diff --git "a/deeplabv3plus_r101_multistep/20230304_140016.log" "b/deeplabv3plus_r101_multistep/20230304_140016.log" new file mode 100644--- /dev/null +++ "b/deeplabv3plus_r101_multistep/20230304_140016.log" @@ -0,0 +1,6387 @@ +2023-03-04 14:00:16,424 - mmseg - INFO - Multi-processing start method is `None` +2023-03-04 14:00:16,436 - mmseg - INFO - OpenCV num_threads is `128 +2023-03-04 14:00:16,436 - mmseg - INFO - OMP num threads is 1 +2023-03-04 14:00:16,503 - 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+d4f0cb3 +------------------------------------------------------------ + +2023-03-04 14:00:16,503 - mmseg - INFO - Distributed training: True +2023-03-04 14:00:17,210 - mmseg - INFO - Config: +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoderDiffusion', + pretrained= + 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth', + backbone=dict( + type='ResNetV1cCustomInitWeights', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DepthwiseSeparableASPPHeadUnetFCHeadMultiStep', + pretrained= + 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth', + dim=128, + out_dim=256, + unet_channels=528, + dim_mults=[1, 1, 1], + cat_embedding_dim=16, + ignore_index=0, + diffusion_timesteps=100, + collect_timesteps=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99], + in_channels=2048, + in_index=3, + channels=512, + dilations=(1, 12, 24, 36), + c1_in_channels=256, + c1_channels=48, + dropout_ratio=0.1, + num_classes=151, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=None, + train_cfg=dict(), + test_cfg=dict(mode='whole'), + freeze_parameters=['backbone', 'decode_head']) +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=20000, + gamma=0.5, + min_lr=1e-06, + by_epoch=False) +runner = dict(type='IterBasedRunner', max_iters=160000) +checkpoint_config = dict(by_epoch=False, interval=16000, max_keep_ckpts=1) +evaluation = dict( + interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU') +checkpoint = 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth' +custom_hooks = [ + dict( + type='ConstantMomentumEMAHook', + momentum=0.01, + interval=25, + eval_interval=16000, + auto_resume=True, + priority=49) +] +work_dir = './work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune' +gpu_ids = range(0, 8) +auto_resume = True + +2023-03-04 14:00:21,541 - mmseg - INFO - Set random seed to 268475637, deterministic: False +2023-03-04 14:00:22,742 - mmseg - INFO - Parameters in backbone freezed! +2023-03-04 14:00:22,743 - mmseg - INFO - Trainable parameters in DepthwiseSeparableASPPHeadUnetFCHeadMultiStep: ['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', 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'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 14:00:22,743 - mmseg - INFO - Parameters in decode_head freezed! +2023-03-04 14:00:22,776 - mmseg - INFO - load checkpoint from local path: work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth +2023-03-04 14:00:24,340 - mmseg - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: decode_head.image_pool.1.conv.weight, decode_head.image_pool.1.bn.weight, decode_head.image_pool.1.bn.bias, decode_head.image_pool.1.bn.running_mean, decode_head.image_pool.1.bn.running_var, decode_head.image_pool.1.bn.num_batches_tracked, decode_head.aspp_modules.0.conv.weight, decode_head.aspp_modules.0.bn.weight, decode_head.aspp_modules.0.bn.bias, decode_head.aspp_modules.0.bn.running_mean, decode_head.aspp_modules.0.bn.running_var, decode_head.aspp_modules.0.bn.num_batches_tracked, decode_head.aspp_modules.1.depthwise_conv.conv.weight, decode_head.aspp_modules.1.depthwise_conv.bn.weight, decode_head.aspp_modules.1.depthwise_conv.bn.bias, decode_head.aspp_modules.1.depthwise_conv.bn.running_mean, 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decode_head.unet.downs.1.1.block1.norm.bias, decode_head.unet.downs.1.1.block2.proj.weight, decode_head.unet.downs.1.1.block2.proj.bias, decode_head.unet.downs.1.1.block2.norm.weight, decode_head.unet.downs.1.1.block2.norm.bias, decode_head.unet.downs.1.2.fn.fn.to_qkv.weight, decode_head.unet.downs.1.2.fn.fn.to_out.0.weight, decode_head.unet.downs.1.2.fn.fn.to_out.0.bias, decode_head.unet.downs.1.2.fn.fn.to_out.1.g, decode_head.unet.downs.1.2.fn.norm.g, decode_head.unet.downs.1.3.weight, decode_head.unet.downs.1.3.bias, decode_head.unet.downs.2.0.mlp.1.weight, decode_head.unet.downs.2.0.mlp.1.bias, decode_head.unet.downs.2.0.block1.proj.weight, decode_head.unet.downs.2.0.block1.proj.bias, decode_head.unet.downs.2.0.block1.norm.weight, decode_head.unet.downs.2.0.block1.norm.bias, decode_head.unet.downs.2.0.block2.proj.weight, decode_head.unet.downs.2.0.block2.proj.bias, decode_head.unet.downs.2.0.block2.norm.weight, decode_head.unet.downs.2.0.block2.norm.bias, decode_head.unet.downs.2.1.mlp.1.weight, decode_head.unet.downs.2.1.mlp.1.bias, decode_head.unet.downs.2.1.block1.proj.weight, decode_head.unet.downs.2.1.block1.proj.bias, decode_head.unet.downs.2.1.block1.norm.weight, decode_head.unet.downs.2.1.block1.norm.bias, decode_head.unet.downs.2.1.block2.proj.weight, decode_head.unet.downs.2.1.block2.proj.bias, decode_head.unet.downs.2.1.block2.norm.weight, decode_head.unet.downs.2.1.block2.norm.bias, decode_head.unet.downs.2.2.fn.fn.to_qkv.weight, decode_head.unet.downs.2.2.fn.fn.to_out.0.weight, decode_head.unet.downs.2.2.fn.fn.to_out.0.bias, decode_head.unet.downs.2.2.fn.fn.to_out.1.g, decode_head.unet.downs.2.2.fn.norm.g, decode_head.unet.downs.2.3.weight, decode_head.unet.downs.2.3.bias, decode_head.unet.ups.0.0.mlp.1.weight, decode_head.unet.ups.0.0.mlp.1.bias, decode_head.unet.ups.0.0.block1.proj.weight, decode_head.unet.ups.0.0.block1.proj.bias, decode_head.unet.ups.0.0.block1.norm.weight, decode_head.unet.ups.0.0.block1.norm.bias, decode_head.unet.ups.0.0.block2.proj.weight, decode_head.unet.ups.0.0.block2.proj.bias, decode_head.unet.ups.0.0.block2.norm.weight, decode_head.unet.ups.0.0.block2.norm.bias, decode_head.unet.ups.0.0.res_conv.weight, decode_head.unet.ups.0.0.res_conv.bias, decode_head.unet.ups.0.1.mlp.1.weight, decode_head.unet.ups.0.1.mlp.1.bias, decode_head.unet.ups.0.1.block1.proj.weight, decode_head.unet.ups.0.1.block1.proj.bias, decode_head.unet.ups.0.1.block1.norm.weight, decode_head.unet.ups.0.1.block1.norm.bias, decode_head.unet.ups.0.1.block2.proj.weight, decode_head.unet.ups.0.1.block2.proj.bias, decode_head.unet.ups.0.1.block2.norm.weight, decode_head.unet.ups.0.1.block2.norm.bias, decode_head.unet.ups.0.1.res_conv.weight, decode_head.unet.ups.0.1.res_conv.bias, decode_head.unet.ups.0.2.fn.fn.to_qkv.weight, decode_head.unet.ups.0.2.fn.fn.to_out.0.weight, decode_head.unet.ups.0.2.fn.fn.to_out.0.bias, decode_head.unet.ups.0.2.fn.fn.to_out.1.g, decode_head.unet.ups.0.2.fn.norm.g, decode_head.unet.ups.0.3.1.weight, decode_head.unet.ups.0.3.1.bias, decode_head.unet.ups.1.0.mlp.1.weight, decode_head.unet.ups.1.0.mlp.1.bias, decode_head.unet.ups.1.0.block1.proj.weight, decode_head.unet.ups.1.0.block1.proj.bias, decode_head.unet.ups.1.0.block1.norm.weight, decode_head.unet.ups.1.0.block1.norm.bias, decode_head.unet.ups.1.0.block2.proj.weight, decode_head.unet.ups.1.0.block2.proj.bias, decode_head.unet.ups.1.0.block2.norm.weight, decode_head.unet.ups.1.0.block2.norm.bias, decode_head.unet.ups.1.0.res_conv.weight, decode_head.unet.ups.1.0.res_conv.bias, decode_head.unet.ups.1.1.mlp.1.weight, decode_head.unet.ups.1.1.mlp.1.bias, decode_head.unet.ups.1.1.block1.proj.weight, decode_head.unet.ups.1.1.block1.proj.bias, decode_head.unet.ups.1.1.block1.norm.weight, decode_head.unet.ups.1.1.block1.norm.bias, decode_head.unet.ups.1.1.block2.proj.weight, decode_head.unet.ups.1.1.block2.proj.bias, decode_head.unet.ups.1.1.block2.norm.weight, decode_head.unet.ups.1.1.block2.norm.bias, decode_head.unet.ups.1.1.res_conv.weight, decode_head.unet.ups.1.1.res_conv.bias, decode_head.unet.ups.1.2.fn.fn.to_qkv.weight, decode_head.unet.ups.1.2.fn.fn.to_out.0.weight, decode_head.unet.ups.1.2.fn.fn.to_out.0.bias, decode_head.unet.ups.1.2.fn.fn.to_out.1.g, decode_head.unet.ups.1.2.fn.norm.g, decode_head.unet.ups.1.3.1.weight, decode_head.unet.ups.1.3.1.bias, decode_head.unet.ups.2.0.mlp.1.weight, decode_head.unet.ups.2.0.mlp.1.bias, decode_head.unet.ups.2.0.block1.proj.weight, decode_head.unet.ups.2.0.block1.proj.bias, decode_head.unet.ups.2.0.block1.norm.weight, decode_head.unet.ups.2.0.block1.norm.bias, decode_head.unet.ups.2.0.block2.proj.weight, decode_head.unet.ups.2.0.block2.proj.bias, decode_head.unet.ups.2.0.block2.norm.weight, decode_head.unet.ups.2.0.block2.norm.bias, decode_head.unet.ups.2.0.res_conv.weight, decode_head.unet.ups.2.0.res_conv.bias, decode_head.unet.ups.2.1.mlp.1.weight, decode_head.unet.ups.2.1.mlp.1.bias, decode_head.unet.ups.2.1.block1.proj.weight, decode_head.unet.ups.2.1.block1.proj.bias, decode_head.unet.ups.2.1.block1.norm.weight, decode_head.unet.ups.2.1.block1.norm.bias, decode_head.unet.ups.2.1.block2.proj.weight, decode_head.unet.ups.2.1.block2.proj.bias, decode_head.unet.ups.2.1.block2.norm.weight, decode_head.unet.ups.2.1.block2.norm.bias, decode_head.unet.ups.2.1.res_conv.weight, decode_head.unet.ups.2.1.res_conv.bias, decode_head.unet.ups.2.2.fn.fn.to_qkv.weight, decode_head.unet.ups.2.2.fn.fn.to_out.0.weight, decode_head.unet.ups.2.2.fn.fn.to_out.0.bias, decode_head.unet.ups.2.2.fn.fn.to_out.1.g, decode_head.unet.ups.2.2.fn.norm.g, decode_head.unet.ups.2.3.weight, decode_head.unet.ups.2.3.bias, decode_head.unet.mid_block1.mlp.1.weight, decode_head.unet.mid_block1.mlp.1.bias, decode_head.unet.mid_block1.block1.proj.weight, decode_head.unet.mid_block1.block1.proj.bias, decode_head.unet.mid_block1.block1.norm.weight, decode_head.unet.mid_block1.block1.norm.bias, decode_head.unet.mid_block1.block2.proj.weight, decode_head.unet.mid_block1.block2.proj.bias, decode_head.unet.mid_block1.block2.norm.weight, decode_head.unet.mid_block1.block2.norm.bias, decode_head.unet.mid_attn.fn.fn.to_qkv.weight, decode_head.unet.mid_attn.fn.fn.to_out.weight, decode_head.unet.mid_attn.fn.fn.to_out.bias, decode_head.unet.mid_attn.fn.norm.g, decode_head.unet.mid_block2.mlp.1.weight, decode_head.unet.mid_block2.mlp.1.bias, decode_head.unet.mid_block2.block1.proj.weight, decode_head.unet.mid_block2.block1.proj.bias, decode_head.unet.mid_block2.block1.norm.weight, decode_head.unet.mid_block2.block1.norm.bias, decode_head.unet.mid_block2.block2.proj.weight, decode_head.unet.mid_block2.block2.proj.bias, decode_head.unet.mid_block2.block2.norm.weight, decode_head.unet.mid_block2.block2.norm.bias, decode_head.unet.final_res_block.mlp.1.weight, decode_head.unet.final_res_block.mlp.1.bias, decode_head.unet.final_res_block.block1.proj.weight, decode_head.unet.final_res_block.block1.proj.bias, decode_head.unet.final_res_block.block1.norm.weight, decode_head.unet.final_res_block.block1.norm.bias, decode_head.unet.final_res_block.block2.proj.weight, decode_head.unet.final_res_block.block2.proj.bias, decode_head.unet.final_res_block.block2.norm.weight, decode_head.unet.final_res_block.block2.norm.bias, decode_head.unet.final_res_block.res_conv.weight, decode_head.unet.final_res_block.res_conv.bias, decode_head.unet.final_conv.weight, decode_head.unet.final_conv.bias, decode_head.conv_seg_new.weight, decode_head.conv_seg_new.bias, decode_head.embed.weight + +2023-03-04 14:00:24,363 - mmseg - INFO - load checkpoint from local path: work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth +2023-03-04 14:00:25,334 - mmseg - WARNING - The model and loaded state dict do not match exactly + +size mismatch for unet.init_conv.weight: copying a param with shape torch.Size([256, 528, 7, 7]) from checkpoint, the shape in current model is torch.Size([128, 528, 7, 7]). +size mismatch for unet.init_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.time_mlp.1.weight: copying a param with shape torch.Size([1024, 256]) from checkpoint, the shape in current model is torch.Size([512, 128]). +size mismatch for unet.time_mlp.1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]). +size mismatch for unet.time_mlp.3.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]). +size mismatch for unet.time_mlp.3.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]). +size mismatch for unet.downs.0.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.downs.0.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.downs.0.0.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.0.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.0.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.downs.0.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.downs.0.1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.0.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.0.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.downs.0.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.downs.0.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.0.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.downs.0.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.downs.0.3.weight: copying a param with shape torch.Size([256, 256, 4, 4]) from checkpoint, the shape in current model is torch.Size([128, 128, 4, 4]). +size mismatch for unet.downs.0.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.downs.1.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.downs.1.0.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.1.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.1.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.downs.1.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.downs.1.1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.1.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.1.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.downs.1.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.downs.1.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.1.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.downs.1.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.downs.1.3.weight: copying a param with shape torch.Size([256, 256, 4, 4]) from checkpoint, the shape in current model is torch.Size([128, 128, 4, 4]). +size mismatch for unet.downs.1.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.downs.2.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.downs.2.0.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.2.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.2.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.downs.2.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.downs.2.1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.2.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.2.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.downs.2.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.downs.2.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.downs.2.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.downs.2.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.downs.2.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.downs.2.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.ups.0.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.ups.0.0.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.ups.0.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.0.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.0.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.ups.0.0.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.ups.0.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.ups.0.1.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.ups.0.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.0.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.1.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.ups.0.1.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.ups.0.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.ups.0.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.0.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.ups.0.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.ups.0.3.1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.0.3.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.ups.1.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.ups.1.0.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.ups.1.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.1.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.0.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.ups.1.0.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.ups.1.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.ups.1.1.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.ups.1.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.1.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.1.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.ups.1.1.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.ups.1.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.ups.1.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.1.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.ups.1.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.ups.1.3.1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.1.3.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.ups.2.0.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.ups.2.0.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.ups.2.0.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.2.0.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.0.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.ups.2.0.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.ups.2.1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.ups.2.1.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.ups.2.1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.2.1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.1.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.ups.2.1.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.2.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.ups.2.2.fn.fn.to_out.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.ups.2.2.fn.fn.to_out.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.ups.2.2.fn.fn.to_out.1.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.ups.2.2.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.ups.2.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.ups.2.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block1.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.mid_block1.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.mid_block1.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.mid_block1.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block1.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block1.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block1.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.mid_block1.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block1.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block1.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_attn.fn.fn.to_qkv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1, 1]). +size mismatch for unet.mid_attn.fn.fn.to_out.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). +size mismatch for unet.mid_attn.fn.fn.to_out.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_attn.fn.norm.g: copying a param with shape torch.Size([1, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). +size mismatch for unet.mid_block2.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.mid_block2.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.mid_block2.block1.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.mid_block2.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block2.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block2.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block2.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.mid_block2.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block2.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.mid_block2.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.mlp.1.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]). +size mismatch for unet.final_res_block.mlp.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]). +size mismatch for unet.final_res_block.block1.proj.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]). +size mismatch for unet.final_res_block.block1.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.block1.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.block1.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.block2.proj.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). +size mismatch for unet.final_res_block.block2.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.block2.norm.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.block2.norm.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_res_block.res_conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). +size mismatch for unet.final_res_block.res_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). +size mismatch for unet.final_conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 128, 1, 1]). +unexpected key in source state_dict: backbone.stem.0.weight, backbone.stem.1.weight, backbone.stem.1.bias, backbone.stem.1.running_mean, backbone.stem.1.running_var, backbone.stem.1.num_batches_tracked, backbone.stem.3.weight, backbone.stem.4.weight, backbone.stem.4.bias, backbone.stem.4.running_mean, backbone.stem.4.running_var, backbone.stem.4.num_batches_tracked, backbone.stem.6.weight, backbone.stem.7.weight, backbone.stem.7.bias, backbone.stem.7.running_mean, backbone.stem.7.running_var, backbone.stem.7.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer1.1.conv3.weight, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.num_batches_tracked, backbone.layer1.2.conv1.weight, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.num_batches_tracked, backbone.layer1.2.conv2.weight, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.num_batches_tracked, backbone.layer1.2.conv3.weight, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.num_batches_tracked, backbone.layer2.0.conv1.weight, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.num_batches_tracked, backbone.layer2.0.conv2.weight, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.num_batches_tracked, backbone.layer2.0.conv3.weight, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.num_batches_tracked, backbone.layer2.0.downsample.0.weight, backbone.layer2.0.downsample.1.weight, backbone.layer2.0.downsample.1.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.0.downsample.1.num_batches_tracked, backbone.layer2.1.conv1.weight, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.num_batches_tracked, backbone.layer2.1.conv2.weight, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.num_batches_tracked, backbone.layer2.1.conv3.weight, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.num_batches_tracked, backbone.layer2.2.conv1.weight, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.num_batches_tracked, backbone.layer2.2.conv2.weight, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.num_batches_tracked, backbone.layer2.2.conv3.weight, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.num_batches_tracked, backbone.layer2.3.conv1.weight, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.num_batches_tracked, backbone.layer2.3.conv2.weight, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.num_batches_tracked, backbone.layer2.3.conv3.weight, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.num_batches_tracked, backbone.layer3.0.conv1.weight, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.num_batches_tracked, backbone.layer3.0.conv2.weight, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.num_batches_tracked, backbone.layer3.0.conv3.weight, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, backbone.layer3.0.bn3.num_batches_tracked, backbone.layer3.0.downsample.0.weight, backbone.layer3.0.downsample.1.weight, backbone.layer3.0.downsample.1.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.0.downsample.1.num_batches_tracked, backbone.layer3.1.conv1.weight, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.num_batches_tracked, backbone.layer3.1.conv2.weight, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.num_batches_tracked, backbone.layer3.1.conv3.weight, backbone.layer3.1.bn3.weight, backbone.layer3.1.bn3.bias, backbone.layer3.1.bn3.running_mean, backbone.layer3.1.bn3.running_var, backbone.layer3.1.bn3.num_batches_tracked, backbone.layer3.2.conv1.weight, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn1.running_mean, backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.num_batches_tracked, backbone.layer3.2.conv2.weight, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.num_batches_tracked, backbone.layer3.2.conv3.weight, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.num_batches_tracked, backbone.layer3.3.conv1.weight, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.num_batches_tracked, backbone.layer3.3.conv2.weight, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.num_batches_tracked, backbone.layer3.3.conv3.weight, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.num_batches_tracked, backbone.layer3.4.conv1.weight, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.num_batches_tracked, backbone.layer3.4.conv2.weight, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.num_batches_tracked, backbone.layer3.4.conv3.weight, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.num_batches_tracked, backbone.layer3.5.conv1.weight, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.num_batches_tracked, backbone.layer3.5.conv2.weight, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.num_batches_tracked, backbone.layer3.5.conv3.weight, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.num_batches_tracked, backbone.layer3.6.conv1.weight, backbone.layer3.6.bn1.weight, backbone.layer3.6.bn1.bias, backbone.layer3.6.bn1.running_mean, backbone.layer3.6.bn1.running_var, backbone.layer3.6.bn1.num_batches_tracked, backbone.layer3.6.conv2.weight, backbone.layer3.6.bn2.weight, backbone.layer3.6.bn2.bias, backbone.layer3.6.bn2.running_mean, backbone.layer3.6.bn2.running_var, backbone.layer3.6.bn2.num_batches_tracked, backbone.layer3.6.conv3.weight, backbone.layer3.6.bn3.weight, backbone.layer3.6.bn3.bias, backbone.layer3.6.bn3.running_mean, backbone.layer3.6.bn3.running_var, backbone.layer3.6.bn3.num_batches_tracked, backbone.layer3.7.conv1.weight, backbone.layer3.7.bn1.weight, backbone.layer3.7.bn1.bias, backbone.layer3.7.bn1.running_mean, backbone.layer3.7.bn1.running_var, 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backbone.layer4.1.bn1.bias, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.num_batches_tracked, backbone.layer4.1.conv2.weight, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.num_batches_tracked, backbone.layer4.1.conv3.weight, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.num_batches_tracked, backbone.layer4.2.conv1.weight, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.num_batches_tracked, backbone.layer4.2.conv2.weight, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.num_batches_tracked, backbone.layer4.2.conv3.weight, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.num_batches_tracked + +missing keys in source state_dict: log_cumprod_at, log_cumprod_bt + +2023-03-04 14:00:25,373 - mmseg - INFO - EncoderDecoderDiffusion( + (backbone): ResNetV1cCustomInitWeights( + (stem): Sequential( + (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) + (1): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (2): ReLU(inplace=True) + (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): SyncBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (5): ReLU(inplace=True) + (6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (7): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (8): ReLU(inplace=True) + ) + (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) + (layer1): ResLayer( + (0): Bottleneck( + (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + (downsample): Sequential( + (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (1): Bottleneck( + (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (2): Bottleneck( + (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + ) + (layer2): ResLayer( + (0): Bottleneck( + (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + (downsample): Sequential( + (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) + (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (1): Bottleneck( + (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (2): Bottleneck( + (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (3): Bottleneck( + (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + ) + (layer3): ResLayer( + (0): Bottleneck( + (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + (downsample): Sequential( + (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (1): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (2): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (3): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (4): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (5): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (6): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (7): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (8): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (9): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (10): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (11): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (12): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (13): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (14): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (15): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (16): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (17): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (18): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (19): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (20): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (21): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (22): Bottleneck( + (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + ) + (layer4): ResLayer( + (0): Bottleneck( + (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) + (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + (downsample): Sequential( + (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) + (1): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (1): Bottleneck( + (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) + (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + (2): Bottleneck( + (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False) + (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (relu): ReLU(inplace=True) + ) + ) + ) + init_cfg={'type': 'Pretrained', 'checkpoint': 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth'} + (decode_head): DepthwiseSeparableASPPHeadUnetFCHeadMultiStep( + input_transform=None, ignore_index=0, align_corners=False + (loss_decode): CrossEntropyLoss(avg_non_ignore=False) + (conv_seg): None + (dropout): Dropout2d(p=0.1, inplace=False) + (image_pool): Sequential( + (0): AdaptiveAvgPool2d(output_size=1) + (1): ConvModule( + (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (aspp_modules): DepthwiseSeparableASPPModule( + (0): ConvModule( + (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (1): DepthwiseSeparableConvModule( + (depthwise_conv): ConvModule( + (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), groups=2048, bias=False) + (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (pointwise_conv): ConvModule( + (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (2): DepthwiseSeparableConvModule( + (depthwise_conv): ConvModule( + (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), groups=2048, bias=False) + (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (pointwise_conv): ConvModule( + (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (3): DepthwiseSeparableConvModule( + (depthwise_conv): ConvModule( + (conv): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), groups=2048, bias=False) + (bn): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (pointwise_conv): ConvModule( + (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + ) + (bottleneck): ConvModule( + (conv): Conv2d(2560, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (c1_bottleneck): ConvModule( + (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (sep_bottleneck): Sequential( + (0): DepthwiseSeparableConvModule( + (depthwise_conv): ConvModule( + (conv): Conv2d(560, 560, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=560, bias=False) + (bn): SyncBatchNorm(560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (pointwise_conv): ConvModule( + (conv): Conv2d(560, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (1): DepthwiseSeparableConvModule( + (depthwise_conv): ConvModule( + (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (pointwise_conv): ConvModule( + (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + ) + (unet): Unet( + (init_conv): Conv2d(528, 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': 'work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_ade_pretrained_freeze_embed_80k_ade20k151/latest.pth'} +) +2023-03-04 14:00:25,820 - mmseg - INFO - Loaded 20210 images +2023-03-04 14:00:30,108 - mmseg - INFO - Loaded 2000 images +2023-03-04 14:00:30,110 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-154, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune +2023-03-04 14:00:30,111 - mmseg - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) StepLrUpdaterHook +(49 ) ConstantMomentumEMAHook +(NORMAL ) CheckpointHook +(LOW ) DistEvalHookMultiSteps +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) StepLrUpdaterHook +(LOW ) IterTimerHook +(LOW ) DistEvalHookMultiSteps +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) StepLrUpdaterHook +(49 ) ConstantMomentumEMAHook +(LOW ) IterTimerHook +(LOW ) DistEvalHookMultiSteps + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(49 ) ConstantMomentumEMAHook +(NORMAL ) CheckpointHook +(LOW ) IterTimerHook +(LOW ) DistEvalHookMultiSteps +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(LOW ) DistEvalHookMultiSteps +(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 14:00:30,111 - mmseg - INFO - workflow: [('train', 1)], max: 160000 iters +2023-03-04 14:00:30,174 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune by HardDiskBackend. +2023-03-04 14:00:54,307 - mmseg - INFO - Swap parameters (before train) before iter [1] +2023-03-04 14:01:29,126 - mmseg - INFO - Iter [50/160000] lr: 7.350e-06, eta: 1 day, 7:15:47, time: 0.704, data_time: 0.014, memory: 38189, decode.loss_ce: 3.6274, decode.acc_seg: 15.4524, loss: 3.6274 +2023-03-04 14:01:42,774 - mmseg - INFO - Iter [100/160000] lr: 1.485e-05, eta: 21:41:14, time: 0.273, data_time: 0.007, memory: 38189, decode.loss_ce: 2.6760, decode.acc_seg: 32.6217, loss: 2.6760 +2023-03-04 14:01:56,331 - mmseg - INFO - Iter [150/160000] lr: 2.235e-05, eta: 18:28:02, time: 0.271, data_time: 0.006, memory: 38189, decode.loss_ce: 2.0337, decode.acc_seg: 47.6477, loss: 2.0337 +2023-03-04 14:02:09,588 - mmseg - INFO - Iter [200/160000] lr: 2.985e-05, eta: 16:47:18, time: 0.265, data_time: 0.006, memory: 38189, decode.loss_ce: 1.4010, decode.acc_seg: 63.2106, loss: 1.4010 +2023-03-04 14:02:22,981 - mmseg - INFO - Iter [250/160000] lr: 3.735e-05, eta: 15:48:13, time: 0.268, data_time: 0.006, memory: 38189, decode.loss_ce: 0.9468, decode.acc_seg: 76.5036, loss: 0.9468 +2023-03-04 14:02:36,293 - mmseg - INFO - Iter [300/160000] lr: 4.485e-05, eta: 15:08:02, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.6681, decode.acc_seg: 82.9471, loss: 0.6681 +2023-03-04 14:02:49,730 - mmseg - INFO - Iter [350/160000] lr: 5.235e-05, eta: 14:40:13, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.4962, decode.acc_seg: 86.4736, loss: 0.4962 +2023-03-04 14:03:03,055 - mmseg - INFO - Iter [400/160000] lr: 5.985e-05, eta: 14:18:34, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.3729, decode.acc_seg: 88.8503, loss: 0.3729 +2023-03-04 14:03:16,343 - mmseg - INFO - Iter [450/160000] lr: 6.735e-05, eta: 14:01:27, time: 0.266, data_time: 0.006, memory: 38189, decode.loss_ce: 0.3321, decode.acc_seg: 89.2140, loss: 0.3321 +2023-03-04 14:03:29,675 - mmseg - INFO - Iter [500/160000] lr: 7.485e-05, eta: 13:47:57, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2968, decode.acc_seg: 89.9531, loss: 0.2968 +2023-03-04 14:03:43,053 - mmseg - INFO - Iter [550/160000] lr: 8.235e-05, eta: 13:37:05, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2647, decode.acc_seg: 90.4478, loss: 0.2647 +2023-03-04 14:03:56,447 - mmseg - INFO - Iter [600/160000] lr: 8.985e-05, eta: 13:28:03, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2639, decode.acc_seg: 90.3483, loss: 0.2639 +2023-03-04 14:04:12,270 - mmseg - INFO - Iter [650/160000] lr: 9.735e-05, eta: 13:30:19, time: 0.316, data_time: 0.056, memory: 38189, decode.loss_ce: 0.2570, decode.acc_seg: 90.3595, loss: 0.2570 +2023-03-04 14:04:25,609 - mmseg - INFO - Iter [700/160000] lr: 1.049e-04, eta: 13:22:47, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2550, decode.acc_seg: 90.5722, loss: 0.2550 +2023-03-04 14:04:39,039 - mmseg - INFO - Iter [750/160000] lr: 1.124e-04, eta: 13:16:34, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2533, decode.acc_seg: 90.5237, loss: 0.2533 +2023-03-04 14:04:52,469 - mmseg - INFO - Iter [800/160000] lr: 1.199e-04, eta: 13:11:05, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2581, decode.acc_seg: 90.3031, loss: 0.2581 +2023-03-04 14:05:05,865 - mmseg - INFO - Iter [850/160000] lr: 1.274e-04, eta: 13:06:07, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2437, decode.acc_seg: 90.8941, loss: 0.2437 +2023-03-04 14:05:19,250 - mmseg - INFO - Iter [900/160000] lr: 1.349e-04, eta: 13:01:39, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2566, decode.acc_seg: 90.3892, loss: 0.2566 +2023-03-04 14:05:32,672 - mmseg - INFO - Iter [950/160000] lr: 1.424e-04, eta: 12:57:43, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2441, decode.acc_seg: 90.5825, loss: 0.2441 +2023-03-04 14:05:46,066 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:05:46,066 - mmseg - INFO - Iter [1000/160000] lr: 1.499e-04, eta: 12:54:06, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2492, decode.acc_seg: 90.5826, loss: 0.2492 +2023-03-04 14:06:01,219 - mmseg - INFO - Iter [1050/160000] lr: 1.500e-04, eta: 12:55:14, time: 0.303, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2325, decode.acc_seg: 91.0546, loss: 0.2325 +2023-03-04 14:06:14,700 - mmseg - INFO - Iter [1100/160000] lr: 1.500e-04, eta: 12:52:13, time: 0.270, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2410, decode.acc_seg: 90.9287, loss: 0.2410 +2023-03-04 14:06:28,075 - mmseg - INFO - Iter [1150/160000] lr: 1.500e-04, eta: 12:49:12, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2398, decode.acc_seg: 91.0427, loss: 0.2398 +2023-03-04 14:06:41,422 - mmseg - INFO - Iter [1200/160000] lr: 1.500e-04, eta: 12:46:21, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2434, decode.acc_seg: 90.6630, loss: 0.2434 +2023-03-04 14:06:54,765 - mmseg - INFO - Iter [1250/160000] lr: 1.500e-04, eta: 12:43:43, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2293, decode.acc_seg: 91.1281, loss: 0.2293 +2023-03-04 14:07:10,705 - mmseg - INFO - Iter [1300/160000] lr: 1.500e-04, eta: 12:46:32, time: 0.319, data_time: 0.056, memory: 38189, decode.loss_ce: 0.2290, decode.acc_seg: 91.0612, loss: 0.2290 +2023-03-04 14:07:24,039 - mmseg - INFO - Iter [1350/160000] lr: 1.500e-04, eta: 12:44:02, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2345, decode.acc_seg: 90.7244, loss: 0.2345 +2023-03-04 14:07:37,311 - mmseg - INFO - Iter [1400/160000] lr: 1.500e-04, eta: 12:41:34, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2299, decode.acc_seg: 91.1303, loss: 0.2299 +2023-03-04 14:07:50,574 - mmseg - INFO - Iter [1450/160000] lr: 1.500e-04, eta: 12:39:15, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2298, decode.acc_seg: 91.0280, loss: 0.2298 +2023-03-04 14:08:03,881 - mmseg - INFO - Iter [1500/160000] lr: 1.500e-04, eta: 12:37:09, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2279, decode.acc_seg: 91.0899, loss: 0.2279 +2023-03-04 14:08:17,187 - mmseg - INFO - Iter [1550/160000] lr: 1.500e-04, eta: 12:35:09, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2309, decode.acc_seg: 90.8859, loss: 0.2309 +2023-03-04 14:08:30,495 - mmseg - INFO - Iter [1600/160000] lr: 1.500e-04, eta: 12:33:17, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2185, decode.acc_seg: 91.3798, loss: 0.2185 +2023-03-04 14:08:43,900 - mmseg - INFO - Iter [1650/160000] lr: 1.500e-04, eta: 12:31:40, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2288, decode.acc_seg: 91.1394, loss: 0.2288 +2023-03-04 14:08:57,340 - mmseg - INFO - Iter [1700/160000] lr: 1.500e-04, eta: 12:30:11, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2317, decode.acc_seg: 90.8848, loss: 0.2317 +2023-03-04 14:09:10,552 - mmseg - INFO - Iter [1750/160000] lr: 1.500e-04, eta: 12:28:26, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2220, decode.acc_seg: 91.3035, loss: 0.2220 +2023-03-04 14:09:23,882 - mmseg - INFO - Iter [1800/160000] lr: 1.500e-04, eta: 12:26:56, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2299, decode.acc_seg: 91.0214, loss: 0.2299 +2023-03-04 14:09:37,121 - mmseg - INFO - Iter [1850/160000] lr: 1.500e-04, eta: 12:25:23, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2149, decode.acc_seg: 91.4560, loss: 0.2149 +2023-03-04 14:09:52,835 - mmseg - INFO - Iter [1900/160000] lr: 1.500e-04, eta: 12:27:20, time: 0.314, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2233, decode.acc_seg: 91.2895, loss: 0.2233 +2023-03-04 14:10:06,104 - mmseg - INFO - Iter [1950/160000] lr: 1.500e-04, eta: 12:25:52, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2277, decode.acc_seg: 91.1986, loss: 0.2277 +2023-03-04 14:10:19,592 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:10:19,592 - mmseg - INFO - Iter [2000/160000] lr: 1.500e-04, eta: 12:24:45, time: 0.270, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2177, decode.acc_seg: 91.6072, loss: 0.2177 +2023-03-04 14:10:32,956 - mmseg - INFO - Iter [2050/160000] lr: 1.500e-04, eta: 12:23:31, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2270, decode.acc_seg: 91.0435, loss: 0.2270 +2023-03-04 14:10:46,170 - mmseg - INFO - Iter [2100/160000] lr: 1.500e-04, eta: 12:22:08, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2218, decode.acc_seg: 91.3448, loss: 0.2218 +2023-03-04 14:10:59,453 - mmseg - INFO - Iter [2150/160000] lr: 1.500e-04, eta: 12:20:54, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2291, decode.acc_seg: 90.9786, loss: 0.2291 +2023-03-04 14:11:12,667 - mmseg - INFO - Iter [2200/160000] lr: 1.500e-04, eta: 12:19:38, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2228, decode.acc_seg: 91.3373, loss: 0.2228 +2023-03-04 14:11:26,015 - mmseg - INFO - Iter [2250/160000] lr: 1.500e-04, eta: 12:18:34, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2275, decode.acc_seg: 91.2087, loss: 0.2275 +2023-03-04 14:11:39,468 - mmseg - INFO - Iter [2300/160000] lr: 1.500e-04, eta: 12:17:39, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2287, decode.acc_seg: 91.2308, loss: 0.2287 +2023-03-04 14:11:52,816 - mmseg - INFO - Iter [2350/160000] lr: 1.500e-04, eta: 12:16:39, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2223, decode.acc_seg: 91.3070, loss: 0.2223 +2023-03-04 14:12:06,051 - mmseg - INFO - Iter [2400/160000] lr: 1.500e-04, eta: 12:15:34, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2152, decode.acc_seg: 91.5788, loss: 0.2152 +2023-03-04 14:12:19,276 - mmseg - INFO - Iter [2450/160000] lr: 1.500e-04, eta: 12:14:30, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2215, decode.acc_seg: 91.3345, loss: 0.2215 +2023-03-04 14:12:32,572 - mmseg - INFO - Iter [2500/160000] lr: 1.500e-04, eta: 12:13:32, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2209, decode.acc_seg: 91.4558, loss: 0.2209 +2023-03-04 14:12:48,384 - mmseg - INFO - Iter [2550/160000] lr: 1.500e-04, eta: 12:15:12, time: 0.316, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2229, decode.acc_seg: 91.2645, loss: 0.2229 +2023-03-04 14:13:01,745 - mmseg - INFO - Iter [2600/160000] lr: 1.500e-04, eta: 12:14:18, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2250, decode.acc_seg: 91.2242, loss: 0.2250 +2023-03-04 14:13:15,147 - mmseg - INFO - Iter [2650/160000] lr: 1.500e-04, eta: 12:13:29, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2216, decode.acc_seg: 91.2192, loss: 0.2216 +2023-03-04 14:13:28,379 - mmseg - INFO - Iter [2700/160000] lr: 1.500e-04, eta: 12:12:31, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2173, decode.acc_seg: 91.3337, loss: 0.2173 +2023-03-04 14:13:41,590 - mmseg - INFO - Iter [2750/160000] lr: 1.500e-04, eta: 12:11:33, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2107, decode.acc_seg: 91.6391, loss: 0.2107 +2023-03-04 14:13:54,981 - mmseg - INFO - Iter [2800/160000] lr: 1.500e-04, eta: 12:10:48, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2116, decode.acc_seg: 91.6246, loss: 0.2116 +2023-03-04 14:14:08,378 - mmseg - INFO - Iter [2850/160000] lr: 1.500e-04, eta: 12:10:04, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2230, decode.acc_seg: 91.3302, loss: 0.2230 +2023-03-04 14:14:21,556 - mmseg - INFO - Iter [2900/160000] lr: 1.500e-04, eta: 12:09:09, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2284, decode.acc_seg: 91.0355, loss: 0.2284 +2023-03-04 14:14:34,770 - mmseg - INFO - Iter [2950/160000] lr: 1.500e-04, eta: 12:08:17, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2249, decode.acc_seg: 91.2322, loss: 0.2249 +2023-03-04 14:14:47,924 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:14:47,925 - mmseg - INFO - Iter [3000/160000] lr: 1.500e-04, eta: 12:07:24, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2199, decode.acc_seg: 91.5682, loss: 0.2199 +2023-03-04 14:15:01,263 - mmseg - INFO - Iter [3050/160000] lr: 1.500e-04, eta: 12:06:41, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2183, decode.acc_seg: 91.3428, loss: 0.2183 +2023-03-04 14:15:14,515 - mmseg - INFO - Iter [3100/160000] lr: 1.500e-04, eta: 12:05:55, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2224, decode.acc_seg: 91.3058, loss: 0.2224 +2023-03-04 14:15:27,760 - mmseg - INFO - Iter [3150/160000] lr: 1.500e-04, eta: 12:05:09, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2148, decode.acc_seg: 91.3633, loss: 0.2148 +2023-03-04 14:15:43,554 - mmseg - INFO - Iter [3200/160000] lr: 1.500e-04, eta: 12:06:29, time: 0.316, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2099, decode.acc_seg: 91.7197, loss: 0.2099 +2023-03-04 14:15:56,730 - mmseg - INFO - Iter [3250/160000] lr: 1.500e-04, eta: 12:05:41, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2139, decode.acc_seg: 91.4688, loss: 0.2139 +2023-03-04 14:16:09,966 - mmseg - INFO - Iter [3300/160000] lr: 1.500e-04, eta: 12:04:56, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2168, decode.acc_seg: 91.3822, loss: 0.2168 +2023-03-04 14:16:23,213 - mmseg - INFO - Iter [3350/160000] lr: 1.500e-04, eta: 12:04:12, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2180, decode.acc_seg: 91.4999, loss: 0.2180 +2023-03-04 14:16:36,435 - mmseg - INFO - Iter [3400/160000] lr: 1.500e-04, eta: 12:03:29, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2188, decode.acc_seg: 91.3017, loss: 0.2188 +2023-03-04 14:16:49,678 - mmseg - INFO - Iter [3450/160000] lr: 1.500e-04, eta: 12:02:47, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2218, decode.acc_seg: 91.3017, loss: 0.2218 +2023-03-04 14:17:02,871 - mmseg - INFO - Iter [3500/160000] lr: 1.500e-04, eta: 12:02:03, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2213, decode.acc_seg: 91.4689, loss: 0.2213 +2023-03-04 14:17:16,179 - mmseg - INFO - Iter [3550/160000] lr: 1.500e-04, eta: 12:01:26, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2165, decode.acc_seg: 91.3512, loss: 0.2165 +2023-03-04 14:17:29,609 - mmseg - INFO - Iter [3600/160000] lr: 1.500e-04, eta: 12:00:55, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2131, decode.acc_seg: 91.5587, loss: 0.2131 +2023-03-04 14:17:42,859 - mmseg - INFO - Iter [3650/160000] lr: 1.500e-04, eta: 12:00:16, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2147, decode.acc_seg: 91.5352, loss: 0.2147 +2023-03-04 14:17:56,102 - mmseg - INFO - Iter [3700/160000] lr: 1.500e-04, eta: 11:59:38, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2175, decode.acc_seg: 91.4450, loss: 0.2175 +2023-03-04 14:18:09,438 - mmseg - INFO - Iter [3750/160000] lr: 1.500e-04, eta: 11:59:04, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2226, decode.acc_seg: 91.2810, loss: 0.2226 +2023-03-04 14:18:25,288 - mmseg - INFO - Iter [3800/160000] lr: 1.500e-04, eta: 12:00:14, time: 0.317, data_time: 0.055, memory: 38189, decode.loss_ce: 0.2097, decode.acc_seg: 91.7362, loss: 0.2097 +2023-03-04 14:18:38,604 - mmseg - INFO - Iter [3850/160000] lr: 1.500e-04, eta: 11:59:40, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2249, decode.acc_seg: 91.1390, loss: 0.2249 +2023-03-04 14:18:51,921 - mmseg - INFO - Iter [3900/160000] lr: 1.500e-04, eta: 11:59:05, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2103, decode.acc_seg: 91.6322, loss: 0.2103 +2023-03-04 14:19:05,124 - mmseg - INFO - Iter [3950/160000] lr: 1.500e-04, eta: 11:58:27, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2121, decode.acc_seg: 91.5576, loss: 0.2121 +2023-03-04 14:19:18,351 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:19:18,351 - mmseg - INFO - Iter [4000/160000] lr: 1.500e-04, eta: 11:57:50, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2185, decode.acc_seg: 91.5271, loss: 0.2185 +2023-03-04 14:19:31,570 - mmseg - INFO - Iter [4050/160000] lr: 1.500e-04, eta: 11:57:14, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2134, decode.acc_seg: 91.5957, loss: 0.2134 +2023-03-04 14:19:44,957 - mmseg - INFO - Iter [4100/160000] lr: 1.500e-04, eta: 11:56:45, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2121, decode.acc_seg: 91.6315, loss: 0.2121 +2023-03-04 14:19:58,192 - mmseg - INFO - Iter [4150/160000] lr: 1.500e-04, eta: 11:56:10, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2101, decode.acc_seg: 91.6888, loss: 0.2101 +2023-03-04 14:20:11,396 - mmseg - INFO - Iter [4200/160000] lr: 1.500e-04, eta: 11:55:35, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2146, decode.acc_seg: 91.5487, loss: 0.2146 +2023-03-04 14:20:24,604 - mmseg - INFO - Iter [4250/160000] lr: 1.500e-04, eta: 11:55:00, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2129, decode.acc_seg: 91.6442, loss: 0.2129 +2023-03-04 14:20:37,828 - mmseg - INFO - Iter [4300/160000] lr: 1.500e-04, eta: 11:54:26, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2153, decode.acc_seg: 91.4479, loss: 0.2153 +2023-03-04 14:20:51,093 - mmseg - INFO - Iter [4350/160000] lr: 1.500e-04, eta: 11:53:54, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2152, decode.acc_seg: 91.5635, loss: 0.2152 +2023-03-04 14:21:04,334 - mmseg - INFO - Iter [4400/160000] lr: 1.500e-04, eta: 11:53:22, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2248, decode.acc_seg: 91.0348, loss: 0.2248 +2023-03-04 14:21:20,130 - mmseg - INFO - Iter [4450/160000] lr: 1.500e-04, eta: 11:54:20, time: 0.316, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2151, decode.acc_seg: 91.3495, loss: 0.2151 +2023-03-04 14:21:33,489 - mmseg - INFO - Iter [4500/160000] lr: 1.500e-04, eta: 11:53:52, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2181, decode.acc_seg: 91.3884, loss: 0.2181 +2023-03-04 14:21:46,747 - mmseg - INFO - Iter [4550/160000] lr: 1.500e-04, eta: 11:53:20, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2136, decode.acc_seg: 91.5700, loss: 0.2136 +2023-03-04 14:22:00,096 - mmseg - INFO - Iter [4600/160000] lr: 1.500e-04, eta: 11:52:52, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2117, decode.acc_seg: 91.7813, loss: 0.2117 +2023-03-04 14:22:13,346 - mmseg - INFO - Iter [4650/160000] lr: 1.500e-04, eta: 11:52:22, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2187, decode.acc_seg: 91.5226, loss: 0.2187 +2023-03-04 14:22:26,680 - mmseg - INFO - Iter [4700/160000] lr: 1.500e-04, eta: 11:51:53, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2062, decode.acc_seg: 91.7121, loss: 0.2062 +2023-03-04 14:22:39,964 - mmseg - INFO - Iter [4750/160000] lr: 1.500e-04, eta: 11:51:25, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2198, decode.acc_seg: 91.3829, loss: 0.2198 +2023-03-04 14:22:53,176 - mmseg - INFO - Iter [4800/160000] lr: 1.500e-04, eta: 11:50:54, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2127, decode.acc_seg: 91.5666, loss: 0.2127 +2023-03-04 14:23:06,380 - mmseg - INFO - Iter [4850/160000] lr: 1.500e-04, eta: 11:50:23, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2152, decode.acc_seg: 91.6473, loss: 0.2152 +2023-03-04 14:23:19,649 - mmseg - INFO - Iter [4900/160000] lr: 1.500e-04, eta: 11:49:54, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2178, decode.acc_seg: 91.3049, loss: 0.2178 +2023-03-04 14:23:32,784 - mmseg - INFO - Iter [4950/160000] lr: 1.500e-04, eta: 11:49:22, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2111, decode.acc_seg: 91.5777, loss: 0.2111 +2023-03-04 14:23:45,953 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:23:45,953 - mmseg - INFO - Iter [5000/160000] lr: 1.500e-04, eta: 11:48:51, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2161, decode.acc_seg: 91.6025, loss: 0.2161 +2023-03-04 14:24:01,837 - mmseg - INFO - Iter [5050/160000] lr: 1.500e-04, eta: 11:49:44, time: 0.318, data_time: 0.055, memory: 38189, decode.loss_ce: 0.2121, decode.acc_seg: 91.6693, loss: 0.2121 +2023-03-04 14:24:15,195 - mmseg - INFO - Iter [5100/160000] lr: 1.500e-04, eta: 11:49:18, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2116, decode.acc_seg: 91.6139, loss: 0.2116 +2023-03-04 14:24:28,501 - mmseg - INFO - Iter [5150/160000] lr: 1.500e-04, eta: 11:48:51, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2139, decode.acc_seg: 91.4186, loss: 0.2139 +2023-03-04 14:24:41,795 - mmseg - INFO - Iter [5200/160000] lr: 1.500e-04, eta: 11:48:25, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2166, decode.acc_seg: 91.5142, loss: 0.2166 +2023-03-04 14:24:54,994 - mmseg - INFO - Iter [5250/160000] lr: 1.500e-04, eta: 11:47:55, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2185, decode.acc_seg: 91.3695, loss: 0.2185 +2023-03-04 14:25:08,210 - mmseg - INFO - Iter [5300/160000] lr: 1.500e-04, eta: 11:47:27, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2154, decode.acc_seg: 91.3819, loss: 0.2154 +2023-03-04 14:25:21,401 - mmseg - INFO - Iter [5350/160000] lr: 1.500e-04, eta: 11:46:58, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2151, decode.acc_seg: 91.5128, loss: 0.2151 +2023-03-04 14:25:34,772 - mmseg - INFO - Iter [5400/160000] lr: 1.500e-04, eta: 11:46:34, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2092, decode.acc_seg: 91.6816, loss: 0.2092 +2023-03-04 14:25:47,973 - mmseg - INFO - Iter [5450/160000] lr: 1.500e-04, eta: 11:46:06, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2167, decode.acc_seg: 91.4963, loss: 0.2167 +2023-03-04 14:26:01,258 - mmseg - INFO - Iter [5500/160000] lr: 1.500e-04, eta: 11:45:40, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2127, decode.acc_seg: 91.3583, loss: 0.2127 +2023-03-04 14:26:14,485 - mmseg - INFO - Iter [5550/160000] lr: 1.500e-04, eta: 11:45:13, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2065, decode.acc_seg: 91.7384, loss: 0.2065 +2023-03-04 14:26:27,833 - mmseg - INFO - Iter [5600/160000] lr: 1.500e-04, eta: 11:44:50, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2186, decode.acc_seg: 91.2266, loss: 0.2186 +2023-03-04 14:26:41,171 - mmseg - INFO - Iter [5650/160000] lr: 1.500e-04, eta: 11:44:27, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2105, decode.acc_seg: 91.5709, loss: 0.2105 +2023-03-04 14:26:56,930 - mmseg - INFO - Iter [5700/160000] lr: 1.500e-04, eta: 11:45:09, time: 0.315, data_time: 0.055, memory: 38189, decode.loss_ce: 0.2092, decode.acc_seg: 91.5487, loss: 0.2092 +2023-03-04 14:27:10,083 - mmseg - INFO - Iter [5750/160000] lr: 1.500e-04, eta: 11:44:40, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2215, decode.acc_seg: 91.2352, loss: 0.2215 +2023-03-04 14:27:23,349 - mmseg - INFO - Iter [5800/160000] lr: 1.500e-04, eta: 11:44:15, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2151, decode.acc_seg: 91.5868, loss: 0.2151 +2023-03-04 14:27:36,705 - mmseg - INFO - Iter [5850/160000] lr: 1.500e-04, eta: 11:43:52, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2067, decode.acc_seg: 91.7707, loss: 0.2067 +2023-03-04 14:27:50,101 - mmseg - INFO - Iter [5900/160000] lr: 1.500e-04, eta: 11:43:30, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2132, decode.acc_seg: 91.6472, loss: 0.2132 +2023-03-04 14:28:03,298 - mmseg - INFO - Iter [5950/160000] lr: 1.500e-04, eta: 11:43:04, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2117, decode.acc_seg: 91.6299, loss: 0.2117 +2023-03-04 14:28:16,517 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:28:16,517 - mmseg - INFO - Iter [6000/160000] lr: 1.500e-04, eta: 11:42:38, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2094, decode.acc_seg: 91.5764, loss: 0.2094 +2023-03-04 14:28:29,878 - mmseg - INFO - Iter [6050/160000] lr: 1.500e-04, eta: 11:42:16, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2131, decode.acc_seg: 91.4198, loss: 0.2131 +2023-03-04 14:28:43,248 - mmseg - INFO - Iter [6100/160000] lr: 1.500e-04, eta: 11:41:54, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2144, decode.acc_seg: 91.5716, loss: 0.2144 +2023-03-04 14:28:56,498 - mmseg - INFO - Iter [6150/160000] lr: 1.500e-04, eta: 11:41:30, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2070, decode.acc_seg: 91.8051, loss: 0.2070 +2023-03-04 14:29:09,755 - mmseg - INFO - Iter [6200/160000] lr: 1.500e-04, eta: 11:41:06, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2065, decode.acc_seg: 91.8054, loss: 0.2065 +2023-03-04 14:29:22,954 - mmseg - INFO - Iter [6250/160000] lr: 1.500e-04, eta: 11:40:40, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2065, decode.acc_seg: 91.7724, loss: 0.2065 +2023-03-04 14:29:36,180 - mmseg - INFO - Iter [6300/160000] lr: 1.500e-04, eta: 11:40:16, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2126, decode.acc_seg: 91.5313, loss: 0.2126 +2023-03-04 14:29:51,894 - mmseg - INFO - Iter [6350/160000] lr: 1.500e-04, eta: 11:40:51, time: 0.314, data_time: 0.052, memory: 38189, decode.loss_ce: 0.2123, decode.acc_seg: 91.5092, loss: 0.2123 +2023-03-04 14:30:05,284 - mmseg - INFO - Iter [6400/160000] lr: 1.500e-04, eta: 11:40:31, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2013, decode.acc_seg: 91.9428, loss: 0.2013 +2023-03-04 14:30:18,724 - mmseg - INFO - Iter [6450/160000] lr: 1.500e-04, eta: 11:40:11, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2038, decode.acc_seg: 91.8930, loss: 0.2038 +2023-03-04 14:30:32,112 - mmseg - INFO - Iter [6500/160000] lr: 1.500e-04, eta: 11:39:51, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2135, decode.acc_seg: 91.5232, loss: 0.2135 +2023-03-04 14:30:45,357 - mmseg - INFO - Iter [6550/160000] lr: 1.500e-04, eta: 11:39:27, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2089, decode.acc_seg: 91.6698, loss: 0.2089 +2023-03-04 14:30:58,539 - mmseg - INFO - Iter [6600/160000] lr: 1.500e-04, eta: 11:39:02, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2172, decode.acc_seg: 91.4183, loss: 0.2172 +2023-03-04 14:31:11,980 - mmseg - INFO - Iter [6650/160000] lr: 1.500e-04, eta: 11:38:43, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2087, decode.acc_seg: 91.6942, loss: 0.2087 +2023-03-04 14:31:25,230 - mmseg - INFO - Iter [6700/160000] lr: 1.500e-04, eta: 11:38:20, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2087, decode.acc_seg: 91.6998, loss: 0.2087 +2023-03-04 14:31:38,434 - mmseg - INFO - Iter [6750/160000] lr: 1.500e-04, eta: 11:37:55, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2161, decode.acc_seg: 91.4643, loss: 0.2161 +2023-03-04 14:31:51,658 - mmseg - INFO - Iter [6800/160000] lr: 1.500e-04, eta: 11:37:32, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2190, decode.acc_seg: 91.4421, loss: 0.2190 +2023-03-04 14:32:04,850 - mmseg - INFO - Iter [6850/160000] lr: 1.500e-04, eta: 11:37:08, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2150, decode.acc_seg: 91.4470, loss: 0.2150 +2023-03-04 14:32:18,128 - mmseg - INFO - Iter [6900/160000] lr: 1.500e-04, eta: 11:36:46, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2089, decode.acc_seg: 91.7796, loss: 0.2089 +2023-03-04 14:32:33,858 - mmseg - INFO - Iter [6950/160000] lr: 1.500e-04, eta: 11:37:18, time: 0.315, data_time: 0.053, memory: 38189, decode.loss_ce: 0.2066, decode.acc_seg: 91.8214, loss: 0.2066 +2023-03-04 14:32:47,055 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:32:47,055 - mmseg - INFO - Iter [7000/160000] lr: 1.500e-04, eta: 11:36:54, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2089, decode.acc_seg: 91.6297, loss: 0.2089 +2023-03-04 14:33:00,255 - mmseg - INFO - Iter [7050/160000] lr: 1.500e-04, eta: 11:36:30, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2117, decode.acc_seg: 91.4697, loss: 0.2117 +2023-03-04 14:33:13,508 - mmseg - INFO - Iter [7100/160000] lr: 1.500e-04, eta: 11:36:08, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2121, decode.acc_seg: 91.6033, loss: 0.2121 +2023-03-04 14:33:27,003 - mmseg - INFO - Iter [7150/160000] lr: 1.500e-04, eta: 11:35:50, time: 0.270, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2110, decode.acc_seg: 91.7063, loss: 0.2110 +2023-03-04 14:33:40,236 - mmseg - INFO - Iter 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time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2142, decode.acc_seg: 91.3298, loss: 0.2142 +2023-03-04 14:38:08,114 - mmseg - INFO - Iter [8200/160000] lr: 1.500e-04, eta: 11:29:18, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2077, decode.acc_seg: 91.7148, loss: 0.2077 +2023-03-04 14:38:23,970 - mmseg - INFO - Iter [8250/160000] lr: 1.500e-04, eta: 11:29:45, time: 0.317, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2077, decode.acc_seg: 91.7273, loss: 0.2077 +2023-03-04 14:38:37,363 - mmseg - INFO - Iter [8300/160000] lr: 1.500e-04, eta: 11:29:27, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2050, decode.acc_seg: 91.7453, loss: 0.2050 +2023-03-04 14:38:50,698 - mmseg - INFO - Iter [8350/160000] lr: 1.500e-04, eta: 11:29:08, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2133, decode.acc_seg: 91.5190, loss: 0.2133 +2023-03-04 14:39:03,930 - mmseg - INFO - Iter [8400/160000] lr: 1.500e-04, eta: 11:28:47, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2195, decode.acc_seg: 91.3351, loss: 0.2195 +2023-03-04 14:39:17,183 - mmseg - INFO - Iter [8450/160000] lr: 1.500e-04, eta: 11:28:27, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2156, decode.acc_seg: 91.3696, loss: 0.2156 +2023-03-04 14:39:30,422 - mmseg - INFO - Iter [8500/160000] lr: 1.500e-04, eta: 11:28:06, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2129, decode.acc_seg: 91.5653, loss: 0.2129 +2023-03-04 14:39:43,637 - mmseg - INFO - Iter [8550/160000] lr: 1.500e-04, eta: 11:27:46, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2155, decode.acc_seg: 91.4016, loss: 0.2155 +2023-03-04 14:39:56,922 - mmseg - INFO - Iter [8600/160000] lr: 1.500e-04, eta: 11:27:26, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2007, decode.acc_seg: 91.8825, loss: 0.2007 +2023-03-04 14:40:10,149 - mmseg - INFO - Iter [8650/160000] lr: 1.500e-04, eta: 11:27:05, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2010, decode.acc_seg: 92.1154, loss: 0.2010 +2023-03-04 14:40:23,436 - mmseg - INFO - Iter [8700/160000] lr: 1.500e-04, eta: 11:26:46, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2195, decode.acc_seg: 91.3321, loss: 0.2195 +2023-03-04 14:40:36,694 - mmseg - INFO - Iter [8750/160000] lr: 1.500e-04, eta: 11:26:26, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2126, decode.acc_seg: 91.6787, loss: 0.2126 +2023-03-04 14:40:49,936 - mmseg - INFO - Iter [8800/160000] lr: 1.500e-04, eta: 11:26:06, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2032, decode.acc_seg: 91.9762, loss: 0.2032 +2023-03-04 14:41:05,732 - mmseg - INFO - Iter [8850/160000] lr: 1.500e-04, eta: 11:26:30, time: 0.316, data_time: 0.057, memory: 38189, decode.loss_ce: 0.2140, decode.acc_seg: 91.4545, loss: 0.2140 +2023-03-04 14:41:18,998 - mmseg - INFO - Iter [8900/160000] lr: 1.500e-04, eta: 11:26:10, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2090, decode.acc_seg: 91.6444, loss: 0.2090 +2023-03-04 14:41:32,383 - mmseg - INFO - Iter [8950/160000] lr: 1.500e-04, eta: 11:25:52, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2112, decode.acc_seg: 91.6612, loss: 0.2112 +2023-03-04 14:41:45,706 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:41:45,707 - mmseg - INFO - Iter [9000/160000] lr: 1.500e-04, eta: 11:25:34, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2147, decode.acc_seg: 91.4125, loss: 0.2147 +2023-03-04 14:41:58,989 - mmseg - INFO - Iter [9050/160000] lr: 1.500e-04, eta: 11:25:14, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2066, decode.acc_seg: 91.7894, loss: 0.2066 +2023-03-04 14:42:12,374 - mmseg - INFO - Iter [9100/160000] lr: 1.500e-04, eta: 11:24:57, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2032, decode.acc_seg: 91.9449, loss: 0.2032 +2023-03-04 14:42:25,610 - mmseg - INFO - Iter [9150/160000] lr: 1.500e-04, eta: 11:24:37, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2114, decode.acc_seg: 91.7151, loss: 0.2114 +2023-03-04 14:42:38,924 - mmseg - INFO - Iter [9200/160000] lr: 1.500e-04, eta: 11:24:19, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2199, decode.acc_seg: 91.1436, loss: 0.2199 +2023-03-04 14:42:52,274 - mmseg - INFO - Iter [9250/160000] lr: 1.500e-04, eta: 11:24:01, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2111, decode.acc_seg: 91.6206, loss: 0.2111 +2023-03-04 14:43:05,550 - mmseg - INFO - Iter [9300/160000] lr: 1.500e-04, eta: 11:23:42, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2003, decode.acc_seg: 92.0271, loss: 0.2003 +2023-03-04 14:43:18,911 - mmseg - INFO - Iter [9350/160000] lr: 1.500e-04, eta: 11:23:24, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2068, decode.acc_seg: 91.8140, loss: 0.2068 +2023-03-04 14:43:32,257 - mmseg - INFO - Iter [9400/160000] lr: 1.500e-04, eta: 11:23:06, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2144, decode.acc_seg: 91.4422, loss: 0.2144 +2023-03-04 14:43:45,521 - mmseg - INFO - Iter [9450/160000] lr: 1.500e-04, eta: 11:22:47, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2119, decode.acc_seg: 91.7085, loss: 0.2119 +2023-03-04 14:44:01,404 - mmseg - INFO - Iter [9500/160000] lr: 1.500e-04, eta: 11:23:09, time: 0.318, data_time: 0.058, memory: 38189, decode.loss_ce: 0.2072, decode.acc_seg: 91.7652, loss: 0.2072 +2023-03-04 14:44:14,686 - mmseg - INFO - Iter [9550/160000] lr: 1.500e-04, eta: 11:22:51, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1996, decode.acc_seg: 91.9466, loss: 0.1996 +2023-03-04 14:44:27,888 - mmseg - INFO - Iter [9600/160000] lr: 1.500e-04, eta: 11:22:30, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2001, decode.acc_seg: 92.0356, loss: 0.2001 +2023-03-04 14:44:41,265 - mmseg - INFO - Iter [9650/160000] lr: 1.500e-04, eta: 11:22:13, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2131, decode.acc_seg: 91.5746, loss: 0.2131 +2023-03-04 14:44:54,554 - mmseg - INFO - Iter [9700/160000] lr: 1.500e-04, eta: 11:21:54, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1988, decode.acc_seg: 91.9596, loss: 0.1988 +2023-03-04 14:45:07,761 - mmseg - INFO - Iter [9750/160000] lr: 1.500e-04, eta: 11:21:35, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2058, decode.acc_seg: 91.7707, loss: 0.2058 +2023-03-04 14:45:21,035 - mmseg - INFO - Iter [9800/160000] lr: 1.500e-04, eta: 11:21:16, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1967, decode.acc_seg: 91.9868, loss: 0.1967 +2023-03-04 14:45:34,331 - mmseg - INFO - Iter [9850/160000] lr: 1.500e-04, eta: 11:20:58, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2137, decode.acc_seg: 91.7457, loss: 0.2137 +2023-03-04 14:45:47,562 - mmseg - INFO - Iter [9900/160000] lr: 1.500e-04, eta: 11:20:38, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2047, decode.acc_seg: 91.7943, loss: 0.2047 +2023-03-04 14:46:00,910 - mmseg - INFO - Iter [9950/160000] lr: 1.500e-04, eta: 11:20:21, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2029, decode.acc_seg: 91.8244, loss: 0.2029 +2023-03-04 14:46:14,256 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:46:14,257 - mmseg - INFO - Iter [10000/160000] lr: 1.500e-04, eta: 11:20:03, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2045, decode.acc_seg: 91.7989, loss: 0.2045 +2023-03-04 14:46:27,496 - mmseg - INFO - Iter [10050/160000] lr: 1.500e-04, eta: 11:19:44, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2108, decode.acc_seg: 91.4761, loss: 0.2108 +2023-03-04 14:46:43,226 - mmseg - INFO - Iter [10100/160000] lr: 1.500e-04, eta: 11:20:02, time: 0.315, data_time: 0.053, memory: 38189, decode.loss_ce: 0.2113, decode.acc_seg: 91.5870, loss: 0.2113 +2023-03-04 14:46:56,533 - mmseg - INFO - Iter [10150/160000] lr: 1.500e-04, eta: 11:19:44, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2053, decode.acc_seg: 91.8367, loss: 0.2053 +2023-03-04 14:47:09,947 - mmseg - INFO - Iter [10200/160000] lr: 1.500e-04, eta: 11:19:28, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2059, decode.acc_seg: 91.7721, loss: 0.2059 +2023-03-04 14:47:23,230 - mmseg - INFO - Iter [10250/160000] lr: 1.500e-04, eta: 11:19:09, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2054, decode.acc_seg: 91.8996, loss: 0.2054 +2023-03-04 14:47:36,442 - mmseg - INFO - Iter [10300/160000] lr: 1.500e-04, eta: 11:18:50, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2020, decode.acc_seg: 91.7797, loss: 0.2020 +2023-03-04 14:47:49,664 - mmseg - INFO - Iter [10350/160000] lr: 1.500e-04, eta: 11:18:31, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2128, decode.acc_seg: 91.4425, loss: 0.2128 +2023-03-04 14:48:02,877 - mmseg - INFO - Iter [10400/160000] lr: 1.500e-04, eta: 11:18:12, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2101, decode.acc_seg: 91.5260, loss: 0.2101 +2023-03-04 14:48:16,240 - mmseg - INFO - Iter [10450/160000] lr: 1.500e-04, eta: 11:17:55, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2015, decode.acc_seg: 92.0580, loss: 0.2015 +2023-03-04 14:48:29,550 - mmseg - INFO - Iter [10500/160000] lr: 1.500e-04, eta: 11:17:37, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2142, decode.acc_seg: 91.5073, loss: 0.2142 +2023-03-04 14:48:42,795 - mmseg - INFO - Iter [10550/160000] lr: 1.500e-04, eta: 11:17:18, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2080, decode.acc_seg: 91.7675, loss: 0.2080 +2023-03-04 14:48:55,966 - mmseg - INFO - Iter [10600/160000] lr: 1.500e-04, eta: 11:16:59, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2138, decode.acc_seg: 91.4543, loss: 0.2138 +2023-03-04 14:49:09,172 - mmseg - INFO - Iter [10650/160000] lr: 1.500e-04, eta: 11:16:40, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2018, decode.acc_seg: 91.9926, loss: 0.2018 +2023-03-04 14:49:22,413 - mmseg - INFO - Iter [10700/160000] lr: 1.500e-04, eta: 11:16:21, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2099, decode.acc_seg: 91.6282, loss: 0.2099 +2023-03-04 14:49:38,291 - mmseg - INFO - Iter [10750/160000] lr: 1.500e-04, eta: 11:16:39, time: 0.318, data_time: 0.058, memory: 38189, decode.loss_ce: 0.1929, decode.acc_seg: 92.2898, loss: 0.1929 +2023-03-04 14:49:51,660 - mmseg - INFO - Iter [10800/160000] lr: 1.500e-04, eta: 11:16:23, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2107, decode.acc_seg: 91.6760, loss: 0.2107 +2023-03-04 14:50:04,880 - mmseg - INFO - Iter [10850/160000] lr: 1.500e-04, eta: 11:16:04, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2110, decode.acc_seg: 91.6334, loss: 0.2110 +2023-03-04 14:50:18,108 - mmseg - INFO - Iter [10900/160000] lr: 1.500e-04, eta: 11:15:45, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1981, decode.acc_seg: 92.0194, loss: 0.1981 +2023-03-04 14:50:31,309 - mmseg - INFO - Iter [10950/160000] lr: 1.500e-04, eta: 11:15:26, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2054, decode.acc_seg: 91.9520, loss: 0.2054 +2023-03-04 14:50:44,520 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:50:44,520 - mmseg - INFO - Iter [11000/160000] lr: 1.500e-04, eta: 11:15:07, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2080, decode.acc_seg: 91.7598, loss: 0.2080 +2023-03-04 14:50:57,731 - mmseg - INFO - Iter [11050/160000] lr: 1.500e-04, eta: 11:14:49, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2055, decode.acc_seg: 91.8926, loss: 0.2055 +2023-03-04 14:51:10,989 - mmseg - INFO - Iter [11100/160000] lr: 1.500e-04, eta: 11:14:31, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2073, decode.acc_seg: 91.6933, loss: 0.2073 +2023-03-04 14:51:24,168 - mmseg - INFO - Iter [11150/160000] lr: 1.500e-04, eta: 11:14:11, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2213, decode.acc_seg: 91.2359, loss: 0.2213 +2023-03-04 14:51:37,422 - mmseg - INFO - Iter [11200/160000] lr: 1.500e-04, eta: 11:13:53, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2093, decode.acc_seg: 91.5867, loss: 0.2093 +2023-03-04 14:51:50,710 - mmseg - INFO - Iter [11250/160000] lr: 1.500e-04, eta: 11:13:36, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2018, decode.acc_seg: 92.1305, loss: 0.2018 +2023-03-04 14:52:03,903 - mmseg - INFO - Iter [11300/160000] lr: 1.500e-04, eta: 11:13:17, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2110, decode.acc_seg: 91.5869, loss: 0.2110 +2023-03-04 14:52:17,153 - mmseg - INFO - Iter [11350/160000] lr: 1.500e-04, eta: 11:12:59, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2087, decode.acc_seg: 91.6236, loss: 0.2087 +2023-03-04 14:52:32,905 - mmseg - INFO - Iter [11400/160000] lr: 1.500e-04, eta: 11:13:14, time: 0.315, data_time: 0.055, memory: 38189, decode.loss_ce: 0.1965, decode.acc_seg: 92.0886, loss: 0.1965 +2023-03-04 14:52:46,219 - mmseg - INFO - Iter [11450/160000] lr: 1.500e-04, eta: 11:12:57, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2071, decode.acc_seg: 91.6666, loss: 0.2071 +2023-03-04 14:52:59,590 - mmseg - INFO - Iter [11500/160000] lr: 1.500e-04, eta: 11:12:40, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2050, decode.acc_seg: 91.8516, loss: 0.2050 +2023-03-04 14:53:12,877 - mmseg - INFO - Iter [11550/160000] lr: 1.500e-04, eta: 11:12:23, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1958, decode.acc_seg: 92.0831, loss: 0.1958 +2023-03-04 14:53:26,164 - mmseg - INFO - Iter [11600/160000] lr: 1.500e-04, eta: 11:12:05, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2057, decode.acc_seg: 91.9179, loss: 0.2057 +2023-03-04 14:53:39,608 - mmseg - INFO - Iter [11650/160000] lr: 1.500e-04, eta: 11:11:50, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2066, decode.acc_seg: 91.8155, loss: 0.2066 +2023-03-04 14:53:52,895 - mmseg - INFO - Iter [11700/160000] lr: 1.500e-04, eta: 11:11:33, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2029, decode.acc_seg: 91.8514, loss: 0.2029 +2023-03-04 14:54:06,345 - mmseg - INFO - Iter [11750/160000] lr: 1.500e-04, eta: 11:11:17, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2032, decode.acc_seg: 91.7572, loss: 0.2032 +2023-03-04 14:54:19,526 - mmseg - INFO - Iter [11800/160000] lr: 1.500e-04, eta: 11:10:59, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2062, decode.acc_seg: 91.6219, loss: 0.2062 +2023-03-04 14:54:32,830 - mmseg - INFO - Iter [11850/160000] lr: 1.500e-04, eta: 11:10:41, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2047, decode.acc_seg: 91.9073, loss: 0.2047 +2023-03-04 14:54:46,162 - mmseg - INFO - Iter [11900/160000] lr: 1.500e-04, eta: 11:10:25, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2085, decode.acc_seg: 91.7500, loss: 0.2085 +2023-03-04 14:54:59,391 - mmseg - INFO - Iter [11950/160000] lr: 1.500e-04, eta: 11:10:07, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2103, decode.acc_seg: 91.6070, loss: 0.2103 +2023-03-04 14:55:15,278 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:55:15,279 - mmseg - INFO - Iter [12000/160000] lr: 1.500e-04, eta: 11:10:22, time: 0.318, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2106, decode.acc_seg: 91.6487, loss: 0.2106 +2023-03-04 14:55:28,651 - mmseg - INFO - Iter [12050/160000] lr: 1.500e-04, eta: 11:10:06, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2052, decode.acc_seg: 91.8083, loss: 0.2052 +2023-03-04 14:55:42,045 - mmseg - INFO - Iter [12100/160000] lr: 1.500e-04, eta: 11:09:50, time: 0.268, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2015, decode.acc_seg: 91.9078, loss: 0.2015 +2023-03-04 14:55:55,287 - mmseg - INFO - Iter [12150/160000] lr: 1.500e-04, eta: 11:09:32, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2048, decode.acc_seg: 91.8257, loss: 0.2048 +2023-03-04 14:56:08,545 - mmseg - INFO - Iter [12200/160000] lr: 1.500e-04, eta: 11:09:14, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2101, decode.acc_seg: 91.6783, loss: 0.2101 +2023-03-04 14:56:21,886 - mmseg - INFO - Iter [12250/160000] lr: 1.500e-04, eta: 11:08:58, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2110, decode.acc_seg: 91.6808, loss: 0.2110 +2023-03-04 14:56:35,239 - mmseg - INFO - Iter [12300/160000] lr: 1.500e-04, eta: 11:08:41, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2118, decode.acc_seg: 91.5835, loss: 0.2118 +2023-03-04 14:56:48,484 - mmseg - INFO - Iter [12350/160000] lr: 1.500e-04, eta: 11:08:24, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2045, decode.acc_seg: 91.8143, loss: 0.2045 +2023-03-04 14:57:01,696 - mmseg - INFO - Iter [12400/160000] lr: 1.500e-04, eta: 11:08:06, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2059, decode.acc_seg: 91.8503, loss: 0.2059 +2023-03-04 14:57:14,988 - mmseg - INFO - Iter [12450/160000] lr: 1.500e-04, eta: 11:07:49, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2114, decode.acc_seg: 91.5628, loss: 0.2114 +2023-03-04 14:57:28,248 - mmseg - INFO - Iter [12500/160000] lr: 1.500e-04, eta: 11:07:31, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2057, decode.acc_seg: 91.7040, loss: 0.2057 +2023-03-04 14:57:41,466 - mmseg - INFO - Iter [12550/160000] lr: 1.500e-04, eta: 11:07:14, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2106, decode.acc_seg: 91.5976, loss: 0.2106 +2023-03-04 14:57:54,697 - mmseg - INFO - Iter [12600/160000] lr: 1.500e-04, eta: 11:06:56, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2054, decode.acc_seg: 91.8730, loss: 0.2054 +2023-03-04 14:58:10,505 - mmseg - INFO - Iter [12650/160000] lr: 1.500e-04, eta: 11:07:08, time: 0.316, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2113, decode.acc_seg: 91.5015, loss: 0.2113 +2023-03-04 14:58:23,822 - mmseg - INFO - Iter [12700/160000] lr: 1.500e-04, eta: 11:06:52, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2079, decode.acc_seg: 91.6466, loss: 0.2079 +2023-03-04 14:58:37,085 - mmseg - INFO - Iter [12750/160000] lr: 1.500e-04, eta: 11:06:35, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2013, decode.acc_seg: 91.7484, loss: 0.2013 +2023-03-04 14:58:50,354 - mmseg - INFO - Iter [12800/160000] lr: 1.500e-04, eta: 11:06:17, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2055, decode.acc_seg: 91.7459, loss: 0.2055 +2023-03-04 14:59:03,554 - mmseg - INFO - Iter [12850/160000] lr: 1.500e-04, eta: 11:05:59, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2053, decode.acc_seg: 91.7823, loss: 0.2053 +2023-03-04 14:59:16,770 - mmseg - INFO - Iter [12900/160000] lr: 1.500e-04, eta: 11:05:42, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2112, decode.acc_seg: 91.6381, loss: 0.2112 +2023-03-04 14:59:30,001 - mmseg - INFO - Iter [12950/160000] lr: 1.500e-04, eta: 11:05:24, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1965, decode.acc_seg: 92.1814, loss: 0.1965 +2023-03-04 14:59:43,167 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 14:59:43,167 - mmseg - INFO - Iter [13000/160000] lr: 1.500e-04, eta: 11:05:06, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1951, decode.acc_seg: 92.1762, loss: 0.1951 +2023-03-04 14:59:56,467 - mmseg - INFO - Iter [13050/160000] lr: 1.500e-04, eta: 11:04:49, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2046, decode.acc_seg: 91.8354, loss: 0.2046 +2023-03-04 15:00:09,637 - mmseg - INFO - Iter [13100/160000] lr: 1.500e-04, eta: 11:04:31, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1997, decode.acc_seg: 91.9822, loss: 0.1997 +2023-03-04 15:00:22,911 - mmseg - INFO - Iter [13150/160000] lr: 1.500e-04, eta: 11:04:14, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2072, decode.acc_seg: 91.7593, loss: 0.2072 +2023-03-04 15:00:36,114 - mmseg - INFO - Iter [13200/160000] lr: 1.500e-04, eta: 11:03:57, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2018, decode.acc_seg: 91.9740, loss: 0.2018 +2023-03-04 15:00:49,272 - mmseg - INFO - Iter [13250/160000] lr: 1.500e-04, eta: 11:03:39, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2036, decode.acc_seg: 91.7812, loss: 0.2036 +2023-03-04 15:01:05,092 - mmseg - INFO - Iter [13300/160000] lr: 1.500e-04, eta: 11:03:50, time: 0.316, data_time: 0.057, memory: 38189, decode.loss_ce: 0.1990, decode.acc_seg: 92.1316, loss: 0.1990 +2023-03-04 15:01:18,439 - mmseg - INFO - Iter [13350/160000] lr: 1.500e-04, eta: 11:03:34, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1992, decode.acc_seg: 91.9116, loss: 0.1992 +2023-03-04 15:01:31,805 - mmseg - INFO - Iter [13400/160000] lr: 1.500e-04, eta: 11:03:18, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2018, decode.acc_seg: 92.0012, loss: 0.2018 +2023-03-04 15:01:45,147 - mmseg - INFO - Iter [13450/160000] lr: 1.500e-04, eta: 11:03:02, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2075, decode.acc_seg: 91.6621, loss: 0.2075 +2023-03-04 15:01:58,347 - mmseg - INFO - Iter [13500/160000] lr: 1.500e-04, eta: 11:02:44, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2111, decode.acc_seg: 91.6571, loss: 0.2111 +2023-03-04 15:02:11,585 - mmseg - INFO - Iter [13550/160000] lr: 1.500e-04, eta: 11:02:27, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2133, decode.acc_seg: 91.5424, loss: 0.2133 +2023-03-04 15:02:24,802 - mmseg - INFO - Iter [13600/160000] lr: 1.500e-04, eta: 11:02:10, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2061, decode.acc_seg: 91.8460, loss: 0.2061 +2023-03-04 15:02:37,988 - mmseg - INFO - Iter [13650/160000] lr: 1.500e-04, eta: 11:01:52, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2103, decode.acc_seg: 91.7169, loss: 0.2103 +2023-03-04 15:02:51,191 - mmseg - INFO - Iter [13700/160000] lr: 1.500e-04, eta: 11:01:34, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2048, decode.acc_seg: 91.8214, loss: 0.2048 +2023-03-04 15:03:04,384 - mmseg - INFO - Iter [13750/160000] lr: 1.500e-04, eta: 11:01:17, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2079, decode.acc_seg: 91.5592, loss: 0.2079 +2023-03-04 15:03:17,591 - mmseg - INFO - Iter [13800/160000] lr: 1.500e-04, eta: 11:01:00, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2016, decode.acc_seg: 91.8728, loss: 0.2016 +2023-03-04 15:03:30,799 - mmseg - INFO - Iter [13850/160000] lr: 1.500e-04, eta: 11:00:42, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2027, decode.acc_seg: 92.0096, loss: 0.2027 +2023-03-04 15:03:46,610 - mmseg - INFO - Iter [13900/160000] lr: 1.500e-04, eta: 11:00:52, time: 0.316, data_time: 0.056, memory: 38189, decode.loss_ce: 0.2076, decode.acc_seg: 91.7107, loss: 0.2076 +2023-03-04 15:03:59,891 - mmseg - INFO - Iter [13950/160000] lr: 1.500e-04, eta: 11:00:36, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2061, decode.acc_seg: 91.8937, loss: 0.2061 +2023-03-04 15:04:13,131 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:04:13,131 - mmseg - INFO - Iter [14000/160000] lr: 1.500e-04, eta: 11:00:19, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2023, decode.acc_seg: 91.9367, loss: 0.2023 +2023-03-04 15:04:26,363 - mmseg - INFO - Iter [14050/160000] lr: 1.500e-04, eta: 11:00:02, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2110, decode.acc_seg: 91.6707, loss: 0.2110 +2023-03-04 15:04:39,617 - mmseg - INFO - Iter [14100/160000] lr: 1.500e-04, eta: 10:59:45, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1950, decode.acc_seg: 92.1704, loss: 0.1950 +2023-03-04 15:04:52,929 - mmseg - INFO - Iter [14150/160000] lr: 1.500e-04, eta: 10:59:29, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2068, decode.acc_seg: 91.7268, loss: 0.2068 +2023-03-04 15:05:06,205 - mmseg - INFO - Iter [14200/160000] lr: 1.500e-04, eta: 10:59:12, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2101, decode.acc_seg: 91.7924, loss: 0.2101 +2023-03-04 15:05:19,418 - mmseg - INFO - Iter [14250/160000] lr: 1.500e-04, eta: 10:58:55, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2124, decode.acc_seg: 91.5775, loss: 0.2124 +2023-03-04 15:05:32,712 - mmseg - INFO - Iter [14300/160000] lr: 1.500e-04, eta: 10:58:39, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2109, decode.acc_seg: 91.6116, loss: 0.2109 +2023-03-04 15:05:45,989 - mmseg - INFO - Iter [14350/160000] lr: 1.500e-04, eta: 10:58:22, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2050, decode.acc_seg: 91.7917, loss: 0.2050 +2023-03-04 15:05:59,240 - mmseg - INFO - Iter [14400/160000] lr: 1.500e-04, eta: 10:58:06, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2030, decode.acc_seg: 91.7692, loss: 0.2030 +2023-03-04 15:06:12,435 - mmseg - INFO - Iter [14450/160000] lr: 1.500e-04, eta: 10:57:48, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2044, decode.acc_seg: 91.8341, loss: 0.2044 +2023-03-04 15:06:25,650 - mmseg - INFO - Iter [14500/160000] lr: 1.500e-04, eta: 10:57:31, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2105, decode.acc_seg: 91.7496, loss: 0.2105 +2023-03-04 15:06:41,447 - mmseg - INFO - Iter [14550/160000] lr: 1.500e-04, eta: 10:57:40, time: 0.316, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2045, decode.acc_seg: 91.9899, loss: 0.2045 +2023-03-04 15:06:54,898 - mmseg - INFO - Iter [14600/160000] lr: 1.500e-04, eta: 10:57:25, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1965, decode.acc_seg: 92.2423, loss: 0.1965 +2023-03-04 15:07:08,172 - mmseg - INFO - Iter [14650/160000] lr: 1.500e-04, eta: 10:57:09, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2148, decode.acc_seg: 91.4377, loss: 0.2148 +2023-03-04 15:07:21,492 - mmseg - INFO - Iter [14700/160000] lr: 1.500e-04, eta: 10:56:53, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2020, decode.acc_seg: 91.9126, loss: 0.2020 +2023-03-04 15:07:34,751 - mmseg - INFO - Iter [14750/160000] lr: 1.500e-04, eta: 10:56:36, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2081, decode.acc_seg: 91.7353, loss: 0.2081 +2023-03-04 15:07:48,105 - mmseg - INFO - Iter [14800/160000] lr: 1.500e-04, eta: 10:56:21, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2001, decode.acc_seg: 91.9372, loss: 0.2001 +2023-03-04 15:08:01,349 - mmseg - INFO - Iter [14850/160000] lr: 1.500e-04, eta: 10:56:04, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1997, decode.acc_seg: 91.9219, loss: 0.1997 +2023-03-04 15:08:14,721 - mmseg - INFO - Iter [14900/160000] lr: 1.500e-04, eta: 10:55:49, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2063, decode.acc_seg: 91.7135, loss: 0.2063 +2023-03-04 15:08:27,910 - mmseg - INFO - Iter [14950/160000] lr: 1.500e-04, eta: 10:55:32, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2097, decode.acc_seg: 91.8645, loss: 0.2097 +2023-03-04 15:08:41,197 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:08:41,197 - mmseg - INFO - Iter [15000/160000] lr: 1.500e-04, eta: 10:55:15, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2034, decode.acc_seg: 91.8847, loss: 0.2034 +2023-03-04 15:08:54,437 - mmseg - INFO - Iter [15050/160000] lr: 1.500e-04, eta: 10:54:59, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2014, decode.acc_seg: 91.9766, loss: 0.2014 +2023-03-04 15:09:07,791 - mmseg - INFO - Iter [15100/160000] lr: 1.500e-04, eta: 10:54:43, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2066, decode.acc_seg: 91.8490, loss: 0.2066 +2023-03-04 15:09:23,426 - mmseg - INFO - Iter [15150/160000] lr: 1.500e-04, eta: 10:54:50, time: 0.313, data_time: 0.053, memory: 38189, decode.loss_ce: 0.2041, decode.acc_seg: 91.8867, loss: 0.2041 +2023-03-04 15:09:36,668 - mmseg - INFO - Iter [15200/160000] lr: 1.500e-04, eta: 10:54:33, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1974, decode.acc_seg: 91.9780, loss: 0.1974 +2023-03-04 15:09:49,900 - mmseg - INFO - Iter [15250/160000] lr: 1.500e-04, eta: 10:54:16, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1977, decode.acc_seg: 91.9868, loss: 0.1977 +2023-03-04 15:10:03,087 - mmseg - INFO - Iter [15300/160000] lr: 