2022/09/12 19:47:11 - 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: 748541906 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/lustre/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/09/12 19:47:13 - 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='UDPHeatmap', 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='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)), test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=False)) 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), use_udp=True), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='UDPHeatmap', 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), use_udp=True), 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), use_udp=True), dict( type='GenerateTarget', target_type='heatmap', encoder=dict( type='UDPHeatmap', 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), use_udp=True), 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), use_udp=True), dict(type='PackPoseInputs') ])) val_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') test_evaluator = dict( type='CocoMetric', ann_file='data/coco/annotations/person_keypoints_val2017.json') launcher = 'slurm' work_dir = '/mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/' 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer wrapper constructor" registry tree. As a workaround, the current "optimizer wrapper constructor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optimizer" registry tree. As a workaround, the current "optimizer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:45 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:49 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:52 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:55 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. 2022/09/12 19:47:55 - mmengine - WARNING - Failed to search registry with scope "mmpose" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmpose" is a correct scope, or whether the registry is initialized. Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.layer1.3.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.0.0.weight - torch.Size([48, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.1.0.0.weight - torch.Size([96, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.1.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition1.1.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage2.0.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition2.2.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition2.2.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition2.2.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.0.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.1.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.2.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage3.3.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition3.3.0.0.weight - torch.Size([384, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition3.3.0.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.transition3.3.0.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.0.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.1.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.2.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.branches.3.3.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.3.0.weight - torch.Size([48, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.3.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.0.3.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.3.0.weight - torch.Size([96, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.3.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.1.3.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.3.0.weight - torch.Size([192, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.3.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.2.3.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.1.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.2.0.weight - torch.Size([384, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.2.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.0.2.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.0.0.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.1.0.weight - torch.Size([384, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.1.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.1.1.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.2.0.0.weight - torch.Size([384, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.2.0.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.0.fuse_layers.3.2.0.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.0.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.1.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.2.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.branches.3.3.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.3.0.weight - torch.Size([48, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.3.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.0.3.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.0.0.0.weight - torch.Size([96, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.0.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.0.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.2.0.weight - torch.Size([96, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.2.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.2.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.3.0.weight - torch.Size([96, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.3.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.1.3.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.1.0.weight - torch.Size([192, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.1.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.0.1.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.1.0.0.weight - torch.Size([192, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.1.0.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.1.0.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.3.0.weight - torch.Size([192, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.3.1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.2.3.1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.0.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.0.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.0.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.1.0.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.2.0.weight - torch.Size([384, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.2.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.0.2.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.0.0.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.0.1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.0.1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.1.0.weight - torch.Size([384, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.1.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.1.1.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.2.0.0.weight - torch.Size([384, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.2.0.1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.1.fuse_layers.3.2.0.1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.0.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.1.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.2.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.conv1.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.conv2.weight - torch.Size([48, 48, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn2.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.0.3.bn2.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.0.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.1.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.2.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.conv1.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn1.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn1.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.conv2.weight - torch.Size([96, 96, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn2.weight - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.1.3.bn2.bias - torch.Size([96]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.0.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.1.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.2.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.conv1.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn1.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn1.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.conv2.weight - torch.Size([192, 192, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn2.weight - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.2.3.bn2.bias - torch.Size([192]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.0.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.1.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.2.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.conv1.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn1.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn1.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.conv2.weight - torch.Size([384, 384, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn2.weight - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.branches.3.3.bn2.bias - torch.Size([384]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.1.0.weight - torch.Size([48, 96, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.1.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.1.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.2.0.weight - torch.Size([48, 192, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.2.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.2.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.3.0.weight - torch.Size([48, 384, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.3.1.weight - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth backbone.stage4.2.fuse_layers.0.3.1.bias - torch.Size([48]): PretrainedInit: load from https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth head.final_layer.weight - torch.Size([17, 48, 1, 1]): NormalInit: mean=0, std=0.001, bias=0 head.final_layer.bias - torch.Size([17]): NormalInit: mean=0, std=0.001, bias=0 2022/09/12 19:48:08 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1 by HardDiskBackend. 2022/09/12 19:49:39 - mmengine - INFO - Epoch(train) [1][50/586] lr: 4.954910e-05 eta: 2 days, 13:59:06 time: 1.814050 data_time: 0.502695 memory: 7489 loss_kpt: 0.002227 acc_pose: 0.183348 loss: 0.002227 2022/09/12 19:50:36 - mmengine - INFO - Epoch(train) [1][100/586] lr: 9.959920e-05 eta: 2 days, 2:26:39 time: 1.139743 data_time: 0.171505 memory: 7489 loss_kpt: 0.001820 acc_pose: 0.323849 loss: 0.001820 2022/09/12 19:51:25 - mmengine - INFO - Epoch(train) [1][150/586] lr: 1.496493e-04 eta: 1 day, 20:52:06 time: 0.988756 data_time: 0.075165 memory: 7489 loss_kpt: 0.001554 acc_pose: 0.502344 loss: 0.001554 2022/09/12 19:52:06 - mmengine - INFO - Epoch(train) [1][200/586] lr: 1.996994e-04 eta: 1 day, 16:34:19 time: 0.812774 data_time: 0.032949 memory: 7489 loss_kpt: 0.001363 acc_pose: 0.542996 loss: 0.001363 2022/09/12 19:52:49 - mmengine - INFO - Epoch(train) [1][250/586] lr: 2.497495e-04 eta: 1 day, 14:16:38 time: 0.854904 data_time: 0.046387 memory: 7489 loss_kpt: 0.001276 acc_pose: 0.581892 loss: 0.001276 2022/09/12 19:53:24 - mmengine - INFO - Epoch(train) [1][300/586] lr: 2.997996e-04 eta: 1 day, 11:55:20 time: 0.710434 data_time: 0.023981 memory: 7489 loss_kpt: 0.001250 acc_pose: 0.585179 loss: 0.001250 2022/09/12 19:53:59 - mmengine - INFO - Epoch(train) [1][350/586] lr: 3.498497e-04 eta: 1 day, 10:08:23 time: 0.690387 data_time: 0.046612 memory: 7489 loss_kpt: 0.001237 acc_pose: 0.648473 loss: 0.001237 2022/09/12 19:54:28 - mmengine - INFO - Epoch(train) [1][400/586] lr: 3.998998e-04 eta: 1 day, 8:21:38 time: 0.587082 data_time: 0.027457 memory: 7489 loss_kpt: 0.001170 acc_pose: 0.576947 loss: 0.001170 2022/09/12 19:54:50 - mmengine - INFO - Epoch(train) [1][450/586] lr: 4.499499e-04 eta: 1 day, 6:25:05 time: 0.439943 data_time: 0.024635 memory: 7489 loss_kpt: 0.001148 acc_pose: 0.569537 loss: 0.001148 2022/09/12 19:55:12 - mmengine - INFO - Epoch(train) [1][500/586] lr: 5.000000e-04 eta: 1 day, 4:53:07 time: 0.446537 data_time: 0.027654 memory: 7489 loss_kpt: 0.001171 acc_pose: 0.626879 loss: 0.001171 2022/09/12 19:55:38 - mmengine - INFO - Epoch(train) [1][550/586] lr: 5.000000e-04 eta: 1 day, 3:51:43 time: 0.521517 data_time: 0.052294 memory: 7489 loss_kpt: 0.001119 acc_pose: 0.683771 loss: 0.001119 2022/09/12 19:55:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 19:55:57 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/12 19:56:21 - mmengine - INFO - Epoch(train) [2][50/586] lr: 5.000000e-04 eta: 1 day, 0:59:04 time: 0.339260 data_time: 0.027030 memory: 7489 loss_kpt: 0.001099 acc_pose: 0.651926 loss: 0.001099 2022/09/12 19:56:37 - mmengine - INFO - Epoch(train) [2][100/586] lr: 5.000000e-04 eta: 23:58:33 time: 0.331711 data_time: 0.027299 memory: 7489 loss_kpt: 0.001056 acc_pose: 0.583752 loss: 0.001056 2022/09/12 19:56:55 - mmengine - INFO - Epoch(train) [2][150/586] lr: 5.000000e-04 eta: 23:08:03 time: 0.344932 data_time: 0.022474 memory: 7489 loss_kpt: 0.001054 acc_pose: 0.628050 loss: 0.001054 2022/09/12 19:57:11 - mmengine - INFO - Epoch(train) [2][200/586] lr: 5.000000e-04 eta: 22:22:08 time: 0.330986 data_time: 0.022346 memory: 7489 loss_kpt: 0.001073 acc_pose: 0.673760 loss: 0.001073 2022/09/12 19:57:28 - mmengine - INFO - Epoch(train) [2][250/586] lr: 5.000000e-04 eta: 21:41:16 time: 0.327718 data_time: 0.023135 memory: 7489 loss_kpt: 0.001041 acc_pose: 0.735943 loss: 0.001041 2022/09/12 19:57:45 - mmengine - INFO - Epoch(train) [2][300/586] lr: 5.000000e-04 eta: 21:06:47 time: 0.343342 data_time: 0.023708 memory: 7489 loss_kpt: 0.001046 acc_pose: 0.728374 loss: 0.001046 2022/09/12 19:58:02 - mmengine - INFO - Epoch(train) [2][350/586] lr: 5.000000e-04 eta: 20:35:07 time: 0.335670 data_time: 0.022830 memory: 7489 loss_kpt: 0.000990 acc_pose: 0.647663 loss: 0.000990 2022/09/12 19:58:18 - mmengine - INFO - Epoch(train) [2][400/586] lr: 5.000000e-04 eta: 20:05:49 time: 0.327752 data_time: 0.022413 memory: 7489 loss_kpt: 0.001009 acc_pose: 0.646405 loss: 0.001009 2022/09/12 19:58:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 19:58:35 - mmengine - INFO - Epoch(train) [2][450/586] lr: 5.000000e-04 eta: 19:40:41 time: 0.341557 data_time: 0.023432 memory: 7489 loss_kpt: 0.000997 acc_pose: 0.709407 loss: 0.000997 2022/09/12 19:58:52 - mmengine - INFO - Epoch(train) [2][500/586] lr: 5.000000e-04 eta: 19:17:08 time: 0.334189 data_time: 0.027940 memory: 7489 loss_kpt: 0.000979 acc_pose: 0.677363 loss: 0.000979 2022/09/12 19:59:08 - mmengine - INFO - Epoch(train) [2][550/586] lr: 5.000000e-04 eta: 18:55:07 time: 0.328379 data_time: 0.022714 memory: 7489 loss_kpt: 0.000978 acc_pose: 0.724420 loss: 0.000978 2022/09/12 19:59:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 19:59:21 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/12 19:59:45 - mmengine - INFO - Epoch(train) [3][50/586] lr: 5.000000e-04 eta: 18:03:03 time: 0.343751 data_time: 0.028167 memory: 7489 loss_kpt: 0.000989 acc_pose: 0.633259 loss: 0.000989 2022/09/12 20:00:01 - mmengine - INFO - Epoch(train) [3][100/586] lr: 5.000000e-04 eta: 17:46:37 time: 0.332946 data_time: 0.029510 memory: 7489 loss_kpt: 0.000966 acc_pose: 0.675253 loss: 0.000966 2022/09/12 20:00:18 - mmengine - INFO - Epoch(train) [3][150/586] lr: 5.000000e-04 eta: 17:32:02 time: 0.341073 data_time: 0.024718 memory: 7489 loss_kpt: 0.000948 acc_pose: 0.669893 loss: 0.000948 2022/09/12 20:00:35 - mmengine - INFO - Epoch(train) [3][200/586] lr: 5.000000e-04 eta: 17:18:07 time: 0.336119 data_time: 0.023911 memory: 7489 loss_kpt: 0.000941 acc_pose: 0.716150 loss: 0.000941 2022/09/12 20:00:52 - mmengine - INFO - Epoch(train) [3][250/586] lr: 5.000000e-04 eta: 17:04:31 time: 0.327122 data_time: 0.023199 memory: 7489 loss_kpt: 0.000937 acc_pose: 0.727500 loss: 0.000937 2022/09/12 20:01:09 - mmengine - INFO - Epoch(train) [3][300/586] lr: 5.000000e-04 eta: 16:52:59 time: 0.343968 data_time: 0.025917 memory: 7489 loss_kpt: 0.000922 acc_pose: 0.613027 loss: 0.000922 2022/09/12 20:01:25 - mmengine - INFO - Epoch(train) [3][350/586] lr: 5.000000e-04 eta: 16:41:32 time: 0.334010 data_time: 0.021711 memory: 7489 loss_kpt: 0.000935 acc_pose: 0.713050 loss: 0.000935 2022/09/12 20:01:42 - mmengine - INFO - Epoch(train) [3][400/586] lr: 5.000000e-04 eta: 16:30:41 time: 0.332340 data_time: 0.022757 memory: 7489 loss_kpt: 0.000905 acc_pose: 0.640954 loss: 0.000905 2022/09/12 20:01:59 - mmengine - INFO - Epoch(train) [3][450/586] lr: 5.000000e-04 eta: 16:21:08 time: 0.342695 data_time: 0.022724 memory: 7489 loss_kpt: 0.000916 acc_pose: 0.624013 loss: 0.000916 2022/09/12 20:02:16 - mmengine - INFO - Epoch(train) [3][500/586] lr: 5.000000e-04 eta: 16:11:44 time: 0.336151 data_time: 0.024592 memory: 7489 loss_kpt: 0.000910 acc_pose: 0.694458 loss: 0.000910 2022/09/12 20:02:32 - mmengine - INFO - Epoch(train) [3][550/586] lr: 5.000000e-04 eta: 16:02:17 time: 0.326268 data_time: 0.023761 memory: 7489 loss_kpt: 0.000914 acc_pose: 0.706437 loss: 0.000914 2022/09/12 20:02:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:02:45 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/12 20:03:09 - mmengine - INFO - Epoch(train) [4][50/586] lr: 5.000000e-04 eta: 15:34:52 time: 0.339797 data_time: 0.032469 memory: 7489 loss_kpt: 0.000909 acc_pose: 0.608319 loss: 0.000909 2022/09/12 20:03:25 - mmengine - INFO - Epoch(train) [4][100/586] lr: 5.000000e-04 eta: 15:27:16 time: 0.329929 data_time: 0.027487 memory: 7489 loss_kpt: 0.000912 acc_pose: 0.667784 loss: 0.000912 2022/09/12 20:03:42 - mmengine - INFO - Epoch(train) [4][150/586] lr: 5.000000e-04 eta: 15:20:29 time: 0.338247 data_time: 0.022959 memory: 7489 loss_kpt: 0.000908 acc_pose: 0.753247 loss: 0.000908 2022/09/12 20:03:59 - mmengine - INFO - Epoch(train) [4][200/586] lr: 5.000000e-04 eta: 15:13:49 time: 0.333971 data_time: 0.023877 memory: 7489 loss_kpt: 0.000879 acc_pose: 0.738032 loss: 0.000879 2022/09/12 20:04:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:04:15 - mmengine - INFO - Epoch(train) [4][250/586] lr: 5.000000e-04 eta: 15:07:13 time: 0.328788 data_time: 0.022745 memory: 7489 loss_kpt: 0.000909 acc_pose: 0.699002 loss: 0.000909 2022/09/12 20:04:32 - mmengine - INFO - Epoch(train) [4][300/586] lr: 5.000000e-04 eta: 15:01:13 time: 0.334865 data_time: 0.023137 memory: 7489 loss_kpt: 0.000866 acc_pose: 0.684646 loss: 0.000866 2022/09/12 20:04:49 - mmengine - INFO - Epoch(train) [4][350/586] lr: 5.000000e-04 eta: 14:55:44 time: 0.339877 data_time: 0.023021 memory: 7489 loss_kpt: 0.000856 acc_pose: 0.852522 loss: 0.000856 2022/09/12 20:05:06 - mmengine - INFO - Epoch(train) [4][400/586] lr: 5.000000e-04 eta: 14:50:03 time: 0.330475 data_time: 0.027463 memory: 7489 loss_kpt: 0.000884 acc_pose: 0.624841 loss: 0.000884 2022/09/12 20:05:23 - mmengine - INFO - Epoch(train) [4][450/586] lr: 5.000000e-04 eta: 14:45:01 time: 0.339466 data_time: 0.022473 memory: 7489 loss_kpt: 0.000858 acc_pose: 0.714516 loss: 0.000858 2022/09/12 20:05:39 - mmengine - INFO - Epoch(train) [4][500/586] lr: 5.000000e-04 eta: 14:39:58 time: 0.334386 data_time: 0.023204 memory: 7489 loss_kpt: 0.000880 acc_pose: 0.638740 loss: 0.000880 2022/09/12 20:05:56 - mmengine - INFO - Epoch(train) [4][550/586] lr: 5.000000e-04 eta: 14:34:53 time: 0.328892 data_time: 0.027576 memory: 7489 loss_kpt: 0.000841 acc_pose: 0.688362 loss: 0.000841 2022/09/12 20:06:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:06:08 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/12 20:06:31 - mmengine - INFO - Epoch(train) [5][50/586] lr: 5.000000e-04 eta: 14:17:11 time: 0.341331 data_time: 0.031857 memory: 7489 loss_kpt: 0.000867 acc_pose: 0.780781 loss: 0.000867 2022/09/12 20:06:48 - mmengine - INFO - Epoch(train) [5][100/586] lr: 5.000000e-04 eta: 14:12:40 time: 0.325018 data_time: 0.023232 memory: 7489 loss_kpt: 0.000858 acc_pose: 0.722435 loss: 0.000858 2022/09/12 20:07:04 - mmengine - INFO - Epoch(train) [5][150/586] lr: 5.000000e-04 eta: 14:08:39 time: 0.333272 data_time: 0.022781 memory: 7489 loss_kpt: 0.000840 acc_pose: 0.672802 loss: 0.000840 2022/09/12 20:07:21 - mmengine - INFO - Epoch(train) [5][200/586] lr: 5.000000e-04 eta: 14:04:52 time: 0.335039 data_time: 0.022955 memory: 7489 loss_kpt: 0.000875 acc_pose: 0.689532 loss: 0.000875 2022/09/12 20:07:37 - mmengine - INFO - Epoch(train) [5][250/586] lr: 5.000000e-04 eta: 14:00:50 time: 0.325630 data_time: 0.023120 memory: 7489 loss_kpt: 0.000862 acc_pose: 0.783996 loss: 0.000862 2022/09/12 20:07:54 - mmengine - INFO - Epoch(train) [5][300/586] lr: 5.000000e-04 eta: 13:57:15 time: 0.333755 data_time: 0.024850 memory: 7489 loss_kpt: 0.000835 acc_pose: 0.736676 loss: 0.000835 2022/09/12 20:08:11 - mmengine - INFO - Epoch(train) [5][350/586] lr: 5.000000e-04 eta: 13:54:03 time: 0.340433 data_time: 0.026003 memory: 7489 loss_kpt: 0.000848 acc_pose: 0.696549 loss: 0.000848 2022/09/12 20:08:28 - mmengine - INFO - Epoch(train) [5][400/586] lr: 5.000000e-04 eta: 13:50:42 time: 0.333749 data_time: 0.023636 memory: 7489 loss_kpt: 0.000865 acc_pose: 0.733435 loss: 0.000865 2022/09/12 20:08:45 - mmengine - INFO - Epoch(train) [5][450/586] lr: 5.000000e-04 eta: 13:47:45 time: 0.341322 data_time: 0.022633 memory: 7489 loss_kpt: 0.000815 acc_pose: 0.755834 loss: 0.000815 2022/09/12 20:09:02 - mmengine - INFO - Epoch(train) [5][500/586] lr: 5.000000e-04 eta: 13:44:55 time: 0.342221 data_time: 0.022779 memory: 7489 loss_kpt: 0.000828 acc_pose: 0.750292 loss: 0.000828 2022/09/12 20:09:18 - mmengine - INFO - Epoch(train) [5][550/586] lr: 5.000000e-04 eta: 13:41:45 time: 0.330539 data_time: 0.023268 memory: 7489 loss_kpt: 0.000823 acc_pose: 0.771405 loss: 0.000823 2022/09/12 20:09:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:09:30 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/12 20:09:55 - mmengine - INFO - Epoch(train) [6][50/586] lr: 5.000000e-04 eta: 13:29:08 time: 0.347346 data_time: 0.030813 memory: 7489 loss_kpt: 0.000856 acc_pose: 0.743243 loss: 0.000856 2022/09/12 20:10:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:10:12 - mmengine - INFO - Epoch(train) [6][100/586] lr: 5.000000e-04 eta: 13:26:39 time: 0.339167 data_time: 0.024464 memory: 7489 loss_kpt: 0.000817 acc_pose: 0.728488 loss: 0.000817 2022/09/12 20:10:29 - mmengine - INFO - Epoch(train) [6][150/586] lr: 5.000000e-04 eta: 13:24:13 time: 0.338424 data_time: 0.023223 memory: 7489 loss_kpt: 0.000822 acc_pose: 0.742364 loss: 0.000822 2022/09/12 20:10:45 - mmengine - INFO - Epoch(train) [6][200/586] lr: 5.000000e-04 eta: 13:21:41 time: 0.333580 data_time: 0.023184 memory: 7489 loss_kpt: 0.000813 acc_pose: 0.726477 loss: 0.000813 2022/09/12 20:11:02 - mmengine - INFO - Epoch(train) [6][250/586] lr: 5.000000e-04 eta: 13:19:10 time: 0.331367 data_time: 0.022806 memory: 7489 loss_kpt: 0.000848 acc_pose: 0.709904 loss: 0.000848 2022/09/12 20:11:19 - mmengine - INFO - Epoch(train) [6][300/586] lr: 5.000000e-04 eta: 13:17:05 time: 0.343886 data_time: 0.023370 memory: 7489 loss_kpt: 0.000820 acc_pose: 0.748201 loss: 0.000820 2022/09/12 20:11:36 - mmengine - INFO - Epoch(train) [6][350/586] lr: 5.000000e-04 eta: 13:14:41 time: 0.330850 data_time: 0.022130 memory: 7489 loss_kpt: 0.000832 acc_pose: 0.766845 loss: 0.000832 2022/09/12 20:11:53 - mmengine - INFO - Epoch(train) [6][400/586] lr: 5.000000e-04 eta: 13:12:31 time: 0.336671 data_time: 0.023848 memory: 7489 loss_kpt: 0.000814 acc_pose: 0.727378 loss: 0.000814 2022/09/12 20:12:10 - mmengine - INFO - Epoch(train) [6][450/586] lr: 5.000000e-04 eta: 13:10:37 time: 0.344327 data_time: 0.024041 memory: 7489 loss_kpt: 0.000835 acc_pose: 0.800360 loss: 0.000835 2022/09/12 20:12:27 - mmengine - INFO - Epoch(train) [6][500/586] lr: 5.000000e-04 eta: 13:08:41 time: 0.341141 data_time: 0.023701 memory: 7489 loss_kpt: 0.000827 acc_pose: 0.768919 loss: 0.000827 2022/09/12 20:12:44 - mmengine - INFO - Epoch(train) [6][550/586] lr: 5.000000e-04 eta: 13:06:39 time: 0.336131 data_time: 0.022516 memory: 7489 loss_kpt: 0.000803 acc_pose: 0.756848 loss: 0.000803 2022/09/12 20:12:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:12:56 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/12 20:13:20 - mmengine - INFO - Epoch(train) [7][50/586] lr: 5.000000e-04 eta: 12:56:54 time: 0.349978 data_time: 0.029387 memory: 7489 loss_kpt: 0.000817 acc_pose: 0.808575 loss: 0.000817 2022/09/12 20:13:36 - mmengine - INFO - Epoch(train) [7][100/586] lr: 5.000000e-04 eta: 12:54:52 time: 0.328043 data_time: 0.023530 memory: 7489 loss_kpt: 0.000840 acc_pose: 0.786909 loss: 0.000840 2022/09/12 20:13:53 - mmengine - INFO - Epoch(train) [7][150/586] lr: 5.000000e-04 eta: 12:53:07 time: 0.336527 data_time: 0.023751 memory: 7489 loss_kpt: 0.000806 acc_pose: 0.822967 loss: 0.000806 2022/09/12 20:14:10 - mmengine - INFO - Epoch(train) [7][200/586] lr: 5.000000e-04 eta: 12:51:34 time: 0.342813 data_time: 0.022740 memory: 7489 loss_kpt: 0.000808 acc_pose: 0.692880 loss: 0.000808 2022/09/12 20:14:27 - mmengine - INFO - Epoch(train) [7][250/586] lr: 5.000000e-04 eta: 12:49:44 time: 0.330794 data_time: 0.022681 memory: 7489 loss_kpt: 0.000811 acc_pose: 0.739196 loss: 0.000811 2022/09/12 20:14:43 - mmengine - INFO - Epoch(train) [7][300/586] lr: 5.000000e-04 eta: 12:48:07 time: 0.337366 data_time: 0.023538 memory: 7489 loss_kpt: 0.000799 acc_pose: 0.743938 loss: 0.000799 2022/09/12 20:15:01 - mmengine - INFO - Epoch(train) [7][350/586] lr: 5.000000e-04 eta: 12:46:40 time: 0.342827 data_time: 0.022716 memory: 7489 loss_kpt: 0.000797 acc_pose: 0.706788 loss: 0.000797 2022/09/12 20:15:17 - mmengine - INFO - Epoch(train) [7][400/586] lr: 5.000000e-04 eta: 12:45:06 time: 0.336383 data_time: 0.023120 memory: 7489 loss_kpt: 0.000808 acc_pose: 0.785892 loss: 0.000808 2022/09/12 20:15:34 - mmengine - INFO - Epoch(train) [7][450/586] lr: 5.000000e-04 eta: 12:43:26 time: 0.331934 data_time: 0.023370 memory: 7489 loss_kpt: 0.000818 acc_pose: 0.731871 loss: 0.000818 2022/09/12 20:15:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:15:51 - mmengine - INFO - Epoch(train) [7][500/586] lr: 5.000000e-04 eta: 12:42:15 time: 0.349507 data_time: 0.024828 memory: 7489 loss_kpt: 0.000816 acc_pose: 0.733265 loss: 0.000816 2022/09/12 20:16:08 - mmengine - INFO - Epoch(train) [7][550/586] lr: 5.000000e-04 eta: 12:40:41 time: 0.333296 data_time: 0.023263 memory: 7489 loss_kpt: 0.000826 acc_pose: 0.706214 loss: 0.000826 2022/09/12 20:16:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:16:20 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/12 20:16:45 - mmengine - INFO - Epoch(train) [8][50/586] lr: 5.000000e-04 eta: 12:32:41 time: 0.347466 data_time: 0.029546 memory: 7489 loss_kpt: 0.000798 acc_pose: 0.806890 loss: 0.000798 2022/09/12 20:17:01 - mmengine - INFO - Epoch(train) [8][100/586] lr: 5.000000e-04 eta: 12:31:08 time: 0.327032 data_time: 0.024692 memory: 7489 loss_kpt: 0.000817 acc_pose: 0.773743 loss: 0.000817 2022/09/12 20:17:18 - mmengine - INFO - Epoch(train) [8][150/586] lr: 5.000000e-04 eta: 12:29:43 time: 0.332156 data_time: 0.023037 memory: 7489 loss_kpt: 0.000793 acc_pose: 0.631178 loss: 0.000793 2022/09/12 20:17:35 - mmengine - INFO - Epoch(train) [8][200/586] lr: 5.000000e-04 eta: 12:28:24 time: 0.334920 data_time: 0.023349 memory: 7489 loss_kpt: 0.000800 acc_pose: 0.792790 loss: 0.000800 2022/09/12 20:17:51 - mmengine - INFO - Epoch(train) [8][250/586] lr: 5.000000e-04 eta: 12:27:01 time: 0.331694 data_time: 0.023009 memory: 7489 loss_kpt: 0.000806 acc_pose: 0.755042 loss: 0.000806 2022/09/12 20:18:08 - mmengine - INFO - Epoch(train) [8][300/586] lr: 5.000000e-04 eta: 12:25:52 time: 0.339966 data_time: 0.023786 memory: 7489 loss_kpt: 0.000822 acc_pose: 0.783726 loss: 0.000822 2022/09/12 20:18:25 - mmengine - INFO - Epoch(train) [8][350/586] lr: 5.000000e-04 eta: 12:24:46 time: 0.341741 data_time: 0.024322 memory: 7489 loss_kpt: 0.000789 acc_pose: 0.715367 loss: 0.000789 2022/09/12 20:18:42 - mmengine - INFO - Epoch(train) [8][400/586] lr: 5.000000e-04 eta: 12:23:26 time: 0.330713 data_time: 0.023749 memory: 7489 loss_kpt: 0.000786 acc_pose: 0.666540 loss: 0.000786 2022/09/12 20:18:59 - mmengine - INFO - Epoch(train) [8][450/586] lr: 5.000000e-04 eta: 12:22:19 time: 0.338796 data_time: 0.028127 memory: 7489 loss_kpt: 0.000764 acc_pose: 0.790104 loss: 0.000764 2022/09/12 20:19:16 - mmengine - INFO - Epoch(train) [8][500/586] lr: 5.000000e-04 eta: 12:21:16 time: 0.341947 data_time: 0.023227 memory: 7489 loss_kpt: 0.000786 acc_pose: 0.757397 loss: 0.000786 2022/09/12 20:19:32 - mmengine - INFO - Epoch(train) [8][550/586] lr: 5.000000e-04 eta: 12:20:01 time: 0.331019 data_time: 0.022795 memory: 7489 loss_kpt: 0.000800 acc_pose: 0.695452 loss: 0.000800 2022/09/12 20:19:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:19:45 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/12 20:20:09 - mmengine - INFO - Epoch(train) [9][50/586] lr: 5.000000e-04 eta: 12:13:21 time: 0.350192 data_time: 0.035236 memory: 7489 loss_kpt: 0.000788 acc_pose: 0.685267 loss: 0.000788 2022/09/12 20:20:25 - mmengine - INFO - Epoch(train) [9][100/586] lr: 5.000000e-04 eta: 12:12:09 time: 0.329149 data_time: 0.023043 memory: 7489 loss_kpt: 0.000770 acc_pose: 0.789321 loss: 0.000770 2022/09/12 20:20:42 - mmengine - INFO - Epoch(train) [9][150/586] lr: 5.000000e-04 eta: 12:11:03 time: 0.332305 data_time: 0.023122 memory: 7489 loss_kpt: 0.000773 acc_pose: 0.850579 loss: 0.000773 2022/09/12 20:20:59 - mmengine - INFO - Epoch(train) [9][200/586] lr: 5.000000e-04 eta: 12:10:07 time: 0.339917 data_time: 0.022409 memory: 7489 loss_kpt: 0.000755 acc_pose: 0.769454 loss: 0.000755 2022/09/12 20:21:15 - mmengine - INFO - Epoch(train) [9][250/586] lr: 5.000000e-04 eta: 12:08:57 time: 0.327441 data_time: 0.023262 memory: 7489 loss_kpt: 0.000780 acc_pose: 0.776064 loss: 0.000780 2022/09/12 20:21:33 - mmengine - INFO - Epoch(train) [9][300/586] lr: 5.000000e-04 eta: 12:08:10 time: 0.346487 data_time: 0.025397 memory: 7489 loss_kpt: 0.000779 acc_pose: 0.770867 loss: 0.000779 2022/09/12 20:21:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:21:50 - mmengine - INFO - Epoch(train) [9][350/586] lr: 5.000000e-04 eta: 12:07:21 time: 0.343830 data_time: 0.023795 memory: 7489 loss_kpt: 0.000806 acc_pose: 0.773659 loss: 0.000806 2022/09/12 20:22:07 - mmengine - INFO - Epoch(train) [9][400/586] lr: 5.000000e-04 eta: 12:06:16 time: 0.329985 data_time: 0.023831 memory: 7489 loss_kpt: 0.000791 acc_pose: 0.823106 loss: 0.000791 2022/09/12 20:22:23 - mmengine - INFO - Epoch(train) [9][450/586] lr: 5.000000e-04 eta: 12:05:22 time: 0.338027 data_time: 0.023266 memory: 7489 loss_kpt: 0.000778 acc_pose: 0.802924 loss: 0.000778 2022/09/12 20:22:41 - mmengine - INFO - Epoch(train) [9][500/586] lr: 5.000000e-04 eta: 12:04:33 time: 0.342366 data_time: 0.027526 memory: 7489 loss_kpt: 0.000773 acc_pose: 0.741475 loss: 0.000773 2022/09/12 20:22:57 - mmengine - INFO - Epoch(train) [9][550/586] lr: 5.000000e-04 eta: 12:03:31 time: 0.329983 data_time: 0.023770 memory: 7489 loss_kpt: 0.000787 acc_pose: 0.792193 loss: 0.000787 2022/09/12 20:23:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:23:09 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/12 20:23:33 - mmengine - INFO - Epoch(train) [10][50/586] lr: 5.000000e-04 eta: 11:57:47 time: 0.351183 data_time: 0.030172 memory: 7489 loss_kpt: 0.000787 acc_pose: 0.733165 loss: 0.000787 2022/09/12 20:23:50 - mmengine - INFO - Epoch(train) [10][100/586] lr: 5.000000e-04 eta: 11:56:50 time: 0.330561 data_time: 0.023013 memory: 7489 loss_kpt: 0.000761 acc_pose: 0.742395 loss: 0.000761 2022/09/12 20:24:07 - mmengine - INFO - Epoch(train) [10][150/586] lr: 5.000000e-04 eta: 11:55:57 time: 0.333383 data_time: 0.022937 memory: 7489 loss_kpt: 0.000762 acc_pose: 0.766149 loss: 0.000762 2022/09/12 20:24:24 - mmengine - INFO - Epoch(train) [10][200/586] lr: 5.000000e-04 eta: 11:55:13 time: 0.341846 data_time: 0.023237 memory: 7489 loss_kpt: 0.000772 acc_pose: 0.770199 loss: 0.000772 2022/09/12 20:24:40 - mmengine - INFO - Epoch(train) [10][250/586] lr: 5.000000e-04 eta: 11:54:18 time: 0.329806 data_time: 0.024717 memory: 7489 loss_kpt: 0.000743 acc_pose: 0.819114 loss: 0.000743 2022/09/12 20:24:57 - mmengine - INFO - Epoch(train) [10][300/586] lr: 5.000000e-04 eta: 11:53:27 time: 0.333925 data_time: 0.022166 memory: 7489 loss_kpt: 0.000781 acc_pose: 0.755173 loss: 0.000781 2022/09/12 20:25:14 - mmengine - INFO - Epoch(train) [10][350/586] lr: 5.000000e-04 eta: 11:52:47 time: 0.343166 data_time: 0.022297 memory: 7489 loss_kpt: 0.000777 acc_pose: 0.684463 loss: 0.000777 2022/09/12 20:25:31 - mmengine - INFO - Epoch(train) [10][400/586] lr: 5.000000e-04 eta: 11:52:02 time: 0.338026 data_time: 0.023558 memory: 7489 loss_kpt: 0.000775 acc_pose: 0.784560 loss: 0.000775 2022/09/12 20:25:47 - mmengine - INFO - Epoch(train) [10][450/586] lr: 5.000000e-04 eta: 11:51:08 time: 0.329703 data_time: 0.025892 memory: 7489 loss_kpt: 0.000753 acc_pose: 0.722810 loss: 0.000753 2022/09/12 20:26:05 - mmengine - INFO - Epoch(train) [10][500/586] lr: 5.000000e-04 eta: 11:50:28 time: 0.342314 data_time: 0.022215 memory: 7489 loss_kpt: 0.000771 acc_pose: 0.625621 loss: 0.000771 2022/09/12 20:26:21 - mmengine - INFO - Epoch(train) [10][550/586] lr: 5.000000e-04 eta: 11:49:35 time: 0.328574 data_time: 0.023136 memory: 7489 loss_kpt: 0.000779 acc_pose: 0.709534 loss: 0.000779 2022/09/12 20:26:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:26:33 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/12 20:26:54 - mmengine - INFO - Epoch(val) [10][50/407] eta: 0:01:23 time: 0.234194 data_time: 0.062974 memory: 7489 2022/09/12 20:27:06 - mmengine - INFO - Epoch(val) [10][100/407] eta: 0:01:14 time: 0.244168 data_time: 0.073158 memory: 1657 2022/09/12 20:27:18 - mmengine - INFO - Epoch(val) [10][150/407] eta: 0:01:02 time: 0.241837 data_time: 0.068872 memory: 1657 2022/09/12 20:27:29 - mmengine - INFO - Epoch(val) [10][200/407] eta: 0:00:44 time: 0.217252 data_time: 0.043796 memory: 1657 2022/09/12 20:27:40 - mmengine - INFO - Epoch(val) [10][250/407] eta: 0:00:34 time: 0.217416 data_time: 0.045701 memory: 1657 2022/09/12 20:27:50 - mmengine - INFO - Epoch(val) [10][300/407] eta: 0:00:20 time: 0.192598 data_time: 0.021089 memory: 1657 2022/09/12 20:28:00 - mmengine - INFO - Epoch(val) [10][350/407] eta: 0:00:11 time: 0.207018 data_time: 0.035856 memory: 1657 2022/09/12 20:28:11 - mmengine - INFO - Epoch(val) [10][400/407] eta: 0:00:01 time: 0.225589 data_time: 0.054597 memory: 1657 2022/09/12 20:28:48 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 20:29:02 - mmengine - INFO - Epoch(val) [10][407/407] coco/AP: 0.678863 coco/AP .5: 0.872561 coco/AP .75: 0.747368 coco/AP (M): 0.640355 coco/AP (L): 0.746163 coco/AR: 0.735390 coco/AR .5: 0.911996 coco/AR .75: 0.798016 coco/AR (M): 0.691259 coco/AR (L): 0.798179 2022/09/12 20:29:06 - mmengine - INFO - The best checkpoint with 0.6789 coco/AP at 10 epoch is saved to best_coco/AP_epoch_10.pth. 2022/09/12 20:29:23 - mmengine - INFO - Epoch(train) [11][50/586] lr: 5.000000e-04 eta: 11:44:23 time: 0.341124 data_time: 0.033953 memory: 7489 loss_kpt: 0.000761 acc_pose: 0.757054 loss: 0.000761 2022/09/12 20:29:39 - mmengine - INFO - Epoch(train) [11][100/586] lr: 5.000000e-04 eta: 11:43:39 time: 0.334514 data_time: 0.027567 memory: 7489 loss_kpt: 0.000766 acc_pose: 0.801474 loss: 0.000766 2022/09/12 20:29:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:29:56 - mmengine - INFO - Epoch(train) [11][150/586] lr: 5.000000e-04 eta: 11:42:55 time: 0.334040 data_time: 0.023827 memory: 7489 loss_kpt: 0.000768 acc_pose: 0.726292 loss: 0.000768 2022/09/12 20:30:13 - mmengine - INFO - Epoch(train) [11][200/586] lr: 5.000000e-04 eta: 11:42:17 time: 0.339332 data_time: 0.023399 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.750704 loss: 0.000736 2022/09/12 20:30:30 - mmengine - INFO - Epoch(train) [11][250/586] lr: 5.000000e-04 eta: 11:41:30 time: 0.329749 data_time: 0.029069 memory: 7489 loss_kpt: 0.000759 acc_pose: 0.771189 loss: 0.000759 2022/09/12 20:30:46 - mmengine - INFO - Epoch(train) [11][300/586] lr: 5.000000e-04 eta: 11:40:49 time: 0.336161 data_time: 0.023737 memory: 7489 loss_kpt: 0.000763 acc_pose: 0.761784 loss: 0.000763 2022/09/12 20:31:04 - mmengine - INFO - Epoch(train) [11][350/586] lr: 