg - INFO - Trainable parameters in SegformerHeadUnetFCHeadSingleStep: ['unet.init_conv.weight', 'unet.init_conv.bias', 'unet.time_mlp.1.weight', 'unet.time_mlp.1.bias', 'unet.time_mlp.3.weight', 'unet.time_mlp.3.bias', 'unet.downs.0.0.mlp.1.weight', 'unet.downs.0.0.mlp.1.bias', 'unet.downs.0.0.block1.proj.weight', 'unet.downs.0.0.block1.proj.bias', 'unet.downs.0.0.block1.norm.weight', 'unet.downs.0.0.block1.norm.bias', 'unet.downs.0.0.block2.proj.weight', 'unet.downs.0.0.block2.proj.bias', 'unet.downs.0.0.block2.norm.weight', 'unet.downs.0.0.block2.norm.bias', 'unet.downs.0.1.mlp.1.weight', 'unet.downs.0.1.mlp.1.bias', 'unet.downs.0.1.block1.proj.weight', 'unet.downs.0.1.block1.proj.bias', 'unet.downs.0.1.block1.norm.weight', 'unet.downs.0.1.block1.norm.bias', 'unet.downs.0.1.block2.proj.weight', 'unet.downs.0.1.block2.proj.bias', 'unet.downs.0.1.block2.norm.weight', 'unet.downs.0.1.block2.norm.bias', 'unet.downs.0.2.fn.fn.to_qkv.weight', 'unet.downs.0.2.fn.fn.to_out.0.weight', 'unet.downs.0.2.fn.fn.to_out.0.bias', 'unet.downs.0.2.fn.fn.to_out.1.g', 'unet.downs.0.2.fn.norm.g', 'unet.downs.0.3.weight', 'unet.downs.0.3.bias', 'unet.downs.1.0.mlp.1.weight', 'unet.downs.1.0.mlp.1.bias', 'unet.downs.1.0.block1.proj.weight', 'unet.downs.1.0.block1.proj.bias', 'unet.downs.1.0.block1.norm.weight', 'unet.downs.1.0.block1.norm.bias', 'unet.downs.1.0.block2.proj.weight', 'unet.downs.1.0.block2.proj.bias', 'unet.downs.1.0.block2.norm.weight', 'unet.downs.1.0.block2.norm.bias', 'unet.downs.1.1.mlp.1.weight', 'unet.downs.1.1.mlp.1.bias', 'unet.downs.1.1.block1.proj.weight', 'unet.downs.1.1.block1.proj.bias', 'unet.downs.1.1.block1.norm.weight', 'unet.downs.1.1.block1.norm.bias', 'unet.downs.1.1.block2.proj.weight', 'unet.downs.1.1.block2.proj.bias', 'unet.downs.1.1.block2.norm.weight', 'unet.downs.1.1.block2.norm.bias', 'unet.downs.1.2.fn.fn.to_qkv.weight', 'unet.downs.1.2.fn.fn.to_out.0.weight', 'unet.downs.1.2.fn.fn.to_out.0.bias', 'unet.downs.1.2.fn.fn.to_out.1.g', 'unet.downs.1.2.fn.norm.g', 'unet.downs.1.3.weight', 'unet.downs.1.3.bias', 'unet.downs.2.0.mlp.1.weight', 'unet.downs.2.0.mlp.1.bias', 'unet.downs.2.0.block1.proj.weight', 'unet.downs.2.0.block1.proj.bias', 'unet.downs.2.0.block1.norm.weight', 'unet.downs.2.0.block1.norm.bias', 'unet.downs.2.0.block2.proj.weight', 'unet.downs.2.0.block2.proj.bias', 'unet.downs.2.0.block2.norm.weight', 'unet.downs.2.0.block2.norm.bias', 'unet.downs.2.1.mlp.1.weight', 'unet.downs.2.1.mlp.1.bias', 'unet.downs.2.1.block1.proj.weight', 'unet.downs.2.1.block1.proj.bias', 'unet.downs.2.1.block1.norm.weight', 'unet.downs.2.1.block1.norm.bias', 'unet.downs.2.1.block2.proj.weight', 'unet.downs.2.1.block2.proj.bias', 'unet.downs.2.1.block2.norm.weight', 'unet.downs.2.1.block2.norm.bias', 'unet.downs.2.2.fn.fn.to_qkv.weight', 'unet.downs.2.2.fn.fn.to_out.0.weight', 'unet.downs.2.2.fn.fn.to_out.0.bias', 'unet.downs.2.2.fn.fn.to_out.1.g', 'unet.downs.2.2.fn.norm.g', 'unet.downs.2.3.weight', 'unet.downs.2.3.bias', 'unet.ups.0.0.mlp.1.weight', 'unet.ups.0.0.mlp.1.bias', 'unet.ups.0.0.block1.proj.weight', 'unet.ups.0.0.block1.proj.bias', 'unet.ups.0.0.block1.norm.weight', 'unet.ups.0.0.block1.norm.bias', 'unet.ups.0.0.block2.proj.weight', 'unet.ups.0.0.block2.proj.bias', 'unet.ups.0.0.block2.norm.weight', 'unet.ups.0.0.block2.norm.bias', 'unet.ups.0.0.res_conv.weight', 'unet.ups.0.0.res_conv.bias', 'unet.ups.0.1.mlp.1.weight', 'unet.ups.0.1.mlp.1.bias', 'unet.ups.0.1.block1.proj.weight', 'unet.ups.0.1.block1.proj.bias', 'unet.ups.0.1.block1.norm.weight', 'unet.ups.0.1.block1.norm.bias', 'unet.ups.0.1.block2.proj.weight', 'unet.ups.0.1.block2.proj.bias', 'unet.ups.0.1.block2.norm.weight', 'unet.ups.0.1.block2.norm.bias', 'unet.ups.0.1.res_conv.weight', 'unet.ups.0.1.res_conv.bias', 'unet.ups.0.2.fn.fn.to_qkv.weight', 'unet.ups.0.2.fn.fn.to_out.0.weight', 'unet.ups.0.2.fn.fn.to_out.0.bias', 'unet.ups.0.2.fn.fn.to_out.1.g', 'unet.ups.0.2.fn.norm.g', 'unet.ups.0.3.1.weight', 'unet.ups.0.3.1.bias', 'unet.ups.1.0.mlp.1.weight', 'unet.ups.1.0.mlp.1.bias', 'unet.ups.1.0.block1.proj.weight', 'unet.ups.1.0.block1.proj.bias', 'unet.ups.1.0.block1.norm.weight', 'unet.ups.1.0.block1.norm.bias', 'unet.ups.1.0.block2.proj.weight', 'unet.ups.1.0.block2.proj.bias', 'unet.ups.1.0.block2.norm.weight', 'unet.ups.1.0.block2.norm.bias', 'unet.ups.1.0.res_conv.weight', 'unet.ups.1.0.res_conv.bias', 'unet.ups.1.1.mlp.1.weight', 'unet.ups.1.1.mlp.1.bias', 'unet.ups.1.1.block1.proj.weight', 'unet.ups.1.1.block1.proj.bias', 'unet.ups.1.1.block1.norm.weight', 'unet.ups.1.1.block1.norm.bias', 'unet.ups.1.1.block2.proj.weight', 'unet.ups.1.1.block2.proj.bias', 'unet.ups.1.1.block2.norm.weight', 'unet.ups.1.1.block2.norm.bias', 'unet.ups.1.1.res_conv.weight', 'unet.ups.1.1.res_conv.bias', 'unet.ups.1.2.fn.fn.to_qkv.weight', 'unet.ups.1.2.fn.fn.to_out.0.weight', 'unet.ups.1.2.fn.fn.to_out.0.bias', 'unet.ups.1.2.fn.fn.to_out.1.g', 'unet.ups.1.2.fn.norm.g', 'unet.ups.1.3.1.weight', 'unet.ups.1.3.1.bias', 'unet.ups.2.0.mlp.1.weight', 'unet.ups.2.0.mlp.1.bias', 'unet.ups.2.0.block1.proj.weight', 'unet.ups.2.0.block1.proj.bias', 'unet.ups.2.0.block1.norm.weight', 'unet.ups.2.0.block1.norm.bias', 'unet.ups.2.0.block2.proj.weight', 'unet.ups.2.0.block2.proj.bias', 'unet.ups.2.0.block2.norm.weight', 'unet.ups.2.0.block2.norm.bias', 'unet.ups.2.0.res_conv.weight', 'unet.ups.2.0.res_conv.bias', 'unet.ups.2.1.mlp.1.weight', 'unet.ups.2.1.mlp.1.bias', 'unet.ups.2.1.block1.proj.weight', 'unet.ups.2.1.block1.proj.bias', 'unet.ups.2.1.block1.norm.weight', 'unet.ups.2.1.block1.norm.bias', 'unet.ups.2.1.block2.proj.weight', 'unet.ups.2.1.block2.proj.bias', 'unet.ups.2.1.block2.norm.weight', 'unet.ups.2.1.block2.norm.bias', 'unet.ups.2.1.res_conv.weight', 'unet.ups.2.1.res_conv.bias', 'unet.ups.2.2.fn.fn.to_qkv.weight', 'unet.ups.2.2.fn.fn.to_out.0.weight', 'unet.ups.2.2.fn.fn.to_out.0.bias', 'unet.ups.2.2.fn.fn.to_out.1.g', 'unet.ups.2.2.fn.norm.g', 'unet.ups.2.3.weight', 'unet.ups.2.3.bias', 'unet.mid_block1.mlp.1.weight', 'unet.mid_block1.mlp.1.bias', 'unet.mid_block1.block1.proj.weight', 'unet.mid_block1.block1.proj.bias', 'unet.mid_block1.block1.norm.weight', 'unet.mid_block1.block1.norm.bias', 'unet.mid_block1.block2.proj.weight', 'unet.mid_block1.block2.proj.bias', 'unet.mid_block1.block2.norm.weight', 'unet.mid_block1.block2.norm.bias', 'unet.mid_attn.fn.fn.to_qkv.weight', 'unet.mid_attn.fn.fn.to_out.weight', 'unet.mid_attn.fn.fn.to_out.bias', 'unet.mid_attn.fn.norm.g', 'unet.mid_block2.mlp.1.weight', 'unet.mid_block2.mlp.1.bias', 'unet.mid_block2.block1.proj.weight', 'unet.mid_block2.block1.proj.bias', 'unet.mid_block2.block1.norm.weight', 'unet.mid_block2.block1.norm.bias', 'unet.mid_block2.block2.proj.weight', 'unet.mid_block2.block2.proj.bias', 'unet.mid_block2.block2.norm.weight', 'unet.mid_block2.block2.norm.bias', 'unet.final_res_block.mlp.1.weight', 'unet.final_res_block.mlp.1.bias', 'unet.final_res_block.block1.proj.weight', 'unet.final_res_block.block1.proj.bias', 'unet.final_res_block.block1.norm.weight', 'unet.final_res_block.block1.norm.bias', 'unet.final_res_block.block2.proj.weight', 'unet.final_res_block.block2.proj.bias', 'unet.final_res_block.block2.norm.weight', 'unet.final_res_block.block2.norm.bias', 'unet.final_res_block.res_conv.weight', 'unet.final_res_block.res_conv.bias', 'unet.final_conv.weight', 'unet.final_conv.bias', 'conv_seg_new.weight', 'conv_seg_new.bias']
+2023-03-04 21:12:34,299 - mmseg - INFO - Parameters in decode_head freezed!
+2023-03-04 21:12:34,321 - mmseg - INFO - load checkpoint from local path: pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth
+2023-03-04 21:12:34,592 - mmseg - WARNING - The model and loaded state dict do not match exactly
+
+unexpected key in source state_dict: decode_head.conv_seg.weight, decode_head.conv_seg.bias, decode_head.convs.0.conv.weight, decode_head.convs.0.bn.weight, decode_head.convs.0.bn.bias, decode_head.convs.0.bn.running_mean, decode_head.convs.0.bn.running_var, decode_head.convs.0.bn.num_batches_tracked, decode_head.convs.1.conv.weight, decode_head.convs.1.bn.weight, decode_head.convs.1.bn.bias, decode_head.convs.1.bn.running_mean, decode_head.convs.1.bn.running_var, decode_head.convs.1.bn.num_batches_tracked, decode_head.convs.2.conv.weight, decode_head.convs.2.bn.weight, decode_head.convs.2.bn.bias, decode_head.convs.2.bn.running_mean, decode_head.convs.2.bn.running_var, decode_head.convs.2.bn.num_batches_tracked, decode_head.convs.3.conv.weight, decode_head.convs.3.bn.weight, decode_head.convs.3.bn.bias, decode_head.convs.3.bn.running_mean, decode_head.convs.3.bn.running_var, decode_head.convs.3.bn.num_batches_tracked, decode_head.fusion_conv.conv.weight, decode_head.fusion_conv.bn.weight, decode_head.fusion_conv.bn.bias, decode_head.fusion_conv.bn.running_mean, decode_head.fusion_conv.bn.running_var, decode_head.fusion_conv.bn.num_batches_tracked
+
+2023-03-04 21:12:34,604 - mmseg - INFO - load checkpoint from local path: pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth
+2023-03-04 21:12:34,840 - mmseg - WARNING - The model and loaded state dict do not match exactly
+
+unexpected key in source state_dict: backbone.layers.0.0.projection.weight, backbone.layers.0.0.projection.bias, backbone.layers.0.0.norm.weight, backbone.layers.0.0.norm.bias, backbone.layers.0.1.0.norm1.weight, backbone.layers.0.1.0.norm1.bias, backbone.layers.0.1.0.attn.attn.in_proj_weight, backbone.layers.0.1.0.attn.attn.in_proj_bias, backbone.layers.0.1.0.attn.attn.out_proj.weight, backbone.layers.0.1.0.attn.attn.out_proj.bias, backbone.layers.0.1.0.attn.sr.weight, backbone.layers.0.1.0.attn.sr.bias, backbone.layers.0.1.0.attn.norm.weight, backbone.layers.0.1.0.attn.norm.bias, backbone.layers.0.1.0.norm2.weight, backbone.layers.0.1.0.norm2.bias, backbone.layers.0.1.0.ffn.layers.0.weight, backbone.layers.0.1.0.ffn.layers.0.bias, backbone.layers.0.1.0.ffn.layers.1.weight, backbone.layers.0.1.0.ffn.layers.1.bias, backbone.layers.0.1.0.ffn.layers.4.weight, backbone.layers.0.1.0.ffn.layers.4.bias, backbone.layers.0.1.1.norm1.weight, backbone.layers.0.1.1.norm1.bias, backbone.layers.0.1.1.attn.attn.in_proj_weight, backbone.layers.0.1.1.attn.attn.in_proj_bias, backbone.layers.0.1.1.attn.attn.out_proj.weight, backbone.layers.0.1.1.attn.attn.out_proj.bias, backbone.layers.0.1.1.attn.sr.weight, backbone.layers.0.1.1.attn.sr.bias, backbone.layers.0.1.1.attn.norm.weight, backbone.layers.0.1.1.attn.norm.bias, backbone.layers.0.1.1.norm2.weight, backbone.layers.0.1.1.norm2.bias, backbone.layers.0.1.1.ffn.layers.0.weight, backbone.layers.0.1.1.ffn.layers.0.bias, backbone.layers.0.1.1.ffn.layers.1.weight, backbone.layers.0.1.1.ffn.layers.1.bias, backbone.layers.0.1.1.ffn.layers.4.weight, backbone.layers.0.1.1.ffn.layers.4.bias, backbone.layers.0.1.2.norm1.weight, backbone.layers.0.1.2.norm1.bias, backbone.layers.0.1.2.attn.attn.in_proj_weight, backbone.layers.0.1.2.attn.attn.in_proj_bias, backbone.layers.0.1.2.attn.attn.out_proj.weight, backbone.layers.0.1.2.attn.attn.out_proj.bias, backbone.layers.0.1.2.attn.sr.weight, backbone.layers.0.1.2.attn.sr.bias, backbone.layers.0.1.2.attn.norm.weight, backbone.layers.0.1.2.attn.norm.bias, backbone.layers.0.1.2.norm2.weight, backbone.layers.0.1.2.norm2.bias, backbone.layers.0.1.2.ffn.layers.0.weight, backbone.layers.0.1.2.ffn.layers.0.bias, backbone.layers.0.1.2.ffn.layers.1.weight, backbone.layers.0.1.2.ffn.layers.1.bias, backbone.layers.0.1.2.ffn.layers.4.weight, backbone.layers.0.1.2.ffn.layers.4.bias, backbone.layers.0.2.weight, backbone.layers.0.2.bias, backbone.layers.1.0.projection.weight, backbone.layers.1.0.projection.bias, backbone.layers.1.0.norm.weight, backbone.layers.1.0.norm.bias, backbone.layers.1.1.0.norm1.weight, backbone.layers.1.1.0.norm1.bias, backbone.layers.1.1.0.attn.attn.in_proj_weight, backbone.layers.1.1.0.attn.attn.in_proj_bias, backbone.layers.1.1.0.attn.attn.out_proj.weight, backbone.layers.1.1.0.attn.attn.out_proj.bias, backbone.layers.1.1.0.attn.sr.weight, backbone.layers.1.1.0.attn.sr.bias, backbone.layers.1.1.0.attn.norm.weight, backbone.layers.1.1.0.attn.norm.bias, backbone.layers.1.1.0.norm2.weight, backbone.layers.1.1.0.norm2.bias, backbone.layers.1.1.0.ffn.layers.0.weight, backbone.layers.1.1.0.ffn.layers.0.bias, backbone.layers.1.1.0.ffn.layers.1.weight, backbone.layers.1.1.0.ffn.layers.1.bias, backbone.layers.1.1.0.ffn.layers.4.weight, backbone.layers.1.1.0.ffn.layers.4.bias, backbone.layers.1.1.1.norm1.weight, backbone.layers.1.1.1.norm1.bias, backbone.layers.1.1.1.attn.attn.in_proj_weight, backbone.layers.1.1.1.attn.attn.in_proj_bias, backbone.layers.1.1.1.attn.attn.out_proj.weight, backbone.layers.1.1.1.attn.attn.out_proj.bias, backbone.layers.1.1.1.attn.sr.weight, backbone.layers.1.1.1.attn.sr.bias, backbone.layers.1.1.1.attn.norm.weight, backbone.layers.1.1.1.attn.norm.bias, backbone.layers.1.1.1.norm2.weight, backbone.layers.1.1.1.norm2.bias, backbone.layers.1.1.1.ffn.layers.0.weight, backbone.layers.1.1.1.ffn.layers.0.bias, backbone.layers.1.1.1.ffn.layers.1.weight, backbone.layers.1.1.1.ffn.layers.1.bias, backbone.layers.1.1.1.ffn.layers.4.weight, backbone.layers.1.1.1.ffn.layers.4.bias, backbone.layers.1.1.2.norm1.weight, backbone.layers.1.1.2.norm1.bias, backbone.layers.1.1.2.attn.attn.in_proj_weight, backbone.layers.1.1.2.attn.attn.in_proj_bias, backbone.layers.1.1.2.attn.attn.out_proj.weight, backbone.layers.1.1.2.attn.attn.out_proj.bias, backbone.layers.1.1.2.attn.sr.weight, backbone.layers.1.1.2.attn.sr.bias, backbone.layers.1.1.2.attn.norm.weight, backbone.layers.1.1.2.attn.norm.bias, backbone.layers.1.1.2.norm2.weight, backbone.layers.1.1.2.norm2.bias, backbone.layers.1.1.2.ffn.layers.0.weight, backbone.layers.1.1.2.ffn.layers.0.bias, backbone.layers.1.1.2.ffn.layers.1.weight, backbone.layers.1.1.2.ffn.layers.1.bias, backbone.layers.1.1.2.ffn.layers.4.weight, backbone.layers.1.1.2.ffn.layers.4.bias, backbone.layers.1.1.3.norm1.weight, backbone.layers.1.1.3.norm1.bias, backbone.layers.1.1.3.attn.attn.in_proj_weight, backbone.layers.1.1.3.attn.attn.in_proj_bias, backbone.layers.1.1.3.attn.attn.out_proj.weight, backbone.layers.1.1.3.attn.attn.out_proj.bias, backbone.layers.1.1.3.attn.sr.weight, backbone.layers.1.1.3.attn.sr.bias, backbone.layers.1.1.3.attn.norm.weight, backbone.layers.1.1.3.attn.norm.bias, backbone.layers.1.1.3.norm2.weight, backbone.layers.1.1.3.norm2.bias, backbone.layers.1.1.3.ffn.layers.0.weight, backbone.layers.1.1.3.ffn.layers.0.bias, backbone.layers.1.1.3.ffn.layers.1.weight, backbone.layers.1.1.3.ffn.layers.1.bias, backbone.layers.1.1.3.ffn.layers.4.weight, backbone.layers.1.1.3.ffn.layers.4.bias, backbone.layers.1.2.weight, backbone.layers.1.2.bias, backbone.layers.2.0.projection.weight, backbone.layers.2.0.projection.bias, backbone.layers.2.0.norm.weight, backbone.layers.2.0.norm.bias, backbone.layers.2.1.0.norm1.weight, backbone.layers.2.1.0.norm1.bias, backbone.layers.2.1.0.attn.attn.in_proj_weight, backbone.layers.2.1.0.attn.attn.in_proj_bias, backbone.layers.2.1.0.attn.attn.out_proj.weight, backbone.layers.2.1.0.attn.attn.out_proj.bias, backbone.layers.2.1.0.attn.sr.weight, backbone.layers.2.1.0.attn.sr.bias, backbone.layers.2.1.0.attn.norm.weight, backbone.layers.2.1.0.attn.norm.bias, backbone.layers.2.1.0.norm2.weight, backbone.layers.2.1.0.norm2.bias, backbone.layers.2.1.0.ffn.layers.0.weight, backbone.layers.2.1.0.ffn.layers.0.bias, backbone.layers.2.1.0.ffn.layers.1.weight, backbone.layers.2.1.0.ffn.layers.1.bias, backbone.layers.2.1.0.ffn.layers.4.weight, backbone.layers.2.1.0.ffn.layers.4.bias, backbone.layers.2.1.1.norm1.weight, backbone.layers.2.1.1.norm1.bias, backbone.layers.2.1.1.attn.attn.in_proj_weight, backbone.layers.2.1.1.attn.attn.in_proj_bias, backbone.layers.2.1.1.attn.attn.out_proj.weight, backbone.layers.2.1.1.attn.attn.out_proj.bias, backbone.layers.2.1.1.attn.sr.weight, backbone.layers.2.1.1.attn.sr.bias, backbone.layers.2.1.1.attn.norm.weight, backbone.layers.2.1.1.attn.norm.bias, backbone.layers.2.1.1.norm2.weight, backbone.layers.2.1.1.norm2.bias, backbone.layers.2.1.1.ffn.layers.0.weight, backbone.layers.2.1.1.ffn.layers.0.bias, backbone.layers.2.1.1.ffn.layers.1.weight, backbone.layers.2.1.1.ffn.layers.1.bias, backbone.layers.2.1.1.ffn.layers.4.weight, backbone.layers.2.1.1.ffn.layers.4.bias, backbone.layers.2.1.2.norm1.weight, backbone.layers.2.1.2.norm1.bias, backbone.layers.2.1.2.attn.attn.in_proj_weight, backbone.layers.2.1.2.attn.attn.in_proj_bias, backbone.layers.2.1.2.attn.attn.out_proj.weight, backbone.layers.2.1.2.attn.attn.out_proj.bias, backbone.layers.2.1.2.attn.sr.weight, backbone.layers.2.1.2.attn.sr.bias, backbone.layers.2.1.2.attn.norm.weight, backbone.layers.2.1.2.attn.norm.bias, backbone.layers.2.1.2.norm2.weight, backbone.layers.2.1.2.norm2.bias, backbone.layers.2.1.2.ffn.layers.0.weight, backbone.layers.2.1.2.ffn.layers.0.bias, backbone.layers.2.1.2.ffn.layers.1.weight, backbone.layers.2.1.2.ffn.layers.1.bias, backbone.layers.2.1.2.ffn.layers.4.weight, backbone.layers.2.1.2.ffn.layers.4.bias, backbone.layers.2.1.3.norm1.weight, backbone.layers.2.1.3.norm1.bias, backbone.layers.2.1.3.attn.attn.in_proj_weight, backbone.layers.2.1.3.attn.attn.in_proj_bias, backbone.layers.2.1.3.attn.attn.out_proj.weight, backbone.layers.2.1.3.attn.attn.out_proj.bias, backbone.layers.2.1.3.attn.sr.weight, backbone.layers.2.1.3.attn.sr.bias, backbone.layers.2.1.3.attn.norm.weight, backbone.layers.2.1.3.attn.norm.bias, backbone.layers.2.1.3.norm2.weight, backbone.layers.2.1.3.norm2.bias, backbone.layers.2.1.3.ffn.layers.0.weight, backbone.layers.2.1.3.ffn.layers.0.bias, backbone.layers.2.1.3.ffn.layers.1.weight, backbone.layers.2.1.3.ffn.layers.1.bias, backbone.layers.2.1.3.ffn.layers.4.weight, backbone.layers.2.1.3.ffn.layers.4.bias, backbone.layers.2.1.4.norm1.weight, backbone.layers.2.1.4.norm1.bias, backbone.layers.2.1.4.attn.attn.in_proj_weight, backbone.layers.2.1.4.attn.attn.in_proj_bias, backbone.layers.2.1.4.attn.attn.out_proj.weight, backbone.layers.2.1.4.attn.attn.out_proj.bias, backbone.layers.2.1.4.attn.sr.weight, backbone.layers.2.1.4.attn.sr.bias, backbone.layers.2.1.4.attn.norm.weight, backbone.layers.2.1.4.attn.norm.bias, backbone.layers.2.1.4.norm2.weight, backbone.layers.2.1.4.norm2.bias, backbone.layers.2.1.4.ffn.layers.0.weight, backbone.layers.2.1.4.ffn.layers.0.bias, backbone.layers.2.1.4.ffn.layers.1.weight, backbone.layers.2.1.4.ffn.layers.1.bias, backbone.layers.2.1.4.ffn.layers.4.weight, backbone.layers.2.1.4.ffn.layers.4.bias, backbone.layers.2.1.5.norm1.weight, backbone.layers.2.1.5.norm1.bias, backbone.layers.2.1.5.attn.attn.in_proj_weight, backbone.layers.2.1.5.attn.attn.in_proj_bias, backbone.layers.2.1.5.attn.attn.out_proj.weight, backbone.layers.2.1.5.attn.attn.out_proj.bias, backbone.layers.2.1.5.attn.sr.weight, backbone.layers.2.1.5.attn.sr.bias, backbone.layers.2.1.5.attn.norm.weight, backbone.layers.2.1.5.attn.norm.bias, backbone.layers.2.1.5.norm2.weight, backbone.layers.2.1.5.norm2.bias, backbone.layers.2.1.5.ffn.layers.0.weight, backbone.layers.2.1.5.ffn.layers.0.bias, backbone.layers.2.1.5.ffn.layers.1.weight, backbone.layers.2.1.5.ffn.layers.1.bias, backbone.layers.2.1.5.ffn.layers.4.weight, backbone.layers.2.1.5.ffn.layers.4.bias, backbone.layers.2.2.weight, backbone.layers.2.2.bias, backbone.layers.3.0.projection.weight, backbone.layers.3.0.projection.bias, backbone.layers.3.0.norm.weight, backbone.layers.3.0.norm.bias, backbone.layers.3.1.0.norm1.weight, backbone.layers.3.1.0.norm1.bias, backbone.layers.3.1.0.attn.attn.in_proj_weight, backbone.layers.3.1.0.attn.attn.in_proj_bias, backbone.layers.3.1.0.attn.attn.out_proj.weight, backbone.layers.3.1.0.attn.attn.out_proj.bias, backbone.layers.3.1.0.norm2.weight, backbone.layers.3.1.0.norm2.bias, backbone.layers.3.1.0.ffn.layers.0.weight, backbone.layers.3.1.0.ffn.layers.0.bias, backbone.layers.3.1.0.ffn.layers.1.weight, backbone.layers.3.1.0.ffn.layers.1.bias, backbone.layers.3.1.0.ffn.layers.4.weight, backbone.layers.3.1.0.ffn.layers.4.bias, backbone.layers.3.1.1.norm1.weight, backbone.layers.3.1.1.norm1.bias, backbone.layers.3.1.1.attn.attn.in_proj_weight, backbone.layers.3.1.1.attn.attn.in_proj_bias, backbone.layers.3.1.1.attn.attn.out_proj.weight, backbone.layers.3.1.1.attn.attn.out_proj.bias, backbone.layers.3.1.1.norm2.weight, backbone.layers.3.1.1.norm2.bias, backbone.layers.3.1.1.ffn.layers.0.weight, backbone.layers.3.1.1.ffn.layers.0.bias, backbone.layers.3.1.1.ffn.layers.1.weight, backbone.layers.3.1.1.ffn.layers.1.bias, backbone.layers.3.1.1.ffn.layers.4.weight, backbone.layers.3.1.1.ffn.layers.4.bias, backbone.layers.3.1.2.norm1.weight, backbone.layers.3.1.2.norm1.bias, backbone.layers.3.1.2.attn.attn.in_proj_weight, backbone.layers.3.1.2.attn.attn.in_proj_bias, backbone.layers.3.1.2.attn.attn.out_proj.weight, backbone.layers.3.1.2.attn.attn.out_proj.bias, backbone.layers.3.1.2.norm2.weight, backbone.layers.3.1.2.norm2.bias, backbone.layers.3.1.2.ffn.layers.0.weight, backbone.layers.3.1.2.ffn.layers.0.bias, backbone.layers.3.1.2.ffn.layers.1.weight, backbone.layers.3.1.2.ffn.layers.1.bias, backbone.layers.3.1.2.ffn.layers.4.weight, backbone.layers.3.1.2.ffn.layers.4.bias, backbone.layers.3.2.weight, backbone.layers.3.2.bias
+
+missing keys in source state_dict: unet.init_conv.weight, unet.init_conv.bias, unet.time_mlp.1.weight, unet.time_mlp.1.bias, unet.time_mlp.3.weight, unet.time_mlp.3.bias, unet.downs.0.0.mlp.1.weight, unet.downs.0.0.mlp.1.bias, unet.downs.0.0.block1.proj.weight, unet.downs.0.0.block1.proj.bias, unet.downs.0.0.block1.norm.weight, unet.downs.0.0.block1.norm.bias, unet.downs.0.0.block2.proj.weight, unet.downs.0.0.block2.proj.bias, unet.downs.0.0.block2.norm.weight, unet.downs.0.0.block2.norm.bias, unet.downs.0.1.mlp.1.weight, unet.downs.0.1.mlp.1.bias, unet.downs.0.1.block1.proj.weight, unet.downs.0.1.block1.proj.bias, unet.downs.0.1.block1.norm.weight, unet.downs.0.1.block1.norm.bias, unet.downs.0.1.block2.proj.weight, unet.downs.0.1.block2.proj.bias, unet.downs.0.1.block2.norm.weight, unet.downs.0.1.block2.norm.bias, unet.downs.0.2.fn.fn.to_qkv.weight, unet.downs.0.2.fn.fn.to_out.0.weight, unet.downs.0.2.fn.fn.to_out.0.bias, unet.downs.0.2.fn.fn.to_out.1.g, unet.downs.0.2.fn.norm.g, unet.downs.0.3.weight, unet.downs.0.3.bias, unet.downs.1.0.mlp.1.weight, unet.downs.1.0.mlp.1.bias, unet.downs.1.0.block1.proj.weight, unet.downs.1.0.block1.proj.bias, unet.downs.1.0.block1.norm.weight, unet.downs.1.0.block1.norm.bias, unet.downs.1.0.block2.proj.weight, unet.downs.1.0.block2.proj.bias, unet.downs.1.0.block2.norm.weight, unet.downs.1.0.block2.norm.bias, unet.downs.1.1.mlp.1.weight, unet.downs.1.1.mlp.1.bias, unet.downs.1.1.block1.proj.weight, unet.downs.1.1.block1.proj.bias, unet.downs.1.1.block1.norm.weight, unet.downs.1.1.block1.norm.bias, unet.downs.1.1.block2.proj.weight, unet.downs.1.1.block2.proj.bias, unet.downs.1.1.block2.norm.weight, unet.downs.1.1.block2.norm.bias, unet.downs.1.2.fn.fn.to_qkv.weight, unet.downs.1.2.fn.fn.to_out.0.weight, unet.downs.1.2.fn.fn.to_out.0.bias, unet.downs.1.2.fn.fn.to_out.1.g, unet.downs.1.2.fn.norm.g, unet.downs.1.3.weight, unet.downs.1.3.bias, unet.downs.2.0.mlp.1.weight, unet.downs.2.0.mlp.1.bias, unet.downs.2.0.block1.proj.weight, unet.downs.2.0.block1.proj.bias, unet.downs.2.0.block1.norm.weight, unet.downs.2.0.block1.norm.bias, unet.downs.2.0.block2.proj.weight, unet.downs.2.0.block2.proj.bias, unet.downs.2.0.block2.norm.weight, unet.downs.2.0.block2.norm.bias, unet.downs.2.1.mlp.1.weight, unet.downs.2.1.mlp.1.bias, unet.downs.2.1.block1.proj.weight, unet.downs.2.1.block1.proj.bias, unet.downs.2.1.block1.norm.weight, unet.downs.2.1.block1.norm.bias, unet.downs.2.1.block2.proj.weight, unet.downs.2.1.block2.proj.bias, unet.downs.2.1.block2.norm.weight, unet.downs.2.1.block2.norm.bias, unet.downs.2.2.fn.fn.to_qkv.weight, unet.downs.2.2.fn.fn.to_out.0.weight, unet.downs.2.2.fn.fn.to_out.0.bias, unet.downs.2.2.fn.fn.to_out.1.g, unet.downs.2.2.fn.norm.g, unet.downs.2.3.weight, unet.downs.2.3.bias, unet.ups.0.0.mlp.1.weight, unet.ups.0.0.mlp.1.bias, unet.ups.0.0.block1.proj.weight, unet.ups.0.0.block1.proj.bias, unet.ups.0.0.block1.norm.weight, unet.ups.0.0.block1.norm.bias, unet.ups.0.0.block2.proj.weight, unet.ups.0.0.block2.proj.bias, unet.ups.0.0.block2.norm.weight, unet.ups.0.0.block2.norm.bias, unet.ups.0.0.res_conv.weight, unet.ups.0.0.res_conv.bias, unet.ups.0.1.mlp.1.weight, unet.ups.0.1.mlp.1.bias, unet.ups.0.1.block1.proj.weight, unet.ups.0.1.block1.proj.bias, unet.ups.0.1.block1.norm.weight, unet.ups.0.1.block1.norm.bias, unet.ups.0.1.block2.proj.weight, unet.ups.0.1.block2.proj.bias, unet.ups.0.1.block2.norm.weight, unet.ups.0.1.block2.norm.bias, unet.ups.0.1.res_conv.weight, unet.ups.0.1.res_conv.bias, unet.ups.0.2.fn.fn.to_qkv.weight, unet.ups.0.2.fn.fn.to_out.0.weight, unet.ups.0.2.fn.fn.to_out.0.bias, unet.ups.0.2.fn.fn.to_out.1.g, unet.ups.0.2.fn.norm.g, unet.ups.0.3.1.weight, unet.ups.0.3.1.bias, unet.ups.1.0.mlp.1.weight, unet.ups.1.0.mlp.1.bias, unet.ups.1.0.block1.proj.weight, unet.ups.1.0.block1.proj.bias, unet.ups.1.0.block1.norm.weight, unet.ups.1.0.block1.norm.bias, unet.ups.1.0.block2.proj.weight, unet.ups.1.0.block2.proj.bias, unet.ups.1.0.block2.norm.weight, unet.ups.1.0.block2.norm.bias, unet.ups.1.0.res_conv.weight, unet.ups.1.0.res_conv.bias, unet.ups.1.1.mlp.1.weight, unet.ups.1.1.mlp.1.bias, unet.ups.1.1.block1.proj.weight, unet.ups.1.1.block1.proj.bias, unet.ups.1.1.block1.norm.weight, unet.ups.1.1.block1.norm.bias, unet.ups.1.1.block2.proj.weight, unet.ups.1.1.block2.proj.bias, unet.ups.1.1.block2.norm.weight, unet.ups.1.1.block2.norm.bias, unet.ups.1.1.res_conv.weight, unet.ups.1.1.res_conv.bias, unet.ups.1.2.fn.fn.to_qkv.weight, unet.ups.1.2.fn.fn.to_out.0.weight, unet.ups.1.2.fn.fn.to_out.0.bias, unet.ups.1.2.fn.fn.to_out.1.g, unet.ups.1.2.fn.norm.g, unet.ups.1.3.1.weight, unet.ups.1.3.1.bias, unet.ups.2.0.mlp.1.weight, unet.ups.2.0.mlp.1.bias, unet.ups.2.0.block1.proj.weight, unet.ups.2.0.block1.proj.bias, unet.ups.2.0.block1.norm.weight, unet.ups.2.0.block1.norm.bias, unet.ups.2.0.block2.proj.weight, unet.ups.2.0.block2.proj.bias, unet.ups.2.0.block2.norm.weight, unet.ups.2.0.block2.norm.bias, unet.ups.2.0.res_conv.weight, unet.ups.2.0.res_conv.bias, unet.ups.2.1.mlp.1.weight, unet.ups.2.1.mlp.1.bias, unet.ups.2.1.block1.proj.weight, unet.ups.2.1.block1.proj.bias, unet.ups.2.1.block1.norm.weight, unet.ups.2.1.block1.norm.bias, unet.ups.2.1.block2.proj.weight, unet.ups.2.1.block2.proj.bias, unet.ups.2.1.block2.norm.weight, unet.ups.2.1.block2.norm.bias, unet.ups.2.1.res_conv.weight, unet.ups.2.1.res_conv.bias, unet.ups.2.2.fn.fn.to_qkv.weight, unet.ups.2.2.fn.fn.to_out.0.weight, unet.ups.2.2.fn.fn.to_out.0.bias, unet.ups.2.2.fn.fn.to_out.1.g, unet.ups.2.2.fn.norm.g, unet.ups.2.3.weight, unet.ups.2.3.bias, unet.mid_block1.mlp.1.weight, unet.mid_block1.mlp.1.bias, unet.mid_block1.block1.proj.weight, unet.mid_block1.block1.proj.bias, unet.mid_block1.block1.norm.weight, unet.mid_block1.block1.norm.bias, unet.mid_block1.block2.proj.weight, unet.mid_block1.block2.proj.bias, unet.mid_block1.block2.norm.weight, unet.mid_block1.block2.norm.bias, unet.mid_attn.fn.fn.to_qkv.weight, unet.mid_attn.fn.fn.to_out.weight, unet.mid_attn.fn.fn.to_out.bias, unet.mid_attn.fn.norm.g, unet.mid_block2.mlp.1.weight, unet.mid_block2.mlp.1.bias, unet.mid_block2.block1.proj.weight, unet.mid_block2.block1.proj.bias, unet.mid_block2.block1.norm.weight, unet.mid_block2.block1.norm.bias, unet.mid_block2.block2.proj.weight, unet.mid_block2.block2.proj.bias, unet.mid_block2.block2.norm.weight, unet.mid_block2.block2.norm.bias, unet.final_res_block.mlp.1.weight, unet.final_res_block.mlp.1.bias, unet.final_res_block.block1.proj.weight, unet.final_res_block.block1.proj.bias, unet.final_res_block.block1.norm.weight, unet.final_res_block.block1.norm.bias, unet.final_res_block.block2.proj.weight, unet.final_res_block.block2.proj.bias, unet.final_res_block.block2.norm.weight, unet.final_res_block.block2.norm.bias, unet.final_res_block.res_conv.weight, unet.final_res_block.res_conv.bias, unet.final_conv.weight, unet.final_conv.bias, conv_seg_new.weight, conv_seg_new.bias, embed.weight
+
+2023-03-04 21:12:34,865 - mmseg - INFO - EncoderDecoderFreeze(
+  (backbone): MixVisionTransformerCustomInitWeights(
+    (layers): ModuleList(
+      (0): ModuleList(
+        (0): PatchEmbed(
+          (projection): Conv2d(3, 64, kernel_size=(7, 7), stride=(4, 4), padding=(3, 3))
+          (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+        )
+        (1): ModuleList(
+          (0): TransformerEncoderLayer(
+            (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8))
+              (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (1): TransformerEncoderLayer(
+            (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8))
+              (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (2): TransformerEncoderLayer(
+            (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8))
+              (norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+        )
+        (2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
+      )
+      (1): ModuleList(
+        (0): PatchEmbed(
+          (projection): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+          (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+        )
+        (1): ModuleList(
+          (0): TransformerEncoderLayer(
+            (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4))
+              (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (1): TransformerEncoderLayer(
+            (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4))
+              (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (2): TransformerEncoderLayer(
+            (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4))
+              (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (3): TransformerEncoderLayer(
+            (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4))
+              (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+        )
+        (2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
+      )
+      (2): ModuleList(
+        (0): PatchEmbed(
+          (projection): Conv2d(128, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+          (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+        )
+        (1): ModuleList(
+          (0): TransformerEncoderLayer(
+            (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
+              (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (1): TransformerEncoderLayer(
+            (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
+              (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (2): TransformerEncoderLayer(
+            (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
+              (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (3): TransformerEncoderLayer(
+            (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
+              (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (4): TransformerEncoderLayer(
+            (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
+              (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (5): TransformerEncoderLayer(
+            (norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=320, out_features=320, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+              (sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
+              (norm): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            )
+            (norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+        )
+        (2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
+      )
+      (3): ModuleList(
+        (0): PatchEmbed(
+          (projection): Conv2d(320, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+        )
+        (1): ModuleList(
+          (0): TransformerEncoderLayer(
+            (norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+            )
+            (norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (1): TransformerEncoderLayer(
+            (norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+            )
+            (norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+          (2): TransformerEncoderLayer(
+            (norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+            (attn): EfficientMultiheadAttention(
+              (attn): MultiheadAttention(
+                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
+              )
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (dropout_layer): DropPath()
+            )
+            (norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+            (ffn): MixFFN(
+              (activate): GELU(approximate='none')
+              (layers): Sequential(
+                (0): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1))
+                (1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048)
+                (2): GELU(approximate='none')
+                (3): Dropout(p=0.0, inplace=False)
+                (4): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1))
+                (5): Dropout(p=0.0, inplace=False)
+              )
+              (dropout_layer): DropPath()
+            )
+          )
+        )
+        (2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
+      )
+    )
+  )
+  init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'}
+  (decode_head): SegformerHeadUnetFCHeadSingleStepLogits(
+    input_transform=multiple_select, ignore_index=0, align_corners=False
+    (loss_decode): CrossEntropyLoss(avg_non_ignore=False)
+    (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1))
+    (dropout): Dropout2d(p=0.1, inplace=False)
+    (convs): ModuleList(
+      (0): ConvModule(
+        (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (activate): ReLU(inplace=True)
+      )
+      (1): ConvModule(
+        (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (activate): ReLU(inplace=True)
+      )
+      (2): ConvModule(
+        (conv): Conv2d(320, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (activate): ReLU(inplace=True)
+      )
+      (3): ConvModule(
+        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (activate): ReLU(inplace=True)
+      )
+    )
+    (fusion_conv): ConvModule(
+      (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (activate): ReLU(inplace=True)
+    )
+    (unet): Unet(
+      (init_conv): Conv2d(166, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
+      (time_mlp): Sequential(
+        (0): SinusoidalPosEmb()
+        (1): Linear(in_features=128, out_features=512, bias=True)
+        (2): GELU(approximate='none')
+        (3): Linear(in_features=512, out_features=512, bias=True)
+      )
+      (downs): ModuleList(
+        (0): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+        )
+        (1): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+        )
+        (2): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Identity()
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (ups): ModuleList(
+        (0): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Sequential(
+            (0): Upsample(scale_factor=2.0, mode=nearest)
+            (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (1): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Sequential(
+            (0): Upsample(scale_factor=2.0, mode=nearest)
+            (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (2): ModuleList(
+          (0): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (1): ResnetBlock(
+            (mlp): Sequential(
+              (0): SiLU()
+              (1): Linear(in_features=512, out_features=256, bias=True)
+            )
+            (block1): Block(
+              (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (block2): Block(
+              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+              (act): SiLU()
+            )
+            (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (2): Residual(
+            (fn): PreNorm(
+              (fn): LinearAttention(
+                (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+                (to_out): Sequential(
+                  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+                  (1): LayerNorm()
+                )
+              )
+              (norm): LayerNorm()
+            )
+          )
+          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (mid_block1): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=512, out_features=256, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Identity()
+      )
+      (mid_attn): Residual(
+        (fn): PreNorm(
+          (fn): Attention(
+            (to_qkv): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+            (to_out): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (norm): LayerNorm()
+        )
+      )
+      (mid_block2): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=512, out_features=256, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Identity()
+      )
+      (final_res_block): ResnetBlock(
+        (mlp): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=512, out_features=256, bias=True)
+        )
+        (block1): Block(
+          (proj): WeightStandardizedConv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (block2): Block(
+          (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          (norm): GroupNorm(8, 128, eps=1e-05, affine=True)
+          (act): SiLU()
+        )
+        (res_conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+      )
+      (final_conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+    )
+    (conv_seg_new): Conv2d(256, 151, kernel_size=(1, 1), stride=(1, 1))
+    (embed): Embedding(151, 16)
+  )
+  init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'}
+)
+2023-03-04 21:12:35,752 - mmseg - INFO - Loaded 20210 images
+2023-03-04 21:12:36,822 - mmseg - INFO - Loaded 2000 images
+2023-03-04 21:12:36,823 - mmseg - INFO - load checkpoint from local path: ./work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/latest.pth
+2023-03-04 21:12:37,482 - mmseg - INFO - resumed from epoch: 13, iter 7999
+2023-03-04 21:12:37,483 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-143, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits
+2023-03-04 21:12:37,484 - mmseg - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(NORMAL      ) CheckpointHook                     
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+ -------------------- 
+after_train_iter:
+(ABOVE_NORMAL) OptimizerHook                      
+(NORMAL      ) CheckpointHook                     
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) CheckpointHook                     
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_val_epoch:
+(LOW         ) IterTimerHook                      
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_val_iter:
+(LOW         ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(LOW         ) IterTimerHook                      
+ -------------------- 
+after_val_epoch:
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_run:
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+2023-03-04 21:12:37,484 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters
+2023-03-04 21:12:37,484 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits by HardDiskBackend.
+2023-03-04 21:13:09,427 - mmseg - INFO - Saving checkpoint at 8000 iterations
+2023-03-04 21:13:10,074 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:13:10,075 - mmseg - INFO - Iter [8000/80000]	lr: 1.500e-04, eta: 272 days, 4:02:38, time: 6.532, data_time: 0.475, memory: 19833, decode.loss_ce: 0.3976, decode.acc_seg: 85.8395, loss: 0.3976
+2023-03-04 21:16:54,685 - mmseg - INFO - per class results:
+2023-03-04 21:16:54,691 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 74.83 | 85.97 |
+|       building      | 77.77 | 93.78 |
+|         sky         | 93.63 | 96.58 |
+|        floor        | 79.77 |  89.5 |
+|         tree        | 67.49 | 79.16 |
+|       ceiling       |  81.2 | 93.02 |
+|         road        |  79.2 | 85.18 |
+|         bed         | 85.61 | 93.84 |
+|      windowpane     | 58.48 |  73.7 |
+|        grass        | 61.25 | 68.61 |
+|       cabinet       | 55.95 | 63.86 |
+|       sidewalk      | 58.07 | 83.18 |
+|        person       |  73.5 |  93.2 |
+|        earth        | 34.75 |  47.6 |
+|         door        | 39.73 | 48.78 |
+|        table        | 52.65 | 78.69 |
+|       mountain      | 55.07 | 67.36 |
+|        plant        | 47.89 | 58.52 |
+|       curtain       | 71.05 | 83.03 |
+|        chair        | 50.16 |  67.9 |
+|         car         | 75.35 | 93.55 |
+|        water        | 55.94 | 73.54 |
+|       painting      | 67.36 | 83.94 |
+|         sofa        | 59.65 | 70.43 |
+|        shelf        | 39.74 |  76.4 |
+|        house        | 41.92 | 61.72 |
+|         sea         |  59.4 | 76.16 |
+|        mirror       | 60.83 | 77.74 |
+|         rug         |  60.8 | 73.66 |
+|        field        |  27.3 |  45.1 |
+|       armchair      |  36.0 | 60.34 |
+|         seat        | 62.78 | 83.27 |
+|        fence        | 24.38 | 26.57 |
+|         desk        | 42.04 | 65.61 |
+|         rock        |  35.8 | 58.81 |
+|       wardrobe      |  52.3 | 77.16 |
+|         lamp        | 49.04 | 81.65 |
+|       bathtub       | 61.96 | 64.58 |
+|       railing       | 23.39 | 24.44 |
+|       cushion       | 50.35 | 66.19 |
+|         base        | 18.98 | 22.95 |
+|         box         | 16.72 | 22.21 |
+|        column       | 42.36 | 55.04 |
+|      signboard      | 32.95 | 39.27 |
+|   chest of drawers  |  36.2 | 47.64 |
+|       counter       | 22.66 | 25.74 |
+|         sand        | 39.19 | 65.82 |
+|         sink        | 60.89 | 76.98 |
+|      skyscraper     | 47.41 | 65.55 |
+|      fireplace      | 69.71 | 82.62 |
+|     refrigerator    | 70.05 | 79.97 |
+|      grandstand     |  47.0 | 59.61 |
+|         path        | 17.19 | 27.09 |
+|        stairs       | 14.19 | 14.94 |
+|        runway       | 66.41 | 88.41 |
+|         case        | 51.99 | 67.67 |
+|      pool table     | 88.81 |  91.7 |
+|        pillow       | 51.19 | 59.68 |
+|     screen door     | 53.77 | 55.34 |
+|       stairway      | 21.69 | 35.54 |
+|        river        | 10.33 | 19.12 |
+|        bridge       | 24.52 | 26.33 |
+|       bookcase      | 37.78 | 44.04 |
+|        blind        | 34.38 | 37.82 |
+|     coffee table    | 51.25 | 76.73 |
+|        toilet       | 76.06 | 90.25 |
+|        flower       | 32.78 | 46.56 |
+|         book        | 43.13 | 59.06 |
+|         hill        | 13.46 | 21.31 |
+|        bench        | 38.33 |  47.6 |
+|      countertop     | 49.16 |  59.6 |
+|        stove        | 67.62 | 84.92 |
+|         palm        | 47.42 | 74.35 |
+|    kitchen island   |  34.0 | 48.49 |
+|       computer      | 56.35 | 71.13 |
+|     swivel chair    | 36.06 | 46.94 |
+|         boat        |  62.8 | 84.53 |
+|         bar         | 23.61 | 32.57 |
+|    arcade machine   | 65.06 | 95.42 |
+|        hovel        | 20.42 | 22.63 |
+|         bus         | 75.67 | 82.36 |
+|        towel        | 58.28 | 66.95 |
+|        light        | 37.98 | 44.34 |
+|        truck        | 14.38 | 17.91 |
+|        tower        | 10.04 | 16.58 |
+|      chandelier     | 55.22 | 80.76 |
+|        awning       | 13.79 | 14.98 |
+|     streetlight     | 18.04 | 23.22 |
+|        booth        | 43.96 | 55.17 |
+| television receiver | 62.41 | 78.74 |
+|       airplane      |  54.8 | 63.61 |
+|      dirt track     | 10.99 | 33.67 |
+|       apparel       | 28.12 | 58.02 |
+|         pole        | 11.95 | 15.72 |
+|         land        |  2.81 |  4.09 |
+|      bannister      |  4.18 |  4.83 |
+|      escalator      | 20.23 | 22.12 |
+|       ottoman       | 35.75 | 63.84 |
+|        bottle       | 24.73 | 35.25 |
+|        buffet       | 43.51 | 49.35 |
+|        poster       | 20.96 | 26.44 |
+|        stage        | 13.26 |  19.8 |
+|         van         | 30.71 | 36.45 |
+|         ship        | 76.13 | 85.15 |
+|       fountain      |  6.64 |  6.75 |
+|    conveyer belt    | 81.65 | 86.12 |
+|        canopy       | 22.37 |  28.0 |
+|        washer       | 81.41 | 85.26 |
+|      plaything      | 18.51 | 38.11 |
+|    swimming pool    | 71.65 | 80.49 |
+|        stool        | 32.55 |  54.8 |
+|        barrel       | 24.59 | 45.67 |
+|        basket       | 20.24 | 41.61 |
+|      waterfall      |  50.1 | 64.36 |
+|         tent        | 94.09 | 97.92 |
+|         bag         |  8.9  |  9.58 |
+|       minibike      | 54.59 | 81.84 |
+|        cradle       | 79.96 | 88.17 |
+|         oven        | 40.96 | 61.46 |
+|         ball        | 38.52 |  60.0 |
+|         food        | 36.86 | 52.66 |
+|         step        |  4.94 |  5.66 |
+|         tank        | 49.27 | 54.14 |
+|      trade name     | 16.39 | 16.96 |
+|      microwave      | 71.89 | 84.75 |
+|         pot         | 30.55 |  42.3 |
+|        animal       | 47.84 | 65.29 |
+|       bicycle       |  36.9 | 52.91 |
+|         lake        | 56.28 | 61.52 |
+|      dishwasher     | 60.81 | 69.48 |
+|        screen       | 58.49 | 64.75 |
+|       blanket       | 13.24 | 14.62 |
+|      sculpture      | 42.28 | 78.65 |
+|         hood        | 44.18 | 71.23 |
+|        sconce       | 22.76 | 25.18 |
+|         vase        | 26.66 |  34.3 |
+|    traffic light    | 23.04 | 30.58 |
+|         tray        |  1.17 |  1.41 |
+|        ashcan       | 30.79 | 38.39 |
+|         fan         | 48.04 | 71.72 |
+|         pier        | 24.42 | 77.96 |
+|      crt screen     |  8.86 | 29.36 |
+|        plate        | 27.39 | 78.99 |
+|       monitor       |  7.61 |  8.99 |
+|    bulletin board   | 41.51 | 56.58 |
+|        shower       |  0.29 |  1.38 |
+|       radiator      | 35.54 | 36.98 |
+|        glass        |  2.59 |  2.73 |
+|        clock        | 24.31 | 28.24 |
+|         flag        | 25.15 | 26.03 |
++---------------------+-------+-------+
+2023-03-04 21:16:54,692 - mmseg - INFO - Summary:
+2023-03-04 21:16:54,692 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 80.16 | 42.62 | 55.19 |
++-------+-------+-------+
+2023-03-04 21:16:55,343 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_8000.pth.
+2023-03-04 21:16:55,343 - mmseg - INFO - Best mIoU is 0.4262 at 8000 iter.