1.500e-04, eta: 10:53:59, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2088, decode.acc_seg: 91.8463, loss: 0.2088 +2023-03-04 15:10:16,263 - mmseg - INFO - Iter [15350/160000] lr: 1.500e-04, eta: 10:53:42, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2076, decode.acc_seg: 91.6882, loss: 0.2076 +2023-03-04 15:10:29,696 - mmseg - INFO - Iter [15400/160000] lr: 1.500e-04, eta: 10:53:27, time: 0.269, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1983, decode.acc_seg: 92.1180, loss: 0.1983 +2023-03-04 15:10:42,869 - mmseg - INFO - Iter [15450/160000] lr: 1.500e-04, eta: 10:53:10, time: 0.263, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2086, decode.acc_seg: 91.7001, loss: 0.2086 +2023-03-04 15:10:56,092 - mmseg - INFO - Iter [15500/160000] lr: 1.500e-04, eta: 10:52:54, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2097, decode.acc_seg: 91.7239, loss: 0.2097 +2023-03-04 15:11:09,332 - mmseg - INFO - Iter [15550/160000] lr: 1.500e-04, eta: 10:52:37, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2045, decode.acc_seg: 91.8061, loss: 0.2045 +2023-03-04 15:11:22,520 - mmseg - INFO - Iter [15600/160000] lr: 1.500e-04, eta: 10:52:20, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2151, decode.acc_seg: 91.5162, loss: 0.2151 +2023-03-04 15:11:35,788 - mmseg - INFO - Iter [15650/160000] lr: 1.500e-04, eta: 10:52:04, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2096, decode.acc_seg: 91.7905, loss: 0.2096 +2023-03-04 15:11:49,071 - mmseg - INFO - Iter [15700/160000] lr: 1.500e-04, eta: 10:51:48, time: 0.266, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2031, decode.acc_seg: 91.8814, loss: 0.2031 +2023-03-04 15:12:02,300 - mmseg - INFO - Iter [15750/160000] lr: 1.500e-04, eta: 10:51:31, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2043, decode.acc_seg: 91.8506, loss: 0.2043 +2023-03-04 15:12:18,124 - mmseg - INFO - Iter [15800/160000] lr: 1.500e-04, eta: 10:51:39, time: 0.316, data_time: 0.054, memory: 38189, decode.loss_ce: 0.2019, decode.acc_seg: 91.7174, loss: 0.2019 +2023-03-04 15:12:31,464 - mmseg - INFO - Iter [15850/160000] lr: 1.500e-04, eta: 10:51:23, time: 0.267, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2052, decode.acc_seg: 91.8553, loss: 0.2052 +2023-03-04 15:12:44,729 - mmseg - INFO - Iter [15900/160000] lr: 1.500e-04, eta: 10:51:07, time: 0.265, data_time: 0.007, memory: 38189, decode.loss_ce: 0.1979, decode.acc_seg: 92.1853, loss: 0.1979 +2023-03-04 15:12:57,928 - mmseg - INFO - Iter [15950/160000] lr: 1.500e-04, eta: 10:50:50, time: 0.264, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2114, decode.acc_seg: 91.6330, loss: 0.2114 +2023-03-04 15:13:11,211 - mmseg - INFO - Swap parameters (after train) after iter [16000] +2023-03-04 15:13:11,233 - mmseg - INFO - Saving checkpoint at 16000 iterations +2023-03-04 15:13:13,110 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:13:13,111 - mmseg - INFO - Iter [16000/160000] lr: 1.500e-04, eta: 10:50:51, time: 0.303, data_time: 0.007, memory: 38189, decode.loss_ce: 0.2027, decode.acc_seg: 91.8813, loss: 0.2027 +2023-03-04 15:36:34,842 - mmseg - INFO - per class results: +2023-03-04 15:36:34,851 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.01,76.02,76.01,76.01,76.01,76.02,76.01,76.0,75.99,76.0,76.01 | +| building | 81.11,81.1,81.11,81.1,81.11,81.09,81.09,81.09,81.09,81.07,81.06 | +| sky | 94.18,94.19,94.19,94.18,94.18,94.18,94.17,94.16,94.16,94.15,94.17 | +| floor | 79.88,79.88,79.88,79.89,79.89,79.89,79.9,79.88,79.89,79.88,79.86 | +| tree | 72.62,72.61,72.61,72.6,72.6,72.58,72.58,72.58,72.57,72.54,72.48 | +| ceiling | 82.27,82.27,82.27,82.28,82.26,82.28,82.27,82.26,82.26,82.26,82.28 | +| road | 81.94,81.95,81.96,81.96,81.97,81.97,81.97,81.99,81.99,82.0,82.02 | +| bed | 88.15,88.16,88.15,88.15,88.15,88.14,88.13,88.15,88.17,88.17,88.12 | +| windowpane | 60.73,60.72,60.71,60.7,60.7,60.7,60.68,60.67,60.68,60.68,60.66 | +| grass | 65.94,65.97,65.94,65.95,65.96,65.98,65.99,65.99,65.97,65.97,65.9 | +| cabinet | 59.51,59.52,59.5,59.48,59.48,59.46,59.45,59.44,59.45,59.44,59.44 | +| sidewalk | 65.58,65.58,65.59,65.61,65.61,65.62,65.63,65.64,65.67,65.68,65.6 | +| person | 78.96,78.98,78.98,78.99,78.98,78.98,79.0,78.97,78.93,78.91,78.96 | +| earth | 33.67,33.63,33.66,33.64,33.64,33.64,33.61,33.66,33.61,33.62,33.57 | +| door | 47.87,47.86,47.85,47.82,47.8,47.8,47.78,47.79,47.73,47.72,47.64 | +| table | 60.95,60.94,60.94,60.92,60.87,60.89,60.84,60.82,60.85,60.85,60.74 | +| mountain | 51.64,51.65,51.63,51.66,51.61,51.63,51.63,51.62,51.63,51.57,51.53 | +| plant | 50.28,50.24,50.23,50.19,50.18,50.15,50.13,50.15,50.12,50.1,49.91 | +| curtain | 70.1,70.1,70.13,70.09,70.1,70.12,70.1,70.07,70.14,70.1,70.04 | +| chair | 57.84,57.83,57.84,57.81,57.79,57.8,57.76,57.73,57.7,57.7,57.65 | +| car | 83.04,83.06,83.03,83.03,83.01,83.0,83.02,83.02,83.02,83.04,82.94 | +| water | 46.82,46.8,46.79,46.79,46.77,46.75,46.79,46.73,46.69,46.65,46.69 | +| painting | 69.18,69.19,69.2,69.17,69.2,69.16,69.16,69.13,69.16,69.15,69.0 | +| sofa | 64.92,64.9,64.88,64.88,64.86,64.86,64.83,64.82,64.85,64.83,64.92 | +| shelf | 40.1,40.09,40.1,40.03,40.03,40.08,40.04,40.05,40.03,40.05,39.96 | +| house | 43.41,43.36,43.31,43.25,43.16,43.05,43.05,42.93,42.78,42.65,42.9 | +| sea | 45.08,45.03,45.02,45.02,45.01,44.98,44.96,44.96,44.95,44.94,44.84 | +| mirror | 65.05,65.07,65.06,65.04,65.0,65.01,64.98,64.96,64.97,64.92,64.96 | +| rug | 54.63,54.62,54.62,54.65,54.58,54.66,54.75,54.67,54.62,54.56,54.71 | +| field | 28.3,28.3,28.29,28.32,28.32,28.33,28.36,28.38,28.37,28.38,28.27 | +| armchair | 43.58,43.6,43.58,43.59,43.65,43.64,43.66,43.67,43.69,43.72,43.6 | +| seat | 54.88,54.82,54.77,54.7,54.62,54.65,54.58,54.55,54.55,54.5,54.19 | +| fence | 40.79,40.78,40.81,40.81,40.77,40.78,40.83,40.78,40.8,40.78,40.63 | +| desk | 49.13,49.04,49.05,49.01,48.97,48.98,48.94,48.95,48.87,48.85,48.8 | +| rock | 29.97,29.96,29.86,29.92,29.91,29.89,29.92,29.82,29.83,29.75,29.67 | +| wardrobe | 49.0,48.99,49.04,49.01,49.03,49.13,49.15,49.16,49.19,49.23,49.23 | +| lamp | 63.13,63.14,63.16,63.13,63.17,63.16,63.18,63.14,63.16,63.16,63.11 | +| bathtub | 75.27,75.18,75.2,75.23,75.19,75.2,75.13,75.17,75.11,75.09,74.93 | +| railing | 31.35,31.26,31.24,31.22,31.14,31.11,31.11,31.03,30.9,30.86,30.95 | +| cushion | 54.54,54.58,54.53,54.54,54.57,54.6,54.55,54.5,54.51,54.46,54.65 | +| base | 26.99,27.07,27.0,27.12,27.25,27.22,27.3,27.31,27.38,27.27,27.48 | +| box | 23.41,23.45,23.47,23.49,23.53,23.5,23.49,23.55,23.64,23.72,23.26 | +| column | 44.69,44.71,44.72,44.7,44.69,44.72,44.83,44.69,44.67,44.7,44.55 | +| signboard | 35.84,35.82,35.77,35.8,35.76,35.71,35.76,35.75,35.72,35.68,35.42 | +| chest of drawers | 39.7,39.72,39.64,39.64,39.77,39.67,39.72,39.73,39.72,39.68,39.81 | +| counter | 27.56,27.52,27.41,27.43,27.52,27.31,27.31,27.21,27.13,27.01,27.42 | +| sand | 32.15,32.07,32.15,32.12,32.11,32.13,32.05,32.12,32.1,32.2,31.96 | +| sink | 69.77,69.75,69.78,69.75,69.71,69.72,69.69,69.7,69.68,69.66,69.61 | +| skyscraper | 48.44,48.41,48.4,48.4,48.37,48.33,48.29,48.2,48.08,47.99,48.29 | +| fireplace | 65.88,65.91,65.91,65.89,65.92,66.04,65.99,65.86,65.97,65.96,65.94 | +| refrigerator | 76.2,76.18,76.29,76.24,76.21,76.26,76.18,76.26,76.26,76.27,76.15 | +| grandstand | 42.37,42.38,42.23,42.28,42.2,42.18,42.18,42.29,42.21,42.23,42.05 | +| path | 15.99,15.97,15.95,15.93,15.92,15.94,15.93,15.95,15.94,15.91,15.94 | +| stairs | 32.54,32.56,32.6,32.55,32.61,32.59,32.58,32.55,32.57,32.55,32.63 | +| runway | 63.35,63.35,63.38,63.39,63.38,63.41,63.42,63.4,63.44,63.46,63.42 | +| case | 49.79,49.8,49.77,49.87,49.84,49.81,49.81,49.88,49.92,49.96,50.0 | +| pool table | 92.4,92.3,92.37,92.35,92.34,92.32,92.32,92.29,92.23,92.25,92.33 | +| pillow | 56.06,56.12,56.11,56.14,56.16,56.06,56.09,56.1,56.06,56.01,56.23 | +| screen door | 65.54,65.54,65.63,65.4,65.34,65.06,65.25,65.05,65.04,64.84,65.23 | +| stairway | 25.58,25.58,25.55,25.54,25.54,25.57,25.57,25.54,25.56,25.6,25.36 | +| river | 9.34,9.4,9.36,9.36,9.41,9.39,9.42,9.44,9.46,9.47,9.43 | +| bridge | 47.51,47.48,47.54,47.47,47.77,48.16,47.91,47.77,47.64,47.66,46.96 | +| bookcase | 40.29,40.25,40.44,40.48,40.52,40.66,40.88,40.92,40.96,41.07,41.2 | +| blind | 46.67,46.61,46.5,46.56,46.52,46.56,46.47,46.37,46.34,46.36,46.39 | +| coffee table | 65.94,65.89,65.94,65.94,65.9,65.88,65.88,65.9,65.9,65.89,65.87 | +| toilet | 86.1,86.07,86.09,86.12,86.07,86.07,86.07,86.08,86.1,86.08,86.31 | +| flower | 32.04,32.14,32.08,32.11,32.18,32.1,32.08,32.05,32.08,31.95,32.18 | +| book | 45.7,45.76,45.75,45.73,45.77,45.8,45.75,45.78,45.77,45.81,45.9 | +| hill | 8.08,8.11,8.09,8.09,8.06,8.06,8.07,8.02,8.01,7.99,8.01 | +| bench | 43.83,43.84,43.82,43.84,43.87,43.87,43.88,43.82,43.81,43.77,43.69 | +| countertop | 52.59,52.7,52.67,52.6,52.7,52.6,52.67,52.61,52.6,52.69,52.8 | +| stove | 72.96,72.93,72.91,72.89,72.93,72.89,72.74,72.66,72.71,72.77,72.58 | +| palm | 50.15,50.25,50.23,50.12,50.22,50.38,50.31,50.32,50.42,50.45,50.03 | +| kitchen island | 45.51,45.56,45.65,45.66,45.6,45.72,45.87,45.83,45.93,45.95,45.57 | +| computer | 56.78,56.76,56.78,56.78,56.84,56.84,56.82,56.82,56.78,56.76,56.87 | +| swivel chair | 45.01,44.95,44.98,44.85,44.8,44.83,44.87,44.86,44.82,44.98,44.5 | +| boat | 39.72,39.7,39.72,39.7,39.71,39.75,39.77,39.72,39.72,39.74,39.38 | +| bar | 26.39,26.42,26.43,26.4,26.42,26.41,26.44,26.34,26.35,26.28,26.42 | +| arcade machine | 26.66,26.77,26.66,26.75,26.81,26.9,26.98,26.65,26.7,26.6,26.83 | +| hovel | 31.39,31.31,31.22,31.24,31.11,31.07,31.01,30.83,30.79,30.63,30.78 | +| bus | 87.87,87.83,87.88,87.84,87.87,87.83,87.86,87.88,87.88,87.84,87.73 | +| towel | 58.44,58.51,58.55,58.59,58.6,58.72,58.6,58.7,58.72,58.82,58.85 | +| light | 55.01,54.95,54.94,54.95,54.87,54.93,54.87,54.77,54.78,54.68,54.75 | +| truck | 33.97,33.95,34.08,34.12,34.18,34.2,34.26,34.26,34.39,34.38,34.24 | +| tower | 22.91,22.92,23.09,23.14,23.19,23.21,23.37,23.36,23.48,23.57,23.68 | +| chandelier | 66.15,66.12,66.17,66.09,66.14,66.13,66.13,66.04,66.0,66.01,65.98 | +| awning | 23.18,23.14,23.13,23.28,23.25,23.27,23.27,23.33,23.32,23.25,23.53 | +| streetlight | 27.84,27.88,27.82,27.8,27.76,27.71,27.7,27.63,27.57,27.57,27.64 | +| booth | 51.89,51.89,51.79,51.81,51.9,51.71,51.78,51.77,51.6,51.47,51.88 | +| television receiver | 68.44,68.48,68.5,68.48,68.54,68.47,68.51,68.47,68.49,68.51,68.52 | +| airplane | 50.5,50.4,50.4,50.3,50.3,50.23,50.34,50.24,50.18,50.19,50.19 | +| dirt track | 9.45,9.57,9.62,9.65,9.52,9.61,9.59,9.73,9.67,9.61,9.63 | +| apparel | 29.22,29.21,29.19,29.1,29.55,29.4,29.35,29.4,29.32,29.24,29.72 | +| pole | 24.15,24.19,24.19,24.19,24.15,24.16,24.11,24.12,24.16,24.14,24.13 | +| land | 10.34,10.33,10.34,10.35,10.31,10.39,10.43,10.35,10.34,10.42,10.53 | +| bannister | 5.83,5.82,5.89,5.85,5.76,5.74,5.59,5.51,5.29,5.31,6.14 | +| escalator | 23.0,23.13,23.12,23.16,23.25,23.3,23.35,23.43,23.52,23.48,23.07 | +| ottoman | 48.13,48.12,48.15,48.1,47.99,48.07,47.96,47.89,47.99,48.01,47.76 | +| bottle | 15.49,15.44,15.49,15.4,15.3,15.53,15.31,15.38,15.38,15.3,15.3 | +| buffet | 51.55,51.74,51.77,51.94,52.09,52.11,52.34,52.42,52.44,52.57,52.47 | +| poster | 27.36,27.45,27.35,27.35,27.31,27.16,27.1,26.87,26.75,26.64,27.08 | +| stage | 17.08,17.09,17.12,17.11,17.16,17.27,17.26,17.23,17.21,17.29,17.32 | +| van | 47.32,47.36,47.33,47.39,47.36,47.38,47.29,47.26,47.36,47.36,47.48 | +| ship | 27.94,27.99,28.37,28.58,29.26,29.52,29.81,29.87,29.6,30.16,30.28 | +| fountain | 7.55,7.55,7.51,7.52,7.49,7.56,7.57,7.4,7.39,7.38,7.53 | +| conveyer belt | 76.25,76.37,76.39,76.35,76.37,76.47,76.49,76.63,76.69,76.78,76.74 | +| canopy | 14.56,14.62,14.75,14.78,14.77,14.78,14.87,14.83,14.9,14.92,15.03 | +| washer | 66.26,66.32,66.42,66.53,66.57,66.67,66.71,66.73,66.77,66.81,66.9 | +| plaything | 21.83,21.96,21.87,21.91,21.92,22.07,22.1,21.99,21.97,21.98,22.29 | +| swimming pool | 42.63,42.48,42.51,42.53,42.65,42.63,42.45,42.18,42.26,42.25,43.08 | +| stool | 40.2,40.26,40.25,40.27,40.28,40.22,40.3,40.3,40.42,40.41,39.88 | +| barrel | 44.45,44.64,43.73,43.79,43.64,43.58,43.32,43.27,44.01,43.39,43.59 | +| basket | 27.38,27.4,27.47,27.42,27.45,27.48,27.53,27.5,27.51,27.45,27.63 | +| waterfall | 57.53,57.86,57.89,58.0,58.42,58.55,58.78,58.87,59.08,59.38,59.75 | +| tent | 94.52,94.53,94.52,94.48,94.44,94.45,94.51,94.53,94.53,94.55,94.35 | +| bag | 11.87,11.93,11.91,12.07,12.11,12.16,12.32,12.32,12.34,12.34,12.34 | +| minibike | 62.24,62.19,62.14,62.17,62.02,62.02,61.83,61.87,61.95,61.94,61.88 | +| cradle | 80.39,80.45,80.45,80.47,80.53,80.58,80.54,80.57,80.65,80.66,80.54 | +| oven | 27.15,27.12,27.19,27.14,27.21,27.16,27.11,27.14,27.11,27.07,27.05 | +| ball | 45.95,46.06,46.05,46.03,46.11,45.99,46.04,45.94,45.95,45.9,45.98 | +| food | 55.39,55.43,55.38,55.37,55.39,55.31,55.27,55.21,55.14,55.05,55.04 | +| step | 13.39,13.47,13.64,13.5,13.59,13.54,13.6,13.55,13.52,13.51,13.81 | +| tank | 41.07,41.15,41.09,41.24,41.27,41.33,41.43,41.5,41.55,41.58,41.64 | +| trade name | 24.66,24.55,24.69,24.69,24.71,24.64,24.75,24.54,24.58,24.57,24.67 | +| microwave | 37.48,37.46,37.46,37.47,37.48,37.43,37.47,37.45,37.47,37.48,37.44 | +| pot | 40.53,40.52,40.53,40.57,40.57,40.56,40.55,40.52,40.55,40.54,40.56 | +| animal | 51.54,51.68,51.84,51.95,51.98,52.1,52.19,52.3,52.43,52.51,52.41 | +| bicycle | 43.89,43.93,44.02,44.15,44.1,44.16,44.11,44.28,44.02,44.1,44.34 | +| lake | 59.55,59.5,59.52,59.44,59.44,59.41,59.34,59.4,59.37,59.35,59.33 | +| dishwasher | 77.44,77.47,77.47,77.45,77.53,77.45,77.4,77.53,77.39,77.4,77.52 | +| screen | 67.65,67.71,67.69,67.7,67.71,67.72,67.76,67.87,67.89,68.02,68.22 | +| blanket | 12.06,12.12,12.15,12.16,12.21,12.22,12.29,12.44,12.45,12.47,12.19 | +| sculpture | 35.74,35.77,35.72,35.86,35.89,35.89,35.85,35.92,36.07,36.06,36.53 | +| hood | 55.62,55.59,55.65,55.7,55.57,55.45,55.46,55.46,55.4,55.55,55.44 | +| sconce | 41.56,41.68,41.49,41.55,41.52,41.47,41.5,41.37,41.38,41.28,41.13 | +| vase | 36.42,36.45,36.49,36.4,36.29,36.36,36.39,36.35,36.28,36.26,36.41 | +| traffic light | 28.97,29.23,29.23,29.2,29.33,29.35,29.47,29.54,29.54,29.6,29.65 | +| tray | 5.65,5.65,5.63,5.63,5.64,5.64,5.65,5.65,5.65,5.65,5.64 | +| ashcan | 39.15,39.17,39.2,39.23,39.21,39.28,39.25,39.22,39.22,39.29,39.17 | +| fan | 56.75,56.81,56.79,56.8,56.82,56.89,56.86,56.86,56.86,56.83,56.87 | +| pier | 11.38,11.17,11.2,11.03,10.97,11.04,11.06,10.76,10.6,10.65,10.8 | +| crt screen | 4.13,4.19,4.2,4.09,4.2,4.21,4.16,4.14,4.16,4.19,4.22 | +| plate | 39.31,39.35,39.29,39.22,39.26,39.21,39.25,39.22,39.3,39.24,39.23 | +| monitor | 24.72,24.5,24.62,24.46,24.46,24.43,24.5,24.45,24.42,24.43,24.23 | +| bulletin board | 45.83,45.66,45.94,45.65,45.83,45.6,45.45,45.23,45.36,45.08,45.68 | +| shower | 0.86,0.85,0.83,1.1,0.86,1.1,1.16,1.08,1.13,1.27,0.78 | +| radiator | 44.96,45.21,45.06,45.2,45.33,45.32,45.42,45.3,45.36,45.35,45.85 | +| glass | 11.88,11.88,11.87,11.9,11.86,11.95,11.95,11.91,11.9,11.93,11.85 | +| clock | 25.3,25.12,25.14,25.14,25.17,25.24,25.2,25.13,25.19,25.23,25.08 | +| flag | 37.35,37.45,37.41,37.42,37.48,37.52,37.41,37.55,37.59,37.41,37.7 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 15:36:34,851 - mmseg - INFO - Summary: +2023-03-04 15:36:34,851 - mmseg - INFO - ++-------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++-------------------------------------------------------------------+ +| 45.82,45.83,45.83,45.83,45.84,45.85,45.85,45.83,45.84,45.83,45.85 | ++-------------------------------------------------------------------+ +2023-03-04 15:36:36,602 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_16000.pth. +2023-03-04 15:36:36,602 - mmseg - INFO - Best mIoU is 0.4585 at 16000 iter. +2023-03-04 15:36:36,602 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:36:36,602 - mmseg - INFO - Iter(val) [250] mIoU: [0.4582, 0.4583, 0.4583, 0.4583, 0.4584, 0.4585, 0.4585, 0.4583, 0.4584, 0.4583, 0.4585], copy_paste: 45.82,45.83,45.83,45.83,45.84,45.85,45.85,45.83,45.84,45.83,45.85 +2023-03-04 15:36:36,608 - mmseg - INFO - Swap parameters (before train) before iter [16001] +2023-03-04 15:36:50,336 - mmseg - INFO - Iter [16050/160000] lr: 1.500e-04, eta: 14:20:27, time: 28.345, data_time: 28.078, memory: 67559, decode.loss_ce: 0.2039, decode.acc_seg: 91.7591, loss: 0.2039 +2023-03-04 15:37:03,880 - mmseg - INFO - Iter [16100/160000] lr: 1.500e-04, eta: 14:19:30, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2077, decode.acc_seg: 91.6528, loss: 0.2077 +2023-03-04 15:37:17,256 - mmseg - INFO - Iter [16150/160000] lr: 1.500e-04, eta: 14:18:31, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2088, decode.acc_seg: 91.6704, loss: 0.2088 +2023-03-04 15:37:30,712 - mmseg - INFO - Iter [16200/160000] lr: 1.500e-04, eta: 14:17:34, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2080, decode.acc_seg: 91.8435, loss: 0.2080 +2023-03-04 15:37:43,966 - mmseg - INFO - Iter [16250/160000] lr: 1.500e-04, eta: 14:16:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2043, decode.acc_seg: 91.8086, loss: 0.2043 +2023-03-04 15:37:57,347 - mmseg - INFO - Iter [16300/160000] lr: 1.500e-04, eta: 14:15:37, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2013, decode.acc_seg: 91.9200, loss: 0.2013 +2023-03-04 15:38:10,894 - mmseg - INFO - Iter [16350/160000] lr: 1.500e-04, eta: 14:14:42, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1985, decode.acc_seg: 92.0175, loss: 0.1985 +2023-03-04 15:38:24,198 - mmseg - INFO - Iter [16400/160000] lr: 1.500e-04, eta: 14:13:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2098, decode.acc_seg: 91.7893, loss: 0.2098 +2023-03-04 15:38:40,155 - mmseg - INFO - Iter [16450/160000] lr: 1.500e-04, eta: 14:13:10, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.2014, decode.acc_seg: 91.9828, loss: 0.2014 +2023-03-04 15:38:53,423 - mmseg - INFO - Iter [16500/160000] lr: 1.500e-04, eta: 14:12:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1998, decode.acc_seg: 91.9485, loss: 0.1998 +2023-03-04 15:39:06,667 - mmseg - INFO - Iter [16550/160000] lr: 1.500e-04, eta: 14:11:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2026, decode.acc_seg: 92.0777, loss: 0.2026 +2023-03-04 15:39:20,007 - mmseg - INFO - Iter [16600/160000] lr: 1.500e-04, eta: 14:10:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2036, decode.acc_seg: 91.8302, loss: 0.2036 +2023-03-04 15:39:33,507 - mmseg - INFO - Iter [16650/160000] lr: 1.500e-04, eta: 14:09:24, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2096, decode.acc_seg: 91.6720, loss: 0.2096 +2023-03-04 15:39:46,890 - mmseg - INFO - Iter [16700/160000] lr: 1.500e-04, eta: 14:08:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2128, decode.acc_seg: 91.4441, loss: 0.2128 +2023-03-04 15:40:00,317 - mmseg - INFO - Iter [16750/160000] lr: 1.500e-04, eta: 14:07:33, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2115, decode.acc_seg: 91.7273, loss: 0.2115 +2023-03-04 15:40:13,579 - mmseg - INFO - Iter [16800/160000] lr: 1.500e-04, eta: 14:06:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2099, decode.acc_seg: 91.7032, loss: 0.2099 +2023-03-04 15:40:26,894 - mmseg - INFO - Iter [16850/160000] lr: 1.500e-04, eta: 14:05:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2025, decode.acc_seg: 91.8162, loss: 0.2025 +2023-03-04 15:40:40,169 - mmseg - INFO - Iter [16900/160000] lr: 1.500e-04, eta: 14:04:47, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2020, decode.acc_seg: 91.8901, loss: 0.2020 +2023-03-04 15:40:53,516 - mmseg - INFO - Iter [16950/160000] lr: 1.500e-04, eta: 14:03:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2133, decode.acc_seg: 91.5757, loss: 0.2133 +2023-03-04 15:41:06,937 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:41:06,937 - mmseg - INFO - Iter [17000/160000] lr: 1.500e-04, eta: 14:02:59, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2014, decode.acc_seg: 92.0277, loss: 0.2014 +2023-03-04 15:41:22,755 - mmseg - INFO - Iter [17050/160000] lr: 1.500e-04, eta: 14:02:25, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.2058, decode.acc_seg: 91.7177, loss: 0.2058 +2023-03-04 15:41:36,081 - mmseg - INFO - Iter [17100/160000] lr: 1.500e-04, eta: 14:01:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2069, decode.acc_seg: 91.7808, loss: 0.2069 +2023-03-04 15:41:49,418 - mmseg - INFO - Iter [17150/160000] lr: 1.500e-04, eta: 14:00:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2029, decode.acc_seg: 91.8118, loss: 0.2029 +2023-03-04 15:42:02,713 - mmseg - INFO - Iter [17200/160000] lr: 1.500e-04, eta: 13:59:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2038, decode.acc_seg: 91.7839, loss: 0.2038 +2023-03-04 15:42:16,106 - mmseg - INFO - Iter [17250/160000] lr: 1.500e-04, eta: 13:58:51, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2012, decode.acc_seg: 91.8936, loss: 0.2012 +2023-03-04 15:42:29,412 - mmseg - INFO - Iter [17300/160000] lr: 1.500e-04, eta: 13:57:57, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2008, decode.acc_seg: 92.0410, loss: 0.2008 +2023-03-04 15:42:42,673 - mmseg - INFO - Iter [17350/160000] lr: 1.500e-04, eta: 13:57:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2126, decode.acc_seg: 91.6265, loss: 0.2126 +2023-03-04 15:42:55,998 - mmseg - INFO - Iter [17400/160000] lr: 1.500e-04, eta: 13:56:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2084, decode.acc_seg: 91.8434, loss: 0.2084 +2023-03-04 15:43:09,393 - mmseg - INFO - Iter [17450/160000] lr: 1.500e-04, eta: 13:55:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2027, decode.acc_seg: 91.9635, loss: 0.2027 +2023-03-04 15:43:22,725 - mmseg - INFO - Iter [17500/160000] lr: 1.500e-04, eta: 13:54:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2042, decode.acc_seg: 91.8195, loss: 0.2042 +2023-03-04 15:43:36,079 - mmseg - INFO - Iter [17550/160000] lr: 1.500e-04, eta: 13:53:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2058, decode.acc_seg: 91.7517, loss: 0.2058 +2023-03-04 15:43:49,418 - mmseg - INFO - Iter [17600/160000] lr: 1.500e-04, eta: 13:52:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2017, decode.acc_seg: 91.9854, loss: 0.2017 +2023-03-04 15:44:02,792 - mmseg - INFO - Iter [17650/160000] lr: 1.500e-04, eta: 13:51:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2044, decode.acc_seg: 91.8629, loss: 0.2044 +2023-03-04 15:44:18,725 - mmseg - INFO - Iter [17700/160000] lr: 1.500e-04, eta: 13:51:22, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.2112, decode.acc_seg: 91.6880, loss: 0.2112 +2023-03-04 15:44:32,196 - mmseg - INFO - Iter [17750/160000] lr: 1.500e-04, eta: 13:50:32, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2015, decode.acc_seg: 91.9362, loss: 0.2015 +2023-03-04 15:44:45,508 - mmseg - INFO - Iter [17800/160000] lr: 1.500e-04, eta: 13:49:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2047, decode.acc_seg: 91.8488, loss: 0.2047 +2023-03-04 15:44:59,021 - mmseg - INFO - Iter [17850/160000] lr: 1.500e-04, eta: 13:48:52, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1967, decode.acc_seg: 92.4072, loss: 0.1967 +2023-03-04 15:45:12,380 - mmseg - INFO - Iter [17900/160000] lr: 1.500e-04, eta: 13:48:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2021, decode.acc_seg: 91.8835, loss: 0.2021 +2023-03-04 15:45:25,710 - mmseg - INFO - Iter [17950/160000] lr: 1.500e-04, eta: 13:47:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2017, decode.acc_seg: 91.8557, loss: 0.2017 +2023-03-04 15:45:39,272 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:45:39,272 - mmseg - INFO - Iter [18000/160000] lr: 1.500e-04, eta: 13:46:23, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2028, decode.acc_seg: 91.8959, loss: 0.2028 +2023-03-04 15:45:52,540 - mmseg - INFO - Iter [18050/160000] lr: 1.500e-04, eta: 13:45:32, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1999, decode.acc_seg: 91.9419, loss: 0.1999 +2023-03-04 15:46:05,770 - mmseg - INFO - Iter [18100/160000] lr: 1.500e-04, eta: 13:44:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2078, decode.acc_seg: 91.7540, loss: 0.2078 +2023-03-04 15:46:19,121 - mmseg - INFO - Iter [18150/160000] lr: 1.500e-04, eta: 13:43:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2050, decode.acc_seg: 91.7986, loss: 0.2050 +2023-03-04 15:46:32,472 - mmseg - INFO - Iter [18200/160000] lr: 1.500e-04, eta: 13:43:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2071, decode.acc_seg: 91.7907, loss: 0.2071 +2023-03-04 15:46:45,716 - mmseg - INFO - Iter [18250/160000] lr: 1.500e-04, eta: 13:42:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2122, decode.acc_seg: 91.5854, loss: 0.2122 +2023-03-04 15:47:01,366 - mmseg - INFO - Iter [18300/160000] lr: 1.500e-04, eta: 13:41:43, time: 0.313, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.0920, loss: 0.1968 +2023-03-04 15:47:14,754 - mmseg - INFO - Iter [18350/160000] lr: 1.500e-04, eta: 13:40:54, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1984, decode.acc_seg: 92.0226, loss: 0.1984 +2023-03-04 15:47:28,076 - mmseg - INFO - Iter [18400/160000] lr: 1.500e-04, eta: 13:40:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2034, decode.acc_seg: 91.8864, loss: 0.2034 +2023-03-04 15:47:41,417 - mmseg - INFO - Iter [18450/160000] lr: 1.500e-04, eta: 13:39:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2048, decode.acc_seg: 91.8378, loss: 0.2048 +2023-03-04 15:47:54,735 - mmseg - INFO - Iter [18500/160000] lr: 1.500e-04, eta: 13:38:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2078, decode.acc_seg: 91.6946, loss: 0.2078 +2023-03-04 15:48:08,047 - mmseg - INFO - Iter [18550/160000] lr: 1.500e-04, eta: 13:37:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2109, decode.acc_seg: 91.7610, loss: 0.2109 +2023-03-04 15:48:21,376 - mmseg - INFO - Iter [18600/160000] lr: 1.500e-04, eta: 13:36:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1998, decode.acc_seg: 91.9383, loss: 0.1998 +2023-03-04 15:48:34,788 - mmseg - INFO - Iter [18650/160000] lr: 1.500e-04, eta: 13:36:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1923, decode.acc_seg: 92.1795, loss: 0.1923 +2023-03-04 15:48:48,403 - mmseg - INFO - Iter [18700/160000] lr: 1.500e-04, eta: 13:35:21, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2061, decode.acc_seg: 91.7434, loss: 0.2061 +2023-03-04 15:49:01,654 - mmseg - INFO - Iter [18750/160000] lr: 1.500e-04, eta: 13:34:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2066, decode.acc_seg: 91.8253, loss: 0.2066 +2023-03-04 15:49:14,983 - mmseg - INFO - Iter [18800/160000] lr: 1.500e-04, eta: 13:33:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2033, decode.acc_seg: 91.9469, loss: 0.2033 +2023-03-04 15:49:28,311 - mmseg - INFO - Iter [18850/160000] lr: 1.500e-04, eta: 13:32:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1963, decode.acc_seg: 92.0686, loss: 0.1963 +2023-03-04 15:49:41,646 - mmseg - INFO - Iter [18900/160000] lr: 1.500e-04, eta: 13:32:12, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1959, decode.acc_seg: 92.1848, loss: 0.1959 +2023-03-04 15:49:57,510 - mmseg - INFO - Iter [18950/160000] lr: 1.500e-04, eta: 13:31:44, time: 0.317, data_time: 0.053, memory: 67559, decode.loss_ce: 0.2127, decode.acc_seg: 91.5739, loss: 0.2127 +2023-03-04 15:50:10,873 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:50:10,873 - mmseg - INFO - Iter [19000/160000] lr: 1.500e-04, eta: 13:30:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2016, decode.acc_seg: 92.0675, loss: 0.2016 +2023-03-04 15:50:24,203 - mmseg - INFO - Iter [19050/160000] lr: 1.500e-04, eta: 13:30:12, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.1502, loss: 0.1955 +2023-03-04 15:50:37,503 - mmseg - INFO - Iter [19100/160000] lr: 1.500e-04, eta: 13:29:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2046, decode.acc_seg: 91.7898, loss: 0.2046 +2023-03-04 15:50:50,816 - mmseg - INFO - Iter [19150/160000] lr: 1.500e-04, eta: 13:28:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2063, decode.acc_seg: 91.8386, loss: 0.2063 +2023-03-04 15:51:04,213 - mmseg - INFO - Iter [19200/160000] lr: 1.500e-04, eta: 13:27:54, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1971, decode.acc_seg: 92.0925, loss: 0.1971 +2023-03-04 15:51:17,648 - mmseg - INFO - Iter [19250/160000] lr: 1.500e-04, eta: 13:27:09, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2116, decode.acc_seg: 91.5572, loss: 0.2116 +2023-03-04 15:51:30,941 - mmseg - INFO - Iter [19300/160000] lr: 1.500e-04, eta: 13:26:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1991, decode.acc_seg: 91.9369, loss: 0.1991 +2023-03-04 15:51:44,367 - mmseg - INFO - Iter [19350/160000] lr: 1.500e-04, eta: 13:25:39, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2034, decode.acc_seg: 91.7698, loss: 0.2034 +2023-03-04 15:51:57,589 - mmseg - INFO - Iter [19400/160000] lr: 1.500e-04, eta: 13:24:53, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2022, decode.acc_seg: 91.9037, loss: 0.2022 +2023-03-04 15:52:11,042 - mmseg - INFO - Iter [19450/160000] lr: 1.500e-04, eta: 13:24:09, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2079, decode.acc_seg: 91.9452, loss: 0.2079 +2023-03-04 15:52:24,366 - mmseg - INFO - Iter [19500/160000] lr: 1.500e-04, eta: 13:23:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1999, decode.acc_seg: 91.9976, loss: 0.1999 +2023-03-04 15:52:37,618 - mmseg - INFO - Iter [19550/160000] lr: 1.500e-04, eta: 13:22:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2000, decode.acc_seg: 91.8342, loss: 0.2000 +2023-03-04 15:52:53,610 - mmseg - INFO - Iter [19600/160000] lr: 1.500e-04, eta: 13:22:13, time: 0.320, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1989, decode.acc_seg: 91.8978, loss: 0.1989 +2023-03-04 15:53:06,931 - mmseg - INFO - Iter [19650/160000] lr: 1.500e-04, eta: 13:21:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2013, decode.acc_seg: 91.9514, loss: 0.2013 +2023-03-04 15:53:20,314 - mmseg - INFO - Iter [19700/160000] lr: 1.500e-04, eta: 13:20:45, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2042, decode.acc_seg: 91.8082, loss: 0.2042 +2023-03-04 15:53:33,676 - mmseg - INFO - Iter [19750/160000] lr: 1.500e-04, eta: 13:20:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2050, decode.acc_seg: 91.8310, loss: 0.2050 +2023-03-04 15:53:47,143 - mmseg - INFO - Iter [19800/160000] lr: 1.500e-04, eta: 13:19:18, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2057, decode.acc_seg: 91.8201, loss: 0.2057 +2023-03-04 15:54:00,443 - mmseg - INFO - Iter [19850/160000] lr: 1.500e-04, eta: 13:18:34, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2013, decode.acc_seg: 92.0040, loss: 0.2013 +2023-03-04 15:54:13,772 - mmseg - INFO - Iter [19900/160000] lr: 1.500e-04, eta: 13:17:51, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2046, decode.acc_seg: 91.7623, loss: 0.2046 +2023-03-04 15:54:27,239 - mmseg - INFO - Iter [19950/160000] lr: 1.500e-04, eta: 13:17:08, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2063, decode.acc_seg: 91.7016, loss: 0.2063 +2023-03-04 15:54:40,691 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:54:40,691 - mmseg - INFO - Iter [20000/160000] lr: 1.500e-04, eta: 13:16:26, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2016, decode.acc_seg: 91.8543, loss: 0.2016 +2023-03-04 15:54:54,175 - mmseg - INFO - Iter [20050/160000] lr: 7.500e-05, eta: 13:15:44, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2018, decode.acc_seg: 92.0280, loss: 0.2018 +2023-03-04 15:55:07,544 - mmseg - INFO - Iter [20100/160000] lr: 7.500e-05, eta: 13:15:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.0376, loss: 0.1958 +2023-03-04 15:55:20,980 - mmseg - INFO - Iter [20150/160000] lr: 7.500e-05, eta: 13:14:19, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2005, decode.acc_seg: 91.9220, loss: 0.2005 +2023-03-04 15:55:36,743 - mmseg - INFO - Iter [20200/160000] lr: 7.500e-05, eta: 13:13:53, time: 0.315, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1983, decode.acc_seg: 91.9893, loss: 0.1983 +2023-03-04 15:55:50,179 - mmseg - INFO - Iter [20250/160000] lr: 7.500e-05, eta: 13:13:11, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2062, decode.acc_seg: 91.7318, loss: 0.2062 +2023-03-04 15:56:03,600 - mmseg - INFO - Iter [20300/160000] lr: 7.500e-05, eta: 13:12:29, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.1241, loss: 0.1974 +2023-03-04 15:56:17,003 - mmseg - INFO - Iter [20350/160000] lr: 7.500e-05, eta: 13:11:47, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1977, decode.acc_seg: 92.0678, loss: 0.1977 +2023-03-04 15:56:30,209 - mmseg - INFO - Iter [20400/160000] lr: 7.500e-05, eta: 13:11:04, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1964, decode.acc_seg: 91.9788, loss: 0.1964 +2023-03-04 15:56:43,593 - mmseg - INFO - Iter [20450/160000] lr: 7.500e-05, eta: 13:10:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2010, decode.acc_seg: 91.8788, loss: 0.2010 +2023-03-04 15:56:57,041 - mmseg - INFO - Iter [20500/160000] lr: 7.500e-05, eta: 13:09:42, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.3452, loss: 0.1922 +2023-03-04 15:57:10,519 - mmseg - INFO - Iter [20550/160000] lr: 7.500e-05, eta: 13:09:01, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1930, decode.acc_seg: 92.2719, loss: 0.1930 +2023-03-04 15:57:23,884 - mmseg - INFO - Iter [20600/160000] lr: 7.500e-05, eta: 13:08:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1934, decode.acc_seg: 92.2586, loss: 0.1934 +2023-03-04 15:57:37,243 - mmseg - INFO - Iter [20650/160000] lr: 7.500e-05, eta: 13:07:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.1630, loss: 0.1951 +2023-03-04 15:57:50,705 - mmseg - INFO - Iter [20700/160000] lr: 7.500e-05, eta: 13:06:58, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.0233, loss: 0.1947 +2023-03-04 15:58:03,882 - mmseg - INFO - Iter [20750/160000] lr: 7.500e-05, eta: 13:06:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2008, decode.acc_seg: 91.9613, loss: 0.2008 +2023-03-04 15:58:17,287 - mmseg - INFO - Iter [20800/160000] lr: 7.500e-05, eta: 13:05:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.0739, loss: 0.1955 +2023-03-04 15:58:33,131 - mmseg - INFO - Iter [20850/160000] lr: 7.500e-05, eta: 13:05:11, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1933, decode.acc_seg: 92.2052, loss: 0.1933 +2023-03-04 15:58:46,411 - mmseg - INFO - Iter [20900/160000] lr: 7.500e-05, eta: 13:04:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1928, decode.acc_seg: 92.1744, loss: 0.1928 +2023-03-04 15:58:59,813 - mmseg - INFO - Iter [20950/160000] lr: 7.500e-05, eta: 13:03:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2027, decode.acc_seg: 91.8384, loss: 0.2027 +2023-03-04 15:59:13,283 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 15:59:13,284 - mmseg - INFO - Iter [21000/160000] lr: 7.500e-05, eta: 13:03:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2014, decode.acc_seg: 91.8568, loss: 0.2014 +2023-03-04 15:59:26,547 - mmseg - INFO - Iter [21050/160000] lr: 7.500e-05, eta: 13:02:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.2447, loss: 0.1946 +2023-03-04 15:59:39,917 - mmseg - INFO - Iter [21100/160000] lr: 7.500e-05, eta: 13:01:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2008, decode.acc_seg: 91.9215, loss: 0.2008 +2023-03-04 15:59:53,318 - mmseg - INFO - Iter [21150/160000] lr: 7.500e-05, eta: 13:01:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1894, decode.acc_seg: 92.3382, loss: 0.1894 +2023-03-04 16:00:06,569 - mmseg - INFO - Iter [21200/160000] lr: 7.500e-05, eta: 13:00:28, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1990, decode.acc_seg: 91.9758, loss: 0.1990 +2023-03-04 16:00:19,822 - mmseg - INFO - Iter [21250/160000] lr: 7.500e-05, eta: 12:59:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1973, decode.acc_seg: 92.0102, loss: 0.1973 +2023-03-04 16:00:33,061 - mmseg - INFO - Iter [21300/160000] lr: 7.500e-05, eta: 12:59:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.1075, loss: 0.1919 +2023-03-04 16:00:46,422 - mmseg - INFO - Iter [21350/160000] lr: 7.500e-05, eta: 12:58:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.2216, loss: 0.1948 +2023-03-04 16:00:59,890 - mmseg - INFO - Iter [21400/160000] lr: 7.500e-05, eta: 12:57:49, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1942, decode.acc_seg: 92.0775, loss: 0.1942 +2023-03-04 16:01:13,222 - mmseg - INFO - Iter [21450/160000] lr: 7.500e-05, eta: 12:57:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1941, decode.acc_seg: 92.2024, loss: 0.1941 +2023-03-04 16:01:29,034 - mmseg - INFO - Iter [21500/160000] lr: 7.500e-05, eta: 12:56:46, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1997, decode.acc_seg: 91.9721, loss: 0.1997 +2023-03-04 16:01:42,419 - mmseg - INFO - Iter [21550/160000] lr: 7.500e-05, eta: 12:56:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2018, decode.acc_seg: 91.9807, loss: 0.2018 +2023-03-04 16:01:55,769 - mmseg - INFO - Iter [21600/160000] lr: 7.500e-05, eta: 12:55:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1999, decode.acc_seg: 92.1120, loss: 0.1999 +2023-03-04 16:02:09,208 - mmseg - INFO - Iter [21650/160000] lr: 7.500e-05, eta: 12:54:50, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2028, decode.acc_seg: 92.0195, loss: 0.2028 +2023-03-04 16:02:22,486 - mmseg - INFO - Iter [21700/160000] lr: 7.500e-05, eta: 12:54:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1963, decode.acc_seg: 92.0895, loss: 0.1963 +2023-03-04 16:02:35,804 - mmseg - INFO - Iter [21750/160000] lr: 7.500e-05, eta: 12:53:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1986, decode.acc_seg: 92.1759, loss: 0.1986 +2023-03-04 16:02:49,139 - mmseg - INFO - Iter [21800/160000] lr: 7.500e-05, eta: 12:52:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1944, decode.acc_seg: 92.2100, loss: 0.1944 +2023-03-04 16:03:02,565 - mmseg - INFO - Iter [21850/160000] lr: 7.500e-05, eta: 12:52:15, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1977, decode.acc_seg: 92.0344, loss: 0.1977 +2023-03-04 16:03:15,879 - mmseg - INFO - Iter [21900/160000] lr: 7.500e-05, eta: 12:51:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.1599, loss: 0.1937 +2023-03-04 16:03:29,129 - mmseg - INFO - Iter [21950/160000] lr: 7.500e-05, eta: 12:50:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.1342, loss: 0.1948 +2023-03-04 16:03:42,600 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:03:42,600 - mmseg - INFO - Iter [22000/160000] lr: 7.500e-05, eta: 12:50:20, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1999, decode.acc_seg: 92.0022, loss: 0.1999 +2023-03-04 16:03:55,805 - mmseg - INFO - Iter [22050/160000] lr: 7.500e-05, eta: 12:49:41, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1899, decode.acc_seg: 92.4606, loss: 0.1899 +2023-03-04 16:04:11,582 - mmseg - INFO - Iter [22100/160000] lr: 7.500e-05, eta: 12:49:19, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1964, decode.acc_seg: 92.2088, loss: 0.1964 +2023-03-04 16:04:24,898 - mmseg - INFO - Iter [22150/160000] lr: 7.500e-05, eta: 12:48:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.1427, loss: 0.1937 +2023-03-04 16:04:38,152 - mmseg - INFO - Iter [22200/160000] lr: 7.500e-05, eta: 12:48:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2029, decode.acc_seg: 91.7401, loss: 0.2029 +2023-03-04 16:04:51,565 - mmseg - INFO - Iter [22250/160000] lr: 7.500e-05, eta: 12:47:25, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2002, decode.acc_seg: 91.9378, loss: 0.2002 +2023-03-04 16:05:04,900 - mmseg - INFO - Iter [22300/160000] lr: 7.500e-05, eta: 12:46:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2018, decode.acc_seg: 91.9960, loss: 0.2018 +2023-03-04 16:05:18,223 - mmseg - INFO - Iter [22350/160000] lr: 7.500e-05, eta: 12:46:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2037, decode.acc_seg: 91.8858, loss: 0.2037 +2023-03-04 16:05:31,548 - mmseg - INFO - Iter [22400/160000] lr: 7.500e-05, eta: 12:45:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.4192, loss: 0.1920 +2023-03-04 16:05:44,778 - mmseg - INFO - Iter [22450/160000] lr: 7.500e-05, eta: 12:44:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.0477, loss: 0.1936 +2023-03-04 16:05:58,039 - mmseg - INFO - Iter [22500/160000] lr: 7.500e-05, eta: 12:44:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.4291, loss: 0.1848 +2023-03-04 16:06:11,521 - mmseg - INFO - Iter [22550/160000] lr: 7.500e-05, eta: 12:43:41, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2040, decode.acc_seg: 91.8473, loss: 0.2040 +2023-03-04 16:06:24,891 - mmseg - INFO - Iter [22600/160000] lr: 7.500e-05, eta: 12:43:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1942, decode.acc_seg: 92.1106, loss: 0.1942 +2023-03-04 16:06:38,392 - mmseg - INFO - Iter [22650/160000] lr: 7.500e-05, eta: 12:42:28, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.0535, loss: 0.1968 +2023-03-04 16:06:51,831 - mmseg - INFO - Iter [22700/160000] lr: 7.500e-05, eta: 12:41:52, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.1832, loss: 0.1935 +2023-03-04 16:07:07,703 - mmseg - INFO - Iter [22750/160000] lr: 7.500e-05, eta: 12:41:31, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1977, decode.acc_seg: 92.0840, loss: 0.1977 +2023-03-04 16:07:21,136 - mmseg - INFO - Iter [22800/160000] lr: 7.500e-05, eta: 12:40:55, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1934, decode.acc_seg: 92.2842, loss: 0.1934 +2023-03-04 16:07:34,459 - mmseg - INFO - Iter [22850/160000] lr: 7.500e-05, eta: 12:40:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1959, decode.acc_seg: 92.0192, loss: 0.1959 +2023-03-04 16:07:47,866 - mmseg - INFO - Iter [22900/160000] lr: 7.500e-05, eta: 12:39:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.2204, loss: 0.1943 +2023-03-04 16:08:01,214 - mmseg - INFO - Iter [22950/160000] lr: 7.500e-05, eta: 12:39:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1971, decode.acc_seg: 92.0818, loss: 0.1971 +2023-03-04 16:08:14,551 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:08:14,551 - mmseg - INFO - Iter [23000/160000] lr: 7.500e-05, eta: 12:38:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2000, decode.acc_seg: 91.9540, loss: 0.2000 +2023-03-04 16:08:27,838 - mmseg - INFO - Iter [23050/160000] lr: 7.500e-05, eta: 12:37:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1978, decode.acc_seg: 92.0756, loss: 0.1978 +2023-03-04 16:08:41,213 - mmseg - INFO - Iter [23100/160000] lr: 7.500e-05, eta: 12:37:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1978, decode.acc_seg: 91.9967, loss: 0.1978 +2023-03-04 16:08:54,535 - mmseg - INFO - Iter [23150/160000] lr: 7.500e-05, eta: 12:36:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2026, decode.acc_seg: 91.8469, loss: 0.2026 +2023-03-04 16:09:07,802 - mmseg - INFO - Iter [23200/160000] lr: 7.500e-05, eta: 12:36:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1969, decode.acc_seg: 92.0498, loss: 0.1969 +2023-03-04 16:09:21,323 - mmseg - INFO - Iter [23250/160000] lr: 7.500e-05, eta: 12:35:31, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1991, decode.acc_seg: 91.9458, loss: 0.1991 +2023-03-04 16:09:34,614 - mmseg - INFO - Iter [23300/160000] lr: 7.500e-05, eta: 12:34:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.0932, loss: 0.1955 +2023-03-04 16:09:50,492 - mmseg - INFO - Iter [23350/160000] lr: 7.500e-05, eta: 12:34:35, time: 0.318, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1987, decode.acc_seg: 92.0547, loss: 0.1987 +2023-03-04 16:10:04,060 - mmseg - INFO - Iter [23400/160000] lr: 7.500e-05, eta: 12:34:01, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2084, decode.acc_seg: 91.6455, loss: 0.2084 +2023-03-04 16:10:17,450 - mmseg - INFO - Iter [23450/160000] lr: 7.500e-05, eta: 12:33:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1971, decode.acc_seg: 91.9637, loss: 0.1971 +2023-03-04 16:10:30,833 - mmseg - INFO - Iter [23500/160000] lr: 7.500e-05, eta: 12:32:51, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1934, decode.acc_seg: 92.2707, loss: 0.1934 +2023-03-04 16:10:44,127 - mmseg - INFO - Iter [23550/160000] lr: 7.500e-05, eta: 12:32:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1987, decode.acc_seg: 92.0839, loss: 0.1987 +2023-03-04 16:10:57,560 - mmseg - INFO - Iter [23600/160000] lr: 7.500e-05, eta: 12:31:41, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.1835, loss: 0.1946 +2023-03-04 16:11:10,938 - mmseg - INFO - Iter [23650/160000] lr: 7.500e-05, eta: 12:31:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.4957, loss: 0.1848 +2023-03-04 16:11:24,168 - mmseg - INFO - Iter [23700/160000] lr: 7.500e-05, eta: 12:30:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1996, decode.acc_seg: 92.0972, loss: 0.1996 +2023-03-04 16:11:37,506 - mmseg - INFO - Iter [23750/160000] lr: 7.500e-05, eta: 12:29:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1987, decode.acc_seg: 91.9724, loss: 0.1987 +2023-03-04 16:11:50,850 - mmseg - INFO - Iter [23800/160000] lr: 7.500e-05, eta: 12:29:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1939, decode.acc_seg: 92.1809, loss: 0.1939 +2023-03-04 16:12:04,163 - mmseg - INFO - Iter [23850/160000] lr: 7.500e-05, eta: 12:28:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2050, decode.acc_seg: 91.7922, loss: 0.2050 +2023-03-04 16:12:17,441 - mmseg - INFO - Iter [23900/160000] lr: 7.500e-05, eta: 12:28:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.2052, loss: 0.1948 +2023-03-04 16:12:30,791 - mmseg - INFO - Iter [23950/160000] lr: 7.500e-05, eta: 12:27:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.4863, loss: 0.1846 +2023-03-04 16:12:46,830 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:12:46,830 - mmseg - INFO - Iter [24000/160000] lr: 7.500e-05, eta: 12:27:18, time: 0.321, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1980, decode.acc_seg: 92.1923, loss: 0.1980 +2023-03-04 16:13:00,166 - mmseg - INFO - Iter [24050/160000] lr: 7.500e-05, eta: 12:26:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1992, decode.acc_seg: 92.0617, loss: 0.1992 +2023-03-04 16:13:13,491 - mmseg - INFO - Iter [24100/160000] lr: 7.500e-05, eta: 12:26:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.3012, loss: 0.1883 +2023-03-04 16:13:26,830 - mmseg - INFO - Iter [24150/160000] lr: 7.500e-05, eta: 12:25:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1927, decode.acc_seg: 92.2384, loss: 0.1927 +2023-03-04 16:13:40,110 - mmseg - INFO - Iter [24200/160000] lr: 7.500e-05, eta: 12:25:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.2840, loss: 0.1914 +2023-03-04 16:13:53,425 - mmseg - INFO - Iter [24250/160000] lr: 7.500e-05, eta: 12:24:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.4762, loss: 0.1865 +2023-03-04 16:14:06,694 - mmseg - INFO - Iter [24300/160000] lr: 7.500e-05, eta: 12:23:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2022, decode.acc_seg: 91.8125, loss: 0.2022 +2023-03-04 16:14:20,071 - mmseg - INFO - Iter [24350/160000] lr: 7.500e-05, eta: 12:23:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1960, decode.acc_seg: 92.1880, loss: 0.1960 +2023-03-04 16:14:33,504 - mmseg - INFO - Iter [24400/160000] lr: 7.500e-05, eta: 12:22:46, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.0020, loss: 0.1968 +2023-03-04 16:14:46,887 - mmseg - INFO - Iter [24450/160000] lr: 7.500e-05, eta: 12:22:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.0156, loss: 0.1974 +2023-03-04 16:15:00,258 - mmseg - INFO - Iter [24500/160000] lr: 7.500e-05, eta: 12:21:40, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1983, decode.acc_seg: 91.9965, loss: 0.1983 +2023-03-04 16:15:13,554 - mmseg - INFO - Iter [24550/160000] lr: 7.500e-05, eta: 12:21:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1979, decode.acc_seg: 92.1287, loss: 0.1979 +2023-03-04 16:15:26,883 - mmseg - INFO - Iter [24600/160000] lr: 7.500e-05, eta: 12:20:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.2709, loss: 0.1919 +2023-03-04 16:15:42,549 - mmseg - INFO - Iter [24650/160000] lr: 7.500e-05, eta: 12:20:12, time: 0.313, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1957, decode.acc_seg: 92.1437, loss: 0.1957 +2023-03-04 16:15:55,830 - mmseg - INFO - Iter [24700/160000] lr: 7.500e-05, eta: 12:19:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2042, decode.acc_seg: 91.8017, loss: 0.2042 +2023-03-04 16:16:09,065 - mmseg - INFO - Iter [24750/160000] lr: 7.500e-05, eta: 12:19:05, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2017, decode.acc_seg: 92.0148, loss: 0.2017 +2023-03-04 16:16:22,328 - mmseg - INFO - Iter [24800/160000] lr: 7.500e-05, eta: 12:18:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.3365, loss: 0.1874 +2023-03-04 16:16:35,733 - mmseg - INFO - Iter [24850/160000] lr: 7.500e-05, eta: 12:17:59, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2001, decode.acc_seg: 91.9553, loss: 0.2001 +2023-03-04 16:16:49,128 - mmseg - INFO - Iter [24900/160000] lr: 7.500e-05, eta: 12:17:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1973, decode.acc_seg: 92.0079, loss: 0.1973 +2023-03-04 16:17:02,554 - mmseg - INFO - Iter [24950/160000] lr: 7.500e-05, eta: 12:16:54, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1975, decode.acc_seg: 92.1436, loss: 0.1975 +2023-03-04 16:17:15,899 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:17:15,899 - mmseg - INFO - Iter [25000/160000] lr: 7.500e-05, eta: 12:16:21, time: 0.267, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.2000, loss: 0.1925 +2023-03-04 16:17:29,160 - mmseg - INFO - Iter [25050/160000] lr: 7.500e-05, eta: 12:15:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2523, loss: 0.1909 +2023-03-04 16:17:42,395 - mmseg - INFO - Iter [25100/160000] lr: 7.500e-05, eta: 12:15:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1970, decode.acc_seg: 92.1117, loss: 0.1970 +2023-03-04 16:17:55,715 - mmseg - INFO - Iter [25150/160000] lr: 7.500e-05, eta: 12:14:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.2794, loss: 0.1921 +2023-03-04 16:18:09,050 - mmseg - INFO - Iter [25200/160000] lr: 7.500e-05, eta: 12:14:10, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2038, decode.acc_seg: 91.9199, loss: 0.2038 +2023-03-04 16:18:24,744 - mmseg - INFO - Iter [25250/160000] lr: 7.500e-05, eta: 12:13:50, time: 0.314, data_time: 0.055, memory: 67559, decode.loss_ce: 0.2044, decode.acc_seg: 91.8098, loss: 0.2044 +2023-03-04 16:18:38,075 - mmseg - INFO - Iter [25300/160000] lr: 7.500e-05, eta: 12:13:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.3566, loss: 0.1922 +2023-03-04 16:18:51,342 - mmseg - INFO - Iter [25350/160000] lr: 7.500e-05, eta: 12:12:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.1831, loss: 0.1938 +2023-03-04 16:19:04,670 - mmseg - INFO - Iter [25400/160000] lr: 7.500e-05, eta: 12:12:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.1536, loss: 0.1915 +2023-03-04 16:19:17,954 - mmseg - INFO - Iter [25450/160000] lr: 7.500e-05, eta: 12:11:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2017, decode.acc_seg: 92.0309, loss: 0.2017 +2023-03-04 16:19:31,286 - mmseg - INFO - Iter [25500/160000] lr: 7.500e-05, eta: 12:11:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1929, decode.acc_seg: 92.1291, loss: 0.1929 +2023-03-04 16:19:44,630 - mmseg - INFO - Iter [25550/160000] lr: 7.500e-05, eta: 12:10:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.1420, loss: 0.1943 +2023-03-04 16:19:57,831 - mmseg - INFO - Iter [25600/160000] lr: 7.500e-05, eta: 12:10:04, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1962, decode.acc_seg: 92.1552, loss: 0.1962 +2023-03-04 16:20:11,141 - mmseg - INFO - Iter [25650/160000] lr: 7.500e-05, eta: 12:09:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1960, decode.acc_seg: 91.9674, loss: 0.1960 +2023-03-04 16:20:24,420 - mmseg - INFO - Iter [25700/160000] lr: 7.500e-05, eta: 12:09:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2066, decode.acc_seg: 91.7882, loss: 0.2066 +2023-03-04 16:20:37,702 - mmseg - INFO - Iter [25750/160000] lr: 7.500e-05, eta: 12:08:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1997, decode.acc_seg: 92.0055, loss: 0.1997 +2023-03-04 16:20:51,066 - mmseg - INFO - Iter [25800/160000] lr: 7.500e-05, eta: 12:07:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.1940, loss: 0.1938 +2023-03-04 16:21:04,364 - mmseg - INFO - Iter [25850/160000] lr: 7.500e-05, eta: 12:07:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.1468, loss: 0.1946 +2023-03-04 16:21:20,309 - mmseg - INFO - Iter [25900/160000] lr: 7.500e-05, eta: 12:07:07, time: 0.319, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.2548, loss: 0.1921 +2023-03-04 16:21:33,574 - mmseg - INFO - Iter [25950/160000] lr: 7.500e-05, eta: 12:06:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.4889, loss: 0.1901 +2023-03-04 16:21:46,892 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:21:46,892 - mmseg - INFO - Iter [26000/160000] lr: 7.500e-05, eta: 12:06:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.3323, loss: 0.1890 +2023-03-04 16:22:00,172 - mmseg - INFO - Iter [26050/160000] lr: 7.500e-05, eta: 12:05:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.2669, loss: 0.1947 +2023-03-04 16:22:13,441 - mmseg - INFO - Iter [26100/160000] lr: 7.500e-05, eta: 12:05:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1897, decode.acc_seg: 92.3777, loss: 0.1897 +2023-03-04 16:22:26,823 - mmseg - INFO - Iter [26150/160000] lr: 7.500e-05, eta: 12:04:30, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2047, decode.acc_seg: 91.8299, loss: 0.2047 +2023-03-04 16:22:40,039 - mmseg - INFO - Iter [26200/160000] lr: 7.500e-05, eta: 12:03:58, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1966, decode.acc_seg: 91.9978, loss: 0.1966 +2023-03-04 16:22:53,312 - mmseg - INFO - Iter [26250/160000] lr: 7.500e-05, eta: 12:03:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1899, decode.acc_seg: 92.3971, loss: 0.1899 +2023-03-04 16:23:06,741 - mmseg - INFO - Iter [26300/160000] lr: 7.500e-05, eta: 12:02:56, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2001, decode.acc_seg: 91.9605, loss: 0.2001 +2023-03-04 16:23:20,078 - mmseg - INFO - Iter [26350/160000] lr: 7.500e-05, eta: 12:02:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1996, decode.acc_seg: 91.9142, loss: 0.1996 +2023-03-04 16:23:33,445 - mmseg - INFO - Iter [26400/160000] lr: 7.500e-05, eta: 12:01:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1961, decode.acc_seg: 92.0666, loss: 0.1961 +2023-03-04 16:23:46,634 - mmseg - INFO - Iter [26450/160000] lr: 7.500e-05, eta: 12:01:23, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1984, decode.acc_seg: 91.9351, loss: 0.1984 +2023-03-04 16:24:00,050 - mmseg - INFO - Iter [26500/160000] lr: 7.500e-05, eta: 12:00:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1979, decode.acc_seg: 91.9619, loss: 0.1979 +2023-03-04 16:24:16,007 - mmseg - INFO - Iter [26550/160000] lr: 7.500e-05, eta: 12:00:36, time: 0.319, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1942, decode.acc_seg: 92.0975, loss: 0.1942 +2023-03-04 16:24:29,236 - mmseg - INFO - Iter [26600/160000] lr: 7.500e-05, eta: 12:00:05, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1964, decode.acc_seg: 92.0287, loss: 0.1964 +2023-03-04 16:24:42,669 - mmseg - INFO - Iter [26650/160000] lr: 7.500e-05, eta: 11:59:35, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1924, decode.acc_seg: 92.2595, loss: 0.1924 +2023-03-04 16:24:55,937 - mmseg - INFO - Iter [26700/160000] lr: 7.500e-05, eta: 11:59:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1941, decode.acc_seg: 92.2395, loss: 0.1941 +2023-03-04 16:25:09,174 - mmseg - INFO - Iter [26750/160000] lr: 7.500e-05, eta: 11:58:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1952, decode.acc_seg: 92.0656, loss: 0.1952 +2023-03-04 16:25:22,600 - mmseg - INFO - Iter [26800/160000] lr: 7.500e-05, eta: 11:58:03, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.2062, loss: 0.1932 +2023-03-04 16:25:35,989 - mmseg - INFO - Iter [26850/160000] lr: 7.500e-05, eta: 11:57:33, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2010, decode.acc_seg: 91.8980, loss: 0.2010 +2023-03-04 16:25:49,347 - mmseg - INFO - Iter [26900/160000] lr: 7.500e-05, eta: 11:57:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1939, decode.acc_seg: 92.0920, loss: 0.1939 +2023-03-04 16:26:02,693 - mmseg - INFO - Iter [26950/160000] lr: 7.500e-05, eta: 11:56:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1927, decode.acc_seg: 92.3755, loss: 0.1927 +2023-03-04 16:26:15,965 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:26:15,965 - mmseg - INFO - Iter [27000/160000] lr: 7.500e-05, eta: 11:56:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1980, decode.acc_seg: 91.9802, loss: 0.1980 +2023-03-04 16:26:29,201 - mmseg - INFO - Iter [27050/160000] lr: 7.500e-05, eta: 11:55:32, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2001, decode.acc_seg: 91.9174, loss: 0.2001 +2023-03-04 16:26:42,811 - mmseg - INFO - Iter [27100/160000] lr: 7.500e-05, eta: 11:55:04, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1952, decode.acc_seg: 92.1839, loss: 0.1952 +2023-03-04 16:26:58,768 - mmseg - INFO - Iter [27150/160000] lr: 7.500e-05, eta: 11:54:47, time: 0.319, data_time: 0.056, memory: 67559, decode.loss_ce: 0.2018, decode.acc_seg: 92.0637, loss: 0.2018 +2023-03-04 16:27:12,216 - mmseg - INFO - Iter [27200/160000] lr: 7.500e-05, eta: 11:54:17, time: 0.269, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1941, decode.acc_seg: 92.0889, loss: 0.1941 +2023-03-04 16:27:25,532 - mmseg - INFO - Iter [27250/160000] lr: 7.500e-05, eta: 11:53:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.2429, loss: 0.1946 +2023-03-04 16:27:38,899 - mmseg - INFO - Iter [27300/160000] lr: 7.500e-05, eta: 11:53:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1999, decode.acc_seg: 91.8892, loss: 0.1999 +2023-03-04 16:27:52,315 - mmseg - INFO - Iter [27350/160000] lr: 7.500e-05, eta: 11:52:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1933, decode.acc_seg: 92.1763, loss: 0.1933 +2023-03-04 16:28:05,702 - mmseg - INFO - Iter [27400/160000] lr: 7.500e-05, eta: 11:52:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.1698, loss: 0.1932 +2023-03-04 16:28:19,011 - mmseg - INFO - Iter [27450/160000] lr: 7.500e-05, eta: 11:51:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1960, decode.acc_seg: 92.1901, loss: 0.1960 +2023-03-04 16:28:32,294 - mmseg - INFO - Iter [27500/160000] lr: 7.500e-05, eta: 11:51:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.3056, loss: 0.1900 +2023-03-04 16:28:45,581 - mmseg - INFO - Iter [27550/160000] lr: 7.500e-05, eta: 11:50:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1992, decode.acc_seg: 91.9583, loss: 0.1992 +2023-03-04 16:28:58,936 - mmseg - INFO - Iter [27600/160000] lr: 7.500e-05, eta: 11:50:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.2084, loss: 0.1958 +2023-03-04 16:29:12,270 - mmseg - INFO - Iter [27650/160000] lr: 7.500e-05, eta: 11:49:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.3069, loss: 0.1955 +2023-03-04 16:29:25,598 - mmseg - INFO - Iter [27700/160000] lr: 7.500e-05, eta: 11:49:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1988, decode.acc_seg: 91.9731, loss: 0.1988 +2023-03-04 16:29:39,040 - mmseg - INFO - Iter [27750/160000] lr: 7.500e-05, eta: 11:48:54, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.2575, loss: 0.1912 +2023-03-04 16:29:54,918 - mmseg - INFO - Iter [27800/160000] lr: 7.500e-05, eta: 11:48:37, time: 0.318, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1953, decode.acc_seg: 92.2020, loss: 0.1953 +2023-03-04 16:30:08,288 - mmseg - INFO - Iter [27850/160000] lr: 7.500e-05, eta: 11:48:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.3597, loss: 0.1911 +2023-03-04 16:30:21,625 - mmseg - INFO - Iter [27900/160000] lr: 7.500e-05, eta: 11:47:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1934, decode.acc_seg: 92.1006, loss: 0.1934 +2023-03-04 16:30:34,924 - mmseg - INFO - Iter [27950/160000] lr: 7.500e-05, eta: 11:47:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.2403, loss: 0.1915 +2023-03-04 16:30:48,111 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:30:48,112 - mmseg - INFO - Iter [28000/160000] lr: 7.500e-05, eta: 11:46:40, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.2068, loss: 0.1951 +2023-03-04 16:31:01,428 - mmseg - INFO - Iter [28050/160000] lr: 7.500e-05, eta: 11:46:11, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1897, decode.acc_seg: 92.2668, loss: 0.1897 +2023-03-04 16:31:14,712 - mmseg - INFO - Iter [28100/160000] lr: 7.500e-05, eta: 11:45:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3165, loss: 0.1901 +2023-03-04 16:31:28,046 - mmseg - INFO - Iter [28150/160000] lr: 7.500e-05, eta: 11:45:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.0537, loss: 0.1958 +2023-03-04 16:31:41,556 - mmseg - INFO - Iter [28200/160000] lr: 7.500e-05, eta: 11:44:45, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1923, decode.acc_seg: 92.3454, loss: 0.1923 +2023-03-04 16:31:54,786 - mmseg - INFO - Iter [28250/160000] lr: 7.500e-05, eta: 11:44:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1918, decode.acc_seg: 92.4604, loss: 0.1918 +2023-03-04 16:32:08,164 - mmseg - INFO - Iter [28300/160000] lr: 7.500e-05, eta: 11:43:47, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2007, decode.acc_seg: 91.9552, loss: 0.2007 +2023-03-04 16:32:21,461 - mmseg - INFO - Iter [28350/160000] lr: 7.500e-05, eta: 11:43:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1986, decode.acc_seg: 92.0719, loss: 0.1986 +2023-03-04 16:32:37,299 - mmseg - INFO - Iter [28400/160000] lr: 7.500e-05, eta: 11:43:02, time: 0.317, data_time: 0.053, memory: 67559, decode.loss_ce: 0.2015, decode.acc_seg: 92.0116, loss: 0.2015 +2023-03-04 16:32:50,674 - mmseg - INFO - Iter [28450/160000] lr: 7.500e-05, eta: 11:42:34, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1963, decode.acc_seg: 92.1137, loss: 0.1963 +2023-03-04 16:33:04,027 - mmseg - INFO - Iter [28500/160000] lr: 7.500e-05, eta: 11:42:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1897, decode.acc_seg: 92.3767, loss: 0.1897 +2023-03-04 16:33:17,436 - mmseg - INFO - Iter [28550/160000] lr: 7.500e-05, eta: 11:41:37, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.3760, loss: 0.1873 +2023-03-04 16:33:30,786 - mmseg - INFO - Iter [28600/160000] lr: 7.500e-05, eta: 11:41:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1930, decode.acc_seg: 92.2044, loss: 0.1930 +2023-03-04 16:33:44,114 - mmseg - INFO - Iter [28650/160000] lr: 7.500e-05, eta: 11:40:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1952, decode.acc_seg: 92.2769, loss: 0.1952 +2023-03-04 16:33:57,384 - mmseg - INFO - Iter [28700/160000] lr: 7.500e-05, eta: 11:40:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.2042, loss: 0.1948 +2023-03-04 16:34:10,649 - mmseg - INFO - Iter [28750/160000] lr: 7.500e-05, eta: 11:39:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1969, decode.acc_seg: 92.1277, loss: 0.1969 +2023-03-04 16:34:23,863 - mmseg - INFO - Iter [28800/160000] lr: 7.500e-05, eta: 11:39:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1953, decode.acc_seg: 92.1110, loss: 0.1953 +2023-03-04 16:34:37,043 - mmseg - INFO - Iter [28850/160000] lr: 7.500e-05, eta: 11:38:46, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1973, decode.acc_seg: 91.9602, loss: 0.1973 +2023-03-04 16:34:50,391 - mmseg - INFO - Iter [28900/160000] lr: 7.500e-05, eta: 11:38:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.3915, loss: 0.1914 +2023-03-04 16:35:03,747 - mmseg - INFO - Iter [28950/160000] lr: 7.500e-05, eta: 11:37:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2012, decode.acc_seg: 91.9934, loss: 0.2012 +2023-03-04 16:35:17,051 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:35:17,051 - mmseg - INFO - Iter [29000/160000] lr: 7.500e-05, eta: 11:37:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.1173, loss: 0.1958 +2023-03-04 16:35:32,978 - mmseg - INFO - Iter [29050/160000] lr: 7.500e-05, eta: 11:37:06, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4148, loss: 0.1866 +2023-03-04 16:35:46,308 - mmseg - INFO - Iter [29100/160000] lr: 7.500e-05, eta: 11:36:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2017, decode.acc_seg: 91.8236, loss: 0.2017 +2023-03-04 16:35:59,810 - mmseg - INFO - Iter [29150/160000] lr: 7.500e-05, eta: 11:36:11, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.2418, loss: 0.1921 +2023-03-04 16:36:13,177 - mmseg - INFO - Iter [29200/160000] lr: 7.500e-05, eta: 11:35:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1941, decode.acc_seg: 92.1023, loss: 0.1941 +2023-03-04 16:36:26,473 - mmseg - INFO - Iter [29250/160000] lr: 7.500e-05, eta: 11:35:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1966, decode.acc_seg: 92.0842, loss: 0.1966 +2023-03-04 16:36:39,716 - mmseg - INFO - Iter [29300/160000] lr: 7.500e-05, eta: 11:34:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1954, decode.acc_seg: 92.1461, loss: 0.1954 +2023-03-04 16:36:53,019 - mmseg - INFO - Iter [29350/160000] lr: 7.500e-05, eta: 11:34:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1979, decode.acc_seg: 91.9441, loss: 0.1979 +2023-03-04 16:37:06,371 - mmseg - INFO - Iter [29400/160000] lr: 7.500e-05, eta: 11:33:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.4508, loss: 0.1886 +2023-03-04 16:37:19,680 - mmseg - INFO - Iter [29450/160000] lr: 7.500e-05, eta: 11:33:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.4489, loss: 0.1905 +2023-03-04 16:37:32,913 - mmseg - INFO - Iter [29500/160000] lr: 7.500e-05, eta: 11:32:57, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1942, decode.acc_seg: 92.2011, loss: 0.1942 +2023-03-04 16:37:46,294 - mmseg - INFO - Iter [29550/160000] lr: 7.500e-05, eta: 11:32:30, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4798, loss: 0.1872 +2023-03-04 16:37:59,664 - mmseg - INFO - Iter [29600/160000] lr: 7.500e-05, eta: 11:32:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1971, decode.acc_seg: 92.0899, loss: 0.1971 +2023-03-04 16:38:12,987 - mmseg - INFO - Iter [29650/160000] lr: 7.500e-05, eta: 11:31:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2003, decode.acc_seg: 92.0048, loss: 0.2003 +2023-03-04 16:38:28,828 - mmseg - INFO - Iter [29700/160000] lr: 7.500e-05, eta: 11:31:19, time: 0.317, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1971, decode.acc_seg: 92.1102, loss: 0.1971 +2023-03-04 16:38:42,157 - mmseg - INFO - Iter [29750/160000] lr: 7.500e-05, eta: 11:30:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.3674, loss: 0.1892 +2023-03-04 16:38:55,581 - mmseg - INFO - Iter [29800/160000] lr: 7.500e-05, eta: 11:30:25, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1891, decode.acc_seg: 92.3826, loss: 0.1891 +2023-03-04 16:39:08,894 - mmseg - INFO - Iter [29850/160000] lr: 7.500e-05, eta: 11:29:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2027, decode.acc_seg: 91.9231, loss: 0.2027 +2023-03-04 16:39:22,110 - mmseg - INFO - Iter [29900/160000] lr: 7.500e-05, eta: 11:29:30, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.6074, loss: 0.1839 +2023-03-04 16:39:35,407 - mmseg - INFO - Iter [29950/160000] lr: 7.500e-05, eta: 11:29:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2029, decode.acc_seg: 91.9648, loss: 0.2029 +2023-03-04 16:39:48,713 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:39:48,713 - mmseg - INFO - Iter [30000/160000] lr: 7.500e-05, eta: 11:28:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2050, decode.acc_seg: 91.6548, loss: 0.2050 +2023-03-04 16:40:02,051 - mmseg - INFO - Iter [30050/160000] lr: 7.500e-05, eta: 11:28:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3289, loss: 0.1887 +2023-03-04 16:40:15,389 - mmseg - INFO - Iter [30100/160000] lr: 7.500e-05, eta: 11:27:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1956, decode.acc_seg: 92.2550, loss: 0.1956 +2023-03-04 16:40:28,636 - mmseg - INFO - Iter [30150/160000] lr: 7.500e-05, eta: 11:27:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1961, decode.acc_seg: 92.1296, loss: 0.1961 +2023-03-04 16:40:41,953 - mmseg - INFO - Iter [30200/160000] lr: 7.500e-05, eta: 11:26:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1985, decode.acc_seg: 92.0323, loss: 0.1985 +2023-03-04 16:40:55,202 - mmseg - INFO - Iter [30250/160000] lr: 7.500e-05, eta: 11:26:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.5539, loss: 0.1869 +2023-03-04 16:41:10,910 - mmseg - INFO - Iter [30300/160000] lr: 7.500e-05, eta: 11:26:04, time: 0.314, data_time: 0.054, memory: 67559, decode.loss_ce: 0.2000, decode.acc_seg: 91.9306, loss: 0.2000 +2023-03-04 16:41:24,178 - mmseg - INFO - Iter [30350/160000] lr: 7.500e-05, eta: 11:25:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.5488, loss: 0.1863 +2023-03-04 16:41:37,573 - mmseg - INFO - Iter [30400/160000] lr: 7.500e-05, eta: 11:25:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2031, decode.acc_seg: 91.8808, loss: 0.2031 +2023-03-04 16:41:50,843 - mmseg - INFO - Iter [30450/160000] lr: 7.500e-05, eta: 11:24:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2039, decode.acc_seg: 91.9257, loss: 0.2039 +2023-03-04 16:42:04,092 - mmseg - INFO - Iter [30500/160000] lr: 7.500e-05, eta: 11:24:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.2631, loss: 0.1920 +2023-03-04 16:42:17,301 - mmseg - INFO - Iter [30550/160000] lr: 7.500e-05, eta: 11:23:50, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3737, loss: 0.1908 +2023-03-04 16:42:30,589 - mmseg - INFO - Iter [30600/160000] lr: 7.500e-05, eta: 11:23:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1977, decode.acc_seg: 92.1189, loss: 0.1977 +2023-03-04 16:42:43,970 - mmseg - INFO - Iter [30650/160000] lr: 7.500e-05, eta: 11:22:57, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2004, decode.acc_seg: 91.9460, loss: 0.2004 +2023-03-04 16:42:57,410 - mmseg - INFO - Iter [30700/160000] lr: 7.500e-05, eta: 11:22:31, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.3695, loss: 0.1900 +2023-03-04 16:43:10,744 - mmseg - INFO - Iter [30750/160000] lr: 7.500e-05, eta: 11:22:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1965, decode.acc_seg: 92.3290, loss: 0.1965 +2023-03-04 16:43:24,103 - mmseg - INFO - Iter [30800/160000] lr: 7.500e-05, eta: 11:21:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.3432, loss: 0.1872 +2023-03-04 16:43:37,291 - mmseg - INFO - Iter [30850/160000] lr: 7.500e-05, eta: 11:21:12, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.1428, loss: 0.1936 +2023-03-04 16:43:50,676 - mmseg - INFO - Iter [30900/160000] lr: 7.500e-05, eta: 11:20:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1985, decode.acc_seg: 91.9779, loss: 0.1985 +2023-03-04 16:44:06,483 - mmseg - INFO - Iter [30950/160000] lr: 7.500e-05, eta: 11:20:30, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.2085, loss: 0.1910 +2023-03-04 16:44:19,824 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:44:19,824 - mmseg - INFO - Iter [31000/160000] lr: 7.500e-05, eta: 11:20:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1953, decode.acc_seg: 92.1107, loss: 0.1953 +2023-03-04 16:44:33,179 - mmseg - INFO - Iter [31050/160000] lr: 7.500e-05, eta: 11:19:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1898, decode.acc_seg: 92.2779, loss: 0.1898 +2023-03-04 16:44:46,412 - mmseg - INFO - Iter [31100/160000] lr: 7.500e-05, eta: 11:19:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.3326, loss: 0.1911 +2023-03-04 16:44:59,728 - mmseg - INFO - Iter [31150/160000] lr: 7.500e-05, eta: 11:18:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.3869, loss: 0.1905 +2023-03-04 16:45:12,950 - mmseg - INFO - Iter [31200/160000] lr: 7.500e-05, eta: 11:18:19, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.2887, loss: 0.1922 +2023-03-04 16:45:26,320 - mmseg - INFO - Iter [31250/160000] lr: 7.500e-05, eta: 11:17:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.2898, loss: 0.1943 +2023-03-04 16:45:39,613 - mmseg - INFO - Iter [31300/160000] lr: 7.500e-05, eta: 11:17:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1976, decode.acc_seg: 92.1920, loss: 0.1976 +2023-03-04 16:45:52,898 - mmseg - INFO - Iter [31350/160000] lr: 7.500e-05, eta: 11:17:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1964, decode.acc_seg: 92.0535, loss: 0.1964 +2023-03-04 16:46:06,204 - mmseg - INFO - Iter [31400/160000] lr: 7.500e-05, eta: 11:16:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.3135, loss: 0.1919 +2023-03-04 16:46:19,542 - mmseg - INFO - Iter [31450/160000] lr: 7.500e-05, eta: 11:16:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1994, decode.acc_seg: 92.0032, loss: 0.1994 +2023-03-04 16:46:32,779 - mmseg - INFO - Iter [31500/160000] lr: 7.500e-05, eta: 11:15:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1976, decode.acc_seg: 92.1199, loss: 0.1976 +2023-03-04 16:46:45,940 - mmseg - INFO - Iter [31550/160000] lr: 7.500e-05, eta: 11:15:16, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2001, decode.acc_seg: 91.9566, loss: 0.2001 +2023-03-04 16:47:01,802 - mmseg - INFO - Iter [31600/160000] lr: 7.500e-05, eta: 11:15:01, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.3743, loss: 0.1886 +2023-03-04 16:47:15,212 - mmseg - INFO - Iter [31650/160000] lr: 7.500e-05, eta: 11:14:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1982, decode.acc_seg: 91.9907, loss: 0.1982 +2023-03-04 16:47:28,442 - mmseg - INFO - Iter [31700/160000] lr: 7.500e-05, eta: 11:14:10, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.3486, loss: 0.1909 +2023-03-04 16:47:41,770 - mmseg - INFO - Iter [31750/160000] lr: 7.500e-05, eta: 11:13:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.2587, loss: 0.1903 +2023-03-04 16:47:55,019 - mmseg - INFO - Iter [31800/160000] lr: 7.500e-05, eta: 11:13:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1986, decode.acc_seg: 92.0057, loss: 0.1986 +2023-03-04 16:48:08,335 - mmseg - INFO - Iter [31850/160000] lr: 7.500e-05, eta: 11:12:53, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.0040, loss: 0.1958 +2023-03-04 16:48:21,650 - mmseg - INFO - Iter [31900/160000] lr: 7.500e-05, eta: 11:12:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1961, decode.acc_seg: 92.1971, loss: 0.1961 +2023-03-04 16:48:34,916 - mmseg - INFO - Iter [31950/160000] lr: 7.500e-05, eta: 11:12:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1956, decode.acc_seg: 92.0969, loss: 0.1956 +2023-03-04 16:48:48,221 - mmseg - INFO - Swap parameters (after train) after iter [32000] +2023-03-04 16:48:48,243 - mmseg - INFO - Saving checkpoint at 32000 iterations +2023-03-04 16:48:50,004 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:48:50,004 - mmseg - INFO - Iter [32000/160000] lr: 7.500e-05, eta: 11:11:43, time: 0.302, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.0973, loss: 0.1968 +2023-03-04 16:59:50,128 - mmseg - INFO - per class results: +2023-03-04 16:59:50,137 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.22,76.22,76.22,76.22,76.21,76.22,76.21,76.22,76.21,76.22,76.2 | +| building | 81.32,81.32,81.32,81.31,81.32,81.31,81.3,81.31,81.31,81.3,81.3 | +| sky | 94.26,94.26,94.26,94.26,94.25,94.25,94.26,94.24,94.24,94.24,94.24 | +| floor | 80.1,80.1,80.09,80.08,80.09,80.06,80.06,80.05,80.04,80.05,80.04 | +| tree | 72.83,72.83,72.81,72.8,72.8,72.78,72.78,72.77,72.76,72.74,72.71 | +| ceiling | 82.53,82.51,82.51,82.52,82.51,82.51,82.48,82.49,82.48,82.47,82.5 | +| road | 82.22,82.23,82.24,82.25,82.23,82.24,82.21,82.22,82.22,82.23,82.23 | +| bed | 88.45,88.45,88.46,88.45,88.47,88.45,88.45,88.44,88.44,88.43,88.46 | +| windowpane | 61.03,61.03,61.03,61.01,61.04,61.02,61.04,61.03,61.03,61.02,61.05 | +| grass | 66.22,66.2,66.2,66.2,66.22,66.22,66.21,66.21,66.2,66.21,66.16 | +| cabinet | 59.63,59.59,59.57,59.57,59.55,59.54,59.53,59.53,59.49,59.47,59.45 | +| sidewalk | 66.06,66.06,66.09,66.11,66.1,66.09,66.04,66.1,66.1,66.09,66.09 | +| person | 79.23,79.25,79.26,79.26,79.26,79.24,79.25,79.25,79.23,79.22,79.26 | +| earth | 33.6,33.55,33.55,33.56,33.53,33.49,33.55,33.5,33.51,33.48,33.57 | +| door | 47.99,48.06,48.06,48.04,48.04,48.04,48.07,48.05,48.01,48.01,47.97 | +| table | 61.42,61.41,61.4,61.42,61.39,61.43,61.4,61.41,61.37,61.38,61.38 | +| mountain | 51.81,51.82,51.79,51.83,51.83,51.85,51.88,51.83,51.85,51.86,51.83 | +| plant | 50.39,50.37,50.36,50.33,50.31,50.29,50.28,50.27,50.25,50.21,50.22 | +| curtain | 70.51,70.55,70.61,70.67,70.72,70.73,70.79,70.78,70.77,70.78,70.63 | +| chair | 58.07,58.07,58.06,58.04,58.04,58.05,58.02,58.01,58.0,57.98,58.01 | +| car | 83.19,83.2,83.19,83.2,83.21,83.18,83.21,83.18,83.19,83.19,83.16 | +| water | 46.79,46.77,46.77,46.76,46.75,46.75,46.75,46.71,46.7,46.71,46.66 | +| painting | 69.47,69.44,69.43,69.48,69.47,69.46,69.43,69.41,69.39,69.38,69.45 | +| sofa | 65.83,65.8,65.8,65.79,65.8,65.84,65.81,65.81,65.82,65.81,65.81 | +| shelf | 40.29,40.29,40.27,40.24,40.27,40.21,40.22,40.12,40.12,40.07,40.07 | +| house | 43.56,43.58,43.6,43.56,43.58,43.54,43.56,43.61,43.6,43.54,43.66 | +| sea | 44.85,44.83,44.79,44.76,44.71,44.7,44.66,44.61,44.56,44.54,44.5 | +| mirror | 65.22,65.18,65.22,65.26,65.2,65.19,65.23,65.22,65.21,65.21,65.15 | +| rug | 56.08,56.06,55.96,55.95,55.98,55.87,55.84,55.83,55.85,55.86,55.76 | +| field | 28.22,28.24,28.3,28.34,28.38,28.42,28.42,28.47,28.52,28.57,28.55 | +| armchair | 44.57,44.56,44.56,44.56,44.57,44.59,44.57,44.58,44.57,44.6,44.67 | +| seat | 54.7,54.58,54.58,54.47,54.43,54.4,54.29,54.25,54.25,54.11,54.07 | +| fence | 40.91,40.94,40.91,40.84,40.9,40.89,40.87,40.87,40.91,40.94,40.95 | +| desk | 49.32,49.26,49.31,49.23,49.24,49.22,49.22,49.17,49.15,49.15,49.06 | +| rock | 30.12,30.17,30.05,30.1,30.15,30.15,30.15,30.12,30.11,30.11,30.13 | +| wardrobe | 49.27,49.27,49.27,49.18,49.22,49.21,49.15,49.23,49.15,49.17,49.47 | +| lamp | 63.53,63.53,63.51,63.51,63.52,63.5,63.52,63.51,63.49,63.47,63.53 | +| bathtub | 76.06,76.01,76.07,76.0,76.02,76.08,76.08,75.96,75.98,75.99,75.66 | +| railing | 31.58,31.49,31.44,31.39,31.33,31.23,31.12,31.1,31.01,30.94,31.04 | +| cushion | 55.02,55.04,55.07,55.02,55.06,55.04,55.09,55.12,55.08,55.13,55.01 | +| base | 28.41,28.51,28.51,28.57,28.59,28.51,28.61,28.67,28.59,28.71,28.68 | +| box | 24.13,24.13,24.15,24.18,24.16,24.13,24.2,24.17,24.16,24.09,24.31 | +| column | 46.13,46.19,46.2,46.2,46.21,46.19,46.26,46.34,46.42,46.42,46.16 | +| signboard | 36.11,36.07,36.04,36.11,36.09,36.06,36.1,36.05,36.1,36.1,35.99 | +| chest of drawers | 40.14,40.13,40.12,39.96,39.82,39.6,39.61,39.38,39.56,39.45,39.21 | +| counter | 27.79,27.83,27.86,27.88,27.87,27.88,27.81,27.75,27.8,27.88,27.55 | +| sand | 31.59,31.48,31.46,31.47,31.4,31.43,31.42,31.47,31.53,31.49,31.5 | +| sink | 70.55,70.49,70.54,70.49,70.5,70.43,70.49,70.46,70.43,70.42,70.26 | +| skyscraper | 47.8,47.81,47.8,47.81,47.84,47.84,47.79,47.88,47.92,47.9,47.88 | +| fireplace | 65.63,65.64,65.66,65.71,65.63,65.64,65.79,65.7,65.72,65.75,65.88 | +| refrigerator | 76.68,76.65,76.68,76.7,76.71,76.72,76.86,76.84,76.86,76.8,76.88 | +| grandstand | 41.92,41.88,41.84,41.83,41.8,41.9,41.85,41.78,41.73,41.74,41.66 | +| path | 16.08,16.06,16.1,16.07,16.04,16.06,16.06,16.09,16.07,16.03,16.02 | +| stairs | 32.46,32.44,32.47,32.47,32.42,32.44,32.46,32.36,32.4,32.41,32.42 | +| runway | 63.84,63.85,63.84,63.84,63.85,63.84,63.84,63.85,63.85,63.83,63.84 | +| case | 48.2,48.13,48.22,48.17,48.12,48.18,48.11,48.09,48.05,48.02,48.01 | +| pool table | 92.61,92.63,92.61,92.62,92.61,92.62,92.6,92.61,92.64,92.62,92.57 | +| pillow | 56.69,56.69,56.82,56.75,56.85,56.75,56.75,56.82,56.84,56.83,56.78 | +| screen door | 67.38,67.34,67.22,67.2,67.24,67.04,67.09,67.02,66.92,66.85,66.66 | +| stairway | 25.49,25.47,25.49,25.51,25.46,25.42,25.44,25.47,25.41,25.42,25.26 | +| river | 9.45,9.46,9.43,9.43,9.4,9.4,9.41,9.45,9.45,9.43,9.25 | +| bridge | 52.51,52.83,53.05,53.12,53.29,53.56,53.54,53.6,53.63,53.79,53.84 | +| bookcase | 40.31,40.32,40.32,40.45,40.27,40.36,40.22,40.43,40.41,40.48,40.34 | +| blind | 46.86,46.9,46.72,46.57,46.57,46.29,46.25,46.14,46.1,45.95,46.15 | +| coffee table | 67.14,67.14,67.17,67.15,67.21,67.19,67.25,67.29,67.33,67.31,67.24 | +| toilet | 86.23,86.21,86.18,86.22,86.24,86.24,86.26,86.24,86.24,86.22,86.25 | +| flower | 31.47,31.49,31.56,31.61,31.63,31.58,31.57,31.51,31.48,31.42,31.81 | +| book | 46.2,46.23,46.24,46.3,46.22,46.38,46.28,46.29,46.29,46.33,46.46 | +| hill | 8.42,8.45,8.43,8.48,8.47,8.49,8.51,8.56,8.56,8.58,8.54 | +| bench | 44.12,44.12,44.1,44.13,44.11,44.05,44.16,44.13,44.1,44.1,44.06 | +| countertop | 52.72,52.6,52.62,52.54,52.69,52.58,52.79,52.71,52.62,52.63,52.59 | +| stove | 72.62,72.54,72.48,72.48,72.41,72.54,72.34,72.36,72.42,72.36,72.37 | +| palm | 50.47,50.41,50.45,50.41,50.45,50.42,50.42,50.5,50.51,50.43,50.36 | +| kitchen island | 47.0,46.95,46.98,47.11,47.18,47.31,47.17,47.19,47.21,47.17,47.74 | +| computer | 56.89,56.88,56.87,56.93,56.95,56.97,57.01,56.99,57.02,56.95,57.12 | +| swivel chair | 44.7,44.65,44.7,44.69,44.64,44.55,44.52,44.54,44.58,44.55,44.33 | +| boat | 39.09,39.14,39.05,39.0,39.06,39.05,39.03,39.02,38.95,38.95,38.99 | +| bar | 27.04,27.1,27.08,27.02,26.99,26.93,26.84,26.85,26.77,26.82,26.89 | +| arcade machine | 27.18,27.35,27.29,27.35,27.39,27.39,27.66,27.71,27.88,28.13,27.36 | +| hovel | 31.86,31.81,31.73,31.74,31.56,31.55,31.49,31.45,31.35,31.33,31.36 | +| bus | 88.29,88.31,88.26,88.29,88.34,88.32,88.32,88.3,88.28,88.24,88.27 | +| towel | 59.88,59.78,59.87,59.97,59.93,59.83,59.64,59.73,59.86,59.79,60.07 | +| light | 55.83,55.81,55.73,55.84,55.84,55.78,55.73,55.71,55.78,55.73,55.68 | +| truck | 34.31,34.31,34.35,34.33,34.54,34.45,34.48,34.56,34.54,34.56,34.56 | +| tower | 23.44,23.56,23.71,23.71,23.89,23.53,23.82,23.88,23.79,24.11,24.13 | +| chandelier | 66.61,66.57,66.58,66.59,66.59,66.57,66.57,66.59,66.57,66.57,66.56 | +| awning | 23.07,23.18,23.2,23.26,23.34,23.47,23.48,23.58,23.63,23.75,23.79 | +| streetlight | 28.34,28.3,28.32,28.31,28.2,28.23,28.16,28.09,28.11,28.08,28.02 | +| booth | 51.44,51.55,51.43,51.47,51.5,51.51,51.55,51.3,51.25,51.2,51.95 | +| television receiver | 69.28,69.39,69.43,69.31,69.45,69.41,69.42,69.44,69.51,69.54,69.52 | +| airplane | 51.74,51.8,51.65,51.64,51.62,51.49,51.48,51.43,51.36,51.37,51.13 | +| dirt track | 9.85,9.79,9.84,9.86,9.89,9.85,9.83,9.85,9.88,9.78,9.78 | +| apparel | 29.25,29.16,29.27,29.34,29.11,29.2,28.99,29.02,28.91,28.87,29.82 | +| pole | 24.59,24.59,24.64,24.6,24.56,24.55,24.55,24.57,24.58,24.6,24.48 | +| land | 10.06,10.09,10.16,10.21,10.2,10.21,10.31,10.34,10.39,10.56,10.44 | +| bannister | 5.86,5.83,5.82,5.82,5.85,5.74,5.82,5.71,5.64,5.62,6.17 | +| escalator | 22.44,22.48,22.61,22.53,22.65,22.67,22.77,22.86,22.87,22.97,22.88 | +| ottoman | 48.41,48.48,48.43,48.42,48.5,48.44,48.41,48.35,48.44,48.47,48.4 | +| bottle | 16.0,15.89,16.0,15.89,15.94,15.88,15.88,15.72,15.61,15.53,15.84 | +| buffet | 50.2,50.54,50.53,50.78,50.68,50.62,50.9,50.77,50.97,51.0,50.9 | +| poster | 26.88,26.94,26.95,26.96,26.98,26.95,26.86,26.86,26.85,26.78,26.99 | +| stage | 18.05,18.04,18.1,18.13,18.14,18.13,18.22,18.22,18.22,18.29,18.29 | +| van | 47.45,47.57,47.52,47.61,47.78,47.71,47.87,47.78,47.88,47.86,48.0 | +| ship | 37.02,37.52,38.15,37.99,37.77,37.74,38.14,38.22,38.16,38.43,37.91 | +| fountain | 5.97,5.97,5.9,5.97,5.96,5.89,5.87,5.77,5.77,5.83,5.63 | +| conveyer belt | 75.61,75.69,75.69,75.78,75.71,75.64,75.75,75.74,75.75,75.7,75.7 | +| canopy | 15.55,15.6,15.63,15.68,15.68,15.69,15.65,15.66,15.62,15.75,15.72 | +| washer | 66.1,66.13,66.16,66.16,66.19,66.11,66.17,66.19,66.18,66.26,66.44 | +| plaything | 22.85,22.88,22.82,22.87,22.79,22.71,22.68,22.66,22.56,22.4,23.23 | +| swimming pool | 43.7,43.8,43.82,43.92,44.05,43.99,44.02,44.02,44.13,44.22,44.68 | +| stool | 41.51,41.33,41.35,41.48,41.43,41.47,41.36,41.29,41.37,41.27,41.57 | +| barrel | 41.62,41.37,40.85,40.88,40.9,40.41,40.54,40.41,40.16,41.3,39.84 | +| basket | 28.19,28.2,28.2,28.17,28.16,28.15,28.16,28.23,28.15,28.13,28.2 | +| waterfall | 58.58,58.61,58.81,58.8,58.87,59.41,59.53,59.7,59.58,59.71,59.68 | +| tent | 94.32,94.28,94.26,94.3,94.25,94.26,94.27,94.28,94.25,94.26,94.24 | +| bag | 12.03,12.11,12.2,12.21,12.31,12.31,12.31,12.39,12.45,12.46,12.39 | +| minibike | 62.4,62.38,62.27,62.23,62.23,62.19,62.02,62.0,61.98,61.92,62.0 | +| cradle | 80.34,80.35,80.39,80.37,80.39,80.37,80.41,80.35,80.36,80.39,80.37 | +| oven | 28.39,28.39,28.4,28.36,28.29,28.21,28.24,28.25,28.12,28.09,28.19 | +| ball | 46.81,46.85,46.81,46.77,46.8,46.8,46.85,46.81,46.66,46.68,46.71 | +| food | 52.68,52.5,52.4,52.35,52.16,52.09,51.94,51.81,51.64,51.48,51.7 | +| step | 13.88,13.96,13.94,13.99,14.05,14.1,14.18,14.25,14.27,14.37,14.12 | +| tank | 42.18,42.16,42.22,42.26,42.13,42.17,42.23,42.27,42.25,42.26,42.08 | +| trade name | 24.38,24.43,24.43,24.48,24.45,24.51,24.56,24.54,24.59,24.73,24.59 | +| microwave | 37.5,37.53,37.51,37.56,37.53,37.5,37.52,37.56,37.51,37.54,37.46 | +| pot | 40.92,40.92,40.95,41.02,41.0,40.94,40.99,40.95,41.09,41.03,41.0 | +| animal | 52.07,52.17,52.29,52.27,52.48,52.4,52.41,52.45,52.51,52.61,52.75 | +| bicycle | 44.74,44.85,45.04,45.07,45.09,45.25,45.24,45.17,45.19,45.34,45.33 | +| lake | 59.67,59.66,59.64,59.61,59.56,59.64,59.55,59.52,59.57,59.52,59.46 | +| dishwasher | 77.08,77.18,77.17,77.22,77.18,77.19,77.22,77.23,77.12,77.3,77.31 | +| screen | 67.5,67.42,67.37,67.44,67.24,67.26,67.14,67.01,67.0,67.0,67.25 | +| blanket | 12.83,12.88,12.96,12.92,12.89,12.93,12.92,12.91,12.88,12.89,13.1 | +| sculpture | 36.55,36.6,36.81,36.95,36.82,36.95,37.14,37.11,37.08,37.32,37.52 | +| hood | 55.93,55.79,55.76,55.81,55.75,55.69,55.7,55.59,55.59,55.6,55.84 | +| sconce | 41.42,41.43,41.29,41.37,41.18,40.94,41.06,40.96,41.02,40.95,40.73 | +| vase | 36.73,36.66,36.62,36.74,36.7,36.67,36.64,36.72,36.61,36.54,36.63 | +| traffic light | 29.49,29.51,29.46,29.59,29.59,29.68,29.72,29.78,29.72,29.83,29.86 | +| tray | 5.38,5.39,5.43,5.41,5.43,5.46,5.45,5.45,5.45,5.46,5.44 | +| ashcan | 38.59,38.81,38.86,38.79,38.81,38.92,38.91,39.05,39.12,39.13,39.05 | +| fan | 57.78,57.81,57.81,57.75,57.75,57.72,57.79,57.73,57.74,57.74,57.67 | +| pier | 11.85,11.87,11.68,11.76,11.64,11.75,11.85,11.79,11.77,11.81,11.62 | +| crt screen | 4.36,4.35,4.36,4.33,4.33,4.32,4.31,4.3,4.31,4.29,4.32 | +| plate | 38.8,38.88,38.87,38.87,38.98,38.95,39.01,39.01,38.99,39.1,39.03 | +| monitor | 22.7,22.6,22.47,22.36,22.32,22.39,22.39,22.11,22.28,22.25,22.15 | +| bulletin board | 46.57,46.51,46.57,46.43,46.62,46.27,46.33,46.3,46.28,46.34,46.47 | +| shower | 1.58,1.58,1.73,1.67,1.68,1.67,1.67,1.72,1.71,1.73,1.67 | +| radiator | 45.33,45.69,45.45,45.53,45.39,45.5,45.26,45.01,45.33,45.3,45.89 | +| glass | 11.65,11.7,11.68,11.79,11.77,11.76,11.82,11.83,11.85,11.87,11.83 | +| clock | 25.15,25.18,25.16,25.26,25.16,25.14,25.07,25.06,25.08,25.11,25.08 | +| flag | 38.18,38.26,38.41,38.4,38.42,38.39,38.55,38.52,38.52,38.59,38.8 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 16:59:50,137 - mmseg - INFO - Summary: +2023-03-04 16:59:50,137 - mmseg - INFO - ++-------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++-------------------------------------------------------------------+ +| 46.13,46.15,46.15,46.16,46.15,46.14,46.15,46.14,46.14,46.15,46.16 | ++-------------------------------------------------------------------+ +2023-03-04 16:59:50,198 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune/best_mIoU_iter_16000.pth was removed +2023-03-04 16:59:52,043 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth. +2023-03-04 16:59:52,046 - mmseg - INFO - Best mIoU is 0.4616 at 32000 iter. +2023-03-04 16:59:52,046 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 16:59:52,046 - mmseg - INFO - Iter(val) [250] mIoU: [0.4613, 0.4615, 0.4615, 0.4616, 0.4615, 0.4614, 0.4615, 0.4614, 0.4614, 0.4615, 0.4616], copy_paste: 46.13,46.15,46.15,46.16,46.15,46.14,46.15,46.14,46.14,46.15,46.16 +2023-03-04 16:59:52,052 - mmseg - INFO - Swap parameters (before train) before iter [32001] +2023-03-04 17:00:05,944 - mmseg - INFO - Iter [32050/160000] lr: 7.500e-05, eta: 11:55:23, time: 13.519, data_time: 13.249, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.3551, loss: 0.1917 +2023-03-04 17:00:19,655 - mmseg - INFO - Iter [32100/160000] lr: 7.500e-05, eta: 11:54:54, time: 0.274, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.2275, loss: 0.1915 +2023-03-04 17:00:33,002 - mmseg - INFO - Iter [32150/160000] lr: 7.500e-05, eta: 11:54:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4322, loss: 0.1873 +2023-03-04 17:00:48,868 - mmseg - INFO - Iter [32200/160000] lr: 7.500e-05, eta: 11:54:03, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1985, decode.acc_seg: 92.0126, loss: 0.1985 +2023-03-04 17:01:02,152 - mmseg - INFO - Iter [32250/160000] lr: 7.500e-05, eta: 11:53:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1975, decode.acc_seg: 91.9235, loss: 0.1975 +2023-03-04 17:01:15,402 - mmseg - INFO - Iter [32300/160000] lr: 7.500e-05, eta: 11:53:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.2735, loss: 0.1908 +2023-03-04 17:01:28,835 - mmseg - INFO - Iter [32350/160000] lr: 7.500e-05, eta: 11:52:32, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1998, decode.acc_seg: 92.0125, loss: 0.1998 +2023-03-04 17:01:42,174 - mmseg - INFO - Iter [32400/160000] lr: 7.500e-05, eta: 11:52:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1998, decode.acc_seg: 91.9016, loss: 0.1998 +2023-03-04 17:01:55,526 - mmseg - INFO - Iter [32450/160000] lr: 7.500e-05, eta: 11:51:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.2063, loss: 0.1937 +2023-03-04 17:02:08,920 - mmseg - INFO - Iter [32500/160000] lr: 7.500e-05, eta: 