5.000000e-04 eta: 11:40:15 time: 0.342844 data_time: 0.023616 memory: 7489 loss_kpt: 0.000777 acc_pose: 0.771438 loss: 0.000777 2022/09/12 20:31:20 - mmengine - INFO - Epoch(train) [11][400/586] lr: 5.000000e-04 eta: 11:39:32 time: 0.331731 data_time: 0.027011 memory: 7489 loss_kpt: 0.000735 acc_pose: 0.779240 loss: 0.000735 2022/09/12 20:31:37 - mmengine - INFO - Epoch(train) [11][450/586] lr: 5.000000e-04 eta: 11:38:47 time: 0.330911 data_time: 0.023575 memory: 7489 loss_kpt: 0.000751 acc_pose: 0.835458 loss: 0.000751 2022/09/12 20:31:54 - mmengine - INFO - Epoch(train) [11][500/586] lr: 5.000000e-04 eta: 11:38:11 time: 0.338734 data_time: 0.022177 memory: 7489 loss_kpt: 0.000756 acc_pose: 0.808205 loss: 0.000756 2022/09/12 20:32:10 - mmengine - INFO - Epoch(train) [11][550/586] lr: 5.000000e-04 eta: 11:37:29 time: 0.332946 data_time: 0.026811 memory: 7489 loss_kpt: 0.000761 acc_pose: 0.782576 loss: 0.000761 2022/09/12 20:32:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:32:23 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/12 20:32:47 - mmengine - INFO - Epoch(train) [12][50/586] lr: 5.000000e-04 eta: 11:32:55 time: 0.346238 data_time: 0.030109 memory: 7489 loss_kpt: 0.000779 acc_pose: 0.747746 loss: 0.000779 2022/09/12 20:33:03 - mmengine - INFO - Epoch(train) [12][100/586] lr: 5.000000e-04 eta: 11:32:12 time: 0.327797 data_time: 0.024143 memory: 7489 loss_kpt: 0.000760 acc_pose: 0.816556 loss: 0.000760 2022/09/12 20:33:20 - mmengine - INFO - Epoch(train) [12][150/586] lr: 5.000000e-04 eta: 11:31:42 time: 0.343634 data_time: 0.026448 memory: 7489 loss_kpt: 0.000763 acc_pose: 0.736804 loss: 0.000763 2022/09/12 20:33:37 - mmengine - INFO - Epoch(train) [12][200/586] lr: 5.000000e-04 eta: 11:31:07 time: 0.336666 data_time: 0.024169 memory: 7489 loss_kpt: 0.000739 acc_pose: 0.773448 loss: 0.000739 2022/09/12 20:33:54 - mmengine - INFO - Epoch(train) [12][250/586] lr: 5.000000e-04 eta: 11:30:25 time: 0.328036 data_time: 0.023563 memory: 7489 loss_kpt: 0.000747 acc_pose: 0.754790 loss: 0.000747 2022/09/12 20:34:11 - mmengine - INFO - Epoch(train) [12][300/586] lr: 5.000000e-04 eta: 11:29:51 time: 0.336964 data_time: 0.023200 memory: 7489 loss_kpt: 0.000763 acc_pose: 0.701494 loss: 0.000763 2022/09/12 20:34:27 - mmengine - INFO - Epoch(train) [12][350/586] lr: 5.000000e-04 eta: 11:29:18 time: 0.338041 data_time: 0.022525 memory: 7489 loss_kpt: 0.000741 acc_pose: 0.693301 loss: 0.000741 2022/09/12 20:34:44 - mmengine - INFO - Epoch(train) [12][400/586] lr: 5.000000e-04 eta: 11:28:34 time: 0.324854 data_time: 0.023407 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.746757 loss: 0.000719 2022/09/12 20:35:00 - mmengine - INFO - Epoch(train) [12][450/586] lr: 5.000000e-04 eta: 11:27:59 time: 0.335097 data_time: 0.023135 memory: 7489 loss_kpt: 0.000783 acc_pose: 0.843325 loss: 0.000783 2022/09/12 20:35:18 - mmengine - INFO - Epoch(train) [12][500/586] lr: 5.000000e-04 eta: 11:27:34 time: 0.346450 data_time: 0.024462 memory: 7489 loss_kpt: 0.000739 acc_pose: 0.811627 loss: 0.000739 2022/09/12 20:35:34 - mmengine - INFO - Epoch(train) [12][550/586] lr: 5.000000e-04 eta: 11:26:55 time: 0.330257 data_time: 0.024115 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.789213 loss: 0.000728 2022/09/12 20:35:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:35:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:35:46 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/12 20:36:11 - mmengine - INFO - Epoch(train) [13][50/586] lr: 5.000000e-04 eta: 11:22:49 time: 0.347773 data_time: 0.030836 memory: 7489 loss_kpt: 0.000739 acc_pose: 0.739989 loss: 0.000739 2022/09/12 20:36:27 - mmengine - INFO - Epoch(train) [13][100/586] lr: 5.000000e-04 eta: 11:22:14 time: 0.331198 data_time: 0.023664 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.799935 loss: 0.000736 2022/09/12 20:36:44 - mmengine - INFO - Epoch(train) [13][150/586] lr: 5.000000e-04 eta: 11:21:42 time: 0.335227 data_time: 0.023168 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.796296 loss: 0.000716 2022/09/12 20:37:01 - mmengine - INFO - Epoch(train) [13][200/586] lr: 5.000000e-04 eta: 11:21:10 time: 0.335028 data_time: 0.023234 memory: 7489 loss_kpt: 0.000745 acc_pose: 0.710599 loss: 0.000745 2022/09/12 20:37:17 - mmengine - INFO - Epoch(train) [13][250/586] lr: 5.000000e-04 eta: 11:20:34 time: 0.330548 data_time: 0.023949 memory: 7489 loss_kpt: 0.000737 acc_pose: 0.800944 loss: 0.000737 2022/09/12 20:37:34 - mmengine - INFO - Epoch(train) [13][300/586] lr: 5.000000e-04 eta: 11:20:02 time: 0.334721 data_time: 0.023628 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.765144 loss: 0.000736 2022/09/12 20:37:51 - mmengine - INFO - Epoch(train) [13][350/586] lr: 5.000000e-04 eta: 11:19:31 time: 0.334701 data_time: 0.022858 memory: 7489 loss_kpt: 0.000760 acc_pose: 0.803720 loss: 0.000760 2022/09/12 20:38:08 - mmengine - INFO - Epoch(train) [13][400/586] lr: 5.000000e-04 eta: 11:18:59 time: 0.334066 data_time: 0.024801 memory: 7489 loss_kpt: 0.000763 acc_pose: 0.805448 loss: 0.000763 2022/09/12 20:38:24 - mmengine - INFO - Epoch(train) [13][450/586] lr: 5.000000e-04 eta: 11:18:27 time: 0.334263 data_time: 0.025913 memory: 7489 loss_kpt: 0.000740 acc_pose: 0.776414 loss: 0.000740 2022/09/12 20:38:41 - mmengine - INFO - Epoch(train) [13][500/586] lr: 5.000000e-04 eta: 11:18:00 time: 0.339209 data_time: 0.028649 memory: 7489 loss_kpt: 0.000769 acc_pose: 0.680867 loss: 0.000769 2022/09/12 20:38:58 - mmengine - INFO - Epoch(train) [13][550/586] lr: 5.000000e-04 eta: 11:17:29 time: 0.334667 data_time: 0.024530 memory: 7489 loss_kpt: 0.000747 acc_pose: 0.755143 loss: 0.000747 2022/09/12 20:39:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:39:10 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/09/12 20:39:34 - mmengine - INFO - Epoch(train) [14][50/586] lr: 5.000000e-04 eta: 11:13:39 time: 0.340675 data_time: 0.030206 memory: 7489 loss_kpt: 0.000735 acc_pose: 0.801296 loss: 0.000735 2022/09/12 20:39:51 - mmengine - INFO - Epoch(train) [14][100/586] lr: 5.000000e-04 eta: 11:13:12 time: 0.337517 data_time: 0.024175 memory: 7489 loss_kpt: 0.000713 acc_pose: 0.724485 loss: 0.000713 2022/09/12 20:40:08 - mmengine - INFO - Epoch(train) [14][150/586] lr: 5.000000e-04 eta: 11:12:44 time: 0.336060 data_time: 0.028467 memory: 7489 loss_kpt: 0.000735 acc_pose: 0.794634 loss: 0.000735 2022/09/12 20:40:24 - mmengine - INFO - Epoch(train) [14][200/586] lr: 5.000000e-04 eta: 11:12:13 time: 0.330999 data_time: 0.024012 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.745010 loss: 0.000730 2022/09/12 20:40:41 - mmengine - INFO - Epoch(train) [14][250/586] lr: 5.000000e-04 eta: 11:11:42 time: 0.332543 data_time: 0.024689 memory: 7489 loss_kpt: 0.000735 acc_pose: 0.770650 loss: 0.000735 2022/09/12 20:40:58 - mmengine - INFO - Epoch(train) [14][300/586] lr: 5.000000e-04 eta: 11:11:12 time: 0.332729 data_time: 0.022136 memory: 7489 loss_kpt: 0.000701 acc_pose: 0.775439 loss: 0.000701 2022/09/12 20:41:14 - mmengine - INFO - Epoch(train) [14][350/586] lr: 5.000000e-04 eta: 11:10:43 time: 0.333093 data_time: 0.023483 memory: 7489 loss_kpt: 0.000734 acc_pose: 0.817468 loss: 0.000734 2022/09/12 20:41:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:41:31 - mmengine - INFO - Epoch(train) [14][400/586] lr: 5.000000e-04 eta: 11:10:15 time: 0.335367 data_time: 0.025261 memory: 7489 loss_kpt: 0.000741 acc_pose: 0.781969 loss: 0.000741 2022/09/12 20:41:48 - mmengine - INFO - Epoch(train) [14][450/586] lr: 5.000000e-04 eta: 11:09:47 time: 0.334541 data_time: 0.023366 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.755870 loss: 0.000724 2022/09/12 20:42:05 - mmengine - INFO - Epoch(train) [14][500/586] lr: 5.000000e-04 eta: 11:09:23 time: 0.339776 data_time: 0.024552 memory: 7489 loss_kpt: 0.000723 acc_pose: 0.801749 loss: 0.000723 2022/09/12 20:42:22 - mmengine - INFO - Epoch(train) [14][550/586] lr: 5.000000e-04 eta: 11:09:02 time: 0.344342 data_time: 0.023733 memory: 7489 loss_kpt: 0.000729 acc_pose: 0.799470 loss: 0.000729 2022/09/12 20:42:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:42:34 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/09/12 20:42:59 - mmengine - INFO - Epoch(train) [15][50/586] lr: 5.000000e-04 eta: 11:05:38 time: 0.351804 data_time: 0.032303 memory: 7489 loss_kpt: 0.000727 acc_pose: 0.778795 loss: 0.000727 2022/09/12 20:43:15 - mmengine - INFO - Epoch(train) [15][100/586] lr: 5.000000e-04 eta: 11:05:09 time: 0.331071 data_time: 0.026969 memory: 7489 loss_kpt: 0.000727 acc_pose: 0.808303 loss: 0.000727 2022/09/12 20:43:32 - mmengine - INFO - Epoch(train) [15][150/586] lr: 5.000000e-04 eta: 11:04:40 time: 0.330719 data_time: 0.024719 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.753889 loss: 0.000725 2022/09/12 20:43:49 - mmengine - INFO - Epoch(train) [15][200/586] lr: 5.000000e-04 eta: 11:04:20 time: 0.344257 data_time: 0.024001 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.839079 loss: 0.000721 2022/09/12 20:44:06 - mmengine - INFO - Epoch(train) [15][250/586] lr: 5.000000e-04 eta: 11:03:51 time: 0.330030 data_time: 0.023889 memory: 7489 loss_kpt: 0.000749 acc_pose: 0.727270 loss: 0.000749 2022/09/12 20:44:22 - mmengine - INFO - Epoch(train) [15][300/586] lr: 5.000000e-04 eta: 11:03:22 time: 0.330741 data_time: 0.023429 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.754222 loss: 0.000730 2022/09/12 20:44:39 - mmengine - INFO - Epoch(train) [15][350/586] lr: 5.000000e-04 eta: 11:03:00 time: 0.340365 data_time: 0.028119 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.795898 loss: 0.000725 2022/09/12 20:44:56 - mmengine - INFO - Epoch(train) [15][400/586] lr: 5.000000e-04 eta: 11:02:33 time: 0.332270 data_time: 0.023461 memory: 7489 loss_kpt: 0.000736 acc_pose: 0.820157 loss: 0.000736 2022/09/12 20:45:12 - mmengine - INFO - Epoch(train) [15][450/586] lr: 5.000000e-04 eta: 11:02:06 time: 0.332930 data_time: 0.022674 memory: 7489 loss_kpt: 0.000715 acc_pose: 0.765949 loss: 0.000715 2022/09/12 20:45:29 - mmengine - INFO - Epoch(train) [15][500/586] lr: 5.000000e-04 eta: 11:01:43 time: 0.338409 data_time: 0.026095 memory: 7489 loss_kpt: 0.000728 acc_pose: 0.808841 loss: 0.000728 2022/09/12 20:45:46 - mmengine - INFO - Epoch(train) [15][550/586] lr: 5.000000e-04 eta: 11:01:17 time: 0.334023 data_time: 0.024454 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.837154 loss: 0.000725 2022/09/12 20:45:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:45:58 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/09/12 20:46:22 - mmengine - INFO - Epoch(train) [16][50/586] lr: 5.000000e-04 eta: 10:58:05 time: 0.346269 data_time: 0.030570 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.772764 loss: 0.000699 2022/09/12 20:46:39 - mmengine - INFO - Epoch(train) [16][100/586] lr: 5.000000e-04 eta: 10:57:42 time: 0.337575 data_time: 0.023480 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.808610 loss: 0.000709 2022/09/12 20:46:56 - mmengine - INFO - Epoch(train) [16][150/586] lr: 5.000000e-04 eta: 10:57:17 time: 0.332189 data_time: 0.026092 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.778668 loss: 0.000712 2022/09/12 20:47:12 - mmengine - INFO - Epoch(train) [16][200/586] lr: 5.000000e-04 eta: 10:56:52 time: 0.334238 data_time: 0.025202 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.806905 loss: 0.000730 2022/09/12 20:47:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:47:29 - mmengine - INFO - Epoch(train) [16][250/586] lr: 5.000000e-04 eta: 10:56:29 time: 0.336129 data_time: 0.023417 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.778533 loss: 0.000732 2022/09/12 20:47:46 - mmengine - INFO - Epoch(train) [16][300/586] lr: 5.000000e-04 eta: 10:56:04 time: 0.332958 data_time: 0.024287 memory: 7489 loss_kpt: 0.000720 acc_pose: 0.804049 loss: 0.000720 2022/09/12 20:48:03 - mmengine - INFO - Epoch(train) [16][350/586] lr: 5.000000e-04 eta: 10:55:39 time: 0.333522 data_time: 0.023269 memory: 7489 loss_kpt: 0.000718 acc_pose: 0.730945 loss: 0.000718 2022/09/12 20:48:19 - mmengine - INFO - Epoch(train) [16][400/586] lr: 5.000000e-04 eta: 10:55:17 time: 0.337260 data_time: 0.023707 memory: 7489 loss_kpt: 0.000734 acc_pose: 0.734229 loss: 0.000734 2022/09/12 20:48:36 - mmengine - INFO - Epoch(train) [16][450/586] lr: 5.000000e-04 eta: 10:54:54 time: 0.335979 data_time: 0.023706 memory: 7489 loss_kpt: 0.000734 acc_pose: 0.721756 loss: 0.000734 2022/09/12 20:48:53 - mmengine - INFO - Epoch(train) [16][500/586] lr: 5.000000e-04 eta: 10:54:32 time: 0.337438 data_time: 0.024100 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.833792 loss: 0.000719 2022/09/12 20:49:10 - mmengine - INFO - Epoch(train) [16][550/586] lr: 5.000000e-04 eta: 10:54:08 time: 0.334940 data_time: 0.022448 memory: 7489 loss_kpt: 0.000714 acc_pose: 0.796355 loss: 0.000714 2022/09/12 20:49:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:49:22 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/09/12 20:49:46 - mmengine - INFO - Epoch(train) [17][50/586] lr: 5.000000e-04 eta: 10:51:06 time: 0.340941 data_time: 0.032244 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.786990 loss: 0.000675 2022/09/12 20:50:03 - mmengine - INFO - Epoch(train) [17][100/586] lr: 5.000000e-04 eta: 10:50:47 time: 0.340629 data_time: 0.024136 memory: 7489 loss_kpt: 0.000700 acc_pose: 0.817848 loss: 0.000700 2022/09/12 20:50:19 - mmengine - INFO - Epoch(train) [17][150/586] lr: 5.000000e-04 eta: 10:50:21 time: 0.327861 data_time: 0.022413 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.771347 loss: 0.000706 2022/09/12 20:50:36 - mmengine - INFO - Epoch(train) [17][200/586] lr: 5.000000e-04 eta: 10:50:01 time: 0.340061 data_time: 0.023630 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.803391 loss: 0.000697 2022/09/12 20:50:53 - mmengine - INFO - Epoch(train) [17][250/586] lr: 5.000000e-04 eta: 10:49:43 time: 0.341226 data_time: 0.024525 memory: 7489 loss_kpt: 0.000721 acc_pose: 0.778029 loss: 0.000721 2022/09/12 20:51:10 - mmengine - INFO - Epoch(train) [17][300/586] lr: 5.000000e-04 eta: 10:49:15 time: 0.325229 data_time: 0.023408 memory: 7489 loss_kpt: 0.000712 acc_pose: 0.846673 loss: 0.000712 2022/09/12 20:51:26 - mmengine - INFO - Epoch(train) [17][350/586] lr: 5.000000e-04 eta: 10:48:54 time: 0.337794 data_time: 0.028536 memory: 7489 loss_kpt: 0.000727 acc_pose: 0.775099 loss: 0.000727 2022/09/12 20:51:44 - mmengine - INFO - Epoch(train) [17][400/586] lr: 5.000000e-04 eta: 10:48:36 time: 0.341174 data_time: 0.023315 memory: 7489 loss_kpt: 0.000732 acc_pose: 0.787494 loss: 0.000732 2022/09/12 20:52:00 - mmengine - INFO - Epoch(train) [17][450/586] lr: 5.000000e-04 eta: 10:48:08 time: 0.324855 data_time: 0.022862 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.817225 loss: 0.000730 2022/09/12 20:52:16 - mmengine - INFO - Epoch(train) [17][500/586] lr: 5.000000e-04 eta: 10:47:44 time: 0.331388 data_time: 0.023188 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.782359 loss: 0.000719 2022/09/12 20:52:34 - mmengine - INFO - Epoch(train) [17][550/586] lr: 5.000000e-04 eta: 10:47:27 time: 0.344465 data_time: 0.024420 memory: 7489 loss_kpt: 0.000735 acc_pose: 0.784322 loss: 0.000735 2022/09/12 20:52:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:52:45 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/09/12 20:53:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:53:09 - mmengine - INFO - Epoch(train) [18][50/586] lr: 5.000000e-04 eta: 10:44:34 time: 0.336519 data_time: 0.030877 memory: 7489 loss_kpt: 0.000730 acc_pose: 0.743334 loss: 0.000730 2022/09/12 20:53:26 - mmengine - INFO - Epoch(train) [18][100/586] lr: 5.000000e-04 eta: 10:44:14 time: 0.337044 data_time: 0.023432 memory: 7489 loss_kpt: 0.000727 acc_pose: 0.728099 loss: 0.000727 2022/09/12 20:53:43 - mmengine - INFO - Epoch(train) [18][150/586] lr: 5.000000e-04 eta: 10:43:50 time: 0.329746 data_time: 0.023254 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.796661 loss: 0.000703 2022/09/12 20:53:59 - mmengine - INFO - Epoch(train) [18][200/586] lr: 5.000000e-04 eta: 10:43:28 time: 0.332950 data_time: 0.024802 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.781321 loss: 0.000708 2022/09/12 20:54:16 - mmengine - INFO - Epoch(train) [18][250/586] lr: 5.000000e-04 eta: 10:43:11 time: 0.342960 data_time: 0.023730 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.756008 loss: 0.000691 2022/09/12 20:54:33 - mmengine - INFO - Epoch(train) [18][300/586] lr: 5.000000e-04 eta: 10:42:48 time: 0.330604 data_time: 0.023475 memory: 7489 loss_kpt: 0.000710 acc_pose: 0.816376 loss: 0.000710 2022/09/12 20:54:50 - mmengine - INFO - Epoch(train) [18][350/586] lr: 5.000000e-04 eta: 10:42:26 time: 0.333298 data_time: 0.023792 memory: 7489 loss_kpt: 0.000720 acc_pose: 0.724768 loss: 0.000720 2022/09/12 20:55:07 - mmengine - INFO - Epoch(train) [18][400/586] lr: 5.000000e-04 eta: 10:42:10 time: 0.343868 data_time: 0.024115 memory: 7489 loss_kpt: 0.000724 acc_pose: 0.746269 loss: 0.000724 2022/09/12 20:55:23 - mmengine - INFO - Epoch(train) [18][450/586] lr: 5.000000e-04 eta: 10:41:47 time: 0.329925 data_time: 0.027035 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.789529 loss: 0.000707 2022/09/12 20:55:40 - mmengine - INFO - Epoch(train) [18][500/586] lr: 5.000000e-04 eta: 10:41:25 time: 0.333803 data_time: 0.023583 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.671662 loss: 0.000694 2022/09/12 20:55:57 - mmengine - INFO - Epoch(train) [18][550/586] lr: 5.000000e-04 eta: 10:41:08 time: 0.340981 data_time: 0.022981 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.779041 loss: 0.000719 2022/09/12 20:56:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:56:09 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/09/12 20:56:33 - mmengine - INFO - Epoch(train) [19][50/586] lr: 5.000000e-04 eta: 10:38:26 time: 0.338939 data_time: 0.029210 memory: 7489 loss_kpt: 0.000707 acc_pose: 0.714480 loss: 0.000707 2022/09/12 20:56:50 - mmengine - INFO - Epoch(train) [19][100/586] lr: 5.000000e-04 eta: 10:38:12 time: 0.345678 data_time: 0.024963 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.816727 loss: 0.000694 2022/09/12 20:57:06 - mmengine - INFO - Epoch(train) [19][150/586] lr: 5.000000e-04 eta: 10:37:48 time: 0.327317 data_time: 0.023204 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.745791 loss: 0.000706 2022/09/12 20:57:23 - mmengine - INFO - Epoch(train) [19][200/586] lr: 5.000000e-04 eta: 10:37:27 time: 0.333124 data_time: 0.023131 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.789575 loss: 0.000686 2022/09/12 20:57:40 - mmengine - INFO - Epoch(train) [19][250/586] lr: 5.000000e-04 eta: 10:37:13 time: 0.345905 data_time: 0.023220 memory: 7489 loss_kpt: 0.000719 acc_pose: 0.800387 loss: 0.000719 2022/09/12 20:57:57 - mmengine - INFO - Epoch(train) [19][300/586] lr: 5.000000e-04 eta: 10:36:51 time: 0.332087 data_time: 0.023317 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.831874 loss: 0.000699 2022/09/12 20:58:13 - mmengine - INFO - Epoch(train) [19][350/586] lr: 5.000000e-04 eta: 10:36:30 time: 0.331578 data_time: 0.024394 memory: 7489 loss_kpt: 0.000720 acc_pose: 0.887537 loss: 0.000720 2022/09/12 20:58:30 - mmengine - INFO - Epoch(train) [19][400/586] lr: 5.000000e-04 eta: 10:36:13 time: 0.341741 data_time: 0.023365 memory: 7489 loss_kpt: 0.000693 acc_pose: 0.871140 loss: 0.000693 2022/09/12 20:58:47 - mmengine - INFO - Epoch(train) [19][450/586] lr: 5.000000e-04 eta: 10:35:55 time: 0.337904 data_time: 0.024365 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.768712 loss: 0.000709 2022/09/12 20:58:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:59:04 - mmengine - INFO - Epoch(train) [19][500/586] lr: 5.000000e-04 eta: 10:35:36 time: 0.337025 data_time: 0.023922 memory: 7489 loss_kpt: 0.000715 acc_pose: 0.798263 loss: 0.000715 2022/09/12 20:59:21 - mmengine - INFO - Epoch(train) [19][550/586] lr: 5.000000e-04 eta: 10:35:18 time: 0.339248 data_time: 0.027779 memory: 7489 loss_kpt: 0.000723 acc_pose: 0.787889 loss: 0.000723 2022/09/12 20:59:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 20:59:33 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/09/12 20:59:57 - mmengine - INFO - Epoch(train) [20][50/586] lr: 5.000000e-04 eta: 10:32:47 time: 0.340933 data_time: 0.033401 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.819234 loss: 0.000702 2022/09/12 21:00:14 - mmengine - INFO - Epoch(train) [20][100/586] lr: 5.000000e-04 eta: 10:32:30 time: 0.339203 data_time: 0.024242 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.690395 loss: 0.000697 2022/09/12 21:00:31 - mmengine - INFO - Epoch(train) [20][150/586] lr: 5.000000e-04 eta: 10:32:11 time: 0.334849 data_time: 0.023291 memory: 7489 loss_kpt: 0.000704 acc_pose: 0.765700 loss: 0.000704 2022/09/12 21:00:48 - mmengine - INFO - Epoch(train) [20][200/586] lr: 5.000000e-04 eta: 10:31:53 time: 0.338759 data_time: 0.027594 memory: 7489 loss_kpt: 0.000700 acc_pose: 0.804773 loss: 0.000700 2022/09/12 21:01:05 - mmengine - INFO - Epoch(train) [20][250/586] lr: 5.000000e-04 eta: 10:31:37 time: 0.341248 data_time: 0.023626 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.851960 loss: 0.000709 2022/09/12 21:01:22 - mmengine - INFO - Epoch(train) [20][300/586] lr: 5.000000e-04 eta: 10:31:18 time: 0.335305 data_time: 0.023231 memory: 7489 loss_kpt: 0.000716 acc_pose: 0.749870 loss: 0.000716 2022/09/12 21:01:38 - mmengine - INFO - Epoch(train) [20][350/586] lr: 5.000000e-04 eta: 10:31:00 time: 0.336746 data_time: 0.025979 memory: 7489 loss_kpt: 0.000717 acc_pose: 0.824268 loss: 0.000717 2022/09/12 21:01:55 - mmengine - INFO - Epoch(train) [20][400/586] lr: 5.000000e-04 eta: 10:30:42 time: 0.336447 data_time: 0.023784 memory: 7489 loss_kpt: 0.000703 acc_pose: 0.804541 loss: 0.000703 2022/09/12 21:02:12 - mmengine - INFO - Epoch(train) [20][450/586] lr: 5.000000e-04 eta: 10:30:22 time: 0.334201 data_time: 0.023998 memory: 7489 loss_kpt: 0.000708 acc_pose: 0.757547 loss: 0.000708 2022/09/12 21:02:29 - mmengine - INFO - Epoch(train) [20][500/586] lr: 5.000000e-04 eta: 10:30:04 time: 0.336472 data_time: 0.022851 memory: 7489 loss_kpt: 0.000725 acc_pose: 0.771628 loss: 0.000725 2022/09/12 21:02:46 - mmengine - INFO - Epoch(train) [20][550/586] lr: 5.000000e-04 eta: 10:29:47 time: 0.338609 data_time: 0.024518 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.821767 loss: 0.000684 2022/09/12 21:02:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:02:58 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/09/12 21:03:14 - mmengine - INFO - Epoch(val) [20][50/407] eta: 0:01:06 time: 0.184942 data_time: 0.012183 memory: 7489 2022/09/12 21:03:23 - mmengine - INFO - Epoch(val) [20][100/407] eta: 0:00:55 time: 0.179455 data_time: 0.007845 memory: 1657 2022/09/12 21:03:32 - mmengine - INFO - Epoch(val) [20][150/407] eta: 0:00:46 time: 0.179669 data_time: 0.008261 memory: 1657 2022/09/12 21:03:41 - mmengine - INFO - Epoch(val) [20][200/407] eta: 0:00:37 time: 0.179251 data_time: 0.007578 memory: 1657 2022/09/12 21:03:50 - mmengine - INFO - Epoch(val) [20][250/407] eta: 0:00:28 time: 0.182428 data_time: 0.010970 memory: 1657 2022/09/12 21:03:59 - mmengine - INFO - Epoch(val) [20][300/407] eta: 0:00:19 time: 0.178858 data_time: 0.007793 memory: 1657 2022/09/12 21:04:08 - mmengine - INFO - Epoch(val) [20][350/407] eta: 0:00:10 time: 0.178052 data_time: 0.008054 memory: 1657 2022/09/12 21:04:17 - mmengine - INFO - Epoch(val) [20][400/407] eta: 0:00:01 time: 0.176906 data_time: 0.007315 memory: 1657 2022/09/12 21:04:53 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 21:05:07 - mmengine - INFO - Epoch(val) [20][407/407] coco/AP: 0.712735 coco/AP .5: 0.885670 coco/AP .75: 0.783816 coco/AP (M): 0.678687 coco/AP (L): 0.778160 coco/AR: 0.767475 coco/AR .5: 0.925850 coco/AR .75: 0.831864 coco/AR (M): 0.725785 coco/AR (L): 0.827685 2022/09/12 21:05:07 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_10.pth is removed 2022/09/12 21:05:12 - mmengine - INFO - The best checkpoint with 0.7127 coco/AP at 20 epoch is saved to best_coco/AP_epoch_20.pth. 2022/09/12 21:05:29 - mmengine - INFO - Epoch(train) [21][50/586] lr: 5.000000e-04 eta: 10:27:21 time: 0.336767 data_time: 0.029082 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.788406 loss: 0.000709 2022/09/12 21:05:46 - mmengine - INFO - Epoch(train) [21][100/586] lr: 5.000000e-04 eta: 10:27:09 time: 0.348004 data_time: 0.029303 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.831532 loss: 0.000697 2022/09/12 21:06:03 - mmengine - INFO - Epoch(train) [21][150/586] lr: 5.000000e-04 eta: 10:26:51 time: 0.336833 data_time: 0.023570 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.814644 loss: 0.000698 2022/09/12 21:06:19 - mmengine - INFO - Epoch(train) [21][200/586] lr: 5.000000e-04 eta: 10:26:31 time: 0.331769 data_time: 0.023984 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.849922 loss: 0.000697 2022/09/12 21:06:37 - mmengine - INFO - Epoch(train) [21][250/586] lr: 5.000000e-04 eta: 10:26:17 time: 0.344222 data_time: 0.024177 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.752107 loss: 0.000702 2022/09/12 21:06:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:06:53 - mmengine - INFO - Epoch(train) [21][300/586] lr: 5.000000e-04 eta: 10:25:59 time: 0.335349 data_time: 0.024706 memory: 7489 loss_kpt: 0.000710 acc_pose: 0.779985 loss: 0.000710 2022/09/12 21:07:10 - mmengine - INFO - Epoch(train) [21][350/586] lr: 5.000000e-04 eta: 10:25:37 time: 0.328849 data_time: 0.024550 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.800447 loss: 0.000699 2022/09/12 21:07:27 - mmengine - INFO - Epoch(train) [21][400/586] lr: 5.000000e-04 eta: 10:25:22 time: 0.341243 data_time: 0.028405 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.812781 loss: 0.000680 2022/09/12 21:07:44 - mmengine - INFO - Epoch(train) [21][450/586] lr: 5.000000e-04 eta: 10:25:03 time: 0.334321 data_time: 0.023230 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.786845 loss: 0.000694 2022/09/12 21:08:00 - mmengine - INFO - Epoch(train) [21][500/586] lr: 5.000000e-04 eta: 10:24:43 time: 0.329947 data_time: 0.023616 memory: 7489 loss_kpt: 0.000695 acc_pose: 0.782324 loss: 0.000695 2022/09/12 21:08:17 - mmengine - INFO - Epoch(train) [21][550/586] lr: 5.000000e-04 eta: 10:24:27 time: 0.341576 data_time: 0.023781 memory: 7489 loss_kpt: 0.000697 acc_pose: 0.753845 loss: 0.000697 2022/09/12 21:08:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:08:29 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/09/12 21:08:53 - mmengine - INFO - Epoch(train) [22][50/586] lr: 5.000000e-04 eta: 10:22:11 time: 0.343308 data_time: 0.032070 memory: 7489 loss_kpt: 0.000711 acc_pose: 0.752787 loss: 0.000711 2022/09/12 21:09:10 - mmengine - INFO - Epoch(train) [22][100/586] lr: 5.000000e-04 eta: 10:21:57 time: 0.344099 data_time: 0.024301 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.789688 loss: 0.000684 2022/09/12 21:09:27 - mmengine - INFO - Epoch(train) [22][150/586] lr: 5.000000e-04 eta: 10:21:37 time: 0.329281 data_time: 0.023340 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.796866 loss: 0.000685 2022/09/12 21:09:44 - mmengine - INFO - Epoch(train) [22][200/586] lr: 5.000000e-04 eta: 10:21:23 time: 0.343246 data_time: 0.023452 memory: 7489 loss_kpt: 0.000696 acc_pose: 0.759075 loss: 0.000696 2022/09/12 21:10:01 - mmengine - INFO - Epoch(train) [22][250/586] lr: 5.000000e-04 eta: 10:21:08 time: 0.341271 data_time: 0.023084 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.742117 loss: 0.000686 2022/09/12 21:10:18 - mmengine - INFO - Epoch(train) [22][300/586] lr: 5.000000e-04 eta: 10:20:50 time: 0.336174 data_time: 0.024273 memory: 7489 loss_kpt: 0.000698 acc_pose: 0.772522 loss: 0.000698 2022/09/12 21:10:34 - mmengine - INFO - Epoch(train) [22][350/586] lr: 5.000000e-04 eta: 10:20:31 time: 0.331883 data_time: 0.023672 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.774186 loss: 0.000675 2022/09/12 21:10:52 - mmengine - INFO - Epoch(train) [22][400/586] lr: 5.000000e-04 eta: 10:20:20 time: 0.351006 data_time: 0.023541 memory: 7489 loss_kpt: 0.000699 acc_pose: 0.747749 loss: 0.000699 2022/09/12 21:11:08 - mmengine - INFO - Epoch(train) [22][450/586] lr: 5.000000e-04 eta: 10:20:00 time: 0.329851 data_time: 0.022998 memory: 7489 loss_kpt: 0.000693 acc_pose: 0.829696 loss: 0.000693 2022/09/12 21:11:25 - mmengine - INFO - Epoch(train) [22][500/586] lr: 5.000000e-04 eta: 10:19:42 time: 0.333566 data_time: 0.026744 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.777490 loss: 0.000678 2022/09/12 21:11:42 - mmengine - INFO - Epoch(train) [22][550/586] lr: 5.000000e-04 eta: 10:19:27 time: 0.341529 data_time: 0.023558 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.793682 loss: 0.000679 2022/09/12 21:11:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:11:54 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/09/12 21:12:18 - mmengine - INFO - Epoch(train) [23][50/586] lr: 5.000000e-04 eta: 10:17:15 time: 0.338423 data_time: 0.030081 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.826828 loss: 0.000687 2022/09/12 21:12:35 - mmengine - INFO - Epoch(train) [23][100/586] lr: 5.000000e-04 eta: 10:17:02 time: 0.345104 data_time: 0.024340 memory: 7489 loss_kpt: 0.000702 acc_pose: 0.825381 loss: 0.000702 2022/09/12 21:12:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:12:52 - mmengine - INFO - Epoch(train) [23][150/586] lr: 5.000000e-04 eta: 10:16:46 time: 0.337611 data_time: 0.025914 memory: 7489 loss_kpt: 0.000709 acc_pose: 0.833580 loss: 0.000709 2022/09/12 21:13:09 - mmengine - INFO - Epoch(train) [23][200/586] lr: 5.000000e-04 eta: 10:16:27 time: 0.333106 data_time: 0.023690 memory: 7489 loss_kpt: 0.000687 acc_pose: 0.826802 loss: 0.000687 2022/09/12 21:13:26 - mmengine - INFO - Epoch(train) [23][250/586] lr: 5.000000e-04 eta: 10:16:13 time: 0.341285 data_time: 0.024411 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.773834 loss: 0.000684 2022/09/12 21:13:42 - mmengine - INFO - Epoch(train) [23][300/586] lr: 5.000000e-04 eta: 10:15:55 time: 0.333230 data_time: 0.026794 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.808688 loss: 0.000663 2022/09/12 21:13:59 - mmengine - INFO - Epoch(train) [23][350/586] lr: 5.000000e-04 eta: 10:15:36 time: 0.331192 data_time: 0.023645 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.703655 loss: 0.000680 2022/09/12 21:14:16 - mmengine - INFO - Epoch(train) [23][400/586] lr: 5.000000e-04 eta: 10:15:22 time: 0.343381 data_time: 0.023280 memory: 7489 loss_kpt: 0.000693 acc_pose: 0.765525 loss: 0.000693 2022/09/12 21:14:33 - mmengine - INFO - Epoch(train) [23][450/586] lr: 5.000000e-04 eta: 10:15:04 time: 0.333393 data_time: 0.024775 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.802724 loss: 0.000685 2022/09/12 21:14:49 - mmengine - INFO - Epoch(train) [23][500/586] lr: 5.000000e-04 eta: 10:14:44 time: 0.329323 data_time: 0.024739 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.808482 loss: 0.000685 2022/09/12 21:15:06 - mmengine - INFO - Epoch(train) [23][550/586] lr: 5.000000e-04 eta: 10:14:29 time: 0.341933 data_time: 0.023802 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.807112 loss: 0.000684 2022/09/12 21:15:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:15:19 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/09/12 21:15:42 - mmengine - INFO - Epoch(train) [24][50/586] lr: 5.000000e-04 eta: 10:12:23 time: 0.337939 data_time: 0.030666 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.822365 loss: 0.000688 2022/09/12 21:15:59 - mmengine - INFO - Epoch(train) [24][100/586] lr: 5.000000e-04 eta: 10:12:07 time: 0.337124 data_time: 0.023447 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.811270 loss: 0.000659 2022/09/12 21:16:16 - mmengine - INFO - Epoch(train) [24][150/586] lr: 5.000000e-04 eta: 10:11:52 time: 0.338868 data_time: 0.022864 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.824123 loss: 0.000688 2022/09/12 21:16:33 - mmengine - INFO - Epoch(train) [24][200/586] lr: 5.000000e-04 eta: 10:11:35 time: 0.336721 data_time: 0.028782 memory: 7489 loss_kpt: 0.000684 acc_pose: 0.739712 loss: 0.000684 2022/09/12 21:16:50 - mmengine - INFO - Epoch(train) [24][250/586] lr: 5.000000e-04 eta: 10:11:18 time: 0.334858 data_time: 0.022635 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.755838 loss: 0.000662 2022/09/12 21:17:06 - mmengine - INFO - Epoch(train) [24][300/586] lr: 5.000000e-04 eta: 10:11:00 time: 0.330599 data_time: 0.023264 memory: 7489 loss_kpt: 0.000665 acc_pose: 0.750268 loss: 0.000665 2022/09/12 21:17:23 - mmengine - INFO - Epoch(train) [24][350/586] lr: 5.000000e-04 eta: 10:10:41 time: 0.330877 data_time: 0.023577 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.808074 loss: 0.000668 2022/09/12 21:17:40 - mmengine - INFO - Epoch(train) [24][400/586] lr: 5.000000e-04 eta: 10:10:26 time: 0.339243 data_time: 0.023802 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.827448 loss: 0.000672 2022/09/12 21:17:56 - mmengine - INFO - Epoch(train) [24][450/586] lr: 5.000000e-04 eta: 10:10:06 time: 0.328859 data_time: 0.024312 memory: 7489 loss_kpt: 0.000680 acc_pose: 0.798514 loss: 0.000680 2022/09/12 21:18:13 - mmengine - INFO - Epoch(train) [24][500/586] lr: 5.000000e-04 eta: 10:09:48 time: 0.332495 data_time: 0.028808 memory: 7489 loss_kpt: 0.000689 acc_pose: 0.878090 loss: 0.000689 2022/09/12 21:18:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:18:30 - mmengine - INFO - Epoch(train) [24][550/586] lr: 5.000000e-04 eta: 10:09:33 time: 0.337639 data_time: 0.022887 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.860324 loss: 0.000682 2022/09/12 21:18:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:18:42 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/09/12 21:19:06 - mmengine - INFO - Epoch(train) [25][50/586] lr: 5.000000e-04 eta: 10:07:32 time: 0.338199 data_time: 0.028525 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.764467 loss: 0.000671 2022/09/12 21:19:23 - mmengine - INFO - Epoch(train) [25][100/586] lr: 5.000000e-04 eta: 10:07:16 time: 0.336993 data_time: 0.023954 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.792049 loss: 0.000677 2022/09/12 21:19:39 - mmengine - INFO - Epoch(train) [25][150/586] lr: 5.000000e-04 eta: 10:07:00 time: 0.336024 data_time: 0.024129 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.744627 loss: 0.000673 2022/09/12 21:19:56 - mmengine - INFO - Epoch(train) [25][200/586] lr: 5.000000e-04 eta: 10:06:44 time: 0.337605 data_time: 0.024574 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.779388 loss: 0.000666 2022/09/12 21:20:13 - mmengine - INFO - Epoch(train) [25][250/586] lr: 5.000000e-04 eta: 10:06:27 time: 0.332789 data_time: 0.023503 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.736622 loss: 0.000678 2022/09/12 21:20:30 - mmengine - INFO - Epoch(train) [25][300/586] lr: 5.000000e-04 eta: 10:06:12 time: 0.339709 data_time: 0.027786 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.792366 loss: 0.000685 2022/09/12 21:20:47 - mmengine - INFO - Epoch(train) [25][350/586] lr: 5.000000e-04 eta: 10:05:53 time: 0.330060 data_time: 0.022800 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.855001 loss: 0.000672 2022/09/12 21:21:03 - mmengine - INFO - Epoch(train) [25][400/586] lr: 5.000000e-04 eta: 10:05:37 time: 0.336490 data_time: 0.024635 memory: 7489 loss_kpt: 0.000706 acc_pose: 0.747818 loss: 0.000706 2022/09/12 21:21:20 - mmengine - INFO - Epoch(train) [25][450/586] lr: 5.000000e-04 eta: 10:05:23 time: 0.340422 data_time: 0.025071 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.841417 loss: 0.000673 2022/09/12 21:21:37 - mmengine - INFO - Epoch(train) [25][500/586] lr: 5.000000e-04 eta: 10:05:04 time: 0.330099 data_time: 0.023710 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.781918 loss: 0.000651 2022/09/12 21:21:54 - mmengine - INFO - Epoch(train) [25][550/586] lr: 5.000000e-04 eta: 10:04:52 time: 0.347340 data_time: 0.023977 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.792020 loss: 0.000674 2022/09/12 21:22:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:22:06 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/09/12 21:22:30 - mmengine - INFO - Epoch(train) [26][50/586] lr: 5.000000e-04 eta: 10:02:57 time: 0.341424 data_time: 0.032964 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.733520 loss: 0.000660 2022/09/12 21:22:47 - mmengine - INFO - Epoch(train) [26][100/586] lr: 5.000000e-04 eta: 10:02:42 time: 0.337109 data_time: 0.027380 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.802602 loss: 