+2023-03-04 21:16:55,343 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:16:55,344 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8016, mIoU: 0.4262, mAcc: 0.5519, IoU.background: nan, IoU.wall: 0.7483, IoU.building: 0.7777, IoU.sky: 0.9363, IoU.floor: 0.7977, IoU.tree: 0.6749, IoU.ceiling: 0.8120, IoU.road: 0.7920, IoU.bed : 0.8561, IoU.windowpane: 0.5848, IoU.grass: 0.6125, IoU.cabinet: 0.5595, IoU.sidewalk: 0.5807, IoU.person: 0.7350, IoU.earth: 0.3475, IoU.door: 0.3973, IoU.table: 0.5265, IoU.mountain: 0.5507, IoU.plant: 0.4789, IoU.curtain: 0.7105, IoU.chair: 0.5016, IoU.car: 0.7535, IoU.water: 0.5594, IoU.painting: 0.6736, IoU.sofa: 0.5965, IoU.shelf: 0.3974, IoU.house: 0.4192, IoU.sea: 0.5940, IoU.mirror: 0.6083, IoU.rug: 0.6080, IoU.field: 0.2730, IoU.armchair: 0.3600, IoU.seat: 0.6278, IoU.fence: 0.2438, IoU.desk: 0.4204, IoU.rock: 0.3580, IoU.wardrobe: 0.5230, IoU.lamp: 0.4904, IoU.bathtub: 0.6196, IoU.railing: 0.2339, IoU.cushion: 0.5035, IoU.base: 0.1898, IoU.box: 0.1672, IoU.column: 0.4236, IoU.signboard: 0.3295, IoU.chest of drawers: 0.3620, IoU.counter: 0.2266, IoU.sand: 0.3919, IoU.sink: 0.6089, IoU.skyscraper: 0.4741, IoU.fireplace: 0.6971, IoU.refrigerator: 0.7005, IoU.grandstand: 0.4700, IoU.path: 0.1719, IoU.stairs: 0.1419, IoU.runway: 0.6641, IoU.case: 0.5199, IoU.pool table: 0.8881, IoU.pillow: 0.5119, IoU.screen door: 0.5377, IoU.stairway: 0.2169, IoU.river: 0.1033, IoU.bridge: 0.2452, IoU.bookcase: 0.3778, IoU.blind: 0.3438, IoU.coffee table: 0.5125, IoU.toilet: 0.7606, IoU.flower: 0.3278, IoU.book: 0.4313, IoU.hill: 0.1346, IoU.bench: 0.3833, IoU.countertop: 0.4916, IoU.stove: 0.6762, IoU.palm: 0.4742, IoU.kitchen island: 0.3400, IoU.computer: 0.5635, IoU.swivel chair: 0.3606, IoU.boat: 0.6280, IoU.bar: 0.2361, IoU.arcade machine: 0.6506, IoU.hovel: 0.2042, IoU.bus: 0.7567, IoU.towel: 0.5828, IoU.light: 0.3798, IoU.truck: 0.1438, IoU.tower: 0.1004, IoU.chandelier: 0.5522, IoU.awning: 0.1379, IoU.streetlight: 0.1804, IoU.booth: 0.4396, IoU.television receiver: 0.6241, IoU.airplane: 0.5480, IoU.dirt track: 0.1099, IoU.apparel: 0.2812, IoU.pole: 0.1195, IoU.land: 0.0281, IoU.bannister: 0.0418, IoU.escalator: 0.2023, IoU.ottoman: 0.3575, IoU.bottle: 0.2473, IoU.buffet: 0.4351, IoU.poster: 0.2096, IoU.stage: 0.1326, IoU.van: 0.3071, IoU.ship: 0.7613, IoU.fountain: 0.0664, IoU.conveyer belt: 0.8165, IoU.canopy: 0.2237, IoU.washer: 0.8141, IoU.plaything: 0.1851, IoU.swimming pool: 0.7165, IoU.stool: 0.3255, IoU.barrel: 0.2459, IoU.basket: 0.2024, IoU.waterfall: 0.5010, IoU.tent: 0.9409, IoU.bag: 0.0890, IoU.minibike: 0.5459, IoU.cradle: 0.7996, IoU.oven: 0.4096, IoU.ball: 0.3852, IoU.food: 0.3686, IoU.step: 0.0494, IoU.tank: 0.4927, IoU.trade name: 0.1639, IoU.microwave: 0.7189, IoU.pot: 0.3055, IoU.animal: 0.4784, IoU.bicycle: 0.3690, IoU.lake: 0.5628, IoU.dishwasher: 0.6081, IoU.screen: 0.5849, IoU.blanket: 0.1324, IoU.sculpture: 0.4228, IoU.hood: 0.4418, IoU.sconce: 0.2276, IoU.vase: 0.2666, IoU.traffic light: 0.2304, IoU.tray: 0.0117, IoU.ashcan: 0.3079, IoU.fan: 0.4804, IoU.pier: 0.2442, IoU.crt screen: 0.0886, IoU.plate: 0.2739, IoU.monitor: 0.0761, IoU.bulletin board: 0.4151, IoU.shower: 0.0029, IoU.radiator: 0.3554, IoU.glass: 0.0259, IoU.clock: 0.2431, IoU.flag: 0.2515, Acc.background: nan, Acc.wall: 0.8597, Acc.building: 0.9378, Acc.sky: 0.9658, Acc.floor: 0.8950, Acc.tree: 0.7916, Acc.ceiling: 0.9302, Acc.road: 0.8518, Acc.bed : 0.9384, Acc.windowpane: 0.7370, Acc.grass: 0.6861, Acc.cabinet: 0.6386, Acc.sidewalk: 0.8318, Acc.person: 0.9320, Acc.earth: 0.4760, Acc.door: 0.4878, Acc.table: 0.7869, Acc.mountain: 0.6736, Acc.plant: 0.5852, Acc.curtain: 0.8303, Acc.chair: 0.6790, Acc.car: 0.9355, Acc.water: 0.7354, Acc.painting: 0.8394, Acc.sofa: 0.7043, Acc.shelf: 0.7640, Acc.house: 0.6172, Acc.sea: 0.7616, Acc.mirror: 0.7774, Acc.rug: 0.7366, Acc.field: 0.4510, Acc.armchair: 0.6034, Acc.seat: 0.8327, Acc.fence: 0.2657, Acc.desk: 0.6561, Acc.rock: 0.5881, Acc.wardrobe: 0.7716, Acc.lamp: 0.8165, Acc.bathtub: 0.6458, Acc.railing: 0.2444, Acc.cushion: 0.6619, Acc.base: 0.2295, Acc.box: 0.2221, Acc.column: 0.5504, Acc.signboard: 0.3927, Acc.chest of drawers: 0.4764, Acc.counter: 0.2574, Acc.sand: 0.6582, Acc.sink: 0.7698, Acc.skyscraper: 0.6555, Acc.fireplace: 0.8262, Acc.refrigerator: 0.7997, Acc.grandstand: 0.5961, Acc.path: 0.2709, Acc.stairs: 0.1494, Acc.runway: 0.8841, Acc.case: 0.6767, Acc.pool table: 0.9170, Acc.pillow: 0.5968, Acc.screen door: 0.5534, Acc.stairway: 0.3554, Acc.river: 0.1912, Acc.bridge: 0.2633, Acc.bookcase: 0.4404, Acc.blind: 0.3782, Acc.coffee table: 0.7673, Acc.toilet: 0.9025, Acc.flower: 0.4656, Acc.book: 0.5906, Acc.hill: 0.2131, Acc.bench: 0.4760, Acc.countertop: 0.5960, Acc.stove: 0.8492, Acc.palm: 0.7435, Acc.kitchen island: 0.4849, Acc.computer: 0.7113, Acc.swivel chair: 0.4694, Acc.boat: 0.8453, Acc.bar: 0.3257, Acc.arcade machine: 0.9542, Acc.hovel: 0.2263, Acc.bus: 0.8236, Acc.towel: 0.6695, Acc.light: 0.4434, Acc.truck: 0.1791, Acc.tower: 0.1658, Acc.chandelier: 0.8076, Acc.awning: 0.1498, Acc.streetlight: 0.2322, Acc.booth: 0.5517, Acc.television receiver: 0.7874, Acc.airplane: 0.6361, Acc.dirt track: 0.3367, Acc.apparel: 0.5802, Acc.pole: 0.1572, Acc.land: 0.0409, Acc.bannister: 0.0483, Acc.escalator: 0.2212, Acc.ottoman: 0.6384, Acc.bottle: 0.3525, Acc.buffet: 0.4935, Acc.poster: 0.2644, Acc.stage: 0.1980, Acc.van: 0.3645, Acc.ship: 0.8515, Acc.fountain: 0.0675, Acc.conveyer belt: 0.8612, Acc.canopy: 0.2800, Acc.washer: 0.8526, Acc.plaything: 0.3811, Acc.swimming pool: 0.8049, Acc.stool: 0.5480, Acc.barrel: 0.4567, Acc.basket: 0.4161, Acc.waterfall: 0.6436, Acc.tent: 0.9792, Acc.bag: 0.0958, Acc.minibike: 0.8184, Acc.cradle: 0.8817, Acc.oven: 0.6146, Acc.ball: 0.6000, Acc.food: 0.5266, Acc.step: 0.0566, Acc.tank: 0.5414, Acc.trade name: 0.1696, Acc.microwave: 0.8475, Acc.pot: 0.4230, Acc.animal: 0.6529, Acc.bicycle: 0.5291, Acc.lake: 0.6152, Acc.dishwasher: 0.6948, Acc.screen: 0.6475, Acc.blanket: 0.1462, Acc.sculpture: 0.7865, Acc.hood: 0.7123, Acc.sconce: 0.2518, Acc.vase: 0.3430, Acc.traffic light: 0.3058, Acc.tray: 0.0141, Acc.ashcan: 0.3839, Acc.fan: 0.7172, Acc.pier: 0.7796, Acc.crt screen: 0.2936, Acc.plate: 0.7899, Acc.monitor: 0.0899, Acc.bulletin board: 0.5658, Acc.shower: 0.0138, Acc.radiator: 0.3698, Acc.glass: 0.0273, Acc.clock: 0.2824, Acc.flag: 0.2603
+2023-03-04 21:17:05,047 - mmseg - INFO - Iter [8050/80000]	lr: 1.500e-04, eta: 9 days, 4:04:20, time: 4.699, data_time: 4.512, memory: 52390, decode.loss_ce: 0.2807, decode.acc_seg: 89.1072, loss: 0.2807
+2023-03-04 21:17:13,940 - mmseg - INFO - Iter [8100/80000]	lr: 1.500e-04, eta: 4 days, 16:48:23, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2809, decode.acc_seg: 89.0361, loss: 0.2809
+2023-03-04 21:17:23,321 - mmseg - INFO - Iter [8150/80000]	lr: 1.500e-04, eta: 3 days, 4:38:21, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2906, decode.acc_seg: 88.7049, loss: 0.2906
+2023-03-04 21:17:32,350 - mmseg - INFO - Iter [8200/80000]	lr: 1.500e-04, eta: 2 days, 10:25:54, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2889, decode.acc_seg: 88.4757, loss: 0.2889
+2023-03-04 21:17:41,069 - mmseg - INFO - Iter [8250/80000]	lr: 1.500e-04, eta: 1 day, 23:27:06, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2741, decode.acc_seg: 89.1700, loss: 0.2741
+2023-03-04 21:17:49,794 - mmseg - INFO - Iter [8300/80000]	lr: 1.500e-04, eta: 1 day, 16:07:08, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2810, decode.acc_seg: 88.8051, loss: 0.2810
+2023-03-04 21:17:58,809 - mmseg - INFO - Iter [8350/80000]	lr: 1.500e-04, eta: 1 day, 10:53:28, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2762, decode.acc_seg: 89.1290, loss: 0.2762
+2023-03-04 21:18:07,537 - mmseg - INFO - Iter [8400/80000]	lr: 1.500e-04, eta: 1 day, 6:57:08, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2719, decode.acc_seg: 89.2059, loss: 0.2719
+2023-03-04 21:18:16,520 - mmseg - INFO - Iter [8450/80000]	lr: 1.500e-04, eta: 1 day, 3:53:50, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2908, decode.acc_seg: 88.7177, loss: 0.2908
+2023-03-04 21:18:25,143 - mmseg - INFO - Iter [8500/80000]	lr: 1.500e-04, eta: 1 day, 1:26:15, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2867, decode.acc_seg: 88.6489, loss: 0.2867
+2023-03-04 21:18:34,629 - mmseg - INFO - Iter [8550/80000]	lr: 1.500e-04, eta: 23:27:17, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.3005, decode.acc_seg: 88.2068, loss: 0.3005
+2023-03-04 21:18:43,811 - mmseg - INFO - Iter [8600/80000]	lr: 1.500e-04, eta: 21:47:27, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2836, decode.acc_seg: 88.8064, loss: 0.2836
+2023-03-04 21:18:55,225 - mmseg - INFO - Iter [8650/80000]	lr: 1.500e-04, eta: 20:27:03, time: 0.229, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2848, decode.acc_seg: 88.9089, loss: 0.2848
+2023-03-04 21:19:04,210 - mmseg - INFO - Iter [8700/80000]	lr: 1.500e-04, eta: 19:13:57, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2806, decode.acc_seg: 88.8507, loss: 0.2806
+2023-03-04 21:19:12,873 - mmseg - INFO - Iter [8750/80000]	lr: 1.500e-04, eta: 18:10:04, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2675, decode.acc_seg: 89.5451, loss: 0.2675
+2023-03-04 21:19:21,601 - mmseg - INFO - Iter [8800/80000]	lr: 1.500e-04, eta: 17:14:15, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2999, decode.acc_seg: 88.1176, loss: 0.2999
+2023-03-04 21:19:30,201 - mmseg - INFO - Iter [8850/80000]	lr: 1.500e-04, eta: 16:24:47, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2929, decode.acc_seg: 88.5527, loss: 0.2929
+2023-03-04 21:19:38,931 - mmseg - INFO - Iter [8900/80000]	lr: 1.500e-04, eta: 15:40:57, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2920, decode.acc_seg: 88.3922, loss: 0.2920
+2023-03-04 21:19:47,590 - mmseg - INFO - Iter [8950/80000]	lr: 1.500e-04, eta: 15:01:38, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2830, decode.acc_seg: 88.8432, loss: 0.2830
+2023-03-04 21:19:56,359 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:19:56,359 - mmseg - INFO - Iter [9000/80000]	lr: 1.500e-04, eta: 14:26:21, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2888, decode.acc_seg: 88.6539, loss: 0.2888
+2023-03-04 21:20:05,884 - mmseg - INFO - Iter [9050/80000]	lr: 1.500e-04, eta: 13:55:17, time: 0.191, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2719, decode.acc_seg: 89.1343, loss: 0.2719
+2023-03-04 21:20:15,205 - mmseg - INFO - Iter [9100/80000]	lr: 1.500e-04, eta: 13:26:48, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2806, decode.acc_seg: 89.0121, loss: 0.2806
+2023-03-04 21:20:24,079 - mmseg - INFO - Iter [9150/80000]	lr: 1.500e-04, eta: 13:00:17, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2733, decode.acc_seg: 89.1222, loss: 0.2733
+2023-03-04 21:20:33,030 - mmseg - INFO - Iter [9200/80000]	lr: 1.500e-04, eta: 12:36:05, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2883, decode.acc_seg: 88.7369, loss: 0.2883
+2023-03-04 21:20:42,179 - mmseg - INFO - Iter [9250/80000]	lr: 1.500e-04, eta: 12:13:59, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2724, decode.acc_seg: 89.2535, loss: 0.2724
+2023-03-04 21:20:53,646 - mmseg - INFO - Iter [9300/80000]	lr: 1.500e-04, eta: 11:55:39, time: 0.229, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2796, decode.acc_seg: 89.0421, loss: 0.2796
+2023-03-04 21:21:02,231 - mmseg - INFO - Iter [9350/80000]	lr: 1.500e-04, eta: 11:36:10, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.3059, decode.acc_seg: 87.9949, loss: 0.3059
+2023-03-04 21:21:11,683 - mmseg - INFO - Iter [9400/80000]	lr: 1.500e-04, eta: 11:18:46, time: 0.189, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2713, decode.acc_seg: 89.1700, loss: 0.2713
+2023-03-04 21:21:20,255 - mmseg - INFO - Iter [9450/80000]	lr: 1.500e-04, eta: 11:01:52, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2793, decode.acc_seg: 88.9274, loss: 0.2793
+2023-03-04 21:21:29,017 - mmseg - INFO - Iter [9500/80000]	lr: 1.500e-04, eta: 10:46:14, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2768, decode.acc_seg: 89.1274, loss: 0.2768
+2023-03-04 21:21:38,127 - mmseg - INFO - Iter [9550/80000]	lr: 1.500e-04, eta: 10:31:51, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2801, decode.acc_seg: 88.7214, loss: 0.2801
+2023-03-04 21:21:47,205 - mmseg - INFO - Iter [9600/80000]	lr: 1.500e-04, eta: 10:18:20, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2833, decode.acc_seg: 88.7062, loss: 0.2833
+2023-03-04 21:21:56,086 - mmseg - INFO - Iter [9650/80000]	lr: 1.500e-04, eta: 10:05:29, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2737, decode.acc_seg: 89.1313, loss: 0.2737
+2023-03-04 21:22:04,844 - mmseg - INFO - Iter [9700/80000]	lr: 1.500e-04, eta: 9:53:18, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2808, decode.acc_seg: 88.9108, loss: 0.2808
+2023-03-04 21:22:13,518 - mmseg - INFO - Iter [9750/80000]	lr: 1.500e-04, eta: 9:41:45, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2829, decode.acc_seg: 88.7986, loss: 0.2829
+2023-03-04 21:22:22,785 - mmseg - INFO - Iter [9800/80000]	lr: 1.500e-04, eta: 9:31:13, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2585, decode.acc_seg: 89.7189, loss: 0.2585
+2023-03-04 21:22:32,190 - mmseg - INFO - Iter [9850/80000]	lr: 1.500e-04, eta: 9:21:20, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2647, decode.acc_seg: 89.4993, loss: 0.2647
+2023-03-04 21:22:43,849 - mmseg - INFO - Iter [9900/80000]	lr: 1.500e-04, eta: 9:13:20, time: 0.233, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2857, decode.acc_seg: 88.9626, loss: 0.2857
+2023-03-04 21:22:53,220 - mmseg - INFO - Iter [9950/80000]	lr: 1.500e-04, eta: 9:04:23, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2690, decode.acc_seg: 89.2528, loss: 0.2690
+2023-03-04 21:23:02,443 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:23:02,443 - mmseg - INFO - Iter [10000/80000]	lr: 1.500e-04, eta: 8:55:46, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2717, decode.acc_seg: 89.3573, loss: 0.2717
+2023-03-04 21:23:11,510 - mmseg - INFO - Iter [10050/80000]	lr: 7.500e-05, eta: 8:47:30, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2685, decode.acc_seg: 89.3855, loss: 0.2685
+2023-03-04 21:23:20,208 - mmseg - INFO - Iter [10100/80000]	lr: 7.500e-05, eta: 8:39:24, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2546, decode.acc_seg: 89.7670, loss: 0.2546
+2023-03-04 21:23:28,940 - mmseg - INFO - Iter [10150/80000]	lr: 7.500e-05, eta: 8:31:41, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2637, decode.acc_seg: 89.6583, loss: 0.2637
+2023-03-04 21:23:37,570 - mmseg - INFO - Iter [10200/80000]	lr: 7.500e-05, eta: 8:24:16, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2543, decode.acc_seg: 89.6032, loss: 0.2543
+2023-03-04 21:23:46,261 - mmseg - INFO - Iter [10250/80000]	lr: 7.500e-05, eta: 8:17:12, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2566, decode.acc_seg: 89.7486, loss: 0.2566
+2023-03-04 21:23:54,939 - mmseg - INFO - Iter [10300/80000]	lr: 7.500e-05, eta: 8:10:26, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2559, decode.acc_seg: 89.6812, loss: 0.2559
+2023-03-04 21:24:03,633 - mmseg - INFO - Iter [10350/80000]	lr: 7.500e-05, eta: 8:03:57, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2523, decode.acc_seg: 89.7583, loss: 0.2523
+2023-03-04 21:24:12,573 - mmseg - INFO - Iter [10400/80000]	lr: 7.500e-05, eta: 7:57:51, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2460, decode.acc_seg: 90.1729, loss: 0.2460
+2023-03-04 21:24:21,298 - mmseg - INFO - Iter [10450/80000]	lr: 7.500e-05, eta: 7:51:53, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2572, decode.acc_seg: 89.6036, loss: 0.2572
+2023-03-04 21:24:29,910 - mmseg - INFO - Iter [10500/80000]	lr: 7.500e-05, eta: 7:46:06, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2403, decode.acc_seg: 90.3185, loss: 0.2403
+2023-03-04 21:24:41,523 - mmseg - INFO - Iter [10550/80000]	lr: 7.500e-05, eta: 7:41:55, time: 0.232, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2546, decode.acc_seg: 89.7669, loss: 0.2546
+2023-03-04 21:24:50,236 - mmseg - INFO - Iter [10600/80000]	lr: 7.500e-05, eta: 7:36:35, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2648, decode.acc_seg: 89.4639, loss: 0.2648
+2023-03-04 21:24:58,820 - mmseg - INFO - Iter [10650/80000]	lr: 7.500e-05, eta: 7:31:24, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2406, decode.acc_seg: 90.1788, loss: 0.2406
+2023-03-04 21:25:07,652 - mmseg - INFO - Iter [10700/80000]	lr: 7.500e-05, eta: 7:26:30, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2447, decode.acc_seg: 90.2119, loss: 0.2447
+2023-03-04 21:25:16,294 - mmseg - INFO - Iter [10750/80000]	lr: 7.500e-05, eta: 7:21:41, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2528, decode.acc_seg: 89.8402, loss: 0.2528
+2023-03-04 21:25:25,213 - mmseg - INFO - Iter [10800/80000]	lr: 7.500e-05, eta: 7:17:10, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2517, decode.acc_seg: 89.9256, loss: 0.2517
+2023-03-04 21:25:34,143 - mmseg - INFO - Iter [10850/80000]	lr: 7.500e-05, eta: 7:12:48, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2606, decode.acc_seg: 89.6733, loss: 0.2606
+2023-03-04 21:25:43,121 - mmseg - INFO - Iter [10900/80000]	lr: 7.500e-05, eta: 7:08:36, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2449, decode.acc_seg: 90.0060, loss: 0.2449
+2023-03-04 21:25:52,110 - mmseg - INFO - Iter [10950/80000]	lr: 7.500e-05, eta: 7:04:32, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2447, decode.acc_seg: 90.0594, loss: 0.2447
+2023-03-04 21:26:01,114 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:26:01,114 - mmseg - INFO - Iter [11000/80000]	lr: 7.500e-05, eta: 7:00:36, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2475, decode.acc_seg: 89.9945, loss: 0.2475
+2023-03-04 21:26:09,964 - mmseg - INFO - Iter [11050/80000]	lr: 7.500e-05, eta: 6:56:45, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2459, decode.acc_seg: 90.0773, loss: 0.2459
+2023-03-04 21:26:18,545 - mmseg - INFO - Iter [11100/80000]	lr: 7.500e-05, eta: 6:52:54, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2588, decode.acc_seg: 89.6659, loss: 0.2588
+2023-03-04 21:26:27,464 - mmseg - INFO - Iter [11150/80000]	lr: 7.500e-05, eta: 6:49:18, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2461, decode.acc_seg: 90.1098, loss: 0.2461
+2023-03-04 21:26:38,855 - mmseg - INFO - Iter [11200/80000]	lr: 7.500e-05, eta: 6:46:42, time: 0.228, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2596, decode.acc_seg: 89.7951, loss: 0.2596
+2023-03-04 21:26:47,860 - mmseg - INFO - Iter [11250/80000]	lr: 7.500e-05, eta: 6:43:20, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2491, decode.acc_seg: 90.0127, loss: 0.2491
+2023-03-04 21:26:57,075 - mmseg - INFO - Iter [11300/80000]	lr: 7.500e-05, eta: 6:40:08, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2382, decode.acc_seg: 90.2450, loss: 0.2382
+2023-03-04 21:27:05,962 - mmseg - INFO - Iter [11350/80000]	lr: 7.500e-05, eta: 6:36:54, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2510, decode.acc_seg: 89.8415, loss: 0.2510
+2023-03-04 21:27:14,510 - mmseg - INFO - Iter [11400/80000]	lr: 7.500e-05, eta: 6:33:39, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2450, decode.acc_seg: 89.9924, loss: 0.2450
+2023-03-04 21:27:23,461 - mmseg - INFO - Iter [11450/80000]	lr: 7.500e-05, eta: 6:30:38, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2562, decode.acc_seg: 89.6791, loss: 0.2562
+2023-03-04 21:27:32,232 - mmseg - INFO - Iter [11500/80000]	lr: 7.500e-05, eta: 6:27:38, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2565, decode.acc_seg: 89.5447, loss: 0.2565
+2023-03-04 21:27:41,281 - mmseg - INFO - Iter [11550/80000]	lr: 7.500e-05, eta: 6:24:48, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2604, decode.acc_seg: 89.6149, loss: 0.2604
+2023-03-04 21:27:50,584 - mmseg - INFO - Iter [11600/80000]	lr: 7.500e-05, eta: 6:22:08, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2571, decode.acc_seg: 89.7752, loss: 0.2571
+2023-03-04 21:27:59,311 - mmseg - INFO - Iter [11650/80000]	lr: 7.500e-05, eta: 6:19:21, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2430, decode.acc_seg: 90.0725, loss: 0.2430
+2023-03-04 21:28:08,115 - mmseg - INFO - Iter [11700/80000]	lr: 7.500e-05, eta: 6:16:39, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2567, decode.acc_seg: 89.5966, loss: 0.2567
+2023-03-04 21:28:16,856 - mmseg - INFO - Iter [11750/80000]	lr: 7.500e-05, eta: 6:14:00, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2580, decode.acc_seg: 89.7323, loss: 0.2580
+2023-03-04 21:28:28,681 - mmseg - INFO - Iter [11800/80000]	lr: 7.500e-05, eta: 6:12:21, time: 0.237, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2487, decode.acc_seg: 90.0867, loss: 0.2487
+2023-03-04 21:28:37,947 - mmseg - INFO - Iter [11850/80000]	lr: 7.500e-05, eta: 6:09:59, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2612, decode.acc_seg: 89.7611, loss: 0.2612
+2023-03-04 21:28:46,651 - mmseg - INFO - Iter [11900/80000]	lr: 7.500e-05, eta: 6:07:30, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2345, decode.acc_seg: 90.4173, loss: 0.2345
+2023-03-04 21:28:55,594 - mmseg - INFO - Iter [11950/80000]	lr: 7.500e-05, eta: 6:05:09, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2319, decode.acc_seg: 90.4980, loss: 0.2319
+2023-03-04 21:29:04,692 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:29:04,692 - mmseg - INFO - Iter [12000/80000]	lr: 7.500e-05, eta: 6:02:54, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2431, decode.acc_seg: 90.2366, loss: 0.2431
+2023-03-04 21:29:14,105 - mmseg - INFO - Iter [12050/80000]	lr: 7.500e-05, eta: 6:00:48, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2542, decode.acc_seg: 89.8204, loss: 0.2542
+2023-03-04 21:29:22,728 - mmseg - INFO - Iter [12100/80000]	lr: 7.500e-05, eta: 5:58:31, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2657, decode.acc_seg: 89.4135, loss: 0.2657
+2023-03-04 21:29:31,568 - mmseg - INFO - Iter [12150/80000]	lr: 7.500e-05, eta: 5:56:20, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2484, decode.acc_seg: 90.0789, loss: 0.2484
+2023-03-04 21:29:40,339 - mmseg - INFO - Iter [12200/80000]	lr: 7.500e-05, eta: 5:54:12, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2387, decode.acc_seg: 90.3215, loss: 0.2387
+2023-03-04 21:29:49,361 - mmseg - INFO - Iter [12250/80000]	lr: 7.500e-05, eta: 5:52:10, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2342, decode.acc_seg: 90.4158, loss: 0.2342
+2023-03-04 21:29:58,057 - mmseg - INFO - Iter [12300/80000]	lr: 7.500e-05, eta: 5:50:06, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2423, decode.acc_seg: 90.2287, loss: 0.2423
+2023-03-04 21:30:07,015 - mmseg - INFO - Iter [12350/80000]	lr: 7.500e-05, eta: 5:48:09, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2507, decode.acc_seg: 89.8662, loss: 0.2507
+2023-03-04 21:30:15,644 - mmseg - INFO - Iter [12400/80000]	lr: 7.500e-05, eta: 5:46:09, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2453, decode.acc_seg: 90.0944, loss: 0.2453
+2023-03-04 21:30:26,860 - mmseg - INFO - Iter [12450/80000]	lr: 7.500e-05, eta: 5:44:50, time: 0.224, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2423, decode.acc_seg: 90.2559, loss: 0.2423
+2023-03-04 21:30:36,071 - mmseg - INFO - Iter [12500/80000]	lr: 7.500e-05, eta: 5:43:03, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2405, decode.acc_seg: 90.3924, loss: 0.2405
+2023-03-04 21:30:44,694 - mmseg - INFO - Iter [12550/80000]	lr: 7.500e-05, eta: 5:41:10, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2537, decode.acc_seg: 89.9150, loss: 0.2537
+2023-03-04 21:30:53,344 - mmseg - INFO - Iter [12600/80000]	lr: 7.500e-05, eta: 5:39:19, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2577, decode.acc_seg: 89.5941, loss: 0.2577
+2023-03-04 21:31:02,099 - mmseg - INFO - Iter [12650/80000]	lr: 7.500e-05, eta: 5:37:32, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2628, decode.acc_seg: 89.3820, loss: 0.2628
+2023-03-04 21:31:11,163 - mmseg - INFO - Iter [12700/80000]	lr: 7.500e-05, eta: 5:35:52, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2542, decode.acc_seg: 89.8577, loss: 0.2542
+2023-03-04 21:31:20,137 - mmseg - INFO - Iter [12750/80000]	lr: 7.500e-05, eta: 5:34:12, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2464, decode.acc_seg: 90.3744, loss: 0.2464
+2023-03-04 21:31:28,815 - mmseg - INFO - Iter [12800/80000]	lr: 7.500e-05, eta: 5:32:30, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2473, decode.acc_seg: 89.9718, loss: 0.2473
+2023-03-04 21:31:37,598 - mmseg - INFO - Iter [12850/80000]	lr: 7.500e-05, eta: 5:30:51, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2418, decode.acc_seg: 90.2286, loss: 0.2418
+2023-03-04 21:31:46,328 - mmseg - INFO - Iter [12900/80000]	lr: 7.500e-05, eta: 5:29:13, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2501, decode.acc_seg: 89.9908, loss: 0.2501
+2023-03-04 21:31:55,314 - mmseg - INFO - Iter [12950/80000]	lr: 7.500e-05, eta: 5:27:41, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2461, decode.acc_seg: 89.8815, loss: 0.2461
+2023-03-04 21:32:04,245 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:32:04,245 - mmseg - INFO - Iter [13000/80000]	lr: 7.500e-05, eta: 5:26:09, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2450, decode.acc_seg: 90.1816, loss: 0.2450
+2023-03-04 21:32:15,305 - mmseg - INFO - Iter [13050/80000]	lr: 7.500e-05, eta: 5:25:08, time: 0.221, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2377, decode.acc_seg: 90.1840, loss: 0.2377
+2023-03-04 21:32:24,183 - mmseg - INFO - Iter [13100/80000]	lr: 7.500e-05, eta: 5:23:39, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2416, decode.acc_seg: 90.1596, loss: 0.2416
+2023-03-04 21:32:32,983 - mmseg - INFO - Iter [13150/80000]	lr: 7.500e-05, eta: 5:22:10, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2392, decode.acc_seg: 90.2603, loss: 0.2392
+2023-03-04 21:32:42,076 - mmseg - INFO - Iter [13200/80000]	lr: 7.500e-05, eta: 5:20:47, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2481, decode.acc_seg: 90.0269, loss: 0.2481
+2023-03-04 21:32:50,771 - mmseg - INFO - Iter [13250/80000]	lr: 7.500e-05, eta: 5:19:20, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2530, decode.acc_seg: 89.8482, loss: 0.2530
+2023-03-04 21:32:59,482 - mmseg - INFO - Iter [13300/80000]	lr: 7.500e-05, eta: 5:17:54, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2450, decode.acc_seg: 90.0450, loss: 0.2450
+2023-03-04 21:33:08,045 - mmseg - INFO - Iter [13350/80000]	lr: 7.500e-05, eta: 5:16:29, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2452, decode.acc_seg: 90.2252, loss: 0.2452
+2023-03-04 21:33:17,181 - mmseg - INFO - Iter [13400/80000]	lr: 7.500e-05, eta: 5:15:11, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2477, decode.acc_seg: 90.0464, loss: 0.2477
+2023-03-04 21:33:26,198 - mmseg - INFO - Iter [13450/80000]	lr: 7.500e-05, eta: 5:13:54, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2417, decode.acc_seg: 90.1883, loss: 0.2417
+2023-03-04 21:33:35,471 - mmseg - INFO - Iter [13500/80000]	lr: 7.500e-05, eta: 5:12:41, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2555, decode.acc_seg: 89.7734, loss: 0.2555
+2023-03-04 21:33:44,278 - mmseg - INFO - Iter [13550/80000]	lr: 7.500e-05, eta: 5:11:23, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2582, decode.acc_seg: 89.7345, loss: 0.2582
+2023-03-04 21:33:53,493 - mmseg - INFO - Iter [13600/80000]	lr: 7.500e-05, eta: 5:10:12, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2454, decode.acc_seg: 90.2413, loss: 0.2454
+2023-03-04 21:34:02,211 - mmseg - INFO - Iter [13650/80000]	lr: 7.500e-05, eta: 5:08:55, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2471, decode.acc_seg: 90.1016, loss: 0.2471
+2023-03-04 21:34:13,378 - mmseg - INFO - Iter [13700/80000]	lr: 7.500e-05, eta: 5:08:09, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2484, decode.acc_seg: 89.9380, loss: 0.2484
+2023-03-04 21:34:21,936 - mmseg - INFO - Iter [13750/80000]	lr: 7.500e-05, eta: 5:06:53, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2342, decode.acc_seg: 90.5387, loss: 0.2342
+2023-03-04 21:34:30,509 - mmseg - INFO - Iter [13800/80000]	lr: 7.500e-05, eta: 5:05:38, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2515, decode.acc_seg: 90.0692, loss: 0.2515
+2023-03-04 21:34:39,193 - mmseg - INFO - Iter [13850/80000]	lr: 7.500e-05, eta: 5:04:26, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2521, decode.acc_seg: 89.7605, loss: 0.2521
+2023-03-04 21:34:47,852 - mmseg - INFO - Iter [13900/80000]	lr: 7.500e-05, eta: 5:03:15, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2436, decode.acc_seg: 90.2257, loss: 0.2436
+2023-03-04 21:34:56,617 - mmseg - INFO - Iter [13950/80000]	lr: 7.500e-05, eta: 5:02:05, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2468, decode.acc_seg: 90.1253, loss: 0.2468
+2023-03-04 21:35:06,142 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:35:06,142 - mmseg - INFO - Iter [14000/80000]	lr: 7.500e-05, eta: 5:01:05, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2450, decode.acc_seg: 90.0117, loss: 0.2450
+2023-03-04 21:35:14,828 - mmseg - INFO - Iter [14050/80000]	lr: 7.500e-05, eta: 4:59:57, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2491, decode.acc_seg: 90.0216, loss: 0.2491
+2023-03-04 21:35:23,761 - mmseg - INFO - Iter [14100/80000]	lr: 7.500e-05, eta: 4:58:53, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2568, decode.acc_seg: 89.8928, loss: 0.2568
+2023-03-04 21:35:32,825 - mmseg - INFO - Iter [14150/80000]	lr: 7.500e-05, eta: 4:57:50, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2507, decode.acc_seg: 89.8296, loss: 0.2507
+2023-03-04 21:35:41,643 - mmseg - INFO - Iter [14200/80000]	lr: 7.500e-05, eta: 4:56:46, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2508, decode.acc_seg: 90.0403, loss: 0.2508
+2023-03-04 21:35:50,447 - mmseg - INFO - Iter [14250/80000]	lr: 7.500e-05, eta: 4:55:43, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2438, decode.acc_seg: 90.1474, loss: 0.2438
+2023-03-04 21:35:59,738 - mmseg - INFO - Iter [14300/80000]	lr: 7.500e-05, eta: 4:54:46, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2385, decode.acc_seg: 90.4585, loss: 0.2385
+2023-03-04 21:36:11,012 - mmseg - INFO - Iter [14350/80000]	lr: 7.500e-05, eta: 4:54:10, time: 0.226, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2393, decode.acc_seg: 90.2266, loss: 0.2393
+2023-03-04 21:36:20,306 - mmseg - INFO - Iter [14400/80000]	lr: 7.500e-05, eta: 4:53:14, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2466, decode.acc_seg: 90.0766, loss: 0.2466
+2023-03-04 21:36:28,941 - mmseg - INFO - Iter [14450/80000]	lr: 7.500e-05, eta: 4:52:12, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2419, decode.acc_seg: 90.3091, loss: 0.2419
+2023-03-04 21:36:37,731 - mmseg - INFO - Iter [14500/80000]	lr: 7.500e-05, eta: 4:51:12, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2348, decode.acc_seg: 90.5120, loss: 0.2348
+2023-03-04 21:36:46,340 - mmseg - INFO - Iter [14550/80000]	lr: 7.500e-05, eta: 4:50:12, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2408, decode.acc_seg: 90.2053, loss: 0.2408
+2023-03-04 21:36:55,267 - mmseg - INFO - Iter [14600/80000]	lr: 7.500e-05, eta: 4:49:15, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2406, decode.acc_seg: 90.3744, loss: 0.2406
+2023-03-04 21:37:04,480 - mmseg - INFO - Iter [14650/80000]	lr: 7.500e-05, eta: 4:48:22, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2487, decode.acc_seg: 89.8760, loss: 0.2487
+2023-03-04 21:37:13,851 - mmseg - INFO - Iter [14700/80000]	lr: 7.500e-05, eta: 4:47:31, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2517, decode.acc_seg: 89.8134, loss: 0.2517
+2023-03-04 21:37:22,876 - mmseg - INFO - Iter [14750/80000]	lr: 7.500e-05, eta: 4:46:37, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2490, decode.acc_seg: 90.0197, loss: 0.2490
+2023-03-04 21:37:31,716 - mmseg - INFO - Iter [14800/80000]	lr: 7.500e-05, eta: 4:45:43, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2463, decode.acc_seg: 90.0005, loss: 0.2463
+2023-03-04 21:37:40,575 - mmseg - INFO - Iter [14850/80000]	lr: 7.500e-05, eta: 4:44:49, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2488, decode.acc_seg: 89.9955, loss: 0.2488
+2023-03-04 21:37:49,301 - mmseg - INFO - Iter [14900/80000]	lr: 7.500e-05, eta: 4:43:54, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2504, decode.acc_seg: 89.9667, loss: 0.2504
+2023-03-04 21:38:00,417 - mmseg - INFO - Iter [14950/80000]	lr: 7.500e-05, eta: 4:43:23, time: 0.222, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2575, decode.acc_seg: 89.7279, loss: 0.2575
+2023-03-04 21:38:09,081 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:38:09,081 - mmseg - INFO - Iter [15000/80000]	lr: 7.500e-05, eta: 4:42:29, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2354, decode.acc_seg: 90.4466, loss: 0.2354
+2023-03-04 21:38:17,742 - mmseg - INFO - Iter [15050/80000]	lr: 7.500e-05, eta: 4:41:35, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2401, decode.acc_seg: 90.3564, loss: 0.2401
+2023-03-04 21:38:26,726 - mmseg - INFO - Iter [15100/80000]	lr: 7.500e-05, eta: 4:40:46, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2540, decode.acc_seg: 89.7952, loss: 0.2540
+2023-03-04 21:38:35,735 - mmseg - INFO - Iter [15150/80000]	lr: 7.500e-05, eta: 4:39:57, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2546, decode.acc_seg: 89.8361, loss: 0.2546
+2023-03-04 21:38:45,094 - mmseg - INFO - Iter [15200/80000]	lr: 7.500e-05, eta: 4:39:11, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2326, decode.acc_seg: 90.4672, loss: 0.2326
+2023-03-04 21:38:53,761 - mmseg - INFO - Iter [15250/80000]	lr: 7.500e-05, eta: 4:38:20, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2486, decode.acc_seg: 89.8273, loss: 0.2486
+2023-03-04 21:39:02,549 - mmseg - INFO - Iter [15300/80000]	lr: 7.500e-05, eta: 4:37:31, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2354, decode.acc_seg: 90.3973, loss: 0.2354
+2023-03-04 21:39:11,030 - mmseg - INFO - Iter [15350/80000]	lr: 7.500e-05, eta: 4:36:40, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2420, decode.acc_seg: 90.2344, loss: 0.2420
+2023-03-04 21:39:19,893 - mmseg - INFO - Iter [15400/80000]	lr: 7.500e-05, eta: 4:35:52, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2449, decode.acc_seg: 90.1771, loss: 0.2449
+2023-03-04 21:39:28,669 - mmseg - INFO - Iter [15450/80000]	lr: 7.500e-05, eta: 4:35:04, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2459, decode.acc_seg: 90.1088, loss: 0.2459
+2023-03-04 21:39:37,596 - mmseg - INFO - Iter [15500/80000]	lr: 7.500e-05, eta: 4:34:18, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2491, decode.acc_seg: 89.8247, loss: 0.2491
+2023-03-04 21:39:46,900 - mmseg - INFO - Iter [15550/80000]	lr: 7.500e-05, eta: 4:33:36, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2522, decode.acc_seg: 89.9476, loss: 0.2522
+2023-03-04 21:39:58,058 - mmseg - INFO - Iter [15600/80000]	lr: 7.500e-05, eta: 4:33:10, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2412, decode.acc_seg: 90.3723, loss: 0.2412
+2023-03-04 21:40:06,839 - mmseg - INFO - Iter [15650/80000]	lr: 7.500e-05, eta: 4:32:24, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2534, decode.acc_seg: 89.8438, loss: 0.2534
+2023-03-04 21:40:16,186 - mmseg - INFO - Iter [15700/80000]	lr: 7.500e-05, eta: 4:31:43, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2456, decode.acc_seg: 90.2012, loss: 0.2456
+2023-03-04 21:40:25,080 - mmseg - INFO - Iter [15750/80000]	lr: 7.500e-05, eta: 4:30:59, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2498, decode.acc_seg: 90.1504, loss: 0.2498
+2023-03-04 21:40:33,917 - mmseg - INFO - Iter [15800/80000]	lr: 7.500e-05, eta: 4:30:15, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2483, decode.acc_seg: 90.0708, loss: 0.2483
+2023-03-04 21:40:43,115 - mmseg - INFO - Iter [15850/80000]	lr: 7.500e-05, eta: 4:29:35, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2358, decode.acc_seg: 90.4640, loss: 0.2358
+2023-03-04 21:40:52,073 - mmseg - INFO - Iter [15900/80000]	lr: 7.500e-05, eta: 4:28:52, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2547, decode.acc_seg: 89.6807, loss: 0.2547
+2023-03-04 21:41:01,055 - mmseg - INFO - Iter [15950/80000]	lr: 7.500e-05, eta: 4:28:11, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2479, decode.acc_seg: 89.9908, loss: 0.2479
+2023-03-04 21:41:10,102 - mmseg - INFO - Saving checkpoint at 16000 iterations
+2023-03-04 21:41:10,706 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:41:10,706 - mmseg - INFO - Iter [16000/80000]	lr: 7.500e-05, eta: 4:27:35, time: 0.193, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2444, decode.acc_seg: 90.1117, loss: 0.2444
+2023-03-04 21:41:26,396 - mmseg - INFO - per class results:
+2023-03-04 21:41:26,402 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        |  74.9 |  86.4 |
+|       building      | 80.97 | 90.31 |
+|         sky         | 93.16 | 94.68 |
+|        floor        |  80.3 | 89.76 |
+|         tree        | 71.85 | 91.06 |
+|       ceiling       | 82.47 | 86.78 |
+|         road        | 80.39 | 92.28 |
+|         bed         | 86.33 | 92.86 |
+|      windowpane     | 56.44 | 84.73 |
+|        grass        | 66.41 | 80.78 |
+|       cabinet       | 57.34 | 70.43 |
+|       sidewalk      |  61.9 | 72.45 |
+|        person       | 77.33 | 89.32 |
+|        earth        | 35.92 | 49.26 |
+|         door        | 41.04 |  67.6 |
+|        table        | 53.57 | 74.34 |
+|       mountain      | 54.73 | 64.12 |
+|        plant        | 48.78 | 60.68 |
+|       curtain       | 70.41 | 80.79 |
+|        chair        | 51.52 | 72.95 |
+|         car         | 77.72 | 93.14 |
+|        water        |  56.6 | 76.16 |
+|       painting      | 65.39 | 86.57 |
+|         sofa        | 61.49 | 82.16 |
+|        shelf        | 35.69 | 43.08 |
+|        house        |  43.6 | 65.15 |
+|         sea         | 55.87 | 68.45 |
+|        mirror       | 60.72 | 68.56 |
+|         rug         | 63.59 |  76.1 |
+|        field        | 29.41 | 43.77 |
+|       armchair      |  33.1 | 46.12 |
+|         seat        | 64.38 | 81.31 |
+|        fence        | 38.21 | 49.16 |
+|         desk        | 43.64 | 59.78 |
+|         rock        |  39.2 | 65.49 |
+|       wardrobe      | 55.14 | 66.32 |
+|         lamp        | 51.45 |  79.2 |
+|       bathtub       | 72.53 | 82.01 |
+|       railing       | 32.33 | 41.61 |
+|       cushion       |  52.0 | 71.43 |
+|         base        | 16.14 |  19.4 |
+|         box         | 18.91 | 21.97 |
+|        column       | 44.05 | 54.56 |
+|      signboard      |  32.1 | 58.41 |
+|   chest of drawers  | 34.24 | 62.23 |
+|       counter       | 14.02 | 14.97 |
+|         sand        | 39.95 | 55.97 |
+|         sink        | 63.61 | 73.26 |
+|      skyscraper     | 59.59 | 84.67 |
+|      fireplace      | 71.58 | 85.89 |
+|     refrigerator    | 70.17 | 84.13 |
+|      grandstand     | 53.01 | 61.03 |
+|         path        | 22.15 | 28.95 |
+|        stairs       | 38.49 | 54.34 |
+|        runway       | 65.68 | 83.96 |
+|         case        | 45.16 | 60.13 |
+|      pool table     | 90.22 | 93.53 |
+|        pillow       | 51.99 | 61.04 |
+|     screen door     | 41.87 | 43.19 |
+|       stairway      |  26.5 |  37.5 |
+|        river        | 10.12 | 24.84 |
+|        bridge       | 31.87 | 36.15 |
+|       bookcase      | 38.23 |  58.1 |
+|        blind        | 28.25 | 29.45 |
+|     coffee table    | 47.21 | 80.44 |
+|        toilet       | 81.61 | 89.23 |
+|        flower       | 35.73 | 44.15 |
+|         book        | 41.85 | 58.91 |
+|         hill        |  9.71 | 10.92 |
+|        bench        | 38.41 |  50.1 |
+|      countertop     |  41.1 | 44.99 |
+|        stove        | 68.15 | 82.51 |
+|         palm        | 45.17 | 55.48 |
+|    kitchen island   | 34.58 | 64.41 |
+|       computer      | 58.22 | 67.72 |
+|     swivel chair    | 42.87 |  57.7 |
+|         boat        | 58.86 | 85.67 |
+|         bar         | 21.48 | 29.38 |
+|    arcade machine   | 71.58 | 74.82 |
+|        hovel        | 13.96 | 14.51 |
+|         bus         | 76.62 | 89.44 |
+|        towel        |  59.6 | 70.17 |
+|        light        | 47.53 | 65.71 |
+|        truck        | 13.53 |  17.4 |
+|        tower        |  5.18 |  7.87 |
+|      chandelier     | 47.96 |  88.7 |
+|        awning       |  19.8 | 25.92 |
+|     streetlight     | 23.14 | 34.12 |
+|        booth        | 31.49 |  31.7 |
+| television receiver | 61.48 | 81.89 |
+|       airplane      | 64.41 | 73.65 |
+|      dirt track     |  4.29 |  7.9  |
+|       apparel       | 29.49 | 53.99 |
+|         pole        |  9.99 | 12.52 |
+|         land        |  8.77 | 28.05 |
+|      bannister      |  8.88 | 11.68 |
+|      escalator      | 23.84 | 26.86 |
+|       ottoman       | 35.43 | 64.61 |
+|        bottle       |  32.7 | 60.58 |
+|        buffet       | 43.03 | 51.23 |
+|        poster       | 20.87 | 27.38 |
+|        stage        | 13.28 | 18.68 |
+|         van         | 37.11 | 50.19 |
+|         ship        | 73.68 | 86.88 |
+|       fountain      |  4.94 |  4.98 |
+|    conveyer belt    | 82.77 | 86.94 |
+|        canopy       | 20.92 | 22.84 |
+|        washer       | 82.28 | 84.83 |
+|      plaything      | 20.14 | 31.17 |
+|    swimming pool    | 68.13 | 79.61 |
+|        stool        | 34.09 | 56.23 |
+|        barrel       | 23.63 | 36.42 |
+|        basket       | 21.66 | 28.48 |
+|      waterfall      | 45.79 | 60.83 |
+|         tent        | 91.45 | 98.24 |
+|         bag         | 12.68 | 14.71 |
+|       minibike      | 53.91 | 62.46 |
+|        cradle       | 80.37 | 95.59 |
+|         oven        | 44.87 | 58.02 |
+|         ball        | 48.07 | 56.08 |
+|         food        | 28.88 | 32.43 |
+|         step        |  3.48 |  3.65 |
+|         tank        | 50.02 | 54.32 |
+|      trade name     |  9.3  |  9.71 |
+|      microwave      | 74.59 | 80.28 |
+|         pot         | 34.13 | 44.61 |
+|        animal       | 53.77 | 61.65 |
+|       bicycle       | 48.39 | 65.34 |
+|         lake        | 56.62 | 62.88 |
+|      dishwasher     | 63.43 | 74.15 |
+|        screen       | 58.69 | 63.68 |
+|       blanket       |  11.9 | 14.02 |
+|      sculpture      | 57.98 | 75.36 |
+|         hood        | 55.09 | 66.01 |
+|        sconce       | 37.79 | 49.74 |
+|         vase        | 27.87 | 40.75 |
+|    traffic light    | 26.35 |  53.5 |
+|         tray        |  4.63 |  7.01 |
+|        ashcan       | 37.44 | 52.39 |
+|         fan         | 54.08 | 69.42 |
+|         pier        | 26.86 | 73.73 |
+|      crt screen     |  8.85 | 34.59 |
+|        plate        | 43.09 | 54.38 |
+|       monitor       |  0.22 |  0.23 |
+|    bulletin board   | 30.76 | 42.82 |
+|        shower       |  0.64 |  2.84 |
+|       radiator      | 57.65 | 67.33 |
+|        glass        |  7.55 |  8.01 |
+|        clock        | 27.63 | 31.54 |
+|         flag        | 33.89 | 42.74 |
++---------------------+-------+-------+
+2023-03-04 21:41:26,403 - mmseg - INFO - Summary:
+2023-03-04 21:41:26,403 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 80.92 | 44.24 | 56.26 |
++-------+-------+-------+
+2023-03-04 21:41:26,425 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_8000.pth was removed
+2023-03-04 21:41:27,047 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth.
+2023-03-04 21:41:27,047 - mmseg - INFO - Best mIoU is 0.4424 at 16000 iter.
+2023-03-04 21:41:27,048 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:41:27,048 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8092, mIoU: 0.4424, mAcc: 0.5626, IoU.background: nan, IoU.wall: 0.7490, IoU.building: 0.8097, IoU.sky: 0.9316, IoU.floor: 0.8030, IoU.tree: 0.7185, IoU.ceiling: 0.8247, IoU.road: 0.8039, IoU.bed : 0.8633, IoU.windowpane: 0.5644, IoU.grass: 0.6641, IoU.cabinet: 0.5734, IoU.sidewalk: 0.6190, IoU.person: 0.7733, IoU.earth: 0.3592, IoU.door: 0.4104, IoU.table: 0.5357, IoU.mountain: 0.5473, IoU.plant: 0.4878, IoU.curtain: 0.7041, IoU.chair: 0.5152, IoU.car: 0.7772, IoU.water: 0.5660, IoU.painting: 0.6539, IoU.sofa: 0.6149, IoU.shelf: 0.3569, IoU.house: 0.4360, IoU.sea: 0.5587, IoU.mirror: 0.6072, IoU.rug: 0.6359, IoU.field: 0.2941, IoU.armchair: 0.3310, IoU.seat: 0.6438, IoU.fence: 0.3821, IoU.desk: 0.4364, IoU.rock: 0.3920, IoU.wardrobe: 0.5514, IoU.lamp: 0.5145, IoU.bathtub: 0.7253, IoU.railing: 0.3233, IoU.cushion: 0.5200, IoU.base: 0.1614, IoU.box: 0.1891, IoU.column: 0.4405, IoU.signboard: 0.3210, IoU.chest of drawers: 0.3424, IoU.counter: 0.1402, IoU.sand: 0.3995, IoU.sink: 0.6361, IoU.skyscraper: 0.5959, IoU.fireplace: 0.7158, IoU.refrigerator: 0.7017, IoU.grandstand: 0.5301, IoU.path: 0.2215, IoU.stairs: 0.3849, IoU.runway: 0.6568, IoU.case: 0.4516, IoU.pool table: 0.9022, IoU.pillow: 0.5199, IoU.screen door: 0.4187, IoU.stairway: 0.2650, IoU.river: 0.1012, IoU.bridge: 0.3187, IoU.bookcase: 0.3823, IoU.blind: 0.2825, IoU.coffee table: 0.4721, IoU.toilet: 0.8161, IoU.flower: 0.3573, IoU.book: 0.4185, IoU.hill: 0.0971, IoU.bench: 0.3841, IoU.countertop: 0.4110, IoU.stove: 0.6815, IoU.palm: 0.4517, IoU.kitchen island: 0.3458, IoU.computer: 0.5822, IoU.swivel chair: 0.4287, IoU.boat: 0.5886, IoU.bar: 0.2148, IoU.arcade machine: 0.7158, IoU.hovel: 0.1396, IoU.bus: 0.7662, IoU.towel: 0.5960, IoU.light: 0.4753, IoU.truck: 0.1353, IoU.tower: 0.0518, IoU.chandelier: 0.4796, IoU.awning: 0.1980, IoU.streetlight: 0.2314, IoU.booth: 0.3149, IoU.television receiver: 0.6148, IoU.airplane: 0.6441, IoU.dirt track: 0.0429, IoU.apparel: 0.2949, IoU.pole: 0.0999, IoU.land: 0.0877, IoU.bannister: 0.0888, IoU.escalator: 0.2384, IoU.ottoman: 0.3543, IoU.bottle: 0.3270, IoU.buffet: 0.4303, IoU.poster: 0.2087, IoU.stage: 0.1328, IoU.van: 0.3711, IoU.ship: 0.7368, IoU.fountain: 0.0494, IoU.conveyer belt: 0.8277, IoU.canopy: 0.2092, IoU.washer: 0.8228, IoU.plaything: 0.2014, IoU.swimming pool: 0.6813, IoU.stool: 0.3409, IoU.barrel: 0.2363, IoU.basket: 0.2166, IoU.waterfall: 0.4579, IoU.tent: 0.9145, IoU.bag: 0.1268, IoU.minibike: 0.5391, IoU.cradle: 0.8037, IoU.oven: 0.4487, IoU.ball: 0.4807, IoU.food: 0.2888, IoU.step: 0.0348, IoU.tank: 0.5002, IoU.trade name: 0.0930, IoU.microwave: 0.7459, IoU.pot: 0.3413, IoU.animal: 0.5377, IoU.bicycle: 0.4839, IoU.lake: 0.5662, IoU.dishwasher: 0.6343, IoU.screen: 0.5869, IoU.blanket: 0.1190, IoU.sculpture: 0.5798, IoU.hood: 0.5509, IoU.sconce: 0.3779, IoU.vase: 0.2787, IoU.traffic light: 0.2635, IoU.tray: 0.0463, IoU.ashcan: 0.3744, IoU.fan: 0.5408, IoU.pier: 0.2686, IoU.crt screen: 0.0885, IoU.plate: 0.4309, IoU.monitor: 0.0022, IoU.bulletin board: 0.3076, IoU.shower: 0.0064, IoU.radiator: 0.5765, IoU.glass: 0.0755, IoU.clock: 0.2763, IoU.flag: 0.3389, Acc.background: nan, Acc.wall: 0.8640, Acc.building: 0.9031, Acc.sky: 0.9468, Acc.floor: 0.8976, Acc.tree: 0.9106, Acc.ceiling: 0.8678, Acc.road: 0.9228, Acc.bed : 0.9286, Acc.windowpane: 0.8473, Acc.grass: 0.8078, Acc.cabinet: 0.7043, Acc.sidewalk: 0.7245, Acc.person: 0.8932, Acc.earth: 0.4926, Acc.door: 0.6760, Acc.table: 0.7434, Acc.mountain: 0.6412, Acc.plant: 0.6068, Acc.curtain: 0.8079, Acc.chair: 0.7295, Acc.car: 0.9314, Acc.water: 0.7616, Acc.painting: 0.8657, Acc.sofa: 0.8216, Acc.shelf: 0.4308, Acc.house: 0.6515, Acc.sea: 0.6845, Acc.mirror: 0.6856, Acc.rug: 0.7610, Acc.field: 0.4377, Acc.armchair: 0.4612, Acc.seat: 0.8131, Acc.fence: 0.4916, Acc.desk: 0.5978, Acc.rock: 0.6549, Acc.wardrobe: 0.6632, Acc.lamp: 0.7920, Acc.bathtub: 0.8201, Acc.railing: 0.4161, Acc.cushion: 0.7143, Acc.base: 0.1940, Acc.box: 0.2197, Acc.column: 0.5456, Acc.signboard: 0.5841, Acc.chest of drawers: 0.6223, Acc.counter: 0.1497, Acc.sand: 0.5597, Acc.sink: 0.7326, Acc.skyscraper: 0.8467, Acc.fireplace: 0.8589, Acc.refrigerator: 0.8413, Acc.grandstand: 0.6103, Acc.path: 0.2895, Acc.stairs: 0.5434, Acc.runway: 0.8396, Acc.case: 0.6013, Acc.pool table: 0.9353, Acc.pillow: 0.6104, Acc.screen door: 0.4319, Acc.stairway: 0.3750, Acc.river: 0.2484, Acc.bridge: 0.3615, Acc.bookcase: 0.5810, Acc.blind: 0.2945, Acc.coffee table: 0.8044, Acc.toilet: 0.8923, Acc.flower: 0.4415, Acc.book: 0.5891, Acc.hill: 0.1092, Acc.bench: 0.5010, Acc.countertop: 0.4499, Acc.stove: 0.8251, Acc.palm: 0.5548, Acc.kitchen island: 0.6441, Acc.computer: 0.6772, Acc.swivel chair: 0.5770, Acc.boat: 0.8567, Acc.bar: 0.2938, Acc.arcade machine: 0.7482, Acc.hovel: 0.1451, Acc.bus: 0.8944, Acc.towel: 0.7017, Acc.light: 0.6571, Acc.truck: 0.1740, Acc.tower: 0.0787, Acc.chandelier: 0.8870, Acc.awning: 0.2592, Acc.streetlight: 0.3412, Acc.booth: 0.3170, Acc.television receiver: 0.8189, Acc.airplane: 0.7365, Acc.dirt track: 0.0790, Acc.apparel: 0.5399, Acc.pole: 0.1252, Acc.land: 0.2805, Acc.bannister: 0.1168, Acc.escalator: 0.2686, Acc.ottoman: 0.6461, Acc.bottle: 0.6058, Acc.buffet: 0.5123, Acc.poster: 0.2738, Acc.stage: 0.1868, Acc.van: 0.5019, Acc.ship: 0.8688, Acc.fountain: 0.0498, Acc.conveyer belt: 0.8694, Acc.canopy: 0.2284, Acc.washer: 0.8483, Acc.plaything: 0.3117, Acc.swimming pool: 0.7961, Acc.stool: 0.5623, Acc.barrel: 0.3642, Acc.basket: 0.2848, Acc.waterfall: 0.6083, Acc.tent: 0.9824, Acc.bag: 0.1471, Acc.minibike: 0.6246, Acc.cradle: 0.9559, Acc.oven: 0.5802, Acc.ball: 0.5608, Acc.food: 0.3243, Acc.step: 0.0365, Acc.tank: 0.5432, Acc.trade name: 0.0971, Acc.microwave: 0.8028, Acc.pot: 0.4461, Acc.animal: 0.6165, Acc.bicycle: 0.6534, Acc.lake: 0.6288, Acc.dishwasher: 0.7415, Acc.screen: 0.6368, Acc.blanket: 0.1402, Acc.sculpture: 0.7536, Acc.hood: 0.6601, Acc.sconce: 0.4974, Acc.vase: 0.4075, Acc.traffic light: 0.5350, Acc.tray: 0.0701, Acc.ashcan: 0.5239, Acc.fan: 0.6942, Acc.pier: 0.7373, Acc.crt screen: 0.3459, Acc.plate: 0.5438, Acc.monitor: 0.0023, Acc.bulletin board: 0.4282, Acc.shower: 0.0284, Acc.radiator: 0.6733, Acc.glass: 0.0801, Acc.clock: 0.3154, Acc.flag: 0.4274
+2023-03-04 21:41:36,524 - mmseg - INFO - Iter [16050/80000]	lr: 7.500e-05, eta: 4:29:08, time: 0.516, data_time: 0.334, memory: 52390, decode.loss_ce: 0.2450, decode.acc_seg: 90.1803, loss: 0.2450
+2023-03-04 21:41:45,429 - mmseg - INFO - Iter [16100/80000]	lr: 7.500e-05, eta: 4:28:26, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2411, decode.acc_seg: 90.3288, loss: 0.2411
+2023-03-04 21:41:54,184 - mmseg - INFO - Iter [16150/80000]	lr: 7.500e-05, eta: 4:27:43, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2535, decode.acc_seg: 89.9807, loss: 0.2535
+2023-03-04 21:42:03,018 - mmseg - INFO - Iter [16200/80000]	lr: 7.500e-05, eta: 4:27:01, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2439, decode.acc_seg: 90.1473, loss: 0.2439
+2023-03-04 21:42:14,511 - mmseg - INFO - Iter [16250/80000]	lr: 7.500e-05, eta: 4:26:41, time: 0.230, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2423, decode.acc_seg: 90.2902, loss: 0.2423
+2023-03-04 21:42:23,132 - mmseg - INFO - Iter [16300/80000]	lr: 7.500e-05, eta: 4:25:58, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2384, decode.acc_seg: 90.4680, loss: 0.2384
+2023-03-04 21:42:31,962 - mmseg - INFO - Iter [16350/80000]	lr: 7.500e-05, eta: 4:25:17, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2426, decode.acc_seg: 90.1364, loss: 0.2426
+2023-03-04 21:42:40,887 - mmseg - INFO - Iter [16400/80000]	lr: 7.500e-05, eta: 4:24:38, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2481, decode.acc_seg: 90.0383, loss: 0.2481
+2023-03-04 21:42:49,665 - mmseg - INFO - Iter [16450/80000]	lr: 7.500e-05, eta: 4:23:57, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2386, decode.acc_seg: 90.2003, loss: 0.2386
+2023-03-04 21:42:58,559 - mmseg - INFO - Iter [16500/80000]	lr: 7.500e-05, eta: 4:23:18, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2498, decode.acc_seg: 90.0039, loss: 0.2498
+2023-03-04 21:43:07,285 - mmseg - INFO - Iter [16550/80000]	lr: 7.500e-05, eta: 4:22:38, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2493, decode.acc_seg: 89.9933, loss: 0.2493
+2023-03-04 21:43:15,971 - mmseg - INFO - Iter [16600/80000]	lr: 7.500e-05, eta: 4:21:58, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2511, decode.acc_seg: 89.9722, loss: 0.2511
+2023-03-04 21:43:24,594 - mmseg - INFO - Iter [16650/80000]	lr: 7.500e-05, eta: 4:21:18, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2382, decode.acc_seg: 90.3006, loss: 0.2382
+2023-03-04 21:43:33,544 - mmseg - INFO - Iter [16700/80000]	lr: 7.500e-05, eta: 4:20:41, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2372, decode.acc_seg: 90.3647, loss: 0.2372
+2023-03-04 21:43:42,584 - mmseg - INFO - Iter [16750/80000]	lr: 7.500e-05, eta: 4:20:04, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2426, decode.acc_seg: 90.2645, loss: 0.2426
+2023-03-04 21:43:51,407 - mmseg - INFO - Iter [16800/80000]	lr: 7.500e-05, eta: 4:19:27, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2506, decode.acc_seg: 89.9649, loss: 0.2506
+2023-03-04 21:44:02,696 - mmseg - INFO - Iter [16850/80000]	lr: 7.500e-05, eta: 4:19:07, time: 0.226, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2358, decode.acc_seg: 90.2620, loss: 0.2358
+2023-03-04 21:44:11,997 - mmseg - INFO - Iter [16900/80000]	lr: 7.500e-05, eta: 4:18:34, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2332, decode.acc_seg: 90.4453, loss: 0.2332
+2023-03-04 21:44:21,450 - mmseg - INFO - Iter [16950/80000]	lr: 7.500e-05, eta: 4:18:01, time: 0.189, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2368, decode.acc_seg: 90.4151, loss: 0.2368
+2023-03-04 21:44:30,166 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:44:30,166 - mmseg - INFO - Iter [17000/80000]	lr: 7.500e-05, eta: 4:17:24, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2471, decode.acc_seg: 89.9184, loss: 0.2471
+2023-03-04 21:44:39,102 - mmseg - INFO - Iter [17050/80000]	lr: 7.500e-05, eta: 4:16:49, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2354, decode.acc_seg: 90.4892, loss: 0.2354
+2023-03-04 21:44:48,116 - mmseg - INFO - Iter [17100/80000]	lr: 7.500e-05, eta: 4:16:14, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2434, decode.acc_seg: 90.1590, loss: 0.2434
+2023-03-04 21:44:56,995 - mmseg - INFO - Iter [17150/80000]	lr: 7.500e-05, eta: 4:15:39, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2525, decode.acc_seg: 89.6948, loss: 0.2525
+2023-03-04 21:45:06,080 - mmseg - INFO - Iter [17200/80000]	lr: 7.500e-05, eta: 4:15:06, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2467, decode.acc_seg: 90.1480, loss: 0.2467
+2023-03-04 21:45:14,839 - mmseg - INFO - Iter [17250/80000]	lr: 7.500e-05, eta: 4:14:30, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2449, decode.acc_seg: 89.9683, loss: 0.2449
+2023-03-04 21:45:23,742 - mmseg - INFO - Iter [17300/80000]	lr: 7.500e-05, eta: 4:13:56, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2479, decode.acc_seg: 90.0318, loss: 0.2479
+2023-03-04 21:45:32,662 - mmseg - INFO - Iter [17350/80000]	lr: 7.500e-05, eta: 4:13:22, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2421, decode.acc_seg: 90.1619, loss: 0.2421
+2023-03-04 21:45:41,338 - mmseg - INFO - Iter [17400/80000]	lr: 7.500e-05, eta: 4:12:47, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2396, decode.acc_seg: 90.3449, loss: 0.2396
+2023-03-04 21:45:50,123 - mmseg - INFO - Iter [17450/80000]	lr: 7.500e-05, eta: 4:12:13, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2419, decode.acc_seg: 90.2554, loss: 0.2419
+2023-03-04 21:46:01,360 - mmseg - INFO - Iter [17500/80000]	lr: 7.500e-05, eta: 4:11:55, time: 0.225, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2398, decode.acc_seg: 90.3347, loss: 0.2398
+2023-03-04 21:46:10,093 - mmseg - INFO - Iter [17550/80000]	lr: 7.500e-05, eta: 4:11:21, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2488, decode.acc_seg: 90.2422, loss: 0.2488
+2023-03-04 21:46:19,041 - mmseg - INFO - Iter [17600/80000]	lr: 7.500e-05, eta: 4:10:49, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2493, decode.acc_seg: 89.9775, loss: 0.2493
+2023-03-04 21:46:27,861 - mmseg - INFO - Iter [17650/80000]	lr: 7.500e-05, eta: 4:10:16, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2481, decode.acc_seg: 89.9307, loss: 0.2481
+2023-03-04 21:46:36,847 - mmseg - INFO - Iter [17700/80000]	lr: 7.500e-05, eta: 4:09:44, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2388, decode.acc_seg: 90.2801, loss: 0.2388
+2023-03-04 21:46:46,329 - mmseg - INFO - Iter [17750/80000]	lr: 7.500e-05, eta: 4:09:16, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2466, decode.acc_seg: 90.1023, loss: 0.2466
+2023-03-04 21:46:55,166 - mmseg - INFO - Iter [17800/80000]	lr: 7.500e-05, eta: 4:08:44, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2397, decode.acc_seg: 90.3521, loss: 0.2397
+2023-03-04 21:47:04,142 - mmseg - INFO - Iter [17850/80000]	lr: 7.500e-05, eta: 4:08:13, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2432, decode.acc_seg: 90.0917, loss: 0.2432
+2023-03-04 21:47:12,720 - mmseg - INFO - Iter [17900/80000]	lr: 7.500e-05, eta: 4:07:39, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2364, decode.acc_seg: 90.3916, loss: 0.2364
+2023-03-04 21:47:21,252 - mmseg - INFO - Iter [17950/80000]	lr: 7.500e-05, eta: 4:07:06, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2389, decode.acc_seg: 90.4480, loss: 0.2389
+2023-03-04 21:47:30,354 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:47:30,354 - mmseg - INFO - Iter [18000/80000]	lr: 7.500e-05, eta: 4:06:36, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2412, decode.acc_seg: 90.1971, loss: 0.2412
+2023-03-04 21:47:39,726 - mmseg - INFO - Iter [18050/80000]	lr: 7.500e-05, eta: 4:06:09, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2499, decode.acc_seg: 89.9948, loss: 0.2499
+2023-03-04 21:47:50,987 - mmseg - INFO - Iter [18100/80000]	lr: 7.500e-05, eta: 4:05:53, time: 0.225, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2417, decode.acc_seg: 90.1764, loss: 0.2417
+2023-03-04 21:47:59,603 - mmseg - INFO - Iter [18150/80000]	lr: 7.500e-05, eta: 4:05:21, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2431, decode.acc_seg: 90.2766, loss: 0.2431
+2023-03-04 21:48:08,369 - mmseg - INFO - Iter [18200/80000]	lr: 7.500e-05, eta: 4:04:50, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2371, decode.acc_seg: 90.4067, loss: 0.2371
+2023-03-04 21:48:17,484 - mmseg - INFO - Iter [18250/80000]	lr: 7.500e-05, eta: 4:04:21, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2353, decode.acc_seg: 90.6240, loss: 0.2353
+2023-03-04 21:48:26,579 - mmseg - INFO - Iter [18300/80000]	lr: 7.500e-05, eta: 4:03:53, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2362, decode.acc_seg: 90.4132, loss: 0.2362
+2023-03-04 21:48:35,606 - mmseg - INFO - Iter [18350/80000]	lr: 7.500e-05, eta: 4:03:24, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2387, decode.acc_seg: 90.2660, loss: 0.2387
+2023-03-04 21:48:44,323 - mmseg - INFO - Iter [18400/80000]	lr: 7.500e-05, eta: 4:02:53, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2386, decode.acc_seg: 90.3258, loss: 0.2386
+2023-03-04 21:48:53,083 - mmseg - INFO - Iter [18450/80000]	lr: 7.500e-05, eta: 4:02:24, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2428, decode.acc_seg: 90.1263, loss: 0.2428
+2023-03-04 21:49:01,804 - mmseg - INFO - Iter [18500/80000]	lr: 7.500e-05, eta: 4:01:54, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2358, decode.acc_seg: 90.3586, loss: 0.2358
+2023-03-04 21:49:11,004 - mmseg - INFO - Iter [18550/80000]	lr: 7.500e-05, eta: 4:01:27, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2365, decode.acc_seg: 90.3993, loss: 0.2365
+2023-03-04 21:49:19,933 - mmseg - INFO - Iter [18600/80000]	lr: 7.500e-05, eta: 4:00:58, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2344, decode.acc_seg: 90.4355, loss: 0.2344
+2023-03-04 21:49:28,672 - mmseg - INFO - Iter [18650/80000]	lr: 7.500e-05, eta: 4:00:29, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2420, decode.acc_seg: 90.2012, loss: 0.2420
+2023-03-04 21:49:37,891 - mmseg - INFO - Iter [18700/80000]	lr: 7.500e-05, eta: 4:00:03, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2396, decode.acc_seg: 90.2203, loss: 0.2396
+2023-03-04 21:49:49,023 - mmseg - INFO - Iter [18750/80000]	lr: 7.500e-05, eta: 3:59:48, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2421, decode.acc_seg: 90.2611, loss: 0.2421
+2023-03-04 21:49:57,804 - mmseg - INFO - Iter [18800/80000]	lr: 7.500e-05, eta: 3:59:19, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2293, decode.acc_seg: 90.6530, loss: 0.2293
+2023-03-04 21:50:06,629 - mmseg - INFO - Iter [18850/80000]	lr: 7.500e-05, eta: 3:58:51, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2491, decode.acc_seg: 90.1351, loss: 0.2491
+2023-03-04 21:50:15,348 - mmseg - INFO - Iter [18900/80000]	lr: 7.500e-05, eta: 3:58:22, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2404, decode.acc_seg: 90.3022, loss: 0.2404
+2023-03-04 21:50:24,749 - mmseg - INFO - Iter [18950/80000]	lr: 7.500e-05, eta: 3:57:58, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2383, decode.acc_seg: 90.3163, loss: 0.2383
+2023-03-04 21:50:33,573 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:50:33,573 - mmseg - INFO - Iter [19000/80000]	lr: 7.500e-05, eta: 3:57:30, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2361, decode.acc_seg: 90.4826, loss: 0.2361
+2023-03-04 21:50:42,447 - mmseg - INFO - Iter [19050/80000]	lr: 7.500e-05, eta: 3:57:03, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2359, decode.acc_seg: 90.1750, loss: 0.2359
+2023-03-04 21:50:51,680 - mmseg - INFO - Iter [19100/80000]	lr: 7.500e-05, eta: 3:56:38, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2434, decode.acc_seg: 90.3321, loss: 0.2434
+2023-03-04 21:51:00,676 - mmseg - INFO - Iter [19150/80000]	lr: 7.500e-05, eta: 3:56:12, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2404, decode.acc_seg: 90.3012, loss: 0.2404
+2023-03-04 21:51:09,288 - mmseg - INFO - Iter [19200/80000]	lr: 7.500e-05, eta: 3:55:44, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2422, decode.acc_seg: 90.3458, loss: 0.2422
+2023-03-04 21:51:18,639 - mmseg - INFO - Iter [19250/80000]	lr: 7.500e-05, eta: 3:55:20, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2489, decode.acc_seg: 90.1636, loss: 0.2489
+2023-03-04 21:51:27,431 - mmseg - INFO - Iter [19300/80000]	lr: 7.500e-05, eta: 3:54:53, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2346, decode.acc_seg: 90.5048, loss: 0.2346
+2023-03-04 21:51:36,234 - mmseg - INFO - Iter [19350/80000]	lr: 7.500e-05, eta: 3:54:26, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2500, decode.acc_seg: 90.0496, loss: 0.2500
+2023-03-04 21:51:47,952 - mmseg - INFO - Iter [19400/80000]	lr: 7.500e-05, eta: 3:54:15, time: 0.234, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2443, decode.acc_seg: 90.2580, loss: 0.2443
+2023-03-04 21:51:56,900 - mmseg - INFO - Iter [19450/80000]	lr: 7.500e-05, eta: 3:53:50, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2397, decode.acc_seg: 90.2295, loss: 0.2397
+2023-03-04 21:52:05,701 - mmseg - INFO - Iter [19500/80000]	lr: 7.500e-05, eta: 3:53:24, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2445, decode.acc_seg: 90.3550, loss: 0.2445
+2023-03-04 21:52:15,060 - mmseg - INFO - Iter [19550/80000]	lr: 7.500e-05, eta: 3:53:00, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2343, decode.acc_seg: 90.4147, loss: 0.2343
+2023-03-04 21:52:23,811 - mmseg - INFO - Iter [19600/80000]	lr: 7.500e-05, eta: 3:52:34, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2395, decode.acc_seg: 90.4351, loss: 0.2395
+2023-03-04 21:52:32,964 - mmseg - INFO - Iter [19650/80000]	lr: 7.500e-05, eta: 3:52:10, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2383, decode.acc_seg: 90.4845, loss: 0.2383
+2023-03-04 21:52:41,695 - mmseg - INFO - Iter [19700/80000]	lr: 7.500e-05, eta: 3:51:44, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2380, decode.acc_seg: 90.4813, loss: 0.2380
+2023-03-04 21:52:50,819 - mmseg - INFO - Iter [19750/80000]	lr: 7.500e-05, eta: 3:51:20, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2409, decode.acc_seg: 90.2625, loss: 0.2409
+2023-03-04 21:52:59,862 - mmseg - INFO - Iter [19800/80000]	lr: 7.500e-05, eta: 3:50:56, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2387, decode.acc_seg: 90.5107, loss: 0.2387
+2023-03-04 21:53:08,740 - mmseg - INFO - Iter [19850/80000]	lr: 7.500e-05, eta: 3:50:31, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2477, decode.acc_seg: 90.0089, loss: 0.2477
+2023-03-04 21:53:17,305 - mmseg - INFO - Iter [19900/80000]	lr: 7.500e-05, eta: 3:50:05, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2424, decode.acc_seg: 90.2224, loss: 0.2424
+2023-03-04 21:53:26,192 - mmseg - INFO - Iter [19950/80000]	lr: 7.500e-05, eta: 3:49:40, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2433, decode.acc_seg: 90.2791, loss: 0.2433
+2023-03-04 21:53:37,435 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:53:37,435 - mmseg - INFO - Iter [20000/80000]	lr: 7.500e-05, eta: 3:49:28, time: 0.225, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2351, decode.acc_seg: 90.3959, loss: 0.2351
