diff --git "a/cityscapes/deeplabv3plus_r50_singlestep/20230303_152127.log" "b/cityscapes/deeplabv3plus_r50_singlestep/20230303_152127.log" new file mode 100644--- /dev/null +++ "b/cityscapes/deeplabv3plus_r50_singlestep/20230303_152127.log" @@ -0,0 +1,3001 @@ +2023-03-03 15:21:27,526 - mmseg - INFO - Multi-processing start method is `None` +2023-03-03 15:21:27,543 - mmseg - INFO - OpenCV num_threads is `128 +2023-03-03 15:21:27,543 - mmseg - INFO - OMP num threads is 1 +2023-03-03 15:21:27,613 - 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+663c855 +------------------------------------------------------------ + +2023-03-03 15:21:27,613 - mmseg - INFO - Distributed training: True +2023-03-03 15:21:28,236 - mmseg - INFO - Config: +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoderFreeze', + pretrained= + 'pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth', + backbone=dict( + type='ResNetV1cCustomInitWeights', + depth=50, + 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='DepthwiseSeparableASPPHeadUnetFCHeadSingleStep', + pretrained= + 'pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth', + dim=128, + out_dim=256, + unet_channels=528, + dim_mults=[1, 1, 1], + cat_embedding_dim=16, + ignore_index=0, + 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=20, + 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 = 'Cityscapes20Dataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (512, 1024) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotationsCityscapes20'), + dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=(512, 1024), 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, 1024), 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, 1024), + 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='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='Cityscapes20Dataset', + data_root='data/cityscapes/', + img_dir='leftImg8bit/train', + ann_dir='gtFine/train', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotationsCityscapes20'), + dict( + type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=(512, 1024), 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, 1024), pad_val=0, seg_pad_val=0), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) + ]), + val=dict( + type='Cityscapes20Dataset', + data_root='data/cityscapes/', + img_dir='leftImg8bit/val', + ann_dir='gtFine/val', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + 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='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( + type='Cityscapes20Dataset', + data_root='data/cityscapes/', + img_dir='leftImg8bit/val', + ann_dir='gtFine/val', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + 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='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ])) +log_config = dict( + interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +cudnn_benchmark = True +optimizer = dict( + type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045) +optimizer_config = dict() +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1e-06, + step=10000, + gamma=0.5, + min_lr=1e-06, + by_epoch=False) +runner = dict(type='IterBasedRunner', max_iters=80000) +checkpoint_config = dict(by_epoch=False, interval=8000, max_keep_ckpts=1) +evaluation = dict( + interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU') +checkpoint = 'pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth' +work_dir = './work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20' +gpu_ids = range(0, 8) +auto_resume = True + +2023-03-03 15:21:32,491 - mmseg - INFO - Set random seed to 335380886, deterministic: False +2023-03-03 15:21:33,564 - mmseg - INFO - Parameters in backbone freezed! +2023-03-03 15:21:33,565 - mmseg - INFO - Trainable parameters in DepthwiseSeparableASPPHeadUnetFCHeadSingleStep: ['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', 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'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'] +2023-03-03 15:21:33,565 - mmseg - INFO - Parameters in decode_head freezed! +2023-03-03 15:21:33,610 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth +2023-03-03 15:21:34,022 - mmseg - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: decode_head.conv_seg.weight, decode_head.conv_seg.bias, decode_head.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, decode_head.aspp_modules.1.depthwise_conv.bn.running_var, decode_head.aspp_modules.1.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.1.pointwise_conv.conv.weight, decode_head.aspp_modules.1.pointwise_conv.bn.weight, decode_head.aspp_modules.1.pointwise_conv.bn.bias, decode_head.aspp_modules.1.pointwise_conv.bn.running_mean, decode_head.aspp_modules.1.pointwise_conv.bn.running_var, decode_head.aspp_modules.1.pointwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.2.depthwise_conv.conv.weight, decode_head.aspp_modules.2.depthwise_conv.bn.weight, decode_head.aspp_modules.2.depthwise_conv.bn.bias, decode_head.aspp_modules.2.depthwise_conv.bn.running_mean, decode_head.aspp_modules.2.depthwise_conv.bn.running_var, decode_head.aspp_modules.2.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.2.pointwise_conv.conv.weight, decode_head.aspp_modules.2.pointwise_conv.bn.weight, decode_head.aspp_modules.2.pointwise_conv.bn.bias, decode_head.aspp_modules.2.pointwise_conv.bn.running_mean, decode_head.aspp_modules.2.pointwise_conv.bn.running_var, decode_head.aspp_modules.2.pointwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.3.depthwise_conv.conv.weight, decode_head.aspp_modules.3.depthwise_conv.bn.weight, decode_head.aspp_modules.3.depthwise_conv.bn.bias, decode_head.aspp_modules.3.depthwise_conv.bn.running_mean, decode_head.aspp_modules.3.depthwise_conv.bn.running_var, decode_head.aspp_modules.3.depthwise_conv.bn.num_batches_tracked, decode_head.aspp_modules.3.pointwise_conv.conv.weight, decode_head.aspp_modules.3.pointwise_conv.bn.weight, decode_head.aspp_modules.3.pointwise_conv.bn.bias, decode_head.aspp_modules.3.pointwise_conv.bn.running_mean, decode_head.aspp_modules.3.pointwise_conv.bn.running_var, decode_head.aspp_modules.3.pointwise_conv.bn.num_batches_tracked, decode_head.bottleneck.conv.weight, decode_head.bottleneck.bn.weight, decode_head.bottleneck.bn.bias, decode_head.bottleneck.bn.running_mean, decode_head.bottleneck.bn.running_var, decode_head.bottleneck.bn.num_batches_tracked, decode_head.c1_bottleneck.conv.weight, decode_head.c1_bottleneck.bn.weight, decode_head.c1_bottleneck.bn.bias, decode_head.c1_bottleneck.bn.running_mean, decode_head.c1_bottleneck.bn.running_var, decode_head.c1_bottleneck.bn.num_batches_tracked, decode_head.sep_bottleneck.0.depthwise_conv.conv.weight, decode_head.sep_bottleneck.0.depthwise_conv.bn.weight, decode_head.sep_bottleneck.0.depthwise_conv.bn.bias, decode_head.sep_bottleneck.0.depthwise_conv.bn.running_mean, decode_head.sep_bottleneck.0.depthwise_conv.bn.running_var, decode_head.sep_bottleneck.0.depthwise_conv.bn.num_batches_tracked, decode_head.sep_bottleneck.0.pointwise_conv.conv.weight, decode_head.sep_bottleneck.0.pointwise_conv.bn.weight, decode_head.sep_bottleneck.0.pointwise_conv.bn.bias, decode_head.sep_bottleneck.0.pointwise_conv.bn.running_mean, decode_head.sep_bottleneck.0.pointwise_conv.bn.running_var, decode_head.sep_bottleneck.0.pointwise_conv.bn.num_batches_tracked, decode_head.sep_bottleneck.1.depthwise_conv.conv.weight, decode_head.sep_bottleneck.1.depthwise_conv.bn.weight, decode_head.sep_bottleneck.1.depthwise_conv.bn.bias, decode_head.sep_bottleneck.1.depthwise_conv.bn.running_mean, decode_head.sep_bottleneck.1.depthwise_conv.bn.running_var, decode_head.sep_bottleneck.1.depthwise_conv.bn.num_batches_tracked, decode_head.sep_bottleneck.1.pointwise_conv.conv.weight, decode_head.sep_bottleneck.1.pointwise_conv.bn.weight, decode_head.sep_bottleneck.1.pointwise_conv.bn.bias, decode_head.sep_bottleneck.1.pointwise_conv.bn.running_mean, decode_head.sep_bottleneck.1.pointwise_conv.bn.running_var, decode_head.sep_bottleneck.1.pointwise_conv.bn.num_batches_tracked, auxiliary_head.conv_seg.weight, auxiliary_head.conv_seg.bias, auxiliary_head.convs.0.conv.weight, auxiliary_head.convs.0.bn.weight, auxiliary_head.convs.0.bn.bias, auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked + +2023-03-03 15:21:34,035 - mmseg - INFO - load checkpoint from local path: pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth +2023-03-03 15:21:34,356 - mmseg - WARNING - The model and loaded state dict do not match exactly + +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.layer4.0.conv1.weight, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.num_batches_tracked, backbone.layer4.0.conv2.weight, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.num_batches_tracked, backbone.layer4.0.conv3.weight, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.num_batches_tracked, backbone.layer4.0.downsample.0.weight, backbone.layer4.0.downsample.1.weight, backbone.layer4.0.downsample.1.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.0.downsample.1.num_batches_tracked, backbone.layer4.1.conv1.weight, backbone.layer4.1.bn1.weight, 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, auxiliary_head.conv_seg.weight, auxiliary_head.conv_seg.bias, auxiliary_head.convs.0.conv.weight, auxiliary_head.convs.0.bn.weight, auxiliary_head.convs.0.bn.bias, auxiliary_head.convs.0.bn.running_mean, auxiliary_head.convs.0.bn.running_var, auxiliary_head.convs.0.bn.num_batches_tracked + +missing keys in source state_dict: unet.init_conv.weight, unet.init_conv.bias, unet.time_mlp.1.weight, unet.time_mlp.1.bias, unet.time_mlp.3.weight, unet.time_mlp.3.bias, unet.downs.0.0.mlp.1.weight, unet.downs.0.0.mlp.1.bias, unet.downs.0.0.block1.proj.weight, unet.downs.0.0.block1.proj.bias, unet.downs.0.0.block1.norm.weight, unet.downs.0.0.block1.norm.bias, unet.downs.0.0.block2.proj.weight, 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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) + ) + ) + (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': 'pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth'} + (decode_head): DepthwiseSeparableASPPHeadUnetFCHeadSingleStep( + 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, 20, kernel_size=(1, 1), stride=(1, 1)) + (embed): Embedding(20, 16) + ) + init_cfg={'type': 'Pretrained', 'checkpoint': 'pretrained/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth'} +) +2023-03-03 15:21:34,460 - mmseg - INFO - Loaded 2975 images +2023-03-03 15:21:35,286 - mmseg - INFO - Loaded 500 images +2023-03-03 15:21:35,292 - mmseg - INFO - Start running, host: laizeqiang@SH-IDC1-10-140-37-154, work_dir: /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20 +2023-03-03 15:21:35,292 - mmseg - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) StepLrUpdaterHook +(NORMAL ) CheckpointHook +(LOW ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) StepLrUpdaterHook +(LOW ) IterTimerHook +(LOW ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) StepLrUpdaterHook +(LOW ) IterTimerHook +(LOW ) DistEvalHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(LOW ) IterTimerHook +(LOW ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(LOW ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2023-03-03 15:21:35,292 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters +2023-03-03 15:21:35,292 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20 by HardDiskBackend. +2023-03-03 15:22:14,870 - mmseg - INFO - Iter [50/80000] lr: 7.350e-06, eta: 11:44:30, time: 0.529, data_time: 0.013, memory: 33997, decode.loss_ce: 2.3233, decode.acc_seg: 50.2628, loss: 2.3233 +2023-03-03 15:22:25,419 - mmseg - INFO - Iter [100/80000] lr: 1.485e-05, eta: 8:12:30, time: 0.211, data_time: 0.007, memory: 33997, decode.loss_ce: 1.1025, decode.acc_seg: 86.8536, loss: 1.1025 +2023-03-03 15:22:35,919 - mmseg - INFO - Iter [150/80000] lr: 2.235e-05, eta: 7:01:16, time: 0.210, data_time: 0.007, memory: 33997, decode.loss_ce: 0.4559, decode.acc_seg: 91.0801, loss: 0.4559 +2023-03-03 15:22:48,726 - mmseg - INFO - Iter [200/80000] lr: 2.985e-05, eta: 6:40:54, time: 0.256, data_time: 0.052, memory: 33997, decode.loss_ce: 0.2246, decode.acc_seg: 94.5655, loss: 0.2246 +2023-03-03 15:22:59,350 - mmseg - INFO - Iter [250/80000] lr: 3.735e-05, eta: 6:17:01, time: 0.213, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1359, decode.acc_seg: 95.6645, loss: 0.1359 +2023-03-03 15:23:09,876 - mmseg - INFO - Iter [300/80000] lr: 4.485e-05, eta: 6:00:36, time: 0.211, data_time: 0.007, memory: 33997, decode.loss_ce: 0.1166, decode.acc_seg: 95.8358, loss: 0.1166 +2023-03-03 15:23:20,264 - mmseg - INFO - Iter [350/80000] lr: 5.235e-05, eta: 5:48:16, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1097, decode.acc_seg: 95.9706, loss: 0.1097 +2023-03-03 15:23:33,052 - mmseg - INFO - Iter [400/80000] lr: 5.985e-05, eta: 5:46:58, time: 0.256, data_time: 0.053, memory: 33997, decode.loss_ce: 0.1091, decode.acc_seg: 95.9509, loss: 0.1091 +2023-03-03 15:23:43,678 - mmseg - INFO - Iter [450/80000] lr: 6.735e-05, eta: 5:39:31, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1071, decode.acc_seg: 95.9633, loss: 0.1071 +2023-03-03 15:23:54,090 - mmseg - INFO - Iter [500/80000] lr: 7.485e-05, eta: 5:32:58, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1034, decode.acc_seg: 96.0609, loss: 0.1034 +2023-03-03 15:24:04,541 - mmseg - INFO - Iter [550/80000] lr: 8.235e-05, eta: 5:27:40, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1011, decode.acc_seg: 96.1141, loss: 0.1011 +2023-03-03 15:24:17,358 - mmseg - INFO - Iter [600/80000] lr: 8.985e-05, eta: 5:28:27, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0974, decode.acc_seg: 96.2479, loss: 0.0974 +2023-03-03 15:24:27,839 - mmseg - INFO - Iter [650/80000] lr: 9.735e-05, eta: 5:24:18, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0970, decode.acc_seg: 96.2672, loss: 0.0970 +2023-03-03 15:24:38,324 - mmseg - INFO - Iter [700/80000] lr: 1.049e-04, eta: 5:20:45, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1012, decode.acc_seg: 96.0989, loss: 0.1012 +2023-03-03 15:24:51,313 - mmseg - INFO - Iter [750/80000] lr: 1.124e-04, eta: 5:22:02, time: 0.260, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0979, decode.acc_seg: 96.1914, loss: 0.0979 +2023-03-03 15:25:01,764 - mmseg - INFO - Iter [800/80000] lr: 1.199e-04, eta: 5:18:58, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0962, decode.acc_seg: 96.2572, loss: 0.0962 +2023-03-03 15:25:12,351 - mmseg - INFO - Iter [850/80000] lr: 1.274e-04, eta: 5:16:27, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0938, decode.acc_seg: 96.3718, loss: 0.0938 +2023-03-03 15:25:22,748 - mmseg - INFO - Iter [900/80000] lr: 1.349e-04, eta: 5:13:55, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0932, decode.acc_seg: 96.3590, loss: 0.0932 +2023-03-03 15:25:35,500 - mmseg - INFO - Iter [950/80000] lr: 1.424e-04, eta: 5:14:53, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0956, decode.acc_seg: 96.3263, loss: 0.0956 +2023-03-03 15:25:45,929 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:25:45,929 - mmseg - INFO - Iter [1000/80000] lr: 1.499e-04, eta: 5:12:41, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0993, decode.acc_seg: 96.1708, loss: 0.0993 +2023-03-03 15:25:56,484 - mmseg - INFO - Iter [1050/80000] lr: 1.500e-04, eta: 5:10:50, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0982, decode.acc_seg: 96.1549, loss: 0.0982 +2023-03-03 15:26:06,997 - mmseg - INFO - Iter [1100/80000] lr: 1.500e-04, eta: 5:09:05, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0915, decode.acc_seg: 96.4098, loss: 0.0915 +2023-03-03 15:26:19,685 - mmseg - INFO - Iter [1150/80000] lr: 1.500e-04, eta: 5:09:57, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0940, decode.acc_seg: 96.3285, loss: 0.0940 +2023-03-03 15:26:30,113 - mmseg - INFO - Iter [1200/80000] lr: 1.500e-04, eta: 5:08:16, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.1005, decode.acc_seg: 96.1259, loss: 0.1005 +2023-03-03 15:26:40,736 - mmseg - INFO - Iter [1250/80000] lr: 1.500e-04, eta: 5:06:54, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0983, decode.acc_seg: 96.2159, loss: 0.0983 +2023-03-03 15:26:51,111 - mmseg - INFO - Iter [1300/80000] lr: 1.500e-04, eta: 5:05:22, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0926, decode.acc_seg: 96.4295, loss: 0.0926 +2023-03-03 15:27:03,797 - mmseg - INFO - Iter [1350/80000] lr: 1.500e-04, eta: 5:06:12, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0936, decode.acc_seg: 96.3425, loss: 0.0936 +2023-03-03 15:27:14,073 - mmseg - INFO - Iter [1400/80000] lr: 1.500e-04, eta: 5:04:41, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0925, decode.acc_seg: 96.4145, loss: 0.0925 +2023-03-03 15:27:24,574 - mmseg - INFO - Iter [1450/80000] lr: 1.500e-04, eta: 5:03:28, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0929, decode.acc_seg: 96.3872, loss: 0.0929 +2023-03-03 15:27:37,225 - mmseg - INFO - Iter [1500/80000] lr: 1.500e-04, eta: 5:04:12, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0977, decode.acc_seg: 96.2470, loss: 0.0977 +2023-03-03 15:27:47,627 - mmseg - INFO - Iter [1550/80000] lr: 1.500e-04, eta: 5:02:58, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0958, decode.acc_seg: 96.3099, loss: 0.0958 +2023-03-03 15:27:57,950 - mmseg - INFO - Iter [1600/80000] lr: 1.500e-04, eta: 5:01:45, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0914, decode.acc_seg: 96.4479, loss: 0.0914 +2023-03-03 15:28:08,324 - mmseg - INFO - Iter [1650/80000] lr: 1.500e-04, eta: 5:00:38, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0981, decode.acc_seg: 96.1267, loss: 0.0981 +2023-03-03 15:28:21,011 - mmseg - INFO - Iter [1700/80000] lr: 1.500e-04, eta: 5:01:20, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0964, decode.acc_seg: 96.2725, loss: 0.0964 +2023-03-03 15:28:31,403 - mmseg - INFO - Iter [1750/80000] lr: 1.500e-04, eta: 5:00:17, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0936, decode.acc_seg: 96.3799, loss: 0.0936 +2023-03-03 15:28:41,790 - mmseg - INFO - Iter [1800/80000] lr: 1.500e-04, eta: 4:59:17, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5848, loss: 0.0870 +2023-03-03 15:28:52,223 - mmseg - INFO - Iter [1850/80000] lr: 1.500e-04, eta: 4:58:21, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0915, decode.acc_seg: 96.4406, loss: 0.0915 +2023-03-03 15:29:04,942 - mmseg - INFO - Iter [1900/80000] lr: 1.500e-04, eta: 4:59:02, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0921, decode.acc_seg: 96.3974, loss: 0.0921 +2023-03-03 15:29:15,251 - mmseg - INFO - Iter [1950/80000] lr: 1.500e-04, eta: 4:58:03, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0921, decode.acc_seg: 96.4486, loss: 0.0921 +2023-03-03 15:29:25,579 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:29:25,580 - mmseg - INFO - Iter [2000/80000] lr: 1.500e-04, eta: 4:57:08, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0903, decode.acc_seg: 96.4594, loss: 0.0903 +2023-03-03 15:29:38,128 - mmseg - INFO - Iter [2050/80000] lr: 1.500e-04, eta: 4:57:39, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6274, loss: 0.0865 +2023-03-03 15:29:48,574 - mmseg - INFO - Iter [2100/80000] lr: 1.500e-04, eta: 4:56:50, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0948, decode.acc_seg: 96.2810, loss: 0.0948 +2023-03-03 15:29:58,855 - mmseg - INFO - Iter [2150/80000] lr: 1.500e-04, eta: 4:55:57, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5439, loss: 0.0887 +2023-03-03 15:30:09,357 - mmseg - INFO - Iter [2200/80000] lr: 1.500e-04, eta: 4:55:13, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0945, decode.acc_seg: 96.3342, loss: 0.0945 +2023-03-03 15:30:21,936 - mmseg - INFO - Iter [2250/80000] lr: 1.500e-04, eta: 4:55:43, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0960, decode.acc_seg: 96.2317, loss: 0.0960 +2023-03-03 15:30:32,500 - mmseg - INFO - Iter [2300/80000] lr: 1.500e-04, eta: 4:55:03, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0895, decode.acc_seg: 96.5132, loss: 0.0895 +2023-03-03 15:30:42,767 - mmseg - INFO - Iter [2350/80000] lr: 1.500e-04, eta: 4:54:14, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5911, loss: 0.0879 +2023-03-03 15:30:53,080 - mmseg - INFO - Iter [2400/80000] lr: 1.500e-04, eta: 4:53:29, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5318, loss: 0.0887 +2023-03-03 15:31:05,816 - mmseg - INFO - Iter [2450/80000] lr: 1.500e-04, eta: 4:54:02, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0952, decode.acc_seg: 96.2896, loss: 0.0952 +2023-03-03 15:31:16,135 - mmseg - INFO - Iter [2500/80000] lr: 1.500e-04, eta: 4:53:18, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0950, decode.acc_seg: 96.2931, loss: 0.0950 +2023-03-03 15:31:26,392 - mmseg - INFO - Iter [2550/80000] lr: 1.500e-04, eta: 4:52:33, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0922, decode.acc_seg: 96.4122, loss: 0.0922 +2023-03-03 15:31:36,692 - mmseg - INFO - Iter [2600/80000] lr: 1.500e-04, eta: 4:51:51, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0950, decode.acc_seg: 96.2895, loss: 0.0950 +2023-03-03 15:31:49,240 - mmseg - INFO - Iter [2650/80000] lr: 1.500e-04, eta: 4:52:16, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4270, loss: 0.0908 +2023-03-03 15:31:59,505 - mmseg - INFO - Iter [2700/80000] lr: 1.500e-04, eta: 4:51:34, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0910, decode.acc_seg: 96.4501, loss: 0.0910 +2023-03-03 15:32:09,827 - mmseg - INFO - Iter [2750/80000] lr: 1.500e-04, eta: 4:50:54, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.4908, loss: 0.0890 +2023-03-03 15:32:22,435 - mmseg - INFO - Iter [2800/80000] lr: 1.500e-04, eta: 4:51:19, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4423, loss: 0.0908 +2023-03-03 15:32:32,788 - mmseg - INFO - Iter [2850/80000] lr: 1.500e-04, eta: 4:50:42, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0931, decode.acc_seg: 96.3763, loss: 0.0931 +2023-03-03 15:32:43,273 - mmseg - INFO - Iter [2900/80000] lr: 1.500e-04, eta: 4:50:09, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5248, loss: 0.0886 +2023-03-03 15:32:53,622 - mmseg - INFO - Iter [2950/80000] lr: 1.500e-04, eta: 4:49:33, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4628, loss: 0.0908 +2023-03-03 15:33:06,161 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:33:06,162 - mmseg - INFO - Iter [3000/80000] lr: 1.500e-04, eta: 4:49:54, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0942, decode.acc_seg: 96.3309, loss: 0.0942 +2023-03-03 15:33:16,545 - mmseg - INFO - Iter [3050/80000] lr: 1.500e-04, eta: 4:49:20, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0923, decode.acc_seg: 96.4568, loss: 0.0923 +2023-03-03 15:33:26,942 - mmseg - INFO - Iter [3100/80000] lr: 1.500e-04, eta: 4:48:46, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0904, decode.acc_seg: 96.4400, loss: 0.0904 +2023-03-03 15:33:37,294 - mmseg - INFO - Iter [3150/80000] lr: 1.500e-04, eta: 4:48:13, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0926, decode.acc_seg: 96.3700, loss: 0.0926 +2023-03-03 15:33:49,924 - mmseg - INFO - Iter [3200/80000] lr: 1.500e-04, eta: 4:48:35, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0916, decode.acc_seg: 96.3917, loss: 0.0916 +2023-03-03 15:34:00,280 - mmseg - INFO - Iter [3250/80000] lr: 1.500e-04, eta: 4:48:02, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0926, decode.acc_seg: 96.4357, loss: 0.0926 +2023-03-03 15:34:10,646 - mmseg - INFO - Iter [3300/80000] lr: 1.500e-04, eta: 4:47:30, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0915, decode.acc_seg: 96.4371, loss: 0.0915 +2023-03-03 15:34:23,195 - mmseg - INFO - Iter [3350/80000] lr: 1.500e-04, eta: 4:47:48, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5475, loss: 0.0887 +2023-03-03 15:34:33,471 - mmseg - INFO - Iter [3400/80000] lr: 1.500e-04, eta: 4:47:15, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0912, decode.acc_seg: 96.4668, loss: 0.0912 +2023-03-03 15:34:43,897 - mmseg - INFO - Iter [3450/80000] lr: 1.500e-04, eta: 4:46:45, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.5241, loss: 0.0892 +2023-03-03 15:34:54,423 - mmseg - INFO - Iter [3500/80000] lr: 1.500e-04, eta: 4:46:18, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0947, decode.acc_seg: 96.3331, loss: 0.0947 +2023-03-03 15:35:07,283 - mmseg - INFO - Iter [3550/80000] lr: 1.500e-04, eta: 4:46:42, time: 0.257, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0903, decode.acc_seg: 96.4991, loss: 0.0903 +2023-03-03 15:35:17,566 - mmseg - INFO - Iter [3600/80000] lr: 1.500e-04, eta: 4:46:10, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5232, loss: 0.0886 +2023-03-03 15:35:27,843 - mmseg - INFO - Iter [3650/80000] lr: 1.500e-04, eta: 4:45:39, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0917, decode.acc_seg: 96.4120, loss: 0.0917 +2023-03-03 15:35:38,209 - mmseg - INFO - Iter [3700/80000] lr: 1.500e-04, eta: 4:45:10, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0903, decode.acc_seg: 96.4478, loss: 0.0903 +2023-03-03 15:35:50,883 - mmseg - INFO - Iter [3750/80000] lr: 1.500e-04, eta: 4:45:29, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5387, loss: 0.0880 +2023-03-03 15:36:01,177 - mmseg - INFO - Iter [3800/80000] lr: 1.500e-04, eta: 4:44:59, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0889, decode.acc_seg: 96.5080, loss: 0.0889 +2023-03-03 15:36:11,627 - mmseg - INFO - Iter [3850/80000] lr: 1.500e-04, eta: 4:44:32, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4982, loss: 0.0900 +2023-03-03 15:36:21,990 - mmseg - INFO - Iter [3900/80000] lr: 1.500e-04, eta: 4:44:04, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0934, decode.acc_seg: 96.3287, loss: 0.0934 +2023-03-03 15:36:34,580 - mmseg - INFO - Iter [3950/80000] lr: 1.500e-04, eta: 4:44:20, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.5203, loss: 0.0891 +2023-03-03 15:36:44,918 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:36:44,918 - mmseg - INFO - Iter [4000/80000] lr: 1.500e-04, eta: 4:43:52, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5420, loss: 0.0886 +2023-03-03 15:36:55,247 - mmseg - INFO - Iter [4050/80000] lr: 1.500e-04, eta: 4:43:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0919, decode.acc_seg: 96.3895, loss: 0.0919 +2023-03-03 15:37:07,896 - mmseg - INFO - Iter [4100/80000] lr: 1.500e-04, eta: 4:43:40, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0873, decode.acc_seg: 96.5865, loss: 0.0873 +2023-03-03 15:37:18,364 - mmseg - INFO - Iter [4150/80000] lr: 1.500e-04, eta: 4:43:15, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6440, loss: 0.0850 +2023-03-03 15:37:28,597 - mmseg - INFO - Iter [4200/80000] lr: 1.500e-04, eta: 4:42:47, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0938, decode.acc_seg: 96.3202, loss: 0.0938 +2023-03-03 15:37:38,887 - mmseg - INFO - Iter [4250/80000] lr: 1.500e-04, eta: 4:42:19, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5548, loss: 0.0883 +2023-03-03 15:37:51,524 - mmseg - INFO - Iter [4300/80000] lr: 1.500e-04, eta: 4:42:34, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0904, decode.acc_seg: 96.4621, loss: 0.0904 +2023-03-03 15:38:01,861 - mmseg - INFO - Iter [4350/80000] lr: 1.500e-04, eta: 4:42:07, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0933, decode.acc_seg: 96.3396, loss: 0.0933 +2023-03-03 15:38:12,323 - mmseg - INFO - Iter [4400/80000] lr: 1.500e-04, eta: 4:41:44, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0928, decode.acc_seg: 96.3725, loss: 0.0928 +2023-03-03 15:38:22,514 - mmseg - INFO - Iter [4450/80000] lr: 1.500e-04, eta: 4:41:16, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0932, decode.acc_seg: 96.3869, loss: 0.0932 +2023-03-03 15:38:35,169 - mmseg - INFO - Iter [4500/80000] lr: 1.500e-04, eta: 4:41:30, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0916, decode.acc_seg: 96.4161, loss: 0.0916 +2023-03-03 15:38:45,466 - mmseg - INFO - Iter [4550/80000] lr: 1.500e-04, eta: 4:41:04, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.5361, loss: 0.0892 +2023-03-03 15:38:55,772 - mmseg - INFO - Iter [4600/80000] lr: 1.500e-04, eta: 4:40:38, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0901, decode.acc_seg: 96.5099, loss: 0.0901 +2023-03-03 15:39:06,051 - mmseg - INFO - Iter [4650/80000] lr: 1.500e-04, eta: 4:40:13, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0902, decode.acc_seg: 96.4513, loss: 0.0902 +2023-03-03 15:39:18,608 - mmseg - INFO - Iter [4700/80000] lr: 1.500e-04, eta: 4:40:24, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5411, loss: 0.0884 +2023-03-03 15:39:28,992 - mmseg - INFO - Iter [4750/80000] lr: 1.500e-04, eta: 4:40:00, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5084, loss: 0.0890 +2023-03-03 15:39:39,254 - mmseg - INFO - Iter [4800/80000] lr: 1.500e-04, eta: 4:39:35, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5664, loss: 0.0877 +2023-03-03 15:39:51,920 - mmseg - INFO - Iter [4850/80000] lr: 1.500e-04, eta: 4:39:47, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0931, decode.acc_seg: 96.3801, loss: 0.0931 +2023-03-03 15:40:02,241 - mmseg - INFO - Iter [4900/80000] lr: 1.500e-04, eta: 4:39:23, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.4754, loss: 0.0891 +2023-03-03 15:40:12,708 - mmseg - INFO - Iter [4950/80000] lr: 1.500e-04, eta: 4:39:01, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0901, decode.acc_seg: 96.4946, loss: 0.0901 +2023-03-03 15:40:23,008 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:40:23,009 - mmseg - INFO - Iter [5000/80000] lr: 1.500e-04, eta: 4:38:38, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0921, decode.acc_seg: 96.3881, loss: 0.0921 +2023-03-03 15:40:35,702 - mmseg - INFO - Iter [5050/80000] lr: 1.500e-04, eta: 4:38:49, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.5143, loss: 0.0885 +2023-03-03 15:40:46,079 - mmseg - INFO - Iter [5100/80000] lr: 1.500e-04, eta: 4:38:27, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5427, loss: 0.0887 +2023-03-03 15:40:56,307 - mmseg - INFO - Iter [5150/80000] lr: 1.500e-04, eta: 4:38:02, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0914, decode.acc_seg: 96.4040, loss: 0.0914 +2023-03-03 15:41:06,651 - mmseg - INFO - Iter [5200/80000] lr: 1.500e-04, eta: 4:37:39, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5655, loss: 0.0877 +2023-03-03 15:41:19,367 - mmseg - INFO - Iter [5250/80000] lr: 1.500e-04, eta: 4:37:51, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0922, decode.acc_seg: 96.4073, loss: 0.0922 +2023-03-03 15:41:29,878 - mmseg - INFO - Iter [5300/80000] lr: 1.500e-04, eta: 4:37:31, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0950, decode.acc_seg: 96.2532, loss: 0.0950 +2023-03-03 15:41:40,270 - mmseg - INFO - Iter [5350/80000] lr: 1.500e-04, eta: 4:37:09, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0905, decode.acc_seg: 96.4474, loss: 0.0905 +2023-03-03 15:41:52,860 - mmseg - INFO - Iter [5400/80000] lr: 1.500e-04, eta: 4:37:18, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.6057, loss: 0.0867 +2023-03-03 15:42:03,396 - mmseg - INFO - Iter [5450/80000] lr: 1.500e-04, eta: 4:36:58, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5817, loss: 0.0884 +2023-03-03 15:42:13,669 - mmseg - INFO - Iter [5500/80000] lr: 1.500e-04, eta: 4:36:35, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5936, loss: 0.0876 +2023-03-03 15:42:24,034 - mmseg - INFO - Iter [5550/80000] lr: 1.500e-04, eta: 4:36:14, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5092, loss: 0.0890 +2023-03-03 15:42:36,661 - mmseg - INFO - Iter [5600/80000] lr: 1.500e-04, eta: 4:36:22, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5518, loss: 0.0886 +2023-03-03 15:42:46,966 - mmseg - INFO - Iter [5650/80000] lr: 1.500e-04, eta: 4:36:00, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0888, decode.acc_seg: 96.4977, loss: 0.0888 +2023-03-03 15:42:57,319 - mmseg - INFO - Iter [5700/80000] lr: 1.500e-04, eta: 4:35:39, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0895, decode.acc_seg: 96.4838, loss: 0.0895 +2023-03-03 15:43:07,659 - mmseg - INFO - Iter [5750/80000] lr: 1.500e-04, eta: 4:35:18, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5551, loss: 0.0872 +2023-03-03 15:43:20,169 - mmseg - INFO - Iter [5800/80000] lr: 1.500e-04, eta: 4:35:24, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0940, decode.acc_seg: 96.3767, loss: 0.0940 +2023-03-03 15:43:30,443 - mmseg - INFO - Iter [5850/80000] lr: 1.500e-04, eta: 4:35:02, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5208, loss: 0.0879 +2023-03-03 15:43:40,846 - mmseg - INFO - Iter [5900/80000] lr: 1.500e-04, eta: 4:34:42, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4625, loss: 0.0900 +2023-03-03 15:43:51,301 - mmseg - INFO - Iter [5950/80000] lr: 1.500e-04, eta: 4:34:23, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0917, decode.acc_seg: 96.4284, loss: 0.0917 +2023-03-03 15:44:03,976 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:44:03,976 - mmseg - INFO - Iter [6000/80000] lr: 1.500e-04, eta: 4:34:31, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0898, decode.acc_seg: 96.5309, loss: 0.0898 +2023-03-03 15:44:14,287 - mmseg - INFO - Iter [6050/80000] lr: 1.500e-04, eta: 4:34:09, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5311, loss: 0.0887 +2023-03-03 15:44:24,575 - mmseg - INFO - Iter [6100/80000] lr: 1.500e-04, eta: 4:33:48, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.5178, loss: 0.0892 +2023-03-03 15:44:37,135 - mmseg - INFO - Iter [6150/80000] lr: 1.500e-04, eta: 4:33:54, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0902, decode.acc_seg: 96.5172, loss: 0.0902 +2023-03-03 15:44:47,438 - mmseg - INFO - Iter [6200/80000] lr: 1.500e-04, eta: 4:33:34, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0913, decode.acc_seg: 96.4186, loss: 0.0913 +2023-03-03 15:44:57,835 - mmseg - INFO - Iter [6250/80000] lr: 1.500e-04, eta: 4:33:14, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0930, decode.acc_seg: 96.3455, loss: 0.0930 +2023-03-03 15:45:08,258 - mmseg - INFO - Iter [6300/80000] lr: 1.500e-04, eta: 4:32:55, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5640, loss: 0.0879 +2023-03-03 15:45:20,890 - mmseg - INFO - Iter [6350/80000] lr: 1.500e-04, eta: 4:33:01, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0898, decode.acc_seg: 96.5294, loss: 0.0898 +2023-03-03 15:45:31,116 - mmseg - INFO - Iter [6400/80000] lr: 1.500e-04, eta: 4:32:40, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0905, decode.acc_seg: 96.4545, loss: 0.0905 +2023-03-03 15:45:41,429 - mmseg - INFO - Iter [6450/80000] lr: 1.500e-04, eta: 4:32:20, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0922, decode.acc_seg: 96.3624, loss: 0.0922 +2023-03-03 15:45:52,006 - mmseg - INFO - Iter [6500/80000] lr: 1.500e-04, eta: 4:32:02, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5868, loss: 0.0865 +2023-03-03 15:46:04,858 - mmseg - INFO - Iter [6550/80000] lr: 1.500e-04, eta: 4:32:11, time: 0.257, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5666, loss: 0.0871 +2023-03-03 15:46:15,153 - mmseg - INFO - Iter [6600/80000] lr: 1.500e-04, eta: 4:31:51, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0913, decode.acc_seg: 96.4296, loss: 0.0913 +2023-03-03 15:46:25,464 - mmseg - INFO - Iter [6650/80000] lr: 1.500e-04, eta: 4:31:31, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5483, loss: 0.0872 +2023-03-03 15:46:38,026 - mmseg - INFO - Iter [6700/80000] lr: 1.500e-04, eta: 4:31:36, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0910, decode.acc_seg: 96.5034, loss: 0.0910 +2023-03-03 15:46:48,349 - mmseg - INFO - Iter [6750/80000] lr: 1.500e-04, eta: 4:31:16, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5942, loss: 0.0865 +2023-03-03 15:46:58,707 - mmseg - INFO - Iter [6800/80000] lr: 1.500e-04, eta: 4:30:57, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0899, decode.acc_seg: 96.4830, loss: 0.0899 +2023-03-03 15:47:09,212 - mmseg - INFO - Iter [6850/80000] lr: 1.500e-04, eta: 4:30:39, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0901, decode.acc_seg: 96.5024, loss: 0.0901 +2023-03-03 15:47:22,054 - mmseg - INFO - Iter [6900/80000] lr: 1.500e-04, eta: 4:30:46, time: 0.257, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6219, loss: 0.0865 +2023-03-03 15:47:32,364 - mmseg - INFO - Iter [6950/80000] lr: 1.500e-04, eta: 4:30:27, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4209, loss: 0.0908 +2023-03-03 15:47:42,664 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:47:42,665 - mmseg - INFO - Iter [7000/80000] lr: 1.500e-04, eta: 4:30:07, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5475, loss: 0.0881 +2023-03-03 15:47:53,245 - mmseg - INFO - Iter [7050/80000] lr: 1.500e-04, eta: 4:29:51, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0919, decode.acc_seg: 96.4109, loss: 0.0919 +2023-03-03 15:48:05,823 - mmseg - INFO - Iter [7100/80000] lr: 1.500e-04, eta: 4:29:55, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.5142, loss: 0.0900 +2023-03-03 15:48:16,105 - mmseg - INFO - Iter [7150/80000] lr: 1.500e-04, eta: 4:29:35, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5493, loss: 0.0876 +2023-03-03 15:48:26,500 - mmseg - INFO - Iter [7200/80000] lr: 1.500e-04, eta: 4:29:17, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4383, loss: 0.0900 +2023-03-03 15:48:36,896 - mmseg - INFO - Iter [7250/80000] lr: 1.500e-04, eta: 4:28:59, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5516, loss: 0.0875 +2023-03-03 15:48:49,437 - mmseg - INFO - Iter [7300/80000] lr: 1.500e-04, eta: 4:29:02, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0915, decode.acc_seg: 96.4310, loss: 0.0915 +2023-03-03 15:48:59,848 - mmseg - INFO - Iter [7350/80000] lr: 1.500e-04, eta: 4:28:44, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5125, loss: 0.0887 +2023-03-03 15:49:10,170 - mmseg - INFO - Iter [7400/80000] lr: 1.500e-04, eta: 4:28:26, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5419, loss: 0.0884 +2023-03-03 15:49:22,760 - mmseg - INFO - Iter [7450/80000] lr: 1.500e-04, eta: 4:28:29, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0893, decode.acc_seg: 96.4763, loss: 0.0893 +2023-03-03 15:49:33,165 - mmseg - INFO - Iter [7500/80000] lr: 1.500e-04, eta: 4:28:11, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5603, loss: 0.0875 +2023-03-03 15:49:43,619 - mmseg - INFO - Iter [7550/80000] lr: 1.500e-04, eta: 4:27:54, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5004, loss: 0.0890 +2023-03-03 15:49:53,971 - mmseg - INFO - Iter [7600/80000] lr: 1.500e-04, eta: 4:27:36, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5489, loss: 0.0884 +2023-03-03 15:50:06,595 - mmseg - INFO - Iter [7650/80000] lr: 1.500e-04, eta: 4:27:39, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6293, loss: 0.0860 +2023-03-03 15:50:16,904 - mmseg - INFO - Iter [7700/80000] lr: 1.500e-04, eta: 4:27:21, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0903, decode.acc_seg: 96.4208, loss: 0.0903 +2023-03-03 15:50:27,242 - mmseg - INFO - Iter [7750/80000] lr: 1.500e-04, eta: 4:27:03, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5528, loss: 0.0882 +2023-03-03 15:50:37,528 - mmseg - INFO - Iter [7800/80000] lr: 1.500e-04, eta: 4:26:44, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0912, decode.acc_seg: 96.4317, loss: 0.0912 +2023-03-03 15:50:50,191 - mmseg - INFO - Iter [7850/80000] lr: 1.500e-04, eta: 4:26:48, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.4774, loss: 0.0887 +2023-03-03 15:51:00,483 - mmseg - INFO - Iter [7900/80000] lr: 1.500e-04, eta: 4:26:29, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0889, decode.acc_seg: 96.4582, loss: 0.0889 +2023-03-03 15:51:10,912 - mmseg - INFO - Iter [7950/80000] lr: 1.500e-04, eta: 4:26:12, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0913, decode.acc_seg: 96.3891, loss: 0.0913 +2023-03-03 15:51:23,537 - mmseg - INFO - Saving checkpoint at 8000 iterations +2023-03-03 15:51:24,420 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:51:24,420 - mmseg - INFO - Iter [8000/80000] lr: 1.500e-04, eta: 4:26:23, time: 0.270, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0911, decode.acc_seg: 96.4402, loss: 0.0911 +2023-03-03 15:52:03,693 - mmseg - INFO - per class results: +2023-03-03 15:52:03,694 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.42 | 99.21 | +| sidewalk | 86.54 | 92.82 | +| building | 93.07 | 97.42 | +| wall | 52.8 | 59.14 | +| fence | 62.58 | 70.68 | +| pole | 69.18 | 78.03 | +| traffic light | 74.42 | 86.86 | +| traffic sign | 81.51 | 85.41 | +| vegetation | 92.68 | 96.34 | +| terrain | 63.58 | 72.46 | +| sky | 95.15 | 98.33 | +| person | 83.8 | 93.43 | +| rider | 65.93 | 75.68 | +| car | 95.72 | 97.99 | +| truck | 80.79 | 89.04 | +| bus | 91.51 | 95.2 | +| train | 85.42 | 90.25 | +| motorcycle | 68.62 | 77.64 | +| bicycle | 79.44 | 90.42 | ++---------------+-------+-------+ +2023-03-03 15:52:03,694 - mmseg - INFO - Summary: +2023-03-03 15:52:03,694 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.43 | 80.06 | 86.65 | ++-------+-------+-------+ +2023-03-03 15:52:04,513 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_8000.pth. +2023-03-03 15:52:04,513 - mmseg - INFO - Best mIoU is 0.8006 at 8000 iter. +2023-03-03 15:52:04,513 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:52:04,513 - mmseg - INFO - Iter(val) [63] aAcc: 0.9643, mIoU: 0.8006, mAcc: 0.8665, IoU.background: nan, IoU.road: 0.9842, IoU.sidewalk: 0.8654, IoU.building: 0.9307, IoU.wall: 0.5280, IoU.fence: 0.6258, IoU.pole: 0.6918, IoU.traffic light: 0.7442, IoU.traffic sign: 0.8151, IoU.vegetation: 0.9268, IoU.terrain: 0.6358, IoU.sky: 0.9515, IoU.person: 0.8380, IoU.rider: 0.6593, IoU.car: 0.9572, IoU.truck: 0.8079, IoU.bus: 0.9151, IoU.train: 0.8542, IoU.motorcycle: 0.6862, IoU.bicycle: 0.7944, Acc.background: nan, Acc.road: 0.9921, Acc.sidewalk: 0.9282, Acc.building: 0.9742, Acc.wall: 0.5914, Acc.fence: 0.7068, Acc.pole: 0.7803, Acc.traffic light: 0.8686, Acc.traffic sign: 0.8541, Acc.vegetation: 0.9634, Acc.terrain: 0.7246, Acc.sky: 0.9833, Acc.person: 0.9343, Acc.rider: 0.7568, Acc.car: 0.9799, Acc.truck: 0.8904, Acc.bus: 0.9520, Acc.train: 0.9025, Acc.motorcycle: 0.7764, Acc.bicycle: 0.9042 +2023-03-03 15:52:15,015 - mmseg - INFO - Iter [8050/80000] lr: 1.500e-04, eta: 4:32:05, time: 1.012, data_time: 0.810, memory: 33997, decode.loss_ce: 0.0928, decode.acc_seg: 96.4198, loss: 0.0928 +2023-03-03 15:52:25,583 - mmseg - INFO - Iter [8100/80000] lr: 1.500e-04, eta: 4:31:46, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.4584, loss: 0.0892 +2023-03-03 15:52:36,163 - mmseg - INFO - Iter [8150/80000] lr: 1.500e-04, eta: 4:31:28, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6430, loss: 0.0856 +2023-03-03 15:52:48,742 - mmseg - INFO - Iter [8200/80000] lr: 1.500e-04, eta: 4:31:28, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0907, decode.acc_seg: 96.4499, loss: 0.0907 +2023-03-03 15:52:59,232 - mmseg - INFO - Iter [8250/80000] lr: 1.500e-04, eta: 4:31:09, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6653, loss: 0.0858 +2023-03-03 15:53:09,733 - mmseg - INFO - Iter [8300/80000] lr: 1.500e-04, eta: 4:30:51, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.5501, loss: 0.0885 +2023-03-03 15:53:20,219 - mmseg - INFO - Iter [8350/80000] lr: 1.500e-04, eta: 4:30:32, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.4865, loss: 0.0890 +2023-03-03 15:53:32,941 - mmseg - INFO - Iter [8400/80000] lr: 1.500e-04, eta: 4:30:33, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.6023, loss: 0.0870 +2023-03-03 15:53:43,380 - mmseg - INFO - Iter [8450/80000] lr: 1.500e-04, eta: 4:30:14, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0914, decode.acc_seg: 96.4200, loss: 0.0914 +2023-03-03 15:53:53,902 - mmseg - INFO - Iter [8500/80000] lr: 1.500e-04, eta: 4:29:55, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5493, loss: 0.0877 +2023-03-03 15:54:04,446 - mmseg - INFO - Iter [8550/80000] lr: 1.500e-04, eta: 4:29:38, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0910, decode.acc_seg: 96.4765, loss: 0.0910 +2023-03-03 15:54:17,109 - mmseg - INFO - Iter [8600/80000] lr: 1.500e-04, eta: 4:29:37, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0907, decode.acc_seg: 96.4517, loss: 0.0907 +2023-03-03 15:54:27,498 - mmseg - INFO - Iter [8650/80000] lr: 1.500e-04, eta: 4:29:18, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5891, loss: 0.0868 +2023-03-03 15:54:37,915 - mmseg - INFO - Iter [8700/80000] lr: 1.500e-04, eta: 4:29:00, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.5172, loss: 0.0891 +2023-03-03 15:54:50,724 - mmseg - INFO - Iter [8750/80000] lr: 1.500e-04, eta: 4:29:00, time: 0.256, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5945, loss: 0.0863 +2023-03-03 15:55:01,104 - mmseg - INFO - Iter [8800/80000] lr: 1.500e-04, eta: 4:28:41, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5753, loss: 0.0867 +2023-03-03 15:55:11,704 - mmseg - INFO - Iter [8850/80000] lr: 1.500e-04, eta: 4:28:24, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5685, loss: 0.0884 +2023-03-03 15:55:22,091 - mmseg - INFO - Iter [8900/80000] lr: 1.500e-04, eta: 4:28:06, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7501, loss: 0.0824 +2023-03-03 15:55:34,722 - mmseg - INFO - Iter [8950/80000] lr: 1.500e-04, eta: 4:28:05, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0936, decode.acc_seg: 96.3421, loss: 0.0936 +2023-03-03 15:55:44,965 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:55:44,965 - mmseg - INFO - Iter [9000/80000] lr: 1.500e-04, eta: 4:27:45, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6097, loss: 0.0860 +2023-03-03 15:55:55,250 - mmseg - INFO - Iter [9050/80000] lr: 1.500e-04, eta: 4:27:26, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.4760, loss: 0.0890 +2023-03-03 15:56:05,604 - mmseg - INFO - Iter [9100/80000] lr: 1.500e-04, eta: 4:27:07, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5679, loss: 0.0876 +2023-03-03 15:56:18,287 - mmseg - INFO - Iter [9150/80000] lr: 1.500e-04, eta: 4:27:06, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5168, loss: 0.0881 +2023-03-03 15:56:28,655 - mmseg - INFO - Iter [9200/80000] lr: 1.500e-04, eta: 4:26:48, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0940, decode.acc_seg: 96.3629, loss: 0.0940 +2023-03-03 15:56:39,150 - mmseg - INFO - Iter [9250/80000] lr: 1.500e-04, eta: 4:26:30, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5977, loss: 0.0865 +2023-03-03 15:56:49,527 - mmseg - INFO - Iter [9300/80000] lr: 1.500e-04, eta: 4:26:12, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5149, loss: 0.0887 +2023-03-03 15:57:02,258 - mmseg - INFO - Iter [9350/80000] lr: 1.500e-04, eta: 4:26:11, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0898, decode.acc_seg: 96.4913, loss: 0.0898 +2023-03-03 15:57:12,529 - mmseg - INFO - Iter [9400/80000] lr: 1.500e-04, eta: 4:25:52, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4213, loss: 0.0908 +2023-03-03 15:57:22,958 - mmseg - INFO - Iter [9450/80000] lr: 1.500e-04, eta: 4:25:34, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5666, loss: 0.0876 +2023-03-03 15:57:35,564 - mmseg - INFO - Iter [9500/80000] lr: 1.500e-04, eta: 4:25:33, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.5997, loss: 0.0864 +2023-03-03 15:57:46,020 - mmseg - INFO - Iter [9550/80000] lr: 1.500e-04, eta: 4:25:15, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4684, loss: 0.0900 +2023-03-03 15:57:56,355 - mmseg - INFO - Iter [9600/80000] lr: 1.500e-04, eta: 4:24:57, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0898, decode.acc_seg: 96.5034, loss: 0.0898 +2023-03-03 15:58:06,664 - mmseg - INFO - Iter [9650/80000] lr: 1.500e-04, eta: 4:24:39, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0899, decode.acc_seg: 96.4910, loss: 0.0899 +2023-03-03 15:58:19,358 - mmseg - INFO - Iter [9700/80000] lr: 1.500e-04, eta: 4:24:38, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5263, loss: 0.0874 +2023-03-03 15:58:29,788 - mmseg - INFO - Iter [9750/80000] lr: 1.500e-04, eta: 4:24:20, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6198, loss: 0.0849 +2023-03-03 15:58:40,213 - mmseg - INFO - Iter [9800/80000] lr: 1.500e-04, eta: 4:24:02, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0912, decode.acc_seg: 96.4410, loss: 0.0912 +2023-03-03 15:58:50,546 - mmseg - INFO - Iter [9850/80000] lr: 1.500e-04, eta: 4:23:44, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.6079, loss: 0.0867 +2023-03-03 15:59:03,369 - mmseg - INFO - Iter [9900/80000] lr: 1.500e-04, eta: 4:23:44, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0911, decode.acc_seg: 96.4387, loss: 0.0911 +2023-03-03 15:59:13,688 - mmseg - INFO - Iter [9950/80000] lr: 1.500e-04, eta: 4:23:26, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6709, loss: 0.0850 +2023-03-03 15:59:24,108 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 15:59:24,108 - mmseg - INFO - Iter [10000/80000] lr: 1.500e-04, eta: 4:23:09, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5427, loss: 0.0875 +2023-03-03 15:59:36,751 - mmseg - INFO - Iter [10050/80000] lr: 7.500e-05, eta: 4:23:07, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6697, loss: 0.0850 +2023-03-03 15:59:47,346 - mmseg - INFO - Iter [10100/80000] lr: 7.500e-05, eta: 4:22:51, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.5915, loss: 0.0859 +2023-03-03 15:59:57,753 - mmseg - INFO - Iter [10150/80000] lr: 7.500e-05, eta: 4:22:34, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0903, decode.acc_seg: 96.4980, loss: 0.0903 +2023-03-03 16:00:08,139 - mmseg - INFO - Iter [10200/80000] lr: 7.500e-05, eta: 4:22:16, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5174, loss: 0.0881 +2023-03-03 16:00:20,824 - mmseg - INFO - Iter [10250/80000] lr: 7.500e-05, eta: 4:22:14, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5925, loss: 0.0872 +2023-03-03 16:00:31,196 - mmseg - INFO - Iter [10300/80000] lr: 7.500e-05, eta: 4:21:57, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.5250, loss: 0.0885 +2023-03-03 16:00:41,589 - mmseg - INFO - Iter [10350/80000] lr: 7.500e-05, eta: 4:21:40, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0907, decode.acc_seg: 96.4442, loss: 0.0907 +2023-03-03 16:00:51,814 - mmseg - INFO - Iter [10400/80000] lr: 7.500e-05, eta: 4:21:22, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6430, loss: 0.0853 +2023-03-03 16:01:04,545 - mmseg - INFO - Iter [10450/80000] lr: 7.500e-05, eta: 4:21:20, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0894, decode.acc_seg: 96.5495, loss: 0.0894 +2023-03-03 16:01:14,961 - mmseg - INFO - Iter [10500/80000] lr: 7.500e-05, eta: 4:21:03, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0911, decode.acc_seg: 96.4232, loss: 0.0911 +2023-03-03 16:01:25,321 - mmseg - INFO - Iter [10550/80000] lr: 7.500e-05, eta: 4:20:46, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6859, loss: 0.0838 +2023-03-03 16:01:35,628 - mmseg - INFO - Iter [10600/80000] lr: 7.500e-05, eta: 4:20:28, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5628, loss: 0.0872 +2023-03-03 16:01:48,341 - mmseg - INFO - Iter 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data_time: 0.009, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5638, loss: 0.0876 +2023-03-03 16:03:36,193 - mmseg - INFO - Iter [11150/80000] lr: 7.500e-05, eta: 4:18:04, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5392, loss: 0.0879 +2023-03-03 16:03:49,055 - mmseg - INFO - Iter [11200/80000] lr: 7.500e-05, eta: 4:18:03, time: 0.257, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6041, loss: 0.0868 +2023-03-03 16:03:59,363 - mmseg - INFO - Iter [11250/80000] lr: 7.500e-05, eta: 4:17:46, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5605, loss: 0.0860 +2023-03-03 16:04:09,674 - mmseg - INFO - Iter [11300/80000] lr: 7.500e-05, eta: 4:17:29, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5928, loss: 0.0869 +2023-03-03 16:04:22,350 - mmseg - INFO - Iter [11350/80000] lr: 7.500e-05, eta: 4:17:26, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4863, loss: 0.0908 +2023-03-03 16:04:32,883 - mmseg - INFO - Iter [11400/80000] lr: 7.500e-05, eta: 4:17:11, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5240, loss: 0.0883 +2023-03-03 16:04:43,187 - mmseg - INFO - Iter [11450/80000] lr: 7.500e-05, eta: 4:16:54, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6971, loss: 0.0839 +2023-03-03 16:04:53,402 - mmseg - INFO - Iter [11500/80000] lr: 7.500e-05, eta: 4:16:36, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5860, loss: 0.0863 +2023-03-03 16:05:06,023 - mmseg - INFO - Iter [11550/80000] lr: 7.500e-05, eta: 4:16:33, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6782, loss: 0.0840 +2023-03-03 16:05:16,315 - mmseg - INFO - Iter [11600/80000] lr: 7.500e-05, eta: 4:16:16, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6031, loss: 0.0853 +2023-03-03 16:05:26,641 - mmseg - INFO - Iter [11650/80000] lr: 7.500e-05, eta: 4:16:00, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.6590, loss: 0.0862 +2023-03-03 16:05:36,882 - mmseg - INFO - Iter [11700/80000] lr: 7.500e-05, eta: 4:15:43, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5958, loss: 0.0865 +2023-03-03 16:05:49,447 - mmseg - INFO - Iter [11750/80000] lr: 7.500e-05, eta: 4:15:39, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0896, decode.acc_seg: 96.5741, loss: 0.0896 +2023-03-03 16:05:59,752 - mmseg - INFO - Iter [11800/80000] lr: 7.500e-05, eta: 4:15:23, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0878, decode.acc_seg: 96.5534, loss: 0.0878 +2023-03-03 16:06:09,961 - mmseg - INFO - Iter [11850/80000] lr: 7.500e-05, eta: 4:15:06, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6416, loss: 0.0851 +2023-03-03 16:06:20,449 - mmseg - INFO - Iter [11900/80000] lr: 7.500e-05, eta: 4:14:50, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5424, loss: 0.0879 +2023-03-03 16:06:33,108 - mmseg - INFO - Iter [11950/80000] lr: 7.500e-05, eta: 4:14:47, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.4954, loss: 0.0883 +2023-03-03 16:06:43,524 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:06:43,524 - mmseg - INFO - Iter [12000/80000] lr: 7.500e-05, eta: 4:14:31, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6363, loss: 0.0859 +2023-03-03 16:06:53,962 - mmseg - INFO - Iter [12050/80000] lr: 7.500e-05, eta: 4:14:15, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6547, loss: 0.0856 +2023-03-03 16:07:06,450 - mmseg - INFO - Iter [12100/80000] lr: 7.500e-05, eta: 4:14:11, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6353, loss: 0.0856 +2023-03-03 16:07:16,767 - mmseg - INFO - Iter [12150/80000] lr: 7.500e-05, eta: 4:13:55, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5763, loss: 0.0871 +2023-03-03 16:07:27,056 - mmseg - INFO - Iter [12200/80000] lr: 7.500e-05, eta: 4:13:39, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0873, decode.acc_seg: 96.5710, loss: 0.0873 +2023-03-03 16:07:37,401 - mmseg - INFO - Iter [12250/80000] lr: 7.500e-05, eta: 4:13:22, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5493, loss: 0.0883 +2023-03-03 16:07:50,033 - mmseg - INFO - Iter [12300/80000] lr: 7.500e-05, eta: 4:13:19, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6094, loss: 0.0853 +2023-03-03 16:08:00,266 - mmseg - INFO - Iter [12350/80000] lr: 7.500e-05, eta: 4:13:02, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5547, loss: 0.0862 +2023-03-03 16:08:10,568 - mmseg - INFO - Iter [12400/80000] lr: 7.500e-05, eta: 4:12:46, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6048, loss: 0.0853 +2023-03-03 16:08:20,829 - mmseg - INFO - Iter [12450/80000] lr: 7.500e-05, eta: 4:12:30, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7249, loss: 0.0837 +2023-03-03 16:08:33,544 - mmseg - INFO - Iter [12500/80000] lr: 7.500e-05, eta: 4:12:27, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5748, loss: 0.0871 +2023-03-03 16:08:43,976 - mmseg - INFO - Iter [12550/80000] lr: 7.500e-05, eta: 4:12:11, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5794, loss: 0.0871 +2023-03-03 16:08:54,277 - mmseg - INFO - Iter [12600/80000] lr: 7.500e-05, eta: 4:11:55, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6406, loss: 0.0854 +2023-03-03 16:09:06,746 - mmseg - INFO - Iter [12650/80000] lr: 7.500e-05, eta: 4:11:50, time: 0.249, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5598, loss: 0.0875 +2023-03-03 16:09:16,982 - mmseg - INFO - Iter [12700/80000] lr: 7.500e-05, eta: 4:11:34, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5701, loss: 0.0869 +2023-03-03 16:09:27,235 - mmseg - INFO - Iter [12750/80000] lr: 7.500e-05, eta: 4:11:18, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6444, loss: 0.0865 +2023-03-03 16:09:37,435 - mmseg - INFO - Iter [12800/80000] lr: 7.500e-05, eta: 4:11:01, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6257, loss: 0.0860 +2023-03-03 16:09:50,040 - mmseg - INFO - Iter [12850/80000] lr: 7.500e-05, eta: 4:10:57, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5520, loss: 0.0865 +2023-03-03 16:10:00,374 - mmseg - INFO - Iter [12900/80000] lr: 7.500e-05, eta: 4:10:42, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.4936, loss: 0.0891 +2023-03-03 16:10:10,671 - mmseg - INFO - Iter [12950/80000] lr: 7.500e-05, eta: 4:10:26, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.7030, loss: 0.0845 +2023-03-03 16:10:20,881 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:10:20,881 - mmseg - INFO - Iter [13000/80000] lr: 7.500e-05, eta: 4:10:09, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0878, decode.acc_seg: 96.5881, loss: 0.0878 +2023-03-03 16:10:33,524 - mmseg - INFO - Iter 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[13800/80000] lr: 7.500e-05, eta: 4:06:57, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5807, loss: 0.0867 +2023-03-03 16:13:27,758 - mmseg - INFO - Iter [13850/80000] lr: 7.500e-05, eta: 4:06:42, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6266, loss: 0.0863 +2023-03-03 16:13:37,986 - mmseg - INFO - Iter [13900/80000] lr: 7.500e-05, eta: 4:06:26, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6516, loss: 0.0844 +2023-03-03 16:13:48,296 - mmseg - INFO - Iter [13950/80000] lr: 7.500e-05, eta: 4:06:11, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5091, loss: 0.0890 +2023-03-03 16:14:01,068 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:14:01,068 - mmseg - INFO - Iter [14000/80000] lr: 7.500e-05, eta: 4:06:07, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0897, decode.acc_seg: 96.4420, loss: 0.0897 +2023-03-03 16:14:11,354 - mmseg - INFO - Iter [14050/80000] lr: 7.500e-05, eta: 4:05:52, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.6158, loss: 0.0866 +2023-03-03 16:14:21,599 - mmseg - INFO - Iter [14100/80000] lr: 7.500e-05, eta: 4:05:36, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.4975, loss: 0.0891 +2023-03-03 16:14:34,203 - mmseg - INFO - Iter [14150/80000] lr: 7.500e-05, eta: 4:05:32, time: 0.252, data_time: 0.052, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5393, loss: 0.0872 +2023-03-03 16:14:44,537 - mmseg - INFO - Iter [14200/80000] lr: 7.500e-05, eta: 4:05:16, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5249, loss: 0.0881 +2023-03-03 16:14:54,835 - mmseg - INFO - Iter [14250/80000] lr: 7.500e-05, eta: 4:05:01, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6314, loss: 0.0860 +2023-03-03 16:15:05,135 - mmseg - INFO - Iter [14300/80000] lr: 7.500e-05, eta: 4:04:46, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.5016, loss: 0.0900 +2023-03-03 16:15:17,650 - mmseg - INFO - Iter [14350/80000] lr: 7.500e-05, eta: 4:04:41, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6009, loss: 0.0864 +2023-03-03 16:15:27,921 - mmseg - INFO - Iter [14400/80000] lr: 7.500e-05, eta: 4:04:26, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0898, decode.acc_seg: 96.4775, loss: 0.0898 +2023-03-03 16:15:38,206 - mmseg - INFO - Iter [14450/80000] lr: 7.500e-05, eta: 4:04:10, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0896, decode.acc_seg: 96.4531, loss: 0.0896 +2023-03-03 16:15:48,576 - mmseg - INFO - Iter [14500/80000] lr: 7.500e-05, eta: 4:03:56, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5522, loss: 0.0874 +2023-03-03 16:16:01,329 - mmseg - INFO - Iter [14550/80000] lr: 7.500e-05, eta: 4:03:51, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6195, loss: 0.0855 +2023-03-03 16:16:11,879 - mmseg - INFO - Iter [14600/80000] lr: 7.500e-05, eta: 4:03:37, time: 0.211, data_time: 0.010, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6094, loss: 0.0852 +2023-03-03 16:16:22,256 - mmseg - INFO - Iter [14650/80000] lr: 7.500e-05, eta: 4:03:23, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5404, loss: 0.0886 +2023-03-03 16:16:34,974 - mmseg - INFO - Iter [14700/80000] lr: 7.500e-05, eta: 4:03:18, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5920, loss: 0.0877 +2023-03-03 16:16:45,185 - mmseg - INFO - Iter [14750/80000] lr: 7.500e-05, eta: 4:03:03, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5634, loss: 0.0883 +2023-03-03 16:16:55,575 - mmseg - INFO - Iter [14800/80000] lr: 7.500e-05, eta: 4:02:48, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5610, loss: 0.0883 +2023-03-03 16:17:05,943 - mmseg - INFO - Iter [14850/80000] lr: 7.500e-05, eta: 4:02:34, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6563, loss: 0.0839 +2023-03-03 16:17:18,422 - mmseg - INFO - Iter [14900/80000] lr: 7.500e-05, eta: 4:02:28, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5881, loss: 0.0871 +2023-03-03 16:17:28,748 - mmseg - INFO - Iter [14950/80000] lr: 7.500e-05, eta: 4:02:13, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5609, loss: 0.0877 +2023-03-03 16:17:39,066 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:17:39,066 - mmseg - INFO - Iter [15000/80000] lr: 7.500e-05, eta: 4:01:58, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6053, loss: 0.0863 +2023-03-03 16:17:49,441 - mmseg - INFO - Iter [15050/80000] lr: 7.500e-05, eta: 4:01:44, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7038, loss: 0.0837 +2023-03-03 16:18:02,304 - mmseg - INFO - Iter [15100/80000] lr: 7.500e-05, eta: 4:01:40, time: 0.257, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6378, loss: 0.0850 +2023-03-03 16:18:12,638 - mmseg - INFO - Iter [15150/80000] lr: 7.500e-05, eta: 4:01:25, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0894, decode.acc_seg: 96.5319, loss: 0.0894 +2023-03-03 16:18:22,911 - mmseg - INFO - Iter [15200/80000] lr: 7.500e-05, eta: 4:01:10, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6225, loss: 0.0850 +2023-03-03 16:18:33,172 - mmseg - INFO - Iter [15250/80000] lr: 7.500e-05, eta: 4:00:55, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5469, loss: 0.0884 +2023-03-03 16:18:45,746 - mmseg - INFO - Iter [15300/80000] lr: 7.500e-05, eta: 4:00:50, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6499, loss: 0.0852 +2023-03-03 16:18:56,066 - mmseg - INFO - Iter [15350/80000] lr: 7.500e-05, eta: 4:00:35, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5763, loss: 0.0880 +2023-03-03 16:19:06,396 - mmseg - INFO - Iter [15400/80000] lr: 7.500e-05, eta: 4:00:21, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7205, loss: 0.0824 +2023-03-03 16:19:19,213 - mmseg - INFO - Iter [15450/80000] lr: 7.500e-05, eta: 4:00:16, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0912, decode.acc_seg: 96.4720, loss: 0.0912 +2023-03-03 16:19:29,488 - mmseg - INFO - Iter [15500/80000] lr: 7.500e-05, eta: 4:00:01, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5757, loss: 0.0866 +2023-03-03 16:19:39,965 - mmseg - INFO - Iter [15550/80000] lr: 7.500e-05, eta: 3:59:47, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0896, decode.acc_seg: 96.4917, loss: 0.0896 +2023-03-03 16:19:50,183 - mmseg - INFO - Iter [15600/80000] lr: 7.500e-05, eta: 3:59:32, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6554, loss: 0.0843 +2023-03-03 16:20:02,708 - mmseg - INFO - Iter [15650/80000] lr: 7.500e-05, eta: 3:59:27, time: 0.250, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.4903, loss: 0.0880 +2023-03-03 16:20:12,992 - mmseg - INFO - Iter [15700/80000] lr: 7.500e-05, eta: 3:59:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4849, loss: 0.0900 +2023-03-03 16:20:23,257 - mmseg - INFO - Iter [15750/80000] lr: 7.500e-05, eta: 3:58:57, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5523, loss: 0.0874 +2023-03-03 16:20:33,494 - mmseg - INFO - Iter [15800/80000] lr: 7.500e-05, eta: 3:58:42, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6550, loss: 0.0849 +2023-03-03 16:20:46,111 - mmseg - INFO - Iter [15850/80000] lr: 7.500e-05, eta: 3:58:37, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0899, decode.acc_seg: 96.5598, loss: 0.0899 +2023-03-03 16:20:56,416 - mmseg - INFO - Iter [15900/80000] lr: 7.500e-05, eta: 3:58:23, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6604, loss: 0.0847 +2023-03-03 16:21:06,763 - mmseg - INFO - Iter [15950/80000] lr: 7.500e-05, eta: 3:58:08, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7701, loss: 0.0825 +2023-03-03 16:21:19,342 - mmseg - INFO - Saving checkpoint at 16000 iterations +2023-03-03 16:21:20,297 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:21:20,298 - mmseg - INFO - Iter [16000/80000] lr: 7.500e-05, eta: 3:58:06, time: 0.271, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0908, decode.acc_seg: 96.4256, loss: 0.0908 +2023-03-03 16:21:40,429 - mmseg - INFO - per class results: +2023-03-03 16:21:40,430 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.45 | 99.22 | +| sidewalk | 86.97 | 92.99 | +| building | 93.02 | 97.1 | +| wall | 48.6 | 52.24 | +| fence | 62.31 | 71.7 | +| pole | 69.61 | 80.06 | +| traffic light | 74.63 | 85.9 | +| traffic sign | 82.69 | 89.48 | +| vegetation | 92.74 | 96.4 | +| terrain | 64.32 | 77.56 | +| sky | 95.07 | 98.4 | +| person | 83.95 | 93.8 | +| rider | 65.53 | 77.74 | +| car | 95.55 | 98.02 | +| truck | 76.92 | 79.6 | +| bus | 91.68 | 95.11 | +| train | 86.02 | 91.93 | +| motorcycle | 68.41 | 77.06 | +| bicycle | 79.43 | 89.93 | ++---------------+-------+-------+ +2023-03-03 16:21:40,430 - mmseg - INFO - Summary: +2023-03-03 16:21:40,430 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.42 | 79.78 | 86.54 | ++-------+-------+-------+ +2023-03-03 16:21:40,430 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:21:40,430 - mmseg - INFO - Iter(val) [63] aAcc: 0.9642, mIoU: 0.7978, mAcc: 0.8654, IoU.background: nan, IoU.road: 0.9845, IoU.sidewalk: 0.8697, IoU.building: 0.9302, IoU.wall: 0.4860, IoU.fence: 0.6231, IoU.pole: 0.6961, IoU.traffic light: 0.7463, IoU.traffic sign: 0.8269, IoU.vegetation: 0.9274, IoU.terrain: 0.6432, IoU.sky: 0.9507, IoU.person: 0.8395, IoU.rider: 0.6553, IoU.car: 0.9555, IoU.truck: 0.7692, IoU.bus: 0.9168, IoU.train: 0.8602, IoU.motorcycle: 0.6841, IoU.bicycle: 0.7943, Acc.background: nan, Acc.road: 0.9922, Acc.sidewalk: 0.9299, Acc.building: 0.9710, Acc.wall: 0.5224, Acc.fence: 0.7170, Acc.pole: 0.8006, Acc.traffic light: 0.8590, Acc.traffic sign: 0.8948, Acc.vegetation: 0.9640, Acc.terrain: 0.7756, Acc.sky: 0.9840, Acc.person: 0.9380, Acc.rider: 0.7774, Acc.car: 0.9802, Acc.truck: 0.7960, Acc.bus: 0.9511, Acc.train: 0.9193, Acc.motorcycle: 0.7706, Acc.bicycle: 0.8993 +2023-03-03 16:21:50,841 - mmseg - INFO - Iter [16050/80000] lr: 7.500e-05, eta: 3:59:13, time: 0.611, data_time: 0.411, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6580, loss: 0.0853 +2023-03-03 16:22:01,226 - mmseg - INFO - Iter [16100/80000] lr: 7.500e-05, eta: 3:58:58, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0897, decode.acc_seg: 96.4684, loss: 0.0897 +2023-03-03 16:22:11,551 - mmseg - INFO - Iter [16150/80000] lr: 7.500e-05, eta: 3:58:43, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6276, loss: 0.0861 +2023-03-03 16:22:24,174 - mmseg - INFO - Iter [16200/80000] lr: 7.500e-05, eta: 3:58:38, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6276, loss: 0.0847 +2023-03-03 16:22:34,392 - mmseg - INFO - Iter [16250/80000] lr: 7.500e-05, eta: 3:58:22, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4991, loss: 0.0900 +2023-03-03 16:22:44,610 - mmseg - INFO - Iter [16300/80000] lr: 7.500e-05, eta: 3:58:07, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6113, loss: 0.0861 +2023-03-03 16:22:55,027 - mmseg - INFO - Iter [16350/80000] lr: 7.500e-05, eta: 3:57:53, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6638, loss: 0.0850 +2023-03-03 16:23:07,581 - mmseg - INFO - Iter [16400/80000] lr: 7.500e-05, eta: 3:57:47, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.4817, loss: 0.0891 +2023-03-03 16:23:17,923 - mmseg - INFO - Iter [16450/80000] lr: 7.500e-05, eta: 3:57:32, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6695, loss: 0.0844 +2023-03-03 16:23:28,162 - mmseg - INFO - Iter [16500/80000] lr: 7.500e-05, eta: 3:57:17, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6061, loss: 0.0864 +2023-03-03 16:23:38,400 - mmseg - INFO - Iter [16550/80000] lr: 7.500e-05, eta: 3:57:02, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5784, loss: 0.0866 +2023-03-03 16:23:51,050 - mmseg - INFO - Iter [16600/80000] lr: 7.500e-05, eta: 3:56:57, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5649, loss: 0.0867 +2023-03-03 16:24:01,572 - mmseg - INFO - Iter [16650/80000] lr: 7.500e-05, eta: 3:56:43, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5262, loss: 0.0883 +2023-03-03 16:24:11,984 - mmseg - INFO - Iter [16700/80000] lr: 7.500e-05, eta: 3:56:29, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6582, loss: 0.0851 +2023-03-03 16:24:24,715 - mmseg - INFO - Iter [16750/80000] lr: 7.500e-05, eta: 3:56:23, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6580, loss: 0.0853 +2023-03-03 16:24:35,036 - mmseg - INFO - Iter [16800/80000] lr: 7.500e-05, eta: 3:56:09, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6496, loss: 0.0854 +2023-03-03 16:24:45,388 - mmseg - INFO - Iter [16850/80000] lr: 7.500e-05, eta: 3:55:54, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4055, loss: 0.0900 +2023-03-03 16:24:55,726 - mmseg - INFO - Iter [16900/80000] lr: 7.500e-05, eta: 3:55:40, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6409, loss: 0.0853 +2023-03-03 16:25:08,342 - mmseg - INFO - Iter [16950/80000] lr: 7.500e-05, eta: 3:55:34, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6597, loss: 0.0844 +2023-03-03 16:25:18,737 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:25:18,737 - mmseg - INFO - Iter [17000/80000] lr: 7.500e-05, eta: 3:55:20, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6321, loss: 0.0852 +2023-03-03 16:25:29,081 - mmseg - INFO - Iter [17050/80000] lr: 7.500e-05, eta: 3:55:05, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6799, loss: 0.0833 +2023-03-03 16:25:39,373 - mmseg - INFO - Iter [17100/80000] lr: 7.500e-05, eta: 3:54:51, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5546, loss: 0.0874 +2023-03-03 16:25:51,925 - mmseg - INFO - Iter [17150/80000] lr: 7.500e-05, eta: 3:54:44, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5339, loss: 0.0875 +2023-03-03 16:26:02,308 - mmseg - INFO - Iter [17200/80000] lr: 7.500e-05, eta: 3:54:30, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0902, decode.acc_seg: 96.3798, loss: 0.0902 +2023-03-03 16:26:12,650 - mmseg - INFO - Iter [17250/80000] lr: 7.500e-05, eta: 3:54:16, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5410, loss: 0.0870 +2023-03-03 16:26:25,300 - mmseg - INFO - Iter [17300/80000] lr: 7.500e-05, eta: 3:54:10, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6812, loss: 