2022/09/12 19:29:29 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 1904684629 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/lustre/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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_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 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, 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, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/09/12 19:29:30 - mmengine - INFO - Config: default_scope = 'mmpose' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=1, save_best='coco/AP', rule='greater', max_keep_ckpts=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='PoseVisualizationHook', enable=False)) custom_hooks = [dict(type='SyncBuffersHook')] env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='PoseLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict( type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) log_level = 'INFO' load_from = None resume = False file_client_args = dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' })) train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) val_cfg = dict() test_cfg = dict() optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 200], gamma=0.1, by_epoch=True) ] auto_scale_lr = dict(base_batch_size=512) codec = dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2, unbiased=True) model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='HRNet', in_channels=3, extra=dict( stage1=dict( num_modules=1, num_branches=1, block='BOTTLENECK', num_blocks=(4, ), num_channels=(64, )), stage2=dict( num_modules=1, num_branches=2, block='BASIC', num_blocks=(4, 4), num_channels=(48, 96)), stage3=dict( num_modules=4, num_branches=3, block='BASIC', num_blocks=(4, 4, 4), num_channels=(48, 96, 192)), stage4=dict( num_modules=3, num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), num_channels=(48, 96, 192, 384))), init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth' )), head=dict( type='HeatmapHead', in_channels=48, out_channels=17, deconv_out_channels=None, loss=dict(type='KeypointMSELoss', use_target_weight=True), decoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2, unbiased=True)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/' train_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2, unbiased=True)), dict(type='PackPoseInputs') ] test_pipeline = [ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ] train_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=(192, 256)), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2, unbiased=True)), dict(type='PackPoseInputs') ])) val_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) test_dataloader = dict( batch_size=32, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root='data/coco/', data_mode='topdown', ann_file='annotations/person_keypoints_val2017.json', bbox_file= 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=[ dict( type='LoadImage', file_client_args=dict( backend='petrel', path_mapping=dict({ './data/': 's3://openmmlab/datasets/detection/', 'data/': 's3://openmmlab/datasets/detection/' }))), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=(192, 256)), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = '/mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/' 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer wrapper constructor" registry tree. As a workaround, the current "optimizer wrapper constructor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer" registry tree. As a workaround, the current "optimizer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:04 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:08 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:10 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:12 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:30:12 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.0.0.weight - torch.Size([48, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.1.0.0.weight - torch.Size([96, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.1.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.1.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition2.2.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition2.2.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition2.2.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition3.3.0.0.weight - torch.Size([384, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition3.3.0.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition3.3.0.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.3.0.weight - torch.Size([48, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.3.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.3.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.3.0.weight - torch.Size([96, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.3.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.3.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.3.0.weight - torch.Size([192, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.3.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.3.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.1.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.2.0.weight - torch.Size([384, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.2.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.2.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.0.0.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.1.0.weight - torch.Size([384, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.1.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.1.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.2.0.0.weight - torch.Size([384, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.2.0.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.2.0.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.3.0.weight - torch.Size([48, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.3.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.3.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.3.0.weight - torch.Size([96, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.3.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.3.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.3.0.weight - torch.Size([192, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.3.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.3.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.1.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.2.0.weight - torch.Size([384, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.2.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.2.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.0.0.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.1.0.weight - torch.Size([384, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.1.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.1.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.2.0.0.weight - torch.Size([384, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.2.0.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.2.0.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.3.0.weight - torch.Size([48, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.3.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.3.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth head.final_layer.weight - torch.Size([17, 48, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 head.final_layer.bias - torch.Size([17]): NormalInit: mean=0, std=0.001, bias=0 2022/09/12 19:30:25 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1 by HardDiskBackend. 2022/09/12 19:32:04 - mmengine - INFO - Epoch(train) [1][50/586] lr: 4.954910e-05 eta: 2 days, 20:03:28 time: 1.991775 data_time: 0.561532 memory: 7489 loss_kpt: 0.002154 acc_pose: 0.158008 loss: 0.002154 2022/09/12 19:33:02 - mmengine - INFO - Epoch(train) [1][100/586] lr: 9.959920e-05 eta: 2 days, 5:46:35 time: 1.157149 data_time: 0.029506 memory: 7489 loss_kpt: 0.001784 acc_pose: 0.397767 loss: 0.001784 2022/09/12 19:33:55 - mmengine - INFO - Epoch(train) [1][150/586] lr: 1.496493e-04 eta: 1 day, 23:47:35 time: 1.050623 data_time: 0.022882 memory: 7489 loss_kpt: 0.001555 acc_pose: 0.537527 loss: 0.001555 2022/09/12 19:34:44 - mmengine - INFO - Epoch(train) [1][200/586] lr: 1.996994e-04 eta: 1 day, 20:14:33 time: 0.985973 data_time: 0.028252 memory: 7489 loss_kpt: 0.001365 acc_pose: 0.488756 loss: 0.001365 2022/09/12 19:35:24 - mmengine - INFO - Epoch(train) [1][250/586] lr: 2.497495e-04 eta: 1 day, 16:47:44 time: 0.793833 data_time: 0.156728 memory: 7489 loss_kpt: 0.001298 acc_pose: 0.564427 loss: 0.001298 2022/09/12 19:36:02 - mmengine - INFO - Epoch(train) [1][300/586] lr: 2.997996e-04 eta: 1 day, 14:19:46 time: 0.764869 data_time: 0.023078 memory: 7489 loss_kpt: 0.001229 acc_pose: 0.566638 loss: 0.001229 2022/09/12 19:36:33 - mmengine - INFO - Epoch(train) [1][350/586] lr: 3.498497e-04 eta: 1 day, 11:54:00 time: 0.628304 data_time: 0.031987 memory: 7489 loss_kpt: 0.001203 acc_pose: 0.592382 loss: 0.001203 2022/09/12 19:37:04 - mmengine - INFO - Epoch(train) [1][400/586] lr: 3.998998e-04 eta: 1 day, 10:00:28 time: 0.612399 data_time: 0.024171 memory: 7489 loss_kpt: 0.001173 acc_pose: 0.590565 loss: 0.001173 2022/09/12 19:37:37 - mmengine - INFO - Epoch(train) [1][450/586] lr: 4.499499e-04 eta: 1 day, 8:42:04 time: 0.656475 data_time: 0.077865 memory: 7489 loss_kpt: 0.001162 acc_pose: 0.524421 loss: 0.001162 2022/09/12 19:38:06 - mmengine - INFO - Epoch(train) [1][500/586] lr: 5.000000e-04 eta: 1 day, 7:24:00 time: 0.581878 data_time: 0.030240 memory: 7489 loss_kpt: 0.001151 acc_pose: 0.659503 loss: 0.001151 2022/09/12 19:38:33 - mmengine - INFO - Epoch(train) [1][550/586] lr: 5.000000e-04 eta: 1 day, 6:12:59 time: 0.543863 data_time: 0.033180 memory: 7489 loss_kpt: 0.001122 acc_pose: 0.562030 loss: 0.001122 2022/09/12 19:38:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:38:53 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/12 19:39:16 - mmengine - INFO - Epoch(train) [2][50/586] lr: 5.000000e-04 eta: 1 day, 3:00:25 time: 0.334673 data_time: 0.026563 memory: 7489 loss_kpt: 0.001097 acc_pose: 0.583762 loss: 0.001097 2022/09/12 19:39:32 - mmengine - INFO - Epoch(train) [2][100/586] lr: 5.000000e-04 eta: 1 day, 1:50:50 time: 0.330616 data_time: 0.027793 memory: 7489 loss_kpt: 0.001085 acc_pose: 0.634021 loss: 0.001085 2022/09/12 19:39:50 - mmengine - INFO - Epoch(train) [2][150/586] lr: 5.000000e-04 eta: 1 day, 0:53:02 time: 0.347526 data_time: 0.022492 memory: 7489 loss_kpt: 0.001053 acc_pose: 0.674970 loss: 0.001053 2022/09/12 19:40:06 - mmengine - INFO - Epoch(train) [2][200/586] lr: 5.000000e-04 eta: 23:59:59 time: 0.327922 data_time: 0.023552 memory: 7489 loss_kpt: 0.001049 acc_pose: 0.595388 loss: 0.001049 2022/09/12 19:40:23 - mmengine - INFO - Epoch(train) [2][250/586] lr: 5.000000e-04 eta: 23:13:40 time: 0.331232 data_time: 0.030588 memory: 7489 loss_kpt: 0.001046 acc_pose: 0.689762 loss: 0.001046 2022/09/12 19:40:40 - mmengine - INFO - Epoch(train) [2][300/586] lr: 5.000000e-04 eta: 22:33:21 time: 0.338234 data_time: 0.022801 memory: 7489 loss_kpt: 0.001034 acc_pose: 0.650442 loss: 0.001034 2022/09/12 19:40:57 - mmengine - INFO - Epoch(train) [2][350/586] lr: 5.000000e-04 eta: 21:57:11 time: 0.337105 data_time: 0.023185 memory: 7489 loss_kpt: 0.001007 acc_pose: 0.699255 loss: 0.001007 2022/09/12 19:41:14 - mmengine - INFO - Epoch(train) [2][400/586] lr: 5.000000e-04 eta: 21:25:04 time: 0.341102 data_time: 0.026181 memory: 7489 loss_kpt: 0.001011 acc_pose: 0.669451 loss: 0.001011 2022/09/12 19:41:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:41:31 - mmengine - INFO - Epoch(train) [2][450/586] lr: 5.000000e-04 eta: 20:56:54 time: 0.350018 data_time: 0.023399 memory: 7489 loss_kpt: 0.000989 acc_pose: 0.693208 loss: 0.000989 2022/09/12 19:41:47 - mmengine - INFO - Epoch(train) [2][500/586] lr: 5.000000e-04 eta: 20:29:02 time: 0.325764 data_time: 0.022994 memory: 7489 loss_kpt: 0.000971 acc_pose: 0.695056 loss: 0.000971 2022/09/12 19:42:04 - mmengine - INFO - Epoch(train) [2][550/586] lr: 5.000000e-04 eta: 20:04:28 time: 0.335571 data_time: 0.028272 memory: 7489 loss_kpt: 0.000961 acc_pose: 0.708486 loss: 0.000961 2022/09/12 19:42:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:42:17 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/12 19:42:41 - mmengine - INFO - Epoch(train) [3][50/586] lr: 5.000000e-04 eta: 19:07:23 time: 0.342675 data_time: 0.028751 memory: 7489 loss_kpt: 0.000983 acc_pose: 0.732535 loss: 0.000983 2022/09/12 19:42:58 - mmengine - INFO - Epoch(train) [3][100/586] lr: 5.000000e-04 eta: 18:48:53 time: 0.339075 data_time: 0.023852 memory: 7489 loss_kpt: 0.000950 acc_pose: 0.669890 loss: 0.000950 2022/09/12 19:43:15 - mmengine - INFO - Epoch(train) [3][150/586] lr: 5.000000e-04 eta: 18:31:38 time: 0.337490 data_time: 0.022555 memory: 7489 loss_kpt: 0.000968 acc_pose: 0.669291 loss: 0.000968 2022/09/12 19:43:31 - mmengine - INFO - Epoch(train) [3][200/586] lr: 5.000000e-04 eta: 18:15:42 time: 0.338546 data_time: 0.022206 memory: 7489 loss_kpt: 0.000948 acc_pose: 0.759509 loss: 0.000948 2022/09/12 19:43:48 - mmengine - INFO - Epoch(train) [3][250/586] lr: 5.000000e-04 eta: 18:00:14 time: 0.329432 data_time: 0.023010 memory: 7489 loss_kpt: 0.000927 acc_pose: 0.693246 loss: 0.000927 2022/09/12 19:44:05 - mmengine - INFO - Epoch(train) [3][300/586] lr: 5.000000e-04 eta: 17:46:22 time: 0.337944 data_time: 0.022360 memory: 7489 loss_kpt: 0.000948 acc_pose: 0.631207 loss: 0.000948 2022/09/12 19:44:22 - mmengine - INFO - Epoch(train) [3][350/586] lr: 5.000000e-04 eta: 17:33:30 time: 0.339385 data_time: 0.022001 memory: 7489 loss_kpt: 0.000927 acc_pose: 0.670272 loss: 0.000927 2022/09/12 19:44:39 - mmengine - INFO - Epoch(train) [3][400/586] lr: 5.000000e-04 eta: 17:21:18 time: 0.337384 data_time: 0.024070 memory: 7489 loss_kpt: 0.000924 acc_pose: 0.695192 loss: 0.000924 2022/09/12 19:44:56 - mmengine - INFO - Epoch(train) [3][450/586] lr: 5.000000e-04 eta: 17:09:55 time: 0.338842 data_time: 0.021878 memory: 7489 loss_kpt: 0.000937 acc_pose: 0.723738 loss: 0.000937 2022/09/12 19:45:13 - mmengine - INFO - Epoch(train) [3][500/586] lr: 5.000000e-04 eta: 16:59:28 time: 0.342914 data_time: 0.023344 memory: 7489 loss_kpt: 0.000904 acc_pose: 0.671033 loss: 0.000904 2022/09/12 19:45:29 - mmengine - INFO - Epoch(train) [3][550/586] lr: 5.000000e-04 eta: 16:48:43 time: 0.328106 data_time: 0.022455 memory: 7489 loss_kpt: 0.000891 acc_pose: 0.688125 loss: 0.000891 2022/09/12 19:45:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:45:41 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/12 19:46:06 - mmengine - INFO - Epoch(train) [4][50/586] lr: 5.000000e-04 eta: 16:19:32 time: 0.348634 data_time: 0.030285 memory: 7489 loss_kpt: 0.000899 acc_pose: 0.642983 loss: 0.000899 2022/09/12 19:46:22 - mmengine - INFO - Epoch(train) [4][100/586] lr: 5.000000e-04 eta: 16:10:58 time: 0.334404 data_time: 0.023766 memory: 7489 loss_kpt: 0.000909 acc_pose: 0.735459 loss: 0.000909 2022/09/12 19:46:39 - mmengine - INFO - Epoch(train) [4][150/586] lr: 5.000000e-04 eta: 16:03:01 time: 0.338187 data_time: 0.022791 memory: 7489 loss_kpt: 0.000898 acc_pose: 0.667795 loss: 0.000898 2022/09/12 19:46:57 - mmengine - INFO - Epoch(train) [4][200/586] lr: 5.000000e-04 eta: 15:55:52 time: 0.345957 data_time: 0.022879 memory: 7489 loss_kpt: 0.000898 acc_pose: 0.749838 loss: 0.000898 2022/09/12 19:47:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:47:13 - mmengine - INFO - Epoch(train) [4][250/586] lr: 5.000000e-04 eta: 15:48:30 time: 0.334716 data_time: 0.030812 memory: 7489 loss_kpt: 0.000870 acc_pose: 0.673352 loss: 0.000870 2022/09/12 19:47:30 - mmengine - INFO - Epoch(train) [4][300/586] lr: 5.000000e-04 eta: 15:41:39 time: 0.338141 data_time: 0.022968 memory: 7489 loss_kpt: 0.000877 acc_pose: 0.765471 loss: 0.000877 2022/09/12 19:47:47 - mmengine - INFO - Epoch(train) [4][350/586] lr: 5.000000e-04 eta: 15:35:10 time: 0.339668 data_time: 0.023109 memory: 7489 loss_kpt: 0.000896 acc_pose: 0.696497 loss: 0.000896 2022/09/12 19:48:04 - mmengine - INFO - Epoch(train) [4][400/586] lr: 5.000000e-04 eta: 15:28:34 time: 0.330865 data_time: 0.027133 memory: 7489 loss_kpt: 0.000879 acc_pose: 0.768315 loss: 0.000879 2022/09/12 19:48:20 - mmengine - INFO - Epoch(train) [4][450/586] lr: 5.000000e-04 eta: 15:22:17 time: 0.331221 data_time: 0.023387 memory: 7489 loss_kpt: 0.000845 acc_pose: 0.810724 loss: 0.000845 2022/09/12 19:48:38 - mmengine - INFO - Epoch(train) [4][500/586] lr: 5.000000e-04 eta: 15:16:57 time: 0.346824 data_time: 0.022934 memory: 7489 loss_kpt: 0.000866 acc_pose: 0.692671 loss: 0.000866 2022/09/12 19:48:54 - mmengine - INFO - Epoch(train) [4][550/586] lr: 5.000000e-04 eta: 15:11:12 time: 0.332420 data_time: 0.028285 memory: 7489 loss_kpt: 0.000887 acc_pose: 0.750875 loss: 0.000887 2022/09/12 19:49:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:49:07 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/12 19:49:31 - mmengine - INFO - Epoch(train) [5][50/586] lr: 5.000000e-04 eta: 14:52:39 time: 0.352548 data_time: 0.034357 memory: 7489 loss_kpt: 0.000856 acc_pose: 0.735647 loss: 0.000856 2022/09/12 19:49:47 - mmengine - INFO - Epoch(train) [5][100/586] lr: 5.000000e-04 eta: 14:47:34 time: 0.329197 data_time: 0.023301 memory: 7489 loss_kpt: 0.000863 acc_pose: 0.742021 loss: 0.000863 2022/09/12 19:50:04 - mmengine - INFO - Epoch(train) [5][150/586] lr: 5.000000e-04 eta: 14:43:15 time: 0.343388 data_time: 0.024594 memory: 7489 loss_kpt: 0.000863 acc_pose: 0.719037 loss: 0.000863 2022/09/12 19:50:21 - mmengine - INFO - Epoch(train) [5][200/586] lr: 5.000000e-04 eta: 14:39:07 time: 0.344237 data_time: 0.023055 memory: 7489 loss_kpt: 0.000848 acc_pose: 0.768678 loss: 0.000848 2022/09/12 19:50:38 - mmengine - INFO - Epoch(train) [5][250/586] lr: 5.000000e-04 eta: 14:34:27 time: 0.326796 data_time: 0.023360 memory: 7489 loss_kpt: 0.000863 acc_pose: 0.758899 loss: 0.000863 2022/09/12 19:50:54 - mmengine - INFO - Epoch(train) [5][300/586] lr: 5.000000e-04 eta: 14:30:15 time: 0.334198 data_time: 0.023352 memory: 7489 loss_kpt: 0.000859 acc_pose: 0.727105 loss: 0.000859 2022/09/12 19:51:11 - mmengine - INFO - Epoch(train) [5][350/586] lr: 5.000000e-04 eta: 14:26:26 time: 0.340767 data_time: 0.022856 memory: 7489 loss_kpt: 0.000854 acc_pose: 0.772733 loss: 0.000854 2022/09/12 19:51:28 - mmengine - INFO - Epoch(train) [5][400/586] lr: 5.000000e-04 eta: 14:22:36 time: 0.337111 data_time: 0.022755 memory: 7489 loss_kpt: 0.000837 acc_pose: 0.746772 loss: 0.000837 2022/09/12 19:51:45 - mmengine - INFO - Epoch(train) [5][450/586] lr: 5.000000e-04 eta: 14:19:01 time: 0.340169 data_time: 0.022750 memory: 7489 loss_kpt: 0.000827 acc_pose: 0.759592 loss: 0.000827 2022/09/12 19:52:02 - mmengine - INFO - Epoch(train) [5][500/586] lr: 5.000000e-04 eta: 14:15:33 time: 0.340334 data_time: 0.022861 memory: 7489 loss_kpt: 0.000808 acc_pose: 0.777542 loss: 0.000808 2022/09/12 19:52:19 - mmengine - INFO - Epoch(train) [5][550/586] lr: 5.000000e-04 eta: 14:11:59 time: 0.334038 data_time: 0.022618 memory: 7489 loss_kpt: 0.000848 acc_pose: 0.719551 loss: 0.000848 2022/09/12 19:52:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:52:31 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/12 19:52:55 - mmengine - INFO - Epoch(train) [6][50/586] lr: 5.000000e-04 eta: 13:58:27 time: 0.346939 data_time: 0.028755 memory: 7489 loss_kpt: 0.000844 acc_pose: 0.726227 loss: 0.000844 2022/09/12 19:53:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:53:12 - mmengine - INFO - Epoch(train) [6][100/586] lr: 5.000000e-04 eta: 13:55:20 time: 0.335282 data_time: 0.025910 memory: 7489 loss_kpt: 0.000860 acc_pose: 0.703668 loss: 0.000860 2022/09/12 19:53:29 - mmengine - INFO - Epoch(train) [6][150/586] lr: 5.000000e-04 eta: 13:52:17 time: 0.334103 data_time: 0.023147 memory: 7489 loss_kpt: 0.000834 acc_pose: 0.731057 loss: 0.000834 2022/09/12 19:53:46 - mmengine - INFO - Epoch(train) [6][200/586] lr: 5.000000e-04 eta: 13:49:31 time: 0.340549 data_time: 0.023381 memory: 7489 loss_kpt: 0.000823 acc_pose: 0.718536 loss: 0.000823 2022/09/12 19:54:03 - mmengine - INFO - Epoch(train) [6][250/586] lr: 5.000000e-04 eta: 13:46:42 time: 0.336491 data_time: 0.026957 memory: 7489 loss_kpt: 0.000840 acc_pose: 0.677140 loss: 0.000840 2022/09/12 19:54:19 - mmengine - INFO - Epoch(train) [6][300/586] lr: 5.000000e-04 eta: 13:43:58 time: 0.336217 data_time: 0.022505 memory: 7489 loss_kpt: 0.000850 acc_pose: 0.733448 loss: 0.000850 2022/09/12 19:54:36 - mmengine - INFO - Epoch(train) [6][350/586] lr: 5.000000e-04 eta: 13:41:23 time: 0.338865 data_time: 0.023303 memory: 7489 loss_kpt: 0.000812 acc_pose: 0.713505 loss: 0.000812 2022/09/12 19:54:53 - mmengine - INFO - Epoch(train) [6][400/586] lr: 5.000000e-04 eta: 13:38:47 time: 0.336531 data_time: 0.027437 memory: 7489 loss_kpt: 0.000847 acc_pose: 0.721993 loss: 0.000847 2022/09/12 19:55:10 - mmengine - INFO - Epoch(train) [6][450/586] lr: 5.000000e-04 eta: 13:36:10 time: 0.333210 data_time: 0.023431 memory: 7489 loss_kpt: 0.000805 acc_pose: 0.698450 loss: 0.000805 2022/09/12 19:55:27 - mmengine - INFO - Epoch(train) [6][500/586] lr: 5.000000e-04 eta: 13:33:45 time: 0.337528 data_time: 0.023180 memory: 7489 loss_kpt: 0.000825 acc_pose: 0.693268 loss: 0.000825 2022/09/12 19:55:44 - mmengine - INFO - Epoch(train) [6][550/586] lr: 5.000000e-04 eta: 13:31:27 time: 0.339647 data_time: 0.026115 memory: 7489 loss_kpt: 0.000810 acc_pose: 0.770226 loss: 0.000810 2022/09/12 19:55:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:55:56 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/12 19:56:20 - mmengine - INFO - Epoch(train) [7][50/586] lr: 5.000000e-04 eta: 13:20:47 time: 0.339082 data_time: 0.026949 memory: 7489 loss_kpt: 0.000839 acc_pose: 0.808459 loss: 0.000839 2022/09/12 19:56:36 - mmengine - INFO - Epoch(train) [7][100/586] lr: 5.000000e-04 eta: 13:18:39 time: 0.336809 data_time: 0.022675 memory: 7489 loss_kpt: 0.000812 acc_pose: 0.801063 loss: 0.000812 2022/09/12 19:56:53 - mmengine - INFO - Epoch(train) [7][150/586] lr: 5.000000e-04 eta: 13:16:26 time: 0.331913 data_time: 0.022300 memory: 7489 loss_kpt: 0.000801 acc_pose: 0.758500 loss: 0.000801 2022/09/12 19:57:10 - mmengine - INFO - Epoch(train) [7][200/586] lr: 5.000000e-04 eta: 13:14:32 time: 0.342046 data_time: 0.022609 memory: 7489 loss_kpt: 0.000814 acc_pose: 0.793062 loss: 0.000814 2022/09/12 19:57:27 - mmengine - INFO - Epoch(train) [7][250/586] lr: 5.000000e-04 eta: 13:12:42 time: 0.342556 data_time: 0.023375 memory: 7489 loss_kpt: 0.000808 acc_pose: 0.783364 loss: 0.000808 2022/09/12 19:57:44 - mmengine - INFO - Epoch(train) [7][300/586] lr: 5.000000e-04 eta: 13:10:40 time: 0.332957 data_time: 0.022934 memory: 7489 loss_kpt: 0.000804 acc_pose: 0.795419 loss: 0.000804 2022/09/12 19:58:01 - mmengine - INFO - Epoch(train) [7][350/586] lr: 5.000000e-04 eta: 13:08:52 time: 0.340783 data_time: 0.025827 memory: 7489 loss_kpt: 0.000820 acc_pose: 0.737227 loss: 0.000820 2022/09/12 19:58:18 - mmengine - INFO - Epoch(train) [7][400/586] lr: 5.000000e-04 eta: 13:07:01 time: 0.337364 data_time: 0.022903 memory: 7489 loss_kpt: 0.000811 acc_pose: 0.687145 loss: 0.000811 2022/09/12 19:58:35 - mmengine - INFO - Epoch(train) [7][450/586] lr: 5.000000e-04 eta: 13:05:10 time: 0.335443 data_time: 0.023918 memory: 7489 loss_kpt: 0.000805 acc_pose: 0.819591 loss: 0.000805 2022/09/12 19:58:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:58:52 - mmengine - INFO - Epoch(train) [7][500/586] lr: 5.000000e-04 eta: 13:03:26 time: 0.339103 data_time: 0.022996 memory: 7489 loss_kpt: 0.000805 acc_pose: 0.808976 loss: 0.000805 2022/09/12 19:59:09 - mmengine - INFO - Epoch(train) [7][550/586] lr: 5.000000e-04 eta: 13:01:54 time: 0.345048 data_time: 0.023902 memory: 7489 loss_kpt: 0.000795 acc_pose: 0.747114 loss: 0.000795 2022/09/12 19:59:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 19:59:21 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/12 19:59:44 - mmengine - INFO - Epoch(train) [8][50/586] lr: 5.000000e-04 eta: 12:53:19 time: 0.342029 data_time: 0.030630 memory: 7489 loss_kpt: 0.000792 acc_pose: 0.801571 loss: 0.000792 2022/09/12 20:00:01 - mmengine - INFO - Epoch(train) [8][100/586] lr: 5.000000e-04 eta: 12:51:48 time: 0.339765 data_time: 0.023577 memory: 7489 loss_kpt: 0.000806 acc_pose: 0.723215 loss: 0.000806 2022/09/12 20:00:18 - mmengine - INFO - Epoch(train) [8][150/586] lr: 5.000000e-04 eta: 12:50:14 time: 0.336179 data_time: 0.026144 memory: 7489 loss_kpt: 0.000802 acc_pose: 0.836307 loss: 0.000802 2022/09/12 20:00:35 - mmengine - INFO - Epoch(train) [8][200/586] lr: 5.000000e-04 eta: 12:48:38 time: 0.333659 data_time: 0.023110 memory: 7489 loss_kpt: 0.000784 acc_pose: 0.748095 loss: 0.000784 2022/09/12 20:00:52 - mmengine - INFO - Epoch(train) [8][250/586] lr: 5.000000e-04 eta: 12:47:05 time: 0.334507 data_time: 0.022797 memory: 7489 loss_kpt: 0.000793 acc_pose: 0.815673 loss: 0.000793 2022/09/12 20:01:09 - mmengine - INFO - Epoch(train) [8][300/586] lr: 5.000000e-04 eta: 12:45:40 time: 0.338969 data_time: 0.023426 memory: 7489 loss_kpt: 0.000775 acc_pose: 0.714464 loss: 0.000775 2022/09/12 20:01:25 - mmengine - INFO - Epoch(train) [8][350/586] lr: 5.000000e-04 eta: 12:44:11 time: 0.334982 data_time: 0.026591 memory: 7489 loss_kpt: 0.000771 acc_pose: 0.700939 loss: 0.000771 2022/09/12 20:01:43 - mmengine - INFO - Epoch(train) [8][400/586] lr: 5.000000e-04 eta: 12:42:56 time: 0.344045 data_time: 0.022436 memory: 7489 loss_kpt: 0.000795 acc_pose: 0.780776 loss: 0.000795 2022/09/12 20:01:59 - mmengine - INFO - Epoch(train) [8][450/586] lr: 5.000000e-04 eta: 12:41:25 time: 0.331449 data_time: 0.023583 memory: 7489 loss_kpt: 0.000800 acc_pose: 0.727560 loss: 0.000800 2022/09/12 20:02:16 - mmengine - INFO - Epoch(train) [8][500/586] lr: 5.000000e-04 eta: 12:40:13 time: 0.344448 data_time: 0.027288 memory: 7489 loss_kpt: 0.000789 acc_pose: 0.773686 loss: 0.000789 2022/09/12 20:02:33 - mmengine - INFO - Epoch(train) [8][550/586] lr: 5.000000e-04 eta: 12:38:50 time: 0.335221 data_time: 0.022308 memory: 7489 loss_kpt: 0.000794 acc_pose: 0.663411 loss: 0.000794 2022/09/12 20:02:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:02:45 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/12 20:03:09 - mmengine - INFO - Epoch(train) [9][50/586] lr: 5.000000e-04 eta: 12:31:36 time: 0.339605 data_time: 0.027006 memory: 7489 loss_kpt: 0.000788 acc_pose: 0.756961 loss: 0.000788 2022/09/12 20:03:26 - mmengine - INFO - Epoch(train) [9][100/586] lr: 5.000000e-04 eta: 12:30:35 time: 0.347439 data_time: 0.023292 memory: 7489 loss_kpt: 0.000795 acc_pose: 0.815922 loss: 0.000795 2022/09/12 20:03:43 - mmengine - INFO - Epoch(train) [9][150/586] lr: 5.000000e-04 eta: 12:29:19 time: 0.334131 data_time: 0.022570 memory: 7489 loss_kpt: 0.000754 acc_pose: 0.791632 loss: 0.000754 2022/09/12 20:03:59 - mmengine - INFO - Epoch(train) [9][200/586] lr: 5.000000e-04 eta: 12:28:00 time: 0.331081 data_time: 0.022617 memory: 7489 loss_kpt: 0.000782 acc_pose: 0.828182 loss: 0.000782 2022/09/12 20:04:16 - mmengine - INFO - Epoch(train) [9][250/586] lr: 5.000000e-04 eta: 12:26:56 time: 0.341620 data_time: 0.028209 memory: 7489 loss_kpt: 0.000789 acc_pose: 0.802032 loss: 0.000789 2022/09/12 20:04:33 - mmengine - INFO - Epoch(train) [9][300/586] lr: 5.000000e-04 eta: 12:25:44 time: 0.334997 data_time: 0.022345 memory: 7489 loss_kpt: 0.000770 acc_pose: 0.743970 loss: 0.000770 2022/09/12 20:04:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:04:50 - mmengine - INFO - Epoch(train) [9][350/586] lr: 5.000000e-04 eta: 12:24:30 time: 0.332180 data_time: 0.022281 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.837709 loss: 0.000778 2022/09/12 20:05:07 - mmengine - INFO - Epoch(train) [9][400/586] lr: 5.000000e-04 eta: 12:23:27 time: 0.339831 data_time: 0.022902 memory: 7489 loss_kpt: 0.000765 acc_pose: 0.613386 loss: 0.000765 2022/09/12 20:05:24 - mmengine - INFO - Epoch(train) [9][450/586] lr: 5.000000e-04 eta: 12:22:25 time: 0.340945 data_time: 0.022667 memory: 7489 loss_kpt: 0.000797 acc_pose: 0.776869 loss: 0.000797 2022/09/12 20:05:40 - mmengine - INFO - Epoch(train) [9][500/586] lr: 5.000000e-04 eta: 12:21:15 time: 0.332590 data_time: 0.022713 memory: 7489 loss_kpt: 0.000791 acc_pose: 0.669648 loss: 0.000791 2022/09/12 20:05:57 - mmengine - INFO - Epoch(train) [9][550/586] lr: 5.000000e-04 eta: 12:20:17 time: 0.342765 data_time: 0.026771 memory: 7489 loss_kpt: 0.000782 acc_pose: 0.784752 loss: 0.000782 2022/09/12 20:06:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:06:10 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/12 20:06:34 - mmengine - INFO - Epoch(train) [10][50/586] lr: 5.000000e-04 eta: 12:14:04 time: 0.340650 data_time: 0.030077 memory: 7489 loss_kpt: 0.000754 acc_pose: 0.791393 loss: 0.000754 2022/09/12 20:06:51 - mmengine - INFO - Epoch(train) [10][100/586] lr: 5.000000e-04 eta: 12:13:09 time: 0.340885 data_time: 0.025633 memory: 7489 loss_kpt: 0.000769 acc_pose: 0.814448 loss: 0.000769 2022/09/12 20:07:08 - mmengine - INFO - Epoch(train) [10][150/586] lr: 5.000000e-04 eta: 12:12:13 time: 0.339065 data_time: 0.022188 memory: 7489 loss_kpt: 0.000752 acc_pose: 0.775375 loss: 0.000752 2022/09/12 20:07:24 - mmengine - INFO - Epoch(train) [10][200/586] lr: 5.000000e-04 eta: 12:11:11 time: 0.332826 data_time: 0.022186 memory: 7489 loss_kpt: 0.000788 acc_pose: 0.738280 loss: 0.000788 2022/09/12 20:07:41 - mmengine - INFO - Epoch(train) [10][250/586] lr: 5.000000e-04 eta: 12:10:18 time: 0.340918 data_time: 0.022875 memory: 7489 loss_kpt: 0.000753 acc_pose: 0.695841 loss: 0.000753 2022/09/12 20:07:58 - mmengine - INFO - Epoch(train) [10][300/586] lr: 5.000000e-04 eta: 12:09:19 time: 0.334890 data_time: 0.022798 memory: 7489 loss_kpt: 0.000804 acc_pose: 0.782034 loss: 0.000804 2022/09/12 20:08:15 - mmengine - INFO - Epoch(train) [10][350/586] lr: 5.000000e-04 eta: 12:08:19 time: 0.332726 data_time: 0.021926 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.796855 loss: 0.000778 2022/09/12 20:08:32 - mmengine - INFO - Epoch(train) [10][400/586] lr: 5.000000e-04 eta: 12:07:28 time: 0.340874 data_time: 0.022648 memory: 7489 loss_kpt: 0.000761 acc_pose: 0.713601 loss: 0.000761 2022/09/12 20:08:49 - mmengine - INFO - Epoch(train) [10][450/586] lr: 5.000000e-04 eta: 12:06:40 time: 0.342669 data_time: 0.022750 memory: 7489 loss_kpt: 0.000764 acc_pose: 0.777151 loss: 0.000764 2022/09/12 20:09:06 - mmengine - INFO - Epoch(train) [10][500/586] lr: 5.000000e-04 eta: 12:05:47 time: 0.338300 data_time: 0.022254 memory: 7489 loss_kpt: 0.000757 acc_pose: 0.852252 loss: 0.000757 2022/09/12 20:09:23 - mmengine - INFO - Epoch(train) [10][550/586] lr: 5.000000e-04 eta: 12:04:56 time: 0.338880 data_time: 0.023215 memory: 7489 loss_kpt: 0.000750 acc_pose: 0.775387 loss: 0.000750 2022/09/12 20:09:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:09:35 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/12 20:09:57 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:35 time: 0.266613 data_time: 0.057833 memory: 7489 2022/09/12 20:10:09 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:01:13 time: 0.239229 data_time: 0.035625 memory: 1657 2022/09/12 20:10:21 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:00:59 time: 0.231803 data_time: 0.027133 memory: 1657 2022/09/12 20:10:33 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:51 time: 0.248279 data_time: 0.045503 memory: 1657 2022/09/12 20:10:45 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:36 time: 0.232002 data_time: 0.023930 memory: 1657 2022/09/12 20:10:59 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:31 time: 0.292505 data_time: 0.088735 memory: 1657 2022/09/12 20:11:10 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:11 time: 0.209456 data_time: 0.007225 memory: 1657 2022/09/12 20:11:21 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:01 time: 0.217898 data_time: 0.015577 memory: 1657 2022/09/12 20:11:59 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 20:12:13 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.677691 coco/AP .5: 0.873855 coco/AP .75: 0.749297 coco/AP (M): 0.640576 coco/AP (L): 0.743573 coco/AR: 0.734902 coco/AR .5: 0.913256 coco/AR .75: 0.801322 coco/AR (M): 0.692762 coco/AR (L): 0.794946 2022/09/12 20:12:17 - mmengine - INFO - The best checkpoint with 0.6777 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/09/12 20:12:34 - mmengine - INFO - Epoch(train) [11][50/586] lr: 5.000000e-04 eta: 11:59:32 time: 0.342951 data_time: 0.026240 memory: 7489 loss_kpt: 0.000789 acc_pose: 0.700238 loss: 0.000789 2022/09/12 20:12:51 - mmengine - INFO - Epoch(train) [11][100/586] lr: 5.000000e-04 eta: 11:58:42 time: 0.337195 data_time: 0.022570 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.681304 loss: 0.000756 2022/09/12 20:13:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:13:07 - mmengine - INFO - Epoch(train) [11][150/586] lr: 5.000000e-04 eta: 11:57:48 time: 0.331502 data_time: 0.022244 memory: 7489 loss_kpt: 0.000772 acc_pose: 0.770834 loss: 0.000772 2022/09/12 20:13:24 - mmengine - INFO - Epoch(train) [11][200/586] lr: 5.000000e-04 eta: 11:57:00 time: 0.337444 data_time: 0.022214 memory: 7489 loss_kpt: 0.000760 acc_pose: 0.749752 loss: 0.000760 2022/09/12 20:13:41 - mmengine - INFO - Epoch(train) [11][250/586] lr: 5.000000e-04 eta: 11:56:08 time: 0.332052 data_time: 0.022358 memory: 7489 loss_kpt: 0.000776 acc_pose: 0.662312 loss: 0.000776 2022/09/12 20:13:57 - mmengine - INFO - Epoch(train) [11][300/586] lr: 5.000000e-04 eta: 11:55:20 time: 0.336611 data_time: 0.023025 memory: 7489 loss_kpt: 0.000754 acc_pose: 0.784762 loss: 0.000754 2022/09/12 20:14:15 - mmengine - INFO - Epoch(train) [11][350/586] lr: 5.000000e-04 eta: 11:54:39 time: 0.343286 data_time: 0.025497 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.762721 loss: 0.000778 2022/09/12 20:14:31 - mmengine - INFO - Epoch(train) [11][400/586] lr: 5.000000e-04 eta: 11:53:51 time: 0.334568 data_time: 0.022256 memory: 7489 loss_kpt: 0.000772 acc_pose: 0.726517 loss: 0.000772 2022/09/12 20:14:49 - mmengine - INFO - Epoch(train) [11][450/586] lr: 5.000000e-04 eta: 11:53:12 time: 0.344026 data_time: 0.022174 memory: 7489 loss_kpt: 0.000752 acc_pose: 0.663873 loss: 0.000752 2022/09/12 20:15:06 - mmengine - INFO - Epoch(train) [11][500/586] lr: 5.000000e-04 eta: 11:52:29 time: 0.340253 data_time: 0.023198 memory: 7489 loss_kpt: 0.000783 acc_pose: 0.843509 loss: 0.000783 2022/09/12 20:15:23 - mmengine - INFO - Epoch(train) [11][550/586] lr: 5.000000e-04 eta: 11:51:48 time: 0.340919 data_time: 0.022545 memory: 7489 loss_kpt: 0.000744 acc_pose: 0.776190 loss: 0.000744 2022/09/12 20:15:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:15:35 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/12 20:15:58 - mmengine - INFO - Epoch(train) [12][50/586] lr: 5.000000e-04 eta: 11:46:58 time: 0.341841 data_time: 0.029625 memory: 7489 loss_kpt: 0.000744 acc_pose: 0.791760 loss: 0.000744 2022/09/12 20:16:15 - mmengine - INFO - Epoch(train) [12][100/586] lr: 5.000000e-04 eta: 11:46:12 time: 0.332881 data_time: 0.022488 memory: 7489 loss_kpt: 0.000742 acc_pose: 0.800988 loss: 0.000742 2022/09/12 20:16:32 - mmengine - INFO - Epoch(train) [12][150/586] lr: 5.000000e-04 eta: 11:45:31 time: 0.337610 data_time: 0.023016 memory: 7489 loss_kpt: 0.000768 acc_pose: 0.796149 loss: 0.000768 2022/09/12 20:16:49 - mmengine - INFO - Epoch(train) [12][200/586] lr: 5.000000e-04 eta: 11:44:56 time: 0.344288 data_time: 0.023038 memory: 7489 loss_kpt: 0.000742 acc_pose: 0.794502 loss: 0.000742 2022/09/12 20:17:06 - mmengine - INFO - Epoch(train) [12][250/586] lr: 5.000000e-04 eta: 11:44:15 time: 0.336794 data_time: 0.023006 memory: 7489 loss_kpt: 0.000738 acc_pose: 0.780687 loss: 0.000738 2022/09/12 20:17:23 - mmengine - INFO - Epoch(train) [12][300/586] lr: 5.000000e-04 eta: 11:43:32 time: 0.335213 data_time: 0.022799 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.807387 loss: 0.000728 2022/09/12 20:17:40 - mmengine - INFO - Epoch(train) [12][350/586] lr: 5.000000e-04 eta: 11:42:55 time: 0.339815 data_time: 0.022829 memory: 7489 loss_kpt: 0.000743 acc_pose: 0.759979 loss: 0.000743 2022/09/12 20:17:57 - mmengine - INFO - Epoch(train) [12][400/586] lr: 5.000000e-04 eta: 11:42:15 time: 0.337527 data_time: 0.022410 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.781737 loss: 0.000730 2022/09/12 20:18:13 - mmengine - INFO - Epoch(train) [12][450/586] lr: 5.000000e-04 eta: 11:41:31 time: 0.332156 data_time: 0.022501 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.759073 loss: 0.000732 2022/09/12 20:18:30 - mmengine - INFO - Epoch(train) [12][500/586] lr: 5.000000e-04 eta: 11:40:58 time: 0.344234 data_time: 0.022444 memory: 7489 loss_kpt: 0.000750 acc_pose: 0.839187 loss: 0.000750 2022/09/12 20:18:48 - mmengine - INFO - Epoch(train) [12][550/586] lr: 5.000000e-04 eta: 11:40:23 time: 0.341935 data_time: 0.023139 memory: 7489 loss_kpt: 0.000733 acc_pose: 0.775047 loss: 0.000733 2022/09/12 20:18:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:18:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:18:59 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/12 20:19:23 - mmengine - INFO - Epoch(train) [13][50/586] lr: 5.000000e-04 eta: 11:36:05 time: 0.345207 data_time: 0.028995 memory: 7489 loss_kpt: 0.000743 acc_pose: 0.740389 loss: 0.000743 2022/09/12 20:19:40 - mmengine - INFO - Epoch(train) [13][100/586] lr: 5.000000e-04 eta: 11:35:26 time: 0.334862 data_time: 0.022354 memory: 7489 loss_kpt: 0.000722 acc_pose: 0.775176 loss: 0.000722 2022/09/12 20:19:57 - mmengine - INFO - Epoch(train) [13][150/586] lr: 5.000000e-04 eta: 11:34:42 time: 0.327759 data_time: 0.023541 memory: 7489 loss_kpt: 0.000768 acc_pose: 0.782131 loss: 0.000768 2022/09/12 20:20:13 - mmengine - INFO - Epoch(train) [13][200/586] lr: 5.000000e-04 eta: 11:34:06 time: 0.337157 data_time: 0.029739 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.763928 loss: 0.000756 2022/09/12 20:20:30 - mmengine - INFO - Epoch(train) [13][250/586] lr: 5.000000e-04 eta: 11:33:30 time: 0.337065 data_time: 0.022131 memory: 7489 loss_kpt: 0.000760 acc_pose: 0.720735 loss: 0.000760 2022/09/12 20:20:47 - mmengine - INFO - Epoch(train) [13][300/586] lr: 5.000000e-04 eta: 11:32:52 time: 0.333017 data_time: 0.023217 memory: 7489 loss_kpt: 0.000749 acc_pose: 0.738291 loss: 0.000749 2022/09/12 20:21:04 - mmengine - INFO - Epoch(train) [13][350/586] lr: 5.000000e-04 eta: 11:32:21 time: 0.342766 data_time: 0.022575 memory: 7489 loss_kpt: 0.000731 acc_pose: 0.784351 loss: 0.000731 2022/09/12 20:21:21 - mmengine - INFO - Epoch(train) [13][400/586] lr: 5.000000e-04 eta: 11:31:48 time: 0.340844 data_time: 0.023108 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.731466 loss: 0.000736 2022/09/12 20:21:38 - mmengine - INFO - Epoch(train) [13][450/586] lr: 5.000000e-04 eta: 11:31:11 time: 0.333157 data_time: 0.025975 memory: 7489 loss_kpt: 0.000718 acc_pose: 0.822918 loss: 0.000718 2022/09/12 20:21:54 - mmengine - INFO - Epoch(train) [13][500/586] lr: 5.000000e-04 eta: 11:30:32 time: 0.332456 data_time: 0.022291 memory: 7489 loss_kpt: 0.000718 acc_pose: 0.781304 loss: 0.000718 2022/09/12 20:22:11 - mmengine - INFO - Epoch(train) [13][550/586] lr: 5.000000e-04 eta: 11:30:01 time: 0.340525 data_time: 0.022096 memory: 7489 loss_kpt: 0.000754 acc_pose: 0.743103 loss: 0.000754 2022/09/12 20:22:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:22:23 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/12 20:22:48 - mmengine - INFO - Epoch(train) [14][50/586] lr: 5.000000e-04 eta: 11:26:09 time: 0.349571 data_time: 0.032098 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.790559 loss: 0.000728 2022/09/12 20:23:05 - mmengine - INFO - Epoch(train) [14][100/586] lr: 5.000000e-04 eta: 11:25:35 time: 0.335864 data_time: 0.027877 memory: 7489 loss_kpt: 0.000739 acc_pose: 0.805672 loss: 0.000739 2022/09/12 20:23:21 - mmengine - INFO - Epoch(train) [14][150/586] lr: 5.000000e-04 eta: 11:24:59 time: 0.331730 data_time: 0.022494 memory: 7489 loss_kpt: 0.000762 acc_pose: 0.758227 loss: 0.000762 2022/09/12 20:23:39 - mmengine - INFO - Epoch(train) [14][200/586] lr: 5.000000e-04 eta: 11:24:32 time: 0.343384 data_time: 0.022844 memory: 7489 loss_kpt: 0.000722 acc_pose: 0.783315 loss: 0.000722 2022/09/12 20:23:55 - mmengine - INFO - Epoch(train) [14][250/586] lr: 5.000000e-04 eta: 11:23:58 time: 0.334565 data_time: 0.026077 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.803392 loss: 0.000732 2022/09/12 20:24:12 - mmengine - INFO - Epoch(train) [14][300/586] lr: 5.000000e-04 eta: 11:23:22 time: 0.332080 data_time: 0.023019 memory: 7489 loss_kpt: 0.000741 acc_pose: 0.829885 loss: 0.000741 2022/09/12 20:24:29 - mmengine - INFO - Epoch(train) [14][350/586] lr: 5.000000e-04 eta: 11:22:51 time: 0.337649 data_time: 0.022771 memory: 7489 loss_kpt: 0.000745 acc_pose: 0.711324 loss: 0.000745 2022/09/12 20:24:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:24:46 - mmengine - INFO - Epoch(train) [14][400/586] lr: 5.000000e-04 eta: 11:22:30 time: 0.351418 data_time: 0.026923 memory: 7489 loss_kpt: 0.000746 acc_pose: 0.801472 loss: 0.000746 2022/09/12 20:25:03 - mmengine - INFO - Epoch(train) [14][450/586] lr: 5.000000e-04 eta: 11:21:54 time: 0.329880 data_time: 0.022940 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.769518 loss: 0.000702 2022/09/12 20:25:19 - mmengine - INFO - Epoch(train) [14][500/586] lr: 5.000000e-04 eta: 11:21:20 time: 0.332796 data_time: 0.022766 memory: 7489 loss_kpt: 0.000742 acc_pose: 0.796635 loss: 0.000742 2022/09/12 20:25:37 - mmengine - INFO - Epoch(train) [14][550/586] lr: 5.000000e-04 eta: 11:20:56 time: 0.346743 data_time: 0.026127 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.756795 loss: 0.000721 2022/09/12 20:25:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:25:49 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/12 20:26:13 - mmengine - INFO - Epoch(train) [15][50/586] lr: 5.000000e-04 eta: 11:17:18 time: 0.342684 data_time: 0.027075 memory: 7489 loss_kpt: 0.000740 acc_pose: 0.873758 loss: 0.000740 2022/09/12 20:26:30 - mmengine - INFO - Epoch(train) [15][100/586] lr: 5.000000e-04 eta: 11:16:56 time: 0.348349 data_time: 0.022862 memory: 7489 loss_kpt: 0.000726 acc_pose: 0.735893 loss: 0.000726 2022/09/12 20:26:47 - mmengine - INFO - Epoch(train) [15][150/586] lr: 5.000000e-04 eta: 11:16:26 time: 0.335854 data_time: 0.022934 memory: 7489 loss_kpt: 0.000713 acc_pose: 0.770240 loss: 0.000713 2022/09/12 20:27:04 - mmengine - INFO - Epoch(train) [15][200/586] lr: 5.000000e-04 eta: 11:16:01 time: 0.343142 data_time: 0.022426 memory: 7489 loss_kpt: 0.000705 acc_pose: 0.806016 loss: 0.000705 2022/09/12 20:27:21 - mmengine - INFO - Epoch(train) [15][250/586] lr: 5.000000e-04 eta: 11:15:33 time: 0.338192 data_time: 0.023310 memory: 7489 loss_kpt: 0.000745 acc_pose: 0.692501 loss: 0.000745 2022/09/12 20:27:38 - mmengine - INFO - Epoch(train) [15][300/586] lr: 5.000000e-04 eta: 11:15:03 time: 0.335686 data_time: 0.022924 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.803981 loss: 0.000724 2022/09/12 20:27:55 - mmengine - INFO - Epoch(train) [15][350/586] lr: 5.000000e-04 eta: 11:14:40 time: 0.345843 data_time: 0.027323 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.775744 loss: 0.000711 2022/09/12 20:28:12 - mmengine - INFO - Epoch(train) [15][400/586] lr: 5.000000e-04 eta: 11:14:13 time: 0.338049 data_time: 0.022305 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.650258 loss: 0.000756 2022/09/12 20:28:28 - mmengine - INFO - Epoch(train) [15][450/586] lr: 5.000000e-04 eta: 11:13:39 time: 0.330145 data_time: 0.023011 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.761590 loss: 0.000736 2022/09/12 20:28:45 - mmengine - INFO - Epoch(train) [15][500/586] lr: 5.000000e-04 eta: 11:13:09 time: 0.334244 data_time: 0.022771 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.740954 loss: 0.000719 2022/09/12 20:29:02 - mmengine - INFO - Epoch(train) [15][550/586] lr: 5.000000e-04 eta: 11:12:46 time: 0.343885 data_time: 0.022364 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.822765 loss: 0.000719 2022/09/12 20:29:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:29:14 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/12 20:29:38 - mmengine - INFO - Epoch(train) [16][50/586] lr: 5.000000e-04 eta: 11:09:21 time: 0.337146 data_time: 0.028274 memory: 7489 loss_kpt: 0.000734 acc_pose: 0.808365 loss: 0.000734 2022/09/12 20:29:55 - mmengine - INFO - Epoch(train) [16][100/586] lr: 5.000000e-04 eta: 11:08:57 time: 0.341776 data_time: 0.026684 memory: 7489 loss_kpt: 0.000722 acc_pose: 0.767955 loss: 0.000722 2022/09/12 20:30:12 - mmengine - INFO - Epoch(train) [16][150/586] lr: 5.000000e-04 eta: 11:08:35 time: 0.345617 data_time: 0.026436 memory: 7489 loss_kpt: 0.000723 acc_pose: 0.731874 loss: 0.000723 2022/09/12 20:30:29 - mmengine - INFO - Epoch(train) [16][200/586] lr: 5.000000e-04 eta: 11:08:05 time: 0.332202 data_time: 0.023959 memory: 7489 loss_kpt: 0.000722 acc_pose: 0.747036 loss: 0.000722 2022/09/12 20:30:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:30:46 - mmengine - INFO - Epoch(train) [16][250/586] lr: 5.000000e-04 eta: 11:07:41 time: 0.340830 data_time: 0.023658 memory: 7489 loss_kpt: 0.000723 acc_pose: 0.733919 loss: 0.000723 2022/09/12 20:31:03 - mmengine - INFO - Epoch(train) [16][300/586] lr: 5.000000e-04 eta: 11:07:18 time: 0.342301 data_time: 0.023602 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.735210 loss: 0.000724 2022/09/12 20:31:20 - mmengine - INFO - Epoch(train) [16][350/586] lr: 5.000000e-04 eta: 11:06:52 time: 0.337026 data_time: 0.023219 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.825766 loss: 0.000716 2022/09/12 20:31:37 - mmengine - INFO - Epoch(train) [16][400/586] lr: 5.000000e-04 eta: 11:06:24 time: 0.335483 data_time: 0.023002 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.847092 loss: 0.000694 2022/09/12 20:31:54 - mmengine - INFO - Epoch(train) [16][450/586] lr: 5.000000e-04 eta: 11:06:01 time: 0.342735 data_time: 0.026359 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.775811 loss: 0.000699 2022/09/12 20:32:10 - mmengine - INFO - Epoch(train) [16][500/586] lr: 5.000000e-04 eta: 11:05:34 time: 0.334691 data_time: 0.022959 memory: 7489 loss_kpt: 0.000727 acc_pose: 0.799073 loss: 0.000727 2022/09/12 20:32:27 - mmengine - INFO - Epoch(train) [16][550/586] lr: 5.000000e-04 eta: 11:05:07 time: 0.336074 data_time: 0.022581 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.730916 loss: 0.000708 2022/09/12 20:32:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:32:40 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/12 20:33:03 - mmengine - INFO - Epoch(train) [17][50/586] lr: 5.000000e-04 eta: 11:02:00 time: 0.342925 data_time: 0.026819 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.765814 loss: 0.000711 2022/09/12 20:33:20 - mmengine - INFO - Epoch(train) [17][100/586] lr: 5.000000e-04 eta: 11:01:35 time: 0.337477 data_time: 0.026606 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.744236 loss: 0.000684 2022/09/12 20:33:37 - mmengine - INFO - Epoch(train) [17][150/586] lr: 5.000000e-04 eta: 11:01:10 time: 0.336605 data_time: 0.022724 memory: 7489 loss_kpt: 0.000752 acc_pose: 0.810814 loss: 0.000752 2022/09/12 20:33:54 - mmengine - INFO - Epoch(train) [17][200/586] lr: 5.000000e-04 eta: 11:00:43 time: 0.333531 data_time: 0.022798 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.848921 loss: 0.000716 2022/09/12 20:34:11 - mmengine - INFO - Epoch(train) [17][250/586] lr: 5.000000e-04 eta: 11:00:23 time: 0.344055 data_time: 0.026116 memory: 7489 loss_kpt: 0.000738 acc_pose: 0.774353 loss: 0.000738 2022/09/12 20:34:27 - mmengine - INFO - Epoch(train) [17][300/586] lr: 5.000000e-04 eta: 10:59:57 time: 0.335638 data_time: 0.023116 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.770640 loss: 0.000698 2022/09/12 20:34:44 - mmengine - INFO - Epoch(train) [17][350/586] lr: 5.000000e-04 eta: 10:59:30 time: 0.333046 data_time: 0.022476 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.781451 loss: 0.000695 2022/09/12 20:35:01 - mmengine - INFO - Epoch(train) [17][400/586] lr: 5.000000e-04 eta: 10:59:10 time: 0.344108 data_time: 0.026562 memory: 7489 loss_kpt: 0.000742 acc_pose: 0.780374 loss: 0.000742 2022/09/12 20:35:19 - mmengine - INFO - Epoch(train) [17][450/586] lr: 5.000000e-04 eta: 10:58:49 time: 0.343935 data_time: 0.023354 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.766706 loss: 0.000712 2022/09/12 20:35:35 - mmengine - INFO - Epoch(train) [17][500/586] lr: 5.000000e-04 eta: 10:58:21 time: 0.330569 data_time: 0.022302 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.744232 loss: 0.000709 2022/09/12 20:35:52 - mmengine - INFO - Epoch(train) [17][550/586] lr: 5.000000e-04 eta: 10:58:01 time: 0.343705 data_time: 0.022732 memory: 7489 loss_kpt: 0.000740 acc_pose: 0.761789 loss: 0.000740 2022/09/12 20:36:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:36:04 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/12 20:36:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:36:28 - mmengine - INFO - Epoch(train) [18][50/586] lr: 5.000000e-04 eta: 10:55:03 time: 0.338770 data_time: 0.028515 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.791192 loss: 0.000709 2022/09/12 20:36:45 - mmengine - INFO - Epoch(train) [18][100/586] lr: 5.000000e-04 eta: 10:54:38 time: 0.334012 data_time: 0.022375 memory: 7489 loss_kpt: 0.000722 acc_pose: 0.828303 loss: 0.000722 2022/09/12 20:37:02 - mmengine - INFO - Epoch(train) [18][150/586] lr: 5.000000e-04 eta: 10:54:15 time: 0.336389 data_time: 0.022393 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.873151 loss: 0.000707 2022/09/12 20:37:18 - mmengine - INFO - Epoch(train) [18][200/586] lr: 5.000000e-04 eta: 10:53:50 time: 0.335011 data_time: 0.027271 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.794516 loss: 0.000679 2022/09/12 20:37:35 - mmengine - INFO - Epoch(train) [18][250/586] lr: 5.000000e-04 eta: 10:53:30 time: 0.342302 data_time: 0.022624 memory: 7489 loss_kpt: 0.000718 acc_pose: 0.834323 loss: 0.000718 2022/09/12 20:37:52 - mmengine - INFO - Epoch(train) [18][300/586] lr: 5.000000e-04 eta: 10:53:08 time: 0.338775 data_time: 0.023259 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.718109 loss: 0.000724 2022/09/12 20:38:09 - mmengine - INFO - Epoch(train) [18][350/586] lr: 5.000000e-04 eta: 10:52:41 time: 0.331027 data_time: 0.021907 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.806218 loss: 0.000708 2022/09/12 20:38:26 - mmengine - INFO - Epoch(train) [18][400/586] lr: 5.000000e-04 eta: 10:52:19 time: 0.337437 data_time: 0.023191 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.740226 loss: 0.000732 2022/09/12 20:38:42 - mmengine - INFO - Epoch(train) [18][450/586] lr: 5.000000e-04 eta: 10:51:53 time: 0.331233 data_time: 0.022956 memory: 7489 loss_kpt: 0.000715 acc_pose: 0.821541 loss: 0.000715 2022/09/12 20:38:59 - mmengine - INFO - Epoch(train) [18][500/586] lr: 5.000000e-04 eta: 10:51:31 time: 0.340036 data_time: 0.022664 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.777296 loss: 0.000706 2022/09/12 20:39:16 - mmengine - INFO - Epoch(train) [18][550/586] lr: 5.000000e-04 eta: 10:51:11 time: 0.341211 data_time: 0.022897 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.863324 loss: 0.000670 2022/09/12 20:39:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:39:28 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/12 20:39:52 - mmengine - INFO - Epoch(train) [19][50/586] lr: 5.000000e-04 eta: 10:48:22 time: 0.335997 data_time: 0.026376 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.807170 loss: 0.000708 2022/09/12 20:40:09 - mmengine - INFO - Epoch(train) [19][100/586] lr: 5.000000e-04 eta: 10:48:02 time: 0.339105 data_time: 0.022463 memory: 7489 loss_kpt: 0.000734 acc_pose: 0.813515 loss: 0.000734 2022/09/12 20:40:25 - mmengine - INFO - Epoch(train) [19][150/586] lr: 5.000000e-04 eta: 10:47:36 time: 0.329721 data_time: 0.027616 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.737050 loss: 0.000691 2022/09/12 20:40:42 - mmengine - INFO - Epoch(train) [19][200/586] lr: 5.000000e-04 eta: 10:47:12 time: 0.334181 data_time: 0.022485 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.781466 loss: 0.000695 2022/09/12 20:40:59 - mmengine - INFO - Epoch(train) [19][250/586] lr: 5.000000e-04 eta: 10:46:53 time: 0.342514 data_time: 0.022416 memory: 7489 loss_kpt: 0.000717 acc_pose: 0.745691 loss: 0.000717 2022/09/12 20:41:16 - mmengine - INFO - Epoch(train) [19][300/586] lr: 5.000000e-04 eta: 10:46:34 time: 0.342355 data_time: 0.023830 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.774436 loss: 0.000697 2022/09/12 20:41:33 - mmengine - INFO - Epoch(train) [19][350/586] lr: 5.000000e-04 eta: 10:46:16 time: 0.342740 data_time: 0.023181 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.778306 loss: 0.000699 2022/09/12 20:41:50 - mmengine - INFO - Epoch(train) [19][400/586] lr: 5.000000e-04 eta: 10:45:54 time: 0.337563 data_time: 0.022716 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.798107 loss: 0.000724 2022/09/12 20:42:07 - mmengine - INFO - Epoch(train) [19][450/586] lr: 5.000000e-04 eta: 10:45:31 time: 0.333436 data_time: 0.022923 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.784786 loss: 0.000676 2022/09/12 20:42:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:42:24 - mmengine - INFO - Epoch(train) [19][500/586] lr: 5.000000e-04 eta: 10:45:09 time: 0.337974 data_time: 0.021835 memory: 7489 loss_kpt: 0.000696 acc_pose: 0.781949 loss: 0.000696 2022/09/12 20:42:41 - mmengine - INFO - Epoch(train) [19][550/586] lr: 5.000000e-04 eta: 10:44:48 time: 0.338034 data_time: 0.023261 memory: 7489 loss_kpt: 0.000693 acc_pose: 0.799025 loss: 0.000693 2022/09/12 20:42:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:42:53 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/12 20:43:17 - mmengine - INFO - Epoch(train) [20][50/586] lr: 5.000000e-04 eta: 10:42:13 time: 0.343761 data_time: 0.033661 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.708545 loss: 0.000681 2022/09/12 20:43:34 - mmengine - INFO - Epoch(train) [20][100/586] lr: 5.000000e-04 eta: 10:41:56 time: 0.343914 data_time: 0.022881 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.745794 loss: 0.000694 2022/09/12 20:43:51 - mmengine - INFO - Epoch(train) [20][150/586] lr: 5.000000e-04 eta: 10:41:35 time: 0.337097 data_time: 0.024009 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.760793 loss: 0.000682 2022/09/12 20:44:08 - mmengine - INFO - Epoch(train) [20][200/586] lr: 5.000000e-04 eta: 10:41:12 time: 0.333233 data_time: 0.022820 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.803672 loss: 0.000699 2022/09/12 20:44:25 - mmengine - INFO - Epoch(train) [20][250/586] lr: 5.000000e-04 eta: 10:40:55 time: 0.344636 data_time: 0.023083 memory: 7489 loss_kpt: 0.000715 acc_pose: 0.757446 loss: 0.000715 2022/09/12 20:44:42 - mmengine - INFO - Epoch(train) [20][300/586] lr: 5.000000e-04 eta: 10:40:34 time: 0.336807 data_time: 0.023695 memory: 7489 loss_kpt: 0.000689 acc_pose: 0.874469 loss: 0.000689 2022/09/12 20:44:59 - mmengine - INFO - Epoch(train) [20][350/586] lr: 5.000000e-04 eta: 10:40:13 time: 0.336004 data_time: 0.021869 memory: 7489 loss_kpt: 0.000700 acc_pose: 0.744534 loss: 0.000700 2022/09/12 20:45:16 - mmengine - INFO - Epoch(train) [20][400/586] lr: 5.000000e-04 eta: 10:39:55 time: 0.342290 data_time: 0.027006 memory: 7489 loss_kpt: 0.000705 acc_pose: 0.789124 loss: 0.000705 2022/09/12 20:45:33 - mmengine - INFO - Epoch(train) [20][450/586] lr: 5.000000e-04 eta: 10:39:35 time: 0.339749 data_time: 0.023073 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.803083 loss: 0.000688 2022/09/12 20:45:50 - mmengine - INFO - Epoch(train) [20][500/586] lr: 5.000000e-04 eta: 10:39:17 time: 0.340772 data_time: 0.022752 memory: 7489 loss_kpt: 0.000692 acc_pose: 0.803580 loss: 0.000692 2022/09/12 20:46:07 - mmengine - INFO - Epoch(train) [20][550/586] lr: 5.000000e-04 eta: 10:38:57 time: 0.339628 data_time: 0.028712 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.793595 loss: 0.000707 2022/09/12 20:46:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:46:19 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/12 20:46:37 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:01:16 time: 0.215484 data_time: 0.012396 memory: 7489 2022/09/12 20:46:47 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:01:04 time: 0.211251 data_time: 0.007806 memory: 1657 2022/09/12 20:46:58 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:53 time: 0.209685 data_time: 0.007703 memory: 1657 2022/09/12 20:47:08 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:43 time: 0.209535 data_time: 0.007323 memory: 1657 2022/09/12 20:47:19 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:32 time: 0.209975 data_time: 0.007622 memory: 1657 2022/09/12 20:47:29 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:22 time: 0.211060 data_time: 0.007375 memory: 1657 2022/09/12 20:47:40 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:12 time: 0.210708 data_time: 0.007968 memory: 1657 2022/09/12 20:47:50 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:01 time: 0.209173 data_time: 0.007180 memory: 1657 2022/09/12 20:48:26 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 20:48:40 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.704072 coco/AP .5: 0.883244 coco/AP .75: 0.773047 coco/AP (M): 0.667404 coco/AP (L): 0.772010 coco/AR: 0.760390 coco/AR .5: 0.923016 coco/AR .75: 0.823520 coco/AR (M): 0.715296 coco/AR (L): 0.824861 2022/09/12 20:48:40 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_10.pth is removed 2022/09/12 20:48:44 - mmengine - INFO - The best checkpoint with 0.7041 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/09/12 20:49:02 - mmengine - INFO - Epoch(train) [21][50/586] lr: 5.000000e-04 eta: 10:36:33 time: 0.349150 data_time: 0.031601 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.804242 loss: 0.000684 2022/09/12 20:49:18 - mmengine - INFO - Epoch(train) [21][100/586] lr: 5.000000e-04 eta: 10:36:10 time: 0.332005 data_time: 0.022961 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.776674 loss: 0.000680 2022/09/12 20:49:35 - mmengine - INFO - Epoch(train) [21][150/586] lr: 5.000000e-04 eta: 10:35:48 time: 0.331907 data_time: 0.022558 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.829528 loss: 0.000691 2022/09/12 20:49:52 - mmengine - INFO - Epoch(train) [21][200/586] lr: 5.000000e-04 eta: 10:35:32 time: 0.346277 data_time: 0.023321 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.763976 loss: 0.000697 2022/09/12 20:50:09 - mmengine - INFO - Epoch(train) [21][250/586] lr: 5.000000e-04 eta: 10:35:11 time: 0.333711 data_time: 0.023351 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.729454 loss: 0.000695 2022/09/12 20:50:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:50:26 - mmengine - INFO - Epoch(train) [21][300/586] lr: 5.000000e-04 eta: 10:34:49 time: 0.334138 data_time: 0.023330 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.718751 loss: 0.000695 2022/09/12 20:50:43 - mmengine - INFO - Epoch(train) [21][350/586] lr: 5.000000e-04 eta: 10:34:36 time: 0.350734 data_time: 0.026071 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.771153 loss: 0.000688 2022/09/12 20:51:00 - mmengine - INFO - Epoch(train) [21][400/586] lr: 5.000000e-04 eta: 10:34:14 time: 0.333236 data_time: 0.022844 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.898025 loss: 0.000695 2022/09/12 20:51:17 - mmengine - INFO - Epoch(train) [21][450/586] lr: 5.000000e-04 eta: 10:33:54 time: 0.336170 data_time: 0.022165 memory: 7489 loss_kpt: 0.000692 acc_pose: 0.839218 loss: 0.000692 2022/09/12 20:51:34 - mmengine - INFO - Epoch(train) [21][500/586] lr: 5.000000e-04 eta: 10:33:36 time: 0.342109 data_time: 0.021965 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.817855 loss: 0.000681 2022/09/12 20:51:51 - mmengine - INFO - Epoch(train) [21][550/586] lr: 5.000000e-04 eta: 10:33:18 time: 0.339973 data_time: 0.023078 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.836294 loss: 0.000699 2022/09/12 20:52:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:52:03 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/09/12 20:52:27 - mmengine - INFO - Epoch(train) [22][50/586] lr: 5.000000e-04 eta: 10:31:00 time: 0.347890 data_time: 0.027041 memory: 7489 loss_kpt: 0.000683 acc_pose: 0.791302 loss: 0.000683 2022/09/12 20:52:43 - mmengine - INFO - Epoch(train) [22][100/586] lr: 5.000000e-04 eta: 10:30:40 time: 0.335087 data_time: 0.023642 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.799323 loss: 0.000703 2022/09/12 20:53:00 - mmengine - INFO - Epoch(train) [22][150/586] lr: 5.000000e-04 eta: 10:30:21 time: 0.339028 data_time: 0.022468 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.815768 loss: 0.000670 2022/09/12 20:53:17 - mmengine - INFO - Epoch(train) [22][200/586] lr: 5.000000e-04 eta: 10:30:00 time: 0.332959 data_time: 0.022132 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.813935 loss: 0.000675 2022/09/12 20:53:34 - mmengine - INFO - Epoch(train) [22][250/586] lr: 5.000000e-04 eta: 10:29:42 time: 0.339401 data_time: 0.022283 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.800914 loss: 0.000694 2022/09/12 20:53:51 - mmengine - INFO - Epoch(train) [22][300/586] lr: 5.000000e-04 eta: 10:29:23 time: 0.336376 data_time: 0.023154 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.784821 loss: 0.000670 2022/09/12 20:54:08 - mmengine - INFO - Epoch(train) [22][350/586] lr: 5.000000e-04 eta: 10:29:06 time: 0.342396 data_time: 0.023410 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.830549 loss: 0.000679 2022/09/12 20:54:25 - mmengine - INFO - Epoch(train) [22][400/586] lr: 5.000000e-04 eta: 10:28:44 time: 0.332342 data_time: 0.022456 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.810181 loss: 0.000669 2022/09/12 20:54:42 - mmengine - INFO - Epoch(train) [22][450/586] lr: 5.000000e-04 eta: 10:28:26 time: 0.339370 data_time: 0.025758 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.816117 loss: 0.000667 2022/09/12 20:54:59 - mmengine - INFO - Epoch(train) [22][500/586] lr: 5.000000e-04 eta: 10:28:08 time: 0.338843 data_time: 0.022704 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.881747 loss: 0.000675 2022/09/12 20:55:15 - mmengine - INFO - Epoch(train) [22][550/586] lr: 5.000000e-04 eta: 10:27:48 time: 0.336508 data_time: 0.022592 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.817853 loss: 0.000685 2022/09/12 20:55:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:55:28 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/09/12 20:55:51 - mmengine - INFO - Epoch(train) [23][50/586] lr: 5.000000e-04 eta: 10:25:31 time: 0.334695 data_time: 0.028930 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.866085 loss: 0.000687 2022/09/12 20:56:09 - mmengine - INFO - Epoch(train) [23][100/586] lr: 5.000000e-04 eta: 10:25:19 time: 0.350797 data_time: 0.029366 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.713654 loss: 0.000685 2022/09/12 20:56:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:56:26 - mmengine - INFO - Epoch(train) [23][150/586] lr: 5.000000e-04 eta: 10:24:59 time: 0.334140 data_time: 0.022933 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.709237 loss: 0.000708 2022/09/12 20:56:42 - mmengine - INFO - Epoch(train) [23][200/586] lr: 5.000000e-04 eta: 10:24:37 time: 0.330281 data_time: 0.023304 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.818537 loss: 0.000662 2022/09/12 20:57:00 - mmengine - INFO - Epoch(train) [23][250/586] lr: 5.000000e-04 eta: 10:24:25 time: 0.353501 data_time: 0.022971 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.746876 loss: 0.000651 2022/09/12 20:57:16 - mmengine - INFO - Epoch(train) [23][300/586] lr: 5.000000e-04 eta: 10:24:04 time: 0.329448 data_time: 0.023572 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.873745 loss: 0.000674 2022/09/12 20:57:33 - mmengine - INFO - Epoch(train) [23][350/586] lr: 5.000000e-04 eta: 10:23:45 time: 0.335862 data_time: 0.023149 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.778622 loss: 0.000686 2022/09/12 20:57:50 - mmengine - INFO - Epoch(train) [23][400/586] lr: 5.000000e-04 eta: 10:23:30 time: 0.347295 data_time: 0.030164 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.790465 loss: 0.000698 2022/09/12 20:58:07 - mmengine - INFO - Epoch(train) [23][450/586] lr: 5.000000e-04 eta: 10:23:09 time: 0.330470 data_time: 0.023425 memory: 7489 loss_kpt: 0.000689 acc_pose: 0.814775 loss: 0.000689 2022/09/12 20:58:24 - mmengine - INFO - Epoch(train) [23][500/586] lr: 5.000000e-04 eta: 10:22:53 time: 0.343488 data_time: 0.023822 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.809658 loss: 0.000695 2022/09/12 20:58:41 - mmengine - INFO - Epoch(train) [23][550/586] lr: 5.000000e-04 eta: 10:22:37 time: 0.343423 data_time: 0.029131 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.829479 loss: 0.000708 2022/09/12 20:58:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 20:58:53 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/09/12 20:59:18 - mmengine - INFO - Epoch(train) [24][50/586] lr: 5.000000e-04 eta: 10:20:30 time: 0.344316 data_time: 0.031880 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.782159 loss: 0.000682 2022/09/12 20:59:35 - mmengine - INFO - Epoch(train) [24][100/586] lr: 5.000000e-04 eta: 10:20:13 time: 0.340872 data_time: 0.025217 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.745510 loss: 0.000684 2022/09/12 20:59:52 - mmengine - INFO - Epoch(train) [24][150/586] lr: 5.000000e-04 eta: 10:19:56 time: 0.339437 data_time: 0.022069 memory: 7489 loss_kpt: 0.000690 acc_pose: 0.719424 loss: 0.000690 2022/09/12 21:00:08 - mmengine - INFO - Epoch(train) [24][200/586] lr: 5.000000e-04 eta: 10:19:37 time: 0.333662 data_time: 0.026737 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.719397 loss: 0.000679 2022/09/12 21:00:26 - mmengine - INFO - Epoch(train) [24][250/586] lr: 5.000000e-04 eta: 10:19:21 time: 0.342368 data_time: 0.022689 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.827844 loss: 0.000682 2022/09/12 21:00:42 - mmengine - INFO - Epoch(train) [24][300/586] lr: 5.000000e-04 eta: 10:19:01 time: 0.334208 data_time: 0.022881 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.854375 loss: 0.000694 2022/09/12 21:00:59 - mmengine - INFO - Epoch(train) [24][350/586] lr: 5.000000e-04 eta: 10:18:44 time: 0.339195 data_time: 0.022929 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.810391 loss: 0.000656 2022/09/12 21:01:16 - mmengine - INFO - Epoch(train) [24][400/586] lr: 5.000000e-04 eta: 10:18:26 time: 0.337375 data_time: 0.023148 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.873491 loss: 0.000688 2022/09/12 21:01:33 - mmengine - INFO - Epoch(train) [24][450/586] lr: 5.000000e-04 eta: 10:18:06 time: 0.333072 data_time: 0.022813 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.815282 loss: 0.000686 2022/09/12 21:01:50 - mmengine - INFO - Epoch(train) [24][500/586] lr: 5.000000e-04 eta: 10:17:52 time: 0.345512 data_time: 0.022721 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.821028 loss: 0.000687 2022/09/12 21:01:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:02:07 - mmengine - INFO - Epoch(train) [24][550/586] lr: 5.000000e-04 eta: 10:17:34 time: 0.337556 data_time: 0.022629 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.779036 loss: 0.000677 2022/09/12 21:02:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:02:19 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/09/12 21:02:43 - mmengine - INFO - Epoch(train) [25][50/586] lr: 5.000000e-04 eta: 10:15:37 time: 0.356774 data_time: 0.030704 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.731936 loss: 0.000670 2022/09/12 21:02:59 - mmengine - INFO - Epoch(train) [25][100/586] lr: 5.000000e-04 eta: 10:15:16 time: 0.328832 data_time: 0.022700 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.833324 loss: 0.000663 2022/09/12 21:03:17 - mmengine - INFO - Epoch(train) [25][150/586] lr: 5.000000e-04 eta: 10:15:01 time: 0.344102 data_time: 0.025973 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.849443 loss: 0.000681 2022/09/12 21:03:33 - mmengine - INFO - Epoch(train) [25][200/586] lr: 5.000000e-04 eta: 10:14:42 time: 0.333783 data_time: 0.023903 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.806495 loss: 0.000678 2022/09/12 21:03:50 - mmengine - INFO - Epoch(train) [25][250/586] lr: 5.000000e-04 eta: 10:14:22 time: 0.332798 data_time: 0.022144 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.781905 loss: 0.000688 2022/09/12 21:04:07 - mmengine - INFO - Epoch(train) [25][300/586] lr: 5.000000e-04 eta: 10:14:05 time: 0.338877 data_time: 0.022946 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.821044 loss: 0.000666 2022/09/12 21:04:24 - mmengine - INFO - Epoch(train) [25][350/586] lr: 