11:51:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1963, decode.acc_seg: 92.1744, loss: 0.1963 +2023-03-04 17:02:22,313 - mmseg - INFO - Iter [32550/160000] lr: 7.500e-05, eta: 11:50:32, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.2681, loss: 0.1901 +2023-03-04 17:02:35,562 - mmseg - INFO - Iter [32600/160000] lr: 7.500e-05, eta: 11:50:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1929, decode.acc_seg: 92.3861, loss: 0.1929 +2023-03-04 17:02:48,875 - mmseg - INFO - Iter [32650/160000] lr: 7.500e-05, eta: 11:49:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.4354, loss: 0.1840 +2023-03-04 17:03:02,264 - mmseg - INFO - Iter [32700/160000] lr: 7.500e-05, eta: 11:49:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.3592, loss: 0.1916 +2023-03-04 17:03:15,620 - mmseg - INFO - Iter [32750/160000] lr: 7.500e-05, eta: 11:48:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4126, loss: 0.1862 +2023-03-04 17:03:28,882 - mmseg - INFO - Iter [32800/160000] lr: 7.500e-05, eta: 11:48:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1999, decode.acc_seg: 91.9853, loss: 0.1999 +2023-03-04 17:03:44,700 - mmseg - INFO - Iter [32850/160000] lr: 7.500e-05, eta: 11:47:42, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.2902, loss: 0.1921 +2023-03-04 17:03:57,961 - mmseg - INFO - Iter [32900/160000] lr: 7.500e-05, eta: 11:47:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.2456, loss: 0.1937 +2023-03-04 17:04:11,326 - mmseg - INFO - Iter [32950/160000] lr: 7.500e-05, eta: 11:46:43, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.2675, loss: 0.1920 +2023-03-04 17:04:24,740 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:04:24,741 - mmseg - INFO - Iter [33000/160000] lr: 7.500e-05, eta: 11:46:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.2339, loss: 0.1932 +2023-03-04 17:04:37,998 - mmseg - INFO - Iter [33050/160000] lr: 7.500e-05, eta: 11:45:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4144, loss: 0.1862 +2023-03-04 17:04:51,234 - mmseg - INFO - Iter [33100/160000] lr: 7.500e-05, eta: 11:45:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2014, decode.acc_seg: 91.9563, loss: 0.2014 +2023-03-04 17:05:04,488 - mmseg - INFO - Iter [33150/160000] lr: 7.500e-05, eta: 11:44:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.1301, loss: 0.1947 +2023-03-04 17:05:17,828 - mmseg - INFO - Iter [33200/160000] lr: 7.500e-05, eta: 11:44:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.2314, loss: 0.1948 +2023-03-04 17:05:31,298 - mmseg - INFO - Iter [33250/160000] lr: 7.500e-05, eta: 11:43:46, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2001, decode.acc_seg: 91.9541, loss: 0.2001 +2023-03-04 17:05:44,555 - mmseg - INFO - Iter [33300/160000] lr: 7.500e-05, eta: 11:43:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1926, decode.acc_seg: 92.1908, loss: 0.1926 +2023-03-04 17:05:57,866 - mmseg - INFO - Iter [33350/160000] lr: 7.500e-05, eta: 11:42:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2014, decode.acc_seg: 92.0716, loss: 0.2014 +2023-03-04 17:06:11,111 - mmseg - INFO - Iter [33400/160000] lr: 7.500e-05, eta: 11:42:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1950, decode.acc_seg: 92.2208, loss: 0.1950 +2023-03-04 17:06:26,815 - mmseg - INFO - Iter [33450/160000] lr: 7.500e-05, eta: 11:41:57, time: 0.314, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1899, decode.acc_seg: 92.3721, loss: 0.1899 +2023-03-04 17:06:40,128 - mmseg - INFO - Iter [33500/160000] lr: 7.500e-05, eta: 11:41:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1991, decode.acc_seg: 92.0351, loss: 0.1991 +2023-03-04 17:06:53,364 - mmseg - INFO - Iter [33550/160000] lr: 7.500e-05, eta: 11:40:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.1544, loss: 0.1974 +2023-03-04 17:07:06,563 - mmseg - INFO - Iter [33600/160000] lr: 7.500e-05, eta: 11:40:29, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.2574, loss: 0.1922 +2023-03-04 17:07:19,760 - mmseg - INFO - Iter [33650/160000] lr: 7.500e-05, eta: 11:40:00, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2722, loss: 0.1909 +2023-03-04 17:07:33,176 - mmseg - INFO - Iter [33700/160000] lr: 7.500e-05, eta: 11:39:31, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1962, decode.acc_seg: 92.2181, loss: 0.1962 +2023-03-04 17:07:46,490 - mmseg - INFO - Iter [33750/160000] lr: 7.500e-05, eta: 11:39:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.3353, loss: 0.1883 +2023-03-04 17:07:59,852 - mmseg - INFO - Iter [33800/160000] lr: 7.500e-05, eta: 11:38:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1965, decode.acc_seg: 92.1452, loss: 0.1965 +2023-03-04 17:08:13,121 - mmseg - INFO - Iter [33850/160000] lr: 7.500e-05, eta: 11:38:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.2950, loss: 0.1943 +2023-03-04 17:08:26,358 - mmseg - INFO - Iter [33900/160000] lr: 7.500e-05, eta: 11:37:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.1385, loss: 0.1958 +2023-03-04 17:08:39,775 - mmseg - INFO - Iter [33950/160000] lr: 7.500e-05, eta: 11:37:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1924, decode.acc_seg: 92.3366, loss: 0.1924 +2023-03-04 17:08:53,045 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:08:53,045 - mmseg - INFO - Iter [34000/160000] lr: 7.500e-05, eta: 11:36:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3624, loss: 0.1887 +2023-03-04 17:09:06,352 - mmseg - INFO - Iter [34050/160000] lr: 7.500e-05, eta: 11:36:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.5284, loss: 0.1864 +2023-03-04 17:09:22,008 - mmseg - INFO - Iter [34100/160000] lr: 7.500e-05, eta: 11:35:49, time: 0.313, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.1057, loss: 0.1968 +2023-03-04 17:09:35,324 - mmseg - INFO - Iter [34150/160000] lr: 7.500e-05, eta: 11:35:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2015, decode.acc_seg: 91.8150, loss: 0.2015 +2023-03-04 17:09:48,602 - mmseg - INFO - Iter [34200/160000] lr: 7.500e-05, eta: 11:34:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.3461, loss: 0.1915 +2023-03-04 17:10:02,050 - mmseg - INFO - Iter [34250/160000] lr: 7.500e-05, eta: 11:34:24, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.2847, loss: 0.1938 +2023-03-04 17:10:15,394 - mmseg - INFO - Iter [34300/160000] lr: 7.500e-05, eta: 11:33:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.0138, loss: 0.1955 +2023-03-04 17:10:28,820 - mmseg - INFO - Iter [34350/160000] lr: 7.500e-05, eta: 11:33:27, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.2256, loss: 0.1912 +2023-03-04 17:10:42,295 - mmseg - INFO - Iter [34400/160000] lr: 7.500e-05, eta: 11:32:59, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 91.9283, loss: 0.1958 +2023-03-04 17:10:55,624 - mmseg - INFO - Iter [34450/160000] lr: 7.500e-05, eta: 11:32:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5433, loss: 0.1859 +2023-03-04 17:11:09,010 - mmseg - INFO - Iter [34500/160000] lr: 7.500e-05, eta: 11:32:03, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1975, decode.acc_seg: 92.2022, loss: 0.1975 +2023-03-04 17:11:22,342 - mmseg - INFO - Iter [34550/160000] lr: 7.500e-05, eta: 11:31:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.5019, loss: 0.1867 +2023-03-04 17:11:35,694 - mmseg - INFO - Iter [34600/160000] lr: 7.500e-05, eta: 11:31:07, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2038, decode.acc_seg: 91.8037, loss: 0.2038 +2023-03-04 17:11:49,106 - mmseg - INFO - Iter [34650/160000] lr: 7.500e-05, eta: 11:30:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2024, decode.acc_seg: 91.9553, loss: 0.2024 +2023-03-04 17:12:02,433 - mmseg - INFO - Iter [34700/160000] lr: 7.500e-05, eta: 11:30:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4786, loss: 0.1880 +2023-03-04 17:12:18,403 - mmseg - INFO - Iter [34750/160000] lr: 7.500e-05, eta: 11:29:52, time: 0.319, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1977, decode.acc_seg: 92.1163, loss: 0.1977 +2023-03-04 17:12:31,712 - mmseg - INFO - Iter [34800/160000] lr: 7.500e-05, eta: 11:29:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2000, decode.acc_seg: 91.8559, loss: 0.2000 +2023-03-04 17:12:45,019 - mmseg - INFO - Iter [34850/160000] lr: 7.500e-05, eta: 11:28:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.2689, loss: 0.1903 +2023-03-04 17:12:58,290 - mmseg - INFO - Iter [34900/160000] lr: 7.500e-05, eta: 11:28:28, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1973, decode.acc_seg: 92.0280, loss: 0.1973 +2023-03-04 17:13:11,640 - mmseg - INFO - Iter [34950/160000] lr: 7.500e-05, eta: 11:28:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1960, decode.acc_seg: 92.1031, loss: 0.1960 +2023-03-04 17:13:25,010 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:13:25,010 - mmseg - INFO - Iter [35000/160000] lr: 7.500e-05, eta: 11:27:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.0597, loss: 0.1974 +2023-03-04 17:13:38,445 - mmseg - INFO - Iter [35050/160000] lr: 7.500e-05, eta: 11:27:05, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.1445, loss: 0.1958 +2023-03-04 17:13:51,719 - mmseg - INFO - Iter [35100/160000] lr: 7.500e-05, eta: 11:26:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.2642, loss: 0.1890 +2023-03-04 17:14:04,922 - mmseg - INFO - Iter [35150/160000] lr: 7.500e-05, eta: 11:26:09, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.2812, loss: 0.1947 +2023-03-04 17:14:18,414 - mmseg - INFO - Iter [35200/160000] lr: 7.500e-05, eta: 11:25:42, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1940, decode.acc_seg: 92.1909, loss: 0.1940 +2023-03-04 17:14:31,764 - mmseg - INFO - Iter [35250/160000] lr: 7.500e-05, eta: 11:25:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1891, decode.acc_seg: 92.4147, loss: 0.1891 +2023-03-04 17:14:45,232 - mmseg - INFO - Iter [35300/160000] lr: 7.500e-05, eta: 11:24:47, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1995, decode.acc_seg: 91.8816, loss: 0.1995 +2023-03-04 17:15:01,111 - mmseg - INFO - Iter [35350/160000] lr: 7.500e-05, eta: 11:24:29, time: 0.318, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4282, loss: 0.1871 +2023-03-04 17:15:14,563 - mmseg - INFO - Iter [35400/160000] lr: 7.500e-05, eta: 11:24:01, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.0199, loss: 0.1935 +2023-03-04 17:15:27,824 - mmseg - INFO - Iter [35450/160000] lr: 7.500e-05, eta: 11:23:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2030, decode.acc_seg: 92.0087, loss: 0.2030 +2023-03-04 17:15:41,211 - mmseg - INFO - Iter [35500/160000] lr: 7.500e-05, eta: 11:23:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1899, decode.acc_seg: 92.3512, loss: 0.1899 +2023-03-04 17:15:54,613 - mmseg - INFO - Iter [35550/160000] lr: 7.500e-05, eta: 11:22:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2038, decode.acc_seg: 91.8813, loss: 0.2038 +2023-03-04 17:16:07,934 - mmseg - INFO - Iter [35600/160000] lr: 7.500e-05, eta: 11:22:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3748, loss: 0.1880 +2023-03-04 17:16:21,238 - mmseg - INFO - Iter [35650/160000] lr: 7.500e-05, eta: 11:21:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1969, decode.acc_seg: 92.1675, loss: 0.1969 +2023-03-04 17:16:34,751 - mmseg - INFO - Iter [35700/160000] lr: 7.500e-05, eta: 11:21:18, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1928, decode.acc_seg: 92.1997, loss: 0.1928 +2023-03-04 17:16:48,039 - mmseg - INFO - Iter [35750/160000] lr: 7.500e-05, eta: 11:20:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.3039, loss: 0.1893 +2023-03-04 17:17:01,340 - mmseg - INFO - Iter [35800/160000] lr: 7.500e-05, eta: 11:20:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1989, decode.acc_seg: 92.0784, loss: 0.1989 +2023-03-04 17:17:14,621 - mmseg - INFO - Iter [35850/160000] lr: 7.500e-05, eta: 11:19:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1953, decode.acc_seg: 92.1564, loss: 0.1953 +2023-03-04 17:17:27,832 - mmseg - INFO - Iter [35900/160000] lr: 7.500e-05, eta: 11:19:28, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1985, decode.acc_seg: 92.0015, loss: 0.1985 +2023-03-04 17:17:41,213 - mmseg - INFO - Iter [35950/160000] lr: 7.500e-05, eta: 11:19:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.2724, loss: 0.1922 +2023-03-04 17:17:57,100 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:17:57,100 - mmseg - INFO - Iter [36000/160000] lr: 7.500e-05, eta: 11:18:43, time: 0.318, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1954, decode.acc_seg: 92.2586, loss: 0.1954 +2023-03-04 17:18:10,345 - mmseg - INFO - Iter [36050/160000] lr: 7.500e-05, eta: 11:18:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1941, decode.acc_seg: 92.0481, loss: 0.1941 +2023-03-04 17:18:23,603 - mmseg - INFO - Iter [36100/160000] lr: 7.500e-05, eta: 11:17:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.1977, loss: 0.1910 +2023-03-04 17:18:36,948 - mmseg - INFO - Iter [36150/160000] lr: 7.500e-05, eta: 11:17:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.3673, loss: 0.1874 +2023-03-04 17:18:50,147 - mmseg - INFO - Iter [36200/160000] lr: 7.500e-05, eta: 11:16:54, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1939, decode.acc_seg: 92.2197, loss: 0.1939 +2023-03-04 17:19:03,476 - mmseg - INFO - Iter [36250/160000] lr: 7.500e-05, eta: 11:16:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.2427, loss: 0.1925 +2023-03-04 17:19:16,867 - mmseg - INFO - Iter [36300/160000] lr: 7.500e-05, eta: 11:16:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2041, decode.acc_seg: 91.9679, loss: 0.2041 +2023-03-04 17:19:30,061 - mmseg - INFO - Iter [36350/160000] lr: 7.500e-05, eta: 11:15:33, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4796, loss: 0.1882 +2023-03-04 17:19:43,352 - mmseg - INFO - Iter [36400/160000] lr: 7.500e-05, eta: 11:15:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2032, decode.acc_seg: 91.9146, loss: 0.2032 +2023-03-04 17:19:56,747 - mmseg - INFO - Iter [36450/160000] lr: 7.500e-05, eta: 11:14:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1888, decode.acc_seg: 92.4924, loss: 0.1888 +2023-03-04 17:20:10,084 - mmseg - INFO - Iter [36500/160000] lr: 7.500e-05, eta: 11:14:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.2843, loss: 0.1911 +2023-03-04 17:20:23,436 - mmseg - INFO - Iter [36550/160000] lr: 7.500e-05, eta: 11:13:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.4726, loss: 0.1905 +2023-03-04 17:20:39,259 - mmseg - INFO - Iter [36600/160000] lr: 7.500e-05, eta: 11:13:28, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3881, loss: 0.1908 +2023-03-04 17:20:52,634 - mmseg - INFO - Iter [36650/160000] lr: 7.500e-05, eta: 11:13:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1967, decode.acc_seg: 92.1161, loss: 0.1967 +2023-03-04 17:21:05,848 - mmseg - INFO - Iter [36700/160000] lr: 7.500e-05, eta: 11:12:35, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.0285, loss: 0.1958 +2023-03-04 17:21:19,278 - mmseg - INFO - Iter [36750/160000] lr: 7.500e-05, eta: 11:12:09, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.3159, loss: 0.1925 +2023-03-04 17:21:32,648 - mmseg - INFO - Iter [36800/160000] lr: 7.500e-05, eta: 11:11:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1945, decode.acc_seg: 92.0488, loss: 0.1945 +2023-03-04 17:21:45,959 - mmseg - INFO - Iter [36850/160000] lr: 7.500e-05, eta: 11:11:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.1790, loss: 0.1947 +2023-03-04 17:21:59,303 - mmseg - INFO - Iter [36900/160000] lr: 7.500e-05, eta: 11:10:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1970, decode.acc_seg: 92.0238, loss: 0.1970 +2023-03-04 17:22:12,616 - mmseg - INFO - Iter [36950/160000] lr: 7.500e-05, eta: 11:10:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.3587, loss: 0.1896 +2023-03-04 17:22:25,831 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:22:25,831 - mmseg - INFO - Iter [37000/160000] lr: 7.500e-05, eta: 11:09:56, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.3752, loss: 0.1876 +2023-03-04 17:22:39,215 - mmseg - INFO - Iter [37050/160000] lr: 7.500e-05, eta: 11:09:30, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1967, decode.acc_seg: 92.1689, loss: 0.1967 +2023-03-04 17:22:52,547 - mmseg - INFO - Iter [37100/160000] lr: 7.500e-05, eta: 11:09:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.4237, loss: 0.1874 +2023-03-04 17:23:06,066 - mmseg - INFO - Iter [37150/160000] lr: 7.500e-05, eta: 11:08:38, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.2666, loss: 0.1870 +2023-03-04 17:23:19,361 - mmseg - INFO - Iter [37200/160000] lr: 7.500e-05, eta: 11:08:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.3732, loss: 0.1883 +2023-03-04 17:23:35,198 - mmseg - INFO - Iter [37250/160000] lr: 7.500e-05, eta: 11:07:54, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.3770, loss: 0.1896 +2023-03-04 17:23:48,666 - mmseg - INFO - Iter [37300/160000] lr: 7.500e-05, eta: 11:07:28, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2018, decode.acc_seg: 91.9597, loss: 0.2018 +2023-03-04 17:24:01,932 - mmseg - INFO - Iter [37350/160000] lr: 7.500e-05, eta: 11:07:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.2766, loss: 0.1946 +2023-03-04 17:24:15,454 - mmseg - INFO - Iter [37400/160000] lr: 7.500e-05, eta: 11:06:36, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.2301, loss: 0.1919 +2023-03-04 17:24:28,735 - mmseg - INFO - Iter [37450/160000] lr: 7.500e-05, eta: 11:06:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1945, decode.acc_seg: 92.2594, loss: 0.1945 +2023-03-04 17:24:42,096 - mmseg - INFO - Iter [37500/160000] lr: 7.500e-05, eta: 11:05:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1944, decode.acc_seg: 92.0608, loss: 0.1944 +2023-03-04 17:24:55,458 - mmseg - INFO - Iter [37550/160000] lr: 7.500e-05, eta: 11:05:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4704, loss: 0.1852 +2023-03-04 17:25:08,898 - mmseg - INFO - Iter [37600/160000] lr: 7.500e-05, eta: 11:04:53, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3308, loss: 0.1880 +2023-03-04 17:25:22,255 - mmseg - INFO - Iter [37650/160000] lr: 7.500e-05, eta: 11:04:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3694, loss: 0.1908 +2023-03-04 17:25:35,507 - mmseg - INFO - Iter [37700/160000] lr: 7.500e-05, eta: 11:04:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.2451, loss: 0.1937 +2023-03-04 17:25:48,850 - mmseg - INFO - Iter [37750/160000] lr: 7.500e-05, eta: 11:03:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.2892, loss: 0.1900 +2023-03-04 17:26:02,107 - mmseg - INFO - Iter [37800/160000] lr: 7.500e-05, eta: 11:03:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1964, decode.acc_seg: 92.0688, loss: 0.1964 +2023-03-04 17:26:15,385 - mmseg - INFO - Iter [37850/160000] lr: 7.500e-05, eta: 11:02:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3832, loss: 0.1895 +2023-03-04 17:26:31,158 - mmseg - INFO - Iter [37900/160000] lr: 7.500e-05, eta: 11:02:25, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.2078, decode.acc_seg: 91.7371, loss: 0.2078 +2023-03-04 17:26:44,693 - mmseg - INFO - Iter [37950/160000] lr: 7.500e-05, eta: 11:02:00, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1986, decode.acc_seg: 91.9454, loss: 0.1986 +2023-03-04 17:26:57,961 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:26:57,961 - mmseg - INFO - Iter [38000/160000] lr: 7.500e-05, eta: 11:01:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5429, loss: 0.1848 +2023-03-04 17:27:11,284 - mmseg - INFO - Iter [38050/160000] lr: 7.500e-05, eta: 11:01:08, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1992, decode.acc_seg: 92.0756, loss: 0.1992 +2023-03-04 17:27:24,684 - mmseg - INFO - Iter [38100/160000] lr: 7.500e-05, eta: 11:00:43, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4034, loss: 0.1860 +2023-03-04 17:27:38,082 - mmseg - INFO - Iter [38150/160000] lr: 7.500e-05, eta: 11:00:17, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1913, decode.acc_seg: 92.3319, loss: 0.1913 +2023-03-04 17:27:51,342 - mmseg - INFO - Iter [38200/160000] lr: 7.500e-05, eta: 10:59:51, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.4821, loss: 0.1827 +2023-03-04 17:28:04,695 - mmseg - INFO - Iter [38250/160000] lr: 7.500e-05, eta: 10:59:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.4060, loss: 0.1911 +2023-03-04 17:28:18,018 - mmseg - INFO - Iter [38300/160000] lr: 7.500e-05, eta: 10:59:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2368, loss: 0.1909 +2023-03-04 17:28:31,317 - mmseg - INFO - Iter [38350/160000] lr: 7.500e-05, eta: 10:58:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.2087, loss: 0.1946 +2023-03-04 17:28:44,603 - mmseg - INFO - Iter [38400/160000] lr: 7.500e-05, eta: 10:58:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.2309, loss: 0.1936 +2023-03-04 17:28:57,825 - mmseg - INFO - Iter [38450/160000] lr: 7.500e-05, eta: 10:57:43, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.1719, loss: 0.1974 +2023-03-04 17:29:13,611 - mmseg - INFO - Iter [38500/160000] lr: 7.500e-05, eta: 10:57:26, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.6051, loss: 0.1837 +2023-03-04 17:29:26,856 - mmseg - INFO - Iter [38550/160000] lr: 7.500e-05, eta: 10:57:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.2990, loss: 0.1915 +2023-03-04 17:29:40,140 - mmseg - INFO - Iter [38600/160000] lr: 7.500e-05, eta: 10:56:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1965, decode.acc_seg: 92.0655, loss: 0.1965 +2023-03-04 17:29:53,575 - mmseg - INFO - Iter [38650/160000] lr: 7.500e-05, eta: 10:56:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4031, loss: 0.1858 +2023-03-04 17:30:06,939 - mmseg - INFO - Iter [38700/160000] lr: 7.500e-05, eta: 10:55:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.3477, loss: 0.1878 +2023-03-04 17:30:20,256 - mmseg - INFO - Iter [38750/160000] lr: 7.500e-05, eta: 10:55:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1991, decode.acc_seg: 91.9755, loss: 0.1991 +2023-03-04 17:30:33,464 - mmseg - INFO - Iter [38800/160000] lr: 7.500e-05, eta: 10:54:54, time: 0.265, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1929, decode.acc_seg: 92.2014, loss: 0.1929 +2023-03-04 17:30:46,797 - mmseg - INFO - Iter [38850/160000] lr: 7.500e-05, eta: 10:54:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.1989, loss: 0.1955 +2023-03-04 17:31:00,047 - mmseg - INFO - Iter [38900/160000] lr: 7.500e-05, eta: 10:54:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.3227, loss: 0.1893 +2023-03-04 17:31:13,343 - mmseg - INFO - Iter [38950/160000] lr: 7.500e-05, eta: 10:53:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1988, decode.acc_seg: 92.0532, loss: 0.1988 +2023-03-04 17:31:26,658 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:31:26,658 - mmseg - INFO - Iter [39000/160000] lr: 7.500e-05, eta: 10:53:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1952, decode.acc_seg: 92.0781, loss: 0.1952 +2023-03-04 17:31:39,921 - mmseg - INFO - Iter [39050/160000] lr: 7.500e-05, eta: 10:52:47, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1898, decode.acc_seg: 92.3298, loss: 0.1898 +2023-03-04 17:31:53,427 - mmseg - INFO - Iter [39100/160000] lr: 7.500e-05, eta: 10:52:23, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2015, decode.acc_seg: 91.8467, loss: 0.2015 +2023-03-04 17:32:09,215 - mmseg - INFO - Iter [39150/160000] lr: 7.500e-05, eta: 10:52:05, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.1665, loss: 0.1912 +2023-03-04 17:32:22,518 - mmseg - INFO - Iter [39200/160000] lr: 7.500e-05, eta: 10:51:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1980, decode.acc_seg: 92.0683, loss: 0.1980 +2023-03-04 17:32:35,921 - mmseg - INFO - Iter [39250/160000] lr: 7.500e-05, eta: 10:51:16, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1940, decode.acc_seg: 92.1434, loss: 0.1940 +2023-03-04 17:32:49,163 - mmseg - INFO - Iter [39300/160000] lr: 7.500e-05, eta: 10:50:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.2642, loss: 0.1885 +2023-03-04 17:33:02,468 - mmseg - INFO - Iter [39350/160000] lr: 7.500e-05, eta: 10:50:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.2150, loss: 0.1911 +2023-03-04 17:33:15,789 - mmseg - INFO - Iter [39400/160000] lr: 7.500e-05, eta: 10:50:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.2192, loss: 0.1946 +2023-03-04 17:33:29,088 - mmseg - INFO - Iter [39450/160000] lr: 7.500e-05, eta: 10:49:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1930, decode.acc_seg: 92.2023, loss: 0.1930 +2023-03-04 17:33:42,483 - mmseg - INFO - Iter [39500/160000] lr: 7.500e-05, eta: 10:49:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.3205, loss: 0.1921 +2023-03-04 17:33:55,828 - mmseg - INFO - Iter [39550/160000] lr: 7.500e-05, eta: 10:48:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1966, decode.acc_seg: 92.0508, loss: 0.1966 +2023-03-04 17:34:09,353 - mmseg - INFO - Iter [39600/160000] lr: 7.500e-05, eta: 10:48:22, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2019, decode.acc_seg: 92.0044, loss: 0.2019 +2023-03-04 17:34:22,700 - mmseg - INFO - Iter [39650/160000] lr: 7.500e-05, eta: 10:47:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1975, decode.acc_seg: 92.0089, loss: 0.1975 +2023-03-04 17:34:36,015 - mmseg - INFO - Iter [39700/160000] lr: 7.500e-05, eta: 10:47:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.4420, loss: 0.1843 +2023-03-04 17:34:49,346 - mmseg - INFO - Iter [39750/160000] lr: 7.500e-05, eta: 10:47:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5625, loss: 0.1834 +2023-03-04 17:35:05,217 - mmseg - INFO - Iter [39800/160000] lr: 7.500e-05, eta: 10:46:51, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.4124, loss: 0.1896 +2023-03-04 17:35:18,606 - mmseg - INFO - Iter [39850/160000] lr: 7.500e-05, eta: 10:46:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2002, decode.acc_seg: 92.0426, loss: 0.2002 +2023-03-04 17:35:32,090 - mmseg - INFO - Iter [39900/160000] lr: 7.500e-05, eta: 10:46:02, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2003, decode.acc_seg: 92.0030, loss: 0.2003 +2023-03-04 17:35:45,481 - mmseg - INFO - Iter [39950/160000] lr: 7.500e-05, eta: 10:45:38, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1981, decode.acc_seg: 92.1464, loss: 0.1981 +2023-03-04 17:35:58,923 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:35:58,923 - mmseg - INFO - Iter [40000/160000] lr: 7.500e-05, eta: 10:45:14, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.5556, loss: 0.1861 +2023-03-04 17:36:12,146 - mmseg - INFO - Iter [40050/160000] lr: 3.750e-05, eta: 10:44:49, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.3619, loss: 0.1916 +2023-03-04 17:36:25,458 - mmseg - INFO - Iter [40100/160000] lr: 3.750e-05, eta: 10:44:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.5028, loss: 0.1871 +2023-03-04 17:36:38,768 - mmseg - INFO - Iter [40150/160000] lr: 3.750e-05, eta: 10:44:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.3991, loss: 0.1853 +2023-03-04 17:36:52,114 - mmseg - INFO - Iter [40200/160000] lr: 3.750e-05, eta: 10:43:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1956, decode.acc_seg: 92.1953, loss: 0.1956 +2023-03-04 17:37:05,494 - mmseg - INFO - Iter [40250/160000] lr: 3.750e-05, eta: 10:43:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1952, decode.acc_seg: 92.0511, loss: 0.1952 +2023-03-04 17:37:18,665 - mmseg - INFO - Iter [40300/160000] lr: 3.750e-05, eta: 10:42:46, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.3644, loss: 0.1921 +2023-03-04 17:37:31,980 - mmseg - INFO - Iter [40350/160000] lr: 3.750e-05, eta: 10:42:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2727, loss: 0.1909 +2023-03-04 17:37:47,872 - mmseg - INFO - Iter [40400/160000] lr: 3.750e-05, eta: 10:42:05, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.4361, loss: 0.1867 +2023-03-04 17:38:01,276 - mmseg - INFO - Iter [40450/160000] lr: 3.750e-05, eta: 10:41:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1906, decode.acc_seg: 92.2398, loss: 0.1906 +2023-03-04 17:38:14,626 - mmseg - INFO - Iter [40500/160000] lr: 3.750e-05, eta: 10:41:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.1648, loss: 0.1932 +2023-03-04 17:38:27,869 - mmseg - INFO - Iter [40550/160000] lr: 3.750e-05, eta: 10:40:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1933, decode.acc_seg: 92.1429, loss: 0.1933 +2023-03-04 17:38:41,115 - mmseg - INFO - Iter [40600/160000] lr: 3.750e-05, eta: 10:40:28, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5188, loss: 0.1844 +2023-03-04 17:38:54,391 - mmseg - INFO - Iter [40650/160000] lr: 3.750e-05, eta: 10:40:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.2631, loss: 0.1916 +2023-03-04 17:39:07,678 - mmseg - INFO - Iter [40700/160000] lr: 3.750e-05, eta: 10:39:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1994, decode.acc_seg: 92.1843, loss: 0.1994 +2023-03-04 17:39:21,060 - mmseg - INFO - Iter [40750/160000] lr: 3.750e-05, eta: 10:39:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2013, decode.acc_seg: 91.8945, loss: 0.2013 +2023-03-04 17:39:34,712 - mmseg - INFO - Iter [40800/160000] lr: 3.750e-05, eta: 10:38:52, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1953, decode.acc_seg: 92.2616, loss: 0.1953 +2023-03-04 17:39:47,991 - mmseg - INFO - Iter [40850/160000] lr: 3.750e-05, eta: 10:38:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1924, decode.acc_seg: 92.3291, loss: 0.1924 +2023-03-04 17:40:01,405 - mmseg - INFO - Iter [40900/160000] lr: 3.750e-05, eta: 10:38:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.5447, loss: 0.1855 +2023-03-04 17:40:14,830 - mmseg - INFO - Iter [40950/160000] lr: 3.750e-05, eta: 10:37:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1944, decode.acc_seg: 92.2486, loss: 0.1944 +2023-03-04 17:40:28,133 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:40:28,133 - mmseg - INFO - Iter [41000/160000] lr: 3.750e-05, eta: 10:37:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1977, decode.acc_seg: 92.1308, loss: 0.1977 +2023-03-04 17:40:43,901 - mmseg - INFO - Iter [41050/160000] lr: 3.750e-05, eta: 10:36:59, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.2161, loss: 0.1904 +2023-03-04 17:40:57,233 - mmseg - INFO - Iter [41100/160000] lr: 3.750e-05, eta: 10:36:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2945, loss: 0.1909 +2023-03-04 17:41:10,664 - mmseg - INFO - Iter [41150/160000] lr: 3.750e-05, eta: 10:36:11, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2009, decode.acc_seg: 91.9292, loss: 0.2009 +2023-03-04 17:41:24,021 - mmseg - INFO - Iter [41200/160000] lr: 3.750e-05, eta: 10:35:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.3825, loss: 0.1883 +2023-03-04 17:41:37,361 - mmseg - INFO - Iter [41250/160000] lr: 3.750e-05, eta: 10:35:24, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7068, loss: 0.1801 +2023-03-04 17:41:50,785 - mmseg - INFO - Iter [41300/160000] lr: 3.750e-05, eta: 10:35:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1973, decode.acc_seg: 92.0313, loss: 0.1973 +2023-03-04 17:42:04,160 - mmseg - INFO - Iter [41350/160000] lr: 3.750e-05, eta: 10:34:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.1900, loss: 0.1937 +2023-03-04 17:42:17,410 - mmseg - INFO - Iter [41400/160000] lr: 3.750e-05, eta: 10:34:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3366, loss: 0.1880 +2023-03-04 17:42:30,747 - mmseg - INFO - Iter [41450/160000] lr: 3.750e-05, eta: 10:33:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.2417, loss: 0.1925 +2023-03-04 17:42:44,113 - mmseg - INFO - Iter [41500/160000] lr: 3.750e-05, eta: 10:33:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.2464, loss: 0.1936 +2023-03-04 17:42:57,485 - mmseg - INFO - Iter [41550/160000] lr: 3.750e-05, eta: 10:33:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2014, decode.acc_seg: 91.9364, loss: 0.2014 +2023-03-04 17:43:10,841 - mmseg - INFO - Iter [41600/160000] lr: 3.750e-05, eta: 10:32:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4505, loss: 0.1860 +2023-03-04 17:43:26,835 - mmseg - INFO - Iter [41650/160000] lr: 3.750e-05, eta: 10:32:21, time: 0.320, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.7095, loss: 0.1813 +2023-03-04 17:43:40,180 - mmseg - INFO - Iter [41700/160000] lr: 3.750e-05, eta: 10:31:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1930, decode.acc_seg: 92.1235, loss: 0.1930 +2023-03-04 17:43:53,438 - mmseg - INFO - Iter [41750/160000] lr: 3.750e-05, eta: 10:31:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.1450, loss: 0.1936 +2023-03-04 17:44:06,741 - mmseg - INFO - Iter [41800/160000] lr: 3.750e-05, eta: 10:31:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.2742, loss: 0.1917 +2023-03-04 17:44:19,974 - mmseg - INFO - Iter [41850/160000] lr: 3.750e-05, eta: 10:30:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7114, loss: 0.1795 +2023-03-04 17:44:33,206 - mmseg - INFO - Iter [41900/160000] lr: 3.750e-05, eta: 10:30:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.6358, loss: 0.1843 +2023-03-04 17:44:46,619 - mmseg - INFO - Iter [41950/160000] lr: 3.750e-05, eta: 10:29:59, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.2999, loss: 0.1901 +2023-03-04 17:44:59,934 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:44:59,934 - mmseg - INFO - Iter [42000/160000] lr: 3.750e-05, eta: 10:29:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.4542, loss: 0.1834 +2023-03-04 17:45:13,354 - mmseg - INFO - Iter [42050/160000] lr: 3.750e-05, eta: 10:29:12, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.4017, loss: 0.1932 +2023-03-04 17:45:26,658 - mmseg - INFO - Iter [42100/160000] lr: 3.750e-05, eta: 10:28:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1965, decode.acc_seg: 92.1784, loss: 0.1965 +2023-03-04 17:45:39,976 - mmseg - INFO - Iter [42150/160000] lr: 3.750e-05, eta: 10:28:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.4408, loss: 0.1885 +2023-03-04 17:45:53,403 - mmseg - INFO - Iter [42200/160000] lr: 3.750e-05, eta: 10:28:02, time: 0.269, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1927, decode.acc_seg: 92.2464, loss: 0.1927 +2023-03-04 17:46:06,612 - mmseg - INFO - Iter [42250/160000] lr: 3.750e-05, eta: 10:27:38, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.6502, loss: 0.1789 +2023-03-04 17:46:22,431 - mmseg - INFO - Iter [42300/160000] lr: 3.750e-05, eta: 10:27:22, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.1944, loss: 0.1951 +2023-03-04 17:46:35,792 - mmseg - INFO - Iter [42350/160000] lr: 3.750e-05, eta: 10:26:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.3626, loss: 0.1884 +2023-03-04 17:46:49,089 - mmseg - INFO - Iter [42400/160000] lr: 3.750e-05, eta: 10:26:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.2772, loss: 0.1886 +2023-03-04 17:47:02,301 - mmseg - INFO - Iter [42450/160000] lr: 3.750e-05, eta: 10:26:11, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5528, loss: 0.1847 +2023-03-04 17:47:15,553 - mmseg - INFO - Iter [42500/160000] lr: 3.750e-05, eta: 10:25:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1906, decode.acc_seg: 92.3830, loss: 0.1906 +2023-03-04 17:47:28,840 - mmseg - INFO - Iter [42550/160000] lr: 3.750e-05, eta: 10:25:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.4218, loss: 0.1881 +2023-03-04 17:47:42,138 - mmseg - INFO - Iter [42600/160000] lr: 3.750e-05, eta: 10:25:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5321, loss: 0.1838 +2023-03-04 17:47:55,488 - mmseg - INFO - Iter [42650/160000] lr: 3.750e-05, eta: 10:24:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2847, loss: 0.1909 +2023-03-04 17:48:08,932 - mmseg - INFO - Iter [42700/160000] lr: 3.750e-05, eta: 10:24:15, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6099, loss: 0.1830 +2023-03-04 17:48:22,280 - mmseg - INFO - Iter [42750/160000] lr: 3.750e-05, eta: 10:23:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.3798, loss: 0.1911 +2023-03-04 17:48:35,753 - mmseg - INFO - Iter [42800/160000] lr: 3.750e-05, eta: 10:23:29, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.2669, loss: 0.1904 +2023-03-04 17:48:49,052 - mmseg - INFO - Iter [42850/160000] lr: 3.750e-05, eta: 10:23:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1930, decode.acc_seg: 92.1568, loss: 0.1930 +2023-03-04 17:49:02,403 - mmseg - INFO - Iter [42900/160000] lr: 3.750e-05, eta: 10:22:43, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.3744, loss: 0.1935 +2023-03-04 17:49:18,304 - mmseg - INFO - Iter [42950/160000] lr: 3.750e-05, eta: 10:22:27, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5507, loss: 0.1809 +2023-03-04 17:49:31,630 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:49:31,630 - mmseg - INFO - Iter [43000/160000] lr: 3.750e-05, eta: 10:22:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1913, decode.acc_seg: 92.2390, loss: 0.1913 +2023-03-04 17:49:44,921 - mmseg - INFO - Iter [43050/160000] lr: 3.750e-05, eta: 10:21:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.5234, loss: 0.1880 +2023-03-04 17:49:58,259 - mmseg - INFO - Iter [43100/160000] lr: 3.750e-05, eta: 10:21:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.7180, loss: 0.1819 +2023-03-04 17:50:11,574 - mmseg - INFO - Iter [43150/160000] lr: 3.750e-05, eta: 10:20:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.3274, loss: 0.1902 +2023-03-04 17:50:24,918 - mmseg - INFO - Iter [43200/160000] lr: 3.750e-05, eta: 10:20:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4867, loss: 0.1882 +2023-03-04 17:50:38,151 - mmseg - INFO - Iter [43250/160000] lr: 3.750e-05, eta: 10:20:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.2666, loss: 0.1910 +2023-03-04 17:50:51,378 - mmseg - INFO - Iter [43300/160000] lr: 3.750e-05, eta: 10:19:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1965, decode.acc_seg: 92.1954, loss: 0.1965 +2023-03-04 17:51:04,730 - mmseg - INFO - Iter [43350/160000] lr: 3.750e-05, eta: 10:19:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.2764, loss: 0.1925 +2023-03-04 17:51:17,963 - mmseg - INFO - Iter [43400/160000] lr: 3.750e-05, eta: 10:18:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.5476, loss: 0.1863 +2023-03-04 17:51:31,315 - mmseg - INFO - Iter [43450/160000] lr: 3.750e-05, eta: 10:18:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.2554, loss: 0.1919 +2023-03-04 17:51:44,760 - mmseg - INFO - Iter [43500/160000] lr: 3.750e-05, eta: 10:18:13, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.4281, loss: 0.1896 +2023-03-04 17:52:00,554 - mmseg - INFO - Iter [43550/160000] lr: 3.750e-05, eta: 10:17:57, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.5022, loss: 0.1884 +2023-03-04 17:52:13,924 - mmseg - INFO - Iter [43600/160000] lr: 3.750e-05, eta: 10:17:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.2274, loss: 0.1955 +2023-03-04 17:52:27,231 - mmseg - INFO - Iter [43650/160000] lr: 3.750e-05, eta: 10:17:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6159, loss: 0.1806 +2023-03-04 17:52:40,638 - mmseg - INFO - Iter [43700/160000] lr: 3.750e-05, eta: 10:16:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4688, loss: 0.1859 +2023-03-04 17:52:53,929 - mmseg - INFO - Iter [43750/160000] lr: 3.750e-05, eta: 10:16:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.3347, loss: 0.1902 +2023-03-04 17:53:07,156 - mmseg - INFO - Iter [43800/160000] lr: 3.750e-05, eta: 10:16:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1906, decode.acc_seg: 92.2366, loss: 0.1906 +2023-03-04 17:53:20,479 - mmseg - INFO - Iter [43850/160000] lr: 3.750e-05, eta: 10:15:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.6612, loss: 0.1821 +2023-03-04 17:53:33,884 - mmseg - INFO - Iter [43900/160000] lr: 3.750e-05, eta: 10:15:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4285, loss: 0.1868 +2023-03-04 17:53:47,159 - mmseg - INFO - Iter [43950/160000] lr: 3.750e-05, eta: 10:14:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.3435, loss: 0.1912 +2023-03-04 17:54:00,626 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:54:00,627 - mmseg - INFO - Iter [44000/160000] lr: 3.750e-05, eta: 10:14:33, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5087, loss: 0.1847 +2023-03-04 17:54:13,986 - mmseg - INFO - Iter [44050/160000] lr: 3.750e-05, eta: 10:14:10, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.3921, loss: 0.1834 +2023-03-04 17:54:27,265 - mmseg - INFO - Iter [44100/160000] lr: 3.750e-05, eta: 10:13:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3448, loss: 0.1901 +2023-03-04 17:54:40,488 - mmseg - INFO - Iter [44150/160000] lr: 3.750e-05, eta: 10:13:25, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4654, loss: 0.1852 +2023-03-04 17:54:56,402 - mmseg - INFO - Iter [44200/160000] lr: 3.750e-05, eta: 10:13:09, time: 0.318, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.5040, loss: 0.1867 +2023-03-04 17:55:09,639 - mmseg - INFO - Iter [44250/160000] lr: 3.750e-05, eta: 10:12:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.6200, loss: 0.1819 +2023-03-04 17:55:22,902 - mmseg - INFO - Iter [44300/160000] lr: 3.750e-05, eta: 10:12:23, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5072, loss: 0.1843 +2023-03-04 17:55:36,100 - mmseg - INFO - Iter [44350/160000] lr: 3.750e-05, eta: 10:12:00, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1888, decode.acc_seg: 92.2968, loss: 0.1888 +2023-03-04 17:55:49,395 - mmseg - INFO - Iter [44400/160000] lr: 3.750e-05, eta: 10:11:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.5156, loss: 0.1908 +2023-03-04 17:56:02,592 - mmseg - INFO - Iter [44450/160000] lr: 3.750e-05, eta: 10:11:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.7331, loss: 0.1803 +2023-03-04 17:56:15,922 - mmseg - INFO - Iter [44500/160000] lr: 3.750e-05, eta: 10:10:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1955, decode.acc_seg: 92.1507, loss: 0.1955 +2023-03-04 17:56:29,286 - mmseg - INFO - Iter [44550/160000] lr: 3.750e-05, eta: 10:10:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.3816, loss: 0.1866 +2023-03-04 17:56:42,646 - mmseg - INFO - Iter [44600/160000] lr: 3.750e-05, eta: 10:10:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2001, decode.acc_seg: 92.0261, loss: 0.2001 +2023-03-04 17:56:55,939 - mmseg - INFO - Iter [44650/160000] lr: 3.750e-05, eta: 10:09:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1930, decode.acc_seg: 92.3746, loss: 0.1930 +2023-03-04 17:57:09,206 - mmseg - INFO - Iter [44700/160000] lr: 3.750e-05, eta: 10:09:23, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.3044, loss: 0.1911 +2023-03-04 17:57:22,621 - mmseg - INFO - Iter [44750/160000] lr: 3.750e-05, eta: 10:09:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1906, decode.acc_seg: 92.2178, loss: 0.1906 +2023-03-04 17:57:36,021 - mmseg - INFO - Iter [44800/160000] lr: 3.750e-05, eta: 10:08:38, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.3596, loss: 0.1876 +2023-03-04 17:57:51,837 - mmseg - INFO - Iter [44850/160000] lr: 3.750e-05, eta: 10:08:22, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.4897, loss: 0.1895 +2023-03-04 17:58:05,092 - mmseg - INFO - Iter [44900/160000] lr: 3.750e-05, eta: 10:08:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4293, loss: 0.1859 +2023-03-04 17:58:18,413 - mmseg - INFO - Iter [44950/160000] lr: 3.750e-05, eta: 10:07:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1898, decode.acc_seg: 92.2358, loss: 0.1898 +2023-03-04 17:58:31,778 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 17:58:31,778 - mmseg - INFO - Iter [45000/160000] lr: 3.750e-05, eta: 10:07:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1899, decode.acc_seg: 92.2839, loss: 0.1899 +2023-03-04 17:58:45,154 - mmseg - INFO - Iter [45050/160000] lr: 3.750e-05, eta: 10:06:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.3550, loss: 0.1886 +2023-03-04 17:58:58,400 - mmseg - INFO - Iter [45100/160000] lr: 3.750e-05, eta: 10:06:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1889, decode.acc_seg: 92.4454, loss: 0.1889 +2023-03-04 17:59:11,763 - mmseg - INFO - Iter [45150/160000] lr: 3.750e-05, eta: 10:06:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.0164, loss: 0.1968 +2023-03-04 17:59:24,971 - mmseg - INFO - Iter [45200/160000] lr: 3.750e-05, eta: 10:05:46, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5357, loss: 0.1840 +2023-03-04 17:59:38,292 - mmseg - INFO - Iter [45250/160000] lr: 3.750e-05, eta: 10:05:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.6151, loss: 0.1796 +2023-03-04 17:59:51,682 - mmseg - INFO - Iter [45300/160000] lr: 3.750e-05, eta: 10:05:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1980, decode.acc_seg: 92.0653, loss: 0.1980 +2023-03-04 18:00:05,153 - mmseg - INFO - Iter [45350/160000] lr: 3.750e-05, eta: 10:04:40, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3861, loss: 0.1869 +2023-03-04 18:00:18,527 - mmseg - INFO - Iter [45400/160000] lr: 3.750e-05, eta: 10:04:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.5162, loss: 0.1885 +2023-03-04 18:00:34,339 - mmseg - INFO - Iter [45450/160000] lr: 3.750e-05, eta: 10:04:02, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.1568, loss: 0.1910 +2023-03-04 18:00:47,713 - mmseg - INFO - Iter [45500/160000] lr: 3.750e-05, eta: 10:03:40, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.2952, loss: 0.1912 +2023-03-04 18:01:01,055 - mmseg - INFO - Iter [45550/160000] lr: 3.750e-05, eta: 10:03:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4806, loss: 0.1870 +2023-03-04 18:01:14,419 - mmseg - INFO - Iter [45600/160000] lr: 3.750e-05, eta: 10:02:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1940, decode.acc_seg: 92.1471, loss: 0.1940 +2023-03-04 18:01:27,585 - mmseg - INFO - Iter [45650/160000] lr: 3.750e-05, eta: 10:02:34, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.2658, loss: 0.1922 +2023-03-04 18:01:40,965 - mmseg - INFO - Iter [45700/160000] lr: 3.750e-05, eta: 10:02:12, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.2495, loss: 0.1900 +2023-03-04 18:01:54,218 - mmseg - INFO - Iter [45750/160000] lr: 3.750e-05, eta: 10:01:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.2789, loss: 0.1885 +2023-03-04 18:02:07,657 - mmseg - INFO - Iter [45800/160000] lr: 3.750e-05, eta: 10:01:28, time: 0.269, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3259, loss: 0.1880 +2023-03-04 18:02:20,919 - mmseg - INFO - Iter [45850/160000] lr: 3.750e-05, eta: 10:01:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6981, loss: 0.1803 +2023-03-04 18:02:34,234 - mmseg - INFO - Iter [45900/160000] lr: 3.750e-05, eta: 10:00:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2000, decode.acc_seg: 92.1104, loss: 0.2000 +2023-03-04 18:02:47,594 - mmseg - INFO - Iter [45950/160000] lr: 3.750e-05, eta: 10:00:22, time: 0.267, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.4533, loss: 0.1881 +2023-03-04 18:03:00,994 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:03:00,994 - mmseg - INFO - Iter [46000/160000] lr: 3.750e-05, eta: 10:00:01, time: 0.268, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.2801, loss: 0.1922 +2023-03-04 18:03:14,471 - mmseg - INFO - Iter [46050/160000] lr: 3.750e-05, eta: 9:59:39, time: 0.270, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.3014, loss: 0.1914 +2023-03-04 18:03:30,242 - mmseg - INFO - Iter [46100/160000] lr: 3.750e-05, eta: 9:59:23, time: 0.315, data_time: 0.052, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.4872, loss: 0.1892 +2023-03-04 18:03:43,586 - mmseg - INFO - Iter [46150/160000] lr: 3.750e-05, eta: 9:59:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.4104, loss: 0.1864 +2023-03-04 18:03:56,877 - mmseg - INFO - Iter [46200/160000] lr: 3.750e-05, eta: 9:58:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.5369, loss: 0.1874 +2023-03-04 18:04:10,125 - mmseg - INFO - Iter [46250/160000] lr: 3.750e-05, eta: 9:58:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5949, loss: 0.1839 +2023-03-04 18:04:23,481 - mmseg - INFO - Iter [46300/160000] lr: 3.750e-05, eta: 9:57:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.3813, loss: 0.1849 +2023-03-04 18:04:36,692 - mmseg - INFO - Iter [46350/160000] lr: 3.750e-05, eta: 9:57:34, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.3350, loss: 0.1903 +2023-03-04 18:04:50,080 - mmseg - INFO - Iter [46400/160000] lr: 3.750e-05, eta: 9:57:12, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1950, decode.acc_seg: 92.2145, loss: 0.1950 +2023-03-04 18:05:03,308 - mmseg - INFO - Iter [46450/160000] lr: 3.750e-05, eta: 9:56:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.2907, loss: 0.1935 +2023-03-04 18:05:16,707 - mmseg - INFO - Iter [46500/160000] lr: 3.750e-05, eta: 9:56:29, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5885, loss: 0.1828 +2023-03-04 18:05:30,044 - mmseg - INFO - Iter [46550/160000] lr: 3.750e-05, eta: 9:56:07, time: 0.267, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4263, loss: 0.1862 +2023-03-04 18:05:43,352 - mmseg - INFO - Iter [46600/160000] lr: 3.750e-05, eta: 9:55:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5466, loss: 0.1847 +2023-03-04 18:05:56,698 - mmseg - INFO - Iter [46650/160000] lr: 3.750e-05, eta: 9:55:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.4143, loss: 0.1910 +2023-03-04 18:06:12,538 - mmseg - INFO - Iter [46700/160000] lr: 3.750e-05, eta: 9:55:08, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.3896, loss: 0.1863 +2023-03-04 18:06:25,872 - mmseg - INFO - Iter [46750/160000] lr: 3.750e-05, eta: 9:54:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6608, loss: 0.1815 +2023-03-04 18:06:39,510 - mmseg - INFO - Iter [46800/160000] lr: 3.750e-05, eta: 9:54:25, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.2755, loss: 0.1880 +2023-03-04 18:06:52,770 - mmseg - INFO - Iter [46850/160000] lr: 3.750e-05, eta: 9:54:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.6237, loss: 0.1855 +2023-03-04 18:07:06,396 - mmseg - INFO - Iter [46900/160000] lr: 3.750e-05, eta: 9:53:43, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1941, decode.acc_seg: 92.1949, loss: 0.1941 +2023-03-04 18:07:19,712 - mmseg - INFO - Iter [46950/160000] lr: 3.750e-05, eta: 9:53:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.5267, loss: 0.1869 +2023-03-04 18:07:32,968 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:07:32,969 - mmseg - INFO - Iter [47000/160000] lr: 3.750e-05, eta: 9:52:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.3998, loss: 0.1846 +2023-03-04 18:07:46,338 - mmseg - INFO - Iter [47050/160000] lr: 3.750e-05, eta: 9:52:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.1071, loss: 0.1910 +2023-03-04 18:07:59,703 - mmseg - INFO - Iter [47100/160000] lr: 3.750e-05, eta: 9:52:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.3218, loss: 0.1932 +2023-03-04 18:08:12,974 - mmseg - INFO - Iter [47150/160000] lr: 3.750e-05, eta: 9:51:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1967, decode.acc_seg: 92.2026, loss: 0.1967 +2023-03-04 18:08:26,265 - mmseg - INFO - Iter [47200/160000] lr: 3.750e-05, eta: 9:51:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3729, loss: 0.1887 +2023-03-04 18:08:39,525 - mmseg - INFO - Iter [47250/160000] lr: 3.750e-05, eta: 9:51:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1927, decode.acc_seg: 92.1904, loss: 0.1927 +2023-03-04 18:08:52,979 - mmseg - INFO - Iter [47300/160000] lr: 3.750e-05, eta: 9:50:51, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.2941, loss: 0.1935 +2023-03-04 18:09:08,936 - mmseg - INFO - Iter [47350/160000] lr: 3.750e-05, eta: 9:50:35, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.6284, loss: 0.1839 +2023-03-04 18:09:22,149 - mmseg - INFO - Iter [47400/160000] lr: 3.750e-05, eta: 9:50:14, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5539, loss: 0.1824 +2023-03-04 18:09:35,541 - mmseg - INFO - Iter [47450/160000] lr: 3.750e-05, eta: 9:49:52, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.5821, loss: 0.1822 +2023-03-04 18:09:48,821 - mmseg - INFO - Iter [47500/160000] lr: 3.750e-05, eta: 9:49:31, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3426, loss: 0.1887 +2023-03-04 18:10:02,165 - mmseg - INFO - Iter [47550/160000] lr: 3.750e-05, eta: 9:49:10, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.6014, loss: 0.1821 +2023-03-04 18:10:15,618 - mmseg - INFO - Iter [47600/160000] lr: 3.750e-05, eta: 9:48:48, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.2021, decode.acc_seg: 91.9812, loss: 0.2021 +2023-03-04 18:10:28,914 - mmseg - INFO - Iter [47650/160000] lr: 3.750e-05, eta: 9:48:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.3786, loss: 0.1882 +2023-03-04 18:10:42,181 - mmseg - INFO - Iter [47700/160000] lr: 3.750e-05, eta: 9:48:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.4188, loss: 0.1842 +2023-03-04 18:10:55,421 - mmseg - INFO - Iter [47750/160000] lr: 3.750e-05, eta: 9:47:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7213, loss: 0.1801 +2023-03-04 18:11:08,835 - mmseg - INFO - Iter [47800/160000] lr: 3.750e-05, eta: 9:47:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.4462, loss: 0.1914 +2023-03-04 18:11:22,068 - mmseg - INFO - Iter [47850/160000] lr: 3.750e-05, eta: 9:47:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.3774, loss: 0.1870 +2023-03-04 18:11:35,391 - mmseg - INFO - Iter [47900/160000] lr: 3.750e-05, eta: 9:46:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.1729, loss: 0.1925 +2023-03-04 18:11:48,641 - mmseg - INFO - Iter [47950/160000] lr: 3.750e-05, eta: 9:46:19, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.1813, loss: 0.1948 +2023-03-04 18:12:04,561 - mmseg - INFO - Swap parameters (after train) after iter [48000] +2023-03-04 18:12:04,583 - mmseg - INFO - Saving checkpoint at 48000 iterations +2023-03-04 18:12:06,423 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:12:06,423 - mmseg - INFO - Iter [48000/160000] lr: 3.750e-05, eta: 9:46:08, time: 0.356, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.4437, loss: 0.1878 +2023-03-04 18:23:13,119 - mmseg - INFO - per class results: +2023-03-04 18:23:13,128 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.41,76.4,76.4,76.38,76.38,76.38,76.38,76.37,76.37,76.37,76.39 | +| building | 81.47,81.47,81.47,81.47,81.47,81.48,81.48,81.46,81.47,81.46,81.45 | +| sky | 94.26,94.26,94.25,94.26,94.25,94.25,94.25,94.25,94.25,94.24,94.25 | +| floor | 80.13,80.12,80.11,80.12,80.12,80.1,80.09,80.07,80.07,80.06,80.07 | +| tree | 72.87,72.85,72.86,72.85,72.84,72.82,72.81,72.8,72.8,72.79,72.79 | +| ceiling | 82.76,82.76,82.75,82.76,82.75,82.74,82.74,82.73,82.73,82.72,82.74 | +| road | 81.96,81.97,81.96,81.96,81.95,81.95,81.94,81.95,81.94,81.94,81.94 | +| bed | 88.62,88.62,88.62,88.62,88.62,88.61,88.61,88.61,88.6,88.59,88.63 | +| windowpane | 61.12,61.13,61.13,61.12,61.13,61.14,61.11,61.13,61.14,61.15,61.11 | +| grass | 66.05,66.05,66.05,66.06,66.05,66.08,66.07,66.05,66.04,66.05,66.01 | +| cabinet | 59.54,59.54,59.55,59.55,59.56,59.58,59.6,59.62,59.64,59.66,59.73 | +| sidewalk | 65.96,65.97,65.97,65.98,65.97,65.98,65.99,66.02,66.05,66.02,66.05 | +| person | 79.32,79.34,79.33,79.33,79.33,79.34,79.34,79.33,79.33,79.34,79.33 | +| earth | 33.31,33.36,33.41,33.41,33.46,33.41,33.39,33.46,33.44,33.43,33.61 | +| door | 48.33,48.37,48.45,48.45,48.51,48.52,48.52,48.56,48.63,48.63,48.66 | +| table | 61.58,61.57,61.57,61.61,61.62,61.62,61.64,61.67,61.69,61.69,61.57 | +| mountain | 51.64,51.65,51.69,51.71,51.71,51.7,51.72,51.72,51.72,51.76,51.83 | +| plant | 50.26,50.24,50.24,50.25,50.26,50.22,50.22,50.2,50.18,50.17,50.2 | +| curtain | 69.83,69.97,70.08,70.23,70.27,70.25,70.2,70.15,70.15,70.12,70.05 | +| chair | 58.04,58.05,58.04,58.06,58.05,58.07,58.07,58.09,58.08,58.1,58.04 | +| car | 83.28,83.29,83.29,83.28,83.29,83.31,83.31,83.31,83.32,83.3,83.27 | +| water | 47.19,47.17,47.18,47.15,47.13,47.15,47.13,47.12,47.11,47.1,47.05 | +| painting | 69.67,69.65,69.67,69.66,69.62,69.65,69.63,69.62,69.65,69.6,69.59 | +| sofa | 65.87,65.83,65.85,65.87,65.88,65.91,65.89,65.92,65.87,65.89,65.97 | +| shelf | 40.38,40.35,40.32,40.33,40.33,40.34,40.24,40.23,40.22,40.22,40.24 | +| house | 44.32,44.27,44.23,44.23,44.15,44.13,44.1,44.02,44.06,44.03,43.96 | +| sea | 44.89,44.84,44.78,44.76,44.73,44.71,44.65,44.59,44.54,44.49,44.51 | +| mirror | 65.11,65.14,65.08,65.09,65.14,65.1,65.11,65.09,65.09,65.11,65.07 | +| rug | 56.05,56.13,56.11,56.17,56.18,56.13,55.96,55.84,55.81,55.58,55.53 | +| field | 28.47,28.53,28.57,28.58,28.62,28.61,28.63,28.67,28.71,28.72,28.65 | +| armchair | 44.84,44.81,44.81,44.93,44.95,44.95,44.87,45.0,44.96,45.01,44.85 | +| seat | 54.62,54.53,54.53,54.47,54.46,54.44,54.3,54.27,54.26,54.23,54.1 | +| fence | 40.91,40.89,40.85,40.89,40.96,40.89,40.9,40.83,40.81,40.78,40.8 | +| desk | 49.03,49.02,49.02,49.1,49.05,49.07,49.11,49.08,49.06,49.04,48.98 | +| rock | 29.86,29.88,29.82,29.72,29.87,29.91,29.92,29.83,29.75,29.71,29.82 | +| wardrobe | 49.09,49.16,49.16,49.24,49.24,49.33,49.32,49.35,49.4,49.36,49.44 | +| lamp | 63.66,63.66,63.65,63.65,63.66,63.65,63.65,63.66,63.68,63.66,63.69 | +| bathtub | 77.03,76.98,76.97,77.03,76.94,76.91,76.89,76.72,76.64,76.62,76.76 | +| railing | 31.87,31.81,31.77,31.74,31.78,31.73,31.69,31.59,31.62,31.56,31.57 | +| cushion | 55.38,55.4,55.41,55.41,55.47,55.49,55.52,55.52,55.55,55.58,55.38 | +| base | 28.17,28.15,28.19,28.24,28.25,28.18,28.2,28.28,28.25,28.31,28.33 | +| box | 24.62,24.63,24.66,24.63,24.62,24.66,24.63,24.62,24.57,24.6,24.45 | +| column | 46.49,46.47,46.5,46.53,46.49,46.59,46.57,46.54,46.58,46.52,46.58 | +| signboard | 36.01,35.97,35.95,35.92,35.91,35.91,35.84,35.87,35.79,35.81,35.8 | +| chest of drawers | 39.13,39.22,39.23,39.18,39.1,39.17,39.15,39.2,39.28,39.27,39.43 | +| counter | 27.76,27.73,27.72,27.7,27.75,27.72,27.64,27.63,27.57,27.54,27.26 | +| sand | 32.34,32.36,32.4,32.4,32.34,32.37,32.41,32.31,32.32,32.31,32.6 | +| sink | 70.96,70.97,70.94,70.88,70.88,70.89,70.84,70.82,70.79,70.75,70.9 | +| skyscraper | 48.56,48.58,48.61,48.68,48.58,48.69,48.76,48.72,48.71,48.71,48.75 | +| fireplace | 66.23,66.26,66.19,66.26,66.15,66.22,66.14,66.05,66.03,66.0,66.36 | +| refrigerator | 77.61,77.61,77.6,77.59,77.69,77.59,77.56,77.63,77.72,77.71,77.5 | +| grandstand | 41.72,41.75,41.64,41.58,41.61,41.68,41.7,41.68,41.69,41.7,41.77 | +| path | 15.78,15.76,15.75,15.74,15.74,15.71,15.72,15.72,15.74,15.72,15.72 | +| stairs | 32.09,32.07,32.09,32.09,32.11,32.15,32.16,32.15,32.17,32.2,32.1 | +| runway | 63.78,63.79,63.79,63.79,63.76,63.79,63.79,63.78,63.79,63.78,63.76 | +| case | 48.99,48.97,48.86,48.78,48.74,48.7,48.62,48.55,48.52,48.52,48.38 | +| pool table | 92.71,92.69,92.7,92.72,92.71,92.68,92.68,92.66,92.64,92.62,92.68 | +| pillow | 56.94,56.9,56.91,56.91,56.94,56.97,56.95,56.97,56.95,56.91,56.91 | +| screen door | 66.91,66.98,66.85,66.86,66.82,66.84,66.71,66.64,66.48,66.55,66.58 | +| stairway | 25.23,25.21,25.17,25.15,25.13,25.13,25.09,25.1,25.12,25.04,24.97 | +| river | 9.94,9.93,9.89,9.87,9.9,9.86,9.84,9.81,9.77,9.74,9.82 | +| bridge | 55.08,55.42,55.72,56.05,56.49,56.72,57.11,57.41,57.66,57.84,58.1 | +| bookcase | 41.03,41.08,41.19,41.29,41.35,41.39,41.37,41.51,41.6,41.63,41.55 | +| blind | 45.93,45.79,45.66,45.44,45.35,45.32,45.13,45.02,45.01,44.9,44.76 | +| coffee table | 67.5,67.43,67.45,67.54,67.59,67.48,67.54,67.58,67.62,67.62,67.53 | +| toilet | 86.06,86.04,86.06,86.12,86.07,86.11,86.1,86.06,86.08,86.08,86.19 | +| flower | 31.33,31.39,31.24,31.37,31.39,31.31,31.28,31.32,31.35,31.38,31.26 | +| book | 46.76,46.8,46.8,46.74,46.77,46.66,46.67,46.71,46.69,46.7,46.63 | +| hill | 8.06,8.12,8.11,8.18,8.15,8.22,8.16,8.27,8.29,8.29,8.29 | +| bench | 44.16,44.15,44.16,44.2,44.15,44.19,44.1,44.09,44.04,44.0,44.19 | +| countertop | 53.23,53.28,53.21,53.14,53.04,53.0,53.11,53.02,52.98,52.96,53.04 | +| stove | 72.96,72.97,73.05,73.15,73.15,73.12,73.11,73.14,73.13,73.27,73.0 | +| palm | 50.83,50.88,50.88,50.98,50.95,51.09,51.06,50.96,51.04,50.95,51.22 | +| kitchen island | 46.47,46.46,46.47,46.48,46.34,46.47,46.52,46.67,46.62,46.86,46.59 | +| computer | 57.15,57.19,57.2,57.23,57.24,57.26,57.23,57.25,57.27,57.26,57.23 | +| swivel chair | 44.71,44.76,44.65,44.74,44.64,44.66,44.7,44.66,44.6,44.6,44.69 | +| boat | 38.68,38.71,38.74,38.75,38.74,38.77,38.78,38.83,38.82,38.84,39.03 | +| bar | 27.01,26.97,26.96,26.94,26.98,26.92,26.9,26.94,27.03,26.96,26.84 | +| arcade machine | 26.36,26.57,26.81,27.07,26.99,27.53,27.76,27.77,28.21,28.38,28.59 | +| hovel | 32.4,32.28,32.12,32.07,31.98,31.88,31.8,31.66,31.6,31.5,31.45 | +| bus | 88.73,88.78,88.84,88.85,88.83,88.82,88.87,88.89,88.88,88.92,88.8 | +| towel | 60.09,60.17,60.17,60.2,60.18,60.17,60.15,60.11,60.0,59.94,60.41 | +| light | 56.07,56.04,56.02,55.96,56.05,56.04,55.98,55.91,55.92,55.9,55.97 | +| truck | 34.04,33.96,34.0,34.0,33.99,34.09,34.05,34.05,33.95,34.0,34.06 | +| tower | 23.35,23.45,23.41,23.54,23.51,23.5,23.66,23.47,23.55,23.71,23.29 | +| chandelier | 66.41,66.41,66.39,66.43,66.48,66.46,66.45,66.46,66.4,66.42,66.51 | +| awning | 23.4,23.48,23.54,23.51,23.6,23.65,23.67,23.71,23.78,23.85,23.89 | +| streetlight | 28.41,28.35,28.34,28.31,28.25,28.19,28.19,28.22,28.16,28.1,28.0 | +| booth | 53.3,53.39,53.36,53.28,53.38,53.35,53.29,53.34,53.14,53.03,53.75 | +| television receiver | 68.72,68.73,68.7,68.68,68.7,68.73,68.72,68.75,68.67,68.76,68.8 | +| airplane | 52.45,52.31,52.29,52.12,52.15,51.92,51.85,51.94,51.78,51.69,51.73 | +| dirt track | 8.41,8.46,8.39,8.38,8.45,8.69,8.57,8.69,8.64,8.65,8.62 | +| apparel | 29.69,29.8,29.72,29.79,29.49,29.46,29.4,29.42,29.42,29.4,29.51 | +| pole | 24.23,24.15,24.26,24.21,24.11,24.07,24.09,24.06,24.04,24.02,24.02 | +| land | 9.26,9.13,9.36,9.35,9.28,9.29,9.3,9.29,9.28,9.34,9.43 | +| bannister | 5.97,5.92,5.84,5.88,5.96,5.75,5.93,5.91,5.91,5.76,6.06 | +| escalator | 22.96,23.02,22.92,22.97,22.98,23.09,23.1,23.09,23.17,23.14,22.94 | +| ottoman | 49.46,49.4,49.27,49.45,49.24,49.35,49.51,49.4,49.19,49.14,49.48 | +| bottle | 16.17,16.07,16.09,16.06,15.93,15.86,15.83,15.85,15.83,15.92,15.64 | +| buffet | 52.13,52.62,52.86,53.09,53.53,53.78,54.25,54.47,55.0,55.62,56.3 | +| poster | 27.05,27.06,27.19,27.21,27.13,27.21,27.14,26.94,26.92,26.88,27.08 | +| stage | 18.13,18.16,18.15,18.16,18.18,18.2,18.27,18.32,18.33,18.38,18.39 | +| van | 48.26,48.43,48.47,48.55,48.53,48.69,48.59,48.71,48.83,48.62,48.71 | +| ship | 38.69,38.56,38.82,38.88,39.45,39.07,38.71,38.6,38.42,37.62,37.29 | +| fountain | 7.68,7.72,7.68,7.64,7.69,7.67,7.57,7.53,7.39,7.22,7.3 | +| conveyer belt | 75.98,75.97,75.86,75.99,75.79,75.8,75.79,75.77,75.67,75.65,75.55 | +| canopy | 15.0,15.08,15.11,15.17,15.16,15.2,15.22,15.31,15.37,15.38,15.17 | +| washer | 66.15,66.18,66.12,66.18,66.22,66.12,66.11,66.12,66.14,66.14,66.1 | +| plaything | 22.24,22.22,22.27,22.32,22.37,22.38,22.45,22.41,22.5,22.45,22.65 | +| swimming pool | 40.5,40.52,40.93,40.87,40.95,40.54,41.09,41.08,41.08,41.33,41.01 | +| stool | 41.25,41.31,41.39,41.4,41.2,41.21,41.22,41.23,41.2,41.36,41.12 | +| barrel | 41.33,42.17,41.24,42.18,41.41,41.14,41.38,42.29,41.72,41.39,41.1 | +| basket | 28.37,28.32,28.33,28.34,28.3,28.28,28.21,28.27,28.23,28.24,28.29 | +| waterfall | 61.03,61.23,60.98,60.93,61.04,60.94,60.51,60.88,61.03,60.72,61.03 | +| tent | 93.83,93.82,93.83,93.81,93.76,93.79,93.79,93.8,93.8,93.81,93.8 | +| bag | 11.67,11.75,11.73,11.79,11.78,11.75,11.86,11.81,11.87,11.86,11.85 | +| minibike | 61.32,61.4,61.33,61.31,61.35,61.35,61.19,61.16,61.16,61.07,61.16 | +| cradle | 80.76,80.67,80.52,80.59,80.59,80.56,80.53,80.48,80.45,80.41,80.44 | +| oven | 27.55,27.51,27.54,27.52,27.46,27.45,27.33,27.37,27.31,27.24,27.29 | +| ball | 47.68,47.76,47.79,47.84,47.95,48.03,47.97,47.94,48.02,47.97,47.96 | +| food | 53.6,53.51,53.54,53.41,53.45,53.32,53.29,53.29,53.07,53.11,52.83 | +| step | 16.07,15.97,15.92,16.23,15.9,15.81,15.99,16.27,16.33,16.34,16.72 | +| tank | 41.8,41.78,41.82,41.78,41.75,41.72,41.72,41.71,41.69,41.69,41.82 | +| trade name | 24.77,24.74,24.74,24.7,24.71,24.65,24.65,24.68,24.61,24.61,24.62 | +| microwave | 37.55,37.56,37.54,37.56,37.52,37.53,37.54,37.55,37.53,37.54,37.5 | +| pot | 41.29,41.25,41.32,41.39,41.32,41.37,41.28,41.43,41.41,41.36,41.48 | +| animal | 51.8,51.98,52.05,52.16,52.2,52.33,52.32,52.54,52.57,52.74,52.73 | +| bicycle | 45.17,45.2,45.29,45.43,45.59,45.42,45.51,45.51,45.49,45.52,45.86 | +| lake | 60.05,60.0,59.99,59.96,59.94,59.9,59.89,59.86,59.83,59.8,59.78 | +| dishwasher | 76.96,76.96,76.99,77.05,77.06,77.09,77.07,76.99,77.15,77.04,77.38 | +| screen | 65.63,65.58,65.45,65.47,65.45,65.24,65.24,65.26,65.13,64.96,64.95 | +| blanket | 13.6,13.53,13.57,13.65,13.67,13.76,13.68,13.73,13.7,13.63,13.73 | +| sculpture | 36.39,36.43,36.33,36.58,36.65,36.8,36.74,36.84,36.84,37.0,37.21 | +| hood | 56.82,56.7,56.86,56.77,56.86,56.77,56.47,56.46,56.34,56.21,56.47 | +| sconce | 41.96,41.95,41.78,41.81,41.6,41.53,41.44,41.46,41.58,41.41,40.99 | +| vase | 36.83,36.91,36.9,36.89,36.85,36.76,36.77,36.81,36.69,36.58,36.8 | +| traffic light | 29.41,29.51,29.45,29.63,29.55,29.64,29.68,29.79,29.9,29.77,29.88 | +| tray | 5.5,5.5,5.51,5.52,5.51,5.48,5.52,5.53,5.55,5.55,5.54 | +| ashcan | 38.04,38.0,38.14,38.3,38.38,38.42,38.54,38.69,38.83,38.81,38.79 | +| fan | 57.69,57.72,57.61,57.63,57.69,57.57,57.58,57.48,57.53,57.55,57.47 | +| pier | 12.36,12.35,12.16,12.15,11.95,12.02,11.94,12.02,11.89,11.9,11.8 | +| crt screen | 4.47,4.43,4.44,4.52,4.52,4.49,4.51,4.55,4.5,4.48,4.53 | +| plate | 39.56,39.66,39.58,39.68,39.6,39.62,39.63,39.67,39.74,39.68,39.76 | +| monitor | 29.05,29.03,28.86,28.6,28.58,28.41,28.28,28.24,28.06,28.13,28.15 | +| bulletin board | 44.91,45.33,45.21,45.5,45.33,45.43,45.37,45.22,45.66,45.73,45.11 | +| shower | 1.67,1.75,1.73,1.68,1.72,1.74,1.71,1.78,1.72,1.76,1.77 | +| radiator | 46.04,46.24,46.24,46.21,46.29,46.47,46.41,46.36,46.31,46.47,46.58 | +| glass | 12.0,12.0,12.02,12.05,12.0,12.06,12.05,12.07,12.1,12.1,12.0 | +| clock | 24.5,24.48,24.52,24.41,24.31,24.36,24.35,24.38,24.34,24.34,24.34 | +| flag | 37.86,37.93,37.89,37.83,38.0,37.93,37.96,38.04,38.13,38.07,38.09 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 18:23:13,128 - mmseg - INFO - Summary: +2023-03-04 18:23:13,129 - mmseg - INFO - ++------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++------------------------------------------------------------------+ +| 46.29,46.31,46.3,46.33,46.32,46.32,46.32,46.33,46.33,46.31,46.33 | ++------------------------------------------------------------------+ +2023-03-04 18:23:13,193 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune/best_mIoU_iter_32000.pth was removed +2023-03-04 18:23:15,262 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_48000.pth. +2023-03-04 18:23:15,263 - mmseg - INFO - Best mIoU is 0.4633 at 48000 iter. +2023-03-04 18:23:15,264 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:23:15,264 - mmseg - INFO - Iter(val) [250] mIoU: [0.4629, 0.4631, 0.463, 0.4633, 0.4632, 0.4632, 0.4632, 0.4633, 0.4633, 0.4631, 0.4633], copy_paste: 46.29,46.31,46.3,46.33,46.32,46.32,46.32,46.33,46.33,46.31,46.33 +2023-03-04 18:23:15,271 - mmseg - INFO - Swap parameters (before train) before iter [48001] +2023-03-04 18:23:29,172 - mmseg - INFO - Iter [48050/160000] lr: 3.750e-05, eta: 10:11:46, time: 13.655, data_time: 13.385, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.3124, loss: 0.1914 +2023-03-04 18:23:42,710 - mmseg - INFO - Iter [48100/160000] lr: 3.750e-05, eta: 10:11:23, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.3444, loss: 0.1892 +2023-03-04 18:23:56,027 - mmseg - INFO - Iter [48150/160000] lr: 3.750e-05, eta: 10:11:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.6075, loss: 0.1826 +2023-03-04 18:24:09,371 - mmseg - INFO - Iter [48200/160000] lr: 3.750e-05, eta: 10:10:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.0920, loss: 0.1943 +2023-03-04 18:24:22,656 - mmseg - INFO - Iter [48250/160000] lr: 3.750e-05, eta: 10:10:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.1708, loss: 0.1958 +2023-03-04 18:24:35,883 - mmseg - INFO - Iter [48300/160000] lr: 3.750e-05, eta: 10:09:49, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.4696, loss: 0.1856 +2023-03-04 18:24:49,135 - mmseg - INFO - Iter [48350/160000] lr: 3.750e-05, eta: 10:09:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4621, loss: 0.1871 +2023-03-04 18:25:02,418 - mmseg - INFO - Iter [48400/160000] lr: 3.750e-05, eta: 10:09:02, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6566, loss: 0.1818 +2023-03-04 18:25:15,839 - mmseg - INFO - Iter [48450/160000] lr: 3.750e-05, eta: 10:08:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.0884, loss: 0.1922 +2023-03-04 18:25:29,115 - mmseg - INFO - Iter [48500/160000] lr: 3.750e-05, eta: 10:08:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.3499, loss: 0.1905 +2023-03-04 18:25:42,355 - mmseg - INFO - Iter [48550/160000] lr: 3.750e-05, eta: 10:07:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4933, loss: 0.1861 +2023-03-04 18:25:58,287 - mmseg - INFO - Iter [48600/160000] lr: 3.750e-05, eta: 10:07:34, time: 0.319, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.5053, loss: 0.1869 +2023-03-04 18:26:11,669 - mmseg - INFO - Iter [48650/160000] lr: 3.750e-05, eta: 10:07:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1940, decode.acc_seg: 92.1703, loss: 0.1940 +2023-03-04 18:26:24,921 - mmseg - INFO - Iter [48700/160000] lr: 3.750e-05, eta: 10:06:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.2857, loss: 0.1885 +2023-03-04 18:26:38,245 - mmseg - INFO - Iter [48750/160000] lr: 3.750e-05, eta: 10:06:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.4067, loss: 0.1904 +2023-03-04 18:26:51,575 - mmseg - INFO - Iter [48800/160000] lr: 3.750e-05, eta: 10:06:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.2919, loss: 0.1916 +2023-03-04 18:27:04,883 - mmseg - INFO - Iter [48850/160000] lr: 3.750e-05, eta: 10:05:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.6351, loss: 0.1772 +2023-03-04 18:27:18,278 - mmseg - INFO - Iter [48900/160000] lr: 3.750e-05, eta: 10:05:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4990, loss: 0.1858 +2023-03-04 18:27:31,553 - mmseg - INFO - Iter [48950/160000] lr: 3.750e-05, eta: 10:04:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3902, loss: 0.1908 +2023-03-04 18:27:44,982 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:27:44,982 - mmseg - INFO - Iter [49000/160000] lr: 3.750e-05, eta: 10:04:29, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.5027, loss: 0.1873 +2023-03-04 18:27:58,276 - mmseg - INFO - Iter [49050/160000] lr: 3.750e-05, eta: 10:04:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6288, loss: 0.1817 +2023-03-04 18:28:11,735 - mmseg - INFO - Iter [49100/160000] lr: 3.750e-05, eta: 10:03:43, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4537, loss: 0.1858 +2023-03-04 18:28:24,959 - mmseg - INFO - Iter [49150/160000] lr: 3.750e-05, eta: 10:03:19, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1940, decode.acc_seg: 92.2170, loss: 0.1940 +2023-03-04 18:28:38,231 - mmseg - INFO - Iter [49200/160000] lr: 3.750e-05, eta: 10:02:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1949, decode.acc_seg: 92.1274, loss: 0.1949 +2023-03-04 18:28:54,202 - mmseg - INFO - Iter [49250/160000] lr: 3.750e-05, eta: 10:02:39, time: 0.319, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5147, loss: 0.1825 +2023-03-04 18:29:07,444 - mmseg - INFO - Iter [49300/160000] lr: 3.750e-05, eta: 10:02:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.1509, loss: 0.1935 +2023-03-04 18:29:20,822 - mmseg - INFO - Iter [49350/160000] lr: 3.750e-05, eta: 10:01:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1971, decode.acc_seg: 92.1103, loss: 0.1971 +2023-03-04 18:29:34,222 - mmseg - INFO - Iter [49400/160000] lr: 3.750e-05, eta: 10:01:30, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.5443, loss: 0.1863 +2023-03-04 18:29:47,447 - mmseg - INFO - Iter [49450/160000] lr: 3.750e-05, eta: 10:01:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.2109, loss: 0.1951 +2023-03-04 18:30:00,748 - mmseg - INFO - Iter [49500/160000] lr: 3.750e-05, eta: 10:00:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5418, loss: 0.1834 +2023-03-04 18:30:14,141 - mmseg - INFO - Iter [49550/160000] lr: 3.750e-05, eta: 10:00:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.4666, loss: 0.1841 +2023-03-04 18:30:27,456 - mmseg - INFO - Iter [49600/160000] lr: 3.750e-05, eta: 9:59:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1891, decode.acc_seg: 92.4608, loss: 0.1891 +2023-03-04 18:30:41,048 - mmseg - INFO - Iter [49650/160000] lr: 3.750e-05, eta: 9:59:36, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5737, loss: 0.1865 +2023-03-04 18:30:54,316 - mmseg - INFO - Iter [49700/160000] lr: 3.750e-05, eta: 9:59:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5116, loss: 0.1823 +2023-03-04 18:31:07,658 - mmseg - INFO - Iter [49750/160000] lr: 3.750e-05, eta: 9:58:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.5196, loss: 0.1815 +2023-03-04 18:31:20,969 - mmseg - INFO - Iter [49800/160000] lr: 3.750e-05, eta: 9:58:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.2204, loss: 0.1893 +2023-03-04 18:31:36,762 - mmseg - INFO - Iter [49850/160000] lr: 3.750e-05, eta: 9:58:09, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.6049, loss: 0.1836 +2023-03-04 18:31:50,126 - mmseg - INFO - Iter [49900/160000] lr: 3.750e-05, eta: 9:57:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.2913, loss: 0.1884 +2023-03-04 18:32:03,367 - mmseg - INFO - Iter [49950/160000] lr: 3.750e-05, eta: 9:57:24, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1898, decode.acc_seg: 92.4013, loss: 0.1898 +2023-03-04 18:32:16,672 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:32:16,672 - mmseg - INFO - Iter [50000/160000] lr: 3.750e-05, eta: 9:57:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1945, decode.acc_seg: 92.1564, loss: 0.1945 +2023-03-04 18:32:29,908 - mmseg - INFO - Iter [50050/160000] lr: 3.750e-05, eta: 9:56:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.2891, loss: 0.1915 +2023-03-04 18:32:43,308 - mmseg - INFO - Iter [50100/160000] lr: 3.750e-05, eta: 9:56:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1918, decode.acc_seg: 92.3336, loss: 0.1918 +2023-03-04 18:32:56,656 - mmseg - INFO - Iter [50150/160000] lr: 3.750e-05, eta: 9:55:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5095, loss: 0.1835 +2023-03-04 18:33:10,015 - mmseg - INFO - Iter [50200/160000] lr: 3.750e-05, eta: 9:55:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.3565, loss: 0.1915 +2023-03-04 18:33:23,451 - mmseg - INFO - Iter [50250/160000] lr: 3.750e-05, eta: 9:55:07, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.4462, loss: 0.1847 +2023-03-04 18:33:36,817 - mmseg - INFO - Iter [50300/160000] lr: 3.750e-05, eta: 9:54:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.3778, loss: 0.1874 +2023-03-04 18:33:50,189 - mmseg - INFO - Iter [50350/160000] lr: 3.750e-05, eta: 9:54:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.0530, loss: 0.1951 +2023-03-04 18:34:03,578 - mmseg - INFO - Iter [50400/160000] lr: 3.750e-05, eta: 9:54:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.3169, loss: 0.1843 +2023-03-04 18:34:16,984 - mmseg - INFO - Iter [50450/160000] lr: 3.750e-05, eta: 9:53:37, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.6417, loss: 0.1848 +2023-03-04 18:34:32,671 - mmseg - INFO - Iter [50500/160000] lr: 3.750e-05, eta: 9:53:20, time: 0.314, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6863, loss: 0.1810 +2023-03-04 18:34:45,996 - mmseg - INFO - Iter [50550/160000] lr: 3.750e-05, eta: 9:52:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.6002, loss: 0.1874 +2023-03-04 18:34:59,296 - mmseg - INFO - Iter [50600/160000] lr: 3.750e-05, eta: 9:52:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4476, loss: 0.1875 +2023-03-04 18:35:12,695 - mmseg - INFO - Iter [50650/160000] lr: 3.750e-05, eta: 9:52:12, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4697, loss: 0.1858 +2023-03-04 18:35:26,150 - mmseg - INFO - Iter [50700/160000] lr: 3.750e-05, eta: 9:51:50, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.3854, loss: 0.1864 +2023-03-04 18:35:39,514 - mmseg - INFO - Iter [50750/160000] lr: 3.750e-05, eta: 9:51:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1934, decode.acc_seg: 92.2994, loss: 0.1934 +2023-03-04 18:35:52,858 - mmseg - INFO - Iter [50800/160000] lr: 3.750e-05, eta: 9:51:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.2381, loss: 0.1902 +2023-03-04 18:36:06,094 - mmseg - INFO - Iter [50850/160000] lr: 3.750e-05, eta: 9:50:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1961, decode.acc_seg: 92.1070, loss: 0.1961 +2023-03-04 18:36:19,467 - mmseg - INFO - Iter [50900/160000] lr: 3.750e-05, eta: 9:50:20, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5405, loss: 0.1825 +2023-03-04 18:36:32,833 - mmseg - INFO - Iter [50950/160000] lr: 3.750e-05, eta: 9:49:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.3169, loss: 0.1943 +2023-03-04 18:36:46,228 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:36:46,228 - mmseg - INFO - Iter [51000/160000] lr: 3.750e-05, eta: 9:49:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5422, loss: 0.1859 +2023-03-04 18:36:59,503 - mmseg - INFO - Iter [51050/160000] lr: 3.750e-05, eta: 9:49:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3492, loss: 0.1901 +2023-03-04 18:37:12,776 - mmseg - INFO - Iter [51100/160000] lr: 3.750e-05, eta: 9:48:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.6060, loss: 0.1839 +2023-03-04 18:37:28,636 - mmseg - INFO - Iter [51150/160000] lr: 3.750e-05, eta: 9:48:33, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1983, decode.acc_seg: 91.9701, loss: 0.1983 +2023-03-04 18:37:42,039 - mmseg - INFO - Iter [51200/160000] lr: 3.750e-05, eta: 9:48:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.6905, loss: 0.1791 +2023-03-04 18:37:55,335 - mmseg - INFO - Iter [51250/160000] lr: 3.750e-05, eta: 9:47:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4354, loss: 0.1866 +2023-03-04 18:38:08,703 - mmseg - INFO - Iter [51300/160000] lr: 3.750e-05, eta: 9:47:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.3102, loss: 0.1896 +2023-03-04 18:38:22,043 - mmseg - INFO - Iter [51350/160000] lr: 3.750e-05, eta: 9:47:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.4878, loss: 0.1886 +2023-03-04 18:38:35,308 - mmseg - INFO - Iter [51400/160000] lr: 3.750e-05, eta: 9:46:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5538, loss: 0.1849 +2023-03-04 18:38:48,614 - mmseg - INFO - Iter [51450/160000] lr: 3.750e-05, eta: 9:46:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.4014, loss: 0.1911 +2023-03-04 18:39:02,098 - mmseg - INFO - Iter [51500/160000] lr: 3.750e-05, eta: 9:45:57, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6976, loss: 0.1808 +2023-03-04 18:39:15,379 - mmseg - INFO - Iter [51550/160000] lr: 3.750e-05, eta: 9:45:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.4916, loss: 0.1854 +2023-03-04 18:39:28,628 - mmseg - INFO - Iter [51600/160000] lr: 3.750e-05, eta: 9:45:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1990, decode.acc_seg: 91.9302, loss: 0.1990 +2023-03-04 18:39:42,093 - mmseg - INFO - Iter [51650/160000] lr: 3.750e-05, eta: 9:44:51, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1907, decode.acc_seg: 92.3275, loss: 0.1907 +2023-03-04 18:39:55,692 - mmseg - INFO - Iter [51700/160000] lr: 3.750e-05, eta: 9:44:29, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1939, decode.acc_seg: 92.2491, loss: 0.1939 +2023-03-04 18:40:11,513 - mmseg - INFO - Iter [51750/160000] lr: 3.750e-05, eta: 9:44:12, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1968, decode.acc_seg: 92.0710, loss: 0.1968 +2023-03-04 18:40:25,039 - mmseg - INFO - Iter [51800/160000] lr: 3.750e-05, eta: 9:43:50, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.4987, loss: 0.1863 +2023-03-04 18:40:38,539 - mmseg - INFO - Iter [51850/160000] lr: 3.750e-05, eta: 9:43:29, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.3203, loss: 0.1890 +2023-03-04 18:40:51,956 - mmseg - INFO - Iter [51900/160000] lr: 3.750e-05, eta: 9:43:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.6459, loss: 0.1822 +2023-03-04 18:41:05,310 - mmseg - INFO - Iter [51950/160000] lr: 3.750e-05, eta: 9:42:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4768, loss: 0.1871 +2023-03-04 18:41:18,651 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:41:18,651 - mmseg - INFO - Iter [52000/160000] lr: 3.750e-05, eta: 9:42:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5225, loss: 0.1870 +2023-03-04 18:41:31,905 - mmseg - INFO - Iter [52050/160000] lr: 3.750e-05, eta: 9:42:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3638, loss: 0.1880 +2023-03-04 18:41:45,266 - mmseg - INFO - Iter [52100/160000] lr: 3.750e-05, eta: 9:41:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.3608, loss: 0.1903 +2023-03-04 18:41:58,586 - mmseg - INFO - Iter [52150/160000] lr: 3.750e-05, eta: 9:41:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.1690, loss: 0.1947 +2023-03-04 18:42:11,876 - mmseg - INFO - Iter [52200/160000] lr: 3.750e-05, eta: 9:40:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.3139, loss: 0.1916 +2023-03-04 18:42:25,288 - mmseg - INFO - Iter [52250/160000] lr: 3.750e-05, eta: 9:40:32, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.3058, loss: 0.1885 +2023-03-04 18:42:38,667 - mmseg - INFO - Iter [52300/160000] lr: 3.750e-05, eta: 9:40:10, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4172, loss: 0.1852 +2023-03-04 18:42:52,085 - mmseg - INFO - Iter [52350/160000] lr: 3.750e-05, eta: 9:39:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3982, loss: 0.1880 +2023-03-04 18:43:07,826 - mmseg - INFO - Iter [52400/160000] lr: 3.750e-05, eta: 9:39:31, time: 0.315, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.3798, loss: 0.1900 +2023-03-04 18:43:21,097 - mmseg - INFO - Iter [52450/160000] lr: 3.750e-05, eta: 9:39:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.5763, loss: 0.1881 +2023-03-04 18:43:34,492 - mmseg - INFO - Iter [52500/160000] lr: 3.750e-05, eta: 9:38:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.2981, loss: 0.1920 +2023-03-04 18:43:47,763 - mmseg - INFO - Iter [52550/160000] lr: 3.750e-05, eta: 9:38:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.4795, loss: 0.1854 +2023-03-04 18:44:01,000 - mmseg - INFO - Iter [52600/160000] lr: 3.750e-05, eta: 9:38:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3647, loss: 0.1901 +2023-03-04 18:44:14,354 - mmseg - INFO - Iter [52650/160000] lr: 3.750e-05, eta: 9:37:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1898, decode.acc_seg: 92.3998, loss: 0.1898 +2023-03-04 18:44:27,665 - mmseg - INFO - Iter [52700/160000] lr: 3.750e-05, eta: 9:37:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.2808, loss: 0.1905 +2023-03-04 18:44:40,965 - mmseg - INFO - Iter [52750/160000] lr: 3.750e-05, eta: 9:36:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.1944, loss: 0.1914 +2023-03-04 18:44:54,343 - mmseg - INFO - Iter [52800/160000] lr: 3.750e-05, eta: 9:36:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6234, loss: 0.1813 +2023-03-04 18:45:07,765 - mmseg - INFO - Iter [52850/160000] lr: 3.750e-05, eta: 9:36:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.3889, loss: 0.1874 +2023-03-04 18:45:21,157 - mmseg - INFO - Iter [52900/160000] lr: 3.750e-05, eta: 9:35:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6473, loss: 0.1834 +2023-03-04 18:45:34,700 - mmseg - INFO - Iter [52950/160000] lr: 3.750e-05, eta: 9:35:31, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3248, loss: 0.1869 +2023-03-04 18:45:48,008 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:45:48,009 - mmseg - INFO - Iter [53000/160000] lr: 3.750e-05, eta: 9:35:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1928, decode.acc_seg: 92.3305, loss: 0.1928 +2023-03-04 18:46:04,081 - mmseg - INFO - Iter [53050/160000] lr: 3.750e-05, eta: 9:34:53, time: 0.321, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.4424, loss: 0.1890 +2023-03-04 18:46:17,531 - mmseg - INFO - Iter [53100/160000] lr: 3.750e-05, eta: 9:34:32, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.5064, loss: 0.1874 +2023-03-04 18:46:30,852 - mmseg - INFO - Iter [53150/160000] lr: 3.750e-05, eta: 9:34:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4320, loss: 0.1852 +2023-03-04 18:46:44,229 - mmseg - INFO - Iter [53200/160000] lr: 3.750e-05, eta: 9:33:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.3825, loss: 0.1841 +2023-03-04 18:46:57,510 - mmseg - INFO - Iter [53250/160000] lr: 3.750e-05, eta: 9:33:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.1321, loss: 0.1974 +2023-03-04 18:47:10,927 - mmseg - INFO - Iter [53300/160000] lr: 3.750e-05, eta: 9:33:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.0916, loss: 0.1935 +2023-03-04 18:47:24,413 - mmseg - INFO - Iter [53350/160000] lr: 3.750e-05, eta: 9:32:44, time: 0.270, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1967, decode.acc_seg: 92.0611, loss: 0.1967 +2023-03-04 18:47:37,691 - mmseg - INFO - Iter [53400/160000] lr: 3.750e-05, eta: 9:32:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.3582, loss: 0.1854 +2023-03-04 18:47:51,010 - mmseg - INFO - Iter [53450/160000] lr: 3.750e-05, eta: 9:32:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.4457, loss: 0.1902 +2023-03-04 18:48:04,271 - mmseg - INFO - Iter [53500/160000] lr: 3.750e-05, eta: 9:31:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.4809, loss: 0.1869 +2023-03-04 18:48:17,526 - mmseg - INFO - Iter [53550/160000] lr: 3.750e-05, eta: 9:31:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.4759, loss: 0.1878 +2023-03-04 18:48:30,795 - mmseg - INFO - Iter [53600/160000] lr: 3.750e-05, eta: 9:30:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.5811, loss: 0.1846 +2023-03-04 18:48:46,538 - mmseg - INFO - Iter [53650/160000] lr: 3.750e-05, eta: 9:30:38, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.4618, loss: 0.1865 +2023-03-04 18:48:59,888 - mmseg - INFO - Iter [53700/160000] lr: 3.750e-05, eta: 9:30:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.5574, loss: 0.1860 +2023-03-04 18:49:13,157 - mmseg - INFO - Iter [53750/160000] lr: 3.750e-05, eta: 9:29:55, time: 0.265, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.4057, loss: 0.1874 +2023-03-04 18:49:26,573 - mmseg - INFO - Iter [53800/160000] lr: 3.750e-05, eta: 9:29:34, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.5139, loss: 0.1868 +2023-03-04 18:49:39,948 - mmseg - INFO - Iter [53850/160000] lr: 3.750e-05, eta: 9:29:12, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1944, decode.acc_seg: 92.2042, loss: 0.1944 +2023-03-04 18:49:53,236 - mmseg - INFO - Iter [53900/160000] lr: 3.750e-05, eta: 9:28:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3979, loss: 0.1908 +2023-03-04 18:50:06,539 - mmseg - INFO - Iter [53950/160000] lr: 3.750e-05, eta: 9:28:29, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6564, loss: 0.1782 +2023-03-04 18:50:19,737 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:50:19,737 - mmseg - INFO - Iter [54000/160000] lr: 3.750e-05, eta: 9:28:07, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1942, decode.acc_seg: 92.1871, loss: 0.1942 +2023-03-04 18:50:33,059 - mmseg - INFO - Iter [54050/160000] lr: 3.750e-05, eta: 9:27:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4182, loss: 0.1861 +2023-03-04 18:50:46,373 - mmseg - INFO - Iter [54100/160000] lr: 3.750e-05, eta: 9:27:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.4192, loss: 0.1920 +2023-03-04 18:50:59,630 - mmseg - INFO - Iter [54150/160000] lr: 3.750e-05, eta: 9:27:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1945, decode.acc_seg: 92.1237, loss: 0.1945 +2023-03-04 18:51:12,904 - mmseg - INFO - Iter [54200/160000] lr: 3.750e-05, eta: 9:26:41, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.3147, loss: 0.1916 +2023-03-04 18:51:26,295 - mmseg - INFO - Iter [54250/160000] lr: 3.750e-05, eta: 9:26:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.5964, loss: 0.1875 +2023-03-04 18:51:42,208 - mmseg - INFO - Iter [54300/160000] lr: 3.750e-05, eta: 9:26:03, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5430, loss: 0.1840 +2023-03-04 18:51:55,489 - mmseg - INFO - Iter [54350/160000] lr: 3.750e-05, eta: 9:25:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.4623, loss: 0.1855 +2023-03-04 18:52:08,867 - mmseg - INFO - Iter [54400/160000] lr: 3.750e-05, eta: 9:25:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.6249, loss: 0.1837 +2023-03-04 18:52:22,180 - mmseg - INFO - Iter [54450/160000] lr: 3.750e-05, eta: 9:24:59, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.4517, loss: 0.1835 +2023-03-04 18:52:35,401 - mmseg - INFO - Iter [54500/160000] lr: 3.750e-05, eta: 9:24:38, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.4424, loss: 0.1883 +2023-03-04 18:52:48,750 - mmseg - INFO - Iter [54550/160000] lr: 3.750e-05, eta: 9:24:16, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.3309, loss: 0.1910 +2023-03-04 18:53:02,033 - mmseg - INFO - Iter [54600/160000] lr: 3.750e-05, eta: 9:23:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.4932, loss: 0.1837 +2023-03-04 18:53:15,576 - mmseg - INFO - Iter [54650/160000] lr: 3.750e-05, eta: 9:23:34, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1947, decode.acc_seg: 92.1897, loss: 0.1947 +2023-03-04 18:53:28,831 - mmseg - INFO - Iter [54700/160000] lr: 3.750e-05, eta: 