+2023-03-04 21:53:46,092 - mmseg - INFO - Iter [20050/80000]	lr: 3.750e-05, eta: 3:49:02, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2247, decode.acc_seg: 90.8940, loss: 0.2247
+2023-03-04 21:53:55,382 - mmseg - INFO - Iter [20100/80000]	lr: 3.750e-05, eta: 3:48:40, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2330, decode.acc_seg: 90.5019, loss: 0.2330
+2023-03-04 21:54:04,037 - mmseg - INFO - Iter [20150/80000]	lr: 3.750e-05, eta: 3:48:15, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2279, decode.acc_seg: 90.6772, loss: 0.2279
+2023-03-04 21:54:12,629 - mmseg - INFO - Iter [20200/80000]	lr: 3.750e-05, eta: 3:47:49, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2238, decode.acc_seg: 90.9701, loss: 0.2238
+2023-03-04 21:54:21,285 - mmseg - INFO - Iter [20250/80000]	lr: 3.750e-05, eta: 3:47:24, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2284, decode.acc_seg: 90.6964, loss: 0.2284
+2023-03-04 21:54:31,172 - mmseg - INFO - Iter [20300/80000]	lr: 3.750e-05, eta: 3:47:06, time: 0.198, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2208, decode.acc_seg: 91.1377, loss: 0.2208
+2023-03-04 21:54:40,081 - mmseg - INFO - Iter [20350/80000]	lr: 3.750e-05, eta: 3:46:42, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2343, decode.acc_seg: 90.3907, loss: 0.2343
+2023-03-04 21:54:48,777 - mmseg - INFO - Iter [20400/80000]	lr: 3.750e-05, eta: 3:46:18, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2324, decode.acc_seg: 90.6101, loss: 0.2324
+2023-03-04 21:54:57,760 - mmseg - INFO - Iter [20450/80000]	lr: 3.750e-05, eta: 3:45:55, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2344, decode.acc_seg: 90.5622, loss: 0.2344
+2023-03-04 21:55:06,604 - mmseg - INFO - Iter [20500/80000]	lr: 3.750e-05, eta: 3:45:31, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2320, decode.acc_seg: 90.5129, loss: 0.2320
+2023-03-04 21:55:15,513 - mmseg - INFO - Iter [20550/80000]	lr: 3.750e-05, eta: 3:45:08, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2279, decode.acc_seg: 90.6911, loss: 0.2279
+2023-03-04 21:55:24,218 - mmseg - INFO - Iter [20600/80000]	lr: 3.750e-05, eta: 3:44:44, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2298, decode.acc_seg: 90.6372, loss: 0.2298
+2023-03-04 21:55:35,811 - mmseg - INFO - Iter [20650/80000]	lr: 3.750e-05, eta: 3:44:34, time: 0.232, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2322, decode.acc_seg: 90.5811, loss: 0.2322
+2023-03-04 21:55:44,752 - mmseg - INFO - Iter [20700/80000]	lr: 3.750e-05, eta: 3:44:12, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2239, decode.acc_seg: 90.8512, loss: 0.2239
+2023-03-04 21:55:53,743 - mmseg - INFO - Iter [20750/80000]	lr: 3.750e-05, eta: 3:43:49, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2308, decode.acc_seg: 90.5741, loss: 0.2308
+2023-03-04 21:56:02,862 - mmseg - INFO - Iter [20800/80000]	lr: 3.750e-05, eta: 3:43:28, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2287, decode.acc_seg: 90.6064, loss: 0.2287
+2023-03-04 21:56:11,938 - mmseg - INFO - Iter [20850/80000]	lr: 3.750e-05, eta: 3:43:06, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2235, decode.acc_seg: 90.8916, loss: 0.2235
+2023-03-04 21:56:21,300 - mmseg - INFO - Iter [20900/80000]	lr: 3.750e-05, eta: 3:42:46, time: 0.187, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2359, decode.acc_seg: 90.6776, loss: 0.2359
+2023-03-04 21:56:30,137 - mmseg - INFO - Iter [20950/80000]	lr: 3.750e-05, eta: 3:42:23, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 91.0236, loss: 0.2200
+2023-03-04 21:56:38,736 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:56:38,736 - mmseg - INFO - Iter [21000/80000]	lr: 3.750e-05, eta: 3:42:00, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2372, decode.acc_seg: 90.3494, loss: 0.2372
+2023-03-04 21:56:47,859 - mmseg - INFO - Iter [21050/80000]	lr: 3.750e-05, eta: 3:41:39, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2242, decode.acc_seg: 90.7723, loss: 0.2242
+2023-03-04 21:56:56,888 - mmseg - INFO - Iter [21100/80000]	lr: 3.750e-05, eta: 3:41:17, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2303, decode.acc_seg: 90.6808, loss: 0.2303
+2023-03-04 21:57:05,522 - mmseg - INFO - Iter [21150/80000]	lr: 3.750e-05, eta: 3:40:54, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2387, decode.acc_seg: 90.4564, loss: 0.2387
+2023-03-04 21:57:14,757 - mmseg - INFO - Iter [21200/80000]	lr: 3.750e-05, eta: 3:40:34, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2334, decode.acc_seg: 90.6136, loss: 0.2334
+2023-03-04 21:57:23,744 - mmseg - INFO - Iter [21250/80000]	lr: 3.750e-05, eta: 3:40:13, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2279, decode.acc_seg: 90.6662, loss: 0.2279
+2023-03-04 21:57:35,106 - mmseg - INFO - Iter [21300/80000]	lr: 3.750e-05, eta: 3:40:02, time: 0.227, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.2841, loss: 0.2134
+2023-03-04 21:57:44,254 - mmseg - INFO - Iter [21350/80000]	lr: 3.750e-05, eta: 3:39:41, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2263, decode.acc_seg: 90.8244, loss: 0.2263
+2023-03-04 21:57:53,542 - mmseg - INFO - Iter [21400/80000]	lr: 3.750e-05, eta: 3:39:22, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2228, decode.acc_seg: 90.8622, loss: 0.2228
+2023-03-04 21:58:02,774 - mmseg - INFO - Iter [21450/80000]	lr: 3.750e-05, eta: 3:39:02, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2321, decode.acc_seg: 90.8150, loss: 0.2321
+2023-03-04 21:58:11,636 - mmseg - INFO - Iter [21500/80000]	lr: 3.750e-05, eta: 3:38:40, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2238, decode.acc_seg: 90.9510, loss: 0.2238
+2023-03-04 21:58:20,899 - mmseg - INFO - Iter [21550/80000]	lr: 3.750e-05, eta: 3:38:21, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2316, decode.acc_seg: 90.6954, loss: 0.2316
+2023-03-04 21:58:30,167 - mmseg - INFO - Iter [21600/80000]	lr: 3.750e-05, eta: 3:38:01, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2371, decode.acc_seg: 90.4047, loss: 0.2371
+2023-03-04 21:58:38,908 - mmseg - INFO - Iter [21650/80000]	lr: 3.750e-05, eta: 3:37:39, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2475, decode.acc_seg: 90.0897, loss: 0.2475
+2023-03-04 21:58:47,623 - mmseg - INFO - Iter [21700/80000]	lr: 3.750e-05, eta: 3:37:18, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2286, decode.acc_seg: 90.7269, loss: 0.2286
+2023-03-04 21:58:56,519 - mmseg - INFO - Iter [21750/80000]	lr: 3.750e-05, eta: 3:36:57, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2329, decode.acc_seg: 90.5526, loss: 0.2329
+2023-03-04 21:59:05,288 - mmseg - INFO - Iter [21800/80000]	lr: 3.750e-05, eta: 3:36:35, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2291, decode.acc_seg: 90.6981, loss: 0.2291
+2023-03-04 21:59:14,159 - mmseg - INFO - Iter [21850/80000]	lr: 3.750e-05, eta: 3:36:15, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2230, decode.acc_seg: 90.7660, loss: 0.2230
+2023-03-04 21:59:25,357 - mmseg - INFO - Iter [21900/80000]	lr: 3.750e-05, eta: 3:36:04, time: 0.224, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2330, decode.acc_seg: 90.5109, loss: 0.2330
+2023-03-04 21:59:34,103 - mmseg - INFO - Iter [21950/80000]	lr: 3.750e-05, eta: 3:35:42, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2203, decode.acc_seg: 91.2207, loss: 0.2203
+2023-03-04 21:59:43,046 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 21:59:43,046 - mmseg - INFO - Iter [22000/80000]	lr: 3.750e-05, eta: 3:35:22, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2288, decode.acc_seg: 90.7427, loss: 0.2288
+2023-03-04 21:59:51,592 - mmseg - INFO - Iter [22050/80000]	lr: 3.750e-05, eta: 3:35:00, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2357, decode.acc_seg: 90.4073, loss: 0.2357
+2023-03-04 22:00:00,744 - mmseg - INFO - Iter [22100/80000]	lr: 3.750e-05, eta: 3:34:41, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2241, decode.acc_seg: 90.9299, loss: 0.2241
+2023-03-04 22:00:10,075 - mmseg - INFO - Iter [22150/80000]	lr: 3.750e-05, eta: 3:34:23, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2235, decode.acc_seg: 91.0735, loss: 0.2235
+2023-03-04 22:00:18,799 - mmseg - INFO - Iter [22200/80000]	lr: 3.750e-05, eta: 3:34:02, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2345, decode.acc_seg: 90.5477, loss: 0.2345
+2023-03-04 22:00:27,914 - mmseg - INFO - Iter [22250/80000]	lr: 3.750e-05, eta: 3:33:43, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2288, decode.acc_seg: 90.8522, loss: 0.2288
+2023-03-04 22:00:36,733 - mmseg - INFO - Iter [22300/80000]	lr: 3.750e-05, eta: 3:33:22, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2220, decode.acc_seg: 90.9620, loss: 0.2220
+2023-03-04 22:00:45,457 - mmseg - INFO - Iter [22350/80000]	lr: 3.750e-05, eta: 3:33:02, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2288, decode.acc_seg: 90.6528, loss: 0.2288
+2023-03-04 22:00:54,284 - mmseg - INFO - Iter [22400/80000]	lr: 3.750e-05, eta: 3:32:41, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2334, decode.acc_seg: 90.5380, loss: 0.2334
+2023-03-04 22:01:03,019 - mmseg - INFO - Iter [22450/80000]	lr: 3.750e-05, eta: 3:32:21, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2261, decode.acc_seg: 90.8577, loss: 0.2261
+2023-03-04 22:01:12,016 - mmseg - INFO - Iter [22500/80000]	lr: 3.750e-05, eta: 3:32:02, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2266, decode.acc_seg: 90.6496, loss: 0.2266
+2023-03-04 22:01:23,690 - mmseg - INFO - Iter [22550/80000]	lr: 3.750e-05, eta: 3:31:53, time: 0.233, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2267, decode.acc_seg: 90.7438, loss: 0.2267
+2023-03-04 22:01:32,374 - mmseg - INFO - Iter [22600/80000]	lr: 3.750e-05, eta: 3:31:33, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2401, decode.acc_seg: 90.3623, loss: 0.2401
+2023-03-04 22:01:41,574 - mmseg - INFO - Iter [22650/80000]	lr: 3.750e-05, eta: 3:31:14, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2404, decode.acc_seg: 90.3277, loss: 0.2404
+2023-03-04 22:01:51,092 - mmseg - INFO - Iter [22700/80000]	lr: 3.750e-05, eta: 3:30:57, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2297, decode.acc_seg: 90.7354, loss: 0.2297
+2023-03-04 22:01:59,926 - mmseg - INFO - Iter [22750/80000]	lr: 3.750e-05, eta: 3:30:38, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2285, decode.acc_seg: 90.6680, loss: 0.2285
+2023-03-04 22:02:08,916 - mmseg - INFO - Iter [22800/80000]	lr: 3.750e-05, eta: 3:30:19, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2289, decode.acc_seg: 90.6487, loss: 0.2289
+2023-03-04 22:02:17,651 - mmseg - INFO - Iter [22850/80000]	lr: 3.750e-05, eta: 3:29:59, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2247, decode.acc_seg: 90.9414, loss: 0.2247
+2023-03-04 22:02:26,506 - mmseg - INFO - Iter [22900/80000]	lr: 3.750e-05, eta: 3:29:40, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 90.7966, loss: 0.2189
+2023-03-04 22:02:35,245 - mmseg - INFO - Iter [22950/80000]	lr: 3.750e-05, eta: 3:29:20, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2176, decode.acc_seg: 91.1516, loss: 0.2176
+2023-03-04 22:02:43,941 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:02:43,941 - mmseg - INFO - Iter [23000/80000]	lr: 3.750e-05, eta: 3:29:00, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2290, decode.acc_seg: 90.8727, loss: 0.2290
+2023-03-04 22:02:52,529 - mmseg - INFO - Iter [23050/80000]	lr: 3.750e-05, eta: 3:28:40, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2183, decode.acc_seg: 91.1977, loss: 0.2183
+2023-03-04 22:03:01,252 - mmseg - INFO - Iter [23100/80000]	lr: 3.750e-05, eta: 3:28:20, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2291, decode.acc_seg: 90.8125, loss: 0.2291
+2023-03-04 22:03:12,621 - mmseg - INFO - Iter [23150/80000]	lr: 3.750e-05, eta: 3:28:11, time: 0.227, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2208, decode.acc_seg: 90.9941, loss: 0.2208
+2023-03-04 22:03:21,549 - mmseg - INFO - Iter [23200/80000]	lr: 3.750e-05, eta: 3:27:52, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2355, decode.acc_seg: 90.3589, loss: 0.2355
+2023-03-04 22:03:30,717 - mmseg - INFO - Iter [23250/80000]	lr: 3.750e-05, eta: 3:27:34, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2315, decode.acc_seg: 90.7375, loss: 0.2315
+2023-03-04 22:03:39,286 - mmseg - INFO - Iter [23300/80000]	lr: 3.750e-05, eta: 3:27:15, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2226, decode.acc_seg: 90.9246, loss: 0.2226
+2023-03-04 22:03:48,336 - mmseg - INFO - Iter [23350/80000]	lr: 3.750e-05, eta: 3:26:57, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2175, decode.acc_seg: 91.1108, loss: 0.2175
+2023-03-04 22:03:56,962 - mmseg - INFO - Iter [23400/80000]	lr: 3.750e-05, eta: 3:26:37, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2286, decode.acc_seg: 90.7066, loss: 0.2286
+2023-03-04 22:04:06,042 - mmseg - INFO - Iter [23450/80000]	lr: 3.750e-05, eta: 3:26:19, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2260, decode.acc_seg: 90.9040, loss: 0.2260
+2023-03-04 22:04:14,817 - mmseg - INFO - Iter [23500/80000]	lr: 3.750e-05, eta: 3:26:00, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2270, decode.acc_seg: 90.7285, loss: 0.2270
+2023-03-04 22:04:23,535 - mmseg - INFO - Iter [23550/80000]	lr: 3.750e-05, eta: 3:25:41, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2269, decode.acc_seg: 90.8139, loss: 0.2269
+2023-03-04 22:04:32,331 - mmseg - INFO - Iter [23600/80000]	lr: 3.750e-05, eta: 3:25:23, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2308, decode.acc_seg: 90.6950, loss: 0.2308
+2023-03-04 22:04:41,060 - mmseg - INFO - Iter [23650/80000]	lr: 3.750e-05, eta: 3:25:04, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2231, decode.acc_seg: 90.8981, loss: 0.2231
+2023-03-04 22:04:49,767 - mmseg - INFO - Iter [23700/80000]	lr: 3.750e-05, eta: 3:24:45, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2322, decode.acc_seg: 90.5128, loss: 0.2322
+2023-03-04 22:04:58,427 - mmseg - INFO - Iter [23750/80000]	lr: 3.750e-05, eta: 3:24:26, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2353, decode.acc_seg: 90.4130, loss: 0.2353
+2023-03-04 22:05:09,755 - mmseg - INFO - Iter [23800/80000]	lr: 3.750e-05, eta: 3:24:17, time: 0.227, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2275, decode.acc_seg: 90.6399, loss: 0.2275
+2023-03-04 22:05:19,147 - mmseg - INFO - Iter [23850/80000]	lr: 3.750e-05, eta: 3:24:00, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2228, decode.acc_seg: 90.8473, loss: 0.2228
+2023-03-04 22:05:27,972 - mmseg - INFO - Iter [23900/80000]	lr: 3.750e-05, eta: 3:23:42, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2250, decode.acc_seg: 90.8478, loss: 0.2250
+2023-03-04 22:05:36,775 - mmseg - INFO - Iter [23950/80000]	lr: 3.750e-05, eta: 3:23:24, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2316, decode.acc_seg: 90.8318, loss: 0.2316
+2023-03-04 22:05:45,549 - mmseg - INFO - Saving checkpoint at 24000 iterations
+2023-03-04 22:05:46,168 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:05:46,168 - mmseg - INFO - Iter [24000/80000]	lr: 3.750e-05, eta: 3:23:08, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2323, decode.acc_seg: 90.5718, loss: 0.2323
+2023-03-04 22:06:02,057 - mmseg - INFO - per class results:
+2023-03-04 22:06:02,063 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.05 | 89.69 |
+|       building      | 80.55 | 92.92 |
+|         sky         | 94.18 | 96.89 |
+|        floor        | 80.79 | 91.07 |
+|         tree        |  73.1 | 86.96 |
+|       ceiling       | 83.98 | 91.55 |
+|         road        | 81.81 | 89.66 |
+|         bed         | 86.75 | 93.61 |
+|      windowpane     | 58.94 | 79.78 |
+|        grass        | 64.37 | 83.97 |
+|       cabinet       | 59.41 | 74.04 |
+|       sidewalk      | 62.69 | 75.61 |
+|        person       | 77.64 | 91.43 |
+|        earth        | 34.58 |  45.5 |
+|         door        | 38.22 | 43.66 |
+|        table        | 56.44 | 75.61 |
+|       mountain      |  55.8 | 71.29 |
+|        plant        | 49.47 | 61.42 |
+|       curtain       | 72.71 | 83.39 |
+|        chair        | 52.76 | 67.75 |
+|         car         | 81.15 | 90.14 |
+|        water        | 57.87 |  76.1 |
+|       painting      |  69.5 | 82.73 |
+|         sofa        | 63.17 | 78.93 |
+|        shelf        | 41.66 | 65.73 |
+|        house        | 36.22 | 44.43 |
+|         sea         | 60.99 | 75.85 |
+|        mirror       | 58.23 | 63.29 |
+|         rug         | 59.89 | 65.56 |
+|        field        | 29.45 | 45.27 |
+|       armchair      |  35.3 | 51.78 |
+|         seat        | 64.79 | 82.38 |
+|        fence        | 41.42 | 53.69 |
+|         desk        | 43.83 | 68.07 |
+|         rock        |  36.1 | 57.39 |
+|       wardrobe      |  56.5 | 67.44 |
+|         lamp        | 58.34 | 71.83 |
+|       bathtub       | 75.26 | 80.94 |
+|       railing       | 31.98 | 42.63 |
+|       cushion       | 53.98 |  66.8 |
+|         base        | 23.34 | 29.39 |
+|         box         | 22.18 |  31.8 |
+|        column       |  41.0 | 46.32 |
+|      signboard      | 35.34 | 45.38 |
+|   chest of drawers  |  37.1 | 53.49 |
+|       counter       | 31.52 | 39.56 |
+|         sand        | 38.07 | 55.73 |
+|         sink        | 64.47 | 73.85 |
+|      skyscraper     | 49.18 | 60.86 |
+|      fireplace      | 72.72 | 82.38 |
+|     refrigerator    | 70.03 | 79.44 |
+|      grandstand     | 45.58 | 69.57 |
+|         path        | 23.28 | 34.54 |
+|        stairs       |  33.7 | 44.07 |
+|        runway       | 68.72 | 88.64 |
+|         case        | 44.18 | 49.07 |
+|      pool table     | 90.36 | 92.11 |
+|        pillow       | 57.87 | 69.06 |
+|     screen door     | 67.15 | 76.33 |
+|       stairway      | 26.13 | 34.17 |
+|        river        | 11.39 | 21.41 |
+|        bridge       | 34.82 |  40.5 |
+|       bookcase      | 42.65 | 59.25 |
+|        blind        | 36.05 | 39.81 |
+|     coffee table    | 51.49 | 78.47 |
+|        toilet       | 80.63 |  89.9 |
+|        flower       | 37.65 | 54.87 |
+|         book        | 42.03 | 66.84 |
+|         hill        | 13.64 | 22.05 |
+|        bench        | 36.92 | 53.94 |
+|      countertop     | 50.83 |  67.5 |
+|        stove        | 68.28 |  77.6 |
+|         palm        | 47.79 |  63.1 |
+|    kitchen island   | 33.45 | 45.57 |
+|       computer      | 59.01 | 69.73 |
+|     swivel chair    | 42.94 | 58.78 |
+|         boat        | 65.34 | 81.09 |
+|         bar         | 21.26 | 28.06 |
+|    arcade machine   | 61.98 | 62.82 |
+|        hovel        | 32.12 | 34.78 |
+|         bus         |  75.9 | 88.94 |
+|        towel        | 62.09 | 68.73 |
+|        light        | 44.89 | 49.92 |
+|        truck        | 16.72 | 22.86 |
+|        tower        |  7.54 | 11.87 |
+|      chandelier     | 61.91 | 80.24 |
+|        awning       | 17.52 | 19.24 |
+|     streetlight     | 21.91 | 30.77 |
+|        booth        | 42.45 | 44.79 |
+| television receiver | 64.03 | 73.49 |
+|       airplane      | 55.08 | 61.38 |
+|      dirt track     | 13.73 | 42.93 |
+|       apparel       | 29.93 | 46.51 |
+|         pole        |  8.02 |  9.02 |
+|         land        |  2.28 |  2.85 |
+|      bannister      |  7.58 |  9.54 |
+|      escalator      | 22.67 | 24.34 |
+|       ottoman       | 41.53 | 56.07 |
+|        bottle       |  33.5 | 57.27 |
+|        buffet       | 41.42 | 48.85 |
+|        poster       | 24.97 |  36.3 |
+|        stage        | 12.34 | 15.93 |
+|         van         | 37.51 | 56.48 |
+|         ship        | 73.34 | 96.87 |
+|       fountain      | 13.18 | 13.43 |
+|    conveyer belt    | 82.85 | 87.69 |
+|        canopy       | 23.11 | 24.33 |
+|        washer       | 76.91 |  78.9 |
+|      plaything      | 21.76 | 34.48 |
+|    swimming pool    | 66.25 |  81.7 |
+|        stool        | 39.18 | 54.73 |
+|        barrel       | 37.33 | 59.46 |
+|        basket       | 21.54 | 31.25 |
+|      waterfall      | 51.31 | 67.39 |
+|         tent        | 93.04 | 97.75 |
+|         bag         | 10.55 | 12.41 |
+|       minibike      | 61.39 | 74.11 |
+|        cradle       | 82.18 |  95.6 |
+|         oven        | 45.91 | 53.62 |
+|         ball        | 40.58 |  47.3 |
+|         food        | 47.66 | 56.63 |
+|         step        |  8.94 | 10.12 |
+|         tank        | 50.79 |  55.2 |
+|      trade name     | 17.03 | 18.14 |
+|      microwave      | 76.07 | 80.25 |
+|         pot         | 29.24 | 34.68 |
+|        animal       | 52.01 | 56.62 |
+|       bicycle       | 52.49 | 67.97 |
+|         lake        | 56.62 | 62.36 |
+|      dishwasher     | 63.28 | 75.04 |
+|        screen       | 64.92 | 86.56 |
+|       blanket       |  15.4 | 17.39 |
+|      sculpture      |  55.6 | 77.58 |
+|         hood        |  50.5 | 52.06 |
+|        sconce       | 37.44 | 43.14 |
+|         vase        | 29.55 | 51.64 |
+|    traffic light    | 23.81 | 30.05 |
+|         tray        |  4.27 |  6.68 |
+|        ashcan       | 36.01 | 46.19 |
+|         fan         | 54.44 | 63.64 |
+|         pier        | 43.34 | 52.34 |
+|      crt screen     |  3.87 |  8.77 |
+|        plate        | 45.27 | 54.55 |
+|       monitor       | 19.17 | 22.72 |
+|    bulletin board   | 27.77 | 32.24 |
+|        shower       |  1.3  |  3.54 |
+|       radiator      |  57.3 | 63.99 |
+|        glass        |  7.28 |  7.57 |
+|        clock        | 30.25 | 31.47 |
+|         flag        |  30.1 | 31.93 |
++---------------------+-------+-------+
+2023-03-04 22:06:02,063 - mmseg - INFO - Summary:
+2023-03-04 22:06:02,063 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.84 | 45.91 | 56.39 |
++-------+-------+-------+
+2023-03-04 22:06:02,083 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_16000.pth was removed
+2023-03-04 22:06:02,664 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_24000.pth.
+2023-03-04 22:06:02,664 - mmseg - INFO - Best mIoU is 0.4591 at 24000 iter.
+2023-03-04 22:06:02,664 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:06:02,665 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8184, mIoU: 0.4591, mAcc: 0.5639, IoU.background: nan, IoU.wall: 0.7605, IoU.building: 0.8055, IoU.sky: 0.9418, IoU.floor: 0.8079, IoU.tree: 0.7310, IoU.ceiling: 0.8398, IoU.road: 0.8181, IoU.bed : 0.8675, IoU.windowpane: 0.5894, IoU.grass: 0.6437, IoU.cabinet: 0.5941, IoU.sidewalk: 0.6269, IoU.person: 0.7764, IoU.earth: 0.3458, IoU.door: 0.3822, IoU.table: 0.5644, IoU.mountain: 0.5580, IoU.plant: 0.4947, IoU.curtain: 0.7271, IoU.chair: 0.5276, IoU.car: 0.8115, IoU.water: 0.5787, IoU.painting: 0.6950, IoU.sofa: 0.6317, IoU.shelf: 0.4166, IoU.house: 0.3622, IoU.sea: 0.6099, IoU.mirror: 0.5823, IoU.rug: 0.5989, IoU.field: 0.2945, IoU.armchair: 0.3530, IoU.seat: 0.6479, IoU.fence: 0.4142, IoU.desk: 0.4383, IoU.rock: 0.3610, IoU.wardrobe: 0.5650, IoU.lamp: 0.5834, IoU.bathtub: 0.7526, IoU.railing: 0.3198, IoU.cushion: 0.5398, IoU.base: 0.2334, IoU.box: 0.2218, IoU.column: 0.4100, IoU.signboard: 0.3534, IoU.chest of drawers: 0.3710, IoU.counter: 0.3152, IoU.sand: 0.3807, IoU.sink: 0.6447, IoU.skyscraper: 0.4918, IoU.fireplace: 0.7272, IoU.refrigerator: 0.7003, IoU.grandstand: 0.4558, IoU.path: 0.2328, IoU.stairs: 0.3370, IoU.runway: 0.6872, IoU.case: 0.4418, IoU.pool table: 0.9036, IoU.pillow: 0.5787, IoU.screen door: 0.6715, IoU.stairway: 0.2613, IoU.river: 0.1139, IoU.bridge: 0.3482, IoU.bookcase: 0.4265, IoU.blind: 0.3605, IoU.coffee table: 0.5149, IoU.toilet: 0.8063, IoU.flower: 0.3765, IoU.book: 0.4203, IoU.hill: 0.1364, IoU.bench: 0.3692, IoU.countertop: 0.5083, IoU.stove: 0.6828, IoU.palm: 0.4779, IoU.kitchen island: 0.3345, IoU.computer: 0.5901, IoU.swivel chair: 0.4294, IoU.boat: 0.6534, IoU.bar: 0.2126, IoU.arcade machine: 0.6198, IoU.hovel: 0.3212, IoU.bus: 0.7590, IoU.towel: 0.6209, IoU.light: 0.4489, IoU.truck: 0.1672, IoU.tower: 0.0754, IoU.chandelier: 0.6191, IoU.awning: 0.1752, IoU.streetlight: 0.2191, IoU.booth: 0.4245, IoU.television receiver: 0.6403, IoU.airplane: 0.5508, IoU.dirt track: 0.1373, IoU.apparel: 0.2993, IoU.pole: 0.0802, IoU.land: 0.0228, IoU.bannister: 0.0758, IoU.escalator: 0.2267, IoU.ottoman: 0.4153, IoU.bottle: 0.3350, IoU.buffet: 0.4142, IoU.poster: 0.2497, IoU.stage: 0.1234, IoU.van: 0.3751, IoU.ship: 0.7334, IoU.fountain: 0.1318, IoU.conveyer belt: 0.8285, IoU.canopy: 0.2311, IoU.washer: 0.7691, IoU.plaything: 0.2176, IoU.swimming pool: 0.6625, IoU.stool: 0.3918, IoU.barrel: 0.3733, IoU.basket: 0.2154, IoU.waterfall: 0.5131, IoU.tent: 0.9304, IoU.bag: 0.1055, IoU.minibike: 0.6139, IoU.cradle: 0.8218, IoU.oven: 0.4591, IoU.ball: 0.4058, IoU.food: 0.4766, IoU.step: 0.0894, IoU.tank: 0.5079, IoU.trade name: 0.1703, IoU.microwave: 0.7607, IoU.pot: 0.2924, IoU.animal: 0.5201, IoU.bicycle: 0.5249, IoU.lake: 0.5662, IoU.dishwasher: 0.6328, IoU.screen: 0.6492, IoU.blanket: 0.1540, IoU.sculpture: 0.5560, IoU.hood: 0.5050, IoU.sconce: 0.3744, IoU.vase: 0.2955, IoU.traffic light: 0.2381, IoU.tray: 0.0427, IoU.ashcan: 0.3601, IoU.fan: 0.5444, IoU.pier: 0.4334, IoU.crt screen: 0.0387, IoU.plate: 0.4527, IoU.monitor: 0.1917, IoU.bulletin board: 0.2777, IoU.shower: 0.0130, IoU.radiator: 0.5730, IoU.glass: 0.0728, IoU.clock: 0.3025, IoU.flag: 0.3010, Acc.background: nan, Acc.wall: 0.8969, Acc.building: 0.9292, Acc.sky: 0.9689, Acc.floor: 0.9107, Acc.tree: 0.8696, Acc.ceiling: 0.9155, Acc.road: 0.8966, Acc.bed : 0.9361, Acc.windowpane: 0.7978, Acc.grass: 0.8397, Acc.cabinet: 0.7404, Acc.sidewalk: 0.7561, Acc.person: 0.9143, Acc.earth: 0.4550, Acc.door: 0.4366, Acc.table: 0.7561, Acc.mountain: 0.7129, Acc.plant: 0.6142, Acc.curtain: 0.8339, Acc.chair: 0.6775, Acc.car: 0.9014, Acc.water: 0.7610, Acc.painting: 0.8273, Acc.sofa: 0.7893, Acc.shelf: 0.6573, Acc.house: 0.4443, Acc.sea: 0.7585, Acc.mirror: 0.6329, Acc.rug: 0.6556, Acc.field: 0.4527, Acc.armchair: 0.5178, Acc.seat: 0.8238, Acc.fence: 0.5369, Acc.desk: 0.6807, Acc.rock: 0.5739, Acc.wardrobe: 0.6744, Acc.lamp: 0.7183, Acc.bathtub: 0.8094, Acc.railing: 0.4263, Acc.cushion: 0.6680, Acc.base: 0.2939, Acc.box: 0.3180, Acc.column: 0.4632, Acc.signboard: 0.4538, Acc.chest of drawers: 0.5349, Acc.counter: 0.3956, Acc.sand: 0.5573, Acc.sink: 0.7385, Acc.skyscraper: 0.6086, Acc.fireplace: 0.8238, Acc.refrigerator: 0.7944, Acc.grandstand: 0.6957, Acc.path: 0.3454, Acc.stairs: 0.4407, Acc.runway: 0.8864, Acc.case: 0.4907, Acc.pool table: 0.9211, Acc.pillow: 0.6906, Acc.screen door: 0.7633, Acc.stairway: 0.3417, Acc.river: 0.2141, Acc.bridge: 0.4050, Acc.bookcase: 0.5925, Acc.blind: 0.3981, Acc.coffee table: 0.7847, Acc.toilet: 0.8990, Acc.flower: 0.5487, Acc.book: 0.6684, Acc.hill: 0.2205, Acc.bench: 0.5394, Acc.countertop: 0.6750, Acc.stove: 0.7760, Acc.palm: 0.6310, Acc.kitchen island: 0.4557, Acc.computer: 0.6973, Acc.swivel chair: 0.5878, Acc.boat: 0.8109, Acc.bar: 0.2806, Acc.arcade machine: 0.6282, Acc.hovel: 0.3478, Acc.bus: 0.8894, Acc.towel: 0.6873, Acc.light: 0.4992, Acc.truck: 0.2286, Acc.tower: 0.1187, Acc.chandelier: 0.8024, Acc.awning: 0.1924, Acc.streetlight: 0.3077, Acc.booth: 0.4479, Acc.television receiver: 0.7349, Acc.airplane: 0.6138, Acc.dirt track: 0.4293, Acc.apparel: 0.4651, Acc.pole: 0.0902, Acc.land: 0.0285, Acc.bannister: 0.0954, Acc.escalator: 0.2434, Acc.ottoman: 0.5607, Acc.bottle: 0.5727, Acc.buffet: 0.4885, Acc.poster: 0.3630, Acc.stage: 0.1593, Acc.van: 0.5648, Acc.ship: 0.9687, Acc.fountain: 0.1343, Acc.conveyer belt: 0.8769, Acc.canopy: 0.2433, Acc.washer: 0.7890, Acc.plaything: 0.3448, Acc.swimming pool: 0.8170, Acc.stool: 0.5473, Acc.barrel: 0.5946, Acc.basket: 0.3125, Acc.waterfall: 0.6739, Acc.tent: 0.9775, Acc.bag: 0.1241, Acc.minibike: 0.7411, Acc.cradle: 0.9560, Acc.oven: 0.5362, Acc.ball: 0.4730, Acc.food: 0.5663, Acc.step: 0.1012, Acc.tank: 0.5520, Acc.trade name: 0.1814, Acc.microwave: 0.8025, Acc.pot: 0.3468, Acc.animal: 0.5662, Acc.bicycle: 0.6797, Acc.lake: 0.6236, Acc.dishwasher: 0.7504, Acc.screen: 0.8656, Acc.blanket: 0.1739, Acc.sculpture: 0.7758, Acc.hood: 0.5206, Acc.sconce: 0.4314, Acc.vase: 0.5164, Acc.traffic light: 0.3005, Acc.tray: 0.0668, Acc.ashcan: 0.4619, Acc.fan: 0.6364, Acc.pier: 0.5234, Acc.crt screen: 0.0877, Acc.plate: 0.5455, Acc.monitor: 0.2272, Acc.bulletin board: 0.3224, Acc.shower: 0.0354, Acc.radiator: 0.6399, Acc.glass: 0.0757, Acc.clock: 0.3147, Acc.flag: 0.3193
+2023-03-04 22:06:11,725 - mmseg - INFO - Iter [24050/80000]	lr: 3.750e-05, eta: 3:23:48, time: 0.511, data_time: 0.337, memory: 52390, decode.loss_ce: 0.2271, decode.acc_seg: 90.8791, loss: 0.2271
+2023-03-04 22:06:20,964 - mmseg - INFO - Iter [24100/80000]	lr: 3.750e-05, eta: 3:23:31, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2172, decode.acc_seg: 91.0624, loss: 0.2172
+2023-03-04 22:06:30,273 - mmseg - INFO - Iter [24150/80000]	lr: 3.750e-05, eta: 3:23:15, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2285, decode.acc_seg: 90.6728, loss: 0.2285
+2023-03-04 22:06:38,859 - mmseg - INFO - Iter [24200/80000]	lr: 3.750e-05, eta: 3:22:56, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2337, decode.acc_seg: 90.4080, loss: 0.2337
+2023-03-04 22:06:47,974 - mmseg - INFO - Iter [24250/80000]	lr: 3.750e-05, eta: 3:22:39, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2264, decode.acc_seg: 90.9179, loss: 0.2264
+2023-03-04 22:06:57,601 - mmseg - INFO - Iter [24300/80000]	lr: 3.750e-05, eta: 3:22:23, time: 0.193, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2209, decode.acc_seg: 90.8872, loss: 0.2209
+2023-03-04 22:07:06,446 - mmseg - INFO - Iter [24350/80000]	lr: 3.750e-05, eta: 3:22:06, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2250, decode.acc_seg: 91.0014, loss: 0.2250
+2023-03-04 22:07:15,388 - mmseg - INFO - Iter [24400/80000]	lr: 3.750e-05, eta: 3:21:48, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2345, decode.acc_seg: 90.4250, loss: 0.2345
+2023-03-04 22:07:27,022 - mmseg - INFO - Iter [24450/80000]	lr: 3.750e-05, eta: 3:21:40, time: 0.232, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2271, decode.acc_seg: 90.7144, loss: 0.2271
+2023-03-04 22:07:35,860 - mmseg - INFO - Iter [24500/80000]	lr: 3.750e-05, eta: 3:21:22, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2244, decode.acc_seg: 90.8669, loss: 0.2244
+2023-03-04 22:07:45,460 - mmseg - INFO - Iter [24550/80000]	lr: 3.750e-05, eta: 3:21:07, time: 0.192, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2236, decode.acc_seg: 90.8935, loss: 0.2236
+2023-03-04 22:07:54,595 - mmseg - INFO - Iter [24600/80000]	lr: 3.750e-05, eta: 3:20:50, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2253, decode.acc_seg: 90.9530, loss: 0.2253
+2023-03-04 22:08:03,159 - mmseg - INFO - Iter [24650/80000]	lr: 3.750e-05, eta: 3:20:31, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2222, decode.acc_seg: 91.0541, loss: 0.2222
+2023-03-04 22:08:11,674 - mmseg - INFO - Iter [24700/80000]	lr: 3.750e-05, eta: 3:20:13, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2391, decode.acc_seg: 90.4759, loss: 0.2391
+2023-03-04 22:08:20,993 - mmseg - INFO - Iter [24750/80000]	lr: 3.750e-05, eta: 3:19:57, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2293, decode.acc_seg: 90.7074, loss: 0.2293
+2023-03-04 22:08:30,117 - mmseg - INFO - Iter [24800/80000]	lr: 3.750e-05, eta: 3:19:40, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2232, decode.acc_seg: 90.8891, loss: 0.2232
+2023-03-04 22:08:38,849 - mmseg - INFO - Iter [24850/80000]	lr: 3.750e-05, eta: 3:19:22, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2356, decode.acc_seg: 90.5185, loss: 0.2356
+2023-03-04 22:08:47,706 - mmseg - INFO - Iter [24900/80000]	lr: 3.750e-05, eta: 3:19:05, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2218, decode.acc_seg: 90.8613, loss: 0.2218
+2023-03-04 22:08:56,941 - mmseg - INFO - Iter [24950/80000]	lr: 3.750e-05, eta: 3:18:49, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 90.8899, loss: 0.2200
+2023-03-04 22:09:06,650 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:09:06,650 - mmseg - INFO - Iter [25000/80000]	lr: 3.750e-05, eta: 3:18:35, time: 0.194, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2363, decode.acc_seg: 90.5865, loss: 0.2363
+2023-03-04 22:09:17,763 - mmseg - INFO - Iter [25050/80000]	lr: 3.750e-05, eta: 3:18:25, time: 0.222, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2315, decode.acc_seg: 90.6250, loss: 0.2315
+2023-03-04 22:09:26,482 - mmseg - INFO - Iter [25100/80000]	lr: 3.750e-05, eta: 3:18:07, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2244, decode.acc_seg: 91.0009, loss: 0.2244
+2023-03-04 22:09:35,725 - mmseg - INFO - Iter [25150/80000]	lr: 3.750e-05, eta: 3:17:51, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2321, decode.acc_seg: 90.6282, loss: 0.2321
+2023-03-04 22:09:44,555 - mmseg - INFO - Iter [25200/80000]	lr: 3.750e-05, eta: 3:17:34, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2296, decode.acc_seg: 90.6127, loss: 0.2296
+2023-03-04 22:09:53,802 - mmseg - INFO - Iter [25250/80000]	lr: 3.750e-05, eta: 3:17:18, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2251, decode.acc_seg: 90.9308, loss: 0.2251
+2023-03-04 22:10:02,908 - mmseg - INFO - Iter [25300/80000]	lr: 3.750e-05, eta: 3:17:02, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2223, decode.acc_seg: 91.0198, loss: 0.2223
+2023-03-04 22:10:11,947 - mmseg - INFO - Iter [25350/80000]	lr: 3.750e-05, eta: 3:16:46, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2231, decode.acc_seg: 90.7354, loss: 0.2231
+2023-03-04 22:10:21,322 - mmseg - INFO - Iter [25400/80000]	lr: 3.750e-05, eta: 3:16:30, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2231, decode.acc_seg: 91.0585, loss: 0.2231
+2023-03-04 22:10:30,377 - mmseg - INFO - Iter [25450/80000]	lr: 3.750e-05, eta: 3:16:14, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2234, decode.acc_seg: 90.9460, loss: 0.2234
+2023-03-04 22:10:39,349 - mmseg - INFO - Iter [25500/80000]	lr: 3.750e-05, eta: 3:15:58, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2287, decode.acc_seg: 90.7159, loss: 0.2287
+2023-03-04 22:10:48,387 - mmseg - INFO - Iter [25550/80000]	lr: 3.750e-05, eta: 3:15:41, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2256, decode.acc_seg: 90.7539, loss: 0.2256
+2023-03-04 22:10:57,071 - mmseg - INFO - Iter [25600/80000]	lr: 3.750e-05, eta: 3:15:24, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.1046, loss: 0.2163
+2023-03-04 22:11:05,737 - mmseg - INFO - Iter [25650/80000]	lr: 3.750e-05, eta: 3:15:07, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2274, decode.acc_seg: 90.7404, loss: 0.2274
+2023-03-04 22:11:16,970 - mmseg - INFO - Iter [25700/80000]	lr: 3.750e-05, eta: 3:14:58, time: 0.225, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2332, decode.acc_seg: 90.4475, loss: 0.2332
+2023-03-04 22:11:26,014 - mmseg - INFO - Iter [25750/80000]	lr: 3.750e-05, eta: 3:14:41, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2386, decode.acc_seg: 90.3401, loss: 0.2386
+2023-03-04 22:11:35,087 - mmseg - INFO - Iter [25800/80000]	lr: 3.750e-05, eta: 3:14:26, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2300, decode.acc_seg: 90.9369, loss: 0.2300
+2023-03-04 22:11:44,065 - mmseg - INFO - Iter [25850/80000]	lr: 3.750e-05, eta: 3:14:09, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2368, decode.acc_seg: 90.3931, loss: 0.2368
+2023-03-04 22:11:53,563 - mmseg - INFO - Iter [25900/80000]	lr: 3.750e-05, eta: 3:13:55, time: 0.190, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2222, decode.acc_seg: 91.0510, loss: 0.2222
+2023-03-04 22:12:02,369 - mmseg - INFO - Iter [25950/80000]	lr: 3.750e-05, eta: 3:13:38, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2245, decode.acc_seg: 90.9073, loss: 0.2245
+2023-03-04 22:12:11,280 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:12:11,280 - mmseg - INFO - Iter [26000/80000]	lr: 3.750e-05, eta: 3:13:22, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2229, decode.acc_seg: 90.8225, loss: 0.2229
+2023-03-04 22:12:20,307 - mmseg - INFO - Iter [26050/80000]	lr: 3.750e-05, eta: 3:13:06, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2285, decode.acc_seg: 90.7698, loss: 0.2285
+2023-03-04 22:12:29,190 - mmseg - INFO - Iter [26100/80000]	lr: 3.750e-05, eta: 3:12:50, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2269, decode.acc_seg: 90.7035, loss: 0.2269
+2023-03-04 22:12:38,212 - mmseg - INFO - Iter [26150/80000]	lr: 3.750e-05, eta: 3:12:34, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2270, decode.acc_seg: 90.9766, loss: 0.2270
+2023-03-04 22:12:47,511 - mmseg - INFO - Iter [26200/80000]	lr: 3.750e-05, eta: 3:12:19, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2151, decode.acc_seg: 91.2388, loss: 0.2151
+2023-03-04 22:12:56,355 - mmseg - INFO - Iter [26250/80000]	lr: 3.750e-05, eta: 3:12:03, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2274, decode.acc_seg: 90.9189, loss: 0.2274
+2023-03-04 22:13:07,723 - mmseg - INFO - Iter [26300/80000]	lr: 3.750e-05, eta: 3:11:54, time: 0.227, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 90.7969, loss: 0.2196
+2023-03-04 22:13:17,267 - mmseg - INFO - Iter [26350/80000]	lr: 3.750e-05, eta: 3:11:40, time: 0.191, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2155, decode.acc_seg: 91.1265, loss: 0.2155
+2023-03-04 22:13:26,694 - mmseg - INFO - Iter [26400/80000]	lr: 3.750e-05, eta: 3:11:25, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2293, decode.acc_seg: 90.6828, loss: 0.2293
+2023-03-04 22:13:35,267 - mmseg - INFO - Iter [26450/80000]	lr: 3.750e-05, eta: 3:11:08, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2255, decode.acc_seg: 90.7630, loss: 0.2255
+2023-03-04 22:13:44,098 - mmseg - INFO - Iter [26500/80000]	lr: 3.750e-05, eta: 3:10:52, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2252, decode.acc_seg: 90.8824, loss: 0.2252
+2023-03-04 22:13:52,956 - mmseg - INFO - Iter [26550/80000]	lr: 3.750e-05, eta: 3:10:36, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2205, decode.acc_seg: 90.9360, loss: 0.2205
+2023-03-04 22:14:01,899 - mmseg - INFO - Iter [26600/80000]	lr: 3.750e-05, eta: 3:10:20, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2275, decode.acc_seg: 90.8218, loss: 0.2275
+2023-03-04 22:14:10,412 - mmseg - INFO - Iter [26650/80000]	lr: 3.750e-05, eta: 3:10:04, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2269, decode.acc_seg: 90.6286, loss: 0.2269
+2023-03-04 22:14:18,986 - mmseg - INFO - Iter [26700/80000]	lr: 3.750e-05, eta: 3:09:47, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2279, decode.acc_seg: 90.7602, loss: 0.2279
+2023-03-04 22:14:27,669 - mmseg - INFO - Iter [26750/80000]	lr: 3.750e-05, eta: 3:09:30, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2315, decode.acc_seg: 90.6802, loss: 0.2315
+2023-03-04 22:14:36,278 - mmseg - INFO - Iter [26800/80000]	lr: 3.750e-05, eta: 3:09:14, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2269, decode.acc_seg: 90.8370, loss: 0.2269
+2023-03-04 22:14:45,020 - mmseg - INFO - Iter [26850/80000]	lr: 3.750e-05, eta: 3:08:58, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2324, decode.acc_seg: 90.6237, loss: 0.2324
+2023-03-04 22:14:54,454 - mmseg - INFO - Iter [26900/80000]	lr: 3.750e-05, eta: 3:08:44, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2293, decode.acc_seg: 90.7841, loss: 0.2293
+2023-03-04 22:15:06,290 - mmseg - INFO - Iter [26950/80000]	lr: 3.750e-05, eta: 3:08:36, time: 0.237, data_time: 0.057, memory: 52390, decode.loss_ce: 0.2249, decode.acc_seg: 91.0213, loss: 0.2249
+2023-03-04 22:15:15,824 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:15:15,825 - mmseg - INFO - Iter [27000/80000]	lr: 3.750e-05, eta: 3:08:22, time: 0.191, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2291, decode.acc_seg: 90.8774, loss: 0.2291
+2023-03-04 22:15:24,785 - mmseg - INFO - Iter [27050/80000]	lr: 3.750e-05, eta: 3:08:07, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2281, decode.acc_seg: 90.7398, loss: 0.2281
+2023-03-04 22:15:33,589 - mmseg - INFO - Iter [27100/80000]	lr: 3.750e-05, eta: 3:07:51, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2257, decode.acc_seg: 90.8538, loss: 0.2257
+2023-03-04 22:15:42,523 - mmseg - INFO - Iter [27150/80000]	lr: 3.750e-05, eta: 3:07:36, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2283, decode.acc_seg: 90.7039, loss: 0.2283
+2023-03-04 22:15:51,376 - mmseg - INFO - Iter [27200/80000]	lr: 3.750e-05, eta: 3:07:20, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2241, decode.acc_seg: 90.9420, loss: 0.2241
+2023-03-04 22:16:00,111 - mmseg - INFO - Iter [27250/80000]	lr: 3.750e-05, eta: 3:07:04, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2222, decode.acc_seg: 90.8394, loss: 0.2222
+2023-03-04 22:16:09,095 - mmseg - INFO - Iter [27300/80000]	lr: 3.750e-05, eta: 3:06:49, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2328, decode.acc_seg: 90.6432, loss: 0.2328
+2023-03-04 22:16:18,036 - mmseg - INFO - Iter [27350/80000]	lr: 3.750e-05, eta: 3:06:34, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2278, decode.acc_seg: 90.7440, loss: 0.2278
+2023-03-04 22:16:26,641 - mmseg - INFO - Iter [27400/80000]	lr: 3.750e-05, eta: 3:06:18, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.2218, loss: 0.2114
+2023-03-04 22:16:35,399 - mmseg - INFO - Iter [27450/80000]	lr: 3.750e-05, eta: 3:06:02, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2177, decode.acc_seg: 91.1981, loss: 0.2177
+2023-03-04 22:16:44,071 - mmseg - INFO - Iter [27500/80000]	lr: 3.750e-05, eta: 3:05:46, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2222, decode.acc_seg: 91.0046, loss: 0.2222
+2023-03-04 22:16:53,019 - mmseg - INFO - Iter [27550/80000]	lr: 3.750e-05, eta: 3:05:31, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2198, decode.acc_seg: 91.1280, loss: 0.2198
+2023-03-04 22:17:04,221 - mmseg - INFO - Iter [27600/80000]	lr: 3.750e-05, eta: 3:05:22, time: 0.224, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2317, decode.acc_seg: 90.6863, loss: 0.2317
+2023-03-04 22:17:13,217 - mmseg - INFO - Iter [27650/80000]	lr: 3.750e-05, eta: 3:05:07, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2215, decode.acc_seg: 90.8965, loss: 0.2215
+2023-03-04 22:17:21,853 - mmseg - INFO - Iter [27700/80000]	lr: 3.750e-05, eta: 3:04:51, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2218, decode.acc_seg: 91.0740, loss: 0.2218
+2023-03-04 22:17:30,943 - mmseg - INFO - Iter [27750/80000]	lr: 3.750e-05, eta: 3:04:37, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2352, decode.acc_seg: 90.7477, loss: 0.2352
+2023-03-04 22:17:40,290 - mmseg - INFO - Iter [27800/80000]	lr: 3.750e-05, eta: 3:04:23, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2232, decode.acc_seg: 90.8676, loss: 0.2232
+2023-03-04 22:17:48,838 - mmseg - INFO - Iter [27850/80000]	lr: 3.750e-05, eta: 3:04:07, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2429, decode.acc_seg: 90.2012, loss: 0.2429
+2023-03-04 22:17:57,582 - mmseg - INFO - Iter [27900/80000]	lr: 3.750e-05, eta: 3:03:52, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2388, decode.acc_seg: 90.4088, loss: 0.2388
+2023-03-04 22:18:06,298 - mmseg - INFO - Iter [27950/80000]	lr: 3.750e-05, eta: 3:03:36, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2252, decode.acc_seg: 90.8142, loss: 0.2252
+2023-03-04 22:18:15,698 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:18:15,699 - mmseg - INFO - Iter [28000/80000]	lr: 3.750e-05, eta: 3:03:22, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2246, decode.acc_seg: 91.1932, loss: 0.2246
+2023-03-04 22:18:24,233 - mmseg - INFO - Iter [28050/80000]	lr: 3.750e-05, eta: 3:03:07, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2270, decode.acc_seg: 90.7911, loss: 0.2270
+2023-03-04 22:18:32,838 - mmseg - INFO - Iter [28100/80000]	lr: 3.750e-05, eta: 3:02:51, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2311, decode.acc_seg: 90.5420, loss: 0.2311
+2023-03-04 22:18:42,045 - mmseg - INFO - Iter [28150/80000]	lr: 3.750e-05, eta: 3:02:37, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2245, decode.acc_seg: 90.8891, loss: 0.2245
+2023-03-04 22:18:53,601 - mmseg - INFO - Iter [28200/80000]	lr: 3.750e-05, eta: 3:02:29, time: 0.231, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2227, decode.acc_seg: 90.9633, loss: 0.2227
+2023-03-04 22:19:02,627 - mmseg - INFO - Iter [28250/80000]	lr: 3.750e-05, eta: 3:02:14, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2258, decode.acc_seg: 90.8696, loss: 0.2258
+2023-03-04 22:19:11,722 - mmseg - INFO - Iter [28300/80000]	lr: 3.750e-05, eta: 3:02:00, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2243, decode.acc_seg: 90.8111, loss: 0.2243
+2023-03-04 22:19:20,398 - mmseg - INFO - Iter [28350/80000]	lr: 3.750e-05, eta: 3:01:45, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2260, decode.acc_seg: 90.8808, loss: 0.2260
+2023-03-04 22:19:29,140 - mmseg - INFO - Iter [28400/80000]	lr: 3.750e-05, eta: 3:01:29, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 91.0418, loss: 0.2200
+2023-03-04 22:19:38,270 - mmseg - INFO - Iter [28450/80000]	lr: 3.750e-05, eta: 3:01:15, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.1748, loss: 0.2128
+2023-03-04 22:19:47,187 - mmseg - INFO - Iter [28500/80000]	lr: 3.750e-05, eta: 3:01:01, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2276, decode.acc_seg: 90.6521, loss: 0.2276
+2023-03-04 22:19:55,875 - mmseg - INFO - Iter [28550/80000]	lr: 3.750e-05, eta: 3:00:45, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2227, decode.acc_seg: 90.8706, loss: 0.2227
+2023-03-04 22:20:04,523 - mmseg - INFO - Iter [28600/80000]	lr: 3.750e-05, eta: 3:00:30, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2284, decode.acc_seg: 90.6040, loss: 0.2284
+2023-03-04 22:20:13,381 - mmseg - INFO - Iter [28650/80000]	lr: 3.750e-05, eta: 3:00:15, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2254, decode.acc_seg: 90.9301, loss: 0.2254
+2023-03-04 22:20:22,015 - mmseg - INFO - Iter [28700/80000]	lr: 3.750e-05, eta: 3:00:00, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2405, decode.acc_seg: 90.2749, loss: 0.2405
+2023-03-04 22:20:31,389 - mmseg - INFO - Iter [28750/80000]	lr: 3.750e-05, eta: 2:59:47, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.3815, loss: 0.2132
+2023-03-04 22:20:41,295 - mmseg - INFO - Iter [28800/80000]	lr: 3.750e-05, eta: 2:59:35, time: 0.198, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2213, decode.acc_seg: 91.0521, loss: 0.2213
+2023-03-04 22:20:52,281 - mmseg - INFO - Iter [28850/80000]	lr: 3.750e-05, eta: 2:59:25, time: 0.220, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2259, decode.acc_seg: 90.8183, loss: 0.2259
+2023-03-04 22:21:00,802 - mmseg - INFO - Iter [28900/80000]	lr: 3.750e-05, eta: 2:59:10, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2369, decode.acc_seg: 90.4460, loss: 0.2369
+2023-03-04 22:21:09,678 - mmseg - INFO - Iter [28950/80000]	lr: 3.750e-05, eta: 2:58:55, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2254, decode.acc_seg: 90.7957, loss: 0.2254
+2023-03-04 22:21:18,185 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:21:18,185 - mmseg - INFO - Iter [29000/80000]	lr: 3.750e-05, eta: 2:58:40, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2362, decode.acc_seg: 90.3573, loss: 0.2362
+2023-03-04 22:21:27,079 - mmseg - INFO - Iter [29050/80000]	lr: 3.750e-05, eta: 2:58:26, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2263, decode.acc_seg: 90.7654, loss: 0.2263
+2023-03-04 22:21:35,973 - mmseg - INFO - Iter [29100/80000]	lr: 3.750e-05, eta: 2:58:11, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2260, decode.acc_seg: 90.7809, loss: 0.2260
+2023-03-04 22:21:44,789 - mmseg - INFO - Iter [29150/80000]	lr: 3.750e-05, eta: 2:57:57, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2240, decode.acc_seg: 90.9701, loss: 0.2240
+2023-03-04 22:21:53,583 - mmseg - INFO - Iter [29200/80000]	lr: 3.750e-05, eta: 2:57:42, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2238, decode.acc_seg: 90.8246, loss: 0.2238
+2023-03-04 22:22:02,419 - mmseg - INFO - Iter [29250/80000]	lr: 3.750e-05, eta: 2:57:28, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.0685, loss: 0.2189
+2023-03-04 22:22:11,076 - mmseg - INFO - Iter [29300/80000]	lr: 3.750e-05, eta: 2:57:13, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2283, decode.acc_seg: 90.6791, loss: 0.2283
+2023-03-04 22:22:19,891 - mmseg - INFO - Iter [29350/80000]	lr: 3.750e-05, eta: 2:56:58, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2223, decode.acc_seg: 90.8330, loss: 0.2223
+2023-03-04 22:22:29,142 - mmseg - INFO - Iter [29400/80000]	lr: 3.750e-05, eta: 2:56:45, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2243, decode.acc_seg: 91.0542, loss: 0.2243
+2023-03-04 22:22:38,332 - mmseg - INFO - Iter [29450/80000]	lr: 3.750e-05, eta: 2:56:31, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2286, decode.acc_seg: 90.8432, loss: 0.2286
+2023-03-04 22:22:49,582 - mmseg - INFO - Iter [29500/80000]	lr: 3.750e-05, eta: 2:56:23, time: 0.225, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2386, decode.acc_seg: 90.4632, loss: 0.2386
+2023-03-04 22:22:58,758 - mmseg - INFO - Iter [29550/80000]	lr: 3.750e-05, eta: 2:56:09, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2301, decode.acc_seg: 90.5910, loss: 0.2301
+2023-03-04 22:23:07,771 - mmseg - INFO - Iter [29600/80000]	lr: 3.750e-05, eta: 2:55:55, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2339, decode.acc_seg: 90.6082, loss: 0.2339
+2023-03-04 22:23:16,402 - mmseg - INFO - Iter [29650/80000]	lr: 3.750e-05, eta: 2:55:41, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2180, decode.acc_seg: 91.0996, loss: 0.2180
+2023-03-04 22:23:25,442 - mmseg - INFO - Iter [29700/80000]	lr: 3.750e-05, eta: 2:55:27, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2260, decode.acc_seg: 90.8336, loss: 0.2260
+2023-03-04 22:23:34,197 - mmseg - INFO - Iter [29750/80000]	lr: 3.750e-05, eta: 2:55:12, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2287, decode.acc_seg: 90.6887, loss: 0.2287
+2023-03-04 22:23:42,814 - mmseg - INFO - Iter [29800/80000]	lr: 3.750e-05, eta: 2:54:58, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2283, decode.acc_seg: 90.6967, loss: 0.2283
+2023-03-04 22:23:51,530 - mmseg - INFO - Iter [29850/80000]	lr: 3.750e-05, eta: 2:54:43, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2255, decode.acc_seg: 90.8018, loss: 0.2255
+2023-03-04 22:24:00,990 - mmseg - INFO - Iter [29900/80000]	lr: 3.750e-05, eta: 2:54:31, time: 0.189, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2203, decode.acc_seg: 91.0016, loss: 0.2203
+2023-03-04 22:24:09,978 - mmseg - INFO - Iter [29950/80000]	lr: 3.750e-05, eta: 2:54:17, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2266, decode.acc_seg: 90.9260, loss: 0.2266
+2023-03-04 22:24:18,709 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:24:18,710 - mmseg - INFO - Iter [30000/80000]	lr: 3.750e-05, eta: 2:54:02, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2248, decode.acc_seg: 90.8778, loss: 0.2248
+2023-03-04 22:24:27,391 - mmseg - INFO - Iter [30050/80000]	lr: 1.875e-05, eta: 2:53:48, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2260, decode.acc_seg: 90.9266, loss: 0.2260
+2023-03-04 22:24:38,540 - mmseg - INFO - Iter [30100/80000]	lr: 1.875e-05, eta: 2:53:39, time: 0.223, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 90.9933, loss: 0.2171
+2023-03-04 22:24:47,517 - mmseg - INFO - Iter [30150/80000]	lr: 1.875e-05, eta: 2:53:25, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2304, decode.acc_seg: 90.7407, loss: 0.2304
+2023-03-04 22:24:56,853 - mmseg - INFO - Iter [30200/80000]	lr: 1.875e-05, eta: 2:53:12, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2249, decode.acc_seg: 90.9029, loss: 0.2249
+2023-03-04 22:25:05,488 - mmseg - INFO - Iter [30250/80000]	lr: 1.875e-05, eta: 2:52:58, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2149, decode.acc_seg: 91.0975, loss: 0.2149
+2023-03-04 22:25:14,192 - mmseg - INFO - Iter [30300/80000]	lr: 1.875e-05, eta: 2:52:44, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2220, decode.acc_seg: 90.8498, loss: 0.2220
+2023-03-04 22:25:23,042 - mmseg - INFO - Iter [30350/80000]	lr: 1.875e-05, eta: 2:52:30, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.3184, loss: 0.2103
+2023-03-04 22:25:32,585 - mmseg - INFO - Iter [30400/80000]	lr: 1.875e-05, eta: 2:52:17, time: 0.191, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.2494, loss: 0.2139
+2023-03-04 22:25:41,596 - mmseg - INFO - Iter [30450/80000]	lr: 1.875e-05, eta: 2:52:04, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2271, decode.acc_seg: 90.8270, loss: 0.2271
+2023-03-04 22:25:51,139 - mmseg - INFO - Iter [30500/80000]	lr: 1.875e-05, eta: 2:51:52, time: 0.191, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2164, decode.acc_seg: 91.1309, loss: 0.2164
+2023-03-04 22:25:59,859 - mmseg - INFO - Iter [30550/80000]	lr: 1.875e-05, eta: 2:51:37, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2263, decode.acc_seg: 90.7484, loss: 0.2263
+2023-03-04 22:26:08,822 - mmseg - INFO - Iter [30600/80000]	lr: 1.875e-05, eta: 2:51:24, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2063, decode.acc_seg: 91.3936, loss: 0.2063
+2023-03-04 22:26:17,924 - mmseg - INFO - Iter [30650/80000]	lr: 1.875e-05, eta: 2:51:11, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2087, decode.acc_seg: 91.4475, loss: 0.2087
+2023-03-04 22:26:26,556 - mmseg - INFO - Iter [30700/80000]	lr: 1.875e-05, eta: 2:50:56, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 91.0828, loss: 0.2200
+2023-03-04 22:26:37,729 - mmseg - INFO - Iter [30750/80000]	lr: 1.875e-05, eta: 2:50:48, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2203, decode.acc_seg: 91.1178, loss: 0.2203
+2023-03-04 22:26:46,764 - mmseg - INFO - Iter [30800/80000]	lr: 1.875e-05, eta: 2:50:34, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2122, decode.acc_seg: 91.4311, loss: 0.2122
+2023-03-04 22:26:55,901 - mmseg - INFO - Iter [30850/80000]	lr: 1.875e-05, eta: 2:50:21, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2115, decode.acc_seg: 91.3756, loss: 0.2115
+2023-03-04 22:27:04,800 - mmseg - INFO - Iter [30900/80000]	lr: 1.875e-05, eta: 2:50:08, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2183, decode.acc_seg: 91.1035, loss: 0.2183
+2023-03-04 22:27:13,437 - mmseg - INFO - Iter [30950/80000]	lr: 1.875e-05, eta: 2:49:53, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2286, decode.acc_seg: 90.8342, loss: 0.2286
+2023-03-04 22:27:22,190 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:27:22,190 - mmseg - INFO - Iter [31000/80000]	lr: 1.875e-05, eta: 2:49:39, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2274, decode.acc_seg: 90.7450, loss: 0.2274
+2023-03-04 22:27:30,974 - mmseg - INFO - Iter [31050/80000]	lr: 1.875e-05, eta: 2:49:26, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.1823, loss: 0.2171
+2023-03-04 22:27:39,932 - mmseg - INFO - Iter [31100/80000]	lr: 1.875e-05, eta: 2:49:12, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2309, decode.acc_seg: 90.6342, loss: 0.2309
+2023-03-04 22:27:48,921 - mmseg - INFO - Iter [31150/80000]	lr: 1.875e-05, eta: 2:48:59, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2241, decode.acc_seg: 90.9442, loss: 0.2241
+2023-03-04 22:27:57,950 - mmseg - INFO - Iter [31200/80000]	lr: 1.875e-05, eta: 2:48:46, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2203, decode.acc_seg: 91.1327, loss: 0.2203
+2023-03-04 22:28:06,749 - mmseg - INFO - Iter [31250/80000]	lr: 1.875e-05, eta: 2:48:32, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2230, decode.acc_seg: 90.9598, loss: 0.2230
+2023-03-04 22:28:15,302 - mmseg - INFO - Iter [31300/80000]	lr: 1.875e-05, eta: 2:48:18, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2226, decode.acc_seg: 90.7734, loss: 0.2226
+2023-03-04 22:28:26,787 - mmseg - INFO - Iter [31350/80000]	lr: 1.875e-05, eta: 2:48:10, time: 0.229, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.1966, loss: 0.2127
+2023-03-04 22:28:35,480 - mmseg - INFO - Iter [31400/80000]	lr: 1.875e-05, eta: 2:47:56, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2155, decode.acc_seg: 91.2143, loss: 0.2155
+2023-03-04 22:28:44,434 - mmseg - INFO - Iter [31450/80000]	lr: 1.875e-05, eta: 2:47:43, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2112, decode.acc_seg: 91.3110, loss: 0.2112
+2023-03-04 22:28:53,465 - mmseg - INFO - Iter [31500/80000]	lr: 1.875e-05, eta: 2:47:30, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.0963, loss: 0.2165
+2023-03-04 22:29:02,500 - mmseg - INFO - Iter [31550/80000]	lr: 1.875e-05, eta: 2:47:16, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2210, decode.acc_seg: 91.0376, loss: 0.2210
+2023-03-04 22:29:11,889 - mmseg - INFO - Iter [31600/80000]	lr: 1.875e-05, eta: 2:47:04, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.1495, loss: 0.2163
+2023-03-04 22:29:20,806 - mmseg - INFO - Iter [31650/80000]	lr: 1.875e-05, eta: 2:46:51, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.0530, loss: 0.2140
+2023-03-04 22:29:29,676 - mmseg - INFO - Iter [31700/80000]	lr: 1.875e-05, eta: 2:46:37, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.0940, loss: 0.2168
+2023-03-04 22:29:38,781 - mmseg - INFO - Iter [31750/80000]	lr: 1.875e-05, eta: 2:46:25, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2252, decode.acc_seg: 90.8659, loss: 0.2252
+2023-03-04 22:29:47,855 - mmseg - INFO - Iter [31800/80000]	lr: 1.875e-05, eta: 2:46:12, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2169, decode.acc_seg: 91.1824, loss: 0.2169
+2023-03-04 22:29:56,676 - mmseg - INFO - Iter [31850/80000]	lr: 1.875e-05, eta: 2:45:58, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2216, decode.acc_seg: 91.0426, loss: 0.2216
+2023-03-04 22:30:05,441 - mmseg - INFO - Iter [31900/80000]	lr: 1.875e-05, eta: 2:45:45, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2241, decode.acc_seg: 90.9069, loss: 0.2241
+2023-03-04 22:30:14,905 - mmseg - INFO - Iter [31950/80000]	lr: 1.875e-05, eta: 2:45:33, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2144, decode.acc_seg: 91.1203, loss: 0.2144
+2023-03-04 22:30:25,981 - mmseg - INFO - Saving checkpoint at 32000 iterations
+2023-03-04 22:30:26,804 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:30:26,805 - mmseg - INFO - Iter [32000/80000]	lr: 1.875e-05, eta: 2:45:25, time: 0.238, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2337, decode.acc_seg: 90.6063, loss: 0.2337
+2023-03-04 22:30:42,386 - mmseg - INFO - per class results:
+2023-03-04 22:30:42,392 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.47 | 87.22 |
+|       building      | 81.04 | 91.99 |
+|         sky         | 94.18 | 97.73 |
+|        floor        | 80.74 | 91.08 |
+|         tree        | 72.89 | 86.73 |
+|       ceiling       |  83.3 | 93.92 |
+|         road        | 81.71 | 90.08 |
+|         bed         | 86.39 | 94.57 |
+|      windowpane     | 59.17 | 77.93 |
+|        grass        | 64.67 | 79.06 |
+|       cabinet       | 59.59 | 72.74 |
+|       sidewalk      | 63.32 | 79.04 |
+|        person       | 77.46 | 92.18 |
+|        earth        | 35.09 |  47.8 |
+|         door        | 44.17 | 57.68 |
+|        table        | 57.57 | 75.28 |
+|       mountain      | 56.14 | 73.11 |
+|        plant        | 48.24 | 57.35 |
+|       curtain       | 73.21 | 82.57 |
+|        chair        | 53.32 | 66.53 |
+|         car         | 80.33 | 91.77 |
+|        water        |  57.7 | 77.29 |
+|       painting      | 68.75 | 84.64 |
+|         sofa        | 61.85 | 81.78 |
+|        shelf        | 41.23 | 55.94 |
+|        house        | 43.33 | 61.41 |
+|         sea         | 60.72 | 74.53 |
+|        mirror       | 63.85 | 72.94 |
+|         rug         | 64.94 | 74.44 |
+|        field        | 27.77 | 46.17 |
+|       armchair      | 35.48 | 51.86 |
+|         seat        |  64.6 | 83.99 |
+|        fence        | 42.37 | 56.82 |
+|         desk        | 46.22 | 66.47 |
+|         rock        | 35.47 | 56.39 |
+|       wardrobe      | 56.06 | 67.35 |
+|         lamp        | 58.03 | 70.63 |
+|       bathtub       | 74.85 |  82.6 |
+|       railing       |  33.5 | 46.66 |
+|       cushion       | 53.47 | 72.42 |
+|         base        | 23.81 | 29.14 |
+|         box         | 22.87 | 33.05 |
+|        column       | 44.15 | 61.94 |
+|      signboard      | 34.88 | 42.08 |
+|   chest of drawers  | 36.14 | 58.57 |
+|       counter       |  33.7 | 47.79 |
+|         sand        | 42.63 | 61.86 |
+|         sink        | 63.82 | 78.17 |
+|      skyscraper     | 52.95 | 66.51 |
+|      fireplace      | 73.63 | 82.69 |
+|     refrigerator    | 68.73 | 84.81 |
+|      grandstand     | 46.02 | 68.53 |
+|         path        | 21.93 | 29.59 |
+|        stairs       | 30.53 | 39.11 |
+|        runway       | 67.08 | 85.72 |
+|         case        | 48.85 | 57.01 |
+|      pool table     | 90.99 | 94.14 |
+|        pillow       | 55.07 | 64.23 |
+|     screen door     | 67.26 | 76.09 |
+|       stairway      | 24.35 | 38.39 |
+|        river        | 11.41 | 20.15 |
+|        bridge       | 31.08 | 35.74 |
+|       bookcase      | 40.37 | 62.23 |
+|        blind        | 38.26 | 42.42 |
+|     coffee table    | 52.32 | 77.48 |
+|        toilet       | 80.05 | 90.08 |
+|        flower       | 36.98 | 53.09 |
+|         book        | 41.82 | 63.43 |
+|         hill        | 14.14 | 25.86 |
+|        bench        |  40.0 |  52.2 |
+|      countertop     |  52.3 | 73.86 |
+|        stove        | 69.45 | 81.73 |
+|         palm        | 46.71 |  62.8 |
+|    kitchen island   | 36.89 |  62.0 |
+|       computer      | 59.29 | 67.15 |
+|     swivel chair    | 43.15 | 58.45 |
+|         boat        | 68.78 | 79.68 |
+|         bar         | 20.23 | 26.68 |
+|    arcade machine   | 69.41 | 71.89 |
+|        hovel        | 27.73 | 31.45 |
+|         bus         | 77.05 | 90.53 |
+|        towel        | 60.29 | 68.66 |
+|        light        | 48.62 | 56.69 |
+|        truck        | 14.39 |  19.6 |
+|        tower        |  6.35 | 10.03 |
+|      chandelier     | 61.67 | 78.05 |
+|        awning       | 21.04 | 23.91 |
+|     streetlight     | 20.25 | 25.01 |
+|        booth        | 43.34 | 45.27 |
+| television receiver | 64.28 | 73.73 |
+|       airplane      | 56.64 | 61.83 |
+|      dirt track     | 12.47 | 38.28 |
+|       apparel       | 33.06 | 60.89 |
+|         pole        | 14.08 | 17.23 |
+|         land        |  1.74 |  2.14 |
+|      bannister      |  10.4 | 14.45 |
+|      escalator      | 24.54 | 27.07 |
+|       ottoman       | 41.52 | 62.83 |
+|        bottle       | 35.11 |  60.3 |
+|        buffet       | 41.83 | 48.69 |
+|        poster       | 21.79 | 30.29 |
+|        stage        | 14.35 |  18.7 |
+|         van         | 37.77 | 50.37 |
+|         ship        |  74.1 | 93.94 |
+|       fountain      |  11.0 | 11.31 |
+|    conveyer belt    | 79.53 | 89.33 |
+|        canopy       | 23.84 |  25.1 |
+|        washer       | 81.48 | 84.43 |
+|      plaything      | 19.64 | 26.31 |
+|    swimming pool    | 73.88 | 79.73 |
+|        stool        | 40.49 | 50.15 |
+|        barrel       | 40.27 | 54.23 |
+|        basket       | 23.41 | 34.26 |
+|      waterfall      | 51.92 | 68.38 |
+|         tent        | 90.09 | 98.25 |
+|         bag         | 17.17 | 23.23 |
+|       minibike      |  58.3 | 68.41 |
+|        cradle       | 82.93 | 95.23 |
+|         oven        | 46.91 | 54.05 |
+|         ball        | 40.08 | 46.08 |
+|         food        | 51.49 | 61.74 |
+|         step        |  9.24 | 10.48 |
+|         tank        | 49.88 | 55.54 |
+|      trade name     | 20.91 | 21.93 |
+|      microwave      | 77.45 | 85.39 |
+|         pot         | 28.44 | 32.16 |
+|        animal       | 52.78 | 57.28 |
+|       bicycle       | 50.26 | 61.91 |
+|         lake        | 57.37 | 63.19 |
+|      dishwasher     | 66.31 | 74.75 |
+|        screen       | 64.67 |  80.1 |
+|       blanket       | 14.01 | 15.78 |
+|      sculpture      | 55.24 | 79.25 |
+|         hood        | 53.67 |  58.0 |
+|        sconce       |  40.8 | 50.13 |
+|         vase        | 29.73 | 49.94 |
+|    traffic light    | 22.99 |  27.0 |
+|         tray        |  3.85 |  5.44 |
+|        ashcan       | 39.18 | 46.24 |
+|         fan         | 54.48 | 64.32 |
+|         pier        | 47.41 | 68.37 |
+|      crt screen     |  9.02 | 27.11 |
+|        plate        |  47.3 | 63.35 |
+|       monitor       |  6.93 |  7.86 |
+|    bulletin board   | 43.27 | 59.85 |
+|        shower       |  1.25 |  4.63 |
+|       radiator      | 55.09 |  60.4 |
+|        glass        |  9.01 |  9.56 |
+|        clock        | 25.51 | 26.48 |
+|         flag        | 35.14 | 39.46 |
++---------------------+-------+-------+
+2023-03-04 22:30:42,392 - mmseg - INFO - Summary:
+2023-03-04 22:30:42,392 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.98 | 46.59 | 57.75 |
++-------+-------+-------+
+2023-03-04 22:30:42,413 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_24000.pth was removed
+2023-03-04 22:30:43,012 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth.
