2022/09/09 17:28:23 - 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: 1788588451 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 ------------------------------------------------------------ 2022/09/09 17:28:25 - 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) 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)), 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)), 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)), 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/20220909/hrnet_w48_256/' 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:00 - 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. 2022/09/09 17:29:04 - 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. 2022/09/09 17:29:07 - 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. 2022/09/09 17:29:10 - 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. 2022/09/09 17:29:10 - 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. 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 2022/09/09 17:29:23 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256 by HardDiskBackend. 2022/09/09 17:31:33 - mmengine - INFO - Epoch(train) [1][50/586] lr: 4.954910e-05 eta: 3 days, 16:57:43 time: 2.603553 data_time: 0.902638 memory: 7489 loss_kpt: 0.002187 acc_pose: 0.134573 loss: 0.002187 2022/09/09 17:32:32 - mmengine - INFO - Epoch(train) [1][100/586] lr: 9.959920e-05 eta: 2 days, 16:31:49 time: 1.175061 data_time: 0.229367 memory: 7489 loss_kpt: 0.001857 acc_pose: 0.403810 loss: 0.001857 2022/09/09 17:33:26 - mmengine - INFO - Epoch(train) [1][150/586] lr: 1.496493e-04 eta: 2 days, 7:22:38 time: 1.087360 data_time: 0.079658 memory: 7489 loss_kpt: 0.001539 acc_pose: 0.487142 loss: 0.001539 2022/09/09 17:34:02 - mmengine - INFO - Epoch(train) [1][200/586] lr: 1.996994e-04 eta: 1 day, 23:40:42 time: 0.722264 data_time: 0.131291 memory: 7489 loss_kpt: 0.001422 acc_pose: 0.513435 loss: 0.001422 2022/09/09 17:34:27 - mmengine - INFO - Epoch(train) [1][250/586] lr: 2.497495e-04 eta: 1 day, 17:28:00 time: 0.489482 data_time: 0.097388 memory: 7489 loss_kpt: 0.001289 acc_pose: 0.637447 loss: 0.001289 2022/09/09 17:35:19 - mmengine - INFO - Epoch(train) [1][300/586] lr: 2.997996e-04 eta: 1 day, 16:26:00 time: 1.036673 data_time: 0.371905 memory: 7489 loss_kpt: 0.001246 acc_pose: 0.587776 loss: 0.001246 2022/09/09 17:36:07 - mmengine - INFO - Epoch(train) [1][350/586] lr: 3.498497e-04 eta: 1 day, 15:22:48 time: 0.972797 data_time: 0.147335 memory: 7489 loss_kpt: 0.001229 acc_pose: 0.594775 loss: 0.001229 2022/09/09 17:36:41 - mmengine - INFO - Epoch(train) [1][400/586] lr: 3.998998e-04 eta: 1 day, 13:17:26 time: 0.668494 data_time: 0.025077 memory: 7489 loss_kpt: 0.001210 acc_pose: 0.513701 loss: 0.001210 2022/09/09 17:37:12 - mmengine - INFO - Epoch(train) [1][450/586] lr: 4.499499e-04 eta: 1 day, 11:30:31 time: 0.627555 data_time: 0.049889 memory: 7489 loss_kpt: 0.001171 acc_pose: 0.637383 loss: 0.001171 2022/09/09 17:37:46 - mmengine - INFO - Epoch(train) [1][500/586] lr: 5.000000e-04 eta: 1 day, 10:14:53 time: 0.676634 data_time: 0.025521 memory: 7489 loss_kpt: 0.001148 acc_pose: 0.585740 loss: 0.001148 2022/09/09 17:38:21 - mmengine - INFO - Epoch(train) [1][550/586] lr: 5.000000e-04 eta: 1 day, 9:16:12 time: 0.694326 data_time: 0.027113 memory: 7489 loss_kpt: 0.001163 acc_pose: 0.563675 loss: 0.001163 2022/09/09 17:38:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:38:48 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/09 17:39:11 - mmengine - INFO - Epoch(train) [2][50/586] lr: 5.000000e-04 eta: 1 day, 5:38:17 time: 0.331801 data_time: 0.029177 memory: 7489 loss_kpt: 0.001133 acc_pose: 0.664253 loss: 0.001133 2022/09/09 17:39:28 - mmengine - INFO - Epoch(train) [2][100/586] lr: 5.000000e-04 eta: 1 day, 4:17:09 time: 0.330649 data_time: 0.028013 memory: 7489 loss_kpt: 0.001076 acc_pose: 0.631992 loss: 0.001076 2022/09/09 17:39:45 - mmengine - INFO - Epoch(train) [2][150/586] lr: 5.000000e-04 eta: 1 day, 3:08:29 time: 0.341375 data_time: 0.024135 memory: 7489 loss_kpt: 0.001057 acc_pose: 0.706958 loss: 0.001057 2022/09/09 17:40:01 - mmengine - INFO - Epoch(train) [2][200/586] lr: 5.000000e-04 eta: 1 day, 2:07:11 time: 0.330957 data_time: 0.023596 memory: 7489 loss_kpt: 0.001073 acc_pose: 0.677708 loss: 0.001073 2022/09/09 17:40:18 - mmengine - INFO - Epoch(train) [2][250/586] lr: 5.000000e-04 eta: 1 day, 1:12:35 time: 0.326215 data_time: 0.023762 memory: 7489 loss_kpt: 0.001090 acc_pose: 0.626716 loss: 0.001090 2022/09/09 17:40:35 - mmengine - INFO - Epoch(train) [2][300/586] lr: 5.000000e-04 eta: 1 day, 0:26:35 time: 0.347639 data_time: 0.022361 memory: 7489 loss_kpt: 0.001051 acc_pose: 0.659847 loss: 0.001051 2022/09/09 17:40:51 - mmengine - INFO - Epoch(train) [2][350/586] lr: 5.000000e-04 eta: 23:43:09 time: 0.326178 data_time: 0.022869 memory: 7489 loss_kpt: 0.001063 acc_pose: 0.729651 loss: 0.001063 2022/09/09 17:41:08 - mmengine - INFO - Epoch(train) [2][400/586] lr: 5.000000e-04 eta: 23:04:37 time: 0.331503 data_time: 0.024576 memory: 7489 loss_kpt: 0.001055 acc_pose: 0.685020 loss: 0.001055 2022/09/09 17:41:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:41:25 - mmengine - INFO - Epoch(train) [2][450/586] lr: 5.000000e-04 eta: 22:30:29 time: 0.338427 data_time: 0.028762 memory: 7489 loss_kpt: 0.001022 acc_pose: 0.630242 loss: 0.001022 2022/09/09 17:41:41 - mmengine - INFO - Epoch(train) [2][500/586] lr: 5.000000e-04 eta: 21:58:11 time: 0.324987 data_time: 0.023054 memory: 7489 loss_kpt: 0.001011 acc_pose: 0.668651 loss: 0.001011 2022/09/09 17:41:58 - mmengine - INFO - Epoch(train) [2][550/586] lr: 5.000000e-04 eta: 21:29:32 time: 0.334137 data_time: 0.025095 memory: 7489 loss_kpt: 0.001002 acc_pose: 0.740880 loss: 0.001002 2022/09/09 17:42:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:42:10 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/09 17:42:33 - mmengine - INFO - Epoch(train) [3][50/586] lr: 5.000000e-04 eta: 20:25:24 time: 0.330513 data_time: 0.027941 memory: 7489 loss_kpt: 0.000976 acc_pose: 0.683295 loss: 0.000976 2022/09/09 17:42:50 - mmengine - INFO - Epoch(train) [3][100/586] lr: 5.000000e-04 eta: 20:03:35 time: 0.336273 data_time: 0.023418 memory: 7489 loss_kpt: 0.000980 acc_pose: 0.671453 loss: 0.000980 2022/09/09 17:43:07 - mmengine - INFO - Epoch(train) [3][150/586] lr: 5.000000e-04 eta: 19:43:51 time: 0.342405 data_time: 0.029235 memory: 7489 loss_kpt: 0.000979 acc_pose: 0.710109 loss: 0.000979 2022/09/09 17:43:24 - mmengine - INFO - Epoch(train) [3][200/586] lr: 5.000000e-04 eta: 19:24:23 time: 0.326542 data_time: 0.023707 memory: 7489 loss_kpt: 0.000980 acc_pose: 0.736401 loss: 0.000980 2022/09/09 17:43:40 - mmengine - INFO - Epoch(train) [3][250/586] lr: 5.000000e-04 eta: 19:06:31 time: 0.330232 data_time: 0.022795 memory: 7489 loss_kpt: 0.000967 acc_pose: 0.737928 loss: 0.000967 2022/09/09 17:43:58 - mmengine - INFO - Epoch(train) [3][300/586] lr: 5.000000e-04 eta: 18:51:14 time: 0.350403 data_time: 0.024028 memory: 7489 loss_kpt: 0.000979 acc_pose: 0.734562 loss: 0.000979 2022/09/09 17:44:14 - mmengine - INFO - Epoch(train) [3][350/586] lr: 5.000000e-04 eta: 18:35:17 time: 0.325606 data_time: 0.024347 memory: 7489 loss_kpt: 0.000954 acc_pose: 0.673213 loss: 0.000954 2022/09/09 17:44:31 - mmengine - INFO - Epoch(train) [3][400/586] lr: 5.000000e-04 eta: 18:20:54 time: 0.334284 data_time: 0.024049 memory: 7489 loss_kpt: 0.000935 acc_pose: 0.756462 loss: 0.000935 2022/09/09 17:44:48 - mmengine - INFO - Epoch(train) [3][450/586] lr: 5.000000e-04 eta: 18:08:15 time: 0.348221 data_time: 0.026791 memory: 7489 loss_kpt: 0.000951 acc_pose: 0.751730 loss: 0.000951 2022/09/09 17:45:04 - mmengine - INFO - Epoch(train) [3][500/586] lr: 5.000000e-04 eta: 17:55:04 time: 0.327076 data_time: 0.022656 memory: 7489 loss_kpt: 0.000945 acc_pose: 0.704041 loss: 0.000945 2022/09/09 17:45:21 - mmengine - INFO - Epoch(train) [3][550/586] lr: 5.000000e-04 eta: 17:42:42 time: 0.328546 data_time: 0.022084 memory: 7489 loss_kpt: 0.000963 acc_pose: 0.681146 loss: 0.000963 2022/09/09 17:45:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:45:33 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/09 17:45:58 - mmengine - INFO - Epoch(train) [4][50/586] lr: 5.000000e-04 eta: 17:10:35 time: 0.342436 data_time: 0.028846 memory: 7489 loss_kpt: 0.000953 acc_pose: 0.760205 loss: 0.000953 2022/09/09 17:46:14 - mmengine - INFO - Epoch(train) [4][100/586] lr: 5.000000e-04 eta: 17:00:12 time: 0.326823 data_time: 0.023758 memory: 7489 loss_kpt: 0.000914 acc_pose: 0.721716 loss: 0.000914 2022/09/09 17:46:31 - mmengine - INFO - Epoch(train) [4][150/586] lr: 5.000000e-04 eta: 16:51:00 time: 0.339100 data_time: 0.023675 memory: 7489 loss_kpt: 0.000926 acc_pose: 0.767745 loss: 0.000926 2022/09/09 17:46:47 - mmengine - INFO - Epoch(train) [4][200/586] lr: 5.000000e-04 eta: 16:41:50 time: 0.331000 data_time: 0.022287 memory: 7489 loss_kpt: 0.000932 acc_pose: 0.754005 loss: 0.000932 2022/09/09 17:47:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:47:04 - mmengine - INFO - Epoch(train) [4][250/586] lr: 5.000000e-04 eta: 16:32:52 time: 0.326133 data_time: 0.023494 memory: 7489 loss_kpt: 0.000918 acc_pose: 0.720414 loss: 0.000918 2022/09/09 17:47:20 - mmengine - INFO - Epoch(train) [4][300/586] lr: 5.000000e-04 eta: 16:24:43 time: 0.334168 data_time: 0.022128 memory: 7489 loss_kpt: 0.000917 acc_pose: 0.750432 loss: 0.000917 2022/09/09 17:47:37 - mmengine - INFO - Epoch(train) [4][350/586] lr: 5.000000e-04 eta: 16:16:37 time: 0.327400 data_time: 0.027023 memory: 7489 loss_kpt: 0.000918 acc_pose: 0.775461 loss: 0.000918 2022/09/09 17:47:53 - mmengine - INFO - Epoch(train) [4][400/586] lr: 5.000000e-04 eta: 16:08:55 time: 0.328112 data_time: 0.022885 memory: 7489 loss_kpt: 0.000892 acc_pose: 0.735785 loss: 0.000892 2022/09/09 17:48:10 - mmengine - INFO - Epoch(train) [4][450/586] lr: 5.000000e-04 eta: 16:01:58 time: 0.337435 data_time: 0.022345 memory: 7489 loss_kpt: 0.000894 acc_pose: 0.671064 loss: 0.000894 2022/09/09 17:48:27 - mmengine - INFO - Epoch(train) [4][500/586] lr: 5.000000e-04 eta: 15:55:06 time: 0.332402 data_time: 0.029188 memory: 7489 loss_kpt: 0.000886 acc_pose: 0.673753 loss: 0.000886 2022/09/09 17:48:43 - mmengine - INFO - Epoch(train) [4][550/586] lr: 5.000000e-04 eta: 15:48:19 time: 0.327791 data_time: 0.024242 memory: 7489 loss_kpt: 0.000912 acc_pose: 0.704962 loss: 0.000912 2022/09/09 17:48:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:48:55 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/09 17:49:19 - mmengine - INFO - Epoch(train) [5][50/586] lr: 5.000000e-04 eta: 15:27:50 time: 0.339093 data_time: 0.028282 memory: 7489 loss_kpt: 0.000884 acc_pose: 0.710322 loss: 0.000884 2022/09/09 17:49:36 - mmengine - INFO - Epoch(train) [5][100/586] lr: 5.000000e-04 eta: 15:21:58 time: 0.327971 data_time: 0.027808 memory: 7489 loss_kpt: 0.000882 acc_pose: 0.706421 loss: 0.000882 2022/09/09 17:49:53 - mmengine - INFO - Epoch(train) [5][150/586] lr: 5.000000e-04 eta: 15:16:53 time: 0.341968 data_time: 0.026023 memory: 7489 loss_kpt: 0.000904 acc_pose: 0.755675 loss: 0.000904 2022/09/09 17:50:09 - mmengine - INFO - Epoch(train) [5][200/586] lr: 5.000000e-04 eta: 15:11:31 time: 0.330015 data_time: 0.023211 memory: 7489 loss_kpt: 0.000884 acc_pose: 0.729093 loss: 0.000884 2022/09/09 17:50:26 - mmengine - INFO - Epoch(train) [5][250/586] lr: 5.000000e-04 eta: 15:06:18 time: 0.328882 data_time: 0.023069 memory: 7489 loss_kpt: 0.000894 acc_pose: 0.713260 loss: 0.000894 2022/09/09 17:50:43 - mmengine - INFO - Epoch(train) [5][300/586] lr: 5.000000e-04 eta: 15:01:40 time: 0.338919 data_time: 0.026357 memory: 7489 loss_kpt: 0.000887 acc_pose: 0.699407 loss: 0.000887 2022/09/09 17:50:59 - mmengine - INFO - Epoch(train) [5][350/586] lr: 5.000000e-04 eta: 14:57:05 time: 0.336385 data_time: 0.023326 memory: 7489 loss_kpt: 0.000905 acc_pose: 0.715900 loss: 0.000905 2022/09/09 17:51:16 - mmengine - INFO - Epoch(train) [5][400/586] lr: 5.000000e-04 eta: 14:52:24 time: 0.329102 data_time: 0.023522 memory: 7489 loss_kpt: 0.000896 acc_pose: 0.718836 loss: 0.000896 2022/09/09 17:51:32 - mmengine - INFO - Epoch(train) [5][450/586] lr: 5.000000e-04 eta: 14:48:01 time: 0.333304 data_time: 0.023769 memory: 7489 loss_kpt: 0.000871 acc_pose: 0.764064 loss: 0.000871 2022/09/09 17:51:49 - mmengine - INFO - Epoch(train) [5][500/586] lr: 5.000000e-04 eta: 14:43:36 time: 0.327969 data_time: 0.024067 memory: 7489 loss_kpt: 0.000882 acc_pose: 0.692305 loss: 0.000882 2022/09/09 17:52:06 - mmengine - INFO - Epoch(train) [5][550/586] lr: 5.000000e-04 eta: 14:39:34 time: 0.335369 data_time: 0.022771 memory: 7489 loss_kpt: 0.000872 acc_pose: 0.720478 loss: 0.000872 2022/09/09 17:52:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:52:18 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/09 17:52:42 - mmengine - INFO - Epoch(train) [6][50/586] lr: 5.000000e-04 eta: 14:25:01 time: 0.340582 data_time: 0.033882 memory: 7489 loss_kpt: 0.000856 acc_pose: 0.655154 loss: 0.000856 2022/09/09 17:52:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:52:58 - mmengine - INFO - Epoch(train) [6][100/586] lr: 5.000000e-04 eta: 14:21:17 time: 0.330294 data_time: 0.023863 memory: 7489 loss_kpt: 0.000878 acc_pose: 0.717687 loss: 0.000878 2022/09/09 17:53:15 - mmengine - INFO - Epoch(train) [6][150/586] lr: 5.000000e-04 eta: 14:17:54 time: 0.336954 data_time: 0.023175 memory: 7489 loss_kpt: 0.000862 acc_pose: 0.717931 loss: 0.000862 2022/09/09 17:53:32 - mmengine - INFO - Epoch(train) [6][200/586] lr: 5.000000e-04 eta: 14:14:19 time: 0.328277 data_time: 0.023478 memory: 7489 loss_kpt: 0.000839 acc_pose: 0.697328 loss: 0.000839 2022/09/09 17:53:48 - mmengine - INFO - Epoch(train) [6][250/586] lr: 5.000000e-04 eta: 14:10:57 time: 0.331643 data_time: 0.022841 memory: 7489 loss_kpt: 0.000866 acc_pose: 0.735260 loss: 0.000866 2022/09/09 17:54:05 - mmengine - INFO - Epoch(train) [6][300/586] lr: 5.000000e-04 eta: 14:07:39 time: 0.330581 data_time: 0.022839 memory: 7489 loss_kpt: 0.000851 acc_pose: 0.756893 loss: 0.000851 2022/09/09 17:54:21 - mmengine - INFO - Epoch(train) [6][350/586] lr: 5.000000e-04 eta: 14:04:33 time: 0.334196 data_time: 0.026688 memory: 7489 loss_kpt: 0.000864 acc_pose: 0.790038 loss: 0.000864 2022/09/09 17:54:38 - mmengine - INFO - Epoch(train) [6][400/586] lr: 5.000000e-04 eta: 14:01:30 time: 0.333077 data_time: 0.022823 memory: 7489 loss_kpt: 0.000848 acc_pose: 0.733768 loss: 0.000848 2022/09/09 17:54:55 - mmengine - INFO - Epoch(train) [6][450/586] lr: 5.000000e-04 eta: 13:58:32 time: 0.332966 data_time: 0.023542 memory: 7489 loss_kpt: 0.000857 acc_pose: 0.764472 loss: 0.000857 2022/09/09 17:55:11 - mmengine - INFO - Epoch(train) [6][500/586] lr: 5.000000e-04 eta: 13:55:34 time: 0.330610 data_time: 0.022541 memory: 7489 loss_kpt: 0.000844 acc_pose: 0.764381 loss: 0.000844 2022/09/09 17:55:28 - mmengine - INFO - Epoch(train) [6][550/586] lr: 5.000000e-04 eta: 13:52:54 time: 0.337822 data_time: 0.022426 memory: 7489 loss_kpt: 0.000840 acc_pose: 0.783130 loss: 0.000840 2022/09/09 17:55:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:55:40 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/09 17:56:04 - mmengine - INFO - Epoch(train) [7][50/586] lr: 5.000000e-04 eta: 13:41:47 time: 0.342506 data_time: 0.030243 memory: 7489 loss_kpt: 0.000884 acc_pose: 0.747901 loss: 0.000884 2022/09/09 17:56:21 - mmengine - INFO - Epoch(train) [7][100/586] lr: 5.000000e-04 eta: 13:39:26 time: 0.339252 data_time: 0.023697 memory: 7489 loss_kpt: 0.000863 acc_pose: 0.791390 loss: 0.000863 2022/09/09 17:56:38 - mmengine - INFO - Epoch(train) [7][150/586] lr: 5.000000e-04 eta: 13:37:12 time: 0.342398 data_time: 0.023211 memory: 7489 loss_kpt: 0.000860 acc_pose: 0.762333 loss: 0.000860 2022/09/09 17:56:54 - mmengine - INFO - Epoch(train) [7][200/586] lr: 5.000000e-04 eta: 13:34:34 time: 0.324743 data_time: 0.023384 memory: 7489 loss_kpt: 0.000856 acc_pose: 0.663835 loss: 0.000856 2022/09/09 17:57:11 - mmengine - INFO - Epoch(train) [7][250/586] lr: 5.000000e-04 eta: 13:32:16 time: 0.335614 data_time: 0.022484 memory: 7489 loss_kpt: 0.000855 acc_pose: 0.789005 loss: 0.000855 2022/09/09 17:57:28 - mmengine - INFO - Epoch(train) [7][300/586] lr: 5.000000e-04 eta: 13:30:01 time: 0.334940 data_time: 0.022932 memory: 7489 loss_kpt: 0.000833 acc_pose: 0.769849 loss: 0.000833 2022/09/09 17:57:44 - mmengine - INFO - Epoch(train) [7][350/586] lr: 5.000000e-04 eta: 13:27:40 time: 0.329171 data_time: 0.023121 memory: 7489 loss_kpt: 0.000834 acc_pose: 0.739492 loss: 0.000834 2022/09/09 17:58:01 - mmengine - INFO - Epoch(train) [7][400/586] lr: 5.000000e-04 eta: 13:25:34 time: 0.337551 data_time: 0.023485 memory: 7489 loss_kpt: 0.000835 acc_pose: 0.690650 loss: 0.000835 2022/09/09 17:58:18 - mmengine - INFO - Epoch(train) [7][450/586] lr: 5.000000e-04 eta: 13:23:42 time: 0.344702 data_time: 0.027867 memory: 7489 loss_kpt: 0.000869 acc_pose: 0.718072 loss: 0.000869 2022/09/09 17:58:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:58:35 - mmengine - INFO - Epoch(train) [7][500/586] lr: 5.000000e-04 eta: 13:21:29 time: 0.328901 data_time: 0.023018 memory: 7489 loss_kpt: 0.000827 acc_pose: 0.684552 loss: 0.000827 2022/09/09 17:58:52 - mmengine - INFO - Epoch(train) [7][550/586] lr: 5.000000e-04 eta: 13:19:27 time: 0.334380 data_time: 0.022969 memory: 7489 loss_kpt: 0.000819 acc_pose: 0.707330 loss: 0.000819 2022/09/09 17:59:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 17:59:04 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/09 17:59:28 - mmengine - INFO - Epoch(train) [8][50/586] lr: 5.000000e-04 eta: 13:10:23 time: 0.337053 data_time: 0.027786 memory: 7489 loss_kpt: 0.000827 acc_pose: 0.788843 loss: 0.000827 2022/09/09 17:59:45 - mmengine - INFO - Epoch(train) [8][100/586] lr: 5.000000e-04 eta: 13:08:31 time: 0.333898 data_time: 0.027444 memory: 7489 loss_kpt: 0.000823 acc_pose: 0.764651 loss: 0.000823 2022/09/09 18:00:04 - mmengine - INFO - Epoch(train) [8][150/586] lr: 5.000000e-04 eta: 13:07:57 time: 0.387851 data_time: 0.024825 memory: 7489 loss_kpt: 0.000827 acc_pose: 0.691493 loss: 0.000827 2022/09/09 18:00:20 - mmengine - INFO - Epoch(train) [8][200/586] lr: 5.000000e-04 eta: 13:05:59 time: 0.327416 data_time: 0.023736 memory: 7489 loss_kpt: 0.000835 acc_pose: 0.830257 loss: 0.000835 2022/09/09 18:00:37 - mmengine - INFO - Epoch(train) [8][250/586] lr: 5.000000e-04 eta: 13:04:13 time: 0.333793 data_time: 0.026415 memory: 7489 loss_kpt: 0.000847 acc_pose: 0.766629 loss: 0.000847 2022/09/09 18:00:54 - mmengine - INFO - Epoch(train) [8][300/586] lr: 5.000000e-04 eta: 13:02:31 time: 0.335456 data_time: 0.024660 memory: 7489 loss_kpt: 0.000815 acc_pose: 0.753634 loss: 0.000815 2022/09/09 18:01:10 - mmengine - INFO - Epoch(train) [8][350/586] lr: 5.000000e-04 eta: 13:00:47 time: 0.331894 data_time: 0.023292 memory: 7489 loss_kpt: 0.000828 acc_pose: 0.734971 loss: 0.000828 2022/09/09 18:01:27 - mmengine - INFO - Epoch(train) [8][400/586] lr: 5.000000e-04 eta: 12:59:06 time: 0.333380 data_time: 0.027358 memory: 7489 loss_kpt: 0.000823 acc_pose: 0.781416 loss: 0.000823 2022/09/09 18:01:44 - mmengine - INFO - Epoch(train) [8][450/586] lr: 5.000000e-04 eta: 12:57:22 time: 0.329829 data_time: 0.025000 memory: 7489 loss_kpt: 0.000802 acc_pose: 0.790526 loss: 0.000802 2022/09/09 18:02:00 - mmengine - INFO - Epoch(train) [8][500/586] lr: 5.000000e-04 eta: 12:55:45 time: 0.333219 data_time: 0.023569 memory: 7489 loss_kpt: 0.000818 acc_pose: 0.655274 loss: 0.000818 2022/09/09 18:02:17 - mmengine - INFO - Epoch(train) [8][550/586] lr: 5.000000e-04 eta: 12:54:14 time: 0.337406 data_time: 0.026088 memory: 7489 loss_kpt: 0.000826 acc_pose: 0.736576 loss: 0.000826 2022/09/09 18:02:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:02:30 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/09 18:02:53 - mmengine - INFO - Epoch(train) [9][50/586] lr: 5.000000e-04 eta: 12:46:33 time: 0.332072 data_time: 0.029296 memory: 7489 loss_kpt: 0.000808 acc_pose: 0.759233 loss: 0.000808 2022/09/09 18:03:10 - mmengine - INFO - Epoch(train) [9][100/586] lr: 5.000000e-04 eta: 12:45:00 time: 0.329759 data_time: 0.023680 memory: 7489 loss_kpt: 0.000812 acc_pose: 0.668339 loss: 0.000812 2022/09/09 18:03:27 - mmengine - INFO - Epoch(train) [9][150/586] lr: 5.000000e-04 eta: 12:43:41 time: 0.338981 data_time: 0.022957 memory: 7489 loss_kpt: 0.000833 acc_pose: 0.785022 loss: 0.000833 2022/09/09 18:03:43 - mmengine - INFO - Epoch(train) [9][200/586] lr: 5.000000e-04 eta: 12:42:13 time: 0.330428 data_time: 0.022571 memory: 7489 loss_kpt: 0.000815 acc_pose: 0.745234 loss: 0.000815 2022/09/09 18:04:00 - mmengine - INFO - Epoch(train) [9][250/586] lr: 5.000000e-04 eta: 12:40:52 time: 0.335802 data_time: 0.025588 memory: 7489 loss_kpt: 0.000816 acc_pose: 0.780091 loss: 0.000816 2022/09/09 18:04:17 - mmengine - INFO - Epoch(train) [9][300/586] lr: 5.000000e-04 eta: 12:39:35 time: 0.337665 data_time: 0.028035 memory: 7489 loss_kpt: 0.000820 acc_pose: 0.741206 loss: 0.000820 2022/09/09 18:04:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:04:33 - mmengine - INFO - Epoch(train) [9][350/586] lr: 5.000000e-04 eta: 12:38:10 time: 0.329999 data_time: 0.023647 memory: 7489 loss_kpt: 0.000825 acc_pose: 0.718834 loss: 0.000825 2022/09/09 18:04:50 - mmengine - INFO - Epoch(train) [9][400/586] lr: 5.000000e-04 eta: 12:36:54 time: 0.336740 data_time: 0.023526 memory: 7489 loss_kpt: 0.000823 acc_pose: 0.762360 loss: 0.000823 2022/09/09 18:05:07 - mmengine - INFO - Epoch(train) [9][450/586] lr: 5.000000e-04 eta: 12:35:32 time: 0.330176 data_time: 0.023951 memory: 7489 loss_kpt: 0.000835 acc_pose: 0.819486 loss: 0.000835 2022/09/09 18:05:23 - mmengine - INFO - Epoch(train) [9][500/586] lr: 5.000000e-04 eta: 12:34:18 time: 0.336424 data_time: 0.024343 memory: 7489 loss_kpt: 0.000827 acc_pose: 0.615197 loss: 0.000827 2022/09/09 18:05:40 - mmengine - INFO - Epoch(train) [9][550/586] lr: 5.000000e-04 eta: 12:33:03 time: 0.333772 data_time: 0.022953 memory: 7489 loss_kpt: 0.000826 acc_pose: 0.734283 loss: 0.000826 2022/09/09 18:05:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:05:52 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/09 18:06:16 - mmengine - INFO - Epoch(train) [10][50/586] lr: 5.000000e-04 eta: 12:26:36 time: 0.339911 data_time: 0.028614 memory: 7489 loss_kpt: 0.000807 acc_pose: 0.785717 loss: 0.000807 2022/09/09 18:06:33 - mmengine - INFO - Epoch(train) [10][100/586] lr: 5.000000e-04 eta: 12:25:31 time: 0.337853 data_time: 0.025544 memory: 7489 loss_kpt: 0.000795 acc_pose: 0.799598 loss: 0.000795 2022/09/09 18:06:49 - mmengine - INFO - Epoch(train) [10][150/586] lr: 5.000000e-04 eta: 12:24:17 time: 0.330055 data_time: 0.032076 memory: 7489 loss_kpt: 0.000793 acc_pose: 0.792569 loss: 0.000793 2022/09/09 18:07:06 - mmengine - INFO - Epoch(train) [10][200/586] lr: 5.000000e-04 eta: 12:23:07 time: 0.332085 data_time: 0.023997 memory: 7489 loss_kpt: 0.000803 acc_pose: 0.765172 loss: 0.000803 2022/09/09 18:07:23 - mmengine - INFO - Epoch(train) [10][250/586] lr: 5.000000e-04 eta: 12:22:03 time: 0.336643 data_time: 0.028247 memory: 7489 loss_kpt: 0.000801 acc_pose: 0.778342 loss: 0.000801 2022/09/09 18:07:40 - mmengine - INFO - Epoch(train) [10][300/586] lr: 5.000000e-04 eta: 12:20:59 time: 0.336101 data_time: 0.022693 memory: 7489 loss_kpt: 0.000802 acc_pose: 0.736128 loss: 0.000802 2022/09/09 18:07:57 - mmengine - INFO - Epoch(train) [10][350/586] lr: 5.000000e-04 eta: 12:20:02 time: 0.342012 data_time: 0.023316 memory: 7489 loss_kpt: 0.000770 acc_pose: 0.777972 loss: 0.000770 2022/09/09 18:08:13 - mmengine - INFO - Epoch(train) [10][400/586] lr: 5.000000e-04 eta: 12:18:53 time: 0.329549 data_time: 0.025963 memory: 7489 loss_kpt: 0.000785 acc_pose: 0.772950 loss: 0.000785 2022/09/09 18:08:30 - mmengine - INFO - Epoch(train) [10][450/586] lr: 5.000000e-04 eta: 12:17:48 time: 0.332569 data_time: 0.022272 memory: 7489 loss_kpt: 0.000815 acc_pose: 0.777143 loss: 0.000815 2022/09/09 18:08:46 - mmengine - INFO - Epoch(train) [10][500/586] lr: 5.000000e-04 eta: 12:16:38 time: 0.327323 data_time: 0.022961 memory: 7489 loss_kpt: 0.000757 acc_pose: 0.804788 loss: 0.000757 2022/09/09 18:09:03 - mmengine - INFO - Epoch(train) [10][550/586] lr: 5.000000e-04 eta: 12:15:39 time: 0.336936 data_time: 0.026316 memory: 7489 loss_kpt: 0.000784 acc_pose: 0.757902 loss: 0.000784 2022/09/09 18:09:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:09:15 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/09 18:09:35 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:17 time: 0.218438 data_time: 0.061522 memory: 7489 2022/09/09 18:09:47 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:01:13 time: 0.240467 data_time: 0.082919 memory: 1657 2022/09/09 18:10:03 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:01:21 time: 0.318147 data_time: 0.160326 memory: 1657 2022/09/09 18:10:22 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:01:20 time: 0.389249 data_time: 0.230990 memory: 1657 2022/09/09 18:10:38 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:48 time: 0.308164 data_time: 0.151605 memory: 1657 2022/09/09 18:10:51 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:28 time: 0.262600 data_time: 0.106328 memory: 1657 2022/09/09 18:11:03 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:13 time: 0.235028 data_time: 0.078965 memory: 1657 2022/09/09 18:11:13 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:01 time: 0.208496 data_time: 0.051392 memory: 1657 2022/09/09 18:11:53 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 18:12:06 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.668994 coco/AP .5: 0.872463 coco/AP .75: 0.741367 coco/AP (M): 0.636044 coco/AP (L): 0.733205 coco/AR: 0.729140 coco/AR .5: 0.914987 coco/AR .75: 0.795970 coco/AR (M): 0.687818 coco/AR (L): 0.787811 2022/09/09 18:12:10 - mmengine - INFO - The best checkpoint with 0.6690 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/09/09 18:12:27 - mmengine - INFO - Epoch(train) [11][50/586] lr: 5.000000e-04 eta: 12:09:58 time: 0.335709 data_time: 0.029868 memory: 7489 loss_kpt: 0.000802 acc_pose: 0.768218 loss: 0.000802 2022/09/09 18:12:44 - mmengine - INFO - Epoch(train) [11][100/586] lr: 5.000000e-04 eta: 12:08:58 time: 0.331534 data_time: 0.025703 memory: 7489 loss_kpt: 0.000786 acc_pose: 0.747917 loss: 0.000786 2022/09/09 18:12:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:13:00 - mmengine - INFO - Epoch(train) [11][150/586] lr: 5.000000e-04 eta: 12:07:59 time: 0.332997 data_time: 0.028614 memory: 7489 loss_kpt: 0.000812 acc_pose: 0.812630 loss: 0.000812 2022/09/09 18:13:17 - mmengine - INFO - Epoch(train) [11][200/586] lr: 5.000000e-04 eta: 12:07:06 time: 0.337596 data_time: 0.024988 memory: 7489 loss_kpt: 0.000787 acc_pose: 0.650612 loss: 0.000787 2022/09/09 18:13:34 - mmengine - INFO - Epoch(train) [11][250/586] lr: 5.000000e-04 eta: 12:06:04 time: 0.326966 data_time: 0.023346 memory: 7489 loss_kpt: 0.000800 acc_pose: 0.670842 loss: 0.000800 2022/09/09 18:13:50 - mmengine - INFO - Epoch(train) [11][300/586] lr: 5.000000e-04 eta: 12:05:10 time: 0.335174 data_time: 0.024796 memory: 7489 loss_kpt: 0.000795 acc_pose: 0.732765 loss: 0.000795 2022/09/09 18:14:07 - mmengine - INFO - Epoch(train) [11][350/586] lr: 5.000000e-04 eta: 12:04:15 time: 0.334216 data_time: 0.023797 memory: 7489 loss_kpt: 0.000783 acc_pose: 0.708532 loss: 0.000783 2022/09/09 18:14:23 - mmengine - INFO - Epoch(train) [11][400/586] lr: 5.000000e-04 eta: 12:03:13 time: 0.325099 data_time: 0.023405 memory: 7489 loss_kpt: 0.000779 acc_pose: 0.732797 loss: 0.000779 2022/09/09 18:14:40 - mmengine - INFO - Epoch(train) [11][450/586] lr: 5.000000e-04 eta: 12:02:23 time: 0.337004 data_time: 0.029496 memory: 7489 loss_kpt: 0.000780 acc_pose: 0.751504 loss: 0.000780 2022/09/09 18:14:57 - mmengine - INFO - Epoch(train) [11][500/586] lr: 5.000000e-04 eta: 12:01:29 time: 0.332671 data_time: 0.023167 memory: 7489 loss_kpt: 0.000788 acc_pose: 0.770851 loss: 0.000788 2022/09/09 18:15:14 - mmengine - INFO - Epoch(train) [11][550/586] lr: 5.000000e-04 eta: 12:00:38 time: 0.335343 data_time: 0.024767 memory: 7489 loss_kpt: 0.000763 acc_pose: 0.797500 loss: 0.000763 2022/09/09 18:15:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:15:26 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/09 18:15:50 - mmengine - INFO - Epoch(train) [12][50/586] lr: 5.000000e-04 eta: 11:55:44 time: 0.345775 data_time: 0.032173 memory: 7489 loss_kpt: 0.000791 acc_pose: 0.814596 loss: 0.000791 2022/09/09 18:16:07 - mmengine - INFO - Epoch(train) [12][100/586] lr: 5.000000e-04 eta: 11:54:55 time: 0.334207 data_time: 0.026342 memory: 7489 loss_kpt: 0.000785 acc_pose: 0.746036 loss: 0.000785 2022/09/09 18:16:23 - mmengine - INFO - Epoch(train) [12][150/586] lr: 5.000000e-04 eta: 11:54:03 time: 0.329281 data_time: 0.024217 memory: 7489 loss_kpt: 0.000794 acc_pose: 0.760547 loss: 0.000794 2022/09/09 18:16:40 - mmengine - INFO - Epoch(train) [12][200/586] lr: 5.000000e-04 eta: 11:53:10 time: 0.329462 data_time: 0.023115 memory: 7489 loss_kpt: 0.000792 acc_pose: 0.804767 loss: 0.000792 2022/09/09 18:16:57 - mmengine - INFO - Epoch(train) [12][250/586] lr: 5.000000e-04 eta: 11:52:28 time: 0.339712 data_time: 0.027747 memory: 7489 loss_kpt: 0.000790 acc_pose: 0.687904 loss: 0.000790 2022/09/09 18:17:13 - mmengine - INFO - Epoch(train) [12][300/586] lr: 5.000000e-04 eta: 11:51:39 time: 0.332395 data_time: 0.023383 memory: 7489 loss_kpt: 0.000783 acc_pose: 0.792412 loss: 0.000783 2022/09/09 18:17:30 - mmengine - INFO - Epoch(train) [12][350/586] lr: 5.000000e-04 eta: 11:50:53 time: 0.334859 data_time: 0.022921 memory: 7489 loss_kpt: 0.000784 acc_pose: 0.763068 loss: 0.000784 2022/09/09 18:17:47 - mmengine - INFO - Epoch(train) [12][400/586] lr: 5.000000e-04 eta: 11:50:05 time: 0.331717 data_time: 0.022642 memory: 7489 loss_kpt: 0.000771 acc_pose: 0.712323 loss: 0.000771 2022/09/09 18:18:03 - mmengine - INFO - Epoch(train) [12][450/586] lr: 5.000000e-04 eta: 11:49:18 time: 0.332622 data_time: 0.022976 memory: 7489 loss_kpt: 0.000764 acc_pose: 0.830614 loss: 0.000764 2022/09/09 18:18:20 - mmengine - INFO - Epoch(train) [12][500/586] lr: 5.000000e-04 eta: 11:48:31 time: 0.332286 data_time: 0.022734 memory: 7489 loss_kpt: 0.000767 acc_pose: 0.779616 loss: 0.000767 2022/09/09 18:18:37 - mmengine - INFO - Epoch(train) [12][550/586] lr: 5.000000e-04 eta: 11:47:47 time: 0.334921 data_time: 0.023001 memory: 7489 loss_kpt: 0.000793 acc_pose: 0.779090 loss: 0.000793 2022/09/09 18:18:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:18:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:18:48 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/09 18:19:12 - mmengine - INFO - Epoch(train) [13][50/586] lr: 5.000000e-04 eta: 11:43:22 time: 0.343335 data_time: 0.028165 memory: 7489 loss_kpt: 0.000773 acc_pose: 0.763005 loss: 0.000773 2022/09/09 18:19:29 - mmengine - INFO - Epoch(train) [13][100/586] lr: 5.000000e-04 eta: 11:42:38 time: 0.332524 data_time: 0.023839 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.832574 loss: 0.000778 2022/09/09 18:19:45 - mmengine - INFO - Epoch(train) [13][150/586] lr: 5.000000e-04 eta: 11:41:53 time: 0.330715 data_time: 0.024115 memory: 7489 loss_kpt: 0.000788 acc_pose: 0.802042 loss: 0.000788 2022/09/09 18:20:02 - mmengine - INFO - Epoch(train) [13][200/586] lr: 5.000000e-04 eta: 11:41:14 time: 0.337096 data_time: 0.023645 memory: 7489 loss_kpt: 0.000745 acc_pose: 0.839874 loss: 0.000745 2022/09/09 18:20:19 - mmengine - INFO - Epoch(train) [13][250/586] lr: 5.000000e-04 eta: 11:40:31 time: 0.331744 data_time: 0.023990 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.776791 loss: 0.000778 2022/09/09 18:20:35 - mmengine - INFO - Epoch(train) [13][300/586] lr: 5.000000e-04 eta: 11:39:43 time: 0.326080 data_time: 0.023496 memory: 7489 loss_kpt: 0.000801 acc_pose: 0.800963 loss: 0.000801 2022/09/09 18:20:52 - mmengine - INFO - Epoch(train) [13][350/586] lr: 5.000000e-04 eta: 11:39:01 time: 0.332026 data_time: 0.027072 memory: 7489 loss_kpt: 0.000758 acc_pose: 0.814095 loss: 0.000758 2022/09/09 18:21:09 - mmengine - INFO - Epoch(train) [13][400/586] lr: 5.000000e-04 eta: 11:38:24 time: 0.338360 data_time: 0.023644 memory: 7489 loss_kpt: 0.000768 acc_pose: 0.746662 loss: 0.000768 2022/09/09 18:21:25 - mmengine - INFO - Epoch(train) [13][450/586] lr: 5.000000e-04 eta: 11:37:45 time: 0.334553 data_time: 0.023106 memory: 7489 loss_kpt: 0.000755 acc_pose: 0.737988 loss: 0.000755 2022/09/09 18:21:42 - mmengine - INFO - Epoch(train) [13][500/586] lr: 5.000000e-04 eta: 11:37:04 time: 0.332430 data_time: 0.022734 memory: 7489 loss_kpt: 0.000770 acc_pose: 0.780736 loss: 0.000770 2022/09/09 18:21:59 - mmengine - INFO - Epoch(train) [13][550/586] lr: 5.000000e-04 eta: 11:36:24 time: 0.334199 data_time: 0.024493 memory: 7489 loss_kpt: 0.000767 acc_pose: 0.734368 loss: 0.000767 2022/09/09 18:22:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:22:11 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/09 18:22:35 - mmengine - INFO - Epoch(train) [14][50/586] lr: 5.000000e-04 eta: 11:32:23 time: 0.343249 data_time: 0.037238 memory: 7489 loss_kpt: 0.000798 acc_pose: 0.751725 loss: 0.000798 2022/09/09 18:22:52 - mmengine - INFO - Epoch(train) [14][100/586] lr: 5.000000e-04 eta: 11:31:43 time: 0.329876 data_time: 0.024043 memory: 7489 loss_kpt: 0.000762 acc_pose: 0.844142 loss: 0.000762 2022/09/09 18:23:09 - mmengine - INFO - Epoch(train) [14][150/586] lr: 5.000000e-04 eta: 11:31:08 time: 0.337827 data_time: 0.024530 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.714957 loss: 0.000778 2022/09/09 18:23:25 - mmengine - INFO - Epoch(train) [14][200/586] lr: 5.000000e-04 eta: 11:30:29 time: 0.331249 data_time: 0.025699 memory: 7489 loss_kpt: 0.000765 acc_pose: 0.724451 loss: 0.000765 2022/09/09 18:23:42 - mmengine - INFO - Epoch(train) [14][250/586] lr: 5.000000e-04 eta: 11:29:51 time: 0.331887 data_time: 0.026761 memory: 7489 loss_kpt: 0.000773 acc_pose: 0.726871 loss: 0.000773 2022/09/09 18:23:59 - mmengine - INFO - Epoch(train) [14][300/586] lr: 5.000000e-04 eta: 11:29:18 time: 0.338457 data_time: 0.023195 memory: 7489 loss_kpt: 0.000770 acc_pose: 0.802764 loss: 0.000770 2022/09/09 18:24:15 - mmengine - INFO - Epoch(train) [14][350/586] lr: 5.000000e-04 eta: 11:28:40 time: 0.330914 data_time: 0.023764 memory: 7489 loss_kpt: 0.000759 acc_pose: 0.789682 loss: 0.000759 2022/09/09 18:24:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:24:32 - mmengine - INFO - Epoch(train) [14][400/586] lr: 5.000000e-04 eta: 11:28:02 time: 0.331526 data_time: 0.026593 memory: 7489 loss_kpt: 0.000773 acc_pose: 0.753619 loss: 0.000773 2022/09/09 18:24:49 - mmengine - INFO - Epoch(train) [14][450/586] lr: 5.000000e-04 eta: 11:27:31 time: 0.340235 data_time: 0.023233 memory: 7489 loss_kpt: 0.000762 acc_pose: 0.825267 loss: 0.000762 2022/09/09 18:25:05 - mmengine - INFO - Epoch(train) [14][500/586] lr: 5.000000e-04 eta: 11:26:54 time: 0.331393 data_time: 0.022787 memory: 7489 loss_kpt: 0.000751 acc_pose: 0.772088 loss: 0.000751 2022/09/09 18:25:22 - mmengine - INFO - Epoch(train) [14][550/586] lr: 5.000000e-04 eta: 11:26:12 time: 0.325564 data_time: 0.026191 memory: 7489 loss_kpt: 0.000755 acc_pose: 0.800880 loss: 0.000755 2022/09/09 18:25:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:25:34 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/09 18:25:57 - mmengine - INFO - Epoch(train) [15][50/586] lr: 5.000000e-04 eta: 11:22:26 time: 0.334817 data_time: 0.030236 memory: 7489 loss_kpt: 0.000747 acc_pose: 0.812105 loss: 0.000747 2022/09/09 18:26:14 - mmengine - INFO - Epoch(train) [15][100/586] lr: 5.000000e-04 eta: 11:21:52 time: 0.333537 data_time: 0.023362 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.770752 loss: 0.000756 2022/09/09 18:26:31 - mmengine - INFO - Epoch(train) [15][150/586] lr: 5.000000e-04 eta: 11:21:23 time: 0.339783 data_time: 0.023200 memory: 7489 loss_kpt: 0.000769 acc_pose: 0.810625 loss: 0.000769 2022/09/09 18:26:47 - mmengine - INFO - Epoch(train) [15][200/586] lr: 5.000000e-04 eta: 11:20:46 time: 0.328764 data_time: 0.022931 memory: 7489 loss_kpt: 0.000771 acc_pose: 0.762035 loss: 0.000771 2022/09/09 18:27:04 - mmengine - INFO - Epoch(train) [15][250/586] lr: 5.000000e-04 eta: 11:20:12 time: 0.332005 data_time: 0.022916 memory: 7489 loss_kpt: 0.000748 acc_pose: 0.701789 loss: 0.000748 2022/09/09 18:27:21 - mmengine - INFO - Epoch(train) [15][300/586] lr: 5.000000e-04 eta: 11:19:44 time: 0.341202 data_time: 0.024023 memory: 7489 loss_kpt: 0.000748 acc_pose: 0.771906 loss: 0.000748 2022/09/09 18:27:38 - mmengine - INFO - Epoch(train) [15][350/586] lr: 5.000000e-04 eta: 11:19:10 time: 0.332328 data_time: 0.026670 memory: 7489 loss_kpt: 0.000748 acc_pose: 0.761004 loss: 0.000748 2022/09/09 18:27:55 - mmengine - INFO - Epoch(train) [15][400/586] lr: 5.000000e-04 eta: 11:18:44 time: 0.343688 data_time: 0.022654 memory: 7489 loss_kpt: 0.000763 acc_pose: 0.834624 loss: 0.000763 2022/09/09 18:28:12 - mmengine - INFO - Epoch(train) [15][450/586] lr: 5.000000e-04 eta: 11:18:13 time: 0.334971 data_time: 0.025063 memory: 7489 loss_kpt: 0.000758 acc_pose: 0.759214 loss: 0.000758 2022/09/09 18:28:28 - mmengine - INFO - Epoch(train) [15][500/586] lr: 5.000000e-04 eta: 11:17:42 time: 0.336676 data_time: 0.023375 memory: 7489 loss_kpt: 0.000752 acc_pose: 0.803395 loss: 0.000752 2022/09/09 18:28:45 - mmengine - INFO - Epoch(train) [15][550/586] lr: 5.000000e-04 eta: 11:17:10 time: 0.333115 data_time: 0.028929 memory: 7489 loss_kpt: 0.000767 acc_pose: 0.814771 loss: 0.000767 2022/09/09 18:28:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:28:57 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/09 18:29:21 - mmengine - INFO - Epoch(train) [16][50/586] lr: 5.000000e-04 eta: 11:13:44 time: 0.340552 data_time: 0.030266 memory: 7489 loss_kpt: 0.000752 acc_pose: 0.830196 loss: 0.000752 2022/09/09 18:29:37 - mmengine - INFO - Epoch(train) [16][100/586] lr: 5.000000e-04 eta: 11:13:10 time: 0.327274 data_time: 0.027433 memory: 7489 loss_kpt: 0.000748 acc_pose: 0.778412 loss: 0.000748 2022/09/09 18:29:54 - mmengine - INFO - Epoch(train) [16][150/586] lr: 5.000000e-04 eta: 11:12:36 time: 0.329418 data_time: 0.028820 memory: 7489 loss_kpt: 0.000731 acc_pose: 0.739034 loss: 0.000731 2022/09/09 18:30:11 - mmengine - INFO - Epoch(train) [16][200/586] lr: 5.000000e-04 eta: 11:12:08 time: 0.337136 data_time: 0.024638 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.784067 loss: 0.000728 2022/09/09 18:30:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:30:27 - mmengine - INFO - Epoch(train) [16][250/586] lr: 5.000000e-04 eta: 11:11:36 time: 0.330778 data_time: 0.022480 memory: 7489 loss_kpt: 0.000743 acc_pose: 0.725564 loss: 0.000743 2022/09/09 18:30:45 - mmengine - INFO - Epoch(train) [16][300/586] lr: 5.000000e-04 eta: 11:11:14 time: 0.346088 data_time: 0.023819 memory: 7489 loss_kpt: 0.000754 acc_pose: 0.808901 loss: 0.000754 2022/09/09 18:31:01 - mmengine - INFO - Epoch(train) [16][350/586] lr: 5.000000e-04 eta: 11:10:46 time: 0.336101 data_time: 0.023798 memory: 7489 loss_kpt: 0.000754 acc_pose: 0.765749 loss: 0.000754 2022/09/09 18:31:18 - mmengine - INFO - Epoch(train) [16][400/586] lr: 5.000000e-04 eta: 11:10:13 time: 0.329881 data_time: 0.023640 memory: 7489 loss_kpt: 0.000735 acc_pose: 0.767033 loss: 0.000735 2022/09/09 18:31:34 - mmengine - INFO - Epoch(train) [16][450/586] lr: 5.000000e-04 eta: 11:09:42 time: 0.330582 data_time: 0.028384 memory: 7489 loss_kpt: 0.000753 acc_pose: 0.760297 loss: 0.000753 2022/09/09 18:31:51 - mmengine - INFO - Epoch(train) [16][500/586] lr: 5.000000e-04 eta: 11:09:17 time: 0.341289 data_time: 0.023449 memory: 7489 loss_kpt: 0.000740 acc_pose: 0.798943 loss: 0.000740 2022/09/09 18:32:08 - mmengine - INFO - Epoch(train) [16][550/586] lr: 5.000000e-04 eta: 11:08:48 time: 0.334256 data_time: 0.022572 memory: 7489 loss_kpt: 0.000729 acc_pose: 0.776629 loss: 0.000729 2022/09/09 18:32:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:32:20 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/09 18:32:44 - mmengine - INFO - Epoch(train) [17][50/586] lr: 5.000000e-04 eta: 11:05:38 time: 0.342712 data_time: 0.028236 memory: 7489 loss_kpt: 0.000767 acc_pose: 0.718778 loss: 0.000767 2022/09/09 18:33:01 - mmengine - INFO - Epoch(train) [17][100/586] lr: 5.000000e-04 eta: 11:05:18 time: 0.347367 data_time: 0.029051 memory: 7489 loss_kpt: 0.000740 acc_pose: 0.723068 loss: 0.000740 2022/09/09 18:33:18 - mmengine - INFO - Epoch(train) [17][150/586] lr: 5.000000e-04 eta: 11:04:46 time: 0.326056 data_time: 0.024427 memory: 7489 loss_kpt: 0.000741 acc_pose: 0.715598 loss: 0.000741 2022/09/09 18:33:34 - mmengine - INFO - Epoch(train) [17][200/586] lr: 5.000000e-04 eta: 11:04:20 time: 0.337446 data_time: 0.024977 memory: 7489 loss_kpt: 0.000746 acc_pose: 0.808203 loss: 0.000746 2022/09/09 18:33:51 - mmengine - INFO - Epoch(train) [17][250/586] lr: 5.000000e-04 eta: 11:03:53 time: 0.335499 data_time: 0.023457 memory: 7489 loss_kpt: 0.000729 acc_pose: 0.811596 loss: 0.000729 2022/09/09 18:34:08 - mmengine - INFO - Epoch(train) [17][300/586] lr: 5.000000e-04 eta: 11:03:21 time: 0.327105 data_time: 0.025146 memory: 7489 loss_kpt: 0.000755 acc_pose: 0.760850 loss: 0.000755 2022/09/09 18:34:25 - mmengine - INFO - Epoch(train) [17][350/586] lr: 5.000000e-04 eta: 11:02:57 time: 0.339141 data_time: 0.023773 memory: 7489 loss_kpt: 0.000766 acc_pose: 0.763025 loss: 0.000766 2022/09/09 18:34:41 - mmengine - INFO - Epoch(train) [17][400/586] lr: 5.000000e-04 eta: 11:02:29 time: 0.333498 data_time: 0.024090 memory: 7489 loss_kpt: 0.000757 acc_pose: 0.709281 loss: 0.000757 2022/09/09 18:34:58 - mmengine - INFO - Epoch(train) [17][450/586] lr: 5.000000e-04 eta: 11:01:57 time: 0.325157 data_time: 0.024345 memory: 7489 loss_kpt: 0.000739 acc_pose: 0.774943 loss: 0.000739 2022/09/09 18:35:14 - mmengine - INFO - Epoch(train) [17][500/586] lr: 5.000000e-04 eta: 11:01:32 time: 0.337554 data_time: 0.023313 memory: 7489 loss_kpt: 0.000746 acc_pose: 0.829307 loss: 0.000746 2022/09/09 18:35:31 - mmengine - INFO - Epoch(train) [17][550/586] lr: 5.000000e-04 eta: 11:01:07 time: 0.337769 data_time: 0.027725 memory: 7489 loss_kpt: 0.000746 acc_pose: 0.785312 loss: 0.000746 2022/09/09 18:35:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:35:43 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/09 18:36:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:36:07 - mmengine - INFO - Epoch(train) [18][50/586] lr: 5.000000e-04 eta: 10:58:10 time: 0.343702 data_time: 0.030331 memory: 7489 loss_kpt: 0.000744 acc_pose: 0.769020 loss: 0.000744 2022/09/09 18:36:24 - mmengine - INFO - Epoch(train) [18][100/586] lr: 5.000000e-04 eta: 10:57:43 time: 0.331510 data_time: 0.024441 memory: 7489 loss_kpt: 0.000714 acc_pose: 0.814252 loss: 0.000714 2022/09/09 18:36:40 - mmengine - INFO - Epoch(train) [18][150/586] lr: 5.000000e-04 eta: 10:57:16 time: 0.332268 data_time: 0.024314 memory: 7489 loss_kpt: 0.000718 acc_pose: 0.692555 loss: 0.000718 2022/09/09 18:36:57 - mmengine - INFO - Epoch(train) [18][200/586] lr: 5.000000e-04 eta: 10:56:50 time: 0.334063 data_time: 0.023233 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.842554 loss: 0.000725 2022/09/09 18:37:14 - mmengine - INFO - Epoch(train) [18][250/586] lr: 5.000000e-04 eta: 10:56:23 time: 0.332376 data_time: 0.024162 memory: 7489 loss_kpt: 0.000742 acc_pose: 0.743443 loss: 0.000742 2022/09/09 18:37:30 - mmengine - INFO - Epoch(train) [18][300/586] lr: 5.000000e-04 eta: 10:55:57 time: 0.332110 data_time: 0.025182 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.777363 loss: 0.000716 2022/09/09 18:37:47 - mmengine - INFO - Epoch(train) [18][350/586] lr: 5.000000e-04 eta: 10:55:34 time: 0.339071 data_time: 0.023809 memory: 7489 loss_kpt: 0.000740 acc_pose: 0.740081 loss: 0.000740 2022/09/09 18:38:04 - mmengine - INFO - Epoch(train) [18][400/586] lr: 5.000000e-04 eta: 10:55:06 time: 0.330749 data_time: 0.023194 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.858658 loss: 0.000756 2022/09/09 18:38:20 - mmengine - INFO - Epoch(train) [18][450/586] lr: 5.000000e-04 eta: 10:54:39 time: 0.330642 data_time: 0.027767 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.668630 loss: 0.000756 2022/09/09 18:38:37 - mmengine - INFO - Epoch(train) [18][500/586] lr: 5.000000e-04 eta: 10:54:12 time: 0.330519 data_time: 0.023022 memory: 7489 loss_kpt: 0.000749 acc_pose: 0.756551 loss: 0.000749 2022/09/09 18:38:53 - mmengine - INFO - Epoch(train) [18][550/586] lr: 5.000000e-04 eta: 10:53:44 time: 0.329657 data_time: 0.023709 memory: 7489 loss_kpt: 0.000744 acc_pose: 0.763098 loss: 0.000744 2022/09/09 18:39:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:39:05 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/09 18:39:30 - mmengine - INFO - Epoch(train) [19][50/586] lr: 5.000000e-04 eta: 10:51:02 time: 0.350468 data_time: 0.033352 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.856985 loss: 0.000732 2022/09/09 18:39:46 - mmengine - INFO - Epoch(train) [19][100/586] lr: 5.000000e-04 eta: 10:50:36 time: 0.331277 data_time: 0.023783 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.737700 loss: 0.000730 2022/09/09 18:40:03 - mmengine - INFO - Epoch(train) [19][150/586] lr: 5.000000e-04 eta: 10:50:11 time: 0.332286 data_time: 0.025561 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.736544 loss: 0.000725 2022/09/09 18:40:20 - mmengine - INFO - Epoch(train) [19][200/586] lr: 5.000000e-04 eta: 10:49:47 time: 0.334742 data_time: 0.026732 memory: 7489 loss_kpt: 0.000748 acc_pose: 0.750567 loss: 0.000748 2022/09/09 18:40:36 - mmengine - INFO - Epoch(train) [19][250/586] lr: 5.000000e-04 eta: 10:49:20 time: 0.327072 data_time: 0.023569 memory: 7489 loss_kpt: 0.000726 acc_pose: 0.781946 loss: 0.000726 2022/09/09 18:40:52 - mmengine - INFO - Epoch(train) [19][300/586] lr: 5.000000e-04 eta: 10:48:54 time: 0.330395 data_time: 0.023771 memory: 7489 loss_kpt: 0.000733 acc_pose: 0.756914 loss: 0.000733 2022/09/09 18:41:09 - mmengine - INFO - Epoch(train) [19][350/586] lr: 5.000000e-04 eta: 10:48:30 time: 0.333992 data_time: 0.024060 memory: 7489 loss_kpt: 0.000729 acc_pose: 0.818594 loss: 0.000729 2022/09/09 18:41:25 - mmengine - INFO - Epoch(train) [19][400/586] lr: 5.000000e-04 eta: 10:48:02 time: 0.326061 data_time: 0.024482 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.824361 loss: 0.000732 2022/09/09 18:41:42 - mmengine - INFO - Epoch(train) [19][450/586] lr: 5.000000e-04 eta: 10:47:38 time: 0.334419 data_time: 0.025621 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.712003 loss: 0.000704 2022/09/09 18:41:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:41:59 - mmengine - INFO - Epoch(train) [19][500/586] lr: 5.000000e-04 eta: 10:47:18 time: 0.342516 data_time: 0.026992 memory: 7489 loss_kpt: 0.000741 acc_pose: 0.801950 loss: 0.000741 2022/09/09 18:42:16 - mmengine - INFO - Epoch(train) [19][550/586] lr: 5.000000e-04 eta: 10:46:52 time: 0.328749 data_time: 0.023978 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.775950 loss: 0.000707 2022/09/09 18:42:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:42:28 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/09 18:42:52 - mmengine - INFO - Epoch(train) [20][50/586] lr: 5.000000e-04 eta: 10:44:12 time: 0.336360 data_time: 0.031964 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.789958 loss: 0.000721 2022/09/09 18:43:08 - mmengine - INFO - Epoch(train) [20][100/586] lr: 5.000000e-04 eta: 10:43:47 time: 0.329323 data_time: 0.024955 memory: 7489 loss_kpt: 0.000737 acc_pose: 0.749715 loss: 0.000737 2022/09/09 18:43:25 - mmengine - INFO - Epoch(train) [20][150/586] lr: 5.000000e-04 eta: 10:43:27 time: 0.340180 data_time: 0.023303 memory: 7489 loss_kpt: 0.000731 acc_pose: 0.785908 loss: 0.000731 2022/09/09 18:43:42 - mmengine - INFO - Epoch(train) [20][200/586] lr: 5.000000e-04 eta: 10:43:03 time: 0.331669 data_time: 0.023100 memory: 7489 loss_kpt: 0.000750 acc_pose: 0.792430 loss: 0.000750 2022/09/09 18:43:59 - mmengine - INFO - Epoch(train) [20][250/586] lr: 5.000000e-04 eta: 10:42:40 time: 0.333876 data_time: 0.024816 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.785188 loss: 0.000712 2022/09/09 18:44:15 - mmengine - INFO - Epoch(train) [20][300/586] lr: 5.000000e-04 eta: 10:42:17 time: 0.333368 data_time: 0.023303 memory: 7489 loss_kpt: 0.000746 acc_pose: 0.770394 loss: 0.000746 2022/09/09 18:44:32 - mmengine - INFO - Epoch(train) [20][350/586] lr: 5.000000e-04 eta: 10:41:54 time: 0.334280 data_time: 0.023507 memory: 7489 loss_kpt: 0.000727 acc_pose: 0.826993 loss: 0.000727 2022/09/09 18:44:49 - mmengine - INFO - Epoch(train) [20][400/586] lr: 5.000000e-04 eta: 10:41:32 time: 0.335113 data_time: 0.030162 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.808063 loss: 0.000736 2022/09/09 18:45:06 - mmengine - INFO - Epoch(train) [20][450/586] lr: 5.000000e-04 eta: 10:41:11 time: 0.336020 data_time: 0.022692 memory: 7489 loss_kpt: 0.000748 acc_pose: 0.800885 loss: 0.000748 2022/09/09 18:45:23 - mmengine - INFO - Epoch(train) [20][500/586] lr: 5.000000e-04 eta: 10:40:52 time: 0.341841 data_time: 0.022879 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.761053 loss: 0.000699 2022/09/09 18:45:39 - mmengine - INFO - Epoch(train) [20][550/586] lr: 5.000000e-04 eta: 10:40:27 time: 0.328574 data_time: 0.023708 memory: 7489 loss_kpt: 0.000710 acc_pose: 0.817813 loss: 0.000710 2022/09/09 18:45:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:45:51 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/09 18:46:07 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:01:01 time: 0.171760 data_time: 0.013285 memory: 7489 2022/09/09 18:46:15 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:50 time: 0.163486 data_time: 0.007240 memory: 1657 2022/09/09 18:46:24 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:42 time: 0.164302 data_time: 0.007361 memory: 1657 2022/09/09 18:46:32 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:34 time: 0.167947 data_time: 0.012123 memory: 1657 2022/09/09 18:46:40 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:25 time: 0.163501 data_time: 0.007314 memory: 1657 2022/09/09 18:46:48 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:17 time: 0.164741 data_time: 0.007897 memory: 1657 2022/09/09 18:46:57 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:09 time: 0.163486 data_time: 0.007608 memory: 1657 2022/09/09 18:47:05 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:01 time: 0.161181 data_time: 0.006656 memory: 1657 2022/09/09 18:47:40 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 18:47:55 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.697196 coco/AP .5: 0.884955 coco/AP .75: 0.773381 coco/AP (M): 0.663442 coco/AP (L): 0.760239 coco/AR: 0.755101 coco/AR .5: 0.925220 coco/AR .75: 0.825409 coco/AR (M): 0.714286 coco/AR (L): 0.813378 2022/09/09 18:47:55 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_10.pth is removed 2022/09/09 18:47:58 - mmengine - INFO - The best checkpoint with 0.6972 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/09/09 18:48:15 - mmengine - INFO - Epoch(train) [21][50/586] lr: 5.000000e-04 eta: 10:37:58 time: 0.342107 data_time: 0.029835 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.811668 loss: 0.000721 2022/09/09 18:48:32 - mmengine - INFO - Epoch(train) [21][100/586] lr: 5.000000e-04 eta: 10:37:34 time: 0.329062 data_time: 0.023114 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.860826 loss: 0.000730 2022/09/09 18:48:49 - mmengine - INFO - Epoch(train) [21][150/586] lr: 5.000000e-04 eta: 10:37:11 time: 0.332106 data_time: 0.022849 memory: 7489 loss_kpt: 0.000729 acc_pose: 0.777099 loss: 0.000729 2022/09/09 18:49:05 - mmengine - INFO - Epoch(train) [21][200/586] lr: 5.000000e-04 eta: 10:36:50 time: 0.334628 data_time: 0.023721 memory: 7489 loss_kpt: 0.000738 acc_pose: 0.725129 loss: 0.000738 2022/09/09 18:49:22 - mmengine - INFO - Epoch(train) [21][250/586] lr: 5.000000e-04 eta: 10:36:27 time: 0.331865 data_time: 0.024535 memory: 7489 loss_kpt: 0.000729 acc_pose: 0.777366 loss: 0.000729 2022/09/09 18:49:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:49:39 - mmengine - INFO - Epoch(train) [21][300/586] lr: 5.000000e-04 eta: 10:36:07 time: 0.336955 data_time: 0.023354 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.762826 loss: 0.000728 2022/09/09 18:49:55 - mmengine - INFO - Epoch(train) [21][350/586] lr: 5.000000e-04 eta: 10:35:44 time: 0.332352 data_time: 0.023012 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.871613 loss: 0.000728 2022/09/09 18:50:12 - mmengine - INFO - Epoch(train) [21][400/586] lr: 5.000000e-04 eta: 10:35:21 time: 0.330491 data_time: 0.023577 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.830838 loss: 0.000721 2022/09/09 18:50:29 - mmengine - INFO - Epoch(train) [21][450/586] lr: 5.000000e-04 eta: 10:34:59 time: 0.333232 data_time: 0.025657 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.773312 loss: 0.000704 2022/09/09 18:50:45 - mmengine - INFO - Epoch(train) [21][500/586] lr: 5.000000e-04 eta: 10:34:40 time: 0.339405 data_time: 0.026986 memory: 7489 loss_kpt: 0.000714 acc_pose: 0.777959 loss: 0.000714 2022/09/09 18:51:02 - mmengine - INFO - Epoch(train) [21][550/586] lr: 5.000000e-04 eta: 10:34:13 time: 0.321751 data_time: 0.024477 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.763913 loss: 0.000711 2022/09/09 18:51:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:51:14 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/09/09 18:51:37 - mmengine - INFO - Epoch(train) [22][50/586] lr: 5.000000e-04 eta: 10:31:52 time: 0.341918 data_time: 0.033255 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.759170 loss: 0.000716 2022/09/09 18:51:54 - mmengine - INFO - Epoch(train) [22][100/586] lr: 5.000000e-04 eta: 10:31:33 time: 0.337959 data_time: 0.025029 memory: 7489 loss_kpt: 0.000733 acc_pose: 0.763641 loss: 0.000733 2022/09/09 18:52:11 - mmengine - INFO - Epoch(train) [22][150/586] lr: 5.000000e-04 eta: 10:31:13 time: 0.335120 data_time: 0.025188 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.816077 loss: 0.000730 2022/09/09 18:52:28 - mmengine - INFO - Epoch(train) [22][200/586] lr: 5.000000e-04 eta: 10:30:51 time: 0.333229 data_time: 0.030169 memory: 7489 loss_kpt: 0.000718 acc_pose: 0.724553 loss: 0.000718 2022/09/09 18:52:44 - mmengine - INFO - Epoch(train) [22][250/586] lr: 5.000000e-04 eta: 10:30:28 time: 0.328349 data_time: 0.023324 memory: 7489 loss_kpt: 0.000714 acc_pose: 0.783029 loss: 0.000714 2022/09/09 18:53:00 - mmengine - INFO - Epoch(train) [22][300/586] lr: 5.000000e-04 eta: 10:30:05 time: 0.328227 data_time: 0.023675 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.836500 loss: 0.000725 2022/09/09 18:53:17 - mmengine - INFO - Epoch(train) [22][350/586] lr: 5.000000e-04 eta: 10:29:45 time: 0.335509 data_time: 0.023144 memory: 7489 loss_kpt: 0.000726 acc_pose: 0.828842 loss: 0.000726 2022/09/09 18:53:34 - mmengine - INFO - Epoch(train) [22][400/586] lr: 5.000000e-04 eta: 10:29:23 time: 0.331004 data_time: 0.023755 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.752546 loss: 0.000724 2022/09/09 18:53:50 - mmengine - INFO - Epoch(train) [22][450/586] lr: 5.000000e-04 eta: 10:28:58 time: 0.324874 data_time: 0.023056 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.813469 loss: 0.000716 2022/09/09 18:54:07 - mmengine - INFO - Epoch(train) [22][500/586] lr: 5.000000e-04 eta: 10:28:39 time: 0.336860 data_time: 0.023215 memory: 7489 loss_kpt: 0.000717 acc_pose: 0.778754 loss: 0.000717 2022/09/09 18:54:24 - mmengine - INFO - Epoch(train) [22][550/586] lr: 5.000000e-04 eta: 10:28:18 time: 0.333309 data_time: 0.023641 memory: 7489 loss_kpt: 0.000742 acc_pose: 0.774964 loss: 0.000742 2022/09/09 18:54:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:54:35 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/09/09 18:54:59 - mmengine - INFO - Epoch(train) [23][50/586] lr: 5.000000e-04 eta: 10:26:03 time: 0.340468 data_time: 0.028521 memory: 7489 loss_kpt: 0.000722 acc_pose: 0.772404 loss: 0.000722 2022/09/09 18:55:16 - mmengine - INFO - Epoch(train) [23][100/586] lr: 5.000000e-04 eta: 10:25:40 time: 0.326518 data_time: 0.023765 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.751716 loss: 0.000724 2022/09/09 18:55:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:55:33 - mmengine - INFO - Epoch(train) [23][150/586] lr: 5.000000e-04 eta: 10:25:21 time: 0.338058 data_time: 0.026417 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.836143 loss: 0.000721 2022/09/09 18:55:49 - mmengine - INFO - Epoch(train) [23][200/586] lr: 5.000000e-04 eta: 10:24:59 time: 0.328781 data_time: 0.023816 memory: 7489 loss_kpt: 0.000696 acc_pose: 0.787160 loss: 0.000696 2022/09/09 18:56:06 - mmengine - INFO - Epoch(train) [23][250/586] lr: 5.000000e-04 eta: 10:24:40 time: 0.335546 data_time: 0.023789 memory: 7489 loss_kpt: 0.000705 acc_pose: 0.760254 loss: 0.000705 2022/09/09 18:56:23 - mmengine - INFO - Epoch(train) [23][300/586] lr: 5.000000e-04 eta: 10:24:20 time: 0.334546 data_time: 0.026529 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.782712 loss: 0.000716 2022/09/09 18:56:39 - mmengine - INFO - Epoch(train) [23][350/586] lr: 5.000000e-04 eta: 10:23:59 time: 0.332018 data_time: 0.022952 memory: 7489 loss_kpt: 0.000689 acc_pose: 0.800049 loss: 0.000689 2022/09/09 18:56:56 - mmengine - INFO - Epoch(train) [23][400/586] lr: 5.000000e-04 eta: 10:23:38 time: 0.330643 data_time: 0.024836 memory: 7489 loss_kpt: 0.000723 acc_pose: 0.860000 loss: 0.000723 2022/09/09 18:57:12 - mmengine - INFO - Epoch(train) [23][450/586] lr: 5.000000e-04 eta: 10:23:18 time: 0.332865 data_time: 0.028798 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.797036 loss: 0.000703 2022/09/09 18:57:29 - mmengine - INFO - Epoch(train) [23][500/586] lr: 5.000000e-04 eta: 10:22:58 time: 0.335233 data_time: 0.023399 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.772147 loss: 0.000728 2022/09/09 18:57:45 - mmengine - INFO - Epoch(train) [23][550/586] lr: 5.000000e-04 eta: 10:22:36 time: 0.326622 data_time: 0.022589 memory: 7489 loss_kpt: 0.000731 acc_pose: 0.844773 loss: 0.000731 2022/09/09 18:57:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 18:57:58 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/09/09 18:58:22 - mmengine - INFO - Epoch(train) [24][50/586] lr: 5.000000e-04 eta: 10:20:34 time: 0.358415 data_time: 0.037503 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.854218 loss: 0.000712 2022/09/09 18:58:39 - mmengine - INFO - Epoch(train) [24][100/586] lr: 5.000000e-04 eta: 10:20:15 time: 0.334279 data_time: 0.025040 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.881991 loss: 0.000707 2022/09/09 18:58:56 - mmengine - INFO - Epoch(train) [24][150/586] lr: 5.000000e-04 eta: 10:19:53 time: 0.329280 data_time: 0.024547 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.830318 loss: 0.000698 2022/09/09 18:59:13 - mmengine - INFO - Epoch(train) [24][200/586] lr: 5.000000e-04 eta: 10:19:37 time: 0.340314 data_time: 0.024616 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.881550 loss: 0.000695 2022/09/09 18:59:29 - mmengine - INFO - Epoch(train) [24][250/586] lr: 5.000000e-04 eta: 10:19:14 time: 0.324901 data_time: 0.024163 memory: 7489 loss_kpt: 0.000714 acc_pose: 0.784371 loss: 0.000714 2022/09/09 18:59:45 - mmengine - INFO - Epoch(train) [24][300/586] lr: 5.000000e-04 eta: 10:18:53 time: 0.330324 data_time: 0.023036 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.789238 loss: 0.000704 2022/09/09 19:00:02 - mmengine - INFO - Epoch(train) [24][350/586] lr: 5.000000e-04 eta: 10:18:35 time: 0.336736 data_time: 0.023299 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.841450 loss: 0.000698 2022/09/09 19:00:18 - mmengine - INFO - Epoch(train) [24][400/586] lr: 5.000000e-04 eta: 10:18:11 time: 0.322550 data_time: 0.023722 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.830856 loss: 0.000721 2022/09/09 19:00:35 - mmengine - INFO - Epoch(train) [24][450/586] lr: 5.000000e-04 eta: 10:17:51 time: 0.331265 data_time: 0.023543 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.806835 loss: 0.000708 2022/09/09 19:00:52 - mmengine - INFO - Epoch(train) [24][500/586] lr: 5.000000e-04 eta: 10:17:36 time: 0.344914 data_time: 0.023984 memory: 7489 loss_kpt: 0.000731 acc_pose: 0.709967 loss: 0.000731 2022/09/09 19:00:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:01:08 - mmengine - INFO - Epoch(train) [24][550/586] lr: 5.000000e-04 eta: 10:17:13 time: 0.324413 data_time: 0.023054 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.845299 loss: 0.000724 2022/09/09 19:01:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:01:20 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/09/09 19:01:45 - mmengine - INFO - Epoch(train) [25][50/586] lr: 5.000000e-04 eta: 10:15:12 time: 0.345615 data_time: 0.028289 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.764388 loss: 0.000728 2022/09/09 19:02:01 - mmengine - INFO - Epoch(train) [25][100/586] lr: 5.000000e-04 eta: 10:14:49 time: 0.324356 data_time: 0.024177 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.800659 loss: 0.000724 2022/09/09 19:02:18 - mmengine - INFO - Epoch(train) [25][150/586] lr: 5.000000e-04 eta: 10:14:30 time: 0.333123 data_time: 0.023548 memory: 7489 loss_kpt: 0.000689 acc_pose: 0.913419 loss: 0.000689 2022/09/09 19:02:34 - mmengine - INFO - Epoch(train) [25][200/586] lr: 5.000000e-04 eta: 10:14:12 time: 0.337193 data_time: 0.023346 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.840345 loss: 0.000698 2022/09/09 19:02:51 - mmengine - INFO - Epoch(train) [25][250/586] lr: 5.000000e-04 eta: 10:13:51 time: 0.327540 data_time: 0.023986 memory: 7489 loss_kpt: 0.000713 acc_pose: 0.891752 loss: 0.000713 2022/09/09 19:03:08 - mmengine - INFO - Epoch(train) [25][300/586] lr: 5.000000e-04 eta: 10:13:34 time: 0.337744 data_time: 0.027121 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.831703 loss: 0.000725 2022/09/09 19:03:25 - mmengine - INFO - Epoch(train) [25][350/586] lr: 5.000000e-04 eta: 10:13:17 time: 0.338195 data_time: 0.024244 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.755958 loss: 0.000712 2022/09/09 19:03:41 - mmengine - INFO - Epoch(train) [25][400/586] lr: 5.000000e-04 eta: 10:12:54 time: 0.323960 data_time: 0.024056 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.808754 loss: 0.000685 2022/09/09 19:03:57 - mmengine - INFO - Epoch(train) [25][450/586] lr: 5.000000e-04 eta: 10:12:35 time: 0.331008 data_time: 0.024330 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.762246 loss: 0.000721 2022/09/09 19:04:14 - mmengine - INFO - Epoch(train) [25][500/586] lr: 5.000000e-04 eta: 10:12:16 time: 0.332948 data_time: 0.024405 memory: 7489 loss_kpt: 0.000714 acc_pose: 0.857147 loss: 0.000714 2022/09/09 19:04:30 - mmengine - INFO - Epoch(train) [25][550/586] lr: 5.000000e-04 eta: 10:11:53 time: 0.324411 data_time: 0.023077 memory: 7489 loss_kpt: 0.000723 acc_pose: 0.868253 loss: 0.000723 2022/09/09 19:04:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:04:42 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/09/09 19:05:06 - mmengine - INFO - Epoch(train) [26][50/586] lr: 5.000000e-04 eta: 10:09:54 time: 0.337857 data_time: 0.028854 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.729989 loss: 0.000681 2022/09/09 19:05:23 - mmengine - INFO - Epoch(train) [26][100/586] lr: 5.000000e-04 eta: 10:09:34 time: 0.329786 data_time: 0.029701 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.768493 loss: 0.000706 2022/09/09 19:05:40 - mmengine - INFO - Epoch(train) [26][150/586] lr: 5.000000e-04 eta: 10:09:17 time: 0.337054 data_time: 0.023658 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.746455 loss: 0.000719 2022/09/09 19:05:56 - mmengine - INFO - Epoch(train) [26][200/586] lr: 5.000000e-04 eta: 10:09:00 time: 0.336994 data_time: 0.026418 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.886037 loss: 0.000706 2022/09/09 19:06:13 - mmengine - INFO - Epoch(train) [26][250/586] lr: 5.000000e-04 eta: 10:08:41 time: 0.332757 data_time: 0.027052 memory: 7489 loss_kpt: 0.000692 acc_pose: 0.795509 loss: 0.000692 2022/09/09 19:06:30 - mmengine - INFO - Epoch(train) [26][300/586] lr: 5.000000e-04 eta: 10:08:23 time: 0.333277 data_time: 0.023715 memory: 7489 loss_kpt: 0.000700 acc_pose: 0.703972 loss: 0.000700 2022/09/09 19:06:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:06:46 - mmengine - INFO - Epoch(train) [26][350/586] lr: 5.000000e-04 eta: 10:08:04 time: 0.332038 data_time: 0.023215 memory: 7489 loss_kpt: 0.000720 acc_pose: 0.782310 loss: 0.000720 2022/09/09 19:07:03 - mmengine - INFO - Epoch(train) [26][400/586] lr: 5.000000e-04 eta: 10:07:46 time: 0.335627 data_time: 0.025842 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.857922 loss: 0.000708 2022/09/09 19:07:19 - mmengine - INFO - Epoch(train) [26][450/586] lr: 5.000000e-04 eta: 10:07:24 time: 0.323522 data_time: 0.023472 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.816746 loss: 0.000695 2022/09/09 19:07:36 - mmengine - INFO - Epoch(train) [26][500/586] lr: 5.000000e-04 eta: 10:07:07 time: 0.335636 data_time: 0.022600 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.822360 loss: 0.000708 2022/09/09 19:07:53 - mmengine - INFO - Epoch(train) [26][550/586] lr: 5.000000e-04 eta: 10:06:49 time: 0.335117 data_time: 0.027517 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.788216 loss: 0.000712 2022/09/09 19:08:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:08:05 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/09/09 19:08:28 - mmengine - INFO - Epoch(train) [27][50/586] lr: 5.000000e-04 eta: 10:04:54 time: 0.336127 data_time: 0.030631 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.806171 loss: 0.000712 2022/09/09 19:08:45 - mmengine - INFO - Epoch(train) [27][100/586] lr: 5.000000e-04 eta: 10:04:35 time: 0.330647 data_time: 0.023678 memory: 7489 loss_kpt: 0.000701 acc_pose: 0.787211 loss: 0.000701 2022/09/09 19:09:02 - mmengine - INFO - Epoch(train) [27][150/586] lr: 5.000000e-04 eta: 10:04:17 time: 0.332716 data_time: 0.023513 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.848019 loss: 0.000724 2022/09/09 19:09:18 - mmengine - INFO - Epoch(train) [27][200/586] lr: 5.000000e-04 eta: 10:03:59 time: 0.335358 data_time: 0.023105 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.848756 loss: 0.000707 2022/09/09 19:09:35 - mmengine - INFO - Epoch(train) [27][250/586] lr: 5.000000e-04 eta: 10:03:41 time: 0.332519 data_time: 0.022815 memory: 7489 loss_kpt: 0.000713 acc_pose: 0.829087 loss: 0.000713 2022/09/09 19:09:52 - mmengine - INFO - Epoch(train) [27][300/586] lr: 5.000000e-04 eta: 10:03:22 time: 0.330666 data_time: 0.022662 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.812918 loss: 0.000698 2022/09/09 19:10:08 - mmengine - INFO - Epoch(train) [27][350/586] lr: 5.000000e-04 eta: 10:03:05 time: 0.334657 data_time: 0.024753 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.778426 loss: 0.000712 2022/09/09 19:10:25 - mmengine - INFO - Epoch(train) [27][400/586] lr: 5.000000e-04 eta: 10:02:48 time: 0.336792 data_time: 0.025465 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.817824 loss: 0.000711 2022/09/09 19:10:42 - mmengine - INFO - Epoch(train) [27][450/586] lr: 5.000000e-04 eta: 10:02:28 time: 0.329040 data_time: 0.023267 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.862637 loss: 0.000679 2022/09/09 19:10:59 - mmengine - INFO - Epoch(train) [27][500/586] lr: 5.000000e-04 eta: 10:02:15 time: 0.346207 data_time: 0.024008 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.771819 loss: 0.000698 2022/09/09 19:11:16 - mmengine - INFO - Epoch(train) [27][550/586] lr: 5.000000e-04 eta: 10:01:58 time: 0.335826 data_time: 0.024069 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.757256 loss: 0.000691 2022/09/09 19:11:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:11:28 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/09/09 19:11:51 - mmengine - INFO - Epoch(train) [28][50/586] lr: 5.000000e-04 eta: 10:00:09 time: 0.343317 data_time: 0.029910 memory: 7489 loss_kpt: 0.000696 acc_pose: 0.818331 