5.000000e-04 eta: 10:13:50 time: 0.341968 data_time: 0.022710 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.812239 loss: 0.000663 2022/09/12 21:04:41 - mmengine - INFO - Epoch(train) [25][400/586] lr: 5.000000e-04 eta: 10:13:32 time: 0.338403 data_time: 0.022722 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.833131 loss: 0.000658 2022/09/12 21:04:58 - mmengine - INFO - Epoch(train) [25][450/586] lr: 5.000000e-04 eta: 10:13:15 time: 0.337251 data_time: 0.022285 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.843693 loss: 0.000680 2022/09/12 21:05:15 - mmengine - INFO - Epoch(train) [25][500/586] lr: 5.000000e-04 eta: 10:12:59 time: 0.340932 data_time: 0.023891 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.714938 loss: 0.000703 2022/09/12 21:05:32 - mmengine - INFO - Epoch(train) [25][550/586] lr: 5.000000e-04 eta: 10:12:41 time: 0.337983 data_time: 0.022590 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.770460 loss: 0.000671 2022/09/12 21:05:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:05:44 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/09/12 21:06:08 - mmengine - INFO - Epoch(train) [26][50/586] lr: 5.000000e-04 eta: 10:10:47 time: 0.350650 data_time: 0.031041 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.852023 loss: 0.000688 2022/09/12 21:06:26 - mmengine - INFO - Epoch(train) [26][100/586] lr: 5.000000e-04 eta: 10:10:32 time: 0.343989 data_time: 0.022846 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.799300 loss: 0.000674 2022/09/12 21:06:43 - mmengine - INFO - Epoch(train) [26][150/586] lr: 5.000000e-04 eta: 10:10:16 time: 0.341247 data_time: 0.022233 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.733594 loss: 0.000671 2022/09/12 21:07:00 - mmengine - INFO - Epoch(train) [26][200/586] lr: 5.000000e-04 eta: 10:10:01 time: 0.342396 data_time: 0.022611 memory: 7489 loss_kpt: 0.000701 acc_pose: 0.810743 loss: 0.000701 2022/09/12 21:07:17 - mmengine - INFO - Epoch(train) [26][250/586] lr: 5.000000e-04 eta: 10:09:44 time: 0.338373 data_time: 0.022739 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.830277 loss: 0.000665 2022/09/12 21:07:34 - mmengine - INFO - Epoch(train) [26][300/586] lr: 5.000000e-04 eta: 10:09:27 time: 0.337965 data_time: 0.022635 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.804391 loss: 0.000669 2022/09/12 21:07:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:07:50 - mmengine - INFO - Epoch(train) [26][350/586] lr: 5.000000e-04 eta: 10:09:09 time: 0.336525 data_time: 0.022980 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.759877 loss: 0.000685 2022/09/12 21:08:08 - mmengine - INFO - Epoch(train) [26][400/586] lr: 5.000000e-04 eta: 10:08:54 time: 0.343177 data_time: 0.022881 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.837058 loss: 0.000674 2022/09/12 21:08:24 - mmengine - INFO - Epoch(train) [26][450/586] lr: 5.000000e-04 eta: 10:08:34 time: 0.328823 data_time: 0.021921 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.771768 loss: 0.000674 2022/09/12 21:08:41 - mmengine - INFO - Epoch(train) [26][500/586] lr: 5.000000e-04 eta: 10:08:17 time: 0.338127 data_time: 0.026066 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.743263 loss: 0.000664 2022/09/12 21:08:58 - mmengine - INFO - Epoch(train) [26][550/586] lr: 5.000000e-04 eta: 10:08:01 time: 0.340529 data_time: 0.022919 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.738144 loss: 0.000680 2022/09/12 21:09:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:09:10 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/09/12 21:09:34 - mmengine - INFO - Epoch(train) [27][50/586] lr: 5.000000e-04 eta: 10:06:07 time: 0.342496 data_time: 0.029298 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.867751 loss: 0.000676 2022/09/12 21:09:51 - mmengine - INFO - Epoch(train) [27][100/586] lr: 5.000000e-04 eta: 10:05:48 time: 0.331872 data_time: 0.022800 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.796681 loss: 0.000686 2022/09/12 21:10:07 - mmengine - INFO - Epoch(train) [27][150/586] lr: 5.000000e-04 eta: 10:05:32 time: 0.337328 data_time: 0.026135 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.798687 loss: 0.000691 2022/09/12 21:10:24 - mmengine - INFO - Epoch(train) [27][200/586] lr: 5.000000e-04 eta: 10:05:15 time: 0.337071 data_time: 0.022292 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.814455 loss: 0.000665 2022/09/12 21:10:41 - mmengine - INFO - Epoch(train) [27][250/586] lr: 5.000000e-04 eta: 10:04:56 time: 0.331590 data_time: 0.022593 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.878028 loss: 0.000674 2022/09/12 21:10:58 - mmengine - INFO - Epoch(train) [27][300/586] lr: 5.000000e-04 eta: 10:04:39 time: 0.338824 data_time: 0.022787 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.811932 loss: 0.000672 2022/09/12 21:11:15 - mmengine - INFO - Epoch(train) [27][350/586] lr: 5.000000e-04 eta: 10:04:26 time: 0.347071 data_time: 0.025901 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.786841 loss: 0.000681 2022/09/12 21:11:32 - mmengine - INFO - Epoch(train) [27][400/586] lr: 5.000000e-04 eta: 10:04:07 time: 0.330686 data_time: 0.022494 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.739025 loss: 0.000682 2022/09/12 21:11:48 - mmengine - INFO - Epoch(train) [27][450/586] lr: 5.000000e-04 eta: 10:03:49 time: 0.336187 data_time: 0.022516 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.824554 loss: 0.000663 2022/09/12 21:12:06 - mmengine - INFO - Epoch(train) [27][500/586] lr: 5.000000e-04 eta: 10:03:38 time: 0.353597 data_time: 0.026708 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.721090 loss: 0.000680 2022/09/12 21:12:23 - mmengine - INFO - Epoch(train) [27][550/586] lr: 5.000000e-04 eta: 10:03:19 time: 0.332471 data_time: 0.022562 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.842600 loss: 0.000666 2022/09/12 21:12:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:12:35 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/09/12 21:12:58 - mmengine - INFO - Epoch(train) [28][50/586] lr: 5.000000e-04 eta: 10:01:31 time: 0.344871 data_time: 0.029624 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.775187 loss: 0.000644 2022/09/12 21:13:16 - mmengine - INFO - Epoch(train) [28][100/586] lr: 5.000000e-04 eta: 10:01:16 time: 0.343810 data_time: 0.027241 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.792401 loss: 0.000658 2022/09/12 21:13:32 - mmengine - INFO - Epoch(train) [28][150/586] lr: 5.000000e-04 eta: 10:00:58 time: 0.331580 data_time: 0.022219 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.830295 loss: 0.000687 2022/09/12 21:13:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:13:49 - mmengine - INFO - Epoch(train) [28][200/586] lr: 5.000000e-04 eta: 10:00:39 time: 0.331801 data_time: 0.022248 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.767359 loss: 0.000675 2022/09/12 21:14:06 - mmengine - INFO - Epoch(train) [28][250/586] lr: 5.000000e-04 eta: 10:00:24 time: 0.342814 data_time: 0.025955 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.823901 loss: 0.000677 2022/09/12 21:14:23 - mmengine - INFO - Epoch(train) [28][300/586] lr: 5.000000e-04 eta: 10:00:08 time: 0.336478 data_time: 0.023411 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.825070 loss: 0.000669 2022/09/12 21:14:39 - mmengine - INFO - Epoch(train) [28][350/586] lr: 5.000000e-04 eta: 9:59:50 time: 0.334793 data_time: 0.022374 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.787506 loss: 0.000651 2022/09/12 21:14:57 - mmengine - INFO - Epoch(train) [28][400/586] lr: 5.000000e-04 eta: 9:59:35 time: 0.342057 data_time: 0.026086 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.809168 loss: 0.000669 2022/09/12 21:15:13 - mmengine - INFO - Epoch(train) [28][450/586] lr: 5.000000e-04 eta: 9:59:18 time: 0.335403 data_time: 0.022890 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.827973 loss: 0.000671 2022/09/12 21:15:30 - mmengine - INFO - Epoch(train) [28][500/586] lr: 5.000000e-04 eta: 9:59:01 time: 0.337446 data_time: 0.022731 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.818739 loss: 0.000662 2022/09/12 21:15:48 - mmengine - INFO - Epoch(train) [28][550/586] lr: 5.000000e-04 eta: 9:58:49 time: 0.352081 data_time: 0.022996 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.850491 loss: 0.000665 2022/09/12 21:16:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:16:00 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/09/12 21:16:24 - mmengine - INFO - Epoch(train) [29][50/586] lr: 5.000000e-04 eta: 9:57:04 time: 0.342535 data_time: 0.034129 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.870482 loss: 0.000677 2022/09/12 21:16:41 - mmengine - INFO - Epoch(train) [29][100/586] lr: 5.000000e-04 eta: 9:56:50 time: 0.344451 data_time: 0.023359 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.824676 loss: 0.000667 2022/09/12 21:16:58 - mmengine - INFO - Epoch(train) [29][150/586] lr: 5.000000e-04 eta: 9:56:35 time: 0.341843 data_time: 0.027122 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.854669 loss: 0.000671 2022/09/12 21:17:15 - mmengine - INFO - Epoch(train) [29][200/586] lr: 5.000000e-04 eta: 9:56:18 time: 0.337009 data_time: 0.023811 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.788468 loss: 0.000675 2022/09/12 21:17:32 - mmengine - INFO - Epoch(train) [29][250/586] lr: 5.000000e-04 eta: 9:56:04 time: 0.343083 data_time: 0.022705 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.744951 loss: 0.000674 2022/09/12 21:17:49 - mmengine - INFO - Epoch(train) [29][300/586] lr: 5.000000e-04 eta: 9:55:46 time: 0.335321 data_time: 0.022479 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.837259 loss: 0.000676 2022/09/12 21:18:06 - mmengine - INFO - Epoch(train) [29][350/586] lr: 5.000000e-04 eta: 9:55:31 time: 0.339371 data_time: 0.023644 memory: 7489 loss_kpt: 0.000645 acc_pose: 0.781778 loss: 0.000645 2022/09/12 21:18:23 - mmengine - INFO - Epoch(train) [29][400/586] lr: 5.000000e-04 eta: 9:55:15 time: 0.341231 data_time: 0.022595 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.737389 loss: 0.000677 2022/09/12 21:18:40 - mmengine - INFO - Epoch(train) [29][450/586] lr: 5.000000e-04 eta: 9:54:58 time: 0.332550 data_time: 0.025581 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.803714 loss: 0.000656 2022/09/12 21:18:57 - mmengine - INFO - Epoch(train) [29][500/586] lr: 5.000000e-04 eta: 9:54:41 time: 0.337476 data_time: 0.022722 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.827447 loss: 0.000659 2022/09/12 21:19:14 - mmengine - INFO - Epoch(train) [29][550/586] lr: 5.000000e-04 eta: 9:54:26 time: 0.340798 data_time: 0.022646 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.837391 loss: 0.000646 2022/09/12 21:19:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:19:26 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/09/12 21:19:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:19:50 - mmengine - INFO - Epoch(train) [30][50/586] lr: 5.000000e-04 eta: 9:52:43 time: 0.340088 data_time: 0.027518 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.769244 loss: 0.000657 2022/09/12 21:20:07 - mmengine - INFO - Epoch(train) [30][100/586] lr: 5.000000e-04 eta: 9:52:29 time: 0.346328 data_time: 0.026842 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.787402 loss: 0.000678 2022/09/12 21:20:24 - mmengine - INFO - Epoch(train) [30][150/586] lr: 5.000000e-04 eta: 9:52:13 time: 0.336445 data_time: 0.022687 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.831206 loss: 0.000671 2022/09/12 21:20:41 - mmengine - INFO - Epoch(train) [30][200/586] lr: 5.000000e-04 eta: 9:51:56 time: 0.336222 data_time: 0.023142 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.867243 loss: 0.000651 2022/09/12 21:20:58 - mmengine - INFO - Epoch(train) [30][250/586] lr: 5.000000e-04 eta: 9:51:42 time: 0.343820 data_time: 0.025246 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.831252 loss: 0.000652 2022/09/12 21:21:15 - mmengine - INFO - Epoch(train) [30][300/586] lr: 5.000000e-04 eta: 9:51:27 time: 0.340901 data_time: 0.023071 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.857896 loss: 0.000647 2022/09/12 21:21:32 - mmengine - INFO - Epoch(train) [30][350/586] lr: 5.000000e-04 eta: 9:51:10 time: 0.336009 data_time: 0.023352 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.837784 loss: 0.000669 2022/09/12 21:21:49 - mmengine - INFO - Epoch(train) [30][400/586] lr: 5.000000e-04 eta: 9:50:54 time: 0.336115 data_time: 0.022591 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.811574 loss: 0.000664 2022/09/12 21:22:05 - mmengine - INFO - Epoch(train) [30][450/586] lr: 5.000000e-04 eta: 9:50:36 time: 0.331960 data_time: 0.021812 memory: 7489 loss_kpt: 0.000645 acc_pose: 0.817742 loss: 0.000645 2022/09/12 21:22:22 - mmengine - INFO - Epoch(train) [30][500/586] lr: 5.000000e-04 eta: 9:50:19 time: 0.334794 data_time: 0.022952 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.738069 loss: 0.000648 2022/09/12 21:22:39 - mmengine - INFO - Epoch(train) [30][550/586] lr: 5.000000e-04 eta: 9:50:04 time: 0.342851 data_time: 0.023555 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.772750 loss: 0.000676 2022/09/12 21:22:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:22:51 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/09/12 21:23:09 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:01:17 time: 0.216808 data_time: 0.012470 memory: 7489 2022/09/12 21:23:19 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:01:05 time: 0.213628 data_time: 0.010857 memory: 1657 2022/09/12 21:23:30 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:54 time: 0.211298 data_time: 0.007845 memory: 1657 2022/09/12 21:23:40 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:43 time: 0.209416 data_time: 0.007580 memory: 1657 2022/09/12 21:23:51 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:33 time: 0.211168 data_time: 0.008191 memory: 1657 2022/09/12 21:24:02 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:22 time: 0.210455 data_time: 0.008145 memory: 1657 2022/09/12 21:24:12 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:12 time: 0.211199 data_time: 0.008600 memory: 1657 2022/09/12 21:24:23 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:01 time: 0.209265 data_time: 0.007697 memory: 1657 2022/09/12 21:24:59 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 21:25:12 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.720302 coco/AP .5: 0.888499 coco/AP .75: 0.789530 coco/AP (M): 0.687313 coco/AP (L): 0.783902 coco/AR: 0.774386 coco/AR .5: 0.927897 coco/AR .75: 0.836272 coco/AR (M): 0.734253 coco/AR (L): 0.832887 2022/09/12 21:25:12 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_20.pth is removed 2022/09/12 21:25:16 - mmengine - INFO - The best checkpoint with 0.7203 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/09/12 21:25:33 - mmengine - INFO - Epoch(train) [31][50/586] lr: 5.000000e-04 eta: 9:48:24 time: 0.337863 data_time: 0.026981 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.789690 loss: 0.000647 2022/09/12 21:25:50 - mmengine - INFO - Epoch(train) [31][100/586] lr: 5.000000e-04 eta: 9:48:09 time: 0.339969 data_time: 0.022178 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.821222 loss: 0.000657 2022/09/12 21:26:07 - mmengine - INFO - Epoch(train) [31][150/586] lr: 5.000000e-04 eta: 9:47:52 time: 0.334708 data_time: 0.025747 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.806407 loss: 0.000657 2022/09/12 21:26:24 - mmengine - INFO - Epoch(train) [31][200/586] lr: 5.000000e-04 eta: 9:47:36 time: 0.338486 data_time: 0.023346 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.812184 loss: 0.000664 2022/09/12 21:26:41 - mmengine - INFO - Epoch(train) [31][250/586] lr: 5.000000e-04 eta: 9:47:22 time: 0.343194 data_time: 0.022468 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.841238 loss: 0.000646 2022/09/12 21:26:58 - mmengine - INFO - Epoch(train) [31][300/586] lr: 5.000000e-04 eta: 9:47:05 time: 0.334288 data_time: 0.023108 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.822655 loss: 0.000650 2022/09/12 21:27:15 - mmengine - INFO - Epoch(train) [31][350/586] lr: 5.000000e-04 eta: 9:46:50 time: 0.339606 data_time: 0.022512 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.836721 loss: 0.000649 2022/09/12 21:27:31 - mmengine - INFO - Epoch(train) [31][400/586] lr: 5.000000e-04 eta: 9:46:34 time: 0.336782 data_time: 0.022134 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.782632 loss: 0.000667 2022/09/12 21:27:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:27:48 - mmengine - INFO - Epoch(train) [31][450/586] lr: 5.000000e-04 eta: 9:46:19 time: 0.342387 data_time: 0.022547 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.827900 loss: 0.000647 2022/09/12 21:28:05 - mmengine - INFO - Epoch(train) [31][500/586] lr: 5.000000e-04 eta: 9:46:03 time: 0.336214 data_time: 0.023561 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.823482 loss: 0.000665 2022/09/12 21:28:22 - mmengine - INFO - Epoch(train) [31][550/586] lr: 5.000000e-04 eta: 9:45:46 time: 0.335085 data_time: 0.022060 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.844849 loss: 0.000653 2022/09/12 21:28:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:28:34 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/09/12 21:28:58 - mmengine - INFO - Epoch(train) [32][50/586] lr: 5.000000e-04 eta: 9:44:12 time: 0.348298 data_time: 0.030847 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.789810 loss: 0.000654 2022/09/12 21:29:15 - mmengine - INFO - Epoch(train) [32][100/586] lr: 5.000000e-04 eta: 9:43:57 time: 0.341058 data_time: 0.022367 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.768730 loss: 0.000667 2022/09/12 21:29:31 - mmengine - INFO - Epoch(train) [32][150/586] lr: 5.000000e-04 eta: 9:43:39 time: 0.331695 data_time: 0.022632 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.816842 loss: 0.000663 2022/09/12 21:29:48 - mmengine - INFO - Epoch(train) [32][200/586] lr: 5.000000e-04 eta: 9:43:24 time: 0.339229 data_time: 0.022408 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.765349 loss: 0.000632 2022/09/12 21:30:05 - mmengine - INFO - Epoch(train) [32][250/586] lr: 5.000000e-04 eta: 9:43:09 time: 0.339838 data_time: 0.023526 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.726218 loss: 0.000648 2022/09/12 21:30:22 - mmengine - INFO - Epoch(train) [32][300/586] lr: 5.000000e-04 eta: 9:42:53 time: 0.338029 data_time: 0.022743 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.862990 loss: 0.000656 2022/09/12 21:30:39 - mmengine - INFO - Epoch(train) [32][350/586] lr: 5.000000e-04 eta: 9:42:37 time: 0.337873 data_time: 0.022216 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.788866 loss: 0.000658 2022/09/12 21:30:56 - mmengine - INFO - Epoch(train) [32][400/586] lr: 5.000000e-04 eta: 9:42:21 time: 0.336784 data_time: 0.022909 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.798075 loss: 0.000647 2022/09/12 21:31:13 - mmengine - INFO - Epoch(train) [32][450/586] lr: 5.000000e-04 eta: 9:42:04 time: 0.333687 data_time: 0.022828 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.739140 loss: 0.000651 2022/09/12 21:31:30 - mmengine - INFO - Epoch(train) [32][500/586] lr: 5.000000e-04 eta: 9:41:49 time: 0.340596 data_time: 0.026222 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.795383 loss: 0.000664 2022/09/12 21:31:47 - mmengine - INFO - Epoch(train) [32][550/586] lr: 5.000000e-04 eta: 9:41:36 time: 0.346017 data_time: 0.023725 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.819056 loss: 0.000666 2022/09/12 21:31:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:31:59 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/09/12 21:32:23 - mmengine - INFO - Epoch(train) [33][50/586] lr: 5.000000e-04 eta: 9:40:03 time: 0.344065 data_time: 0.027649 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.813142 loss: 0.000643 2022/09/12 21:32:39 - mmengine - INFO - Epoch(train) [33][100/586] lr: 5.000000e-04 eta: 9:39:47 time: 0.336203 data_time: 0.022395 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.807406 loss: 0.000642 2022/09/12 21:32:57 - mmengine - INFO - Epoch(train) [33][150/586] lr: 5.000000e-04 eta: 9:39:32 time: 0.341286 data_time: 0.022421 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.801638 loss: 0.000644 2022/09/12 21:33:13 - mmengine - INFO - Epoch(train) [33][200/586] lr: 5.000000e-04 eta: 9:39:15 time: 0.332022 data_time: 0.022834 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.829053 loss: 0.000639 2022/09/12 21:33:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:33:30 - mmengine - INFO - Epoch(train) [33][250/586] lr: 5.000000e-04 eta: 9:39:00 time: 0.340879 data_time: 0.022398 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.844615 loss: 0.000655 2022/09/12 21:33:47 - mmengine - INFO - Epoch(train) [33][300/586] lr: 5.000000e-04 eta: 9:38:45 time: 0.339424 data_time: 0.023483 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.813070 loss: 0.000639 2022/09/12 21:34:04 - mmengine - INFO - Epoch(train) [33][350/586] lr: 5.000000e-04 eta: 9:38:28 time: 0.333320 data_time: 0.022068 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.821376 loss: 0.000644 2022/09/12 21:34:21 - mmengine - INFO - Epoch(train) [33][400/586] lr: 5.000000e-04 eta: 9:38:14 time: 0.341765 data_time: 0.022604 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.724641 loss: 0.000643 2022/09/12 21:34:38 - mmengine - INFO - Epoch(train) [33][450/586] lr: 5.000000e-04 eta: 9:37:57 time: 0.334064 data_time: 0.023430 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.868007 loss: 0.000642 2022/09/12 21:34:54 - mmengine - INFO - Epoch(train) [33][500/586] lr: 5.000000e-04 eta: 9:37:40 time: 0.333109 data_time: 0.022626 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.814481 loss: 0.000626 2022/09/12 21:35:11 - mmengine - INFO - Epoch(train) [33][550/586] lr: 5.000000e-04 eta: 9:37:25 time: 0.340823 data_time: 0.022447 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.842903 loss: 0.000642 2022/09/12 21:35:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:35:24 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/09/12 21:35:48 - mmengine - INFO - Epoch(train) [34][50/586] lr: 5.000000e-04 eta: 9:35:53 time: 0.336835 data_time: 0.026166 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.868793 loss: 0.000652 2022/09/12 21:36:05 - mmengine - INFO - Epoch(train) [34][100/586] lr: 5.000000e-04 eta: 9:35:38 time: 0.339432 data_time: 0.023581 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.857191 loss: 0.000637 2022/09/12 21:36:22 - mmengine - INFO - Epoch(train) [34][150/586] lr: 5.000000e-04 eta: 9:35:24 time: 0.341097 data_time: 0.022366 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.802132 loss: 0.000647 2022/09/12 21:36:39 - mmengine - INFO - Epoch(train) [34][200/586] lr: 5.000000e-04 eta: 9:35:07 time: 0.334362 data_time: 0.023032 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.774731 loss: 0.000663 2022/09/12 21:36:55 - mmengine - INFO - Epoch(train) [34][250/586] lr: 5.000000e-04 eta: 9:34:51 time: 0.335614 data_time: 0.023497 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.803688 loss: 0.000657 2022/09/12 21:37:12 - mmengine - INFO - Epoch(train) [34][300/586] lr: 5.000000e-04 eta: 9:34:35 time: 0.336440 data_time: 0.022581 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.793504 loss: 0.000661 2022/09/12 21:37:29 - mmengine - INFO - Epoch(train) [34][350/586] lr: 5.000000e-04 eta: 9:34:19 time: 0.334724 data_time: 0.023207 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.756977 loss: 0.000640 2022/09/12 21:37:46 - mmengine - INFO - Epoch(train) [34][400/586] lr: 5.000000e-04 eta: 9:34:03 time: 0.335403 data_time: 0.026954 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.753205 loss: 0.000649 2022/09/12 21:38:02 - mmengine - INFO - Epoch(train) [34][450/586] lr: 5.000000e-04 eta: 9:33:45 time: 0.330247 data_time: 0.022189 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.868085 loss: 0.000667 2022/09/12 21:38:19 - mmengine - INFO - Epoch(train) [34][500/586] lr: 5.000000e-04 eta: 9:33:28 time: 0.329263 data_time: 0.023043 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.829230 loss: 0.000661 2022/09/12 21:38:36 - mmengine - INFO - Epoch(train) [34][550/586] lr: 5.000000e-04 eta: 9:33:14 time: 0.342967 data_time: 0.026047 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.802283 loss: 0.000662 2022/09/12 21:38:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:38:48 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/09/12 21:39:12 - mmengine - INFO - Epoch(train) [35][50/586] lr: 5.000000e-04 eta: 9:31:44 time: 0.338570 data_time: 0.028891 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.821921 loss: 0.000639 2022/09/12 21:39:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:39:28 - mmengine - INFO - Epoch(train) [35][100/586] lr: 5.000000e-04 eta: 9:31:28 time: 0.335871 data_time: 0.022932 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.850051 loss: 0.000664 2022/09/12 21:39:45 - mmengine - INFO - Epoch(train) [35][150/586] lr: 5.000000e-04 eta: 9:31:14 time: 0.339569 data_time: 0.026024 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.805464 loss: 0.000652 2022/09/12 21:40:02 - mmengine - INFO - Epoch(train) [35][200/586] lr: 5.000000e-04 eta: 9:30:56 time: 0.331132 data_time: 0.022470 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.811516 loss: 0.000646 2022/09/12 21:40:19 - mmengine - INFO - Epoch(train) [35][250/586] lr: 5.000000e-04 eta: 9:30:42 time: 0.342752 data_time: 0.023400 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.758725 loss: 0.000643 2022/09/12 21:40:36 - mmengine - INFO - Epoch(train) [35][300/586] lr: 5.000000e-04 eta: 9:30:27 time: 0.336928 data_time: 0.027040 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.792930 loss: 0.000647 2022/09/12 21:40:53 - mmengine - INFO - Epoch(train) [35][350/586] lr: 5.000000e-04 eta: 9:30:11 time: 0.336575 data_time: 0.023268 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.828652 loss: 0.000665 2022/09/12 21:41:10 - mmengine - INFO - Epoch(train) [35][400/586] lr: 5.000000e-04 eta: 9:29:56 time: 0.337519 data_time: 0.022203 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.808477 loss: 0.000664 2022/09/12 21:41:27 - mmengine - INFO - Epoch(train) [35][450/586] lr: 5.000000e-04 eta: 9:29:41 time: 0.342385 data_time: 0.022763 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.830400 loss: 0.000669 2022/09/12 21:41:43 - mmengine - INFO - Epoch(train) [35][500/586] lr: 5.000000e-04 eta: 9:29:24 time: 0.330111 data_time: 0.021851 memory: 7489 loss_kpt: 0.000645 acc_pose: 0.816434 loss: 0.000645 2022/09/12 21:42:00 - mmengine - INFO - Epoch(train) [35][550/586] lr: 5.000000e-04 eta: 9:29:07 time: 0.333185 data_time: 0.022235 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.818468 loss: 0.000646 2022/09/12 21:42:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:42:12 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/09/12 21:42:36 - mmengine - INFO - Epoch(train) [36][50/586] lr: 5.000000e-04 eta: 9:27:42 time: 0.343612 data_time: 0.034573 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.853027 loss: 0.000636 2022/09/12 21:42:53 - mmengine - INFO - Epoch(train) [36][100/586] lr: 5.000000e-04 eta: 9:27:26 time: 0.337707 data_time: 0.026963 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.810276 loss: 0.000651 2022/09/12 21:43:10 - mmengine - INFO - Epoch(train) [36][150/586] lr: 5.000000e-04 eta: 9:27:11 time: 0.338834 data_time: 0.022736 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.747875 loss: 0.000636 2022/09/12 21:43:27 - mmengine - INFO - Epoch(train) [36][200/586] lr: 5.000000e-04 eta: 9:26:55 time: 0.333962 data_time: 0.022210 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.876279 loss: 0.000647 2022/09/12 21:43:44 - mmengine - INFO - Epoch(train) [36][250/586] lr: 5.000000e-04 eta: 9:26:41 time: 0.342847 data_time: 0.025689 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.778993 loss: 0.000633 2022/09/12 21:44:01 - mmengine - INFO - Epoch(train) [36][300/586] lr: 5.000000e-04 eta: 9:26:24 time: 0.330818 data_time: 0.022699 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.769485 loss: 0.000632 2022/09/12 21:44:17 - mmengine - INFO - Epoch(train) [36][350/586] lr: 5.000000e-04 eta: 9:26:08 time: 0.335699 data_time: 0.022263 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.758012 loss: 0.000640 2022/09/12 21:44:34 - mmengine - INFO - Epoch(train) [36][400/586] lr: 5.000000e-04 eta: 9:25:54 time: 0.341991 data_time: 0.026307 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.801557 loss: 0.000630 2022/09/12 21:44:51 - mmengine - INFO - Epoch(train) [36][450/586] lr: 5.000000e-04 eta: 9:25:39 time: 0.337393 data_time: 0.022698 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.708979 loss: 0.000655 2022/09/12 21:45:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:45:08 - mmengine - INFO - Epoch(train) [36][500/586] lr: 5.000000e-04 eta: 9:25:23 time: 0.334798 data_time: 0.022670 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.852682 loss: 0.000643 2022/09/12 21:45:25 - mmengine - INFO - Epoch(train) [36][550/586] lr: 5.000000e-04 eta: 9:25:09 time: 0.345554 data_time: 0.027218 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.793569 loss: 0.000664 2022/09/12 21:45:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:45:37 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/09/12 21:46:01 - mmengine - INFO - Epoch(train) [37][50/586] lr: 5.000000e-04 eta: 9:23:45 time: 0.342719 data_time: 0.031620 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.801061 loss: 0.000646 2022/09/12 21:46:18 - mmengine - INFO - Epoch(train) [37][100/586] lr: 5.000000e-04 eta: 9:23:30 time: 0.336891 data_time: 0.023742 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.872603 loss: 0.000648 2022/09/12 21:46:35 - mmengine - INFO - Epoch(train) [37][150/586] lr: 5.000000e-04 eta: 9:23:14 time: 0.334334 data_time: 0.022866 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.841496 loss: 0.000652 2022/09/12 21:46:52 - mmengine - INFO - Epoch(train) [37][200/586] lr: 5.000000e-04 eta: 9:22:59 time: 0.336954 data_time: 0.022547 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.790539 loss: 0.000628 2022/09/12 21:47:08 - mmengine - INFO - Epoch(train) [37][250/586] lr: 5.000000e-04 eta: 9:22:43 time: 0.337038 data_time: 0.022786 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.797596 loss: 0.000639 2022/09/12 21:47:25 - mmengine - INFO - Epoch(train) [37][300/586] lr: 5.000000e-04 eta: 9:22:27 time: 0.332782 data_time: 0.024649 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.816903 loss: 0.000636 2022/09/12 21:47:42 - mmengine - INFO - Epoch(train) [37][350/586] lr: 5.000000e-04 eta: 9:22:12 time: 0.339036 data_time: 0.022825 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.854623 loss: 0.000612 2022/09/12 21:47:59 - mmengine - INFO - Epoch(train) [37][400/586] lr: 5.000000e-04 eta: 9:21:56 time: 0.335263 data_time: 0.022408 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.764383 loss: 0.000641 2022/09/12 21:48:16 - mmengine - INFO - Epoch(train) [37][450/586] lr: 5.000000e-04 eta: 9:21:41 time: 0.339826 data_time: 0.023759 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.860403 loss: 0.000647 2022/09/12 21:48:32 - mmengine - INFO - Epoch(train) [37][500/586] lr: 5.000000e-04 eta: 9:21:25 time: 0.332476 data_time: 0.021822 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.841495 loss: 0.000623 2022/09/12 21:48:50 - mmengine - INFO - Epoch(train) [37][550/586] lr: 5.000000e-04 eta: 9:21:13 time: 0.350589 data_time: 0.022084 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.820246 loss: 0.000647 2022/09/12 21:49:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:49:02 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/09/12 21:49:26 - mmengine - INFO - Epoch(train) [38][50/586] lr: 5.000000e-04 eta: 9:19:51 time: 0.345188 data_time: 0.028227 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.824241 loss: 0.000651 2022/09/12 21:49:43 - mmengine - INFO - Epoch(train) [38][100/586] lr: 5.000000e-04 eta: 9:19:38 time: 0.345845 data_time: 0.027806 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.777323 loss: 0.000629 2022/09/12 21:50:00 - mmengine - INFO - Epoch(train) [38][150/586] lr: 5.000000e-04 eta: 9:19:22 time: 0.331723 data_time: 0.023303 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.787685 loss: 0.000642 2022/09/12 21:50:17 - mmengine - INFO - Epoch(train) [38][200/586] lr: 5.000000e-04 eta: 9:19:06 time: 0.334526 data_time: 0.023012 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.789694 loss: 0.000630 2022/09/12 21:50:34 - mmengine - INFO - Epoch(train) [38][250/586] lr: 5.000000e-04 eta: 9:18:52 time: 0.345772 data_time: 0.022268 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.796643 loss: 0.000656 2022/09/12 21:50:50 - mmengine - INFO - Epoch(train) [38][300/586] lr: 5.000000e-04 eta: 9:18:35 time: 0.328579 data_time: 0.022252 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.822245 loss: 0.000651 2022/09/12 21:50:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:51:07 - mmengine - INFO - Epoch(train) [38][350/586] lr: 5.000000e-04 eta: 9:18:19 time: 0.333286 data_time: 0.023303 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.862603 loss: 0.000635 2022/09/12 21:51:24 - mmengine - INFO - Epoch(train) [38][400/586] lr: 5.000000e-04 eta: 9:18:06 time: 0.345712 data_time: 0.026211 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.917292 loss: 0.000632 2022/09/12 21:51:41 - mmengine - INFO - Epoch(train) [38][450/586] lr: 5.000000e-04 eta: 9:17:48 time: 0.327699 data_time: 0.022438 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.789897 loss: 0.000643 2022/09/12 21:51:58 - mmengine - INFO - Epoch(train) [38][500/586] lr: 5.000000e-04 eta: 9:17:34 time: 0.343199 data_time: 0.022780 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.776348 loss: 0.000626 2022/09/12 21:52:15 - mmengine - INFO - Epoch(train) [38][550/586] lr: 5.000000e-04 eta: 9:17:20 time: 0.341448 data_time: 0.022513 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.856451 loss: 0.000628 2022/09/12 21:52:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:52:27 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/09/12 21:52:51 - mmengine - INFO - Epoch(train) [39][50/586] lr: 5.000000e-04 eta: 9:16:01 time: 0.347242 data_time: 0.026260 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.818633 loss: 0.000640 2022/09/12 21:53:07 - mmengine - INFO - Epoch(train) [39][100/586] lr: 5.000000e-04 eta: 9:15:45 time: 0.335771 data_time: 0.022676 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.783090 loss: 0.000632 2022/09/12 21:53:24 - mmengine - INFO - Epoch(train) [39][150/586] lr: 5.000000e-04 eta: 9:15:29 time: 0.331043 data_time: 0.022450 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.843170 loss: 0.000616 2022/09/12 21:53:41 - mmengine - INFO - Epoch(train) [39][200/586] lr: 5.000000e-04 eta: 9:15:13 time: 0.336144 data_time: 0.027186 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.835711 loss: 0.000652 2022/09/12 21:53:58 - mmengine - INFO - Epoch(train) [39][250/586] lr: 5.000000e-04 eta: 9:14:59 time: 0.339564 data_time: 0.022601 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.737012 loss: 0.000638 2022/09/12 21:54:15 - mmengine - INFO - Epoch(train) [39][300/586] lr: 5.000000e-04 eta: 9:14:44 time: 0.337293 data_time: 0.022938 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.804840 loss: 0.000625 2022/09/12 21:54:31 - mmengine - INFO - Epoch(train) [39][350/586] lr: 5.000000e-04 eta: 9:14:28 time: 0.337362 data_time: 0.022745 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.800169 loss: 0.000635 2022/09/12 21:54:48 - mmengine - INFO - Epoch(train) [39][400/586] lr: 5.000000e-04 eta: 9:14:13 time: 0.337298 data_time: 0.022238 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.847829 loss: 0.000633 2022/09/12 21:55:05 - mmengine - INFO - Epoch(train) [39][450/586] lr: 5.000000e-04 eta: 9:13:59 time: 0.344186 data_time: 0.022887 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.777737 loss: 0.000638 2022/09/12 21:55:22 - mmengine - INFO - Epoch(train) [39][500/586] lr: 5.000000e-04 eta: 9:13:45 time: 0.340152 data_time: 0.023069 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.788512 loss: 0.000649 2022/09/12 21:55:40 - mmengine - INFO - Epoch(train) [39][550/586] lr: 5.000000e-04 eta: 9:13:30 time: 0.340584 data_time: 0.023203 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.808843 loss: 0.000659 2022/09/12 21:55:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:55:51 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/09/12 21:56:15 - mmengine - INFO - Epoch(train) [40][50/586] lr: 5.000000e-04 eta: 9:12:13 time: 0.344764 data_time: 0.029520 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.793269 loss: 0.000641 2022/09/12 21:56:32 - mmengine - INFO - Epoch(train) [40][100/586] lr: 5.000000e-04 eta: 9:11:57 time: 0.336509 data_time: 0.023057 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.821235 loss: 0.000631 2022/09/12 21:56:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:56:49 - mmengine - INFO - Epoch(train) [40][150/586] lr: 5.000000e-04 eta: 9:11:41 time: 0.332370 data_time: 0.023586 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.769433 loss: 0.000626 2022/09/12 21:57:05 - mmengine - INFO - Epoch(train) [40][200/586] lr: 5.000000e-04 eta: 9:11:26 time: 0.338638 data_time: 0.025256 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.809435 loss: 0.000657 2022/09/12 21:57:22 - mmengine - INFO - Epoch(train) [40][250/586] lr: 5.000000e-04 eta: 9:11:12 time: 0.339364 data_time: 0.022851 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.775456 loss: 0.000644 2022/09/12 21:57:39 - mmengine - INFO - Epoch(train) [40][300/586] lr: 5.000000e-04 eta: 9:10:55 time: 0.329363 data_time: 0.022455 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.803689 loss: 0.000641 2022/09/12 21:57:56 - mmengine - INFO - Epoch(train) [40][350/586] lr: 5.000000e-04 eta: 9:10:39 time: 0.336941 data_time: 0.022855 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.828892 loss: 0.000640 2022/09/12 21:58:13 - mmengine - INFO - Epoch(train) [40][400/586] lr: 5.000000e-04 eta: 9:10:25 time: 0.340549 data_time: 0.023707 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.849381 loss: 0.000634 2022/09/12 21:58:29 - mmengine - INFO - Epoch(train) [40][450/586] lr: 5.000000e-04 eta: 9:10:09 time: 0.332543 data_time: 0.022213 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.773516 loss: 0.000637 2022/09/12 21:58:47 - mmengine - INFO - Epoch(train) [40][500/586] lr: 5.000000e-04 eta: 9:09:54 time: 0.341286 data_time: 0.023939 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.820051 loss: 0.000627 2022/09/12 21:59:03 - mmengine - INFO - Epoch(train) [40][550/586] lr: 5.000000e-04 eta: 9:09:38 time: 0.333538 data_time: 0.026260 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.777147 loss: 0.000623 2022/09/12 21:59:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 21:59:15 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/12 21:59:33 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:01:19 time: 0.221503 data_time: 0.014895 memory: 7489 2022/09/12 21:59:43 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:01:04 time: 0.211548 data_time: 0.008280 memory: 1657 2022/09/12 21:59:54 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:54 time: 0.210488 data_time: 0.008336 memory: 1657 2022/09/12 22:00:04 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:43 time: 0.210961 data_time: 0.008683 memory: 1657 2022/09/12 22:00:15 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:32 time: 0.210070 data_time: 0.007773 memory: 1657 2022/09/12 22:00:25 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:22 time: 0.210129 data_time: 0.007526 memory: 1657 2022/09/12 22:00:36 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:12 time: 0.210584 data_time: 0.008171 memory: 1657 2022/09/12 22:00:46 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:01 time: 0.207531 data_time: 0.007247 memory: 1657 2022/09/12 22:01:22 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 22:01:36 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.731309 coco/AP .5: 0.896538 coco/AP .75: 0.803280 coco/AP (M): 0.697855 coco/AP (L): 0.794765 coco/AR: 0.782368 coco/AR .5: 0.933407 coco/AR .75: 0.846348 coco/AR (M): 0.742611 coco/AR (L): 0.840951 2022/09/12 22:01:36 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_30.pth is removed 2022/09/12 22:01:39 - mmengine - INFO - The best checkpoint with 0.7313 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/09/12 22:01:57 - mmengine - INFO - Epoch(train) [41][50/586] lr: 5.000000e-04 eta: 9:08:23 time: 0.348464 data_time: 0.027122 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.839078 loss: 0.000627 2022/09/12 22:02:14 - mmengine - INFO - Epoch(train) [41][100/586] lr: 5.000000e-04 eta: 9:08:08 time: 0.336825 data_time: 0.026903 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.803589 loss: 0.000627 2022/09/12 22:02:30 - mmengine - INFO - Epoch(train) [41][150/586] lr: 5.000000e-04 eta: 9:07:52 time: 0.331764 data_time: 0.022883 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.841126 loss: 0.000641 2022/09/12 22:02:47 - mmengine - INFO - Epoch(train) [41][200/586] lr: 5.000000e-04 eta: 9:07:37 time: 0.337553 data_time: 0.023128 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.772167 loss: 0.000638 2022/09/12 22:03:04 - mmengine - INFO - Epoch(train) [41][250/586] lr: 5.000000e-04 eta: 9:07:20 time: 0.331725 data_time: 0.022553 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.853712 loss: 0.000624 2022/09/12 22:03:21 - mmengine - INFO - Epoch(train) [41][300/586] lr: 5.000000e-04 eta: 9:07:05 time: 0.336512 data_time: 0.022261 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.764069 loss: 0.000635 2022/09/12 22:03:38 - mmengine - INFO - Epoch(train) [41][350/586] lr: 5.000000e-04 eta: 9:06:52 time: 0.347665 data_time: 0.023608 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.852590 loss: 0.000607 2022/09/12 22:03:55 - mmengine - INFO - Epoch(train) [41][400/586] lr: 5.000000e-04 eta: 9:06:36 time: 0.332016 data_time: 0.022819 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.815176 loss: 0.000635 2022/09/12 22:04:12 - mmengine - INFO - Epoch(train) [41][450/586] lr: 5.000000e-04 eta: 9:06:22 time: 0.340395 data_time: 0.022418 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.857956 loss: 0.000637 2022/09/12 22:04:29 - mmengine - INFO - Epoch(train) [41][500/586] lr: 5.000000e-04 eta: 9:06:08 time: 0.344710 data_time: 0.023531 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.857350 loss: 0.000625 2022/09/12 22:04:46 - mmengine - INFO - Epoch(train) [41][550/586] lr: 5.000000e-04 eta: 9:05:53 time: 0.339201 data_time: 0.027291 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.795195 loss: 0.000630 2022/09/12 22:04:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:04:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:04:58 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/09/12 22:05:22 - mmengine - INFO - Epoch(train) [42][50/586] lr: 5.000000e-04 eta: 9:04:39 time: 0.346747 data_time: 0.032165 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.805827 loss: 0.000614 2022/09/12 22:05:39 - mmengine - INFO - Epoch(train) [42][100/586] lr: 5.000000e-04 eta: 9:04:24 time: 0.338645 data_time: 0.023154 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.828972 loss: 0.000648 2022/09/12 22:05:55 - mmengine - INFO - Epoch(train) [42][150/586] lr: 5.000000e-04 eta: 9:04:08 time: 0.328952 data_time: 0.022204 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.803950 loss: 0.000612 2022/09/12 22:06:12 - mmengine - INFO - Epoch(train) [42][200/586] lr: 5.000000e-04 eta: 9:03:54 time: 0.346397 data_time: 0.023428 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.823829 loss: 0.000596 2022/09/12 22:06:29 - mmengine - INFO - Epoch(train) [42][250/586] lr: 5.000000e-04 eta: 9:03:38 time: 0.332208 data_time: 0.022403 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.762330 loss: 0.000625 2022/09/12 22:06:46 - mmengine - INFO - Epoch(train) [42][300/586] lr: 5.000000e-04 eta: 9:03:23 time: 0.334718 data_time: 0.022306 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.848018 loss: 0.000613 2022/09/12 22:07:03 - mmengine - INFO - Epoch(train) [42][350/586] lr: 5.000000e-04 eta: 9:03:08 time: 0.339325 data_time: 0.025882 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.811074 loss: 0.000631 2022/09/12 22:07:20 - mmengine - INFO - Epoch(train) [42][400/586] lr: 5.000000e-04 eta: 9:02:53 time: 0.337669 data_time: 0.022261 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.798894 loss: 0.000626 2022/09/12 22:07:36 - mmengine - INFO - Epoch(train) [42][450/586] lr: 5.000000e-04 eta: 9:02:37 time: 0.332975 data_time: 0.021752 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.782264 loss: 0.000649 2022/09/12 22:07:53 - mmengine - INFO - Epoch(train) [42][500/586] lr: 5.000000e-04 eta: 9:02:22 time: 0.338266 data_time: 0.022280 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.858722 loss: 0.000647 2022/09/12 22:08:10 - mmengine - INFO - Epoch(train) [42][550/586] lr: 5.000000e-04 eta: 9:02:08 time: 0.339814 data_time: 0.022955 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.801609 loss: 0.000654 2022/09/12 22:08:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:08:22 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/09/12 22:08:46 - mmengine - INFO - Epoch(train) [43][50/586] lr: 5.000000e-04 eta: 9:00:54 time: 0.341257 data_time: 0.029489 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.814093 loss: 0.000634 2022/09/12 22:09:03 - mmengine - INFO - Epoch(train) [43][100/586] lr: 5.000000e-04 eta: 9:00:40 time: 0.341986 data_time: 0.021921 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.859299 loss: 0.000625 2022/09/12 22:09:20 - mmengine - INFO - Epoch(train) [43][150/586] lr: 5.000000e-04 eta: 9:00:26 time: 0.340708 data_time: 0.022421 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.851296 loss: 0.000627 2022/09/12 22:09:37 - mmengine - INFO - Epoch(train) [43][200/586] lr: 5.000000e-04 eta: 9:00:10 time: 0.332465 data_time: 0.022628 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.772805 loss: 0.000632 2022/09/12 22:09:54 - mmengine - INFO - Epoch(train) [43][250/586] lr: 5.000000e-04 eta: 8:59:55 time: 0.338899 data_time: 0.022148 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.791548 loss: 0.000656 2022/09/12 22:10:11 - mmengine - INFO - Epoch(train) [43][300/586] lr: 5.000000e-04 eta: 8:59:40 time: 0.339304 data_time: 0.022526 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.789459 loss: 0.000612 2022/09/12 22:10:28 - mmengine - INFO - Epoch(train) [43][350/586] lr: 5.000000e-04 eta: 8:59:26 time: 0.339732 data_time: 0.022465 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.761918 loss: 0.000629 2022/09/12 22:10:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:10:45 - mmengine - INFO - Epoch(train) [43][400/586] lr: 5.000000e-04 eta: 8:59:11 time: 0.339039 data_time: 0.022015 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.780549 loss: 0.000626 2022/09/12 22:11:01 - mmengine - INFO - Epoch(train) [43][450/586] lr: 5.000000e-04 eta: 8:58:55 time: 0.330410 data_time: 0.021611 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.800053 loss: 0.000643 2022/09/12 22:11:18 - mmengine - INFO - Epoch(train) [43][500/586] lr: 5.000000e-04 eta: 8:58:40 time: 0.338018 data_time: 0.022051 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.746842 loss: 0.000616 2022/09/12 22:11:35 - mmengine - INFO - Epoch(train) [43][550/586] lr: 5.000000e-04 eta: 8:58:24 time: 0.334971 data_time: 0.022522 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.819621 loss: 0.000630 2022/09/12 22:11:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:11:47 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/09/12 22:12:11 - mmengine - INFO - Epoch(train) [44][50/586] lr: 5.000000e-04 eta: 8:57:12 time: 0.341582 data_time: 0.027568 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.796636 loss: 0.000656 2022/09/12 22:12:28 - mmengine - INFO - Epoch(train) [44][100/586] lr: 5.000000e-04 eta: 8:56:58 time: 0.340182 data_time: 0.023663 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.809090 loss: 0.000630 2022/09/12 22:12:45 - mmengine - INFO - Epoch(train) [44][150/586] lr: 5.000000e-04 eta: 8:56:42 time: 0.336174 data_time: 0.022148 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.861105 loss: 0.000596 2022/09/12 22:13:02 - mmengine - INFO - Epoch(train) [44][200/586] lr: 5.000000e-04 eta: 8:56:28 time: 0.341305 data_time: 0.021759 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.780137 loss: 0.000625 2022/09/12 22:13:19 - mmengine - INFO - Epoch(train) [44][250/586] lr: 5.000000e-04 eta: 8:56:12 time: 0.333594 data_time: 0.022098 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.824503 loss: 0.000642 2022/09/12 22:13:36 - mmengine - INFO - Epoch(train) [44][300/586] lr: 5.000000e-04 eta: 8:55:59 time: 0.346776 data_time: 0.025983 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.822444 loss: 0.000631 2022/09/12 22:13:53 - mmengine - INFO - Epoch(train) [44][350/586] lr: 5.000000e-04 eta: 8:55:43 time: 0.332448 data_time: 0.023350 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.815582 loss: 0.000635 2022/09/12 22:14:09 - mmengine - INFO - Epoch(train) [44][400/586] lr: 5.000000e-04 eta: 8:55:28 time: 0.336694 data_time: 0.022142 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.824171 loss: 0.000613 2022/09/12 22:14:26 - mmengine - INFO - Epoch(train) [44][450/586] lr: 5.000000e-04 eta: 8:55:13 time: 0.334707 data_time: 0.022148 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.749597 loss: 0.000616 2022/09/12 22:14:43 - mmengine - INFO - Epoch(train) [44][500/586] lr: 5.000000e-04 eta: 8:54:57 time: 0.336157 data_time: 0.022679 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.821069 loss: 0.000616 2022/09/12 22:15:00 - mmengine - INFO - Epoch(train) [44][550/586] lr: 5.000000e-04 eta: 8:54:42 time: 0.334755 data_time: 0.022534 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.815601 loss: 0.000625 2022/09/12 22:15:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:15:12 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/09/12 22:15:36 - mmengine - INFO - Epoch(train) [45][50/586] lr: 5.000000e-04 eta: 8:53:29 time: 0.332535 data_time: 0.026993 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.801662 loss: 0.000609 2022/09/12 22:15:53 - mmengine - INFO - Epoch(train) [45][100/586] lr: 5.000000e-04 eta: 8:53:16 time: 0.348065 data_time: 0.028826 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.818309 loss: 0.000624 2022/09/12 22:16:10 - mmengine - INFO - Epoch(train) [45][150/586] lr: 5.000000e-04 eta: 8:53:01 time: 0.336253 data_time: 0.021896 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.794361 loss: 0.000624 2022/09/12 22:16:26 - mmengine - INFO - Epoch(train) [45][200/586] lr: 5.000000e-04 eta: 8:52:45 time: 0.330064 data_time: 0.022464 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.777224 loss: 0.000614 2022/09/12 22:16:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:16:44 - mmengine - INFO - Epoch(train) [45][250/586] lr: 5.000000e-04 eta: 8:52:31 time: 0.342793 data_time: 0.022573 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.883060 loss: 0.000618 2022/09/12 22:17:00 - mmengine - INFO - Epoch(train) [45][300/586] lr: 5.000000e-04 eta: 8:52:16 time: 0.338724 data_time: 0.022756 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.838318 loss: 0.000617 2022/09/12 22:17:17 - mmengine - INFO - Epoch(train) [45][350/586] lr: 5.000000e-04 eta: 8:51:59 time: 0.327058 data_time: 0.022843 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.758387 loss: 0.000625 2022/09/12 22:17:34 - mmengine - INFO - Epoch(train) [45][400/586] lr: 5.000000e-04 eta: 8:51:45 time: 0.342187 data_time: 0.025839 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.812427 loss: 0.000631 2022/09/12 22:17:51 - mmengine - INFO - Epoch(train) [45][450/586] lr: 5.000000e-04 eta: 8:51:31 time: 0.341682 data_time: 0.022180 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.776306 loss: 0.000629 2022/09/12 22:18:07 - mmengine - INFO - Epoch(train) [45][500/586] lr: 5.000000e-04 eta: 8:51:14 time: 0.327682 data_time: 0.022008 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.859504 loss: 0.000615 2022/09/12 22:18:25 - mmengine - INFO - Epoch(train) [45][550/586] lr: 5.000000e-04 eta: 8:51:00 time: 0.341991 data_time: 0.022154 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.808451 loss: 0.000619 2022/09/12 22:18:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:18:37 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/09/12 22:19:00 - mmengine - INFO - Epoch(train) [46][50/586] lr: 5.000000e-04 eta: 8:49:49 time: 0.333508 data_time: 0.030345 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.830857 loss: 0.000615 2022/09/12 22:19:16 - mmengine - INFO - Epoch(train) [46][100/586] lr: 5.000000e-04 eta: 8:49:34 time: 0.336860 data_time: 0.022606 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.813583 loss: 0.000628 2022/09/12 22:19:33 - mmengine - INFO - Epoch(train) [46][150/586] lr: 5.000000e-04 eta: 8:49:20 time: 0.338701 data_time: 0.026375 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.796421 loss: 0.000623 2022/09/12 22:19:50 - mmengine - INFO - Epoch(train) [46][200/586] lr: 5.000000e-04 eta: 8:49:05 time: 0.337917 data_time: 0.023369 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.805322 loss: 0.000604 2022/09/12 22:20:07 - mmengine - INFO - Epoch(train) [46][250/586] lr: 5.000000e-04 eta: 8:48:50 time: 0.339645 data_time: 0.022103 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.839297 loss: 0.000630 2022/09/12 22:20:24 - mmengine - INFO - Epoch(train) [46][300/586] lr: 5.000000e-04 eta: 8:48:35 time: 0.334331 data_time: 0.029059 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.793551 loss: 0.000619 2022/09/12 22:20:41 - mmengine - INFO - Epoch(train) [46][350/586] lr: 5.000000e-04 eta: 8:48:19 time: 0.333965 data_time: 0.023137 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.857528 loss: 0.000614 2022/09/12 22:20:58 - mmengine - INFO - Epoch(train) [46][400/586] lr: 5.000000e-04 eta: 8:48:05 time: 0.340687 data_time: 0.022225 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.742866 loss: 0.000600 2022/09/12 22:21:14 - mmengine - INFO - Epoch(train) [46][450/586] lr: 5.000000e-04 eta: 8:47:49 time: 0.333839 data_time: 0.023340 memory: 7489 loss_kpt: 0.000645 acc_pose: 0.902642 loss: 0.000645 2022/09/12 22:21:31 - mmengine - INFO - Epoch(train) [46][500/586] lr: 5.000000e-04 eta: 8:47:34 time: 0.338467 data_time: 0.022965 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.820050 loss: 0.000620 2022/09/12 22:21:48 - mmengine - INFO - Epoch(train) [46][550/586] lr: 5.000000e-04 eta: 8:47:19 time: 0.335691 data_time: 0.023249 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.834593 loss: 0.000623 2022/09/12 22:22:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:22:00 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/09/12 22:22:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:22:25 - mmengine - INFO - Epoch(train) [47][50/586] lr: 5.000000e-04 eta: 8:46:10 time: 0.338470 data_time: 0.029799 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.835854 loss: 0.000616 2022/09/12 22:22:42 - mmengine - INFO - Epoch(train) [47][100/586] lr: 5.000000e-04 eta: 8:45:56 time: 0.342463 data_time: 0.026151 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.837321 loss: 0.000624 2022/09/12 22:22:58 - mmengine - INFO - Epoch(train) [47][150/586] lr: 5.000000e-04 eta: 8:45:41 time: 0.332703 data_time: 0.022253 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.805224 loss: 0.000639 2022/09/12 22:23:15 - mmengine - INFO - Epoch(train) [47][200/586] lr: 5.000000e-04 eta: 8:45:25 time: 0.333554 data_time: 0.022262 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.768061 loss: 0.000616 2022/09/12 22:23:32 - mmengine - INFO - Epoch(train) [47][250/586] lr: 5.000000e-04 eta: 8:45:11 time: 0.339794 data_time: 0.022864 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.848240 loss: 0.000627 2022/09/12 22:23:49 - mmengine - INFO - Epoch(train) [47][300/586] lr: 5.000000e-04 eta: 8:44:56 time: 0.338352 data_time: 0.023853 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.766349 loss: 0.000625 2022/09/12 22:24:06 - mmengine - INFO - Epoch(train) [47][350/586] lr: 5.000000e-04 eta: 8:44:40 time: 0.334980 data_time: 0.023489 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.764187 loss: 0.000611 2022/09/12 22:24:23 - mmengine - INFO - Epoch(train) [47][400/586] lr: 5.000000e-04 eta: 8:44:26 time: 0.337647 data_time: 0.026793 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.811233 loss: 0.000639 2022/09/12 22:24:39 - mmengine - INFO - Epoch(train) [47][450/586] lr: 5.000000e-04 eta: 8:44:10 time: 0.333312 data_time: 0.023182 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.848447 loss: 0.000613 2022/09/12 22:24:56 - mmengine - INFO - Epoch(train) [47][500/586] lr: 5.000000e-04 eta: 8:43:54 time: 0.332216 data_time: 0.023904 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.874953 loss: 0.000620 2022/09/12 22:25:13 - mmengine - INFO - Epoch(train) [47][550/586] lr: 5.000000e-04 eta: 8:43:41 time: 0.347117 data_time: 0.029493 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.830360 loss: 0.000622 2022/09/12 22:25:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:25:25 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/09/12 22:25:49 - mmengine - INFO - Epoch(train) [48][50/586] lr: 5.000000e-04 eta: 8:42:34 time: 0.344980 data_time: 0.031327 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.790703 loss: 0.000646 2022/09/12 22:26:05 - mmengine - INFO - Epoch(train) [48][100/586] lr: 5.000000e-04 eta: 8:42:19 time: 0.335504 data_time: 0.023594 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.841170 loss: 0.000629 2022/09/12 22:26:22 - mmengine - INFO - Epoch(train) [48][150/586] lr: 5.000000e-04 eta: 8:42:04 time: 0.336499 data_time: 0.022258 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.796371 loss: 0.000609 2022/09/12 22:26:39 - mmengine - INFO - Epoch(train) [48][200/586] lr: 5.000000e-04 eta: 8:41:50 time: 0.344242 data_time: 0.023363 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.888053 loss: 0.000607 2022/09/12 22:26:56 - mmengine - INFO - Epoch(train) [48][250/586] lr: 5.000000e-04 eta: 8:41:35 time: 0.335399 data_time: 0.022397 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.843392 loss: 0.000618 2022/09/12 22:27:13 - mmengine - INFO - Epoch(train) [48][300/586] lr: 5.000000e-04 eta: 8:41:20 time: 0.337912 data_time: 0.022148 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.789720 loss: 0.000637 2022/09/12 22:27:30 - mmengine - INFO - Epoch(train) [48][350/586] lr: 5.000000e-04 eta: 8:41:05 time: 0.332968 data_time: 0.022556 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.817735 loss: 0.000606 2022/09/12 22:27:46 - mmengine - INFO - Epoch(train) [48][400/586] lr: 5.000000e-04 eta: 8:40:49 time: 0.335023 data_time: 0.022697 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.825442 loss: 0.000631 2022/09/12 22:28:03 - mmengine - INFO - Epoch(train) [48][450/586] lr: 5.000000e-04 eta: 8:40:35 time: 0.338295 data_time: 0.022210 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.779692 loss: 0.000611 2022/09/12 22:28:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:28:20 - mmengine - INFO - Epoch(train) [48][500/586] lr: 5.000000e-04 eta: 8:40:19 time: 0.333495 data_time: 0.022485 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.823388 loss: 0.000612 2022/09/12 22:28:37 - mmengine - INFO - Epoch(train) [48][550/586] lr: 5.000000e-04 eta: 8:40:04 time: 0.334136 data_time: 0.022631 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.829425 loss: 0.000626 2022/09/12 22:28:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:28:49 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/09/12 22:29:13 - mmengine - INFO - Epoch(train) [49][50/586] lr: 5.000000e-04 eta: 8:38:58 time: 0.345048 data_time: 0.026877 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.838697 loss: 0.000639 2022/09/12 22:29:30 - mmengine - INFO - Epoch(train) [49][100/586] lr: 5.000000e-04 eta: 8:38:43 time: 0.333802 data_time: 0.022584 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.835245 loss: 0.000629 2022/09/12 22:29:47 - mmengine - INFO - Epoch(train) [49][150/586] lr: 5.000000e-04 eta: 8:38:29 time: 0.343149 data_time: 0.022400 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.791512 loss: 0.000619 2022/09/12 22:30:04 - mmengine - INFO - Epoch(train) [49][200/586] lr: 5.000000e-04 eta: 8:38:13 time: 0.331711 data_time: 0.022298 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.717927 loss: 0.000621 2022/09/12 22:30:21 - mmengine - INFO - Epoch(train) [49][250/586] lr: 5.000000e-04 eta: 8:37:59 time: 0.340987 data_time: 0.022424 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.888715 loss: 0.000626 2022/09/12 22:30:38 - mmengine - INFO - Epoch(train) [49][300/586] lr: 5.000000e-04 eta: 8:37:44 time: 0.336614 data_time: 0.025839 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.884932 loss: 0.000602 2022/09/12 22:30:55 - mmengine - INFO - Epoch(train) [49][350/586] lr: 5.000000e-04 eta: 8:37:28 time: 0.334307 data_time: 0.022108 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.752973 loss: 0.000605 2022/09/12 22:31:11 - mmengine - INFO - Epoch(train) [49][400/586] lr: 5.000000e-04 eta: 8:37:13 time: 0.335708 data_time: 0.023011 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.810121 loss: 0.000596 2022/09/12 22:31:29 - mmengine - INFO - Epoch(train) [49][450/586] lr: 5.000000e-04 eta: 8:37:00 time: 0.348990 data_time: 0.023734 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.805397 loss: 0.000608 2022/09/12 22:31:45 - mmengine - INFO - Epoch(train) [49][500/586] lr: 5.000000e-04 eta: 8:36:45 time: 0.333699 data_time: 0.021891 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.853910 loss: 0.000603 2022/09/12 22:32:02 - mmengine - INFO - Epoch(train) [49][550/586] lr: 5.000000e-04 eta: 8:36:29 time: 0.330150 data_time: 0.021904 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.840885 loss: 0.000641 2022/09/12 22:32:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:32:15 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/09/12 22:32:39 - mmengine - INFO - Epoch(train) [50][50/586] lr: 5.000000e-04 eta: 8:35:25 time: 0.349356 data_time: 0.032996 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.822235 loss: 0.000613 2022/09/12 22:32:55 - mmengine - INFO - Epoch(train) [50][100/586] lr: 5.000000e-04 eta: 8:35:09 time: 0.330547 data_time: 0.022788 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.790356 loss: 0.000622 2022/09/12 22:33:12 - mmengine - INFO - Epoch(train) [50][150/586] lr: 5.000000e-04 eta: 8:34:55 time: 0.345454 data_time: 0.022156 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.805148 loss: 0.000611 2022/09/12 22:33:29 - mmengine - INFO - Epoch(train) [50][200/586] lr: 5.000000e-04 eta: 8:34:40 time: 0.334491 data_time: 0.022710 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.828802 loss: 0.000600 2022/09/12 22:33:46 - mmengine - INFO - Epoch(train) [50][250/586] lr: 5.000000e-04 eta: 8:34:25 time: 0.335605 data_time: 0.022251 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.811786 loss: 0.000609 2022/09/12 22:33:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:34:03 - mmengine - INFO - Epoch(train) [50][300/586] lr: 5.000000e-04 eta: 8:34:10 time: 0.336848 data_time: 0.022869 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.842841 loss: 0.000612 2022/09/12 22:34:20 - mmengine - INFO - Epoch(train) [50][350/586] lr: 5.000000e-04 eta: 8:33:57 time: 0.346522 data_time: 0.022766 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.835696 loss: 0.000622 2022/09/12 22:34:37 - mmengine - INFO - Epoch(train) [50][400/586] lr: 5.000000e-04 eta: 8:33:42 time: 0.338798 data_time: 0.021940 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.842611 loss: 0.000600 2022/09/12 22:34:54 - mmengine - INFO - Epoch(train) [50][450/586] lr: 5.000000e-04 eta: 8:33:27 time: 0.336695 data_time: 0.022113 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.826683 loss: 0.000617 2022/09/12 22:35:11 - mmengine - INFO - Epoch(train) [50][500/586] lr: 5.000000e-04 eta: 8:33:12 time: 0.334941 data_time: 0.022230 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.876508 loss: 0.000609 2022/09/12 22:35:27 - mmengine - INFO - Epoch(train) [50][550/586] lr: 5.000000e-04 eta: 8:32:55 time: 0.329426 data_time: 0.022815 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.764136 loss: 0.000603 2022/09/12 22:35:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:35:39 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/09/12 22:35:58 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:01:18 time: 0.219117 data_time: 0.012264 memory: 7489 2022/09/12 22:36:08 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:01:04 time: 0.210293 data_time: 0.008606 memory: 1657 2022/09/12 22:36:19 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:54 time: 0.210855 data_time: 0.007587 memory: 1657 2022/09/12 22:36:29 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:43 time: 0.210398 data_time: 0.007621 memory: 1657 2022/09/12 22:36:40 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:33 time: 0.211024 data_time: 0.007745 memory: 1657 2022/09/12 22:36:50 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:22 time: 0.209719 data_time: 0.007553 memory: 1657 2022/09/12 22:37:01 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:11 time: 0.210073 data_time: 0.007745 memory: 1657 2022/09/12 22:37:12 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:01 time: 0.212869 data_time: 0.010928 memory: 1657 2022/09/12 22:37:46 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 22:38:00 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.734015 coco/AP .5: 0.892453 coco/AP .75: 0.803241 coco/AP (M): 0.699504 coco/AP (L): 0.802315 coco/AR: 0.786666 coco/AR .5: 0.931045 coco/AR .75: 0.849496 coco/AR (M): 0.745288 coco/AR (L): 0.846600 2022/09/12 22:38:00 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_40.pth is removed 2022/09/12 22:38:04 - mmengine - INFO - The best checkpoint with 0.7340 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/09/12 22:38:21 - mmengine - INFO - Epoch(train) [51][50/586] lr: 5.000000e-04 eta: 8:31:53 time: 0.350219 data_time: 0.027774 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.766278 loss: 0.000624 2022/09/12 22:38:38 - mmengine - INFO - Epoch(train) [51][100/586] lr: 5.000000e-04 eta: 8:31:37 time: 0.329982 data_time: 0.022247 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.832680 loss: 0.000610 2022/09/12 22:38:55 - mmengine - INFO - Epoch(train) [51][150/586] lr: 5.000000e-04 eta: 8:31:23 time: 0.342117 data_time: 0.022569 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.783690 loss: 0.000625 2022/09/12 22:39:12 - mmengine - INFO - Epoch(train) [51][200/586] lr: 5.000000e-04 eta: 8:31:09 time: 0.345199 data_time: 0.025610 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.820709 loss: 0.000625 2022/09/12 22:39:30 - mmengine - INFO - Epoch(train) [51][250/586] lr: 5.000000e-04 eta: 8:30:55 time: 0.344311 data_time: 0.023550 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.747601 loss: 0.000624 2022/09/12 22:39:46 - mmengine - INFO - Epoch(train) [51][300/586] lr: 5.000000e-04 eta: 8:30:41 time: 0.337302 data_time: 0.022764 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.793790 loss: 0.000613 2022/09/12 22:40:03 - mmengine - INFO - Epoch(train) [51][350/586] lr: 5.000000e-04 eta: 8:30:26 time: 0.341517 data_time: 0.022131 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.798992 loss: 0.000614 2022/09/12 22:40:20 - mmengine - INFO - Epoch(train) [51][400/586] lr: 5.000000e-04 eta: 8:30:11 time: 0.333630 data_time: 0.023239 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.826704 loss: 0.000620 2022/09/12 22:40:37 - mmengine - INFO - Epoch(train) [51][450/586] lr: 5.000000e-04 eta: 8:29:56 time: 0.340161 data_time: 0.022042 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.832105 loss: 0.000619 2022/09/12 22:40:54 - mmengine - INFO - Epoch(train) [51][500/586] lr: 5.000000e-04 eta: 8:29:42 time: 0.339745 data_time: 0.026454 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.841638 loss: 0.000605 2022/09/12 22:41:11 - mmengine - INFO - Epoch(train) [51][550/586] lr: 5.000000e-04 eta: 8:29:25 time: 0.327998 data_time: 0.023270 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.840623 