0.0837 +2023-03-03 16:26:35,844 - mmseg - INFO - Iter [17350/80000] lr: 7.500e-05, eta: 3:53:56, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5243, loss: 0.0886 +2023-03-03 16:26:46,259 - mmseg - INFO - Iter [17400/80000] lr: 7.500e-05, eta: 3:53:42, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6275, loss: 0.0841 +2023-03-03 16:26:56,596 - mmseg - INFO - Iter [17450/80000] lr: 7.500e-05, eta: 3:53:28, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6592, loss: 0.0849 +2023-03-03 16:27:09,224 - mmseg - INFO - Iter [17500/80000] lr: 7.500e-05, eta: 3:53:22, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6500, loss: 0.0860 +2023-03-03 16:27:19,522 - mmseg - INFO - Iter [17550/80000] lr: 7.500e-05, eta: 3:53:07, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5574, loss: 0.0867 +2023-03-03 16:27:29,767 - mmseg - INFO - Iter [17600/80000] lr: 7.500e-05, eta: 3:52:53, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.4963, loss: 0.0876 +2023-03-03 16:27:40,043 - mmseg - INFO - Iter [17650/80000] lr: 7.500e-05, eta: 3:52:38, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6538, loss: 0.0852 +2023-03-03 16:27:52,696 - mmseg - INFO - Iter [17700/80000] lr: 7.500e-05, eta: 3:52:32, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5750, loss: 0.0866 +2023-03-03 16:28:02,936 - mmseg - INFO - Iter [17750/80000] lr: 7.500e-05, eta: 3:52:18, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5881, loss: 0.0862 +2023-03-03 16:28:13,336 - mmseg - INFO - Iter [17800/80000] lr: 7.500e-05, eta: 3:52:04, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0897, decode.acc_seg: 96.4615, loss: 0.0897 +2023-03-03 16:28:23,678 - mmseg - INFO - Iter [17850/80000] lr: 7.500e-05, eta: 3:51:50, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.4986, loss: 0.0885 +2023-03-03 16:28:36,325 - mmseg - INFO - Iter [17900/80000] lr: 7.500e-05, eta: 3:51:43, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5730, loss: 0.0868 +2023-03-03 16:28:46,603 - mmseg - INFO - Iter [17950/80000] lr: 7.500e-05, eta: 3:51:29, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5499, loss: 0.0886 +2023-03-03 16:28:56,879 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:28:56,880 - mmseg - INFO - Iter [18000/80000] lr: 7.500e-05, eta: 3:51:15, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5675, loss: 0.0865 +2023-03-03 16:29:09,429 - mmseg - INFO - Iter [18050/80000] lr: 7.500e-05, eta: 3:51:08, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6638, loss: 0.0841 +2023-03-03 16:29:19,864 - mmseg - INFO - Iter [18100/80000] lr: 7.500e-05, eta: 3:50:54, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5448, loss: 0.0880 +2023-03-03 16:29:30,122 - mmseg - INFO - Iter [18150/80000] lr: 7.500e-05, eta: 3:50:40, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0950, decode.acc_seg: 96.4137, loss: 0.0950 +2023-03-03 16:29:40,507 - mmseg - INFO - Iter [18200/80000] lr: 7.500e-05, eta: 3:50:26, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5783, loss: 0.0874 +2023-03-03 16:29:53,110 - mmseg - INFO - Iter [18250/80000] lr: 7.500e-05, eta: 3:50:20, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6569, loss: 0.0850 +2023-03-03 16:30:03,326 - mmseg - INFO - Iter [18300/80000] lr: 7.500e-05, eta: 3:50:05, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5474, loss: 0.0865 +2023-03-03 16:30:13,671 - mmseg - INFO - Iter [18350/80000] lr: 7.500e-05, eta: 3:49:51, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0904, decode.acc_seg: 96.4788, loss: 0.0904 +2023-03-03 16:30:23,895 - mmseg - INFO - Iter [18400/80000] lr: 7.500e-05, eta: 3:49:37, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6922, loss: 0.0843 +2023-03-03 16:30:36,452 - mmseg - INFO - Iter [18450/80000] lr: 7.500e-05, eta: 3:49:30, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.5328, loss: 0.0891 +2023-03-03 16:30:46,933 - mmseg - INFO - Iter [18500/80000] lr: 7.500e-05, eta: 3:49:17, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6814, loss: 0.0841 +2023-03-03 16:30:57,204 - mmseg - INFO - Iter [18550/80000] lr: 7.500e-05, eta: 3:49:03, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6883, loss: 0.0837 +2023-03-03 16:31:07,608 - mmseg - INFO - Iter [18600/80000] lr: 7.500e-05, eta: 3:48:49, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5332, loss: 0.0872 +2023-03-03 16:31:20,211 - mmseg - INFO - Iter [18650/80000] lr: 7.500e-05, eta: 3:48:42, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7093, loss: 0.0831 +2023-03-03 16:31:30,455 - mmseg - INFO - Iter [18700/80000] lr: 7.500e-05, eta: 3:48:28, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6391, loss: 0.0846 +2023-03-03 16:31:40,730 - mmseg - INFO - Iter [18750/80000] lr: 7.500e-05, eta: 3:48:14, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5986, loss: 0.0860 +2023-03-03 16:31:53,369 - mmseg - INFO - Iter [18800/80000] lr: 7.500e-05, eta: 3:48:07, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6188, loss: 0.0864 +2023-03-03 16:32:03,615 - mmseg - INFO - Iter [18850/80000] lr: 7.500e-05, eta: 3:47:53, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5985, loss: 0.0868 +2023-03-03 16:32:13,836 - mmseg - INFO - Iter [18900/80000] lr: 7.500e-05, eta: 3:47:39, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.7254, loss: 0.0836 +2023-03-03 16:32:24,100 - mmseg - INFO - Iter [18950/80000] lr: 7.500e-05, eta: 3:47:25, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0887, decode.acc_seg: 96.5004, loss: 0.0887 +2023-03-03 16:32:36,706 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:32:36,706 - mmseg - INFO - Iter [19000/80000] lr: 7.500e-05, eta: 3:47:18, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0916, decode.acc_seg: 96.3900, loss: 0.0916 +2023-03-03 16:32:47,063 - mmseg - INFO - Iter [19050/80000] lr: 7.500e-05, eta: 3:47:04, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0897, decode.acc_seg: 96.4713, loss: 0.0897 +2023-03-03 16:32:57,335 - mmseg - INFO - Iter [19100/80000] lr: 7.500e-05, eta: 3:46:50, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5248, loss: 0.0882 +2023-03-03 16:33:07,611 - mmseg - INFO - Iter [19150/80000] lr: 7.500e-05, eta: 3:46:36, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6743, loss: 0.0846 +2023-03-03 16:33:20,212 - mmseg - INFO - Iter [19200/80000] lr: 7.500e-05, eta: 3:46:30, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5926, loss: 0.0881 +2023-03-03 16:33:30,469 - mmseg - INFO - Iter [19250/80000] lr: 7.500e-05, eta: 3:46:16, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6772, loss: 0.0848 +2023-03-03 16:33:40,726 - mmseg - INFO - Iter [19300/80000] lr: 7.500e-05, eta: 3:46:02, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5657, loss: 0.0875 +2023-03-03 16:33:53,581 - mmseg - INFO - Iter [19350/80000] lr: 7.500e-05, eta: 3:45:56, time: 0.257, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5997, loss: 0.0877 +2023-03-03 16:34:04,100 - mmseg - INFO - Iter [19400/80000] lr: 7.500e-05, eta: 3:45:42, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5760, loss: 0.0869 +2023-03-03 16:34:14,338 - mmseg - INFO - Iter [19450/80000] lr: 7.500e-05, eta: 3:45:28, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7003, loss: 0.0831 +2023-03-03 16:34:24,595 - mmseg - INFO - Iter [19500/80000] lr: 7.500e-05, eta: 3:45:14, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5672, loss: 0.0871 +2023-03-03 16:34:37,092 - mmseg - INFO - Iter [19550/80000] lr: 7.500e-05, eta: 3:45:07, time: 0.250, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5563, loss: 0.0870 +2023-03-03 16:34:47,522 - mmseg - INFO - Iter [19600/80000] lr: 7.500e-05, eta: 3:44:54, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6514, loss: 0.0852 +2023-03-03 16:34:57,946 - mmseg - INFO - Iter [19650/80000] lr: 7.500e-05, eta: 3:44:40, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5215, loss: 0.0881 +2023-03-03 16:35:08,196 - mmseg - INFO - Iter [19700/80000] lr: 7.500e-05, eta: 3:44:26, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6494, loss: 0.0850 +2023-03-03 16:35:20,678 - mmseg - INFO - Iter [19750/80000] lr: 7.500e-05, eta: 3:44:19, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5483, loss: 0.0879 +2023-03-03 16:35:30,981 - mmseg - INFO - Iter [19800/80000] lr: 7.500e-05, eta: 3:44:05, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6018, loss: 0.0864 +2023-03-03 16:35:41,326 - mmseg - INFO - Iter [19850/80000] lr: 7.500e-05, eta: 3:43:52, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6933, loss: 0.0844 +2023-03-03 16:35:51,725 - mmseg - INFO - Iter [19900/80000] lr: 7.500e-05, eta: 3:43:38, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0878, decode.acc_seg: 96.5407, loss: 0.0878 +2023-03-03 16:36:04,414 - mmseg - INFO - Iter [19950/80000] lr: 7.500e-05, eta: 3:43:32, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5849, loss: 0.0860 +2023-03-03 16:36:14,732 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:36:14,732 - mmseg - INFO - Iter 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[20250/80000] lr: 3.750e-05, eta: 3:42:16, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6773, loss: 0.0838 +2023-03-03 16:37:21,016 - mmseg - INFO - Iter [20300/80000] lr: 3.750e-05, eta: 3:42:09, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6566, loss: 0.0852 +2023-03-03 16:37:31,263 - mmseg - INFO - Iter [20350/80000] lr: 3.750e-05, eta: 3:41:55, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7520, loss: 0.0816 +2023-03-03 16:37:41,555 - mmseg - INFO - Iter [20400/80000] lr: 3.750e-05, eta: 3:41:41, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0895, decode.acc_seg: 96.5115, loss: 0.0895 +2023-03-03 16:37:51,870 - mmseg - INFO - Iter [20450/80000] lr: 3.750e-05, eta: 3:41:28, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5673, loss: 0.0869 +2023-03-03 16:38:04,376 - mmseg - INFO - Iter [20500/80000] lr: 3.750e-05, eta: 3:41:20, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.6038, loss: 0.0867 +2023-03-03 16:38:14,628 - mmseg - INFO - Iter [20550/80000] lr: 3.750e-05, eta: 3:41:07, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6846, loss: 0.0850 +2023-03-03 16:38:24,931 - mmseg - INFO - Iter [20600/80000] lr: 3.750e-05, eta: 3:40:53, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7413, loss: 0.0826 +2023-03-03 16:38:37,516 - mmseg - INFO - Iter [20650/80000] lr: 3.750e-05, eta: 3:40:46, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7391, loss: 0.0833 +2023-03-03 16:38:47,935 - mmseg - INFO - Iter [20700/80000] lr: 3.750e-05, eta: 3:40:33, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6605, loss: 0.0849 +2023-03-03 16:38:58,146 - mmseg - INFO - Iter [20750/80000] lr: 3.750e-05, eta: 3:40:19, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5786, loss: 0.0877 +2023-03-03 16:39:08,405 - mmseg - INFO - Iter [20800/80000] lr: 3.750e-05, eta: 3:40:05, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5064, loss: 0.0880 +2023-03-03 16:39:21,049 - mmseg - INFO - Iter [20850/80000] lr: 3.750e-05, eta: 3:39:58, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6372, loss: 0.0854 +2023-03-03 16:39:31,396 - mmseg - INFO - Iter [20900/80000] lr: 3.750e-05, eta: 3:39:45, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0878, decode.acc_seg: 96.5513, loss: 0.0878 +2023-03-03 16:39:41,752 - mmseg - INFO - Iter [20950/80000] lr: 3.750e-05, eta: 3:39:31, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6553, loss: 0.0850 +2023-03-03 16:39:52,162 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:39:52,162 - mmseg - INFO - Iter [21000/80000] lr: 3.750e-05, eta: 3:39:18, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6744, loss: 0.0849 +2023-03-03 16:40:04,996 - mmseg - INFO - Iter [21050/80000] lr: 3.750e-05, eta: 3:39:12, time: 0.257, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5141, loss: 0.0884 +2023-03-03 16:40:15,292 - mmseg - INFO - Iter [21100/80000] lr: 3.750e-05, eta: 3:38:58, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6984, loss: 0.0844 +2023-03-03 16:40:25,494 - mmseg - INFO - Iter [21150/80000] lr: 3.750e-05, eta: 3:38:44, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6118, loss: 0.0863 +2023-03-03 16:40:35,662 - mmseg - INFO - Iter [21200/80000] lr: 3.750e-05, eta: 3:38:30, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6902, loss: 0.0840 +2023-03-03 16:40:48,269 - mmseg - INFO - Iter [21250/80000] lr: 3.750e-05, eta: 3:38:23, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5398, loss: 0.0874 +2023-03-03 16:40:58,539 - mmseg - INFO - Iter [21300/80000] lr: 3.750e-05, eta: 3:38:10, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5873, loss: 0.0884 +2023-03-03 16:41:08,843 - mmseg - INFO - Iter [21350/80000] lr: 3.750e-05, eta: 3:37:56, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7269, loss: 0.0830 +2023-03-03 16:41:21,471 - mmseg - INFO - Iter [21400/80000] lr: 3.750e-05, eta: 3:37:49, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6212, loss: 0.0859 +2023-03-03 16:41:31,646 - mmseg - INFO - Iter [21450/80000] lr: 3.750e-05, eta: 3:37:35, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6244, loss: 0.0847 +2023-03-03 16:41:42,041 - mmseg - INFO - Iter [21500/80000] lr: 3.750e-05, eta: 3:37:22, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6229, loss: 0.0847 +2023-03-03 16:41:52,381 - mmseg - INFO - Iter [21550/80000] lr: 3.750e-05, eta: 3:37:09, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6715, loss: 0.0845 +2023-03-03 16:42:04,926 - mmseg - INFO - Iter [21600/80000] lr: 3.750e-05, eta: 3:37:01, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6489, loss: 0.0846 +2023-03-03 16:42:15,271 - mmseg - INFO - Iter [21650/80000] lr: 3.750e-05, eta: 3:36:48, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6745, loss: 0.0844 +2023-03-03 16:42:25,511 - mmseg - INFO - Iter [21700/80000] lr: 3.750e-05, eta: 3:36:35, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0927, decode.acc_seg: 96.4350, loss: 0.0927 +2023-03-03 16:42:35,692 - mmseg - INFO - Iter [21750/80000] lr: 3.750e-05, eta: 3:36:21, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6713, loss: 0.0839 +2023-03-03 16:42:48,134 - mmseg - INFO - Iter [21800/80000] lr: 3.750e-05, eta: 3:36:13, time: 0.249, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6072, loss: 0.0858 +2023-03-03 16:42:58,277 - mmseg - INFO - Iter [21850/80000] lr: 3.750e-05, eta: 3:35:59, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7019, loss: 0.0838 +2023-03-03 16:43:08,467 - mmseg - INFO - Iter [21900/80000] lr: 3.750e-05, eta: 3:35:46, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6305, loss: 0.0850 +2023-03-03 16:43:20,951 - mmseg - INFO - Iter [21950/80000] lr: 3.750e-05, eta: 3:35:38, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7538, loss: 0.0815 +2023-03-03 16:43:31,237 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:43:31,237 - mmseg - INFO - Iter [22000/80000] lr: 3.750e-05, eta: 3:35:25, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5811, loss: 0.0862 +2023-03-03 16:43:41,425 - mmseg - INFO - Iter [22050/80000] lr: 3.750e-05, eta: 3:35:11, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0917, decode.acc_seg: 96.4909, loss: 0.0917 +2023-03-03 16:43:51,549 - mmseg - INFO - Iter [22100/80000] lr: 3.750e-05, eta: 3:34:57, time: 0.202, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5879, loss: 0.0863 +2023-03-03 16:44:04,088 - mmseg - INFO - Iter [22150/80000] lr: 3.750e-05, eta: 3:34:50, time: 0.251, data_time: 0.056, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5420, loss: 0.0874 +2023-03-03 16:44:14,175 - mmseg - INFO - Iter [22200/80000] lr: 3.750e-05, eta: 3:34:36, time: 0.202, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5782, loss: 0.0860 +2023-03-03 16:44:24,399 - mmseg - INFO - Iter [22250/80000] lr: 3.750e-05, eta: 3:34:22, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6735, loss: 0.0848 +2023-03-03 16:44:34,605 - mmseg - INFO - Iter [22300/80000] lr: 3.750e-05, eta: 3:34:09, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7178, loss: 0.0823 +2023-03-03 16:44:47,281 - mmseg - INFO - Iter [22350/80000] lr: 3.750e-05, eta: 3:34:02, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6539, loss: 0.0837 +2023-03-03 16:44:57,464 - mmseg - INFO - Iter [22400/80000] lr: 3.750e-05, eta: 3:33:48, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6137, loss: 0.0858 +2023-03-03 16:45:07,835 - mmseg - INFO - Iter [22450/80000] lr: 3.750e-05, eta: 3:33:35, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6165, loss: 0.0860 +2023-03-03 16:45:18,111 - mmseg - INFO - Iter [22500/80000] lr: 3.750e-05, eta: 3:33:22, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6934, loss: 0.0831 +2023-03-03 16:45:30,667 - mmseg - INFO - Iter [22550/80000] lr: 3.750e-05, eta: 3:33:14, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8238, loss: 0.0801 +2023-03-03 16:45:40,919 - mmseg - INFO - Iter [22600/80000] lr: 3.750e-05, eta: 3:33:01, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6625, loss: 0.0859 +2023-03-03 16:45:51,066 - mmseg - INFO - Iter [22650/80000] lr: 3.750e-05, eta: 3:32:47, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6837, loss: 0.0845 +2023-03-03 16:46:03,608 - mmseg - INFO - Iter [22700/80000] lr: 3.750e-05, eta: 3:32:39, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6223, loss: 0.0854 +2023-03-03 16:46:13,865 - mmseg - INFO - Iter [22750/80000] lr: 3.750e-05, eta: 3:32:26, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6149, loss: 0.0857 +2023-03-03 16:46:24,094 - mmseg - INFO - Iter [22800/80000] lr: 3.750e-05, eta: 3:32:13, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6765, loss: 0.0841 +2023-03-03 16:46:34,250 - mmseg - INFO - Iter [22850/80000] lr: 3.750e-05, eta: 3:31:59, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7785, loss: 0.0815 +2023-03-03 16:46:46,788 - mmseg - INFO - Iter [22900/80000] lr: 3.750e-05, eta: 3:31:52, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6056, loss: 0.0855 +2023-03-03 16:46:56,967 - mmseg - INFO - Iter [22950/80000] lr: 3.750e-05, eta: 3:31:38, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6743, loss: 0.0851 +2023-03-03 16:47:07,118 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:47:07,118 - mmseg - INFO - Iter [23000/80000] lr: 3.750e-05, eta: 3:31:24, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7444, loss: 0.0834 +2023-03-03 16:47:17,308 - mmseg - INFO - Iter [23050/80000] lr: 3.750e-05, eta: 3:31:11, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.5800, loss: 0.0858 +2023-03-03 16:47:29,792 - mmseg - INFO - Iter [23100/80000] lr: 3.750e-05, eta: 3:31:03, time: 0.250, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5530, loss: 0.0869 +2023-03-03 16:47:39,878 - mmseg - INFO - Iter [23150/80000] lr: 3.750e-05, eta: 3:30:50, time: 0.202, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0797, decode.acc_seg: 96.8405, loss: 0.0797 +2023-03-03 16:47:49,998 - mmseg - INFO - Iter [23200/80000] lr: 3.750e-05, eta: 3:30:36, time: 0.202, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0915, decode.acc_seg: 96.4912, loss: 0.0915 +2023-03-03 16:48:00,284 - mmseg - INFO - Iter [23250/80000] lr: 3.750e-05, eta: 3:30:23, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5213, loss: 0.0882 +2023-03-03 16:48:13,035 - mmseg - INFO - Iter [23300/80000] lr: 3.750e-05, eta: 3:30:16, time: 0.255, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5095, loss: 0.0890 +2023-03-03 16:48:23,300 - mmseg - INFO - Iter [23350/80000] lr: 3.750e-05, eta: 3:30:02, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6811, loss: 0.0844 +2023-03-03 16:48:33,513 - mmseg - INFO - Iter [23400/80000] lr: 3.750e-05, eta: 3:29:49, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5426, loss: 0.0872 +2023-03-03 16:48:45,940 - mmseg - INFO - Iter [23450/80000] lr: 3.750e-05, eta: 3:29:41, time: 0.249, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7252, loss: 0.0822 +2023-03-03 16:48:56,195 - mmseg - INFO - Iter [23500/80000] lr: 3.750e-05, eta: 3:29:28, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.5145, loss: 0.0885 +2023-03-03 16:49:06,448 - mmseg - INFO - Iter [23550/80000] lr: 3.750e-05, eta: 3:29:15, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5791, loss: 0.0869 +2023-03-03 16:49:16,640 - mmseg - INFO - Iter [23600/80000] lr: 3.750e-05, eta: 3:29:01, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6004, loss: 0.0860 +2023-03-03 16:49:29,248 - mmseg - INFO - Iter [23650/80000] lr: 3.750e-05, eta: 3:28:54, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.8322, loss: 0.0803 +2023-03-03 16:49:39,592 - mmseg - INFO - Iter [23700/80000] lr: 3.750e-05, eta: 3:28:41, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.5884, loss: 0.0864 +2023-03-03 16:49:49,735 - mmseg - INFO - Iter [23750/80000] lr: 3.750e-05, eta: 3:28:27, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.5886, loss: 0.0857 +2023-03-03 16:49:59,990 - mmseg - INFO - Iter [23800/80000] lr: 3.750e-05, eta: 3:28:14, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6312, loss: 0.0853 +2023-03-03 16:50:12,550 - mmseg - INFO - Iter [23850/80000] lr: 3.750e-05, eta: 3:28:07, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6264, loss: 0.0851 +2023-03-03 16:50:22,701 - mmseg - INFO - Iter [23900/80000] lr: 3.750e-05, eta: 3:27:53, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6369, loss: 0.0850 +2023-03-03 16:50:32,968 - mmseg - INFO - Iter [23950/80000] lr: 3.750e-05, eta: 3:27:40, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.5794, loss: 0.0892 +2023-03-03 16:50:45,526 - mmseg - INFO - Saving checkpoint at 24000 iterations +2023-03-03 16:50:46,446 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:50:46,446 - mmseg - INFO - Iter [24000/80000] lr: 3.750e-05, eta: 3:27:34, time: 0.270, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6957, loss: 0.0836 +2023-03-03 16:51:06,755 - mmseg - INFO - per class results: +2023-03-03 16:51:06,756 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.48 | 99.17 | +| sidewalk | 87.17 | 93.31 | +| building | 93.28 | 96.93 | +| wall | 53.75 | 59.42 | +| fence | 63.05 | 72.54 | +| pole | 70.08 | 80.15 | +| traffic light | 74.44 | 87.61 | +| traffic sign | 82.94 | 90.05 | +| vegetation | 92.89 | 96.91 | +| terrain | 65.35 | 75.58 | +| sky | 95.21 | 98.29 | +| person | 84.57 | 92.16 | +| rider | 66.44 | 78.72 | +| car | 95.66 | 98.25 | +| truck | 79.5 | 85.59 | +| bus | 91.38 | 94.3 | +| train | 86.4 | 91.03 | +| motorcycle | 68.56 | 79.19 | +| bicycle | 79.43 | 89.75 | ++---------------+-------+-------+ +2023-03-03 16:51:06,756 - mmseg - INFO - Summary: +2023-03-03 16:51:06,756 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.53 | 80.45 | 87.31 | ++-------+-------+-------+ +2023-03-03 16:51:06,783 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_8000.pth was removed +2023-03-03 16:51:07,609 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_24000.pth. +2023-03-03 16:51:07,609 - mmseg - INFO - Best mIoU is 0.8045 at 24000 iter. +2023-03-03 16:51:07,609 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:51:07,609 - mmseg - INFO - Iter(val) [63] aAcc: 0.9653, mIoU: 0.8045, mAcc: 0.8731, IoU.background: nan, IoU.road: 0.9848, IoU.sidewalk: 0.8717, IoU.building: 0.9328, IoU.wall: 0.5375, IoU.fence: 0.6305, IoU.pole: 0.7008, IoU.traffic light: 0.7444, IoU.traffic sign: 0.8294, IoU.vegetation: 0.9289, IoU.terrain: 0.6535, IoU.sky: 0.9521, IoU.person: 0.8457, IoU.rider: 0.6644, IoU.car: 0.9566, IoU.truck: 0.7950, IoU.bus: 0.9138, IoU.train: 0.8640, IoU.motorcycle: 0.6856, IoU.bicycle: 0.7943, Acc.background: nan, Acc.road: 0.9917, Acc.sidewalk: 0.9331, Acc.building: 0.9693, Acc.wall: 0.5942, Acc.fence: 0.7254, Acc.pole: 0.8015, Acc.traffic light: 0.8761, Acc.traffic sign: 0.9005, Acc.vegetation: 0.9691, Acc.terrain: 0.7558, Acc.sky: 0.9829, Acc.person: 0.9216, Acc.rider: 0.7872, Acc.car: 0.9825, Acc.truck: 0.8559, Acc.bus: 0.9430, Acc.train: 0.9103, Acc.motorcycle: 0.7919, Acc.bicycle: 0.8975 +2023-03-03 16:51:18,073 - mmseg - INFO - Iter [24050/80000] lr: 3.750e-05, eta: 3:28:11, time: 0.633, data_time: 0.432, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6868, loss: 0.0840 +2023-03-03 16:51:28,384 - mmseg - INFO - Iter [24100/80000] lr: 3.750e-05, eta: 3:27:58, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5201, loss: 0.0882 +2023-03-03 16:51:38,750 - mmseg - INFO - Iter [24150/80000] lr: 3.750e-05, eta: 3:27:45, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6782, loss: 0.0841 +2023-03-03 16:51:51,389 - mmseg - INFO - Iter [24200/80000] lr: 3.750e-05, eta: 3:27:37, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6555, loss: 0.0844 +2023-03-03 16:52:01,755 - mmseg - INFO - Iter [24250/80000] lr: 3.750e-05, eta: 3:27:24, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6265, loss: 0.0849 +2023-03-03 16:52:12,052 - mmseg - INFO - Iter [24300/80000] lr: 3.750e-05, eta: 3:27:11, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5833, loss: 0.0869 +2023-03-03 16:52:22,318 - mmseg - INFO - Iter [24350/80000] lr: 3.750e-05, eta: 3:26:58, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6380, loss: 0.0854 +2023-03-03 16:52:34,908 - mmseg - INFO - Iter [24400/80000] lr: 3.750e-05, eta: 3:26:50, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6197, loss: 0.0844 +2023-03-03 16:52:45,386 - mmseg - INFO - Iter [24450/80000] lr: 3.750e-05, eta: 3:26:37, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6618, loss: 0.0842 +2023-03-03 16:52:55,686 - mmseg - INFO - Iter [24500/80000] lr: 3.750e-05, eta: 3:26:24, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7014, loss: 0.0837 +2023-03-03 16:53:06,101 - mmseg - INFO - Iter [24550/80000] lr: 3.750e-05, eta: 3:26:11, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6353, loss: 0.0849 +2023-03-03 16:53:18,830 - mmseg - INFO - Iter [24600/80000] lr: 3.750e-05, eta: 3:26:04, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6330, loss: 0.0843 +2023-03-03 16:53:29,186 - mmseg - INFO - Iter [24650/80000] lr: 3.750e-05, eta: 3:25:51, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6618, loss: 0.0848 +2023-03-03 16:53:39,467 - mmseg - INFO - Iter [24700/80000] lr: 3.750e-05, eta: 3:25:38, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6213, loss: 0.0853 +2023-03-03 16:53:52,214 - mmseg - INFO - Iter [24750/80000] lr: 3.750e-05, eta: 3:25:30, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6871, loss: 0.0846 +2023-03-03 16:54:02,514 - mmseg - INFO - Iter [24800/80000] lr: 3.750e-05, eta: 3:25:17, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.4903, loss: 0.0879 +2023-03-03 16:54:12,956 - mmseg - INFO - Iter [24850/80000] lr: 3.750e-05, eta: 3:25:04, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5096, loss: 0.0879 +2023-03-03 16:54:23,397 - mmseg - INFO - Iter [24900/80000] lr: 3.750e-05, eta: 3:24:51, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6352, loss: 0.0857 +2023-03-03 16:54:36,027 - mmseg - INFO - Iter [24950/80000] lr: 3.750e-05, eta: 3:24:43, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.7763, loss: 0.0807 +2023-03-03 16:54:46,436 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:54:46,437 - mmseg - INFO - Iter [25000/80000] lr: 3.750e-05, eta: 3:24:31, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6263, loss: 0.0853 +2023-03-03 16:54:56,745 - mmseg - INFO - Iter [25050/80000] lr: 3.750e-05, eta: 3:24:18, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6065, loss: 0.0868 +2023-03-03 16:55:07,071 - mmseg - INFO - Iter [25100/80000] lr: 3.750e-05, eta: 3:24:05, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6150, loss: 0.0865 +2023-03-03 16:55:19,755 - mmseg - INFO - Iter [25150/80000] lr: 3.750e-05, eta: 3:23:57, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6121, loss: 0.0856 +2023-03-03 16:55:30,605 - mmseg - INFO - Iter [25200/80000] lr: 3.750e-05, eta: 3:23:45, time: 0.217, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6600, loss: 0.0853 +2023-03-03 16:55:41,080 - mmseg - INFO - Iter [25250/80000] lr: 3.750e-05, eta: 3:23:32, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6958, loss: 0.0833 +2023-03-03 16:55:53,616 - mmseg - INFO - Iter [25300/80000] lr: 3.750e-05, eta: 3:23:24, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.7161, loss: 0.0842 +2023-03-03 16:56:03,905 - mmseg - INFO - Iter [25350/80000] lr: 3.750e-05, eta: 3:23:11, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6271, loss: 0.0841 +2023-03-03 16:56:14,467 - mmseg - INFO - Iter [25400/80000] lr: 3.750e-05, eta: 3:22:59, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0881, decode.acc_seg: 96.5675, loss: 0.0881 +2023-03-03 16:56:24,701 - mmseg - INFO - Iter [25450/80000] lr: 3.750e-05, eta: 3:22:46, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6380, loss: 0.0854 +2023-03-03 16:56:37,339 - mmseg - INFO - Iter [25500/80000] lr: 3.750e-05, eta: 3:22:38, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6964, loss: 0.0837 +2023-03-03 16:56:47,627 - mmseg - INFO - Iter [25550/80000] lr: 3.750e-05, eta: 3:22:25, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.5934, loss: 0.0853 +2023-03-03 16:56:57,951 - mmseg - INFO - Iter [25600/80000] lr: 3.750e-05, eta: 3:22:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6220, loss: 0.0854 +2023-03-03 16:57:08,211 - mmseg - INFO - Iter [25650/80000] lr: 3.750e-05, eta: 3:21:59, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7579, loss: 0.0818 +2023-03-03 16:57:20,853 - mmseg - INFO - Iter [25700/80000] lr: 3.750e-05, eta: 3:21:51, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.7087, loss: 0.0836 +2023-03-03 16:57:31,177 - mmseg - INFO - Iter [25750/80000] lr: 3.750e-05, eta: 3:21:38, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6791, loss: 0.0833 +2023-03-03 16:57:41,446 - mmseg - INFO - Iter [25800/80000] lr: 3.750e-05, eta: 3:21:25, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6507, loss: 0.0856 +2023-03-03 16:57:51,763 - mmseg - INFO - Iter [25850/80000] lr: 3.750e-05, eta: 3:21:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6803, loss: 0.0840 +2023-03-03 16:58:04,314 - mmseg - INFO - Iter [25900/80000] lr: 3.750e-05, eta: 3:21:04, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6319, loss: 0.0860 +2023-03-03 16:58:14,614 - mmseg - INFO - Iter [25950/80000] lr: 3.750e-05, eta: 3:20:51, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.6965, loss: 0.0827 +2023-03-03 16:58:24,812 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 16:58:24,812 - mmseg - INFO - Iter [26000/80000] lr: 3.750e-05, eta: 3:20:38, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6918, loss: 0.0844 +2023-03-03 16:58:37,396 - mmseg - INFO - Iter [26050/80000] lr: 3.750e-05, eta: 3:20:30, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6545, loss: 0.0851 +2023-03-03 16:58:47,677 - mmseg - INFO - Iter [26100/80000] lr: 3.750e-05, eta: 3:20:17, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6108, loss: 0.0846 +2023-03-03 16:58:57,969 - mmseg - INFO - Iter [26150/80000] lr: 3.750e-05, eta: 3:20:04, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5471, loss: 0.0882 +2023-03-03 16:59:08,174 - mmseg - INFO - Iter [26200/80000] lr: 3.750e-05, eta: 3:19:51, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7095, loss: 0.0838 +2023-03-03 16:59:20,786 - mmseg - INFO - Iter [26250/80000] lr: 3.750e-05, eta: 3:19:42, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6481, loss: 0.0851 +2023-03-03 16:59:31,241 - mmseg - INFO - Iter [26300/80000] lr: 3.750e-05, eta: 3:19:30, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5909, loss: 0.0870 +2023-03-03 16:59:41,690 - mmseg - INFO - Iter [26350/80000] lr: 3.750e-05, eta: 3:19:17, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7190, loss: 0.0820 +2023-03-03 16:59:52,010 - mmseg - INFO - Iter [26400/80000] lr: 3.750e-05, eta: 3:19:05, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6493, loss: 0.0848 +2023-03-03 17:00:04,622 - mmseg - INFO - Iter [26450/80000] lr: 3.750e-05, eta: 3:18:56, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6899, loss: 0.0841 +2023-03-03 17:00:14,941 - mmseg - INFO - Iter [26500/80000] lr: 3.750e-05, eta: 3:18:44, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5911, loss: 0.0863 +2023-03-03 17:00:25,173 - mmseg - INFO - Iter [26550/80000] lr: 3.750e-05, eta: 3:18:31, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5745, loss: 0.0862 +2023-03-03 17:00:38,035 - mmseg - INFO - Iter [26600/80000] lr: 3.750e-05, eta: 3:18:23, time: 0.257, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6509, loss: 0.0847 +2023-03-03 17:00:48,370 - mmseg - INFO - Iter [26650/80000] lr: 3.750e-05, eta: 3:18:10, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0800, decode.acc_seg: 96.8227, loss: 0.0800 +2023-03-03 17:00:58,817 - mmseg - INFO - Iter [26700/80000] lr: 3.750e-05, eta: 3:17:58, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0889, decode.acc_seg: 96.4951, loss: 0.0889 +2023-03-03 17:01:09,137 - mmseg - INFO - Iter [26750/80000] lr: 3.750e-05, eta: 3:17:45, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5786, loss: 0.0870 +2023-03-03 17:01:21,789 - mmseg - INFO - Iter [26800/80000] lr: 3.750e-05, eta: 3:17:37, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.4877, loss: 0.0890 +2023-03-03 17:01:32,042 - mmseg - INFO - Iter [26850/80000] lr: 3.750e-05, eta: 3:17:24, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6263, loss: 0.0864 +2023-03-03 17:01:42,347 - mmseg - INFO - Iter [26900/80000] lr: 3.750e-05, eta: 3:17:11, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5743, loss: 0.0868 +2023-03-03 17:01:52,641 - mmseg - INFO - Iter [26950/80000] lr: 3.750e-05, eta: 3:16:58, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6318, loss: 0.0839 +2023-03-03 17:02:05,195 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:02:05,195 - mmseg - INFO - Iter [27000/80000] lr: 3.750e-05, eta: 3:16:50, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.4907, loss: 0.0892 +2023-03-03 17:02:15,487 - mmseg - INFO - Iter [27050/80000] lr: 3.750e-05, eta: 3:16:37, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0900, decode.acc_seg: 96.4696, loss: 0.0900 +2023-03-03 17:02:25,746 - mmseg - INFO - Iter [27100/80000] lr: 3.750e-05, eta: 3:16:24, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6598, loss: 0.0839 +2023-03-03 17:02:36,019 - mmseg - INFO - Iter [27150/80000] lr: 3.750e-05, eta: 3:16:11, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6953, loss: 0.0833 +2023-03-03 17:02:48,762 - mmseg - INFO - Iter [27200/80000] lr: 3.750e-05, eta: 3:16:03, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7056, loss: 0.0833 +2023-03-03 17:02:59,088 - mmseg - INFO - Iter [27250/80000] lr: 3.750e-05, eta: 3:15:51, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6210, loss: 0.0836 +2023-03-03 17:03:09,430 - mmseg - INFO - Iter [27300/80000] lr: 3.750e-05, eta: 3:15:38, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6041, loss: 0.0863 +2023-03-03 17:03:22,025 - mmseg - INFO - Iter [27350/80000] lr: 3.750e-05, eta: 3:15:30, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5249, loss: 0.0890 +2023-03-03 17:03:32,265 - mmseg - INFO - Iter [27400/80000] lr: 3.750e-05, eta: 3:15:17, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5539, loss: 0.0865 +2023-03-03 17:03:42,650 - mmseg - INFO - Iter [27450/80000] lr: 3.750e-05, eta: 3:15:04, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6017, loss: 0.0857 +2023-03-03 17:03:52,949 - mmseg - INFO - Iter [27500/80000] lr: 3.750e-05, eta: 3:14:51, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6471, loss: 0.0855 +2023-03-03 17:04:05,511 - mmseg - INFO - Iter [27550/80000] lr: 3.750e-05, eta: 3:14:43, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7178, loss: 0.0828 +2023-03-03 17:04:15,968 - mmseg - INFO - Iter [27600/80000] lr: 3.750e-05, eta: 3:14:30, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6592, loss: 