+2023-03-04 22:30:43,013 - mmseg - INFO - Best mIoU is 0.4659 at 32000 iter.
+2023-03-04 22:30:43,013 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:30:43,013 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8198, mIoU: 0.4659, mAcc: 0.5775, IoU.background: nan, IoU.wall: 0.7647, IoU.building: 0.8104, IoU.sky: 0.9418, IoU.floor: 0.8074, IoU.tree: 0.7289, IoU.ceiling: 0.8330, IoU.road: 0.8171, IoU.bed : 0.8639, IoU.windowpane: 0.5917, IoU.grass: 0.6467, IoU.cabinet: 0.5959, IoU.sidewalk: 0.6332, IoU.person: 0.7746, IoU.earth: 0.3509, IoU.door: 0.4417, IoU.table: 0.5757, IoU.mountain: 0.5614, IoU.plant: 0.4824, IoU.curtain: 0.7321, IoU.chair: 0.5332, IoU.car: 0.8033, IoU.water: 0.5770, IoU.painting: 0.6875, IoU.sofa: 0.6185, IoU.shelf: 0.4123, IoU.house: 0.4333, IoU.sea: 0.6072, IoU.mirror: 0.6385, IoU.rug: 0.6494, IoU.field: 0.2777, IoU.armchair: 0.3548, IoU.seat: 0.6460, IoU.fence: 0.4237, IoU.desk: 0.4622, IoU.rock: 0.3547, IoU.wardrobe: 0.5606, IoU.lamp: 0.5803, IoU.bathtub: 0.7485, IoU.railing: 0.3350, IoU.cushion: 0.5347, IoU.base: 0.2381, IoU.box: 0.2287, IoU.column: 0.4415, IoU.signboard: 0.3488, IoU.chest of drawers: 0.3614, IoU.counter: 0.3370, IoU.sand: 0.4263, IoU.sink: 0.6382, IoU.skyscraper: 0.5295, IoU.fireplace: 0.7363, IoU.refrigerator: 0.6873, IoU.grandstand: 0.4602, IoU.path: 0.2193, IoU.stairs: 0.3053, IoU.runway: 0.6708, IoU.case: 0.4885, IoU.pool table: 0.9099, IoU.pillow: 0.5507, IoU.screen door: 0.6726, IoU.stairway: 0.2435, IoU.river: 0.1141, IoU.bridge: 0.3108, IoU.bookcase: 0.4037, IoU.blind: 0.3826, IoU.coffee table: 0.5232, IoU.toilet: 0.8005, IoU.flower: 0.3698, IoU.book: 0.4182, IoU.hill: 0.1414, IoU.bench: 0.4000, IoU.countertop: 0.5230, IoU.stove: 0.6945, IoU.palm: 0.4671, IoU.kitchen island: 0.3689, IoU.computer: 0.5929, IoU.swivel chair: 0.4315, IoU.boat: 0.6878, IoU.bar: 0.2023, IoU.arcade machine: 0.6941, IoU.hovel: 0.2773, IoU.bus: 0.7705, IoU.towel: 0.6029, IoU.light: 0.4862, IoU.truck: 0.1439, IoU.tower: 0.0635, IoU.chandelier: 0.6167, IoU.awning: 0.2104, IoU.streetlight: 0.2025, IoU.booth: 0.4334, IoU.television receiver: 0.6428, IoU.airplane: 0.5664, IoU.dirt track: 0.1247, IoU.apparel: 0.3306, IoU.pole: 0.1408, IoU.land: 0.0174, IoU.bannister: 0.1040, IoU.escalator: 0.2454, IoU.ottoman: 0.4152, IoU.bottle: 0.3511, IoU.buffet: 0.4183, IoU.poster: 0.2179, IoU.stage: 0.1435, IoU.van: 0.3777, IoU.ship: 0.7410, IoU.fountain: 0.1100, IoU.conveyer belt: 0.7953, IoU.canopy: 0.2384, IoU.washer: 0.8148, IoU.plaything: 0.1964, IoU.swimming pool: 0.7388, IoU.stool: 0.4049, IoU.barrel: 0.4027, IoU.basket: 0.2341, IoU.waterfall: 0.5192, IoU.tent: 0.9009, IoU.bag: 0.1717, IoU.minibike: 0.5830, IoU.cradle: 0.8293, IoU.oven: 0.4691, IoU.ball: 0.4008, IoU.food: 0.5149, IoU.step: 0.0924, IoU.tank: 0.4988, IoU.trade name: 0.2091, IoU.microwave: 0.7745, IoU.pot: 0.2844, IoU.animal: 0.5278, IoU.bicycle: 0.5026, IoU.lake: 0.5737, IoU.dishwasher: 0.6631, IoU.screen: 0.6467, IoU.blanket: 0.1401, IoU.sculpture: 0.5524, IoU.hood: 0.5367, IoU.sconce: 0.4080, IoU.vase: 0.2973, IoU.traffic light: 0.2299, IoU.tray: 0.0385, IoU.ashcan: 0.3918, IoU.fan: 0.5448, IoU.pier: 0.4741, IoU.crt screen: 0.0902, IoU.plate: 0.4730, IoU.monitor: 0.0693, IoU.bulletin board: 0.4327, IoU.shower: 0.0125, IoU.radiator: 0.5509, IoU.glass: 0.0901, IoU.clock: 0.2551, IoU.flag: 0.3514, Acc.background: nan, Acc.wall: 0.8722, Acc.building: 0.9199, Acc.sky: 0.9773, Acc.floor: 0.9108, Acc.tree: 0.8673, Acc.ceiling: 0.9392, Acc.road: 0.9008, Acc.bed : 0.9457, Acc.windowpane: 0.7793, Acc.grass: 0.7906, Acc.cabinet: 0.7274, Acc.sidewalk: 0.7904, Acc.person: 0.9218, Acc.earth: 0.4780, Acc.door: 0.5768, Acc.table: 0.7528, Acc.mountain: 0.7311, Acc.plant: 0.5735, Acc.curtain: 0.8257, Acc.chair: 0.6653, Acc.car: 0.9177, Acc.water: 0.7729, Acc.painting: 0.8464, Acc.sofa: 0.8178, Acc.shelf: 0.5594, Acc.house: 0.6141, Acc.sea: 0.7453, Acc.mirror: 0.7294, Acc.rug: 0.7444, Acc.field: 0.4617, Acc.armchair: 0.5186, Acc.seat: 0.8399, Acc.fence: 0.5682, Acc.desk: 0.6647, Acc.rock: 0.5639, Acc.wardrobe: 0.6735, Acc.lamp: 0.7063, Acc.bathtub: 0.8260, Acc.railing: 0.4666, Acc.cushion: 0.7242, Acc.base: 0.2914, Acc.box: 0.3305, Acc.column: 0.6194, Acc.signboard: 0.4208, Acc.chest of drawers: 0.5857, Acc.counter: 0.4779, Acc.sand: 0.6186, Acc.sink: 0.7817, Acc.skyscraper: 0.6651, Acc.fireplace: 0.8269, Acc.refrigerator: 0.8481, Acc.grandstand: 0.6853, Acc.path: 0.2959, Acc.stairs: 0.3911, Acc.runway: 0.8572, Acc.case: 0.5701, Acc.pool table: 0.9414, Acc.pillow: 0.6423, Acc.screen door: 0.7609, Acc.stairway: 0.3839, Acc.river: 0.2015, Acc.bridge: 0.3574, Acc.bookcase: 0.6223, Acc.blind: 0.4242, Acc.coffee table: 0.7748, Acc.toilet: 0.9008, Acc.flower: 0.5309, Acc.book: 0.6343, Acc.hill: 0.2586, Acc.bench: 0.5220, Acc.countertop: 0.7386, Acc.stove: 0.8173, Acc.palm: 0.6280, Acc.kitchen island: 0.6200, Acc.computer: 0.6715, Acc.swivel chair: 0.5845, Acc.boat: 0.7968, Acc.bar: 0.2668, Acc.arcade machine: 0.7189, Acc.hovel: 0.3145, Acc.bus: 0.9053, Acc.towel: 0.6866, Acc.light: 0.5669, Acc.truck: 0.1960, Acc.tower: 0.1003, Acc.chandelier: 0.7805, Acc.awning: 0.2391, Acc.streetlight: 0.2501, Acc.booth: 0.4527, Acc.television receiver: 0.7373, Acc.airplane: 0.6183, Acc.dirt track: 0.3828, Acc.apparel: 0.6089, Acc.pole: 0.1723, Acc.land: 0.0214, Acc.bannister: 0.1445, Acc.escalator: 0.2707, Acc.ottoman: 0.6283, Acc.bottle: 0.6030, Acc.buffet: 0.4869, Acc.poster: 0.3029, Acc.stage: 0.1870, Acc.van: 0.5037, Acc.ship: 0.9394, Acc.fountain: 0.1131, Acc.conveyer belt: 0.8933, Acc.canopy: 0.2510, Acc.washer: 0.8443, Acc.plaything: 0.2631, Acc.swimming pool: 0.7973, Acc.stool: 0.5015, Acc.barrel: 0.5423, Acc.basket: 0.3426, Acc.waterfall: 0.6838, Acc.tent: 0.9825, Acc.bag: 0.2323, Acc.minibike: 0.6841, Acc.cradle: 0.9523, Acc.oven: 0.5405, Acc.ball: 0.4608, Acc.food: 0.6174, Acc.step: 0.1048, Acc.tank: 0.5554, Acc.trade name: 0.2193, Acc.microwave: 0.8539, Acc.pot: 0.3216, Acc.animal: 0.5728, Acc.bicycle: 0.6191, Acc.lake: 0.6319, Acc.dishwasher: 0.7475, Acc.screen: 0.8010, Acc.blanket: 0.1578, Acc.sculpture: 0.7925, Acc.hood: 0.5800, Acc.sconce: 0.5013, Acc.vase: 0.4994, Acc.traffic light: 0.2700, Acc.tray: 0.0544, Acc.ashcan: 0.4624, Acc.fan: 0.6432, Acc.pier: 0.6837, Acc.crt screen: 0.2711, Acc.plate: 0.6335, Acc.monitor: 0.0786, Acc.bulletin board: 0.5985, Acc.shower: 0.0463, Acc.radiator: 0.6040, Acc.glass: 0.0956, Acc.clock: 0.2648, Acc.flag: 0.3946
+2023-03-04 22:30:52,426 - mmseg - INFO - Iter [32050/80000]	lr: 1.875e-05, eta: 2:45:46, time: 0.512, data_time: 0.331, memory: 52390, decode.loss_ce: 0.2152, decode.acc_seg: 91.2146, loss: 0.2152
+2023-03-04 22:31:01,509 - mmseg - INFO - Iter [32100/80000]	lr: 1.875e-05, eta: 2:45:33, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2148, decode.acc_seg: 91.2789, loss: 0.2148
+2023-03-04 22:31:10,579 - mmseg - INFO - Iter [32150/80000]	lr: 1.875e-05, eta: 2:45:20, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2284, decode.acc_seg: 90.7256, loss: 0.2284
+2023-03-04 22:31:19,220 - mmseg - INFO - Iter [32200/80000]	lr: 1.875e-05, eta: 2:45:06, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.3518, loss: 0.2111
+2023-03-04 22:31:28,351 - mmseg - INFO - Iter [32250/80000]	lr: 1.875e-05, eta: 2:44:53, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.1527, loss: 0.2154
+2023-03-04 22:31:37,220 - mmseg - INFO - Iter [32300/80000]	lr: 1.875e-05, eta: 2:44:40, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.1565, loss: 0.2161
+2023-03-04 22:31:46,292 - mmseg - INFO - Iter [32350/80000]	lr: 1.875e-05, eta: 2:44:27, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.3776, loss: 0.2127
+2023-03-04 22:31:54,917 - mmseg - INFO - Iter [32400/80000]	lr: 1.875e-05, eta: 2:44:13, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2173, decode.acc_seg: 91.1447, loss: 0.2173
+2023-03-04 22:32:04,287 - mmseg - INFO - Iter [32450/80000]	lr: 1.875e-05, eta: 2:44:01, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2244, decode.acc_seg: 90.8485, loss: 0.2244
+2023-03-04 22:32:12,927 - mmseg - INFO - Iter [32500/80000]	lr: 1.875e-05, eta: 2:43:47, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2177, decode.acc_seg: 91.1001, loss: 0.2177
+2023-03-04 22:32:21,492 - mmseg - INFO - Iter [32550/80000]	lr: 1.875e-05, eta: 2:43:34, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2252, decode.acc_seg: 90.9435, loss: 0.2252
+2023-03-04 22:32:30,088 - mmseg - INFO - Iter [32600/80000]	lr: 1.875e-05, eta: 2:43:20, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2344, decode.acc_seg: 90.5792, loss: 0.2344
+2023-03-04 22:32:41,371 - mmseg - INFO - Iter [32650/80000]	lr: 1.875e-05, eta: 2:43:11, time: 0.226, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2249, decode.acc_seg: 90.8984, loss: 0.2249
+2023-03-04 22:32:50,018 - mmseg - INFO - Iter [32700/80000]	lr: 1.875e-05, eta: 2:42:58, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2147, decode.acc_seg: 91.3366, loss: 0.2147
+2023-03-04 22:32:59,237 - mmseg - INFO - Iter [32750/80000]	lr: 1.875e-05, eta: 2:42:45, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 90.9553, loss: 0.2196
+2023-03-04 22:33:08,324 - mmseg - INFO - Iter [32800/80000]	lr: 1.875e-05, eta: 2:42:33, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2152, decode.acc_seg: 91.2358, loss: 0.2152
+2023-03-04 22:33:16,977 - mmseg - INFO - Iter [32850/80000]	lr: 1.875e-05, eta: 2:42:19, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2283, decode.acc_seg: 90.7135, loss: 0.2283
+2023-03-04 22:33:25,568 - mmseg - INFO - Iter [32900/80000]	lr: 1.875e-05, eta: 2:42:05, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.2260, loss: 0.2128
+2023-03-04 22:33:34,748 - mmseg - INFO - Iter [32950/80000]	lr: 1.875e-05, eta: 2:41:53, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2187, decode.acc_seg: 91.2253, loss: 0.2187
+2023-03-04 22:33:43,428 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:33:43,429 - mmseg - INFO - Iter [33000/80000]	lr: 1.875e-05, eta: 2:41:40, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2235, decode.acc_seg: 91.0319, loss: 0.2235
+2023-03-04 22:33:52,604 - mmseg - INFO - Iter [33050/80000]	lr: 1.875e-05, eta: 2:41:27, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2169, decode.acc_seg: 91.2653, loss: 0.2169
+2023-03-04 22:34:01,607 - mmseg - INFO - Iter [33100/80000]	lr: 1.875e-05, eta: 2:41:14, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2185, decode.acc_seg: 91.0301, loss: 0.2185
+2023-03-04 22:34:10,650 - mmseg - INFO - Iter [33150/80000]	lr: 1.875e-05, eta: 2:41:02, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2144, decode.acc_seg: 91.2462, loss: 0.2144
+2023-03-04 22:34:20,092 - mmseg - INFO - Iter [33200/80000]	lr: 1.875e-05, eta: 2:40:50, time: 0.189, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2180, decode.acc_seg: 91.0588, loss: 0.2180
+2023-03-04 22:34:31,361 - mmseg - INFO - Iter [33250/80000]	lr: 1.875e-05, eta: 2:40:41, time: 0.225, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2173, decode.acc_seg: 90.9803, loss: 0.2173
+2023-03-04 22:34:40,887 - mmseg - INFO - Iter [33300/80000]	lr: 1.875e-05, eta: 2:40:29, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2244, decode.acc_seg: 91.0236, loss: 0.2244
+2023-03-04 22:34:50,245 - mmseg - INFO - Iter [33350/80000]	lr: 1.875e-05, eta: 2:40:17, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2177, decode.acc_seg: 91.0898, loss: 0.2177
+2023-03-04 22:34:59,164 - mmseg - INFO - Iter [33400/80000]	lr: 1.875e-05, eta: 2:40:04, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2198, decode.acc_seg: 90.9474, loss: 0.2198
+2023-03-04 22:35:08,150 - mmseg - INFO - Iter [33450/80000]	lr: 1.875e-05, eta: 2:39:52, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2232, decode.acc_seg: 91.1457, loss: 0.2232
+2023-03-04 22:35:17,727 - mmseg - INFO - Iter [33500/80000]	lr: 1.875e-05, eta: 2:39:40, time: 0.191, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2226, decode.acc_seg: 90.7943, loss: 0.2226
+2023-03-04 22:35:26,578 - mmseg - INFO - Iter [33550/80000]	lr: 1.875e-05, eta: 2:39:27, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2246, decode.acc_seg: 90.9592, loss: 0.2246
+2023-03-04 22:35:35,164 - mmseg - INFO - Iter [33600/80000]	lr: 1.875e-05, eta: 2:39:14, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.1779, loss: 0.2143
+2023-03-04 22:35:43,877 - mmseg - INFO - Iter [33650/80000]	lr: 1.875e-05, eta: 2:39:01, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2184, decode.acc_seg: 91.1574, loss: 0.2184
+2023-03-04 22:35:52,933 - mmseg - INFO - Iter [33700/80000]	lr: 1.875e-05, eta: 2:38:48, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.1968, loss: 0.2163
+2023-03-04 22:36:01,898 - mmseg - INFO - Iter [33750/80000]	lr: 1.875e-05, eta: 2:38:35, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2137, decode.acc_seg: 91.2231, loss: 0.2137
+2023-03-04 22:36:10,697 - mmseg - INFO - Iter [33800/80000]	lr: 1.875e-05, eta: 2:38:22, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2133, decode.acc_seg: 91.3463, loss: 0.2133
+2023-03-04 22:36:19,689 - mmseg - INFO - Iter [33850/80000]	lr: 1.875e-05, eta: 2:38:10, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.3510, loss: 0.2120
+2023-03-04 22:36:31,232 - mmseg - INFO - Iter [33900/80000]	lr: 1.875e-05, eta: 2:38:02, time: 0.231, data_time: 0.057, memory: 52390, decode.loss_ce: 0.2164, decode.acc_seg: 91.2669, loss: 0.2164
+2023-03-04 22:36:39,930 - mmseg - INFO - Iter [33950/80000]	lr: 1.875e-05, eta: 2:37:49, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2267, decode.acc_seg: 90.7576, loss: 0.2267
+2023-03-04 22:36:48,713 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:36:48,714 - mmseg - INFO - Iter [34000/80000]	lr: 1.875e-05, eta: 2:37:36, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2049, decode.acc_seg: 91.6357, loss: 0.2049
+2023-03-04 22:36:57,567 - mmseg - INFO - Iter [34050/80000]	lr: 1.875e-05, eta: 2:37:23, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.1980, loss: 0.2165
+2023-03-04 22:37:06,369 - mmseg - INFO - Iter [34100/80000]	lr: 1.875e-05, eta: 2:37:10, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2231, decode.acc_seg: 91.0008, loss: 0.2231
+2023-03-04 22:37:15,292 - mmseg - INFO - Iter [34150/80000]	lr: 1.875e-05, eta: 2:36:57, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2194, decode.acc_seg: 90.7200, loss: 0.2194
+2023-03-04 22:37:24,269 - mmseg - INFO - Iter [34200/80000]	lr: 1.875e-05, eta: 2:36:45, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2130, decode.acc_seg: 91.1992, loss: 0.2130
+2023-03-04 22:37:33,064 - mmseg - INFO - Iter [34250/80000]	lr: 1.875e-05, eta: 2:36:32, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2186, decode.acc_seg: 91.0727, loss: 0.2186
+2023-03-04 22:37:41,688 - mmseg - INFO - Iter [34300/80000]	lr: 1.875e-05, eta: 2:36:19, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2247, decode.acc_seg: 90.9573, loss: 0.2247
+2023-03-04 22:37:50,248 - mmseg - INFO - Iter [34350/80000]	lr: 1.875e-05, eta: 2:36:06, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2152, decode.acc_seg: 91.3493, loss: 0.2152
+2023-03-04 22:37:59,023 - mmseg - INFO - Iter [34400/80000]	lr: 1.875e-05, eta: 2:35:53, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2281, decode.acc_seg: 90.6821, loss: 0.2281
+2023-03-04 22:38:08,059 - mmseg - INFO - Iter [34450/80000]	lr: 1.875e-05, eta: 2:35:41, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2191, decode.acc_seg: 91.0799, loss: 0.2191
+2023-03-04 22:38:16,633 - mmseg - INFO - Iter [34500/80000]	lr: 1.875e-05, eta: 2:35:27, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2303, decode.acc_seg: 90.6383, loss: 0.2303
+2023-03-04 22:38:28,081 - mmseg - INFO - Iter [34550/80000]	lr: 1.875e-05, eta: 2:35:19, time: 0.229, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2231, decode.acc_seg: 90.8839, loss: 0.2231
+2023-03-04 22:38:36,882 - mmseg - INFO - Iter [34600/80000]	lr: 1.875e-05, eta: 2:35:07, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.2642, loss: 0.2140
+2023-03-04 22:38:45,671 - mmseg - INFO - Iter [34650/80000]	lr: 1.875e-05, eta: 2:34:54, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2250, decode.acc_seg: 90.7500, loss: 0.2250
+2023-03-04 22:38:54,552 - mmseg - INFO - Iter [34700/80000]	lr: 1.875e-05, eta: 2:34:41, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.4974, loss: 0.2076
+2023-03-04 22:39:03,613 - mmseg - INFO - Iter [34750/80000]	lr: 1.875e-05, eta: 2:34:29, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2162, decode.acc_seg: 91.1648, loss: 0.2162
+2023-03-04 22:39:12,408 - mmseg - INFO - Iter [34800/80000]	lr: 1.875e-05, eta: 2:34:16, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2336, decode.acc_seg: 90.7295, loss: 0.2336
+2023-03-04 22:39:21,473 - mmseg - INFO - Iter [34850/80000]	lr: 1.875e-05, eta: 2:34:04, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.3928, loss: 0.2099
+2023-03-04 22:39:30,435 - mmseg - INFO - Iter [34900/80000]	lr: 1.875e-05, eta: 2:33:52, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.3326, loss: 0.2134
+2023-03-04 22:39:39,234 - mmseg - INFO - Iter [34950/80000]	lr: 1.875e-05, eta: 2:33:39, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2129, decode.acc_seg: 91.2637, loss: 0.2129
+2023-03-04 22:39:47,958 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:39:47,959 - mmseg - INFO - Iter [35000/80000]	lr: 1.875e-05, eta: 2:33:26, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2251, decode.acc_seg: 91.0145, loss: 0.2251
+2023-03-04 22:39:56,793 - mmseg - INFO - Iter [35050/80000]	lr: 1.875e-05, eta: 2:33:14, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2276, decode.acc_seg: 90.7860, loss: 0.2276
+2023-03-04 22:40:05,912 - mmseg - INFO - Iter [35100/80000]	lr: 1.875e-05, eta: 2:33:02, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2204, decode.acc_seg: 91.0984, loss: 0.2204
+2023-03-04 22:40:17,217 - mmseg - INFO - Iter [35150/80000]	lr: 1.875e-05, eta: 2:32:53, time: 0.226, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.2404, loss: 0.2134
+2023-03-04 22:40:26,100 - mmseg - INFO - Iter [35200/80000]	lr: 1.875e-05, eta: 2:32:41, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2205, decode.acc_seg: 91.0586, loss: 0.2205
+2023-03-04 22:40:35,028 - mmseg - INFO - Iter [35250/80000]	lr: 1.875e-05, eta: 2:32:28, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.3581, loss: 0.2109
+2023-03-04 22:40:43,813 - mmseg - INFO - Iter [35300/80000]	lr: 1.875e-05, eta: 2:32:16, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2218, decode.acc_seg: 90.9918, loss: 0.2218
+2023-03-04 22:40:52,657 - mmseg - INFO - Iter [35350/80000]	lr: 1.875e-05, eta: 2:32:03, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2159, decode.acc_seg: 91.1863, loss: 0.2159
+2023-03-04 22:41:01,332 - mmseg - INFO - Iter [35400/80000]	lr: 1.875e-05, eta: 2:31:51, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2192, decode.acc_seg: 91.0699, loss: 0.2192
+2023-03-04 22:41:10,069 - mmseg - INFO - Iter [35450/80000]	lr: 1.875e-05, eta: 2:31:38, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2204, decode.acc_seg: 91.0723, loss: 0.2204
+2023-03-04 22:41:18,924 - mmseg - INFO - Iter [35500/80000]	lr: 1.875e-05, eta: 2:31:26, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2182, decode.acc_seg: 91.1872, loss: 0.2182
+2023-03-04 22:41:28,286 - mmseg - INFO - Iter [35550/80000]	lr: 1.875e-05, eta: 2:31:14, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2192, decode.acc_seg: 91.0252, loss: 0.2192
+2023-03-04 22:41:37,080 - mmseg - INFO - Iter [35600/80000]	lr: 1.875e-05, eta: 2:31:02, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.1868, loss: 0.2153
+2023-03-04 22:41:45,791 - mmseg - INFO - Iter [35650/80000]	lr: 1.875e-05, eta: 2:30:49, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2142, decode.acc_seg: 91.2284, loss: 0.2142
+2023-03-04 22:41:54,773 - mmseg - INFO - Iter [35700/80000]	lr: 1.875e-05, eta: 2:30:37, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2092, decode.acc_seg: 91.4351, loss: 0.2092
+2023-03-04 22:42:03,522 - mmseg - INFO - Iter [35750/80000]	lr: 1.875e-05, eta: 2:30:24, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2252, decode.acc_seg: 90.7603, loss: 0.2252
+2023-03-04 22:42:15,393 - mmseg - INFO - Iter [35800/80000]	lr: 1.875e-05, eta: 2:30:17, time: 0.237, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2173, decode.acc_seg: 91.0343, loss: 0.2173
+2023-03-04 22:42:24,288 - mmseg - INFO - Iter [35850/80000]	lr: 1.875e-05, eta: 2:30:05, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2174, decode.acc_seg: 91.1305, loss: 0.2174
+2023-03-04 22:42:32,858 - mmseg - INFO - Iter [35900/80000]	lr: 1.875e-05, eta: 2:29:52, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2202, decode.acc_seg: 91.0582, loss: 0.2202
+2023-03-04 22:42:42,286 - mmseg - INFO - Iter [35950/80000]	lr: 1.875e-05, eta: 2:29:40, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2207, decode.acc_seg: 91.0472, loss: 0.2207
+2023-03-04 22:42:51,589 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:42:51,590 - mmseg - INFO - Iter [36000/80000]	lr: 1.875e-05, eta: 2:29:29, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.2423, loss: 0.2153
+2023-03-04 22:43:00,327 - mmseg - INFO - Iter [36050/80000]	lr: 1.875e-05, eta: 2:29:16, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.5449, loss: 0.2072
+2023-03-04 22:43:09,484 - mmseg - INFO - Iter [36100/80000]	lr: 1.875e-05, eta: 2:29:04, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.1247, loss: 0.2196
+2023-03-04 22:43:18,242 - mmseg - INFO - Iter [36150/80000]	lr: 1.875e-05, eta: 2:28:52, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2213, decode.acc_seg: 91.0007, loss: 0.2213
+2023-03-04 22:43:27,123 - mmseg - INFO - Iter [36200/80000]	lr: 1.875e-05, eta: 2:28:40, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.1203, loss: 0.2196
+2023-03-04 22:43:35,981 - mmseg - INFO - Iter [36250/80000]	lr: 1.875e-05, eta: 2:28:28, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2179, decode.acc_seg: 91.1570, loss: 0.2179
+2023-03-04 22:43:45,296 - mmseg - INFO - Iter [36300/80000]	lr: 1.875e-05, eta: 2:28:16, time: 0.186, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2167, decode.acc_seg: 91.1952, loss: 0.2167
+2023-03-04 22:43:54,099 - mmseg - INFO - Iter [36350/80000]	lr: 1.875e-05, eta: 2:28:04, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2192, decode.acc_seg: 90.9653, loss: 0.2192
+2023-03-04 22:44:05,611 - mmseg - INFO - Iter [36400/80000]	lr: 1.875e-05, eta: 2:27:56, time: 0.230, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2248, decode.acc_seg: 90.7616, loss: 0.2248
+2023-03-04 22:44:14,186 - mmseg - INFO - Iter [36450/80000]	lr: 1.875e-05, eta: 2:27:43, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2276, decode.acc_seg: 90.7281, loss: 0.2276
+2023-03-04 22:44:23,292 - mmseg - INFO - Iter [36500/80000]	lr: 1.875e-05, eta: 2:27:31, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 91.0101, loss: 0.2200
+2023-03-04 22:44:32,677 - mmseg - INFO - Iter [36550/80000]	lr: 1.875e-05, eta: 2:27:20, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2126, decode.acc_seg: 91.2654, loss: 0.2126
+2023-03-04 22:44:41,817 - mmseg - INFO - Iter [36600/80000]	lr: 1.875e-05, eta: 2:27:08, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2199, decode.acc_seg: 91.0639, loss: 0.2199
+2023-03-04 22:44:50,417 - mmseg - INFO - Iter [36650/80000]	lr: 1.875e-05, eta: 2:26:56, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2083, decode.acc_seg: 91.3764, loss: 0.2083
+2023-03-04 22:44:59,085 - mmseg - INFO - Iter [36700/80000]	lr: 1.875e-05, eta: 2:26:43, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2243, decode.acc_seg: 90.8732, loss: 0.2243
+2023-03-04 22:45:07,948 - mmseg - INFO - Iter [36750/80000]	lr: 1.875e-05, eta: 2:26:31, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2212, decode.acc_seg: 91.0053, loss: 0.2212
+2023-03-04 22:45:16,421 - mmseg - INFO - Iter [36800/80000]	lr: 1.875e-05, eta: 2:26:18, time: 0.169, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2193, decode.acc_seg: 91.3119, loss: 0.2193
+2023-03-04 22:45:25,627 - mmseg - INFO - Iter [36850/80000]	lr: 1.875e-05, eta: 2:26:07, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.0734, loss: 0.2196
+2023-03-04 22:45:34,522 - mmseg - INFO - Iter [36900/80000]	lr: 1.875e-05, eta: 2:25:55, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2210, decode.acc_seg: 90.9840, loss: 0.2210
+2023-03-04 22:45:43,478 - mmseg - INFO - Iter [36950/80000]	lr: 1.875e-05, eta: 2:25:43, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2197, decode.acc_seg: 90.9859, loss: 0.2197
+2023-03-04 22:45:52,514 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:45:52,514 - mmseg - INFO - Iter [37000/80000]	lr: 1.875e-05, eta: 2:25:31, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2129, decode.acc_seg: 91.3421, loss: 0.2129
+2023-03-04 22:46:03,700 - mmseg - INFO - Iter [37050/80000]	lr: 1.875e-05, eta: 2:25:22, time: 0.224, data_time: 0.058, memory: 52390, decode.loss_ce: 0.2263, decode.acc_seg: 90.8099, loss: 0.2263
+2023-03-04 22:46:12,539 - mmseg - INFO - Iter [37100/80000]	lr: 1.875e-05, eta: 2:25:10, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2220, decode.acc_seg: 90.8442, loss: 0.2220
+2023-03-04 22:46:21,334 - mmseg - INFO - Iter [37150/80000]	lr: 1.875e-05, eta: 2:24:58, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2227, decode.acc_seg: 90.8533, loss: 0.2227
+2023-03-04 22:46:30,425 - mmseg - INFO - Iter [37200/80000]	lr: 1.875e-05, eta: 2:24:46, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2275, decode.acc_seg: 90.7906, loss: 0.2275
+2023-03-04 22:46:39,383 - mmseg - INFO - Iter [37250/80000]	lr: 1.875e-05, eta: 2:24:34, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2085, decode.acc_seg: 91.5176, loss: 0.2085
+2023-03-04 22:46:48,377 - mmseg - INFO - Iter [37300/80000]	lr: 1.875e-05, eta: 2:24:23, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.2629, loss: 0.2168
+2023-03-04 22:46:57,091 - mmseg - INFO - Iter [37350/80000]	lr: 1.875e-05, eta: 2:24:10, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2238, decode.acc_seg: 90.8644, loss: 0.2238
+2023-03-04 22:47:06,240 - mmseg - INFO - Iter [37400/80000]	lr: 1.875e-05, eta: 2:23:59, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.3755, loss: 0.2127
+2023-03-04 22:47:14,986 - mmseg - INFO - Iter [37450/80000]	lr: 1.875e-05, eta: 2:23:47, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.3888, loss: 0.2107
+2023-03-04 22:47:23,829 - mmseg - INFO - Iter [37500/80000]	lr: 1.875e-05, eta: 2:23:35, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2232, decode.acc_seg: 90.9190, loss: 0.2232
+2023-03-04 22:47:32,981 - mmseg - INFO - Iter [37550/80000]	lr: 1.875e-05, eta: 2:23:23, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2181, decode.acc_seg: 91.0353, loss: 0.2181
+2023-03-04 22:47:41,974 - mmseg - INFO - Iter [37600/80000]	lr: 1.875e-05, eta: 2:23:11, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.4613, loss: 0.2123
+2023-03-04 22:47:51,104 - mmseg - INFO - Iter [37650/80000]	lr: 1.875e-05, eta: 2:23:00, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2176, decode.acc_seg: 91.0425, loss: 0.2176
+2023-03-04 22:48:02,259 - mmseg - INFO - Iter [37700/80000]	lr: 1.875e-05, eta: 2:22:51, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2174, decode.acc_seg: 91.1510, loss: 0.2174
+2023-03-04 22:48:11,070 - mmseg - INFO - Iter [37750/80000]	lr: 1.875e-05, eta: 2:22:39, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2197, decode.acc_seg: 90.9812, loss: 0.2197
+2023-03-04 22:48:20,163 - mmseg - INFO - Iter [37800/80000]	lr: 1.875e-05, eta: 2:22:28, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.2572, loss: 0.2165
+2023-03-04 22:48:29,430 - mmseg - INFO - Iter [37850/80000]	lr: 1.875e-05, eta: 2:22:16, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2087, decode.acc_seg: 91.4791, loss: 0.2087
+2023-03-04 22:48:38,288 - mmseg - INFO - Iter [37900/80000]	lr: 1.875e-05, eta: 2:22:04, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2201, decode.acc_seg: 91.0402, loss: 0.2201
+2023-03-04 22:48:47,409 - mmseg - INFO - Iter [37950/80000]	lr: 1.875e-05, eta: 2:21:53, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.3116, loss: 0.2109
+2023-03-04 22:48:56,261 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:48:56,262 - mmseg - INFO - Iter [38000/80000]	lr: 1.875e-05, eta: 2:21:41, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2136, decode.acc_seg: 91.2019, loss: 0.2136
+2023-03-04 22:49:05,205 - mmseg - INFO - Iter [38050/80000]	lr: 1.875e-05, eta: 2:21:29, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2179, decode.acc_seg: 91.0819, loss: 0.2179
+2023-03-04 22:49:13,830 - mmseg - INFO - Iter [38100/80000]	lr: 1.875e-05, eta: 2:21:17, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2080, decode.acc_seg: 91.5021, loss: 0.2080
+2023-03-04 22:49:23,132 - mmseg - INFO - Iter [38150/80000]	lr: 1.875e-05, eta: 2:21:06, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.0837, loss: 0.2165
+2023-03-04 22:49:32,127 - mmseg - INFO - Iter [38200/80000]	lr: 1.875e-05, eta: 2:20:54, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.1669, loss: 0.2154
+2023-03-04 22:49:41,420 - mmseg - INFO - Iter [38250/80000]	lr: 1.875e-05, eta: 2:20:43, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2197, decode.acc_seg: 91.0148, loss: 0.2197
+2023-03-04 22:49:52,507 - mmseg - INFO - Iter [38300/80000]	lr: 1.875e-05, eta: 2:20:34, time: 0.222, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.0830, loss: 0.2161
+2023-03-04 22:50:01,413 - mmseg - INFO - Iter [38350/80000]	lr: 1.875e-05, eta: 2:20:22, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.3267, loss: 0.2099
+2023-03-04 22:50:10,421 - mmseg - INFO - Iter [38400/80000]	lr: 1.875e-05, eta: 2:20:11, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2021, decode.acc_seg: 91.6298, loss: 0.2021
+2023-03-04 22:50:19,424 - mmseg - INFO - Iter [38450/80000]	lr: 1.875e-05, eta: 2:19:59, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2225, decode.acc_seg: 91.1262, loss: 0.2225
+2023-03-04 22:50:28,072 - mmseg - INFO - Iter [38500/80000]	lr: 1.875e-05, eta: 2:19:47, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.2708, loss: 0.2132
+2023-03-04 22:50:37,676 - mmseg - INFO - Iter [38550/80000]	lr: 1.875e-05, eta: 2:19:36, time: 0.192, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2138, decode.acc_seg: 91.2321, loss: 0.2138
+2023-03-04 22:50:46,933 - mmseg - INFO - Iter [38600/80000]	lr: 1.875e-05, eta: 2:19:25, time: 0.185, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.1991, loss: 0.2114
+2023-03-04 22:50:55,887 - mmseg - INFO - Iter [38650/80000]	lr: 1.875e-05, eta: 2:19:13, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2167, decode.acc_seg: 91.0965, loss: 0.2167
+2023-03-04 22:51:04,407 - mmseg - INFO - Iter [38700/80000]	lr: 1.875e-05, eta: 2:19:01, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2197, decode.acc_seg: 91.1550, loss: 0.2197
+2023-03-04 22:51:13,297 - mmseg - INFO - Iter [38750/80000]	lr: 1.875e-05, eta: 2:18:49, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2239, decode.acc_seg: 90.9726, loss: 0.2239
+2023-03-04 22:51:22,076 - mmseg - INFO - Iter [38800/80000]	lr: 1.875e-05, eta: 2:18:37, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2125, decode.acc_seg: 91.2953, loss: 0.2125
+2023-03-04 22:51:30,778 - mmseg - INFO - Iter [38850/80000]	lr: 1.875e-05, eta: 2:18:25, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.3738, loss: 0.2121
+2023-03-04 22:51:39,912 - mmseg - INFO - Iter [38900/80000]	lr: 1.875e-05, eta: 2:18:14, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2224, decode.acc_seg: 90.9501, loss: 0.2224
+2023-03-04 22:51:51,381 - mmseg - INFO - Iter [38950/80000]	lr: 1.875e-05, eta: 2:18:06, time: 0.229, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2131, decode.acc_seg: 91.2399, loss: 0.2131
+2023-03-04 22:52:00,402 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:52:00,402 - mmseg - INFO - Iter [39000/80000]	lr: 1.875e-05, eta: 2:17:54, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.3224, loss: 0.2111
+2023-03-04 22:52:09,256 - mmseg - INFO - Iter [39050/80000]	lr: 1.875e-05, eta: 2:17:43, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2065, decode.acc_seg: 91.5145, loss: 0.2065
+2023-03-04 22:52:18,070 - mmseg - INFO - Iter [39100/80000]	lr: 1.875e-05, eta: 2:17:31, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2158, decode.acc_seg: 91.2465, loss: 0.2158
+2023-03-04 22:52:27,581 - mmseg - INFO - Iter [39150/80000]	lr: 1.875e-05, eta: 2:17:20, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.0305, loss: 0.2196
+2023-03-04 22:52:36,197 - mmseg - INFO - Iter [39200/80000]	lr: 1.875e-05, eta: 2:17:08, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2220, decode.acc_seg: 91.1065, loss: 0.2220
+2023-03-04 22:52:45,033 - mmseg - INFO - Iter [39250/80000]	lr: 1.875e-05, eta: 2:16:56, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.3540, loss: 0.2075
+2023-03-04 22:52:53,860 - mmseg - INFO - Iter [39300/80000]	lr: 1.875e-05, eta: 2:16:44, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2174, decode.acc_seg: 91.0893, loss: 0.2174
+2023-03-04 22:53:02,548 - mmseg - INFO - Iter [39350/80000]	lr: 1.875e-05, eta: 2:16:33, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2233, decode.acc_seg: 90.9002, loss: 0.2233
+2023-03-04 22:53:11,326 - mmseg - INFO - Iter [39400/80000]	lr: 1.875e-05, eta: 2:16:21, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2178, decode.acc_seg: 91.1507, loss: 0.2178
+2023-03-04 22:53:19,954 - mmseg - INFO - Iter [39450/80000]	lr: 1.875e-05, eta: 2:16:09, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.4065, loss: 0.2107
+2023-03-04 22:53:28,849 - mmseg - INFO - Iter [39500/80000]	lr: 1.875e-05, eta: 2:15:57, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2253, decode.acc_seg: 90.6936, loss: 0.2253
+2023-03-04 22:53:40,109 - mmseg - INFO - Iter [39550/80000]	lr: 1.875e-05, eta: 2:15:49, time: 0.225, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2186, decode.acc_seg: 91.0434, loss: 0.2186
+2023-03-04 22:53:49,381 - mmseg - INFO - Iter [39600/80000]	lr: 1.875e-05, eta: 2:15:38, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2233, decode.acc_seg: 90.9964, loss: 0.2233
+2023-03-04 22:53:57,856 - mmseg - INFO - Iter [39650/80000]	lr: 1.875e-05, eta: 2:15:26, time: 0.169, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2210, decode.acc_seg: 90.9008, loss: 0.2210
+2023-03-04 22:54:06,672 - mmseg - INFO - Iter [39700/80000]	lr: 1.875e-05, eta: 2:15:14, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2281, decode.acc_seg: 90.8401, loss: 0.2281
+2023-03-04 22:54:15,162 - mmseg - INFO - Iter [39750/80000]	lr: 1.875e-05, eta: 2:15:02, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2063, decode.acc_seg: 91.5539, loss: 0.2063
+2023-03-04 22:54:24,118 - mmseg - INFO - Iter [39800/80000]	lr: 1.875e-05, eta: 2:14:50, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2133, decode.acc_seg: 91.1781, loss: 0.2133
+2023-03-04 22:54:33,141 - mmseg - INFO - Iter [39850/80000]	lr: 1.875e-05, eta: 2:14:39, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.3665, loss: 0.2114
+2023-03-04 22:54:41,966 - mmseg - INFO - Iter [39900/80000]	lr: 1.875e-05, eta: 2:14:27, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.2742, loss: 0.2134
+2023-03-04 22:54:50,861 - mmseg - INFO - Iter [39950/80000]	lr: 1.875e-05, eta: 2:14:16, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.1435, loss: 0.2153
+2023-03-04 22:54:59,754 - mmseg - INFO - Saving checkpoint at 40000 iterations
+2023-03-04 22:55:00,381 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:55:00,381 - mmseg - INFO - Iter [40000/80000]	lr: 1.875e-05, eta: 2:14:05, time: 0.191, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2035, decode.acc_seg: 91.6602, loss: 0.2035
+2023-03-04 22:55:16,151 - mmseg - INFO - per class results:
+2023-03-04 22:55:16,157 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.34 | 87.83 |
+|       building      | 81.41 |  90.9 |
+|         sky         | 94.12 | 97.89 |
+|        floor        | 81.01 | 90.72 |
+|         tree        | 73.15 | 86.36 |
+|       ceiling       | 83.14 | 94.21 |
+|         road        | 81.25 | 88.17 |
+|         bed         | 86.71 | 94.39 |
+|      windowpane     | 58.87 | 76.24 |
+|        grass        | 64.32 | 83.73 |
+|       cabinet       | 59.88 | 72.21 |
+|       sidewalk      |  61.6 | 82.05 |
+|        person       | 78.05 | 90.24 |
+|        earth        | 34.62 | 46.19 |
+|         door        | 43.78 |  58.2 |
+|        table        | 57.13 | 77.79 |
+|       mountain      |  56.5 | 72.13 |
+|        plant        | 49.23 | 59.73 |
+|       curtain       | 72.81 | 85.71 |
+|        chair        |  52.6 | 62.98 |
+|         car         | 81.13 | 92.03 |
+|        water        | 57.59 | 76.93 |
+|       painting      | 69.65 | 82.77 |
+|         sofa        | 62.44 | 82.73 |
+|        shelf        | 42.68 | 62.78 |
+|        house        | 42.01 | 55.39 |
+|         sea         | 60.84 | 77.15 |
+|        mirror       | 60.87 | 67.15 |
+|         rug         | 65.33 | 76.01 |
+|        field        | 29.89 | 44.83 |
+|       armchair      | 35.54 | 52.59 |
+|         seat        | 60.58 |  87.0 |
+|        fence        | 40.53 | 52.61 |
+|         desk        | 45.15 | 65.03 |
+|         rock        | 36.15 |  62.3 |
+|       wardrobe      | 56.48 |  67.3 |
+|         lamp        | 58.01 | 73.21 |
+|       bathtub       | 74.74 |  80.4 |
+|       railing       | 33.02 | 47.05 |
+|       cushion       | 54.02 | 67.64 |
+|         base        |  23.8 | 29.98 |
+|         box         | 22.71 | 32.75 |
+|        column       | 44.91 | 57.11 |
+|      signboard      | 36.62 | 53.03 |
+|   chest of drawers  | 36.36 | 53.69 |
+|       counter       | 28.62 |  34.4 |
+|         sand        | 41.02 | 54.99 |
+|         sink        | 63.99 | 77.37 |
+|      skyscraper     | 60.72 | 79.79 |
+|      fireplace      | 72.76 | 84.99 |
+|     refrigerator    | 68.31 | 85.49 |
+|      grandstand     | 44.13 |  70.1 |
+|         path        | 20.95 | 28.63 |
+|        stairs       | 31.87 |  43.0 |
+|        runway       | 68.35 | 88.49 |
+|         case        |  47.3 | 54.76 |
+|      pool table     | 91.33 | 93.81 |
+|        pillow       | 59.93 |  76.5 |
+|     screen door     |  64.0 | 69.91 |
+|       stairway      | 26.05 | 36.46 |
+|        river        | 11.59 | 19.86 |
+|        bridge       | 39.52 |  48.3 |
+|       bookcase      | 44.47 | 64.18 |
+|        blind        | 35.51 | 38.25 |
+|     coffee table    | 53.51 | 74.59 |
+|        toilet       |  81.6 | 89.09 |
+|        flower       | 36.04 | 48.56 |
+|         book        | 42.24 | 59.96 |
+|         hill        | 11.47 | 16.55 |
+|        bench        | 38.65 | 51.04 |
+|      countertop     | 51.64 | 71.94 |
+|        stove        | 69.14 | 82.78 |
+|         palm        | 49.07 | 66.75 |
+|    kitchen island   | 38.14 | 61.04 |
+|       computer      |  59.3 | 69.71 |
+|     swivel chair    | 43.76 |  60.2 |
+|         boat        | 66.25 | 84.93 |
+|         bar         | 23.58 | 33.66 |
+|    arcade machine   | 72.37 | 76.08 |
+|        hovel        | 25.78 | 28.23 |
+|         bus         | 77.25 | 91.57 |
+|        towel        | 61.66 | 70.95 |
+|        light        | 43.43 |  47.0 |
+|        truck        | 13.47 | 16.93 |
+|        tower        |  7.89 | 12.46 |
+|      chandelier     | 61.38 | 75.26 |
+|        awning       | 23.73 | 27.87 |
+|     streetlight     | 24.59 | 33.93 |
+|        booth        | 43.82 | 47.43 |
+| television receiver | 64.65 | 77.14 |
+|       airplane      | 56.91 | 63.52 |
+|      dirt track     | 12.23 | 25.16 |
+|       apparel       | 32.51 | 53.28 |
+|         pole        | 20.59 | 30.64 |
+|         land        |  3.1  |  4.54 |
+|      bannister      | 12.11 |  20.2 |
+|      escalator      | 24.73 | 27.77 |
+|       ottoman       | 41.77 | 55.23 |
+|        bottle       | 33.63 | 55.45 |
+|        buffet       | 38.72 | 45.06 |
+|        poster       | 23.39 | 32.92 |
+|        stage        | 13.51 | 17.38 |
+|         van         | 38.65 | 53.07 |
+|         ship        | 73.85 | 94.74 |
+|       fountain      | 20.87 |  22.3 |
+|    conveyer belt    | 79.55 | 89.98 |
+|        canopy       | 30.56 | 34.03 |
+|        washer       |  76.9 | 79.07 |
+|      plaything      | 18.94 | 24.28 |
+|    swimming pool    |  72.2 | 80.99 |
+|        stool        | 38.82 | 48.39 |
+|        barrel       | 39.95 | 54.69 |
+|        basket       | 23.77 | 34.58 |
+|      waterfall      |  49.8 | 67.81 |
+|         tent        | 94.16 | 97.24 |
+|         bag         |  16.3 | 24.21 |
+|       minibike      | 59.03 | 69.78 |
+|        cradle       | 83.72 | 94.69 |
+|         oven        | 45.87 | 54.91 |
+|         ball        | 46.73 | 56.99 |
+|         food        | 43.76 | 50.03 |
+|         step        |  9.1  |  10.3 |
+|         tank        | 50.35 | 54.39 |
+|      trade name     | 28.32 | 34.33 |
+|      microwave      | 72.77 | 79.87 |
+|         pot         | 28.65 | 31.93 |
+|        animal       | 51.82 | 55.46 |
+|       bicycle       | 51.31 | 68.02 |
+|         lake        | 57.75 | 63.33 |
+|      dishwasher     |  62.1 | 76.16 |
+|        screen       |  66.2 | 83.61 |
+|       blanket       | 15.93 | 18.33 |
+|      sculpture      | 57.42 | 78.46 |
+|         hood        | 51.68 | 53.87 |
+|        sconce       | 41.18 | 50.29 |
+|         vase        | 30.82 |  42.9 |
+|    traffic light    | 31.64 | 49.61 |
+|         tray        |  4.29 |  6.3  |
+|        ashcan       | 38.74 | 52.47 |
+|         fan         | 47.05 | 49.54 |
+|         pier        | 43.13 | 68.76 |
+|      crt screen     |  7.96 | 18.81 |
+|        plate        | 45.68 | 58.34 |
+|       monitor       | 18.74 | 24.49 |
+|    bulletin board   | 40.89 | 55.86 |
+|        shower       |  1.15 |  4.38 |
+|       radiator      | 56.89 | 62.83 |
+|        glass        |  9.82 | 10.57 |
+|        clock        | 28.66 | 30.03 |
+|         flag        | 37.78 | 43.18 |
++---------------------+-------+-------+
+2023-03-04 22:55:16,157 - mmseg - INFO - Summary:
+2023-03-04 22:55:16,157 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 81.99 | 46.91 | 58.14 |
++-------+-------+-------+
+2023-03-04 22:55:16,178 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_32000.pth was removed
+2023-03-04 22:55:16,747 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_40000.pth.
+2023-03-04 22:55:16,748 - mmseg - INFO - Best mIoU is 0.4691 at 40000 iter.