loss: 0.000623 2022/09/12 22:41:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:41:23 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/09/12 22:41:47 - mmengine - INFO - Epoch(train) [52][50/586] lr: 5.000000e-04 eta: 8:28:24 time: 0.348351 data_time: 0.026586 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.801094 loss: 0.000636 2022/09/12 22:42:04 - mmengine - INFO - Epoch(train) [52][100/586] lr: 5.000000e-04 eta: 8:28:08 time: 0.330520 data_time: 0.025051 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.821909 loss: 0.000624 2022/09/12 22:42:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:42:21 - mmengine - INFO - Epoch(train) [52][150/586] lr: 5.000000e-04 eta: 8:27:54 time: 0.341519 data_time: 0.022241 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.798037 loss: 0.000626 2022/09/12 22:42:38 - mmengine - INFO - Epoch(train) [52][200/586] lr: 5.000000e-04 eta: 8:27:38 time: 0.335981 data_time: 0.022462 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.805938 loss: 0.000605 2022/09/12 22:42:54 - mmengine - INFO - Epoch(train) [52][250/586] lr: 5.000000e-04 eta: 8:27:23 time: 0.336105 data_time: 0.022387 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.820665 loss: 0.000594 2022/09/12 22:43:11 - mmengine - INFO - Epoch(train) [52][300/586] lr: 5.000000e-04 eta: 8:27:08 time: 0.334275 data_time: 0.023216 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.797769 loss: 0.000598 2022/09/12 22:43:28 - mmengine - INFO - Epoch(train) [52][350/586] lr: 5.000000e-04 eta: 8:26:54 time: 0.346088 data_time: 0.021967 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.838445 loss: 0.000602 2022/09/12 22:43:45 - mmengine - INFO - Epoch(train) [52][400/586] lr: 5.000000e-04 eta: 8:26:40 time: 0.337031 data_time: 0.022827 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.827982 loss: 0.000621 2022/09/12 22:44:02 - mmengine - INFO - Epoch(train) [52][450/586] lr: 5.000000e-04 eta: 8:26:25 time: 0.338845 data_time: 0.022473 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.772535 loss: 0.000615 2022/09/12 22:44:19 - mmengine - INFO - Epoch(train) [52][500/586] lr: 5.000000e-04 eta: 8:26:09 time: 0.334520 data_time: 0.022105 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.831400 loss: 0.000618 2022/09/12 22:44:36 - mmengine - INFO - Epoch(train) [52][550/586] lr: 5.000000e-04 eta: 8:25:55 time: 0.338503 data_time: 0.022300 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.791416 loss: 0.000616 2022/09/12 22:44:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:44:48 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/09/12 22:45:12 - mmengine - INFO - Epoch(train) [53][50/586] lr: 5.000000e-04 eta: 8:24:53 time: 0.342115 data_time: 0.027875 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.856213 loss: 0.000605 2022/09/12 22:45:29 - mmengine - INFO - Epoch(train) [53][100/586] lr: 5.000000e-04 eta: 8:24:38 time: 0.334345 data_time: 0.027084 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.812438 loss: 0.000603 2022/09/12 22:45:46 - mmengine - INFO - Epoch(train) [53][150/586] lr: 5.000000e-04 eta: 8:24:24 time: 0.344701 data_time: 0.022369 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.785976 loss: 0.000610 2022/09/12 22:46:03 - mmengine - INFO - Epoch(train) [53][200/586] lr: 5.000000e-04 eta: 8:24:08 time: 0.332399 data_time: 0.022462 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.790542 loss: 0.000630 2022/09/12 22:46:20 - mmengine - INFO - Epoch(train) [53][250/586] lr: 5.000000e-04 eta: 8:23:53 time: 0.337382 data_time: 0.022024 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.836823 loss: 0.000588 2022/09/12 22:46:37 - mmengine - INFO - Epoch(train) [53][300/586] lr: 5.000000e-04 eta: 8:23:39 time: 0.337740 data_time: 0.022037 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.762824 loss: 0.000600 2022/09/12 22:46:53 - mmengine - INFO - Epoch(train) [53][350/586] lr: 5.000000e-04 eta: 8:23:24 time: 0.336450 data_time: 0.022783 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.699042 loss: 0.000614 2022/09/12 22:47:10 - mmengine - INFO - Epoch(train) [53][400/586] lr: 5.000000e-04 eta: 8:23:08 time: 0.335590 data_time: 0.027088 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.782742 loss: 0.000608 2022/09/12 22:47:27 - mmengine - INFO - Epoch(train) [53][450/586] lr: 5.000000e-04 eta: 8:22:53 time: 0.335866 data_time: 0.022909 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.773374 loss: 0.000613 2022/09/12 22:47:44 - mmengine - INFO - Epoch(train) [53][500/586] lr: 5.000000e-04 eta: 8:22:39 time: 0.343274 data_time: 0.022997 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.814602 loss: 0.000605 2022/09/12 22:47:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:48:01 - mmengine - INFO - Epoch(train) [53][550/586] lr: 5.000000e-04 eta: 8:22:24 time: 0.333016 data_time: 0.022580 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.799733 loss: 0.000609 2022/09/12 22:48:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:48:13 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/09/12 22:48:38 - mmengine - INFO - Epoch(train) [54][50/586] lr: 5.000000e-04 eta: 8:21:25 time: 0.355073 data_time: 0.035094 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.815179 loss: 0.000602 2022/09/12 22:48:54 - mmengine - INFO - Epoch(train) [54][100/586] lr: 5.000000e-04 eta: 8:21:09 time: 0.330946 data_time: 0.023734 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.819458 loss: 0.000619 2022/09/12 22:49:11 - mmengine - INFO - Epoch(train) [54][150/586] lr: 5.000000e-04 eta: 8:20:53 time: 0.332552 data_time: 0.022667 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.782621 loss: 0.000618 2022/09/12 22:49:28 - mmengine - INFO - Epoch(train) [54][200/586] lr: 5.000000e-04 eta: 8:20:39 time: 0.337872 data_time: 0.022968 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.843372 loss: 0.000593 2022/09/12 22:49:44 - mmengine - INFO - Epoch(train) [54][250/586] lr: 5.000000e-04 eta: 8:20:24 time: 0.337172 data_time: 0.021466 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.865562 loss: 0.000615 2022/09/12 22:50:01 - mmengine - INFO - Epoch(train) [54][300/586] lr: 5.000000e-04 eta: 8:20:09 time: 0.336584 data_time: 0.022358 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.841900 loss: 0.000612 2022/09/12 22:50:18 - mmengine - INFO - Epoch(train) [54][350/586] lr: 5.000000e-04 eta: 8:19:54 time: 0.335707 data_time: 0.022240 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.796254 loss: 0.000620 2022/09/12 22:50:35 - mmengine - INFO - Epoch(train) [54][400/586] lr: 5.000000e-04 eta: 8:19:39 time: 0.337356 data_time: 0.022107 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.813838 loss: 0.000600 2022/09/12 22:50:52 - mmengine - INFO - Epoch(train) [54][450/586] lr: 5.000000e-04 eta: 8:19:24 time: 0.339967 data_time: 0.022987 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.858255 loss: 0.000606 2022/09/12 22:51:09 - mmengine - INFO - Epoch(train) [54][500/586] lr: 5.000000e-04 eta: 8:19:09 time: 0.338923 data_time: 0.022214 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.793912 loss: 0.000624 2022/09/12 22:51:26 - mmengine - INFO - Epoch(train) [54][550/586] lr: 5.000000e-04 eta: 8:18:55 time: 0.337184 data_time: 0.023416 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.802026 loss: 0.000630 2022/09/12 22:51:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:51:38 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/09/12 22:52:02 - mmengine - INFO - Epoch(train) [55][50/586] lr: 5.000000e-04 eta: 8:17:54 time: 0.341159 data_time: 0.032289 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.816415 loss: 0.000606 2022/09/12 22:52:19 - mmengine - INFO - Epoch(train) [55][100/586] lr: 5.000000e-04 eta: 8:17:40 time: 0.338035 data_time: 0.022124 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.814060 loss: 0.000604 2022/09/12 22:52:36 - mmengine - INFO - Epoch(train) [55][150/586] lr: 5.000000e-04 eta: 8:17:25 time: 0.337498 data_time: 0.023050 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.845647 loss: 0.000602 2022/09/12 22:52:53 - mmengine - INFO - Epoch(train) [55][200/586] lr: 5.000000e-04 eta: 8:17:10 time: 0.341199 data_time: 0.022587 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.847340 loss: 0.000609 2022/09/12 22:53:10 - mmengine - INFO - Epoch(train) [55][250/586] lr: 5.000000e-04 eta: 8:16:55 time: 0.331724 data_time: 0.026256 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.807660 loss: 0.000627 2022/09/12 22:53:26 - mmengine - INFO - Epoch(train) [55][300/586] lr: 5.000000e-04 eta: 8:16:40 time: 0.335828 data_time: 0.023267 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.826479 loss: 0.000599 2022/09/12 22:53:44 - mmengine - INFO - Epoch(train) [55][350/586] lr: 5.000000e-04 eta: 8:16:26 time: 0.343447 data_time: 0.023098 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.853843 loss: 0.000615 2022/09/12 22:53:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:54:00 - mmengine - INFO - Epoch(train) [55][400/586] lr: 5.000000e-04 eta: 8:16:10 time: 0.333410 data_time: 0.023259 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.771966 loss: 0.000599 2022/09/12 22:54:17 - mmengine - INFO - Epoch(train) [55][450/586] lr: 5.000000e-04 eta: 8:15:55 time: 0.333276 data_time: 0.022032 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.808383 loss: 0.000592 2022/09/12 22:54:34 - mmengine - INFO - Epoch(train) [55][500/586] lr: 5.000000e-04 eta: 8:15:40 time: 0.339123 data_time: 0.022981 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.821721 loss: 0.000636 2022/09/12 22:54:51 - mmengine - INFO - Epoch(train) [55][550/586] lr: 5.000000e-04 eta: 8:15:24 time: 0.332500 data_time: 0.025162 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.835545 loss: 0.000620 2022/09/12 22:55:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:55:03 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/09/12 22:55:27 - mmengine - INFO - Epoch(train) [56][50/586] lr: 5.000000e-04 eta: 8:14:26 time: 0.344197 data_time: 0.030077 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.779071 loss: 0.000619 2022/09/12 22:55:44 - mmengine - INFO - Epoch(train) [56][100/586] lr: 5.000000e-04 eta: 8:14:11 time: 0.341696 data_time: 0.022942 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.850077 loss: 0.000625 2022/09/12 22:56:01 - mmengine - INFO - Epoch(train) [56][150/586] lr: 5.000000e-04 eta: 8:13:56 time: 0.334281 data_time: 0.023171 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.770878 loss: 0.000616 2022/09/12 22:56:18 - mmengine - INFO - Epoch(train) [56][200/586] lr: 5.000000e-04 eta: 8:13:41 time: 0.337748 data_time: 0.022282 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.785945 loss: 0.000618 2022/09/12 22:56:35 - mmengine - INFO - Epoch(train) [56][250/586] lr: 5.000000e-04 eta: 8:13:26 time: 0.335813 data_time: 0.022865 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.667379 loss: 0.000634 2022/09/12 22:56:51 - mmengine - INFO - Epoch(train) [56][300/586] lr: 5.000000e-04 eta: 8:13:11 time: 0.331847 data_time: 0.022352 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.740761 loss: 0.000615 2022/09/12 22:57:08 - mmengine - INFO - Epoch(train) [56][350/586] lr: 5.000000e-04 eta: 8:12:56 time: 0.341627 data_time: 0.025496 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.785534 loss: 0.000610 2022/09/12 22:57:25 - mmengine - INFO - Epoch(train) [56][400/586] lr: 5.000000e-04 eta: 8:12:41 time: 0.336361 data_time: 0.022290 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.795446 loss: 0.000612 2022/09/12 22:57:42 - mmengine - INFO - Epoch(train) [56][450/586] lr: 5.000000e-04 eta: 8:12:26 time: 0.335331 data_time: 0.022368 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.805866 loss: 0.000621 2022/09/12 22:57:59 - mmengine - INFO - Epoch(train) [56][500/586] lr: 5.000000e-04 eta: 8:12:11 time: 0.334794 data_time: 0.025604 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.801589 loss: 0.000593 2022/09/12 22:58:16 - mmengine - INFO - Epoch(train) [56][550/586] lr: 5.000000e-04 eta: 8:11:56 time: 0.337102 data_time: 0.022793 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.778538 loss: 0.000617 2022/09/12 22:58:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:58:28 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/09/12 22:58:52 - mmengine - INFO - Epoch(train) [57][50/586] lr: 5.000000e-04 eta: 8:10:58 time: 0.345307 data_time: 0.031802 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.863732 loss: 0.000594 2022/09/12 22:59:09 - mmengine - INFO - Epoch(train) [57][100/586] lr: 5.000000e-04 eta: 8:10:44 time: 0.346163 data_time: 0.023128 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.827118 loss: 0.000599 2022/09/12 22:59:26 - mmengine - INFO - Epoch(train) [57][150/586] lr: 5.000000e-04 eta: 8:10:29 time: 0.335008 data_time: 0.022406 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.822827 loss: 0.000606 2022/09/12 22:59:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 22:59:43 - mmengine - INFO - Epoch(train) [57][200/586] lr: 5.000000e-04 eta: 8:10:14 time: 0.337167 data_time: 0.022903 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.851256 loss: 0.000607 2022/09/12 23:00:00 - mmengine - INFO - Epoch(train) [57][250/586] lr: 5.000000e-04 eta: 8:09:59 time: 0.337687 data_time: 0.025937 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.816879 loss: 0.000603 2022/09/12 23:00:17 - mmengine - INFO - Epoch(train) [57][300/586] lr: 5.000000e-04 eta: 8:09:44 time: 0.336826 data_time: 0.021637 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.841290 loss: 0.000586 2022/09/12 23:00:33 - mmengine - INFO - Epoch(train) [57][350/586] lr: 5.000000e-04 eta: 8:09:29 time: 0.331089 data_time: 0.023021 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.842167 loss: 0.000588 2022/09/12 23:00:50 - mmengine - INFO - Epoch(train) [57][400/586] lr: 5.000000e-04 eta: 8:09:14 time: 0.337859 data_time: 0.026475 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.829225 loss: 0.000579 2022/09/12 23:01:07 - mmengine - INFO - Epoch(train) [57][450/586] lr: 5.000000e-04 eta: 8:08:59 time: 0.339352 data_time: 0.022616 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.808495 loss: 0.000597 2022/09/12 23:01:24 - mmengine - INFO - Epoch(train) [57][500/586] lr: 5.000000e-04 eta: 8:08:44 time: 0.335428 data_time: 0.022260 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.825461 loss: 0.000624 2022/09/12 23:01:41 - mmengine - INFO - Epoch(train) [57][550/586] lr: 5.000000e-04 eta: 8:08:29 time: 0.337074 data_time: 0.022106 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.830897 loss: 0.000613 2022/09/12 23:01:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:01:53 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/09/12 23:02:16 - mmengine - INFO - Epoch(train) [58][50/586] lr: 5.000000e-04 eta: 8:07:31 time: 0.335676 data_time: 0.026634 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.831314 loss: 0.000600 2022/09/12 23:02:32 - mmengine - INFO - Epoch(train) [58][100/586] lr: 5.000000e-04 eta: 8:07:16 time: 0.336435 data_time: 0.022517 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.820609 loss: 0.000600 2022/09/12 23:02:49 - mmengine - INFO - Epoch(train) [58][150/586] lr: 5.000000e-04 eta: 8:07:00 time: 0.331105 data_time: 0.026157 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.807116 loss: 0.000607 2022/09/12 23:03:06 - mmengine - INFO - Epoch(train) [58][200/586] lr: 5.000000e-04 eta: 8:06:45 time: 0.336802 data_time: 0.022905 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.884152 loss: 0.000620 2022/09/12 23:03:23 - mmengine - INFO - Epoch(train) [58][250/586] lr: 5.000000e-04 eta: 8:06:30 time: 0.339417 data_time: 0.022293 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.848304 loss: 0.000599 2022/09/12 23:03:40 - mmengine - INFO - Epoch(train) [58][300/586] lr: 5.000000e-04 eta: 8:06:15 time: 0.334463 data_time: 0.025125 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.768950 loss: 0.000601 2022/09/12 23:03:57 - mmengine - INFO - Epoch(train) [58][350/586] lr: 5.000000e-04 eta: 8:06:02 time: 0.350938 data_time: 0.022645 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.879361 loss: 0.000611 2022/09/12 23:04:14 - mmengine - INFO - Epoch(train) [58][400/586] lr: 5.000000e-04 eta: 8:05:47 time: 0.335407 data_time: 0.022878 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.820293 loss: 0.000595 2022/09/12 23:04:31 - mmengine - INFO - Epoch(train) [58][450/586] lr: 5.000000e-04 eta: 8:05:32 time: 0.336447 data_time: 0.024258 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.802259 loss: 0.000577 2022/09/12 23:04:48 - mmengine - INFO - Epoch(train) [58][500/586] lr: 5.000000e-04 eta: 8:05:18 time: 0.342242 data_time: 0.022629 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.865939 loss: 0.000611 2022/09/12 23:05:04 - mmengine - INFO - Epoch(train) [58][550/586] lr: 5.000000e-04 eta: 8:05:02 time: 0.329111 data_time: 0.022574 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.816892 loss: 0.000600 2022/09/12 23:05:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:05:16 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/09/12 23:05:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:05:40 - mmengine - INFO - Epoch(train) [59][50/586] lr: 5.000000e-04 eta: 8:04:05 time: 0.347128 data_time: 0.030805 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.792780 loss: 0.000600 2022/09/12 23:05:57 - mmengine - INFO - Epoch(train) [59][100/586] lr: 5.000000e-04 eta: 8:03:51 time: 0.341622 data_time: 0.023158 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.803811 loss: 0.000605 2022/09/12 23:06:14 - mmengine - INFO - Epoch(train) [59][150/586] lr: 5.000000e-04 eta: 8:03:36 time: 0.336490 data_time: 0.022964 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.849746 loss: 0.000621 2022/09/12 23:06:31 - mmengine - INFO - Epoch(train) [59][200/586] lr: 5.000000e-04 eta: 8:03:21 time: 0.335743 data_time: 0.022642 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.753741 loss: 0.000604 2022/09/12 23:06:48 - mmengine - INFO - Epoch(train) [59][250/586] lr: 5.000000e-04 eta: 8:03:07 time: 0.345033 data_time: 0.026069 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.770669 loss: 0.000615 2022/09/12 23:07:04 - mmengine - INFO - Epoch(train) [59][300/586] lr: 5.000000e-04 eta: 8:02:51 time: 0.323960 data_time: 0.022198 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.802750 loss: 0.000612 2022/09/12 23:07:21 - mmengine - INFO - Epoch(train) [59][350/586] lr: 5.000000e-04 eta: 8:02:36 time: 0.340469 data_time: 0.021960 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.891518 loss: 0.000603 2022/09/12 23:07:38 - mmengine - INFO - Epoch(train) [59][400/586] lr: 5.000000e-04 eta: 8:02:21 time: 0.335681 data_time: 0.023114 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.865316 loss: 0.000618 2022/09/12 23:07:54 - mmengine - INFO - Epoch(train) [59][450/586] lr: 5.000000e-04 eta: 8:02:05 time: 0.328829 data_time: 0.022453 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.845491 loss: 0.000598 2022/09/12 23:08:12 - mmengine - INFO - Epoch(train) [59][500/586] lr: 5.000000e-04 eta: 8:01:52 time: 0.353966 data_time: 0.023159 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.851755 loss: 0.000613 2022/09/12 23:08:29 - mmengine - INFO - Epoch(train) [59][550/586] lr: 5.000000e-04 eta: 8:01:38 time: 0.342983 data_time: 0.023431 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.808050 loss: 0.000603 2022/09/12 23:08:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:08:41 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/09/12 23:09:06 - mmengine - INFO - Epoch(train) [60][50/586] lr: 5.000000e-04 eta: 8:00:42 time: 0.343471 data_time: 0.027306 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.763495 loss: 0.000605 2022/09/12 23:09:23 - mmengine - INFO - Epoch(train) [60][100/586] lr: 5.000000e-04 eta: 8:00:28 time: 0.342079 data_time: 0.023103 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.887363 loss: 0.000604 2022/09/12 23:09:39 - mmengine - INFO - Epoch(train) [60][150/586] lr: 5.000000e-04 eta: 8:00:12 time: 0.328981 data_time: 0.022316 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.897618 loss: 0.000594 2022/09/12 23:09:56 - mmengine - INFO - Epoch(train) [60][200/586] lr: 5.000000e-04 eta: 7:59:57 time: 0.340496 data_time: 0.022298 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.842464 loss: 0.000597 2022/09/12 23:10:13 - mmengine - INFO - Epoch(train) [60][250/586] lr: 5.000000e-04 eta: 7:59:42 time: 0.334912 data_time: 0.022306 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.815906 loss: 0.000619 2022/09/12 23:10:30 - mmengine - INFO - Epoch(train) [60][300/586] lr: 5.000000e-04 eta: 7:59:27 time: 0.334118 data_time: 0.022364 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.875304 loss: 0.000603 2022/09/12 23:10:46 - mmengine - INFO - Epoch(train) [60][350/586] lr: 5.000000e-04 eta: 7:59:11 time: 0.333508 data_time: 0.022017 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.864289 loss: 0.000596 2022/09/12 23:11:03 - mmengine - INFO - Epoch(train) [60][400/586] lr: 5.000000e-04 eta: 7:58:57 time: 0.341939 data_time: 0.022635 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.778745 loss: 0.000599 2022/09/12 23:11:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:11:20 - mmengine - INFO - Epoch(train) [60][450/586] lr: 5.000000e-04 eta: 7:58:41 time: 0.331323 data_time: 0.021987 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.842476 loss: 0.000603 2022/09/12 23:11:37 - mmengine - INFO - Epoch(train) [60][500/586] lr: 5.000000e-04 eta: 7:58:27 time: 0.340644 data_time: 0.022379 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.819331 loss: 0.000573 2022/09/12 23:11:54 - mmengine - INFO - Epoch(train) [60][550/586] lr: 5.000000e-04 eta: 7:58:12 time: 0.340591 data_time: 0.022046 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.751374 loss: 0.000600 2022/09/12 23:12:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:12:06 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/09/12 23:12:24 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:01:18 time: 0.218791 data_time: 0.012607 memory: 7489 2022/09/12 23:12:35 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:01:04 time: 0.210632 data_time: 0.008111 memory: 1657 2022/09/12 23:12:45 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:54 time: 0.210380 data_time: 0.007610 memory: 1657 2022/09/12 23:12:56 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:43 time: 0.210061 data_time: 0.007628 memory: 1657 2022/09/12 23:13:06 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:33 time: 0.210697 data_time: 0.007526 memory: 1657 2022/09/12 23:13:17 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:22 time: 0.210772 data_time: 0.008544 memory: 1657 2022/09/12 23:13:28 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:12 time: 0.213370 data_time: 0.007614 memory: 1657 2022/09/12 23:13:38 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:01 time: 0.207074 data_time: 0.007148 memory: 1657 2022/09/12 23:14:14 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 23:14:27 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.740634 coco/AP .5: 0.898378 coco/AP .75: 0.810193 coco/AP (M): 0.709381 coco/AP (L): 0.802104 coco/AR: 0.792459 coco/AR .5: 0.936398 coco/AR .75: 0.855479 coco/AR (M): 0.754329 coco/AR (L): 0.848309 2022/09/12 23:14:27 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_50.pth is removed 2022/09/12 23:14:32 - mmengine - INFO - The best checkpoint with 0.7406 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/09/12 23:14:49 - mmengine - INFO - Epoch(train) [61][50/586] lr: 5.000000e-04 eta: 7:57:17 time: 0.338376 data_time: 0.026537 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.852009 loss: 0.000616 2022/09/12 23:15:06 - mmengine - INFO - Epoch(train) [61][100/586] lr: 5.000000e-04 eta: 7:57:02 time: 0.340129 data_time: 0.022408 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.843193 loss: 0.000604 2022/09/12 23:15:22 - mmengine - INFO - Epoch(train) [61][150/586] lr: 5.000000e-04 eta: 7:56:47 time: 0.336621 data_time: 0.022707 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.835340 loss: 0.000589 2022/09/12 23:15:39 - mmengine - INFO - Epoch(train) [61][200/586] lr: 5.000000e-04 eta: 7:56:32 time: 0.334870 data_time: 0.023119 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.871127 loss: 0.000625 2022/09/12 23:15:56 - mmengine - INFO - Epoch(train) [61][250/586] lr: 5.000000e-04 eta: 7:56:17 time: 0.335609 data_time: 0.022224 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.819588 loss: 0.000604 2022/09/12 23:16:13 - mmengine - INFO - Epoch(train) [61][300/586] lr: 5.000000e-04 eta: 7:56:01 time: 0.333877 data_time: 0.026718 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.847249 loss: 0.000587 2022/09/12 23:16:29 - mmengine - INFO - Epoch(train) [61][350/586] lr: 5.000000e-04 eta: 7:55:46 time: 0.333459 data_time: 0.023253 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.815412 loss: 0.000597 2022/09/12 23:16:46 - mmengine - INFO - Epoch(train) [61][400/586] lr: 5.000000e-04 eta: 7:55:31 time: 0.339111 data_time: 0.023347 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.745163 loss: 0.000614 2022/09/12 23:17:03 - mmengine - INFO - Epoch(train) [61][450/586] lr: 5.000000e-04 eta: 7:55:16 time: 0.332910 data_time: 0.025683 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.800056 loss: 0.000602 2022/09/12 23:17:20 - mmengine - INFO - Epoch(train) [61][500/586] lr: 5.000000e-04 eta: 7:55:01 time: 0.338158 data_time: 0.022794 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.720415 loss: 0.000603 2022/09/12 23:17:36 - mmengine - INFO - Epoch(train) [61][550/586] lr: 5.000000e-04 eta: 7:54:45 time: 0.329113 data_time: 0.022034 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.815792 loss: 0.000611 2022/09/12 23:17:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:17:48 - mmengine - INFO - Saving checkpoint at 61 epochs 2022/09/12 23:18:13 - mmengine - INFO - Epoch(train) [62][50/586] lr: 5.000000e-04 eta: 7:53:50 time: 0.342102 data_time: 0.033050 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.809508 loss: 0.000612 2022/09/12 23:18:30 - mmengine - INFO - Epoch(train) [62][100/586] lr: 5.000000e-04 eta: 7:53:36 time: 0.340493 data_time: 0.022589 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.864867 loss: 0.000606 2022/09/12 23:18:47 - mmengine - INFO - Epoch(train) [62][150/586] lr: 5.000000e-04 eta: 7:53:20 time: 0.332470 data_time: 0.022268 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.837573 loss: 0.000601 2022/09/12 23:19:03 - mmengine - INFO - Epoch(train) [62][200/586] lr: 5.000000e-04 eta: 7:53:05 time: 0.337530 data_time: 0.022394 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.846870 loss: 0.000597 2022/09/12 23:19:20 - mmengine - INFO - Epoch(train) [62][250/586] lr: 5.000000e-04 eta: 7:52:50 time: 0.333891 data_time: 0.022321 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.860678 loss: 0.000591 2022/09/12 23:19:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:19:37 - mmengine - INFO - Epoch(train) [62][300/586] lr: 5.000000e-04 eta: 7:52:35 time: 0.331361 data_time: 0.023584 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.797600 loss: 0.000602 2022/09/12 23:19:54 - mmengine - INFO - Epoch(train) [62][350/586] lr: 5.000000e-04 eta: 7:52:20 time: 0.341248 data_time: 0.022548 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.857177 loss: 0.000599 2022/09/12 23:20:11 - mmengine - INFO - Epoch(train) [62][400/586] lr: 5.000000e-04 eta: 7:52:05 time: 0.337753 data_time: 0.022453 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.815437 loss: 0.000619 2022/09/12 23:20:27 - mmengine - INFO - Epoch(train) [62][450/586] lr: 5.000000e-04 eta: 7:51:50 time: 0.331979 data_time: 0.022548 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.840835 loss: 0.000617 2022/09/12 23:20:44 - mmengine - INFO - Epoch(train) [62][500/586] lr: 5.000000e-04 eta: 7:51:35 time: 0.336943 data_time: 0.022814 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.813858 loss: 0.000610 2022/09/12 23:21:01 - mmengine - INFO - Epoch(train) [62][550/586] lr: 5.000000e-04 eta: 7:51:21 time: 0.343956 data_time: 0.022085 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.850373 loss: 0.000604 2022/09/12 23:21:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:21:13 - mmengine - INFO - Saving checkpoint at 62 epochs 2022/09/12 23:21:38 - mmengine - INFO - Epoch(train) [63][50/586] lr: 5.000000e-04 eta: 7:50:27 time: 0.348111 data_time: 0.026869 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.862313 loss: 0.000595 2022/09/12 23:21:55 - mmengine - INFO - Epoch(train) [63][100/586] lr: 5.000000e-04 eta: 7:50:13 time: 0.341859 data_time: 0.023655 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.865772 loss: 0.000594 2022/09/12 23:22:12 - mmengine - INFO - Epoch(train) [63][150/586] lr: 5.000000e-04 eta: 7:49:57 time: 0.330167 data_time: 0.023009 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.804151 loss: 0.000599 2022/09/12 23:22:29 - mmengine - INFO - Epoch(train) [63][200/586] lr: 5.000000e-04 eta: 7:49:44 time: 0.349425 data_time: 0.025805 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.864459 loss: 0.000608 2022/09/12 23:22:46 - mmengine - INFO - Epoch(train) [63][250/586] lr: 5.000000e-04 eta: 7:49:29 time: 0.336768 data_time: 0.022335 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.765191 loss: 0.000608 2022/09/12 23:23:03 - mmengine - INFO - Epoch(train) [63][300/586] lr: 5.000000e-04 eta: 7:49:14 time: 0.343666 data_time: 0.023840 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.829545 loss: 0.000609 2022/09/12 23:23:21 - mmengine - INFO - Epoch(train) [63][350/586] lr: 5.000000e-04 eta: 7:49:01 time: 0.348416 data_time: 0.023969 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.817391 loss: 0.000601 2022/09/12 23:23:38 - mmengine - INFO - Epoch(train) [63][400/586] lr: 5.000000e-04 eta: 7:48:46 time: 0.338976 data_time: 0.022780 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.853454 loss: 0.000585 2022/09/12 23:23:54 - mmengine - INFO - Epoch(train) [63][450/586] lr: 5.000000e-04 eta: 7:48:30 time: 0.331748 data_time: 0.022918 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.799586 loss: 0.000603 2022/09/12 23:24:11 - mmengine - INFO - Epoch(train) [63][500/586] lr: 5.000000e-04 eta: 7:48:16 time: 0.341187 data_time: 0.027539 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.865392 loss: 0.000610 2022/09/12 23:24:28 - mmengine - INFO - Epoch(train) [63][550/586] lr: 5.000000e-04 eta: 7:48:01 time: 0.333911 data_time: 0.022350 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.829473 loss: 0.000609 2022/09/12 23:24:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:24:40 - mmengine - INFO - Saving checkpoint at 63 epochs 2022/09/12 23:25:05 - mmengine - INFO - Epoch(train) [64][50/586] lr: 5.000000e-04 eta: 7:47:08 time: 0.346874 data_time: 0.027247 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.812645 loss: 0.000597 2022/09/12 23:25:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:25:22 - mmengine - INFO - Epoch(train) [64][100/586] lr: 5.000000e-04 eta: 7:46:54 time: 0.347883 data_time: 0.023751 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.843652 loss: 0.000596 2022/09/12 23:25:39 - mmengine - INFO - Epoch(train) [64][150/586] lr: 5.000000e-04 eta: 7:46:38 time: 0.329754 data_time: 0.022084 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.827335 loss: 0.000585 2022/09/12 23:25:55 - mmengine - INFO - Epoch(train) [64][200/586] lr: 5.000000e-04 eta: 7:46:23 time: 0.334004 data_time: 0.022190 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.822301 loss: 0.000592 2022/09/12 23:26:12 - mmengine - INFO - Epoch(train) [64][250/586] lr: 5.000000e-04 eta: 7:46:08 time: 0.340412 data_time: 0.022014 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.849900 loss: 0.000586 2022/09/12 23:26:29 - mmengine - INFO - Epoch(train) [64][300/586] lr: 5.000000e-04 eta: 7:45:52 time: 0.328549 data_time: 0.021991 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.870246 loss: 0.000616 2022/09/12 23:26:46 - mmengine - INFO - Epoch(train) [64][350/586] lr: 5.000000e-04 eta: 7:45:37 time: 0.336157 data_time: 0.022617 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.809960 loss: 0.000602 2022/09/12 23:27:03 - mmengine - INFO - Epoch(train) [64][400/586] lr: 5.000000e-04 eta: 7:45:23 time: 0.341572 data_time: 0.026339 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.861131 loss: 0.000607 2022/09/12 23:27:19 - mmengine - INFO - Epoch(train) [64][450/586] lr: 5.000000e-04 eta: 7:45:07 time: 0.330373 data_time: 0.022424 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.845046 loss: 0.000602 2022/09/12 23:27:37 - mmengine - INFO - Epoch(train) [64][500/586] lr: 5.000000e-04 eta: 7:44:54 time: 0.353115 data_time: 0.023064 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.819532 loss: 0.000604 2022/09/12 23:27:54 - mmengine - INFO - Epoch(train) [64][550/586] lr: 5.000000e-04 eta: 7:44:39 time: 0.335821 data_time: 0.022645 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.874133 loss: 0.000587 2022/09/12 23:28:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:28:05 - mmengine - INFO - Saving checkpoint at 64 epochs 2022/09/12 23:28:30 - mmengine - INFO - Epoch(train) [65][50/586] lr: 5.000000e-04 eta: 7:43:46 time: 0.339026 data_time: 0.026227 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.838782 loss: 0.000614 2022/09/12 23:28:47 - mmengine - INFO - Epoch(train) [65][100/586] lr: 5.000000e-04 eta: 7:43:31 time: 0.341867 data_time: 0.023194 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.809326 loss: 0.000593 2022/09/12 23:29:03 - mmengine - INFO - Epoch(train) [65][150/586] lr: 5.000000e-04 eta: 7:43:15 time: 0.323273 data_time: 0.022299 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.889460 loss: 0.000613 2022/09/12 23:29:20 - mmengine - INFO - Epoch(train) [65][200/586] lr: 5.000000e-04 eta: 7:43:00 time: 0.339395 data_time: 0.022666 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.857054 loss: 0.000585 2022/09/12 23:29:37 - mmengine - INFO - Epoch(train) [65][250/586] lr: 5.000000e-04 eta: 7:42:46 time: 0.340668 data_time: 0.022853 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.871102 loss: 0.000581 2022/09/12 23:29:53 - mmengine - INFO - Epoch(train) [65][300/586] lr: 5.000000e-04 eta: 7:42:30 time: 0.330462 data_time: 0.022431 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.844155 loss: 0.000596 2022/09/12 23:30:10 - mmengine - INFO - Epoch(train) [65][350/586] lr: 5.000000e-04 eta: 7:42:15 time: 0.334799 data_time: 0.022639 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.862129 loss: 0.000594 2022/09/12 23:30:27 - mmengine - INFO - Epoch(train) [65][400/586] lr: 5.000000e-04 eta: 7:42:00 time: 0.340757 data_time: 0.022459 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.823220 loss: 0.000602 2022/09/12 23:30:44 - mmengine - INFO - Epoch(train) [65][450/586] lr: 5.000000e-04 eta: 7:41:44 time: 0.331942 data_time: 0.022536 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.859182 loss: 0.000571 2022/09/12 23:30:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:31:01 - mmengine - INFO - Epoch(train) [65][500/586] lr: 5.000000e-04 eta: 7:41:29 time: 0.336094 data_time: 0.023136 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.862105 loss: 0.000599 2022/09/12 23:31:18 - mmengine - INFO - Epoch(train) [65][550/586] lr: 5.000000e-04 eta: 7:41:15 time: 0.341575 data_time: 0.022178 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.888341 loss: 0.000593 2022/09/12 23:31:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:31:30 - mmengine - INFO - Saving checkpoint at 65 epochs 2022/09/12 23:31:53 - mmengine - INFO - Epoch(train) [66][50/586] lr: 5.000000e-04 eta: 7:40:23 time: 0.341125 data_time: 0.029863 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.784993 loss: 0.000566 2022/09/12 23:32:11 - mmengine - INFO - Epoch(train) [66][100/586] lr: 5.000000e-04 eta: 7:40:09 time: 0.351486 data_time: 0.023409 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.834617 loss: 0.000586 2022/09/12 23:32:28 - mmengine - INFO - Epoch(train) [66][150/586] lr: 5.000000e-04 eta: 7:39:54 time: 0.332205 data_time: 0.023683 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.842084 loss: 0.000585 2022/09/12 23:32:44 - mmengine - INFO - Epoch(train) [66][200/586] lr: 5.000000e-04 eta: 7:39:38 time: 0.333324 data_time: 0.022229 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.841534 loss: 0.000587 2022/09/12 23:33:01 - mmengine - INFO - Epoch(train) [66][250/586] lr: 5.000000e-04 eta: 7:39:24 time: 0.341095 data_time: 0.025823 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.853491 loss: 0.000580 2022/09/12 23:33:18 - mmengine - INFO - Epoch(train) [66][300/586] lr: 5.000000e-04 eta: 7:39:08 time: 0.330129 data_time: 0.022586 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.769935 loss: 0.000596 2022/09/12 23:33:34 - mmengine - INFO - Epoch(train) [66][350/586] lr: 5.000000e-04 eta: 7:38:52 time: 0.328520 data_time: 0.022177 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.817929 loss: 0.000592 2022/09/12 23:33:52 - mmengine - INFO - Epoch(train) [66][400/586] lr: 5.000000e-04 eta: 7:38:38 time: 0.344146 data_time: 0.022656 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.824253 loss: 0.000577 2022/09/12 23:34:08 - mmengine - INFO - Epoch(train) [66][450/586] lr: 5.000000e-04 eta: 7:38:22 time: 0.331617 data_time: 0.023367 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.838480 loss: 0.000597 2022/09/12 23:34:25 - mmengine - INFO - Epoch(train) [66][500/586] lr: 5.000000e-04 eta: 7:38:06 time: 0.328172 data_time: 0.022421 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.823484 loss: 0.000601 2022/09/12 23:34:42 - mmengine - INFO - Epoch(train) [66][550/586] lr: 5.000000e-04 eta: 7:37:52 time: 0.343993 data_time: 0.022539 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.859463 loss: 0.000595 2022/09/12 23:34:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:34:54 - mmengine - INFO - Saving checkpoint at 66 epochs 2022/09/12 23:35:17 - mmengine - INFO - Epoch(train) [67][50/586] lr: 5.000000e-04 eta: 7:37:00 time: 0.336499 data_time: 0.028183 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.746478 loss: 0.000590 2022/09/12 23:35:35 - mmengine - INFO - Epoch(train) [67][100/586] lr: 5.000000e-04 eta: 7:36:46 time: 0.351607 data_time: 0.022428 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.828944 loss: 0.000590 2022/09/12 23:35:52 - mmengine - INFO - Epoch(train) [67][150/586] lr: 5.000000e-04 eta: 7:36:31 time: 0.334308 data_time: 0.021975 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.836829 loss: 0.000591 2022/09/12 23:36:09 - mmengine - INFO - Epoch(train) [67][200/586] lr: 5.000000e-04 eta: 7:36:16 time: 0.334620 data_time: 0.025796 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.856039 loss: 0.000606 2022/09/12 23:36:26 - mmengine - INFO - Epoch(train) [67][250/586] lr: 5.000000e-04 eta: 7:36:01 time: 0.340858 data_time: 0.022810 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.834659 loss: 0.000595 2022/09/12 23:36:42 - mmengine - INFO - Epoch(train) [67][300/586] lr: 5.000000e-04 eta: 7:35:46 time: 0.335538 data_time: 0.022026 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.888976 loss: 0.000582 2022/09/12 23:36:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:36:59 - mmengine - INFO - Epoch(train) [67][350/586] lr: 5.000000e-04 eta: 7:35:31 time: 0.333689 data_time: 0.022725 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.768061 loss: 0.000565 2022/09/12 23:37:16 - mmengine - INFO - Epoch(train) [67][400/586] lr: 5.000000e-04 eta: 7:35:16 time: 0.339586 data_time: 0.022789 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.770528 loss: 0.000595 2022/09/12 23:37:33 - mmengine - INFO - Epoch(train) [67][450/586] lr: 5.000000e-04 eta: 7:35:00 time: 0.329047 data_time: 0.022725 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.839011 loss: 0.000594 2022/09/12 23:37:49 - mmengine - INFO - Epoch(train) [67][500/586] lr: 5.000000e-04 eta: 7:34:45 time: 0.335494 data_time: 0.027275 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.803237 loss: 0.000624 2022/09/12 23:38:06 - mmengine - INFO - Epoch(train) [67][550/586] lr: 5.000000e-04 eta: 7:34:31 time: 0.343933 data_time: 0.022698 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.782401 loss: 0.000579 2022/09/12 23:38:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:38:19 - mmengine - INFO - Saving checkpoint at 67 epochs 2022/09/12 23:38:42 - mmengine - INFO - Epoch(train) [68][50/586] lr: 5.000000e-04 eta: 7:33:40 time: 0.341373 data_time: 0.026847 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.793294 loss: 0.000596 2022/09/12 23:38:59 - mmengine - INFO - Epoch(train) [68][100/586] lr: 5.000000e-04 eta: 7:33:25 time: 0.339813 data_time: 0.025965 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.828697 loss: 0.000585 2022/09/12 23:39:16 - mmengine - INFO - Epoch(train) [68][150/586] lr: 5.000000e-04 eta: 7:33:10 time: 0.339306 data_time: 0.022410 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.837520 loss: 0.000573 2022/09/12 23:39:32 - mmengine - INFO - Epoch(train) [68][200/586] lr: 5.000000e-04 eta: 7:32:54 time: 0.324293 data_time: 0.022683 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.843080 loss: 0.000593 2022/09/12 23:39:49 - mmengine - INFO - Epoch(train) [68][250/586] lr: 5.000000e-04 eta: 7:32:40 time: 0.342885 data_time: 0.026303 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.810284 loss: 0.000588 2022/09/12 23:40:07 - mmengine - INFO - Epoch(train) [68][300/586] lr: 5.000000e-04 eta: 7:32:25 time: 0.340630 data_time: 0.022243 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.795617 loss: 0.000592 2022/09/12 23:40:23 - mmengine - INFO - Epoch(train) [68][350/586] lr: 5.000000e-04 eta: 7:32:09 time: 0.328336 data_time: 0.022348 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.790403 loss: 0.000599 2022/09/12 23:40:40 - mmengine - INFO - Epoch(train) [68][400/586] lr: 5.000000e-04 eta: 7:31:54 time: 0.337848 data_time: 0.022540 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.808969 loss: 0.000594 2022/09/12 23:40:56 - mmengine - INFO - Epoch(train) [68][450/586] lr: 5.000000e-04 eta: 7:31:39 time: 0.332199 data_time: 0.022322 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.825449 loss: 0.000568 2022/09/12 23:41:13 - mmengine - INFO - Epoch(train) [68][500/586] lr: 5.000000e-04 eta: 7:31:22 time: 0.326965 data_time: 0.021962 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.833859 loss: 0.000588 2022/09/12 23:41:30 - mmengine - INFO - Epoch(train) [68][550/586] lr: 5.000000e-04 eta: 7:31:08 time: 0.343035 data_time: 0.023225 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.803014 loss: 0.000585 2022/09/12 23:41:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:41:42 - mmengine - INFO - Saving checkpoint at 68 epochs 2022/09/12 23:42:06 - mmengine - INFO - Epoch(train) [69][50/586] lr: 5.000000e-04 eta: 7:30:17 time: 0.339302 data_time: 0.026028 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.812772 loss: 0.000608 2022/09/12 23:42:23 - mmengine - INFO - Epoch(train) [69][100/586] lr: 5.000000e-04 eta: 7:30:03 time: 0.344618 data_time: 0.023241 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.811746 loss: 0.000578 2022/09/12 23:42:40 - mmengine - INFO - Epoch(train) [69][150/586] lr: 5.000000e-04 eta: 7:29:48 time: 0.338739 data_time: 0.022688 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.869939 loss: 0.000611 2022/09/12 23:42:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:42:57 - mmengine - INFO - Epoch(train) [69][200/586] lr: 5.000000e-04 eta: 7:29:33 time: 0.330797 data_time: 0.026173 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.794719 loss: 0.000601 2022/09/12 23:43:13 - mmengine - INFO - Epoch(train) [69][250/586] lr: 5.000000e-04 eta: 7:29:18 time: 0.337846 data_time: 0.022936 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.910175 loss: 0.000572 2022/09/12 23:43:31 - mmengine - INFO - Epoch(train) [69][300/586] lr: 5.000000e-04 eta: 7:29:03 time: 0.344818 data_time: 0.022954 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.783017 loss: 0.000625 2022/09/12 23:43:47 - mmengine - INFO - Epoch(train) [69][350/586] lr: 5.000000e-04 eta: 7:28:48 time: 0.334122 data_time: 0.022237 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.831957 loss: 0.000593 2022/09/12 23:44:04 - mmengine - INFO - Epoch(train) [69][400/586] lr: 5.000000e-04 eta: 7:28:33 time: 0.339885 data_time: 0.022462 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.872047 loss: 0.000582 2022/09/12 23:44:21 - mmengine - INFO - Epoch(train) [69][450/586] lr: 5.000000e-04 eta: 7:28:19 time: 0.338373 data_time: 0.023124 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.847037 loss: 0.000595 2022/09/12 23:44:38 - mmengine - INFO - Epoch(train) [69][500/586] lr: 5.000000e-04 eta: 7:28:04 time: 0.339321 data_time: 0.026227 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.819315 loss: 0.000608 2022/09/12 23:44:56 - mmengine - INFO - Epoch(train) [69][550/586] lr: 5.000000e-04 eta: 7:27:49 time: 0.343996 data_time: 0.022439 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.805354 loss: 0.000597 2022/09/12 23:45:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:45:08 - mmengine - INFO - Saving checkpoint at 69 epochs 2022/09/12 23:45:31 - mmengine - INFO - Epoch(train) [70][50/586] lr: 5.000000e-04 eta: 7:26:59 time: 0.336787 data_time: 0.027681 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.803057 loss: 0.000597 2022/09/12 23:45:48 - mmengine - INFO - Epoch(train) [70][100/586] lr: 5.000000e-04 eta: 7:26:44 time: 0.343141 data_time: 0.026769 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.917170 loss: 0.000589 2022/09/12 23:46:05 - mmengine - INFO - Epoch(train) [70][150/586] lr: 5.000000e-04 eta: 7:26:29 time: 0.336303 data_time: 0.022174 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.790958 loss: 0.000603 2022/09/12 23:46:22 - mmengine - INFO - Epoch(train) [70][200/586] lr: 5.000000e-04 eta: 7:26:14 time: 0.329259 data_time: 0.023196 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.851783 loss: 0.000583 2022/09/12 23:46:39 - mmengine - INFO - Epoch(train) [70][250/586] lr: 5.000000e-04 eta: 7:25:59 time: 0.342216 data_time: 0.023369 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.854468 loss: 0.000590 2022/09/12 23:46:56 - mmengine - INFO - Epoch(train) [70][300/586] lr: 5.000000e-04 eta: 7:25:44 time: 0.336876 data_time: 0.023072 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.816063 loss: 0.000594 2022/09/12 23:47:12 - mmengine - INFO - Epoch(train) [70][350/586] lr: 5.000000e-04 eta: 7:25:28 time: 0.330643 data_time: 0.022884 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.818413 loss: 0.000593 2022/09/12 23:47:29 - mmengine - INFO - Epoch(train) [70][400/586] lr: 5.000000e-04 eta: 7:25:14 time: 0.341216 data_time: 0.022386 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.826419 loss: 0.000577 2022/09/12 23:47:46 - mmengine - INFO - Epoch(train) [70][450/586] lr: 5.000000e-04 eta: 7:24:59 time: 0.337875 data_time: 0.022890 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.815344 loss: 0.000606 2022/09/12 23:48:03 - mmengine - INFO - Epoch(train) [70][500/586] lr: 5.000000e-04 eta: 7:24:44 time: 0.334607 data_time: 0.022065 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.900057 loss: 0.000600 2022/09/12 23:48:20 - mmengine - INFO - Epoch(train) [70][550/586] lr: 5.000000e-04 eta: 7:24:29 time: 0.336083 data_time: 0.022768 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.884079 loss: 0.000595 2022/09/12 23:48:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:48:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:48:32 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/09/12 23:48:50 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:01:18 time: 0.219152 data_time: 0.012013 memory: 7489 2022/09/12 23:49:01 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:01:05 time: 0.212141 data_time: 0.007979 memory: 1657 2022/09/12 23:49:11 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:53 time: 0.209477 data_time: 0.008298 memory: 1657 2022/09/12 23:49:22 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:43 time: 0.209143 data_time: 0.007984 memory: 1657 2022/09/12 23:49:32 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:33 time: 0.210368 data_time: 0.007535 memory: 1657 2022/09/12 23:49:43 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:22 time: 0.211012 data_time: 0.007695 memory: 1657 2022/09/12 23:49:53 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:11 time: 0.209591 data_time: 0.007777 memory: 1657 2022/09/12 23:50:04 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:01 time: 0.206105 data_time: 0.007015 memory: 1657 2022/09/12 23:50:40 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 23:50:53 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.740752 coco/AP .5: 0.898053 coco/AP .75: 0.807518 coco/AP (M): 0.705486 coco/AP (L): 0.806632 coco/AR: 0.792302 coco/AR .5: 0.936555 coco/AR .75: 0.851858 coco/AR (M): 0.751625 coco/AR (L): 0.851691 2022/09/12 23:50:53 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_60.pth is removed 2022/09/12 23:50:57 - mmengine - INFO - The best checkpoint with 0.7408 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/09/12 23:51:14 - mmengine - INFO - Epoch(train) [71][50/586] lr: 5.000000e-04 eta: 7:23:39 time: 0.342159 data_time: 0.027404 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.869628 loss: 0.000605 2022/09/12 23:51:31 - mmengine - INFO - Epoch(train) [71][100/586] lr: 5.000000e-04 eta: 7:23:23 time: 0.331973 data_time: 0.023509 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.901135 loss: 0.000594 2022/09/12 23:51:47 - mmengine - INFO - Epoch(train) [71][150/586] lr: 5.000000e-04 eta: 7:23:08 time: 0.334701 data_time: 0.022806 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.839121 loss: 0.000569 2022/09/12 23:52:04 - mmengine - INFO - Epoch(train) [71][200/586] lr: 5.000000e-04 eta: 7:22:53 time: 0.335187 data_time: 0.021752 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.708741 loss: 0.000589 2022/09/12 23:52:21 - mmengine - INFO - Epoch(train) [71][250/586] lr: 5.000000e-04 eta: 7:22:39 time: 0.343861 data_time: 0.027991 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.804522 loss: 0.000577 2022/09/12 23:52:39 - mmengine - INFO - Epoch(train) [71][300/586] lr: 5.000000e-04 eta: 7:22:24 time: 0.343290 data_time: 0.023708 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.861751 loss: 0.000565 2022/09/12 23:52:55 - mmengine - INFO - Epoch(train) [71][350/586] lr: 5.000000e-04 eta: 7:22:09 time: 0.335353 data_time: 0.024267 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.875726 loss: 0.000587 2022/09/12 23:53:12 - mmengine - INFO - Epoch(train) [71][400/586] lr: 5.000000e-04 eta: 7:21:54 time: 0.335575 data_time: 0.026959 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.855282 loss: 0.000585 2022/09/12 23:53:29 - mmengine - INFO - Epoch(train) [71][450/586] lr: 5.000000e-04 eta: 7:21:39 time: 0.342514 data_time: 0.022871 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.807900 loss: 0.000574 2022/09/12 23:53:46 - mmengine - INFO - Epoch(train) [71][500/586] lr: 5.000000e-04 eta: 7:21:24 time: 0.333418 data_time: 0.021881 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.834946 loss: 0.000600 2022/09/12 23:54:03 - mmengine - INFO - Epoch(train) [71][550/586] lr: 5.000000e-04 eta: 7:21:09 time: 0.333375 data_time: 0.022485 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.809959 loss: 0.000586 2022/09/12 23:54:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:54:15 - mmengine - INFO - Saving checkpoint at 71 epochs 2022/09/12 23:54:39 - mmengine - INFO - Epoch(train) [72][50/586] lr: 5.000000e-04 eta: 7:20:20 time: 0.344786 data_time: 0.034020 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.834716 loss: 0.000585 2022/09/12 23:54:55 - mmengine - INFO - Epoch(train) [72][100/586] lr: 5.000000e-04 eta: 7:20:05 time: 0.335444 data_time: 0.022617 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.876053 loss: 0.000599 2022/09/12 23:55:12 - mmengine - INFO - Epoch(train) [72][150/586] lr: 5.000000e-04 eta: 7:19:49 time: 0.333613 data_time: 0.023280 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.802577 loss: 0.000573 2022/09/12 23:55:29 - mmengine - INFO - Epoch(train) [72][200/586] lr: 5.000000e-04 eta: 7:19:34 time: 0.337626 data_time: 0.022357 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.825715 loss: 0.000604 2022/09/12 23:55:46 - mmengine - INFO - Epoch(train) [72][250/586] lr: 5.000000e-04 eta: 7:19:19 time: 0.334419 data_time: 0.022661 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.801795 loss: 0.000586 2022/09/12 23:56:02 - mmengine - INFO - Epoch(train) [72][300/586] lr: 5.000000e-04 eta: 7:19:04 time: 0.333634 data_time: 0.022708 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.807784 loss: 0.000591 2022/09/12 23:56:19 - mmengine - INFO - Epoch(train) [72][350/586] lr: 5.000000e-04 eta: 7:18:49 time: 0.336333 data_time: 0.027410 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.818518 loss: 0.000590 2022/09/12 23:56:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:56:36 - mmengine - INFO - Epoch(train) [72][400/586] lr: 5.000000e-04 eta: 7:18:33 time: 0.335586 data_time: 0.022313 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.832790 loss: 0.000585 2022/09/12 23:56:53 - mmengine - INFO - Epoch(train) [72][450/586] lr: 5.000000e-04 eta: 7:18:18 time: 0.330941 data_time: 0.021711 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.803051 loss: 0.000604 2022/09/12 23:57:09 - mmengine - INFO - Epoch(train) [72][500/586] lr: 5.000000e-04 eta: 7:18:03 time: 0.338731 data_time: 0.023605 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.855358 loss: 0.000586 2022/09/12 23:57:26 - mmengine - INFO - Epoch(train) [72][550/586] lr: 5.000000e-04 eta: 7:17:47 time: 0.331438 data_time: 0.022458 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.829573 loss: 0.000575 2022/09/12 23:57:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/12 23:57:38 - mmengine - INFO - Saving checkpoint at 72 epochs 2022/09/12 23:58:03 - mmengine - INFO - Epoch(train) [73][50/586] lr: 5.000000e-04 eta: 7:16:59 time: 0.340965 data_time: 0.026973 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.887937 loss: 0.000577 2022/09/12 23:58:20 - mmengine - INFO - Epoch(train) [73][100/586] lr: 5.000000e-04 eta: 7:16:45 time: 0.347055 data_time: 0.025697 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.842574 loss: 0.000591 2022/09/12 23:58:36 - mmengine - INFO - Epoch(train) [73][150/586] lr: 5.000000e-04 eta: 7:16:29 time: 0.328309 data_time: 0.022755 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.768834 loss: 0.000591 2022/09/12 23:58:53 - mmengine - INFO - Epoch(train) [73][200/586] lr: 5.000000e-04 eta: 7:16:14 time: 0.336021 data_time: 0.022699 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.823412 loss: 0.000595 2022/09/12 23:59:10 - mmengine - INFO - Epoch(train) [73][250/586] lr: 5.000000e-04 eta: 7:15:59 time: 0.342401 data_time: 0.022642 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.856032 loss: 0.000579 2022/09/12 23:59:27 - mmengine - INFO - Epoch(train) [73][300/586] lr: 5.000000e-04 eta: 7:15:44 time: 0.333771 data_time: 0.022730 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.868851 loss: 0.000592 2022/09/12 23:59:44 - mmengine - INFO - Epoch(train) [73][350/586] lr: 5.000000e-04 eta: 7:15:29 time: 0.339361 data_time: 0.022504 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.867773 loss: 0.000589 2022/09/13 00:00:02 - mmengine - INFO - Epoch(train) [73][400/586] lr: 5.000000e-04 eta: 7:15:16 time: 0.360564 data_time: 0.021943 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.816738 loss: 0.000589 2022/09/13 00:00:21 - mmengine - INFO - Epoch(train) [73][450/586] lr: 5.000000e-04 eta: 7:15:04 time: 0.373066 data_time: 0.025900 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.781298 loss: 0.000636 2022/09/13 00:00:37 - mmengine - INFO - Epoch(train) [73][500/586] lr: 5.000000e-04 eta: 7:14:49 time: 0.335542 data_time: 0.022238 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.881499 loss: 0.000584 2022/09/13 00:00:54 - mmengine - INFO - Epoch(train) [73][550/586] lr: 5.000000e-04 eta: 7:14:34 time: 0.339498 data_time: 0.026526 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.774073 loss: 0.000589 2022/09/13 00:01:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:01:07 - mmengine - INFO - Saving checkpoint at 73 epochs 2022/09/13 00:01:31 - mmengine - INFO - Epoch(train) [74][50/586] lr: 5.000000e-04 eta: 7:13:46 time: 0.340861 data_time: 0.028059 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.829659 loss: 0.000597 2022/09/13 00:01:48 - mmengine - INFO - Epoch(train) [74][100/586] lr: 5.000000e-04 eta: 7:13:31 time: 0.340743 data_time: 0.023890 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.854729 loss: 0.000583 2022/09/13 00:02:05 - mmengine - INFO - Epoch(train) [74][150/586] lr: 5.000000e-04 eta: 7:13:16 time: 0.336154 data_time: 0.022088 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.844974 loss: 0.000600 2022/09/13 00:02:21 - mmengine - INFO - Epoch(train) [74][200/586] lr: 5.000000e-04 eta: 7:13:01 time: 0.333489 data_time: 0.026094 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.870091 loss: 0.000569 2022/09/13 00:02:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:02:39 - mmengine - INFO - Epoch(train) [74][250/586] lr: 5.000000e-04 eta: 7:12:46 time: 0.342724 data_time: 0.022064 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.810418 loss: 0.000592 2022/09/13 00:02:55 - mmengine - INFO - Epoch(train) [74][300/586] lr: 5.000000e-04 eta: 7:12:30 time: 0.325463 data_time: 0.022260 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.842225 loss: 0.000565 2022/09/13 00:03:12 - mmengine - INFO - Epoch(train) [74][350/586] lr: 5.000000e-04 eta: 7:12:16 time: 0.341972 data_time: 0.023214 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.825424 loss: 0.000585 2022/09/13 00:03:29 - mmengine - INFO - Epoch(train) [74][400/586] lr: 5.000000e-04 eta: 7:12:01 time: 0.342149 data_time: 0.022748 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.855132 loss: 0.000574 2022/09/13 00:03:45 - mmengine - INFO - Epoch(train) [74][450/586] lr: 5.000000e-04 eta: 7:11:45 time: 0.327460 data_time: 0.022332 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.883585 loss: 0.000592 2022/09/13 00:04:02 - mmengine - INFO - Epoch(train) [74][500/586] lr: 5.000000e-04 eta: 7:11:30 time: 0.336123 data_time: 0.026301 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.815907 loss: 0.000581 2022/09/13 00:04:19 - mmengine - INFO - Epoch(train) [74][550/586] lr: 5.000000e-04 eta: 7:11:15 time: 0.336759 data_time: 0.021835 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.769963 loss: 0.000588 2022/09/13 00:04:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:04:31 - mmengine - INFO - Saving checkpoint at 74 epochs 2022/09/13 00:04:55 - mmengine - INFO - Epoch(train) [75][50/586] lr: 5.000000e-04 eta: 7:10:27 time: 0.344265 data_time: 0.030881 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.865311 loss: 0.000593 2022/09/13 00:05:13 - mmengine - INFO - Epoch(train) [75][100/586] lr: 5.000000e-04 eta: 7:10:13 time: 0.348062 data_time: 0.030375 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.779479 loss: 0.000571 2022/09/13 00:05:29 - mmengine - INFO - Epoch(train) [75][150/586] lr: 5.000000e-04 eta: 7:09:57 time: 0.328328 data_time: 0.022367 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.801894 loss: 0.000585 2022/09/13 00:05:46 - mmengine - INFO - Epoch(train) [75][200/586] lr: 5.000000e-04 eta: 7:09:42 time: 0.337295 data_time: 0.022861 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.814117 loss: 0.000603 2022/09/13 00:06:03 - mmengine - INFO - Epoch(train) [75][250/586] lr: 5.000000e-04 eta: 7:09:28 time: 0.343389 data_time: 0.022362 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.834210 loss: 0.000573 2022/09/13 00:06:20 - mmengine - INFO - Epoch(train) [75][300/586] lr: 5.000000e-04 eta: 7:09:12 time: 0.331122 data_time: 0.022534 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.843799 loss: 0.000600 2022/09/13 00:06:37 - mmengine - INFO - Epoch(train) [75][350/586] lr: 5.000000e-04 eta: 7:08:57 time: 0.339575 data_time: 0.023228 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.813173 loss: 0.000582 2022/09/13 00:06:54 - mmengine - INFO - Epoch(train) [75][400/586] lr: 5.000000e-04 eta: 7:08:43 time: 0.346807 data_time: 0.026300 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.893792 loss: 0.000613 2022/09/13 00:07:11 - mmengine - INFO - Epoch(train) [75][450/586] lr: 5.000000e-04 eta: 7:08:28 time: 0.333195 data_time: 0.022872 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.849793 loss: 0.000579 2022/09/13 00:07:27 - mmengine - INFO - Epoch(train) [75][500/586] lr: 5.000000e-04 eta: 7:08:12 time: 0.332583 data_time: 0.022620 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.833013 loss: 0.000588 2022/09/13 00:07:45 - mmengine - INFO - Epoch(train) [75][550/586] lr: 5.000000e-04 eta: 7:07:58 time: 0.350449 data_time: 0.023760 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.837050 loss: 0.000588 2022/09/13 00:07:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:07:57 - mmengine - INFO - Saving checkpoint at 75 epochs 2022/09/13 00:08:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:08:20 - mmengine - INFO - Epoch(train) [76][50/586] lr: 5.000000e-04 eta: 7:07:11 time: 0.338392 data_time: 0.031203 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.770808 loss: 0.000598 2022/09/13 00:08:37 - mmengine - INFO - Epoch(train) [76][100/586] lr: 5.000000e-04 eta: 7:06:56 time: 0.340528 data_time: 0.022548 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.857688 loss: 0.000580 2022/09/13 00:08:54 - mmengine - INFO - Epoch(train) [76][150/586] lr: 5.000000e-04 eta: 7:06:41 time: 0.333517 data_time: 0.023165 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.816642 loss: 0.000576 2022/09/13 00:09:11 - mmengine - INFO - Epoch(train) [76][200/586] lr: 5.000000e-04 eta: 7:06:26 time: 0.343204 data_time: 0.025932 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.881435 loss: 0.000585 2022/09/13 00:09:28 - mmengine - INFO - Epoch(train) [76][250/586] lr: 5.000000e-04 eta: 7:06:11 time: 0.336190 data_time: 0.023218 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.835865 loss: 0.000578 2022/09/13 00:09:44 - mmengine - INFO - Epoch(train) [76][300/586] lr: 5.000000e-04 eta: 7:05:56 time: 0.332475 data_time: 0.023090 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.859484 loss: 0.000581 2022/09/13 00:10:01 - mmengine - INFO - Epoch(train) [76][350/586] lr: 5.000000e-04 eta: 7:05:40 time: 0.334185 data_time: 0.025902 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.884460 loss: 0.000570 2022/09/13 00:10:18 - mmengine - INFO - Epoch(train) [76][400/586] lr: 5.000000e-04 eta: 7:05:25 time: 0.338894 data_time: 0.022773 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.783146 loss: 0.000593 2022/09/13 00:10:35 - mmengine - INFO - Epoch(train) [76][450/586] lr: 5.000000e-04 eta: 7:05:10 time: 0.333990 data_time: 0.023109 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.795174 loss: 0.000590 2022/09/13 00:10:52 - mmengine - INFO - Epoch(train) [76][500/586] lr: 5.000000e-04 eta: 7:04:55 time: 0.336929 data_time: 0.027955 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.783997 loss: 0.000588 2022/09/13 00:11:09 - mmengine - INFO - Epoch(train) [76][550/586] lr: 5.000000e-04 eta: 7:04:40 time: 0.338887 data_time: 0.022627 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.788417 loss: 0.000578 2022/09/13 00:11:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:11:20 - mmengine - INFO - Saving checkpoint at 76 epochs 2022/09/13 00:11:45 - mmengine - INFO - Epoch(train) [77][50/586] lr: 5.000000e-04 eta: 7:03:53 time: 0.343976 data_time: 0.029277 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.852923 loss: 0.000593 2022/09/13 00:12:02 - mmengine - INFO - Epoch(train) [77][100/586] lr: 5.000000e-04 eta: 7:03:38 time: 0.337283 data_time: 0.022645 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.818238 loss: 0.000593 2022/09/13 00:12:19 - mmengine - INFO - Epoch(train) [77][150/586] lr: 5.000000e-04 eta: 7:03:23 time: 0.336015 data_time: 0.025963 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.812696 loss: 0.000585 2022/09/13 00:12:35 - mmengine - INFO - Epoch(train) [77][200/586] lr: 5.000000e-04 eta: 7:03:08 time: 0.336235 data_time: 0.022484 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.859490 loss: 0.000576 2022/09/13 00:12:52 - mmengine - INFO - Epoch(train) [77][250/586] lr: 5.000000e-04 eta: 7:02:53 time: 0.334100 data_time: 0.022799 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.831186 loss: 0.000587 2022/09/13 00:13:09 - mmengine - INFO - Epoch(train) [77][300/586] lr: 5.000000e-04 eta: 7:02:38 time: 0.340710 data_time: 0.022313 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.877375 loss: 0.000589 2022/09/13 00:13:26 - mmengine - INFO - Epoch(train) [77][350/586] lr: 5.000000e-04 eta: 7:02:22 time: 0.326028 data_time: 0.021785 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.867535 loss: 0.000597 2022/09/13 00:13:43 - mmengine - INFO - Epoch(train) [77][400/586] lr: 5.000000e-04 eta: 7:02:07 time: 0.338875 data_time: 0.022410 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.858533 loss: 0.000601 2022/09/13 00:13:59 - mmengine - INFO - Epoch(train) [77][450/586] lr: 5.000000e-04 eta: 7:01:51 time: 0.333291 data_time: 0.022565 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.801439 loss: 0.000607 2022/09/13 00:14:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:14:16 - mmengine - INFO - Epoch(train) [77][500/586] lr: 5.000000e-04 eta: 7:01:36 time: 0.334475 data_time: 0.022038 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.827004 loss: 0.000558 2022/09/13 00:14:33 - mmengine - INFO - Epoch(train) [77][550/586] lr: 5.000000e-04 eta: 7:01:21 time: 0.337237 data_time: 0.022675 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.823178 loss: 0.000569 2022/09/13 00:14:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:14:45 - mmengine - INFO - Saving checkpoint at 77 epochs 2022/09/13 00:15:09 - mmengine - INFO - Epoch(train) [78][50/586] lr: 5.000000e-04 eta: 7:00:34 time: 0.337438 data_time: 0.030494 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.826015 loss: 0.000579 2022/09/13 00:15:25 - mmengine - INFO - Epoch(train) [78][100/586] lr: 5.000000e-04 eta: 7:00:19 time: 0.333897 data_time: 0.026831 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.859792 loss: 0.000586 2022/09/13 00:15:42 - mmengine - INFO - Epoch(train) [78][150/586] lr: 5.000000e-04 eta: 7:00:04 time: 0.338279 data_time: 0.022997 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.788041 loss: 0.000588 2022/09/13 00:15:59 - mmengine - INFO - Epoch(train) [78][200/586] lr: 5.000000e-04 eta: 6:59:48 time: 0.329957 data_time: 0.023764 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.887532 loss: 0.000590 2022/09/13 00:16:16 - mmengine - INFO - Epoch(train) [78][250/586] lr: 5.000000e-04 eta: 6:59:34 time: 0.345846 data_time: 0.026002 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.810775 loss: 0.000574 2022/09/13 00:16:33 - mmengine - INFO - Epoch(train) [78][300/586] lr: 5.000000e-04 eta: 6:59:18 time: 0.332527 data_time: 0.023017 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.839672 loss: 0.000596 2022/09/13 00:16:49 - mmengine - INFO - Epoch(train) [78][350/586] lr: 5.000000e-04 eta: 6:59:03 time: 0.329409 data_time: 0.023116 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.743235 loss: 0.000616 2022/09/13 00:17:06 - mmengine - INFO - Epoch(train) [78][400/586] lr: 5.000000e-04 eta: 6:58:48 time: 0.338490 data_time: 0.022230 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.866918 loss: 0.000588 2022/09/13 00:17:23 - mmengine - INFO - Epoch(train) [78][450/586] lr: 5.000000e-04 eta: 6:58:33 time: 0.338077 data_time: 0.022822 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.843540 loss: 0.000565 2022/09/13 00:17:40 - mmengine - INFO - Epoch(train) [78][500/586] lr: 5.000000e-04 eta: 6:58:17 time: 0.331160 data_time: 0.022247 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.804023 loss: 0.000582 2022/09/13 00:17:57 - mmengine - INFO - Epoch(train) [78][550/586] lr: 5.000000e-04 eta: 6:58:02 time: 0.343450 data_time: 