0.000659 2022/09/12 21:23:04 - mmengine - INFO - Epoch(train) [26][150/586] lr: 5.000000e-04 eta: 10:02:27 time: 0.338225 data_time: 0.023773 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.826912 loss: 0.000676 2022/09/12 21:23:21 - mmengine - INFO - Epoch(train) [26][200/586] lr: 5.000000e-04 eta: 10:02:10 time: 0.333291 data_time: 0.023622 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.703609 loss: 0.000691 2022/09/12 21:23:37 - mmengine - INFO - Epoch(train) [26][250/586] lr: 5.000000e-04 eta: 10:01:54 time: 0.335640 data_time: 0.024216 memory: 7489 loss_kpt: 0.000701 acc_pose: 0.790762 loss: 0.000701 2022/09/12 21:23:54 - mmengine - INFO - Epoch(train) [26][300/586] lr: 5.000000e-04 eta: 10:01:38 time: 0.336691 data_time: 0.023594 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.835598 loss: 0.000663 2022/09/12 21:24:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:24:11 - mmengine - INFO - Epoch(train) [26][350/586] lr: 5.000000e-04 eta: 10:01:20 time: 0.331010 data_time: 0.023020 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.783992 loss: 0.000659 2022/09/12 21:24:28 - mmengine - INFO - Epoch(train) [26][400/586] lr: 5.000000e-04 eta: 10:01:06 time: 0.341231 data_time: 0.023550 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.854320 loss: 0.000673 2022/09/12 21:24:45 - mmengine - INFO - Epoch(train) [26][450/586] lr: 5.000000e-04 eta: 10:00:50 time: 0.336687 data_time: 0.023313 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.771907 loss: 0.000651 2022/09/12 21:25:01 - mmengine - INFO - Epoch(train) [26][500/586] lr: 5.000000e-04 eta: 10:00:34 time: 0.333547 data_time: 0.023935 memory: 7489 loss_kpt: 0.000691 acc_pose: 0.770595 loss: 0.000691 2022/09/12 21:25:18 - mmengine - INFO - Epoch(train) [26][550/586] lr: 5.000000e-04 eta: 10:00:18 time: 0.338337 data_time: 0.027114 memory: 7489 loss_kpt: 0.000686 acc_pose: 0.788925 loss: 0.000686 2022/09/12 21:25:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:25:31 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/09/12 21:25:54 - mmengine - INFO - Epoch(train) [27][50/586] lr: 5.000000e-04 eta: 9:58:27 time: 0.339113 data_time: 0.032397 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.791581 loss: 0.000654 2022/09/12 21:26:11 - mmengine - INFO - Epoch(train) [27][100/586] lr: 5.000000e-04 eta: 9:58:11 time: 0.334520 data_time: 0.023139 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.803143 loss: 0.000676 2022/09/12 21:26:28 - mmengine - INFO - Epoch(train) [27][150/586] lr: 5.000000e-04 eta: 9:57:55 time: 0.335772 data_time: 0.023041 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.794194 loss: 0.000661 2022/09/12 21:26:45 - mmengine - INFO - Epoch(train) [27][200/586] lr: 5.000000e-04 eta: 9:57:38 time: 0.334043 data_time: 0.025613 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.888247 loss: 0.000671 2022/09/12 21:27:01 - mmengine - INFO - Epoch(train) [27][250/586] lr: 5.000000e-04 eta: 9:57:22 time: 0.334761 data_time: 0.023933 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.806688 loss: 0.000664 2022/09/12 21:27:18 - mmengine - INFO - Epoch(train) [27][300/586] lr: 5.000000e-04 eta: 9:57:07 time: 0.335847 data_time: 0.023885 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.719183 loss: 0.000660 2022/09/12 21:27:35 - mmengine - INFO - Epoch(train) [27][350/586] lr: 5.000000e-04 eta: 9:56:49 time: 0.330672 data_time: 0.024003 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.824222 loss: 0.000668 2022/09/12 21:27:52 - mmengine - INFO - Epoch(train) [27][400/586] lr: 5.000000e-04 eta: 9:56:35 time: 0.341341 data_time: 0.023377 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.825151 loss: 0.000671 2022/09/12 21:28:09 - mmengine - INFO - Epoch(train) [27][450/586] lr: 5.000000e-04 eta: 9:56:20 time: 0.336921 data_time: 0.023990 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.749065 loss: 0.000662 2022/09/12 21:28:25 - mmengine - INFO - Epoch(train) [27][500/586] lr: 5.000000e-04 eta: 9:56:04 time: 0.334836 data_time: 0.024157 memory: 7489 loss_kpt: 0.000677 acc_pose: 0.826149 loss: 0.000677 2022/09/12 21:28:42 - mmengine - INFO - Epoch(train) [27][550/586] lr: 5.000000e-04 eta: 9:55:47 time: 0.333733 data_time: 0.023709 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.775242 loss: 0.000660 2022/09/12 21:28:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:28:54 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/09/12 21:29:18 - mmengine - INFO - Epoch(train) [28][50/586] lr: 5.000000e-04 eta: 9:53:57 time: 0.332161 data_time: 0.029506 memory: 7489 loss_kpt: 0.000694 acc_pose: 0.855041 loss: 0.000694 2022/09/12 21:29:35 - mmengine - INFO - Epoch(train) [28][100/586] lr: 5.000000e-04 eta: 9:53:44 time: 0.341374 data_time: 0.025111 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.862989 loss: 0.000660 2022/09/12 21:29:52 - mmengine - INFO - Epoch(train) [28][150/586] lr: 5.000000e-04 eta: 9:53:28 time: 0.335201 data_time: 0.022680 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.790257 loss: 0.000675 2022/09/12 21:30:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:30:09 - mmengine - INFO - Epoch(train) [28][200/586] lr: 5.000000e-04 eta: 9:53:14 time: 0.341306 data_time: 0.022580 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.883228 loss: 0.000669 2022/09/12 21:30:26 - mmengine - INFO - Epoch(train) [28][250/586] lr: 5.000000e-04 eta: 9:53:00 time: 0.340727 data_time: 0.022697 memory: 7489 loss_kpt: 0.000688 acc_pose: 0.872054 loss: 0.000688 2022/09/12 21:30:43 - mmengine - INFO - Epoch(train) [28][300/586] lr: 5.000000e-04 eta: 9:52:44 time: 0.334158 data_time: 0.023414 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.744481 loss: 0.000672 2022/09/12 21:30:59 - mmengine - INFO - Epoch(train) [28][350/586] lr: 5.000000e-04 eta: 9:52:29 time: 0.336219 data_time: 0.023168 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.827277 loss: 0.000640 2022/09/12 21:31:17 - mmengine - INFO - Epoch(train) [28][400/586] lr: 5.000000e-04 eta: 9:52:16 time: 0.343375 data_time: 0.024117 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.806293 loss: 0.000682 2022/09/12 21:31:33 - mmengine - INFO - Epoch(train) [28][450/586] lr: 5.000000e-04 eta: 9:51:59 time: 0.333750 data_time: 0.023817 memory: 7489 loss_kpt: 0.000682 acc_pose: 0.856913 loss: 0.000682 2022/09/12 21:31:50 - mmengine - INFO - Epoch(train) [28][500/586] lr: 5.000000e-04 eta: 9:51:43 time: 0.332100 data_time: 0.022751 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.754958 loss: 0.000673 2022/09/12 21:32:07 - mmengine - INFO - Epoch(train) [28][550/586] lr: 5.000000e-04 eta: 9:51:29 time: 0.340970 data_time: 0.028224 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.815119 loss: 0.000685 2022/09/12 21:32:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:32:19 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/09/12 21:32:43 - mmengine - INFO - Epoch(train) [29][50/586] lr: 5.000000e-04 eta: 9:49:45 time: 0.340674 data_time: 0.033936 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.783859 loss: 0.000661 2022/09/12 21:33:00 - mmengine - INFO - Epoch(train) [29][100/586] lr: 5.000000e-04 eta: 9:49:29 time: 0.334890 data_time: 0.023523 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.833368 loss: 0.000654 2022/09/12 21:33:17 - mmengine - INFO - Epoch(train) [29][150/586] lr: 5.000000e-04 eta: 9:49:15 time: 0.339239 data_time: 0.023996 memory: 7489 loss_kpt: 0.000692 acc_pose: 0.805923 loss: 0.000692 2022/09/12 21:33:34 - mmengine - INFO - Epoch(train) [29][200/586] lr: 5.000000e-04 eta: 9:49:00 time: 0.336471 data_time: 0.024323 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.775649 loss: 0.000672 2022/09/12 21:33:51 - mmengine - INFO - Epoch(train) [29][250/586] lr: 5.000000e-04 eta: 9:48:43 time: 0.331522 data_time: 0.024399 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.760339 loss: 0.000672 2022/09/12 21:34:07 - mmengine - INFO - Epoch(train) [29][300/586] lr: 5.000000e-04 eta: 9:48:28 time: 0.337563 data_time: 0.022490 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.866745 loss: 0.000657 2022/09/12 21:34:24 - mmengine - INFO - Epoch(train) [29][350/586] lr: 5.000000e-04 eta: 9:48:13 time: 0.335556 data_time: 0.022816 memory: 7489 loss_kpt: 0.000679 acc_pose: 0.761674 loss: 0.000679 2022/09/12 21:34:41 - mmengine - INFO - Epoch(train) [29][400/586] lr: 5.000000e-04 eta: 9:47:57 time: 0.334048 data_time: 0.024028 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.900266 loss: 0.000657 2022/09/12 21:34:58 - mmengine - INFO - Epoch(train) [29][450/586] lr: 5.000000e-04 eta: 9:47:41 time: 0.334108 data_time: 0.023440 memory: 7489 loss_kpt: 0.000671 acc_pose: 0.836695 loss: 0.000671 2022/09/12 21:35:14 - mmengine - INFO - Epoch(train) [29][500/586] lr: 5.000000e-04 eta: 9:47:26 time: 0.335975 data_time: 0.029192 memory: 7489 loss_kpt: 0.000670 acc_pose: 0.819426 loss: 0.000670 2022/09/12 21:35:31 - mmengine - INFO - Epoch(train) [29][550/586] lr: 5.000000e-04 eta: 9:47:10 time: 0.334701 data_time: 0.022479 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.825818 loss: 0.000649 2022/09/12 21:35:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:35:43 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/09/12 21:35:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:36:08 - mmengine - INFO - Epoch(train) [30][50/586] lr: 5.000000e-04 eta: 9:45:32 time: 0.346439 data_time: 0.033167 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.793456 loss: 0.000657 2022/09/12 21:36:24 - mmengine - INFO - Epoch(train) [30][100/586] lr: 5.000000e-04 eta: 9:45:15 time: 0.332965 data_time: 0.023775 memory: 7489 loss_kpt: 0.000669 acc_pose: 0.809645 loss: 0.000669 2022/09/12 21:36:42 - mmengine - INFO - Epoch(train) [30][150/586] lr: 5.000000e-04 eta: 9:45:05 time: 0.349588 data_time: 0.028332 memory: 7489 loss_kpt: 0.000672 acc_pose: 0.774450 loss: 0.000672 2022/09/12 21:36:58 - mmengine - INFO - Epoch(train) [30][200/586] lr: 5.000000e-04 eta: 9:44:47 time: 0.329041 data_time: 0.023144 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.824685 loss: 0.000673 2022/09/12 21:37:15 - mmengine - INFO - Epoch(train) [30][250/586] lr: 5.000000e-04 eta: 9:44:32 time: 0.336173 data_time: 0.025056 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.831587 loss: 0.000663 2022/09/12 21:37:32 - mmengine - INFO - Epoch(train) [30][300/586] lr: 5.000000e-04 eta: 9:44:17 time: 0.335898 data_time: 0.023839 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.829703 loss: 0.000654 2022/09/12 21:37:48 - mmengine - INFO - Epoch(train) [30][350/586] lr: 5.000000e-04 eta: 9:44:00 time: 0.330666 data_time: 0.023931 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.800679 loss: 0.000637 2022/09/12 21:38:05 - mmengine - INFO - Epoch(train) [30][400/586] lr: 5.000000e-04 eta: 9:43:45 time: 0.336457 data_time: 0.023203 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.790919 loss: 0.000642 2022/09/12 21:38:22 - mmengine - INFO - Epoch(train) [30][450/586] lr: 5.000000e-04 eta: 9:43:30 time: 0.337952 data_time: 0.023332 memory: 7489 loss_kpt: 0.000655 acc_pose: 0.812957 loss: 0.000655 2022/09/12 21:38:39 - mmengine - INFO - Epoch(train) [30][500/586] lr: 5.000000e-04 eta: 9:43:15 time: 0.334162 data_time: 0.024322 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.801891 loss: 0.000657 2022/09/12 21:38:56 - mmengine - INFO - Epoch(train) [30][550/586] lr: 5.000000e-04 eta: 9:42:59 time: 0.334968 data_time: 0.023426 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.817286 loss: 0.000662 2022/09/12 21:39:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:39:08 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/09/12 21:39:25 - mmengine - INFO - Epoch(val) [30][50/407] eta: 0:01:05 time: 0.183860 data_time: 0.012328 memory: 7489 2022/09/12 21:39:34 - mmengine - INFO - Epoch(val) [30][100/407] eta: 0:00:56 time: 0.185126 data_time: 0.010053 memory: 1657 2022/09/12 21:39:43 - mmengine - INFO - Epoch(val) [30][150/407] eta: 0:00:45 time: 0.178332 data_time: 0.008035 memory: 1657 2022/09/12 21:39:52 - mmengine - INFO - Epoch(val) [30][200/407] eta: 0:00:36 time: 0.178185 data_time: 0.007750 memory: 1657 2022/09/12 21:40:01 - mmengine - INFO - Epoch(val) [30][250/407] eta: 0:00:27 time: 0.177101 data_time: 0.007597 memory: 1657 2022/09/12 21:40:09 - mmengine - INFO - Epoch(val) [30][300/407] eta: 0:00:18 time: 0.176940 data_time: 0.007615 memory: 1657 2022/09/12 21:40:18 - mmengine - INFO - Epoch(val) [30][350/407] eta: 0:00:10 time: 0.179063 data_time: 0.008336 memory: 1657 2022/09/12 21:40:27 - mmengine - INFO - Epoch(val) [30][400/407] eta: 0:00:01 time: 0.175410 data_time: 0.007150 memory: 1657 2022/09/12 21:41:04 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 21:41:18 - mmengine - INFO - Epoch(val) [30][407/407] coco/AP: 0.729058 coco/AP .5: 0.893404 coco/AP .75: 0.797792 coco/AP (M): 0.695278 coco/AP (L): 0.792610 coco/AR: 0.781785 coco/AR .5: 0.931990 coco/AR .75: 0.844773 coco/AR (M): 0.741710 coco/AR (L): 0.839725 2022/09/12 21:41:18 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_20.pth is removed 2022/09/12 21:41:22 - mmengine - INFO - The best checkpoint with 0.7291 coco/AP at 30 epoch is saved to best_coco/AP_epoch_30.pth. 2022/09/12 21:41:39 - mmengine - INFO - Epoch(train) [31][50/586] lr: 5.000000e-04 eta: 9:41:21 time: 0.337560 data_time: 0.028369 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.821708 loss: 0.000663 2022/09/12 21:41:56 - mmengine - INFO - Epoch(train) [31][100/586] lr: 5.000000e-04 eta: 9:41:05 time: 0.333300 data_time: 0.024095 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.775035 loss: 0.000657 2022/09/12 21:42:12 - mmengine - INFO - Epoch(train) [31][150/586] lr: 5.000000e-04 eta: 9:40:51 time: 0.337325 data_time: 0.023648 memory: 7489 loss_kpt: 0.000674 acc_pose: 0.816969 loss: 0.000674 2022/09/12 21:42:29 - mmengine - INFO - Epoch(train) [31][200/586] lr: 5.000000e-04 eta: 9:40:35 time: 0.334481 data_time: 0.023133 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.800065 loss: 0.000668 2022/09/12 21:42:46 - mmengine - INFO - Epoch(train) [31][250/586] lr: 5.000000e-04 eta: 9:40:20 time: 0.335917 data_time: 0.023653 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.741999 loss: 0.000676 2022/09/12 21:43:03 - mmengine - INFO - Epoch(train) [31][300/586] lr: 5.000000e-04 eta: 9:40:06 time: 0.338593 data_time: 0.022204 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.812483 loss: 0.000662 2022/09/12 21:43:20 - mmengine - INFO - Epoch(train) [31][350/586] lr: 5.000000e-04 eta: 9:39:50 time: 0.334685 data_time: 0.022645 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.776501 loss: 0.000646 2022/09/12 21:43:36 - mmengine - INFO - Epoch(train) [31][400/586] lr: 5.000000e-04 eta: 9:39:36 time: 0.336912 data_time: 0.022052 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.773442 loss: 0.000660 2022/09/12 21:43:43 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:43:53 - mmengine - INFO - Epoch(train) [31][450/586] lr: 5.000000e-04 eta: 9:39:20 time: 0.334949 data_time: 0.022401 memory: 7489 loss_kpt: 0.000660 acc_pose: 0.787199 loss: 0.000660 2022/09/12 21:44:10 - mmengine - INFO - Epoch(train) [31][500/586] lr: 5.000000e-04 eta: 9:39:03 time: 0.329671 data_time: 0.022700 memory: 7489 loss_kpt: 0.000663 acc_pose: 0.808860 loss: 0.000663 2022/09/12 21:44:27 - mmengine - INFO - Epoch(train) [31][550/586] lr: 5.000000e-04 eta: 9:38:48 time: 0.336646 data_time: 0.023117 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.860412 loss: 0.000651 2022/09/12 21:44:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:44:39 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/09/12 21:45:03 - mmengine - INFO - Epoch(train) [32][50/586] lr: 5.000000e-04 eta: 9:37:16 time: 0.346497 data_time: 0.029726 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.821213 loss: 0.000649 2022/09/12 21:45:20 - mmengine - INFO - Epoch(train) [32][100/586] lr: 5.000000e-04 eta: 9:37:01 time: 0.335987 data_time: 0.023738 memory: 7489 loss_kpt: 0.000659 acc_pose: 0.899416 loss: 0.000659 2022/09/12 21:45:37 - mmengine - INFO - Epoch(train) [32][150/586] lr: 5.000000e-04 eta: 9:36:45 time: 0.332399 data_time: 0.022757 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.783485 loss: 0.000664 2022/09/12 21:45:53 - mmengine - INFO - Epoch(train) [32][200/586] lr: 5.000000e-04 eta: 9:36:28 time: 0.329736 data_time: 0.022358 memory: 7489 loss_kpt: 0.000676 acc_pose: 0.796879 loss: 0.000676 2022/09/12 21:46:10 - mmengine - INFO - Epoch(train) [32][250/586] lr: 5.000000e-04 eta: 9:36:14 time: 0.337145 data_time: 0.027326 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.809220 loss: 0.000646 2022/09/12 21:46:27 - mmengine - INFO - Epoch(train) [32][300/586] lr: 5.000000e-04 eta: 9:35:58 time: 0.334621 data_time: 0.023459 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.813957 loss: 0.000661 2022/09/12 21:46:43 - mmengine - INFO - Epoch(train) [32][350/586] lr: 5.000000e-04 eta: 9:35:43 time: 0.334284 data_time: 0.023927 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.790555 loss: 0.000658 2022/09/12 21:47:00 - mmengine - INFO - Epoch(train) [32][400/586] lr: 5.000000e-04 eta: 9:35:30 time: 0.342423 data_time: 0.027162 memory: 7489 loss_kpt: 0.000685 acc_pose: 0.728339 loss: 0.000685 2022/09/12 21:47:17 - mmengine - INFO - Epoch(train) [32][450/586] lr: 5.000000e-04 eta: 9:35:15 time: 0.337169 data_time: 0.022850 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.850220 loss: 0.000646 2022/09/12 21:47:34 - mmengine - INFO - Epoch(train) [32][500/586] lr: 5.000000e-04 eta: 9:34:58 time: 0.329767 data_time: 0.023412 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.846188 loss: 0.000640 2022/09/12 21:47:51 - mmengine - INFO - Epoch(train) [32][550/586] lr: 5.000000e-04 eta: 9:34:44 time: 0.338736 data_time: 0.026702 memory: 7489 loss_kpt: 0.000667 acc_pose: 0.790531 loss: 0.000667 2022/09/12 21:48:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:48:03 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/09/12 21:48:27 - mmengine - INFO - Epoch(train) [33][50/586] lr: 5.000000e-04 eta: 9:33:12 time: 0.339884 data_time: 0.030658 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.820862 loss: 0.000657 2022/09/12 21:48:44 - mmengine - INFO - Epoch(train) [33][100/586] lr: 5.000000e-04 eta: 9:32:57 time: 0.335744 data_time: 0.023551 memory: 7489 loss_kpt: 0.000675 acc_pose: 0.848457 loss: 0.000675 2022/09/12 21:49:01 - mmengine - INFO - Epoch(train) [33][150/586] lr: 5.000000e-04 eta: 9:32:45 time: 0.344445 data_time: 0.027138 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.852540 loss: 0.000657 2022/09/12 21:49:18 - mmengine - INFO - Epoch(train) [33][200/586] lr: 5.000000e-04 eta: 9:32:29 time: 0.333408 data_time: 0.023570 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.804136 loss: 0.000641 2022/09/12 21:49:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:49:35 - mmengine - INFO - Epoch(train) [33][250/586] lr: 5.000000e-04 eta: 9:32:16 time: 0.341874 data_time: 0.023215 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.784630 loss: 0.000644 2022/09/12 21:49:51 - mmengine - INFO - Epoch(train) [33][300/586] lr: 5.000000e-04 eta: 9:32:01 time: 0.334581 data_time: 0.026882 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.776052 loss: 0.000656 2022/09/12 21:50:08 - mmengine - INFO - Epoch(train) [33][350/586] lr: 5.000000e-04 eta: 9:31:46 time: 0.336705 data_time: 0.023659 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.865367 loss: 0.000654 2022/09/12 21:50:25 - mmengine - INFO - Epoch(train) [33][400/586] lr: 5.000000e-04 eta: 9:31:32 time: 0.338366 data_time: 0.023458 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.792805 loss: 0.000652 2022/09/12 21:50:42 - mmengine - INFO - Epoch(train) [33][450/586] lr: 5.000000e-04 eta: 9:31:18 time: 0.338525 data_time: 0.022151 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.842955 loss: 0.000664 2022/09/12 21:50:59 - mmengine - INFO - Epoch(train) [33][500/586] lr: 5.000000e-04 eta: 9:31:04 time: 0.341688 data_time: 0.022802 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.755196 loss: 0.000654 2022/09/12 21:51:16 - mmengine - INFO - Epoch(train) [33][550/586] lr: 5.000000e-04 eta: 9:30:50 time: 0.337547 data_time: 0.023550 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.797683 loss: 0.000644 2022/09/12 21:51:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:51:28 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/09/12 21:51:52 - mmengine - INFO - Epoch(train) [34][50/586] lr: 5.000000e-04 eta: 9:29:21 time: 0.340992 data_time: 0.038736 memory: 7489 loss_kpt: 0.000661 acc_pose: 0.766457 loss: 0.000661 2022/09/12 21:52:09 - mmengine - INFO - Epoch(train) [34][100/586] lr: 5.000000e-04 eta: 9:29:09 time: 0.344610 data_time: 0.023127 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.851764 loss: 0.000634 2022/09/12 21:52:26 - mmengine - INFO - Epoch(train) [34][150/586] lr: 5.000000e-04 eta: 9:28:53 time: 0.334182 data_time: 0.023628 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.779159 loss: 0.000648 2022/09/12 21:52:43 - mmengine - INFO - Epoch(train) [34][200/586] lr: 5.000000e-04 eta: 9:28:37 time: 0.331955 data_time: 0.022684 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.811943 loss: 0.000639 2022/09/12 21:53:00 - mmengine - INFO - Epoch(train) [34][250/586] lr: 5.000000e-04 eta: 9:28:22 time: 0.335070 data_time: 0.022024 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.782155 loss: 0.000664 2022/09/12 21:53:16 - mmengine - INFO - Epoch(train) [34][300/586] lr: 5.000000e-04 eta: 9:28:06 time: 0.330990 data_time: 0.023146 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.816670 loss: 0.000652 2022/09/12 21:53:33 - mmengine - INFO - Epoch(train) [34][350/586] lr: 5.000000e-04 eta: 9:27:50 time: 0.330626 data_time: 0.024448 memory: 7489 loss_kpt: 0.000668 acc_pose: 0.815656 loss: 0.000668 2022/09/12 21:53:50 - mmengine - INFO - Epoch(train) [34][400/586] lr: 5.000000e-04 eta: 9:27:36 time: 0.340089 data_time: 0.022714 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.832237 loss: 0.000650 2022/09/12 21:54:06 - mmengine - INFO - Epoch(train) [34][450/586] lr: 5.000000e-04 eta: 9:27:20 time: 0.330697 data_time: 0.023693 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.778224 loss: 0.000639 2022/09/12 21:54:23 - mmengine - INFO - Epoch(train) [34][500/586] lr: 5.000000e-04 eta: 9:27:05 time: 0.336067 data_time: 0.023367 memory: 7489 loss_kpt: 0.000678 acc_pose: 0.724205 loss: 0.000678 2022/09/12 21:54:40 - mmengine - INFO - Epoch(train) [34][550/586] lr: 5.000000e-04 eta: 9:26:50 time: 0.333571 data_time: 0.026487 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.769534 loss: 0.000648 2022/09/12 21:54:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:54:52 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/09/12 21:55:16 - mmengine - INFO - Epoch(train) [35][50/586] lr: 5.000000e-04 eta: 9:25:23 time: 0.341376 data_time: 0.030921 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.779410 loss: 0.000650 2022/09/12 21:55:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:55:33 - mmengine - INFO - Epoch(train) [35][100/586] lr: 5.000000e-04 eta: 9:25:09 time: 0.336476 data_time: 0.023974 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.754599 loss: 0.000648 2022/09/12 21:55:50 - mmengine - INFO - Epoch(train) [35][150/586] lr: 5.000000e-04 eta: 9:24:55 time: 0.341104 data_time: 0.022625 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.843866 loss: 0.000615 2022/09/12 21:56:07 - mmengine - INFO - Epoch(train) [35][200/586] lr: 5.000000e-04 eta: 9:24:43 time: 0.343393 data_time: 0.024060 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.872862 loss: 0.000652 2022/09/12 21:56:24 - mmengine - INFO - Epoch(train) [35][250/586] lr: 5.000000e-04 eta: 9:24:28 time: 0.335840 data_time: 0.023218 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.828392 loss: 0.000634 2022/09/12 21:56:41 - mmengine - INFO - Epoch(train) [35][300/586] lr: 5.000000e-04 eta: 9:24:15 time: 0.345230 data_time: 0.022470 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.772768 loss: 0.000626 2022/09/12 21:56:58 - mmengine - INFO - Epoch(train) [35][350/586] lr: 5.000000e-04 eta: 9:24:01 time: 0.338850 data_time: 0.023044 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.763630 loss: 0.000653 2022/09/12 21:57:15 - mmengine - INFO - Epoch(train) [35][400/586] lr: 5.000000e-04 eta: 9:23:45 time: 0.330663 data_time: 0.022951 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.809117 loss: 0.000646 2022/09/12 21:57:32 - mmengine - INFO - Epoch(train) [35][450/586] lr: 5.000000e-04 eta: 9:23:32 time: 0.341275 data_time: 0.028258 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.768948 loss: 0.000649 2022/09/12 21:57:49 - mmengine - INFO - Epoch(train) [35][500/586] lr: 5.000000e-04 eta: 9:23:17 time: 0.336515 data_time: 0.022640 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.756324 loss: 0.000631 2022/09/12 21:58:05 - mmengine - INFO - Epoch(train) [35][550/586] lr: 5.000000e-04 eta: 9:23:02 time: 0.335424 data_time: 0.023285 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.830262 loss: 0.000638 2022/09/12 21:58:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 21:58:18 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/09/12 21:58:42 - mmengine - INFO - Epoch(train) [36][50/586] lr: 5.000000e-04 eta: 9:21:37 time: 0.339658 data_time: 0.027082 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.782865 loss: 0.000634 2022/09/12 21:58:59 - mmengine - INFO - Epoch(train) [36][100/586] lr: 5.000000e-04 eta: 9:21:24 time: 0.340963 data_time: 0.026365 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.822830 loss: 0.000624 2022/09/12 21:59:16 - mmengine - INFO - Epoch(train) [36][150/586] lr: 5.000000e-04 eta: 9:21:10 time: 0.340092 data_time: 0.023191 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.824687 loss: 0.000642 2022/09/12 21:59:33 - mmengine - INFO - Epoch(train) [36][200/586] lr: 5.000000e-04 eta: 9:20:55 time: 0.333669 data_time: 0.022979 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.734515 loss: 0.000662 2022/09/12 21:59:50 - mmengine - INFO - Epoch(train) [36][250/586] lr: 5.000000e-04 eta: 9:20:42 time: 0.341539 data_time: 0.026214 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.791023 loss: 0.000640 2022/09/12 22:00:06 - mmengine - INFO - Epoch(train) [36][300/586] lr: 5.000000e-04 eta: 9:20:27 time: 0.334029 data_time: 0.022646 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.787858 loss: 0.000638 2022/09/12 22:00:23 - mmengine - INFO - Epoch(train) [36][350/586] lr: 5.000000e-04 eta: 9:20:11 time: 0.330282 data_time: 0.022382 memory: 7489 loss_kpt: 0.000657 acc_pose: 0.794755 loss: 0.000657 2022/09/12 22:00:40 - mmengine - INFO - Epoch(train) [36][400/586] lr: 5.000000e-04 eta: 9:19:56 time: 0.337891 data_time: 0.026283 memory: 7489 loss_kpt: 0.000664 acc_pose: 0.856738 loss: 0.000664 2022/09/12 22:00:57 - mmengine - INFO - Epoch(train) [36][450/586] lr: 5.000000e-04 eta: 9:19:41 time: 0.334733 data_time: 0.023554 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.858250 loss: 0.000644 2022/09/12 22:01:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:01:13 - mmengine - INFO - Epoch(train) [36][500/586] lr: 5.000000e-04 eta: 9:19:24 time: 0.325825 data_time: 0.023910 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.812275 loss: 0.000649 2022/09/12 22:01:30 - mmengine - INFO - Epoch(train) [36][550/586] lr: 5.000000e-04 eta: 9:19:12 time: 0.346308 data_time: 0.023855 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.776940 loss: 0.000641 2022/09/12 22:01:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:01:42 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/09/12 22:02:06 - mmengine - INFO - Epoch(train) [37][50/586] lr: 5.000000e-04 eta: 9:17:49 time: 0.337841 data_time: 0.033355 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.826854 loss: 0.000638 2022/09/12 22:02:24 - mmengine - INFO - Epoch(train) [37][100/586] lr: 5.000000e-04 eta: 9:17:37 time: 0.347506 data_time: 0.023394 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.776290 loss: 0.000636 2022/09/12 22:02:41 - mmengine - INFO - Epoch(train) [37][150/586] lr: 5.000000e-04 eta: 9:17:23 time: 0.339858 data_time: 0.025786 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.840382 loss: 0.000649 2022/09/12 22:02:58 - mmengine - INFO - Epoch(train) [37][200/586] lr: 5.000000e-04 eta: 9:17:08 time: 0.335626 data_time: 0.022628 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.774764 loss: 0.000638 2022/09/12 22:03:14 - mmengine - INFO - Epoch(train) [37][250/586] lr: 5.000000e-04 eta: 9:16:54 time: 0.336242 data_time: 0.024194 memory: 7489 loss_kpt: 0.000645 acc_pose: 0.771692 loss: 0.000645 2022/09/12 22:03:31 - mmengine - INFO - Epoch(train) [37][300/586] lr: 5.000000e-04 eta: 9:16:39 time: 0.333209 data_time: 0.022548 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.839840 loss: 0.000648 2022/09/12 22:03:48 - mmengine - INFO - Epoch(train) [37][350/586] lr: 5.000000e-04 eta: 9:16:25 time: 0.338552 data_time: 0.023199 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.767678 loss: 0.000634 2022/09/12 22:04:05 - mmengine - INFO - Epoch(train) [37][400/586] lr: 5.000000e-04 eta: 9:16:10 time: 0.336526 data_time: 0.022801 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.789520 loss: 0.000618 2022/09/12 22:04:22 - mmengine - INFO - Epoch(train) [37][450/586] lr: 5.000000e-04 eta: 9:15:56 time: 0.338791 data_time: 0.021981 memory: 7489 loss_kpt: 0.000653 acc_pose: 0.824553 loss: 0.000653 2022/09/12 22:04:39 - mmengine - INFO - Epoch(train) [37][500/586] lr: 5.000000e-04 eta: 9:15:42 time: 0.339394 data_time: 0.022297 memory: 7489 loss_kpt: 0.000656 acc_pose: 0.796678 loss: 0.000656 2022/09/12 22:04:56 - mmengine - INFO - Epoch(train) [37][550/586] lr: 5.000000e-04 eta: 9:15:27 time: 0.335037 data_time: 0.022263 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.777793 loss: 0.000651 2022/09/12 22:05:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:05:08 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/09/12 22:05:32 - mmengine - INFO - Epoch(train) [38][50/586] lr: 5.000000e-04 eta: 9:14:07 time: 0.343227 data_time: 0.030953 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.749943 loss: 0.000642 2022/09/12 22:05:49 - mmengine - INFO - Epoch(train) [38][100/586] lr: 5.000000e-04 eta: 9:13:52 time: 0.334144 data_time: 0.024330 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.827276 loss: 0.000624 2022/09/12 22:06:05 - mmengine - INFO - Epoch(train) [38][150/586] lr: 5.000000e-04 eta: 9:13:37 time: 0.335049 data_time: 0.023249 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.838618 loss: 0.000634 2022/09/12 22:06:22 - mmengine - INFO - Epoch(train) [38][200/586] lr: 5.000000e-04 eta: 9:13:21 time: 0.328819 data_time: 0.023205 memory: 7489 loss_kpt: 0.000652 acc_pose: 0.793257 loss: 0.000652 2022/09/12 22:06:39 - mmengine - INFO - Epoch(train) [38][250/586] lr: 5.000000e-04 eta: 9:13:09 time: 0.345001 data_time: 0.022811 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.819882 loss: 0.000638 2022/09/12 22:06:56 - mmengine - INFO - Epoch(train) [38][300/586] lr: 5.000000e-04 eta: 9:12:53 time: 0.332133 data_time: 0.022668 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.871377 loss: 0.000638 2022/09/12 22:07:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:07:12 - mmengine - INFO - Epoch(train) [38][350/586] lr: 5.000000e-04 eta: 9:12:37 time: 0.331870 data_time: 0.022555 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.782952 loss: 0.000633 2022/09/12 22:07:29 - mmengine - INFO - Epoch(train) [38][400/586] lr: 5.000000e-04 eta: 9:12:25 time: 0.344534 data_time: 0.023335 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.818991 loss: 0.000644 2022/09/12 22:07:46 - mmengine - INFO - Epoch(train) [38][450/586] lr: 5.000000e-04 eta: 9:12:09 time: 0.332600 data_time: 0.023676 memory: 7489 loss_kpt: 0.000666 acc_pose: 0.778639 loss: 0.000666 2022/09/12 22:08:03 - mmengine - INFO - Epoch(train) [38][500/586] lr: 5.000000e-04 eta: 9:11:54 time: 0.330687 data_time: 0.022364 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.817860 loss: 0.000650 2022/09/12 22:08:20 - mmengine - INFO - Epoch(train) [38][550/586] lr: 5.000000e-04 eta: 9:11:40 time: 0.340276 data_time: 0.023130 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.805728 loss: 0.000642 2022/09/12 22:08:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:08:31 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/09/12 22:08:56 - mmengine - INFO - Epoch(train) [39][50/586] lr: 5.000000e-04 eta: 9:10:22 time: 0.343385 data_time: 0.032058 memory: 7489 loss_kpt: 0.000647 acc_pose: 0.774614 loss: 0.000647 2022/09/12 22:09:12 - mmengine - INFO - Epoch(train) [39][100/586] lr: 5.000000e-04 eta: 9:10:07 time: 0.334690 data_time: 0.026213 memory: 7489 loss_kpt: 0.000673 acc_pose: 0.760713 loss: 0.000673 2022/09/12 22:09:29 - mmengine - INFO - Epoch(train) [39][150/586] lr: 5.000000e-04 eta: 9:09:53 time: 0.337379 data_time: 0.023198 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.800610 loss: 0.000648 2022/09/12 22:09:46 - mmengine - INFO - Epoch(train) [39][200/586] lr: 5.000000e-04 eta: 9:09:38 time: 0.334146 data_time: 0.023993 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.781284 loss: 0.000650 2022/09/12 22:10:03 - mmengine - INFO - Epoch(train) [39][250/586] lr: 5.000000e-04 eta: 9:09:23 time: 0.335864 data_time: 0.022488 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.842559 loss: 0.000648 2022/09/12 22:10:19 - mmengine - INFO - Epoch(train) [39][300/586] lr: 5.000000e-04 eta: 9:09:08 time: 0.335457 data_time: 0.023551 memory: 7489 loss_kpt: 0.000654 acc_pose: 0.761123 loss: 0.000654 2022/09/12 22:10:36 - mmengine - INFO - Epoch(train) [39][350/586] lr: 5.000000e-04 eta: 9:08:54 time: 0.335868 data_time: 0.022408 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.781360 loss: 0.000634 2022/09/12 22:10:53 - mmengine - INFO - Epoch(train) [39][400/586] lr: 5.000000e-04 eta: 9:08:39 time: 0.333659 data_time: 0.023449 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.808569 loss: 0.000635 2022/09/12 22:11:10 - mmengine - INFO - Epoch(train) [39][450/586] lr: 5.000000e-04 eta: 9:08:24 time: 0.336588 data_time: 0.023451 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.813605 loss: 0.000643 2022/09/12 22:11:27 - mmengine - INFO - Epoch(train) [39][500/586] lr: 5.000000e-04 eta: 9:08:10 time: 0.337367 data_time: 0.023773 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.735773 loss: 0.000662 2022/09/12 22:11:43 - mmengine - INFO - Epoch(train) [39][550/586] lr: 5.000000e-04 eta: 9:07:55 time: 0.336673 data_time: 0.023887 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.831091 loss: 0.000637 2022/09/12 22:11:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:11:55 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/09/12 22:12:20 - mmengine - INFO - Epoch(train) [40][50/586] lr: 5.000000e-04 eta: 9:06:38 time: 0.341106 data_time: 0.032966 memory: 7489 loss_kpt: 0.000651 acc_pose: 0.772115 loss: 0.000651 2022/09/12 22:12:36 - mmengine - INFO - Epoch(train) [40][100/586] lr: 5.000000e-04 eta: 9:06:23 time: 0.333136 data_time: 0.022576 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.817552 loss: 0.000634 2022/09/12 22:12:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:12:53 - mmengine - INFO - Epoch(train) [40][150/586] lr: 5.000000e-04 eta: 9:06:09 time: 0.337760 data_time: 0.023558 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.817120 loss: 0.000639 2022/09/12 22:13:10 - mmengine - INFO - Epoch(train) [40][200/586] lr: 5.000000e-04 eta: 9:05:53 time: 0.328796 data_time: 0.022939 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.832912 loss: 0.000633 2022/09/12 22:13:27 - mmengine - INFO - Epoch(train) [40][250/586] lr: 5.000000e-04 eta: 9:05:39 time: 0.340247 data_time: 0.026587 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.840746 loss: 0.000627 2022/09/12 22:13:44 - mmengine - INFO - Epoch(train) [40][300/586] lr: 5.000000e-04 eta: 9:05:26 time: 0.340468 data_time: 0.022665 memory: 7489 loss_kpt: 0.000649 acc_pose: 0.774960 loss: 0.000649 2022/09/12 22:14:00 - mmengine - INFO - Epoch(train) [40][350/586] lr: 5.000000e-04 eta: 9:05:10 time: 0.332327 data_time: 0.026641 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.811532 loss: 0.000641 2022/09/12 22:14:17 - mmengine - INFO - Epoch(train) [40][400/586] lr: 