+2023-03-04 22:55:16,748 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:55:16,748 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8199, mIoU: 0.4691, mAcc: 0.5814, IoU.background: nan, IoU.wall: 0.7634, IoU.building: 0.8141, IoU.sky: 0.9412, IoU.floor: 0.8101, IoU.tree: 0.7315, IoU.ceiling: 0.8314, IoU.road: 0.8125, IoU.bed : 0.8671, IoU.windowpane: 0.5887, IoU.grass: 0.6432, IoU.cabinet: 0.5988, IoU.sidewalk: 0.6160, IoU.person: 0.7805, IoU.earth: 0.3462, IoU.door: 0.4378, IoU.table: 0.5713, IoU.mountain: 0.5650, IoU.plant: 0.4923, IoU.curtain: 0.7281, IoU.chair: 0.5260, IoU.car: 0.8113, IoU.water: 0.5759, IoU.painting: 0.6965, IoU.sofa: 0.6244, IoU.shelf: 0.4268, IoU.house: 0.4201, IoU.sea: 0.6084, IoU.mirror: 0.6087, IoU.rug: 0.6533, IoU.field: 0.2989, IoU.armchair: 0.3554, IoU.seat: 0.6058, IoU.fence: 0.4053, IoU.desk: 0.4515, IoU.rock: 0.3615, IoU.wardrobe: 0.5648, IoU.lamp: 0.5801, IoU.bathtub: 0.7474, IoU.railing: 0.3302, IoU.cushion: 0.5402, IoU.base: 0.2380, IoU.box: 0.2271, IoU.column: 0.4491, IoU.signboard: 0.3662, IoU.chest of drawers: 0.3636, IoU.counter: 0.2862, IoU.sand: 0.4102, IoU.sink: 0.6399, IoU.skyscraper: 0.6072, IoU.fireplace: 0.7276, IoU.refrigerator: 0.6831, IoU.grandstand: 0.4413, IoU.path: 0.2095, IoU.stairs: 0.3187, IoU.runway: 0.6835, IoU.case: 0.4730, IoU.pool table: 0.9133, IoU.pillow: 0.5993, IoU.screen door: 0.6400, IoU.stairway: 0.2605, IoU.river: 0.1159, IoU.bridge: 0.3952, IoU.bookcase: 0.4447, IoU.blind: 0.3551, IoU.coffee table: 0.5351, IoU.toilet: 0.8160, IoU.flower: 0.3604, IoU.book: 0.4224, IoU.hill: 0.1147, IoU.bench: 0.3865, IoU.countertop: 0.5164, IoU.stove: 0.6914, IoU.palm: 0.4907, IoU.kitchen island: 0.3814, IoU.computer: 0.5930, IoU.swivel chair: 0.4376, IoU.boat: 0.6625, IoU.bar: 0.2358, IoU.arcade machine: 0.7237, IoU.hovel: 0.2578, IoU.bus: 0.7725, IoU.towel: 0.6166, IoU.light: 0.4343, IoU.truck: 0.1347, IoU.tower: 0.0789, IoU.chandelier: 0.6138, IoU.awning: 0.2373, IoU.streetlight: 0.2459, IoU.booth: 0.4382, IoU.television receiver: 0.6465, IoU.airplane: 0.5691, IoU.dirt track: 0.1223, IoU.apparel: 0.3251, IoU.pole: 0.2059, IoU.land: 0.0310, IoU.bannister: 0.1211, IoU.escalator: 0.2473, IoU.ottoman: 0.4177, IoU.bottle: 0.3363, IoU.buffet: 0.3872, IoU.poster: 0.2339, IoU.stage: 0.1351, IoU.van: 0.3865, IoU.ship: 0.7385, IoU.fountain: 0.2087, IoU.conveyer belt: 0.7955, IoU.canopy: 0.3056, IoU.washer: 0.7690, IoU.plaything: 0.1894, IoU.swimming pool: 0.7220, IoU.stool: 0.3882, IoU.barrel: 0.3995, IoU.basket: 0.2377, IoU.waterfall: 0.4980, IoU.tent: 0.9416, IoU.bag: 0.1630, IoU.minibike: 0.5903, IoU.cradle: 0.8372, IoU.oven: 0.4587, IoU.ball: 0.4673, IoU.food: 0.4376, IoU.step: 0.0910, IoU.tank: 0.5035, IoU.trade name: 0.2832, IoU.microwave: 0.7277, IoU.pot: 0.2865, IoU.animal: 0.5182, IoU.bicycle: 0.5131, IoU.lake: 0.5775, IoU.dishwasher: 0.6210, IoU.screen: 0.6620, IoU.blanket: 0.1593, IoU.sculpture: 0.5742, IoU.hood: 0.5168, IoU.sconce: 0.4118, IoU.vase: 0.3082, IoU.traffic light: 0.3164, IoU.tray: 0.0429, IoU.ashcan: 0.3874, IoU.fan: 0.4705, IoU.pier: 0.4313, IoU.crt screen: 0.0796, IoU.plate: 0.4568, IoU.monitor: 0.1874, IoU.bulletin board: 0.4089, IoU.shower: 0.0115, IoU.radiator: 0.5689, IoU.glass: 0.0982, IoU.clock: 0.2866, IoU.flag: 0.3778, Acc.background: nan, Acc.wall: 0.8783, Acc.building: 0.9090, Acc.sky: 0.9789, Acc.floor: 0.9072, Acc.tree: 0.8636, Acc.ceiling: 0.9421, Acc.road: 0.8817, Acc.bed : 0.9439, Acc.windowpane: 0.7624, Acc.grass: 0.8373, Acc.cabinet: 0.7221, Acc.sidewalk: 0.8205, Acc.person: 0.9024, Acc.earth: 0.4619, Acc.door: 0.5820, Acc.table: 0.7779, Acc.mountain: 0.7213, Acc.plant: 0.5973, Acc.curtain: 0.8571, Acc.chair: 0.6298, Acc.car: 0.9203, Acc.water: 0.7693, Acc.painting: 0.8277, Acc.sofa: 0.8273, Acc.shelf: 0.6278, Acc.house: 0.5539, Acc.sea: 0.7715, Acc.mirror: 0.6715, Acc.rug: 0.7601, Acc.field: 0.4483, Acc.armchair: 0.5259, Acc.seat: 0.8700, Acc.fence: 0.5261, Acc.desk: 0.6503, Acc.rock: 0.6230, Acc.wardrobe: 0.6730, Acc.lamp: 0.7321, Acc.bathtub: 0.8040, Acc.railing: 0.4705, Acc.cushion: 0.6764, Acc.base: 0.2998, Acc.box: 0.3275, Acc.column: 0.5711, Acc.signboard: 0.5303, Acc.chest of drawers: 0.5369, Acc.counter: 0.3440, Acc.sand: 0.5499, Acc.sink: 0.7737, Acc.skyscraper: 0.7979, Acc.fireplace: 0.8499, Acc.refrigerator: 0.8549, Acc.grandstand: 0.7010, Acc.path: 0.2863, Acc.stairs: 0.4300, Acc.runway: 0.8849, Acc.case: 0.5476, Acc.pool table: 0.9381, Acc.pillow: 0.7650, Acc.screen door: 0.6991, Acc.stairway: 0.3646, Acc.river: 0.1986, Acc.bridge: 0.4830, Acc.bookcase: 0.6418, Acc.blind: 0.3825, Acc.coffee table: 0.7459, Acc.toilet: 0.8909, Acc.flower: 0.4856, Acc.book: 0.5996, Acc.hill: 0.1655, Acc.bench: 0.5104, Acc.countertop: 0.7194, Acc.stove: 0.8278, Acc.palm: 0.6675, Acc.kitchen island: 0.6104, Acc.computer: 0.6971, Acc.swivel chair: 0.6020, Acc.boat: 0.8493, Acc.bar: 0.3366, Acc.arcade machine: 0.7608, Acc.hovel: 0.2823, Acc.bus: 0.9157, Acc.towel: 0.7095, Acc.light: 0.4700, Acc.truck: 0.1693, Acc.tower: 0.1246, Acc.chandelier: 0.7526, Acc.awning: 0.2787, Acc.streetlight: 0.3393, Acc.booth: 0.4743, Acc.television receiver: 0.7714, Acc.airplane: 0.6352, Acc.dirt track: 0.2516, Acc.apparel: 0.5328, Acc.pole: 0.3064, Acc.land: 0.0454, Acc.bannister: 0.2020, Acc.escalator: 0.2777, Acc.ottoman: 0.5523, Acc.bottle: 0.5545, Acc.buffet: 0.4506, Acc.poster: 0.3292, Acc.stage: 0.1738, Acc.van: 0.5307, Acc.ship: 0.9474, Acc.fountain: 0.2230, Acc.conveyer belt: 0.8998, Acc.canopy: 0.3403, Acc.washer: 0.7907, Acc.plaything: 0.2428, Acc.swimming pool: 0.8099, Acc.stool: 0.4839, Acc.barrel: 0.5469, Acc.basket: 0.3458, Acc.waterfall: 0.6781, Acc.tent: 0.9724, Acc.bag: 0.2421, Acc.minibike: 0.6978, Acc.cradle: 0.9469, Acc.oven: 0.5491, Acc.ball: 0.5699, Acc.food: 0.5003, Acc.step: 0.1030, Acc.tank: 0.5439, Acc.trade name: 0.3433, Acc.microwave: 0.7987, Acc.pot: 0.3193, Acc.animal: 0.5546, Acc.bicycle: 0.6802, Acc.lake: 0.6333, Acc.dishwasher: 0.7616, Acc.screen: 0.8361, Acc.blanket: 0.1833, Acc.sculpture: 0.7846, Acc.hood: 0.5387, Acc.sconce: 0.5029, Acc.vase: 0.4290, Acc.traffic light: 0.4961, Acc.tray: 0.0630, Acc.ashcan: 0.5247, Acc.fan: 0.4954, Acc.pier: 0.6876, Acc.crt screen: 0.1881, Acc.plate: 0.5834, Acc.monitor: 0.2449, Acc.bulletin board: 0.5586, Acc.shower: 0.0438, Acc.radiator: 0.6283, Acc.glass: 0.1057, Acc.clock: 0.3003, Acc.flag: 0.4318
+2023-03-04 22:55:25,685 - mmseg - INFO - Iter [40050/80000]	lr: 9.375e-06, eta: 2:14:14, time: 0.506, data_time: 0.335, memory: 52390, decode.loss_ce: 0.2020, decode.acc_seg: 91.6472, loss: 0.2020
+2023-03-04 22:55:34,542 - mmseg - INFO - Iter [40100/80000]	lr: 9.375e-06, eta: 2:14:02, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.2846, loss: 0.2103
+2023-03-04 22:55:43,755 - mmseg - INFO - Iter [40150/80000]	lr: 9.375e-06, eta: 2:13:51, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.2399, loss: 0.2165
+2023-03-04 22:55:54,923 - mmseg - INFO - Iter [40200/80000]	lr: 9.375e-06, eta: 2:13:43, time: 0.223, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2096, decode.acc_seg: 91.4209, loss: 0.2096
+2023-03-04 22:56:03,802 - mmseg - INFO - Iter [40250/80000]	lr: 9.375e-06, eta: 2:13:31, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2158, decode.acc_seg: 91.1862, loss: 0.2158
+2023-03-04 22:56:12,808 - mmseg - INFO - Iter [40300/80000]	lr: 9.375e-06, eta: 2:13:20, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2091, decode.acc_seg: 91.5292, loss: 0.2091
+2023-03-04 22:56:21,940 - mmseg - INFO - Iter [40350/80000]	lr: 9.375e-06, eta: 2:13:08, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2210, decode.acc_seg: 90.8687, loss: 0.2210
+2023-03-04 22:56:30,960 - mmseg - INFO - Iter [40400/80000]	lr: 9.375e-06, eta: 2:12:57, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.3378, loss: 0.2139
+2023-03-04 22:56:39,790 - mmseg - INFO - Iter [40450/80000]	lr: 9.375e-06, eta: 2:12:45, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.4184, loss: 0.2098
+2023-03-04 22:56:48,561 - mmseg - INFO - Iter [40500/80000]	lr: 9.375e-06, eta: 2:12:34, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2195, decode.acc_seg: 91.0435, loss: 0.2195
+2023-03-04 22:56:57,534 - mmseg - INFO - Iter [40550/80000]	lr: 9.375e-06, eta: 2:12:22, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2260, decode.acc_seg: 90.8824, loss: 0.2260
+2023-03-04 22:57:06,437 - mmseg - INFO - Iter [40600/80000]	lr: 9.375e-06, eta: 2:12:11, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2129, decode.acc_seg: 91.4249, loss: 0.2129
+2023-03-04 22:57:15,107 - mmseg - INFO - Iter [40650/80000]	lr: 9.375e-06, eta: 2:11:59, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2177, decode.acc_seg: 91.1577, loss: 0.2177
+2023-03-04 22:57:23,800 - mmseg - INFO - Iter [40700/80000]	lr: 9.375e-06, eta: 2:11:47, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.2828, loss: 0.2128
+2023-03-04 22:57:32,415 - mmseg - INFO - Iter [40750/80000]	lr: 9.375e-06, eta: 2:11:36, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.2056, loss: 0.2171
+2023-03-04 22:57:40,908 - mmseg - INFO - Iter [40800/80000]	lr: 9.375e-06, eta: 2:11:24, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2198, decode.acc_seg: 91.0695, loss: 0.2198
+2023-03-04 22:57:52,883 - mmseg - INFO - Iter [40850/80000]	lr: 9.375e-06, eta: 2:11:16, time: 0.240, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2184, decode.acc_seg: 91.0507, loss: 0.2184
+2023-03-04 22:58:01,953 - mmseg - INFO - Iter [40900/80000]	lr: 9.375e-06, eta: 2:11:05, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2254, decode.acc_seg: 90.9304, loss: 0.2254
+2023-03-04 22:58:11,117 - mmseg - INFO - Iter [40950/80000]	lr: 9.375e-06, eta: 2:10:54, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2070, decode.acc_seg: 91.5970, loss: 0.2070
+2023-03-04 22:58:19,731 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 22:58:19,731 - mmseg - INFO - Iter [41000/80000]	lr: 9.375e-06, eta: 2:10:42, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2085, decode.acc_seg: 91.4283, loss: 0.2085
+2023-03-04 22:58:28,936 - mmseg - INFO - Iter [41050/80000]	lr: 9.375e-06, eta: 2:10:31, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.3637, loss: 0.2089
+2023-03-04 22:58:37,951 - mmseg - INFO - Iter [41100/80000]	lr: 9.375e-06, eta: 2:10:20, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2129, decode.acc_seg: 91.3761, loss: 0.2129
+2023-03-04 22:58:47,130 - mmseg - INFO - Iter [41150/80000]	lr: 9.375e-06, eta: 2:10:08, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2250, decode.acc_seg: 90.7465, loss: 0.2250
+2023-03-04 22:58:56,006 - mmseg - INFO - Iter [41200/80000]	lr: 9.375e-06, eta: 2:09:57, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.3389, loss: 0.2101
+2023-03-04 22:59:05,213 - mmseg - INFO - Iter [41250/80000]	lr: 9.375e-06, eta: 2:09:46, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2125, decode.acc_seg: 91.3383, loss: 0.2125
+2023-03-04 22:59:13,773 - mmseg - INFO - Iter [41300/80000]	lr: 9.375e-06, eta: 2:09:34, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2194, decode.acc_seg: 91.2946, loss: 0.2194
+2023-03-04 22:59:22,411 - mmseg - INFO - Iter [41350/80000]	lr: 9.375e-06, eta: 2:09:23, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.4364, loss: 0.2140
+2023-03-04 22:59:31,134 - mmseg - INFO - Iter [41400/80000]	lr: 9.375e-06, eta: 2:09:11, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2122, decode.acc_seg: 91.2976, loss: 0.2122
+2023-03-04 22:59:42,226 - mmseg - INFO - Iter [41450/80000]	lr: 9.375e-06, eta: 2:09:02, time: 0.222, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2130, decode.acc_seg: 91.3170, loss: 0.2130
+2023-03-04 22:59:50,906 - mmseg - INFO - Iter [41500/80000]	lr: 9.375e-06, eta: 2:08:51, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2135, decode.acc_seg: 91.3117, loss: 0.2135
+2023-03-04 23:00:00,220 - mmseg - INFO - Iter [41550/80000]	lr: 9.375e-06, eta: 2:08:40, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2086, decode.acc_seg: 91.6022, loss: 0.2086
+2023-03-04 23:00:09,490 - mmseg - INFO - Iter [41600/80000]	lr: 9.375e-06, eta: 2:08:29, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2048, decode.acc_seg: 91.6387, loss: 0.2048
+2023-03-04 23:00:18,274 - mmseg - INFO - Iter [41650/80000]	lr: 9.375e-06, eta: 2:08:17, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2210, decode.acc_seg: 90.9073, loss: 0.2210
+2023-03-04 23:00:27,333 - mmseg - INFO - Iter [41700/80000]	lr: 9.375e-06, eta: 2:08:06, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2241, decode.acc_seg: 90.8343, loss: 0.2241
+2023-03-04 23:00:35,996 - mmseg - INFO - Iter [41750/80000]	lr: 9.375e-06, eta: 2:07:55, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2174, decode.acc_seg: 91.1698, loss: 0.2174
+2023-03-04 23:00:45,108 - mmseg - INFO - Iter [41800/80000]	lr: 9.375e-06, eta: 2:07:43, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2225, decode.acc_seg: 90.9759, loss: 0.2225
+2023-03-04 23:00:53,808 - mmseg - INFO - Iter [41850/80000]	lr: 9.375e-06, eta: 2:07:32, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2220, decode.acc_seg: 90.9865, loss: 0.2220
+2023-03-04 23:01:02,313 - mmseg - INFO - Iter [41900/80000]	lr: 9.375e-06, eta: 2:07:20, time: 0.170, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2201, decode.acc_seg: 91.0366, loss: 0.2201
+2023-03-04 23:01:11,135 - mmseg - INFO - Iter [41950/80000]	lr: 9.375e-06, eta: 2:07:09, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.3449, loss: 0.2116
+2023-03-04 23:01:19,858 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:01:19,858 - mmseg - INFO - Iter [42000/80000]	lr: 9.375e-06, eta: 2:06:57, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2142, decode.acc_seg: 91.3029, loss: 0.2142
+2023-03-04 23:01:29,105 - mmseg - INFO - Iter [42050/80000]	lr: 9.375e-06, eta: 2:06:46, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2237, decode.acc_seg: 90.9890, loss: 0.2237
+2023-03-04 23:01:40,549 - mmseg - INFO - Iter [42100/80000]	lr: 9.375e-06, eta: 2:06:38, time: 0.229, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.4708, loss: 0.2109
+2023-03-04 23:01:49,706 - mmseg - INFO - Iter [42150/80000]	lr: 9.375e-06, eta: 2:06:27, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.1181, loss: 0.2189
+2023-03-04 23:01:59,023 - mmseg - INFO - Iter [42200/80000]	lr: 9.375e-06, eta: 2:06:16, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.0025, loss: 0.2189
+2023-03-04 23:02:08,043 - mmseg - INFO - Iter [42250/80000]	lr: 9.375e-06, eta: 2:06:05, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2136, decode.acc_seg: 91.2802, loss: 0.2136
+2023-03-04 23:02:17,067 - mmseg - INFO - Iter [42300/80000]	lr: 9.375e-06, eta: 2:05:54, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.2023, loss: 0.2168
+2023-03-04 23:02:25,678 - mmseg - INFO - Iter [42350/80000]	lr: 9.375e-06, eta: 2:05:42, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.3929, loss: 0.2171
+2023-03-04 23:02:34,672 - mmseg - INFO - Iter [42400/80000]	lr: 9.375e-06, eta: 2:05:31, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.3363, loss: 0.2139
+2023-03-04 23:02:43,820 - mmseg - INFO - Iter [42450/80000]	lr: 9.375e-06, eta: 2:05:20, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2016, decode.acc_seg: 91.5365, loss: 0.2016
+2023-03-04 23:02:52,474 - mmseg - INFO - Iter [42500/80000]	lr: 9.375e-06, eta: 2:05:09, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2199, decode.acc_seg: 91.0835, loss: 0.2199
+2023-03-04 23:03:01,112 - mmseg - INFO - Iter [42550/80000]	lr: 9.375e-06, eta: 2:04:57, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2217, decode.acc_seg: 91.0222, loss: 0.2217
+2023-03-04 23:03:09,671 - mmseg - INFO - Iter [42600/80000]	lr: 9.375e-06, eta: 2:04:46, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.4112, loss: 0.2121
+2023-03-04 23:03:18,655 - mmseg - INFO - Iter [42650/80000]	lr: 9.375e-06, eta: 2:04:35, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.2088, loss: 0.2163
+2023-03-04 23:03:27,211 - mmseg - INFO - Iter [42700/80000]	lr: 9.375e-06, eta: 2:04:23, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.3213, loss: 0.2103
+2023-03-04 23:03:38,499 - mmseg - INFO - Iter [42750/80000]	lr: 9.375e-06, eta: 2:04:14, time: 0.226, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2204, decode.acc_seg: 91.0450, loss: 0.2204
+2023-03-04 23:03:47,504 - mmseg - INFO - Iter [42800/80000]	lr: 9.375e-06, eta: 2:04:03, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.0920, loss: 0.2171
+2023-03-04 23:03:56,153 - mmseg - INFO - Iter [42850/80000]	lr: 9.375e-06, eta: 2:03:52, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2069, decode.acc_seg: 91.5068, loss: 0.2069
+2023-03-04 23:04:05,609 - mmseg - INFO - Iter [42900/80000]	lr: 9.375e-06, eta: 2:03:41, time: 0.189, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.4034, loss: 0.2114
+2023-03-04 23:04:14,524 - mmseg - INFO - Iter [42950/80000]	lr: 9.375e-06, eta: 2:03:30, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2145, decode.acc_seg: 91.2322, loss: 0.2145
+2023-03-04 23:04:23,270 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:04:23,270 - mmseg - INFO - Iter [43000/80000]	lr: 9.375e-06, eta: 2:03:19, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2110, decode.acc_seg: 91.3531, loss: 0.2110
+2023-03-04 23:04:32,085 - mmseg - INFO - Iter [43050/80000]	lr: 9.375e-06, eta: 2:03:08, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2193, decode.acc_seg: 91.0155, loss: 0.2193
+2023-03-04 23:04:40,983 - mmseg - INFO - Iter [43100/80000]	lr: 9.375e-06, eta: 2:02:56, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2221, decode.acc_seg: 91.1640, loss: 0.2221
+2023-03-04 23:04:50,337 - mmseg - INFO - Iter [43150/80000]	lr: 9.375e-06, eta: 2:02:46, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.1484, loss: 0.2107
+2023-03-04 23:05:00,013 - mmseg - INFO - Iter [43200/80000]	lr: 9.375e-06, eta: 2:02:35, time: 0.194, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2225, decode.acc_seg: 90.9786, loss: 0.2225
+2023-03-04 23:05:09,017 - mmseg - INFO - Iter [43250/80000]	lr: 9.375e-06, eta: 2:02:24, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2061, decode.acc_seg: 91.4315, loss: 0.2061
+2023-03-04 23:05:17,632 - mmseg - INFO - Iter [43300/80000]	lr: 9.375e-06, eta: 2:02:13, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2020, decode.acc_seg: 91.7450, loss: 0.2020
+2023-03-04 23:05:29,208 - mmseg - INFO - Iter [43350/80000]	lr: 9.375e-06, eta: 2:02:05, time: 0.232, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2149, decode.acc_seg: 91.2231, loss: 0.2149
+2023-03-04 23:05:38,109 - mmseg - INFO - Iter [43400/80000]	lr: 9.375e-06, eta: 2:01:54, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2227, decode.acc_seg: 91.0537, loss: 0.2227
+2023-03-04 23:05:47,289 - mmseg - INFO - Iter [43450/80000]	lr: 9.375e-06, eta: 2:01:43, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2057, decode.acc_seg: 91.5595, loss: 0.2057
+2023-03-04 23:05:56,099 - mmseg - INFO - Iter [43500/80000]	lr: 9.375e-06, eta: 2:01:31, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.1212, loss: 0.2161
+2023-03-04 23:06:05,392 - mmseg - INFO - Iter [43550/80000]	lr: 9.375e-06, eta: 2:01:21, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.5143, loss: 0.2076
+2023-03-04 23:06:14,679 - mmseg - INFO - Iter [43600/80000]	lr: 9.375e-06, eta: 2:01:10, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2176, decode.acc_seg: 91.2150, loss: 0.2176
+2023-03-04 23:06:23,214 - mmseg - INFO - Iter [43650/80000]	lr: 9.375e-06, eta: 2:00:59, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2178, decode.acc_seg: 91.1913, loss: 0.2178
+2023-03-04 23:06:31,798 - mmseg - INFO - Iter [43700/80000]	lr: 9.375e-06, eta: 2:00:47, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.3958, loss: 0.2107
+2023-03-04 23:06:40,699 - mmseg - INFO - Iter [43750/80000]	lr: 9.375e-06, eta: 2:00:36, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2144, decode.acc_seg: 91.3353, loss: 0.2144
+2023-03-04 23:06:49,940 - mmseg - INFO - Iter [43800/80000]	lr: 9.375e-06, eta: 2:00:25, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2085, decode.acc_seg: 91.3011, loss: 0.2085
+2023-03-04 23:06:59,136 - mmseg - INFO - Iter [43850/80000]	lr: 9.375e-06, eta: 2:00:15, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2172, decode.acc_seg: 91.1012, loss: 0.2172
+2023-03-04 23:07:08,113 - mmseg - INFO - Iter [43900/80000]	lr: 9.375e-06, eta: 2:00:04, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2174, decode.acc_seg: 91.0901, loss: 0.2174
+2023-03-04 23:07:17,178 - mmseg - INFO - Iter [43950/80000]	lr: 9.375e-06, eta: 1:59:53, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2063, decode.acc_seg: 91.4514, loss: 0.2063
+2023-03-04 23:07:28,829 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:07:28,830 - mmseg - INFO - Iter [44000/80000]	lr: 9.375e-06, eta: 1:59:44, time: 0.233, data_time: 0.051, memory: 52390, decode.loss_ce: 0.2178, decode.acc_seg: 91.1073, loss: 0.2178
+2023-03-04 23:07:37,565 - mmseg - INFO - Iter [44050/80000]	lr: 9.375e-06, eta: 1:59:33, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2077, decode.acc_seg: 91.4396, loss: 0.2077
+2023-03-04 23:07:47,111 - mmseg - INFO - Iter [44100/80000]	lr: 9.375e-06, eta: 1:59:23, time: 0.191, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2079, decode.acc_seg: 91.4162, loss: 0.2079
+2023-03-04 23:07:56,095 - mmseg - INFO - Iter [44150/80000]	lr: 9.375e-06, eta: 1:59:12, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.0846, loss: 0.2171
+2023-03-04 23:08:04,936 - mmseg - INFO - Iter [44200/80000]	lr: 9.375e-06, eta: 1:59:01, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2105, decode.acc_seg: 91.2987, loss: 0.2105
+2023-03-04 23:08:14,372 - mmseg - INFO - Iter [44250/80000]	lr: 9.375e-06, eta: 1:58:50, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2184, decode.acc_seg: 91.1020, loss: 0.2184
+2023-03-04 23:08:23,060 - mmseg - INFO - Iter [44300/80000]	lr: 9.375e-06, eta: 1:58:39, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2110, decode.acc_seg: 91.3431, loss: 0.2110
+2023-03-04 23:08:31,872 - mmseg - INFO - Iter [44350/80000]	lr: 9.375e-06, eta: 1:58:28, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2009, decode.acc_seg: 91.6994, loss: 0.2009
+2023-03-04 23:08:40,750 - mmseg - INFO - Iter [44400/80000]	lr: 9.375e-06, eta: 1:58:17, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.0983, loss: 0.2189
+2023-03-04 23:08:49,485 - mmseg - INFO - Iter [44450/80000]	lr: 9.375e-06, eta: 1:58:06, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2145, decode.acc_seg: 91.2275, loss: 0.2145
+2023-03-04 23:08:58,188 - mmseg - INFO - Iter [44500/80000]	lr: 9.375e-06, eta: 1:57:54, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2206, decode.acc_seg: 90.9644, loss: 0.2206
+2023-03-04 23:09:07,019 - mmseg - INFO - Iter [44550/80000]	lr: 9.375e-06, eta: 1:57:43, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2149, decode.acc_seg: 91.2301, loss: 0.2149
+2023-03-04 23:09:18,249 - mmseg - INFO - Iter [44600/80000]	lr: 9.375e-06, eta: 1:57:35, time: 0.225, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2069, decode.acc_seg: 91.4067, loss: 0.2069
+2023-03-04 23:09:27,374 - mmseg - INFO - Iter [44650/80000]	lr: 9.375e-06, eta: 1:57:24, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2135, decode.acc_seg: 91.3157, loss: 0.2135
+2023-03-04 23:09:36,426 - mmseg - INFO - Iter [44700/80000]	lr: 9.375e-06, eta: 1:57:13, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.3667, loss: 0.2132
+2023-03-04 23:09:45,274 - mmseg - INFO - Iter [44750/80000]	lr: 9.375e-06, eta: 1:57:02, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2217, decode.acc_seg: 91.1447, loss: 0.2217
+2023-03-04 23:09:54,113 - mmseg - INFO - Iter [44800/80000]	lr: 9.375e-06, eta: 1:56:51, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2199, decode.acc_seg: 91.0624, loss: 0.2199
+2023-03-04 23:10:03,198 - mmseg - INFO - Iter [44850/80000]	lr: 9.375e-06, eta: 1:56:40, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2146, decode.acc_seg: 91.2402, loss: 0.2146
+2023-03-04 23:10:11,997 - mmseg - INFO - Iter [44900/80000]	lr: 9.375e-06, eta: 1:56:29, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2175, decode.acc_seg: 91.2804, loss: 0.2175
+2023-03-04 23:10:20,784 - mmseg - INFO - Iter [44950/80000]	lr: 9.375e-06, eta: 1:56:18, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.4342, loss: 0.2076
+2023-03-04 23:10:29,421 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:10:29,421 - mmseg - INFO - Iter [45000/80000]	lr: 9.375e-06, eta: 1:56:07, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2187, decode.acc_seg: 91.0742, loss: 0.2187
+2023-03-04 23:10:38,474 - mmseg - INFO - Iter [45050/80000]	lr: 9.375e-06, eta: 1:55:56, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2084, decode.acc_seg: 91.4204, loss: 0.2084
+2023-03-04 23:10:47,283 - mmseg - INFO - Iter [45100/80000]	lr: 9.375e-06, eta: 1:55:45, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2180, decode.acc_seg: 91.0390, loss: 0.2180
+2023-03-04 23:10:55,980 - mmseg - INFO - Iter [45150/80000]	lr: 9.375e-06, eta: 1:55:34, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2158, decode.acc_seg: 91.1239, loss: 0.2158
+2023-03-04 23:11:04,737 - mmseg - INFO - Iter [45200/80000]	lr: 9.375e-06, eta: 1:55:23, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2071, decode.acc_seg: 91.3599, loss: 0.2071
+2023-03-04 23:11:15,882 - mmseg - INFO - Iter [45250/80000]	lr: 9.375e-06, eta: 1:55:14, time: 0.223, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2185, decode.acc_seg: 91.1787, loss: 0.2185
+2023-03-04 23:11:24,582 - mmseg - INFO - Iter [45300/80000]	lr: 9.375e-06, eta: 1:55:03, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2160, decode.acc_seg: 91.1664, loss: 0.2160
+2023-03-04 23:11:33,159 - mmseg - INFO - Iter [45350/80000]	lr: 9.375e-06, eta: 1:54:52, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.1324, loss: 0.2127
+2023-03-04 23:11:42,401 - mmseg - INFO - Iter [45400/80000]	lr: 9.375e-06, eta: 1:54:41, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.4200, loss: 0.2106
+2023-03-04 23:11:51,338 - mmseg - INFO - Iter [45450/80000]	lr: 9.375e-06, eta: 1:54:30, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.5120, loss: 0.2074
+2023-03-04 23:12:00,530 - mmseg - INFO - Iter [45500/80000]	lr: 9.375e-06, eta: 1:54:20, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2126, decode.acc_seg: 91.2792, loss: 0.2126
+2023-03-04 23:12:09,197 - mmseg - INFO - Iter [45550/80000]	lr: 9.375e-06, eta: 1:54:08, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.3416, loss: 0.2116
+2023-03-04 23:12:17,957 - mmseg - INFO - Iter [45600/80000]	lr: 9.375e-06, eta: 1:53:57, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2166, decode.acc_seg: 91.2205, loss: 0.2166
+2023-03-04 23:12:27,219 - mmseg - INFO - Iter [45650/80000]	lr: 9.375e-06, eta: 1:53:47, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.0295, loss: 0.2161
+2023-03-04 23:12:36,422 - mmseg - INFO - Iter [45700/80000]	lr: 9.375e-06, eta: 1:53:36, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.2047, loss: 0.2116
+2023-03-04 23:12:45,326 - mmseg - INFO - Iter [45750/80000]	lr: 9.375e-06, eta: 1:53:25, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.0791, loss: 0.2143
+2023-03-04 23:12:54,056 - mmseg - INFO - Iter [45800/80000]	lr: 9.375e-06, eta: 1:53:14, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2173, decode.acc_seg: 90.9864, loss: 0.2173
+2023-03-04 23:13:02,609 - mmseg - INFO - Iter [45850/80000]	lr: 9.375e-06, eta: 1:53:03, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.3948, loss: 0.2098
+2023-03-04 23:13:14,279 - mmseg - INFO - Iter [45900/80000]	lr: 9.375e-06, eta: 1:52:55, time: 0.233, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2280, decode.acc_seg: 90.7707, loss: 0.2280
+2023-03-04 23:13:23,410 - mmseg - INFO - Iter [45950/80000]	lr: 9.375e-06, eta: 1:52:44, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.1087, loss: 0.2161
+2023-03-04 23:13:32,301 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:13:32,301 - mmseg - INFO - Iter [46000/80000]	lr: 9.375e-06, eta: 1:52:33, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2070, decode.acc_seg: 91.5460, loss: 0.2070
+2023-03-04 23:13:41,271 - mmseg - INFO - Iter [46050/80000]	lr: 9.375e-06, eta: 1:52:23, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.0440, loss: 0.2189
+2023-03-04 23:13:50,692 - mmseg - INFO - Iter [46100/80000]	lr: 9.375e-06, eta: 1:52:12, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2148, decode.acc_seg: 91.2120, loss: 0.2148
+2023-03-04 23:13:59,427 - mmseg - INFO - Iter [46150/80000]	lr: 9.375e-06, eta: 1:52:01, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2131, decode.acc_seg: 91.1495, loss: 0.2131
+2023-03-04 23:14:08,235 - mmseg - INFO - Iter [46200/80000]	lr: 9.375e-06, eta: 1:51:50, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.3373, loss: 0.2127
+2023-03-04 23:14:16,966 - mmseg - INFO - Iter [46250/80000]	lr: 9.375e-06, eta: 1:51:39, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2125, decode.acc_seg: 91.2649, loss: 0.2125
+2023-03-04 23:14:25,731 - mmseg - INFO - Iter [46300/80000]	lr: 9.375e-06, eta: 1:51:28, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2138, decode.acc_seg: 91.1221, loss: 0.2138
+2023-03-04 23:14:34,489 - mmseg - INFO - Iter [46350/80000]	lr: 9.375e-06, eta: 1:51:17, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.2246, loss: 0.2165
+2023-03-04 23:14:43,086 - mmseg - INFO - Iter [46400/80000]	lr: 9.375e-06, eta: 1:51:06, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2104, decode.acc_seg: 91.2238, loss: 0.2104
+2023-03-04 23:14:51,726 - mmseg - INFO - Iter [46450/80000]	lr: 9.375e-06, eta: 1:50:55, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 91.0627, loss: 0.2200
+2023-03-04 23:15:03,257 - mmseg - INFO - Iter [46500/80000]	lr: 9.375e-06, eta: 1:50:47, time: 0.230, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2158, decode.acc_seg: 91.0867, loss: 0.2158
+2023-03-04 23:15:12,060 - mmseg - INFO - Iter [46550/80000]	lr: 9.375e-06, eta: 1:50:36, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.1761, loss: 0.2171
+2023-03-04 23:15:20,912 - mmseg - INFO - Iter [46600/80000]	lr: 9.375e-06, eta: 1:50:25, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.1705, loss: 0.2153
+2023-03-04 23:15:29,929 - mmseg - INFO - Iter [46650/80000]	lr: 9.375e-06, eta: 1:50:14, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2179, decode.acc_seg: 90.9337, loss: 0.2179
+2023-03-04 23:15:39,065 - mmseg - INFO - Iter [46700/80000]	lr: 9.375e-06, eta: 1:50:04, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2233, decode.acc_seg: 90.9734, loss: 0.2233
+2023-03-04 23:15:47,628 - mmseg - INFO - Iter [46750/80000]	lr: 9.375e-06, eta: 1:49:53, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2058, decode.acc_seg: 91.5668, loss: 0.2058
+2023-03-04 23:15:56,373 - mmseg - INFO - Iter [46800/80000]	lr: 9.375e-06, eta: 1:49:42, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2152, decode.acc_seg: 91.2063, loss: 0.2152
+2023-03-04 23:16:04,891 - mmseg - INFO - Iter [46850/80000]	lr: 9.375e-06, eta: 1:49:31, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.2460, loss: 0.2127
+2023-03-04 23:16:13,608 - mmseg - INFO - Iter [46900/80000]	lr: 9.375e-06, eta: 1:49:20, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.3399, loss: 0.2107
+2023-03-04 23:16:22,281 - mmseg - INFO - Iter [46950/80000]	lr: 9.375e-06, eta: 1:49:09, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.2350, loss: 0.2143
+2023-03-04 23:16:30,767 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:16:30,767 - mmseg - INFO - Iter [47000/80000]	lr: 9.375e-06, eta: 1:48:58, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.5104, loss: 0.2076
+2023-03-04 23:16:39,931 - mmseg - INFO - Iter [47050/80000]	lr: 9.375e-06, eta: 1:48:47, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.3466, loss: 0.2103
+2023-03-04 23:16:48,789 - mmseg - INFO - Iter [47100/80000]	lr: 9.375e-06, eta: 1:48:36, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2157, decode.acc_seg: 91.2077, loss: 0.2157
+2023-03-04 23:17:00,104 - mmseg - INFO - Iter [47150/80000]	lr: 9.375e-06, eta: 1:48:28, time: 0.226, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2118, decode.acc_seg: 91.3227, loss: 0.2118
+2023-03-04 23:17:08,778 - mmseg - INFO - Iter [47200/80000]	lr: 9.375e-06, eta: 1:48:17, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2050, decode.acc_seg: 91.5840, loss: 0.2050
+2023-03-04 23:17:17,608 - mmseg - INFO - Iter [47250/80000]	lr: 9.375e-06, eta: 1:48:06, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2086, decode.acc_seg: 91.5494, loss: 0.2086
+2023-03-04 23:17:26,445 - mmseg - INFO - Iter [47300/80000]	lr: 9.375e-06, eta: 1:47:55, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2022, decode.acc_seg: 91.7508, loss: 0.2022
+2023-03-04 23:17:35,211 - mmseg - INFO - Iter [47350/80000]	lr: 9.375e-06, eta: 1:47:44, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.3905, loss: 0.2109
+2023-03-04 23:17:43,927 - mmseg - INFO - Iter [47400/80000]	lr: 9.375e-06, eta: 1:47:33, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.4876, loss: 0.2128
+2023-03-04 23:17:53,377 - mmseg - INFO - Iter [47450/80000]	lr: 9.375e-06, eta: 1:47:23, time: 0.189, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.4655, loss: 0.2116
+2023-03-04 23:18:02,628 - mmseg - INFO - Iter [47500/80000]	lr: 9.375e-06, eta: 1:47:13, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2129, decode.acc_seg: 91.2132, loss: 0.2129
+2023-03-04 23:18:11,511 - mmseg - INFO - Iter [47550/80000]	lr: 9.375e-06, eta: 1:47:02, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2218, decode.acc_seg: 91.0995, loss: 0.2218
+2023-03-04 23:18:20,186 - mmseg - INFO - Iter [47600/80000]	lr: 9.375e-06, eta: 1:46:51, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2125, decode.acc_seg: 91.3154, loss: 0.2125
+2023-03-04 23:18:29,162 - mmseg - INFO - Iter [47650/80000]	lr: 9.375e-06, eta: 1:46:40, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2141, decode.acc_seg: 91.2911, loss: 0.2141
+2023-03-04 23:18:38,305 - mmseg - INFO - Iter [47700/80000]	lr: 9.375e-06, eta: 1:46:30, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.3524, loss: 0.2120
+2023-03-04 23:18:47,603 - mmseg - INFO - Iter [47750/80000]	lr: 9.375e-06, eta: 1:46:19, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2124, decode.acc_seg: 91.2169, loss: 0.2124
+2023-03-04 23:18:58,769 - mmseg - INFO - Iter [47800/80000]	lr: 9.375e-06, eta: 1:46:11, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2151, decode.acc_seg: 91.2572, loss: 0.2151
+2023-03-04 23:19:07,627 - mmseg - INFO - Iter [47850/80000]	lr: 9.375e-06, eta: 1:46:00, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2125, decode.acc_seg: 91.3318, loss: 0.2125
+2023-03-04 23:19:16,583 - mmseg - INFO - Iter [47900/80000]	lr: 9.375e-06, eta: 1:45:49, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2067, decode.acc_seg: 91.4831, loss: 0.2067
+2023-03-04 23:19:25,725 - mmseg - INFO - Iter [47950/80000]	lr: 9.375e-06, eta: 1:45:39, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2175, decode.acc_seg: 91.2625, loss: 0.2175
+2023-03-04 23:19:34,680 - mmseg - INFO - Saving checkpoint at 48000 iterations
+2023-03-04 23:19:35,416 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:19:35,416 - mmseg - INFO - Iter [48000/80000]	lr: 9.375e-06, eta: 1:45:29, time: 0.194, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2185, decode.acc_seg: 90.9800, loss: 0.2185
+2023-03-04 23:19:50,883 - mmseg - INFO - per class results:
+2023-03-04 23:19:50,889 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.52 | 89.62 |
+|       building      | 80.86 | 92.79 |
+|         sky         | 94.21 | 97.73 |
+|        floor        | 81.09 | 89.86 |
+|         tree        | 72.74 | 86.66 |
+|       ceiling       | 84.12 | 93.07 |
+|         road        | 81.56 | 90.41 |
+|         bed         | 86.19 | 95.31 |
+|      windowpane     | 59.59 |  74.3 |
+|        grass        | 64.29 | 79.24 |
+|       cabinet       | 59.62 | 71.45 |
+|       sidewalk      | 63.01 | 78.99 |
+|        person       | 78.21 | 91.54 |
+|        earth        | 34.32 |  45.9 |
+|         door        | 42.77 | 52.46 |
+|        table        | 57.96 | 70.42 |
+|       mountain      | 55.47 | 70.26 |
+|        plant        | 48.39 | 57.93 |
+|       curtain       | 73.23 | 82.25 |
+|        chair        | 53.78 | 68.23 |
+|         car         | 80.19 | 92.19 |
+|        water        | 56.98 | 74.43 |
+|       painting      | 69.55 | 83.71 |
+|         sofa        | 61.92 | 83.73 |
+|        shelf        | 42.02 | 58.36 |
+|        house        | 40.37 | 53.86 |
+|         sea         | 59.58 | 77.18 |
+|        mirror       | 63.31 | 73.38 |
+|         rug         | 66.51 | 77.16 |
+|        field        | 27.45 | 46.87 |
+|       armchair      | 33.89 | 46.98 |
+|         seat        | 65.91 | 82.72 |
+|        fence        | 40.29 | 51.42 |
+|         desk        | 44.63 | 69.21 |
+|         rock        | 36.27 | 56.67 |
+|       wardrobe      |  56.6 |  66.8 |
+|         lamp        | 58.31 | 71.96 |
+|       bathtub       | 75.18 | 80.81 |
+|       railing       | 32.95 | 43.05 |
+|       cushion       |  54.0 | 63.97 |
+|         base        | 22.54 | 27.56 |
+|         box         | 22.45 |  31.0 |
+|        column       | 45.28 | 55.31 |
+|      signboard      |  36.3 | 48.14 |
+|   chest of drawers  | 35.89 | 59.04 |
+|       counter       | 29.42 | 38.47 |
+|         sand        |  43.4 | 62.31 |
+|         sink        |  64.1 | 75.18 |
+|      skyscraper     | 48.65 | 60.54 |
+|      fireplace      | 72.98 | 84.47 |
+|     refrigerator    | 70.89 | 83.43 |
+|      grandstand     | 46.91 | 65.09 |
+|         path        | 22.57 | 29.63 |
+|        stairs       | 30.43 | 36.76 |
+|        runway       |  67.9 | 87.47 |
+|         case        | 49.15 | 59.66 |
+|      pool table     | 91.49 | 94.85 |
+|        pillow       | 56.54 |  65.3 |
+|     screen door     | 63.68 | 70.78 |
+|       stairway      | 24.43 |  36.4 |
+|        river        |  11.5 | 22.16 |
+|        bridge       | 29.35 | 33.92 |
+|       bookcase      |  42.4 |  61.6 |
+|        blind        | 44.19 | 50.97 |
+|     coffee table    | 48.44 | 81.74 |
+|        toilet       | 81.81 | 89.43 |
+|        flower       | 36.56 | 49.06 |
+|         book        | 42.46 | 63.71 |
+|         hill        | 14.02 | 25.87 |
+|        bench        | 40.46 | 52.56 |
+|      countertop     | 52.76 | 71.52 |
+|        stove        | 69.25 | 82.17 |
+|         palm        |  48.5 | 70.37 |
+|    kitchen island   | 37.51 | 61.97 |
+|       computer      | 59.35 | 68.43 |
+|     swivel chair    | 44.62 | 67.92 |
+|         boat        | 69.65 | 80.96 |
+|         bar         | 23.36 | 32.34 |
+|    arcade machine   | 70.43 | 72.81 |
+|        hovel        | 24.68 | 27.01 |
+|         bus         | 78.19 | 89.78 |
+|        towel        | 63.07 | 70.69 |
+|        light        | 45.65 | 49.54 |
+|        truck        | 14.87 | 19.99 |
+|        tower        |  6.62 | 10.53 |
+|      chandelier     | 62.41 | 78.71 |
+|        awning       |  20.6 | 23.61 |
+|     streetlight     | 23.87 | 34.21 |
+|        booth        | 39.06 | 40.02 |
+| television receiver | 63.57 | 75.62 |
+|       airplane      | 56.67 | 61.77 |
+|      dirt track     | 12.46 | 26.48 |
+|       apparel       | 34.54 | 56.15 |
+|         pole        | 14.94 | 18.52 |
+|         land        |  2.61 |  3.44 |
+|      bannister      |  9.85 | 12.39 |
+|      escalator      | 23.86 | 26.13 |
+|       ottoman       | 41.44 | 62.03 |
+|        bottle       | 34.43 | 56.23 |
+|        buffet       | 39.87 | 45.84 |
+|        poster       |  22.7 | 37.47 |
+|        stage        | 14.47 | 18.26 |
+|         van         | 38.01 |  50.6 |
+|         ship        | 75.17 | 95.38 |
+|       fountain      | 15.03 | 15.31 |
+|    conveyer belt    | 82.47 | 88.65 |
+|        canopy       | 26.52 | 28.49 |
+|        washer       | 77.54 | 79.07 |
+|      plaything      | 20.67 | 26.79 |
+|    swimming pool    | 75.54 | 82.09 |
+|        stool        | 39.99 | 57.68 |
+|        barrel       | 38.54 | 61.33 |
+|        basket       | 23.93 | 36.71 |
+|      waterfall      | 52.04 | 67.65 |
+|         tent        | 93.93 |  97.3 |
+|         bag         | 15.62 | 20.22 |
+|       minibike      | 60.56 | 74.85 |
+|        cradle       | 82.46 | 95.95 |
+|         oven        | 46.56 | 65.09 |
+|         ball        | 45.26 | 52.57 |
+|         food        | 50.05 | 59.66 |
+|         step        |  5.87 |  6.61 |
+|         tank        |  52.1 | 57.66 |
+|      trade name     | 25.95 | 29.08 |
+|      microwave      | 74.13 | 80.14 |
+|         pot         | 29.66 | 33.98 |
+|        animal       | 52.85 |  59.4 |
+|       bicycle       | 51.32 | 64.88 |
+|         lake        | 56.89 | 62.92 |
+|      dishwasher     |  63.9 | 76.32 |
+|        screen       | 68.49 | 80.32 |
+|       blanket       | 16.27 | 18.78 |
+|      sculpture      | 56.41 | 78.07 |
+|         hood        |  54.5 |  60.8 |
+|        sconce       | 40.42 | 47.92 |
+|         vase        | 31.34 | 49.41 |
+|    traffic light    | 29.65 | 42.87 |
+|         tray        |  4.83 |  8.04 |
+|        ashcan       | 39.62 | 50.41 |
+|         fan         | 55.98 | 67.22 |
+|         pier        | 50.96 | 69.35 |
+|      crt screen     |  8.92 | 22.28 |
+|        plate        | 48.18 | 67.34 |
+|       monitor       | 14.55 | 17.06 |
+|    bulletin board   | 41.13 | 55.87 |
+|        shower       |  1.22 |  5.44 |
+|       radiator      | 56.71 | 63.73 |
+|        glass        | 12.53 | 14.37 |
+|        clock        | 33.51 | 37.88 |
+|         flag        | 36.45 | 41.02 |
++---------------------+-------+-------+
+2023-03-04 23:19:50,889 - mmseg - INFO - Summary:
+2023-03-04 23:19:50,889 - mmseg - INFO - 
++-------+------+-------+
+|  aAcc | mIoU |  mAcc |
++-------+------+-------+
+| 82.07 | 47.0 | 58.08 |
++-------+------+-------+
+2023-03-04 23:19:50,912 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_40000.pth was removed
+2023-03-04 23:19:51,658 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_48000.pth.
+2023-03-04 23:19:51,658 - mmseg - INFO - Best mIoU is 0.4700 at 48000 iter.