loss: 0.000696 2022/09/09 19:12:08 - mmengine - INFO - Epoch(train) [28][100/586] lr: 5.000000e-04 eta: 9:59:52 time: 0.334451 data_time: 0.027001 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.830999 loss: 0.000686 2022/09/09 19:12:25 - mmengine - INFO - Epoch(train) [28][150/586] lr: 5.000000e-04 eta: 9:59:33 time: 0.330097 data_time: 0.023671 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.742501 loss: 0.000677 2022/09/09 19:12:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:12:41 - mmengine - INFO - Epoch(train) [28][200/586] lr: 5.000000e-04 eta: 9:59:15 time: 0.330794 data_time: 0.025065 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.837232 loss: 0.000698 2022/09/09 19:12:58 - mmengine - INFO - Epoch(train) [28][250/586] lr: 5.000000e-04 eta: 9:58:57 time: 0.335014 data_time: 0.027481 memory: 7489 loss_kpt: 0.000713 acc_pose: 0.801525 loss: 0.000713 2022/09/09 19:13:15 - mmengine - INFO - Epoch(train) [28][300/586] lr: 5.000000e-04 eta: 9:58:39 time: 0.331200 data_time: 0.023927 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.814059 loss: 0.000667 2022/09/09 19:13:32 - mmengine - INFO - Epoch(train) [28][350/586] lr: 5.000000e-04 eta: 9:58:24 time: 0.339806 data_time: 0.024891 memory: 7489 loss_kpt: 0.000713 acc_pose: 0.802461 loss: 0.000713 2022/09/09 19:13:48 - mmengine - INFO - Epoch(train) [28][400/586] lr: 5.000000e-04 eta: 9:58:05 time: 0.331615 data_time: 0.028374 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.848532 loss: 0.000688 2022/09/09 19:14:05 - mmengine - INFO - Epoch(train) [28][450/586] lr: 5.000000e-04 eta: 9:57:48 time: 0.332761 data_time: 0.022783 memory: 7489 loss_kpt: 0.000701 acc_pose: 0.842248 loss: 0.000701 2022/09/09 19:14:22 - mmengine - INFO - Epoch(train) [28][500/586] lr: 5.000000e-04 eta: 9:57:31 time: 0.335563 data_time: 0.023042 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.744950 loss: 0.000685 2022/09/09 19:14:38 - mmengine - INFO - Epoch(train) [28][550/586] lr: 5.000000e-04 eta: 9:57:13 time: 0.333655 data_time: 0.026122 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.814950 loss: 0.000699 2022/09/09 19:14:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:14:50 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/09/09 19:15:15 - mmengine - INFO - Epoch(train) [29][50/586] lr: 5.000000e-04 eta: 9:55:29 time: 0.346528 data_time: 0.032325 memory: 7489 loss_kpt: 0.000689 acc_pose: 0.829356 loss: 0.000689 2022/09/09 19:15:31 - mmengine - INFO - Epoch(train) [29][100/586] lr: 5.000000e-04 eta: 9:55:12 time: 0.333311 data_time: 0.023049 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.828517 loss: 0.000702 2022/09/09 19:15:48 - mmengine - INFO - Epoch(train) [29][150/586] lr: 5.000000e-04 eta: 9:54:54 time: 0.331927 data_time: 0.024868 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.780899 loss: 0.000686 2022/09/09 19:16:05 - mmengine - INFO - Epoch(train) [29][200/586] lr: 5.000000e-04 eta: 9:54:39 time: 0.340009 data_time: 0.023684 memory: 7489 loss_kpt: 0.000715 acc_pose: 0.707719 loss: 0.000715 2022/09/09 19:16:21 - mmengine - INFO - Epoch(train) [29][250/586] lr: 5.000000e-04 eta: 9:54:22 time: 0.332917 data_time: 0.024983 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.799235 loss: 0.000688 2022/09/09 19:16:38 - mmengine - INFO - Epoch(train) [29][300/586] lr: 5.000000e-04 eta: 9:54:05 time: 0.334495 data_time: 0.028754 memory: 7489 loss_kpt: 0.000700 acc_pose: 0.755147 loss: 0.000700 2022/09/09 19:16:55 - mmengine - INFO - Epoch(train) [29][350/586] lr: 5.000000e-04 eta: 9:53:48 time: 0.336036 data_time: 0.023707 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.746951 loss: 0.000682 2022/09/09 19:17:11 - mmengine - INFO - Epoch(train) [29][400/586] lr: 5.000000e-04 eta: 9:53:30 time: 0.329550 data_time: 0.022664 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.802487 loss: 0.000685 2022/09/09 19:17:28 - mmengine - INFO - Epoch(train) [29][450/586] lr: 5.000000e-04 eta: 9:53:12 time: 0.332419 data_time: 0.026031 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.799037 loss: 0.000711 2022/09/09 19:17:45 - mmengine - INFO - Epoch(train) [29][500/586] lr: 5.000000e-04 eta: 9:52:55 time: 0.335934 data_time: 0.023441 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.807489 loss: 0.000702 2022/09/09 19:18:01 - mmengine - INFO - Epoch(train) [29][550/586] lr: 5.000000e-04 eta: 9:52:38 time: 0.331996 data_time: 0.026392 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.814258 loss: 0.000694 2022/09/09 19:18:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:18:14 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/09/09 19:18:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:18:38 - mmengine - INFO - Epoch(train) [30][50/586] lr: 5.000000e-04 eta: 9:50:57 time: 0.344737 data_time: 0.030816 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.790741 loss: 0.000698 2022/09/09 19:18:54 - mmengine - INFO - Epoch(train) [30][100/586] lr: 5.000000e-04 eta: 9:50:37 time: 0.325608 data_time: 0.022933 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.719784 loss: 0.000702 2022/09/09 19:19:11 - mmengine - INFO - Epoch(train) [30][150/586] lr: 5.000000e-04 eta: 9:50:20 time: 0.330845 data_time: 0.023587 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.785494 loss: 0.000703 2022/09/09 19:19:28 - mmengine - INFO - Epoch(train) [30][200/586] lr: 5.000000e-04 eta: 9:50:04 time: 0.338708 data_time: 0.026676 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.835644 loss: 0.000706 2022/09/09 19:19:44 - mmengine - INFO - Epoch(train) [30][250/586] lr: 5.000000e-04 eta: 9:49:46 time: 0.330709 data_time: 0.023692 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.774621 loss: 0.000694 2022/09/09 19:20:01 - mmengine - INFO - Epoch(train) [30][300/586] lr: 5.000000e-04 eta: 9:49:28 time: 0.329094 data_time: 0.024047 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.800098 loss: 0.000668 2022/09/09 19:20:18 - mmengine - INFO - Epoch(train) [30][350/586] lr: 5.000000e-04 eta: 9:49:13 time: 0.338984 data_time: 0.022560 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.748262 loss: 0.000674 2022/09/09 19:20:34 - mmengine - INFO - Epoch(train) [30][400/586] lr: 5.000000e-04 eta: 9:48:55 time: 0.331001 data_time: 0.024472 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.822867 loss: 0.000691 2022/09/09 19:20:51 - mmengine - INFO - Epoch(train) [30][450/586] lr: 5.000000e-04 eta: 9:48:38 time: 0.335463 data_time: 0.023207 memory: 7489 loss_kpt: 0.000693 acc_pose: 0.883556 loss: 0.000693 2022/09/09 19:21:08 - mmengine - INFO - Epoch(train) [30][500/586] lr: 5.000000e-04 eta: 9:48:21 time: 0.333266 data_time: 0.025462 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.807592 loss: 0.000679 2022/09/09 19:21:25 - mmengine - INFO - Epoch(train) [30][550/586] lr: 5.000000e-04 eta: 9:48:04 time: 0.333430 data_time: 0.023433 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.795372 loss: 0.000665 2022/09/09 19:21:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:21:36 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/09/09 19:21:53 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:01:01 time: 0.173286 data_time: 0.016036 memory: 7489 2022/09/09 19:22:01 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:50 time: 0.163811 data_time: 0.007314 memory: 1657 2022/09/09 19:22:09 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:42 time: 0.164158 data_time: 0.007925 memory: 1657 2022/09/09 19:22:18 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:34 time: 0.165269 data_time: 0.007891 memory: 1657 2022/09/09 19:22:26 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:26 time: 0.168412 data_time: 0.007229 memory: 1657 2022/09/09 19:22:34 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:17 time: 0.164765 data_time: 0.007928 memory: 1657 2022/09/09 19:22:42 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:09 time: 0.164405 data_time: 0.008022 memory: 1657 2022/09/09 19:22:50 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:01 time: 0.161042 data_time: 0.006873 memory: 1657 2022/09/09 19:23:27 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 19:23:41 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.713341 coco/AP .5: 0.889164 coco/AP .75: 0.787351 coco/AP (M): 0.679807 coco/AP (L): 0.776259 coco/AR: 0.768813 coco/AR .5: 0.929156 coco/AR .75: 0.835013 coco/AR (M): 0.727807 coco/AR (L): 0.827722 2022/09/09 19:23:41 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_20.pth is removed 2022/09/09 19:23:44 - mmengine - INFO - The best checkpoint with 0.7133 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/09/09 19:24:01 - mmengine - INFO - Epoch(train) [31][50/586] lr: 5.000000e-04 eta: 9:46:25 time: 0.339761 data_time: 0.030662 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.818346 loss: 0.000704 2022/09/09 19:24:18 - mmengine - INFO - Epoch(train) [31][100/586] lr: 5.000000e-04 eta: 9:46:09 time: 0.336871 data_time: 0.023107 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.819174 loss: 0.000679 2022/09/09 19:24:35 - mmengine - INFO - Epoch(train) [31][150/586] lr: 5.000000e-04 eta: 9:45:53 time: 0.334813 data_time: 0.023402 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.790356 loss: 0.000699 2022/09/09 19:24:52 - mmengine - INFO - Epoch(train) [31][200/586] lr: 5.000000e-04 eta: 9:45:39 time: 0.341172 data_time: 0.023439 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.742041 loss: 0.000672 2022/09/09 19:25:09 - mmengine - INFO - Epoch(train) [31][250/586] lr: 5.000000e-04 eta: 9:45:21 time: 0.331927 data_time: 0.023371 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.769892 loss: 0.000698 2022/09/09 19:25:25 - mmengine - INFO - Epoch(train) [31][300/586] lr: 5.000000e-04 eta: 9:45:05 time: 0.334273 data_time: 0.023834 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.755840 loss: 0.000711 2022/09/09 19:25:42 - mmengine - INFO - Epoch(train) [31][350/586] lr: 5.000000e-04 eta: 9:44:49 time: 0.336229 data_time: 0.027399 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.783376 loss: 0.000685 2022/09/09 19:25:59 - mmengine - INFO - Epoch(train) [31][400/586] lr: 5.000000e-04 eta: 9:44:32 time: 0.332501 data_time: 0.023574 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.813679 loss: 0.000669 2022/09/09 19:26:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:26:16 - mmengine - INFO - Epoch(train) [31][450/586] lr: 5.000000e-04 eta: 9:44:16 time: 0.337859 data_time: 0.027151 memory: 7489 loss_kpt: 0.000683 acc_pose: 0.791348 loss: 0.000683 2022/09/09 19:26:32 - mmengine - INFO - Epoch(train) [31][500/586] lr: 5.000000e-04 eta: 9:43:59 time: 0.332661 data_time: 0.022982 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.752378 loss: 0.000687 2022/09/09 19:26:49 - mmengine - INFO - Epoch(train) [31][550/586] lr: 5.000000e-04 eta: 9:43:41 time: 0.328293 data_time: 0.023982 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.716274 loss: 0.000691 2022/09/09 19:27:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:27:01 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/09/09 19:27:25 - mmengine - INFO - Epoch(train) [32][50/586] lr: 5.000000e-04 eta: 9:42:06 time: 0.345148 data_time: 0.031480 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.723493 loss: 0.000702 2022/09/09 19:27:42 - mmengine - INFO - Epoch(train) [32][100/586] lr: 5.000000e-04 eta: 9:41:50 time: 0.335454 data_time: 0.027520 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.817792 loss: 0.000698 2022/09/09 19:27:59 - mmengine - INFO - Epoch(train) [32][150/586] lr: 5.000000e-04 eta: 9:41:34 time: 0.333505 data_time: 0.023555 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.775391 loss: 0.000691 2022/09/09 19:28:16 - mmengine - INFO - Epoch(train) [32][200/586] lr: 5.000000e-04 eta: 9:41:19 time: 0.339429 data_time: 0.023390 memory: 7489 loss_kpt: 0.000701 acc_pose: 0.757630 loss: 0.000701 2022/09/09 19:28:32 - mmengine - INFO - Epoch(train) [32][250/586] lr: 5.000000e-04 eta: 9:41:02 time: 0.331057 data_time: 0.023052 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.767644 loss: 0.000697 2022/09/09 19:28:49 - mmengine - INFO - Epoch(train) [32][300/586] lr: 5.000000e-04 eta: 9:40:43 time: 0.326221 data_time: 0.025365 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.841295 loss: 0.000682 2022/09/09 19:29:06 - mmengine - INFO - Epoch(train) [32][350/586] lr: 5.000000e-04 eta: 9:40:29 time: 0.343760 data_time: 0.024303 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.862885 loss: 0.000680 2022/09/09 19:29:22 - mmengine - INFO - Epoch(train) [32][400/586] lr: 5.000000e-04 eta: 9:40:12 time: 0.330438 data_time: 0.023435 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.807646 loss: 0.000703 2022/09/09 19:29:39 - mmengine - INFO - Epoch(train) [32][450/586] lr: 5.000000e-04 eta: 9:39:55 time: 0.331607 data_time: 0.023902 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.779941 loss: 0.000677 2022/09/09 19:29:56 - mmengine - INFO - Epoch(train) [32][500/586] lr: 5.000000e-04 eta: 9:39:39 time: 0.335982 data_time: 0.028915 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.747647 loss: 0.000668 2022/09/09 19:30:12 - mmengine - INFO - Epoch(train) [32][550/586] lr: 5.000000e-04 eta: 9:39:21 time: 0.327395 data_time: 0.023266 memory: 7489 loss_kpt: 0.000717 acc_pose: 0.789054 loss: 0.000717 2022/09/09 19:30:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:30:24 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/09/09 19:30:48 - mmengine - INFO - Epoch(train) [33][50/586] lr: 5.000000e-04 eta: 9:37:48 time: 0.341396 data_time: 0.030111 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.816545 loss: 0.000671 2022/09/09 19:31:04 - mmengine - INFO - Epoch(train) [33][100/586] lr: 5.000000e-04 eta: 9:37:32 time: 0.333157 data_time: 0.023867 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.828920 loss: 0.000681 2022/09/09 19:31:21 - mmengine - INFO - Epoch(train) [33][150/586] lr: 5.000000e-04 eta: 9:37:15 time: 0.332298 data_time: 0.022891 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.716810 loss: 0.000694 2022/09/09 19:31:38 - mmengine - INFO - Epoch(train) [33][200/586] lr: 5.000000e-04 eta: 9:36:59 time: 0.336952 data_time: 0.025033 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.803447 loss: 0.000685 2022/09/09 19:31:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:31:54 - mmengine - INFO - Epoch(train) [33][250/586] lr: 5.000000e-04 eta: 9:36:42 time: 0.329787 data_time: 0.022801 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.825642 loss: 0.000670 2022/09/09 19:32:11 - mmengine - INFO - Epoch(train) [33][300/586] lr: 5.000000e-04 eta: 9:36:25 time: 0.330852 data_time: 0.026952 memory: 7489 loss_kpt: 0.000696 acc_pose: 0.764875 loss: 0.000696 2022/09/09 19:32:28 - mmengine - INFO - Epoch(train) [33][350/586] lr: 5.000000e-04 eta: 9:36:10 time: 0.337383 data_time: 0.022856 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.802643 loss: 0.000678 2022/09/09 19:32:44 - mmengine - INFO - Epoch(train) [33][400/586] lr: 5.000000e-04 eta: 9:35:52 time: 0.327031 data_time: 0.023832 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.847465 loss: 0.000662 2022/09/09 19:33:01 - mmengine - INFO - Epoch(train) [33][450/586] lr: 5.000000e-04 eta: 9:35:35 time: 0.332143 data_time: 0.023897 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.838580 loss: 0.000691 2022/09/09 19:33:18 - mmengine - INFO - Epoch(train) [33][500/586] lr: 5.000000e-04 eta: 9:35:21 time: 0.343129 data_time: 0.024574 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.807210 loss: 0.000681 2022/09/09 19:33:34 - mmengine - INFO - Epoch(train) [33][550/586] lr: 5.000000e-04 eta: 9:35:04 time: 0.329257 data_time: 0.022959 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.837078 loss: 0.000704 2022/09/09 19:33:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:33:46 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/09/09 19:34:10 - mmengine - INFO - Epoch(train) [34][50/586] lr: 5.000000e-04 eta: 9:33:34 time: 0.342940 data_time: 0.028700 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.816358 loss: 0.000677 2022/09/09 19:34:26 - mmengine - INFO - Epoch(train) [34][100/586] lr: 5.000000e-04 eta: 9:33:16 time: 0.328156 data_time: 0.027056 memory: 7489 loss_kpt: 0.000683 acc_pose: 0.861501 loss: 0.000683 2022/09/09 19:34:43 - mmengine - INFO - Epoch(train) [34][150/586] lr: 5.000000e-04 eta: 9:33:00 time: 0.333216 data_time: 0.023210 memory: 7489 loss_kpt: 0.000710 acc_pose: 0.828723 loss: 0.000710 2022/09/09 19:35:00 - mmengine - INFO - Epoch(train) [34][200/586] lr: 5.000000e-04 eta: 9:32:43 time: 0.329364 data_time: 0.022854 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.755930 loss: 0.000682 2022/09/09 19:35:19 - mmengine - INFO - Epoch(train) [34][250/586] lr: 5.000000e-04 eta: 9:32:39 time: 0.380369 data_time: 0.038816 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.815964 loss: 0.000667 2022/09/09 19:35:37 - mmengine - INFO - Epoch(train) [34][300/586] lr: 5.000000e-04 eta: 9:32:30 time: 0.361286 data_time: 0.029681 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.823341 loss: 0.000680 2022/09/09 19:35:53 - mmengine - INFO - Epoch(train) [34][350/586] lr: 5.000000e-04 eta: 9:32:12 time: 0.326176 data_time: 0.023236 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.875076 loss: 0.000671 2022/09/09 19:36:10 - mmengine - INFO - Epoch(train) [34][400/586] lr: 5.000000e-04 eta: 9:31:55 time: 0.330518 data_time: 0.023253 memory: 7489 loss_kpt: 0.000690 acc_pose: 0.820111 loss: 0.000690 2022/09/09 19:36:26 - mmengine - INFO - Epoch(train) [34][450/586] lr: 5.000000e-04 eta: 9:31:39 time: 0.334544 data_time: 0.023466 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.824861 loss: 0.000669 2022/09/09 19:36:43 - mmengine - INFO - Epoch(train) [34][500/586] lr: 5.000000e-04 eta: 9:31:24 time: 0.338000 data_time: 0.024770 memory: 7489 loss_kpt: 0.000683 acc_pose: 0.881585 loss: 0.000683 2022/09/09 19:37:00 - mmengine - INFO - Epoch(train) [34][550/586] lr: 5.000000e-04 eta: 9:31:08 time: 0.336328 data_time: 0.024978 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.834583 loss: 0.000699 2022/09/09 19:37:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:37:12 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/09/09 19:37:36 - mmengine - INFO - Epoch(train) [35][50/586] lr: 5.000000e-04 eta: 9:29:40 time: 0.339366 data_time: 0.032756 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.828263 loss: 0.000659 2022/09/09 19:37:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:37:53 - mmengine - INFO - Epoch(train) [35][100/586] lr: 5.000000e-04 eta: 9:29:23 time: 0.331327 data_time: 0.024346 memory: 7489 loss_kpt: 0.000700 acc_pose: 0.820306 loss: 0.000700 2022/09/09 19:38:10 - mmengine - INFO - Epoch(train) [35][150/586] lr: 5.000000e-04 eta: 9:29:07 time: 0.334821 data_time: 0.023544 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.896059 loss: 0.000677 2022/09/09 19:38:26 - mmengine - INFO - Epoch(train) [35][200/586] lr: 5.000000e-04 eta: 9:28:51 time: 0.332458 data_time: 0.023312 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.852372 loss: 0.000685 2022/09/09 19:38:43 - mmengine - INFO - Epoch(train) [35][250/586] lr: 5.000000e-04 eta: 9:28:34 time: 0.331653 data_time: 0.025138 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.800024 loss: 0.000694 2022/09/09 19:39:00 - mmengine - INFO - Epoch(train) [35][300/586] lr: 5.000000e-04 eta: 9:28:18 time: 0.334384 data_time: 0.026834 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.746972 loss: 0.000665 2022/09/09 19:39:16 - mmengine - INFO - Epoch(train) [35][350/586] lr: 5.000000e-04 eta: 9:28:02 time: 0.330453 data_time: 0.024299 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.809038 loss: 0.000685 2022/09/09 19:39:32 - mmengine - INFO - Epoch(train) [35][400/586] lr: 5.000000e-04 eta: 9:27:44 time: 0.328780 data_time: 0.025037 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.852351 loss: 0.000669 2022/09/09 19:39:50 - mmengine - INFO - Epoch(train) [35][450/586] lr: 5.000000e-04 eta: 9:27:31 time: 0.345120 data_time: 0.022712 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.804173 loss: 0.000687 2022/09/09 19:40:06 - mmengine - INFO - Epoch(train) [35][500/586] lr: 5.000000e-04 eta: 9:27:15 time: 0.334270 data_time: 0.026990 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.818932 loss: 0.000704 2022/09/09 19:40:23 - mmengine - INFO - Epoch(train) [35][550/586] lr: 5.000000e-04 eta: 9:26:58 time: 0.328353 data_time: 0.025322 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.791902 loss: 0.000687 2022/09/09 19:40:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:40:35 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/09/09 19:41:00 - mmengine - INFO - Epoch(train) [36][50/586] lr: 5.000000e-04 eta: 9:25:34 time: 0.348236 data_time: 0.029621 memory: 7489 loss_kpt: 0.000683 acc_pose: 0.796432 loss: 0.000683 2022/09/09 19:41:17 - mmengine - INFO - Epoch(train) [36][100/586] lr: 5.000000e-04 eta: 9:25:18 time: 0.334512 data_time: 0.028012 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.668901 loss: 0.000671 2022/09/09 19:41:33 - mmengine - INFO - Epoch(train) [36][150/586] lr: 5.000000e-04 eta: 9:25:01 time: 0.328319 data_time: 0.023383 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.844574 loss: 0.000697 2022/09/09 19:41:50 - mmengine - INFO - Epoch(train) [36][200/586] lr: 5.000000e-04 eta: 9:24:44 time: 0.330580 data_time: 0.023081 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.784243 loss: 0.000684 2022/09/09 19:42:06 - mmengine - INFO - Epoch(train) [36][250/586] lr: 5.000000e-04 eta: 9:24:27 time: 0.326808 data_time: 0.023874 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.827195 loss: 0.000681 2022/09/09 19:42:22 - mmengine - INFO - Epoch(train) [36][300/586] lr: 5.000000e-04 eta: 9:24:10 time: 0.331775 data_time: 0.023294 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.813881 loss: 0.000664 2022/09/09 19:42:39 - mmengine - INFO - Epoch(train) [36][350/586] lr: 5.000000e-04 eta: 9:23:55 time: 0.333881 data_time: 0.026431 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.777098 loss: 0.000670 2022/09/09 19:42:56 - mmengine - INFO - Epoch(train) [36][400/586] lr: 5.000000e-04 eta: 9:23:37 time: 0.327097 data_time: 0.023142 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.824671 loss: 0.000672 2022/09/09 19:43:12 - mmengine - INFO - Epoch(train) [36][450/586] lr: 5.000000e-04 eta: 9:23:21 time: 0.334748 data_time: 0.028574 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.787320 loss: 0.000667 2022/09/09 19:43:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:43:29 - mmengine - INFO - Epoch(train) [36][500/586] lr: 5.000000e-04 eta: 9:23:05 time: 0.333804 data_time: 0.023319 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.782815 loss: 0.000667 2022/09/09 19:43:46 - mmengine - INFO - Epoch(train) [36][550/586] lr: 5.000000e-04 eta: 9:22:50 time: 0.334025 data_time: 0.023032 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.792640 loss: 0.000668 2022/09/09 19:44:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:44:01 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/09/09 19:44:29 - mmengine - INFO - Epoch(train) [37][50/586] lr: 5.000000e-04 eta: 9:21:45 time: 0.419202 data_time: 0.105151 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.753968 loss: 0.000671 2022/09/09 19:44:45 - mmengine - INFO - Epoch(train) [37][100/586] lr: 5.000000e-04 eta: 9:21:28 time: 0.330039 data_time: 0.025376 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.803547 loss: 0.000646 2022/09/09 19:45:02 - mmengine - INFO - Epoch(train) [37][150/586] lr: 5.000000e-04 eta: 9:21:12 time: 0.330623 data_time: 0.024257 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.793364 loss: 0.000667 2022/09/09 19:45:19 - mmengine - INFO - Epoch(train) [37][200/586] lr: 5.000000e-04 eta: 9:20:57 time: 0.338057 data_time: 0.027076 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.725916 loss: 0.000674 2022/09/09 19:45:35 - mmengine - INFO - Epoch(train) [37][250/586] lr: 5.000000e-04 eta: 9:20:40 time: 0.330007 data_time: 0.023267 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.799147 loss: 0.000667 2022/09/09 19:45:52 - mmengine - INFO - Epoch(train) [37][300/586] lr: 5.000000e-04 eta: 9:20:24 time: 0.332171 data_time: 0.024400 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.783581 loss: 0.000684 2022/09/09 19:46:08 - mmengine - INFO - Epoch(train) [37][350/586] lr: 5.000000e-04 eta: 9:20:08 time: 0.333008 data_time: 0.027353 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.850209 loss: 0.000669 2022/09/09 19:46:25 - mmengine - INFO - Epoch(train) [37][400/586] lr: 5.000000e-04 eta: 9:19:52 time: 0.333435 data_time: 0.024039 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.845710 loss: 0.000663 2022/09/09 19:46:42 - mmengine - INFO - Epoch(train) [37][450/586] lr: 5.000000e-04 eta: 9:19:35 time: 0.329893 data_time: 0.022944 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.806441 loss: 0.000659 2022/09/09 19:46:58 - mmengine - INFO - Epoch(train) [37][500/586] lr: 5.000000e-04 eta: 9:19:20 time: 0.335342 data_time: 0.026430 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.810898 loss: 0.000656 2022/09/09 19:47:15 - mmengine - INFO - Epoch(train) [37][550/586] lr: 5.000000e-04 eta: 9:19:04 time: 0.331993 data_time: 0.023453 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.833963 loss: 0.000658 2022/09/09 19:47:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:47:27 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/09/09 19:47:52 - mmengine - INFO - Epoch(train) [38][50/586] lr: 5.000000e-04 eta: 9:17:43 time: 0.347201 data_time: 0.029361 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.842418 loss: 0.000694 2022/09/09 19:48:08 - mmengine - INFO - Epoch(train) [38][100/586] lr: 5.000000e-04 eta: 9:17:27 time: 0.330573 data_time: 0.022986 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.831098 loss: 0.000687 2022/09/09 19:48:25 - mmengine - INFO - Epoch(train) [38][150/586] lr: 5.000000e-04 eta: 9:17:11 time: 0.331624 data_time: 0.022309 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.821284 loss: 0.000662 2022/09/09 19:48:41 - mmengine - INFO - Epoch(train) [38][200/586] lr: 5.000000e-04 eta: 9:16:55 time: 0.333314 data_time: 0.022818 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.847367 loss: 0.000668 2022/09/09 19:48:58 - mmengine - INFO - Epoch(train) [38][250/586] lr: 5.000000e-04 eta: 9:16:39 time: 0.333124 data_time: 0.024131 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.747549 loss: 0.000663 2022/09/09 19:49:15 - mmengine - INFO - Epoch(train) [38][300/586] lr: 5.000000e-04 eta: 9:16:25 time: 0.338353 data_time: 0.023078 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.730835 loss: 0.000682 2022/09/09 19:49:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:49:32 - mmengine - INFO - Epoch(train) [38][350/586] lr: 5.000000e-04 eta: 9:16:09 time: 0.332951 data_time: 0.023491 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.836698 loss: 0.000661 2022/09/09 19:49:48 - mmengine - INFO - Epoch(train) [38][400/586] lr: 5.000000e-04 eta: 9:15:53 time: 0.335384 data_time: 0.023996 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.803601 loss: 0.000688 2022/09/09 19:50:05 - mmengine - INFO - Epoch(train) [38][450/586] lr: 5.000000e-04 eta: 9:15:37 time: 0.330709 data_time: 0.025871 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.813549 loss: 0.000669 2022/09/09 19:50:22 - mmengine - INFO - Epoch(train) [38][500/586] lr: 5.000000e-04 eta: 9:15:24 time: 0.344825 data_time: 0.024046 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.767093 loss: 0.000682 2022/09/09 19:50:39 - mmengine - INFO - Epoch(train) [38][550/586] lr: 5.000000e-04 eta: 9:15:08 time: 0.332971 data_time: 0.023134 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.847296 loss: 0.000664 2022/09/09 19:50:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:50:51 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/09/09 19:51:15 - mmengine - INFO - Epoch(train) [39][50/586] lr: 5.000000e-04 eta: 9:13:49 time: 0.343373 data_time: 0.033453 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.839287 loss: 0.000677 2022/09/09 19:51:32 - mmengine - INFO - Epoch(train) [39][100/586] lr: 5.000000e-04 eta: 9:13:35 time: 0.343312 data_time: 0.030869 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.825286 loss: 0.000661 2022/09/09 19:51:48 - mmengine - INFO - Epoch(train) [39][150/586] lr: 5.000000e-04 eta: 9:13:19 time: 0.330933 data_time: 0.023607 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.748848 loss: 0.000662 2022/09/09 19:52:05 - mmengine - INFO - Epoch(train) [39][200/586] lr: 5.000000e-04 eta: 9:13:03 time: 0.331200 data_time: 0.026333 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.787442 loss: 0.000669 2022/09/09 19:52:22 - mmengine - INFO - Epoch(train) [39][250/586] lr: 5.000000e-04 eta: 9:12:48 time: 0.338976 data_time: 0.023072 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.805359 loss: 0.000669 2022/09/09 19:52:39 - mmengine - INFO - Epoch(train) [39][300/586] lr: 5.000000e-04 eta: 9:12:33 time: 0.337577 data_time: 0.024340 memory: 7489 loss_kpt: 0.000681 acc_pose: 0.729324 loss: 0.000681 2022/09/09 19:52:56 - mmengine - INFO - Epoch(train) [39][350/586] lr: 5.000000e-04 eta: 9:12:19 time: 0.340021 data_time: 0.024500 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.816812 loss: 0.000677 2022/09/09 19:53:13 - mmengine - INFO - Epoch(train) [39][400/586] lr: 5.000000e-04 eta: 9:12:04 time: 0.334215 data_time: 0.023326 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.839324 loss: 0.000685 2022/09/09 19:53:29 - mmengine - INFO - Epoch(train) [39][450/586] lr: 5.000000e-04 eta: 9:11:47 time: 0.329291 data_time: 0.022984 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.787853 loss: 0.000662 2022/09/09 19:53:46 - mmengine - INFO - Epoch(train) [39][500/586] lr: 5.000000e-04 eta: 9:11:31 time: 0.332341 data_time: 0.026839 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.731288 loss: 0.000686 2022/09/09 19:54:02 - mmengine - INFO - Epoch(train) [39][550/586] lr: 5.000000e-04 eta: 9:11:15 time: 0.331354 data_time: 0.023598 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.793194 loss: 0.000674 2022/09/09 19:54:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:54:14 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/09/09 19:54:39 - mmengine - INFO - Epoch(train) [40][50/586] lr: 5.000000e-04 eta: 9:09:58 time: 0.344778 data_time: 0.037235 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.900844 loss: 0.000663 2022/09/09 19:54:56 - mmengine - INFO - Epoch(train) [40][100/586] lr: 5.000000e-04 eta: 9:09:43 time: 0.336729 data_time: 0.023020 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.765914 loss: 0.000680 2022/09/09 19:55:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:55:12 - mmengine - INFO - Epoch(train) [40][150/586] lr: 5.000000e-04 eta: 9:09:28 time: 0.334884 data_time: 0.023754 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.851485 loss: 0.000680 2022/09/09 19:55:29 - mmengine - INFO - Epoch(train) [40][200/586] lr: 5.000000e-04 eta: 9:09:12 time: 0.332146 data_time: 0.023015 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.790449 loss: 0.000661 2022/09/09 19:55:46 - mmengine - INFO - Epoch(train) [40][250/586] lr: 5.000000e-04 eta: 9:08:56 time: 0.332786 data_time: 0.023549 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.735739 loss: 0.000673 2022/09/09 19:56:03 - mmengine - INFO - Epoch(train) [40][300/586] lr: 5.000000e-04 eta: 9:08:41 time: 0.337425 data_time: 0.029242 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.828191 loss: 0.000667 2022/09/09 19:56:19 - mmengine - INFO - Epoch(train) [40][350/586] lr: 5.000000e-04 eta: 9:08:24 time: 0.328259 data_time: 0.025019 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.764612 loss: 0.000646 2022/09/09 19:56:36 - mmengine - INFO - Epoch(train) [40][400/586] lr: 5.000000e-04 eta: 9:08:08 time: 0.332108 data_time: 0.028721 memory: 7489 loss_kpt: 0.000683 acc_pose: 0.787465 loss: 0.000683 2022/09/09 19:56:53 - mmengine - INFO - Epoch(train) [40][450/586] lr: 5.000000e-04 eta: 9:07:54 time: 0.338161 data_time: 0.027949 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.866922 loss: 0.000649 2022/09/09 19:57:09 - mmengine - INFO - Epoch(train) [40][500/586] lr: 5.000000e-04 eta: 9:07:36 time: 0.324266 data_time: 0.024117 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.768634 loss: 0.000679 2022/09/09 19:57:25 - mmengine - INFO - Epoch(train) [40][550/586] lr: 5.000000e-04 eta: 9:07:21 time: 0.333567 data_time: 0.025365 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.865601 loss: 0.000663 2022/09/09 19:57:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 19:57:38 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/09 19:57:54 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:01:00 time: 0.170133 data_time: 0.012319 memory: 7489 2022/09/09 19:58:02 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:50 time: 0.165158 data_time: 0.007655 memory: 1657 2022/09/09 19:58:10 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:42 time: 0.166953 data_time: 0.007603 memory: 1657 2022/09/09 19:58:19 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:34 time: 0.164523 data_time: 0.007981 memory: 1657 2022/09/09 19:58:27 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:25 time: 0.165012 data_time: 0.008821 memory: 1657 2022/09/09 19:58:35 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:17 time: 0.164213 data_time: 0.007527 memory: 1657 2022/09/09 19:58:43 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:09 time: 0.162869 data_time: 0.007164 memory: 1657 2022/09/09 19:58:51 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:01 time: 0.161059 data_time: 0.006723 memory: 1657 2022/09/09 19:59:27 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 19:59:41 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.719428 coco/AP .5: 0.893516 coco/AP .75: 0.795419 coco/AP (M): 0.686360 coco/AP (L): 0.784821 coco/AR: 0.776228 coco/AR .5: 0.933564 coco/AR .75: 0.843514 coco/AR (M): 0.734908 coco/AR (L): 0.835786 2022/09/09 19:59:41 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_30.pth is removed 2022/09/09 19:59:44 - mmengine - INFO - The best checkpoint with 0.7194 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/09/09 20:00:01 - mmengine - INFO - Epoch(train) [41][50/586] lr: 5.000000e-04 eta: 9:06:02 time: 0.331315 data_time: 0.027654 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.768930 loss: 0.000682 2022/09/09 20:00:18 - mmengine - INFO - Epoch(train) [41][100/586] lr: 5.000000e-04 eta: 9:05:48 time: 0.338282 data_time: 0.023981 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.770137 loss: 0.000656 2022/09/09 20:00:34 - mmengine - INFO - Epoch(train) [41][150/586] lr: 5.000000e-04 eta: 9:05:32 time: 0.331728 data_time: 0.027219 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.792813 loss: 0.000666 2022/09/09 20:00:51 - mmengine - INFO - Epoch(train) [41][200/586] lr: 5.000000e-04 eta: 9:05:15 time: 0.328717 data_time: 0.023525 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.819078 loss: 0.000663 2022/09/09 20:01:08 - mmengine - INFO - Epoch(train) [41][250/586] lr: 5.000000e-04 eta: 9:05:02 time: 0.342298 data_time: 0.024035 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.817208 loss: 0.000643 2022/09/09 20:01:24 - mmengine - INFO - Epoch(train) [41][300/586] lr: 5.000000e-04 eta: 9:04:46 time: 0.330468 data_time: 0.024796 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.773401 loss: 0.000671 2022/09/09 20:01:41 - mmengine - INFO - Epoch(train) [41][350/586] lr: 5.000000e-04 eta: 9:04:29 time: 0.326977 data_time: 0.023804 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.622577 loss: 0.000658 2022/09/09 20:01:58 - mmengine - INFO - Epoch(train) [41][400/586] lr: 5.000000e-04 eta: 9:04:14 time: 0.338934 data_time: 0.024471 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.856611 loss: 0.000663 2022/09/09 20:02:14 - mmengine - INFO - Epoch(train) [41][450/586] lr: 5.000000e-04 eta: 9:03:59 time: 0.332491 data_time: 0.023648 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.814781 loss: 0.000664 2022/09/09 20:02:31 - mmengine - INFO - Epoch(train) [41][500/586] lr: 5.000000e-04 eta: 9:03:42 time: 0.328237 data_time: 0.024430 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.778009 loss: 0.000680 2022/09/09 20:02:48 - mmengine - INFO - Epoch(train) [41][550/586] lr: 5.000000e-04 eta: 9:03:26 time: 0.334251 data_time: 0.024183 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.812162 loss: 0.000666 2022/09/09 20:02:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:03:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:03:00 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/09/09 20:03:24 - mmengine - INFO - Epoch(train) [42][50/586] lr: 5.000000e-04 eta: 9:02:12 time: 0.343212 data_time: 0.034963 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.832864 loss: 0.000671 2022/09/09 20:03:41 - mmengine - INFO - Epoch(train) [42][100/586] lr: 5.000000e-04 eta: 9:01:59 time: 0.344190 data_time: 0.022480 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.819036 loss: 0.000661 2022/09/09 20:03:57 - mmengine - INFO - Epoch(train) [42][150/586] lr: 5.000000e-04 eta: 9:01:42 time: 0.324316 data_time: 0.023101 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.803932 loss: 0.000642 2022/09/09 20:04:14 - mmengine - INFO - Epoch(train) [42][200/586] lr: 5.000000e-04 eta: 9:01:26 time: 0.331742 data_time: 0.028452 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.801271 loss: 0.000695 2022/09/09 20:04:31 - mmengine - INFO - Epoch(train) [42][250/586] lr: 5.000000e-04 eta: 9:01:12 time: 0.340924 data_time: 0.024963 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.744950 loss: 0.000671 2022/09/09 20:04:47 - mmengine - INFO - Epoch(train) [42][300/586] lr: 5.000000e-04 eta: 9:00:55 time: 0.328024 data_time: 0.023357 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.824571 loss: 0.000672 2022/09/09 20:05:04 - mmengine - INFO - Epoch(train) [42][350/586] lr: 5.000000e-04 eta: 9:00:39 time: 0.327288 data_time: 0.028966 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.779342 loss: 0.000666 2022/09/09 20:05:21 - mmengine - INFO - Epoch(train) [42][400/586] lr: 5.000000e-04 eta: 9:00:25 time: 0.343414 data_time: 0.023514 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.787802 loss: 0.000691 2022/09/09 20:05:37 - mmengine - INFO - Epoch(train) [42][450/586] lr: 5.000000e-04 eta: 9:00:08 time: 0.325325 data_time: 0.023754 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.796359 loss: 0.000664 2022/09/09 20:05:54 - mmengine - INFO - Epoch(train) [42][500/586] lr: 5.000000e-04 eta: 8:59:53 time: 0.333828 data_time: 0.027243 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.821891 loss: 0.000664 2022/09/09 20:06:11 - mmengine - INFO - Epoch(train) [42][550/586] lr: 5.000000e-04 eta: 8:59:38 time: 0.337350 data_time: 0.024863 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.742939 loss: 0.000661 2022/09/09 20:06:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:06:22 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/09/09 20:06:46 - mmengine - INFO - Epoch(train) [43][50/586] lr: 5.000000e-04 eta: 8:58:24 time: 0.338614 data_time: 0.033306 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.810363 loss: 0.000670 2022/09/09 20:07:03 - mmengine - INFO - Epoch(train) [43][100/586] lr: 5.000000e-04 eta: 8:58:10 time: 0.340702 data_time: 0.029075 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.799547 loss: 0.000663 2022/09/09 20:07:20 - mmengine - INFO - Epoch(train) [43][150/586] lr: 5.000000e-04 eta: 8:57:55 time: 0.333389 data_time: 0.023216 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.804720 loss: 0.000648 2022/09/09 20:07:36 - mmengine - INFO - Epoch(train) [43][200/586] lr: 5.000000e-04 eta: 8:57:39 time: 0.331175 data_time: 0.022681 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.823971 loss: 0.000671 2022/09/09 20:07:53 - mmengine - INFO - Epoch(train) [43][250/586] lr: 5.000000e-04 eta: 8:57:25 time: 0.337735 data_time: 0.024082 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.832375 loss: 0.000664 2022/09/09 20:08:10 - mmengine - INFO - Epoch(train) [43][300/586] lr: 5.000000e-04 eta: 8:57:09 time: 0.331638 data_time: 0.022911 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.875646 loss: 0.000678 2022/09/09 20:08:26 - mmengine - INFO - Epoch(train) [43][350/586] lr: 5.000000e-04 eta: 8:56:52 time: 0.326608 data_time: 0.023722 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.784623 loss: 0.000677 2022/09/09 20:08:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:08:44 - mmengine - INFO - Epoch(train) [43][400/586] lr: 5.000000e-04 eta: 8:56:39 time: 0.347629 data_time: 0.022730 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.792293 loss: 0.000670 2022/09/09 20:09:00 - mmengine - INFO - Epoch(train) [43][450/586] lr: 5.000000e-04 eta: 8:56:24 time: 0.332374 data_time: 0.023540 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.786109 loss: 0.000660 2022/09/09 20:09:17 - mmengine - INFO - Epoch(train) [43][500/586] lr: 5.000000e-04 eta: 8:56:08 time: 0.333497 data_time: 0.024264 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.779695 loss: 0.000685 2022/09/09 20:09:34 - mmengine - INFO - Epoch(train) [43][550/586] lr: 5.000000e-04 eta: 8:55:55 time: 0.343955 data_time: 0.026492 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.806931 loss: 0.000651 2022/09/09 20:09:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:09:46 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/09/09 20:10:10 - mmengine - INFO - Epoch(train) [44][50/586] lr: 5.000000e-04 eta: 8:54:43 time: 0.339248 data_time: 0.029440 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.774037 loss: 0.000655 2022/09/09 20:10:27 - mmengine - INFO - Epoch(train) [44][100/586] lr: 5.000000e-04 eta: 8:54:28 time: 0.332813 data_time: 0.022271 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.812965 loss: 0.000639 2022/09/09 20:10:43 - mmengine - INFO - Epoch(train) [44][150/586] lr: 5.000000e-04 eta: 8:54:11 time: 0.329072 data_time: 0.023928 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.835273 loss: 0.000646 2022/09/09 20:11:00 - mmengine - INFO - Epoch(train) [44][200/586] lr: 5.000000e-04 eta: 8:53:55 time: 0.329784 data_time: 0.023725 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.822584 loss: 0.000654 2022/09/09 20:11:16 - mmengine - INFO - Epoch(train) [44][250/586] lr: 5.000000e-04 eta: 8:53:39 time: 0.330998 data_time: 0.023077 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.808280 loss: 0.000678 2022/09/09 20:11:33 - mmengine - INFO - Epoch(train) [44][300/586] lr: 5.000000e-04 eta: 8:53:25 time: 0.337364 data_time: 0.024458 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.781321 loss: 0.000675 2022/09/09 20:11:50 - mmengine - INFO - Epoch(train) [44][350/586] lr: 5.000000e-04 eta: 8:53:09 time: 0.330024 data_time: 0.023341 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.819124 loss: 0.000662 2022/09/09 20:12:06 - mmengine - INFO - Epoch(train) [44][400/586] lr: 5.000000e-04 eta: 8:52:54 time: 0.337100 data_time: 0.025865 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.839910 loss: 0.000634 2022/09/09 20:12:23 - mmengine - INFO - Epoch(train) [44][450/586] lr: 5.000000e-04 eta: 8:52:39 time: 0.333318 data_time: 0.026597 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.797628 loss: 0.000660 2022/09/09 20:12:40 - mmengine - INFO - Epoch(train) [44][500/586] lr: 5.000000e-04 eta: 8:52:23 time: 0.330345 data_time: 0.023310 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.791685 loss: 0.000650 2022/09/09 20:12:56 - mmengine - INFO - Epoch(train) [44][550/586] lr: 5.000000e-04 eta: 8:52:07 time: 0.331179 data_time: 0.022440 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.881377 loss: 0.000664 2022/09/09 20:13:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:13:08 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/09/09 20:13:32 - mmengine - INFO - Epoch(train) [45][50/586] lr: 5.000000e-04 eta: 8:50:57 time: 0.342141 data_time: 0.032606 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.904029 loss: 0.000662 2022/09/09 20:13:49 - mmengine - INFO - Epoch(train) [45][100/586] lr: 5.000000e-04 eta: 8:50:42 time: 0.337624 data_time: 0.029114 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.843741 loss: 0.000646 2022/09/09 20:14:06 - mmengine - INFO - Epoch(train) [45][150/586] lr: 5.000000e-04 eta: 8:50:26 time: 0.326447 data_time: 0.023518 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.766352 loss: 0.000678 2022/09/09 20:14:23 - mmengine - INFO - Epoch(train) [45][200/586] lr: 5.000000e-04 eta: 8:50:12 time: 0.339980 data_time: 0.022898 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.881526 loss: 0.000651 2022/09/09 20:14:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:14:39 - mmengine - INFO - Epoch(train) [45][250/586] lr: 5.000000e-04 eta: 8:49:56 time: 0.332142 data_time: 0.023636 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.811725 loss: 0.000654 2022/09/09 20:14:56 - mmengine - INFO - Epoch(train) [45][300/586] lr: 5.000000e-04 eta: 8:49:41 time: 0.332514 data_time: 0.029745 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.821978 loss: 0.000635 2022/09/09 20:15:13 - mmengine - INFO - Epoch(train) [45][350/586] lr: 5.000000e-04 eta: 8:49:26 time: 0.338174 data_time: 0.022582 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.831074 loss: 0.000658 2022/09/09 20:15:29 - mmengine - INFO - Epoch(train) [45][400/586] lr: 5.000000e-04 eta: 8:49:11 time: 0.334717 data_time: 0.023483 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.811802 loss: 0.000666 2022/09/09 20:15:46 - mmengine - INFO - Epoch(train) [45][450/586] lr: 5.000000e-04 eta: 8:48:54 time: 0.326430 data_time: 0.023802 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.809661 loss: 0.000666 2022/09/09 20:16:03 - mmengine - INFO - Epoch(train) [45][500/586] lr: 5.000000e-04 eta: 8:48:40 time: 0.338211 data_time: 0.022943 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.841154 loss: 0.000648 2022/09/09 20:16:20 - mmengine - INFO - Epoch(train) [45][550/586] lr: 5.000000e-04 eta: 8:48:26 time: 0.338572 data_time: 0.024254 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.745169 loss: 0.000641 2022/09/09 20:16:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:16:32 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/09/09 20:16:55 - mmengine - INFO - Epoch(train) [46][50/586] lr: 5.000000e-04 eta: 8:47:15 time: 0.334533 data_time: 0.032764 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.788789 loss: 0.000660 2022/09/09 20:17:13 - mmengine - INFO - Epoch(train) [46][100/586] lr: 5.000000e-04 eta: 8:47:03 time: 0.346818 data_time: 0.026133 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.800841 loss: 0.000629 2022/09/09 20:17:29 - mmengine - INFO - Epoch(train) [46][150/586] lr: 5.000000e-04 eta: 8:46:47 time: 0.331140 data_time: 0.022899 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.774230 loss: 0.000667 2022/09/09 20:17:46 - mmengine - INFO - Epoch(train) [46][200/586] lr: 5.000000e-04 eta: 8:46:31 time: 0.329537 data_time: 0.023310 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.755600 loss: 0.000667 2022/09/09 20:18:03 - mmengine - INFO - Epoch(train) [46][250/586] lr: 5.000000e-04 eta: 8:46:16 time: 0.337399 data_time: 0.023841 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.744176 loss: 0.000649 2022/09/09 20:18:20 - mmengine - INFO - Epoch(train) [46][300/586] lr: 5.000000e-04 eta: 8:46:02 time: 0.339588 data_time: 0.023943 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.791101 loss: 0.000660 2022/09/09 20:18:36 - mmengine - INFO - Epoch(train) [46][350/586] lr: 5.000000e-04 eta: 8:45:47 time: 0.333534 data_time: 0.023127 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.846582 loss: 0.000654 2022/09/09 20:18:54 - mmengine - INFO - Epoch(train) [46][400/586] lr: 5.000000e-04 eta: 8:45:34 time: 0.347818 data_time: 0.033690 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.814496 loss: 0.000657 2022/09/09 20:19:10 - mmengine - INFO - Epoch(train) [46][450/586] lr: 5.000000e-04 eta: 8:45:19 time: 0.335009 data_time: 0.023469 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.844693 loss: 0.000669 2022/09/09 20:19:27 - mmengine - INFO - Epoch(train) [46][500/586] lr: 5.000000e-04 eta: 8:45:04 time: 0.331042 data_time: 0.024453 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.803199 loss: 0.000672 2022/09/09 20:19:44 - mmengine - INFO - Epoch(train) [46][550/586] lr: 5.000000e-04 eta: 8:44:49 time: 0.339400 data_time: 0.022779 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.813415 loss: 0.000655 2022/09/09 20:19:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:19:56 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/09/09 20:20:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:20:20 - mmengine - INFO - Epoch(train) [47][50/586] lr: 5.000000e-04 eta: 8:43:41 time: 0.340787 data_time: 0.038670 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.784146 loss: 0.000659 2022/09/09 20:20:37 - mmengine - INFO - Epoch(train) [47][100/586] lr: 5.000000e-04 eta: 8:43:27 time: 0.338609 data_time: 0.023179 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.830659 loss: 0.000650 2022/09/09 20:20:53 - mmengine - INFO - Epoch(train) [47][150/586] lr: 5.000000e-04 eta: 8:43:11 time: 0.326597 data_time: 0.023270 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.884696 loss: 0.000663 2022/09/09 20:21:10 - mmengine - INFO - Epoch(train) [47][200/586] lr: 5.000000e-04 eta: 8:42:55 time: 0.333543 data_time: 0.023437 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.824299 loss: 0.000650 2022/09/09 20:21:27 - mmengine - INFO - Epoch(train) [47][250/586] lr: 5.000000e-04 eta: 8:42:40 time: 0.334255 data_time: 0.023167 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.840103 loss: 0.000643 2022/09/09 20:21:43 - mmengine - INFO - Epoch(train) [47][300/586] lr: 5.000000e-04 eta: 8:42:24 time: 0.327865 data_time: 0.022811 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.795864 loss: 0.000647 2022/09/09 20:21:59 - mmengine - INFO - Epoch(train) [47][350/586] lr: 5.000000e-04 eta: 8:42:09 time: 0.331753 data_time: 0.025512 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.824421 loss: 0.000665 2022/09/09 20:22:16 - mmengine - INFO - Epoch(train) [47][400/586] lr: 5.000000e-04 eta: 8:41:54 time: 0.337501 data_time: 0.023233 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.793046 loss: 0.000660 2022/09/09 20:22:33 - mmengine - INFO - Epoch(train) [47][450/586] lr: 5.000000e-04 eta: 8:41:40 time: 0.340783 data_time: 0.022020 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.772648 loss: 0.000654 2022/09/09 20:22:50 - mmengine - INFO - Epoch(train) [47][500/586] lr: 5.000000e-04 eta: 8:41:25 time: 0.332748 data_time: 0.026291 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.799322 loss: 0.000660 2022/09/09 20:23:07 - mmengine - INFO - Epoch(train) [47][550/586] lr: 5.000000e-04 eta: 8:41:09 time: 0.332717 data_time: 0.023689 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.702569 loss: 0.000653 2022/09/09 20:23:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:23:19 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/09/09 20:23:43 - mmengine - INFO - Epoch(train) [48][50/586] lr: 5.000000e-04 eta: 8:40:03 time: 0.339865 data_time: 0.029105 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.848300 loss: 0.000650 2022/09/09 20:24:00 - mmengine - INFO - Epoch(train) [48][100/586] lr: 5.000000e-04 eta: 8:39:49 time: 0.341238 data_time: 0.025538 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.887701 loss: 0.000655 2022/09/09 20:24:16 - mmengine - INFO - Epoch(train) [48][150/586] lr: 5.000000e-04 eta: 8:39:33 time: 0.328040 data_time: 0.023350 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.865773 loss: 0.000641 2022/09/09 20:24:33 - mmengine - INFO - Epoch(train) [48][200/586] lr: 5.000000e-04 eta: 8:39:17 time: 0.332955 data_time: 0.023086 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.826566 loss: 0.000671 2022/09/09 20:24:50 - mmengine - INFO - Epoch(train) [48][250/586] lr: 5.000000e-04 eta: 8:39:03 time: 0.337167 data_time: 0.027110 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.828368 loss: 0.000644 2022/09/09 20:25:06 - mmengine - INFO - Epoch(train) [48][300/586] lr: 5.000000e-04 eta: 8:38:46 time: 0.326818 data_time: 0.024695 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.785657 loss: 0.000668 2022/09/09 20:25:23 - mmengine - INFO - Epoch(train) [48][350/586] lr: 5.000000e-04 eta: 8:38:31 time: 0.334918 data_time: 0.023120 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.787701 loss: 0.000662 2022/09/09 20:25:39 - mmengine - INFO - Epoch(train) [48][400/586] lr: 5.000000e-04 eta: 8:38:15 time: 0.326842 data_time: 0.023493 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.880156 loss: 0.000647 2022/09/09 20:25:56 - mmengine - INFO - Epoch(train) [48][450/586] lr: 5.000000e-04 eta: 8:37:59 time: 0.327619 data_time: 0.024065 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.863860 loss: 0.000658 2022/09/09 20:25:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:26:13 - mmengine - INFO - Epoch(train) [48][500/586] lr: 5.000000e-04 eta: 8:37:46 time: 0.346590 data_time: 0.024696 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.808360 loss: 0.000650 2022/09/09 20:26:30 - mmengine - INFO - Epoch(train) [48][550/586] lr: 5.000000e-04 eta: 8:37:31 time: 0.334389 data_time: 0.023329 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.857963 loss: 0.000651 2022/09/09 20:26:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:26:42 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/09/09 20:27:06 - mmengine - INFO - Epoch(train) [49][50/586] lr: 5.000000e-04 eta: 8:36:26 time: 0.348260 data_time: 0.033057 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.873511 loss: 0.000648 2022/09/09 20:27:22 - mmengine - INFO - Epoch(train) [49][100/586] lr: 5.000000e-04 eta: 8:36:10 time: 0.328521 data_time: 0.023403 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.807581 loss: 0.000643 2022/09/09 20:27:39 - mmengine - INFO - Epoch(train) [49][150/586] lr: 5.000000e-04 eta: 8:35:55 time: 0.331112 data_time: 0.023885 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.866598 loss: 0.000653 2022/09/09 20:27:56 - mmengine - INFO - Epoch(train) [49][200/586] lr: 5.000000e-04 eta: 8:35:40 time: 0.337640 data_time: 0.025423 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.794137 loss: 0.000650 2022/09/09 20:28:12 - mmengine - INFO - Epoch(train) [49][250/586] lr: 5.000000e-04 eta: 8:35:25 time: 0.334003 data_time: 0.023439 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.874912 loss: 0.000651 2022/09/09 20:28:29 - mmengine - INFO - Epoch(train) [49][300/586] lr: 5.000000e-04 eta: 8:35:09 time: 0.328650 data_time: 0.023955 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.812886 loss: 0.000655 2022/09/09 20:28:46 - mmengine - INFO - Epoch(train) [49][350/586] lr: 5.000000e-04 eta: 8:34:56 time: 0.345744 data_time: 0.022810 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.769068 loss: 0.000653 2022/09/09 20:29:03 - mmengine - INFO - Epoch(train) [49][400/586] lr: 5.000000e-04 eta: 8:34:41 time: 0.333715 data_time: 0.024405 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.818804 loss: 0.000656 2022/09/09 20:29:19 - mmengine - INFO - Epoch(train) [49][450/586] lr: 5.000000e-04 eta: 8:34:25 time: 0.331400 data_time: 0.022818 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.880737 loss: 0.000650 2022/09/09 20:29:36 - mmengine - INFO - Epoch(train) [49][500/586] lr: 5.000000e-04 eta: 8:34:10 time: 0.334284 data_time: 0.023311 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.795679 loss: 0.000662 2022/09/09 20:29:53 - mmengine - INFO - Epoch(train) [49][550/586] lr: 5.000000e-04 eta: 8:33:55 time: 0.330307 data_time: 0.024501 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.809494 loss: 0.000644 2022/09/09 20:30:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:30:05 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/09/09 20:30:29 - mmengine - INFO - Epoch(train) [50][50/586] lr: 5.000000e-04 eta: 8:32:51 time: 0.343708 data_time: 0.032164 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.845061 loss: 0.000652 2022/09/09 20:30:46 - mmengine - INFO - Epoch(train) [50][100/586] lr: 5.000000e-04 eta: 8:32:37 time: 0.339113 data_time: 0.027623 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.819579 loss: 0.000635 2022/09/09 20:31:02 - mmengine - INFO - Epoch(train) [50][150/586] lr: 5.000000e-04 eta: 8:32:21 time: 0.328612 data_time: 0.023694 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.822886 loss: 0.000660 2022/09/09 20:31:19 - mmengine - INFO - Epoch(train) [50][200/586] lr: 5.000000e-04 eta: 8:32:05 time: 0.331820 data_time: 0.022825 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.751798 loss: 0.000658 2022/09/09 20:31:35 - mmengine - INFO - Epoch(train) [50][250/586] lr: 5.000000e-04 eta: 8:31:50 time: 0.331984 data_time: 0.022758 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.851353 loss: 0.000668 2022/09/09 20:31:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:31:56 - mmengine - INFO - Epoch(train) [50][300/586] lr: 5.000000e-04 eta: 8:31:49 time: 0.423689 data_time: 0.031124 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.796507 loss: 0.000653 2022/09/09 20:32:18 - mmengine - INFO - Epoch(train) [50][350/586] lr: 5.000000e-04 eta: 8:31:49 time: 0.425563 data_time: 0.027410 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.753099 loss: 0.000658 2022/09/09 20:32:35 - mmengine - INFO - Epoch(train) [50][400/586] lr: 5.000000e-04 eta: 8:31:34 time: 0.336506 data_time: 0.026605 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.800070 loss: 0.000668 2022/09/09 20:32:51 - mmengine - INFO - Epoch(train) [50][450/586] lr: 5.000000e-04 eta: 8:31:20 time: 0.337473 data_time: 0.025094 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.873178 loss: 0.000625 2022/09/09 20:33:08 - mmengine - INFO - Epoch(train) [50][500/586] lr: 5.000000e-04 eta: 8:31:04 time: 0.334829 data_time: 0.024357 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.856784 loss: 0.000676 2022/09/09 20:33:25 - mmengine - INFO - Epoch(train) [50][550/586] lr: 5.000000e-04 eta: 8:30:49 time: 0.333574 data_time: 0.024738 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.825794 loss: 0.000628 2022/09/09 20:33:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:33:37 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/09/09 20:33:53 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:01:07 time: 0.189628 data_time: 0.032901 memory: 7489 2022/09/09 20:34:02 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:50 time: 0.163422 data_time: 0.007304 memory: 1657 2022/09/09 20:34:10 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:41 time: 0.163028 data_time: 0.007176 memory: 1657 2022/09/09 20:34:18 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:33 time: 0.164152 data_time: 0.007502 memory: 1657 2022/09/09 20:34:26 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:25 time: 0.162705 data_time: 0.007077 memory: 1657 2022/09/09 20:34:34 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:17 time: 0.165470 data_time: 0.009151 memory: 1657 2022/09/09 20:34:43 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:09 time: 0.169144 data_time: 0.012384 memory: 1657 2022/09/09 20:34:51 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:01 time: 0.161048 data_time: 0.006772 memory: 1657 2022/09/09 20:35:27 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 20:35:41 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.722828 coco/AP .5: 0.894506 coco/AP .75: 0.796594 coco/AP (M): 0.687519 coco/AP (L): 0.787892 coco/AR: 0.778904 coco/AR .5: 0.932305 coco/AR .75: 0.845088 coco/AR (M): 0.737066 coco/AR (L): 0.838573 2022/09/09 20:35:41 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_40.pth is removed 2022/09/09 20:35:45 - mmengine - INFO - The best checkpoint with 0.7228 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/09/09 20:36:01 - mmengine - INFO - Epoch(train) [51][50/586] lr: 5.000000e-04 eta: 8:29:45 time: 0.336927 data_time: 0.030720 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.818645 loss: 0.000653 2022/09/09 20:36:18 - mmengine - INFO - Epoch(train) [51][100/586] lr: 5.000000e-04 eta: 8:29:30 time: 0.335547 data_time: 0.024780 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.800351 loss: 0.000651 2022/09/09 20:36:35 - mmengine - INFO - Epoch(train) [51][150/586] lr: 5.000000e-04 eta: 8:29:16 time: 0.336671 data_time: 0.024246 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.815456 loss: 0.000643 2022/09/09 20:36:52 - mmengine - INFO - Epoch(train) [51][200/586] lr: 5.000000e-04 eta: 8:29:02 time: 0.340261 data_time: 0.022728 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.845127 loss: 0.000631 2022/09/09 20:37:09 - mmengine - INFO - Epoch(train) [51][250/586] lr: 5.000000e-04 eta: 8:28:46 time: 0.334516 data_time: 0.023260 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.819447 loss: 0.000656 2022/09/09 20:37:25 - mmengine - INFO - Epoch(train) [51][300/586] lr: 5.000000e-04 eta: 8:28:31 time: 0.332728 data_time: 0.022599 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.790714 loss: 0.000661 2022/09/09 20:37:42 - mmengine - INFO - Epoch(train) [51][350/586] lr: 5.000000e-04 eta: 8:28:16 time: 0.334835 data_time: 0.024438 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.853265 loss: 0.000653 2022/09/09 20:37:59 - mmengine - INFO - Epoch(train) [51][400/586] lr: 5.000000e-04 eta: 8:28:00 time: 0.328075 data_time: 0.027351 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.936660 loss: 0.000662 2022/09/09 20:38:15 - mmengine - INFO - Epoch(train) [51][450/586] lr: 5.000000e-04 eta: 8:27:45 time: 0.331753 data_time: 0.025011 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.780687 loss: 0.000641 2022/09/09 20:38:32 - mmengine - INFO - Epoch(train) [51][500/586] lr: 5.000000e-04 eta: 8:27:30 time: 0.335104 data_time: 0.022674 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.801096 loss: 0.000658 2022/09/09 20:38:49 - mmengine - INFO - Epoch(train) [51][550/586] lr: 5.000000e-04 eta: 8:27:15 time: 0.339306 data_time: 0.023412 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.811926 loss: 0.000643 2022/09/09 20:39:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:39:01 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/09/09 20:39:25 - mmengine - INFO - Epoch(train) [52][50/586] lr: 5.000000e-04 eta: 8:26:12 time: 0.335411 data_time: 0.028842 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.890470 loss: 0.000631 2022/09/09 20:39:42 - mmengine - INFO - Epoch(train) [52][100/586] lr: 5.000000e-04 eta: 8:25:57 time: 0.335357 data_time: 0.025670 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.752861 loss: 0.000631 2022/09/09 20:39:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:39:58 - mmengine - INFO - Epoch(train) [52][150/586] lr: 5.000000e-04 eta: 8:25:42 time: 0.332304 data_time: 0.023834 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.831916 loss: 0.000630 2022/09/09 20:40:15 - mmengine - INFO - Epoch(train) [52][200/586] lr: 5.000000e-04 eta: 8:25:26 time: 0.330480 data_time: 0.023482 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.775539 loss: 0.000644 2022/09/09 20:40:31 - mmengine - INFO - Epoch(train) [52][250/586] lr: 5.000000e-04 eta: 8:25:11 time: 0.331248 data_time: 0.022621 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.839795 loss: 0.000632 2022/09/09 20:40:49 - mmengine - INFO - Epoch(train) [52][300/586] lr: 5.000000e-04 eta: 8:24:57 time: 0.346024 data_time: 0.030013 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.817362 loss: 0.000652 2022/09/09 20:41:05 - mmengine - INFO - Epoch(train) [52][350/586] lr: 5.000000e-04 eta: 8:24:43 time: 0.336587 data_time: 0.028776 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.782702 loss: 0.000635 2022/09/09 20:41:22 - mmengine - INFO - Epoch(train) [52][400/586] lr: 5.000000e-04 eta: 8:24:27 time: 0.330634 data_time: 0.023222 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.788629 loss: 0.000639 2022/09/09 20:41:39 - mmengine - INFO - Epoch(train) [52][450/586] lr: 5.000000e-04 eta: 8:24:12 time: 0.333633 data_time: 0.024482 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.856875 loss: 0.000636 2022/09/09 20:41:56 - mmengine - INFO - Epoch(train) [52][500/586] lr: 5.000000e-04 eta: 8:23:57 time: 0.337707 data_time: 0.025676 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.832299 loss: 0.000656 2022/09/09 20:42:12 - mmengine - INFO - Epoch(train) [52][550/586] lr: 5.000000e-04 eta: 8:23:41 time: 0.326746 data_time: 0.022550 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.821027 loss: 0.000665 2022/09/09 20:42:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:42:24 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/09/09 20:42:48 - mmengine - INFO - Epoch(train) [53][50/586] lr: 5.000000e-04 eta: 8:22:40 time: 0.344562 data_time: 0.035456 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.814181 loss: 0.000666 2022/09/09 20:43:05 - mmengine - INFO - Epoch(train) [53][100/586] lr: 5.000000e-04 eta: 8:22:25 time: 0.333185 data_time: 0.028020 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.770184 loss: 0.000651 2022/09/09 20:43:21 - mmengine - INFO - Epoch(train) [53][150/586] lr: 5.000000e-04 eta: 8:22:09 time: 0.331468 data_time: 0.025123 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.812429 loss: 0.000609 2022/09/09 20:43:38 - mmengine - INFO - Epoch(train) [53][200/586] lr: 5.000000e-04 eta: 8:21:54 time: 0.330390 data_time: 0.026290 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.782449 loss: 0.000636 2022/09/09 20:43:55 - mmengine - INFO - Epoch(train) [53][250/586] lr: 5.000000e-04 eta: 8:21:40 time: 0.340460 data_time: 0.023905 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.788027 loss: 0.000650 2022/09/09 20:44:11 - mmengine - INFO - Epoch(train) [53][300/586] lr: 5.000000e-04 eta: 8:21:24 time: 0.329401 data_time: 0.024415 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.809878 loss: 0.000644 2022/09/09 20:44:28 - mmengine - INFO - Epoch(train) [53][350/586] lr: 5.000000e-04 eta: 8:21:09 time: 0.334225 data_time: 0.026172 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.807128 loss: 0.000632 2022/09/09 20:44:45 - mmengine - INFO - Epoch(train) [53][400/586] lr: 5.000000e-04 eta: 8:20:54 time: 0.338382 data_time: 0.027423 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.865014 loss: 0.000653 2022/09/09 20:45:02 - mmengine - INFO - Epoch(train) [53][450/586] lr: 5.000000e-04 eta: 8:20:40 time: 0.340467 data_time: 0.024953 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.781533 loss: 0.000644 2022/09/09 20:45:19 - mmengine - INFO - Epoch(train) [53][500/586] lr: 5.000000e-04 eta: 8:20:26 time: 0.339698 data_time: 0.031501 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.787944 loss: 0.000648 2022/09/09 20:45:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:45:35 - mmengine - INFO - Epoch(train) [53][550/586] lr: 5.000000e-04 eta: 8:20:10 time: 0.330398 data_time: 0.023584 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.854701 loss: 0.000661 2022/09/09 20:45:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:45:47 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/09/09 20:46:11 - mmengine - INFO - Epoch(train) [54][50/586] lr: 5.000000e-04 eta: 8:19:09 time: 0.335517 data_time: 0.031108 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.786131 loss: 0.000655 2022/09/09 20:46:28 - mmengine - INFO - Epoch(train) [54][100/586] lr: 5.000000e-04 eta: 8:18:53 time: 0.329551 data_time: 0.023477 