5.000000e-04 eta: 9:04:55 time: 0.331877 data_time: 0.023205 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.841216 loss: 0.000639 2022/09/12 22:14:34 - mmengine - INFO - Epoch(train) [40][450/586] lr: 5.000000e-04 eta: 9:04:41 time: 0.338083 data_time: 0.022407 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.778838 loss: 0.000631 2022/09/12 22:14:51 - mmengine - INFO - Epoch(train) [40][500/586] lr: 5.000000e-04 eta: 9:04:26 time: 0.334615 data_time: 0.026886 memory: 7489 loss_kpt: 0.000662 acc_pose: 0.785111 loss: 0.000662 2022/09/12 22:15:07 - mmengine - INFO - Epoch(train) [40][550/586] lr: 5.000000e-04 eta: 9:04:11 time: 0.336371 data_time: 0.022039 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.807560 loss: 0.000627 2022/09/12 22:15:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:15:20 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/09/12 22:15:37 - mmengine - INFO - Epoch(val) [40][50/407] eta: 0:01:05 time: 0.184653 data_time: 0.012782 memory: 7489 2022/09/12 22:15:46 - mmengine - INFO - Epoch(val) [40][100/407] eta: 0:00:56 time: 0.183652 data_time: 0.011245 memory: 1657 2022/09/12 22:15:55 - mmengine - INFO - Epoch(val) [40][150/407] eta: 0:00:46 time: 0.180700 data_time: 0.007780 memory: 1657 2022/09/12 22:16:04 - mmengine - INFO - Epoch(val) [40][200/407] eta: 0:00:36 time: 0.178644 data_time: 0.008066 memory: 1657 2022/09/12 22:16:13 - mmengine - INFO - Epoch(val) [40][250/407] eta: 0:00:27 time: 0.178201 data_time: 0.007553 memory: 1657 2022/09/12 22:16:22 - mmengine - INFO - Epoch(val) [40][300/407] eta: 0:00:19 time: 0.181713 data_time: 0.008020 memory: 1657 2022/09/12 22:16:31 - mmengine - INFO - Epoch(val) [40][350/407] eta: 0:00:10 time: 0.179031 data_time: 0.007900 memory: 1657 2022/09/12 22:16:40 - mmengine - INFO - Epoch(val) [40][400/407] eta: 0:00:01 time: 0.175597 data_time: 0.008016 memory: 1657 2022/09/12 22:17:15 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 22:17:28 - mmengine - INFO - Epoch(val) [40][407/407] coco/AP: 0.735082 coco/AP .5: 0.894658 coco/AP .75: 0.802646 coco/AP (M): 0.700597 coco/AP (L): 0.800511 coco/AR: 0.788350 coco/AR .5: 0.934666 coco/AR .75: 0.849654 coco/AR (M): 0.746763 coco/AR (L): 0.848532 2022/09/12 22:17:28 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_30.pth is removed 2022/09/12 22:17:32 - mmengine - INFO - The best checkpoint with 0.7351 coco/AP at 40 epoch is saved to best_coco/AP_epoch_40.pth. 2022/09/12 22:17:49 - mmengine - INFO - Epoch(train) [41][50/586] lr: 5.000000e-04 eta: 9:02:54 time: 0.331866 data_time: 0.026513 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.755147 loss: 0.000640 2022/09/12 22:18:06 - mmengine - INFO - Epoch(train) [41][100/586] lr: 5.000000e-04 eta: 9:02:40 time: 0.336678 data_time: 0.022882 memory: 7489 loss_kpt: 0.000644 acc_pose: 0.769888 loss: 0.000644 2022/09/12 22:18:23 - mmengine - INFO - Epoch(train) [41][150/586] lr: 5.000000e-04 eta: 9:02:28 time: 0.350198 data_time: 0.023121 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.824325 loss: 0.000639 2022/09/12 22:18:40 - mmengine - INFO - Epoch(train) [41][200/586] lr: 5.000000e-04 eta: 9:02:13 time: 0.334054 data_time: 0.022757 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.722655 loss: 0.000635 2022/09/12 22:18:57 - mmengine - INFO - Epoch(train) [41][250/586] lr: 5.000000e-04 eta: 9:01:58 time: 0.333046 data_time: 0.022980 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.852191 loss: 0.000637 2022/09/12 22:19:13 - mmengine - INFO - Epoch(train) [41][300/586] lr: 5.000000e-04 eta: 9:01:44 time: 0.337307 data_time: 0.026095 memory: 7489 loss_kpt: 0.000650 acc_pose: 0.798847 loss: 0.000650 2022/09/12 22:19:30 - mmengine - INFO - Epoch(train) [41][350/586] lr: 5.000000e-04 eta: 9:01:29 time: 0.332703 data_time: 0.023004 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.840280 loss: 0.000634 2022/09/12 22:19:47 - mmengine - INFO - Epoch(train) [41][400/586] lr: 5.000000e-04 eta: 9:01:14 time: 0.333044 data_time: 0.022146 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.704176 loss: 0.000633 2022/09/12 22:20:04 - mmengine - INFO - Epoch(train) [41][450/586] lr: 5.000000e-04 eta: 9:00:59 time: 0.336725 data_time: 0.025877 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.821017 loss: 0.000629 2022/09/12 22:20:20 - mmengine - INFO - Epoch(train) [41][500/586] lr: 5.000000e-04 eta: 9:00:43 time: 0.330133 data_time: 0.022745 memory: 7489 loss_kpt: 0.000658 acc_pose: 0.822669 loss: 0.000658 2022/09/12 22:20:37 - mmengine - INFO - Epoch(train) [41][550/586] lr: 5.000000e-04 eta: 9:00:28 time: 0.330825 data_time: 0.022365 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.816393 loss: 0.000630 2022/09/12 22:20:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:20:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:20:50 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/09/12 22:21:14 - mmengine - INFO - Epoch(train) [42][50/586] lr: 5.000000e-04 eta: 8:59:14 time: 0.341425 data_time: 0.035203 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.855376 loss: 0.000641 2022/09/12 22:21:30 - mmengine - INFO - Epoch(train) [42][100/586] lr: 5.000000e-04 eta: 8:58:59 time: 0.333362 data_time: 0.023790 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.808927 loss: 0.000626 2022/09/12 22:21:47 - mmengine - INFO - Epoch(train) [42][150/586] lr: 5.000000e-04 eta: 8:58:44 time: 0.333446 data_time: 0.023512 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.839188 loss: 0.000641 2022/09/12 22:22:04 - mmengine - INFO - Epoch(train) [42][200/586] lr: 5.000000e-04 eta: 8:58:29 time: 0.335788 data_time: 0.027444 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.837192 loss: 0.000629 2022/09/12 22:22:21 - mmengine - INFO - Epoch(train) [42][250/586] lr: 5.000000e-04 eta: 8:58:17 time: 0.346054 data_time: 0.022620 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.794192 loss: 0.000633 2022/09/12 22:22:38 - mmengine - INFO - Epoch(train) [42][300/586] lr: 5.000000e-04 eta: 8:58:01 time: 0.331012 data_time: 0.023665 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.787099 loss: 0.000636 2022/09/12 22:22:54 - mmengine - INFO - Epoch(train) [42][350/586] lr: 5.000000e-04 eta: 8:57:47 time: 0.335453 data_time: 0.025862 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.864427 loss: 0.000622 2022/09/12 22:23:12 - mmengine - INFO - Epoch(train) [42][400/586] lr: 5.000000e-04 eta: 8:57:35 time: 0.347031 data_time: 0.022920 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.777002 loss: 0.000614 2022/09/12 22:23:28 - mmengine - INFO - Epoch(train) [42][450/586] lr: 5.000000e-04 eta: 8:57:20 time: 0.333741 data_time: 0.022413 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.781828 loss: 0.000626 2022/09/12 22:23:45 - mmengine - INFO - Epoch(train) [42][500/586] lr: 5.000000e-04 eta: 8:57:04 time: 0.331916 data_time: 0.021954 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.861880 loss: 0.000624 2022/09/12 22:24:02 - mmengine - INFO - Epoch(train) [42][550/586] lr: 5.000000e-04 eta: 8:56:49 time: 0.333905 data_time: 0.026170 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.798389 loss: 0.000622 2022/09/12 22:24:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:24:14 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/09/12 22:24:38 - mmengine - INFO - Epoch(train) [43][50/586] lr: 5.000000e-04 eta: 8:55:37 time: 0.343609 data_time: 0.031447 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.845339 loss: 0.000614 2022/09/12 22:24:55 - mmengine - INFO - Epoch(train) [43][100/586] lr: 5.000000e-04 eta: 8:55:23 time: 0.337192 data_time: 0.022783 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.814758 loss: 0.000628 2022/09/12 22:25:12 - mmengine - INFO - Epoch(train) [43][150/586] lr: 5.000000e-04 eta: 8:55:12 time: 0.350275 data_time: 0.022525 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.833378 loss: 0.000633 2022/09/12 22:25:29 - mmengine - INFO - Epoch(train) [43][200/586] lr: 5.000000e-04 eta: 8:54:57 time: 0.333502 data_time: 0.023600 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.752469 loss: 0.000634 2022/09/12 22:25:46 - mmengine - INFO - Epoch(train) [43][250/586] lr: 5.000000e-04 eta: 8:54:41 time: 0.332623 data_time: 0.022447 memory: 7489 loss_kpt: 0.000646 acc_pose: 0.866256 loss: 0.000646 2022/09/12 22:26:03 - mmengine - INFO - Epoch(train) [43][300/586] lr: 5.000000e-04 eta: 8:54:28 time: 0.341948 data_time: 0.027033 memory: 7489 loss_kpt: 0.000630 acc_pose: 0.791813 loss: 0.000630 2022/09/12 22:26:19 - mmengine - INFO - Epoch(train) [43][350/586] lr: 5.000000e-04 eta: 8:54:12 time: 0.329074 data_time: 0.022815 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.836727 loss: 0.000612 2022/09/12 22:26:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:26:36 - mmengine - INFO - Epoch(train) [43][400/586] lr: 5.000000e-04 eta: 8:53:57 time: 0.332586 data_time: 0.023235 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.795349 loss: 0.000616 2022/09/12 22:26:53 - mmengine - INFO - Epoch(train) [43][450/586] lr: 5.000000e-04 eta: 8:53:45 time: 0.350931 data_time: 0.026797 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.804500 loss: 0.000618 2022/09/12 22:27:10 - mmengine - INFO - Epoch(train) [43][500/586] lr: 5.000000e-04 eta: 8:53:31 time: 0.335439 data_time: 0.023733 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.883515 loss: 0.000631 2022/09/12 22:27:27 - mmengine - INFO - Epoch(train) [43][550/586] lr: 5.000000e-04 eta: 8:53:16 time: 0.337107 data_time: 0.022283 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.877421 loss: 0.000634 2022/09/12 22:27:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:27:39 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/09/12 22:28:03 - mmengine - INFO - Epoch(train) [44][50/586] lr: 5.000000e-04 eta: 8:52:05 time: 0.340358 data_time: 0.032215 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.794355 loss: 0.000620 2022/09/12 22:28:20 - mmengine - INFO - Epoch(train) [44][100/586] lr: 5.000000e-04 eta: 8:51:51 time: 0.337242 data_time: 0.027187 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.777424 loss: 0.000634 2022/09/12 22:28:37 - mmengine - INFO - Epoch(train) [44][150/586] lr: 5.000000e-04 eta: 8:51:37 time: 0.335454 data_time: 0.022466 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.754088 loss: 0.000638 2022/09/12 22:28:54 - mmengine - INFO - Epoch(train) [44][200/586] lr: 5.000000e-04 eta: 8:51:22 time: 0.335715 data_time: 0.022856 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.875908 loss: 0.000604 2022/09/12 22:29:10 - mmengine - INFO - Epoch(train) [44][250/586] lr: 5.000000e-04 eta: 8:51:07 time: 0.331037 data_time: 0.022570 memory: 7489 loss_kpt: 0.000635 acc_pose: 0.868515 loss: 0.000635 2022/09/12 22:29:27 - mmengine - INFO - Epoch(train) [44][300/586] lr: 5.000000e-04 eta: 8:50:53 time: 0.338807 data_time: 0.023025 memory: 7489 loss_kpt: 0.000640 acc_pose: 0.896892 loss: 0.000640 2022/09/12 22:29:44 - mmengine - INFO - Epoch(train) [44][350/586] lr: 5.000000e-04 eta: 8:50:37 time: 0.331326 data_time: 0.022901 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.748150 loss: 0.000622 2022/09/12 22:30:01 - mmengine - INFO - Epoch(train) [44][400/586] lr: 5.000000e-04 eta: 8:50:23 time: 0.335626 data_time: 0.025684 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.812735 loss: 0.000622 2022/09/12 22:30:18 - mmengine - INFO - Epoch(train) [44][450/586] lr: 5.000000e-04 eta: 8:50:09 time: 0.338908 data_time: 0.022581 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.819631 loss: 0.000608 2022/09/12 22:30:35 - mmengine - INFO - Epoch(train) [44][500/586] lr: 5.000000e-04 eta: 8:49:55 time: 0.338545 data_time: 0.022574 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.762970 loss: 0.000623 2022/09/12 22:30:51 - mmengine - INFO - Epoch(train) [44][550/586] lr: 5.000000e-04 eta: 8:49:40 time: 0.335823 data_time: 0.023038 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.834710 loss: 0.000610 2022/09/12 22:31:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:31:03 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/09/12 22:31:28 - mmengine - INFO - Epoch(train) [45][50/586] lr: 5.000000e-04 eta: 8:48:32 time: 0.351605 data_time: 0.030481 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.771796 loss: 0.000632 2022/09/12 22:31:44 - mmengine - INFO - Epoch(train) [45][100/586] lr: 5.000000e-04 eta: 8:48:17 time: 0.330127 data_time: 0.023649 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.831428 loss: 0.000619 2022/09/12 22:32:01 - mmengine - INFO - Epoch(train) [45][150/586] lr: 5.000000e-04 eta: 8:48:02 time: 0.333831 data_time: 0.022733 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.862802 loss: 0.000612 2022/09/12 22:32:18 - mmengine - INFO - Epoch(train) [45][200/586] lr: 5.000000e-04 eta: 8:47:48 time: 0.338477 data_time: 0.025457 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.785077 loss: 0.000612 2022/09/12 22:32:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:32:35 - mmengine - INFO - Epoch(train) [45][250/586] lr: 5.000000e-04 eta: 8:47:33 time: 0.335509 data_time: 0.022104 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.774227 loss: 0.000615 2022/09/12 22:32:51 - mmengine - INFO - Epoch(train) [45][300/586] lr: 5.000000e-04 eta: 8:47:18 time: 0.332883 data_time: 0.023184 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.879427 loss: 0.000609 2022/09/12 22:33:08 - mmengine - INFO - Epoch(train) [45][350/586] lr: 5.000000e-04 eta: 8:47:04 time: 0.337817 data_time: 0.023247 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.723203 loss: 0.000638 2022/09/12 22:33:25 - mmengine - INFO - Epoch(train) [45][400/586] lr: 5.000000e-04 eta: 8:46:49 time: 0.334332 data_time: 0.022416 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.849949 loss: 0.000613 2022/09/12 22:33:42 - mmengine - INFO - Epoch(train) [45][450/586] lr: 5.000000e-04 eta: 8:46:35 time: 0.335220 data_time: 0.022915 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.770807 loss: 0.000616 2022/09/12 22:33:58 - mmengine - INFO - Epoch(train) [45][500/586] lr: 5.000000e-04 eta: 8:46:20 time: 0.334476 data_time: 0.026286 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.854210 loss: 0.000618 2022/09/12 22:34:15 - mmengine - INFO - Epoch(train) [45][550/586] lr: 5.000000e-04 eta: 8:46:05 time: 0.332250 data_time: 0.022614 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.770331 loss: 0.000615 2022/09/12 22:34:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:34:27 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/09/12 22:34:51 - mmengine - INFO - Epoch(train) [46][50/586] lr: 5.000000e-04 eta: 8:44:57 time: 0.344996 data_time: 0.029553 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.790949 loss: 0.000617 2022/09/12 22:35:08 - mmengine - INFO - Epoch(train) [46][100/586] lr: 5.000000e-04 eta: 8:44:42 time: 0.335169 data_time: 0.023913 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.787743 loss: 0.000612 2022/09/12 22:35:25 - mmengine - INFO - Epoch(train) [46][150/586] lr: 5.000000e-04 eta: 8:44:28 time: 0.335149 data_time: 0.023174 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.804330 loss: 0.000621 2022/09/12 22:35:41 - mmengine - INFO - Epoch(train) [46][200/586] lr: 5.000000e-04 eta: 8:44:13 time: 0.332209 data_time: 0.022069 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.870389 loss: 0.000621 2022/09/12 22:35:58 - mmengine - INFO - Epoch(train) [46][250/586] lr: 5.000000e-04 eta: 8:43:58 time: 0.338357 data_time: 0.022501 memory: 7489 loss_kpt: 0.000627 acc_pose: 0.847185 loss: 0.000627 2022/09/12 22:36:15 - mmengine - INFO - Epoch(train) [46][300/586] lr: 5.000000e-04 eta: 8:43:44 time: 0.334649 data_time: 0.022577 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.788123 loss: 0.000636 2022/09/12 22:36:32 - mmengine - INFO - Epoch(train) [46][350/586] lr: 5.000000e-04 eta: 8:43:30 time: 0.337683 data_time: 0.022157 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.774604 loss: 0.000617 2022/09/12 22:36:49 - mmengine - INFO - Epoch(train) [46][400/586] lr: 5.000000e-04 eta: 8:43:15 time: 0.337258 data_time: 0.022644 memory: 7489 loss_kpt: 0.000626 acc_pose: 0.836965 loss: 0.000626 2022/09/12 22:37:06 - mmengine - INFO - Epoch(train) [46][450/586] lr: 5.000000e-04 eta: 8:43:01 time: 0.336930 data_time: 0.022165 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.820656 loss: 0.000614 2022/09/12 22:37:22 - mmengine - INFO - Epoch(train) [46][500/586] lr: 5.000000e-04 eta: 8:42:47 time: 0.337982 data_time: 0.023009 memory: 7489 loss_kpt: 0.000643 acc_pose: 0.837401 loss: 0.000643 2022/09/12 22:37:39 - mmengine - INFO - Epoch(train) [46][550/586] lr: 5.000000e-04 eta: 8:42:33 time: 0.339268 data_time: 0.022352 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.799664 loss: 0.000642 2022/09/12 22:37:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:37:52 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/09/12 22:38:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:38:16 - mmengine - INFO - Epoch(train) [47][50/586] lr: 5.000000e-04 eta: 8:41:27 time: 0.347852 data_time: 0.030853 memory: 7489 loss_kpt: 0.000634 acc_pose: 0.825715 loss: 0.000634 2022/09/12 22:38:33 - mmengine - INFO - Epoch(train) [47][100/586] lr: 5.000000e-04 eta: 8:41:14 time: 0.345288 data_time: 0.027056 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.763706 loss: 0.000638 2022/09/12 22:38:50 - mmengine - INFO - Epoch(train) [47][150/586] lr: 5.000000e-04 eta: 8:40:59 time: 0.332262 data_time: 0.023049 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.781644 loss: 0.000616 2022/09/12 22:39:07 - mmengine - INFO - Epoch(train) [47][200/586] lr: 5.000000e-04 eta: 8:40:46 time: 0.346186 data_time: 0.022518 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.761106 loss: 0.000605 2022/09/12 22:39:24 - mmengine - INFO - Epoch(train) [47][250/586] lr: 5.000000e-04 eta: 8:40:32 time: 0.336593 data_time: 0.022534 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.835674 loss: 0.000618 2022/09/12 22:39:41 - mmengine - INFO - Epoch(train) [47][300/586] lr: 5.000000e-04 eta: 8:40:17 time: 0.333769 data_time: 0.023488 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.801841 loss: 0.000622 2022/09/12 22:39:57 - mmengine - INFO - Epoch(train) [47][350/586] lr: 5.000000e-04 eta: 8:40:02 time: 0.335228 data_time: 0.022247 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.855904 loss: 0.000636 2022/09/12 22:40:14 - mmengine - INFO - Epoch(train) [47][400/586] lr: 5.000000e-04 eta: 8:39:49 time: 0.340599 data_time: 0.022870 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.875370 loss: 0.000615 2022/09/12 22:40:31 - mmengine - INFO - Epoch(train) [47][450/586] lr: 5.000000e-04 eta: 8:39:34 time: 0.333615 data_time: 0.022455 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.836499 loss: 0.000617 2022/09/12 22:40:48 - mmengine - INFO - Epoch(train) [47][500/586] lr: 5.000000e-04 eta: 8:39:18 time: 0.331115 data_time: 0.023030 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.787491 loss: 0.000631 2022/09/12 22:41:05 - mmengine - INFO - Epoch(train) [47][550/586] lr: 5.000000e-04 eta: 8:39:04 time: 0.339083 data_time: 0.025874 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.825024 loss: 0.000621 2022/09/12 22:41:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:41:17 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/09/12 22:41:41 - mmengine - INFO - Epoch(train) [48][50/586] lr: 5.000000e-04 eta: 8:38:00 time: 0.348899 data_time: 0.031849 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.696185 loss: 0.000611 2022/09/12 22:41:58 - mmengine - INFO - Epoch(train) [48][100/586] lr: 5.000000e-04 eta: 8:37:46 time: 0.341750 data_time: 0.026868 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.854319 loss: 0.000637 2022/09/12 22:42:14 - mmengine - INFO - Epoch(train) [48][150/586] lr: 5.000000e-04 eta: 8:37:31 time: 0.333195 data_time: 0.024000 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.799239 loss: 0.000621 2022/09/12 22:42:31 - mmengine - INFO - Epoch(train) [48][200/586] lr: 5.000000e-04 eta: 8:37:17 time: 0.336667 data_time: 0.022559 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.813052 loss: 0.000607 2022/09/12 22:42:48 - mmengine - INFO - Epoch(train) [48][250/586] lr: 5.000000e-04 eta: 8:37:02 time: 0.335280 data_time: 0.023757 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.807361 loss: 0.000601 2022/09/12 22:43:05 - mmengine - INFO - Epoch(train) [48][300/586] lr: 5.000000e-04 eta: 8:36:48 time: 0.340155 data_time: 0.026931 memory: 7489 loss_kpt: 0.000629 acc_pose: 0.812934 loss: 0.000629 2022/09/12 22:43:22 - mmengine - INFO - Epoch(train) [48][350/586] lr: 5.000000e-04 eta: 8:36:34 time: 0.334546 data_time: 0.022579 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.874038 loss: 0.000623 2022/09/12 22:43:39 - mmengine - INFO - Epoch(train) [48][400/586] lr: 5.000000e-04 eta: 8:36:20 time: 0.340068 data_time: 0.022723 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.880889 loss: 0.000631 2022/09/12 22:43:56 - mmengine - INFO - Epoch(train) [48][450/586] lr: 5.000000e-04 eta: 8:36:05 time: 0.336109 data_time: 0.022487 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.783926 loss: 0.000628 2022/09/12 22:43:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:44:12 - mmengine - INFO - Epoch(train) [48][500/586] lr: 5.000000e-04 eta: 8:35:50 time: 0.332284 data_time: 0.022143 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.830073 loss: 0.000618 2022/09/12 22:44:29 - mmengine - INFO - Epoch(train) [48][550/586] lr: 5.000000e-04 eta: 8:35:36 time: 0.341421 data_time: 0.022712 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.784861 loss: 0.000617 2022/09/12 22:44:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:44:41 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/09/12 22:45:05 - mmengine - INFO - Epoch(train) [49][50/586] lr: 5.000000e-04 eta: 8:34:31 time: 0.337426 data_time: 0.030651 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.893791 loss: 0.000614 2022/09/12 22:45:22 - mmengine - INFO - Epoch(train) [49][100/586] lr: 5.000000e-04 eta: 8:34:17 time: 0.340848 data_time: 0.022491 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.817921 loss: 0.000603 2022/09/12 22:45:38 - mmengine - INFO - Epoch(train) [49][150/586] lr: 5.000000e-04 eta: 8:34:01 time: 0.326821 data_time: 0.022404 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.840889 loss: 0.000612 2022/09/12 22:45:55 - mmengine - INFO - Epoch(train) [49][200/586] lr: 5.000000e-04 eta: 8:33:46 time: 0.333216 data_time: 0.022569 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.813571 loss: 0.000615 2022/09/12 22:46:12 - mmengine - INFO - Epoch(train) [49][250/586] lr: 5.000000e-04 eta: 8:33:33 time: 0.345897 data_time: 0.023660 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.839718 loss: 0.000613 2022/09/12 22:46:29 - mmengine - INFO - Epoch(train) [49][300/586] lr: 5.000000e-04 eta: 8:33:18 time: 0.329149 data_time: 0.022664 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.841802 loss: 0.000612 2022/09/12 22:46:46 - mmengine - INFO - Epoch(train) [49][350/586] lr: 5.000000e-04 eta: 8:33:03 time: 0.334099 data_time: 0.022384 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.825730 loss: 0.000632 2022/09/12 22:47:02 - mmengine - INFO - Epoch(train) [49][400/586] lr: 5.000000e-04 eta: 8:32:49 time: 0.337706 data_time: 0.026025 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.840960 loss: 0.000631 2022/09/12 22:47:19 - mmengine - INFO - Epoch(train) [49][450/586] lr: 5.000000e-04 eta: 8:32:33 time: 0.331931 data_time: 0.023116 memory: 7489 loss_kpt: 0.000636 acc_pose: 0.815740 loss: 0.000636 2022/09/12 22:47:36 - mmengine - INFO - Epoch(train) [49][500/586] lr: 5.000000e-04 eta: 8:32:18 time: 0.329631 data_time: 0.022671 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.823016 loss: 0.000622 2022/09/12 22:47:53 - mmengine - INFO - Epoch(train) [49][550/586] lr: 5.000000e-04 eta: 8:32:04 time: 0.341489 data_time: 0.022106 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.838632 loss: 0.000632 2022/09/12 22:48:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:48:05 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/09/12 22:48:29 - mmengine - INFO - Epoch(train) [50][50/586] lr: 5.000000e-04 eta: 8:31:01 time: 0.346675 data_time: 0.037732 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.823182 loss: 0.000599 2022/09/12 22:48:46 - mmengine - INFO - Epoch(train) [50][100/586] lr: 5.000000e-04 eta: 8:30:48 time: 0.343333 data_time: 0.023391 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.772821 loss: 0.000617 2022/09/12 22:49:03 - mmengine - INFO - Epoch(train) [50][150/586] lr: 5.000000e-04 eta: 8:30:33 time: 0.333284 data_time: 0.022459 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.873821 loss: 0.000632 2022/09/12 22:49:20 - mmengine - INFO - Epoch(train) [50][200/586] lr: 5.000000e-04 eta: 8:30:19 time: 0.338200 data_time: 0.027581 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.795225 loss: 0.000621 2022/09/12 22:49:37 - mmengine - INFO - Epoch(train) [50][250/586] lr: 5.000000e-04 eta: 8:30:04 time: 0.334739 data_time: 0.022849 memory: 7489 loss_kpt: 0.000632 acc_pose: 0.767414 loss: 0.000632 2022/09/12 22:49:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:49:53 - mmengine - INFO - Epoch(train) [50][300/586] lr: 5.000000e-04 eta: 8:29:50 time: 0.336807 data_time: 0.022213 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.853400 loss: 0.000609 2022/09/12 22:50:10 - mmengine - INFO - Epoch(train) [50][350/586] lr: 5.000000e-04 eta: 8:29:35 time: 0.336654 data_time: 0.026665 memory: 7489 loss_kpt: 0.000648 acc_pose: 0.836973 loss: 0.000648 2022/09/12 22:50:27 - mmengine - INFO - Epoch(train) [50][400/586] lr: 5.000000e-04 eta: 8:29:21 time: 0.336821 data_time: 0.022248 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.776594 loss: 0.000590 2022/09/12 22:50:44 - mmengine - INFO - Epoch(train) [50][450/586] lr: 5.000000e-04 eta: 8:29:06 time: 0.333756 data_time: 0.022676 memory: 7489 loss_kpt: 0.000620 acc_pose: 0.822055 loss: 0.000620 2022/09/12 22:51:01 - mmengine - INFO - Epoch(train) [50][500/586] lr: 5.000000e-04 eta: 8:28:51 time: 0.333436 data_time: 0.023146 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.755777 loss: 0.000618 2022/09/12 22:51:18 - mmengine - INFO - Epoch(train) [50][550/586] lr: 5.000000e-04 eta: 8:28:38 time: 0.345993 data_time: 0.023008 memory: 7489 loss_kpt: 0.000633 acc_pose: 0.811925 loss: 0.000633 2022/09/12 22:51:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:51:30 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/09/12 22:51:47 - mmengine - INFO - Epoch(val) [50][50/407] eta: 0:01:05 time: 0.184444 data_time: 0.012133 memory: 7489 2022/09/12 22:51:56 - mmengine - INFO - Epoch(val) [50][100/407] eta: 0:00:55 time: 0.179762 data_time: 0.007887 memory: 1657 2022/09/12 22:52:05 - mmengine - INFO - Epoch(val) [50][150/407] eta: 0:00:46 time: 0.180407 data_time: 0.008118 memory: 1657 2022/09/12 22:52:14 - mmengine - INFO - Epoch(val) [50][200/407] eta: 0:00:36 time: 0.178589 data_time: 0.007337 memory: 1657 2022/09/12 22:52:23 - mmengine - INFO - Epoch(val) [50][250/407] eta: 0:00:28 time: 0.178602 data_time: 0.007513 memory: 1657 2022/09/12 22:52:31 - mmengine - INFO - Epoch(val) [50][300/407] eta: 0:00:19 time: 0.178815 data_time: 0.007835 memory: 1657 2022/09/12 22:52:41 - mmengine - INFO - Epoch(val) [50][350/407] eta: 0:00:10 time: 0.183269 data_time: 0.012535 memory: 1657 2022/09/12 22:52:49 - mmengine - INFO - Epoch(val) [50][400/407] eta: 0:00:01 time: 0.174984 data_time: 0.006950 memory: 1657 2022/09/12 22:53:24 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 22:53:38 - mmengine - INFO - Epoch(val) [50][407/407] coco/AP: 0.740400 coco/AP .5: 0.896227 coco/AP .75: 0.806720 coco/AP (M): 0.703882 coco/AP (L): 0.808490 coco/AR: 0.791987 coco/AR .5: 0.935926 coco/AR .75: 0.850598 coco/AR (M): 0.748784 coco/AR (L): 0.854329 2022/09/12 22:53:38 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_40.pth is removed 2022/09/12 22:53:42 - mmengine - INFO - The best checkpoint with 0.7404 coco/AP at 50 epoch is saved to best_coco/AP_epoch_50.pth. 2022/09/12 22:53:58 - mmengine - INFO - Epoch(train) [51][50/586] lr: 5.000000e-04 eta: 8:27:34 time: 0.331147 data_time: 0.026637 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.800251 loss: 0.000589 2022/09/12 22:54:15 - mmengine - INFO - Epoch(train) [51][100/586] lr: 5.000000e-04 eta: 8:27:20 time: 0.342576 data_time: 0.022680 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.848524 loss: 0.000616 2022/09/12 22:54:32 - mmengine - INFO - Epoch(train) [51][150/586] lr: 5.000000e-04 eta: 8:27:05 time: 0.331982 data_time: 0.022223 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.806965 loss: 0.000601 2022/09/12 22:54:49 - mmengine - INFO - Epoch(train) [51][200/586] lr: 5.000000e-04 eta: 8:26:51 time: 0.340527 data_time: 0.027394 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.844015 loss: 0.000619 2022/09/12 22:55:06 - mmengine - INFO - Epoch(train) [51][250/586] lr: 5.000000e-04 eta: 8:26:37 time: 0.340900 data_time: 0.022439 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.822659 loss: 0.000607 2022/09/12 22:55:23 - mmengine - INFO - Epoch(train) [51][300/586] lr: 5.000000e-04 eta: 8:26:23 time: 0.335145 data_time: 0.028264 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.848749 loss: 0.000609 2022/09/12 22:55:40 - mmengine - INFO - Epoch(train) [51][350/586] lr: 5.000000e-04 eta: 8:26:08 time: 0.336158 data_time: 0.023771 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.765316 loss: 0.000613 2022/09/12 22:55:56 - mmengine - INFO - Epoch(train) [51][400/586] lr: 5.000000e-04 eta: 8:25:54 time: 0.336514 data_time: 0.022543 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.769482 loss: 0.000628 2022/09/12 22:56:13 - mmengine - INFO - Epoch(train) [51][450/586] lr: 5.000000e-04 eta: 8:25:39 time: 0.336323 data_time: 0.027635 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.818833 loss: 0.000618 2022/09/12 22:56:30 - mmengine - INFO - Epoch(train) [51][500/586] lr: 5.000000e-04 eta: 8:25:24 time: 0.328102 data_time: 0.022716 memory: 7489 loss_kpt: 0.000638 acc_pose: 0.849246 loss: 0.000638 2022/09/12 22:56:46 - mmengine - INFO - Epoch(train) [51][550/586] lr: 5.000000e-04 eta: 8:25:09 time: 0.334804 data_time: 0.022885 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.862829 loss: 0.000599 2022/09/12 22:56:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:56:59 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/09/12 22:57:23 - mmengine - INFO - Epoch(train) [52][50/586] lr: 5.000000e-04 eta: 8:24:06 time: 0.337395 data_time: 0.032543 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.870505 loss: 0.000608 2022/09/12 22:57:40 - mmengine - INFO - Epoch(train) [52][100/586] lr: 5.000000e-04 eta: 8:23:52 time: 0.340536 data_time: 0.027029 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.862453 loss: 0.000599 2022/09/12 22:57:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 22:57:56 - mmengine - INFO - Epoch(train) [52][150/586] lr: 5.000000e-04 eta: 8:23:38 time: 0.337281 data_time: 0.022721 memory: 7489 loss_kpt: 0.000642 acc_pose: 0.798530 loss: 0.000642 2022/09/12 22:58:13 - mmengine - INFO - Epoch(train) [52][200/586] lr: 5.000000e-04 eta: 8:23:23 time: 0.334156 data_time: 0.021970 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.831617 loss: 0.000610 2022/09/12 22:58:30 - mmengine - INFO - Epoch(train) [52][250/586] lr: 5.000000e-04 eta: 8:23:09 time: 0.335582 data_time: 0.023312 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.772244 loss: 0.000608 2022/09/12 22:58:47 - mmengine - INFO - Epoch(train) [52][300/586] lr: 5.000000e-04 eta: 8:22:54 time: 0.333555 data_time: 0.022508 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.794704 loss: 0.000614 2022/09/12 22:59:03 - mmengine - INFO - Epoch(train) [52][350/586] lr: 5.000000e-04 eta: 8:22:39 time: 0.336872 data_time: 0.022783 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.765890 loss: 0.000639 2022/09/12 22:59:20 - mmengine - INFO - Epoch(train) [52][400/586] lr: 5.000000e-04 eta: 8:22:25 time: 0.337052 data_time: 0.022372 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.832144 loss: 0.000602 2022/09/12 22:59:37 - mmengine - INFO - Epoch(train) [52][450/586] lr: 5.000000e-04 eta: 8:22:11 time: 0.336124 data_time: 0.022241 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.802838 loss: 0.000603 2022/09/12 22:59:54 - mmengine - INFO - Epoch(train) [52][500/586] lr: 5.000000e-04 eta: 8:21:57 time: 0.341535 data_time: 0.022801 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.773476 loss: 0.000623 2022/09/12 23:00:11 - mmengine - INFO - Epoch(train) [52][550/586] lr: 5.000000e-04 eta: 8:21:43 time: 0.340365 data_time: 0.027381 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.852684 loss: 0.000605 2022/09/12 23:00:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:00:23 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/09/12 23:00:48 - mmengine - INFO - Epoch(train) [53][50/586] lr: 5.000000e-04 eta: 8:20:43 time: 0.348024 data_time: 0.031386 memory: 7489 loss_kpt: 0.000639 acc_pose: 0.822628 loss: 0.000639 2022/09/12 23:01:05 - mmengine - INFO - Epoch(train) [53][100/586] lr: 5.000000e-04 eta: 8:20:29 time: 0.337781 data_time: 0.025829 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.806364 loss: 0.000622 2022/09/12 23:01:22 - mmengine - INFO - Epoch(train) [53][150/586] lr: 5.000000e-04 eta: 8:20:14 time: 0.337005 data_time: 0.026080 memory: 7489 loss_kpt: 0.000628 acc_pose: 0.802987 loss: 0.000628 2022/09/12 23:01:39 - mmengine - INFO - Epoch(train) [53][200/586] lr: 5.000000e-04 eta: 8:20:00 time: 0.339719 data_time: 0.023454 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.841791 loss: 0.000607 2022/09/12 23:01:55 - mmengine - INFO - Epoch(train) [53][250/586] lr: 5.000000e-04 eta: 8:19:45 time: 0.333402 data_time: 0.022223 memory: 7489 loss_kpt: 0.000624 acc_pose: 0.865168 loss: 0.000624 2022/09/12 23:02:12 - mmengine - INFO - Epoch(train) [53][300/586] lr: 5.000000e-04 eta: 8:19:32 time: 0.343665 data_time: 0.026396 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.846056 loss: 0.000595 2022/09/12 23:02:29 - mmengine - INFO - Epoch(train) [53][350/586] lr: 5.000000e-04 eta: 8:19:17 time: 0.331482 data_time: 0.022885 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.780980 loss: 0.000614 2022/09/12 23:02:46 - mmengine - INFO - Epoch(train) [53][400/586] lr: 5.000000e-04 eta: 8:19:03 time: 0.338693 data_time: 0.023082 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.827648 loss: 0.000610 2022/09/12 23:03:03 - mmengine - INFO - Epoch(train) [53][450/586] lr: 5.000000e-04 eta: 8:18:49 time: 0.340851 data_time: 0.022895 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.810012 loss: 0.000617 2022/09/12 23:03:19 - mmengine - INFO - Epoch(train) [53][500/586] lr: 5.000000e-04 eta: 8:18:33 time: 0.330088 data_time: 0.021665 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.889220 loss: 0.000612 2022/09/12 23:03:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:03:36 - mmengine - INFO - Epoch(train) [53][550/586] lr: 5.000000e-04 eta: 8:18:19 time: 0.337415 data_time: 0.022189 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.829937 loss: 0.000591 2022/09/12 23:03:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:03:49 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/09/12 23:04:12 - mmengine - INFO - Epoch(train) [54][50/586] lr: 5.000000e-04 eta: 8:17:19 time: 0.340187 data_time: 0.031009 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.868304 loss: 0.000610 2022/09/12 23:04:29 - mmengine - INFO - Epoch(train) [54][100/586] lr: 5.000000e-04 eta: 8:17:05 time: 0.339499 data_time: 0.023142 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.831760 loss: 0.000613 2022/09/12 23:04:46 - mmengine - INFO - Epoch(train) [54][150/586] lr: 5.000000e-04 eta: 8:16:50 time: 0.337007 data_time: 0.023680 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.803359 loss: 0.000604 2022/09/12 23:05:03 - mmengine - INFO - Epoch(train) [54][200/586] lr: 5.000000e-04 eta: 8:16:36 time: 0.338171 data_time: 0.024160 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.860681 loss: 0.000605 2022/09/12 23:05:20 - mmengine - INFO - Epoch(train) [54][250/586] lr: 5.000000e-04 eta: 8:16:23 time: 0.345517 data_time: 0.022959 memory: 7489 loss_kpt: 0.000612 acc_pose: 0.787413 