0.022461 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.835263 loss: 0.000605 2022/09/13 00:18:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:18:09 - mmengine - INFO - Saving checkpoint at 78 epochs 2022/09/13 00:18:33 - mmengine - INFO - Epoch(train) [79][50/586] lr: 5.000000e-04 eta: 6:57:16 time: 0.338246 data_time: 0.029730 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.800054 loss: 0.000588 2022/09/13 00:18:50 - mmengine - INFO - Epoch(train) [79][100/586] lr: 5.000000e-04 eta: 6:57:01 time: 0.342702 data_time: 0.023979 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.740032 loss: 0.000596 2022/09/13 00:19:07 - mmengine - INFO - Epoch(train) [79][150/586] lr: 5.000000e-04 eta: 6:56:46 time: 0.332253 data_time: 0.022323 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.789929 loss: 0.000557 2022/09/13 00:19:24 - mmengine - INFO - Epoch(train) [79][200/586] lr: 5.000000e-04 eta: 6:56:31 time: 0.336996 data_time: 0.022291 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.841483 loss: 0.000573 2022/09/13 00:19:40 - mmengine - INFO - Epoch(train) [79][250/586] lr: 5.000000e-04 eta: 6:56:16 time: 0.337542 data_time: 0.022741 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.919975 loss: 0.000581 2022/09/13 00:19:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:19:57 - mmengine - INFO - Epoch(train) [79][300/586] lr: 5.000000e-04 eta: 6:56:01 time: 0.337118 data_time: 0.022695 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.812364 loss: 0.000579 2022/09/13 00:20:14 - mmengine - INFO - Epoch(train) [79][350/586] lr: 5.000000e-04 eta: 6:55:45 time: 0.335414 data_time: 0.023010 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.755268 loss: 0.000583 2022/09/13 00:20:31 - mmengine - INFO - Epoch(train) [79][400/586] lr: 5.000000e-04 eta: 6:55:31 time: 0.341324 data_time: 0.022356 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.905007 loss: 0.000571 2022/09/13 00:20:48 - mmengine - INFO - Epoch(train) [79][450/586] lr: 5.000000e-04 eta: 6:55:15 time: 0.331588 data_time: 0.021774 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.808575 loss: 0.000576 2022/09/13 00:21:05 - mmengine - INFO - Epoch(train) [79][500/586] lr: 5.000000e-04 eta: 6:55:00 time: 0.339181 data_time: 0.027912 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.913087 loss: 0.000584 2022/09/13 00:21:22 - mmengine - INFO - Epoch(train) [79][550/586] lr: 5.000000e-04 eta: 6:54:45 time: 0.339313 data_time: 0.022610 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.893109 loss: 0.000575 2022/09/13 00:21:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:21:34 - mmengine - INFO - Saving checkpoint at 79 epochs 2022/09/13 00:21:58 - mmengine - INFO - Epoch(train) [80][50/586] lr: 5.000000e-04 eta: 6:53:59 time: 0.339128 data_time: 0.028849 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.832732 loss: 0.000573 2022/09/13 00:22:15 - mmengine - INFO - Epoch(train) [80][100/586] lr: 5.000000e-04 eta: 6:53:44 time: 0.339856 data_time: 0.026067 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.815873 loss: 0.000573 2022/09/13 00:22:32 - mmengine - INFO - Epoch(train) [80][150/586] lr: 5.000000e-04 eta: 6:53:29 time: 0.337223 data_time: 0.024225 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.886173 loss: 0.000577 2022/09/13 00:22:48 - mmengine - INFO - Epoch(train) [80][200/586] lr: 5.000000e-04 eta: 6:53:14 time: 0.330777 data_time: 0.023465 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.785631 loss: 0.000578 2022/09/13 00:23:05 - mmengine - INFO - Epoch(train) [80][250/586] lr: 5.000000e-04 eta: 6:52:59 time: 0.336351 data_time: 0.022473 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.794237 loss: 0.000576 2022/09/13 00:23:22 - mmengine - INFO - Epoch(train) [80][300/586] lr: 5.000000e-04 eta: 6:52:44 time: 0.342975 data_time: 0.023799 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.827686 loss: 0.000574 2022/09/13 00:23:39 - mmengine - INFO - Epoch(train) [80][350/586] lr: 5.000000e-04 eta: 6:52:28 time: 0.328014 data_time: 0.022550 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.855442 loss: 0.000593 2022/09/13 00:23:56 - mmengine - INFO - Epoch(train) [80][400/586] lr: 5.000000e-04 eta: 6:52:13 time: 0.341644 data_time: 0.025796 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.880164 loss: 0.000575 2022/09/13 00:24:12 - mmengine - INFO - Epoch(train) [80][450/586] lr: 5.000000e-04 eta: 6:51:58 time: 0.333983 data_time: 0.022625 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.819301 loss: 0.000568 2022/09/13 00:24:29 - mmengine - INFO - Epoch(train) [80][500/586] lr: 5.000000e-04 eta: 6:51:42 time: 0.330947 data_time: 0.022209 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.845989 loss: 0.000585 2022/09/13 00:24:46 - mmengine - INFO - Epoch(train) [80][550/586] lr: 5.000000e-04 eta: 6:51:28 time: 0.340893 data_time: 0.024090 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.832221 loss: 0.000576 2022/09/13 00:24:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:24:58 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/09/13 00:25:16 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:01:18 time: 0.220639 data_time: 0.015169 memory: 7489 2022/09/13 00:25:26 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:01:04 time: 0.210243 data_time: 0.007708 memory: 1657 2022/09/13 00:25:37 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:53 time: 0.209663 data_time: 0.008257 memory: 1657 2022/09/13 00:25:47 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:43 time: 0.210867 data_time: 0.007673 memory: 1657 2022/09/13 00:25:58 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:32 time: 0.209735 data_time: 0.007545 memory: 1657 2022/09/13 00:26:08 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:22 time: 0.210917 data_time: 0.007808 memory: 1657 2022/09/13 00:26:19 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:11 time: 0.208210 data_time: 0.007414 memory: 1657 2022/09/13 00:26:29 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:01 time: 0.209988 data_time: 0.007354 memory: 1657 2022/09/13 00:27:05 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 00:27:18 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.746107 coco/AP .5: 0.898889 coco/AP .75: 0.817495 coco/AP (M): 0.710905 coco/AP (L): 0.811750 coco/AR: 0.797292 coco/AR .5: 0.936398 coco/AR .75: 0.862248 coco/AR (M): 0.756760 coco/AR (L): 0.856596 2022/09/13 00:27:18 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_70.pth is removed 2022/09/13 00:27:22 - mmengine - INFO - The best checkpoint with 0.7461 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/09/13 00:27:39 - mmengine - INFO - Epoch(train) [81][50/586] lr: 5.000000e-04 eta: 6:50:42 time: 0.335666 data_time: 0.026504 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.807579 loss: 0.000565 2022/09/13 00:27:56 - mmengine - INFO - Epoch(train) [81][100/586] lr: 5.000000e-04 eta: 6:50:27 time: 0.341508 data_time: 0.022998 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.814395 loss: 0.000584 2022/09/13 00:28:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:28:13 - mmengine - INFO - Epoch(train) [81][150/586] lr: 5.000000e-04 eta: 6:50:12 time: 0.336258 data_time: 0.022382 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.818100 loss: 0.000597 2022/09/13 00:28:30 - mmengine - INFO - Epoch(train) [81][200/586] lr: 5.000000e-04 eta: 6:49:57 time: 0.334636 data_time: 0.023935 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.846537 loss: 0.000583 2022/09/13 00:28:47 - mmengine - INFO - Epoch(train) [81][250/586] lr: 5.000000e-04 eta: 6:49:42 time: 0.346109 data_time: 0.025518 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.881140 loss: 0.000575 2022/09/13 00:29:04 - mmengine - INFO - Epoch(train) [81][300/586] lr: 5.000000e-04 eta: 6:49:27 time: 0.338398 data_time: 0.022548 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.785262 loss: 0.000570 2022/09/13 00:29:21 - mmengine - INFO - Epoch(train) [81][350/586] lr: 5.000000e-04 eta: 6:49:12 time: 0.336060 data_time: 0.022966 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.844800 loss: 0.000585 2022/09/13 00:29:37 - mmengine - INFO - Epoch(train) [81][400/586] lr: 5.000000e-04 eta: 6:48:57 time: 0.336248 data_time: 0.022307 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.811559 loss: 0.000580 2022/09/13 00:29:54 - mmengine - INFO - Epoch(train) [81][450/586] lr: 5.000000e-04 eta: 6:48:41 time: 0.335454 data_time: 0.023008 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.889961 loss: 0.000584 2022/09/13 00:30:11 - mmengine - INFO - Epoch(train) [81][500/586] lr: 5.000000e-04 eta: 6:48:26 time: 0.337433 data_time: 0.022455 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.871198 loss: 0.000557 2022/09/13 00:30:28 - mmengine - INFO - Epoch(train) [81][550/586] lr: 5.000000e-04 eta: 6:48:12 time: 0.342483 data_time: 0.025422 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.859979 loss: 0.000599 2022/09/13 00:30:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:30:40 - mmengine - INFO - Saving checkpoint at 81 epochs 2022/09/13 00:31:05 - mmengine - INFO - Epoch(train) [82][50/586] lr: 5.000000e-04 eta: 6:47:27 time: 0.342544 data_time: 0.029952 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.867734 loss: 0.000575 2022/09/13 00:31:21 - mmengine - INFO - Epoch(train) [82][100/586] lr: 5.000000e-04 eta: 6:47:11 time: 0.333985 data_time: 0.024170 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.851290 loss: 0.000582 2022/09/13 00:31:38 - mmengine - INFO - Epoch(train) [82][150/586] lr: 5.000000e-04 eta: 6:46:56 time: 0.334619 data_time: 0.022636 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.832877 loss: 0.000591 2022/09/13 00:31:55 - mmengine - INFO - Epoch(train) [82][200/586] lr: 5.000000e-04 eta: 6:46:41 time: 0.337632 data_time: 0.023279 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.874849 loss: 0.000593 2022/09/13 00:32:12 - mmengine - INFO - Epoch(train) [82][250/586] lr: 5.000000e-04 eta: 6:46:25 time: 0.331369 data_time: 0.022420 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.876255 loss: 0.000576 2022/09/13 00:32:28 - mmengine - INFO - Epoch(train) [82][300/586] lr: 5.000000e-04 eta: 6:46:10 time: 0.336298 data_time: 0.021902 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.826905 loss: 0.000584 2022/09/13 00:32:46 - mmengine - INFO - Epoch(train) [82][350/586] lr: 5.000000e-04 eta: 6:45:55 time: 0.341190 data_time: 0.023277 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.877978 loss: 0.000573 2022/09/13 00:33:02 - mmengine - INFO - Epoch(train) [82][400/586] lr: 5.000000e-04 eta: 6:45:40 time: 0.335132 data_time: 0.022134 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.838991 loss: 0.000582 2022/09/13 00:33:19 - mmengine - INFO - Epoch(train) [82][450/586] lr: 5.000000e-04 eta: 6:45:25 time: 0.342229 data_time: 0.022467 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.839653 loss: 0.000576 2022/09/13 00:33:36 - mmengine - INFO - Epoch(train) [82][500/586] lr: 5.000000e-04 eta: 6:45:10 time: 0.334242 data_time: 0.026552 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.851305 loss: 0.000577 2022/09/13 00:33:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:33:53 - mmengine - INFO - Epoch(train) [82][550/586] lr: 5.000000e-04 eta: 6:44:55 time: 0.335254 data_time: 0.023436 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.801818 loss: 0.000575 2022/09/13 00:34:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:34:05 - mmengine - INFO - Saving checkpoint at 82 epochs 2022/09/13 00:34:29 - mmengine - INFO - Epoch(train) [83][50/586] lr: 5.000000e-04 eta: 6:44:10 time: 0.342898 data_time: 0.032022 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.849615 loss: 0.000594 2022/09/13 00:34:46 - mmengine - INFO - Epoch(train) [83][100/586] lr: 5.000000e-04 eta: 6:43:55 time: 0.337074 data_time: 0.022551 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.821183 loss: 0.000587 2022/09/13 00:35:03 - mmengine - INFO - Epoch(train) [83][150/586] lr: 5.000000e-04 eta: 6:43:40 time: 0.336755 data_time: 0.022827 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.860228 loss: 0.000579 2022/09/13 00:35:19 - mmengine - INFO - Epoch(train) [83][200/586] lr: 5.000000e-04 eta: 6:43:24 time: 0.331367 data_time: 0.022446 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.869271 loss: 0.000590 2022/09/13 00:35:36 - mmengine - INFO - Epoch(train) [83][250/586] lr: 5.000000e-04 eta: 6:43:09 time: 0.335850 data_time: 0.022438 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.859822 loss: 0.000571 2022/09/13 00:35:53 - mmengine - INFO - Epoch(train) [83][300/586] lr: 5.000000e-04 eta: 6:42:54 time: 0.339295 data_time: 0.022877 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.856608 loss: 0.000580 2022/09/13 00:36:10 - mmengine - INFO - Epoch(train) [83][350/586] lr: 5.000000e-04 eta: 6:42:39 time: 0.341421 data_time: 0.023038 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.821031 loss: 0.000601 2022/09/13 00:36:27 - mmengine - INFO - Epoch(train) [83][400/586] lr: 5.000000e-04 eta: 6:42:24 time: 0.333859 data_time: 0.022266 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.760667 loss: 0.000581 2022/09/13 00:36:44 - mmengine - INFO - Epoch(train) [83][450/586] lr: 5.000000e-04 eta: 6:42:09 time: 0.336477 data_time: 0.026167 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.853689 loss: 0.000577 2022/09/13 00:37:01 - mmengine - INFO - Epoch(train) [83][500/586] lr: 5.000000e-04 eta: 6:41:54 time: 0.338843 data_time: 0.022147 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.868398 loss: 0.000560 2022/09/13 00:37:17 - mmengine - INFO - Epoch(train) [83][550/586] lr: 5.000000e-04 eta: 6:41:38 time: 0.330586 data_time: 0.025624 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.871402 loss: 0.000576 2022/09/13 00:37:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:37:29 - mmengine - INFO - Saving checkpoint at 83 epochs 2022/09/13 00:37:53 - mmengine - INFO - Epoch(train) [84][50/586] lr: 5.000000e-04 eta: 6:40:54 time: 0.345821 data_time: 0.032546 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.802692 loss: 0.000562 2022/09/13 00:38:09 - mmengine - INFO - Epoch(train) [84][100/586] lr: 5.000000e-04 eta: 6:40:38 time: 0.326832 data_time: 0.022467 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.835472 loss: 0.000587 2022/09/13 00:38:26 - mmengine - INFO - Epoch(train) [84][150/586] lr: 5.000000e-04 eta: 6:40:23 time: 0.340422 data_time: 0.022727 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.825507 loss: 0.000566 2022/09/13 00:38:44 - mmengine - INFO - Epoch(train) [84][200/586] lr: 5.000000e-04 eta: 6:40:09 time: 0.342002 data_time: 0.022727 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.815407 loss: 0.000577 2022/09/13 00:39:00 - mmengine - INFO - Epoch(train) [84][250/586] lr: 5.000000e-04 eta: 6:39:53 time: 0.331676 data_time: 0.021757 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.795728 loss: 0.000578 2022/09/13 00:39:17 - mmengine - INFO - Epoch(train) [84][300/586] lr: 5.000000e-04 eta: 6:39:38 time: 0.330804 data_time: 0.022491 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.767970 loss: 0.000584 2022/09/13 00:39:34 - mmengine - INFO - Epoch(train) [84][350/586] lr: 5.000000e-04 eta: 6:39:23 time: 0.341483 data_time: 0.023373 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.818774 loss: 0.000565 2022/09/13 00:39:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:39:50 - mmengine - INFO - Epoch(train) [84][400/586] lr: 5.000000e-04 eta: 6:39:07 time: 0.334330 data_time: 0.023268 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.831616 loss: 0.000586 2022/09/13 00:40:08 - mmengine - INFO - Epoch(train) [84][450/586] lr: 5.000000e-04 eta: 6:38:52 time: 0.341132 data_time: 0.022287 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.793052 loss: 0.000583 2022/09/13 00:40:25 - mmengine - INFO - Epoch(train) [84][500/586] lr: 5.000000e-04 eta: 6:38:38 time: 0.340742 data_time: 0.022767 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.818984 loss: 0.000575 2022/09/13 00:40:41 - mmengine - INFO - Epoch(train) [84][550/586] lr: 5.000000e-04 eta: 6:38:22 time: 0.333639 data_time: 0.023453 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.833843 loss: 0.000585 2022/09/13 00:40:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:40:53 - mmengine - INFO - Saving checkpoint at 84 epochs 2022/09/13 00:41:17 - mmengine - INFO - Epoch(train) [85][50/586] lr: 5.000000e-04 eta: 6:37:38 time: 0.337199 data_time: 0.027386 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.890064 loss: 0.000581 2022/09/13 00:41:34 - mmengine - INFO - Epoch(train) [85][100/586] lr: 5.000000e-04 eta: 6:37:23 time: 0.340771 data_time: 0.022137 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.882144 loss: 0.000553 2022/09/13 00:41:51 - mmengine - INFO - Epoch(train) [85][150/586] lr: 5.000000e-04 eta: 6:37:08 time: 0.337362 data_time: 0.027115 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.875656 loss: 0.000588 2022/09/13 00:42:08 - mmengine - INFO - Epoch(train) [85][200/586] lr: 5.000000e-04 eta: 6:36:53 time: 0.335936 data_time: 0.022364 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.788455 loss: 0.000596 2022/09/13 00:42:25 - mmengine - INFO - Epoch(train) [85][250/586] lr: 5.000000e-04 eta: 6:36:37 time: 0.333857 data_time: 0.022276 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.902585 loss: 0.000589 2022/09/13 00:42:42 - mmengine - INFO - Epoch(train) [85][300/586] lr: 5.000000e-04 eta: 6:36:22 time: 0.338558 data_time: 0.022757 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.864750 loss: 0.000566 2022/09/13 00:42:59 - mmengine - INFO - Epoch(train) [85][350/586] lr: 5.000000e-04 eta: 6:36:08 time: 0.343428 data_time: 0.023300 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.795671 loss: 0.000570 2022/09/13 00:43:16 - mmengine - INFO - Epoch(train) [85][400/586] lr: 5.000000e-04 eta: 6:35:53 time: 0.338325 data_time: 0.023990 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.878759 loss: 0.000564 2022/09/13 00:43:33 - mmengine - INFO - Epoch(train) [85][450/586] lr: 5.000000e-04 eta: 6:35:38 time: 0.339408 data_time: 0.025913 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.809231 loss: 0.000570 2022/09/13 00:43:49 - mmengine - INFO - Epoch(train) [85][500/586] lr: 5.000000e-04 eta: 6:35:22 time: 0.334894 data_time: 0.023131 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.876535 loss: 0.000564 2022/09/13 00:44:06 - mmengine - INFO - Epoch(train) [85][550/586] lr: 5.000000e-04 eta: 6:35:07 time: 0.339307 data_time: 0.022566 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.829967 loss: 0.000574 2022/09/13 00:44:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:44:19 - mmengine - INFO - Saving checkpoint at 85 epochs 2022/09/13 00:44:42 - mmengine - INFO - Epoch(train) [86][50/586] lr: 5.000000e-04 eta: 6:34:23 time: 0.337067 data_time: 0.027102 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.749833 loss: 0.000576 2022/09/13 00:44:59 - mmengine - INFO - Epoch(train) [86][100/586] lr: 5.000000e-04 eta: 6:34:08 time: 0.337276 data_time: 0.022359 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.797642 loss: 0.000571 2022/09/13 00:45:16 - mmengine - INFO - Epoch(train) [86][150/586] lr: 5.000000e-04 eta: 6:33:53 time: 0.330920 data_time: 0.023208 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.790190 loss: 0.000561 2022/09/13 00:45:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:45:33 - mmengine - INFO - Epoch(train) [86][200/586] lr: 5.000000e-04 eta: 6:33:37 time: 0.338068 data_time: 0.025362 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.876703 loss: 0.000566 2022/09/13 00:45:50 - mmengine - INFO - Epoch(train) [86][250/586] lr: 5.000000e-04 eta: 6:33:23 time: 0.341039 data_time: 0.027593 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.866077 loss: 0.000556 2022/09/13 00:46:06 - mmengine - INFO - Epoch(train) [86][300/586] lr: 5.000000e-04 eta: 6:33:07 time: 0.329330 data_time: 0.022331 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.818339 loss: 0.000587 2022/09/13 00:46:23 - mmengine - INFO - Epoch(train) [86][350/586] lr: 5.000000e-04 eta: 6:32:52 time: 0.339331 data_time: 0.023614 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.852502 loss: 0.000586 2022/09/13 00:46:40 - mmengine - INFO - Epoch(train) [86][400/586] lr: 5.000000e-04 eta: 6:32:37 time: 0.337759 data_time: 0.022666 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.823090 loss: 0.000560 2022/09/13 00:46:56 - mmengine - INFO - Epoch(train) [86][450/586] lr: 5.000000e-04 eta: 6:32:21 time: 0.328258 data_time: 0.023110 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.807814 loss: 0.000588 2022/09/13 00:47:13 - mmengine - INFO - Epoch(train) [86][500/586] lr: 5.000000e-04 eta: 6:32:06 time: 0.339893 data_time: 0.022705 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.859830 loss: 0.000577 2022/09/13 00:47:30 - mmengine - INFO - Epoch(train) [86][550/586] lr: 5.000000e-04 eta: 6:31:50 time: 0.332270 data_time: 0.021790 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.831151 loss: 0.000564 2022/09/13 00:47:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:47:42 - mmengine - INFO - Saving checkpoint at 86 epochs 2022/09/13 00:48:06 - mmengine - INFO - Epoch(train) [87][50/586] lr: 5.000000e-04 eta: 6:31:07 time: 0.344798 data_time: 0.026460 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.821184 loss: 0.000563 2022/09/13 00:48:23 - mmengine - INFO - Epoch(train) [87][100/586] lr: 5.000000e-04 eta: 6:30:53 time: 0.348506 data_time: 0.023320 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.853515 loss: 0.000579 2022/09/13 00:48:40 - mmengine - INFO - Epoch(train) [87][150/586] lr: 5.000000e-04 eta: 6:30:37 time: 0.330640 data_time: 0.022443 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.834860 loss: 0.000560 2022/09/13 00:48:57 - mmengine - INFO - Epoch(train) [87][200/586] lr: 5.000000e-04 eta: 6:30:23 time: 0.341144 data_time: 0.027230 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.801065 loss: 0.000553 2022/09/13 00:49:14 - mmengine - INFO - Epoch(train) [87][250/586] lr: 5.000000e-04 eta: 6:30:08 time: 0.341275 data_time: 0.022599 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.829268 loss: 0.000576 2022/09/13 00:49:31 - mmengine - INFO - Epoch(train) [87][300/586] lr: 5.000000e-04 eta: 6:29:52 time: 0.329825 data_time: 0.023113 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.844201 loss: 0.000566 2022/09/13 00:49:48 - mmengine - INFO - Epoch(train) [87][350/586] lr: 5.000000e-04 eta: 6:29:37 time: 0.339917 data_time: 0.025824 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.836644 loss: 0.000571 2022/09/13 00:50:04 - mmengine - INFO - Epoch(train) [87][400/586] lr: 5.000000e-04 eta: 6:29:22 time: 0.335797 data_time: 0.022548 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.824143 loss: 0.000573 2022/09/13 00:50:21 - mmengine - INFO - Epoch(train) [87][450/586] lr: 5.000000e-04 eta: 6:29:06 time: 0.333921 data_time: 0.022173 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.801430 loss: 0.000586 2022/09/13 00:50:38 - mmengine - INFO - Epoch(train) [87][500/586] lr: 5.000000e-04 eta: 6:28:51 time: 0.336174 data_time: 0.022835 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.837640 loss: 0.000570 2022/09/13 00:50:55 - mmengine - INFO - Epoch(train) [87][550/586] lr: 5.000000e-04 eta: 6:28:36 time: 0.335800 data_time: 0.022134 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.882188 loss: 0.000575 2022/09/13 00:51:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:51:07 - mmengine - INFO - Saving checkpoint at 87 epochs 2022/09/13 00:51:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:51:31 - mmengine - INFO - Epoch(train) [88][50/586] lr: 5.000000e-04 eta: 6:27:52 time: 0.337074 data_time: 0.030681 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.828160 loss: 0.000576 2022/09/13 00:51:48 - mmengine - INFO - Epoch(train) [88][100/586] lr: 5.000000e-04 eta: 6:27:38 time: 0.341015 data_time: 0.023308 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.803846 loss: 0.000575 2022/09/13 00:52:05 - mmengine - INFO - Epoch(train) [88][150/586] lr: 5.000000e-04 eta: 6:27:22 time: 0.335795 data_time: 0.022442 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.844299 loss: 0.000570 2022/09/13 00:52:21 - mmengine - INFO - Epoch(train) [88][200/586] lr: 5.000000e-04 eta: 6:27:07 time: 0.330782 data_time: 0.022628 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.802258 loss: 0.000584 2022/09/13 00:52:38 - mmengine - INFO - Epoch(train) [88][250/586] lr: 5.000000e-04 eta: 6:26:52 time: 0.342444 data_time: 0.022967 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.908775 loss: 0.000588 2022/09/13 00:52:55 - mmengine - INFO - Epoch(train) [88][300/586] lr: 5.000000e-04 eta: 6:26:37 time: 0.334884 data_time: 0.022142 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.837563 loss: 0.000569 2022/09/13 00:53:11 - mmengine - INFO - Epoch(train) [88][350/586] lr: 5.000000e-04 eta: 6:26:21 time: 0.328538 data_time: 0.022231 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.847298 loss: 0.000554 2022/09/13 00:53:28 - mmengine - INFO - Epoch(train) [88][400/586] lr: 5.000000e-04 eta: 6:26:06 time: 0.339924 data_time: 0.023693 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.860702 loss: 0.000558 2022/09/13 00:53:45 - mmengine - INFO - Epoch(train) [88][450/586] lr: 5.000000e-04 eta: 6:25:51 time: 0.337128 data_time: 0.023257 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.877081 loss: 0.000567 2022/09/13 00:54:02 - mmengine - INFO - Epoch(train) [88][500/586] lr: 5.000000e-04 eta: 6:25:35 time: 0.334012 data_time: 0.022446 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.836427 loss: 0.000572 2022/09/13 00:54:19 - mmengine - INFO - Epoch(train) [88][550/586] lr: 5.000000e-04 eta: 6:25:20 time: 0.339944 data_time: 0.024003 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.868251 loss: 0.000572 2022/09/13 00:54:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:54:31 - mmengine - INFO - Saving checkpoint at 88 epochs 2022/09/13 00:54:54 - mmengine - INFO - Epoch(train) [89][50/586] lr: 5.000000e-04 eta: 6:24:37 time: 0.335745 data_time: 0.025927 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.907942 loss: 0.000590 2022/09/13 00:55:11 - mmengine - INFO - Epoch(train) [89][100/586] lr: 5.000000e-04 eta: 6:24:22 time: 0.337572 data_time: 0.022601 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.864833 loss: 0.000568 2022/09/13 00:55:28 - mmengine - INFO - Epoch(train) [89][150/586] lr: 5.000000e-04 eta: 6:24:07 time: 0.334373 data_time: 0.022098 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.867778 loss: 0.000562 2022/09/13 00:55:45 - mmengine - INFO - Epoch(train) [89][200/586] lr: 5.000000e-04 eta: 6:23:51 time: 0.337443 data_time: 0.027506 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.877035 loss: 0.000578 2022/09/13 00:56:02 - mmengine - INFO - Epoch(train) [89][250/586] lr: 5.000000e-04 eta: 6:23:37 time: 0.342311 data_time: 0.022637 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.826830 loss: 0.000580 2022/09/13 00:56:19 - mmengine - INFO - Epoch(train) [89][300/586] lr: 5.000000e-04 eta: 6:23:22 time: 0.342749 data_time: 0.022729 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.860397 loss: 0.000583 2022/09/13 00:56:36 - mmengine - INFO - Epoch(train) [89][350/586] lr: 5.000000e-04 eta: 6:23:07 time: 0.337204 data_time: 0.027900 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.821588 loss: 0.000581 2022/09/13 00:56:53 - mmengine - INFO - Epoch(train) [89][400/586] lr: 5.000000e-04 eta: 6:22:52 time: 0.344729 data_time: 0.022083 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.799342 loss: 0.000556 2022/09/13 00:57:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:57:10 - mmengine - INFO - Epoch(train) [89][450/586] lr: 5.000000e-04 eta: 6:22:36 time: 0.331444 data_time: 0.022091 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.836287 loss: 0.000570 2022/09/13 00:57:27 - mmengine - INFO - Epoch(train) [89][500/586] lr: 5.000000e-04 eta: 6:22:21 time: 0.334657 data_time: 0.028776 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.834031 loss: 0.000585 2022/09/13 00:57:44 - mmengine - INFO - Epoch(train) [89][550/586] lr: 5.000000e-04 eta: 6:22:06 time: 0.339440 data_time: 0.022660 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.827644 loss: 0.000565 2022/09/13 00:57:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 00:57:56 - mmengine - INFO - Saving checkpoint at 89 epochs 2022/09/13 00:58:19 - mmengine - INFO - Epoch(train) [90][50/586] lr: 5.000000e-04 eta: 6:21:23 time: 0.336690 data_time: 0.028889 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.737645 loss: 0.000578 2022/09/13 00:58:37 - mmengine - INFO - Epoch(train) [90][100/586] lr: 5.000000e-04 eta: 6:21:09 time: 0.348512 data_time: 0.022817 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.888329 loss: 0.000589 2022/09/13 00:58:53 - mmengine - INFO - Epoch(train) [90][150/586] lr: 5.000000e-04 eta: 6:20:54 time: 0.334693 data_time: 0.024355 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.857489 loss: 0.000568 2022/09/13 00:59:10 - mmengine - INFO - Epoch(train) [90][200/586] lr: 5.000000e-04 eta: 6:20:38 time: 0.333045 data_time: 0.023150 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.878198 loss: 0.000570 2022/09/13 00:59:27 - mmengine - INFO - Epoch(train) [90][250/586] lr: 5.000000e-04 eta: 6:20:23 time: 0.338992 data_time: 0.023030 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.791374 loss: 0.000590 2022/09/13 00:59:44 - mmengine - INFO - Epoch(train) [90][300/586] lr: 5.000000e-04 eta: 6:20:08 time: 0.337586 data_time: 0.025088 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.876351 loss: 0.000585 2022/09/13 01:00:00 - mmengine - INFO - Epoch(train) [90][350/586] lr: 5.000000e-04 eta: 6:19:52 time: 0.332399 data_time: 0.023950 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.852138 loss: 0.000585 2022/09/13 01:00:17 - mmengine - INFO - Epoch(train) [90][400/586] lr: 5.000000e-04 eta: 6:19:37 time: 0.338986 data_time: 0.025294 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.856498 loss: 0.000575 2022/09/13 01:00:34 - mmengine - INFO - Epoch(train) [90][450/586] lr: 5.000000e-04 eta: 6:19:22 time: 0.341491 data_time: 0.023586 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.894771 loss: 0.000570 2022/09/13 01:00:51 - mmengine - INFO - Epoch(train) [90][500/586] lr: 5.000000e-04 eta: 6:19:07 time: 0.331021 data_time: 0.023430 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.816154 loss: 0.000554 2022/09/13 01:01:08 - mmengine - INFO - Epoch(train) [90][550/586] lr: 5.000000e-04 eta: 6:18:52 time: 0.344161 data_time: 0.023612 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.877180 loss: 0.000557 2022/09/13 01:01:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:01:20 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/09/13 01:01:39 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:01:17 time: 0.217359 data_time: 0.013817 memory: 7489 2022/09/13 01:01:49 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:01:05 time: 0.212010 data_time: 0.008126 memory: 1657 2022/09/13 01:02:00 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:54 time: 0.212250 data_time: 0.008022 memory: 1657 2022/09/13 01:02:11 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:43 time: 0.211986 data_time: 0.008020 memory: 1657 2022/09/13 01:02:21 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:33 time: 0.210595 data_time: 0.007618 memory: 1657 2022/09/13 01:02:32 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:22 time: 0.209666 data_time: 0.007993 memory: 1657 2022/09/13 01:02:42 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:12 time: 0.215519 data_time: 0.008068 memory: 1657 2022/09/13 01:02:53 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:01 time: 0.207504 data_time: 0.007190 memory: 1657 2022/09/13 01:03:29 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 01:03:42 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.747071 coco/AP .5: 0.898033 coco/AP .75: 0.818866 coco/AP (M): 0.712185 coco/AP (L): 0.813208 coco/AR: 0.799480 coco/AR .5: 0.935768 coco/AR .75: 0.863035 coco/AR (M): 0.758563 coco/AR (L): 0.858863 2022/09/13 01:03:42 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_80.pth is removed 2022/09/13 01:03:46 - mmengine - INFO - The best checkpoint with 0.7471 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/09/13 01:04:03 - mmengine - INFO - Epoch(train) [91][50/586] lr: 5.000000e-04 eta: 6:18:09 time: 0.334669 data_time: 0.028288 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.783389 loss: 0.000562 2022/09/13 01:04:20 - mmengine - INFO - Epoch(train) [91][100/586] lr: 5.000000e-04 eta: 6:17:55 time: 0.349548 data_time: 0.023517 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.856788 loss: 0.000570 2022/09/13 01:04:37 - mmengine - INFO - Epoch(train) [91][150/586] lr: 5.000000e-04 eta: 6:17:39 time: 0.325376 data_time: 0.022331 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.805856 loss: 0.000577 2022/09/13 01:04:53 - mmengine - INFO - Epoch(train) [91][200/586] lr: 5.000000e-04 eta: 6:17:24 time: 0.334461 data_time: 0.022926 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.823310 loss: 0.000569 2022/09/13 01:05:10 - mmengine - INFO - Epoch(train) [91][250/586] lr: 5.000000e-04 eta: 6:17:09 time: 0.341889 data_time: 0.023772 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.835713 loss: 0.000580 2022/09/13 01:05:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:05:27 - mmengine - INFO - Epoch(train) [91][300/586] lr: 5.000000e-04 eta: 6:16:53 time: 0.327819 data_time: 0.022160 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.855284 loss: 0.000569 2022/09/13 01:05:44 - mmengine - INFO - Epoch(train) [91][350/586] lr: 5.000000e-04 eta: 6:16:38 time: 0.338149 data_time: 0.022958 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.864810 loss: 0.000576 2022/09/13 01:06:01 - mmengine - INFO - Epoch(train) [91][400/586] lr: 5.000000e-04 eta: 6:16:23 time: 0.335980 data_time: 0.022549 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.880708 loss: 0.000583 2022/09/13 01:06:17 - mmengine - INFO - Epoch(train) [91][450/586] lr: 5.000000e-04 eta: 6:16:07 time: 0.332204 data_time: 0.022178 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.852289 loss: 0.000578 2022/09/13 01:06:34 - mmengine - INFO - Epoch(train) [91][500/586] lr: 5.000000e-04 eta: 6:15:52 time: 0.335393 data_time: 0.026214 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.878059 loss: 0.000570 2022/09/13 01:06:51 - mmengine - INFO - Epoch(train) [91][550/586] lr: 5.000000e-04 eta: 6:15:36 time: 0.332144 data_time: 0.022526 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.803049 loss: 0.000565 2022/09/13 01:07:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:07:03 - mmengine - INFO - Saving checkpoint at 91 epochs 2022/09/13 01:07:27 - mmengine - INFO - Epoch(train) [92][50/586] lr: 5.000000e-04 eta: 6:14:55 time: 0.344207 data_time: 0.027380 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.926145 loss: 0.000557 2022/09/13 01:07:44 - mmengine - INFO - Epoch(train) [92][100/586] lr: 5.000000e-04 eta: 6:14:39 time: 0.336074 data_time: 0.022274 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.826918 loss: 0.000562 2022/09/13 01:08:01 - mmengine - INFO - Epoch(train) [92][150/586] lr: 5.000000e-04 eta: 6:14:24 time: 0.337742 data_time: 0.028423 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.807780 loss: 0.000584 2022/09/13 01:08:17 - mmengine - INFO - Epoch(train) [92][200/586] lr: 5.000000e-04 eta: 6:14:08 time: 0.331051 data_time: 0.022351 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.889097 loss: 0.000561 2022/09/13 01:08:34 - mmengine - INFO - Epoch(train) [92][250/586] lr: 5.000000e-04 eta: 6:13:53 time: 0.340099 data_time: 0.022417 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.861656 loss: 0.000563 2022/09/13 01:08:51 - mmengine - INFO - Epoch(train) [92][300/586] lr: 5.000000e-04 eta: 6:13:38 time: 0.336446 data_time: 0.022539 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.824839 loss: 0.000552 2022/09/13 01:09:08 - mmengine - INFO - Epoch(train) [92][350/586] lr: 5.000000e-04 eta: 6:13:23 time: 0.335045 data_time: 0.022728 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.852396 loss: 0.000566 2022/09/13 01:09:25 - mmengine - INFO - Epoch(train) [92][400/586] lr: 5.000000e-04 eta: 6:13:08 time: 0.340207 data_time: 0.022136 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.861966 loss: 0.000572 2022/09/13 01:09:41 - mmengine - INFO - Epoch(train) [92][450/586] lr: 5.000000e-04 eta: 6:12:52 time: 0.332931 data_time: 0.022146 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.838210 loss: 0.000578 2022/09/13 01:09:58 - mmengine - INFO - Epoch(train) [92][500/586] lr: 5.000000e-04 eta: 6:12:37 time: 0.330838 data_time: 0.022255 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.836437 loss: 0.000579 2022/09/13 01:10:15 - mmengine - INFO - Epoch(train) [92][550/586] lr: 5.000000e-04 eta: 6:12:21 time: 0.334294 data_time: 0.022190 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.855804 loss: 0.000573 2022/09/13 01:10:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:10:27 - mmengine - INFO - Saving checkpoint at 92 epochs 2022/09/13 01:10:51 - mmengine - INFO - Epoch(train) [93][50/586] lr: 5.000000e-04 eta: 6:11:41 time: 0.356200 data_time: 0.030580 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.849104 loss: 0.000593 2022/09/13 01:11:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:11:08 - mmengine - INFO - Epoch(train) [93][100/586] lr: 5.000000e-04 eta: 6:11:25 time: 0.334933 data_time: 0.023564 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.860716 loss: 0.000564 2022/09/13 01:11:24 - mmengine - INFO - Epoch(train) [93][150/586] lr: 5.000000e-04 eta: 6:11:10 time: 0.335952 data_time: 0.022550 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.854106 loss: 0.000561 2022/09/13 01:11:41 - mmengine - INFO - Epoch(train) [93][200/586] lr: 5.000000e-04 eta: 6:10:55 time: 0.335771 data_time: 0.022704 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.843873 loss: 0.000556 2022/09/13 01:11:58 - mmengine - INFO - Epoch(train) [93][250/586] lr: 5.000000e-04 eta: 6:10:39 time: 0.336469 data_time: 0.024413 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.866256 loss: 0.000564 2022/09/13 01:12:15 - mmengine - INFO - Epoch(train) [93][300/586] lr: 5.000000e-04 eta: 6:10:24 time: 0.337514 data_time: 0.022249 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.846865 loss: 0.000578 2022/09/13 01:12:32 - mmengine - INFO - Epoch(train) [93][350/586] lr: 5.000000e-04 eta: 6:10:09 time: 0.335874 data_time: 0.024101 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.880775 loss: 0.000566 2022/09/13 01:12:49 - mmengine - INFO - Epoch(train) [93][400/586] lr: 5.000000e-04 eta: 6:09:54 time: 0.343014 data_time: 0.022324 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.867282 loss: 0.000566 2022/09/13 01:13:06 - mmengine - INFO - Epoch(train) [93][450/586] lr: 5.000000e-04 eta: 6:09:39 time: 0.343228 data_time: 0.022323 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.856301 loss: 0.000560 2022/09/13 01:13:23 - mmengine - INFO - Epoch(train) [93][500/586] lr: 5.000000e-04 eta: 6:09:24 time: 0.337913 data_time: 0.022930 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.884150 loss: 0.000583 2022/09/13 01:13:40 - mmengine - INFO - Epoch(train) [93][550/586] lr: 5.000000e-04 eta: 6:09:09 time: 0.334663 data_time: 0.023019 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.872926 loss: 0.000562 2022/09/13 01:13:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:13:52 - mmengine - INFO - Saving checkpoint at 93 epochs 2022/09/13 01:14:16 - mmengine - INFO - Epoch(train) [94][50/586] lr: 5.000000e-04 eta: 6:08:28 time: 0.347169 data_time: 0.033624 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.844236 loss: 0.000558 2022/09/13 01:14:33 - mmengine - INFO - Epoch(train) [94][100/586] lr: 5.000000e-04 eta: 6:08:12 time: 0.335545 data_time: 0.022306 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.814233 loss: 0.000567 2022/09/13 01:14:50 - mmengine - INFO - Epoch(train) [94][150/586] lr: 5.000000e-04 eta: 6:07:57 time: 0.333584 data_time: 0.022328 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.816481 loss: 0.000554 2022/09/13 01:15:07 - mmengine - INFO - Epoch(train) [94][200/586] lr: 5.000000e-04 eta: 6:07:42 time: 0.338285 data_time: 0.026726 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.826655 loss: 0.000581 2022/09/13 01:15:23 - mmengine - INFO - Epoch(train) [94][250/586] lr: 5.000000e-04 eta: 6:07:26 time: 0.332765 data_time: 0.022761 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.778483 loss: 0.000575 2022/09/13 01:15:40 - mmengine - INFO - Epoch(train) [94][300/586] lr: 5.000000e-04 eta: 6:07:11 time: 0.337895 data_time: 0.023171 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.833028 loss: 0.000565 2022/09/13 01:15:57 - mmengine - INFO - Epoch(train) [94][350/586] lr: 5.000000e-04 eta: 6:06:56 time: 0.342310 data_time: 0.022881 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.869557 loss: 0.000547 2022/09/13 01:16:14 - mmengine - INFO - Epoch(train) [94][400/586] lr: 5.000000e-04 eta: 6:06:40 time: 0.329067 data_time: 0.022116 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.849960 loss: 0.000557 2022/09/13 01:16:31 - mmengine - INFO - Epoch(train) [94][450/586] lr: 5.000000e-04 eta: 6:06:25 time: 0.340421 data_time: 0.023627 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.846623 loss: 0.000567 2022/09/13 01:16:48 - mmengine - INFO - Epoch(train) [94][500/586] lr: 5.000000e-04 eta: 6:06:11 time: 0.344459 data_time: 0.023152 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.817633 loss: 0.000570 2022/09/13 01:16:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:17:05 - mmengine - INFO - Epoch(train) [94][550/586] lr: 5.000000e-04 eta: 6:05:55 time: 0.334380 data_time: 0.022658 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.875593 loss: 0.000564 2022/09/13 01:17:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:17:17 - mmengine - INFO - Saving checkpoint at 94 epochs 2022/09/13 01:17:41 - mmengine - INFO - Epoch(train) [95][50/586] lr: 5.000000e-04 eta: 6:05:14 time: 0.335700 data_time: 0.027032 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.898520 loss: 0.000573 2022/09/13 01:17:58 - mmengine - INFO - Epoch(train) [95][100/586] lr: 5.000000e-04 eta: 6:04:59 time: 0.343285 data_time: 0.027045 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.820932 loss: 0.000578 2022/09/13 01:18:15 - mmengine - INFO - Epoch(train) [95][150/586] lr: 5.000000e-04 eta: 6:04:44 time: 0.338255 data_time: 0.023126 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.831580 loss: 0.000592 2022/09/13 01:18:32 - mmengine - INFO - Epoch(train) [95][200/586] lr: 5.000000e-04 eta: 6:04:29 time: 0.335140 data_time: 0.022523 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.830952 loss: 0.000566 2022/09/13 01:18:49 - mmengine - INFO - Epoch(train) [95][250/586] lr: 5.000000e-04 eta: 6:04:14 time: 0.342283 data_time: 0.022574 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.787906 loss: 0.000569 2022/09/13 01:19:06 - mmengine - INFO - Epoch(train) [95][300/586] lr: 5.000000e-04 eta: 6:03:58 time: 0.336969 data_time: 0.022835 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.819465 loss: 0.000572 2022/09/13 01:19:23 - mmengine - INFO - Epoch(train) [95][350/586] lr: 5.000000e-04 eta: 6:03:43 time: 0.340367 data_time: 0.022613 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.872127 loss: 0.000566 2022/09/13 01:19:39 - mmengine - INFO - Epoch(train) [95][400/586] lr: 5.000000e-04 eta: 6:03:28 time: 0.337578 data_time: 0.026884 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.858401 loss: 0.000566 2022/09/13 01:19:56 - mmengine - INFO - Epoch(train) [95][450/586] lr: 5.000000e-04 eta: 6:03:13 time: 0.335578 data_time: 0.022766 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.829713 loss: 0.000585 2022/09/13 01:20:13 - mmengine - INFO - Epoch(train) [95][500/586] lr: 5.000000e-04 eta: 6:02:58 time: 0.338627 data_time: 0.022606 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.859061 loss: 0.000567 2022/09/13 01:20:30 - mmengine - INFO - Epoch(train) [95][550/586] lr: 5.000000e-04 eta: 6:02:42 time: 0.340239 data_time: 0.022239 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.875636 loss: 0.000571 2022/09/13 01:20:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:20:42 - mmengine - INFO - Saving checkpoint at 95 epochs 2022/09/13 01:21:06 - mmengine - INFO - Epoch(train) [96][50/586] lr: 5.000000e-04 eta: 6:02:02 time: 0.344114 data_time: 0.029050 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.714811 loss: 0.000563 2022/09/13 01:21:24 - mmengine - INFO - Epoch(train) [96][100/586] lr: 5.000000e-04 eta: 6:01:47 time: 0.348840 data_time: 0.023390 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.846503 loss: 0.000553 2022/09/13 01:21:40 - mmengine - INFO - Epoch(train) [96][150/586] lr: 5.000000e-04 eta: 6:01:32 time: 0.332792 data_time: 0.023417 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.865153 loss: 0.000570 2022/09/13 01:21:57 - mmengine - INFO - Epoch(train) [96][200/586] lr: 5.000000e-04 eta: 6:01:17 time: 0.335905 data_time: 0.022221 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.812268 loss: 0.000564 2022/09/13 01:22:14 - mmengine - INFO - Epoch(train) [96][250/586] lr: 5.000000e-04 eta: 6:01:01 time: 0.334866 data_time: 0.022762 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.825848 loss: 0.000566 2022/09/13 01:22:31 - mmengine - INFO - Epoch(train) [96][300/586] lr: 5.000000e-04 eta: 6:00:46 time: 0.335593 data_time: 0.022945 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.885884 loss: 0.000573 2022/09/13 01:22:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:22:48 - mmengine - INFO - Epoch(train) [96][350/586] lr: 5.000000e-04 eta: 6:00:30 time: 0.334375 data_time: 0.022052 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.813499 loss: 0.000566 2022/09/13 01:23:04 - mmengine - INFO - Epoch(train) [96][400/586] lr: 5.000000e-04 eta: 6:00:15 time: 0.334222 data_time: 0.022106 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.883636 loss: 0.000566 2022/09/13 01:23:21 - mmengine - INFO - Epoch(train) [96][450/586] lr: 5.000000e-04 eta: 5:59:59 time: 0.333891 data_time: 0.022669 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.721346 loss: 0.000558 2022/09/13 01:23:38 - mmengine - INFO - Epoch(train) [96][500/586] lr: 5.000000e-04 eta: 5:59:44 time: 0.339397 data_time: 0.023373 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.858620 loss: 0.000571 2022/09/13 01:23:55 - mmengine - INFO - Epoch(train) [96][550/586] lr: 5.000000e-04 eta: 5:59:29 time: 0.336316 data_time: 0.022530 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.862105 loss: 0.000547 2022/09/13 01:24:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:24:07 - mmengine - INFO - Saving checkpoint at 96 epochs 2022/09/13 01:24:30 - mmengine - INFO - Epoch(train) [97][50/586] lr: 5.000000e-04 eta: 5:58:48 time: 0.333417 data_time: 0.026490 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.860954 loss: 0.000552 2022/09/13 01:24:47 - mmengine - INFO - Epoch(train) [97][100/586] lr: 5.000000e-04 eta: 5:58:33 time: 0.347312 data_time: 0.022921 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.822917 loss: 0.000569 2022/09/13 01:25:04 - mmengine - INFO - Epoch(train) [97][150/586] lr: 5.000000e-04 eta: 5:58:18 time: 0.337238 data_time: 0.022670 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.873523 loss: 0.000556 2022/09/13 01:25:21 - mmengine - INFO - Epoch(train) [97][200/586] lr: 5.000000e-04 eta: 5:58:03 time: 0.335317 data_time: 0.022632 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.879499 loss: 0.000567 2022/09/13 01:25:38 - mmengine - INFO - Epoch(train) [97][250/586] lr: 5.000000e-04 eta: 5:57:47 time: 0.334387 data_time: 0.023472 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.832848 loss: 0.000552 2022/09/13 01:25:55 - mmengine - INFO - Epoch(train) [97][300/586] lr: 5.000000e-04 eta: 5:57:33 time: 0.344403 data_time: 0.022622 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.823865 loss: 0.000577 2022/09/13 01:26:12 - mmengine - INFO - Epoch(train) [97][350/586] lr: 5.000000e-04 eta: 5:57:17 time: 0.331536 data_time: 0.022701 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.843991 loss: 0.000557 2022/09/13 01:26:29 - mmengine - INFO - Epoch(train) [97][400/586] lr: 5.000000e-04 eta: 5:57:02 time: 0.343540 data_time: 0.022424 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.886933 loss: 0.000582 2022/09/13 01:26:46 - mmengine - INFO - Epoch(train) [97][450/586] lr: 5.000000e-04 eta: 5:56:47 time: 0.340599 data_time: 0.027862 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.827347 loss: 0.000575 2022/09/13 01:27:02 - mmengine - INFO - Epoch(train) [97][500/586] lr: 5.000000e-04 eta: 5:56:31 time: 0.331240 data_time: 0.022871 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.866660 loss: 0.000576 2022/09/13 01:27:19 - mmengine - INFO - Epoch(train) [97][550/586] lr: 5.000000e-04 eta: 5:56:16 time: 0.333979 data_time: 0.022322 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.824699 loss: 0.000571 2022/09/13 01:27:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:27:31 - mmengine - INFO - Saving checkpoint at 97 epochs 2022/09/13 01:27:56 - mmengine - INFO - Epoch(train) [98][50/586] lr: 5.000000e-04 eta: 5:55:36 time: 0.348238 data_time: 0.029401 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.831950 loss: 0.000564 2022/09/13 01:28:13 - mmengine - INFO - Epoch(train) [98][100/586] lr: 5.000000e-04 eta: 5:55:21 time: 0.341593 data_time: 0.022637 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.914990 loss: 0.000548 2022/09/13 01:28:30 - mmengine - INFO - Epoch(train) [98][150/586] lr: 5.000000e-04 eta: 5:55:06 time: 0.343664 data_time: 0.022067 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.874248 loss: 0.000551 2022/09/13 01:28:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:28:47 - mmengine - INFO - Epoch(train) [98][200/586] lr: 5.000000e-04 eta: 5:54:51 time: 0.337782 data_time: 0.022952 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.856811 loss: 0.000565 2022/09/13 01:29:04 - mmengine - INFO - Epoch(train) [98][250/586] lr: 5.000000e-04 eta: 5:54:36 time: 0.339825 data_time: 0.022108 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.789019 loss: 0.000573 2022/09/13 01:29:21 - mmengine - INFO - Epoch(train) [98][300/586] lr: 5.000000e-04 eta: 5:54:21 time: 0.343267 data_time: 0.022144 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.849755 loss: 0.000588 2022/09/13 01:29:38 - mmengine - INFO - Epoch(train) [98][350/586] lr: 5.000000e-04 eta: 5:54:06 time: 0.336888 data_time: 0.022780 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.820730 loss: 0.000550 2022/09/13 01:29:55 - mmengine - INFO - Epoch(train) [98][400/586] lr: 5.000000e-04 eta: 5:53:50 time: 0.339924 data_time: 0.026921 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.806264 loss: 0.000578 2022/09/13 01:30:12 - mmengine - INFO - Epoch(train) [98][450/586] lr: 5.000000e-04 eta: 5:53:35 time: 0.337236 data_time: 0.022630 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.871887 loss: 0.000572 2022/09/13 01:30:28 - mmengine - INFO - Epoch(train) [98][500/586] lr: 5.000000e-04 eta: 5:53:19 time: 0.330353 data_time: 0.022546 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.833641 loss: 0.000578 2022/09/13 01:30:45 - mmengine - INFO - Epoch(train) [98][550/586] lr: 5.000000e-04 eta: 5:53:04 time: 0.338215 data_time: 0.027267 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.877135 loss: 0.000562 2022/09/13 01:30:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:30:57 - mmengine - INFO - Saving checkpoint at 98 epochs 2022/09/13 01:31:21 - mmengine - INFO - Epoch(train) [99][50/586] lr: 5.000000e-04 eta: 5:52:24 time: 0.336460 data_time: 0.030340 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.787714 loss: 0.000590 2022/09/13 01:31:38 - mmengine - INFO - Epoch(train) [99][100/586] lr: 5.000000e-04 eta: 5:52:09 time: 0.334539 data_time: 0.022793 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.801631 loss: 0.000544 2022/09/13 01:31:55 - mmengine - INFO - Epoch(train) [99][150/586] lr: 5.000000e-04 eta: 5:51:53 time: 0.334995 data_time: 0.022765 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.828008 loss: 0.000559 2022/09/13 01:32:12 - mmengine - INFO - Epoch(train) [99][200/586] lr: 5.000000e-04 eta: 5:51:38 time: 0.344104 data_time: 0.022362 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.869595 loss: 0.000565 2022/09/13 01:32:28 - mmengine - INFO - Epoch(train) [99][250/586] lr: 5.000000e-04 eta: 5:51:22 time: 0.326122 data_time: 0.022367 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.796790 loss: 0.000545 2022/09/13 01:32:45 - mmengine - INFO - Epoch(train) [99][300/586] lr: 5.000000e-04 eta: 5:51:07 time: 0.337611 data_time: 0.022337 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.780550 loss: 0.000573 2022/09/13 01:33:02 - mmengine - INFO - Epoch(train) [99][350/586] lr: 5.000000e-04 eta: 5:50:52 time: 0.342349 data_time: 0.027395 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.818785 loss: 0.000590 2022/09/13 01:33:19 - mmengine - INFO - Epoch(train) [99][400/586] lr: 5.000000e-04 eta: 5:50:37 time: 0.335503 data_time: 0.023140 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.775358 loss: 0.000577 2022/09/13 01:33:36 - mmengine - INFO - Epoch(train) [99][450/586] lr: 5.000000e-04 eta: 5:50:21 time: 0.335946 data_time: 0.022462 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.837571 loss: 0.000579 2022/09/13 01:33:53 - mmengine - INFO - Epoch(train) [99][500/586] lr: 5.000000e-04 eta: 5:50:06 time: 0.337932 data_time: 0.025911 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.876782 loss: 0.000557 2022/09/13 01:34:10 - mmengine - INFO - Epoch(train) [99][550/586] lr: 5.000000e-04 eta: 5:49:51 time: 0.338246 data_time: 0.022698 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.884738 loss: 0.000556 2022/09/13 01:34:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:34:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:34:22 - mmengine - INFO - Saving checkpoint at 99 epochs 2022/09/13 01:34:46 - mmengine - INFO - Epoch(train) [100][50/586] lr: 5.000000e-04 eta: 5:49:11 time: 0.336598 data_time: 0.027213 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.759478 loss: 0.000566 2022/09/13 01:35:02 - mmengine - INFO - Epoch(train) [100][100/586] lr: 5.000000e-04 eta: 5:48:56 time: 0.335732 data_time: 0.023287 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.800528 loss: 0.000573 2022/09/13 01:35:19 - mmengine - INFO - Epoch(train) [100][150/586] lr: 5.000000e-04 eta: 5:48:40 time: 0.336614 data_time: 0.023302 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.878024 loss: 0.000564 2022/09/13 01:35:36 - mmengine - INFO - Epoch(train) [100][200/586] lr: 5.000000e-04 eta: 5:48:25 time: 0.336470 data_time: 0.022215 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.872974 loss: 0.000554 2022/09/13 01:35:53 - mmengine - INFO - Epoch(train) [100][250/586] lr: 5.000000e-04 eta: 5:48:10 time: 0.341155 data_time: 0.027882 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.823711 loss: 0.000564 2022/09/13 01:36:10 - mmengine - INFO - Epoch(train) [100][300/586] lr: 5.000000e-04 eta: 5:47:54 time: 0.336076 data_time: 0.022359 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.830740 loss: 0.000558 2022/09/13 01:36:27 - mmengine - INFO - Epoch(train) [100][350/586] lr: 5.000000e-04 eta: 5:47:39 time: 0.334624 data_time: 0.023339 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.799825 loss: 0.000554 2022/09/13 01:36:44 - mmengine - INFO - Epoch(train) [100][400/586] lr: 5.000000e-04 eta: 5:47:24 time: 0.338665 data_time: 0.022033 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.853040 loss: 0.000570 2022/09/13 01:37:01 - mmengine - INFO - Epoch(train) [100][450/586] lr: 5.000000e-04 eta: 5:47:09 time: 0.345224 data_time: 0.023924 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.808332 loss: 0.000572 2022/09/13 01:37:18 - mmengine - INFO - Epoch(train) [100][500/586] lr: 5.000000e-04 eta: 5:46:53 time: 0.335001 data_time: 0.022933 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.870555 loss: 0.000576 2022/09/13 01:37:35 - mmengine - INFO - Epoch(train) [100][550/586] lr: 5.000000e-04 eta: 5:46:38 time: 0.339869 data_time: 0.023283 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.845433 loss: 0.000549 2022/09/13 01:37:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:37:47 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/09/13 01:38:04 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:01:16 time: 0.215159 data_time: 0.012244 memory: 7489 2022/09/13 01:38:15 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:01:06 time: 0.215271 data_time: 0.007721 memory: 1657 2022/09/13 01:38:25 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:54 time: 0.210326 data_time: 0.007787 memory: 1657 2022/09/13 01:38:36 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:43 time: 0.209333 data_time: 0.007503 memory: 1657 2022/09/13 01:38:46 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:32 time: 0.209806 data_time: 0.008047 memory: 1657 2022/09/13 01:38:57 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:22 time: 0.209934 data_time: 0.007591 memory: 1657 2022/09/13 01:39:07 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:11 time: 0.209580 data_time: 0.008041 memory: 1657 2022/09/13 01:39:18 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:01 time: 0.208000 data_time: 0.007125 memory: 1657 2022/09/13 01:39:53 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 01:40:07 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.749833 coco/AP .5: 0.899550 coco/AP .75: 0.816541 coco/AP (M): 0.714861 coco/AP (L): 0.816216 coco/AR: 0.800268 coco/AR .5: 0.938917 coco/AR .75: 0.860516 coco/AR (M): 0.759082 coco/AR (L): 0.860164 2022/09/13 01:40:07 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_90.pth is removed 2022/09/13 01:40:11 - mmengine - INFO - The best checkpoint with 0.7498 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/09/13 01:40:28 - mmengine - INFO - Epoch(train) [101][50/586] lr: 5.000000e-04 eta: 5:45:59 time: 0.340558 data_time: 0.026379 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.863794 loss: 0.000540 2022/09/13 01:40:45 - mmengine - INFO - Epoch(train) [101][100/586] lr: 5.000000e-04 eta: 5:45:43 time: 0.335498 data_time: 0.026291 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.829008 loss: 0.000567 2022/09/13 01:41:02 - mmengine - INFO - Epoch(train) [101][150/586] lr: 5.000000e-04 eta: 5:45:28 time: 0.339424 data_time: 0.022166 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.892569 loss: 0.000553 2022/09/13 01:41:19 - mmengine - INFO - Epoch(train) [101][200/586] lr: 5.000000e-04 eta: 5:45:13 time: 0.339593 data_time: 0.022657 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.791594 loss: 0.000572 2022/09/13 01:41:35 - mmengine - INFO - Epoch(train) [101][250/586] lr: 5.000000e-04 eta: 5:44:58 time: 0.332886 data_time: 0.026733 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.817560 loss: 0.000554 2022/09/13 01:41:52 - mmengine - INFO - Epoch(train) [101][300/586] lr: 5.000000e-04 eta: 5:44:42 time: 0.340650 data_time: 0.022414 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.832155 loss: 0.000566 2022/09/13 01:42:09 - mmengine - INFO - Epoch(train) [101][350/586] lr: 5.000000e-04 eta: 5:44:27 time: 0.337130 data_time: 0.023353 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.842386 loss: 0.000572 2022/09/13 01:42:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:42:26 - mmengine - INFO - Epoch(train) [101][400/586] lr: 5.000000e-04 eta: 5:44:11 time: 0.328542 data_time: 0.022673 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.820317 loss: 0.000563 2022/09/13 01:42:43 - mmengine - INFO - Epoch(train) [101][450/586] lr: 5.000000e-04 eta: 5:43:57 time: 0.348203 data_time: 0.023527 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.857524 loss: 0.000567 2022/09/13 01:43:00 - mmengine - INFO - Epoch(train) [101][500/586] lr: 5.000000e-04 eta: 5:43:41 time: 0.337167 data_time: 0.023277 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.881075 loss: 0.000556 2022/09/13 01:43:17 - mmengine - INFO - Epoch(train) [101][550/586] lr: 5.000000e-04 eta: 5:43:26 time: 0.335231 data_time: 0.022864 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.878563 loss: 0.000564 2022/09/13 01:43:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:43:29 - mmengine - INFO - Saving checkpoint at 101 epochs 2022/09/13 01:43:53 - mmengine - INFO - Epoch(train) [102][50/586] lr: 5.000000e-04 eta: 5:42:47 time: 0.347635 data_time: 0.030532 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.866346 loss: 0.000573 2022/09/13 01:44:10 - mmengine - INFO - Epoch(train) [102][100/586] lr: 5.000000e-04 eta: 5:42:32 time: 0.335573 data_time: 0.022988 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.819437 loss: 0.000564 2022/09/13 01:44:27 - mmengine - INFO - Epoch(train) [102][150/586] lr: 5.000000e-04 eta: 5:42:16 time: 0.341210 data_time: 0.023113 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.850598 loss: 0.000568 2022/09/13 01:44:44 - mmengine - INFO - Epoch(train) [102][200/586] lr: 5.000000e-04 eta: 5:42:01 time: 0.341498 data_time: 0.022912 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.868349 loss: 0.000552 2022/09/13 01:45:00 - mmengine - INFO - Epoch(train) [102][250/586] lr: 5.000000e-04 eta: 5:41:46 time: 0.328345 data_time: 0.022767 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.908134 loss: 0.000555 2022/09/13 01:45:18 - mmengine - INFO - Epoch(train) [102][300/586] lr: 5.000000e-04 eta: 5:41:31 time: 0.341867 data_time: 0.022886 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.842663 loss: 0.000555 2022/09/13 01:45:35 - mmengine - INFO - Epoch(train) [102][350/586] lr: 5.000000e-04 eta: 5:41:16 time: 0.344827 data_time: 0.026646 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.781525 loss: 0.000562 2022/09/13 01:45:52 - mmengine - INFO - Epoch(train) [102][400/586] lr: 5.000000e-04 eta: 5:41:00 time: 0.332868 data_time: 0.022972 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.843616 loss: 0.000565 2022/09/13 01:46:08 - mmengine - INFO - Epoch(train) [102][450/586] lr: 5.000000e-04 eta: 5:40:45 time: 0.339144 data_time: 0.022803 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.791968 loss: 0.000544 2022/09/13 01:46:25 - mmengine - INFO - Epoch(train) [102][500/586] lr: 5.000000e-04 eta: 5:40:30 time: 0.339596 data_time: 0.022517 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.811092 loss: 0.000562 2022/09/13 01:46:42 - mmengine - INFO - Epoch(train) [102][550/586] lr: 5.000000e-04 eta: 5:40:14 time: 0.336539 data_time: 0.022158 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.828304 loss: 0.000567 2022/09/13 01:46:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:46:54 - mmengine - INFO - Saving checkpoint at 102 epochs 2022/09/13 01:47:18 - mmengine - INFO - Epoch(train) [103][50/586] lr: 5.000000e-04 eta: 5:39:35 time: 0.338706 data_time: 0.027407 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.856905 loss: 0.000572 2022/09/13 01:47:36 - mmengine - INFO - Epoch(train) [103][100/586] lr: 5.000000e-04 eta: 5:39:20 time: 0.343657 data_time: 0.027988 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.835896 loss: 0.000542 2022/09/13 01:47:52 - mmengine - INFO - Epoch(train) [103][150/586] lr: 5.000000e-04 eta: 5:39:05 time: 0.333678 data_time: 0.023597 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.866966 loss: 0.000570 2022/09/13 01:48:09 - mmengine - INFO - Epoch(train) [103][200/586] lr: 5.000000e-04 eta: 5:38:49 time: 0.339884 data_time: 0.022892 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.776201 loss: 0.000558 2022/09/13 01:48:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:48:26 - mmengine - INFO - Epoch(train) [103][250/586] lr: 5.000000e-04 eta: 5:38:34 time: 0.331832 data_time: 0.021977 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.871970 loss: 0.000551 2022/09/13 01:48:43 - mmengine - INFO - Epoch(train) [103][300/586] lr: 5.000000e-04 eta: 5:38:19 time: 0.344643 data_time: 0.026345 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.798807 loss: 0.000568 2022/09/13 01:49:00 - mmengine - INFO - Epoch(train) [103][350/586] lr: 5.000000e-04 eta: 5:38:04 time: 0.341911 data_time: 0.022472 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.874149 loss: 0.000563 2022/09/13 01:49:17 - mmengine - INFO - Epoch(train) [103][400/586] lr: 5.000000e-04 eta: 5:37:49 time: 0.341492 data_time: 0.026516 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.778218 loss: 0.000562 2022/09/13 01:49:34 - mmengine - INFO - Epoch(train) [103][450/586] lr: 5.000000e-04 eta: 5:37:33 time: 0.332167 data_time: 0.022064 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.849331 loss: 0.000573 2022/09/13 01:49:51 - mmengine - INFO - Epoch(train) [103][500/586] lr: 5.000000e-04 eta: 5:37:18 time: 0.338020 data_time: 0.022381 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.839996 loss: 0.000569 2022/09/13 01:50:08 - mmengine - INFO - Epoch(train) [103][550/586] lr: 5.000000e-04 eta: 5:37:02 time: 0.333708 data_time: 0.022281 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.883583 loss: 0.000567 2022/09/13 01:50:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:50:20 - mmengine - INFO - Saving checkpoint at 103 epochs 2022/09/13 01:50:44 - mmengine - INFO - Epoch(train) [104][50/586] lr: 5.000000e-04 eta: 5:36:24 time: 0.349672 data_time: 0.026838 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.810272 loss: 0.000545 2022/09/13 01:51:01 - mmengine - INFO - Epoch(train) [104][100/586] lr: 5.000000e-04 eta: 5:36:09 time: 0.336814 data_time: 0.023191 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.843099 loss: 0.000557 2022/09/13 01:51:17 - mmengine - INFO - Epoch(train) [104][150/586] lr: 5.000000e-04 eta: 5:35:53 time: 0.331405 data_time: 0.023866 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.863696 loss: 0.000549 2022/09/13 01:51:34 - mmengine - INFO - Epoch(train) [104][200/586] lr: 5.000000e-04 eta: 5:35:38 time: 0.342026 data_time: 0.022852 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.814359 loss: 0.000561 2022/09/13 01:51:52 - mmengine - INFO - Epoch(train) [104][250/586] lr: 5.000000e-04 eta: 5:35:23 time: 0.342412 data_time: 0.023824 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.851559 loss: 0.000535 2022/09/13 01:52:08 - mmengine - INFO - Epoch(train) [104][300/586] lr: 5.000000e-04 eta: 5:35:07 time: 0.326699 data_time: 0.022439 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.811871 loss: 0.000559 2022/09/13 01:52:25 - mmengine - INFO - Epoch(train) [104][350/586] lr: 5.000000e-04 eta: 5:34:52 time: 0.349406 data_time: 0.028274 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.851860 loss: 0.000568 2022/09/13 01:52:42 - mmengine - INFO - Epoch(train) [104][400/586] lr: 5.000000e-04 eta: 5:34:36 time: 0.329129 data_time: 0.022270 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.877931 loss: 0.000540 2022/09/13 01:52:58 - mmengine - INFO - Epoch(train) [104][450/586] lr: 5.000000e-04 eta: 5:34:21 time: 0.330911 data_time: 0.022443 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.924558 loss: 0.000564 2022/09/13 01:53:16 - mmengine - INFO - Epoch(train) [104][500/586] lr: 5.000000e-04 eta: 5:34:06 time: 0.348775 data_time: 0.022675 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.823733 loss: 0.000557 2022/09/13 01:53:32 - mmengine - INFO - Epoch(train) [104][550/586] lr: 5.000000e-04 eta: 5:33:50 time: 0.328406 data_time: 0.022172 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.831733 loss: 0.000578 2022/09/13 01:53:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:53:44 - mmengine - INFO - Saving checkpoint at 104 epochs 2022/09/13 01:54:09 - mmengine - INFO - Epoch(train) [105][50/586] lr: 5.000000e-04 eta: 5:33:12 time: 0.346249 data_time: 0.027913 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.880185 loss: 0.000563 2022/09/13 01:54:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:54:26 - mmengine - INFO - Epoch(train) [105][100/586] lr: 5.000000e-04 eta: 5:32:57 time: 0.337571 data_time: 0.023190 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.827880 loss: 0.000551 2022/09/13 01:54:43 - mmengine - INFO - Epoch(train) [105][150/586] lr: 5.000000e-04 eta: 5:32:41 time: 0.334098 data_time: 