9:23:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1926, decode.acc_seg: 92.3116, loss: 0.1926 +2023-03-04 18:53:42,322 - mmseg - INFO - Iter [54750/160000] lr: 3.750e-05, eta: 9:22:52, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1879, decode.acc_seg: 92.3072, loss: 0.1879 +2023-03-04 18:53:55,559 - mmseg - INFO - Iter [54800/160000] lr: 3.750e-05, eta: 9:22:30, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1948, decode.acc_seg: 92.0845, loss: 0.1948 +2023-03-04 18:54:08,847 - mmseg - INFO - Iter [54850/160000] lr: 3.750e-05, eta: 9:22:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.5396, loss: 0.1852 +2023-03-04 18:54:24,588 - mmseg - INFO - Iter [54900/160000] lr: 3.750e-05, eta: 9:21:52, time: 0.315, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3642, loss: 0.1901 +2023-03-04 18:54:38,107 - mmseg - INFO - Iter [54950/160000] lr: 3.750e-05, eta: 9:21:32, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.5798, loss: 0.1852 +2023-03-04 18:54:51,428 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:54:51,428 - mmseg - INFO - Iter [55000/160000] lr: 3.750e-05, eta: 9:21:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5953, loss: 0.1825 +2023-03-04 18:55:04,918 - mmseg - INFO - Iter [55050/160000] lr: 3.750e-05, eta: 9:20:49, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.3880, loss: 0.1886 +2023-03-04 18:55:18,240 - mmseg - INFO - Iter [55100/160000] lr: 3.750e-05, eta: 9:20:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1946, decode.acc_seg: 92.0574, loss: 0.1946 +2023-03-04 18:55:31,461 - mmseg - INFO - Iter [55150/160000] lr: 3.750e-05, eta: 9:20:07, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.5250, loss: 0.1851 +2023-03-04 18:55:44,749 - mmseg - INFO - Iter [55200/160000] lr: 3.750e-05, eta: 9:19:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.4127, loss: 0.1914 +2023-03-04 18:55:58,097 - mmseg - INFO - Iter [55250/160000] lr: 3.750e-05, eta: 9:19:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.4256, loss: 0.1908 +2023-03-04 18:56:11,491 - mmseg - INFO - Iter [55300/160000] lr: 3.750e-05, eta: 9:19:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.4617, loss: 0.1883 +2023-03-04 18:56:24,742 - mmseg - INFO - Iter [55350/160000] lr: 3.750e-05, eta: 9:18:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3958, loss: 0.1887 +2023-03-04 18:56:38,011 - mmseg - INFO - Iter [55400/160000] lr: 3.750e-05, eta: 9:18:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4421, loss: 0.1858 +2023-03-04 18:56:51,350 - mmseg - INFO - Iter [55450/160000] lr: 3.750e-05, eta: 9:18:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6828, loss: 0.1782 +2023-03-04 18:57:04,749 - mmseg - INFO - Iter [55500/160000] lr: 3.750e-05, eta: 9:17:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.4663, loss: 0.1813 +2023-03-04 18:57:20,713 - mmseg - INFO - Iter [55550/160000] lr: 3.750e-05, eta: 9:17:23, time: 0.320, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1926, decode.acc_seg: 92.2229, loss: 0.1926 +2023-03-04 18:57:34,086 - mmseg 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INFO - Iter [55850/160000] lr: 3.750e-05, eta: 9:15:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.5050, loss: 0.1880 +2023-03-04 18:58:53,979 - mmseg - INFO - Iter [55900/160000] lr: 3.750e-05, eta: 9:14:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.3585, loss: 0.1900 +2023-03-04 18:59:07,357 - mmseg - INFO - Iter [55950/160000] lr: 3.750e-05, eta: 9:14:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.3261, loss: 0.1851 +2023-03-04 18:59:20,575 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 18:59:20,576 - mmseg - INFO - Iter [56000/160000] lr: 3.750e-05, eta: 9:14:14, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.0909, loss: 0.1951 +2023-03-04 18:59:33,840 - mmseg - INFO - Iter [56050/160000] lr: 3.750e-05, eta: 9:13:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.4794, loss: 0.1847 +2023-03-04 18:59:47,168 - mmseg - INFO - Iter [56100/160000] lr: 3.750e-05, eta: 9:13:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.3279, loss: 0.1875 +2023-03-04 19:00:00,646 - mmseg - INFO - Iter [56150/160000] lr: 3.750e-05, eta: 9:13:11, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.5375, loss: 0.1874 +2023-03-04 19:00:16,644 - mmseg - INFO - Iter [56200/160000] lr: 3.750e-05, eta: 9:12:55, time: 0.320, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.3087, loss: 0.1890 +2023-03-04 19:00:29,984 - mmseg - INFO - Iter [56250/160000] lr: 3.750e-05, eta: 9:12:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3736, loss: 0.1895 +2023-03-04 19:00:43,284 - mmseg - INFO - Iter [56300/160000] lr: 3.750e-05, eta: 9:12:14, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4562, loss: 0.1858 +2023-03-04 19:00:56,675 - mmseg - INFO - Iter [56350/160000] lr: 3.750e-05, eta: 9:11:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.2768, loss: 0.1902 +2023-03-04 19:01:09,935 - mmseg - INFO - Iter [56400/160000] lr: 3.750e-05, eta: 9:11:32, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4181, loss: 0.1866 +2023-03-04 19:01:23,306 - mmseg - INFO - Iter [56450/160000] lr: 3.750e-05, eta: 9:11:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.4044, loss: 0.1912 +2023-03-04 19:01:36,675 - mmseg - INFO - Iter [56500/160000] lr: 3.750e-05, eta: 9:10:51, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.3518, loss: 0.1922 +2023-03-04 19:01:49,960 - mmseg - INFO - Iter [56550/160000] lr: 3.750e-05, eta: 9:10:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1924, decode.acc_seg: 92.2600, loss: 0.1924 +2023-03-04 19:02:03,316 - mmseg - INFO - Iter [56600/160000] lr: 3.750e-05, eta: 9:10:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1829, decode.acc_seg: 92.5032, loss: 0.1829 +2023-03-04 19:02:16,721 - mmseg - INFO - Iter [56650/160000] lr: 3.750e-05, eta: 9:09:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.2395, loss: 0.1901 +2023-03-04 19:02:30,008 - mmseg - INFO - Iter [56700/160000] lr: 3.750e-05, eta: 9:09:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.7994, loss: 0.1766 +2023-03-04 19:02:43,414 - mmseg - INFO - Iter [56750/160000] lr: 3.750e-05, eta: 9:09:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1944, decode.acc_seg: 92.2266, loss: 0.1944 +2023-03-04 19:02:59,203 - mmseg - INFO - Iter [56800/160000] lr: 3.750e-05, eta: 9:08:51, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.4490, loss: 0.1842 +2023-03-04 19:03:12,558 - mmseg - INFO - Iter [56850/160000] lr: 3.750e-05, eta: 9:08:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.4707, loss: 0.1841 +2023-03-04 19:03:25,950 - mmseg - INFO - Iter [56900/160000] lr: 3.750e-05, eta: 9:08:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3960, loss: 0.1895 +2023-03-04 19:03:39,330 - mmseg - INFO - Iter [56950/160000] lr: 3.750e-05, eta: 9:07:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1924, decode.acc_seg: 92.2845, loss: 0.1924 +2023-03-04 19:03:52,746 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:03:52,747 - mmseg - INFO - Iter [57000/160000] lr: 3.750e-05, eta: 9:07:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.6029, loss: 0.1849 +2023-03-04 19:04:06,109 - mmseg - INFO - Iter [57050/160000] lr: 3.750e-05, eta: 9:07:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.4072, loss: 0.1869 +2023-03-04 19:04:19,443 - mmseg - INFO - Iter [57100/160000] lr: 3.750e-05, eta: 9:06:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1899, decode.acc_seg: 92.3110, loss: 0.1899 +2023-03-04 19:04:32,731 - mmseg - INFO - Iter [57150/160000] lr: 3.750e-05, eta: 9:06:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1891, decode.acc_seg: 92.2855, loss: 0.1891 +2023-03-04 19:04:46,067 - mmseg - INFO - Iter [57200/160000] lr: 3.750e-05, eta: 9:06:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.2725, loss: 0.1943 +2023-03-04 19:04:59,457 - mmseg - INFO - Iter [57250/160000] lr: 3.750e-05, eta: 9:05:45, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5038, loss: 0.1847 +2023-03-04 19:05:12,682 - mmseg - INFO - Iter [57300/160000] lr: 3.750e-05, eta: 9:05:24, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1978, decode.acc_seg: 91.9091, loss: 0.1978 +2023-03-04 19:05:26,003 - mmseg - INFO - Iter [57350/160000] lr: 3.750e-05, eta: 9:05:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.2425, loss: 0.1904 +2023-03-04 19:05:39,394 - mmseg - INFO - Iter [57400/160000] lr: 3.750e-05, eta: 9:04:43, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5823, loss: 0.1841 +2023-03-04 19:05:55,158 - mmseg - INFO - Iter [57450/160000] lr: 3.750e-05, eta: 9:04:27, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.2775, loss: 0.1920 +2023-03-04 19:06:08,446 - mmseg - INFO - Iter [57500/160000] lr: 3.750e-05, eta: 9:04:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5013, loss: 0.1849 +2023-03-04 19:06:21,699 - mmseg - INFO - Iter [57550/160000] lr: 3.750e-05, eta: 9:03:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.4107, loss: 0.1892 +2023-03-04 19:06:34,983 - mmseg - INFO - Iter [57600/160000] lr: 3.750e-05, eta: 9:03:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1942, decode.acc_seg: 92.0255, loss: 0.1942 +2023-03-04 19:06:48,310 - mmseg - INFO - Iter [57650/160000] lr: 3.750e-05, eta: 9:03:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.3844, loss: 0.1868 +2023-03-04 19:07:01,540 - mmseg - INFO - Iter [57700/160000] lr: 3.750e-05, eta: 9:02:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.3896, loss: 0.1912 +2023-03-04 19:07:14,919 - mmseg - INFO - Iter [57750/160000] lr: 3.750e-05, eta: 9:02:24, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.2291, loss: 0.1915 +2023-03-04 19:07:28,377 - mmseg - INFO - Iter [57800/160000] lr: 3.750e-05, eta: 9:02:03, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.7745, loss: 0.1772 +2023-03-04 19:07:41,677 - mmseg - INFO - Iter [57850/160000] lr: 3.750e-05, eta: 9:01:43, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1889, decode.acc_seg: 92.3319, loss: 0.1889 +2023-03-04 19:07:55,140 - mmseg - INFO - Iter [57900/160000] lr: 3.750e-05, eta: 9:01:22, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.3858, loss: 0.1909 +2023-03-04 19:08:08,349 - mmseg - INFO - Iter [57950/160000] lr: 3.750e-05, eta: 9:01:02, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.4903, loss: 0.1856 +2023-03-04 19:08:21,774 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:08:21,774 - mmseg - INFO - Iter [58000/160000] lr: 3.750e-05, eta: 9:00:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.2962, loss: 0.1910 +2023-03-04 19:08:35,226 - mmseg - INFO - Iter [58050/160000] lr: 3.750e-05, eta: 9:00:21, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6723, loss: 0.1830 +2023-03-04 19:08:51,234 - mmseg - INFO - Iter [58100/160000] lr: 3.750e-05, eta: 9:00:06, time: 0.320, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1924, decode.acc_seg: 92.4161, loss: 0.1924 +2023-03-04 19:09:04,644 - mmseg - INFO - Iter [58150/160000] lr: 3.750e-05, eta: 8:59:45, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.4870, loss: 0.1864 +2023-03-04 19:09:17,990 - mmseg - INFO - Iter [58200/160000] lr: 3.750e-05, eta: 8:59:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1888, decode.acc_seg: 92.3719, loss: 0.1888 +2023-03-04 19:09:31,273 - mmseg - INFO - Iter [58250/160000] lr: 3.750e-05, eta: 8:59:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.2935, loss: 0.1903 +2023-03-04 19:09:44,610 - mmseg - INFO - Iter [58300/160000] lr: 3.750e-05, eta: 8:58:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.3836, loss: 0.1873 +2023-03-04 19:09:57,986 - mmseg - INFO - Iter [58350/160000] lr: 3.750e-05, eta: 8:58:24, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1746, decode.acc_seg: 92.8759, loss: 0.1746 +2023-03-04 19:10:11,318 - mmseg - INFO - Iter [58400/160000] lr: 3.750e-05, eta: 8:58:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1934, decode.acc_seg: 92.2707, loss: 0.1934 +2023-03-04 19:10:24,720 - mmseg - INFO - Iter [58450/160000] lr: 3.750e-05, eta: 8:57:43, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4135, loss: 0.1870 +2023-03-04 19:10:37,906 - mmseg - INFO - Iter [58500/160000] lr: 3.750e-05, eta: 8:57:23, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.4753, loss: 0.1857 +2023-03-04 19:10:51,224 - mmseg - INFO - Iter [58550/160000] lr: 3.750e-05, eta: 8:57:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.5198, loss: 0.1852 +2023-03-04 19:11:04,571 - mmseg - INFO - Iter [58600/160000] lr: 3.750e-05, eta: 8:56:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1943, decode.acc_seg: 92.2797, loss: 0.1943 +2023-03-04 19:11:17,865 - mmseg - INFO - Iter [58650/160000] lr: 3.750e-05, eta: 8:56:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.1290, loss: 0.1958 +2023-03-04 19:11:33,715 - mmseg - INFO - Iter [58700/160000] lr: 3.750e-05, eta: 8:56:06, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.4950, loss: 0.1878 +2023-03-04 19:11:47,055 - mmseg - INFO - Iter [58750/160000] lr: 3.750e-05, eta: 8:55:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.3062, loss: 0.1909 +2023-03-04 19:12:00,584 - mmseg - INFO - Iter [58800/160000] lr: 3.750e-05, eta: 8:55:26, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5086, loss: 0.1865 +2023-03-04 19:12:14,041 - mmseg - INFO - Iter [58850/160000] lr: 3.750e-05, eta: 8:55:06, time: 0.269, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3511, loss: 0.1895 +2023-03-04 19:12:27,447 - mmseg - INFO - Iter [58900/160000] lr: 3.750e-05, eta: 8:54:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.5604, loss: 0.1857 +2023-03-04 19:12:40,810 - mmseg - INFO - Iter [58950/160000] lr: 3.750e-05, eta: 8:54:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4156, loss: 0.1862 +2023-03-04 19:12:54,089 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:12:54,089 - mmseg - INFO - Iter [59000/160000] lr: 3.750e-05, eta: 8:54:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.3182, loss: 0.1938 +2023-03-04 19:13:07,348 - mmseg - INFO - Iter [59050/160000] lr: 3.750e-05, eta: 8:53:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.4408, loss: 0.1893 +2023-03-04 19:13:20,673 - mmseg - INFO - Iter [59100/160000] lr: 3.750e-05, eta: 8:53:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.3811, loss: 0.1892 +2023-03-04 19:13:33,927 - mmseg - INFO - Iter [59150/160000] lr: 3.750e-05, eta: 8:53:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.3698, loss: 0.1883 +2023-03-04 19:13:47,379 - mmseg - INFO - Iter [59200/160000] lr: 3.750e-05, eta: 8:52:44, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4722, loss: 0.1882 +2023-03-04 19:14:00,601 - mmseg - INFO - Iter [59250/160000] lr: 3.750e-05, eta: 8:52:24, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.5348, loss: 0.1836 +2023-03-04 19:14:13,863 - mmseg - INFO - Iter [59300/160000] lr: 3.750e-05, eta: 8:52:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.5213, loss: 0.1858 +2023-03-04 19:14:29,515 - mmseg - INFO - Iter [59350/160000] lr: 3.750e-05, eta: 8:51:48, time: 0.313, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.4978, loss: 0.1863 +2023-03-04 19:14:42,816 - mmseg - INFO - Iter [59400/160000] lr: 3.750e-05, eta: 8:51:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6866, loss: 0.1807 +2023-03-04 19:14:56,093 - mmseg - INFO - Iter [59450/160000] lr: 3.750e-05, eta: 8:51:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.2861, loss: 0.1901 +2023-03-04 19:15:09,337 - mmseg - INFO - Iter [59500/160000] lr: 3.750e-05, eta: 8:50:47, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.4500, loss: 0.1902 +2023-03-04 19:15:22,787 - mmseg - INFO - Iter [59550/160000] lr: 3.750e-05, eta: 8:50:27, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.2342, loss: 0.1936 +2023-03-04 19:15:36,115 - mmseg - INFO - Iter [59600/160000] lr: 3.750e-05, eta: 8:50:07, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6775, loss: 0.1812 +2023-03-04 19:15:49,507 - mmseg - INFO - Iter [59650/160000] lr: 3.750e-05, eta: 8:49:47, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.2025, loss: 0.1903 +2023-03-04 19:16:02,845 - mmseg - INFO - Iter [59700/160000] lr: 3.750e-05, eta: 8:49:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.3221, loss: 0.1925 +2023-03-04 19:16:16,101 - mmseg - INFO - Iter [59750/160000] lr: 3.750e-05, eta: 8:49:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.3536, loss: 0.1878 +2023-03-04 19:16:29,389 - mmseg - INFO - Iter [59800/160000] lr: 3.750e-05, eta: 8:48:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.3558, loss: 0.1938 +2023-03-04 19:16:42,893 - mmseg - INFO - Iter [59850/160000] lr: 3.750e-05, eta: 8:48:27, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.3192, loss: 0.1902 +2023-03-04 19:16:56,593 - mmseg - INFO - Iter [59900/160000] lr: 3.750e-05, eta: 8:48:08, time: 0.274, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6065, loss: 0.1820 +2023-03-04 19:17:12,462 - mmseg - INFO - Iter [59950/160000] lr: 3.750e-05, eta: 8:47:52, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.4000, loss: 0.1909 +2023-03-04 19:17:25,831 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:17:25,831 - mmseg - INFO - Iter [60000/160000] lr: 3.750e-05, eta: 8:47:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.1930, loss: 0.1919 +2023-03-04 19:17:39,143 - mmseg - INFO - Iter [60050/160000] lr: 1.875e-05, eta: 8:47:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5703, loss: 0.1847 +2023-03-04 19:17:52,637 - mmseg - INFO - Iter [60100/160000] lr: 1.875e-05, eta: 8:46:52, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.5131, loss: 0.1883 +2023-03-04 19:18:05,928 - mmseg - INFO - Iter [60150/160000] lr: 1.875e-05, eta: 8:46:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.3211, loss: 0.1890 +2023-03-04 19:18:19,419 - mmseg - INFO - Iter [60200/160000] lr: 1.875e-05, eta: 8:46:13, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.6083, loss: 0.1838 +2023-03-04 19:18:32,947 - mmseg - INFO - Iter [60250/160000] lr: 1.875e-05, eta: 8:45:53, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3083, loss: 0.1901 +2023-03-04 19:18:46,372 - mmseg - INFO - Iter [60300/160000] lr: 1.875e-05, eta: 8:45:33, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6603, loss: 0.1786 +2023-03-04 19:18:59,637 - mmseg - INFO - Iter [60350/160000] lr: 1.875e-05, eta: 8:45:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1888, decode.acc_seg: 92.4526, loss: 0.1888 +2023-03-04 19:19:12,960 - mmseg - INFO - Iter [60400/160000] lr: 1.875e-05, eta: 8:44:53, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.2540, loss: 0.1895 +2023-03-04 19:19:26,344 - mmseg - INFO - Iter [60450/160000] lr: 1.875e-05, eta: 8:44:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4193, loss: 0.1873 +2023-03-04 19:19:39,783 - mmseg - INFO - Iter [60500/160000] lr: 1.875e-05, eta: 8:44:14, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6631, loss: 0.1834 +2023-03-04 19:19:53,214 - mmseg - INFO - Iter [60550/160000] lr: 1.875e-05, eta: 8:43:54, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.3622, loss: 0.1896 +2023-03-04 19:20:09,236 - mmseg - INFO - Iter [60600/160000] lr: 1.875e-05, eta: 8:43:39, time: 0.320, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.4737, loss: 0.1838 +2023-03-04 19:20:22,613 - mmseg - INFO - Iter [60650/160000] lr: 1.875e-05, eta: 8:43:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.3299, loss: 0.1862 +2023-03-04 19:20:35,940 - mmseg - INFO - Iter [60700/160000] lr: 1.875e-05, eta: 8:42:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1931, decode.acc_seg: 92.2122, loss: 0.1931 +2023-03-04 19:20:49,258 - mmseg - INFO - Iter [60750/160000] lr: 1.875e-05, eta: 8:42:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.3759, loss: 0.1893 +2023-03-04 19:21:02,555 - mmseg - INFO - Iter [60800/160000] lr: 1.875e-05, eta: 8:42:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.4663, loss: 0.1836 +2023-03-04 19:21:15,938 - mmseg - INFO - Iter [60850/160000] lr: 1.875e-05, eta: 8:41:59, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4852, loss: 0.1870 +2023-03-04 19:21:29,205 - mmseg - INFO - Iter [60900/160000] lr: 1.875e-05, eta: 8:41:40, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5257, loss: 0.1821 +2023-03-04 19:21:42,635 - mmseg - INFO - Iter [60950/160000] lr: 1.875e-05, eta: 8:41:20, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1927, decode.acc_seg: 92.2346, loss: 0.1927 +2023-03-04 19:21:55,921 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:21:55,921 - mmseg - INFO - Iter [61000/160000] lr: 1.875e-05, eta: 8:41:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.2535, loss: 0.1920 +2023-03-04 19:22:09,339 - mmseg - INFO - Iter [61050/160000] lr: 1.875e-05, eta: 8:40:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4356, loss: 0.1866 +2023-03-04 19:22:22,607 - mmseg - INFO - Iter [61100/160000] lr: 1.875e-05, eta: 8:40:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.4692, loss: 0.1841 +2023-03-04 19:22:35,883 - mmseg - INFO - Iter [61150/160000] lr: 1.875e-05, eta: 8:40:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.6243, loss: 0.1857 +2023-03-04 19:22:49,170 - mmseg - INFO - Iter [61200/160000] lr: 1.875e-05, eta: 8:39:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.4442, loss: 0.1874 +2023-03-04 19:23:05,127 - mmseg - INFO - Iter [61250/160000] lr: 1.875e-05, eta: 8:39:25, time: 0.319, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.2623, loss: 0.1904 +2023-03-04 19:23:18,471 - mmseg - INFO - Iter [61300/160000] lr: 1.875e-05, eta: 8:39:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.3610, loss: 0.1877 +2023-03-04 19:23:31,808 - mmseg - INFO - Iter [61350/160000] lr: 1.875e-05, eta: 8:38:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5023, loss: 0.1844 +2023-03-04 19:23:45,146 - mmseg - INFO - Iter [61400/160000] lr: 1.875e-05, eta: 8:38:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.3774, loss: 0.1870 +2023-03-04 19:23:58,540 - mmseg - INFO - Iter [61450/160000] lr: 1.875e-05, eta: 8:38:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6679, loss: 0.1814 +2023-03-04 19:24:11,811 - mmseg - INFO - Iter [61500/160000] lr: 1.875e-05, eta: 8:37:47, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.5714, loss: 0.1815 +2023-03-04 19:24:25,032 - mmseg - INFO - Iter [61550/160000] lr: 1.875e-05, eta: 8:37:27, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5017, loss: 0.1850 +2023-03-04 19:24:38,368 - mmseg - INFO - Iter [61600/160000] lr: 1.875e-05, eta: 8:37:07, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.6586, loss: 0.1841 +2023-03-04 19:24:51,730 - mmseg - INFO - Iter [61650/160000] lr: 1.875e-05, eta: 8:36:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1740, decode.acc_seg: 92.9025, loss: 0.1740 +2023-03-04 19:25:05,097 - mmseg - INFO - Iter [61700/160000] lr: 1.875e-05, eta: 8:36:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.6731, loss: 0.1751 +2023-03-04 19:25:18,357 - mmseg - INFO - Iter [61750/160000] lr: 1.875e-05, eta: 8:36:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1954, decode.acc_seg: 92.1628, loss: 0.1954 +2023-03-04 19:25:31,587 - mmseg - INFO - Iter [61800/160000] lr: 1.875e-05, eta: 8:35:49, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.4225, loss: 0.1832 +2023-03-04 19:25:47,485 - mmseg - INFO - Iter [61850/160000] lr: 1.875e-05, eta: 8:35:33, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1894, decode.acc_seg: 92.4403, loss: 0.1894 +2023-03-04 19:26:00,890 - mmseg - INFO - Iter [61900/160000] lr: 1.875e-05, eta: 8:35:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.5619, loss: 0.1833 +2023-03-04 19:26:14,337 - mmseg - INFO - Iter [61950/160000] lr: 1.875e-05, eta: 8:34:54, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4613, loss: 0.1872 +2023-03-04 19:26:27,724 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:26:27,724 - mmseg - INFO - Iter [62000/160000] lr: 1.875e-05, eta: 8:34:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.3955, loss: 0.1874 +2023-03-04 19:26:41,136 - mmseg - INFO - Iter [62050/160000] lr: 1.875e-05, eta: 8:34:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6794, loss: 0.1811 +2023-03-04 19:26:54,413 - mmseg - INFO - Iter [62100/160000] lr: 1.875e-05, eta: 8:33:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7192, loss: 0.1808 +2023-03-04 19:27:07,631 - mmseg - INFO - Iter [62150/160000] lr: 1.875e-05, eta: 8:33:36, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5068, loss: 0.1827 +2023-03-04 19:27:20,894 - mmseg - INFO - Iter [62200/160000] lr: 1.875e-05, eta: 8:33:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.3218, loss: 0.1878 +2023-03-04 19:27:34,263 - mmseg - INFO - Iter [62250/160000] lr: 1.875e-05, eta: 8:32:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1964, decode.acc_seg: 91.9976, loss: 0.1964 +2023-03-04 19:27:47,569 - mmseg - INFO - Iter [62300/160000] lr: 1.875e-05, eta: 8:32:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5315, loss: 0.1844 +2023-03-04 19:28:00,829 - mmseg - INFO - Iter [62350/160000] lr: 1.875e-05, eta: 8:32:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.7031, loss: 0.1822 +2023-03-04 19:28:14,042 - mmseg - INFO - Iter [62400/160000] lr: 1.875e-05, eta: 8:31:58, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.4244, loss: 0.1842 +2023-03-04 19:28:27,460 - mmseg - INFO - Iter [62450/160000] lr: 1.875e-05, eta: 8:31:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.3490, loss: 0.1872 +2023-03-04 19:28:43,332 - mmseg - INFO - Iter [62500/160000] lr: 1.875e-05, eta: 8:31:23, time: 0.317, data_time: 0.058, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.6032, loss: 0.1809 +2023-03-04 19:28:56,583 - mmseg - INFO - Iter [62550/160000] lr: 1.875e-05, eta: 8:31:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1829, decode.acc_seg: 92.5911, loss: 0.1829 +2023-03-04 19:29:09,921 - mmseg - INFO - Iter [62600/160000] lr: 1.875e-05, eta: 8:30:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4640, loss: 0.1860 +2023-03-04 19:29:23,219 - mmseg - INFO - Iter [62650/160000] lr: 1.875e-05, eta: 8:30:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.3620, loss: 0.1890 +2023-03-04 19:29:36,661 - mmseg - INFO - Iter [62700/160000] lr: 1.875e-05, eta: 8:30:05, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4638, loss: 0.1861 +2023-03-04 19:29:50,006 - mmseg - INFO - Iter [62750/160000] lr: 1.875e-05, eta: 8:29:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1894, decode.acc_seg: 92.3505, loss: 0.1894 +2023-03-04 19:30:03,270 - mmseg - INFO - Iter [62800/160000] lr: 1.875e-05, eta: 8:29:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5917, loss: 0.1850 +2023-03-04 19:30:16,513 - mmseg - INFO - Iter [62850/160000] lr: 1.875e-05, eta: 8:29:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.3747, loss: 0.1884 +2023-03-04 19:30:30,136 - mmseg - INFO - Iter [62900/160000] lr: 1.875e-05, eta: 8:28:48, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.4454, loss: 0.1874 +2023-03-04 19:30:43,500 - mmseg - INFO - Iter [62950/160000] lr: 1.875e-05, eta: 8:28:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.6137, loss: 0.1844 +2023-03-04 19:30:56,843 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:30:56,843 - mmseg - INFO - Iter [63000/160000] lr: 1.875e-05, eta: 8:28:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.4567, loss: 0.1855 +2023-03-04 19:31:10,133 - mmseg - INFO - Iter [63050/160000] lr: 1.875e-05, eta: 8:27:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.4939, loss: 0.1876 +2023-03-04 19:31:23,404 - mmseg - INFO - Iter [63100/160000] lr: 1.875e-05, eta: 8:27:30, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6000, loss: 0.1833 +2023-03-04 19:31:39,358 - mmseg - INFO - Iter [63150/160000] lr: 1.875e-05, eta: 8:27:15, time: 0.319, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.3752, loss: 0.1876 +2023-03-04 19:31:52,715 - mmseg - INFO - Iter [63200/160000] lr: 1.875e-05, eta: 8:26:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5518, loss: 0.1839 +2023-03-04 19:32:05,996 - mmseg - INFO - Iter [63250/160000] lr: 1.875e-05, eta: 8:26:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.7339, loss: 0.1796 +2023-03-04 19:32:19,547 - mmseg - INFO - Iter [63300/160000] lr: 1.875e-05, eta: 8:26:17, time: 0.271, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4816, loss: 0.1860 +2023-03-04 19:32:32,921 - mmseg - INFO - Iter [63350/160000] lr: 1.875e-05, eta: 8:25:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.3201, loss: 0.1910 +2023-03-04 19:32:46,225 - mmseg - INFO - Iter [63400/160000] lr: 1.875e-05, eta: 8:25:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.2721, loss: 0.1896 +2023-03-04 19:32:59,654 - mmseg - INFO - Iter [63450/160000] lr: 1.875e-05, eta: 8:25:19, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.6637, loss: 0.1785 +2023-03-04 19:33:12,889 - mmseg - INFO - Iter [63500/160000] lr: 1.875e-05, eta: 8:25:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5799, loss: 0.1839 +2023-03-04 19:33:26,172 - mmseg - INFO - Iter [63550/160000] lr: 1.875e-05, eta: 8:24:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.4410, loss: 0.1850 +2023-03-04 19:33:39,406 - mmseg - INFO - Iter [63600/160000] lr: 1.875e-05, eta: 8:24:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1888, decode.acc_seg: 92.5430, loss: 0.1888 +2023-03-04 19:33:52,758 - mmseg - INFO - Iter [63650/160000] lr: 1.875e-05, eta: 8:24:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5527, loss: 0.1848 +2023-03-04 19:34:06,021 - mmseg - INFO - Iter [63700/160000] lr: 1.875e-05, eta: 8:23:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5794, loss: 0.1828 +2023-03-04 19:34:21,862 - mmseg - INFO - Iter [63750/160000] lr: 1.875e-05, eta: 8:23:27, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1935, decode.acc_seg: 92.2522, loss: 0.1935 +2023-03-04 19:34:35,231 - mmseg - INFO - Iter [63800/160000] lr: 1.875e-05, eta: 8:23:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6230, loss: 0.1812 +2023-03-04 19:34:48,577 - mmseg - INFO - Iter [63850/160000] lr: 1.875e-05, eta: 8:22:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.3711, loss: 0.1917 +2023-03-04 19:35:01,869 - mmseg - INFO - Iter [63900/160000] lr: 1.875e-05, eta: 8:22:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.4947, loss: 0.1854 +2023-03-04 19:35:15,285 - mmseg - INFO - Iter [63950/160000] lr: 1.875e-05, eta: 8:22:10, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1907, decode.acc_seg: 92.3649, loss: 0.1907 +2023-03-04 19:35:28,612 - mmseg - INFO - Swap parameters (after train) after iter [64000] +2023-03-04 19:35:28,635 - mmseg - INFO - Saving checkpoint at 64000 iterations +2023-03-04 19:35:30,629 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:35:30,629 - mmseg - INFO - Iter [64000/160000] lr: 1.875e-05, eta: 8:21:54, time: 0.307, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.3388, loss: 0.1872 +2023-03-04 19:46:41,504 - mmseg - INFO - per class results: +2023-03-04 19:46:41,513 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.44,76.44,76.43,76.43,76.43,76.43,76.42,76.42,76.41,76.41,76.43 | +| building | 81.45,81.47,81.46,81.47,81.48,81.49,81.49,81.48,81.49,81.49,81.48 | +| sky | 94.27,94.27,94.27,94.27,94.27,94.26,94.26,94.26,94.26,94.26,94.25 | +| floor | 80.01,80.0,80.0,80.0,80.02,80.0,79.99,79.99,79.99,79.98,79.96 | +| tree | 72.88,72.87,72.88,72.87,72.84,72.82,72.82,72.79,72.8,72.8,72.76 | +| ceiling | 82.81,82.81,82.82,82.81,82.81,82.8,82.8,82.81,82.78,82.78,82.8 | +| road | 82.15,82.16,82.17,82.13,82.13,82.13,82.12,82.14,82.12,82.11,82.09 | +| bed | 88.62,88.63,88.64,88.63,88.64,88.63,88.63,88.65,88.63,88.62,88.6 | +| windowpane | 61.17,61.17,61.18,61.18,61.2,61.21,61.17,61.17,61.16,61.14,61.17 | +| grass | 65.79,65.82,65.83,65.87,65.88,65.91,65.88,65.93,65.91,65.93,65.98 | +| cabinet | 59.55,59.51,59.55,59.53,59.6,59.55,59.55,59.53,59.48,59.47,59.46 | +| sidewalk | 66.18,66.22,66.27,66.23,66.23,66.22,66.25,66.29,66.27,66.26,66.26 | +| person | 79.44,79.44,79.45,79.45,79.44,79.46,79.45,79.45,79.46,79.44,79.45 | +| earth | 33.46,33.48,33.54,33.5,33.49,33.44,33.52,33.56,33.57,33.55,33.53 | +| door | 48.54,48.55,48.6,48.63,48.65,48.69,48.67,48.7,48.67,48.64,48.62 | +| table | 61.64,61.64,61.68,61.69,61.68,61.7,61.75,61.79,61.78,61.79,61.78 | +| mountain | 51.54,51.62,51.63,51.7,51.73,51.77,51.75,51.75,51.81,51.81,51.98 | +| plant | 50.36,50.36,50.34,50.33,50.34,50.3,50.3,50.31,50.27,50.31,50.23 | +| curtain | 69.67,69.78,69.95,70.02,70.23,70.22,70.28,70.28,70.27,70.25,70.23 | +| chair | 58.19,58.24,58.26,58.3,58.3,58.36,58.36,58.36,58.36,58.37,58.38 | +| car | 83.27,83.28,83.3,83.28,83.28,83.3,83.27,83.27,83.25,83.27,83.26 | +| water | 47.1,47.11,47.15,47.11,47.1,47.11,47.07,47.05,47.04,47.04,47.06 | +| painting | 69.61,69.63,69.61,69.62,69.63,69.6,69.54,69.54,69.53,69.51,69.47 | +| sofa | 65.48,65.44,65.43,65.43,65.53,65.58,65.56,65.63,65.63,65.69,65.65 | +| shelf | 40.4,40.37,40.41,40.34,40.31,40.29,40.27,40.26,40.22,40.27,40.24 | +| house | 44.37,44.36,44.35,44.33,44.28,44.33,44.2,44.2,44.22,44.24,44.04 | +| sea | 45.13,45.13,45.14,45.14,45.12,45.07,45.06,45.02,44.98,44.98,44.98 | +| mirror | 65.36,65.34,65.37,65.3,65.29,65.27,65.31,65.26,65.24,65.24,65.21 | +| rug | 55.74,55.85,55.98,55.9,56.04,56.04,55.85,55.93,55.9,55.77,55.38 | +| field | 28.96,29.01,29.05,29.06,29.1,29.1,29.11,29.12,29.1,29.12,29.2 | +| armchair | 44.15,44.1,44.05,44.02,44.18,44.26,44.09,44.12,44.12,44.08,44.16 | +| seat | 54.28,54.24,54.24,54.16,54.08,54.1,54.01,53.97,53.95,53.91,53.92 | +| fence | 40.7,40.65,40.66,40.65,40.58,40.67,40.62,40.66,40.63,40.63,40.65 | +| desk | 49.34,49.36,49.29,49.19,49.31,49.27,49.32,49.4,49.37,49.4,49.06 | +| rock | 28.31,28.33,28.36,28.4,28.45,28.61,28.47,28.35,28.5,28.45,29.0 | +| wardrobe | 48.4,48.27,48.3,48.34,48.39,48.27,48.18,48.2,48.03,48.05,48.18 | +| lamp | 63.79,63.8,63.8,63.8,63.79,63.8,63.8,63.83,63.82,63.82,63.82 | +| bathtub | 77.26,77.25,77.28,77.27,77.2,77.21,77.23,77.21,77.19,77.24,76.82 | +| railing | 31.88,31.83,31.84,31.74,31.69,31.69,31.61,31.65,31.54,31.51,31.47 | +| cushion | 55.57,55.52,55.53,55.54,55.57,55.55,55.63,55.71,55.67,55.71,55.57 | +| base | 28.52,28.48,28.45,28.59,28.54,28.57,28.5,28.41,28.51,28.51,28.51 | +| box | 24.62,24.65,24.61,24.61,24.65,24.68,24.64,24.61,24.64,24.56,24.65 | +| column | 46.33,46.35,46.51,46.35,46.52,46.52,46.5,46.53,46.51,46.56,46.63 | +| signboard | 35.97,36.03,35.97,35.95,35.93,35.88,35.89,35.86,35.84,35.88,35.79 | +| chest of drawers | 39.97,39.73,39.77,39.79,39.98,39.93,39.72,39.63,39.76,39.82,40.01 | +| counter | 26.64,26.66,26.64,26.75,26.79,26.69,26.74,26.46,26.4,26.3,26.33 | +| sand | 32.34,32.3,32.28,32.23,32.14,32.11,32.17,32.29,32.27,32.28,32.25 | +| sink | 70.76,70.74,70.74,70.75,70.72,70.66,70.65,70.69,70.69,70.71,70.73 | +| skyscraper | 48.4,48.54,48.53,48.66,48.64,48.61,48.71,48.76,48.77,48.83,48.97 | +| fireplace | 66.14,66.15,66.17,66.13,66.24,66.16,66.23,66.21,66.17,66.29,66.19 | +| refrigerator | 77.93,77.96,77.95,77.88,77.89,77.88,77.91,77.88,77.94,77.9,77.91 | +| grandstand | 41.96,42.08,42.04,42.07,42.15,42.12,42.1,42.17,42.18,42.22,42.13 | +| path | 17.6,17.59,17.64,17.77,17.72,17.78,17.77,17.89,17.94,17.94,17.87 | +| stairs | 31.79,31.83,31.85,31.77,31.79,31.82,31.78,31.8,31.81,31.78,31.81 | +| runway | 63.85,63.84,63.87,63.87,63.86,63.88,63.87,63.88,63.88,63.87,63.89 | +| case | 48.43,48.32,48.3,48.17,48.07,47.98,48.0,47.91,47.87,47.8,47.62 | +| pool table | 92.56,92.57,92.58,92.56,92.6,92.58,92.6,92.56,92.55,92.59,92.59 | +| pillow | 57.14,57.08,57.1,57.13,57.17,57.14,57.14,57.18,57.09,57.0,57.23 | +| screen door | 67.16,67.1,67.06,66.99,67.26,67.12,67.16,67.14,67.03,67.02,67.03 | +| stairway | 25.31,25.3,25.33,25.25,25.21,25.26,25.23,25.24,25.21,25.23,25.16 | +| river | 10.03,10.01,10.0,9.96,9.91,9.9,9.88,9.87,9.86,9.85,9.76 | +| bridge | 54.82,55.28,55.67,56.15,56.53,57.05,57.12,57.35,57.73,58.05,58.58 | +| bookcase | 41.43,41.47,41.59,41.81,41.77,41.74,41.8,41.79,41.95,41.99,42.36 | +| blind | 46.09,45.85,45.8,45.63,45.64,45.55,45.31,45.25,45.33,45.26,45.28 | +| coffee table | 66.87,66.89,66.84,66.89,66.94,66.95,67.01,67.08,67.09,67.13,67.26 | +| toilet | 86.25,86.31,86.26,86.32,86.26,86.26,86.23,86.19,86.22,86.23,86.39 | +| flower | 31.23,31.23,31.18,31.27,31.28,31.24,31.36,31.31,31.43,31.42,31.5 | +| book | 47.13,47.12,47.11,47.06,47.07,47.03,47.01,46.97,46.91,46.8,46.86 | +| hill | 8.0,7.9,7.86,7.91,7.96,8.01,7.97,7.87,8.08,8.11,8.07 | +| bench | 44.4,44.42,44.44,44.43,44.4,44.47,44.4,44.41,44.46,44.46,44.41 | +| countertop | 53.77,53.85,53.88,53.86,53.9,53.81,53.9,53.96,53.91,53.96,53.67 | +| stove | 72.94,73.04,73.06,72.96,73.1,73.15,73.17,73.2,73.25,73.2,73.19 | +| palm | 50.78,50.76,50.79,50.82,50.86,50.86,50.9,50.8,50.92,50.92,50.96 | +| kitchen island | 46.75,46.67,46.66,46.5,46.94,46.9,46.97,46.96,46.87,46.47,46.6 | +| computer | 57.41,57.41,57.43,57.47,57.51,57.46,57.44,57.46,57.47,57.48,57.53 | +| swivel chair | 44.91,44.96,45.03,45.02,45.04,45.02,45.06,45.03,45.05,45.06,45.27 | +| boat | 39.46,39.41,39.41,39.57,39.57,39.53,39.59,39.52,39.6,39.55,39.67 | +| bar | 27.69,27.58,27.53,27.43,27.3,27.24,27.03,26.85,26.72,26.67,26.8 | +| arcade machine | 25.97,26.02,26.04,26.35,26.49,26.85,27.09,27.17,27.48,27.71,27.31 | +| hovel | 32.62,32.51,32.49,32.36,32.3,32.23,32.15,32.0,31.93,31.74,31.72 | +| bus | 88.73,88.7,88.67,88.65,88.73,88.86,88.77,88.74,88.78,88.84,88.75 | +| towel | 60.22,60.35,60.56,60.37,60.52,60.48,60.53,60.51,60.6,60.56,60.54 | +| light | 56.38,56.42,56.3,56.32,56.24,56.24,56.18,56.14,56.17,56.1,56.14 | +| truck | 34.45,34.47,34.59,34.42,34.32,34.42,34.45,34.35,34.43,34.46,34.47 | +| tower | 24.04,24.06,24.04,24.18,24.3,24.35,24.25,24.15,24.31,24.25,23.35 | +| chandelier | 66.34,66.33,66.36,66.4,66.4,66.41,66.45,66.46,66.44,66.46,66.44 | +| awning | 22.77,22.88,22.9,23.01,23.01,23.02,23.0,23.3,23.08,23.33,23.38 | +| streetlight | 28.55,28.44,28.37,28.38,28.29,28.3,28.21,28.2,28.17,28.15,28.03 | +| booth | 56.61,56.45,56.35,56.34,56.45,56.39,56.22,56.29,56.14,56.02,56.29 | +| television receiver | 68.17,68.22,68.28,68.2,68.27,68.2,68.18,68.19,68.27,68.14,68.2 | +| airplane | 51.92,51.97,51.81,51.73,51.6,51.47,51.52,51.47,51.39,51.26,51.15 | +| dirt track | 9.69,9.74,9.8,9.81,9.72,9.79,9.89,9.9,9.95,9.99,10.11 | +| apparel | 29.1,29.12,28.89,28.77,29.03,29.13,29.0,28.99,29.08,29.01,28.45 | +| pole | 24.82,24.78,24.77,24.73,24.69,24.71,24.68,24.61,24.61,24.61,24.46 | +| land | 8.76,8.79,8.62,8.49,8.45,8.3,8.2,8.06,8.09,7.98,7.84 | +| bannister | 5.95,5.92,5.99,5.98,5.91,6.0,6.05,6.03,6.24,6.07,6.1 | +| escalator | 22.47,22.4,22.44,22.52,22.5,22.55,22.61,22.69,22.78,22.8,22.28 | +| ottoman | 49.04,49.1,48.99,49.07,49.06,48.98,48.81,48.97,48.98,49.08,48.75 | +| bottle | 16.16,16.16,16.03,15.92,15.96,15.92,15.95,15.83,15.82,15.83,15.63 | +| buffet | 51.7,51.84,52.16,52.47,52.63,53.07,52.91,53.63,54.26,54.66,55.02 | +| poster | 27.01,27.14,27.17,27.21,27.11,27.05,27.26,26.79,26.62,26.5,27.52 | +| stage | 17.65,17.68,17.73,17.76,17.81,17.81,17.97,18.04,17.91,18.03,18.08 | +| van | 48.59,48.59,48.72,48.78,48.77,48.55,48.68,48.77,48.69,48.72,48.43 | +| ship | 36.55,36.7,36.74,37.07,37.67,37.11,37.13,36.61,36.69,36.55,36.38 | +| fountain | 8.3,8.21,8.24,8.06,8.06,7.89,7.84,7.68,7.57,7.6,7.3 | +| conveyer belt | 76.6,76.51,76.42,76.25,76.29,76.09,75.88,75.81,75.8,75.51,75.29 | +| canopy | 15.41,15.43,15.36,15.44,15.51,15.51,15.52,15.43,15.3,15.27,15.55 | +| washer | 66.23,66.14,66.14,66.16,66.03,66.1,66.17,66.12,66.27,66.24,66.02 | +| plaything | 23.2,23.15,23.14,23.13,23.11,23.11,23.1,23.21,23.18,23.23,23.24 | +| swimming pool | 41.34,41.22,41.37,41.64,41.72,41.59,41.27,41.61,41.58,41.7,41.68 | +| stool | 41.0,40.99,41.1,41.01,41.0,41.2,41.13,41.14,41.15,41.17,40.85 | +| barrel | 39.73,39.97,39.2,39.45,39.35,39.33,38.31,38.81,38.67,38.98,38.41 | +| basket | 28.58,28.61,28.59,28.6,28.6,28.57,28.54,28.59,28.57,28.52,28.48 | +| waterfall | 58.61,59.32,59.48,59.19,59.08,60.13,59.86,59.84,59.99,59.76,60.58 | +| tent | 93.79,93.84,93.74,93.74,93.75,93.78,93.77,93.75,93.75,93.74,93.74 | +| bag | 11.7,11.76,11.75,11.82,11.8,11.81,11.8,11.83,11.83,11.86,11.82 | +| minibike | 62.02,61.94,61.99,61.96,62.05,61.92,61.94,61.95,61.98,62.03,61.95 | +| cradle | 81.2,81.23,81.17,81.15,81.15,81.12,81.04,80.98,80.93,80.93,80.86 | +| oven | 26.82,26.8,26.82,26.8,26.84,26.71,26.73,26.75,26.79,26.73,26.69 | +| ball | 47.58,47.6,47.73,47.87,47.93,47.96,47.95,47.94,48.17,48.14,48.11 | +| food | 54.3,54.3,54.35,54.23,54.14,54.16,54.2,54.2,54.23,54.23,53.82 | +| step | 16.11,15.68,15.98,15.98,16.2,15.97,16.35,16.5,16.52,16.4,16.73 | +| tank | 41.87,41.83,41.87,41.81,41.82,41.82,41.79,41.79,41.77,41.79,41.92 | +| trade name | 24.58,24.57,24.6,24.66,24.64,24.69,24.64,24.7,24.67,24.72,24.71 | +| microwave | 37.55,37.58,37.57,37.59,37.64,37.59,37.61,37.61,37.63,37.61,37.61 | +| pot | 41.11,41.14,41.15,41.2,41.21,41.19,41.23,41.29,41.3,41.32,41.32 | +| animal | 51.65,51.66,51.81,51.88,51.86,51.98,51.98,52.1,52.1,52.22,52.18 | +| bicycle | 46.41,46.31,46.44,46.49,46.52,46.48,46.48,46.55,46.4,46.58,46.36 | +| lake | 59.94,59.95,59.87,59.79,59.76,59.7,59.6,59.51,59.49,59.45,59.35 | +| dishwasher | 76.9,76.94,76.99,77.06,77.12,77.09,77.11,77.2,77.22,77.27,77.31 | +| screen | 64.85,64.66,64.58,64.33,64.25,63.97,63.81,63.69,63.37,63.25,62.71 | +| blanket | 14.13,14.22,14.15,14.19,14.07,14.18,14.19,14.34,14.26,14.23,13.95 | +| sculpture | 35.12,35.16,35.38,35.29,35.26,35.38,35.5,35.5,35.53,35.71,35.52 | +| hood | 56.95,57.18,56.97,57.2,57.19,56.78,56.81,56.66,56.42,56.47,56.62 | +| sconce | 42.03,41.84,41.94,41.82,41.86,41.46,41.48,41.29,41.1,41.0,41.85 | +| vase | 37.09,37.16,37.02,37.06,36.95,36.94,37.0,37.05,37.02,36.96,36.98 | +| traffic light | 29.46,29.5,29.54,29.56,29.63,29.64,29.67,29.68,29.72,29.73,29.75 | +| tray | 5.57,5.58,5.59,5.6,5.62,5.62,5.64,5.64,5.66,5.64,5.64 | +| ashcan | 37.29,37.33,37.42,37.31,37.25,37.25,37.52,37.4,37.71,37.67,37.64 | +| fan | 58.17,58.07,58.13,58.17,58.11,58.16,58.07,58.13,58.16,58.12,58.11 | +| pier | 13.57,13.57,13.61,13.74,13.03,13.24,13.06,13.01,12.98,12.93,12.85 | +| crt screen | 4.17,4.25,4.28,4.29,4.26,4.24,4.28,4.27,4.27,4.32,4.3 | +| plate | 39.43,39.49,39.6,39.57,39.61,39.7,39.59,39.74,39.67,39.75,39.88 | +| monitor | 25.65,25.61,25.41,25.16,25.24,25.09,24.78,24.68,24.45,24.44,24.36 | +| bulletin board | 46.84,46.76,47.07,47.12,47.02,47.55,46.89,47.43,47.02,47.05,47.47 | +| shower | 1.53,1.48,1.56,1.58,1.71,1.69,1.75,1.71,1.75,1.75,1.82 | +| radiator | 46.89,46.99,46.9,46.81,46.93,47.1,46.99,47.19,46.94,46.83,47.3 | +| glass | 12.28,12.26,12.32,12.27,12.26,12.35,12.34,12.4,12.41,12.4,12.38 | +| clock | 24.82,24.75,24.73,24.81,24.7,24.73,24.79,24.76,24.74,24.77,24.68 | +| flag | 38.1,38.1,38.2,38.33,38.44,38.43,38.49,38.58,38.61,38.66,38.69 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 19:46:41,513 - mmseg - INFO - Summary: +2023-03-04 19:46:41,513 - mmseg - INFO - ++-------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++-------------------------------------------------------------------+ +| 46.32,46.32,46.33,46.34,46.35,46.35,46.33,46.34,46.34,46.34,46.33 | ++-------------------------------------------------------------------+ +2023-03-04 19:46:41,514 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:46:41,514 - mmseg - INFO - Iter(val) [250] mIoU: [0.4632, 0.4632, 0.4633, 0.4634, 0.4635, 0.4635, 0.4633, 0.4634, 0.4634, 0.4634, 0.4633], copy_paste: 46.32,46.32,46.33,46.34,46.35,46.35,46.33,46.34,46.34,46.34,46.33 +2023-03-04 19:46:41,521 - mmseg - INFO - Swap parameters (before train) before iter [64001] +2023-03-04 19:46:55,527 - mmseg - INFO - Iter [64050/160000] lr: 1.875e-05, eta: 8:38:21, time: 13.698, data_time: 13.427, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5834, loss: 0.1831 +2023-03-04 19:47:09,221 - mmseg - INFO - Iter [64100/160000] lr: 1.875e-05, eta: 8:38:01, time: 0.274, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6656, loss: 0.1800 +2023-03-04 19:47:22,876 - mmseg - INFO - Iter [64150/160000] lr: 1.875e-05, eta: 8:37:41, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.4090, loss: 0.1904 +2023-03-04 19:47:36,307 - mmseg - INFO - Iter [64200/160000] lr: 1.875e-05, eta: 8:37:20, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6905, loss: 0.1782 +2023-03-04 19:47:49,549 - mmseg - INFO - Iter [64250/160000] lr: 1.875e-05, eta: 8:37:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.5087, loss: 0.1851 +2023-03-04 19:48:03,056 - mmseg - INFO - Iter [64300/160000] lr: 1.875e-05, eta: 8:36:40, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6144, loss: 0.1833 +2023-03-04 19:48:16,418 - mmseg - INFO - Iter [64350/160000] lr: 1.875e-05, eta: 8:36:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.5380, loss: 0.1826 +2023-03-04 19:48:32,204 - mmseg - INFO - Iter [64400/160000] lr: 1.875e-05, eta: 8:36:02, time: 0.316, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5730, loss: 0.1830 +2023-03-04 19:48:45,614 - mmseg - INFO - Iter [64450/160000] lr: 1.875e-05, eta: 8:35:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.5717, loss: 0.1866 +2023-03-04 19:48:58,946 - mmseg - INFO - Iter [64500/160000] lr: 1.875e-05, eta: 8:35:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7539, loss: 0.1793 +2023-03-04 19:49:12,240 - mmseg - INFO - Iter [64550/160000] lr: 1.875e-05, eta: 8:35:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4562, loss: 0.1875 +2023-03-04 19:49:25,595 - mmseg - INFO - Iter [64600/160000] lr: 1.875e-05, eta: 8:34:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.7699, loss: 0.1775 +2023-03-04 19:49:39,001 - mmseg - INFO - Iter [64650/160000] lr: 1.875e-05, eta: 8:34:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7250, loss: 0.1768 +2023-03-04 19:49:52,387 - mmseg - INFO - Iter [64700/160000] lr: 1.875e-05, eta: 8:34:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5256, loss: 0.1865 +2023-03-04 19:50:05,694 - mmseg - INFO - Iter [64750/160000] lr: 1.875e-05, eta: 8:33:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3845, loss: 0.1908 +2023-03-04 19:50:19,067 - mmseg - INFO - Iter [64800/160000] lr: 1.875e-05, eta: 8:33:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1923, decode.acc_seg: 92.1461, loss: 0.1923 +2023-03-04 19:50:32,353 - mmseg - INFO - Iter [64850/160000] lr: 1.875e-05, eta: 8:32:59, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.4932, loss: 0.1831 +2023-03-04 19:50:45,608 - mmseg - INFO - Iter [64900/160000] lr: 1.875e-05, eta: 8:32:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.6842, loss: 0.1785 +2023-03-04 19:50:59,003 - mmseg - INFO - Iter [64950/160000] lr: 1.875e-05, eta: 8:32:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.7822, loss: 0.1835 +2023-03-04 19:51:14,990 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:51:14,990 - mmseg - INFO - Iter [65000/160000] lr: 1.875e-05, eta: 8:32:02, time: 0.320, data_time: 0.056, memory: 67559, decode.loss_ce: 0.2021, decode.acc_seg: 92.0876, loss: 0.2021 +2023-03-04 19:51:28,485 - mmseg - INFO - Iter [65050/160000] lr: 1.875e-05, eta: 8:31:42, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5065, loss: 0.1839 +2023-03-04 19:51:41,806 - mmseg - INFO - Iter [65100/160000] lr: 1.875e-05, eta: 8:31:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.6266, loss: 0.1871 +2023-03-04 19:51:55,119 - mmseg - INFO - Iter [65150/160000] lr: 1.875e-05, eta: 8:31:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7304, loss: 0.1804 +2023-03-04 19:52:08,431 - mmseg - INFO - Iter [65200/160000] lr: 1.875e-05, eta: 8:30:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5063, loss: 0.1831 +2023-03-04 19:52:21,699 - mmseg - INFO - Iter [65250/160000] lr: 1.875e-05, eta: 8:30:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.2952, loss: 0.1908 +2023-03-04 19:52:35,042 - mmseg - INFO - Iter [65300/160000] lr: 1.875e-05, eta: 8:30:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.6232, loss: 0.1823 +2023-03-04 19:52:48,461 - mmseg - INFO - Iter [65350/160000] lr: 1.875e-05, eta: 8:29:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1889, decode.acc_seg: 92.3957, loss: 0.1889 +2023-03-04 19:53:01,837 - mmseg - INFO - Iter [65400/160000] lr: 1.875e-05, eta: 8:29:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.4311, loss: 0.1912 +2023-03-04 19:53:15,146 - mmseg - INFO - Iter [65450/160000] lr: 1.875e-05, eta: 8:29:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6540, loss: 0.1803 +2023-03-04 19:53:28,467 - mmseg - INFO - Iter [65500/160000] lr: 1.875e-05, eta: 8:28:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.5760, loss: 0.1804 +2023-03-04 19:53:41,771 - mmseg - INFO - Iter [65550/160000] lr: 1.875e-05, eta: 8:28:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1974, decode.acc_seg: 92.2930, loss: 0.1974 +2023-03-04 19:53:55,128 - mmseg - INFO - Iter [65600/160000] lr: 1.875e-05, eta: 8:27:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4338, loss: 0.1844 +2023-03-04 19:54:10,915 - mmseg - INFO - Iter [65650/160000] lr: 1.875e-05, eta: 8:27:42, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4875, loss: 0.1859 +2023-03-04 19:54:24,187 - mmseg - INFO - Iter [65700/160000] lr: 1.875e-05, eta: 8:27:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6878, loss: 0.1808 +2023-03-04 19:54:37,540 - mmseg - INFO - Iter [65750/160000] lr: 1.875e-05, eta: 8:27:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7037, loss: 0.1797 +2023-03-04 19:54:50,765 - mmseg - INFO - Iter [65800/160000] lr: 1.875e-05, eta: 8:26:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7832, loss: 0.1789 +2023-03-04 19:55:04,136 - mmseg - INFO - Iter [65850/160000] lr: 1.875e-05, eta: 8:26:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.3671, loss: 0.1890 +2023-03-04 19:55:17,343 - mmseg - INFO - Iter [65900/160000] lr: 1.875e-05, eta: 8:26:01, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6708, loss: 0.1797 +2023-03-04 19:55:30,713 - mmseg - INFO - Iter [65950/160000] lr: 1.875e-05, eta: 8:25:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4157, loss: 0.1859 +2023-03-04 19:55:43,990 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 19:55:43,991 - mmseg - INFO - Iter [66000/160000] lr: 1.875e-05, eta: 8:25:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5232, loss: 0.1845 +2023-03-04 19:55:57,265 - mmseg - INFO - Iter [66050/160000] lr: 1.875e-05, eta: 8:25:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4404, loss: 0.1882 +2023-03-04 19:56:10,638 - mmseg - INFO - Iter [66100/160000] lr: 1.875e-05, eta: 8:24:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.5145, loss: 0.1864 +2023-03-04 19:56:24,087 - mmseg - INFO - Iter [66150/160000] lr: 1.875e-05, eta: 8:24:21, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.4412, loss: 0.1865 +2023-03-04 19:56:37,484 - mmseg - INFO - Iter [66200/160000] lr: 1.875e-05, eta: 8:24:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5369, loss: 0.1828 +2023-03-04 19:56:50,758 - mmseg - INFO - Iter [66250/160000] lr: 1.875e-05, eta: 8:23:41, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4242, loss: 0.1873 +2023-03-04 19:57:06,583 - mmseg - INFO - Iter [66300/160000] lr: 1.875e-05, eta: 8:23:24, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.7363, loss: 0.1827 +2023-03-04 19:57:19,949 - mmseg - INFO - Iter [66350/160000] lr: 1.875e-05, eta: 8:23:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1902, decode.acc_seg: 92.4022, loss: 0.1902 +2023-03-04 19:57:33,242 - mmseg - INFO - Iter [66400/160000] lr: 1.875e-05, eta: 8:22:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.2477, loss: 0.1895 +2023-03-04 19:57:46,576 - mmseg - INFO - Iter [66450/160000] lr: 1.875e-05, eta: 8:22:24, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1889, decode.acc_seg: 92.3178, loss: 0.1889 +2023-03-04 19:57:59,924 - mmseg - INFO - Iter [66500/160000] lr: 1.875e-05, eta: 8:22:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.4156, loss: 0.1863 +2023-03-04 19:58:13,305 - mmseg - INFO - Iter [66550/160000] lr: 1.875e-05, eta: 8:21:44, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4216, loss: 0.1875 +2023-03-04 19:58:26,618 - mmseg - INFO - Iter [66600/160000] lr: 1.875e-05, eta: 8:21:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.4592, loss: 0.1867 +2023-03-04 19:58:40,122 - mmseg - INFO - Iter [66650/160000] lr: 1.875e-05, eta: 8:21:04, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1897, decode.acc_seg: 92.3572, loss: 0.1897 +2023-03-04 19:58:53,493 - mmseg - INFO - Iter [66700/160000] lr: 1.875e-05, eta: 8:20:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4045, loss: 0.1882 +2023-03-04 19:59:06,878 - mmseg - INFO - Iter [66750/160000] lr: 1.875e-05, eta: 8:20:25, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5249, loss: 0.1835 +2023-03-04 19:59:20,186 - mmseg - INFO - Iter [66800/160000] lr: 1.875e-05, eta: 8:20:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.6844, loss: 0.1844 +2023-03-04 19:59:33,529 - mmseg - INFO - Iter [66850/160000] lr: 1.875e-05, eta: 8:19:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.4941, loss: 0.1851 +2023-03-04 19:59:49,300 - mmseg - INFO - Iter [66900/160000] lr: 1.875e-05, eta: 8:19:28, time: 0.315, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4627, loss: 0.1870 +2023-03-04 20:00:02,698 - mmseg - INFO - Iter [66950/160000] lr: 1.875e-05, eta: 8:19:08, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.8490, loss: 0.1768 +2023-03-04 20:00:16,133 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:00:16,133 - mmseg - INFO - Iter [67000/160000] lr: 1.875e-05, eta: 8:18:48, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4509, loss: 0.1880 +2023-03-04 20:00:29,635 - mmseg - INFO - Iter [67050/160000] lr: 1.875e-05, eta: 8:18:29, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6259, loss: 0.1833 +2023-03-04 20:00:42,918 - mmseg - INFO - Iter [67100/160000] lr: 1.875e-05, eta: 8:18:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.4979, loss: 0.1806 +2023-03-04 20:00:56,420 - mmseg - INFO - Iter [67150/160000] lr: 1.875e-05, eta: 8:17:49, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1929, decode.acc_seg: 92.2185, loss: 0.1929 +2023-03-04 20:01:09,790 - mmseg - INFO - Iter [67200/160000] lr: 1.875e-05, eta: 8:17:29, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6422, loss: 0.1833 +2023-03-04 20:01:23,056 - mmseg - INFO - Iter [67250/160000] lr: 1.875e-05, eta: 8:17:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1972, decode.acc_seg: 92.1543, loss: 0.1972 +2023-03-04 20:01:36,414 - mmseg - INFO - Iter [67300/160000] lr: 1.875e-05, eta: 8:16:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5682, loss: 0.1830 +2023-03-04 20:01:49,787 - mmseg - INFO - Iter [67350/160000] lr: 1.875e-05, eta: 8:16:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.7340, loss: 0.1838 +2023-03-04 20:02:03,059 - mmseg - INFO - Iter [67400/160000] lr: 1.875e-05, eta: 8:16:10, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.6692, loss: 0.1826 +2023-03-04 20:02:16,342 - mmseg - INFO - Iter [67450/160000] lr: 1.875e-05, eta: 8:15:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4591, loss: 0.1860 +2023-03-04 20:02:29,861 - mmseg - INFO - Iter [67500/160000] lr: 1.875e-05, eta: 8:15:30, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.8720, loss: 0.1770 +2023-03-04 20:02:45,718 - mmseg - INFO - Iter [67550/160000] lr: 1.875e-05, eta: 8:15:14, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.3642, loss: 0.1884 +2023-03-04 20:02:59,003 - mmseg - INFO - Iter [67600/160000] lr: 1.875e-05, eta: 8:14:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5114, loss: 0.1844 +2023-03-04 20:03:12,474 - mmseg - INFO - Iter [67650/160000] lr: 1.875e-05, eta: 8:14:34, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1960, decode.acc_seg: 92.1271, loss: 0.1960 +2023-03-04 20:03:25,780 - mmseg - INFO - Iter [67700/160000] lr: 1.875e-05, eta: 8:14:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.3098, loss: 0.1892 +2023-03-04 20:03:39,131 - mmseg - INFO - Iter [67750/160000] lr: 1.875e-05, eta: 8:13:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.4761, loss: 0.1838 +2023-03-04 20:03:52,605 - mmseg - INFO - Iter [67800/160000] lr: 1.875e-05, eta: 8:13:35, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5121, loss: 0.1825 +2023-03-04 20:04:05,943 - mmseg - INFO - Iter [67850/160000] lr: 1.875e-05, eta: 8:13:16, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5626, loss: 0.1845 +2023-03-04 20:04:19,259 - mmseg - INFO - Iter [67900/160000] lr: 1.875e-05, eta: 8:12:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.6353, loss: 0.1769 +2023-03-04 20:04:32,518 - mmseg - INFO - Iter [67950/160000] lr: 1.875e-05, eta: 8:12:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1933, decode.acc_seg: 92.3000, loss: 0.1933 +2023-03-04 20:04:45,814 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:04:45,814 - mmseg - INFO - Iter [68000/160000] lr: 1.875e-05, eta: 8:12:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.4972, loss: 0.1838 +2023-03-04 20:04:59,138 - mmseg - INFO - Iter [68050/160000] lr: 1.875e-05, eta: 8:11:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.3865, loss: 0.1921 +2023-03-04 20:05:12,440 - mmseg - INFO - Iter [68100/160000] lr: 1.875e-05, eta: 8:11:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5851, loss: 0.1843 +2023-03-04 20:05:28,307 - mmseg - INFO - Iter [68150/160000] lr: 1.875e-05, eta: 8:11:20, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.5963, loss: 0.1812 +2023-03-04 20:05:41,657 - mmseg - INFO - Iter [68200/160000] lr: 1.875e-05, eta: 8:11:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4236, loss: 0.1871 +2023-03-04 20:05:55,241 - mmseg - INFO - Iter [68250/160000] lr: 1.875e-05, eta: 8:10:41, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5680, loss: 0.1830 +2023-03-04 20:06:08,464 - mmseg - INFO - Iter [68300/160000] lr: 1.875e-05, eta: 8:10:21, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6726, loss: 0.1802 +2023-03-04 20:06:21,890 - mmseg - INFO - Iter [68350/160000] lr: 1.875e-05, eta: 8:10:02, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.6154, loss: 0.1837 +2023-03-04 20:06:35,113 - mmseg - INFO - Iter [68400/160000] lr: 1.875e-05, eta: 8:09:42, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.3209, loss: 0.1873 +2023-03-04 20:06:48,437 - mmseg - INFO - Iter [68450/160000] lr: 1.875e-05, eta: 8:09:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.5185, loss: 0.1884 +2023-03-04 20:07:01,904 - mmseg - INFO - Iter [68500/160000] lr: 1.875e-05, eta: 8:09:03, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.1621, loss: 0.1936 +2023-03-04 20:07:15,334 - mmseg - INFO - Iter [68550/160000] lr: 1.875e-05, eta: 8:08:43, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.4368, loss: 0.1823 +2023-03-04 20:07:28,601 - mmseg - INFO - Iter [68600/160000] lr: 1.875e-05, eta: 8:08:24, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3526, loss: 0.1869 +2023-03-04 20:07:41,974 - mmseg - INFO - Iter [68650/160000] lr: 1.875e-05, eta: 8:08:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.3560, loss: 0.1912 +2023-03-04 20:07:55,318 - mmseg - INFO - Iter [68700/160000] lr: 1.875e-05, eta: 8:07:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.4792, loss: 0.1877 +2023-03-04 20:08:08,657 - mmseg - INFO - Iter [68750/160000] lr: 1.875e-05, eta: 8:07:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.9484, loss: 0.1751 +2023-03-04 20:08:24,519 - mmseg - INFO - Iter [68800/160000] lr: 1.875e-05, eta: 8:07:09, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.3452, loss: 0.1872 +2023-03-04 20:08:37,799 - mmseg - INFO - Iter [68850/160000] lr: 1.875e-05, eta: 8:06:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.7134, loss: 0.1786 +2023-03-04 20:08:51,040 - mmseg - INFO - Iter [68900/160000] lr: 1.875e-05, eta: 8:06:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.4220, loss: 0.1854 +2023-03-04 20:09:04,339 - mmseg - INFO - Iter [68950/160000] lr: 1.875e-05, eta: 8:06:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.6042, loss: 0.1858 +2023-03-04 20:09:17,710 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:09:17,710 - mmseg - INFO - Iter [69000/160000] lr: 1.875e-05, eta: 8:05:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.5278, loss: 0.1813 +2023-03-04 20:09:31,144 - mmseg - INFO - Iter [69050/160000] lr: 1.875e-05, eta: 8:05:31, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.4147, loss: 0.1887 +2023-03-04 20:09:44,419 - mmseg - INFO - Iter [69100/160000] lr: 1.875e-05, eta: 8:05:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6372, loss: 0.1795 +2023-03-04 20:09:57,758 - mmseg - INFO - Iter [69150/160000] lr: 1.875e-05, eta: 8:04:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1897, decode.acc_seg: 92.3526, loss: 0.1897 +2023-03-04 20:10:11,086 - mmseg - INFO - Iter [69200/160000] lr: 1.875e-05, eta: 8:04:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4149, loss: 0.1860 +2023-03-04 20:10:24,481 - mmseg - INFO - Iter [69250/160000] lr: 1.875e-05, eta: 8:04:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.8525, loss: 0.1797 +2023-03-04 20:10:37,866 - mmseg - INFO - Iter [69300/160000] lr: 1.875e-05, eta: 8:03:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3657, loss: 0.1869 +2023-03-04 20:10:51,077 - mmseg - INFO - Iter [69350/160000] lr: 1.875e-05, eta: 8:03:34, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7450, loss: 0.1789 +2023-03-04 20:11:04,435 - mmseg - INFO - Iter [69400/160000] lr: 1.875e-05, eta: 8:03:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6421, loss: 0.1820 +2023-03-04 20:11:20,267 - mmseg - INFO - Iter [69450/160000] lr: 1.875e-05, eta: 8:02:58, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.5025, loss: 0.1876 +2023-03-04 20:11:33,648 - mmseg - INFO - Iter [69500/160000] lr: 1.875e-05, eta: 8:02:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.6156, loss: 0.1824 +2023-03-04 20:11:46,973 - mmseg - INFO - Iter [69550/160000] lr: 1.875e-05, eta: 8:02:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.3997, loss: 0.1877 +2023-03-04 20:12:00,332 - mmseg - INFO - Iter [69600/160000] lr: 1.875e-05, eta: 8:02:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8746, loss: 0.1775 +2023-03-04 20:12:13,610 - mmseg - INFO - Iter [69650/160000] lr: 1.875e-05, eta: 8:01:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.4132, loss: 0.1874 +2023-03-04 20:12:26,865 - mmseg - INFO - Iter [69700/160000] lr: 1.875e-05, eta: 8:01:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.5622, loss: 0.1878 +2023-03-04 20:12:40,178 - mmseg - INFO - Iter [69750/160000] lr: 1.875e-05, eta: 8:01:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8432, loss: 0.1752 +2023-03-04 20:12:53,537 - mmseg - INFO - Iter [69800/160000] lr: 1.875e-05, eta: 8:00:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6831, loss: 0.1816 +2023-03-04 20:13:06,827 - mmseg - INFO - Iter [69850/160000] lr: 1.875e-05, eta: 8:00:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1954, decode.acc_seg: 92.1130, loss: 0.1954 +2023-03-04 20:13:20,073 - mmseg - INFO - Iter [69900/160000] lr: 1.875e-05, eta: 8:00:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6119, loss: 0.1825 +2023-03-04 20:13:33,341 - mmseg - INFO - Iter [69950/160000] lr: 1.875e-05, eta: 7:59:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5096, loss: 0.1850 +2023-03-04 20:13:46,558 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:13:46,558 - mmseg - INFO - Iter [70000/160000] lr: 1.875e-05, eta: 7:59:24, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5879, loss: 0.1809 +2023-03-04 20:14:02,549 - mmseg - INFO - Iter [70050/160000] lr: 1.875e-05, eta: 7:59:08, time: 0.320, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5433, loss: 0.1825 +2023-03-04 20:14:15,870 - mmseg - INFO - Iter [70100/160000] lr: 1.875e-05, eta: 7:58:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.3922, loss: 0.1917 +2023-03-04 20:14:29,243 - mmseg - INFO - Iter [70150/160000] lr: 1.875e-05, eta: 7:58:29, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6630, loss: 0.1806 +2023-03-04 20:14:42,546 - mmseg - INFO - Iter [70200/160000] lr: 1.875e-05, eta: 7:58:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.6087, loss: 0.1845 +2023-03-04 20:14:55,897 - mmseg - INFO - Iter [70250/160000] lr: 1.875e-05, eta: 7:57:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.4736, loss: 0.1848 +2023-03-04 20:15:09,142 - mmseg - INFO - Iter [70300/160000] lr: 1.875e-05, eta: 7:57:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6245, loss: 0.1794 +2023-03-04 20:15:22,449 - mmseg - INFO - Iter [70350/160000] lr: 1.875e-05, eta: 7:57:11, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6344, loss: 0.1825 +2023-03-04 20:15:35,764 - mmseg - INFO - Iter [70400/160000] lr: 1.875e-05, eta: 7:56:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6891, loss: 0.1799 +2023-03-04 20:15:49,072 - mmseg - INFO - Iter [70450/160000] lr: 1.875e-05, eta: 7:56:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.6755, loss: 0.1766 +2023-03-04 20:16:02,433 - mmseg - INFO - Iter [70500/160000] lr: 1.875e-05, eta: 7:56:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.4882, loss: 0.1823 +2023-03-04 20:16:15,950 - mmseg - INFO - Iter [70550/160000] lr: 1.875e-05, eta: 7:55:54, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.3766, loss: 0.1915 +2023-03-04 20:16:29,302 - mmseg - INFO - Iter [70600/160000] lr: 1.875e-05, eta: 7:55:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7867, loss: 0.1789 +2023-03-04 20:16:42,769 - mmseg - INFO - Iter [70650/160000] lr: 1.875e-05, eta: 7:55:16, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1920, decode.acc_seg: 92.3657, loss: 0.1920 +2023-03-04 20:16:58,675 - mmseg - INFO - Iter [70700/160000] lr: 1.875e-05, eta: 7:55:00, time: 0.318, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6610, loss: 0.1834 +2023-03-04 20:17:11,974 - mmseg - INFO - Iter [70750/160000] lr: 1.875e-05, eta: 7:54:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.8439, loss: 0.1771 +2023-03-04 20:17:25,368 - mmseg - INFO - Iter [70800/160000] lr: 1.875e-05, eta: 7:54:22, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.4880, loss: 0.1830 +2023-03-04 20:17:38,671 - mmseg - INFO - Iter [70850/160000] lr: 1.875e-05, eta: 7:54:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.5260, loss: 0.1858 +2023-03-04 20:17:51,910 - mmseg - INFO - Iter [70900/160000] lr: 1.875e-05, eta: 7:53:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5484, loss: 0.1816 +2023-03-04 20:18:05,162 - mmseg - INFO - Iter [70950/160000] lr: 1.875e-05, eta: 7:53:24, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.6725, loss: 0.1778 +2023-03-04 20:18:18,405 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:18:18,405 - mmseg - INFO - Iter [71000/160000] lr: 1.875e-05, eta: 7:53:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7659, loss: 0.1788 +2023-03-04 20:18:31,808 - mmseg - INFO - Iter [71050/160000] lr: 1.875e-05, eta: 7:52:45, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.2918, loss: 0.1904 +2023-03-04 20:18:45,050 - mmseg - INFO - Iter [71100/160000] lr: 1.875e-05, eta: 7:52:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.4028, loss: 0.1890 +2023-03-04 20:18:58,339 - mmseg - INFO - Iter [71150/160000] lr: 1.875e-05, eta: 7:52:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.7161, loss: 0.1845 +2023-03-04 20:19:11,814 - mmseg - INFO - Iter [71200/160000] lr: 1.875e-05, eta: 7:51:47, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1959, decode.acc_seg: 92.0533, loss: 0.1959 +2023-03-04 20:19:25,267 - mmseg - INFO - Iter [71250/160000] lr: 1.875e-05, eta: 7:51:28, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.4847, loss: 0.1916 +2023-03-04 20:19:38,605 - mmseg - INFO - Iter [71300/160000] lr: 1.875e-05, eta: 7:51:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5928, loss: 0.1816 +2023-03-04 20:19:54,453 - mmseg - INFO - Iter [71350/160000] lr: 1.875e-05, eta: 7:50:53, time: 0.317, data_time: 0.051, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.5991, loss: 0.1802 +2023-03-04 20:20:07,817 - mmseg - INFO - Iter [71400/160000] lr: 1.875e-05, eta: 7:50:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.7532, loss: 0.1813 +2023-03-04 20:20:21,144 - mmseg - INFO - Iter [71450/160000] lr: 1.875e-05, eta: 7:50:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.4929, loss: 0.1837 +2023-03-04 20:20:34,789 - mmseg - INFO - Iter [71500/160000] lr: 1.875e-05, eta: 7:49:56, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.4763, loss: 0.1848 +2023-03-04 20:20:48,026 - mmseg - INFO - Iter [71550/160000] lr: 1.875e-05, eta: 7:49:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.3156, loss: 0.1885 +2023-03-04 20:21:01,259 - mmseg - INFO - Iter [71600/160000] lr: 1.875e-05, eta: 7:49:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.3315, loss: 0.1912 +2023-03-04 20:21:14,471 - mmseg - INFO - Iter [71650/160000] lr: 1.875e-05, eta: 7:48:58, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6254, loss: 0.1804 +2023-03-04 20:21:27,867 - mmseg - INFO - Iter [71700/160000] lr: 1.875e-05, eta: 7:48:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6606, loss: 0.1813 +2023-03-04 20:21:41,128 - mmseg - INFO - Iter [71750/160000] lr: 1.875e-05, eta: 7:48:20, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.5443, loss: 0.1874 +2023-03-04 20:21:54,314 - mmseg - INFO - Iter [71800/160000] lr: 1.875e-05, eta: 7:48:01, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5700, loss: 0.1816 +2023-03-04 20:22:07,612 - mmseg - INFO - Iter [71850/160000] lr: 1.875e-05, eta: 7:47:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4035, loss: 0.1880 +2023-03-04 20:22:20,955 - mmseg - INFO - Iter [71900/160000] lr: 1.875e-05, eta: 7:47:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5298, loss: 0.1841 +2023-03-04 20:22:36,963 - mmseg - INFO - Iter [71950/160000] lr: 1.875e-05, eta: 7:47:07, time: 0.320, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.2848, loss: 0.1905 +2023-03-04 20:22:50,347 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:22:50,348 - mmseg - INFO - Iter [72000/160000] lr: 1.875e-05, eta: 7:46:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1755, decode.acc_seg: 92.8785, loss: 0.1755 +2023-03-04 20:23:03,782 - mmseg - INFO - Iter [72050/160000] lr: 1.875e-05, eta: 7:46:29, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.3141, loss: 0.1867 +2023-03-04 20:23:17,072 - mmseg - INFO - Iter [72100/160000] lr: 1.875e-05, eta: 7:46:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5457, loss: 0.1841 +2023-03-04 20:23:30,306 - mmseg - INFO - Iter [72150/160000] lr: 1.875e-05, eta: 7:45:51, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.8066, loss: 0.1790 +2023-03-04 20:23:43,541 - mmseg - INFO - Iter [72200/160000] lr: 1.875e-05, eta: 7:45:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4743, loss: 0.1875 +2023-03-04 20:23:56,740 - mmseg - INFO - Iter [72250/160000] lr: 1.875e-05, eta: 7:45:12, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1913, decode.acc_seg: 92.2557, loss: 0.1913 +2023-03-04 20:24:10,134 - mmseg - INFO - Iter [72300/160000] lr: 1.875e-05, eta: 7:44:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.5823, loss: 0.1858 +2023-03-04 20:24:23,461 - mmseg - INFO - Iter [72350/160000] lr: 1.875e-05, eta: 7:44:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4672, loss: 0.1858 +2023-03-04 20:24:36,843 - mmseg - INFO - Iter [72400/160000] lr: 1.875e-05, eta: 7:44:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5682, loss: 0.1827 +2023-03-04 20:24:50,090 - mmseg - INFO - Iter [72450/160000] lr: 1.875e-05, eta: 7:43:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5212, loss: 0.1835 +2023-03-04 20:25:03,458 - mmseg - INFO - Iter [72500/160000] lr: 1.875e-05, eta: 7:43:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7289, loss: 0.1797 +2023-03-04 20:25:16,807 - mmseg - INFO - Iter [72550/160000] lr: 1.875e-05, eta: 7:43:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.7700, loss: 0.1796 +2023-03-04 20:25:32,608 - mmseg - INFO - Iter [72600/160000] lr: 1.875e-05, eta: 7:43:02, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.5204, loss: 0.1875 +2023-03-04 20:25:45,855 - mmseg - INFO - Iter [72650/160000] lr: 1.875e-05, eta: 7:42:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.6662, loss: 0.1840 +2023-03-04 20:25:59,184 - mmseg - INFO - Iter [72700/160000] lr: 1.875e-05, eta: 7:42:24, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.2723, loss: 0.1870 +2023-03-04 20:26:12,422 - mmseg - INFO - Iter [72750/160000] lr: 1.875e-05, eta: 7:42:05, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.5950, loss: 0.1820 +2023-03-04 20:26:25,905 - mmseg - INFO - Iter [72800/160000] lr: 1.875e-05, eta: 7:41:46, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.4596, loss: 0.1828 +2023-03-04 20:26:39,264 - mmseg - INFO - Iter [72850/160000] lr: 1.875e-05, eta: 7:41:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6186, loss: 0.1817 +2023-03-04 20:26:52,631 - mmseg - INFO - Iter [72900/160000] lr: 1.875e-05, eta: 7:41:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.6962, loss: 0.1781 +2023-03-04 20:27:05,846 - mmseg - INFO - Iter [72950/160000] lr: 1.875e-05, eta: 7:40:50, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.3636, loss: 0.1861 +2023-03-04 20:27:19,235 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:27:19,235 - mmseg - INFO - Iter [73000/160000] lr: 1.875e-05, eta: 7:40:31, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.8140, loss: 0.1790 +2023-03-04 20:27:32,516 - mmseg - INFO - Iter [73050/160000] lr: 1.875e-05, eta: 7:40:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7555, loss: 0.1795 +2023-03-04 20:27:45,786 - mmseg - INFO - Iter [73100/160000] lr: 1.875e-05, eta: 7:39:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5310, loss: 0.1859 +2023-03-04 20:27:59,019 - mmseg - INFO - Iter [73150/160000] lr: 1.875e-05, eta: 7:39:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.6935, loss: 0.1843 +2023-03-04 20:28:14,784 - mmseg - INFO - Iter [73200/160000] lr: 1.875e-05, eta: 7:39:18, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4897, loss: 0.1859 +2023-03-04 20:28:28,202 - mmseg - INFO - Iter [73250/160000] lr: 1.875e-05, eta: 7:38:59, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.5596, loss: 0.1800 +2023-03-04 20:28:41,577 - mmseg - INFO - Iter [73300/160000] lr: 1.875e-05, eta: 7:38:40, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.4909, loss: 0.1886 +2023-03-04 20:28:54,896 - mmseg - INFO - Iter [73350/160000] lr: 1.875e-05, eta: 7:38:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7139, loss: 0.1801 +2023-03-04 20:29:08,102 - mmseg - INFO - Iter [73400/160000] lr: 1.875e-05, eta: 7:38:02, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7125, loss: 0.1788 +2023-03-04 20:29:21,432 - mmseg - INFO - Iter [73450/160000] lr: 1.875e-05, eta: 7:37:43, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.3605, loss: 0.1908 +2023-03-04 20:29:34,714 - mmseg - INFO - Iter [73500/160000] lr: 1.875e-05, eta: 7:37:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.7601, loss: 0.1767 +2023-03-04 20:29:48,051 - mmseg - INFO - Iter [73550/160000] lr: 1.875e-05, eta: 7:37:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 92.7713, loss: 0.1759 +2023-03-04 20:30:01,407 - mmseg - INFO - Iter [73600/160000] lr: 1.875e-05, eta: 7:36:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5047, loss: 0.1865 +2023-03-04 20:30:14,704 - mmseg - INFO - Iter [73650/160000] lr: 1.875e-05, eta: 7:36:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6355, loss: 0.1786 +2023-03-04 20:30:28,011 - mmseg - INFO - Iter [73700/160000] lr: 1.875e-05, eta: 7:36:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1894, decode.acc_seg: 92.3572, loss: 0.1894 +2023-03-04 20:30:41,218 - mmseg - INFO - Iter [73750/160000] lr: 1.875e-05, eta: 7:35:50, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1874, decode.acc_seg: 92.4100, loss: 0.1874 +2023-03-04 20:30:54,479 - mmseg - INFO - Iter [73800/160000] lr: 1.875e-05, eta: 7:35:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6848, loss: 0.1803 +2023-03-04 20:31:10,348 - mmseg - INFO - Iter [73850/160000] lr: 1.875e-05, eta: 7:35:15, time: 0.317, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5814, loss: 0.1821 +2023-03-04 20:31:23,884 - mmseg - INFO - Iter [73900/160000] lr: 1.875e-05, eta: 7:34:57, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.4849, loss: 0.1850 +2023-03-04 20:31:37,186 - mmseg - INFO - Iter [73950/160000] lr: 1.875e-05, eta: 7:34:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.2636, loss: 0.1909 +2023-03-04 20:31:50,406 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:31:50,407 - mmseg - INFO - Iter [74000/160000] lr: 1.875e-05, eta: 7:34:19, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.4571, loss: 0.1884 +2023-03-04 20:32:03,717 - mmseg - INFO - Iter [74050/160000] lr: 1.875e-05, eta: 7:34:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.6209, loss: 0.1784 +2023-03-04 20:32:16,986 - mmseg - INFO - Iter [74100/160000] lr: 1.875e-05, eta: 7:33:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4514, loss: 0.1880 +2023-03-04 20:32:30,271 - mmseg - INFO - Iter [74150/160000] lr: 1.875e-05, eta: 7:33:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.5234, loss: 0.1813 +2023-03-04 20:32:43,628 - mmseg - INFO - Iter [74200/160000] lr: 1.875e-05, eta: 7:33:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.6844, loss: 0.1841 +2023-03-04 20:32:56,882 - mmseg - INFO - Iter [74250/160000] lr: 1.875e-05, eta: 7:32:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6358, loss: 0.1828 +2023-03-04 20:33:10,078 - mmseg - INFO - Iter [74300/160000] lr: 1.875e-05, eta: 7:32:26, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1890, decode.acc_seg: 92.2289, loss: 0.1890 +2023-03-04 20:33:23,355 - mmseg - INFO - Iter [74350/160000] lr: 1.875e-05, eta: 7:32:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.5214, loss: 0.1884 +2023-03-04 20:33:36,689 - mmseg - INFO - Iter [74400/160000] lr: 1.875e-05, eta: 7:31:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5661, loss: 0.1850 +2023-03-04 20:33:49,974 - mmseg - INFO - Iter [74450/160000] lr: 1.875e-05, eta: 7:31:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5755, loss: 0.1845 +2023-03-04 20:34:05,787 - mmseg - INFO - Iter [74500/160000] lr: 1.875e-05, eta: 7:31:14, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4962, loss: 0.1844 +2023-03-04 20:34:19,085 - mmseg - INFO - Iter [74550/160000] lr: 1.875e-05, eta: 7:30:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1936, decode.acc_seg: 92.3488, loss: 0.1936 +2023-03-04 20:34:32,383 - mmseg - INFO - Iter [74600/160000] lr: 1.875e-05, eta: 7:30:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1746, decode.acc_seg: 92.8647, loss: 0.1746 +2023-03-04 20:34:45,718 - mmseg - INFO - Iter [74650/160000] lr: 1.875e-05, eta: 7:30:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1957, decode.acc_seg: 92.1846, loss: 0.1957 +2023-03-04 20:34:58,977 - mmseg - INFO - Iter [74700/160000] lr: 1.875e-05, eta: 7:29:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6826, loss: 0.1812 +2023-03-04 20:35:12,401 - mmseg - INFO - Iter [74750/160000] lr: 1.875e-05, eta: 7:29:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.4750, loss: 0.1867 +2023-03-04 20:35:25,635 - mmseg - INFO - Iter [74800/160000] lr: 1.875e-05, eta: 7:29:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.2194, loss: 0.1871 +2023-03-04 20:35:38,938 - mmseg - INFO - Iter [74850/160000] lr: 1.875e-05, eta: 7:29:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6208, loss: 0.1817 +2023-03-04 20:35:52,186 - mmseg - INFO - Iter [74900/160000] lr: 1.875e-05, eta: 7:28:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.4801, loss: 0.1820 +2023-03-04 20:36:05,412 - mmseg - INFO - Iter [74950/160000] lr: 1.875e-05, eta: 7:28:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4415, loss: 0.1880 +2023-03-04 20:36:18,678 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:36:18,678 - mmseg - INFO - Iter [75000/160000] lr: 1.875e-05, eta: 7:28:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.8457, loss: 0.1742 +2023-03-04 20:36:31,884 - mmseg - INFO - Iter [75050/160000] lr: 1.875e-05, eta: 7:27:48, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.8960, loss: 0.1784 +2023-03-04 20:36:47,654 - mmseg - INFO - Iter [75100/160000] lr: 1.875e-05, eta: 7:27:32, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6051, loss: 0.1817 +2023-03-04 20:37:01,079 - mmseg - INFO - Iter [75150/160000] lr: 1.875e-05, eta: 7:27:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.4749, loss: 0.1849 +2023-03-04 20:37:14,407 - mmseg - INFO - Iter [75200/160000] lr: 1.875e-05, eta: 7:26:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.4555, loss: 0.1865 +2023-03-04 20:37:27,712 - mmseg - INFO - Iter [75250/160000] lr: 1.875e-05, eta: 7:26:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.3686, loss: 0.1841 +2023-03-04 20:37:41,047 - mmseg - INFO - Iter [75300/160000] lr: 1.875e-05, eta: 7:26:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.3489, loss: 0.1914 +2023-03-04 20:37:54,233 - mmseg - INFO - Iter [75350/160000] lr: 1.875e-05, eta: 7:25:59, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6300, loss: 0.1810 +2023-03-04 20:38:07,562 - mmseg - INFO - Iter [75400/160000] lr: 1.875e-05, eta: 7:25:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.7200, loss: 0.1835 +2023-03-04 20:38:20,801 - mmseg - INFO - Iter [75450/160000] lr: 1.875e-05, eta: 7:25:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5832, loss: 0.1835 +2023-03-04 20:38:34,087 - mmseg - INFO - Iter [75500/160000] lr: 1.875e-05, eta: 7:25:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3533, loss: 0.1880 +2023-03-04 20:38:47,547 - mmseg - INFO - Iter [75550/160000] lr: 1.875e-05, eta: 7:24:45, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6210, loss: 0.1820 +2023-03-04 20:39:00,978 - mmseg - INFO - Iter [75600/160000] lr: 1.875e-05, eta: 7:24:26, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.6247, loss: 0.1854 +2023-03-04 20:39:14,327 - mmseg - INFO - Iter [75650/160000] lr: 1.875e-05, eta: 7:24:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.5807, loss: 0.1842 +2023-03-04 20:39:27,653 - mmseg - INFO - Iter [75700/160000] lr: 1.875e-05, eta: 7:23:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.2962, loss: 0.1863 +2023-03-04 20:39:43,399 - mmseg - INFO - Iter [75750/160000] lr: 1.875e-05, eta: 7:23:34, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6917, loss: 0.1806 +2023-03-04 20:39:56,711 - mmseg - INFO - Iter [75800/160000] lr: 1.875e-05, eta: 7:23:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1734, decode.acc_seg: 93.0329, loss: 0.1734 +2023-03-04 20:40:09,983 - mmseg - INFO - Iter [75850/160000] lr: 1.875e-05, eta: 7:22:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.5934, loss: 0.1812 +2023-03-04 20:40:23,376 - mmseg - INFO - Iter [75900/160000] lr: 1.875e-05, eta: 7:22:38, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.4073, loss: 0.1901 +2023-03-04 20:40:36,818 - mmseg - INFO - Iter [75950/160000] lr: 1.875e-05, eta: 7:22:20, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.3385, loss: 0.1876 +2023-03-04 20:40:50,172 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:40:50,172 - mmseg - INFO - Iter [76000/160000] lr: 1.875e-05, eta: 7:22:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1927, decode.acc_seg: 92.0430, loss: 0.1927 +2023-03-04 20:41:03,633 - mmseg - INFO - Iter [76050/160000] lr: 1.875e-05, eta: 7:21:43, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.2736, loss: 0.1932 +2023-03-04 20:41:16,933 - mmseg - INFO - Iter [76100/160000] lr: 1.875e-05, eta: 7:21:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6097, loss: 0.1803 +2023-03-04 20:41:30,345 - mmseg - INFO - Iter [76150/160000] lr: 1.875e-05, eta: 7:21:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4107, loss: 0.1852 +2023-03-04 20:41:43,782 - mmseg - INFO - Iter [76200/160000] lr: 1.875e-05, eta: 7:20:47, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.6273, loss: 0.1847 +2023-03-04 20:41:57,160 - mmseg - INFO - Iter [76250/160000] lr: 1.875e-05, eta: 7:20:29, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.3773, loss: 0.1881 +2023-03-04 20:42:10,423 - mmseg - INFO - Iter [76300/160000] lr: 1.875e-05, eta: 7:20:10, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6691, loss: 0.1797 +2023-03-04 20:42:23,699 - mmseg - INFO - Iter [76350/160000] lr: 1.875e-05, eta: 7:19:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.7699, loss: 0.1803 +2023-03-04 20:42:39,688 - mmseg - INFO - Iter [76400/160000] lr: 1.875e-05, eta: 7:19:36, time: 0.320, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.6827, loss: 0.1796 +2023-03-04 20:42:53,026 - mmseg - INFO - Iter [76450/160000] lr: 1.875e-05, eta: 7:19:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1951, decode.acc_seg: 92.2165, loss: 0.1951 +2023-03-04 20:43:06,325 - mmseg - INFO - Iter [76500/160000] lr: 1.875e-05, eta: 7:19:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.6358, loss: 0.1836 +2023-03-04 20:43:19,664 - mmseg - INFO - Iter [76550/160000] lr: 1.875e-05, eta: 7:18:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.3502, loss: 0.1868 +2023-03-04 20:43:32,853 - mmseg - INFO - Iter [76600/160000] lr: 1.875e-05, eta: 7:18:23, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.5613, loss: 0.1867 +2023-03-04 20:43:46,276 - mmseg - INFO - Iter [76650/160000] lr: 1.875e-05, eta: 7:18:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4957, loss: 0.1860 +2023-03-04 20:43:59,636 - mmseg - INFO - Iter [76700/160000] lr: 1.875e-05, eta: 7:17:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.5857, loss: 0.1799 +2023-03-04 20:44:12,895 - mmseg - INFO - Iter [76750/160000] lr: 1.875e-05, eta: 7:17:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7468, loss: 0.1768 +2023-03-04 20:44:26,220 - mmseg - INFO - Iter [76800/160000] lr: 1.875e-05, eta: 7:17:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4466, loss: 0.1871 +2023-03-04 20:44:39,419 - mmseg - INFO - Iter [76850/160000] lr: 1.875e-05, eta: 7:16:50, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.4775, loss: 0.1865 +2023-03-04 20:44:52,726 - mmseg - INFO - Iter [76900/160000] lr: 1.875e-05, eta: 7:16:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.8512, loss: 0.1760 +2023-03-04 20:45:06,019 - mmseg - INFO - Iter [76950/160000] lr: 1.875e-05, eta: 7:16:14, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6326, loss: 0.1803 +2023-03-04 20:45:21,751 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:45:21,751 - mmseg - INFO - Iter [77000/160000] lr: 1.875e-05, eta: 7:15:58, time: 0.315, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.4783, loss: 0.1833 +2023-03-04 20:45:34,999 - mmseg - INFO - Iter [77050/160000] lr: 1.875e-05, eta: 7:15:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4164, loss: 0.1861 +2023-03-04 20:45:48,277 - mmseg - INFO - Iter [77100/160000] lr: 1.875e-05, eta: 7:15:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5556, loss: 0.1849 +2023-03-04 20:46:01,638 - mmseg - INFO - Iter [77150/160000] lr: 1.875e-05, eta: 7:15:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5667, loss: 0.1809 +2023-03-04 20:46:15,005 - mmseg - INFO - Iter [77200/160000] lr: 1.875e-05, eta: 7:14:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6161, loss: 0.1816 +2023-03-04 20:46:28,406 - mmseg - INFO - Iter [77250/160000] lr: 1.875e-05, eta: 7:14:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6657, loss: 0.1808 +2023-03-04 20:46:41,816 - mmseg - INFO - Iter [77300/160000] lr: 1.875e-05, eta: 7:14:08, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4005, loss: 0.1859 +2023-03-04 20:46:55,130 - mmseg - INFO - Iter [77350/160000] lr: 1.875e-05, eta: 7:13:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.4728, loss: 0.1841 +2023-03-04 20:47:08,383 - mmseg - INFO - Iter [77400/160000] lr: 1.875e-05, eta: 7:13:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5407, loss: 0.1839 +2023-03-04 20:47:21,677 - mmseg - INFO - Iter [77450/160000] lr: 1.875e-05, eta: 7:13:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6542, loss: 0.1794 +2023-03-04 20:47:35,003 - mmseg - INFO - Iter [77500/160000] lr: 1.875e-05, eta: 7:12:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.5016, loss: 0.1836 +2023-03-04 20:47:48,321 - mmseg - INFO - Iter [77550/160000] lr: 1.875e-05, eta: 7:12:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5482, loss: 0.1848 +2023-03-04 20:48:01,747 - mmseg - INFO - Iter [77600/160000] lr: 1.875e-05, eta: 7:12:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.8009, loss: 0.1793 +2023-03-04 20:48:17,657 - mmseg - INFO - Iter [77650/160000] lr: 1.875e-05, eta: 7:12:02, time: 0.318, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.5572, loss: 0.1854 +2023-03-04 20:48:31,048 - mmseg - INFO - Iter [77700/160000] lr: 1.875e-05, eta: 7:11:44, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4076, loss: 0.1860 +2023-03-04 20:48:44,368 - mmseg - INFO - Iter [77750/160000] lr: 1.875e-05, eta: 7:11:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1933, decode.acc_seg: 92.1373, loss: 0.1933 +2023-03-04 20:48:57,641 - mmseg - INFO - Iter [77800/160000] lr: 