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.844058 loss: 0.000631 2022/09/09 20:46:45 - mmengine - INFO - Epoch(train) [54][150/586] lr: 5.000000e-04 eta: 8:18:40 time: 0.343977 data_time: 0.023296 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.854748 loss: 0.000627 2022/09/09 20:47:02 - mmengine - INFO - Epoch(train) [54][200/586] lr: 5.000000e-04 eta: 8:18:24 time: 0.333705 data_time: 0.023133 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.821920 loss: 0.000648 2022/09/09 20:47:18 - mmengine - INFO - Epoch(train) [54][250/586] lr: 5.000000e-04 eta: 8:18:10 time: 0.335723 data_time: 0.022723 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.821589 loss: 0.000629 2022/09/09 20:47:35 - mmengine - INFO - Epoch(train) [54][300/586] lr: 5.000000e-04 eta: 8:17:54 time: 0.330649 data_time: 0.023354 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.808759 loss: 0.000623 2022/09/09 20:47:51 - mmengine - INFO - Epoch(train) [54][350/586] lr: 5.000000e-04 eta: 8:17:38 time: 0.328103 data_time: 0.024761 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.792451 loss: 0.000650 2022/09/09 20:48:08 - mmengine - INFO - Epoch(train) [54][400/586] lr: 5.000000e-04 eta: 8:17:23 time: 0.336884 data_time: 0.023819 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.842130 loss: 0.000632 2022/09/09 20:48:25 - mmengine - INFO - Epoch(train) [54][450/586] lr: 5.000000e-04 eta: 8:17:09 time: 0.334985 data_time: 0.023519 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.785604 loss: 0.000664 2022/09/09 20:48:42 - mmengine - INFO - Epoch(train) [54][500/586] lr: 5.000000e-04 eta: 8:16:53 time: 0.332719 data_time: 0.022737 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.819410 loss: 0.000631 2022/09/09 20:48:58 - mmengine - INFO - Epoch(train) [54][550/586] lr: 5.000000e-04 eta: 8:16:38 time: 0.336169 data_time: 0.029070 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.856784 loss: 0.000633 2022/09/09 20:49:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:49:10 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/09/09 20:49:35 - mmengine - INFO - Epoch(train) [55][50/586] lr: 5.000000e-04 eta: 8:15:39 time: 0.345672 data_time: 0.032245 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.811523 loss: 0.000642 2022/09/09 20:49:51 - mmengine - INFO - Epoch(train) [55][100/586] lr: 5.000000e-04 eta: 8:15:24 time: 0.330662 data_time: 0.023445 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.784642 loss: 0.000642 2022/09/09 20:50:09 - mmengine - INFO - Epoch(train) [55][150/586] lr: 5.000000e-04 eta: 8:15:11 time: 0.347532 data_time: 0.026685 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.812492 loss: 0.000639 2022/09/09 20:50:25 - mmengine - INFO - Epoch(train) [55][200/586] lr: 5.000000e-04 eta: 8:14:56 time: 0.332719 data_time: 0.025345 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.758713 loss: 0.000630 2022/09/09 20:50:42 - mmengine - INFO - Epoch(train) [55][250/586] lr: 5.000000e-04 eta: 8:14:40 time: 0.331224 data_time: 0.025448 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.832426 loss: 0.000640 2022/09/09 20:50:59 - mmengine - INFO - Epoch(train) [55][300/586] lr: 5.000000e-04 eta: 8:14:25 time: 0.334758 data_time: 0.023800 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.808327 loss: 0.000635 2022/09/09 20:51:15 - mmengine - INFO - Epoch(train) [55][350/586] lr: 5.000000e-04 eta: 8:14:09 time: 0.325998 data_time: 0.026669 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.812091 loss: 0.000639 2022/09/09 20:51:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:51:32 - mmengine - INFO - Epoch(train) [55][400/586] lr: 5.000000e-04 eta: 8:13:54 time: 0.334990 data_time: 0.023100 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.821014 loss: 0.000637 2022/09/09 20:51:49 - mmengine - INFO - Epoch(train) [55][450/586] lr: 5.000000e-04 eta: 8:13:40 time: 0.338778 data_time: 0.027462 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.797061 loss: 0.000655 2022/09/09 20:52:05 - mmengine - INFO - Epoch(train) [55][500/586] lr: 5.000000e-04 eta: 8:13:24 time: 0.330955 data_time: 0.023031 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.867708 loss: 0.000624 2022/09/09 20:52:22 - mmengine - INFO - Epoch(train) [55][550/586] lr: 5.000000e-04 eta: 8:13:09 time: 0.332642 data_time: 0.023943 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.785147 loss: 0.000628 2022/09/09 20:52:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:52:34 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/09/09 20:52:57 - mmengine - INFO - Epoch(train) [56][50/586] lr: 5.000000e-04 eta: 8:12:09 time: 0.335402 data_time: 0.027984 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.870463 loss: 0.000648 2022/09/09 20:53:14 - mmengine - INFO - Epoch(train) [56][100/586] lr: 5.000000e-04 eta: 8:11:54 time: 0.332429 data_time: 0.023983 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.788110 loss: 0.000642 2022/09/09 20:53:31 - mmengine - INFO - Epoch(train) [56][150/586] lr: 5.000000e-04 eta: 8:11:40 time: 0.340716 data_time: 0.024529 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.814874 loss: 0.000610 2022/09/09 20:53:47 - mmengine - INFO - Epoch(train) [56][200/586] lr: 5.000000e-04 eta: 8:11:24 time: 0.326261 data_time: 0.023380 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.795571 loss: 0.000643 2022/09/09 20:54:04 - mmengine - INFO - Epoch(train) [56][250/586] lr: 5.000000e-04 eta: 8:11:09 time: 0.335312 data_time: 0.023964 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.765067 loss: 0.000624 2022/09/09 20:54:21 - mmengine - INFO - Epoch(train) [56][300/586] lr: 5.000000e-04 eta: 8:10:54 time: 0.336669 data_time: 0.024377 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.792543 loss: 0.000627 2022/09/09 20:54:38 - mmengine - INFO - Epoch(train) [56][350/586] lr: 5.000000e-04 eta: 8:10:38 time: 0.329257 data_time: 0.023299 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.862135 loss: 0.000638 2022/09/09 20:54:54 - mmengine - INFO - Epoch(train) [56][400/586] lr: 5.000000e-04 eta: 8:10:24 time: 0.335961 data_time: 0.024858 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.888846 loss: 0.000632 2022/09/09 20:55:11 - mmengine - INFO - Epoch(train) [56][450/586] lr: 5.000000e-04 eta: 8:10:08 time: 0.332692 data_time: 0.023420 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.874804 loss: 0.000634 2022/09/09 20:55:28 - mmengine - INFO - Epoch(train) [56][500/586] lr: 5.000000e-04 eta: 8:09:53 time: 0.330445 data_time: 0.024357 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.793867 loss: 0.000615 2022/09/09 20:55:44 - mmengine - INFO - Epoch(train) [56][550/586] lr: 5.000000e-04 eta: 8:09:38 time: 0.336388 data_time: 0.028824 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.869513 loss: 0.000637 2022/09/09 20:55:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:55:56 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/09/09 20:56:20 - mmengine - INFO - Epoch(train) [57][50/586] lr: 5.000000e-04 eta: 8:08:39 time: 0.334158 data_time: 0.030276 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.854988 loss: 0.000641 2022/09/09 20:56:37 - mmengine - INFO - Epoch(train) [57][100/586] lr: 5.000000e-04 eta: 8:08:25 time: 0.338495 data_time: 0.026542 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.794801 loss: 0.000638 2022/09/09 20:56:54 - mmengine - INFO - Epoch(train) [57][150/586] lr: 5.000000e-04 eta: 8:08:10 time: 0.336959 data_time: 0.022707 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.801723 loss: 0.000649 2022/09/09 20:57:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:57:11 - mmengine - INFO - Epoch(train) [57][200/586] lr: 5.000000e-04 eta: 8:07:54 time: 0.329245 data_time: 0.022845 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.756824 loss: 0.000630 2022/09/09 20:57:28 - mmengine - INFO - Epoch(train) [57][250/586] lr: 5.000000e-04 eta: 8:07:40 time: 0.343329 data_time: 0.025241 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.867004 loss: 0.000630 2022/09/09 20:57:45 - mmengine - INFO - Epoch(train) [57][300/586] lr: 5.000000e-04 eta: 8:07:26 time: 0.339040 data_time: 0.027144 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.837810 loss: 0.000653 2022/09/09 20:58:01 - mmengine - INFO - Epoch(train) [57][350/586] lr: 5.000000e-04 eta: 8:07:11 time: 0.330521 data_time: 0.023596 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.800225 loss: 0.000648 2022/09/09 20:58:18 - mmengine - INFO - Epoch(train) [57][400/586] lr: 5.000000e-04 eta: 8:06:55 time: 0.333130 data_time: 0.023601 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.831072 loss: 0.000631 2022/09/09 20:58:34 - mmengine - INFO - Epoch(train) [57][450/586] lr: 5.000000e-04 eta: 8:06:40 time: 0.331434 data_time: 0.027249 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.877026 loss: 0.000626 2022/09/09 20:58:51 - mmengine - INFO - Epoch(train) [57][500/586] lr: 5.000000e-04 eta: 8:06:25 time: 0.335176 data_time: 0.023174 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.874167 loss: 0.000654 2022/09/09 20:59:08 - mmengine - INFO - Epoch(train) [57][550/586] lr: 5.000000e-04 eta: 8:06:09 time: 0.328760 data_time: 0.022350 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.858244 loss: 0.000616 2022/09/09 20:59:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 20:59:20 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/09/09 20:59:44 - mmengine - INFO - Epoch(train) [58][50/586] lr: 5.000000e-04 eta: 8:05:12 time: 0.340737 data_time: 0.032120 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.813543 loss: 0.000644 2022/09/09 21:00:01 - mmengine - INFO - Epoch(train) [58][100/586] lr: 5.000000e-04 eta: 8:04:58 time: 0.338717 data_time: 0.023752 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.778261 loss: 0.000617 2022/09/09 21:00:18 - mmengine - INFO - Epoch(train) [58][150/586] lr: 5.000000e-04 eta: 8:04:44 time: 0.345702 data_time: 0.023671 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.808853 loss: 0.000636 2022/09/09 21:00:35 - mmengine - INFO - Epoch(train) [58][200/586] lr: 5.000000e-04 eta: 8:04:30 time: 0.339878 data_time: 0.023093 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.814530 loss: 0.000630 2022/09/09 21:00:52 - mmengine - INFO - Epoch(train) [58][250/586] lr: 5.000000e-04 eta: 8:04:15 time: 0.335925 data_time: 0.026198 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.851430 loss: 0.000625 2022/09/09 21:01:08 - mmengine - INFO - Epoch(train) [58][300/586] lr: 5.000000e-04 eta: 8:04:00 time: 0.332531 data_time: 0.023563 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.829353 loss: 0.000649 2022/09/09 21:01:25 - mmengine - INFO - Epoch(train) [58][350/586] lr: 5.000000e-04 eta: 8:03:45 time: 0.332322 data_time: 0.023607 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.808866 loss: 0.000653 2022/09/09 21:01:42 - mmengine - INFO - Epoch(train) [58][400/586] lr: 5.000000e-04 eta: 8:03:30 time: 0.335815 data_time: 0.025571 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.818675 loss: 0.000635 2022/09/09 21:01:58 - mmengine - INFO - Epoch(train) [58][450/586] lr: 5.000000e-04 eta: 8:03:14 time: 0.327994 data_time: 0.024536 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.838412 loss: 0.000611 2022/09/09 21:02:15 - mmengine - INFO - Epoch(train) [58][500/586] lr: 5.000000e-04 eta: 8:02:59 time: 0.334094 data_time: 0.024124 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.818874 loss: 0.000640 2022/09/09 21:02:32 - mmengine - INFO - Epoch(train) [58][550/586] lr: 5.000000e-04 eta: 8:02:44 time: 0.336545 data_time: 0.023236 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.656055 loss: 0.000646 2022/09/09 21:02:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:02:44 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/09/09 21:02:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:03:08 - mmengine - INFO - Epoch(train) [59][50/586] lr: 5.000000e-04 eta: 8:01:49 time: 0.348611 data_time: 0.038569 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.812146 loss: 0.000637 2022/09/09 21:03:25 - mmengine - INFO - Epoch(train) [59][100/586] lr: 5.000000e-04 eta: 8:01:34 time: 0.337081 data_time: 0.024192 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.851562 loss: 0.000628 2022/09/09 21:03:42 - mmengine - INFO - Epoch(train) [59][150/586] lr: 5.000000e-04 eta: 8:01:18 time: 0.329677 data_time: 0.023518 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.821340 loss: 0.000625 2022/09/09 21:03:58 - mmengine - INFO - Epoch(train) [59][200/586] lr: 5.000000e-04 eta: 8:01:03 time: 0.333882 data_time: 0.022720 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.763931 loss: 0.000631 2022/09/09 21:04:15 - mmengine - INFO - Epoch(train) [59][250/586] lr: 5.000000e-04 eta: 8:00:49 time: 0.336189 data_time: 0.023806 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.820920 loss: 0.000649 2022/09/09 21:04:32 - mmengine - INFO - Epoch(train) [59][300/586] lr: 5.000000e-04 eta: 8:00:33 time: 0.331275 data_time: 0.026663 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.780679 loss: 0.000634 2022/09/09 21:04:48 - mmengine - INFO - Epoch(train) [59][350/586] lr: 5.000000e-04 eta: 8:00:18 time: 0.330463 data_time: 0.024300 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.788645 loss: 0.000633 2022/09/09 21:05:05 - mmengine - INFO - Epoch(train) [59][400/586] lr: 5.000000e-04 eta: 8:00:03 time: 0.339119 data_time: 0.023153 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.786174 loss: 0.000631 2022/09/09 21:05:22 - mmengine - INFO - Epoch(train) [59][450/586] lr: 5.000000e-04 eta: 7:59:48 time: 0.331649 data_time: 0.023986 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.843591 loss: 0.000632 2022/09/09 21:05:39 - mmengine - INFO - Epoch(train) [59][500/586] lr: 5.000000e-04 eta: 7:59:33 time: 0.336058 data_time: 0.023660 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.829587 loss: 0.000612 2022/09/09 21:05:55 - mmengine - INFO - Epoch(train) [59][550/586] lr: 5.000000e-04 eta: 7:59:18 time: 0.334631 data_time: 0.023402 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.848077 loss: 0.000632 2022/09/09 21:06:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:06:07 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/09/09 21:06:32 - mmengine - INFO - Epoch(train) [60][50/586] lr: 5.000000e-04 eta: 7:58:24 time: 0.351251 data_time: 0.028431 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.831363 loss: 0.000614 2022/09/09 21:06:49 - mmengine - INFO - Epoch(train) [60][100/586] lr: 5.000000e-04 eta: 7:58:09 time: 0.340263 data_time: 0.028386 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.821484 loss: 0.000627 2022/09/09 21:07:05 - mmengine - INFO - Epoch(train) [60][150/586] lr: 5.000000e-04 eta: 7:57:54 time: 0.330301 data_time: 0.024171 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.823826 loss: 0.000657 2022/09/09 21:07:22 - mmengine - INFO - Epoch(train) [60][200/586] lr: 5.000000e-04 eta: 7:57:40 time: 0.339291 data_time: 0.024860 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.831927 loss: 0.000638 2022/09/09 21:07:39 - mmengine - INFO - Epoch(train) [60][250/586] lr: 5.000000e-04 eta: 7:57:25 time: 0.337417 data_time: 0.023947 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.777373 loss: 0.000646 2022/09/09 21:07:56 - mmengine - INFO - Epoch(train) [60][300/586] lr: 5.000000e-04 eta: 7:57:10 time: 0.331235 data_time: 0.027563 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.823957 loss: 0.000620 2022/09/09 21:08:12 - mmengine - INFO - Epoch(train) [60][350/586] lr: 5.000000e-04 eta: 7:56:54 time: 0.332080 data_time: 0.023771 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.780079 loss: 0.000637 2022/09/09 21:08:29 - mmengine - INFO - Epoch(train) [60][400/586] lr: 5.000000e-04 eta: 7:56:40 time: 0.339637 data_time: 0.023449 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.844412 loss: 0.000624 2022/09/09 21:08:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:08:46 - mmengine - INFO - Epoch(train) [60][450/586] lr: 5.000000e-04 eta: 7:56:25 time: 0.334849 data_time: 0.025017 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.778618 loss: 0.000615 2022/09/09 21:09:03 - mmengine - INFO - Epoch(train) [60][500/586] lr: 5.000000e-04 eta: 7:56:10 time: 0.332321 data_time: 0.023115 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.773724 loss: 0.000630 2022/09/09 21:09:19 - mmengine - INFO - Epoch(train) [60][550/586] lr: 5.000000e-04 eta: 7:55:55 time: 0.333084 data_time: 0.022609 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.804982 loss: 0.000636 2022/09/09 21:09:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:09:31 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/09/09 21:09:47 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:01:01 time: 0.172831 data_time: 0.012049 memory: 7489 2022/09/09 21:09:55 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:50 time: 0.163502 data_time: 0.007357 memory: 1657 2022/09/09 21:10:03 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:42 time: 0.165314 data_time: 0.008018 memory: 1657 2022/09/09 21:10:11 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:33 time: 0.164217 data_time: 0.007745 memory: 1657 2022/09/09 21:10:19 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:25 time: 0.163525 data_time: 0.007122 memory: 1657 2022/09/09 21:10:28 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:17 time: 0.164300 data_time: 0.007306 memory: 1657 2022/09/09 21:10:36 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:09 time: 0.164578 data_time: 0.007611 memory: 1657 2022/09/09 21:10:44 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:01 time: 0.163298 data_time: 0.007222 memory: 1657 2022/09/09 21:11:20 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 21:11:34 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.729470 coco/AP .5: 0.894193 coco/AP .75: 0.804131 coco/AP (M): 0.693518 coco/AP (L): 0.796314 coco/AR: 0.783249 coco/AR .5: 0.931832 coco/AR .75: 0.850126 coco/AR (M): 0.741300 coco/AR (L): 0.844259 2022/09/09 21:11:34 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_50.pth is removed 2022/09/09 21:11:38 - mmengine - INFO - The best checkpoint with 0.7295 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/09/09 21:11:55 - mmengine - INFO - Epoch(train) [61][50/586] lr: 5.000000e-04 eta: 7:55:00 time: 0.344038 data_time: 0.027840 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.844232 loss: 0.000621 2022/09/09 21:12:12 - mmengine - INFO - Epoch(train) [61][100/586] lr: 5.000000e-04 eta: 7:54:44 time: 0.327982 data_time: 0.027739 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.771517 loss: 0.000636 2022/09/09 21:12:28 - mmengine - INFO - Epoch(train) [61][150/586] lr: 5.000000e-04 eta: 7:54:29 time: 0.332102 data_time: 0.022964 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.848384 loss: 0.000620 2022/09/09 21:12:45 - mmengine - INFO - Epoch(train) [61][200/586] lr: 5.000000e-04 eta: 7:54:14 time: 0.338833 data_time: 0.023917 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.816222 loss: 0.000632 2022/09/09 21:13:02 - mmengine - INFO - Epoch(train) [61][250/586] lr: 5.000000e-04 eta: 7:53:58 time: 0.326513 data_time: 0.028620 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.825650 loss: 0.000641 2022/09/09 21:13:18 - mmengine - INFO - Epoch(train) [61][300/586] lr: 5.000000e-04 eta: 7:53:43 time: 0.334641 data_time: 0.023075 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.886405 loss: 0.000651 2022/09/09 21:13:35 - mmengine - INFO - Epoch(train) [61][350/586] lr: 5.000000e-04 eta: 7:53:29 time: 0.336154 data_time: 0.023676 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.783596 loss: 0.000627 2022/09/09 21:13:52 - mmengine - INFO - Epoch(train) [61][400/586] lr: 5.000000e-04 eta: 7:53:13 time: 0.328432 data_time: 0.022556 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.848406 loss: 0.000672 2022/09/09 21:14:08 - mmengine - INFO - Epoch(train) [61][450/586] lr: 5.000000e-04 eta: 7:52:58 time: 0.333434 data_time: 0.023065 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.853162 loss: 0.000628 2022/09/09 21:14:25 - mmengine - INFO - Epoch(train) [61][500/586] lr: 5.000000e-04 eta: 7:52:43 time: 0.333024 data_time: 0.022544 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.823751 loss: 0.000646 2022/09/09 21:14:41 - mmengine - INFO - Epoch(train) [61][550/586] lr: 5.000000e-04 eta: 7:52:27 time: 0.331764 data_time: 0.024416 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.836567 loss: 0.000665 2022/09/09 21:14:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:14:53 - mmengine - INFO - Saving checkpoint at 61 epochs 2022/09/09 21:15:17 - mmengine - INFO - Epoch(train) [62][50/586] lr: 5.000000e-04 eta: 7:51:33 time: 0.344249 data_time: 0.032480 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.807064 loss: 0.000620 2022/09/09 21:15:34 - mmengine - INFO - Epoch(train) [62][100/586] lr: 5.000000e-04 eta: 7:51:18 time: 0.330504 data_time: 0.025186 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.821565 loss: 0.000633 2022/09/09 21:15:51 - mmengine - INFO - Epoch(train) [62][150/586] lr: 5.000000e-04 eta: 7:51:03 time: 0.333172 data_time: 0.023868 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.812594 loss: 0.000625 2022/09/09 21:16:07 - mmengine - INFO - Epoch(train) [62][200/586] lr: 5.000000e-04 eta: 7:50:48 time: 0.335252 data_time: 0.027991 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.834342 loss: 0.000643 2022/09/09 21:16:24 - mmengine - INFO - Epoch(train) [62][250/586] lr: 5.000000e-04 eta: 7:50:32 time: 0.331405 data_time: 0.029055 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.856729 loss: 0.000661 2022/09/09 21:16:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:16:41 - mmengine - INFO - Epoch(train) [62][300/586] lr: 5.000000e-04 eta: 7:50:18 time: 0.339377 data_time: 0.027352 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.840172 loss: 0.000630 2022/09/09 21:16:58 - mmengine - INFO - Epoch(train) [62][350/586] lr: 5.000000e-04 eta: 7:50:03 time: 0.330137 data_time: 0.023251 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.847135 loss: 0.000642 2022/09/09 21:17:14 - mmengine - INFO - Epoch(train) [62][400/586] lr: 5.000000e-04 eta: 7:49:47 time: 0.329889 data_time: 0.023638 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.878734 loss: 0.000646 2022/09/09 21:17:31 - mmengine - INFO - Epoch(train) [62][450/586] lr: 5.000000e-04 eta: 7:49:32 time: 0.337014 data_time: 0.024983 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.838261 loss: 0.000621 2022/09/09 21:17:47 - mmengine - INFO - Epoch(train) [62][500/586] lr: 5.000000e-04 eta: 7:49:17 time: 0.332481 data_time: 0.022781 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.845481 loss: 0.000642 2022/09/09 21:18:04 - mmengine - INFO - Epoch(train) [62][550/586] lr: 5.000000e-04 eta: 7:49:02 time: 0.337547 data_time: 0.025736 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.714452 loss: 0.000636 2022/09/09 21:18:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:18:16 - mmengine - INFO - Saving checkpoint at 62 epochs 2022/09/09 21:18:40 - mmengine - INFO - Epoch(train) [63][50/586] lr: 5.000000e-04 eta: 7:48:09 time: 0.340408 data_time: 0.027660 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.824322 loss: 0.000624 2022/09/09 21:18:57 - mmengine - INFO - Epoch(train) [63][100/586] lr: 5.000000e-04 eta: 7:47:54 time: 0.336915 data_time: 0.026879 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.805474 loss: 0.000628 2022/09/09 21:19:14 - mmengine - INFO - Epoch(train) [63][150/586] lr: 5.000000e-04 eta: 7:47:39 time: 0.331168 data_time: 0.022722 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.828507 loss: 0.000617 2022/09/09 21:19:31 - mmengine - INFO - Epoch(train) [63][200/586] lr: 5.000000e-04 eta: 7:47:24 time: 0.337466 data_time: 0.024231 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.855627 loss: 0.000632 2022/09/09 21:19:47 - mmengine - INFO - Epoch(train) [63][250/586] lr: 5.000000e-04 eta: 7:47:09 time: 0.334902 data_time: 0.023537 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.852798 loss: 0.000616 2022/09/09 21:20:04 - mmengine - INFO - Epoch(train) [63][300/586] lr: 5.000000e-04 eta: 7:46:54 time: 0.336808 data_time: 0.026015 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.851818 loss: 0.000624 2022/09/09 21:20:21 - mmengine - INFO - Epoch(train) [63][350/586] lr: 5.000000e-04 eta: 7:46:39 time: 0.330920 data_time: 0.023085 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.841832 loss: 0.000608 2022/09/09 21:20:38 - mmengine - INFO - Epoch(train) [63][400/586] lr: 5.000000e-04 eta: 7:46:24 time: 0.338326 data_time: 0.026223 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.863352 loss: 0.000644 2022/09/09 21:20:54 - mmengine - INFO - Epoch(train) [63][450/586] lr: 5.000000e-04 eta: 7:46:09 time: 0.329660 data_time: 0.024534 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.838149 loss: 0.000648 2022/09/09 21:21:11 - mmengine - INFO - Epoch(train) [63][500/586] lr: 5.000000e-04 eta: 7:45:54 time: 0.332110 data_time: 0.024854 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.795377 loss: 0.000642 2022/09/09 21:21:27 - mmengine - INFO - Epoch(train) [63][550/586] lr: 5.000000e-04 eta: 7:45:38 time: 0.330839 data_time: 0.023742 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.749046 loss: 0.000628 2022/09/09 21:21:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:21:39 - mmengine - INFO - Saving checkpoint at 63 epochs 2022/09/09 21:22:04 - mmengine - INFO - Epoch(train) [64][50/586] lr: 5.000000e-04 eta: 7:44:45 time: 0.342148 data_time: 0.033658 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.829728 loss: 0.000636 2022/09/09 21:22:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:22:20 - mmengine - INFO - Epoch(train) [64][100/586] lr: 5.000000e-04 eta: 7:44:30 time: 0.332692 data_time: 0.025362 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.850648 loss: 0.000621 2022/09/09 21:22:37 - mmengine - INFO - Epoch(train) [64][150/586] lr: 5.000000e-04 eta: 7:44:15 time: 0.332757 data_time: 0.023887 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.849941 loss: 0.000630 2022/09/09 21:22:54 - mmengine - INFO - Epoch(train) [64][200/586] lr: 5.000000e-04 eta: 7:44:00 time: 0.334864 data_time: 0.023130 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.833870 loss: 0.000624 2022/09/09 21:23:10 - mmengine - INFO - Epoch(train) [64][250/586] lr: 5.000000e-04 eta: 7:43:44 time: 0.330428 data_time: 0.023194 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.778032 loss: 0.000642 2022/09/09 21:23:27 - mmengine - INFO - Epoch(train) [64][300/586] lr: 5.000000e-04 eta: 7:43:30 time: 0.338982 data_time: 0.023614 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.818411 loss: 0.000627 2022/09/09 21:23:44 - mmengine - INFO - Epoch(train) [64][350/586] lr: 5.000000e-04 eta: 7:43:15 time: 0.333391 data_time: 0.023727 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.820348 loss: 0.000623 2022/09/09 21:24:01 - mmengine - INFO - Epoch(train) [64][400/586] lr: 5.000000e-04 eta: 7:43:00 time: 0.332862 data_time: 0.023315 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.834828 loss: 0.000629 2022/09/09 21:24:17 - mmengine - INFO - Epoch(train) [64][450/586] lr: 5.000000e-04 eta: 7:42:45 time: 0.339100 data_time: 0.026836 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.803646 loss: 0.000624 2022/09/09 21:24:34 - mmengine - INFO - Epoch(train) [64][500/586] lr: 5.000000e-04 eta: 7:42:29 time: 0.326954 data_time: 0.023516 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.860705 loss: 0.000611 2022/09/09 21:24:51 - mmengine - INFO - Epoch(train) [64][550/586] lr: 5.000000e-04 eta: 7:42:14 time: 0.334733 data_time: 0.023730 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.837291 loss: 0.000620 2022/09/09 21:25:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:25:03 - mmengine - INFO - Saving checkpoint at 64 epochs 2022/09/09 21:25:27 - mmengine - INFO - Epoch(train) [65][50/586] lr: 5.000000e-04 eta: 7:41:22 time: 0.341192 data_time: 0.029670 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.790002 loss: 0.000632 2022/09/09 21:25:44 - mmengine - INFO - Epoch(train) [65][100/586] lr: 5.000000e-04 eta: 7:41:07 time: 0.337823 data_time: 0.024399 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.795700 loss: 0.000615 2022/09/09 21:26:01 - mmengine - INFO - Epoch(train) [65][150/586] lr: 5.000000e-04 eta: 7:40:53 time: 0.339846 data_time: 0.023048 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.815202 loss: 0.000622 2022/09/09 21:26:17 - mmengine - INFO - Epoch(train) [65][200/586] lr: 5.000000e-04 eta: 7:40:37 time: 0.330131 data_time: 0.022882 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.824634 loss: 0.000615 2022/09/09 21:26:34 - mmengine - INFO - Epoch(train) [65][250/586] lr: 5.000000e-04 eta: 7:40:23 time: 0.338186 data_time: 0.023317 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.809216 loss: 0.000635 2022/09/09 21:26:51 - mmengine - INFO - Epoch(train) [65][300/586] lr: 5.000000e-04 eta: 7:40:08 time: 0.339325 data_time: 0.023175 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.810842 loss: 0.000631 2022/09/09 21:27:08 - mmengine - INFO - Epoch(train) [65][350/586] lr: 5.000000e-04 eta: 7:39:53 time: 0.326498 data_time: 0.023249 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.745846 loss: 0.000638 2022/09/09 21:27:25 - mmengine - INFO - Epoch(train) [65][400/586] lr: 5.000000e-04 eta: 7:39:38 time: 0.339927 data_time: 0.030304 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.881380 loss: 0.000627 2022/09/09 21:27:41 - mmengine - INFO - Epoch(train) [65][450/586] lr: 5.000000e-04 eta: 7:39:23 time: 0.331669 data_time: 0.024145 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.768478 loss: 0.000631 2022/09/09 21:27:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:27:58 - mmengine - INFO - Epoch(train) [65][500/586] lr: 5.000000e-04 eta: 7:39:08 time: 0.332267 data_time: 0.024318 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.862299 loss: 0.000631 2022/09/09 21:28:15 - mmengine - INFO - Epoch(train) [65][550/586] lr: 5.000000e-04 eta: 7:38:53 time: 0.335240 data_time: 0.024410 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.836851 loss: 0.000625 2022/09/09 21:28:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:28:27 - mmengine - INFO - Saving checkpoint at 65 epochs 2022/09/09 21:28:50 - mmengine - INFO - Epoch(train) [66][50/586] lr: 5.000000e-04 eta: 7:38:01 time: 0.339062 data_time: 0.030196 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.820429 loss: 0.000625 2022/09/09 21:29:07 - mmengine - INFO - Epoch(train) [66][100/586] lr: 5.000000e-04 eta: 7:37:45 time: 0.329621 data_time: 0.024489 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.759199 loss: 0.000631 2022/09/09 21:29:23 - mmengine - INFO - Epoch(train) [66][150/586] lr: 5.000000e-04 eta: 7:37:29 time: 0.326943 data_time: 0.024609 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.789111 loss: 0.000639 2022/09/09 21:29:40 - mmengine - INFO - Epoch(train) [66][200/586] lr: 5.000000e-04 eta: 7:37:15 time: 0.337628 data_time: 0.023817 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.823501 loss: 0.000627 2022/09/09 21:29:57 - mmengine - INFO - Epoch(train) [66][250/586] lr: 5.000000e-04 eta: 7:36:59 time: 0.333651 data_time: 0.023954 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.850032 loss: 0.000631 2022/09/09 21:30:14 - mmengine - INFO - Epoch(train) [66][300/586] lr: 5.000000e-04 eta: 7:36:44 time: 0.334390 data_time: 0.029625 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.823124 loss: 0.000618 2022/09/09 21:30:31 - mmengine - INFO - Epoch(train) [66][350/586] lr: 5.000000e-04 eta: 7:36:30 time: 0.339043 data_time: 0.027802 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.849478 loss: 0.000647 2022/09/09 21:30:47 - mmengine - INFO - Epoch(train) [66][400/586] lr: 5.000000e-04 eta: 7:36:15 time: 0.337686 data_time: 0.023971 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.794108 loss: 0.000627 2022/09/09 21:31:04 - mmengine - INFO - Epoch(train) [66][450/586] lr: 5.000000e-04 eta: 7:36:01 time: 0.339856 data_time: 0.023797 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.838063 loss: 0.000622 2022/09/09 21:31:21 - mmengine - INFO - Epoch(train) [66][500/586] lr: 5.000000e-04 eta: 7:35:46 time: 0.332239 data_time: 0.023385 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.807172 loss: 0.000619 2022/09/09 21:31:38 - mmengine - INFO - Epoch(train) [66][550/586] lr: 5.000000e-04 eta: 7:35:31 time: 0.333442 data_time: 0.024897 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.909627 loss: 0.000611 2022/09/09 21:31:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:31:50 - mmengine - INFO - Saving checkpoint at 66 epochs 2022/09/09 21:32:14 - mmengine - INFO - Epoch(train) [67][50/586] lr: 5.000000e-04 eta: 7:34:39 time: 0.337616 data_time: 0.033519 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.783324 loss: 0.000644 2022/09/09 21:32:31 - mmengine - INFO - Epoch(train) [67][100/586] lr: 5.000000e-04 eta: 7:34:24 time: 0.338240 data_time: 0.024278 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.888568 loss: 0.000630 2022/09/09 21:32:47 - mmengine - INFO - Epoch(train) [67][150/586] lr: 5.000000e-04 eta: 7:34:10 time: 0.336602 data_time: 0.023392 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.765031 loss: 0.000648 2022/09/09 21:33:04 - mmengine - INFO - Epoch(train) [67][200/586] lr: 5.000000e-04 eta: 7:33:54 time: 0.331076 data_time: 0.023429 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.813064 loss: 0.000615 2022/09/09 21:33:21 - mmengine - INFO - Epoch(train) [67][250/586] lr: 5.000000e-04 eta: 7:33:39 time: 0.333795 data_time: 0.024030 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.811304 loss: 0.000617 2022/09/09 21:33:37 - mmengine - INFO - Epoch(train) [67][300/586] lr: 5.000000e-04 eta: 7:33:24 time: 0.330413 data_time: 0.023470 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.843899 loss: 0.000604 2022/09/09 21:33:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:33:54 - mmengine - INFO - Epoch(train) [67][350/586] lr: 5.000000e-04 eta: 7:33:09 time: 0.335911 data_time: 0.023684 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.832746 loss: 0.000625 2022/09/09 21:34:11 - mmengine - INFO - Epoch(train) [67][400/586] lr: 5.000000e-04 eta: 7:32:54 time: 0.331553 data_time: 0.024328 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.795269 loss: 0.000630 2022/09/09 21:34:27 - mmengine - INFO - Epoch(train) [67][450/586] lr: 5.000000e-04 eta: 7:32:38 time: 0.329903 data_time: 0.023759 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.806957 loss: 0.000627 2022/09/09 21:34:44 - mmengine - INFO - Epoch(train) [67][500/586] lr: 5.000000e-04 eta: 7:32:23 time: 0.334671 data_time: 0.022980 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.827138 loss: 0.000620 2022/09/09 21:35:00 - mmengine - INFO - Epoch(train) [67][550/586] lr: 5.000000e-04 eta: 7:32:08 time: 0.333092 data_time: 0.022354 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.824165 loss: 0.000614 2022/09/09 21:35:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:35:12 - mmengine - INFO - Saving checkpoint at 67 epochs 2022/09/09 21:35:36 - mmengine - INFO - Epoch(train) [68][50/586] lr: 5.000000e-04 eta: 7:31:17 time: 0.342615 data_time: 0.035324 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.753861 loss: 0.000637 2022/09/09 21:35:53 - mmengine - INFO - Epoch(train) [68][100/586] lr: 5.000000e-04 eta: 7:31:02 time: 0.329858 data_time: 0.023411 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.846031 loss: 0.000627 2022/09/09 21:36:10 - mmengine - INFO - Epoch(train) [68][150/586] lr: 5.000000e-04 eta: 7:30:47 time: 0.333675 data_time: 0.023344 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.794196 loss: 0.000599 2022/09/09 21:36:26 - mmengine - INFO - Epoch(train) [68][200/586] lr: 5.000000e-04 eta: 7:30:32 time: 0.334715 data_time: 0.024427 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.815299 loss: 0.000640 2022/09/09 21:36:43 - mmengine - INFO - Epoch(train) [68][250/586] lr: 5.000000e-04 eta: 7:30:17 time: 0.334398 data_time: 0.025284 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.865191 loss: 0.000630 2022/09/09 21:37:00 - mmengine - INFO - Epoch(train) [68][300/586] lr: 5.000000e-04 eta: 7:30:02 time: 0.333221 data_time: 0.029187 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.796925 loss: 0.000632 2022/09/09 21:37:17 - mmengine - INFO - Epoch(train) [68][350/586] lr: 5.000000e-04 eta: 7:29:47 time: 0.337216 data_time: 0.022554 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.751043 loss: 0.000635 2022/09/09 21:37:33 - mmengine - INFO - Epoch(train) [68][400/586] lr: 5.000000e-04 eta: 7:29:31 time: 0.328318 data_time: 0.022628 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.854974 loss: 0.000620 2022/09/09 21:37:50 - mmengine - INFO - Epoch(train) [68][450/586] lr: 5.000000e-04 eta: 7:29:17 time: 0.337868 data_time: 0.022975 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.867272 loss: 0.000618 2022/09/09 21:38:07 - mmengine - INFO - Epoch(train) [68][500/586] lr: 5.000000e-04 eta: 7:29:01 time: 0.331701 data_time: 0.023404 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.766098 loss: 0.000629 2022/09/09 21:38:23 - mmengine - INFO - Epoch(train) [68][550/586] lr: 5.000000e-04 eta: 7:28:46 time: 0.332916 data_time: 0.023348 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.857313 loss: 0.000640 2022/09/09 21:38:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:38:35 - mmengine - INFO - Saving checkpoint at 68 epochs 2022/09/09 21:38:59 - mmengine - INFO - Epoch(train) [69][50/586] lr: 5.000000e-04 eta: 7:27:56 time: 0.340816 data_time: 0.029374 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.855723 loss: 0.000618 2022/09/09 21:39:16 - mmengine - INFO - Epoch(train) [69][100/586] lr: 5.000000e-04 eta: 7:27:41 time: 0.332047 data_time: 0.026388 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.802715 loss: 0.000635 2022/09/09 21:39:33 - mmengine - INFO - Epoch(train) [69][150/586] lr: 5.000000e-04 eta: 7:27:26 time: 0.335431 data_time: 0.023844 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.762441 loss: 0.000637 2022/09/09 21:39:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:39:49 - mmengine - INFO - Epoch(train) [69][200/586] lr: 5.000000e-04 eta: 7:27:11 time: 0.332634 data_time: 0.023436 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.847899 loss: 0.000628 2022/09/09 21:40:06 - mmengine - INFO - Epoch(train) [69][250/586] lr: 5.000000e-04 eta: 7:26:55 time: 0.334078 data_time: 0.023363 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.782463 loss: 0.000632 2022/09/09 21:40:23 - mmengine - INFO - Epoch(train) [69][300/586] lr: 5.000000e-04 eta: 7:26:41 time: 0.336466 data_time: 0.024060 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.866977 loss: 0.000619 2022/09/09 21:40:39 - mmengine - INFO - Epoch(train) [69][350/586] lr: 5.000000e-04 eta: 7:26:25 time: 0.331506 data_time: 0.023874 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.814144 loss: 0.000613 2022/09/09 21:40:56 - mmengine - INFO - Epoch(train) [69][400/586] lr: 5.000000e-04 eta: 7:26:10 time: 0.331153 data_time: 0.023658 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.848517 loss: 0.000623 2022/09/09 21:41:13 - mmengine - INFO - Epoch(train) [69][450/586] lr: 5.000000e-04 eta: 7:25:55 time: 0.334376 data_time: 0.022186 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.866978 loss: 0.000626 2022/09/09 21:41:29 - mmengine - INFO - Epoch(train) [69][500/586] lr: 5.000000e-04 eta: 7:25:39 time: 0.329544 data_time: 0.025786 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.814595 loss: 0.000622 2022/09/09 21:41:46 - mmengine - INFO - Epoch(train) [69][550/586] lr: 5.000000e-04 eta: 7:25:24 time: 0.329671 data_time: 0.025102 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.822769 loss: 0.000640 2022/09/09 21:41:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:41:58 - mmengine - INFO - Saving checkpoint at 69 epochs 2022/09/09 21:42:21 - mmengine - INFO - Epoch(train) [70][50/586] lr: 5.000000e-04 eta: 7:24:33 time: 0.331114 data_time: 0.031945 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.746152 loss: 0.000628 2022/09/09 21:42:38 - mmengine - INFO - Epoch(train) [70][100/586] lr: 5.000000e-04 eta: 7:24:18 time: 0.334032 data_time: 0.023897 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.860744 loss: 0.000614 2022/09/09 21:42:55 - mmengine - INFO - Epoch(train) [70][150/586] lr: 5.000000e-04 eta: 7:24:03 time: 0.335659 data_time: 0.023440 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.857968 loss: 0.000631 2022/09/09 21:43:12 - mmengine - INFO - Epoch(train) [70][200/586] lr: 5.000000e-04 eta: 7:23:48 time: 0.334640 data_time: 0.022885 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.845288 loss: 0.000618 2022/09/09 21:43:28 - mmengine - INFO - Epoch(train) [70][250/586] lr: 5.000000e-04 eta: 7:23:33 time: 0.329417 data_time: 0.027172 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.849373 loss: 0.000621 2022/09/09 21:43:45 - mmengine - INFO - Epoch(train) [70][300/586] lr: 5.000000e-04 eta: 7:23:18 time: 0.337428 data_time: 0.022720 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.859713 loss: 0.000610 2022/09/09 21:44:02 - mmengine - INFO - Epoch(train) [70][350/586] lr: 5.000000e-04 eta: 7:23:03 time: 0.333604 data_time: 0.023080 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.830102 loss: 0.000621 2022/09/09 21:44:18 - mmengine - INFO - Epoch(train) [70][400/586] lr: 5.000000e-04 eta: 7:22:47 time: 0.326351 data_time: 0.025499 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.828709 loss: 0.000622 2022/09/09 21:44:35 - mmengine - INFO - Epoch(train) [70][450/586] lr: 5.000000e-04 eta: 7:22:33 time: 0.342160 data_time: 0.024331 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.797806 loss: 0.000623 2022/09/09 21:44:52 - mmengine - INFO - Epoch(train) [70][500/586] lr: 5.000000e-04 eta: 7:22:18 time: 0.330245 data_time: 0.022560 memory: 7489 loss_kpt: 0.000645 acc_pose: 0.855612 loss: 0.000645 2022/09/09 21:45:08 - mmengine - INFO - Epoch(train) [70][550/586] lr: 5.000000e-04 eta: 7:22:02 time: 0.330667 data_time: 0.023413 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.867909 loss: 0.000639 2022/09/09 21:45:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:45:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:45:20 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/09/09 21:45:37 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:01:00 time: 0.170478 data_time: 0.012286 memory: 7489 2022/09/09 21:45:46 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:50 time: 0.164147 data_time: 0.007528 memory: 1657 2022/09/09 21:45:54 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:42 time: 0.164805 data_time: 0.007591 memory: 1657 2022/09/09 21:46:02 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:35 time: 0.169667 data_time: 0.007679 memory: 1657 2022/09/09 21:46:11 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:26 time: 0.167841 data_time: 0.011789 memory: 1657 2022/09/09 21:46:19 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:17 time: 0.164016 data_time: 0.007413 memory: 1657 2022/09/09 21:46:27 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:09 time: 0.163127 data_time: 0.007171 memory: 1657 2022/09/09 21:46:35 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:01 time: 0.160700 data_time: 0.006517 memory: 1657 2022/09/09 21:47:11 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 21:47:25 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.735394 coco/AP .5: 0.898534 coco/AP .75: 0.809552 coco/AP (M): 0.700711 coco/AP (L): 0.799827 coco/AR: 0.788256 coco/AR .5: 0.935139 coco/AR .75: 0.854691 coco/AR (M): 0.747637 coco/AR (L): 0.847529 2022/09/09 21:47:25 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_60.pth is removed 2022/09/09 21:47:29 - mmengine - INFO - The best checkpoint with 0.7354 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/09/09 21:47:46 - mmengine - INFO - Epoch(train) [71][50/586] lr: 5.000000e-04 eta: 7:21:13 time: 0.342483 data_time: 0.034521 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.854816 loss: 0.000613 2022/09/09 21:48:03 - mmengine - INFO - Epoch(train) [71][100/586] lr: 5.000000e-04 eta: 7:20:58 time: 0.338718 data_time: 0.026631 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.797061 loss: 0.000622 2022/09/09 21:48:19 - mmengine - INFO - Epoch(train) [71][150/586] lr: 5.000000e-04 eta: 7:20:43 time: 0.330855 data_time: 0.024137 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.816223 loss: 0.000651 2022/09/09 21:48:36 - mmengine - INFO - Epoch(train) [71][200/586] lr: 5.000000e-04 eta: 7:20:28 time: 0.331223 data_time: 0.023224 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.827124 loss: 0.000607 2022/09/09 21:48:53 - mmengine - INFO - Epoch(train) [71][250/586] lr: 5.000000e-04 eta: 7:20:14 time: 0.342572 data_time: 0.023485 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.799687 loss: 0.000640 2022/09/09 21:49:10 - mmengine - INFO - Epoch(train) [71][300/586] lr: 5.000000e-04 eta: 7:19:58 time: 0.332129 data_time: 0.022863 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.800741 loss: 0.000644 2022/09/09 21:49:27 - mmengine - INFO - Epoch(train) [71][350/586] lr: 5.000000e-04 eta: 7:19:44 time: 0.342445 data_time: 0.023011 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.864851 loss: 0.000647 2022/09/09 21:49:44 - mmengine - INFO - Epoch(train) [71][400/586] lr: 5.000000e-04 eta: 7:19:29 time: 0.337438 data_time: 0.024497 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.869033 loss: 0.000614 2022/09/09 21:50:01 - mmengine - INFO - Epoch(train) [71][450/586] lr: 5.000000e-04 eta: 7:19:14 time: 0.334817 data_time: 0.023296 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.831085 loss: 0.000613 2022/09/09 21:50:17 - mmengine - INFO - Epoch(train) [71][500/586] lr: 5.000000e-04 eta: 7:18:59 time: 0.335056 data_time: 0.022568 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.829460 loss: 0.000637 2022/09/09 21:50:35 - mmengine - INFO - Epoch(train) [71][550/586] lr: 5.000000e-04 eta: 7:18:46 time: 0.346232 data_time: 0.028963 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.817685 loss: 0.000612 2022/09/09 21:50:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:50:46 - mmengine - INFO - Saving checkpoint at 71 epochs 2022/09/09 21:51:10 - mmengine - INFO - Epoch(train) [72][50/586] lr: 5.000000e-04 eta: 7:17:57 time: 0.345874 data_time: 0.031337 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.836342 loss: 0.000621 2022/09/09 21:51:27 - mmengine - INFO - Epoch(train) [72][100/586] lr: 5.000000e-04 eta: 7:17:42 time: 0.333534 data_time: 0.027809 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.885221 loss: 0.000621 2022/09/09 21:51:44 - mmengine - INFO - Epoch(train) [72][150/586] lr: 5.000000e-04 eta: 7:17:27 time: 0.335017 data_time: 0.022678 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.832398 loss: 0.000620 2022/09/09 21:52:00 - mmengine - INFO - Epoch(train) [72][200/586] lr: 5.000000e-04 eta: 7:17:12 time: 0.333467 data_time: 0.023826 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.845199 loss: 0.000613 2022/09/09 21:52:17 - mmengine - INFO - Epoch(train) [72][250/586] lr: 5.000000e-04 eta: 7:16:57 time: 0.334516 data_time: 0.023165 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.756129 loss: 0.000630 2022/09/09 21:52:33 - mmengine - INFO - Epoch(train) [72][300/586] lr: 5.000000e-04 eta: 7:16:42 time: 0.329252 data_time: 0.027794 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.863874 loss: 0.000629 2022/09/09 21:52:50 - mmengine - INFO - Epoch(train) [72][350/586] lr: 5.000000e-04 eta: 7:16:26 time: 0.327400 data_time: 0.023103 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.745149 loss: 0.000625 2022/09/09 21:53:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:53:06 - mmengine - INFO - Epoch(train) [72][400/586] lr: 5.000000e-04 eta: 7:16:11 time: 0.332214 data_time: 0.025625 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.862998 loss: 0.000617 2022/09/09 21:53:23 - mmengine - INFO - Epoch(train) [72][450/586] lr: 5.000000e-04 eta: 7:15:55 time: 0.330671 data_time: 0.022487 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.853932 loss: 0.000596 2022/09/09 21:53:40 - mmengine - INFO - Epoch(train) [72][500/586] lr: 5.000000e-04 eta: 7:15:40 time: 0.333968 data_time: 0.022832 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.830609 loss: 0.000626 2022/09/09 21:53:56 - mmengine - INFO - Epoch(train) [72][550/586] lr: 5.000000e-04 eta: 7:15:25 time: 0.334955 data_time: 0.025458 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.856437 loss: 0.000642 2022/09/09 21:54:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:54:08 - mmengine - INFO - Saving checkpoint at 72 epochs 2022/09/09 21:54:32 - mmengine - INFO - Epoch(train) [73][50/586] lr: 5.000000e-04 eta: 7:14:37 time: 0.341796 data_time: 0.030758 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.762481 loss: 0.000628 2022/09/09 21:54:49 - mmengine - INFO - Epoch(train) [73][100/586] lr: 5.000000e-04 eta: 7:14:23 time: 0.341316 data_time: 0.023549 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.881524 loss: 0.000603 2022/09/09 21:55:06 - mmengine - INFO - Epoch(train) [73][150/586] lr: 5.000000e-04 eta: 7:14:07 time: 0.330168 data_time: 0.023603 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.853565 loss: 0.000604 2022/09/09 21:55:23 - mmengine - INFO - Epoch(train) [73][200/586] lr: 5.000000e-04 eta: 7:13:52 time: 0.333012 data_time: 0.024823 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.859174 loss: 0.000627 2022/09/09 21:55:39 - mmengine - INFO - Epoch(train) [73][250/586] lr: 5.000000e-04 eta: 7:13:37 time: 0.333985 data_time: 0.028639 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.855204 loss: 0.000623 2022/09/09 21:55:56 - mmengine - INFO - Epoch(train) [73][300/586] lr: 5.000000e-04 eta: 7:13:21 time: 0.327532 data_time: 0.023330 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.871058 loss: 0.000606 2022/09/09 21:56:12 - mmengine - INFO - Epoch(train) [73][350/586] lr: 5.000000e-04 eta: 7:13:06 time: 0.329732 data_time: 0.023665 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.750699 loss: 0.000629 2022/09/09 21:56:29 - mmengine - INFO - Epoch(train) [73][400/586] lr: 5.000000e-04 eta: 7:12:51 time: 0.339929 data_time: 0.024975 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.817195 loss: 0.000609 2022/09/09 21:56:46 - mmengine - INFO - Epoch(train) [73][450/586] lr: 5.000000e-04 eta: 7:12:36 time: 0.326629 data_time: 0.022906 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.906352 loss: 0.000618 2022/09/09 21:57:02 - mmengine - INFO - Epoch(train) [73][500/586] lr: 5.000000e-04 eta: 7:12:20 time: 0.332791 data_time: 0.022394 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.892904 loss: 0.000601 2022/09/09 21:57:19 - mmengine - INFO - Epoch(train) [73][550/586] lr: 5.000000e-04 eta: 7:12:05 time: 0.334180 data_time: 0.023316 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.815756 loss: 0.000621 2022/09/09 21:57:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:57:31 - mmengine - INFO - Saving checkpoint at 73 epochs 2022/09/09 21:57:56 - mmengine - INFO - Epoch(train) [74][50/586] lr: 5.000000e-04 eta: 7:11:18 time: 0.349171 data_time: 0.039418 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.823591 loss: 0.000623 2022/09/09 21:58:12 - mmengine - INFO - Epoch(train) [74][100/586] lr: 5.000000e-04 eta: 7:11:03 time: 0.333733 data_time: 0.027309 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.841109 loss: 0.000636 2022/09/09 21:58:29 - mmengine - INFO - Epoch(train) [74][150/586] lr: 5.000000e-04 eta: 7:10:48 time: 0.332453 data_time: 0.030244 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.875233 loss: 0.000618 2022/09/09 21:58:46 - mmengine - INFO - Epoch(train) [74][200/586] lr: 5.000000e-04 eta: 7:10:33 time: 0.335270 data_time: 0.023454 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.836857 loss: 0.000625 2022/09/09 21:58:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 21:59:02 - mmengine - INFO - Epoch(train) [74][250/586] lr: 5.000000e-04 eta: 7:10:18 time: 0.330479 data_time: 0.023809 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.840216 loss: 0.000624 2022/09/09 21:59:19 - mmengine - INFO - Epoch(train) [74][300/586] lr: 5.000000e-04 eta: 7:10:03 time: 0.342415 data_time: 0.022788 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.794812 loss: 0.000620 2022/09/09 21:59:36 - mmengine - INFO - Epoch(train) [74][350/586] lr: 5.000000e-04 eta: 7:09:48 time: 0.330865 data_time: 0.023605 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.802325 loss: 0.000621 2022/09/09 21:59:53 - mmengine - INFO - Epoch(train) [74][400/586] lr: 5.000000e-04 eta: 7:09:33 time: 0.333973 data_time: 0.023312 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.809111 loss: 0.000628 2022/09/09 22:00:09 - mmengine - INFO - Epoch(train) [74][450/586] lr: 5.000000e-04 eta: 7:09:18 time: 0.334232 data_time: 0.023005 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.720116 loss: 0.000619 2022/09/09 22:00:26 - mmengine - INFO - Epoch(train) [74][500/586] lr: 5.000000e-04 eta: 7:09:03 time: 0.334384 data_time: 0.023967 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.868986 loss: 0.000614 2022/09/09 22:00:43 - mmengine - INFO - Epoch(train) [74][550/586] lr: 5.000000e-04 eta: 7:08:47 time: 0.330197 data_time: 0.024113 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.811033 loss: 0.000620 2022/09/09 22:00:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:00:55 - mmengine - INFO - Saving checkpoint at 74 epochs 2022/09/09 22:01:19 - mmengine - INFO - Epoch(train) [75][50/586] lr: 5.000000e-04 eta: 7:08:00 time: 0.344323 data_time: 0.031085 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.804799 loss: 0.000600 2022/09/09 22:01:35 - mmengine - INFO - Epoch(train) [75][100/586] lr: 5.000000e-04 eta: 7:07:45 time: 0.329852 data_time: 0.026389 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.875528 loss: 0.000605 2022/09/09 22:01:52 - mmengine - INFO - Epoch(train) [75][150/586] lr: 5.000000e-04 eta: 7:07:30 time: 0.333032 data_time: 0.023278 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.881451 loss: 0.000627 2022/09/09 22:02:09 - mmengine - INFO - Epoch(train) [75][200/586] lr: 5.000000e-04 eta: 7:07:14 time: 0.333079 data_time: 0.023204 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.844397 loss: 0.000621 2022/09/09 22:02:26 - mmengine - INFO - Epoch(train) [75][250/586] lr: 5.000000e-04 eta: 7:07:00 time: 0.337447 data_time: 0.025887 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.833892 loss: 0.000620 2022/09/09 22:02:42 - mmengine - INFO - Epoch(train) [75][300/586] lr: 5.000000e-04 eta: 7:06:44 time: 0.326698 data_time: 0.024603 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.762216 loss: 0.000646 2022/09/09 22:02:59 - mmengine - INFO - Epoch(train) [75][350/586] lr: 5.000000e-04 eta: 7:06:29 time: 0.335461 data_time: 0.022391 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.816496 loss: 0.000610 2022/09/09 22:03:16 - mmengine - INFO - Epoch(train) [75][400/586] lr: 5.000000e-04 eta: 7:06:15 time: 0.341442 data_time: 0.023227 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.768005 loss: 0.000626 2022/09/09 22:03:32 - mmengine - INFO - Epoch(train) [75][450/586] lr: 5.000000e-04 eta: 7:05:59 time: 0.323645 data_time: 0.023408 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.880434 loss: 0.000629 2022/09/09 22:03:49 - mmengine - INFO - Epoch(train) [75][500/586] lr: 5.000000e-04 eta: 7:05:43 time: 0.332716 data_time: 0.023354 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.817942 loss: 0.000609 2022/09/09 22:04:05 - mmengine - INFO - Epoch(train) [75][550/586] lr: 5.000000e-04 eta: 7:05:29 time: 0.337390 data_time: 0.024660 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.867397 loss: 0.000598 2022/09/09 22:04:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:04:17 - mmengine - INFO - Saving checkpoint at 75 epochs 2022/09/09 22:04:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:04:42 - mmengine - INFO - Epoch(train) [76][50/586] lr: 5.000000e-04 eta: 7:04:42 time: 0.347395 data_time: 0.032299 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.789397 loss: 0.000596 2022/09/09 22:04:59 - mmengine - INFO - Epoch(train) [76][100/586] lr: 5.000000e-04 eta: 7:04:28 time: 0.337876 data_time: 0.024658 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.808700 loss: 0.000628 2022/09/09 22:05:15 - mmengine - INFO - Epoch(train) [76][150/586] lr: 5.000000e-04 eta: 7:04:13 time: 0.336588 data_time: 0.028214 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.845744 loss: 0.000626 2022/09/09 22:05:32 - mmengine - INFO - Epoch(train) [76][200/586] lr: 5.000000e-04 eta: 7:03:57 time: 0.329042 data_time: 0.023712 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.890886 loss: 0.000591 2022/09/09 22:05:49 - mmengine - INFO - Epoch(train) [76][250/586] lr: 5.000000e-04 eta: 7:03:42 time: 0.333106 data_time: 0.023604 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.854486 loss: 0.000631 2022/09/09 22:06:05 - mmengine - INFO - Epoch(train) [76][300/586] lr: 5.000000e-04 eta: 7:03:27 time: 0.332856 data_time: 0.022515 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.814837 loss: 0.000597 2022/09/09 22:06:22 - mmengine - INFO - Epoch(train) [76][350/586] lr: 5.000000e-04 eta: 7:03:12 time: 0.334226 data_time: 0.023417 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.910625 loss: 0.000630 2022/09/09 22:06:39 - mmengine - INFO - Epoch(train) [76][400/586] lr: 5.000000e-04 eta: 7:02:56 time: 0.332114 data_time: 0.022475 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.872136 loss: 0.000605 2022/09/09 22:06:55 - mmengine - INFO - Epoch(train) [76][450/586] lr: 5.000000e-04 eta: 7:02:41 time: 0.332956 data_time: 0.027451 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.860458 loss: 0.000602 2022/09/09 22:07:12 - mmengine - INFO - Epoch(train) [76][500/586] lr: 5.000000e-04 eta: 7:02:26 time: 0.334718 data_time: 0.023345 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.835402 loss: 0.000608 2022/09/09 22:07:29 - mmengine - INFO - Epoch(train) [76][550/586] lr: 5.000000e-04 eta: 7:02:11 time: 0.336378 data_time: 0.023632 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.889359 loss: 0.000625 2022/09/09 22:07:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:07:41 - mmengine - INFO - Saving checkpoint at 76 epochs 2022/09/09 22:08:05 - mmengine - INFO - Epoch(train) [77][50/586] lr: 5.000000e-04 eta: 7:01:25 time: 0.344918 data_time: 0.030173 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.833270 loss: 0.000633 2022/09/09 22:08:22 - mmengine - INFO - Epoch(train) [77][100/586] lr: 5.000000e-04 eta: 7:01:10 time: 0.335495 data_time: 0.027142 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.802027 loss: 0.000607 2022/09/09 22:08:38 - mmengine - INFO - Epoch(train) [77][150/586] lr: 5.000000e-04 eta: 7:00:55 time: 0.331286 data_time: 0.024236 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.806435 loss: 0.000619 2022/09/09 22:08:55 - mmengine - INFO - Epoch(train) [77][200/586] lr: 5.000000e-04 eta: 7:00:40 time: 0.331196 data_time: 0.026822 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.807487 loss: 0.000621 2022/09/09 22:09:12 - mmengine - INFO - Epoch(train) [77][250/586] lr: 5.000000e-04 eta: 7:00:25 time: 0.344993 data_time: 0.024677 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.803072 loss: 0.000614 2022/09/09 22:09:29 - mmengine - INFO - Epoch(train) [77][300/586] lr: 5.000000e-04 eta: 7:00:10 time: 0.334634 data_time: 0.027385 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.822962 loss: 0.000617 2022/09/09 22:09:45 - mmengine - INFO - Epoch(train) [77][350/586] lr: 5.000000e-04 eta: 6:59:55 time: 0.329143 data_time: 0.022869 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.791163 loss: 0.000625 2022/09/09 22:10:02 - mmengine - INFO - Epoch(train) [77][400/586] lr: 5.000000e-04 eta: 6:59:40 time: 0.334638 data_time: 0.025381 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.844322 loss: 0.000633 2022/09/09 22:10:19 - mmengine - INFO - Epoch(train) [77][450/586] lr: 5.000000e-04 eta: 6:59:25 time: 0.336634 data_time: 0.024017 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.769993 loss: 0.000602 2022/09/09 22:10:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:10:36 - mmengine - INFO - Epoch(train) [77][500/586] lr: 5.000000e-04 eta: 6:59:11 time: 0.342189 data_time: 0.025309 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.835903 loss: 0.000612 2022/09/09 22:10:53 - mmengine - INFO - Epoch(train) [77][550/586] lr: 5.000000e-04 eta: 6:58:55 time: 0.334162 data_time: 0.026670 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.842163 loss: 0.000607 2022/09/09 22:11:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:11:05 - mmengine - INFO - Saving checkpoint at 77 epochs 2022/09/09 22:11:28 - mmengine - INFO - Epoch(train) [78][50/586] lr: 5.000000e-04 eta: 6:58:10 time: 0.344002 data_time: 0.033333 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.816010 loss: 0.000600 2022/09/09 22:11:45 - mmengine - INFO - Epoch(train) [78][100/586] lr: 5.000000e-04 eta: 6:57:54 time: 0.332420 data_time: 0.026016 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.811414 loss: 0.000627 2022/09/09 22:12:02 - mmengine - INFO - Epoch(train) [78][150/586] lr: 5.000000e-04 eta: 6:57:39 time: 0.332680 data_time: 0.022912 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.748934 loss: 0.000611 2022/09/09 22:12:18 - mmengine - INFO - Epoch(train) [78][200/586] lr: 5.000000e-04 eta: 6:57:24 time: 0.334689 data_time: 0.026443 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.834745 loss: 0.000617 2022/09/09 22:12:35 - mmengine - INFO - Epoch(train) [78][250/586] lr: 5.000000e-04 eta: 6:57:09 time: 0.336756 data_time: 0.022454 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.842504 loss: 0.000618 2022/09/09 22:12:52 - mmengine - INFO - Epoch(train) [78][300/586] lr: 5.000000e-04 eta: 6:56:54 time: 0.332165 data_time: 0.028323 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.815844 loss: 0.000619 2022/09/09 22:13:08 - mmengine - INFO - Epoch(train) [78][350/586] lr: 5.000000e-04 eta: 6:56:39 time: 0.329297 data_time: 0.023132 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.829989 loss: 0.000597 2022/09/09 22:13:25 - mmengine - INFO - Epoch(train) [78][400/586] lr: 5.000000e-04 eta: 6:56:24 time: 0.334774 data_time: 0.025419 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.826430 loss: 0.000604 2022/09/09 22:13:42 - mmengine - INFO - Epoch(train) [78][450/586] lr: 5.000000e-04 eta: 6:56:08 time: 0.333793 data_time: 0.028088 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.805609 loss: 0.000627 2022/09/09 22:13:59 - mmengine - INFO - Epoch(train) [78][500/586] lr: 5.000000e-04 eta: 6:55:54 time: 0.338404 data_time: 0.023811 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.863726 loss: 0.000609 2022/09/09 22:14:15 - mmengine - INFO - Epoch(train) [78][550/586] lr: 5.000000e-04 eta: 6:55:38 time: 0.333077 data_time: 0.023881 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.859731 loss: 0.000623 2022/09/09 22:14:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:14:27 - mmengine - INFO - Saving checkpoint at 78 epochs 2022/09/09 22:14:51 - mmengine - INFO - Epoch(train) [79][50/586] lr: 5.000000e-04 eta: 6:54:53 time: 0.344509 data_time: 0.034692 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.845040 loss: 0.000604 2022/09/09 22:15:08 - mmengine - INFO - Epoch(train) [79][100/586] lr: 5.000000e-04 eta: 6:54:38 time: 0.333440 data_time: 0.025832 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.760826 loss: 0.000620 2022/09/09 22:15:24 - mmengine - INFO - Epoch(train) [79][150/586] lr: 5.000000e-04 eta: 6:54:23 time: 0.333819 data_time: 0.022793 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.870615 loss: 0.000612 2022/09/09 22:15:41 - mmengine - INFO - Epoch(train) [79][200/586] lr: 5.000000e-04 eta: 6:54:07 time: 0.330940 data_time: 0.023779 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.768149 loss: 0.000619 2022/09/09 22:15:58 - mmengine - INFO - Epoch(train) [79][250/586] lr: 5.000000e-04 eta: 6:53:53 time: 0.336941 data_time: 0.027445 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.847265 loss: 0.000602 2022/09/09 22:16:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:16:14 - mmengine - INFO - Epoch(train) [79][300/586] lr: 5.000000e-04 eta: 6:53:37 time: 0.333897 data_time: 0.024872 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.739476 loss: 0.000603 2022/09/09 22:16:31 - mmengine - INFO - Epoch(train) [79][350/586] lr: 5.000000e-04 eta: 6:53:22 time: 0.332000 data_time: 0.023292 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.794700 loss: 0.000611 2022/09/09 22:16:48 - mmengine - INFO - Epoch(train) [79][400/586] lr: 5.000000e-04 eta: 6:53:08 time: 0.339899 data_time: 0.023749 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.823441 loss: 0.000626 2022/09/09 22:17:05 - mmengine - INFO - Epoch(train) [79][450/586] lr: 5.000000e-04 eta: 6:52:52 time: 0.332735 data_time: 0.023970 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.790742 loss: 0.000626 2022/09/09 22:17:22 - mmengine - INFO - Epoch(train) [79][500/586] lr: 5.000000e-04 eta: 6:52:38 time: 0.338274 data_time: 0.026534 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.848346 loss: 0.000609 2022/09/09 22:17:38 - mmengine - INFO - Epoch(train) [79][550/586] lr: 5.000000e-04 eta: 6:52:23 time: 0.335545 data_time: 0.027160 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.832488 loss: 0.000621 2022/09/09 22:17:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:17:50 - mmengine - INFO - Saving checkpoint at 79 epochs 2022/09/09 22:18:14 - mmengine - INFO - Epoch(train) [80][50/586] lr: 5.000000e-04 eta: 6:51:38 time: 0.344863 data_time: 0.031153 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.836106 loss: 0.000603 2022/09/09 22:18:31 - mmengine - INFO - Epoch(train) [80][100/586] lr: 5.000000e-04 eta: 6:51:22 time: 0.330982 data_time: 0.024042 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.758321 loss: 0.000637 2022/09/09 22:18:47 - mmengine - INFO - Epoch(train) [80][150/586] lr: 5.000000e-04 eta: 6:51:07 time: 0.334116 data_time: 0.022969 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.812785 loss: 0.000613 2022/09/09 22:19:04 - mmengine - INFO - Epoch(train) [80][200/586] lr: 5.000000e-04 eta: 6:50:52 time: 0.334143 data_time: 0.023728 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.838775 loss: 0.000613 2022/09/09 22:19:21 - mmengine - INFO - Epoch(train) [80][250/586] lr: 5.000000e-04 eta: 6:50:37 time: 0.330065 data_time: 0.023414 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.724203 loss: 0.000609 2022/09/09 22:19:37 - mmengine - INFO - Epoch(train) [80][300/586] lr: 5.000000e-04 eta: 6:50:21 time: 0.330495 data_time: 0.027045 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.783209 loss: 0.000610 2022/09/09 22:19:54 - mmengine - INFO - Epoch(train) [80][350/586] lr: 5.000000e-04 eta: 6:50:06 time: 0.328653 data_time: 0.023141 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.793339 loss: 0.000588 2022/09/09 22:20:10 - mmengine - INFO - Epoch(train) [80][400/586] lr: 5.000000e-04 eta: 6:49:51 time: 0.335520 data_time: 0.022996 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.836501 loss: 0.000620 2022/09/09 22:20:27 - mmengine - INFO - Epoch(train) [80][450/586] lr: 5.000000e-04 eta: 6:49:36 time: 0.336560 data_time: 0.023137 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.812068 loss: 0.000598 2022/09/09 22:20:44 - mmengine - INFO - Epoch(train) [80][500/586] lr: 5.000000e-04 eta: 6:49:20 time: 0.329663 data_time: 0.021984 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.839734 loss: 0.000612 2022/09/09 22:21:00 - mmengine - INFO - Epoch(train) [80][550/586] lr: 5.000000e-04 eta: 6:49:05 time: 0.332576 data_time: 0.023767 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.805394 loss: 0.000610 2022/09/09 22:21:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:21:12 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/09/09 22:21:28 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:01:00 time: 0.170192 data_time: 0.013411 memory: 7489 2022/09/09 22:21:36 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:50 time: 0.164732 data_time: 0.007704 memory: 1657 2022/09/09 22:21:44 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:42 time: 0.163968 data_time: 0.007560 memory: 1657 2022/09/09 22:21:53 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:34 time: 0.164843 data_time: 0.007352 memory: 1657 2022/09/09 22:22:01 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:26 time: 0.167099 data_time: 0.010819 memory: 1657 2022/09/09 22:22:09 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:17 time: 0.164693 data_time: 0.008143 memory: 1657 2022/09/09 22:22:17 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:09 time: 0.165017 data_time: 0.008047 memory: 1657 2022/09/09 22:22:25 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:01 time: 0.161231 data_time: 0.006916 memory: 1657 2022/09/09 22:23:02 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 22:23:16 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.737415 coco/AP .5: 0.899661 coco/AP .75: 0.813140 coco/AP (M): 0.702322 coco/AP (L): 0.800835 coco/AR: 0.790475 coco/AR .5: 0.937657 coco/AR .75: 0.856266 coco/AR (M): 0.750068 coco/AR (L): 0.848941 2022/09/09 22:23:16 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_70.pth is removed 2022/09/09 22:23:20 - mmengine - INFO - The best checkpoint with 0.7374 coco/AP at 80 epoch is saved to best_coco/AP_epoch_80.pth. 2022/09/09 22:23:37 - mmengine - INFO - Epoch(train) [81][50/586] lr: 5.000000e-04 eta: 6:48:20 time: 0.337223 data_time: 0.030679 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.824323 loss: 0.000617 2022/09/09 22:23:54 - mmengine - INFO - Epoch(train) [81][100/586] lr: 5.000000e-04 eta: 6:48:05 time: 0.342166 data_time: 0.022969 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.824430 loss: 0.000613 2022/09/09 22:24:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:24:10 - mmengine - INFO - Epoch(train) [81][150/586] lr: 5.000000e-04 eta: 6:47:50 time: 0.326863 data_time: 0.023401 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.870105 loss: 0.000599 2022/09/09 22:24:27 - mmengine - INFO - Epoch(train) [81][200/586] lr: 5.000000e-04 eta: 6:47:34 time: 0.328174 data_time: 0.023223 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.785651 loss: 0.000627 2022/09/09 22:24:44 - mmengine - INFO - Epoch(train) [81][250/586] lr: 5.000000e-04 eta: 6:47:19 time: 0.336478 data_time: 0.023598 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.840912 loss: 0.000611 2022/09/09 22:25:00 - mmengine - INFO - Epoch(train) [81][300/586] lr: 5.000000e-04 eta: 6:47:04 time: 0.331684 data_time: 0.025446 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.803105 loss: 0.000597 2022/09/09 22:25:17 - mmengine - INFO - Epoch(train) [81][350/586] lr: 5.000000e-04 eta: 6:46:49 time: 0.339317 data_time: 0.026351 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.809211 loss: 0.000613 2022/09/09 22:25:34 - mmengine - INFO - Epoch(train) [81][400/586] lr: 5.000000e-04 eta: 6:46:34 time: 0.337126 data_time: 0.024548 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.880802 loss: 0.000608 2022/09/09 22:25:51 - mmengine - INFO - Epoch(train) [81][450/586] lr: 5.000000e-04 eta: 6:46:19 time: 0.329179 data_time: 0.024918 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.807279 loss: 0.000605 2022/09/09 22:26:07 - mmengine - INFO - Epoch(train) [81][500/586] lr: 5.000000e-04 eta: 6:46:03 time: 0.331931 data_time: 0.027785 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.825838 loss: 0.000599 2022/09/09 22:26:24 - mmengine - INFO - Epoch(train) [81][550/586] lr: 5.000000e-04 eta: 6:45:48 time: 0.330569 data_time: 0.022789 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.856928 loss: 0.000604 2022/09/09 22:26:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:26:36 - mmengine - INFO - Saving checkpoint at 81 epochs 2022/09/09 22:27:00 - mmengine - INFO - Epoch(train) [82][50/586] lr: 5.000000e-04 eta: 6:45:03 time: 0.341735 data_time: 0.035187 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.859204 loss: 0.000629 2022/09/09 22:27:17 - mmengine - INFO - Epoch(train) [82][100/586] lr: 5.000000e-04 eta: 6:44:49 time: 0.341889 data_time: 0.028475 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.832647 loss: 0.000601 2022/09/09 22:27:33 - mmengine - INFO - Epoch(train) [82][150/586] lr: 5.000000e-04 eta: 6:44:33 time: 0.329357 data_time: 0.023904 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.834738 loss: 0.000627 2022/09/09 22:27:50 - mmengine - INFO - Epoch(train) [82][200/586] lr: 5.000000e-04 eta: 6:44:18 time: 0.329594 data_time: 0.025880 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.806908 loss: 0.000608 2022/09/09 22:28:07 - mmengine - INFO - Epoch(train) [82][250/586] lr: 5.000000e-04 eta: 6:44:03 time: 0.336665 data_time: 0.024246 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.834342 loss: 0.000615 2022/09/09 22:28:23 - mmengine - INFO - Epoch(train) [82][300/586] lr: 5.000000e-04 eta: 6:43:48 time: 0.329689 data_time: 0.024099 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.881320 loss: 0.000605 2022/09/09 22:28:40 - mmengine - INFO - Epoch(train) [82][350/586] lr: 5.000000e-04 eta: 6:43:32 time: 0.333650 data_time: 0.023164 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.809574 loss: 0.000621 2022/09/09 22:28:57 - mmengine - INFO - Epoch(train) [82][400/586] lr: 5.000000e-04 eta: 6:43:17 time: 0.335859 data_time: 0.023158 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.914112 loss: 0.000606 2022/09/09 22:29:13 - mmengine - INFO - Epoch(train) [82][450/586] lr: 5.000000e-04 eta: 6:43:02 time: 0.326945 data_time: 0.022936 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.824658 loss: 0.000608 2022/09/09 22:29:30 - mmengine - INFO - Epoch(train) [82][500/586] lr: 5.000000e-04 eta: 6:42:47 time: 0.334020 data_time: 0.022971 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.797974 loss: 0.000623 2022/09/09 22:29:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:29:47 - mmengine - INFO - Epoch(train) [82][550/586] lr: 5.000000e-04 eta: 6:42:32 time: 0.341195 data_time: 0.023872 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.865372 loss: 0.000599 2022/09/09 22:29:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:29:59 - mmengine - INFO - Saving checkpoint at 82 epochs 2022/09/09 22:30:23 - mmengine - INFO - Epoch(train) [83][50/586] lr: 5.000000e-04 eta: 6:41:47 time: 0.334342 data_time: 0.030808 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.818359 loss: 0.000603 2022/09/09 22:30:40 - mmengine - INFO - Epoch(train) [83][100/586] lr: 5.000000e-04 eta: 6:41:33 time: 0.341105 data_time: 0.022866 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.847048 loss: 0.000609 2022/09/09 22:30:56 - mmengine - INFO - Epoch(train) [83][150/586] lr: 5.000000e-04 eta: 6:41:18 time: 0.332748 data_time: 0.026508 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.806186 loss: 0.000601 2022/09/09 22:31:13 - mmengine - INFO - Epoch(train) [83][200/586] lr: 5.000000e-04 eta: 6:41:03 time: 0.336445 data_time: 0.024550 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.817484 loss: 0.000613 2022/09/09 22:31:30 - mmengine - INFO - Epoch(train) [83][250/586] lr: 5.000000e-04 eta: 6:40:47 time: 0.330407 data_time: 0.024001 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.835422 loss: 0.000609 2022/09/09 22:31:46 - mmengine - INFO - Epoch(train) [83][300/586] lr: 5.000000e-04 eta: 6:40:32 time: 0.331846 data_time: 0.023808 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.874876 loss: 0.000617 2022/09/09 22:32:03 - mmengine - INFO - Epoch(train) [83][350/586] lr: 5.000000e-04 eta: 6:40:17 time: 0.339771 data_time: 0.023538 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.852906 loss: 0.000583 2022/09/09 22:32:20 - mmengine - INFO - Epoch(train) [83][400/586] lr: 5.000000e-04 eta: 6:40:02 time: 0.329505 data_time: 0.023882 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.762685 loss: 0.000641 2022/09/09 22:32:36 - mmengine - INFO - Epoch(train) [83][450/586] lr: 5.000000e-04 eta: 6:39:46 time: 0.332760 data_time: 0.022514 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.797198 loss: 0.000607 2022/09/09 22:32:53 - mmengine - INFO - Epoch(train) [83][500/586] lr: 5.000000e-04 eta: 6:39:32 time: 0.337614 data_time: 0.026869 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.828980 loss: 0.000616 2022/09/09 22:33:10 - mmengine - INFO - Epoch(train) [83][550/586] lr: 5.000000e-04 eta: 6:39:16 time: 0.332388 data_time: 0.023719 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.793002 loss: 0.000611 2022/09/09 22:33:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:33:22 - mmengine - INFO - Saving checkpoint at 83 epochs 2022/09/09 22:33:46 - mmengine - INFO - Epoch(train) [84][50/586] lr: 5.000000e-04 eta: 6:38:32 time: 0.336380 data_time: 0.027906 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.832816 loss: 0.000596 2022/09/09 22:34:03 - mmengine - INFO - Epoch(train) [84][100/586] lr: 5.000000e-04 eta: 6:38:17 time: 0.333595 data_time: 0.024637 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.855322 loss: 0.000607 2022/09/09 22:34:19 - mmengine - INFO - Epoch(train) [84][150/586] lr: 5.000000e-04 eta: 6:38:01 time: 0.328833 data_time: 0.027245 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.822277 loss: 0.000612 2022/09/09 22:34:36 - mmengine - INFO - Epoch(train) [84][200/586] lr: 5.000000e-04 eta: 6:37:46 time: 0.334515 data_time: 0.023365 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.794515 loss: 0.000606 2022/09/09 22:34:53 - mmengine - INFO - Epoch(train) [84][250/586] lr: 5.000000e-04 eta: 6:37:32 time: 0.346986 data_time: 0.029638 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.840398 loss: 0.000616 2022/09/09 22:35:10 - mmengine - INFO - Epoch(train) [84][300/586] lr: 5.000000e-04 eta: 6:37:17 time: 0.331519 data_time: 0.024214 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.803622 loss: 0.000609 2022/09/09 22:35:27 - mmengine - INFO - Epoch(train) [84][350/586] lr: 5.000000e-04 eta: 6:37:03 time: 0.345365 data_time: 0.024481 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.856226 loss: 0.000595 2022/09/09 22:35:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:35:45 - mmengine - INFO - Epoch(train) [84][400/586] lr: 5.000000e-04 eta: 6:36:49 time: 0.350602 data_time: 0.027979 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.859854 loss: 0.000619 2022/09/09 22:36:02 - mmengine - INFO - Epoch(train) [84][450/586] lr: 5.000000e-04 eta: 6:36:34 time: 0.346641 data_time: 0.028005 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.770080 loss: 0.000613 2022/09/09 22:36:20 - mmengine - INFO - Epoch(train) [84][500/586] lr: 5.000000e-04 eta: 6:36:21 time: 0.352118 data_time: 0.025097 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.922450 loss: 0.000575 2022/09/09 22:36:37 - mmengine - INFO - Epoch(train) [84][550/586] lr: 5.000000e-04 eta: 6:36:06 time: 0.337951 data_time: 0.034077 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.806433 loss: 0.000609 2022/09/09 22:36:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:36:49 - mmengine - INFO - Saving checkpoint at 84 epochs 2022/09/09 22:37:12 - mmengine - INFO - Epoch(train) [85][50/586] lr: 5.000000e-04 eta: 6:35:22 time: 0.337738 data_time: 0.029390 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.789530 loss: 0.000597 2022/09/09 22:37:29 - mmengine - INFO - Epoch(train) [85][100/586] lr: 5.000000e-04 eta: 6:35:07 time: 0.333076 data_time: 0.024018 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.822850 loss: 0.000614 2022/09/09 22:37:46 - mmengine - INFO - Epoch(train) [85][150/586] lr: 5.000000e-04 eta: 6:34:52 time: 0.337905 data_time: 0.028323 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.787512 loss: 0.000608 2022/09/09 22:38:03 - mmengine - INFO - Epoch(train) [85][200/586] lr: 5.000000e-04 eta: 6:34:38 time: 0.343569 data_time: 0.034112 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.842886 loss: 0.000588 2022/09/09 22:38:20 - mmengine - INFO - Epoch(train) [85][250/586] lr: 5.000000e-04 eta: 6:34:22 time: 0.329311 data_time: 0.025463 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.852346 loss: 0.000598 2022/09/09 22:38:36 - mmengine - INFO - Epoch(train) [85][300/586] lr: 5.000000e-04 eta: 6:34:07 time: 0.333254 data_time: 0.023035 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.891398 loss: 0.000614 2022/09/09 22:38:53 - mmengine - INFO - Epoch(train) [85][350/586] lr: 5.000000e-04 eta: 6:33:52 time: 0.339241 data_time: 0.024467 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.843678 loss: 0.000583 2022/09/09 22:39:12 - mmengine - INFO - Epoch(train) [85][400/586] lr: 5.000000e-04 eta: 6:33:40 time: 0.378618 data_time: 0.052049 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.860604 loss: 0.000619 2022/09/09 22:39:29 - mmengine - INFO - Epoch(train) [85][450/586] lr: 5.000000e-04 eta: 6:33:25 time: 0.339992 data_time: 0.023796 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.839362 loss: 0.000607 2022/09/09 22:39:46 - mmengine - INFO - Epoch(train) [85][500/586] lr: 5.000000e-04 eta: 6:33:11 time: 0.343453 data_time: 0.024270 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.811999 loss: 0.000628 2022/09/09 22:40:03 - mmengine - INFO - Epoch(train) [85][550/586] lr: 5.000000e-04 eta: 6:32:55 time: 0.327276 data_time: 0.022916 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.867757 loss: 0.000597 2022/09/09 22:40:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:40:15 - mmengine - INFO - Saving checkpoint at 85 epochs 2022/09/09 22:40:39 - mmengine - INFO - Epoch(train) [86][50/586] lr: 5.000000e-04 eta: 6:32:12 time: 0.342010 data_time: 0.034675 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.811260 loss: 0.000607 2022/09/09 22:40:55 - mmengine - INFO - Epoch(train) [86][100/586] lr: 5.000000e-04 eta: 6:31:57 time: 0.329436 data_time: 0.024908 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.819628 loss: 0.000612 2022/09/09 22:41:13 - mmengine - INFO - Epoch(train) [86][150/586] lr: 5.000000e-04 eta: 6:31:42 time: 0.345109 data_time: 0.025967 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.923955 loss: 0.000610 2022/09/09 22:41:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:41:30 - mmengine - INFO - Epoch(train) [86][200/586] lr: 5.000000e-04 eta: 6:31:27 time: 0.336376 data_time: 0.024969 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.844434 loss: 0.000613 2022/09/09 22:41:48 - mmengine - INFO - Epoch(train) [86][250/586] lr: 5.000000e-04 eta: 6:31:15 time: 0.371520 data_time: 0.037768 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.760731 loss: 0.000614 2022/09/09 22:42:08 - mmengine - INFO - Epoch(train) [86][300/586] lr: 5.000000e-04 eta: 6:31:04 time: 0.388942 data_time: 0.045998 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.865549 loss: 0.000594 2022/09/09 22:42:24 - mmengine - INFO - Epoch(train) [86][350/586] lr: 5.000000e-04 eta: 6:30:49 time: 0.336486 data_time: 0.024698 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.855479 loss: 0.000591 2022/09/09 22:42:42 - mmengine - INFO - Epoch(train) [86][400/586] lr: 5.000000e-04 eta: 6:30:35 time: 0.351795 data_time: 0.042437 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.836928 loss: 0.000590 2022/09/09 22:42:59 - mmengine - INFO - Epoch(train) [86][450/586] lr: 5.000000e-04 eta: 6:30:20 time: 0.341366 data_time: 0.029485 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.846087 loss: 0.000608 2022/09/09 22:43:17 - mmengine - INFO - Epoch(train) [86][500/586] lr: 5.000000e-04 eta: 6:30:06 time: 0.351006 data_time: 0.032956 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.841602 loss: 0.000635 2022/09/09 22:43:34 - mmengine - INFO - Epoch(train) [86][550/586] lr: 5.000000e-04 eta: 6:29:52 time: 0.350162 data_time: 0.041157 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.840707 loss: 0.000619 2022/09/09 22:43:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:43:46 - mmengine - INFO - Saving checkpoint at 86 epochs 2022/09/09 22:44:10 - mmengine - INFO - Epoch(train) [87][50/586] lr: 5.000000e-04 eta: 6:29:09 time: 0.336887 data_time: 0.028509 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.850314 loss: 0.000599 2022/09/09 22:44:28 - mmengine - INFO - Epoch(train) [87][100/586] lr: 5.000000e-04 eta: 6:28:55 time: 0.359061 data_time: 0.043341 memory: 7489 loss_kpt: 0.000625 acc_pose: 0.828488 loss: 0.000625 2022/09/09 22:44:44 - mmengine - INFO - Epoch(train) [87][150/586] lr: 5.000000e-04 eta: 6:28:40 time: 0.330494 data_time: 0.025842 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.846378 loss: 0.000599 2022/09/09 22:45:01 - mmengine - INFO - Epoch(train) [87][200/586] lr: 5.000000e-04 eta: 6:28:25 time: 0.339621 data_time: 0.025726 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.863306 loss: 0.000590 2022/09/09 22:45:21 - mmengine - INFO - Epoch(train) [87][250/586] lr: 5.000000e-04 eta: 6:28:15 time: 0.398535 data_time: 0.026315 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.872685 loss: 0.000591 2022/09/09 22:45:39 - mmengine - INFO - Epoch(train) [87][300/586] lr: 5.000000e-04 eta: 6:28:00 time: 0.346574 data_time: 0.024813 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.842444 loss: 0.000619 2022/09/09 22:45:55 - mmengine - INFO - Epoch(train) [87][350/586] lr: 5.000000e-04 eta: 6:27:45 time: 0.333318 data_time: 0.024876 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.819840 loss: 0.000627 2022/09/09 22:46:12 - mmengine - INFO - Epoch(train) [87][400/586] lr: 5.000000e-04 eta: 6:27:30 time: 0.337109 data_time: 0.024362 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.827707 loss: 0.000607 2022/09/09 22:46:29 - mmengine - INFO - Epoch(train) [87][450/586] lr: 5.000000e-04 eta: 6:27:15 time: 0.335623 data_time: 0.031913 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.813025 loss: 0.000589 2022/09/09 22:46:46 - mmengine - INFO - Epoch(train) [87][500/586] lr: 5.000000e-04 eta: 6:27:00 time: 0.343443 data_time: 0.025120 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.859924 loss: 0.000612 2022/09/09 22:47:03 - mmengine - INFO - Epoch(train) [87][550/586] lr: 5.000000e-04 eta: 6:26:45 time: 0.333345 data_time: 0.027099 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.821555 loss: 0.000606 2022/09/09 22:47:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:47:15 - mmengine - INFO - Saving checkpoint at 87 epochs 2022/09/09 22:47:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:47:39 - mmengine - INFO - Epoch(train) [88][50/586] lr: 5.000000e-04 eta: 6:26:03 time: 0.346445 data_time: 0.028263 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.810452 loss: 0.000616 2022/09/09 22:47:55 - mmengine - INFO - Epoch(train) [88][100/586] lr: 5.000000e-04 eta: 6:25:48 time: 0.331863 data_time: 0.023580 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.776858 loss: 0.000624 2022/09/09 22:48:12 - mmengine - INFO - Epoch(train) [88][150/586] lr: 5.000000e-04 eta: 6:25:32 time: 0.333754 data_time: 0.023070 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.723130 loss: 0.000613 2022/09/09 22:48:31 - mmengine - INFO - Epoch(train) [88][200/586] lr: 5.000000e-04 eta: 6:25:20 time: 0.380780 data_time: 0.043599 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.865573 loss: 0.000582 2022/09/09 22:48:48 - mmengine - INFO - Epoch(train) [88][250/586] lr: 5.000000e-04 eta: 6:25:05 time: 0.337900 data_time: 0.023573 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.846401 loss: 0.000618 2022/09/09 22:49:06 - mmengine - INFO - Epoch(train) [88][300/586] lr: 5.000000e-04 eta: 6:24:51 time: 0.348253 data_time: 0.026926 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.832308 loss: 0.000638 2022/09/09 22:49:23 - mmengine - INFO - Epoch(train) [88][350/586] lr: 5.000000e-04 eta: 6:24:37 time: 0.350500 data_time: 0.024775 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.814275 loss: 0.000627 2022/09/09 22:49:40 - mmengine - INFO - Epoch(train) [88][400/586] lr: 5.000000e-04 eta: 6:24:22 time: 0.331400 data_time: 0.024957 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.846628 loss: 0.000595 2022/09/09 22:49:57 - mmengine - INFO - Epoch(train) [88][450/586] lr: 5.000000e-04 eta: 6:24:07 time: 0.341193 data_time: 0.030462 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.851978 loss: 0.000616 2022/09/09 22:50:14 - mmengine - INFO - Epoch(train) [88][500/586] lr: 5.000000e-04 eta: 6:23:52 time: 0.335395 data_time: 0.024801 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.765365 loss: 0.000607 2022/09/09 22:50:30 - mmengine - INFO - Epoch(train) [88][550/586] lr: 5.000000e-04 eta: 6:23:37 time: 0.332265 data_time: 0.024321 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.884298 loss: 0.000608 2022/09/09 22:50:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:50:42 - mmengine - INFO - Saving checkpoint at 88 epochs 2022/09/09 22:51:08 - mmengine - INFO - Epoch(train) [89][50/586] lr: 5.000000e-04 eta: 6:22:57 time: 0.379706 data_time: 0.068456 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.856700 loss: 0.000591 2022/09/09 22:51:24 - mmengine - INFO - Epoch(train) [89][100/586] lr: 5.000000e-04 eta: 6:22:41 time: 0.330411 data_time: 0.027511 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.875691 loss: 0.000616 2022/09/09 22:51:41 - mmengine - INFO - Epoch(train) [89][150/586] lr: 5.000000e-04 eta: 6:22:26 time: 0.336488 data_time: 0.024729 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.837261 loss: 0.000619 2022/09/09 22:51:58 - mmengine - INFO - Epoch(train) [89][200/586] lr: 5.000000e-04 eta: 6:22:11 time: 0.338926 data_time: 0.024529 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.832183 loss: 0.000584 2022/09/09 22:52:15 - mmengine - INFO - Epoch(train) [89][250/586] lr: 5.000000e-04 eta: 6:21:56 time: 0.335254 data_time: 0.023644 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.846037 loss: 0.000615 2022/09/09 22:52:32 - mmengine - INFO - Epoch(train) [89][300/586] lr: 5.000000e-04 eta: 6:21:41 time: 0.336610 data_time: 0.024512 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.798941 loss: 0.000593 2022/09/09 22:52:49 - mmengine - INFO - Epoch(train) [89][350/586] lr: 5.000000e-04 eta: 6:21:26 time: 0.337719 data_time: 0.023466 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.867296 loss: 0.000584 2022/09/09 22:53:06 - mmengine - INFO - Epoch(train) [89][400/586] lr: 5.000000e-04 eta: 6:21:12 time: 0.342941 data_time: 0.024952 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.813882 loss: 0.000619 2022/09/09 22:53:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:53:23 - mmengine - INFO - Epoch(train) [89][450/586] lr: 5.000000e-04 eta: 6:20:56 time: 0.334416 data_time: 0.031048 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.850258 loss: 0.000601 2022/09/09 22:53:39 - mmengine - INFO - Epoch(train) [89][500/586] lr: 5.000000e-04 eta: 6:20:41 time: 0.335186 data_time: 0.025682 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.800823 loss: 0.000622 2022/09/09 22:53:56 - mmengine - INFO - Epoch(train) [89][550/586] lr: 5.000000e-04 eta: 6:20:26 time: 0.337326 data_time: 0.026133 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.861042 loss: 0.000607 2022/09/09 22:54:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:54:08 - mmengine - INFO - Saving checkpoint at 89 epochs 2022/09/09 22:54:32 - mmengine - INFO - Epoch(train) [90][50/586] lr: 5.000000e-04 eta: 6:19:44 time: 0.345554 data_time: 0.031867 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.817910 loss: 0.000615 2022/09/09 22:54:49 - mmengine - INFO - Epoch(train) [90][100/586] lr: 5.000000e-04 eta: 6:19:29 time: 0.329681 data_time: 0.024571 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.921751 loss: 0.000604 2022/09/09 22:55:06 - mmengine - INFO - Epoch(train) [90][150/586] lr: 5.000000e-04 eta: 6:19:14 time: 0.342135 data_time: 0.026269 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.771090 loss: 0.000595 2022/09/09 22:55:23 - mmengine - INFO - Epoch(train) [90][200/586] lr: 5.000000e-04 eta: 6:18:59 time: 0.333951 data_time: 0.024197 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.858145 loss: 0.000605 2022/09/09 22:55:39 - mmengine - INFO - Epoch(train) [90][250/586] lr: 5.000000e-04 eta: 6:18:44 time: 0.332327 data_time: 0.022579 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.798462 loss: 0.000600 2022/09/09 22:55:56 - mmengine - INFO - Epoch(train) [90][300/586] lr: 5.000000e-04 eta: 6:18:29 time: 0.339934 data_time: 0.024239 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.851287 loss: 0.000604 2022/09/09 22:56:13 - mmengine - INFO - Epoch(train) [90][350/586] lr: 5.000000e-04 eta: 6:18:13 time: 0.331081 data_time: 0.023399 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.802176 loss: 0.000617 2022/09/09 22:56:30 - mmengine - INFO - Epoch(train) [90][400/586] lr: 5.000000e-04 eta: 6:17:58 time: 0.336145 data_time: 0.023832 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.817627 loss: 0.000607 2022/09/09 22:56:46 - mmengine - INFO - Epoch(train) [90][450/586] lr: 5.000000e-04 eta: 6:17:43 time: 0.334815 data_time: 0.026704 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.883260 loss: 0.000591 2022/09/09 22:57:03 - mmengine - INFO - Epoch(train) [90][500/586] lr: 5.000000e-04 eta: 6:17:28 time: 0.337816 data_time: 0.023868 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.877541 loss: 0.000609 2022/09/09 22:57:20 - mmengine - INFO - Epoch(train) [90][550/586] lr: 5.000000e-04 eta: 6:17:13 time: 0.333844 data_time: 0.027313 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.807436 loss: 0.000588 2022/09/09 22:57:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 22:57:32 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/09/09 22:57:47 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:01:00 time: 0.169595 data_time: 0.012309 memory: 7489 2022/09/09 22:57:56 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:50 time: 0.163796 data_time: 0.007441 memory: 1657 2022/09/09 22:58:04 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:42 time: 0.166797 data_time: 0.007203 memory: 1657 2022/09/09 22:58:12 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:33 time: 0.163637 data_time: 0.007441 memory: 1657 2022/09/09 22:58:20 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:25 time: 0.164330 data_time: 0.007400 memory: 1657 2022/09/09 22:58:29 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:17 time: 0.164286 data_time: 0.007392 memory: 1657 2022/09/09 22:58:37 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:09 time: 0.163976 data_time: 0.007780 memory: 1657 2022/09/09 22:58:45 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:01 time: 0.162156 data_time: 0.007378 memory: 1657 2022/09/09 22:59:22 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 22:59:35 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.739213 coco/AP .5: 0.900121 coco/AP .75: 0.812722 coco/AP (M): 0.702271 coco/AP (L): 0.805594 coco/AR: 0.791971 coco/AR .5: 0.937343 coco/AR .75: 0.856266 coco/AR (M): 0.750560 coco/AR (L): 0.852025 2022/09/09 22:59:36 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_80.pth is removed 2022/09/09 22:59:40 - mmengine - INFO - The best checkpoint with 0.7392 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/09/09 22:59:56 - mmengine - INFO - Epoch(train) [91][50/586] lr: 5.000000e-04 eta: 6:16:31 time: 0.336848 data_time: 0.028252 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.759271 loss: 0.000596 2022/09/09 23:00:13 - mmengine - INFO - Epoch(train) [91][100/586] lr: 5.000000e-04 eta: 6:16:15 time: 0.330355 data_time: 0.028305 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.812517 loss: 0.000579 2022/09/09 23:00:30 - mmengine - INFO - Epoch(train) [91][150/586] lr: 5.000000e-04 eta: 6:16:00 time: 0.332923 data_time: 0.022975 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.835624 loss: 0.000609 2022/09/09 23:00:46 - mmengine - INFO - Epoch(train) [91][200/586] lr: 5.000000e-04 eta: 6:15:44 time: 0.331574 data_time: 0.024258 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.846908 loss: 0.000616 2022/09/09 23:01:03 - mmengine - INFO - Epoch(train) [91][250/586] lr: 5.000000e-04 eta: 6:15:29 time: 0.335477 data_time: 0.024386 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.867443 loss: 0.000616 2022/09/09 23:01:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:01:20 - mmengine - INFO - Epoch(train) [91][300/586] lr: 5.000000e-04 eta: 6:15:14 time: 0.330835 data_time: 0.027342 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.835498 loss: 0.000611 2022/09/09 23:01:36 - mmengine - INFO - Epoch(train) [91][350/586] lr: 5.000000e-04 eta: 6:14:58 time: 0.333697 data_time: 0.023502 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.834028 loss: 0.000621 2022/09/09 23:01:53 - mmengine - INFO - Epoch(train) [91][400/586] lr: 5.000000e-04 eta: 6:14:43 time: 0.331212 data_time: 0.026760 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.875049 loss: 0.000602 2022/09/09 23:02:10 - mmengine - INFO - Epoch(train) [91][450/586] lr: 5.000000e-04 eta: 6:14:28 time: 0.335270 data_time: 0.023362 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.837954 loss: 0.000591 2022/09/09 23:02:27 - mmengine - INFO - Epoch(train) [91][500/586] lr: 5.000000e-04 eta: 6:14:13 time: 0.340712 data_time: 0.023861 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.857640 loss: 0.000617 2022/09/09 23:02:44 - mmengine - INFO - Epoch(train) [91][550/586] lr: 5.000000e-04 eta: 6:13:58 time: 0.341014 data_time: 0.022951 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.830762 loss: 0.000614 2022/09/09 23:02:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:02:55 - mmengine - INFO - Saving checkpoint at 91 epochs 2022/09/09 23:03:19 - mmengine - INFO - Epoch(train) [92][50/586] lr: 5.000000e-04 eta: 6:13:17 time: 0.342104 data_time: 0.029656 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.863308 loss: 0.000595 2022/09/09 23:03:36 - mmengine - INFO - Epoch(train) [92][100/586] lr: 5.000000e-04 eta: 6:13:01 time: 0.331475 data_time: 0.024180 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.875055 loss: 0.000603 2022/09/09 23:03:52 - mmengine - INFO - Epoch(train) [92][150/586] lr: 5.000000e-04 eta: 6:12:46 time: 0.330088 data_time: 0.022971 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.833083 loss: 0.000620 2022/09/09 23:04:10 - mmengine - INFO - Epoch(train) [92][200/586] lr: 5.000000e-04 eta: 6:12:31 time: 0.343890 data_time: 0.023037 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.844782 loss: 0.000601 2022/09/09 23:04:26 - mmengine - INFO - Epoch(train) [92][250/586] lr: 5.000000e-04 eta: 6:12:16 time: 0.329088 data_time: 0.024958 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.850418 loss: 0.000605 2022/09/09 23:04:43 - mmengine - INFO - Epoch(train) [92][300/586] lr: 5.000000e-04 eta: 6:12:01 time: 0.336497 data_time: 0.030323 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.817855 loss: 0.000599 2022/09/09 23:05:00 - mmengine - INFO - Epoch(train) [92][350/586] lr: 5.000000e-04 eta: 6:11:46 time: 0.340260 data_time: 0.023086 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.836703 loss: 0.000609 2022/09/09 23:05:16 - mmengine - INFO - Epoch(train) [92][400/586] lr: 5.000000e-04 eta: 6:11:30 time: 0.328842 data_time: 0.022457 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.804756 loss: 0.000615 2022/09/09 23:05:33 - mmengine - INFO - Epoch(train) [92][450/586] lr: 5.000000e-04 eta: 6:11:15 time: 0.335057 data_time: 0.023047 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.848876 loss: 0.000614 2022/09/09 23:05:50 - mmengine - INFO - Epoch(train) [92][500/586] lr: 5.000000e-04 eta: 6:11:00 time: 0.333536 data_time: 0.023176 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.866915 loss: 0.000600 2022/09/09 23:06:07 - mmengine - INFO - Epoch(train) [92][550/586] lr: 5.000000e-04 eta: 6:10:44 time: 0.334842 data_time: 0.025685 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.829333 loss: 0.000593 2022/09/09 23:06:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:06:19 - mmengine - INFO - Saving checkpoint at 92 epochs 2022/09/09 23:06:42 - mmengine - INFO - Epoch(train) [93][50/586] lr: 5.000000e-04 eta: 6:10:03 time: 0.342241 data_time: 0.030101 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.843361 loss: 0.000609 2022/09/09 23:06:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:06:59 - mmengine - INFO - Epoch(train) [93][100/586] lr: 5.000000e-04 eta: 6:09:48 time: 0.335896 data_time: 0.024933 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.877775 loss: 0.000617 2022/09/09 23:07:16 - mmengine - INFO - Epoch(train) [93][150/586] lr: 5.000000e-04 eta: 6:09:32 time: 0.327704 data_time: 0.022829 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.834713 loss: 0.000604 2022/09/09 23:07:33 - mmengine - INFO - Epoch(train) [93][200/586] lr: 5.000000e-04 eta: 6:09:17 time: 0.338563 data_time: 0.023589 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.862964 loss: 0.000580 2022/09/09 23:07:49 - mmengine - INFO - Epoch(train) [93][250/586] lr: 5.000000e-04 eta: 6:09:02 time: 0.331880 data_time: 0.023041 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.837882 loss: 0.000618 2022/09/09 23:08:06 - mmengine - INFO - Epoch(train) [93][300/586] lr: 5.000000e-04 eta: 6:08:47 time: 0.342759 data_time: 0.023036 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.837390 loss: 0.000588 2022/09/09 23:08:23 - mmengine - INFO - Epoch(train) [93][350/586] lr: 5.000000e-04 eta: 6:08:32 time: 0.336299 data_time: 0.022871 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.779754 loss: 0.000607 2022/09/09 23:08:40 - mmengine - INFO - Epoch(train) [93][400/586] lr: 5.000000e-04 eta: 6:08:17 time: 0.332765 data_time: 0.026818 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.819986 loss: 0.000605 2022/09/09 23:08:57 - mmengine - INFO - Epoch(train) [93][450/586] lr: 5.000000e-04 eta: 6:08:02 time: 0.335826 data_time: 0.024053 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.868914 loss: 0.000599 2022/09/09 23:09:14 - mmengine - INFO - Epoch(train) [93][500/586] lr: 5.000000e-04 eta: 6:07:47 time: 0.339486 data_time: 0.023801 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.845522 loss: 0.000599 2022/09/09 23:09:30 - mmengine - INFO - Epoch(train) [93][550/586] lr: 5.000000e-04 eta: 6:07:31 time: 0.331701 data_time: 0.025425 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.878909 loss: 0.000617 2022/09/09 23:09:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:09:42 - mmengine - INFO - Saving checkpoint at 93 epochs 2022/09/09 23:10:07 - mmengine - INFO - Epoch(train) [94][50/586] lr: 5.000000e-04 eta: 6:06:51 time: 0.350141 data_time: 0.031454 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.855532 loss: 0.000599 2022/09/09 23:10:23 - mmengine - INFO - Epoch(train) [94][100/586] lr: 5.000000e-04 eta: 6:06:36 time: 0.330649 data_time: 0.024530 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.824906 loss: 0.000594 2022/09/09 23:10:40 - mmengine - INFO - Epoch(train) [94][150/586] lr: 5.000000e-04 eta: 6:06:20 time: 0.336920 data_time: 0.022967 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.793530 loss: 0.000613 2022/09/09 23:10:58 - mmengine - INFO - Epoch(train) [94][200/586] lr: 5.000000e-04 eta: 6:06:07 time: 0.359798 data_time: 0.023404 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.776332 loss: 0.000608 2022/09/09 23:11:14 - mmengine - INFO - Epoch(train) [94][250/586] lr: 5.000000e-04 eta: 6:05:51 time: 0.323293 data_time: 0.024319 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.875252 loss: 0.000608 2022/09/09 23:11:31 - mmengine - INFO - Epoch(train) [94][300/586] lr: 5.000000e-04 eta: 6:05:35 time: 0.327827 data_time: 0.025292 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.852802 loss: 0.000598 2022/09/09 23:11:48 - mmengine - INFO - Epoch(train) [94][350/586] lr: 5.000000e-04 eta: 6:05:21 time: 0.345801 data_time: 0.026436 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.850160 loss: 0.000597 2022/09/09 23:12:05 - mmengine - INFO - Epoch(train) [94][400/586] lr: 5.000000e-04 eta: 6:05:05 time: 0.328993 data_time: 0.023679 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.819989 loss: 0.000615 2022/09/09 23:12:22 - mmengine - INFO - Epoch(train) [94][450/586] lr: 5.000000e-04 eta: 6:04:50 time: 0.345307 data_time: 0.026667 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.785610 loss: 0.000586 2022/09/09 23:12:39 - mmengine - INFO - Epoch(train) [94][500/586] lr: 5.000000e-04 eta: 6:04:35 time: 0.334788 data_time: 0.023587 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.850977 loss: 0.000593 2022/09/09 23:12:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:12:55 - mmengine - INFO - Epoch(train) [94][550/586] lr: 5.000000e-04 eta: 6:04:20 time: 0.337555 data_time: 0.022876 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.826398 loss: 0.000613 2022/09/09 23:13:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:13:07 - mmengine - INFO - Saving checkpoint at 94 epochs 2022/09/09 23:13:32 - mmengine - INFO - Epoch(train) [95][50/586] lr: 5.000000e-04 eta: 6:03:40 time: 0.349041 data_time: 0.026966 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.823008 loss: 0.000587 2022/09/09 23:13:49 - mmengine - INFO - Epoch(train) [95][100/586] lr: 5.000000e-04 eta: 6:03:25 time: 0.340217 data_time: 0.027308 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.893837 loss: 0.000612 2022/09/09 23:14:05 - mmengine - INFO - Epoch(train) [95][150/586] lr: 5.000000e-04 eta: 6:03:09 time: 0.326770 data_time: 0.023756 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.870244 loss: 0.000594 2022/09/09 23:14:22 - mmengine - INFO - Epoch(train) [95][200/586] lr: 5.000000e-04 eta: 6:02:54 time: 0.337766 data_time: 0.023870 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.853770 loss: 0.000603 2022/09/09 23:14:39 - mmengine - INFO - Epoch(train) [95][250/586] lr: 5.000000e-04 eta: 6:02:39 time: 0.331929 data_time: 0.027896 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.836777 loss: 0.000601 2022/09/09 23:14:55 - mmengine - INFO - Epoch(train) [95][300/586] lr: 5.000000e-04 eta: 6:02:23 time: 0.329623 data_time: 0.026351 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.843855 loss: 0.000599 2022/09/09 23:15:13 - mmengine - INFO - Epoch(train) [95][350/586] lr: 5.000000e-04 eta: 6:02:08 time: 0.341846 data_time: 0.031109 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.836948 loss: 0.000612 2022/09/09 23:15:29 - mmengine - INFO - Epoch(train) [95][400/586] lr: 5.000000e-04 eta: 6:01:53 time: 0.328841 data_time: 0.024578 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.803969 loss: 0.000587 2022/09/09 23:15:46 - mmengine - INFO - Epoch(train) [95][450/586] lr: 5.000000e-04 eta: 6:01:38 time: 0.335510 data_time: 0.028758 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.828727 loss: 0.000612 2022/09/09 23:16:03 - mmengine - INFO - Epoch(train) [95][500/586] lr: 5.000000e-04 eta: 6:01:23 time: 0.341730 data_time: 0.023707 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.828209 loss: 0.000591 2022/09/09 23:16:19 - mmengine - INFO - Epoch(train) [95][550/586] lr: 5.000000e-04 eta: 6:01:07 time: 0.329036 data_time: 0.024453 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.812843 loss: 0.000584 2022/09/09 23:16:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:16:31 - mmengine - INFO - Saving checkpoint at 95 epochs 2022/09/09 23:16:55 - mmengine - INFO - Epoch(train) [96][50/586] lr: 5.000000e-04 eta: 6:00:27 time: 0.339474 data_time: 0.029719 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.915503 loss: 0.000597 2022/09/09 23:17:12 - mmengine - INFO - Epoch(train) [96][100/586] lr: 5.000000e-04 eta: 6:00:11 time: 0.333838 data_time: 0.023558 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.848168 loss: 0.000599 2022/09/09 23:17:28 - mmengine - INFO - Epoch(train) [96][150/586] lr: 5.000000e-04 eta: 5:59:56 time: 0.334533 data_time: 0.027229 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.863197 loss: 0.000596 2022/09/09 23:17:46 - mmengine - INFO - Epoch(train) [96][200/586] lr: 5.000000e-04 eta: 5:59:42 time: 0.344475 data_time: 0.024209 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.856145 loss: 0.000586 2022/09/09 23:18:02 - mmengine - INFO - Epoch(train) [96][250/586] lr: 5.000000e-04 eta: 5:59:26 time: 0.329989 data_time: 0.024530 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.835891 loss: 0.000605 2022/09/09 23:18:19 - mmengine - INFO - Epoch(train) [96][300/586] lr: 5.000000e-04 eta: 5:59:11 time: 0.338610 data_time: 0.027848 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.896548 loss: 0.000589 2022/09/09 23:18:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:18:35 - mmengine - INFO - Epoch(train) [96][350/586] lr: 5.000000e-04 eta: 5:58:55 time: 0.327776 data_time: 0.023509 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.854108 loss: 0.000599 2022/09/09 23:18:52 - mmengine - INFO - Epoch(train) [96][400/586] lr: 5.000000e-04 eta: 5:58:40 time: 0.329663 data_time: 0.023385 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.782663 loss: 0.000597 2022/09/09 23:19:09 - mmengine - INFO - Epoch(train) [96][450/586] lr: 5.000000e-04 eta: 5:58:25 time: 0.343093 data_time: 0.023003 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.856977 loss: 0.000607 2022/09/09 23:19:26 - mmengine - INFO - Epoch(train) [96][500/586] lr: 5.000000e-04 eta: 5:58:10 time: 0.334103 data_time: 0.022796 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.831033 loss: 0.000614 2022/09/09 23:19:42 - mmengine - INFO - Epoch(train) [96][550/586] lr: 5.000000e-04 eta: 5:57:54 time: 0.329205 data_time: 0.022518 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.835848 loss: 0.000573 2022/09/09 23:19:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:19:55 - mmengine - INFO - Saving checkpoint at 96 epochs 2022/09/09 23:20:19 - mmengine - INFO - Epoch(train) [97][50/586] lr: 5.000000e-04 eta: 5:57:14 time: 0.342271 data_time: 0.033149 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.790257 loss: 0.000595 2022/09/09 23:20:35 - mmengine - INFO - Epoch(train) [97][100/586] lr: 5.000000e-04 eta: 5:56:58 time: 0.328633 data_time: 0.024336 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.859146 loss: 0.000601 2022/09/09 23:20:52 - mmengine - INFO - Epoch(train) [97][150/586] lr: 5.000000e-04 eta: 5:56:43 time: 0.333304 data_time: 0.022698 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.860415 loss: 0.000609 2022/09/09 23:21:09 - mmengine - INFO - Epoch(train) [97][200/586] lr: 5.000000e-04 eta: 5:56:28 time: 0.337748 data_time: 0.023605 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.845995 loss: 0.000590 2022/09/09 23:21:26 - mmengine - INFO - Epoch(train) [97][250/586] lr: 5.000000e-04 eta: 5:56:13 time: 0.333395 data_time: 0.023280 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.867850 loss: 0.000597 2022/09/09 23:21:42 - mmengine - INFO - Epoch(train) [97][300/586] lr: 5.000000e-04 eta: 5:55:57 time: 0.334552 data_time: 0.022929 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.803104 loss: 0.000597 2022/09/09 23:21:59 - mmengine - INFO - Epoch(train) [97][350/586] lr: 5.000000e-04 eta: 5:55:42 time: 0.340158 data_time: 0.023085 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.880236 loss: 0.000603 2022/09/09 23:22:16 - mmengine - INFO - Epoch(train) [97][400/586] lr: 5.000000e-04 eta: 5:55:27 time: 0.337995 data_time: 0.023645 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.815085 loss: 0.000594 2022/09/09 23:22:33 - mmengine - INFO - Epoch(train) [97][450/586] lr: 5.000000e-04 eta: 5:55:12 time: 0.331593 data_time: 0.024274 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.873406 loss: 0.000583 2022/09/09 23:22:50 - mmengine - INFO - Epoch(train) [97][500/586] lr: 5.000000e-04 eta: 5:54:57 time: 0.343881 data_time: 0.022949 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.831378 loss: 0.000598 2022/09/09 23:23:07 - mmengine - INFO - Epoch(train) [97][550/586] lr: 5.000000e-04 eta: 5:54:42 time: 0.338181 data_time: 0.023865 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.844507 loss: 0.000612 2022/09/09 23:23:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:23:19 - mmengine - INFO - Saving checkpoint at 97 epochs 2022/09/09 23:23:43 - mmengine - INFO - Epoch(train) [98][50/586] lr: 5.000000e-04 eta: 5:54:03 time: 0.349183 data_time: 0.029127 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.873813 loss: 0.000594 2022/09/09 23:24:00 - mmengine - INFO - Epoch(train) [98][100/586] lr: 5.000000e-04 eta: 5:53:47 time: 0.332744 data_time: 0.023078 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.827680 loss: 0.000589 2022/09/09 23:24:17 - mmengine - INFO - Epoch(train) [98][150/586] lr: 5.000000e-04 eta: 5:53:32 time: 0.332076 data_time: 0.022658 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.835694 loss: 0.000593 2022/09/09 23:24:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:24:33 - mmengine - INFO - Epoch(train) [98][200/586] lr: 5.000000e-04 eta: 5:53:17 time: 0.334978 data_time: 0.023046 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.827276 loss: 0.000586 2022/09/09 23:24:50 - mmengine - INFO - Epoch(train) [98][250/586] lr: 5.000000e-04 eta: 5:53:01 time: 0.329736 data_time: 0.023936 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.822462 loss: 0.000602 2022/09/09 23:25:07 - mmengine - INFO - Epoch(train) [98][300/586] lr: 5.000000e-04 eta: 5:52:46 time: 0.335711 data_time: 0.027692 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.838605 loss: 0.000598 2022/09/09 23:25:23 - mmengine - INFO - Epoch(train) [98][350/586] lr: 5.000000e-04 eta: 5:52:31 time: 0.334718 data_time: 0.023866 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.862725 loss: 0.000578 2022/09/09 23:25:40 - mmengine - INFO - Epoch(train) [98][400/586] lr: 5.000000e-04 eta: 5:52:15 time: 0.329822 data_time: 0.022601 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.894990 loss: 0.000574 2022/09/09 23:25:57 - mmengine - INFO - Epoch(train) [98][450/586] lr: 5.000000e-04 eta: 5:52:00 time: 0.344181 data_time: 0.022818 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.857705 loss: 0.000596 2022/09/09 23:26:14 - mmengine - INFO - Epoch(train) [98][500/586] lr: 5.000000e-04 eta: 5:51:45 time: 0.332724 data_time: 0.022650 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.809328 loss: 0.000591 2022/09/09 23:26:31 - mmengine - INFO - Epoch(train) [98][550/586] lr: 5.000000e-04 eta: 5:51:30 time: 0.342336 data_time: 0.023117 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.810276 loss: 0.000611 2022/09/09 23:26:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:26:43 - mmengine - INFO - Saving checkpoint at 98 epochs 2022/09/09 23:27:07 - mmengine - INFO - Epoch(train) [99][50/586] lr: 5.000000e-04 eta: 5:50:50 time: 0.331635 data_time: 0.028750 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.820484 loss: 0.000594 2022/09/09 23:27:24 - mmengine - INFO - Epoch(train) [99][100/586] lr: 5.000000e-04 eta: 5:50:35 time: 0.341131 data_time: 0.027500 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.850199 loss: 0.000578 2022/09/09 23:27:40 - mmengine - INFO - Epoch(train) [99][150/586] lr: 5.000000e-04 eta: 5:50:20 time: 0.332252 data_time: 0.022779 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.860359 loss: 0.000595 2022/09/09 23:27:57 - mmengine - INFO - Epoch(train) [99][200/586] lr: 5.000000e-04 eta: 5:50:04 time: 0.332118 data_time: 0.025456 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.757975 loss: 0.000592 2022/09/09 23:28:14 - mmengine - INFO - Epoch(train) [99][250/586] lr: 5.000000e-04 eta: 5:49:49 time: 0.336152 data_time: 0.022914 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.828098 loss: 0.000598 2022/09/09 23:28:31 - mmengine - INFO - Epoch(train) [99][300/586] lr: 5.000000e-04 eta: 5:49:34 time: 0.339432 data_time: 0.024803 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.823158 loss: 0.000600 2022/09/09 23:28:47 - mmengine - INFO - Epoch(train) [99][350/586] lr: 5.000000e-04 eta: 5:49:18 time: 0.332611 data_time: 0.024452 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.840483 loss: 0.000578 2022/09/09 23:29:04 - mmengine - INFO - Epoch(train) [99][400/586] lr: 5.000000e-04 eta: 5:49:03 time: 0.336053 data_time: 0.026697 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.830974 loss: 0.000591 2022/09/09 23:29:21 - mmengine - INFO - Epoch(train) [99][450/586] lr: 5.000000e-04 eta: 5:48:48 time: 0.336266 data_time: 0.022861 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.864944 loss: 0.000593 2022/09/09 23:29:38 - mmengine - INFO - Epoch(train) [99][500/586] lr: 5.000000e-04 eta: 5:48:33 time: 0.337792 data_time: 0.022989 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.820376 loss: 0.000570 2022/09/09 23:29:54 - mmengine - INFO - Epoch(train) [99][550/586] lr: 5.000000e-04 eta: 5:48:18 time: 0.331717 data_time: 0.022757 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.842610 loss: 0.000583 2022/09/09 23:30:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:30:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:30:07 - mmengine - INFO - Saving checkpoint at 99 epochs 2022/09/09 23:30:31 - mmengine - INFO - Epoch(train) [100][50/586] lr: 5.000000e-04 eta: 5:47:38 time: 0.343900 data_time: 0.028399 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.838277 loss: 0.000599 2022/09/09 23:30:48 - mmengine - INFO - Epoch(train) [100][100/586] lr: 5.000000e-04 eta: 5:47:23 time: 0.330978 data_time: 0.023795 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.880948 loss: 0.000593 2022/09/09 23:31:05 - mmengine - INFO - Epoch(train) [100][150/586] lr: 5.000000e-04 eta: 5:47:08 time: 0.335755 data_time: 0.022774 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.849759 loss: 0.000603 2022/09/09 23:31:22 - mmengine - INFO - Epoch(train) [100][200/586] lr: 5.000000e-04 eta: 5:46:53 time: 0.341143 data_time: 0.027263 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.806797 loss: 0.000613 2022/09/09 23:31:38 - mmengine - INFO - Epoch(train) [100][250/586] lr: 5.000000e-04 eta: 5:46:37 time: 0.332653 data_time: 0.023609 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.854280 loss: 0.000587 2022/09/09 23:31:55 - mmengine - INFO - Epoch(train) [100][300/586] lr: 5.000000e-04 eta: 5:46:22 time: 0.331052 data_time: 0.023172 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.864298 loss: 0.000596 2022/09/09 23:32:12 - mmengine - INFO - Epoch(train) [100][350/586] lr: 5.000000e-04 eta: 5:46:06 time: 0.333595 data_time: 0.023251 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.836676 loss: 0.000601 2022/09/09 23:32:28 - mmengine - INFO - Epoch(train) [100][400/586] lr: 5.000000e-04 eta: 5:45:51 time: 0.331107 data_time: 0.023450 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.867585 loss: 0.000597 2022/09/09 23:32:45 - mmengine - INFO - Epoch(train) [100][450/586] lr: 5.000000e-04 eta: 5:45:35 time: 0.331289 data_time: 0.022877 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.793567 loss: 0.000595 2022/09/09 23:33:02 - mmengine - INFO - Epoch(train) [100][500/586] lr: 5.000000e-04 eta: 5:45:21 time: 0.345445 data_time: 0.026519 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.813955 loss: 0.000593 2022/09/09 23:33:18 - mmengine - INFO - Epoch(train) [100][550/586] lr: 5.000000e-04 eta: 5:45:05 time: 0.329368 data_time: 0.023436 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.816612 loss: 0.000610 2022/09/09 23:33:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:33:31 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/09/09 23:33:46 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:01:01 time: 0.171387 data_time: 0.012839 memory: 7489 2022/09/09 23:33:55 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:51 time: 0.166621 data_time: 0.008086 memory: 1657 2022/09/09 23:34:03 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:43 time: 0.169004 data_time: 0.007924 memory: 1657 2022/09/09 23:34:11 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:34 time: 0.165113 data_time: 0.007786 memory: 1657 2022/09/09 23:34:20 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:26 time: 0.166478 data_time: 0.008838 memory: 1657 2022/09/09 23:34:28 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:17 time: 0.168208 data_time: 0.011019 memory: 1657 2022/09/09 23:34:36 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:09 time: 0.164910 data_time: 0.007808 memory: 1657 2022/09/09 23:34:45 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:01 time: 0.162788 data_time: 0.007005 memory: 1657 2022/09/09 23:35:20 - mmengine - INFO - Evaluating CocoMetric... 2022/09/09 23:35:34 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.742089 coco/AP .5: 0.900710 coco/AP .75: 0.814851 coco/AP (M): 0.708365 coco/AP (L): 0.805923 coco/AR: 0.795009 coco/AR .5: 0.938130 coco/AR .75: 0.860516 coco/AR (M): 0.754493 coco/AR (L): 0.854701 2022/09/09 23:35:34 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_90.pth is removed 2022/09/09 23:35:38 - mmengine - INFO - The best checkpoint with 0.7421 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/09/09 23:35:55 - mmengine - INFO - Epoch(train) [101][50/586] lr: 5.000000e-04 eta: 5:44:26 time: 0.342509 data_time: 0.033082 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.890254 loss: 0.000606 2022/09/09 23:36:12 - mmengine - INFO - Epoch(train) [101][100/586] lr: 5.000000e-04 eta: 5:44:10 time: 0.330579 data_time: 0.023029 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.817341 loss: 0.000603 2022/09/09 23:36:29 - mmengine - INFO - Epoch(train) [101][150/586] lr: 5.000000e-04 eta: 5:43:56 time: 0.342694 data_time: 0.023216 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.743565 loss: 0.000610 2022/09/09 23:36:45 - mmengine - INFO - Epoch(train) [101][200/586] lr: 5.000000e-04 eta: 5:43:40 time: 0.326927 data_time: 0.024439 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.785545 loss: 0.000593 2022/09/09 23:37:02 - mmengine - INFO - Epoch(train) [101][250/586] lr: 5.000000e-04 eta: 5:43:25 time: 0.333910 data_time: 0.024066 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.878098 loss: 0.000582 2022/09/09 23:37:19 - mmengine - INFO - Epoch(train) [101][300/586] lr: 5.000000e-04 eta: 5:43:09 time: 0.338077 data_time: 0.027277 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.815459 loss: 0.000608 2022/09/09 23:37:35 - mmengine - INFO - Epoch(train) [101][350/586] lr: 5.000000e-04 eta: 5:42:54 time: 0.330105 data_time: 0.023455 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.859289 loss: 0.000596 2022/09/09 23:37:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:37:52 - mmengine - INFO - Epoch(train) [101][400/586] lr: 5.000000e-04 eta: 5:42:39 time: 0.339611 data_time: 0.028758 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.838874 loss: 0.000583 2022/09/09 23:38:09 - mmengine - INFO - Epoch(train) [101][450/586] lr: 5.000000e-04 eta: 5:42:24 time: 0.334892 data_time: 0.023079 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.755215 loss: 0.000597 2022/09/09 23:38:25 - mmengine - INFO - Epoch(train) [101][500/586] lr: 5.000000e-04 eta: 5:42:08 time: 0.328236 data_time: 0.023375 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.845450 loss: 0.000605 2022/09/09 23:38:42 - mmengine - INFO - Epoch(train) [101][550/586] lr: 5.000000e-04 eta: 5:41:53 time: 0.340023 data_time: 0.023808 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.867798 loss: 0.000604 2022/09/09 23:38:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:38:55 - mmengine - INFO - Saving checkpoint at 101 epochs 2022/09/09 23:39:19 - mmengine - INFO - Epoch(train) [102][50/586] lr: 5.000000e-04 eta: 5:41:14 time: 0.342808 data_time: 0.037004 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.768741 loss: 0.000595 2022/09/09 23:39:35 - mmengine - INFO - Epoch(train) [102][100/586] lr: 5.000000e-04 eta: 5:40:59 time: 0.333090 data_time: 0.024156 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.836627 loss: 0.000600 2022/09/09 23:39:52 - mmengine - INFO - Epoch(train) [102][150/586] lr: 5.000000e-04 eta: 5:40:43 time: 0.336217 data_time: 0.023916 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.862086 loss: 0.000605 2022/09/09 23:40:09 - mmengine - INFO - Epoch(train) [102][200/586] lr: 5.000000e-04 eta: 5:40:28 time: 0.337340 data_time: 0.023194 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.866708 loss: 0.000598 2022/09/09 23:40:26 - mmengine - INFO - Epoch(train) [102][250/586] lr: 5.000000e-04 eta: 5:40:13 time: 0.335840 data_time: 0.024497 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.864418 loss: 0.000595 2022/09/09 23:40:43 - mmengine - INFO - Epoch(train) [102][300/586] lr: 5.000000e-04 eta: 5:39:58 time: 0.334422 data_time: 0.022744 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.878714 loss: 0.000595 2022/09/09 23:40:59 - mmengine - INFO - Epoch(train) [102][350/586] lr: 5.000000e-04 eta: 5:39:43 time: 0.336549 data_time: 0.022673 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.816752 loss: 0.000602 2022/09/09 23:41:16 - mmengine - INFO - Epoch(train) [102][400/586] lr: 5.000000e-04 eta: 5:39:27 time: 0.331035 data_time: 0.023795 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.834282 loss: 0.000579 2022/09/09 23:41:33 - mmengine - INFO - Epoch(train) [102][450/586] lr: 5.000000e-04 eta: 5:39:12 time: 0.336194 data_time: 0.025396 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.835176 loss: 0.000596 2022/09/09 23:41:50 - mmengine - INFO - Epoch(train) [102][500/586] lr: 5.000000e-04 eta: 5:38:57 time: 0.337703 data_time: 0.022910 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.877461 loss: 0.000587 2022/09/09 23:42:06 - mmengine - INFO - Epoch(train) [102][550/586] lr: 5.000000e-04 eta: 5:38:41 time: 0.332523 data_time: 0.022996 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.815193 loss: 0.000588 2022/09/09 23:42:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:42:19 - mmengine - INFO - Saving checkpoint at 102 epochs 2022/09/09 23:42:42 - mmengine - INFO - Epoch(train) [103][50/586] lr: 5.000000e-04 eta: 5:38:02 time: 0.334533 data_time: 0.027537 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.823308 loss: 0.000604 2022/09/09 23:42:59 - mmengine - INFO - Epoch(train) [103][100/586] lr: 5.000000e-04 eta: 5:37:47 time: 0.338538 data_time: 0.026262 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.891324 loss: 0.000587 2022/09/09 23:43:16 - mmengine - INFO - Epoch(train) [103][150/586] lr: 5.000000e-04 eta: 5:37:32 time: 0.333204 data_time: 0.022908 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.764550 loss: 0.000572 2022/09/09 23:43:33 - mmengine - INFO - Epoch(train) [103][200/586] lr: 5.000000e-04 eta: 5:37:16 time: 0.335513 data_time: 0.023314 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.823603 loss: 0.000588 2022/09/09 23:43:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:43:49 - mmengine - INFO - Epoch(train) [103][250/586] lr: 5.000000e-04 eta: 5:37:01 time: 0.330847 data_time: 0.028603 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.889891 loss: 0.000581 2022/09/09 23:44:07 - mmengine - INFO - Epoch(train) [103][300/586] lr: 5.000000e-04 eta: 5:36:46 time: 0.343399 data_time: 0.028101 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.849257 loss: 0.000580 2022/09/09 23:44:23 - mmengine - INFO - Epoch(train) [103][350/586] lr: 5.000000e-04 eta: 5:36:31 time: 0.338596 data_time: 0.023199 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.864855 loss: 0.000588 2022/09/09 23:44:40 - mmengine - INFO - Epoch(train) [103][400/586] lr: 5.000000e-04 eta: 5:36:16 time: 0.336681 data_time: 0.024451 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.818284 loss: 0.000604 2022/09/09 23:44:57 - mmengine - INFO - Epoch(train) [103][450/586] lr: 5.000000e-04 eta: 5:36:00 time: 0.331594 data_time: 0.023129 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.808761 loss: 0.000583 2022/09/09 23:45:14 - mmengine - INFO - Epoch(train) [103][500/586] lr: 5.000000e-04 eta: 5:35:45 time: 0.334376 data_time: 0.023511 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.810776 loss: 0.000604 2022/09/09 23:45:31 - mmengine - INFO - Epoch(train) [103][550/586] lr: 5.000000e-04 eta: 5:35:30 time: 0.337761 data_time: 0.024310 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.821491 loss: 0.000590 2022/09/09 23:45:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:45:42 - mmengine - INFO - Saving checkpoint at 103 epochs 2022/09/09 23:46:07 - mmengine - INFO - Epoch(train) [104][50/586] lr: 5.000000e-04 eta: 5:34:51 time: 0.342660 data_time: 0.029283 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.876858 loss: 0.000579 2022/09/09 23:46:24 - mmengine - INFO - Epoch(train) [104][100/586] lr: 5.000000e-04 eta: 5:34:36 time: 0.340103 data_time: 0.026792 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.834958 loss: 0.000591 2022/09/09 23:46:40 - mmengine - INFO - Epoch(train) [104][150/586] lr: 5.000000e-04 eta: 5:34:21 time: 0.333176 data_time: 0.026037 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.864175 loss: 0.000575 2022/09/09 23:46:57 - mmengine - INFO - Epoch(train) [104][200/586] lr: 5.000000e-04 eta: 5:34:06 time: 0.338850 data_time: 0.029300 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.839897 loss: 0.000596 2022/09/09 23:47:14 - mmengine - INFO - Epoch(train) [104][250/586] lr: 5.000000e-04 eta: 5:33:50 time: 0.331281 data_time: 0.023276 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.803875 loss: 0.000605 2022/09/09 23:47:31 - mmengine - INFO - Epoch(train) [104][300/586] lr: 5.000000e-04 eta: 5:33:35 time: 0.340115 data_time: 0.022925 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.877723 loss: 0.000594 2022/09/09 23:47:47 - mmengine - INFO - Epoch(train) [104][350/586] lr: 5.000000e-04 eta: 5:33:20 time: 0.331678 data_time: 0.022535 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.886396 loss: 0.000579 2022/09/09 23:48:04 - mmengine - INFO - Epoch(train) [104][400/586] lr: 5.000000e-04 eta: 5:33:04 time: 0.330297 data_time: 0.024034 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.803773 loss: 0.000589 2022/09/09 23:48:21 - mmengine - INFO - Epoch(train) [104][450/586] lr: 5.000000e-04 eta: 5:32:49 time: 0.335707 data_time: 0.026898 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.845898 loss: 0.000592 2022/09/09 23:48:37 - mmengine - INFO - Epoch(train) [104][500/586] lr: 5.000000e-04 eta: 5:32:34 time: 0.335496 data_time: 0.022838 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.845172 loss: 0.000603 2022/09/09 23:48:54 - mmengine - INFO - Epoch(train) [104][550/586] lr: 5.000000e-04 eta: 5:32:19 time: 0.340130 data_time: 0.022681 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.816592 loss: 0.000580 2022/09/09 23:49:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:49:06 - mmengine - INFO - Saving checkpoint at 104 epochs 2022/09/09 23:49:31 - mmengine - INFO - Epoch(train) [105][50/586] lr: 5.000000e-04 eta: 5:31:41 time: 0.345577 data_time: 0.029890 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.875613 loss: 0.000567 2022/09/09 23:49:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:49:48 - mmengine - INFO - Epoch(train) [105][100/586] lr: 5.000000e-04 eta: 5:31:25 time: 0.335511 data_time: 0.025080 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.919906 loss: 0.000589 2022/09/09 23:50:04 - mmengine - INFO - Epoch(train) [105][150/586] lr: 5.000000e-04 eta: 5:31:10 time: 0.329672 data_time: 0.022971 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.869178 loss: 0.000586 2022/09/09 23:50:21 - mmengine - INFO - Epoch(train) [105][200/586] lr: 5.000000e-04 eta: 5:30:54 time: 0.336544 data_time: 0.022708 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.882046 loss: 0.000582 2022/09/09 23:50:38 - mmengine - INFO - Epoch(train) [105][250/586] lr: 5.000000e-04 eta: 5:30:39 time: 0.332011 data_time: 0.026127 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.861577 loss: 0.000572 2022/09/09 23:50:54 - mmengine - INFO - Epoch(train) [105][300/586] lr: 5.000000e-04 eta: 5:30:24 time: 0.336447 data_time: 0.023219 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.835292 loss: 0.000601 2022/09/09 23:51:11 - mmengine - INFO - Epoch(train) [105][350/586] lr: 5.000000e-04 eta: 5:30:09 time: 0.336798 data_time: 0.022498 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.793449 loss: 0.000598 2022/09/09 23:51:28 - mmengine - INFO - Epoch(train) [105][400/586] lr: 5.000000e-04 eta: 5:29:53 time: 0.338933 data_time: 0.022799 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.823294 loss: 0.000591 2022/09/09 23:51:45 - mmengine - INFO - Epoch(train) [105][450/586] lr: 5.000000e-04 eta: 5:29:38 time: 0.330421 data_time: 0.022518 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.855122 loss: 0.000599 2022/09/09 23:52:01 - mmengine - INFO - Epoch(train) [105][500/586] lr: 5.000000e-04 eta: 5:29:22 time: 0.330697 data_time: 0.023551 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.888246 loss: 0.000582 2022/09/09 23:52:18 - mmengine - INFO - Epoch(train) [105][550/586] lr: 5.000000e-04 eta: 5:29:07 time: 0.334034 data_time: 0.027881 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.858188 loss: 0.000584 2022/09/09 23:52:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:52:30 - mmengine - INFO - Saving checkpoint at 105 epochs 2022/09/09 23:52:54 - mmengine - INFO - Epoch(train) [106][50/586] lr: 5.000000e-04 eta: 5:28:29 time: 0.346891 data_time: 0.031101 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.861552 loss: 0.000576 2022/09/09 23:53:11 - mmengine - INFO - Epoch(train) [106][100/586] lr: 5.000000e-04 eta: 5:28:13 time: 0.327529 data_time: 0.022708 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.846764 loss: 0.000590 2022/09/09 23:53:27 - mmengine - INFO - Epoch(train) [106][150/586] lr: 5.000000e-04 eta: 5:27:58 time: 0.329816 data_time: 0.027115 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.835675 loss: 0.000591 2022/09/09 23:53:44 - mmengine - INFO - Epoch(train) [106][200/586] lr: 5.000000e-04 eta: 5:27:43 time: 0.341913 data_time: 0.028956 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.792224 loss: 0.000590 2022/09/09 23:54:01 - mmengine - INFO - Epoch(train) [106][250/586] lr: 5.000000e-04 eta: 5:27:27 time: 0.328869 data_time: 0.023229 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.815031 loss: 0.000602 2022/09/09 23:54:18 - mmengine - INFO - Epoch(train) [106][300/586] lr: 5.000000e-04 eta: 5:27:12 time: 0.338682 data_time: 0.022565 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.842730 loss: 0.000574 2022/09/09 23:54:35 - mmengine - INFO - Epoch(train) [106][350/586] lr: 5.000000e-04 eta: 5:26:57 time: 0.337867 data_time: 0.025736 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.848671 loss: 0.000564 2022/09/09 23:54:51 - mmengine - INFO - Epoch(train) [106][400/586] lr: 5.000000e-04 eta: 5:26:42 time: 0.333112 data_time: 0.024183 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.865275 loss: 0.000588 2022/09/09 23:55:08 - mmengine - INFO - Epoch(train) [106][450/586] lr: 5.000000e-04 eta: 5:26:26 time: 0.337367 data_time: 0.022399 