0.022572 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.874990 loss: 0.000564 2022/09/13 01:54:59 - mmengine - INFO - Epoch(train) [105][200/586] lr: 5.000000e-04 eta: 5:32:26 time: 0.335607 data_time: 0.023093 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.802949 loss: 0.000555 2022/09/13 01:55:16 - mmengine - INFO - Epoch(train) [105][250/586] lr: 5.000000e-04 eta: 5:32:10 time: 0.334990 data_time: 0.023466 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.881456 loss: 0.000564 2022/09/13 01:55:33 - mmengine - INFO - Epoch(train) [105][300/586] lr: 5.000000e-04 eta: 5:31:55 time: 0.335855 data_time: 0.022217 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.874377 loss: 0.000550 2022/09/13 01:55:50 - mmengine - INFO - Epoch(train) [105][350/586] lr: 5.000000e-04 eta: 5:31:40 time: 0.345030 data_time: 0.022691 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.837784 loss: 0.000558 2022/09/13 01:56:07 - mmengine - INFO - Epoch(train) [105][400/586] lr: 5.000000e-04 eta: 5:31:24 time: 0.336883 data_time: 0.023049 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.893428 loss: 0.000540 2022/09/13 01:56:24 - mmengine - INFO - Epoch(train) [105][450/586] lr: 5.000000e-04 eta: 5:31:09 time: 0.338125 data_time: 0.025684 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.818512 loss: 0.000561 2022/09/13 01:56:41 - mmengine - INFO - Epoch(train) [105][500/586] lr: 5.000000e-04 eta: 5:30:54 time: 0.335066 data_time: 0.022909 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.787862 loss: 0.000571 2022/09/13 01:56:57 - mmengine - INFO - Epoch(train) [105][550/586] lr: 5.000000e-04 eta: 5:30:38 time: 0.330429 data_time: 0.022201 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.836654 loss: 0.000559 2022/09/13 01:57:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 01:57:10 - mmengine - INFO - Saving checkpoint at 105 epochs 2022/09/13 01:57:34 - mmengine - INFO - Epoch(train) [106][50/586] lr: 5.000000e-04 eta: 5:30:00 time: 0.342271 data_time: 0.026683 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.869516 loss: 0.000575 2022/09/13 01:57:51 - mmengine - INFO - Epoch(train) [106][100/586] lr: 5.000000e-04 eta: 5:29:44 time: 0.336078 data_time: 0.025722 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.824535 loss: 0.000543 2022/09/13 01:58:07 - mmengine - INFO - Epoch(train) [106][150/586] lr: 5.000000e-04 eta: 5:29:29 time: 0.329024 data_time: 0.022598 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.825381 loss: 0.000557 2022/09/13 01:58:24 - mmengine - INFO - Epoch(train) [106][200/586] lr: 5.000000e-04 eta: 5:29:13 time: 0.337102 data_time: 0.022059 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.873002 loss: 0.000549 2022/09/13 01:58:41 - mmengine - INFO - Epoch(train) [106][250/586] lr: 5.000000e-04 eta: 5:28:58 time: 0.342362 data_time: 0.022786 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.862871 loss: 0.000558 2022/09/13 01:58:58 - mmengine - INFO - Epoch(train) [106][300/586] lr: 5.000000e-04 eta: 5:28:42 time: 0.330490 data_time: 0.022865 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.787443 loss: 0.000557 2022/09/13 01:59:14 - mmengine - INFO - Epoch(train) [106][350/586] lr: 5.000000e-04 eta: 5:28:27 time: 0.337112 data_time: 0.022120 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.868882 loss: 0.000579 2022/09/13 01:59:31 - mmengine - INFO - Epoch(train) [106][400/586] lr: 5.000000e-04 eta: 5:28:12 time: 0.338009 data_time: 0.027474 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.855569 loss: 0.000582 2022/09/13 01:59:48 - mmengine - INFO - Epoch(train) [106][450/586] lr: 5.000000e-04 eta: 5:27:56 time: 0.333808 data_time: 0.021689 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.825127 loss: 0.000532 2022/09/13 01:59:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:00:07 - mmengine - INFO - Epoch(train) [106][500/586] lr: 5.000000e-04 eta: 5:27:43 time: 0.377703 data_time: 0.026115 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.835246 loss: 0.000570 2022/09/13 02:00:24 - mmengine - INFO - Epoch(train) [106][550/586] lr: 5.000000e-04 eta: 5:27:27 time: 0.337865 data_time: 0.022238 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.854800 loss: 0.000559 2022/09/13 02:00:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:00:36 - mmengine - INFO - Saving checkpoint at 106 epochs 2022/09/13 02:01:00 - mmengine - INFO - Epoch(train) [107][50/586] lr: 5.000000e-04 eta: 5:26:49 time: 0.343012 data_time: 0.031074 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.858211 loss: 0.000547 2022/09/13 02:01:17 - mmengine - INFO - Epoch(train) [107][100/586] lr: 5.000000e-04 eta: 5:26:34 time: 0.336472 data_time: 0.022466 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.814037 loss: 0.000555 2022/09/13 02:01:34 - mmengine - INFO - Epoch(train) [107][150/586] lr: 5.000000e-04 eta: 5:26:18 time: 0.331182 data_time: 0.022675 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.862409 loss: 0.000533 2022/09/13 02:01:51 - mmengine - INFO - Epoch(train) [107][200/586] lr: 5.000000e-04 eta: 5:26:03 time: 0.340026 data_time: 0.022383 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.801217 loss: 0.000557 2022/09/13 02:02:07 - mmengine - INFO - Epoch(train) [107][250/586] lr: 5.000000e-04 eta: 5:25:48 time: 0.336477 data_time: 0.023393 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.838405 loss: 0.000551 2022/09/13 02:02:24 - mmengine - INFO - Epoch(train) [107][300/586] lr: 5.000000e-04 eta: 5:25:32 time: 0.335482 data_time: 0.022870 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.869077 loss: 0.000559 2022/09/13 02:02:41 - mmengine - INFO - Epoch(train) [107][350/586] lr: 5.000000e-04 eta: 5:25:17 time: 0.337762 data_time: 0.022174 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.826499 loss: 0.000532 2022/09/13 02:02:58 - mmengine - INFO - Epoch(train) [107][400/586] lr: 5.000000e-04 eta: 5:25:02 time: 0.339027 data_time: 0.022285 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.761701 loss: 0.000533 2022/09/13 02:03:15 - mmengine - INFO - Epoch(train) [107][450/586] lr: 5.000000e-04 eta: 5:24:47 time: 0.342257 data_time: 0.023647 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.832402 loss: 0.000546 2022/09/13 02:03:32 - mmengine - INFO - Epoch(train) [107][500/586] lr: 5.000000e-04 eta: 5:24:31 time: 0.339429 data_time: 0.027352 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.872690 loss: 0.000557 2022/09/13 02:03:49 - mmengine - INFO - Epoch(train) [107][550/586] lr: 5.000000e-04 eta: 5:24:16 time: 0.334195 data_time: 0.022687 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.830733 loss: 0.000562 2022/09/13 02:04:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:04:01 - mmengine - INFO - Saving checkpoint at 107 epochs 2022/09/13 02:04:25 - mmengine - INFO - Epoch(train) [108][50/586] lr: 5.000000e-04 eta: 5:23:37 time: 0.330823 data_time: 0.028106 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.852349 loss: 0.000548 2022/09/13 02:04:42 - mmengine - INFO - Epoch(train) [108][100/586] lr: 5.000000e-04 eta: 5:23:22 time: 0.335113 data_time: 0.022578 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.914350 loss: 0.000566 2022/09/13 02:04:59 - mmengine - INFO - Epoch(train) [108][150/586] lr: 5.000000e-04 eta: 5:23:07 time: 0.339594 data_time: 0.021677 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.808479 loss: 0.000558 2022/09/13 02:05:15 - mmengine - INFO - Epoch(train) [108][200/586] lr: 5.000000e-04 eta: 5:22:51 time: 0.334283 data_time: 0.022772 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.901604 loss: 0.000561 2022/09/13 02:05:32 - mmengine - INFO - Epoch(train) [108][250/586] lr: 5.000000e-04 eta: 5:22:36 time: 0.339656 data_time: 0.022480 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.843793 loss: 0.000548 2022/09/13 02:05:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:05:49 - mmengine - INFO - Epoch(train) [108][300/586] lr: 5.000000e-04 eta: 5:22:20 time: 0.332090 data_time: 0.022305 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.835953 loss: 0.000563 2022/09/13 02:06:06 - mmengine - INFO - Epoch(train) [108][350/586] lr: 5.000000e-04 eta: 5:22:05 time: 0.337047 data_time: 0.023603 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.848116 loss: 0.000563 2022/09/13 02:06:23 - mmengine - INFO - Epoch(train) [108][400/586] lr: 5.000000e-04 eta: 5:21:49 time: 0.338330 data_time: 0.022596 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.847565 loss: 0.000540 2022/09/13 02:06:39 - mmengine - INFO - Epoch(train) [108][450/586] lr: 5.000000e-04 eta: 5:21:34 time: 0.337263 data_time: 0.026497 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.832988 loss: 0.000567 2022/09/13 02:06:56 - mmengine - INFO - Epoch(train) [108][500/586] lr: 5.000000e-04 eta: 5:21:19 time: 0.337649 data_time: 0.022485 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.858877 loss: 0.000562 2022/09/13 02:07:13 - mmengine - INFO - Epoch(train) [108][550/586] lr: 5.000000e-04 eta: 5:21:03 time: 0.334225 data_time: 0.022927 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.777128 loss: 0.000562 2022/09/13 02:07:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:07:25 - mmengine - INFO - Saving checkpoint at 108 epochs 2022/09/13 02:07:50 - mmengine - INFO - Epoch(train) [109][50/586] lr: 5.000000e-04 eta: 5:20:26 time: 0.347181 data_time: 0.036022 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.827262 loss: 0.000551 2022/09/13 02:08:07 - mmengine - INFO - Epoch(train) [109][100/586] lr: 5.000000e-04 eta: 5:20:10 time: 0.337035 data_time: 0.024220 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.873593 loss: 0.000572 2022/09/13 02:08:23 - mmengine - INFO - Epoch(train) [109][150/586] lr: 5.000000e-04 eta: 5:19:55 time: 0.330709 data_time: 0.021814 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.869999 loss: 0.000542 2022/09/13 02:08:40 - mmengine - INFO - Epoch(train) [109][200/586] lr: 5.000000e-04 eta: 5:19:39 time: 0.336117 data_time: 0.026265 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.831716 loss: 0.000537 2022/09/13 02:08:57 - mmengine - INFO - Epoch(train) [109][250/586] lr: 5.000000e-04 eta: 5:19:24 time: 0.336212 data_time: 0.022563 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.799807 loss: 0.000569 2022/09/13 02:09:13 - mmengine - INFO - Epoch(train) [109][300/586] lr: 5.000000e-04 eta: 5:19:08 time: 0.330222 data_time: 0.022586 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.825911 loss: 0.000534 2022/09/13 02:09:30 - mmengine - INFO - Epoch(train) [109][350/586] lr: 5.000000e-04 eta: 5:18:53 time: 0.338476 data_time: 0.022901 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.861484 loss: 0.000543 2022/09/13 02:09:47 - mmengine - INFO - Epoch(train) [109][400/586] lr: 5.000000e-04 eta: 5:18:37 time: 0.335199 data_time: 0.023244 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.823778 loss: 0.000570 2022/09/13 02:10:03 - mmengine - INFO - Epoch(train) [109][450/586] lr: 5.000000e-04 eta: 5:18:22 time: 0.328940 data_time: 0.022874 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.852594 loss: 0.000561 2022/09/13 02:10:21 - mmengine - INFO - Epoch(train) [109][500/586] lr: 5.000000e-04 eta: 5:18:07 time: 0.347831 data_time: 0.023114 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.842883 loss: 0.000558 2022/09/13 02:10:37 - mmengine - INFO - Epoch(train) [109][550/586] lr: 5.000000e-04 eta: 5:17:51 time: 0.329793 data_time: 0.022908 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.824730 loss: 0.000558 2022/09/13 02:10:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:10:49 - mmengine - INFO - Saving checkpoint at 109 epochs 2022/09/13 02:11:13 - mmengine - INFO - Epoch(train) [110][50/586] lr: 5.000000e-04 eta: 5:17:13 time: 0.343441 data_time: 0.028129 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.912334 loss: 0.000539 2022/09/13 02:11:30 - mmengine - INFO - Epoch(train) [110][100/586] lr: 5.000000e-04 eta: 5:16:58 time: 0.333962 data_time: 0.022919 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.803684 loss: 0.000556 2022/09/13 02:11:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:11:47 - mmengine - INFO - Epoch(train) [110][150/586] lr: 5.000000e-04 eta: 5:16:42 time: 0.330850 data_time: 0.022425 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.902409 loss: 0.000553 2022/09/13 02:12:04 - mmengine - INFO - Epoch(train) [110][200/586] lr: 5.000000e-04 eta: 5:16:27 time: 0.342975 data_time: 0.022886 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.806534 loss: 0.000557 2022/09/13 02:12:21 - mmengine - INFO - Epoch(train) [110][250/586] lr: 5.000000e-04 eta: 5:16:12 time: 0.335479 data_time: 0.022212 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.864480 loss: 0.000558 2022/09/13 02:12:38 - mmengine - INFO - Epoch(train) [110][300/586] lr: 5.000000e-04 eta: 5:15:56 time: 0.339870 data_time: 0.027552 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.868067 loss: 0.000553 2022/09/13 02:12:55 - mmengine - INFO - Epoch(train) [110][350/586] lr: 5.000000e-04 eta: 5:15:41 time: 0.340676 data_time: 0.022664 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.867853 loss: 0.000552 2022/09/13 02:13:11 - mmengine - INFO - Epoch(train) [110][400/586] lr: 5.000000e-04 eta: 5:15:26 time: 0.335485 data_time: 0.022357 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.869953 loss: 0.000555 2022/09/13 02:13:28 - mmengine - INFO - Epoch(train) [110][450/586] lr: 5.000000e-04 eta: 5:15:10 time: 0.331443 data_time: 0.026823 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.909459 loss: 0.000548 2022/09/13 02:13:45 - mmengine - INFO - Epoch(train) [110][500/586] lr: 5.000000e-04 eta: 5:14:55 time: 0.341580 data_time: 0.022652 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.790954 loss: 0.000559 2022/09/13 02:14:02 - mmengine - INFO - Epoch(train) [110][550/586] lr: 5.000000e-04 eta: 5:14:39 time: 0.336625 data_time: 0.022370 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.871265 loss: 0.000573 2022/09/13 02:14:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:14:14 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/09/13 02:14:32 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:01:17 time: 0.217306 data_time: 0.012578 memory: 7489 2022/09/13 02:14:42 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:01:04 time: 0.210600 data_time: 0.008011 memory: 1657 2022/09/13 02:14:53 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:54 time: 0.213933 data_time: 0.007898 memory: 1657 2022/09/13 02:15:04 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:43 time: 0.209548 data_time: 0.008258 memory: 1657 2022/09/13 02:15:14 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:32 time: 0.208335 data_time: 0.007616 memory: 1657 2022/09/13 02:15:24 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:22 time: 0.209277 data_time: 0.007684 memory: 1657 2022/09/13 02:15:35 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:11 time: 0.209461 data_time: 0.007592 memory: 1657 2022/09/13 02:15:45 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:01 time: 0.206972 data_time: 0.006919 memory: 1657 2022/09/13 02:16:20 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 02:16:33 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.750141 coco/AP .5: 0.901022 coco/AP .75: 0.818790 coco/AP (M): 0.717548 coco/AP (L): 0.816868 coco/AR: 0.801354 coco/AR .5: 0.940334 coco/AR .75: 0.861776 coco/AR (M): 0.760940 coco/AR (L): 0.860870 2022/09/13 02:16:33 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_100.pth is removed 2022/09/13 02:16:37 - mmengine - INFO - The best checkpoint with 0.7501 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/09/13 02:16:54 - mmengine - INFO - Epoch(train) [111][50/586] lr: 5.000000e-04 eta: 5:14:02 time: 0.341555 data_time: 0.026462 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.841961 loss: 0.000534 2022/09/13 02:17:12 - mmengine - INFO - Epoch(train) [111][100/586] lr: 5.000000e-04 eta: 5:13:47 time: 0.347470 data_time: 0.023084 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.790373 loss: 0.000555 2022/09/13 02:17:28 - mmengine - INFO - Epoch(train) [111][150/586] lr: 5.000000e-04 eta: 5:13:31 time: 0.329888 data_time: 0.022392 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.872012 loss: 0.000552 2022/09/13 02:17:45 - mmengine - INFO - Epoch(train) [111][200/586] lr: 5.000000e-04 eta: 5:13:16 time: 0.343764 data_time: 0.022668 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.850727 loss: 0.000541 2022/09/13 02:18:02 - mmengine - INFO - Epoch(train) [111][250/586] lr: 5.000000e-04 eta: 5:13:01 time: 0.340015 data_time: 0.023520 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.898734 loss: 0.000557 2022/09/13 02:18:19 - mmengine - INFO - Epoch(train) [111][300/586] lr: 5.000000e-04 eta: 5:12:45 time: 0.328502 data_time: 0.024013 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.897098 loss: 0.000554 2022/09/13 02:18:36 - mmengine - INFO - Epoch(train) [111][350/586] lr: 5.000000e-04 eta: 5:12:30 time: 0.343283 data_time: 0.022533 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.885001 loss: 0.000556 2022/09/13 02:18:53 - mmengine - INFO - Epoch(train) [111][400/586] lr: 5.000000e-04 eta: 5:12:15 time: 0.334726 data_time: 0.022525 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.868928 loss: 0.000534 2022/09/13 02:19:09 - mmengine - INFO - Epoch(train) [111][450/586] lr: 5.000000e-04 eta: 5:11:59 time: 0.327153 data_time: 0.023204 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.830931 loss: 0.000569 2022/09/13 02:19:26 - mmengine - INFO - Epoch(train) [111][500/586] lr: 5.000000e-04 eta: 5:11:43 time: 0.341021 data_time: 0.022931 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.854035 loss: 0.000559 2022/09/13 02:19:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:19:43 - mmengine - INFO - Epoch(train) [111][550/586] lr: 5.000000e-04 eta: 5:11:28 time: 0.336164 data_time: 0.022941 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.820915 loss: 0.000545 2022/09/13 02:19:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:19:55 - mmengine - INFO - Saving checkpoint at 111 epochs 2022/09/13 02:20:19 - mmengine - INFO - Epoch(train) [112][50/586] lr: 5.000000e-04 eta: 5:10:51 time: 0.349955 data_time: 0.031847 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.837502 loss: 0.000556 2022/09/13 02:20:36 - mmengine - INFO - Epoch(train) [112][100/586] lr: 5.000000e-04 eta: 5:10:36 time: 0.332378 data_time: 0.022859 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.881295 loss: 0.000536 2022/09/13 02:20:52 - mmengine - INFO - Epoch(train) [112][150/586] lr: 5.000000e-04 eta: 5:10:20 time: 0.331221 data_time: 0.022673 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.823656 loss: 0.000537 2022/09/13 02:21:09 - mmengine - INFO - Epoch(train) [112][200/586] lr: 5.000000e-04 eta: 5:10:05 time: 0.345947 data_time: 0.023761 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.846624 loss: 0.000528 2022/09/13 02:21:26 - mmengine - INFO - Epoch(train) [112][250/586] lr: 5.000000e-04 eta: 5:09:50 time: 0.337435 data_time: 0.023457 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.889605 loss: 0.000552 2022/09/13 02:21:43 - mmengine - INFO - Epoch(train) [112][300/586] lr: 5.000000e-04 eta: 5:09:34 time: 0.330994 data_time: 0.023190 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.835376 loss: 0.000564 2022/09/13 02:22:00 - mmengine - INFO - Epoch(train) [112][350/586] lr: 5.000000e-04 eta: 5:09:19 time: 0.342271 data_time: 0.023680 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.769993 loss: 0.000556 2022/09/13 02:22:17 - mmengine - INFO - Epoch(train) [112][400/586] lr: 5.000000e-04 eta: 5:09:03 time: 0.334506 data_time: 0.024790 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.843324 loss: 0.000553 2022/09/13 02:22:34 - mmengine - INFO - Epoch(train) [112][450/586] lr: 5.000000e-04 eta: 5:08:48 time: 0.334825 data_time: 0.024523 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.846130 loss: 0.000545 2022/09/13 02:22:51 - mmengine - INFO - Epoch(train) [112][500/586] lr: 5.000000e-04 eta: 5:08:32 time: 0.341026 data_time: 0.022610 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.847947 loss: 0.000560 2022/09/13 02:23:07 - mmengine - INFO - Epoch(train) [112][550/586] lr: 5.000000e-04 eta: 5:08:17 time: 0.332369 data_time: 0.023451 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.792320 loss: 0.000571 2022/09/13 02:23:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:23:19 - mmengine - INFO - Saving checkpoint at 112 epochs 2022/09/13 02:23:43 - mmengine - INFO - Epoch(train) [113][50/586] lr: 5.000000e-04 eta: 5:07:40 time: 0.346690 data_time: 0.028018 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.824247 loss: 0.000561 2022/09/13 02:24:00 - mmengine - INFO - Epoch(train) [113][100/586] lr: 5.000000e-04 eta: 5:07:24 time: 0.331731 data_time: 0.023584 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.832116 loss: 0.000556 2022/09/13 02:24:16 - mmengine - INFO - Epoch(train) [113][150/586] lr: 5.000000e-04 eta: 5:07:09 time: 0.332511 data_time: 0.022660 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.909831 loss: 0.000552 2022/09/13 02:24:33 - mmengine - INFO - Epoch(train) [113][200/586] lr: 5.000000e-04 eta: 5:06:53 time: 0.341844 data_time: 0.022632 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.895505 loss: 0.000548 2022/09/13 02:24:50 - mmengine - INFO - Epoch(train) [113][250/586] lr: 5.000000e-04 eta: 5:06:38 time: 0.326730 data_time: 0.024892 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.802057 loss: 0.000543 2022/09/13 02:25:07 - mmengine - INFO - Epoch(train) [113][300/586] lr: 5.000000e-04 eta: 5:06:22 time: 0.333487 data_time: 0.023129 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.847303 loss: 0.000573 2022/09/13 02:25:24 - mmengine - INFO - Epoch(train) [113][350/586] lr: 5.000000e-04 eta: 5:06:07 time: 0.343566 data_time: 0.023488 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.888586 loss: 0.000548 2022/09/13 02:25:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:25:40 - mmengine - INFO - Epoch(train) [113][400/586] lr: 5.000000e-04 eta: 5:05:51 time: 0.332414 data_time: 0.023623 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.850222 loss: 0.000578 2022/09/13 02:25:57 - mmengine - INFO - Epoch(train) [113][450/586] lr: 5.000000e-04 eta: 5:05:36 time: 0.334012 data_time: 0.023701 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.845380 loss: 0.000562 2022/09/13 02:26:14 - mmengine - INFO - Epoch(train) [113][500/586] lr: 5.000000e-04 eta: 5:05:21 time: 0.342892 data_time: 0.023513 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.785753 loss: 0.000555 2022/09/13 02:26:31 - mmengine - INFO - Epoch(train) [113][550/586] lr: 5.000000e-04 eta: 5:05:05 time: 0.333311 data_time: 0.026102 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.842218 loss: 0.000559 2022/09/13 02:26:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:26:43 - mmengine - INFO - Saving checkpoint at 113 epochs 2022/09/13 02:27:07 - mmengine - INFO - Epoch(train) [114][50/586] lr: 5.000000e-04 eta: 5:04:28 time: 0.344477 data_time: 0.027111 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.881027 loss: 0.000562 2022/09/13 02:27:24 - mmengine - INFO - Epoch(train) [114][100/586] lr: 5.000000e-04 eta: 5:04:13 time: 0.336314 data_time: 0.023824 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.746159 loss: 0.000546 2022/09/13 02:27:41 - mmengine - INFO - Epoch(train) [114][150/586] lr: 5.000000e-04 eta: 5:03:57 time: 0.333107 data_time: 0.022497 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.774091 loss: 0.000572 2022/09/13 02:27:58 - mmengine - INFO - Epoch(train) [114][200/586] lr: 5.000000e-04 eta: 5:03:42 time: 0.344985 data_time: 0.026246 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.875558 loss: 0.000551 2022/09/13 02:28:15 - mmengine - INFO - Epoch(train) [114][250/586] lr: 5.000000e-04 eta: 5:03:26 time: 0.330223 data_time: 0.023370 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.829323 loss: 0.000537 2022/09/13 02:28:31 - mmengine - INFO - Epoch(train) [114][300/586] lr: 5.000000e-04 eta: 5:03:11 time: 0.331428 data_time: 0.022030 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.852859 loss: 0.000544 2022/09/13 02:28:49 - mmengine - INFO - Epoch(train) [114][350/586] lr: 5.000000e-04 eta: 5:02:56 time: 0.348990 data_time: 0.027347 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.841653 loss: 0.000559 2022/09/13 02:29:05 - mmengine - INFO - Epoch(train) [114][400/586] lr: 5.000000e-04 eta: 5:02:40 time: 0.325456 data_time: 0.022882 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.851592 loss: 0.000562 2022/09/13 02:29:21 - mmengine - INFO - Epoch(train) [114][450/586] lr: 5.000000e-04 eta: 5:02:24 time: 0.329920 data_time: 0.022452 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.891801 loss: 0.000540 2022/09/13 02:29:39 - mmengine - INFO - Epoch(train) [114][500/586] lr: 5.000000e-04 eta: 5:02:09 time: 0.345082 data_time: 0.022631 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.829834 loss: 0.000558 2022/09/13 02:29:55 - mmengine - INFO - Epoch(train) [114][550/586] lr: 5.000000e-04 eta: 5:01:53 time: 0.329501 data_time: 0.022375 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.878541 loss: 0.000546 2022/09/13 02:30:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:30:07 - mmengine - INFO - Saving checkpoint at 114 epochs 2022/09/13 02:30:32 - mmengine - INFO - Epoch(train) [115][50/586] lr: 5.000000e-04 eta: 5:01:17 time: 0.349783 data_time: 0.028755 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.803344 loss: 0.000552 2022/09/13 02:30:48 - mmengine - INFO - Epoch(train) [115][100/586] lr: 5.000000e-04 eta: 5:01:01 time: 0.330617 data_time: 0.023321 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.803654 loss: 0.000559 2022/09/13 02:31:05 - mmengine - INFO - Epoch(train) [115][150/586] lr: 5.000000e-04 eta: 5:00:46 time: 0.334231 data_time: 0.022721 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.891864 loss: 0.000544 2022/09/13 02:31:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:31:22 - mmengine - INFO - Epoch(train) [115][200/586] lr: 5.000000e-04 eta: 5:00:31 time: 0.344265 data_time: 0.022659 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.890372 loss: 0.000538 2022/09/13 02:31:39 - mmengine - INFO - Epoch(train) [115][250/586] lr: 5.000000e-04 eta: 5:00:15 time: 0.335776 data_time: 0.022769 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.891941 loss: 0.000579 2022/09/13 02:31:55 - mmengine - INFO - Epoch(train) [115][300/586] lr: 5.000000e-04 eta: 4:59:59 time: 0.323502 data_time: 0.022490 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.856141 loss: 0.000564 2022/09/13 02:32:12 - mmengine - INFO - Epoch(train) [115][350/586] lr: 5.000000e-04 eta: 4:59:44 time: 0.342798 data_time: 0.022250 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.824951 loss: 0.000538 2022/09/13 02:32:29 - mmengine - INFO - Epoch(train) [115][400/586] lr: 5.000000e-04 eta: 4:59:28 time: 0.333342 data_time: 0.022476 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.811306 loss: 0.000548 2022/09/13 02:32:46 - mmengine - INFO - Epoch(train) [115][450/586] lr: 5.000000e-04 eta: 4:59:13 time: 0.331235 data_time: 0.022125 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.827385 loss: 0.000562 2022/09/13 02:33:03 - mmengine - INFO - Epoch(train) [115][500/586] lr: 5.000000e-04 eta: 4:58:57 time: 0.342928 data_time: 0.023178 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.920641 loss: 0.000552 2022/09/13 02:33:19 - mmengine - INFO - Epoch(train) [115][550/586] lr: 5.000000e-04 eta: 4:58:42 time: 0.333724 data_time: 0.022171 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.855733 loss: 0.000550 2022/09/13 02:33:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:33:31 - mmengine - INFO - Saving checkpoint at 115 epochs 2022/09/13 02:33:56 - mmengine - INFO - Epoch(train) [116][50/586] lr: 5.000000e-04 eta: 4:58:05 time: 0.342834 data_time: 0.027091 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.864708 loss: 0.000542 2022/09/13 02:34:12 - mmengine - INFO - Epoch(train) [116][100/586] lr: 5.000000e-04 eta: 4:57:50 time: 0.329517 data_time: 0.022581 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.888160 loss: 0.000558 2022/09/13 02:34:29 - mmengine - INFO - Epoch(train) [116][150/586] lr: 5.000000e-04 eta: 4:57:34 time: 0.336451 data_time: 0.022926 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.840028 loss: 0.000563 2022/09/13 02:34:46 - mmengine - INFO - Epoch(train) [116][200/586] lr: 5.000000e-04 eta: 4:57:19 time: 0.335852 data_time: 0.021970 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.864141 loss: 0.000559 2022/09/13 02:35:02 - mmengine - INFO - Epoch(train) [116][250/586] lr: 5.000000e-04 eta: 4:57:03 time: 0.337176 data_time: 0.022448 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.841765 loss: 0.000558 2022/09/13 02:35:19 - mmengine - INFO - Epoch(train) [116][300/586] lr: 5.000000e-04 eta: 4:56:48 time: 0.331102 data_time: 0.022327 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.858904 loss: 0.000545 2022/09/13 02:35:36 - mmengine - INFO - Epoch(train) [116][350/586] lr: 5.000000e-04 eta: 4:56:32 time: 0.343866 data_time: 0.022538 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.834701 loss: 0.000541 2022/09/13 02:35:53 - mmengine - INFO - Epoch(train) [116][400/586] lr: 5.000000e-04 eta: 4:56:17 time: 0.335534 data_time: 0.022927 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.820263 loss: 0.000552 2022/09/13 02:36:10 - mmengine - INFO - Epoch(train) [116][450/586] lr: 5.000000e-04 eta: 4:56:02 time: 0.337579 data_time: 0.025808 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.874779 loss: 0.000541 2022/09/13 02:36:27 - mmengine - INFO - Epoch(train) [116][500/586] lr: 5.000000e-04 eta: 4:55:46 time: 0.346178 data_time: 0.022907 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.860807 loss: 0.000548 2022/09/13 02:36:44 - mmengine - INFO - Epoch(train) [116][550/586] lr: 5.000000e-04 eta: 4:55:31 time: 0.334198 data_time: 0.023678 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.844068 loss: 0.000537 2022/09/13 02:36:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:36:56 - mmengine - INFO - Saving checkpoint at 116 epochs 2022/09/13 02:37:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:37:20 - mmengine - INFO - Epoch(train) [117][50/586] lr: 5.000000e-04 eta: 4:54:55 time: 0.343492 data_time: 0.030378 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.851651 loss: 0.000548 2022/09/13 02:37:38 - mmengine - INFO - Epoch(train) [117][100/586] lr: 5.000000e-04 eta: 4:54:40 time: 0.343437 data_time: 0.025819 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.837473 loss: 0.000543 2022/09/13 02:37:54 - mmengine - INFO - Epoch(train) [117][150/586] lr: 5.000000e-04 eta: 4:54:24 time: 0.327841 data_time: 0.022711 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.873204 loss: 0.000552 2022/09/13 02:38:11 - mmengine - INFO - Epoch(train) [117][200/586] lr: 5.000000e-04 eta: 4:54:08 time: 0.336560 data_time: 0.023465 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.883204 loss: 0.000581 2022/09/13 02:38:28 - mmengine - INFO - Epoch(train) [117][250/586] lr: 5.000000e-04 eta: 4:53:53 time: 0.337254 data_time: 0.022740 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.797704 loss: 0.000544 2022/09/13 02:38:45 - mmengine - INFO - Epoch(train) [117][300/586] lr: 5.000000e-04 eta: 4:53:37 time: 0.338237 data_time: 0.022382 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.862280 loss: 0.000561 2022/09/13 02:39:02 - mmengine - INFO - Epoch(train) [117][350/586] lr: 5.000000e-04 eta: 4:53:22 time: 0.338080 data_time: 0.022470 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.880060 loss: 0.000555 2022/09/13 02:39:19 - mmengine - INFO - Epoch(train) [117][400/586] lr: 5.000000e-04 eta: 4:53:07 time: 0.339080 data_time: 0.027027 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.882182 loss: 0.000532 2022/09/13 02:39:35 - mmengine - INFO - Epoch(train) [117][450/586] lr: 5.000000e-04 eta: 4:52:51 time: 0.333101 data_time: 0.022111 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.840412 loss: 0.000547 2022/09/13 02:39:52 - mmengine - INFO - Epoch(train) [117][500/586] lr: 5.000000e-04 eta: 4:52:35 time: 0.333940 data_time: 0.022191 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.850100 loss: 0.000546 2022/09/13 02:40:09 - mmengine - INFO - Epoch(train) [117][550/586] lr: 5.000000e-04 eta: 4:52:20 time: 0.337013 data_time: 0.022469 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.805487 loss: 0.000555 2022/09/13 02:40:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:40:21 - mmengine - INFO - Saving checkpoint at 117 epochs 2022/09/13 02:40:44 - mmengine - INFO - Epoch(train) [118][50/586] lr: 5.000000e-04 eta: 4:51:44 time: 0.348448 data_time: 0.028370 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.783405 loss: 0.000538 2022/09/13 02:41:01 - mmengine - INFO - Epoch(train) [118][100/586] lr: 5.000000e-04 eta: 4:51:29 time: 0.338628 data_time: 0.022495 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.844266 loss: 0.000551 2022/09/13 02:41:18 - mmengine - INFO - Epoch(train) [118][150/586] lr: 5.000000e-04 eta: 4:51:13 time: 0.332595 data_time: 0.022558 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.833593 loss: 0.000546 2022/09/13 02:41:35 - mmengine - INFO - Epoch(train) [118][200/586] lr: 5.000000e-04 eta: 4:50:58 time: 0.338562 data_time: 0.022306 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.831792 loss: 0.000544 2022/09/13 02:41:52 - mmengine - INFO - Epoch(train) [118][250/586] lr: 5.000000e-04 eta: 4:50:42 time: 0.335323 data_time: 0.022952 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.900595 loss: 0.000548 2022/09/13 02:42:08 - mmengine - INFO - Epoch(train) [118][300/586] lr: 5.000000e-04 eta: 4:50:27 time: 0.336095 data_time: 0.026408 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.871604 loss: 0.000553 2022/09/13 02:42:25 - mmengine - INFO - Epoch(train) [118][350/586] lr: 5.000000e-04 eta: 4:50:11 time: 0.339093 data_time: 0.022862 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.873022 loss: 0.000542 2022/09/13 02:42:42 - mmengine - INFO - Epoch(train) [118][400/586] lr: 5.000000e-04 eta: 4:49:56 time: 0.333257 data_time: 0.022522 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.863211 loss: 0.000535 2022/09/13 02:42:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:42:59 - mmengine - INFO - Epoch(train) [118][450/586] lr: 5.000000e-04 eta: 4:49:40 time: 0.341694 data_time: 0.026705 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.838277 loss: 0.000548 2022/09/13 02:43:16 - mmengine - INFO - Epoch(train) [118][500/586] lr: 5.000000e-04 eta: 4:49:25 time: 0.330380 data_time: 0.022700 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.788925 loss: 0.000546 2022/09/13 02:43:32 - mmengine - INFO - Epoch(train) [118][550/586] lr: 5.000000e-04 eta: 4:49:09 time: 0.327524 data_time: 0.022159 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.890508 loss: 0.000551 2022/09/13 02:43:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:43:44 - mmengine - INFO - Saving checkpoint at 118 epochs 2022/09/13 02:44:08 - mmengine - INFO - Epoch(train) [119][50/586] lr: 5.000000e-04 eta: 4:48:33 time: 0.340593 data_time: 0.030700 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.862040 loss: 0.000544 2022/09/13 02:44:25 - mmengine - INFO - Epoch(train) [119][100/586] lr: 5.000000e-04 eta: 4:48:18 time: 0.346866 data_time: 0.023062 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.885065 loss: 0.000558 2022/09/13 02:44:42 - mmengine - INFO - Epoch(train) [119][150/586] lr: 5.000000e-04 eta: 4:48:02 time: 0.336408 data_time: 0.023090 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.810754 loss: 0.000547 2022/09/13 02:44:59 - mmengine - INFO - Epoch(train) [119][200/586] lr: 5.000000e-04 eta: 4:47:47 time: 0.332673 data_time: 0.022537 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.895442 loss: 0.000537 2022/09/13 02:45:16 - mmengine - INFO - Epoch(train) [119][250/586] lr: 5.000000e-04 eta: 4:47:31 time: 0.343462 data_time: 0.022512 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.912294 loss: 0.000540 2022/09/13 02:45:33 - mmengine - INFO - Epoch(train) [119][300/586] lr: 5.000000e-04 eta: 4:47:16 time: 0.333185 data_time: 0.022236 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.877431 loss: 0.000547 2022/09/13 02:45:49 - mmengine - INFO - Epoch(train) [119][350/586] lr: 5.000000e-04 eta: 4:47:00 time: 0.332099 data_time: 0.023215 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.866297 loss: 0.000529 2022/09/13 02:46:07 - mmengine - INFO - Epoch(train) [119][400/586] lr: 5.000000e-04 eta: 4:46:45 time: 0.341344 data_time: 0.023170 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.849241 loss: 0.000541 2022/09/13 02:46:23 - mmengine - INFO - Epoch(train) [119][450/586] lr: 5.000000e-04 eta: 4:46:29 time: 0.334087 data_time: 0.022813 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.867849 loss: 0.000552 2022/09/13 02:46:40 - mmengine - INFO - Epoch(train) [119][500/586] lr: 5.000000e-04 eta: 4:46:14 time: 0.336119 data_time: 0.022765 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.812072 loss: 0.000569 2022/09/13 02:46:57 - mmengine - INFO - Epoch(train) [119][550/586] lr: 5.000000e-04 eta: 4:45:58 time: 0.341964 data_time: 0.023137 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.858767 loss: 0.000542 2022/09/13 02:47:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:47:09 - mmengine - INFO - Saving checkpoint at 119 epochs 2022/09/13 02:47:32 - mmengine - INFO - Epoch(train) [120][50/586] lr: 5.000000e-04 eta: 4:45:22 time: 0.336926 data_time: 0.029046 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.894609 loss: 0.000549 2022/09/13 02:47:49 - mmengine - INFO - Epoch(train) [120][100/586] lr: 5.000000e-04 eta: 4:45:07 time: 0.336579 data_time: 0.022934 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.807464 loss: 0.000546 2022/09/13 02:48:06 - mmengine - INFO - Epoch(train) [120][150/586] lr: 5.000000e-04 eta: 4:44:51 time: 0.337174 data_time: 0.022426 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.829815 loss: 0.000539 2022/09/13 02:48:23 - mmengine - INFO - Epoch(train) [120][200/586] lr: 5.000000e-04 eta: 4:44:36 time: 0.329400 data_time: 0.026146 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.827167 loss: 0.000548 2022/09/13 02:48:40 - mmengine - INFO - Epoch(train) [120][250/586] lr: 5.000000e-04 eta: 4:44:20 time: 0.339290 data_time: 0.022916 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.909418 loss: 0.000544 2022/09/13 02:48:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:48:57 - mmengine - INFO - Epoch(train) [120][300/586] lr: 5.000000e-04 eta: 4:44:05 time: 0.341351 data_time: 0.023197 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.866259 loss: 0.000537 2022/09/13 02:49:13 - mmengine - INFO - Epoch(train) [120][350/586] lr: 5.000000e-04 eta: 4:43:50 time: 0.337045 data_time: 0.026112 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.794641 loss: 0.000545 2022/09/13 02:49:30 - mmengine - INFO - Epoch(train) [120][400/586] lr: 5.000000e-04 eta: 4:43:34 time: 0.339223 data_time: 0.023243 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.795971 loss: 0.000539 2022/09/13 02:49:47 - mmengine - INFO - Epoch(train) [120][450/586] lr: 5.000000e-04 eta: 4:43:19 time: 0.338536 data_time: 0.022437 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.886677 loss: 0.000570 2022/09/13 02:50:04 - mmengine - INFO - Epoch(train) [120][500/586] lr: 5.000000e-04 eta: 4:43:03 time: 0.335407 data_time: 0.026586 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.848907 loss: 0.000551 2022/09/13 02:50:21 - mmengine - INFO - Epoch(train) [120][550/586] lr: 5.000000e-04 eta: 4:42:48 time: 0.338228 data_time: 0.022827 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.744399 loss: 0.000551 2022/09/13 02:50:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:50:33 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/09/13 02:50:51 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:01:16 time: 0.214568 data_time: 0.012354 memory: 7489 2022/09/13 02:51:01 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:01:04 time: 0.209582 data_time: 0.008271 memory: 1657 2022/09/13 02:51:12 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:53 time: 0.209286 data_time: 0.008165 memory: 1657 2022/09/13 02:51:22 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:43 time: 0.210378 data_time: 0.008296 memory: 1657 2022/09/13 02:51:33 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:32 time: 0.208884 data_time: 0.007609 memory: 1657 2022/09/13 02:51:43 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:22 time: 0.214593 data_time: 0.008015 memory: 1657 2022/09/13 02:51:54 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:11 time: 0.209939 data_time: 0.007985 memory: 1657 2022/09/13 02:52:04 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:01 time: 0.206478 data_time: 0.007217 memory: 1657 2022/09/13 02:52:40 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 02:52:53 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.750679 coco/AP .5: 0.900679 coco/AP .75: 0.817772 coco/AP (M): 0.716559 coco/AP (L): 0.816536 coco/AR: 0.801842 coco/AR .5: 0.939704 coco/AR .75: 0.861618 coco/AR (M): 0.761322 coco/AR (L): 0.861799 2022/09/13 02:52:54 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_110.pth is removed 2022/09/13 02:52:57 - mmengine - INFO - The best checkpoint with 0.7507 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/09/13 02:53:15 - mmengine - INFO - Epoch(train) [121][50/586] lr: 5.000000e-04 eta: 4:42:12 time: 0.342888 data_time: 0.026759 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.905899 loss: 0.000544 2022/09/13 02:53:31 - mmengine - INFO - Epoch(train) [121][100/586] lr: 5.000000e-04 eta: 4:41:57 time: 0.336182 data_time: 0.022216 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.762183 loss: 0.000547 2022/09/13 02:53:48 - mmengine - INFO - Epoch(train) [121][150/586] lr: 5.000000e-04 eta: 4:41:41 time: 0.332716 data_time: 0.022742 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.804634 loss: 0.000553 2022/09/13 02:54:05 - mmengine - INFO - Epoch(train) [121][200/586] lr: 5.000000e-04 eta: 4:41:26 time: 0.337388 data_time: 0.024180 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.855701 loss: 0.000580 2022/09/13 02:54:22 - mmengine - INFO - Epoch(train) [121][250/586] lr: 5.000000e-04 eta: 4:41:10 time: 0.340309 data_time: 0.025632 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.899416 loss: 0.000539 2022/09/13 02:54:39 - mmengine - INFO - Epoch(train) [121][300/586] lr: 5.000000e-04 eta: 4:40:55 time: 0.339480 data_time: 0.022434 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.851684 loss: 0.000527 2022/09/13 02:54:55 - mmengine - INFO - Epoch(train) [121][350/586] lr: 5.000000e-04 eta: 4:40:39 time: 0.329100 data_time: 0.023278 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.815845 loss: 0.000573 2022/09/13 02:55:13 - mmengine - INFO - Epoch(train) [121][400/586] lr: 5.000000e-04 eta: 4:40:24 time: 0.344007 data_time: 0.023445 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.921522 loss: 0.000544 2022/09/13 02:55:30 - mmengine - INFO - Epoch(train) [121][450/586] lr: 5.000000e-04 eta: 4:40:09 time: 0.345677 data_time: 0.022511 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.882768 loss: 0.000546 2022/09/13 02:55:47 - mmengine - INFO - Epoch(train) [121][500/586] lr: 5.000000e-04 eta: 4:39:53 time: 0.333441 data_time: 0.022955 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.837585 loss: 0.000545 2022/09/13 02:56:04 - mmengine - INFO - Epoch(train) [121][550/586] lr: 5.000000e-04 eta: 4:39:38 time: 0.338979 data_time: 0.022089 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.909849 loss: 0.000563 2022/09/13 02:56:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:56:16 - mmengine - INFO - Saving checkpoint at 121 epochs 2022/09/13 02:56:39 - mmengine - INFO - Epoch(train) [122][50/586] lr: 5.000000e-04 eta: 4:39:02 time: 0.339711 data_time: 0.026261 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.880550 loss: 0.000567 2022/09/13 02:56:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:56:56 - mmengine - INFO - Epoch(train) [122][100/586] lr: 5.000000e-04 eta: 4:38:47 time: 0.339281 data_time: 0.023138 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.850838 loss: 0.000539 2022/09/13 02:57:13 - mmengine - INFO - Epoch(train) [122][150/586] lr: 5.000000e-04 eta: 4:38:31 time: 0.335967 data_time: 0.023041 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.811920 loss: 0.000557 2022/09/13 02:57:30 - mmengine - INFO - Epoch(train) [122][200/586] lr: 5.000000e-04 eta: 4:38:16 time: 0.342391 data_time: 0.022379 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.900654 loss: 0.000547 2022/09/13 02:57:47 - mmengine - INFO - Epoch(train) [122][250/586] lr: 5.000000e-04 eta: 4:38:00 time: 0.332301 data_time: 0.021973 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.854184 loss: 0.000547 2022/09/13 02:58:03 - mmengine - INFO - Epoch(train) [122][300/586] lr: 5.000000e-04 eta: 4:37:45 time: 0.334026 data_time: 0.023313 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.874803 loss: 0.000540 2022/09/13 02:58:20 - mmengine - INFO - Epoch(train) [122][350/586] lr: 5.000000e-04 eta: 4:37:29 time: 0.338360 data_time: 0.022740 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.822407 loss: 0.000555 2022/09/13 02:58:37 - mmengine - INFO - Epoch(train) [122][400/586] lr: 5.000000e-04 eta: 4:37:14 time: 0.337819 data_time: 0.022993 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.791453 loss: 0.000545 2022/09/13 02:58:54 - mmengine - INFO - Epoch(train) [122][450/586] lr: 5.000000e-04 eta: 4:36:58 time: 0.337467 data_time: 0.023412 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.865350 loss: 0.000545 2022/09/13 02:59:11 - mmengine - INFO - Epoch(train) [122][500/586] lr: 5.000000e-04 eta: 4:36:43 time: 0.338903 data_time: 0.027452 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.878335 loss: 0.000534 2022/09/13 02:59:28 - mmengine - INFO - Epoch(train) [122][550/586] lr: 5.000000e-04 eta: 4:36:27 time: 0.333489 data_time: 0.022402 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.842580 loss: 0.000553 2022/09/13 02:59:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 02:59:40 - mmengine - INFO - Saving checkpoint at 122 epochs 2022/09/13 03:00:04 - mmengine - INFO - Epoch(train) [123][50/586] lr: 5.000000e-04 eta: 4:35:52 time: 0.341676 data_time: 0.028858 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.776776 loss: 0.000531 2022/09/13 03:00:21 - mmengine - INFO - Epoch(train) [123][100/586] lr: 5.000000e-04 eta: 4:35:36 time: 0.333516 data_time: 0.022670 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.859181 loss: 0.000550 2022/09/13 03:00:38 - mmengine - INFO - Epoch(train) [123][150/586] lr: 5.000000e-04 eta: 4:35:21 time: 0.339013 data_time: 0.023197 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.788759 loss: 0.000554 2022/09/13 03:00:54 - mmengine - INFO - Epoch(train) [123][200/586] lr: 5.000000e-04 eta: 4:35:05 time: 0.332410 data_time: 0.022825 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.808532 loss: 0.000534 2022/09/13 03:01:11 - mmengine - INFO - Epoch(train) [123][250/586] lr: 5.000000e-04 eta: 4:34:49 time: 0.331880 data_time: 0.023770 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.835543 loss: 0.000555 2022/09/13 03:01:28 - mmengine - INFO - Epoch(train) [123][300/586] lr: 5.000000e-04 eta: 4:34:34 time: 0.341108 data_time: 0.022446 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.860052 loss: 0.000532 2022/09/13 03:01:45 - mmengine - INFO - Epoch(train) [123][350/586] lr: 5.000000e-04 eta: 4:34:19 time: 0.345680 data_time: 0.023110 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.795577 loss: 0.000553 2022/09/13 03:02:02 - mmengine - INFO - Epoch(train) [123][400/586] lr: 5.000000e-04 eta: 4:34:03 time: 0.337662 data_time: 0.024409 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.870564 loss: 0.000546 2022/09/13 03:02:20 - mmengine - INFO - Epoch(train) [123][450/586] lr: 5.000000e-04 eta: 4:33:48 time: 0.348997 data_time: 0.023005 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.854328 loss: 0.000538 2022/09/13 03:02:36 - mmengine - INFO - Epoch(train) [123][500/586] lr: 5.000000e-04 eta: 4:33:33 time: 0.333573 data_time: 0.023615 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.887882 loss: 0.000548 2022/09/13 03:02:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:02:53 - mmengine - INFO - Epoch(train) [123][550/586] lr: 5.000000e-04 eta: 4:33:17 time: 0.336997 data_time: 0.022714 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.907322 loss: 0.000538 2022/09/13 03:03:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:03:05 - mmengine - INFO - Saving checkpoint at 123 epochs 2022/09/13 03:03:29 - mmengine - INFO - Epoch(train) [124][50/586] lr: 5.000000e-04 eta: 4:32:42 time: 0.341086 data_time: 0.029275 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.893140 loss: 0.000550 2022/09/13 03:03:46 - mmengine - INFO - Epoch(train) [124][100/586] lr: 5.000000e-04 eta: 4:32:27 time: 0.341805 data_time: 0.022359 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.859972 loss: 0.000546 2022/09/13 03:04:03 - mmengine - INFO - Epoch(train) [124][150/586] lr: 5.000000e-04 eta: 4:32:11 time: 0.330632 data_time: 0.022328 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.845817 loss: 0.000553 2022/09/13 03:04:20 - mmengine - INFO - Epoch(train) [124][200/586] lr: 5.000000e-04 eta: 4:31:56 time: 0.345431 data_time: 0.025806 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.868039 loss: 0.000541 2022/09/13 03:04:37 - mmengine - INFO - Epoch(train) [124][250/586] lr: 5.000000e-04 eta: 4:31:40 time: 0.336253 data_time: 0.022523 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.813312 loss: 0.000552 2022/09/13 03:04:54 - mmengine - INFO - Epoch(train) [124][300/586] lr: 5.000000e-04 eta: 4:31:24 time: 0.332276 data_time: 0.023615 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.860476 loss: 0.000554 2022/09/13 03:05:10 - mmengine - INFO - Epoch(train) [124][350/586] lr: 5.000000e-04 eta: 4:31:09 time: 0.332624 data_time: 0.026118 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.831057 loss: 0.000554 2022/09/13 03:05:27 - mmengine - INFO - Epoch(train) [124][400/586] lr: 5.000000e-04 eta: 4:30:53 time: 0.340371 data_time: 0.022844 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.812579 loss: 0.000542 2022/09/13 03:05:44 - mmengine - INFO - Epoch(train) [124][450/586] lr: 5.000000e-04 eta: 4:30:38 time: 0.339363 data_time: 0.023618 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.803333 loss: 0.000549 2022/09/13 03:06:01 - mmengine - INFO - Epoch(train) [124][500/586] lr: 5.000000e-04 eta: 4:30:22 time: 0.335506 data_time: 0.027716 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.869803 loss: 0.000564 2022/09/13 03:06:18 - mmengine - INFO - Epoch(train) [124][550/586] lr: 5.000000e-04 eta: 4:30:07 time: 0.337365 data_time: 0.022486 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.861789 loss: 0.000536 2022/09/13 03:06:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:06:30 - mmengine - INFO - Saving checkpoint at 124 epochs 2022/09/13 03:06:55 - mmengine - INFO - Epoch(train) [125][50/586] lr: 5.000000e-04 eta: 4:29:32 time: 0.346917 data_time: 0.035346 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.885465 loss: 0.000545 2022/09/13 03:07:12 - mmengine - INFO - Epoch(train) [125][100/586] lr: 5.000000e-04 eta: 4:29:17 time: 0.340397 data_time: 0.025321 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.850476 loss: 0.000551 2022/09/13 03:07:29 - mmengine - INFO - Epoch(train) [125][150/586] lr: 5.000000e-04 eta: 4:29:01 time: 0.343314 data_time: 0.023125 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.896554 loss: 0.000551 2022/09/13 03:07:45 - mmengine - INFO - Epoch(train) [125][200/586] lr: 5.000000e-04 eta: 4:28:46 time: 0.333017 data_time: 0.022611 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.829999 loss: 0.000556 2022/09/13 03:08:02 - mmengine - INFO - Epoch(train) [125][250/586] lr: 5.000000e-04 eta: 4:28:30 time: 0.337056 data_time: 0.023062 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.811480 loss: 0.000532 2022/09/13 03:08:19 - mmengine - INFO - Epoch(train) [125][300/586] lr: 5.000000e-04 eta: 4:28:15 time: 0.341642 data_time: 0.023007 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.828008 loss: 0.000561 2022/09/13 03:08:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:08:36 - mmengine - INFO - Epoch(train) [125][350/586] lr: 5.000000e-04 eta: 4:27:59 time: 0.333378 data_time: 0.023863 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.872294 loss: 0.000542 2022/09/13 03:08:53 - mmengine - INFO - Epoch(train) [125][400/586] lr: 5.000000e-04 eta: 4:27:44 time: 0.340345 data_time: 0.022573 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.767487 loss: 0.000537 2022/09/13 03:09:10 - mmengine - INFO - Epoch(train) [125][450/586] lr: 5.000000e-04 eta: 4:27:28 time: 0.337569 data_time: 0.022519 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.803273 loss: 0.000558 2022/09/13 03:09:27 - mmengine - INFO - Epoch(train) [125][500/586] lr: 5.000000e-04 eta: 4:27:13 time: 0.337058 data_time: 0.023191 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.853961 loss: 0.000543 2022/09/13 03:09:43 - mmengine - INFO - Epoch(train) [125][550/586] lr: 5.000000e-04 eta: 4:26:57 time: 0.332430 data_time: 0.022515 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.898297 loss: 0.000541 2022/09/13 03:09:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:09:56 - mmengine - INFO - Saving checkpoint at 125 epochs 2022/09/13 03:10:20 - mmengine - INFO - Epoch(train) [126][50/586] lr: 5.000000e-04 eta: 4:26:22 time: 0.346685 data_time: 0.029511 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.812815 loss: 0.000541 2022/09/13 03:10:37 - mmengine - INFO - Epoch(train) [126][100/586] lr: 5.000000e-04 eta: 4:26:07 time: 0.338480 data_time: 0.022535 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.879694 loss: 0.000547 2022/09/13 03:10:54 - mmengine - INFO - Epoch(train) [126][150/586] lr: 5.000000e-04 eta: 4:25:51 time: 0.338224 data_time: 0.023609 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.926896 loss: 0.000561 2022/09/13 03:11:11 - mmengine - INFO - Epoch(train) [126][200/586] lr: 5.000000e-04 eta: 4:25:36 time: 0.334657 data_time: 0.024250 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.913920 loss: 0.000541 2022/09/13 03:11:28 - mmengine - INFO - Epoch(train) [126][250/586] lr: 5.000000e-04 eta: 4:25:20 time: 0.339140 data_time: 0.027231 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.856230 loss: 0.000544 2022/09/13 03:11:45 - mmengine - INFO - Epoch(train) [126][300/586] lr: 5.000000e-04 eta: 4:25:05 time: 0.339357 data_time: 0.023281 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.748937 loss: 0.000550 2022/09/13 03:12:01 - mmengine - INFO - Epoch(train) [126][350/586] lr: 5.000000e-04 eta: 4:24:49 time: 0.331303 data_time: 0.023069 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.841336 loss: 0.000537 2022/09/13 03:12:18 - mmengine - INFO - Epoch(train) [126][400/586] lr: 5.000000e-04 eta: 4:24:34 time: 0.335127 data_time: 0.026125 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.852101 loss: 0.000551 2022/09/13 03:12:35 - mmengine - INFO - Epoch(train) [126][450/586] lr: 5.000000e-04 eta: 4:24:18 time: 0.335581 data_time: 0.022876 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.689928 loss: 0.000551 2022/09/13 03:12:52 - mmengine - INFO - Epoch(train) [126][500/586] lr: 5.000000e-04 eta: 4:24:03 time: 0.340119 data_time: 0.023041 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.811660 loss: 0.000536 2022/09/13 03:13:09 - mmengine - INFO - Epoch(train) [126][550/586] lr: 5.000000e-04 eta: 4:23:47 time: 0.335065 data_time: 0.026915 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.828922 loss: 0.000554 2022/09/13 03:13:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:13:21 - mmengine - INFO - Saving checkpoint at 126 epochs 2022/09/13 03:13:45 - mmengine - INFO - Epoch(train) [127][50/586] lr: 5.000000e-04 eta: 4:23:13 time: 0.348817 data_time: 0.030742 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.801652 loss: 0.000548 2022/09/13 03:14:01 - mmengine - INFO - Epoch(train) [127][100/586] lr: 5.000000e-04 eta: 4:22:57 time: 0.330376 data_time: 0.026921 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.893338 loss: 0.000540 2022/09/13 03:14:18 - mmengine - INFO - Epoch(train) [127][150/586] lr: 5.000000e-04 eta: 4:22:41 time: 0.342053 data_time: 0.023058 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.825249 loss: 0.000560 2022/09/13 03:14:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:14:35 - mmengine - INFO - Epoch(train) [127][200/586] lr: 5.000000e-04 eta: 4:22:26 time: 0.339714 data_time: 0.026301 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.909940 loss: 0.000544 2022/09/13 03:14:52 - mmengine - INFO - Epoch(train) [127][250/586] lr: 5.000000e-04 eta: 4:22:10 time: 0.327100 data_time: 0.021939 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.852109 loss: 0.000542 2022/09/13 03:15:09 - mmengine - INFO - Epoch(train) [127][300/586] lr: 5.000000e-04 eta: 4:21:55 time: 0.345949 data_time: 0.022813 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.898109 loss: 0.000535 2022/09/13 03:15:26 - mmengine - INFO - Epoch(train) [127][350/586] lr: 5.000000e-04 eta: 4:21:39 time: 0.338679 data_time: 0.026256 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.884873 loss: 0.000542 2022/09/13 03:15:42 - mmengine - INFO - Epoch(train) [127][400/586] lr: 5.000000e-04 eta: 4:21:24 time: 0.330606 data_time: 0.022657 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.869938 loss: 0.000540 2022/09/13 03:16:00 - mmengine - INFO - Epoch(train) [127][450/586] lr: 5.000000e-04 eta: 4:21:08 time: 0.343346 data_time: 0.022414 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.864667 loss: 0.000533 2022/09/13 03:16:17 - mmengine - INFO - Epoch(train) [127][500/586] lr: 5.000000e-04 eta: 4:20:53 time: 0.341165 data_time: 0.023053 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.803820 loss: 0.000545 2022/09/13 03:16:33 - mmengine - INFO - Epoch(train) [127][550/586] lr: 5.000000e-04 eta: 4:20:37 time: 0.330780 data_time: 0.022795 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.813488 loss: 0.000534 2022/09/13 03:16:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:16:45 - mmengine - INFO - Saving checkpoint at 127 epochs 2022/09/13 03:17:09 - mmengine - INFO - Epoch(train) [128][50/586] lr: 5.000000e-04 eta: 4:20:03 time: 0.343514 data_time: 0.026802 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.741865 loss: 0.000553 2022/09/13 03:17:26 - mmengine - INFO - Epoch(train) [128][100/586] lr: 5.000000e-04 eta: 4:19:47 time: 0.332374 data_time: 0.026031 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.815747 loss: 0.000543 2022/09/13 03:17:42 - mmengine - INFO - Epoch(train) [128][150/586] lr: 5.000000e-04 eta: 4:19:31 time: 0.334896 data_time: 0.022913 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.864293 loss: 0.000539 2022/09/13 03:18:00 - mmengine - INFO - Epoch(train) [128][200/586] lr: 5.000000e-04 eta: 4:19:16 time: 0.343392 data_time: 0.023187 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.849007 loss: 0.000547 2022/09/13 03:18:16 - mmengine - INFO - Epoch(train) [128][250/586] lr: 5.000000e-04 eta: 4:19:00 time: 0.331885 data_time: 0.022217 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.796658 loss: 0.000549 2022/09/13 03:18:33 - mmengine - INFO - Epoch(train) [128][300/586] lr: 5.000000e-04 eta: 4:18:45 time: 0.343257 data_time: 0.022409 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.829665 loss: 0.000562 2022/09/13 03:18:50 - mmengine - INFO - Epoch(train) [128][350/586] lr: 5.000000e-04 eta: 4:18:30 time: 0.340345 data_time: 0.022688 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.848244 loss: 0.000548 2022/09/13 03:19:07 - mmengine - INFO - Epoch(train) [128][400/586] lr: 5.000000e-04 eta: 4:18:14 time: 0.332184 data_time: 0.022988 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.914133 loss: 0.000550 2022/09/13 03:19:24 - mmengine - INFO - Epoch(train) [128][450/586] lr: 5.000000e-04 eta: 4:17:58 time: 0.339827 data_time: 0.026300 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.804331 loss: 0.000526 2022/09/13 03:19:41 - mmengine - INFO - Epoch(train) [128][500/586] lr: 5.000000e-04 eta: 4:17:43 time: 0.336250 data_time: 0.022465 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.876501 loss: 0.000544 2022/09/13 03:19:58 - mmengine - INFO - Epoch(train) [128][550/586] lr: 5.000000e-04 eta: 4:17:27 time: 0.332689 data_time: 0.022699 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.811192 loss: 0.000556 2022/09/13 03:20:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:20:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:20:10 - mmengine - INFO - Saving checkpoint at 128 epochs 2022/09/13 03:20:34 - mmengine - INFO - Epoch(train) [129][50/586] lr: 5.000000e-04 eta: 4:16:53 time: 0.340285 data_time: 0.029461 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.775311 loss: 0.000549 2022/09/13 03:20:51 - mmengine - INFO - Epoch(train) [129][100/586] lr: 5.000000e-04 eta: 4:16:37 time: 0.337716 data_time: 0.029296 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.861355 loss: 0.000542 2022/09/13 03:21:08 - mmengine - INFO - Epoch(train) [129][150/586] lr: 5.000000e-04 eta: 4:16:22 time: 0.340716 data_time: 0.023316 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.848789 loss: 0.000536 2022/09/13 03:21:24 - mmengine - INFO - Epoch(train) [129][200/586] lr: 5.000000e-04 eta: 4:16:06 time: 0.336407 data_time: 0.026336 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.875373 loss: 0.000558 2022/09/13 03:21:41 - mmengine - INFO - Epoch(train) [129][250/586] lr: 5.000000e-04 eta: 4:15:51 time: 0.338966 data_time: 0.023232 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.856715 loss: 0.000542 2022/09/13 03:21:58 - mmengine - INFO - Epoch(train) [129][300/586] lr: 5.000000e-04 eta: 4:15:35 time: 0.333389 data_time: 0.022924 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.840460 loss: 0.000552 2022/09/13 03:22:15 - mmengine - INFO - Epoch(train) [129][350/586] lr: 5.000000e-04 eta: 4:15:20 time: 0.339991 data_time: 0.022464 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.870926 loss: 0.000526 2022/09/13 03:22:32 - mmengine - INFO - Epoch(train) [129][400/586] lr: 5.000000e-04 eta: 4:15:04 time: 0.334407 data_time: 0.022777 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.819332 loss: 0.000547 2022/09/13 03:22:49 - mmengine - INFO - Epoch(train) [129][450/586] lr: 5.000000e-04 eta: 4:14:49 time: 0.340304 data_time: 0.022661 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.836482 loss: 0.000546 2022/09/13 03:23:06 - mmengine - INFO - Epoch(train) [129][500/586] lr: 5.000000e-04 eta: 4:14:33 time: 0.340340 data_time: 0.022568 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.824668 loss: 0.000551 2022/09/13 03:23:23 - mmengine - INFO - Epoch(train) [129][550/586] lr: 5.000000e-04 eta: 4:14:17 time: 0.331947 data_time: 0.022913 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.787840 loss: 0.000561 2022/09/13 03:23:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:23:35 - mmengine - INFO - Saving checkpoint at 129 epochs 2022/09/13 03:23:59 - mmengine - INFO - Epoch(train) [130][50/586] lr: 5.000000e-04 eta: 4:13:43 time: 0.347158 data_time: 0.028450 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.845907 loss: 0.000527 2022/09/13 03:24:16 - mmengine - INFO - Epoch(train) [130][100/586] lr: 5.000000e-04 eta: 4:13:28 time: 0.342249 data_time: 0.022811 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.774008 loss: 0.000546 2022/09/13 03:24:34 - mmengine - INFO - Epoch(train) [130][150/586] lr: 5.000000e-04 eta: 4:13:13 time: 0.345370 data_time: 0.023500 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.805521 loss: 0.000566 2022/09/13 03:24:50 - mmengine - INFO - Epoch(train) [130][200/586] lr: 5.000000e-04 eta: 4:12:57 time: 0.334329 data_time: 0.023483 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.858743 loss: 0.000547 2022/09/13 03:25:07 - mmengine - INFO - Epoch(train) [130][250/586] lr: 5.000000e-04 eta: 4:12:42 time: 0.342113 data_time: 0.022500 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.780988 loss: 0.000548 2022/09/13 03:25:24 - mmengine - INFO - Epoch(train) [130][300/586] lr: 5.000000e-04 eta: 4:12:26 time: 0.339916 data_time: 0.022561 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.875776 loss: 0.000529 2022/09/13 03:25:41 - mmengine - INFO - Epoch(train) [130][350/586] lr: 5.000000e-04 eta: 4:12:10 time: 0.333671 data_time: 0.023474 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.825864 loss: 0.000529 2022/09/13 03:25:58 - mmengine - INFO - Epoch(train) [130][400/586] lr: 5.000000e-04 eta: 4:11:55 time: 0.336934 data_time: 0.022464 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.891092 loss: 0.000550 2022/09/13 03:26:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:26:15 - mmengine - INFO - Epoch(train) [130][450/586] lr: 5.000000e-04 eta: 4:11:39 time: 0.334277 data_time: 0.026067 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.818571 loss: 0.000533 2022/09/13 03:26:31 - mmengine - INFO - Epoch(train) [130][500/586] lr: 5.000000e-04 eta: 4:11:24 time: 0.334018 data_time: 0.024457 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.836432 loss: 0.000546 2022/09/13 03:26:48 - mmengine - INFO - Epoch(train) [130][550/586] lr: 5.000000e-04 eta: 4:11:08 time: 0.336220 data_time: 0.022825 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.783246 loss: 0.000557 2022/09/13 03:27:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:27:01 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/09/13 03:27:18 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:01:17 time: 0.216300 data_time: 0.012112 memory: 7489 2022/09/13 03:27:29 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:01:04 time: 0.209411 data_time: 0.007774 memory: 1657 2022/09/13 03:27:39 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:53 time: 0.210038 data_time: 0.007743 memory: 1657 2022/09/13 03:27:50 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:43 time: 0.209493 data_time: 0.007779 memory: 1657 2022/09/13 03:28:00 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:33 time: 0.213567 data_time: 0.011628 memory: 1657 2022/09/13 03:28:11 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:22 time: 0.209513 data_time: 0.007568 memory: 1657 2022/09/13 03:28:21 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:12 time: 0.211072 data_time: 0.007957 memory: 1657 2022/09/13 03:28:32 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:01 time: 0.207085 data_time: 0.007219 memory: 1657 2022/09/13 03:29:07 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 03:29:20 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.748283 coco/AP .5: 0.899527 coco/AP .75: 0.814067 coco/AP (M): 0.713380 coco/AP (L): 0.816166 coco/AR: 0.799669 coco/AR .5: 0.937972 coco/AR .75: 0.857525 coco/AR (M): 0.759082 coco/AR (L): 0.859346 2022/09/13 03:29:38 - mmengine - INFO - Epoch(train) [131][50/586] lr: 5.000000e-04 eta: 4:10:34 time: 0.349751 data_time: 0.026689 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.836687 loss: 0.000534 2022/09/13 03:29:55 - mmengine - INFO - Epoch(train) [131][100/586] lr: 5.000000e-04 eta: 4:10:19 time: 0.338308 data_time: 0.022401 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.821960 loss: 0.000556 2022/09/13 03:30:12 - mmengine - INFO - Epoch(train) [131][150/586] lr: 5.000000e-04 eta: 4:10:03 time: 0.335506 data_time: 0.022152 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.860895 loss: 0.000533 2022/09/13 03:30:28 - mmengine - INFO - Epoch(train) [131][200/586] lr: 5.000000e-04 eta: 4:09:47 time: 0.335907 data_time: 0.022641 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.858475 loss: 0.000549 2022/09/13 03:30:45 - mmengine - INFO - Epoch(train) [131][250/586] lr: 5.000000e-04 eta: 4:09:32 time: 0.338172 data_time: 0.023439 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.863903 loss: 0.000537 2022/09/13 03:31:02 - mmengine - INFO - Epoch(train) [131][300/586] lr: 5.000000e-04 eta: 4:09:16 time: 0.336378 data_time: 0.023455 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.860001 loss: 0.000552 2022/09/13 03:31:19 - mmengine - INFO - Epoch(train) [131][350/586] lr: 