1.875e-05, eta: 7:11:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.7546, loss: 0.1817 +2023-03-04 20:49:11,063 - mmseg - INFO - Iter [77850/160000] lr: 1.875e-05, eta: 7:10:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.4280, loss: 0.1885 +2023-03-04 20:49:24,480 - mmseg - INFO - Iter [77900/160000] lr: 1.875e-05, eta: 7:10:31, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6405, loss: 0.1814 +2023-03-04 20:49:37,936 - mmseg - INFO - Iter [77950/160000] lr: 1.875e-05, eta: 7:10:13, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.7047, loss: 0.1822 +2023-03-04 20:49:51,207 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:49:51,207 - mmseg - INFO - Iter [78000/160000] lr: 1.875e-05, eta: 7:09:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3878, loss: 0.1901 +2023-03-04 20:50:04,474 - mmseg - INFO - Iter [78050/160000] lr: 1.875e-05, eta: 7:09:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.6676, loss: 0.1840 +2023-03-04 20:50:17,864 - mmseg - INFO - Iter [78100/160000] lr: 1.875e-05, eta: 7:09:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6022, loss: 0.1833 +2023-03-04 20:50:31,098 - mmseg - INFO - Iter [78150/160000] lr: 1.875e-05, eta: 7:09:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.3301, loss: 0.1881 +2023-03-04 20:50:44,481 - mmseg - INFO - Iter [78200/160000] lr: 1.875e-05, eta: 7:08:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5142, loss: 0.1835 +2023-03-04 20:51:00,204 - mmseg - INFO - Iter [78250/160000] lr: 1.875e-05, eta: 7:08:26, time: 0.314, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.3942, loss: 0.1847 +2023-03-04 20:51:13,447 - mmseg - INFO - Iter [78300/160000] lr: 1.875e-05, eta: 7:08:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.3537, loss: 0.1867 +2023-03-04 20:51:26,779 - mmseg - INFO - Iter [78350/160000] lr: 1.875e-05, eta: 7:07:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1925, decode.acc_seg: 92.1996, loss: 0.1925 +2023-03-04 20:51:39,995 - mmseg - INFO - Iter [78400/160000] lr: 1.875e-05, eta: 7:07:31, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.4578, loss: 0.1869 +2023-03-04 20:51:53,329 - mmseg - INFO - Iter [78450/160000] lr: 1.875e-05, eta: 7:07:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.6683, loss: 0.1819 +2023-03-04 20:52:06,511 - mmseg - INFO - Iter [78500/160000] lr: 1.875e-05, eta: 7:06:54, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.7005, loss: 0.1791 +2023-03-04 20:52:19,762 - mmseg - INFO - Iter [78550/160000] lr: 1.875e-05, eta: 7:06:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.5413, loss: 0.1855 +2023-03-04 20:52:33,204 - mmseg - INFO - Iter [78600/160000] lr: 1.875e-05, eta: 7:06:18, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6193, loss: 0.1814 +2023-03-04 20:52:46,440 - mmseg - INFO - Iter [78650/160000] lr: 1.875e-05, eta: 7:06:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5685, loss: 0.1818 +2023-03-04 20:52:59,967 - mmseg - INFO - Iter [78700/160000] lr: 1.875e-05, eta: 7:05:42, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.3807, loss: 0.1873 +2023-03-04 20:53:13,263 - mmseg - INFO - Iter [78750/160000] lr: 1.875e-05, eta: 7:05:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6833, loss: 0.1818 +2023-03-04 20:53:26,668 - mmseg - INFO - Iter [78800/160000] lr: 1.875e-05, eta: 7:05:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5708, loss: 0.1816 +2023-03-04 20:53:39,977 - mmseg - INFO - Iter [78850/160000] lr: 1.875e-05, eta: 7:04:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.5493, loss: 0.1893 +2023-03-04 20:53:55,873 - mmseg - INFO - Iter [78900/160000] lr: 1.875e-05, eta: 7:04:32, time: 0.318, data_time: 0.052, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6567, loss: 0.1825 +2023-03-04 20:54:09,303 - mmseg - INFO - Iter [78950/160000] lr: 1.875e-05, eta: 7:04:14, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7052, loss: 0.1795 +2023-03-04 20:54:22,615 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:54:22,615 - mmseg - INFO - Iter [79000/160000] lr: 1.875e-05, eta: 7:03:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5339, loss: 0.1840 +2023-03-04 20:54:35,963 - mmseg - INFO - Iter [79050/160000] lr: 1.875e-05, eta: 7:03:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6830, loss: 0.1799 +2023-03-04 20:54:49,222 - mmseg - INFO - Iter [79100/160000] lr: 1.875e-05, eta: 7:03:19, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.4532, loss: 0.1864 +2023-03-04 20:55:02,637 - mmseg - INFO - Iter [79150/160000] lr: 1.875e-05, eta: 7:03:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7978, loss: 0.1778 +2023-03-04 20:55:16,059 - mmseg - INFO - Iter [79200/160000] lr: 1.875e-05, eta: 7:02:43, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.2952, loss: 0.1915 +2023-03-04 20:55:29,311 - mmseg - INFO - Iter [79250/160000] lr: 1.875e-05, eta: 7:02:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1889, decode.acc_seg: 92.3768, loss: 0.1889 +2023-03-04 20:55:42,540 - mmseg - INFO - Iter [79300/160000] lr: 1.875e-05, eta: 7:02:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1966, decode.acc_seg: 92.0666, loss: 0.1966 +2023-03-04 20:55:55,876 - mmseg - INFO - Iter [79350/160000] lr: 1.875e-05, eta: 7:01:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.3095, loss: 0.1915 +2023-03-04 20:56:09,113 - mmseg - INFO - Iter [79400/160000] lr: 1.875e-05, eta: 7:01:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.6874, loss: 0.1826 +2023-03-04 20:56:22,385 - mmseg - INFO - Iter [79450/160000] lr: 1.875e-05, eta: 7:01:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.7200, loss: 0.1763 +2023-03-04 20:56:35,716 - mmseg - INFO - Iter [79500/160000] lr: 1.875e-05, eta: 7:00:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.6560, loss: 0.1835 +2023-03-04 20:56:51,606 - mmseg - INFO - Iter [79550/160000] lr: 1.875e-05, eta: 7:00:39, time: 0.318, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5618, loss: 0.1831 +2023-03-04 20:57:04,967 - mmseg - INFO - Iter [79600/160000] lr: 1.875e-05, eta: 7:00:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.1600, loss: 0.1938 +2023-03-04 20:57:18,428 - mmseg - INFO - Iter [79650/160000] lr: 1.875e-05, eta: 7:00:03, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8324, loss: 0.1752 +2023-03-04 20:57:31,692 - mmseg - INFO - Iter [79700/160000] lr: 1.875e-05, eta: 6:59:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.4459, loss: 0.1885 +2023-03-04 20:57:45,163 - mmseg - INFO - Iter [79750/160000] lr: 1.875e-05, eta: 6:59:27, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.4369, loss: 0.1901 +2023-03-04 20:57:58,541 - mmseg - INFO - Iter [79800/160000] lr: 1.875e-05, eta: 6:59:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6924, loss: 0.1806 +2023-03-04 20:58:11,814 - mmseg - INFO - Iter [79850/160000] lr: 1.875e-05, eta: 6:58:51, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4042, loss: 0.1880 +2023-03-04 20:58:25,009 - mmseg - INFO - Iter [79900/160000] lr: 1.875e-05, eta: 6:58:33, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.4672, loss: 0.1867 +2023-03-04 20:58:38,375 - mmseg - INFO - Iter [79950/160000] lr: 1.875e-05, eta: 6:58:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6388, loss: 0.1810 +2023-03-04 20:58:51,651 - mmseg - INFO - Swap parameters (after train) after iter [80000] +2023-03-04 20:58:51,674 - mmseg - INFO - Saving checkpoint at 80000 iterations +2023-03-04 20:58:53,518 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 20:58:53,519 - mmseg - INFO - Iter [80000/160000] lr: 1.875e-05, eta: 6:57:59, time: 0.303, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.5589, loss: 0.1833 +2023-03-04 21:10:00,665 - mmseg - INFO - per class results: +2023-03-04 21:10:00,674 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.43,76.43,76.43,76.43,76.42,76.42,76.41,76.4,76.41,76.41,76.38 | +| building | 81.45,81.45,81.46,81.47,81.47,81.46,81.46,81.45,81.45,81.46,81.44 | +| sky | 94.29,94.28,94.28,94.28,94.28,94.28,94.28,94.27,94.28,94.27,94.26 | +| floor | 80.0,80.01,80.0,80.01,80.04,80.04,80.03,80.03,80.03,80.04,80.03 | +| tree | 72.9,72.88,72.87,72.86,72.85,72.83,72.84,72.84,72.82,72.81,72.82 | +| ceiling | 82.82,82.84,82.84,82.85,82.84,82.84,82.83,82.83,82.83,82.83,82.81 | +| road | 82.26,82.28,82.28,82.28,82.3,82.24,82.23,82.19,82.15,82.12,82.17 | +| bed | 88.57,88.59,88.58,88.58,88.62,88.59,88.59,88.59,88.58,88.59,88.57 | +| windowpane | 61.1,61.11,61.1,61.12,61.12,61.15,61.15,61.15,61.18,61.17,61.14 | +| grass | 65.65,65.64,65.68,65.67,65.7,65.77,65.75,65.8,65.8,65.83,65.85 | +| cabinet | 59.52,59.54,59.57,59.58,59.53,59.56,59.51,59.49,59.47,59.48,59.41 | +| sidewalk | 66.36,66.43,66.46,66.46,66.53,66.47,66.47,66.44,66.39,66.38,66.34 | +| person | 79.48,79.51,79.5,79.53,79.51,79.52,79.51,79.51,79.52,79.52,79.48 | +| earth | 33.25,33.25,33.31,33.33,33.25,33.17,33.15,33.15,33.06,33.06,33.21 | +| door | 48.37,48.39,48.46,48.47,48.51,48.53,48.55,48.58,48.62,48.65,48.62 | +| table | 61.67,61.71,61.7,61.74,61.75,61.79,61.75,61.74,61.7,61.74,61.86 | +| mountain | 51.85,51.96,51.96,52.08,52.1,52.21,52.26,52.2,52.3,52.33,52.12 | +| plant | 50.29,50.26,50.23,50.22,50.21,50.19,50.18,50.15,50.16,50.15,50.14 | +| curtain | 70.27,70.42,70.66,70.72,70.8,70.9,70.93,70.93,70.94,70.92,70.8 | +| chair | 58.29,58.34,58.38,58.41,58.45,58.49,58.5,58.5,58.49,58.52,58.57 | +| car | 83.18,83.2,83.22,83.19,83.22,83.2,83.21,83.22,83.21,83.2,83.24 | +| water | 47.43,47.44,47.46,47.47,47.48,47.44,47.45,47.41,47.37,47.37,47.44 | +| painting | 69.8,69.8,69.85,69.82,69.81,69.83,69.84,69.86,69.82,69.81,69.77 | +| sofa | 65.64,65.61,65.63,65.66,65.69,65.71,65.77,65.8,65.88,66.01,65.97 | +| shelf | 40.73,40.73,40.74,40.74,40.72,40.66,40.62,40.58,40.58,40.57,40.65 | +| house | 44.63,44.51,44.54,44.55,44.56,44.51,44.5,44.47,44.5,44.5,44.25 | +| sea | 44.7,44.63,44.61,44.56,44.56,44.52,44.46,44.47,44.38,44.37,44.29 | +| mirror | 65.35,65.35,65.35,65.38,65.34,65.36,65.32,65.33,65.28,65.26,65.35 | +| rug | 55.26,55.41,55.35,55.48,55.81,55.81,55.79,55.72,55.71,55.73,56.06 | +| field | 28.23,28.26,28.28,28.35,28.32,28.3,28.29,28.29,28.26,28.26,28.21 | +| armchair | 43.98,44.01,44.12,44.13,44.11,44.1,44.11,44.13,44.18,44.28,44.25 | +| seat | 54.03,54.06,54.04,53.94,53.99,53.92,53.91,53.83,53.78,53.77,53.82 | +| fence | 40.92,40.88,40.93,40.86,40.93,40.88,40.87,40.85,40.85,40.79,40.84 | +| desk | 49.3,49.33,49.31,49.27,49.24,49.31,49.32,49.23,49.14,49.01,49.01 | +| rock | 28.16,28.28,28.02,28.19,28.25,28.51,28.6,28.51,28.66,28.82,28.18 | +| wardrobe | 47.89,47.92,47.92,47.87,47.77,47.74,47.68,47.66,47.6,47.64,47.24 | +| lamp | 63.78,63.8,63.82,63.83,63.81,63.85,63.84,63.86,63.88,63.88,63.87 | +| bathtub | 77.1,77.12,77.12,77.0,76.98,76.87,76.86,76.75,76.73,76.66,76.93 | +| railing | 32.04,31.98,31.92,31.85,31.75,31.72,31.65,31.65,31.59,31.57,31.56 | +| cushion | 55.53,55.62,55.6,55.64,55.63,55.69,55.72,55.73,55.76,55.84,55.69 | +| base | 28.82,28.89,28.95,28.93,29.16,29.05,29.11,29.06,29.09,29.16,29.33 | +| box | 24.25,24.26,24.31,24.32,24.27,24.4,24.38,24.39,24.4,24.39,24.37 | +| column | 46.08,46.1,46.06,46.06,46.16,46.16,46.07,46.03,45.99,46.03,45.88 | +| signboard | 36.02,36.0,36.05,36.0,35.97,36.01,36.01,35.94,35.92,35.96,35.93 | +| chest of drawers | 39.08,38.91,38.97,38.88,38.93,38.92,38.98,38.98,38.92,38.98,39.01 | +| counter | 27.18,27.33,27.11,27.34,27.09,27.22,26.99,26.91,26.69,26.47,26.54 | +| sand | 32.01,31.95,32.0,32.07,31.98,31.94,31.92,31.93,31.97,32.01,32.09 | +| sink | 71.03,71.0,71.02,70.99,70.91,70.96,71.04,71.02,71.0,70.95,70.83 | +| skyscraper | 48.7,48.73,48.73,48.77,48.81,48.7,48.87,48.88,48.9,48.93,49.0 | +| fireplace | 66.12,66.13,66.16,66.15,66.13,66.24,66.13,66.2,66.2,66.18,66.2 | +| refrigerator | 78.23,78.25,78.24,78.31,78.25,78.24,78.2,78.36,78.3,78.3,78.45 | +| grandstand | 42.01,42.06,42.04,42.06,42.05,42.21,42.1,42.14,42.26,42.17,42.22 | +| path | 17.91,17.88,17.92,17.92,17.95,17.99,17.99,18.0,18.04,18.02,18.1 | +| stairs | 31.81,31.81,31.83,31.79,31.79,31.75,31.73,31.77,31.75,31.74,31.79 | +| runway | 63.88,63.89,63.89,63.89,63.9,63.9,63.9,63.91,63.91,63.9,63.92 | +| case | 48.26,48.23,48.18,48.09,48.03,48.01,47.97,47.88,47.85,47.76,47.79 | +| pool table | 92.66,92.68,92.69,92.71,92.68,92.68,92.74,92.71,92.71,92.69,92.67 | +| pillow | 57.06,57.06,57.14,57.08,57.14,57.1,57.1,57.08,56.98,56.96,57.04 | +| screen door | 66.26,66.6,66.4,66.41,66.54,66.57,66.51,66.57,66.74,66.78,66.68 | +| stairway | 25.62,25.58,25.62,25.58,25.63,25.6,25.62,25.6,25.62,25.67,25.65 | +| river | 10.06,9.99,9.93,9.88,9.81,9.77,9.67,9.58,9.52,9.43,9.38 | +| bridge | 53.28,54.22,54.76,55.19,56.02,56.67,56.91,57.06,57.61,58.32,58.62 | +| bookcase | 41.59,41.8,41.9,42.03,42.27,42.5,42.41,42.52,42.59,42.83,42.09 | +| blind | 45.27,45.15,44.96,44.84,44.73,44.73,44.67,44.5,44.58,44.62,44.54 | +| coffee table | 66.84,66.76,66.83,66.84,66.74,66.9,66.86,66.99,66.99,67.05,67.04 | +| toilet | 86.45,86.47,86.46,86.48,86.51,86.56,86.59,86.53,86.53,86.5,86.52 | +| flower | 31.46,31.48,31.47,31.56,31.47,31.52,31.59,31.61,31.7,31.7,31.6 | +| book | 47.03,47.05,47.01,47.0,47.0,46.88,46.85,46.81,46.79,46.71,46.81 | +| hill | 7.54,7.51,7.52,7.5,7.56,7.49,7.52,7.51,7.64,7.64,7.45 | +| bench | 44.38,44.4,44.43,44.46,44.46,44.51,44.44,44.51,44.55,44.54,44.5 | +| countertop | 54.64,54.64,54.69,54.69,54.69,54.77,54.85,54.91,54.87,54.81,54.87 | +| stove | 72.59,72.62,72.62,72.72,72.61,72.72,72.72,72.69,72.79,72.82,72.99 | +| palm | 50.53,50.58,50.58,50.68,50.65,50.67,50.77,50.79,50.91,50.96,50.81 | +| kitchen island | 46.54,46.54,46.48,46.55,46.39,46.58,46.46,46.26,45.97,45.89,46.76 | +| computer | 57.27,57.27,57.32,57.29,57.29,57.35,57.28,57.31,57.4,57.43,57.25 | +| swivel chair | 45.02,45.07,45.18,45.2,45.31,45.35,45.44,45.43,45.36,45.42,45.52 | +| boat | 38.49,38.56,38.58,38.51,38.45,38.54,38.65,38.66,38.77,38.77,38.78 | +| bar | 27.18,27.09,26.99,26.96,26.76,26.6,26.59,26.53,26.35,26.44,26.49 | +| arcade machine | 25.88,26.02,26.17,26.55,26.78,27.14,27.2,27.35,27.95,28.16,28.28 | +| hovel | 31.46,31.41,31.33,31.24,31.14,31.08,30.86,30.81,30.76,30.64,30.44 | +| bus | 88.51,88.59,88.48,88.55,88.61,88.58,88.59,88.68,88.68,88.7,88.71 | +| towel | 60.49,60.5,60.55,60.49,60.59,60.48,60.54,60.54,60.56,60.75,60.37 | +| light | 56.57,56.56,56.56,56.55,56.51,56.54,56.5,56.47,56.53,56.52,56.49 | +| truck | 34.36,34.56,34.31,34.28,34.29,34.32,34.35,34.46,34.45,34.47,34.34 | +| tower | 23.26,23.33,23.27,23.29,23.1,23.06,22.83,22.48,22.35,22.29,22.36 | +| chandelier | 66.29,66.32,66.33,66.32,66.34,66.42,66.39,66.37,66.34,66.42,66.4 | +| awning | 23.71,23.7,23.69,23.62,23.84,23.76,23.69,23.8,23.91,24.0,23.85 | +| streetlight | 28.46,28.37,28.34,28.32,28.21,28.22,28.2,28.14,28.1,28.11,28.03 | +| booth | 57.17,57.29,57.22,57.2,57.34,57.18,57.18,57.36,57.21,57.29,57.15 | +| television receiver | 68.52,68.5,68.31,68.38,68.39,68.38,68.35,68.45,68.35,68.38,68.34 | +| airplane | 52.23,52.24,52.22,52.17,52.0,52.1,52.03,51.99,52.01,51.81,51.89 | +| dirt track | 10.29,10.16,10.21,10.4,10.31,10.36,10.55,10.77,10.69,10.75,10.57 | +| apparel | 29.74,29.58,29.54,29.59,29.7,29.62,29.48,29.36,29.66,29.49,28.73 | +| pole | 24.49,24.47,24.4,24.38,24.35,24.36,24.32,24.25,24.29,24.26,24.27 | +| land | 7.75,7.7,7.65,7.6,7.44,7.33,7.27,7.23,7.18,7.1,7.1 | +| bannister | 5.79,5.82,5.76,5.85,5.78,5.92,5.94,5.9,6.02,5.99,5.85 | +| escalator | 22.85,22.92,22.98,22.92,22.94,22.94,22.95,22.92,23.03,23.17,22.82 | +| ottoman | 48.4,48.35,48.19,48.13,48.36,48.23,48.1,47.82,47.82,47.71,48.37 | +| bottle | 15.32,15.27,15.34,15.25,15.23,15.2,15.21,15.16,15.08,15.17,15.02 | +| buffet | 52.55,53.02,53.11,53.66,54.1,54.73,55.02,55.56,56.23,56.72,56.35 | +| poster | 27.53,27.52,27.55,27.52,27.67,27.72,27.6,27.78,27.8,27.76,27.02 | +| stage | 17.41,17.53,17.48,17.6,17.6,17.63,17.64,17.74,17.71,17.79,17.75 | +| van | 47.84,47.85,48.13,47.84,48.22,47.9,48.21,48.26,48.05,48.16,48.39 | +| ship | 32.34,32.72,33.02,32.67,33.37,33.3,32.6,31.93,32.67,32.97,31.0 | +| fountain | 8.52,8.55,8.52,8.69,8.62,8.68,8.55,8.36,8.14,8.29,8.01 | +| conveyer belt | 75.63,75.59,75.54,75.47,75.5,75.41,75.43,75.44,75.21,75.19,75.06 | +| canopy | 15.24,15.31,15.34,15.37,15.45,15.43,15.41,15.4,15.33,15.46,15.34 | +| washer | 66.41,66.42,66.43,66.37,66.34,66.33,66.35,66.22,66.38,66.42,66.08 | +| plaything | 23.0,22.98,22.98,23.05,23.01,23.02,23.05,23.08,23.23,23.22,23.14 | +| swimming pool | 40.81,41.18,41.74,41.89,42.06,42.56,43.05,42.92,42.63,43.1,43.46 | +| stool | 41.63,41.69,41.56,41.55,41.57,41.73,41.59,41.72,41.5,41.61,41.53 | +| barrel | 40.26,40.11,40.3,40.49,40.0,40.54,40.6,39.65,39.95,39.91,39.79 | +| basket | 28.4,28.38,28.4,28.32,28.34,28.33,28.34,28.38,28.37,28.36,28.32 | +| waterfall | 58.65,58.9,59.29,59.54,59.85,59.55,59.51,59.04,59.02,59.01,60.74 | +| tent | 93.64,93.62,93.64,93.65,93.67,93.61,93.67,93.76,93.73,93.76,93.78 | +| bag | 11.58,11.51,11.64,11.64,11.68,11.69,11.65,11.69,11.76,11.84,11.73 | +| minibike | 61.95,61.86,61.89,61.88,61.83,61.83,61.8,61.8,61.77,61.86,61.68 | +| cradle | 81.39,81.34,81.25,81.22,81.22,81.13,81.16,81.06,80.97,80.93,80.88 | +| oven | 27.44,27.44,27.42,27.41,27.41,27.4,27.36,27.3,27.31,27.28,27.28 | +| ball | 47.92,47.87,48.03,48.16,48.19,48.01,48.07,48.2,48.17,48.28,48.59 | +| food | 53.32,53.42,53.4,53.34,53.35,53.48,53.46,53.32,53.33,53.31,53.38 | +| step | 15.98,16.02,16.05,16.45,16.33,16.33,16.5,16.47,16.33,16.21,16.4 | +| tank | 41.69,41.64,41.64,41.61,41.54,41.53,41.48,41.47,41.46,41.45,41.54 | +| trade name | 25.12,25.14,25.09,24.91,25.05,25.05,24.89,25.0,24.87,24.92,24.98 | +| microwave | 37.46,37.49,37.49,37.48,37.47,37.48,37.48,37.52,37.52,37.5,37.49 | +| pot | 41.27,41.32,41.4,41.39,41.48,41.4,41.43,41.49,41.48,41.46,41.57 | +| animal | 51.49,51.55,51.56,51.65,51.66,51.74,51.74,51.82,51.91,51.94,51.93 | +| bicycle | 45.85,45.95,45.8,45.91,45.96,45.91,45.99,45.87,45.97,45.97,45.9 | +| lake | 60.25,60.2,60.13,60.12,59.95,59.92,59.84,59.73,59.64,59.5,59.38 | +| dishwasher | 76.92,76.89,76.98,77.07,77.06,77.1,77.15,77.23,77.14,77.22,77.27 | +| screen | 64.57,64.22,64.18,63.84,63.73,63.54,63.17,62.99,62.64,62.32,62.58 | +| blanket | 14.37,14.36,14.43,14.43,14.41,14.46,14.39,14.49,14.36,14.34,14.31 | +| sculpture | 36.01,36.08,36.1,36.13,36.07,36.13,36.26,36.22,36.34,36.38,36.36 | +| hood | 57.71,57.71,57.69,57.62,57.32,57.39,57.36,57.35,57.26,57.24,57.07 | +| sconce | 42.06,42.05,41.99,41.9,41.98,41.78,41.75,41.51,41.48,41.41,41.77 | +| vase | 37.32,37.49,37.44,37.46,37.42,37.38,37.47,37.39,37.42,37.44,37.3 | +| traffic light | 29.48,29.41,29.41,29.45,29.53,29.52,29.54,29.46,29.54,29.56,29.57 | +| tray | 5.46,5.43,5.49,5.47,5.48,5.5,5.52,5.54,5.53,5.55,5.53 | +| ashcan | 37.72,37.58,37.57,37.66,37.87,37.51,37.68,37.47,37.69,37.93,37.63 | +| fan | 58.17,58.04,58.06,58.05,58.19,58.09,58.13,58.13,58.13,58.17,58.12 | +| pier | 12.21,12.05,11.92,12.04,11.9,11.86,11.93,11.8,11.75,11.85,11.56 | +| crt screen | 4.38,4.31,4.37,4.36,4.34,4.56,4.72,4.75,4.71,4.76,4.8 | +| plate | 39.71,39.8,39.84,39.92,39.98,39.96,39.93,40.02,40.04,40.1,40.21 | +| monitor | 28.07,27.88,27.57,27.35,27.56,27.48,27.23,27.16,26.99,26.93,26.92 | +| bulletin board | 45.08,45.02,45.13,45.15,45.1,45.15,45.39,45.55,45.28,45.49,45.51 | +| shower | 1.49,1.51,1.51,1.58,1.55,1.58,1.67,1.63,1.67,1.69,1.71 | +| radiator | 46.69,46.67,46.77,46.81,46.65,46.77,46.57,46.76,46.9,47.01,46.36 | +| glass | 12.18,12.19,12.21,12.24,12.28,12.23,12.3,12.32,12.3,12.36,12.35 | +| clock | 24.64,24.62,24.67,24.61,24.59,24.56,24.55,24.58,24.52,24.54,24.53 | +| flag | 37.84,37.81,37.9,37.86,38.06,38.09,38.16,38.18,38.28,38.33,38.44 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 21:10:00,674 - mmseg - INFO - Summary: +2023-03-04 21:10:00,674 - mmseg - INFO - ++------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++------------------------------------------------------------------+ +| 46.25,46.27,46.28,46.3,46.31,46.33,46.32,46.31,46.31,46.34,46.31 | ++------------------------------------------------------------------+ +2023-03-04 21:10:00,675 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:10:00,675 - mmseg - INFO - Iter(val) [250] mIoU: [0.4625, 0.4627, 0.4628, 0.463, 0.4631, 0.4633, 0.4632, 0.4631, 0.4631, 0.4634, 0.4631], copy_paste: 46.25,46.27,46.28,46.3,46.31,46.33,46.32,46.31,46.31,46.34,46.31 +2023-03-04 21:10:00,682 - mmseg - INFO - Swap parameters (before train) before iter [80001] +2023-03-04 21:10:14,486 - mmseg - INFO - Iter [80050/160000] lr: 9.375e-06, eta: 7:08:47, time: 13.619, data_time: 13.351, memory: 67559, decode.loss_ce: 0.1829, decode.acc_seg: 92.5775, loss: 0.1829 +2023-03-04 21:10:27,818 - mmseg - INFO - Iter [80100/160000] lr: 9.375e-06, eta: 7:08:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7693, loss: 0.1785 +2023-03-04 21:10:43,558 - mmseg - INFO - Iter [80150/160000] lr: 9.375e-06, eta: 7:08:12, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5155, loss: 0.1843 +2023-03-04 21:10:56,938 - mmseg - INFO - Iter [80200/160000] lr: 9.375e-06, eta: 7:07:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.8353, loss: 0.1769 +2023-03-04 21:11:10,178 - mmseg - INFO - Iter [80250/160000] lr: 9.375e-06, eta: 7:07:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.8418, loss: 0.1791 +2023-03-04 21:11:23,487 - mmseg - INFO - Iter [80300/160000] lr: 9.375e-06, eta: 7:07:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.4491, loss: 0.1864 +2023-03-04 21:11:36,687 - mmseg - INFO - Iter [80350/160000] lr: 9.375e-06, eta: 7:06:57, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.5377, loss: 0.1822 +2023-03-04 21:11:50,059 - mmseg - INFO - Iter [80400/160000] lr: 9.375e-06, eta: 7:06:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1918, decode.acc_seg: 92.2026, loss: 0.1918 +2023-03-04 21:12:03,436 - mmseg - INFO - Iter [80450/160000] lr: 9.375e-06, eta: 7:06:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1720, decode.acc_seg: 92.9724, loss: 0.1720 +2023-03-04 21:12:16,839 - mmseg - INFO - Iter [80500/160000] lr: 9.375e-06, eta: 7:06:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.4966, loss: 0.1857 +2023-03-04 21:12:30,213 - mmseg - INFO - Iter [80550/160000] lr: 9.375e-06, eta: 7:05:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1911, decode.acc_seg: 92.3471, loss: 0.1911 +2023-03-04 21:12:43,712 - mmseg - INFO - Iter [80600/160000] lr: 9.375e-06, eta: 7:05:23, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.6355, loss: 0.1837 +2023-03-04 21:12:56,987 - mmseg - INFO - Iter [80650/160000] lr: 9.375e-06, eta: 7:05:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6763, loss: 0.1828 +2023-03-04 21:13:10,278 - mmseg - INFO - Iter [80700/160000] lr: 9.375e-06, eta: 7:04:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6720, loss: 0.1820 +2023-03-04 21:13:23,841 - mmseg - INFO - Iter [80750/160000] lr: 9.375e-06, eta: 7:04:27, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.3509, loss: 0.1878 +2023-03-04 21:13:39,655 - mmseg - INFO - Iter [80800/160000] lr: 9.375e-06, eta: 7:04:11, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1755, decode.acc_seg: 92.8555, loss: 0.1755 +2023-03-04 21:13:53,014 - mmseg - INFO - Iter [80850/160000] lr: 9.375e-06, eta: 7:03:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4917, loss: 0.1861 +2023-03-04 21:14:06,371 - mmseg - INFO - Iter [80900/160000] lr: 9.375e-06, eta: 7:03:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1734, decode.acc_seg: 92.9608, loss: 0.1734 +2023-03-04 21:14:19,848 - mmseg - INFO - Iter [80950/160000] lr: 9.375e-06, eta: 7:03:14, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6428, loss: 0.1804 +2023-03-04 21:14:33,154 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:14:33,154 - mmseg - INFO - Iter [81000/160000] lr: 9.375e-06, eta: 7:02:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1916, decode.acc_seg: 92.2458, loss: 0.1916 +2023-03-04 21:14:46,468 - mmseg - INFO - Iter [81050/160000] lr: 9.375e-06, eta: 7:02:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1956, decode.acc_seg: 92.1307, loss: 0.1956 +2023-03-04 21:14:59,670 - mmseg - INFO - Iter [81100/160000] lr: 9.375e-06, eta: 7:02:18, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.7392, loss: 0.1752 +2023-03-04 21:15:12,959 - mmseg - INFO - Iter [81150/160000] lr: 9.375e-06, eta: 7:01:59, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7069, loss: 0.1808 +2023-03-04 21:15:26,357 - mmseg - INFO - Iter [81200/160000] lr: 9.375e-06, eta: 7:01:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1910, decode.acc_seg: 92.5382, loss: 0.1910 +2023-03-04 21:15:39,751 - mmseg - INFO - Iter [81250/160000] lr: 9.375e-06, eta: 7:01:22, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5633, loss: 0.1825 +2023-03-04 21:15:52,989 - mmseg - INFO - Iter [81300/160000] lr: 9.375e-06, eta: 7:01:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.3688, loss: 0.1852 +2023-03-04 21:16:06,280 - mmseg - INFO - Iter [81350/160000] lr: 9.375e-06, eta: 7:00:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6359, loss: 0.1798 +2023-03-04 21:16:21,992 - mmseg - INFO - Iter [81400/160000] lr: 9.375e-06, eta: 7:00:28, time: 0.314, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.5782, loss: 0.1805 +2023-03-04 21:16:35,274 - mmseg - INFO - Iter [81450/160000] lr: 9.375e-06, eta: 7:00:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.4525, loss: 0.1854 +2023-03-04 21:16:48,644 - mmseg - INFO - Iter [81500/160000] lr: 9.375e-06, eta: 6:59:51, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6662, loss: 0.1799 +2023-03-04 21:17:01,995 - mmseg - INFO - Iter [81550/160000] lr: 9.375e-06, eta: 6:59:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.3686, loss: 0.1878 +2023-03-04 21:17:15,248 - mmseg - INFO - Iter [81600/160000] lr: 9.375e-06, eta: 6:59:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.4905, loss: 0.1819 +2023-03-04 21:17:28,577 - mmseg - INFO - Iter [81650/160000] lr: 9.375e-06, eta: 6:58:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.3325, loss: 0.1856 +2023-03-04 21:17:41,987 - mmseg - INFO - Iter [81700/160000] lr: 9.375e-06, eta: 6:58:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3076, loss: 0.1901 +2023-03-04 21:17:55,275 - mmseg - INFO - Iter [81750/160000] lr: 9.375e-06, eta: 6:58:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.6377, loss: 0.1789 +2023-03-04 21:18:08,651 - mmseg - INFO - Iter [81800/160000] lr: 9.375e-06, eta: 6:57:59, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1909, decode.acc_seg: 92.3362, loss: 0.1909 +2023-03-04 21:18:22,028 - mmseg - INFO - Iter [81850/160000] lr: 9.375e-06, eta: 6:57:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.7256, loss: 0.1776 +2023-03-04 21:18:35,455 - mmseg - INFO - Iter [81900/160000] lr: 9.375e-06, eta: 6:57:22, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6969, loss: 0.1814 +2023-03-04 21:18:48,742 - mmseg - INFO - Iter [81950/160000] lr: 9.375e-06, eta: 6:57:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.9479, loss: 0.1754 +2023-03-04 21:19:02,001 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:19:02,001 - mmseg - INFO - Iter [82000/160000] lr: 9.375e-06, eta: 6:56:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6590, loss: 0.1828 +2023-03-04 21:19:18,079 - mmseg - INFO - Iter [82050/160000] lr: 9.375e-06, eta: 6:56:29, time: 0.322, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.2490, loss: 0.1917 +2023-03-04 21:19:31,363 - mmseg - INFO - Iter [82100/160000] lr: 9.375e-06, eta: 6:56:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6718, loss: 0.1799 +2023-03-04 21:19:44,741 - mmseg - INFO - Iter [82150/160000] lr: 9.375e-06, eta: 6:55:52, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7286, loss: 0.1808 +2023-03-04 21:19:58,080 - mmseg - INFO - Iter [82200/160000] lr: 9.375e-06, eta: 6:55:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.5900, loss: 0.1861 +2023-03-04 21:20:11,486 - mmseg - INFO - Iter [82250/160000] lr: 9.375e-06, eta: 6:55:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.9084, loss: 0.1763 +2023-03-04 21:20:24,857 - mmseg - INFO - Iter [82300/160000] lr: 9.375e-06, eta: 6:54:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.5147, loss: 0.1820 +2023-03-04 21:20:38,129 - mmseg - INFO - Iter [82350/160000] lr: 9.375e-06, eta: 6:54:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.4728, loss: 0.1850 +2023-03-04 21:20:51,473 - mmseg - INFO - Iter [82400/160000] lr: 9.375e-06, eta: 6:54:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7832, loss: 0.1790 +2023-03-04 21:21:04,870 - mmseg - INFO - Iter [82450/160000] lr: 9.375e-06, eta: 6:54:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.5212, loss: 0.1871 +2023-03-04 21:21:18,185 - mmseg - INFO - Iter [82500/160000] lr: 9.375e-06, eta: 6:53:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5771, loss: 0.1847 +2023-03-04 21:21:31,498 - mmseg - INFO - Iter [82550/160000] lr: 9.375e-06, eta: 6:53:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4231, loss: 0.1868 +2023-03-04 21:21:44,989 - mmseg - INFO - Iter [82600/160000] lr: 9.375e-06, eta: 6:53:05, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.4655, loss: 0.1826 +2023-03-04 21:21:58,217 - mmseg - INFO - Iter [82650/160000] lr: 9.375e-06, eta: 6:52:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.3675, loss: 0.1846 +2023-03-04 21:22:13,962 - mmseg - INFO - Iter [82700/160000] lr: 9.375e-06, eta: 6:52:30, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6647, loss: 0.1817 +2023-03-04 21:22:27,302 - mmseg - INFO - Iter [82750/160000] lr: 9.375e-06, eta: 6:52:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1753, decode.acc_seg: 92.8111, loss: 0.1753 +2023-03-04 21:22:40,764 - mmseg - INFO - Iter [82800/160000] lr: 9.375e-06, eta: 6:51:53, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6691, loss: 0.1820 +2023-03-04 21:22:54,121 - mmseg - INFO - Iter [82850/160000] lr: 9.375e-06, eta: 6:51:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.7233, loss: 0.1826 +2023-03-04 21:23:07,419 - mmseg - INFO - Iter [82900/160000] lr: 9.375e-06, eta: 6:51:16, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5007, loss: 0.1859 +2023-03-04 21:23:20,752 - mmseg - INFO - Iter [82950/160000] lr: 9.375e-06, eta: 6:50:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7119, loss: 0.1801 +2023-03-04 21:23:34,144 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:23:34,144 - mmseg - INFO - Iter [83000/160000] lr: 9.375e-06, eta: 6:50:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1725, decode.acc_seg: 92.8377, loss: 0.1725 +2023-03-04 21:23:47,544 - mmseg - INFO - Iter [83050/160000] lr: 9.375e-06, eta: 6:50:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.5534, loss: 0.1853 +2023-03-04 21:24:00,817 - mmseg - INFO - Iter [83100/160000] lr: 9.375e-06, eta: 6:50:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.4817, loss: 0.1851 +2023-03-04 21:24:14,080 - mmseg - INFO - Iter [83150/160000] lr: 9.375e-06, eta: 6:49:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.4966, loss: 0.1828 +2023-03-04 21:24:27,363 - mmseg - INFO - Iter [83200/160000] lr: 9.375e-06, eta: 6:49:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6364, loss: 0.1814 +2023-03-04 21:24:40,632 - mmseg - INFO - Iter [83250/160000] lr: 9.375e-06, eta: 6:49:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.5101, loss: 0.1812 +2023-03-04 21:24:56,575 - mmseg - INFO - Iter [83300/160000] lr: 9.375e-06, eta: 6:48:51, time: 0.319, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5782, loss: 0.1849 +2023-03-04 21:25:10,026 - mmseg - INFO - Iter [83350/160000] lr: 9.375e-06, eta: 6:48:32, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.8870, loss: 0.1769 +2023-03-04 21:25:23,340 - mmseg - INFO - Iter [83400/160000] lr: 9.375e-06, eta: 6:48:14, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5818, loss: 0.1817 +2023-03-04 21:25:36,625 - mmseg - INFO - Iter [83450/160000] lr: 9.375e-06, eta: 6:47:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.6690, loss: 0.1765 +2023-03-04 21:25:49,915 - mmseg - INFO - Iter [83500/160000] lr: 9.375e-06, eta: 6:47:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7099, loss: 0.1785 +2023-03-04 21:26:03,368 - mmseg - INFO - Iter [83550/160000] lr: 9.375e-06, eta: 6:47:19, time: 0.269, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.4926, loss: 0.1857 +2023-03-04 21:26:16,748 - mmseg - INFO - Iter [83600/160000] lr: 9.375e-06, eta: 6:47:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.8022, loss: 0.1804 +2023-03-04 21:26:30,166 - mmseg - INFO - Iter [83650/160000] lr: 9.375e-06, eta: 6:46:42, time: 0.268, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7439, loss: 0.1797 +2023-03-04 21:26:43,392 - mmseg - INFO - Iter [83700/160000] lr: 9.375e-06, eta: 6:46:23, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.4065, loss: 0.1932 +2023-03-04 21:26:56,841 - mmseg - INFO - Iter [83750/160000] lr: 9.375e-06, eta: 6:46:05, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.5604, loss: 0.1855 +2023-03-04 21:27:10,148 - mmseg - INFO - Iter [83800/160000] lr: 9.375e-06, eta: 6:45:47, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.5344, loss: 0.1860 +2023-03-04 21:27:23,523 - mmseg - INFO - Iter [83850/160000] lr: 9.375e-06, eta: 6:45:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.6361, loss: 0.1842 +2023-03-04 21:27:36,780 - mmseg - INFO - Iter [83900/160000] lr: 9.375e-06, eta: 6:45:10, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6465, loss: 0.1799 +2023-03-04 21:27:52,626 - mmseg - INFO - Iter [83950/160000] lr: 9.375e-06, eta: 6:44:54, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4293, loss: 0.1872 +2023-03-04 21:28:06,075 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:28:06,075 - mmseg - INFO - Iter [84000/160000] lr: 9.375e-06, eta: 6:44:36, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6448, loss: 0.1795 +2023-03-04 21:28:19,534 - mmseg - INFO - Iter [84050/160000] lr: 9.375e-06, eta: 6:44:17, time: 0.269, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.8591, loss: 0.1778 +2023-03-04 21:28:32,875 - mmseg - INFO - Iter [84100/160000] lr: 9.375e-06, eta: 6:43:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.3634, loss: 0.1883 +2023-03-04 21:28:46,157 - mmseg - INFO - Iter [84150/160000] lr: 9.375e-06, eta: 6:43:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4778, loss: 0.1868 +2023-03-04 21:28:59,533 - mmseg - INFO - Iter [84200/160000] lr: 9.375e-06, eta: 6:43:22, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5038, loss: 0.1859 +2023-03-04 21:29:12,867 - mmseg - INFO - Iter [84250/160000] lr: 9.375e-06, eta: 6:43:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.5729, loss: 0.1793 +2023-03-04 21:29:26,101 - mmseg - INFO - Iter [84300/160000] lr: 9.375e-06, eta: 6:42:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.2715, loss: 0.1877 +2023-03-04 21:29:39,497 - mmseg - INFO - Iter [84350/160000] lr: 9.375e-06, eta: 6:42:27, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6811, loss: 0.1816 +2023-03-04 21:29:52,806 - mmseg - INFO - Iter [84400/160000] lr: 9.375e-06, eta: 6:42:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.4829, loss: 0.1876 +2023-03-04 21:30:06,242 - mmseg - INFO - Iter [84450/160000] lr: 9.375e-06, eta: 6:41:51, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.6986, loss: 0.1792 +2023-03-04 21:30:19,536 - mmseg - INFO - Iter [84500/160000] lr: 9.375e-06, eta: 6:41:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.7956, loss: 0.1813 +2023-03-04 21:30:32,744 - mmseg - INFO - Iter [84550/160000] lr: 9.375e-06, eta: 6:41:14, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.3442, loss: 0.1886 +2023-03-04 21:30:48,613 - mmseg - INFO - Iter [84600/160000] lr: 9.375e-06, eta: 6:40:58, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5002, loss: 0.1814 +2023-03-04 21:31:02,010 - mmseg - INFO - Iter [84650/160000] lr: 9.375e-06, eta: 6:40:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.4626, loss: 0.1865 +2023-03-04 21:31:15,461 - mmseg - INFO - Iter [84700/160000] lr: 9.375e-06, eta: 6:40:22, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.5511, loss: 0.1812 +2023-03-04 21:31:28,875 - mmseg - INFO - Iter [84750/160000] lr: 9.375e-06, eta: 6:40:03, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.6814, loss: 0.1784 +2023-03-04 21:31:42,249 - mmseg - INFO - Iter [84800/160000] lr: 9.375e-06, eta: 6:39:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.5570, loss: 0.1869 +2023-03-04 21:31:55,671 - mmseg - INFO - Iter [84850/160000] lr: 9.375e-06, eta: 6:39:27, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5265, loss: 0.1865 +2023-03-04 21:32:09,082 - mmseg - INFO - Iter [84900/160000] lr: 9.375e-06, eta: 6:39:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.4751, loss: 0.1830 +2023-03-04 21:32:22,359 - mmseg - INFO - Iter [84950/160000] lr: 9.375e-06, eta: 6:38:51, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.5305, loss: 0.1858 +2023-03-04 21:32:35,693 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:32:35,693 - mmseg - INFO - Iter [85000/160000] lr: 9.375e-06, eta: 6:38:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.6235, loss: 0.1792 +2023-03-04 21:32:48,946 - mmseg - INFO - Iter [85050/160000] lr: 9.375e-06, eta: 6:38:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.7637, loss: 0.1751 +2023-03-04 21:33:02,338 - mmseg - INFO - Iter [85100/160000] lr: 9.375e-06, eta: 6:37:56, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.5519, loss: 0.1837 +2023-03-04 21:33:15,717 - mmseg - INFO - Iter [85150/160000] lr: 9.375e-06, eta: 6:37:38, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6230, loss: 0.1814 +2023-03-04 21:33:31,612 - mmseg - INFO - Iter [85200/160000] lr: 9.375e-06, eta: 6:37:22, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.4205, loss: 0.1864 +2023-03-04 21:33:44,992 - mmseg - INFO - Iter [85250/160000] lr: 9.375e-06, eta: 6:37:03, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.8604, loss: 0.1799 +2023-03-04 21:33:58,351 - mmseg - INFO - Iter [85300/160000] lr: 9.375e-06, eta: 6:36:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.7707, loss: 0.1800 +2023-03-04 21:34:11,643 - mmseg - INFO - Iter [85350/160000] lr: 9.375e-06, eta: 6:36:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4218, loss: 0.1872 +2023-03-04 21:34:25,131 - mmseg - INFO - Iter [85400/160000] lr: 9.375e-06, eta: 6:36:09, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.7276, loss: 0.1805 +2023-03-04 21:34:38,430 - mmseg - INFO - Iter [85450/160000] lr: 9.375e-06, eta: 6:35:51, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.3701, loss: 0.1854 +2023-03-04 21:34:51,727 - mmseg - INFO - Iter [85500/160000] lr: 9.375e-06, eta: 6:35:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.3280, loss: 0.1904 +2023-03-04 21:35:04,962 - mmseg - INFO - Iter [85550/160000] lr: 9.375e-06, eta: 6:35:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.2534, loss: 0.1885 +2023-03-04 21:35:18,343 - mmseg - INFO - Iter [85600/160000] lr: 9.375e-06, eta: 6:34:56, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1726, decode.acc_seg: 92.9669, loss: 0.1726 +2023-03-04 21:35:31,706 - mmseg - INFO - Iter [85650/160000] lr: 9.375e-06, eta: 6:34:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.4040, loss: 0.1885 +2023-03-04 21:35:45,340 - mmseg - INFO - Iter [85700/160000] lr: 9.375e-06, eta: 6:34:20, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1718, decode.acc_seg: 93.0239, loss: 0.1718 +2023-03-04 21:35:58,703 - mmseg - INFO - Iter [85750/160000] lr: 9.375e-06, eta: 6:34:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1733, decode.acc_seg: 92.8008, loss: 0.1733 +2023-03-04 21:36:11,948 - mmseg - INFO - Iter [85800/160000] lr: 9.375e-06, eta: 6:33:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.6293, loss: 0.1805 +2023-03-04 21:36:27,851 - mmseg - INFO - Iter [85850/160000] lr: 9.375e-06, eta: 6:33:28, time: 0.318, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7214, loss: 0.1785 +2023-03-04 21:36:41,294 - mmseg - INFO - Iter [85900/160000] lr: 9.375e-06, eta: 6:33:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.7099, loss: 0.1776 +2023-03-04 21:36:54,685 - mmseg - INFO - Iter [85950/160000] lr: 9.375e-06, eta: 6:32:52, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7677, loss: 0.1801 +2023-03-04 21:37:07,955 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:37:07,955 - mmseg - INFO - Iter [86000/160000] lr: 9.375e-06, eta: 6:32:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5960, loss: 0.1824 +2023-03-04 21:37:21,538 - mmseg - INFO - Iter [86050/160000] lr: 9.375e-06, eta: 6:32:15, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.4768, loss: 0.1855 +2023-03-04 21:37:34,990 - mmseg - INFO - Iter [86100/160000] lr: 9.375e-06, eta: 6:31:57, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.4135, loss: 0.1896 +2023-03-04 21:37:48,222 - mmseg - INFO - Iter [86150/160000] lr: 9.375e-06, eta: 6:31:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5301, loss: 0.1859 +2023-03-04 21:38:01,497 - mmseg - INFO - Iter [86200/160000] lr: 9.375e-06, eta: 6:31:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.6282, loss: 0.1819 +2023-03-04 21:38:14,710 - mmseg - INFO - Iter [86250/160000] lr: 9.375e-06, eta: 6:31:03, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1720, decode.acc_seg: 93.0148, loss: 0.1720 +2023-03-04 21:38:27,933 - mmseg - INFO - Iter [86300/160000] lr: 9.375e-06, eta: 6:30:45, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.4393, loss: 0.1847 +2023-03-04 21:38:41,149 - mmseg - INFO - Iter [86350/160000] lr: 9.375e-06, eta: 6:30:26, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5490, loss: 0.1865 +2023-03-04 21:38:54,346 - mmseg - INFO - Iter [86400/160000] lr: 9.375e-06, eta: 6:30:08, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.7986, loss: 0.1773 +2023-03-04 21:39:10,101 - mmseg - INFO - Iter [86450/160000] lr: 9.375e-06, eta: 6:29:52, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.7391, loss: 0.1783 +2023-03-04 21:39:23,356 - mmseg - INFO - Iter [86500/160000] lr: 9.375e-06, eta: 6:29:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.5335, loss: 0.1869 +2023-03-04 21:39:36,743 - mmseg - INFO - Iter [86550/160000] lr: 9.375e-06, eta: 6:29:16, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7009, loss: 0.1798 +2023-03-04 21:39:49,956 - mmseg - INFO - Iter [86600/160000] lr: 9.375e-06, eta: 6:28:58, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7601, loss: 0.1780 +2023-03-04 21:40:03,309 - mmseg - INFO - Iter [86650/160000] lr: 9.375e-06, eta: 6:28:40, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6867, loss: 0.1827 +2023-03-04 21:40:16,566 - mmseg - INFO - Iter [86700/160000] lr: 9.375e-06, eta: 6:28:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.5551, loss: 0.1794 +2023-03-04 21:40:29,816 - mmseg - INFO - Iter [86750/160000] lr: 9.375e-06, eta: 6:28:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.7925, loss: 0.1796 +2023-03-04 21:40:43,036 - mmseg - INFO - Iter [86800/160000] lr: 9.375e-06, eta: 6:27:45, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.5345, loss: 0.1876 +2023-03-04 21:40:56,263 - mmseg - INFO - Iter [86850/160000] lr: 9.375e-06, eta: 6:27:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.8771, loss: 0.1779 +2023-03-04 21:41:09,531 - mmseg - INFO - Iter [86900/160000] lr: 9.375e-06, eta: 6:27:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6218, loss: 0.1803 +2023-03-04 21:41:22,903 - mmseg - INFO - Iter [86950/160000] lr: 9.375e-06, eta: 6:26:51, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1962, decode.acc_seg: 92.2146, loss: 0.1962 +2023-03-04 21:41:36,166 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:41:36,166 - mmseg - INFO - Iter [87000/160000] lr: 9.375e-06, eta: 6:26:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.7797, loss: 0.1765 +2023-03-04 21:41:49,382 - mmseg - INFO - Iter [87050/160000] lr: 9.375e-06, eta: 6:26:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.5446, loss: 0.1842 +2023-03-04 21:42:05,112 - mmseg - INFO - Iter [87100/160000] lr: 9.375e-06, eta: 6:25:59, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1932, decode.acc_seg: 92.1616, loss: 0.1932 +2023-03-04 21:42:18,427 - mmseg - INFO - Iter [87150/160000] lr: 9.375e-06, eta: 6:25:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3620, loss: 0.1895 +2023-03-04 21:42:31,732 - mmseg - INFO - Iter [87200/160000] lr: 9.375e-06, eta: 6:25:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7487, loss: 0.1795 +2023-03-04 21:42:45,025 - mmseg - INFO - Iter [87250/160000] lr: 9.375e-06, eta: 6:25:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.5799, loss: 0.1867 +2023-03-04 21:42:58,304 - mmseg - INFO - Iter [87300/160000] lr: 9.375e-06, eta: 6:24:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.4641, loss: 0.1839 +2023-03-04 21:43:11,660 - mmseg - INFO - Iter [87350/160000] lr: 9.375e-06, eta: 6:24:29, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.4414, loss: 0.1878 +2023-03-04 21:43:25,034 - mmseg - INFO - Iter [87400/160000] lr: 9.375e-06, eta: 6:24:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.5976, loss: 0.1794 +2023-03-04 21:43:38,566 - mmseg - INFO - Iter [87450/160000] lr: 9.375e-06, eta: 6:23:53, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.4964, loss: 0.1841 +2023-03-04 21:43:51,834 - mmseg - INFO - Iter [87500/160000] lr: 9.375e-06, eta: 6:23:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.7159, loss: 0.1799 +2023-03-04 21:44:05,202 - mmseg - INFO - Iter [87550/160000] lr: 9.375e-06, eta: 6:23:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.7164, loss: 0.1802 +2023-03-04 21:44:18,546 - mmseg - INFO - Iter [87600/160000] lr: 9.375e-06, eta: 6:22:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.5736, loss: 0.1832 +2023-03-04 21:44:31,791 - mmseg - INFO - Iter [87650/160000] lr: 9.375e-06, eta: 6:22:41, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5029, loss: 0.1850 +2023-03-04 21:44:45,140 - mmseg - INFO - Iter [87700/160000] lr: 9.375e-06, eta: 6:22:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6454, loss: 0.1808 +2023-03-04 21:45:00,950 - mmseg - INFO - Iter [87750/160000] lr: 9.375e-06, eta: 6:22:07, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6915, loss: 0.1786 +2023-03-04 21:45:14,322 - mmseg - INFO - Iter [87800/160000] lr: 9.375e-06, eta: 6:21:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1741, decode.acc_seg: 92.8304, loss: 0.1741 +2023-03-04 21:45:27,768 - mmseg - INFO - Iter [87850/160000] lr: 9.375e-06, eta: 6:21:31, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1913, decode.acc_seg: 92.3726, loss: 0.1913 +2023-03-04 21:45:41,158 - mmseg - INFO - Iter [87900/160000] lr: 9.375e-06, eta: 6:21:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.5463, loss: 0.1861 +2023-03-04 21:45:54,725 - mmseg - INFO - Iter [87950/160000] lr: 9.375e-06, eta: 6:20:56, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.7454, loss: 0.1803 +2023-03-04 21:46:07,973 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:46:07,973 - mmseg - INFO - Iter [88000/160000] lr: 9.375e-06, eta: 6:20:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5581, loss: 0.1835 +2023-03-04 21:46:21,333 - mmseg - INFO - Iter [88050/160000] lr: 9.375e-06, eta: 6:20:20, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.4375, loss: 0.1855 +2023-03-04 21:46:34,631 - mmseg - INFO - Iter [88100/160000] lr: 9.375e-06, eta: 6:20:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5636, loss: 0.1834 +2023-03-04 21:46:48,031 - mmseg - INFO - Iter [88150/160000] lr: 9.375e-06, eta: 6:19:44, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8769, loss: 0.1758 +2023-03-04 21:47:01,362 - mmseg - INFO - Iter [88200/160000] lr: 9.375e-06, eta: 6:19:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1891, decode.acc_seg: 92.6075, loss: 0.1891 +2023-03-04 21:47:14,753 - mmseg - INFO - Iter [88250/160000] lr: 9.375e-06, eta: 6:19:08, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.6851, loss: 0.1823 +2023-03-04 21:47:28,172 - mmseg - INFO - Iter [88300/160000] lr: 9.375e-06, eta: 6:18:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.7159, loss: 0.1779 +2023-03-04 21:47:44,106 - mmseg - INFO - Iter [88350/160000] lr: 9.375e-06, eta: 6:18:35, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6639, loss: 0.1827 +2023-03-04 21:47:57,513 - mmseg - INFO - Iter [88400/160000] lr: 9.375e-06, eta: 6:18:17, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7492, loss: 0.1798 +2023-03-04 21:48:10,855 - mmseg - INFO - Iter [88450/160000] lr: 9.375e-06, eta: 6:17:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5959, loss: 0.1821 +2023-03-04 21:48:24,234 - mmseg - INFO - Iter [88500/160000] lr: 9.375e-06, eta: 6:17:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1904, decode.acc_seg: 92.4490, loss: 0.1904 +2023-03-04 21:48:37,608 - mmseg - INFO - Iter [88550/160000] lr: 9.375e-06, eta: 6:17:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.5509, loss: 0.1819 +2023-03-04 21:48:50,924 - mmseg - INFO - Iter [88600/160000] lr: 9.375e-06, eta: 6:17:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.4763, loss: 0.1851 +2023-03-04 21:49:04,348 - mmseg - INFO - Iter [88650/160000] lr: 9.375e-06, eta: 6:16:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.7890, loss: 0.1758 +2023-03-04 21:49:17,667 - mmseg - INFO - Iter [88700/160000] lr: 9.375e-06, eta: 6:16:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.5965, loss: 0.1868 +2023-03-04 21:49:31,082 - mmseg - INFO - Iter [88750/160000] lr: 9.375e-06, eta: 6:16:12, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1931, decode.acc_seg: 92.2062, loss: 0.1931 +2023-03-04 21:49:44,334 - mmseg - INFO - Iter [88800/160000] lr: 9.375e-06, eta: 6:15:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.5328, loss: 0.1854 +2023-03-04 21:49:57,592 - mmseg - INFO - Iter [88850/160000] lr: 9.375e-06, eta: 6:15:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5414, loss: 0.1844 +2023-03-04 21:50:10,895 - mmseg - INFO - Iter [88900/160000] lr: 9.375e-06, eta: 6:15:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5528, loss: 0.1850 +2023-03-04 21:50:24,166 - mmseg - INFO - Iter [88950/160000] lr: 9.375e-06, eta: 6:15:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.4945, loss: 0.1813 +2023-03-04 21:50:40,006 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:50:40,006 - mmseg - INFO - Iter [89000/160000] lr: 9.375e-06, eta: 6:14:45, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1922, decode.acc_seg: 92.2390, loss: 0.1922 +2023-03-04 21:50:53,337 - mmseg - INFO - Iter [89050/160000] lr: 9.375e-06, eta: 6:14:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6212, loss: 0.1820 +2023-03-04 21:51:06,647 - mmseg - INFO - Iter [89100/160000] lr: 9.375e-06, eta: 6:14:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.7454, loss: 0.1840 +2023-03-04 21:51:19,866 - mmseg - INFO - Iter [89150/160000] lr: 9.375e-06, eta: 6:13:51, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.4001, loss: 0.1881 +2023-03-04 21:51:33,293 - mmseg - INFO - Iter [89200/160000] lr: 9.375e-06, eta: 6:13:33, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.8063, loss: 0.1777 +2023-03-04 21:51:46,769 - mmseg - INFO - Iter [89250/160000] lr: 9.375e-06, eta: 6:13:16, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.4251, loss: 0.1851 +2023-03-04 21:52:00,007 - mmseg - INFO - Iter [89300/160000] lr: 9.375e-06, eta: 6:12:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.5203, loss: 0.1832 +2023-03-04 21:52:13,324 - mmseg - INFO - Iter [89350/160000] lr: 9.375e-06, eta: 6:12:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.7071, loss: 0.1825 +2023-03-04 21:52:26,613 - mmseg - INFO - Iter [89400/160000] lr: 9.375e-06, eta: 6:12:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.7967, loss: 0.1805 +2023-03-04 21:52:39,904 - mmseg - INFO - Iter [89450/160000] lr: 9.375e-06, eta: 6:12:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5168, loss: 0.1845 +2023-03-04 21:52:53,097 - mmseg - INFO - Iter [89500/160000] lr: 9.375e-06, eta: 6:11:46, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7703, loss: 0.1801 +2023-03-04 21:53:06,439 - mmseg - INFO - Iter [89550/160000] lr: 9.375e-06, eta: 6:11:29, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5801, loss: 0.1825 +2023-03-04 21:53:19,715 - mmseg - INFO - Iter [89600/160000] lr: 9.375e-06, eta: 6:11:11, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.7361, loss: 0.1807 +2023-03-04 21:53:35,817 - mmseg - INFO - Iter [89650/160000] lr: 9.375e-06, eta: 6:10:55, time: 0.322, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5384, loss: 0.1848 +2023-03-04 21:53:49,151 - mmseg - INFO - Iter [89700/160000] lr: 9.375e-06, eta: 6:10:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.7976, loss: 0.1806 +2023-03-04 21:54:02,518 - mmseg - INFO - Iter [89750/160000] lr: 9.375e-06, eta: 6:10:20, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6415, loss: 0.1827 +2023-03-04 21:54:15,837 - mmseg - INFO - Iter [89800/160000] lr: 9.375e-06, eta: 6:10:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4539, loss: 0.1873 +2023-03-04 21:54:29,170 - mmseg - INFO - Iter [89850/160000] lr: 9.375e-06, eta: 6:09:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1898, decode.acc_seg: 92.3722, loss: 0.1898 +2023-03-04 21:54:42,423 - mmseg - INFO - Iter [89900/160000] lr: 9.375e-06, eta: 6:09:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6492, loss: 0.1806 +2023-03-04 21:54:55,651 - mmseg - INFO - Iter [89950/160000] lr: 9.375e-06, eta: 6:09:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.5745, loss: 0.1836 +2023-03-04 21:55:08,887 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:55:08,887 - mmseg - INFO - Iter [90000/160000] lr: 9.375e-06, eta: 6:08:51, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4859, loss: 0.1858 +2023-03-04 21:55:22,148 - mmseg - INFO - Iter [90050/160000] lr: 9.375e-06, eta: 6:08:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.4482, loss: 0.1914 +2023-03-04 21:55:35,351 - mmseg - INFO - Iter [90100/160000] lr: 9.375e-06, eta: 6:08:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5243, loss: 0.1835 +2023-03-04 21:55:48,562 - mmseg - INFO - Iter [90150/160000] lr: 9.375e-06, eta: 6:07:57, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7279, loss: 0.1789 +2023-03-04 21:56:01,816 - mmseg - INFO - Iter [90200/160000] lr: 9.375e-06, eta: 6:07:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7005, loss: 0.1797 +2023-03-04 21:56:17,587 - mmseg - INFO - Iter [90250/160000] lr: 9.375e-06, eta: 6:07:24, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.8112, loss: 0.1761 +2023-03-04 21:56:30,900 - mmseg - INFO - Iter [90300/160000] lr: 9.375e-06, eta: 6:07:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3480, loss: 0.1895 +2023-03-04 21:56:44,305 - mmseg - INFO - Iter [90350/160000] lr: 9.375e-06, eta: 6:06:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6316, loss: 0.1807 +2023-03-04 21:56:57,652 - mmseg - INFO - Iter [90400/160000] lr: 9.375e-06, eta: 6:06:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.5265, loss: 0.1860 +2023-03-04 21:57:11,105 - mmseg - INFO - Iter [90450/160000] lr: 9.375e-06, eta: 6:06:13, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4450, loss: 0.1859 +2023-03-04 21:57:24,404 - mmseg - INFO - Iter [90500/160000] lr: 9.375e-06, eta: 6:05:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.4984, loss: 0.1820 +2023-03-04 21:57:37,640 - mmseg - INFO - Iter [90550/160000] lr: 9.375e-06, eta: 6:05:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1938, decode.acc_seg: 92.2840, loss: 0.1938 +2023-03-04 21:57:50,989 - mmseg - INFO - Iter [90600/160000] lr: 9.375e-06, eta: 6:05:20, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.3886, loss: 0.1876 +2023-03-04 21:58:04,182 - mmseg - INFO - Iter [90650/160000] lr: 9.375e-06, eta: 6:05:02, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6160, loss: 0.1820 +2023-03-04 21:58:17,484 - mmseg - INFO - Iter [90700/160000] lr: 9.375e-06, eta: 6:04:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6190, loss: 0.1827 +2023-03-04 21:58:30,859 - mmseg - INFO - Iter [90750/160000] lr: 9.375e-06, eta: 6:04:27, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.4445, loss: 0.1836 +2023-03-04 21:58:44,246 - mmseg - INFO - Iter [90800/160000] lr: 9.375e-06, eta: 6:04:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.4451, loss: 0.1849 +2023-03-04 21:58:57,622 - mmseg - INFO - Iter [90850/160000] lr: 9.375e-06, eta: 6:03:51, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.6984, loss: 0.1790 +2023-03-04 21:59:13,567 - mmseg - INFO - Iter [90900/160000] lr: 9.375e-06, eta: 6:03:36, time: 0.319, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6509, loss: 0.1832 +2023-03-04 21:59:26,961 - mmseg - INFO - Iter [90950/160000] lr: 9.375e-06, eta: 6:03:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6221, loss: 0.1833 +2023-03-04 21:59:40,379 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 21:59:40,380 - mmseg - INFO - Iter [91000/160000] lr: 9.375e-06, eta: 6:03:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5107, loss: 0.1847 +2023-03-04 21:59:53,628 - mmseg - INFO - Iter [91050/160000] lr: 9.375e-06, eta: 6:02:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1721, decode.acc_seg: 92.9107, loss: 0.1721 +2023-03-04 22:00:06,908 - mmseg - INFO - Iter [91100/160000] lr: 9.375e-06, eta: 6:02:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7608, loss: 0.1804 +2023-03-04 22:00:20,232 - mmseg - INFO - Iter [91150/160000] lr: 9.375e-06, eta: 6:02:08, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.4696, loss: 0.1850 +2023-03-04 22:00:33,657 - mmseg - INFO - Iter [91200/160000] lr: 9.375e-06, eta: 6:01:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1921, decode.acc_seg: 92.2414, loss: 0.1921 +2023-03-04 22:00:47,054 - mmseg - INFO - Iter [91250/160000] lr: 9.375e-06, eta: 6:01:32, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.5082, loss: 0.1861 +2023-03-04 22:01:00,487 - mmseg - INFO - Iter [91300/160000] lr: 9.375e-06, eta: 6:01:15, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1764, decode.acc_seg: 92.8925, loss: 0.1764 +2023-03-04 22:01:13,835 - mmseg - INFO - Iter [91350/160000] lr: 9.375e-06, eta: 6:00:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6936, loss: 0.1786 +2023-03-04 22:01:27,037 - mmseg - INFO - Iter [91400/160000] lr: 9.375e-06, eta: 6:00:40, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4982, loss: 0.1873 +2023-03-04 22:01:40,346 - mmseg - INFO - Iter [91450/160000] lr: 9.375e-06, eta: 6:00:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5483, loss: 0.1848 +2023-03-04 22:01:56,128 - mmseg - INFO - Iter [91500/160000] lr: 9.375e-06, eta: 6:00:06, time: 0.316, data_time: 0.052, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.4564, loss: 0.1857 +2023-03-04 22:02:09,601 - mmseg - INFO - Iter [91550/160000] lr: 9.375e-06, eta: 5:59:49, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6427, loss: 0.1797 +2023-03-04 22:02:22,918 - mmseg - INFO - Iter [91600/160000] lr: 9.375e-06, eta: 5:59:31, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.6762, loss: 0.1789 +2023-03-04 22:02:36,220 - mmseg - INFO - Iter [91650/160000] lr: 9.375e-06, eta: 5:59:14, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5796, loss: 0.1841 +2023-03-04 22:02:49,561 - mmseg - INFO - Iter [91700/160000] lr: 9.375e-06, eta: 5:58:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5374, loss: 0.1870 +2023-03-04 22:03:02,841 - mmseg - INFO - Iter [91750/160000] lr: 9.375e-06, eta: 5:58:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.8606, loss: 0.1774 +2023-03-04 22:03:16,096 - mmseg - INFO - Iter [91800/160000] lr: 9.375e-06, eta: 5:58:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.5827, loss: 0.1813 +2023-03-04 22:03:29,376 - mmseg - INFO - Iter [91850/160000] lr: 9.375e-06, eta: 5:58:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7396, loss: 0.1790 +2023-03-04 22:03:42,647 - mmseg - INFO - Iter [91900/160000] lr: 9.375e-06, eta: 5:57:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6582, loss: 0.1801 +2023-03-04 22:03:55,988 - mmseg - INFO - Iter [91950/160000] lr: 9.375e-06, eta: 5:57:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.7414, loss: 0.1811 +2023-03-04 22:04:09,235 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:04:09,236 - mmseg - INFO - Iter [92000/160000] lr: 9.375e-06, eta: 5:57:10, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7531, loss: 0.1778 +2023-03-04 22:04:22,616 - mmseg - INFO - Iter [92050/160000] lr: 9.375e-06, eta: 5:56:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5379, loss: 0.1817 +2023-03-04 22:04:35,824 - mmseg - INFO - Iter [92100/160000] lr: 9.375e-06, eta: 5:56:35, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.5476, loss: 0.1855 +2023-03-04 22:04:51,509 - mmseg - INFO - Iter [92150/160000] lr: 9.375e-06, eta: 5:56:19, time: 0.314, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1755, decode.acc_seg: 92.7389, loss: 0.1755 +2023-03-04 22:05:04,830 - mmseg - INFO - Iter [92200/160000] lr: 9.375e-06, eta: 5:56:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5694, loss: 0.1823 +2023-03-04 22:05:18,178 - mmseg - INFO - Iter [92250/160000] lr: 9.375e-06, eta: 5:55:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.4779, loss: 0.1857 +2023-03-04 22:05:31,435 - mmseg - INFO - Iter [92300/160000] lr: 9.375e-06, eta: 5:55:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5698, loss: 0.1827 +2023-03-04 22:05:44,718 - mmseg - INFO - Iter [92350/160000] lr: 9.375e-06, eta: 5:55:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.6634, loss: 0.1809 +2023-03-04 22:05:57,982 - mmseg - INFO - Iter [92400/160000] lr: 9.375e-06, eta: 5:54:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6443, loss: 0.1798 +2023-03-04 22:06:11,303 - mmseg - INFO - Iter [92450/160000] lr: 9.375e-06, eta: 5:54:34, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5627, loss: 0.1840 +2023-03-04 22:06:24,653 - mmseg - INFO - Iter [92500/160000] lr: 9.375e-06, eta: 5:54:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.8095, loss: 0.1754 +2023-03-04 22:06:37,831 - mmseg - INFO - Iter [92550/160000] lr: 9.375e-06, eta: 5:53:59, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7340, loss: 0.1774 +2023-03-04 22:06:51,074 - mmseg - INFO - Iter [92600/160000] lr: 9.375e-06, eta: 5:53:41, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6561, loss: 0.1817 +2023-03-04 22:07:04,277 - mmseg - INFO - Iter [92650/160000] lr: 9.375e-06, eta: 5:53:24, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.6301, loss: 0.1841 +2023-03-04 22:07:17,532 - mmseg - INFO - Iter [92700/160000] lr: 9.375e-06, eta: 5:53:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6785, loss: 0.1802 +2023-03-04 22:07:30,753 - mmseg - INFO - Iter [92750/160000] lr: 9.375e-06, eta: 5:52:49, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.4950, loss: 0.1840 +2023-03-04 22:07:46,619 - mmseg - INFO - Iter [92800/160000] lr: 9.375e-06, eta: 5:52:33, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.5591, loss: 0.1846 +2023-03-04 22:08:00,092 - mmseg - INFO - Iter [92850/160000] lr: 9.375e-06, eta: 5:52:16, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.4754, loss: 0.1854 +2023-03-04 22:08:13,426 - mmseg - INFO - Iter [92900/160000] lr: 9.375e-06, eta: 5:51:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.6573, loss: 0.1776 +2023-03-04 22:08:26,783 - mmseg - INFO - Iter [92950/160000] lr: 9.375e-06, eta: 5:51:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6732, loss: 0.1807 +2023-03-04 22:08:40,093 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:08:40,093 - mmseg - INFO - Iter [93000/160000] lr: 9.375e-06, eta: 5:51:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.9507, loss: 0.1756 +2023-03-04 22:08:53,482 - mmseg - INFO - Iter [93050/160000] lr: 9.375e-06, eta: 5:51:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7576, loss: 0.1784 +2023-03-04 22:09:06,904 - mmseg - INFO - Iter [93100/160000] lr: 9.375e-06, eta: 5:50:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.3302, loss: 0.1884 +2023-03-04 22:09:20,199 - mmseg - INFO - Iter [93150/160000] lr: 9.375e-06, eta: 5:50:31, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.5720, loss: 0.1857 +2023-03-04 22:09:33,652 - mmseg - INFO - Iter [93200/160000] lr: 9.375e-06, eta: 5:50:14, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.4932, loss: 0.1856 +2023-03-04 22:09:46,924 - mmseg - INFO - Iter [93250/160000] lr: 9.375e-06, eta: 5:49:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6797, loss: 0.1815 +2023-03-04 22:10:00,222 - mmseg - INFO - Iter [93300/160000] lr: 9.375e-06, eta: 5:49:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5537, loss: 0.1828 +2023-03-04 22:10:13,436 - mmseg - INFO - Iter [93350/160000] lr: 9.375e-06, eta: 5:49:21, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6023, loss: 0.1812 +2023-03-04 22:10:29,170 - mmseg - INFO - Iter [93400/160000] lr: 9.375e-06, eta: 5:49:05, time: 0.315, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.8296, loss: 0.1751 +2023-03-04 22:10:42,414 - mmseg - INFO - Iter [93450/160000] lr: 9.375e-06, eta: 5:48:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5483, loss: 0.1870 +2023-03-04 22:10:55,674 - mmseg - INFO - Iter [93500/160000] lr: 9.375e-06, eta: 5:48:30, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1905, decode.acc_seg: 92.3766, loss: 0.1905 +2023-03-04 22:11:09,065 - mmseg - INFO - Iter [93550/160000] lr: 9.375e-06, eta: 5:48:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.8086, loss: 0.1766 +2023-03-04 22:11:22,423 - mmseg - INFO - Iter [93600/160000] lr: 9.375e-06, eta: 5:47:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5033, loss: 0.1870 +2023-03-04 22:11:35,766 - mmseg - INFO - Iter [93650/160000] lr: 9.375e-06, eta: 5:47:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.8395, loss: 0.1767 +2023-03-04 22:11:49,470 - mmseg - INFO - Iter [93700/160000] lr: 9.375e-06, eta: 5:47:21, time: 0.274, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.5087, loss: 0.1858 +2023-03-04 22:12:02,712 - mmseg - INFO - Iter [93750/160000] lr: 9.375e-06, eta: 5:47:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6968, loss: 0.1797 +2023-03-04 22:12:16,198 - mmseg - INFO - Iter [93800/160000] lr: 9.375e-06, eta: 5:46:46, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.3729, loss: 0.1856 +2023-03-04 22:12:29,629 - mmseg - INFO - Iter [93850/160000] lr: 9.375e-06, eta: 5:46:29, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.6662, loss: 0.1784 +2023-03-04 22:12:43,228 - mmseg - INFO - Iter [93900/160000] lr: 9.375e-06, eta: 5:46:12, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.8332, loss: 0.1768 +2023-03-04 22:12:56,584 - mmseg - INFO - Iter [93950/160000] lr: 9.375e-06, eta: 5:45:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.6336, loss: 0.1787 +2023-03-04 22:13:10,025 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:13:10,025 - mmseg - INFO - Iter [94000/160000] lr: 9.375e-06, eta: 5:45:37, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6146, loss: 0.1820 +2023-03-04 22:13:25,900 - mmseg - INFO - Iter [94050/160000] lr: 9.375e-06, eta: 5:45:22, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5107, loss: 0.1849 +2023-03-04 22:13:39,235 - mmseg - INFO - Iter [94100/160000] lr: 9.375e-06, eta: 5:45:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.4073, loss: 0.1828 +2023-03-04 22:13:52,659 - mmseg - INFO - Iter [94150/160000] lr: 9.375e-06, eta: 5:44:47, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.3426, loss: 0.1856 +2023-03-04 22:14:05,995 - mmseg - INFO - Iter [94200/160000] lr: 9.375e-06, eta: 5:44:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1748, decode.acc_seg: 92.8467, loss: 0.1748 +2023-03-04 22:14:19,240 - mmseg - INFO - Iter [94250/160000] lr: 9.375e-06, eta: 5:44:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.8582, loss: 0.1803 +2023-03-04 22:14:32,664 - mmseg - INFO - Iter [94300/160000] lr: 9.375e-06, eta: 5:43:55, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 92.8341, loss: 0.1759 +2023-03-04 22:14:46,031 - mmseg - INFO - Iter [94350/160000] lr: 9.375e-06, eta: 5:43:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7633, loss: 0.1785 +2023-03-04 22:14:59,442 - mmseg - INFO - Iter [94400/160000] lr: 9.375e-06, eta: 5:43:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1829, decode.acc_seg: 92.5157, loss: 0.1829 +2023-03-04 22:15:12,774 - mmseg - INFO - Iter [94450/160000] lr: 9.375e-06, eta: 5:43:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.6869, loss: 0.1843 +2023-03-04 22:15:26,062 - mmseg - INFO - Iter [94500/160000] lr: 9.375e-06, eta: 5:42:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.6234, loss: 0.1790 +2023-03-04 22:15:39,401 - mmseg - INFO - Iter [94550/160000] lr: 9.375e-06, eta: 5:42:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1892, decode.acc_seg: 92.3535, loss: 0.1892 +2023-03-04 22:15:52,742 - mmseg - INFO - Iter [94600/160000] lr: 9.375e-06, eta: 5:42:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.4050, loss: 0.1877 +2023-03-04 22:16:06,052 - mmseg - INFO - Iter [94650/160000] lr: 9.375e-06, eta: 5:41:53, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8520, loss: 0.1775 +2023-03-04 22:16:21,855 - mmseg - INFO - Iter [94700/160000] lr: 9.375e-06, eta: 5:41:38, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6257, loss: 0.1798 +2023-03-04 22:16:35,340 - mmseg - INFO - Iter [94750/160000] lr: 9.375e-06, eta: 5:41:21, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6615, loss: 0.1811 +2023-03-04 22:16:48,707 - mmseg - INFO - Iter [94800/160000] lr: 9.375e-06, eta: 5:41:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7818, loss: 0.1770 +2023-03-04 22:17:01,974 - mmseg - INFO - Iter [94850/160000] lr: 9.375e-06, eta: 5:40:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.5800, loss: 0.1810 +2023-03-04 22:17:15,182 - mmseg - INFO - Iter [94900/160000] lr: 9.375e-06, eta: 5:40:29, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.7251, loss: 0.1831 +2023-03-04 22:17:28,521 - mmseg - INFO - Iter [94950/160000] lr: 9.375e-06, eta: 5:40:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6710, loss: 0.1810 +2023-03-04 22:17:41,878 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:17:41,878 - mmseg - INFO - Iter [95000/160000] lr: 9.375e-06, eta: 5:39:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.6726, loss: 0.1847 +2023-03-04 22:17:55,099 - mmseg - INFO - Iter [95050/160000] lr: 9.375e-06, eta: 5:39:37, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6058, loss: 0.1813 +2023-03-04 22:18:08,436 - mmseg - INFO - Iter [95100/160000] lr: 9.375e-06, eta: 5:39:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.5818, loss: 0.1842 +2023-03-04 22:18:21,733 - mmseg - INFO - Iter [95150/160000] lr: 9.375e-06, eta: 5:39:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5703, loss: 0.1824 +2023-03-04 22:18:34,940 - mmseg - INFO - Iter [95200/160000] lr: 9.375e-06, eta: 5:38:45, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6708, loss: 0.1797 +2023-03-04 22:18:48,313 - mmseg - INFO - Iter [95250/160000] lr: 9.375e-06, eta: 5:38:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.3871, loss: 0.1896 +2023-03-04 22:19:04,146 - mmseg - INFO - Iter [95300/160000] lr: 9.375e-06, eta: 5:38:12, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.4546, loss: 0.1859 +2023-03-04 22:19:17,609 - mmseg - INFO - Iter [95350/160000] lr: 9.375e-06, eta: 5:37:55, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1734, decode.acc_seg: 93.0281, loss: 0.1734 +2023-03-04 22:19:30,952 - mmseg - INFO - Iter [95400/160000] lr: 9.375e-06, eta: 5:37:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6240, loss: 0.1834 +2023-03-04 22:19:44,195 - mmseg - INFO - Iter [95450/160000] lr: 9.375e-06, eta: 5:37:20, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1908, decode.acc_seg: 92.4114, loss: 0.1908 +2023-03-04 22:19:57,693 - mmseg - INFO - Iter [95500/160000] lr: 9.375e-06, eta: 5:37:03, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6730, loss: 0.1825 +2023-03-04 22:20:11,109 - mmseg - INFO - Iter [95550/160000] lr: 9.375e-06, eta: 5:36:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3112, loss: 0.1887 +2023-03-04 22:20:24,521 - mmseg - INFO - Iter [95600/160000] lr: 9.375e-06, eta: 5:36:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.3925, loss: 0.1885 +2023-03-04 22:20:37,861 - mmseg - INFO - Iter [95650/160000] lr: 9.375e-06, eta: 5:36:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4429, loss: 0.1844 +2023-03-04 22:20:51,225 - mmseg - INFO - Iter [95700/160000] lr: 9.375e-06, eta: 5:35:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7561, loss: 0.1774 +2023-03-04 22:21:04,531 - mmseg - INFO - Iter [95750/160000] lr: 9.375e-06, eta: 5:35:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.5937, loss: 0.1820 +2023-03-04 22:21:17,873 - mmseg - INFO - Iter [95800/160000] lr: 9.375e-06, eta: 5:35:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.7158, loss: 0.1781 +2023-03-04 22:21:31,219 - mmseg - INFO - Iter [95850/160000] lr: 9.375e-06, eta: 5:35:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.7593, loss: 0.1815 +2023-03-04 22:21:44,471 - mmseg - INFO - Iter [95900/160000] lr: 9.375e-06, eta: 5:34:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4962, loss: 0.1844 +2023-03-04 22:22:00,339 - mmseg - INFO - Iter [95950/160000] lr: 9.375e-06, eta: 5:34:29, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7204, loss: 0.1790 +2023-03-04 22:22:13,650 - mmseg - INFO - Swap parameters (after train) after iter [96000] +2023-03-04 22:22:13,672 - mmseg - INFO - Saving checkpoint at 96000 iterations +2023-03-04 22:22:15,633 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:22:15,633 - mmseg - INFO - Iter [96000/160000] lr: 9.375e-06, eta: 5:34:14, time: 0.306, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6409, loss: 0.1800 +2023-03-04 22:33:24,252 - mmseg - INFO - per class results: +2023-03-04 22:33:24,261 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.43,76.43,76.42,76.42,76.42,76.41,76.4,76.37,76.37,76.36,76.37 | +| building | 81.46,81.47,81.47,81.49,81.47,81.46,81.47,81.45,81.46,81.46,81.43 | +| sky | 94.27,94.27,94.27,94.27,94.26,94.27,94.27,94.26,94.26,94.26,94.26 | +| floor | 80.03,80.05,80.06,80.07,80.09,80.09,80.09,80.09,80.1,80.09,80.04 | +| tree | 72.88,72.87,72.87,72.89,72.85,72.86,72.85,72.85,72.81,72.81,72.82 | +| ceiling | 82.87,82.88,82.87,82.86,82.86,82.86,82.86,82.85,82.86,82.88,82.9 | +| road | 81.87,81.87,81.85,81.78,81.79,81.75,81.82,81.87,81.89,81.91,81.87 | +| bed | 88.57,88.58,88.59,88.59,88.59,88.59,88.6,88.59,88.58,88.57,88.6 | +| windowpane | 61.16,61.17,61.17,61.2,61.2,61.22,61.22,61.24,61.18,61.19,61.13 | +| grass | 65.42,65.46,65.46,65.47,65.49,65.51,65.53,65.56,65.51,65.55,65.61 | +| cabinet | 59.33,59.32,59.33,59.34,59.38,59.31,59.28,59.26,59.26,59.28,59.28 | +| sidewalk | 66.18,66.14,66.14,66.04,66.06,66.04,66.15,66.34,66.4,66.47,66.28 | +| person | 79.54,79.54,79.54,79.56,79.54,79.55,79.54,79.54,79.54,79.54,79.56 | +| earth | 33.11,33.17,33.12,33.17,33.07,32.97,33.01,33.07,32.99,32.9,32.95 | +| door | 48.4,48.5,48.57,48.65,48.71,48.71,48.72,48.72,48.68,48.73,48.69 | +| table | 61.65,61.64,61.64,61.64,61.66,61.69,61.76,61.75,61.77,61.78,61.77 | +| mountain | 51.4,51.5,51.53,51.62,51.74,51.84,51.83,51.84,51.86,51.87,51.93 | +| plant | 49.96,49.96,49.94,49.92,49.9,49.89,49.91,49.94,49.85,49.85,49.93 | +| curtain | 69.83,69.96,70.25,70.4,70.52,70.62,70.69,70.77,70.79,70.85,70.83 | +| chair | 58.33,58.4,58.46,58.5,58.49,58.54,58.56,58.62,58.62,58.64,58.67 | +| car | 83.2,83.21,83.21,83.2,83.2,83.16,83.19,83.18,83.21,83.19,83.15 | +| water | 47.47,47.48,47.52,47.5,47.51,47.53,47.55,47.57,47.53,47.55,47.53 | +| painting | 69.78,69.79,69.81,69.81,69.82,69.85,69.83,69.87,69.9,69.9,69.84 | +| sofa | 65.11,65.14,65.2,65.22,65.33,65.44,65.46,65.44,65.47,65.49,65.5 | +| shelf | 40.66,40.55,40.56,40.58,40.54,40.53,40.46,40.45,40.43,40.44,40.31 | +| house | 44.51,44.44,44.46,44.36,44.32,44.29,44.22,44.13,44.11,44.1,43.89 | +| sea | 45.15,45.18,45.15,45.11,45.09,45.07,45.06,45.03,44.99,44.98,44.92 | +| mirror | 65.52,65.52,65.53,65.49,65.47,65.48,65.45,65.45,65.47,65.45,65.37 | +| rug | 54.57,54.88,54.65,54.88,54.93,55.1,55.18,55.1,55.23,55.18,55.16 | +| field | 28.02,28.05,28.05,28.06,28.04,27.93,28.0,27.99,27.97,27.99,27.89 | +| armchair | 43.59,43.67,43.66,43.69,43.71,43.77,43.81,43.75,43.73,43.69,43.72 | +| seat | 53.11,53.1,53.06,53.13,53.1,53.07,53.12,53.07,52.96,52.91,53.04 | +| fence | 40.98,40.94,40.93,40.89,40.94,40.88,40.94,40.92,40.9,40.9,40.78 | +| desk | 49.26,49.19,49.26,49.28,49.3,49.26,49.2,49.07,48.99,48.97,48.99 | +| rock | 26.49,26.77,26.59,26.59,26.45,26.42,26.71,26.37,26.5,26.47,26.62 | +| wardrobe | 48.07,48.08,47.99,48.08,48.13,48.04,47.95,47.85,47.81,47.83,47.73 | +| lamp | 63.89,63.9,63.89,63.93,63.95,63.97,63.96,63.96,63.98,63.97,64.02 | +| bathtub | 77.78,77.66,77.65,77.65,77.52,77.49,77.39,77.45,77.37,77.36,77.19 | +| railing | 31.83,31.76,31.76,31.58,31.63,31.52,31.49,31.44,31.3,31.29,31.34 | +| cushion | 55.3,55.32,55.34,55.39,55.41,55.45,55.5,55.56,55.56,55.58,55.77 | +| base | 28.29,28.36,28.33,28.4,28.51,28.46,28.42,28.39,28.46,28.48,28.45 | +| box | 24.21,24.18,24.21,24.28,24.28,24.27,24.34,24.29,24.4,24.44,24.29 | +| column | 46.1,46.24,46.22,46.23,46.34,46.47,46.3,46.31,46.34,46.36,45.97 | +| signboard | 35.49,35.5,35.51,35.51,35.53,35.45,35.55,35.51,35.48,35.55,35.47 | +| chest of drawers | 39.44,39.37,39.29,39.34,39.26,39.15,39.32,39.26,39.26,39.21,39.4 | +| counter | 26.29,26.34,26.32,26.1,26.23,26.23,26.18,25.92,25.98,25.93,26.06 | +| sand | 31.87,31.95,31.7,31.82,31.79,31.7,31.72,31.82,31.73,31.69,31.79 | +| sink | 71.08,71.07,71.11,71.08,71.17,71.15,71.15,71.31,71.26,71.19,71.36 | +| skyscraper | 48.55,48.63,48.63,48.74,48.79,48.75,48.87,48.91,48.97,49.04,48.75 | +| fireplace | 66.16,66.25,66.26,66.22,66.26,66.19,66.19,66.19,66.17,66.28,66.14 | +| refrigerator | 78.34,78.34,78.35,78.43,78.34,78.41,78.37,78.42,78.43,78.45,78.36 | +| grandstand | 42.05,42.08,42.06,42.04,42.11,42.14,42.16,42.1,42.14,42.14,42.21 | +| path | 18.04,18.01,18.01,18.02,18.04,18.05,18.07,18.09,18.09,18.12,18.16 | +| stairs | 31.55,31.56,31.57,31.53,31.54,31.57,31.52,31.51,31.47,31.5,31.5 | +| runway | 63.98,63.97,63.97,63.98,63.97,63.99,64.0,63.99,63.99,63.99,63.99 | +| case | 48.46,48.4,48.31,48.29,48.31,48.32,48.28,48.24,48.12,48.06,48.07 | +| pool table | 92.68,92.7,92.7,92.72,92.66,92.71,92.71,92.73,92.73,92.76,92.64 | +| pillow | 57.19,57.12,57.11,57.19,57.13,57.17,57.16,57.21,57.16,57.1,57.14 | +| screen door | 67.09,67.21,67.11,67.14,67.3,67.09,67.17,67.22,67.38,67.47,67.27 | +| stairway | 25.6,25.58,25.58,25.59,25.55,25.59,25.57,25.6,25.57,25.56,25.44 | +| river | 9.94,9.89,9.82,9.79,9.73,9.67,9.63,9.62,9.57,9.6,9.54 | +| bridge | 55.25,56.19,56.62,57.17,57.52,57.85,58.46,59.15,59.56,60.09,59.58 | +| bookcase | 42.24,42.58,42.61,42.73,43.09,42.95,43.11,43.31,43.52,43.46,42.6 | +| blind | 45.24,45.22,44.95,44.93,44.8,44.73,44.72,44.67,44.64,44.59,44.53 | +| coffee table | 66.68,66.71,66.6,66.66,66.6,66.58,66.53,66.67,66.52,66.55,66.49 | +| toilet | 86.43,86.43,86.42,86.47,86.42,86.41,86.43,86.41,86.34,86.32,86.53 | +| flower | 30.64,30.65,30.64,30.75,30.74,30.7,30.86,30.9,30.87,30.94,31.0 | +| book | 47.09,47.08,47.06,47.01,46.96,46.99,46.96,46.9,46.84,46.78,46.7 | +| hill | 8.04,8.02,7.99,7.94,7.97,8.0,7.92,7.9,7.96,7.97,7.85 | +| bench | 44.33,44.34,44.37,44.38,44.38,44.42,44.45,44.5,44.46,44.47,44.51 | +| countertop | 54.63,54.6,54.7,54.62,54.67,54.75,54.72,54.78,54.74,54.74,55.01 | +| stove | 72.23,72.13,72.12,72.27,72.45,72.3,72.38,72.49,72.62,72.7,72.55 | +| palm | 50.69,50.65,50.72,50.74,50.79,50.78,50.84,50.85,50.94,50.94,51.07 | +| kitchen island | 47.92,48.0,48.05,47.84,48.04,48.09,48.42,48.41,48.52,48.63,48.34 | +| computer | 57.14,57.18,57.12,57.23,57.19,57.22,57.23,57.17,57.26,57.28,57.09 | +| swivel chair | 45.31,45.35,45.37,45.56,45.53,45.5,45.45,45.61,45.62,45.58,46.04 | +| boat | 37.94,38.09,38.02,38.14,38.11,38.16,38.29,38.25,38.28,38.29,38.55 | +| bar | 27.86,27.8,27.75,27.66,27.63,27.62,27.49,27.31,27.28,27.06,27.09 | +| arcade machine | 25.32,25.49,25.97,26.01,26.3,26.42,26.7,27.15,27.29,27.6,27.69 | +| hovel | 31.47,31.4,31.23,31.15,31.14,30.98,30.83,30.74,30.72,30.62,30.47 | +| bus | 88.51,88.63,88.63,88.6,88.55,88.55,88.55,88.6,88.59,88.58,88.64 | +| towel | 60.79,60.77,60.89,60.93,60.92,60.95,61.03,60.82,61.0,60.91,61.04 | +| light | 56.5,56.49,56.44,56.47,56.47,56.49,56.47,56.43,56.46,56.44,56.51 | +| truck | 35.05,35.1,34.95,35.25,35.34,35.12,35.12,35.11,35.11,35.16,35.13 | +| tower | 24.62,24.83,24.95,24.98,24.83,24.77,24.39,24.53,24.34,24.07,23.82 | +| chandelier | 66.29,66.38,66.38,66.43,66.41,66.42,66.46,66.46,66.47,66.49,66.45 | +| awning | 23.21,23.16,23.23,23.22,23.17,23.11,23.19,23.14,23.15,23.33,23.23 | +| streetlight | 28.38,28.3,28.26,28.26,28.17,28.18,28.13,28.1,28.1,28.0,28.03 | +| booth | 57.38,57.46,57.48,57.48,57.58,57.29,57.15,57.31,57.38,57.27,57.1 | +| television receiver | 68.25,68.21,68.19,68.26,68.24,68.19,68.2,68.16,68.1,68.12,68.12 | +| airplane | 51.93,51.96,51.78,51.81,51.65,51.67,51.61,51.23,51.36,51.19,51.05 | +| dirt track | 10.55,10.59,10.6,10.49,10.59,10.5,10.6,10.77,10.83,10.82,10.52 | +| apparel | 29.03,29.03,28.86,28.96,28.58,28.61,28.71,28.39,28.41,28.34,29.07 | +| pole | 24.6,24.55,24.49,24.58,24.5,24.46,24.49,24.43,24.46,24.46,24.42 | +| land | 6.98,6.88,6.77,6.64,6.52,6.5,6.5,6.56,6.5,6.5,6.31 | +| bannister | 5.43,5.45,5.46,5.55,5.48,5.49,5.47,5.51,5.55,5.48,5.64 | +| escalator | 22.05,22.09,22.09,22.1,22.17,22.14,22.1,22.34,22.23,22.2,22.3 | +| ottoman | 48.48,48.32,48.42,48.42,48.36,48.49,48.47,48.32,48.26,48.25,48.85 | +| bottle | 15.2,15.39,15.25,15.22,15.21,15.12,15.04,14.94,14.89,14.89,15.14 | +| buffet | 47.1,47.41,47.14,46.38,46.47,46.39,46.97,47.71,48.05,48.14,48.63 | +| poster | 27.45,27.34,27.55,27.55,27.46,27.65,27.51,27.56,27.6,27.61,27.21 | +| stage | 16.91,17.0,17.04,17.13,17.16,17.21,17.3,17.45,17.53,17.63,17.47 | +| van | 48.44,48.52,48.4,48.32,48.26,47.99,48.29,48.28,48.62,48.38,47.93 | +| ship | 30.4,30.85,32.32,31.12,32.84,31.96,32.28,32.84,33.3,33.39,33.59 | +| fountain | 8.78,8.64,8.67,8.6,8.66,8.58,8.44,8.3,8.06,7.93,7.83 | +| conveyer belt | 76.16,76.14,76.03,75.87,75.85,75.82,75.75,75.76,75.66,75.39,75.17 | +| canopy | 14.95,14.94,14.87,15.03,14.95,14.99,15.0,15.11,15.06,15.18,14.99 | +| washer | 66.1,66.08,66.04,66.05,66.01,66.0,66.05,65.85,65.87,65.89,65.93 | +| plaything | 23.1,23.08,23.13,23.11,23.09,23.13,23.11,23.11,23.16,23.28,23.28 | +| swimming pool | 42.76,43.07,44.1,44.11,44.59,45.07,45.22,45.58,46.17,46.27,46.78 | +| stool | 41.91,41.81,41.81,41.78,41.73,41.78,41.72,41.72,41.67,41.63,41.75 | +| barrel | 38.33,38.78,37.54,38.56,38.57,39.08,38.16,38.8,38.28,38.47,38.54 | +| basket | 28.56,28.55,28.6,28.57,28.58,28.6,28.67,28.67,28.59,28.58,28.69 | +| waterfall | 54.05,54.37,54.47,54.36,54.77,54.79,55.63,56.2,55.67,56.58,56.25 | +| tent | 93.75,93.77,93.73,93.75,93.73,93.72,93.74,93.77,93.76,93.76,93.78 | +| bag | 11.41,11.41,11.46,11.39,11.41,11.47,11.51,11.46,11.46,11.45,11.46 | +| minibike | 61.7,61.86,61.74,61.77,61.75,61.67,61.74,61.79,61.72,61.69,61.71 | +| cradle | 81.16,81.16,81.06,80.93,80.96,80.84,80.82,80.75,80.59,80.53,80.59 | +| oven | 27.28,27.32,27.24,27.25,27.3,27.2,27.14,27.14,27.09,27.03,27.01 | +| ball | 47.76,47.93,48.02,47.98,48.04,48.02,48.09,48.15,48.24,48.32,48.44 | +| food | 53.32,53.44,53.35,53.35,53.56,53.59,53.65,53.57,53.4,53.59,53.75 | +| step | 16.63,16.77,17.0,16.83,17.08,17.02,17.02,16.95,17.2,17.2,17.54 | +| tank | 41.5,41.44,41.44,41.39,41.38,41.36,41.27,41.23,41.23,41.25,41.33 | +| trade name | 24.89,24.88,24.79,24.87,24.8,24.83,24.8,24.76,24.82,24.77,24.76 | +| microwave | 37.39,37.39,37.37,37.38,37.37,37.37,37.4,37.39,37.37,37.38,37.38 | +| pot | 41.22,41.26,41.26,41.31,41.31,41.29,41.36,41.39,41.38,41.42,41.39 | +| animal | 51.75,51.78,51.81,51.76,51.79,51.91,51.87,51.87,51.93,51.91,51.92 | +| bicycle | 46.18,46.17,46.18,46.14,46.21,46.28,46.3,46.27,46.31,46.29,46.2 | +| lake | 59.85,59.75,59.67,59.64,59.55,59.42,59.3,59.25,59.15,59.11,59.0 | +| dishwasher | 76.76,76.76,76.81,76.86,76.98,77.0,77.01,77.11,77.13,77.22,77.23 | +| screen | 64.12,63.9,63.68,63.36,63.3,63.0,62.82,62.74,62.57,62.62,62.56 | +| blanket | 14.83,14.75,14.75,14.62,14.8,14.76,14.71,14.74,14.65,14.68,14.54 | +| sculpture | 35.68,35.68,35.84,35.86,35.87,36.05,36.02,35.98,36.08,36.09,36.14 | +| hood | 57.44,57.46,57.44,57.66,57.42,57.43,57.07,57.13,56.97,56.99,57.05 | +| sconce | 41.95,41.97,41.78,41.89,41.83,41.8,41.88,41.64,41.56,41.42,41.85 | +| vase | 37.27,37.29,37.26,37.33,37.22,37.18,37.14,37.23,37.13,37.13,37.14 | +| traffic light | 29.5,29.47,29.47,29.54,29.52,29.57,29.51,29.5,29.5,29.51,29.53 | +| tray | 5.51,5.52,5.49,5.55,5.55,5.54,5.56,5.56,5.59,5.59,5.62 | +| ashcan | 37.8,37.84,37.65,37.66,37.7,37.5,37.52,37.44,37.64,37.73,37.52 | +| fan | 58.23,58.31,58.25,58.34,58.28,58.39,58.26,58.33,58.32,58.29,58.34 | +| pier | 12.75,12.59,12.66,12.39,12.37,12.4,12.31,12.27,12.1,12.19,11.89 | +| crt screen | 4.59,4.63,4.67,4.77,4.91,5.05,5.48,5.41,5.94,6.21,6.69 | +| plate | 39.02,38.98,39.14,39.21,39.15,39.15,39.24,39.26,39.4,39.33,39.48 | +| monitor | 25.85,25.65,25.44,25.41,25.49,25.36,25.34,25.18,25.08,25.13,25.15 | +| bulletin board | 45.7,45.98,45.91,46.11,46.79,46.86,46.56,46.97,47.11,47.9,47.26 | +| shower | 1.18,1.2,1.24,1.31,1.26,1.25,1.37,1.31,1.36,1.38,1.44 | +| radiator | 46.01,46.22,46.34,46.22,46.45,46.34,46.66,46.32,46.61,46.65,46.32 | +| glass | 12.08,12.08,12.13,12.15,12.13,12.18,12.18,12.23,12.27,12.28,12.31 | +| clock | 24.72,24.74,24.74,24.7,24.71,24.68,24.7,24.67,24.65,24.69,24.73 | +| flag | 37.74,37.64,37.78,37.84,38.07,38.11,38.26,38.36,38.39,38.48,38.41 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 22:33:24,261 - mmseg - INFO - Summary: +2023-03-04 22:33:24,261 - mmseg - INFO - ++------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++------------------------------------------------------------------+ +| 46.12,46.16,46.16,46.16,46.2,46.19,46.21,46.22,46.23,46.25,46.24 | ++------------------------------------------------------------------+ +2023-03-04 22:33:24,261 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:33:24,261 - mmseg - INFO - Iter(val) [250] mIoU: [0.4612, 0.4616, 0.4616, 0.4616, 0.462, 0.4619, 0.4621, 0.4622, 0.4623, 0.4625, 0.4624], copy_paste: 46.12,46.16,46.16,46.16,46.2,46.19,46.21,46.22,46.23,46.25,46.24 +2023-03-04 22:33:24,269 - mmseg - INFO - Swap parameters (before train) before iter [96001] +2023-03-04 22:33:38,031 - mmseg - INFO - Iter [96050/160000] lr: 9.375e-06, eta: 5:41:22, time: 13.648, data_time: 13.381, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5623, loss: 0.1827 +2023-03-04 22:33:51,507 - mmseg - INFO - Iter [96100/160000] lr: 9.375e-06, eta: 5:41:04, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.7478, loss: 0.1805 +2023-03-04 22:34:04,775 - mmseg - INFO - Iter [96150/160000] lr: 9.375e-06, eta: 5:40:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6136, loss: 0.1828 +2023-03-04 22:34:18,069 - mmseg - INFO - Iter [96200/160000] lr: 9.375e-06, eta: 5:40:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7393, loss: 0.1761 +2023-03-04 22:34:31,341 - mmseg - INFO - Iter [96250/160000] lr: 9.375e-06, eta: 5:40:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7447, loss: 0.1784 +2023-03-04 22:34:44,788 - mmseg - INFO - Iter [96300/160000] lr: 9.375e-06, eta: 5:39:53, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.5798, loss: 0.1810 +2023-03-04 22:34:58,103 - mmseg - INFO - Iter [96350/160000] lr: 9.375e-06, eta: 5:39:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5859, loss: 0.1823 +2023-03-04 22:35:11,406 - mmseg - INFO - Iter [96400/160000] lr: 9.375e-06, eta: 5:39:17, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4747, loss: 0.1852 +2023-03-04 22:35:24,715 - mmseg - INFO - Iter [96450/160000] lr: 9.375e-06, eta: 5:39:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4664, loss: 0.1844 +2023-03-04 22:35:38,004 - mmseg - INFO - Iter [96500/160000] lr: 9.375e-06, eta: 5:38:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5920, loss: 0.1830 +2023-03-04 22:35:53,873 - mmseg - INFO - Iter [96550/160000] lr: 9.375e-06, eta: 5:38:26, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.3429, loss: 0.1866 +2023-03-04 22:36:07,242 - mmseg - INFO - Iter [96600/160000] lr: 9.375e-06, eta: 5:38:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7580, loss: 0.1761 +2023-03-04 22:36:20,559 - mmseg - INFO - Iter [96650/160000] lr: 9.375e-06, eta: 5:37:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1753, decode.acc_seg: 92.8293, loss: 0.1753 +2023-03-04 22:36:33,835 - mmseg - INFO - Iter [96700/160000] lr: 9.375e-06, eta: 5:37:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6911, loss: 0.1810 +2023-03-04 22:36:47,071 - mmseg - INFO - Iter [96750/160000] lr: 9.375e-06, eta: 5:37:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5851, loss: 0.1859 +2023-03-04 22:37:00,350 - mmseg - INFO - Iter [96800/160000] lr: 9.375e-06, eta: 5:36:57, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.6222, loss: 0.1824 +2023-03-04 22:37:13,642 - mmseg - INFO - Iter [96850/160000] lr: 9.375e-06, eta: 