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.838843 loss: 0.000582 2022/09/09 23:55:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:55:25 - mmengine - INFO - Epoch(train) [106][500/586] lr: 5.000000e-04 eta: 5:26:11 time: 0.338940 data_time: 0.026427 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.840976 loss: 0.000581 2022/09/09 23:55:42 - mmengine - INFO - Epoch(train) [106][550/586] lr: 5.000000e-04 eta: 5:25:56 time: 0.334663 data_time: 0.022880 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.863462 loss: 0.000586 2022/09/09 23:55:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:55:54 - mmengine - INFO - Saving checkpoint at 106 epochs 2022/09/09 23:56:18 - mmengine - INFO - Epoch(train) [107][50/586] lr: 5.000000e-04 eta: 5:25:18 time: 0.345542 data_time: 0.028224 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.797713 loss: 0.000592 2022/09/09 23:56:36 - mmengine - INFO - Epoch(train) [107][100/586] lr: 5.000000e-04 eta: 5:25:03 time: 0.346691 data_time: 0.026373 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.849706 loss: 0.000588 2022/09/09 23:56:52 - mmengine - INFO - Epoch(train) [107][150/586] lr: 5.000000e-04 eta: 5:24:48 time: 0.332174 data_time: 0.022609 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.862113 loss: 0.000584 2022/09/09 23:57:09 - mmengine - INFO - Epoch(train) [107][200/586] lr: 5.000000e-04 eta: 5:24:33 time: 0.332962 data_time: 0.022266 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.800586 loss: 0.000575 2022/09/09 23:57:26 - mmengine - INFO - Epoch(train) [107][250/586] lr: 5.000000e-04 eta: 5:24:17 time: 0.337838 data_time: 0.026818 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.829292 loss: 0.000605 2022/09/09 23:57:43 - mmengine - INFO - Epoch(train) [107][300/586] lr: 5.000000e-04 eta: 5:24:02 time: 0.334728 data_time: 0.023493 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.778030 loss: 0.000604 2022/09/09 23:57:59 - mmengine - INFO - Epoch(train) [107][350/586] lr: 5.000000e-04 eta: 5:23:47 time: 0.332469 data_time: 0.022841 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.897556 loss: 0.000586 2022/09/09 23:58:16 - mmengine - INFO - Epoch(train) [107][400/586] lr: 5.000000e-04 eta: 5:23:31 time: 0.334601 data_time: 0.022663 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.889387 loss: 0.000577 2022/09/09 23:58:33 - mmengine - INFO - Epoch(train) [107][450/586] lr: 5.000000e-04 eta: 5:23:16 time: 0.340382 data_time: 0.022786 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.875549 loss: 0.000564 2022/09/09 23:58:50 - mmengine - INFO - Epoch(train) [107][500/586] lr: 5.000000e-04 eta: 5:23:01 time: 0.336173 data_time: 0.022652 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.882126 loss: 0.000582 2022/09/09 23:59:06 - mmengine - INFO - Epoch(train) [107][550/586] lr: 5.000000e-04 eta: 5:22:45 time: 0.330450 data_time: 0.023141 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.835049 loss: 0.000587 2022/09/09 23:59:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/09 23:59:19 - mmengine - INFO - Saving checkpoint at 107 epochs 2022/09/09 23:59:43 - mmengine - INFO - Epoch(train) [108][50/586] lr: 5.000000e-04 eta: 5:22:08 time: 0.343191 data_time: 0.029376 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.829667 loss: 0.000584 2022/09/10 00:00:00 - mmengine - INFO - Epoch(train) [108][100/586] lr: 5.000000e-04 eta: 5:21:52 time: 0.328854 data_time: 0.024856 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.854138 loss: 0.000569 2022/09/10 00:00:18 - mmengine - INFO - Epoch(train) [108][150/586] lr: 5.000000e-04 eta: 5:21:39 time: 0.379021 data_time: 0.025056 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.786011 loss: 0.000594 2022/09/10 00:00:36 - mmengine - INFO - Epoch(train) [108][200/586] lr: 5.000000e-04 eta: 5:21:24 time: 0.343040 data_time: 0.026580 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.895664 loss: 0.000595 2022/09/10 00:00:53 - mmengine - INFO - Epoch(train) [108][250/586] lr: 5.000000e-04 eta: 5:21:09 time: 0.339363 data_time: 0.024108 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.937580 loss: 0.000576 2022/09/10 00:01:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:01:09 - mmengine - INFO - Epoch(train) [108][300/586] lr: 5.000000e-04 eta: 5:20:53 time: 0.332583 data_time: 0.022795 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.886457 loss: 0.000585 2022/09/10 00:01:26 - mmengine - INFO - Epoch(train) [108][350/586] lr: 5.000000e-04 eta: 5:20:38 time: 0.334826 data_time: 0.022563 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.786599 loss: 0.000598 2022/09/10 00:01:42 - mmengine - INFO - Epoch(train) [108][400/586] lr: 5.000000e-04 eta: 5:20:22 time: 0.328377 data_time: 0.025186 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.853198 loss: 0.000601 2022/09/10 00:01:59 - mmengine - INFO - Epoch(train) [108][450/586] lr: 5.000000e-04 eta: 5:20:07 time: 0.334039 data_time: 0.024672 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.848490 loss: 0.000591 2022/09/10 00:02:16 - mmengine - INFO - Epoch(train) [108][500/586] lr: 5.000000e-04 eta: 5:19:52 time: 0.339709 data_time: 0.022318 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.882440 loss: 0.000592 2022/09/10 00:02:32 - mmengine - INFO - Epoch(train) [108][550/586] lr: 5.000000e-04 eta: 5:19:36 time: 0.326493 data_time: 0.022979 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.842575 loss: 0.000589 2022/09/10 00:02:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:02:44 - mmengine - INFO - Saving checkpoint at 108 epochs 2022/09/10 00:03:09 - mmengine - INFO - Epoch(train) [109][50/586] lr: 5.000000e-04 eta: 5:18:59 time: 0.349157 data_time: 0.029977 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.726855 loss: 0.000601 2022/09/10 00:03:26 - mmengine - INFO - Epoch(train) [109][100/586] lr: 5.000000e-04 eta: 5:18:44 time: 0.336439 data_time: 0.026646 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.842938 loss: 0.000594 2022/09/10 00:03:42 - mmengine - INFO - Epoch(train) [109][150/586] lr: 5.000000e-04 eta: 5:18:28 time: 0.334050 data_time: 0.022436 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.897613 loss: 0.000581 2022/09/10 00:04:00 - mmengine - INFO - Epoch(train) [109][200/586] lr: 5.000000e-04 eta: 5:18:13 time: 0.343497 data_time: 0.022623 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.889273 loss: 0.000594 2022/09/10 00:04:16 - mmengine - INFO - Epoch(train) [109][250/586] lr: 5.000000e-04 eta: 5:17:58 time: 0.336110 data_time: 0.026250 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.821192 loss: 0.000604 2022/09/10 00:04:33 - mmengine - INFO - Epoch(train) [109][300/586] lr: 5.000000e-04 eta: 5:17:43 time: 0.333117 data_time: 0.022926 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.826976 loss: 0.000578 2022/09/10 00:04:50 - mmengine - INFO - Epoch(train) [109][350/586] lr: 5.000000e-04 eta: 5:17:27 time: 0.337371 data_time: 0.023560 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.814058 loss: 0.000589 2022/09/10 00:05:06 - mmengine - INFO - Epoch(train) [109][400/586] lr: 5.000000e-04 eta: 5:17:12 time: 0.331588 data_time: 0.022700 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.858326 loss: 0.000576 2022/09/10 00:05:23 - mmengine - INFO - Epoch(train) [109][450/586] lr: 5.000000e-04 eta: 5:16:56 time: 0.333754 data_time: 0.023090 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.759830 loss: 0.000602 2022/09/10 00:05:40 - mmengine - INFO - Epoch(train) [109][500/586] lr: 5.000000e-04 eta: 5:16:41 time: 0.341240 data_time: 0.025876 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.856169 loss: 0.000596 2022/09/10 00:05:57 - mmengine - INFO - Epoch(train) [109][550/586] lr: 5.000000e-04 eta: 5:16:26 time: 0.328582 data_time: 0.023674 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.834208 loss: 0.000587 2022/09/10 00:06:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:06:09 - mmengine - INFO - Saving checkpoint at 109 epochs 2022/09/10 00:06:33 - mmengine - INFO - Epoch(train) [110][50/586] lr: 5.000000e-04 eta: 5:15:49 time: 0.352674 data_time: 0.030516 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.910672 loss: 0.000588 2022/09/10 00:06:50 - mmengine - INFO - Epoch(train) [110][100/586] lr: 5.000000e-04 eta: 5:15:33 time: 0.327990 data_time: 0.023414 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.865386 loss: 0.000576 2022/09/10 00:06:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:07:07 - mmengine - INFO - Epoch(train) [110][150/586] lr: 5.000000e-04 eta: 5:15:18 time: 0.336724 data_time: 0.023324 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.811096 loss: 0.000590 2022/09/10 00:07:23 - mmengine - INFO - Epoch(train) [110][200/586] lr: 5.000000e-04 eta: 5:15:03 time: 0.339277 data_time: 0.023022 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.846734 loss: 0.000583 2022/09/10 00:07:40 - mmengine - INFO - Epoch(train) [110][250/586] lr: 5.000000e-04 eta: 5:14:47 time: 0.329004 data_time: 0.025286 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.776505 loss: 0.000608 2022/09/10 00:07:56 - mmengine - INFO - Epoch(train) [110][300/586] lr: 5.000000e-04 eta: 5:14:32 time: 0.328677 data_time: 0.024513 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.766889 loss: 0.000588 2022/09/10 00:08:14 - mmengine - INFO - Epoch(train) [110][350/586] lr: 5.000000e-04 eta: 5:14:17 time: 0.350077 data_time: 0.023102 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.887673 loss: 0.000603 2022/09/10 00:08:30 - mmengine - INFO - Epoch(train) [110][400/586] lr: 5.000000e-04 eta: 5:14:01 time: 0.326937 data_time: 0.022448 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.832491 loss: 0.000598 2022/09/10 00:08:47 - mmengine - INFO - Epoch(train) [110][450/586] lr: 5.000000e-04 eta: 5:13:46 time: 0.336902 data_time: 0.022459 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.884114 loss: 0.000596 2022/09/10 00:09:04 - mmengine - INFO - Epoch(train) [110][500/586] lr: 5.000000e-04 eta: 5:13:31 time: 0.341543 data_time: 0.023179 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.830659 loss: 0.000592 2022/09/10 00:09:20 - mmengine - INFO - Epoch(train) [110][550/586] lr: 5.000000e-04 eta: 5:13:15 time: 0.324502 data_time: 0.022742 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.863830 loss: 0.000591 2022/09/10 00:09:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:09:32 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/09/10 00:09:48 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:01:00 time: 0.170289 data_time: 0.012428 memory: 7489 2022/09/10 00:09:57 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:50 time: 0.165492 data_time: 0.007884 memory: 1657 2022/09/10 00:10:05 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:43 time: 0.168158 data_time: 0.007418 memory: 1657 2022/09/10 00:10:13 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:34 time: 0.165115 data_time: 0.007306 memory: 1657 2022/09/10 00:10:22 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:25 time: 0.164858 data_time: 0.007748 memory: 1657 2022/09/10 00:10:30 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:17 time: 0.164520 data_time: 0.007468 memory: 1657 2022/09/10 00:10:38 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:09 time: 0.165984 data_time: 0.007680 memory: 1657 2022/09/10 00:10:46 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:01 time: 0.162110 data_time: 0.007086 memory: 1657 2022/09/10 00:11:22 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 00:11:36 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.742025 coco/AP .5: 0.899314 coco/AP .75: 0.812741 coco/AP (M): 0.706332 coco/AP (L): 0.808965 coco/AR: 0.795828 coco/AR .5: 0.938917 coco/AR .75: 0.857683 coco/AR (M): 0.753455 coco/AR (L): 0.857228 2022/09/10 00:11:53 - mmengine - INFO - Epoch(train) [111][50/586] lr: 5.000000e-04 eta: 5:12:38 time: 0.353344 data_time: 0.027772 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.850318 loss: 0.000611 2022/09/10 00:12:10 - mmengine - INFO - Epoch(train) [111][100/586] lr: 5.000000e-04 eta: 5:12:23 time: 0.325074 data_time: 0.023334 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.810427 loss: 0.000575 2022/09/10 00:12:26 - mmengine - INFO - Epoch(train) [111][150/586] lr: 5.000000e-04 eta: 5:12:07 time: 0.332749 data_time: 0.023414 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.893401 loss: 0.000589 2022/09/10 00:12:43 - mmengine - INFO - Epoch(train) [111][200/586] lr: 5.000000e-04 eta: 5:11:52 time: 0.337926 data_time: 0.022765 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.895458 loss: 0.000583 2022/09/10 00:13:00 - mmengine - INFO - Epoch(train) [111][250/586] lr: 5.000000e-04 eta: 5:11:37 time: 0.334680 data_time: 0.025829 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.857980 loss: 0.000585 2022/09/10 00:13:17 - mmengine - INFO - Epoch(train) [111][300/586] lr: 5.000000e-04 eta: 5:11:21 time: 0.339126 data_time: 0.022848 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.844079 loss: 0.000581 2022/09/10 00:13:34 - mmengine - INFO - Epoch(train) [111][350/586] lr: 5.000000e-04 eta: 5:11:06 time: 0.336995 data_time: 0.022979 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.837786 loss: 0.000599 2022/09/10 00:13:50 - mmengine - INFO - Epoch(train) [111][400/586] lr: 5.000000e-04 eta: 5:10:51 time: 0.334706 data_time: 0.026419 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.833574 loss: 0.000578 2022/09/10 00:14:07 - mmengine - INFO - Epoch(train) [111][450/586] lr: 5.000000e-04 eta: 5:10:36 time: 0.340460 data_time: 0.023642 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.875958 loss: 0.000608 2022/09/10 00:14:24 - mmengine - INFO - Epoch(train) [111][500/586] lr: 5.000000e-04 eta: 5:10:20 time: 0.340163 data_time: 0.023166 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.813657 loss: 0.000564 2022/09/10 00:14:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:14:41 - mmengine - INFO - Epoch(train) [111][550/586] lr: 5.000000e-04 eta: 5:10:05 time: 0.328860 data_time: 0.027039 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.842908 loss: 0.000587 2022/09/10 00:14:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:14:53 - mmengine - INFO - Saving checkpoint at 111 epochs 2022/09/10 00:15:17 - mmengine - INFO - Epoch(train) [112][50/586] lr: 5.000000e-04 eta: 5:09:28 time: 0.345595 data_time: 0.030137 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.859456 loss: 0.000608 2022/09/10 00:15:34 - mmengine - INFO - Epoch(train) [112][100/586] lr: 5.000000e-04 eta: 5:09:12 time: 0.331314 data_time: 0.024650 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.815349 loss: 0.000604 2022/09/10 00:15:51 - mmengine - INFO - Epoch(train) [112][150/586] lr: 5.000000e-04 eta: 5:08:57 time: 0.341409 data_time: 0.024064 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.873741 loss: 0.000601 2022/09/10 00:16:08 - mmengine - INFO - Epoch(train) [112][200/586] lr: 5.000000e-04 eta: 5:08:42 time: 0.336008 data_time: 0.022902 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.904773 loss: 0.000592 2022/09/10 00:16:24 - mmengine - INFO - Epoch(train) [112][250/586] lr: 5.000000e-04 eta: 5:08:27 time: 0.334572 data_time: 0.024513 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.811157 loss: 0.000589 2022/09/10 00:16:42 - mmengine - INFO - Epoch(train) [112][300/586] lr: 5.000000e-04 eta: 5:08:12 time: 0.339775 data_time: 0.027034 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.816752 loss: 0.000595 2022/09/10 00:16:58 - mmengine - INFO - Epoch(train) [112][350/586] lr: 5.000000e-04 eta: 5:07:56 time: 0.328316 data_time: 0.023488 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.882757 loss: 0.000565 2022/09/10 00:17:14 - mmengine - INFO - Epoch(train) [112][400/586] lr: 5.000000e-04 eta: 5:07:40 time: 0.327566 data_time: 0.023473 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.856073 loss: 0.000590 2022/09/10 00:17:32 - mmengine - INFO - Epoch(train) [112][450/586] lr: 5.000000e-04 eta: 5:07:26 time: 0.351536 data_time: 0.026500 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.934754 loss: 0.000588 2022/09/10 00:17:48 - mmengine - INFO - Epoch(train) [112][500/586] lr: 5.000000e-04 eta: 5:07:10 time: 0.329808 data_time: 0.022977 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.788123 loss: 0.000581 2022/09/10 00:18:05 - mmengine - INFO - Epoch(train) [112][550/586] lr: 5.000000e-04 eta: 5:06:54 time: 0.330663 data_time: 0.023319 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.816081 loss: 0.000581 2022/09/10 00:18:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:18:17 - mmengine - INFO - Saving checkpoint at 112 epochs 2022/09/10 00:18:41 - mmengine - INFO - Epoch(train) [113][50/586] lr: 5.000000e-04 eta: 5:06:18 time: 0.340389 data_time: 0.032977 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.781262 loss: 0.000605 2022/09/10 00:18:58 - mmengine - INFO - Epoch(train) [113][100/586] lr: 5.000000e-04 eta: 5:06:02 time: 0.337104 data_time: 0.027092 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.829985 loss: 0.000586 2022/09/10 00:19:16 - mmengine - INFO - Epoch(train) [113][150/586] lr: 5.000000e-04 eta: 5:05:47 time: 0.345748 data_time: 0.024023 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.850003 loss: 0.000604 2022/09/10 00:19:32 - mmengine - INFO - Epoch(train) [113][200/586] lr: 5.000000e-04 eta: 5:05:32 time: 0.328126 data_time: 0.024713 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.842556 loss: 0.000582 2022/09/10 00:19:49 - mmengine - INFO - Epoch(train) [113][250/586] lr: 5.000000e-04 eta: 5:05:16 time: 0.335090 data_time: 0.028672 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.837300 loss: 0.000576 2022/09/10 00:20:06 - mmengine - INFO - Epoch(train) [113][300/586] lr: 5.000000e-04 eta: 5:05:01 time: 0.344037 data_time: 0.024868 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.845134 loss: 0.000597 2022/09/10 00:20:22 - mmengine - INFO - Epoch(train) [113][350/586] lr: 5.000000e-04 eta: 5:04:46 time: 0.326549 data_time: 0.023542 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.804530 loss: 0.000555 2022/09/10 00:20:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:20:39 - mmengine - INFO - Epoch(train) [113][400/586] lr: 5.000000e-04 eta: 5:04:30 time: 0.340655 data_time: 0.029700 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.876713 loss: 0.000577 2022/09/10 00:20:56 - mmengine - INFO - Epoch(train) [113][450/586] lr: 5.000000e-04 eta: 5:04:15 time: 0.337675 data_time: 0.025190 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.783590 loss: 0.000585 2022/09/10 00:21:13 - mmengine - INFO - Epoch(train) [113][500/586] lr: 5.000000e-04 eta: 5:04:00 time: 0.333615 data_time: 0.025146 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.901327 loss: 0.000582 2022/09/10 00:21:30 - mmengine - INFO - Epoch(train) [113][550/586] lr: 5.000000e-04 eta: 5:03:45 time: 0.346239 data_time: 0.031482 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.869668 loss: 0.000614 2022/09/10 00:21:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:21:42 - mmengine - INFO - Saving checkpoint at 113 epochs 2022/09/10 00:22:07 - mmengine - INFO - Epoch(train) [114][50/586] lr: 5.000000e-04 eta: 5:03:08 time: 0.344094 data_time: 0.031323 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.859619 loss: 0.000584 2022/09/10 00:22:23 - mmengine - INFO - Epoch(train) [114][100/586] lr: 5.000000e-04 eta: 5:02:53 time: 0.334520 data_time: 0.024166 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.861257 loss: 0.000575 2022/09/10 00:22:41 - mmengine - INFO - Epoch(train) [114][150/586] lr: 5.000000e-04 eta: 5:02:38 time: 0.346172 data_time: 0.024531 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.827833 loss: 0.000585 2022/09/10 00:22:57 - mmengine - INFO - Epoch(train) [114][200/586] lr: 5.000000e-04 eta: 5:02:23 time: 0.332941 data_time: 0.024137 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.825599 loss: 0.000581 2022/09/10 00:23:14 - mmengine - INFO - Epoch(train) [114][250/586] lr: 5.000000e-04 eta: 5:02:07 time: 0.335483 data_time: 0.023800 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.855562 loss: 0.000593 2022/09/10 00:23:32 - mmengine - INFO - Epoch(train) [114][300/586] lr: 5.000000e-04 eta: 5:01:52 time: 0.345545 data_time: 0.027794 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.788441 loss: 0.000571 2022/09/10 00:23:48 - mmengine - INFO - Epoch(train) [114][350/586] lr: 5.000000e-04 eta: 5:01:37 time: 0.335018 data_time: 0.024090 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.873970 loss: 0.000610 2022/09/10 00:24:05 - mmengine - INFO - Epoch(train) [114][400/586] lr: 5.000000e-04 eta: 5:01:21 time: 0.335086 data_time: 0.026887 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.899959 loss: 0.000576 2022/09/10 00:24:22 - mmengine - INFO - Epoch(train) [114][450/586] lr: 5.000000e-04 eta: 5:01:06 time: 0.335800 data_time: 0.027020 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.905277 loss: 0.000603 2022/09/10 00:24:38 - mmengine - INFO - Epoch(train) [114][500/586] lr: 5.000000e-04 eta: 5:00:50 time: 0.329115 data_time: 0.023854 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.824614 loss: 0.000588 2022/09/10 00:24:55 - mmengine - INFO - Epoch(train) [114][550/586] lr: 5.000000e-04 eta: 5:00:35 time: 0.336076 data_time: 0.027225 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.837206 loss: 0.000579 2022/09/10 00:25:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:25:08 - mmengine - INFO - Saving checkpoint at 114 epochs 2022/09/10 00:25:32 - mmengine - INFO - Epoch(train) [115][50/586] lr: 5.000000e-04 eta: 4:59:59 time: 0.343986 data_time: 0.032376 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.843431 loss: 0.000575 2022/09/10 00:25:49 - mmengine - INFO - Epoch(train) [115][100/586] lr: 5.000000e-04 eta: 4:59:44 time: 0.342207 data_time: 0.028508 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.874850 loss: 0.000590 2022/09/10 00:26:06 - mmengine - INFO - Epoch(train) [115][150/586] lr: 5.000000e-04 eta: 4:59:29 time: 0.338877 data_time: 0.026776 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.883690 loss: 0.000575 2022/09/10 00:26:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:26:22 - mmengine - INFO - Epoch(train) [115][200/586] lr: 5.000000e-04 eta: 4:59:13 time: 0.330504 data_time: 0.024928 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.827517 loss: 0.000581 2022/09/10 00:26:39 - mmengine - INFO - Epoch(train) [115][250/586] lr: 5.000000e-04 eta: 4:58:58 time: 0.341201 data_time: 0.027826 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.893401 loss: 0.000588 2022/09/10 00:26:56 - mmengine - INFO - Epoch(train) [115][300/586] lr: 5.000000e-04 eta: 4:58:42 time: 0.332463 data_time: 0.026423 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.862143 loss: 0.000587 2022/09/10 00:27:13 - mmengine - INFO - Epoch(train) [115][350/586] lr: 5.000000e-04 eta: 4:58:27 time: 0.333930 data_time: 0.026408 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.872094 loss: 0.000594 2022/09/10 00:27:30 - mmengine - INFO - Epoch(train) [115][400/586] lr: 5.000000e-04 eta: 4:58:12 time: 0.341825 data_time: 0.026100 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.786771 loss: 0.000601 2022/09/10 00:27:46 - mmengine - INFO - Epoch(train) [115][450/586] lr: 5.000000e-04 eta: 4:57:56 time: 0.330584 data_time: 0.024250 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.824315 loss: 0.000597 2022/09/10 00:28:03 - mmengine - INFO - Epoch(train) [115][500/586] lr: 5.000000e-04 eta: 4:57:41 time: 0.334206 data_time: 0.028583 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.798647 loss: 0.000572 2022/09/10 00:28:20 - mmengine - INFO - Epoch(train) [115][550/586] lr: 5.000000e-04 eta: 4:57:25 time: 0.329871 data_time: 0.024735 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.840497 loss: 0.000585 2022/09/10 00:28:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:28:32 - mmengine - INFO - Saving checkpoint at 115 epochs 2022/09/10 00:28:56 - mmengine - INFO - Epoch(train) [116][50/586] lr: 5.000000e-04 eta: 4:56:49 time: 0.336507 data_time: 0.029495 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.843831 loss: 0.000584 2022/09/10 00:29:13 - mmengine - INFO - Epoch(train) [116][100/586] lr: 5.000000e-04 eta: 4:56:34 time: 0.341575 data_time: 0.027103 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.875260 loss: 0.000587 2022/09/10 00:29:30 - mmengine - INFO - Epoch(train) [116][150/586] lr: 5.000000e-04 eta: 4:56:18 time: 0.339416 data_time: 0.024481 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.862143 loss: 0.000563 2022/09/10 00:29:46 - mmengine - INFO - Epoch(train) [116][200/586] lr: 5.000000e-04 eta: 4:56:02 time: 0.324963 data_time: 0.023273 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.850526 loss: 0.000575 2022/09/10 00:30:03 - mmengine - INFO - Epoch(train) [116][250/586] lr: 5.000000e-04 eta: 4:55:47 time: 0.335688 data_time: 0.022949 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.852150 loss: 0.000571 2022/09/10 00:30:20 - mmengine - INFO - Epoch(train) [116][300/586] lr: 5.000000e-04 eta: 4:55:32 time: 0.339592 data_time: 0.023625 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.851821 loss: 0.000586 2022/09/10 00:30:36 - mmengine - INFO - Epoch(train) [116][350/586] lr: 5.000000e-04 eta: 4:55:16 time: 0.324423 data_time: 0.023913 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.877792 loss: 0.000578 2022/09/10 00:30:53 - mmengine - INFO - Epoch(train) [116][400/586] lr: 5.000000e-04 eta: 4:55:01 time: 0.339179 data_time: 0.023968 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.915589 loss: 0.000574 2022/09/10 00:31:10 - mmengine - INFO - Epoch(train) [116][450/586] lr: 5.000000e-04 eta: 4:54:45 time: 0.330241 data_time: 0.023492 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.766658 loss: 0.000589 2022/09/10 00:31:26 - mmengine - INFO - Epoch(train) [116][500/586] lr: 5.000000e-04 eta: 4:54:30 time: 0.332920 data_time: 0.023015 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.824795 loss: 0.000584 2022/09/10 00:31:43 - mmengine - INFO - Epoch(train) [116][550/586] lr: 5.000000e-04 eta: 4:54:15 time: 0.341429 data_time: 0.026364 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.836681 loss: 0.000586 2022/09/10 00:31:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:31:56 - mmengine - INFO - Saving checkpoint at 116 epochs 2022/09/10 00:32:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:32:20 - mmengine - INFO - Epoch(train) [117][50/586] lr: 5.000000e-04 eta: 4:53:38 time: 0.338077 data_time: 0.034567 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.830975 loss: 0.000580 2022/09/10 00:32:37 - mmengine - INFO - Epoch(train) [117][100/586] lr: 5.000000e-04 eta: 4:53:23 time: 0.345735 data_time: 0.026140 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.803544 loss: 0.000596 2022/09/10 00:32:54 - mmengine - INFO - Epoch(train) [117][150/586] lr: 5.000000e-04 eta: 4:53:08 time: 0.331554 data_time: 0.024722 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.913967 loss: 0.000548 2022/09/10 00:33:11 - mmengine - INFO - Epoch(train) [117][200/586] lr: 5.000000e-04 eta: 4:52:53 time: 0.342929 data_time: 0.027750 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.859879 loss: 0.000597 2022/09/10 00:33:28 - mmengine - INFO - Epoch(train) [117][250/586] lr: 5.000000e-04 eta: 4:52:38 time: 0.344017 data_time: 0.027441 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.829777 loss: 0.000597 2022/09/10 00:33:45 - mmengine - INFO - Epoch(train) [117][300/586] lr: 5.000000e-04 eta: 4:52:22 time: 0.333254 data_time: 0.025277 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.869840 loss: 0.000566 2022/09/10 00:34:02 - mmengine - INFO - Epoch(train) [117][350/586] lr: 5.000000e-04 eta: 4:52:07 time: 0.340753 data_time: 0.028348 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.855005 loss: 0.000580 2022/09/10 00:34:19 - mmengine - INFO - Epoch(train) [117][400/586] lr: 5.000000e-04 eta: 4:51:52 time: 0.337612 data_time: 0.023386 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.867908 loss: 0.000597 2022/09/10 00:34:35 - mmengine - INFO - Epoch(train) [117][450/586] lr: 5.000000e-04 eta: 4:51:36 time: 0.335886 data_time: 0.023472 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.850334 loss: 0.000571 2022/09/10 00:34:52 - mmengine - INFO - Epoch(train) [117][500/586] lr: 5.000000e-04 eta: 4:51:21 time: 0.334482 data_time: 0.023075 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.760813 loss: 0.000571 2022/09/10 00:35:09 - mmengine - INFO - Epoch(train) [117][550/586] lr: 5.000000e-04 eta: 4:51:06 time: 0.340150 data_time: 0.024127 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.901882 loss: 0.000576 2022/09/10 00:35:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:35:21 - mmengine - INFO - Saving checkpoint at 117 epochs 2022/09/10 00:35:45 - mmengine - INFO - Epoch(train) [118][50/586] lr: 5.000000e-04 eta: 4:50:30 time: 0.337251 data_time: 0.030392 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.843164 loss: 0.000572 2022/09/10 00:36:02 - mmengine - INFO - Epoch(train) [118][100/586] lr: 5.000000e-04 eta: 4:50:14 time: 0.335316 data_time: 0.023074 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.767580 loss: 0.000591 2022/09/10 00:36:19 - mmengine - INFO - Epoch(train) [118][150/586] lr: 5.000000e-04 eta: 4:49:59 time: 0.338545 data_time: 0.027677 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.845812 loss: 0.000592 2022/09/10 00:36:36 - mmengine - INFO - Epoch(train) [118][200/586] lr: 5.000000e-04 eta: 4:49:44 time: 0.337680 data_time: 0.023245 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.863323 loss: 0.000586 2022/09/10 00:36:52 - mmengine - INFO - Epoch(train) [118][250/586] lr: 5.000000e-04 eta: 4:49:28 time: 0.332507 data_time: 0.027171 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.801350 loss: 0.000575 2022/09/10 00:37:09 - mmengine - INFO - Epoch(train) [118][300/586] lr: 5.000000e-04 eta: 4:49:13 time: 0.333508 data_time: 0.024342 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.779549 loss: 0.000585 2022/09/10 00:37:26 - mmengine - INFO - Epoch(train) [118][350/586] lr: 5.000000e-04 eta: 4:48:57 time: 0.334834 data_time: 0.027751 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.896309 loss: 0.000575 2022/09/10 00:37:42 - mmengine - INFO - Epoch(train) [118][400/586] lr: 5.000000e-04 eta: 4:48:42 time: 0.336166 data_time: 0.031252 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.829959 loss: 0.000578 2022/09/10 00:37:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:37:59 - mmengine - INFO - Epoch(train) [118][450/586] lr: 5.000000e-04 eta: 4:48:26 time: 0.335823 data_time: 0.024119 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.863556 loss: 0.000589 2022/09/10 00:38:16 - mmengine - INFO - Epoch(train) [118][500/586] lr: 5.000000e-04 eta: 4:48:11 time: 0.335664 data_time: 0.025867 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.874033 loss: 0.000592 2022/09/10 00:38:33 - mmengine - INFO - Epoch(train) [118][550/586] lr: 5.000000e-04 eta: 4:47:56 time: 0.334182 data_time: 0.026122 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.865146 loss: 0.000584 2022/09/10 00:38:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:38:45 - mmengine - INFO - Saving checkpoint at 118 epochs 2022/09/10 00:39:10 - mmengine - INFO - Epoch(train) [119][50/586] lr: 5.000000e-04 eta: 4:47:20 time: 0.346934 data_time: 0.032794 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.895478 loss: 0.000587 2022/09/10 00:39:26 - mmengine - INFO - Epoch(train) [119][100/586] lr: 5.000000e-04 eta: 4:47:05 time: 0.331420 data_time: 0.026472 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.777110 loss: 0.000574 2022/09/10 00:39:43 - mmengine - INFO - Epoch(train) [119][150/586] lr: 5.000000e-04 eta: 4:46:49 time: 0.334182 data_time: 0.024603 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.810614 loss: 0.000585 2022/09/10 00:40:00 - mmengine - INFO - Epoch(train) [119][200/586] lr: 5.000000e-04 eta: 4:46:34 time: 0.337133 data_time: 0.024196 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.878606 loss: 0.000572 2022/09/10 00:40:17 - mmengine - INFO - Epoch(train) [119][250/586] lr: 5.000000e-04 eta: 4:46:18 time: 0.335276 data_time: 0.027662 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.813604 loss: 0.000577 2022/09/10 00:40:34 - mmengine - INFO - Epoch(train) [119][300/586] lr: 5.000000e-04 eta: 4:46:03 time: 0.337144 data_time: 0.025919 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.745520 loss: 0.000566 2022/09/10 00:40:50 - mmengine - INFO - Epoch(train) [119][350/586] lr: 5.000000e-04 eta: 4:45:47 time: 0.334075 data_time: 0.029843 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.886449 loss: 0.000569 2022/09/10 00:41:08 - mmengine - INFO - Epoch(train) [119][400/586] lr: 5.000000e-04 eta: 4:45:32 time: 0.342485 data_time: 0.025184 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.823934 loss: 0.000570 2022/09/10 00:41:24 - mmengine - INFO - Epoch(train) [119][450/586] lr: 5.000000e-04 eta: 4:45:17 time: 0.335987 data_time: 0.025299 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.875140 loss: 0.000577 2022/09/10 00:41:41 - mmengine - INFO - Epoch(train) [119][500/586] lr: 5.000000e-04 eta: 4:45:02 time: 0.342995 data_time: 0.023207 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.856504 loss: 0.000582 2022/09/10 00:41:59 - mmengine - INFO - Epoch(train) [119][550/586] lr: 5.000000e-04 eta: 4:44:47 time: 0.345215 data_time: 0.022968 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.882377 loss: 0.000588 2022/09/10 00:42:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:42:11 - mmengine - INFO - Saving checkpoint at 119 epochs 2022/09/10 00:42:35 - mmengine - INFO - Epoch(train) [120][50/586] lr: 5.000000e-04 eta: 4:44:11 time: 0.338999 data_time: 0.031399 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.788111 loss: 0.000595 2022/09/10 00:42:52 - mmengine - INFO - Epoch(train) [120][100/586] lr: 5.000000e-04 eta: 4:43:56 time: 0.338117 data_time: 0.022539 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.843487 loss: 0.000592 2022/09/10 00:43:09 - mmengine - INFO - Epoch(train) [120][150/586] lr: 5.000000e-04 eta: 4:43:40 time: 0.335145 data_time: 0.022518 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.848363 loss: 0.000575 2022/09/10 00:43:25 - mmengine - INFO - Epoch(train) [120][200/586] lr: 5.000000e-04 eta: 4:43:25 time: 0.328478 data_time: 0.023142 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.898813 loss: 0.000580 2022/09/10 00:43:42 - mmengine - INFO - Epoch(train) [120][250/586] lr: 5.000000e-04 eta: 4:43:09 time: 0.337467 data_time: 0.023585 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.899123 loss: 0.000596 2022/09/10 00:43:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:43:59 - mmengine - INFO - Epoch(train) [120][300/586] lr: 5.000000e-04 eta: 4:42:54 time: 0.334412 data_time: 0.027712 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.819934 loss: 0.000598 2022/09/10 00:44:15 - mmengine - INFO - Epoch(train) [120][350/586] lr: 5.000000e-04 eta: 4:42:38 time: 0.336137 data_time: 0.024014 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.841773 loss: 0.000569 2022/09/10 00:44:32 - mmengine - INFO - Epoch(train) [120][400/586] lr: 5.000000e-04 eta: 4:42:23 time: 0.332565 data_time: 0.024314 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.852375 loss: 0.000568 2022/09/10 00:44:49 - mmengine - INFO - Epoch(train) [120][450/586] lr: 5.000000e-04 eta: 4:42:07 time: 0.331717 data_time: 0.023787 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.804384 loss: 0.000589 2022/09/10 00:45:05 - mmengine - INFO - Epoch(train) [120][500/586] lr: 5.000000e-04 eta: 4:41:52 time: 0.335915 data_time: 0.023190 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.892165 loss: 0.000604 2022/09/10 00:45:22 - mmengine - INFO - Epoch(train) [120][550/586] lr: 5.000000e-04 eta: 4:41:36 time: 0.334500 data_time: 0.023100 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.851038 loss: 0.000587 2022/09/10 00:45:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:45:35 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/09/10 00:45:50 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:01:02 time: 0.174939 data_time: 0.015951 memory: 7489 2022/09/10 00:45:59 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:52 time: 0.170268 data_time: 0.013008 memory: 1657 2022/09/10 00:46:07 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:42 time: 0.164373 data_time: 0.007563 memory: 1657 2022/09/10 00:46:15 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:34 time: 0.168048 data_time: 0.010664 memory: 1657 2022/09/10 00:46:24 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:25 time: 0.164923 data_time: 0.007828 memory: 1657 2022/09/10 00:46:32 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:17 time: 0.165444 data_time: 0.007905 memory: 1657 2022/09/10 00:46:40 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:09 time: 0.164329 data_time: 0.007603 memory: 1657 2022/09/10 00:46:48 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:01 time: 0.163407 data_time: 0.008825 memory: 1657 2022/09/10 00:47:24 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 00:47:38 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.743921 coco/AP .5: 0.901994 coco/AP .75: 0.816677 coco/AP (M): 0.709741 coco/AP (L): 0.808415 coco/AR: 0.796914 coco/AR .5: 0.939704 coco/AR .75: 0.861461 coco/AR (M): 0.756023 coco/AR (L): 0.855890 2022/09/10 00:47:38 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_100.pth is removed 2022/09/10 00:47:42 - mmengine - INFO - The best checkpoint with 0.7439 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/09/10 00:47:59 - mmengine - INFO - Epoch(train) [121][50/586] lr: 5.000000e-04 eta: 4:41:01 time: 0.340529 data_time: 0.025988 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.822057 loss: 0.000604 2022/09/10 00:48:16 - mmengine - INFO - Epoch(train) [121][100/586] lr: 5.000000e-04 eta: 4:40:46 time: 0.335188 data_time: 0.022863 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.889911 loss: 0.000564 2022/09/10 00:48:32 - mmengine - INFO - Epoch(train) [121][150/586] lr: 5.000000e-04 eta: 4:40:30 time: 0.325625 data_time: 0.023094 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.833843 loss: 0.000567 2022/09/10 00:48:49 - mmengine - INFO - Epoch(train) [121][200/586] lr: 5.000000e-04 eta: 4:40:15 time: 0.339236 data_time: 0.025751 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.860094 loss: 0.000577 2022/09/10 00:49:06 - mmengine - INFO - Epoch(train) [121][250/586] lr: 5.000000e-04 eta: 4:39:59 time: 0.336519 data_time: 0.022499 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.873521 loss: 0.000578 2022/09/10 00:49:22 - mmengine - INFO - Epoch(train) [121][300/586] lr: 5.000000e-04 eta: 4:39:44 time: 0.332493 data_time: 0.026912 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.860954 loss: 0.000584 2022/09/10 00:49:39 - mmengine - INFO - Epoch(train) [121][350/586] lr: 5.000000e-04 eta: 4:39:28 time: 0.338803 data_time: 0.023533 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.893135 loss: 0.000584 2022/09/10 00:49:56 - mmengine - INFO - Epoch(train) [121][400/586] lr: 5.000000e-04 eta: 4:39:13 time: 0.333387 data_time: 0.023695 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.923653 loss: 0.000579 2022/09/10 00:50:12 - mmengine - INFO - Epoch(train) [121][450/586] lr: 5.000000e-04 eta: 4:38:57 time: 0.327431 data_time: 0.022488 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.802015 loss: 0.000581 2022/09/10 00:50:30 - mmengine - INFO - Epoch(train) [121][500/586] lr: 5.000000e-04 eta: 4:38:42 time: 0.349191 data_time: 0.026290 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.902810 loss: 0.000578 2022/09/10 00:50:47 - mmengine - INFO - Epoch(train) [121][550/586] lr: 5.000000e-04 eta: 4:38:27 time: 0.333818 data_time: 0.024461 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.857414 loss: 0.000580 2022/09/10 00:50:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:50:59 - mmengine - INFO - Saving checkpoint at 121 epochs 2022/09/10 00:51:23 - mmengine - INFO - Epoch(train) [122][50/586] lr: 5.000000e-04 eta: 4:37:52 time: 0.345428 data_time: 0.030339 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.805488 loss: 0.000589 2022/09/10 00:51:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:51:40 - mmengine - INFO - Epoch(train) [122][100/586] lr: 5.000000e-04 eta: 4:37:36 time: 0.337237 data_time: 0.028532 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.837607 loss: 0.000578 2022/09/10 00:51:56 - mmengine - INFO - Epoch(train) [122][150/586] lr: 5.000000e-04 eta: 4:37:21 time: 0.332091 data_time: 0.022820 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.801388 loss: 0.000589 2022/09/10 00:52:13 - mmengine - INFO - Epoch(train) [122][200/586] lr: 5.000000e-04 eta: 4:37:05 time: 0.337780 data_time: 0.022700 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.871623 loss: 0.000581 2022/09/10 00:52:30 - mmengine - INFO - Epoch(train) [122][250/586] lr: 5.000000e-04 eta: 4:36:50 time: 0.334842 data_time: 0.022654 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.818137 loss: 0.000589 2022/09/10 00:52:47 - mmengine - INFO - Epoch(train) [122][300/586] lr: 5.000000e-04 eta: 4:36:35 time: 0.339076 data_time: 0.022825 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.915505 loss: 0.000573 2022/09/10 00:53:04 - mmengine - INFO - Epoch(train) [122][350/586] lr: 5.000000e-04 eta: 4:36:19 time: 0.332933 data_time: 0.022377 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.869841 loss: 0.000575 2022/09/10 00:53:20 - mmengine - INFO - Epoch(train) [122][400/586] lr: 5.000000e-04 eta: 4:36:04 time: 0.334909 data_time: 0.022433 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.808021 loss: 0.000581 2022/09/10 00:53:37 - mmengine - INFO - Epoch(train) [122][450/586] lr: 5.000000e-04 eta: 4:35:48 time: 0.333848 data_time: 0.024034 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.837661 loss: 0.000591 2022/09/10 00:53:54 - mmengine - INFO - Epoch(train) [122][500/586] lr: 5.000000e-04 eta: 4:35:33 time: 0.341412 data_time: 0.022697 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.877891 loss: 0.000578 2022/09/10 00:54:11 - mmengine - INFO - Epoch(train) [122][550/586] lr: 5.000000e-04 eta: 4:35:17 time: 0.329787 data_time: 0.026662 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.818268 loss: 0.000610 2022/09/10 00:54:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:54:23 - mmengine - INFO - Saving checkpoint at 122 epochs 2022/09/10 00:54:47 - mmengine - INFO - Epoch(train) [123][50/586] lr: 5.000000e-04 eta: 4:34:42 time: 0.349879 data_time: 0.031540 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.830776 loss: 0.000579 2022/09/10 00:55:04 - mmengine - INFO - Epoch(train) [123][100/586] lr: 5.000000e-04 eta: 4:34:27 time: 0.332533 data_time: 0.022364 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.858001 loss: 0.000584 2022/09/10 00:55:21 - mmengine - INFO - Epoch(train) [123][150/586] lr: 5.000000e-04 eta: 4:34:11 time: 0.337178 data_time: 0.026091 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.876467 loss: 0.000576 2022/09/10 00:55:37 - mmengine - INFO - Epoch(train) [123][200/586] lr: 5.000000e-04 eta: 4:33:56 time: 0.337003 data_time: 0.022844 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.817191 loss: 0.000595 2022/09/10 00:55:54 - mmengine - INFO - Epoch(train) [123][250/586] lr: 5.000000e-04 eta: 4:33:40 time: 0.328056 data_time: 0.022382 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.868761 loss: 0.000600 2022/09/10 00:56:11 - mmengine - INFO - Epoch(train) [123][300/586] lr: 5.000000e-04 eta: 4:33:25 time: 0.336784 data_time: 0.027490 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.814126 loss: 0.000563 2022/09/10 00:56:28 - mmengine - INFO - Epoch(train) [123][350/586] lr: 5.000000e-04 eta: 4:33:10 time: 0.346540 data_time: 0.023277 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.888592 loss: 0.000572 2022/09/10 00:56:45 - mmengine - INFO - Epoch(train) [123][400/586] lr: 5.000000e-04 eta: 4:32:55 time: 0.335609 data_time: 0.023631 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.794268 loss: 0.000573 2022/09/10 00:57:02 - mmengine - INFO - Epoch(train) [123][450/586] lr: 5.000000e-04 eta: 4:32:39 time: 0.335089 data_time: 0.026346 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.903920 loss: 0.000572 2022/09/10 00:57:19 - mmengine - INFO - Epoch(train) [123][500/586] lr: 5.000000e-04 eta: 4:32:24 time: 0.339915 data_time: 0.026567 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.831810 loss: 0.000567 2022/09/10 00:57:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:57:35 - mmengine - INFO - Epoch(train) [123][550/586] lr: 5.000000e-04 eta: 4:32:08 time: 0.331931 data_time: 0.023402 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.857353 loss: 0.000579 2022/09/10 00:57:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 00:57:47 - mmengine - INFO - Saving checkpoint at 123 epochs 2022/09/10 00:58:12 - mmengine - INFO - Epoch(train) [124][50/586] lr: 5.000000e-04 eta: 4:31:34 time: 0.349385 data_time: 0.029184 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.861632 loss: 0.000597 2022/09/10 00:58:28 - mmengine - INFO - Epoch(train) [124][100/586] lr: 5.000000e-04 eta: 4:31:18 time: 0.336084 data_time: 0.023560 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.859896 loss: 0.000567 2022/09/10 00:58:45 - mmengine - INFO - Epoch(train) [124][150/586] lr: 5.000000e-04 eta: 4:31:02 time: 0.330922 data_time: 0.022926 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.855016 loss: 0.000558 2022/09/10 00:59:02 - mmengine - INFO - Epoch(train) [124][200/586] lr: 5.000000e-04 eta: 4:30:47 time: 0.340606 data_time: 0.022709 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.834433 loss: 0.000588 2022/09/10 00:59:18 - mmengine - INFO - Epoch(train) [124][250/586] lr: 5.000000e-04 eta: 4:30:32 time: 0.329259 data_time: 0.022285 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.814981 loss: 0.000567 2022/09/10 00:59:35 - mmengine - INFO - Epoch(train) [124][300/586] lr: 5.000000e-04 eta: 4:30:16 time: 0.324129 data_time: 0.023359 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.795123 loss: 0.000579 2022/09/10 00:59:51 - mmengine - INFO - Epoch(train) [124][350/586] lr: 5.000000e-04 eta: 4:30:00 time: 0.334907 data_time: 0.026140 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.854758 loss: 0.000576 2022/09/10 01:00:08 - mmengine - INFO - Epoch(train) [124][400/586] lr: 5.000000e-04 eta: 4:29:45 time: 0.335384 data_time: 0.023168 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.881913 loss: 0.000579 2022/09/10 01:00:25 - mmengine - INFO - Epoch(train) [124][450/586] lr: 5.000000e-04 eta: 4:29:29 time: 0.330865 data_time: 0.023784 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.852117 loss: 0.000568 2022/09/10 01:00:42 - mmengine - INFO - Epoch(train) [124][500/586] lr: 5.000000e-04 eta: 4:29:14 time: 0.342739 data_time: 0.023135 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.858068 loss: 0.000583 2022/09/10 01:00:59 - mmengine - INFO - Epoch(train) [124][550/586] lr: 5.000000e-04 eta: 4:28:59 time: 0.340834 data_time: 0.022348 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.835244 loss: 0.000572 2022/09/10 01:01:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:01:11 - mmengine - INFO - Saving checkpoint at 124 epochs 2022/09/10 01:01:35 - mmengine - INFO - Epoch(train) [125][50/586] lr: 5.000000e-04 eta: 4:28:24 time: 0.348682 data_time: 0.033701 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.828923 loss: 0.000581 2022/09/10 01:01:52 - mmengine - INFO - Epoch(train) [125][100/586] lr: 5.000000e-04 eta: 4:28:09 time: 0.336791 data_time: 0.024354 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.880209 loss: 0.000583 2022/09/10 01:02:09 - mmengine - INFO - Epoch(train) [125][150/586] lr: 5.000000e-04 eta: 4:27:53 time: 0.332821 data_time: 0.026352 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.813540 loss: 0.000569 2022/09/10 01:02:26 - mmengine - INFO - Epoch(train) [125][200/586] lr: 5.000000e-04 eta: 4:27:38 time: 0.336782 data_time: 0.024891 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.834926 loss: 0.000585 2022/09/10 01:02:43 - mmengine - INFO - Epoch(train) [125][250/586] lr: 5.000000e-04 eta: 4:27:22 time: 0.340237 data_time: 0.023788 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.811347 loss: 0.000589 2022/09/10 01:03:00 - mmengine - INFO - Epoch(train) [125][300/586] lr: 5.000000e-04 eta: 4:27:07 time: 0.338435 data_time: 0.023514 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.861214 loss: 0.000574 2022/09/10 01:03:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:03:16 - mmengine - INFO - Epoch(train) [125][350/586] lr: 5.000000e-04 eta: 4:26:52 time: 0.335206 data_time: 0.024659 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.862186 loss: 0.000581 2022/09/10 01:03:33 - mmengine - INFO - Epoch(train) [125][400/586] lr: 5.000000e-04 eta: 4:26:36 time: 0.336507 data_time: 0.022664 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.816601 loss: 0.000583 2022/09/10 01:03:50 - mmengine - INFO - Epoch(train) [125][450/586] lr: 5.000000e-04 eta: 4:26:21 time: 0.335786 data_time: 0.023055 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.841197 loss: 0.000569 2022/09/10 01:04:07 - mmengine - INFO - Epoch(train) [125][500/586] lr: 5.000000e-04 eta: 4:26:05 time: 0.334464 data_time: 0.022723 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.866267 loss: 0.000594 2022/09/10 01:04:24 - mmengine - INFO - Epoch(train) [125][550/586] lr: 5.000000e-04 eta: 4:25:50 time: 0.339213 data_time: 0.023647 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.792708 loss: 0.000592 2022/09/10 01:04:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:04:36 - mmengine - INFO - Saving checkpoint at 125 epochs 2022/09/10 01:05:00 - mmengine - INFO - Epoch(train) [126][50/586] lr: 5.000000e-04 eta: 4:25:15 time: 0.336017 data_time: 0.026557 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.750760 loss: 0.000579 2022/09/10 01:05:17 - mmengine - INFO - Epoch(train) [126][100/586] lr: 5.000000e-04 eta: 4:25:00 time: 0.338136 data_time: 0.026888 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.839272 loss: 0.000572 2022/09/10 01:05:33 - mmengine - INFO - Epoch(train) [126][150/586] lr: 5.000000e-04 eta: 4:24:44 time: 0.327060 data_time: 0.022512 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.882082 loss: 0.000582 2022/09/10 01:05:50 - mmengine - INFO - Epoch(train) [126][200/586] lr: 5.000000e-04 eta: 4:24:29 time: 0.337290 data_time: 0.023146 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.881595 loss: 0.000576 2022/09/10 01:06:07 - mmengine - INFO - Epoch(train) [126][250/586] lr: 5.000000e-04 eta: 4:24:13 time: 0.333134 data_time: 0.026846 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.854701 loss: 0.000584 2022/09/10 01:06:23 - mmengine - INFO - Epoch(train) [126][300/586] lr: 5.000000e-04 eta: 4:23:57 time: 0.328412 data_time: 0.023266 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.886944 loss: 0.000584 2022/09/10 01:06:40 - mmengine - INFO - Epoch(train) [126][350/586] lr: 5.000000e-04 eta: 4:23:42 time: 0.336233 data_time: 0.022762 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.873036 loss: 0.000574 2022/09/10 01:06:57 - mmengine - INFO - Epoch(train) [126][400/586] lr: 5.000000e-04 eta: 4:23:26 time: 0.336967 data_time: 0.025940 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.818696 loss: 0.000569 2022/09/10 01:07:13 - mmengine - INFO - Epoch(train) [126][450/586] lr: 5.000000e-04 eta: 4:23:11 time: 0.327827 data_time: 0.023134 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.806946 loss: 0.000593 2022/09/10 01:07:30 - mmengine - INFO - Epoch(train) [126][500/586] lr: 5.000000e-04 eta: 4:22:55 time: 0.334335 data_time: 0.022797 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.856131 loss: 0.000577 2022/09/10 01:07:47 - mmengine - INFO - Epoch(train) [126][550/586] lr: 5.000000e-04 eta: 4:22:40 time: 0.334768 data_time: 0.025515 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.847195 loss: 0.000573 2022/09/10 01:07:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:07:58 - mmengine - INFO - Saving checkpoint at 126 epochs 2022/09/10 01:08:22 - mmengine - INFO - Epoch(train) [127][50/586] lr: 5.000000e-04 eta: 4:22:05 time: 0.340612 data_time: 0.032417 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.840179 loss: 0.000575 2022/09/10 01:08:39 - mmengine - INFO - Epoch(train) [127][100/586] lr: 5.000000e-04 eta: 4:21:50 time: 0.333457 data_time: 0.023774 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.840715 loss: 0.000571 2022/09/10 01:08:56 - mmengine - INFO - Epoch(train) [127][150/586] lr: 5.000000e-04 eta: 4:21:34 time: 0.333912 data_time: 0.022616 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.800529 loss: 0.000582 2022/09/10 01:09:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:09:13 - mmengine - INFO - Epoch(train) [127][200/586] lr: 5.000000e-04 eta: 4:21:19 time: 0.336898 data_time: 0.023156 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.838703 loss: 0.000583 2022/09/10 01:09:29 - mmengine - INFO - Epoch(train) [127][250/586] lr: 5.000000e-04 eta: 4:21:03 time: 0.331986 data_time: 0.024763 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.855922 loss: 0.000584 2022/09/10 01:09:46 - mmengine - INFO - Epoch(train) [127][300/586] lr: 5.000000e-04 eta: 4:20:48 time: 0.339257 data_time: 0.024212 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.812816 loss: 0.000586 2022/09/10 01:10:03 - mmengine - INFO - Epoch(train) [127][350/586] lr: 5.000000e-04 eta: 4:20:33 time: 0.343396 data_time: 0.022269 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.847263 loss: 0.000592 2022/09/10 01:10:20 - mmengine - INFO - Epoch(train) [127][400/586] lr: 5.000000e-04 eta: 4:20:17 time: 0.335708 data_time: 0.022926 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.839667 loss: 0.000582 2022/09/10 01:10:37 - mmengine - INFO - Epoch(train) [127][450/586] lr: 5.000000e-04 eta: 4:20:02 time: 0.335990 data_time: 0.023002 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.882495 loss: 0.000595 2022/09/10 01:10:54 - mmengine - INFO - Epoch(train) [127][500/586] lr: 5.000000e-04 eta: 4:19:46 time: 0.332298 data_time: 0.022864 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.851294 loss: 0.000564 2022/09/10 01:11:11 - mmengine - INFO - Epoch(train) [127][550/586] lr: 5.000000e-04 eta: 4:19:31 time: 0.344793 data_time: 0.022751 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.815955 loss: 0.000564 2022/09/10 01:11:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:11:23 - mmengine - INFO - Saving checkpoint at 127 epochs 2022/09/10 01:11:47 - mmengine - INFO - Epoch(train) [128][50/586] lr: 5.000000e-04 eta: 4:18:56 time: 0.337930 data_time: 0.028649 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.856889 loss: 0.000566 2022/09/10 01:12:04 - mmengine - INFO - Epoch(train) [128][100/586] lr: 5.000000e-04 eta: 4:18:41 time: 0.337728 data_time: 0.022825 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.814305 loss: 0.000565 2022/09/10 01:12:21 - mmengine - INFO - Epoch(train) [128][150/586] lr: 5.000000e-04 eta: 4:18:25 time: 0.332836 data_time: 0.024126 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.849887 loss: 0.000564 2022/09/10 01:12:38 - mmengine - INFO - Epoch(train) [128][200/586] lr: 5.000000e-04 eta: 4:18:10 time: 0.334624 data_time: 0.027422 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.777912 loss: 0.000581 2022/09/10 01:12:55 - mmengine - INFO - Epoch(train) [128][250/586] lr: 5.000000e-04 eta: 4:17:55 time: 0.342424 data_time: 0.022511 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.853249 loss: 0.000601 2022/09/10 01:13:11 - mmengine - INFO - Epoch(train) [128][300/586] lr: 5.000000e-04 eta: 4:17:39 time: 0.331721 data_time: 0.022553 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.845336 loss: 0.000573 2022/09/10 01:13:28 - mmengine - INFO - Epoch(train) [128][350/586] lr: 5.000000e-04 eta: 4:17:24 time: 0.334470 data_time: 0.026470 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.862035 loss: 0.000577 2022/09/10 01:13:45 - mmengine - INFO - Epoch(train) [128][400/586] lr: 5.000000e-04 eta: 4:17:08 time: 0.339130 data_time: 0.023440 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.861844 loss: 0.000574 2022/09/10 01:14:02 - mmengine - INFO - Epoch(train) [128][450/586] lr: 5.000000e-04 eta: 4:16:53 time: 0.333488 data_time: 0.023785 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.852405 loss: 0.000575 2022/09/10 01:14:18 - mmengine - INFO - Epoch(train) [128][500/586] lr: 5.000000e-04 eta: 4:16:37 time: 0.328294 data_time: 0.027680 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.850603 loss: 0.000602 2022/09/10 01:14:35 - mmengine - INFO - Epoch(train) [128][550/586] lr: 5.000000e-04 eta: 4:16:21 time: 0.336863 data_time: 0.023757 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.796876 loss: 0.000585 2022/09/10 01:14:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:14:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:14:47 - mmengine - INFO - Saving checkpoint at 128 epochs 2022/09/10 01:15:11 - mmengine - INFO - Epoch(train) [129][50/586] lr: 5.000000e-04 eta: 4:15:47 time: 0.334345 data_time: 0.026460 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.763752 loss: 0.000558 2022/09/10 01:15:28 - mmengine - INFO - Epoch(train) [129][100/586] lr: 5.000000e-04 eta: 4:15:32 time: 0.332886 data_time: 0.023345 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.864671 loss: 0.000573 2022/09/10 01:15:45 - mmengine - INFO - Epoch(train) [129][150/586] lr: 5.000000e-04 eta: 4:15:16 time: 0.338419 data_time: 0.030637 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.831817 loss: 0.000583 2022/09/10 01:16:01 - mmengine - INFO - Epoch(train) [129][200/586] lr: 5.000000e-04 eta: 4:15:01 time: 0.332321 data_time: 0.024911 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.888216 loss: 0.000564 2022/09/10 01:16:18 - mmengine - INFO - Epoch(train) [129][250/586] lr: 5.000000e-04 eta: 4:14:45 time: 0.331841 data_time: 0.026010 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.888116 loss: 0.000541 2022/09/10 01:16:35 - mmengine - INFO - Epoch(train) [129][300/586] lr: 5.000000e-04 eta: 4:14:30 time: 0.338090 data_time: 0.029815 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.739913 loss: 0.000579 2022/09/10 01:16:51 - mmengine - INFO - Epoch(train) [129][350/586] lr: 5.000000e-04 eta: 4:14:14 time: 0.334641 data_time: 0.025305 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.843582 loss: 0.000565 2022/09/10 01:17:09 - mmengine - INFO - Epoch(train) [129][400/586] lr: 5.000000e-04 eta: 4:13:59 time: 0.343228 data_time: 0.027522 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.821522 loss: 0.000566 2022/09/10 01:17:25 - mmengine - INFO - Epoch(train) [129][450/586] lr: 5.000000e-04 eta: 4:13:43 time: 0.333796 data_time: 0.025251 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.830978 loss: 0.000580 2022/09/10 01:17:42 - mmengine - INFO - Epoch(train) [129][500/586] lr: 5.000000e-04 eta: 4:13:28 time: 0.336977 data_time: 0.023670 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.852781 loss: 0.000577 2022/09/10 01:17:59 - mmengine - INFO - Epoch(train) [129][550/586] lr: 5.000000e-04 eta: 4:13:12 time: 0.335860 data_time: 0.023659 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.830021 loss: 0.000573 2022/09/10 01:18:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:18:11 - mmengine - INFO - Saving checkpoint at 129 epochs 2022/09/10 01:18:35 - mmengine - INFO - Epoch(train) [130][50/586] lr: 5.000000e-04 eta: 4:12:38 time: 0.340509 data_time: 0.029019 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.883710 loss: 0.000578 2022/09/10 01:18:52 - mmengine - INFO - Epoch(train) [130][100/586] lr: 5.000000e-04 eta: 4:12:23 time: 0.338405 data_time: 0.025185 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.850888 loss: 0.000576 2022/09/10 01:19:09 - mmengine - INFO - Epoch(train) [130][150/586] lr: 5.000000e-04 eta: 4:12:07 time: 0.335358 data_time: 0.024710 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.769945 loss: 0.000571 2022/09/10 01:19:25 - mmengine - INFO - Epoch(train) [130][200/586] lr: 5.000000e-04 eta: 4:11:52 time: 0.337407 data_time: 0.028606 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.872779 loss: 0.000575 2022/09/10 01:19:42 - mmengine - INFO - Epoch(train) [130][250/586] lr: 5.000000e-04 eta: 4:11:36 time: 0.331983 data_time: 0.023623 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.809207 loss: 0.000566 2022/09/10 01:19:59 - mmengine - INFO - Epoch(train) [130][300/586] lr: 5.000000e-04 eta: 4:11:21 time: 0.342065 data_time: 0.023553 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.835213 loss: 0.000583 2022/09/10 01:20:16 - mmengine - INFO - Epoch(train) [130][350/586] lr: 5.000000e-04 eta: 4:11:06 time: 0.337743 data_time: 0.023690 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.736250 loss: 0.000584 2022/09/10 01:20:33 - mmengine - INFO - Epoch(train) [130][400/586] lr: 5.000000e-04 eta: 4:10:50 time: 0.333774 data_time: 0.024081 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.878222 loss: 0.000593 2022/09/10 01:20:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:20:50 - mmengine - INFO - Epoch(train) [130][450/586] lr: 5.000000e-04 eta: 4:10:35 time: 0.338505 data_time: 0.023672 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.893451 loss: 0.000580 2022/09/10 01:21:06 - mmengine - INFO - Epoch(train) [130][500/586] lr: 5.000000e-04 eta: 4:10:19 time: 0.334116 data_time: 0.024890 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.829203 loss: 0.000571 2022/09/10 01:21:23 - mmengine - INFO - Epoch(train) [130][550/586] lr: 5.000000e-04 eta: 4:10:03 time: 0.331165 data_time: 0.023785 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.887710 loss: 0.000571 2022/09/10 01:21:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:21:35 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/09/10 01:21:51 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:01:00 time: 0.169895 data_time: 0.012383 memory: 7489 2022/09/10 01:21:59 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:50 time: 0.163628 data_time: 0.007384 memory: 1657 2022/09/10 01:22:07 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:41 time: 0.163278 data_time: 0.007239 memory: 1657 2022/09/10 01:22:15 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:33 time: 0.163488 data_time: 0.007388 memory: 1657 2022/09/10 01:22:24 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:26 time: 0.166874 data_time: 0.007971 memory: 1657 2022/09/10 01:22:32 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:17 time: 0.168147 data_time: 0.007621 memory: 1657 2022/09/10 01:22:40 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:09 time: 0.165274 data_time: 0.007482 memory: 1657 2022/09/10 01:22:49 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:01 time: 0.161555 data_time: 0.006903 memory: 1657 2022/09/10 01:23:25 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 01:23:39 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.745004 coco/AP .5: 0.900324 coco/AP .75: 0.816799 coco/AP (M): 0.708391 coco/AP (L): 0.812623 coco/AR: 0.798079 coco/AR .5: 0.938130 coco/AR .75: 0.863193 coco/AR (M): 0.755695 coco/AR (L): 0.859234 2022/09/10 01:23:39 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_120.pth is removed 2022/09/10 01:23:44 - mmengine - INFO - The best checkpoint with 0.7450 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/09/10 01:24:01 - mmengine - INFO - Epoch(train) [131][50/586] lr: 5.000000e-04 eta: 4:09:29 time: 0.341668 data_time: 0.027251 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.857705 loss: 0.000574 2022/09/10 01:24:17 - mmengine - INFO - Epoch(train) [131][100/586] lr: 5.000000e-04 eta: 4:09:14 time: 0.325675 data_time: 0.025888 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.849289 loss: 0.000580 2022/09/10 01:24:35 - mmengine - INFO - Epoch(train) [131][150/586] lr: 5.000000e-04 eta: 4:08:58 time: 0.341752 data_time: 0.030842 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.791303 loss: 0.000569 2022/09/10 01:24:51 - mmengine - INFO - Epoch(train) [131][200/586] lr: 5.000000e-04 eta: 4:08:43 time: 0.333060 data_time: 0.022554 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.856967 loss: 0.000560 2022/09/10 01:25:08 - mmengine - INFO - Epoch(train) [131][250/586] lr: 5.000000e-04 eta: 4:08:27 time: 0.331949 data_time: 0.027869 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.837537 loss: 0.000561 2022/09/10 01:25:25 - mmengine - INFO - Epoch(train) [131][300/586] lr: 5.000000e-04 eta: 4:08:12 time: 0.341277 data_time: 0.024548 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.828219 loss: 0.000586 2022/09/10 01:25:42 - mmengine - INFO - Epoch(train) [131][350/586] lr: 5.000000e-04 eta: 4:07:56 time: 0.338292 data_time: 0.023639 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.837914 loss: 0.000589 2022/09/10 01:25:59 - mmengine - INFO - Epoch(train) [131][400/586] lr: 5.000000e-04 eta: 4:07:41 time: 0.334777 data_time: 0.026966 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.786505 loss: 0.000581 2022/09/10 01:26:16 - mmengine - INFO - Epoch(train) [131][450/586] lr: 5.000000e-04 eta: 4:07:26 time: 0.339499 data_time: 0.023494 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.820833 loss: 0.000589 2022/09/10 01:26:32 - mmengine - INFO - Epoch(train) [131][500/586] lr: 5.000000e-04 eta: 4:07:10 time: 0.336907 data_time: 0.023994 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.873108 loss: 0.000557 2022/09/10 01:26:49 - mmengine - INFO - Epoch(train) [131][550/586] lr: 5.000000e-04 eta: 4:06:55 time: 0.339086 data_time: 0.023354 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.893348 loss: 0.000582 2022/09/10 01:27:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:27:01 - mmengine - INFO - Saving checkpoint at 131 epochs 2022/09/10 01:27:26 - mmengine - INFO - Epoch(train) [132][50/586] lr: 5.000000e-04 eta: 4:06:21 time: 0.346465 data_time: 0.033308 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.883062 loss: 0.000579 2022/09/10 01:27:42 - mmengine - INFO - Epoch(train) [132][100/586] lr: 5.000000e-04 eta: 4:06:05 time: 0.328439 data_time: 0.024884 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.905907 loss: 0.000566 2022/09/10 01:27:59 - mmengine - INFO - Epoch(train) [132][150/586] lr: 5.000000e-04 eta: 4:05:50 time: 0.335404 data_time: 0.022414 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.845745 loss: 0.000584 2022/09/10 01:28:16 - mmengine - INFO - Epoch(train) [132][200/586] lr: 5.000000e-04 eta: 4:05:34 time: 0.341450 data_time: 0.022943 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.897579 loss: 0.000573 2022/09/10 01:28:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:28:33 - mmengine - INFO - Epoch(train) [132][250/586] lr: 5.000000e-04 eta: 4:05:19 time: 0.333781 data_time: 0.023788 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.880473 loss: 0.000568 2022/09/10 01:28:50 - mmengine - INFO - Epoch(train) [132][300/586] lr: 5.000000e-04 eta: 4:05:04 time: 0.344983 data_time: 0.022621 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.884298 loss: 0.000573 2022/09/10 01:29:07 - mmengine - INFO - Epoch(train) [132][350/586] lr: 5.000000e-04 eta: 4:04:48 time: 0.340564 data_time: 0.027552 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.845014 loss: 0.000565 2022/09/10 01:29:24 - mmengine - INFO - Epoch(train) [132][400/586] lr: 5.000000e-04 eta: 4:04:33 time: 0.331122 data_time: 0.023418 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.903350 