5.000000e-04 eta: 4:09:01 time: 0.337968 data_time: 0.027062 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.844836 loss: 0.000541 2022/09/13 03:31:36 - mmengine - INFO - Epoch(train) [131][400/586] lr: 5.000000e-04 eta: 4:08:45 time: 0.329651 data_time: 0.022014 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.886134 loss: 0.000523 2022/09/13 03:31:53 - mmengine - INFO - Epoch(train) [131][450/586] lr: 5.000000e-04 eta: 4:08:29 time: 0.343117 data_time: 0.022415 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.817608 loss: 0.000534 2022/09/13 03:32:09 - mmengine - INFO - Epoch(train) [131][500/586] lr: 5.000000e-04 eta: 4:08:14 time: 0.333440 data_time: 0.026008 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.889116 loss: 0.000530 2022/09/13 03:32:26 - mmengine - INFO - Epoch(train) [131][550/586] lr: 5.000000e-04 eta: 4:07:58 time: 0.330573 data_time: 0.021881 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.812761 loss: 0.000551 2022/09/13 03:32:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:32:39 - mmengine - INFO - Saving checkpoint at 131 epochs 2022/09/13 03:33:03 - mmengine - INFO - Epoch(train) [132][50/586] lr: 5.000000e-04 eta: 4:07:24 time: 0.338362 data_time: 0.026331 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.857496 loss: 0.000542 2022/09/13 03:33:20 - mmengine - INFO - Epoch(train) [132][100/586] lr: 5.000000e-04 eta: 4:07:08 time: 0.337737 data_time: 0.022771 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.864743 loss: 0.000540 2022/09/13 03:33:37 - mmengine - INFO - Epoch(train) [132][150/586] lr: 5.000000e-04 eta: 4:06:53 time: 0.340921 data_time: 0.022591 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.797535 loss: 0.000550 2022/09/13 03:33:53 - mmengine - INFO - Epoch(train) [132][200/586] lr: 5.000000e-04 eta: 4:06:37 time: 0.336791 data_time: 0.022792 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.887507 loss: 0.000551 2022/09/13 03:34:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:34:10 - mmengine - INFO - Epoch(train) [132][250/586] lr: 5.000000e-04 eta: 4:06:22 time: 0.334119 data_time: 0.023241 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.850684 loss: 0.000538 2022/09/13 03:34:27 - mmengine - INFO - Epoch(train) [132][300/586] lr: 5.000000e-04 eta: 4:06:06 time: 0.337694 data_time: 0.022609 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.850305 loss: 0.000529 2022/09/13 03:34:44 - mmengine - INFO - Epoch(train) [132][350/586] lr: 5.000000e-04 eta: 4:05:50 time: 0.333166 data_time: 0.022301 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.814498 loss: 0.000534 2022/09/13 03:35:00 - mmengine - INFO - Epoch(train) [132][400/586] lr: 5.000000e-04 eta: 4:05:35 time: 0.332668 data_time: 0.023429 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.852675 loss: 0.000545 2022/09/13 03:35:17 - mmengine - INFO - Epoch(train) [132][450/586] lr: 5.000000e-04 eta: 4:05:19 time: 0.335790 data_time: 0.025756 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.841534 loss: 0.000554 2022/09/13 03:35:34 - mmengine - INFO - Epoch(train) [132][500/586] lr: 5.000000e-04 eta: 4:05:04 time: 0.337367 data_time: 0.021911 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.804555 loss: 0.000534 2022/09/13 03:35:51 - mmengine - INFO - Epoch(train) [132][550/586] lr: 5.000000e-04 eta: 4:04:48 time: 0.332824 data_time: 0.023137 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.854788 loss: 0.000546 2022/09/13 03:36:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:36:03 - mmengine - INFO - Saving checkpoint at 132 epochs 2022/09/13 03:36:26 - mmengine - INFO - Epoch(train) [133][50/586] lr: 5.000000e-04 eta: 4:04:14 time: 0.339639 data_time: 0.027695 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.836732 loss: 0.000546 2022/09/13 03:36:43 - mmengine - INFO - Epoch(train) [133][100/586] lr: 5.000000e-04 eta: 4:03:58 time: 0.338335 data_time: 0.023258 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.801052 loss: 0.000547 2022/09/13 03:37:00 - mmengine - INFO - Epoch(train) [133][150/586] lr: 5.000000e-04 eta: 4:03:43 time: 0.335784 data_time: 0.022721 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.845291 loss: 0.000536 2022/09/13 03:37:17 - mmengine - INFO - Epoch(train) [133][200/586] lr: 5.000000e-04 eta: 4:03:27 time: 0.333751 data_time: 0.024854 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.877653 loss: 0.000543 2022/09/13 03:37:34 - mmengine - INFO - Epoch(train) [133][250/586] lr: 5.000000e-04 eta: 4:03:12 time: 0.347104 data_time: 0.023320 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.872361 loss: 0.000539 2022/09/13 03:37:51 - mmengine - INFO - Epoch(train) [133][300/586] lr: 5.000000e-04 eta: 4:02:56 time: 0.335880 data_time: 0.023178 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.783889 loss: 0.000533 2022/09/13 03:38:08 - mmengine - INFO - Epoch(train) [133][350/586] lr: 5.000000e-04 eta: 4:02:41 time: 0.344323 data_time: 0.022483 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.883681 loss: 0.000544 2022/09/13 03:38:25 - mmengine - INFO - Epoch(train) [133][400/586] lr: 5.000000e-04 eta: 4:02:25 time: 0.334477 data_time: 0.025959 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.846545 loss: 0.000536 2022/09/13 03:38:42 - mmengine - INFO - Epoch(train) [133][450/586] lr: 5.000000e-04 eta: 4:02:10 time: 0.335988 data_time: 0.022957 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.804683 loss: 0.000526 2022/09/13 03:38:59 - mmengine - INFO - Epoch(train) [133][500/586] lr: 5.000000e-04 eta: 4:01:54 time: 0.338743 data_time: 0.023779 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.823630 loss: 0.000537 2022/09/13 03:39:16 - mmengine - INFO - Epoch(train) [133][550/586] lr: 5.000000e-04 eta: 4:01:38 time: 0.335595 data_time: 0.023372 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.760700 loss: 0.000549 2022/09/13 03:39:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:39:28 - mmengine - INFO - Saving checkpoint at 133 epochs 2022/09/13 03:39:52 - mmengine - INFO - Epoch(train) [134][50/586] lr: 5.000000e-04 eta: 4:01:05 time: 0.346305 data_time: 0.026973 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.864685 loss: 0.000548 2022/09/13 03:39:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:40:08 - mmengine - INFO - Epoch(train) [134][100/586] lr: 5.000000e-04 eta: 4:00:49 time: 0.327636 data_time: 0.023193 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.877028 loss: 0.000531 2022/09/13 03:40:25 - mmengine - INFO - Epoch(train) [134][150/586] lr: 5.000000e-04 eta: 4:00:34 time: 0.341056 data_time: 0.022504 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.880210 loss: 0.000539 2022/09/13 03:40:43 - mmengine - INFO - Epoch(train) [134][200/586] lr: 5.000000e-04 eta: 4:00:18 time: 0.341954 data_time: 0.022923 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.903161 loss: 0.000547 2022/09/13 03:40:59 - mmengine - INFO - Epoch(train) [134][250/586] lr: 5.000000e-04 eta: 4:00:03 time: 0.336099 data_time: 0.022513 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.850675 loss: 0.000534 2022/09/13 03:41:16 - mmengine - INFO - Epoch(train) [134][300/586] lr: 5.000000e-04 eta: 3:59:47 time: 0.332987 data_time: 0.023562 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.822873 loss: 0.000552 2022/09/13 03:41:33 - mmengine - INFO - Epoch(train) [134][350/586] lr: 5.000000e-04 eta: 3:59:31 time: 0.345811 data_time: 0.027120 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.847089 loss: 0.000552 2022/09/13 03:41:50 - mmengine - INFO - Epoch(train) [134][400/586] lr: 5.000000e-04 eta: 3:59:16 time: 0.332105 data_time: 0.022643 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.790383 loss: 0.000531 2022/09/13 03:42:07 - mmengine - INFO - Epoch(train) [134][450/586] lr: 5.000000e-04 eta: 3:59:00 time: 0.333510 data_time: 0.023096 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.861818 loss: 0.000541 2022/09/13 03:42:24 - mmengine - INFO - Epoch(train) [134][500/586] lr: 5.000000e-04 eta: 3:58:45 time: 0.346855 data_time: 0.022229 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.812151 loss: 0.000527 2022/09/13 03:42:41 - mmengine - INFO - Epoch(train) [134][550/586] lr: 5.000000e-04 eta: 3:58:29 time: 0.332171 data_time: 0.022261 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.845365 loss: 0.000532 2022/09/13 03:42:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:42:53 - mmengine - INFO - Saving checkpoint at 134 epochs 2022/09/13 03:43:17 - mmengine - INFO - Epoch(train) [135][50/586] lr: 5.000000e-04 eta: 3:57:56 time: 0.345372 data_time: 0.031540 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.852331 loss: 0.000519 2022/09/13 03:43:33 - mmengine - INFO - Epoch(train) [135][100/586] lr: 5.000000e-04 eta: 3:57:40 time: 0.330199 data_time: 0.023474 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.886951 loss: 0.000544 2022/09/13 03:43:50 - mmengine - INFO - Epoch(train) [135][150/586] lr: 5.000000e-04 eta: 3:57:24 time: 0.338757 data_time: 0.022633 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.937574 loss: 0.000533 2022/09/13 03:44:07 - mmengine - INFO - Epoch(train) [135][200/586] lr: 5.000000e-04 eta: 3:57:09 time: 0.336109 data_time: 0.022963 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.878967 loss: 0.000539 2022/09/13 03:44:24 - mmengine - INFO - Epoch(train) [135][250/586] lr: 5.000000e-04 eta: 3:56:53 time: 0.336244 data_time: 0.022509 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.855579 loss: 0.000545 2022/09/13 03:44:40 - mmengine - INFO - Epoch(train) [135][300/586] lr: 5.000000e-04 eta: 3:56:37 time: 0.329298 data_time: 0.022676 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.816592 loss: 0.000544 2022/09/13 03:44:57 - mmengine - INFO - Epoch(train) [135][350/586] lr: 5.000000e-04 eta: 3:56:22 time: 0.341831 data_time: 0.022629 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.887349 loss: 0.000534 2022/09/13 03:45:14 - mmengine - INFO - Epoch(train) [135][400/586] lr: 5.000000e-04 eta: 3:56:06 time: 0.336060 data_time: 0.022124 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.881594 loss: 0.000531 2022/09/13 03:45:31 - mmengine - INFO - Epoch(train) [135][450/586] lr: 5.000000e-04 eta: 3:55:50 time: 0.335432 data_time: 0.027237 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.833552 loss: 0.000533 2022/09/13 03:45:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:45:48 - mmengine - INFO - Epoch(train) [135][500/586] lr: 5.000000e-04 eta: 3:55:35 time: 0.344334 data_time: 0.023197 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.888388 loss: 0.000537 2022/09/13 03:46:05 - mmengine - INFO - Epoch(train) [135][550/586] lr: 5.000000e-04 eta: 3:55:19 time: 0.337348 data_time: 0.022947 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.823943 loss: 0.000534 2022/09/13 03:46:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:46:17 - mmengine - INFO - Saving checkpoint at 135 epochs 2022/09/13 03:46:41 - mmengine - INFO - Epoch(train) [136][50/586] lr: 5.000000e-04 eta: 3:54:46 time: 0.346133 data_time: 0.027794 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.849487 loss: 0.000524 2022/09/13 03:46:59 - mmengine - INFO - Epoch(train) [136][100/586] lr: 5.000000e-04 eta: 3:54:31 time: 0.343834 data_time: 0.026970 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.847012 loss: 0.000555 2022/09/13 03:47:15 - mmengine - INFO - Epoch(train) [136][150/586] lr: 5.000000e-04 eta: 3:54:15 time: 0.330692 data_time: 0.022820 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.897381 loss: 0.000561 2022/09/13 03:47:32 - mmengine - INFO - Epoch(train) [136][200/586] lr: 5.000000e-04 eta: 3:53:59 time: 0.341364 data_time: 0.022342 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.842528 loss: 0.000542 2022/09/13 03:47:49 - mmengine - INFO - Epoch(train) [136][250/586] lr: 5.000000e-04 eta: 3:53:44 time: 0.338321 data_time: 0.027113 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.824730 loss: 0.000540 2022/09/13 03:48:06 - mmengine - INFO - Epoch(train) [136][300/586] lr: 5.000000e-04 eta: 3:53:28 time: 0.334879 data_time: 0.022523 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.841165 loss: 0.000537 2022/09/13 03:48:23 - mmengine - INFO - Epoch(train) [136][350/586] lr: 5.000000e-04 eta: 3:53:13 time: 0.335825 data_time: 0.022790 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.849871 loss: 0.000531 2022/09/13 03:48:40 - mmengine - INFO - Epoch(train) [136][400/586] lr: 5.000000e-04 eta: 3:52:57 time: 0.340243 data_time: 0.023760 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.872831 loss: 0.000545 2022/09/13 03:48:57 - mmengine - INFO - Epoch(train) [136][450/586] lr: 5.000000e-04 eta: 3:52:41 time: 0.338461 data_time: 0.022884 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.886413 loss: 0.000525 2022/09/13 03:49:13 - mmengine - INFO - Epoch(train) [136][500/586] lr: 5.000000e-04 eta: 3:52:26 time: 0.333548 data_time: 0.021966 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.822098 loss: 0.000532 2022/09/13 03:49:31 - mmengine - INFO - Epoch(train) [136][550/586] lr: 5.000000e-04 eta: 3:52:10 time: 0.342332 data_time: 0.025883 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.881922 loss: 0.000550 2022/09/13 03:49:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:49:42 - mmengine - INFO - Saving checkpoint at 136 epochs 2022/09/13 03:50:07 - mmengine - INFO - Epoch(train) [137][50/586] lr: 5.000000e-04 eta: 3:51:37 time: 0.346679 data_time: 0.031114 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.862609 loss: 0.000516 2022/09/13 03:50:24 - mmengine - INFO - Epoch(train) [137][100/586] lr: 5.000000e-04 eta: 3:51:22 time: 0.340932 data_time: 0.022668 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.859950 loss: 0.000528 2022/09/13 03:50:40 - mmengine - INFO - Epoch(train) [137][150/586] lr: 5.000000e-04 eta: 3:51:06 time: 0.335505 data_time: 0.022377 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.877795 loss: 0.000551 2022/09/13 03:50:58 - mmengine - INFO - Epoch(train) [137][200/586] lr: 5.000000e-04 eta: 3:50:51 time: 0.344331 data_time: 0.026259 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.871233 loss: 0.000529 2022/09/13 03:51:15 - mmengine - INFO - Epoch(train) [137][250/586] lr: 5.000000e-04 eta: 3:50:35 time: 0.341549 data_time: 0.021820 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.888474 loss: 0.000543 2022/09/13 03:51:32 - mmengine - INFO - Epoch(train) [137][300/586] lr: 5.000000e-04 eta: 3:50:20 time: 0.341302 data_time: 0.022373 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.899265 loss: 0.000537 2022/09/13 03:51:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:51:49 - mmengine - INFO - Epoch(train) [137][350/586] lr: 5.000000e-04 eta: 3:50:04 time: 0.342226 data_time: 0.025577 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.850919 loss: 0.000547 2022/09/13 03:52:05 - mmengine - INFO - Epoch(train) [137][400/586] lr: 5.000000e-04 eta: 3:49:48 time: 0.328045 data_time: 0.022434 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.810295 loss: 0.000537 2022/09/13 03:52:23 - mmengine - INFO - Epoch(train) [137][450/586] lr: 5.000000e-04 eta: 3:49:33 time: 0.348199 data_time: 0.022067 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.806856 loss: 0.000533 2022/09/13 03:52:40 - mmengine - INFO - Epoch(train) [137][500/586] lr: 5.000000e-04 eta: 3:49:17 time: 0.341244 data_time: 0.022822 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.830010 loss: 0.000535 2022/09/13 03:52:57 - mmengine - INFO - Epoch(train) [137][550/586] lr: 5.000000e-04 eta: 3:49:02 time: 0.334917 data_time: 0.022698 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.832055 loss: 0.000556 2022/09/13 03:53:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:53:09 - mmengine - INFO - Saving checkpoint at 137 epochs 2022/09/13 03:53:33 - mmengine - INFO - Epoch(train) [138][50/586] lr: 5.000000e-04 eta: 3:48:29 time: 0.341972 data_time: 0.026065 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.861724 loss: 0.000543 2022/09/13 03:53:50 - mmengine - INFO - Epoch(train) [138][100/586] lr: 5.000000e-04 eta: 3:48:13 time: 0.341412 data_time: 0.022761 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.847653 loss: 0.000533 2022/09/13 03:54:08 - mmengine - INFO - Epoch(train) [138][150/586] lr: 5.000000e-04 eta: 3:47:58 time: 0.343669 data_time: 0.022288 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.843736 loss: 0.000545 2022/09/13 03:54:24 - mmengine - INFO - Epoch(train) [138][200/586] lr: 5.000000e-04 eta: 3:47:42 time: 0.337287 data_time: 0.022341 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.799247 loss: 0.000541 2022/09/13 03:54:41 - mmengine - INFO - Epoch(train) [138][250/586] lr: 5.000000e-04 eta: 3:47:26 time: 0.337882 data_time: 0.022711 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.878332 loss: 0.000539 2022/09/13 03:54:58 - mmengine - INFO - Epoch(train) [138][300/586] lr: 5.000000e-04 eta: 3:47:11 time: 0.338224 data_time: 0.026141 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.826428 loss: 0.000526 2022/09/13 03:55:15 - mmengine - INFO - Epoch(train) [138][350/586] lr: 5.000000e-04 eta: 3:46:55 time: 0.340829 data_time: 0.021508 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.794369 loss: 0.000543 2022/09/13 03:55:32 - mmengine - INFO - Epoch(train) [138][400/586] lr: 5.000000e-04 eta: 3:46:40 time: 0.337035 data_time: 0.022361 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.840332 loss: 0.000542 2022/09/13 03:55:49 - mmengine - INFO - Epoch(train) [138][450/586] lr: 5.000000e-04 eta: 3:46:24 time: 0.341374 data_time: 0.022105 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.819047 loss: 0.000529 2022/09/13 03:56:06 - mmengine - INFO - Epoch(train) [138][500/586] lr: 5.000000e-04 eta: 3:46:09 time: 0.341066 data_time: 0.022341 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.853268 loss: 0.000549 2022/09/13 03:56:23 - mmengine - INFO - Epoch(train) [138][550/586] lr: 5.000000e-04 eta: 3:45:53 time: 0.337025 data_time: 0.022233 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.852240 loss: 0.000538 2022/09/13 03:56:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:56:35 - mmengine - INFO - Saving checkpoint at 138 epochs 2022/09/13 03:57:00 - mmengine - INFO - Epoch(train) [139][50/586] lr: 5.000000e-04 eta: 3:45:20 time: 0.342720 data_time: 0.026622 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.844751 loss: 0.000546 2022/09/13 03:57:16 - mmengine - INFO - Epoch(train) [139][100/586] lr: 5.000000e-04 eta: 3:45:04 time: 0.336251 data_time: 0.022067 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.849670 loss: 0.000542 2022/09/13 03:57:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 03:57:33 - mmengine - INFO - Epoch(train) [139][150/586] lr: 5.000000e-04 eta: 3:44:49 time: 0.339189 data_time: 0.022445 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.809919 loss: 0.000545 2022/09/13 03:57:50 - mmengine - INFO - Epoch(train) [139][200/586] lr: 5.000000e-04 eta: 3:44:33 time: 0.336830 data_time: 0.022945 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.853272 loss: 0.000533 2022/09/13 03:58:07 - mmengine - INFO - Epoch(train) [139][250/586] lr: 5.000000e-04 eta: 3:44:18 time: 0.341218 data_time: 0.022281 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.872385 loss: 0.000523 2022/09/13 03:58:24 - mmengine - INFO - Epoch(train) [139][300/586] lr: 5.000000e-04 eta: 3:44:02 time: 0.338212 data_time: 0.022409 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.903533 loss: 0.000550 2022/09/13 03:58:42 - mmengine - INFO - Epoch(train) [139][350/586] lr: 5.000000e-04 eta: 3:43:47 time: 0.346239 data_time: 0.023178 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.882941 loss: 0.000546 2022/09/13 03:58:58 - mmengine - INFO - Epoch(train) [139][400/586] lr: 5.000000e-04 eta: 3:43:31 time: 0.337099 data_time: 0.026152 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.879829 loss: 0.000529 2022/09/13 03:59:16 - mmengine - INFO - Epoch(train) [139][450/586] lr: 5.000000e-04 eta: 3:43:16 time: 0.345437 data_time: 0.022420 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.885789 loss: 0.000528 2022/09/13 03:59:32 - mmengine - INFO - Epoch(train) [139][500/586] lr: 5.000000e-04 eta: 3:43:00 time: 0.332479 data_time: 0.022133 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.828591 loss: 0.000535 2022/09/13 03:59:49 - mmengine - INFO - Epoch(train) [139][550/586] lr: 5.000000e-04 eta: 3:42:44 time: 0.336613 data_time: 0.022304 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.862703 loss: 0.000555 2022/09/13 04:00:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:00:01 - mmengine - INFO - Saving checkpoint at 139 epochs 2022/09/13 04:00:26 - mmengine - INFO - Epoch(train) [140][50/586] lr: 5.000000e-04 eta: 3:42:11 time: 0.343845 data_time: 0.027338 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.877929 loss: 0.000522 2022/09/13 04:00:43 - mmengine - INFO - Epoch(train) [140][100/586] lr: 5.000000e-04 eta: 3:41:56 time: 0.338251 data_time: 0.022180 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.853011 loss: 0.000536 2022/09/13 04:00:59 - mmengine - INFO - Epoch(train) [140][150/586] lr: 5.000000e-04 eta: 3:41:40 time: 0.336645 data_time: 0.022283 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.879084 loss: 0.000540 2022/09/13 04:01:16 - mmengine - INFO - Epoch(train) [140][200/586] lr: 5.000000e-04 eta: 3:41:24 time: 0.335150 data_time: 0.022865 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.817411 loss: 0.000530 2022/09/13 04:01:33 - mmengine - INFO - Epoch(train) [140][250/586] lr: 5.000000e-04 eta: 3:41:09 time: 0.340276 data_time: 0.021862 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.901665 loss: 0.000523 2022/09/13 04:01:50 - mmengine - INFO - Epoch(train) [140][300/586] lr: 5.000000e-04 eta: 3:40:53 time: 0.337877 data_time: 0.022703 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.831461 loss: 0.000525 2022/09/13 04:02:07 - mmengine - INFO - Epoch(train) [140][350/586] lr: 5.000000e-04 eta: 3:40:38 time: 0.344892 data_time: 0.022435 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.893036 loss: 0.000542 2022/09/13 04:02:24 - mmengine - INFO - Epoch(train) [140][400/586] lr: 5.000000e-04 eta: 3:40:22 time: 0.338808 data_time: 0.022937 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.872303 loss: 0.000530 2022/09/13 04:02:41 - mmengine - INFO - Epoch(train) [140][450/586] lr: 5.000000e-04 eta: 3:40:07 time: 0.337332 data_time: 0.025594 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.851028 loss: 0.000535 2022/09/13 04:02:58 - mmengine - INFO - Epoch(train) [140][500/586] lr: 5.000000e-04 eta: 3:39:51 time: 0.336503 data_time: 0.022072 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.794582 loss: 0.000540 2022/09/13 04:03:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:03:15 - mmengine - INFO - Epoch(train) [140][550/586] lr: 5.000000e-04 eta: 3:39:35 time: 0.335899 data_time: 0.022154 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.919300 loss: 0.000536 2022/09/13 04:03:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:03:27 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/09/13 04:03:45 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:01:17 time: 0.216033 data_time: 0.012196 memory: 7489 2022/09/13 04:03:55 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:01:04 time: 0.209238 data_time: 0.007492 memory: 1657 2022/09/13 04:04:06 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:53 time: 0.208744 data_time: 0.007618 memory: 1657 2022/09/13 04:04:16 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:43 time: 0.208018 data_time: 0.007903 memory: 1657 2022/09/13 04:04:27 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:33 time: 0.213088 data_time: 0.011744 memory: 1657 2022/09/13 04:04:37 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:22 time: 0.208601 data_time: 0.007539 memory: 1657 2022/09/13 04:04:48 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:11 time: 0.208181 data_time: 0.007289 memory: 1657 2022/09/13 04:04:58 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:01 time: 0.206318 data_time: 0.006836 memory: 1657 2022/09/13 04:05:34 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 04:05:48 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.753680 coco/AP .5: 0.903319 coco/AP .75: 0.819409 coco/AP (M): 0.719229 coco/AP (L): 0.818575 coco/AR: 0.803684 coco/AR .5: 0.940019 coco/AR .75: 0.862563 coco/AR (M): 0.763425 coco/AR (L): 0.862616 2022/09/13 04:05:48 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_120.pth is removed 2022/09/13 04:05:51 - mmengine - INFO - The best checkpoint with 0.7537 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/09/13 04:06:08 - mmengine - INFO - Epoch(train) [141][50/586] lr: 5.000000e-04 eta: 3:39:02 time: 0.341483 data_time: 0.025681 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.857648 loss: 0.000534 2022/09/13 04:06:25 - mmengine - INFO - Epoch(train) [141][100/586] lr: 5.000000e-04 eta: 3:38:47 time: 0.338109 data_time: 0.022254 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.837021 loss: 0.000533 2022/09/13 04:06:42 - mmengine - INFO - Epoch(train) [141][150/586] lr: 5.000000e-04 eta: 3:38:31 time: 0.333893 data_time: 0.022487 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.858806 loss: 0.000524 2022/09/13 04:06:59 - mmengine - INFO - Epoch(train) [141][200/586] lr: 5.000000e-04 eta: 3:38:16 time: 0.341695 data_time: 0.025938 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.851256 loss: 0.000537 2022/09/13 04:07:16 - mmengine - INFO - Epoch(train) [141][250/586] lr: 5.000000e-04 eta: 3:38:00 time: 0.335286 data_time: 0.022650 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.862936 loss: 0.000546 2022/09/13 04:07:33 - mmengine - INFO - Epoch(train) [141][300/586] lr: 5.000000e-04 eta: 3:37:44 time: 0.342995 data_time: 0.022440 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.832964 loss: 0.000538 2022/09/13 04:07:50 - mmengine - INFO - Epoch(train) [141][350/586] lr: 5.000000e-04 eta: 3:37:29 time: 0.342801 data_time: 0.025998 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.863794 loss: 0.000539 2022/09/13 04:08:07 - mmengine - INFO - Epoch(train) [141][400/586] lr: 5.000000e-04 eta: 3:37:13 time: 0.337392 data_time: 0.022324 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.922534 loss: 0.000524 2022/09/13 04:08:24 - mmengine - INFO - Epoch(train) [141][450/586] lr: 5.000000e-04 eta: 3:36:57 time: 0.330367 data_time: 0.021949 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.831418 loss: 0.000536 2022/09/13 04:08:41 - mmengine - INFO - Epoch(train) [141][500/586] lr: 5.000000e-04 eta: 3:36:42 time: 0.344429 data_time: 0.022559 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.904277 loss: 0.000544 2022/09/13 04:08:57 - mmengine - INFO - Epoch(train) [141][550/586] lr: 5.000000e-04 eta: 3:36:26 time: 0.331185 data_time: 0.022540 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.857673 loss: 0.000525 2022/09/13 04:09:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:09:10 - mmengine - INFO - Saving checkpoint at 141 epochs 2022/09/13 04:09:33 - mmengine - INFO - Epoch(train) [142][50/586] lr: 5.000000e-04 eta: 3:35:54 time: 0.351765 data_time: 0.035047 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.815979 loss: 0.000557 2022/09/13 04:09:50 - mmengine - INFO - Epoch(train) [142][100/586] lr: 5.000000e-04 eta: 3:35:38 time: 0.335783 data_time: 0.026009 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.832384 loss: 0.000534 2022/09/13 04:10:07 - mmengine - INFO - Epoch(train) [142][150/586] lr: 5.000000e-04 eta: 3:35:22 time: 0.335061 data_time: 0.022261 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.872975 loss: 0.000551 2022/09/13 04:10:25 - mmengine - INFO - Epoch(train) [142][200/586] lr: 5.000000e-04 eta: 3:35:07 time: 0.349756 data_time: 0.024209 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.908862 loss: 0.000528 2022/09/13 04:10:41 - mmengine - INFO - Epoch(train) [142][250/586] lr: 5.000000e-04 eta: 3:34:51 time: 0.336464 data_time: 0.023935 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.848337 loss: 0.000547 2022/09/13 04:10:58 - mmengine - INFO - Epoch(train) [142][300/586] lr: 5.000000e-04 eta: 3:34:36 time: 0.335630 data_time: 0.021997 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.838446 loss: 0.000531 2022/09/13 04:11:15 - mmengine - INFO - Epoch(train) [142][350/586] lr: 5.000000e-04 eta: 3:34:20 time: 0.343335 data_time: 0.022424 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.853486 loss: 0.000536 2022/09/13 04:11:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:11:32 - mmengine - INFO - Epoch(train) [142][400/586] lr: 5.000000e-04 eta: 3:34:05 time: 0.340688 data_time: 0.023223 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.888886 loss: 0.000533 2022/09/13 04:11:49 - mmengine - INFO - Epoch(train) [142][450/586] lr: 5.000000e-04 eta: 3:33:49 time: 0.331632 data_time: 0.022482 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.849014 loss: 0.000527 2022/09/13 04:12:06 - mmengine - INFO - Epoch(train) [142][500/586] lr: 5.000000e-04 eta: 3:33:33 time: 0.341482 data_time: 0.022572 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.862130 loss: 0.000540 2022/09/13 04:12:23 - mmengine - INFO - Epoch(train) [142][550/586] lr: 5.000000e-04 eta: 3:33:18 time: 0.336817 data_time: 0.023241 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.851174 loss: 0.000540 2022/09/13 04:12:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:12:35 - mmengine - INFO - Saving checkpoint at 142 epochs 2022/09/13 04:12:59 - mmengine - INFO - Epoch(train) [143][50/586] lr: 5.000000e-04 eta: 3:32:45 time: 0.348627 data_time: 0.028910 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.866813 loss: 0.000543 2022/09/13 04:13:17 - mmengine - INFO - Epoch(train) [143][100/586] lr: 5.000000e-04 eta: 3:32:30 time: 0.342337 data_time: 0.023751 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.847020 loss: 0.000528 2022/09/13 04:13:33 - mmengine - INFO - Epoch(train) [143][150/586] lr: 5.000000e-04 eta: 3:32:14 time: 0.331977 data_time: 0.022420 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.846125 loss: 0.000534 2022/09/13 04:13:50 - mmengine - INFO - Epoch(train) [143][200/586] lr: 5.000000e-04 eta: 3:31:58 time: 0.338088 data_time: 0.022646 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.838160 loss: 0.000535 2022/09/13 04:14:07 - mmengine - INFO - Epoch(train) [143][250/586] lr: 5.000000e-04 eta: 3:31:43 time: 0.345090 data_time: 0.022640 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.891389 loss: 0.000521 2022/09/13 04:14:24 - mmengine - INFO - Epoch(train) [143][300/586] lr: 5.000000e-04 eta: 3:31:27 time: 0.338541 data_time: 0.023104 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.887732 loss: 0.000527 2022/09/13 04:14:42 - mmengine - INFO - Epoch(train) [143][350/586] lr: 5.000000e-04 eta: 3:31:12 time: 0.346605 data_time: 0.021968 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.893869 loss: 0.000536 2022/09/13 04:14:59 - mmengine - INFO - Epoch(train) [143][400/586] lr: 5.000000e-04 eta: 3:30:56 time: 0.339175 data_time: 0.021653 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.843621 loss: 0.000535 2022/09/13 04:15:16 - mmengine - INFO - Epoch(train) [143][450/586] lr: 5.000000e-04 eta: 3:30:41 time: 0.348509 data_time: 0.022052 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.785890 loss: 0.000525 2022/09/13 04:15:33 - mmengine - INFO - Epoch(train) [143][500/586] lr: 5.000000e-04 eta: 3:30:25 time: 0.344282 data_time: 0.026189 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.893238 loss: 0.000550 2022/09/13 04:15:50 - mmengine - INFO - Epoch(train) [143][550/586] lr: 5.000000e-04 eta: 3:30:09 time: 0.332581 data_time: 0.022216 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.832329 loss: 0.000540 2022/09/13 04:16:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:16:03 - mmengine - INFO - Saving checkpoint at 143 epochs 2022/09/13 04:16:27 - mmengine - INFO - Epoch(train) [144][50/586] lr: 5.000000e-04 eta: 3:29:37 time: 0.338123 data_time: 0.028742 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.787062 loss: 0.000544 2022/09/13 04:16:44 - mmengine - INFO - Epoch(train) [144][100/586] lr: 5.000000e-04 eta: 3:29:21 time: 0.341601 data_time: 0.025531 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.874088 loss: 0.000519 2022/09/13 04:17:01 - mmengine - INFO - Epoch(train) [144][150/586] lr: 5.000000e-04 eta: 3:29:06 time: 0.334858 data_time: 0.022470 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.880218 loss: 0.000507 2022/09/13 04:17:18 - mmengine - INFO - Epoch(train) [144][200/586] lr: 5.000000e-04 eta: 3:28:50 time: 0.339054 data_time: 0.023127 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.866907 loss: 0.000537 2022/09/13 04:17:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:17:35 - mmengine - INFO - Epoch(train) [144][250/586] lr: 5.000000e-04 eta: 3:28:34 time: 0.341936 data_time: 0.025970 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.906269 loss: 0.000512 2022/09/13 04:17:51 - mmengine - INFO - Epoch(train) [144][300/586] lr: 5.000000e-04 eta: 3:28:19 time: 0.333358 data_time: 0.022645 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.856728 loss: 0.000542 2022/09/13 04:18:08 - mmengine - INFO - Epoch(train) [144][350/586] lr: 5.000000e-04 eta: 3:28:03 time: 0.339257 data_time: 0.022137 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.897376 loss: 0.000521 2022/09/13 04:18:25 - mmengine - INFO - Epoch(train) [144][400/586] lr: 5.000000e-04 eta: 3:27:47 time: 0.339187 data_time: 0.025454 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.851838 loss: 0.000530 2022/09/13 04:18:42 - mmengine - INFO - Epoch(train) [144][450/586] lr: 5.000000e-04 eta: 3:27:32 time: 0.338606 data_time: 0.022062 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.837596 loss: 0.000538 2022/09/13 04:18:59 - mmengine - INFO - Epoch(train) [144][500/586] lr: 5.000000e-04 eta: 3:27:16 time: 0.335872 data_time: 0.022405 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.817091 loss: 0.000532 2022/09/13 04:19:16 - mmengine - INFO - Epoch(train) [144][550/586] lr: 5.000000e-04 eta: 3:27:00 time: 0.337101 data_time: 0.026148 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.850008 loss: 0.000530 2022/09/13 04:19:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:19:28 - mmengine - INFO - Saving checkpoint at 144 epochs 2022/09/13 04:19:52 - mmengine - INFO - Epoch(train) [145][50/586] lr: 5.000000e-04 eta: 3:26:28 time: 0.341041 data_time: 0.030856 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.798840 loss: 0.000531 2022/09/13 04:20:09 - mmengine - INFO - Epoch(train) [145][100/586] lr: 5.000000e-04 eta: 3:26:12 time: 0.338633 data_time: 0.022533 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.899102 loss: 0.000532 2022/09/13 04:20:26 - mmengine - INFO - Epoch(train) [145][150/586] lr: 5.000000e-04 eta: 3:25:57 time: 0.336812 data_time: 0.022772 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.832265 loss: 0.000526 2022/09/13 04:20:42 - mmengine - INFO - Epoch(train) [145][200/586] lr: 5.000000e-04 eta: 3:25:41 time: 0.333088 data_time: 0.022509 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.825209 loss: 0.000535 2022/09/13 04:20:59 - mmengine - INFO - Epoch(train) [145][250/586] lr: 5.000000e-04 eta: 3:25:25 time: 0.337078 data_time: 0.022133 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.881625 loss: 0.000541 2022/09/13 04:21:16 - mmengine - INFO - Epoch(train) [145][300/586] lr: 5.000000e-04 eta: 3:25:10 time: 0.338425 data_time: 0.021878 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.852771 loss: 0.000548 2022/09/13 04:21:33 - mmengine - INFO - Epoch(train) [145][350/586] lr: 5.000000e-04 eta: 3:24:54 time: 0.341057 data_time: 0.023602 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.869410 loss: 0.000539 2022/09/13 04:21:50 - mmengine - INFO - Epoch(train) [145][400/586] lr: 5.000000e-04 eta: 3:24:38 time: 0.333724 data_time: 0.022623 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.862758 loss: 0.000522 2022/09/13 04:22:07 - mmengine - INFO - Epoch(train) [145][450/586] lr: 5.000000e-04 eta: 3:24:23 time: 0.336698 data_time: 0.027778 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.813060 loss: 0.000527 2022/09/13 04:22:24 - mmengine - INFO - Epoch(train) [145][500/586] lr: 5.000000e-04 eta: 3:24:07 time: 0.337955 data_time: 0.022492 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.784800 loss: 0.000544 2022/09/13 04:22:41 - mmengine - INFO - Epoch(train) [145][550/586] lr: 5.000000e-04 eta: 3:23:51 time: 0.341217 data_time: 0.022414 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.868518 loss: 0.000529 2022/09/13 04:22:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:22:53 - mmengine - INFO - Saving checkpoint at 145 epochs 2022/09/13 04:23:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:23:17 - mmengine - INFO - Epoch(train) [146][50/586] lr: 5.000000e-04 eta: 3:23:19 time: 0.351282 data_time: 0.027184 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.824790 loss: 0.000524 2022/09/13 04:23:34 - mmengine - INFO - Epoch(train) [146][100/586] lr: 5.000000e-04 eta: 3:23:04 time: 0.339838 data_time: 0.022302 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.882154 loss: 0.000512 2022/09/13 04:23:51 - mmengine - INFO - Epoch(train) [146][150/586] lr: 5.000000e-04 eta: 3:22:48 time: 0.332233 data_time: 0.022280 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.821213 loss: 0.000522 2022/09/13 04:24:08 - mmengine - INFO - Epoch(train) [146][200/586] lr: 5.000000e-04 eta: 3:22:32 time: 0.345013 data_time: 0.022082 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.889391 loss: 0.000526 2022/09/13 04:24:25 - mmengine - INFO - Epoch(train) [146][250/586] lr: 5.000000e-04 eta: 3:22:17 time: 0.331738 data_time: 0.021952 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.809297 loss: 0.000537 2022/09/13 04:24:42 - mmengine - INFO - Epoch(train) [146][300/586] lr: 5.000000e-04 eta: 3:22:01 time: 0.336024 data_time: 0.021761 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.813534 loss: 0.000523 2022/09/13 04:24:58 - mmengine - INFO - Epoch(train) [146][350/586] lr: 5.000000e-04 eta: 3:21:45 time: 0.337832 data_time: 0.022480 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.831325 loss: 0.000530 2022/09/13 04:25:16 - mmengine - INFO - Epoch(train) [146][400/586] lr: 5.000000e-04 eta: 3:21:30 time: 0.341563 data_time: 0.024912 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.806605 loss: 0.000540 2022/09/13 04:25:32 - mmengine - INFO - Epoch(train) [146][450/586] lr: 5.000000e-04 eta: 3:21:14 time: 0.329626 data_time: 0.022287 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.805752 loss: 0.000548 2022/09/13 04:25:49 - mmengine - INFO - Epoch(train) [146][500/586] lr: 5.000000e-04 eta: 3:20:58 time: 0.341461 data_time: 0.023071 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.840086 loss: 0.000522 2022/09/13 04:26:06 - mmengine - INFO - Epoch(train) [146][550/586] lr: 5.000000e-04 eta: 3:20:43 time: 0.343772 data_time: 0.021982 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.909402 loss: 0.000521 2022/09/13 04:26:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:26:18 - mmengine - INFO - Saving checkpoint at 146 epochs 2022/09/13 04:26:42 - mmengine - INFO - Epoch(train) [147][50/586] lr: 5.000000e-04 eta: 3:20:11 time: 0.348029 data_time: 0.031960 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.862814 loss: 0.000515 2022/09/13 04:26:59 - mmengine - INFO - Epoch(train) [147][100/586] lr: 5.000000e-04 eta: 3:19:55 time: 0.340219 data_time: 0.023621 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.845177 loss: 0.000535 2022/09/13 04:27:16 - mmengine - INFO - Epoch(train) [147][150/586] lr: 5.000000e-04 eta: 3:19:39 time: 0.335618 data_time: 0.023933 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.887496 loss: 0.000523 2022/09/13 04:27:33 - mmengine - INFO - Epoch(train) [147][200/586] lr: 5.000000e-04 eta: 3:19:24 time: 0.339636 data_time: 0.022038 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.886218 loss: 0.000545 2022/09/13 04:27:49 - mmengine - INFO - Epoch(train) [147][250/586] lr: 5.000000e-04 eta: 3:19:08 time: 0.333242 data_time: 0.022511 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.858939 loss: 0.000539 2022/09/13 04:28:06 - mmengine - INFO - Epoch(train) [147][300/586] lr: 5.000000e-04 eta: 3:18:52 time: 0.335531 data_time: 0.022475 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.877341 loss: 0.000538 2022/09/13 04:28:23 - mmengine - INFO - Epoch(train) [147][350/586] lr: 5.000000e-04 eta: 3:18:37 time: 0.341605 data_time: 0.026593 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.821021 loss: 0.000525 2022/09/13 04:28:40 - mmengine - INFO - Epoch(train) [147][400/586] lr: 5.000000e-04 eta: 3:18:21 time: 0.340259 data_time: 0.022382 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.913053 loss: 0.000528 2022/09/13 04:28:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:28:57 - mmengine - INFO - Epoch(train) [147][450/586] lr: 5.000000e-04 eta: 3:18:05 time: 0.340833 data_time: 0.022365 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.854922 loss: 0.000531 2022/09/13 04:29:14 - mmengine - INFO - Epoch(train) [147][500/586] lr: 5.000000e-04 eta: 3:17:50 time: 0.342545 data_time: 0.022195 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.911082 loss: 0.000537 2022/09/13 04:29:31 - mmengine - INFO - Epoch(train) [147][550/586] lr: 5.000000e-04 eta: 3:17:34 time: 0.338416 data_time: 0.021736 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.818121 loss: 0.000545 2022/09/13 04:29:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:29:43 - mmengine - INFO - Saving checkpoint at 147 epochs 2022/09/13 04:30:07 - mmengine - INFO - Epoch(train) [148][50/586] lr: 5.000000e-04 eta: 3:17:02 time: 0.345234 data_time: 0.026846 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.877445 loss: 0.000557 2022/09/13 04:30:24 - mmengine - INFO - Epoch(train) [148][100/586] lr: 5.000000e-04 eta: 3:16:47 time: 0.338106 data_time: 0.022905 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.851449 loss: 0.000531 2022/09/13 04:30:41 - mmengine - INFO - Epoch(train) [148][150/586] lr: 5.000000e-04 eta: 3:16:31 time: 0.338358 data_time: 0.022365 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.730791 loss: 0.000546 2022/09/13 04:30:58 - mmengine - INFO - Epoch(train) [148][200/586] lr: 5.000000e-04 eta: 3:16:15 time: 0.336825 data_time: 0.022193 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.777159 loss: 0.000521 2022/09/13 04:31:15 - mmengine - INFO - Epoch(train) [148][250/586] lr: 5.000000e-04 eta: 3:16:00 time: 0.343238 data_time: 0.022293 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.882898 loss: 0.000542 2022/09/13 04:31:32 - mmengine - INFO - Epoch(train) [148][300/586] lr: 5.000000e-04 eta: 3:15:44 time: 0.338724 data_time: 0.022047 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.838643 loss: 0.000534 2022/09/13 04:31:49 - mmengine - INFO - Epoch(train) [148][350/586] lr: 5.000000e-04 eta: 3:15:28 time: 0.341989 data_time: 0.022372 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.862036 loss: 0.000531 2022/09/13 04:32:06 - mmengine - INFO - Epoch(train) [148][400/586] lr: 5.000000e-04 eta: 3:15:13 time: 0.341681 data_time: 0.027049 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.815201 loss: 0.000536 2022/09/13 04:32:23 - mmengine - INFO - Epoch(train) [148][450/586] lr: 5.000000e-04 eta: 3:14:57 time: 0.332463 data_time: 0.021621 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.748894 loss: 0.000541 2022/09/13 04:32:40 - mmengine - INFO - Epoch(train) [148][500/586] lr: 5.000000e-04 eta: 3:14:41 time: 0.335757 data_time: 0.022723 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.809471 loss: 0.000533 2022/09/13 04:32:57 - mmengine - INFO - Epoch(train) [148][550/586] lr: 5.000000e-04 eta: 3:14:26 time: 0.338159 data_time: 0.022023 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.854778 loss: 0.000536 2022/09/13 04:33:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:33:09 - mmengine - INFO - Saving checkpoint at 148 epochs 2022/09/13 04:33:34 - mmengine - INFO - Epoch(train) [149][50/586] lr: 5.000000e-04 eta: 3:13:54 time: 0.347385 data_time: 0.031945 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.906803 loss: 0.000534 2022/09/13 04:33:50 - mmengine - INFO - Epoch(train) [149][100/586] lr: 5.000000e-04 eta: 3:13:38 time: 0.335266 data_time: 0.022036 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.891781 loss: 0.000537 2022/09/13 04:34:07 - mmengine - INFO - Epoch(train) [149][150/586] lr: 5.000000e-04 eta: 3:13:22 time: 0.337780 data_time: 0.022552 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.850252 loss: 0.000534 2022/09/13 04:34:24 - mmengine - INFO - Epoch(train) [149][200/586] lr: 5.000000e-04 eta: 3:13:07 time: 0.336717 data_time: 0.026449 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.828977 loss: 0.000541 2022/09/13 04:34:41 - mmengine - INFO - Epoch(train) [149][250/586] lr: 5.000000e-04 eta: 3:12:51 time: 0.335854 data_time: 0.022419 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.820706 loss: 0.000531 2022/09/13 04:34:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:34:58 - mmengine - INFO - Epoch(train) [149][300/586] lr: 5.000000e-04 eta: 3:12:35 time: 0.337291 data_time: 0.022356 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.871046 loss: 0.000532 2022/09/13 04:35:15 - mmengine - INFO - Epoch(train) [149][350/586] lr: 5.000000e-04 eta: 3:12:20 time: 0.342304 data_time: 0.026581 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.907499 loss: 0.000527 2022/09/13 04:35:32 - mmengine - INFO - Epoch(train) [149][400/586] lr: 5.000000e-04 eta: 3:12:04 time: 0.334495 data_time: 0.023258 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.854246 loss: 0.000531 2022/09/13 04:35:49 - mmengine - INFO - Epoch(train) [149][450/586] lr: 5.000000e-04 eta: 3:11:48 time: 0.338317 data_time: 0.022525 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.861911 loss: 0.000523 2022/09/13 04:36:05 - mmengine - INFO - Epoch(train) [149][500/586] lr: 5.000000e-04 eta: 3:11:33 time: 0.335705 data_time: 0.022286 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.862348 loss: 0.000520 2022/09/13 04:36:22 - mmengine - INFO - Epoch(train) [149][550/586] lr: 5.000000e-04 eta: 3:11:17 time: 0.342318 data_time: 0.022136 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.894695 loss: 0.000536 2022/09/13 04:36:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:36:34 - mmengine - INFO - Saving checkpoint at 149 epochs 2022/09/13 04:36:59 - mmengine - INFO - Epoch(train) [150][50/586] lr: 5.000000e-04 eta: 3:10:45 time: 0.344748 data_time: 0.026907 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.796783 loss: 0.000519 2022/09/13 04:37:16 - mmengine - INFO - Epoch(train) [150][100/586] lr: 5.000000e-04 eta: 3:10:30 time: 0.345900 data_time: 0.022826 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.898594 loss: 0.000538 2022/09/13 04:37:33 - mmengine - INFO - Epoch(train) [150][150/586] lr: 5.000000e-04 eta: 3:10:14 time: 0.341229 data_time: 0.026399 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.873887 loss: 0.000522 2022/09/13 04:37:50 - mmengine - INFO - Epoch(train) [150][200/586] lr: 5.000000e-04 eta: 3:09:58 time: 0.333185 data_time: 0.023156 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.860764 loss: 0.000520 2022/09/13 04:38:07 - mmengine - INFO - Epoch(train) [150][250/586] lr: 5.000000e-04 eta: 3:09:43 time: 0.343499 data_time: 0.021829 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.817221 loss: 0.000540 2022/09/13 04:38:24 - mmengine - INFO - Epoch(train) [150][300/586] lr: 5.000000e-04 eta: 3:09:27 time: 0.340519 data_time: 0.025568 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.840667 loss: 0.000550 2022/09/13 04:38:41 - mmengine - INFO - Epoch(train) [150][350/586] lr: 5.000000e-04 eta: 3:09:11 time: 0.333910 data_time: 0.022106 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.872977 loss: 0.000548 2022/09/13 04:38:58 - mmengine - INFO - Epoch(train) [150][400/586] lr: 5.000000e-04 eta: 3:08:56 time: 0.341242 data_time: 0.022936 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.817306 loss: 0.000529 2022/09/13 04:39:15 - mmengine - INFO - Epoch(train) [150][450/586] lr: 5.000000e-04 eta: 3:08:40 time: 0.341857 data_time: 0.025605 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.902941 loss: 0.000528 2022/09/13 04:39:32 - mmengine - INFO - Epoch(train) [150][500/586] lr: 5.000000e-04 eta: 3:08:24 time: 0.338236 data_time: 0.022245 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.846173 loss: 0.000529 2022/09/13 04:39:49 - mmengine - INFO - Epoch(train) [150][550/586] lr: 5.000000e-04 eta: 3:08:09 time: 0.344137 data_time: 0.022393 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.819553 loss: 0.000519 2022/09/13 04:40:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:40:01 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/09/13 04:40:19 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:01:16 time: 0.214635 data_time: 0.012620 memory: 7489 2022/09/13 04:40:30 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:01:04 time: 0.209541 data_time: 0.007952 memory: 1657 2022/09/13 04:40:40 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:53 time: 0.209397 data_time: 0.007694 memory: 1657 2022/09/13 04:40:51 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:44 time: 0.213524 data_time: 0.007389 memory: 1657 2022/09/13 04:41:01 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:32 time: 0.208563 data_time: 0.007808 memory: 1657 2022/09/13 04:41:12 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:22 time: 0.209991 data_time: 0.007459 memory: 1657 2022/09/13 04:41:22 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:11 time: 0.209149 data_time: 0.007566 memory: 1657 2022/09/13 04:41:33 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:01 time: 0.206933 data_time: 0.006867 memory: 1657 2022/09/13 04:42:08 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 04:42:22 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.751631 coco/AP .5: 0.899724 coco/AP .75: 0.816326 coco/AP (M): 0.716804 coco/AP (L): 0.818938 coco/AR: 0.801779 coco/AR .5: 0.938917 coco/AR .75: 0.859099 coco/AR (M): 0.760585 coco/AR (L): 0.862765 2022/09/13 04:42:39 - mmengine - INFO - Epoch(train) [151][50/586] lr: 5.000000e-04 eta: 3:07:37 time: 0.348238 data_time: 0.028354 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.903377 loss: 0.000537 2022/09/13 04:42:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:42:56 - mmengine - INFO - Epoch(train) [151][100/586] lr: 5.000000e-04 eta: 3:07:21 time: 0.342160 data_time: 0.022544 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.886571 loss: 0.000521 2022/09/13 04:43:13 - mmengine - INFO - Epoch(train) [151][150/586] lr: 5.000000e-04 eta: 3:07:06 time: 0.334762 data_time: 0.021927 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.857082 loss: 0.000527 2022/09/13 04:43:30 - mmengine - INFO - Epoch(train) [151][200/586] lr: 5.000000e-04 eta: 3:06:50 time: 0.340139 data_time: 0.023154 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.887178 loss: 0.000502 2022/09/13 04:43:47 - mmengine - INFO - Epoch(train) [151][250/586] lr: 5.000000e-04 eta: 3:06:34 time: 0.338724 data_time: 0.028927 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.814901 loss: 0.000542 2022/09/13 04:44:04 - mmengine - INFO - Epoch(train) [151][300/586] lr: 5.000000e-04 eta: 3:06:19 time: 0.338356 data_time: 0.022193 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.869276 loss: 0.000541 2022/09/13 04:44:21 - mmengine - INFO - Epoch(train) [151][350/586] lr: 5.000000e-04 eta: 3:06:03 time: 0.331740 data_time: 0.022604 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.821668 loss: 0.000527 2022/09/13 04:44:38 - mmengine - INFO - Epoch(train) [151][400/586] lr: 5.000000e-04 eta: 3:05:47 time: 0.342912 data_time: 0.025255 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.880189 loss: 0.000530 2022/09/13 04:44:55 - mmengine - INFO - Epoch(train) [151][450/586] lr: 5.000000e-04 eta: 3:05:32 time: 0.335997 data_time: 0.022029 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.863048 loss: 0.000544 2022/09/13 04:45:11 - mmengine - INFO - Epoch(train) [151][500/586] lr: 5.000000e-04 eta: 3:05:16 time: 0.328541 data_time: 0.022094 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.842821 loss: 0.000556 2022/09/13 04:45:28 - mmengine - INFO - Epoch(train) [151][550/586] lr: 5.000000e-04 eta: 3:05:00 time: 0.337952 data_time: 0.022624 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.843151 loss: 0.000517 2022/09/13 04:45:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:45:40 - mmengine - INFO - Saving checkpoint at 151 epochs 2022/09/13 04:46:04 - mmengine - INFO - Epoch(train) [152][50/586] lr: 5.000000e-04 eta: 3:04:28 time: 0.345443 data_time: 0.027042 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.820749 loss: 0.000527 2022/09/13 04:46:21 - mmengine - INFO - Epoch(train) [152][100/586] lr: 5.000000e-04 eta: 3:04:13 time: 0.338988 data_time: 0.022551 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.862162 loss: 0.000529 2022/09/13 04:46:38 - mmengine - INFO - Epoch(train) [152][150/586] lr: 5.000000e-04 eta: 3:03:57 time: 0.338771 data_time: 0.026936 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.854101 loss: 0.000526 2022/09/13 04:46:55 - mmengine - INFO - Epoch(train) [152][200/586] lr: 5.000000e-04 eta: 3:03:41 time: 0.335838 data_time: 0.022694 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.852379 loss: 0.000526 2022/09/13 04:47:11 - mmengine - INFO - Epoch(train) [152][250/586] lr: 5.000000e-04 eta: 3:03:25 time: 0.327969 data_time: 0.021999 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.832026 loss: 0.000537 2022/09/13 04:47:29 - mmengine - INFO - Epoch(train) [152][300/586] lr: 5.000000e-04 eta: 3:03:10 time: 0.345044 data_time: 0.022491 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.866527 loss: 0.000552 2022/09/13 04:47:45 - mmengine - INFO - Epoch(train) [152][350/586] lr: 5.000000e-04 eta: 3:02:54 time: 0.335883 data_time: 0.021798 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.834417 loss: 0.000514 2022/09/13 04:48:02 - mmengine - INFO - Epoch(train) [152][400/586] lr: 5.000000e-04 eta: 3:02:38 time: 0.334362 data_time: 0.023160 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.853638 loss: 0.000523 2022/09/13 04:48:19 - mmengine - INFO - Epoch(train) [152][450/586] lr: 5.000000e-04 eta: 3:02:23 time: 0.337617 data_time: 0.026009 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.875038 loss: 0.000550 2022/09/13 04:48:36 - mmengine - INFO - Epoch(train) [152][500/586] lr: 5.000000e-04 eta: 3:02:07 time: 0.338430 data_time: 0.022426 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.880111 loss: 0.000535 2022/09/13 04:48:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:48:53 - mmengine - INFO - Epoch(train) [152][550/586] lr: 5.000000e-04 eta: 3:01:51 time: 0.336056 data_time: 0.022364 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.834309 loss: 0.000532 2022/09/13 04:49:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:49:05 - mmengine - INFO - Saving checkpoint at 152 epochs 2022/09/13 04:49:29 - mmengine - INFO - Epoch(train) [153][50/586] lr: 5.000000e-04 eta: 3:01:20 time: 0.339484 data_time: 0.025995 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.871456 loss: 0.000513 2022/09/13 04:49:46 - mmengine - INFO - Epoch(train) [153][100/586] lr: 5.000000e-04 eta: 3:01:04 time: 0.344175 data_time: 0.027119 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.801515 loss: 0.000513 2022/09/13 04:50:03 - mmengine - INFO - Epoch(train) [153][150/586] lr: 5.000000e-04 eta: 3:00:48 time: 0.336503 data_time: 0.022037 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.875120 loss: 0.000518 2022/09/13 04:50:20 - mmengine - INFO - Epoch(train) [153][200/586] lr: 5.000000e-04 eta: 3:00:33 time: 0.335315 data_time: 0.023032 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.835956 loss: 0.000534 2022/09/13 04:50:37 - mmengine - INFO - Epoch(train) [153][250/586] lr: 5.000000e-04 eta: 3:00:17 time: 0.337800 data_time: 0.022188 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.869604 loss: 0.000513 2022/09/13 04:50:54 - mmengine - INFO - Epoch(train) [153][300/586] lr: 5.000000e-04 eta: 3:00:01 time: 0.340043 data_time: 0.022261 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.866277 loss: 0.000539 2022/09/13 04:51:11 - mmengine - INFO - Epoch(train) [153][350/586] lr: 5.000000e-04 eta: 2:59:45 time: 0.333865 data_time: 0.022180 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.844185 loss: 0.000533 2022/09/13 04:51:28 - mmengine - INFO - Epoch(train) [153][400/586] lr: 5.000000e-04 eta: 2:59:30 time: 0.340618 data_time: 0.022351 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.845447 loss: 0.000518 2022/09/13 04:51:44 - mmengine - INFO - Epoch(train) [153][450/586] lr: 5.000000e-04 eta: 2:59:14 time: 0.332422 data_time: 0.022257 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.848515 loss: 0.000518 2022/09/13 04:52:01 - mmengine - INFO - Epoch(train) [153][500/586] lr: 5.000000e-04 eta: 2:58:58 time: 0.331098 data_time: 0.022134 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.891851 loss: 0.000546 2022/09/13 04:52:18 - mmengine - INFO - Epoch(train) [153][550/586] lr: 5.000000e-04 eta: 2:58:42 time: 0.338855 data_time: 0.025345 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.873006 loss: 0.000520 2022/09/13 04:52:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:52:30 - mmengine - INFO - Saving checkpoint at 153 epochs 2022/09/13 04:52:53 - mmengine - INFO - Epoch(train) [154][50/586] lr: 5.000000e-04 eta: 2:58:11 time: 0.338828 data_time: 0.030717 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.821102 loss: 0.000539 2022/09/13 04:53:10 - mmengine - INFO - Epoch(train) [154][100/586] lr: 5.000000e-04 eta: 2:57:55 time: 0.337397 data_time: 0.021969 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.891692 loss: 0.000514 2022/09/13 04:53:27 - mmengine - INFO - Epoch(train) [154][150/586] lr: 5.000000e-04 eta: 2:57:39 time: 0.338252 data_time: 0.022141 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.854800 loss: 0.000549 2022/09/13 04:53:44 - mmengine - INFO - Epoch(train) [154][200/586] lr: 5.000000e-04 eta: 2:57:24 time: 0.338676 data_time: 0.022434 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.876147 loss: 0.000523 2022/09/13 04:54:01 - mmengine - INFO - Epoch(train) [154][250/586] lr: 5.000000e-04 eta: 2:57:08 time: 0.334355 data_time: 0.022375 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.894869 loss: 0.000539 2022/09/13 04:54:18 - mmengine - INFO - Epoch(train) [154][300/586] lr: 5.000000e-04 eta: 2:56:52 time: 0.342639 data_time: 0.022690 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.868363 loss: 0.000523 2022/09/13 04:54:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:54:35 - mmengine - INFO - Epoch(train) [154][350/586] lr: 5.000000e-04 eta: 2:56:37 time: 0.345340 data_time: 0.027836 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.809362 loss: 0.000537 2022/09/13 04:54:52 - mmengine - INFO - Epoch(train) [154][400/586] lr: 5.000000e-04 eta: 2:56:21 time: 0.333681 data_time: 0.022152 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.893777 loss: 0.000540 2022/09/13 04:55:09 - mmengine - INFO - Epoch(train) [154][450/586] lr: 5.000000e-04 eta: 2:56:05 time: 0.337136 data_time: 0.023127 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.854146 loss: 0.000540 2022/09/13 04:55:26 - mmengine - INFO - Epoch(train) [154][500/586] lr: 5.000000e-04 eta: 2:55:50 time: 0.346591 data_time: 0.023356 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.818765 loss: 0.000536 2022/09/13 04:55:43 - mmengine - INFO - Epoch(train) [154][550/586] lr: 5.000000e-04 eta: 2:55:34 time: 0.332664 data_time: 0.022956 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.857531 loss: 0.000520 2022/09/13 04:55:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:55:55 - mmengine - INFO - Saving checkpoint at 154 epochs 2022/09/13 04:56:18 - mmengine - INFO - Epoch(train) [155][50/586] lr: 5.000000e-04 eta: 2:55:02 time: 0.337378 data_time: 0.026166 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.866759 loss: 0.000513 2022/09/13 04:56:35 - mmengine - INFO - Epoch(train) [155][100/586] lr: 5.000000e-04 eta: 2:54:47 time: 0.341600 data_time: 0.023332 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.870822 loss: 0.000532 2022/09/13 04:56:52 - mmengine - INFO - Epoch(train) [155][150/586] lr: 5.000000e-04 eta: 2:54:31 time: 0.339269 data_time: 0.022374 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.859127 loss: 0.000528 2022/09/13 04:57:09 - mmengine - INFO - Epoch(train) [155][200/586] lr: 5.000000e-04 eta: 2:54:15 time: 0.337934 data_time: 0.022615 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.891671 loss: 0.000517 2022/09/13 04:57:26 - mmengine - INFO - Epoch(train) [155][250/586] lr: 5.000000e-04 eta: 2:54:00 time: 0.344063 data_time: 0.025640 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.846966 loss: 0.000507 2022/09/13 04:57:43 - mmengine - INFO - Epoch(train) [155][300/586] lr: 5.000000e-04 eta: 2:53:44 time: 0.334360 data_time: 0.023007 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.823080 loss: 0.000532 2022/09/13 04:58:01 - mmengine - INFO - Epoch(train) [155][350/586] lr: 5.000000e-04 eta: 2:53:28 time: 0.347832 data_time: 0.022891 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.885299 loss: 0.000533 2022/09/13 04:58:18 - mmengine - INFO - Epoch(train) [155][400/586] lr: 5.000000e-04 eta: 2:53:13 time: 0.350600 data_time: 0.027183 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.912396 loss: 0.000518 2022/09/13 04:58:35 - mmengine - INFO - Epoch(train) [155][450/586] lr: 5.000000e-04 eta: 2:52:57 time: 0.337619 data_time: 0.022047 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.865344 loss: 0.000521 2022/09/13 04:58:52 - mmengine - INFO - Epoch(train) [155][500/586] lr: 5.000000e-04 eta: 2:52:41 time: 0.339140 data_time: 0.022556 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.863834 loss: 0.000529 2022/09/13 04:59:09 - mmengine - INFO - Epoch(train) [155][550/586] lr: 5.000000e-04 eta: 2:52:26 time: 0.333085 data_time: 0.026368 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.862883 loss: 0.000538 2022/09/13 04:59:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 04:59:21 - mmengine - INFO - Saving checkpoint at 155 epochs 2022/09/13 04:59:46 - mmengine - INFO - Epoch(train) [156][50/586] lr: 5.000000e-04 eta: 2:51:54 time: 0.347581 data_time: 0.029769 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.857939 loss: 0.000533 2022/09/13 05:00:02 - mmengine - INFO - Epoch(train) [156][100/586] lr: 5.000000e-04 eta: 2:51:39 time: 0.336695 data_time: 0.022425 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.868514 loss: 0.000527 2022/09/13 05:00:19 - mmengine - INFO - Epoch(train) [156][150/586] lr: 5.000000e-04 eta: 2:51:23 time: 0.339813 data_time: 0.022785 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.913695 loss: 0.000513 2022/09/13 05:00:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:00:36 - mmengine - INFO - Epoch(train) [156][200/586] lr: 5.000000e-04 eta: 2:51:07 time: 0.332009 data_time: 0.022303 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.887500 loss: 0.000521 2022/09/13 05:00:53 - mmengine - INFO - Epoch(train) [156][250/586] lr: 5.000000e-04 eta: 2:50:52 time: 0.338663 data_time: 0.022127 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.918464 loss: 0.000534 2022/09/13 05:01:10 - mmengine - INFO - Epoch(train) [156][300/586] lr: 5.000000e-04 eta: 2:50:36 time: 0.344025 data_time: 0.022572 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.827594 loss: 0.000542 2022/09/13 05:01:27 - mmengine - INFO - Epoch(train) [156][350/586] lr: 5.000000e-04 eta: 2:50:20 time: 0.335576 data_time: 0.022304 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.847398 loss: 0.000521 2022/09/13 05:01:44 - mmengine - INFO - Epoch(train) [156][400/586] lr: 5.000000e-04 eta: 2:50:05 time: 0.347342 data_time: 0.022503 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.839564 loss: 0.000521 2022/09/13 05:02:01 - mmengine - INFO - Epoch(train) [156][450/586] lr: 5.000000e-04 eta: 2:49:49 time: 0.338925 data_time: 0.025886 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.861528 loss: 0.000543 2022/09/13 05:02:18 - mmengine - INFO - Epoch(train) [156][500/586] lr: 5.000000e-04 eta: 2:49:33 time: 0.338351 data_time: 0.023007 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.865357 loss: 0.000543 2022/09/13 05:02:35 - mmengine - INFO - Epoch(train) [156][550/586] lr: 5.000000e-04 eta: 2:49:17 time: 0.336766 data_time: 0.022081 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.898337 loss: 0.000526 2022/09/13 05:02:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:02:47 - mmengine - INFO - Saving checkpoint at 156 epochs 2022/09/13 05:03:12 - mmengine - INFO - Epoch(train) [157][50/586] lr: 5.000000e-04 eta: 2:48:46 time: 0.346047 data_time: 0.027787 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.824830 loss: 0.000527 2022/09/13 05:03:29 - mmengine - INFO - Epoch(train) [157][100/586] lr: 5.000000e-04 eta: 2:48:31 time: 0.339192 data_time: 0.026189 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.802602 loss: 0.000542 2022/09/13 05:03:46 - mmengine - INFO - Epoch(train) [157][150/586] lr: 5.000000e-04 eta: 2:48:15 time: 0.337344 data_time: 0.021718 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.849858 loss: 0.000540 2022/09/13 05:04:02 - mmengine - INFO - Epoch(train) [157][200/586] lr: 5.000000e-04 eta: 2:47:59 time: 0.329452 data_time: 0.022931 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.885476 loss: 0.000528 2022/09/13 05:04:20 - mmengine - INFO - Epoch(train) [157][250/586] lr: 5.000000e-04 eta: 2:47:43 time: 0.343077 data_time: 0.022566 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.868031 loss: 0.000520 2022/09/13 05:04:36 - mmengine - INFO - Epoch(train) [157][300/586] lr: 5.000000e-04 eta: 2:47:28 time: 0.334989 data_time: 0.022146 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.876838 loss: 0.000524 2022/09/13 05:04:53 - mmengine - INFO - Epoch(train) [157][350/586] lr: 5.000000e-04 eta: 2:47:12 time: 0.331836 data_time: 0.022060 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.890295 loss: 0.000529 2022/09/13 05:05:10 - mmengine - INFO - Epoch(train) [157][400/586] lr: 5.000000e-04 eta: 2:46:56 time: 0.350212 data_time: 0.022278 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.834226 loss: 0.000531 2022/09/13 05:05:27 - mmengine - INFO - Epoch(train) [157][450/586] lr: 5.000000e-04 eta: 2:46:40 time: 0.333419 data_time: 0.022248 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.787162 loss: 0.000532 2022/09/13 05:05:44 - mmengine - INFO - Epoch(train) [157][500/586] lr: 5.000000e-04 eta: 2:46:25 time: 0.338527 data_time: 0.023668 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.838215 loss: 0.000541 2022/09/13 05:06:01 - mmengine - INFO - Epoch(train) [157][550/586] lr: 5.000000e-04 eta: 2:46:09 time: 0.345752 data_time: 0.025876 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.918548 loss: 0.000530 2022/09/13 05:06:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:06:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:06:13 - mmengine - INFO - Saving checkpoint at 157 epochs 2022/09/13 05:06:37 - mmengine - INFO - Epoch(train) [158][50/586] lr: 5.000000e-04 eta: 2:45:38 time: 0.341931 data_time: 0.034924 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.872515 loss: 0.000517 2022/09/13 05:06:55 - mmengine - INFO - Epoch(train) [158][100/586] lr: 5.000000e-04 eta: 2:45:22 time: 0.344778 data_time: 0.023401 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.847535 loss: 0.000516 2022/09/13 05:07:11 - mmengine - INFO - Epoch(train) [158][150/586] lr: 5.000000e-04 eta: 2:45:06 time: 0.333991 data_time: 0.022309 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.843452 loss: 0.000516 2022/09/13 05:07:29 - mmengine - INFO - Epoch(train) [158][200/586] lr: 5.000000e-04 eta: 2:44:51 time: 0.347114 data_time: 0.025933 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.875444 loss: 0.000536 2022/09/13 05:07:45 - mmengine - INFO - Epoch(train) [158][250/586] lr: 5.000000e-04 eta: 2:44:35 time: 0.334024 data_time: 0.021902 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.847788 loss: 0.000538 2022/09/13 05:08:02 - mmengine - INFO - Epoch(train) [158][300/586] lr: 5.000000e-04 eta: 2:44:19 time: 