+2023-03-04 23:19:51,659 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:19:51,659 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8207, mIoU: 0.4700, mAcc: 0.5808, IoU.background: nan, IoU.wall: 0.7652, IoU.building: 0.8086, IoU.sky: 0.9421, IoU.floor: 0.8109, IoU.tree: 0.7274, IoU.ceiling: 0.8412, IoU.road: 0.8156, IoU.bed : 0.8619, IoU.windowpane: 0.5959, IoU.grass: 0.6429, IoU.cabinet: 0.5962, IoU.sidewalk: 0.6301, IoU.person: 0.7821, IoU.earth: 0.3432, IoU.door: 0.4277, IoU.table: 0.5796, IoU.mountain: 0.5547, IoU.plant: 0.4839, IoU.curtain: 0.7323, IoU.chair: 0.5378, IoU.car: 0.8019, IoU.water: 0.5698, IoU.painting: 0.6955, IoU.sofa: 0.6192, IoU.shelf: 0.4202, IoU.house: 0.4037, IoU.sea: 0.5958, IoU.mirror: 0.6331, IoU.rug: 0.6651, IoU.field: 0.2745, IoU.armchair: 0.3389, IoU.seat: 0.6591, IoU.fence: 0.4029, IoU.desk: 0.4463, IoU.rock: 0.3627, IoU.wardrobe: 0.5660, IoU.lamp: 0.5831, IoU.bathtub: 0.7518, IoU.railing: 0.3295, IoU.cushion: 0.5400, IoU.base: 0.2254, IoU.box: 0.2245, IoU.column: 0.4528, IoU.signboard: 0.3630, IoU.chest of drawers: 0.3589, IoU.counter: 0.2942, IoU.sand: 0.4340, IoU.sink: 0.6410, IoU.skyscraper: 0.4865, IoU.fireplace: 0.7298, IoU.refrigerator: 0.7089, IoU.grandstand: 0.4691, IoU.path: 0.2257, IoU.stairs: 0.3043, IoU.runway: 0.6790, IoU.case: 0.4915, IoU.pool table: 0.9149, IoU.pillow: 0.5654, IoU.screen door: 0.6368, IoU.stairway: 0.2443, IoU.river: 0.1150, IoU.bridge: 0.2935, IoU.bookcase: 0.4240, IoU.blind: 0.4419, IoU.coffee table: 0.4844, IoU.toilet: 0.8181, IoU.flower: 0.3656, IoU.book: 0.4246, IoU.hill: 0.1402, IoU.bench: 0.4046, IoU.countertop: 0.5276, IoU.stove: 0.6925, IoU.palm: 0.4850, IoU.kitchen island: 0.3751, IoU.computer: 0.5935, IoU.swivel chair: 0.4462, IoU.boat: 0.6965, IoU.bar: 0.2336, IoU.arcade machine: 0.7043, IoU.hovel: 0.2468, IoU.bus: 0.7819, IoU.towel: 0.6307, IoU.light: 0.4565, IoU.truck: 0.1487, IoU.tower: 0.0662, IoU.chandelier: 0.6241, IoU.awning: 0.2060, IoU.streetlight: 0.2387, IoU.booth: 0.3906, IoU.television receiver: 0.6357, IoU.airplane: 0.5667, IoU.dirt track: 0.1246, IoU.apparel: 0.3454, IoU.pole: 0.1494, IoU.land: 0.0261, IoU.bannister: 0.0985, IoU.escalator: 0.2386, IoU.ottoman: 0.4144, IoU.bottle: 0.3443, IoU.buffet: 0.3987, IoU.poster: 0.2270, IoU.stage: 0.1447, IoU.van: 0.3801, IoU.ship: 0.7517, IoU.fountain: 0.1503, IoU.conveyer belt: 0.8247, IoU.canopy: 0.2652, IoU.washer: 0.7754, IoU.plaything: 0.2067, IoU.swimming pool: 0.7554, IoU.stool: 0.3999, IoU.barrel: 0.3854, IoU.basket: 0.2393, IoU.waterfall: 0.5204, IoU.tent: 0.9393, IoU.bag: 0.1562, IoU.minibike: 0.6056, IoU.cradle: 0.8246, IoU.oven: 0.4656, IoU.ball: 0.4526, IoU.food: 0.5005, IoU.step: 0.0587, IoU.tank: 0.5210, IoU.trade name: 0.2595, IoU.microwave: 0.7413, IoU.pot: 0.2966, IoU.animal: 0.5285, IoU.bicycle: 0.5132, IoU.lake: 0.5689, IoU.dishwasher: 0.6390, IoU.screen: 0.6849, IoU.blanket: 0.1627, IoU.sculpture: 0.5641, IoU.hood: 0.5450, IoU.sconce: 0.4042, IoU.vase: 0.3134, IoU.traffic light: 0.2965, IoU.tray: 0.0483, IoU.ashcan: 0.3962, IoU.fan: 0.5598, IoU.pier: 0.5096, IoU.crt screen: 0.0892, IoU.plate: 0.4818, IoU.monitor: 0.1455, IoU.bulletin board: 0.4113, IoU.shower: 0.0122, IoU.radiator: 0.5671, IoU.glass: 0.1253, IoU.clock: 0.3351, IoU.flag: 0.3645, Acc.background: nan, Acc.wall: 0.8962, Acc.building: 0.9279, Acc.sky: 0.9773, Acc.floor: 0.8986, Acc.tree: 0.8666, Acc.ceiling: 0.9307, Acc.road: 0.9041, Acc.bed : 0.9531, Acc.windowpane: 0.7430, Acc.grass: 0.7924, Acc.cabinet: 0.7145, Acc.sidewalk: 0.7899, Acc.person: 0.9154, Acc.earth: 0.4590, Acc.door: 0.5246, Acc.table: 0.7042, Acc.mountain: 0.7026, Acc.plant: 0.5793, Acc.curtain: 0.8225, Acc.chair: 0.6823, Acc.car: 0.9219, Acc.water: 0.7443, Acc.painting: 0.8371, Acc.sofa: 0.8373, Acc.shelf: 0.5836, Acc.house: 0.5386, Acc.sea: 0.7718, Acc.mirror: 0.7338, Acc.rug: 0.7716, Acc.field: 0.4687, Acc.armchair: 0.4698, Acc.seat: 0.8272, Acc.fence: 0.5142, Acc.desk: 0.6921, Acc.rock: 0.5667, Acc.wardrobe: 0.6680, Acc.lamp: 0.7196, Acc.bathtub: 0.8081, Acc.railing: 0.4305, Acc.cushion: 0.6397, Acc.base: 0.2756, Acc.box: 0.3100, Acc.column: 0.5531, Acc.signboard: 0.4814, Acc.chest of drawers: 0.5904, Acc.counter: 0.3847, Acc.sand: 0.6231, Acc.sink: 0.7518, Acc.skyscraper: 0.6054, Acc.fireplace: 0.8447, Acc.refrigerator: 0.8343, Acc.grandstand: 0.6509, Acc.path: 0.2963, Acc.stairs: 0.3676, Acc.runway: 0.8747, Acc.case: 0.5966, Acc.pool table: 0.9485, Acc.pillow: 0.6530, Acc.screen door: 0.7078, Acc.stairway: 0.3640, Acc.river: 0.2216, Acc.bridge: 0.3392, Acc.bookcase: 0.6160, Acc.blind: 0.5097, Acc.coffee table: 0.8174, Acc.toilet: 0.8943, Acc.flower: 0.4906, Acc.book: 0.6371, Acc.hill: 0.2587, Acc.bench: 0.5256, Acc.countertop: 0.7152, Acc.stove: 0.8217, Acc.palm: 0.7037, Acc.kitchen island: 0.6197, Acc.computer: 0.6843, Acc.swivel chair: 0.6792, Acc.boat: 0.8096, Acc.bar: 0.3234, Acc.arcade machine: 0.7281, Acc.hovel: 0.2701, Acc.bus: 0.8978, Acc.towel: 0.7069, Acc.light: 0.4954, Acc.truck: 0.1999, Acc.tower: 0.1053, Acc.chandelier: 0.7871, Acc.awning: 0.2361, Acc.streetlight: 0.3421, Acc.booth: 0.4002, Acc.television receiver: 0.7562, Acc.airplane: 0.6177, Acc.dirt track: 0.2648, Acc.apparel: 0.5615, Acc.pole: 0.1852, Acc.land: 0.0344, Acc.bannister: 0.1239, Acc.escalator: 0.2613, Acc.ottoman: 0.6203, Acc.bottle: 0.5623, Acc.buffet: 0.4584, Acc.poster: 0.3747, Acc.stage: 0.1826, Acc.van: 0.5060, Acc.ship: 0.9538, Acc.fountain: 0.1531, Acc.conveyer belt: 0.8865, Acc.canopy: 0.2849, Acc.washer: 0.7907, Acc.plaything: 0.2679, Acc.swimming pool: 0.8209, Acc.stool: 0.5768, Acc.barrel: 0.6133, Acc.basket: 0.3671, Acc.waterfall: 0.6765, Acc.tent: 0.9730, Acc.bag: 0.2022, Acc.minibike: 0.7485, Acc.cradle: 0.9595, Acc.oven: 0.6509, Acc.ball: 0.5257, Acc.food: 0.5966, Acc.step: 0.0661, Acc.tank: 0.5766, Acc.trade name: 0.2908, Acc.microwave: 0.8014, Acc.pot: 0.3398, Acc.animal: 0.5940, Acc.bicycle: 0.6488, Acc.lake: 0.6292, Acc.dishwasher: 0.7632, Acc.screen: 0.8032, Acc.blanket: 0.1878, Acc.sculpture: 0.7807, Acc.hood: 0.6080, Acc.sconce: 0.4792, Acc.vase: 0.4941, Acc.traffic light: 0.4287, Acc.tray: 0.0804, Acc.ashcan: 0.5041, Acc.fan: 0.6722, Acc.pier: 0.6935, Acc.crt screen: 0.2228, Acc.plate: 0.6734, Acc.monitor: 0.1706, Acc.bulletin board: 0.5587, Acc.shower: 0.0544, Acc.radiator: 0.6373, Acc.glass: 0.1437, Acc.clock: 0.3788, Acc.flag: 0.4102
+2023-03-04 23:20:00,711 - mmseg - INFO - Iter [48050/80000]	lr: 9.375e-06, eta: 1:45:31, time: 0.506, data_time: 0.332, memory: 52390, decode.loss_ce: 0.2229, decode.acc_seg: 91.0077, loss: 0.2229
+2023-03-04 23:20:09,684 - mmseg - INFO - Iter [48100/80000]	lr: 9.375e-06, eta: 1:45:20, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2155, decode.acc_seg: 91.1637, loss: 0.2155
+2023-03-04 23:20:18,424 - mmseg - INFO - Iter [48150/80000]	lr: 9.375e-06, eta: 1:45:10, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2137, decode.acc_seg: 91.1289, loss: 0.2137
+2023-03-04 23:20:27,112 - mmseg - INFO - Iter [48200/80000]	lr: 9.375e-06, eta: 1:44:59, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.3869, loss: 0.2154
+2023-03-04 23:20:35,854 - mmseg - INFO - Iter [48250/80000]	lr: 9.375e-06, eta: 1:44:48, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.2176, loss: 0.2140
+2023-03-04 23:20:44,652 - mmseg - INFO - Iter [48300/80000]	lr: 9.375e-06, eta: 1:44:37, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2170, decode.acc_seg: 91.0636, loss: 0.2170
+2023-03-04 23:20:53,457 - mmseg - INFO - Iter [48350/80000]	lr: 9.375e-06, eta: 1:44:26, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2137, decode.acc_seg: 91.3780, loss: 0.2137
+2023-03-04 23:21:04,922 - mmseg - INFO - Iter [48400/80000]	lr: 9.375e-06, eta: 1:44:18, time: 0.229, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2081, decode.acc_seg: 91.5687, loss: 0.2081
+2023-03-04 23:21:13,617 - mmseg - INFO - Iter [48450/80000]	lr: 9.375e-06, eta: 1:44:07, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2148, decode.acc_seg: 91.4316, loss: 0.2148
+2023-03-04 23:21:22,222 - mmseg - INFO - Iter [48500/80000]	lr: 9.375e-06, eta: 1:43:56, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.4385, loss: 0.2127
+2023-03-04 23:21:31,675 - mmseg - INFO - Iter [48550/80000]	lr: 9.375e-06, eta: 1:43:46, time: 0.189, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.1213, loss: 0.2163
+2023-03-04 23:21:40,546 - mmseg - INFO - Iter [48600/80000]	lr: 9.375e-06, eta: 1:43:35, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2142, decode.acc_seg: 91.1816, loss: 0.2142
+2023-03-04 23:21:49,031 - mmseg - INFO - Iter [48650/80000]	lr: 9.375e-06, eta: 1:43:24, time: 0.170, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.2467, loss: 0.2116
+2023-03-04 23:21:57,768 - mmseg - INFO - Iter [48700/80000]	lr: 9.375e-06, eta: 1:43:13, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.4711, loss: 0.2072
+2023-03-04 23:22:06,724 - mmseg - INFO - Iter [48750/80000]	lr: 9.375e-06, eta: 1:43:03, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2119, decode.acc_seg: 91.2681, loss: 0.2119
+2023-03-04 23:22:15,500 - mmseg - INFO - Iter [48800/80000]	lr: 9.375e-06, eta: 1:42:52, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.2411, loss: 0.2123
+2023-03-04 23:22:24,073 - mmseg - INFO - Iter [48850/80000]	lr: 9.375e-06, eta: 1:42:41, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2201, decode.acc_seg: 90.9742, loss: 0.2201
+2023-03-04 23:22:32,545 - mmseg - INFO - Iter [48900/80000]	lr: 9.375e-06, eta: 1:42:30, time: 0.169, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2136, decode.acc_seg: 91.3882, loss: 0.2136
+2023-03-04 23:22:41,383 - mmseg - INFO - Iter [48950/80000]	lr: 9.375e-06, eta: 1:42:19, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2224, decode.acc_seg: 90.8354, loss: 0.2224
+2023-03-04 23:22:50,389 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:22:50,389 - mmseg - INFO - Iter [49000/80000]	lr: 9.375e-06, eta: 1:42:09, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2145, decode.acc_seg: 91.3921, loss: 0.2145
+2023-03-04 23:23:01,846 - mmseg - INFO - Iter [49050/80000]	lr: 9.375e-06, eta: 1:42:00, time: 0.229, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2048, decode.acc_seg: 91.4949, loss: 0.2048
+2023-03-04 23:23:10,533 - mmseg - INFO - Iter [49100/80000]	lr: 9.375e-06, eta: 1:41:49, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.2823, loss: 0.2107
+2023-03-04 23:23:19,539 - mmseg - INFO - Iter [49150/80000]	lr: 9.375e-06, eta: 1:41:39, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.1219, loss: 0.2196
+2023-03-04 23:23:28,413 - mmseg - INFO - Iter [49200/80000]	lr: 9.375e-06, eta: 1:41:28, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.3964, loss: 0.2098
+2023-03-04 23:23:37,729 - mmseg - INFO - Iter [49250/80000]	lr: 9.375e-06, eta: 1:41:18, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2172, decode.acc_seg: 91.2802, loss: 0.2172
+2023-03-04 23:23:46,399 - mmseg - INFO - Iter [49300/80000]	lr: 9.375e-06, eta: 1:41:07, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2057, decode.acc_seg: 91.5823, loss: 0.2057
+2023-03-04 23:23:55,158 - mmseg - INFO - Iter [49350/80000]	lr: 9.375e-06, eta: 1:40:56, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2187, decode.acc_seg: 90.9884, loss: 0.2187
+2023-03-04 23:24:03,737 - mmseg - INFO - Iter [49400/80000]	lr: 9.375e-06, eta: 1:40:45, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2070, decode.acc_seg: 91.3621, loss: 0.2070
+2023-03-04 23:24:12,655 - mmseg - INFO - Iter [49450/80000]	lr: 9.375e-06, eta: 1:40:35, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2155, decode.acc_seg: 91.3086, loss: 0.2155
+2023-03-04 23:24:21,612 - mmseg - INFO - Iter [49500/80000]	lr: 9.375e-06, eta: 1:40:24, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.1398, loss: 0.2189
+2023-03-04 23:24:30,230 - mmseg - INFO - Iter [49550/80000]	lr: 9.375e-06, eta: 1:40:13, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2119, decode.acc_seg: 91.3226, loss: 0.2119
+2023-03-04 23:24:39,413 - mmseg - INFO - Iter [49600/80000]	lr: 9.375e-06, eta: 1:40:03, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2052, decode.acc_seg: 91.7124, loss: 0.2052
+2023-03-04 23:24:50,753 - mmseg - INFO - Iter [49650/80000]	lr: 9.375e-06, eta: 1:39:54, time: 0.227, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2184, decode.acc_seg: 91.2062, loss: 0.2184
+2023-03-04 23:24:59,604 - mmseg - INFO - Iter [49700/80000]	lr: 9.375e-06, eta: 1:39:44, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2138, decode.acc_seg: 91.2878, loss: 0.2138
+2023-03-04 23:25:08,328 - mmseg - INFO - Iter [49750/80000]	lr: 9.375e-06, eta: 1:39:33, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.3070, loss: 0.2140
+2023-03-04 23:25:16,957 - mmseg - INFO - Iter [49800/80000]	lr: 9.375e-06, eta: 1:39:22, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2118, decode.acc_seg: 91.4111, loss: 0.2118
+2023-03-04 23:25:26,058 - mmseg - INFO - Iter [49850/80000]	lr: 9.375e-06, eta: 1:39:12, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.3156, loss: 0.2127
+2023-03-04 23:25:35,167 - mmseg - INFO - Iter [49900/80000]	lr: 9.375e-06, eta: 1:39:01, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.3517, loss: 0.2114
+2023-03-04 23:25:44,175 - mmseg - INFO - Iter [49950/80000]	lr: 9.375e-06, eta: 1:38:51, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.2622, loss: 0.2117
+2023-03-04 23:25:52,816 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:25:52,816 - mmseg - INFO - Iter [50000/80000]	lr: 9.375e-06, eta: 1:38:40, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2192, decode.acc_seg: 91.0601, loss: 0.2192
+2023-03-04 23:26:01,564 - mmseg - INFO - Iter [50050/80000]	lr: 4.687e-06, eta: 1:38:29, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2135, decode.acc_seg: 91.1247, loss: 0.2135
+2023-03-04 23:26:10,083 - mmseg - INFO - Iter [50100/80000]	lr: 4.687e-06, eta: 1:38:19, time: 0.170, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2162, decode.acc_seg: 91.1288, loss: 0.2162
+2023-03-04 23:26:19,074 - mmseg - INFO - Iter [50150/80000]	lr: 4.687e-06, eta: 1:38:08, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.2457, loss: 0.2089
+2023-03-04 23:26:27,742 - mmseg - INFO - Iter [50200/80000]	lr: 4.687e-06, eta: 1:37:57, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2150, decode.acc_seg: 91.2249, loss: 0.2150
+2023-03-04 23:26:36,906 - mmseg - INFO - Iter [50250/80000]	lr: 4.687e-06, eta: 1:37:47, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.3460, loss: 0.2120
+2023-03-04 23:26:48,681 - mmseg - INFO - Iter [50300/80000]	lr: 4.687e-06, eta: 1:37:39, time: 0.236, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2201, decode.acc_seg: 91.2775, loss: 0.2201
+2023-03-04 23:26:57,814 - mmseg - INFO - Iter [50350/80000]	lr: 4.687e-06, eta: 1:37:28, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2133, decode.acc_seg: 91.3738, loss: 0.2133
+2023-03-04 23:27:07,020 - mmseg - INFO - Iter [50400/80000]	lr: 4.687e-06, eta: 1:37:18, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2086, decode.acc_seg: 91.4866, loss: 0.2086
+2023-03-04 23:27:15,992 - mmseg - INFO - Iter [50450/80000]	lr: 4.687e-06, eta: 1:37:07, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.2962, loss: 0.2099
+2023-03-04 23:27:24,982 - mmseg - INFO - Iter [50500/80000]	lr: 4.687e-06, eta: 1:36:57, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.3165, loss: 0.2154
+2023-03-04 23:27:33,810 - mmseg - INFO - Iter [50550/80000]	lr: 4.687e-06, eta: 1:36:46, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2159, decode.acc_seg: 91.1529, loss: 0.2159
+2023-03-04 23:27:43,209 - mmseg - INFO - Iter [50600/80000]	lr: 4.687e-06, eta: 1:36:36, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2093, decode.acc_seg: 91.3882, loss: 0.2093
+2023-03-04 23:27:51,959 - mmseg - INFO - Iter [50650/80000]	lr: 4.687e-06, eta: 1:36:26, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2209, decode.acc_seg: 90.9538, loss: 0.2209
+2023-03-04 23:28:00,991 - mmseg - INFO - Iter [50700/80000]	lr: 4.687e-06, eta: 1:36:15, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2177, decode.acc_seg: 91.2588, loss: 0.2177
+2023-03-04 23:28:09,985 - mmseg - INFO - Iter [50750/80000]	lr: 4.687e-06, eta: 1:36:05, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.4821, loss: 0.2075
+2023-03-04 23:28:19,022 - mmseg - INFO - Iter [50800/80000]	lr: 4.687e-06, eta: 1:35:54, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2131, decode.acc_seg: 91.3175, loss: 0.2131
+2023-03-04 23:28:28,101 - mmseg - INFO - Iter [50850/80000]	lr: 4.687e-06, eta: 1:35:44, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2084, decode.acc_seg: 91.4318, loss: 0.2084
+2023-03-04 23:28:36,822 - mmseg - INFO - Iter [50900/80000]	lr: 4.687e-06, eta: 1:35:33, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.4474, loss: 0.2123
+2023-03-04 23:28:48,274 - mmseg - INFO - Iter [50950/80000]	lr: 4.687e-06, eta: 1:35:24, time: 0.229, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.1639, loss: 0.2168
+2023-03-04 23:28:56,789 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:28:56,789 - mmseg - INFO - Iter [51000/80000]	lr: 4.687e-06, eta: 1:35:14, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2070, decode.acc_seg: 91.3381, loss: 0.2070
+2023-03-04 23:29:05,691 - mmseg - INFO - Iter [51050/80000]	lr: 4.687e-06, eta: 1:35:03, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2080, decode.acc_seg: 91.4810, loss: 0.2080
+2023-03-04 23:29:14,220 - mmseg - INFO - Iter [51100/80000]	lr: 4.687e-06, eta: 1:34:53, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2096, decode.acc_seg: 91.3048, loss: 0.2096
+2023-03-04 23:29:22,861 - mmseg - INFO - Iter [51150/80000]	lr: 4.687e-06, eta: 1:34:42, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2158, decode.acc_seg: 91.1119, loss: 0.2158
+2023-03-04 23:29:32,154 - mmseg - INFO - Iter [51200/80000]	lr: 4.687e-06, eta: 1:34:32, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2045, decode.acc_seg: 91.6616, loss: 0.2045
+2023-03-04 23:29:40,882 - mmseg - INFO - Iter [51250/80000]	lr: 4.687e-06, eta: 1:34:21, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2207, decode.acc_seg: 91.1678, loss: 0.2207
+2023-03-04 23:29:50,061 - mmseg - INFO - Iter [51300/80000]	lr: 4.687e-06, eta: 1:34:11, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2166, decode.acc_seg: 91.2597, loss: 0.2166
+2023-03-04 23:29:59,441 - mmseg - INFO - Iter [51350/80000]	lr: 4.687e-06, eta: 1:34:01, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2100, decode.acc_seg: 91.4526, loss: 0.2100
+2023-03-04 23:30:08,438 - mmseg - INFO - Iter [51400/80000]	lr: 4.687e-06, eta: 1:33:50, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2157, decode.acc_seg: 91.1766, loss: 0.2157
+2023-03-04 23:30:17,451 - mmseg - INFO - Iter [51450/80000]	lr: 4.687e-06, eta: 1:33:40, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2028, decode.acc_seg: 91.6946, loss: 0.2028
+2023-03-04 23:30:26,286 - mmseg - INFO - Iter [51500/80000]	lr: 4.687e-06, eta: 1:33:29, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.4103, loss: 0.2103
+2023-03-04 23:30:37,488 - mmseg - INFO - Iter [51550/80000]	lr: 4.687e-06, eta: 1:33:20, time: 0.224, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2157, decode.acc_seg: 91.1423, loss: 0.2157
+2023-03-04 23:30:46,434 - mmseg - INFO - Iter [51600/80000]	lr: 4.687e-06, eta: 1:33:10, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.2374, loss: 0.2117
+2023-03-04 23:30:55,003 - mmseg - INFO - Iter [51650/80000]	lr: 4.687e-06, eta: 1:32:59, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.4033, loss: 0.2143
+2023-03-04 23:31:04,038 - mmseg - INFO - Iter [51700/80000]	lr: 4.687e-06, eta: 1:32:49, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2048, decode.acc_seg: 91.4812, loss: 0.2048
+2023-03-04 23:31:12,744 - mmseg - INFO - Iter [51750/80000]	lr: 4.687e-06, eta: 1:32:38, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2044, decode.acc_seg: 91.6512, loss: 0.2044
+2023-03-04 23:31:21,267 - mmseg - INFO - Iter [51800/80000]	lr: 4.687e-06, eta: 1:32:28, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2025, decode.acc_seg: 91.6347, loss: 0.2025
+2023-03-04 23:31:31,133 - mmseg - INFO - Iter [51850/80000]	lr: 4.687e-06, eta: 1:32:18, time: 0.197, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.4029, loss: 0.2143
+2023-03-04 23:31:40,077 - mmseg - INFO - Iter [51900/80000]	lr: 4.687e-06, eta: 1:32:07, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2122, decode.acc_seg: 91.3528, loss: 0.2122
+2023-03-04 23:31:48,788 - mmseg - INFO - Iter [51950/80000]	lr: 4.687e-06, eta: 1:31:57, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2090, decode.acc_seg: 91.4430, loss: 0.2090
+2023-03-04 23:31:57,889 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:31:57,889 - mmseg - INFO - Iter [52000/80000]	lr: 4.687e-06, eta: 1:31:47, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.4448, loss: 0.2073
+2023-03-04 23:32:06,928 - mmseg - INFO - Iter [52050/80000]	lr: 4.687e-06, eta: 1:31:36, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2141, decode.acc_seg: 91.2116, loss: 0.2141
+2023-03-04 23:32:16,037 - mmseg - INFO - Iter [52100/80000]	lr: 4.687e-06, eta: 1:31:26, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.4021, loss: 0.2076
+2023-03-04 23:32:24,920 - mmseg - INFO - Iter [52150/80000]	lr: 4.687e-06, eta: 1:31:15, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2149, decode.acc_seg: 91.2880, loss: 0.2149
+2023-03-04 23:32:36,121 - mmseg - INFO - Iter [52200/80000]	lr: 4.687e-06, eta: 1:31:07, time: 0.224, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2149, decode.acc_seg: 91.2081, loss: 0.2149
+2023-03-04 23:32:44,772 - mmseg - INFO - Iter [52250/80000]	lr: 4.687e-06, eta: 1:30:56, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2141, decode.acc_seg: 91.3942, loss: 0.2141
+2023-03-04 23:32:53,237 - mmseg - INFO - Iter [52300/80000]	lr: 4.687e-06, eta: 1:30:45, time: 0.169, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.4429, loss: 0.2121
+2023-03-04 23:33:01,955 - mmseg - INFO - Iter [52350/80000]	lr: 4.687e-06, eta: 1:30:35, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2034, decode.acc_seg: 91.7013, loss: 0.2034
+2023-03-04 23:33:10,965 - mmseg - INFO - Iter [52400/80000]	lr: 4.687e-06, eta: 1:30:24, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2160, decode.acc_seg: 91.2413, loss: 0.2160
+2023-03-04 23:33:20,294 - mmseg - INFO - Iter [52450/80000]	lr: 4.687e-06, eta: 1:30:14, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.3547, loss: 0.2099
+2023-03-04 23:33:29,008 - mmseg - INFO - Iter [52500/80000]	lr: 4.687e-06, eta: 1:30:04, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.4088, loss: 0.2103
+2023-03-04 23:33:37,840 - mmseg - INFO - Iter [52550/80000]	lr: 4.687e-06, eta: 1:29:53, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.3228, loss: 0.2163
+2023-03-04 23:33:46,453 - mmseg - INFO - Iter [52600/80000]	lr: 4.687e-06, eta: 1:29:43, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.4044, loss: 0.2123
+2023-03-04 23:33:55,552 - mmseg - INFO - Iter [52650/80000]	lr: 4.687e-06, eta: 1:29:32, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2164, decode.acc_seg: 91.2623, loss: 0.2164
+2023-03-04 23:34:04,162 - mmseg - INFO - Iter [52700/80000]	lr: 4.687e-06, eta: 1:29:22, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2036, decode.acc_seg: 91.5536, loss: 0.2036
+2023-03-04 23:34:13,320 - mmseg - INFO - Iter [52750/80000]	lr: 4.687e-06, eta: 1:29:12, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2058, decode.acc_seg: 91.5680, loss: 0.2058
+2023-03-04 23:34:22,027 - mmseg - INFO - Iter [52800/80000]	lr: 4.687e-06, eta: 1:29:01, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2126, decode.acc_seg: 91.2637, loss: 0.2126
+2023-03-04 23:34:33,333 - mmseg - INFO - Iter [52850/80000]	lr: 4.687e-06, eta: 1:28:52, time: 0.226, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2044, decode.acc_seg: 91.7135, loss: 0.2044
+2023-03-04 23:34:42,393 - mmseg - INFO - Iter [52900/80000]	lr: 4.687e-06, eta: 1:28:42, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.3235, loss: 0.2111
+2023-03-04 23:34:51,110 - mmseg - INFO - Iter [52950/80000]	lr: 4.687e-06, eta: 1:28:31, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2137, decode.acc_seg: 91.3276, loss: 0.2137
+2023-03-04 23:34:59,819 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:34:59,820 - mmseg - INFO - Iter [53000/80000]	lr: 4.687e-06, eta: 1:28:21, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.1776, loss: 0.2165
+2023-03-04 23:35:08,426 - mmseg - INFO - Iter [53050/80000]	lr: 4.687e-06, eta: 1:28:10, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2166, decode.acc_seg: 91.0884, loss: 0.2166
+2023-03-04 23:35:17,147 - mmseg - INFO - Iter [53100/80000]	lr: 4.687e-06, eta: 1:28:00, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2038, decode.acc_seg: 91.6527, loss: 0.2038
+2023-03-04 23:35:26,077 - mmseg - INFO - Iter [53150/80000]	lr: 4.687e-06, eta: 1:27:50, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2012, decode.acc_seg: 91.7073, loss: 0.2012
+2023-03-04 23:35:35,167 - mmseg - INFO - Iter [53200/80000]	lr: 4.687e-06, eta: 1:27:39, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2104, decode.acc_seg: 91.4637, loss: 0.2104
+2023-03-04 23:35:44,037 - mmseg - INFO - Iter [53250/80000]	lr: 4.687e-06, eta: 1:27:29, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2144, decode.acc_seg: 91.2575, loss: 0.2144
+2023-03-04 23:35:52,697 - mmseg - INFO - Iter [53300/80000]	lr: 4.687e-06, eta: 1:27:19, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2124, decode.acc_seg: 91.4345, loss: 0.2124
+2023-03-04 23:36:01,484 - mmseg - INFO - Iter [53350/80000]	lr: 4.687e-06, eta: 1:27:08, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2097, decode.acc_seg: 91.4610, loss: 0.2097
+2023-03-04 23:36:10,049 - mmseg - INFO - Iter [53400/80000]	lr: 4.687e-06, eta: 1:26:58, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2194, decode.acc_seg: 91.1690, loss: 0.2194
+2023-03-04 23:36:21,055 - mmseg - INFO - Iter [53450/80000]	lr: 4.687e-06, eta: 1:26:48, time: 0.220, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.5742, loss: 0.2074
+2023-03-04 23:36:30,216 - mmseg - INFO - Iter [53500/80000]	lr: 4.687e-06, eta: 1:26:38, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.3264, loss: 0.2128
+2023-03-04 23:36:39,285 - mmseg - INFO - Iter [53550/80000]	lr: 4.687e-06, eta: 1:26:28, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.4200, loss: 0.2106
+2023-03-04 23:36:48,176 - mmseg - INFO - Iter [53600/80000]	lr: 4.687e-06, eta: 1:26:18, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2231, decode.acc_seg: 90.9899, loss: 0.2231
+2023-03-04 23:36:57,116 - mmseg - INFO - Iter [53650/80000]	lr: 4.687e-06, eta: 1:26:07, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2179, decode.acc_seg: 91.1326, loss: 0.2179
+2023-03-04 23:37:05,749 - mmseg - INFO - Iter [53700/80000]	lr: 4.687e-06, eta: 1:25:57, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.3074, loss: 0.2123
+2023-03-04 23:37:15,068 - mmseg - INFO - Iter [53750/80000]	lr: 4.687e-06, eta: 1:25:47, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.2301, loss: 0.2154
+2023-03-04 23:37:23,773 - mmseg - INFO - Iter [53800/80000]	lr: 4.687e-06, eta: 1:25:36, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.5356, loss: 0.2107
+2023-03-04 23:37:32,393 - mmseg - INFO - Iter [53850/80000]	lr: 4.687e-06, eta: 1:25:26, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2058, decode.acc_seg: 91.4564, loss: 0.2058
+2023-03-04 23:37:41,286 - mmseg - INFO - Iter [53900/80000]	lr: 4.687e-06, eta: 1:25:16, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.2914, loss: 0.2098
+2023-03-04 23:37:50,042 - mmseg - INFO - Iter [53950/80000]	lr: 4.687e-06, eta: 1:25:05, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2156, decode.acc_seg: 91.3066, loss: 0.2156
+2023-03-04 23:37:58,793 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:37:58,793 - mmseg - INFO - Iter [54000/80000]	lr: 4.687e-06, eta: 1:24:55, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.5048, loss: 0.2074
+2023-03-04 23:38:07,858 - mmseg - INFO - Iter [54050/80000]	lr: 4.687e-06, eta: 1:24:45, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2235, decode.acc_seg: 91.0564, loss: 0.2235
+2023-03-04 23:38:19,543 - mmseg - INFO - Iter [54100/80000]	lr: 4.687e-06, eta: 1:24:36, time: 0.234, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2046, decode.acc_seg: 91.6248, loss: 0.2046
+2023-03-04 23:38:28,549 - mmseg - INFO - Iter [54150/80000]	lr: 4.687e-06, eta: 1:24:26, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.2885, loss: 0.2114
+2023-03-04 23:38:37,322 - mmseg - INFO - Iter [54200/80000]	lr: 4.687e-06, eta: 1:24:15, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2094, decode.acc_seg: 91.4475, loss: 0.2094
+2023-03-04 23:38:46,066 - mmseg - INFO - Iter [54250/80000]	lr: 4.687e-06, eta: 1:24:05, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.3126, loss: 0.2106
+2023-03-04 23:38:55,069 - mmseg - INFO - Iter [54300/80000]	lr: 4.687e-06, eta: 1:23:55, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2160, decode.acc_seg: 91.1996, loss: 0.2160
+2023-03-04 23:39:04,285 - mmseg - INFO - Iter [54350/80000]	lr: 4.687e-06, eta: 1:23:45, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.5677, loss: 0.2075
+2023-03-04 23:39:13,118 - mmseg - INFO - Iter [54400/80000]	lr: 4.687e-06, eta: 1:23:34, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.3261, loss: 0.2163
+2023-03-04 23:39:21,729 - mmseg - INFO - Iter [54450/80000]	lr: 4.687e-06, eta: 1:23:24, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2144, decode.acc_seg: 91.3284, loss: 0.2144
+2023-03-04 23:39:30,538 - mmseg - INFO - Iter [54500/80000]	lr: 4.687e-06, eta: 1:23:13, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2085, decode.acc_seg: 91.3911, loss: 0.2085
+2023-03-04 23:39:39,147 - mmseg - INFO - Iter [54550/80000]	lr: 4.687e-06, eta: 1:23:03, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2141, decode.acc_seg: 91.3491, loss: 0.2141
+2023-03-04 23:39:48,755 - mmseg - INFO - Iter [54600/80000]	lr: 4.687e-06, eta: 1:22:53, time: 0.192, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2197, decode.acc_seg: 91.0489, loss: 0.2197
+2023-03-04 23:39:57,676 - mmseg - INFO - Iter [54650/80000]	lr: 4.687e-06, eta: 1:22:43, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.4604, loss: 0.2111
+2023-03-04 23:40:08,996 - mmseg - INFO - Iter [54700/80000]	lr: 4.687e-06, eta: 1:22:34, time: 0.226, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.3374, loss: 0.2103
+2023-03-04 23:40:17,934 - mmseg - INFO - Iter [54750/80000]	lr: 4.687e-06, eta: 1:22:24, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.4181, loss: 0.2109
+2023-03-04 23:40:26,813 - mmseg - INFO - Iter [54800/80000]	lr: 4.687e-06, eta: 1:22:13, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.2383, loss: 0.2139
+2023-03-04 23:40:35,427 - mmseg - INFO - Iter [54850/80000]	lr: 4.687e-06, eta: 1:22:03, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2090, decode.acc_seg: 91.3476, loss: 0.2090
+2023-03-04 23:40:44,390 - mmseg - INFO - Iter [54900/80000]	lr: 4.687e-06, eta: 1:21:53, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.1648, loss: 0.2165
+2023-03-04 23:40:53,175 - mmseg - INFO - Iter [54950/80000]	lr: 4.687e-06, eta: 1:21:42, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2007, decode.acc_seg: 91.7213, loss: 0.2007
+2023-03-04 23:41:01,875 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:41:01,875 - mmseg - INFO - Iter [55000/80000]	lr: 4.687e-06, eta: 1:21:32, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2130, decode.acc_seg: 91.4236, loss: 0.2130
+2023-03-04 23:41:10,517 - mmseg - INFO - Iter [55050/80000]	lr: 4.687e-06, eta: 1:21:22, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2144, decode.acc_seg: 91.2798, loss: 0.2144
+2023-03-04 23:41:19,422 - mmseg - INFO - Iter [55100/80000]	lr: 4.687e-06, eta: 1:21:11, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.5471, loss: 0.2075
+2023-03-04 23:41:28,115 - mmseg - INFO - Iter [55150/80000]	lr: 4.687e-06, eta: 1:21:01, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2205, decode.acc_seg: 91.0060, loss: 0.2205
+2023-03-04 23:41:36,823 - mmseg - INFO - Iter [55200/80000]	lr: 4.687e-06, eta: 1:20:51, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2093, decode.acc_seg: 91.4515, loss: 0.2093
+2023-03-04 23:41:45,563 - mmseg - INFO - Iter [55250/80000]	lr: 4.687e-06, eta: 1:20:40, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2097, decode.acc_seg: 91.3620, loss: 0.2097
+2023-03-04 23:41:54,817 - mmseg - INFO - Iter [55300/80000]	lr: 4.687e-06, eta: 1:20:30, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.1438, loss: 0.2168
+2023-03-04 23:42:06,027 - mmseg - INFO - Iter [55350/80000]	lr: 4.687e-06, eta: 1:20:21, time: 0.224, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2094, decode.acc_seg: 91.4722, loss: 0.2094
+2023-03-04 23:42:14,565 - mmseg - INFO - Iter [55400/80000]	lr: 4.687e-06, eta: 1:20:11, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2094, decode.acc_seg: 91.4826, loss: 0.2094
+2023-03-04 23:42:23,952 - mmseg - INFO - Iter [55450/80000]	lr: 4.687e-06, eta: 1:20:01, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2181, decode.acc_seg: 91.2196, loss: 0.2181
+2023-03-04 23:42:32,866 - mmseg - INFO - Iter [55500/80000]	lr: 4.687e-06, eta: 1:19:51, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2057, decode.acc_seg: 91.5081, loss: 0.2057
+2023-03-04 23:42:41,896 - mmseg - INFO - Iter [55550/80000]	lr: 4.687e-06, eta: 1:19:40, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2145, decode.acc_seg: 91.0811, loss: 0.2145
+2023-03-04 23:42:50,488 - mmseg - INFO - Iter [55600/80000]	lr: 4.687e-06, eta: 1:19:30, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2083, decode.acc_seg: 91.4288, loss: 0.2083
+2023-03-04 23:42:59,737 - mmseg - INFO - Iter [55650/80000]	lr: 4.687e-06, eta: 1:19:20, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.3183, loss: 0.2128
+2023-03-04 23:43:08,205 - mmseg - INFO - Iter [55700/80000]	lr: 4.687e-06, eta: 1:19:10, time: 0.169, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2151, decode.acc_seg: 91.0388, loss: 0.2151
+2023-03-04 23:43:16,779 - mmseg - INFO - Iter [55750/80000]	lr: 4.687e-06, eta: 1:18:59, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2113, decode.acc_seg: 91.1988, loss: 0.2113
+2023-03-04 23:43:25,560 - mmseg - INFO - Iter [55800/80000]	lr: 4.687e-06, eta: 1:18:49, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2172, decode.acc_seg: 91.2663, loss: 0.2172
+2023-03-04 23:43:34,655 - mmseg - INFO - Iter [55850/80000]	lr: 4.687e-06, eta: 1:18:39, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2003, decode.acc_seg: 91.6497, loss: 0.2003
+2023-03-04 23:43:43,250 - mmseg - INFO - Iter [55900/80000]	lr: 4.687e-06, eta: 1:18:28, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2055, decode.acc_seg: 91.6949, loss: 0.2055
+2023-03-04 23:43:51,769 - mmseg - INFO - Iter [55950/80000]	lr: 4.687e-06, eta: 1:18:18, time: 0.170, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2044, decode.acc_seg: 91.7155, loss: 0.2044
+2023-03-04 23:44:03,388 - mmseg - INFO - Saving checkpoint at 56000 iterations
+2023-03-04 23:44:04,039 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:44:04,039 - mmseg - INFO - Iter [56000/80000]	lr: 4.687e-06, eta: 1:18:10, time: 0.245, data_time: 0.058, memory: 52390, decode.loss_ce: 0.2108, decode.acc_seg: 91.2908, loss: 0.2108
+2023-03-04 23:44:20,008 - mmseg - INFO - per class results:
+2023-03-04 23:44:20,014 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.35 | 89.55 |
+|       building      | 81.31 | 91.04 |
+|         sky         |  94.3 | 97.29 |
+|        floor        | 81.16 | 90.73 |
+|         tree        | 73.44 | 88.38 |
+|       ceiling       | 84.63 | 91.91 |
+|         road        | 81.89 | 90.91 |
+|         bed         | 86.95 |  94.1 |
+|      windowpane     | 59.71 |  77.1 |
+|        grass        | 65.89 | 82.77 |
+|       cabinet       | 59.94 | 73.91 |
+|       sidewalk      | 63.89 | 77.54 |
+|        person       | 77.78 | 92.15 |
+|        earth        | 34.23 | 44.97 |
+|         door        |  43.7 |  57.6 |
+|        table        | 58.46 | 74.31 |
+|       mountain      | 55.27 | 69.18 |
+|        plant        | 49.26 | 60.82 |
+|       curtain       | 73.21 |  84.1 |
+|        chair        | 53.95 | 67.55 |
+|         car         | 80.79 | 92.14 |
+|        water        | 57.69 | 76.45 |
+|       painting      | 69.76 | 83.94 |
+|         sofa        | 63.32 | 79.56 |
+|        shelf        | 42.24 | 60.39 |
+|        house        | 43.26 |  60.0 |
+|         sea         | 60.99 | 77.11 |
+|        mirror       | 61.41 | 68.05 |
+|         rug         | 64.55 | 73.37 |
+|        field        | 29.47 | 45.14 |
+|       armchair      | 36.13 | 52.67 |
+|         seat        | 65.89 |  81.3 |
+|        fence        | 40.37 | 53.23 |
+|         desk        | 45.64 | 65.56 |
+|         rock        | 37.28 | 61.67 |
+|       wardrobe      | 56.14 | 65.62 |
+|         lamp        | 58.68 | 72.33 |
+|       bathtub       | 75.72 | 81.82 |
+|       railing       | 33.19 | 46.68 |
+|       cushion       |  55.1 | 70.06 |
+|         base        | 23.01 |  28.4 |
+|         box         | 22.34 | 30.83 |
+|        column       |  45.4 | 57.76 |
+|      signboard      | 36.68 |  50.8 |
+|   chest of drawers  | 36.18 | 56.43 |
+|       counter       | 31.51 | 40.88 |
+|         sand        | 44.63 | 63.26 |
+|         sink        |  64.6 | 77.65 |
+|      skyscraper     | 50.91 | 63.47 |
+|      fireplace      | 72.88 | 85.12 |
+|     refrigerator    | 71.38 | 82.64 |
+|      grandstand     |  47.8 |  62.2 |
+|         path        | 23.82 | 34.35 |
+|        stairs       | 33.15 | 42.75 |
+|        runway       |  67.9 | 87.98 |
+|         case        | 46.35 |  52.9 |
+|      pool table     | 91.39 | 94.54 |
+|        pillow       | 59.74 | 70.72 |
+|     screen door     | 64.11 | 69.89 |
+|       stairway      | 23.25 |  35.9 |
+|        river        | 11.46 | 20.76 |
+|        bridge       | 34.29 | 40.32 |
+|       bookcase      | 43.49 | 61.38 |
+|        blind        | 41.37 | 46.26 |
+|     coffee table    | 52.76 | 77.28 |
+|        toilet       | 81.94 | 89.32 |
+|        flower       | 37.49 |  51.9 |
+|         book        | 42.97 | 63.73 |
+|         hill        | 13.79 | 21.02 |
+|        bench        | 40.41 | 53.52 |
+|      countertop     | 52.74 |  67.5 |
+|        stove        | 69.28 | 80.78 |
+|         palm        |  49.4 | 70.22 |
+|    kitchen island   | 37.69 | 60.36 |
+|       computer      | 59.79 | 69.76 |
+|     swivel chair    | 42.84 | 58.62 |
+|         boat        | 69.14 | 81.25 |
+|         bar         | 22.11 |  29.8 |
+|    arcade machine   | 67.88 | 69.58 |
+|        hovel        |  25.6 | 28.62 |
+|         bus         | 78.61 | 89.92 |
+|        towel        |  62.0 | 72.58 |
+|        light        | 49.75 |  56.7 |
+|        truck        | 15.78 | 21.34 |
+|        tower        |  8.25 | 13.17 |
+|      chandelier     | 62.12 | 75.49 |
+|        awning       | 23.62 | 27.25 |
+|     streetlight     | 23.62 | 30.74 |
+|        booth        | 39.01 | 40.12 |
+| television receiver | 64.19 | 75.13 |
+|       airplane      | 57.31 | 63.06 |
+|      dirt track     | 16.46 | 52.89 |
+|       apparel       | 33.22 | 54.39 |
+|         pole        | 17.77 | 23.02 |
+|         land        |  3.39 |  4.85 |
+|      bannister      | 10.36 |  13.8 |
+|      escalator      | 22.51 | 24.12 |
+|       ottoman       | 40.97 | 61.75 |
+|        bottle       | 33.86 | 54.27 |
+|        buffet       | 37.02 | 41.85 |
+|        poster       | 22.93 | 31.77 |
+|        stage        | 13.39 | 16.86 |
+|         van         | 38.11 |  53.5 |
+|         ship        | 75.09 | 94.97 |
+|       fountain      | 13.71 |  14.1 |
+|    conveyer belt    | 81.37 | 89.02 |
+|        canopy       | 24.64 | 26.33 |
+|        washer       |  77.3 | 79.39 |
+|      plaything      | 20.53 | 28.72 |
+|    swimming pool    |  74.8 | 80.88 |
+|        stool        | 40.61 | 54.02 |
+|        barrel       | 38.88 | 54.24 |
+|        basket       | 24.45 | 35.99 |
+|      waterfall      |  50.8 | 67.45 |
+|         tent        | 94.05 |  97.4 |
+|         bag         | 14.59 | 17.87 |
+|       minibike      | 60.63 | 73.91 |
+|        cradle       | 82.81 | 96.01 |
+|         oven        | 47.97 | 63.23 |
+|         ball        | 43.27 | 51.04 |
+|         food        | 47.84 | 55.96 |
+|         step        |  5.67 |  6.34 |
+|         tank        | 50.37 | 54.73 |
+|      trade name     | 24.08 | 26.96 |
+|      microwave      | 75.19 | 81.38 |
+|         pot         | 29.84 | 33.81 |
+|        animal       |  51.8 | 57.65 |
+|       bicycle       | 52.51 | 67.58 |
+|         lake        |  57.3 | 63.28 |
+|      dishwasher     | 64.74 | 76.35 |
+|        screen       | 66.88 | 82.14 |
+|       blanket       |  15.1 | 17.94 |
+|      sculpture      | 56.82 |  77.9 |
+|         hood        | 53.67 | 58.18 |
+|        sconce       | 40.77 | 48.32 |
+|         vase        | 31.14 | 48.57 |
+|    traffic light    | 30.61 | 46.37 |
+|         tray        |  4.76 |  7.47 |
+|        ashcan       | 39.34 | 52.52 |
+|         fan         | 56.27 | 64.55 |
+|         pier        | 49.97 | 68.38 |
+|      crt screen     |  8.92 | 22.42 |
+|        plate        | 47.51 | 62.47 |
+|       monitor       | 16.38 | 19.71 |
+|    bulletin board   | 39.62 |  53.7 |
+|        shower       |  1.15 |  4.42 |
+|       radiator      | 56.68 | 63.54 |
+|        glass        |  9.65 | 10.29 |
+|        clock        | 31.74 | 34.99 |
+|         flag        | 35.83 | 40.33 |
++---------------------+-------+-------+
+2023-03-04 23:44:20,014 - mmseg - INFO - Summary:
+2023-03-04 23:44:20,014 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 82.23 | 47.19 | 58.17 |
++-------+-------+-------+
+2023-03-04 23:44:20,036 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_48000.pth was removed
+2023-03-04 23:44:20,596 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_56000.pth.
+2023-03-04 23:44:20,597 - mmseg - INFO - Best mIoU is 0.4719 at 56000 iter.
+2023-03-04 23:44:20,597 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:44:20,597 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8223, mIoU: 0.4719, mAcc: 0.5817, IoU.background: nan, IoU.wall: 0.7635, IoU.building: 0.8131, IoU.sky: 0.9430, IoU.floor: 0.8116, IoU.tree: 0.7344, IoU.ceiling: 0.8463, IoU.road: 0.8189, IoU.bed : 0.8695, IoU.windowpane: 0.5971, IoU.grass: 0.6589, IoU.cabinet: 0.5994, IoU.sidewalk: 0.6389, IoU.person: 0.7778, IoU.earth: 0.3423, IoU.door: 0.4370, IoU.table: 0.5846, IoU.mountain: 0.5527, IoU.plant: 0.4926, IoU.curtain: 0.7321, IoU.chair: 0.5395, IoU.car: 0.8079, IoU.water: 0.5769, IoU.painting: 0.6976, IoU.sofa: 0.6332, IoU.shelf: 0.4224, IoU.house: 0.4326, IoU.sea: 0.6099, IoU.mirror: 0.6141, IoU.rug: 0.6455, IoU.field: 0.2947, IoU.armchair: 0.3613, IoU.seat: 0.6589, IoU.fence: 0.4037, IoU.desk: 0.4564, IoU.rock: 0.3728, IoU.wardrobe: 0.5614, IoU.lamp: 0.5868, IoU.bathtub: 0.7572, IoU.railing: 0.3319, IoU.cushion: 0.5510, IoU.base: 0.2301, IoU.box: 0.2234, IoU.column: 0.4540, IoU.signboard: 0.3668, IoU.chest of drawers: 0.3618, IoU.counter: 0.3151, IoU.sand: 0.4463, IoU.sink: 0.6460, IoU.skyscraper: 0.5091, IoU.fireplace: 0.7288, IoU.refrigerator: 0.7138, IoU.grandstand: 0.4780, IoU.path: 0.2382, IoU.stairs: 0.3315, IoU.runway: 0.6790, IoU.case: 0.4635, IoU.pool table: 0.9139, IoU.pillow: 0.5974, IoU.screen door: 0.6411, IoU.stairway: 0.2325, IoU.river: 0.1146, IoU.bridge: 0.3429, IoU.bookcase: 0.4349, IoU.blind: 0.4137, IoU.coffee table: 0.5276, IoU.toilet: 0.8194, IoU.flower: 0.3749, IoU.book: 0.4297, IoU.hill: 0.1379, IoU.bench: 0.4041, IoU.countertop: 0.5274, IoU.stove: 0.6928, IoU.palm: 0.4940, IoU.kitchen island: 0.3769, IoU.computer: 0.5979, IoU.swivel chair: 0.4284, IoU.boat: 0.6914, IoU.bar: 0.2211, IoU.arcade machine: 0.6788, IoU.hovel: 0.2560, IoU.bus: 0.7861, IoU.towel: 0.6200, IoU.light: 0.4975, IoU.truck: 0.1578, IoU.tower: 0.0825, IoU.chandelier: 0.6212, IoU.awning: 0.2362, IoU.streetlight: 0.2362, IoU.booth: 0.3901, IoU.television receiver: 0.6419, IoU.airplane: 0.5731, IoU.dirt track: 0.1646, IoU.apparel: 0.3322, IoU.pole: 0.1777, IoU.land: 0.0339, IoU.bannister: 0.1036, IoU.escalator: 0.2251, IoU.ottoman: 0.4097, IoU.bottle: 0.3386, IoU.buffet: 0.3702, IoU.poster: 0.2293, IoU.stage: 0.1339, IoU.van: 0.3811, IoU.ship: 0.7509, IoU.fountain: 0.1371, IoU.conveyer belt: 0.8137, IoU.canopy: 0.2464, IoU.washer: 0.7730, IoU.plaything: 0.2053, IoU.swimming pool: 0.7480, IoU.stool: 0.4061, IoU.barrel: 0.3888, IoU.basket: 0.2445, IoU.waterfall: 0.5080, IoU.tent: 0.9405, IoU.bag: 0.1459, IoU.minibike: 0.6063, IoU.cradle: 0.8281, IoU.oven: 0.4797, IoU.ball: 0.4327, IoU.food: 0.4784, IoU.step: 0.0567, IoU.tank: 0.5037, IoU.trade name: 0.2408, IoU.microwave: 0.7519, IoU.pot: 0.2984, IoU.animal: 0.5180, IoU.bicycle: 0.5251, IoU.lake: 0.5730, IoU.dishwasher: 0.6474, IoU.screen: 0.6688, IoU.blanket: 0.1510, IoU.sculpture: 0.5682, IoU.hood: 0.5367, IoU.sconce: 0.4077, IoU.vase: 0.3114, IoU.traffic light: 0.3061, IoU.tray: 0.0476, IoU.ashcan: 0.3934, IoU.fan: 0.5627, IoU.pier: 0.4997, IoU.crt screen: 0.0892, IoU.plate: 0.4751, IoU.monitor: 0.1638, IoU.bulletin board: 0.3962, IoU.shower: 0.0115, IoU.radiator: 0.5668, IoU.glass: 0.0965, IoU.clock: 0.3174, IoU.flag: 0.3583, Acc.background: nan, Acc.wall: 0.8955, Acc.building: 0.9104, Acc.sky: 0.9729, Acc.floor: 0.9073, Acc.tree: 0.8838, Acc.ceiling: 0.9191, Acc.road: 0.9091, Acc.bed : 0.9410, Acc.windowpane: 0.7710, Acc.grass: 0.8277, Acc.cabinet: 0.7391, Acc.sidewalk: 0.7754, Acc.person: 0.9215, Acc.earth: 0.4497, Acc.door: 0.5760, Acc.table: 0.7431, Acc.mountain: 0.6918, Acc.plant: 0.6082, Acc.curtain: 0.8410, Acc.chair: 0.6755, Acc.car: 0.9214, Acc.water: 0.7645, Acc.painting: 0.8394, Acc.sofa: 0.7956, Acc.shelf: 0.6039, Acc.house: 0.6000, Acc.sea: 0.7711, Acc.mirror: 0.6805, Acc.rug: 0.7337, Acc.field: 0.4514, Acc.armchair: 0.5267, Acc.seat: 0.8130, Acc.fence: 0.5323, Acc.desk: 0.6556, Acc.rock: 0.6167, Acc.wardrobe: 0.6562, Acc.lamp: 0.7233, Acc.bathtub: 0.8182, Acc.railing: 0.4668, Acc.cushion: 0.7006, Acc.base: 0.2840, Acc.box: 0.3083, Acc.column: 0.5776, Acc.signboard: 0.5080, Acc.chest of drawers: 0.5643, Acc.counter: 0.4088, Acc.sand: 0.6326, Acc.sink: 0.7765, Acc.skyscraper: 0.6347, Acc.fireplace: 0.8512, Acc.refrigerator: 0.8264, Acc.grandstand: 0.6220, Acc.path: 0.3435, Acc.stairs: 0.4275, Acc.runway: 0.8798, Acc.case: 0.5290, Acc.pool table: 0.9454, Acc.pillow: 0.7072, Acc.screen door: 0.6989, Acc.stairway: 0.3590, Acc.river: 0.2076, Acc.bridge: 0.4032, Acc.bookcase: 0.6138, Acc.blind: 0.4626, Acc.coffee table: 0.7728, Acc.toilet: 0.8932, Acc.flower: 0.5190, Acc.book: 0.6373, Acc.hill: 0.2102, Acc.bench: 0.5352, Acc.countertop: 0.6750, Acc.stove: 0.8078, Acc.palm: 0.7022, Acc.kitchen island: 0.6036, Acc.computer: 0.6976, Acc.swivel chair: 0.5862, Acc.boat: 0.8125, Acc.bar: 0.2980, Acc.arcade machine: 0.6958, Acc.hovel: 0.2862, Acc.bus: 0.8992, Acc.towel: 0.7258, Acc.light: 0.5670, Acc.truck: 0.2134, Acc.tower: 0.1317, Acc.chandelier: 0.7549, Acc.awning: 0.2725, Acc.streetlight: 0.3074, Acc.booth: 0.4012, Acc.television receiver: 0.7513, Acc.airplane: 0.6306, Acc.dirt track: 0.5289, Acc.apparel: 0.5439, Acc.pole: 0.2302, Acc.land: 0.0485, Acc.bannister: 0.1380, Acc.escalator: 0.2412, Acc.ottoman: 0.6175, Acc.bottle: 0.5427, Acc.buffet: 0.4185, Acc.poster: 0.3177, Acc.stage: 0.1686, Acc.van: 0.5350, Acc.ship: 0.9497, Acc.fountain: 0.1410, Acc.conveyer belt: 0.8902, Acc.canopy: 0.2633, Acc.washer: 0.7939, Acc.plaything: 0.2872, Acc.swimming pool: 0.8088, Acc.stool: 0.5402, Acc.barrel: 0.5424, Acc.basket: 0.3599, Acc.waterfall: 0.6745, Acc.tent: 0.9740, Acc.bag: 0.1787, Acc.minibike: 0.7391, Acc.cradle: 0.9601, Acc.oven: 0.6323, Acc.ball: 0.5104, Acc.food: 0.5596, Acc.step: 0.0634, Acc.tank: 0.5473, Acc.trade name: 0.2696, Acc.microwave: 0.8138, Acc.pot: 0.3381, Acc.animal: 0.5765, Acc.bicycle: 0.6758, Acc.lake: 0.6328, Acc.dishwasher: 0.7635, Acc.screen: 0.8214, Acc.blanket: 0.1794, Acc.sculpture: 0.7790, Acc.hood: 0.5818, Acc.sconce: 0.4832, Acc.vase: 0.4857, Acc.traffic light: 0.4637, Acc.tray: 0.0747, Acc.ashcan: 0.5252, Acc.fan: 0.6455, Acc.pier: 0.6838, Acc.crt screen: 0.2242, Acc.plate: 0.6247, Acc.monitor: 0.1971, Acc.bulletin board: 0.5370, Acc.shower: 0.0442, Acc.radiator: 0.6354, Acc.glass: 0.1029, Acc.clock: 0.3499, Acc.flag: 0.4033
+2023-03-04 23:44:29,855 - mmseg - INFO - Iter [56050/80000]	lr: 4.687e-06, eta: 1:18:08, time: 0.516, data_time: 0.338, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.4588, loss: 0.2099
+2023-03-04 23:44:39,198 - mmseg - INFO - Iter [56100/80000]	lr: 4.687e-06, eta: 1:17:58, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.4796, loss: 0.2106
+2023-03-04 23:44:47,971 - mmseg - INFO - Iter [56150/80000]	lr: 4.687e-06, eta: 1:17:47, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.4413, loss: 0.2127
+2023-03-04 23:44:57,096 - mmseg - INFO - Iter [56200/80000]	lr: 4.687e-06, eta: 1:17:37, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2055, decode.acc_seg: 91.4171, loss: 0.2055
+2023-03-04 23:45:05,685 - mmseg - INFO - Iter [56250/80000]	lr: 4.687e-06, eta: 1:17:27, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.1621, loss: 0.2163
+2023-03-04 23:45:14,223 - mmseg - INFO - Iter [56300/80000]	lr: 4.687e-06, eta: 1:17:17, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.2481, loss: 0.2139
+2023-03-04 23:45:23,676 - mmseg - INFO - Iter [56350/80000]	lr: 4.687e-06, eta: 1:17:07, time: 0.189, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2090, decode.acc_seg: 91.6371, loss: 0.2090
+2023-03-04 23:45:32,458 - mmseg - INFO - Iter [56400/80000]	lr: 4.687e-06, eta: 1:16:56, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2053, decode.acc_seg: 91.4824, loss: 0.2053
+2023-03-04 23:45:41,184 - mmseg - INFO - Iter [56450/80000]	lr: 4.687e-06, eta: 1:16:46, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2086, decode.acc_seg: 91.4639, loss: 0.2086
+2023-03-04 23:45:49,837 - mmseg - INFO - Iter [56500/80000]	lr: 4.687e-06, eta: 1:16:36, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2048, decode.acc_seg: 91.5848, loss: 0.2048
+2023-03-04 23:45:58,484 - mmseg - INFO - Iter [56550/80000]	lr: 4.687e-06, eta: 1:16:25, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2224, decode.acc_seg: 90.9787, loss: 0.2224
+2023-03-04 23:46:10,112 - mmseg - INFO - Iter [56600/80000]	lr: 4.687e-06, eta: 1:16:17, time: 0.233, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.4639, loss: 0.2098
+2023-03-04 23:46:19,120 - mmseg - INFO - Iter [56650/80000]	lr: 4.687e-06, eta: 1:16:06, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2108, decode.acc_seg: 91.4597, loss: 0.2108
+2023-03-04 23:46:28,329 - mmseg - INFO - Iter [56700/80000]	lr: 4.687e-06, eta: 1:15:56, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2142, decode.acc_seg: 91.2485, loss: 0.2142
+2023-03-04 23:46:37,276 - mmseg - INFO - Iter [56750/80000]	lr: 4.687e-06, eta: 1:15:46, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2159, decode.acc_seg: 91.0422, loss: 0.2159
+2023-03-04 23:46:46,211 - mmseg - INFO - Iter [56800/80000]	lr: 4.687e-06, eta: 1:15:36, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2178, decode.acc_seg: 91.2481, loss: 0.2178
+2023-03-04 23:46:55,565 - mmseg - INFO - Iter [56850/80000]	lr: 4.687e-06, eta: 1:15:26, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2201, decode.acc_seg: 91.2814, loss: 0.2201
+2023-03-04 23:47:04,427 - mmseg - INFO - Iter [56900/80000]	lr: 4.687e-06, eta: 1:15:16, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.6137, loss: 0.2095
+2023-03-04 23:47:13,334 - mmseg - INFO - Iter [56950/80000]	lr: 4.687e-06, eta: 1:15:06, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.2280, loss: 0.2121
+2023-03-04 23:47:22,264 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:47:22,264 - mmseg - INFO - Iter [57000/80000]	lr: 4.687e-06, eta: 1:14:55, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.2457, loss: 0.2134
+2023-03-04 23:47:31,361 - mmseg - INFO - Iter [57050/80000]	lr: 4.687e-06, eta: 1:14:45, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2067, decode.acc_seg: 91.4778, loss: 0.2067
+2023-03-04 23:47:40,384 - mmseg - INFO - Iter [57100/80000]	lr: 4.687e-06, eta: 1:14:35, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2092, decode.acc_seg: 91.4709, loss: 0.2092
+2023-03-04 23:47:49,038 - mmseg - INFO - Iter [57150/80000]	lr: 4.687e-06, eta: 1:14:25, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2029, decode.acc_seg: 91.5967, loss: 0.2029
+2023-03-04 23:47:58,206 - mmseg - INFO - Iter [57200/80000]	lr: 4.687e-06, eta: 1:14:15, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2071, decode.acc_seg: 91.4996, loss: 0.2071
+2023-03-04 23:48:09,744 - mmseg - INFO - Iter [57250/80000]	lr: 4.687e-06, eta: 1:14:06, time: 0.231, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.4399, loss: 0.2073
+2023-03-04 23:48:18,647 - mmseg - INFO - Iter [57300/80000]	lr: 4.687e-06, eta: 1:13:56, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2022, decode.acc_seg: 91.7299, loss: 0.2022
+2023-03-04 23:48:27,805 - mmseg - INFO - Iter [57350/80000]	lr: 4.687e-06, eta: 1:13:46, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2135, decode.acc_seg: 91.2607, loss: 0.2135
+2023-03-04 23:48:36,656 - mmseg - INFO - Iter [57400/80000]	lr: 4.687e-06, eta: 1:13:35, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.3870, loss: 0.2101
+2023-03-04 23:48:45,200 - mmseg - INFO - Iter [57450/80000]	lr: 4.687e-06, eta: 1:13:25, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2108, decode.acc_seg: 91.2275, loss: 0.2108
+2023-03-04 23:48:54,151 - mmseg - INFO - Iter [57500/80000]	lr: 4.687e-06, eta: 1:13:15, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.3190, loss: 0.2106
+2023-03-04 23:49:03,176 - mmseg - INFO - Iter [57550/80000]	lr: 4.687e-06, eta: 1:13:05, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.3835, loss: 0.2089
+2023-03-04 23:49:11,964 - mmseg - INFO - Iter [57600/80000]	lr: 4.687e-06, eta: 1:12:55, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2190, decode.acc_seg: 91.1027, loss: 0.2190
+2023-03-04 23:49:20,924 - mmseg - INFO - Iter [57650/80000]	lr: 4.687e-06, eta: 1:12:45, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2118, decode.acc_seg: 91.4081, loss: 0.2118
+2023-03-04 23:49:29,927 - mmseg - INFO - Iter [57700/80000]	lr: 4.687e-06, eta: 1:12:34, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2051, decode.acc_seg: 91.7027, loss: 0.2051
+2023-03-04 23:49:38,828 - mmseg - INFO - Iter [57750/80000]	lr: 4.687e-06, eta: 1:12:24, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.2009, loss: 0.2171
+2023-03-04 23:49:47,550 - mmseg - INFO - Iter [57800/80000]	lr: 4.687e-06, eta: 1:12:14, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2133, decode.acc_seg: 91.1696, loss: 0.2133
+2023-03-04 23:49:58,940 - mmseg - INFO - Iter [57850/80000]	lr: 4.687e-06, eta: 1:12:05, time: 0.228, data_time: 0.057, memory: 52390, decode.loss_ce: 0.2059, decode.acc_seg: 91.5202, loss: 0.2059
+2023-03-04 23:50:07,684 - mmseg - INFO - Iter [57900/80000]	lr: 4.687e-06, eta: 1:11:55, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1985, decode.acc_seg: 91.7814, loss: 0.1985
+2023-03-04 23:50:16,550 - mmseg - INFO - Iter [57950/80000]	lr: 4.687e-06, eta: 1:11:45, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2219, decode.acc_seg: 90.9666, loss: 0.2219
+2023-03-04 23:50:25,416 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:50:25,416 - mmseg - INFO - Iter [58000/80000]	lr: 4.687e-06, eta: 1:11:35, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2183, decode.acc_seg: 91.2614, loss: 0.2183
+2023-03-04 23:50:33,989 - mmseg - INFO - Iter [58050/80000]	lr: 4.687e-06, eta: 1:11:24, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.3363, loss: 0.2075
+2023-03-04 23:50:43,401 - mmseg - INFO - Iter [58100/80000]	lr: 4.687e-06, eta: 1:11:14, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2207, decode.acc_seg: 91.1484, loss: 0.2207
+2023-03-04 23:50:52,154 - mmseg - INFO - Iter [58150/80000]	lr: 4.687e-06, eta: 1:11:04, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2119, decode.acc_seg: 91.3870, loss: 0.2119
+2023-03-04 23:51:00,829 - mmseg - INFO - Iter [58200/80000]	lr: 4.687e-06, eta: 1:10:54, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2042, decode.acc_seg: 91.7324, loss: 0.2042
+2023-03-04 23:51:09,481 - mmseg - INFO - Iter [58250/80000]	lr: 4.687e-06, eta: 1:10:44, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.6765, loss: 0.2072
+2023-03-04 23:51:18,554 - mmseg - INFO - Iter [58300/80000]	lr: 4.687e-06, eta: 1:10:34, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2126, decode.acc_seg: 91.1569, loss: 0.2126
+2023-03-04 23:51:27,148 - mmseg - INFO - Iter [58350/80000]	lr: 4.687e-06, eta: 1:10:23, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2029, decode.acc_seg: 91.4310, loss: 0.2029
+2023-03-04 23:51:35,959 - mmseg - INFO - Iter [58400/80000]	lr: 4.687e-06, eta: 1:10:13, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2016, decode.acc_seg: 91.7035, loss: 0.2016
+2023-03-04 23:51:44,621 - mmseg - INFO - Iter [58450/80000]	lr: 4.687e-06, eta: 1:10:03, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2067, decode.acc_seg: 91.4871, loss: 0.2067
+2023-03-04 23:51:55,887 - mmseg - INFO - Iter [58500/80000]	lr: 4.687e-06, eta: 1:09:54, time: 0.225, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2035, decode.acc_seg: 91.7160, loss: 0.2035
+2023-03-04 23:52:04,629 - mmseg - INFO - Iter [58550/80000]	lr: 4.687e-06, eta: 1:09:44, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2166, decode.acc_seg: 91.2468, loss: 0.2166
+2023-03-04 23:52:13,288 - mmseg - INFO - Iter [58600/80000]	lr: 4.687e-06, eta: 1:09:33, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2062, decode.acc_seg: 91.4892, loss: 0.2062
+2023-03-04 23:52:22,533 - mmseg - INFO - Iter [58650/80000]	lr: 4.687e-06, eta: 1:09:23, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2055, decode.acc_seg: 91.5980, loss: 0.2055
+2023-03-04 23:52:31,122 - mmseg - INFO - Iter [58700/80000]	lr: 4.687e-06, eta: 1:09:13, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.3816, loss: 0.2107
+2023-03-04 23:52:40,108 - mmseg - INFO - Iter [58750/80000]	lr: 4.687e-06, eta: 1:09:03, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2146, decode.acc_seg: 91.1631, loss: 0.2146
+2023-03-04 23:52:48,587 - mmseg - INFO - Iter [58800/80000]	lr: 4.687e-06, eta: 1:08:53, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1999, decode.acc_seg: 91.7900, loss: 0.1999
+2023-03-04 23:52:57,946 - mmseg - INFO - Iter [58850/80000]	lr: 4.687e-06, eta: 1:08:43, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2222, decode.acc_seg: 91.0294, loss: 0.2222
+2023-03-04 23:53:06,474 - mmseg - INFO - Iter [58900/80000]	lr: 4.687e-06, eta: 1:08:33, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2195, decode.acc_seg: 91.1135, loss: 0.2195
+2023-03-04 23:53:15,316 - mmseg - INFO - Iter [58950/80000]	lr: 4.687e-06, eta: 1:08:23, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.2355, loss: 0.2168
+2023-03-04 23:53:24,342 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:53:24,342 - mmseg - INFO - Iter [59000/80000]	lr: 4.687e-06, eta: 1:08:13, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.3452, loss: 0.2120
+2023-03-04 23:53:33,646 - mmseg - INFO - Iter [59050/80000]	lr: 4.687e-06, eta: 1:08:03, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2186, decode.acc_seg: 91.1835, loss: 0.2186
+2023-03-04 23:53:42,429 - mmseg - INFO - Iter [59100/80000]	lr: 4.687e-06, eta: 1:07:53, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2104, decode.acc_seg: 91.4580, loss: 0.2104
+2023-03-04 23:53:54,216 - mmseg - INFO - Iter [59150/80000]	lr: 4.687e-06, eta: 1:07:44, time: 0.235, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2170, decode.acc_seg: 91.1469, loss: 0.2170
+2023-03-04 23:54:02,946 - mmseg - INFO - Iter [59200/80000]	lr: 4.687e-06, eta: 1:07:33, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.1995, decode.acc_seg: 91.7477, loss: 0.1995
+2023-03-04 23:54:11,555 - mmseg - INFO - Iter [59250/80000]	lr: 4.687e-06, eta: 1:07:23, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.3010, loss: 0.2101
+2023-03-04 23:54:20,266 - mmseg - INFO - Iter [59300/80000]	lr: 4.687e-06, eta: 1:07:13, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.3119, loss: 0.2106
+2023-03-04 23:54:29,157 - mmseg - INFO - Iter [59350/80000]	lr: 4.687e-06, eta: 1:07:03, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2090, decode.acc_seg: 91.4624, loss: 0.2090
+2023-03-04 23:54:37,885 - mmseg - INFO - Iter [59400/80000]	lr: 4.687e-06, eta: 1:06:53, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2048, decode.acc_seg: 91.5652, loss: 0.2048
+2023-03-04 23:54:46,939 - mmseg - INFO - Iter [59450/80000]	lr: 4.687e-06, eta: 1:06:43, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2148, decode.acc_seg: 91.1767, loss: 0.2148
+2023-03-04 23:54:55,716 - mmseg - INFO - Iter [59500/80000]	lr: 4.687e-06, eta: 1:06:33, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2023, decode.acc_seg: 91.6459, loss: 0.2023
+2023-03-04 23:55:04,187 - mmseg - INFO - Iter [59550/80000]	lr: 4.687e-06, eta: 1:06:22, time: 0.169, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2200, decode.acc_seg: 91.0378, loss: 0.2200
+2023-03-04 23:55:13,041 - mmseg - INFO - Iter [59600/80000]	lr: 4.687e-06, eta: 1:06:12, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2086, decode.acc_seg: 91.4497, loss: 0.2086
+2023-03-04 23:55:21,809 - mmseg - INFO - Iter [59650/80000]	lr: 4.687e-06, eta: 1:06:02, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.4725, loss: 0.2072
+2023-03-04 23:55:30,365 - mmseg - INFO - Iter [59700/80000]	lr: 4.687e-06, eta: 1:05:52, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.5239, loss: 0.2089
+2023-03-04 23:55:41,715 - mmseg - INFO - Iter [59750/80000]	lr: 4.687e-06, eta: 1:05:43, time: 0.227, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2021, decode.acc_seg: 91.7596, loss: 0.2021
+2023-03-04 23:55:50,702 - mmseg - INFO - Iter [59800/80000]	lr: 4.687e-06, eta: 1:05:33, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2190, decode.acc_seg: 91.0914, loss: 0.2190
+2023-03-04 23:55:59,855 - mmseg - INFO - Iter [59850/80000]	lr: 4.687e-06, eta: 1:05:23, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2081, decode.acc_seg: 91.5751, loss: 0.2081
+2023-03-04 23:56:08,417 - mmseg - INFO - Iter [59900/80000]	lr: 4.687e-06, eta: 1:05:13, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.1660, loss: 0.2171
+2023-03-04 23:56:17,356 - mmseg - INFO - Iter [59950/80000]	lr: 4.687e-06, eta: 1:05:03, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2112, decode.acc_seg: 91.3021, loss: 0.2112
+2023-03-04 23:56:26,067 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:56:26,067 - mmseg - INFO - Iter [60000/80000]	lr: 4.687e-06, eta: 1:04:53, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.1159, loss: 0.2120
+2023-03-04 23:56:35,635 - mmseg - INFO - Iter [60050/80000]	lr: 2.344e-06, eta: 1:04:43, time: 0.192, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.2331, loss: 0.2127
+2023-03-04 23:56:44,613 - mmseg - INFO - Iter [60100/80000]	lr: 2.344e-06, eta: 1:04:33, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.1197, loss: 0.2196
+2023-03-04 23:56:54,288 - mmseg - INFO - Iter [60150/80000]	lr: 2.344e-06, eta: 1:04:23, time: 0.194, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2071, decode.acc_seg: 91.5376, loss: 0.2071
+2023-03-04 23:57:03,366 - mmseg - INFO - Iter [60200/80000]	lr: 2.344e-06, eta: 1:04:13, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.3038, loss: 0.2143
+2023-03-04 23:57:12,443 - mmseg - INFO - Iter [60250/80000]	lr: 2.344e-06, eta: 1:04:03, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2071, decode.acc_seg: 91.5069, loss: 0.2071
+2023-03-04 23:57:21,164 - mmseg - INFO - Iter [60300/80000]	lr: 2.344e-06, eta: 1:03:53, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5750, loss: 0.2060
+2023-03-04 23:57:29,975 - mmseg - INFO - Iter [60350/80000]	lr: 2.344e-06, eta: 1:03:43, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2047, decode.acc_seg: 91.5680, loss: 0.2047
+2023-03-04 23:57:41,118 - mmseg - INFO - Iter [60400/80000]	lr: 2.344e-06, eta: 1:03:34, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.2285, loss: 0.2117
+2023-03-04 23:57:49,774 - mmseg - INFO - Iter [60450/80000]	lr: 2.344e-06, eta: 1:03:24, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2037, decode.acc_seg: 91.4301, loss: 0.2037
+2023-03-04 23:57:58,614 - mmseg - INFO - Iter [60500/80000]	lr: 2.344e-06, eta: 1:03:13, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2190, decode.acc_seg: 91.2294, loss: 0.2190
+2023-03-04 23:58:07,305 - mmseg - INFO - Iter [60550/80000]	lr: 2.344e-06, eta: 1:03:03, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2050, decode.acc_seg: 91.5921, loss: 0.2050
+2023-03-04 23:58:16,616 - mmseg - INFO - Iter [60600/80000]	lr: 2.344e-06, eta: 1:02:53, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.2665, loss: 0.2099
+2023-03-04 23:58:25,715 - mmseg - INFO - Iter [60650/80000]	lr: 2.344e-06, eta: 1:02:44, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2112, decode.acc_seg: 91.3021, loss: 0.2112
+2023-03-04 23:58:34,689 - mmseg - INFO - Iter [60700/80000]	lr: 2.344e-06, eta: 1:02:34, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2042, decode.acc_seg: 91.7060, loss: 0.2042
+2023-03-04 23:58:43,449 - mmseg - INFO - Iter [60750/80000]	lr: 2.344e-06, eta: 1:02:23, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.5236, loss: 0.2095
+2023-03-04 23:58:51,991 - mmseg - INFO - Iter [60800/80000]	lr: 2.344e-06, eta: 1:02:13, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.3957, loss: 0.2114
+2023-03-04 23:59:00,738 - mmseg - INFO - Iter [60850/80000]	lr: 2.344e-06, eta: 1:02:03, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2105, decode.acc_seg: 91.4992, loss: 0.2105
+2023-03-04 23:59:09,361 - mmseg - INFO - Iter [60900/80000]	lr: 2.344e-06, eta: 1:01:53, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.6519, loss: 0.2078
+2023-03-04 23:59:18,365 - mmseg - INFO - Iter [60950/80000]	lr: 2.344e-06, eta: 1:01:43, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.3567, loss: 0.2117
+2023-03-04 23:59:27,209 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-04 23:59:27,209 - mmseg - INFO - Iter [61000/80000]	lr: 2.344e-06, eta: 1:01:33, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.4425, loss: 0.2103
+2023-03-04 23:59:38,182 - mmseg - INFO - Iter [61050/80000]	lr: 2.344e-06, eta: 1:01:24, time: 0.219, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2039, decode.acc_seg: 91.7025, loss: 0.2039
+2023-03-04 23:59:46,745 - mmseg - INFO - Iter [61100/80000]	lr: 2.344e-06, eta: 1:01:14, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.3032, loss: 0.2106
+2023-03-04 23:59:55,648 - mmseg - INFO - Iter [61150/80000]	lr: 2.344e-06, eta: 1:01:04, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2039, decode.acc_seg: 91.5827, loss: 0.2039
+2023-03-05 00:00:04,641 - mmseg - INFO - Iter [61200/80000]	lr: 2.344e-06, eta: 1:00:54, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2053, decode.acc_seg: 91.5819, loss: 0.2053
+2023-03-05 00:00:13,339 - mmseg - INFO - Iter [61250/80000]	lr: 2.344e-06, eta: 1:00:44, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.4199, loss: 0.2076
+2023-03-05 00:00:22,050 - mmseg - INFO - Iter [61300/80000]	lr: 2.344e-06, eta: 1:00:34, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.3224, loss: 0.2117
+2023-03-05 00:00:30,729 - mmseg - INFO - Iter [61350/80000]	lr: 2.344e-06, eta: 1:00:23, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2037, decode.acc_seg: 91.6402, loss: 0.2037
+2023-03-05 00:00:39,808 - mmseg - INFO - Iter [61400/80000]	lr: 2.344e-06, eta: 1:00:14, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.4045, loss: 0.2120
+2023-03-05 00:00:48,671 - mmseg - INFO - Iter [61450/80000]	lr: 2.344e-06, eta: 1:00:04, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.3762, loss: 0.2073
+2023-03-05 00:00:57,316 - mmseg - INFO - Iter [61500/80000]	lr: 2.344e-06, eta: 0:59:53, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2102, decode.acc_seg: 91.5390, loss: 0.2102
+2023-03-05 00:01:06,196 - mmseg - INFO - Iter [61550/80000]	lr: 2.344e-06, eta: 0:59:43, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2169, decode.acc_seg: 91.1651, loss: 0.2169
+2023-03-05 00:01:15,209 - mmseg - INFO - Iter [61600/80000]	lr: 2.344e-06, eta: 0:59:33, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.3157, loss: 0.2121
+2023-03-05 00:01:26,633 - mmseg - INFO - Iter [61650/80000]	lr: 2.344e-06, eta: 0:59:24, time: 0.228, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.2965, loss: 0.2196
+2023-03-05 00:01:36,050 - mmseg - INFO - Iter [61700/80000]	lr: 2.344e-06, eta: 0:59:15, time: 0.189, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.5689, loss: 0.2089
+2023-03-05 00:01:44,575 - mmseg - INFO - Iter [61750/80000]	lr: 2.344e-06, eta: 0:59:04, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1992, decode.acc_seg: 91.7668, loss: 0.1992
+2023-03-05 00:01:53,701 - mmseg - INFO - Iter [61800/80000]	lr: 2.344e-06, eta: 0:58:55, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.3434, loss: 0.2095
+2023-03-05 00:02:02,854 - mmseg - INFO - Iter [61850/80000]	lr: 2.344e-06, eta: 0:58:45, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2004, decode.acc_seg: 91.7138, loss: 0.2004
+2023-03-05 00:02:11,580 - mmseg - INFO - Iter [61900/80000]	lr: 2.344e-06, eta: 0:58:35, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2066, decode.acc_seg: 91.4689, loss: 0.2066
+2023-03-05 00:02:20,401 - mmseg - INFO - Iter [61950/80000]	lr: 2.344e-06, eta: 0:58:25, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.5015, loss: 0.2074
+2023-03-05 00:02:29,080 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:02:29,080 - mmseg - INFO - Iter [62000/80000]	lr: 2.344e-06, eta: 0:58:15, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.3981, loss: 0.2103
+2023-03-05 00:02:38,022 - mmseg - INFO - Iter [62050/80000]	lr: 2.344e-06, eta: 0:58:05, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2039, decode.acc_seg: 91.5254, loss: 0.2039
+2023-03-05 00:02:46,971 - mmseg - INFO - Iter [62100/80000]	lr: 2.344e-06, eta: 0:57:55, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2166, decode.acc_seg: 91.1137, loss: 0.2166
+2023-03-05 00:02:56,155 - mmseg - INFO - Iter [62150/80000]	lr: 2.344e-06, eta: 0:57:45, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.2021, loss: 0.2127
+2023-03-05 00:03:04,805 - mmseg - INFO - Iter [62200/80000]	lr: 2.344e-06, eta: 0:57:35, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2064, decode.acc_seg: 91.6493, loss: 0.2064
+2023-03-05 00:03:13,658 - mmseg - INFO - Iter [62250/80000]	lr: 2.344e-06, eta: 0:57:25, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2113, decode.acc_seg: 91.3145, loss: 0.2113
+2023-03-05 00:03:24,871 - mmseg - INFO - Iter [62300/80000]	lr: 2.344e-06, eta: 0:57:15, time: 0.224, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.3426, loss: 0.2139
+2023-03-05 00:03:33,503 - mmseg - INFO - Iter [62350/80000]	lr: 2.344e-06, eta: 0:57:05, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.5542, loss: 0.2074
+2023-03-05 00:03:42,186 - mmseg - INFO - Iter [62400/80000]	lr: 2.344e-06, eta: 0:56:55, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2097, decode.acc_seg: 91.5222, loss: 0.2097
+2023-03-05 00:03:50,960 - mmseg - INFO - Iter [62450/80000]	lr: 2.344e-06, eta: 0:56:45, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2170, decode.acc_seg: 91.1157, loss: 0.2170
+2023-03-05 00:03:59,733 - mmseg - INFO - Iter [62500/80000]	lr: 2.344e-06, eta: 0:56:35, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.2194, loss: 0.2114
+2023-03-05 00:04:08,449 - mmseg - INFO - Iter [62550/80000]	lr: 2.344e-06, eta: 0:56:25, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2045, decode.acc_seg: 91.6445, loss: 0.2045
+2023-03-05 00:04:17,503 - mmseg - INFO - Iter [62600/80000]	lr: 2.344e-06, eta: 0:56:15, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2091, decode.acc_seg: 91.5380, loss: 0.2091
+2023-03-05 00:04:26,446 - mmseg - INFO - Iter [62650/80000]	lr: 2.344e-06, eta: 0:56:05, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.5141, loss: 0.2095
+2023-03-05 00:04:35,238 - mmseg - INFO - Iter [62700/80000]	lr: 2.344e-06, eta: 0:55:56, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.4471, loss: 0.2116
+2023-03-05 00:04:44,084 - mmseg - INFO - Iter [62750/80000]	lr: 2.344e-06, eta: 0:55:46, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5162, loss: 0.2060
+2023-03-05 00:04:53,158 - mmseg - INFO - Iter [62800/80000]	lr: 2.344e-06, eta: 0:55:36, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2190, decode.acc_seg: 91.2362, loss: 0.2190
+2023-03-05 00:05:02,022 - mmseg - INFO - Iter [62850/80000]	lr: 2.344e-06, eta: 0:55:26, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.2186, loss: 0.2168
+2023-03-05 00:05:13,210 - mmseg - INFO - Iter [62900/80000]	lr: 2.344e-06, eta: 0:55:16, time: 0.224, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.3476, loss: 0.2132
+2023-03-05 00:05:22,377 - mmseg - INFO - Iter [62950/80000]	lr: 2.344e-06, eta: 0:55:07, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2092, decode.acc_seg: 91.3792, loss: 0.2092
+2023-03-05 00:05:31,320 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:05:31,320 - mmseg - INFO - Iter [63000/80000]	lr: 2.344e-06, eta: 0:54:57, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2080, decode.acc_seg: 91.3796, loss: 0.2080
+2023-03-05 00:05:40,383 - mmseg - INFO - Iter [63050/80000]	lr: 2.344e-06, eta: 0:54:47, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.3903, loss: 0.2111
+2023-03-05 00:05:49,322 - mmseg - INFO - Iter [63100/80000]	lr: 2.344e-06, eta: 0:54:37, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.0725, loss: 0.2165
+2023-03-05 00:05:59,089 - mmseg - INFO - Iter [63150/80000]	lr: 2.344e-06, eta: 0:54:27, time: 0.195, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2172, decode.acc_seg: 91.1070, loss: 0.2172
+2023-03-05 00:06:08,206 - mmseg - INFO - Iter [63200/80000]	lr: 2.344e-06, eta: 0:54:17, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.3924, loss: 0.2106
+2023-03-05 00:06:16,801 - mmseg - INFO - Iter [63250/80000]	lr: 2.344e-06, eta: 0:54:07, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2045, decode.acc_seg: 91.3973, loss: 0.2045
+2023-03-05 00:06:25,447 - mmseg - INFO - Iter [63300/80000]	lr: 2.344e-06, eta: 0:53:57, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.3333, loss: 0.2107
+2023-03-05 00:06:34,913 - mmseg - INFO - Iter [63350/80000]	lr: 2.344e-06, eta: 0:53:47, time: 0.189, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2183, decode.acc_seg: 91.1859, loss: 0.2183
+2023-03-05 00:06:43,835 - mmseg - INFO - Iter [63400/80000]	lr: 2.344e-06, eta: 0:53:38, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2156, decode.acc_seg: 91.2849, loss: 0.2156
+2023-03-05 00:06:53,150 - mmseg - INFO - Iter [63450/80000]	lr: 2.344e-06, eta: 0:53:28, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2126, decode.acc_seg: 91.2747, loss: 0.2126
+2023-03-05 00:07:01,984 - mmseg - INFO - Iter [63500/80000]	lr: 2.344e-06, eta: 0:53:18, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.4512, loss: 0.2095
+2023-03-05 00:07:13,119 - mmseg - INFO - Iter [63550/80000]	lr: 2.344e-06, eta: 0:53:09, time: 0.223, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2093, decode.acc_seg: 91.3691, loss: 0.2093
+2023-03-05 00:07:22,079 - mmseg - INFO - Iter [63600/80000]	lr: 2.344e-06, eta: 0:52:59, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2110, decode.acc_seg: 91.4194, loss: 0.2110
+2023-03-05 00:07:30,792 - mmseg - INFO - Iter [63650/80000]	lr: 2.344e-06, eta: 0:52:49, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2124, decode.acc_seg: 91.4099, loss: 0.2124
+2023-03-05 00:07:39,554 - mmseg - INFO - Iter [63700/80000]	lr: 2.344e-06, eta: 0:52:39, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2191, decode.acc_seg: 91.1353, loss: 0.2191
+2023-03-05 00:07:48,600 - mmseg - INFO - Iter [63750/80000]	lr: 2.344e-06, eta: 0:52:29, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2021, decode.acc_seg: 91.7015, loss: 0.2021
+2023-03-05 00:07:57,368 - mmseg - INFO - Iter [63800/80000]	lr: 2.344e-06, eta: 0:52:19, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2113, decode.acc_seg: 91.2496, loss: 0.2113
+2023-03-05 00:08:06,232 - mmseg - INFO - Iter [63850/80000]	lr: 2.344e-06, eta: 0:52:09, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2184, decode.acc_seg: 91.1078, loss: 0.2184
+2023-03-05 00:08:14,890 - mmseg - INFO - Iter [63900/80000]	lr: 2.344e-06, eta: 0:51:59, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2129, decode.acc_seg: 91.4291, loss: 0.2129
+2023-03-05 00:08:24,117 - mmseg - INFO - Iter [63950/80000]	lr: 2.344e-06, eta: 0:51:49, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2026, decode.acc_seg: 91.7049, loss: 0.2026
+2023-03-05 00:08:32,937 - mmseg - INFO - Saving checkpoint at 64000 iterations
+2023-03-05 00:08:33,609 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:08:33,609 - mmseg - INFO - Iter [64000/80000]	lr: 2.344e-06, eta: 0:51:39, time: 0.190, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5443, loss: 0.2060
+2023-03-05 00:08:49,445 - mmseg - INFO - per class results:
+2023-03-05 00:08:49,452 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.72 | 88.59 |
+|       building      | 81.39 | 91.59 |
+|         sky         | 94.34 | 97.35 |
+|        floor        | 81.19 | 90.76 |
+|         tree        | 73.55 | 87.48 |
+|       ceiling       | 84.25 |  93.1 |
+|         road        | 81.81 | 90.06 |
+|         bed         | 86.76 | 95.07 |
+|      windowpane     | 59.62 | 76.91 |
+|        grass        | 65.49 | 81.35 |
+|       cabinet       | 59.44 | 71.81 |
+|       sidewalk      | 63.55 | 79.21 |
+|        person       | 78.18 | 91.65 |
+|        earth        | 35.62 | 49.57 |
+|         door        |  44.2 |  58.1 |
+|        table        |  58.4 | 75.38 |
+|       mountain      | 55.95 | 70.34 |
+|        plant        | 50.12 | 62.29 |
+|       curtain       |  73.3 | 83.68 |
+|        chair        | 54.01 | 67.26 |
+|         car         | 79.92 | 92.76 |
+|        water        | 57.34 |  76.6 |
+|       painting      | 69.21 | 84.54 |
+|         sofa        | 62.85 |  82.1 |
+|        shelf        | 42.72 | 60.67 |
+|        house        | 42.63 | 58.61 |
+|         sea         | 60.96 | 77.18 |
+|        mirror       | 63.48 | 72.67 |
+|         rug         | 65.25 | 74.33 |
+|        field        | 29.03 | 45.81 |
+|       armchair      | 35.71 | 51.02 |
+|         seat        | 66.11 | 82.54 |
+|        fence        | 41.49 | 56.62 |
+|         desk        | 45.42 | 66.75 |
+|         rock        | 37.15 | 58.33 |
+|       wardrobe      | 56.61 | 69.22 |
+|         lamp        | 58.71 | 73.29 |
+|       bathtub       | 75.44 | 84.07 |
+|       railing       | 33.15 | 45.28 |
+|       cushion       | 55.05 | 67.81 |
+|         base        |  21.8 | 26.53 |
+|         box         |  22.1 | 29.75 |
+|        column       | 44.88 | 54.34 |
+|      signboard      | 36.86 |  48.8 |
+|   chest of drawers  | 36.27 | 57.61 |
+|       counter       | 31.35 | 42.25 |
+|         sand        | 41.54 |  59.3 |
+|         sink        | 64.95 | 76.23 |
+|      skyscraper     | 54.32 | 68.68 |
+|      fireplace      | 73.45 | 85.33 |
+|     refrigerator    | 69.55 | 84.25 |
+|      grandstand     | 48.24 | 63.09 |
+|         path        | 22.61 | 29.48 |
+|        stairs       | 32.83 | 40.35 |
+|        runway       | 66.93 | 85.76 |
+|         case        | 48.62 | 60.24 |
+|      pool table     | 91.38 |  94.5 |
+|        pillow       | 60.45 | 72.18 |
+|     screen door     | 65.34 | 73.82 |
+|       stairway      | 23.86 | 35.71 |
+|        river        | 11.52 | 21.54 |
+|        bridge       | 34.75 |  41.0 |
+|       bookcase      | 43.17 | 63.31 |
+|        blind        |  42.5 | 48.09 |
+|     coffee table    | 52.56 | 76.98 |
+|        toilet       |  82.0 | 89.65 |
+|        flower       | 37.38 | 51.14 |
+|         book        | 42.84 |  61.3 |
+|         hill        |  14.1 | 21.71 |
+|        bench        | 40.02 | 53.71 |
+|      countertop     | 53.02 | 68.97 |
+|        stove        | 69.47 | 80.49 |
+|         palm        | 48.98 | 67.73 |
+|    kitchen island   | 38.66 | 60.38 |
+|       computer      | 59.76 | 68.95 |
+|     swivel chair    | 43.34 | 59.61 |
+|         boat        | 68.93 | 84.32 |
+|         bar         | 22.24 |  30.1 |
+|    arcade machine   | 68.03 | 69.78 |
+|        hovel        | 23.91 | 26.21 |
+|         bus         | 77.81 | 89.98 |
+|        towel        | 62.08 | 70.48 |
+|        light        | 49.52 | 55.97 |
+|        truck        | 15.74 | 21.37 |
+|        tower        |  6.21 |  9.83 |
+|      chandelier     | 62.49 | 77.43 |
+|        awning       | 24.68 | 29.22 |
+|     streetlight     |  23.6 | 31.11 |
+|        booth        | 41.91 | 42.94 |
+| television receiver | 63.89 | 77.03 |
+|       airplane      | 57.03 | 62.59 |
+|      dirt track     | 12.96 | 37.15 |
+|       apparel       | 32.79 |  51.9 |
+|         pole        | 18.12 | 22.98 |
+|         land        |  3.07 |  4.26 |
+|      bannister      | 10.12 | 13.53 |
+|      escalator      | 23.59 | 25.52 |
+|       ottoman       | 41.76 | 60.68 |
+|        bottle       |  34.3 | 55.08 |
+|        buffet       | 39.72 | 45.62 |
+|        poster       |  21.8 | 31.66 |
+|        stage        |  13.4 | 16.98 |
+|         van         | 37.75 | 54.03 |
+|         ship        | 75.58 |  91.8 |
+|       fountain      |  9.13 |  9.31 |
+|    conveyer belt    | 82.92 |  88.6 |
+|        canopy       | 24.97 | 26.66 |
+|        washer       | 78.83 | 81.33 |
+|      plaything      | 20.91 | 29.48 |
+|    swimming pool    | 75.53 | 82.33 |
+|        stool        | 41.25 | 53.57 |
+|        barrel       | 37.85 | 58.43 |
+|        basket       | 24.57 |  37.2 |
+|      waterfall      | 49.68 | 66.33 |
+|         tent        | 93.43 | 97.44 |
+|         bag         | 14.02 | 16.88 |
+|       minibike      | 60.63 |  73.7 |
+|        cradle       | 81.84 | 96.67 |
+|         oven        | 47.83 | 61.11 |
+|         ball        | 41.13 | 46.67 |
+|         food        | 48.81 | 57.95 |
+|         step        |  6.1  |  6.83 |
+|         tank        | 50.41 | 56.81 |
+|      trade name     | 25.15 | 28.03 |
+|      microwave      | 75.84 | 83.05 |
+|         pot         | 31.12 | 36.02 |
+|        animal       | 53.31 | 60.07 |
+|       bicycle       | 52.16 | 65.29 |
+|         lake        | 57.08 | 62.95 |
+|      dishwasher     | 62.14 | 76.99 |
+|        screen       | 67.29 | 82.32 |
+|       blanket       | 14.68 | 16.28 |
+|      sculpture      | 58.49 | 77.29 |
+|         hood        | 54.85 | 61.41 |
+|        sconce       | 42.91 | 54.83 |
+|         vase        | 31.04 |  46.9 |
+|    traffic light    | 30.09 | 42.37 |
+|         tray        |  4.54 |  6.7  |
+|        ashcan       | 38.95 | 52.66 |
+|         fan         | 55.85 |  68.3 |
+|         pier        | 49.13 | 66.96 |
+|      crt screen     |  8.78 | 22.38 |
+|        plate        | 47.38 | 64.35 |
+|       monitor       | 14.66 | 17.31 |
+|    bulletin board   | 37.33 | 49.01 |
+|        shower       |  1.49 |  5.4  |
+|       radiator      | 58.59 |  67.1 |
+|        glass        | 11.05 | 12.06 |
+|        clock        | 32.31 | 35.51 |
+|         flag        | 34.44 | 37.51 |
++---------------------+-------+-------+
+2023-03-05 00:08:49,452 - mmseg - INFO - Summary:
+2023-03-05 00:08:49,452 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 82.28 | 47.21 | 58.27 |
++-------+-------+-------+
+2023-03-05 00:08:49,474 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_56000.pth was removed
+2023-03-05 00:08:50,087 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_64000.pth.