loss: 0.000585 2022/09/10 01:29:40 - mmengine - INFO - Epoch(train) [132][450/586] lr: 5.000000e-04 eta: 4:04:17 time: 0.335955 data_time: 0.022505 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.816615 loss: 0.000578 2022/09/10 01:29:57 - mmengine - INFO - Epoch(train) [132][500/586] lr: 5.000000e-04 eta: 4:04:02 time: 0.338507 data_time: 0.022810 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.834806 loss: 0.000580 2022/09/10 01:30:14 - mmengine - INFO - Epoch(train) [132][550/586] lr: 5.000000e-04 eta: 4:03:46 time: 0.333314 data_time: 0.022684 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.845739 loss: 0.000574 2022/09/10 01:30:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:30:26 - mmengine - INFO - Saving checkpoint at 132 epochs 2022/09/10 01:30:50 - mmengine - INFO - Epoch(train) [133][50/586] lr: 5.000000e-04 eta: 4:03:12 time: 0.337425 data_time: 0.028857 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.871392 loss: 0.000569 2022/09/10 01:31:06 - mmengine - INFO - Epoch(train) [133][100/586] lr: 5.000000e-04 eta: 4:02:57 time: 0.329182 data_time: 0.022802 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.861793 loss: 0.000564 2022/09/10 01:31:23 - mmengine - INFO - Epoch(train) [133][150/586] lr: 5.000000e-04 eta: 4:02:41 time: 0.336013 data_time: 0.026156 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.776645 loss: 0.000579 2022/09/10 01:31:40 - mmengine - INFO - Epoch(train) [133][200/586] lr: 5.000000e-04 eta: 4:02:26 time: 0.339461 data_time: 0.022596 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.767250 loss: 0.000584 2022/09/10 01:31:57 - mmengine - INFO - Epoch(train) [133][250/586] lr: 5.000000e-04 eta: 4:02:10 time: 0.332239 data_time: 0.025833 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.818640 loss: 0.000579 2022/09/10 01:32:14 - mmengine - INFO - Epoch(train) [133][300/586] lr: 5.000000e-04 eta: 4:01:55 time: 0.341044 data_time: 0.023218 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.871832 loss: 0.000585 2022/09/10 01:32:30 - mmengine - INFO - Epoch(train) [133][350/586] lr: 5.000000e-04 eta: 4:01:39 time: 0.333301 data_time: 0.022878 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.821069 loss: 0.000578 2022/09/10 01:32:47 - mmengine - INFO - Epoch(train) [133][400/586] lr: 5.000000e-04 eta: 4:01:24 time: 0.333255 data_time: 0.022842 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.860165 loss: 0.000579 2022/09/10 01:33:04 - mmengine - INFO - Epoch(train) [133][450/586] lr: 5.000000e-04 eta: 4:01:08 time: 0.337736 data_time: 0.023713 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.854329 loss: 0.000574 2022/09/10 01:33:21 - mmengine - INFO - Epoch(train) [133][500/586] lr: 5.000000e-04 eta: 4:00:53 time: 0.338554 data_time: 0.022804 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.786659 loss: 0.000592 2022/09/10 01:33:38 - mmengine - INFO - Epoch(train) [133][550/586] lr: 5.000000e-04 eta: 4:00:37 time: 0.336121 data_time: 0.023063 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.908675 loss: 0.000572 2022/09/10 01:33:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:33:50 - mmengine - INFO - Saving checkpoint at 133 epochs 2022/09/10 01:34:14 - mmengine - INFO - Epoch(train) [134][50/586] lr: 5.000000e-04 eta: 4:00:04 time: 0.340703 data_time: 0.027555 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.837132 loss: 0.000578 2022/09/10 01:34:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:34:31 - mmengine - INFO - Epoch(train) [134][100/586] lr: 5.000000e-04 eta: 3:59:48 time: 0.334574 data_time: 0.026858 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.853219 loss: 0.000575 2022/09/10 01:34:47 - mmengine - INFO - Epoch(train) [134][150/586] lr: 5.000000e-04 eta: 3:59:32 time: 0.329859 data_time: 0.023050 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.839930 loss: 0.000582 2022/09/10 01:35:04 - mmengine - INFO - Epoch(train) [134][200/586] lr: 5.000000e-04 eta: 3:59:17 time: 0.341313 data_time: 0.022589 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.861670 loss: 0.000570 2022/09/10 01:35:21 - mmengine - INFO - Epoch(train) [134][250/586] lr: 5.000000e-04 eta: 3:59:02 time: 0.338545 data_time: 0.024098 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.846120 loss: 0.000578 2022/09/10 01:35:38 - mmengine - INFO - Epoch(train) [134][300/586] lr: 5.000000e-04 eta: 3:58:46 time: 0.340803 data_time: 0.023906 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.883549 loss: 0.000555 2022/09/10 01:35:55 - mmengine - INFO - Epoch(train) [134][350/586] lr: 5.000000e-04 eta: 3:58:31 time: 0.340630 data_time: 0.024440 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.895424 loss: 0.000557 2022/09/10 01:36:12 - mmengine - INFO - Epoch(train) [134][400/586] lr: 5.000000e-04 eta: 3:58:15 time: 0.335800 data_time: 0.024806 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.912809 loss: 0.000589 2022/09/10 01:36:29 - mmengine - INFO - Epoch(train) [134][450/586] lr: 5.000000e-04 eta: 3:58:00 time: 0.336288 data_time: 0.025129 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.877122 loss: 0.000581 2022/09/10 01:36:46 - mmengine - INFO - Epoch(train) [134][500/586] lr: 5.000000e-04 eta: 3:57:44 time: 0.339966 data_time: 0.034537 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.841887 loss: 0.000586 2022/09/10 01:37:03 - mmengine - INFO - Epoch(train) [134][550/586] lr: 5.000000e-04 eta: 3:57:29 time: 0.333024 data_time: 0.024339 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.869109 loss: 0.000573 2022/09/10 01:37:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:37:15 - mmengine - INFO - Saving checkpoint at 134 epochs 2022/09/10 01:37:39 - mmengine - INFO - Epoch(train) [135][50/586] lr: 5.000000e-04 eta: 3:56:56 time: 0.346878 data_time: 0.028476 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.784444 loss: 0.000551 2022/09/10 01:37:56 - mmengine - INFO - Epoch(train) [135][100/586] lr: 5.000000e-04 eta: 3:56:40 time: 0.341836 data_time: 0.029422 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.843145 loss: 0.000581 2022/09/10 01:38:13 - mmengine - INFO - Epoch(train) [135][150/586] lr: 5.000000e-04 eta: 3:56:25 time: 0.331534 data_time: 0.022605 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.855138 loss: 0.000564 2022/09/10 01:38:30 - mmengine - INFO - Epoch(train) [135][200/586] lr: 5.000000e-04 eta: 3:56:09 time: 0.341468 data_time: 0.023869 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.893195 loss: 0.000560 2022/09/10 01:38:47 - mmengine - INFO - Epoch(train) [135][250/586] lr: 5.000000e-04 eta: 3:55:54 time: 0.335611 data_time: 0.024857 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.817961 loss: 0.000571 2022/09/10 01:39:03 - mmengine - INFO - Epoch(train) [135][300/586] lr: 5.000000e-04 eta: 3:55:38 time: 0.334125 data_time: 0.023468 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.831511 loss: 0.000576 2022/09/10 01:39:20 - mmengine - INFO - Epoch(train) [135][350/586] lr: 5.000000e-04 eta: 3:55:23 time: 0.340149 data_time: 0.023928 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.826331 loss: 0.000589 2022/09/10 01:39:37 - mmengine - INFO - Epoch(train) [135][400/586] lr: 5.000000e-04 eta: 3:55:07 time: 0.329133 data_time: 0.023573 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.883138 loss: 0.000571 2022/09/10 01:39:53 - mmengine - INFO - Epoch(train) [135][450/586] lr: 5.000000e-04 eta: 3:54:51 time: 0.333378 data_time: 0.022106 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.834838 loss: 0.000570 2022/09/10 01:40:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:40:11 - mmengine - INFO - Epoch(train) [135][500/586] lr: 5.000000e-04 eta: 3:54:36 time: 0.341703 data_time: 0.022899 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.865187 loss: 0.000563 2022/09/10 01:40:27 - mmengine - INFO - Epoch(train) [135][550/586] lr: 5.000000e-04 eta: 3:54:20 time: 0.329201 data_time: 0.022085 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.880402 loss: 0.000569 2022/09/10 01:40:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:40:39 - mmengine - INFO - Saving checkpoint at 135 epochs 2022/09/10 01:41:03 - mmengine - INFO - Epoch(train) [136][50/586] lr: 5.000000e-04 eta: 3:53:47 time: 0.351000 data_time: 0.034844 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.825469 loss: 0.000570 2022/09/10 01:41:20 - mmengine - INFO - Epoch(train) [136][100/586] lr: 5.000000e-04 eta: 3:53:32 time: 0.330468 data_time: 0.025463 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.786288 loss: 0.000569 2022/09/10 01:41:37 - mmengine - INFO - Epoch(train) [136][150/586] lr: 5.000000e-04 eta: 3:53:16 time: 0.332763 data_time: 0.025934 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.921006 loss: 0.000576 2022/09/10 01:41:53 - mmengine - INFO - Epoch(train) [136][200/586] lr: 5.000000e-04 eta: 3:53:00 time: 0.330498 data_time: 0.025139 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.913423 loss: 0.000584 2022/09/10 01:42:10 - mmengine - INFO - Epoch(train) [136][250/586] lr: 5.000000e-04 eta: 3:52:45 time: 0.338101 data_time: 0.028920 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.815550 loss: 0.000569 2022/09/10 01:42:27 - mmengine - INFO - Epoch(train) [136][300/586] lr: 5.000000e-04 eta: 3:52:29 time: 0.328614 data_time: 0.026373 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.804293 loss: 0.000567 2022/09/10 01:42:44 - mmengine - INFO - Epoch(train) [136][350/586] lr: 5.000000e-04 eta: 3:52:14 time: 0.343540 data_time: 0.027353 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.853316 loss: 0.000583 2022/09/10 01:43:00 - mmengine - INFO - Epoch(train) [136][400/586] lr: 5.000000e-04 eta: 3:51:58 time: 0.329896 data_time: 0.023055 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.834708 loss: 0.000582 2022/09/10 01:43:17 - mmengine - INFO - Epoch(train) [136][450/586] lr: 5.000000e-04 eta: 3:51:42 time: 0.330324 data_time: 0.022708 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.812634 loss: 0.000571 2022/09/10 01:43:34 - mmengine - INFO - Epoch(train) [136][500/586] lr: 5.000000e-04 eta: 3:51:27 time: 0.343049 data_time: 0.026572 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.853180 loss: 0.000544 2022/09/10 01:43:50 - mmengine - INFO - Epoch(train) [136][550/586] lr: 5.000000e-04 eta: 3:51:11 time: 0.327953 data_time: 0.023121 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.800643 loss: 0.000554 2022/09/10 01:44:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:44:02 - mmengine - INFO - Saving checkpoint at 136 epochs 2022/09/10 01:44:26 - mmengine - INFO - Epoch(train) [137][50/586] lr: 5.000000e-04 eta: 3:50:38 time: 0.342191 data_time: 0.031588 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.814613 loss: 0.000567 2022/09/10 01:44:43 - mmengine - INFO - Epoch(train) [137][100/586] lr: 5.000000e-04 eta: 3:50:23 time: 0.332742 data_time: 0.023417 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.811036 loss: 0.000582 2022/09/10 01:45:00 - mmengine - INFO - Epoch(train) [137][150/586] lr: 5.000000e-04 eta: 3:50:07 time: 0.329371 data_time: 0.027168 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.845808 loss: 0.000579 2022/09/10 01:45:16 - mmengine - INFO - Epoch(train) [137][200/586] lr: 5.000000e-04 eta: 3:49:51 time: 0.334085 data_time: 0.022864 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.800106 loss: 0.000568 2022/09/10 01:45:34 - mmengine - INFO - Epoch(train) [137][250/586] lr: 5.000000e-04 eta: 3:49:36 time: 0.345161 data_time: 0.023300 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.880667 loss: 0.000566 2022/09/10 01:45:50 - mmengine - INFO - Epoch(train) [137][300/586] lr: 5.000000e-04 eta: 3:49:20 time: 0.332912 data_time: 0.026973 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.829027 loss: 0.000572 2022/09/10 01:45:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:46:07 - mmengine - INFO - Epoch(train) [137][350/586] lr: 5.000000e-04 eta: 3:49:05 time: 0.340077 data_time: 0.022347 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.880549 loss: 0.000568 2022/09/10 01:46:24 - mmengine - INFO - Epoch(train) [137][400/586] lr: 5.000000e-04 eta: 3:48:50 time: 0.336305 data_time: 0.022799 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.867714 loss: 0.000586 2022/09/10 01:46:41 - mmengine - INFO - Epoch(train) [137][450/586] lr: 5.000000e-04 eta: 3:48:34 time: 0.333451 data_time: 0.026839 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.869513 loss: 0.000590 2022/09/10 01:46:58 - mmengine - INFO - Epoch(train) [137][500/586] lr: 5.000000e-04 eta: 3:48:18 time: 0.339288 data_time: 0.025462 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.818918 loss: 0.000586 2022/09/10 01:47:14 - mmengine - INFO - Epoch(train) [137][550/586] lr: 5.000000e-04 eta: 3:48:03 time: 0.331924 data_time: 0.025490 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.818450 loss: 0.000573 2022/09/10 01:47:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:47:26 - mmengine - INFO - Saving checkpoint at 137 epochs 2022/09/10 01:47:51 - mmengine - INFO - Epoch(train) [138][50/586] lr: 5.000000e-04 eta: 3:47:30 time: 0.352439 data_time: 0.033898 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.860764 loss: 0.000576 2022/09/10 01:48:08 - mmengine - INFO - Epoch(train) [138][100/586] lr: 5.000000e-04 eta: 3:47:15 time: 0.339107 data_time: 0.023568 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.786091 loss: 0.000568 2022/09/10 01:48:24 - mmengine - INFO - Epoch(train) [138][150/586] lr: 5.000000e-04 eta: 3:46:59 time: 0.333271 data_time: 0.024481 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.799319 loss: 0.000587 2022/09/10 01:48:41 - mmengine - INFO - Epoch(train) [138][200/586] lr: 5.000000e-04 eta: 3:46:44 time: 0.338098 data_time: 0.022579 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.861823 loss: 0.000580 2022/09/10 01:48:58 - mmengine - INFO - Epoch(train) [138][250/586] lr: 5.000000e-04 eta: 3:46:28 time: 0.331193 data_time: 0.023882 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.875649 loss: 0.000570 2022/09/10 01:49:15 - mmengine - INFO - Epoch(train) [138][300/586] lr: 5.000000e-04 eta: 3:46:12 time: 0.335620 data_time: 0.025386 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.888388 loss: 0.000569 2022/09/10 01:49:32 - mmengine - INFO - Epoch(train) [138][350/586] lr: 5.000000e-04 eta: 3:45:57 time: 0.338588 data_time: 0.023555 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.845341 loss: 0.000577 2022/09/10 01:49:48 - mmengine - INFO - Epoch(train) [138][400/586] lr: 5.000000e-04 eta: 3:45:41 time: 0.333761 data_time: 0.027595 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.889183 loss: 0.000573 2022/09/10 01:50:05 - mmengine - INFO - Epoch(train) [138][450/586] lr: 5.000000e-04 eta: 3:45:26 time: 0.333742 data_time: 0.022274 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.888346 loss: 0.000542 2022/09/10 01:50:22 - mmengine - INFO - Epoch(train) [138][500/586] lr: 5.000000e-04 eta: 3:45:10 time: 0.338898 data_time: 0.022921 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.842418 loss: 0.000566 2022/09/10 01:50:39 - mmengine - INFO - Epoch(train) [138][550/586] lr: 5.000000e-04 eta: 3:44:55 time: 0.334174 data_time: 0.022257 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.912450 loss: 0.000571 2022/09/10 01:50:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:50:50 - mmengine - INFO - Saving checkpoint at 138 epochs 2022/09/10 01:51:15 - mmengine - INFO - Epoch(train) [139][50/586] lr: 5.000000e-04 eta: 3:44:22 time: 0.351438 data_time: 0.028387 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.907121 loss: 0.000575 2022/09/10 01:51:32 - mmengine - INFO - Epoch(train) [139][100/586] lr: 5.000000e-04 eta: 3:44:06 time: 0.337074 data_time: 0.023330 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.855322 loss: 0.000568 2022/09/10 01:51:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:51:48 - mmengine - INFO - Epoch(train) [139][150/586] lr: 5.000000e-04 eta: 3:43:51 time: 0.328476 data_time: 0.022539 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.849543 loss: 0.000571 2022/09/10 01:52:05 - mmengine - INFO - Epoch(train) [139][200/586] lr: 5.000000e-04 eta: 3:43:35 time: 0.341444 data_time: 0.026217 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.860198 loss: 0.000561 2022/09/10 01:52:22 - mmengine - INFO - Epoch(train) [139][250/586] lr: 5.000000e-04 eta: 3:43:20 time: 0.330117 data_time: 0.022593 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.879280 loss: 0.000583 2022/09/10 01:52:38 - mmengine - INFO - Epoch(train) [139][300/586] lr: 5.000000e-04 eta: 3:43:04 time: 0.325380 data_time: 0.023020 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.878116 loss: 0.000579 2022/09/10 01:52:56 - mmengine - INFO - Epoch(train) [139][350/586] lr: 5.000000e-04 eta: 3:42:48 time: 0.345961 data_time: 0.022514 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.830130 loss: 0.000566 2022/09/10 01:53:12 - mmengine - INFO - Epoch(train) [139][400/586] lr: 5.000000e-04 eta: 3:42:33 time: 0.329764 data_time: 0.022496 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.877590 loss: 0.000583 2022/09/10 01:53:29 - mmengine - INFO - Epoch(train) [139][450/586] lr: 5.000000e-04 eta: 3:42:17 time: 0.332966 data_time: 0.022491 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.834733 loss: 0.000563 2022/09/10 01:53:46 - mmengine - INFO - Epoch(train) [139][500/586] lr: 5.000000e-04 eta: 3:42:02 time: 0.338145 data_time: 0.022021 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.840228 loss: 0.000582 2022/09/10 01:54:03 - mmengine - INFO - Epoch(train) [139][550/586] lr: 5.000000e-04 eta: 3:41:46 time: 0.339763 data_time: 0.022868 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.834322 loss: 0.000570 2022/09/10 01:54:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:54:14 - mmengine - INFO - Saving checkpoint at 139 epochs 2022/09/10 01:54:38 - mmengine - INFO - Epoch(train) [140][50/586] lr: 5.000000e-04 eta: 3:41:13 time: 0.345517 data_time: 0.031072 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.844938 loss: 0.000570 2022/09/10 01:54:55 - mmengine - INFO - Epoch(train) [140][100/586] lr: 5.000000e-04 eta: 3:40:58 time: 0.332754 data_time: 0.023723 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.803800 loss: 0.000573 2022/09/10 01:55:12 - mmengine - INFO - Epoch(train) [140][150/586] lr: 5.000000e-04 eta: 3:40:42 time: 0.333605 data_time: 0.022665 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.846007 loss: 0.000580 2022/09/10 01:55:29 - mmengine - INFO - Epoch(train) [140][200/586] lr: 5.000000e-04 eta: 3:40:27 time: 0.341377 data_time: 0.022711 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.810558 loss: 0.000548 2022/09/10 01:55:46 - mmengine - INFO - Epoch(train) [140][250/586] lr: 5.000000e-04 eta: 3:40:11 time: 0.338200 data_time: 0.027997 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.845884 loss: 0.000568 2022/09/10 01:56:03 - mmengine - INFO - Epoch(train) [140][300/586] lr: 5.000000e-04 eta: 3:39:56 time: 0.333612 data_time: 0.022838 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.864166 loss: 0.000555 2022/09/10 01:56:20 - mmengine - INFO - Epoch(train) [140][350/586] lr: 5.000000e-04 eta: 3:39:40 time: 0.339797 data_time: 0.022568 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.825413 loss: 0.000561 2022/09/10 01:56:36 - mmengine - INFO - Epoch(train) [140][400/586] lr: 5.000000e-04 eta: 3:39:25 time: 0.337214 data_time: 0.022450 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.783662 loss: 0.000581 2022/09/10 01:56:53 - mmengine - INFO - Epoch(train) [140][450/586] lr: 5.000000e-04 eta: 3:39:09 time: 0.331622 data_time: 0.022957 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.752898 loss: 0.000583 2022/09/10 01:57:10 - mmengine - INFO - Epoch(train) [140][500/586] lr: 5.000000e-04 eta: 3:38:54 time: 0.339466 data_time: 0.022431 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.846220 loss: 0.000585 2022/09/10 01:57:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:57:26 - mmengine - INFO - Epoch(train) [140][550/586] lr: 5.000000e-04 eta: 3:38:38 time: 0.330289 data_time: 0.022727 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.871115 loss: 0.000587 2022/09/10 01:57:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 01:57:39 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/09/10 01:57:54 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:01:01 time: 0.171624 data_time: 0.012882 memory: 7489 2022/09/10 01:58:03 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:52 time: 0.171286 data_time: 0.007944 memory: 1657 2022/09/10 01:58:11 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:42 time: 0.166185 data_time: 0.007629 memory: 1657 2022/09/10 01:58:20 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:34 time: 0.166328 data_time: 0.007662 memory: 1657 2022/09/10 01:58:28 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:25 time: 0.165255 data_time: 0.007585 memory: 1657 2022/09/10 01:58:36 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:17 time: 0.167725 data_time: 0.008025 memory: 1657 2022/09/10 01:58:45 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:09 time: 0.164818 data_time: 0.007684 memory: 1657 2022/09/10 01:58:53 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:01 time: 0.161776 data_time: 0.007067 memory: 1657 2022/09/10 01:59:28 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 01:59:42 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.743795 coco/AP .5: 0.900435 coco/AP .75: 0.811486 coco/AP (M): 0.708504 coco/AP (L): 0.811161 coco/AR: 0.797544 coco/AR .5: 0.939861 coco/AR .75: 0.857525 coco/AR (M): 0.755231 coco/AR (L): 0.858231 2022/09/10 02:00:00 - mmengine - INFO - Epoch(train) [141][50/586] lr: 5.000000e-04 eta: 3:38:05 time: 0.350903 data_time: 0.028011 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.848899 loss: 0.000580 2022/09/10 02:00:18 - mmengine - INFO - Epoch(train) [141][100/586] lr: 5.000000e-04 eta: 3:37:51 time: 0.371165 data_time: 0.024321 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.917686 loss: 0.000558 2022/09/10 02:00:35 - mmengine - INFO - Epoch(train) [141][150/586] lr: 5.000000e-04 eta: 3:37:35 time: 0.344909 data_time: 0.022893 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.788530 loss: 0.000583 2022/09/10 02:00:52 - mmengine - INFO - Epoch(train) [141][200/586] lr: 5.000000e-04 eta: 3:37:20 time: 0.331039 data_time: 0.025560 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.898750 loss: 0.000562 2022/09/10 02:01:09 - mmengine - INFO - Epoch(train) [141][250/586] lr: 5.000000e-04 eta: 3:37:04 time: 0.338342 data_time: 0.022485 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.873894 loss: 0.000571 2022/09/10 02:01:26 - mmengine - INFO - Epoch(train) [141][300/586] lr: 5.000000e-04 eta: 3:36:49 time: 0.331052 data_time: 0.022430 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.838136 loss: 0.000574 2022/09/10 02:01:43 - mmengine - INFO - Epoch(train) [141][350/586] lr: 5.000000e-04 eta: 3:36:33 time: 0.342634 data_time: 0.022822 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.843335 loss: 0.000564 2022/09/10 02:01:59 - mmengine - INFO - Epoch(train) [141][400/586] lr: 5.000000e-04 eta: 3:36:17 time: 0.334389 data_time: 0.022207 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.837606 loss: 0.000580 2022/09/10 02:02:16 - mmengine - INFO - Epoch(train) [141][450/586] lr: 5.000000e-04 eta: 3:36:02 time: 0.335677 data_time: 0.023145 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.884462 loss: 0.000565 2022/09/10 02:02:33 - mmengine - INFO - Epoch(train) [141][500/586] lr: 5.000000e-04 eta: 3:35:47 time: 0.346025 data_time: 0.023220 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.865671 loss: 0.000575 2022/09/10 02:02:50 - mmengine - INFO - Epoch(train) [141][550/586] lr: 5.000000e-04 eta: 3:35:31 time: 0.330288 data_time: 0.022679 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.814835 loss: 0.000576 2022/09/10 02:03:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:03:02 - mmengine - INFO - Saving checkpoint at 141 epochs 2022/09/10 02:03:26 - mmengine - INFO - Epoch(train) [142][50/586] lr: 5.000000e-04 eta: 3:34:58 time: 0.343005 data_time: 0.028345 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.869006 loss: 0.000562 2022/09/10 02:03:43 - mmengine - INFO - Epoch(train) [142][100/586] lr: 5.000000e-04 eta: 3:34:43 time: 0.333583 data_time: 0.022316 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.852762 loss: 0.000556 2022/09/10 02:04:00 - mmengine - INFO - Epoch(train) [142][150/586] lr: 5.000000e-04 eta: 3:34:27 time: 0.336968 data_time: 0.022629 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.895936 loss: 0.000577 2022/09/10 02:04:16 - mmengine - INFO - Epoch(train) [142][200/586] lr: 5.000000e-04 eta: 3:34:12 time: 0.332544 data_time: 0.023320 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.819768 loss: 0.000563 2022/09/10 02:04:33 - mmengine - INFO - Epoch(train) [142][250/586] lr: 5.000000e-04 eta: 3:33:56 time: 0.337829 data_time: 0.022632 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.827650 loss: 0.000579 2022/09/10 02:04:50 - mmengine - INFO - Epoch(train) [142][300/586] lr: 5.000000e-04 eta: 3:33:40 time: 0.327959 data_time: 0.022474 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.867561 loss: 0.000581 2022/09/10 02:05:07 - mmengine - INFO - Epoch(train) [142][350/586] lr: 5.000000e-04 eta: 3:33:25 time: 0.338164 data_time: 0.024031 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.902534 loss: 0.000554 2022/09/10 02:05:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:05:24 - mmengine - INFO - Epoch(train) [142][400/586] lr: 5.000000e-04 eta: 3:33:09 time: 0.335892 data_time: 0.023434 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.838978 loss: 0.000571 2022/09/10 02:05:40 - mmengine - INFO - Epoch(train) [142][450/586] lr: 5.000000e-04 eta: 3:32:54 time: 0.334025 data_time: 0.022235 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.863069 loss: 0.000565 2022/09/10 02:05:57 - mmengine - INFO - Epoch(train) [142][500/586] lr: 5.000000e-04 eta: 3:32:38 time: 0.334944 data_time: 0.022361 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.822553 loss: 0.000566 2022/09/10 02:06:14 - mmengine - INFO - Epoch(train) [142][550/586] lr: 5.000000e-04 eta: 3:32:22 time: 0.337694 data_time: 0.026140 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.848549 loss: 0.000565 2022/09/10 02:06:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:06:26 - mmengine - INFO - Saving checkpoint at 142 epochs 2022/09/10 02:06:51 - mmengine - INFO - Epoch(train) [143][50/586] lr: 5.000000e-04 eta: 3:31:50 time: 0.353838 data_time: 0.035745 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.864551 loss: 0.000557 2022/09/10 02:07:08 - mmengine - INFO - Epoch(train) [143][100/586] lr: 5.000000e-04 eta: 3:31:35 time: 0.337853 data_time: 0.025815 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.846511 loss: 0.000565 2022/09/10 02:07:24 - mmengine - INFO - Epoch(train) [143][150/586] lr: 5.000000e-04 eta: 3:31:19 time: 0.334444 data_time: 0.022760 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.892191 loss: 0.000568 2022/09/10 02:07:41 - mmengine - INFO - Epoch(train) [143][200/586] lr: 5.000000e-04 eta: 3:31:03 time: 0.330231 data_time: 0.022492 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.891496 loss: 0.000566 2022/09/10 02:07:58 - mmengine - INFO - Epoch(train) [143][250/586] lr: 5.000000e-04 eta: 3:30:48 time: 0.333934 data_time: 0.023218 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.907148 loss: 0.000574 2022/09/10 02:08:14 - mmengine - INFO - Epoch(train) [143][300/586] lr: 5.000000e-04 eta: 3:30:32 time: 0.337595 data_time: 0.026594 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.804351 loss: 0.000565 2022/09/10 02:08:31 - mmengine - INFO - Epoch(train) [143][350/586] lr: 5.000000e-04 eta: 3:30:16 time: 0.329535 data_time: 0.022832 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.823504 loss: 0.000564 2022/09/10 02:08:48 - mmengine - INFO - Epoch(train) [143][400/586] lr: 5.000000e-04 eta: 3:30:01 time: 0.337720 data_time: 0.022490 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.894398 loss: 0.000566 2022/09/10 02:09:04 - mmengine - INFO - Epoch(train) [143][450/586] lr: 5.000000e-04 eta: 3:29:45 time: 0.333450 data_time: 0.026242 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.845967 loss: 0.000568 2022/09/10 02:09:22 - mmengine - INFO - Epoch(train) [143][500/586] lr: 5.000000e-04 eta: 3:29:30 time: 0.342515 data_time: 0.023612 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.858636 loss: 0.000569 2022/09/10 02:09:38 - mmengine - INFO - Epoch(train) [143][550/586] lr: 5.000000e-04 eta: 3:29:14 time: 0.334422 data_time: 0.022922 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.878337 loss: 0.000575 2022/09/10 02:09:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:09:50 - mmengine - INFO - Saving checkpoint at 143 epochs 2022/09/10 02:10:15 - mmengine - INFO - Epoch(train) [144][50/586] lr: 5.000000e-04 eta: 3:28:42 time: 0.343549 data_time: 0.031280 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.868727 loss: 0.000560 2022/09/10 02:10:32 - mmengine - INFO - Epoch(train) [144][100/586] lr: 5.000000e-04 eta: 3:28:26 time: 0.338054 data_time: 0.027731 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.878351 loss: 0.000562 2022/09/10 02:10:48 - mmengine - INFO - Epoch(train) [144][150/586] lr: 5.000000e-04 eta: 3:28:11 time: 0.330617 data_time: 0.022251 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.811796 loss: 0.000551 2022/09/10 02:11:05 - mmengine - INFO - Epoch(train) [144][200/586] lr: 5.000000e-04 eta: 3:27:55 time: 0.339028 data_time: 0.023404 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.908900 loss: 0.000546 2022/09/10 02:11:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:11:22 - mmengine - INFO - Epoch(train) [144][250/586] lr: 5.000000e-04 eta: 3:27:40 time: 0.333726 data_time: 0.022659 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.863833 loss: 0.000586 2022/09/10 02:11:39 - mmengine - INFO - Epoch(train) [144][300/586] lr: 5.000000e-04 eta: 3:27:24 time: 0.335845 data_time: 0.022405 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.873861 loss: 0.000570 2022/09/10 02:11:56 - mmengine - INFO - Epoch(train) [144][350/586] lr: 5.000000e-04 eta: 3:27:09 time: 0.342672 data_time: 0.023211 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.841762 loss: 0.000570 2022/09/10 02:12:12 - mmengine - INFO - Epoch(train) [144][400/586] lr: 5.000000e-04 eta: 3:26:53 time: 0.328204 data_time: 0.022593 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.845508 loss: 0.000545 2022/09/10 02:12:30 - mmengine - INFO - Epoch(train) [144][450/586] lr: 5.000000e-04 eta: 3:26:37 time: 0.345218 data_time: 0.022286 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.891275 loss: 0.000554 2022/09/10 02:12:46 - mmengine - INFO - Epoch(train) [144][500/586] lr: 5.000000e-04 eta: 3:26:22 time: 0.330722 data_time: 0.022707 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.815363 loss: 0.000571 2022/09/10 02:13:03 - mmengine - INFO - Epoch(train) [144][550/586] lr: 5.000000e-04 eta: 3:26:06 time: 0.343946 data_time: 0.026704 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.859296 loss: 0.000558 2022/09/10 02:13:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:13:15 - mmengine - INFO - Saving checkpoint at 144 epochs 2022/09/10 02:13:39 - mmengine - INFO - Epoch(train) [145][50/586] lr: 5.000000e-04 eta: 3:25:34 time: 0.339222 data_time: 0.029826 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.838157 loss: 0.000568 2022/09/10 02:13:56 - mmengine - INFO - Epoch(train) [145][100/586] lr: 5.000000e-04 eta: 3:25:19 time: 0.342982 data_time: 0.023112 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.869573 loss: 0.000573 2022/09/10 02:14:13 - mmengine - INFO - Epoch(train) [145][150/586] lr: 5.000000e-04 eta: 3:25:03 time: 0.330201 data_time: 0.022631 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.841919 loss: 0.000561 2022/09/10 02:14:30 - mmengine - INFO - Epoch(train) [145][200/586] lr: 5.000000e-04 eta: 3:24:47 time: 0.333554 data_time: 0.027141 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.745545 loss: 0.000582 2022/09/10 02:14:47 - mmengine - INFO - Epoch(train) [145][250/586] lr: 5.000000e-04 eta: 3:24:32 time: 0.337259 data_time: 0.024237 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.889823 loss: 0.000575 2022/09/10 02:15:04 - mmengine - INFO - Epoch(train) [145][300/586] lr: 5.000000e-04 eta: 3:24:16 time: 0.339849 data_time: 0.024747 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.849604 loss: 0.000555 2022/09/10 02:15:20 - mmengine - INFO - Epoch(train) [145][350/586] lr: 5.000000e-04 eta: 3:24:01 time: 0.337168 data_time: 0.023875 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.830783 loss: 0.000555 2022/09/10 02:15:37 - mmengine - INFO - Epoch(train) [145][400/586] lr: 5.000000e-04 eta: 3:23:45 time: 0.333859 data_time: 0.022814 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.906105 loss: 0.000570 2022/09/10 02:15:54 - mmengine - INFO - Epoch(train) [145][450/586] lr: 5.000000e-04 eta: 3:23:29 time: 0.336741 data_time: 0.023262 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.814823 loss: 0.000560 2022/09/10 02:16:11 - mmengine - INFO - Epoch(train) [145][500/586] lr: 5.000000e-04 eta: 3:23:14 time: 0.333746 data_time: 0.028027 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.804413 loss: 0.000571 2022/09/10 02:16:28 - mmengine - INFO - Epoch(train) [145][550/586] lr: 5.000000e-04 eta: 3:22:58 time: 0.337116 data_time: 0.022485 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.773876 loss: 0.000575 2022/09/10 02:16:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:16:40 - mmengine - INFO - Saving checkpoint at 145 epochs 2022/09/10 02:16:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:17:03 - mmengine - INFO - Epoch(train) [146][50/586] lr: 5.000000e-04 eta: 3:22:26 time: 0.333343 data_time: 0.032377 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.797572 loss: 0.000577 2022/09/10 02:17:20 - mmengine - INFO - Epoch(train) [146][100/586] lr: 5.000000e-04 eta: 3:22:10 time: 0.331856 data_time: 0.022470 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.876200 loss: 0.000566 2022/09/10 02:17:37 - mmengine - INFO - Epoch(train) [146][150/586] lr: 5.000000e-04 eta: 3:21:55 time: 0.348208 data_time: 0.027476 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.821999 loss: 0.000574 2022/09/10 02:17:54 - mmengine - INFO - Epoch(train) [146][200/586] lr: 5.000000e-04 eta: 3:21:39 time: 0.328793 data_time: 0.024271 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.829870 loss: 0.000555 2022/09/10 02:18:11 - mmengine - INFO - Epoch(train) [146][250/586] lr: 5.000000e-04 eta: 3:21:24 time: 0.339324 data_time: 0.022197 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.842931 loss: 0.000579 2022/09/10 02:18:28 - mmengine - INFO - Epoch(train) [146][300/586] lr: 5.000000e-04 eta: 3:21:08 time: 0.335843 data_time: 0.022577 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.793537 loss: 0.000593 2022/09/10 02:18:44 - mmengine - INFO - Epoch(train) [146][350/586] lr: 5.000000e-04 eta: 3:20:52 time: 0.336788 data_time: 0.023747 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.851751 loss: 0.000556 2022/09/10 02:19:01 - mmengine - INFO - Epoch(train) [146][400/586] lr: 5.000000e-04 eta: 3:20:37 time: 0.332080 data_time: 0.022938 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.819355 loss: 0.000567 2022/09/10 02:19:18 - mmengine - INFO - Epoch(train) [146][450/586] lr: 5.000000e-04 eta: 3:20:21 time: 0.345088 data_time: 0.023317 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.892735 loss: 0.000557 2022/09/10 02:19:35 - mmengine - INFO - Epoch(train) [146][500/586] lr: 5.000000e-04 eta: 3:20:06 time: 0.341022 data_time: 0.022937 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.835898 loss: 0.000581 2022/09/10 02:19:52 - mmengine - INFO - Epoch(train) [146][550/586] lr: 5.000000e-04 eta: 3:19:50 time: 0.330175 data_time: 0.022347 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.888815 loss: 0.000561 2022/09/10 02:20:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:20:04 - mmengine - INFO - Saving checkpoint at 146 epochs 2022/09/10 02:20:29 - mmengine - INFO - Epoch(train) [147][50/586] lr: 5.000000e-04 eta: 3:19:18 time: 0.343877 data_time: 0.034914 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.846881 loss: 0.000553 2022/09/10 02:20:46 - mmengine - INFO - Epoch(train) [147][100/586] lr: 5.000000e-04 eta: 3:19:03 time: 0.341565 data_time: 0.023231 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.867685 loss: 0.000569 2022/09/10 02:21:02 - mmengine - INFO - Epoch(train) [147][150/586] lr: 5.000000e-04 eta: 3:18:47 time: 0.333784 data_time: 0.023756 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.808210 loss: 0.000561 2022/09/10 02:21:19 - mmengine - INFO - Epoch(train) [147][200/586] lr: 5.000000e-04 eta: 3:18:31 time: 0.330883 data_time: 0.022983 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.831985 loss: 0.000562 2022/09/10 02:21:36 - mmengine - INFO - Epoch(train) [147][250/586] lr: 5.000000e-04 eta: 3:18:16 time: 0.340870 data_time: 0.022904 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.843048 loss: 0.000567 2022/09/10 02:21:53 - mmengine - INFO - Epoch(train) [147][300/586] lr: 5.000000e-04 eta: 3:18:00 time: 0.334101 data_time: 0.023631 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.830788 loss: 0.000569 2022/09/10 02:22:10 - mmengine - INFO - Epoch(train) [147][350/586] lr: 5.000000e-04 eta: 3:17:45 time: 0.343766 data_time: 0.027891 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.847039 loss: 0.000567 2022/09/10 02:22:27 - mmengine - INFO - Epoch(train) [147][400/586] lr: 5.000000e-04 eta: 3:17:29 time: 0.338794 data_time: 0.022947 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.869832 loss: 0.000586 2022/09/10 02:22:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:22:44 - mmengine - INFO - Epoch(train) [147][450/586] lr: 5.000000e-04 eta: 3:17:13 time: 0.334550 data_time: 0.023409 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.835343 loss: 0.000556 2022/09/10 02:23:00 - mmengine - INFO - Epoch(train) [147][500/586] lr: 5.000000e-04 eta: 3:16:58 time: 0.331677 data_time: 0.022951 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.851202 loss: 0.000572 2022/09/10 02:23:17 - mmengine - INFO - Epoch(train) [147][550/586] lr: 5.000000e-04 eta: 3:16:42 time: 0.334150 data_time: 0.023209 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.830614 loss: 0.000565 2022/09/10 02:23:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:23:29 - mmengine - INFO - Saving checkpoint at 147 epochs 2022/09/10 02:23:53 - mmengine - INFO - Epoch(train) [148][50/586] lr: 5.000000e-04 eta: 3:16:10 time: 0.336458 data_time: 0.030656 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.793235 loss: 0.000580 2022/09/10 02:24:10 - mmengine - INFO - Epoch(train) [148][100/586] lr: 5.000000e-04 eta: 3:15:55 time: 0.337522 data_time: 0.024060 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.862598 loss: 0.000565 2022/09/10 02:24:27 - mmengine - INFO - Epoch(train) [148][150/586] lr: 5.000000e-04 eta: 3:15:39 time: 0.333893 data_time: 0.023372 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.863209 loss: 0.000558 2022/09/10 02:24:44 - mmengine - INFO - Epoch(train) [148][200/586] lr: 5.000000e-04 eta: 3:15:23 time: 0.335740 data_time: 0.024395 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.867923 loss: 0.000574 2022/09/10 02:25:01 - mmengine - INFO - Epoch(train) [148][250/586] lr: 5.000000e-04 eta: 3:15:08 time: 0.336588 data_time: 0.022918 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.884943 loss: 0.000568 2022/09/10 02:25:18 - mmengine - INFO - Epoch(train) [148][300/586] lr: 5.000000e-04 eta: 3:14:52 time: 0.345024 data_time: 0.026804 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.781732 loss: 0.000579 2022/09/10 02:25:35 - mmengine - INFO - Epoch(train) [148][350/586] lr: 5.000000e-04 eta: 3:14:37 time: 0.333777 data_time: 0.024428 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.853564 loss: 0.000575 2022/09/10 02:25:51 - mmengine - INFO - Epoch(train) [148][400/586] lr: 5.000000e-04 eta: 3:14:21 time: 0.334137 data_time: 0.023195 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.853969 loss: 0.000557 2022/09/10 02:26:08 - mmengine - INFO - Epoch(train) [148][450/586] lr: 5.000000e-04 eta: 3:14:05 time: 0.338836 data_time: 0.022509 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.886118 loss: 0.000579 2022/09/10 02:26:25 - mmengine - INFO - Epoch(train) [148][500/586] lr: 5.000000e-04 eta: 3:13:50 time: 0.334248 data_time: 0.022997 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.879204 loss: 0.000558 2022/09/10 02:26:41 - mmengine - INFO - Epoch(train) [148][550/586] lr: 5.000000e-04 eta: 3:13:34 time: 0.328867 data_time: 0.022627 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.890289 loss: 0.000556 2022/09/10 02:26:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:26:54 - mmengine - INFO - Saving checkpoint at 148 epochs 2022/09/10 02:27:18 - mmengine - INFO - Epoch(train) [149][50/586] lr: 5.000000e-04 eta: 3:13:02 time: 0.339822 data_time: 0.028469 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.803469 loss: 0.000562 2022/09/10 02:27:35 - mmengine - INFO - Epoch(train) [149][100/586] lr: 5.000000e-04 eta: 3:12:46 time: 0.332357 data_time: 0.024018 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.832297 loss: 0.000563 2022/09/10 02:27:51 - mmengine - INFO - Epoch(train) [149][150/586] lr: 5.000000e-04 eta: 3:12:31 time: 0.336004 data_time: 0.022182 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.870267 loss: 0.000572 2022/09/10 02:28:08 - mmengine - INFO - Epoch(train) [149][200/586] lr: 5.000000e-04 eta: 3:12:15 time: 0.339701 data_time: 0.022941 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.829685 loss: 0.000553 2022/09/10 02:28:25 - mmengine - INFO - Epoch(train) [149][250/586] lr: 5.000000e-04 eta: 3:12:00 time: 0.332873 data_time: 0.022596 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.921907 loss: 0.000576 2022/09/10 02:28:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:28:42 - mmengine - INFO - Epoch(train) [149][300/586] lr: 5.000000e-04 eta: 3:11:44 time: 0.333898 data_time: 0.023099 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.821385 loss: 0.000565 2022/09/10 02:28:59 - mmengine - INFO - Epoch(train) [149][350/586] lr: 5.000000e-04 eta: 3:11:28 time: 0.338080 data_time: 0.027482 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.890023 loss: 0.000559 2022/09/10 02:29:15 - mmengine - INFO - Epoch(train) [149][400/586] lr: 5.000000e-04 eta: 3:11:13 time: 0.336359 data_time: 0.022858 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.871534 loss: 0.000560 2022/09/10 02:29:32 - mmengine - INFO - Epoch(train) [149][450/586] lr: 5.000000e-04 eta: 3:10:57 time: 0.332838 data_time: 0.022814 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.802535 loss: 0.000562 2022/09/10 02:29:49 - mmengine - INFO - Epoch(train) [149][500/586] lr: 5.000000e-04 eta: 3:10:42 time: 0.341685 data_time: 0.022760 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.861217 loss: 0.000555 2022/09/10 02:30:06 - mmengine - INFO - Epoch(train) [149][550/586] lr: 5.000000e-04 eta: 3:10:26 time: 0.334189 data_time: 0.026030 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.896893 loss: 0.000568 2022/09/10 02:30:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:30:18 - mmengine - INFO - Saving checkpoint at 149 epochs 2022/09/10 02:30:42 - mmengine - INFO - Epoch(train) [150][50/586] lr: 5.000000e-04 eta: 3:09:54 time: 0.336983 data_time: 0.029127 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.889555 loss: 0.000556 2022/09/10 02:30:59 - mmengine - INFO - Epoch(train) [150][100/586] lr: 5.000000e-04 eta: 3:09:38 time: 0.334931 data_time: 0.023313 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.864397 loss: 0.000572 2022/09/10 02:31:15 - mmengine - INFO - Epoch(train) [150][150/586] lr: 5.000000e-04 eta: 3:09:23 time: 0.335761 data_time: 0.026536 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.924599 loss: 0.000547 2022/09/10 02:31:32 - mmengine - INFO - Epoch(train) [150][200/586] lr: 5.000000e-04 eta: 3:09:07 time: 0.334222 data_time: 0.022612 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.870627 loss: 0.000545 2022/09/10 02:31:49 - mmengine - INFO - Epoch(train) [150][250/586] lr: 5.000000e-04 eta: 3:08:51 time: 0.334319 data_time: 0.023355 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.837645 loss: 0.000573 2022/09/10 02:32:06 - mmengine - INFO - Epoch(train) [150][300/586] lr: 5.000000e-04 eta: 3:08:36 time: 0.339918 data_time: 0.022916 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.870531 loss: 0.000562 2022/09/10 02:32:22 - mmengine - INFO - Epoch(train) [150][350/586] lr: 5.000000e-04 eta: 3:08:20 time: 0.332407 data_time: 0.026414 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.899855 loss: 0.000570 2022/09/10 02:32:40 - mmengine - INFO - Epoch(train) [150][400/586] lr: 5.000000e-04 eta: 3:08:05 time: 0.344564 data_time: 0.022951 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.849236 loss: 0.000555 2022/09/10 02:32:57 - mmengine - INFO - Epoch(train) [150][450/586] lr: 5.000000e-04 eta: 3:07:49 time: 0.341872 data_time: 0.027023 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.809262 loss: 0.000582 2022/09/10 02:33:13 - mmengine - INFO - Epoch(train) [150][500/586] lr: 5.000000e-04 eta: 3:07:34 time: 0.330271 data_time: 0.022299 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.881557 loss: 0.000565 2022/09/10 02:33:30 - mmengine - INFO - Epoch(train) [150][550/586] lr: 5.000000e-04 eta: 3:07:18 time: 0.333285 data_time: 0.022795 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.862937 loss: 0.000553 2022/09/10 02:33:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:33:43 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/09/10 02:33:58 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:01:00 time: 0.169830 data_time: 0.012518 memory: 7489 2022/09/10 02:34:07 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:51 time: 0.167365 data_time: 0.009031 memory: 1657 2022/09/10 02:34:15 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:42 time: 0.165746 data_time: 0.008026 memory: 1657 2022/09/10 02:34:23 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:34 time: 0.167913 data_time: 0.010930 memory: 1657 2022/09/10 02:34:32 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:25 time: 0.164135 data_time: 0.007466 memory: 1657 2022/09/10 02:34:40 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:17 time: 0.166060 data_time: 0.008058 memory: 1657 2022/09/10 02:34:48 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:09 time: 0.166032 data_time: 0.007892 memory: 1657 2022/09/10 02:34:56 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:01 time: 0.163629 data_time: 0.007416 memory: 1657 2022/09/10 02:35:32 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 02:35:46 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.745137 coco/AP .5: 0.900308 coco/AP .75: 0.817479 coco/AP (M): 0.709808 coco/AP (L): 0.810794 coco/AR: 0.798158 coco/AR .5: 0.938759 coco/AR .75: 0.862248 coco/AR (M): 0.756405 coco/AR (L): 0.858417 2022/09/10 02:35:46 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_130.pth is removed 2022/09/10 02:35:50 - mmengine - INFO - The best checkpoint with 0.7451 coco/AP at 150 epoch is saved to best_coco/AP_epoch_150.pth. 2022/09/10 02:36:07 - mmengine - INFO - Epoch(train) [151][50/586] lr: 5.000000e-04 eta: 3:06:46 time: 0.334148 data_time: 0.027023 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.879828 loss: 0.000550 2022/09/10 02:36:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:36:24 - mmengine - INFO - Epoch(train) [151][100/586] lr: 5.000000e-04 eta: 3:06:30 time: 0.335579 data_time: 0.022579 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.855204 loss: 0.000567 2022/09/10 02:36:40 - mmengine - INFO - Epoch(train) [151][150/586] lr: 5.000000e-04 eta: 3:06:15 time: 0.335513 data_time: 0.023117 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.809796 loss: 0.000571 2022/09/10 02:36:57 - mmengine - INFO - Epoch(train) [151][200/586] lr: 5.000000e-04 eta: 3:05:59 time: 0.339852 data_time: 0.026654 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.855379 loss: 0.000554 2022/09/10 02:37:14 - mmengine - INFO - Epoch(train) [151][250/586] lr: 5.000000e-04 eta: 3:05:44 time: 0.335712 data_time: 0.023365 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.876533 loss: 0.000531 2022/09/10 02:37:31 - mmengine - INFO - Epoch(train) [151][300/586] lr: 5.000000e-04 eta: 3:05:28 time: 0.336137 data_time: 0.022717 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.910241 loss: 0.000568 2022/09/10 02:37:48 - mmengine - INFO - Epoch(train) [151][350/586] lr: 5.000000e-04 eta: 3:05:12 time: 0.337520 data_time: 0.022872 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.877972 loss: 0.000566 2022/09/10 02:38:05 - mmengine - INFO - Epoch(train) [151][400/586] lr: 5.000000e-04 eta: 3:04:57 time: 0.337807 data_time: 0.023107 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.846645 loss: 0.000557 2022/09/10 02:38:21 - mmengine - INFO - Epoch(train) [151][450/586] lr: 5.000000e-04 eta: 3:04:41 time: 0.330279 data_time: 0.022357 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.876661 loss: 0.000566 2022/09/10 02:38:38 - mmengine - INFO - Epoch(train) [151][500/586] lr: 5.000000e-04 eta: 3:04:25 time: 0.336412 data_time: 0.026067 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.901857 loss: 0.000582 2022/09/10 02:38:55 - mmengine - INFO - Epoch(train) [151][550/586] lr: 5.000000e-04 eta: 3:04:10 time: 0.338939 data_time: 0.022621 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.838980 loss: 0.000575 2022/09/10 02:39:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:39:07 - mmengine - INFO - Saving checkpoint at 151 epochs 2022/09/10 02:39:31 - mmengine - INFO - Epoch(train) [152][50/586] lr: 5.000000e-04 eta: 3:03:38 time: 0.339802 data_time: 0.027739 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.905991 loss: 0.000565 2022/09/10 02:39:48 - mmengine - INFO - Epoch(train) [152][100/586] lr: 5.000000e-04 eta: 3:03:23 time: 0.342493 data_time: 0.026688 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.783400 loss: 0.000563 2022/09/10 02:40:05 - mmengine - INFO - Epoch(train) [152][150/586] lr: 5.000000e-04 eta: 3:03:07 time: 0.329835 data_time: 0.023686 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.912638 loss: 0.000563 2022/09/10 02:40:22 - mmengine - INFO - Epoch(train) [152][200/586] lr: 5.000000e-04 eta: 3:02:51 time: 0.333429 data_time: 0.022558 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.835057 loss: 0.000583 2022/09/10 02:40:39 - mmengine - INFO - Epoch(train) [152][250/586] lr: 5.000000e-04 eta: 3:02:36 time: 0.338181 data_time: 0.025508 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.856143 loss: 0.000562 2022/09/10 02:40:55 - mmengine - INFO - Epoch(train) [152][300/586] lr: 5.000000e-04 eta: 3:02:20 time: 0.333442 data_time: 0.025622 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.877426 loss: 0.000558 2022/09/10 02:41:12 - mmengine - INFO - Epoch(train) [152][350/586] lr: 5.000000e-04 eta: 3:02:04 time: 0.336394 data_time: 0.031767 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.887766 loss: 0.000556 2022/09/10 02:41:29 - mmengine - INFO - Epoch(train) [152][400/586] lr: 5.000000e-04 eta: 3:01:49 time: 0.341624 data_time: 0.024245 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.915551 loss: 0.000548 2022/09/10 02:41:46 - mmengine - INFO - Epoch(train) [152][450/586] lr: 5.000000e-04 eta: 3:01:33 time: 0.335811 data_time: 0.022574 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.845734 loss: 0.000564 2022/09/10 02:42:03 - mmengine - INFO - Epoch(train) [152][500/586] lr: 5.000000e-04 eta: 3:01:18 time: 0.338615 data_time: 0.026602 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.889696 loss: 0.000554 2022/09/10 02:42:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:42:20 - mmengine - INFO - Epoch(train) [152][550/586] lr: 5.000000e-04 eta: 3:01:02 time: 0.335713 data_time: 0.026367 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.838982 loss: 0.000564 2022/09/10 02:42:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:42:32 - mmengine - INFO - Saving checkpoint at 152 epochs 2022/09/10 02:42:56 - mmengine - INFO - Epoch(train) [153][50/586] lr: 5.000000e-04 eta: 3:00:31 time: 0.335805 data_time: 0.030085 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.867673 loss: 0.000560 2022/09/10 02:43:12 - mmengine - INFO - Epoch(train) [153][100/586] lr: 5.000000e-04 eta: 3:00:15 time: 0.338361 data_time: 0.028302 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.929051 loss: 0.000564 2022/09/10 02:43:29 - mmengine - INFO - Epoch(train) [153][150/586] lr: 5.000000e-04 eta: 2:59:59 time: 0.339275 data_time: 0.024940 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.842477 loss: 0.000561 2022/09/10 02:43:46 - mmengine - INFO - Epoch(train) [153][200/586] lr: 5.000000e-04 eta: 2:59:44 time: 0.333299 data_time: 0.024701 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.817244 loss: 0.000559 2022/09/10 02:44:03 - mmengine - INFO - Epoch(train) [153][250/586] lr: 5.000000e-04 eta: 2:59:28 time: 0.328234 data_time: 0.022986 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.869817 loss: 0.000561 2022/09/10 02:44:19 - mmengine - INFO - Epoch(train) [153][300/586] lr: 5.000000e-04 eta: 2:59:12 time: 0.335390 data_time: 0.022580 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.838747 loss: 0.000558 2022/09/10 02:44:36 - mmengine - INFO - Epoch(train) [153][350/586] lr: 5.000000e-04 eta: 2:58:57 time: 0.343054 data_time: 0.023212 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.898542 loss: 0.000558 2022/09/10 02:44:53 - mmengine - INFO - Epoch(train) [153][400/586] lr: 5.000000e-04 eta: 2:58:41 time: 0.329451 data_time: 0.023456 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.814397 loss: 0.000593 2022/09/10 02:45:10 - mmengine - INFO - Epoch(train) [153][450/586] lr: 5.000000e-04 eta: 2:58:25 time: 0.332794 data_time: 0.023228 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.846289 loss: 0.000575 2022/09/10 02:45:27 - mmengine - INFO - Epoch(train) [153][500/586] lr: 5.000000e-04 eta: 2:58:10 time: 0.349324 data_time: 0.027052 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.885572 loss: 0.000570 2022/09/10 02:45:44 - mmengine - INFO - Epoch(train) [153][550/586] lr: 5.000000e-04 eta: 2:57:54 time: 0.332014 data_time: 0.023709 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.864720 loss: 0.000565 2022/09/10 02:45:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:45:56 - mmengine - INFO - Saving checkpoint at 153 epochs 2022/09/10 02:46:20 - mmengine - INFO - Epoch(train) [154][50/586] lr: 5.000000e-04 eta: 2:57:23 time: 0.343159 data_time: 0.033256 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.803913 loss: 0.000544 2022/09/10 02:46:37 - mmengine - INFO - Epoch(train) [154][100/586] lr: 5.000000e-04 eta: 2:57:07 time: 0.333325 data_time: 0.025500 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.867108 loss: 0.000576 2022/09/10 02:46:53 - mmengine - INFO - Epoch(train) [154][150/586] lr: 5.000000e-04 eta: 2:56:52 time: 0.334999 data_time: 0.023162 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.857910 loss: 0.000557 2022/09/10 02:47:10 - mmengine - INFO - Epoch(train) [154][200/586] lr: 5.000000e-04 eta: 2:56:36 time: 0.334438 data_time: 0.023280 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.814457 loss: 0.000557 2022/09/10 02:47:27 - mmengine - INFO - Epoch(train) [154][250/586] lr: 5.000000e-04 eta: 2:56:20 time: 0.337955 data_time: 0.026124 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.841680 loss: 0.000556 2022/09/10 02:47:44 - mmengine - INFO - Epoch(train) [154][300/586] lr: 5.000000e-04 eta: 2:56:05 time: 0.335567 data_time: 0.023018 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.865708 loss: 0.000551 2022/09/10 02:47:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:48:01 - mmengine - INFO - Epoch(train) [154][350/586] lr: 5.000000e-04 eta: 2:55:49 time: 0.335218 data_time: 0.023158 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.894829 loss: 0.000571 2022/09/10 02:48:17 - mmengine - INFO - Epoch(train) [154][400/586] lr: 5.000000e-04 eta: 2:55:33 time: 0.336802 data_time: 0.023353 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.817949 loss: 0.000568 2022/09/10 02:48:34 - mmengine - INFO - Epoch(train) [154][450/586] lr: 5.000000e-04 eta: 2:55:18 time: 0.337104 data_time: 0.023941 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.870016 loss: 0.000570 2022/09/10 02:48:51 - mmengine - INFO - Epoch(train) [154][500/586] lr: 5.000000e-04 eta: 2:55:02 time: 0.332136 data_time: 0.024597 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.861171 loss: 0.000575 2022/09/10 02:49:08 - mmengine - INFO - Epoch(train) [154][550/586] lr: 5.000000e-04 eta: 2:54:46 time: 0.339505 data_time: 0.031598 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.817898 loss: 0.000566 2022/09/10 02:49:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:49:20 - mmengine - INFO - Saving checkpoint at 154 epochs 2022/09/10 02:49:44 - mmengine - INFO - Epoch(train) [155][50/586] lr: 5.000000e-04 eta: 2:54:15 time: 0.344663 data_time: 0.034892 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.814078 loss: 0.000559 2022/09/10 02:50:01 - mmengine - INFO - Epoch(train) [155][100/586] lr: 5.000000e-04 eta: 2:54:00 time: 0.337718 data_time: 0.022906 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.821312 loss: 0.000567 2022/09/10 02:50:18 - mmengine - INFO - Epoch(train) [155][150/586] lr: 5.000000e-04 eta: 2:53:44 time: 0.336859 data_time: 0.023843 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.858992 loss: 0.000557 2022/09/10 02:50:34 - mmengine - INFO - Epoch(train) [155][200/586] lr: 5.000000e-04 eta: 2:53:28 time: 0.334969 data_time: 0.027387 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.869737 loss: 0.000554 2022/09/10 02:50:51 - mmengine - INFO - Epoch(train) [155][250/586] lr: 5.000000e-04 eta: 2:53:13 time: 0.336075 data_time: 0.023347 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.850983 loss: 0.000551 2022/09/10 02:51:08 - mmengine - INFO - Epoch(train) [155][300/586] lr: 5.000000e-04 eta: 2:52:57 time: 0.337828 data_time: 0.027461 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.793266 loss: 0.000580 2022/09/10 02:51:25 - mmengine - INFO - Epoch(train) [155][350/586] lr: 5.000000e-04 eta: 2:52:41 time: 0.331420 data_time: 0.023816 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.895544 loss: 0.000558 2022/09/10 02:51:41 - mmengine - INFO - Epoch(train) [155][400/586] lr: 5.000000e-04 eta: 2:52:25 time: 0.330886 data_time: 0.022576 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.857445 loss: 0.000563 2022/09/10 02:51:58 - mmengine - INFO - Epoch(train) [155][450/586] lr: 5.000000e-04 eta: 2:52:10 time: 0.332574 data_time: 0.026284 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.883436 loss: 0.000541 2022/09/10 02:52:15 - mmengine - INFO - Epoch(train) [155][500/586] lr: 5.000000e-04 eta: 2:51:54 time: 0.338709 data_time: 0.022960 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.904141 loss: 0.000552 2022/09/10 02:52:32 - mmengine - INFO - Epoch(train) [155][550/586] lr: 5.000000e-04 eta: 2:51:39 time: 0.337130 data_time: 0.026044 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.778177 loss: 0.000578 2022/09/10 02:52:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:52:44 - mmengine - INFO - Saving checkpoint at 155 epochs 2022/09/10 02:53:08 - mmengine - INFO - Epoch(train) [156][50/586] lr: 5.000000e-04 eta: 2:51:07 time: 0.343046 data_time: 0.029165 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.827073 loss: 0.000581 2022/09/10 02:53:25 - mmengine - INFO - Epoch(train) [156][100/586] lr: 5.000000e-04 eta: 2:50:52 time: 0.334477 data_time: 0.023581 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.841095 loss: 0.000569 2022/09/10 02:53:42 - mmengine - INFO - Epoch(train) [156][150/586] lr: 5.000000e-04 eta: 2:50:36 time: 0.333675 data_time: 0.024205 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.794545 loss: 0.000584 2022/09/10 02:53:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:53:59 - mmengine - INFO - Epoch(train) [156][200/586] lr: 5.000000e-04 eta: 2:50:20 time: 0.338845 data_time: 0.023752 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.840946 loss: 0.000564 2022/09/10 02:54:15 - mmengine - INFO - Epoch(train) [156][250/586] lr: 5.000000e-04 eta: 2:50:05 time: 0.329431 data_time: 0.031576 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.868784 loss: 0.000561 2022/09/10 02:54:32 - mmengine - INFO - Epoch(train) [156][300/586] lr: 5.000000e-04 eta: 2:49:49 time: 0.342139 data_time: 0.027747 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.860005 loss: 0.000557 2022/09/10 02:54:49 - mmengine - INFO - Epoch(train) [156][350/586] lr: 5.000000e-04 eta: 2:49:34 time: 0.340388 data_time: 0.023796 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.838983 loss: 0.000551 2022/09/10 02:55:06 - mmengine - INFO - Epoch(train) [156][400/586] lr: 5.000000e-04 eta: 2:49:18 time: 0.339024 data_time: 0.024958 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.844193 loss: 0.000540 2022/09/10 02:55:23 - mmengine - INFO - Epoch(train) [156][450/586] lr: 5.000000e-04 eta: 2:49:02 time: 0.333215 data_time: 0.026159 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.878071 loss: 0.000549 2022/09/10 02:55:40 - mmengine - INFO - Epoch(train) [156][500/586] lr: 5.000000e-04 eta: 2:48:47 time: 0.341425 data_time: 0.023920 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.842336 loss: 0.000561 2022/09/10 02:55:57 - mmengine - INFO - Epoch(train) [156][550/586] lr: 5.000000e-04 eta: 2:48:31 time: 0.333669 data_time: 0.025197 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.878766 loss: 0.000565 2022/09/10 02:56:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:56:09 - mmengine - INFO - Saving checkpoint at 156 epochs 2022/09/10 02:56:33 - mmengine - INFO - Epoch(train) [157][50/586] lr: 5.000000e-04 eta: 2:48:00 time: 0.337910 data_time: 0.028334 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.841491 loss: 0.000565 2022/09/10 02:56:50 - mmengine - INFO - Epoch(train) [157][100/586] lr: 5.000000e-04 eta: 2:47:44 time: 0.339458 data_time: 0.027039 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.856367 loss: 0.000576 2022/09/10 02:57:07 - mmengine - INFO - Epoch(train) [157][150/586] lr: 5.000000e-04 eta: 2:47:29 time: 0.338499 data_time: 0.024027 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.880133 loss: 0.000573 2022/09/10 02:57:23 - mmengine - INFO - Epoch(train) [157][200/586] lr: 5.000000e-04 eta: 2:47:13 time: 0.329706 data_time: 0.024312 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.804308 loss: 0.000560 2022/09/10 02:57:41 - mmengine - INFO - Epoch(train) [157][250/586] lr: 5.000000e-04 eta: 2:46:57 time: 0.346535 data_time: 0.027958 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.870208 loss: 0.000557 2022/09/10 02:57:57 - mmengine - INFO - Epoch(train) [157][300/586] lr: 5.000000e-04 eta: 2:46:42 time: 0.333289 data_time: 0.024061 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.843446 loss: 0.000570 2022/09/10 02:58:14 - mmengine - INFO - Epoch(train) [157][350/586] lr: 5.000000e-04 eta: 2:46:26 time: 0.328665 data_time: 0.024793 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.858559 loss: 0.000564 2022/09/10 02:58:31 - mmengine - INFO - Epoch(train) [157][400/586] lr: 5.000000e-04 eta: 2:46:10 time: 0.343197 data_time: 0.028829 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.906296 loss: 0.000569 2022/09/10 02:58:48 - mmengine - INFO - Epoch(train) [157][450/586] lr: 5.000000e-04 eta: 2:45:55 time: 0.332338 data_time: 0.024762 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.922935 loss: 0.000562 2022/09/10 02:59:04 - mmengine - INFO - Epoch(train) [157][500/586] lr: 5.000000e-04 eta: 2:45:39 time: 0.333417 data_time: 0.024228 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.867634 loss: 0.000552 2022/09/10 02:59:22 - mmengine - INFO - Epoch(train) [157][550/586] lr: 5.000000e-04 eta: 2:45:24 time: 0.357470 data_time: 0.033194 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.862179 loss: 0.000548 2022/09/10 02:59:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:59:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 02:59:34 - mmengine - INFO - Saving checkpoint at 157 epochs 2022/09/10 02:59:59 - mmengine - INFO - Epoch(train) [158][50/586] lr: 5.000000e-04 eta: 2:44:53 time: 0.339677 data_time: 0.032661 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.886844 loss: 0.000561 2022/09/10 03:00:16 - mmengine - INFO - Epoch(train) [158][100/586] lr: 5.000000e-04 eta: 2:44:37 time: 0.339933 data_time: 0.024649 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.893463 loss: 0.000550 2022/09/10 03:00:32 - mmengine - INFO - Epoch(train) [158][150/586] lr: 5.000000e-04 eta: 2:44:21 time: 0.327443 data_time: 0.025397 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.836193 loss: 0.000558 2022/09/10 03:00:49 - mmengine - INFO - Epoch(train) [158][200/586] lr: 5.000000e-04 eta: 2:44:05 time: 0.335248 data_time: 0.024628 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.811601 loss: 0.000557 2022/09/10 03:01:06 - mmengine - INFO - Epoch(train) [158][250/586] lr: 5.000000e-04 eta: 2:43:50 time: 0.338852 data_time: 0.024809 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.891099 loss: 0.000546 2022/09/10 03:01:23 - mmengine - INFO - Epoch(train) [158][300/586] lr: 5.000000e-04 eta: 2:43:34 time: 0.339160 data_time: 0.028694 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.865676 loss: 0.000570 2022/09/10 03:01:40 - mmengine - INFO - Epoch(train) [158][350/586] lr: 5.000000e-04 eta: 2:43:19 time: 0.337579 data_time: 0.030318 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.846148 loss: 0.000578 2022/09/10 03:01:56 - mmengine - INFO - Epoch(train) [158][400/586] lr: 5.000000e-04 eta: 2:43:03 time: 0.338995 data_time: 0.024750 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.880429 loss: 0.000581 2022/09/10 03:02:13 - mmengine - INFO - Epoch(train) [158][450/586] lr: 5.000000e-04 eta: 2:42:47 time: 0.334417 data_time: 0.024393 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.767064 loss: 0.000551 2022/09/10 03:02:31 - mmengine - INFO - Epoch(train) [158][500/586] lr: 5.000000e-04 eta: 2:42:32 time: 0.357530 data_time: 0.024818 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.849280 loss: 0.000564 2022/09/10 03:02:48 - mmengine - INFO - Epoch(train) [158][550/586] lr: 5.000000e-04 eta: 2:42:16 time: 0.330075 data_time: 0.024515 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.874005 loss: 0.000564 2022/09/10 03:03:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:03:00 - mmengine - INFO - Saving checkpoint at 158 epochs 2022/09/10 03:03:25 - mmengine - INFO - Epoch(train) [159][50/586] lr: 5.000000e-04 eta: 2:41:46 time: 0.359870 data_time: 0.038031 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.860772 loss: 0.000550 2022/09/10 03:03:41 - mmengine - INFO - Epoch(train) [159][100/586] lr: 5.000000e-04 eta: 2:41:30 time: 0.332609 data_time: 0.024147 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.889828 loss: 0.000550 2022/09/10 03:03:58 - mmengine - INFO - Epoch(train) [159][150/586] lr: 5.000000e-04 eta: 2:41:14 time: 0.335718 data_time: 0.025004 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.860258 loss: 0.000580 2022/09/10 03:04:15 - mmengine - INFO - Epoch(train) [159][200/586] lr: 5.000000e-04 eta: 2:40:58 time: 0.333227 data_time: 0.029793 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.825880 loss: 0.000568 2022/09/10 03:04:32 - mmengine - INFO - Epoch(train) [159][250/586] lr: 5.000000e-04 eta: 2:40:43 time: 0.335683 data_time: 0.026843 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.867673 loss: 0.000558 2022/09/10 03:04:48 - mmengine - INFO - Epoch(train) [159][300/586] lr: 5.000000e-04 eta: 2:40:27 time: 0.333991 data_time: 0.025342 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.822597 loss: 0.000559 2022/09/10 03:05:05 - mmengine - INFO - Epoch(train) [159][350/586] lr: 5.000000e-04 eta: 2:40:11 time: 0.336815 data_time: 0.027028 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.821466 loss: 0.000568 2022/09/10 03:05:22 - mmengine - INFO - Epoch(train) [159][400/586] lr: 5.000000e-04 eta: 2:39:56 time: 0.337754 data_time: 0.023275 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.882356 loss: 0.000577 2022/09/10 03:05:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:05:39 - mmengine - INFO - Epoch(train) [159][450/586] lr: 5.000000e-04 eta: 2:39:40 time: 0.333261 data_time: 0.023561 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.866380 loss: 0.000547 2022/09/10 03:05:56 - mmengine - INFO - Epoch(train) [159][500/586] lr: 5.000000e-04 eta: 2:39:24 time: 0.339077 data_time: 0.024949 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.788720 loss: 0.000584 2022/09/10 03:06:12 - mmengine - INFO - Epoch(train) [159][550/586] lr: 5.000000e-04 eta: 2:39:09 time: 0.334448 data_time: 0.030386 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.838449 loss: 0.000549 2022/09/10 03:06:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:06:25 - mmengine - INFO - Saving checkpoint at 159 epochs 2022/09/10 03:06:49 - mmengine - INFO - Epoch(train) [160][50/586] lr: 5.000000e-04 eta: 2:38:38 time: 0.348828 data_time: 0.032484 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.854280 loss: 0.000563 2022/09/10 03:07:06 - mmengine - INFO - Epoch(train) [160][100/586] lr: 5.000000e-04 eta: 2:38:22 time: 0.332935 data_time: 0.029879 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.849570 loss: 0.000566 2022/09/10 03:07:23 - mmengine - INFO - Epoch(train) [160][150/586] lr: 5.000000e-04 eta: 2:38:07 time: 0.335652 data_time: 0.023538 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.829195 loss: 0.000574 2022/09/10 03:07:39 - mmengine - INFO - Epoch(train) [160][200/586] lr: 5.000000e-04 eta: 2:37:51 time: 0.335345 data_time: 0.024288 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.851336 loss: 0.000557 2022/09/10 03:07:56 - mmengine - INFO - Epoch(train) [160][250/586] lr: 5.000000e-04 eta: 2:37:35 time: 0.331336 data_time: 0.022997 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.868417 loss: 0.000555 2022/09/10 03:08:13 - mmengine - INFO - Epoch(train) [160][300/586] lr: 5.000000e-04 eta: 2:37:19 time: 0.335234 data_time: 0.027050 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.882834 loss: 0.000557 2022/09/10 03:08:30 - mmengine - INFO - Epoch(train) [160][350/586] lr: 5.000000e-04 eta: 2:37:04 time: 0.341737 data_time: 0.024907 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.858023 loss: 0.000548 2022/09/10 03:08:46 - mmengine - INFO - Epoch(train) [160][400/586] lr: 5.000000e-04 eta: 2:36:48 time: 0.332135 data_time: 0.024274 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.920071 loss: 0.000559 2022/09/10 03:09:03 - mmengine - INFO - Epoch(train) [160][450/586] lr: 5.000000e-04 eta: 2:36:32 time: 0.338708 data_time: 0.024493 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.870023 loss: 0.000581 2022/09/10 03:09:20 - mmengine - INFO - Epoch(train) [160][500/586] lr: 5.000000e-04 eta: 2:36:17 time: 0.337537 data_time: 0.023402 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.766385 loss: 0.000550 2022/09/10 03:09:37 - mmengine - INFO - Epoch(train) [160][550/586] lr: 5.000000e-04 eta: 2:36:01 time: 0.334547 data_time: 0.027815 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.866412 loss: 0.000550 2022/09/10 03:09:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:09:49 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/09/10 03:10:05 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:01:02 time: 0.175257 data_time: 0.016913 memory: 7489 2022/09/10 03:10:13 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:51 time: 0.168716 data_time: 0.011819 memory: 1657 2022/09/10 03:10:21 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:42 time: 0.163552 data_time: 0.007608 memory: 1657 2022/09/10 03:10:30 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:33 time: 0.164153 data_time: 0.007908 memory: 1657 2022/09/10 03:10:38 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:26 time: 0.165658 data_time: 0.008009 memory: 1657 2022/09/10 03:10:46 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:17 time: 0.164891 data_time: 0.007434 memory: 1657 2022/09/10 03:10:54 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:09 time: 0.165934 data_time: 0.008080 memory: 1657 2022/09/10 03:11:03 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:01 time: 0.162654 data_time: 0.007277 memory: 1657 2022/09/10 03:11:39 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 03:11:53 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.746865 coco/AP .5: 0.902422 coco/AP .75: 0.817799 coco/AP (M): 0.711218 coco/AP (L): 0.814685 coco/AR: 0.799780 coco/AR .5: 0.940176 coco/AR .75: 0.862563 coco/AR (M): 0.758208 coco/AR (L): 0.860164 2022/09/10 03:11:53 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_150.pth is removed 2022/09/10 03:11:57 - mmengine - INFO - The best checkpoint with 0.7469 coco/AP at 160 epoch is saved to best_coco/AP_epoch_160.pth. 2022/09/10 03:12:14 - mmengine - INFO - Epoch(train) [161][50/586] lr: 5.000000e-04 eta: 2:35:30 time: 0.343952 data_time: 0.028471 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.878263 loss: 0.000544 2022/09/10 03:12:31 - mmengine - INFO - Epoch(train) [161][100/586] lr: 5.000000e-04 eta: 2:35:15 time: 0.335628 data_time: 0.023275 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.909572 loss: 0.000562 2022/09/10 03:12:48 - mmengine - INFO - Epoch(train) [161][150/586] lr: 5.000000e-04 eta: 2:34:59 time: 0.336015 data_time: 0.023176 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.867112 loss: 0.000562 2022/09/10 03:13:04 - mmengine - INFO - Epoch(train) [161][200/586] lr: 5.000000e-04 eta: 2:34:43 time: 0.327866 data_time: 0.022679 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.855216 loss: 0.000579 2022/09/10 03:13:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:13:21 - mmengine - INFO - Epoch(train) [161][250/586] lr: 5.000000e-04 eta: 2:34:28 time: 0.336373 data_time: 0.023141 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.861864 loss: 0.000556 2022/09/10 03:13:38 - mmengine - INFO - Epoch(train) [161][300/586] lr: 5.000000e-04 eta: 2:34:12 time: 0.336734 data_time: 0.022909 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.811446 loss: 0.000566 2022/09/10 03:13:55 - mmengine - INFO - Epoch(train) [161][350/586] lr: 5.000000e-04 eta: 2:33:56 time: 0.335370 data_time: 0.023690 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.907544 loss: 0.000541 2022/09/10 03:14:11 - mmengine - INFO - Epoch(train) [161][400/586] lr: 5.000000e-04 eta: 2:33:41 time: 0.337644 data_time: 0.023077 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.872821 loss: 0.000579 2022/09/10 03:14:29 - mmengine - INFO - Epoch(train) [161][450/586] lr: 5.000000e-04 eta: 2:33:25 time: 0.340228 data_time: 0.028400 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.884291 loss: 0.000555 2022/09/10 03:14:45 - mmengine - INFO - Epoch(train) [161][500/586] lr: 5.000000e-04 eta: 2:33:09 time: 0.327484 data_time: 0.023478 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.907479 loss: 0.000570 2022/09/10 03:15:02 - mmengine - INFO - Epoch(train) [161][550/586] lr: 5.000000e-04 eta: 2:32:53 time: 0.341876 data_time: 0.023369 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.865400 loss: 0.000559 2022/09/10 03:15:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:15:14 - mmengine - INFO - Saving checkpoint at 161 epochs 2022/09/10 03:15:38 - mmengine - INFO - Epoch(train) [162][50/586] lr: 5.000000e-04 eta: 2:32:23 time: 0.340635 data_time: 0.028134 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.895598 loss: 0.000560 2022/09/10 03:15:55 - mmengine - INFO - Epoch(train) [162][100/586] lr: 5.000000e-04 eta: 2:32:07 time: 0.338776 data_time: 0.023151 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.884679 loss: 0.000570 2022/09/10 03:16:12 - mmengine - INFO - Epoch(train) [162][150/586] lr: 5.000000e-04 eta: 2:31:52 time: 0.347815 data_time: 0.023574 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.894126 loss: 0.000548 2022/09/10 03:16:28 - mmengine - INFO - Epoch(train) [162][200/586] lr: 5.000000e-04 eta: 2:31:36 time: 0.323463 data_time: 0.023416 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.827349 loss: 0.000578 2022/09/10 03:16:46 - mmengine - INFO - Epoch(train) [162][250/586] lr: 5.000000e-04 eta: 2:31:20 time: 0.340866 data_time: 0.027830 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.868763 loss: 0.000568 2022/09/10 03:17:03 - mmengine - INFO - Epoch(train) [162][300/586] lr: 5.000000e-04 eta: 2:31:05 time: 0.338861 data_time: 0.023319 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.899119 loss: 0.000569 2022/09/10 03:17:19 - mmengine - INFO - Epoch(train) [162][350/586] lr: 5.000000e-04 eta: 2:30:49 time: 0.329593 data_time: 0.022421 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.874156 loss: 0.000555 2022/09/10 03:17:36 - mmengine - INFO - Epoch(train) [162][400/586] lr: 5.000000e-04 eta: 2:30:33 time: 0.337132 data_time: 0.026359 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.864586 loss: 0.000550 2022/09/10 03:17:53 - mmengine - INFO - Epoch(train) [162][450/586] lr: 5.000000e-04 eta: 2:30:17 time: 0.343360 data_time: 0.023862 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.845957 loss: 0.000546 2022/09/10 03:18:10 - mmengine - INFO - Epoch(train) [162][500/586] lr: 5.000000e-04 eta: 2:30:02 time: 0.329276 data_time: 0.024306 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.850613 loss: 0.000566 2022/09/10 03:18:26 - mmengine - INFO - Epoch(train) [162][550/586] lr: 5.000000e-04 eta: 2:29:46 time: 0.334183 data_time: 0.028490 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.883576 loss: 0.000568 2022/09/10 03:18:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:18:39 - mmengine - INFO - Saving checkpoint at 162 epochs 2022/09/10 03:19:03 - mmengine - INFO - Epoch(train) [163][50/586] lr: 5.000000e-04 eta: 2:29:15 time: 0.333609 data_time: 0.030575 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.860573 loss: 0.000538 2022/09/10 03:19:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:19:20 - mmengine - INFO - Epoch(train) [163][100/586] lr: 5.000000e-04 eta: 2:29:00 time: 0.340761 data_time: 0.023297 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.892582 loss: 0.000561 2022/09/10 03:19:37 - mmengine - INFO - Epoch(train) [163][150/586] lr: 5.000000e-04 eta: 2:28:44 time: 0.338501 data_time: 0.026713 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.859128 loss: 0.000554 2022/09/10 03:19:53 - mmengine - INFO - Epoch(train) [163][200/586] lr: 5.000000e-04 eta: 2:28:28 time: 0.327682 data_time: 0.022359 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.878376 loss: 0.000552 2022/09/10 03:20:10 - mmengine - INFO - Epoch(train) [163][250/586] lr: 5.000000e-04 eta: 2:28:13 time: 0.336214 data_time: 0.024418 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.866576 loss: 0.000565 2022/09/10 03:20:27 - mmengine - INFO - Epoch(train) [163][300/586] lr: 5.000000e-04 eta: 2:27:57 time: 0.340091 data_time: 0.030166 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.904924 loss: 0.000560 2022/09/10 03:20:43 - mmengine - INFO - Epoch(train) [163][350/586] lr: 5.000000e-04 eta: 2:27:41 time: 0.327871 data_time: 0.023902 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.853339 loss: 0.000569 2022/09/10 03:21:00 - mmengine - INFO - Epoch(train) [163][400/586] lr: 5.000000e-04 eta: 2:27:25 time: 0.337373 data_time: 0.024665 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.888011 loss: 0.000567 2022/09/10 03:21:17 - mmengine - INFO - Epoch(train) [163][450/586] lr: 5.000000e-04 eta: 2:27:10 time: 0.334062 data_time: 0.026697 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.855533 loss: 0.000565 2022/09/10 03:21:34 - mmengine - INFO - Epoch(train) [163][500/586] lr: 5.000000e-04 eta: 2:26:54 time: 0.329764 data_time: 0.023156 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.911908 loss: 0.000557 2022/09/10 03:21:51 - mmengine - INFO - Epoch(train) [163][550/586] lr: 5.000000e-04 eta: 2:26:38 time: 0.339097 data_time: 0.022541 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.868299 loss: 0.000568 2022/09/10 03:22:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:22:03 - mmengine - INFO - Saving checkpoint at 163 epochs 2022/09/10 03:22:28 - mmengine - INFO - Epoch(train) [164][50/586] lr: 5.000000e-04 eta: 2:26:08 time: 0.344527 data_time: 0.027725 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.895531 loss: 0.000553 2022/09/10 03:22:44 - mmengine - INFO - Epoch(train) [164][100/586] lr: 5.000000e-04 eta: 2:25:52 time: 0.334914 data_time: 0.024027 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.847824 loss: 0.000566 2022/09/10 03:23:01 - mmengine - INFO - Epoch(train) [164][150/586] lr: 5.000000e-04 eta: 2:25:36 time: 0.339414 data_time: 0.022782 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.821365 loss: 0.000545 2022/09/10 03:23:18 - mmengine - INFO - Epoch(train) [164][200/586] lr: 5.000000e-04 eta: 2:25:21 time: 0.327015 data_time: 0.022863 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.789421 loss: 0.000554 2022/09/10 03:23:35 - mmengine - INFO - Epoch(train) [164][250/586] lr: 5.000000e-04 eta: 2:25:05 time: 0.336431 data_time: 0.023146 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.868322 loss: 0.000562 2022/09/10 03:23:52 - mmengine - INFO - Epoch(train) [164][300/586] lr: 5.000000e-04 eta: 2:24:49 time: 0.340394 data_time: 0.023327 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.781758 loss: 0.000554 2022/09/10 03:24:08 - mmengine - INFO - Epoch(train) [164][350/586] lr: 5.000000e-04 eta: 2:24:34 time: 0.335909 data_time: 0.026519 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.842843 loss: 0.000562 2022/09/10 03:24:25 - mmengine - INFO - Epoch(train) [164][400/586] lr: 5.000000e-04 eta: 2:24:18 time: 0.334810 data_time: 0.022391 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.876910 loss: 0.000557 2022/09/10 03:24:42 - mmengine - INFO - Epoch(train) [164][450/586] lr: 5.000000e-04 eta: 2:24:02 time: 0.340905 data_time: 0.023264 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.895898 loss: 0.000538 2022/09/10 03:24:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:24:59 - mmengine - INFO - Epoch(train) [164][500/586] lr: 5.000000e-04 eta: 2:23:47 time: 0.341057 data_time: 0.025656 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.862712 loss: 0.000556 2022/09/10 03:25:16 - mmengine - INFO - Epoch(train) [164][550/586] lr: 5.000000e-04 eta: 2:23:31 time: 0.329228 data_time: 0.022586 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.811684 loss: 0.000565 2022/09/10 03:25:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:25:28 - mmengine - INFO - Saving checkpoint at 164 epochs 2022/09/10 03:25:52 - mmengine - INFO - Epoch(train) [165][50/586] lr: 5.000000e-04 eta: 2:23:00 time: 0.343629 data_time: 0.031159 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.816835 loss: 0.000546 2022/09/10 03:26:09 - mmengine - INFO - Epoch(train) [165][100/586] lr: 5.000000e-04 eta: 2:22:45 time: 0.329801 data_time: 0.023103 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.821660 loss: 0.000549 2022/09/10 03:26:25 - mmengine - INFO - Epoch(train) [165][150/586] lr: 5.000000e-04 eta: 2:22:29 time: 0.338999 data_time: 0.023404 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.884345 loss: 0.000535 2022/09/10 03:26:42 - mmengine - INFO - Epoch(train) [165][200/586] lr: 5.000000e-04 eta: 2:22:13 time: 0.336941 data_time: 0.024213 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.824486 loss: 0.000566 2022/09/10 03:26:59 - mmengine - INFO - Epoch(train) [165][250/586] lr: 5.000000e-04 eta: 2:21:58 time: 0.331433 data_time: 0.022736 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.834884 loss: 0.000567 2022/09/10 03:27:16 - mmengine - INFO - Epoch(train) [165][300/586] lr: 5.000000e-04 eta: 2:21:42 time: 0.338555 data_time: 0.022851 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.879199 loss: 0.000563 2022/09/10 03:27:33 - mmengine - INFO - Epoch(train) [165][350/586] lr: 5.000000e-04 eta: 2:21:26 time: 0.337789 data_time: 0.028736 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.846933 loss: 0.000542 2022/09/10 03:27:50 - mmengine - INFO - Epoch(train) [165][400/586] lr: 5.000000e-04 eta: 2:21:10 time: 0.334605 data_time: 0.028752 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.868585 loss: 0.000547 2022/09/10 03:28:06 - mmengine - INFO - Epoch(train) [165][450/586] lr: 5.000000e-04 eta: 2:20:55 time: 0.338342 data_time: 0.022403 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.880509 loss: 0.000571 2022/09/10 03:28:23 - mmengine - INFO - Epoch(train) [165][500/586] lr: 5.000000e-04 eta: 2:20:39 time: 0.331438 data_time: 0.023146 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.842733 loss: 0.000570 2022/09/10 03:28:40 - mmengine - INFO - Epoch(train) [165][550/586] lr: 5.000000e-04 eta: 2:20:23 time: 0.334674 data_time: 0.022905 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.869573 loss: 0.000547 2022/09/10 03:28:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:28:52 - mmengine - INFO - Saving checkpoint at 165 epochs 2022/09/10 03:29:16 - mmengine - INFO - Epoch(train) [166][50/586] lr: 5.000000e-04 eta: 2:19:53 time: 0.339363 data_time: 0.027864 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.852188 loss: 0.000552 2022/09/10 03:29:33 - mmengine - INFO - Epoch(train) [166][100/586] lr: 5.000000e-04 eta: 2:19:37 time: 0.341422 data_time: 0.023218 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.917580 loss: 0.000557 2022/09/10 03:29:50 - mmengine - INFO - Epoch(train) [166][150/586] lr: 5.000000e-04 eta: 2:19:22 time: 0.337066 data_time: 0.023883 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.872705 loss: 0.000565 2022/09/10 03:30:07 - mmengine - INFO - Epoch(train) [166][200/586] lr: 5.000000e-04 eta: 2:19:06 time: 0.334581 data_time: 0.022929 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.856482 loss: 0.000571 2022/09/10 03:30:24 - mmengine - INFO - Epoch(train) [166][250/586] lr: 5.000000e-04 eta: 2:18:50 time: 0.338114 data_time: 0.022821 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.868311 loss: 0.000566 2022/09/10 03:30:40 - mmengine - INFO - Epoch(train) [166][300/586] lr: 5.000000e-04 eta: 2:18:34 time: 0.331919 data_time: 0.022819 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.856820 loss: 0.000552 2022/09/10 03:30:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:30:57 - mmengine - INFO - Epoch(train) [166][350/586] lr: 5.000000e-04 eta: 2:18:19 time: 0.335567 data_time: 0.022469 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.798103 loss: 0.000567 2022/09/10 03:31:14 - mmengine - INFO - Epoch(train) [166][400/586] lr: 5.000000e-04 eta: 2:18:03 time: 0.338120 data_time: 0.022940 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.890656 loss: 0.000562 2022/09/10 03:31:31 - mmengine - INFO - Epoch(train) [166][450/586] lr: 5.000000e-04 eta: 2:17:47 time: 0.333285 data_time: 0.023212 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.853082 loss: 0.000550 2022/09/10 03:31:47 - mmengine - INFO - Epoch(train) [166][500/586] lr: 5.000000e-04 eta: 2:17:31 time: 0.330634 data_time: 0.022634 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.821568 loss: 0.000555 2022/09/10 03:32:05 - mmengine - INFO - Epoch(train) [166][550/586] lr: 5.000000e-04 eta: 2:17:16 time: 0.346389 data_time: 0.022921 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.856214 loss: 0.000553 2022/09/10 03:32:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:32:17 - mmengine - INFO - Saving checkpoint at 166 epochs 2022/09/10 03:32:41 - mmengine - INFO - Epoch(train) [167][50/586] lr: 5.000000e-04 eta: 2:16:46 time: 0.342382 data_time: 0.035647 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.834257 loss: 0.000553 2022/09/10 03:32:58 - mmengine - INFO - Epoch(train) [167][100/586] lr: 5.000000e-04 eta: 2:16:30 time: 0.338215 data_time: 0.022622 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.856397 loss: 0.000560 2022/09/10 03:33:15 - mmengine - INFO - Epoch(train) [167][150/586] lr: 5.000000e-04 eta: 2:16:14 time: 0.338586 data_time: 0.026697 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.816196 loss: 0.000549 2022/09/10 03:33:32 - mmengine - INFO - Epoch(train) [167][200/586] lr: 5.000000e-04 eta: 2:15:58 time: 0.328817 data_time: 0.023487 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.847113 loss: 0.000558 2022/09/10 03:33:49 - mmengine - INFO - Epoch(train) [167][250/586] lr: 5.000000e-04 eta: 2:15:43 time: 0.343315 data_time: 0.022703 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.853011 loss: 0.000550 2022/09/10 03:34:05 - mmengine - INFO - Epoch(train) [167][300/586] lr: 5.000000e-04 eta: 2:15:27 time: 0.334930 data_time: 0.025733 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.826845 loss: 0.000540 2022/09/10 03:34:22 - mmengine - INFO - Epoch(train) [167][350/586] lr: 5.000000e-04 eta: 2:15:11 time: 0.329977 data_time: 0.024460 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.854441 loss: 0.000556 2022/09/10 03:34:39 - mmengine - INFO - Epoch(train) [167][400/586] lr: 5.000000e-04 eta: 2:14:56 time: 0.336087 data_time: 0.022724 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.879497 loss: 0.000558 2022/09/10 03:34:56 - mmengine - INFO - Epoch(train) [167][450/586] lr: 5.000000e-04 eta: 2:14:40 time: 0.336374 data_time: 0.026250 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.841366 loss: 0.000548 2022/09/10 03:35:12 - mmengine - INFO - Epoch(train) [167][500/586] lr: 5.000000e-04 eta: 2:14:24 time: 0.334249 data_time: 0.022711 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.885778 loss: 0.000552 2022/09/10 03:35:29 - mmengine - INFO - Epoch(train) [167][550/586] lr: 5.000000e-04 eta: 2:14:08 time: 0.331933 data_time: 0.023264 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.893969 loss: 0.000561 2022/09/10 03:35:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:35:41 - mmengine - INFO - Saving checkpoint at 167 epochs 2022/09/10 03:36:05 - mmengine - INFO - Epoch(train) [168][50/586] lr: 5.000000e-04 eta: 2:13:38 time: 0.335225 data_time: 0.028931 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.870226 loss: 0.000545 2022/09/10 03:36:22 - mmengine - INFO - Epoch(train) [168][100/586] lr: 5.000000e-04 eta: 2:13:23 time: 0.341561 data_time: 0.023814 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.898146 loss: 0.000575 2022/09/10 03:36:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:36:39 - mmengine - INFO - Epoch(train) [168][150/586] lr: 5.000000e-04 eta: 2:13:07 time: 0.334815 data_time: 0.022823 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.907325 loss: 0.000568 2022/09/10 03:36:56 - mmengine - INFO - Epoch(train) [168][200/586] lr: 5.000000e-04 eta: 2:12:51 time: 0.330929 data_time: 0.023150 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.908234 loss: 0.000545 2022/09/10 03:37:13 - mmengine - INFO - Epoch(train) [168][250/586] lr: 5.000000e-04 eta: 2:12:35 time: 0.340968 data_time: 0.026741 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.910012 loss: 0.000563 2022/09/10 03:37:30 - mmengine - INFO - Epoch(train) [168][300/586] lr: 5.000000e-04 eta: 2:12:20 time: 0.333155 data_time: 0.022738 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.876933 loss: 0.000555 2022/09/10 03:37:46 - mmengine - INFO - Epoch(train) [168][350/586] lr: 5.000000e-04 eta: 2:12:04 time: 0.337925 data_time: 0.025455 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.868578 loss: 0.000570 2022/09/10 03:38:03 - mmengine - INFO - Epoch(train) [168][400/586] lr: 5.000000e-04 eta: 2:11:48 time: 0.331733 data_time: 0.022974 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.889735 loss: 0.000560 2022/09/10 03:38:20 - mmengine - INFO - Epoch(train) [168][450/586] lr: 5.000000e-04 eta: 2:11:32 time: 0.338856 data_time: 0.022342 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.774173 loss: 0.000566 2022/09/10 03:38:36 - mmengine - INFO - Epoch(train) [168][500/586] lr: 5.000000e-04 eta: 2:11:17 time: 0.327327 data_time: 0.023149 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.850371 loss: 0.000560 2022/09/10 03:38:53 - mmengine - INFO - Epoch(train) [168][550/586] lr: 5.000000e-04 eta: 2:11:01 time: 0.338325 data_time: 0.023120 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.918644 loss: 0.000544 2022/09/10 03:39:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:39:05 - mmengine - INFO - Saving checkpoint at 168 epochs 2022/09/10 03:39:30 - mmengine - INFO - Epoch(train) [169][50/586] lr: 5.000000e-04 eta: 2:10:31 time: 0.345081 data_time: 0.032539 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.883058 loss: 0.000549 2022/09/10 03:39:46 - mmengine - INFO - Epoch(train) [169][100/586] lr: 5.000000e-04 eta: 2:10:15 time: 0.335274 data_time: 0.024748 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.842569 loss: 0.000561 2022/09/10 03:40:03 - mmengine - INFO - Epoch(train) [169][150/586] lr: 5.000000e-04 eta: 2:09:59 time: 0.337098 data_time: 0.023210 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.841659 loss: 0.000554 2022/09/10 03:40:20 - mmengine - INFO - Epoch(train) [169][200/586] lr: 5.000000e-04 eta: 2:09:44 time: 0.331790 data_time: 0.022539 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.865450 loss: 0.000570 2022/09/10 03:40:37 - mmengine - INFO - Epoch(train) [169][250/586] lr: 5.000000e-04 eta: 2:09:28 time: 0.336868 data_time: 0.022484 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.851974 loss: 0.000558 2022/09/10 03:40:54 - mmengine - INFO - Epoch(train) [169][300/586] lr: 5.000000e-04 eta: 2:09:12 time: 0.334509 data_time: 0.023060 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.850569 loss: 0.000555 2022/09/10 03:41:10 - mmengine - INFO - Epoch(train) [169][350/586] lr: 5.000000e-04 eta: 2:08:56 time: 0.332341 data_time: 0.022264 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.815637 loss: 0.000566 2022/09/10 03:41:27 - mmengine - INFO - Epoch(train) [169][400/586] lr: 5.000000e-04 eta: 2:08:41 time: 0.338398 data_time: 0.023614 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.853654 loss: 0.000536 2022/09/10 03:41:44 - mmengine - INFO - Epoch(train) [169][450/586] lr: 5.000000e-04 eta: 2:08:25 time: 0.342287 data_time: 0.023230 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.885741 loss: 0.000567 2022/09/10 03:42:01 - mmengine - INFO - Epoch(train) [169][500/586] lr: 5.000000e-04 eta: 2:08:09 time: 0.330364 data_time: 0.022590 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.845262 loss: 0.000582 2022/09/10 03:42:18 - mmengine - INFO - Epoch(train) [169][550/586] lr: 5.000000e-04 eta: 2:07:54 time: 0.348341 data_time: 0.023248 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.801670 loss: 0.000550 2022/09/10 03:42:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:42:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:42:31 - mmengine - INFO - Saving checkpoint at 169 epochs 2022/09/10 03:42:55 - mmengine - INFO - Epoch(train) [170][50/586] lr: 5.000000e-04 eta: 2:07:24 time: 0.343605 data_time: 0.028737 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.837796 loss: 0.000559 2022/09/10 03:43:12 - mmengine - INFO - Epoch(train) [170][100/586] lr: 5.000000e-04 eta: 2:07:08 time: 0.348485 data_time: 0.028721 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.820935 loss: 0.000539 2022/09/10 03:43:29 - mmengine - INFO - Epoch(train) [170][150/586] lr: 5.000000e-04 eta: 2:06:52 time: 0.331145 data_time: 0.022430 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.905183 loss: 0.000575 2022/09/10 03:43:46 - mmengine - INFO - Epoch(train) [170][200/586] lr: 5.000000e-04 eta: 2:06:37 time: 0.335084 data_time: 0.027183 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.885332 loss: 0.000556 2022/09/10 03:44:02 - mmengine - INFO - Epoch(train) [170][250/586] lr: 5.000000e-04 eta: 2:06:21 time: 0.335700 data_time: 0.023611 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.843863 loss: 0.000553 2022/09/10 03:44:19 - mmengine - INFO - Epoch(train) [170][300/586] lr: 5.000000e-04 eta: 2:06:05 time: 0.333017 data_time: 0.022733 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.862486 loss: 0.000553 2022/09/10 03:44:36 - mmengine - INFO - Epoch(train) [170][350/586] lr: 5.000000e-04 eta: 2:05:49 time: 0.337924 data_time: 0.022682 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.842220 loss: 0.000562 2022/09/10 03:44:53 - mmengine - INFO - Epoch(train) [170][400/586] lr: 5.000000e-04 eta: 2:05:34 time: 0.336955 data_time: 0.022837 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.875984 loss: 0.000547 2022/09/10 03:45:10 - mmengine - INFO - Epoch(train) [170][450/586] lr: 5.000000e-04 eta: 2:05:18 time: 0.334260 data_time: 0.023121 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.854772 loss: 0.000570 2022/09/10 03:45:26 - mmengine - INFO - Epoch(train) [170][500/586] lr: 5.000000e-04 eta: 2:05:02 time: 0.336769 data_time: 0.023068 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.908937 loss: 0.000576 2022/09/10 03:45:43 - mmengine - INFO - Epoch(train) [170][550/586] lr: 5.000000e-04 eta: 2:04:46 time: 0.333360 data_time: 0.024772 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.880404 loss: 0.000563 2022/09/10 03:45:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:45:55 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/09/10 03:46:11 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:01:01 time: 0.171578 data_time: 0.012366 memory: 7489 2022/09/10 03:46:19 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:51 time: 0.168338 data_time: 0.008410 memory: 1657 2022/09/10 03:46:28 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:42 time: 0.164714 data_time: 0.007764 memory: 1657 2022/09/10 03:46:36 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:34 time: 0.165109 data_time: 0.007469 memory: 1657 2022/09/10 03:46:44 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:26 time: 0.165708 data_time: 0.007944 memory: 1657 2022/09/10 03:46:53 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:17 time: 0.166345 data_time: 0.007638 memory: 1657 2022/09/10 03:47:01 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:09 time: 0.167030 data_time: 0.007414 memory: 1657 2022/09/10 03:47:09 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:01 time: 0.161814 data_time: 0.006896 memory: 1657 2022/09/10 03:47:45 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 03:47:58 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.746275 coco/AP .5: 0.902519 coco/AP .75: 0.819502 coco/AP (M): 0.711773 coco/AP (L): 0.812448 coco/AR: 0.799717 coco/AR .5: 0.940649 coco/AR .75: 0.864295 coco/AR (M): 0.757853 coco/AR (L): 0.860312 2022/09/10 03:48:16 - mmengine - INFO - Epoch(train) [171][50/586] lr: 5.000000e-05 eta: 2:04:17 time: 0.349609 data_time: 0.029060 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.832584 loss: 0.000573 2022/09/10 03:48:33 - mmengine - INFO - Epoch(train) [171][100/586] lr: 5.000000e-05 eta: 2:04:01 time: 0.340370 data_time: 0.023980 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.910010 loss: 0.000566 2022/09/10 03:48:49 - mmengine - INFO - Epoch(train) [171][150/586] lr: 5.000000e-05 eta: 2:03:45 time: 0.334083 data_time: 0.022494 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.847164 loss: 0.000549 2022/09/10 03:49:06 - mmengine - INFO - Epoch(train) [171][200/586] lr: 5.000000e-05 eta: 2:03:29 time: 0.329236 data_time: 0.023685 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.845167 loss: 0.000535 2022/09/10 03:49:23 - mmengine - INFO - Epoch(train) [171][250/586] lr: 5.000000e-05 eta: 2:03:14 time: 0.351217 data_time: 0.022971 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.907062 loss: 0.000538 2022/09/10 03:49:40 - mmengine - INFO - Epoch(train) [171][300/586] lr: 5.000000e-05 eta: 2:02:58 time: 0.331296 data_time: 0.023344 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.779656 loss: 0.000550 2022/09/10 03:49:57 - mmengine - INFO - Epoch(train) [171][350/586] lr: 5.000000e-05 eta: 2:02:42 time: 0.330038 data_time: 0.022835 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.799365 loss: 0.000551 2022/09/10 03:50:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:50:14 - mmengine - INFO - Epoch(train) [171][400/586] lr: 5.000000e-05 eta: 2:02:26 time: 0.344034 data_time: 0.025929 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.837663 loss: 0.000519 2022/09/10 03:50:30 - mmengine - INFO - Epoch(train) [171][450/586] lr: 5.000000e-05 eta: 2:02:11 time: 0.329963 data_time: 0.022612 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.885141 loss: 0.000539 2022/09/10 03:50:47 - mmengine - INFO - Epoch(train) [171][500/586] lr: 5.000000e-05 eta: 2:01:55 time: 0.339656 data_time: 0.022158 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.930453 loss: 0.000530 2022/09/10 03:51:04 - mmengine - INFO - Epoch(train) [171][550/586] lr: 5.000000e-05 eta: 2:01:39 time: 0.341814 data_time: 0.026050 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.922582 loss: 0.000539 2022/09/10 03:51:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:51:16 - mmengine - INFO - Saving checkpoint at 171 epochs 2022/09/10 03:51:41 - mmengine - INFO - Epoch(train) [172][50/586] lr: 5.000000e-05 eta: 2:01:10 time: 0.342972 data_time: 0.031133 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.884961 loss: 0.000535 2022/09/10 03:51:58 - mmengine - INFO - Epoch(train) [172][100/586] lr: 5.000000e-05 eta: 2:00:54 time: 0.337710 data_time: 0.023108 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.862017 loss: 0.000543 2022/09/10 03:52:15 - mmengine - INFO - Epoch(train) [172][150/586] lr: 5.000000e-05 eta: 2:00:38 time: 0.346746 data_time: 0.026427 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.829831 loss: 0.000546 2022/09/10 03:52:32 - mmengine - INFO - Epoch(train) [172][200/586] lr: 5.000000e-05 eta: 2:00:22 time: 0.333919 data_time: 0.022868 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.883429 loss: 0.000514 2022/09/10 03:52:48 - mmengine - INFO - Epoch(train) [172][250/586] lr: 5.000000e-05 eta: 2:00:07 time: 0.334184 data_time: 0.022623 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.842972 loss: 0.000531 2022/09/10 03:53:05 - mmengine - INFO - Epoch(train) [172][300/586] lr: 5.000000e-05 eta: 1:59:51 time: 0.335417 data_time: 0.028292 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.854618 loss: 0.000528 2022/09/10 03:53:22 - mmengine - INFO - Epoch(train) [172][350/586] lr: 5.000000e-05 eta: 1:59:35 time: 0.333757 data_time: 0.022999 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.818749 loss: 0.000542 2022/09/10 03:53:39 - mmengine - INFO - Epoch(train) [172][400/586] lr: 5.000000e-05 eta: 1:59:19 time: 0.335156 data_time: 0.024669 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.862905 loss: 0.000561 2022/09/10 03:53:56 - mmengine - INFO - Epoch(train) [172][450/586] lr: 5.000000e-05 eta: 1:59:04 time: 0.340654 data_time: 0.022997 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.855416 loss: 0.000543 2022/09/10 03:54:12 - mmengine - INFO - Epoch(train) [172][500/586] lr: 5.000000e-05 eta: 1:58:48 time: 0.330631 data_time: 0.022728 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.869737 loss: 0.000540 2022/09/10 03:54:29 - mmengine - INFO - Epoch(train) [172][550/586] lr: 5.000000e-05 eta: 1:58:32 time: 0.339789 data_time: 0.022859 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.839078 loss: 0.000530 2022/09/10 03:54:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:54:41 - mmengine - INFO - Saving checkpoint at 172 epochs 2022/09/10 03:55:05 - mmengine - INFO - Epoch(train) [173][50/586] lr: 5.000000e-05 eta: 1:58:02 time: 0.342095 data_time: 0.031562 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.895241 loss: 0.000549 2022/09/10 03:55:22 - mmengine - INFO - Epoch(train) [173][100/586] lr: 5.000000e-05 eta: 1:57:47 time: 0.335929 data_time: 0.023133 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.865790 loss: 0.000519 2022/09/10 03:55:39 - mmengine - INFO - Epoch(train) [173][150/586] lr: 5.000000e-05 eta: 1:57:31 time: 0.335805 data_time: 0.022324 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.854746 loss: 0.000525 2022/09/10 03:55:56 - mmengine - INFO - Epoch(train) [173][200/586] lr: 5.000000e-05 eta: 1:57:15 time: 0.332817 data_time: 0.022943 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.878447 loss: 0.000536 2022/09/10 03:55:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:56:13 - mmengine - INFO - Epoch(train) [173][250/586] lr: 5.000000e-05 eta: 1:56:59 time: 0.339718 data_time: 0.022632 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.850376 loss: 0.000535 2022/09/10 03:56:30 - mmengine - INFO - Epoch(train) [173][300/586] lr: 5.000000e-05 eta: 1:56:44 time: 0.335902 data_time: 0.023208 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.848058 loss: 0.000532 2022/09/10 03:56:46 - mmengine - INFO - Epoch(train) [173][350/586] lr: 5.000000e-05 eta: 1:56:28 time: 0.333257 data_time: 0.022768 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.845716 loss: 0.000524 2022/09/10 03:57:03 - mmengine - INFO - Epoch(train) [173][400/586] lr: 5.000000e-05 eta: 1:56:12 time: 0.336834 data_time: 0.027530 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.897278 loss: 0.000526 2022/09/10 03:57:20 - mmengine - INFO - Epoch(train) [173][450/586] lr: 5.000000e-05 eta: 1:55:56 time: 0.334514 data_time: 0.022546 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.861525 loss: 0.000549 2022/09/10 03:57:36 - mmengine - INFO - Epoch(train) [173][500/586] lr: 5.000000e-05 eta: 1:55:41 time: 0.329095 data_time: 0.022145 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.848041 loss: 0.000547 2022/09/10 03:57:53 - mmengine - INFO - Epoch(train) [173][550/586] lr: 5.000000e-05 eta: 1:55:25 time: 0.338934 data_time: 0.022784 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.779820 loss: 0.000536 2022/09/10 03:58:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 03:58:05 - mmengine - INFO - Saving checkpoint at 173 epochs 2022/09/10 03:58:29 - mmengine - INFO - Epoch(train) [174][50/586] lr: 5.000000e-05 eta: 1:54:55 time: 0.341491 data_time: 0.032945 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.888920 loss: 0.000532 2022/09/10 03:58:46 - mmengine - INFO - Epoch(train) [174][100/586] lr: 5.000000e-05 eta: 1:54:39 time: 0.336098 data_time: 0.023469 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.861818 loss: 0.000516 2022/09/10 03:59:03 - mmengine - INFO - Epoch(train) [174][150/586] lr: 5.000000e-05 eta: 1:54:24 time: 0.337756 data_time: 0.026580 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.883853 loss: 0.000523 2022/09/10 03:59:20 - mmengine - INFO - Epoch(train) [174][200/586] lr: 5.000000e-05 eta: 1:54:08 time: 0.330531 data_time: 0.023308 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.881346 loss: 0.000533 2022/09/10 03:59:36 - mmengine - INFO - Epoch(train) [174][250/586] lr: 5.000000e-05 eta: 1:53:52 time: 0.332768 data_time: 0.022795 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.857718 loss: 0.000514 2022/09/10 03:59:53 - mmengine - INFO - Epoch(train) [174][300/586] lr: 5.000000e-05 eta: 1:53:36 time: 0.340629 data_time: 0.026370 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.886216 loss: 0.000522 2022/09/10 04:00:10 - mmengine - INFO - Epoch(train) [174][350/586] lr: 5.000000e-05 eta: 1:53:20 time: 0.327033 data_time: 0.022786 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.855447 loss: 0.000510 2022/09/10 04:00:26 - mmengine - INFO - Epoch(train) [174][400/586] lr: 5.000000e-05 eta: 1:53:05 time: 0.331066 data_time: 0.022558 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.934514 loss: 0.000510 2022/09/10 04:00:43 - mmengine - INFO - Epoch(train) [174][450/586] lr: 5.000000e-05 eta: 1:52:49 time: 0.339064 data_time: 0.022151 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.903388 loss: 0.000535 2022/09/10 04:01:00 - mmengine - INFO - Epoch(train) [174][500/586] lr: 5.000000e-05 eta: 1:52:33 time: 0.328551 data_time: 0.022847 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.879215 loss: 0.000532 2022/09/10 04:01:16 - mmengine - INFO - Epoch(train) [174][550/586] lr: 5.000000e-05 eta: 1:52:17 time: 0.334478 data_time: 0.022823 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.824010 loss: 0.000527 2022/09/10 04:01:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:01:29 - mmengine - INFO - Saving checkpoint at 174 epochs 2022/09/10 04:01:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:01:52 - mmengine - INFO - Epoch(train) [175][50/586] lr: 5.000000e-05 eta: 1:51:48 time: 0.331428 data_time: 0.026593 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.882489 loss: 0.000544 2022/09/10 04:02:09 - mmengine - INFO - Epoch(train) [175][100/586] lr: 5.000000e-05 eta: 1:51:32 time: 0.342231 data_time: 0.028514 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.857683 loss: 0.000535 2022/09/10 04:02:27 - mmengine - INFO - Epoch(train) [175][150/586] lr: 5.000000e-05 eta: 1:51:16 time: 0.341637 data_time: 0.023230 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.902509 loss: 0.000528 2022/09/10 04:02:43 - mmengine - INFO - Epoch(train) [175][200/586] lr: 5.000000e-05 eta: 1:51:00 time: 0.326941 data_time: 0.023327 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.821480 loss: 0.000527 2022/09/10 04:03:00 - mmengine - INFO - Epoch(train) [175][250/586] lr: 5.000000e-05 eta: 1:50:45 time: 0.340690 data_time: 0.022396 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.897949 loss: 0.000530 2022/09/10 04:03:17 - mmengine - INFO - Epoch(train) [175][300/586] lr: 5.000000e-05 eta: 1:50:29 time: 0.332649 data_time: 0.022444 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.866052 loss: 0.000519 2022/09/10 04:03:33 - mmengine - INFO - Epoch(train) [175][350/586] lr: 5.000000e-05 eta: 1:50:13 time: 0.331339 data_time: 0.023318 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.893008 loss: 0.000526 2022/09/10 04:03:50 - mmengine - INFO - Epoch(train) [175][400/586] lr: 5.000000e-05 eta: 1:49:57 time: 0.339420 data_time: 0.025739 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.875559 loss: 0.000522 2022/09/10 04:04:07 - mmengine - INFO - Epoch(train) [175][450/586] lr: 5.000000e-05 eta: 1:49:42 time: 0.338085 data_time: 0.024019 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.872366 loss: 0.000534 2022/09/10 04:04:23 - mmengine - INFO - Epoch(train) [175][500/586] lr: 5.000000e-05 eta: 1:49:26 time: 0.327373 data_time: 0.022619 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.824314 loss: 0.000538 2022/09/10 04:04:41 - mmengine - INFO - Epoch(train) [175][550/586] lr: 5.000000e-05 eta: 1:49:10 time: 0.343475 data_time: 0.024318 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.856155 loss: 0.000514 2022/09/10 04:04:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:04:53 - mmengine - INFO - Saving checkpoint at 175 epochs 2022/09/10 04:05:16 - mmengine - INFO - Epoch(train) [176][50/586] lr: 5.000000e-05 eta: 1:48:41 time: 0.337980 data_time: 0.028405 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.938118 loss: 0.000512 2022/09/10 04:05:33 - mmengine - INFO - Epoch(train) [176][100/586] lr: 5.000000e-05 eta: 1:48:25 time: 0.339688 data_time: 0.024784 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.837792 loss: 0.000523 2022/09/10 04:05:50 - mmengine - INFO - Epoch(train) [176][150/586] lr: 5.000000e-05 eta: 1:48:09 time: 0.338108 data_time: 0.024170 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.892940 loss: 0.000523 2022/09/10 04:06:07 - mmengine - INFO - Epoch(train) [176][200/586] lr: 5.000000e-05 eta: 1:47:53 time: 0.329376 data_time: 0.027692 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.938359 loss: 0.000525 2022/09/10 04:06:24 - mmengine - INFO - Epoch(train) [176][250/586] lr: 5.000000e-05 eta: 1:47:38 time: 0.341841 data_time: 0.024193 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.821205 loss: 0.000538 2022/09/10 04:06:41 - mmengine - INFO - Epoch(train) [176][300/586] lr: 5.000000e-05 eta: 1:47:22 time: 0.335574 data_time: 0.022359 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.874914 loss: 0.000520 2022/09/10 04:06:57 - mmengine - INFO - Epoch(train) [176][350/586] lr: 5.000000e-05 eta: 1:47:06 time: 0.333572 data_time: 0.023334 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.866414 loss: 0.000531 2022/09/10 04:07:14 - mmengine - INFO - Epoch(train) [176][400/586] lr: 5.000000e-05 eta: 1:46:50 time: 0.333329 data_time: 0.023049 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.889085 loss: 0.000532 2022/09/10 04:07:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:07:31 - mmengine - INFO - Epoch(train) [176][450/586] lr: 5.000000e-05 eta: 1:46:34 time: 0.334688 data_time: 0.023252 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.883298 loss: 0.000529 2022/09/10 04:07:47 - mmengine - INFO - Epoch(train) [176][500/586] lr: 5.000000e-05 eta: 1:46:19 time: 0.331223 data_time: 0.023454 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.868075 loss: 0.000526 2022/09/10 04:08:04 - mmengine - INFO - Epoch(train) [176][550/586] lr: 5.000000e-05 eta: 1:46:03 time: 0.334423 data_time: 0.023181 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.828310 loss: 0.000530 2022/09/10 04:08:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:08:16 - mmengine - INFO - Saving checkpoint at 176 epochs 2022/09/10 04:08:40 - mmengine - INFO - Epoch(train) [177][50/586] lr: 5.000000e-05 eta: 1:45:33 time: 0.329937 data_time: 0.027854 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.920787 loss: 0.000521 2022/09/10 04:08:57 - mmengine - INFO - Epoch(train) [177][100/586] lr: 5.000000e-05 eta: 1:45:18 time: 0.341901 data_time: 0.025914 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.904882 loss: 0.000535 2022/09/10 04:09:14 - mmengine - INFO - Epoch(train) [177][150/586] lr: 5.000000e-05 eta: 1:45:02 time: 0.334733 data_time: 0.022885 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.862823 loss: 0.000530 2022/09/10 04:09:30 - mmengine - INFO - Epoch(train) [177][200/586] lr: 5.000000e-05 eta: 1:44:46 time: 0.325394 data_time: 0.023555 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.791264 loss: 0.000532 2022/09/10 04:09:47 - mmengine - INFO - Epoch(train) [177][250/586] lr: 5.000000e-05 eta: 1:44:30 time: 0.337179 data_time: 0.022742 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.908198 loss: 0.000535 2022/09/10 04:10:04 - mmengine - INFO - Epoch(train) [177][300/586] lr: 5.000000e-05 eta: 1:44:14 time: 0.339916 data_time: 0.022902 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.883447 loss: 0.000552 2022/09/10 04:10:20 - mmengine - INFO - Epoch(train) [177][350/586] lr: 5.000000e-05 eta: 1:43:59 time: 0.329143 data_time: 0.023551 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.898377 loss: 0.000507 2022/09/10 04:10:37 - mmengine - INFO - Epoch(train) [177][400/586] lr: 5.000000e-05 eta: 1:43:43 time: 0.334679 data_time: 0.022974 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.854205 loss: 0.000516 2022/09/10 04:10:54 - mmengine - INFO - Epoch(train) [177][450/586] lr: 5.000000e-05 eta: 1:43:27 time: 0.340213 data_time: 0.022196 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.881573 loss: 0.000522 2022/09/10 04:11:11 - mmengine - INFO - Epoch(train) [177][500/586] lr: 5.000000e-05 eta: 1:43:11 time: 0.330593 data_time: 0.022612 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.872062 loss: 0.000520 2022/09/10 04:11:28 - mmengine - INFO - Epoch(train) [177][550/586] lr: 5.000000e-05 eta: 1:42:55 time: 0.336730 data_time: 0.023119 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.876977 loss: 0.000518 2022/09/10 04:11:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:11:40 - mmengine - INFO - Saving checkpoint at 177 epochs 2022/09/10 04:12:04 - mmengine - INFO - Epoch(train) [178][50/586] lr: 5.000000e-05 eta: 1:42:26 time: 0.334749 data_time: 0.032397 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.882221 loss: 0.000531 2022/09/10 04:12:21 - mmengine - INFO - Epoch(train) [178][100/586] lr: 5.000000e-05 eta: 1:42:10 time: 0.340351 data_time: 0.026068 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.898122 loss: 0.000512 2022/09/10 04:12:38 - mmengine - INFO - Epoch(train) [178][150/586] lr: 5.000000e-05 eta: 1:41:55 time: 0.335620 data_time: 0.023057 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.884078 loss: 0.000543 2022/09/10 04:12:55 - mmengine - INFO - Epoch(train) [178][200/586] lr: 5.000000e-05 eta: 1:41:39 time: 0.338070 data_time: 0.026875 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.879948 loss: 0.000528 2022/09/10 04:13:11 - mmengine - INFO - Epoch(train) [178][250/586] lr: 5.000000e-05 eta: 1:41:23 time: 0.336066 data_time: 0.023779 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.871078 loss: 0.000532 2022/09/10 04:13:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:13:28 - mmengine - INFO - Epoch(train) [178][300/586] lr: 5.000000e-05 eta: 1:41:07 time: 0.338587 data_time: 0.022723 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.834554 loss: 0.000524 2022/09/10 04:13:46 - mmengine - INFO - Epoch(train) [178][350/586] lr: 5.000000e-05 eta: 1:40:52 time: 0.343563 data_time: 0.026122 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.845017 loss: 0.000522 2022/09/10 04:14:02 - mmengine - INFO - Epoch(train) [178][400/586] lr: 5.000000e-05 eta: 1:40:36 time: 0.333065 data_time: 0.022482 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.821642 loss: 0.000535 2022/09/10 04:14:19 - mmengine - INFO - Epoch(train) [178][450/586] lr: 5.000000e-05 eta: 1:40:20 time: 0.333949 data_time: 0.022424 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.861608 loss: 0.000528 2022/09/10 04:14:36 - mmengine - INFO - Epoch(train) [178][500/586] lr: 5.000000e-05 eta: 1:40:04 time: 0.338345 data_time: 0.022869 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.904353 loss: 0.000514 2022/09/10 04:14:53 - mmengine - INFO - Epoch(train) [178][550/586] lr: 5.000000e-05 eta: 1:39:48 time: 0.333631 data_time: 0.022799 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.865727 loss: 0.000531 2022/09/10 04:15:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:15:05 - mmengine - INFO - Saving checkpoint at 178 epochs 2022/09/10 04:15:29 - mmengine - INFO - Epoch(train) [179][50/586] lr: 5.000000e-05 eta: 1:39:19 time: 0.330447 data_time: 0.029355 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.843810 loss: 0.000516 2022/09/10 04:15:46 - mmengine - INFO - Epoch(train) [179][100/586] lr: 5.000000e-05 eta: 1:39:03 time: 0.342504 data_time: 0.029122 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.848969 loss: 0.000527 2022/09/10 04:16:02 - mmengine - INFO - Epoch(train) [179][150/586] lr: 5.000000e-05 eta: 1:38:47 time: 0.332185 data_time: 0.023091 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.850511 loss: 0.000533 2022/09/10 04:16:19 - mmengine - INFO - Epoch(train) [179][200/586] lr: 5.000000e-05 eta: 1:38:32 time: 0.332134 data_time: 0.023475 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.875997 loss: 0.000528 2022/09/10 04:16:36 - mmengine - INFO - Epoch(train) [179][250/586] lr: 5.000000e-05 eta: 1:38:16 time: 0.346504 data_time: 0.024242 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.857581 loss: 0.000540 2022/09/10 04:16:53 - mmengine - INFO - Epoch(train) [179][300/586] lr: 5.000000e-05 eta: 1:38:00 time: 0.333402 data_time: 0.023258 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.882841 loss: 0.000524 2022/09/10 04:17:10 - mmengine - INFO - Epoch(train) [179][350/586] lr: 5.000000e-05 eta: 1:37:44 time: 0.330352 data_time: 0.022435 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.896078 loss: 0.000518 2022/09/10 04:17:27 - mmengine - INFO - Epoch(train) [179][400/586] lr: 5.000000e-05 eta: 1:37:29 time: 0.342782 data_time: 0.022622 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.850014 loss: 0.000526 2022/09/10 04:17:43 - mmengine - INFO - Epoch(train) [179][450/586] lr: 5.000000e-05 eta: 1:37:13 time: 0.333848 data_time: 0.022426 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.839249 loss: 0.000524 2022/09/10 04:18:00 - mmengine - INFO - Epoch(train) [179][500/586] lr: 5.000000e-05 eta: 1:36:57 time: 0.330988 data_time: 0.023217 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.875750 loss: 0.000525 2022/09/10 04:18:17 - mmengine - INFO - Epoch(train) [179][550/586] lr: 5.000000e-05 eta: 1:36:41 time: 0.336517 data_time: 0.025772 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.897932 loss: 0.000526 2022/09/10 04:18:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:18:29 - mmengine - INFO - Saving checkpoint at 179 epochs 2022/09/10 04:18:53 - mmengine - INFO - Epoch(train) [180][50/586] lr: 5.000000e-05 eta: 1:36:12 time: 0.335169 data_time: 0.026983 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.930015 loss: 0.000525 2022/09/10 04:19:10 - mmengine - INFO - Epoch(train) [180][100/586] lr: 5.000000e-05 eta: 1:35:56 time: 0.338176 data_time: 0.024113 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.818451 loss: 0.000523 2022/09/10 04:19:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:19:26 - mmengine - INFO - Epoch(train) [180][150/586] lr: 5.000000e-05 eta: 1:35:40 time: 0.329737 data_time: 0.022529 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.843275 loss: 0.000538 2022/09/10 04:19:43 - mmengine - INFO - Epoch(train) [180][200/586] lr: 5.000000e-05 eta: 1:35:25 time: 0.346727 data_time: 0.024539 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.925732 loss: 0.000511 2022/09/10 04:20:00 - mmengine - INFO - Epoch(train) [180][250/586] lr: 5.000000e-05 eta: 1:35:09 time: 0.337505 data_time: 0.023051 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.880746 loss: 0.000524 2022/09/10 04:20:17 - mmengine - INFO - Epoch(train) [180][300/586] lr: 5.000000e-05 eta: 1:34:53 time: 0.331068 data_time: 0.022972 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.876858 loss: 0.000521 2022/09/10 04:20:34 - mmengine - INFO - Epoch(train) [180][350/586] lr: 5.000000e-05 eta: 1:34:37 time: 0.334203 data_time: 0.027310 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.898651 loss: 0.000528 2022/09/10 04:20:50 - mmengine - INFO - Epoch(train) [180][400/586] lr: 5.000000e-05 eta: 1:34:21 time: 0.335299 data_time: 0.023416 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.779987 loss: 0.000541 2022/09/10 04:21:07 - mmengine - INFO - Epoch(train) [180][450/586] lr: 5.000000e-05 eta: 1:34:06 time: 0.333114 data_time: 0.022289 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.894196 loss: 0.000523 2022/09/10 04:21:24 - mmengine - INFO - Epoch(train) [180][500/586] lr: 5.000000e-05 eta: 1:33:50 time: 0.327719 data_time: 0.023068 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.859218 loss: 0.000516 2022/09/10 04:21:40 - mmengine - INFO - Epoch(train) [180][550/586] lr: 5.000000e-05 eta: 1:33:34 time: 0.337355 data_time: 0.022792 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.887954 loss: 0.000522 2022/09/10 04:21:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:21:52 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/09/10 04:22:09 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:01:01 time: 0.172321 data_time: 0.012912 memory: 7489 2022/09/10 04:22:17 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:50 time: 0.166040 data_time: 0.007895 memory: 1657 2022/09/10 04:22:25 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:43 time: 0.167612 data_time: 0.008186 memory: 1657 2022/09/10 04:22:34 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:34 time: 0.164503 data_time: 0.007566 memory: 1657 2022/09/10 04:22:42 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:26 time: 0.166558 data_time: 0.007867 memory: 1657 2022/09/10 04:22:50 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:17 time: 0.168183 data_time: 0.007569 memory: 1657 2022/09/10 04:22:59 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:09 time: 0.165768 data_time: 0.007816 memory: 1657 2022/09/10 04:23:07 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:01 time: 0.162838 data_time: 0.008110 memory: 1657 2022/09/10 04:23:42 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 04:23:56 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.753674 coco/AP .5: 0.906426 coco/AP .75: 0.824842 coco/AP (M): 0.720484 coco/AP (L): 0.819204 coco/AR: 0.806045 coco/AR .5: 0.943482 coco/AR .75: 0.868703 coco/AR (M): 0.765447 coco/AR (L): 0.865143 2022/09/10 04:23:56 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_160.pth is removed 2022/09/10 04:24:00 - mmengine - INFO - The best checkpoint with 0.7537 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/09/10 04:24:17 - mmengine - INFO - Epoch(train) [181][50/586] lr: 5.000000e-05 eta: 1:33:05 time: 0.346499 data_time: 0.026404 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.913599 loss: 0.000510 2022/09/10 04:24:34 - mmengine - INFO - Epoch(train) [181][100/586] lr: 5.000000e-05 eta: 1:32:49 time: 0.328395 data_time: 0.022467 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.892807 loss: 0.000520 2022/09/10 04:24:51 - mmengine - INFO - Epoch(train) [181][150/586] lr: 5.000000e-05 eta: 1:32:33 time: 0.342398 data_time: 0.024327 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.869792 loss: 0.000519 2022/09/10 04:25:08 - mmengine - INFO - Epoch(train) [181][200/586] lr: 5.000000e-05 eta: 1:32:18 time: 0.345708 data_time: 0.026395 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.868365 loss: 0.000527 2022/09/10 04:25:25 - mmengine - INFO - Epoch(train) [181][250/586] lr: 5.000000e-05 eta: 1:32:02 time: 0.332013 data_time: 0.023264 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.845755 loss: 0.000516 2022/09/10 04:25:42 - mmengine - INFO - Epoch(train) [181][300/586] lr: 5.000000e-05 eta: 1:31:46 time: 0.339036 data_time: 0.023254 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.877843 loss: 0.000529 2022/09/10 04:25:59 - mmengine - INFO - Epoch(train) [181][350/586] lr: 5.000000e-05 eta: 1:31:30 time: 0.336461 data_time: 0.022864 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.842226 loss: 0.000507 2022/09/10 04:26:15 - mmengine - INFO - Epoch(train) [181][400/586] lr: 5.000000e-05 eta: 1:31:14 time: 0.328869 data_time: 0.023228 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.898703 loss: 0.000526 2022/09/10 04:26:32 - mmengine - INFO - Epoch(train) [181][450/586] lr: 5.000000e-05 eta: 1:30:59 time: 0.336067 data_time: 0.023107 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.898478 loss: 0.000525 2022/09/10 04:26:49 - mmengine - INFO - Epoch(train) [181][500/586] lr: 5.000000e-05 eta: 1:30:43 time: 0.337897 data_time: 0.022516 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.918638 loss: 0.000519 2022/09/10 04:26:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:27:05 - mmengine - INFO - Epoch(train) [181][550/586] lr: 5.000000e-05 eta: 1:30:27 time: 0.330748 data_time: 0.023996 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.876054 loss: 0.000515 2022/09/10 04:27:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:27:18 - mmengine - INFO - Saving checkpoint at 181 epochs 2022/09/10 04:27:42 - mmengine - INFO - Epoch(train) [182][50/586] lr: 5.000000e-05 eta: 1:29:58 time: 0.340492 data_time: 0.029202 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.907037 loss: 0.000527 2022/09/10 04:27:59 - mmengine - INFO - Epoch(train) [182][100/586] lr: 5.000000e-05 eta: 1:29:42 time: 0.344703 data_time: 0.023036 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.887962 loss: 0.000524 2022/09/10 04:28:16 - mmengine - INFO - Epoch(train) [182][150/586] lr: 5.000000e-05 eta: 1:29:26 time: 0.336516 data_time: 0.024426 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.868308 loss: 0.000513 2022/09/10 04:28:33 - mmengine - INFO - Epoch(train) [182][200/586] lr: 5.000000e-05 eta: 1:29:11 time: 0.337398 data_time: 0.023063 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.830721 loss: 0.000520 2022/09/10 04:28:50 - mmengine - INFO - Epoch(train) [182][250/586] lr: 5.000000e-05 eta: 1:28:55 time: 0.330992 data_time: 0.023214 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.896852 loss: 0.000522 2022/09/10 04:29:06 - mmengine - INFO - Epoch(train) [182][300/586] lr: 5.000000e-05 eta: 1:28:39 time: 0.329418 data_time: 0.022731 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.909666 loss: 0.000526 2022/09/10 04:29:23 - mmengine - INFO - Epoch(train) [182][350/586] lr: 5.000000e-05 eta: 1:28:23 time: 0.339210 data_time: 0.023306 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.893249 loss: 0.000499 2022/09/10 04:29:40 - mmengine - INFO - Epoch(train) [182][400/586] lr: 5.000000e-05 eta: 1:28:07 time: 0.335835 data_time: 0.022563 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.859039 loss: 0.000521 2022/09/10 04:29:56 - mmengine - INFO - Epoch(train) [182][450/586] lr: 5.000000e-05 eta: 1:27:52 time: 0.332746 data_time: 0.022641 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.829383 loss: 0.000534 2022/09/10 04:30:13 - mmengine - INFO - Epoch(train) [182][500/586] lr: 5.000000e-05 eta: 1:27:36 time: 0.336466 data_time: 0.022863 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.882376 loss: 0.000521 2022/09/10 04:30:30 - mmengine - INFO - Epoch(train) [182][550/586] lr: 5.000000e-05 eta: 1:27:20 time: 0.337228 data_time: 0.023028 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.880779 loss: 0.000512 2022/09/10 04:30:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:30:42 - mmengine - INFO - Saving checkpoint at 182 epochs 2022/09/10 04:31:06 - mmengine - INFO - Epoch(train) [183][50/586] lr: 5.000000e-05 eta: 1:26:51 time: 0.339294 data_time: 0.029123 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.867949 loss: 0.000507 2022/09/10 04:31:23 - mmengine - INFO - Epoch(train) [183][100/586] lr: 5.000000e-05 eta: 1:26:35 time: 0.330820 data_time: 0.022635 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.816594 loss: 0.000530 2022/09/10 04:31:39 - mmengine - INFO - Epoch(train) [183][150/586] lr: 5.000000e-05 eta: 1:26:19 time: 0.334396 data_time: 0.022702 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.867923 loss: 0.000526 2022/09/10 04:31:56 - mmengine - INFO - Epoch(train) [183][200/586] lr: 5.000000e-05 eta: 1:26:04 time: 0.339271 data_time: 0.026523 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.907926 loss: 0.000506 2022/09/10 04:32:13 - mmengine - INFO - Epoch(train) [183][250/586] lr: 5.000000e-05 eta: 1:25:48 time: 0.328814 data_time: 0.022592 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.842173 loss: 0.000525 2022/09/10 04:32:30 - mmengine - INFO - Epoch(train) [183][300/586] lr: 5.000000e-05 eta: 1:25:32 time: 0.337641 data_time: 0.023176 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.893173 loss: 0.000546 2022/09/10 04:32:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:32:47 - mmengine - INFO - Epoch(train) [183][350/586] lr: 5.000000e-05 eta: 1:25:16 time: 0.338167 data_time: 0.022675 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.783896 loss: 0.000504 2022/09/10 04:33:03 - mmengine - INFO - Epoch(train) [183][400/586] lr: 5.000000e-05 eta: 1:25:00 time: 0.335418 data_time: 0.024210 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.864692 loss: 0.000515 2022/09/10 04:33:20 - mmengine - INFO - Epoch(train) [183][450/586] lr: 5.000000e-05 eta: 1:24:44 time: 0.330361 data_time: 0.022377 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.820433 loss: 0.000515 2022/09/10 04:33:37 - mmengine - INFO - Epoch(train) [183][500/586] lr: 5.000000e-05 eta: 1:24:29 time: 0.346810 data_time: 0.023557 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.840904 loss: 0.000503 2022/09/10 04:33:54 - mmengine - INFO - Epoch(train) [183][550/586] lr: 5.000000e-05 eta: 1:24:13 time: 0.331471 data_time: 0.022558 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.868651 loss: 0.000520 2022/09/10 04:34:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:34:06 - mmengine - INFO - Saving checkpoint at 183 epochs 2022/09/10 04:34:31 - mmengine - INFO - Epoch(train) [184][50/586] lr: 5.000000e-05 eta: 1:23:44 time: 0.343603 data_time: 0.027171 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.866068 loss: 0.000530 2022/09/10 04:34:47 - mmengine - INFO - Epoch(train) [184][100/586] lr: 5.000000e-05 eta: 1:23:28 time: 0.336258 data_time: 0.022980 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.761934 loss: 0.000525 2022/09/10 04:35:04 - mmengine - INFO - Epoch(train) [184][150/586] lr: 5.000000e-05 eta: 1:23:12 time: 0.333193 data_time: 0.023190 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.892737 loss: 0.000526 2022/09/10 04:35:21 - mmengine - INFO - Epoch(train) [184][200/586] lr: 5.000000e-05 eta: 1:22:57 time: 0.336691 data_time: 0.023057 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.885885 loss: 0.000505 2022/09/10 04:35:38 - mmengine - INFO - Epoch(train) [184][250/586] lr: 5.000000e-05 eta: 1:22:41 time: 0.335998 data_time: 0.022931 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.843410 loss: 0.000508 2022/09/10 04:35:54 - mmengine - INFO - Epoch(train) [184][300/586] lr: 5.000000e-05 eta: 1:22:25 time: 0.333621 data_time: 0.027532 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.887882 loss: 0.000519 2022/09/10 04:36:11 - mmengine - INFO - Epoch(train) [184][350/586] lr: 5.000000e-05 eta: 1:22:09 time: 0.335752 data_time: 0.022107 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.871525 loss: 0.000515 2022/09/10 04:36:28 - mmengine - INFO - Epoch(train) [184][400/586] lr: 5.000000e-05 eta: 1:21:53 time: 0.336114 data_time: 0.022697 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.912956 loss: 0.000513 2022/09/10 04:36:45 - mmengine - INFO - Epoch(train) [184][450/586] lr: 5.000000e-05 eta: 1:21:37 time: 0.332338 data_time: 0.023484 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.942206 loss: 0.000516 2022/09/10 04:37:02 - mmengine - INFO - Epoch(train) [184][500/586] lr: 5.000000e-05 eta: 1:21:22 time: 0.342237 data_time: 0.023266 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.826421 loss: 0.000513 2022/09/10 04:37:19 - mmengine - INFO - Epoch(train) [184][550/586] lr: 5.000000e-05 eta: 1:21:06 time: 0.336691 data_time: 0.022580 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.947965 loss: 0.000504 2022/09/10 04:37:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:37:31 - mmengine - INFO - Saving checkpoint at 184 epochs 2022/09/10 04:37:55 - mmengine - INFO - Epoch(train) [185][50/586] lr: 5.000000e-05 eta: 1:20:37 time: 0.344541 data_time: 0.030866 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.865197 loss: 0.000532 2022/09/10 04:38:12 - mmengine - INFO - Epoch(train) [185][100/586] lr: 5.000000e-05 eta: 1:20:21 time: 0.340249 data_time: 0.023237 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.878388 loss: 0.000521 2022/09/10 04:38:28 - mmengine - INFO - Epoch(train) [185][150/586] lr: 5.000000e-05 eta: 1:20:05 time: 0.328454 