0.334393 data_time: 0.022914 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.846363 loss: 0.000512 2022/09/13 05:08:20 - mmengine - INFO - Epoch(train) [158][350/586] lr: 5.000000e-04 eta: 2:44:04 time: 0.350220 data_time: 0.025375 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.861557 loss: 0.000537 2022/09/13 05:08:36 - mmengine - INFO - Epoch(train) [158][400/586] lr: 5.000000e-04 eta: 2:43:48 time: 0.337276 data_time: 0.022248 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.879824 loss: 0.000522 2022/09/13 05:08:54 - mmengine - INFO - Epoch(train) [158][450/586] lr: 5.000000e-04 eta: 2:43:32 time: 0.341465 data_time: 0.023230 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.850599 loss: 0.000533 2022/09/13 05:09:10 - mmengine - INFO - Epoch(train) [158][500/586] lr: 5.000000e-04 eta: 2:43:17 time: 0.338600 data_time: 0.022283 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.815303 loss: 0.000534 2022/09/13 05:09:28 - mmengine - INFO - Epoch(train) [158][550/586] lr: 5.000000e-04 eta: 2:43:01 time: 0.344221 data_time: 0.022080 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.844801 loss: 0.000536 2022/09/13 05:09:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:09:40 - mmengine - INFO - Saving checkpoint at 158 epochs 2022/09/13 05:10:04 - mmengine - INFO - Epoch(train) [159][50/586] lr: 5.000000e-04 eta: 2:42:30 time: 0.345523 data_time: 0.030733 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.813897 loss: 0.000514 2022/09/13 05:10:21 - mmengine - INFO - Epoch(train) [159][100/586] lr: 5.000000e-04 eta: 2:42:14 time: 0.343928 data_time: 0.025527 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.881405 loss: 0.000524 2022/09/13 05:10:38 - mmengine - INFO - Epoch(train) [159][150/586] lr: 5.000000e-04 eta: 2:41:59 time: 0.334316 data_time: 0.022200 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.844741 loss: 0.000529 2022/09/13 05:10:55 - mmengine - INFO - Epoch(train) [159][200/586] lr: 5.000000e-04 eta: 2:41:43 time: 0.340998 data_time: 0.023508 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.872422 loss: 0.000516 2022/09/13 05:11:12 - mmengine - INFO - Epoch(train) [159][250/586] lr: 5.000000e-04 eta: 2:41:27 time: 0.337900 data_time: 0.022189 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.870962 loss: 0.000518 2022/09/13 05:11:29 - mmengine - INFO - Epoch(train) [159][300/586] lr: 5.000000e-04 eta: 2:41:11 time: 0.340801 data_time: 0.027072 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.868862 loss: 0.000532 2022/09/13 05:11:46 - mmengine - INFO - Epoch(train) [159][350/586] lr: 5.000000e-04 eta: 2:40:56 time: 0.336226 data_time: 0.021898 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.868908 loss: 0.000519 2022/09/13 05:12:03 - mmengine - INFO - Epoch(train) [159][400/586] lr: 5.000000e-04 eta: 2:40:40 time: 0.340280 data_time: 0.022021 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.864843 loss: 0.000535 2022/09/13 05:12:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:12:20 - mmengine - INFO - Epoch(train) [159][450/586] lr: 5.000000e-04 eta: 2:40:24 time: 0.344371 data_time: 0.026699 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.833823 loss: 0.000528 2022/09/13 05:12:37 - mmengine - INFO - Epoch(train) [159][500/586] lr: 5.000000e-04 eta: 2:40:08 time: 0.333557 data_time: 0.022376 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.862260 loss: 0.000525 2022/09/13 05:12:54 - mmengine - INFO - Epoch(train) [159][550/586] lr: 5.000000e-04 eta: 2:39:53 time: 0.341828 data_time: 0.022599 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.807615 loss: 0.000530 2022/09/13 05:13:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:13:06 - mmengine - INFO - Saving checkpoint at 159 epochs 2022/09/13 05:13:30 - mmengine - INFO - Epoch(train) [160][50/586] lr: 5.000000e-04 eta: 2:39:22 time: 0.342036 data_time: 0.030076 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.873039 loss: 0.000517 2022/09/13 05:13:47 - mmengine - INFO - Epoch(train) [160][100/586] lr: 5.000000e-04 eta: 2:39:06 time: 0.343328 data_time: 0.026181 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.877085 loss: 0.000526 2022/09/13 05:14:03 - mmengine - INFO - Epoch(train) [160][150/586] lr: 5.000000e-04 eta: 2:38:50 time: 0.329827 data_time: 0.022613 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.921087 loss: 0.000527 2022/09/13 05:14:20 - mmengine - INFO - Epoch(train) [160][200/586] lr: 5.000000e-04 eta: 2:38:35 time: 0.336570 data_time: 0.022396 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.856014 loss: 0.000535 2022/09/13 05:14:38 - mmengine - INFO - Epoch(train) [160][250/586] lr: 5.000000e-04 eta: 2:38:19 time: 0.345543 data_time: 0.025394 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.862783 loss: 0.000505 2022/09/13 05:14:54 - mmengine - INFO - Epoch(train) [160][300/586] lr: 5.000000e-04 eta: 2:38:03 time: 0.331402 data_time: 0.023097 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.865894 loss: 0.000526 2022/09/13 05:15:11 - mmengine - INFO - Epoch(train) [160][350/586] lr: 5.000000e-04 eta: 2:37:47 time: 0.338844 data_time: 0.022446 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.916014 loss: 0.000506 2022/09/13 05:15:28 - mmengine - INFO - Epoch(train) [160][400/586] lr: 5.000000e-04 eta: 2:37:32 time: 0.340954 data_time: 0.026244 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.915968 loss: 0.000512 2022/09/13 05:15:45 - mmengine - INFO - Epoch(train) [160][450/586] lr: 5.000000e-04 eta: 2:37:16 time: 0.332362 data_time: 0.023301 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.867122 loss: 0.000529 2022/09/13 05:16:02 - mmengine - INFO - Epoch(train) [160][500/586] lr: 5.000000e-04 eta: 2:37:00 time: 0.335273 data_time: 0.022864 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.845919 loss: 0.000526 2022/09/13 05:16:19 - mmengine - INFO - Epoch(train) [160][550/586] lr: 5.000000e-04 eta: 2:36:44 time: 0.341513 data_time: 0.025861 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.873387 loss: 0.000519 2022/09/13 05:16:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:16:31 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/09/13 05:16:49 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:01:18 time: 0.218527 data_time: 0.011686 memory: 7489 2022/09/13 05:16:59 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:01:04 time: 0.208762 data_time: 0.007715 memory: 1657 2022/09/13 05:17:10 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:53 time: 0.209172 data_time: 0.007608 memory: 1657 2022/09/13 05:17:20 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:43 time: 0.208167 data_time: 0.007542 memory: 1657 2022/09/13 05:17:31 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:32 time: 0.209521 data_time: 0.007900 memory: 1657 2022/09/13 05:17:41 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:22 time: 0.209551 data_time: 0.008119 memory: 1657 2022/09/13 05:17:52 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:11 time: 0.209825 data_time: 0.008459 memory: 1657 2022/09/13 05:18:02 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:01 time: 0.208550 data_time: 0.006937 memory: 1657 2022/09/13 05:18:38 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 05:18:51 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.756819 coco/AP .5: 0.904061 coco/AP .75: 0.822462 coco/AP (M): 0.720722 coco/AP (L): 0.824672 coco/AR: 0.807809 coco/AR .5: 0.941593 coco/AR .75: 0.866814 coco/AR (M): 0.765884 coco/AR (L): 0.869045 2022/09/13 05:18:52 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_140.pth is removed 2022/09/13 05:18:55 - mmengine - INFO - The best checkpoint with 0.7568 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/09/13 05:19:12 - mmengine - INFO - Epoch(train) [161][50/586] lr: 5.000000e-04 eta: 2:36:13 time: 0.337172 data_time: 0.026904 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.902454 loss: 0.000522 2022/09/13 05:19:30 - mmengine - INFO - Epoch(train) [161][100/586] lr: 5.000000e-04 eta: 2:35:58 time: 0.355295 data_time: 0.022644 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.822454 loss: 0.000529 2022/09/13 05:19:46 - mmengine - INFO - Epoch(train) [161][150/586] lr: 5.000000e-04 eta: 2:35:42 time: 0.332713 data_time: 0.022647 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.896089 loss: 0.000514 2022/09/13 05:20:03 - mmengine - INFO - Epoch(train) [161][200/586] lr: 5.000000e-04 eta: 2:35:26 time: 0.334534 data_time: 0.022100 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.853666 loss: 0.000512 2022/09/13 05:20:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:20:20 - mmengine - INFO - Epoch(train) [161][250/586] lr: 5.000000e-04 eta: 2:35:11 time: 0.343787 data_time: 0.026588 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.784280 loss: 0.000545 2022/09/13 05:20:37 - mmengine - INFO - Epoch(train) [161][300/586] lr: 5.000000e-04 eta: 2:34:55 time: 0.333673 data_time: 0.022896 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.916232 loss: 0.000517 2022/09/13 05:20:54 - mmengine - INFO - Epoch(train) [161][350/586] lr: 5.000000e-04 eta: 2:34:39 time: 0.334330 data_time: 0.022513 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.838246 loss: 0.000539 2022/09/13 05:21:11 - mmengine - INFO - Epoch(train) [161][400/586] lr: 5.000000e-04 eta: 2:34:23 time: 0.345466 data_time: 0.022639 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.868038 loss: 0.000537 2022/09/13 05:21:28 - mmengine - INFO - Epoch(train) [161][450/586] lr: 5.000000e-04 eta: 2:34:07 time: 0.333269 data_time: 0.021735 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.852686 loss: 0.000563 2022/09/13 05:21:45 - mmengine - INFO - Epoch(train) [161][500/586] lr: 5.000000e-04 eta: 2:33:52 time: 0.336840 data_time: 0.022853 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.839323 loss: 0.000512 2022/09/13 05:22:02 - mmengine - INFO - Epoch(train) [161][550/586] lr: 5.000000e-04 eta: 2:33:36 time: 0.346223 data_time: 0.022293 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.874549 loss: 0.000521 2022/09/13 05:22:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:22:14 - mmengine - INFO - Saving checkpoint at 161 epochs 2022/09/13 05:22:39 - mmengine - INFO - Epoch(train) [162][50/586] lr: 5.000000e-04 eta: 2:33:05 time: 0.342501 data_time: 0.029925 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.808438 loss: 0.000518 2022/09/13 05:22:56 - mmengine - INFO - Epoch(train) [162][100/586] lr: 5.000000e-04 eta: 2:32:50 time: 0.347564 data_time: 0.022095 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.845285 loss: 0.000521 2022/09/13 05:23:13 - mmengine - INFO - Epoch(train) [162][150/586] lr: 5.000000e-04 eta: 2:32:34 time: 0.338674 data_time: 0.022924 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.802420 loss: 0.000545 2022/09/13 05:23:30 - mmengine - INFO - Epoch(train) [162][200/586] lr: 5.000000e-04 eta: 2:32:18 time: 0.333242 data_time: 0.022702 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.819564 loss: 0.000526 2022/09/13 05:23:46 - mmengine - INFO - Epoch(train) [162][250/586] lr: 5.000000e-04 eta: 2:32:02 time: 0.338014 data_time: 0.022224 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.906811 loss: 0.000517 2022/09/13 05:24:04 - mmengine - INFO - Epoch(train) [162][300/586] lr: 5.000000e-04 eta: 2:31:47 time: 0.341319 data_time: 0.022666 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.864680 loss: 0.000531 2022/09/13 05:24:21 - mmengine - INFO - Epoch(train) [162][350/586] lr: 5.000000e-04 eta: 2:31:31 time: 0.339810 data_time: 0.023162 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.855470 loss: 0.000549 2022/09/13 05:24:38 - mmengine - INFO - Epoch(train) [162][400/586] lr: 5.000000e-04 eta: 2:31:15 time: 0.342201 data_time: 0.022213 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.877896 loss: 0.000530 2022/09/13 05:24:55 - mmengine - INFO - Epoch(train) [162][450/586] lr: 5.000000e-04 eta: 2:30:59 time: 0.336210 data_time: 0.022076 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.873161 loss: 0.000526 2022/09/13 05:25:12 - mmengine - INFO - Epoch(train) [162][500/586] lr: 5.000000e-04 eta: 2:30:44 time: 0.344864 data_time: 0.026864 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.800288 loss: 0.000530 2022/09/13 05:25:29 - mmengine - INFO - Epoch(train) [162][550/586] lr: 5.000000e-04 eta: 2:30:28 time: 0.338150 data_time: 0.022413 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.848535 loss: 0.000535 2022/09/13 05:25:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:25:41 - mmengine - INFO - Saving checkpoint at 162 epochs 2022/09/13 05:26:05 - mmengine - INFO - Epoch(train) [163][50/586] lr: 5.000000e-04 eta: 2:29:57 time: 0.342195 data_time: 0.027282 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.874275 loss: 0.000545 2022/09/13 05:26:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:26:22 - mmengine - INFO - Epoch(train) [163][100/586] lr: 5.000000e-04 eta: 2:29:42 time: 0.340730 data_time: 0.026015 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.916599 loss: 0.000515 2022/09/13 05:26:39 - mmengine - INFO - Epoch(train) [163][150/586] lr: 5.000000e-04 eta: 2:29:26 time: 0.333509 data_time: 0.022862 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.831353 loss: 0.000529 2022/09/13 05:26:55 - mmengine - INFO - Epoch(train) [163][200/586] lr: 5.000000e-04 eta: 2:29:10 time: 0.331741 data_time: 0.023023 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.867849 loss: 0.000524 2022/09/13 05:27:13 - mmengine - INFO - Epoch(train) [163][250/586] lr: 5.000000e-04 eta: 2:28:54 time: 0.344436 data_time: 0.022128 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.911063 loss: 0.000502 2022/09/13 05:27:30 - mmengine - INFO - Epoch(train) [163][300/586] lr: 5.000000e-04 eta: 2:28:39 time: 0.340288 data_time: 0.022372 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.853415 loss: 0.000541 2022/09/13 05:27:46 - mmengine - INFO - Epoch(train) [163][350/586] lr: 5.000000e-04 eta: 2:28:23 time: 0.331409 data_time: 0.022130 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.904640 loss: 0.000515 2022/09/13 05:28:03 - mmengine - INFO - Epoch(train) [163][400/586] lr: 5.000000e-04 eta: 2:28:07 time: 0.340653 data_time: 0.022187 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.831695 loss: 0.000515 2022/09/13 05:28:20 - mmengine - INFO - Epoch(train) [163][450/586] lr: 5.000000e-04 eta: 2:27:51 time: 0.339549 data_time: 0.022323 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.913684 loss: 0.000507 2022/09/13 05:28:37 - mmengine - INFO - Epoch(train) [163][500/586] lr: 5.000000e-04 eta: 2:27:35 time: 0.342441 data_time: 0.023152 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.855802 loss: 0.000528 2022/09/13 05:28:55 - mmengine - INFO - Epoch(train) [163][550/586] lr: 5.000000e-04 eta: 2:27:20 time: 0.341582 data_time: 0.021964 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.896269 loss: 0.000516 2022/09/13 05:29:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:29:07 - mmengine - INFO - Saving checkpoint at 163 epochs 2022/09/13 05:29:30 - mmengine - INFO - Epoch(train) [164][50/586] lr: 5.000000e-04 eta: 2:26:49 time: 0.348684 data_time: 0.029930 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.853074 loss: 0.000513 2022/09/13 05:29:47 - mmengine - INFO - Epoch(train) [164][100/586] lr: 5.000000e-04 eta: 2:26:33 time: 0.334862 data_time: 0.022165 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.873961 loss: 0.000514 2022/09/13 05:30:04 - mmengine - INFO - Epoch(train) [164][150/586] lr: 5.000000e-04 eta: 2:26:18 time: 0.341328 data_time: 0.021974 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.798389 loss: 0.000539 2022/09/13 05:30:21 - mmengine - INFO - Epoch(train) [164][200/586] lr: 5.000000e-04 eta: 2:26:02 time: 0.344181 data_time: 0.022658 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.875199 loss: 0.000531 2022/09/13 05:30:38 - mmengine - INFO - Epoch(train) [164][250/586] lr: 5.000000e-04 eta: 2:25:46 time: 0.333475 data_time: 0.022377 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.890840 loss: 0.000525 2022/09/13 05:30:55 - mmengine - INFO - Epoch(train) [164][300/586] lr: 5.000000e-04 eta: 2:25:30 time: 0.341717 data_time: 0.022255 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.859526 loss: 0.000524 2022/09/13 05:31:12 - mmengine - INFO - Epoch(train) [164][350/586] lr: 5.000000e-04 eta: 2:25:15 time: 0.342081 data_time: 0.022015 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.857315 loss: 0.000523 2022/09/13 05:31:29 - mmengine - INFO - Epoch(train) [164][400/586] lr: 5.000000e-04 eta: 2:24:59 time: 0.335154 data_time: 0.022791 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.857594 loss: 0.000534 2022/09/13 05:31:46 - mmengine - INFO - Epoch(train) [164][450/586] lr: 5.000000e-04 eta: 2:24:43 time: 0.334078 data_time: 0.026065 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.855420 loss: 0.000524 2022/09/13 05:31:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:32:03 - mmengine - INFO - Epoch(train) [164][500/586] lr: 5.000000e-04 eta: 2:24:27 time: 0.342058 data_time: 0.022514 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.856791 loss: 0.000536 2022/09/13 05:32:20 - mmengine - INFO - Epoch(train) [164][550/586] lr: 5.000000e-04 eta: 2:24:12 time: 0.335697 data_time: 0.022298 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.816177 loss: 0.000519 2022/09/13 05:32:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:32:32 - mmengine - INFO - Saving checkpoint at 164 epochs 2022/09/13 05:32:56 - mmengine - INFO - Epoch(train) [165][50/586] lr: 5.000000e-04 eta: 2:23:41 time: 0.347434 data_time: 0.026635 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.842878 loss: 0.000514 2022/09/13 05:33:13 - mmengine - INFO - Epoch(train) [165][100/586] lr: 5.000000e-04 eta: 2:23:25 time: 0.339148 data_time: 0.022625 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.867101 loss: 0.000512 2022/09/13 05:33:30 - mmengine - INFO - Epoch(train) [165][150/586] lr: 5.000000e-04 eta: 2:23:10 time: 0.338342 data_time: 0.022395 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.914663 loss: 0.000511 2022/09/13 05:33:47 - mmengine - INFO - Epoch(train) [165][200/586] lr: 5.000000e-04 eta: 2:22:54 time: 0.338362 data_time: 0.022209 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.823383 loss: 0.000526 2022/09/13 05:34:04 - mmengine - INFO - Epoch(train) [165][250/586] lr: 5.000000e-04 eta: 2:22:38 time: 0.337051 data_time: 0.022031 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.788139 loss: 0.000512 2022/09/13 05:34:21 - mmengine - INFO - Epoch(train) [165][300/586] lr: 5.000000e-04 eta: 2:22:22 time: 0.341433 data_time: 0.022655 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.867741 loss: 0.000518 2022/09/13 05:34:37 - mmengine - INFO - Epoch(train) [165][350/586] lr: 5.000000e-04 eta: 2:22:06 time: 0.330307 data_time: 0.022159 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.808841 loss: 0.000524 2022/09/13 05:34:55 - mmengine - INFO - Epoch(train) [165][400/586] lr: 5.000000e-04 eta: 2:21:51 time: 0.342575 data_time: 0.025473 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.838691 loss: 0.000517 2022/09/13 05:35:11 - mmengine - INFO - Epoch(train) [165][450/586] lr: 5.000000e-04 eta: 2:21:35 time: 0.338208 data_time: 0.023104 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.886165 loss: 0.000510 2022/09/13 05:35:28 - mmengine - INFO - Epoch(train) [165][500/586] lr: 5.000000e-04 eta: 2:21:19 time: 0.333830 data_time: 0.022250 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.917634 loss: 0.000536 2022/09/13 05:35:46 - mmengine - INFO - Epoch(train) [165][550/586] lr: 5.000000e-04 eta: 2:21:03 time: 0.346952 data_time: 0.022468 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.852481 loss: 0.000539 2022/09/13 05:35:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:35:58 - mmengine - INFO - Saving checkpoint at 165 epochs 2022/09/13 05:36:21 - mmengine - INFO - Epoch(train) [166][50/586] lr: 5.000000e-04 eta: 2:20:33 time: 0.338438 data_time: 0.035579 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.889735 loss: 0.000530 2022/09/13 05:36:39 - mmengine - INFO - Epoch(train) [166][100/586] lr: 5.000000e-04 eta: 2:20:17 time: 0.347146 data_time: 0.023356 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.868753 loss: 0.000514 2022/09/13 05:36:55 - mmengine - INFO - Epoch(train) [166][150/586] lr: 5.000000e-04 eta: 2:20:01 time: 0.329774 data_time: 0.022096 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.853829 loss: 0.000513 2022/09/13 05:37:12 - mmengine - INFO - Epoch(train) [166][200/586] lr: 5.000000e-04 eta: 2:19:46 time: 0.344791 data_time: 0.023507 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.897566 loss: 0.000522 2022/09/13 05:37:30 - mmengine - INFO - Epoch(train) [166][250/586] lr: 5.000000e-04 eta: 2:19:30 time: 0.346513 data_time: 0.022026 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.861318 loss: 0.000523 2022/09/13 05:37:47 - mmengine - INFO - Epoch(train) [166][300/586] lr: 5.000000e-04 eta: 2:19:14 time: 0.335615 data_time: 0.022268 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.866360 loss: 0.000531 2022/09/13 05:37:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:38:04 - mmengine - INFO - Epoch(train) [166][350/586] lr: 5.000000e-04 eta: 2:18:58 time: 0.342497 data_time: 0.025978 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.901331 loss: 0.000520 2022/09/13 05:38:21 - mmengine - INFO - Epoch(train) [166][400/586] lr: 5.000000e-04 eta: 2:18:43 time: 0.343361 data_time: 0.023064 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.861786 loss: 0.000514 2022/09/13 05:38:38 - mmengine - INFO - Epoch(train) [166][450/586] lr: 5.000000e-04 eta: 2:18:27 time: 0.339144 data_time: 0.022410 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.825948 loss: 0.000548 2022/09/13 05:38:55 - mmengine - INFO - Epoch(train) [166][500/586] lr: 5.000000e-04 eta: 2:18:11 time: 0.337890 data_time: 0.021998 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.868038 loss: 0.000541 2022/09/13 05:39:12 - mmengine - INFO - Epoch(train) [166][550/586] lr: 5.000000e-04 eta: 2:17:55 time: 0.340694 data_time: 0.022409 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.875079 loss: 0.000531 2022/09/13 05:39:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:39:24 - mmengine - INFO - Saving checkpoint at 166 epochs 2022/09/13 05:39:48 - mmengine - INFO - Epoch(train) [167][50/586] lr: 5.000000e-04 eta: 2:17:25 time: 0.341895 data_time: 0.028342 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.870930 loss: 0.000518 2022/09/13 05:40:05 - mmengine - INFO - Epoch(train) [167][100/586] lr: 5.000000e-04 eta: 2:17:09 time: 0.342522 data_time: 0.026131 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.883984 loss: 0.000530 2022/09/13 05:40:22 - mmengine - INFO - Epoch(train) [167][150/586] lr: 5.000000e-04 eta: 2:16:54 time: 0.337720 data_time: 0.022191 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.818955 loss: 0.000511 2022/09/13 05:40:39 - mmengine - INFO - Epoch(train) [167][200/586] lr: 5.000000e-04 eta: 2:16:38 time: 0.333992 data_time: 0.022115 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.851670 loss: 0.000525 2022/09/13 05:40:57 - mmengine - INFO - Epoch(train) [167][250/586] lr: 5.000000e-04 eta: 2:16:22 time: 0.351788 data_time: 0.023167 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.830518 loss: 0.000516 2022/09/13 05:41:13 - mmengine - INFO - Epoch(train) [167][300/586] lr: 5.000000e-04 eta: 2:16:06 time: 0.331445 data_time: 0.022404 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.897056 loss: 0.000496 2022/09/13 05:41:30 - mmengine - INFO - Epoch(train) [167][350/586] lr: 5.000000e-04 eta: 2:15:50 time: 0.340712 data_time: 0.023279 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.860882 loss: 0.000534 2022/09/13 05:41:47 - mmengine - INFO - Epoch(train) [167][400/586] lr: 5.000000e-04 eta: 2:15:35 time: 0.335760 data_time: 0.022837 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.858153 loss: 0.000515 2022/09/13 05:42:04 - mmengine - INFO - Epoch(train) [167][450/586] lr: 5.000000e-04 eta: 2:15:19 time: 0.338954 data_time: 0.026999 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.861654 loss: 0.000539 2022/09/13 05:42:21 - mmengine - INFO - Epoch(train) [167][500/586] lr: 5.000000e-04 eta: 2:15:03 time: 0.334969 data_time: 0.022924 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.858033 loss: 0.000524 2022/09/13 05:42:38 - mmengine - INFO - Epoch(train) [167][550/586] lr: 5.000000e-04 eta: 2:14:47 time: 0.344305 data_time: 0.022294 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.833743 loss: 0.000523 2022/09/13 05:42:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:42:50 - mmengine - INFO - Saving checkpoint at 167 epochs 2022/09/13 05:43:14 - mmengine - INFO - Epoch(train) [168][50/586] lr: 5.000000e-04 eta: 2:14:17 time: 0.346351 data_time: 0.028604 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.884300 loss: 0.000510 2022/09/13 05:43:31 - mmengine - INFO - Epoch(train) [168][100/586] lr: 5.000000e-04 eta: 2:14:01 time: 0.343911 data_time: 0.025844 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.834616 loss: 0.000518 2022/09/13 05:43:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:43:48 - mmengine - INFO - Epoch(train) [168][150/586] lr: 5.000000e-04 eta: 2:13:46 time: 0.338631 data_time: 0.021822 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.793904 loss: 0.000512 2022/09/13 05:44:05 - mmengine - INFO - Epoch(train) [168][200/586] lr: 5.000000e-04 eta: 2:13:30 time: 0.335002 data_time: 0.022287 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.890226 loss: 0.000512 2022/09/13 05:44:22 - mmengine - INFO - Epoch(train) [168][250/586] lr: 5.000000e-04 eta: 2:13:14 time: 0.339811 data_time: 0.022873 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.788215 loss: 0.000524 2022/09/13 05:44:39 - mmengine - INFO - Epoch(train) [168][300/586] lr: 5.000000e-04 eta: 2:12:58 time: 0.339270 data_time: 0.022498 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.879716 loss: 0.000533 2022/09/13 05:44:55 - mmengine - INFO - Epoch(train) [168][350/586] lr: 5.000000e-04 eta: 2:12:42 time: 0.335919 data_time: 0.022557 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.891930 loss: 0.000523 2022/09/13 05:45:12 - mmengine - INFO - Epoch(train) [168][400/586] lr: 5.000000e-04 eta: 2:12:27 time: 0.340422 data_time: 0.025881 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.874999 loss: 0.000531 2022/09/13 05:45:30 - mmengine - INFO - Epoch(train) [168][450/586] lr: 5.000000e-04 eta: 2:12:11 time: 0.342685 data_time: 0.023118 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.831336 loss: 0.000535 2022/09/13 05:45:46 - mmengine - INFO - Epoch(train) [168][500/586] lr: 5.000000e-04 eta: 2:11:55 time: 0.335811 data_time: 0.023234 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.866275 loss: 0.000514 2022/09/13 05:46:04 - mmengine - INFO - Epoch(train) [168][550/586] lr: 5.000000e-04 eta: 2:11:39 time: 0.359647 data_time: 0.022915 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.850104 loss: 0.000519 2022/09/13 05:46:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:46:16 - mmengine - INFO - Saving checkpoint at 168 epochs 2022/09/13 05:46:41 - mmengine - INFO - Epoch(train) [169][50/586] lr: 5.000000e-04 eta: 2:11:09 time: 0.343877 data_time: 0.031411 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.877916 loss: 0.000521 2022/09/13 05:46:58 - mmengine - INFO - Epoch(train) [169][100/586] lr: 5.000000e-04 eta: 2:10:54 time: 0.340345 data_time: 0.025005 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.887399 loss: 0.000523 2022/09/13 05:47:15 - mmengine - INFO - Epoch(train) [169][150/586] lr: 5.000000e-04 eta: 2:10:38 time: 0.343247 data_time: 0.026630 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.842298 loss: 0.000520 2022/09/13 05:47:32 - mmengine - INFO - Epoch(train) [169][200/586] lr: 5.000000e-04 eta: 2:10:22 time: 0.335465 data_time: 0.022648 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.844294 loss: 0.000536 2022/09/13 05:47:49 - mmengine - INFO - Epoch(train) [169][250/586] lr: 5.000000e-04 eta: 2:10:06 time: 0.334169 data_time: 0.021741 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.865551 loss: 0.000530 2022/09/13 05:48:06 - mmengine - INFO - Epoch(train) [169][300/586] lr: 5.000000e-04 eta: 2:09:50 time: 0.346803 data_time: 0.022679 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.862991 loss: 0.000516 2022/09/13 05:48:23 - mmengine - INFO - Epoch(train) [169][350/586] lr: 5.000000e-04 eta: 2:09:35 time: 0.338235 data_time: 0.022678 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.869081 loss: 0.000506 2022/09/13 05:48:40 - mmengine - INFO - Epoch(train) [169][400/586] lr: 5.000000e-04 eta: 2:09:19 time: 0.335505 data_time: 0.021952 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.865803 loss: 0.000532 2022/09/13 05:48:57 - mmengine - INFO - Epoch(train) [169][450/586] lr: 5.000000e-04 eta: 2:09:03 time: 0.340522 data_time: 0.025668 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.862312 loss: 0.000541 2022/09/13 05:49:13 - mmengine - INFO - Epoch(train) [169][500/586] lr: 5.000000e-04 eta: 2:08:47 time: 0.335894 data_time: 0.022638 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.811294 loss: 0.000511 2022/09/13 05:49:31 - mmengine - INFO - Epoch(train) [169][550/586] lr: 5.000000e-04 eta: 2:08:32 time: 0.352224 data_time: 0.022802 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.905717 loss: 0.000512 2022/09/13 05:49:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:49:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:49:43 - mmengine - INFO - Saving checkpoint at 169 epochs 2022/09/13 05:50:07 - mmengine - INFO - Epoch(train) [170][50/586] lr: 5.000000e-04 eta: 2:08:01 time: 0.341998 data_time: 0.027731 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.876657 loss: 0.000519 2022/09/13 05:50:24 - mmengine - INFO - Epoch(train) [170][100/586] lr: 5.000000e-04 eta: 2:07:46 time: 0.341068 data_time: 0.026646 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.826804 loss: 0.000523 2022/09/13 05:50:41 - mmengine - INFO - Epoch(train) [170][150/586] lr: 5.000000e-04 eta: 2:07:30 time: 0.332284 data_time: 0.022475 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.854383 loss: 0.000512 2022/09/13 05:50:58 - mmengine - INFO - Epoch(train) [170][200/586] lr: 5.000000e-04 eta: 2:07:14 time: 0.336041 data_time: 0.022745 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.763333 loss: 0.000518 2022/09/13 05:51:15 - mmengine - INFO - Epoch(train) [170][250/586] lr: 5.000000e-04 eta: 2:06:58 time: 0.341842 data_time: 0.021767 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.895990 loss: 0.000517 2022/09/13 05:51:32 - mmengine - INFO - Epoch(train) [170][300/586] lr: 5.000000e-04 eta: 2:06:42 time: 0.340006 data_time: 0.022664 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.799043 loss: 0.000521 2022/09/13 05:51:49 - mmengine - INFO - Epoch(train) [170][350/586] lr: 5.000000e-04 eta: 2:06:27 time: 0.338426 data_time: 0.022767 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.857612 loss: 0.000533 2022/09/13 05:52:07 - mmengine - INFO - Epoch(train) [170][400/586] lr: 5.000000e-04 eta: 2:06:11 time: 0.355156 data_time: 0.025703 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.853207 loss: 0.000530 2022/09/13 05:52:24 - mmengine - INFO - Epoch(train) [170][450/586] lr: 5.000000e-04 eta: 2:05:55 time: 0.337788 data_time: 0.022088 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.841016 loss: 0.000513 2022/09/13 05:52:40 - mmengine - INFO - Epoch(train) [170][500/586] lr: 5.000000e-04 eta: 2:05:39 time: 0.332210 data_time: 0.022434 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.849802 loss: 0.000510 2022/09/13 05:52:58 - mmengine - INFO - Epoch(train) [170][550/586] lr: 5.000000e-04 eta: 2:05:24 time: 0.345285 data_time: 0.022555 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.889813 loss: 0.000516 2022/09/13 05:53:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:53:10 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/09/13 05:53:28 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:01:17 time: 0.216308 data_time: 0.012509 memory: 7489 2022/09/13 05:53:39 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:01:05 time: 0.214138 data_time: 0.007394 memory: 1657 2022/09/13 05:53:49 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:54 time: 0.210218 data_time: 0.007201 memory: 1657 2022/09/13 05:54:00 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:43 time: 0.210471 data_time: 0.007622 memory: 1657 2022/09/13 05:54:10 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:32 time: 0.210080 data_time: 0.007819 memory: 1657 2022/09/13 05:54:21 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:22 time: 0.209583 data_time: 0.007300 memory: 1657 2022/09/13 05:54:31 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:12 time: 0.210698 data_time: 0.007422 memory: 1657 2022/09/13 05:54:42 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:01 time: 0.207770 data_time: 0.007485 memory: 1657 2022/09/13 05:55:17 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 05:55:31 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.755446 coco/AP .5: 0.901537 coco/AP .75: 0.821738 coco/AP (M): 0.718661 coco/AP (L): 0.824597 coco/AR: 0.806329 coco/AR .5: 0.939547 coco/AR .75: 0.864137 coco/AR (M): 0.764054 coco/AR (L): 0.867781 2022/09/13 05:55:48 - mmengine - INFO - Epoch(train) [171][50/586] lr: 5.000000e-05 eta: 2:04:54 time: 0.344038 data_time: 0.026440 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.868112 loss: 0.000518 2022/09/13 05:56:05 - mmengine - INFO - Epoch(train) [171][100/586] lr: 5.000000e-05 eta: 2:04:38 time: 0.341971 data_time: 0.022414 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.889337 loss: 0.000508 2022/09/13 05:56:22 - mmengine - INFO - Epoch(train) [171][150/586] lr: 5.000000e-05 eta: 2:04:22 time: 0.338562 data_time: 0.023000 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.792824 loss: 0.000502 2022/09/13 05:56:39 - mmengine - INFO - Epoch(train) [171][200/586] lr: 5.000000e-05 eta: 2:04:06 time: 0.344211 data_time: 0.026211 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.897959 loss: 0.000499 2022/09/13 05:56:56 - mmengine - INFO - Epoch(train) [171][250/586] lr: 5.000000e-05 eta: 2:03:50 time: 0.340808 data_time: 0.022450 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.851957 loss: 0.000518 2022/09/13 05:57:13 - mmengine - INFO - Epoch(train) [171][300/586] lr: 5.000000e-05 eta: 2:03:35 time: 0.340865 data_time: 0.022355 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.764860 loss: 0.000504 2022/09/13 05:57:30 - mmengine - INFO - Epoch(train) [171][350/586] lr: 5.000000e-05 eta: 2:03:19 time: 0.341132 data_time: 0.022229 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.784995 loss: 0.000507 2022/09/13 05:57:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:57:48 - mmengine - INFO - Epoch(train) [171][400/586] lr: 5.000000e-05 eta: 2:03:03 time: 0.344260 data_time: 0.022642 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.926594 loss: 0.000507 2022/09/13 05:58:05 - mmengine - INFO - Epoch(train) [171][450/586] lr: 5.000000e-05 eta: 2:02:47 time: 0.337048 data_time: 0.022282 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.724739 loss: 0.000509 2022/09/13 05:58:22 - mmengine - INFO - Epoch(train) [171][500/586] lr: 5.000000e-05 eta: 2:02:32 time: 0.344530 data_time: 0.026400 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.857293 loss: 0.000502 2022/09/13 05:58:39 - mmengine - INFO - Epoch(train) [171][550/586] lr: 5.000000e-05 eta: 2:02:16 time: 0.335104 data_time: 0.022918 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.874223 loss: 0.000512 2022/09/13 05:58:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 05:58:51 - mmengine - INFO - Saving checkpoint at 171 epochs 2022/09/13 05:59:15 - mmengine - INFO - Epoch(train) [172][50/586] lr: 5.000000e-05 eta: 2:01:46 time: 0.336073 data_time: 0.026538 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.871047 loss: 0.000483 2022/09/13 05:59:32 - mmengine - INFO - Epoch(train) [172][100/586] lr: 5.000000e-05 eta: 2:01:30 time: 0.347467 data_time: 0.026142 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.911050 loss: 0.000501 2022/09/13 05:59:49 - mmengine - INFO - Epoch(train) [172][150/586] lr: 5.000000e-05 eta: 2:01:14 time: 0.329996 data_time: 0.022190 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.758470 loss: 0.000501 2022/09/13 06:00:07 - mmengine - INFO - Epoch(train) [172][200/586] lr: 5.000000e-05 eta: 2:00:59 time: 0.371284 data_time: 0.022949 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.892956 loss: 0.000508 2022/09/13 06:00:24 - mmengine - INFO - Epoch(train) [172][250/586] lr: 5.000000e-05 eta: 2:00:43 time: 0.341439 data_time: 0.023529 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.915000 loss: 0.000487 2022/09/13 06:00:41 - mmengine - INFO - Epoch(train) [172][300/586] lr: 5.000000e-05 eta: 2:00:27 time: 0.334104 data_time: 0.022568 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.821295 loss: 0.000512 2022/09/13 06:00:58 - mmengine - INFO - Epoch(train) [172][350/586] lr: 5.000000e-05 eta: 2:00:11 time: 0.340632 data_time: 0.022259 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.876491 loss: 0.000500 2022/09/13 06:01:15 - mmengine - INFO - Epoch(train) [172][400/586] lr: 5.000000e-05 eta: 1:59:55 time: 0.342039 data_time: 0.023294 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.881019 loss: 0.000483 2022/09/13 06:01:32 - mmengine - INFO - Epoch(train) [172][450/586] lr: 5.000000e-05 eta: 1:59:39 time: 0.330259 data_time: 0.021689 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.823156 loss: 0.000491 2022/09/13 06:01:49 - mmengine - INFO - Epoch(train) [172][500/586] lr: 5.000000e-05 eta: 1:59:24 time: 0.336957 data_time: 0.022167 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.855278 loss: 0.000488 2022/09/13 06:02:06 - mmengine - INFO - Epoch(train) [172][550/586] lr: 5.000000e-05 eta: 1:59:08 time: 0.342037 data_time: 0.021981 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.867415 loss: 0.000479 2022/09/13 06:02:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:02:18 - mmengine - INFO - Saving checkpoint at 172 epochs 2022/09/13 06:02:43 - mmengine - INFO - Epoch(train) [173][50/586] lr: 5.000000e-05 eta: 1:58:38 time: 0.346864 data_time: 0.034577 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.870169 loss: 0.000508 2022/09/13 06:02:59 - mmengine - INFO - Epoch(train) [173][100/586] lr: 5.000000e-05 eta: 1:58:22 time: 0.337582 data_time: 0.022265 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.872171 loss: 0.000485 2022/09/13 06:03:16 - mmengine - INFO - Epoch(train) [173][150/586] lr: 5.000000e-05 eta: 1:58:06 time: 0.340103 data_time: 0.022300 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.859360 loss: 0.000500 2022/09/13 06:03:33 - mmengine - INFO - Epoch(train) [173][200/586] lr: 5.000000e-05 eta: 1:57:51 time: 0.336287 data_time: 0.022716 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.882975 loss: 0.000490 2022/09/13 06:03:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:03:50 - mmengine - INFO - Epoch(train) [173][250/586] lr: 5.000000e-05 eta: 1:57:35 time: 0.335500 data_time: 0.021951 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.891681 loss: 0.000493 2022/09/13 06:04:07 - mmengine - INFO - Epoch(train) [173][300/586] lr: 5.000000e-05 eta: 1:57:19 time: 0.336597 data_time: 0.021796 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.867902 loss: 0.000493 2022/09/13 06:04:24 - mmengine - INFO - Epoch(train) [173][350/586] lr: 5.000000e-05 eta: 1:57:03 time: 0.347200 data_time: 0.023267 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.862188 loss: 0.000504 2022/09/13 06:04:41 - mmengine - INFO - Epoch(train) [173][400/586] lr: 5.000000e-05 eta: 1:56:47 time: 0.343449 data_time: 0.023928 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.929871 loss: 0.000491 2022/09/13 06:04:58 - mmengine - INFO - Epoch(train) [173][450/586] lr: 5.000000e-05 eta: 1:56:32 time: 0.338966 data_time: 0.022290 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.908148 loss: 0.000477 2022/09/13 06:05:16 - mmengine - INFO - Epoch(train) [173][500/586] lr: 5.000000e-05 eta: 1:56:16 time: 0.342977 data_time: 0.026805 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.869379 loss: 0.000500 2022/09/13 06:05:32 - mmengine - INFO - Epoch(train) [173][550/586] lr: 5.000000e-05 eta: 1:56:00 time: 0.336091 data_time: 0.022125 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.847224 loss: 0.000490 2022/09/13 06:05:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:05:45 - mmengine - INFO - Saving checkpoint at 173 epochs 2022/09/13 06:06:08 - mmengine - INFO - Epoch(train) [174][50/586] lr: 5.000000e-05 eta: 1:55:30 time: 0.340609 data_time: 0.025926 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.874599 loss: 0.000485 2022/09/13 06:06:26 - mmengine - INFO - Epoch(train) [174][100/586] lr: 5.000000e-05 eta: 1:55:14 time: 0.341899 data_time: 0.022846 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.863853 loss: 0.000485 2022/09/13 06:06:43 - mmengine - INFO - Epoch(train) [174][150/586] lr: 5.000000e-05 eta: 1:54:59 time: 0.344176 data_time: 0.022104 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.893168 loss: 0.000499 2022/09/13 06:07:00 - mmengine - INFO - Epoch(train) [174][200/586] lr: 5.000000e-05 eta: 1:54:43 time: 0.337602 data_time: 0.021737 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.862378 loss: 0.000497 2022/09/13 06:07:17 - mmengine - INFO - Epoch(train) [174][250/586] lr: 5.000000e-05 eta: 1:54:27 time: 0.338728 data_time: 0.022201 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.908965 loss: 0.000487 2022/09/13 06:07:33 - mmengine - INFO - Epoch(train) [174][300/586] lr: 5.000000e-05 eta: 1:54:11 time: 0.336253 data_time: 0.022039 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.852876 loss: 0.000490 2022/09/13 06:07:50 - mmengine - INFO - Epoch(train) [174][350/586] lr: 5.000000e-05 eta: 1:53:55 time: 0.340503 data_time: 0.022268 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.840936 loss: 0.000501 2022/09/13 06:08:07 - mmengine - INFO - Epoch(train) [174][400/586] lr: 5.000000e-05 eta: 1:53:39 time: 0.339629 data_time: 0.022388 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.875895 loss: 0.000485 2022/09/13 06:08:24 - mmengine - INFO - Epoch(train) [174][450/586] lr: 5.000000e-05 eta: 1:53:23 time: 0.330888 data_time: 0.022248 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.799441 loss: 0.000521 2022/09/13 06:08:41 - mmengine - INFO - Epoch(train) [174][500/586] lr: 5.000000e-05 eta: 1:53:08 time: 0.337982 data_time: 0.022016 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.901254 loss: 0.000511 2022/09/13 06:08:58 - mmengine - INFO - Epoch(train) [174][550/586] lr: 5.000000e-05 eta: 1:52:52 time: 0.344383 data_time: 0.025748 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.870435 loss: 0.000505 2022/09/13 06:09:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:09:10 - mmengine - INFO - Saving checkpoint at 174 epochs 2022/09/13 06:09:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:09:34 - mmengine - INFO - Epoch(train) [175][50/586] lr: 5.000000e-05 eta: 1:52:22 time: 0.347996 data_time: 0.032706 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.877292 loss: 0.000494 2022/09/13 06:09:51 - mmengine - INFO - Epoch(train) [175][100/586] lr: 5.000000e-05 eta: 1:52:06 time: 0.337334 data_time: 0.022132 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.884621 loss: 0.000488 2022/09/13 06:10:08 - mmengine - INFO - Epoch(train) [175][150/586] lr: 5.000000e-05 eta: 1:51:51 time: 0.340027 data_time: 0.021979 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.853335 loss: 0.000490 2022/09/13 06:10:25 - mmengine - INFO - Epoch(train) [175][200/586] lr: 5.000000e-05 eta: 1:51:35 time: 0.341638 data_time: 0.026637 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.905020 loss: 0.000478 2022/09/13 06:10:41 - mmengine - INFO - Epoch(train) [175][250/586] lr: 5.000000e-05 eta: 1:51:19 time: 0.328128 data_time: 0.022847 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.871982 loss: 0.000486 2022/09/13 06:10:58 - mmengine - INFO - Epoch(train) [175][300/586] lr: 5.000000e-05 eta: 1:51:03 time: 0.340533 data_time: 0.022871 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.848999 loss: 0.000499 2022/09/13 06:11:15 - mmengine - INFO - Epoch(train) [175][350/586] lr: 5.000000e-05 eta: 1:50:47 time: 0.334113 data_time: 0.023258 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.886938 loss: 0.000485 2022/09/13 06:11:32 - mmengine - INFO - Epoch(train) [175][400/586] lr: 5.000000e-05 eta: 1:50:31 time: 0.339919 data_time: 0.022834 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.834556 loss: 0.000493 2022/09/13 06:11:49 - mmengine - INFO - Epoch(train) [175][450/586] lr: 5.000000e-05 eta: 1:50:15 time: 0.338034 data_time: 0.021923 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.840421 loss: 0.000485 2022/09/13 06:12:06 - mmengine - INFO - Epoch(train) [175][500/586] lr: 5.000000e-05 eta: 1:50:00 time: 0.333170 data_time: 0.022208 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.828911 loss: 0.000511 2022/09/13 06:12:23 - mmengine - INFO - Epoch(train) [175][550/586] lr: 5.000000e-05 eta: 1:49:44 time: 0.337129 data_time: 0.022156 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.899923 loss: 0.000493 2022/09/13 06:12:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:12:35 - mmengine - INFO - Saving checkpoint at 175 epochs 2022/09/13 06:13:00 - mmengine - INFO - Epoch(train) [176][50/586] lr: 5.000000e-05 eta: 1:49:14 time: 0.342985 data_time: 0.030905 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.851519 loss: 0.000499 2022/09/13 06:13:17 - mmengine - INFO - Epoch(train) [176][100/586] lr: 5.000000e-05 eta: 1:48:58 time: 0.347299 data_time: 0.027659 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.804056 loss: 0.000480 2022/09/13 06:13:34 - mmengine - INFO - Epoch(train) [176][150/586] lr: 5.000000e-05 eta: 1:48:42 time: 0.332960 data_time: 0.022632 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.880313 loss: 0.000501 2022/09/13 06:13:51 - mmengine - INFO - Epoch(train) [176][200/586] lr: 5.000000e-05 eta: 1:48:27 time: 0.340680 data_time: 0.022264 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.888678 loss: 0.000494 2022/09/13 06:14:08 - mmengine - INFO - Epoch(train) [176][250/586] lr: 5.000000e-05 eta: 1:48:11 time: 0.342522 data_time: 0.025054 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.841029 loss: 0.000481 2022/09/13 06:14:25 - mmengine - INFO - Epoch(train) [176][300/586] lr: 5.000000e-05 eta: 1:47:55 time: 0.341532 data_time: 0.022444 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.875206 loss: 0.000494 2022/09/13 06:14:41 - mmengine - INFO - Epoch(train) [176][350/586] lr: 5.000000e-05 eta: 1:47:39 time: 0.332386 data_time: 0.021561 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.840080 loss: 0.000487 2022/09/13 06:14:58 - mmengine - INFO - Epoch(train) [176][400/586] lr: 5.000000e-05 eta: 1:47:23 time: 0.335189 data_time: 0.022026 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.863829 loss: 0.000488 2022/09/13 06:15:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:15:15 - mmengine - INFO - Epoch(train) [176][450/586] lr: 5.000000e-05 eta: 1:47:07 time: 0.341338 data_time: 0.026750 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.848818 loss: 0.000492 2022/09/13 06:15:32 - mmengine - INFO - Epoch(train) [176][500/586] lr: 5.000000e-05 eta: 1:46:51 time: 0.333867 data_time: 0.022439 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.835128 loss: 0.000496 2022/09/13 06:15:49 - mmengine - INFO - Epoch(train) [176][550/586] lr: 5.000000e-05 eta: 1:46:36 time: 0.335409 data_time: 0.022835 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.896995 loss: 0.000500 2022/09/13 06:16:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:16:01 - mmengine - INFO - Saving checkpoint at 176 epochs 2022/09/13 06:16:25 - mmengine - INFO - Epoch(train) [177][50/586] lr: 5.000000e-05 eta: 1:46:06 time: 0.342909 data_time: 0.027318 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.825369 loss: 0.000490 2022/09/13 06:16:42 - mmengine - INFO - Epoch(train) [177][100/586] lr: 5.000000e-05 eta: 1:45:50 time: 0.349544 data_time: 0.022031 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.835374 loss: 0.000487 2022/09/13 06:16:59 - mmengine - INFO - Epoch(train) [177][150/586] lr: 5.000000e-05 eta: 1:45:34 time: 0.334393 data_time: 0.022662 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.872355 loss: 0.000491 2022/09/13 06:17:16 - mmengine - INFO - Epoch(train) [177][200/586] lr: 5.000000e-05 eta: 1:45:19 time: 0.343088 data_time: 0.022662 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.920485 loss: 0.000492 2022/09/13 06:17:33 - mmengine - INFO - Epoch(train) [177][250/586] lr: 5.000000e-05 eta: 1:45:03 time: 0.339146 data_time: 0.021979 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.896911 loss: 0.000503 2022/09/13 06:17:50 - mmengine - INFO - Epoch(train) [177][300/586] lr: 5.000000e-05 eta: 1:44:47 time: 0.338426 data_time: 0.022799 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.795452 loss: 0.000503 2022/09/13 06:18:07 - mmengine - INFO - Epoch(train) [177][350/586] lr: 5.000000e-05 eta: 1:44:31 time: 0.347674 data_time: 0.025780 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.806305 loss: 0.000489 2022/09/13 06:18:24 - mmengine - INFO - Epoch(train) [177][400/586] lr: 5.000000e-05 eta: 1:44:15 time: 0.333313 data_time: 0.022527 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.898294 loss: 0.000494 2022/09/13 06:18:41 - mmengine - INFO - Epoch(train) [177][450/586] lr: 5.000000e-05 eta: 1:43:59 time: 0.337993 data_time: 0.022202 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.858376 loss: 0.000499 2022/09/13 06:18:58 - mmengine - INFO - Epoch(train) [177][500/586] lr: 5.000000e-05 eta: 1:43:44 time: 0.342720 data_time: 0.022586 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.822222 loss: 0.000488 2022/09/13 06:19:15 - mmengine - INFO - Epoch(train) [177][550/586] lr: 5.000000e-05 eta: 1:43:28 time: 0.332371 data_time: 0.023142 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.870922 loss: 0.000489 2022/09/13 06:19:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:19:27 - mmengine - INFO - Saving checkpoint at 177 epochs 2022/09/13 06:19:52 - mmengine - INFO - Epoch(train) [178][50/586] lr: 5.000000e-05 eta: 1:42:58 time: 0.341718 data_time: 0.029305 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.893214 loss: 0.000489 2022/09/13 06:20:09 - mmengine - INFO - Epoch(train) [178][100/586] lr: 5.000000e-05 eta: 1:42:42 time: 0.339980 data_time: 0.025192 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.907314 loss: 0.000485 2022/09/13 06:20:26 - mmengine - INFO - Epoch(train) [178][150/586] lr: 5.000000e-05 eta: 1:42:27 time: 0.346809 data_time: 0.022203 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.884128 loss: 0.000497 2022/09/13 06:20:43 - mmengine - INFO - Epoch(train) [178][200/586] lr: 5.000000e-05 eta: 1:42:11 time: 0.339979 data_time: 0.022730 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.916742 loss: 0.000496 2022/09/13 06:21:00 - mmengine - INFO - Epoch(train) [178][250/586] lr: 5.000000e-05 eta: 1:41:55 time: 0.338821 data_time: 0.021739 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.877346 loss: 0.000483 2022/09/13 06:21:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:21:17 - mmengine - INFO - Epoch(train) [178][300/586] lr: 5.000000e-05 eta: 1:41:39 time: 0.334038 data_time: 0.025520 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.867894 loss: 0.000482 2022/09/13 06:21:34 - mmengine - INFO - Epoch(train) [178][350/586] lr: 5.000000e-05 eta: 1:41:23 time: 0.343621 data_time: 0.022625 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.833728 loss: 0.000482 2022/09/13 06:21:51 - mmengine - INFO - Epoch(train) [178][400/586] lr: 5.000000e-05 eta: 1:41:07 time: 0.335252 data_time: 0.022349 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.880206 loss: 0.000493 2022/09/13 06:22:08 - mmengine - INFO - Epoch(train) [178][450/586] lr: 5.000000e-05 eta: 1:40:51 time: 0.343770 data_time: 0.022160 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.898387 loss: 0.000471 2022/09/13 06:22:25 - mmengine - INFO - Epoch(train) [178][500/586] lr: 5.000000e-05 eta: 1:40:36 time: 0.333101 data_time: 0.022056 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.842449 loss: 0.000483 2022/09/13 06:22:42 - mmengine - INFO - Epoch(train) [178][550/586] lr: 5.000000e-05 eta: 1:40:20 time: 0.337286 data_time: 0.022589 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.915023 loss: 0.000475 2022/09/13 06:22:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:22:54 - mmengine - INFO - Saving checkpoint at 178 epochs 2022/09/13 06:23:18 - mmengine - INFO - Epoch(train) [179][50/586] lr: 5.000000e-05 eta: 1:39:50 time: 0.344420 data_time: 0.026504 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.879909 loss: 0.000487 2022/09/13 06:23:35 - mmengine - INFO - Epoch(train) [179][100/586] lr: 5.000000e-05 eta: 1:39:34 time: 0.337322 data_time: 0.026476 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.796664 loss: 0.000497 2022/09/13 06:23:52 - mmengine - INFO - Epoch(train) [179][150/586] lr: 5.000000e-05 eta: 1:39:19 time: 0.340980 data_time: 0.022097 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.896241 loss: 0.000482 2022/09/13 06:24:10 - mmengine - INFO - Epoch(train) [179][200/586] lr: 5.000000e-05 eta: 1:39:03 time: 0.345747 data_time: 0.023265 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.920330 loss: 0.000495 2022/09/13 06:24:27 - mmengine - INFO - Epoch(train) [179][250/586] lr: 5.000000e-05 eta: 1:38:47 time: 0.346212 data_time: 0.027788 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.876405 loss: 0.000483 2022/09/13 06:24:44 - mmengine - INFO - Epoch(train) [179][300/586] lr: 5.000000e-05 eta: 1:38:31 time: 0.333635 data_time: 0.022746 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.837759 loss: 0.000487 2022/09/13 06:25:00 - mmengine - INFO - Epoch(train) [179][350/586] lr: 5.000000e-05 eta: 1:38:15 time: 0.335279 data_time: 0.022184 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.868530 loss: 0.000493 2022/09/13 06:25:18 - mmengine - INFO - Epoch(train) [179][400/586] lr: 5.000000e-05 eta: 1:37:59 time: 0.344218 data_time: 0.026795 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.862757 loss: 0.000502 2022/09/13 06:25:34 - mmengine - INFO - Epoch(train) [179][450/586] lr: 5.000000e-05 eta: 1:37:44 time: 0.336840 data_time: 0.022077 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.873166 loss: 0.000492 2022/09/13 06:25:51 - mmengine - INFO - Epoch(train) [179][500/586] lr: 5.000000e-05 eta: 1:37:28 time: 0.339838 data_time: 0.022072 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.898861 loss: 0.000493 2022/09/13 06:26:08 - mmengine - INFO - Epoch(train) [179][550/586] lr: 5.000000e-05 eta: 1:37:12 time: 0.338216 data_time: 0.026370 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.852124 loss: 0.000500 2022/09/13 06:26:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:26:20 - mmengine - INFO - Saving checkpoint at 179 epochs 2022/09/13 06:26:45 - mmengine - INFO - Epoch(train) [180][50/586] lr: 5.000000e-05 eta: 1:36:42 time: 0.345625 data_time: 0.029035 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.928692 loss: 0.000471 2022/09/13 06:27:02 - mmengine - INFO - Epoch(train) [180][100/586] lr: 5.000000e-05 eta: 1:36:27 time: 0.343884 data_time: 0.022018 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.860414 loss: 0.000493 2022/09/13 06:27:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:27:19 - mmengine - INFO - Epoch(train) [180][150/586] lr: 5.000000e-05 eta: 1:36:11 time: 0.340460 data_time: 0.021855 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.834586 loss: 0.000494 2022/09/13 06:27:36 - mmengine - INFO - Epoch(train) [180][200/586] lr: 5.000000e-05 eta: 1:35:55 time: 0.339059 data_time: 0.022786 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.862100 loss: 0.000485 2022/09/13 06:27:53 - mmengine - INFO - Epoch(train) [180][250/586] lr: 5.000000e-05 eta: 1:35:39 time: 0.337374 data_time: 0.022806 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.910134 loss: 0.000508 2022/09/13 06:28:10 - mmengine - INFO - Epoch(train) [180][300/586] lr: 5.000000e-05 eta: 1:35:23 time: 0.339450 data_time: 0.022221 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.889894 loss: 0.000479 2022/09/13 06:28:27 - mmengine - INFO - Epoch(train) [180][350/586] lr: 5.000000e-05 eta: 1:35:07 time: 0.339206 data_time: 0.022448 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.898340 loss: 0.000490 2022/09/13 06:28:44 - mmengine - INFO - Epoch(train) [180][400/586] lr: 5.000000e-05 eta: 1:34:52 time: 0.343879 data_time: 0.021908 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.911939 loss: 0.000470 2022/09/13 06:29:01 - mmengine - INFO - Epoch(train) [180][450/586] lr: 5.000000e-05 eta: 1:34:36 time: 0.336583 data_time: 0.022822 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.850651 loss: 0.000473 2022/09/13 06:29:18 - mmengine - INFO - Epoch(train) [180][500/586] lr: 5.000000e-05 eta: 1:34:20 time: 0.335059 data_time: 0.022347 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.813370 loss: 0.000499 2022/09/13 06:29:35 - mmengine - INFO - Epoch(train) [180][550/586] lr: 5.000000e-05 eta: 1:34:04 time: 0.337996 data_time: 0.022388 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.861277 loss: 0.000486 2022/09/13 06:29:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:29:47 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/09/13 06:30:05 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:01:16 time: 0.215035 data_time: 0.012439 memory: 7489 2022/09/13 06:30:15 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:01:04 time: 0.209231 data_time: 0.007507 memory: 1657 2022/09/13 06:30:26 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:53 time: 0.209494 data_time: 0.007580 memory: 1657 2022/09/13 06:30:36 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:43 time: 0.212233 data_time: 0.011226 memory: 1657 2022/09/13 06:30:47 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:32 time: 0.208457 data_time: 0.007405 memory: 1657 2022/09/13 06:30:57 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:22 time: 0.208078 data_time: 0.007583 memory: 1657 2022/09/13 06:31:08 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:11 time: 0.209002 data_time: 0.007715 memory: 1657 2022/09/13 06:31:18 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:01 time: 0.207368 data_time: 0.007101 memory: 1657 2022/09/13 06:31:54 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 06:32:07 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.762342 coco/AP .5: 0.906055 coco/AP .75: 0.826950 coco/AP (M): 0.725687 coco/AP (L): 0.829752 coco/AR: 0.811288 coco/AR .5: 0.940963 coco/AR .75: 0.869805 coco/AR (M): 0.770227 coco/AR (L): 0.871275 2022/09/13 06:32:08 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_160.pth is removed 2022/09/13 06:32:12 - mmengine - INFO - The best checkpoint with 0.7623 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/09/13 06:32:29 - mmengine - INFO - Epoch(train) [181][50/586] lr: 5.000000e-05 eta: 1:33:35 time: 0.341562 data_time: 0.027170 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.845812 loss: 0.000511 2022/09/13 06:32:46 - mmengine - INFO - Epoch(train) [181][100/586] lr: 5.000000e-05 eta: 1:33:19 time: 0.342692 data_time: 0.023296 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.849568 loss: 0.000492 2022/09/13 06:33:03 - mmengine - INFO - Epoch(train) [181][150/586] lr: 5.000000e-05 eta: 1:33:03 time: 0.332529 data_time: 0.022286 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.832553 loss: 0.000463 2022/09/13 06:33:20 - mmengine - INFO - Epoch(train) [181][200/586] lr: 5.000000e-05 eta: 1:32:47 time: 0.339777 data_time: 0.022056 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.916576 loss: 0.000497 2022/09/13 06:33:36 - mmengine - INFO - Epoch(train) [181][250/586] lr: 5.000000e-05 eta: 1:32:31 time: 0.336414 data_time: 0.022528 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.866300 loss: 0.000472 2022/09/13 06:33:53 - mmengine - INFO - Epoch(train) [181][300/586] lr: 5.000000e-05 eta: 1:32:15 time: 0.333242 data_time: 0.025365 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.804107 loss: 0.000483 2022/09/13 06:34:10 - mmengine - INFO - Epoch(train) [181][350/586] lr: 5.000000e-05 eta: 1:31:59 time: 0.343835 data_time: 0.023013 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.892271 loss: 0.000487 2022/09/13 06:34:27 - mmengine - INFO - Epoch(train) [181][400/586] lr: 5.000000e-05 eta: 1:31:44 time: 0.339501 data_time: 0.023322 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.855195 loss: 0.000505 2022/09/13 06:34:44 - mmengine - INFO - Epoch(train) [181][450/586] lr: 5.000000e-05 eta: 1:31:28 time: 0.333520 data_time: 0.022157 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.834554 loss: 0.000477 2022/09/13 06:35:01 - mmengine - INFO - Epoch(train) [181][500/586] lr: 5.000000e-05 eta: 1:31:12 time: 0.339318 data_time: 0.022649 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.896968 loss: 0.000476 2022/09/13 06:35:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:35:18 - mmengine - INFO - Epoch(train) [181][550/586] lr: 5.000000e-05 eta: 1:30:56 time: 0.336362 data_time: 0.022909 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.821292 loss: 0.000476 2022/09/13 06:35:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:35:30 - mmengine - INFO - Saving checkpoint at 181 epochs 2022/09/13 06:35:54 - mmengine - INFO - Epoch(train) [182][50/586] lr: 5.000000e-05 eta: 1:30:27 time: 0.346035 data_time: 0.030074 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.834745 loss: 0.000483 2022/09/13 06:36:11 - mmengine - INFO - Epoch(train) [182][100/586] lr: 5.000000e-05 eta: 1:30:11 time: 0.343401 data_time: 0.029114 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.875715 loss: 0.000465 2022/09/13 06:36:28 - mmengine - INFO - Epoch(train) [182][150/586] lr: 5.000000e-05 eta: 1:29:55 time: 0.336154 data_time: 0.023071 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.849649 loss: 0.000483 2022/09/13 06:36:45 - mmengine - INFO - Epoch(train) [182][200/586] lr: 5.000000e-05 eta: 1:29:39 time: 0.339424 data_time: 0.022693 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.910627 loss: 0.000479 2022/09/13 06:37:02 - mmengine - INFO - Epoch(train) [182][250/586] lr: 5.000000e-05 eta: 1:29:23 time: 0.341885 data_time: 0.022723 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.884805 loss: 0.000499 2022/09/13 06:37:18 - mmengine - INFO - Epoch(train) [182][300/586] lr: 5.000000e-05 eta: 1:29:07 time: 0.329199 data_time: 0.021885 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.917205 loss: 0.000492 2022/09/13 06:37:36 - mmengine - INFO - Epoch(train) [182][350/586] lr: 5.000000e-05 eta: 1:28:51 time: 0.345417 data_time: 0.022139 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.902026 loss: 0.000469 2022/09/13 06:37:52 - mmengine - INFO - Epoch(train) [182][400/586] lr: 5.000000e-05 eta: 1:28:36 time: 0.337161 data_time: 0.022283 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.898201 loss: 0.000488 2022/09/13 06:38:09 - mmengine - INFO - Epoch(train) [182][450/586] lr: 5.000000e-05 eta: 1:28:20 time: 0.331327 data_time: 0.022289 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.891605 loss: 0.000484 2022/09/13 06:38:26 - mmengine - INFO - Epoch(train) [182][500/586] lr: 5.000000e-05 eta: 1:28:04 time: 0.347344 data_time: 0.022023 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.891758 loss: 0.000493 2022/09/13 06:38:43 - mmengine - INFO - Epoch(train) [182][550/586] lr: 5.000000e-05 eta: 1:27:48 time: 0.340934 data_time: 0.025464 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.814934 loss: 0.000481 2022/09/13 06:38:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:38:56 - mmengine - INFO - Saving checkpoint at 182 epochs 2022/09/13 06:39:20 - mmengine - INFO - Epoch(train) [183][50/586] lr: 5.000000e-05 eta: 1:27:19 time: 0.342476 data_time: 0.029477 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.869754 loss: 0.000475 2022/09/13 06:39:37 - mmengine - INFO - Epoch(train) [183][100/586] lr: 5.000000e-05 eta: 1:27:03 time: 0.341619 data_time: 0.022646 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.899896 loss: 0.000483 2022/09/13 06:39:54 - mmengine - INFO - Epoch(train) [183][150/586] lr: 5.000000e-05 eta: 1:26:47 time: 0.332378 data_time: 0.022069 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.906465 loss: 0.000485 2022/09/13 06:40:11 - mmengine - INFO - Epoch(train) [183][200/586] lr: 5.000000e-05 eta: 1:26:31 time: 0.337798 data_time: 0.025425 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.898634 loss: 0.000488 2022/09/13 06:40:28 - mmengine - INFO - Epoch(train) [183][250/586] lr: 5.000000e-05 eta: 1:26:15 time: 0.340886 data_time: 0.022917 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.904823 loss: 0.000485 2022/09/13 06:40:44 - mmengine - INFO - Epoch(train) [183][300/586] lr: 5.000000e-05 eta: 1:25:59 time: 0.330235 data_time: 0.022270 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.843443 loss: 0.000495 2022/09/13 06:41:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:41:01 - mmengine - INFO - Epoch(train) [183][350/586] lr: 5.000000e-05 eta: 1:25:43 time: 0.340551 data_time: 0.022547 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.852206 loss: 0.000482 2022/09/13 06:41:18 - mmengine - INFO - Epoch(train) [183][400/586] lr: 5.000000e-05 eta: 1:25:28 time: 0.339997 data_time: 0.022048 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.864635 loss: 0.000483 2022/09/13 06:41:35 - mmengine - INFO - Epoch(train) [183][450/586] lr: 5.000000e-05 eta: 1:25:12 time: 0.328691 data_time: 0.022309 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.894788 loss: 0.000485 2022/09/13 06:41:52 - mmengine - INFO - Epoch(train) [183][500/586] lr: 5.000000e-05 eta: 1:24:56 time: 0.338093 data_time: 0.025153 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.876220 loss: 0.000489 2022/09/13 06:42:09 - mmengine - INFO - Epoch(train) [183][550/586] lr: 5.000000e-05 eta: 1:24:40 time: 0.343883 data_time: 0.022918 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.909317 loss: 0.000488 2022/09/13 06:42:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:42:21 - mmengine - INFO - Saving checkpoint at 183 epochs 2022/09/13 06:42:44 - mmengine - INFO - Epoch(train) [184][50/586] lr: 5.000000e-05 eta: 1:24:11 time: 0.339412 data_time: 0.026912 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.864278 loss: 0.000491 2022/09/13 06:43:01 - mmengine - INFO - Epoch(train) [184][100/586] lr: 5.000000e-05 eta: 1:23:55 time: 0.344547 data_time: 0.026360 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.865911 loss: 0.000486 2022/09/13 06:43:18 - mmengine - INFO - Epoch(train) [184][150/586] lr: 5.000000e-05 eta: 1:23:39 time: 0.334451 data_time: 0.022120 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.885051 loss: 0.000487 2022/09/13 06:43:35 - mmengine - INFO - Epoch(train) [184][200/586] lr: 5.000000e-05 eta: 1:23:23 time: 0.337994 data_time: 0.022804 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.865182 loss: 0.000490 2022/09/13 06:43:52 - mmengine - INFO - Epoch(train) [184][250/586] lr: 5.000000e-05 eta: 1:23:07 time: 0.340182 data_time: 0.025519 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.921192 loss: 0.000479 2022/09/13 06:44:09 - mmengine - INFO - Epoch(train) [184][300/586] lr: 5.000000e-05 eta: 1:22:51 time: 0.340004 data_time: 0.022555 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.889604 loss: 0.000507 2022/09/13 06:44:26 - mmengine - INFO - Epoch(train) [184][350/586] lr: 5.000000e-05 eta: 1:22:36 time: 0.334119 data_time: 0.021738 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.902677 loss: 0.000478 2022/09/13 06:44:43 - mmengine - INFO - Epoch(train) [184][400/586] lr: 5.000000e-05 eta: 1:22:20 time: 0.343139 data_time: 0.026148 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.890377 loss: 0.000473 2022/09/13 06:45:00 - mmengine - INFO - Epoch(train) [184][450/586] lr: 5.000000e-05 eta: 1:22:04 time: 0.336366 data_time: 0.022342 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.842354 loss: 0.000471 2022/09/13 06:45:17 - mmengine - INFO - Epoch(train) [184][500/586] lr: 5.000000e-05 eta: 1:21:48 time: 0.338207 data_time: 0.022428 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.877558 loss: 0.000475 2022/09/13 06:45:34 - mmengine - INFO - Epoch(train) [184][550/586] lr: 5.000000e-05 eta: 1:21:32 time: 0.344832 data_time: 0.023413 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.808739 loss: 0.000490 2022/09/13 06:45:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:45:46 - mmengine - INFO - Saving checkpoint at 184 epochs 2022/09/13 06:46:09 - mmengine - INFO - Epoch(train) [185][50/586] lr: 5.000000e-05 eta: 1:21:03 time: 0.338464 data_time: 0.027678 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.823927 loss: 0.000506 2022/09/13 06:46:26 - mmengine - INFO - Epoch(train) [185][100/586] lr: 5.000000e-05 eta: 1:20:47 time: 0.338753 data_time: 0.022204 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.813570 loss: 0.000479 2022/09/13 06:46:44 - mmengine - INFO - Epoch(train) [185][150/586] lr: 5.000000e-05 eta: 1:20:31 time: 0.351807 data_time: 0.023575 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.881089 loss: 0.000502 2022/09/13 06:46:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:47:01 - mmengine - INFO - Epoch(train) [185][200/586] lr: 5.000000e-05 eta: 1:20:15 time: 0.335832 data_time: 0.022766 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.889106 loss: 0.000498 2022/09/13 06:47:18 - mmengine - INFO - Epoch(train) [185][250/586] lr: 5.000000e-05 eta: 1:20:00 time: 0.337731 data_time: 0.022361 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.844886 loss: 0.000495 2022/09/13 06:47:34 - mmengine - INFO - Epoch(train) [185][300/586] lr: 5.000000e-05 eta: 1:19:44 time: 0.336871 data_time: 0.021801 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.857816 loss: 0.000477 2022/09/13 06:47:51 - mmengine - INFO - Epoch(train) [185][350/586] lr: 5.000000e-05 eta: 1:19:28 time: 0.336242 data_time: 0.021932 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.917301 loss: 0.000480 2022/09/13 06:48:08 - mmengine - INFO - Epoch(train) [185][400/586] lr: 5.000000e-05 eta: 1:19:12 time: 0.340892 data_time: 0.022281 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.876685 loss: 0.000476 2022/09/13 06:48:25 - mmengine - INFO - Epoch(train) [185][450/586] lr: 5.000000e-05 eta: 1:18:56 time: 0.334587 data_time: 0.022055 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.805260 loss: 0.000485 2022/09/13 06:48:42 - mmengine - INFO - Epoch(train) [185][500/586] lr: 5.000000e-05 eta: 1:18:40 time: 0.336467 data_time: 0.026582 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.859998 loss: 0.000472 2022/09/13 06:48:59 - mmengine - INFO - Epoch(train) [185][550/586] lr: 5.000000e-05 eta: 1:18:24 time: 0.339403 data_time: 0.022408 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.848962 loss: 0.000477 2022/09/13 06:49:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:49:11 - mmengine - INFO - Saving checkpoint at 185 epochs 2022/09/13 06:49:35 - mmengine - INFO - Epoch(train) [186][50/586] lr: 5.000000e-05 eta: 1:17:55 time: 0.336723 data_time: 0.027426 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.842098 loss: 0.000492 2022/09/13 06:49:52 - mmengine - INFO - Epoch(train) [186][100/586] lr: 5.000000e-05 eta: 1:17:39 time: 0.342203 data_time: 0.022525 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.919386 loss: 0.000476 2022/09/13 06:50:08 - mmengine - INFO - Epoch(train) [186][150/586] lr: 5.000000e-05 eta: 1:17:23 time: 0.336113 data_time: 0.022396 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.893573 loss: 0.000472 2022/09/13 06:50:25 - mmengine - INFO - Epoch(train) [186][200/586] lr: 5.000000e-05 eta: 1:17:07 time: 0.331496 data_time: 0.022214 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.860198 loss: 0.000485 2022/09/13 06:50:42 - mmengine - INFO - Epoch(train) [186][250/586] lr: 5.000000e-05 eta: 1:16:52 time: 0.341389 data_time: 0.022542 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.851895 loss: 0.000488 2022/09/13 06:50:59 - mmengine - INFO - Epoch(train) [186][300/586] lr: 5.000000e-05 eta: 1:16:36 time: 0.343861 data_time: 0.022105 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.888207 loss: 0.000479 2022/09/13 06:51:16 - mmengine - INFO - Epoch(train) [186][350/586] lr: 5.000000e-05 eta: 1:16:20 time: 0.338905 data_time: 0.022734 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.849798 loss: 0.000493 2022/09/13 06:51:33 - mmengine - INFO - Epoch(train) [186][400/586] lr: 5.000000e-05 eta: 1:16:04 time: 0.336844 data_time: 0.022670 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.897790 loss: 0.000482 2022/09/13 06:51:50 - mmengine - INFO - Epoch(train) [186][450/586] lr: 5.000000e-05 eta: 1:15:48 time: 0.333856 data_time: 0.022374 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.822672 loss: 0.000483 2022/09/13 06:52:06 - mmengine - INFO - Epoch(train) [186][500/586] lr: 5.000000e-05 eta: 1:15:32 time: 0.327737 data_time: 0.022239 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.893562 loss: 0.000478 2022/09/13 06:52:24 - mmengine - INFO - Epoch(train) [186][550/586] lr: 5.000000e-05 eta: 1:15:16 time: 0.351120 data_time: 0.022870 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.924513 loss: 0.000495 2022/09/13 06:52:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:52:36 - mmengine - INFO - Saving checkpoint at 186 epochs 2022/09/13 06:52:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:53:00 - mmengine - INFO - Epoch(train) [187][50/586] lr: 5.000000e-05 eta: 1:14:47 time: 0.337806 data_time: 0.026710 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.875335 loss: 0.000468 2022/09/13 06:53:17 - mmengine - INFO - Epoch(train) [187][100/586] lr: 5.000000e-05 eta: 1:14:31 time: 0.340227 data_time: 0.023565 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.852046 loss: 0.000488 2022/09/13 06:53:34 - mmengine - INFO - Epoch(train) [187][150/586] lr: 5.000000e-05 eta: 1:14:15 time: 0.337703 data_time: 0.023263 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.900146 loss: 0.000463 2022/09/13 06:53:51 - mmengine - INFO - Epoch(train) [187][200/586] lr: 5.000000e-05 eta: 1:14:00 time: 0.331025 data_time: 0.022600 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.853477 loss: 0.000492 2022/09/13 06:54:07 - mmengine - INFO - Epoch(train) [187][250/586] lr: 5.000000e-05 eta: 1:13:44 time: 0.337994 data_time: 0.022603 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.880553 loss: 0.000503 2022/09/13 06:54:25 - mmengine - INFO - Epoch(train) [187][300/586] lr: 5.000000e-05 eta: 1:13:28 time: 0.343875 data_time: 0.022110 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.873192 loss: 0.000488 2022/09/13 06:54:41 - mmengine - INFO - Epoch(train) [187][350/586] lr: 5.000000e-05 eta: 1:13:12 time: 0.332341 data_time: 0.026164 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.876804 loss: 0.000463 2022/09/13 06:54:59 - mmengine - INFO - Epoch(train) [187][400/586] lr: 5.000000e-05 eta: 1:12:56 time: 0.343416 data_time: 0.022200 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.936974 loss: 0.000482 2022/09/13 06:55:15 - mmengine - INFO - Epoch(train) [187][450/586] lr: 5.000000e-05 eta: 1:12:40 time: 0.333763 data_time: 0.022290 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.864601 loss: 0.000476 2022/09/13 06:55:32 - mmengine - INFO - Epoch(train) [187][500/586] lr: 5.000000e-05 eta: 1:12:24 time: 0.337005 data_time: 0.023270 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.866467 loss: 0.000494 2022/09/13 06:55:49 - mmengine - INFO - Epoch(train) [187][550/586] lr: 5.000000e-05 eta: 1:12:08 time: 0.343339 data_time: 0.022366 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.825533 loss: 0.000487 2022/09/13 06:56:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:56:02 - mmengine - INFO - Saving checkpoint at 187 epochs 2022/09/13 06:56:26 - mmengine - INFO - Epoch(train) [188][50/586] lr: 5.000000e-05 eta: 1:11:39 time: 0.337515 data_time: 0.029376 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.927910 loss: 0.000468 2022/09/13 06:56:43 - mmengine - INFO - Epoch(train) [188][100/586] lr: 5.000000e-05 eta: 1:11:24 time: 0.346543 data_time: 0.027389 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.870902 loss: 0.000469 2022/09/13 06:57:00 - mmengine - INFO - Epoch(train) [188][150/586] lr: 5.000000e-05 eta: 1:11:08 time: 0.341684 data_time: 0.022452 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.809452 loss: 0.000487 2022/09/13 06:57:17 - mmengine - INFO - Epoch(train) [188][200/586] lr: 5.000000e-05 eta: 1:10:52 time: 0.332109 data_time: 0.022274 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.860714 loss: 0.000492 2022/09/13 06:57:33 - mmengine - INFO - Epoch(train) [188][250/586] lr: 5.000000e-05 eta: 1:10:36 time: 0.332576 data_time: 0.022137 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.874057 loss: 0.000495 2022/09/13 06:57:51 - mmengine - INFO - Epoch(train) [188][300/586] lr: 5.000000e-05 eta: 1:10:20 time: 0.347989 data_time: 0.022474 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.824364 loss: 0.000480 2022/09/13 06:58:08 - mmengine - INFO - Epoch(train) [188][350/586] lr: 5.000000e-05 eta: 1:10:04 time: 0.335829 data_time: 0.022435 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.901740 loss: 0.000488 2022/09/13 06:58:24 - mmengine - INFO - Epoch(train) [188][400/586] lr: 5.000000e-05 eta: 1:09:48 time: 0.333976 data_time: 0.022725 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.888114 loss: 0.000484 2022/09/13 06:58:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:58:42 - mmengine - INFO - Epoch(train) [188][450/586] lr: 5.000000e-05 eta: 1:09:32 time: 0.347843 data_time: 0.022649 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.923894 loss: 0.000464 2022/09/13 06:58:59 - mmengine - INFO - Epoch(train) [188][500/586] lr: 5.000000e-05 eta: 1:09:16 time: 0.339007 data_time: 0.023845 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.877560 loss: 0.000474 2022/09/13 06:59:16 - mmengine - INFO - Epoch(train) [188][550/586] lr: 5.000000e-05 eta: 1:09:00 time: 0.340619 data_time: 0.026471 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.830085 loss: 0.000502 2022/09/13 06:59:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 06:59:28 - mmengine - INFO - Saving checkpoint at 188 epochs 2022/09/13 06:59:52 - mmengine - INFO - Epoch(train) [189][50/586] lr: 5.000000e-05 eta: 1:08:32 time: 0.346708 data_time: 0.032742 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.906166 loss: 0.000488 2022/09/13 07:00:09 - mmengine - INFO - Epoch(train) [189][100/586] lr: 5.000000e-05 eta: 1:08:16 time: 0.332856 data_time: 0.022671 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.830443 loss: 0.000481 2022/09/13 07:00:26 - mmengine - INFO - Epoch(train) [189][150/586] lr: 5.000000e-05 eta: 1:08:00 time: 0.342423 data_time: 0.023408 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.784718 loss: 0.000505 2022/09/13 07:00:43 - mmengine - INFO - Epoch(train) [189][200/586] lr: 5.000000e-05 eta: 1:07:44 time: 0.338149 data_time: 0.022240 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.906444 loss: 0.000476 2022/09/13 07:01:00 - mmengine - INFO - Epoch(train) [189][250/586] lr: 5.000000e-05 eta: 1:07:28 time: 0.334309 data_time: 0.022736 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.848363 loss: 0.000500 2022/09/13 07:01:17 - mmengine - INFO - Epoch(train) [189][300/586] lr: 5.000000e-05 eta: 1:07:12 time: 0.341959 data_time: 0.023613 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.888066 loss: 0.000489 2022/09/13 07:01:34 - mmengine - INFO - Epoch(train) [189][350/586] lr: 5.000000e-05 eta: 1:06:56 time: 0.340562 data_time: 0.025700 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.884401 loss: 0.000493 2022/09/13 07:01:50 - mmengine - INFO - Epoch(train) [189][400/586] lr: 5.000000e-05 eta: 1:06:40 time: 0.332151 data_time: 0.022854 memory: 7489 loss_kpt: 0.000456 acc_pose: 0.838017 loss: 0.000456 2022/09/13 07:02:08 - mmengine - INFO - Epoch(train) [189][450/586] lr: 5.000000e-05 eta: 1:06:24 time: 0.344099 data_time: 0.023013 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.843059 loss: 0.000477 2022/09/13 07:02:24 - mmengine - INFO - Epoch(train) [189][500/586] lr: 5.000000e-05 eta: 1:06:08 time: 0.333274 data_time: 0.025240 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.860717 loss: 0.000480 2022/09/13 07:02:41 - mmengine - INFO - Epoch(train) [189][550/586] lr: 5.000000e-05 eta: 1:05:53 time: 0.331709 data_time: 0.023189 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.852436 loss: 0.000504 2022/09/13 07:02:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:02:53 - mmengine - INFO - Saving checkpoint at 189 epochs 2022/09/13 07:03:18 - mmengine - INFO - Epoch(train) [190][50/586] lr: 5.000000e-05 eta: 1:05:24 time: 0.346365 data_time: 0.028619 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.844897 loss: 0.000486 2022/09/13 07:03:35 - mmengine - INFO - Epoch(train) [190][100/586] lr: 5.000000e-05 eta: 1:05:08 time: 0.334301 data_time: 0.022290 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.904403 loss: 0.000479 2022/09/13 07:03:52 - mmengine - INFO - Epoch(train) [190][150/586] lr: 5.000000e-05 eta: 1:04:52 time: 0.337560 data_time: 0.023540 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.897528 loss: 0.000479 2022/09/13 07:04:09 - mmengine - INFO - Epoch(train) [190][200/586] lr: 5.000000e-05 eta: 1:04:36 time: 0.338288 data_time: 0.023835 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.885022 loss: 0.000473 2022/09/13 07:04:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:04:25 - mmengine - INFO - Epoch(train) [190][250/586] lr: 5.000000e-05 eta: 1:04:20 time: 0.336304 data_time: 0.022340 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.902294 loss: 0.000464 2022/09/13 07:04:42 - mmengine - INFO - Epoch(train) [190][300/586] lr: 5.000000e-05 eta: 1:04:04 time: 0.338036 data_time: 0.022233 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.933291 loss: 0.000489 2022/09/13 07:05:00 - mmengine - INFO - Epoch(train) [190][350/586] lr: 5.000000e-05 eta: 1:03:48 time: 0.344193 data_time: 0.022497 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.870297 loss: 0.000477 2022/09/13 07:05:16 - mmengine - INFO - Epoch(train) [190][400/586] lr: 5.000000e-05 eta: 1:03:32 time: 0.333889 data_time: 0.022592 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.926796 loss: 0.000495 2022/09/13 07:05:33 - mmengine - INFO - Epoch(train) [190][450/586] lr: 5.000000e-05 eta: 1:03:17 time: 0.344210 data_time: 0.023434 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.862857 loss: 0.000480 2022/09/13 07:05:51 - mmengine - INFO - Epoch(train) [190][500/586] lr: 5.000000e-05 eta: 1:03:01 time: 0.345536 data_time: 0.026933 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.878209 loss: 0.000486 2022/09/13 07:06:07 - mmengine - INFO - Epoch(train) [190][550/586] lr: 5.000000e-05 eta: 1:02:45 time: 0.333628 data_time: 0.021765 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.904283 loss: 0.000492 2022/09/13 07:06:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:06:20 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/09/13 07:06:37 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:01:16 time: 0.214995 data_time: 0.012821 memory: 7489 2022/09/13 07:06:48 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:01:04 time: 0.210218 data_time: 0.007482 memory: 1657 2022/09/13 07:06:58 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:53 time: 0.209612 data_time: 0.007787 memory: 1657 2022/09/13 07:07:09 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:43 time: 0.208180 data_time: 0.007299 memory: 1657 2022/09/13 07:07:19 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:32 time: 0.208922 data_time: 0.008354 memory: 1657 2022/09/13 07:07:30 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:22 time: 0.209165 data_time: 0.007591 memory: 1657 2022/09/13 07:07:40 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:11 time: 0.208573 data_time: 0.007467 memory: 1657 2022/09/13 07:07:51 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:01 time: 0.210719 data_time: 0.011230 memory: 1657 2022/09/13 07:08:27 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 07:08:40 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.762364 coco/AP .5: 0.906491 coco/AP .75: 0.828976 coco/AP (M): 0.725853 coco/AP (L): 0.830405 coco/AR: 0.811366 coco/AR .5: 0.942223 coco/AR .75: 0.870435 coco/AR (M): 0.769790 coco/AR (L): 0.872278 2022/09/13 07:08:40 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_180.pth is removed 2022/09/13 07:08:44 - mmengine - INFO - The best checkpoint with 0.7624 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/09/13 07:09:01 - mmengine - INFO - Epoch(train) [191][50/586] lr: 5.000000e-05 eta: 1:02:16 time: 0.340453 data_time: 0.026696 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.871392 loss: 0.000488 2022/09/13 07:09:18 - mmengine - INFO - Epoch(train) [191][100/586] lr: 5.000000e-05 eta: 1:02:00 time: 0.337217 data_time: 0.022914 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.881420 loss: 0.000489 2022/09/13 07:09:35 - mmengine - INFO - Epoch(train) [191][150/586] lr: 5.000000e-05 eta: 1:01:44 time: 0.336209 data_time: 0.022108 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.850322 loss: 0.000481 2022/09/13 07:09:53 - mmengine - INFO - Epoch(train) [191][200/586] lr: 5.000000e-05 eta: 1:01:28 time: 0.348899 data_time: 0.027285 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.930463 loss: 0.000466 2022/09/13 07:10:10 - mmengine - INFO - Epoch(train) [191][250/586] lr: 5.000000e-05 eta: 1:01:12 time: 0.343133 data_time: 0.022790 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.830465 loss: 0.000492 2022/09/13 07:10:27 - mmengine - INFO - Epoch(train) [191][300/586] lr: 5.000000e-05 eta: 1:00:57 time: 0.336059 data_time: 0.022341 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.851312 loss: 0.000479 2022/09/13 07:10:43 - mmengine - INFO - Epoch(train) [191][350/586] lr: 5.000000e-05 eta: 1:00:41 time: 0.338985 data_time: 0.022129 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.863160 loss: 0.000472 2022/09/13 07:11:01 - mmengine - INFO - Epoch(train) [191][400/586] lr: 5.000000e-05 eta: 1:00:25 time: 0.341368 data_time: 0.022270 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.870263 loss: 0.000475 2022/09/13 07:11:17 - mmengine - INFO - Epoch(train) [191][450/586] lr: 5.000000e-05 eta: 1:00:09 time: 0.333306 data_time: 0.022545 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.820031 loss: 0.000464 2022/09/13 07:11:34 - mmengine - INFO - Epoch(train) [191][500/586] lr: 5.000000e-05 eta: 0:59:53 time: 0.332067 data_time: 0.021916 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.895026 loss: 0.000492 2022/09/13 07:11:51 - mmengine - INFO - Epoch(train) [191][550/586] lr: 5.000000e-05 eta: 0:59:37 time: 0.339793 data_time: 0.022578 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.886435 loss: 0.000482 2022/09/13 07:12:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:12:03 - mmengine - INFO - Saving checkpoint at 191 epochs 2022/09/13 07:12:27 - mmengine - INFO - Epoch(train) [192][50/586] lr: 5.000000e-05 eta: 0:59:08 time: 0.341726 data_time: 0.030175 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.856005 loss: 0.000489 2022/09/13 07:12:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:12:44 - mmengine - INFO - Epoch(train) [192][100/586] lr: 5.000000e-05 eta: 0:58:52 time: 0.344211 data_time: 0.023142 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.904415 loss: 0.000480 2022/09/13 07:13:01 - mmengine - INFO - Epoch(train) [192][150/586] lr: 5.000000e-05 eta: 0:58:37 time: 0.339618 data_time: 0.023547 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.886298 loss: 0.000490 2022/09/13 07:13:18 - mmengine - INFO - Epoch(train) [192][200/586] lr: 5.000000e-05 eta: 0:58:21 time: 0.331525 data_time: 0.021954 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.922371 loss: 0.000487 2022/09/13 07:13:35 - mmengine - INFO - Epoch(train) [192][250/586] lr: 5.000000e-05 eta: 0:58:05 time: 0.336422 data_time: 0.022184 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.885824 loss: 0.000478 2022/09/13 07:13:52 - mmengine - INFO - Epoch(train) [192][300/586] lr: 5.000000e-05 eta: 0:57:49 time: 0.343483 data_time: 0.022165 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.874166 loss: 0.000481 2022/09/13 07:14:09 - mmengine - INFO - Epoch(train) [192][350/586] lr: 5.000000e-05 eta: 0:57:33 time: 0.339315 data_time: 0.022887 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.870041 loss: 0.000473 2022/09/13 07:14:27 - mmengine - INFO - Epoch(train) [192][400/586] lr: 5.000000e-05 eta: 0:57:17 time: 0.350266 data_time: 0.022832 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.857530 loss: 0.000486 2022/09/13 07:14:44 - mmengine - INFO - Epoch(train) [192][450/586] lr: 5.000000e-05 eta: 0:57:01 time: 0.339332 data_time: 0.026400 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.925183 loss: 0.000490 2022/09/13 07:15:00 - mmengine - INFO - Epoch(train) [192][500/586] lr: 5.000000e-05 eta: 0:56:45 time: 0.334188 data_time: 0.022691 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.903208 loss: 0.000498 2022/09/13 07:15:17 - mmengine - INFO - Epoch(train) [192][550/586] lr: 5.000000e-05 eta: 0:56:29 time: 0.339726 data_time: 0.021974 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.912148 loss: 0.000465 2022/09/13 07:15:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:15:30 - mmengine - INFO - Saving checkpoint at 192 epochs 2022/09/13 07:15:54 - mmengine - INFO - Epoch(train) [193][50/586] lr: 5.000000e-05 eta: 0:56:01 time: 0.347960 data_time: 0.026278 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.906919 loss: 0.000475 2022/09/13 07:16:11 - mmengine - INFO - Epoch(train) [193][100/586] lr: 5.000000e-05 eta: 0:55:45 time: 0.350827 data_time: 0.022245 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.848060 loss: 0.000481 2022/09/13 07:16:28 - mmengine - INFO - Epoch(train) [193][150/586] lr: 5.000000e-05 eta: 0:55:29 time: 0.334073 data_time: 0.021732 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.887275 loss: 0.000496 2022/09/13 07:16:45 - mmengine - INFO - Epoch(train) [193][200/586] lr: 5.000000e-05 eta: 0:55:13 time: 0.335281 data_time: 0.022872 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.904654 loss: 0.000481 2022/09/13 07:17:02 - mmengine - INFO - Epoch(train) [193][250/586] lr: 5.000000e-05 eta: 0:54:57 time: 0.337656 data_time: 0.022532 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.880975 loss: 0.000493 2022/09/13 07:17:19 - mmengine - INFO - Epoch(train) [193][300/586] lr: 5.000000e-05 eta: 0:54:41 time: 0.340334 data_time: 0.022938 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.918971 loss: 0.000479 2022/09/13 07:17:36 - mmengine - INFO - Epoch(train) [193][350/586] lr: 5.000000e-05 eta: 0:54:25 time: 0.334566 data_time: 0.022663 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.892652 loss: 0.000481 2022/09/13 07:17:53 - mmengine - INFO - Epoch(train) [193][400/586] lr: 5.000000e-05 eta: 0:54:09 time: 0.349928 data_time: 0.022532 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.906657 loss: 0.000488 2022/09/13 07:18:10 - mmengine - INFO - Epoch(train) [193][450/586] lr: 5.000000e-05 eta: 0:53:53 time: 0.344686 data_time: 0.026511 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.799245 loss: 0.000485 2022/09/13 07:18:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:18:27 - mmengine - INFO - Epoch(train) [193][500/586] lr: 5.000000e-05 eta: 0:53:37 time: 0.336984 data_time: 0.021936 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.897147 loss: 0.000484 2022/09/13 07:18:45 - mmengine - INFO - Epoch(train) [193][550/586] lr: 5.000000e-05 eta: 0:53:22 time: 0.346928 data_time: 0.022343 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.872934 loss: 0.000491 2022/09/13 07:18:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:18:57 - mmengine - INFO - Saving checkpoint at 193 epochs 2022/09/13 07:19:22 - mmengine - INFO - Epoch(train) [194][50/586] lr: 5.000000e-05 eta: 0:52:53 time: 0.349594 data_time: 0.029041 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.831245 loss: 0.000468 2022/09/13 07:19:39 - mmengine - INFO - Epoch(train) [194][100/586] lr: 5.000000e-05 eta: 0:52:37 time: 0.340545 data_time: 0.022502 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.877908 loss: 0.000484 2022/09/13 07:19:56 - mmengine - INFO - Epoch(train) [194][150/586] lr: 5.000000e-05 eta: 0:52:21 time: 0.343019 data_time: 0.022251 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.918649 loss: 0.000461 2022/09/13 07:20:13 - mmengine - INFO - Epoch(train) [194][200/586] lr: 5.000000e-05 eta: 0:52:05 time: 0.343011 data_time: 0.022183 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.866147 loss: 0.000482 2022/09/13 07:20:30 - mmengine - INFO - Epoch(train) [194][250/586] lr: 5.000000e-05 eta: 0:51:49 time: 0.339294 data_time: 0.022291 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.888433 loss: 0.000481 2022/09/13 07:20:47 - mmengine - INFO - Epoch(train) [194][300/586] lr: 5.000000e-05 eta: 0:51:33 time: 0.337536 data_time: 0.022422 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.861976 loss: 0.000483 2022/09/13 07:21:04 - mmengine - INFO - Epoch(train) [194][350/586] lr: 5.000000e-05 eta: 0:51:18 time: 0.332280 data_time: 0.022515 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.875991 loss: 0.000484 2022/09/13 07:21:21 - mmengine - INFO - Epoch(train) [194][400/586] lr: 5.000000e-05 eta: 0:51:02 time: 0.340421 data_time: 0.022504 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.885444 loss: 0.000491 2022/09/13 07:21:37 - mmengine - INFO - Epoch(train) [194][450/586] lr: 5.000000e-05 eta: 0:50:46 time: 0.335876 data_time: 0.022486 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.915507 loss: 0.000491 2022/09/13 07:21:54 - mmengine - INFO - Epoch(train) [194][500/586] lr: 5.000000e-05 eta: 0:50:30 time: 0.334893 data_time: 0.022294 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.895180 loss: 0.000492 2022/09/13 07:22:11 - mmengine - INFO - Epoch(train) [194][550/586] lr: 5.000000e-05 eta: 0:50:14 time: 0.340402 data_time: 0.022693 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.893897 loss: 0.000481 2022/09/13 07:22:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:22:23 - mmengine - INFO - Saving checkpoint at 194 epochs 2022/09/13 07:22:48 - mmengine - INFO - Epoch(train) [195][50/586] lr: 5.000000e-05 eta: 0:49:45 time: 0.352069 data_time: 0.030546 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.860529 loss: 0.000471 2022/09/13 07:23:05 - mmengine - INFO - Epoch(train) [195][100/586] lr: 5.000000e-05 eta: 0:49:30 time: 0.341240 data_time: 0.022645 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.871275 loss: 0.000472 2022/09/13 07:23:22 - mmengine - INFO - Epoch(train) [195][150/586] lr: 5.000000e-05 eta: 0:49:14 time: 0.340316 data_time: 0.022331 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.912745 loss: 0.000487 2022/09/13 07:23:39 - mmengine - INFO - Epoch(train) [195][200/586] lr: 5.000000e-05 eta: 0:48:58 time: 0.334225 data_time: 0.023274 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.898535 loss: 0.000485 2022/09/13 07:23:56 - mmengine - INFO - Epoch(train) [195][250/586] lr: 5.000000e-05 eta: 0:48:42 time: 0.341708 data_time: 0.022280 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.887159 loss: 0.000477 2022/09/13 07:24:13 - mmengine - INFO - Epoch(train) [195][300/586] lr: 5.000000e-05 eta: 0:48:26 time: 0.336104 data_time: 0.022044 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.876477 loss: 0.000465 2022/09/13 07:24:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:24:30 - mmengine - INFO - Epoch(train) [195][350/586] lr: 5.000000e-05 eta: 0:48:10 time: 0.337363 data_time: 0.022428 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.927407 loss: 0.000475 2022/09/13 07:24:46 - mmengine - INFO - Epoch(train) [195][400/586] lr: 5.000000e-05 eta: 0:47:54 time: 0.338282 data_time: 0.022194 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.883650 loss: 0.000486 2022/09/13 07:25:04 - mmengine - INFO - Epoch(train) [195][450/586] lr: 5.000000e-05 eta: 0:47:38 time: 0.343204 data_time: 0.025452 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.859401 loss: 0.000477 2022/09/13 07:25:20 - mmengine - INFO - Epoch(train) [195][500/586] lr: 5.000000e-05 eta: 0:47:22 time: 0.331155 data_time: 0.022976 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.886417 loss: 0.000486 2022/09/13 07:25:38 - mmengine - INFO - Epoch(train) [195][550/586] lr: 5.000000e-05 eta: 0:47:06 time: 0.347251 data_time: 0.021998 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.897531 loss: 0.000480 2022/09/13 07:25:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:25:50 - mmengine - INFO - Saving checkpoint at 195 epochs 2022/09/13 07:26:14 - mmengine - INFO - Epoch(train) [196][50/586] lr: 5.000000e-05 eta: 0:46:38 time: 0.343761 data_time: 0.026914 memory: 7489 loss_kpt: 0.000459 acc_pose: 0.874092 loss: 0.000459 2022/09/13 07:26:31 - mmengine - INFO - Epoch(train) [196][100/586] lr: 5.000000e-05 eta: 0:46:22 time: 0.342398 data_time: 0.026200 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.890742 loss: 0.000476 2022/09/13 07:26:48 - mmengine - INFO - Epoch(train) [196][150/586] lr: 5.000000e-05 eta: 0:46:06 time: 0.335609 data_time: 0.023258 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.858520 loss: 0.000479 2022/09/13 07:27:04 - mmengine - INFO - Epoch(train) [196][200/586] lr: 5.000000e-05 eta: 0:45:50 time: 0.335948 data_time: 0.022426 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.869962 loss: 0.000472 2022/09/13 07:27:21 - mmengine - INFO - Epoch(train) [196][250/586] lr: 5.000000e-05 eta: 0:45:34 time: 0.339904 data_time: 0.025716 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.890271 loss: 0.000476 2022/09/13 07:27:38 - mmengine - INFO - Epoch(train) [196][300/586] lr: 5.000000e-05 eta: 0:45:18 time: 0.337174 data_time: 0.021860 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.838727 loss: 0.000471 2022/09/13 07:27:55 - mmengine - INFO - Epoch(train) [196][350/586] lr: 5.000000e-05 eta: 0:45:02 time: 0.337117 data_time: 0.022775 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.852845 loss: 0.000472 2022/09/13 07:28:13 - mmengine - INFO - Epoch(train) [196][400/586] lr: 5.000000e-05 eta: 0:44:46 time: 0.350083 data_time: 0.026763 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.915217 loss: 0.000491 2022/09/13 07:28:30 - mmengine - INFO - Epoch(train) [196][450/586] lr: 5.000000e-05 eta: 0:44:30 time: 0.339619 data_time: 0.022666 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.854539 loss: 0.000486 2022/09/13 07:28:47 - mmengine - INFO - Epoch(train) [196][500/586] lr: 5.000000e-05 eta: 0:44:14 time: 0.338137 data_time: 0.023106 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.846867 loss: 0.000470 2022/09/13 07:29:04 - mmengine - INFO - Epoch(train) [196][550/586] lr: 5.000000e-05 eta: 0:43:58 time: 0.344844 data_time: 0.022957 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.876379 loss: 0.000493 2022/09/13 07:29:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:29:16 - mmengine - INFO - Saving checkpoint at 196 epochs 2022/09/13 07:29:40 - mmengine - INFO - Epoch(train) [197][50/586] lr: 5.000000e-05 eta: 0:43:30 time: 0.345363 data_time: 0.033590 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.825346 loss: 0.000484 2022/09/13 07:29:58 - mmengine - INFO - Epoch(train) [197][100/586] lr: 5.000000e-05 eta: 0:43:14 time: 0.344385 data_time: 0.022216 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.817907 loss: 0.000475 2022/09/13 07:30:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:30:14 - mmengine - INFO - Epoch(train) [197][150/586] lr: 5.000000e-05 eta: 0:42:58 time: 0.335760 data_time: 0.022399 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.891367 loss: 0.000465 2022/09/13 07:30:32 - mmengine - INFO - Epoch(train) [197][200/586] lr: 5.000000e-05 eta: 0:42:42 time: 0.343316 data_time: 0.022280 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.849438 loss: 0.000473 2022/09/13 07:30:48 - mmengine - INFO - Epoch(train) [197][250/586] lr: 5.000000e-05 eta: 0:42:26 time: 0.336265 data_time: 0.022044 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.925340 loss: 0.000488 2022/09/13 07:31:05 - mmengine - INFO - Epoch(train) [197][300/586] lr: 5.000000e-05 eta: 0:42:10 time: 0.333882 data_time: 0.022687 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.871814 loss: 0.000477 2022/09/13 07:31:22 - mmengine - INFO - Epoch(train) [197][350/586] lr: 5.000000e-05 eta: 0:41:54 time: 0.339698 data_time: 0.025509 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.913224 loss: 0.000480 2022/09/13 07:31:39 - mmengine - INFO - Epoch(train) [197][400/586] lr: 5.000000e-05 eta: 0:41:39 time: 0.344678 data_time: 0.021972 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.923256 loss: 0.000486 2022/09/13 07:31:56 - mmengine - INFO - Epoch(train) [197][450/586] lr: 5.000000e-05 eta: 0:41:23 time: 0.333390 data_time: 0.022068 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.848373 loss: 0.000486 2022/09/13 07:32:13 - mmengine - INFO - Epoch(train) [197][500/586] lr: 5.000000e-05 eta: 0:41:07 time: 0.338822 data_time: 0.022742 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.863965 loss: 0.000471 2022/09/13 07:32:30 - mmengine - INFO - Epoch(train) [197][550/586] lr: 5.000000e-05 eta: 0:40:51 time: 0.341135 data_time: 0.023053 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.828974 loss: 0.000476 2022/09/13 07:32:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:32:42 - mmengine - INFO - Saving checkpoint at 197 epochs 2022/09/13 07:33:08 - mmengine - INFO - Epoch(train) [198][50/586] lr: 5.000000e-05 eta: 0:40:22 time: 0.345906 data_time: 0.030230 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.847422 loss: 0.000490 2022/09/13 07:33:25 - mmengine - INFO - Epoch(train) [198][100/586] lr: 5.000000e-05 eta: 0:40:07 time: 0.343465 data_time: 0.023370 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.832117 loss: 0.000476 2022/09/13 07:33:42 - mmengine - INFO - Epoch(train) [198][150/586] lr: 5.000000e-05 eta: 0:39:51 time: 0.333219 data_time: 0.022427 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.868643 loss: 0.000479 2022/09/13 07:33:59 - mmengine - INFO - Epoch(train) [198][200/586] lr: 5.000000e-05 eta: 0:39:35 time: 0.337684 data_time: 0.022226 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.896321 loss: 0.000495 2022/09/13 07:34:16 - mmengine - INFO - Epoch(train) [198][250/586] lr: 5.000000e-05 eta: 0:39:19 time: 0.341400 data_time: 0.022802 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.888294 loss: 0.000479 2022/09/13 07:34:33 - mmengine - INFO - Epoch(train) [198][300/586] lr: 5.000000e-05 eta: 0:39:03 time: 0.332508 data_time: 0.022794 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.908479 loss: 0.000473 2022/09/13 07:34:50 - mmengine - INFO - Epoch(train) [198][350/586] lr: 5.000000e-05 eta: 0:38:47 time: 0.338354 data_time: 0.022533 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.838425 loss: 0.000476 2022/09/13 07:35:06 - mmengine - INFO - Epoch(train) [198][400/586] lr: 5.000000e-05 eta: 0:38:31 time: 0.335607 data_time: 0.025881 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.876251 loss: 0.000488 2022/09/13 07:35:23 - mmengine - INFO - Epoch(train) [198][450/586] lr: 5.000000e-05 eta: 0:38:15 time: 0.336405 data_time: 0.022144 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.838934 loss: 0.000467 2022/09/13 07:35:40 - mmengine - INFO - Epoch(train) [198][500/586] lr: 5.000000e-05 eta: 0:37:59 time: 0.329485 data_time: 0.021877 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.887677 loss: 0.000481 2022/09/13 07:35:57 - mmengine - INFO - Epoch(train) [198][550/586] lr: 5.000000e-05 eta: 0:37:43 time: 0.337433 data_time: 0.025061 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.818579 loss: 0.000481 2022/09/13 07:35:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:36:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:36:09 - mmengine - INFO - Saving checkpoint at 198 epochs 2022/09/13 07:36:33 - mmengine - INFO - Epoch(train) [199][50/586] lr: 5.000000e-05 eta: 0:37:15 time: 0.350232 data_time: 0.035208 memory: 7489 loss_kpt: 0.000459 acc_pose: 0.864697 loss: 0.000459 2022/09/13 07:36:50 - mmengine - INFO - Epoch(train) [199][100/586] lr: 5.000000e-05 eta: 0:36:59 time: 0.339545 data_time: 0.022921 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.875724 loss: 0.000478 2022/09/13 07:37:07 - mmengine - INFO - Epoch(train) [199][150/586] lr: 5.000000e-05 eta: 0:36:43 time: 0.340623 data_time: 0.026551 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.841112 loss: 0.000468 2022/09/13 07:37:24 - mmengine - INFO - Epoch(train) [199][200/586] lr: 5.000000e-05 eta: 0:36:27 time: 0.336375 data_time: 0.022966 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.925311 loss: 0.000484 2022/09/13 07:37:41 - mmengine - INFO - Epoch(train) [199][250/586] lr: 5.000000e-05 eta: 0:36:11 time: 0.338229 data_time: 0.022306 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.840933 loss: 0.000474 2022/09/13 07:37:58 - mmengine - INFO - Epoch(train) [199][300/586] lr: 5.000000e-05 eta: 0:35:55 time: 0.336107 data_time: 0.022240 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.871715 loss: 0.000493 2022/09/13 07:38:15 - mmengine - INFO - Epoch(train) [199][350/586] lr: 5.000000e-05 eta: 0:35:39 time: 0.342231 data_time: 0.026356 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.849808 loss: 0.000474 2022/09/13 07:38:32 - mmengine - INFO - Epoch(train) [199][400/586] lr: 5.000000e-05 eta: 0:35:23 time: 0.335283 data_time: 0.023218 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.877798 loss: 0.000481 2022/09/13 07:38:49 - mmengine - INFO - Epoch(train) [199][450/586] lr: 5.000000e-05 eta: 0:35:07 time: 0.340267 data_time: 0.025973 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.867287 loss: 0.000473 2022/09/13 07:39:05 - mmengine - INFO - Epoch(train) [199][500/586] lr: 5.000000e-05 eta: 0:34:51 time: 0.335845 data_time: 0.021856 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.858374 loss: 0.000467 2022/09/13 07:39:23 - mmengine - INFO - Epoch(train) [199][550/586] lr: 5.000000e-05 eta: 0:34:35 time: 0.342178 data_time: 0.023210 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.885948 loss: 0.000468 2022/09/13 07:39:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:39:35 - mmengine - INFO - Saving checkpoint at 199 epochs 2022/09/13 07:39:58 - mmengine - INFO - Epoch(train) [200][50/586] lr: 5.000000e-05 eta: 0:34:07 time: 0.339225 data_time: 0.029328 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.898326 loss: 0.000472 2022/09/13 07:40:16 - mmengine - INFO - Epoch(train) [200][100/586] lr: 5.000000e-05 eta: 0:33:51 time: 0.350576 data_time: 0.026960 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.871959 loss: 0.000482 2022/09/13 07:40:33 - mmengine - INFO - Epoch(train) [200][150/586] lr: 5.000000e-05 eta: 0:33:35 time: 0.338768 data_time: 0.022063 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.815096 loss: 0.000484 2022/09/13 07:40:50 - mmengine - INFO - Epoch(train) [200][200/586] lr: 5.000000e-05 eta: 0:33:19 time: 0.334147 data_time: 0.022435 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.837740 loss: 0.000496 2022/09/13 07:41:07 - mmengine - INFO - Epoch(train) [200][250/586] lr: 5.000000e-05 eta: 0:33:03 time: 0.347651 data_time: 0.022773 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.880134 loss: 0.000472 2022/09/13 07:41:24 - mmengine - INFO - Epoch(train) [200][300/586] lr: 5.000000e-05 eta: 0:32:47 time: 0.343344 data_time: 0.023577 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.830752 loss: 0.000473 2022/09/13 07:41:41 - mmengine - INFO - Epoch(train) [200][350/586] lr: 5.000000e-05 eta: 0:32:31 time: 0.336359 data_time: 0.023048 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.921335 loss: 0.000468 2022/09/13 07:41:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:41:58 - mmengine - INFO - Epoch(train) [200][400/586] lr: 5.000000e-05 eta: 0:32:15 time: 0.341502 data_time: 0.023638 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.846112 loss: 0.000491 2022/09/13 07:42:15 - mmengine - INFO - Epoch(train) [200][450/586] lr: 5.000000e-05 eta: 0:31:59 time: 0.335800 data_time: 0.022819 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.912262 loss: 0.000475 2022/09/13 07:42:32 - mmengine - INFO - Epoch(train) [200][500/586] lr: 5.000000e-05 eta: 0:31:43 time: 0.340191 data_time: 0.022068 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.847807 loss: 0.000497 2022/09/13 07:42:49 - mmengine - INFO - Epoch(train) [200][550/586] lr: 5.000000e-05 eta: 0:31:27 time: 0.339175 data_time: 0.022447 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.881189 loss: 0.000475 2022/09/13 07:43:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:43:02 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/09/13 07:43:20 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:01:17 time: 0.218018 data_time: 0.015710 memory: 7489 2022/09/13 07:43:30 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:01:04 time: 0.209522 data_time: 0.008618 memory: 1657 2022/09/13 07:43:41 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:53 time: 0.209228 data_time: 0.007845 memory: 1657 2022/09/13 07:43:51 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:43 time: 0.207732 data_time: 0.007480 memory: 1657 2022/09/13 07:44:01 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:32 time: 0.208867 data_time: 0.007823 memory: 1657 2022/09/13 07:44:12 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:22 time: 0.208883 data_time: 0.007660 memory: 1657 2022/09/13 07:44:22 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:11 time: 0.209511 data_time: 0.007828 memory: 1657 2022/09/13 07:44:33 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:01 time: 0.207264 data_time: 0.007373 memory: 1657 2022/09/13 07:45:09 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 07:45:22 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.763728 coco/AP .5: 0.905210 coco/AP .75: 0.831213 coco/AP (M): 0.727146 coco/AP (L): 0.831055 coco/AR: 0.812500 coco/AR .5: 0.940334 coco/AR .75: 0.872166 coco/AR (M): 0.771347 coco/AR (L): 0.872835 2022/09/13 07:45:22 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_190.pth is removed 2022/09/13 07:45:26 - mmengine - INFO - The best checkpoint with 0.7637 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/09/13 07:45:43 - mmengine - INFO - Epoch(train) [201][50/586] lr: 5.000000e-06 eta: 0:30:59 time: 0.338010 data_time: 0.026564 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.848229 loss: 0.000484 2022/09/13 07:46:00 - mmengine - INFO - Epoch(train) [201][100/586] lr: 5.000000e-06 eta: 0:30:43 time: 0.342823 data_time: 0.022442 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.852544 loss: 0.000496 2022/09/13 07:46:17 - mmengine - INFO - Epoch(train) [201][150/586] lr: 5.000000e-06 eta: 0:30:28 time: 0.333906 data_time: 0.022143 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.870907 loss: 0.000491 2022/09/13 07:46:34 - mmengine - INFO - Epoch(train) [201][200/586] lr: 5.000000e-06 eta: 0:30:12 time: 0.339339 data_time: 0.022335 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.871443 loss: 0.000469 2022/09/13 07:46:51 - mmengine - INFO - Epoch(train) [201][250/586] lr: 5.000000e-06 eta: 0:29:56 time: 0.342396 data_time: 0.021949 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.916551 loss: 0.000479 2022/09/13 07:47:08 - mmengine - INFO - Epoch(train) [201][300/586] lr: 5.000000e-06 eta: 0:29:40 time: 0.337294 data_time: 0.022944 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.872639 loss: 0.000482 2022/09/13 07:47:25 - mmengine - INFO - Epoch(train) [201][350/586] lr: 5.000000e-06 eta: 0:29:24 time: 0.339696 data_time: 0.022905 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.884374 loss: 0.000492 2022/09/13 07:47:42 - mmengine - INFO - Epoch(train) [201][400/586] lr: 5.000000e-06 eta: 0:29:08 time: 0.343716 data_time: 0.026078 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.871564 loss: 0.000478 2022/09/13 07:47:59 - mmengine - INFO - Epoch(train) [201][450/586] lr: 5.000000e-06 eta: 0:28:52 time: 0.334125 data_time: 0.022508 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.892550 loss: 0.000479 2022/09/13 07:48:16 - mmengine - INFO - Epoch(train) [201][500/586] lr: 5.000000e-06 eta: 0:28:36 time: 0.340547 data_time: 0.022103 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.898137 loss: 0.000479 2022/09/13 07:48:33 - mmengine - INFO - Epoch(train) [201][550/586] lr: 5.000000e-06 eta: 0:28:20 time: 0.346444 data_time: 0.023668 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.892737 loss: 0.000461 2022/09/13 07:48:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:48:46 - mmengine - INFO - Saving checkpoint at 201 epochs 2022/09/13 07:49:09 - mmengine - INFO - Epoch(train) [202][50/586] lr: 5.000000e-06 eta: 0:27:52 time: 0.339741 data_time: 0.026478 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.874709 loss: 0.000473 2022/09/13 07:49:26 - mmengine - INFO - Epoch(train) [202][100/586] lr: 5.000000e-06 eta: 0:27:36 time: 0.342201 data_time: 0.022338 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.841219 loss: 0.000477 2022/09/13 07:49:43 - mmengine - INFO - Epoch(train) [202][150/586] lr: 5.000000e-06 eta: 0:27:20 time: 0.334281 data_time: 0.022092 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.872057 loss: 0.000485 2022/09/13 07:50:00 - mmengine - INFO - Epoch(train) [202][200/586] lr: 5.000000e-06 eta: 0:27:04 time: 0.340827 data_time: 0.021668 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.906221 loss: 0.000473 2022/09/13 07:50:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:50:17 - mmengine - INFO - Epoch(train) [202][250/586] lr: 5.000000e-06 eta: 0:26:48 time: 0.339123 data_time: 0.022319 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.892679 loss: 0.000478 2022/09/13 07:50:34 - mmengine - INFO - Epoch(train) [202][300/586] lr: 5.000000e-06 eta: 0:26:32 time: 0.334225 data_time: 0.023107 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.869395 loss: 0.000481 2022/09/13 07:50:51 - mmengine - INFO - Epoch(train) [202][350/586] lr: 5.000000e-06 eta: 0:26:16 time: 0.343036 data_time: 0.025557 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.912896 loss: 0.000468 2022/09/13 07:51:08 - mmengine - INFO - Epoch(train) [202][400/586] lr: 5.000000e-06 eta: 0:26:00 time: 0.337343 data_time: 0.022744 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.880639 loss: 0.000499 2022/09/13 07:51:25 - mmengine - INFO - Epoch(train) [202][450/586] lr: 5.000000e-06 eta: 0:25:44 time: 0.336547 data_time: 0.023001 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.856434 loss: 0.000489 2022/09/13 07:51:42 - mmengine - INFO - Epoch(train) [202][500/586] lr: 5.000000e-06 eta: 0:25:28 time: 0.340216 data_time: 0.021896 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.908828 loss: 0.000463 2022/09/13 07:51:59 - mmengine - INFO - Epoch(train) [202][550/586] lr: 5.000000e-06 eta: 0:25:12 time: 0.342651 data_time: 0.021961 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.887364 loss: 0.000481 2022/09/13 07:52:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:52:11 - mmengine - INFO - Saving checkpoint at 202 epochs 2022/09/13 07:52:35 - mmengine - INFO - Epoch(train) [203][50/586] lr: 5.000000e-06 eta: 0:24:44 time: 0.342352 data_time: 0.032101 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.882137 loss: 0.000479 2022/09/13 07:52:52 - mmengine - INFO - Epoch(train) [203][100/586] lr: 5.000000e-06 eta: 0:24:28 time: 0.340588 data_time: 0.025630 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.870709 loss: 0.000475 2022/09/13 07:53:09 - mmengine - INFO - Epoch(train) [203][150/586] lr: 5.000000e-06 eta: 0:24:12 time: 0.343279 data_time: 0.022391 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.795732 loss: 0.000488 2022/09/13 07:53:27 - mmengine - INFO - Epoch(train) [203][200/586] lr: 5.000000e-06 eta: 0:23:56 time: 0.346090 data_time: 0.023388 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.896159 loss: 0.000479 2022/09/13 07:53:44 - mmengine - INFO - Epoch(train) [203][250/586] lr: 5.000000e-06 eta: 0:23:40 time: 0.345149 data_time: 0.022672 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.860172 loss: 0.000468 2022/09/13 07:54:00 - mmengine - INFO - Epoch(train) [203][300/586] lr: 5.000000e-06 eta: 0:23:24 time: 0.330140 data_time: 0.022392 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.892314 loss: 0.000480 2022/09/13 07:54:17 - mmengine - INFO - Epoch(train) [203][350/586] lr: 5.000000e-06 eta: 0:23:08 time: 0.339973 data_time: 0.022385 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.853397 loss: 0.000468 2022/09/13 07:54:34 - mmengine - INFO - Epoch(train) [203][400/586] lr: 5.000000e-06 eta: 0:22:52 time: 0.335658 data_time: 0.025574 memory: 7489 loss_kpt: 0.000452 acc_pose: 0.902648 loss: 0.000452 2022/09/13 07:54:51 - mmengine - INFO - Epoch(train) [203][450/586] lr: 5.000000e-06 eta: 0:22:36 time: 0.338526 data_time: 0.022366 memory: 7489 loss_kpt: 0.000462 acc_pose: 0.852050 loss: 0.000462 2022/09/13 07:55:08 - mmengine - INFO - Epoch(train) [203][500/586] lr: 5.000000e-06 eta: 0:22:20 time: 0.339621 data_time: 0.023727 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.878127 loss: 0.000475 2022/09/13 07:55:26 - mmengine - INFO - Epoch(train) [203][550/586] lr: 5.000000e-06 eta: 0:22:04 time: 0.344886 data_time: 0.026764 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.865182 loss: 0.000464 2022/09/13 07:55:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:55:37 - mmengine - INFO - Saving checkpoint at 203 epochs 2022/09/13 07:55:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:56:02 - mmengine - INFO - Epoch(train) [204][50/586] lr: 5.000000e-06 eta: 0:21:37 time: 0.349550 data_time: 0.030227 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.924128 loss: 0.000474 2022/09/13 07:56:19 - mmengine - INFO - Epoch(train) [204][100/586] lr: 5.000000e-06 eta: 0:21:21 time: 0.341705 data_time: 0.023152 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.854714 loss: 0.000465 2022/09/13 07:56:36 - mmengine - INFO - Epoch(train) [204][150/586] lr: 5.000000e-06 eta: 0:21:05 time: 0.339185 data_time: 0.022606 memory: 7489 loss_kpt: 0.000459 acc_pose: 0.893102 loss: 0.000459 2022/09/13 07:56:53 - mmengine - INFO - Epoch(train) [204][200/586] lr: 5.000000e-06 eta: 0:20:49 time: 0.342864 data_time: 0.022330 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.873039 loss: 0.000480 2022/09/13 07:57:10 - mmengine - INFO - Epoch(train) [204][250/586] lr: 5.000000e-06 eta: 0:20:33 time: 0.338173 data_time: 0.022513 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.912663 loss: 0.000463 2022/09/13 07:57:27 - mmengine - INFO - Epoch(train) [204][300/586] lr: 5.000000e-06 eta: 0:20:17 time: 0.338473 data_time: 0.026540 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.839291 loss: 0.000472 2022/09/13 07:57:44 - mmengine - INFO - Epoch(train) [204][350/586] lr: 5.000000e-06 eta: 0:20:01 time: 0.336165 data_time: 0.021864 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.833168 loss: 0.000472 2022/09/13 07:58:01 - mmengine - INFO - Epoch(train) [204][400/586] lr: 5.000000e-06 eta: 0:19:45 time: 0.343517 data_time: 0.022996 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.814392 loss: 0.000476 2022/09/13 07:58:18 - mmengine - INFO - Epoch(train) [204][450/586] lr: 5.000000e-06 eta: 0:19:29 time: 0.337625 data_time: 0.022932 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.824761 loss: 0.000494 2022/09/13 07:58:35 - mmengine - INFO - Epoch(train) [204][500/586] lr: 5.000000e-06 eta: 0:19:13 time: 0.342081 data_time: 0.022444 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.878769 loss: 0.000484 2022/09/13 07:58:52 - mmengine - INFO - Epoch(train) [204][550/586] lr: 5.000000e-06 eta: 0:18:57 time: 0.337991 data_time: 0.022511 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.888551 loss: 0.000481 2022/09/13 07:59:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 07:59:04 - mmengine - INFO - Saving checkpoint at 204 epochs 2022/09/13 07:59:29 - mmengine - INFO - Epoch(train) [205][50/586] lr: 5.000000e-06 eta: 0:18:29 time: 0.346289 data_time: 0.026053 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.900522 loss: 0.000486 2022/09/13 07:59:46 - mmengine - INFO - Epoch(train) [205][100/586] lr: 5.000000e-06 eta: 0:18:13 time: 0.343203 data_time: 0.022642 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.871358 loss: 0.000485 2022/09/13 08:00:03 - mmengine - INFO - Epoch(train) [205][150/586] lr: 5.000000e-06 eta: 0:17:57 time: 0.342458 data_time: 0.023685 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.854547 loss: 0.000469 2022/09/13 08:00:20 - mmengine - INFO - Epoch(train) [205][200/586] lr: 5.000000e-06 eta: 0:17:41 time: 0.338095 data_time: 0.021785 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.852708 loss: 0.000477 2022/09/13 08:00:38 - mmengine - INFO - Epoch(train) [205][250/586] lr: 5.000000e-06 eta: 0:17:25 time: 0.350864 data_time: 0.026986 memory: 7489 loss_kpt: 0.000462 acc_pose: 0.889172 loss: 0.000462 2022/09/13 08:00:55 - mmengine - INFO - Epoch(train) [205][300/586] lr: 5.000000e-06 eta: 0:17:09 time: 0.336472 data_time: 0.022337 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.878680 loss: 0.000481 2022/09/13 08:01:12 - mmengine - INFO - Epoch(train) [205][350/586] lr: 5.000000e-06 eta: 0:16:53 time: 0.348882 data_time: 0.022315 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.834045 loss: 0.000466 2022/09/13 08:01:29 - mmengine - INFO - Epoch(train) [205][400/586] lr: 5.000000e-06 eta: 0:16:37 time: 0.335215 data_time: 0.021964 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.898102 loss: 0.000485 2022/09/13 08:01:46 - mmengine - INFO - Epoch(train) [205][450/586] lr: 5.000000e-06 eta: 0:16:21 time: 0.343839 data_time: 0.026847 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.864853 loss: 0.000485 2022/09/13 08:01:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:02:03 - mmengine - INFO - Epoch(train) [205][500/586] lr: 5.000000e-06 eta: 0:16:05 time: 0.335724 data_time: 0.022306 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.924241 loss: 0.000477 2022/09/13 08:02:20 - mmengine - INFO - Epoch(train) [205][550/586] lr: 5.000000e-06 eta: 0:15:49 time: 0.339725 data_time: 0.023196 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.817890 loss: 0.000474 2022/09/13 08:02:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:02:32 - mmengine - INFO - Saving checkpoint at 205 epochs 2022/09/13 08:02:56 - mmengine - INFO - Epoch(train) [206][50/586] lr: 5.000000e-06 eta: 0:15:21 time: 0.347995 data_time: 0.031000 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.880168 loss: 0.000475 2022/09/13 08:03:13 - mmengine - INFO - Epoch(train) [206][100/586] lr: 5.000000e-06 eta: 0:15:05 time: 0.332763 data_time: 0.022271 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.882658 loss: 0.000473 2022/09/13 08:03:30 - mmengine - INFO - Epoch(train) [206][150/586] lr: 5.000000e-06 eta: 0:14:49 time: 0.337480 data_time: 0.023159 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.904200 loss: 0.000466 2022/09/13 08:03:47 - mmengine - INFO - Epoch(train) [206][200/586] lr: 5.000000e-06 eta: 0:14:33 time: 0.337225 data_time: 0.022737 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.899790 loss: 0.000491 2022/09/13 08:04:04 - mmengine - INFO - Epoch(train) [206][250/586] lr: 5.000000e-06 eta: 0:14:17 time: 0.344928 data_time: 0.022343 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.858256 loss: 0.000483 2022/09/13 08:04:21 - mmengine - INFO - Epoch(train) [206][300/586] lr: 5.000000e-06 eta: 0:14:02 time: 0.346170 data_time: 0.022962 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.852910 loss: 0.000487 2022/09/13 08:04:38 - mmengine - INFO - Epoch(train) [206][350/586] lr: 5.000000e-06 eta: 0:13:46 time: 0.338003 data_time: 0.025635 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.904684 loss: 0.000472 2022/09/13 08:04:55 - mmengine - INFO - Epoch(train) [206][400/586] lr: 5.000000e-06 eta: 0:13:30 time: 0.335999 data_time: 0.022084 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.857466 loss: 0.000490 2022/09/13 08:05:12 - mmengine - INFO - Epoch(train) [206][450/586] lr: 5.000000e-06 eta: 0:13:14 time: 0.336793 data_time: 0.022604 memory: 7489 loss_kpt: 0.000456 acc_pose: 0.836786 loss: 0.000456 2022/09/13 08:05:29 - mmengine - INFO - Epoch(train) [206][500/586] lr: 5.000000e-06 eta: 0:12:58 time: 0.337654 data_time: 0.023440 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.789206 loss: 0.000489 2022/09/13 08:05:46 - mmengine - INFO - Epoch(train) [206][550/586] lr: 5.000000e-06 eta: 0:12:42 time: 0.336539 data_time: 0.021690 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.902395 loss: 0.000481 2022/09/13 08:05:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:05:58 - mmengine - INFO - Saving checkpoint at 206 epochs 2022/09/13 08:06:23 - mmengine - INFO - Epoch(train) [207][50/586] lr: 5.000000e-06 eta: 0:12:14 time: 0.352801 data_time: 0.029671 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.853420 loss: 0.000485 2022/09/13 08:06:40 - mmengine - INFO - Epoch(train) [207][100/586] lr: 5.000000e-06 eta: 0:11:58 time: 0.343535 data_time: 0.027338 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.895797 loss: 0.000471 2022/09/13 08:06:57 - mmengine - INFO - Epoch(train) [207][150/586] lr: 5.000000e-06 eta: 0:11:42 time: 0.341850 data_time: 0.025721 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.869349 loss: 0.000467 2022/09/13 08:07:14 - mmengine - INFO - Epoch(train) [207][200/586] lr: 5.000000e-06 eta: 0:11:26 time: 0.338128 data_time: 0.022786 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.845375 loss: 0.000478 2022/09/13 08:07:31 - mmengine - INFO - Epoch(train) [207][250/586] lr: 5.000000e-06 eta: 0:11:10 time: 0.340356 data_time: 0.023103 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.836431 loss: 0.000488 2022/09/13 08:07:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:07:48 - mmengine - INFO - Epoch(train) [207][300/586] lr: 5.000000e-06 eta: 0:10:54 time: 0.342453 data_time: 0.022527 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.852769 loss: 0.000491 2022/09/13 08:08:05 - mmengine - INFO - Epoch(train) [207][350/586] lr: 5.000000e-06 eta: 0:10:38 time: 0.339747 data_time: 0.022278 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.916479 loss: 0.000474 2022/09/13 08:08:22 - mmengine - INFO - Epoch(train) [207][400/586] lr: 5.000000e-06 eta: 0:10:22 time: 0.338819 data_time: 0.023014 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.852601 loss: 0.000461 2022/09/13 08:08:39 - mmengine - INFO - Epoch(train) [207][450/586] lr: 5.000000e-06 eta: 0:10:06 time: 0.338275 data_time: 0.022217 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.871685 loss: 0.000472 2022/09/13 08:08:56 - mmengine - INFO - Epoch(train) [207][500/586] lr: 5.000000e-06 eta: 0:09:50 time: 0.333941 data_time: 0.023839 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.872752 loss: 0.000466 2022/09/13 08:09:13 - mmengine - INFO - Epoch(train) [207][550/586] lr: 5.000000e-06 eta: 0:09:34 time: 0.344391 data_time: 0.022643 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.877990 loss: 0.000489 2022/09/13 08:09:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:09:25 - mmengine - INFO - Saving checkpoint at 207 epochs 2022/09/13 08:09:49 - mmengine - INFO - Epoch(train) [208][50/586] lr: 5.000000e-06 eta: 0:09:06 time: 0.344407 data_time: 0.026673 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.868067 loss: 0.000484 2022/09/13 08:10:06 - mmengine - INFO - Epoch(train) [208][100/586] lr: 5.000000e-06 eta: 0:08:50 time: 0.343166 data_time: 0.022329 memory: 7489 loss_kpt: 0.000457 acc_pose: 0.863490 loss: 0.000457 2022/09/13 08:10:23 - mmengine - INFO - Epoch(train) [208][150/586] lr: 5.000000e-06 eta: 0:08:34 time: 0.334536 data_time: 0.022687 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.865489 loss: 0.000471 2022/09/13 08:10:40 - mmengine - INFO - Epoch(train) [208][200/586] lr: 5.000000e-06 eta: 0:08:18 time: 0.336771 data_time: 0.021937 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.890742 loss: 0.000465 2022/09/13 08:10:57 - mmengine - INFO - Epoch(train) [208][250/586] lr: 5.000000e-06 eta: 0:08:02 time: 0.339350 data_time: 0.027253 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.812978 loss: 0.000475 2022/09/13 08:11:13 - mmengine - INFO - Epoch(train) [208][300/586] lr: 5.000000e-06 eta: 0:07:46 time: 0.334653 data_time: 0.022077 memory: 7489 loss_kpt: 0.000456 acc_pose: 0.871744 loss: 0.000456 2022/09/13 08:11:30 - mmengine - INFO - Epoch(train) [208][350/586] lr: 5.000000e-06 eta: 0:07:30 time: 0.337013 data_time: 0.022552 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.875796 loss: 0.000496 2022/09/13 08:11:47 - mmengine - INFO - Epoch(train) [208][400/586] lr: 5.000000e-06 eta: 0:07:14 time: 0.342077 data_time: 0.022691 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.858371 loss: 0.000478 2022/09/13 08:12:04 - mmengine - INFO - Epoch(train) [208][450/586] lr: 5.000000e-06 eta: 0:06:58 time: 0.338028 data_time: 0.022421 memory: 7489 loss_kpt: 0.000455 acc_pose: 0.915572 loss: 0.000455 2022/09/13 08:12:21 - mmengine - INFO - Epoch(train) [208][500/586] lr: 5.000000e-06 eta: 0:06:42 time: 0.334522 data_time: 0.023069 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.842456 loss: 0.000466 2022/09/13 08:12:38 - mmengine - INFO - Epoch(train) [208][550/586] lr: 5.000000e-06 eta: 0:06:26 time: 0.337939 data_time: 0.026167 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.840031 loss: 0.000483 2022/09/13 08:12:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:12:50 - mmengine - INFO - Saving checkpoint at 208 epochs 2022/09/13 08:13:14 - mmengine - INFO - Epoch(train) [209][50/586] lr: 5.000000e-06 eta: 0:05:59 time: 0.352862 data_time: 0.032773 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.905817 loss: 0.000493 2022/09/13 08:13:31 - mmengine - INFO - Epoch(train) [209][100/586] lr: 5.000000e-06 eta: 0:05:43 time: 0.338271 data_time: 0.023086 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.903748 loss: 0.000477 2022/09/13 08:13:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:13:48 - mmengine - INFO - Epoch(train) [209][150/586] lr: 5.000000e-06 eta: 0:05:27 time: 0.339331 data_time: 0.021916 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.867326 loss: 0.000483 2022/09/13 08:14:05 - mmengine - INFO - Epoch(train) [209][200/586] lr: 5.000000e-06 eta: 0:05:11 time: 0.335821 data_time: 0.022223 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.904235 loss: 0.000493 2022/09/13 08:14:22 - mmengine - INFO - Epoch(train) [209][250/586] lr: 5.000000e-06 eta: 0:04:55 time: 0.339904 data_time: 0.022752 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.897103 loss: 0.000486 2022/09/13 08:14:39 - mmengine - INFO - Epoch(train) [209][300/586] lr: 5.000000e-06 eta: 0:04:39 time: 0.341038 data_time: 0.022042 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.906029 loss: 0.000478 2022/09/13 08:14:56 - mmengine - INFO - Epoch(train) [209][350/586] lr: 5.000000e-06 eta: 0:04:23 time: 0.343461 data_time: 0.027038 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.905787 loss: 0.000473 2022/09/13 08:15:13 - mmengine - INFO - Epoch(train) [209][400/586] lr: 5.000000e-06 eta: 0:04:07 time: 0.336631 data_time: 0.023146 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.860326 loss: 0.000476 2022/09/13 08:15:30 - mmengine - INFO - Epoch(train) [209][450/586] lr: 5.000000e-06 eta: 0:03:51 time: 0.341651 data_time: 0.022568 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.854095 loss: 0.000479 2022/09/13 08:15:47 - mmengine - INFO - Epoch(train) [209][500/586] lr: 5.000000e-06 eta: 0:03:35 time: 0.335365 data_time: 0.022861 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.860439 loss: 0.000485 2022/09/13 08:16:04 - mmengine - INFO - Epoch(train) [209][550/586] lr: 5.000000e-06 eta: 0:03:19 time: 0.333994 data_time: 0.022757 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.868913 loss: 0.000486 2022/09/13 08:16:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:16:16 - mmengine - INFO - Saving checkpoint at 209 epochs 2022/09/13 08:16:41 - mmengine - INFO - Epoch(train) [210][50/586] lr: 5.000000e-06 eta: 0:02:51 time: 0.342117 data_time: 0.030288 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.886653 loss: 0.000463 2022/09/13 08:16:58 - mmengine - INFO - Epoch(train) [210][100/586] lr: 5.000000e-06 eta: 0:02:35 time: 0.340103 data_time: 0.022927 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.900368 loss: 0.000481 2022/09/13 08:17:14 - mmengine - INFO - Epoch(train) [210][150/586] lr: 5.000000e-06 eta: 0:02:19 time: 0.335741 data_time: 0.022537 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.842959 loss: 0.000475 2022/09/13 08:17:31 - mmengine - INFO - Epoch(train) [210][200/586] lr: 5.000000e-06 eta: 0:02:03 time: 0.334042 data_time: 0.022335 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.858312 loss: 0.000473 2022/09/13 08:17:48 - mmengine - INFO - Epoch(train) [210][250/586] lr: 5.000000e-06 eta: 0:01:47 time: 0.339192 data_time: 0.022315 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.931061 loss: 0.000475 2022/09/13 08:18:05 - mmengine - INFO - Epoch(train) [210][300/586] lr: 5.000000e-06 eta: 0:01:31 time: 0.339953 data_time: 0.022553 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.882232 loss: 0.000474 2022/09/13 08:18:22 - mmengine - INFO - Epoch(train) [210][350/586] lr: 5.000000e-06 eta: 0:01:15 time: 0.337247 data_time: 0.022438 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.844021 loss: 0.000471 2022/09/13 08:18:39 - mmengine - INFO - Epoch(train) [210][400/586] lr: 5.000000e-06 eta: 0:00:59 time: 0.343271 data_time: 0.022088 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.898898 loss: 0.000487 2022/09/13 08:18:56 - mmengine - INFO - Epoch(train) [210][450/586] lr: 5.000000e-06 eta: 0:00:43 time: 0.336166 data_time: 0.022464 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.894322 loss: 0.000484 2022/09/13 08:19:13 - mmengine - INFO - Epoch(train) [210][500/586] lr: 5.000000e-06 eta: 0:00:27 time: 0.333638 data_time: 0.022276 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.896490 loss: 0.000480 2022/09/13 08:19:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:19:30 - mmengine - INFO - Epoch(train) [210][550/586] lr: 5.000000e-06 eta: 0:00:11 time: 0.339830 data_time: 0.022159 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.905889 loss: 0.000467 2022/09/13 08:19:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_dark-8xb32-210e_coco-256x192_20220912_192919 2022/09/13 08:19:42 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/09/13 08:20:00 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:01:16 time: 0.215564 data_time: 0.013226 memory: 7489 2022/09/13 08:20:11 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:01:04 time: 0.209329 data_time: 0.007617 memory: 1657 2022/09/13 08:20:21 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:53 time: 0.208927 data_time: 0.007352 memory: 1657 2022/09/13 08:20:31 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:43 time: 0.209268 data_time: 0.007363 memory: 1657 2022/09/13 08:20:42 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:32 time: 0.208943 data_time: 0.007753 memory: 1657 2022/09/13 08:20:52 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:22 time: 0.208029 data_time: 0.007348 memory: 1657 2022/09/13 08:21:03 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:12 time: 0.211092 data_time: 0.007265 memory: 1657 2022/09/13 08:21:13 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:01 time: 0.206906 data_time: 0.006907 memory: 1657 2022/09/13 08:21:49 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 08:22:02 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.763810 coco/AP .5: 0.906619 coco/AP .75: 0.831251 coco/AP (M): 0.727441 coco/AP (L): 0.831247 coco/AR: 0.813571 coco/AR .5: 0.942380 coco/AR .75: 0.873111 coco/AR (M): 0.771866 coco/AR (L): 0.874508 2022/09/13 08:22:02 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/dark_w48_256_v1/best_coco/AP_epoch_200.pth is removed 2022/09/13 08:22:06 - mmengine - INFO - The best checkpoint with 0.7638 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.