5:36:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6260, loss: 0.1820 +2023-03-04 22:37:26,938 - mmseg - INFO - Iter [96900/160000] lr: 9.375e-06, eta: 5:36:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.5476, loss: 0.1833 +2023-03-04 22:37:40,302 - mmseg - INFO - Iter [96950/160000] lr: 9.375e-06, eta: 5:36:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3424, loss: 0.1901 +2023-03-04 22:37:53,618 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:37:53,618 - mmseg - INFO - Iter [97000/160000] lr: 9.375e-06, eta: 5:35:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7493, loss: 0.1790 +2023-03-04 22:38:07,051 - mmseg - INFO - Iter [97050/160000] lr: 9.375e-06, eta: 5:35:28, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.8673, loss: 0.1782 +2023-03-04 22:38:20,381 - mmseg - INFO - Iter [97100/160000] lr: 9.375e-06, eta: 5:35:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.5729, loss: 0.1842 +2023-03-04 22:38:33,716 - mmseg - INFO - Iter [97150/160000] lr: 9.375e-06, eta: 5:34:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7836, loss: 0.1808 +2023-03-04 22:38:49,525 - mmseg - INFO - Iter [97200/160000] lr: 9.375e-06, eta: 5:34:37, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5137, loss: 0.1845 +2023-03-04 22:39:02,908 - mmseg - INFO - Iter [97250/160000] lr: 9.375e-06, eta: 5:34:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6305, loss: 0.1798 +2023-03-04 22:39:16,171 - mmseg - INFO - Iter [97300/160000] lr: 9.375e-06, eta: 5:34:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6180, loss: 0.1810 +2023-03-04 22:39:29,429 - mmseg - INFO - Iter [97350/160000] lr: 9.375e-06, eta: 5:33:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7671, loss: 0.1784 +2023-03-04 22:39:42,715 - mmseg - INFO - Iter [97400/160000] lr: 9.375e-06, eta: 5:33:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.9223, loss: 0.1745 +2023-03-04 22:39:55,962 - mmseg - INFO - Iter [97450/160000] lr: 9.375e-06, eta: 5:33:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6130, loss: 0.1832 +2023-03-04 22:40:09,341 - mmseg - INFO - Iter [97500/160000] lr: 9.375e-06, eta: 5:32:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5304, loss: 0.1843 +2023-03-04 22:40:22,525 - mmseg - INFO - Iter [97550/160000] lr: 9.375e-06, eta: 5:32:33, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.8621, loss: 0.1749 +2023-03-04 22:40:35,800 - mmseg - INFO - Iter [97600/160000] lr: 9.375e-06, eta: 5:32:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.3809, loss: 0.1872 +2023-03-04 22:40:49,089 - mmseg - INFO - Iter [97650/160000] lr: 9.375e-06, eta: 5:31:57, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.3899, loss: 0.1875 +2023-03-04 22:41:02,400 - mmseg - INFO - Iter [97700/160000] lr: 9.375e-06, eta: 5:31:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4286, loss: 0.1861 +2023-03-04 22:41:15,624 - mmseg - INFO - Iter [97750/160000] lr: 9.375e-06, eta: 5:31:22, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5689, loss: 0.1835 +2023-03-04 22:41:28,929 - mmseg - INFO - Iter [97800/160000] lr: 9.375e-06, eta: 5:31:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5865, loss: 0.1823 +2023-03-04 22:41:44,996 - mmseg - INFO - Iter [97850/160000] lr: 9.375e-06, eta: 5:30:48, time: 0.321, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.5805, loss: 0.1820 +2023-03-04 22:41:58,380 - mmseg - INFO - Iter [97900/160000] lr: 9.375e-06, eta: 5:30:31, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7001, loss: 0.1808 +2023-03-04 22:42:11,787 - mmseg - INFO - Iter [97950/160000] lr: 9.375e-06, eta: 5:30:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.8062, loss: 0.1795 +2023-03-04 22:42:25,062 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:42:25,062 - mmseg - INFO - Iter [98000/160000] lr: 9.375e-06, eta: 5:29:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.7933, loss: 0.1762 +2023-03-04 22:42:38,435 - mmseg - INFO - Iter [98050/160000] lr: 9.375e-06, eta: 5:29:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.6952, loss: 0.1785 +2023-03-04 22:42:51,744 - mmseg - INFO - Iter [98100/160000] lr: 9.375e-06, eta: 5:29:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1736, decode.acc_seg: 92.8494, loss: 0.1736 +2023-03-04 22:43:05,235 - mmseg - INFO - Iter [98150/160000] lr: 9.375e-06, eta: 5:29:03, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6255, loss: 0.1808 +2023-03-04 22:43:18,513 - mmseg - INFO - Iter [98200/160000] lr: 9.375e-06, eta: 5:28:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.7620, loss: 0.1809 +2023-03-04 22:43:31,741 - mmseg - INFO - Iter [98250/160000] lr: 9.375e-06, eta: 5:28:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.4473, loss: 0.1833 +2023-03-04 22:43:44,949 - mmseg - INFO - Iter [98300/160000] lr: 9.375e-06, eta: 5:28:10, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5155, loss: 0.1838 +2023-03-04 22:43:58,464 - mmseg - INFO - Iter [98350/160000] lr: 9.375e-06, eta: 5:27:52, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.6868, loss: 0.1787 +2023-03-04 22:44:11,842 - mmseg - INFO - Iter [98400/160000] lr: 9.375e-06, eta: 5:27:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.5185, loss: 0.1842 +2023-03-04 22:44:27,730 - mmseg - INFO - Iter [98450/160000] lr: 9.375e-06, eta: 5:27:19, time: 0.318, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.4598, loss: 0.1869 +2023-03-04 22:44:41,133 - mmseg - INFO - Iter [98500/160000] lr: 9.375e-06, eta: 5:27:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.6996, loss: 0.1778 +2023-03-04 22:44:54,443 - mmseg - INFO - Iter [98550/160000] lr: 9.375e-06, eta: 5:26:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.7253, loss: 0.1826 +2023-03-04 22:45:07,703 - mmseg - INFO - Iter [98600/160000] lr: 9.375e-06, eta: 5:26:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6287, loss: 0.1812 +2023-03-04 22:45:21,009 - mmseg - INFO - Iter [98650/160000] lr: 9.375e-06, eta: 5:26:08, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1931, decode.acc_seg: 92.2692, loss: 0.1931 +2023-03-04 22:45:34,395 - mmseg - INFO - Iter [98700/160000] lr: 9.375e-06, eta: 5:25:51, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8981, loss: 0.1775 +2023-03-04 22:45:47,612 - mmseg - INFO - Iter [98750/160000] lr: 9.375e-06, eta: 5:25:33, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.4824, loss: 0.1847 +2023-03-04 22:46:00,828 - mmseg - INFO - Iter [98800/160000] lr: 9.375e-06, eta: 5:25:16, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8263, loss: 0.1752 +2023-03-04 22:46:14,363 - mmseg - INFO - Iter [98850/160000] lr: 9.375e-06, eta: 5:24:58, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1887, decode.acc_seg: 92.3536, loss: 0.1887 +2023-03-04 22:46:27,748 - mmseg - INFO - Iter [98900/160000] lr: 9.375e-06, eta: 5:24:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1705, decode.acc_seg: 93.0479, loss: 0.1705 +2023-03-04 22:46:41,099 - mmseg - INFO - Iter [98950/160000] lr: 9.375e-06, eta: 5:24:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1969, decode.acc_seg: 92.0968, loss: 0.1969 +2023-03-04 22:46:54,428 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:46:54,428 - mmseg - INFO - Iter [99000/160000] lr: 9.375e-06, eta: 5:24:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5338, loss: 0.1840 +2023-03-04 22:47:07,662 - mmseg - INFO - Iter [99050/160000] lr: 9.375e-06, eta: 5:23:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.5376, loss: 0.1866 +2023-03-04 22:47:23,478 - mmseg - INFO - Iter [99100/160000] lr: 9.375e-06, eta: 5:23:32, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.4692, loss: 0.1877 +2023-03-04 22:47:36,746 - mmseg - INFO - Iter [99150/160000] lr: 9.375e-06, eta: 5:23:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6543, loss: 0.1802 +2023-03-04 22:47:50,113 - mmseg - INFO - Iter [99200/160000] lr: 9.375e-06, eta: 5:22:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.9082, loss: 0.1786 +2023-03-04 22:48:03,586 - mmseg - INFO - Iter [99250/160000] lr: 9.375e-06, eta: 5:22:39, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.7147, loss: 0.1827 +2023-03-04 22:48:17,017 - mmseg - INFO - Iter [99300/160000] lr: 9.375e-06, eta: 5:22:22, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4435, loss: 0.1844 +2023-03-04 22:48:30,319 - mmseg - INFO - Iter [99350/160000] lr: 9.375e-06, eta: 5:22:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.7745, loss: 0.1773 +2023-03-04 22:48:43,654 - mmseg - INFO - Iter [99400/160000] lr: 9.375e-06, eta: 5:21:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.8600, loss: 0.1767 +2023-03-04 22:48:57,054 - mmseg - INFO - Iter [99450/160000] lr: 9.375e-06, eta: 5:21:29, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.3706, loss: 0.1873 +2023-03-04 22:49:10,320 - mmseg - INFO - Iter [99500/160000] lr: 9.375e-06, eta: 5:21:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1881, decode.acc_seg: 92.4253, loss: 0.1881 +2023-03-04 22:49:23,769 - mmseg - INFO - Iter [99550/160000] lr: 9.375e-06, eta: 5:20:54, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6639, loss: 0.1818 +2023-03-04 22:49:37,030 - mmseg - INFO - Iter [99600/160000] lr: 9.375e-06, eta: 5:20:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.8378, loss: 0.1769 +2023-03-04 22:49:50,453 - mmseg - INFO - Iter [99650/160000] lr: 9.375e-06, eta: 5:20:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6516, loss: 0.1808 +2023-03-04 22:50:06,092 - mmseg - INFO - Iter [99700/160000] lr: 9.375e-06, eta: 5:20:03, time: 0.313, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.7783, loss: 0.1787 +2023-03-04 22:50:19,422 - mmseg - INFO - Iter [99750/160000] lr: 9.375e-06, eta: 5:19:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5090, loss: 0.1818 +2023-03-04 22:50:32,828 - mmseg - INFO - Iter [99800/160000] lr: 9.375e-06, eta: 5:19:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.4547, loss: 0.1816 +2023-03-04 22:50:46,210 - mmseg - INFO - Iter [99850/160000] lr: 9.375e-06, eta: 5:19:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.7758, loss: 0.1792 +2023-03-04 22:50:59,441 - mmseg - INFO - Iter [99900/160000] lr: 9.375e-06, eta: 5:18:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.7254, loss: 0.1802 +2023-03-04 22:51:12,807 - mmseg - INFO - Iter [99950/160000] lr: 9.375e-06, eta: 5:18:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.5450, loss: 0.1846 +2023-03-04 22:51:26,092 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:51:26,092 - mmseg - INFO - Iter [100000/160000] lr: 9.375e-06, eta: 5:18:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.3591, loss: 0.1917 +2023-03-04 22:51:39,315 - mmseg - INFO - Iter [100050/160000] lr: 4.687e-06, eta: 5:18:01, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5816, loss: 0.1838 +2023-03-04 22:51:52,525 - mmseg - INFO - Iter [100100/160000] lr: 4.687e-06, eta: 5:17:43, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 92.7679, loss: 0.1759 +2023-03-04 22:52:05,828 - mmseg - INFO - Iter [100150/160000] lr: 4.687e-06, eta: 5:17:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.7365, loss: 0.1811 +2023-03-04 22:52:19,198 - mmseg - INFO - Iter [100200/160000] lr: 4.687e-06, eta: 5:17:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6998, loss: 0.1825 +2023-03-04 22:52:32,409 - mmseg - INFO - Iter [100250/160000] lr: 4.687e-06, eta: 5:16:51, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.6924, loss: 0.1776 +2023-03-04 22:52:45,643 - mmseg - INFO - Iter [100300/160000] lr: 4.687e-06, eta: 5:16:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.8070, loss: 0.1802 +2023-03-04 22:53:01,474 - mmseg - INFO - Iter [100350/160000] lr: 4.687e-06, eta: 5:16:17, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1888, decode.acc_seg: 92.4581, loss: 0.1888 +2023-03-04 22:53:14,718 - mmseg - INFO - Iter [100400/160000] lr: 4.687e-06, eta: 5:16:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.6347, loss: 0.1792 +2023-03-04 22:53:28,047 - mmseg - INFO - Iter [100450/160000] lr: 4.687e-06, eta: 5:15:43, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1738, decode.acc_seg: 92.7935, loss: 0.1738 +2023-03-04 22:53:41,393 - mmseg - INFO - Iter [100500/160000] lr: 4.687e-06, eta: 5:15:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.6374, loss: 0.1835 +2023-03-04 22:53:54,739 - mmseg - INFO - Iter [100550/160000] lr: 4.687e-06, eta: 5:15:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.6330, loss: 0.1831 +2023-03-04 22:54:07,927 - mmseg - INFO - Iter [100600/160000] lr: 4.687e-06, eta: 5:14:50, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.4821, loss: 0.1832 +2023-03-04 22:54:21,249 - mmseg - INFO - Iter [100650/160000] lr: 4.687e-06, eta: 5:14:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.6043, loss: 0.1826 +2023-03-04 22:54:34,567 - mmseg - INFO - Iter [100700/160000] lr: 4.687e-06, eta: 5:14:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5207, loss: 0.1818 +2023-03-04 22:54:47,832 - mmseg - INFO - Iter [100750/160000] lr: 4.687e-06, eta: 5:13:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5657, loss: 0.1830 +2023-03-04 22:55:01,104 - mmseg - INFO - Iter [100800/160000] lr: 4.687e-06, eta: 5:13:40, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.7238, loss: 0.1763 +2023-03-04 22:55:14,503 - mmseg - INFO - Iter [100850/160000] lr: 4.687e-06, eta: 5:13:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.6605, loss: 0.1835 +2023-03-04 22:55:27,768 - mmseg - INFO - Iter [100900/160000] lr: 4.687e-06, eta: 5:13:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5302, loss: 0.1821 +2023-03-04 22:55:41,148 - mmseg - INFO - Iter [100950/160000] lr: 4.687e-06, eta: 5:12:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5761, loss: 0.1827 +2023-03-04 22:55:56,963 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 22:55:56,963 - mmseg - INFO - Iter [101000/160000] lr: 4.687e-06, eta: 5:12:32, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6929, loss: 0.1801 +2023-03-04 22:56:10,312 - mmseg - INFO - Iter [101050/160000] lr: 4.687e-06, eta: 5:12:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6199, loss: 0.1804 +2023-03-04 22:56:23,548 - mmseg - INFO - Iter [101100/160000] lr: 4.687e-06, eta: 5:11:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6271, loss: 0.1808 +2023-03-04 22:56:36,766 - mmseg - INFO - Iter [101150/160000] lr: 4.687e-06, eta: 5:11:40, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4458, loss: 0.1873 +2023-03-04 22:56:49,991 - mmseg - INFO - Iter [101200/160000] lr: 4.687e-06, eta: 5:11:23, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.5877, loss: 0.1803 +2023-03-04 22:57:03,398 - mmseg - INFO - Iter [101250/160000] lr: 4.687e-06, eta: 5:11:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5108, loss: 0.1831 +2023-03-04 22:57:16,659 - mmseg - INFO - Iter [101300/160000] lr: 4.687e-06, eta: 5:10:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6259, loss: 0.1816 +2023-03-04 22:57:30,046 - mmseg - INFO - Iter [101350/160000] lr: 4.687e-06, eta: 5:10:31, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.7836, loss: 0.1775 +2023-03-04 22:57:43,351 - mmseg - INFO - Iter [101400/160000] lr: 4.687e-06, eta: 5:10:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.5814, loss: 0.1820 +2023-03-04 22:57:56,562 - mmseg - INFO - Iter [101450/160000] lr: 4.687e-06, eta: 5:09:56, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.6932, loss: 0.1846 +2023-03-04 22:58:09,829 - mmseg - INFO - Iter [101500/160000] lr: 4.687e-06, eta: 5:09:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6041, loss: 0.1811 +2023-03-04 22:58:23,165 - mmseg - INFO - Iter [101550/160000] lr: 4.687e-06, eta: 5:09:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6610, loss: 0.1810 +2023-03-04 22:58:39,025 - mmseg - INFO - Iter [101600/160000] lr: 4.687e-06, eta: 5:09:05, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5318, loss: 0.1827 +2023-03-04 22:58:52,265 - mmseg - INFO - Iter [101650/160000] lr: 4.687e-06, eta: 5:08:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5240, loss: 0.1847 +2023-03-04 22:59:05,577 - mmseg - INFO - Iter [101700/160000] lr: 4.687e-06, eta: 5:08:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6623, loss: 0.1817 +2023-03-04 22:59:18,845 - mmseg - INFO - Iter [101750/160000] lr: 4.687e-06, eta: 5:08:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6421, loss: 0.1811 +2023-03-04 22:59:32,082 - mmseg - INFO - Iter [101800/160000] lr: 4.687e-06, eta: 5:07:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.6474, loss: 0.1859 +2023-03-04 22:59:45,453 - mmseg - INFO - Iter [101850/160000] lr: 4.687e-06, eta: 5:07:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5534, loss: 0.1814 +2023-03-04 22:59:58,745 - mmseg - INFO - Iter [101900/160000] lr: 4.687e-06, eta: 5:07:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4505, loss: 0.1875 +2023-03-04 23:00:12,049 - mmseg - INFO - Iter [101950/160000] lr: 4.687e-06, eta: 5:07:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 92.7988, loss: 0.1759 +2023-03-04 23:00:25,367 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:00:25,367 - mmseg - INFO - Iter [102000/160000] lr: 4.687e-06, eta: 5:06:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5316, loss: 0.1830 +2023-03-04 23:00:38,637 - mmseg - INFO - Iter [102050/160000] lr: 4.687e-06, eta: 5:06:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.8887, loss: 0.1765 +2023-03-04 23:00:51,912 - mmseg - INFO - Iter [102100/160000] lr: 4.687e-06, eta: 5:06:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1726, decode.acc_seg: 92.8476, loss: 0.1726 +2023-03-04 23:01:05,286 - mmseg - INFO - Iter [102150/160000] lr: 4.687e-06, eta: 5:05:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.9820, loss: 0.1745 +2023-03-04 23:01:18,597 - mmseg - INFO - Iter [102200/160000] lr: 4.687e-06, eta: 5:05:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4458, loss: 0.1852 +2023-03-04 23:01:34,382 - mmseg - INFO - Iter [102250/160000] lr: 4.687e-06, eta: 5:05:21, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.4869, loss: 0.1848 +2023-03-04 23:01:47,659 - mmseg - INFO - Iter [102300/160000] lr: 4.687e-06, eta: 5:05:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1681, decode.acc_seg: 93.0663, loss: 0.1681 +2023-03-04 23:02:00,931 - mmseg - INFO - Iter [102350/160000] lr: 4.687e-06, eta: 5:04:47, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.6398, loss: 0.1796 +2023-03-04 23:02:14,187 - mmseg - INFO - Iter [102400/160000] lr: 4.687e-06, eta: 5:04:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.4782, loss: 0.1839 +2023-03-04 23:02:27,487 - mmseg - INFO - Iter [102450/160000] lr: 4.687e-06, eta: 5:04:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5847, loss: 0.1824 +2023-03-04 23:02:40,821 - mmseg - INFO - Iter [102500/160000] lr: 4.687e-06, eta: 5:03:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.7041, loss: 0.1805 +2023-03-04 23:02:54,175 - mmseg - INFO - Iter [102550/160000] lr: 4.687e-06, eta: 5:03:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.5402, loss: 0.1819 +2023-03-04 23:03:07,370 - mmseg - INFO - Iter [102600/160000] lr: 4.687e-06, eta: 5:03:20, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5499, loss: 0.1834 +2023-03-04 23:03:20,650 - mmseg - INFO - Iter [102650/160000] lr: 4.687e-06, eta: 5:03:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.5924, loss: 0.1861 +2023-03-04 23:03:33,967 - mmseg - INFO - Iter [102700/160000] lr: 4.687e-06, eta: 5:02:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.7908, loss: 0.1742 +2023-03-04 23:03:47,360 - mmseg - INFO - Iter [102750/160000] lr: 4.687e-06, eta: 5:02:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6196, loss: 0.1806 +2023-03-04 23:04:00,563 - mmseg - INFO - Iter [102800/160000] lr: 4.687e-06, eta: 5:02:11, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6710, loss: 0.1803 +2023-03-04 23:04:13,763 - mmseg - INFO - Iter [102850/160000] lr: 4.687e-06, eta: 5:01:54, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7185, loss: 0.1788 +2023-03-04 23:04:29,737 - mmseg - INFO - Iter [102900/160000] lr: 4.687e-06, eta: 5:01:38, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6841, loss: 0.1795 +2023-03-04 23:04:43,097 - mmseg - INFO - Iter [102950/160000] lr: 4.687e-06, eta: 5:01:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1917, decode.acc_seg: 92.2224, loss: 0.1917 +2023-03-04 23:04:56,361 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:04:56,361 - mmseg - INFO - Iter [103000/160000] lr: 4.687e-06, eta: 5:01:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.7201, loss: 0.1820 +2023-03-04 23:05:09,717 - mmseg - INFO - Iter [103050/160000] lr: 4.687e-06, eta: 5:00:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1670, decode.acc_seg: 93.1626, loss: 0.1670 +2023-03-04 23:05:23,032 - mmseg - INFO - Iter [103100/160000] lr: 4.687e-06, eta: 5:00:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6579, loss: 0.1816 +2023-03-04 23:05:36,464 - mmseg - INFO - Iter [103150/160000] lr: 4.687e-06, eta: 5:00:12, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5729, loss: 0.1830 +2023-03-04 23:05:49,739 - mmseg - INFO - Iter [103200/160000] lr: 4.687e-06, eta: 4:59:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.4819, loss: 0.1856 +2023-03-04 23:06:02,978 - mmseg - INFO - Iter [103250/160000] lr: 4.687e-06, eta: 4:59:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1879, decode.acc_seg: 92.3458, loss: 0.1879 +2023-03-04 23:06:16,208 - mmseg - INFO - Iter [103300/160000] lr: 4.687e-06, eta: 4:59:20, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5556, loss: 0.1841 +2023-03-04 23:06:29,443 - mmseg - INFO - Iter [103350/160000] lr: 4.687e-06, eta: 4:59:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.8202, loss: 0.1779 +2023-03-04 23:06:42,760 - mmseg - INFO - Iter [103400/160000] lr: 4.687e-06, eta: 4:58:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1884, decode.acc_seg: 92.3538, loss: 0.1884 +2023-03-04 23:06:56,018 - mmseg - INFO - Iter [103450/160000] lr: 4.687e-06, eta: 4:58:28, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6325, loss: 0.1804 +2023-03-04 23:07:11,824 - mmseg - INFO - Iter [103500/160000] lr: 4.687e-06, eta: 4:58:12, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.7504, loss: 0.1769 +2023-03-04 23:07:25,374 - mmseg - INFO - Iter [103550/160000] lr: 4.687e-06, eta: 4:57:55, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5616, loss: 0.1831 +2023-03-04 23:07:38,608 - mmseg - INFO - Iter [103600/160000] lr: 4.687e-06, eta: 4:57:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.7363, loss: 0.1825 +2023-03-04 23:07:52,004 - mmseg - INFO - Iter [103650/160000] lr: 4.687e-06, eta: 4:57:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1907, decode.acc_seg: 92.3447, loss: 0.1907 +2023-03-04 23:08:05,325 - mmseg - INFO - Iter [103700/160000] lr: 4.687e-06, eta: 4:57:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1744, decode.acc_seg: 92.9244, loss: 0.1744 +2023-03-04 23:08:18,693 - mmseg - INFO - Iter [103750/160000] lr: 4.687e-06, eta: 4:56:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.1610, loss: 0.1903 +2023-03-04 23:08:31,977 - mmseg - INFO - Iter [103800/160000] lr: 4.687e-06, eta: 4:56:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.6779, loss: 0.1791 +2023-03-04 23:08:45,502 - mmseg - INFO - Iter [103850/160000] lr: 4.687e-06, eta: 4:56:12, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5450, loss: 0.1849 +2023-03-04 23:08:58,840 - mmseg - INFO - Iter [103900/160000] lr: 4.687e-06, eta: 4:55:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.7107, loss: 0.1820 +2023-03-04 23:09:12,070 - mmseg - INFO - Iter [103950/160000] lr: 4.687e-06, eta: 4:55:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.9543, loss: 0.1799 +2023-03-04 23:09:25,298 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:09:25,298 - mmseg - INFO - Iter [104000/160000] lr: 4.687e-06, eta: 4:55:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7593, loss: 0.1774 +2023-03-04 23:09:38,599 - mmseg - INFO - Iter [104050/160000] lr: 4.687e-06, eta: 4:55:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4184, loss: 0.1866 +2023-03-04 23:09:51,983 - mmseg - INFO - Iter [104100/160000] lr: 4.687e-06, eta: 4:54:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1741, decode.acc_seg: 92.8885, loss: 0.1741 +2023-03-04 23:10:07,891 - mmseg - INFO - Iter [104150/160000] lr: 4.687e-06, eta: 4:54:31, time: 0.318, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6673, loss: 0.1815 +2023-03-04 23:10:21,331 - mmseg - INFO - Iter [104200/160000] lr: 4.687e-06, eta: 4:54:13, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7975, loss: 0.1768 +2023-03-04 23:10:34,724 - mmseg - INFO - Iter [104250/160000] lr: 4.687e-06, eta: 4:53:56, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1738, decode.acc_seg: 92.9241, loss: 0.1738 +2023-03-04 23:10:48,066 - mmseg - INFO - Iter [104300/160000] lr: 4.687e-06, eta: 4:53:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1937, decode.acc_seg: 92.2845, loss: 0.1937 +2023-03-04 23:11:01,350 - mmseg - INFO - Iter [104350/160000] lr: 4.687e-06, eta: 4:53:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.6339, loss: 0.1809 +2023-03-04 23:11:14,712 - mmseg - INFO - Iter [104400/160000] lr: 4.687e-06, eta: 4:53:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6922, loss: 0.1806 +2023-03-04 23:11:28,041 - mmseg - INFO - Iter [104450/160000] lr: 4.687e-06, eta: 4:52:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.5958, loss: 0.1797 +2023-03-04 23:11:41,308 - mmseg - INFO - Iter [104500/160000] lr: 4.687e-06, eta: 4:52:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.6942, loss: 0.1762 +2023-03-04 23:11:54,678 - mmseg - INFO - Iter [104550/160000] lr: 4.687e-06, eta: 4:52:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1719, decode.acc_seg: 92.8247, loss: 0.1719 +2023-03-04 23:12:08,062 - mmseg - INFO - Iter [104600/160000] lr: 4.687e-06, eta: 4:51:56, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5910, loss: 0.1827 +2023-03-04 23:12:21,404 - mmseg - INFO - Iter [104650/160000] lr: 4.687e-06, eta: 4:51:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5959, loss: 0.1844 +2023-03-04 23:12:34,746 - mmseg - INFO - Iter [104700/160000] lr: 4.687e-06, eta: 4:51:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6112, loss: 0.1818 +2023-03-04 23:12:50,633 - mmseg - INFO - Iter [104750/160000] lr: 4.687e-06, eta: 4:51:06, time: 0.318, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.8646, loss: 0.1742 +2023-03-04 23:13:03,980 - mmseg - INFO - Iter [104800/160000] lr: 4.687e-06, eta: 4:50:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.7814, loss: 0.1763 +2023-03-04 23:13:17,270 - mmseg - INFO - Iter [104850/160000] lr: 4.687e-06, eta: 4:50:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5553, loss: 0.1840 +2023-03-04 23:13:30,549 - mmseg - INFO - Iter [104900/160000] lr: 4.687e-06, eta: 4:50:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5533, loss: 0.1814 +2023-03-04 23:13:43,801 - mmseg - INFO - Iter [104950/160000] lr: 4.687e-06, eta: 4:49:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7908, loss: 0.1798 +2023-03-04 23:13:57,014 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:13:57,014 - mmseg - INFO - Iter [105000/160000] lr: 4.687e-06, eta: 4:49:41, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.6728, loss: 0.1763 +2023-03-04 23:14:10,433 - mmseg - INFO - Iter [105050/160000] lr: 4.687e-06, eta: 4:49:24, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.8065, loss: 0.1767 +2023-03-04 23:14:23,733 - mmseg - INFO - Iter [105100/160000] lr: 4.687e-06, eta: 4:49:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.6958, loss: 0.1826 +2023-03-04 23:14:36,954 - mmseg - INFO - Iter [105150/160000] lr: 4.687e-06, eta: 4:48:49, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.1726, loss: 0.1871 +2023-03-04 23:14:50,244 - mmseg - INFO - Iter [105200/160000] lr: 4.687e-06, eta: 4:48:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.7510, loss: 0.1760 +2023-03-04 23:15:03,555 - mmseg - INFO - Iter [105250/160000] lr: 4.687e-06, eta: 4:48:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.6415, loss: 0.1831 +2023-03-04 23:15:16,913 - mmseg - INFO - Iter [105300/160000] lr: 4.687e-06, eta: 4:47:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.6783, loss: 0.1785 +2023-03-04 23:15:30,362 - mmseg - INFO - Iter [105350/160000] lr: 4.687e-06, eta: 4:47:41, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4800, loss: 0.1866 +2023-03-04 23:15:46,185 - mmseg - INFO - Iter [105400/160000] lr: 4.687e-06, eta: 4:47:25, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.3654, loss: 0.1915 +2023-03-04 23:15:59,666 - mmseg - INFO - Iter [105450/160000] lr: 4.687e-06, eta: 4:47:08, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.8298, loss: 0.1786 +2023-03-04 23:16:12,887 - mmseg - INFO - Iter [105500/160000] lr: 4.687e-06, eta: 4:46:51, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7710, loss: 0.1780 +2023-03-04 23:16:26,248 - mmseg - INFO - Iter [105550/160000] lr: 4.687e-06, eta: 4:46:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5144, loss: 0.1824 +2023-03-04 23:16:39,493 - mmseg - INFO - Iter [105600/160000] lr: 4.687e-06, eta: 4:46:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1895, decode.acc_seg: 92.3621, loss: 0.1895 +2023-03-04 23:16:52,791 - mmseg - INFO - Iter [105650/160000] lr: 4.687e-06, eta: 4:46:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4885, loss: 0.1873 +2023-03-04 23:17:06,099 - mmseg - INFO - Iter [105700/160000] lr: 4.687e-06, eta: 4:45:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.8458, loss: 0.1761 +2023-03-04 23:17:19,358 - mmseg - INFO - Iter [105750/160000] lr: 4.687e-06, eta: 4:45:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7417, loss: 0.1788 +2023-03-04 23:17:32,739 - mmseg - INFO - Iter [105800/160000] lr: 4.687e-06, eta: 4:45:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.7495, loss: 0.1773 +2023-03-04 23:17:45,960 - mmseg - INFO - Iter [105850/160000] lr: 4.687e-06, eta: 4:44:52, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.4792, loss: 0.1863 +2023-03-04 23:17:59,259 - mmseg - INFO - Iter [105900/160000] lr: 4.687e-06, eta: 4:44:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6191, loss: 0.1795 +2023-03-04 23:18:12,542 - mmseg - INFO - Iter [105950/160000] lr: 4.687e-06, eta: 4:44:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.8883, loss: 0.1756 +2023-03-04 23:18:25,809 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:18:25,809 - mmseg - INFO - Iter [106000/160000] lr: 4.687e-06, eta: 4:44:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6463, loss: 0.1828 +2023-03-04 23:18:41,618 - mmseg - INFO - Iter [106050/160000] lr: 4.687e-06, eta: 4:43:45, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6264, loss: 0.1803 +2023-03-04 23:18:55,060 - mmseg - INFO - Iter [106100/160000] lr: 4.687e-06, eta: 4:43:28, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6972, loss: 0.1799 +2023-03-04 23:19:08,369 - mmseg - INFO - Iter [106150/160000] lr: 4.687e-06, eta: 4:43:11, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 93.0395, loss: 0.1759 +2023-03-04 23:19:21,682 - mmseg - INFO - Iter [106200/160000] lr: 4.687e-06, eta: 4:42:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6190, loss: 0.1795 +2023-03-04 23:19:34,949 - mmseg - INFO - Iter [106250/160000] lr: 4.687e-06, eta: 4:42:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5447, loss: 0.1870 +2023-03-04 23:19:48,218 - mmseg - INFO - Iter [106300/160000] lr: 4.687e-06, eta: 4:42:20, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5877, loss: 0.1809 +2023-03-04 23:20:01,654 - mmseg - INFO - Iter [106350/160000] lr: 4.687e-06, eta: 4:42:03, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6153, loss: 0.1794 +2023-03-04 23:20:15,018 - mmseg - INFO - Iter [106400/160000] lr: 4.687e-06, eta: 4:41:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.8187, loss: 0.1813 +2023-03-04 23:20:28,300 - mmseg - INFO - Iter [106450/160000] lr: 4.687e-06, eta: 4:41:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4489, loss: 0.1871 +2023-03-04 23:20:41,610 - mmseg - INFO - Iter [106500/160000] lr: 4.687e-06, eta: 4:41:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.7559, loss: 0.1779 +2023-03-04 23:20:54,900 - mmseg - INFO - Iter [106550/160000] lr: 4.687e-06, eta: 4:40:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.7736, loss: 0.1794 +2023-03-04 23:21:08,178 - mmseg - INFO - Iter [106600/160000] lr: 4.687e-06, eta: 4:40:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7237, loss: 0.1778 +2023-03-04 23:21:24,036 - mmseg - INFO - Iter [106650/160000] lr: 4.687e-06, eta: 4:40:22, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5607, loss: 0.1817 +2023-03-04 23:21:37,449 - mmseg - INFO - Iter [106700/160000] lr: 4.687e-06, eta: 4:40:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.7482, loss: 0.1786 +2023-03-04 23:21:50,836 - mmseg - INFO - Iter [106750/160000] lr: 4.687e-06, eta: 4:39:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.7002, loss: 0.1803 +2023-03-04 23:22:04,167 - mmseg - INFO - Iter [106800/160000] lr: 4.687e-06, eta: 4:39:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.6975, loss: 0.1823 +2023-03-04 23:22:17,500 - mmseg - INFO - Iter [106850/160000] lr: 4.687e-06, eta: 4:39:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.8098, loss: 0.1765 +2023-03-04 23:22:30,744 - mmseg - INFO - Iter [106900/160000] lr: 4.687e-06, eta: 4:38:57, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1720, decode.acc_seg: 93.0262, loss: 0.1720 +2023-03-04 23:22:44,132 - mmseg - INFO - Iter [106950/160000] lr: 4.687e-06, eta: 4:38:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.7107, loss: 0.1796 +2023-03-04 23:22:57,394 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:22:57,395 - mmseg - INFO - Iter [107000/160000] lr: 4.687e-06, eta: 4:38:23, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.4247, loss: 0.1878 +2023-03-04 23:23:10,675 - mmseg - INFO - Iter [107050/160000] lr: 4.687e-06, eta: 4:38:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4090, loss: 0.1872 +2023-03-04 23:23:23,989 - mmseg - INFO - Iter [107100/160000] lr: 4.687e-06, eta: 4:37:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.3763, loss: 0.1833 +2023-03-04 23:23:37,295 - mmseg - INFO - Iter [107150/160000] lr: 4.687e-06, eta: 4:37:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.8299, loss: 0.1782 +2023-03-04 23:23:50,550 - mmseg - INFO - Iter [107200/160000] lr: 4.687e-06, eta: 4:37:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.6827, loss: 0.1787 +2023-03-04 23:24:03,783 - mmseg - INFO - Iter [107250/160000] lr: 4.687e-06, eta: 4:36:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4178, loss: 0.1844 +2023-03-04 23:24:19,569 - mmseg - INFO - Iter [107300/160000] lr: 4.687e-06, eta: 4:36:43, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.7219, loss: 0.1812 +2023-03-04 23:24:32,895 - mmseg - INFO - Iter [107350/160000] lr: 4.687e-06, eta: 4:36:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.7126, loss: 0.1775 +2023-03-04 23:24:46,031 - mmseg - INFO - Iter [107400/160000] lr: 4.687e-06, eta: 4:36:09, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.7205, loss: 0.1817 +2023-03-04 23:24:59,313 - mmseg - INFO - Iter [107450/160000] lr: 4.687e-06, eta: 4:35:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5604, loss: 0.1814 +2023-03-04 23:25:12,609 - mmseg - INFO - Iter [107500/160000] lr: 4.687e-06, eta: 4:35:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6206, loss: 0.1828 +2023-03-04 23:25:25,839 - mmseg - INFO - Iter [107550/160000] lr: 4.687e-06, eta: 4:35:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5511, loss: 0.1823 +2023-03-04 23:25:39,132 - mmseg - INFO - Iter [107600/160000] lr: 4.687e-06, eta: 4:35:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.7113, loss: 0.1818 +2023-03-04 23:25:52,412 - mmseg - INFO - Iter [107650/160000] lr: 4.687e-06, eta: 4:34:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.8027, loss: 0.1788 +2023-03-04 23:26:05,654 - mmseg - INFO - Iter [107700/160000] lr: 4.687e-06, eta: 4:34:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.4346, loss: 0.1851 +2023-03-04 23:26:19,095 - mmseg - INFO - Iter [107750/160000] lr: 4.687e-06, eta: 4:34:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.5489, loss: 0.1811 +2023-03-04 23:26:32,419 - mmseg - INFO - Iter [107800/160000] lr: 4.687e-06, eta: 4:33:53, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.7201, loss: 0.1860 +2023-03-04 23:26:45,602 - mmseg - INFO - Iter [107850/160000] lr: 4.687e-06, eta: 4:33:36, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.8168, loss: 0.1779 +2023-03-04 23:26:58,852 - mmseg - INFO - Iter [107900/160000] lr: 4.687e-06, eta: 4:33:19, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.6951, loss: 0.1783 +2023-03-04 23:27:14,570 - mmseg - INFO - Iter [107950/160000] lr: 4.687e-06, eta: 4:33:04, time: 0.314, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.7892, loss: 0.1783 +2023-03-04 23:27:27,933 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:27:27,933 - mmseg - INFO - Iter [108000/160000] lr: 4.687e-06, eta: 4:32:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7815, loss: 0.1780 +2023-03-04 23:27:41,235 - mmseg - INFO - Iter [108050/160000] lr: 4.687e-06, eta: 4:32:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.8833, loss: 0.1779 +2023-03-04 23:27:54,511 - mmseg - INFO - Iter [108100/160000] lr: 4.687e-06, eta: 4:32:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.7424, loss: 0.1772 +2023-03-04 23:28:07,860 - mmseg - INFO - Iter [108150/160000] lr: 4.687e-06, eta: 4:31:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6475, loss: 0.1800 +2023-03-04 23:28:21,152 - mmseg - INFO - Iter [108200/160000] lr: 4.687e-06, eta: 4:31:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6345, loss: 0.1806 +2023-03-04 23:28:34,446 - mmseg - INFO - Iter [108250/160000] lr: 4.687e-06, eta: 4:31:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6143, loss: 0.1834 +2023-03-04 23:28:47,930 - mmseg - INFO - Iter [108300/160000] lr: 4.687e-06, eta: 4:31:05, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6487, loss: 0.1802 +2023-03-04 23:29:01,191 - mmseg - INFO - Iter [108350/160000] lr: 4.687e-06, eta: 4:30:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.8135, loss: 0.1809 +2023-03-04 23:29:14,522 - mmseg - INFO - Iter [108400/160000] lr: 4.687e-06, eta: 4:30:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7085, loss: 0.1785 +2023-03-04 23:29:27,816 - mmseg - INFO - Iter [108450/160000] lr: 4.687e-06, eta: 4:30:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5121, loss: 0.1845 +2023-03-04 23:29:41,192 - mmseg - INFO - Iter [108500/160000] lr: 4.687e-06, eta: 4:29:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.5170, loss: 0.1860 +2023-03-04 23:29:57,090 - mmseg - INFO - Iter [108550/160000] lr: 4.687e-06, eta: 4:29:42, time: 0.318, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.5203, loss: 0.1826 +2023-03-04 23:30:10,345 - mmseg - INFO - Iter [108600/160000] lr: 4.687e-06, eta: 4:29:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5867, loss: 0.1844 +2023-03-04 23:30:23,719 - mmseg - INFO - Iter [108650/160000] lr: 4.687e-06, eta: 4:29:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6106, loss: 0.1820 +2023-03-04 23:30:37,076 - mmseg - INFO - Iter [108700/160000] lr: 4.687e-06, eta: 4:28:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.5957, loss: 0.1802 +2023-03-04 23:30:50,333 - mmseg - INFO - Iter [108750/160000] lr: 4.687e-06, eta: 4:28:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.5313, loss: 0.1807 +2023-03-04 23:31:03,552 - mmseg - INFO - Iter [108800/160000] lr: 4.687e-06, eta: 4:28:18, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.6210, loss: 0.1857 +2023-03-04 23:31:16,801 - mmseg - INFO - Iter [108850/160000] lr: 4.687e-06, eta: 4:28:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.7581, loss: 0.1817 +2023-03-04 23:31:30,152 - mmseg - INFO - Iter [108900/160000] lr: 4.687e-06, eta: 4:27:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.7879, loss: 0.1762 +2023-03-04 23:31:43,463 - mmseg - INFO - Iter [108950/160000] lr: 4.687e-06, eta: 4:27:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6053, loss: 0.1782 +2023-03-04 23:31:56,923 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:31:56,923 - mmseg - INFO - Iter [109000/160000] lr: 4.687e-06, eta: 4:27:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6814, loss: 0.1811 +2023-03-04 23:32:10,254 - mmseg - INFO - Iter [109050/160000] lr: 4.687e-06, eta: 4:26:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.6335, loss: 0.1844 +2023-03-04 23:32:23,498 - mmseg - INFO - Iter [109100/160000] lr: 4.687e-06, eta: 4:26:37, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6074, loss: 0.1810 +2023-03-04 23:32:36,911 - mmseg - INFO - Iter [109150/160000] lr: 4.687e-06, eta: 4:26:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.7273, loss: 0.1814 +2023-03-04 23:32:52,733 - mmseg - INFO - Iter [109200/160000] lr: 4.687e-06, eta: 4:26:04, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5652, loss: 0.1848 +2023-03-04 23:33:05,955 - mmseg - INFO - Iter [109250/160000] lr: 4.687e-06, eta: 4:25:47, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.9091, loss: 0.1745 +2023-03-04 23:33:19,303 - mmseg - INFO - Iter [109300/160000] lr: 4.687e-06, eta: 4:25:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6933, loss: 0.1804 +2023-03-04 23:33:32,765 - mmseg - INFO - Iter [109350/160000] lr: 4.687e-06, eta: 4:25:14, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.6257, loss: 0.1805 +2023-03-04 23:33:46,222 - mmseg - INFO - Iter [109400/160000] lr: 4.687e-06, eta: 4:24:57, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.2753, loss: 0.1868 +2023-03-04 23:33:59,613 - mmseg - INFO - Iter [109450/160000] lr: 4.687e-06, eta: 4:24:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.7007, loss: 0.1800 +2023-03-04 23:34:12,929 - mmseg - INFO - Iter [109500/160000] lr: 4.687e-06, eta: 4:24:24, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6314, loss: 0.1827 +2023-03-04 23:34:26,219 - mmseg - INFO - Iter [109550/160000] lr: 4.687e-06, eta: 4:24:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.7238, loss: 0.1787 +2023-03-04 23:34:39,533 - mmseg - INFO - Iter [109600/160000] lr: 4.687e-06, eta: 4:23:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7858, loss: 0.1795 +2023-03-04 23:34:52,872 - mmseg - INFO - Iter [109650/160000] lr: 4.687e-06, eta: 4:23:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7309, loss: 0.1793 +2023-03-04 23:35:06,095 - mmseg - INFO - Iter [109700/160000] lr: 4.687e-06, eta: 4:23:16, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6800, loss: 0.1814 +2023-03-04 23:35:19,439 - mmseg - INFO - Iter [109750/160000] lr: 4.687e-06, eta: 4:22:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.4827, loss: 0.1850 +2023-03-04 23:35:35,137 - mmseg - INFO - Iter [109800/160000] lr: 4.687e-06, eta: 4:22:44, time: 0.314, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5454, loss: 0.1828 +2023-03-04 23:35:48,479 - mmseg - INFO - Iter [109850/160000] lr: 4.687e-06, eta: 4:22:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8196, loss: 0.1758 +2023-03-04 23:36:01,842 - mmseg - INFO - Iter [109900/160000] lr: 4.687e-06, eta: 4:22:10, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6926, loss: 0.1811 +2023-03-04 23:36:15,214 - mmseg - INFO - Iter [109950/160000] lr: 4.687e-06, eta: 4:21:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7332, loss: 0.1782 +2023-03-04 23:36:28,525 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:36:28,526 - mmseg - INFO - Iter [110000/160000] lr: 4.687e-06, eta: 4:21:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.8496, loss: 0.1761 +2023-03-04 23:36:41,947 - mmseg - INFO - Iter [110050/160000] lr: 4.687e-06, eta: 4:21:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.7233, loss: 0.1776 +2023-03-04 23:36:55,332 - mmseg - INFO - Iter [110100/160000] lr: 4.687e-06, eta: 4:21:03, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6199, loss: 0.1799 +2023-03-04 23:37:08,598 - mmseg - INFO - Iter [110150/160000] lr: 4.687e-06, eta: 4:20:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.7056, loss: 0.1833 +2023-03-04 23:37:21,839 - mmseg - INFO - Iter [110200/160000] lr: 4.687e-06, eta: 4:20:30, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7569, loss: 0.1788 +2023-03-04 23:37:35,146 - mmseg - INFO - Iter [110250/160000] lr: 4.687e-06, eta: 4:20:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6720, loss: 0.1813 +2023-03-04 23:37:48,510 - mmseg - INFO - Iter [110300/160000] lr: 4.687e-06, eta: 4:19:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.7092, loss: 0.1794 +2023-03-04 23:38:01,780 - mmseg - INFO - Iter [110350/160000] lr: 4.687e-06, eta: 4:19:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.3931, loss: 0.1871 +2023-03-04 23:38:15,073 - mmseg - INFO - Iter [110400/160000] lr: 4.687e-06, eta: 4:19:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7311, loss: 0.1798 +2023-03-04 23:38:30,867 - mmseg - INFO - Iter [110450/160000] lr: 4.687e-06, eta: 4:19:07, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.5246, loss: 0.1864 +2023-03-04 23:38:44,222 - mmseg - INFO - Iter [110500/160000] lr: 4.687e-06, eta: 4:18:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6613, loss: 0.1812 +2023-03-04 23:38:57,545 - mmseg - INFO - Iter [110550/160000] lr: 4.687e-06, eta: 4:18:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.7696, loss: 0.1796 +2023-03-04 23:39:10,898 - mmseg - INFO - Iter [110600/160000] lr: 4.687e-06, eta: 4:18:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.6857, loss: 0.1790 +2023-03-04 23:39:24,149 - mmseg - INFO - Iter [110650/160000] lr: 4.687e-06, eta: 4:18:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1728, decode.acc_seg: 92.8968, loss: 0.1728 +2023-03-04 23:39:37,433 - mmseg - INFO - Iter [110700/160000] lr: 4.687e-06, eta: 4:17:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.5326, loss: 0.1852 +2023-03-04 23:39:50,672 - mmseg - INFO - Iter [110750/160000] lr: 4.687e-06, eta: 4:17:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1713, decode.acc_seg: 92.9427, loss: 0.1713 +2023-03-04 23:40:04,149 - mmseg - INFO - Iter [110800/160000] lr: 4.687e-06, eta: 4:17:10, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5018, loss: 0.1841 +2023-03-04 23:40:17,358 - mmseg - INFO - Iter [110850/160000] lr: 4.687e-06, eta: 4:16:53, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1744, decode.acc_seg: 92.9311, loss: 0.1744 +2023-03-04 23:40:30,675 - mmseg - INFO - Iter [110900/160000] lr: 4.687e-06, eta: 4:16:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.5001, loss: 0.1862 +2023-03-04 23:40:44,052 - mmseg - INFO - Iter [110950/160000] lr: 4.687e-06, eta: 4:16:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6627, loss: 0.1818 +2023-03-04 23:40:57,457 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:40:57,457 - mmseg - INFO - Iter [111000/160000] lr: 4.687e-06, eta: 4:16:03, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.6683, loss: 0.1841 +2023-03-04 23:41:10,678 - mmseg - INFO - Iter [111050/160000] lr: 4.687e-06, eta: 4:15:46, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5094, loss: 0.1838 +2023-03-04 23:41:26,590 - mmseg - INFO - Iter [111100/160000] lr: 4.687e-06, eta: 4:15:31, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5400, loss: 0.1840 +2023-03-04 23:41:39,866 - mmseg - INFO - Iter [111150/160000] lr: 4.687e-06, eta: 4:15:14, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.5463, loss: 0.1806 +2023-03-04 23:41:53,247 - mmseg - INFO - Iter [111200/160000] lr: 4.687e-06, eta: 4:14:57, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.3856, loss: 0.1896 +2023-03-04 23:42:06,566 - mmseg - INFO - Iter [111250/160000] lr: 4.687e-06, eta: 4:14:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1730, decode.acc_seg: 92.8613, loss: 0.1730 +2023-03-04 23:42:19,913 - mmseg - INFO - Iter [111300/160000] lr: 4.687e-06, eta: 4:14:24, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7341, loss: 0.1774 +2023-03-04 23:42:33,164 - mmseg - INFO - Iter [111350/160000] lr: 4.687e-06, eta: 4:14:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.7846, loss: 0.1813 +2023-03-04 23:42:46,538 - mmseg - INFO - Iter [111400/160000] lr: 4.687e-06, eta: 4:13:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7025, loss: 0.1798 +2023-03-04 23:42:59,801 - mmseg - INFO - Iter [111450/160000] lr: 4.687e-06, eta: 4:13:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.8676, loss: 0.1777 +2023-03-04 23:43:13,204 - mmseg - INFO - Iter [111500/160000] lr: 4.687e-06, eta: 4:13:17, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4823, loss: 0.1862 +2023-03-04 23:43:26,444 - mmseg - INFO - Iter [111550/160000] lr: 4.687e-06, eta: 4:13:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.4848, loss: 0.1860 +2023-03-04 23:43:39,862 - mmseg - INFO - Iter [111600/160000] lr: 4.687e-06, eta: 4:12:44, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6479, loss: 0.1834 +2023-03-04 23:43:53,135 - mmseg - INFO - Iter [111650/160000] lr: 4.687e-06, eta: 4:12:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.9092, loss: 0.1758 +2023-03-04 23:44:08,872 - mmseg - INFO - Iter [111700/160000] lr: 4.687e-06, eta: 4:12:11, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8533, loss: 0.1758 +2023-03-04 23:44:22,199 - mmseg - INFO - Iter [111750/160000] lr: 4.687e-06, eta: 4:11:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.5087, loss: 0.1869 +2023-03-04 23:44:35,445 - mmseg - INFO - Iter [111800/160000] lr: 4.687e-06, eta: 4:11:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4826, loss: 0.1852 +2023-03-04 23:44:48,744 - mmseg - INFO - Iter [111850/160000] lr: 4.687e-06, eta: 4:11:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6166, loss: 0.1800 +2023-03-04 23:45:02,145 - mmseg - INFO - Iter [111900/160000] lr: 4.687e-06, eta: 4:11:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.7111, loss: 0.1824 +2023-03-04 23:45:15,542 - mmseg - INFO - Iter [111950/160000] lr: 4.687e-06, eta: 4:10:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.8747, loss: 0.1779 +2023-03-04 23:45:28,908 - mmseg - INFO - Swap parameters (after train) after iter [112000] +2023-03-04 23:45:28,930 - mmseg - INFO - Saving checkpoint at 112000 iterations +2023-03-04 23:45:30,744 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:45:30,744 - mmseg - INFO - Iter [112000/160000] lr: 4.687e-06, eta: 4:10:32, time: 0.304, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.4935, loss: 0.1867 +2023-03-04 23:56:25,852 - mmseg - INFO - per class results: +2023-03-04 23:56:25,861 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.34,76.36,76.37,76.36,76.37,76.35,76.36,76.34,76.34,76.34,76.36 | +| building | 81.42,81.42,81.42,81.43,81.42,81.41,81.42,81.42,81.42,81.43,81.45 | +| sky | 94.29,94.28,94.28,94.28,94.27,94.28,94.28,94.27,94.27,94.27,94.28 | +| floor | 80.0,80.01,80.01,80.03,80.04,80.04,80.07,80.06,80.08,80.08,80.03 | +| tree | 72.93,72.93,72.92,72.91,72.88,72.89,72.87,72.88,72.87,72.86,72.88 | +| ceiling | 82.86,82.86,82.86,82.86,82.87,82.88,82.86,82.88,82.9,82.9,82.9 | +| road | 82.0,81.95,81.92,81.88,81.88,81.86,81.89,81.89,81.87,81.83,82.01 | +| bed | 88.51,88.54,88.53,88.54,88.53,88.53,88.55,88.52,88.49,88.48,88.5 | +| windowpane | 61.01,61.0,61.01,61.01,61.03,61.03,61.05,61.06,61.07,61.13,61.08 | +| grass | 65.41,65.41,65.44,65.47,65.54,65.59,65.58,65.56,65.58,65.65,65.74 | +| cabinet | 59.43,59.42,59.41,59.42,59.42,59.43,59.44,59.45,59.52,59.51,59.44 | +| sidewalk | 66.33,66.23,66.22,66.16,66.21,66.31,66.4,66.45,66.49,66.49,66.63 | +| person | 79.56,79.56,79.57,79.59,79.57,79.58,79.57,79.57,79.56,79.58,79.56 | +| earth | 33.04,32.87,32.77,32.74,32.68,32.57,32.61,32.67,32.63,32.55,32.86 | +| door | 48.33,48.44,48.48,48.5,48.63,48.61,48.67,48.67,48.68,48.75,48.78 | +| table | 61.62,61.7,61.68,61.69,61.72,61.69,61.75,61.7,61.73,61.7,61.83 | +| mountain | 51.44,51.56,51.67,51.75,51.77,51.87,51.88,51.86,51.91,51.94,52.05 | +| plant | 50.2,50.14,50.17,50.13,50.09,50.11,50.06,50.07,50.05,50.07,50.11 | +| curtain | 70.34,70.48,70.5,70.63,70.71,70.75,70.75,70.74,70.8,70.8,70.73 | +| chair | 58.31,58.36,58.38,58.42,58.44,58.47,58.48,58.54,58.57,58.61,58.55 | +| car | 83.17,83.2,83.18,83.21,83.16,83.17,83.16,83.17,83.16,83.14,83.17 | +| water | 47.29,47.35,47.36,47.36,47.37,47.39,47.39,47.39,47.39,47.42,47.38 | +| painting | 69.81,69.76,69.79,69.82,69.85,69.84,69.88,69.84,69.86,69.86,69.86 | +| sofa | 65.43,65.48,65.51,65.58,65.55,65.63,65.69,65.78,65.86,65.9,65.95 | +| shelf | 40.73,40.7,40.65,40.61,40.55,40.51,40.42,40.43,40.4,40.42,40.22 | +| house | 44.03,43.94,43.91,43.78,43.81,43.73,43.61,43.59,43.58,43.59,43.57 | +| sea | 44.74,44.71,44.72,44.68,44.65,44.6,44.57,44.54,44.48,44.52,44.53 | +| mirror | 65.58,65.55,65.55,65.54,65.56,65.54,65.53,65.55,65.51,65.53,65.44 | +| rug | 54.5,54.65,54.48,54.62,54.73,54.84,55.0,55.03,55.29,55.3,55.0 | +| field | 28.07,28.0,27.99,28.0,27.98,28.01,28.03,28.06,28.1,28.08,28.23 | +| armchair | 43.73,43.88,43.78,43.88,43.84,43.85,43.99,43.9,43.93,43.91,43.72 | +| seat | 53.46,53.39,53.44,53.37,53.37,53.34,53.32,53.22,53.17,53.18,53.05 | +| fence | 40.78,40.75,40.74,40.74,40.73,40.65,40.74,40.74,40.77,40.78,40.79 | +| desk | 49.55,49.6,49.5,49.5,49.43,49.4,49.34,49.3,49.28,49.25,49.14 | +| rock | 26.23,26.35,26.3,25.97,26.1,26.22,26.23,25.97,26.06,26.28,26.98 | +| wardrobe | 48.15,48.15,48.04,48.09,47.97,47.99,47.77,47.76,47.7,47.71,47.6 | +| lamp | 63.86,63.89,63.88,63.91,63.91,63.96,63.94,63.95,63.96,63.97,63.96 | +| bathtub | 77.72,77.54,77.58,77.47,77.58,77.3,77.15,77.33,77.23,77.24,77.37 | +| railing | 31.98,31.98,31.94,31.83,31.74,31.68,31.73,31.63,31.61,31.55,31.53 | +| cushion | 55.56,55.62,55.66,55.64,55.63,55.65,55.75,55.74,55.83,55.85,55.72 | +| base | 27.94,27.88,27.93,27.93,28.0,28.02,28.07,28.11,28.08,28.1,28.1 | +| box | 24.37,24.36,24.43,24.46,24.49,24.49,24.52,24.47,24.53,24.61,24.55 | +| column | 46.0,45.99,46.14,46.14,46.28,46.0,45.83,45.87,45.78,45.76,45.77 | +| signboard | 35.59,35.54,35.57,35.57,35.53,35.55,35.58,35.5,35.54,35.54,35.52 | +| chest of drawers | 39.47,39.55,39.43,39.53,39.49,39.38,39.37,39.42,39.56,39.5,39.57 | +| counter | 26.35,26.66,26.53,26.49,26.27,26.21,25.98,25.98,26.05,26.01,26.38 | +| sand | 32.54,32.22,32.14,32.23,32.13,32.14,32.14,32.07,32.1,32.04,32.11 | +| sink | 71.23,71.16,71.19,71.28,71.33,71.28,71.31,71.36,71.4,71.37,71.46 | +| skyscraper | 48.7,48.74,48.85,48.86,48.9,48.79,48.86,48.88,48.84,48.82,49.14 | +| fireplace | 66.22,66.21,66.16,66.19,66.23,66.03,66.13,66.02,65.95,66.09,66.04 | +| refrigerator | 78.57,78.55,78.64,78.55,78.69,78.59,78.62,78.64,78.66,78.66,78.63 | +| grandstand | 42.05,42.1,42.1,42.1,42.14,42.22,42.18,42.18,42.1,42.17,42.21 | +| path | 17.88,17.9,17.95,17.94,17.93,17.93,17.96,17.98,18.05,18.02,18.12 | +| stairs | 31.51,31.52,31.51,31.5,31.49,31.49,31.49,31.48,31.49,31.5,31.38 | +| runway | 63.88,63.87,63.87,63.89,63.9,63.91,63.9,63.92,63.93,63.91,63.91 | +| case | 48.82,48.9,48.82,48.86,48.88,48.91,48.91,48.91,48.93,48.92,48.74 | +| pool table | 92.69,92.72,92.72,92.71,92.7,92.72,92.73,92.71,92.74,92.7,92.8 | +| pillow | 57.32,57.43,57.34,57.3,57.25,57.24,57.31,57.19,57.22,57.09,57.23 | +| screen door | 66.72,66.77,66.59,66.62,66.73,66.79,66.82,66.94,66.94,66.84,67.02 | +| stairway | 25.69,25.71,25.71,25.74,25.65,25.67,25.62,25.69,25.67,25.69,25.61 | +| river | 9.64,9.59,9.51,9.49,9.41,9.37,9.29,9.25,9.24,9.18,9.16 | +| bridge | 53.99,54.96,55.48,56.15,56.85,57.6,58.21,58.39,58.92,59.47,59.91 | +| bookcase | 41.92,42.06,42.2,42.67,42.68,43.25,43.2,43.34,43.34,43.29,42.63 | +| blind | 44.94,44.83,44.48,44.43,44.33,44.41,44.36,44.46,44.53,44.47,44.01 | +| coffee table | 66.18,66.13,66.11,66.13,66.14,66.06,66.13,66.2,66.21,66.22,66.25 | +| toilet | 86.44,86.43,86.45,86.47,86.44,86.48,86.4,86.41,86.41,86.36,86.53 | +| flower | 31.32,31.27,31.45,31.51,31.44,31.58,31.64,31.63,31.69,31.81,31.95 | +| book | 47.35,47.41,47.41,47.29,47.34,47.32,47.34,47.29,47.28,47.23,47.16 | +| hill | 7.82,7.79,7.78,7.81,7.82,7.85,7.9,7.85,7.88,7.88,7.82 | +| bench | 44.3,44.3,44.31,44.36,44.31,44.37,44.4,44.43,44.47,44.43,44.41 | +| countertop | 54.42,54.45,54.41,54.5,54.65,54.58,54.7,54.66,54.63,54.65,54.57 | +| stove | 71.83,71.81,71.95,71.85,72.1,72.03,71.98,72.08,72.12,72.22,72.46 | +| palm | 50.67,50.69,50.76,50.81,50.9,50.89,50.95,51.01,51.04,50.98,51.15 | +| kitchen island | 47.28,47.28,47.16,47.13,47.18,47.3,47.15,46.98,47.08,46.9,47.4 | +| computer | 57.13,57.14,57.22,57.16,57.23,57.25,57.24,57.25,57.28,57.27,57.24 | +| swivel chair | 45.57,45.61,45.58,45.83,45.74,45.86,45.75,45.75,45.94,45.96,46.13 | +| boat | 37.97,38.08,38.08,38.13,38.19,38.17,38.22,38.29,38.31,38.34,38.43 | +| bar | 26.71,26.76,26.69,26.53,26.4,26.24,26.05,25.91,25.8,25.57,25.33 | +| arcade machine | 24.82,24.96,25.17,25.54,25.56,25.87,26.24,26.31,26.61,27.1,27.14 | +| hovel | 31.49,31.33,31.23,31.09,30.99,30.83,30.75,30.69,30.63,30.53,30.5 | +| bus | 88.41,88.48,88.4,88.44,88.35,88.41,88.46,88.51,88.47,88.44,88.53 | +| towel | 60.6,60.68,60.78,60.71,60.76,60.62,60.61,60.55,60.51,60.71,60.65 | +| light | 56.68,56.74,56.64,56.65,56.59,56.57,56.59,56.54,56.56,56.49,56.56 | +| truck | 34.68,34.8,34.61,34.74,34.86,34.72,34.67,34.75,34.74,34.65,34.8 | +| tower | 25.51,25.67,25.37,25.55,25.24,24.81,24.72,24.76,24.6,24.64,24.66 | +| chandelier | 66.22,66.26,66.33,66.33,66.39,66.42,66.46,66.41,66.42,66.43,66.46 | +| awning | 23.78,23.78,23.71,23.75,23.71,23.78,23.79,23.78,23.75,23.69,23.83 | +| streetlight | 28.47,28.37,28.24,28.29,28.2,28.17,28.15,28.13,28.05,28.02,27.98 | +| booth | 57.37,57.43,57.53,57.3,57.56,57.4,57.44,57.56,57.48,57.41,57.24 | +| television receiver | 68.32,68.31,68.35,68.36,68.37,68.32,68.39,68.35,68.36,68.34,68.26 | +| airplane | 52.09,52.08,52.16,51.9,51.79,51.84,51.74,51.84,51.87,51.65,51.44 | +| dirt track | 10.38,10.42,10.49,10.57,10.52,10.55,10.56,10.73,10.66,10.77,10.39 | +| apparel | 29.01,29.05,29.01,29.0,28.84,28.95,28.74,28.8,28.98,28.86,28.81 | +| pole | 24.63,24.49,24.49,24.47,24.42,24.38,24.33,24.37,24.46,24.42,24.25 | +| land | 7.05,6.95,6.83,6.8,6.7,6.62,6.61,6.6,6.56,6.55,6.61 | +| bannister | 5.65,5.69,5.77,5.76,5.71,5.7,5.79,5.78,5.86,5.91,5.8 | +| escalator | 22.65,22.64,22.65,22.73,22.71,22.75,22.7,22.71,22.8,22.89,22.86 | +| ottoman | 47.07,46.89,46.8,47.14,47.04,47.11,47.32,46.98,46.87,46.74,47.09 | +| bottle | 15.01,14.96,14.89,14.88,14.82,14.73,14.85,14.82,14.96,14.9,14.32 | +| buffet | 48.64,48.67,48.91,48.63,48.47,49.4,49.44,50.2,51.35,51.51,50.84 | +| poster | 27.29,27.32,27.46,27.38,27.51,27.61,27.66,27.33,27.54,27.24,27.6 | +| stage | 17.18,17.3,17.38,17.47,17.53,17.64,17.72,17.72,17.77,17.81,17.91 | +| van | 47.45,47.61,47.36,47.63,47.26,47.32,47.14,47.25,47.32,47.14,47.17 | +| ship | 24.38,24.94,26.83,27.1,27.68,27.65,29.35,28.51,28.62,28.86,27.6 | +| fountain | 7.1,7.66,8.23,8.09,8.16,8.06,8.06,7.96,7.82,7.78,7.61 | +| conveyer belt | 76.47,76.4,76.25,76.3,76.18,76.32,76.3,76.17,76.11,76.1,75.82 | +| canopy | 15.01,15.04,14.93,14.93,14.91,15.07,14.94,15.05,14.99,15.06,14.78 | +| washer | 65.82,65.8,65.74,65.78,65.77,65.74,65.72,65.72,65.7,65.69,65.63 | +| plaything | 23.01,23.05,23.23,23.09,23.19,23.22,23.22,23.18,23.18,23.28,23.29 | +| swimming pool | 43.94,44.36,44.86,45.47,45.94,46.31,46.82,48.82,49.31,50.4,49.11 | +| stool | 41.95,41.94,41.98,41.89,41.9,41.84,41.85,41.83,41.77,41.72,41.77 | +| barrel | 40.08,39.72,40.81,39.46,39.91,40.27,40.66,39.37,40.31,40.29,40.1 | +| basket | 28.51,28.49,28.47,28.45,28.49,28.63,28.53,28.56,28.59,28.64,28.59 | +| waterfall | 48.92,49.06,47.9,48.01,48.63,48.32,48.41,47.78,47.17,46.88,47.76 | +| tent | 93.65,93.65,93.62,93.66,93.57,93.58,93.61,93.67,93.6,93.63,93.66 | +| bag | 11.43,11.42,11.47,11.52,11.48,11.5,11.55,11.55,11.57,11.59,11.5 | +| minibike | 61.52,61.55,61.54,61.54,61.42,61.4,61.46,61.48,61.46,61.46,61.36 | +| cradle | 81.86,81.9,81.76,81.71,81.76,81.68,81.63,81.55,81.46,81.41,81.32 | +| oven | 27.27,27.17,27.21,27.17,27.17,27.12,27.12,27.11,27.13,27.08,26.98 | +| ball | 47.42,47.62,47.61,47.56,47.62,47.6,47.72,47.81,47.84,47.85,48.09 | +| food | 52.73,53.0,53.07,53.0,53.18,53.23,53.48,53.48,53.58,53.85,53.34 | +| step | 16.76,16.92,17.01,16.68,17.54,16.75,17.09,16.98,16.94,16.94,17.39 | +| tank | 41.37,41.36,41.35,41.32,41.27,41.21,41.21,41.15,41.18,41.2,41.17 | +| trade name | 25.42,25.34,25.26,25.21,25.19,25.16,25.13,24.88,25.0,24.94,25.05 | +| microwave | 37.5,37.46,37.48,37.47,37.5,37.49,37.49,37.47,37.49,37.47,37.48 | +| pot | 41.19,41.23,41.22,41.25,41.28,41.25,41.31,41.28,41.32,41.35,41.34 | +| animal | 51.59,51.54,51.55,51.59,51.5,51.61,51.61,51.51,51.57,51.57,51.6 | +| bicycle | 46.01,46.13,46.14,46.18,46.19,46.14,46.1,46.14,46.22,46.16,46.11 | +| lake | 59.43,59.4,59.35,59.31,59.28,59.19,59.14,59.05,59.02,58.95,58.91 | +| dishwasher | 76.52,76.56,76.71,76.71,76.79,76.71,76.94,77.04,77.11,77.12,77.16 | +| screen | 62.59,62.54,62.35,61.93,62.01,61.74,61.14,61.21,60.93,60.72,61.36 | +| blanket | 14.75,14.94,14.77,14.91,14.93,14.96,15.05,15.03,14.91,14.75,14.85 | +| sculpture | 35.79,35.86,36.01,36.01,36.1,36.11,36.15,36.3,36.22,36.23,36.42 | +| hood | 57.84,57.72,57.81,57.71,57.86,57.84,57.9,57.74,57.72,57.69,57.52 | +| sconce | 42.07,42.27,42.03,42.0,41.84,42.0,42.05,41.93,42.03,42.12,41.68 | +| vase | 37.26,37.19,37.25,37.25,37.25,37.21,37.27,37.21,37.14,37.14,37.24 | +| traffic light | 29.65,29.57,29.61,29.63,29.55,29.58,29.58,29.66,29.7,29.64,29.73 | +| tray | 5.53,5.56,5.59,5.58,5.61,5.63,5.63,5.65,5.68,5.68,5.7 | +| ashcan | 37.55,37.46,37.44,37.57,37.39,37.33,37.46,37.44,37.44,37.41,37.46 | +| fan | 58.68,58.59,58.6,58.56,58.53,58.63,58.62,58.6,58.55,58.62,58.58 | +| pier | 11.69,11.69,11.51,11.61,11.36,11.44,11.36,11.27,11.29,10.98,10.83 | +| crt screen | 4.74,4.71,5.05,4.97,5.16,5.39,5.72,6.08,6.62,7.03,7.44 | +| plate | 38.96,39.02,38.89,39.14,39.1,39.06,39.23,39.15,39.18,39.3,39.29 | +| monitor | 26.94,26.95,26.55,26.54,26.55,26.45,26.34,26.33,26.07,26.14,26.15 | +| bulletin board | 45.37,45.98,46.28,46.35,46.61,46.68,47.05,47.22,47.69,47.98,47.42 | +| shower | 1.12,1.14,1.22,1.3,1.18,1.28,1.31,1.3,1.33,1.4,1.43 | +| radiator | 45.4,45.91,45.44,45.76,45.42,45.79,45.7,45.85,46.04,46.22,45.45 | +| glass | 12.29,12.32,12.33,12.38,12.32,12.33,12.36,12.38,12.37,12.35,12.38 | +| clock | 25.0,25.05,24.97,24.95,24.96,24.91,24.87,24.88,24.86,24.88,24.77 | +| flag | 37.54,37.53,37.65,37.69,37.72,37.74,37.92,38.03,38.02,38.12,38.17 | ++---------------------+-------------------------------------------------------------------+ +2023-03-04 23:56:25,861 - mmseg - INFO - Summary: +2023-03-04 23:56:25,861 - mmseg - INFO - ++------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++------------------------------------------------------------------+ +| 46.03,46.07,46.08,46.09,46.1,46.11,46.15,46.15,46.18,46.19,46.17 | ++------------------------------------------------------------------+ +2023-03-04 23:56:25,861 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-04 23:56:25,862 - mmseg - INFO - Iter(val) [250] mIoU: [0.4603, 0.4607, 0.4608, 0.4609, 0.461, 0.4611, 0.4615, 0.4615, 0.4618, 0.4619, 0.4617], copy_paste: 46.03,46.07,46.08,46.09,46.1,46.11,46.15,46.15,46.18,46.19,46.17 +2023-03-04 23:56:25,868 - mmseg - INFO - Swap parameters (before train) before iter [112001] +2023-03-04 23:56:39,730 - mmseg - INFO - Iter [112050/160000] lr: 4.687e-06, eta: 4:14:56, time: 13.380, data_time: 13.111, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.3655, loss: 0.1862 +2023-03-04 23:56:53,094 - mmseg - INFO - Iter [112100/160000] lr: 4.687e-06, eta: 4:14:39, time: 0.267, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1747, decode.acc_seg: 92.8536, loss: 0.1747 +2023-03-04 23:57:06,607 - mmseg - INFO - Iter [112150/160000] lr: 4.687e-06, eta: 4:14:22, time: 0.270, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.5614, loss: 0.1832 +2023-03-04 23:57:19,981 - mmseg - INFO - Iter [112200/160000] lr: 4.687e-06, eta: 4:14:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.7045, loss: 0.1802 +2023-03-04 23:57:33,363 - mmseg - INFO - Iter [112250/160000] lr: 4.687e-06, eta: 4:13:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6058, loss: 0.1818 +2023-03-04 23:57:46,710 - mmseg - INFO - Iter [112300/160000] lr: 4.687e-06, eta: 4:13:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6330, loss: 0.1813 +2023-03-04 23:58:02,431 - mmseg - INFO - Iter [112350/160000] lr: 4.687e-06, eta: 4:13:15, time: 0.314, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6979, loss: 0.1808 +2023-03-04 23:58:15,703 - mmseg - INFO - Iter [112400/160000] lr: 4.687e-06, eta: 4:12:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7566, loss: 0.1804 +2023-03-04 23:58:29,068 - mmseg - INFO - Iter [112450/160000] lr: 4.687e-06, eta: 4:12:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6644, loss: 0.1804 +2023-03-04 23:58:42,494 - mmseg - INFO - Iter [112500/160000] lr: 4.687e-06, eta: 4:12:24, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.5166, loss: 0.1790 +2023-03-04 23:58:55,738 - mmseg - INFO - Iter [112550/160000] lr: 4.687e-06, eta: 4:12:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1750, decode.acc_seg: 92.7532, loss: 0.1750 +2023-03-04 23:59:09,048 - mmseg - INFO - Iter [112600/160000] lr: 4.687e-06, eta: 4:11:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.9104, loss: 0.1751 +2023-03-04 23:59:22,362 - mmseg - INFO - Iter [112650/160000] lr: 4.687e-06, eta: 4:11:33, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.8303, loss: 0.1807 +2023-03-04 23:59:35,827 - mmseg - INFO - Iter [112700/160000] lr: 4.687e-06, eta: 4:11:16, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1878, decode.acc_seg: 92.3381, loss: 0.1878 +2023-03-04 23:59:49,100 - mmseg - INFO - Iter [112750/160000] lr: 4.687e-06, eta: 4:10:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.6502, loss: 0.1826 +2023-03-05 00:00:02,319 - mmseg - INFO - Iter [112800/160000] lr: 4.687e-06, eta: 4:10:41, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.5864, loss: 0.1813 +2023-03-05 00:00:15,544 - mmseg - INFO - Iter [112850/160000] lr: 4.687e-06, eta: 4:10:24, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.7118, loss: 0.1817 +2023-03-05 00:00:28,839 - mmseg - INFO - Iter [112900/160000] lr: 4.687e-06, eta: 4:10:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.6533, loss: 0.1831 +2023-03-05 00:00:44,634 - mmseg - INFO - Iter [112950/160000] lr: 4.687e-06, eta: 4:09:51, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1747, decode.acc_seg: 92.7553, loss: 0.1747 +2023-03-05 00:00:58,073 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:00:58,073 - mmseg - INFO - Iter [113000/160000] lr: 4.687e-06, eta: 4:09:34, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6537, loss: 0.1833 +2023-03-05 00:01:11,396 - mmseg - INFO - Iter [113050/160000] lr: 4.687e-06, eta: 4:09:17, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.4258, loss: 0.1863 +2023-03-05 00:01:24,692 - mmseg - INFO - Iter [113100/160000] lr: 4.687e-06, eta: 4:09:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.4515, loss: 0.1848 +2023-03-05 00:01:37,960 - mmseg - INFO - Iter [113150/160000] lr: 4.687e-06, eta: 4:08:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7028, loss: 0.1782 +2023-03-05 00:01:51,337 - mmseg - INFO - Iter [113200/160000] lr: 4.687e-06, eta: 4:08:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.7016, loss: 0.1823 +2023-03-05 00:02:04,651 - mmseg - INFO - Iter [113250/160000] lr: 4.687e-06, eta: 4:08:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.8501, loss: 0.1781 +2023-03-05 00:02:17,960 - mmseg - INFO - Iter [113300/160000] lr: 4.687e-06, eta: 4:07:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.5979, loss: 0.1808 +2023-03-05 00:02:31,211 - mmseg - INFO - Iter [113350/160000] lr: 4.687e-06, eta: 4:07:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.9199, loss: 0.1757 +2023-03-05 00:02:44,476 - mmseg - INFO - Iter [113400/160000] lr: 4.687e-06, eta: 4:07:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.6390, loss: 0.1822 +2023-03-05 00:02:57,830 - mmseg - INFO - Iter [113450/160000] lr: 4.687e-06, eta: 4:07:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6566, loss: 0.1810 +2023-03-05 00:03:11,233 - mmseg - INFO - Iter [113500/160000] lr: 4.687e-06, eta: 4:06:44, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.5640, loss: 0.1803 +2023-03-05 00:03:24,403 - mmseg - INFO - Iter [113550/160000] lr: 4.687e-06, eta: 4:06:27, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7012, loss: 0.1784 +2023-03-05 00:03:40,187 - mmseg - INFO - Iter [113600/160000] lr: 4.687e-06, eta: 4:06:11, time: 0.315, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.4666, loss: 0.1833 +2023-03-05 00:03:53,656 - mmseg - INFO - Iter [113650/160000] lr: 4.687e-06, eta: 4:05:54, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.6628, loss: 0.1819 +2023-03-05 00:04:06,958 - mmseg - INFO - Iter [113700/160000] lr: 4.687e-06, eta: 4:05:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7572, loss: 0.1808 +2023-03-05 00:04:20,224 - mmseg - INFO - Iter [113750/160000] lr: 4.687e-06, eta: 4:05:20, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6822, loss: 0.1820 +2023-03-05 00:04:33,725 - mmseg - INFO - Iter [113800/160000] lr: 4.687e-06, eta: 4:05:04, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7467, loss: 0.1780 +2023-03-05 00:04:47,021 - mmseg - INFO - Iter [113850/160000] lr: 4.687e-06, eta: 4:04:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.5014, loss: 0.1857 +2023-03-05 00:05:00,283 - mmseg - INFO - Iter [113900/160000] lr: 4.687e-06, eta: 4:04:30, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4371, loss: 0.1875 +2023-03-05 00:05:13,780 - mmseg - INFO - Iter [113950/160000] lr: 4.687e-06, eta: 4:04:13, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.9344, loss: 0.1754 +2023-03-05 00:05:27,079 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:05:27,079 - mmseg - INFO - Iter [114000/160000] lr: 4.687e-06, eta: 4:03:56, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.8117, loss: 0.1776 +2023-03-05 00:05:40,450 - mmseg - INFO - Iter [114050/160000] lr: 4.687e-06, eta: 4:03:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6195, loss: 0.1802 +2023-03-05 00:05:53,679 - mmseg - INFO - Iter [114100/160000] lr: 4.687e-06, eta: 4:03:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6932, loss: 0.1806 +2023-03-05 00:06:07,067 - mmseg - INFO - Iter [114150/160000] lr: 4.687e-06, eta: 4:03:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.7552, loss: 0.1815 +2023-03-05 00:06:20,377 - mmseg - INFO - Iter [114200/160000] lr: 4.687e-06, eta: 4:02:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6351, loss: 0.1816 +2023-03-05 00:06:36,134 - mmseg - INFO - Iter [114250/160000] lr: 4.687e-06, eta: 4:02:32, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4831, loss: 0.1873 +2023-03-05 00:06:49,400 - mmseg - INFO - Iter [114300/160000] lr: 4.687e-06, eta: 4:02:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6786, loss: 0.1799 +2023-03-05 00:07:02,753 - mmseg - INFO - Iter [114350/160000] lr: 4.687e-06, eta: 4:01:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6519, loss: 0.1820 +2023-03-05 00:07:16,086 - mmseg - INFO - Iter [114400/160000] lr: 4.687e-06, eta: 4:01:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.4779, loss: 0.1833 +2023-03-05 00:07:29,373 - mmseg - INFO - Iter [114450/160000] lr: 4.687e-06, eta: 4:01:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.4982, loss: 0.1856 +2023-03-05 00:07:42,639 - mmseg - INFO - Iter [114500/160000] lr: 4.687e-06, eta: 4:01:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.7179, loss: 0.1823 +2023-03-05 00:07:56,052 - mmseg - INFO - Iter [114550/160000] lr: 4.687e-06, eta: 4:00:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.5452, loss: 0.1854 +2023-03-05 00:08:09,357 - mmseg - INFO - Iter [114600/160000] lr: 4.687e-06, eta: 4:00:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5253, loss: 0.1838 +2023-03-05 00:08:22,680 - mmseg - INFO - Iter [114650/160000] lr: 4.687e-06, eta: 4:00:17, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6991, loss: 0.1825 +2023-03-05 00:08:36,011 - mmseg - INFO - Iter [114700/160000] lr: 4.687e-06, eta: 4:00:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7626, loss: 0.1768 +2023-03-05 00:08:49,389 - mmseg - INFO - Iter [114750/160000] lr: 4.687e-06, eta: 3:59:43, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.8890, loss: 0.1772 +2023-03-05 00:09:02,766 - mmseg - INFO - Iter [114800/160000] lr: 4.687e-06, eta: 3:59:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.6686, loss: 0.1796 +2023-03-05 00:09:18,531 - mmseg - INFO - Iter [114850/160000] lr: 4.687e-06, eta: 3:59:10, time: 0.315, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1764, decode.acc_seg: 92.8287, loss: 0.1764 +2023-03-05 00:09:31,842 - mmseg - INFO - Iter [114900/160000] lr: 4.687e-06, eta: 3:58:53, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5768, loss: 0.1816 +2023-03-05 00:09:45,103 - mmseg - INFO - Iter [114950/160000] lr: 4.687e-06, eta: 3:58:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.5638, loss: 0.1837 +2023-03-05 00:09:58,404 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:09:58,405 - mmseg - INFO - Iter [115000/160000] lr: 4.687e-06, eta: 3:58:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7481, loss: 0.1768 +2023-03-05 00:10:11,735 - mmseg - INFO - Iter [115050/160000] lr: 4.687e-06, eta: 3:58:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6111, loss: 0.1828 +2023-03-05 00:10:25,045 - mmseg - INFO - Iter [115100/160000] lr: 4.687e-06, eta: 3:57:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6551, loss: 0.1834 +2023-03-05 00:10:38,359 - mmseg - INFO - Iter [115150/160000] lr: 4.687e-06, eta: 3:57:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.6765, loss: 0.1792 +2023-03-05 00:10:51,794 - mmseg - INFO - Iter [115200/160000] lr: 4.687e-06, eta: 3:57:12, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5349, loss: 0.1824 +2023-03-05 00:11:05,063 - mmseg - INFO - Iter [115250/160000] lr: 4.687e-06, eta: 3:56:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5242, loss: 0.1838 +2023-03-05 00:11:18,430 - mmseg - INFO - Iter [115300/160000] lr: 4.687e-06, eta: 3:56:38, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6284, loss: 0.1811 +2023-03-05 00:11:31,760 - mmseg - INFO - Iter [115350/160000] lr: 4.687e-06, eta: 3:56:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1735, decode.acc_seg: 93.0323, loss: 0.1735 +2023-03-05 00:11:45,157 - mmseg - INFO - Iter [115400/160000] lr: 4.687e-06, eta: 3:56:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5558, loss: 0.1825 +2023-03-05 00:11:58,573 - mmseg - INFO - Iter [115450/160000] lr: 4.687e-06, eta: 3:55:47, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7362, loss: 0.1761 +2023-03-05 00:12:14,313 - mmseg - INFO - Iter [115500/160000] lr: 4.687e-06, eta: 3:55:31, time: 0.315, data_time: 0.052, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.7217, loss: 0.1796 +2023-03-05 00:12:27,636 - mmseg - INFO - Iter [115550/160000] lr: 4.687e-06, eta: 3:55:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.6654, loss: 0.1851 +2023-03-05 00:12:41,134 - mmseg - INFO - Iter [115600/160000] lr: 4.687e-06, eta: 3:54:58, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5558, loss: 0.1823 +2023-03-05 00:12:54,416 - mmseg - INFO - Iter [115650/160000] lr: 4.687e-06, eta: 3:54:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.6695, loss: 0.1809 +2023-03-05 00:13:07,708 - mmseg - INFO - Iter [115700/160000] lr: 4.687e-06, eta: 3:54:24, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.5149, loss: 0.1876 +2023-03-05 00:13:21,014 - mmseg - INFO - Iter [115750/160000] lr: 4.687e-06, eta: 3:54:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7241, loss: 0.1785 +2023-03-05 00:13:34,261 - mmseg - INFO - Iter [115800/160000] lr: 4.687e-06, eta: 3:53:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.4345, loss: 0.1867 +2023-03-05 00:13:47,488 - mmseg - INFO - Iter [115850/160000] lr: 4.687e-06, eta: 3:53:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.5785, loss: 0.1794 +2023-03-05 00:14:00,786 - mmseg - INFO - Iter [115900/160000] lr: 4.687e-06, eta: 3:53:17, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1731, decode.acc_seg: 92.9136, loss: 0.1731 +2023-03-05 00:14:14,021 - mmseg - INFO - Iter [115950/160000] lr: 4.687e-06, eta: 3:53:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6441, loss: 0.1798 +2023-03-05 00:14:27,343 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:14:27,343 - mmseg - INFO - Iter [116000/160000] lr: 4.687e-06, eta: 3:52:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1750, decode.acc_seg: 92.9897, loss: 0.1750 +2023-03-05 00:14:40,728 - mmseg - INFO - Iter [116050/160000] lr: 4.687e-06, eta: 3:52:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7266, loss: 0.1780 +2023-03-05 00:14:53,938 - mmseg - INFO - Iter [116100/160000] lr: 4.687e-06, eta: 3:52:09, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.8308, loss: 0.1801 +2023-03-05 00:15:09,879 - mmseg - INFO - Iter [116150/160000] lr: 4.687e-06, eta: 3:51:53, time: 0.319, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.6044, loss: 0.1846 +2023-03-05 00:15:23,244 - mmseg - INFO - Iter [116200/160000] lr: 4.687e-06, eta: 3:51:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.5999, loss: 0.1833 +2023-03-05 00:15:36,539 - mmseg - INFO - Iter [116250/160000] lr: 4.687e-06, eta: 3:51:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7415, loss: 0.1793 +2023-03-05 00:15:49,889 - mmseg - INFO - Iter [116300/160000] lr: 4.687e-06, eta: 3:51:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.6782, loss: 0.1778 +2023-03-05 00:16:03,166 - mmseg - INFO - Iter [116350/160000] lr: 4.687e-06, eta: 3:50:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6492, loss: 0.1807 +2023-03-05 00:16:16,453 - mmseg - INFO - Iter [116400/160000] lr: 4.687e-06, eta: 3:50:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7249, loss: 0.1798 +2023-03-05 00:16:29,673 - mmseg - INFO - Iter [116450/160000] lr: 4.687e-06, eta: 3:50:12, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.8121, loss: 0.1760 +2023-03-05 00:16:43,065 - mmseg - INFO - Iter [116500/160000] lr: 4.687e-06, eta: 3:49:56, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5447, loss: 0.1849 +2023-03-05 00:16:56,412 - mmseg - INFO - Iter [116550/160000] lr: 4.687e-06, eta: 3:49:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5103, loss: 0.1828 +2023-03-05 00:17:09,757 - mmseg - INFO - Iter [116600/160000] lr: 4.687e-06, eta: 3:49:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6380, loss: 0.1834 +2023-03-05 00:17:23,152 - mmseg - INFO - Iter [116650/160000] lr: 4.687e-06, eta: 3:49:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1741, decode.acc_seg: 92.9422, loss: 0.1741 +2023-03-05 00:17:36,390 - mmseg - INFO - Iter [116700/160000] lr: 4.687e-06, eta: 3:48:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1728, decode.acc_seg: 92.9750, loss: 0.1728 +2023-03-05 00:17:52,213 - mmseg - INFO - Iter [116750/160000] lr: 4.687e-06, eta: 3:48:33, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6292, loss: 0.1795 +2023-03-05 00:18:05,552 - mmseg - INFO - Iter [116800/160000] lr: 4.687e-06, eta: 3:48:16, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.5878, loss: 0.1801 +2023-03-05 00:18:18,908 - mmseg - INFO - Iter [116850/160000] lr: 4.687e-06, eta: 3:47:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1896, decode.acc_seg: 92.2034, loss: 0.1896 +2023-03-05 00:18:32,316 - mmseg - INFO - Iter [116900/160000] lr: 4.687e-06, eta: 3:47:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.5522, loss: 0.1815 +2023-03-05 00:18:45,515 - mmseg - INFO - Iter [116950/160000] lr: 4.687e-06, eta: 3:47:25, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1740, decode.acc_seg: 92.8919, loss: 0.1740 +2023-03-05 00:18:58,695 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:18:58,695 - mmseg - INFO - Iter [117000/160000] lr: 4.687e-06, eta: 3:47:09, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.7528, loss: 0.1814 +2023-03-05 00:19:11,993 - mmseg - INFO - Iter [117050/160000] lr: 4.687e-06, eta: 3:46:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5924, loss: 0.1840 +2023-03-05 00:19:25,220 - mmseg - INFO - Iter [117100/160000] lr: 4.687e-06, eta: 3:46:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1746, decode.acc_seg: 92.8534, loss: 0.1746 +2023-03-05 00:19:38,459 - mmseg - INFO - Iter [117150/160000] lr: 4.687e-06, eta: 3:46:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7764, loss: 0.1784 +2023-03-05 00:19:51,771 - mmseg - INFO - Iter [117200/160000] lr: 4.687e-06, eta: 3:46:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7057, loss: 0.1789 +2023-03-05 00:20:05,106 - mmseg - INFO - Iter [117250/160000] lr: 4.687e-06, eta: 3:45:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6757, loss: 0.1803 +2023-03-05 00:20:18,483 - mmseg - INFO - Iter [117300/160000] lr: 4.687e-06, eta: 3:45:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.6340, loss: 0.1761 +2023-03-05 00:20:31,900 - mmseg - INFO - Iter [117350/160000] lr: 4.687e-06, eta: 3:45:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4368, loss: 0.1870 +2023-03-05 00:20:47,740 - mmseg - INFO - Iter [117400/160000] lr: 4.687e-06, eta: 3:44:55, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.5713, loss: 0.1867 +2023-03-05 00:21:01,120 - mmseg - INFO - Iter [117450/160000] lr: 4.687e-06, eta: 3:44:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.6281, loss: 0.1846 +2023-03-05 00:21:14,426 - mmseg - INFO - Iter [117500/160000] lr: 4.687e-06, eta: 3:44:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7687, loss: 0.1782 +2023-03-05 00:21:27,750 - mmseg - INFO - Iter [117550/160000] lr: 4.687e-06, eta: 3:44:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7635, loss: 0.1798 +2023-03-05 00:21:40,992 - mmseg - INFO - Iter [117600/160000] lr: 4.687e-06, eta: 3:43:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.4585, loss: 0.1831 +2023-03-05 00:21:54,384 - mmseg - INFO - Iter [117650/160000] lr: 4.687e-06, eta: 3:43:32, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.5123, loss: 0.1824 +2023-03-05 00:22:07,816 - mmseg - INFO - Iter [117700/160000] lr: 4.687e-06, eta: 3:43:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.6485, loss: 0.1773 +2023-03-05 00:22:21,189 - mmseg - INFO - Iter [117750/160000] lr: 4.687e-06, eta: 3:42:58, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.6323, loss: 0.1852 +2023-03-05 00:22:34,513 - mmseg - INFO - Iter [117800/160000] lr: 4.687e-06, eta: 3:42:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.8397, loss: 0.1770 +2023-03-05 00:22:47,804 - mmseg - INFO - Iter [117850/160000] lr: 4.687e-06, eta: 3:42:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1764, decode.acc_seg: 92.7933, loss: 0.1764 +2023-03-05 00:23:01,065 - mmseg - INFO - Iter [117900/160000] lr: 4.687e-06, eta: 3:42:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.4782, loss: 0.1845 +2023-03-05 00:23:14,356 - mmseg - INFO - Iter [117950/160000] lr: 4.687e-06, eta: 3:41:51, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6854, loss: 0.1798 +2023-03-05 00:23:30,375 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:23:30,376 - mmseg - INFO - Iter [118000/160000] lr: 4.687e-06, eta: 3:41:36, time: 0.320, data_time: 0.052, memory: 67559, decode.loss_ce: 0.1860, decode.acc_seg: 92.5009, loss: 0.1860 +2023-03-05 00:23:43,694 - mmseg - INFO - Iter [118050/160000] lr: 4.687e-06, eta: 3:41:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.7865, loss: 0.1792 +2023-03-05 00:23:57,040 - mmseg - INFO - Iter [118100/160000] lr: 4.687e-06, eta: 3:41:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.7283, loss: 0.1818 +2023-03-05 00:24:10,314 - mmseg - INFO - Iter [118150/160000] lr: 4.687e-06, eta: 3:40:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5170, loss: 0.1834 +2023-03-05 00:24:23,848 - mmseg - INFO - Iter [118200/160000] lr: 4.687e-06, eta: 3:40:29, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7174, loss: 0.1780 +2023-03-05 00:24:37,105 - mmseg - INFO - Iter [118250/160000] lr: 4.687e-06, eta: 3:40:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5667, loss: 0.1818 +2023-03-05 00:24:50,565 - mmseg - INFO - Iter [118300/160000] lr: 4.687e-06, eta: 3:39:55, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.8167, loss: 0.1787 +2023-03-05 00:25:03,873 - mmseg - INFO - Iter [118350/160000] lr: 4.687e-06, eta: 3:39:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.8322, loss: 0.1798 +2023-03-05 00:25:17,300 - mmseg - INFO - Iter [118400/160000] lr: 4.687e-06, eta: 3:39:22, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6746, loss: 0.1814 +2023-03-05 00:25:30,603 - mmseg - INFO - Iter [118450/160000] lr: 4.687e-06, eta: 3:39:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6189, loss: 0.1800 +2023-03-05 00:25:43,819 - mmseg - INFO - Iter [118500/160000] lr: 4.687e-06, eta: 3:38:49, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6797, loss: 0.1814 +2023-03-05 00:25:57,164 - mmseg - INFO - Iter [118550/160000] lr: 4.687e-06, eta: 3:38:32, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1720, decode.acc_seg: 92.9268, loss: 0.1720 +2023-03-05 00:26:10,706 - mmseg - INFO - Iter [118600/160000] lr: 4.687e-06, eta: 3:38:15, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.5014, loss: 0.1854 +2023-03-05 00:26:26,708 - mmseg - INFO - Iter [118650/160000] lr: 4.687e-06, eta: 3:38:00, time: 0.320, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.7088, loss: 0.1756 +2023-03-05 00:26:40,031 - mmseg - INFO - Iter [118700/160000] lr: 4.687e-06, eta: 3:37:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.4363, loss: 0.1835 +2023-03-05 00:26:53,310 - mmseg - INFO - Iter [118750/160000] lr: 4.687e-06, eta: 3:37:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.4922, loss: 0.1837 +2023-03-05 00:27:06,629 - mmseg - INFO - Iter [118800/160000] lr: 4.687e-06, eta: 3:37:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8114, loss: 0.1775 +2023-03-05 00:27:20,012 - mmseg - INFO - Iter [118850/160000] lr: 4.687e-06, eta: 3:36:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.6339, loss: 0.1854 +2023-03-05 00:27:33,334 - mmseg - INFO - Iter [118900/160000] lr: 4.687e-06, eta: 3:36:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.8450, loss: 0.1783 +2023-03-05 00:27:46,671 - mmseg - INFO - Iter [118950/160000] lr: 4.687e-06, eta: 3:36:19, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.5633, loss: 0.1854 +2023-03-05 00:27:59,983 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:27:59,983 - mmseg - INFO - Iter [119000/160000] lr: 4.687e-06, eta: 3:36:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1684, decode.acc_seg: 93.1755, loss: 0.1684 +2023-03-05 00:28:13,225 - mmseg - INFO - Iter [119050/160000] lr: 4.687e-06, eta: 3:35:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.7769, loss: 0.1815 +2023-03-05 00:28:26,490 - mmseg - INFO - Iter [119100/160000] lr: 4.687e-06, eta: 3:35:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5268, loss: 0.1816 +2023-03-05 00:28:40,048 - mmseg - INFO - Iter [119150/160000] lr: 4.687e-06, eta: 3:35:13, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1716, decode.acc_seg: 92.9672, loss: 0.1716 +2023-03-05 00:28:53,443 - mmseg - INFO - Iter [119200/160000] lr: 4.687e-06, eta: 3:34:56, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1865, decode.acc_seg: 92.5405, loss: 0.1865 +2023-03-05 00:29:06,889 - mmseg - INFO - Iter [119250/160000] lr: 4.687e-06, eta: 3:34:40, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4256, loss: 0.1852 +2023-03-05 00:29:22,674 - mmseg - INFO - Iter [119300/160000] lr: 4.687e-06, eta: 3:34:24, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1755, decode.acc_seg: 92.8041, loss: 0.1755 +2023-03-05 00:29:35,950 - mmseg - INFO - Iter [119350/160000] lr: 4.687e-06, eta: 3:34:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.6772, loss: 0.1793 +2023-03-05 00:29:49,282 - mmseg - INFO - Iter [119400/160000] lr: 4.687e-06, eta: 3:33:51, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1747, decode.acc_seg: 92.9445, loss: 0.1747 +2023-03-05 00:30:02,537 - mmseg - INFO - Iter [119450/160000] lr: 4.687e-06, eta: 3:33:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6953, loss: 0.1782 +2023-03-05 00:30:15,809 - mmseg - INFO - Iter [119500/160000] lr: 4.687e-06, eta: 3:33:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5653, loss: 0.1838 +2023-03-05 00:30:29,230 - mmseg - INFO - Iter [119550/160000] lr: 4.687e-06, eta: 3:33:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1743, decode.acc_seg: 92.9183, loss: 0.1743 +2023-03-05 00:30:42,611 - mmseg - INFO - Iter [119600/160000] lr: 4.687e-06, eta: 3:32:44, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6231, loss: 0.1802 +2023-03-05 00:30:55,921 - mmseg - INFO - Iter [119650/160000] lr: 4.687e-06, eta: 3:32:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6700, loss: 0.1795 +2023-03-05 00:31:09,166 - mmseg - INFO - Iter [119700/160000] lr: 