loss: 0.000612 2022/09/12 23:05:37 - mmengine - INFO - Epoch(train) [54][300/586] lr: 5.000000e-04 eta: 8:16:09 time: 0.339170 data_time: 0.023018 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.840261 loss: 0.000607 2022/09/12 23:05:54 - mmengine - INFO - Epoch(train) [54][350/586] lr: 5.000000e-04 eta: 8:15:54 time: 0.335698 data_time: 0.023488 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.696930 loss: 0.000614 2022/09/12 23:06:11 - mmengine - INFO - Epoch(train) [54][400/586] lr: 5.000000e-04 eta: 8:15:40 time: 0.339681 data_time: 0.025517 memory: 7489 loss_kpt: 0.000631 acc_pose: 0.841109 loss: 0.000631 2022/09/12 23:06:28 - mmengine - INFO - Epoch(train) [54][450/586] lr: 5.000000e-04 eta: 8:15:26 time: 0.335578 data_time: 0.022795 memory: 7489 loss_kpt: 0.000641 acc_pose: 0.820191 loss: 0.000641 2022/09/12 23:06:44 - mmengine - INFO - Epoch(train) [54][500/586] lr: 5.000000e-04 eta: 8:15:10 time: 0.331763 data_time: 0.023021 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.860270 loss: 0.000616 2022/09/12 23:07:01 - mmengine - INFO - Epoch(train) [54][550/586] lr: 5.000000e-04 eta: 8:14:56 time: 0.337849 data_time: 0.022634 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.852539 loss: 0.000610 2022/09/12 23:07:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:07:13 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/09/12 23:07:37 - mmengine - INFO - Epoch(train) [55][50/586] lr: 5.000000e-04 eta: 8:13:56 time: 0.339021 data_time: 0.029540 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.810039 loss: 0.000615 2022/09/12 23:07:54 - mmengine - INFO - Epoch(train) [55][100/586] lr: 5.000000e-04 eta: 8:13:43 time: 0.341910 data_time: 0.023324 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.837626 loss: 0.000593 2022/09/12 23:08:11 - mmengine - INFO - Epoch(train) [55][150/586] lr: 5.000000e-04 eta: 8:13:28 time: 0.334617 data_time: 0.027193 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.827177 loss: 0.000593 2022/09/12 23:08:28 - mmengine - INFO - Epoch(train) [55][200/586] lr: 5.000000e-04 eta: 8:13:13 time: 0.334418 data_time: 0.022614 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.868475 loss: 0.000601 2022/09/12 23:08:45 - mmengine - INFO - Epoch(train) [55][250/586] lr: 5.000000e-04 eta: 8:12:59 time: 0.335930 data_time: 0.022538 memory: 7489 loss_kpt: 0.000615 acc_pose: 0.878841 loss: 0.000615 2022/09/12 23:09:02 - mmengine - INFO - Epoch(train) [55][300/586] lr: 5.000000e-04 eta: 8:12:45 time: 0.340509 data_time: 0.022584 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.873966 loss: 0.000582 2022/09/12 23:09:19 - mmengine - INFO - Epoch(train) [55][350/586] lr: 5.000000e-04 eta: 8:12:31 time: 0.340140 data_time: 0.021948 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.796893 loss: 0.000608 2022/09/12 23:09:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:09:35 - mmengine - INFO - Epoch(train) [55][400/586] lr: 5.000000e-04 eta: 8:12:15 time: 0.330396 data_time: 0.023590 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.827996 loss: 0.000603 2022/09/12 23:09:53 - mmengine - INFO - Epoch(train) [55][450/586] lr: 5.000000e-04 eta: 8:12:02 time: 0.346261 data_time: 0.029469 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.881402 loss: 0.000594 2022/09/12 23:10:10 - mmengine - INFO - Epoch(train) [55][500/586] lr: 5.000000e-04 eta: 8:11:48 time: 0.338110 data_time: 0.022410 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.819970 loss: 0.000616 2022/09/12 23:10:26 - mmengine - INFO - Epoch(train) [55][550/586] lr: 5.000000e-04 eta: 8:11:33 time: 0.334352 data_time: 0.022941 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.838161 loss: 0.000603 2022/09/12 23:10:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:10:39 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/09/12 23:11:03 - mmengine - INFO - Epoch(train) [56][50/586] lr: 5.000000e-04 eta: 8:10:35 time: 0.342486 data_time: 0.030640 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.737970 loss: 0.000604 2022/09/12 23:11:20 - mmengine - INFO - Epoch(train) [56][100/586] lr: 5.000000e-04 eta: 8:10:20 time: 0.337962 data_time: 0.027027 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.818063 loss: 0.000611 2022/09/12 23:11:36 - mmengine - INFO - Epoch(train) [56][150/586] lr: 5.000000e-04 eta: 8:10:06 time: 0.336700 data_time: 0.022461 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.774205 loss: 0.000605 2022/09/12 23:11:53 - mmengine - INFO - Epoch(train) [56][200/586] lr: 5.000000e-04 eta: 8:09:50 time: 0.328112 data_time: 0.023184 memory: 7489 loss_kpt: 0.000621 acc_pose: 0.743437 loss: 0.000621 2022/09/12 23:12:10 - mmengine - INFO - Epoch(train) [56][250/586] lr: 5.000000e-04 eta: 8:09:37 time: 0.346046 data_time: 0.026545 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.795359 loss: 0.000611 2022/09/12 23:12:27 - mmengine - INFO - Epoch(train) [56][300/586] lr: 5.000000e-04 eta: 8:09:24 time: 0.347736 data_time: 0.023681 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.820646 loss: 0.000585 2022/09/12 23:12:44 - mmengine - INFO - Epoch(train) [56][350/586] lr: 5.000000e-04 eta: 8:09:09 time: 0.333773 data_time: 0.022837 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.813862 loss: 0.000602 2022/09/12 23:13:01 - mmengine - INFO - Epoch(train) [56][400/586] lr: 5.000000e-04 eta: 8:08:54 time: 0.332670 data_time: 0.023364 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.824751 loss: 0.000604 2022/09/12 23:13:18 - mmengine - INFO - Epoch(train) [56][450/586] lr: 5.000000e-04 eta: 8:08:40 time: 0.338992 data_time: 0.022577 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.814757 loss: 0.000609 2022/09/12 23:13:34 - mmengine - INFO - Epoch(train) [56][500/586] lr: 5.000000e-04 eta: 8:08:25 time: 0.330662 data_time: 0.023541 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.802762 loss: 0.000617 2022/09/12 23:13:51 - mmengine - INFO - Epoch(train) [56][550/586] lr: 5.000000e-04 eta: 8:08:10 time: 0.334170 data_time: 0.026506 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.776339 loss: 0.000607 2022/09/12 23:14:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:14:03 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/09/12 23:14:27 - mmengine - INFO - Epoch(train) [57][50/586] lr: 5.000000e-04 eta: 8:07:11 time: 0.333046 data_time: 0.028514 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.842434 loss: 0.000595 2022/09/12 23:14:44 - mmengine - INFO - Epoch(train) [57][100/586] lr: 5.000000e-04 eta: 8:06:56 time: 0.331416 data_time: 0.022093 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.788012 loss: 0.000591 2022/09/12 23:15:00 - mmengine - INFO - Epoch(train) [57][150/586] lr: 5.000000e-04 eta: 8:06:41 time: 0.334431 data_time: 0.022855 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.788616 loss: 0.000619 2022/09/12 23:15:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:15:17 - mmengine - INFO - Epoch(train) [57][200/586] lr: 5.000000e-04 eta: 8:06:26 time: 0.330460 data_time: 0.023290 memory: 7489 loss_kpt: 0.000618 acc_pose: 0.785749 loss: 0.000618 2022/09/12 23:15:34 - mmengine - INFO - Epoch(train) [57][250/586] lr: 5.000000e-04 eta: 8:06:11 time: 0.337584 data_time: 0.021824 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.828048 loss: 0.000611 2022/09/12 23:15:51 - mmengine - INFO - Epoch(train) [57][300/586] lr: 5.000000e-04 eta: 8:05:57 time: 0.336668 data_time: 0.022350 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.827533 loss: 0.000600 2022/09/12 23:16:07 - mmengine - INFO - Epoch(train) [57][350/586] lr: 5.000000e-04 eta: 8:05:42 time: 0.337104 data_time: 0.026781 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.828275 loss: 0.000613 2022/09/12 23:16:24 - mmengine - INFO - Epoch(train) [57][400/586] lr: 5.000000e-04 eta: 8:05:27 time: 0.332037 data_time: 0.022144 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.762671 loss: 0.000609 2022/09/12 23:16:41 - mmengine - INFO - Epoch(train) [57][450/586] lr: 5.000000e-04 eta: 8:05:12 time: 0.335027 data_time: 0.023391 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.859461 loss: 0.000603 2022/09/12 23:16:57 - mmengine - INFO - Epoch(train) [57][500/586] lr: 5.000000e-04 eta: 8:04:57 time: 0.333583 data_time: 0.024039 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.796241 loss: 0.000598 2022/09/12 23:17:14 - mmengine - INFO - Epoch(train) [57][550/586] lr: 5.000000e-04 eta: 8:04:43 time: 0.333959 data_time: 0.022607 memory: 7489 loss_kpt: 0.000637 acc_pose: 0.792961 loss: 0.000637 2022/09/12 23:17:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:17:27 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/09/12 23:17:50 - mmengine - INFO - Epoch(train) [58][50/586] lr: 5.000000e-04 eta: 8:03:45 time: 0.332934 data_time: 0.025869 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.822976 loss: 0.000609 2022/09/12 23:18:07 - mmengine - INFO - Epoch(train) [58][100/586] lr: 5.000000e-04 eta: 8:03:30 time: 0.335581 data_time: 0.022532 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.808828 loss: 0.000604 2022/09/12 23:18:24 - mmengine - INFO - Epoch(train) [58][150/586] lr: 5.000000e-04 eta: 8:03:16 time: 0.340835 data_time: 0.026907 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.756585 loss: 0.000604 2022/09/12 23:18:41 - mmengine - INFO - Epoch(train) [58][200/586] lr: 5.000000e-04 eta: 8:03:01 time: 0.335944 data_time: 0.024002 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.926062 loss: 0.000613 2022/09/12 23:18:58 - mmengine - INFO - Epoch(train) [58][250/586] lr: 5.000000e-04 eta: 8:02:47 time: 0.335459 data_time: 0.022793 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.776115 loss: 0.000592 2022/09/12 23:19:15 - mmengine - INFO - Epoch(train) [58][300/586] lr: 5.000000e-04 eta: 8:02:33 time: 0.343899 data_time: 0.026031 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.830820 loss: 0.000607 2022/09/12 23:19:32 - mmengine - INFO - Epoch(train) [58][350/586] lr: 5.000000e-04 eta: 8:02:18 time: 0.330364 data_time: 0.022185 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.843204 loss: 0.000608 2022/09/12 23:19:48 - mmengine - INFO - Epoch(train) [58][400/586] lr: 5.000000e-04 eta: 8:02:03 time: 0.334221 data_time: 0.021951 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.795637 loss: 0.000591 2022/09/12 23:20:06 - mmengine - INFO - Epoch(train) [58][450/586] lr: 5.000000e-04 eta: 8:01:50 time: 0.351387 data_time: 0.026967 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.871745 loss: 0.000589 2022/09/12 23:20:22 - mmengine - INFO - Epoch(train) [58][500/586] lr: 5.000000e-04 eta: 8:01:35 time: 0.332498 data_time: 0.022466 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.880354 loss: 0.000596 2022/09/12 23:20:39 - mmengine - INFO - Epoch(train) [58][550/586] lr: 5.000000e-04 eta: 8:01:21 time: 0.337432 data_time: 0.022669 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.801994 loss: 0.000607 2022/09/12 23:20:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:20:52 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/09/12 23:21:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:21:16 - mmengine - INFO - Epoch(train) [59][50/586] lr: 5.000000e-04 eta: 8:00:24 time: 0.340260 data_time: 0.029862 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.796366 loss: 0.000602 2022/09/12 23:21:33 - mmengine - INFO - Epoch(train) [59][100/586] lr: 5.000000e-04 eta: 8:00:10 time: 0.338204 data_time: 0.022835 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.831304 loss: 0.000586 2022/09/12 23:21:50 - mmengine - INFO - Epoch(train) [59][150/586] lr: 5.000000e-04 eta: 7:59:56 time: 0.341126 data_time: 0.021903 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.827755 loss: 0.000595 2022/09/12 23:22:06 - mmengine - INFO - Epoch(train) [59][200/586] lr: 5.000000e-04 eta: 7:59:41 time: 0.330147 data_time: 0.023131 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.864961 loss: 0.000586 2022/09/12 23:22:23 - mmengine - INFO - Epoch(train) [59][250/586] lr: 5.000000e-04 eta: 7:59:26 time: 0.336742 data_time: 0.025320 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.792939 loss: 0.000608 2022/09/12 23:22:40 - mmengine - INFO - Epoch(train) [59][300/586] lr: 5.000000e-04 eta: 7:59:12 time: 0.335659 data_time: 0.023777 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.831333 loss: 0.000608 2022/09/12 23:22:57 - mmengine - INFO - Epoch(train) [59][350/586] lr: 5.000000e-04 eta: 7:58:57 time: 0.332490 data_time: 0.023023 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.786542 loss: 0.000597 2022/09/12 23:23:13 - mmengine - INFO - Epoch(train) [59][400/586] lr: 5.000000e-04 eta: 7:58:42 time: 0.333873 data_time: 0.022835 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.806079 loss: 0.000600 2022/09/12 23:23:30 - mmengine - INFO - Epoch(train) [59][450/586] lr: 5.000000e-04 eta: 7:58:27 time: 0.332849 data_time: 0.022198 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.856407 loss: 0.000611 2022/09/12 23:23:47 - mmengine - INFO - Epoch(train) [59][500/586] lr: 5.000000e-04 eta: 7:58:12 time: 0.334529 data_time: 0.023048 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.796281 loss: 0.000591 2022/09/12 23:24:04 - mmengine - INFO - Epoch(train) [59][550/586] lr: 5.000000e-04 eta: 7:57:57 time: 0.337515 data_time: 0.022239 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.788186 loss: 0.000608 2022/09/12 23:24:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:24:16 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/09/12 23:24:40 - mmengine - INFO - Epoch(train) [60][50/586] lr: 5.000000e-04 eta: 7:57:01 time: 0.335926 data_time: 0.030305 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.867186 loss: 0.000607 2022/09/12 23:24:56 - mmengine - INFO - Epoch(train) [60][100/586] lr: 5.000000e-04 eta: 7:56:46 time: 0.333005 data_time: 0.025820 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.776763 loss: 0.000588 2022/09/12 23:25:13 - mmengine - INFO - Epoch(train) [60][150/586] lr: 5.000000e-04 eta: 7:56:31 time: 0.333728 data_time: 0.023354 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.763457 loss: 0.000602 2022/09/12 23:25:30 - mmengine - INFO - Epoch(train) [60][200/586] lr: 5.000000e-04 eta: 7:56:17 time: 0.338731 data_time: 0.026118 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.844380 loss: 0.000606 2022/09/12 23:25:47 - mmengine - INFO - Epoch(train) [60][250/586] lr: 5.000000e-04 eta: 7:56:03 time: 0.338693 data_time: 0.023364 memory: 7489 loss_kpt: 0.000623 acc_pose: 0.830287 loss: 0.000623 2022/09/12 23:26:04 - mmengine - INFO - Epoch(train) [60][300/586] lr: 5.000000e-04 eta: 7:55:48 time: 0.338705 data_time: 0.022360 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.835121 loss: 0.000600 2022/09/12 23:26:21 - mmengine - INFO - Epoch(train) [60][350/586] lr: 5.000000e-04 eta: 7:55:35 time: 0.341322 data_time: 0.023414 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.772473 loss: 0.000614 2022/09/12 23:26:37 - mmengine - INFO - Epoch(train) [60][400/586] lr: 5.000000e-04 eta: 7:55:19 time: 0.330248 data_time: 0.023203 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.856265 loss: 0.000603 2022/09/12 23:26:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:26:54 - mmengine - INFO - Epoch(train) [60][450/586] lr: 5.000000e-04 eta: 7:55:04 time: 0.335425 data_time: 0.022546 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.809451 loss: 0.000616 2022/09/12 23:27:11 - mmengine - INFO - Epoch(train) [60][500/586] lr: 5.000000e-04 eta: 7:54:50 time: 0.337714 data_time: 0.026473 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.667837 loss: 0.000590 2022/09/12 23:27:28 - mmengine - INFO - Epoch(train) [60][550/586] lr: 5.000000e-04 eta: 7:54:36 time: 0.341355 data_time: 0.023465 memory: 7489 loss_kpt: 0.000617 acc_pose: 0.832060 loss: 0.000617 2022/09/12 23:27:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:27:40 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/09/12 23:27:57 - mmengine - INFO - Epoch(val) [60][50/407] eta: 0:01:06 time: 0.185626 data_time: 0.012350 memory: 7489 2022/09/12 23:28:06 - mmengine - INFO - Epoch(val) [60][100/407] eta: 0:00:54 time: 0.177830 data_time: 0.007737 memory: 1657 2022/09/12 23:28:15 - mmengine - INFO - Epoch(val) [60][150/407] eta: 0:00:45 time: 0.178552 data_time: 0.008004 memory: 1657 2022/09/12 23:28:24 - mmengine - INFO - Epoch(val) [60][200/407] eta: 0:00:36 time: 0.178116 data_time: 0.007823 memory: 1657 2022/09/12 23:28:33 - mmengine - INFO - Epoch(val) [60][250/407] eta: 0:00:28 time: 0.178477 data_time: 0.007661 memory: 1657 2022/09/12 23:28:42 - mmengine - INFO - Epoch(val) [60][300/407] eta: 0:00:19 time: 0.178480 data_time: 0.007516 memory: 1657 2022/09/12 23:28:51 - mmengine - INFO - Epoch(val) [60][350/407] eta: 0:00:10 time: 0.182815 data_time: 0.012365 memory: 1657 2022/09/12 23:29:00 - mmengine - INFO - Epoch(val) [60][400/407] eta: 0:00:01 time: 0.177975 data_time: 0.007158 memory: 1657 2022/09/12 23:29:35 - mmengine - INFO - Evaluating CocoMetric... 2022/09/12 23:29:48 - mmengine - INFO - Epoch(val) [60][407/407] coco/AP: 0.740921 coco/AP .5: 0.894543 coco/AP .75: 0.806378 coco/AP (M): 0.705017 coco/AP (L): 0.808982 coco/AR: 0.793120 coco/AR .5: 0.935926 coco/AR .75: 0.850598 coco/AR (M): 0.751188 coco/AR (L): 0.854143 2022/09/12 23:29:48 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_50.pth is removed 2022/09/12 23:29:52 - mmengine - INFO - The best checkpoint with 0.7409 coco/AP at 60 epoch is saved to best_coco/AP_epoch_60.pth. 2022/09/12 23:30:09 - mmengine - INFO - Epoch(train) [61][50/586] lr: 5.000000e-04 eta: 7:53:42 time: 0.344361 data_time: 0.030169 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.826837 loss: 0.000588 2022/09/12 23:30:25 - mmengine - INFO - Epoch(train) [61][100/586] lr: 5.000000e-04 eta: 7:53:26 time: 0.327032 data_time: 0.022586 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.839557 loss: 0.000611 2022/09/12 23:30:43 - mmengine - INFO - Epoch(train) [61][150/586] lr: 5.000000e-04 eta: 7:53:13 time: 0.347032 data_time: 0.023260 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.818114 loss: 0.000575 2022/09/12 23:31:00 - mmengine - INFO - Epoch(train) [61][200/586] lr: 5.000000e-04 eta: 7:52:58 time: 0.336337 data_time: 0.022954 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.808338 loss: 0.000613 2022/09/12 23:31:16 - mmengine - INFO - Epoch(train) [61][250/586] lr: 5.000000e-04 eta: 7:52:43 time: 0.335682 data_time: 0.023135 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.860240 loss: 0.000601 2022/09/12 23:31:33 - mmengine - INFO - Epoch(train) [61][300/586] lr: 5.000000e-04 eta: 7:52:29 time: 0.338142 data_time: 0.022596 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.824253 loss: 0.000595 2022/09/12 23:31:50 - mmengine - INFO - Epoch(train) [61][350/586] lr: 5.000000e-04 eta: 7:52:14 time: 0.333921 data_time: 0.026241 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.812868 loss: 0.000607 2022/09/12 23:32:07 - mmengine - INFO - Epoch(train) [61][400/586] lr: 5.000000e-04 eta: 7:51:59 time: 0.333154 data_time: 0.023572 memory: 7489 loss_kpt: 0.000610 acc_pose: 0.770144 loss: 0.000610 2022/09/12 23:32:23 - mmengine - INFO - Epoch(train) [61][450/586] lr: 5.000000e-04 eta: 7:51:44 time: 0.332725 data_time: 0.022442 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.843190 loss: 0.000607 2022/09/12 23:32:40 - mmengine - INFO - Epoch(train) [61][500/586] lr: 5.000000e-04 eta: 7:51:30 time: 0.338845 data_time: 0.026203 memory: 7489 loss_kpt: 0.000603 acc_pose: 0.846885 loss: 0.000603 2022/09/12 23:32:57 - mmengine - INFO - Epoch(train) [61][550/586] lr: 5.000000e-04 eta: 7:51:14 time: 0.331132 data_time: 0.022889 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.808271 loss: 0.000594 2022/09/12 23:33:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:33:09 - mmengine - INFO - Saving checkpoint at 61 epochs 2022/09/12 23:33:33 - mmengine - INFO - Epoch(train) [62][50/586] lr: 5.000000e-04 eta: 7:50:20 time: 0.337639 data_time: 0.026969 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.820747 loss: 0.000600 2022/09/12 23:33:50 - mmengine - INFO - Epoch(train) [62][100/586] lr: 5.000000e-04 eta: 7:50:06 time: 0.339020 data_time: 0.026313 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.834944 loss: 0.000601 2022/09/12 23:34:07 - mmengine - INFO - Epoch(train) [62][150/586] lr: 5.000000e-04 eta: 7:49:51 time: 0.337589 data_time: 0.022750 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.823084 loss: 0.000589 2022/09/12 23:34:24 - mmengine - INFO - Epoch(train) [62][200/586] lr: 5.000000e-04 eta: 7:49:37 time: 0.336945 data_time: 0.023283 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.812085 loss: 0.000599 2022/09/12 23:34:41 - mmengine - INFO - Epoch(train) [62][250/586] lr: 5.000000e-04 eta: 7:49:22 time: 0.334806 data_time: 0.022210 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.811539 loss: 0.000590 2022/09/12 23:34:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:34:57 - mmengine - INFO - Epoch(train) [62][300/586] lr: 5.000000e-04 eta: 7:49:07 time: 0.339355 data_time: 0.023395 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.861930 loss: 0.000591 2022/09/12 23:35:14 - mmengine - INFO - Epoch(train) [62][350/586] lr: 5.000000e-04 eta: 7:48:53 time: 0.333402 data_time: 0.023757 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.789763 loss: 0.000579 2022/09/12 23:35:31 - mmengine - INFO - Epoch(train) [62][400/586] lr: 5.000000e-04 eta: 7:48:39 time: 0.342722 data_time: 0.025572 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.883747 loss: 0.000598 2022/09/12 23:35:48 - mmengine - INFO - Epoch(train) [62][450/586] lr: 5.000000e-04 eta: 7:48:23 time: 0.330704 data_time: 0.022744 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.811091 loss: 0.000596 2022/09/12 23:36:05 - mmengine - INFO - Epoch(train) [62][500/586] lr: 5.000000e-04 eta: 7:48:09 time: 0.336073 data_time: 0.022601 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.831739 loss: 0.000605 2022/09/12 23:36:21 - mmengine - INFO - Epoch(train) [62][550/586] lr: 5.000000e-04 eta: 7:47:53 time: 0.329797 data_time: 0.022456 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.778930 loss: 0.000601 2022/09/12 23:36:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:36:33 - mmengine - INFO - Saving checkpoint at 62 epochs 2022/09/12 23:36:58 - mmengine - INFO - Epoch(train) [63][50/586] lr: 5.000000e-04 eta: 7:47:00 time: 0.345410 data_time: 0.028615 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.855601 loss: 0.000595 2022/09/12 23:37:15 - mmengine - INFO - Epoch(train) [63][100/586] lr: 5.000000e-04 eta: 7:46:45 time: 0.332565 data_time: 0.022606 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.835857 loss: 0.000604 2022/09/12 23:37:32 - mmengine - INFO - Epoch(train) [63][150/586] lr: 5.000000e-04 eta: 7:46:31 time: 0.337945 data_time: 0.022587 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.852419 loss: 0.000597 2022/09/12 23:37:49 - mmengine - INFO - Epoch(train) [63][200/586] lr: 5.000000e-04 eta: 7:46:17 time: 0.346380 data_time: 0.023043 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.849454 loss: 0.000590 2022/09/12 23:38:06 - mmengine - INFO - Epoch(train) [63][250/586] lr: 5.000000e-04 eta: 7:46:02 time: 0.332980 data_time: 0.024258 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.809771 loss: 0.000598 2022/09/12 23:38:22 - mmengine - INFO - Epoch(train) [63][300/586] lr: 5.000000e-04 eta: 7:45:47 time: 0.333595 data_time: 0.022877 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.809253 loss: 0.000606 2022/09/12 23:38:40 - mmengine - INFO - Epoch(train) [63][350/586] lr: 5.000000e-04 eta: 7:45:34 time: 0.348057 data_time: 0.026031 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.832871 loss: 0.000574 2022/09/12 23:38:56 - mmengine - INFO - Epoch(train) [63][400/586] lr: 5.000000e-04 eta: 7:45:19 time: 0.329329 data_time: 0.024121 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.840167 loss: 0.000596 2022/09/12 23:39:13 - mmengine - INFO - Epoch(train) [63][450/586] lr: 5.000000e-04 eta: 7:45:04 time: 0.335188 data_time: 0.022578 memory: 7489 loss_kpt: 0.000607 acc_pose: 0.835408 loss: 0.000607 2022/09/12 23:39:30 - mmengine - INFO - Epoch(train) [63][500/586] lr: 5.000000e-04 eta: 7:44:50 time: 0.340680 data_time: 0.022878 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.772797 loss: 0.000604 2022/09/12 23:39:47 - mmengine - INFO - Epoch(train) [63][550/586] lr: 5.000000e-04 eta: 7:44:35 time: 0.334193 data_time: 0.023315 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.872541 loss: 0.000591 2022/09/12 23:39:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:39:59 - mmengine - INFO - Saving checkpoint at 63 epochs 2022/09/12 23:40:23 - mmengine - INFO - Epoch(train) [64][50/586] lr: 5.000000e-04 eta: 7:43:42 time: 0.338544 data_time: 0.031579 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.831967 loss: 0.000569 2022/09/12 23:40:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:40:40 - mmengine - INFO - Epoch(train) [64][100/586] lr: 5.000000e-04 eta: 7:43:27 time: 0.336564 data_time: 0.023681 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.882106 loss: 0.000586 2022/09/12 23:40:57 - mmengine - INFO - Epoch(train) [64][150/586] lr: 5.000000e-04 eta: 7:43:13 time: 0.341721 data_time: 0.022173 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.853243 loss: 0.000595 2022/09/12 23:41:14 - mmengine - INFO - Epoch(train) [64][200/586] lr: 5.000000e-04 eta: 7:42:59 time: 0.339143 data_time: 0.023256 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.834641 loss: 0.000584 2022/09/12 23:41:31 - mmengine - INFO - Epoch(train) [64][250/586] lr: 5.000000e-04 eta: 7:42:44 time: 0.339237 data_time: 0.022818 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.819696 loss: 0.000600 2022/09/12 23:41:48 - mmengine - INFO - Epoch(train) [64][300/586] lr: 5.000000e-04 eta: 7:42:30 time: 0.340440 data_time: 0.025545 memory: 7489 loss_kpt: 0.000613 acc_pose: 0.816542 loss: 0.000613 2022/09/12 23:42:05 - mmengine - INFO - Epoch(train) [64][350/586] lr: 5.000000e-04 eta: 7:42:15 time: 0.332698 data_time: 0.023663 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.874225 loss: 0.000600 2022/09/12 23:42:22 - mmengine - INFO - Epoch(train) [64][400/586] lr: 5.000000e-04 eta: 7:42:01 time: 0.342195 data_time: 0.023016 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.841321 loss: 0.000597 2022/09/12 23:42:38 - mmengine - INFO - Epoch(train) [64][450/586] lr: 5.000000e-04 eta: 7:41:46 time: 0.333892 data_time: 0.022943 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.794550 loss: 0.000587 2022/09/12 23:42:55 - mmengine - INFO - Epoch(train) [64][500/586] lr: 5.000000e-04 eta: 7:41:32 time: 0.342053 data_time: 0.022229 memory: 7489 loss_kpt: 0.000611 acc_pose: 0.842794 loss: 0.000611 2022/09/12 23:43:12 - mmengine - INFO - Epoch(train) [64][550/586] lr: 5.000000e-04 eta: 7:41:17 time: 0.333568 data_time: 0.022974 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.835638 loss: 0.000597 2022/09/12 23:43:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:43:24 - mmengine - INFO - Saving checkpoint at 64 epochs 2022/09/12 23:43:48 - mmengine - INFO - Epoch(train) [65][50/586] lr: 5.000000e-04 eta: 7:40:25 time: 0.346284 data_time: 0.031504 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.806860 loss: 0.000601 2022/09/12 23:44:05 - mmengine - INFO - Epoch(train) [65][100/586] lr: 5.000000e-04 eta: 7:40:10 time: 0.329680 data_time: 0.023522 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.843862 loss: 0.000583 2022/09/12 23:44:22 - mmengine - INFO - Epoch(train) [65][150/586] lr: 5.000000e-04 eta: 7:39:56 time: 0.341378 data_time: 0.023388 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.824236 loss: 0.000594 2022/09/12 23:44:39 - mmengine - INFO - Epoch(train) [65][200/586] lr: 5.000000e-04 eta: 7:39:41 time: 0.337290 data_time: 0.026047 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.824998 loss: 0.000593 2022/09/12 23:44:55 - mmengine - INFO - Epoch(train) [65][250/586] lr: 5.000000e-04 eta: 7:39:26 time: 0.331156 data_time: 0.022904 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.844209 loss: 0.000599 2022/09/12 23:45:12 - mmengine - INFO - Epoch(train) [65][300/586] lr: 5.000000e-04 eta: 7:39:12 time: 0.337883 data_time: 0.024340 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.885000 loss: 0.000593 2022/09/12 23:45:29 - mmengine - INFO - Epoch(train) [65][350/586] lr: 5.000000e-04 eta: 7:38:57 time: 0.340428 data_time: 0.023922 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.838052 loss: 0.000602 2022/09/12 23:45:46 - mmengine - INFO - Epoch(train) [65][400/586] lr: 5.000000e-04 eta: 7:38:42 time: 0.334383 data_time: 0.024358 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.791673 loss: 0.000582 2022/09/12 23:46:03 - mmengine - INFO - Epoch(train) [65][450/586] lr: 5.000000e-04 eta: 7:38:28 time: 0.337096 data_time: 0.024655 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.816501 loss: 0.000598 2022/09/12 23:46:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:46:20 - mmengine - INFO - Epoch(train) [65][500/586] lr: 5.000000e-04 eta: 7:38:14 time: 0.342580 data_time: 0.031444 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.883545 loss: 0.000599 2022/09/12 23:46:36 - mmengine - INFO - Epoch(train) [65][550/586] lr: 5.000000e-04 eta: 7:37:58 time: 0.326607 data_time: 0.024759 memory: 7489 loss_kpt: 0.000609 acc_pose: 0.831365 loss: 0.000609 2022/09/12 23:46:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:46:49 - mmengine - INFO - Saving checkpoint at 65 epochs 2022/09/12 23:47:13 - mmengine - INFO - Epoch(train) [66][50/586] lr: 5.000000e-04 eta: 7:37:06 time: 0.337994 data_time: 0.030526 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.804496 loss: 0.000591 2022/09/12 23:47:30 - mmengine - INFO - Epoch(train) [66][100/586] lr: 5.000000e-04 eta: 7:36:53 time: 0.350981 data_time: 0.024225 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.795321 loss: 0.000614 2022/09/12 23:47:47 - mmengine - INFO - Epoch(train) [66][150/586] lr: 5.000000e-04 eta: 7:36:39 time: 0.340675 data_time: 0.024911 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.812752 loss: 0.000608 2022/09/12 23:48:04 - mmengine - INFO - Epoch(train) [66][200/586] lr: 5.000000e-04 eta: 7:36:23 time: 0.329718 data_time: 0.024385 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.783960 loss: 0.000600 2022/09/12 23:48:21 - mmengine - INFO - Epoch(train) [66][250/586] lr: 5.000000e-04 eta: 7:36:09 time: 0.335177 data_time: 0.022901 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.885244 loss: 0.000595 2022/09/12 23:48:37 - mmengine - INFO - Epoch(train) [66][300/586] lr: 5.000000e-04 eta: 7:35:54 time: 0.337704 data_time: 0.028485 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.755559 loss: 0.000593 2022/09/12 23:48:54 - mmengine - INFO - Epoch(train) [66][350/586] lr: 5.000000e-04 eta: 7:35:39 time: 0.335263 data_time: 0.024802 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.783073 loss: 0.000604 2022/09/12 23:49:11 - mmengine - INFO - Epoch(train) [66][400/586] lr: 5.000000e-04 eta: 7:35:25 time: 0.339058 data_time: 0.023561 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.916874 loss: 0.000572 2022/09/12 23:49:28 - mmengine - INFO - Epoch(train) [66][450/586] lr: 5.000000e-04 eta: 7:35:10 time: 0.335155 data_time: 0.027795 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.845057 loss: 0.000599 2022/09/12 23:49:45 - mmengine - INFO - Epoch(train) [66][500/586] lr: 5.000000e-04 eta: 7:34:56 time: 0.340458 data_time: 0.024017 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.819053 loss: 0.000604 2022/09/12 23:50:02 - mmengine - INFO - Epoch(train) [66][550/586] lr: 5.000000e-04 eta: 7:34:41 time: 0.337733 data_time: 0.024042 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.834413 loss: 0.000588 2022/09/12 23:50:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:50:14 - mmengine - INFO - Saving checkpoint at 66 epochs 2022/09/12 23:50:38 - mmengine - INFO - Epoch(train) [67][50/586] lr: 5.000000e-04 eta: 7:33:50 time: 0.339662 data_time: 0.031996 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.878022 loss: 0.000600 2022/09/12 23:50:55 - mmengine - INFO - Epoch(train) [67][100/586] lr: 5.000000e-04 eta: 7:33:36 time: 0.343292 data_time: 0.028657 memory: 7489 loss_kpt: 0.000608 acc_pose: 0.798449 loss: 0.000608 2022/09/12 23:51:12 - mmengine - INFO - Epoch(train) [67][150/586] lr: 5.000000e-04 eta: 7:33:21 time: 0.333253 data_time: 0.024156 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.833756 loss: 0.000596 2022/09/12 23:51:29 - mmengine - INFO - Epoch(train) [67][200/586] lr: 5.000000e-04 eta: 7:33:06 time: 0.335936 data_time: 0.024508 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.857808 loss: 0.000594 2022/09/12 23:51:46 - mmengine - INFO - Epoch(train) [67][250/586] lr: 5.000000e-04 eta: 7:32:52 time: 0.339593 data_time: 0.031673 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.824861 loss: 0.000602 2022/09/12 23:52:02 - mmengine - INFO - Epoch(train) [67][300/586] lr: 5.000000e-04 eta: 7:32:37 time: 0.331892 data_time: 0.023712 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.882685 loss: 0.000583 2022/09/12 23:52:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:52:19 - mmengine - INFO - Epoch(train) [67][350/586] lr: 5.000000e-04 eta: 7:32:22 time: 0.340128 data_time: 0.029808 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.874086 loss: 0.000583 2022/09/12 23:52:36 - mmengine - INFO - Epoch(train) [67][400/586] lr: 5.000000e-04 eta: 7:32:08 time: 0.335906 data_time: 0.025985 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.797951 loss: 0.000587 2022/09/12 23:52:53 - mmengine - INFO - Epoch(train) [67][450/586] lr: 5.000000e-04 eta: 7:31:53 time: 0.337581 data_time: 0.023943 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.845905 loss: 0.000595 2022/09/12 23:53:10 - mmengine - INFO - Epoch(train) [67][500/586] lr: 5.000000e-04 eta: 7:31:38 time: 0.336602 data_time: 0.032219 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.854027 loss: 0.000570 2022/09/12 23:53:27 - mmengine - INFO - Epoch(train) [67][550/586] lr: 5.000000e-04 eta: 7:31:24 time: 0.338661 data_time: 0.023647 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.876736 loss: 0.000581 2022/09/12 23:53:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:53:39 - mmengine - INFO - Saving checkpoint at 67 epochs 2022/09/12 23:54:03 - mmengine - INFO - Epoch(train) [68][50/586] lr: 5.000000e-04 eta: 7:30:32 time: 0.333855 data_time: 0.032486 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.801413 loss: 0.000575 2022/09/12 23:54:20 - mmengine - INFO - Epoch(train) [68][100/586] lr: 5.000000e-04 eta: 7:30:18 time: 0.343351 data_time: 0.024834 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.786202 loss: 0.000599 2022/09/12 23:54:37 - mmengine - INFO - Epoch(train) [68][150/586] lr: 5.000000e-04 eta: 7:30:04 time: 0.340860 data_time: 0.023827 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.793531 loss: 0.000590 2022/09/12 23:54:53 - mmengine - INFO - Epoch(train) [68][200/586] lr: 5.000000e-04 eta: 7:29:49 time: 0.331644 data_time: 0.024133 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.852074 loss: 0.000587 2022/09/12 23:55:10 - mmengine - INFO - Epoch(train) [68][250/586] lr: 5.000000e-04 eta: 7:29:34 time: 0.337207 data_time: 0.023989 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.866617 loss: 0.000590 2022/09/12 23:55:27 - mmengine - INFO - Epoch(train) [68][300/586] lr: 5.000000e-04 eta: 7:29:20 time: 0.337686 data_time: 0.023571 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.831790 loss: 0.000591 2022/09/12 23:55:44 - mmengine - INFO - Epoch(train) [68][350/586] lr: 5.000000e-04 eta: 7:29:05 time: 0.338947 data_time: 0.024839 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.860014 loss: 0.000592 2022/09/12 23:56:01 - mmengine - INFO - Epoch(train) [68][400/586] lr: 5.000000e-04 eta: 7:28:51 time: 0.336808 data_time: 0.024173 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.787145 loss: 0.000573 2022/09/12 23:56:18 - mmengine - INFO - Epoch(train) [68][450/586] lr: 5.000000e-04 eta: 7:28:36 time: 0.334526 data_time: 0.028381 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.857542 loss: 0.000582 2022/09/12 23:56:34 - mmengine - INFO - Epoch(train) [68][500/586] lr: 5.000000e-04 eta: 7:28:21 time: 0.333117 