0.0846 +2023-03-03 17:04:26,388 - mmseg - INFO - Iter [27650/80000] lr: 3.750e-05, eta: 3:14:18, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6020, loss: 0.0856 +2023-03-03 17:04:36,677 - mmseg - INFO - Iter [27700/80000] lr: 3.750e-05, eta: 3:14:05, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6406, loss: 0.0850 +2023-03-03 17:04:49,339 - mmseg - INFO - Iter [27750/80000] lr: 3.750e-05, eta: 3:13:57, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6270, loss: 0.0847 +2023-03-03 17:04:59,712 - mmseg - INFO - Iter [27800/80000] lr: 3.750e-05, eta: 3:13:44, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5313, loss: 0.0869 +2023-03-03 17:05:10,054 - mmseg - INFO - Iter [27850/80000] lr: 3.750e-05, eta: 3:13:32, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6011, loss: 0.0856 +2023-03-03 17:05:20,515 - mmseg - INFO - Iter [27900/80000] lr: 3.750e-05, eta: 3:13:19, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7157, loss: 0.0825 +2023-03-03 17:05:33,126 - mmseg - INFO - Iter [27950/80000] lr: 3.750e-05, eta: 3:13:11, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7412, loss: 0.0820 +2023-03-03 17:05:43,477 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:05:43,477 - mmseg - INFO - Iter [28000/80000] lr: 3.750e-05, eta: 3:12:58, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0910, decode.acc_seg: 96.4443, loss: 0.0910 +2023-03-03 17:05:53,863 - mmseg - INFO - Iter [28050/80000] lr: 3.750e-05, eta: 3:12:46, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0878, decode.acc_seg: 96.5362, loss: 0.0878 +2023-03-03 17:06:06,429 - mmseg - INFO - Iter [28100/80000] lr: 3.750e-05, eta: 3:12:37, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5454, loss: 0.0876 +2023-03-03 17:06:16,724 - mmseg - INFO - Iter [28150/80000] lr: 3.750e-05, eta: 3:12:25, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7202, loss: 0.0833 +2023-03-03 17:06:26,848 - mmseg - INFO - Iter [28200/80000] lr: 3.750e-05, eta: 3:12:12, time: 0.202, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6843, loss: 0.0831 +2023-03-03 17:06:37,200 - mmseg - INFO - Iter [28250/80000] lr: 3.750e-05, eta: 3:11:59, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6297, loss: 0.0863 +2023-03-03 17:06:49,927 - mmseg - INFO - Iter [28300/80000] lr: 3.750e-05, eta: 3:11:51, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.5949, loss: 0.0854 +2023-03-03 17:07:00,094 - mmseg - INFO - Iter [28350/80000] lr: 3.750e-05, eta: 3:11:38, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6249, loss: 0.0865 +2023-03-03 17:07:10,384 - mmseg - INFO - Iter [28400/80000] lr: 3.750e-05, eta: 3:11:25, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6105, loss: 0.0868 +2023-03-03 17:07:20,555 - mmseg - INFO - Iter [28450/80000] lr: 3.750e-05, eta: 3:11:13, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.6888, loss: 0.0827 +2023-03-03 17:07:33,132 - mmseg - INFO - Iter [28500/80000] lr: 3.750e-05, eta: 3:11:04, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6362, loss: 0.0856 +2023-03-03 17:07:43,402 - mmseg - INFO - Iter [28550/80000] lr: 3.750e-05, eta: 3:10:51, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0810, decode.acc_seg: 96.7492, loss: 0.0810 +2023-03-03 17:07:53,705 - mmseg - INFO - Iter [28600/80000] lr: 3.750e-05, eta: 3:10:39, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5093, loss: 0.0884 +2023-03-03 17:08:06,333 - mmseg - INFO - Iter [28650/80000] lr: 3.750e-05, eta: 3:10:30, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5811, loss: 0.0860 +2023-03-03 17:08:16,485 - mmseg - INFO - Iter [28700/80000] lr: 3.750e-05, eta: 3:10:17, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7877, loss: 0.0815 +2023-03-03 17:08:26,571 - mmseg - INFO - Iter [28750/80000] lr: 3.750e-05, eta: 3:10:04, time: 0.202, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.5927, loss: 0.0851 +2023-03-03 17:08:36,778 - mmseg - INFO - Iter [28800/80000] lr: 3.750e-05, eta: 3:09:52, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6760, loss: 0.0845 +2023-03-03 17:08:49,312 - mmseg - INFO - Iter [28850/80000] lr: 3.750e-05, eta: 3:09:43, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6616, loss: 0.0843 +2023-03-03 17:08:59,494 - mmseg - INFO - Iter [28900/80000] lr: 3.750e-05, eta: 3:09:30, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6213, loss: 0.0864 +2023-03-03 17:09:09,625 - mmseg - INFO - Iter [28950/80000] lr: 3.750e-05, eta: 3:09:17, time: 0.203, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6829, loss: 0.0843 +2023-03-03 17:09:19,797 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:09:19,797 - mmseg - INFO - Iter [29000/80000] lr: 3.750e-05, eta: 3:09:05, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5745, loss: 0.0868 +2023-03-03 17:09:32,258 - mmseg - INFO - Iter [29050/80000] lr: 3.750e-05, eta: 3:08:56, time: 0.249, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6554, loss: 0.0868 +2023-03-03 17:09:42,679 - mmseg - INFO - Iter [29100/80000] lr: 3.750e-05, eta: 3:08:43, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7077, loss: 0.0837 +2023-03-03 17:09:52,847 - mmseg - INFO - Iter [29150/80000] lr: 3.750e-05, eta: 3:08:31, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6969, loss: 0.0835 +2023-03-03 17:10:03,102 - mmseg - INFO - Iter [29200/80000] lr: 3.750e-05, eta: 3:08:18, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.6497, loss: 0.0871 +2023-03-03 17:10:15,564 - mmseg - INFO - Iter [29250/80000] lr: 3.750e-05, eta: 3:08:09, time: 0.249, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7109, loss: 0.0829 +2023-03-03 17:10:25,810 - mmseg - INFO - Iter [29300/80000] lr: 3.750e-05, eta: 3:07:57, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7262, loss: 0.0826 +2023-03-03 17:10:36,095 - mmseg - INFO - Iter [29350/80000] lr: 3.750e-05, eta: 3:07:44, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7951, loss: 0.0823 +2023-03-03 17:10:48,885 - mmseg - INFO - Iter [29400/80000] lr: 3.750e-05, eta: 3:07:36, time: 0.256, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.5832, loss: 0.0884 +2023-03-03 17:10:59,066 - mmseg - INFO - Iter [29450/80000] lr: 3.750e-05, eta: 3:07:23, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6710, loss: 0.0849 +2023-03-03 17:11:09,485 - mmseg - INFO - Iter [29500/80000] lr: 3.750e-05, eta: 3:07:11, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6140, loss: 0.0863 +2023-03-03 17:11:19,690 - mmseg - INFO - Iter [29550/80000] lr: 3.750e-05, eta: 3:06:58, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6201, loss: 0.0863 +2023-03-03 17:11:32,350 - mmseg - INFO - Iter [29600/80000] lr: 3.750e-05, eta: 3:06:50, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6321, loss: 0.0849 +2023-03-03 17:11:42,665 - mmseg - INFO - Iter [29650/80000] lr: 3.750e-05, eta: 3:06:37, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5365, loss: 0.0883 +2023-03-03 17:11:52,877 - mmseg - INFO - Iter [29700/80000] lr: 3.750e-05, eta: 3:06:24, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6365, loss: 0.0855 +2023-03-03 17:12:03,294 - mmseg - INFO - Iter [29750/80000] lr: 3.750e-05, eta: 3:06:12, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5822, loss: 0.0872 +2023-03-03 17:12:16,056 - mmseg - INFO - Iter [29800/80000] lr: 3.750e-05, eta: 3:06:04, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6196, loss: 0.0857 +2023-03-03 17:12:26,384 - mmseg - INFO - Iter [29850/80000] lr: 3.750e-05, eta: 3:05:51, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6339, loss: 0.0852 +2023-03-03 17:12:36,736 - mmseg - INFO - Iter [29900/80000] lr: 3.750e-05, eta: 3:05:39, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6528, loss: 0.0840 +2023-03-03 17:12:49,219 - mmseg - INFO - Iter [29950/80000] lr: 3.750e-05, eta: 3:05:30, time: 0.250, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7479, loss: 0.0824 +2023-03-03 17:12:59,396 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:12:59,396 - mmseg - INFO - Iter [30000/80000] lr: 3.750e-05, eta: 3:05:17, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7366, loss: 0.0831 +2023-03-03 17:13:09,637 - mmseg - INFO - Iter [30050/80000] lr: 1.875e-05, eta: 3:05:05, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6144, loss: 0.0855 +2023-03-03 17:13:19,986 - mmseg - INFO - Iter [30100/80000] lr: 1.875e-05, eta: 3:04:52, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7438, loss: 0.0833 +2023-03-03 17:13:32,587 - mmseg - INFO - Iter [30150/80000] lr: 1.875e-05, eta: 3:04:44, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7260, loss: 0.0824 +2023-03-03 17:13:42,828 - mmseg - INFO - Iter [30200/80000] lr: 1.875e-05, eta: 3:04:31, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.7111, loss: 0.0839 +2023-03-03 17:13:53,086 - mmseg - INFO - Iter [30250/80000] lr: 1.875e-05, eta: 3:04:19, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5341, loss: 0.0866 +2023-03-03 17:14:03,306 - mmseg - INFO - Iter [30300/80000] lr: 1.875e-05, eta: 3:04:06, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5371, loss: 0.0882 +2023-03-03 17:14:15,829 - mmseg - INFO - Iter [30350/80000] lr: 1.875e-05, eta: 3:03:57, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7673, loss: 0.0825 +2023-03-03 17:14:26,009 - mmseg - INFO - Iter [30400/80000] lr: 1.875e-05, eta: 3:03:45, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7470, loss: 0.0820 +2023-03-03 17:14:36,164 - mmseg - INFO - Iter [30450/80000] lr: 1.875e-05, eta: 3:03:32, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5434, loss: 0.0871 +2023-03-03 17:14:46,350 - mmseg - INFO - Iter [30500/80000] lr: 1.875e-05, eta: 3:03:19, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6461, loss: 0.0854 +2023-03-03 17:14:58,973 - mmseg - INFO - Iter [30550/80000] lr: 1.875e-05, eta: 3:03:11, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.7026, loss: 0.0836 +2023-03-03 17:15:09,141 - mmseg - INFO - Iter [30600/80000] lr: 1.875e-05, eta: 3:02:58, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.6949, loss: 0.0824 +2023-03-03 17:15:19,356 - mmseg - INFO - Iter [30650/80000] lr: 1.875e-05, eta: 3:02:45, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6462, loss: 0.0854 +2023-03-03 17:15:31,867 - mmseg - INFO - Iter [30700/80000] lr: 1.875e-05, eta: 3:02:37, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6474, loss: 0.0840 +2023-03-03 17:15:42,070 - mmseg - INFO - Iter [30750/80000] lr: 1.875e-05, eta: 3:02:24, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5728, loss: 0.0868 +2023-03-03 17:15:52,368 - mmseg - INFO - Iter [30800/80000] lr: 1.875e-05, eta: 3:02:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6786, loss: 0.0836 +2023-03-03 17:16:02,591 - mmseg - INFO - Iter [30850/80000] lr: 1.875e-05, eta: 3:01:59, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7375, loss: 0.0819 +2023-03-03 17:16:15,085 - mmseg - INFO - Iter [30900/80000] lr: 1.875e-05, eta: 3:01:50, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6773, loss: 0.0851 +2023-03-03 17:16:25,287 - mmseg - INFO - Iter [30950/80000] lr: 1.875e-05, eta: 3:01:38, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7209, loss: 0.0830 +2023-03-03 17:16:35,494 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:16:35,494 - mmseg - INFO - Iter [31000/80000] lr: 1.875e-05, eta: 3:01:25, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5987, loss: 0.0860 +2023-03-03 17:16:45,622 - mmseg - INFO - Iter [31050/80000] lr: 1.875e-05, eta: 3:01:12, time: 0.203, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6251, loss: 0.0854 +2023-03-03 17:16:58,107 - mmseg - INFO - Iter [31100/80000] lr: 1.875e-05, eta: 3:01:04, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6646, loss: 0.0844 +2023-03-03 17:17:08,228 - mmseg - INFO - Iter [31150/80000] lr: 1.875e-05, eta: 3:00:51, time: 0.202, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6614, loss: 0.0845 +2023-03-03 17:17:18,428 - mmseg - INFO - Iter [31200/80000] lr: 1.875e-05, eta: 3:00:38, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6241, loss: 0.0855 +2023-03-03 17:17:31,042 - mmseg - INFO - Iter [31250/80000] lr: 1.875e-05, eta: 3:00:30, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.5694, loss: 0.0864 +2023-03-03 17:17:41,176 - mmseg - INFO - Iter [31300/80000] lr: 1.875e-05, eta: 3:00:17, time: 0.203, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6091, loss: 0.0857 +2023-03-03 17:17:51,392 - mmseg - INFO - Iter [31350/80000] lr: 1.875e-05, eta: 3:00:04, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6612, loss: 0.0841 +2023-03-03 17:18:01,529 - mmseg - INFO - Iter [31400/80000] lr: 1.875e-05, eta: 2:59:52, time: 0.203, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7090, loss: 0.0825 +2023-03-03 17:18:14,068 - mmseg - INFO - Iter [31450/80000] lr: 1.875e-05, eta: 2:59:43, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5679, loss: 0.0868 +2023-03-03 17:18:24,457 - mmseg - INFO - Iter [31500/80000] lr: 1.875e-05, eta: 2:59:31, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7284, loss: 0.0819 +2023-03-03 17:18:34,850 - mmseg - INFO - Iter [31550/80000] lr: 1.875e-05, eta: 2:59:19, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5817, loss: 0.0862 +2023-03-03 17:18:45,049 - mmseg - INFO - Iter [31600/80000] lr: 1.875e-05, eta: 2:59:06, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.7451, loss: 0.0840 +2023-03-03 17:18:57,618 - mmseg - INFO - Iter [31650/80000] lr: 1.875e-05, eta: 2:58:57, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6418, loss: 0.0861 +2023-03-03 17:19:07,820 - mmseg - INFO - Iter [31700/80000] lr: 1.875e-05, eta: 2:58:45, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6067, loss: 0.0859 +2023-03-03 17:19:18,069 - mmseg - INFO - Iter [31750/80000] lr: 1.875e-05, eta: 2:58:32, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7013, loss: 0.0834 +2023-03-03 17:19:28,316 - mmseg - INFO - Iter [31800/80000] lr: 1.875e-05, eta: 2:58:20, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6982, loss: 0.0835 +2023-03-03 17:19:41,021 - mmseg - INFO - Iter [31850/80000] lr: 1.875e-05, eta: 2:58:11, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5141, loss: 0.0883 +2023-03-03 17:19:51,214 - mmseg - INFO - Iter [31900/80000] lr: 1.875e-05, eta: 2:57:59, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7210, loss: 0.0827 +2023-03-03 17:20:01,558 - mmseg - INFO - Iter [31950/80000] lr: 1.875e-05, eta: 2:57:47, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5741, loss: 0.0863 +2023-03-03 17:20:14,121 - mmseg - INFO - Saving checkpoint at 32000 iterations +2023-03-03 17:20:15,046 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:20:15,046 - mmseg - INFO - Iter [32000/80000] lr: 1.875e-05, eta: 2:57:39, time: 0.270, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5680, loss: 0.0867 +2023-03-03 17:20:35,015 - mmseg - INFO - per class results: +2023-03-03 17:20:35,016 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.49 | 99.15 | +| sidewalk | 87.23 | 93.57 | +| building | 93.24 | 96.8 | +| wall | 52.59 | 57.22 | +| fence | 63.13 | 72.04 | +| pole | 70.46 | 82.32 | +| traffic light | 74.8 | 84.07 | +| traffic sign | 83.0 | 89.13 | +| vegetation | 92.86 | 97.16 | +| terrain | 64.64 | 72.38 | +| sky | 95.23 | 98.2 | +| person | 84.5 | 92.7 | +| rider | 66.12 | 77.36 | +| car | 95.94 | 98.13 | +| truck | 84.42 | 91.07 | +| bus | 91.97 | 95.32 | +| train | 85.4 | 90.51 | +| motorcycle | 68.9 | 81.38 | +| bicycle | 79.45 | 90.81 | ++---------------+-------+-------+ +2023-03-03 17:20:35,016 - mmseg - INFO - Summary: +2023-03-03 17:20:35,017 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.54 | 80.65 | 87.33 | ++-------+-------+-------+ +2023-03-03 17:20:35,046 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_24000.pth was removed +2023-03-03 17:20:35,862 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_32000.pth. +2023-03-03 17:20:35,862 - mmseg - INFO - Best mIoU is 0.8065 at 32000 iter. +2023-03-03 17:20:35,862 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:20:35,862 - mmseg - INFO - Iter(val) [63] aAcc: 0.9654, mIoU: 0.8065, mAcc: 0.8733, IoU.background: nan, IoU.road: 0.9849, IoU.sidewalk: 0.8723, IoU.building: 0.9324, IoU.wall: 0.5259, IoU.fence: 0.6313, IoU.pole: 0.7046, IoU.traffic light: 0.7480, IoU.traffic sign: 0.8300, IoU.vegetation: 0.9286, IoU.terrain: 0.6464, IoU.sky: 0.9523, IoU.person: 0.8450, IoU.rider: 0.6612, IoU.car: 0.9594, IoU.truck: 0.8442, IoU.bus: 0.9197, IoU.train: 0.8540, IoU.motorcycle: 0.6890, IoU.bicycle: 0.7945, Acc.background: nan, Acc.road: 0.9915, Acc.sidewalk: 0.9357, Acc.building: 0.9680, Acc.wall: 0.5722, Acc.fence: 0.7204, Acc.pole: 0.8232, Acc.traffic light: 0.8407, Acc.traffic sign: 0.8913, Acc.vegetation: 0.9716, Acc.terrain: 0.7238, Acc.sky: 0.9820, Acc.person: 0.9270, Acc.rider: 0.7736, Acc.car: 0.9813, Acc.truck: 0.9107, Acc.bus: 0.9532, Acc.train: 0.9051, Acc.motorcycle: 0.8138, Acc.bicycle: 0.9081 +2023-03-03 17:20:46,242 - mmseg - INFO - Iter [32050/80000] lr: 1.875e-05, eta: 2:57:58, time: 0.624, data_time: 0.425, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.5269, loss: 0.0872 +2023-03-03 17:20:56,634 - mmseg - INFO - Iter [32100/80000] lr: 1.875e-05, eta: 2:57:46, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.7065, loss: 0.0853 +2023-03-03 17:21:07,173 - mmseg - INFO - Iter [32150/80000] lr: 1.875e-05, eta: 2:57:34, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7204, loss: 0.0825 +2023-03-03 17:21:19,732 - mmseg - INFO - Iter [32200/80000] lr: 1.875e-05, eta: 2:57:25, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5183, loss: 0.0883 +2023-03-03 17:21:30,087 - mmseg - INFO - Iter [32250/80000] lr: 1.875e-05, eta: 2:57:12, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0873, decode.acc_seg: 96.5385, loss: 0.0873 +2023-03-03 17:21:40,320 - mmseg - INFO - Iter [32300/80000] lr: 1.875e-05, eta: 2:57:00, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.5870, loss: 0.0859 +2023-03-03 17:21:50,758 - mmseg - INFO - Iter [32350/80000] lr: 1.875e-05, eta: 2:56:48, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.8277, loss: 0.0807 +2023-03-03 17:22:03,357 - mmseg - INFO - Iter [32400/80000] lr: 1.875e-05, eta: 2:56:39, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6245, loss: 0.0841 +2023-03-03 17:22:13,759 - mmseg - INFO - Iter [32450/80000] lr: 1.875e-05, eta: 2:56:27, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7591, loss: 0.0818 +2023-03-03 17:22:24,275 - mmseg - INFO - Iter [32500/80000] lr: 1.875e-05, eta: 2:56:15, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6075, loss: 0.0851 +2023-03-03 17:22:34,542 - mmseg - INFO - Iter [32550/80000] lr: 1.875e-05, eta: 2:56:02, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.5670, loss: 0.0892 +2023-03-03 17:22:47,252 - mmseg - INFO - Iter [32600/80000] lr: 1.875e-05, eta: 2:55:53, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6154, loss: 0.0861 +2023-03-03 17:22:57,733 - mmseg - INFO - Iter [32650/80000] lr: 1.875e-05, eta: 2:55:41, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7132, loss: 0.0833 +2023-03-03 17:23:07,934 - mmseg - INFO - Iter [32700/80000] lr: 1.875e-05, eta: 2:55:29, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6146, loss: 0.0858 +2023-03-03 17:23:20,495 - mmseg - INFO - Iter [32750/80000] lr: 1.875e-05, eta: 2:55:20, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6031, loss: 0.0854 +2023-03-03 17:23:30,748 - mmseg - INFO - Iter [32800/80000] lr: 1.875e-05, eta: 2:55:07, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6276, loss: 0.0861 +2023-03-03 17:23:41,025 - mmseg - INFO - Iter [32850/80000] lr: 1.875e-05, eta: 2:54:55, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6640, loss: 0.0845 +2023-03-03 17:23:51,267 - mmseg - INFO - Iter [32900/80000] lr: 1.875e-05, eta: 2:54:43, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.5926, loss: 0.0854 +2023-03-03 17:24:03,860 - mmseg - INFO - Iter [32950/80000] lr: 1.875e-05, eta: 2:54:34, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6608, loss: 0.0842 +2023-03-03 17:24:14,236 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:24:14,236 - mmseg - INFO - Iter [33000/80000] lr: 1.875e-05, eta: 2:54:21, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6888, loss: 0.0834 +2023-03-03 17:24:24,470 - mmseg - INFO - Iter [33050/80000] lr: 1.875e-05, eta: 2:54:09, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6940, loss: 0.0836 +2023-03-03 17:24:34,841 - mmseg - INFO - Iter [33100/80000] lr: 1.875e-05, eta: 2:53:57, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0787, decode.acc_seg: 96.8764, loss: 0.0787 +2023-03-03 17:24:47,440 - mmseg - INFO - Iter [33150/80000] lr: 1.875e-05, eta: 2:53:48, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7636, loss: 0.0811 +2023-03-03 17:24:57,827 - mmseg - INFO - Iter [33200/80000] lr: 1.875e-05, eta: 2:53:36, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5827, loss: 0.0876 +2023-03-03 17:25:08,421 - mmseg - INFO - Iter [33250/80000] lr: 1.875e-05, eta: 2:53:24, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0902, decode.acc_seg: 96.5843, loss: 0.0902 +2023-03-03 17:25:20,990 - mmseg - INFO - Iter [33300/80000] lr: 1.875e-05, eta: 2:53:15, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7331, loss: 0.0824 +2023-03-03 17:25:31,198 - mmseg - INFO - Iter [33350/80000] lr: 1.875e-05, eta: 2:53:02, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.5942, loss: 0.0864 +2023-03-03 17:25:41,455 - mmseg - INFO - Iter [33400/80000] lr: 1.875e-05, eta: 2:52:50, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6415, loss: 0.0837 +2023-03-03 17:25:51,890 - mmseg - INFO - Iter [33450/80000] lr: 1.875e-05, eta: 2:52:38, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.6960, loss: 0.0826 +2023-03-03 17:26:04,507 - mmseg - INFO - Iter [33500/80000] lr: 1.875e-05, eta: 2:52:29, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6180, loss: 0.0848 +2023-03-03 17:26:14,829 - mmseg - INFO - Iter [33550/80000] lr: 1.875e-05, eta: 2:52:16, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5907, loss: 0.0871 +2023-03-03 17:26:25,159 - mmseg - INFO - Iter [33600/80000] lr: 1.875e-05, eta: 2:52:04, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.5982, loss: 0.0855 +2023-03-03 17:26:35,403 - mmseg - INFO - Iter [33650/80000] lr: 1.875e-05, eta: 2:51:52, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7042, loss: 0.0832 +2023-03-03 17:26:48,040 - mmseg - INFO - Iter [33700/80000] lr: 1.875e-05, eta: 2:51:43, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7263, loss: 0.0822 +2023-03-03 17:26:58,281 - mmseg - INFO - Iter [33750/80000] lr: 1.875e-05, eta: 2:51:30, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.8090, loss: 0.0812 +2023-03-03 17:27:08,535 - mmseg - INFO - Iter [33800/80000] lr: 1.875e-05, eta: 2:51:18, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6548, loss: 0.0854 +2023-03-03 17:27:18,840 - mmseg - INFO - Iter [33850/80000] lr: 1.875e-05, eta: 2:51:06, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6602, loss: 0.0844 +2023-03-03 17:27:31,488 - mmseg - INFO - Iter [33900/80000] lr: 1.875e-05, eta: 2:50:57, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7648, loss: 0.0817 +2023-03-03 17:27:41,828 - mmseg - INFO - Iter [33950/80000] lr: 1.875e-05, eta: 2:50:45, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6282, loss: 0.0851 +2023-03-03 17:27:52,160 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:27:52,160 - mmseg - INFO - Iter [34000/80000] lr: 1.875e-05, eta: 2:50:32, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6217, loss: 0.0848 +2023-03-03 17:28:05,029 - mmseg - INFO - Iter [34050/80000] lr: 1.875e-05, eta: 2:50:24, time: 0.257, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.7070, loss: 0.0845 +2023-03-03 17:28:15,407 - mmseg - INFO - Iter [34100/80000] lr: 1.875e-05, eta: 2:50:12, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6810, loss: 0.0840 +2023-03-03 17:28:25,656 - mmseg - INFO - Iter [34150/80000] lr: 1.875e-05, eta: 2:49:59, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6398, loss: 0.0855 +2023-03-03 17:28:35,972 - mmseg - INFO - Iter [34200/80000] lr: 1.875e-05, eta: 2:49:47, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5655, loss: 0.0875 +2023-03-03 17:28:48,861 - mmseg - INFO - Iter [34250/80000] lr: 1.875e-05, eta: 2:49:38, time: 0.258, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7308, loss: 0.0815 +2023-03-03 17:28:59,273 - mmseg - INFO - Iter [34300/80000] lr: 1.875e-05, eta: 2:49:26, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6478, loss: 0.0857 +2023-03-03 17:29:09,599 - mmseg - INFO - Iter [34350/80000] lr: 1.875e-05, eta: 2:49:14, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6455, loss: 0.0833 +2023-03-03 17:29:19,866 - mmseg - INFO - Iter [34400/80000] lr: 1.875e-05, eta: 2:49:02, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5961, loss: 0.0866 +2023-03-03 17:29:32,510 - mmseg - INFO - Iter [34450/80000] lr: 1.875e-05, eta: 2:48:53, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6518, loss: 0.0845 +2023-03-03 17:29:42,755 - mmseg - INFO - Iter [34500/80000] lr: 1.875e-05, eta: 2:48:40, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7036, loss: 0.0828 +2023-03-03 17:29:53,162 - mmseg - INFO - Iter [34550/80000] lr: 1.875e-05, eta: 2:48:28, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5718, loss: 0.0867 +2023-03-03 17:30:05,717 - mmseg - INFO - Iter [34600/80000] lr: 1.875e-05, eta: 2:48:19, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5637, loss: 0.0876 +2023-03-03 17:30:15,980 - mmseg - INFO - Iter [34650/80000] lr: 1.875e-05, eta: 2:48:07, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.7313, loss: 0.0836 +2023-03-03 17:30:26,256 - mmseg - INFO - Iter [34700/80000] lr: 1.875e-05, eta: 2:47:55, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.5712, loss: 0.0864 +2023-03-03 17:30:36,519 - mmseg - INFO - Iter [34750/80000] lr: 1.875e-05, eta: 2:47:42, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6875, loss: 0.0837 +2023-03-03 17:30:49,265 - mmseg - INFO - Iter [34800/80000] lr: 1.875e-05, eta: 2:47:33, time: 0.255, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.7039, loss: 0.0842 +2023-03-03 17:30:59,446 - mmseg - INFO - Iter [34850/80000] lr: 1.875e-05, eta: 2:47:21, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6445, loss: 0.0857 +2023-03-03 17:31:09,998 - mmseg - INFO - Iter [34900/80000] lr: 1.875e-05, eta: 2:47:09, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6249, loss: 0.0855 +2023-03-03 17:31:20,275 - mmseg - INFO - Iter [34950/80000] lr: 1.875e-05, eta: 2:46:57, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7159, loss: 0.0826 +2023-03-03 17:31:32,925 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:31:32,925 - mmseg - INFO - Iter [35000/80000] lr: 1.875e-05, eta: 2:46:48, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7784, loss: 0.0816 +2023-03-03 17:31:43,454 - mmseg - INFO - Iter [35050/80000] lr: 1.875e-05, eta: 2:46:36, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.6281, loss: 0.0866 +2023-03-03 17:31:54,011 - mmseg - INFO - Iter [35100/80000] lr: 1.875e-05, eta: 2:46:24, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.6117, loss: 0.0875 +2023-03-03 17:32:04,247 - mmseg - INFO - Iter 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data_time: 0.055, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7129, loss: 0.0822 +2023-03-03 17:35:44,344 - mmseg - INFO - Iter [36150/80000] lr: 1.875e-05, eta: 2:42:27, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.6534, loss: 0.0875 +2023-03-03 17:35:54,592 - mmseg - INFO - Iter [36200/80000] lr: 1.875e-05, eta: 2:42:15, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6066, loss: 0.0852 +2023-03-03 17:36:04,760 - mmseg - INFO - Iter [36250/80000] lr: 1.875e-05, eta: 2:42:02, time: 0.203, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6561, loss: 0.0847 +2023-03-03 17:36:17,375 - mmseg - INFO - Iter [36300/80000] lr: 1.875e-05, eta: 2:41:53, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6054, loss: 0.0864 +2023-03-03 17:36:27,751 - mmseg - INFO - Iter [36350/80000] lr: 1.875e-05, eta: 2:41:41, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7054, loss: 0.0834 +2023-03-03 17:36:37,991 - mmseg - INFO - Iter [36400/80000] lr: 1.875e-05, eta: 2:41:29, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5885, loss: 0.0870 +2023-03-03 17:36:48,261 - mmseg - INFO - Iter [36450/80000] lr: 1.875e-05, eta: 2:41:17, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0810, decode.acc_seg: 96.7709, loss: 0.0810 +2023-03-03 17:37:00,751 - mmseg - INFO - Iter [36500/80000] lr: 1.875e-05, eta: 2:41:07, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7025, loss: 0.0832 +2023-03-03 17:37:11,023 - mmseg - INFO - Iter [36550/80000] lr: 1.875e-05, eta: 2:40:55, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6650, loss: 0.0839 +2023-03-03 17:37:21,318 - mmseg - INFO - Iter [36600/80000] lr: 1.875e-05, eta: 2:40:43, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6587, loss: 0.0841 +2023-03-03 17:37:34,073 - mmseg - INFO - Iter [36650/80000] lr: 1.875e-05, eta: 2:40:34, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.5735, loss: 0.0892 +2023-03-03 17:37:44,359 - mmseg - INFO - Iter [36700/80000] lr: 1.875e-05, eta: 2:40:22, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6860, loss: 0.0835 +2023-03-03 17:37:54,548 - mmseg - INFO - Iter [36750/80000] lr: 1.875e-05, eta: 2:40:10, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6869, loss: 0.0836 +2023-03-03 17:38:04,782 - mmseg - INFO - Iter [36800/80000] lr: 1.875e-05, eta: 2:39:57, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6235, loss: 0.0858 +2023-03-03 17:38:17,350 - mmseg - INFO - Iter [36850/80000] lr: 1.875e-05, eta: 2:39:48, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6761, loss: 0.0843 +2023-03-03 17:38:27,656 - mmseg - INFO - Iter [36900/80000] lr: 1.875e-05, eta: 2:39:36, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6764, loss: 0.0837 +2023-03-03 17:38:37,931 - mmseg - INFO - Iter [36950/80000] lr: 1.875e-05, eta: 2:39:24, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7535, loss: 0.0821 +2023-03-03 17:38:48,237 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:38:48,237 - mmseg - INFO - Iter [37000/80000] lr: 1.875e-05, eta: 2:39:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6805, loss: 0.0845 +2023-03-03 17:39:00,807 - mmseg - INFO - Iter [37050/80000] lr: 1.875e-05, eta: 2:39:02, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0922, decode.acc_seg: 96.5315, loss: 0.0922 +2023-03-03 17:39:11,100 - mmseg - INFO - Iter [37100/80000] lr: 1.875e-05, eta: 2:38:50, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6421, loss: 0.0851 +2023-03-03 17:39:21,362 - mmseg - INFO - Iter [37150/80000] lr: 1.875e-05, eta: 2:38:38, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.5580, loss: 0.0864 +2023-03-03 17:39:31,696 - mmseg - INFO - Iter [37200/80000] lr: 1.875e-05, eta: 2:38:26, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5823, loss: 0.0868 +2023-03-03 17:39:44,362 - mmseg - INFO - Iter [37250/80000] lr: 1.875e-05, eta: 2:38:17, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6550, loss: 0.0841 +2023-03-03 17:39:54,871 - mmseg - INFO - Iter [37300/80000] lr: 1.875e-05, eta: 2:38:05, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6789, loss: 0.0839 +2023-03-03 17:40:05,181 - mmseg - INFO - Iter [37350/80000] lr: 1.875e-05, eta: 2:37:53, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7330, loss: 0.0826 +2023-03-03 17:40:17,735 - mmseg - INFO - Iter [37400/80000] lr: 1.875e-05, eta: 2:37:44, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6169, loss: 0.0840 +2023-03-03 17:40:28,179 - mmseg - INFO - Iter [37450/80000] lr: 1.875e-05, eta: 2:37:32, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6717, loss: 0.0865 +2023-03-03 17:40:38,408 - mmseg - INFO - Iter [37500/80000] lr: 1.875e-05, eta: 2:37:20, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7567, loss: 0.0813 +2023-03-03 17:40:48,715 - mmseg - INFO - Iter [37550/80000] lr: 1.875e-05, eta: 2:37:08, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0894, decode.acc_seg: 96.5036, loss: 0.0894 +2023-03-03 17:41:01,217 - mmseg - INFO - Iter [37600/80000] lr: 1.875e-05, eta: 2:36:58, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5776, loss: 0.0866 +2023-03-03 17:41:11,529 - mmseg - INFO - Iter [37650/80000] lr: 1.875e-05, eta: 2:36:46, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6953, loss: 0.0835 +2023-03-03 17:41:21,791 - mmseg - INFO - Iter [37700/80000] lr: 1.875e-05, eta: 2:36:34, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0806, decode.acc_seg: 96.8106, loss: 0.0806 +2023-03-03 17:41:32,352 - mmseg - INFO - Iter [37750/80000] lr: 1.875e-05, eta: 2:36:22, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6602, loss: 0.0849 +2023-03-03 17:41:44,852 - mmseg - INFO - Iter [37800/80000] lr: 1.875e-05, eta: 2:36:13, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7692, loss: 0.0816 +2023-03-03 17:41:55,156 - mmseg - INFO - Iter [37850/80000] lr: 1.875e-05, eta: 2:36:01, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5918, loss: 0.0870 +2023-03-03 17:42:05,574 - mmseg - INFO - Iter [37900/80000] lr: 1.875e-05, eta: 2:35:49, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.5919, loss: 0.0853 +2023-03-03 17:42:18,112 - mmseg - INFO - Iter [37950/80000] lr: 1.875e-05, eta: 2:35:40, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7720, loss: 0.0809 +2023-03-03 17:42:28,303 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:42:28,303 - mmseg - INFO - Iter [38000/80000] lr: 1.875e-05, eta: 2:35:27, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6550, loss: 0.0847 +2023-03-03 17:42:38,685 - mmseg - INFO - Iter [38050/80000] lr: 1.875e-05, eta: 2:35:16, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6376, loss: 0.0860 +2023-03-03 17:42:49,102 - mmseg - INFO - Iter [38100/80000] lr: 1.875e-05, eta: 2:35:04, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7445, loss: 0.0830 +2023-03-03 17:43:01,802 - mmseg - INFO - Iter [38150/80000] lr: 1.875e-05, eta: 2:34:54, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6417, loss: 0.0836 +2023-03-03 17:43:12,016 - mmseg - INFO - Iter [38200/80000] lr: 1.875e-05, eta: 2:34:42, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7981, loss: 0.0812 +2023-03-03 17:43:22,258 - mmseg - INFO - Iter [38250/80000] lr: 1.875e-05, eta: 2:34:30, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5766, loss: 0.0867 +2023-03-03 17:43:32,529 - mmseg - INFO - Iter [38300/80000] lr: 1.875e-05, eta: 2:34:18, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6019, loss: 0.0861 +2023-03-03 17:43:45,075 - mmseg - INFO - Iter [38350/80000] lr: 1.875e-05, eta: 2:34:09, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5640, loss: 0.0880 +2023-03-03 17:43:55,305 - mmseg - INFO - Iter [38400/80000] lr: 1.875e-05, eta: 2:33:57, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.6011, loss: 0.0866 +2023-03-03 17:44:05,736 - mmseg - INFO - Iter [38450/80000] lr: 1.875e-05, eta: 2:33:45, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.6868, loss: 0.0824 +2023-03-03 17:44:15,977 - mmseg - INFO - Iter [38500/80000] lr: 1.875e-05, eta: 2:33:33, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0879, decode.acc_seg: 96.5138, loss: 0.0879 +2023-03-03 17:44:28,552 - mmseg - INFO - Iter [38550/80000] lr: 1.875e-05, eta: 2:33:23, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7455, loss: 0.0818 +2023-03-03 17:44:39,337 - mmseg - INFO - Iter [38600/80000] lr: 1.875e-05, eta: 2:33:12, time: 0.216, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6129, loss: 0.0848 +2023-03-03 17:44:49,518 - mmseg - INFO - Iter [38650/80000] lr: 1.875e-05, eta: 