+2023-03-05 00:08:50,088 - mmseg - INFO - Best mIoU is 0.4721 at 64000 iter.
+2023-03-05 00:08:50,088 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:08:50,088 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8228, mIoU: 0.4721, mAcc: 0.5827, IoU.background: nan, IoU.wall: 0.7672, IoU.building: 0.8139, IoU.sky: 0.9434, IoU.floor: 0.8119, IoU.tree: 0.7355, IoU.ceiling: 0.8425, IoU.road: 0.8181, IoU.bed : 0.8676, IoU.windowpane: 0.5962, IoU.grass: 0.6549, IoU.cabinet: 0.5944, IoU.sidewalk: 0.6355, IoU.person: 0.7818, IoU.earth: 0.3562, IoU.door: 0.4420, IoU.table: 0.5840, IoU.mountain: 0.5595, IoU.plant: 0.5012, IoU.curtain: 0.7330, IoU.chair: 0.5401, IoU.car: 0.7992, IoU.water: 0.5734, IoU.painting: 0.6921, IoU.sofa: 0.6285, IoU.shelf: 0.4272, IoU.house: 0.4263, IoU.sea: 0.6096, IoU.mirror: 0.6348, IoU.rug: 0.6525, IoU.field: 0.2903, IoU.armchair: 0.3571, IoU.seat: 0.6611, IoU.fence: 0.4149, IoU.desk: 0.4542, IoU.rock: 0.3715, IoU.wardrobe: 0.5661, IoU.lamp: 0.5871, IoU.bathtub: 0.7544, IoU.railing: 0.3315, IoU.cushion: 0.5505, IoU.base: 0.2180, IoU.box: 0.2210, IoU.column: 0.4488, IoU.signboard: 0.3686, IoU.chest of drawers: 0.3627, IoU.counter: 0.3135, IoU.sand: 0.4154, IoU.sink: 0.6495, IoU.skyscraper: 0.5432, IoU.fireplace: 0.7345, IoU.refrigerator: 0.6955, IoU.grandstand: 0.4824, IoU.path: 0.2261, IoU.stairs: 0.3283, IoU.runway: 0.6693, IoU.case: 0.4862, IoU.pool table: 0.9138, IoU.pillow: 0.6045, IoU.screen door: 0.6534, IoU.stairway: 0.2386, IoU.river: 0.1152, IoU.bridge: 0.3475, IoU.bookcase: 0.4317, IoU.blind: 0.4250, IoU.coffee table: 0.5256, IoU.toilet: 0.8200, IoU.flower: 0.3738, IoU.book: 0.4284, IoU.hill: 0.1410, IoU.bench: 0.4002, IoU.countertop: 0.5302, IoU.stove: 0.6947, IoU.palm: 0.4898, IoU.kitchen island: 0.3866, IoU.computer: 0.5976, IoU.swivel chair: 0.4334, IoU.boat: 0.6893, IoU.bar: 0.2224, IoU.arcade machine: 0.6803, IoU.hovel: 0.2391, IoU.bus: 0.7781, IoU.towel: 0.6208, IoU.light: 0.4952, IoU.truck: 0.1574, IoU.tower: 0.0621, IoU.chandelier: 0.6249, IoU.awning: 0.2468, IoU.streetlight: 0.2360, IoU.booth: 0.4191, IoU.television receiver: 0.6389, IoU.airplane: 0.5703, IoU.dirt track: 0.1296, IoU.apparel: 0.3279, IoU.pole: 0.1812, IoU.land: 0.0307, IoU.bannister: 0.1012, IoU.escalator: 0.2359, IoU.ottoman: 0.4176, IoU.bottle: 0.3430, IoU.buffet: 0.3972, IoU.poster: 0.2180, IoU.stage: 0.1340, IoU.van: 0.3775, IoU.ship: 0.7558, IoU.fountain: 0.0913, IoU.conveyer belt: 0.8292, IoU.canopy: 0.2497, IoU.washer: 0.7883, IoU.plaything: 0.2091, IoU.swimming pool: 0.7553, IoU.stool: 0.4125, IoU.barrel: 0.3785, IoU.basket: 0.2457, IoU.waterfall: 0.4968, IoU.tent: 0.9343, IoU.bag: 0.1402, IoU.minibike: 0.6063, IoU.cradle: 0.8184, IoU.oven: 0.4783, IoU.ball: 0.4113, IoU.food: 0.4881, IoU.step: 0.0610, IoU.tank: 0.5041, IoU.trade name: 0.2515, IoU.microwave: 0.7584, IoU.pot: 0.3112, IoU.animal: 0.5331, IoU.bicycle: 0.5216, IoU.lake: 0.5708, IoU.dishwasher: 0.6214, IoU.screen: 0.6729, IoU.blanket: 0.1468, IoU.sculpture: 0.5849, IoU.hood: 0.5485, IoU.sconce: 0.4291, IoU.vase: 0.3104, IoU.traffic light: 0.3009, IoU.tray: 0.0454, IoU.ashcan: 0.3895, IoU.fan: 0.5585, IoU.pier: 0.4913, IoU.crt screen: 0.0878, IoU.plate: 0.4738, IoU.monitor: 0.1466, IoU.bulletin board: 0.3733, IoU.shower: 0.0149, IoU.radiator: 0.5859, IoU.glass: 0.1105, IoU.clock: 0.3231, IoU.flag: 0.3444, Acc.background: nan, Acc.wall: 0.8859, Acc.building: 0.9159, Acc.sky: 0.9735, Acc.floor: 0.9076, Acc.tree: 0.8748, Acc.ceiling: 0.9310, Acc.road: 0.9006, Acc.bed : 0.9507, Acc.windowpane: 0.7691, Acc.grass: 0.8135, Acc.cabinet: 0.7181, Acc.sidewalk: 0.7921, Acc.person: 0.9165, Acc.earth: 0.4957, Acc.door: 0.5810, Acc.table: 0.7538, Acc.mountain: 0.7034, Acc.plant: 0.6229, Acc.curtain: 0.8368, Acc.chair: 0.6726, Acc.car: 0.9276, Acc.water: 0.7660, Acc.painting: 0.8454, Acc.sofa: 0.8210, Acc.shelf: 0.6067, Acc.house: 0.5861, Acc.sea: 0.7718, Acc.mirror: 0.7267, Acc.rug: 0.7433, Acc.field: 0.4581, Acc.armchair: 0.5102, Acc.seat: 0.8254, Acc.fence: 0.5662, Acc.desk: 0.6675, Acc.rock: 0.5833, Acc.wardrobe: 0.6922, Acc.lamp: 0.7329, Acc.bathtub: 0.8407, Acc.railing: 0.4528, Acc.cushion: 0.6781, Acc.base: 0.2653, Acc.box: 0.2975, Acc.column: 0.5434, Acc.signboard: 0.4880, Acc.chest of drawers: 0.5761, Acc.counter: 0.4225, Acc.sand: 0.5930, Acc.sink: 0.7623, Acc.skyscraper: 0.6868, Acc.fireplace: 0.8533, Acc.refrigerator: 0.8425, Acc.grandstand: 0.6309, Acc.path: 0.2948, Acc.stairs: 0.4035, Acc.runway: 0.8576, Acc.case: 0.6024, Acc.pool table: 0.9450, Acc.pillow: 0.7218, Acc.screen door: 0.7382, Acc.stairway: 0.3571, Acc.river: 0.2154, Acc.bridge: 0.4100, Acc.bookcase: 0.6331, Acc.blind: 0.4809, Acc.coffee table: 0.7698, Acc.toilet: 0.8965, Acc.flower: 0.5114, Acc.book: 0.6130, Acc.hill: 0.2171, Acc.bench: 0.5371, Acc.countertop: 0.6897, Acc.stove: 0.8049, Acc.palm: 0.6773, Acc.kitchen island: 0.6038, Acc.computer: 0.6895, Acc.swivel chair: 0.5961, Acc.boat: 0.8432, Acc.bar: 0.3010, Acc.arcade machine: 0.6978, Acc.hovel: 0.2621, Acc.bus: 0.8998, Acc.towel: 0.7048, Acc.light: 0.5597, Acc.truck: 0.2137, Acc.tower: 0.0983, Acc.chandelier: 0.7743, Acc.awning: 0.2922, Acc.streetlight: 0.3111, Acc.booth: 0.4294, Acc.television receiver: 0.7703, Acc.airplane: 0.6259, Acc.dirt track: 0.3715, Acc.apparel: 0.5190, Acc.pole: 0.2298, Acc.land: 0.0426, Acc.bannister: 0.1353, Acc.escalator: 0.2552, Acc.ottoman: 0.6068, Acc.bottle: 0.5508, Acc.buffet: 0.4562, Acc.poster: 0.3166, Acc.stage: 0.1698, Acc.van: 0.5403, Acc.ship: 0.9180, Acc.fountain: 0.0931, Acc.conveyer belt: 0.8860, Acc.canopy: 0.2666, Acc.washer: 0.8133, Acc.plaything: 0.2948, Acc.swimming pool: 0.8233, Acc.stool: 0.5357, Acc.barrel: 0.5843, Acc.basket: 0.3720, Acc.waterfall: 0.6633, Acc.tent: 0.9744, Acc.bag: 0.1688, Acc.minibike: 0.7370, Acc.cradle: 0.9667, Acc.oven: 0.6111, Acc.ball: 0.4667, Acc.food: 0.5795, Acc.step: 0.0683, Acc.tank: 0.5681, Acc.trade name: 0.2803, Acc.microwave: 0.8305, Acc.pot: 0.3602, Acc.animal: 0.6007, Acc.bicycle: 0.6529, Acc.lake: 0.6295, Acc.dishwasher: 0.7699, Acc.screen: 0.8232, Acc.blanket: 0.1628, Acc.sculpture: 0.7729, Acc.hood: 0.6141, Acc.sconce: 0.5483, Acc.vase: 0.4690, Acc.traffic light: 0.4237, Acc.tray: 0.0670, Acc.ashcan: 0.5266, Acc.fan: 0.6830, Acc.pier: 0.6696, Acc.crt screen: 0.2238, Acc.plate: 0.6435, Acc.monitor: 0.1731, Acc.bulletin board: 0.4901, Acc.shower: 0.0540, Acc.radiator: 0.6710, Acc.glass: 0.1206, Acc.clock: 0.3551, Acc.flag: 0.3751
+2023-03-05 00:08:59,091 - mmseg - INFO - Iter [64050/80000]	lr: 2.344e-06, eta: 0:51:34, time: 0.510, data_time: 0.337, memory: 52390, decode.loss_ce: 0.2186, decode.acc_seg: 91.2425, loss: 0.2186
+2023-03-05 00:09:07,845 - mmseg - INFO - Iter [64100/80000]	lr: 2.344e-06, eta: 0:51:24, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2189, decode.acc_seg: 91.1121, loss: 0.2189
+2023-03-05 00:09:16,787 - mmseg - INFO - Iter [64150/80000]	lr: 2.344e-06, eta: 0:51:14, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2062, decode.acc_seg: 91.6137, loss: 0.2062
+2023-03-05 00:09:28,192 - mmseg - INFO - Iter [64200/80000]	lr: 2.344e-06, eta: 0:51:05, time: 0.228, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2064, decode.acc_seg: 91.5437, loss: 0.2064
+2023-03-05 00:09:37,777 - mmseg - INFO - Iter [64250/80000]	lr: 2.344e-06, eta: 0:50:55, time: 0.191, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.2895, loss: 0.2153
+2023-03-05 00:09:46,538 - mmseg - INFO - Iter [64300/80000]	lr: 2.344e-06, eta: 0:50:45, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2110, decode.acc_seg: 91.3337, loss: 0.2110
+2023-03-05 00:09:55,380 - mmseg - INFO - Iter [64350/80000]	lr: 2.344e-06, eta: 0:50:35, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2145, decode.acc_seg: 91.1807, loss: 0.2145
+2023-03-05 00:10:04,356 - mmseg - INFO - Iter [64400/80000]	lr: 2.344e-06, eta: 0:50:26, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2066, decode.acc_seg: 91.5124, loss: 0.2066
+2023-03-05 00:10:13,468 - mmseg - INFO - Iter [64450/80000]	lr: 2.344e-06, eta: 0:50:16, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.4704, loss: 0.2074
+2023-03-05 00:10:22,089 - mmseg - INFO - Iter [64500/80000]	lr: 2.344e-06, eta: 0:50:06, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2092, decode.acc_seg: 91.4564, loss: 0.2092
+2023-03-05 00:10:30,912 - mmseg - INFO - Iter [64550/80000]	lr: 2.344e-06, eta: 0:49:56, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2091, decode.acc_seg: 91.3356, loss: 0.2091
+2023-03-05 00:10:39,908 - mmseg - INFO - Iter [64600/80000]	lr: 2.344e-06, eta: 0:49:46, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2137, decode.acc_seg: 91.2774, loss: 0.2137
+2023-03-05 00:10:48,936 - mmseg - INFO - Iter [64650/80000]	lr: 2.344e-06, eta: 0:49:36, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2137, decode.acc_seg: 91.3483, loss: 0.2137
+2023-03-05 00:10:58,173 - mmseg - INFO - Iter [64700/80000]	lr: 2.344e-06, eta: 0:49:26, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.5075, loss: 0.2072
+2023-03-05 00:11:07,264 - mmseg - INFO - Iter [64750/80000]	lr: 2.344e-06, eta: 0:49:16, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2150, decode.acc_seg: 91.2472, loss: 0.2150
+2023-03-05 00:11:18,520 - mmseg - INFO - Iter [64800/80000]	lr: 2.344e-06, eta: 0:49:07, time: 0.225, data_time: 0.057, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.2572, loss: 0.2111
+2023-03-05 00:11:27,561 - mmseg - INFO - Iter [64850/80000]	lr: 2.344e-06, eta: 0:48:57, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2026, decode.acc_seg: 91.7330, loss: 0.2026
+2023-03-05 00:11:36,252 - mmseg - INFO - Iter [64900/80000]	lr: 2.344e-06, eta: 0:48:47, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2146, decode.acc_seg: 91.2451, loss: 0.2146
+2023-03-05 00:11:45,478 - mmseg - INFO - Iter [64950/80000]	lr: 2.344e-06, eta: 0:48:37, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2061, decode.acc_seg: 91.5813, loss: 0.2061
+2023-03-05 00:11:54,426 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:11:54,426 - mmseg - INFO - Iter [65000/80000]	lr: 2.344e-06, eta: 0:48:27, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.4068, loss: 0.2107
+2023-03-05 00:12:03,060 - mmseg - INFO - Iter [65050/80000]	lr: 2.344e-06, eta: 0:48:18, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5080, loss: 0.2060
+2023-03-05 00:12:12,256 - mmseg - INFO - Iter [65100/80000]	lr: 2.344e-06, eta: 0:48:08, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2104, decode.acc_seg: 91.3905, loss: 0.2104
+2023-03-05 00:12:21,091 - mmseg - INFO - Iter [65150/80000]	lr: 2.344e-06, eta: 0:47:58, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.1383, loss: 0.2163
+2023-03-05 00:12:29,983 - mmseg - INFO - Iter [65200/80000]	lr: 2.344e-06, eta: 0:47:48, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.3473, loss: 0.2109
+2023-03-05 00:12:38,740 - mmseg - INFO - Iter [65250/80000]	lr: 2.344e-06, eta: 0:47:38, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2049, decode.acc_seg: 91.7458, loss: 0.2049
+2023-03-05 00:12:47,459 - mmseg - INFO - Iter [65300/80000]	lr: 2.344e-06, eta: 0:47:28, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.2680, loss: 0.2154
+2023-03-05 00:12:56,594 - mmseg - INFO - Iter [65350/80000]	lr: 2.344e-06, eta: 0:47:18, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2044, decode.acc_seg: 91.5896, loss: 0.2044
+2023-03-05 00:13:05,442 - mmseg - INFO - Iter [65400/80000]	lr: 2.344e-06, eta: 0:47:08, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2027, decode.acc_seg: 91.8255, loss: 0.2027
+2023-03-05 00:13:16,827 - mmseg - INFO - Iter [65450/80000]	lr: 2.344e-06, eta: 0:46:59, time: 0.228, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2084, decode.acc_seg: 91.5636, loss: 0.2084
+2023-03-05 00:13:25,789 - mmseg - INFO - Iter [65500/80000]	lr: 2.344e-06, eta: 0:46:49, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.3965, loss: 0.2153
+2023-03-05 00:13:34,908 - mmseg - INFO - Iter [65550/80000]	lr: 2.344e-06, eta: 0:46:39, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2090, decode.acc_seg: 91.4187, loss: 0.2090
+2023-03-05 00:13:43,810 - mmseg - INFO - Iter [65600/80000]	lr: 2.344e-06, eta: 0:46:29, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2080, decode.acc_seg: 91.2978, loss: 0.2080
+2023-03-05 00:13:52,687 - mmseg - INFO - Iter [65650/80000]	lr: 2.344e-06, eta: 0:46:20, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.2850, loss: 0.2140
+2023-03-05 00:14:01,667 - mmseg - INFO - Iter [65700/80000]	lr: 2.344e-06, eta: 0:46:10, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1969, decode.acc_seg: 91.8258, loss: 0.1969
+2023-03-05 00:14:10,783 - mmseg - INFO - Iter [65750/80000]	lr: 2.344e-06, eta: 0:46:00, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.3378, loss: 0.2132
+2023-03-05 00:14:19,753 - mmseg - INFO - Iter [65800/80000]	lr: 2.344e-06, eta: 0:45:50, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.6079, loss: 0.2073
+2023-03-05 00:14:28,792 - mmseg - INFO - Iter [65850/80000]	lr: 2.344e-06, eta: 0:45:40, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.4364, loss: 0.2120
+2023-03-05 00:14:37,916 - mmseg - INFO - Iter [65900/80000]	lr: 2.344e-06, eta: 0:45:30, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2209, decode.acc_seg: 90.8970, loss: 0.2209
+2023-03-05 00:14:46,617 - mmseg - INFO - Iter [65950/80000]	lr: 2.344e-06, eta: 0:45:20, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2048, decode.acc_seg: 91.5487, loss: 0.2048
+2023-03-05 00:14:55,825 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:14:55,825 - mmseg - INFO - Iter [66000/80000]	lr: 2.344e-06, eta: 0:45:11, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2046, decode.acc_seg: 91.6480, loss: 0.2046
+2023-03-05 00:15:04,563 - mmseg - INFO - Iter [66050/80000]	lr: 2.344e-06, eta: 0:45:01, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2154, decode.acc_seg: 91.2226, loss: 0.2154
+2023-03-05 00:15:15,919 - mmseg - INFO - Iter [66100/80000]	lr: 2.344e-06, eta: 0:44:51, time: 0.227, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.1465, loss: 0.2161
+2023-03-05 00:15:25,011 - mmseg - INFO - Iter [66150/80000]	lr: 2.344e-06, eta: 0:44:42, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2105, decode.acc_seg: 91.4936, loss: 0.2105
+2023-03-05 00:15:33,922 - mmseg - INFO - Iter [66200/80000]	lr: 2.344e-06, eta: 0:44:32, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.2924, loss: 0.2128
+2023-03-05 00:15:43,516 - mmseg - INFO - Iter [66250/80000]	lr: 2.344e-06, eta: 0:44:22, time: 0.192, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2180, decode.acc_seg: 91.2750, loss: 0.2180
+2023-03-05 00:15:52,881 - mmseg - INFO - Iter [66300/80000]	lr: 2.344e-06, eta: 0:44:12, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2041, decode.acc_seg: 91.6756, loss: 0.2041
+2023-03-05 00:16:01,874 - mmseg - INFO - Iter [66350/80000]	lr: 2.344e-06, eta: 0:44:02, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.4511, loss: 0.2073
+2023-03-05 00:16:10,650 - mmseg - INFO - Iter [66400/80000]	lr: 2.344e-06, eta: 0:43:53, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2080, decode.acc_seg: 91.4968, loss: 0.2080
+2023-03-05 00:16:19,664 - mmseg - INFO - Iter [66450/80000]	lr: 2.344e-06, eta: 0:43:43, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2113, decode.acc_seg: 91.4558, loss: 0.2113
+2023-03-05 00:16:28,855 - mmseg - INFO - Iter [66500/80000]	lr: 2.344e-06, eta: 0:43:33, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2046, decode.acc_seg: 91.6463, loss: 0.2046
+2023-03-05 00:16:37,681 - mmseg - INFO - Iter [66550/80000]	lr: 2.344e-06, eta: 0:43:23, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.4759, loss: 0.2072
+2023-03-05 00:16:46,577 - mmseg - INFO - Iter [66600/80000]	lr: 2.344e-06, eta: 0:43:13, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2069, decode.acc_seg: 91.5139, loss: 0.2069
+2023-03-05 00:16:55,934 - mmseg - INFO - Iter [66650/80000]	lr: 2.344e-06, eta: 0:43:03, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2170, decode.acc_seg: 91.1143, loss: 0.2170
+2023-03-05 00:17:07,379 - mmseg - INFO - Iter [66700/80000]	lr: 2.344e-06, eta: 0:42:54, time: 0.229, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.3891, loss: 0.2134
+2023-03-05 00:17:16,087 - mmseg - INFO - Iter [66750/80000]	lr: 2.344e-06, eta: 0:42:44, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5344, loss: 0.2060
+2023-03-05 00:17:25,078 - mmseg - INFO - Iter [66800/80000]	lr: 2.344e-06, eta: 0:42:34, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2085, decode.acc_seg: 91.6411, loss: 0.2085
+2023-03-05 00:17:34,182 - mmseg - INFO - Iter [66850/80000]	lr: 2.344e-06, eta: 0:42:25, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.3323, loss: 0.2107
+2023-03-05 00:17:42,952 - mmseg - INFO - Iter [66900/80000]	lr: 2.344e-06, eta: 0:42:15, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2262, decode.acc_seg: 90.8297, loss: 0.2262
+2023-03-05 00:17:51,996 - mmseg - INFO - Iter [66950/80000]	lr: 2.344e-06, eta: 0:42:05, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.3416, loss: 0.2117
+2023-03-05 00:18:00,875 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:18:00,875 - mmseg - INFO - Iter [67000/80000]	lr: 2.344e-06, eta: 0:41:55, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2148, decode.acc_seg: 91.2193, loss: 0.2148
+2023-03-05 00:18:09,843 - mmseg - INFO - Iter [67050/80000]	lr: 2.344e-06, eta: 0:41:45, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2088, decode.acc_seg: 91.4370, loss: 0.2088
+2023-03-05 00:18:19,053 - mmseg - INFO - Iter [67100/80000]	lr: 2.344e-06, eta: 0:41:35, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.4775, loss: 0.2099
+2023-03-05 00:18:27,697 - mmseg - INFO - Iter [67150/80000]	lr: 2.344e-06, eta: 0:41:26, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.3076, loss: 0.2123
+2023-03-05 00:18:36,868 - mmseg - INFO - Iter [67200/80000]	lr: 2.344e-06, eta: 0:41:16, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2161, decode.acc_seg: 91.1521, loss: 0.2161
+2023-03-05 00:18:45,769 - mmseg - INFO - Iter [67250/80000]	lr: 2.344e-06, eta: 0:41:06, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.3861, loss: 0.2075
+2023-03-05 00:18:54,531 - mmseg - INFO - Iter [67300/80000]	lr: 2.344e-06, eta: 0:40:56, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.6265, loss: 0.2060
+2023-03-05 00:19:05,719 - mmseg - INFO - Iter [67350/80000]	lr: 2.344e-06, eta: 0:40:47, time: 0.223, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2043, decode.acc_seg: 91.5143, loss: 0.2043
+2023-03-05 00:19:14,751 - mmseg - INFO - Iter [67400/80000]	lr: 2.344e-06, eta: 0:40:37, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2001, decode.acc_seg: 91.7091, loss: 0.2001
+2023-03-05 00:19:24,047 - mmseg - INFO - Iter [67450/80000]	lr: 2.344e-06, eta: 0:40:27, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2102, decode.acc_seg: 91.4733, loss: 0.2102
+2023-03-05 00:19:33,023 - mmseg - INFO - Iter [67500/80000]	lr: 2.344e-06, eta: 0:40:17, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.1980, decode.acc_seg: 91.8098, loss: 0.1980
+2023-03-05 00:19:42,030 - mmseg - INFO - Iter [67550/80000]	lr: 2.344e-06, eta: 0:40:08, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2105, decode.acc_seg: 91.2576, loss: 0.2105
+2023-03-05 00:19:50,982 - mmseg - INFO - Iter [67600/80000]	lr: 2.344e-06, eta: 0:39:58, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2152, decode.acc_seg: 91.3164, loss: 0.2152
+2023-03-05 00:19:59,734 - mmseg - INFO - Iter [67650/80000]	lr: 2.344e-06, eta: 0:39:48, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2130, decode.acc_seg: 91.1907, loss: 0.2130
+2023-03-05 00:20:09,029 - mmseg - INFO - Iter [67700/80000]	lr: 2.344e-06, eta: 0:39:38, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2054, decode.acc_seg: 91.5440, loss: 0.2054
+2023-03-05 00:20:17,905 - mmseg - INFO - Iter [67750/80000]	lr: 2.344e-06, eta: 0:39:28, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.6433, loss: 0.2072
+2023-03-05 00:20:27,273 - mmseg - INFO - Iter [67800/80000]	lr: 2.344e-06, eta: 0:39:19, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.3497, loss: 0.2114
+2023-03-05 00:20:36,165 - mmseg - INFO - Iter [67850/80000]	lr: 2.344e-06, eta: 0:39:09, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2141, decode.acc_seg: 91.2334, loss: 0.2141
+2023-03-05 00:20:44,933 - mmseg - INFO - Iter [67900/80000]	lr: 2.344e-06, eta: 0:38:59, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2213, decode.acc_seg: 90.8035, loss: 0.2213
+2023-03-05 00:20:56,450 - mmseg - INFO - Iter [67950/80000]	lr: 2.344e-06, eta: 0:38:50, time: 0.230, data_time: 0.054, memory: 52390, decode.loss_ce: 0.1973, decode.acc_seg: 91.8970, loss: 0.1973
+2023-03-05 00:21:05,991 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:21:05,991 - mmseg - INFO - Iter [68000/80000]	lr: 2.344e-06, eta: 0:38:40, time: 0.191, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.1124, loss: 0.2196
+2023-03-05 00:21:14,942 - mmseg - INFO - Iter [68050/80000]	lr: 2.344e-06, eta: 0:38:30, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.1934, loss: 0.2196
+2023-03-05 00:21:24,072 - mmseg - INFO - Iter [68100/80000]	lr: 2.344e-06, eta: 0:38:20, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2152, decode.acc_seg: 91.2895, loss: 0.2152
+2023-03-05 00:21:33,275 - mmseg - INFO - Iter [68150/80000]	lr: 2.344e-06, eta: 0:38:11, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2011, decode.acc_seg: 91.7452, loss: 0.2011
+2023-03-05 00:21:41,814 - mmseg - INFO - Iter [68200/80000]	lr: 2.344e-06, eta: 0:38:01, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2175, decode.acc_seg: 91.1100, loss: 0.2175
+2023-03-05 00:21:50,766 - mmseg - INFO - Iter [68250/80000]	lr: 2.344e-06, eta: 0:37:51, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.2678, loss: 0.2123
+2023-03-05 00:21:59,330 - mmseg - INFO - Iter [68300/80000]	lr: 2.344e-06, eta: 0:37:41, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.5362, loss: 0.2078
+2023-03-05 00:22:08,115 - mmseg - INFO - Iter [68350/80000]	lr: 2.344e-06, eta: 0:37:31, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2110, decode.acc_seg: 91.4514, loss: 0.2110
+2023-03-05 00:22:16,967 - mmseg - INFO - Iter [68400/80000]	lr: 2.344e-06, eta: 0:37:21, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2040, decode.acc_seg: 91.7131, loss: 0.2040
+2023-03-05 00:22:25,565 - mmseg - INFO - Iter [68450/80000]	lr: 2.344e-06, eta: 0:37:11, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2234, decode.acc_seg: 90.9675, loss: 0.2234
+2023-03-05 00:22:34,648 - mmseg - INFO - Iter [68500/80000]	lr: 2.344e-06, eta: 0:37:02, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2057, decode.acc_seg: 91.5847, loss: 0.2057
+2023-03-05 00:22:43,959 - mmseg - INFO - Iter [68550/80000]	lr: 2.344e-06, eta: 0:36:52, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2146, decode.acc_seg: 91.3503, loss: 0.2146
+2023-03-05 00:22:55,433 - mmseg - INFO - Iter [68600/80000]	lr: 2.344e-06, eta: 0:36:43, time: 0.230, data_time: 0.058, memory: 52390, decode.loss_ce: 0.2074, decode.acc_seg: 91.5341, loss: 0.2074
+2023-03-05 00:23:04,555 - mmseg - INFO - Iter [68650/80000]	lr: 2.344e-06, eta: 0:36:33, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2046, decode.acc_seg: 91.6955, loss: 0.2046
+2023-03-05 00:23:13,918 - mmseg - INFO - Iter [68700/80000]	lr: 2.344e-06, eta: 0:36:23, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2136, decode.acc_seg: 91.3183, loss: 0.2136
+2023-03-05 00:23:22,954 - mmseg - INFO - Iter [68750/80000]	lr: 2.344e-06, eta: 0:36:13, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2042, decode.acc_seg: 91.6701, loss: 0.2042
+2023-03-05 00:23:32,194 - mmseg - INFO - Iter [68800/80000]	lr: 2.344e-06, eta: 0:36:04, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.2991, loss: 0.2123
+2023-03-05 00:23:41,369 - mmseg - INFO - Iter [68850/80000]	lr: 2.344e-06, eta: 0:35:54, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2232, decode.acc_seg: 90.9427, loss: 0.2232
+2023-03-05 00:23:50,366 - mmseg - INFO - Iter [68900/80000]	lr: 2.344e-06, eta: 0:35:44, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2023, decode.acc_seg: 91.7046, loss: 0.2023
+2023-03-05 00:23:59,693 - mmseg - INFO - Iter [68950/80000]	lr: 2.344e-06, eta: 0:35:34, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2055, decode.acc_seg: 91.5459, loss: 0.2055
+2023-03-05 00:24:08,946 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:24:08,946 - mmseg - INFO - Iter [69000/80000]	lr: 2.344e-06, eta: 0:35:25, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2085, decode.acc_seg: 91.4680, loss: 0.2085
+2023-03-05 00:24:17,541 - mmseg - INFO - Iter [69050/80000]	lr: 2.344e-06, eta: 0:35:15, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2086, decode.acc_seg: 91.5540, loss: 0.2086
+2023-03-05 00:24:26,764 - mmseg - INFO - Iter [69100/80000]	lr: 2.344e-06, eta: 0:35:05, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2041, decode.acc_seg: 91.5153, loss: 0.2041
+2023-03-05 00:24:35,682 - mmseg - INFO - Iter [69150/80000]	lr: 2.344e-06, eta: 0:34:55, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.4995, loss: 0.2073
+2023-03-05 00:24:44,497 - mmseg - INFO - Iter [69200/80000]	lr: 2.344e-06, eta: 0:34:45, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2015, decode.acc_seg: 91.6900, loss: 0.2015
+2023-03-05 00:24:55,832 - mmseg - INFO - Iter [69250/80000]	lr: 2.344e-06, eta: 0:34:36, time: 0.226, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2045, decode.acc_seg: 91.5865, loss: 0.2045
+2023-03-05 00:25:04,553 - mmseg - INFO - Iter [69300/80000]	lr: 2.344e-06, eta: 0:34:26, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2075, decode.acc_seg: 91.4464, loss: 0.2075
+2023-03-05 00:25:13,673 - mmseg - INFO - Iter [69350/80000]	lr: 2.344e-06, eta: 0:34:17, time: 0.183, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.4653, loss: 0.2076
+2023-03-05 00:25:22,717 - mmseg - INFO - Iter [69400/80000]	lr: 2.344e-06, eta: 0:34:07, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.3455, loss: 0.2106
+2023-03-05 00:25:31,525 - mmseg - INFO - Iter [69450/80000]	lr: 2.344e-06, eta: 0:33:57, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2182, decode.acc_seg: 91.1029, loss: 0.2182
+2023-03-05 00:25:40,227 - mmseg - INFO - Iter [69500/80000]	lr: 2.344e-06, eta: 0:33:47, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.4102, loss: 0.2095
+2023-03-05 00:25:48,945 - mmseg - INFO - Iter [69550/80000]	lr: 2.344e-06, eta: 0:33:37, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2148, decode.acc_seg: 91.1704, loss: 0.2148
+2023-03-05 00:25:57,911 - mmseg - INFO - Iter [69600/80000]	lr: 2.344e-06, eta: 0:33:28, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2172, decode.acc_seg: 91.2883, loss: 0.2172
+2023-03-05 00:26:06,486 - mmseg - INFO - Iter [69650/80000]	lr: 2.344e-06, eta: 0:33:18, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2131, decode.acc_seg: 91.3526, loss: 0.2131
+2023-03-05 00:26:15,166 - mmseg - INFO - Iter [69700/80000]	lr: 2.344e-06, eta: 0:33:08, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2156, decode.acc_seg: 91.2271, loss: 0.2156
+2023-03-05 00:26:24,034 - mmseg - INFO - Iter [69750/80000]	lr: 2.344e-06, eta: 0:32:58, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2190, decode.acc_seg: 91.2658, loss: 0.2190
+2023-03-05 00:26:32,900 - mmseg - INFO - Iter [69800/80000]	lr: 2.344e-06, eta: 0:32:48, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.3667, loss: 0.2101
+2023-03-05 00:26:43,931 - mmseg - INFO - Iter [69850/80000]	lr: 2.344e-06, eta: 0:32:39, time: 0.221, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2158, decode.acc_seg: 91.3178, loss: 0.2158
+2023-03-05 00:26:52,613 - mmseg - INFO - Iter [69900/80000]	lr: 2.344e-06, eta: 0:32:29, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.2470, loss: 0.2132
+2023-03-05 00:27:01,625 - mmseg - INFO - Iter [69950/80000]	lr: 2.344e-06, eta: 0:32:19, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2113, decode.acc_seg: 91.1481, loss: 0.2113
+2023-03-05 00:27:10,242 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:27:10,242 - mmseg - INFO - Iter [70000/80000]	lr: 2.344e-06, eta: 0:32:10, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.2180, loss: 0.2099
+2023-03-05 00:27:18,887 - mmseg - INFO - Iter [70050/80000]	lr: 1.172e-06, eta: 0:32:00, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2163, decode.acc_seg: 91.2933, loss: 0.2163
+2023-03-05 00:27:27,716 - mmseg - INFO - Iter [70100/80000]	lr: 1.172e-06, eta: 0:31:50, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2003, decode.acc_seg: 91.7270, loss: 0.2003
+2023-03-05 00:27:36,490 - mmseg - INFO - Iter [70150/80000]	lr: 1.172e-06, eta: 0:31:40, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.4383, loss: 0.2134
+2023-03-05 00:27:45,812 - mmseg - INFO - Iter [70200/80000]	lr: 1.172e-06, eta: 0:31:30, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2077, decode.acc_seg: 91.4271, loss: 0.2077
+2023-03-05 00:27:54,655 - mmseg - INFO - Iter [70250/80000]	lr: 1.172e-06, eta: 0:31:21, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2159, decode.acc_seg: 91.3902, loss: 0.2159
+2023-03-05 00:28:03,738 - mmseg - INFO - Iter [70300/80000]	lr: 1.172e-06, eta: 0:31:11, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2069, decode.acc_seg: 91.6558, loss: 0.2069
+2023-03-05 00:28:12,661 - mmseg - INFO - Iter [70350/80000]	lr: 1.172e-06, eta: 0:31:01, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2019, decode.acc_seg: 91.7362, loss: 0.2019
+2023-03-05 00:28:21,147 - mmseg - INFO - Iter [70400/80000]	lr: 1.172e-06, eta: 0:30:51, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2082, decode.acc_seg: 91.5318, loss: 0.2082
+2023-03-05 00:28:30,149 - mmseg - INFO - Iter [70450/80000]	lr: 1.172e-06, eta: 0:30:42, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2183, decode.acc_seg: 91.1230, loss: 0.2183
+2023-03-05 00:28:41,543 - mmseg - INFO - Iter [70500/80000]	lr: 1.172e-06, eta: 0:30:32, time: 0.228, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2133, decode.acc_seg: 91.3376, loss: 0.2133
+2023-03-05 00:28:50,598 - mmseg - INFO - Iter [70550/80000]	lr: 1.172e-06, eta: 0:30:22, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2096, decode.acc_seg: 91.5225, loss: 0.2096
+2023-03-05 00:28:59,593 - mmseg - INFO - Iter [70600/80000]	lr: 1.172e-06, eta: 0:30:13, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2092, decode.acc_seg: 91.4298, loss: 0.2092
+2023-03-05 00:29:08,921 - mmseg - INFO - Iter [70650/80000]	lr: 1.172e-06, eta: 0:30:03, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2005, decode.acc_seg: 91.8761, loss: 0.2005
+2023-03-05 00:29:17,850 - mmseg - INFO - Iter [70700/80000]	lr: 1.172e-06, eta: 0:29:53, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1963, decode.acc_seg: 92.0733, loss: 0.1963
+2023-03-05 00:29:26,407 - mmseg - INFO - Iter [70750/80000]	lr: 1.172e-06, eta: 0:29:44, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.2010, loss: 0.2117
+2023-03-05 00:29:35,361 - mmseg - INFO - Iter [70800/80000]	lr: 1.172e-06, eta: 0:29:34, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2023, decode.acc_seg: 91.6752, loss: 0.2023
+2023-03-05 00:29:43,960 - mmseg - INFO - Iter [70850/80000]	lr: 1.172e-06, eta: 0:29:24, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.2926, loss: 0.2127
+2023-03-05 00:29:52,737 - mmseg - INFO - Iter [70900/80000]	lr: 1.172e-06, eta: 0:29:14, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2186, decode.acc_seg: 91.1560, loss: 0.2186
+2023-03-05 00:30:01,307 - mmseg - INFO - Iter [70950/80000]	lr: 1.172e-06, eta: 0:29:04, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2112, decode.acc_seg: 91.3788, loss: 0.2112
+2023-03-05 00:30:09,865 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:30:09,865 - mmseg - INFO - Iter [71000/80000]	lr: 1.172e-06, eta: 0:28:55, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2067, decode.acc_seg: 91.6854, loss: 0.2067
+2023-03-05 00:30:18,658 - mmseg - INFO - Iter [71050/80000]	lr: 1.172e-06, eta: 0:28:45, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5035, loss: 0.2060
+2023-03-05 00:30:30,114 - mmseg - INFO - Iter [71100/80000]	lr: 1.172e-06, eta: 0:28:35, time: 0.229, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.3745, loss: 0.2103
+2023-03-05 00:30:39,152 - mmseg - INFO - Iter [71150/80000]	lr: 1.172e-06, eta: 0:28:26, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2218, decode.acc_seg: 91.0390, loss: 0.2218
+2023-03-05 00:30:47,994 - mmseg - INFO - Iter [71200/80000]	lr: 1.172e-06, eta: 0:28:16, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2082, decode.acc_seg: 91.3943, loss: 0.2082
+2023-03-05 00:30:56,651 - mmseg - INFO - Iter [71250/80000]	lr: 1.172e-06, eta: 0:28:06, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2077, decode.acc_seg: 91.4481, loss: 0.2077
+2023-03-05 00:31:05,749 - mmseg - INFO - Iter [71300/80000]	lr: 1.172e-06, eta: 0:27:57, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2151, decode.acc_seg: 91.1739, loss: 0.2151
+2023-03-05 00:31:14,412 - mmseg - INFO - Iter [71350/80000]	lr: 1.172e-06, eta: 0:27:47, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.4820, loss: 0.2078
+2023-03-05 00:31:23,089 - mmseg - INFO - Iter [71400/80000]	lr: 1.172e-06, eta: 0:27:37, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2133, decode.acc_seg: 91.2838, loss: 0.2133
+2023-03-05 00:31:31,728 - mmseg - INFO - Iter [71450/80000]	lr: 1.172e-06, eta: 0:27:27, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.3499, loss: 0.2099
+2023-03-05 00:31:40,973 - mmseg - INFO - Iter [71500/80000]	lr: 1.172e-06, eta: 0:27:18, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.1191, loss: 0.2139
+2023-03-05 00:31:49,684 - mmseg - INFO - Iter [71550/80000]	lr: 1.172e-06, eta: 0:27:08, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2024, decode.acc_seg: 91.7759, loss: 0.2024
+2023-03-05 00:31:58,336 - mmseg - INFO - Iter [71600/80000]	lr: 1.172e-06, eta: 0:26:58, time: 0.173, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2093, decode.acc_seg: 91.4908, loss: 0.2093
+2023-03-05 00:32:06,972 - mmseg - INFO - Iter [71650/80000]	lr: 1.172e-06, eta: 0:26:48, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1976, decode.acc_seg: 91.7627, loss: 0.1976
+2023-03-05 00:32:15,766 - mmseg - INFO - Iter [71700/80000]	lr: 1.172e-06, eta: 0:26:38, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2053, decode.acc_seg: 91.6169, loss: 0.2053
+2023-03-05 00:32:27,195 - mmseg - INFO - Iter [71750/80000]	lr: 1.172e-06, eta: 0:26:29, time: 0.228, data_time: 0.055, memory: 52390, decode.loss_ce: 0.2227, decode.acc_seg: 90.9332, loss: 0.2227
+2023-03-05 00:32:35,965 - mmseg - INFO - Iter [71800/80000]	lr: 1.172e-06, eta: 0:26:19, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2038, decode.acc_seg: 91.6252, loss: 0.2038
+2023-03-05 00:32:44,450 - mmseg - INFO - Iter [71850/80000]	lr: 1.172e-06, eta: 0:26:10, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.3467, loss: 0.2114
+2023-03-05 00:32:53,719 - mmseg - INFO - Iter [71900/80000]	lr: 1.172e-06, eta: 0:26:00, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2077, decode.acc_seg: 91.5593, loss: 0.2077
+2023-03-05 00:33:03,317 - mmseg - INFO - Iter [71950/80000]	lr: 1.172e-06, eta: 0:25:50, time: 0.192, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2193, decode.acc_seg: 91.0134, loss: 0.2193
+2023-03-05 00:33:12,308 - mmseg - INFO - Saving checkpoint at 72000 iterations
+2023-03-05 00:33:12,968 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:33:12,968 - mmseg - INFO - Iter [72000/80000]	lr: 1.172e-06, eta: 0:25:41, time: 0.193, data_time: 0.007, memory: 52390, decode.loss_ce: 0.1945, decode.acc_seg: 91.8773, loss: 0.1945
+2023-03-05 00:33:28,499 - mmseg - INFO - per class results:
+2023-03-05 00:33:28,505 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.66 | 89.22 |
+|       building      | 81.34 | 91.39 |
+|         sky         | 94.33 | 97.36 |
+|        floor        | 81.17 | 90.53 |
+|         tree        |  73.5 | 87.81 |
+|       ceiling       | 84.41 | 92.77 |
+|         road        | 81.74 | 90.47 |
+|         bed         | 86.77 | 94.95 |
+|      windowpane     | 59.72 | 77.18 |
+|        grass        | 65.75 | 82.01 |
+|       cabinet       | 59.86 | 72.51 |
+|       sidewalk      | 63.45 | 79.37 |
+|        person       | 78.24 | 91.61 |
+|        earth        | 35.29 | 48.42 |
+|         door        | 43.68 | 55.65 |
+|        table        | 58.37 | 75.37 |
+|       mountain      | 55.88 | 70.15 |
+|        plant        |  49.4 | 60.09 |
+|       curtain       | 72.91 | 82.54 |
+|        chair        | 53.82 | 66.68 |
+|         car         | 80.65 | 92.21 |
+|        water        | 58.02 | 75.81 |
+|       painting      | 69.79 | 83.96 |
+|         sofa        | 62.94 | 81.17 |
+|        shelf        | 42.97 | 62.08 |
+|        house        | 43.55 | 60.51 |
+|         sea         | 60.93 | 76.69 |
+|        mirror       | 63.49 | 72.91 |
+|         rug         | 65.08 | 73.97 |
+|        field        | 29.31 | 45.29 |
+|       armchair      | 36.47 | 53.88 |
+|         seat        | 66.08 | 81.87 |
+|        fence        | 40.76 | 53.12 |
+|         desk        | 45.71 | 66.21 |
+|         rock        | 37.25 | 61.27 |
+|       wardrobe      | 56.45 | 67.71 |
+|         lamp        | 58.67 | 72.56 |
+|       bathtub       | 76.02 | 83.63 |
+|       railing       | 33.08 | 45.17 |
+|       cushion       | 55.59 | 70.07 |
+|         base        | 22.52 | 27.75 |
+|         box         | 21.53 | 28.14 |
+|        column       | 45.57 | 56.85 |
+|      signboard      | 37.21 | 50.57 |
+|   chest of drawers  | 36.66 | 56.71 |
+|       counter       | 30.65 | 40.14 |
+|         sand        | 42.02 | 59.24 |
+|         sink        | 64.74 | 76.12 |
+|      skyscraper     | 50.39 | 63.19 |
+|      fireplace      |  72.7 | 86.13 |
+|     refrigerator    | 71.05 | 83.21 |
+|      grandstand     | 48.74 | 62.05 |
+|         path        | 21.64 | 28.83 |
+|        stairs       | 32.71 | 40.88 |
+|        runway       | 67.25 | 86.45 |
+|         case        | 48.46 | 59.62 |
+|      pool table     | 91.39 | 94.49 |
+|        pillow       |  60.1 | 70.66 |
+|     screen door     | 64.63 | 71.93 |
+|       stairway      | 23.03 |  35.2 |
+|        river        | 11.63 | 21.85 |
+|        bridge       | 34.81 | 40.87 |
+|       bookcase      | 44.21 | 63.09 |
+|        blind        | 40.31 | 44.74 |
+|     coffee table    | 51.99 | 77.64 |
+|        toilet       | 81.71 |  89.6 |
+|        flower       | 37.36 | 53.09 |
+|         book        | 43.04 | 62.01 |
+|         hill        | 14.07 | 22.23 |
+|        bench        | 40.31 | 53.67 |
+|      countertop     | 53.01 | 70.54 |
+|        stove        | 69.81 | 81.15 |
+|         palm        | 49.14 | 69.45 |
+|    kitchen island   | 38.47 | 62.48 |
+|       computer      |  59.8 | 69.06 |
+|     swivel chair    | 42.91 |  58.8 |
+|         boat        | 69.26 | 84.59 |
+|         bar         | 23.07 | 31.82 |
+|    arcade machine   | 69.83 | 71.82 |
+|        hovel        | 25.58 | 28.87 |
+|         bus         | 78.65 | 90.32 |
+|        towel        |  62.1 | 71.47 |
+|        light        | 48.26 | 53.68 |
+|        truck        |  14.9 | 19.78 |
+|        tower        |  7.54 | 12.05 |
+|      chandelier     | 62.53 | 77.28 |
+|        awning       | 24.54 | 28.94 |
+|     streetlight     |  23.5 | 30.49 |
+|        booth        | 41.18 | 42.36 |
+| television receiver | 63.47 | 75.75 |
+|       airplane      | 57.08 | 62.94 |
+|      dirt track     | 16.01 | 46.38 |
+|       apparel       | 32.47 | 51.37 |
+|         pole        | 18.12 |  23.3 |
+|         land        |  2.87 |  3.8  |
+|      bannister      | 10.62 | 14.53 |
+|      escalator      | 23.09 | 24.83 |
+|       ottoman       | 41.47 | 61.77 |
+|        bottle       | 33.72 | 53.54 |
+|        buffet       |  40.9 | 47.42 |
+|        poster       |  22.7 | 31.31 |
+|        stage        |  13.1 | 17.11 |
+|         van         |  38.2 |  52.9 |
+|         ship        | 76.78 | 93.49 |
+|       fountain      | 16.24 | 16.69 |
+|    conveyer belt    | 83.02 | 88.86 |
+|        canopy       | 27.63 | 29.89 |
+|        washer       | 78.86 | 81.23 |
+|      plaything      | 20.72 | 30.11 |
+|    swimming pool    | 75.05 |  80.9 |
+|        stool        | 40.97 | 55.53 |
+|        barrel       | 40.17 |  61.3 |
+|        basket       | 24.74 | 38.72 |
+|      waterfall      | 51.33 | 68.05 |
+|         tent        | 93.97 | 97.38 |
+|         bag         | 15.33 | 19.15 |
+|       minibike      | 61.08 | 76.05 |
+|        cradle       | 82.47 | 96.49 |
+|         oven        | 48.13 | 62.48 |
+|         ball        | 44.82 | 52.33 |
+|         food        | 50.46 | 60.84 |
+|         step        |  6.01 |  7.0  |
+|         tank        | 51.42 | 56.82 |
+|      trade name     | 25.91 | 29.25 |
+|      microwave      | 75.69 | 82.48 |
+|         pot         |  29.3 | 32.91 |
+|        animal       | 53.44 | 59.16 |
+|       bicycle       | 52.69 | 66.53 |
+|         lake        | 57.18 | 62.85 |
+|      dishwasher     | 65.49 | 76.11 |
+|        screen       | 67.03 | 79.84 |
+|       blanket       | 15.58 | 17.76 |
+|      sculpture      | 56.19 | 78.56 |
+|         hood        | 53.77 |  58.8 |
+|        sconce       | 40.92 | 48.54 |
+|         vase        |  30.7 | 47.49 |
+|    traffic light    | 30.25 | 42.63 |
+|         tray        |  4.62 |  7.2  |
+|        ashcan       | 39.64 | 49.26 |
+|         fan         |  56.0 | 67.13 |
+|         pier        |  48.6 |  68.4 |
+|      crt screen     |  8.95 | 23.17 |
+|        plate        | 47.98 | 64.96 |
+|       monitor       | 14.42 |  17.1 |
+|    bulletin board   | 36.65 | 48.41 |
+|        shower       |  1.27 |  4.99 |
+|       radiator      | 55.72 | 61.88 |
+|        glass        | 11.08 | 12.16 |
+|        clock        | 31.99 | 35.01 |
+|         flag        | 34.07 | 37.19 |
++---------------------+-------+-------+
+2023-03-05 00:33:28,505 - mmseg - INFO - Summary:
+2023-03-05 00:33:28,505 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 82.29 | 47.36 | 58.39 |
++-------+-------+-------+
+2023-03-05 00:33:28,528 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/best_mIoU_iter_64000.pth was removed
+2023-03-05 00:33:29,122 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_72000.pth.