data_time: 0.024035 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.790590 loss: 0.000592 2022/09/12 23:56:51 - mmengine - INFO - Epoch(train) [68][550/586] lr: 5.000000e-04 eta: 7:28:06 time: 0.337244 data_time: 0.023390 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.803704 loss: 0.000614 2022/09/12 23:57:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:57:03 - mmengine - INFO - Saving checkpoint at 68 epochs 2022/09/12 23:57:27 - mmengine - INFO - Epoch(train) [69][50/586] lr: 5.000000e-04 eta: 7:27:16 time: 0.338696 data_time: 0.029816 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.809874 loss: 0.000589 2022/09/12 23:57:44 - mmengine - INFO - Epoch(train) [69][100/586] lr: 5.000000e-04 eta: 7:27:03 time: 0.354077 data_time: 0.030311 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.827196 loss: 0.000575 2022/09/12 23:58:01 - mmengine - INFO - Epoch(train) [69][150/586] lr: 5.000000e-04 eta: 7:26:47 time: 0.328934 data_time: 0.024243 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.852045 loss: 0.000594 2022/09/12 23:58:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/12 23:58:18 - mmengine - INFO - Epoch(train) [69][200/586] lr: 5.000000e-04 eta: 7:26:32 time: 0.334222 data_time: 0.022862 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.867739 loss: 0.000602 2022/09/12 23:58:35 - mmengine - INFO - Epoch(train) [69][250/586] lr: 5.000000e-04 eta: 7:26:18 time: 0.342350 data_time: 0.027677 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.771625 loss: 0.000605 2022/09/12 23:58:52 - mmengine - INFO - Epoch(train) [69][300/586] lr: 5.000000e-04 eta: 7:26:04 time: 0.337333 data_time: 0.023290 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.921980 loss: 0.000593 2022/09/12 23:59:08 - mmengine - INFO - Epoch(train) [69][350/586] lr: 5.000000e-04 eta: 7:25:48 time: 0.325916 data_time: 0.023248 memory: 7489 loss_kpt: 0.000597 acc_pose: 0.852409 loss: 0.000597 2022/09/12 23:59:25 - mmengine - INFO - Epoch(train) [69][400/586] lr: 5.000000e-04 eta: 7:25:33 time: 0.340675 data_time: 0.024458 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.888510 loss: 0.000578 2022/09/12 23:59:42 - mmengine - INFO - Epoch(train) [69][450/586] lr: 5.000000e-04 eta: 7:25:19 time: 0.337575 data_time: 0.023523 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.837882 loss: 0.000596 2022/09/12 23:59:58 - mmengine - INFO - Epoch(train) [69][500/586] lr: 5.000000e-04 eta: 7:25:03 time: 0.330616 data_time: 0.023619 memory: 7489 loss_kpt: 0.000622 acc_pose: 0.872299 loss: 0.000622 2022/09/13 00:00:18 - mmengine - INFO - Epoch(train) [69][550/586] lr: 5.000000e-04 eta: 7:24:54 time: 0.385502 data_time: 0.025252 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.782021 loss: 0.000596 2022/09/13 00:00:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:00:30 - mmengine - INFO - Saving checkpoint at 69 epochs 2022/09/13 00:00:54 - mmengine - INFO - Epoch(train) [70][50/586] lr: 5.000000e-04 eta: 7:24:05 time: 0.348993 data_time: 0.032716 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.794433 loss: 0.000588 2022/09/13 00:01:11 - mmengine - INFO - Epoch(train) [70][100/586] lr: 5.000000e-04 eta: 7:23:50 time: 0.336087 data_time: 0.024509 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.767085 loss: 0.000587 2022/09/13 00:01:28 - mmengine - INFO - Epoch(train) [70][150/586] lr: 5.000000e-04 eta: 7:23:35 time: 0.336608 data_time: 0.023953 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.844187 loss: 0.000579 2022/09/13 00:01:45 - mmengine - INFO - Epoch(train) [70][200/586] lr: 5.000000e-04 eta: 7:23:21 time: 0.341439 data_time: 0.024233 memory: 7489 loss_kpt: 0.000605 acc_pose: 0.859351 loss: 0.000605 2022/09/13 00:02:02 - mmengine - INFO - Epoch(train) [70][250/586] lr: 5.000000e-04 eta: 7:23:06 time: 0.335836 data_time: 0.023628 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.836309 loss: 0.000590 2022/09/13 00:02:18 - mmengine - INFO - Epoch(train) [70][300/586] lr: 5.000000e-04 eta: 7:22:52 time: 0.335768 data_time: 0.023693 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.888766 loss: 0.000590 2022/09/13 00:02:35 - mmengine - INFO - Epoch(train) [70][350/586] lr: 5.000000e-04 eta: 7:22:37 time: 0.334469 data_time: 0.024665 memory: 7489 loss_kpt: 0.000616 acc_pose: 0.867856 loss: 0.000616 2022/09/13 00:02:52 - mmengine - INFO - Epoch(train) [70][400/586] lr: 5.000000e-04 eta: 7:22:22 time: 0.337557 data_time: 0.023443 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.820988 loss: 0.000594 2022/09/13 00:03:09 - mmengine - INFO - Epoch(train) [70][450/586] lr: 5.000000e-04 eta: 7:22:08 time: 0.348283 data_time: 0.023966 memory: 7489 loss_kpt: 0.000602 acc_pose: 0.833058 loss: 0.000602 2022/09/13 00:03:26 - mmengine - INFO - Epoch(train) [70][500/586] lr: 5.000000e-04 eta: 7:21:53 time: 0.334328 data_time: 0.028455 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.771475 loss: 0.000591 2022/09/13 00:03:43 - mmengine - INFO - Epoch(train) [70][550/586] lr: 5.000000e-04 eta: 7:21:39 time: 0.340813 data_time: 0.023280 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.880425 loss: 0.000606 2022/09/13 00:03:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:03:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:03:55 - mmengine - INFO - Saving checkpoint at 70 epochs 2022/09/13 00:04:12 - mmengine - INFO - Epoch(val) [70][50/407] eta: 0:01:05 time: 0.183698 data_time: 0.011824 memory: 7489 2022/09/13 00:04:21 - mmengine - INFO - Epoch(val) [70][100/407] eta: 0:00:54 time: 0.177605 data_time: 0.007414 memory: 1657 2022/09/13 00:04:30 - mmengine - INFO - Epoch(val) [70][150/407] eta: 0:00:46 time: 0.179840 data_time: 0.008296 memory: 1657 2022/09/13 00:04:39 - mmengine - INFO - Epoch(val) [70][200/407] eta: 0:00:36 time: 0.178716 data_time: 0.007964 memory: 1657 2022/09/13 00:04:48 - mmengine - INFO - Epoch(val) [70][250/407] eta: 0:00:27 time: 0.177618 data_time: 0.007682 memory: 1657 2022/09/13 00:04:57 - mmengine - INFO - Epoch(val) [70][300/407] eta: 0:00:19 time: 0.184236 data_time: 0.011699 memory: 1657 2022/09/13 00:05:06 - mmengine - INFO - Epoch(val) [70][350/407] eta: 0:00:10 time: 0.178127 data_time: 0.007627 memory: 1657 2022/09/13 00:05:15 - mmengine - INFO - Epoch(val) [70][400/407] eta: 0:00:01 time: 0.176183 data_time: 0.008012 memory: 1657 2022/09/13 00:05:51 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 00:06:05 - mmengine - INFO - Epoch(val) [70][407/407] coco/AP: 0.745838 coco/AP .5: 0.899461 coco/AP .75: 0.812601 coco/AP (M): 0.708737 coco/AP (L): 0.812169 coco/AR: 0.796458 coco/AR .5: 0.937500 coco/AR .75: 0.855479 coco/AR (M): 0.754848 coco/AR (L): 0.856633 2022/09/13 00:06:05 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_60.pth is removed 2022/09/13 00:06:10 - mmengine - INFO - The best checkpoint with 0.7458 coco/AP at 70 epoch is saved to best_coco/AP_epoch_70.pth. 2022/09/13 00:06:27 - mmengine - INFO - Epoch(train) [71][50/586] lr: 5.000000e-04 eta: 7:20:49 time: 0.335762 data_time: 0.029183 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.830930 loss: 0.000579 2022/09/13 00:06:44 - mmengine - INFO - Epoch(train) [71][100/586] lr: 5.000000e-04 eta: 7:20:35 time: 0.340729 data_time: 0.023713 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.845270 loss: 0.000562 2022/09/13 00:07:01 - mmengine - INFO - Epoch(train) [71][150/586] lr: 5.000000e-04 eta: 7:20:21 time: 0.341926 data_time: 0.026670 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.812241 loss: 0.000578 2022/09/13 00:07:17 - mmengine - INFO - Epoch(train) [71][200/586] lr: 5.000000e-04 eta: 7:20:05 time: 0.328642 data_time: 0.022273 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.829923 loss: 0.000581 2022/09/13 00:07:34 - mmengine - INFO - Epoch(train) [71][250/586] lr: 5.000000e-04 eta: 7:19:50 time: 0.333892 data_time: 0.022464 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.807051 loss: 0.000588 2022/09/13 00:07:51 - mmengine - INFO - Epoch(train) [71][300/586] lr: 5.000000e-04 eta: 7:19:35 time: 0.335284 data_time: 0.022801 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.872557 loss: 0.000591 2022/09/13 00:08:08 - mmengine - INFO - Epoch(train) [71][350/586] lr: 5.000000e-04 eta: 7:19:21 time: 0.338326 data_time: 0.026165 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.885334 loss: 0.000580 2022/09/13 00:08:25 - mmengine - INFO - Epoch(train) [71][400/586] lr: 5.000000e-04 eta: 7:19:07 time: 0.342211 data_time: 0.025048 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.787752 loss: 0.000591 2022/09/13 00:08:42 - mmengine - INFO - Epoch(train) [71][450/586] lr: 5.000000e-04 eta: 7:18:52 time: 0.342162 data_time: 0.022895 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.756515 loss: 0.000593 2022/09/13 00:08:59 - mmengine - INFO - Epoch(train) [71][500/586] lr: 5.000000e-04 eta: 7:18:37 time: 0.334098 data_time: 0.021982 memory: 7489 loss_kpt: 0.000606 acc_pose: 0.761842 loss: 0.000606 2022/09/13 00:09:16 - mmengine - INFO - Epoch(train) [71][550/586] lr: 5.000000e-04 eta: 7:18:23 time: 0.339791 data_time: 0.021849 memory: 7489 loss_kpt: 0.000614 acc_pose: 0.809592 loss: 0.000614 2022/09/13 00:09:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:09:28 - mmengine - INFO - Saving checkpoint at 71 epochs 2022/09/13 00:09:51 - mmengine - INFO - Epoch(train) [72][50/586] lr: 5.000000e-04 eta: 7:17:34 time: 0.341022 data_time: 0.030628 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.863459 loss: 0.000572 2022/09/13 00:10:08 - mmengine - INFO - Epoch(train) [72][100/586] lr: 5.000000e-04 eta: 7:17:19 time: 0.335570 data_time: 0.022741 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.796970 loss: 0.000589 2022/09/13 00:10:25 - mmengine - INFO - Epoch(train) [72][150/586] lr: 5.000000e-04 eta: 7:17:04 time: 0.334454 data_time: 0.022494 memory: 7489 loss_kpt: 0.000619 acc_pose: 0.860355 loss: 0.000619 2022/09/13 00:10:42 - mmengine - INFO - Epoch(train) [72][200/586] lr: 5.000000e-04 eta: 7:16:49 time: 0.334482 data_time: 0.023518 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.861127 loss: 0.000584 2022/09/13 00:10:58 - mmengine - INFO - Epoch(train) [72][250/586] lr: 5.000000e-04 eta: 7:16:34 time: 0.333753 data_time: 0.023670 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.837076 loss: 0.000584 2022/09/13 00:11:15 - mmengine - INFO - Epoch(train) [72][300/586] lr: 5.000000e-04 eta: 7:16:20 time: 0.338046 data_time: 0.024755 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.826390 loss: 0.000585 2022/09/13 00:11:32 - mmengine - INFO - Epoch(train) [72][350/586] lr: 5.000000e-04 eta: 7:16:05 time: 0.338101 data_time: 0.024027 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.876051 loss: 0.000581 2022/09/13 00:11:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:11:49 - mmengine - INFO - Epoch(train) [72][400/586] lr: 5.000000e-04 eta: 7:15:50 time: 0.337776 data_time: 0.024248 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.871908 loss: 0.000571 2022/09/13 00:12:06 - mmengine - INFO - Epoch(train) [72][450/586] lr: 5.000000e-04 eta: 7:15:36 time: 0.337391 data_time: 0.024423 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.787549 loss: 0.000596 2022/09/13 00:12:23 - mmengine - INFO - Epoch(train) [72][500/586] lr: 5.000000e-04 eta: 7:15:22 time: 0.344039 data_time: 0.025055 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.852812 loss: 0.000561 2022/09/13 00:12:40 - mmengine - INFO - Epoch(train) [72][550/586] lr: 5.000000e-04 eta: 7:15:07 time: 0.333554 data_time: 0.028241 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.817075 loss: 0.000580 2022/09/13 00:12:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:12:52 - mmengine - INFO - Saving checkpoint at 72 epochs 2022/09/13 00:13:17 - mmengine - INFO - Epoch(train) [73][50/586] lr: 5.000000e-04 eta: 7:14:19 time: 0.349203 data_time: 0.032447 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.716874 loss: 0.000586 2022/09/13 00:13:33 - mmengine - INFO - Epoch(train) [73][100/586] lr: 5.000000e-04 eta: 7:14:04 time: 0.332403 data_time: 0.024667 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.848517 loss: 0.000591 2022/09/13 00:13:50 - mmengine - INFO - Epoch(train) [73][150/586] lr: 5.000000e-04 eta: 7:13:49 time: 0.333384 data_time: 0.023655 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.739092 loss: 0.000598 2022/09/13 00:14:07 - mmengine - INFO - Epoch(train) [73][200/586] lr: 5.000000e-04 eta: 7:13:34 time: 0.339516 data_time: 0.028596 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.861352 loss: 0.000585 2022/09/13 00:14:24 - mmengine - INFO - Epoch(train) [73][250/586] lr: 5.000000e-04 eta: 7:13:20 time: 0.339480 data_time: 0.023953 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.837280 loss: 0.000585 2022/09/13 00:14:41 - mmengine - INFO - Epoch(train) [73][300/586] lr: 5.000000e-04 eta: 7:13:05 time: 0.335635 data_time: 0.024073 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.761626 loss: 0.000579 2022/09/13 00:14:58 - mmengine - INFO - Epoch(train) [73][350/586] lr: 5.000000e-04 eta: 7:12:50 time: 0.338029 data_time: 0.024785 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.785774 loss: 0.000576 2022/09/13 00:15:14 - mmengine - INFO - Epoch(train) [73][400/586] lr: 5.000000e-04 eta: 7:12:35 time: 0.333807 data_time: 0.023819 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.900811 loss: 0.000576 2022/09/13 00:15:31 - mmengine - INFO - Epoch(train) [73][450/586] lr: 5.000000e-04 eta: 7:12:20 time: 0.333151 data_time: 0.024345 memory: 7489 loss_kpt: 0.000593 acc_pose: 0.822235 loss: 0.000593 2022/09/13 00:15:48 - mmengine - INFO - Epoch(train) [73][500/586] lr: 5.000000e-04 eta: 7:12:06 time: 0.347284 data_time: 0.024822 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.810528 loss: 0.000570 2022/09/13 00:16:06 - mmengine - INFO - Epoch(train) [73][550/586] lr: 5.000000e-04 eta: 7:11:52 time: 0.342997 data_time: 0.024326 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.818110 loss: 0.000596 2022/09/13 00:16:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:16:17 - mmengine - INFO - Saving checkpoint at 73 epochs 2022/09/13 00:16:42 - mmengine - INFO - Epoch(train) [74][50/586] lr: 5.000000e-04 eta: 7:11:04 time: 0.342497 data_time: 0.031952 memory: 7489 loss_kpt: 0.000599 acc_pose: 0.852730 loss: 0.000599 2022/09/13 00:16:59 - mmengine - INFO - Epoch(train) [74][100/586] lr: 5.000000e-04 eta: 7:10:50 time: 0.337236 data_time: 0.026052 memory: 7489 loss_kpt: 0.000604 acc_pose: 0.836154 loss: 0.000604 2022/09/13 00:17:15 - mmengine - INFO - Epoch(train) [74][150/586] lr: 5.000000e-04 eta: 7:10:35 time: 0.332561 data_time: 0.023732 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.760548 loss: 0.000591 2022/09/13 00:17:32 - mmengine - INFO - Epoch(train) [74][200/586] lr: 5.000000e-04 eta: 7:10:20 time: 0.338909 data_time: 0.024933 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.826276 loss: 0.000570 2022/09/13 00:17:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:17:49 - mmengine - INFO - Epoch(train) [74][250/586] lr: 5.000000e-04 eta: 7:10:05 time: 0.333038 data_time: 0.027103 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.837767 loss: 0.000578 2022/09/13 00:18:05 - mmengine - INFO - Epoch(train) [74][300/586] lr: 5.000000e-04 eta: 7:09:50 time: 0.334269 data_time: 0.025886 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.888616 loss: 0.000574 2022/09/13 00:18:22 - mmengine - INFO - Epoch(train) [74][350/586] lr: 5.000000e-04 eta: 7:09:35 time: 0.335825 data_time: 0.024898 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.869433 loss: 0.000578 2022/09/13 00:18:39 - mmengine - INFO - Epoch(train) [74][400/586] lr: 5.000000e-04 eta: 7:09:20 time: 0.332928 data_time: 0.023601 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.820783 loss: 0.000574 2022/09/13 00:18:55 - mmengine - INFO - Epoch(train) [74][450/586] lr: 5.000000e-04 eta: 7:09:04 time: 0.330315 data_time: 0.024778 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.856571 loss: 0.000560 2022/09/13 00:19:12 - mmengine - INFO - Epoch(train) [74][500/586] lr: 5.000000e-04 eta: 7:08:50 time: 0.337177 data_time: 0.024365 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.827422 loss: 0.000600 2022/09/13 00:19:29 - mmengine - INFO - Epoch(train) [74][550/586] lr: 5.000000e-04 eta: 7:08:35 time: 0.334464 data_time: 0.024914 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.816293 loss: 0.000585 2022/09/13 00:19:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:19:41 - mmengine - INFO - Saving checkpoint at 74 epochs 2022/09/13 00:20:05 - mmengine - INFO - Epoch(train) [75][50/586] lr: 5.000000e-04 eta: 7:07:47 time: 0.343355 data_time: 0.028705 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.839021 loss: 0.000574 2022/09/13 00:20:22 - mmengine - INFO - Epoch(train) [75][100/586] lr: 5.000000e-04 eta: 7:07:33 time: 0.339653 data_time: 0.023552 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.800038 loss: 0.000556 2022/09/13 00:20:39 - mmengine - INFO - Epoch(train) [75][150/586] lr: 5.000000e-04 eta: 7:07:18 time: 0.334618 data_time: 0.024353 memory: 7489 loss_kpt: 0.000588 acc_pose: 0.892998 loss: 0.000588 2022/09/13 00:20:56 - mmengine - INFO - Epoch(train) [75][200/586] lr: 5.000000e-04 eta: 7:07:03 time: 0.337311 data_time: 0.023740 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.823120 loss: 0.000570 2022/09/13 00:21:13 - mmengine - INFO - Epoch(train) [75][250/586] lr: 5.000000e-04 eta: 7:06:48 time: 0.336350 data_time: 0.025629 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.828947 loss: 0.000600 2022/09/13 00:21:30 - mmengine - INFO - Epoch(train) [75][300/586] lr: 5.000000e-04 eta: 7:06:34 time: 0.340466 data_time: 0.023766 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.802983 loss: 0.000592 2022/09/13 00:21:47 - mmengine - INFO - Epoch(train) [75][350/586] lr: 5.000000e-04 eta: 7:06:19 time: 0.331765 data_time: 0.024989 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.796718 loss: 0.000596 2022/09/13 00:22:03 - mmengine - INFO - Epoch(train) [75][400/586] lr: 5.000000e-04 eta: 7:06:03 time: 0.332086 data_time: 0.024207 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.848566 loss: 0.000582 2022/09/13 00:22:20 - mmengine - INFO - Epoch(train) [75][450/586] lr: 5.000000e-04 eta: 7:05:48 time: 0.333339 data_time: 0.027031 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.861836 loss: 0.000580 2022/09/13 00:22:37 - mmengine - INFO - Epoch(train) [75][500/586] lr: 5.000000e-04 eta: 7:05:34 time: 0.342540 data_time: 0.024720 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.817460 loss: 0.000583 2022/09/13 00:22:54 - mmengine - INFO - Epoch(train) [75][550/586] lr: 5.000000e-04 eta: 7:05:19 time: 0.338851 data_time: 0.023657 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.851285 loss: 0.000578 2022/09/13 00:23:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:23:06 - mmengine - INFO - Saving checkpoint at 75 epochs 2022/09/13 00:23:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:23:31 - mmengine - INFO - Epoch(train) [76][50/586] lr: 5.000000e-04 eta: 7:04:32 time: 0.338809 data_time: 0.028980 memory: 7489 loss_kpt: 0.000601 acc_pose: 0.850077 loss: 0.000601 2022/09/13 00:23:47 - mmengine - INFO - Epoch(train) [76][100/586] lr: 5.000000e-04 eta: 7:04:17 time: 0.338004 data_time: 0.028033 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.818728 loss: 0.000578 2022/09/13 00:24:04 - mmengine - INFO - Epoch(train) [76][150/586] lr: 5.000000e-04 eta: 7:04:03 time: 0.338339 data_time: 0.024212 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.861939 loss: 0.000582 2022/09/13 00:24:21 - mmengine - INFO - Epoch(train) [76][200/586] lr: 5.000000e-04 eta: 7:03:48 time: 0.334195 data_time: 0.024745 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.811723 loss: 0.000582 2022/09/13 00:24:38 - mmengine - INFO - Epoch(train) [76][250/586] lr: 5.000000e-04 eta: 7:03:33 time: 0.337861 data_time: 0.024392 memory: 7489 loss_kpt: 0.000595 acc_pose: 0.849641 loss: 0.000595 2022/09/13 00:24:55 - mmengine - INFO - Epoch(train) [76][300/586] lr: 5.000000e-04 eta: 7:03:18 time: 0.335031 data_time: 0.024358 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.832545 loss: 0.000569 2022/09/13 00:25:12 - mmengine - INFO - Epoch(train) [76][350/586] lr: 5.000000e-04 eta: 7:03:04 time: 0.342438 data_time: 0.028227 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.850015 loss: 0.000583 2022/09/13 00:25:29 - mmengine - INFO - Epoch(train) [76][400/586] lr: 5.000000e-04 eta: 7:02:49 time: 0.338997 data_time: 0.023853 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.771663 loss: 0.000592 2022/09/13 00:25:46 - mmengine - INFO - Epoch(train) [76][450/586] lr: 5.000000e-04 eta: 7:02:34 time: 0.334974 data_time: 0.024142 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.845654 loss: 0.000581 2022/09/13 00:26:02 - mmengine - INFO - Epoch(train) [76][500/586] lr: 5.000000e-04 eta: 7:02:19 time: 0.330693 data_time: 0.023357 memory: 7489 loss_kpt: 0.000594 acc_pose: 0.837089 loss: 0.000594 2022/09/13 00:26:19 - mmengine - INFO - Epoch(train) [76][550/586] lr: 5.000000e-04 eta: 7:02:05 time: 0.345194 data_time: 0.023011 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.808999 loss: 0.000598 2022/09/13 00:26:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:26:31 - mmengine - INFO - Saving checkpoint at 76 epochs 2022/09/13 00:26:55 - mmengine - INFO - Epoch(train) [77][50/586] lr: 5.000000e-04 eta: 7:01:18 time: 0.338550 data_time: 0.031702 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.754648 loss: 0.000592 2022/09/13 00:27:12 - mmengine - INFO - Epoch(train) [77][100/586] lr: 5.000000e-04 eta: 7:01:03 time: 0.340331 data_time: 0.024433 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.779640 loss: 0.000596 2022/09/13 00:27:29 - mmengine - INFO - Epoch(train) [77][150/586] lr: 5.000000e-04 eta: 7:00:48 time: 0.335354 data_time: 0.023953 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.817206 loss: 0.000568 2022/09/13 00:27:46 - mmengine - INFO - Epoch(train) [77][200/586] lr: 5.000000e-04 eta: 7:00:34 time: 0.338629 data_time: 0.023842 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.810581 loss: 0.000573 2022/09/13 00:28:03 - mmengine - INFO - Epoch(train) [77][250/586] lr: 5.000000e-04 eta: 7:00:19 time: 0.340116 data_time: 0.026549 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.808020 loss: 0.000566 2022/09/13 00:28:20 - mmengine - INFO - Epoch(train) [77][300/586] lr: 5.000000e-04 eta: 7:00:04 time: 0.329095 data_time: 0.023489 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.842833 loss: 0.000587 2022/09/13 00:28:37 - mmengine - INFO - Epoch(train) [77][350/586] lr: 5.000000e-04 eta: 6:59:49 time: 0.338624 data_time: 0.027371 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.811130 loss: 0.000574 2022/09/13 00:28:54 - mmengine - INFO - Epoch(train) [77][400/586] lr: 5.000000e-04 eta: 6:59:35 time: 0.344756 data_time: 0.024682 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.862897 loss: 0.000589 2022/09/13 00:29:10 - mmengine - INFO - Epoch(train) [77][450/586] lr: 5.000000e-04 eta: 6:59:20 time: 0.332086 data_time: 0.023625 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.826767 loss: 0.000587 2022/09/13 00:29:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:29:27 - mmengine - INFO - Epoch(train) [77][500/586] lr: 5.000000e-04 eta: 6:59:05 time: 0.334990 data_time: 0.025715 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.854765 loss: 0.000568 2022/09/13 00:29:44 - mmengine - INFO - Epoch(train) [77][550/586] lr: 5.000000e-04 eta: 6:58:50 time: 0.343248 data_time: 0.024458 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.838149 loss: 0.000582 2022/09/13 00:29:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:29:56 - mmengine - INFO - Saving checkpoint at 77 epochs 2022/09/13 00:30:21 - mmengine - INFO - Epoch(train) [78][50/586] lr: 5.000000e-04 eta: 6:58:05 time: 0.344391 data_time: 0.031900 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.854788 loss: 0.000577 2022/09/13 00:30:38 - mmengine - INFO - Epoch(train) [78][100/586] lr: 5.000000e-04 eta: 6:57:50 time: 0.335872 data_time: 0.025611 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.828859 loss: 0.000585 2022/09/13 00:30:55 - mmengine - INFO - Epoch(train) [78][150/586] lr: 5.000000e-04 eta: 6:57:35 time: 0.340452 data_time: 0.024850 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.837739 loss: 0.000587 2022/09/13 00:31:12 - mmengine - INFO - Epoch(train) [78][200/586] lr: 5.000000e-04 eta: 6:57:20 time: 0.334051 data_time: 0.024034 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.826224 loss: 0.000582 2022/09/13 00:31:29 - mmengine - INFO - Epoch(train) [78][250/586] lr: 5.000000e-04 eta: 6:57:05 time: 0.339544 data_time: 0.023364 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.851149 loss: 0.000573 2022/09/13 00:31:45 - mmengine - INFO - Epoch(train) [78][300/586] lr: 5.000000e-04 eta: 6:56:51 time: 0.337006 data_time: 0.023542 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.841738 loss: 0.000574 2022/09/13 00:32:02 - mmengine - INFO - Epoch(train) [78][350/586] lr: 5.000000e-04 eta: 6:56:36 time: 0.336007 data_time: 0.025431 memory: 7489 loss_kpt: 0.000596 acc_pose: 0.837734 loss: 0.000596 2022/09/13 00:32:19 - mmengine - INFO - Epoch(train) [78][400/586] lr: 5.000000e-04 eta: 6:56:21 time: 0.342395 data_time: 0.024167 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.833289 loss: 0.000585 2022/09/13 00:32:36 - mmengine - INFO - Epoch(train) [78][450/586] lr: 5.000000e-04 eta: 6:56:07 time: 0.339771 data_time: 0.024042 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.841456 loss: 0.000581 2022/09/13 00:32:53 - mmengine - INFO - Epoch(train) [78][500/586] lr: 5.000000e-04 eta: 6:55:52 time: 0.336778 data_time: 0.024514 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.876111 loss: 0.000566 2022/09/13 00:33:10 - mmengine - INFO - Epoch(train) [78][550/586] lr: 5.000000e-04 eta: 6:55:37 time: 0.340063 data_time: 0.024234 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.845988 loss: 0.000585 2022/09/13 00:33:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:33:22 - mmengine - INFO - Saving checkpoint at 78 epochs 2022/09/13 00:33:47 - mmengine - INFO - Epoch(train) [79][50/586] lr: 5.000000e-04 eta: 6:54:51 time: 0.341582 data_time: 0.031887 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.835010 loss: 0.000600 2022/09/13 00:34:04 - mmengine - INFO - Epoch(train) [79][100/586] lr: 5.000000e-04 eta: 6:54:37 time: 0.338174 data_time: 0.025542 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.821726 loss: 0.000592 2022/09/13 00:34:21 - mmengine - INFO - Epoch(train) [79][150/586] lr: 5.000000e-04 eta: 6:54:22 time: 0.335918 data_time: 0.024046 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.882121 loss: 0.000579 2022/09/13 00:34:37 - mmengine - INFO - Epoch(train) [79][200/586] lr: 5.000000e-04 eta: 6:54:07 time: 0.336479 data_time: 0.024041 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.788425 loss: 0.000592 2022/09/13 00:34:54 - mmengine - INFO - Epoch(train) [79][250/586] lr: 5.000000e-04 eta: 6:53:52 time: 0.337755 data_time: 0.023871 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.805251 loss: 0.000590 2022/09/13 00:35:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:35:11 - mmengine - INFO - Epoch(train) [79][300/586] lr: 5.000000e-04 eta: 6:53:37 time: 0.338853 data_time: 0.023647 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.868403 loss: 0.000578 2022/09/13 00:35:28 - mmengine - INFO - Epoch(train) [79][350/586] lr: 5.000000e-04 eta: 6:53:23 time: 0.338139 data_time: 0.028890 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.797659 loss: 0.000589 2022/09/13 00:35:45 - mmengine - INFO - Epoch(train) [79][400/586] lr: 5.000000e-04 eta: 6:53:08 time: 0.344659 data_time: 0.024428 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.776468 loss: 0.000589 2022/09/13 00:36:02 - mmengine - INFO - Epoch(train) [79][450/586] lr: 5.000000e-04 eta: 6:52:54 time: 0.337937 data_time: 0.025059 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.848830 loss: 0.000575 2022/09/13 00:36:19 - mmengine - INFO - Epoch(train) [79][500/586] lr: 5.000000e-04 eta: 6:52:39 time: 0.337735 data_time: 0.024246 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.829089 loss: 0.000573 2022/09/13 00:36:36 - mmengine - INFO - Epoch(train) [79][550/586] lr: 5.000000e-04 eta: 6:52:23 time: 0.329981 data_time: 0.024667 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.813704 loss: 0.000598 2022/09/13 00:36:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:36:48 - mmengine - INFO - Saving checkpoint at 79 epochs 2022/09/13 00:37:12 - mmengine - INFO - Epoch(train) [80][50/586] lr: 5.000000e-04 eta: 6:51:38 time: 0.344239 data_time: 0.032536 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.848619 loss: 0.000565 2022/09/13 00:37:29 - mmengine - INFO - Epoch(train) [80][100/586] lr: 5.000000e-04 eta: 6:51:23 time: 0.333909 data_time: 0.024604 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.876224 loss: 0.000576 2022/09/13 00:37:46 - mmengine - INFO - Epoch(train) [80][150/586] lr: 5.000000e-04 eta: 6:51:09 time: 0.343394 data_time: 0.023105 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.817390 loss: 0.000578 2022/09/13 00:38:03 - mmengine - INFO - Epoch(train) [80][200/586] lr: 5.000000e-04 eta: 6:50:54 time: 0.334805 data_time: 0.024467 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.899088 loss: 0.000557 2022/09/13 00:38:19 - mmengine - INFO - Epoch(train) [80][250/586] lr: 5.000000e-04 eta: 6:50:39 time: 0.333600 data_time: 0.023474 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.796465 loss: 0.000580 2022/09/13 00:38:36 - mmengine - INFO - Epoch(train) [80][300/586] lr: 5.000000e-04 eta: 6:50:24 time: 0.342439 data_time: 0.024238 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.770284 loss: 0.000582 2022/09/13 00:38:53 - mmengine - INFO - Epoch(train) [80][350/586] lr: 5.000000e-04 eta: 6:50:09 time: 0.336950 data_time: 0.023118 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.873258 loss: 0.000561 2022/09/13 00:39:10 - mmengine - INFO - Epoch(train) [80][400/586] lr: 5.000000e-04 eta: 6:49:54 time: 0.332412 data_time: 0.025269 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.843675 loss: 0.000583 2022/09/13 00:39:27 - mmengine - INFO - Epoch(train) [80][450/586] lr: 5.000000e-04 eta: 6:49:39 time: 0.337568 data_time: 0.028558 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.894893 loss: 0.000585 2022/09/13 00:39:44 - mmengine - INFO - Epoch(train) [80][500/586] lr: 5.000000e-04 eta: 6:49:25 time: 0.339263 data_time: 0.023583 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.783249 loss: 0.000590 2022/09/13 00:40:01 - mmengine - INFO - Epoch(train) [80][550/586] lr: 5.000000e-04 eta: 6:49:10 time: 0.337463 data_time: 0.027890 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.887985 loss: 0.000587 2022/09/13 00:40:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:40:13 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/09/13 00:40:29 - mmengine - INFO - Epoch(val) [80][50/407] eta: 0:01:06 time: 0.185743 data_time: 0.013506 memory: 7489 2022/09/13 00:40:38 - mmengine - INFO - Epoch(val) [80][100/407] eta: 0:00:56 time: 0.182678 data_time: 0.008158 memory: 1657 2022/09/13 00:40:47 - mmengine - INFO - Epoch(val) [80][150/407] eta: 0:00:46 time: 0.179831 data_time: 0.008756 memory: 1657 2022/09/13 00:40:56 - mmengine - INFO - Epoch(val) [80][200/407] eta: 0:00:37 time: 0.179907 data_time: 0.007413 memory: 1657 2022/09/13 00:41:05 - mmengine - INFO - Epoch(val) [80][250/407] eta: 0:00:27 time: 0.177791 data_time: 0.007477 memory: 1657 2022/09/13 00:41:14 - mmengine - INFO - Epoch(val) [80][300/407] eta: 0:00:19 time: 0.178372 data_time: 0.007565 memory: 1657 2022/09/13 00:41:23 - mmengine - INFO - Epoch(val) [80][350/407] eta: 0:00:10 time: 0.177991 data_time: 0.007570 memory: 1657 2022/09/13 00:41:32 - mmengine - INFO - Epoch(val) [80][400/407] eta: 0:00:01 time: 0.176137 data_time: 0.007158 memory: 1657 2022/09/13 00:42:08 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 00:42:21 - mmengine - INFO - Epoch(val) [80][407/407] coco/AP: 0.745837 coco/AP .5: 0.897706 coco/AP .75: 0.809834 coco/AP (M): 0.708957 coco/AP (L): 0.814532 coco/AR: 0.796962 coco/AR .5: 0.935926 coco/AR .75: 0.854062 coco/AR (M): 0.754439 coco/AR (L): 0.859197 2022/09/13 00:42:39 - mmengine - INFO - Epoch(train) [81][50/586] lr: 5.000000e-04 eta: 6:48:26 time: 0.356899 data_time: 0.028786 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.893976 loss: 0.000562 2022/09/13 00:42:56 - mmengine - INFO - Epoch(train) [81][100/586] lr: 5.000000e-04 eta: 6:48:11 time: 0.332700 data_time: 0.022451 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.837521 loss: 0.000589 2022/09/13 00:43:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:43:13 - mmengine - INFO - Epoch(train) [81][150/586] lr: 5.000000e-04 eta: 6:47:56 time: 0.333413 data_time: 0.023017 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.790534 loss: 0.000564 2022/09/13 00:43:30 - mmengine - INFO - Epoch(train) [81][200/586] lr: 5.000000e-04 eta: 6:47:41 time: 0.338328 data_time: 0.022388 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.851733 loss: 0.000581 2022/09/13 00:43:46 - mmengine - INFO - Epoch(train) [81][250/586] lr: 5.000000e-04 eta: 6:47:26 time: 0.334093 data_time: 0.023721 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.900443 loss: 0.000573 2022/09/13 00:44:03 - mmengine - INFO - Epoch(train) [81][300/586] lr: 5.000000e-04 eta: 6:47:11 time: 0.340946 data_time: 0.022966 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.780866 loss: 0.000591 2022/09/13 00:44:20 - mmengine - INFO - Epoch(train) [81][350/586] lr: 5.000000e-04 eta: 6:46:56 time: 0.334617 data_time: 0.023723 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.880939 loss: 0.000563 2022/09/13 00:44:37 - mmengine - INFO - Epoch(train) [81][400/586] lr: 5.000000e-04 eta: 6:46:42 time: 0.342334 data_time: 0.026252 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.839840 loss: 0.000576 2022/09/13 00:44:54 - mmengine - INFO - Epoch(train) [81][450/586] lr: 5.000000e-04 eta: 6:46:27 time: 0.338702 data_time: 0.023269 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.873795 loss: 0.000589 2022/09/13 00:45:11 - mmengine - INFO - Epoch(train) [81][500/586] lr: 5.000000e-04 eta: 6:46:12 time: 0.335918 data_time: 0.023416 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.846151 loss: 0.000577 2022/09/13 00:45:28 - mmengine - INFO - Epoch(train) [81][550/586] lr: 5.000000e-04 eta: 6:45:57 time: 0.335569 data_time: 0.023814 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.824599 loss: 0.000575 2022/09/13 00:45:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:45:40 - mmengine - INFO - Saving checkpoint at 81 epochs 2022/09/13 00:46:04 - mmengine - INFO - Epoch(train) [82][50/586] lr: 5.000000e-04 eta: 6:45:13 time: 0.351739 data_time: 0.031441 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.795635 loss: 0.000592 2022/09/13 00:46:20 - mmengine - INFO - Epoch(train) [82][100/586] lr: 5.000000e-04 eta: 6:44:57 time: 0.327989 data_time: 0.022287 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.821108 loss: 0.000592 2022/09/13 00:46:38 - mmengine - INFO - Epoch(train) [82][150/586] lr: 5.000000e-04 eta: 6:44:43 time: 0.343890 data_time: 0.021925 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.870725 loss: 0.000577 2022/09/13 00:46:55 - mmengine - INFO - Epoch(train) [82][200/586] lr: 5.000000e-04 eta: 6:44:29 time: 0.340742 data_time: 0.021976 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.821963 loss: 0.000571 2022/09/13 00:47:11 - mmengine - INFO - Epoch(train) [82][250/586] lr: 5.000000e-04 eta: 6:44:13 time: 0.327276 data_time: 0.022382 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.891720 loss: 0.000564 2022/09/13 00:47:28 - mmengine - INFO - Epoch(train) [82][300/586] lr: 5.000000e-04 eta: 6:43:58 time: 0.340732 data_time: 0.021967 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.838407 loss: 0.000568 2022/09/13 00:47:45 - mmengine - INFO - Epoch(train) [82][350/586] lr: 5.000000e-04 eta: 6:43:44 time: 0.340977 data_time: 0.026471 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.839220 loss: 0.000580 2022/09/13 00:48:02 - mmengine - INFO - Epoch(train) [82][400/586] lr: 5.000000e-04 eta: 6:43:28 time: 0.329854 data_time: 0.022673 