2:33:00, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6196, loss: 0.0849 +2023-03-03 17:45:02,114 - mmseg - INFO - Iter [38700/80000] lr: 1.875e-05, eta: 2:32:50, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0873, decode.acc_seg: 96.5520, loss: 0.0873 +2023-03-03 17:45:12,334 - mmseg - INFO - Iter [38750/80000] lr: 1.875e-05, eta: 2:32:38, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.5868, loss: 0.0856 +2023-03-03 17:45:22,586 - mmseg - INFO - Iter [38800/80000] lr: 1.875e-05, eta: 2:32:26, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7305, loss: 0.0829 +2023-03-03 17:45:32,960 - mmseg - INFO - Iter [38850/80000] lr: 1.875e-05, eta: 2:32:14, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6002, loss: 0.0860 +2023-03-03 17:45:45,656 - mmseg - INFO - Iter [38900/80000] lr: 1.875e-05, eta: 2:32:05, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6857, loss: 0.0839 +2023-03-03 17:45:55,981 - mmseg - INFO - Iter [38950/80000] lr: 1.875e-05, eta: 2:31:53, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6848, loss: 0.0841 +2023-03-03 17:46:06,190 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:46:06,191 - mmseg - INFO - Iter [39000/80000] lr: 1.875e-05, eta: 2:31:41, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5617, loss: 0.0862 +2023-03-03 17:46:16,418 - mmseg - INFO - Iter [39050/80000] lr: 1.875e-05, eta: 2:31:29, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6008, loss: 0.0868 +2023-03-03 17:46:28,949 - mmseg - INFO - Iter [39100/80000] lr: 1.875e-05, eta: 2:31:19, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7094, loss: 0.0832 +2023-03-03 17:46:39,307 - mmseg - INFO - Iter [39150/80000] lr: 1.875e-05, eta: 2:31:07, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7478, loss: 0.0831 +2023-03-03 17:46:49,638 - mmseg - INFO - Iter [39200/80000] lr: 1.875e-05, eta: 2:30:56, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7329, loss: 0.0816 +2023-03-03 17:47:02,275 - mmseg - INFO - Iter [39250/80000] lr: 1.875e-05, eta: 2:30:46, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7990, loss: 0.0809 +2023-03-03 17:47:12,548 - mmseg - INFO - Iter [39300/80000] lr: 1.875e-05, eta: 2:30:34, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6595, loss: 0.0844 +2023-03-03 17:47:22,902 - mmseg - INFO - Iter [39350/80000] lr: 1.875e-05, eta: 2:30:22, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6179, loss: 0.0854 +2023-03-03 17:47:33,232 - mmseg - INFO - Iter [39400/80000] lr: 1.875e-05, eta: 2:30:10, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6045, loss: 0.0856 +2023-03-03 17:47:45,861 - mmseg - INFO - Iter [39450/80000] lr: 1.875e-05, eta: 2:30:01, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5786, loss: 0.0865 +2023-03-03 17:47:56,136 - mmseg - INFO - Iter [39500/80000] lr: 1.875e-05, eta: 2:29:49, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5340, loss: 0.0868 +2023-03-03 17:48:06,395 - mmseg - INFO - Iter [39550/80000] lr: 1.875e-05, eta: 2:29:37, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6906, loss: 0.0832 +2023-03-03 17:48:16,911 - mmseg - INFO - Iter [39600/80000] lr: 1.875e-05, eta: 2:29:25, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6605, loss: 0.0844 +2023-03-03 17:48:29,539 - mmseg - INFO - Iter [39650/80000] lr: 1.875e-05, eta: 2:29:16, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6784, loss: 0.0842 +2023-03-03 17:48:39,744 - mmseg - INFO - Iter [39700/80000] lr: 1.875e-05, eta: 2:29:04, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6769, loss: 0.0845 +2023-03-03 17:48:50,081 - mmseg - INFO - Iter [39750/80000] lr: 1.875e-05, eta: 2:28:52, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6034, loss: 0.0855 +2023-03-03 17:49:00,337 - mmseg - INFO - Iter [39800/80000] lr: 1.875e-05, eta: 2:28:40, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7594, loss: 0.0813 +2023-03-03 17:49:12,994 - mmseg - INFO - Iter [39850/80000] lr: 1.875e-05, eta: 2:28:30, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6678, loss: 0.0855 +2023-03-03 17:49:23,218 - mmseg - INFO - Iter [39900/80000] lr: 1.875e-05, eta: 2:28:18, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6782, loss: 0.0842 +2023-03-03 17:49:33,493 - mmseg - INFO - Iter [39950/80000] lr: 1.875e-05, eta: 2:28:06, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.6301, loss: 0.0860 +2023-03-03 17:49:46,003 - mmseg - INFO - Saving checkpoint at 40000 iterations +2023-03-03 17:49:46,922 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:49:46,922 - mmseg - INFO - Iter [40000/80000] lr: 1.875e-05, eta: 2:27:58, time: 0.269, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7673, loss: 0.0820 +2023-03-03 17:50:07,238 - mmseg - INFO - per class results: +2023-03-03 17:50:07,239 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.49 | 99.2 | +| sidewalk | 87.23 | 93.3 | +| building | 93.25 | 96.5 | +| wall | 53.53 | 58.89 | +| fence | 63.79 | 74.58 | +| pole | 70.35 | 83.03 | +| traffic light | 74.78 | 85.61 | +| traffic sign | 82.89 | 89.99 | +| vegetation | 92.87 | 97.06 | +| terrain | 64.58 | 73.76 | +| sky | 95.18 | 98.27 | +| person | 84.45 | 92.83 | +| rider | 66.41 | 78.85 | +| car | 95.65 | 98.28 | +| truck | 78.67 | 83.98 | +| bus | 91.9 | 95.12 | +| train | 86.01 | 92.01 | +| motorcycle | 68.92 | 80.67 | +| bicycle | 79.52 | 90.38 | ++---------------+-------+-------+ +2023-03-03 17:50:07,239 - mmseg - INFO - Summary: +2023-03-03 17:50:07,239 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.52 | 80.45 | 87.49 | ++-------+-------+-------+ +2023-03-03 17:50:07,240 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:50:07,240 - mmseg - INFO - Iter(val) [63] aAcc: 0.9652, mIoU: 0.8045, mAcc: 0.8749, IoU.background: nan, IoU.road: 0.9849, IoU.sidewalk: 0.8723, IoU.building: 0.9325, IoU.wall: 0.5353, IoU.fence: 0.6379, IoU.pole: 0.7035, IoU.traffic light: 0.7478, IoU.traffic sign: 0.8289, IoU.vegetation: 0.9287, IoU.terrain: 0.6458, IoU.sky: 0.9518, IoU.person: 0.8445, IoU.rider: 0.6641, IoU.car: 0.9565, IoU.truck: 0.7867, IoU.bus: 0.9190, IoU.train: 0.8601, IoU.motorcycle: 0.6892, IoU.bicycle: 0.7952, Acc.background: nan, Acc.road: 0.9920, Acc.sidewalk: 0.9330, Acc.building: 0.9650, Acc.wall: 0.5889, Acc.fence: 0.7458, Acc.pole: 0.8303, Acc.traffic light: 0.8561, Acc.traffic sign: 0.8999, Acc.vegetation: 0.9706, Acc.terrain: 0.7376, Acc.sky: 0.9827, Acc.person: 0.9283, Acc.rider: 0.7885, Acc.car: 0.9828, Acc.truck: 0.8398, Acc.bus: 0.9512, Acc.train: 0.9201, Acc.motorcycle: 0.8067, Acc.bicycle: 0.9038 +2023-03-03 17:50:17,698 - mmseg - INFO - Iter [40050/80000] lr: 9.375e-06, eta: 2:28:06, time: 0.615, data_time: 0.415, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6969, loss: 0.0836 +2023-03-03 17:50:28,153 - mmseg - INFO - Iter [40100/80000] lr: 9.375e-06, eta: 2:27:54, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6750, loss: 0.0835 +2023-03-03 17:50:38,619 - mmseg - INFO - Iter [40150/80000] lr: 9.375e-06, eta: 2:27:43, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6609, loss: 0.0839 +2023-03-03 17:50:51,363 - mmseg - INFO - Iter [40200/80000] lr: 9.375e-06, eta: 2:27:33, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6621, loss: 0.0847 +2023-03-03 17:51:01,729 - mmseg - INFO - Iter [40250/80000] lr: 9.375e-06, eta: 2:27:21, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7570, loss: 0.0819 +2023-03-03 17:51:12,076 - mmseg - INFO - Iter [40300/80000] lr: 9.375e-06, eta: 2:27:09, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6413, loss: 0.0845 +2023-03-03 17:51:22,345 - mmseg - INFO - Iter [40350/80000] lr: 9.375e-06, eta: 2:26:57, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.5804, loss: 0.0859 +2023-03-03 17:51:34,963 - mmseg - INFO - Iter [40400/80000] lr: 9.375e-06, eta: 2:26:48, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6631, loss: 0.0840 +2023-03-03 17:51:45,273 - mmseg - INFO - Iter [40450/80000] lr: 9.375e-06, eta: 2:26:36, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6754, loss: 0.0841 +2023-03-03 17:51:55,534 - mmseg - INFO - Iter [40500/80000] lr: 9.375e-06, eta: 2:26:24, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7586, loss: 0.0815 +2023-03-03 17:52:08,271 - mmseg - INFO - Iter [40550/80000] lr: 9.375e-06, eta: 2:26:14, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.5819, loss: 0.0868 +2023-03-03 17:52:18,662 - mmseg - INFO - Iter [40600/80000] lr: 9.375e-06, eta: 2:26:03, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6621, loss: 0.0849 +2023-03-03 17:52:28,940 - mmseg - INFO - Iter [40650/80000] lr: 9.375e-06, eta: 2:25:51, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7600, loss: 0.0822 +2023-03-03 17:52:39,337 - mmseg - INFO - Iter [40700/80000] lr: 9.375e-06, eta: 2:25:39, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5636, loss: 0.0876 +2023-03-03 17:52:51,974 - mmseg - INFO - Iter [40750/80000] lr: 9.375e-06, eta: 2:25:29, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6648, loss: 0.0837 +2023-03-03 17:53:02,434 - mmseg - INFO - Iter [40800/80000] lr: 9.375e-06, eta: 2:25:17, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6469, loss: 0.0865 +2023-03-03 17:53:12,794 - mmseg - INFO - Iter [40850/80000] lr: 9.375e-06, eta: 2:25:05, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7430, loss: 0.0827 +2023-03-03 17:53:23,165 - mmseg - INFO - Iter [40900/80000] lr: 9.375e-06, eta: 2:24:54, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6930, loss: 0.0837 +2023-03-03 17:53:35,863 - mmseg - INFO - Iter [40950/80000] lr: 9.375e-06, eta: 2:24:44, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5504, loss: 0.0870 +2023-03-03 17:53:46,191 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:53:46,191 - mmseg - INFO - Iter [41000/80000] lr: 9.375e-06, eta: 2:24:32, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7375, loss: 0.0827 +2023-03-03 17:53:56,734 - mmseg - INFO - Iter [41050/80000] lr: 9.375e-06, eta: 2:24:21, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6317, loss: 0.0847 +2023-03-03 17:54:07,163 - mmseg - INFO - Iter [41100/80000] lr: 9.375e-06, eta: 2:24:09, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7438, loss: 0.0829 +2023-03-03 17:54:19,779 - mmseg - INFO - Iter [41150/80000] lr: 9.375e-06, eta: 2:23:59, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6972, loss: 0.0840 +2023-03-03 17:54:30,223 - mmseg - INFO - Iter [41200/80000] lr: 9.375e-06, eta: 2:23:47, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.6952, loss: 0.0828 +2023-03-03 17:54:40,534 - mmseg - INFO - Iter [41250/80000] lr: 9.375e-06, eta: 2:23:35, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7205, loss: 0.0831 +2023-03-03 17:54:53,154 - mmseg - INFO - Iter [41300/80000] lr: 9.375e-06, eta: 2:23:26, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.8169, loss: 0.0808 +2023-03-03 17:55:03,450 - mmseg - INFO - Iter [41350/80000] lr: 9.375e-06, eta: 2:23:14, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7498, loss: 0.0815 +2023-03-03 17:55:13,923 - mmseg - INFO - Iter [41400/80000] lr: 9.375e-06, eta: 2:23:02, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7411, loss: 0.0811 +2023-03-03 17:55:24,413 - mmseg - INFO - Iter [41450/80000] lr: 9.375e-06, eta: 2:22:50, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6849, loss: 0.0840 +2023-03-03 17:55:37,116 - mmseg - INFO - Iter [41500/80000] lr: 9.375e-06, eta: 2:22:41, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6383, loss: 0.0843 +2023-03-03 17:55:47,470 - mmseg - INFO - Iter [41550/80000] lr: 9.375e-06, eta: 2:22:29, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6317, loss: 0.0845 +2023-03-03 17:55:57,810 - mmseg - INFO - Iter [41600/80000] lr: 9.375e-06, eta: 2:22:17, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7395, loss: 0.0822 +2023-03-03 17:56:08,237 - mmseg - INFO - Iter [41650/80000] lr: 9.375e-06, eta: 2:22:05, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7097, loss: 0.0823 +2023-03-03 17:56:21,051 - mmseg - INFO - Iter [41700/80000] lr: 9.375e-06, eta: 2:21:56, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0798, decode.acc_seg: 96.8462, loss: 0.0798 +2023-03-03 17:56:31,387 - mmseg - INFO - Iter [41750/80000] lr: 9.375e-06, eta: 2:21:44, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6368, loss: 0.0845 +2023-03-03 17:56:41,670 - mmseg - INFO - Iter [41800/80000] lr: 9.375e-06, eta: 2:21:32, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.7082, loss: 0.0844 +2023-03-03 17:56:51,944 - mmseg - INFO - Iter [41850/80000] lr: 9.375e-06, eta: 2:21:20, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6242, loss: 0.0849 +2023-03-03 17:57:04,506 - mmseg - INFO - Iter [41900/80000] lr: 9.375e-06, eta: 2:21:10, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6585, loss: 0.0842 +2023-03-03 17:57:14,887 - mmseg - INFO - Iter [41950/80000] lr: 9.375e-06, eta: 2:20:59, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.7265, loss: 0.0839 +2023-03-03 17:57:25,227 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 17:57:25,227 - mmseg - INFO - Iter [42000/80000] lr: 9.375e-06, eta: 2:20:47, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6895, loss: 0.0839 +2023-03-03 17:57:37,907 - mmseg - INFO - Iter [42050/80000] lr: 9.375e-06, eta: 2:20:37, time: 0.254, data_time: 0.052, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.8179, loss: 0.0807 +2023-03-03 17:57:48,165 - mmseg - INFO - Iter [42100/80000] lr: 9.375e-06, eta: 2:20:25, time: 0.205, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5481, loss: 0.0865 +2023-03-03 17:57:58,396 - mmseg - INFO - Iter [42150/80000] lr: 9.375e-06, eta: 2:20:13, time: 0.205, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7203, loss: 0.0834 +2023-03-03 17:58:08,719 - mmseg - INFO - Iter [42200/80000] lr: 9.375e-06, eta: 2:20:01, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0806, decode.acc_seg: 96.7903, loss: 0.0806 +2023-03-03 17:58:21,378 - mmseg - INFO - Iter [42250/80000] lr: 9.375e-06, eta: 2:19:52, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7552, loss: 0.0820 +2023-03-03 17:58:31,858 - mmseg - INFO - Iter [42300/80000] lr: 9.375e-06, eta: 2:19:40, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7817, loss: 0.0817 +2023-03-03 17:58:42,238 - mmseg - INFO - Iter [42350/80000] lr: 9.375e-06, eta: 2:19:28, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7310, loss: 0.0820 +2023-03-03 17:58:52,491 - mmseg - INFO - Iter [42400/80000] lr: 9.375e-06, eta: 2:19:16, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6408, loss: 0.0852 +2023-03-03 17:59:05,274 - mmseg - INFO - Iter [42450/80000] lr: 9.375e-06, eta: 2:19:07, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7120, loss: 0.0838 +2023-03-03 17:59:15,696 - mmseg - INFO - Iter [42500/80000] lr: 9.375e-06, eta: 2:18:55, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5689, loss: 0.0867 +2023-03-03 17:59:26,138 - mmseg - INFO - Iter [42550/80000] lr: 9.375e-06, eta: 2:18:43, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8159, loss: 0.0801 +2023-03-03 17:59:38,715 - mmseg - INFO - Iter [42600/80000] lr: 9.375e-06, eta: 2:18:33, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7304, loss: 0.0825 +2023-03-03 17:59:49,008 - mmseg - INFO - Iter [42650/80000] lr: 9.375e-06, eta: 2:18:22, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7357, loss: 0.0825 +2023-03-03 17:59:59,365 - mmseg - INFO - Iter [42700/80000] lr: 9.375e-06, eta: 2:18:10, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6573, loss: 0.0849 +2023-03-03 18:00:09,675 - mmseg - INFO - Iter [42750/80000] lr: 9.375e-06, eta: 2:17:58, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7457, loss: 0.0824 +2023-03-03 18:00:22,220 - mmseg - INFO - Iter [42800/80000] lr: 9.375e-06, eta: 2:17:48, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5711, loss: 0.0869 +2023-03-03 18:00:32,485 - mmseg - INFO - Iter [42850/80000] lr: 9.375e-06, eta: 2:17:36, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6943, loss: 0.0831 +2023-03-03 18:00:42,707 - mmseg - INFO - Iter [42900/80000] lr: 9.375e-06, eta: 2:17:24, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7158, loss: 0.0829 +2023-03-03 18:00:52,966 - mmseg - INFO - Iter [42950/80000] lr: 9.375e-06, eta: 2:17:13, time: 0.205, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6435, loss: 0.0851 +2023-03-03 18:01:05,474 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:01:05,474 - mmseg - INFO - Iter [43000/80000] lr: 9.375e-06, eta: 2:17:03, time: 0.250, data_time: 0.052, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7572, loss: 0.0813 +2023-03-03 18:01:15,714 - mmseg - INFO - Iter [43050/80000] lr: 9.375e-06, eta: 2:16:51, time: 0.205, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0873, decode.acc_seg: 96.5845, loss: 0.0873 +2023-03-03 18:01:25,972 - mmseg - INFO - Iter [43100/80000] lr: 9.375e-06, eta: 2:16:39, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7067, loss: 0.0833 +2023-03-03 18:01:36,197 - mmseg - INFO - Iter [43150/80000] lr: 9.375e-06, eta: 2:16:27, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6213, loss: 0.0850 +2023-03-03 18:01:48,762 - mmseg - INFO - Iter [43200/80000] lr: 9.375e-06, eta: 2:16:17, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6505, loss: 0.0844 +2023-03-03 18:01:59,218 - mmseg - INFO - Iter [43250/80000] lr: 9.375e-06, eta: 2:16:06, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6848, loss: 0.0851 +2023-03-03 18:02:09,497 - mmseg - INFO - Iter [43300/80000] lr: 9.375e-06, eta: 2:15:54, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6414, loss: 0.0859 +2023-03-03 18:02:22,058 - mmseg - INFO - Iter [43350/80000] lr: 9.375e-06, eta: 2:15:44, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.6034, loss: 0.0867 +2023-03-03 18:02:32,361 - mmseg - INFO - Iter [43400/80000] lr: 9.375e-06, eta: 2:15:32, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6796, loss: 0.0838 +2023-03-03 18:02:42,721 - mmseg - INFO - Iter [43450/80000] lr: 9.375e-06, eta: 2:15:20, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6936, loss: 0.0831 +2023-03-03 18:02:53,016 - mmseg - INFO - Iter [43500/80000] lr: 9.375e-06, eta: 2:15:09, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7310, loss: 0.0826 +2023-03-03 18:03:05,585 - mmseg - INFO - Iter [43550/80000] lr: 9.375e-06, eta: 2:14:59, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.6233, loss: 0.0865 +2023-03-03 18:03:16,017 - mmseg - INFO - Iter [43600/80000] lr: 9.375e-06, eta: 2:14:47, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7124, loss: 0.0829 +2023-03-03 18:03:26,429 - mmseg - INFO - Iter [43650/80000] lr: 9.375e-06, eta: 2:14:35, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6735, loss: 0.0851 +2023-03-03 18:03:36,757 - mmseg - INFO - Iter [43700/80000] lr: 9.375e-06, eta: 2:14:24, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7626, loss: 0.0824 +2023-03-03 18:03:49,259 - mmseg - INFO - Iter [43750/80000] lr: 9.375e-06, eta: 2:14:14, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6708, loss: 0.0842 +2023-03-03 18:03:59,489 - mmseg - INFO - Iter [43800/80000] lr: 9.375e-06, eta: 2:14:02, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5636, loss: 0.0863 +2023-03-03 18:04:09,753 - mmseg - INFO - Iter [43850/80000] lr: 9.375e-06, eta: 2:13:50, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6733, loss: 0.0838 +2023-03-03 18:04:22,207 - mmseg - INFO - Iter [43900/80000] lr: 9.375e-06, eta: 2:13:40, time: 0.249, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.5953, loss: 0.0858 +2023-03-03 18:04:32,534 - mmseg - INFO - Iter [43950/80000] lr: 9.375e-06, eta: 2:13:28, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7263, loss: 0.0823 +2023-03-03 18:04:42,785 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:04:42,785 - mmseg - INFO - Iter [44000/80000] lr: 9.375e-06, eta: 2:13:16, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.7149, loss: 0.0836 +2023-03-03 18:04:53,093 - mmseg - INFO - Iter 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[44550/80000] lr: 9.375e-06, eta: 2:11:13, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6594, loss: 0.0833 +2023-03-03 18:06:53,935 - mmseg - INFO - Iter [44600/80000] lr: 9.375e-06, eta: 2:11:01, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0814, decode.acc_seg: 96.7737, loss: 0.0814 +2023-03-03 18:07:06,516 - mmseg - INFO - Iter [44650/80000] lr: 9.375e-06, eta: 2:10:51, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6427, loss: 0.0836 +2023-03-03 18:07:16,716 - mmseg - INFO - Iter [44700/80000] lr: 9.375e-06, eta: 2:10:40, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6483, loss: 0.0841 +2023-03-03 18:07:27,014 - mmseg - INFO - Iter [44750/80000] lr: 9.375e-06, eta: 2:10:28, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6506, loss: 0.0859 +2023-03-03 18:07:37,267 - mmseg - INFO - Iter 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data_time: 0.009, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.5902, loss: 0.0885 +2023-03-03 18:08:33,186 - mmseg - INFO - Iter [45050/80000] lr: 9.375e-06, eta: 2:09:21, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6795, loss: 0.0836 +2023-03-03 18:08:43,494 - mmseg - INFO - Iter [45100/80000] lr: 9.375e-06, eta: 2:09:09, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5900, loss: 0.0865 +2023-03-03 18:08:53,750 - mmseg - INFO - Iter [45150/80000] lr: 9.375e-06, eta: 2:08:57, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0798, decode.acc_seg: 96.8510, loss: 0.0798 +2023-03-03 18:09:06,390 - mmseg - INFO - Iter [45200/80000] lr: 9.375e-06, eta: 2:08:48, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7099, loss: 0.0838 +2023-03-03 18:09:16,723 - mmseg - INFO - Iter [45250/80000] lr: 9.375e-06, eta: 2:08:36, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7171, loss: 0.0825 +2023-03-03 18:09:27,074 - mmseg - INFO - Iter [45300/80000] lr: 9.375e-06, eta: 2:08:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6476, loss: 0.0851 +2023-03-03 18:09:37,483 - mmseg - INFO - Iter [45350/80000] lr: 9.375e-06, eta: 2:08:13, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.6047, loss: 0.0866 +2023-03-03 18:09:50,125 - mmseg - INFO - Iter [45400/80000] lr: 9.375e-06, eta: 2:08:03, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6683, loss: 0.0841 +2023-03-03 18:10:00,432 - mmseg - INFO - Iter [45450/80000] lr: 9.375e-06, eta: 2:07:51, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.7017, loss: 0.0840 +2023-03-03 18:10:10,721 - mmseg - INFO - Iter [45500/80000] lr: 9.375e-06, eta: 2:07:39, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7685, loss: 0.0815 +2023-03-03 18:10:21,117 - mmseg - INFO - Iter [45550/80000] lr: 9.375e-06, eta: 2:07:28, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.5529, loss: 0.0862 +2023-03-03 18:10:33,884 - mmseg - INFO - Iter [45600/80000] lr: 9.375e-06, eta: 2:07:18, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7168, loss: 0.0833 +2023-03-03 18:10:44,303 - mmseg - INFO - Iter [45650/80000] lr: 9.375e-06, eta: 2:07:06, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7326, loss: 0.0819 +2023-03-03 18:10:54,625 - mmseg - INFO - Iter [45700/80000] lr: 9.375e-06, eta: 2:06:54, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6994, loss: 0.0831 +2023-03-03 18:11:04,858 - mmseg - INFO - Iter [45750/80000] lr: 9.375e-06, eta: 2:06:43, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6130, loss: 0.0853 +2023-03-03 18:11:17,409 - mmseg - INFO - Iter [45800/80000] lr: 9.375e-06, eta: 2:06:33, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6619, loss: 0.0853 +2023-03-03 18:11:27,722 - mmseg - INFO - Iter [45850/80000] lr: 9.375e-06, eta: 2:06:21, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7449, loss: 0.0822 +2023-03-03 18:11:37,952 - mmseg - INFO - Iter [45900/80000] lr: 9.375e-06, eta: 2:06:09, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6287, loss: 0.0847 +2023-03-03 18:11:50,514 - mmseg - INFO - Iter [45950/80000] lr: 9.375e-06, eta: 2:05:59, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7087, loss: 0.0832 +2023-03-03 18:12:00,758 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:12:00,758 - mmseg - INFO - Iter [46000/80000] lr: 9.375e-06, eta: 2:05:47, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6936, loss: 0.0832 +2023-03-03 18:12:11,009 - mmseg - INFO - Iter [46050/80000] lr: 9.375e-06, eta: 2:05:36, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7580, loss: 0.0813 +2023-03-03 18:12:21,309 - mmseg - INFO - Iter [46100/80000] lr: 9.375e-06, eta: 2:05:24, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7599, loss: 0.0826 +2023-03-03 18:12:33,894 - mmseg - INFO - Iter [46150/80000] lr: 9.375e-06, eta: 2:05:14, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7548, loss: 0.0824 +2023-03-03 18:12:44,219 - mmseg - INFO - Iter [46200/80000] lr: 9.375e-06, eta: 2:05:02, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6058, loss: 0.0848 +2023-03-03 18:12:54,456 - mmseg - INFO - Iter [46250/80000] lr: 9.375e-06, eta: 2:04:51, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6930, loss: 0.0835 +2023-03-03 18:13:04,737 - mmseg - INFO - Iter [46300/80000] lr: 9.375e-06, eta: 2:04:39, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.7181, loss: 0.0839 +2023-03-03 18:13:17,423 - mmseg - INFO - Iter [46350/80000] lr: 9.375e-06, eta: 2:04:29, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6782, loss: 0.0837 +2023-03-03 18:13:27,742 - mmseg - INFO - Iter [46400/80000] lr: 9.375e-06, eta: 2:04:17, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.6941, loss: 0.0826 +2023-03-03 18:13:38,008 - mmseg - INFO - Iter [46450/80000] lr: 9.375e-06, eta: 2:04:06, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6342, loss: 0.0859 +2023-03-03 18:13:48,188 - mmseg - INFO - Iter [46500/80000] lr: 9.375e-06, eta: 2:03:54, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0793, decode.acc_seg: 96.8728, loss: 0.0793 +2023-03-03 18:14:00,826 - mmseg - INFO - Iter [46550/80000] lr: 9.375e-06, eta: 2:03:44, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7949, loss: 0.0813 +2023-03-03 18:14:11,194 - mmseg - INFO - Iter [46600/80000] lr: 9.375e-06, eta: 2:03:32, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5584, loss: 0.0863 +2023-03-03 18:14:21,421 - mmseg - INFO - Iter [46650/80000] lr: 9.375e-06, eta: 2:03:21, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7215, loss: 0.0832 +2023-03-03 18:14:34,008 - mmseg - INFO - Iter [46700/80000] lr: 9.375e-06, eta: 2:03:11, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6342, loss: 0.0850 +2023-03-03 18:14:44,391 - mmseg - INFO - Iter [46750/80000] lr: 9.375e-06, eta: 2:02:59, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6892, loss: 0.0832 +2023-03-03 18:14:54,716 - mmseg - INFO - Iter [46800/80000] lr: 9.375e-06, eta: 2:02:47, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7386, loss: 0.0817 +2023-03-03 18:15:05,052 - mmseg - INFO - Iter [46850/80000] lr: 9.375e-06, eta: 2:02:36, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6679, loss: 0.0840 +2023-03-03 18:15:17,674 - mmseg - INFO - Iter [46900/80000] lr: 9.375e-06, eta: 2:02:26, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0862, decode.acc_seg: 96.6005, loss: 0.0862 +2023-03-03 18:15:28,104 - mmseg - INFO - Iter [46950/80000] lr: 9.375e-06, eta: 2:02:14, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0798, decode.acc_seg: 96.8309, loss: 0.0798 +2023-03-03 18:15:38,368 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:15:38,369 - mmseg - INFO - Iter [47000/80000] lr: 9.375e-06, eta: 2:02:02, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7137, loss: 0.0832 +2023-03-03 18:15:48,651 - mmseg - INFO - Iter [47050/80000] lr: 9.375e-06, eta: 2:01:51, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7425, loss: 0.0830 +2023-03-03 18:16:01,309 - mmseg - INFO - Iter [47100/80000] lr: 9.375e-06, eta: 2:01:41, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6245, loss: 0.0855 +2023-03-03 18:16:11,575 - mmseg - INFO - Iter [47150/80000] lr: 9.375e-06, eta: 2:01:29, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7213, loss: 0.0830 +2023-03-03 18:16:21,928 - mmseg - INFO - Iter [47200/80000] lr: 9.375e-06, eta: 2:01:17, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7293, loss: 0.0831 +2023-03-03 18:16:34,585 - mmseg - INFO - Iter [47250/80000] lr: 9.375e-06, eta: 2:01:07, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7631, loss: 0.0817 +2023-03-03 18:16:44,884 - mmseg - INFO - Iter [47300/80000] lr: 9.375e-06, eta: 2:00:56, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7166, loss: 0.0834 +2023-03-03 18:16:55,220 - mmseg - INFO - Iter [47350/80000] lr: 9.375e-06, eta: 2:00:44, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5648, loss: 0.0871 +2023-03-03 18:17:05,494 - mmseg - INFO - Iter [47400/80000] lr: 9.375e-06, eta: 2:00:32, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6127, loss: 0.0845 +2023-03-03 18:17:18,137 - mmseg - INFO - Iter [47450/80000] lr: 9.375e-06, eta: 2:00:22, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7695, loss: 0.0818 +2023-03-03 18:17:28,389 - mmseg - INFO - Iter [47500/80000] lr: 9.375e-06, eta: 2:00:11, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0896, decode.acc_seg: 96.5779, loss: 0.0896 +2023-03-03 18:17:38,667 - mmseg - INFO - Iter [47550/80000] lr: 9.375e-06, eta: 1:59:59, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6102, loss: 0.0854 +2023-03-03 18:17:48,958 - mmseg - INFO - Iter [47600/80000] lr: 9.375e-06, eta: 1:59:47, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0799, decode.acc_seg: 96.8186, loss: 0.0799 +2023-03-03 18:18:01,598 - mmseg - INFO - Iter [47650/80000] lr: 9.375e-06, eta: 1:59:37, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7488, loss: 0.0824 +2023-03-03 18:18:11,865 - mmseg - INFO - Iter [47700/80000] lr: 9.375e-06, eta: 1:59:26, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6288, loss: 0.0853 +2023-03-03 18:18:22,099 - mmseg - INFO - Iter [47750/80000] lr: 9.375e-06, eta: 1:59:14, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.7109, loss: 0.0836 +2023-03-03 18:18:32,425 - mmseg - INFO - Iter [47800/80000] lr: 9.375e-06, eta: 1:59:03, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6991, loss: 0.0842 +2023-03-03 18:18:45,180 - mmseg - INFO - Iter [47850/80000] lr: 9.375e-06, eta: 1:58:53, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5813, loss: 0.0865 +2023-03-03 18:18:55,626 - mmseg - INFO - Iter [47900/80000] lr: 9.375e-06, eta: 1:58:41, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6424, loss: 0.0855 +2023-03-03 18:19:05,972 - mmseg - INFO - Iter [47950/80000] lr: 9.375e-06, eta: 1:58:29, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6735, loss: 0.0843 +2023-03-03 18:19:18,516 - mmseg - INFO - Saving checkpoint at 48000 iterations +2023-03-03 18:19:19,450 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:19:19,451 - mmseg - INFO - Iter [48000/80000] lr: 9.375e-06, eta: 1:58:20, time: 0.270, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7522, loss: 0.0821 +2023-03-03 18:19:39,666 - mmseg - INFO - per class results: +2023-03-03 18:19:39,667 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.51 | 99.27 | +| sidewalk | 87.42 | 92.95 | +| building | 93.22 | 96.7 | +| wall | 53.39 | 58.25 | +| fence | 63.35 | 72.4 | +| pole | 70.4 | 83.11 | +| traffic light | 74.93 | 86.62 | +| traffic sign | 83.03 | 89.72 | +| vegetation | 92.88 | 96.9 | +| terrain | 65.71 | 76.09 | +| sky | 95.14 | 98.42 | +| person | 84.59 | 92.66 | +| rider | 66.27 | 78.6 | +| car | 95.91 | 98.14 | +| truck | 84.13 | 89.43 | +| bus | 92.05 | 94.97 | +| train | 86.16 | 91.52 | +| motorcycle | 68.95 | 80.74 | +| bicycle | 79.55 | 90.75 | ++---------------+-------+-------+ +2023-03-03 18:19:39,667 - mmseg - INFO - Summary: +2023-03-03 18:19:39,667 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.55 | 80.82 | 87.75 | ++-------+-------+-------+ +2023-03-03 18:19:39,696 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_32000.pth was removed +2023-03-03 18:19:40,480 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_48000.pth. +2023-03-03 18:19:40,481 - mmseg - INFO - Best mIoU is 0.8082 at 48000 iter. +2023-03-03 18:19:40,481 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:19:40,481 - mmseg - INFO - Iter(val) [63] aAcc: 0.9655, mIoU: 0.8082, mAcc: 0.8775, IoU.background: nan, IoU.road: 0.9851, IoU.sidewalk: 0.8742, IoU.building: 0.9322, IoU.wall: 0.5339, IoU.fence: 0.6335, IoU.pole: 0.7040, IoU.traffic light: 0.7493, IoU.traffic sign: 0.8303, IoU.vegetation: 0.9288, IoU.terrain: 0.6571, IoU.sky: 0.9514, IoU.person: 0.8459, IoU.rider: 0.6627, IoU.car: 0.9591, IoU.truck: 0.8413, IoU.bus: 0.9205, IoU.train: 0.8616, IoU.motorcycle: 0.6895, IoU.bicycle: 0.7955, Acc.background: nan, Acc.road: 0.9927, Acc.sidewalk: 0.9295, Acc.building: 0.9670, Acc.wall: 0.5825, Acc.fence: 0.7240, Acc.pole: 0.8311, Acc.traffic light: 0.8662, Acc.traffic sign: 0.8972, Acc.vegetation: 0.9690, Acc.terrain: 0.7609, Acc.sky: 0.9842, Acc.person: 0.9266, Acc.rider: 0.7860, Acc.car: 0.9814, Acc.truck: 0.8943, Acc.bus: 0.9497, Acc.train: 0.9152, Acc.motorcycle: 0.8074, Acc.bicycle: 0.9075 +2023-03-03 18:19:50,905 - mmseg - INFO - Iter [48050/80000] lr: 9.375e-06, eta: 1:58:22, time: 0.629, data_time: 0.429, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7457, loss: 0.0819 +2023-03-03 18:20:01,611 - mmseg - INFO - Iter [48100/80000] lr: 9.375e-06, eta: 1:58:11, time: 0.214, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6794, loss: 0.0833 +2023-03-03 18:20:12,018 - mmseg - INFO - Iter [48150/80000] lr: 9.375e-06, eta: 1:57:59, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6806, loss: 0.0841 +2023-03-03 18:20:24,680 - mmseg - INFO - Iter [48200/80000] lr: 9.375e-06, eta: 1:57:49, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6366, loss: 0.0853 +2023-03-03 18:20:35,014 - mmseg - INFO - Iter [48250/80000] lr: 9.375e-06, eta: 1:57:38, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6938, loss: 0.0833 +2023-03-03 18:20:45,352 - mmseg - INFO - Iter [48300/80000] lr: 9.375e-06, eta: 1:57:26, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6746, loss: 0.0846 +2023-03-03 18:20:55,638 - mmseg - INFO - Iter [48350/80000] lr: 9.375e-06, eta: 1:57:14, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6780, loss: 0.0839 +2023-03-03 18:21:08,473 - mmseg - INFO - Iter [48400/80000] lr: 9.375e-06, eta: 1:57:04, time: 0.257, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6769, loss: 0.0841 +2023-03-03 18:21:18,958 - mmseg - INFO - Iter [48450/80000] lr: 9.375e-06, eta: 1:56:53, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.6151, loss: 0.0866 +2023-03-03 18:21:29,425 - mmseg - INFO - Iter [48500/80000] lr: 9.375e-06, eta: 1:56:41, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7952, loss: 0.0811 +2023-03-03 18:21:41,930 - mmseg - INFO - Iter [48550/80000] lr: 9.375e-06, eta: 1:56:31, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6763, loss: 0.0847 +2023-03-03 18:21:52,280 - mmseg - INFO - Iter [48600/80000] lr: 9.375e-06, eta: 1:56:20, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6838, loss: 0.0837 +2023-03-03 18:22:02,685 - mmseg - INFO - Iter [48650/80000] lr: 9.375e-06, eta: 1:56:08, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0882, decode.acc_seg: 96.5391, loss: 0.0882 +2023-03-03 18:22:13,068 - mmseg - INFO - Iter [48700/80000] lr: 9.375e-06, eta: 1:55:56, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6599, loss: 0.0868 +2023-03-03 18:22:25,756 - mmseg - INFO - Iter [48750/80000] lr: 9.375e-06, eta: 1:55:46, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7242, loss: 0.0828 +2023-03-03 18:22:36,261 - mmseg - INFO - Iter [48800/80000] lr: 9.375e-06, eta: 1:55:35, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6345, loss: 0.0843 +2023-03-03 18:22:46,673 - mmseg - INFO - Iter [48850/80000] lr: 9.375e-06, eta: 