+2023-03-05 00:33:29,122 - mmseg - INFO - Best mIoU is 0.4736 at 72000 iter.
+2023-03-05 00:33:29,122 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:33:29,123 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8229, mIoU: 0.4736, mAcc: 0.5839, IoU.background: nan, IoU.wall: 0.7666, IoU.building: 0.8134, IoU.sky: 0.9433, IoU.floor: 0.8117, IoU.tree: 0.7350, IoU.ceiling: 0.8441, IoU.road: 0.8174, IoU.bed : 0.8677, IoU.windowpane: 0.5972, IoU.grass: 0.6575, IoU.cabinet: 0.5986, IoU.sidewalk: 0.6345, IoU.person: 0.7824, IoU.earth: 0.3529, IoU.door: 0.4368, IoU.table: 0.5837, IoU.mountain: 0.5588, IoU.plant: 0.4940, IoU.curtain: 0.7291, IoU.chair: 0.5382, IoU.car: 0.8065, IoU.water: 0.5802, IoU.painting: 0.6979, IoU.sofa: 0.6294, IoU.shelf: 0.4297, IoU.house: 0.4355, IoU.sea: 0.6093, IoU.mirror: 0.6349, IoU.rug: 0.6508, IoU.field: 0.2931, IoU.armchair: 0.3647, IoU.seat: 0.6608, IoU.fence: 0.4076, IoU.desk: 0.4571, IoU.rock: 0.3725, IoU.wardrobe: 0.5645, IoU.lamp: 0.5867, IoU.bathtub: 0.7602, IoU.railing: 0.3308, IoU.cushion: 0.5559, IoU.base: 0.2252, IoU.box: 0.2153, IoU.column: 0.4557, IoU.signboard: 0.3721, IoU.chest of drawers: 0.3666, IoU.counter: 0.3065, IoU.sand: 0.4202, IoU.sink: 0.6474, IoU.skyscraper: 0.5039, IoU.fireplace: 0.7270, IoU.refrigerator: 0.7105, IoU.grandstand: 0.4874, IoU.path: 0.2164, IoU.stairs: 0.3271, IoU.runway: 0.6725, IoU.case: 0.4846, IoU.pool table: 0.9139, IoU.pillow: 0.6010, IoU.screen door: 0.6463, IoU.stairway: 0.2303, IoU.river: 0.1163, IoU.bridge: 0.3481, IoU.bookcase: 0.4421, IoU.blind: 0.4031, IoU.coffee table: 0.5199, IoU.toilet: 0.8171, IoU.flower: 0.3736, IoU.book: 0.4304, IoU.hill: 0.1407, IoU.bench: 0.4031, IoU.countertop: 0.5301, IoU.stove: 0.6981, IoU.palm: 0.4914, IoU.kitchen island: 0.3847, IoU.computer: 0.5980, IoU.swivel chair: 0.4291, IoU.boat: 0.6926, IoU.bar: 0.2307, IoU.arcade machine: 0.6983, IoU.hovel: 0.2558, IoU.bus: 0.7865, IoU.towel: 0.6210, IoU.light: 0.4826, IoU.truck: 0.1490, IoU.tower: 0.0754, IoU.chandelier: 0.6253, IoU.awning: 0.2454, IoU.streetlight: 0.2350, IoU.booth: 0.4118, IoU.television receiver: 0.6347, IoU.airplane: 0.5708, IoU.dirt track: 0.1601, IoU.apparel: 0.3247, IoU.pole: 0.1812, IoU.land: 0.0287, IoU.bannister: 0.1062, IoU.escalator: 0.2309, IoU.ottoman: 0.4147, IoU.bottle: 0.3372, IoU.buffet: 0.4090, IoU.poster: 0.2270, IoU.stage: 0.1310, IoU.van: 0.3820, IoU.ship: 0.7678, IoU.fountain: 0.1624, IoU.conveyer belt: 0.8302, IoU.canopy: 0.2763, IoU.washer: 0.7886, IoU.plaything: 0.2072, IoU.swimming pool: 0.7505, IoU.stool: 0.4097, IoU.barrel: 0.4017, IoU.basket: 0.2474, IoU.waterfall: 0.5133, IoU.tent: 0.9397, IoU.bag: 0.1533, IoU.minibike: 0.6108, IoU.cradle: 0.8247, IoU.oven: 0.4813, IoU.ball: 0.4482, IoU.food: 0.5046, IoU.step: 0.0601, IoU.tank: 0.5142, IoU.trade name: 0.2591, IoU.microwave: 0.7569, IoU.pot: 0.2930, IoU.animal: 0.5344, IoU.bicycle: 0.5269, IoU.lake: 0.5718, IoU.dishwasher: 0.6549, IoU.screen: 0.6703, IoU.blanket: 0.1558, IoU.sculpture: 0.5619, IoU.hood: 0.5377, IoU.sconce: 0.4092, IoU.vase: 0.3070, IoU.traffic light: 0.3025, IoU.tray: 0.0462, IoU.ashcan: 0.3964, IoU.fan: 0.5600, IoU.pier: 0.4860, IoU.crt screen: 0.0895, IoU.plate: 0.4798, IoU.monitor: 0.1442, IoU.bulletin board: 0.3665, IoU.shower: 0.0127, IoU.radiator: 0.5572, IoU.glass: 0.1108, IoU.clock: 0.3199, IoU.flag: 0.3407, Acc.background: nan, Acc.wall: 0.8922, Acc.building: 0.9139, Acc.sky: 0.9736, Acc.floor: 0.9053, Acc.tree: 0.8781, Acc.ceiling: 0.9277, Acc.road: 0.9047, Acc.bed : 0.9495, Acc.windowpane: 0.7718, Acc.grass: 0.8201, Acc.cabinet: 0.7251, Acc.sidewalk: 0.7937, Acc.person: 0.9161, Acc.earth: 0.4842, Acc.door: 0.5565, Acc.table: 0.7537, Acc.mountain: 0.7015, Acc.plant: 0.6009, Acc.curtain: 0.8254, Acc.chair: 0.6668, Acc.car: 0.9221, Acc.water: 0.7581, Acc.painting: 0.8396, Acc.sofa: 0.8117, Acc.shelf: 0.6208, Acc.house: 0.6051, Acc.sea: 0.7669, Acc.mirror: 0.7291, Acc.rug: 0.7397, Acc.field: 0.4529, Acc.armchair: 0.5388, Acc.seat: 0.8187, Acc.fence: 0.5312, Acc.desk: 0.6621, Acc.rock: 0.6127, Acc.wardrobe: 0.6771, Acc.lamp: 0.7256, Acc.bathtub: 0.8363, Acc.railing: 0.4517, Acc.cushion: 0.7007, Acc.base: 0.2775, Acc.box: 0.2814, Acc.column: 0.5685, Acc.signboard: 0.5057, Acc.chest of drawers: 0.5671, Acc.counter: 0.4014, Acc.sand: 0.5924, Acc.sink: 0.7612, Acc.skyscraper: 0.6319, Acc.fireplace: 0.8613, Acc.refrigerator: 0.8321, Acc.grandstand: 0.6205, Acc.path: 0.2883, Acc.stairs: 0.4088, Acc.runway: 0.8645, Acc.case: 0.5962, Acc.pool table: 0.9449, Acc.pillow: 0.7066, Acc.screen door: 0.7193, Acc.stairway: 0.3520, Acc.river: 0.2185, Acc.bridge: 0.4087, Acc.bookcase: 0.6309, Acc.blind: 0.4474, Acc.coffee table: 0.7764, Acc.toilet: 0.8960, Acc.flower: 0.5309, Acc.book: 0.6201, Acc.hill: 0.2223, Acc.bench: 0.5367, Acc.countertop: 0.7054, Acc.stove: 0.8115, Acc.palm: 0.6945, Acc.kitchen island: 0.6248, Acc.computer: 0.6906, Acc.swivel chair: 0.5880, Acc.boat: 0.8459, Acc.bar: 0.3182, Acc.arcade machine: 0.7182, Acc.hovel: 0.2887, Acc.bus: 0.9032, Acc.towel: 0.7147, Acc.light: 0.5368, Acc.truck: 0.1978, Acc.tower: 0.1205, Acc.chandelier: 0.7728, Acc.awning: 0.2894, Acc.streetlight: 0.3049, Acc.booth: 0.4236, Acc.television receiver: 0.7575, Acc.airplane: 0.6294, Acc.dirt track: 0.4638, Acc.apparel: 0.5137, Acc.pole: 0.2330, Acc.land: 0.0380, Acc.bannister: 0.1453, Acc.escalator: 0.2483, Acc.ottoman: 0.6177, Acc.bottle: 0.5354, Acc.buffet: 0.4742, Acc.poster: 0.3131, Acc.stage: 0.1711, Acc.van: 0.5290, Acc.ship: 0.9349, Acc.fountain: 0.1669, Acc.conveyer belt: 0.8886, Acc.canopy: 0.2989, Acc.washer: 0.8123, Acc.plaything: 0.3011, Acc.swimming pool: 0.8090, Acc.stool: 0.5553, Acc.barrel: 0.6130, Acc.basket: 0.3872, Acc.waterfall: 0.6805, Acc.tent: 0.9738, Acc.bag: 0.1915, Acc.minibike: 0.7605, Acc.cradle: 0.9649, Acc.oven: 0.6248, Acc.ball: 0.5233, Acc.food: 0.6084, Acc.step: 0.0700, Acc.tank: 0.5682, Acc.trade name: 0.2925, Acc.microwave: 0.8248, Acc.pot: 0.3291, Acc.animal: 0.5916, Acc.bicycle: 0.6653, Acc.lake: 0.6285, Acc.dishwasher: 0.7611, Acc.screen: 0.7984, Acc.blanket: 0.1776, Acc.sculpture: 0.7856, Acc.hood: 0.5880, Acc.sconce: 0.4854, Acc.vase: 0.4749, Acc.traffic light: 0.4263, Acc.tray: 0.0720, Acc.ashcan: 0.4926, Acc.fan: 0.6713, Acc.pier: 0.6840, Acc.crt screen: 0.2317, Acc.plate: 0.6496, Acc.monitor: 0.1710, Acc.bulletin board: 0.4841, Acc.shower: 0.0499, Acc.radiator: 0.6188, Acc.glass: 0.1216, Acc.clock: 0.3501, Acc.flag: 0.3719
+2023-03-05 00:33:38,074 - mmseg - INFO - Iter [72050/80000]	lr: 1.172e-06, eta: 0:25:33, time: 0.502, data_time: 0.330, memory: 52390, decode.loss_ce: 0.2081, decode.acc_seg: 91.5510, loss: 0.2081
+2023-03-05 00:33:47,028 - mmseg - INFO - Iter [72100/80000]	lr: 1.172e-06, eta: 0:25:23, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2082, decode.acc_seg: 91.5131, loss: 0.2082
+2023-03-05 00:33:55,791 - mmseg - INFO - Iter [72150/80000]	lr: 1.172e-06, eta: 0:25:13, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2087, decode.acc_seg: 91.2721, loss: 0.2087
+2023-03-05 00:34:04,904 - mmseg - INFO - Iter [72200/80000]	lr: 1.172e-06, eta: 0:25:04, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.4275, loss: 0.2109
+2023-03-05 00:34:14,133 - mmseg - INFO - Iter [72250/80000]	lr: 1.172e-06, eta: 0:24:54, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2140, decode.acc_seg: 91.3155, loss: 0.2140
+2023-03-05 00:34:23,162 - mmseg - INFO - Iter [72300/80000]	lr: 1.172e-06, eta: 0:24:44, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2087, decode.acc_seg: 91.5429, loss: 0.2087
+2023-03-05 00:34:31,922 - mmseg - INFO - Iter [72350/80000]	lr: 1.172e-06, eta: 0:24:35, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2064, decode.acc_seg: 91.5755, loss: 0.2064
+2023-03-05 00:34:43,080 - mmseg - INFO - Iter [72400/80000]	lr: 1.172e-06, eta: 0:24:25, time: 0.223, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2119, decode.acc_seg: 91.4831, loss: 0.2119
+2023-03-05 00:34:51,609 - mmseg - INFO - Iter [72450/80000]	lr: 1.172e-06, eta: 0:24:15, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2095, decode.acc_seg: 91.5041, loss: 0.2095
+2023-03-05 00:35:00,376 - mmseg - INFO - Iter [72500/80000]	lr: 1.172e-06, eta: 0:24:06, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1977, decode.acc_seg: 91.8884, loss: 0.1977
+2023-03-05 00:35:09,108 - mmseg - INFO - Iter [72550/80000]	lr: 1.172e-06, eta: 0:23:56, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2091, decode.acc_seg: 91.4853, loss: 0.2091
+2023-03-05 00:35:18,047 - mmseg - INFO - Iter [72600/80000]	lr: 1.172e-06, eta: 0:23:46, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.3717, loss: 0.2111
+2023-03-05 00:35:27,085 - mmseg - INFO - Iter [72650/80000]	lr: 1.172e-06, eta: 0:23:36, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.3436, loss: 0.2121
+2023-03-05 00:35:35,851 - mmseg - INFO - Iter [72700/80000]	lr: 1.172e-06, eta: 0:23:27, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2149, decode.acc_seg: 91.1495, loss: 0.2149
+2023-03-05 00:35:45,260 - mmseg - INFO - Iter [72750/80000]	lr: 1.172e-06, eta: 0:23:17, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2040, decode.acc_seg: 91.7508, loss: 0.2040
+2023-03-05 00:35:54,089 - mmseg - INFO - Iter [72800/80000]	lr: 1.172e-06, eta: 0:23:07, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2196, decode.acc_seg: 91.0614, loss: 0.2196
+2023-03-05 00:36:03,102 - mmseg - INFO - Iter [72850/80000]	lr: 1.172e-06, eta: 0:22:58, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2145, decode.acc_seg: 91.2259, loss: 0.2145
+2023-03-05 00:36:12,117 - mmseg - INFO - Iter [72900/80000]	lr: 1.172e-06, eta: 0:22:48, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2039, decode.acc_seg: 91.7418, loss: 0.2039
+2023-03-05 00:36:20,659 - mmseg - INFO - Iter [72950/80000]	lr: 1.172e-06, eta: 0:22:38, time: 0.171, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2142, decode.acc_seg: 91.2053, loss: 0.2142
+2023-03-05 00:36:32,048 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:36:32,048 - mmseg - INFO - Iter [73000/80000]	lr: 1.172e-06, eta: 0:22:29, time: 0.227, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2150, decode.acc_seg: 91.2511, loss: 0.2150
+2023-03-05 00:36:41,086 - mmseg - INFO - Iter [73050/80000]	lr: 1.172e-06, eta: 0:22:19, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2013, decode.acc_seg: 91.6749, loss: 0.2013
+2023-03-05 00:36:50,057 - mmseg - INFO - Iter [73100/80000]	lr: 1.172e-06, eta: 0:22:09, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2028, decode.acc_seg: 91.6254, loss: 0.2028
+2023-03-05 00:36:58,916 - mmseg - INFO - Iter [73150/80000]	lr: 1.172e-06, eta: 0:22:00, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2171, decode.acc_seg: 91.0324, loss: 0.2171
+2023-03-05 00:37:08,018 - mmseg - INFO - Iter [73200/80000]	lr: 1.172e-06, eta: 0:21:50, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2123, decode.acc_seg: 91.1386, loss: 0.2123
+2023-03-05 00:37:16,914 - mmseg - INFO - Iter [73250/80000]	lr: 1.172e-06, eta: 0:21:40, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2184, decode.acc_seg: 91.1838, loss: 0.2184
+2023-03-05 00:37:26,068 - mmseg - INFO - Iter [73300/80000]	lr: 1.172e-06, eta: 0:21:30, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2088, decode.acc_seg: 91.5027, loss: 0.2088
+2023-03-05 00:37:35,106 - mmseg - INFO - Iter [73350/80000]	lr: 1.172e-06, eta: 0:21:21, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.3845, loss: 0.2120
+2023-03-05 00:37:44,194 - mmseg - INFO - Iter [73400/80000]	lr: 1.172e-06, eta: 0:21:11, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2071, decode.acc_seg: 91.6016, loss: 0.2071
+2023-03-05 00:37:53,039 - mmseg - INFO - Iter [73450/80000]	lr: 1.172e-06, eta: 0:21:01, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2132, decode.acc_seg: 91.2792, loss: 0.2132
+2023-03-05 00:38:01,717 - mmseg - INFO - Iter [73500/80000]	lr: 1.172e-06, eta: 0:20:52, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.4962, loss: 0.2078
+2023-03-05 00:38:10,734 - mmseg - INFO - Iter [73550/80000]	lr: 1.172e-06, eta: 0:20:42, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.5215, loss: 0.2089
+2023-03-05 00:38:19,432 - mmseg - INFO - Iter [73600/80000]	lr: 1.172e-06, eta: 0:20:32, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.3931, loss: 0.2073
+2023-03-05 00:38:30,697 - mmseg - INFO - Iter [73650/80000]	lr: 1.172e-06, eta: 0:20:23, time: 0.225, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2130, decode.acc_seg: 91.2984, loss: 0.2130
+2023-03-05 00:38:39,922 - mmseg - INFO - Iter [73700/80000]	lr: 1.172e-06, eta: 0:20:13, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2079, decode.acc_seg: 91.4653, loss: 0.2079
+2023-03-05 00:38:49,273 - mmseg - INFO - Iter [73750/80000]	lr: 1.172e-06, eta: 0:20:03, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.4350, loss: 0.2111
+2023-03-05 00:38:57,867 - mmseg - INFO - Iter [73800/80000]	lr: 1.172e-06, eta: 0:19:54, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2135, decode.acc_seg: 91.2753, loss: 0.2135
+2023-03-05 00:39:06,423 - mmseg - INFO - Iter [73850/80000]	lr: 1.172e-06, eta: 0:19:44, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2143, decode.acc_seg: 91.3318, loss: 0.2143
+2023-03-05 00:39:15,335 - mmseg - INFO - Iter [73900/80000]	lr: 1.172e-06, eta: 0:19:34, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2084, decode.acc_seg: 91.4275, loss: 0.2084
+2023-03-05 00:39:24,939 - mmseg - INFO - Iter [73950/80000]	lr: 1.172e-06, eta: 0:19:25, time: 0.192, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2097, decode.acc_seg: 91.3883, loss: 0.2097
+2023-03-05 00:39:33,891 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:39:33,891 - mmseg - INFO - Iter [74000/80000]	lr: 1.172e-06, eta: 0:19:15, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.4876, loss: 0.2078
+2023-03-05 00:39:42,545 - mmseg - INFO - Iter [74050/80000]	lr: 1.172e-06, eta: 0:19:05, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.3001, loss: 0.2120
+2023-03-05 00:39:51,057 - mmseg - INFO - Iter [74100/80000]	lr: 1.172e-06, eta: 0:18:55, time: 0.170, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2183, decode.acc_seg: 91.1227, loss: 0.2183
+2023-03-05 00:39:59,997 - mmseg - INFO - Iter [74150/80000]	lr: 1.172e-06, eta: 0:18:46, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2022, decode.acc_seg: 91.5735, loss: 0.2022
+2023-03-05 00:40:09,030 - mmseg - INFO - Iter [74200/80000]	lr: 1.172e-06, eta: 0:18:36, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2076, decode.acc_seg: 91.4034, loss: 0.2076
+2023-03-05 00:40:17,945 - mmseg - INFO - Iter [74250/80000]	lr: 1.172e-06, eta: 0:18:26, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2037, decode.acc_seg: 91.6137, loss: 0.2037
+2023-03-05 00:40:29,301 - mmseg - INFO - Iter [74300/80000]	lr: 1.172e-06, eta: 0:18:17, time: 0.227, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2116, decode.acc_seg: 91.3582, loss: 0.2116
+2023-03-05 00:40:38,127 - mmseg - INFO - Iter [74350/80000]	lr: 1.172e-06, eta: 0:18:07, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.1970, decode.acc_seg: 91.9788, loss: 0.1970
+2023-03-05 00:40:46,902 - mmseg - INFO - Iter [74400/80000]	lr: 1.172e-06, eta: 0:17:58, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2037, decode.acc_seg: 91.7981, loss: 0.2037
+2023-03-05 00:40:56,089 - mmseg - INFO - Iter [74450/80000]	lr: 1.172e-06, eta: 0:17:48, time: 0.183, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2044, decode.acc_seg: 91.5695, loss: 0.2044
+2023-03-05 00:41:04,862 - mmseg - INFO - Iter [74500/80000]	lr: 1.172e-06, eta: 0:17:38, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.4532, loss: 0.2101
+2023-03-05 00:41:13,496 - mmseg - INFO - Iter [74550/80000]	lr: 1.172e-06, eta: 0:17:29, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2141, decode.acc_seg: 91.0841, loss: 0.2141
+2023-03-05 00:41:22,133 - mmseg - INFO - Iter [74600/80000]	lr: 1.172e-06, eta: 0:17:19, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2124, decode.acc_seg: 91.2778, loss: 0.2124
+2023-03-05 00:41:31,121 - mmseg - INFO - Iter [74650/80000]	lr: 1.172e-06, eta: 0:17:09, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2045, decode.acc_seg: 91.7540, loss: 0.2045
+2023-03-05 00:41:40,541 - mmseg - INFO - Iter [74700/80000]	lr: 1.172e-06, eta: 0:16:59, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2124, decode.acc_seg: 91.3858, loss: 0.2124
+2023-03-05 00:41:49,217 - mmseg - INFO - Iter [74750/80000]	lr: 1.172e-06, eta: 0:16:50, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2040, decode.acc_seg: 91.6299, loss: 0.2040
+2023-03-05 00:41:57,951 - mmseg - INFO - Iter [74800/80000]	lr: 1.172e-06, eta: 0:16:40, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.4491, loss: 0.2101
+2023-03-05 00:42:06,768 - mmseg - INFO - Iter [74850/80000]	lr: 1.172e-06, eta: 0:16:30, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1999, decode.acc_seg: 91.7297, loss: 0.1999
+2023-03-05 00:42:18,149 - mmseg - INFO - Iter [74900/80000]	lr: 1.172e-06, eta: 0:16:21, time: 0.227, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.3300, loss: 0.2127
+2023-03-05 00:42:26,914 - mmseg - INFO - Iter [74950/80000]	lr: 1.172e-06, eta: 0:16:11, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2090, decode.acc_seg: 91.2928, loss: 0.2090
+2023-03-05 00:42:36,224 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:42:36,224 - mmseg - INFO - Iter [75000/80000]	lr: 1.172e-06, eta: 0:16:02, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2093, decode.acc_seg: 91.6254, loss: 0.2093
+2023-03-05 00:42:45,437 - mmseg - INFO - Iter [75050/80000]	lr: 1.172e-06, eta: 0:15:52, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2015, decode.acc_seg: 91.6210, loss: 0.2015
+2023-03-05 00:42:54,736 - mmseg - INFO - Iter [75100/80000]	lr: 1.172e-06, eta: 0:15:42, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2201, decode.acc_seg: 90.9947, loss: 0.2201
+2023-03-05 00:43:03,652 - mmseg - INFO - Iter [75150/80000]	lr: 1.172e-06, eta: 0:15:33, time: 0.179, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2139, decode.acc_seg: 91.2600, loss: 0.2139
+2023-03-05 00:43:12,596 - mmseg - INFO - Iter [75200/80000]	lr: 1.172e-06, eta: 0:15:23, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2038, decode.acc_seg: 91.6847, loss: 0.2038
+2023-03-05 00:43:21,684 - mmseg - INFO - Iter [75250/80000]	lr: 1.172e-06, eta: 0:15:13, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2009, decode.acc_seg: 91.7609, loss: 0.2009
+2023-03-05 00:43:30,502 - mmseg - INFO - Iter [75300/80000]	lr: 1.172e-06, eta: 0:15:04, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2168, decode.acc_seg: 91.2065, loss: 0.2168
+2023-03-05 00:43:39,206 - mmseg - INFO - Iter [75350/80000]	lr: 1.172e-06, eta: 0:14:54, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.2677, loss: 0.2121
+2023-03-05 00:43:47,993 - mmseg - INFO - Iter [75400/80000]	lr: 1.172e-06, eta: 0:14:44, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.1985, decode.acc_seg: 91.7557, loss: 0.1985
+2023-03-05 00:43:57,010 - mmseg - INFO - Iter [75450/80000]	lr: 1.172e-06, eta: 0:14:35, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2058, decode.acc_seg: 91.6286, loss: 0.2058
+2023-03-05 00:44:06,402 - mmseg - INFO - Iter [75500/80000]	lr: 1.172e-06, eta: 0:14:25, time: 0.188, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2046, decode.acc_seg: 91.6625, loss: 0.2046
+2023-03-05 00:44:17,810 - mmseg - INFO - Iter [75550/80000]	lr: 1.172e-06, eta: 0:14:15, time: 0.228, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2067, decode.acc_seg: 91.5933, loss: 0.2067
+2023-03-05 00:44:26,442 - mmseg - INFO - Iter [75600/80000]	lr: 1.172e-06, eta: 0:14:06, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2124, decode.acc_seg: 91.2465, loss: 0.2124
+2023-03-05 00:44:35,255 - mmseg - INFO - Iter [75650/80000]	lr: 1.172e-06, eta: 0:13:56, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2120, decode.acc_seg: 91.2389, loss: 0.2120
+2023-03-05 00:44:44,099 - mmseg - INFO - Iter [75700/80000]	lr: 1.172e-06, eta: 0:13:46, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2070, decode.acc_seg: 91.6143, loss: 0.2070
+2023-03-05 00:44:52,940 - mmseg - INFO - Iter [75750/80000]	lr: 1.172e-06, eta: 0:13:37, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2101, decode.acc_seg: 91.3524, loss: 0.2101
+2023-03-05 00:45:01,688 - mmseg - INFO - Iter [75800/80000]	lr: 1.172e-06, eta: 0:13:27, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1996, decode.acc_seg: 91.7334, loss: 0.1996
+2023-03-05 00:45:10,812 - mmseg - INFO - Iter [75850/80000]	lr: 1.172e-06, eta: 0:13:17, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2000, decode.acc_seg: 91.6346, loss: 0.2000
+2023-03-05 00:45:19,436 - mmseg - INFO - Iter [75900/80000]	lr: 1.172e-06, eta: 0:13:08, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2059, decode.acc_seg: 91.5884, loss: 0.2059
+2023-03-05 00:45:28,160 - mmseg - INFO - Iter [75950/80000]	lr: 1.172e-06, eta: 0:12:58, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2004, decode.acc_seg: 91.7969, loss: 0.2004
+2023-03-05 00:45:37,008 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:45:37,008 - mmseg - INFO - Iter [76000/80000]	lr: 1.172e-06, eta: 0:12:48, time: 0.177, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.2416, loss: 0.2121
+2023-03-05 00:45:46,003 - mmseg - INFO - Iter [76050/80000]	lr: 1.172e-06, eta: 0:12:39, time: 0.180, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2036, decode.acc_seg: 91.6745, loss: 0.2036
+2023-03-05 00:45:54,615 - mmseg - INFO - Iter [76100/80000]	lr: 1.172e-06, eta: 0:12:29, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2067, decode.acc_seg: 91.5910, loss: 0.2067
+2023-03-05 00:46:06,189 - mmseg - INFO - Iter [76150/80000]	lr: 1.172e-06, eta: 0:12:20, time: 0.231, data_time: 0.053, memory: 52390, decode.loss_ce: 0.2064, decode.acc_seg: 91.4846, loss: 0.2064
+2023-03-05 00:46:14,750 - mmseg - INFO - Iter [76200/80000]	lr: 1.172e-06, eta: 0:12:10, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2153, decode.acc_seg: 91.1673, loss: 0.2153
+2023-03-05 00:46:23,363 - mmseg - INFO - Iter [76250/80000]	lr: 1.172e-06, eta: 0:12:00, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2022, decode.acc_seg: 91.7548, loss: 0.2022
+2023-03-05 00:46:32,402 - mmseg - INFO - Iter [76300/80000]	lr: 1.172e-06, eta: 0:11:51, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2014, decode.acc_seg: 91.7348, loss: 0.2014
+2023-03-05 00:46:41,820 - mmseg - INFO - Iter [76350/80000]	lr: 1.172e-06, eta: 0:11:41, time: 0.188, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.3299, loss: 0.2089
+2023-03-05 00:46:51,248 - mmseg - INFO - Iter [76400/80000]	lr: 1.172e-06, eta: 0:11:31, time: 0.189, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2066, decode.acc_seg: 91.5788, loss: 0.2066
+2023-03-05 00:46:59,835 - mmseg - INFO - Iter [76450/80000]	lr: 1.172e-06, eta: 0:11:22, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.5478, loss: 0.2073
+2023-03-05 00:47:09,047 - mmseg - INFO - Iter [76500/80000]	lr: 1.172e-06, eta: 0:11:12, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2099, decode.acc_seg: 91.3152, loss: 0.2099
+2023-03-05 00:47:18,325 - mmseg - INFO - Iter [76550/80000]	lr: 1.172e-06, eta: 0:11:03, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2060, decode.acc_seg: 91.5580, loss: 0.2060
+2023-03-05 00:47:27,519 - mmseg - INFO - Iter [76600/80000]	lr: 1.172e-06, eta: 0:10:53, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.4388, loss: 0.2111
+2023-03-05 00:47:36,103 - mmseg - INFO - Iter [76650/80000]	lr: 1.172e-06, eta: 0:10:43, time: 0.172, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2205, decode.acc_seg: 91.1038, loss: 0.2205
+2023-03-05 00:47:45,170 - mmseg - INFO - Iter [76700/80000]	lr: 1.172e-06, eta: 0:10:34, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2058, decode.acc_seg: 91.3977, loss: 0.2058
+2023-03-05 00:47:53,913 - mmseg - INFO - Iter [76750/80000]	lr: 1.172e-06, eta: 0:10:24, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2228, decode.acc_seg: 90.9825, loss: 0.2228
+2023-03-05 00:48:05,238 - mmseg - INFO - Iter [76800/80000]	lr: 1.172e-06, eta: 0:10:14, time: 0.226, data_time: 0.057, memory: 52390, decode.loss_ce: 0.2119, decode.acc_seg: 91.3838, loss: 0.2119
+2023-03-05 00:48:14,264 - mmseg - INFO - Iter [76850/80000]	lr: 1.172e-06, eta: 0:10:05, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2040, decode.acc_seg: 91.7225, loss: 0.2040
+2023-03-05 00:48:23,946 - mmseg - INFO - Iter [76900/80000]	lr: 1.172e-06, eta: 0:09:55, time: 0.194, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2093, decode.acc_seg: 91.3470, loss: 0.2093
+2023-03-05 00:48:33,001 - mmseg - INFO - Iter [76950/80000]	lr: 1.172e-06, eta: 0:09:46, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.5756, loss: 0.2098
+2023-03-05 00:48:41,858 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:48:41,858 - mmseg - INFO - Iter [77000/80000]	lr: 1.172e-06, eta: 0:09:36, time: 0.177, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2185, decode.acc_seg: 91.3538, loss: 0.2185
+2023-03-05 00:48:50,848 - mmseg - INFO - Iter [77050/80000]	lr: 1.172e-06, eta: 0:09:26, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2005, decode.acc_seg: 91.7941, loss: 0.2005
+2023-03-05 00:48:59,328 - mmseg - INFO - Iter [77100/80000]	lr: 1.172e-06, eta: 0:09:17, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2057, decode.acc_seg: 91.5124, loss: 0.2057
+2023-03-05 00:49:08,259 - mmseg - INFO - Iter [77150/80000]	lr: 1.172e-06, eta: 0:09:07, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2109, decode.acc_seg: 91.3832, loss: 0.2109
+2023-03-05 00:49:16,814 - mmseg - INFO - Iter [77200/80000]	lr: 1.172e-06, eta: 0:08:57, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.5834, loss: 0.2078
+2023-03-05 00:49:25,537 - mmseg - INFO - Iter [77250/80000]	lr: 1.172e-06, eta: 0:08:48, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2044, decode.acc_seg: 91.5573, loss: 0.2044
+2023-03-05 00:49:35,150 - mmseg - INFO - Iter [77300/80000]	lr: 1.172e-06, eta: 0:08:38, time: 0.192, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.3435, loss: 0.2128
+2023-03-05 00:49:44,045 - mmseg - INFO - Iter [77350/80000]	lr: 1.172e-06, eta: 0:08:28, time: 0.178, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2160, decode.acc_seg: 91.2055, loss: 0.2160
+2023-03-05 00:49:52,973 - mmseg - INFO - Iter [77400/80000]	lr: 1.172e-06, eta: 0:08:19, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2155, decode.acc_seg: 91.3004, loss: 0.2155
+2023-03-05 00:50:04,154 - mmseg - INFO - Iter [77450/80000]	lr: 1.172e-06, eta: 0:08:09, time: 0.223, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2114, decode.acc_seg: 91.4000, loss: 0.2114
+2023-03-05 00:50:13,239 - mmseg - INFO - Iter [77500/80000]	lr: 1.172e-06, eta: 0:08:00, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.3620, loss: 0.2078
+2023-03-05 00:50:21,965 - mmseg - INFO - Iter [77550/80000]	lr: 1.172e-06, eta: 0:07:50, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2081, decode.acc_seg: 91.3667, loss: 0.2081
+2023-03-05 00:50:30,905 - mmseg - INFO - Iter [77600/80000]	lr: 1.172e-06, eta: 0:07:40, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2115, decode.acc_seg: 91.3410, loss: 0.2115
+2023-03-05 00:50:40,105 - mmseg - INFO - Iter [77650/80000]	lr: 1.172e-06, eta: 0:07:31, time: 0.184, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2063, decode.acc_seg: 91.5471, loss: 0.2063
+2023-03-05 00:50:49,064 - mmseg - INFO - Iter [77700/80000]	lr: 1.172e-06, eta: 0:07:21, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2010, decode.acc_seg: 91.8180, loss: 0.2010
+2023-03-05 00:50:58,152 - mmseg - INFO - Iter [77750/80000]	lr: 1.172e-06, eta: 0:07:12, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2023, decode.acc_seg: 91.7028, loss: 0.2023
+2023-03-05 00:51:07,256 - mmseg - INFO - Iter [77800/80000]	lr: 1.172e-06, eta: 0:07:02, time: 0.182, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2128, decode.acc_seg: 91.3340, loss: 0.2128
+2023-03-05 00:51:16,234 - mmseg - INFO - Iter [77850/80000]	lr: 1.172e-06, eta: 0:06:52, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2130, decode.acc_seg: 91.2121, loss: 0.2130
+2023-03-05 00:51:26,267 - mmseg - INFO - Iter [77900/80000]	lr: 1.172e-06, eta: 0:06:43, time: 0.201, data_time: 0.008, memory: 52390, decode.loss_ce: 0.2094, decode.acc_seg: 91.5275, loss: 0.2094
+2023-03-05 00:51:35,007 - mmseg - INFO - Iter [77950/80000]	lr: 1.172e-06, eta: 0:06:33, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.3466, loss: 0.2073
+2023-03-05 00:51:43,768 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:51:43,768 - mmseg - INFO - Iter [78000/80000]	lr: 1.172e-06, eta: 0:06:23, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2106, decode.acc_seg: 91.2700, loss: 0.2106
+2023-03-05 00:51:54,935 - mmseg - INFO - Iter [78050/80000]	lr: 1.172e-06, eta: 0:06:14, time: 0.223, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2117, decode.acc_seg: 91.2989, loss: 0.2117
+2023-03-05 00:52:03,727 - mmseg - INFO - Iter [78100/80000]	lr: 1.172e-06, eta: 0:06:04, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2089, decode.acc_seg: 91.3497, loss: 0.2089
+2023-03-05 00:52:12,473 - mmseg - INFO - Iter [78150/80000]	lr: 1.172e-06, eta: 0:05:55, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2121, decode.acc_seg: 91.4699, loss: 0.2121
+2023-03-05 00:52:21,786 - mmseg - INFO - Iter [78200/80000]	lr: 1.172e-06, eta: 0:05:45, time: 0.187, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2064, decode.acc_seg: 91.5125, loss: 0.2064
+2023-03-05 00:52:30,726 - mmseg - INFO - Iter [78250/80000]	lr: 1.172e-06, eta: 0:05:35, time: 0.179, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2055, decode.acc_seg: 91.5233, loss: 0.2055
+2023-03-05 00:52:39,455 - mmseg - INFO - Iter [78300/80000]	lr: 1.172e-06, eta: 0:05:26, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2125, decode.acc_seg: 91.2635, loss: 0.2125
+2023-03-05 00:52:48,722 - mmseg - INFO - Iter [78350/80000]	lr: 1.172e-06, eta: 0:05:16, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2091, decode.acc_seg: 91.5815, loss: 0.2091
+2023-03-05 00:52:57,996 - mmseg - INFO - Iter [78400/80000]	lr: 1.172e-06, eta: 0:05:07, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2073, decode.acc_seg: 91.2822, loss: 0.2073
+2023-03-05 00:53:06,644 - mmseg - INFO - Iter [78450/80000]	lr: 1.172e-06, eta: 0:04:57, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2000, decode.acc_seg: 91.7773, loss: 0.2000
+2023-03-05 00:53:15,337 - mmseg - INFO - Iter [78500/80000]	lr: 1.172e-06, eta: 0:04:47, time: 0.174, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2176, decode.acc_seg: 91.2963, loss: 0.2176
+2023-03-05 00:53:24,016 - mmseg - INFO - Iter [78550/80000]	lr: 1.172e-06, eta: 0:04:38, time: 0.174, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2103, decode.acc_seg: 91.4967, loss: 0.2103
+2023-03-05 00:53:33,343 - mmseg - INFO - Iter [78600/80000]	lr: 1.172e-06, eta: 0:04:28, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2186, decode.acc_seg: 91.1930, loss: 0.2186
+2023-03-05 00:53:42,597 - mmseg - INFO - Iter [78650/80000]	lr: 1.172e-06, eta: 0:04:19, time: 0.185, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2042, decode.acc_seg: 91.7100, loss: 0.2042
+2023-03-05 00:53:53,744 - mmseg - INFO - Iter [78700/80000]	lr: 1.172e-06, eta: 0:04:09, time: 0.223, data_time: 0.056, memory: 52390, decode.loss_ce: 0.2107, decode.acc_seg: 91.4793, loss: 0.2107
+2023-03-05 00:54:03,084 - mmseg - INFO - Iter [78750/80000]	lr: 1.172e-06, eta: 0:03:59, time: 0.187, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2021, decode.acc_seg: 91.6656, loss: 0.2021
+2023-03-05 00:54:11,846 - mmseg - INFO - Iter [78800/80000]	lr: 1.172e-06, eta: 0:03:50, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2006, decode.acc_seg: 91.8114, loss: 0.2006
+2023-03-05 00:54:20,382 - mmseg - INFO - Iter [78850/80000]	lr: 1.172e-06, eta: 0:03:40, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2157, decode.acc_seg: 91.2770, loss: 0.2157
+2023-03-05 00:54:29,423 - mmseg - INFO - Iter [78900/80000]	lr: 1.172e-06, eta: 0:03:31, time: 0.181, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2131, decode.acc_seg: 91.3710, loss: 0.2131
+2023-03-05 00:54:38,515 - mmseg - INFO - Iter [78950/80000]	lr: 1.172e-06, eta: 0:03:21, time: 0.182, data_time: 0.007, memory: 52390, decode.loss_ce: 0.1987, decode.acc_seg: 91.9045, loss: 0.1987
+2023-03-05 00:54:47,437 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:54:47,438 - mmseg - INFO - Iter [79000/80000]	lr: 1.172e-06, eta: 0:03:11, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2098, decode.acc_seg: 91.2359, loss: 0.2098
+2023-03-05 00:54:56,736 - mmseg - INFO - Iter [79050/80000]	lr: 1.172e-06, eta: 0:03:02, time: 0.186, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2096, decode.acc_seg: 91.5336, loss: 0.2096
+2023-03-05 00:55:05,262 - mmseg - INFO - Iter [79100/80000]	lr: 1.172e-06, eta: 0:02:52, time: 0.170, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2052, decode.acc_seg: 91.7004, loss: 0.2052
+2023-03-05 00:55:14,258 - mmseg - INFO - Iter [79150/80000]	lr: 1.172e-06, eta: 0:02:43, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2104, decode.acc_seg: 91.3044, loss: 0.2104
+2023-03-05 00:55:23,196 - mmseg - INFO - Iter [79200/80000]	lr: 1.172e-06, eta: 0:02:33, time: 0.178, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2030, decode.acc_seg: 91.6559, loss: 0.2030
+2023-03-05 00:55:32,177 - mmseg - INFO - Iter [79250/80000]	lr: 1.172e-06, eta: 0:02:23, time: 0.180, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2072, decode.acc_seg: 91.3848, loss: 0.2072
+2023-03-05 00:55:40,992 - mmseg - INFO - Iter [79300/80000]	lr: 1.172e-06, eta: 0:02:14, time: 0.176, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2224, decode.acc_seg: 90.9499, loss: 0.2224
+2023-03-05 00:55:52,365 - mmseg - INFO - Iter [79350/80000]	lr: 1.172e-06, eta: 0:02:04, time: 0.227, data_time: 0.054, memory: 52390, decode.loss_ce: 0.2167, decode.acc_seg: 91.2023, loss: 0.2167
+2023-03-05 00:56:01,003 - mmseg - INFO - Iter [79400/80000]	lr: 1.172e-06, eta: 0:01:55, time: 0.173, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2087, decode.acc_seg: 91.5601, loss: 0.2087
+2023-03-05 00:56:10,322 - mmseg - INFO - Iter [79450/80000]	lr: 1.172e-06, eta: 0:01:45, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2080, decode.acc_seg: 91.3423, loss: 0.2080
+2023-03-05 00:56:19,488 - mmseg - INFO - Iter [79500/80000]	lr: 1.172e-06, eta: 0:01:35, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2078, decode.acc_seg: 91.4549, loss: 0.2078
+2023-03-05 00:56:28,797 - mmseg - INFO - Iter [79550/80000]	lr: 1.172e-06, eta: 0:01:26, time: 0.186, data_time: 0.006, memory: 52390, decode.loss_ce: 0.1959, decode.acc_seg: 91.9260, loss: 0.1959
+2023-03-05 00:56:37,560 - mmseg - INFO - Iter [79600/80000]	lr: 1.172e-06, eta: 0:01:16, time: 0.175, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2115, decode.acc_seg: 91.3793, loss: 0.2115
+2023-03-05 00:56:46,300 - mmseg - INFO - Iter [79650/80000]	lr: 1.172e-06, eta: 0:01:07, time: 0.175, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2127, decode.acc_seg: 91.3909, loss: 0.2127
+2023-03-05 00:56:54,829 - mmseg - INFO - Iter [79700/80000]	lr: 1.172e-06, eta: 0:00:57, time: 0.171, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2165, decode.acc_seg: 91.0291, loss: 0.2165
+2023-03-05 00:57:04,044 - mmseg - INFO - Iter [79750/80000]	lr: 1.172e-06, eta: 0:00:47, time: 0.184, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2031, decode.acc_seg: 91.5889, loss: 0.2031
+2023-03-05 00:57:12,822 - mmseg - INFO - Iter [79800/80000]	lr: 1.172e-06, eta: 0:00:38, time: 0.176, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2061, decode.acc_seg: 91.4753, loss: 0.2061
+2023-03-05 00:57:21,447 - mmseg - INFO - Iter [79850/80000]	lr: 1.172e-06, eta: 0:00:28, time: 0.172, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2134, decode.acc_seg: 91.2239, loss: 0.2134
+2023-03-05 00:57:30,516 - mmseg - INFO - Iter [79900/80000]	lr: 1.172e-06, eta: 0:00:19, time: 0.181, data_time: 0.007, memory: 52390, decode.loss_ce: 0.2126, decode.acc_seg: 91.2939, loss: 0.2126
+2023-03-05 00:57:41,854 - mmseg - INFO - Iter [79950/80000]	lr: 1.172e-06, eta: 0:00:09, time: 0.227, data_time: 0.052, memory: 52390, decode.loss_ce: 0.2057, decode.acc_seg: 91.5927, loss: 0.2057
+2023-03-05 00:57:50,445 - mmseg - INFO - Saving checkpoint at 80000 iterations
+2023-03-05 00:57:51,112 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:57:51,112 - mmseg - INFO - Iter [80000/80000]	lr: 1.172e-06, eta: 0:00:00, time: 0.185, data_time: 0.006, memory: 52390, decode.loss_ce: 0.2111, decode.acc_seg: 91.3744, loss: 0.2111
+2023-03-05 00:58:06,767 - mmseg - INFO - per class results:
+2023-03-05 00:58:06,773 - mmseg - INFO - 
++---------------------+-------+-------+
+|        Class        |  IoU  |  Acc  |
++---------------------+-------+-------+
+|      background     |  nan  |  nan  |
+|         wall        | 76.74 | 88.88 |
+|       building      | 81.34 |  91.7 |
+|         sky         | 94.33 | 97.31 |
+|        floor        | 81.19 | 90.71 |
+|         tree        | 73.51 | 87.93 |
+|       ceiling       | 84.46 | 92.62 |
+|         road        |  81.9 | 90.43 |
+|         bed         | 86.98 | 94.88 |
+|      windowpane     | 59.61 | 77.91 |
+|        grass        | 65.78 | 81.24 |
+|       cabinet       | 60.05 | 72.44 |
+|       sidewalk      | 63.84 | 78.58 |
+|        person       | 78.09 | 91.97 |
+|        earth        | 35.64 | 49.78 |
+|         door        | 43.86 |  56.2 |
+|        table        | 58.61 | 75.33 |
+|       mountain      | 56.32 | 71.01 |
+|        plant        | 49.81 | 61.69 |
+|       curtain       | 73.05 | 82.67 |
+|        chair        | 54.21 | 68.36 |
+|         car         | 80.49 | 92.44 |
+|        water        | 57.49 | 77.16 |
+|       painting      | 69.29 | 84.54 |
+|         sofa        | 63.19 | 80.84 |
+|        shelf        | 42.78 | 61.71 |
+|        house        | 41.51 | 55.64 |
+|         sea         |  61.4 | 76.83 |
+|        mirror       |  63.5 | 72.14 |
+|         rug         | 64.88 | 73.69 |
+|        field        | 29.21 | 45.41 |
+|       armchair      | 36.05 | 52.72 |
+|         seat        | 65.72 | 82.83 |
+|        fence        | 41.22 | 54.61 |
+|         desk        | 45.48 |  66.3 |
+|         rock        | 37.17 | 61.09 |
+|       wardrobe      | 56.64 | 68.08 |
+|         lamp        | 58.76 | 72.85 |
+|       bathtub       | 75.34 | 83.06 |
+|       railing       | 33.28 | 46.27 |
+|       cushion       | 55.22 | 68.63 |
+|         base        | 21.59 |  26.1 |
+|         box         |  22.0 | 29.56 |
+|        column       |  45.2 |  55.5 |
+|      signboard      | 37.12 | 49.64 |
+|   chest of drawers  | 36.55 | 55.54 |
+|       counter       | 30.73 |  40.5 |
+|         sand        | 41.39 | 58.43 |
+|         sink        | 64.85 | 77.39 |
+|      skyscraper     | 50.42 |  62.9 |
+|      fireplace      | 73.27 | 84.87 |
+|     refrigerator    |  70.3 | 83.84 |
+|      grandstand     | 47.51 |  63.1 |
+|         path        | 24.04 | 32.31 |
+|        stairs       | 32.73 | 41.05 |
+|        runway       | 66.84 | 85.52 |
+|         case        | 48.67 | 58.86 |
+|      pool table     | 91.39 | 94.29 |
+|        pillow       | 61.09 | 73.23 |
+|     screen door     | 64.98 | 71.79 |
+|       stairway      | 23.81 | 36.13 |
+|        river        | 11.58 | 20.95 |
+|        bridge       | 33.93 | 39.63 |
+|       bookcase      | 43.71 | 61.35 |
+|        blind        | 38.72 | 42.46 |
+|     coffee table    | 52.65 | 77.26 |
+|        toilet       | 81.64 | 89.46 |
+|        flower       | 37.65 | 53.52 |
+|         book        | 42.55 | 63.72 |
+|         hill        | 13.08 | 19.39 |
+|        bench        |  40.6 | 53.58 |
+|      countertop     | 53.15 | 68.77 |
+|        stove        | 69.59 | 81.56 |
+|         palm        | 49.22 | 67.76 |
+|    kitchen island   | 38.59 | 59.47 |
+|       computer      | 59.64 | 69.66 |
+|     swivel chair    | 42.88 | 58.72 |
+|         boat        | 69.85 |  84.6 |
+|         bar         | 22.54 |  30.8 |
+|    arcade machine   | 69.69 | 71.83 |
+|        hovel        |  25.1 | 27.93 |
+|         bus         | 78.22 | 90.41 |
+|        towel        | 61.95 | 70.81 |
+|        light        | 50.48 | 57.73 |
+|        truck        | 15.15 | 20.33 |
+|        tower        |  6.9  | 10.94 |
+|      chandelier     | 62.66 | 79.93 |
+|        awning       | 24.04 | 28.18 |
+|     streetlight     |  23.9 |  31.5 |
+|        booth        | 42.37 | 43.47 |
+| television receiver | 63.88 | 75.57 |
+|       airplane      | 57.03 | 62.71 |
+|      dirt track     | 14.07 | 33.82 |
+|       apparel       | 33.89 | 55.23 |
+|         pole        | 18.21 | 23.32 |
+|         land        |  2.84 |  3.74 |
+|      bannister      | 10.77 | 14.92 |
+|      escalator      | 23.86 | 25.91 |
+|       ottoman       | 43.49 | 60.01 |
+|        bottle       | 34.35 | 56.33 |
+|        buffet       |  40.8 | 47.31 |
+|        poster       | 22.37 | 32.64 |
+|        stage        | 13.75 | 17.84 |
+|         van         |  38.1 |  52.0 |
+|         ship        | 76.11 | 92.17 |
+|       fountain      | 10.59 | 10.88 |
+|    conveyer belt    | 82.69 | 88.68 |
+|        canopy       |  26.3 |  28.1 |
+|        washer       | 78.87 | 81.27 |
+|      plaything      |  21.2 | 29.97 |
+|    swimming pool    | 75.44 | 82.08 |
+|        stool        | 41.09 | 53.09 |
+|        barrel       | 39.08 | 58.31 |
+|        basket       |  25.0 | 35.68 |
+|      waterfall      | 49.61 | 66.39 |
+|         tent        | 93.74 | 97.46 |
+|         bag         | 16.04 | 20.37 |
+|       minibike      |  61.0 | 74.95 |
+|        cradle       | 82.07 | 96.57 |
+|         oven        | 48.64 | 60.65 |
+|         ball        | 43.75 | 51.23 |
+|         food        | 49.63 | 59.38 |
+|         step        |  5.83 |  6.54 |
+|         tank        |  49.2 |  54.5 |
+|      trade name     | 26.91 | 30.65 |
+|      microwave      | 76.44 | 83.48 |
+|         pot         | 29.62 | 33.34 |
+|        animal       | 53.52 | 59.12 |
+|       bicycle       | 52.82 | 68.07 |
+|         lake        | 57.21 | 63.04 |
+|      dishwasher     | 64.51 | 76.46 |
+|        screen       | 66.37 | 81.64 |
+|       blanket       | 14.99 | 16.79 |
+|      sculpture      | 57.21 | 78.01 |
+|         hood        | 53.44 | 58.82 |
+|        sconce       | 43.01 | 53.98 |
+|         vase        | 30.57 | 46.11 |
+|    traffic light    | 30.24 | 43.25 |
+|         tray        |  4.53 |  7.09 |
+|        ashcan       | 39.71 | 51.59 |
+|         fan         | 55.89 | 65.26 |
+|         pier        | 49.97 | 63.68 |
+|      crt screen     |  8.87 | 23.23 |
+|        plate        | 47.72 | 66.45 |
+|       monitor       | 14.39 | 17.25 |
+|    bulletin board   | 39.64 |  52.5 |
+|        shower       |  1.31 |  5.0  |
+|       radiator      | 56.22 | 62.77 |
+|        glass        | 11.55 | 12.87 |
+|        clock        | 32.27 | 35.54 |
+|         flag        |  35.5 | 39.18 |
++---------------------+-------+-------+
+2023-03-05 00:58:06,773 - mmseg - INFO - Summary:
+2023-03-05 00:58:06,774 - mmseg - INFO - 
++-------+-------+-------+
+|  aAcc |  mIoU |  mAcc |
++-------+-------+-------+
+| 82.31 | 47.35 | 58.26 |
++-------+-------+-------+
+2023-03-05 00:58:06,775 - mmseg - INFO - Exp name: ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py
+2023-03-05 00:58:06,775 - mmseg - INFO - Iter(val) [250]	aAcc: 0.8231, mIoU: 0.4735, mAcc: 0.5826, IoU.background: nan, IoU.wall: 0.7674, IoU.building: 0.8134, IoU.sky: 0.9433, IoU.floor: 0.8119, IoU.tree: 0.7351, IoU.ceiling: 0.8446, IoU.road: 0.8190, IoU.bed : 0.8698, IoU.windowpane: 0.5961, IoU.grass: 0.6578, IoU.cabinet: 0.6005, IoU.sidewalk: 0.6384, IoU.person: 0.7809, IoU.earth: 0.3564, IoU.door: 0.4386, IoU.table: 0.5861, IoU.mountain: 0.5632, IoU.plant: 0.4981, IoU.curtain: 0.7305, IoU.chair: 0.5421, IoU.car: 0.8049, IoU.water: 0.5749, IoU.painting: 0.6929, IoU.sofa: 0.6319, IoU.shelf: 0.4278, IoU.house: 0.4151, IoU.sea: 0.6140, IoU.mirror: 0.6350, IoU.rug: 0.6488, IoU.field: 0.2921, IoU.armchair: 0.3605, IoU.seat: 0.6572, IoU.fence: 0.4122, IoU.desk: 0.4548, IoU.rock: 0.3717, IoU.wardrobe: 0.5664, IoU.lamp: 0.5876, IoU.bathtub: 0.7534, IoU.railing: 0.3328, IoU.cushion: 0.5522, IoU.base: 0.2159, IoU.box: 0.2200, IoU.column: 0.4520, IoU.signboard: 0.3712, IoU.chest of drawers: 0.3655, IoU.counter: 0.3073, IoU.sand: 0.4139, IoU.sink: 0.6485, IoU.skyscraper: 0.5042, IoU.fireplace: 0.7327, IoU.refrigerator: 0.7030, IoU.grandstand: 0.4751, IoU.path: 0.2404, IoU.stairs: 0.3273, IoU.runway: 0.6684, IoU.case: 0.4867, IoU.pool table: 0.9139, IoU.pillow: 0.6109, IoU.screen door: 0.6498, IoU.stairway: 0.2381, IoU.river: 0.1158, IoU.bridge: 0.3393, IoU.bookcase: 0.4371, IoU.blind: 0.3872, IoU.coffee table: 0.5265, IoU.toilet: 0.8164, IoU.flower: 0.3765, IoU.book: 0.4255, IoU.hill: 0.1308, IoU.bench: 0.4060, IoU.countertop: 0.5315, IoU.stove: 0.6959, IoU.palm: 0.4922, IoU.kitchen island: 0.3859, IoU.computer: 0.5964, IoU.swivel chair: 0.4288, IoU.boat: 0.6985, IoU.bar: 0.2254, IoU.arcade machine: 0.6969, IoU.hovel: 0.2510, IoU.bus: 0.7822, IoU.towel: 0.6195, IoU.light: 0.5048, IoU.truck: 0.1515, IoU.tower: 0.0690, IoU.chandelier: 0.6266, IoU.awning: 0.2404, IoU.streetlight: 0.2390, IoU.booth: 0.4237, IoU.television receiver: 0.6388, IoU.airplane: 0.5703, IoU.dirt track: 0.1407, IoU.apparel: 0.3389, IoU.pole: 0.1821, IoU.land: 0.0284, IoU.bannister: 0.1077, IoU.escalator: 0.2386, IoU.ottoman: 0.4349, IoU.bottle: 0.3435, IoU.buffet: 0.4080, IoU.poster: 0.2237, IoU.stage: 0.1375, IoU.van: 0.3810, IoU.ship: 0.7611, IoU.fountain: 0.1059, IoU.conveyer belt: 0.8269, IoU.canopy: 0.2630, IoU.washer: 0.7887, IoU.plaything: 0.2120, IoU.swimming pool: 0.7544, IoU.stool: 0.4109, IoU.barrel: 0.3908, IoU.basket: 0.2500, IoU.waterfall: 0.4961, IoU.tent: 0.9374, IoU.bag: 0.1604, IoU.minibike: 0.6100, IoU.cradle: 0.8207, IoU.oven: 0.4864, IoU.ball: 0.4375, IoU.food: 0.4963, IoU.step: 0.0583, IoU.tank: 0.4920, IoU.trade name: 0.2691, IoU.microwave: 0.7644, IoU.pot: 0.2962, IoU.animal: 0.5352, IoU.bicycle: 0.5282, IoU.lake: 0.5721, IoU.dishwasher: 0.6451, IoU.screen: 0.6637, IoU.blanket: 0.1499, IoU.sculpture: 0.5721, IoU.hood: 0.5344, IoU.sconce: 0.4301, IoU.vase: 0.3057, IoU.traffic light: 0.3024, IoU.tray: 0.0453, IoU.ashcan: 0.3971, IoU.fan: 0.5589, IoU.pier: 0.4997, IoU.crt screen: 0.0887, IoU.plate: 0.4772, IoU.monitor: 0.1439, IoU.bulletin board: 0.3964, IoU.shower: 0.0131, IoU.radiator: 0.5622, IoU.glass: 0.1155, IoU.clock: 0.3227, IoU.flag: 0.3550, Acc.background: nan, Acc.wall: 0.8888, Acc.building: 0.9170, Acc.sky: 0.9731, Acc.floor: 0.9071, Acc.tree: 0.8793, Acc.ceiling: 0.9262, Acc.road: 0.9043, Acc.bed : 0.9488, Acc.windowpane: 0.7791, Acc.grass: 0.8124, Acc.cabinet: 0.7244, Acc.sidewalk: 0.7858, Acc.person: 0.9197, Acc.earth: 0.4978, Acc.door: 0.5620, Acc.table: 0.7533, Acc.mountain: 0.7101, Acc.plant: 0.6169, Acc.curtain: 0.8267, Acc.chair: 0.6836, Acc.car: 0.9244, Acc.water: 0.7716, Acc.painting: 0.8454, Acc.sofa: 0.8084, Acc.shelf: 0.6171, Acc.house: 0.5564, Acc.sea: 0.7683, Acc.mirror: 0.7214, Acc.rug: 0.7369, Acc.field: 0.4541, Acc.armchair: 0.5272, Acc.seat: 0.8283, Acc.fence: 0.5461, Acc.desk: 0.6630, Acc.rock: 0.6109, Acc.wardrobe: 0.6808, Acc.lamp: 0.7285, Acc.bathtub: 0.8306, Acc.railing: 0.4627, Acc.cushion: 0.6863, Acc.base: 0.2610, Acc.box: 0.2956, Acc.column: 0.5550, Acc.signboard: 0.4964, Acc.chest of drawers: 0.5554, Acc.counter: 0.4050, Acc.sand: 0.5843, Acc.sink: 0.7739, Acc.skyscraper: 0.6290, Acc.fireplace: 0.8487, Acc.refrigerator: 0.8384, Acc.grandstand: 0.6310, Acc.path: 0.3231, Acc.stairs: 0.4105, Acc.runway: 0.8552, Acc.case: 0.5886, Acc.pool table: 0.9429, Acc.pillow: 0.7323, Acc.screen door: 0.7179, Acc.stairway: 0.3613, Acc.river: 0.2095, Acc.bridge: 0.3963, Acc.bookcase: 0.6135, Acc.blind: 0.4246, Acc.coffee table: 0.7726, Acc.toilet: 0.8946, Acc.flower: 0.5352, Acc.book: 0.6372, Acc.hill: 0.1939, Acc.bench: 0.5358, Acc.countertop: 0.6877, Acc.stove: 0.8156, Acc.palm: 0.6776, Acc.kitchen island: 0.5947, Acc.computer: 0.6966, Acc.swivel chair: 0.5872, Acc.boat: 0.8460, Acc.bar: 0.3080, Acc.arcade machine: 0.7183, Acc.hovel: 0.2793, Acc.bus: 0.9041, Acc.towel: 0.7081, Acc.light: 0.5773, Acc.truck: 0.2033, Acc.tower: 0.1094, Acc.chandelier: 0.7993, Acc.awning: 0.2818, Acc.streetlight: 0.3150, Acc.booth: 0.4347, Acc.television receiver: 0.7557, Acc.airplane: 0.6271, Acc.dirt track: 0.3382, Acc.apparel: 0.5523, Acc.pole: 0.2332, Acc.land: 0.0374, Acc.bannister: 0.1492, Acc.escalator: 0.2591, Acc.ottoman: 0.6001, Acc.bottle: 0.5633, Acc.buffet: 0.4731, Acc.poster: 0.3264, Acc.stage: 0.1784, Acc.van: 0.5200, Acc.ship: 0.9217, Acc.fountain: 0.1088, Acc.conveyer belt: 0.8868, Acc.canopy: 0.2810, Acc.washer: 0.8127, Acc.plaything: 0.2997, Acc.swimming pool: 0.8208, Acc.stool: 0.5309, Acc.barrel: 0.5831, Acc.basket: 0.3568, Acc.waterfall: 0.6639, Acc.tent: 0.9746, Acc.bag: 0.2037, Acc.minibike: 0.7495, Acc.cradle: 0.9657, Acc.oven: 0.6065, Acc.ball: 0.5123, Acc.food: 0.5938, Acc.step: 0.0654, Acc.tank: 0.5450, Acc.trade name: 0.3065, Acc.microwave: 0.8348, Acc.pot: 0.3334, Acc.animal: 0.5912, Acc.bicycle: 0.6807, Acc.lake: 0.6304, Acc.dishwasher: 0.7646, Acc.screen: 0.8164, Acc.blanket: 0.1679, Acc.sculpture: 0.7801, Acc.hood: 0.5882, Acc.sconce: 0.5398, Acc.vase: 0.4611, Acc.traffic light: 0.4325, Acc.tray: 0.0709, Acc.ashcan: 0.5159, Acc.fan: 0.6526, Acc.pier: 0.6368, Acc.crt screen: 0.2323, Acc.plate: 0.6645, Acc.monitor: 0.1725, Acc.bulletin board: 0.5250, Acc.shower: 0.0500, Acc.radiator: 0.6277, Acc.glass: 0.1287, Acc.clock: 0.3554, Acc.flag: 0.3918