data_time: 0.022676 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.843203 loss: 0.000521 2022/09/10 04:38:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:38:45 - mmengine - INFO - Epoch(train) [185][200/586] lr: 5.000000e-05 eta: 1:19:50 time: 0.339465 data_time: 0.026211 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.807761 loss: 0.000517 2022/09/10 04:39:02 - mmengine - INFO - Epoch(train) [185][250/586] lr: 5.000000e-05 eta: 1:19:34 time: 0.341759 data_time: 0.022306 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.917874 loss: 0.000523 2022/09/10 04:39:19 - mmengine - INFO - Epoch(train) [185][300/586] lr: 5.000000e-05 eta: 1:19:18 time: 0.331084 data_time: 0.022751 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.846804 loss: 0.000510 2022/09/10 04:39:36 - mmengine - INFO - Epoch(train) [185][350/586] lr: 5.000000e-05 eta: 1:19:02 time: 0.338594 data_time: 0.023359 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.875890 loss: 0.000521 2022/09/10 04:39:52 - mmengine - INFO - Epoch(train) [185][400/586] lr: 5.000000e-05 eta: 1:18:46 time: 0.337290 data_time: 0.022909 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.891018 loss: 0.000519 2022/09/10 04:40:09 - mmengine - INFO - Epoch(train) [185][450/586] lr: 5.000000e-05 eta: 1:18:31 time: 0.330657 data_time: 0.022622 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.850448 loss: 0.000501 2022/09/10 04:40:26 - mmengine - INFO - Epoch(train) [185][500/586] lr: 5.000000e-05 eta: 1:18:15 time: 0.337891 data_time: 0.024227 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.864444 loss: 0.000526 2022/09/10 04:40:43 - mmengine - INFO - Epoch(train) [185][550/586] lr: 5.000000e-05 eta: 1:17:59 time: 0.335566 data_time: 0.024650 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.923250 loss: 0.000521 2022/09/10 04:40:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:40:55 - mmengine - INFO - Saving checkpoint at 185 epochs 2022/09/10 04:41:20 - mmengine - INFO - Epoch(train) [186][50/586] lr: 5.000000e-05 eta: 1:17:30 time: 0.349675 data_time: 0.031913 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.863538 loss: 0.000527 2022/09/10 04:41:37 - mmengine - INFO - Epoch(train) [186][100/586] lr: 5.000000e-05 eta: 1:17:15 time: 0.344539 data_time: 0.022911 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.879025 loss: 0.000525 2022/09/10 04:41:53 - mmengine - INFO - Epoch(train) [186][150/586] lr: 5.000000e-05 eta: 1:16:59 time: 0.332783 data_time: 0.024529 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.904627 loss: 0.000518 2022/09/10 04:42:10 - mmengine - INFO - Epoch(train) [186][200/586] lr: 5.000000e-05 eta: 1:16:43 time: 0.337267 data_time: 0.022813 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.848077 loss: 0.000518 2022/09/10 04:42:27 - mmengine - INFO - Epoch(train) [186][250/586] lr: 5.000000e-05 eta: 1:16:27 time: 0.336260 data_time: 0.022599 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.845214 loss: 0.000503 2022/09/10 04:42:44 - mmengine - INFO - Epoch(train) [186][300/586] lr: 5.000000e-05 eta: 1:16:11 time: 0.332298 data_time: 0.022600 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.886347 loss: 0.000497 2022/09/10 04:43:01 - mmengine - INFO - Epoch(train) [186][350/586] lr: 5.000000e-05 eta: 1:15:55 time: 0.334843 data_time: 0.023463 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.903515 loss: 0.000524 2022/09/10 04:43:17 - mmengine - INFO - Epoch(train) [186][400/586] lr: 5.000000e-05 eta: 1:15:40 time: 0.334087 data_time: 0.024065 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.880902 loss: 0.000510 2022/09/10 04:43:34 - mmengine - INFO - Epoch(train) [186][450/586] lr: 5.000000e-05 eta: 1:15:24 time: 0.334387 data_time: 0.026758 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.876133 loss: 0.000511 2022/09/10 04:43:51 - mmengine - INFO - Epoch(train) [186][500/586] lr: 5.000000e-05 eta: 1:15:08 time: 0.335536 data_time: 0.022514 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.925875 loss: 0.000503 2022/09/10 04:44:08 - mmengine - INFO - Epoch(train) [186][550/586] lr: 5.000000e-05 eta: 1:14:52 time: 0.339123 data_time: 0.023527 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.805945 loss: 0.000522 2022/09/10 04:44:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:44:20 - mmengine - INFO - Saving checkpoint at 186 epochs 2022/09/10 04:44:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:44:44 - mmengine - INFO - Epoch(train) [187][50/586] lr: 5.000000e-05 eta: 1:14:23 time: 0.337588 data_time: 0.027307 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.888680 loss: 0.000507 2022/09/10 04:45:01 - mmengine - INFO - Epoch(train) [187][100/586] lr: 5.000000e-05 eta: 1:14:08 time: 0.338386 data_time: 0.025895 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.888734 loss: 0.000518 2022/09/10 04:45:17 - mmengine - INFO - Epoch(train) [187][150/586] lr: 5.000000e-05 eta: 1:13:52 time: 0.329804 data_time: 0.023146 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.913212 loss: 0.000509 2022/09/10 04:45:34 - mmengine - INFO - Epoch(train) [187][200/586] lr: 5.000000e-05 eta: 1:13:36 time: 0.331049 data_time: 0.023225 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.859322 loss: 0.000519 2022/09/10 04:45:51 - mmengine - INFO - Epoch(train) [187][250/586] lr: 5.000000e-05 eta: 1:13:20 time: 0.341697 data_time: 0.023431 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.845960 loss: 0.000525 2022/09/10 04:46:07 - mmengine - INFO - Epoch(train) [187][300/586] lr: 5.000000e-05 eta: 1:13:04 time: 0.329377 data_time: 0.023391 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.882582 loss: 0.000513 2022/09/10 04:46:24 - mmengine - INFO - Epoch(train) [187][350/586] lr: 5.000000e-05 eta: 1:12:48 time: 0.335087 data_time: 0.022973 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.870207 loss: 0.000532 2022/09/10 04:46:41 - mmengine - INFO - Epoch(train) [187][400/586] lr: 5.000000e-05 eta: 1:12:33 time: 0.341284 data_time: 0.023944 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.845474 loss: 0.000534 2022/09/10 04:46:58 - mmengine - INFO - Epoch(train) [187][450/586] lr: 5.000000e-05 eta: 1:12:17 time: 0.329604 data_time: 0.022727 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.899802 loss: 0.000521 2022/09/10 04:47:14 - mmengine - INFO - Epoch(train) [187][500/586] lr: 5.000000e-05 eta: 1:12:01 time: 0.336631 data_time: 0.023752 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.896515 loss: 0.000522 2022/09/10 04:47:31 - mmengine - INFO - Epoch(train) [187][550/586] lr: 5.000000e-05 eta: 1:11:45 time: 0.341090 data_time: 0.026907 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.877264 loss: 0.000504 2022/09/10 04:47:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:47:44 - mmengine - INFO - Saving checkpoint at 187 epochs 2022/09/10 04:48:08 - mmengine - INFO - Epoch(train) [188][50/586] lr: 5.000000e-05 eta: 1:11:16 time: 0.344053 data_time: 0.035196 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.824893 loss: 0.000528 2022/09/10 04:48:25 - mmengine - INFO - Epoch(train) [188][100/586] lr: 5.000000e-05 eta: 1:11:01 time: 0.335115 data_time: 0.022836 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.909103 loss: 0.000503 2022/09/10 04:48:41 - mmengine - INFO - Epoch(train) [188][150/586] lr: 5.000000e-05 eta: 1:10:45 time: 0.330776 data_time: 0.022858 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.860944 loss: 0.000507 2022/09/10 04:48:58 - mmengine - INFO - Epoch(train) [188][200/586] lr: 5.000000e-05 eta: 1:10:29 time: 0.338349 data_time: 0.027056 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.852432 loss: 0.000535 2022/09/10 04:49:15 - mmengine - INFO - Epoch(train) [188][250/586] lr: 5.000000e-05 eta: 1:10:13 time: 0.338491 data_time: 0.022634 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.852599 loss: 0.000521 2022/09/10 04:49:32 - mmengine - INFO - Epoch(train) [188][300/586] lr: 5.000000e-05 eta: 1:09:57 time: 0.337093 data_time: 0.022583 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.867898 loss: 0.000504 2022/09/10 04:49:49 - mmengine - INFO - Epoch(train) [188][350/586] lr: 5.000000e-05 eta: 1:09:42 time: 0.331704 data_time: 0.025979 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.828169 loss: 0.000528 2022/09/10 04:50:06 - mmengine - INFO - Epoch(train) [188][400/586] lr: 5.000000e-05 eta: 1:09:26 time: 0.340573 data_time: 0.022245 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.806880 loss: 0.000531 2022/09/10 04:50:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:50:22 - mmengine - INFO - Epoch(train) [188][450/586] lr: 5.000000e-05 eta: 1:09:10 time: 0.333776 data_time: 0.022429 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.865373 loss: 0.000537 2022/09/10 04:50:39 - mmengine - INFO - Epoch(train) [188][500/586] lr: 5.000000e-05 eta: 1:08:54 time: 0.331432 data_time: 0.022854 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.933563 loss: 0.000519 2022/09/10 04:50:56 - mmengine - INFO - Epoch(train) [188][550/586] lr: 5.000000e-05 eta: 1:08:38 time: 0.345293 data_time: 0.024789 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.879453 loss: 0.000518 2022/09/10 04:51:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:51:08 - mmengine - INFO - Saving checkpoint at 188 epochs 2022/09/10 04:51:32 - mmengine - INFO - Epoch(train) [189][50/586] lr: 5.000000e-05 eta: 1:08:10 time: 0.335970 data_time: 0.027649 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.924300 loss: 0.000518 2022/09/10 04:51:49 - mmengine - INFO - Epoch(train) [189][100/586] lr: 5.000000e-05 eta: 1:07:54 time: 0.339865 data_time: 0.023326 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.895619 loss: 0.000506 2022/09/10 04:52:06 - mmengine - INFO - Epoch(train) [189][150/586] lr: 5.000000e-05 eta: 1:07:38 time: 0.342391 data_time: 0.025791 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.847190 loss: 0.000524 2022/09/10 04:52:22 - mmengine - INFO - Epoch(train) [189][200/586] lr: 5.000000e-05 eta: 1:07:22 time: 0.331156 data_time: 0.022494 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.893269 loss: 0.000515 2022/09/10 04:52:39 - mmengine - INFO - Epoch(train) [189][250/586] lr: 5.000000e-05 eta: 1:07:06 time: 0.336273 data_time: 0.023279 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.888738 loss: 0.000525 2022/09/10 04:52:56 - mmengine - INFO - Epoch(train) [189][300/586] lr: 5.000000e-05 eta: 1:06:51 time: 0.340857 data_time: 0.022292 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.865104 loss: 0.000520 2022/09/10 04:53:13 - mmengine - INFO - Epoch(train) [189][350/586] lr: 5.000000e-05 eta: 1:06:35 time: 0.331642 data_time: 0.022261 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.872070 loss: 0.000523 2022/09/10 04:53:30 - mmengine - INFO - Epoch(train) [189][400/586] lr: 5.000000e-05 eta: 1:06:19 time: 0.345063 data_time: 0.022712 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.877465 loss: 0.000528 2022/09/10 04:53:47 - mmengine - INFO - Epoch(train) [189][450/586] lr: 5.000000e-05 eta: 1:06:03 time: 0.331445 data_time: 0.026650 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.813365 loss: 0.000511 2022/09/10 04:54:03 - mmengine - INFO - Epoch(train) [189][500/586] lr: 5.000000e-05 eta: 1:05:47 time: 0.331939 data_time: 0.022407 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.866857 loss: 0.000521 2022/09/10 04:54:20 - mmengine - INFO - Epoch(train) [189][550/586] lr: 5.000000e-05 eta: 1:05:31 time: 0.334757 data_time: 0.022706 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.863646 loss: 0.000514 2022/09/10 04:54:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:54:32 - mmengine - INFO - Saving checkpoint at 189 epochs 2022/09/10 04:54:57 - mmengine - INFO - Epoch(train) [190][50/586] lr: 5.000000e-05 eta: 1:05:03 time: 0.356932 data_time: 0.035041 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.910747 loss: 0.000506 2022/09/10 04:55:14 - mmengine - INFO - Epoch(train) [190][100/586] lr: 5.000000e-05 eta: 1:04:47 time: 0.349714 data_time: 0.025823 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.790897 loss: 0.000527 2022/09/10 04:55:31 - mmengine - INFO - Epoch(train) [190][150/586] lr: 5.000000e-05 eta: 1:04:31 time: 0.335626 data_time: 0.024099 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.923463 loss: 0.000505 2022/09/10 04:55:48 - mmengine - INFO - Epoch(train) [190][200/586] lr: 5.000000e-05 eta: 1:04:16 time: 0.335979 data_time: 0.023857 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.848261 loss: 0.000499 2022/09/10 04:56:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:56:04 - mmengine - INFO - Epoch(train) [190][250/586] lr: 5.000000e-05 eta: 1:04:00 time: 0.334271 data_time: 0.023148 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.837794 loss: 0.000528 2022/09/10 04:56:21 - mmengine - INFO - Epoch(train) [190][300/586] lr: 5.000000e-05 eta: 1:03:44 time: 0.335172 data_time: 0.022601 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.937792 loss: 0.000514 2022/09/10 04:56:38 - mmengine - INFO - Epoch(train) [190][350/586] lr: 5.000000e-05 eta: 1:03:28 time: 0.335323 data_time: 0.025838 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.857997 loss: 0.000518 2022/09/10 04:56:55 - mmengine - INFO - Epoch(train) [190][400/586] lr: 5.000000e-05 eta: 1:03:12 time: 0.341702 data_time: 0.023886 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.883944 loss: 0.000522 2022/09/10 04:57:12 - mmengine - INFO - Epoch(train) [190][450/586] lr: 5.000000e-05 eta: 1:02:56 time: 0.332643 data_time: 0.023302 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.836479 loss: 0.000537 2022/09/10 04:57:29 - mmengine - INFO - Epoch(train) [190][500/586] lr: 5.000000e-05 eta: 1:02:41 time: 0.343334 data_time: 0.023285 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.921459 loss: 0.000520 2022/09/10 04:57:46 - mmengine - INFO - Epoch(train) [190][550/586] lr: 5.000000e-05 eta: 1:02:25 time: 0.336838 data_time: 0.023661 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.877233 loss: 0.000525 2022/09/10 04:57:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 04:57:58 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/09/10 04:58:14 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:01:00 time: 0.170852 data_time: 0.012126 memory: 7489 2022/09/10 04:58:22 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:51 time: 0.166430 data_time: 0.007864 memory: 1657 2022/09/10 04:58:31 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:42 time: 0.166427 data_time: 0.008534 memory: 1657 2022/09/10 04:58:39 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:34 time: 0.165586 data_time: 0.007839 memory: 1657 2022/09/10 04:58:47 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:26 time: 0.166142 data_time: 0.008086 memory: 1657 2022/09/10 04:58:55 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:17 time: 0.165383 data_time: 0.007829 memory: 1657 2022/09/10 04:59:04 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:09 time: 0.170217 data_time: 0.011316 memory: 1657 2022/09/10 04:59:12 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:01 time: 0.161264 data_time: 0.006769 memory: 1657 2022/09/10 04:59:47 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 05:00:01 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.754261 coco/AP .5: 0.905445 coco/AP .75: 0.824892 coco/AP (M): 0.720970 coco/AP (L): 0.820540 coco/AR: 0.806895 coco/AR .5: 0.942380 coco/AR .75: 0.869175 coco/AR (M): 0.766266 coco/AR (L): 0.866221 2022/09/10 05:00:01 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_180.pth is removed 2022/09/10 05:00:05 - mmengine - INFO - The best checkpoint with 0.7543 coco/AP at 190 epoch is saved to best_coco/AP_epoch_190.pth. 2022/09/10 05:00:22 - mmengine - INFO - Epoch(train) [191][50/586] lr: 5.000000e-05 eta: 1:01:56 time: 0.345395 data_time: 0.029132 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.795472 loss: 0.000524 2022/09/10 05:00:39 - mmengine - INFO - Epoch(train) [191][100/586] lr: 5.000000e-05 eta: 1:01:40 time: 0.334701 data_time: 0.025479 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.889735 loss: 0.000508 2022/09/10 05:00:56 - mmengine - INFO - Epoch(train) [191][150/586] lr: 5.000000e-05 eta: 1:01:25 time: 0.335661 data_time: 0.022939 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.840443 loss: 0.000509 2022/09/10 05:01:13 - mmengine - INFO - Epoch(train) [191][200/586] lr: 5.000000e-05 eta: 1:01:09 time: 0.341598 data_time: 0.022751 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.886640 loss: 0.000510 2022/09/10 05:01:29 - mmengine - INFO - Epoch(train) [191][250/586] lr: 5.000000e-05 eta: 1:00:53 time: 0.330783 data_time: 0.022677 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.874030 loss: 0.000509 2022/09/10 05:01:47 - mmengine - INFO - Epoch(train) [191][300/586] lr: 5.000000e-05 eta: 1:00:37 time: 0.349245 data_time: 0.023124 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.906276 loss: 0.000512 2022/09/10 05:02:04 - mmengine - INFO - Epoch(train) [191][350/586] lr: 5.000000e-05 eta: 1:00:21 time: 0.341446 data_time: 0.022615 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.910416 loss: 0.000506 2022/09/10 05:02:21 - mmengine - INFO - Epoch(train) [191][400/586] lr: 5.000000e-05 eta: 1:00:05 time: 0.330456 data_time: 0.022700 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.882137 loss: 0.000521 2022/09/10 05:02:37 - mmengine - INFO - Epoch(train) [191][450/586] lr: 5.000000e-05 eta: 0:59:50 time: 0.336760 data_time: 0.022238 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.871192 loss: 0.000535 2022/09/10 05:02:54 - mmengine - INFO - Epoch(train) [191][500/586] lr: 5.000000e-05 eta: 0:59:34 time: 0.334272 data_time: 0.022758 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.909488 loss: 0.000517 2022/09/10 05:03:11 - mmengine - INFO - Epoch(train) [191][550/586] lr: 5.000000e-05 eta: 0:59:18 time: 0.340058 data_time: 0.023029 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.872994 loss: 0.000509 2022/09/10 05:03:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:03:23 - mmengine - INFO - Saving checkpoint at 191 epochs 2022/09/10 05:03:48 - mmengine - INFO - Epoch(train) [192][50/586] lr: 5.000000e-05 eta: 0:58:50 time: 0.344279 data_time: 0.027870 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.880394 loss: 0.000517 2022/09/10 05:03:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:04:05 - mmengine - INFO - Epoch(train) [192][100/586] lr: 5.000000e-05 eta: 0:58:34 time: 0.339456 data_time: 0.028028 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.878386 loss: 0.000511 2022/09/10 05:04:21 - mmengine - INFO - Epoch(train) [192][150/586] lr: 5.000000e-05 eta: 0:58:18 time: 0.334150 data_time: 0.022283 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.858046 loss: 0.000516 2022/09/10 05:04:38 - mmengine - INFO - Epoch(train) [192][200/586] lr: 5.000000e-05 eta: 0:58:02 time: 0.335198 data_time: 0.022707 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.881488 loss: 0.000510 2022/09/10 05:04:55 - mmengine - INFO - Epoch(train) [192][250/586] lr: 5.000000e-05 eta: 0:57:46 time: 0.343253 data_time: 0.027520 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.864298 loss: 0.000515 2022/09/10 05:05:12 - mmengine - INFO - Epoch(train) [192][300/586] lr: 5.000000e-05 eta: 0:57:30 time: 0.337724 data_time: 0.022912 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.856662 loss: 0.000508 2022/09/10 05:05:29 - mmengine - INFO - Epoch(train) [192][350/586] lr: 5.000000e-05 eta: 0:57:15 time: 0.337099 data_time: 0.022319 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.854570 loss: 0.000530 2022/09/10 05:05:46 - mmengine - INFO - Epoch(train) [192][400/586] lr: 5.000000e-05 eta: 0:56:59 time: 0.332677 data_time: 0.026213 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.869135 loss: 0.000516 2022/09/10 05:06:03 - mmengine - INFO - Epoch(train) [192][450/586] lr: 5.000000e-05 eta: 0:56:43 time: 0.338915 data_time: 0.022718 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.834078 loss: 0.000516 2022/09/10 05:06:19 - mmengine - INFO - Epoch(train) [192][500/586] lr: 5.000000e-05 eta: 0:56:27 time: 0.333408 data_time: 0.022947 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.901248 loss: 0.000507 2022/09/10 05:06:36 - mmengine - INFO - Epoch(train) [192][550/586] lr: 5.000000e-05 eta: 0:56:11 time: 0.336737 data_time: 0.028433 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.829118 loss: 0.000527 2022/09/10 05:06:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:06:48 - mmengine - INFO - Saving checkpoint at 192 epochs 2022/09/10 05:07:12 - mmengine - INFO - Epoch(train) [193][50/586] lr: 5.000000e-05 eta: 0:55:43 time: 0.343903 data_time: 0.029423 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.906448 loss: 0.000522 2022/09/10 05:07:29 - mmengine - INFO - Epoch(train) [193][100/586] lr: 5.000000e-05 eta: 0:55:27 time: 0.330947 data_time: 0.023380 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.868220 loss: 0.000505 2022/09/10 05:07:46 - mmengine - INFO - Epoch(train) [193][150/586] lr: 5.000000e-05 eta: 0:55:11 time: 0.335516 data_time: 0.023234 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.861649 loss: 0.000513 2022/09/10 05:08:02 - mmengine - INFO - Epoch(train) [193][200/586] lr: 5.000000e-05 eta: 0:54:55 time: 0.335801 data_time: 0.022990 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.816648 loss: 0.000508 2022/09/10 05:08:19 - mmengine - INFO - Epoch(train) [193][250/586] lr: 5.000000e-05 eta: 0:54:39 time: 0.332096 data_time: 0.022462 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.871565 loss: 0.000517 2022/09/10 05:08:36 - mmengine - INFO - Epoch(train) [193][300/586] lr: 5.000000e-05 eta: 0:54:24 time: 0.337284 data_time: 0.027551 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.840394 loss: 0.000508 2022/09/10 05:08:53 - mmengine - INFO - Epoch(train) [193][350/586] lr: 5.000000e-05 eta: 0:54:08 time: 0.340726 data_time: 0.023079 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.860844 loss: 0.000524 2022/09/10 05:09:10 - mmengine - INFO - Epoch(train) [193][400/586] lr: 5.000000e-05 eta: 0:53:52 time: 0.338366 data_time: 0.022487 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.845192 loss: 0.000505 2022/09/10 05:09:26 - mmengine - INFO - Epoch(train) [193][450/586] lr: 5.000000e-05 eta: 0:53:36 time: 0.330402 data_time: 0.023191 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.901275 loss: 0.000534 2022/09/10 05:09:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:09:43 - mmengine - INFO - Epoch(train) [193][500/586] lr: 5.000000e-05 eta: 0:53:20 time: 0.338837 data_time: 0.022940 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.850135 loss: 0.000495 2022/09/10 05:10:00 - mmengine - INFO - Epoch(train) [193][550/586] lr: 5.000000e-05 eta: 0:53:04 time: 0.331686 data_time: 0.022487 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.861776 loss: 0.000509 2022/09/10 05:10:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:10:12 - mmengine - INFO - Saving checkpoint at 193 epochs 2022/09/10 05:10:36 - mmengine - INFO - Epoch(train) [194][50/586] lr: 5.000000e-05 eta: 0:52:36 time: 0.340255 data_time: 0.027288 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.783582 loss: 0.000505 2022/09/10 05:10:53 - mmengine - INFO - Epoch(train) [194][100/586] lr: 5.000000e-05 eta: 0:52:20 time: 0.337997 data_time: 0.028538 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.874612 loss: 0.000507 2022/09/10 05:11:10 - mmengine - INFO - Epoch(train) [194][150/586] lr: 5.000000e-05 eta: 0:52:04 time: 0.338343 data_time: 0.022556 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.885707 loss: 0.000510 2022/09/10 05:11:26 - mmengine - INFO - Epoch(train) [194][200/586] lr: 5.000000e-05 eta: 0:51:48 time: 0.327036 data_time: 0.023253 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.839169 loss: 0.000504 2022/09/10 05:11:43 - mmengine - INFO - Epoch(train) [194][250/586] lr: 5.000000e-05 eta: 0:51:33 time: 0.338775 data_time: 0.022654 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.864455 loss: 0.000523 2022/09/10 05:12:00 - mmengine - INFO - Epoch(train) [194][300/586] lr: 5.000000e-05 eta: 0:51:17 time: 0.338813 data_time: 0.023633 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.890363 loss: 0.000517 2022/09/10 05:12:16 - mmengine - INFO - Epoch(train) [194][350/586] lr: 5.000000e-05 eta: 0:51:01 time: 0.327646 data_time: 0.023061 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.912861 loss: 0.000535 2022/09/10 05:12:34 - mmengine - INFO - Epoch(train) [194][400/586] lr: 5.000000e-05 eta: 0:50:45 time: 0.344310 data_time: 0.029305 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.887668 loss: 0.000516 2022/09/10 05:12:51 - mmengine - INFO - Epoch(train) [194][450/586] lr: 5.000000e-05 eta: 0:50:29 time: 0.339008 data_time: 0.024225 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.812357 loss: 0.000518 2022/09/10 05:13:07 - mmengine - INFO - Epoch(train) [194][500/586] lr: 5.000000e-05 eta: 0:50:13 time: 0.331036 data_time: 0.023792 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.767641 loss: 0.000517 2022/09/10 05:13:24 - mmengine - INFO - Epoch(train) [194][550/586] lr: 5.000000e-05 eta: 0:49:58 time: 0.340497 data_time: 0.022987 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.901779 loss: 0.000501 2022/09/10 05:13:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:13:36 - mmengine - INFO - Saving checkpoint at 194 epochs 2022/09/10 05:14:00 - mmengine - INFO - Epoch(train) [195][50/586] lr: 5.000000e-05 eta: 0:49:29 time: 0.340786 data_time: 0.031576 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.871697 loss: 0.000514 2022/09/10 05:14:17 - mmengine - INFO - Epoch(train) [195][100/586] lr: 5.000000e-05 eta: 0:49:13 time: 0.332621 data_time: 0.022926 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.898243 loss: 0.000518 2022/09/10 05:14:34 - mmengine - INFO - Epoch(train) [195][150/586] lr: 5.000000e-05 eta: 0:48:58 time: 0.338603 data_time: 0.027673 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.849276 loss: 0.000508 2022/09/10 05:14:51 - mmengine - INFO - Epoch(train) [195][200/586] lr: 5.000000e-05 eta: 0:48:42 time: 0.336651 data_time: 0.024327 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.952861 loss: 0.000523 2022/09/10 05:15:07 - mmengine - INFO - Epoch(train) [195][250/586] lr: 5.000000e-05 eta: 0:48:26 time: 0.335591 data_time: 0.022620 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.868137 loss: 0.000508 2022/09/10 05:15:24 - mmengine - INFO - Epoch(train) [195][300/586] lr: 5.000000e-05 eta: 0:48:10 time: 0.339870 data_time: 0.023213 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.880871 loss: 0.000520 2022/09/10 05:15:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:15:41 - mmengine - INFO - Epoch(train) [195][350/586] lr: 5.000000e-05 eta: 0:47:54 time: 0.336311 data_time: 0.023468 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.902136 loss: 0.000515 2022/09/10 05:15:59 - mmengine - INFO - Epoch(train) [195][400/586] lr: 5.000000e-05 eta: 0:47:38 time: 0.344927 data_time: 0.022951 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.896613 loss: 0.000532 2022/09/10 05:16:16 - mmengine - INFO - Epoch(train) [195][450/586] lr: 5.000000e-05 eta: 0:47:23 time: 0.342706 data_time: 0.023534 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.880167 loss: 0.000510 2022/09/10 05:16:33 - mmengine - INFO - Epoch(train) [195][500/586] lr: 5.000000e-05 eta: 0:47:07 time: 0.339468 data_time: 0.023223 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.828443 loss: 0.000519 2022/09/10 05:16:49 - mmengine - INFO - Epoch(train) [195][550/586] lr: 5.000000e-05 eta: 0:46:51 time: 0.328387 data_time: 0.023818 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.916411 loss: 0.000506 2022/09/10 05:17:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:17:02 - mmengine - INFO - Saving checkpoint at 195 epochs 2022/09/10 05:17:26 - mmengine - INFO - Epoch(train) [196][50/586] lr: 5.000000e-05 eta: 0:46:23 time: 0.339378 data_time: 0.029057 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.873366 loss: 0.000522 2022/09/10 05:17:42 - mmengine - INFO - Epoch(train) [196][100/586] lr: 5.000000e-05 eta: 0:46:07 time: 0.334774 data_time: 0.022931 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.911442 loss: 0.000502 2022/09/10 05:17:59 - mmengine - INFO - Epoch(train) [196][150/586] lr: 5.000000e-05 eta: 0:45:51 time: 0.334340 data_time: 0.024309 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.850381 loss: 0.000512 2022/09/10 05:18:16 - mmengine - INFO - Epoch(train) [196][200/586] lr: 5.000000e-05 eta: 0:45:35 time: 0.338521 data_time: 0.023054 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.866883 loss: 0.000515 2022/09/10 05:18:33 - mmengine - INFO - Epoch(train) [196][250/586] lr: 5.000000e-05 eta: 0:45:19 time: 0.335052 data_time: 0.023853 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.859114 loss: 0.000530 2022/09/10 05:18:50 - mmengine - INFO - Epoch(train) [196][300/586] lr: 5.000000e-05 eta: 0:45:03 time: 0.335153 data_time: 0.022831 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.815124 loss: 0.000524 2022/09/10 05:19:06 - mmengine - INFO - Epoch(train) [196][350/586] lr: 5.000000e-05 eta: 0:44:47 time: 0.332983 data_time: 0.022402 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.878625 loss: 0.000500 2022/09/10 05:19:23 - mmengine - INFO - Epoch(train) [196][400/586] lr: 5.000000e-05 eta: 0:44:32 time: 0.337142 data_time: 0.023078 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.889785 loss: 0.000511 2022/09/10 05:19:40 - mmengine - INFO - Epoch(train) [196][450/586] lr: 5.000000e-05 eta: 0:44:16 time: 0.334646 data_time: 0.023086 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.865633 loss: 0.000502 2022/09/10 05:19:57 - mmengine - INFO - Epoch(train) [196][500/586] lr: 5.000000e-05 eta: 0:44:00 time: 0.341421 data_time: 0.023336 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.866890 loss: 0.000520 2022/09/10 05:20:14 - mmengine - INFO - Epoch(train) [196][550/586] lr: 5.000000e-05 eta: 0:43:44 time: 0.336325 data_time: 0.026323 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.915378 loss: 0.000498 2022/09/10 05:20:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:20:26 - mmengine - INFO - Saving checkpoint at 196 epochs 2022/09/10 05:20:50 - mmengine - INFO - Epoch(train) [197][50/586] lr: 5.000000e-05 eta: 0:43:16 time: 0.345817 data_time: 0.031436 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.869958 loss: 0.000516 2022/09/10 05:21:07 - mmengine - INFO - Epoch(train) [197][100/586] lr: 5.000000e-05 eta: 0:43:00 time: 0.331850 data_time: 0.023922 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.913272 loss: 0.000518 2022/09/10 05:21:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:21:23 - mmengine - INFO - Epoch(train) [197][150/586] lr: 5.000000e-05 eta: 0:42:44 time: 0.335680 data_time: 0.023149 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.884933 loss: 0.000520 2022/09/10 05:21:40 - mmengine - INFO - Epoch(train) [197][200/586] lr: 5.000000e-05 eta: 0:42:28 time: 0.335434 data_time: 0.023234 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.862629 loss: 0.000522 2022/09/10 05:21:57 - mmengine - INFO - Epoch(train) [197][250/586] lr: 5.000000e-05 eta: 0:42:13 time: 0.340787 data_time: 0.023397 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.836217 loss: 0.000513 2022/09/10 05:22:14 - mmengine - INFO - Epoch(train) [197][300/586] lr: 5.000000e-05 eta: 0:41:57 time: 0.333845 data_time: 0.022545 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.844254 loss: 0.000502 2022/09/10 05:22:31 - mmengine - INFO - Epoch(train) [197][350/586] lr: 5.000000e-05 eta: 0:41:41 time: 0.340251 data_time: 0.022493 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.919859 loss: 0.000519 2022/09/10 05:22:48 - mmengine - INFO - Epoch(train) [197][400/586] lr: 5.000000e-05 eta: 0:41:25 time: 0.331376 data_time: 0.026178 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.913404 loss: 0.000514 2022/09/10 05:23:05 - mmengine - INFO - Epoch(train) [197][450/586] lr: 5.000000e-05 eta: 0:41:09 time: 0.341411 data_time: 0.022883 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.860385 loss: 0.000498 2022/09/10 05:23:22 - mmengine - INFO - Epoch(train) [197][500/586] lr: 5.000000e-05 eta: 0:40:53 time: 0.344807 data_time: 0.022229 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.894226 loss: 0.000498 2022/09/10 05:23:38 - mmengine - INFO - Epoch(train) [197][550/586] lr: 5.000000e-05 eta: 0:40:37 time: 0.328219 data_time: 0.023187 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.859832 loss: 0.000503 2022/09/10 05:23:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:23:51 - mmengine - INFO - Saving checkpoint at 197 epochs 2022/09/10 05:24:15 - mmengine - INFO - Epoch(train) [198][50/586] lr: 5.000000e-05 eta: 0:40:09 time: 0.339629 data_time: 0.030187 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.876640 loss: 0.000531 2022/09/10 05:24:32 - mmengine - INFO - Epoch(train) [198][100/586] lr: 5.000000e-05 eta: 0:39:53 time: 0.333874 data_time: 0.024537 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.867883 loss: 0.000493 2022/09/10 05:24:49 - mmengine - INFO - Epoch(train) [198][150/586] lr: 5.000000e-05 eta: 0:39:38 time: 0.342351 data_time: 0.023245 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.835297 loss: 0.000507 2022/09/10 05:25:06 - mmengine - INFO - Epoch(train) [198][200/586] lr: 5.000000e-05 eta: 0:39:22 time: 0.340241 data_time: 0.022462 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.860799 loss: 0.000511 2022/09/10 05:25:23 - mmengine - INFO - Epoch(train) [198][250/586] lr: 5.000000e-05 eta: 0:39:06 time: 0.344336 data_time: 0.027900 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.888481 loss: 0.000521 2022/09/10 05:25:40 - mmengine - INFO - Epoch(train) [198][300/586] lr: 5.000000e-05 eta: 0:38:50 time: 0.339721 data_time: 0.022655 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.925589 loss: 0.000515 2022/09/10 05:25:57 - mmengine - INFO - Epoch(train) [198][350/586] lr: 5.000000e-05 eta: 0:38:34 time: 0.342455 data_time: 0.023227 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.859450 loss: 0.000531 2022/09/10 05:26:14 - mmengine - INFO - Epoch(train) [198][400/586] lr: 5.000000e-05 eta: 0:38:18 time: 0.336416 data_time: 0.027442 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.877722 loss: 0.000525 2022/09/10 05:26:31 - mmengine - INFO - Epoch(train) [198][450/586] lr: 5.000000e-05 eta: 0:38:02 time: 0.331981 data_time: 0.022877 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.843673 loss: 0.000512 2022/09/10 05:26:47 - mmengine - INFO - Epoch(train) [198][500/586] lr: 5.000000e-05 eta: 0:37:47 time: 0.337202 data_time: 0.023493 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.852783 loss: 0.000506 2022/09/10 05:27:05 - mmengine - INFO - Epoch(train) [198][550/586] lr: 5.000000e-05 eta: 0:37:31 time: 0.343615 data_time: 0.024072 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.880226 loss: 0.000511 2022/09/10 05:27:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:27:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:27:16 - mmengine - INFO - Saving checkpoint at 198 epochs 2022/09/10 05:27:41 - mmengine - INFO - Epoch(train) [199][50/586] lr: 5.000000e-05 eta: 0:37:03 time: 0.345825 data_time: 0.038269 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.825459 loss: 0.000522 2022/09/10 05:27:58 - mmengine - INFO - Epoch(train) [199][100/586] lr: 5.000000e-05 eta: 0:36:47 time: 0.340160 data_time: 0.024215 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.894820 loss: 0.000519 2022/09/10 05:28:15 - mmengine - INFO - Epoch(train) [199][150/586] lr: 5.000000e-05 eta: 0:36:31 time: 0.336518 data_time: 0.024323 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.880317 loss: 0.000491 2022/09/10 05:28:31 - mmengine - INFO - Epoch(train) [199][200/586] lr: 5.000000e-05 eta: 0:36:15 time: 0.334269 data_time: 0.022861 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.839567 loss: 0.000502 2022/09/10 05:28:49 - mmengine - INFO - Epoch(train) [199][250/586] lr: 5.000000e-05 eta: 0:35:59 time: 0.344037 data_time: 0.022073 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.863284 loss: 0.000514 2022/09/10 05:29:05 - mmengine - INFO - Epoch(train) [199][300/586] lr: 5.000000e-05 eta: 0:35:43 time: 0.334281 data_time: 0.022348 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.816164 loss: 0.000514 2022/09/10 05:29:22 - mmengine - INFO - Epoch(train) [199][350/586] lr: 5.000000e-05 eta: 0:35:27 time: 0.337363 data_time: 0.022930 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.859595 loss: 0.000513 2022/09/10 05:29:39 - mmengine - INFO - Epoch(train) [199][400/586] lr: 5.000000e-05 eta: 0:35:12 time: 0.336366 data_time: 0.022637 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.857110 loss: 0.000520 2022/09/10 05:29:56 - mmengine - INFO - Epoch(train) [199][450/586] lr: 5.000000e-05 eta: 0:34:56 time: 0.338141 data_time: 0.025968 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.911596 loss: 0.000517 2022/09/10 05:30:13 - mmengine - INFO - Epoch(train) [199][500/586] lr: 5.000000e-05 eta: 0:34:40 time: 0.338890 data_time: 0.023063 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.842526 loss: 0.000518 2022/09/10 05:30:30 - mmengine - INFO - Epoch(train) [199][550/586] lr: 5.000000e-05 eta: 0:34:24 time: 0.333996 data_time: 0.022499 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.876707 loss: 0.000507 2022/09/10 05:30:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:30:42 - mmengine - INFO - Saving checkpoint at 199 epochs 2022/09/10 05:31:06 - mmengine - INFO - Epoch(train) [200][50/586] lr: 5.000000e-05 eta: 0:33:56 time: 0.350375 data_time: 0.026604 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.859248 loss: 0.000511 2022/09/10 05:31:23 - mmengine - INFO - Epoch(train) [200][100/586] lr: 5.000000e-05 eta: 0:33:40 time: 0.333435 data_time: 0.027685 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.911013 loss: 0.000523 2022/09/10 05:31:40 - mmengine - INFO - Epoch(train) [200][150/586] lr: 5.000000e-05 eta: 0:33:24 time: 0.332864 data_time: 0.022835 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.848050 loss: 0.000515 2022/09/10 05:31:56 - mmengine - INFO - Epoch(train) [200][200/586] lr: 5.000000e-05 eta: 0:33:08 time: 0.333530 data_time: 0.023171 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.907340 loss: 0.000534 2022/09/10 05:32:13 - mmengine - INFO - Epoch(train) [200][250/586] lr: 5.000000e-05 eta: 0:32:53 time: 0.341778 data_time: 0.026606 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.896030 loss: 0.000517 2022/09/10 05:32:30 - mmengine - INFO - Epoch(train) [200][300/586] lr: 5.000000e-05 eta: 0:32:37 time: 0.333711 data_time: 0.023337 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.842700 loss: 0.000505 2022/09/10 05:32:47 - mmengine - INFO - Epoch(train) [200][350/586] lr: 5.000000e-05 eta: 0:32:21 time: 0.335748 data_time: 0.023344 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.897041 loss: 0.000519 2022/09/10 05:32:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:33:04 - mmengine - INFO - Epoch(train) [200][400/586] lr: 5.000000e-05 eta: 0:32:05 time: 0.333625 data_time: 0.027129 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.830630 loss: 0.000514 2022/09/10 05:33:20 - mmengine - INFO - Epoch(train) [200][450/586] lr: 5.000000e-05 eta: 0:31:49 time: 0.331714 data_time: 0.023084 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.898123 loss: 0.000505 2022/09/10 05:33:37 - mmengine - INFO - Epoch(train) [200][500/586] lr: 5.000000e-05 eta: 0:31:33 time: 0.342700 data_time: 0.022946 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.843612 loss: 0.000524 2022/09/10 05:33:54 - mmengine - INFO - Epoch(train) [200][550/586] lr: 5.000000e-05 eta: 0:31:17 time: 0.334874 data_time: 0.026864 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.883459 loss: 0.000515 2022/09/10 05:34:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:34:06 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/09/10 05:34:22 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:01:02 time: 0.173790 data_time: 0.013229 memory: 7489 2022/09/10 05:34:30 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:50 time: 0.166003 data_time: 0.008097 memory: 1657 2022/09/10 05:34:39 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:42 time: 0.164957 data_time: 0.008158 memory: 1657 2022/09/10 05:34:47 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:34 time: 0.164753 data_time: 0.007699 memory: 1657 2022/09/10 05:34:55 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:26 time: 0.166312 data_time: 0.007767 memory: 1657 2022/09/10 05:35:04 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:17 time: 0.165886 data_time: 0.008071 memory: 1657 2022/09/10 05:35:12 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:09 time: 0.167423 data_time: 0.010356 memory: 1657 2022/09/10 05:35:20 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:01 time: 0.162115 data_time: 0.007305 memory: 1657 2022/09/10 05:35:56 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 05:36:10 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.755331 coco/AP .5: 0.907492 coco/AP .75: 0.825259 coco/AP (M): 0.720458 coco/AP (L): 0.822412 coco/AR: 0.807210 coco/AR .5: 0.944742 coco/AR .75: 0.867758 coco/AR (M): 0.765419 coco/AR (L): 0.868339 2022/09/10 05:36:10 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_190.pth is removed 2022/09/10 05:36:14 - mmengine - INFO - The best checkpoint with 0.7553 coco/AP at 200 epoch is saved to best_coco/AP_epoch_200.pth. 2022/09/10 05:36:31 - mmengine - INFO - Epoch(train) [201][50/586] lr: 5.000000e-06 eta: 0:30:49 time: 0.349473 data_time: 0.031609 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.873925 loss: 0.000507 2022/09/10 05:36:48 - mmengine - INFO - Epoch(train) [201][100/586] lr: 5.000000e-06 eta: 0:30:34 time: 0.336064 data_time: 0.023272 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.923001 loss: 0.000513 2022/09/10 05:37:04 - mmengine - INFO - Epoch(train) [201][150/586] lr: 5.000000e-06 eta: 0:30:18 time: 0.331294 data_time: 0.023252 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.842808 loss: 0.000532 2022/09/10 05:37:21 - mmengine - INFO - Epoch(train) [201][200/586] lr: 5.000000e-06 eta: 0:30:02 time: 0.335730 data_time: 0.022703 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.780372 loss: 0.000522 2022/09/10 05:37:38 - mmengine - INFO - Epoch(train) [201][250/586] lr: 5.000000e-06 eta: 0:29:46 time: 0.330516 data_time: 0.022922 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.851912 loss: 0.000500 2022/09/10 05:37:55 - mmengine - INFO - Epoch(train) [201][300/586] lr: 5.000000e-06 eta: 0:29:30 time: 0.334382 data_time: 0.023565 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.861681 loss: 0.000514 2022/09/10 05:38:11 - mmengine - INFO - Epoch(train) [201][350/586] lr: 5.000000e-06 eta: 0:29:14 time: 0.337631 data_time: 0.027761 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.894247 loss: 0.000515 2022/09/10 05:38:28 - mmengine - INFO - Epoch(train) [201][400/586] lr: 5.000000e-06 eta: 0:28:58 time: 0.331212 data_time: 0.025459 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.885486 loss: 0.000502 2022/09/10 05:38:44 - mmengine - INFO - Epoch(train) [201][450/586] lr: 5.000000e-06 eta: 0:28:42 time: 0.328547 data_time: 0.022613 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.868674 loss: 0.000501 2022/09/10 05:39:02 - mmengine - INFO - Epoch(train) [201][500/586] lr: 5.000000e-06 eta: 0:28:26 time: 0.343084 data_time: 0.022734 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.931025 loss: 0.000511 2022/09/10 05:39:18 - mmengine - INFO - Epoch(train) [201][550/586] lr: 5.000000e-06 eta: 0:28:11 time: 0.330202 data_time: 0.023540 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.897789 loss: 0.000520 2022/09/10 05:39:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:39:30 - mmengine - INFO - Saving checkpoint at 201 epochs 2022/09/10 05:39:55 - mmengine - INFO - Epoch(train) [202][50/586] lr: 5.000000e-06 eta: 0:27:43 time: 0.349025 data_time: 0.033888 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.927264 loss: 0.000495 2022/09/10 05:40:12 - mmengine - INFO - Epoch(train) [202][100/586] lr: 5.000000e-06 eta: 0:27:27 time: 0.336320 data_time: 0.025122 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.903440 loss: 0.000512 2022/09/10 05:40:28 - mmengine - INFO - Epoch(train) [202][150/586] lr: 5.000000e-06 eta: 0:27:11 time: 0.331638 data_time: 0.023637 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.794013 loss: 0.000505 2022/09/10 05:40:45 - mmengine - INFO - Epoch(train) [202][200/586] lr: 5.000000e-06 eta: 0:26:55 time: 0.342022 data_time: 0.024391 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.868703 loss: 0.000515 2022/09/10 05:40:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:41:02 - mmengine - INFO - Epoch(train) [202][250/586] lr: 5.000000e-06 eta: 0:26:39 time: 0.338506 data_time: 0.023509 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.871269 loss: 0.000500 2022/09/10 05:41:19 - mmengine - INFO - Epoch(train) [202][300/586] lr: 5.000000e-06 eta: 0:26:23 time: 0.324727 data_time: 0.022726 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.885680 loss: 0.000514 2022/09/10 05:41:36 - mmengine - INFO - Epoch(train) [202][350/586] lr: 5.000000e-06 eta: 0:26:07 time: 0.341332 data_time: 0.022699 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.925102 loss: 0.000505 2022/09/10 05:41:52 - mmengine - INFO - Epoch(train) [202][400/586] lr: 5.000000e-06 eta: 0:25:52 time: 0.335083 data_time: 0.022960 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.873800 loss: 0.000502 2022/09/10 05:42:09 - mmengine - INFO - Epoch(train) [202][450/586] lr: 5.000000e-06 eta: 0:25:36 time: 0.334280 data_time: 0.022405 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.848347 loss: 0.000505 2022/09/10 05:42:26 - mmengine - INFO - Epoch(train) [202][500/586] lr: 5.000000e-06 eta: 0:25:20 time: 0.338999 data_time: 0.022709 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.854969 loss: 0.000519 2022/09/10 05:42:43 - mmengine - INFO - Epoch(train) [202][550/586] lr: 5.000000e-06 eta: 0:25:04 time: 0.332525 data_time: 0.027435 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.793484 loss: 0.000521 2022/09/10 05:42:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:42:55 - mmengine - INFO - Saving checkpoint at 202 epochs 2022/09/10 05:43:20 - mmengine - INFO - Epoch(train) [203][50/586] lr: 5.000000e-06 eta: 0:24:36 time: 0.362891 data_time: 0.039879 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.906817 loss: 0.000510 2022/09/10 05:43:37 - mmengine - INFO - Epoch(train) [203][100/586] lr: 5.000000e-06 eta: 0:24:20 time: 0.328547 data_time: 0.023366 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.943072 loss: 0.000509 2022/09/10 05:43:54 - mmengine - INFO - Epoch(train) [203][150/586] lr: 5.000000e-06 eta: 0:24:04 time: 0.339616 data_time: 0.023496 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.893677 loss: 0.000521 2022/09/10 05:44:10 - mmengine - INFO - Epoch(train) [203][200/586] lr: 5.000000e-06 eta: 0:23:48 time: 0.336399 data_time: 0.023440 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.907071 loss: 0.000520 2022/09/10 05:44:27 - mmengine - INFO - Epoch(train) [203][250/586] lr: 5.000000e-06 eta: 0:23:33 time: 0.329766 data_time: 0.022730 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.886187 loss: 0.000509 2022/09/10 05:44:44 - mmengine - INFO - Epoch(train) [203][300/586] lr: 5.000000e-06 eta: 0:23:17 time: 0.341512 data_time: 0.022914 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.939323 loss: 0.000495 2022/09/10 05:45:01 - mmengine - INFO - Epoch(train) [203][350/586] lr: 5.000000e-06 eta: 0:23:01 time: 0.336486 data_time: 0.022637 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.917414 loss: 0.000511 2022/09/10 05:45:18 - mmengine - INFO - Epoch(train) [203][400/586] lr: 5.000000e-06 eta: 0:22:45 time: 0.340808 data_time: 0.024115 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.867478 loss: 0.000508 2022/09/10 05:45:35 - mmengine - INFO - Epoch(train) [203][450/586] lr: 5.000000e-06 eta: 0:22:29 time: 0.335244 data_time: 0.027877 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.872207 loss: 0.000504 2022/09/10 05:45:51 - mmengine - INFO - Epoch(train) [203][500/586] lr: 5.000000e-06 eta: 0:22:13 time: 0.327335 data_time: 0.023233 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.871392 loss: 0.000506 2022/09/10 05:46:08 - mmengine - INFO - Epoch(train) [203][550/586] lr: 5.000000e-06 eta: 0:21:57 time: 0.331053 data_time: 0.022636 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.786236 loss: 0.000518 2022/09/10 05:46:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:46:20 - mmengine - INFO - Saving checkpoint at 203 epochs 2022/09/10 05:46:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:46:44 - mmengine - INFO - Epoch(train) [204][50/586] lr: 5.000000e-06 eta: 0:21:29 time: 0.344103 data_time: 0.031446 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.921068 loss: 0.000507 2022/09/10 05:47:01 - mmengine - INFO - Epoch(train) [204][100/586] lr: 5.000000e-06 eta: 0:21:14 time: 0.329833 data_time: 0.026389 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.893972 loss: 0.000529 2022/09/10 05:47:18 - mmengine - INFO - Epoch(train) [204][150/586] lr: 5.000000e-06 eta: 0:20:58 time: 0.337876 data_time: 0.022574 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.849018 loss: 0.000510 2022/09/10 05:47:35 - mmengine - INFO - Epoch(train) [204][200/586] lr: 5.000000e-06 eta: 0:20:42 time: 0.334604 data_time: 0.025637 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.877083 loss: 0.000510 2022/09/10 05:47:51 - mmengine - INFO - Epoch(train) [204][250/586] lr: 5.000000e-06 eta: 0:20:26 time: 0.331974 data_time: 0.024053 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.851717 loss: 0.000514 2022/09/10 05:48:08 - mmengine - INFO - Epoch(train) [204][300/586] lr: 5.000000e-06 eta: 0:20:10 time: 0.336295 data_time: 0.023499 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.831848 loss: 0.000504 2022/09/10 05:48:25 - mmengine - INFO - Epoch(train) [204][350/586] lr: 5.000000e-06 eta: 0:19:54 time: 0.341531 data_time: 0.022989 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.906713 loss: 0.000513 2022/09/10 05:48:42 - mmengine - INFO - Epoch(train) [204][400/586] lr: 5.000000e-06 eta: 0:19:38 time: 0.328812 data_time: 0.027078 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.943436 loss: 0.000509 2022/09/10 05:48:58 - mmengine - INFO - Epoch(train) [204][450/586] lr: 5.000000e-06 eta: 0:19:22 time: 0.335353 data_time: 0.023164 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.873475 loss: 0.000507 2022/09/10 05:49:16 - mmengine - INFO - Epoch(train) [204][500/586] lr: 5.000000e-06 eta: 0:19:06 time: 0.348211 data_time: 0.022848 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.888845 loss: 0.000528 2022/09/10 05:49:32 - mmengine - INFO - Epoch(train) [204][550/586] lr: 5.000000e-06 eta: 0:18:51 time: 0.331473 data_time: 0.026181 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.887227 loss: 0.000480 2022/09/10 05:49:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:49:45 - mmengine - INFO - Saving checkpoint at 204 epochs 2022/09/10 05:50:09 - mmengine - INFO - Epoch(train) [205][50/586] lr: 5.000000e-06 eta: 0:18:23 time: 0.340761 data_time: 0.028161 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.907726 loss: 0.000511 2022/09/10 05:50:26 - mmengine - INFO - Epoch(train) [205][100/586] lr: 5.000000e-06 eta: 0:18:07 time: 0.332699 data_time: 0.022529 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.872562 loss: 0.000520 2022/09/10 05:50:43 - mmengine - INFO - Epoch(train) [205][150/586] lr: 5.000000e-06 eta: 0:17:51 time: 0.332533 data_time: 0.022423 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.864771 loss: 0.000508 2022/09/10 05:51:00 - mmengine - INFO - Epoch(train) [205][200/586] lr: 5.000000e-06 eta: 0:17:35 time: 0.340767 data_time: 0.022683 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.843214 loss: 0.000498 2022/09/10 05:51:17 - mmengine - INFO - Epoch(train) [205][250/586] lr: 5.000000e-06 eta: 0:17:19 time: 0.337356 data_time: 0.022931 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.865911 loss: 0.000495 2022/09/10 05:51:33 - mmengine - INFO - Epoch(train) [205][300/586] lr: 5.000000e-06 eta: 0:17:03 time: 0.336699 data_time: 0.026763 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.871665 loss: 0.000501 2022/09/10 05:51:51 - mmengine - INFO - Epoch(train) [205][350/586] lr: 5.000000e-06 eta: 0:16:48 time: 0.343997 data_time: 0.022843 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.875638 loss: 0.000519 2022/09/10 05:52:08 - mmengine - INFO - Epoch(train) [205][400/586] lr: 5.000000e-06 eta: 0:16:32 time: 0.342785 data_time: 0.024129 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.898461 loss: 0.000526 2022/09/10 05:52:25 - mmengine - INFO - Epoch(train) [205][450/586] lr: 5.000000e-06 eta: 0:16:16 time: 0.349597 data_time: 0.024154 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.853997 loss: 0.000514 2022/09/10 05:52:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:52:42 - mmengine - INFO - Epoch(train) [205][500/586] lr: 5.000000e-06 eta: 0:16:00 time: 0.340311 data_time: 0.022531 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.824856 loss: 0.000514 2022/09/10 05:52:59 - mmengine - INFO - Epoch(train) [205][550/586] lr: 5.000000e-06 eta: 0:15:44 time: 0.333269 data_time: 0.023156 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.917692 loss: 0.000500 2022/09/10 05:53:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:53:11 - mmengine - INFO - Saving checkpoint at 205 epochs 2022/09/10 05:53:35 - mmengine - INFO - Epoch(train) [206][50/586] lr: 5.000000e-06 eta: 0:15:16 time: 0.347288 data_time: 0.031593 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.854358 loss: 0.000511 2022/09/10 05:53:52 - mmengine - INFO - Epoch(train) [206][100/586] lr: 5.000000e-06 eta: 0:15:00 time: 0.339181 data_time: 0.027887 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.917770 loss: 0.000521 2022/09/10 05:54:09 - mmengine - INFO - Epoch(train) [206][150/586] lr: 5.000000e-06 eta: 0:14:45 time: 0.332496 data_time: 0.022972 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.844295 loss: 0.000509 2022/09/10 05:54:26 - mmengine - INFO - Epoch(train) [206][200/586] lr: 5.000000e-06 eta: 0:14:29 time: 0.330416 data_time: 0.023057 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.854908 loss: 0.000510 2022/09/10 05:54:42 - mmengine - INFO - Epoch(train) [206][250/586] lr: 5.000000e-06 eta: 0:14:13 time: 0.337935 data_time: 0.023500 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.917639 loss: 0.000498 2022/09/10 05:54:59 - mmengine - INFO - Epoch(train) [206][300/586] lr: 5.000000e-06 eta: 0:13:57 time: 0.334689 data_time: 0.021987 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.841167 loss: 0.000503 2022/09/10 05:55:16 - mmengine - INFO - Epoch(train) [206][350/586] lr: 5.000000e-06 eta: 0:13:41 time: 0.335894 data_time: 0.023080 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.807181 loss: 0.000508 2022/09/10 05:55:33 - mmengine - INFO - Epoch(train) [206][400/586] lr: 5.000000e-06 eta: 0:13:25 time: 0.338192 data_time: 0.023984 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.863630 loss: 0.000506 2022/09/10 05:55:50 - mmengine - INFO - Epoch(train) [206][450/586] lr: 5.000000e-06 eta: 0:13:09 time: 0.333464 data_time: 0.022454 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.891751 loss: 0.000509 2022/09/10 05:56:07 - mmengine - INFO - Epoch(train) [206][500/586] lr: 5.000000e-06 eta: 0:12:53 time: 0.340664 data_time: 0.022906 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.874320 loss: 0.000507 2022/09/10 05:56:24 - mmengine - INFO - Epoch(train) [206][550/586] lr: 5.000000e-06 eta: 0:12:37 time: 0.336887 data_time: 0.025563 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.912185 loss: 0.000507 2022/09/10 05:56:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:56:36 - mmengine - INFO - Saving checkpoint at 206 epochs 2022/09/10 05:57:00 - mmengine - INFO - Epoch(train) [207][50/586] lr: 5.000000e-06 eta: 0:12:10 time: 0.344041 data_time: 0.030219 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.842542 loss: 0.000513 2022/09/10 05:57:17 - mmengine - INFO - Epoch(train) [207][100/586] lr: 5.000000e-06 eta: 0:11:54 time: 0.341183 data_time: 0.022823 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.859238 loss: 0.000511 2022/09/10 05:57:33 - mmengine - INFO - Epoch(train) [207][150/586] lr: 5.000000e-06 eta: 0:11:38 time: 0.330528 data_time: 0.022164 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.850874 loss: 0.000526 2022/09/10 05:57:50 - mmengine - INFO - Epoch(train) [207][200/586] lr: 5.000000e-06 eta: 0:11:22 time: 0.336251 data_time: 0.022475 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.900565 loss: 0.000509 2022/09/10 05:58:07 - mmengine - INFO - Epoch(train) [207][250/586] lr: 5.000000e-06 eta: 0:11:06 time: 0.339252 data_time: 0.024071 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.892647 loss: 0.000503 2022/09/10 05:58:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 05:58:24 - mmengine - INFO - Epoch(train) [207][300/586] lr: 5.000000e-06 eta: 0:10:50 time: 0.336818 data_time: 0.023469 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.848760 loss: 0.000510 2022/09/10 05:58:41 - mmengine - INFO - Epoch(train) [207][350/586] lr: 5.000000e-06 eta: 0:10:34 time: 0.336872 data_time: 0.026222 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.894777 loss: 0.000518 2022/09/10 05:58:58 - mmengine - INFO - Epoch(train) [207][400/586] lr: 5.000000e-06 eta: 0:10:18 time: 0.333065 data_time: 0.024267 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.926191 loss: 0.000520 2022/09/10 05:59:14 - mmengine - INFO - Epoch(train) [207][450/586] lr: 5.000000e-06 eta: 0:10:03 time: 0.333384 data_time: 0.024917 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.871659 loss: 0.000523 2022/09/10 05:59:31 - mmengine - INFO - Epoch(train) [207][500/586] lr: 5.000000e-06 eta: 0:09:47 time: 0.338864 data_time: 0.028066 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.865523 loss: 0.000518 2022/09/10 05:59:49 - mmengine - INFO - Epoch(train) [207][550/586] lr: 5.000000e-06 eta: 0:09:31 time: 0.348509 data_time: 0.024243 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.884989 loss: 0.000503 2022/09/10 06:00:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 06:00:01 - mmengine - INFO - Saving checkpoint at 207 epochs 2022/09/10 06:00:26 - mmengine - INFO - Epoch(train) [208][50/586] lr: 5.000000e-06 eta: 0:09:03 time: 0.349139 data_time: 0.028252 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.894355 loss: 0.000506 2022/09/10 06:00:42 - mmengine - INFO - Epoch(train) [208][100/586] lr: 5.000000e-06 eta: 0:08:47 time: 0.336442 data_time: 0.026785 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.835532 loss: 0.000517 2022/09/10 06:00:59 - mmengine - INFO - Epoch(train) [208][150/586] lr: 5.000000e-06 eta: 0:08:31 time: 0.329275 data_time: 0.022556 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.929545 loss: 0.000515 2022/09/10 06:01:16 - mmengine - INFO - Epoch(train) [208][200/586] lr: 5.000000e-06 eta: 0:08:15 time: 0.340965 data_time: 0.023379 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.878604 loss: 0.000496 2022/09/10 06:01:33 - mmengine - INFO - Epoch(train) [208][250/586] lr: 5.000000e-06 eta: 0:08:00 time: 0.335252 data_time: 0.026006 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.834968 loss: 0.000510 2022/09/10 06:01:49 - mmengine - INFO - Epoch(train) [208][300/586] lr: 5.000000e-06 eta: 0:07:44 time: 0.332449 data_time: 0.022869 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.883490 loss: 0.000506 2022/09/10 06:02:06 - mmengine - INFO - Epoch(train) [208][350/586] lr: 5.000000e-06 eta: 0:07:28 time: 0.339541 data_time: 0.024040 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.866826 loss: 0.000513 2022/09/10 06:02:23 - mmengine - INFO - Epoch(train) [208][400/586] lr: 5.000000e-06 eta: 0:07:12 time: 0.338869 data_time: 0.028892 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.872550 loss: 0.000486 2022/09/10 06:02:40 - mmengine - INFO - Epoch(train) [208][450/586] lr: 5.000000e-06 eta: 0:06:56 time: 0.333103 data_time: 0.025716 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.916326 loss: 0.000513 2022/09/10 06:02:57 - mmengine - INFO - Epoch(train) [208][500/586] lr: 5.000000e-06 eta: 0:06:40 time: 0.345181 data_time: 0.027943 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.833813 loss: 0.000518 2022/09/10 06:03:14 - mmengine - INFO - Epoch(train) [208][550/586] lr: 5.000000e-06 eta: 0:06:24 time: 0.340985 data_time: 0.024793 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.909848 loss: 0.000523 2022/09/10 06:03:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 06:03:26 - mmengine - INFO - Saving checkpoint at 208 epochs 2022/09/10 06:03:51 - mmengine - INFO - Epoch(train) [209][50/586] lr: 5.000000e-06 eta: 0:05:57 time: 0.350125 data_time: 0.032470 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.855292 loss: 0.000514 2022/09/10 06:04:08 - mmengine - INFO - Epoch(train) [209][100/586] lr: 5.000000e-06 eta: 0:05:41 time: 0.337755 data_time: 0.023469 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.885222 loss: 0.000492 2022/09/10 06:04:12 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 06:04:25 - mmengine - INFO - Epoch(train) [209][150/586] lr: 5.000000e-06 eta: 0:05:25 time: 0.337313 data_time: 0.026757 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.839981 loss: 0.000502 2022/09/10 06:04:42 - mmengine - INFO - Epoch(train) [209][200/586] lr: 5.000000e-06 eta: 0:05:09 time: 0.336265 data_time: 0.023930 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.918043 loss: 0.000507 2022/09/10 06:04:59 - mmengine - INFO - Epoch(train) [209][250/586] lr: 5.000000e-06 eta: 0:04:53 time: 0.339912 data_time: 0.024229 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.871792 loss: 0.000521 2022/09/10 06:05:16 - mmengine - INFO - Epoch(train) [209][300/586] lr: 5.000000e-06 eta: 0:04:37 time: 0.340493 data_time: 0.027913 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.892425 loss: 0.000514 2022/09/10 06:05:33 - mmengine - INFO - Epoch(train) [209][350/586] lr: 5.000000e-06 eta: 0:04:21 time: 0.332761 data_time: 0.022842 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.857002 loss: 0.000532 2022/09/10 06:05:49 - mmengine - INFO - Epoch(train) [209][400/586] lr: 5.000000e-06 eta: 0:04:05 time: 0.331007 data_time: 0.024671 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.909718 loss: 0.000517 2022/09/10 06:06:06 - mmengine - INFO - Epoch(train) [209][450/586] lr: 5.000000e-06 eta: 0:03:49 time: 0.334193 data_time: 0.024631 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.829281 loss: 0.000510 2022/09/10 06:06:22 - mmengine - INFO - Epoch(train) [209][500/586] lr: 5.000000e-06 eta: 0:03:33 time: 0.332772 data_time: 0.022956 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.865501 loss: 0.000510 2022/09/10 06:06:39 - mmengine - INFO - Epoch(train) [209][550/586] lr: 5.000000e-06 eta: 0:03:18 time: 0.333753 data_time: 0.023104 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.840897 loss: 0.000516 2022/09/10 06:06:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 06:06:52 - mmengine - INFO - Saving checkpoint at 209 epochs 2022/09/10 06:07:16 - mmengine - INFO - Epoch(train) [210][50/586] lr: 5.000000e-06 eta: 0:02:50 time: 0.344321 data_time: 0.037762 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.920193 loss: 0.000504 2022/09/10 06:07:33 - mmengine - INFO - Epoch(train) [210][100/586] lr: 5.000000e-06 eta: 0:02:34 time: 0.338102 data_time: 0.023353 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.893265 loss: 0.000527 2022/09/10 06:07:50 - mmengine - INFO - Epoch(train) [210][150/586] lr: 5.000000e-06 eta: 0:02:18 time: 0.338121 data_time: 0.022841 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.888391 loss: 0.000518 2022/09/10 06:08:06 - mmengine - INFO - Epoch(train) [210][200/586] lr: 5.000000e-06 eta: 0:02:02 time: 0.333696 data_time: 0.026026 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.844601 loss: 0.000499 2022/09/10 06:08:23 - mmengine - INFO - Epoch(train) [210][250/586] lr: 5.000000e-06 eta: 0:01:46 time: 0.340990 data_time: 0.025475 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.896642 loss: 0.000510 2022/09/10 06:08:40 - mmengine - INFO - Epoch(train) [210][300/586] lr: 5.000000e-06 eta: 0:01:31 time: 0.333569 data_time: 0.026304 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.808653 loss: 0.000501 2022/09/10 06:08:57 - mmengine - INFO - Epoch(train) [210][350/586] lr: 5.000000e-06 eta: 0:01:15 time: 0.342265 data_time: 0.022751 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.901937 loss: 0.000525 2022/09/10 06:09:14 - mmengine - INFO - Epoch(train) [210][400/586] lr: 5.000000e-06 eta: 0:00:59 time: 0.340538 data_time: 0.022569 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.859438 loss: 0.000501 2022/09/10 06:09:31 - mmengine - INFO - Epoch(train) [210][450/586] lr: 5.000000e-06 eta: 0:00:43 time: 0.328958 data_time: 0.022903 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.889246 loss: 0.000509 2022/09/10 06:09:48 - mmengine - INFO - Epoch(train) [210][500/586] lr: 5.000000e-06 eta: 0:00:27 time: 0.342976 data_time: 0.023035 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.856591 loss: 0.000514 2022/09/10 06:09:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 06:10:04 - mmengine - INFO - Epoch(train) [210][550/586] lr: 5.000000e-06 eta: 0:00:11 time: 0.330554 data_time: 0.022897 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.850265 loss: 0.000522 2022/09/10 06:10:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_8xb32-210e_coco-256x192_20220909_172814 2022/09/10 06:10:17 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/09/10 06:10:33 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:01:01 time: 0.172020 data_time: 0.013321 memory: 7489 2022/09/10 06:10:41 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:51 time: 0.168655 data_time: 0.009586 memory: 1657 2022/09/10 06:10:49 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:42 time: 0.164804 data_time: 0.007853 memory: 1657 2022/09/10 06:10:58 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:34 time: 0.167611 data_time: 0.007867 memory: 1657 2022/09/10 06:11:06 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:26 time: 0.166286 data_time: 0.007812 memory: 1657 2022/09/10 06:11:14 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:18 time: 0.168442 data_time: 0.010593 memory: 1657 2022/09/10 06:11:23 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:09 time: 0.169465 data_time: 0.008251 memory: 1657 2022/09/10 06:11:31 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:01 time: 0.161455 data_time: 0.006918 memory: 1657 2022/09/10 06:12:07 - mmengine - INFO - Evaluating CocoMetric... 2022/09/10 06:12:20 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.755801 coco/AP .5: 0.907956 coco/AP .75: 0.826129 coco/AP (M): 0.721634 coco/AP (L): 0.822957 coco/AR: 0.808486 coco/AR .5: 0.945214 coco/AR .75: 0.869175 coco/AR (M): 0.766976 coco/AR (L): 0.868748 2022/09/10 06:12:20 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220909/hrnet_w48_256/best_coco/AP_epoch_200.pth is removed 2022/09/10 06:12:24 - mmengine - INFO - The best checkpoint with 0.7558 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.