1:55:23, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6824, loss: 0.0838 +2023-03-03 18:22:56,989 - mmseg - INFO - Iter [48900/80000] lr: 9.375e-06, eta: 1:55:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.7053, loss: 0.0835 +2023-03-03 18:23:09,582 - mmseg - INFO - Iter [48950/80000] lr: 9.375e-06, eta: 1:55:01, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7352, loss: 0.0823 +2023-03-03 18:23:19,952 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:23:19,953 - mmseg - INFO - Iter [49000/80000] lr: 9.375e-06, eta: 1:54:50, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7854, loss: 0.0812 +2023-03-03 18:23:30,260 - mmseg - INFO - Iter [49050/80000] lr: 9.375e-06, eta: 1:54:38, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7235, loss: 0.0832 +2023-03-03 18:23:40,566 - mmseg - INFO - Iter [49100/80000] lr: 9.375e-06, eta: 1:54:27, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5587, loss: 0.0863 +2023-03-03 18:23:53,280 - mmseg - INFO - Iter [49150/80000] lr: 9.375e-06, eta: 1:54:16, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6971, loss: 0.0834 +2023-03-03 18:24:03,637 - mmseg - INFO - Iter [49200/80000] lr: 9.375e-06, eta: 1:54:05, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7000, loss: 0.0838 +2023-03-03 18:24:13,980 - mmseg - INFO - Iter [49250/80000] lr: 9.375e-06, eta: 1:53:53, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7505, loss: 0.0827 +2023-03-03 18:24:26,624 - mmseg - INFO - Iter [49300/80000] lr: 9.375e-06, eta: 1:53:43, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0890, decode.acc_seg: 96.5066, loss: 0.0890 +2023-03-03 18:24:36,978 - mmseg - INFO - Iter [49350/80000] lr: 9.375e-06, eta: 1:53:32, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.7229, loss: 0.0842 +2023-03-03 18:24:47,277 - mmseg - INFO - Iter [49400/80000] lr: 9.375e-06, eta: 1:53:20, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6644, loss: 0.0844 +2023-03-03 18:24:57,861 - mmseg - INFO - Iter [49450/80000] lr: 9.375e-06, eta: 1:53:08, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.5848, loss: 0.0859 +2023-03-03 18:25:10,620 - mmseg - INFO - Iter [49500/80000] lr: 9.375e-06, eta: 1:52:58, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6852, loss: 0.0833 +2023-03-03 18:25:20,907 - mmseg - INFO - Iter [49550/80000] lr: 9.375e-06, eta: 1:52:47, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6419, loss: 0.0848 +2023-03-03 18:25:31,306 - mmseg - INFO - Iter [49600/80000] lr: 9.375e-06, eta: 1:52:35, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6128, loss: 0.0854 +2023-03-03 18:25:41,676 - mmseg - INFO - Iter [49650/80000] lr: 9.375e-06, eta: 1:52:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7632, loss: 0.0820 +2023-03-03 18:25:54,377 - mmseg - INFO - Iter [49700/80000] lr: 9.375e-06, eta: 1:52:14, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0794, decode.acc_seg: 96.8537, loss: 0.0794 +2023-03-03 18:26:04,641 - mmseg - INFO - Iter [49750/80000] lr: 9.375e-06, eta: 1:52:02, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.5832, loss: 0.0855 +2023-03-03 18:26:15,023 - mmseg - INFO - Iter [49800/80000] lr: 9.375e-06, eta: 1:51:50, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6737, loss: 0.0846 +2023-03-03 18:26:27,660 - mmseg - INFO - Iter [49850/80000] lr: 9.375e-06, eta: 1:51:40, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6569, loss: 0.0841 +2023-03-03 18:26:38,076 - mmseg - INFO - Iter [49900/80000] lr: 9.375e-06, eta: 1:51:29, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7675, loss: 0.0816 +2023-03-03 18:26:48,545 - mmseg - INFO - Iter [49950/80000] lr: 9.375e-06, eta: 1:51:17, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.5931, loss: 0.0851 +2023-03-03 18:26:58,751 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:26:58,752 - mmseg - INFO - Iter [50000/80000] lr: 9.375e-06, eta: 1:51:05, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6646, loss: 0.0831 +2023-03-03 18:27:11,253 - mmseg - INFO - Iter [50050/80000] lr: 4.687e-06, eta: 1:50:55, time: 0.250, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7866, loss: 0.0812 +2023-03-03 18:27:21,586 - mmseg - INFO - Iter [50100/80000] lr: 4.687e-06, eta: 1:50:44, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6045, loss: 0.0859 +2023-03-03 18:27:31,982 - mmseg - INFO - Iter [50150/80000] lr: 4.687e-06, eta: 1:50:32, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8199, loss: 0.0801 +2023-03-03 18:27:42,361 - mmseg - INFO - Iter [50200/80000] lr: 4.687e-06, eta: 1:50:21, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6970, loss: 0.0843 +2023-03-03 18:27:54,974 - mmseg - INFO - Iter [50250/80000] lr: 4.687e-06, eta: 1:50:10, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7115, loss: 0.0828 +2023-03-03 18:28:05,314 - mmseg - INFO - Iter [50300/80000] lr: 4.687e-06, eta: 1:49:59, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0802, decode.acc_seg: 96.8382, loss: 0.0802 +2023-03-03 18:28:15,593 - mmseg - INFO - Iter [50350/80000] lr: 4.687e-06, eta: 1:49:47, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0805, decode.acc_seg: 96.8589, loss: 0.0805 +2023-03-03 18:28:25,895 - mmseg - INFO - Iter [50400/80000] lr: 4.687e-06, eta: 1:49:36, time: 0.206, data_time: 0.010, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6964, loss: 0.0840 +2023-03-03 18:28:38,428 - mmseg - INFO - Iter [50450/80000] lr: 4.687e-06, eta: 1:49:25, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6958, loss: 0.0836 +2023-03-03 18:28:48,640 - mmseg - INFO - Iter [50500/80000] lr: 4.687e-06, eta: 1:49:14, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7320, loss: 0.0830 +2023-03-03 18:28:58,921 - mmseg - INFO - Iter [50550/80000] lr: 4.687e-06, eta: 1:49:02, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6456, loss: 0.0840 +2023-03-03 18:29:11,592 - mmseg - INFO - Iter [50600/80000] lr: 4.687e-06, eta: 1:48:52, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6255, loss: 0.0861 +2023-03-03 18:29:21,839 - mmseg - INFO - Iter [50650/80000] lr: 4.687e-06, eta: 1:48:40, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6998, loss: 0.0833 +2023-03-03 18:29:32,099 - mmseg - INFO - Iter [50700/80000] lr: 4.687e-06, eta: 1:48:29, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6859, loss: 0.0841 +2023-03-03 18:29:42,350 - mmseg - INFO - Iter [50750/80000] lr: 4.687e-06, eta: 1:48:17, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6174, loss: 0.0851 +2023-03-03 18:29:54,929 - mmseg - INFO - Iter [50800/80000] lr: 4.687e-06, eta: 1:48:07, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6488, loss: 0.0857 +2023-03-03 18:30:05,211 - mmseg - INFO - Iter [50850/80000] lr: 4.687e-06, eta: 1:47:55, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6715, loss: 0.0837 +2023-03-03 18:30:15,510 - mmseg - INFO - Iter [50900/80000] lr: 4.687e-06, eta: 1:47:44, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6584, loss: 0.0841 +2023-03-03 18:30:25,715 - mmseg - INFO - Iter [50950/80000] lr: 4.687e-06, eta: 1:47:32, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7387, loss: 0.0823 +2023-03-03 18:30:38,301 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:30:38,302 - mmseg - INFO - Iter [51000/80000] lr: 4.687e-06, eta: 1:47:22, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6596, loss: 0.0841 +2023-03-03 18:30:48,586 - mmseg - INFO - Iter [51050/80000] lr: 4.687e-06, eta: 1:47:10, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0814, decode.acc_seg: 96.7869, loss: 0.0814 +2023-03-03 18:30:58,941 - mmseg - INFO - Iter [51100/80000] lr: 4.687e-06, eta: 1:46:59, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7128, loss: 0.0826 +2023-03-03 18:31:09,208 - mmseg - INFO - Iter 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data_time: 0.054, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.7953, loss: 0.0807 +2023-03-03 18:34:49,390 - mmseg - INFO - Iter [52150/80000] lr: 4.687e-06, eta: 1:43:04, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.6312, loss: 0.0877 +2023-03-03 18:34:59,663 - mmseg - INFO - Iter [52200/80000] lr: 4.687e-06, eta: 1:42:52, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6968, loss: 0.0837 +2023-03-03 18:35:10,216 - mmseg - INFO - Iter [52250/80000] lr: 4.687e-06, eta: 1:42:41, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5297, loss: 0.0883 +2023-03-03 18:35:22,892 - mmseg - INFO - Iter [52300/80000] lr: 4.687e-06, eta: 1:42:31, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6921, loss: 0.0843 +2023-03-03 18:35:33,347 - mmseg - INFO - Iter [52350/80000] lr: 4.687e-06, eta: 1:42:19, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7116, loss: 0.0828 +2023-03-03 18:35:43,705 - mmseg - INFO - Iter [52400/80000] lr: 4.687e-06, eta: 1:42:08, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6787, loss: 0.0843 +2023-03-03 18:35:53,979 - mmseg - INFO - Iter [52450/80000] lr: 4.687e-06, eta: 1:41:56, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.8070, loss: 0.0811 +2023-03-03 18:36:06,768 - mmseg - INFO - Iter [52500/80000] lr: 4.687e-06, eta: 1:41:46, time: 0.256, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6622, loss: 0.0844 +2023-03-03 18:36:17,013 - mmseg - INFO - Iter [52550/80000] lr: 4.687e-06, eta: 1:41:34, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0790, decode.acc_seg: 96.9022, loss: 0.0790 +2023-03-03 18:36:27,271 - mmseg - INFO - Iter [52600/80000] lr: 4.687e-06, eta: 1:41:23, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7147, loss: 0.0828 +2023-03-03 18:36:39,884 - mmseg - INFO - Iter [52650/80000] lr: 4.687e-06, eta: 1:41:13, time: 0.252, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.6981, loss: 0.0829 +2023-03-03 18:36:50,135 - mmseg - INFO - Iter [52700/80000] lr: 4.687e-06, eta: 1:41:01, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0796, decode.acc_seg: 96.8039, loss: 0.0796 +2023-03-03 18:37:00,483 - mmseg - INFO - Iter [52750/80000] lr: 4.687e-06, eta: 1:40:50, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6880, loss: 0.0837 +2023-03-03 18:37:10,668 - mmseg - INFO - Iter [52800/80000] lr: 4.687e-06, eta: 1:40:38, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5961, loss: 0.0863 +2023-03-03 18:37:23,193 - mmseg - INFO - Iter [52850/80000] lr: 4.687e-06, eta: 1:40:28, time: 0.250, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6395, loss: 0.0854 +2023-03-03 18:37:33,531 - mmseg - INFO - Iter [52900/80000] lr: 4.687e-06, eta: 1:40:16, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7460, loss: 0.0824 +2023-03-03 18:37:43,878 - mmseg - INFO - Iter [52950/80000] lr: 4.687e-06, eta: 1:40:05, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7098, loss: 0.0826 +2023-03-03 18:37:54,144 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:37:54,145 - mmseg - INFO - Iter [53000/80000] lr: 4.687e-06, eta: 1:39:53, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7510, loss: 0.0816 +2023-03-03 18:38:06,718 - mmseg - INFO - Iter [53050/80000] lr: 4.687e-06, eta: 1:39:43, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7404, loss: 0.0812 +2023-03-03 18:38:17,122 - mmseg - INFO - Iter [53100/80000] lr: 4.687e-06, eta: 1:39:31, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.8277, loss: 0.0803 +2023-03-03 18:38:27,423 - mmseg - INFO - Iter [53150/80000] lr: 4.687e-06, eta: 1:39:20, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.5861, loss: 0.0847 +2023-03-03 18:38:39,961 - mmseg - INFO - Iter [53200/80000] lr: 4.687e-06, eta: 1:39:09, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7502, loss: 0.0818 +2023-03-03 18:38:50,376 - mmseg - INFO - Iter [53250/80000] lr: 4.687e-06, eta: 1:38:58, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7740, loss: 0.0817 +2023-03-03 18:39:00,603 - mmseg - INFO - Iter [53300/80000] lr: 4.687e-06, eta: 1:38:46, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7961, loss: 0.0822 +2023-03-03 18:39:11,281 - mmseg - INFO - Iter [53350/80000] lr: 4.687e-06, eta: 1:38:35, time: 0.214, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.6710, loss: 0.0829 +2023-03-03 18:39:24,017 - mmseg - INFO - Iter [53400/80000] lr: 4.687e-06, eta: 1:38:25, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7474, loss: 0.0819 +2023-03-03 18:39:34,337 - mmseg - INFO - Iter [53450/80000] lr: 4.687e-06, eta: 1:38:13, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6544, loss: 0.0839 +2023-03-03 18:39:44,632 - mmseg - INFO - Iter [53500/80000] lr: 4.687e-06, eta: 1:38:02, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5799, loss: 0.0871 +2023-03-03 18:39:55,230 - mmseg - INFO - Iter [53550/80000] lr: 4.687e-06, eta: 1:37:51, time: 0.212, data_time: 0.010, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7286, loss: 0.0831 +2023-03-03 18:40:07,810 - mmseg - INFO - Iter [53600/80000] lr: 4.687e-06, eta: 1:37:40, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7851, loss: 0.0809 +2023-03-03 18:40:18,070 - mmseg - INFO - Iter [53650/80000] lr: 4.687e-06, eta: 1:37:29, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5826, loss: 0.0874 +2023-03-03 18:40:28,350 - mmseg - INFO - Iter [53700/80000] lr: 4.687e-06, eta: 1:37:17, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6698, loss: 0.0840 +2023-03-03 18:40:38,623 - mmseg - INFO - Iter [53750/80000] lr: 4.687e-06, eta: 1:37:06, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6365, loss: 0.0845 +2023-03-03 18:40:51,275 - mmseg - INFO - Iter [53800/80000] lr: 4.687e-06, eta: 1:36:55, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7280, loss: 0.0838 +2023-03-03 18:41:01,671 - mmseg - INFO - Iter [53850/80000] lr: 4.687e-06, eta: 1:36:44, time: 0.208, data_time: 0.010, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7102, loss: 0.0828 +2023-03-03 18:41:12,182 - mmseg - INFO - Iter [53900/80000] lr: 4.687e-06, eta: 1:36:32, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6454, loss: 0.0855 +2023-03-03 18:41:24,903 - mmseg - INFO - Iter [53950/80000] lr: 4.687e-06, eta: 1:36:22, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0865, decode.acc_seg: 96.5323, loss: 0.0865 +2023-03-03 18:41:35,268 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:41:35,268 - mmseg - INFO - Iter [54000/80000] lr: 4.687e-06, eta: 1:36:11, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6492, loss: 0.0856 +2023-03-03 18:41:45,472 - mmseg - INFO - Iter 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[54300/80000] lr: 4.687e-06, eta: 1:35:03, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0806, decode.acc_seg: 96.7897, loss: 0.0806 +2023-03-03 18:42:51,833 - mmseg - INFO - Iter [54350/80000] lr: 4.687e-06, eta: 1:34:53, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.6122, loss: 0.0861 +2023-03-03 18:43:01,990 - mmseg - INFO - Iter [54400/80000] lr: 4.687e-06, eta: 1:34:41, time: 0.203, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7078, loss: 0.0829 +2023-03-03 18:43:12,227 - mmseg - INFO - Iter [54450/80000] lr: 4.687e-06, eta: 1:34:29, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7283, loss: 0.0828 +2023-03-03 18:43:24,685 - mmseg - INFO - Iter [54500/80000] lr: 4.687e-06, eta: 1:34:19, time: 0.249, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7647, loss: 0.0813 +2023-03-03 18:43:34,904 - mmseg - INFO - Iter [54550/80000] lr: 4.687e-06, eta: 1:34:08, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7308, loss: 0.0822 +2023-03-03 18:43:45,194 - mmseg - INFO - Iter [54600/80000] lr: 4.687e-06, eta: 1:33:56, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0876, decode.acc_seg: 96.5599, loss: 0.0876 +2023-03-03 18:43:55,401 - mmseg - INFO - Iter [54650/80000] lr: 4.687e-06, eta: 1:33:45, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7474, loss: 0.0827 +2023-03-03 18:44:07,936 - mmseg - INFO - Iter [54700/80000] lr: 4.687e-06, eta: 1:33:34, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6750, loss: 0.0843 +2023-03-03 18:44:18,316 - mmseg - INFO - Iter [54750/80000] lr: 4.687e-06, eta: 1:33:23, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6502, loss: 0.0847 +2023-03-03 18:44:28,604 - mmseg - INFO - Iter [54800/80000] lr: 4.687e-06, eta: 1:33:11, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6606, loss: 0.0841 +2023-03-03 18:44:38,853 - mmseg - INFO - Iter [54850/80000] lr: 4.687e-06, eta: 1:33:00, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7070, loss: 0.0824 +2023-03-03 18:44:51,583 - mmseg - INFO - Iter [54900/80000] lr: 4.687e-06, eta: 1:32:49, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6883, loss: 0.0851 +2023-03-03 18:45:01,812 - mmseg - INFO - Iter [54950/80000] lr: 4.687e-06, eta: 1:32:38, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6576, loss: 0.0845 +2023-03-03 18:45:12,332 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:45:12,332 - mmseg - INFO - Iter [55000/80000] lr: 4.687e-06, eta: 1:32:27, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7039, loss: 0.0826 +2023-03-03 18:45:22,546 - mmseg - INFO - Iter [55050/80000] lr: 4.687e-06, eta: 1:32:15, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0877, decode.acc_seg: 96.5460, loss: 0.0877 +2023-03-03 18:45:35,000 - mmseg - INFO - Iter [55100/80000] lr: 4.687e-06, eta: 1:32:05, time: 0.249, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6073, loss: 0.0858 +2023-03-03 18:45:45,195 - mmseg - INFO - Iter [55150/80000] lr: 4.687e-06, eta: 1:31:53, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7097, loss: 0.0829 +2023-03-03 18:45:55,386 - mmseg - INFO - Iter [55200/80000] lr: 4.687e-06, eta: 1:31:42, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6641, loss: 0.0863 +2023-03-03 18:46:07,851 - mmseg - INFO - Iter [55250/80000] lr: 4.687e-06, eta: 1:31:31, time: 0.249, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7415, loss: 0.0820 +2023-03-03 18:46:18,079 - mmseg - INFO - Iter [55300/80000] lr: 4.687e-06, eta: 1:31:20, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0810, decode.acc_seg: 96.7959, loss: 0.0810 +2023-03-03 18:46:28,341 - mmseg - INFO - Iter [55350/80000] lr: 4.687e-06, eta: 1:31:08, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6078, loss: 0.0855 +2023-03-03 18:46:38,744 - mmseg - INFO - Iter [55400/80000] lr: 4.687e-06, eta: 1:30:57, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6671, loss: 0.0847 +2023-03-03 18:46:51,322 - mmseg - INFO - Iter [55450/80000] lr: 4.687e-06, eta: 1:30:46, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7017, loss: 0.0827 +2023-03-03 18:47:01,952 - mmseg - INFO - Iter [55500/80000] lr: 4.687e-06, eta: 1:30:35, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6742, loss: 0.0831 +2023-03-03 18:47:12,299 - mmseg - INFO - Iter [55550/80000] lr: 4.687e-06, eta: 1:30:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7243, loss: 0.0823 +2023-03-03 18:47:22,642 - mmseg - INFO - Iter [55600/80000] lr: 4.687e-06, eta: 1:30:12, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.6966, loss: 0.0827 +2023-03-03 18:47:35,255 - mmseg - INFO - Iter [55650/80000] lr: 4.687e-06, eta: 1:30:02, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6603, loss: 0.0850 +2023-03-03 18:47:45,644 - mmseg - INFO - Iter [55700/80000] lr: 4.687e-06, eta: 1:29:50, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7135, loss: 0.0833 +2023-03-03 18:47:55,934 - mmseg - INFO - Iter [55750/80000] lr: 4.687e-06, eta: 1:29:39, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7655, loss: 0.0820 +2023-03-03 18:48:06,211 - mmseg - INFO - Iter [55800/80000] lr: 4.687e-06, eta: 1:29:27, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6528, loss: 0.0848 +2023-03-03 18:48:18,806 - mmseg - INFO - Iter [55850/80000] lr: 4.687e-06, eta: 1:29:17, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.7964, loss: 0.0808 +2023-03-03 18:48:29,011 - mmseg - INFO - Iter [55900/80000] lr: 4.687e-06, eta: 1:29:06, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5786, loss: 0.0866 +2023-03-03 18:48:39,501 - mmseg - INFO - Iter [55950/80000] lr: 4.687e-06, eta: 1:28:54, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6569, loss: 0.0851 +2023-03-03 18:48:52,087 - mmseg - INFO - Saving checkpoint at 56000 iterations +2023-03-03 18:48:53,034 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:48:53,035 - mmseg - INFO - Iter [56000/80000] lr: 4.687e-06, eta: 1:28:44, time: 0.271, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7958, loss: 0.0811 +2023-03-03 18:49:13,332 - mmseg - INFO - per class results: +2023-03-03 18:49:13,333 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.55 | 99.27 | +| sidewalk | 87.58 | 93.23 | +| building | 93.26 | 96.71 | +| wall | 52.47 | 57.22 | +| fence | 63.4 | 73.51 | +| pole | 70.37 | 82.12 | +| traffic light | 74.84 | 85.17 | +| traffic sign | 82.94 | 89.5 | +| vegetation | 92.86 | 97.03 | +| terrain | 65.45 | 74.76 | +| sky | 95.17 | 98.42 | +| person | 84.49 | 92.8 | +| rider | 66.76 | 79.65 | +| car | 95.95 | 98.14 | +| truck | 85.0 | 90.51 | +| bus | 92.16 | 94.76 | +| train | 86.19 | 91.78 | +| motorcycle | 68.89 | 80.21 | +| bicycle | 79.49 | 90.53 | ++---------------+-------+-------+ +2023-03-03 18:49:13,333 - mmseg - INFO - Summary: +2023-03-03 18:49:13,334 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.56 | 80.83 | 87.65 | ++-------+-------+-------+ +2023-03-03 18:49:13,360 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_48000.pth was removed +2023-03-03 18:49:14,240 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_56000.pth. +2023-03-03 18:49:14,240 - mmseg - INFO - Best mIoU is 0.8083 at 56000 iter. +2023-03-03 18:49:14,240 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:49:14,240 - mmseg - INFO - Iter(val) [63] aAcc: 0.9656, mIoU: 0.8083, mAcc: 0.8765, IoU.background: nan, IoU.road: 0.9855, IoU.sidewalk: 0.8758, IoU.building: 0.9326, IoU.wall: 0.5247, IoU.fence: 0.6340, IoU.pole: 0.7037, IoU.traffic light: 0.7484, IoU.traffic sign: 0.8294, IoU.vegetation: 0.9286, IoU.terrain: 0.6545, IoU.sky: 0.9517, IoU.person: 0.8449, IoU.rider: 0.6676, IoU.car: 0.9595, IoU.truck: 0.8500, IoU.bus: 0.9216, IoU.train: 0.8619, IoU.motorcycle: 0.6889, IoU.bicycle: 0.7949, Acc.background: nan, Acc.road: 0.9927, Acc.sidewalk: 0.9323, Acc.building: 0.9671, Acc.wall: 0.5722, Acc.fence: 0.7351, Acc.pole: 0.8212, Acc.traffic light: 0.8517, Acc.traffic sign: 0.8950, Acc.vegetation: 0.9703, Acc.terrain: 0.7476, Acc.sky: 0.9842, Acc.person: 0.9280, Acc.rider: 0.7965, Acc.car: 0.9814, Acc.truck: 0.9051, Acc.bus: 0.9476, Acc.train: 0.9178, Acc.motorcycle: 0.8021, Acc.bicycle: 0.9053 +2023-03-03 18:49:24,633 - mmseg - INFO - Iter [56050/80000] lr: 4.687e-06, eta: 1:28:42, time: 0.632, data_time: 0.433, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6748, loss: 0.0840 +2023-03-03 18:49:35,177 - mmseg - INFO - Iter [56100/80000] lr: 4.687e-06, eta: 1:28:30, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7300, loss: 0.0831 +2023-03-03 18:49:45,463 - mmseg - INFO - Iter [56150/80000] lr: 4.687e-06, eta: 1:28:19, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.8051, loss: 0.0808 +2023-03-03 18:49:58,137 - mmseg - INFO - Iter [56200/80000] lr: 4.687e-06, eta: 1:28:09, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7361, loss: 0.0824 +2023-03-03 18:50:08,422 - mmseg - INFO - Iter [56250/80000] lr: 4.687e-06, eta: 1:27:57, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.8102, loss: 0.0807 +2023-03-03 18:50:18,712 - mmseg - INFO - Iter [56300/80000] lr: 4.687e-06, eta: 1:27:46, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.8031, loss: 0.0807 +2023-03-03 18:50:29,114 - mmseg - INFO - Iter [56350/80000] lr: 4.687e-06, eta: 1:27:34, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0797, decode.acc_seg: 96.8238, loss: 0.0797 +2023-03-03 18:50:41,707 - mmseg - INFO - Iter [56400/80000] lr: 4.687e-06, eta: 1:27:24, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7636, loss: 0.0815 +2023-03-03 18:50:52,004 - mmseg - INFO - Iter [56450/80000] lr: 4.687e-06, eta: 1:27:12, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0885, decode.acc_seg: 96.4945, loss: 0.0885 +2023-03-03 18:51:02,360 - mmseg - INFO - Iter [56500/80000] lr: 4.687e-06, eta: 1:27:01, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0798, decode.acc_seg: 96.8000, loss: 0.0798 +2023-03-03 18:51:14,994 - mmseg - INFO - Iter [56550/80000] lr: 4.687e-06, eta: 1:26:50, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7841, loss: 0.0811 +2023-03-03 18:51:25,365 - mmseg - INFO - Iter [56600/80000] lr: 4.687e-06, eta: 1:26:39, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6853, loss: 0.0832 +2023-03-03 18:51:35,664 - mmseg - INFO - Iter [56650/80000] lr: 4.687e-06, eta: 1:26:28, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7782, loss: 0.0821 +2023-03-03 18:51:46,006 - mmseg - INFO - Iter [56700/80000] lr: 4.687e-06, eta: 1:26:16, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6479, loss: 0.0845 +2023-03-03 18:51:58,818 - mmseg - INFO - Iter [56750/80000] lr: 4.687e-06, eta: 1:26:06, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7702, loss: 0.0812 +2023-03-03 18:52:09,305 - mmseg - INFO - Iter [56800/80000] lr: 4.687e-06, eta: 1:25:54, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6514, loss: 0.0852 +2023-03-03 18:52:19,543 - mmseg - INFO - Iter [56850/80000] lr: 4.687e-06, eta: 1:25:43, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6800, loss: 0.0839 +2023-03-03 18:52:29,895 - mmseg - INFO - Iter [56900/80000] lr: 4.687e-06, eta: 1:25:31, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0792, decode.acc_seg: 96.8441, loss: 0.0792 +2023-03-03 18:52:42,578 - mmseg - INFO - Iter [56950/80000] lr: 4.687e-06, eta: 1:25:21, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6590, loss: 0.0851 +2023-03-03 18:52:52,949 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 18:52:52,949 - mmseg - INFO - Iter [57000/80000] lr: 4.687e-06, eta: 1:25:10, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.7200, loss: 0.0839 +2023-03-03 18:53:03,261 - mmseg - INFO - Iter [57050/80000] lr: 4.687e-06, eta: 1:24:58, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6034, loss: 0.0849 +2023-03-03 18:53:13,663 - mmseg - INFO - Iter [57100/80000] lr: 4.687e-06, eta: 1:24:47, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6402, loss: 0.0855 +2023-03-03 18:53:26,282 - mmseg - INFO - Iter [57150/80000] lr: 4.687e-06, eta: 1:24:36, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7120, loss: 0.0837 +2023-03-03 18:53:36,638 - mmseg - INFO - Iter [57200/80000] lr: 4.687e-06, eta: 1:24:25, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7287, loss: 0.0825 +2023-03-03 18:53:46,865 - mmseg - INFO - Iter [57250/80000] lr: 4.687e-06, eta: 1:24:13, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5501, loss: 0.0866 +2023-03-03 18:53:59,550 - mmseg - INFO - Iter [57300/80000] lr: 4.687e-06, eta: 1:24:03, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6152, loss: 0.0847 +2023-03-03 18:54:09,955 - mmseg - INFO - Iter [57350/80000] lr: 4.687e-06, eta: 1:23:51, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7467, loss: 0.0823 +2023-03-03 18:54:20,421 - mmseg - INFO - Iter [57400/80000] lr: 4.687e-06, eta: 1:23:40, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7436, loss: 0.0824 +2023-03-03 18:54:30,761 - mmseg - INFO - Iter [57450/80000] lr: 4.687e-06, eta: 1:23:29, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0814, decode.acc_seg: 96.7899, loss: 0.0814 +2023-03-03 18:54:43,497 - mmseg - INFO - Iter [57500/80000] lr: 4.687e-06, eta: 1:23:18, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6925, loss: 0.0837 +2023-03-03 18:54:53,963 - mmseg - INFO - Iter [57550/80000] lr: 4.687e-06, eta: 1:23:07, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6331, loss: 0.0853 +2023-03-03 18:55:04,239 - mmseg - INFO - Iter [57600/80000] lr: 4.687e-06, eta: 1:22:55, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6781, loss: 0.0838 +2023-03-03 18:55:14,736 - mmseg - INFO - Iter [57650/80000] lr: 4.687e-06, eta: 1:22:44, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7644, loss: 0.0821 +2023-03-03 18:55:27,422 - mmseg - INFO - Iter [57700/80000] lr: 4.687e-06, eta: 1:22:34, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7475, loss: 0.0827 +2023-03-03 18:55:37,758 - mmseg - INFO - Iter [57750/80000] lr: 4.687e-06, eta: 1:22:22, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6851, loss: 0.0842 +2023-03-03 18:55:48,144 - mmseg - INFO - Iter 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data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.7081, loss: 0.0840 +2023-03-03 18:56:44,478 - mmseg - INFO - Iter [58050/80000] lr: 4.687e-06, eta: 1:21:16, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6220, loss: 0.0845 +2023-03-03 18:56:54,874 - mmseg - INFO - Iter [58100/80000] lr: 4.687e-06, eta: 1:21:04, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6748, loss: 0.0833 +2023-03-03 18:57:05,238 - mmseg - INFO - Iter [58150/80000] lr: 4.687e-06, eta: 1:20:53, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.7521, loss: 0.0807 +2023-03-03 18:57:15,663 - mmseg - INFO - Iter [58200/80000] lr: 4.687e-06, eta: 1:20:41, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.5809, loss: 0.0859 +2023-03-03 18:57:28,243 - mmseg - INFO - Iter [58250/80000] lr: 4.687e-06, eta: 1:20:31, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7819, loss: 0.0811 +2023-03-03 18:57:38,620 - mmseg - INFO - Iter [58300/80000] lr: 4.687e-06, eta: 1:20:20, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.6946, loss: 0.0829 +2023-03-03 18:57:48,960 - mmseg - INFO - Iter [58350/80000] lr: 4.687e-06, eta: 1:20:08, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.5565, loss: 0.0861 +2023-03-03 18:57:59,340 - mmseg - INFO - Iter [58400/80000] lr: 4.687e-06, eta: 1:19:57, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7042, loss: 0.0831 +2023-03-03 18:58:11,977 - mmseg - INFO - Iter [58450/80000] lr: 4.687e-06, eta: 1:19:46, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6580, loss: 0.0844 +2023-03-03 18:58:22,420 - mmseg - INFO - Iter [58500/80000] lr: 4.687e-06, eta: 1:19:35, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0875, decode.acc_seg: 96.5690, loss: 0.0875 +2023-03-03 18:58:32,795 - mmseg - INFO - Iter [58550/80000] lr: 4.687e-06, eta: 1:19:24, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6685, loss: 0.0836 +2023-03-03 18:58:45,407 - mmseg - INFO - Iter [58600/80000] lr: 4.687e-06, eta: 1:19:13, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7571, loss: 0.0821 +2023-03-03 18:58:55,759 - mmseg - INFO - Iter [58650/80000] lr: 4.687e-06, eta: 1:19:02, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6740, loss: 0.0836 +2023-03-03 18:59:06,077 - mmseg - INFO - Iter [58700/80000] lr: 4.687e-06, eta: 1:18:50, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7352, loss: 0.0822 +2023-03-03 18:59:16,407 - mmseg - INFO - Iter [58750/80000] lr: 4.687e-06, eta: 1:18:39, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0889, decode.acc_seg: 96.5198, loss: 0.0889 +2023-03-03 18:59:28,964 - mmseg - INFO - Iter [58800/80000] lr: 4.687e-06, eta: 1:18:28, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7566, loss: 0.0824 +2023-03-03 18:59:39,318 - mmseg - INFO - Iter [58850/80000] lr: 4.687e-06, eta: 1:18:17, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6806, loss: 0.0842 +2023-03-03 18:59:49,624 - mmseg - INFO - Iter [58900/80000] lr: 4.687e-06, eta: 1:18:05, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6853, loss: 0.0831 +2023-03-03 18:59:59,819 - mmseg - INFO - Iter [58950/80000] lr: 4.687e-06, eta: 1:17:54, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6656, loss: 0.0851 +2023-03-03 19:00:12,356 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:00:12,357 - mmseg - INFO - Iter [59000/80000] lr: 4.687e-06, eta: 1:17:43, time: 0.251, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7184, loss: 0.0821 +2023-03-03 19:00:22,904 - mmseg - INFO - Iter [59050/80000] lr: 4.687e-06, eta: 1:17:32, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6219, loss: 0.0854 +2023-03-03 19:00:33,254 - mmseg - INFO - Iter [59100/80000] lr: 4.687e-06, eta: 1:17:21, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6532, loss: 0.0844 +2023-03-03 19:00:46,078 - mmseg - INFO - Iter [59150/80000] lr: 4.687e-06, eta: 1:17:10, time: 0.257, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6500, loss: 0.0843 +2023-03-03 19:00:56,392 - mmseg - INFO - Iter [59200/80000] lr: 4.687e-06, eta: 1:16:59, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7270, loss: 0.0827 +2023-03-03 19:01:06,744 - mmseg - INFO - Iter [59250/80000] lr: 4.687e-06, eta: 1:16:48, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6743, loss: 0.0839 +2023-03-03 19:01:17,158 - mmseg - INFO - Iter [59300/80000] lr: 4.687e-06, eta: 1:16:36, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7492, loss: 0.0831 +2023-03-03 19:01:29,780 - mmseg - INFO - Iter [59350/80000] lr: 4.687e-06, eta: 1:16:26, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0871, decode.acc_seg: 96.5275, loss: 0.0871 +2023-03-03 19:01:40,076 - mmseg - INFO - Iter [59400/80000] lr: 4.687e-06, eta: 1:16:14, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6274, loss: 0.0851 +2023-03-03 19:01:50,403 - mmseg - INFO - Iter [59450/80000] lr: 4.687e-06, eta: 1:16:03, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6737, loss: 0.0844 +2023-03-03 19:02:00,669 - mmseg - INFO - Iter [59500/80000] lr: 4.687e-06, eta: 1:15:51, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7412, loss: 0.0820 +2023-03-03 19:02:13,352 - mmseg - INFO - Iter [59550/80000] lr: 4.687e-06, eta: 1:15:41, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7323, loss: 0.0812 +2023-03-03 19:02:23,731 - mmseg - INFO - Iter [59600/80000] lr: 4.687e-06, eta: 1:15:30, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6134, loss: 0.0858 +2023-03-03 19:02:34,155 - mmseg - INFO - Iter [59650/80000] lr: 4.687e-06, eta: 1:15:18, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7357, loss: 0.0822 +2023-03-03 19:02:44,715 - mmseg - INFO - Iter [59700/80000] lr: 4.687e-06, eta: 1:15:07, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7734, loss: 0.0822 +2023-03-03 19:02:57,400 - mmseg - INFO - Iter [59750/80000] lr: 4.687e-06, eta: 1:14:56, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7115, loss: 0.0828 +2023-03-03 19:03:07,638 - mmseg - INFO - Iter [59800/80000] lr: 4.687e-06, eta: 1:14:45, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6714, loss: 0.0844 +2023-03-03 19:03:18,127 - mmseg - INFO - Iter [59850/80000] lr: 4.687e-06, eta: 1:14:34, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.7012, loss: 0.0841 +2023-03-03 19:03:30,702 - mmseg - INFO - Iter [59900/80000] lr: 4.687e-06, eta: 1:14:23, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7407, loss: 0.0830 +2023-03-03 19:03:40,899 - mmseg - INFO - Iter [59950/80000] lr: 4.687e-06, eta: 1:14:12, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6768, loss: 0.0841 +2023-03-03 19:03:51,306 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:03:51,306 - mmseg - INFO - Iter [60000/80000] lr: 4.687e-06, eta: 1:14:00, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7389, loss: 0.0825 +2023-03-03 19:04:01,573 - mmseg - INFO - Iter [60050/80000] lr: 2.344e-06, eta: 1:13:49, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.5919, loss: 0.0856 +2023-03-03 19:04:14,216 - mmseg - INFO - Iter [60100/80000] lr: 2.344e-06, eta: 1:13:38, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5661, loss: 0.0863 +2023-03-03 19:04:24,483 - mmseg - INFO - Iter [60150/80000] lr: 2.344e-06, eta: 1:13:27, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6559, loss: 0.0845 +2023-03-03 19:04:34,742 - mmseg - INFO - Iter [60200/80000] lr: 2.344e-06, eta: 1:13:16, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6803, loss: 0.0843 +2023-03-03 19:04:45,029 - mmseg - INFO - Iter [60250/80000] lr: 2.344e-06, eta: 