4.687e-06, eta: 3:32:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.6079, loss: 0.1845 +2023-03-05 00:31:22,425 - mmseg - INFO - Iter [119750/160000] lr: 4.687e-06, eta: 3:31:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3787, loss: 0.1901 +2023-03-05 00:31:35,742 - mmseg - INFO - Iter [119800/160000] lr: 4.687e-06, eta: 3:31:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5361, loss: 0.1827 +2023-03-05 00:31:49,024 - mmseg - INFO - Iter [119850/160000] lr: 4.687e-06, eta: 3:31:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7760, loss: 0.1782 +2023-03-05 00:32:04,796 - mmseg - INFO - Iter [119900/160000] lr: 4.687e-06, eta: 3:31:05, time: 0.315, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5403, loss: 0.1816 +2023-03-05 00:32:18,065 - mmseg - INFO - Iter [119950/160000] lr: 4.687e-06, eta: 3:30:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7638, loss: 0.1770 +2023-03-05 00:32:31,388 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:32:31,388 - mmseg - INFO - Iter [120000/160000] lr: 4.687e-06, eta: 3:30:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3587, loss: 0.1869 +2023-03-05 00:32:44,692 - mmseg - INFO - Iter [120050/160000] lr: 2.344e-06, eta: 3:30:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.9094, loss: 0.1757 +2023-03-05 00:32:58,058 - mmseg - INFO - Iter [120100/160000] lr: 2.344e-06, eta: 3:29:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7502, loss: 0.1797 +2023-03-05 00:33:11,494 - mmseg - INFO - Iter [120150/160000] lr: 2.344e-06, eta: 3:29:42, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.7787, loss: 0.1800 +2023-03-05 00:33:24,722 - mmseg - INFO - Iter [120200/160000] lr: 2.344e-06, eta: 3:29:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.7716, loss: 0.1765 +2023-03-05 00:33:37,956 - mmseg - INFO - Iter [120250/160000] lr: 2.344e-06, eta: 3:29:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7258, loss: 0.1798 +2023-03-05 00:33:51,269 - mmseg - INFO - Iter [120300/160000] lr: 2.344e-06, eta: 3:28:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1736, decode.acc_seg: 92.9186, loss: 0.1736 +2023-03-05 00:34:04,581 - mmseg - INFO - Iter [120350/160000] lr: 2.344e-06, eta: 3:28:35, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5035, loss: 0.1834 +2023-03-05 00:34:17,864 - mmseg - INFO - Iter [120400/160000] lr: 2.344e-06, eta: 3:28:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5748, loss: 0.1825 +2023-03-05 00:34:31,204 - mmseg - INFO - Iter [120450/160000] lr: 2.344e-06, eta: 3:28:02, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6851, loss: 0.1810 +2023-03-05 00:34:44,554 - mmseg - INFO - Iter [120500/160000] lr: 2.344e-06, eta: 3:27:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.5012, loss: 0.1845 +2023-03-05 00:35:00,348 - mmseg - INFO - Iter [120550/160000] lr: 2.344e-06, eta: 3:27:30, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.7555, loss: 0.1802 +2023-03-05 00:35:13,768 - mmseg - INFO - Iter [120600/160000] lr: 2.344e-06, eta: 3:27:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5746, loss: 0.1821 +2023-03-05 00:35:27,026 - mmseg - INFO - Iter [120650/160000] lr: 2.344e-06, eta: 3:26:57, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.6125, loss: 0.1868 +2023-03-05 00:35:40,343 - mmseg - INFO - Iter [120700/160000] lr: 2.344e-06, eta: 3:26:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.7592, loss: 0.1814 +2023-03-05 00:35:53,586 - mmseg - INFO - Iter [120750/160000] lr: 2.344e-06, eta: 3:26:24, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.7232, loss: 0.1827 +2023-03-05 00:36:06,811 - mmseg - INFO - Iter [120800/160000] lr: 2.344e-06, eta: 3:26:07, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.6852, loss: 0.1790 +2023-03-05 00:36:20,054 - mmseg - INFO - Iter [120850/160000] lr: 2.344e-06, eta: 3:25:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.7830, loss: 0.1787 +2023-03-05 00:36:33,377 - mmseg - INFO - Iter [120900/160000] lr: 2.344e-06, eta: 3:25:34, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5949, loss: 0.1840 +2023-03-05 00:36:46,647 - mmseg - INFO - Iter [120950/160000] lr: 2.344e-06, eta: 3:25:17, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.8858, loss: 0.1762 +2023-03-05 00:36:59,864 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:36:59,864 - mmseg - INFO - Iter [121000/160000] lr: 2.344e-06, eta: 3:25:01, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.6522, loss: 0.1823 +2023-03-05 00:37:13,095 - mmseg - INFO - Iter [121050/160000] lr: 2.344e-06, eta: 3:24:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5591, loss: 0.1828 +2023-03-05 00:37:26,437 - mmseg - INFO - Iter [121100/160000] lr: 2.344e-06, eta: 3:24:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.6666, loss: 0.1779 +2023-03-05 00:37:39,652 - mmseg - INFO - Iter [121150/160000] lr: 2.344e-06, eta: 3:24:11, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.8082, loss: 0.1790 +2023-03-05 00:37:55,745 - mmseg - INFO - Iter [121200/160000] lr: 2.344e-06, eta: 3:23:55, time: 0.322, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.9333, loss: 0.1768 +2023-03-05 00:38:09,041 - mmseg - INFO - Iter [121250/160000] lr: 2.344e-06, eta: 3:23:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.4836, loss: 0.1824 +2023-03-05 00:38:22,335 - mmseg - INFO - Iter [121300/160000] lr: 2.344e-06, eta: 3:23:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.7301, loss: 0.1799 +2023-03-05 00:38:35,774 - mmseg - INFO - Iter [121350/160000] lr: 2.344e-06, eta: 3:23:06, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.5717, loss: 0.1810 +2023-03-05 00:38:49,174 - mmseg - INFO - Iter [121400/160000] lr: 2.344e-06, eta: 3:22:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6630, loss: 0.1817 +2023-03-05 00:39:02,486 - mmseg - INFO - Iter [121450/160000] lr: 2.344e-06, eta: 3:22:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.5585, loss: 0.1792 +2023-03-05 00:39:15,879 - mmseg - INFO - Iter [121500/160000] lr: 2.344e-06, eta: 3:22:16, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5307, loss: 0.1835 +2023-03-05 00:39:29,257 - mmseg - INFO - Iter [121550/160000] lr: 2.344e-06, eta: 3:22:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1693, decode.acc_seg: 92.9829, loss: 0.1693 +2023-03-05 00:39:42,747 - mmseg - INFO - Iter [121600/160000] lr: 2.344e-06, eta: 3:21:43, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7385, loss: 0.1795 +2023-03-05 00:39:56,035 - mmseg - INFO - Iter [121650/160000] lr: 2.344e-06, eta: 3:21:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6141, loss: 0.1827 +2023-03-05 00:40:09,387 - mmseg - INFO - Iter [121700/160000] lr: 2.344e-06, eta: 3:21:10, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.8692, loss: 0.1776 +2023-03-05 00:40:22,676 - mmseg - INFO - Iter [121750/160000] lr: 2.344e-06, eta: 3:20:53, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.4925, loss: 0.1853 +2023-03-05 00:40:38,444 - mmseg - INFO - Iter [121800/160000] lr: 2.344e-06, eta: 3:20:38, time: 0.315, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.6937, loss: 0.1823 +2023-03-05 00:40:51,826 - mmseg - INFO - Iter [121850/160000] lr: 2.344e-06, eta: 3:20:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.8267, loss: 0.1763 +2023-03-05 00:41:05,066 - mmseg - INFO - Iter [121900/160000] lr: 2.344e-06, eta: 3:20:05, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.7612, loss: 0.1767 +2023-03-05 00:41:18,336 - mmseg - INFO - Iter [121950/160000] lr: 2.344e-06, eta: 3:19:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.7619, loss: 0.1809 +2023-03-05 00:41:31,832 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:41:31,832 - mmseg - INFO - Iter [122000/160000] lr: 2.344e-06, eta: 3:19:32, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1732, decode.acc_seg: 92.9798, loss: 0.1732 +2023-03-05 00:41:45,125 - mmseg - INFO - Iter [122050/160000] lr: 2.344e-06, eta: 3:19:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.4815, loss: 0.1808 +2023-03-05 00:41:58,402 - mmseg - INFO - Iter [122100/160000] lr: 2.344e-06, eta: 3:18:59, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7248, loss: 0.1790 +2023-03-05 00:42:11,693 - mmseg - INFO - Iter [122150/160000] lr: 2.344e-06, eta: 3:18:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1889, decode.acc_seg: 92.3937, loss: 0.1889 +2023-03-05 00:42:25,012 - mmseg - INFO - Iter [122200/160000] lr: 2.344e-06, eta: 3:18:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1720, decode.acc_seg: 92.9916, loss: 0.1720 +2023-03-05 00:42:38,367 - mmseg - INFO - Iter [122250/160000] lr: 2.344e-06, eta: 3:18:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.6280, loss: 0.1841 +2023-03-05 00:42:51,819 - mmseg - INFO - Iter [122300/160000] lr: 2.344e-06, eta: 3:17:53, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1744, decode.acc_seg: 92.8346, loss: 0.1744 +2023-03-05 00:43:05,112 - mmseg - INFO - Iter [122350/160000] lr: 2.344e-06, eta: 3:17:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7928, loss: 0.1785 +2023-03-05 00:43:18,533 - mmseg - INFO - Iter [122400/160000] lr: 2.344e-06, eta: 3:17:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.8192, loss: 0.1783 +2023-03-05 00:43:34,385 - mmseg - INFO - Iter [122450/160000] lr: 2.344e-06, eta: 3:17:04, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4109, loss: 0.1882 +2023-03-05 00:43:47,622 - mmseg - INFO - Iter [122500/160000] lr: 2.344e-06, eta: 3:16:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7783, loss: 0.1770 +2023-03-05 00:44:00,964 - mmseg - INFO - Iter [122550/160000] lr: 2.344e-06, eta: 3:16:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5234, loss: 0.1816 +2023-03-05 00:44:14,249 - mmseg - INFO - Iter [122600/160000] lr: 2.344e-06, eta: 3:16:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.5714, loss: 0.1822 +2023-03-05 00:44:27,578 - mmseg - INFO - Iter [122650/160000] lr: 2.344e-06, eta: 3:15:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6484, loss: 0.1808 +2023-03-05 00:44:40,917 - mmseg - INFO - Iter [122700/160000] lr: 2.344e-06, eta: 3:15:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.5734, loss: 0.1804 +2023-03-05 00:44:54,185 - mmseg - INFO - Iter [122750/160000] lr: 2.344e-06, eta: 3:15:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.5969, loss: 0.1803 +2023-03-05 00:45:07,466 - mmseg - INFO - Iter [122800/160000] lr: 2.344e-06, eta: 3:15:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.7919, loss: 0.1811 +2023-03-05 00:45:20,727 - mmseg - INFO - Iter [122850/160000] lr: 2.344e-06, eta: 3:14:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.7614, loss: 0.1807 +2023-03-05 00:45:34,008 - mmseg - INFO - Iter [122900/160000] lr: 2.344e-06, eta: 3:14:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1728, decode.acc_seg: 92.9582, loss: 0.1728 +2023-03-05 00:45:47,424 - mmseg - INFO - Iter [122950/160000] lr: 2.344e-06, eta: 3:14:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.5638, loss: 0.1858 +2023-03-05 00:46:00,742 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:46:00,742 - mmseg - INFO - Iter [123000/160000] lr: 2.344e-06, eta: 3:14:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1918, decode.acc_seg: 92.2628, loss: 0.1918 +2023-03-05 00:46:16,559 - mmseg - INFO - Iter [123050/160000] lr: 2.344e-06, eta: 3:13:47, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1740, decode.acc_seg: 92.9400, loss: 0.1740 +2023-03-05 00:46:29,836 - mmseg - INFO - Iter [123100/160000] lr: 2.344e-06, eta: 3:13:31, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.5111, loss: 0.1877 +2023-03-05 00:46:43,076 - mmseg - INFO - Iter [123150/160000] lr: 2.344e-06, eta: 3:13:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8476, loss: 0.1752 +2023-03-05 00:46:56,453 - mmseg - INFO - Iter [123200/160000] lr: 2.344e-06, eta: 3:12:58, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1739, decode.acc_seg: 92.8158, loss: 0.1739 +2023-03-05 00:47:09,855 - mmseg - INFO - Iter [123250/160000] lr: 2.344e-06, eta: 3:12:41, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6684, loss: 0.1807 +2023-03-05 00:47:23,050 - mmseg - INFO - Iter [123300/160000] lr: 2.344e-06, eta: 3:12:25, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.5949, loss: 0.1866 +2023-03-05 00:47:36,385 - mmseg - INFO - Iter [123350/160000] lr: 2.344e-06, eta: 3:12:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.4462, loss: 0.1828 +2023-03-05 00:47:49,658 - mmseg - INFO - Iter [123400/160000] lr: 2.344e-06, eta: 3:11:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.6854, loss: 0.1770 +2023-03-05 00:48:03,053 - mmseg - INFO - Iter [123450/160000] lr: 2.344e-06, eta: 3:11:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.5304, loss: 0.1848 +2023-03-05 00:48:16,240 - mmseg - INFO - Iter [123500/160000] lr: 2.344e-06, eta: 3:11:19, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.9099, loss: 0.1758 +2023-03-05 00:48:29,554 - mmseg - INFO - Iter [123550/160000] lr: 2.344e-06, eta: 3:11:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3793, loss: 0.1869 +2023-03-05 00:48:42,858 - mmseg - INFO - Iter [123600/160000] lr: 2.344e-06, eta: 3:10:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1717, decode.acc_seg: 92.9222, loss: 0.1717 +2023-03-05 00:48:56,277 - mmseg - INFO - Iter [123650/160000] lr: 2.344e-06, eta: 3:10:30, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5395, loss: 0.1838 +2023-03-05 00:49:11,994 - mmseg - INFO - Iter [123700/160000] lr: 2.344e-06, eta: 3:10:14, time: 0.314, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7400, loss: 0.1778 +2023-03-05 00:49:25,409 - mmseg - INFO - Iter [123750/160000] lr: 2.344e-06, eta: 3:09:58, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.6421, loss: 0.1787 +2023-03-05 00:49:38,694 - mmseg - INFO - Iter [123800/160000] lr: 2.344e-06, eta: 3:09:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.6380, loss: 0.1870 +2023-03-05 00:49:52,005 - mmseg - INFO - Iter [123850/160000] lr: 2.344e-06, eta: 3:09:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.5683, loss: 0.1855 +2023-03-05 00:50:05,328 - mmseg - INFO - Iter [123900/160000] lr: 2.344e-06, eta: 3:09:08, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7113, loss: 0.1793 +2023-03-05 00:50:18,732 - mmseg - INFO - Iter [123950/160000] lr: 2.344e-06, eta: 3:08:52, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.5508, loss: 0.1826 +2023-03-05 00:50:32,116 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:50:32,116 - mmseg - INFO - Iter [124000/160000] lr: 2.344e-06, eta: 3:08:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.7856, loss: 0.1772 +2023-03-05 00:50:45,672 - mmseg - INFO - Iter [124050/160000] lr: 2.344e-06, eta: 3:08:19, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.6015, loss: 0.1790 +2023-03-05 00:50:58,991 - mmseg - INFO - Iter [124100/160000] lr: 2.344e-06, eta: 3:08:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7202, loss: 0.1789 +2023-03-05 00:51:12,312 - mmseg - INFO - Iter [124150/160000] lr: 2.344e-06, eta: 3:07:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.7234, loss: 0.1781 +2023-03-05 00:51:25,671 - mmseg - INFO - Iter [124200/160000] lr: 2.344e-06, eta: 3:07:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.9538, loss: 0.1745 +2023-03-05 00:51:38,876 - mmseg - INFO - Iter [124250/160000] lr: 2.344e-06, eta: 3:07:14, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.6835, loss: 0.1789 +2023-03-05 00:51:52,241 - mmseg - INFO - Iter [124300/160000] lr: 2.344e-06, eta: 3:06:57, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7983, loss: 0.1782 +2023-03-05 00:52:08,113 - mmseg - INFO - Iter [124350/160000] lr: 2.344e-06, eta: 3:06:41, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1728, decode.acc_seg: 93.0455, loss: 0.1728 +2023-03-05 00:52:21,392 - mmseg - INFO - Iter [124400/160000] lr: 2.344e-06, eta: 3:06:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.6976, loss: 0.1767 +2023-03-05 00:52:34,728 - mmseg - INFO - Iter [124450/160000] lr: 2.344e-06, eta: 3:06:09, time: 0.267, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.9174, loss: 0.1758 +2023-03-05 00:52:48,251 - mmseg - INFO - Iter [124500/160000] lr: 2.344e-06, eta: 3:05:52, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5412, loss: 0.1835 +2023-03-05 00:53:01,626 - mmseg - INFO - Iter [124550/160000] lr: 2.344e-06, eta: 3:05:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.6620, loss: 0.1788 +2023-03-05 00:53:15,023 - mmseg - INFO - Iter [124600/160000] lr: 2.344e-06, eta: 3:05:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1737, decode.acc_seg: 92.8790, loss: 0.1737 +2023-03-05 00:53:28,313 - mmseg - INFO - Iter [124650/160000] lr: 2.344e-06, eta: 3:05:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.9195, loss: 0.1766 +2023-03-05 00:53:41,619 - mmseg - INFO - Iter [124700/160000] lr: 2.344e-06, eta: 3:04:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6712, loss: 0.1810 +2023-03-05 00:53:54,936 - mmseg - INFO - Iter [124750/160000] lr: 2.344e-06, eta: 3:04:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7427, loss: 0.1785 +2023-03-05 00:54:08,274 - mmseg - INFO - Iter [124800/160000] lr: 2.344e-06, eta: 3:04:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.8195, loss: 0.1784 +2023-03-05 00:54:21,611 - mmseg - INFO - Iter [124850/160000] lr: 2.344e-06, eta: 3:03:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.3267, loss: 0.1903 +2023-03-05 00:54:34,933 - mmseg - INFO - Iter [124900/160000] lr: 2.344e-06, eta: 3:03:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6854, loss: 0.1798 +2023-03-05 00:54:50,767 - mmseg - INFO - Iter [124950/160000] lr: 2.344e-06, eta: 3:03:26, time: 0.317, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8296, loss: 0.1758 +2023-03-05 00:55:04,117 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:55:04,117 - mmseg - INFO - Iter [125000/160000] lr: 2.344e-06, eta: 3:03:09, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1731, decode.acc_seg: 92.9985, loss: 0.1731 +2023-03-05 00:55:17,469 - mmseg - INFO - Iter [125050/160000] lr: 2.344e-06, eta: 3:02:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7750, loss: 0.1789 +2023-03-05 00:55:30,747 - mmseg - INFO - Iter [125100/160000] lr: 2.344e-06, eta: 3:02:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5497, loss: 0.1814 +2023-03-05 00:55:43,966 - mmseg - INFO - Iter [125150/160000] lr: 2.344e-06, eta: 3:02:20, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.7055, loss: 0.1837 +2023-03-05 00:55:57,281 - mmseg - INFO - Iter [125200/160000] lr: 2.344e-06, eta: 3:02:04, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4355, loss: 0.1862 +2023-03-05 00:56:10,622 - mmseg - INFO - Iter [125250/160000] lr: 2.344e-06, eta: 3:01:47, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.4294, loss: 0.1855 +2023-03-05 00:56:23,896 - mmseg - INFO - Iter [125300/160000] lr: 2.344e-06, eta: 3:01:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1702, decode.acc_seg: 93.0532, loss: 0.1702 +2023-03-05 00:56:37,126 - mmseg - INFO - Iter [125350/160000] lr: 2.344e-06, eta: 3:01:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.7996, loss: 0.1762 +2023-03-05 00:56:50,366 - mmseg - INFO - Iter [125400/160000] lr: 2.344e-06, eta: 3:00:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5509, loss: 0.1818 +2023-03-05 00:57:03,640 - mmseg - INFO - Iter [125450/160000] lr: 2.344e-06, eta: 3:00:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7399, loss: 0.1785 +2023-03-05 00:57:16,927 - mmseg - INFO - Iter [125500/160000] lr: 2.344e-06, eta: 3:00:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5230, loss: 0.1834 +2023-03-05 00:57:30,144 - mmseg - INFO - Iter [125550/160000] lr: 2.344e-06, eta: 3:00:09, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4603, loss: 0.1871 +2023-03-05 00:57:45,953 - mmseg - INFO - Iter [125600/160000] lr: 2.344e-06, eta: 2:59:54, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4160, loss: 0.1880 +2023-03-05 00:57:59,385 - mmseg - INFO - Iter [125650/160000] lr: 2.344e-06, eta: 2:59:37, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.5058, loss: 0.1867 +2023-03-05 00:58:12,715 - mmseg - INFO - Iter [125700/160000] lr: 2.344e-06, eta: 2:59:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.4062, loss: 0.1893 +2023-03-05 00:58:26,113 - mmseg - INFO - Iter [125750/160000] lr: 2.344e-06, eta: 2:59:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5587, loss: 0.1823 +2023-03-05 00:58:39,411 - mmseg - INFO - Iter [125800/160000] lr: 2.344e-06, eta: 2:58:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4743, loss: 0.1868 +2023-03-05 00:58:52,733 - mmseg - INFO - Iter [125850/160000] lr: 2.344e-06, eta: 2:58:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6792, loss: 0.1801 +2023-03-05 00:59:05,980 - mmseg - INFO - Iter [125900/160000] lr: 2.344e-06, eta: 2:58:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.6699, loss: 0.1770 +2023-03-05 00:59:19,228 - mmseg - INFO - Iter [125950/160000] lr: 2.344e-06, eta: 2:57:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.7118, loss: 0.1823 +2023-03-05 00:59:32,543 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 00:59:32,543 - mmseg - INFO - Iter [126000/160000] lr: 2.344e-06, eta: 2:57:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.8640, loss: 0.1761 +2023-03-05 00:59:45,882 - mmseg - INFO - Iter [126050/160000] lr: 2.344e-06, eta: 2:57:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8859, loss: 0.1752 +2023-03-05 00:59:59,193 - mmseg - INFO - Iter [126100/160000] lr: 2.344e-06, eta: 2:57:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.7679, loss: 0.1766 +2023-03-05 01:00:12,360 - mmseg - INFO - Iter [126150/160000] lr: 2.344e-06, eta: 2:56:54, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6375, loss: 0.1795 +2023-03-05 01:00:25,671 - mmseg - INFO - Iter [126200/160000] lr: 2.344e-06, eta: 2:56:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6058, loss: 0.1795 +2023-03-05 01:00:41,538 - mmseg - INFO - Iter [126250/160000] lr: 2.344e-06, eta: 2:56:22, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7925, loss: 0.1785 +2023-03-05 01:00:54,851 - mmseg - INFO - Iter [126300/160000] lr: 2.344e-06, eta: 2:56:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.6196, loss: 0.1831 +2023-03-05 01:01:08,102 - mmseg - INFO - Iter [126350/160000] lr: 2.344e-06, eta: 2:55:49, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7408, loss: 0.1774 +2023-03-05 01:01:21,677 - mmseg - INFO - Iter [126400/160000] lr: 2.344e-06, eta: 2:55:33, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.7860, loss: 0.1757 +2023-03-05 01:01:35,074 - mmseg - INFO - Iter [126450/160000] lr: 2.344e-06, eta: 2:55:17, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5572, loss: 0.1821 +2023-03-05 01:01:48,381 - mmseg - INFO - Iter [126500/160000] lr: 2.344e-06, eta: 2:55:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.5664, loss: 0.1807 +2023-03-05 01:02:01,689 - mmseg - INFO - Iter [126550/160000] lr: 2.344e-06, eta: 2:54:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.9130, loss: 0.1756 +2023-03-05 01:02:14,934 - mmseg - INFO - Iter [126600/160000] lr: 2.344e-06, eta: 2:54:28, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6248, loss: 0.1811 +2023-03-05 01:02:28,222 - mmseg - INFO - Iter [126650/160000] lr: 2.344e-06, eta: 2:54:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.8031, loss: 0.1784 +2023-03-05 01:02:41,621 - mmseg - INFO - Iter [126700/160000] lr: 2.344e-06, eta: 2:53:55, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7370, loss: 0.1778 +2023-03-05 01:02:54,880 - mmseg - INFO - Iter [126750/160000] lr: 2.344e-06, eta: 2:53:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.7935, loss: 0.1800 +2023-03-05 01:03:08,197 - mmseg - INFO - Iter [126800/160000] lr: 2.344e-06, eta: 2:53:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.4285, loss: 0.1886 +2023-03-05 01:03:23,905 - mmseg - INFO - Iter [126850/160000] lr: 2.344e-06, eta: 2:53:07, time: 0.314, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6334, loss: 0.1832 +2023-03-05 01:03:37,185 - mmseg - INFO - Iter [126900/160000] lr: 2.344e-06, eta: 2:52:51, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6075, loss: 0.1813 +2023-03-05 01:03:50,759 - mmseg - INFO - Iter [126950/160000] lr: 2.344e-06, eta: 2:52:35, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.7447, loss: 0.1767 +2023-03-05 01:04:04,017 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:04:04,017 - mmseg - INFO - Iter [127000/160000] lr: 2.344e-06, eta: 2:52:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.7201, loss: 0.1794 +2023-03-05 01:04:17,252 - mmseg - INFO - Iter [127050/160000] lr: 2.344e-06, eta: 2:52:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.6500, loss: 0.1835 +2023-03-05 01:04:30,658 - mmseg - INFO - Iter [127100/160000] lr: 2.344e-06, eta: 2:51:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8315, loss: 0.1752 +2023-03-05 01:04:43,989 - mmseg - INFO - Iter [127150/160000] lr: 2.344e-06, eta: 2:51:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.5879, loss: 0.1806 +2023-03-05 01:04:57,342 - mmseg - INFO - Iter [127200/160000] lr: 2.344e-06, eta: 2:51:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1737, decode.acc_seg: 93.0721, loss: 0.1737 +2023-03-05 01:05:10,628 - mmseg - INFO - Iter [127250/160000] lr: 2.344e-06, eta: 2:50:57, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.7682, loss: 0.1820 +2023-03-05 01:05:23,894 - mmseg - INFO - Iter [127300/160000] lr: 2.344e-06, eta: 2:50:41, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6430, loss: 0.1811 +2023-03-05 01:05:37,161 - mmseg - INFO - Iter [127350/160000] lr: 2.344e-06, eta: 2:50:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1722, decode.acc_seg: 92.9475, loss: 0.1722 +2023-03-05 01:05:50,482 - mmseg - INFO - Iter [127400/160000] lr: 2.344e-06, eta: 2:50:08, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.4729, loss: 0.1849 +2023-03-05 01:06:03,751 - mmseg - INFO - Iter [127450/160000] lr: 2.344e-06, eta: 2:49:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.3972, loss: 0.1877 +2023-03-05 01:06:19,603 - mmseg - INFO - Iter [127500/160000] lr: 2.344e-06, eta: 2:49:36, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4094, loss: 0.1872 +2023-03-05 01:06:32,985 - mmseg - INFO - Iter [127550/160000] lr: 2.344e-06, eta: 2:49:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5905, loss: 0.1817 +2023-03-05 01:06:46,257 - mmseg - INFO - Iter [127600/160000] lr: 2.344e-06, eta: 2:49:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4801, loss: 0.1872 +2023-03-05 01:06:59,495 - mmseg - INFO - Iter [127650/160000] lr: 2.344e-06, eta: 2:48:48, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.5851, loss: 0.1842 +2023-03-05 01:07:12,810 - mmseg - INFO - Iter [127700/160000] lr: 2.344e-06, eta: 2:48:31, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 92.7834, loss: 0.1759 +2023-03-05 01:07:26,049 - mmseg - INFO - Iter [127750/160000] lr: 2.344e-06, eta: 2:48:15, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4920, loss: 0.1858 +2023-03-05 01:07:39,376 - mmseg - INFO - Iter [127800/160000] lr: 2.344e-06, eta: 2:47:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.6341, loss: 0.1821 +2023-03-05 01:07:52,641 - mmseg - INFO - Iter [127850/160000] lr: 2.344e-06, eta: 2:47:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1879, decode.acc_seg: 92.4512, loss: 0.1879 +2023-03-05 01:08:05,868 - mmseg - INFO - Iter [127900/160000] lr: 2.344e-06, eta: 2:47:26, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.8100, loss: 0.1773 +2023-03-05 01:08:19,144 - mmseg - INFO - Iter [127950/160000] lr: 2.344e-06, eta: 2:47:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.6360, loss: 0.1842 +2023-03-05 01:08:32,466 - mmseg - INFO - Swap parameters (after train) after iter [128000] +2023-03-05 01:08:32,512 - mmseg - INFO - Saving checkpoint at 128000 iterations +2023-03-05 01:08:34,421 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:08:34,421 - mmseg - INFO - Iter [128000/160000] lr: 2.344e-06, eta: 2:46:54, time: 0.306, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1719, decode.acc_seg: 92.9643, loss: 0.1719 +2023-03-05 01:19:40,392 - mmseg - INFO - per class results: +2023-03-05 01:19:40,401 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.38,76.38,76.37,76.38,76.39,76.38,76.38,76.38,76.38,76.38,76.39 | +| building | 81.39,81.4,81.4,81.42,81.41,81.41,81.43,81.42,81.44,81.45,81.44 | +| sky | 94.27,94.27,94.28,94.27,94.27,94.27,94.27,94.26,94.26,94.26,94.25 | +| floor | 80.09,80.12,80.12,80.14,80.15,80.15,80.16,80.16,80.16,80.16,80.19 | +| tree | 72.86,72.85,72.85,72.83,72.81,72.8,72.79,72.82,72.82,72.8,72.77 | +| ceiling | 82.86,82.87,82.86,82.88,82.88,82.88,82.9,82.92,82.93,82.93,82.91 | +| road | 82.16,82.15,82.14,82.15,82.13,82.11,82.1,82.13,82.19,82.16,82.16 | +| bed | 88.55,88.57,88.55,88.55,88.53,88.53,88.54,88.53,88.53,88.54,88.55 | +| windowpane | 61.07,61.08,61.09,61.07,61.1,61.1,61.08,61.09,61.11,61.14,61.08 | +| grass | 65.3,65.28,65.3,65.3,65.28,65.35,65.36,65.39,65.42,65.48,65.49 | +| cabinet | 59.29,59.29,59.38,59.45,59.46,59.41,59.46,59.35,59.34,59.36,59.39 | +| sidewalk | 66.66,66.64,66.67,66.71,66.77,66.73,66.77,66.82,66.93,66.9,66.87 | +| person | 79.53,79.53,79.53,79.56,79.55,79.54,79.54,79.55,79.54,79.56,79.53 | +| earth | 32.49,32.31,32.32,32.21,32.12,32.1,32.09,32.17,32.19,32.16,32.02 | +| door | 48.45,48.56,48.58,48.58,48.73,48.74,48.73,48.68,48.68,48.7,48.73 | +| table | 61.61,61.66,61.68,61.7,61.67,61.68,61.76,61.75,61.8,61.82,61.86 | +| mountain | 51.52,51.68,51.71,51.75,51.82,51.75,51.86,51.97,52.07,52.14,51.9 | +| plant | 50.13,50.15,50.11,50.09,50.09,50.11,50.06,50.06,50.08,50.1,50.08 | +| curtain | 70.32,70.44,70.53,70.6,70.63,70.67,70.72,70.74,70.79,70.81,70.56 | +| chair | 58.25,58.28,58.33,58.34,58.39,58.43,58.46,58.49,58.51,58.53,58.54 | +| car | 83.13,83.16,83.15,83.16,83.12,83.15,83.18,83.12,83.15,83.14,83.18 | +| water | 47.17,47.2,47.22,47.28,47.29,47.29,47.35,47.42,47.43,47.43,47.47 | +| painting | 69.82,69.82,69.88,69.89,69.84,69.91,69.82,69.93,69.9,69.88,69.85 | +| sofa | 65.42,65.43,65.59,65.63,65.49,65.5,65.68,65.85,65.97,66.08,65.71 | +| shelf | 40.8,40.69,40.73,40.65,40.59,40.53,40.54,40.49,40.4,40.36,40.39 | +| house | 43.84,43.7,43.79,43.7,43.77,43.73,43.55,43.51,43.55,43.53,43.65 | +| sea | 44.89,44.88,44.85,44.82,44.81,44.78,44.79,44.74,44.72,44.73,44.71 | +| mirror | 65.54,65.48,65.55,65.54,65.5,65.55,65.49,65.42,65.42,65.42,65.59 | +| rug | 54.91,55.04,54.94,55.03,54.97,55.09,55.19,55.11,55.21,55.19,55.41 | +| field | 27.97,27.96,27.93,27.94,27.96,27.95,27.83,27.88,27.84,27.86,27.91 | +| armchair | 43.79,43.75,43.74,43.81,43.65,43.64,43.8,44.01,43.96,44.1,43.25 | +| seat | 53.6,53.57,53.58,53.5,53.46,53.49,53.52,53.45,53.47,53.51,53.33 | +| fence | 40.32,40.27,40.31,40.29,40.23,40.24,40.25,40.23,40.31,40.34,40.41 | +| desk | 49.37,49.42,49.39,49.4,49.3,49.28,49.28,49.16,49.16,49.16,49.17 | +| rock | 25.85,25.94,25.68,25.42,25.6,25.36,25.63,25.34,25.48,25.66,25.45 | +| wardrobe | 47.72,47.74,47.76,47.66,47.71,47.66,47.66,47.39,47.42,47.56,47.52 | +| lamp | 63.87,63.89,63.94,63.9,63.93,63.96,63.97,63.96,63.98,63.98,63.99 | +| bathtub | 76.91,76.91,76.9,76.99,76.82,76.76,76.58,76.64,76.79,76.82,76.53 | +| railing | 31.93,31.97,31.84,31.84,31.75,31.68,31.7,31.64,31.62,31.55,31.52 | +| cushion | 55.32,55.47,55.44,55.54,55.55,55.63,55.62,55.65,55.64,55.65,55.82 | +| base | 27.76,27.74,27.72,27.7,27.76,27.66,27.81,27.75,27.8,27.73,27.63 | +| box | 24.27,24.27,24.35,24.3,24.28,24.39,24.33,24.39,24.42,24.42,24.4 | +| column | 45.76,45.77,45.81,45.84,45.83,45.87,45.78,45.73,45.71,45.74,46.1 | +| signboard | 35.39,35.45,35.44,35.37,35.44,35.42,35.49,35.49,35.45,35.46,35.47 | +| chest of drawers | 39.54,39.53,39.35,39.15,39.27,39.11,39.19,39.19,39.21,39.25,39.42 | +| counter | 26.08,26.01,25.88,25.69,25.57,25.46,25.52,25.62,25.6,25.53,26.09 | +| sand | 31.16,30.88,30.85,30.72,30.64,30.66,30.6,30.57,30.65,30.71,30.58 | +| sink | 70.97,71.03,71.06,71.07,71.12,71.04,71.17,71.27,71.29,71.3,71.13 | +| skyscraper | 49.15,49.1,49.19,49.24,49.22,49.29,49.25,49.42,49.37,49.38,49.38 | +| fireplace | 66.14,66.18,66.09,66.15,66.11,65.99,66.02,65.91,65.84,65.93,66.05 | +| refrigerator | 78.8,78.74,78.83,78.89,78.81,78.86,78.88,78.88,78.85,78.79,78.82 | +| grandstand | 41.87,41.9,41.87,41.85,41.88,41.95,41.95,41.91,41.96,41.97,41.95 | +| path | 17.51,17.38,17.55,17.68,17.66,17.86,17.82,17.93,17.97,17.92,18.05 | +| stairs | 31.55,31.51,31.51,31.52,31.44,31.47,31.48,31.43,31.46,31.44,31.39 | +| runway | 63.88,63.9,63.9,63.93,63.93,63.94,63.94,63.95,63.96,63.96,63.96 | +| case | 48.76,48.78,48.85,48.76,48.78,48.8,48.81,48.7,48.68,48.77,48.63 | +| pool table | 92.67,92.66,92.67,92.7,92.67,92.68,92.66,92.7,92.69,92.7,92.71 | +| pillow | 57.36,57.42,57.4,57.33,57.38,57.33,57.31,57.32,57.28,57.22,57.21 | +| screen door | 66.18,66.17,66.12,66.14,66.67,66.47,66.37,66.68,66.63,66.56,66.96 | +| stairway | 25.61,25.62,25.61,25.63,25.54,25.62,25.68,25.64,25.66,25.6,25.7 | +| river | 9.94,9.8,9.82,9.71,9.67,9.63,9.54,9.45,9.46,9.37,9.4 | +| bridge | 53.19,54.2,54.72,55.77,56.33,57.03,58.3,58.23,59.0,59.7,59.88 | +| bookcase | 42.11,42.73,42.78,43.08,43.24,43.24,43.08,43.2,43.15,43.16,42.99 | +| blind | 45.48,45.44,45.31,45.07,44.82,44.9,44.9,45.0,45.17,45.24,44.65 | +| coffee table | 66.1,66.05,66.08,66.01,66.11,66.07,66.11,66.15,66.18,66.21,66.13 | +| toilet | 86.19,86.21,86.26,86.28,86.27,86.35,86.27,86.3,86.33,86.3,86.37 | +| flower | 31.16,31.32,31.37,31.35,31.48,31.56,31.67,31.57,31.77,31.82,31.85 | +| book | 47.19,47.32,47.21,47.2,47.19,47.16,47.17,47.19,47.11,47.07,47.1 | +| hill | 7.98,7.85,7.83,7.69,7.67,7.66,7.73,7.6,7.67,7.67,7.79 | +| bench | 44.36,44.38,44.41,44.44,44.48,44.53,44.54,44.59,44.58,44.57,44.64 | +| countertop | 54.61,54.65,54.65,54.69,54.73,54.7,54.75,54.76,55.0,55.01,54.84 | +| stove | 72.5,72.42,72.62,72.48,72.59,72.41,72.48,72.54,72.55,72.69,72.52 | +| palm | 50.61,50.62,50.64,50.76,50.75,50.75,50.75,50.78,50.91,50.85,51.0 | +| kitchen island | 47.41,47.26,47.51,47.46,47.46,47.44,47.7,47.38,47.35,47.32,47.78 | +| computer | 57.31,57.34,57.24,57.35,57.28,57.35,57.34,57.36,57.29,57.26,57.46 | +| swivel chair | 45.61,45.6,45.71,45.87,45.94,45.96,45.92,45.98,46.02,45.99,46.26 | +| boat | 37.18,37.28,37.28,37.29,37.28,37.26,37.47,37.53,37.68,37.7,37.72 | +| bar | 27.23,27.07,27.1,27.02,26.95,26.78,26.77,26.62,26.47,26.33,26.34 | +| arcade machine | 25.72,26.1,26.29,26.7,26.74,27.1,27.33,27.35,27.55,27.54,27.83 | +| hovel | 31.59,31.38,31.28,31.17,31.08,31.0,30.88,30.84,30.75,30.63,30.61 | +| bus | 88.26,88.36,88.32,88.34,88.24,88.19,88.47,88.28,88.38,88.25,88.46 | +| towel | 61.0,61.2,61.05,61.03,61.07,61.01,60.95,61.05,60.95,60.94,60.94 | +| light | 56.64,56.64,56.52,56.6,56.49,56.53,56.54,56.48,56.46,56.41,56.43 | +| truck | 34.92,35.12,34.98,34.96,35.11,35.11,35.28,35.22,35.16,35.18,35.18 | +| tower | 25.15,25.19,25.16,24.97,24.74,24.61,24.51,24.37,24.56,24.54,24.54 | +| chandelier | 66.34,66.44,66.41,66.43,66.49,66.46,66.51,66.54,66.57,66.54,66.56 | +| awning | 23.4,23.37,23.31,23.33,23.28,23.28,23.21,23.18,23.23,23.12,23.58 | +| streetlight | 28.49,28.45,28.4,28.39,28.31,28.35,28.32,28.28,28.23,28.17,28.17 | +| booth | 59.34,59.34,59.4,59.19,59.28,59.22,58.99,59.07,58.86,58.89,58.92 | +| television receiver | 68.23,68.22,68.27,68.22,68.24,68.23,68.22,68.17,68.18,68.15,68.17 | +| airplane | 52.1,52.03,51.96,51.87,51.7,51.69,51.96,51.8,51.78,51.7,51.01 | +| dirt track | 10.13,9.97,10.25,10.21,10.39,10.65,10.7,10.68,10.71,10.46,10.68 | +| apparel | 28.73,28.91,28.69,28.91,28.96,28.82,28.91,28.89,29.05,28.92,28.25 | +| pole | 24.6,24.48,24.42,24.45,24.41,24.44,24.37,24.36,24.35,24.3,24.36 | +| land | 6.53,6.37,6.28,6.2,6.21,6.1,6.1,6.13,6.05,6.01,5.99 | +| bannister | 5.64,5.66,5.66,5.67,5.69,5.65,5.7,5.66,5.66,5.62,5.88 | +| escalator | 22.68,22.63,22.6,22.78,22.77,22.78,22.76,22.79,22.92,22.87,22.9 | +| ottoman | 46.58,46.65,46.5,46.45,46.39,46.52,46.54,46.48,46.61,46.54,46.92 | +| bottle | 14.83,14.76,14.73,14.67,14.71,14.54,14.61,14.68,14.63,14.63,14.33 | +| buffet | 49.85,50.12,50.18,52.0,53.25,53.83,54.61,56.11,56.3,56.26,56.02 | +| poster | 27.66,27.67,27.69,27.85,28.05,27.68,27.94,27.89,27.81,27.71,27.65 | +| stage | 16.77,16.86,16.82,16.94,17.07,17.11,17.2,17.29,17.33,17.47,17.45 | +| van | 47.52,47.63,47.33,47.47,47.11,47.42,47.57,47.04,47.09,47.08,47.57 | +| ship | 33.57,34.72,35.59,36.99,37.14,37.32,37.46,38.26,38.18,38.71,38.06 | +| fountain | 8.61,8.62,8.78,8.85,8.53,8.46,8.27,8.17,7.94,7.85,7.67 | +| conveyer belt | 76.09,76.01,75.87,75.8,75.83,75.77,75.71,75.57,75.44,75.29,74.93 | +| canopy | 15.33,15.3,15.25,15.32,15.24,15.15,15.13,15.08,15.06,15.09,15.4 | +| washer | 66.08,66.0,66.05,65.98,65.9,65.87,65.85,65.8,65.8,65.79,65.74 | +| plaything | 23.16,23.07,23.19,23.23,23.15,23.19,23.22,23.27,23.37,23.27,23.56 | +| swimming pool | 43.4,44.25,44.85,45.36,46.93,47.45,49.24,51.12,50.68,50.89,52.06 | +| stool | 41.86,41.86,41.95,41.82,41.88,41.83,41.76,41.83,41.69,41.72,41.68 | +| barrel | 39.61,39.08,39.79,39.8,39.74,39.58,40.9,39.51,39.8,40.04,39.86 | +| basket | 28.49,28.43,28.46,28.61,28.51,28.52,28.45,28.53,28.59,28.52,28.61 | +| waterfall | 51.69,52.28,52.73,52.71,53.44,53.18,53.99,54.67,54.45,55.18,56.15 | +| tent | 93.61,93.52,93.54,93.51,93.5,93.5,93.45,93.44,93.41,93.39,93.42 | +| bag | 11.47,11.51,11.56,11.61,11.53,11.55,11.63,11.59,11.63,11.56,11.64 | +| minibike | 61.67,61.6,61.77,61.62,61.75,61.72,61.69,61.66,61.65,61.63,61.68 | +| cradle | 81.65,81.64,81.58,81.51,81.48,81.36,81.32,81.21,81.1,80.98,80.99 | +| oven | 27.01,27.05,27.08,27.03,27.02,27.01,26.95,26.96,26.97,26.98,26.95 | +| ball | 47.38,47.52,47.49,47.59,47.56,47.58,47.62,47.74,47.81,47.98,47.84 | +| food | 52.5,52.88,52.89,52.8,53.09,53.13,53.28,53.38,53.41,53.61,53.31 | +| step | 17.27,17.57,17.58,17.62,17.65,17.48,17.69,17.88,17.95,17.84,17.48 | +| tank | 41.39,41.35,41.32,41.28,41.25,41.26,41.18,41.14,41.11,41.09,41.09 | +| trade name | 25.15,25.13,25.05,25.11,24.88,25.03,24.94,24.86,24.88,24.88,24.81 | +| microwave | 37.59,37.6,37.59,37.61,37.62,37.6,37.58,37.6,37.6,37.61,37.58 | +| pot | 41.23,41.2,41.22,41.26,41.25,41.26,41.28,41.3,41.38,41.37,41.32 | +| animal | 51.63,51.58,51.58,51.51,51.49,51.47,51.61,51.59,51.44,51.47,51.59 | +| bicycle | 45.94,45.98,46.01,46.05,46.12,46.13,46.15,46.13,46.09,46.2,46.09 | +| lake | 59.53,59.46,59.43,59.33,59.24,59.14,59.03,58.96,58.9,58.85,58.75 | +| dishwasher | 76.57,76.56,76.58,76.67,76.77,76.91,76.92,77.0,77.07,77.1,77.16 | +| screen | 62.47,62.46,62.1,61.95,62.08,61.34,61.26,60.95,60.72,60.88,61.16 | +| blanket | 15.69,15.62,15.65,15.73,15.62,15.66,15.62,15.57,15.48,15.42,15.48 | +| sculpture | 36.04,36.07,36.22,36.21,36.23,36.33,36.39,36.28,36.42,36.5,36.32 | +| hood | 57.61,57.6,57.43,57.56,57.48,57.56,57.39,57.47,57.51,57.41,57.15 | +| sconce | 42.27,42.2,42.15,42.15,42.2,42.08,42.16,41.97,41.87,41.72,42.18 | +| vase | 37.31,37.25,37.29,37.33,37.22,37.22,37.22,37.23,37.17,37.2,37.14 | +| traffic light | 29.43,29.38,29.41,29.42,29.36,29.38,29.44,29.38,29.39,29.33,29.45 | +| tray | 5.48,5.5,5.53,5.54,5.54,5.57,5.59,5.58,5.6,5.61,5.63 | +| ashcan | 37.6,37.6,37.49,37.42,37.42,37.32,37.48,37.4,37.4,37.35,37.52 | +| fan | 58.5,58.47,58.48,58.52,58.48,58.55,58.48,58.51,58.55,58.54,58.54 | +| pier | 12.22,12.24,12.21,11.88,12.14,11.92,12.07,11.67,11.81,11.77,11.62 | +| crt screen | 4.4,4.44,4.51,4.64,4.67,4.82,5.05,5.12,5.23,5.37,5.97 | +| plate | 39.18,39.24,39.23,39.41,39.34,39.43,39.41,39.37,39.49,39.39,39.7 | +| monitor | 28.42,28.36,28.26,28.15,28.3,28.01,27.94,27.76,27.78,27.8,27.76 | +| bulletin board | 44.48,44.77,44.73,45.01,45.19,44.81,45.46,45.6,45.73,46.23,45.89 | +| shower | 1.34,1.35,1.34,1.38,1.41,1.46,1.45,1.52,1.56,1.53,1.6 | +| radiator | 45.27,45.47,45.27,45.41,45.42,45.24,45.27,45.27,45.2,45.26,45.48 | +| glass | 12.28,12.26,12.29,12.31,12.3,12.27,12.35,12.31,12.34,12.34,12.39 | +| clock | 24.97,24.91,24.9,24.94,24.81,24.86,24.87,24.78,24.75,24.81,24.74 | +| flag | 37.37,37.56,37.67,37.75,37.85,37.88,37.96,38.06,38.06,38.09,38.19 | ++---------------------+-------------------------------------------------------------------+ +2023-03-05 01:19:40,401 - mmseg - INFO - Summary: +2023-03-05 01:19:40,402 - mmseg - INFO - ++------------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++------------------------------------------------------------------+ +| 46.11,46.14,46.16,46.19,46.22,46.22,46.28,46.29,46.3,46.32,46.33 | ++------------------------------------------------------------------+ +2023-03-05 01:19:40,402 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:19:40,402 - mmseg - INFO - Iter(val) [250] mIoU: [0.4611, 0.4614, 0.4616, 0.4619, 0.4622, 0.4622, 0.4628, 0.4629, 0.463, 0.4632, 0.4633], copy_paste: 46.11,46.14,46.16,46.19,46.22,46.22,46.28,46.29,46.3,46.32,46.33 +2023-03-05 01:19:40,408 - mmseg - INFO - Swap parameters (before train) before iter [128001] +2023-03-05 01:19:54,086 - mmseg - INFO - Iter [128050/160000] lr: 2.344e-06, eta: 2:49:24, time: 13.593, data_time: 13.328, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7074, loss: 0.1770 +2023-03-05 01:20:10,138 - mmseg - INFO - Iter [128100/160000] lr: 2.344e-06, eta: 2:49:09, time: 0.321, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.5166, loss: 0.1873 +2023-03-05 01:20:23,499 - mmseg - INFO - Iter [128150/160000] lr: 2.344e-06, eta: 2:48:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.5667, loss: 0.1803 +2023-03-05 01:20:36,868 - mmseg - INFO - Iter [128200/160000] lr: 2.344e-06, eta: 2:48:35, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7750, loss: 0.1770 +2023-03-05 01:20:50,152 - mmseg - INFO - Iter [128250/160000] lr: 2.344e-06, eta: 2:48:19, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.7256, loss: 0.1777 +2023-03-05 01:21:03,622 - mmseg - INFO - Iter [128300/160000] lr: 2.344e-06, eta: 2:48:02, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1743, decode.acc_seg: 92.8617, loss: 0.1743 +2023-03-05 01:21:16,965 - mmseg - INFO - Iter [128350/160000] lr: 2.344e-06, eta: 2:47:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7874, loss: 0.1801 +2023-03-05 01:21:30,233 - mmseg - INFO - Iter [128400/160000] lr: 2.344e-06, eta: 2:47:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5880, loss: 0.1839 +2023-03-05 01:21:43,458 - mmseg - INFO - Iter [128450/160000] lr: 2.344e-06, eta: 2:47:13, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6011, loss: 0.1827 +2023-03-05 01:21:56,681 - mmseg - INFO - Iter [128500/160000] lr: 2.344e-06, eta: 2:46:56, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5069, loss: 0.1850 +2023-03-05 01:22:09,880 - mmseg - INFO - Iter [128550/160000] lr: 2.344e-06, eta: 2:46:40, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1829, decode.acc_seg: 92.5448, loss: 0.1829 +2023-03-05 01:22:23,296 - mmseg - INFO - Iter [128600/160000] lr: 2.344e-06, eta: 2:46:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.8503, loss: 0.1765 +2023-03-05 01:22:36,551 - mmseg - INFO - Iter [128650/160000] lr: 2.344e-06, eta: 2:46:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6397, loss: 0.1806 +2023-03-05 01:22:49,863 - mmseg - INFO - Iter [128700/160000] lr: 2.344e-06, eta: 2:45:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1684, decode.acc_seg: 93.0106, loss: 0.1684 +2023-03-05 01:23:05,728 - mmseg - INFO - Iter [128750/160000] lr: 2.344e-06, eta: 2:45:34, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.8383, loss: 0.1762 +2023-03-05 01:23:19,152 - mmseg - INFO - Iter [128800/160000] lr: 2.344e-06, eta: 2:45:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4666, loss: 0.1858 +2023-03-05 01:23:32,529 - mmseg - INFO - Iter [128850/160000] lr: 2.344e-06, eta: 2:45:01, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7355, loss: 0.1761 +2023-03-05 01:23:45,828 - mmseg - INFO - Iter [128900/160000] lr: 2.344e-06, eta: 2:44:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.7061, loss: 0.1803 +2023-03-05 01:23:59,121 - mmseg - INFO - Iter [128950/160000] lr: 2.344e-06, eta: 2:44:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7840, loss: 0.1795 +2023-03-05 01:24:12,561 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:24:12,561 - mmseg - INFO - Iter [129000/160000] lr: 2.344e-06, eta: 2:44:12, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.7056, loss: 0.1781 +2023-03-05 01:24:25,896 - mmseg - INFO - Iter [129050/160000] lr: 2.344e-06, eta: 2:43:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6332, loss: 0.1812 +2023-03-05 01:24:39,223 - mmseg - INFO - Iter [129100/160000] lr: 2.344e-06, eta: 2:43:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8494, loss: 0.1752 +2023-03-05 01:24:52,476 - mmseg - INFO - Iter [129150/160000] lr: 2.344e-06, eta: 2:43:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7076, loss: 0.1788 +2023-03-05 01:25:05,705 - mmseg - INFO - Iter [129200/160000] lr: 2.344e-06, eta: 2:43:06, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.6889, loss: 0.1783 +2023-03-05 01:25:18,993 - mmseg - INFO - Iter [129250/160000] lr: 2.344e-06, eta: 2:42:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.8506, loss: 0.1773 +2023-03-05 01:25:32,246 - mmseg - INFO - Iter [129300/160000] lr: 2.344e-06, eta: 2:42:33, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7822, loss: 0.1774 +2023-03-05 01:25:45,483 - mmseg - INFO - Iter [129350/160000] lr: 2.344e-06, eta: 2:42:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.7905, loss: 0.1779 +2023-03-05 01:26:01,351 - mmseg - INFO - Iter [129400/160000] lr: 2.344e-06, eta: 2:42:00, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6324, loss: 0.1830 +2023-03-05 01:26:14,724 - mmseg - INFO - Iter [129450/160000] lr: 2.344e-06, eta: 2:41:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.5847, loss: 0.1853 +2023-03-05 01:26:27,967 - mmseg - INFO - Iter [129500/160000] lr: 2.344e-06, eta: 2:41:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.8144, loss: 0.1806 +2023-03-05 01:26:41,191 - mmseg - INFO - Iter [129550/160000] lr: 2.344e-06, eta: 2:41:11, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.8010, loss: 0.1767 +2023-03-05 01:26:54,464 - mmseg - INFO - Iter [129600/160000] lr: 2.344e-06, eta: 2:40:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7456, loss: 0.1782 +2023-03-05 01:27:07,725 - mmseg - INFO - Iter [129650/160000] lr: 2.344e-06, eta: 2:40:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5007, loss: 0.1814 +2023-03-05 01:27:21,166 - mmseg - INFO - Iter [129700/160000] lr: 2.344e-06, eta: 2:40:21, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6364, loss: 0.1832 +2023-03-05 01:27:34,450 - mmseg - INFO - Iter [129750/160000] lr: 2.344e-06, eta: 2:40:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7287, loss: 0.1761 +2023-03-05 01:27:47,778 - mmseg - INFO - Iter [129800/160000] lr: 2.344e-06, eta: 2:39:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1721, decode.acc_seg: 92.9080, loss: 0.1721 +2023-03-05 01:28:01,043 - mmseg - INFO - Iter [129850/160000] lr: 2.344e-06, eta: 2:39:32, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8583, loss: 0.1758 +2023-03-05 01:28:14,459 - mmseg - INFO - Iter [129900/160000] lr: 2.344e-06, eta: 2:39:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.8876, loss: 0.1742 +2023-03-05 01:28:27,772 - mmseg - INFO - Iter [129950/160000] lr: 2.344e-06, eta: 2:38:59, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1886, decode.acc_seg: 92.2743, loss: 0.1886 +2023-03-05 01:28:43,573 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:28:43,573 - mmseg - INFO - Iter [130000/160000] lr: 2.344e-06, eta: 2:38:43, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.7298, loss: 0.1777 +2023-03-05 01:28:56,991 - mmseg - INFO - Iter [130050/160000] lr: 2.344e-06, eta: 2:38:27, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.8142, loss: 0.1790 +2023-03-05 01:29:10,360 - mmseg - INFO - Iter [130100/160000] lr: 2.344e-06, eta: 2:38:10, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.8042, loss: 0.1786 +2023-03-05 01:29:23,610 - mmseg - INFO - Iter [130150/160000] lr: 2.344e-06, eta: 2:37:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1730, decode.acc_seg: 93.0191, loss: 0.1730 +2023-03-05 01:29:36,980 - mmseg - INFO - Iter [130200/160000] lr: 2.344e-06, eta: 2:37:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.6706, loss: 0.1824 +2023-03-05 01:29:50,167 - mmseg - INFO - Iter [130250/160000] lr: 2.344e-06, eta: 2:37:21, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.5876, loss: 0.1805 +2023-03-05 01:30:03,516 - mmseg - INFO - Iter [130300/160000] lr: 2.344e-06, eta: 2:37:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6687, loss: 0.1794 +2023-03-05 01:30:16,897 - mmseg - INFO - Iter [130350/160000] lr: 2.344e-06, eta: 2:36:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7768, loss: 0.1774 +2023-03-05 01:30:30,168 - mmseg - INFO - Iter [130400/160000] lr: 2.344e-06, eta: 2:36:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7604, loss: 0.1797 +2023-03-05 01:30:43,508 - mmseg - INFO - Iter [130450/160000] lr: 2.344e-06, eta: 2:36:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5243, loss: 0.1847 +2023-03-05 01:30:56,772 - mmseg - INFO - Iter [130500/160000] lr: 2.344e-06, eta: 2:35:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7872, loss: 0.1770 +2023-03-05 01:31:10,007 - mmseg - INFO - Iter [130550/160000] lr: 2.344e-06, eta: 2:35:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.3931, loss: 0.1854 +2023-03-05 01:31:23,291 - mmseg - INFO - Iter [130600/160000] lr: 2.344e-06, eta: 2:35:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5072, loss: 0.1838 +2023-03-05 01:31:39,210 - mmseg - INFO - Iter [130650/160000] lr: 2.344e-06, eta: 2:35:10, time: 0.318, data_time: 0.058, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6876, loss: 0.1801 +2023-03-05 01:31:52,454 - mmseg - INFO - Iter [130700/160000] lr: 2.344e-06, eta: 2:34:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.8706, loss: 0.1760 +2023-03-05 01:32:05,733 - mmseg - INFO - Iter [130750/160000] lr: 2.344e-06, eta: 2:34:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6181, loss: 0.1815 +2023-03-05 01:32:18,942 - mmseg - INFO - Iter [130800/160000] lr: 2.344e-06, eta: 2:34:20, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6765, loss: 0.1814 +2023-03-05 01:32:32,147 - mmseg - INFO - Iter [130850/160000] lr: 2.344e-06, eta: 2:34:04, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.6732, loss: 0.1779 +2023-03-05 01:32:45,450 - mmseg - INFO - Iter [130900/160000] lr: 2.344e-06, eta: 2:33:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.5396, loss: 0.1827 +2023-03-05 01:32:58,688 - mmseg - INFO - Iter [130950/160000] lr: 2.344e-06, eta: 2:33:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.8242, loss: 0.1788 +2023-03-05 01:33:11,995 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:33:11,995 - mmseg - INFO - Iter [131000/160000] lr: 2.344e-06, eta: 2:33:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.7933, loss: 0.1757 +2023-03-05 01:33:25,311 - mmseg - INFO - Iter [131050/160000] lr: 2.344e-06, eta: 2:32:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7663, loss: 0.1780 +2023-03-05 01:33:38,544 - mmseg - INFO - Iter [131100/160000] lr: 2.344e-06, eta: 2:32:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6841, loss: 0.1830 +2023-03-05 01:33:51,927 - mmseg - INFO - Iter [131150/160000] lr: 2.344e-06, eta: 2:32:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1857, decode.acc_seg: 92.5025, loss: 0.1857 +2023-03-05 01:34:05,359 - mmseg - INFO - Iter [131200/160000] lr: 2.344e-06, eta: 2:32:09, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.5979, loss: 0.1805 +2023-03-05 01:34:21,129 - mmseg - INFO - Iter [131250/160000] lr: 2.344e-06, eta: 2:31:53, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.8305, loss: 0.1763 +2023-03-05 01:34:34,518 - mmseg - INFO - Iter [131300/160000] lr: 2.344e-06, eta: 2:31:37, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.8870, loss: 0.1761 +2023-03-05 01:34:47,855 - mmseg - INFO - Iter [131350/160000] lr: 2.344e-06, eta: 2:31:20, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5335, loss: 0.1835 +2023-03-05 01:35:01,205 - mmseg - INFO - Iter [131400/160000] lr: 2.344e-06, eta: 2:31:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1744, decode.acc_seg: 92.9419, loss: 0.1744 +2023-03-05 01:35:14,533 - mmseg - INFO - Iter [131450/160000] lr: 2.344e-06, eta: 2:30:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6087, loss: 0.1813 +2023-03-05 01:35:27,762 - mmseg - INFO - Iter [131500/160000] lr: 2.344e-06, eta: 2:30:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.7518, loss: 0.1783 +2023-03-05 01:35:40,973 - mmseg - INFO - Iter [131550/160000] lr: 2.344e-06, eta: 2:30:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6256, loss: 0.1828 +2023-03-05 01:35:54,278 - mmseg - INFO - Iter [131600/160000] lr: 2.344e-06, eta: 2:29:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7568, loss: 0.1788 +2023-03-05 01:36:07,601 - mmseg - INFO - Iter [131650/160000] lr: 2.344e-06, eta: 2:29:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.2989, loss: 0.1859 +2023-03-05 01:36:20,803 - mmseg - INFO - Iter [131700/160000] lr: 2.344e-06, eta: 2:29:26, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.7696, loss: 0.1754 +2023-03-05 01:36:34,208 - mmseg - INFO - Iter [131750/160000] lr: 2.344e-06, eta: 2:29:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.8909, loss: 0.1742 +2023-03-05 01:36:47,591 - mmseg - INFO - Iter [131800/160000] lr: 2.344e-06, eta: 2:28:53, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6964, loss: 0.1834 +2023-03-05 01:37:00,868 - mmseg - INFO - Iter [131850/160000] lr: 2.344e-06, eta: 2:28:37, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.6792, loss: 0.1793 +2023-03-05 01:37:16,831 - mmseg - INFO - Iter [131900/160000] lr: 2.344e-06, eta: 2:28:21, time: 0.319, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.5871, loss: 0.1853 +2023-03-05 01:37:30,244 - mmseg - INFO - Iter [131950/160000] lr: 2.344e-06, eta: 2:28:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.7775, loss: 0.1762 +2023-03-05 01:37:43,468 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:37:43,468 - mmseg - INFO - Iter [132000/160000] lr: 2.344e-06, eta: 2:27:48, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1714, decode.acc_seg: 93.0341, loss: 0.1714 +2023-03-05 01:37:56,713 - mmseg - INFO - Iter [132050/160000] lr: 2.344e-06, eta: 2:27:32, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6087, loss: 0.1825 +2023-03-05 01:38:10,012 - mmseg - INFO - Iter [132100/160000] lr: 2.344e-06, eta: 2:27:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6343, loss: 0.1830 +2023-03-05 01:38:23,368 - mmseg - INFO - Iter [132150/160000] lr: 2.344e-06, eta: 2:26:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4602, loss: 0.1862 +2023-03-05 01:38:36,636 - mmseg - INFO - Iter [132200/160000] lr: 2.344e-06, eta: 2:26:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.7125, loss: 0.1792 +2023-03-05 01:38:50,035 - mmseg - INFO - Iter [132250/160000] lr: 2.344e-06, eta: 2:26:26, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.6419, loss: 0.1831 +2023-03-05 01:39:03,412 - mmseg - INFO - Iter [132300/160000] lr: 2.344e-06, eta: 2:26:10, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.5172, loss: 0.1814 +2023-03-05 01:39:16,869 - mmseg - INFO - Iter [132350/160000] lr: 2.344e-06, eta: 2:25:53, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5560, loss: 0.1818 +2023-03-05 01:39:30,409 - mmseg - INFO - Iter [132400/160000] lr: 2.344e-06, eta: 2:25:37, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6292, loss: 0.1820 +2023-03-05 01:39:43,860 - mmseg - INFO - Iter [132450/160000] lr: 2.344e-06, eta: 2:25:21, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.8796, loss: 0.1793 +2023-03-05 01:39:57,135 - mmseg - INFO - Iter [132500/160000] lr: 2.344e-06, eta: 2:25:05, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6527, loss: 0.1795 +2023-03-05 01:40:13,057 - mmseg - INFO - Iter [132550/160000] lr: 2.344e-06, eta: 2:24:49, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.5521, loss: 0.1789 +2023-03-05 01:40:26,513 - mmseg - INFO - Iter [132600/160000] lr: 2.344e-06, eta: 2:24:32, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.5257, loss: 0.1840 +2023-03-05 01:40:40,017 - mmseg - INFO - Iter [132650/160000] lr: 2.344e-06, eta: 2:24:16, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.8067, loss: 0.1783 +2023-03-05 01:40:53,391 - mmseg - INFO - Iter [132700/160000] lr: 2.344e-06, eta: 2:24:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.7922, loss: 0.1777 +2023-03-05 01:41:06,811 - mmseg - INFO - Iter [132750/160000] lr: 2.344e-06, eta: 2:23:43, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8168, loss: 0.1752 +2023-03-05 01:41:20,175 - mmseg - INFO - Iter [132800/160000] lr: 2.344e-06, eta: 2:23:27, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.7035, loss: 0.1826 +2023-03-05 01:41:33,500 - mmseg - INFO - Iter [132850/160000] lr: 2.344e-06, eta: 2:23:11, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.4859, loss: 0.1849 +2023-03-05 01:41:46,749 - mmseg - INFO - Iter [132900/160000] lr: 2.344e-06, eta: 2:22:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6036, loss: 0.1811 +2023-03-05 01:41:59,979 - mmseg - INFO - Iter [132950/160000] lr: 2.344e-06, eta: 2:22:38, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.7253, loss: 0.1771 +2023-03-05 01:42:13,303 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:42:13,303 - mmseg - INFO - Iter [133000/160000] lr: 2.344e-06, eta: 2:22:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7772, loss: 0.1761 +2023-03-05 01:42:26,712 - mmseg - INFO - Iter [133050/160000] lr: 2.344e-06, eta: 2:22:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5217, loss: 0.1831 +2023-03-05 01:42:39,962 - mmseg - INFO - Iter [133100/160000] lr: 2.344e-06, eta: 2:21:49, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1741, decode.acc_seg: 92.8388, loss: 0.1741 +2023-03-05 01:42:55,870 - mmseg - INFO - Iter [133150/160000] lr: 2.344e-06, eta: 2:21:33, time: 0.318, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.7243, loss: 0.1822 +2023-03-05 01:43:09,269 - mmseg - INFO - Iter [133200/160000] lr: 2.344e-06, eta: 2:21:17, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.5413, loss: 0.1846 +2023-03-05 01:43:22,612 - mmseg - INFO - Iter [133250/160000] lr: 2.344e-06, eta: 2:21:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1697, decode.acc_seg: 92.9705, loss: 0.1697 +2023-03-05 01:43:35,846 - mmseg - INFO - Iter [133300/160000] lr: 2.344e-06, eta: 2:20:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4922, loss: 0.1868 +2023-03-05 01:43:49,035 - mmseg - INFO - Iter [133350/160000] lr: 2.344e-06, eta: 2:20:28, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.5111, loss: 0.1799 +2023-03-05 01:44:02,392 - mmseg - INFO - Iter [133400/160000] lr: 2.344e-06, eta: 2:20:12, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.7917, loss: 0.1757 +2023-03-05 01:44:15,780 - mmseg - INFO - Iter [133450/160000] lr: 2.344e-06, eta: 2:19:55, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.8661, loss: 0.1754 +2023-03-05 01:44:29,129 - mmseg - INFO - Iter [133500/160000] lr: 2.344e-06, eta: 2:19:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6492, loss: 0.1802 +2023-03-05 01:44:42,491 - mmseg - INFO - Iter [133550/160000] lr: 2.344e-06, eta: 2:19:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.7850, loss: 0.1820 +2023-03-05 01:44:55,742 - mmseg - INFO - Iter [133600/160000] lr: 2.344e-06, eta: 2:19:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1741, decode.acc_seg: 92.8152, loss: 0.1741 +2023-03-05 01:45:09,167 - mmseg - INFO - Iter [133650/160000] lr: 2.344e-06, eta: 2:18:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.9003, loss: 0.1760 +2023-03-05 01:45:22,399 - mmseg - INFO - Iter [133700/160000] lr: 2.344e-06, eta: 2:18:34, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7879, loss: 0.1780 +2023-03-05 01:45:35,686 - mmseg - INFO - Iter [133750/160000] lr: 2.344e-06, eta: 2:18:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.9380, loss: 0.1757 +2023-03-05 01:45:51,458 - mmseg - INFO - Iter [133800/160000] lr: 2.344e-06, eta: 2:18:02, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.4910, loss: 0.1853 +2023-03-05 01:46:04,719 - mmseg - INFO - Iter [133850/160000] lr: 2.344e-06, eta: 2:17:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7877, loss: 0.1793 +2023-03-05 01:46:17,955 - mmseg - INFO - Iter [133900/160000] lr: 2.344e-06, eta: 2:17:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.9858, loss: 0.1754 +2023-03-05 01:46:31,194 - mmseg - INFO - Iter [133950/160000] lr: 2.344e-06, eta: 2:17:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.9192, loss: 0.1754 +2023-03-05 01:46:44,640 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:46:44,640 - mmseg - INFO - Iter [134000/160000] lr: 2.344e-06, eta: 2:16:57, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6849, loss: 0.1807 +2023-03-05 01:46:58,101 - mmseg - INFO - Iter [134050/160000] lr: 2.344e-06, eta: 2:16:40, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.7535, loss: 0.1765 +2023-03-05 01:47:11,516 - mmseg - INFO - Iter [134100/160000] lr: 2.344e-06, eta: 2:16:24, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.8569, loss: 0.1749 +2023-03-05 01:47:24,786 - mmseg - INFO - Iter [134150/160000] lr: 2.344e-06, eta: 2:16:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.6961, loss: 0.1793 +2023-03-05 01:47:38,199 - mmseg - INFO - Iter [134200/160000] lr: 2.344e-06, eta: 2:15:52, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.5746, loss: 0.1807 +2023-03-05 01:47:51,426 - mmseg - INFO - Iter [134250/160000] lr: 2.344e-06, eta: 2:15:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.7029, loss: 0.1834 +2023-03-05 01:48:04,859 - mmseg - INFO - Iter [134300/160000] lr: 2.344e-06, eta: 2:15:19, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.4463, loss: 0.1846 +2023-03-05 01:48:18,194 - mmseg - INFO - Iter [134350/160000] lr: 2.344e-06, eta: 2:15:03, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1755, decode.acc_seg: 92.9053, loss: 0.1755 +2023-03-05 01:48:31,518 - mmseg - INFO - Iter [134400/160000] lr: 2.344e-06, eta: 2:14:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.7724, loss: 0.1791 +2023-03-05 01:48:47,368 - mmseg - INFO - Iter [134450/160000] lr: 2.344e-06, eta: 2:14:31, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.6993, loss: 0.1809 +2023-03-05 01:49:00,737 - mmseg - INFO - Iter [134500/160000] lr: 2.344e-06, eta: 2:14:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1732, decode.acc_seg: 92.7156, loss: 0.1732 +2023-03-05 01:49:14,095 - mmseg - INFO - Iter [134550/160000] lr: 2.344e-06, eta: 2:13:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1748, decode.acc_seg: 92.7898, loss: 0.1748 +2023-03-05 01:49:27,498 - mmseg - INFO - Iter [134600/160000] lr: 2.344e-06, eta: 2:13:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7928, loss: 0.1782 +2023-03-05 01:49:40,918 - mmseg - INFO - Iter [134650/160000] lr: 2.344e-06, eta: 2:13:26, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7146, loss: 0.1785 +2023-03-05 01:49:54,351 - mmseg - INFO - Iter [134700/160000] lr: 2.344e-06, eta: 2:13:09, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1919, decode.acc_seg: 92.3131, loss: 0.1919 +2023-03-05 01:50:07,609 - mmseg - INFO - Iter [134750/160000] lr: 2.344e-06, eta: 2:12:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.7140, loss: 0.1809 +2023-03-05 01:50:20,944 - mmseg - INFO - Iter [134800/160000] lr: 2.344e-06, eta: 2:12:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1692, decode.acc_seg: 93.1038, loss: 0.1692 +2023-03-05 01:50:34,205 - mmseg - INFO - Iter [134850/160000] lr: 2.344e-06, eta: 2:12:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.7874, loss: 0.1769 +2023-03-05 01:50:47,445 - mmseg - INFO - Iter [134900/160000] lr: 2.344e-06, eta: 2:12:04, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.4014, loss: 0.1837 +2023-03-05 01:51:00,734 - mmseg - INFO - Iter [134950/160000] lr: 2.344e-06, eta: 2:11:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.5174, loss: 0.1846 +2023-03-05 01:51:14,004 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:51:14,005 - mmseg - INFO - Iter [135000/160000] lr: 2.344e-06, eta: 2:11:32, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.7511, loss: 0.1779 +2023-03-05 01:51:29,807 - mmseg - INFO - Iter [135050/160000] lr: 2.344e-06, eta: 2:11:16, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.7094, loss: 0.1813 +2023-03-05 01:51:43,047 - mmseg - INFO - Iter [135100/160000] lr: 2.344e-06, eta: 2:11:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6501, loss: 0.1782 +2023-03-05 01:51:56,332 - mmseg - INFO - Iter [135150/160000] lr: 2.344e-06, eta: 2:10:44, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6152, loss: 0.1828 +2023-03-05 01:52:09,749 - mmseg - INFO - Iter [135200/160000] lr: 2.344e-06, eta: 2:10:27, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1939, decode.acc_seg: 92.3069, loss: 0.1939 +2023-03-05 01:52:22,955 - mmseg - INFO - Iter [135250/160000] lr: 2.344e-06, eta: 2:10:11, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.5084, loss: 0.1819 +2023-03-05 01:52:36,245 - mmseg - INFO - Iter [135300/160000] lr: 2.344e-06, eta: 2:09:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.8350, loss: 0.1778 +2023-03-05 01:52:49,510 - mmseg - INFO - Iter [135350/160000] lr: 2.344e-06, eta: 2:09:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.6184, loss: 0.1791 +2023-03-05 01:53:02,860 - mmseg - INFO - Iter [135400/160000] lr: 2.344e-06, eta: 2:09:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1696, decode.acc_seg: 93.0765, loss: 0.1696 +2023-03-05 01:53:16,332 - mmseg - INFO - Iter [135450/160000] lr: 2.344e-06, eta: 2:09:06, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6154, loss: 0.1810 +2023-03-05 01:53:29,590 - mmseg - INFO - Iter [135500/160000] lr: 2.344e-06, eta: 2:08:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.8901, loss: 0.1763 +2023-03-05 01:53:42,982 - mmseg - INFO - Iter [135550/160000] lr: 2.344e-06, eta: 2:08:34, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1876, decode.acc_seg: 92.4770, loss: 0.1876 +2023-03-05 01:53:56,252 - mmseg - INFO - Iter [135600/160000] lr: 2.344e-06, eta: 2:08:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.7844, loss: 0.1758 +2023-03-05 01:54:09,574 - mmseg - INFO - Iter [135650/160000] lr: 2.344e-06, eta: 2:08:01, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.5310, loss: 0.1856 +2023-03-05 01:54:25,424 - mmseg - INFO - Iter [135700/160000] lr: 2.344e-06, eta: 2:07:46, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.7687, loss: 0.1760 +2023-03-05 01:54:38,787 - mmseg - INFO - Iter [135750/160000] lr: 2.344e-06, eta: 2:07:29, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.5041, loss: 0.1849 +2023-03-05 01:54:52,207 - mmseg - INFO - Iter [135800/160000] lr: 2.344e-06, eta: 2:07:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.6775, loss: 0.1777 +2023-03-05 01:55:05,427 - mmseg - INFO - Iter [135850/160000] lr: 2.344e-06, eta: 2:06:57, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.9393, loss: 0.1768 +2023-03-05 01:55:18,748 - mmseg - INFO - Iter [135900/160000] lr: 2.344e-06, eta: 2:06:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.6200, loss: 0.1820 +2023-03-05 01:55:32,031 - mmseg - INFO - Iter [135950/160000] lr: 2.344e-06, eta: 2:06:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1715, decode.acc_seg: 92.9940, loss: 0.1715 +2023-03-05 01:55:45,379 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 01:55:45,379 - mmseg - INFO - Iter [136000/160000] lr: 2.344e-06, eta: 2:06:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5494, loss: 0.1825 +2023-03-05 01:55:58,758 - mmseg - INFO - Iter [136050/160000] lr: 2.344e-06, eta: 2:05:52, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.5574, loss: 0.1818 +2023-03-05 01:56:12,105 - mmseg - INFO - Iter [136100/160000] lr: 2.344e-06, eta: 2:05:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6618, loss: 0.1810 +2023-03-05 01:56:25,406 - mmseg - INFO - Iter [136150/160000] lr: 2.344e-06, eta: 2:05:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.5022, loss: 0.1836 +2023-03-05 01:56:38,576 - mmseg - INFO - Iter [136200/160000] lr: 2.344e-06, eta: 2:05:04, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.6434, loss: 0.1791 +2023-03-05 01:56:51,889 - mmseg - INFO - Iter [136250/160000] lr: 2.344e-06, eta: 2:04:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1918, decode.acc_seg: 92.5610, loss: 0.1918 +2023-03-05 01:57:07,676 - mmseg - INFO - Iter [136300/160000] lr: 2.344e-06, eta: 2:04:32, time: 0.316, data_time: 0.058, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.8445, loss: 0.1752 +2023-03-05 01:57:21,078 - mmseg - INFO - Iter [136350/160000] lr: 2.344e-06, eta: 2:04:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7675, loss: 0.1789 +2023-03-05 01:57:34,325 - mmseg - INFO - Iter [136400/160000] lr: 2.344e-06, eta: 2:03:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.6700, loss: 0.1781 +2023-03-05 01:57:47,643 - mmseg - INFO - Iter [136450/160000] lr: 2.344e-06, eta: 2:03:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1915, decode.acc_seg: 92.3840, loss: 0.1915 +2023-03-05 01:58:00,891 - mmseg - INFO - Iter [136500/160000] lr: 2.344e-06, eta: 2:03:27, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7083, loss: 0.1784 +2023-03-05 01:58:14,152 - mmseg - INFO - Iter [136550/160000] lr: 2.344e-06, eta: 2:03:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.6251, loss: 0.1779 +2023-03-05 01:58:27,376 - mmseg - INFO - Iter [136600/160000] lr: 2.344e-06, eta: 2:02:55, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.7915, loss: 0.1776 +2023-03-05 01:58:40,559 - mmseg - INFO - Iter [136650/160000] lr: 2.344e-06, eta: 2:02:38, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6804, loss: 0.1803 +2023-03-05 01:58:53,870 - mmseg - INFO - Iter [136700/160000] lr: 2.344e-06, eta: 2:02:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.5809, loss: 0.1815 +2023-03-05 01:59:07,160 - mmseg - INFO - Iter [136750/160000] lr: 2.344e-06, eta: 2:02:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7000, loss: 0.1793 +2023-03-05 01:59:20,538 - mmseg - INFO - Iter [136800/160000] lr: 2.344e-06, eta: 2:01:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.5835, loss: 0.1823 +2023-03-05 01:59:33,848 - mmseg - INFO - Iter [136850/160000] lr: 2.344e-06, eta: 2:01:34, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.6493, loss: 0.1802 +2023-03-05 01:59:47,228 - mmseg - INFO - Iter [136900/160000] lr: 2.344e-06, eta: 2:01:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6084, loss: 0.1813 +2023-03-05 02:00:02,923 - mmseg - INFO - Iter [136950/160000] lr: 2.344e-06, eta: 2:01:02, time: 0.314, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6918, loss: 0.1794 +2023-03-05 02:00:16,330 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:00:16,330 - mmseg - INFO - Iter [137000/160000] lr: 2.344e-06, eta: 2:00:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.4778, loss: 0.1824 +2023-03-05 02:00:29,713 - mmseg - INFO - Iter [137050/160000] lr: 2.344e-06, eta: 2:00:29, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.7091, loss: 0.1816 +2023-03-05 02:00:43,159 - mmseg - INFO - Iter [137100/160000] lr: 2.344e-06, eta: 2:00:13, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5592, loss: 0.1809 +2023-03-05 02:00:56,494 - mmseg - INFO - Iter [137150/160000] lr: 2.344e-06, eta: 1:59:57, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.8461, loss: 0.1769 +2023-03-05 02:01:09,774 - mmseg - INFO - Iter [137200/160000] lr: 2.344e-06, eta: 1:59:41, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.8433, loss: 0.1781 +2023-03-05 02:01:23,063 - mmseg - INFO - Iter [137250/160000] lr: 2.344e-06, eta: 1:59:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7549, loss: 0.1778 +2023-03-05 02:01:36,544 - mmseg - INFO - Iter [137300/160000] lr: 2.344e-06, eta: 1:59:09, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.8921, loss: 0.1742 +2023-03-05 02:01:49,977 - mmseg - INFO - Iter [137350/160000] lr: 2.344e-06, eta: 1:58:53, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6327, loss: 0.1786 +2023-03-05 02:02:03,257 - mmseg - INFO - Iter [137400/160000] lr: 2.344e-06, eta: 1:58:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.6517, loss: 0.1835 +2023-03-05 02:02:16,731 - mmseg - INFO - Iter [137450/160000] lr: 2.344e-06, eta: 1:58:20, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.6627, loss: 0.1784 +2023-03-05 02:02:30,118 - mmseg - INFO - Iter [137500/160000] lr: 2.344e-06, eta: 1:58:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7459, loss: 0.1797 +2023-03-05 02:02:43,415 - mmseg - INFO - Iter [137550/160000] lr: 2.344e-06, eta: 1:57:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.6953, loss: 0.1793 +2023-03-05 02:02:59,235 - mmseg - INFO - Iter [137600/160000] lr: 2.344e-06, eta: 1:57:32, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.7237, loss: 0.1812 +2023-03-05 02:03:12,523 - mmseg - INFO - Iter [137650/160000] lr: 2.344e-06, eta: 1:57:16, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7959, loss: 0.1768 +2023-03-05 02:03:25,841 - mmseg - INFO - Iter [137700/160000] lr: 2.344e-06, eta: 1:57:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.5960, loss: 0.1819 +2023-03-05 02:03:39,142 - mmseg - INFO - Iter [137750/160000] lr: 2.344e-06, eta: 1:56:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6712, loss: 0.1800 +2023-03-05 02:03:52,550 - mmseg - INFO - Iter [137800/160000] lr: 2.344e-06, eta: 1:56:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1739, decode.acc_seg: 92.9845, loss: 0.1739 +2023-03-05 02:04:06,150 - mmseg - INFO - Iter [137850/160000] lr: 2.344e-06, eta: 1:56:12, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.6946, loss: 0.1789 +2023-03-05 02:04:19,642 - mmseg - INFO - Iter [137900/160000] lr: 2.344e-06, eta: 1:55:56, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6549, loss: 0.1804 +2023-03-05 02:04:33,041 - mmseg - INFO - Iter [137950/160000] lr: 2.344e-06, eta: 1:55:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.7077, loss: 0.1780 +2023-03-05 02:04:46,384 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:04:46,385 - mmseg - INFO - Iter [138000/160000] lr: 2.344e-06, eta: 1:55:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.7600, loss: 0.1775 +2023-03-05 02:04:59,663 - mmseg - INFO - Iter [138050/160000] lr: 2.344e-06, eta: 1:55:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.8015, loss: 0.1772 +2023-03-05 02:05:12,971 - mmseg - INFO - Iter [138100/160000] lr: 2.344e-06, eta: 1:54:51, time: 0.266, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.7159, loss: 0.1825 +2023-03-05 02:05:26,353 - mmseg - INFO - Iter [138150/160000] lr: 2.344e-06, eta: 1:54:35, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1885, decode.acc_seg: 92.4336, loss: 0.1885 +2023-03-05 02:05:42,106 - mmseg - INFO - Iter [138200/160000] lr: 2.344e-06, eta: 1:54:19, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6318, loss: 0.1832 +2023-03-05 02:05:55,584 - mmseg - INFO - Iter [138250/160000] lr: 2.344e-06, eta: 1:54:03, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.6877, loss: 0.1777 +2023-03-05 02:06:08,888 - mmseg - INFO - Iter [138300/160000] lr: 2.344e-06, eta: 1:53:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.4662, loss: 0.1869 +2023-03-05 02:06:22,162 - mmseg - INFO - Iter [138350/160000] lr: 2.344e-06, eta: 1:53:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1903, decode.acc_seg: 92.3763, loss: 0.1903 +2023-03-05 02:06:35,502 - mmseg - INFO - Iter [138400/160000] lr: 2.344e-06, eta: 1:53:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.7928, loss: 0.1771 +2023-03-05 02:06:48,853 - mmseg - INFO - Iter [138450/160000] lr: 2.344e-06, eta: 1:52:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.4882, loss: 0.1851 +2023-03-05 02:07:02,163 - mmseg - INFO - Iter [138500/160000] lr: 2.344e-06, eta: 1:52:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.6508, loss: 0.1817 +2023-03-05 02:07:15,546 - mmseg - INFO - Iter [138550/160000] lr: 2.344e-06, eta: 1:52:27, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1912, decode.acc_seg: 92.4091, loss: 0.1912 +2023-03-05 02:07:28,841 - mmseg - INFO - Iter [138600/160000] lr: 2.344e-06, eta: 1:52:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6990, loss: 0.1800 +2023-03-05 02:07:42,194 - mmseg - INFO - Iter [138650/160000] lr: 2.344e-06, eta: 1:51:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.7858, loss: 0.1765 +2023-03-05 02:07:55,473 - mmseg - INFO - Iter [138700/160000] lr: 2.344e-06, eta: 1:51:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.6262, loss: 0.1848 +2023-03-05 02:08:08,758 - mmseg - INFO - Iter [138750/160000] lr: 2.344e-06, eta: 1:51:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1704, decode.acc_seg: 93.1292, loss: 0.1704 +2023-03-05 02:08:22,164 - mmseg - INFO - Iter [138800/160000] lr: 2.344e-06, eta: 1:51:06, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.5978, loss: 0.1822 +2023-03-05 02:08:38,047 - mmseg - INFO - Iter [138850/160000] lr: 2.344e-06, eta: 1:50:50, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6702, loss: 0.1828 +2023-03-05 02:08:51,419 - mmseg - INFO - Iter [138900/160000] lr: 2.344e-06, eta: 1:50:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.4980, loss: 0.1875 +2023-03-05 02:09:04,794 - mmseg - INFO - Iter [138950/160000] lr: 2.344e-06, eta: 1:50:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6244, loss: 0.1810 +2023-03-05 02:09:18,213 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:09:18,213 - mmseg - INFO - Iter [139000/160000] lr: 2.344e-06, eta: 1:50:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1715, decode.acc_seg: 92.9416, loss: 0.1715 +2023-03-05 02:09:31,538 - mmseg - INFO - Iter [139050/160000] lr: 2.344e-06, eta: 1:49:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.3899, loss: 0.1849 +2023-03-05 02:09:44,811 - mmseg - INFO - Iter [139100/160000] lr: 2.344e-06, eta: 1:49:30, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.5168, loss: 0.1831 +2023-03-05 02:09:58,136 - mmseg - INFO - Iter [139150/160000] lr: 2.344e-06, eta: 1:49:14, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1746, decode.acc_seg: 93.0305, loss: 0.1746 +2023-03-05 02:10:11,453 - mmseg - INFO - Iter [139200/160000] lr: 2.344e-06, eta: 1:48:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7501, loss: 0.1782 +2023-03-05 02:10:24,819 - mmseg - INFO - Iter [139250/160000] lr: 2.344e-06, eta: 1:48:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5406, loss: 0.1844 +2023-03-05 02:10:38,119 - mmseg - INFO - Iter [139300/160000] lr: 2.344e-06, eta: 1:48:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.6342, loss: 0.1822 +2023-03-05 02:10:51,610 - mmseg - INFO - Iter [139350/160000] lr: 2.344e-06, eta: 1:48:10, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.4950, loss: 0.1813 +2023-03-05 02:11:05,080 - mmseg - INFO - Iter [139400/160000] lr: 2.344e-06, eta: 1:47:54, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1863, decode.acc_seg: 92.4907, loss: 0.1863 +2023-03-05 02:11:18,370 - mmseg - INFO - Iter [139450/160000] lr: 2.344e-06, eta: 1:47:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.8163, loss: 0.1780 +2023-03-05 02:11:34,528 - mmseg - INFO - Iter [139500/160000] lr: 2.344e-06, eta: 1:47:22, time: 0.323, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1770, decode.acc_seg: 92.7031, loss: 0.1770 +2023-03-05 02:11:47,901 - mmseg - INFO - Iter [139550/160000] lr: 2.344e-06, eta: 1:47:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1763, decode.acc_seg: 92.8365, loss: 0.1763 +2023-03-05 02:12:01,257 - mmseg - INFO - Iter [139600/160000] lr: 2.344e-06, eta: 1:46:50, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.6959, loss: 0.1791 +2023-03-05 02:12:14,567 - mmseg - INFO - Iter [139650/160000] lr: 2.344e-06, eta: 1:46:34, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1764, decode.acc_seg: 92.8222, loss: 0.1764 +2023-03-05 02:12:27,939 - mmseg - INFO - Iter [139700/160000] lr: 2.344e-06, eta: 1:46:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7861, loss: 0.1804 +2023-03-05 02:12:41,209 - mmseg - INFO - Iter [139750/160000] lr: 2.344e-06, eta: 1:46:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6868, loss: 0.1812 +2023-03-05 02:12:54,536 - mmseg - INFO - Iter [139800/160000] lr: 2.344e-06, eta: 1:45:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6237, loss: 0.1825 +2023-03-05 02:13:07,807 - mmseg - INFO - Iter [139850/160000] lr: 2.344e-06, eta: 1:45:29, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6821, loss: 0.1801 +2023-03-05 02:13:21,061 - mmseg - INFO - Iter [139900/160000] lr: 2.344e-06, eta: 1:45:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6149, loss: 0.1834 +2023-03-05 02:13:34,493 - mmseg - INFO - Iter [139950/160000] lr: 2.344e-06, eta: 1:44:57, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5560, loss: 0.1843 +2023-03-05 02:13:47,667 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:13:47,667 - mmseg - INFO - Iter [140000/160000] lr: 2.344e-06, eta: 1:44:41, time: 0.263, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.9138, loss: 0.1757 +2023-03-05 02:14:01,030 - mmseg - INFO - Iter [140050/160000] lr: 1.172e-06, eta: 1:44:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.6001, loss: 0.1845 +2023-03-05 02:14:17,123 - mmseg - INFO - Iter [140100/160000] lr: 1.172e-06, eta: 1:44:10, time: 0.322, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.7805, loss: 0.1787 +2023-03-05 02:14:30,459 - mmseg - INFO - Iter [140150/160000] lr: 1.172e-06, eta: 1:43:54, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7342, loss: 0.1793 +2023-03-05 02:14:43,739 - mmseg - INFO - Iter [140200/160000] lr: 1.172e-06, eta: 1:43:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7842, loss: 0.1768 +2023-03-05 02:14:57,109 - mmseg - INFO - Iter [140250/160000] lr: 1.172e-06, eta: 1:43:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.8102, loss: 0.1797 +2023-03-05 02:15:10,354 - mmseg - INFO - Iter [140300/160000] lr: 1.172e-06, eta: 1:43:05, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1723, decode.acc_seg: 92.8888, loss: 0.1723 +2023-03-05 02:15:23,630 - mmseg - INFO - Iter [140350/160000] lr: 1.172e-06, eta: 1:42:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.4368, loss: 0.1849 +2023-03-05 02:15:36,999 - mmseg - INFO - Iter [140400/160000] lr: 1.172e-06, eta: 1:42:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.6330, loss: 0.1838 +2023-03-05 02:15:50,332 - mmseg - INFO - Iter [140450/160000] lr: 1.172e-06, eta: 1:42:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1829, decode.acc_seg: 92.4891, loss: 0.1829 +2023-03-05 02:16:03,666 - mmseg - INFO - Iter [140500/160000] lr: 1.172e-06, eta: 1:42:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7398, loss: 0.1789 +2023-03-05 02:16:16,892 - mmseg - INFO - Iter [140550/160000] lr: 1.172e-06, eta: 1:41:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7256, loss: 0.1795 +2023-03-05 02:16:30,183 - mmseg - INFO - Iter [140600/160000] lr: 1.172e-06, eta: 1:41:29, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6892, loss: 0.1800 +2023-03-05 02:16:43,424 - mmseg - INFO - Iter [140650/160000] lr: 1.172e-06, eta: 1:41:13, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.4205, loss: 0.1813 +2023-03-05 02:16:56,823 - mmseg - INFO - Iter [140700/160000] lr: 1.172e-06, eta: 1:40:57, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.8964, loss: 0.1757 +2023-03-05 02:17:12,679 - mmseg - INFO - Iter [140750/160000] lr: 1.172e-06, eta: 1:40:42, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.7528, loss: 0.1774 +2023-03-05 02:17:25,991 - mmseg - INFO - Iter [140800/160000] lr: 1.172e-06, eta: 1:40:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.7477, loss: 0.1783 +2023-03-05 02:17:39,432 - mmseg - INFO - Iter [140850/160000] lr: 1.172e-06, eta: 1:40:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.9340, loss: 0.1801 +2023-03-05 02:17:52,958 - mmseg - INFO - Iter [140900/160000] lr: 1.172e-06, eta: 1:39:54, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.3225, loss: 0.1880 +2023-03-05 02:18:06,280 - mmseg - INFO - Iter [140950/160000] lr: 1.172e-06, eta: 1:39:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1891, decode.acc_seg: 92.5247, loss: 0.1891 +2023-03-05 02:18:19,558 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:18:19,558 - mmseg - INFO - Iter [141000/160000] lr: 1.172e-06, eta: 1:39:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5824, loss: 0.1817 +2023-03-05 02:18:32,983 - mmseg - INFO - Iter [141050/160000] lr: 1.172e-06, eta: 1:39:06, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6982, loss: 0.1786 +2023-03-05 02:18:46,520 - mmseg - INFO - Iter [141100/160000] lr: 1.172e-06, eta: 1:38:50, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1753, decode.acc_seg: 92.8799, loss: 0.1753 +2023-03-05 02:18:59,910 - mmseg - INFO - Iter [141150/160000] lr: 1.172e-06, eta: 1:38:34, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1894, decode.acc_seg: 92.5207, loss: 0.1894 +2023-03-05 02:19:13,295 - mmseg - INFO - Iter [141200/160000] lr: 1.172e-06, eta: 1:38:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.6450, loss: 0.1848 +2023-03-05 02:19:26,608 - mmseg - INFO - Iter [141250/160000] lr: 1.172e-06, eta: 1:38:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.6049, loss: 0.1823 +2023-03-05 02:19:40,003 - mmseg - INFO - Iter [141300/160000] lr: 1.172e-06, eta: 1:37:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6698, loss: 0.1811 +2023-03-05 02:19:55,906 - mmseg - INFO - Iter [141350/160000] lr: 1.172e-06, eta: 1:37:30, time: 0.318, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.5839, loss: 0.1810 +2023-03-05 02:20:09,250 - mmseg - INFO - Iter [141400/160000] lr: 1.172e-06, eta: 1:37:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.6824, loss: 0.1778 +2023-03-05 02:20:22,531 - mmseg - INFO - Iter [141450/160000] lr: 1.172e-06, eta: 1:36:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.7904, loss: 0.1749 +2023-03-05 02:20:35,886 - mmseg - INFO - Iter [141500/160000] lr: 1.172e-06, eta: 1:36:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.0977, loss: 0.1914 +2023-03-05 02:20:49,190 - mmseg - INFO - Iter [141550/160000] lr: 1.172e-06, eta: 1:36:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4476, loss: 0.1870 +2023-03-05 02:21:02,491 - mmseg - INFO - Iter [141600/160000] lr: 1.172e-06, eta: 1:36:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.7791, loss: 0.1809 +2023-03-05 02:21:15,719 - mmseg - INFO - Iter [141650/160000] lr: 1.172e-06, eta: 1:35:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.6512, loss: 0.1822 +2023-03-05 02:21:29,107 - mmseg - INFO - Iter [141700/160000] lr: 1.172e-06, eta: 1:35:38, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.6972, loss: 0.1800 +2023-03-05 02:21:42,451 - mmseg - INFO - Iter [141750/160000] lr: 1.172e-06, eta: 1:35:22, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1810, decode.acc_seg: 92.6391, loss: 0.1810 +2023-03-05 02:21:55,791 - mmseg - INFO - Iter [141800/160000] lr: 1.172e-06, eta: 1:35:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1717, decode.acc_seg: 93.0240, loss: 0.1717 +2023-03-05 02:22:09,183 - mmseg - INFO - Iter [141850/160000] lr: 1.172e-06, eta: 1:34:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5273, loss: 0.1839 +2023-03-05 02:22:22,535 - mmseg - INFO - Iter [141900/160000] lr: 1.172e-06, eta: 1:34:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.6800, loss: 0.1789 +2023-03-05 02:22:35,920 - mmseg - INFO - Iter [141950/160000] lr: 1.172e-06, eta: 1:34:18, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.8204, loss: 0.1765 +2023-03-05 02:22:51,695 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:22:51,695 - mmseg - INFO - Iter [142000/160000] lr: 1.172e-06, eta: 1:34:02, time: 0.315, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1725, decode.acc_seg: 93.0015, loss: 0.1725 +2023-03-05 02:23:04,984 - mmseg - INFO - Iter [142050/160000] lr: 1.172e-06, eta: 1:33:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6260, loss: 0.1827 +2023-03-05 02:23:18,310 - mmseg - INFO - Iter [142100/160000] lr: 1.172e-06, eta: 1:33:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1883, decode.acc_seg: 92.5065, loss: 0.1883 +2023-03-05 02:23:31,617 - mmseg - INFO - Iter [142150/160000] lr: 1.172e-06, eta: 1:33:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1724, decode.acc_seg: 93.0167, loss: 0.1724 +2023-03-05 02:23:44,943 - mmseg - INFO - Iter [142200/160000] lr: 1.172e-06, eta: 1:32:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6823, loss: 0.1815 +2023-03-05 02:23:58,257 - mmseg - INFO - Iter [142250/160000] lr: 1.172e-06, eta: 1:32:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.5445, loss: 0.1807 +2023-03-05 02:24:11,751 - mmseg - INFO - Iter [142300/160000] lr: 1.172e-06, eta: 1:32:27, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1841, decode.acc_seg: 92.5870, loss: 0.1841 +2023-03-05 02:24:25,070 - mmseg - INFO - Iter [142350/160000] lr: 1.172e-06, eta: 1:32:11, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.8038, loss: 0.1780 +2023-03-05 02:24:38,396 - mmseg - INFO - Iter [142400/160000] lr: 1.172e-06, eta: 1:31:55, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8050, loss: 0.1775 +2023-03-05 02:24:51,903 - mmseg - INFO - Iter [142450/160000] lr: 1.172e-06, eta: 1:31:39, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1726, decode.acc_seg: 92.8675, loss: 0.1726 +2023-03-05 02:25:05,283 - mmseg - INFO - Iter [142500/160000] lr: 1.172e-06, eta: 1:31:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6701, loss: 0.1797 +2023-03-05 02:25:18,638 - mmseg - INFO - Iter [142550/160000] lr: 1.172e-06, eta: 1:31:07, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.7513, loss: 0.1799 +2023-03-05 02:25:31,990 - mmseg - INFO - Iter [142600/160000] lr: 1.172e-06, eta: 1:30:51, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.7116, loss: 0.1822 +2023-03-05 02:25:47,936 - mmseg - INFO - Iter [142650/160000] lr: 1.172e-06, eta: 1:30:35, time: 0.319, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1852, decode.acc_seg: 92.4810, loss: 0.1852 +2023-03-05 02:26:01,391 - mmseg - INFO - Iter [142700/160000] lr: 1.172e-06, eta: 1:30:19, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5893, loss: 0.1830 +2023-03-05 02:26:14,652 - mmseg - INFO - Iter [142750/160000] lr: 1.172e-06, eta: 1:30:03, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6173, loss: 0.1798 +2023-03-05 02:26:28,044 - mmseg - INFO - Iter [142800/160000] lr: 1.172e-06, eta: 1:29:48, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7087, loss: 0.1804 +2023-03-05 02:26:41,348 - mmseg - INFO - Iter [142850/160000] lr: 1.172e-06, eta: 1:29:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.6199, loss: 0.1788 +2023-03-05 02:26:54,687 - mmseg - INFO - Iter [142900/160000] lr: 1.172e-06, eta: 1:29:16, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.7560, loss: 0.1786 +2023-03-05 02:27:08,048 - mmseg - INFO - Iter [142950/160000] lr: 1.172e-06, eta: 1:29:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.6609, loss: 0.1773 +2023-03-05 02:27:21,362 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:27:21,362 - mmseg - INFO - Iter [143000/160000] lr: 1.172e-06, eta: 1:28:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5229, loss: 0.1817 +2023-03-05 02:27:34,711 - mmseg - INFO - Iter [143050/160000] lr: 1.172e-06, eta: 1:28:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8788, loss: 0.1758 +2023-03-05 02:27:47,981 - mmseg - INFO - Iter [143100/160000] lr: 1.172e-06, eta: 1:28:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.7347, loss: 0.1861 +2023-03-05 02:28:01,421 - mmseg - INFO - Iter [143150/160000] lr: 1.172e-06, eta: 1:27:56, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.8143, loss: 0.1794 +2023-03-05 02:28:14,800 - mmseg - INFO - Iter [143200/160000] lr: 1.172e-06, eta: 1:27:40, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5057, loss: 0.1859 +2023-03-05 02:28:30,760 - mmseg - INFO - Iter [143250/160000] lr: 1.172e-06, eta: 1:27:24, time: 0.319, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.4866, loss: 0.1880 +2023-03-05 02:28:44,142 - mmseg - INFO - Iter [143300/160000] lr: 1.172e-06, eta: 1:27:09, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1701, decode.acc_seg: 93.1147, loss: 0.1701 +2023-03-05 02:28:57,501 - mmseg - INFO - Iter [143350/160000] lr: 1.172e-06, eta: 1:26:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6512, loss: 0.1798 +2023-03-05 02:29:10,965 - mmseg - INFO - Iter [143400/160000] lr: 1.172e-06, eta: 1:26:37, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.5441, loss: 0.1817 +2023-03-05 02:29:24,323 - mmseg - INFO - Iter [143450/160000] lr: 1.172e-06, eta: 1:26:21, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1764, decode.acc_seg: 92.7498, loss: 0.1764 +2023-03-05 02:29:37,646 - mmseg - INFO - Iter [143500/160000] lr: 1.172e-06, eta: 1:26:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.5003, loss: 0.1853 +2023-03-05 02:29:51,150 - mmseg - INFO - Iter [143550/160000] lr: 1.172e-06, eta: 1:25:49, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.5181, loss: 0.1793 +2023-03-05 02:30:04,535 - mmseg - INFO - Iter [143600/160000] lr: 1.172e-06, eta: 1:25:33, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6302, loss: 0.1832 +2023-03-05 02:30:18,016 - mmseg - INFO - Iter [143650/160000] lr: 1.172e-06, eta: 1:25:17, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.5219, loss: 0.1880 +2023-03-05 02:30:31,363 - mmseg - INFO - Iter [143700/160000] lr: 1.172e-06, eta: 1:25:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.5116, loss: 0.1834 +2023-03-05 02:30:44,861 - mmseg - INFO - Iter [143750/160000] lr: 1.172e-06, eta: 1:24:45, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6739, loss: 0.1833 +2023-03-05 02:30:58,367 - mmseg - INFO - Iter [143800/160000] lr: 1.172e-06, eta: 1:24:29, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.5563, loss: 0.1805 +2023-03-05 02:31:11,770 - mmseg - INFO - Iter [143850/160000] lr: 1.172e-06, eta: 1:24:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.7773, loss: 0.1833 +2023-03-05 02:31:27,640 - mmseg - INFO - Iter [143900/160000] lr: 1.172e-06, eta: 1:23:58, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.7917, loss: 0.1762 +2023-03-05 02:31:40,953 - mmseg - INFO - Iter [143950/160000] lr: 1.172e-06, eta: 1:23:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.6699, loss: 0.1819 +2023-03-05 02:31:54,313 - mmseg - INFO - Swap parameters (after train) after iter [144000] +2023-03-05 02:31:54,335 - mmseg - INFO - Saving checkpoint at 144000 iterations +2023-03-05 02:31:56,215 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:31:56,215 - mmseg - INFO - Iter [144000/160000] lr: 1.172e-06, eta: 1:23:26, time: 0.305, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1786, decode.acc_seg: 92.6946, loss: 0.1786 +2023-03-05 02:42:52,005 - mmseg - INFO - per class results: +2023-03-05 02:42:52,014 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.36,76.37,76.38,76.4,76.39,76.39,76.39,76.38,76.39,76.39,76.38 | +| building | 81.4,81.41,81.41,81.42,81.43,81.43,81.43,81.43,81.44,81.45,81.43 | +| sky | 94.28,94.28,94.28,94.28,94.28,94.28,94.28,94.27,94.26,94.26,94.27 | +| floor | 80.0,80.01,80.03,80.05,80.05,80.09,80.09,80.09,80.12,80.13,80.09 | +| tree | 72.92,72.91,72.92,72.9,72.9,72.87,72.86,72.86,72.85,72.85,72.82 | +| ceiling | 82.87,82.87,82.88,82.89,82.91,82.95,82.95,82.96,82.94,82.95,82.92 | +| road | 82.03,82.01,81.96,81.96,81.97,81.96,82.03,81.98,81.96,81.88,81.94 | +| bed | 88.55,88.56,88.56,88.55,88.56,88.55,88.55,88.55,88.55,88.57,88.52 | +| windowpane | 61.07,61.09,61.06,61.11,61.11,61.11,61.12,61.1,61.11,61.12,61.08 | +| grass | 65.27,65.27,65.31,65.32,65.35,65.42,65.4,65.47,65.45,65.45,65.54 | +| cabinet | 59.27,59.22,59.22,59.27,59.27,59.23,59.22,59.14,59.14,59.15,59.11 | +| sidewalk | 66.56,66.54,66.48,66.59,66.63,66.63,66.74,66.74,66.72,66.71,66.77 | +| person | 79.52,79.53,79.53,79.54,79.52,79.52,79.52,79.53,79.52,79.53,79.52 | +| earth | 32.75,32.63,32.51,32.38,32.25,32.21,32.27,32.27,32.24,32.1,32.02 | +| door | 48.53,48.61,48.65,48.71,48.76,48.71,48.78,48.76,48.77,48.83,48.82 | +| table | 61.61,61.67,61.65,61.66,61.68,61.68,61.73,61.7,61.76,61.79,61.81 | +| mountain | 51.76,51.85,51.99,52.06,52.17,52.24,52.27,52.23,52.37,52.49,52.37 | +| plant | 50.14,50.06,50.08,50.09,50.07,50.06,50.02,50.03,50.03,50.02,50.1 | +| curtain | 70.43,70.51,70.53,70.59,70.62,70.64,70.65,70.65,70.67,70.66,70.57 | +| chair | 58.31,58.34,58.37,58.43,58.44,58.47,58.52,58.52,58.54,58.58,58.59 | +| car | 83.13,83.13,83.12,83.13,83.13,83.15,83.12,83.14,83.13,83.14,83.15 | +| water | 47.38,47.41,47.39,47.43,47.45,47.48,47.48,47.53,47.54,47.56,47.53 | +| painting | 69.77,69.77,69.74,69.8,69.81,69.82,69.82,69.84,69.87,69.88,69.9 | +| sofa | 65.58,65.52,65.46,65.52,65.53,65.6,65.66,65.74,65.82,65.78,65.98 | +| shelf | 40.69,40.65,40.6,40.51,40.55,40.46,40.45,40.41,40.4,40.34,40.45 | +| house | 44.17,44.18,44.17,44.15,44.19,44.16,44.07,43.96,43.91,43.88,43.87 | +| sea | 44.82,44.84,44.78,44.71,44.72,44.63,44.63,44.62,44.6,44.62,44.54 | +| mirror | 65.59,65.63,65.59,65.58,65.55,65.53,65.53,65.58,65.55,65.53,65.52 | +| rug | 54.42,54.47,54.55,54.63,54.6,54.79,54.9,54.94,55.07,55.05,55.02 | +| field | 28.13,28.06,28.01,28.03,28.02,27.99,28.01,28.06,28.08,28.11,28.05 | +| armchair | 43.66,43.54,43.62,43.57,43.63,43.52,43.54,43.63,43.65,43.55,43.56 | +| seat | 53.52,53.57,53.53,53.48,53.6,53.55,53.42,53.45,53.4,53.37,53.41 | +| fence | 40.54,40.5,40.45,40.47,40.47,40.43,40.38,40.39,40.42,40.42,40.54 | +| desk | 49.41,49.41,49.34,49.42,49.26,49.22,49.12,49.11,49.07,49.1,49.19 | +| rock | 26.84,27.03,27.17,27.0,27.21,27.3,27.26,27.1,26.99,27.35,27.19 | +| wardrobe | 47.62,47.53,47.49,47.54,47.59,47.36,47.34,46.99,46.95,46.92,47.06 | +| lamp | 63.88,63.93,63.92,63.92,63.94,63.95,63.97,63.95,63.96,63.98,64.0 | +| bathtub | 77.62,77.44,77.42,77.57,77.47,77.44,77.52,77.13,77.04,77.21,77.15 | +| railing | 32.12,32.1,32.09,31.98,31.98,31.93,31.86,31.88,31.88,31.76,31.8 | +| cushion | 55.43,55.51,55.5,55.59,55.57,55.71,55.72,55.66,55.66,55.72,55.92 | +| base | 28.03,28.05,27.97,27.86,28.05,28.05,28.06,28.02,28.14,28.1,28.21 | +| box | 24.33,24.42,24.32,24.42,24.4,24.39,24.45,24.32,24.39,24.4,24.58 | +| column | 46.18,46.33,46.36,46.5,46.55,46.52,46.4,46.38,46.28,46.35,46.25 | +| signboard | 35.44,35.37,35.43,35.33,35.31,35.43,35.42,35.48,35.47,35.51,35.38 | +| chest of drawers | 39.1,39.09,39.02,39.09,39.06,39.0,39.05,39.2,39.11,39.23,39.22 | +| counter | 26.83,26.77,27.0,26.6,26.55,26.4,26.29,26.32,26.43,26.38,26.14 | +| sand | 31.69,31.41,31.31,31.15,31.05,31.04,31.03,31.06,31.11,31.11,31.05 | +| sink | 71.41,71.3,71.46,71.54,71.59,71.64,71.64,71.45,71.43,71.42,71.49 | +| skyscraper | 49.27,49.34,49.39,49.44,49.5,49.54,49.57,49.59,49.58,49.59,49.85 | +| fireplace | 66.83,66.76,66.78,66.77,66.77,66.7,66.71,66.73,66.56,66.44,66.58 | +| refrigerator | 78.68,78.7,78.79,78.71,78.69,78.7,78.68,78.71,78.75,78.7,78.72 | +| grandstand | 41.85,41.93,41.92,41.94,41.92,41.97,41.89,41.9,42.0,41.96,41.94 | +| path | 17.85,17.89,17.93,17.9,17.93,17.94,17.94,18.0,18.04,18.02,18.08 | +| stairs | 31.47,31.48,31.49,31.48,31.43,31.46,31.41,31.42,31.42,31.39,31.41 | +| runway | 63.94,63.92,63.94,63.95,63.97,63.98,63.99,63.98,63.99,63.98,63.99 | +| case | 48.26,48.27,48.29,48.3,48.18,48.25,48.19,48.19,48.15,48.15,48.24 | +| pool table | 92.7,92.69,92.7,92.67,92.7,92.69,92.71,92.71,92.69,92.69,92.77 | +| pillow | 57.33,57.34,57.28,57.21,57.24,57.23,57.14,57.18,57.1,57.09,57.05 | +| screen door | 66.18,66.56,66.38,66.52,66.95,67.05,67.05,67.1,67.23,67.41,67.1 | +| stairway | 25.63,25.59,25.64,25.67,25.56,25.68,25.71,25.64,25.73,25.74,25.58 | +| river | 9.93,9.82,9.78,9.64,9.62,9.54,9.48,9.38,9.36,9.25,9.22 | +| bridge | 55.39,56.39,57.07,57.9,58.45,58.94,59.37,59.81,60.66,61.07,60.82 | +| bookcase | 41.96,42.31,42.19,42.47,42.97,43.0,43.28,43.37,43.45,43.45,42.97 | +| blind | 44.85,44.86,44.63,44.66,44.64,44.6,44.6,44.43,44.53,44.45,44.39 | +| coffee table | 66.01,66.0,65.9,65.85,65.96,65.89,65.87,65.98,65.98,66.02,65.86 | +| toilet | 86.29,86.38,86.35,86.37,86.32,86.35,86.35,86.33,86.4,86.44,86.32 | +| flower | 31.57,31.51,31.64,31.74,31.88,31.88,31.94,32.05,32.13,32.16,32.24 | +| book | 47.23,47.27,47.34,47.24,47.24,47.23,47.19,47.16,47.09,47.14,47.05 | +| hill | 7.77,7.72,7.75,7.75,7.78,7.84,7.93,7.83,7.92,7.89,7.72 | +| bench | 44.34,44.37,44.4,44.45,44.52,44.48,44.45,44.6,44.63,44.57,44.62 | +| countertop | 54.51,54.5,54.51,54.54,54.6,54.55,54.56,54.54,54.43,54.42,54.57 | +| stove | 72.34,72.19,72.23,72.27,72.34,72.44,72.42,72.54,72.58,72.6,72.35 | +| palm | 50.79,50.84,50.87,50.81,50.88,50.93,50.94,51.0,51.07,51.05,51.14 | +| kitchen island | 47.28,47.29,47.36,47.41,47.47,47.55,47.61,47.44,47.24,47.45,47.38 | +| computer | 57.24,57.23,57.23,57.28,57.31,57.26,57.28,57.26,57.25,57.26,57.35 | +| swivel chair | 45.78,45.69,45.8,45.81,45.89,46.1,46.08,46.09,46.09,46.12,46.33 | +| boat | 38.3,38.25,38.31,38.4,38.37,38.51,38.58,38.61,38.7,38.77,38.71 | +| bar | 27.08,27.06,27.1,26.93,26.98,26.85,26.75,26.7,26.49,26.37,25.77 | +| arcade machine | 24.99,25.0,24.98,25.38,25.39,25.64,25.78,25.74,26.08,26.39,26.75 | +| hovel | 31.03,30.75,30.73,30.64,30.61,30.51,30.5,30.43,30.32,30.28,30.23 | +| bus | 88.17,88.31,88.17,88.17,88.2,88.26,88.14,88.29,88.34,88.42,88.35 | +| towel | 60.61,60.58,60.72,60.65,60.68,60.74,60.74,60.77,60.75,60.75,60.79 | +| light | 56.52,56.52,56.54,56.5,56.51,56.56,56.53,56.46,56.49,56.42,56.46 | +| truck | 35.06,35.13,35.14,35.05,35.12,35.08,34.99,34.99,35.33,35.17,35.28 | +| tower | 25.37,25.22,24.93,25.04,24.99,24.53,24.63,24.41,24.37,24.36,23.99 | +| chandelier | 66.29,66.34,66.39,66.37,66.39,66.43,66.45,66.53,66.47,66.51,66.53 | +| awning | 23.34,23.43,23.25,23.47,23.31,23.36,23.3,23.38,23.5,23.43,23.63 | +| streetlight | 28.4,28.38,28.31,28.37,28.22,28.21,28.16,28.15,28.1,28.07,28.04 | +| booth | 58.45,58.55,58.54,58.6,58.6,58.4,58.52,58.59,58.34,58.37,58.3 | +| television receiver | 68.18,68.14,68.15,68.11,68.16,68.08,68.12,68.12,68.08,68.1,68.05 | +| airplane | 51.77,51.62,51.5,51.24,51.3,51.18,50.97,50.89,50.78,50.67,50.96 | +| dirt track | 11.11,11.11,11.17,11.1,11.12,10.97,10.92,10.99,10.82,10.87,11.05 | +| apparel | 28.54,28.59,28.47,28.37,28.38,28.29,28.36,28.26,27.95,27.94,27.97 | +| pole | 24.7,24.71,24.61,24.66,24.61,24.53,24.49,24.46,24.48,24.42,24.44 | +| land | 6.69,6.66,6.41,6.32,6.25,6.15,6.11,6.11,6.05,6.06,6.03 | +| bannister | 5.45,5.51,5.48,5.59,5.54,5.53,5.58,5.58,5.61,5.59,5.57 | +| escalator | 22.68,22.71,22.79,22.9,22.86,22.92,22.91,22.95,22.95,22.94,23.22 | +| ottoman | 47.39,47.5,47.56,47.6,47.38,47.63,47.75,47.32,47.44,47.85,47.12 | +| bottle | 14.9,14.77,14.58,14.55,14.65,14.5,14.56,14.62,14.51,14.59,14.29 | +| buffet | 47.01,47.09,46.63,46.81,46.69,46.99,47.24,48.11,49.99,50.82,50.55 | +| poster | 27.79,27.85,27.88,27.93,28.03,27.9,28.17,28.03,27.97,27.84,28.13 | +| stage | 16.56,16.72,16.77,17.05,17.03,17.17,17.23,17.26,17.32,17.33,17.42 | +| van | 47.45,47.53,47.48,47.62,47.31,47.61,47.45,47.45,47.31,47.31,47.76 | +| ship | 26.24,27.09,28.04,28.96,30.28,31.8,31.41,31.57,33.52,34.41,32.4 | +| fountain | 8.42,8.55,8.49,8.56,8.48,8.52,8.46,8.25,8.15,8.06,7.85 | +| conveyer belt | 76.38,76.2,76.25,76.16,76.14,76.01,75.96,75.86,75.79,75.75,75.4 | +| canopy | 14.8,14.77,14.69,14.72,14.72,14.8,14.82,14.93,14.88,14.98,14.67 | +| washer | 65.95,65.87,65.9,65.82,65.8,65.69,65.63,65.67,65.69,65.68,65.59 | +| plaything | 23.28,23.25,23.33,23.24,23.22,23.3,23.26,23.33,23.41,23.41,23.34 | +| swimming pool | 44.09,44.34,45.01,45.52,46.18,46.58,47.35,49.1,49.62,50.43,50.08 | +| stool | 42.19,42.09,42.02,42.12,42.1,42.02,41.99,41.99,41.9,41.92,41.84 | +| barrel | 41.02,40.12,40.96,40.46,40.76,40.96,40.55,40.56,40.64,40.55,40.32 | +| basket | 28.53,28.52,28.43,28.45,28.47,28.42,28.41,28.42,28.55,28.55,28.56 | +| waterfall | 52.66,52.95,53.39,53.28,53.76,54.27,53.76,54.33,55.12,55.22,56.27 | +| tent | 93.67,93.65,93.61,93.62,93.63,93.57,93.67,93.59,93.59,93.59,93.61 | +| bag | 11.37,11.34,11.46,11.44,11.46,11.46,11.51,11.51,11.51,11.46,11.54 | +| minibike | 61.6,61.6,61.56,61.41,61.51,61.45,61.48,61.41,61.47,61.39,61.37 | +| cradle | 81.59,81.57,81.52,81.42,81.36,81.33,81.16,81.04,80.97,80.89,80.82 | +| oven | 27.06,27.09,27.11,27.06,27.09,27.01,27.12,26.96,27.0,26.96,26.98 | +| ball | 46.93,46.9,47.07,47.26,47.27,47.25,47.32,47.43,47.49,47.56,47.76 | +| food | 52.11,52.43,52.33,52.55,52.6,52.65,52.89,52.98,53.03,53.25,53.02 | +| step | 18.23,18.28,18.35,18.11,18.04,18.52,18.43,18.13,17.93,17.98,18.81 | +| tank | 41.45,41.4,41.31,41.33,41.28,41.23,41.17,41.16,41.1,41.1,41.15 | +| trade name | 25.05,25.07,24.99,24.96,24.92,24.92,24.82,24.91,24.77,24.81,24.74 | +| microwave | 37.47,37.52,37.46,37.54,37.49,37.52,37.49,37.47,37.48,37.48,37.49 | +| pot | 41.29,41.31,41.35,41.35,41.32,41.35,41.35,41.43,41.4,41.42,41.41 | +| animal | 51.29,51.29,51.26,51.23,51.22,51.23,51.21,51.25,51.14,51.19,51.16 | +| bicycle | 46.02,46.13,46.06,46.0,46.13,46.09,46.13,46.12,46.13,46.14,46.19 | +| lake | 59.89,59.77,59.73,59.63,59.52,59.46,59.38,59.26,59.21,59.13,59.02 | +| dishwasher | 76.59,76.58,76.65,76.76,76.78,76.82,76.97,77.06,77.02,77.08,77.14 | +| screen | 62.34,62.06,62.3,61.88,61.82,61.79,62.07,62.09,62.16,62.33,61.88 | +| blanket | 14.97,15.11,15.09,15.07,15.13,14.94,15.08,15.04,14.86,14.82,14.84 | +| sculpture | 35.92,35.92,36.07,36.06,35.99,36.27,36.15,36.14,36.27,36.21,36.22 | +| hood | 57.59,57.59,57.72,57.63,57.55,57.64,57.66,57.51,57.37,57.38,57.44 | +| sconce | 42.59,42.45,42.55,42.45,42.46,42.41,42.58,42.42,42.37,42.27,42.46 | +| vase | 37.39,37.39,37.38,37.4,37.29,37.43,37.33,37.36,37.32,37.4,37.27 | +| traffic light | 29.69,29.69,29.74,29.72,29.69,29.74,29.71,29.7,29.73,29.69,29.78 | +| tray | 5.4,5.41,5.4,5.43,5.42,5.43,5.46,5.48,5.52,5.56,5.57 | +| ashcan | 37.54,37.65,37.57,37.5,37.5,37.43,37.49,37.55,37.59,37.5,37.39 | +| fan | 58.45,58.51,58.44,58.45,58.43,58.53,58.59,58.56,58.49,58.51,58.48 | +| pier | 11.48,11.58,11.59,11.44,11.3,11.35,11.37,11.26,11.25,11.13,10.99 | +| crt screen | 4.96,4.97,5.23,5.38,5.69,5.95,6.42,6.77,7.19,7.45,7.6 | +| plate | 38.92,38.95,38.9,39.17,39.11,39.18,39.23,39.23,39.17,39.3,39.34 | +| monitor | 23.77,23.75,23.52,23.35,23.52,23.41,23.25,23.24,23.1,23.11,23.1 | +| bulletin board | 45.14,44.96,45.36,45.72,45.54,46.26,46.88,46.76,47.34,47.39,47.1 | +| shower | 1.12,1.09,1.14,1.23,1.26,1.3,1.31,1.37,1.4,1.39,1.41 | +| radiator | 45.35,45.39,45.48,45.44,45.53,45.68,45.82,45.42,45.51,45.87,45.28 | +| glass | 12.27,12.23,12.25,12.29,12.24,12.26,12.25,12.29,12.32,12.33,12.35 | +| clock | 25.11,25.05,25.09,25.07,25.05,24.98,25.0,24.96,25.0,25.02,25.02 | +| flag | 37.42,37.43,37.59,37.66,37.82,37.82,37.95,37.99,38.12,38.17,38.24 | ++---------------------+-------------------------------------------------------------------+ +2023-03-05 02:42:52,014 - mmseg - INFO - Summary: +2023-03-05 02:42:52,014 - mmseg - INFO - ++-----------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++-----------------------------------------------------------------+ +| 46.07,46.08,46.1,46.12,46.15,46.18,46.2,46.21,46.25,46.28,46.25 | ++-----------------------------------------------------------------+ +2023-03-05 02:42:52,014 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:42:52,014 - mmseg - INFO - Iter(val) [250] mIoU: [0.4607, 0.4608, 0.461, 0.4612, 0.4615, 0.4618, 0.462, 0.4621, 0.4625, 0.4628, 0.4625], copy_paste: 46.07,46.08,46.1,46.12,46.15,46.18,46.2,46.21,46.25,46.28,46.25 +2023-03-05 02:42:52,023 - mmseg - INFO - Swap parameters (before train) before iter [144001] +2023-03-05 02:43:05,938 - mmseg - INFO - Iter [144050/160000] lr: 1.172e-06, eta: 1:24:23, time: 13.394, data_time: 13.124, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.7329, loss: 0.1808 +2023-03-05 02:43:19,500 - mmseg - INFO - Iter [144100/160000] lr: 1.172e-06, eta: 1:24:07, time: 0.271, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.6031, loss: 0.1858 +2023-03-05 02:43:33,117 - mmseg - INFO - Iter [144150/160000] lr: 1.172e-06, eta: 1:23:51, time: 0.272, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6470, loss: 0.1828 +2023-03-05 02:43:46,827 - mmseg - INFO - Iter [144200/160000] lr: 1.172e-06, eta: 1:23:35, time: 0.274, data_time: 0.008, memory: 67559, decode.loss_ce: 0.1739, decode.acc_seg: 92.9206, loss: 0.1739 +2023-03-05 02:44:00,254 - mmseg - INFO - Iter [144250/160000] lr: 1.172e-06, eta: 1:23:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.7045, loss: 0.1809 +2023-03-05 02:44:13,638 - mmseg - INFO - Iter [144300/160000] lr: 1.172e-06, eta: 1:23:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.7700, loss: 0.1811 +2023-03-05 02:44:26,948 - mmseg - INFO - Iter [144350/160000] lr: 1.172e-06, eta: 1:22:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.5313, loss: 0.1832 +2023-03-05 02:44:40,269 - mmseg - INFO - Iter [144400/160000] lr: 1.172e-06, eta: 1:22:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.8116, loss: 0.1791 +2023-03-05 02:44:53,724 - mmseg - INFO - Iter [144450/160000] lr: 1.172e-06, eta: 1:22:14, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.7525, loss: 0.1820 +2023-03-05 02:45:09,633 - mmseg - INFO - Iter [144500/160000] lr: 1.172e-06, eta: 1:21:58, time: 0.318, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1871, decode.acc_seg: 92.4106, loss: 0.1871 +2023-03-05 02:45:23,055 - mmseg - INFO - Iter [144550/160000] lr: 1.172e-06, eta: 1:21:42, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6229, loss: 0.1834 +2023-03-05 02:45:36,503 - mmseg - INFO - Iter [144600/160000] lr: 1.172e-06, eta: 1:21:26, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.4432, loss: 0.1853 +2023-03-05 02:45:49,807 - mmseg - INFO - Iter [144650/160000] lr: 1.172e-06, eta: 1:21:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7209, loss: 0.1782 +2023-03-05 02:46:03,079 - mmseg - INFO - Iter [144700/160000] lr: 1.172e-06, eta: 1:20:54, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.7195, loss: 0.1816 +2023-03-05 02:46:16,402 - mmseg - INFO - Iter [144750/160000] lr: 1.172e-06, eta: 1:20:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.6110, loss: 0.1838 +2023-03-05 02:46:29,625 - mmseg - INFO - Iter [144800/160000] lr: 1.172e-06, eta: 1:20:21, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1859, decode.acc_seg: 92.5735, loss: 0.1859 +2023-03-05 02:46:42,945 - mmseg - INFO - Iter [144850/160000] lr: 1.172e-06, eta: 1:20:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1762, decode.acc_seg: 92.8000, loss: 0.1762 +2023-03-05 02:46:56,427 - mmseg - INFO - Iter [144900/160000] lr: 1.172e-06, eta: 1:19:49, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.8868, loss: 0.1761 +2023-03-05 02:47:09,804 - mmseg - INFO - Iter [144950/160000] lr: 1.172e-06, eta: 1:19:33, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1826, decode.acc_seg: 92.5248, loss: 0.1826 +2023-03-05 02:47:23,129 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:47:23,129 - mmseg - INFO - Iter [145000/160000] lr: 1.172e-06, eta: 1:19:17, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.7407, loss: 0.1806 +2023-03-05 02:47:36,387 - mmseg - INFO - Iter [145050/160000] lr: 1.172e-06, eta: 1:19:01, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.9499, loss: 0.1771 +2023-03-05 02:47:49,704 - mmseg - INFO - Iter [145100/160000] lr: 1.172e-06, eta: 1:18:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.4593, loss: 0.1864 +2023-03-05 02:48:05,483 - mmseg - INFO - Iter [145150/160000] lr: 1.172e-06, eta: 1:18:29, time: 0.316, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6716, loss: 0.1815 +2023-03-05 02:48:18,819 - mmseg - INFO - Iter [145200/160000] lr: 1.172e-06, eta: 1:18:13, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.4004, loss: 0.1845 +2023-03-05 02:48:32,051 - mmseg - INFO - Iter [145250/160000] lr: 1.172e-06, eta: 1:17:57, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1806, decode.acc_seg: 92.6296, loss: 0.1806 +2023-03-05 02:48:45,405 - mmseg - INFO - Iter [145300/160000] lr: 1.172e-06, eta: 1:17:40, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6135, loss: 0.1803 +2023-03-05 02:48:58,753 - mmseg - INFO - Iter [145350/160000] lr: 1.172e-06, eta: 1:17:24, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1760, decode.acc_seg: 92.9231, loss: 0.1760 +2023-03-05 02:49:12,163 - mmseg - INFO - Iter [145400/160000] lr: 1.172e-06, eta: 1:17:08, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1781, decode.acc_seg: 92.7200, loss: 0.1781 +2023-03-05 02:49:25,448 - mmseg - INFO - Iter [145450/160000] lr: 1.172e-06, eta: 1:16:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.9070, loss: 0.1767 +2023-03-05 02:49:38,742 - mmseg - INFO - Iter [145500/160000] lr: 1.172e-06, eta: 1:16:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1750, decode.acc_seg: 92.8696, loss: 0.1750 +2023-03-05 02:49:52,144 - mmseg - INFO - Iter [145550/160000] lr: 1.172e-06, eta: 1:16:20, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7521, loss: 0.1804 +2023-03-05 02:50:05,500 - mmseg - INFO - Iter [145600/160000] lr: 1.172e-06, eta: 1:16:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1727, decode.acc_seg: 92.9093, loss: 0.1727 +2023-03-05 02:50:18,869 - mmseg - INFO - Iter [145650/160000] lr: 1.172e-06, eta: 1:15:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1877, decode.acc_seg: 92.4295, loss: 0.1877 +2023-03-05 02:50:32,246 - mmseg - INFO - Iter [145700/160000] lr: 1.172e-06, eta: 1:15:32, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1880, decode.acc_seg: 92.5034, loss: 0.1880 +2023-03-05 02:50:45,629 - mmseg - INFO - Iter [145750/160000] lr: 1.172e-06, eta: 1:15:16, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.8333, loss: 0.1756 +2023-03-05 02:51:01,477 - mmseg - INFO - Iter [145800/160000] lr: 1.172e-06, eta: 1:15:00, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.9008, loss: 0.1752 +2023-03-05 02:51:14,804 - mmseg - INFO - Iter [145850/160000] lr: 1.172e-06, eta: 1:14:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.6622, loss: 0.1787 +2023-03-05 02:51:28,193 - mmseg - INFO - Iter [145900/160000] lr: 1.172e-06, eta: 1:14:28, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.6163, loss: 0.1796 +2023-03-05 02:51:41,621 - mmseg - INFO - Iter [145950/160000] lr: 1.172e-06, eta: 1:14:11, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6361, loss: 0.1811 +2023-03-05 02:51:55,219 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:51:55,220 - mmseg - INFO - Iter [146000/160000] lr: 1.172e-06, eta: 1:13:55, time: 0.272, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.8784, loss: 0.1791 +2023-03-05 02:52:08,431 - mmseg - INFO - Iter [146050/160000] lr: 1.172e-06, eta: 1:13:39, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1740, decode.acc_seg: 93.0463, loss: 0.1740 +2023-03-05 02:52:21,649 - mmseg - INFO - Iter [146100/160000] lr: 1.172e-06, eta: 1:13:23, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6665, loss: 0.1801 +2023-03-05 02:52:35,046 - mmseg - INFO - Iter [146150/160000] lr: 1.172e-06, eta: 1:13:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.4440, loss: 0.1840 +2023-03-05 02:52:48,460 - mmseg - INFO - Iter [146200/160000] lr: 1.172e-06, eta: 1:12:51, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.7783, loss: 0.1779 +2023-03-05 02:53:01,713 - mmseg - INFO - Iter [146250/160000] lr: 1.172e-06, eta: 1:12:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.8109, loss: 0.1776 +2023-03-05 02:53:14,933 - mmseg - INFO - Iter [146300/160000] lr: 1.172e-06, eta: 1:12:19, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.9843, loss: 0.1756 +2023-03-05 02:53:28,422 - mmseg - INFO - Iter [146350/160000] lr: 1.172e-06, eta: 1:12:03, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4825, loss: 0.1868 +2023-03-05 02:53:44,389 - mmseg - INFO - Iter [146400/160000] lr: 1.172e-06, eta: 1:11:47, time: 0.319, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.7713, loss: 0.1783 +2023-03-05 02:53:57,957 - mmseg - INFO - Iter [146450/160000] lr: 1.172e-06, eta: 1:11:31, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7216, loss: 0.1778 +2023-03-05 02:54:11,347 - mmseg - INFO - Iter [146500/160000] lr: 1.172e-06, eta: 1:11:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7705, loss: 0.1788 +2023-03-05 02:54:24,611 - mmseg - INFO - Iter [146550/160000] lr: 1.172e-06, eta: 1:10:59, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6077, loss: 0.1830 +2023-03-05 02:54:37,876 - mmseg - INFO - Iter [146600/160000] lr: 1.172e-06, eta: 1:10:43, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6123, loss: 0.1808 +2023-03-05 02:54:51,376 - mmseg - INFO - Iter [146650/160000] lr: 1.172e-06, eta: 1:10:27, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6862, loss: 0.1816 +2023-03-05 02:55:04,699 - mmseg - INFO - Iter [146700/160000] lr: 1.172e-06, eta: 1:10:11, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.7739, loss: 0.1766 +2023-03-05 02:55:17,899 - mmseg - INFO - Iter [146750/160000] lr: 1.172e-06, eta: 1:09:55, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1739, decode.acc_seg: 92.8627, loss: 0.1739 +2023-03-05 02:55:31,357 - mmseg - INFO - Iter [146800/160000] lr: 1.172e-06, eta: 1:09:39, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1861, decode.acc_seg: 92.4511, loss: 0.1861 +2023-03-05 02:55:44,677 - mmseg - INFO - Iter [146850/160000] lr: 1.172e-06, eta: 1:09:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5660, loss: 0.1825 +2023-03-05 02:55:57,964 - mmseg - INFO - Iter [146900/160000] lr: 1.172e-06, eta: 1:09:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1731, decode.acc_seg: 93.0491, loss: 0.1731 +2023-03-05 02:56:11,362 - mmseg - INFO - Iter [146950/160000] lr: 1.172e-06, eta: 1:08:50, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.8794, loss: 0.1789 +2023-03-05 02:56:24,834 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 02:56:24,834 - mmseg - INFO - Iter [147000/160000] lr: 1.172e-06, eta: 1:08:34, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1845, decode.acc_seg: 92.6141, loss: 0.1845 +2023-03-05 02:56:40,610 - mmseg - INFO - Iter [147050/160000] lr: 1.172e-06, eta: 1:08:19, time: 0.316, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7445, loss: 0.1798 +2023-03-05 02:56:54,114 - mmseg - INFO - Iter [147100/160000] lr: 1.172e-06, eta: 1:08:03, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.6957, loss: 0.1847 +2023-03-05 02:57:07,447 - mmseg - INFO - Iter [147150/160000] lr: 1.172e-06, eta: 1:07:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.7790, loss: 0.1775 +2023-03-05 02:57:20,805 - mmseg - INFO - Iter [147200/160000] lr: 1.172e-06, eta: 1:07:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1842, decode.acc_seg: 92.6128, loss: 0.1842 +2023-03-05 02:57:34,165 - mmseg - INFO - Iter [147250/160000] lr: 1.172e-06, eta: 1:07:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.9054, loss: 0.1751 +2023-03-05 02:57:47,423 - mmseg - INFO - Iter [147300/160000] lr: 1.172e-06, eta: 1:06:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1743, decode.acc_seg: 92.8608, loss: 0.1743 +2023-03-05 02:58:00,816 - mmseg - INFO - Iter [147350/160000] lr: 1.172e-06, eta: 1:06:42, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7212, loss: 0.1801 +2023-03-05 02:58:14,181 - mmseg - INFO - Iter [147400/160000] lr: 1.172e-06, eta: 1:06:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1755, decode.acc_seg: 92.8589, loss: 0.1755 +2023-03-05 02:58:27,635 - mmseg - INFO - Iter [147450/160000] lr: 1.172e-06, eta: 1:06:10, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1726, decode.acc_seg: 92.9430, loss: 0.1726 +2023-03-05 02:58:40,960 - mmseg - INFO - Iter [147500/160000] lr: 1.172e-06, eta: 1:05:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6080, loss: 0.1803 +2023-03-05 02:58:54,268 - mmseg - INFO - Iter [147550/160000] lr: 1.172e-06, eta: 1:05:38, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.6613, loss: 0.1801 +2023-03-05 02:59:07,591 - mmseg - INFO - Iter [147600/160000] lr: 1.172e-06, eta: 1:05:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5804, loss: 0.1825 +2023-03-05 02:59:20,871 - mmseg - INFO - Iter [147650/160000] lr: 1.172e-06, eta: 1:05:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4501, loss: 0.1870 +2023-03-05 02:59:36,856 - mmseg - INFO - Iter [147700/160000] lr: 1.172e-06, eta: 1:04:50, time: 0.320, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6440, loss: 0.1808 +2023-03-05 02:59:50,225 - mmseg - INFO - Iter [147750/160000] lr: 1.172e-06, eta: 1:04:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.7508, loss: 0.1798 +2023-03-05 03:00:03,600 - mmseg - INFO - Iter [147800/160000] lr: 1.172e-06, eta: 1:04:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1761, decode.acc_seg: 92.7084, loss: 0.1761 +2023-03-05 03:00:16,865 - mmseg - INFO - Iter [147850/160000] lr: 1.172e-06, eta: 1:04:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1808, decode.acc_seg: 92.6289, loss: 0.1808 +2023-03-05 03:00:30,203 - mmseg - INFO - Iter [147900/160000] lr: 1.172e-06, eta: 1:03:46, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6319, loss: 0.1794 +2023-03-05 03:00:43,592 - mmseg - INFO - Iter [147950/160000] lr: 1.172e-06, eta: 1:03:30, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7858, loss: 0.1797 +2023-03-05 03:00:56,971 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:00:56,971 - mmseg - INFO - Iter [148000/160000] lr: 1.172e-06, eta: 1:03:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1958, decode.acc_seg: 92.3579, loss: 0.1958 +2023-03-05 03:01:10,265 - mmseg - INFO - Iter [148050/160000] lr: 1.172e-06, eta: 1:02:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1713, decode.acc_seg: 92.8642, loss: 0.1713 +2023-03-05 03:01:23,573 - mmseg - INFO - Iter [148100/160000] lr: 1.172e-06, eta: 1:02:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5813, loss: 0.1809 +2023-03-05 03:01:36,921 - mmseg - INFO - Iter [148150/160000] lr: 1.172e-06, eta: 1:02:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.8893, loss: 0.1745 +2023-03-05 03:01:50,470 - mmseg - INFO - Iter [148200/160000] lr: 1.172e-06, eta: 1:02:10, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.7004, loss: 0.1809 +2023-03-05 03:02:03,780 - mmseg - INFO - Iter [148250/160000] lr: 1.172e-06, eta: 1:01:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6827, loss: 0.1797 +2023-03-05 03:02:19,553 - mmseg - INFO - Iter [148300/160000] lr: 1.172e-06, eta: 1:01:38, time: 0.315, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.8162, loss: 0.1790 +2023-03-05 03:02:32,786 - mmseg - INFO - Iter [148350/160000] lr: 1.172e-06, eta: 1:01:22, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.8406, loss: 0.1784 +2023-03-05 03:02:46,067 - mmseg - INFO - Iter [148400/160000] lr: 1.172e-06, eta: 1:01:06, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.4250, loss: 0.1846 +2023-03-05 03:02:59,377 - mmseg - INFO - Iter [148450/160000] lr: 1.172e-06, eta: 1:00:50, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.8367, loss: 0.1777 +2023-03-05 03:03:12,762 - mmseg - INFO - Iter [148500/160000] lr: 1.172e-06, eta: 1:00:34, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.6980, loss: 0.1830 +2023-03-05 03:03:26,062 - mmseg - INFO - Iter [148550/160000] lr: 1.172e-06, eta: 1:00:18, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.6067, loss: 0.1836 +2023-03-05 03:03:39,357 - mmseg - INFO - Iter [148600/160000] lr: 1.172e-06, eta: 1:00:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.8043, loss: 0.1785 +2023-03-05 03:03:52,788 - mmseg - INFO - Iter [148650/160000] lr: 1.172e-06, eta: 0:59:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.9915, loss: 0.1749 +2023-03-05 03:04:06,260 - mmseg - INFO - Iter [148700/160000] lr: 1.172e-06, eta: 0:59:30, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1827, decode.acc_seg: 92.6923, loss: 0.1827 +2023-03-05 03:04:19,493 - mmseg - INFO - Iter [148750/160000] lr: 1.172e-06, eta: 0:59:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8088, loss: 0.1775 +2023-03-05 03:04:32,823 - mmseg - INFO - Iter [148800/160000] lr: 1.172e-06, eta: 0:58:58, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6209, loss: 0.1832 +2023-03-05 03:04:46,064 - mmseg - INFO - Iter [148850/160000] lr: 1.172e-06, eta: 0:58:42, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.8896, loss: 0.1754 +2023-03-05 03:04:59,470 - mmseg - INFO - Iter [148900/160000] lr: 1.172e-06, eta: 0:58:27, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7102, loss: 0.1801 +2023-03-05 03:05:15,304 - mmseg - INFO - Iter [148950/160000] lr: 1.172e-06, eta: 0:58:11, time: 0.317, data_time: 0.058, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.4902, loss: 0.1805 +2023-03-05 03:05:28,596 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:05:28,596 - mmseg - INFO - Iter [149000/160000] lr: 1.172e-06, eta: 0:57:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1836, decode.acc_seg: 92.5980, loss: 0.1836 +2023-03-05 03:05:41,944 - mmseg - INFO - Iter [149050/160000] lr: 1.172e-06, eta: 0:57:39, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6568, loss: 0.1782 +2023-03-05 03:05:55,269 - mmseg - INFO - Iter [149100/160000] lr: 1.172e-06, eta: 0:57:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.7265, loss: 0.1794 +2023-03-05 03:06:08,591 - mmseg - INFO - Iter [149150/160000] lr: 1.172e-06, eta: 0:57:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1693, decode.acc_seg: 92.9898, loss: 0.1693 +2023-03-05 03:06:22,033 - mmseg - INFO - Iter [149200/160000] lr: 1.172e-06, eta: 0:56:51, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.7104, loss: 0.1816 +2023-03-05 03:06:35,292 - mmseg - INFO - Iter [149250/160000] lr: 1.172e-06, eta: 0:56:35, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.5826, loss: 0.1853 +2023-03-05 03:06:48,788 - mmseg - INFO - Iter [149300/160000] lr: 1.172e-06, eta: 0:56:19, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.5650, loss: 0.1837 +2023-03-05 03:07:02,227 - mmseg - INFO - Iter [149350/160000] lr: 1.172e-06, eta: 0:56:03, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7258, loss: 0.1790 +2023-03-05 03:07:15,543 - mmseg - INFO - Iter [149400/160000] lr: 1.172e-06, eta: 0:55:47, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1725, decode.acc_seg: 92.8764, loss: 0.1725 +2023-03-05 03:07:29,014 - mmseg - INFO - Iter [149450/160000] lr: 1.172e-06, eta: 0:55:31, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.6510, loss: 0.1825 +2023-03-05 03:07:42,301 - mmseg - INFO - Iter [149500/160000] lr: 1.172e-06, eta: 0:55:15, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1914, decode.acc_seg: 92.5113, loss: 0.1914 +2023-03-05 03:07:58,167 - mmseg - INFO - Iter [149550/160000] lr: 1.172e-06, eta: 0:54:59, time: 0.317, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.5951, loss: 0.1782 +2023-03-05 03:08:11,443 - mmseg - INFO - Iter [149600/160000] lr: 1.172e-06, eta: 0:54:43, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.7356, loss: 0.1815 +2023-03-05 03:08:24,727 - mmseg - INFO - Iter [149650/160000] lr: 1.172e-06, eta: 0:54:27, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1699, decode.acc_seg: 92.9617, loss: 0.1699 +2023-03-05 03:08:37,981 - mmseg - INFO - Iter [149700/160000] lr: 1.172e-06, eta: 0:54:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1856, decode.acc_seg: 92.6330, loss: 0.1856 +2023-03-05 03:08:51,213 - mmseg - INFO - Iter [149750/160000] lr: 1.172e-06, eta: 0:53:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.7305, loss: 0.1814 +2023-03-05 03:09:04,642 - mmseg - INFO - Iter [149800/160000] lr: 1.172e-06, eta: 0:53:39, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.9075, loss: 0.1754 +2023-03-05 03:09:17,895 - mmseg - INFO - Iter [149850/160000] lr: 1.172e-06, eta: 0:53:24, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.4070, loss: 0.1866 +2023-03-05 03:09:31,155 - mmseg - INFO - Iter [149900/160000] lr: 1.172e-06, eta: 0:53:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.6921, loss: 0.1839 +2023-03-05 03:09:44,433 - mmseg - INFO - Iter [149950/160000] lr: 1.172e-06, eta: 0:52:52, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1776, decode.acc_seg: 92.7403, loss: 0.1776 +2023-03-05 03:09:57,741 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:09:57,742 - mmseg - INFO - Iter [150000/160000] lr: 1.172e-06, eta: 0:52:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1872, decode.acc_seg: 92.4438, loss: 0.1872 +2023-03-05 03:10:11,269 - mmseg - INFO - Iter [150050/160000] lr: 1.172e-06, eta: 0:52:20, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.8504, loss: 0.1779 +2023-03-05 03:10:24,820 - mmseg - INFO - Iter [150100/160000] lr: 1.172e-06, eta: 0:52:04, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.5926, loss: 0.1811 +2023-03-05 03:10:38,112 - mmseg - INFO - Iter [150150/160000] lr: 1.172e-06, eta: 0:51:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.6293, loss: 0.1828 +2023-03-05 03:10:54,110 - mmseg - INFO - Iter [150200/160000] lr: 1.172e-06, eta: 0:51:32, time: 0.320, data_time: 0.052, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.5716, loss: 0.1819 +2023-03-05 03:11:07,487 - mmseg - INFO - Iter [150250/160000] lr: 1.172e-06, eta: 0:51:16, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.9246, loss: 0.1749 +2023-03-05 03:11:20,857 - mmseg - INFO - Iter [150300/160000] lr: 1.172e-06, eta: 0:51:00, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1901, decode.acc_seg: 92.3311, loss: 0.1901 +2023-03-05 03:11:34,171 - mmseg - INFO - Iter [150350/160000] lr: 1.172e-06, eta: 0:50:44, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1900, decode.acc_seg: 92.3613, loss: 0.1900 +2023-03-05 03:11:47,493 - mmseg - INFO - Iter [150400/160000] lr: 1.172e-06, eta: 0:50:28, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1742, decode.acc_seg: 92.9565, loss: 0.1742 +2023-03-05 03:12:00,724 - mmseg - INFO - Iter [150450/160000] lr: 1.172e-06, eta: 0:50:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5657, loss: 0.1839 +2023-03-05 03:12:14,184 - mmseg - INFO - Iter [150500/160000] lr: 1.172e-06, eta: 0:49:57, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.5492, loss: 0.1816 +2023-03-05 03:12:27,525 - mmseg - INFO - Iter [150550/160000] lr: 1.172e-06, eta: 0:49:41, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1709, decode.acc_seg: 92.9600, loss: 0.1709 +2023-03-05 03:12:40,771 - mmseg - INFO - Iter [150600/160000] lr: 1.172e-06, eta: 0:49:25, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.8304, loss: 0.1793 +2023-03-05 03:12:54,002 - mmseg - INFO - Iter [150650/160000] lr: 1.172e-06, eta: 0:49:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7468, loss: 0.1790 +2023-03-05 03:13:07,342 - mmseg - INFO - Iter [150700/160000] lr: 1.172e-06, eta: 0:48:53, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.6270, loss: 0.1815 +2023-03-05 03:13:20,809 - mmseg - INFO - Iter [150750/160000] lr: 1.172e-06, eta: 0:48:37, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1708, decode.acc_seg: 93.0327, loss: 0.1708 +2023-03-05 03:13:34,111 - mmseg - INFO - Iter [150800/160000] lr: 1.172e-06, eta: 0:48:21, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.6212, loss: 0.1850 +2023-03-05 03:13:49,894 - mmseg - INFO - Iter [150850/160000] lr: 1.172e-06, eta: 0:48:05, time: 0.316, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1735, decode.acc_seg: 92.8961, loss: 0.1735 +2023-03-05 03:14:03,245 - mmseg - INFO - Iter [150900/160000] lr: 1.172e-06, eta: 0:47:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1779, decode.acc_seg: 92.7745, loss: 0.1779 +2023-03-05 03:14:16,667 - mmseg - INFO - Iter [150950/160000] lr: 1.172e-06, eta: 0:47:33, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.7169, loss: 0.1788 +2023-03-05 03:14:30,034 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:14:30,034 - mmseg - INFO - Iter [151000/160000] lr: 1.172e-06, eta: 0:47:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1792, decode.acc_seg: 92.8035, loss: 0.1792 +2023-03-05 03:14:43,439 - mmseg - INFO - Iter [151050/160000] lr: 1.172e-06, eta: 0:47:02, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1840, decode.acc_seg: 92.4664, loss: 0.1840 +2023-03-05 03:14:56,763 - mmseg - INFO - Iter [151100/160000] lr: 1.172e-06, eta: 0:46:46, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1854, decode.acc_seg: 92.5003, loss: 0.1854 +2023-03-05 03:15:10,068 - mmseg - INFO - Iter [151150/160000] lr: 1.172e-06, eta: 0:46:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.5680, loss: 0.1809 +2023-03-05 03:15:23,305 - mmseg - INFO - Iter [151200/160000] lr: 1.172e-06, eta: 0:46:14, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1772, decode.acc_seg: 92.7754, loss: 0.1772 +2023-03-05 03:15:36,541 - mmseg - INFO - Iter [151250/160000] lr: 1.172e-06, eta: 0:45:58, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.5321, loss: 0.1844 +2023-03-05 03:15:50,169 - mmseg - INFO - Iter [151300/160000] lr: 1.172e-06, eta: 0:45:42, time: 0.273, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1678, decode.acc_seg: 93.0937, loss: 0.1678 +2023-03-05 03:16:03,525 - mmseg - INFO - Iter [151350/160000] lr: 1.172e-06, eta: 0:45:26, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.5669, loss: 0.1835 +2023-03-05 03:16:16,774 - mmseg - INFO - Iter [151400/160000] lr: 1.172e-06, eta: 0:45:10, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1784, decode.acc_seg: 92.7451, loss: 0.1784 +2023-03-05 03:16:32,609 - mmseg - INFO - Iter [151450/160000] lr: 1.172e-06, eta: 0:44:54, time: 0.317, data_time: 0.057, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.7163, loss: 0.1816 +2023-03-05 03:16:46,024 - mmseg - INFO - Iter [151500/160000] lr: 1.172e-06, eta: 0:44:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1731, decode.acc_seg: 93.1428, loss: 0.1731 +2023-03-05 03:16:59,412 - mmseg - INFO - Iter [151550/160000] lr: 1.172e-06, eta: 0:44:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.7324, loss: 0.1800 +2023-03-05 03:17:12,810 - mmseg - INFO - Iter [151600/160000] lr: 1.172e-06, eta: 0:44:07, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1774, decode.acc_seg: 92.8174, loss: 0.1774 +2023-03-05 03:17:26,093 - mmseg - INFO - Iter [151650/160000] lr: 1.172e-06, eta: 0:43:51, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.8264, loss: 0.1777 +2023-03-05 03:17:39,521 - mmseg - INFO - Iter [151700/160000] lr: 1.172e-06, eta: 0:43:35, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.7220, loss: 0.1804 +2023-03-05 03:17:52,933 - mmseg - INFO - Iter [151750/160000] lr: 1.172e-06, eta: 0:43:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.5048, loss: 0.1873 +2023-03-05 03:18:06,246 - mmseg - INFO - Iter [151800/160000] lr: 1.172e-06, eta: 0:43:03, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6391, loss: 0.1813 +2023-03-05 03:18:19,485 - mmseg - INFO - Iter [151850/160000] lr: 1.172e-06, eta: 0:42:47, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.5961, loss: 0.1787 +2023-03-05 03:18:32,734 - mmseg - INFO - Iter [151900/160000] lr: 1.172e-06, eta: 0:42:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1882, decode.acc_seg: 92.4992, loss: 0.1882 +2023-03-05 03:18:45,958 - mmseg - INFO - Iter [151950/160000] lr: 1.172e-06, eta: 0:42:16, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6809, loss: 0.1799 +2023-03-05 03:18:59,216 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:18:59,217 - mmseg - INFO - Iter [152000/160000] lr: 1.172e-06, eta: 0:42:00, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1688, decode.acc_seg: 93.0523, loss: 0.1688 +2023-03-05 03:19:12,732 - mmseg - INFO - Iter [152050/160000] lr: 1.172e-06, eta: 0:41:44, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1864, decode.acc_seg: 92.3563, loss: 0.1864 +2023-03-05 03:19:28,621 - mmseg - INFO - Iter [152100/160000] lr: 1.172e-06, eta: 0:41:28, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.4857, loss: 0.1868 +2023-03-05 03:19:41,882 - mmseg - INFO - Iter [152150/160000] lr: 1.172e-06, eta: 0:41:12, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.9015, loss: 0.1782 +2023-03-05 03:19:55,118 - mmseg - INFO - Iter [152200/160000] lr: 1.172e-06, eta: 0:40:56, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4365, loss: 0.1873 +2023-03-05 03:20:08,396 - mmseg - INFO - Iter [152250/160000] lr: 1.172e-06, eta: 0:40:40, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7516, loss: 0.1793 +2023-03-05 03:20:21,712 - mmseg - INFO - Iter [152300/160000] lr: 1.172e-06, eta: 0:40:25, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1746, decode.acc_seg: 92.8528, loss: 0.1746 +2023-03-05 03:20:35,016 - mmseg - INFO - Iter [152350/160000] lr: 1.172e-06, eta: 0:40:09, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.8390, loss: 0.1771 +2023-03-05 03:20:48,280 - mmseg - INFO - Iter [152400/160000] lr: 1.172e-06, eta: 0:39:53, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6732, loss: 0.1811 +2023-03-05 03:21:01,622 - mmseg - INFO - Iter [152450/160000] lr: 1.172e-06, eta: 0:39:37, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1868, decode.acc_seg: 92.5581, loss: 0.1868 +2023-03-05 03:21:14,873 - mmseg - INFO - Iter [152500/160000] lr: 1.172e-06, eta: 0:39:21, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.6284, loss: 0.1843 +2023-03-05 03:21:28,177 - mmseg - INFO - Iter [152550/160000] lr: 1.172e-06, eta: 0:39:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5176, loss: 0.1838 +2023-03-05 03:21:41,491 - mmseg - INFO - Iter [152600/160000] lr: 1.172e-06, eta: 0:38:49, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.5229, loss: 0.1822 +2023-03-05 03:21:54,820 - mmseg - INFO - Iter [152650/160000] lr: 1.172e-06, eta: 0:38:34, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1813, decode.acc_seg: 92.6348, loss: 0.1813 +2023-03-05 03:22:08,048 - mmseg - INFO - Iter [152700/160000] lr: 1.172e-06, eta: 0:38:18, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.5446, loss: 0.1825 +2023-03-05 03:22:24,181 - mmseg - INFO - Iter [152750/160000] lr: 1.172e-06, eta: 0:38:02, time: 0.323, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1875, decode.acc_seg: 92.5530, loss: 0.1875 +2023-03-05 03:22:37,652 - mmseg - INFO - Iter [152800/160000] lr: 1.172e-06, eta: 0:37:46, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1783, decode.acc_seg: 92.9329, loss: 0.1783 +2023-03-05 03:22:50,970 - mmseg - INFO - Iter [152850/160000] lr: 1.172e-06, eta: 0:37:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7882, loss: 0.1790 +2023-03-05 03:23:04,387 - mmseg - INFO - Iter [152900/160000] lr: 1.172e-06, eta: 0:37:14, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1851, decode.acc_seg: 92.6072, loss: 0.1851 +2023-03-05 03:23:17,716 - mmseg - INFO - Iter [152950/160000] lr: 1.172e-06, eta: 0:36:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.7130, loss: 0.1769 +2023-03-05 03:23:31,084 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:23:31,085 - mmseg - INFO - Iter [153000/160000] lr: 1.172e-06, eta: 0:36:43, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.9061, loss: 0.1766 +2023-03-05 03:23:44,512 - mmseg - INFO - Iter [153050/160000] lr: 1.172e-06, eta: 0:36:27, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5125, loss: 0.1843 +2023-03-05 03:23:57,962 - mmseg - INFO - Iter [153100/160000] lr: 1.172e-06, eta: 0:36:11, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.4935, loss: 0.1824 +2023-03-05 03:24:11,263 - mmseg - INFO - Iter [153150/160000] lr: 1.172e-06, eta: 0:35:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1734, decode.acc_seg: 93.0639, loss: 0.1734 +2023-03-05 03:24:24,516 - mmseg - INFO - Iter [153200/160000] lr: 1.172e-06, eta: 0:35:39, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.6929, loss: 0.1771 +2023-03-05 03:24:37,728 - mmseg - INFO - Iter [153250/160000] lr: 1.172e-06, eta: 0:35:24, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1712, decode.acc_seg: 93.0319, loss: 0.1712 +2023-03-05 03:24:50,994 - mmseg - INFO - Iter [153300/160000] lr: 1.172e-06, eta: 0:35:08, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1873, decode.acc_seg: 92.4803, loss: 0.1873 +2023-03-05 03:25:06,868 - mmseg - INFO - Iter [153350/160000] lr: 1.172e-06, eta: 0:34:52, time: 0.317, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.6657, loss: 0.1785 +2023-03-05 03:25:20,420 - mmseg - INFO - Iter [153400/160000] lr: 1.172e-06, eta: 0:34:36, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.8572, loss: 0.1756 +2023-03-05 03:25:33,725 - mmseg - INFO - Iter [153450/160000] lr: 1.172e-06, eta: 0:34:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1823, decode.acc_seg: 92.7002, loss: 0.1823 +2023-03-05 03:25:47,131 - mmseg - INFO - Iter [153500/160000] lr: 1.172e-06, eta: 0:34:04, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1825, decode.acc_seg: 92.4262, loss: 0.1825 +2023-03-05 03:26:00,491 - mmseg - INFO - Iter [153550/160000] lr: 1.172e-06, eta: 0:33:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1816, decode.acc_seg: 92.6195, loss: 0.1816 +2023-03-05 03:26:13,951 - mmseg - INFO - Iter [153600/160000] lr: 1.172e-06, eta: 0:33:33, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.6860, loss: 0.1822 +2023-03-05 03:26:27,239 - mmseg - INFO - Iter [153650/160000] lr: 1.172e-06, eta: 0:33:17, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.6317, loss: 0.1849 +2023-03-05 03:26:40,565 - mmseg - INFO - Iter [153700/160000] lr: 1.172e-06, eta: 0:33:01, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.6391, loss: 0.1793 +2023-03-05 03:26:53,916 - mmseg - INFO - Iter [153750/160000] lr: 1.172e-06, eta: 0:32:45, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1828, decode.acc_seg: 92.5812, loss: 0.1828 +2023-03-05 03:27:07,366 - mmseg - INFO - Iter [153800/160000] lr: 1.172e-06, eta: 0:32:29, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.5566, loss: 0.1843 +2023-03-05 03:27:20,737 - mmseg - INFO - Iter [153850/160000] lr: 1.172e-06, eta: 0:32:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.6989, loss: 0.1782 +2023-03-05 03:27:34,126 - mmseg - INFO - Iter [153900/160000] lr: 1.172e-06, eta: 0:31:58, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1765, decode.acc_seg: 92.8282, loss: 0.1765 +2023-03-05 03:27:47,448 - mmseg - INFO - Iter [153950/160000] lr: 1.172e-06, eta: 0:31:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.7963, loss: 0.1802 +2023-03-05 03:28:03,327 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:28:03,327 - mmseg - INFO - Iter [154000/160000] lr: 1.172e-06, eta: 0:31:26, time: 0.318, data_time: 0.056, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6150, loss: 0.1811 +2023-03-05 03:28:16,704 - mmseg - INFO - Iter [154050/160000] lr: 1.172e-06, eta: 0:31:11, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6897, loss: 0.1798 +2023-03-05 03:28:30,209 - mmseg - INFO - Iter [154100/160000] lr: 1.172e-06, eta: 0:30:55, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7546, loss: 0.1795 +2023-03-05 03:28:43,651 - mmseg - INFO - Iter [154150/160000] lr: 1.172e-06, eta: 0:30:39, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.8919, loss: 0.1766 +2023-03-05 03:28:56,992 - mmseg - INFO - Iter [154200/160000] lr: 1.172e-06, eta: 0:30:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.5964, loss: 0.1839 +2023-03-05 03:29:10,310 - mmseg - INFO - Iter [154250/160000] lr: 1.172e-06, eta: 0:30:07, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1756, decode.acc_seg: 92.9856, loss: 0.1756 +2023-03-05 03:29:23,603 - mmseg - INFO - Iter [154300/160000] lr: 1.172e-06, eta: 0:29:51, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1732, decode.acc_seg: 92.8014, loss: 0.1732 +2023-03-05 03:29:36,950 - mmseg - INFO - Iter [154350/160000] lr: 1.172e-06, eta: 0:29:36, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1844, decode.acc_seg: 92.4485, loss: 0.1844 +2023-03-05 03:29:50,152 - mmseg - INFO - Iter [154400/160000] lr: 1.172e-06, eta: 0:29:20, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1822, decode.acc_seg: 92.6649, loss: 0.1822 +2023-03-05 03:30:03,493 - mmseg - INFO - Iter [154450/160000] lr: 1.172e-06, eta: 0:29:04, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.4876, loss: 0.1838 +2023-03-05 03:30:16,790 - mmseg - INFO - Iter [154500/160000] lr: 1.172e-06, eta: 0:28:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1752, decode.acc_seg: 92.9517, loss: 0.1752 +2023-03-05 03:30:30,087 - mmseg - INFO - Iter [154550/160000] lr: 1.172e-06, eta: 0:28:32, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1766, decode.acc_seg: 92.8383, loss: 0.1766 +2023-03-05 03:30:45,891 - mmseg - INFO - Iter [154600/160000] lr: 1.172e-06, eta: 0:28:17, time: 0.316, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1773, decode.acc_seg: 92.7711, loss: 0.1773 +2023-03-05 03:30:59,445 - mmseg - INFO - Iter [154650/160000] lr: 1.172e-06, eta: 0:28:01, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.7440, loss: 0.1803 +2023-03-05 03:31:12,698 - mmseg - INFO - Iter [154700/160000] lr: 1.172e-06, eta: 0:27:45, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1819, decode.acc_seg: 92.5767, loss: 0.1819 +2023-03-05 03:31:26,110 - mmseg - INFO - Iter [154750/160000] lr: 1.172e-06, eta: 0:27:29, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1839, decode.acc_seg: 92.4443, loss: 0.1839 +2023-03-05 03:31:39,484 - mmseg - INFO - Iter [154800/160000] lr: 1.172e-06, eta: 0:27:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1832, decode.acc_seg: 92.6101, loss: 0.1832 +2023-03-05 03:31:52,870 - mmseg - INFO - Iter [154850/160000] lr: 1.172e-06, eta: 0:26:58, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6149, loss: 0.1798 +2023-03-05 03:32:06,337 - mmseg - INFO - Iter [154900/160000] lr: 1.172e-06, eta: 0:26:42, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1730, decode.acc_seg: 92.9878, loss: 0.1730 +2023-03-05 03:32:19,620 - mmseg - INFO - Iter [154950/160000] lr: 1.172e-06, eta: 0:26:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.5603, loss: 0.1797 +2023-03-05 03:32:32,928 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:32:32,928 - mmseg - INFO - Iter [155000/160000] lr: 1.172e-06, eta: 0:26:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5766, loss: 0.1870 +2023-03-05 03:32:46,201 - mmseg - INFO - Iter [155050/160000] lr: 1.172e-06, eta: 0:25:55, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1757, decode.acc_seg: 92.8460, loss: 0.1757 +2023-03-05 03:32:59,493 - mmseg - INFO - Iter [155100/160000] lr: 1.172e-06, eta: 0:25:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.8607, loss: 0.1789 +2023-03-05 03:33:12,859 - mmseg - INFO - Iter [155150/160000] lr: 1.172e-06, eta: 0:25:23, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6052, loss: 0.1803 +2023-03-05 03:33:26,098 - mmseg - INFO - Iter [155200/160000] lr: 1.172e-06, eta: 0:25:07, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1788, decode.acc_seg: 92.8516, loss: 0.1788 +2023-03-05 03:33:41,926 - mmseg - INFO - Iter [155250/160000] lr: 1.172e-06, eta: 0:24:52, time: 0.317, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.8724, loss: 0.1749 +2023-03-05 03:33:55,313 - mmseg - INFO - Iter [155300/160000] lr: 1.172e-06, eta: 0:24:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1893, decode.acc_seg: 92.3117, loss: 0.1893 +2023-03-05 03:34:08,606 - mmseg - INFO - Iter [155350/160000] lr: 1.172e-06, eta: 0:24:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6201, loss: 0.1834 +2023-03-05 03:34:22,057 - mmseg - INFO - Iter [155400/160000] lr: 1.172e-06, eta: 0:24:04, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7956, loss: 0.1768 +2023-03-05 03:34:35,384 - mmseg - INFO - Iter [155450/160000] lr: 1.172e-06, eta: 0:23:48, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.6235, loss: 0.1858 +2023-03-05 03:34:48,662 - mmseg - INFO - Iter [155500/160000] lr: 1.172e-06, eta: 0:23:33, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1824, decode.acc_seg: 92.6259, loss: 0.1824 +2023-03-05 03:35:02,022 - mmseg - INFO - Iter [155550/160000] lr: 1.172e-06, eta: 0:23:17, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1753, decode.acc_seg: 92.7588, loss: 0.1753 +2023-03-05 03:35:15,480 - mmseg - INFO - Iter [155600/160000] lr: 1.172e-06, eta: 0:23:01, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1729, decode.acc_seg: 92.9641, loss: 0.1729 +2023-03-05 03:35:28,763 - mmseg - INFO - Iter [155650/160000] lr: 1.172e-06, eta: 0:22:45, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.4722, loss: 0.1870 +2023-03-05 03:35:42,051 - mmseg - INFO - Iter [155700/160000] lr: 1.172e-06, eta: 0:22:30, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1764, decode.acc_seg: 92.8243, loss: 0.1764 +2023-03-05 03:35:55,409 - mmseg - INFO - Iter [155750/160000] lr: 1.172e-06, eta: 0:22:14, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1787, decode.acc_seg: 92.6710, loss: 0.1787 +2023-03-05 03:36:08,614 - mmseg - INFO - Iter [155800/160000] lr: 1.172e-06, eta: 0:21:58, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1749, decode.acc_seg: 92.8988, loss: 0.1749 +2023-03-05 03:36:21,936 - mmseg - INFO - Iter [155850/160000] lr: 1.172e-06, eta: 0:21:42, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1811, decode.acc_seg: 92.6681, loss: 0.1811 +2023-03-05 03:36:37,953 - mmseg - INFO - Iter [155900/160000] lr: 1.172e-06, eta: 0:21:27, time: 0.320, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1741, decode.acc_seg: 92.7970, loss: 0.1741 +2023-03-05 03:36:51,421 - mmseg - INFO - Iter [155950/160000] lr: 1.172e-06, eta: 0:21:11, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.8346, loss: 0.1751 +2023-03-05 03:37:04,925 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:37:04,925 - mmseg - INFO - Iter [156000/160000] lr: 1.172e-06, eta: 0:20:55, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7326, loss: 0.1782 +2023-03-05 03:37:18,235 - mmseg - INFO - Iter [156050/160000] lr: 1.172e-06, eta: 0:20:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1855, decode.acc_seg: 92.4991, loss: 0.1855 +2023-03-05 03:37:31,658 - mmseg - INFO - Iter [156100/160000] lr: 1.172e-06, eta: 0:20:24, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1780, decode.acc_seg: 92.8122, loss: 0.1780 +2023-03-05 03:37:45,012 - mmseg - INFO - Iter [156150/160000] lr: 1.172e-06, eta: 0:20:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1800, decode.acc_seg: 92.7723, loss: 0.1800 +2023-03-05 03:37:58,375 - mmseg - INFO - Iter [156200/160000] lr: 1.172e-06, eta: 0:19:52, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1751, decode.acc_seg: 92.8901, loss: 0.1751 +2023-03-05 03:38:11,656 - mmseg - INFO - Iter [156250/160000] lr: 1.172e-06, eta: 0:19:36, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1866, decode.acc_seg: 92.5045, loss: 0.1866 +2023-03-05 03:38:25,034 - mmseg - INFO - Iter [156300/160000] lr: 1.172e-06, eta: 0:19:21, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1846, decode.acc_seg: 92.4555, loss: 0.1846 +2023-03-05 03:38:38,399 - mmseg - INFO - Iter [156350/160000] lr: 1.172e-06, eta: 0:19:05, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1746, decode.acc_seg: 92.9650, loss: 0.1746 +2023-03-05 03:38:51,791 - mmseg - INFO - Iter [156400/160000] lr: 1.172e-06, eta: 0:18:49, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1834, decode.acc_seg: 92.6642, loss: 0.1834 +2023-03-05 03:39:05,216 - mmseg - INFO - Iter [156450/160000] lr: 1.172e-06, eta: 0:18:33, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.7675, loss: 0.1797 +2023-03-05 03:39:21,071 - mmseg - INFO - Iter [156500/160000] lr: 1.172e-06, eta: 0:18:18, time: 0.317, data_time: 0.058, memory: 67559, decode.loss_ce: 0.1843, decode.acc_seg: 92.4590, loss: 0.1843 +2023-03-05 03:39:34,355 - mmseg - INFO - Iter [156550/160000] lr: 1.172e-06, eta: 0:18:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1758, decode.acc_seg: 92.8606, loss: 0.1758 +2023-03-05 03:39:47,614 - mmseg - INFO - Iter [156600/160000] lr: 1.172e-06, eta: 0:17:46, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1747, decode.acc_seg: 92.9108, loss: 0.1747 +2023-03-05 03:40:00,969 - mmseg - INFO - Iter [156650/160000] lr: 1.172e-06, eta: 0:17:30, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.9299, loss: 0.1745 +2023-03-05 03:40:14,386 - mmseg - INFO - Iter [156700/160000] lr: 1.172e-06, eta: 0:17:15, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1801, decode.acc_seg: 92.7570, loss: 0.1801 +2023-03-05 03:40:27,755 - mmseg - INFO - Iter [156750/160000] lr: 1.172e-06, eta: 0:16:59, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1838, decode.acc_seg: 92.5928, loss: 0.1838 +2023-03-05 03:40:41,112 - mmseg - INFO - Iter [156800/160000] lr: 1.172e-06, eta: 0:16:43, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1809, decode.acc_seg: 92.6881, loss: 0.1809 +2023-03-05 03:40:54,476 - mmseg - INFO - Iter [156850/160000] lr: 1.172e-06, eta: 0:16:28, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1853, decode.acc_seg: 92.4542, loss: 0.1853 +2023-03-05 03:41:07,761 - mmseg - INFO - Iter [156900/160000] lr: 1.172e-06, eta: 0:16:12, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1771, decode.acc_seg: 92.7207, loss: 0.1771 +2023-03-05 03:41:21,132 - mmseg - INFO - Iter [156950/160000] lr: 1.172e-06, eta: 0:15:56, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1799, decode.acc_seg: 92.6609, loss: 0.1799 +2023-03-05 03:41:34,389 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:41:34,389 - mmseg - INFO - Iter [157000/160000] lr: 1.172e-06, eta: 0:15:40, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1847, decode.acc_seg: 92.5905, loss: 0.1847 +2023-03-05 03:41:47,750 - mmseg - INFO - Iter [157050/160000] lr: 1.172e-06, eta: 0:15:25, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.6454, loss: 0.1805 +2023-03-05 03:42:01,017 - mmseg - INFO - Iter [157100/160000] lr: 1.172e-06, eta: 0:15:09, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1725, decode.acc_seg: 92.9443, loss: 0.1725 +2023-03-05 03:42:16,726 - mmseg - INFO - Iter [157150/160000] lr: 1.172e-06, eta: 0:14:53, time: 0.314, data_time: 0.055, memory: 67559, decode.loss_ce: 0.1745, decode.acc_seg: 92.8477, loss: 0.1745 +2023-03-05 03:42:30,220 - mmseg - INFO - Iter [157200/160000] lr: 1.172e-06, eta: 0:14:38, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1759, decode.acc_seg: 92.7542, loss: 0.1759 +2023-03-05 03:42:43,529 - mmseg - INFO - Iter [157250/160000] lr: 1.172e-06, eta: 0:14:22, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1790, decode.acc_seg: 92.7961, loss: 0.1790 +2023-03-05 03:42:56,902 - mmseg - INFO - Iter [157300/160000] lr: 1.172e-06, eta: 0:14:06, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1805, decode.acc_seg: 92.6666, loss: 0.1805 +2023-03-05 03:43:10,135 - mmseg - INFO - Iter [157350/160000] lr: 1.172e-06, eta: 0:13:50, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1835, decode.acc_seg: 92.4618, loss: 0.1835 +2023-03-05 03:43:23,584 - mmseg - INFO - Iter [157400/160000] lr: 1.172e-06, eta: 0:13:35, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.9626, loss: 0.1791 +2023-03-05 03:43:36,961 - mmseg - INFO - Iter [157450/160000] lr: 1.172e-06, eta: 0:13:19, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1862, decode.acc_seg: 92.4975, loss: 0.1862 +2023-03-05 03:43:50,169 - mmseg - INFO - Iter [157500/160000] lr: 1.172e-06, eta: 0:13:03, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1817, decode.acc_seg: 92.7355, loss: 0.1817 +2023-03-05 03:44:03,457 - mmseg - INFO - Iter [157550/160000] lr: 1.172e-06, eta: 0:12:48, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1796, decode.acc_seg: 92.6850, loss: 0.1796 +2023-03-05 03:44:16,862 - mmseg - INFO - Iter [157600/160000] lr: 1.172e-06, eta: 0:12:32, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1748, decode.acc_seg: 92.7763, loss: 0.1748 +2023-03-05 03:44:30,115 - mmseg - INFO - Iter [157650/160000] lr: 1.172e-06, eta: 0:12:16, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1785, decode.acc_seg: 92.7177, loss: 0.1785 +2023-03-05 03:44:43,505 - mmseg - INFO - Iter [157700/160000] lr: 1.172e-06, eta: 0:12:00, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1754, decode.acc_seg: 92.8031, loss: 0.1754 +2023-03-05 03:44:56,713 - mmseg - INFO - Iter [157750/160000] lr: 1.172e-06, eta: 0:11:45, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1815, decode.acc_seg: 92.5451, loss: 0.1815 +2023-03-05 03:45:12,566 - mmseg - INFO - Iter [157800/160000] lr: 1.172e-06, eta: 0:11:29, time: 0.317, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1833, decode.acc_seg: 92.6328, loss: 0.1833 +2023-03-05 03:45:25,850 - mmseg - INFO - Iter [157850/160000] lr: 1.172e-06, eta: 0:11:13, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.8267, loss: 0.1775 +2023-03-05 03:45:39,151 - mmseg - INFO - Iter [157900/160000] lr: 1.172e-06, eta: 0:10:58, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1848, decode.acc_seg: 92.6385, loss: 0.1848 +2023-03-05 03:45:52,525 - mmseg - INFO - Iter [157950/160000] lr: 1.172e-06, eta: 0:10:42, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1837, decode.acc_seg: 92.6161, loss: 0.1837 +2023-03-05 03:46:05,745 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:46:05,745 - mmseg - INFO - Iter [158000/160000] lr: 1.172e-06, eta: 0:10:26, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.7018, loss: 0.1821 +2023-03-05 03:46:19,015 - mmseg - INFO - Iter [158050/160000] lr: 1.172e-06, eta: 0:10:11, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1870, decode.acc_seg: 92.5621, loss: 0.1870 +2023-03-05 03:46:32,312 - mmseg - INFO - Iter [158100/160000] lr: 1.172e-06, eta: 0:09:55, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1767, decode.acc_seg: 92.7781, loss: 0.1767 +2023-03-05 03:46:45,602 - mmseg - INFO - Iter [158150/160000] lr: 1.172e-06, eta: 0:09:39, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1831, decode.acc_seg: 92.6238, loss: 0.1831 +2023-03-05 03:46:58,893 - mmseg - INFO - Iter [158200/160000] lr: 1.172e-06, eta: 0:09:23, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1769, decode.acc_seg: 92.8030, loss: 0.1769 +2023-03-05 03:47:12,223 - mmseg - INFO - Iter [158250/160000] lr: 1.172e-06, eta: 0:09:08, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.6030, loss: 0.1850 +2023-03-05 03:47:25,488 - mmseg - INFO - Iter [158300/160000] lr: 1.172e-06, eta: 0:08:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1849, decode.acc_seg: 92.2969, loss: 0.1849 +2023-03-05 03:47:38,900 - mmseg - INFO - Iter [158350/160000] lr: 1.172e-06, eta: 0:08:36, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1798, decode.acc_seg: 92.6672, loss: 0.1798 +2023-03-05 03:47:54,628 - mmseg - INFO - Iter [158400/160000] lr: 1.172e-06, eta: 0:08:21, time: 0.315, data_time: 0.054, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7148, loss: 0.1793 +2023-03-05 03:48:07,940 - mmseg - INFO - Iter [158450/160000] lr: 1.172e-06, eta: 0:08:05, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1748, decode.acc_seg: 92.8934, loss: 0.1748 +2023-03-05 03:48:21,309 - mmseg - INFO - Iter [158500/160000] lr: 1.172e-06, eta: 0:07:49, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1768, decode.acc_seg: 92.7713, loss: 0.1768 +2023-03-05 03:48:34,870 - mmseg - INFO - Iter [158550/160000] lr: 1.172e-06, eta: 0:07:34, time: 0.271, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1812, decode.acc_seg: 92.6603, loss: 0.1812 +2023-03-05 03:48:48,214 - mmseg - INFO - Iter [158600/160000] lr: 1.172e-06, eta: 0:07:18, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1850, decode.acc_seg: 92.5119, loss: 0.1850 +2023-03-05 03:49:01,538 - mmseg - INFO - Iter [158650/160000] lr: 1.172e-06, eta: 0:07:02, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1739, decode.acc_seg: 92.9004, loss: 0.1739 +2023-03-05 03:49:15,058 - mmseg - INFO - Iter [158700/160000] lr: 1.172e-06, eta: 0:06:47, time: 0.270, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1797, decode.acc_seg: 92.6522, loss: 0.1797 +2023-03-05 03:49:28,433 - mmseg - INFO - Iter [158750/160000] lr: 1.172e-06, eta: 0:06:31, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1830, decode.acc_seg: 92.5677, loss: 0.1830 +2023-03-05 03:49:41,781 - mmseg - INFO - Iter [158800/160000] lr: 1.172e-06, eta: 0:06:15, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1782, decode.acc_seg: 92.7497, loss: 0.1782 +2023-03-05 03:49:55,096 - mmseg - INFO - Iter [158850/160000] lr: 1.172e-06, eta: 0:06:00, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1867, decode.acc_seg: 92.3503, loss: 0.1867 +2023-03-05 03:50:08,439 - mmseg - INFO - Iter [158900/160000] lr: 1.172e-06, eta: 0:05:44, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1791, decode.acc_seg: 92.6176, loss: 0.1791 +2023-03-05 03:50:21,869 - mmseg - INFO - Iter [158950/160000] lr: 1.172e-06, eta: 0:05:28, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1807, decode.acc_seg: 92.6591, loss: 0.1807 +2023-03-05 03:50:35,261 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:50:35,261 - mmseg - INFO - Iter [159000/160000] lr: 1.172e-06, eta: 0:05:13, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.6436, loss: 0.1821 +2023-03-05 03:50:51,091 - mmseg - INFO - Iter [159050/160000] lr: 1.172e-06, eta: 0:04:57, time: 0.317, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1869, decode.acc_seg: 92.3930, loss: 0.1869 +2023-03-05 03:51:04,343 - mmseg - INFO - Iter [159100/160000] lr: 1.172e-06, eta: 0:04:41, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1744, decode.acc_seg: 92.9487, loss: 0.1744 +2023-03-05 03:51:17,651 - mmseg - INFO - Iter [159150/160000] lr: 1.172e-06, eta: 0:04:26, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1821, decode.acc_seg: 92.5249, loss: 0.1821 +2023-03-05 03:51:30,971 - mmseg - INFO - Iter [159200/160000] lr: 1.172e-06, eta: 0:04:10, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.7122, loss: 0.1795 +2023-03-05 03:51:44,277 - mmseg - INFO - Iter [159250/160000] lr: 1.172e-06, eta: 0:03:54, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1793, decode.acc_seg: 92.7408, loss: 0.1793 +2023-03-05 03:51:57,681 - mmseg - INFO - Iter [159300/160000] lr: 1.172e-06, eta: 0:03:39, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1814, decode.acc_seg: 92.6846, loss: 0.1814 +2023-03-05 03:52:11,070 - mmseg - INFO - Iter [159350/160000] lr: 1.172e-06, eta: 0:03:23, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.9281, loss: 0.1794 +2023-03-05 03:52:24,444 - mmseg - INFO - Iter [159400/160000] lr: 1.172e-06, eta: 0:03:07, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1820, decode.acc_seg: 92.8953, loss: 0.1820 +2023-03-05 03:52:37,690 - mmseg - INFO - Iter [159450/160000] lr: 1.172e-06, eta: 0:02:52, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1777, decode.acc_seg: 92.8508, loss: 0.1777 +2023-03-05 03:52:50,946 - mmseg - INFO - Iter [159500/160000] lr: 1.172e-06, eta: 0:02:36, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1858, decode.acc_seg: 92.4331, loss: 0.1858 +2023-03-05 03:53:04,240 - mmseg - INFO - Iter [159550/160000] lr: 1.172e-06, eta: 0:02:20, time: 0.266, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1795, decode.acc_seg: 92.6430, loss: 0.1795 +2023-03-05 03:53:17,647 - mmseg - INFO - Iter [159600/160000] lr: 1.172e-06, eta: 0:02:05, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1818, decode.acc_seg: 92.6387, loss: 0.1818 +2023-03-05 03:53:33,336 - mmseg - INFO - Iter [159650/160000] lr: 1.172e-06, eta: 0:01:49, time: 0.314, data_time: 0.053, memory: 67559, decode.loss_ce: 0.1794, decode.acc_seg: 92.6361, loss: 0.1794 +2023-03-05 03:53:46,693 - mmseg - INFO - Iter [159700/160000] lr: 1.172e-06, eta: 0:01:33, time: 0.267, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1803, decode.acc_seg: 92.6375, loss: 0.1803 +2023-03-05 03:54:00,139 - mmseg - INFO - Iter [159750/160000] lr: 1.172e-06, eta: 0:01:18, time: 0.269, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1778, decode.acc_seg: 92.7415, loss: 0.1778 +2023-03-05 03:54:13,392 - mmseg - INFO - Iter [159800/160000] lr: 1.172e-06, eta: 0:01:02, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1775, decode.acc_seg: 92.6582, loss: 0.1775 +2023-03-05 03:54:26,775 - mmseg - INFO - Iter [159850/160000] lr: 1.172e-06, eta: 0:00:46, time: 0.268, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1804, decode.acc_seg: 92.6915, loss: 0.1804 +2023-03-05 03:54:40,026 - mmseg - INFO - Iter [159900/160000] lr: 1.172e-06, eta: 0:00:31, time: 0.265, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1802, decode.acc_seg: 92.7312, loss: 0.1802 +2023-03-05 03:54:53,239 - mmseg - INFO - Iter [159950/160000] lr: 1.172e-06, eta: 0:00:15, time: 0.264, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1748, decode.acc_seg: 92.8734, loss: 0.1748 +2023-03-05 03:55:06,483 - mmseg - INFO - Swap parameters (after train) after iter [160000] +2023-03-05 03:55:06,506 - mmseg - INFO - Saving checkpoint at 160000 iterations +2023-03-05 03:55:08,405 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 03:55:08,405 - mmseg - INFO - Iter [160000/160000] lr: 1.172e-06, eta: 0:00:00, time: 0.303, data_time: 0.007, memory: 67559, decode.loss_ce: 0.1789, decode.acc_seg: 92.7526, loss: 0.1789 +2023-03-05 04:06:17,663 - mmseg - INFO - per class results: +2023-03-05 04:06:17,672 - mmseg - INFO - ++---------------------+-------------------------------------------------------------------+ +| Class | IoU 0,10,20,30,40,50,60,70,80,90,99 | ++---------------------+-------------------------------------------------------------------+ +| background | nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan | +| wall | 76.34,76.36,76.36,76.36,76.37,76.39,76.38,76.37,76.38,76.38,76.37 | +| building | 81.38,81.38,81.4,81.4,81.41,81.4,81.39,81.39,81.4,81.4,81.42 | +| sky | 94.28,94.28,94.28,94.28,94.28,94.28,94.28,94.28,94.27,94.27,94.27 | +| floor | 80.01,80.02,80.03,80.06,80.07,80.08,80.11,80.12,80.13,80.12,80.09 | +| tree | 72.93,72.92,72.9,72.9,72.89,72.89,72.85,72.87,72.86,72.85,72.87 | +| ceiling | 82.83,82.84,82.84,82.84,82.87,82.91,82.9,82.89,82.92,82.94,82.92 | +| road | 82.03,82.02,82.02,81.99,82.05,82.06,82.06,82.05,82.05,81.99,82.03 | +| bed | 88.55,88.57,88.55,88.56,88.56,88.56,88.57,88.55,88.55,88.56,88.55 | +| windowpane | 61.05,61.06,61.07,61.1,61.08,61.14,61.11,61.13,61.12,61.11,61.12 | +| grass | 65.24,65.24,65.28,65.27,65.34,65.38,65.39,65.4,65.38,65.47,65.52 | +| cabinet | 59.43,59.44,59.37,59.36,59.28,59.34,59.32,59.32,59.35,59.41,59.38 | +| sidewalk | 66.6,66.61,66.63,66.69,66.82,66.87,66.9,66.94,66.89,66.83,66.94 | +| person | 79.46,79.48,79.5,79.49,79.47,79.48,79.48,79.49,79.49,79.49,79.44 | +| earth | 33.15,33.1,33.06,33.01,32.95,32.79,32.73,32.74,32.74,32.66,32.66 | +| door | 48.45,48.56,48.66,48.68,48.76,48.76,48.78,48.74,48.75,48.74,48.82 | +| table | 61.56,61.54,61.52,61.56,61.57,61.6,61.65,61.66,61.69,61.7,61.65 | +| mountain | 51.81,51.96,51.99,52.12,52.13,52.3,52.27,52.35,52.34,52.32,52.29 | +| plant | 49.98,49.94,49.97,49.91,49.89,49.92,49.85,49.84,49.86,49.86,49.95 | +| curtain | 70.61,70.64,70.71,70.78,70.76,70.8,70.75,70.82,70.83,70.82,70.8 | +| chair | 58.29,58.33,58.39,58.39,58.4,58.43,58.48,58.5,58.5,58.51,58.61 | +| car | 83.12,83.12,83.11,83.12,83.11,83.15,83.12,83.12,83.13,83.13,83.13 | +| water | 47.41,47.39,47.43,47.45,47.46,47.48,47.47,47.48,47.47,47.53,47.55 | +| painting | 69.85,69.83,69.85,69.88,69.85,69.93,69.93,69.95,69.96,69.97,69.93 | +| sofa | 65.3,65.35,65.36,65.34,65.38,65.49,65.56,65.71,65.95,65.9,65.58 | +| shelf | 40.65,40.57,40.61,40.51,40.44,40.48,40.43,40.44,40.45,40.44,40.15 | +| house | 43.99,43.94,44.01,44.0,44.04,43.99,43.85,43.73,43.69,43.7,43.72 | +| sea | 44.93,44.88,44.86,44.79,44.76,44.7,44.67,44.62,44.6,44.59,44.59 | +| mirror | 65.66,65.6,65.64,65.58,65.57,65.58,65.55,65.57,65.54,65.54,65.47 | +| rug | 54.85,54.9,54.93,54.99,55.06,54.95,55.09,55.07,55.23,55.18,55.21 | +| field | 28.22,28.21,28.06,28.12,28.14,28.08,28.08,28.23,28.26,28.29,28.09 | +| armchair | 43.31,43.29,43.19,43.06,43.13,43.18,43.2,43.44,43.59,43.48,42.99 | +| seat | 53.59,53.68,53.78,53.75,53.77,53.76,53.77,53.79,53.73,53.7,53.64 | +| fence | 40.53,40.5,40.42,40.42,40.43,40.4,40.4,40.34,40.36,40.38,40.45 | +| desk | 49.6,49.58,49.57,49.55,49.54,49.47,49.4,49.31,49.29,49.26,49.29 | +| rock | 25.96,26.12,25.86,25.83,25.64,26.09,26.18,26.05,26.07,26.17,26.01 | +| wardrobe | 47.78,47.83,47.71,47.7,47.47,47.49,47.37,47.4,47.5,47.48,47.48 | +| lamp | 63.93,63.92,63.95,63.94,63.96,63.97,63.98,63.97,64.0,64.0,64.01 | +| bathtub | 77.4,77.42,77.41,77.44,77.43,77.37,77.3,77.37,77.19,77.04,77.29 | +| railing | 31.77,31.79,31.74,31.63,31.62,31.49,31.52,31.47,31.45,31.42,31.38 | +| cushion | 55.39,55.46,55.48,55.52,55.61,55.63,55.71,55.75,55.74,55.8,55.78 | +| base | 27.52,27.66,27.64,27.62,27.58,27.64,27.7,27.83,27.83,27.82,27.74 | +| box | 24.21,24.27,24.17,24.26,24.25,24.27,24.22,24.32,24.41,24.41,24.27 | +| column | 45.74,45.79,45.83,45.69,45.72,45.92,45.84,45.84,45.92,45.98,45.84 | +| signboard | 35.37,35.36,35.38,35.4,35.35,35.36,35.44,35.38,35.44,35.47,35.39 | +| chest of drawers | 39.71,39.75,39.45,39.33,39.34,39.47,39.3,39.51,39.55,39.64,39.42 | +| counter | 26.67,26.71,26.61,26.66,26.67,26.69,26.59,26.69,26.66,26.57,26.24 | +| sand | 32.98,32.93,32.78,32.75,32.7,32.54,32.37,32.31,32.26,32.18,32.14 | +| sink | 71.49,71.56,71.61,71.71,71.73,71.82,71.79,71.85,71.65,71.42,71.72 | +| skyscraper | 49.15,49.19,49.23,49.25,49.43,49.36,49.36,49.47,49.44,49.48,49.65 | +| fireplace | 66.6,66.68,66.6,66.55,66.58,66.55,66.54,66.53,66.5,66.48,66.42 | +| refrigerator | 78.67,78.7,78.66,78.73,78.75,78.67,78.66,78.72,78.74,78.73,78.68 | +| grandstand | 41.64,41.68,41.72,41.75,41.71,41.73,41.72,41.67,41.73,41.73,41.71 | +| path | 17.91,17.87,17.87,17.88,17.91,17.95,17.93,17.97,18.0,18.0,18.05 | +| stairs | 31.42,31.43,31.42,31.41,31.38,31.4,31.37,31.39,31.38,31.36,31.38 | +| runway | 63.93,63.94,63.96,63.98,63.96,63.98,63.99,63.99,64.0,63.97,64.0 | +| case | 48.69,48.79,48.76,48.78,48.84,48.83,48.79,48.74,48.73,48.72,48.68 | +| pool table | 92.67,92.67,92.7,92.67,92.7,92.67,92.7,92.68,92.66,92.66,92.75 | +| pillow | 57.38,57.36,57.28,57.32,57.3,57.25,57.38,57.34,57.3,57.24,57.16 | +| screen door | 66.05,66.17,66.24,66.29,66.67,66.57,66.47,66.71,66.62,66.87,67.23 | +| stairway | 25.5,25.53,25.53,25.57,25.52,25.59,25.58,25.55,25.62,25.52,25.64 | +| river | 9.99,9.97,9.86,9.77,9.69,9.58,9.51,9.36,9.35,9.3,9.22 | +| bridge | 53.8,54.81,55.62,56.06,57.15,57.56,58.29,58.63,59.54,59.92,60.28 | +| bookcase | 42.45,42.32,42.44,42.73,42.95,43.12,43.13,43.32,43.45,43.5,43.22 | +| blind | 44.99,44.94,44.79,44.73,44.68,44.77,44.81,44.71,44.68,44.5,44.46 | +| coffee table | 65.76,65.81,65.69,65.7,65.66,65.73,65.77,65.82,65.88,65.88,65.46 | +| toilet | 86.4,86.39,86.4,86.39,86.42,86.42,86.4,86.39,86.39,86.38,86.49 | +| flower | 31.14,31.17,31.32,31.27,31.29,31.47,31.51,31.5,31.73,31.74,31.89 | +| book | 47.11,47.25,47.17,47.09,47.13,47.07,47.06,47.1,47.09,47.08,46.96 | +| hill | 7.69,7.69,7.67,7.7,7.65,7.69,7.7,7.64,7.72,7.71,7.74 | +| bench | 44.43,44.4,44.5,44.57,44.56,44.63,44.63,44.71,44.67,44.7,44.74 | +| countertop | 54.7,54.67,54.63,54.73,54.7,54.64,54.66,54.69,54.62,54.56,54.62 | +| stove | 72.25,72.26,72.33,72.24,72.27,72.3,72.33,72.17,72.25,72.31,72.42 | +| palm | 50.66,50.72,50.78,50.78,50.83,50.87,50.86,50.94,51.03,51.02,51.05 | +| kitchen island | 47.28,47.36,47.58,47.28,47.37,47.37,47.76,47.49,47.3,47.51,47.35 | +| computer | 57.29,57.23,57.28,57.34,57.26,57.3,57.39,57.37,57.4,57.39,57.24 | +| swivel chair | 45.69,45.83,45.93,45.96,45.97,46.11,46.21,46.26,46.36,46.44,46.0 | +| boat | 37.77,37.78,37.69,37.73,37.9,37.92,38.0,38.04,38.15,38.16,38.2 | +| bar | 27.0,26.96,26.9,26.86,26.73,26.64,26.47,26.37,26.21,25.95,25.64 | +| arcade machine | 24.93,25.12,25.37,25.45,25.62,25.78,25.85,25.75,26.02,26.15,26.51 | +| hovel | 30.93,30.79,30.71,30.69,30.61,30.58,30.48,30.43,30.43,30.31,30.26 | +| bus | 88.2,88.24,88.25,88.3,88.21,88.35,88.26,88.36,88.43,88.42,88.31 | +| towel | 60.69,60.83,60.66,60.66,60.73,60.82,60.83,60.69,60.74,60.82,60.58 | +| light | 56.72,56.75,56.7,56.74,56.67,56.68,56.72,56.66,56.69,56.63,56.71 | +| truck | 35.31,35.42,35.32,35.1,35.37,35.34,35.33,35.54,35.4,35.43,35.44 | +| tower | 25.83,25.57,25.88,25.64,25.01,24.66,24.56,24.22,24.29,24.12,24.3 | +| chandelier | 66.29,66.33,66.35,66.4,66.43,66.41,66.43,66.45,66.45,66.49,66.45 | +| awning | 23.35,23.44,23.39,23.36,23.32,23.34,23.28,23.41,23.36,23.39,23.4 | +| streetlight | 28.33,28.21,28.23,28.2,28.12,28.12,28.07,28.04,28.05,27.99,27.93 | +| booth | 57.86,57.85,57.95,57.97,58.03,58.01,57.91,58.16,57.94,57.88,57.88 | +| television receiver | 68.23,68.2,68.22,68.2,68.2,68.18,68.14,68.17,68.17,68.16,68.15 | +| airplane | 51.68,51.69,51.64,51.35,51.69,51.61,51.38,51.44,51.48,51.24,50.72 | +| dirt track | 11.45,11.32,11.48,11.67,11.45,11.28,11.31,11.19,11.28,11.0,11.48 | +| apparel | 28.32,28.36,28.3,28.22,28.33,28.35,28.03,27.81,27.8,27.81,27.53 | +| pole | 24.48,24.52,24.45,24.44,24.51,24.41,24.46,24.49,24.39,24.36,24.29 | +| land | 6.82,6.77,6.8,6.73,6.73,6.46,6.41,6.44,6.37,6.39,6.37 | +| bannister | 5.54,5.58,5.55,5.61,5.61,5.59,5.66,5.64,5.66,5.64,5.63 | +| escalator | 22.4,22.37,22.5,22.55,22.6,22.66,22.59,22.64,22.7,22.78,22.75 | +| ottoman | 47.36,47.31,47.11,47.24,47.41,47.29,47.32,46.92,46.74,46.86,47.65 | +| bottle | 14.74,14.68,14.64,14.64,14.52,14.51,14.6,14.37,14.4,14.38,14.42 | +| buffet | 46.92,47.38,46.46,46.24,45.86,46.08,46.55,46.72,47.71,49.37,50.35 | +| poster | 27.6,27.58,27.79,27.89,27.84,28.08,28.08,28.04,28.15,28.13,27.81 | +| stage | 16.69,16.88,16.95,17.06,17.13,17.13,17.33,17.42,17.44,17.42,17.59 | +| van | 47.58,47.5,47.58,47.36,47.35,47.39,47.24,47.18,47.32,47.28,47.38 | +| ship | 26.16,27.31,28.83,30.53,29.98,31.37,32.56,32.33,33.16,33.76,33.51 | +| fountain | 8.32,8.34,8.5,8.57,8.53,8.37,8.43,8.32,8.09,8.06,7.88 | +| conveyer belt | 76.45,76.22,76.17,76.15,76.02,76.07,76.1,75.83,75.8,75.51,75.57 | +| canopy | 15.34,15.25,15.24,15.31,15.34,15.41,15.41,15.51,15.46,15.54,15.3 | +| washer | 65.94,65.93,65.83,65.8,65.79,65.71,65.7,65.68,65.65,65.65,65.59 | +| plaything | 23.2,23.31,23.25,23.31,23.24,23.34,23.41,23.28,23.39,23.36,23.55 | +| swimming pool | 43.2,43.94,44.82,45.6,45.7,46.07,46.25,47.12,47.19,48.11,49.53 | +| stool | 42.09,42.01,42.11,41.99,41.99,41.97,41.96,42.01,41.93,41.83,41.83 | +| barrel | 41.33,41.11,39.66,41.53,41.5,40.72,40.73,41.17,40.99,40.97,40.68 | +| basket | 28.41,28.44,28.45,28.48,28.42,28.45,28.44,28.54,28.48,28.54,28.5 | +| waterfall | 51.11,50.5,51.66,51.93,51.89,51.57,51.36,51.54,52.05,52.88,53.41 | +| tent | 93.68,93.68,93.67,93.6,93.68,93.59,93.61,93.59,93.6,93.54,93.56 | +| bag | 11.42,11.4,11.52,11.45,11.46,11.47,11.55,11.56,11.51,11.43,11.57 | +| minibike | 61.56,61.46,61.56,61.6,61.58,61.54,61.5,61.49,61.43,61.45,61.42 | +| cradle | 81.74,81.72,81.61,81.56,81.54,81.44,81.37,81.22,81.21,81.07,81.01 | +| oven | 26.96,27.0,26.98,27.02,26.94,26.91,27.04,26.93,26.93,26.87,26.92 | +| ball | 47.46,47.51,47.7,47.53,47.62,47.71,47.91,47.7,47.91,47.84,48.04 | +| food | 51.31,51.64,51.77,51.74,51.99,52.14,52.24,52.34,52.41,52.58,52.52 | +| step | 18.11,18.23,18.11,18.02,18.22,18.48,18.49,18.39,18.68,18.47,18.03 | +| tank | 41.29,41.29,41.26,41.2,41.17,41.15,41.11,41.14,41.13,41.11,41.04 | +| trade name | 25.11,25.02,25.0,24.97,24.93,24.92,24.86,24.83,24.69,24.72,24.84 | +| microwave | 37.5,37.49,37.5,37.52,37.52,37.52,37.49,37.52,37.5,37.51,37.48 | +| pot | 41.27,41.25,41.23,41.26,41.25,41.28,41.32,41.36,41.34,41.37,41.41 | +| animal | 51.49,51.48,51.42,51.52,51.39,51.38,51.33,51.41,51.34,51.29,51.44 | +| bicycle | 46.08,46.02,46.0,46.1,46.08,46.11,46.16,46.07,46.13,46.04,46.14 | +| lake | 60.42,60.37,60.22,60.09,59.99,59.94,59.77,59.61,59.55,59.45,59.33 | +| dishwasher | 76.71,76.73,76.69,76.8,76.87,76.86,77.03,77.04,77.13,77.18,77.18 | +| screen | 63.54,63.57,63.46,62.94,62.75,62.91,62.83,62.73,62.69,62.85,63.1 | +| blanket | 15.76,15.93,15.91,15.88,15.78,15.88,15.76,15.71,15.73,15.58,15.52 | +| sculpture | 35.97,35.99,36.01,36.15,36.09,36.16,36.15,36.26,36.22,36.27,36.36 | +| hood | 57.41,57.54,57.54,57.55,57.55,57.6,57.37,57.35,57.25,57.21,57.45 | +| sconce | 42.33,42.32,42.2,42.22,42.32,42.04,42.23,42.11,41.89,41.82,42.14 | +| vase | 37.55,37.46,37.54,37.55,37.47,37.54,37.49,37.52,37.47,37.49,37.42 | +| traffic light | 29.66,29.63,29.66,29.69,29.61,29.64,29.6,29.6,29.64,29.6,29.68 | +| tray | 5.5,5.49,5.53,5.55,5.53,5.57,5.62,5.59,5.61,5.61,5.64 | +| ashcan | 37.6,37.64,37.54,37.64,37.43,37.49,37.48,37.46,37.47,37.42,37.5 | +| fan | 58.44,58.39,58.48,58.49,58.46,58.5,58.53,58.41,58.45,58.41,58.42 | +| pier | 11.55,11.5,11.43,11.36,11.38,11.33,11.34,11.18,11.08,11.09,10.97 | +| crt screen | 4.51,4.48,4.45,4.53,4.55,4.9,4.92,5.09,5.38,6.0,6.72 | +| plate | 38.96,38.98,38.92,39.16,39.03,39.08,39.14,39.22,39.28,39.33,39.38 | +| monitor | 25.48,25.52,25.09,25.13,25.2,25.07,24.82,24.75,24.58,24.65,24.61 | +| bulletin board | 45.12,45.38,45.36,45.06,45.36,45.65,46.27,46.51,47.04,47.24,46.84 | +| shower | 1.29,1.29,1.29,1.38,1.34,1.41,1.48,1.44,1.48,1.47,1.56 | +| radiator | 45.34,45.78,45.54,45.41,45.51,45.75,45.45,45.81,46.17,45.68,46.28 | +| glass | 12.13,12.18,12.24,12.23,12.23,12.21,12.26,12.28,12.33,12.31,12.3 | +| clock | 25.0,25.02,24.97,24.93,24.93,24.9,24.89,24.88,24.92,24.89,24.84 | +| flag | 37.68,37.76,37.87,38.02,38.03,38.13,38.21,38.36,38.33,38.37,38.45 | ++---------------------+-------------------------------------------------------------------+ +2023-03-05 04:06:17,672 - mmseg - INFO - Summary: +2023-03-05 04:06:17,672 - mmseg - INFO - ++-----------------------------------------------------------------+ +| mIoU 0,10,20,30,40,50,60,70,80,90,99 | ++-----------------------------------------------------------------+ +| 46.06,46.1,46.1,46.13,46.13,46.16,46.17,46.18,46.21,46.23,46.25 | ++-----------------------------------------------------------------+ +2023-03-05 04:06:17,672 - mmseg - INFO - Exp name: deeplabv3plus_r101-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py +2023-03-05 04:06:17,672 - mmseg - INFO - Iter(val) [250] mIoU: [0.4606, 0.461, 0.461, 0.4613, 0.4613, 0.4616, 0.4617, 0.4618, 0.4621, 0.4623, 0.4625], copy_paste: 46.06,46.1,46.1,46.13,46.13,46.16,46.17,46.18,46.21,46.23,46.25