1:13:04, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7421, loss: 0.0825 +2023-03-03 19:04:57,624 - mmseg - INFO - Iter [60300/80000] lr: 2.344e-06, eta: 1:12:54, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7617, loss: 0.0828 +2023-03-03 19:05:07,819 - mmseg - INFO - Iter [60350/80000] lr: 2.344e-06, eta: 1:12:42, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7156, loss: 0.0825 +2023-03-03 19:05:18,152 - mmseg - INFO - Iter [60400/80000] lr: 2.344e-06, eta: 1:12:31, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6730, loss: 0.0846 +2023-03-03 19:05:28,415 - mmseg - INFO - Iter [60450/80000] lr: 2.344e-06, eta: 1:12:19, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7224, loss: 0.0824 +2023-03-03 19:05:41,067 - mmseg - INFO - Iter [60500/80000] lr: 2.344e-06, eta: 1:12:09, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7022, loss: 0.0838 +2023-03-03 19:05:51,277 - mmseg - INFO - Iter [60550/80000] lr: 2.344e-06, eta: 1:11:57, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.6525, loss: 0.0828 +2023-03-03 19:06:01,775 - mmseg - INFO - Iter [60600/80000] lr: 2.344e-06, eta: 1:11:46, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7172, loss: 0.0826 +2023-03-03 19:06:14,560 - mmseg - INFO - Iter [60650/80000] lr: 2.344e-06, eta: 1:11:36, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.6958, loss: 0.0827 +2023-03-03 19:06:24,894 - mmseg - INFO - Iter [60700/80000] lr: 2.344e-06, eta: 1:11:24, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6949, loss: 0.0842 +2023-03-03 19:06:35,126 - mmseg - INFO - Iter [60750/80000] lr: 2.344e-06, eta: 1:11:13, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7805, loss: 0.0815 +2023-03-03 19:06:45,490 - mmseg - INFO - Iter [60800/80000] lr: 2.344e-06, eta: 1:11:02, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6725, loss: 0.0837 +2023-03-03 19:06:58,079 - mmseg - INFO - Iter [60850/80000] lr: 2.344e-06, eta: 1:10:51, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7719, loss: 0.0812 +2023-03-03 19:07:08,400 - mmseg - INFO - Iter [60900/80000] lr: 2.344e-06, eta: 1:10:40, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6221, loss: 0.0854 +2023-03-03 19:07:18,678 - mmseg - INFO - Iter [60950/80000] lr: 2.344e-06, eta: 1:10:28, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6028, loss: 0.0855 +2023-03-03 19:07:29,082 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:07:29,082 - mmseg - INFO - Iter [61000/80000] lr: 2.344e-06, eta: 1:10:17, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6188, loss: 0.0868 +2023-03-03 19:07:41,641 - mmseg - INFO - Iter [61050/80000] lr: 2.344e-06, eta: 1:10:06, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.8315, loss: 0.0803 +2023-03-03 19:07:51,912 - mmseg - INFO - Iter [61100/80000] lr: 2.344e-06, eta: 1:09:55, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7271, loss: 0.0823 +2023-03-03 19:08:02,207 - mmseg - INFO - Iter [61150/80000] lr: 2.344e-06, eta: 1:09:44, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.6737, loss: 0.0830 +2023-03-03 19:08:14,857 - mmseg - INFO - Iter [61200/80000] lr: 2.344e-06, eta: 1:09:33, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0850, decode.acc_seg: 96.6276, loss: 0.0850 +2023-03-03 19:08:25,039 - mmseg - INFO - Iter [61250/80000] lr: 2.344e-06, eta: 1:09:22, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6966, loss: 0.0834 +2023-03-03 19:08:35,393 - mmseg - INFO - Iter [61300/80000] lr: 2.344e-06, eta: 1:09:10, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6861, loss: 0.0838 +2023-03-03 19:08:45,701 - mmseg - INFO - Iter [61350/80000] lr: 2.344e-06, eta: 1:08:59, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7166, loss: 0.0823 +2023-03-03 19:08:58,339 - mmseg - INFO - Iter [61400/80000] lr: 2.344e-06, eta: 1:08:48, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.5956, loss: 0.0859 +2023-03-03 19:09:08,683 - mmseg - INFO - Iter [61450/80000] lr: 2.344e-06, eta: 1:08:37, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6938, loss: 0.0838 +2023-03-03 19:09:18,974 - mmseg - INFO - Iter [61500/80000] lr: 2.344e-06, eta: 1:08:26, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.5934, loss: 0.0855 +2023-03-03 19:09:29,274 - mmseg - INFO - Iter [61550/80000] lr: 2.344e-06, eta: 1:08:14, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6921, loss: 0.0838 +2023-03-03 19:09:41,858 - mmseg - INFO - Iter [61600/80000] lr: 2.344e-06, eta: 1:08:04, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7986, loss: 0.0817 +2023-03-03 19:09:52,254 - mmseg - INFO - Iter [61650/80000] lr: 2.344e-06, eta: 1:07:52, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7460, loss: 0.0819 +2023-03-03 19:10:02,650 - mmseg - INFO - Iter [61700/80000] lr: 2.344e-06, eta: 1:07:41, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7289, loss: 0.0834 +2023-03-03 19:10:12,976 - mmseg - INFO - Iter [61750/80000] lr: 2.344e-06, eta: 1:07:30, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6404, loss: 0.0839 +2023-03-03 19:10:25,625 - mmseg - INFO - Iter [61800/80000] lr: 2.344e-06, eta: 1:07:19, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7278, loss: 0.0823 +2023-03-03 19:10:35,991 - mmseg - INFO - Iter [61850/80000] lr: 2.344e-06, eta: 1:07:08, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7085, loss: 0.0828 +2023-03-03 19:10:46,677 - mmseg - INFO - Iter [61900/80000] lr: 2.344e-06, eta: 1:06:57, time: 0.214, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7535, loss: 0.0817 +2023-03-03 19:10:59,405 - mmseg - INFO - Iter [61950/80000] lr: 2.344e-06, eta: 1:06:46, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7217, loss: 0.0824 +2023-03-03 19:11:09,721 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:11:09,722 - mmseg - INFO - Iter [62000/80000] lr: 2.344e-06, eta: 1:06:35, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6377, loss: 0.0844 +2023-03-03 19:11:20,076 - mmseg - INFO - Iter [62050/80000] lr: 2.344e-06, eta: 1:06:23, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6397, loss: 0.0852 +2023-03-03 19:11:30,488 - mmseg - INFO - Iter [62100/80000] lr: 2.344e-06, eta: 1:06:12, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.5730, loss: 0.0858 +2023-03-03 19:11:43,108 - mmseg - INFO - Iter [62150/80000] lr: 2.344e-06, eta: 1:06:01, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6131, loss: 0.0837 +2023-03-03 19:11:53,249 - mmseg - INFO - Iter [62200/80000] lr: 2.344e-06, eta: 1:05:50, time: 0.203, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7261, loss: 0.0833 +2023-03-03 19:12:03,655 - mmseg - INFO - Iter [62250/80000] lr: 2.344e-06, eta: 1:05:39, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7503, loss: 0.0823 +2023-03-03 19:12:13,976 - mmseg - INFO - Iter [62300/80000] lr: 2.344e-06, eta: 1:05:27, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.6788, loss: 0.0827 +2023-03-03 19:12:26,542 - mmseg - INFO - Iter [62350/80000] lr: 2.344e-06, eta: 1:05:17, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6407, loss: 0.0843 +2023-03-03 19:12:36,912 - mmseg - INFO - Iter [62400/80000] lr: 2.344e-06, eta: 1:05:05, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6647, loss: 0.0837 +2023-03-03 19:12:47,279 - mmseg - INFO - Iter [62450/80000] lr: 2.344e-06, eta: 1:04:54, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0805, decode.acc_seg: 96.7849, loss: 0.0805 +2023-03-03 19:12:59,813 - mmseg - INFO - Iter [62500/80000] lr: 2.344e-06, eta: 1:04:43, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6618, loss: 0.0836 +2023-03-03 19:13:10,129 - mmseg - INFO - Iter [62550/80000] lr: 2.344e-06, eta: 1:04:32, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7196, loss: 0.0831 +2023-03-03 19:13:20,476 - mmseg - INFO - Iter [62600/80000] lr: 2.344e-06, eta: 1:04:21, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.7914, loss: 0.0808 +2023-03-03 19:13:30,971 - mmseg - INFO - Iter [62650/80000] lr: 2.344e-06, eta: 1:04:09, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7643, loss: 0.0813 +2023-03-03 19:13:43,563 - mmseg - INFO - Iter [62700/80000] lr: 2.344e-06, eta: 1:03:59, time: 0.252, data_time: 0.052, memory: 33997, decode.loss_ce: 0.0802, decode.acc_seg: 96.8139, loss: 0.0802 +2023-03-03 19:13:53,822 - mmseg - INFO - Iter [62750/80000] lr: 2.344e-06, eta: 1:03:47, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5270, loss: 0.0866 +2023-03-03 19:14:04,125 - mmseg - INFO - Iter [62800/80000] lr: 2.344e-06, eta: 1:03:36, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6361, loss: 0.0863 +2023-03-03 19:14:14,437 - mmseg - INFO - Iter [62850/80000] lr: 2.344e-06, eta: 1:03:25, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6988, loss: 0.0831 +2023-03-03 19:14:27,027 - mmseg - INFO - Iter [62900/80000] lr: 2.344e-06, eta: 1:03:14, time: 0.252, data_time: 0.052, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7871, loss: 0.0818 +2023-03-03 19:14:37,369 - mmseg - INFO - Iter [62950/80000] lr: 2.344e-06, eta: 1:03:03, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0804, decode.acc_seg: 96.8282, loss: 0.0804 +2023-03-03 19:14:47,878 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:14:47,878 - mmseg - INFO - Iter [63000/80000] lr: 2.344e-06, eta: 1:02:52, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6833, loss: 0.0838 +2023-03-03 19:14:58,177 - mmseg - INFO - Iter [63050/80000] lr: 2.344e-06, eta: 1:02:40, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6727, loss: 0.0834 +2023-03-03 19:15:10,879 - mmseg - INFO - Iter [63100/80000] lr: 2.344e-06, eta: 1:02:30, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7033, loss: 0.0837 +2023-03-03 19:15:21,152 - mmseg - INFO - Iter [63150/80000] lr: 2.344e-06, eta: 1:02:18, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6301, loss: 0.0855 +2023-03-03 19:15:31,458 - mmseg - INFO - Iter [63200/80000] lr: 2.344e-06, eta: 1:02:07, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6241, loss: 0.0848 +2023-03-03 19:15:44,189 - mmseg - INFO - Iter [63250/80000] lr: 2.344e-06, eta: 1:01:56, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.7214, loss: 0.0839 +2023-03-03 19:15:54,535 - mmseg - INFO - Iter [63300/80000] lr: 2.344e-06, eta: 1:01:45, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5970, loss: 0.0863 +2023-03-03 19:16:04,762 - mmseg - INFO - Iter [63350/80000] lr: 2.344e-06, eta: 1:01:34, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0898, decode.acc_seg: 96.5656, loss: 0.0898 +2023-03-03 19:16:14,983 - mmseg - INFO - Iter [63400/80000] lr: 2.344e-06, eta: 1:01:22, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7440, loss: 0.0821 +2023-03-03 19:16:27,542 - mmseg - INFO - Iter [63450/80000] lr: 2.344e-06, eta: 1:01:12, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7295, loss: 0.0822 +2023-03-03 19:16:37,764 - mmseg - INFO - Iter [63500/80000] lr: 2.344e-06, eta: 1:01:00, time: 0.204, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0798, decode.acc_seg: 96.8021, loss: 0.0798 +2023-03-03 19:16:47,945 - mmseg - INFO - Iter [63550/80000] lr: 2.344e-06, eta: 1:00:49, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.6755, loss: 0.0828 +2023-03-03 19:16:58,144 - mmseg - INFO - Iter [63600/80000] lr: 2.344e-06, eta: 1:00:38, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5540, loss: 0.0869 +2023-03-03 19:17:10,802 - mmseg - INFO - Iter [63650/80000] lr: 2.344e-06, eta: 1:00:27, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7243, loss: 0.0829 +2023-03-03 19:17:21,160 - mmseg - INFO - Iter [63700/80000] lr: 2.344e-06, eta: 1:00:16, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7151, loss: 0.0832 +2023-03-03 19:17:31,687 - mmseg - INFO - Iter [63750/80000] lr: 2.344e-06, eta: 1:00:05, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6074, loss: 0.0859 +2023-03-03 19:17:44,251 - mmseg - INFO - Iter [63800/80000] lr: 2.344e-06, eta: 0:59:54, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7972, loss: 0.0812 +2023-03-03 19:17:54,480 - mmseg - INFO - Iter [63850/80000] lr: 2.344e-06, eta: 0:59:42, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6676, loss: 0.0835 +2023-03-03 19:18:04,761 - mmseg - INFO - Iter [63900/80000] lr: 2.344e-06, eta: 0:59:31, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6706, loss: 0.0835 +2023-03-03 19:18:15,146 - mmseg - INFO - Iter [63950/80000] lr: 2.344e-06, eta: 0:59:20, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6759, loss: 0.0843 +2023-03-03 19:18:27,841 - mmseg - INFO - Saving checkpoint at 64000 iterations +2023-03-03 19:18:28,821 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:18:28,821 - mmseg - INFO - Iter [64000/80000] lr: 2.344e-06, eta: 0:59:09, time: 0.273, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0911, decode.acc_seg: 96.4978, loss: 0.0911 +2023-03-03 19:18:48,985 - mmseg - INFO - per class results: +2023-03-03 19:18:48,986 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.53 | 99.22 | +| sidewalk | 87.52 | 93.39 | +| building | 93.27 | 96.76 | +| wall | 54.39 | 60.05 | +| fence | 63.41 | 72.66 | +| pole | 70.43 | 82.28 | +| traffic light | 74.98 | 85.94 | +| traffic sign | 83.05 | 89.86 | +| vegetation | 92.93 | 96.99 | +| terrain | 65.29 | 74.3 | +| sky | 95.22 | 98.34 | +| person | 84.54 | 92.92 | +| rider | 66.34 | 78.88 | +| car | 95.98 | 98.1 | +| truck | 85.23 | 91.32 | +| bus | 91.75 | 95.9 | +| train | 85.45 | 91.03 | +| motorcycle | 68.82 | 79.33 | +| bicycle | 79.55 | 90.39 | ++---------------+-------+-------+ +2023-03-03 19:18:48,986 - mmseg - INFO - Summary: +2023-03-03 19:18:48,986 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 96.57 | 80.88 | 87.77 | ++-------+-------+-------+ +2023-03-03 19:18:49,015 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_56000.pth was removed +2023-03-03 19:18:49,878 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_64000.pth. +2023-03-03 19:18:49,879 - mmseg - INFO - Best mIoU is 0.8088 at 64000 iter. +2023-03-03 19:18:49,879 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:18:49,879 - mmseg - INFO - Iter(val) [63] aAcc: 0.9657, mIoU: 0.8088, mAcc: 0.8777, IoU.background: nan, IoU.road: 0.9853, IoU.sidewalk: 0.8752, IoU.building: 0.9327, IoU.wall: 0.5439, IoU.fence: 0.6341, IoU.pole: 0.7043, IoU.traffic light: 0.7498, IoU.traffic sign: 0.8305, IoU.vegetation: 0.9293, IoU.terrain: 0.6529, IoU.sky: 0.9522, IoU.person: 0.8454, IoU.rider: 0.6634, IoU.car: 0.9598, IoU.truck: 0.8523, IoU.bus: 0.9175, IoU.train: 0.8545, IoU.motorcycle: 0.6882, IoU.bicycle: 0.7955, Acc.background: nan, Acc.road: 0.9922, Acc.sidewalk: 0.9339, Acc.building: 0.9676, Acc.wall: 0.6005, Acc.fence: 0.7266, Acc.pole: 0.8228, Acc.traffic light: 0.8594, Acc.traffic sign: 0.8986, Acc.vegetation: 0.9699, Acc.terrain: 0.7430, Acc.sky: 0.9834, Acc.person: 0.9292, Acc.rider: 0.7888, Acc.car: 0.9810, Acc.truck: 0.9132, Acc.bus: 0.9590, Acc.train: 0.9103, Acc.motorcycle: 0.7933, Acc.bicycle: 0.9039 +2023-03-03 19:19:00,232 - mmseg - INFO - Iter [64050/80000] lr: 2.344e-06, eta: 0:59:03, time: 0.628, data_time: 0.430, memory: 33997, decode.loss_ce: 0.0795, decode.acc_seg: 96.8404, loss: 0.0795 +2023-03-03 19:19:10,659 - mmseg - INFO - Iter [64100/80000] lr: 2.344e-06, eta: 0:58:52, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7578, loss: 0.0818 +2023-03-03 19:19:20,988 - mmseg - INFO - Iter [64150/80000] lr: 2.344e-06, eta: 0:58:41, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0806, decode.acc_seg: 96.8107, loss: 0.0806 +2023-03-03 19:19:33,695 - mmseg - INFO - Iter [64200/80000] lr: 2.344e-06, eta: 0:58:30, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5877, loss: 0.0866 +2023-03-03 19:19:44,039 - mmseg - INFO - Iter [64250/80000] lr: 2.344e-06, eta: 0:58:19, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6519, loss: 0.0834 +2023-03-03 19:19:54,335 - mmseg - INFO - Iter [64300/80000] lr: 2.344e-06, eta: 0:58:08, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7151, loss: 0.0830 +2023-03-03 19:20:04,639 - mmseg - INFO - Iter [64350/80000] lr: 2.344e-06, eta: 0:57:56, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6709, loss: 0.0843 +2023-03-03 19:20:17,266 - mmseg - INFO - Iter [64400/80000] lr: 2.344e-06, eta: 0:57:45, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0864, decode.acc_seg: 96.6133, loss: 0.0864 +2023-03-03 19:20:27,520 - mmseg - INFO - Iter [64450/80000] lr: 2.344e-06, eta: 0:57:34, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.6251, loss: 0.0858 +2023-03-03 19:20:37,913 - mmseg - INFO - Iter [64500/80000] lr: 2.344e-06, eta: 0:57:23, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6359, loss: 0.0843 +2023-03-03 19:20:50,690 - mmseg - INFO - Iter [64550/80000] lr: 2.344e-06, eta: 0:57:12, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7350, loss: 0.0822 +2023-03-03 19:21:00,897 - mmseg - INFO - Iter [64600/80000] lr: 2.344e-06, eta: 0:57:01, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.7892, loss: 0.0807 +2023-03-03 19:21:11,155 - mmseg - INFO - Iter [64650/80000] lr: 2.344e-06, eta: 0:56:50, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.5431, loss: 0.0886 +2023-03-03 19:21:21,433 - mmseg - INFO - Iter [64700/80000] lr: 2.344e-06, eta: 0:56:38, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6541, loss: 0.0847 +2023-03-03 19:21:33,961 - mmseg - INFO - Iter [64750/80000] lr: 2.344e-06, eta: 0:56:27, time: 0.250, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0794, decode.acc_seg: 96.8049, loss: 0.0794 +2023-03-03 19:21:44,493 - mmseg - INFO - Iter [64800/80000] lr: 2.344e-06, eta: 0:56:16, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.5878, loss: 0.0861 +2023-03-03 19:21:54,974 - mmseg - INFO - Iter [64850/80000] lr: 2.344e-06, eta: 0:56:05, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7131, loss: 0.0820 +2023-03-03 19:22:05,250 - mmseg - INFO - Iter [64900/80000] lr: 2.344e-06, eta: 0:55:54, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.5910, loss: 0.0858 +2023-03-03 19:22:17,901 - mmseg - INFO - Iter [64950/80000] lr: 2.344e-06, eta: 0:55:43, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7591, loss: 0.0812 +2023-03-03 19:22:28,208 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:22:28,208 - mmseg - INFO - Iter [65000/80000] lr: 2.344e-06, eta: 0:55:32, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6383, loss: 0.0854 +2023-03-03 19:22:38,443 - mmseg - INFO - Iter [65050/80000] lr: 2.344e-06, eta: 0:55:20, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6758, loss: 0.0844 +2023-03-03 19:22:48,706 - mmseg - INFO - Iter [65100/80000] lr: 2.344e-06, eta: 0:55:09, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6685, loss: 0.0834 +2023-03-03 19:23:01,273 - mmseg - INFO - Iter [65150/80000] lr: 2.344e-06, eta: 0:54:58, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7326, loss: 0.0824 +2023-03-03 19:23:11,703 - mmseg - INFO - Iter [65200/80000] lr: 2.344e-06, eta: 0:54:47, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6252, loss: 0.0852 +2023-03-03 19:23:22,154 - mmseg - INFO - Iter [65250/80000] lr: 2.344e-06, eta: 0:54:36, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7272, loss: 0.0828 +2023-03-03 19:23:34,960 - mmseg - INFO - Iter [65300/80000] lr: 2.344e-06, eta: 0:54:25, time: 0.256, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.6890, loss: 0.0830 +2023-03-03 19:23:45,192 - mmseg - INFO - Iter [65350/80000] lr: 2.344e-06, eta: 0:54:14, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7097, loss: 0.0828 +2023-03-03 19:23:55,513 - mmseg - INFO - Iter [65400/80000] lr: 2.344e-06, eta: 0:54:02, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6140, loss: 0.0854 +2023-03-03 19:24:05,906 - mmseg - INFO - Iter [65450/80000] lr: 2.344e-06, eta: 0:53:51, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.5784, loss: 0.0858 +2023-03-03 19:24:18,536 - mmseg - INFO - Iter [65500/80000] lr: 2.344e-06, eta: 0:53:40, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6237, loss: 0.0844 +2023-03-03 19:24:28,886 - mmseg - INFO - Iter [65550/80000] lr: 2.344e-06, eta: 0:53:29, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7041, loss: 0.0829 +2023-03-03 19:24:39,280 - mmseg - INFO - Iter [65600/80000] lr: 2.344e-06, eta: 0:53:18, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.8233, loss: 0.0803 +2023-03-03 19:24:49,716 - mmseg - INFO - Iter [65650/80000] lr: 2.344e-06, eta: 0:53:07, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0836, decode.acc_seg: 96.6702, loss: 0.0836 +2023-03-03 19:25:02,711 - mmseg - INFO - Iter [65700/80000] lr: 2.344e-06, eta: 0:52:56, time: 0.260, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6424, loss: 0.0851 +2023-03-03 19:25:13,189 - mmseg - INFO - Iter [65750/80000] lr: 2.344e-06, eta: 0:52:45, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7463, loss: 0.0819 +2023-03-03 19:25:23,473 - mmseg - INFO - Iter [65800/80000] lr: 2.344e-06, eta: 0:52:33, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7078, loss: 0.0838 +2023-03-03 19:25:36,142 - mmseg - INFO - Iter [65850/80000] lr: 2.344e-06, eta: 0:52:23, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7480, loss: 0.0823 +2023-03-03 19:25:46,414 - mmseg - INFO - Iter [65900/80000] lr: 2.344e-06, eta: 0:52:11, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7083, loss: 0.0826 +2023-03-03 19:25:56,842 - mmseg - INFO - Iter [65950/80000] lr: 2.344e-06, eta: 0:52:00, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6892, loss: 0.0835 +2023-03-03 19:26:07,263 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:26:07,263 - mmseg - INFO - Iter [66000/80000] lr: 2.344e-06, eta: 0:51:49, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6866, loss: 0.0834 +2023-03-03 19:26:19,969 - mmseg - INFO - Iter [66050/80000] lr: 2.344e-06, eta: 0:51:38, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7451, loss: 0.0824 +2023-03-03 19:26:30,465 - mmseg - INFO - Iter [66100/80000] lr: 2.344e-06, eta: 0:51:27, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0860, decode.acc_seg: 96.5979, loss: 0.0860 +2023-03-03 19:26:40,833 - mmseg - INFO - Iter [66150/80000] lr: 2.344e-06, eta: 0:51:16, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6550, loss: 0.0839 +2023-03-03 19:26:51,147 - mmseg - INFO - Iter [66200/80000] lr: 2.344e-06, eta: 0:51:04, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7219, loss: 0.0821 +2023-03-03 19:27:03,837 - mmseg - INFO - Iter [66250/80000] lr: 2.344e-06, eta: 0:50:54, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7090, loss: 0.0838 +2023-03-03 19:27:14,206 - mmseg - INFO - Iter [66300/80000] lr: 2.344e-06, eta: 0:50:42, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6539, loss: 0.0838 +2023-03-03 19:27:24,447 - mmseg - INFO - Iter [66350/80000] lr: 2.344e-06, eta: 0:50:31, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0869, decode.acc_seg: 96.5515, loss: 0.0869 +2023-03-03 19:27:34,784 - mmseg - INFO - Iter [66400/80000] lr: 2.344e-06, eta: 0:50:20, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6627, loss: 0.0837 +2023-03-03 19:27:47,395 - mmseg - INFO - Iter [66450/80000] lr: 2.344e-06, eta: 0:50:09, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7186, loss: 0.0832 +2023-03-03 19:27:57,738 - mmseg - INFO - Iter [66500/80000] lr: 2.344e-06, eta: 0:49:58, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6903, loss: 0.0838 +2023-03-03 19:28:08,105 - mmseg - INFO - Iter [66550/80000] lr: 2.344e-06, eta: 0:49:46, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7253, loss: 0.0829 +2023-03-03 19:28:20,745 - mmseg - INFO - Iter [66600/80000] lr: 2.344e-06, eta: 0:49:36, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7046, loss: 0.0826 +2023-03-03 19:28:31,073 - mmseg - INFO - Iter [66650/80000] lr: 2.344e-06, eta: 0:49:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6383, loss: 0.0841 +2023-03-03 19:28:41,389 - mmseg - INFO - Iter [66700/80000] lr: 2.344e-06, eta: 0:49:13, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7011, loss: 0.0831 +2023-03-03 19:28:51,683 - mmseg - INFO - Iter [66750/80000] lr: 2.344e-06, eta: 0:49:02, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7619, loss: 0.0825 +2023-03-03 19:29:04,278 - mmseg - INFO - Iter [66800/80000] lr: 2.344e-06, eta: 0:48:51, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6673, loss: 0.0840 +2023-03-03 19:29:14,728 - mmseg - INFO - Iter [66850/80000] lr: 2.344e-06, eta: 0:48:40, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8089, loss: 0.0801 +2023-03-03 19:29:25,152 - mmseg - INFO - Iter [66900/80000] lr: 2.344e-06, eta: 0:48:29, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0858, decode.acc_seg: 96.5738, loss: 0.0858 +2023-03-03 19:29:35,501 - mmseg - INFO - Iter [66950/80000] lr: 2.344e-06, eta: 0:48:17, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.7104, loss: 0.0837 +2023-03-03 19:29:48,256 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:29:48,256 - mmseg - INFO - Iter [67000/80000] lr: 2.344e-06, eta: 0:48:07, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7319, loss: 0.0818 +2023-03-03 19:29:58,749 - mmseg - INFO - Iter 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data_time: 0.009, memory: 33997, decode.loss_ce: 0.0894, decode.acc_seg: 96.6383, loss: 0.0894 +2023-03-03 19:33:37,727 - mmseg - INFO - Iter [68050/80000] lr: 2.344e-06, eta: 0:44:13, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6884, loss: 0.0832 +2023-03-03 19:33:50,395 - mmseg - INFO - Iter [68100/80000] lr: 2.344e-06, eta: 0:44:02, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7019, loss: 0.0831 +2023-03-03 19:34:00,986 - mmseg - INFO - Iter [68150/80000] lr: 2.344e-06, eta: 0:43:51, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7176, loss: 0.0829 +2023-03-03 19:34:11,251 - mmseg - INFO - Iter [68200/80000] lr: 2.344e-06, eta: 0:43:39, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6758, loss: 0.0834 +2023-03-03 19:34:21,599 - mmseg - INFO - Iter [68250/80000] lr: 2.344e-06, eta: 0:43:28, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6694, loss: 0.0840 +2023-03-03 19:34:34,400 - mmseg - INFO - Iter [68300/80000] lr: 2.344e-06, eta: 0:43:17, time: 0.256, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7377, loss: 0.0826 +2023-03-03 19:34:44,821 - mmseg - INFO - Iter [68350/80000] lr: 2.344e-06, eta: 0:43:06, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7762, loss: 0.0819 +2023-03-03 19:34:55,293 - mmseg - INFO - Iter [68400/80000] lr: 2.344e-06, eta: 0:42:55, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7423, loss: 0.0825 +2023-03-03 19:35:07,857 - mmseg - INFO - Iter [68450/80000] lr: 2.344e-06, eta: 0:42:44, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7875, loss: 0.0813 +2023-03-03 19:35:18,191 - mmseg - INFO - Iter [68500/80000] lr: 2.344e-06, eta: 0:42:33, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0796, decode.acc_seg: 96.8451, loss: 0.0796 +2023-03-03 19:35:28,575 - mmseg - INFO - Iter [68550/80000] lr: 2.344e-06, eta: 0:42:22, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7308, loss: 0.0829 +2023-03-03 19:35:38,950 - mmseg - INFO - Iter [68600/80000] lr: 2.344e-06, eta: 0:42:10, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6753, loss: 0.0840 +2023-03-03 19:35:51,538 - mmseg - INFO - Iter [68650/80000] lr: 2.344e-06, eta: 0:42:00, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6626, loss: 0.0844 +2023-03-03 19:36:01,784 - mmseg - INFO - Iter [68700/80000] lr: 2.344e-06, eta: 0:41:48, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6920, loss: 0.0843 +2023-03-03 19:36:12,214 - mmseg - INFO - Iter [68750/80000] lr: 2.344e-06, eta: 0:41:37, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7398, loss: 0.0817 +2023-03-03 19:36:22,571 - mmseg - INFO - Iter [68800/80000] lr: 2.344e-06, eta: 0:41:26, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6301, loss: 0.0839 +2023-03-03 19:36:35,260 - mmseg - INFO - Iter [68850/80000] lr: 2.344e-06, eta: 0:41:15, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7208, loss: 0.0826 +2023-03-03 19:36:45,678 - mmseg - INFO - Iter [68900/80000] lr: 2.344e-06, eta: 0:41:04, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7436, loss: 0.0824 +2023-03-03 19:36:55,946 - mmseg - INFO - Iter [68950/80000] lr: 2.344e-06, eta: 0:40:53, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7663, loss: 0.0822 +2023-03-03 19:37:06,413 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:37:06,413 - mmseg - INFO - Iter [69000/80000] lr: 2.344e-06, eta: 0:40:41, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7230, loss: 0.0821 +2023-03-03 19:37:19,175 - mmseg - INFO - Iter [69050/80000] lr: 2.344e-06, eta: 0:40:31, time: 0.255, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0892, decode.acc_seg: 96.4501, loss: 0.0892 +2023-03-03 19:37:29,542 - mmseg - INFO - Iter [69100/80000] lr: 2.344e-06, eta: 0:40:19, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6923, loss: 0.0832 +2023-03-03 19:37:39,933 - mmseg - INFO - Iter [69150/80000] lr: 2.344e-06, eta: 0:40:08, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.8010, loss: 0.0803 +2023-03-03 19:37:52,538 - mmseg - INFO - Iter [69200/80000] lr: 2.344e-06, eta: 0:39:57, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7104, loss: 0.0838 +2023-03-03 19:38:02,939 - mmseg - INFO - Iter [69250/80000] lr: 2.344e-06, eta: 0:39:46, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7391, loss: 0.0830 +2023-03-03 19:38:13,248 - mmseg - INFO - Iter [69300/80000] lr: 2.344e-06, eta: 0:39:35, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7522, loss: 0.0809 +2023-03-03 19:38:23,586 - mmseg - INFO - Iter [69350/80000] lr: 2.344e-06, eta: 0:39:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7036, loss: 0.0829 +2023-03-03 19:38:36,284 - mmseg - INFO - Iter [69400/80000] lr: 2.344e-06, eta: 0:39:13, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7127, loss: 0.0820 +2023-03-03 19:38:46,591 - mmseg - INFO - Iter [69450/80000] lr: 2.344e-06, eta: 0:39:02, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7300, loss: 0.0821 +2023-03-03 19:38:57,008 - mmseg - INFO - Iter [69500/80000] lr: 2.344e-06, eta: 0:38:50, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6771, loss: 0.0852 +2023-03-03 19:39:07,193 - mmseg - INFO - Iter [69550/80000] lr: 2.344e-06, eta: 0:38:39, time: 0.204, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7499, loss: 0.0825 +2023-03-03 19:39:19,740 - mmseg - INFO - Iter [69600/80000] lr: 2.344e-06, eta: 0:38:28, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.6436, loss: 0.0863 +2023-03-03 19:39:29,994 - mmseg - INFO - Iter [69650/80000] lr: 2.344e-06, eta: 0:38:17, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6697, loss: 0.0851 +2023-03-03 19:39:40,474 - mmseg - INFO - Iter [69700/80000] lr: 2.344e-06, eta: 0:38:06, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.7866, loss: 0.0808 +2023-03-03 19:39:50,737 - mmseg - INFO - Iter [69750/80000] lr: 2.344e-06, eta: 0:37:55, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7430, loss: 0.0820 +2023-03-03 19:40:03,310 - mmseg - INFO - Iter [69800/80000] lr: 2.344e-06, eta: 0:37:44, time: 0.251, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6895, loss: 0.0840 +2023-03-03 19:40:13,681 - mmseg - INFO - Iter [69850/80000] lr: 2.344e-06, eta: 0:37:33, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7123, loss: 0.0829 +2023-03-03 19:40:24,165 - mmseg - INFO - Iter [69900/80000] lr: 2.344e-06, eta: 0:37:21, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7517, loss: 0.0819 +2023-03-03 19:40:36,890 - mmseg - INFO - Iter [69950/80000] lr: 2.344e-06, eta: 0:37:10, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6271, loss: 0.0847 +2023-03-03 19:40:47,201 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:40:47,201 - mmseg - INFO - Iter [70000/80000] lr: 2.344e-06, eta: 0:36:59, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.7005, loss: 0.0842 +2023-03-03 19:40:57,523 - mmseg - INFO - Iter [70050/80000] lr: 1.172e-06, eta: 0:36:48, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7311, loss: 0.0829 +2023-03-03 19:41:07,915 - mmseg - INFO - Iter [70100/80000] lr: 1.172e-06, eta: 0:36:37, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0873, decode.acc_seg: 96.5672, loss: 0.0873 +2023-03-03 19:41:20,685 - mmseg - INFO - Iter [70150/80000] lr: 1.172e-06, eta: 0:36:26, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6619, loss: 0.0831 +2023-03-03 19:41:30,942 - mmseg - INFO - Iter [70200/80000] lr: 1.172e-06, eta: 0:36:15, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6590, loss: 0.0852 +2023-03-03 19:41:41,348 - mmseg - INFO - Iter [70250/80000] lr: 1.172e-06, eta: 0:36:04, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7378, loss: 0.0818 +2023-03-03 19:41:51,668 - mmseg - INFO - Iter [70300/80000] lr: 1.172e-06, eta: 0:35:52, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7371, loss: 0.0833 +2023-03-03 19:42:04,322 - mmseg - INFO - Iter [70350/80000] lr: 1.172e-06, eta: 0:35:41, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7721, loss: 0.0816 +2023-03-03 19:42:14,760 - mmseg - INFO - Iter [70400/80000] lr: 1.172e-06, eta: 0:35:30, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0800, decode.acc_seg: 96.8182, loss: 0.0800 +2023-03-03 19:42:25,099 - mmseg - INFO - Iter [70450/80000] lr: 1.172e-06, eta: 0:35:19, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7319, loss: 0.0825 +2023-03-03 19:42:37,664 - mmseg - INFO - Iter [70500/80000] lr: 1.172e-06, eta: 0:35:08, time: 0.251, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0798, decode.acc_seg: 96.7999, loss: 0.0798 +2023-03-03 19:42:48,093 - mmseg - INFO - Iter [70550/80000] lr: 1.172e-06, eta: 0:34:57, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6686, loss: 0.0835 +2023-03-03 19:42:58,465 - mmseg - INFO - Iter [70600/80000] lr: 1.172e-06, eta: 0:34:46, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7500, loss: 0.0811 +2023-03-03 19:43:08,827 - mmseg - INFO - Iter [70650/80000] lr: 1.172e-06, eta: 0:34:35, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6249, loss: 0.0856 +2023-03-03 19:43:21,497 - mmseg - INFO - Iter [70700/80000] lr: 1.172e-06, eta: 0:34:24, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6643, loss: 0.0845 +2023-03-03 19:43:31,987 - mmseg - INFO - Iter [70750/80000] lr: 1.172e-06, eta: 0:34:13, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6393, loss: 0.0848 +2023-03-03 19:43:42,340 - mmseg - INFO - Iter [70800/80000] lr: 1.172e-06, eta: 0:34:01, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7751, loss: 0.0819 +2023-03-03 19:43:52,812 - mmseg - INFO - Iter [70850/80000] lr: 1.172e-06, eta: 0:33:50, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.7904, loss: 0.0807 +2023-03-03 19:44:05,463 - mmseg - INFO - Iter [70900/80000] lr: 1.172e-06, eta: 0:33:39, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6351, loss: 0.0851 +2023-03-03 19:44:15,833 - mmseg - INFO - Iter [70950/80000] lr: 1.172e-06, eta: 0:33:28, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7048, loss: 0.0824 +2023-03-03 19:44:26,229 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:44:26,229 - mmseg - INFO - Iter [71000/80000] lr: 1.172e-06, eta: 0:33:17, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6890, loss: 0.0839 +2023-03-03 19:44:36,603 - mmseg - INFO - Iter [71050/80000] lr: 1.172e-06, eta: 0:33:06, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7222, loss: 0.0826 +2023-03-03 19:44:49,309 - mmseg - INFO - Iter [71100/80000] lr: 1.172e-06, eta: 0:32:55, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0810, decode.acc_seg: 96.7554, loss: 0.0810 +2023-03-03 19:44:59,571 - mmseg - INFO - Iter [71150/80000] lr: 1.172e-06, eta: 0:32:44, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7505, loss: 0.0823 +2023-03-03 19:45:10,041 - mmseg - INFO - Iter [71200/80000] lr: 1.172e-06, eta: 0:32:32, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7719, loss: 0.0809 +2023-03-03 19:45:22,633 - mmseg - INFO - Iter [71250/80000] lr: 1.172e-06, eta: 0:32:22, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7129, loss: 0.0829 +2023-03-03 19:45:33,247 - mmseg - INFO - Iter [71300/80000] lr: 1.172e-06, eta: 0:32:10, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6540, loss: 0.0839 +2023-03-03 19:45:43,773 - mmseg - INFO - Iter [71350/80000] lr: 1.172e-06, eta: 0:31:59, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7762, loss: 0.0813 +2023-03-03 19:45:54,195 - mmseg - INFO - Iter [71400/80000] lr: 1.172e-06, eta: 0:31:48, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5587, loss: 0.0863 +2023-03-03 19:46:06,944 - mmseg - INFO - Iter [71450/80000] lr: 1.172e-06, eta: 0:31:37, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5964, loss: 0.0874 +2023-03-03 19:46:17,313 - mmseg - INFO - Iter [71500/80000] lr: 1.172e-06, eta: 0:31:26, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0886, decode.acc_seg: 96.6837, loss: 0.0886 +2023-03-03 19:46:27,598 - mmseg - INFO - Iter [71550/80000] lr: 1.172e-06, eta: 0:31:15, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7484, loss: 0.0813 +2023-03-03 19:46:38,102 - mmseg - INFO - Iter [71600/80000] lr: 1.172e-06, eta: 0:31:04, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8275, loss: 0.0801 +2023-03-03 19:46:50,780 - mmseg - INFO - Iter [71650/80000] lr: 1.172e-06, eta: 0:30:53, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6395, loss: 0.0855 +2023-03-03 19:47:01,135 - mmseg - INFO - Iter [71700/80000] lr: 1.172e-06, eta: 0:30:41, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7552, loss: 0.0822 +2023-03-03 19:47:11,465 - mmseg - INFO - Iter [71750/80000] lr: 1.172e-06, eta: 0:30:30, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0863, decode.acc_seg: 96.5881, loss: 0.0863 +2023-03-03 19:47:24,241 - mmseg - INFO - Iter [71800/80000] lr: 1.172e-06, eta: 0:30:19, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6109, loss: 0.0857 +2023-03-03 19:47:34,601 - mmseg - INFO - Iter [71850/80000] lr: 1.172e-06, eta: 0:30:08, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7390, loss: 0.0823 +2023-03-03 19:47:45,011 - mmseg - INFO - Iter [71900/80000] lr: 1.172e-06, eta: 0:29:57, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0814, decode.acc_seg: 96.7964, loss: 0.0814 +2023-03-03 19:47:55,333 - mmseg - INFO - Iter [71950/80000] lr: 1.172e-06, eta: 0:29:46, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6717, loss: 0.0855 +2023-03-03 19:48:07,947 - mmseg - INFO - Saving checkpoint at 72000 iterations +2023-03-03 19:48:08,874 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:48:08,874 - mmseg - INFO - Iter [72000/80000] lr: 1.172e-06, eta: 0:29:35, time: 0.271, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7272, loss: 0.0832 +2023-03-03 19:48:29,043 - mmseg - INFO - per class results: +2023-03-03 19:48:29,045 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.53 | 99.25 | +| sidewalk | 87.51 | 93.32 | +| building | 93.25 | 96.82 | +| wall | 53.38 | 58.32 | +| fence | 63.17 | 72.2 | +| pole | 70.49 | 82.44 | +| traffic light | 74.93 | 85.16 | +| traffic sign | 83.11 | 89.7 | +| vegetation | 92.95 | 96.93 | +| terrain | 65.72 | 75.09 | +| sky | 95.15 | 98.51 | +| person | 84.63 | 92.73 | +| rider | 66.54 | 79.03 | +| car | 95.93 | 98.14 | +| truck | 84.97 | 90.07 | +| bus | 92.33 | 95.21 | +| train | 85.74 | 91.3 | +| motorcycle | 68.98 | 80.06 | +| bicycle | 79.59 | 90.11 | ++---------------+-------+-------+ +2023-03-03 19:48:29,045 - mmseg - INFO - Summary: +2023-03-03 19:48:29,045 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 96.57 | 80.89 | 87.6 | ++-------+-------+------+ +2023-03-03 19:48:29,074 - mmseg - INFO - The previous best checkpoint /mnt/petrelfs/laizeqiang/mmseg-baseline/work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20/best_mIoU_iter_64000.pth was removed +2023-03-03 19:48:29,906 - mmseg - INFO - Now best checkpoint is saved as best_mIoU_iter_72000.pth. +2023-03-03 19:48:29,906 - mmseg - INFO - Best mIoU is 0.8089 at 72000 iter. +2023-03-03 19:48:29,906 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:48:29,906 - mmseg - INFO - Iter(val) [63] aAcc: 0.9657, mIoU: 0.8089, mAcc: 0.8760, IoU.background: nan, IoU.road: 0.9853, IoU.sidewalk: 0.8751, IoU.building: 0.9325, IoU.wall: 0.5338, IoU.fence: 0.6317, IoU.pole: 0.7049, IoU.traffic light: 0.7493, IoU.traffic sign: 0.8311, IoU.vegetation: 0.9295, IoU.terrain: 0.6572, IoU.sky: 0.9515, IoU.person: 0.8463, IoU.rider: 0.6654, IoU.car: 0.9593, IoU.truck: 0.8497, IoU.bus: 0.9233, IoU.train: 0.8574, IoU.motorcycle: 0.6898, IoU.bicycle: 0.7959, Acc.background: nan, Acc.road: 0.9925, Acc.sidewalk: 0.9332, Acc.building: 0.9682, Acc.wall: 0.5832, Acc.fence: 0.7220, Acc.pole: 0.8244, Acc.traffic light: 0.8516, Acc.traffic sign: 0.8970, Acc.vegetation: 0.9693, Acc.terrain: 0.7509, Acc.sky: 0.9851, Acc.person: 0.9273, Acc.rider: 0.7903, Acc.car: 0.9814, Acc.truck: 0.9007, Acc.bus: 0.9521, Acc.train: 0.9130, Acc.motorcycle: 0.8006, Acc.bicycle: 0.9011 +2023-03-03 19:48:40,481 - mmseg - INFO - Iter [72050/80000] lr: 1.172e-06, eta: 0:29:26, time: 0.632, data_time: 0.429, memory: 33997, decode.loss_ce: 0.0796, decode.acc_seg: 96.8424, loss: 0.0796 +2023-03-03 19:48:50,933 - mmseg - INFO - Iter [72100/80000] lr: 1.172e-06, eta: 0:29:15, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0856, decode.acc_seg: 96.6175, loss: 0.0856 +2023-03-03 19:49:01,374 - mmseg - INFO - Iter [72150/80000] lr: 1.172e-06, eta: 0:29:04, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.8017, loss: 0.0811 +2023-03-03 19:49:14,099 - mmseg - INFO - Iter [72200/80000] lr: 1.172e-06, eta: 0:28:53, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8311, loss: 0.0801 +2023-03-03 19:49:24,551 - mmseg - INFO - Iter [72250/80000] lr: 1.172e-06, eta: 0:28:42, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7240, loss: 0.0821 +2023-03-03 19:49:34,885 - mmseg - INFO - Iter [72300/80000] lr: 1.172e-06, eta: 0:28:31, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6730, loss: 0.0845 +2023-03-03 19:49:45,241 - mmseg - INFO - Iter [72350/80000] lr: 1.172e-06, eta: 0:28:19, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7550, loss: 0.0827 +2023-03-03 19:49:57,899 - mmseg - INFO - Iter [72400/80000] lr: 1.172e-06, eta: 0:28:08, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6512, loss: 0.0839 +2023-03-03 19:50:08,378 - mmseg - INFO - Iter [72450/80000] lr: 1.172e-06, eta: 0:27:57, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0883, decode.acc_seg: 96.5307, loss: 0.0883 +2023-03-03 19:50:18,755 - mmseg - INFO - Iter [72500/80000] lr: 1.172e-06, eta: 0:27:46, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0814, decode.acc_seg: 96.7828, loss: 0.0814 +2023-03-03 19:50:31,528 - mmseg - INFO - Iter [72550/80000] lr: 1.172e-06, eta: 0:27:35, time: 0.255, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7158, loss: 0.0828 +2023-03-03 19:50:41,850 - mmseg - INFO - Iter [72600/80000] lr: 1.172e-06, eta: 0:27:24, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6726, loss: 0.0839 +2023-03-03 19:50:52,277 - mmseg - INFO - Iter [72650/80000] lr: 1.172e-06, eta: 0:27:13, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6163, loss: 0.0854 +2023-03-03 19:51:02,815 - mmseg - INFO - Iter [72700/80000] lr: 1.172e-06, eta: 0:27:02, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8172, loss: 0.0801 +2023-03-03 19:51:15,502 - mmseg - INFO - Iter [72750/80000] lr: 1.172e-06, eta: 0:26:51, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.7047, loss: 0.0835 +2023-03-03 19:51:25,965 - mmseg - INFO - Iter [72800/80000] lr: 1.172e-06, eta: 0:26:39, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6614, loss: 0.0839 +2023-03-03 19:51:36,337 - mmseg - INFO - Iter [72850/80000] lr: 1.172e-06, eta: 0:26:28, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0835, decode.acc_seg: 96.6648, loss: 0.0835 +2023-03-03 19:51:46,959 - mmseg - INFO - Iter [72900/80000] lr: 1.172e-06, eta: 0:26:17, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0867, decode.acc_seg: 96.5776, loss: 0.0867 +2023-03-03 19:51:59,700 - mmseg - INFO - Iter [72950/80000] lr: 1.172e-06, eta: 0:26:06, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7695, loss: 0.0818 +2023-03-03 19:52:10,244 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:52:10,245 - mmseg - INFO - Iter [73000/80000] lr: 1.172e-06, eta: 0:25:55, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5935, loss: 0.0866 +2023-03-03 19:52:20,630 - mmseg - INFO - Iter [73050/80000] lr: 1.172e-06, eta: 0:25:44, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0891, decode.acc_seg: 96.5158, loss: 0.0891 +2023-03-03 19:52:33,290 - mmseg - INFO - Iter [73100/80000] lr: 1.172e-06, eta: 0:25:33, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6126, loss: 0.0849 +2023-03-03 19:52:43,727 - mmseg - INFO - Iter [73150/80000] lr: 1.172e-06, eta: 0:25:22, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6952, loss: 0.0831 +2023-03-03 19:52:54,205 - mmseg - INFO - Iter [73200/80000] lr: 1.172e-06, eta: 0:25:10, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0815, decode.acc_seg: 96.7905, loss: 0.0815 +2023-03-03 19:53:04,716 - mmseg - INFO - Iter [73250/80000] lr: 1.172e-06, eta: 0:24:59, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0868, decode.acc_seg: 96.6429, loss: 0.0868 +2023-03-03 19:53:17,443 - mmseg - INFO - Iter [73300/80000] lr: 1.172e-06, eta: 0:24:48, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.7005, loss: 0.0839 +2023-03-03 19:53:27,891 - mmseg - INFO - Iter [73350/80000] lr: 1.172e-06, eta: 0:24:37, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.7003, loss: 0.0838 +2023-03-03 19:53:38,380 - mmseg - INFO - Iter [73400/80000] lr: 1.172e-06, eta: 0:24:26, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7334, loss: 0.0817 +2023-03-03 19:53:48,792 - mmseg - INFO - Iter [73450/80000] lr: 1.172e-06, eta: 0:24:15, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6063, loss: 0.0857 +2023-03-03 19:54:01,611 - mmseg - INFO - Iter [73500/80000] lr: 1.172e-06, eta: 0:24:04, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7449, loss: 0.0825 +2023-03-03 19:54:12,216 - mmseg - INFO - Iter [73550/80000] lr: 1.172e-06, eta: 0:23:53, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.6208, loss: 0.0853 +2023-03-03 19:54:22,548 - mmseg - INFO - Iter [73600/80000] lr: 1.172e-06, eta: 0:23:42, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.7132, loss: 0.0834 +2023-03-03 19:54:32,991 - mmseg - INFO - Iter [73650/80000] lr: 1.172e-06, eta: 0:23:30, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6155, loss: 0.0841 +2023-03-03 19:54:45,711 - mmseg - INFO - Iter [73700/80000] lr: 1.172e-06, eta: 0:23:19, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7372, loss: 0.0821 +2023-03-03 19:54:56,179 - mmseg - INFO - Iter [73750/80000] lr: 1.172e-06, eta: 0:23:08, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7688, loss: 0.0818 +2023-03-03 19:55:06,482 - mmseg - INFO - Iter [73800/80000] lr: 1.172e-06, eta: 0:22:57, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6644, loss: 0.0833 +2023-03-03 19:55:19,196 - mmseg - INFO - Iter [73850/80000] lr: 1.172e-06, eta: 0:22:46, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6359, loss: 0.0848 +2023-03-03 19:55:29,672 - mmseg - INFO - Iter [73900/80000] lr: 1.172e-06, eta: 0:22:35, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.8285, loss: 0.0808 +2023-03-03 19:55:40,136 - mmseg - INFO - Iter [73950/80000] lr: 1.172e-06, eta: 0:22:24, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.5617, loss: 0.0870 +2023-03-03 19:55:50,639 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:55:50,639 - mmseg - INFO - Iter [74000/80000] lr: 1.172e-06, eta: 0:22:13, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7553, loss: 0.0828 +2023-03-03 19:56:03,250 - mmseg - INFO - Iter [74050/80000] lr: 1.172e-06, eta: 0:22:02, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7117, loss: 0.0827 +2023-03-03 19:56:13,829 - mmseg - INFO - Iter [74100/80000] lr: 1.172e-06, eta: 0:21:50, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6710, loss: 0.0839 +2023-03-03 19:56:24,290 - mmseg - INFO - Iter [74150/80000] lr: 1.172e-06, eta: 0:21:39, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0808, decode.acc_seg: 96.8199, loss: 0.0808 +2023-03-03 19:56:34,617 - mmseg - INFO - Iter [74200/80000] lr: 1.172e-06, eta: 0:21:28, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0818, decode.acc_seg: 96.7598, loss: 0.0818 +2023-03-03 19:56:47,297 - mmseg - INFO - Iter [74250/80000] lr: 1.172e-06, eta: 0:21:17, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0829, decode.acc_seg: 96.7232, loss: 0.0829 +2023-03-03 19:56:57,727 - mmseg - INFO - Iter [74300/80000] lr: 1.172e-06, eta: 0:21:06, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6843, loss: 0.0841 +2023-03-03 19:57:08,120 - mmseg - INFO - Iter [74350/80000] lr: 1.172e-06, eta: 0:20:55, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7210, loss: 0.0831 +2023-03-03 19:57:18,386 - mmseg - INFO - Iter [74400/80000] lr: 1.172e-06, eta: 0:20:44, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.6961, loss: 0.0830 +2023-03-03 19:57:31,176 - mmseg - INFO - Iter [74450/80000] lr: 1.172e-06, eta: 0:20:33, time: 0.256, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0805, decode.acc_seg: 96.8163, loss: 0.0805 +2023-03-03 19:57:41,584 - mmseg - INFO - Iter [74500/80000] lr: 1.172e-06, eta: 0:20:22, time: 0.208, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0870, decode.acc_seg: 96.6095, loss: 0.0870 +2023-03-03 19:57:52,211 - mmseg - INFO - Iter [74550/80000] lr: 1.172e-06, eta: 0:20:10, time: 0.212, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0814, decode.acc_seg: 96.7715, loss: 0.0814 +2023-03-03 19:58:04,876 - mmseg - INFO - Iter [74600/80000] lr: 1.172e-06, eta: 0:19:59, time: 0.253, data_time: 0.052, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5255, loss: 0.0880 +2023-03-03 19:58:15,219 - mmseg - INFO - Iter [74650/80000] lr: 1.172e-06, eta: 0:19:48, time: 0.207, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6318, loss: 0.0838 +2023-03-03 19:58:25,588 - mmseg - INFO - Iter [74700/80000] lr: 1.172e-06, eta: 0:19:37, time: 0.207, data_time: 0.007, memory: 33997, decode.loss_ce: 0.0812, decode.acc_seg: 96.7398, loss: 0.0812 +2023-03-03 19:58:36,073 - mmseg - INFO - Iter [74750/80000] lr: 1.172e-06, eta: 0:19:26, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6910, loss: 0.0843 +2023-03-03 19:58:48,904 - mmseg - INFO - Iter [74800/80000] lr: 1.172e-06, eta: 0:19:15, time: 0.257, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7706, loss: 0.0817 +2023-03-03 19:58:59,324 - mmseg - INFO - Iter [74850/80000] lr: 1.172e-06, eta: 0:19:04, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.7059, loss: 0.0832 +2023-03-03 19:59:09,898 - mmseg - INFO - Iter [74900/80000] lr: 1.172e-06, eta: 0:18:53, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0839, decode.acc_seg: 96.6955, loss: 0.0839 +2023-03-03 19:59:20,341 - mmseg - INFO - Iter [74950/80000] lr: 1.172e-06, eta: 0:18:41, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0822, decode.acc_seg: 96.7303, loss: 0.0822 +2023-03-03 19:59:32,993 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 19:59:32,993 - mmseg - INFO - Iter [75000/80000] lr: 1.172e-06, eta: 0:18:30, time: 0.253, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.6917, loss: 0.0827 +2023-03-03 19:59:43,499 - mmseg - INFO - Iter [75050/80000] lr: 1.172e-06, eta: 0:18:19, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6715, loss: 0.0838 +2023-03-03 19:59:53,855 - mmseg - INFO - Iter [75100/80000] lr: 1.172e-06, eta: 0:18:08, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.7228, loss: 0.0830 +2023-03-03 20:00:06,610 - mmseg - INFO - Iter [75150/80000] lr: 1.172e-06, eta: 0:17:57, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0805, decode.acc_seg: 96.8345, loss: 0.0805 +2023-03-03 20:00:16,922 - mmseg - INFO - Iter [75200/80000] lr: 1.172e-06, eta: 0:17:46, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6679, loss: 0.0834 +2023-03-03 20:00:27,386 - mmseg - INFO - Iter [75250/80000] lr: 1.172e-06, eta: 0:17:35, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.6610, loss: 0.0855 +2023-03-03 20:00:37,735 - mmseg - INFO - Iter [75300/80000] lr: 1.172e-06, eta: 0:17:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6517, loss: 0.0846 +2023-03-03 20:00:50,347 - mmseg - INFO - Iter [75350/80000] lr: 1.172e-06, eta: 0:17:13, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.7991, loss: 0.0803 +2023-03-03 20:01:00,759 - mmseg - INFO - Iter [75400/80000] lr: 1.172e-06, eta: 0:17:01, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.8199, loss: 0.0803 +2023-03-03 20:01:11,116 - mmseg - INFO - Iter [75450/80000] lr: 1.172e-06, eta: 0:16:50, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7830, loss: 0.0809 +2023-03-03 20:01:21,577 - mmseg - INFO - Iter [75500/80000] lr: 1.172e-06, eta: 0:16:39, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0874, decode.acc_seg: 96.5489, loss: 0.0874 +2023-03-03 20:01:34,470 - mmseg - INFO - Iter [75550/80000] lr: 1.172e-06, eta: 0:16:28, time: 0.258, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6338, loss: 0.0851 +2023-03-03 20:01:44,992 - mmseg - INFO - Iter [75600/80000] lr: 1.172e-06, eta: 0:16:17, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0804, decode.acc_seg: 96.7959, loss: 0.0804 +2023-03-03 20:01:55,427 - mmseg - INFO - Iter [75650/80000] lr: 1.172e-06, eta: 0:16:06, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.6687, loss: 0.0828 +2023-03-03 20:02:05,850 - mmseg - INFO - Iter [75700/80000] lr: 1.172e-06, eta: 0:15:55, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6957, loss: 0.0833 +2023-03-03 20:02:18,729 - mmseg - INFO - Iter [75750/80000] lr: 1.172e-06, eta: 0:15:44, time: 0.258, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6818, loss: 0.0831 +2023-03-03 20:02:29,143 - mmseg - INFO - Iter [75800/80000] lr: 1.172e-06, eta: 0:15:33, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7874, loss: 0.0816 +2023-03-03 20:02:39,610 - mmseg - INFO - Iter [75850/80000] lr: 1.172e-06, eta: 0:15:21, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0828, decode.acc_seg: 96.7598, loss: 0.0828 +2023-03-03 20:02:52,397 - mmseg - INFO - Iter [75900/80000] lr: 1.172e-06, eta: 0:15:10, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0861, decode.acc_seg: 96.5910, loss: 0.0861 +2023-03-03 20:03:02,778 - mmseg - INFO - Iter [75950/80000] lr: 1.172e-06, eta: 0:14:59, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0834, decode.acc_seg: 96.6993, loss: 0.0834 +2023-03-03 20:03:13,227 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 20:03:13,228 - mmseg - INFO - Iter [76000/80000] lr: 1.172e-06, eta: 0:14:48, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7733, loss: 0.0817 +2023-03-03 20:03:23,677 - mmseg - INFO - Iter [76050/80000] lr: 1.172e-06, eta: 0:14:37, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0880, decode.acc_seg: 96.5103, loss: 0.0880 +2023-03-03 20:03:36,417 - mmseg - INFO - Iter [76100/80000] lr: 1.172e-06, eta: 0:14:26, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.8021, loss: 0.0809 +2023-03-03 20:03:46,783 - mmseg - INFO - Iter [76150/80000] lr: 1.172e-06, eta: 0:14:15, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0806, decode.acc_seg: 96.8138, loss: 0.0806 +2023-03-03 20:03:57,134 - mmseg - INFO - Iter [76200/80000] lr: 1.172e-06, eta: 0:14:04, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7506, loss: 0.0819 +2023-03-03 20:04:07,621 - mmseg - INFO - Iter [76250/80000] lr: 1.172e-06, eta: 0:13:53, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0878, decode.acc_seg: 96.5575, loss: 0.0878 +2023-03-03 20:04:20,394 - mmseg - INFO - Iter [76300/80000] lr: 1.172e-06, eta: 0:13:42, time: 0.256, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0830, decode.acc_seg: 96.6987, loss: 0.0830 +2023-03-03 20:04:30,823 - mmseg - INFO - Iter [76350/80000] lr: 1.172e-06, eta: 0:13:30, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.6656, loss: 0.0826 +2023-03-03 20:04:41,338 - mmseg - INFO - Iter [76400/80000] lr: 1.172e-06, eta: 0:13:19, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0843, decode.acc_seg: 96.6604, loss: 0.0843 +2023-03-03 20:04:53,999 - mmseg - INFO - Iter [76450/80000] lr: 1.172e-06, eta: 0:13:08, time: 0.253, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7982, loss: 0.0816 +2023-03-03 20:05:04,492 - mmseg - INFO - Iter [76500/80000] lr: 1.172e-06, eta: 0:12:57, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7741, loss: 0.0816 +2023-03-03 20:05:15,062 - mmseg - INFO - Iter [76550/80000] lr: 1.172e-06, eta: 0:12:46, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0841, decode.acc_seg: 96.6541, loss: 0.0841 +2023-03-03 20:05:25,373 - mmseg - INFO - Iter [76600/80000] lr: 1.172e-06, eta: 0:12:35, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0820, decode.acc_seg: 96.7431, loss: 0.0820 +2023-03-03 20:05:38,065 - mmseg - INFO - Iter [76650/80000] lr: 1.172e-06, eta: 0:12:24, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0809, decode.acc_seg: 96.7543, loss: 0.0809 +2023-03-03 20:05:48,302 - mmseg - INFO - Iter [76700/80000] lr: 1.172e-06, eta: 0:12:13, time: 0.205, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0807, decode.acc_seg: 96.7765, loss: 0.0807 +2023-03-03 20:05:58,694 - mmseg - INFO - Iter [76750/80000] lr: 1.172e-06, eta: 0:12:01, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0824, decode.acc_seg: 96.7597, loss: 0.0824 +2023-03-03 20:06:08,997 - mmseg - INFO - Iter [76800/80000] lr: 1.172e-06, eta: 0:11:50, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0840, decode.acc_seg: 96.6646, loss: 0.0840 +2023-03-03 20:06:21,823 - mmseg - INFO - Iter [76850/80000] lr: 1.172e-06, eta: 0:11:39, time: 0.256, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0852, decode.acc_seg: 96.6370, loss: 0.0852 +2023-03-03 20:06:32,219 - mmseg - INFO - Iter [76900/80000] lr: 1.172e-06, eta: 0:11:28, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6778, loss: 0.0844 +2023-03-03 20:06:42,732 - mmseg - INFO - Iter [76950/80000] lr: 1.172e-06, eta: 0:11:17, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.6857, loss: 0.0842 +2023-03-03 20:06:53,312 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 20:06:53,312 - mmseg - INFO - Iter [77000/80000] lr: 1.172e-06, eta: 0:11:06, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6897, loss: 0.0832 +2023-03-03 20:07:06,111 - mmseg - INFO - Iter [77050/80000] lr: 1.172e-06, eta: 0:10:55, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0819, decode.acc_seg: 96.7678, loss: 0.0819 +2023-03-03 20:07:16,685 - mmseg - INFO - Iter [77100/80000] lr: 1.172e-06, eta: 0:10:44, time: 0.212, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0832, decode.acc_seg: 96.6952, loss: 0.0832 +2023-03-03 20:07:27,146 - mmseg - INFO - Iter [77150/80000] lr: 1.172e-06, eta: 0:10:33, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6282, loss: 0.0846 +2023-03-03 20:07:39,873 - mmseg - INFO - Iter [77200/80000] lr: 1.172e-06, eta: 0:10:22, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0854, decode.acc_seg: 96.6224, loss: 0.0854 +2023-03-03 20:07:50,214 - mmseg - INFO - Iter [77250/80000] lr: 1.172e-06, eta: 0:10:10, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6174, loss: 0.0846 +2023-03-03 20:08:00,585 - mmseg - INFO - Iter [77300/80000] lr: 1.172e-06, eta: 0:09:59, time: 0.207, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7307, loss: 0.0826 +2023-03-03 20:08:11,180 - mmseg - INFO - Iter [77350/80000] lr: 1.172e-06, eta: 0:09:48, time: 0.212, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0827, decode.acc_seg: 96.7101, loss: 0.0827 +2023-03-03 20:08:23,897 - mmseg - INFO - Iter [77400/80000] lr: 1.172e-06, eta: 0:09:37, time: 0.254, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6496, loss: 0.0848 +2023-03-03 20:08:34,335 - mmseg - INFO - Iter [77450/80000] lr: 1.172e-06, eta: 0:09:26, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0851, decode.acc_seg: 96.6078, loss: 0.0851 +2023-03-03 20:08:44,762 - mmseg - INFO - Iter [77500/80000] lr: 1.172e-06, eta: 0:09:15, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0857, decode.acc_seg: 96.6118, loss: 0.0857 +2023-03-03 20:08:55,320 - mmseg - INFO - Iter [77550/80000] lr: 1.172e-06, eta: 0:09:04, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6957, loss: 0.0833 +2023-03-03 20:09:08,026 - mmseg - INFO - Iter [77600/80000] lr: 1.172e-06, eta: 0:08:53, time: 0.254, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0855, decode.acc_seg: 96.5877, loss: 0.0855 +2023-03-03 20:09:18,583 - mmseg - INFO - Iter [77650/80000] lr: 1.172e-06, eta: 0:08:42, time: 0.211, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0825, decode.acc_seg: 96.7442, loss: 0.0825 +2023-03-03 20:09:29,139 - mmseg - INFO - Iter [77700/80000] lr: 1.172e-06, eta: 0:08:30, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6272, loss: 0.0848 +2023-03-03 20:09:41,930 - mmseg - INFO - Iter [77750/80000] lr: 1.172e-06, eta: 0:08:19, time: 0.256, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0821, decode.acc_seg: 96.7319, loss: 0.0821 +2023-03-03 20:09:52,426 - mmseg - INFO - Iter [77800/80000] lr: 1.172e-06, eta: 0:08:08, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7034, loss: 0.0831 +2023-03-03 20:10:02,751 - mmseg - INFO - Iter [77850/80000] lr: 1.172e-06, eta: 0:07:57, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0846, decode.acc_seg: 96.6124, loss: 0.0846 +2023-03-03 20:10:13,184 - mmseg - INFO - Iter [77900/80000] lr: 1.172e-06, eta: 0:07:46, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6483, loss: 0.0847 +2023-03-03 20:10:26,010 - mmseg - INFO - Iter [77950/80000] lr: 1.172e-06, eta: 0:07:35, time: 0.257, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.6834, loss: 0.0831 +2023-03-03 20:10:36,493 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 20:10:36,493 - mmseg - INFO - Iter [78000/80000] lr: 1.172e-06, eta: 0:07:24, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0802, decode.acc_seg: 96.8131, loss: 0.0802 +2023-03-03 20:10:47,012 - mmseg - INFO - Iter [78050/80000] lr: 1.172e-06, eta: 0:07:13, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0802, decode.acc_seg: 96.8073, loss: 0.0802 +2023-03-03 20:10:57,462 - mmseg - INFO - Iter [78100/80000] lr: 1.172e-06, eta: 0:07:02, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6641, loss: 0.0844 +2023-03-03 20:11:10,224 - mmseg - INFO - Iter [78150/80000] lr: 1.172e-06, eta: 0:06:50, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0799, decode.acc_seg: 96.8367, loss: 0.0799 +2023-03-03 20:11:20,713 - mmseg - INFO - Iter [78200/80000] lr: 1.172e-06, eta: 0:06:39, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6530, loss: 0.0848 +2023-03-03 20:11:31,224 - mmseg - INFO - Iter [78250/80000] lr: 1.172e-06, eta: 0:06:28, time: 0.210, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0872, decode.acc_seg: 96.6142, loss: 0.0872 +2023-03-03 20:11:41,651 - mmseg - INFO - Iter [78300/80000] lr: 1.172e-06, eta: 0:06:17, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0853, decode.acc_seg: 96.5948, loss: 0.0853 +2023-03-03 20:11:54,368 - mmseg - INFO - Iter [78350/80000] lr: 1.172e-06, eta: 0:06:06, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0805, decode.acc_seg: 96.7981, loss: 0.0805 +2023-03-03 20:12:04,739 - mmseg - INFO - Iter [78400/80000] lr: 1.172e-06, eta: 0:05:55, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0895, decode.acc_seg: 96.5031, loss: 0.0895 +2023-03-03 20:12:15,193 - mmseg - INFO - Iter [78450/80000] lr: 1.172e-06, eta: 0:05:44, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0823, decode.acc_seg: 96.7616, loss: 0.0823 +2023-03-03 20:12:28,144 - mmseg - INFO - Iter [78500/80000] lr: 1.172e-06, eta: 0:05:33, time: 0.259, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0792, decode.acc_seg: 96.8749, loss: 0.0792 +2023-03-03 20:12:38,609 - mmseg - INFO - Iter [78550/80000] lr: 1.172e-06, eta: 0:05:22, time: 0.209, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0794, decode.acc_seg: 96.8502, loss: 0.0794 +2023-03-03 20:12:49,135 - mmseg - INFO - Iter [78600/80000] lr: 1.172e-06, eta: 0:05:10, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.6940, loss: 0.0826 +2023-03-03 20:12:59,628 - mmseg - INFO - Iter [78650/80000] lr: 1.172e-06, eta: 0:04:59, time: 0.210, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0838, decode.acc_seg: 96.6806, loss: 0.0838 +2023-03-03 20:13:12,355 - mmseg - INFO - Iter [78700/80000] lr: 1.172e-06, eta: 0:04:48, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0842, decode.acc_seg: 96.7021, loss: 0.0842 +2023-03-03 20:13:22,667 - mmseg - INFO - Iter [78750/80000] lr: 1.172e-06, eta: 0:04:37, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0790, decode.acc_seg: 96.8469, loss: 0.0790 +2023-03-03 20:13:32,929 - mmseg - INFO - Iter [78800/80000] lr: 1.172e-06, eta: 0:04:26, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7600, loss: 0.0817 +2023-03-03 20:13:43,225 - mmseg - INFO - Iter [78850/80000] lr: 1.172e-06, eta: 0:04:15, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6592, loss: 0.0837 +2023-03-03 20:13:55,953 - mmseg - INFO - Iter [78900/80000] lr: 1.172e-06, eta: 0:04:04, time: 0.255, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0803, decode.acc_seg: 96.7854, loss: 0.0803 +2023-03-03 20:14:06,388 - mmseg - INFO - Iter [78950/80000] lr: 1.172e-06, eta: 0:03:53, time: 0.209, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0844, decode.acc_seg: 96.6590, loss: 0.0844 +2023-03-03 20:14:16,753 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 20:14:16,753 - mmseg - INFO - Iter [79000/80000] lr: 1.172e-06, eta: 0:03:42, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0806, decode.acc_seg: 96.7585, loss: 0.0806 +2023-03-03 20:14:27,048 - mmseg - INFO - Iter [79050/80000] lr: 1.172e-06, eta: 0:03:31, time: 0.206, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0848, decode.acc_seg: 96.6393, loss: 0.0848 +2023-03-03 20:14:39,754 - mmseg - INFO - Iter [79100/80000] lr: 1.172e-06, eta: 0:03:19, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0866, decode.acc_seg: 96.5973, loss: 0.0866 +2023-03-03 20:14:50,169 - mmseg - INFO - Iter [79150/80000] lr: 1.172e-06, eta: 0:03:08, time: 0.208, data_time: 0.008, memory: 33997, decode.loss_ce: 0.0859, decode.acc_seg: 96.6721, loss: 0.0859 +2023-03-03 20:15:00,393 - mmseg - INFO - Iter [79200/80000] lr: 1.172e-06, eta: 0:02:57, time: 0.205, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6821, loss: 0.0845 +2023-03-03 20:15:13,020 - mmseg - INFO - Iter [79250/80000] lr: 1.172e-06, eta: 0:02:46, time: 0.252, data_time: 0.053, memory: 33997, decode.loss_ce: 0.0813, decode.acc_seg: 96.7624, loss: 0.0813 +2023-03-03 20:15:23,542 - mmseg - INFO - Iter [79300/80000] lr: 1.172e-06, eta: 0:02:35, time: 0.211, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0796, decode.acc_seg: 96.8295, loss: 0.0796 +2023-03-03 20:15:33,884 - mmseg - INFO - Iter [79350/80000] lr: 1.172e-06, eta: 0:02:24, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0849, decode.acc_seg: 96.6053, loss: 0.0849 +2023-03-03 20:15:44,250 - mmseg - INFO - Iter [79400/80000] lr: 1.172e-06, eta: 0:02:13, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0801, decode.acc_seg: 96.8307, loss: 0.0801 +2023-03-03 20:15:56,851 - mmseg - INFO - Iter [79450/80000] lr: 1.172e-06, eta: 0:02:02, time: 0.252, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0831, decode.acc_seg: 96.7053, loss: 0.0831 +2023-03-03 20:16:07,193 - mmseg - INFO - Iter [79500/80000] lr: 1.172e-06, eta: 0:01:51, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0817, decode.acc_seg: 96.7310, loss: 0.0817 +2023-03-03 20:16:17,555 - mmseg - INFO - Iter [79550/80000] lr: 1.172e-06, eta: 0:01:39, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.7124, loss: 0.0833 +2023-03-03 20:16:27,949 - mmseg - INFO - Iter [79600/80000] lr: 1.172e-06, eta: 0:01:28, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6706, loss: 0.0833 +2023-03-03 20:16:40,628 - mmseg - INFO - Iter [79650/80000] lr: 1.172e-06, eta: 0:01:17, time: 0.254, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0847, decode.acc_seg: 96.6487, loss: 0.0847 +2023-03-03 20:16:50,981 - mmseg - INFO - Iter [79700/80000] lr: 1.172e-06, eta: 0:01:06, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0833, decode.acc_seg: 96.6806, loss: 0.0833 +2023-03-03 20:17:01,327 - mmseg - INFO - Iter [79750/80000] lr: 1.172e-06, eta: 0:00:55, time: 0.207, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0811, decode.acc_seg: 96.7872, loss: 0.0811 +2023-03-03 20:17:14,000 - mmseg - INFO - Iter [79800/80000] lr: 1.172e-06, eta: 0:00:44, time: 0.253, data_time: 0.055, memory: 33997, decode.loss_ce: 0.0826, decode.acc_seg: 96.7169, loss: 0.0826 +2023-03-03 20:17:24,422 - mmseg - INFO - Iter [79850/80000] lr: 1.172e-06, eta: 0:00:33, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0816, decode.acc_seg: 96.7274, loss: 0.0816 +2023-03-03 20:17:34,838 - mmseg - INFO - Iter [79900/80000] lr: 1.172e-06, eta: 0:00:22, time: 0.208, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0845, decode.acc_seg: 96.6590, loss: 0.0845 +2023-03-03 20:17:45,130 - mmseg - INFO - Iter [79950/80000] lr: 1.172e-06, eta: 0:00:11, time: 0.206, data_time: 0.009, memory: 33997, decode.loss_ce: 0.0837, decode.acc_seg: 96.6569, loss: 0.0837 +2023-03-03 20:17:57,668 - mmseg - INFO - Saving checkpoint at 80000 iterations +2023-03-03 20:17:58,621 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 20:17:58,621 - mmseg - INFO - Iter [80000/80000] lr: 1.172e-06, eta: 0:00:00, time: 0.270, data_time: 0.054, memory: 33997, decode.loss_ce: 0.0884, decode.acc_seg: 96.4932, loss: 0.0884 +2023-03-03 20:18:19,054 - mmseg - INFO - per class results: +2023-03-03 20:18:19,055 - mmseg - INFO - ++---------------+-------+-------+ +| Class | IoU | Acc | ++---------------+-------+-------+ +| background | nan | nan | +| road | 98.52 | 99.24 | +| sidewalk | 87.45 | 93.3 | +| building | 93.23 | 96.81 | +| wall | 53.25 | 57.92 | +| fence | 63.39 | 72.47 | +| pole | 70.46 | 82.26 | +| traffic light | 74.98 | 85.52 | +| traffic sign | 83.09 | 89.61 | +| vegetation | 92.9 | 96.96 | +| terrain | 65.31 | 74.33 | +| sky | 95.13 | 98.47 | +| person | 84.51 | 92.8 | +| rider | 66.37 | 78.7 | +| car | 95.71 | 98.11 | +| truck | 79.19 | 83.48 | +| bus | 91.81 | 95.58 | +| train | 85.59 | 91.02 | +| motorcycle | 69.12 | 79.69 | +| bicycle | 79.5 | 90.43 | ++---------------+-------+-------+ +2023-03-03 20:18:19,055 - mmseg - INFO - Summary: +2023-03-03 20:18:19,056 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 96.54 | 80.5 | 87.19 | ++-------+------+-------+ +2023-03-03 20:18:19,056 - mmseg - INFO - Exp name: deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_cityscapes20.py +2023-03-03 20:18:19,056 - mmseg - INFO - Iter(val) [63] aAcc: 0.9654, mIoU: 0.8050, mAcc: 0.8719, IoU.background: nan, IoU.road: 0.9852, IoU.sidewalk: 0.8745, IoU.building: 0.9323, IoU.wall: 0.5325, IoU.fence: 0.6339, IoU.pole: 0.7046, IoU.traffic light: 0.7498, IoU.traffic sign: 0.8309, IoU.vegetation: 0.9290, IoU.terrain: 0.6531, IoU.sky: 0.9513, IoU.person: 0.8451, IoU.rider: 0.6637, IoU.car: 0.9571, IoU.truck: 0.7919, IoU.bus: 0.9181, IoU.train: 0.8559, IoU.motorcycle: 0.6912, IoU.bicycle: 0.7950, Acc.background: nan, Acc.road: 0.9924, Acc.sidewalk: 0.9330, Acc.building: 0.9681, Acc.wall: 0.5792, Acc.fence: 0.7247, Acc.pole: 0.8226, Acc.traffic light: 0.8552, Acc.traffic sign: 0.8961, Acc.vegetation: 0.9696, Acc.terrain: 0.7433, Acc.sky: 0.9847, Acc.person: 0.9280, Acc.rider: 0.7870, Acc.car: 0.9811, Acc.truck: 0.8348, Acc.bus: 0.9558, Acc.train: 0.9102, Acc.motorcycle: 0.7969, Acc.bicycle: 0.9043