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.807689 loss: 0.000578 2022/09/13 00:48:19 - mmengine - INFO - Epoch(train) [82][450/586] lr: 5.000000e-04 eta: 6:43:13 time: 0.339143 data_time: 0.022365 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.885763 loss: 0.000581 2022/09/13 00:48:35 - mmengine - INFO - Epoch(train) [82][500/586] lr: 5.000000e-04 eta: 6:42:58 time: 0.336634 data_time: 0.023039 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.876322 loss: 0.000566 2022/09/13 00:48:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:48:52 - mmengine - INFO - Epoch(train) [82][550/586] lr: 5.000000e-04 eta: 6:42:43 time: 0.330884 data_time: 0.022853 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.862524 loss: 0.000583 2022/09/13 00:49:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:49:04 - mmengine - INFO - Saving checkpoint at 82 epochs 2022/09/13 00:49:28 - mmengine - INFO - Epoch(train) [83][50/586] lr: 5.000000e-04 eta: 6:41:59 time: 0.340603 data_time: 0.026916 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.848106 loss: 0.000589 2022/09/13 00:49:45 - mmengine - INFO - Epoch(train) [83][100/586] lr: 5.000000e-04 eta: 6:41:43 time: 0.328654 data_time: 0.022945 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.871780 loss: 0.000570 2022/09/13 00:50:02 - mmengine - INFO - Epoch(train) [83][150/586] lr: 5.000000e-04 eta: 6:41:29 time: 0.343906 data_time: 0.022308 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.812026 loss: 0.000583 2022/09/13 00:50:19 - mmengine - INFO - Epoch(train) [83][200/586] lr: 5.000000e-04 eta: 6:41:14 time: 0.337436 data_time: 0.022150 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.853892 loss: 0.000561 2022/09/13 00:50:36 - mmengine - INFO - Epoch(train) [83][250/586] lr: 5.000000e-04 eta: 6:40:59 time: 0.331627 data_time: 0.022373 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.773774 loss: 0.000568 2022/09/13 00:50:53 - mmengine - INFO - Epoch(train) [83][300/586] lr: 5.000000e-04 eta: 6:40:44 time: 0.338913 data_time: 0.025072 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.755528 loss: 0.000554 2022/09/13 00:51:09 - mmengine - INFO - Epoch(train) [83][350/586] lr: 5.000000e-04 eta: 6:40:29 time: 0.337812 data_time: 0.022293 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.909092 loss: 0.000565 2022/09/13 00:51:26 - mmengine - INFO - Epoch(train) [83][400/586] lr: 5.000000e-04 eta: 6:40:14 time: 0.330739 data_time: 0.022755 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.793294 loss: 0.000563 2022/09/13 00:51:43 - mmengine - INFO - Epoch(train) [83][450/586] lr: 5.000000e-04 eta: 6:39:59 time: 0.338626 data_time: 0.025264 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.850177 loss: 0.000585 2022/09/13 00:52:00 - mmengine - INFO - Epoch(train) [83][500/586] lr: 5.000000e-04 eta: 6:39:44 time: 0.335783 data_time: 0.022189 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.881837 loss: 0.000568 2022/09/13 00:52:16 - mmengine - INFO - Epoch(train) [83][550/586] lr: 5.000000e-04 eta: 6:39:28 time: 0.328574 data_time: 0.023201 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.852190 loss: 0.000561 2022/09/13 00:52:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:52:28 - mmengine - INFO - Saving checkpoint at 83 epochs 2022/09/13 00:52:53 - mmengine - INFO - Epoch(train) [84][50/586] lr: 5.000000e-04 eta: 6:38:44 time: 0.344282 data_time: 0.033847 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.797999 loss: 0.000575 2022/09/13 00:53:09 - mmengine - INFO - Epoch(train) [84][100/586] lr: 5.000000e-04 eta: 6:38:29 time: 0.327349 data_time: 0.023272 memory: 7489 loss_kpt: 0.000591 acc_pose: 0.906595 loss: 0.000591 2022/09/13 00:53:26 - mmengine - INFO - Epoch(train) [84][150/586] lr: 5.000000e-04 eta: 6:38:14 time: 0.337979 data_time: 0.022464 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.883584 loss: 0.000570 2022/09/13 00:53:43 - mmengine - INFO - Epoch(train) [84][200/586] lr: 5.000000e-04 eta: 6:37:59 time: 0.337905 data_time: 0.023014 memory: 7489 loss_kpt: 0.000600 acc_pose: 0.867754 loss: 0.000600 2022/09/13 00:53:59 - mmengine - INFO - Epoch(train) [84][250/586] lr: 5.000000e-04 eta: 6:37:44 time: 0.331315 data_time: 0.023475 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.849742 loss: 0.000574 2022/09/13 00:54:16 - mmengine - INFO - Epoch(train) [84][300/586] lr: 5.000000e-04 eta: 6:37:29 time: 0.338872 data_time: 0.023135 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.856426 loss: 0.000579 2022/09/13 00:54:33 - mmengine - INFO - Epoch(train) [84][350/586] lr: 5.000000e-04 eta: 6:37:14 time: 0.337658 data_time: 0.026385 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.861271 loss: 0.000582 2022/09/13 00:54:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:54:50 - mmengine - INFO - Epoch(train) [84][400/586] lr: 5.000000e-04 eta: 6:36:59 time: 0.329676 data_time: 0.023924 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.753878 loss: 0.000574 2022/09/13 00:55:07 - mmengine - INFO - Epoch(train) [84][450/586] lr: 5.000000e-04 eta: 6:36:44 time: 0.338398 data_time: 0.022169 memory: 7489 loss_kpt: 0.000598 acc_pose: 0.793139 loss: 0.000598 2022/09/13 00:55:23 - mmengine - INFO - Epoch(train) [84][500/586] lr: 5.000000e-04 eta: 6:36:29 time: 0.334997 data_time: 0.025812 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.795947 loss: 0.000573 2022/09/13 00:55:40 - mmengine - INFO - Epoch(train) [84][550/586] lr: 5.000000e-04 eta: 6:36:13 time: 0.331617 data_time: 0.022802 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.858887 loss: 0.000581 2022/09/13 00:55:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:55:52 - mmengine - INFO - Saving checkpoint at 84 epochs 2022/09/13 00:56:16 - mmengine - INFO - Epoch(train) [85][50/586] lr: 5.000000e-04 eta: 6:35:30 time: 0.344842 data_time: 0.030053 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.770334 loss: 0.000570 2022/09/13 00:56:33 - mmengine - INFO - Epoch(train) [85][100/586] lr: 5.000000e-04 eta: 6:35:15 time: 0.334822 data_time: 0.023221 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.881889 loss: 0.000566 2022/09/13 00:56:50 - mmengine - INFO - Epoch(train) [85][150/586] lr: 5.000000e-04 eta: 6:35:00 time: 0.338551 data_time: 0.022687 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.804170 loss: 0.000559 2022/09/13 00:57:07 - mmengine - INFO - Epoch(train) [85][200/586] lr: 5.000000e-04 eta: 6:34:45 time: 0.332121 data_time: 0.023258 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.818518 loss: 0.000563 2022/09/13 00:57:23 - mmengine - INFO - Epoch(train) [85][250/586] lr: 5.000000e-04 eta: 6:34:29 time: 0.326802 data_time: 0.022929 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.835531 loss: 0.000568 2022/09/13 00:57:40 - mmengine - INFO - Epoch(train) [85][300/586] lr: 5.000000e-04 eta: 6:34:14 time: 0.341330 data_time: 0.027016 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.819816 loss: 0.000571 2022/09/13 00:57:57 - mmengine - INFO - Epoch(train) [85][350/586] lr: 5.000000e-04 eta: 6:33:59 time: 0.334143 data_time: 0.021784 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.848424 loss: 0.000556 2022/09/13 00:58:13 - mmengine - INFO - Epoch(train) [85][400/586] lr: 5.000000e-04 eta: 6:33:44 time: 0.326075 data_time: 0.022055 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.820510 loss: 0.000581 2022/09/13 00:58:30 - mmengine - INFO - Epoch(train) [85][450/586] lr: 5.000000e-04 eta: 6:33:29 time: 0.343145 data_time: 0.027502 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.852356 loss: 0.000569 2022/09/13 00:58:47 - mmengine - INFO - Epoch(train) [85][500/586] lr: 5.000000e-04 eta: 6:33:14 time: 0.335984 data_time: 0.024095 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.713141 loss: 0.000592 2022/09/13 00:59:04 - mmengine - INFO - Epoch(train) [85][550/586] lr: 5.000000e-04 eta: 6:32:58 time: 0.327532 data_time: 0.022884 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.842316 loss: 0.000574 2022/09/13 00:59:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 00:59:16 - mmengine - INFO - Saving checkpoint at 85 epochs 2022/09/13 00:59:40 - mmengine - INFO - Epoch(train) [86][50/586] lr: 5.000000e-04 eta: 6:32:15 time: 0.344332 data_time: 0.027896 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.883447 loss: 0.000592 2022/09/13 00:59:56 - mmengine - INFO - Epoch(train) [86][100/586] lr: 5.000000e-04 eta: 6:32:00 time: 0.331796 data_time: 0.022533 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.829118 loss: 0.000589 2022/09/13 01:00:13 - mmengine - INFO - Epoch(train) [86][150/586] lr: 5.000000e-04 eta: 6:31:45 time: 0.338773 data_time: 0.022222 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.849694 loss: 0.000552 2022/09/13 01:00:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:00:30 - mmengine - INFO - Epoch(train) [86][200/586] lr: 5.000000e-04 eta: 6:31:30 time: 0.336974 data_time: 0.022012 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.858783 loss: 0.000570 2022/09/13 01:00:47 - mmengine - INFO - Epoch(train) [86][250/586] lr: 5.000000e-04 eta: 6:31:15 time: 0.334108 data_time: 0.022484 memory: 7489 loss_kpt: 0.000584 acc_pose: 0.830103 loss: 0.000584 2022/09/13 01:01:04 - mmengine - INFO - Epoch(train) [86][300/586] lr: 5.000000e-04 eta: 6:31:00 time: 0.338416 data_time: 0.022879 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.877514 loss: 0.000559 2022/09/13 01:01:21 - mmengine - INFO - Epoch(train) [86][350/586] lr: 5.000000e-04 eta: 6:30:46 time: 0.346439 data_time: 0.021586 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.752718 loss: 0.000563 2022/09/13 01:01:38 - mmengine - INFO - Epoch(train) [86][400/586] lr: 5.000000e-04 eta: 6:30:31 time: 0.331397 data_time: 0.026147 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.859998 loss: 0.000572 2022/09/13 01:01:55 - mmengine - INFO - Epoch(train) [86][450/586] lr: 5.000000e-04 eta: 6:30:15 time: 0.335245 data_time: 0.022058 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.899125 loss: 0.000563 2022/09/13 01:02:11 - mmengine - INFO - Epoch(train) [86][500/586] lr: 5.000000e-04 eta: 6:30:00 time: 0.334117 data_time: 0.022638 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.814764 loss: 0.000558 2022/09/13 01:02:28 - mmengine - INFO - Epoch(train) [86][550/586] lr: 5.000000e-04 eta: 6:29:45 time: 0.334078 data_time: 0.022324 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.828932 loss: 0.000578 2022/09/13 01:02:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:02:40 - mmengine - INFO - Saving checkpoint at 86 epochs 2022/09/13 01:03:04 - mmengine - INFO - Epoch(train) [87][50/586] lr: 5.000000e-04 eta: 6:29:02 time: 0.344246 data_time: 0.029518 memory: 7489 loss_kpt: 0.000585 acc_pose: 0.863910 loss: 0.000585 2022/09/13 01:03:21 - mmengine - INFO - Epoch(train) [87][100/586] lr: 5.000000e-04 eta: 6:28:47 time: 0.330992 data_time: 0.023146 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.850350 loss: 0.000571 2022/09/13 01:03:38 - mmengine - INFO - Epoch(train) [87][150/586] lr: 5.000000e-04 eta: 6:28:32 time: 0.334968 data_time: 0.022656 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.858586 loss: 0.000579 2022/09/13 01:03:54 - mmengine - INFO - Epoch(train) [87][200/586] lr: 5.000000e-04 eta: 6:28:17 time: 0.332831 data_time: 0.023574 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.886769 loss: 0.000557 2022/09/13 01:04:11 - mmengine - INFO - Epoch(train) [87][250/586] lr: 5.000000e-04 eta: 6:28:01 time: 0.328876 data_time: 0.023542 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.919394 loss: 0.000576 2022/09/13 01:04:27 - mmengine - INFO - Epoch(train) [87][300/586] lr: 5.000000e-04 eta: 6:27:46 time: 0.336650 data_time: 0.022179 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.842054 loss: 0.000564 2022/09/13 01:04:44 - mmengine - INFO - Epoch(train) [87][350/586] lr: 5.000000e-04 eta: 6:27:31 time: 0.339407 data_time: 0.027215 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.877170 loss: 0.000566 2022/09/13 01:05:01 - mmengine - INFO - Epoch(train) [87][400/586] lr: 5.000000e-04 eta: 6:27:16 time: 0.327018 data_time: 0.022382 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.859845 loss: 0.000579 2022/09/13 01:05:18 - mmengine - INFO - Epoch(train) [87][450/586] lr: 5.000000e-04 eta: 6:27:01 time: 0.338204 data_time: 0.023841 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.822568 loss: 0.000569 2022/09/13 01:05:35 - mmengine - INFO - Epoch(train) [87][500/586] lr: 5.000000e-04 eta: 6:26:46 time: 0.337986 data_time: 0.022282 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.820717 loss: 0.000576 2022/09/13 01:05:51 - mmengine - INFO - Epoch(train) [87][550/586] lr: 5.000000e-04 eta: 6:26:30 time: 0.324678 data_time: 0.022867 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.825905 loss: 0.000556 2022/09/13 01:06:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:06:03 - mmengine - INFO - Saving checkpoint at 87 epochs 2022/09/13 01:06:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:06:28 - mmengine - INFO - Epoch(train) [88][50/586] lr: 5.000000e-04 eta: 6:25:48 time: 0.353755 data_time: 0.031064 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.849701 loss: 0.000572 2022/09/13 01:06:44 - mmengine - INFO - Epoch(train) [88][100/586] lr: 5.000000e-04 eta: 6:25:32 time: 0.324071 data_time: 0.022155 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.866623 loss: 0.000577 2022/09/13 01:07:01 - mmengine - INFO - Epoch(train) [88][150/586] lr: 5.000000e-04 eta: 6:25:18 time: 0.339419 data_time: 0.025890 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.843555 loss: 0.000581 2022/09/13 01:07:18 - mmengine - INFO - Epoch(train) [88][200/586] lr: 5.000000e-04 eta: 6:25:03 time: 0.348331 data_time: 0.022539 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.868115 loss: 0.000569 2022/09/13 01:07:35 - mmengine - INFO - Epoch(train) [88][250/586] lr: 5.000000e-04 eta: 6:24:48 time: 0.329809 data_time: 0.022479 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.821822 loss: 0.000570 2022/09/13 01:07:52 - mmengine - INFO - Epoch(train) [88][300/586] lr: 5.000000e-04 eta: 6:24:33 time: 0.340996 data_time: 0.022940 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.856545 loss: 0.000563 2022/09/13 01:08:09 - mmengine - INFO - Epoch(train) [88][350/586] lr: 5.000000e-04 eta: 6:24:18 time: 0.338282 data_time: 0.021629 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.888371 loss: 0.000582 2022/09/13 01:08:25 - mmengine - INFO - Epoch(train) [88][400/586] lr: 5.000000e-04 eta: 6:24:03 time: 0.329693 data_time: 0.022721 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.844084 loss: 0.000563 2022/09/13 01:08:42 - mmengine - INFO - Epoch(train) [88][450/586] lr: 5.000000e-04 eta: 6:23:48 time: 0.342725 data_time: 0.021901 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.856872 loss: 0.000570 2022/09/13 01:08:59 - mmengine - INFO - Epoch(train) [88][500/586] lr: 5.000000e-04 eta: 6:23:33 time: 0.337326 data_time: 0.021736 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.845970 loss: 0.000552 2022/09/13 01:09:16 - mmengine - INFO - Epoch(train) [88][550/586] lr: 5.000000e-04 eta: 6:23:18 time: 0.328194 data_time: 0.022583 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.839214 loss: 0.000578 2022/09/13 01:09:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:09:28 - mmengine - INFO - Saving checkpoint at 88 epochs 2022/09/13 01:09:52 - mmengine - INFO - Epoch(train) [89][50/586] lr: 5.000000e-04 eta: 6:22:36 time: 0.346579 data_time: 0.030940 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.835586 loss: 0.000552 2022/09/13 01:10:09 - mmengine - INFO - Epoch(train) [89][100/586] lr: 5.000000e-04 eta: 6:22:20 time: 0.329859 data_time: 0.022525 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.890963 loss: 0.000566 2022/09/13 01:10:25 - mmengine - INFO - Epoch(train) [89][150/586] lr: 5.000000e-04 eta: 6:22:05 time: 0.333733 data_time: 0.021820 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.850323 loss: 0.000556 2022/09/13 01:10:42 - mmengine - INFO - Epoch(train) [89][200/586] lr: 5.000000e-04 eta: 6:21:50 time: 0.339024 data_time: 0.021883 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.833396 loss: 0.000574 2022/09/13 01:10:59 - mmengine - INFO - Epoch(train) [89][250/586] lr: 5.000000e-04 eta: 6:21:35 time: 0.333447 data_time: 0.022434 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.840302 loss: 0.000565 2022/09/13 01:11:16 - mmengine - INFO - Epoch(train) [89][300/586] lr: 5.000000e-04 eta: 6:21:20 time: 0.336196 data_time: 0.023204 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.857476 loss: 0.000577 2022/09/13 01:11:33 - mmengine - INFO - Epoch(train) [89][350/586] lr: 5.000000e-04 eta: 6:21:05 time: 0.342310 data_time: 0.022184 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.823337 loss: 0.000576 2022/09/13 01:11:50 - mmengine - INFO - Epoch(train) [89][400/586] lr: 5.000000e-04 eta: 6:20:50 time: 0.332122 data_time: 0.022172 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.861963 loss: 0.000568 2022/09/13 01:12:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:12:06 - mmengine - INFO - Epoch(train) [89][450/586] lr: 5.000000e-04 eta: 6:20:35 time: 0.332668 data_time: 0.022385 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.830709 loss: 0.000587 2022/09/13 01:12:23 - mmengine - INFO - Epoch(train) [89][500/586] lr: 5.000000e-04 eta: 6:20:20 time: 0.340863 data_time: 0.022249 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.841475 loss: 0.000580 2022/09/13 01:12:40 - mmengine - INFO - Epoch(train) [89][550/586] lr: 5.000000e-04 eta: 6:20:04 time: 0.331405 data_time: 0.023400 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.823203 loss: 0.000552 2022/09/13 01:12:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:12:52 - mmengine - INFO - Saving checkpoint at 89 epochs 2022/09/13 01:13:16 - mmengine - INFO - Epoch(train) [90][50/586] lr: 5.000000e-04 eta: 6:19:23 time: 0.344254 data_time: 0.031953 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.842130 loss: 0.000574 2022/09/13 01:13:33 - mmengine - INFO - Epoch(train) [90][100/586] lr: 5.000000e-04 eta: 6:19:07 time: 0.333422 data_time: 0.022881 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.861558 loss: 0.000558 2022/09/13 01:13:50 - mmengine - INFO - Epoch(train) [90][150/586] lr: 5.000000e-04 eta: 6:18:52 time: 0.334638 data_time: 0.026252 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.869512 loss: 0.000576 2022/09/13 01:14:07 - mmengine - INFO - Epoch(train) [90][200/586] lr: 5.000000e-04 eta: 6:18:37 time: 0.336585 data_time: 0.021866 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.762785 loss: 0.000560 2022/09/13 01:14:23 - mmengine - INFO - Epoch(train) [90][250/586] lr: 5.000000e-04 eta: 6:18:22 time: 0.329615 data_time: 0.022370 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.809474 loss: 0.000568 2022/09/13 01:14:40 - mmengine - INFO - Epoch(train) [90][300/586] lr: 5.000000e-04 eta: 6:18:07 time: 0.334867 data_time: 0.026312 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.822855 loss: 0.000567 2022/09/13 01:14:57 - mmengine - INFO - Epoch(train) [90][350/586] lr: 5.000000e-04 eta: 6:17:52 time: 0.339134 data_time: 0.022627 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.844920 loss: 0.000578 2022/09/13 01:15:13 - mmengine - INFO - Epoch(train) [90][400/586] lr: 5.000000e-04 eta: 6:17:36 time: 0.332646 data_time: 0.022825 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.833507 loss: 0.000562 2022/09/13 01:15:30 - mmengine - INFO - Epoch(train) [90][450/586] lr: 5.000000e-04 eta: 6:17:22 time: 0.339792 data_time: 0.025726 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.870133 loss: 0.000562 2022/09/13 01:15:47 - mmengine - INFO - Epoch(train) [90][500/586] lr: 5.000000e-04 eta: 6:17:07 time: 0.338615 data_time: 0.023261 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.853854 loss: 0.000570 2022/09/13 01:16:04 - mmengine - INFO - Epoch(train) [90][550/586] lr: 5.000000e-04 eta: 6:16:51 time: 0.330148 data_time: 0.022399 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.797192 loss: 0.000568 2022/09/13 01:16:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:16:16 - mmengine - INFO - Saving checkpoint at 90 epochs 2022/09/13 01:16:32 - mmengine - INFO - Epoch(val) [90][50/407] eta: 0:01:05 time: 0.183762 data_time: 0.012000 memory: 7489 2022/09/13 01:16:41 - mmengine - INFO - Epoch(val) [90][100/407] eta: 0:00:55 time: 0.180298 data_time: 0.008889 memory: 1657 2022/09/13 01:16:50 - mmengine - INFO - Epoch(val) [90][150/407] eta: 0:00:45 time: 0.178133 data_time: 0.007610 memory: 1657 2022/09/13 01:16:59 - mmengine - INFO - Epoch(val) [90][200/407] eta: 0:00:37 time: 0.182683 data_time: 0.007737 memory: 1657 2022/09/13 01:17:08 - mmengine - INFO - Epoch(val) [90][250/407] eta: 0:00:28 time: 0.178416 data_time: 0.008138 memory: 1657 2022/09/13 01:17:17 - mmengine - INFO - Epoch(val) [90][300/407] eta: 0:00:19 time: 0.179184 data_time: 0.007866 memory: 1657 2022/09/13 01:17:26 - mmengine - INFO - Epoch(val) [90][350/407] eta: 0:00:10 time: 0.179094 data_time: 0.008243 memory: 1657 2022/09/13 01:17:35 - mmengine - INFO - Epoch(val) [90][400/407] eta: 0:00:01 time: 0.174641 data_time: 0.006769 memory: 1657 2022/09/13 01:18:10 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 01:18:24 - mmengine - INFO - Epoch(val) [90][407/407] coco/AP: 0.748300 coco/AP .5: 0.898294 coco/AP .75: 0.813985 coco/AP (M): 0.713697 coco/AP (L): 0.815220 coco/AR: 0.799969 coco/AR .5: 0.936555 coco/AR .75: 0.858942 coco/AR (M): 0.759110 coco/AR (L): 0.859903 2022/09/13 01:18:24 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_70.pth is removed 2022/09/13 01:18:28 - mmengine - INFO - The best checkpoint with 0.7483 coco/AP at 90 epoch is saved to best_coco/AP_epoch_90.pth. 2022/09/13 01:18:46 - mmengine - INFO - Epoch(train) [91][50/586] lr: 5.000000e-04 eta: 6:16:10 time: 0.345611 data_time: 0.026860 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.812021 loss: 0.000563 2022/09/13 01:19:02 - mmengine - INFO - Epoch(train) [91][100/586] lr: 5.000000e-04 eta: 6:15:54 time: 0.331255 data_time: 0.022733 memory: 7489 loss_kpt: 0.000592 acc_pose: 0.856582 loss: 0.000592 2022/09/13 01:19:19 - mmengine - INFO - Epoch(train) [91][150/586] lr: 5.000000e-04 eta: 6:15:39 time: 0.337112 data_time: 0.025250 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.854812 loss: 0.000555 2022/09/13 01:19:36 - mmengine - INFO - Epoch(train) [91][200/586] lr: 5.000000e-04 eta: 6:15:24 time: 0.336444 data_time: 0.022274 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.851977 loss: 0.000551 2022/09/13 01:19:53 - mmengine - INFO - Epoch(train) [91][250/586] lr: 5.000000e-04 eta: 6:15:09 time: 0.334238 data_time: 0.022204 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.830652 loss: 0.000571 2022/09/13 01:19:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:20:09 - mmengine - INFO - Epoch(train) [91][300/586] lr: 5.000000e-04 eta: 6:14:54 time: 0.334534 data_time: 0.023127 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.890600 loss: 0.000569 2022/09/13 01:20:26 - mmengine - INFO - Epoch(train) [91][350/586] lr: 5.000000e-04 eta: 6:14:39 time: 0.336821 data_time: 0.021612 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.843398 loss: 0.000586 2022/09/13 01:20:43 - mmengine - INFO - Epoch(train) [91][400/586] lr: 5.000000e-04 eta: 6:14:23 time: 0.328754 data_time: 0.022098 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.898396 loss: 0.000548 2022/09/13 01:20:59 - mmengine - INFO - Epoch(train) [91][450/586] lr: 5.000000e-04 eta: 6:14:08 time: 0.335325 data_time: 0.025175 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.864551 loss: 0.000562 2022/09/13 01:21:16 - mmengine - INFO - Epoch(train) [91][500/586] lr: 5.000000e-04 eta: 6:13:53 time: 0.342674 data_time: 0.021547 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.851744 loss: 0.000576 2022/09/13 01:21:33 - mmengine - INFO - Epoch(train) [91][550/586] lr: 5.000000e-04 eta: 6:13:38 time: 0.324594 data_time: 0.021956 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.802213 loss: 0.000553 2022/09/13 01:21:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:21:45 - mmengine - INFO - Saving checkpoint at 91 epochs 2022/09/13 01:22:09 - mmengine - INFO - Epoch(train) [92][50/586] lr: 5.000000e-04 eta: 6:12:57 time: 0.349047 data_time: 0.030582 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.805178 loss: 0.000575 2022/09/13 01:22:26 - mmengine - INFO - Epoch(train) [92][100/586] lr: 5.000000e-04 eta: 6:12:42 time: 0.336367 data_time: 0.022224 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.868332 loss: 0.000572 2022/09/13 01:22:43 - mmengine - INFO - Epoch(train) [92][150/586] lr: 5.000000e-04 eta: 6:12:26 time: 0.330758 data_time: 0.022599 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.822995 loss: 0.000572 2022/09/13 01:23:00 - mmengine - INFO - Epoch(train) [92][200/586] lr: 5.000000e-04 eta: 6:12:11 time: 0.340791 data_time: 0.023024 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.762182 loss: 0.000561 2022/09/13 01:23:17 - mmengine - INFO - Epoch(train) [92][250/586] lr: 5.000000e-04 eta: 6:11:56 time: 0.337997 data_time: 0.027118 memory: 7489 loss_kpt: 0.000586 acc_pose: 0.809902 loss: 0.000586 2022/09/13 01:23:33 - mmengine - INFO - Epoch(train) [92][300/586] lr: 5.000000e-04 eta: 6:11:41 time: 0.332925 data_time: 0.023020 memory: 7489 loss_kpt: 0.000582 acc_pose: 0.823638 loss: 0.000582 2022/09/13 01:23:50 - mmengine - INFO - Epoch(train) [92][350/586] lr: 5.000000e-04 eta: 6:11:26 time: 0.339267 data_time: 0.022119 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.891748 loss: 0.000572 2022/09/13 01:24:07 - mmengine - INFO - Epoch(train) [92][400/586] lr: 5.000000e-04 eta: 6:11:11 time: 0.335546 data_time: 0.021539 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.858645 loss: 0.000568 2022/09/13 01:24:24 - mmengine - INFO - Epoch(train) [92][450/586] lr: 5.000000e-04 eta: 6:10:56 time: 0.335035 data_time: 0.021978 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.895306 loss: 0.000587 2022/09/13 01:24:41 - mmengine - INFO - Epoch(train) [92][500/586] lr: 5.000000e-04 eta: 6:10:41 time: 0.337312 data_time: 0.022594 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.861644 loss: 0.000578 2022/09/13 01:24:58 - mmengine - INFO - Epoch(train) [92][550/586] lr: 5.000000e-04 eta: 6:10:26 time: 0.335513 data_time: 0.021709 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.769499 loss: 0.000574 2022/09/13 01:25:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:25:09 - mmengine - INFO - Saving checkpoint at 92 epochs 2022/09/13 01:25:34 - mmengine - INFO - Epoch(train) [93][50/586] lr: 5.000000e-04 eta: 6:09:45 time: 0.344945 data_time: 0.035004 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.807174 loss: 0.000569 2022/09/13 01:25:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:25:51 - mmengine - INFO - Epoch(train) [93][100/586] lr: 5.000000e-04 eta: 6:09:30 time: 0.338884 data_time: 0.023246 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.800363 loss: 0.000567 2022/09/13 01:26:07 - mmengine - INFO - Epoch(train) [93][150/586] lr: 5.000000e-04 eta: 6:09:15 time: 0.331406 data_time: 0.022396 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.880939 loss: 0.000572 2022/09/13 01:26:24 - mmengine - INFO - Epoch(train) [93][200/586] lr: 5.000000e-04 eta: 6:09:00 time: 0.338360 data_time: 0.024327 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.857016 loss: 0.000559 2022/09/13 01:26:41 - mmengine - INFO - Epoch(train) [93][250/586] lr: 5.000000e-04 eta: 6:08:44 time: 0.333447 data_time: 0.022817 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.902716 loss: 0.000575 2022/09/13 01:26:57 - mmengine - INFO - Epoch(train) [93][300/586] lr: 5.000000e-04 eta: 6:08:29 time: 0.331845 data_time: 0.021604 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.845321 loss: 0.000561 2022/09/13 01:27:14 - mmengine - INFO - Epoch(train) [93][350/586] lr: 5.000000e-04 eta: 6:08:14 time: 0.341188 data_time: 0.026578 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.842614 loss: 0.000562 2022/09/13 01:27:31 - mmengine - INFO - Epoch(train) [93][400/586] lr: 5.000000e-04 eta: 6:07:59 time: 0.335522 data_time: 0.022183 memory: 7489 loss_kpt: 0.000581 acc_pose: 0.777444 loss: 0.000581 2022/09/13 01:27:48 - mmengine - INFO - Epoch(train) [93][450/586] lr: 5.000000e-04 eta: 6:07:44 time: 0.330712 data_time: 0.023437 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.824586 loss: 0.000561 2022/09/13 01:28:05 - mmengine - INFO - Epoch(train) [93][500/586] lr: 5.000000e-04 eta: 6:07:28 time: 0.336857 data_time: 0.021696 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.833315 loss: 0.000575 2022/09/13 01:28:21 - mmengine - INFO - Epoch(train) [93][550/586] lr: 5.000000e-04 eta: 6:07:13 time: 0.332926 data_time: 0.022147 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.897868 loss: 0.000573 2022/09/13 01:28:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:28:33 - mmengine - INFO - Saving checkpoint at 93 epochs 2022/09/13 01:28:57 - mmengine - INFO - Epoch(train) [94][50/586] lr: 5.000000e-04 eta: 6:06:32 time: 0.340607 data_time: 0.031548 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.802617 loss: 0.000565 2022/09/13 01:29:14 - mmengine - INFO - Epoch(train) [94][100/586] lr: 5.000000e-04 eta: 6:06:17 time: 0.337821 data_time: 0.022749 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.734233 loss: 0.000566 2022/09/13 01:29:31 - mmengine - INFO - Epoch(train) [94][150/586] lr: 5.000000e-04 eta: 6:06:02 time: 0.333837 data_time: 0.022605 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.834409 loss: 0.000544 2022/09/13 01:29:48 - mmengine - INFO - Epoch(train) [94][200/586] lr: 5.000000e-04 eta: 6:05:47 time: 0.330614 data_time: 0.024739 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.868657 loss: 0.000559 2022/09/13 01:30:05 - mmengine - INFO - Epoch(train) [94][250/586] lr: 5.000000e-04 eta: 6:05:32 time: 0.339304 data_time: 0.022290 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.801536 loss: 0.000569 2022/09/13 01:30:21 - mmengine - INFO - Epoch(train) [94][300/586] lr: 5.000000e-04 eta: 6:05:17 time: 0.336610 data_time: 0.025635 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.841709 loss: 0.000548 2022/09/13 01:30:38 - mmengine - INFO - Epoch(train) [94][350/586] lr: 5.000000e-04 eta: 6:05:02 time: 0.342542 data_time: 0.022080 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.784393 loss: 0.000575 2022/09/13 01:30:55 - mmengine - INFO - Epoch(train) [94][400/586] lr: 5.000000e-04 eta: 6:04:47 time: 0.333410 data_time: 0.023067 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.887631 loss: 0.000553 2022/09/13 01:31:12 - mmengine - INFO - Epoch(train) [94][450/586] lr: 5.000000e-04 eta: 6:04:32 time: 0.345978 data_time: 0.024203 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.783949 loss: 0.000575 2022/09/13 01:31:29 - mmengine - INFO - Epoch(train) [94][500/586] lr: 5.000000e-04 eta: 6:04:17 time: 0.335348 data_time: 0.021974 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.812818 loss: 0.000572 2022/09/13 01:31:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:31:46 - mmengine - INFO - Epoch(train) [94][550/586] lr: 5.000000e-04 eta: 6:04:02 time: 0.337481 data_time: 0.022418 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.785182 loss: 0.000553 2022/09/13 01:31:58 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:31:58 - mmengine - INFO - Saving checkpoint at 94 epochs 2022/09/13 01:32:22 - mmengine - INFO - Epoch(train) [95][50/586] lr: 5.000000e-04 eta: 6:03:21 time: 0.339227 data_time: 0.030554 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.821611 loss: 0.000566 2022/09/13 01:32:39 - mmengine - INFO - Epoch(train) [95][100/586] lr: 5.000000e-04 eta: 6:03:06 time: 0.344272 data_time: 0.027153 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.794727 loss: 0.000578 2022/09/13 01:32:56 - mmengine - INFO - Epoch(train) [95][150/586] lr: 5.000000e-04 eta: 6:02:51 time: 0.330857 data_time: 0.022364 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.879195 loss: 0.000541 2022/09/13 01:33:13 - mmengine - INFO - Epoch(train) [95][200/586] lr: 5.000000e-04 eta: 6:02:36 time: 0.335901 data_time: 0.023189 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.815707 loss: 0.000557 2022/09/13 01:33:30 - mmengine - INFO - Epoch(train) [95][250/586] lr: 5.000000e-04 eta: 6:02:21 time: 0.339623 data_time: 0.022034 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.821787 loss: 0.000572 2022/09/13 01:33:46 - mmengine - INFO - Epoch(train) [95][300/586] lr: 5.000000e-04 eta: 6:02:05 time: 0.328846 data_time: 0.021444 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.792626 loss: 0.000571 2022/09/13 01:34:03 - mmengine - INFO - Epoch(train) [95][350/586] lr: 5.000000e-04 eta: 6:01:50 time: 0.336581 data_time: 0.025914 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.855035 loss: 0.000566 2022/09/13 01:34:20 - mmengine - INFO - Epoch(train) [95][400/586] lr: 5.000000e-04 eta: 6:01:36 time: 0.343770 data_time: 0.022280 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.833917 loss: 0.000566 2022/09/13 01:34:36 - mmengine - INFO - Epoch(train) [95][450/586] lr: 5.000000e-04 eta: 6:01:20 time: 0.325917 data_time: 0.022697 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.814726 loss: 0.000562 2022/09/13 01:34:53 - mmengine - INFO - Epoch(train) [95][500/586] lr: 5.000000e-04 eta: 6:01:05 time: 0.339020 data_time: 0.022565 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.855725 loss: 0.000572 2022/09/13 01:35:10 - mmengine - INFO - Epoch(train) [95][550/586] lr: 5.000000e-04 eta: 6:00:50 time: 0.333656 data_time: 0.021881 memory: 7489 loss_kpt: 0.000583 acc_pose: 0.847507 loss: 0.000583 2022/09/13 01:35:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:35:22 - mmengine - INFO - Saving checkpoint at 95 epochs 2022/09/13 01:35:47 - mmengine - INFO - Epoch(train) [96][50/586] lr: 5.000000e-04 eta: 6:00:09 time: 0.335215 data_time: 0.030175 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.798713 loss: 0.000546 2022/09/13 01:36:04 - mmengine - INFO - Epoch(train) [96][100/586] lr: 5.000000e-04 eta: 5:59:53 time: 0.332513 data_time: 0.022987 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.842535 loss: 0.000557 2022/09/13 01:36:21 - mmengine - INFO - Epoch(train) [96][150/586] lr: 5.000000e-04 eta: 5:59:39 time: 0.339573 data_time: 0.026101 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.823267 loss: 0.000561 2022/09/13 01:36:37 - mmengine - INFO - Epoch(train) [96][200/586] lr: 5.000000e-04 eta: 5:59:23 time: 0.331964 data_time: 0.022499 memory: 7489 loss_kpt: 0.000589 acc_pose: 0.838915 loss: 0.000589 2022/09/13 01:36:54 - mmengine - INFO - Epoch(train) [96][250/586] lr: 5.000000e-04 eta: 5:59:08 time: 0.334253 data_time: 0.022846 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.887728 loss: 0.000544 2022/09/13 01:37:11 - mmengine - INFO - Epoch(train) [96][300/586] lr: 5.000000e-04 eta: 5:58:53 time: 0.332990 data_time: 0.026894 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.872438 loss: 0.000570 2022/09/13 01:37:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:37:28 - mmengine - INFO - Epoch(train) [96][350/586] lr: 5.000000e-04 eta: 5:58:38 time: 0.336485 data_time: 0.023077 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.800741 loss: 0.000552 2022/09/13 01:37:44 - mmengine - INFO - Epoch(train) [96][400/586] lr: 5.000000e-04 eta: 5:58:22 time: 0.337532 data_time: 0.023052 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.863306 loss: 0.000563 2022/09/13 01:38:01 - mmengine - INFO - Epoch(train) [96][450/586] lr: 5.000000e-04 eta: 5:58:07 time: 0.337505 data_time: 0.026218 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.903263 loss: 0.000560 2022/09/13 01:38:18 - mmengine - INFO - Epoch(train) [96][500/586] lr: 5.000000e-04 eta: 5:57:52 time: 0.331826 data_time: 0.021943 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.861193 loss: 0.000559 2022/09/13 01:38:35 - mmengine - INFO - Epoch(train) [96][550/586] lr: 5.000000e-04 eta: 5:57:37 time: 0.331381 data_time: 0.021593 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.825559 loss: 0.000567 2022/09/13 01:38:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:38:47 - mmengine - INFO - Saving checkpoint at 96 epochs 2022/09/13 01:39:10 - mmengine - INFO - Epoch(train) [97][50/586] lr: 5.000000e-04 eta: 5:56:56 time: 0.338915 data_time: 0.029959 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.796363 loss: 0.000570 2022/09/13 01:39:27 - mmengine - INFO - Epoch(train) [97][100/586] lr: 5.000000e-04 eta: 5:56:41 time: 0.336694 data_time: 0.022780 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.796058 loss: 0.000569 2022/09/13 01:39:44 - mmengine - INFO - Epoch(train) [97][150/586] lr: 5.000000e-04 eta: 5:56:26 time: 0.333239 data_time: 0.024891 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.853845 loss: 0.000574 2022/09/13 01:40:00 - mmengine - INFO - Epoch(train) [97][200/586] lr: 5.000000e-04 eta: 5:56:10 time: 0.331737 data_time: 0.026095 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.884139 loss: 0.000560 2022/09/13 01:40:17 - mmengine - INFO - Epoch(train) [97][250/586] lr: 5.000000e-04 eta: 5:55:55 time: 0.337378 data_time: 0.022572 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.842307 loss: 0.000561 2022/09/13 01:40:34 - mmengine - INFO - Epoch(train) [97][300/586] lr: 5.000000e-04 eta: 5:55:40 time: 0.337143 data_time: 0.022862 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.836824 loss: 0.000563 2022/09/13 01:40:51 - mmengine - INFO - Epoch(train) [97][350/586] lr: 5.000000e-04 eta: 5:55:25 time: 0.332829 data_time: 0.025010 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.854664 loss: 0.000550 2022/09/13 01:41:07 - mmengine - INFO - Epoch(train) [97][400/586] lr: 5.000000e-04 eta: 5:55:10 time: 0.339547 data_time: 0.021559 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.849283 loss: 0.000564 2022/09/13 01:41:24 - mmengine - INFO - Epoch(train) [97][450/586] lr: 5.000000e-04 eta: 5:54:55 time: 0.332915 data_time: 0.021612 memory: 7489 loss_kpt: 0.000579 acc_pose: 0.846578 loss: 0.000579 2022/09/13 01:41:41 - mmengine - INFO - Epoch(train) [97][500/586] lr: 5.000000e-04 eta: 5:54:39 time: 0.333536 data_time: 0.025166 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.827625 loss: 0.000570 2022/09/13 01:41:57 - mmengine - INFO - Epoch(train) [97][550/586] lr: 5.000000e-04 eta: 5:54:24 time: 0.332575 data_time: 0.022097 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.835741 loss: 0.000571 2022/09/13 01:42:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:42:10 - mmengine - INFO - Saving checkpoint at 97 epochs 2022/09/13 01:42:34 - mmengine - INFO - Epoch(train) [98][50/586] lr: 5.000000e-04 eta: 5:53:44 time: 0.331311 data_time: 0.029945 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.838199 loss: 0.000545 2022/09/13 01:42:51 - mmengine - INFO - Epoch(train) [98][100/586] lr: 5.000000e-04 eta: 5:53:29 time: 0.339694 data_time: 0.022090 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.781833 loss: 0.000555 2022/09/13 01:43:08 - mmengine - INFO - Epoch(train) [98][150/586] lr: 5.000000e-04 eta: 5:53:14 time: 0.337115 data_time: 0.022436 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.883883 loss: 0.000554 2022/09/13 01:43:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:43:24 - mmengine - INFO - Epoch(train) [98][200/586] lr: 5.000000e-04 eta: 5:52:58 time: 0.329132 data_time: 0.022254 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.851041 loss: 0.000556 2022/09/13 01:43:41 - mmengine - INFO - Epoch(train) [98][250/586] lr: 5.000000e-04 eta: 5:52:43 time: 0.337645 data_time: 0.025477 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.850610 loss: 0.000542 2022/09/13 01:43:58 - mmengine - INFO - Epoch(train) [98][300/586] lr: 5.000000e-04 eta: 5:52:28 time: 0.335766 data_time: 0.022385 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.863551 loss: 0.000557 2022/09/13 01:44:14 - mmengine - INFO - Epoch(train) [98][350/586] lr: 5.000000e-04 eta: 5:52:12 time: 0.326956 data_time: 0.021912 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.883444 loss: 0.000558 2022/09/13 01:44:31 - mmengine - INFO - Epoch(train) [98][400/586] lr: 5.000000e-04 eta: 5:51:57 time: 0.339142 data_time: 0.022871 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.845213 loss: 0.000552 2022/09/13 01:44:48 - mmengine - INFO - Epoch(train) [98][450/586] lr: 5.000000e-04 eta: 5:51:42 time: 0.339882 data_time: 0.023510 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.876140 loss: 0.000569 2022/09/13 01:45:05 - mmengine - INFO - Epoch(train) [98][500/586] lr: 5.000000e-04 eta: 5:51:27 time: 0.333764 data_time: 0.022909 memory: 7489 loss_kpt: 0.000590 acc_pose: 0.854818 loss: 0.000590 2022/09/13 01:45:22 - mmengine - INFO - Epoch(train) [98][550/586] lr: 5.000000e-04 eta: 5:51:12 time: 0.340730 data_time: 0.026428 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.873555 loss: 0.000577 2022/09/13 01:45:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:45:34 - mmengine - INFO - Saving checkpoint at 98 epochs 2022/09/13 01:45:58 - mmengine - INFO - Epoch(train) [99][50/586] lr: 5.000000e-04 eta: 5:50:32 time: 0.336617 data_time: 0.028573 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.851273 loss: 0.000570 2022/09/13 01:46:15 - mmengine - INFO - Epoch(train) [99][100/586] lr: 5.000000e-04 eta: 5:50:17 time: 0.341490 data_time: 0.027292 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.855602 loss: 0.000565 2022/09/13 01:46:31 - mmengine - INFO - Epoch(train) [99][150/586] lr: 5.000000e-04 eta: 5:50:02 time: 0.333527 data_time: 0.022597 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.864185 loss: 0.000578 2022/09/13 01:46:48 - mmengine - INFO - Epoch(train) [99][200/586] lr: 5.000000e-04 eta: 5:49:47 time: 0.337882 data_time: 0.021930 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.863236 loss: 0.000566 2022/09/13 01:47:05 - mmengine - INFO - Epoch(train) [99][250/586] lr: 5.000000e-04 eta: 5:49:31 time: 0.331427 data_time: 0.022272 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.795512 loss: 0.000555 2022/09/13 01:47:21 - mmengine - INFO - Epoch(train) [99][300/586] lr: 5.000000e-04 eta: 5:49:16 time: 0.334307 data_time: 0.022405 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.829452 loss: 0.000568 2022/09/13 01:47:38 - mmengine - INFO - Epoch(train) [99][350/586] lr: 5.000000e-04 eta: 5:49:01 time: 0.333601 data_time: 0.025557 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.874740 loss: 0.000562 2022/09/13 01:47:55 - mmengine - INFO - Epoch(train) [99][400/586] lr: 5.000000e-04 eta: 5:48:45 time: 0.332548 data_time: 0.022647 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.842130 loss: 0.000552 2022/09/13 01:48:12 - mmengine - INFO - Epoch(train) [99][450/586] lr: 5.000000e-04 eta: 5:48:30 time: 0.334649 data_time: 0.022272 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.897971 loss: 0.000554 2022/09/13 01:48:28 - mmengine - INFO - Epoch(train) [99][500/586] lr: 5.000000e-04 eta: 5:48:15 time: 0.337962 data_time: 0.022865 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.836007 loss: 0.000567 2022/09/13 01:48:45 - mmengine - INFO - Epoch(train) [99][550/586] lr: 5.000000e-04 eta: 5:48:00 time: 0.333467 data_time: 0.022942 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.888970 loss: 0.000564 2022/09/13 01:48:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:48:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:48:57 - mmengine - INFO - Saving checkpoint at 99 epochs 2022/09/13 01:49:22 - mmengine - INFO - Epoch(train) [100][50/586] lr: 5.000000e-04 eta: 5:47:21 time: 0.350371 data_time: 0.031199 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.845889 loss: 0.000552 2022/09/13 01:49:38 - mmengine - INFO - Epoch(train) [100][100/586] lr: 5.000000e-04 eta: 5:47:05 time: 0.330968 data_time: 0.022516 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.889860 loss: 0.000577 2022/09/13 01:49:55 - mmengine - INFO - Epoch(train) [100][150/586] lr: 5.000000e-04 eta: 5:46:50 time: 0.336052 data_time: 0.022725 memory: 7489 loss_kpt: 0.000587 acc_pose: 0.861823 loss: 0.000587 2022/09/13 01:50:12 - mmengine - INFO - Epoch(train) [100][200/586] lr: 5.000000e-04 eta: 5:46:35 time: 0.333087 data_time: 0.022072 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.859658 loss: 0.000564 2022/09/13 01:50:28 - mmengine - INFO - Epoch(train) [100][250/586] lr: 5.000000e-04 eta: 5:46:19 time: 0.331529 data_time: 0.022063 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.781467 loss: 0.000560 2022/09/13 01:50:45 - mmengine - INFO - Epoch(train) [100][300/586] lr: 5.000000e-04 eta: 5:46:04 time: 0.339724 data_time: 0.026147 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.833777 loss: 0.000559 2022/09/13 01:51:02 - mmengine - INFO - Epoch(train) [100][350/586] lr: 5.000000e-04 eta: 5:45:49 time: 0.336770 data_time: 0.022853 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.826702 loss: 0.000560 2022/09/13 01:51:19 - mmengine - INFO - Epoch(train) [100][400/586] lr: 5.000000e-04 eta: 5:45:34 time: 0.335048 data_time: 0.022224 memory: 7489 loss_kpt: 0.000576 acc_pose: 0.874118 loss: 0.000576 2022/09/13 01:51:36 - mmengine - INFO - Epoch(train) [100][450/586] lr: 5.000000e-04 eta: 5:45:19 time: 0.337469 data_time: 0.022290 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.798545 loss: 0.000559 2022/09/13 01:51:53 - mmengine - INFO - Epoch(train) [100][500/586] lr: 5.000000e-04 eta: 5:45:04 time: 0.335451 data_time: 0.022071 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.834227 loss: 0.000570 2022/09/13 01:52:09 - mmengine - INFO - Epoch(train) [100][550/586] lr: 5.000000e-04 eta: 5:44:48 time: 0.334161 data_time: 0.021957 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.845875 loss: 0.000568 2022/09/13 01:52:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:52:22 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/09/13 01:52:38 - mmengine - INFO - Epoch(val) [100][50/407] eta: 0:01:05 time: 0.184089 data_time: 0.012192 memory: 7489 2022/09/13 01:52:47 - mmengine - INFO - Epoch(val) [100][100/407] eta: 0:00:54 time: 0.178158 data_time: 0.007508 memory: 1657 2022/09/13 01:52:56 - mmengine - INFO - Epoch(val) [100][150/407] eta: 0:00:46 time: 0.181690 data_time: 0.007771 memory: 1657 2022/09/13 01:53:05 - mmengine - INFO - Epoch(val) [100][200/407] eta: 0:00:37 time: 0.179817 data_time: 0.008263 memory: 1657 2022/09/13 01:53:14 - mmengine - INFO - Epoch(val) [100][250/407] eta: 0:00:27 time: 0.178117 data_time: 0.007616 memory: 1657 2022/09/13 01:53:23 - mmengine - INFO - Epoch(val) [100][300/407] eta: 0:00:19 time: 0.179868 data_time: 0.008098 memory: 1657 2022/09/13 01:53:32 - mmengine - INFO - Epoch(val) [100][350/407] eta: 0:00:10 time: 0.179072 data_time: 0.007956 memory: 1657 2022/09/13 01:53:41 - mmengine - INFO - Epoch(val) [100][400/407] eta: 0:00:01 time: 0.175338 data_time: 0.007059 memory: 1657 2022/09/13 01:54:17 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 01:54:30 - mmengine - INFO - Epoch(val) [100][407/407] coco/AP: 0.749696 coco/AP .5: 0.898296 coco/AP .75: 0.812623 coco/AP (M): 0.712442 coco/AP (L): 0.819170 coco/AR: 0.801307 coco/AR .5: 0.937815 coco/AR .75: 0.858470 coco/AR (M): 0.758072 coco/AR (L): 0.864214 2022/09/13 01:54:30 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_90.pth is removed 2022/09/13 01:54:34 - mmengine - INFO - The best checkpoint with 0.7497 coco/AP at 100 epoch is saved to best_coco/AP_epoch_100.pth. 2022/09/13 01:54:52 - mmengine - INFO - Epoch(train) [101][50/586] lr: 5.000000e-04 eta: 5:44:10 time: 0.345440 data_time: 0.026974 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.849594 loss: 0.000571 2022/09/13 01:55:08 - mmengine - INFO - Epoch(train) [101][100/586] lr: 5.000000e-04 eta: 5:43:54 time: 0.328032 data_time: 0.022636 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.834661 loss: 0.000567 2022/09/13 01:55:25 - mmengine - INFO - Epoch(train) [101][150/586] lr: 5.000000e-04 eta: 5:43:39 time: 0.335567 data_time: 0.022055 memory: 7489 loss_kpt: 0.000575 acc_pose: 0.807481 loss: 0.000575 2022/09/13 01:55:42 - mmengine - INFO - Epoch(train) [101][200/586] lr: 5.000000e-04 eta: 5:43:24 time: 0.337267 data_time: 0.026143 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.841809 loss: 0.000568 2022/09/13 01:55:58 - mmengine - INFO - Epoch(train) [101][250/586] lr: 5.000000e-04 eta: 5:43:08 time: 0.332582 data_time: 0.022110 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.881429 loss: 0.000580 2022/09/13 01:56:15 - mmengine - INFO - Epoch(train) [101][300/586] lr: 5.000000e-04 eta: 5:42:53 time: 0.336743 data_time: 0.022339 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.897127 loss: 0.000550 2022/09/13 01:56:32 - mmengine - INFO - Epoch(train) [101][350/586] lr: 5.000000e-04 eta: 5:42:38 time: 0.344553 data_time: 0.022344 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.869798 loss: 0.000551 2022/09/13 01:56:49 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:56:49 - mmengine - INFO - Epoch(train) [101][400/586] lr: 5.000000e-04 eta: 5:42:23 time: 0.328335 data_time: 0.022172 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.880538 loss: 0.000566 2022/09/13 01:57:06 - mmengine - INFO - Epoch(train) [101][450/586] lr: 5.000000e-04 eta: 5:42:08 time: 0.340388 data_time: 0.022670 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.884024 loss: 0.000554 2022/09/13 01:57:23 - mmengine - INFO - Epoch(train) [101][500/586] lr: 5.000000e-04 eta: 5:41:52 time: 0.334982 data_time: 0.025163 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.778751 loss: 0.000556 2022/09/13 01:57:39 - mmengine - INFO - Epoch(train) [101][550/586] lr: 5.000000e-04 eta: 5:41:37 time: 0.329462 data_time: 0.022032 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.794637 loss: 0.000574 2022/09/13 01:57:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 01:57:51 - mmengine - INFO - Saving checkpoint at 101 epochs 2022/09/13 01:58:15 - mmengine - INFO - Epoch(train) [102][50/586] lr: 5.000000e-04 eta: 5:40:58 time: 0.341775 data_time: 0.028907 memory: 7489 loss_kpt: 0.000574 acc_pose: 0.826319 loss: 0.000574 2022/09/13 01:58:32 - mmengine - INFO - Epoch(train) [102][100/586] lr: 5.000000e-04 eta: 5:40:43 time: 0.338011 data_time: 0.026969 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.808917 loss: 0.000552 2022/09/13 01:58:49 - mmengine - INFO - Epoch(train) [102][150/586] lr: 5.000000e-04 eta: 5:40:28 time: 0.332614 data_time: 0.021463 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.821341 loss: 0.000562 2022/09/13 01:59:06 - mmengine - INFO - Epoch(train) [102][200/586] lr: 5.000000e-04 eta: 5:40:12 time: 0.337946 data_time: 0.021855 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.836504 loss: 0.000562 2022/09/13 01:59:22 - mmengine - INFO - Epoch(train) [102][250/586] lr: 5.000000e-04 eta: 5:39:57 time: 0.337454 data_time: 0.025426 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.837799 loss: 0.000571 2022/09/13 01:59:39 - mmengine - INFO - Epoch(train) [102][300/586] lr: 5.000000e-04 eta: 5:39:42 time: 0.339226 data_time: 0.022857 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.868989 loss: 0.000531 2022/09/13 01:59:56 - mmengine - INFO - Epoch(train) [102][350/586] lr: 5.000000e-04 eta: 5:39:27 time: 0.336434 data_time: 0.022423 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.852159 loss: 0.000563 2022/09/13 02:00:13 - mmengine - INFO - Epoch(train) [102][400/586] lr: 5.000000e-04 eta: 5:39:12 time: 0.331921 data_time: 0.023931 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.895866 loss: 0.000552 2022/09/13 02:00:31 - mmengine - INFO - Epoch(train) [102][450/586] lr: 5.000000e-04 eta: 5:38:58 time: 0.358152 data_time: 0.024434 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.847440 loss: 0.000551 2022/09/13 02:00:47 - mmengine - INFO - Epoch(train) [102][500/586] lr: 5.000000e-04 eta: 5:38:42 time: 0.334364 data_time: 0.022200 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.836444 loss: 0.000564 2022/09/13 02:01:04 - mmengine - INFO - Epoch(train) [102][550/586] lr: 5.000000e-04 eta: 5:38:27 time: 0.328598 data_time: 0.024974 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.811339 loss: 0.000545 2022/09/13 02:01:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:01:16 - mmengine - INFO - Saving checkpoint at 102 epochs 2022/09/13 02:01:41 - mmengine - INFO - Epoch(train) [103][50/586] lr: 5.000000e-04 eta: 5:37:48 time: 0.350190 data_time: 0.037862 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.843477 loss: 0.000566 2022/09/13 02:01:57 - mmengine - INFO - Epoch(train) [103][100/586] lr: 5.000000e-04 eta: 5:37:33 time: 0.327405 data_time: 0.023938 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.803749 loss: 0.000559 2022/09/13 02:02:14 - mmengine - INFO - Epoch(train) [103][150/586] lr: 5.000000e-04 eta: 5:37:18 time: 0.339612 data_time: 0.022456 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.857200 loss: 0.000566 2022/09/13 02:02:31 - mmengine - INFO - Epoch(train) [103][200/586] lr: 5.000000e-04 eta: 5:37:03 time: 0.334839 data_time: 0.022365 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.801166 loss: 0.000563 2022/09/13 02:02:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:02:48 - mmengine - INFO - Epoch(train) [103][250/586] lr: 5.000000e-04 eta: 5:36:47 time: 0.337118 data_time: 0.021902 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.839483 loss: 0.000544 2022/09/13 02:03:05 - mmengine - INFO - Epoch(train) [103][300/586] lr: 5.000000e-04 eta: 5:36:32 time: 0.337262 data_time: 0.021657 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.898513 loss: 0.000557 2022/09/13 02:03:22 - mmengine - INFO - Epoch(train) [103][350/586] lr: 5.000000e-04 eta: 5:36:17 time: 0.335560 data_time: 0.025926 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.894637 loss: 0.000564 2022/09/13 02:03:38 - mmengine - INFO - Epoch(train) [103][400/586] lr: 5.000000e-04 eta: 5:36:02 time: 0.333541 data_time: 0.022406 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.839806 loss: 0.000558 2022/09/13 02:03:55 - mmengine - INFO - Epoch(train) [103][450/586] lr: 5.000000e-04 eta: 5:35:46 time: 0.333199 data_time: 0.021615 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.869518 loss: 0.000558 2022/09/13 02:04:12 - mmengine - INFO - Epoch(train) [103][500/586] lr: 5.000000e-04 eta: 5:35:32 time: 0.347543 data_time: 0.027008 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.828941 loss: 0.000567 2022/09/13 02:04:29 - mmengine - INFO - Epoch(train) [103][550/586] lr: 5.000000e-04 eta: 5:35:17 time: 0.338601 data_time: 0.021566 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.856124 loss: 0.000566 2022/09/13 02:04:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:04:41 - mmengine - INFO - Saving checkpoint at 103 epochs 2022/09/13 02:05:06 - mmengine - INFO - Epoch(train) [104][50/586] lr: 5.000000e-04 eta: 5:34:39 time: 0.352167 data_time: 0.032669 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.860214 loss: 0.000565 2022/09/13 02:05:23 - mmengine - INFO - Epoch(train) [104][100/586] lr: 5.000000e-04 eta: 5:34:23 time: 0.335331 data_time: 0.023579 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.820693 loss: 0.000528 2022/09/13 02:05:39 - mmengine - INFO - Epoch(train) [104][150/586] lr: 5.000000e-04 eta: 5:34:08 time: 0.335884 data_time: 0.022495 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.848989 loss: 0.000577 2022/09/13 02:05:56 - mmengine - INFO - Epoch(train) [104][200/586] lr: 5.000000e-04 eta: 5:33:53 time: 0.332089 data_time: 0.022457 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.810608 loss: 0.000541 2022/09/13 02:06:13 - mmengine - INFO - Epoch(train) [104][250/586] lr: 5.000000e-04 eta: 5:33:37 time: 0.336733 data_time: 0.025270 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.865285 loss: 0.000557 2022/09/13 02:06:30 - mmengine - INFO - Epoch(train) [104][300/586] lr: 5.000000e-04 eta: 5:33:22 time: 0.339464 data_time: 0.022165 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.797212 loss: 0.000543 2022/09/13 02:06:46 - mmengine - INFO - Epoch(train) [104][350/586] lr: 5.000000e-04 eta: 5:33:07 time: 0.329454 data_time: 0.026185 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.835020 loss: 0.000554 2022/09/13 02:07:04 - mmengine - INFO - Epoch(train) [104][400/586] lr: 5.000000e-04 eta: 5:32:52 time: 0.348973 data_time: 0.022828 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.833582 loss: 0.000526 2022/09/13 02:07:20 - mmengine - INFO - Epoch(train) [104][450/586] lr: 5.000000e-04 eta: 5:32:37 time: 0.328194 data_time: 0.021629 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.823703 loss: 0.000560 2022/09/13 02:07:37 - mmengine - INFO - Epoch(train) [104][500/586] lr: 5.000000e-04 eta: 5:32:22 time: 0.338585 data_time: 0.021882 memory: 7489 loss_kpt: 0.000567 acc_pose: 0.827258 loss: 0.000567 2022/09/13 02:07:54 - mmengine - INFO - Epoch(train) [104][550/586] lr: 5.000000e-04 eta: 5:32:07 time: 0.345257 data_time: 0.022499 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.853207 loss: 0.000551 2022/09/13 02:08:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:08:06 - mmengine - INFO - Saving checkpoint at 104 epochs 2022/09/13 02:08:30 - mmengine - INFO - Epoch(train) [105][50/586] lr: 5.000000e-04 eta: 5:31:29 time: 0.346759 data_time: 0.029332 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.844765 loss: 0.000578 2022/09/13 02:08:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:08:47 - mmengine - INFO - Epoch(train) [105][100/586] lr: 5.000000e-04 eta: 5:31:14 time: 0.339874 data_time: 0.022975 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.838725 loss: 0.000545 2022/09/13 02:09:05 - mmengine - INFO - Epoch(train) [105][150/586] lr: 5.000000e-04 eta: 5:30:59 time: 0.343766 data_time: 0.026667 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.859540 loss: 0.000577 2022/09/13 02:09:21 - mmengine - INFO - Epoch(train) [105][200/586] lr: 5.000000e-04 eta: 5:30:44 time: 0.335789 data_time: 0.022436 memory: 7489 loss_kpt: 0.000577 acc_pose: 0.861546 loss: 0.000577 2022/09/13 02:09:39 - mmengine - INFO - Epoch(train) [105][250/586] lr: 5.000000e-04 eta: 5:30:29 time: 0.342272 data_time: 0.021950 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.872192 loss: 0.000554 2022/09/13 02:09:56 - mmengine - INFO - Epoch(train) [105][300/586] lr: 5.000000e-04 eta: 5:30:14 time: 0.340039 data_time: 0.023683 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.833530 loss: 0.000539 2022/09/13 02:10:13 - mmengine - INFO - Epoch(train) [105][350/586] lr: 5.000000e-04 eta: 5:29:59 time: 0.339300 data_time: 0.023016 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.845278 loss: 0.000554 2022/09/13 02:10:29 - mmengine - INFO - Epoch(train) [105][400/586] lr: 5.000000e-04 eta: 5:29:43 time: 0.336101 data_time: 0.022164 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.828519 loss: 0.000548 2022/09/13 02:10:46 - mmengine - INFO - Epoch(train) [105][450/586] lr: 5.000000e-04 eta: 5:29:28 time: 0.338210 data_time: 0.025731 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.826758 loss: 0.000558 2022/09/13 02:11:03 - mmengine - INFO - Epoch(train) [105][500/586] lr: 5.000000e-04 eta: 5:29:13 time: 0.333697 data_time: 0.021877 memory: 7489 loss_kpt: 0.000572 acc_pose: 0.819654 loss: 0.000572 2022/09/13 02:11:20 - mmengine - INFO - Epoch(train) [105][550/586] lr: 5.000000e-04 eta: 5:28:58 time: 0.335157 data_time: 0.022215 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.808731 loss: 0.000542 2022/09/13 02:11:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:11:32 - mmengine - INFO - Saving checkpoint at 105 epochs 2022/09/13 02:11:56 - mmengine - INFO - Epoch(train) [106][50/586] lr: 5.000000e-04 eta: 5:28:20 time: 0.342592 data_time: 0.027231 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.893499 loss: 0.000555 2022/09/13 02:12:12 - mmengine - INFO - Epoch(train) [106][100/586] lr: 5.000000e-04 eta: 5:28:04 time: 0.335855 data_time: 0.027818 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.786881 loss: 0.000554 2022/09/13 02:12:29 - mmengine - INFO - Epoch(train) [106][150/586] lr: 5.000000e-04 eta: 5:27:49 time: 0.342243 data_time: 0.021680 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.832652 loss: 0.000546 2022/09/13 02:12:46 - mmengine - INFO - Epoch(train) [106][200/586] lr: 5.000000e-04 eta: 5:27:34 time: 0.330238 data_time: 0.021793 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.821003 loss: 0.000554 2022/09/13 02:13:03 - mmengine - INFO - Epoch(train) [106][250/586] lr: 5.000000e-04 eta: 5:27:19 time: 0.337709 data_time: 0.023727 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.806157 loss: 0.000558 2022/09/13 02:13:20 - mmengine - INFO - Epoch(train) [106][300/586] lr: 5.000000e-04 eta: 5:27:03 time: 0.336643 data_time: 0.023365 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.777743 loss: 0.000570 2022/09/13 02:13:36 - mmengine - INFO - Epoch(train) [106][350/586] lr: 5.000000e-04 eta: 5:26:48 time: 0.333976 data_time: 0.022615 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.895665 loss: 0.000540 2022/09/13 02:13:53 - mmengine - INFO - Epoch(train) [106][400/586] lr: 5.000000e-04 eta: 5:26:33 time: 0.331738 data_time: 0.022236 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.838387 loss: 0.000566 2022/09/13 02:14:10 - mmengine - INFO - Epoch(train) [106][450/586] lr: 5.000000e-04 eta: 5:26:18 time: 0.342300 data_time: 0.023130 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.865876 loss: 0.000549 2022/09/13 02:14:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:14:27 - mmengine - INFO - Epoch(train) [106][500/586] lr: 5.000000e-04 eta: 5:26:02 time: 0.333975 data_time: 0.022143 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.795730 loss: 0.000553 2022/09/13 02:14:44 - mmengine - INFO - Epoch(train) [106][550/586] lr: 5.000000e-04 eta: 5:25:47 time: 0.333363 data_time: 0.022449 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.893343 loss: 0.000559 2022/09/13 02:14:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:14:56 - mmengine - INFO - Saving checkpoint at 106 epochs 2022/09/13 02:15:20 - mmengine - INFO - Epoch(train) [107][50/586] lr: 5.000000e-04 eta: 5:25:09 time: 0.344416 data_time: 0.034644 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.851538 loss: 0.000548 2022/09/13 02:15:37 - mmengine - INFO - Epoch(train) [107][100/586] lr: 5.000000e-04 eta: 5:24:54 time: 0.330216 data_time: 0.023282 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.810186 loss: 0.000546 2022/09/13 02:15:54 - mmengine - INFO - Epoch(train) [107][150/586] lr: 5.000000e-04 eta: 5:24:39 time: 0.345697 data_time: 0.025703 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.783855 loss: 0.000539 2022/09/13 02:16:11 - mmengine - INFO - Epoch(train) [107][200/586] lr: 5.000000e-04 eta: 5:24:23 time: 0.332198 data_time: 0.022739 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.870486 loss: 0.000540 2022/09/13 02:16:27 - mmengine - INFO - Epoch(train) [107][250/586] lr: 5.000000e-04 eta: 5:24:08 time: 0.329481 data_time: 0.022015 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.871267 loss: 0.000542 2022/09/13 02:16:44 - mmengine - INFO - Epoch(train) [107][300/586] lr: 5.000000e-04 eta: 5:23:53 time: 0.339513 data_time: 0.026380 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.816065 loss: 0.000549 2022/09/13 02:17:01 - mmengine - INFO - Epoch(train) [107][350/586] lr: 5.000000e-04 eta: 5:23:37 time: 0.331874 data_time: 0.021830 memory: 7489 loss_kpt: 0.000566 acc_pose: 0.836067 loss: 0.000566 2022/09/13 02:17:18 - mmengine - INFO - Epoch(train) [107][400/586] lr: 5.000000e-04 eta: 5:23:22 time: 0.342532 data_time: 0.022465 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.844296 loss: 0.000556 2022/09/13 02:17:35 - mmengine - INFO - Epoch(train) [107][450/586] lr: 5.000000e-04 eta: 5:23:07 time: 0.336472 data_time: 0.025606 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.879216 loss: 0.000556 2022/09/13 02:17:52 - mmengine - INFO - Epoch(train) [107][500/586] lr: 5.000000e-04 eta: 5:22:52 time: 0.343346 data_time: 0.023735 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.880436 loss: 0.000557 2022/09/13 02:18:09 - mmengine - INFO - Epoch(train) [107][550/586] lr: 5.000000e-04 eta: 5:22:37 time: 0.332108 data_time: 0.021652 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.846148 loss: 0.000548 2022/09/13 02:18:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:18:21 - mmengine - INFO - Saving checkpoint at 107 epochs 2022/09/13 02:18:45 - mmengine - INFO - Epoch(train) [108][50/586] lr: 5.000000e-04 eta: 5:21:59 time: 0.344510 data_time: 0.029153 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.885995 loss: 0.000546 2022/09/13 02:19:02 - mmengine - INFO - Epoch(train) [108][100/586] lr: 5.000000e-04 eta: 5:21:44 time: 0.331906 data_time: 0.024525 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.754654 loss: 0.000565 2022/09/13 02:19:19 - mmengine - INFO - Epoch(train) [108][150/586] lr: 5.000000e-04 eta: 5:21:29 time: 0.339427 data_time: 0.022718 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.850511 loss: 0.000556 2022/09/13 02:19:36 - mmengine - INFO - Epoch(train) [108][200/586] lr: 5.000000e-04 eta: 5:21:14 time: 0.339882 data_time: 0.022380 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.832219 loss: 0.000559 2022/09/13 02:19:52 - mmengine - INFO - Epoch(train) [108][250/586] lr: 5.000000e-04 eta: 5:20:58 time: 0.331149 data_time: 0.022207 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.823417 loss: 0.000552 2022/09/13 02:20:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:20:09 - mmengine - INFO - Epoch(train) [108][300/586] lr: 5.000000e-04 eta: 5:20:43 time: 0.341965 data_time: 0.022392 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.864704 loss: 0.000545 2022/09/13 02:20:26 - mmengine - INFO - Epoch(train) [108][350/586] lr: 5.000000e-04 eta: 5:20:28 time: 0.335418 data_time: 0.026332 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.864148 loss: 0.000530 2022/09/13 02:20:43 - mmengine - INFO - Epoch(train) [108][400/586] lr: 5.000000e-04 eta: 5:20:13 time: 0.339182 data_time: 0.022204 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.876099 loss: 0.000533 2022/09/13 02:21:00 - mmengine - INFO - Epoch(train) [108][450/586] lr: 5.000000e-04 eta: 5:19:57 time: 0.333929 data_time: 0.022440 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.818819 loss: 0.000545 2022/09/13 02:21:17 - mmengine - INFO - Epoch(train) [108][500/586] lr: 5.000000e-04 eta: 5:19:42 time: 0.336819 data_time: 0.021820 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.833006 loss: 0.000551 2022/09/13 02:21:33 - mmengine - INFO - Epoch(train) [108][550/586] lr: 5.000000e-04 eta: 5:19:27 time: 0.338667 data_time: 0.022315 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.858168 loss: 0.000540 2022/09/13 02:21:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:21:46 - mmengine - INFO - Saving checkpoint at 108 epochs 2022/09/13 02:22:10 - mmengine - INFO - Epoch(train) [109][50/586] lr: 5.000000e-04 eta: 5:18:49 time: 0.342360 data_time: 0.027968 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.820717 loss: 0.000550 2022/09/13 02:22:27 - mmengine - INFO - Epoch(train) [109][100/586] lr: 5.000000e-04 eta: 5:18:34 time: 0.338882 data_time: 0.023565 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.846664 loss: 0.000537 2022/09/13 02:22:43 - mmengine - INFO - Epoch(train) [109][150/586] lr: 5.000000e-04 eta: 5:18:19 time: 0.332129 data_time: 0.023096 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.881656 loss: 0.000552 2022/09/13 02:23:00 - mmengine - INFO - Epoch(train) [109][200/586] lr: 5.000000e-04 eta: 5:18:03 time: 0.335652 data_time: 0.022922 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.850953 loss: 0.000569 2022/09/13 02:23:17 - mmengine - INFO - Epoch(train) [109][250/586] lr: 5.000000e-04 eta: 5:17:48 time: 0.340003 data_time: 0.021778 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.805247 loss: 0.000535 2022/09/13 02:23:34 - mmengine - INFO - Epoch(train) [109][300/586] lr: 5.000000e-04 eta: 5:17:33 time: 0.337337 data_time: 0.025389 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.839815 loss: 0.000570 2022/09/13 02:23:51 - mmengine - INFO - Epoch(train) [109][350/586] lr: 5.000000e-04 eta: 5:17:18 time: 0.337606 data_time: 0.021673 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.842068 loss: 0.000560 2022/09/13 02:24:08 - mmengine - INFO - Epoch(train) [109][400/586] lr: 5.000000e-04 eta: 5:17:03 time: 0.333931 data_time: 0.021599 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.806458 loss: 0.000560 2022/09/13 02:24:24 - mmengine - INFO - Epoch(train) [109][450/586] lr: 5.000000e-04 eta: 5:16:47 time: 0.334126 data_time: 0.023198 memory: 7489 loss_kpt: 0.000580 acc_pose: 0.810084 loss: 0.000580 2022/09/13 02:24:41 - mmengine - INFO - Epoch(train) [109][500/586] lr: 5.000000e-04 eta: 5:16:32 time: 0.337182 data_time: 0.022055 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.766023 loss: 0.000557 2022/09/13 02:24:58 - mmengine - INFO - Epoch(train) [109][550/586] lr: 5.000000e-04 eta: 5:16:16 time: 0.332662 data_time: 0.021746 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.849852 loss: 0.000561 2022/09/13 02:25:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:25:10 - mmengine - INFO - Saving checkpoint at 109 epochs 2022/09/13 02:25:34 - mmengine - INFO - Epoch(train) [110][50/586] lr: 5.000000e-04 eta: 5:15:39 time: 0.339388 data_time: 0.030785 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.754226 loss: 0.000551 2022/09/13 02:25:51 - mmengine - INFO - Epoch(train) [110][100/586] lr: 5.000000e-04 eta: 5:15:24 time: 0.341660 data_time: 0.022901 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.838244 loss: 0.000563 2022/09/13 02:25:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:26:07 - mmengine - INFO - Epoch(train) [110][150/586] lr: 5.000000e-04 eta: 5:15:09 time: 0.329830 data_time: 0.022171 memory: 7489 loss_kpt: 0.000570 acc_pose: 0.820667 loss: 0.000570 2022/09/13 02:26:24 - mmengine - INFO - Epoch(train) [110][200/586] lr: 5.000000e-04 eta: 5:14:53 time: 0.335986 data_time: 0.023691 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.831858 loss: 0.000550 2022/09/13 02:26:41 - mmengine - INFO - Epoch(train) [110][250/586] lr: 5.000000e-04 eta: 5:14:38 time: 0.340354 data_time: 0.025866 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.788712 loss: 0.000561 2022/09/13 02:26:58 - mmengine - INFO - Epoch(train) [110][300/586] lr: 5.000000e-04 eta: 5:14:23 time: 0.340416 data_time: 0.024479 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.860210 loss: 0.000555 2022/09/13 02:27:15 - mmengine - INFO - Epoch(train) [110][350/586] lr: 5.000000e-04 eta: 5:14:08 time: 0.336945 data_time: 0.021814 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.856111 loss: 0.000545 2022/09/13 02:27:32 - mmengine - INFO - Epoch(train) [110][400/586] lr: 5.000000e-04 eta: 5:13:53 time: 0.337012 data_time: 0.022572 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.844078 loss: 0.000548 2022/09/13 02:27:49 - mmengine - INFO - Epoch(train) [110][450/586] lr: 5.000000e-04 eta: 5:13:37 time: 0.339632 data_time: 0.023130 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.787206 loss: 0.000548 2022/09/13 02:28:06 - mmengine - INFO - Epoch(train) [110][500/586] lr: 5.000000e-04 eta: 5:13:22 time: 0.332144 data_time: 0.021960 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.861205 loss: 0.000538 2022/09/13 02:28:23 - mmengine - INFO - Epoch(train) [110][550/586] lr: 5.000000e-04 eta: 5:13:07 time: 0.339544 data_time: 0.022184 memory: 7489 loss_kpt: 0.000571 acc_pose: 0.835004 loss: 0.000571 2022/09/13 02:28:34 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:28:34 - mmengine - INFO - Saving checkpoint at 110 epochs 2022/09/13 02:28:51 - mmengine - INFO - Epoch(val) [110][50/407] eta: 0:01:07 time: 0.189889 data_time: 0.018162 memory: 7489 2022/09/13 02:29:00 - mmengine - INFO - Epoch(val) [110][100/407] eta: 0:00:54 time: 0.177982 data_time: 0.007526 memory: 1657 2022/09/13 02:29:09 - mmengine - INFO - Epoch(val) [110][150/407] eta: 0:00:45 time: 0.178957 data_time: 0.007739 memory: 1657 2022/09/13 02:29:18 - mmengine - INFO - Epoch(val) [110][200/407] eta: 0:00:36 time: 0.178265 data_time: 0.007642 memory: 1657 2022/09/13 02:29:27 - mmengine - INFO - Epoch(val) [110][250/407] eta: 0:00:28 time: 0.178869 data_time: 0.007300 memory: 1657 2022/09/13 02:29:36 - mmengine - INFO - Epoch(val) [110][300/407] eta: 0:00:19 time: 0.178022 data_time: 0.007736 memory: 1657 2022/09/13 02:29:45 - mmengine - INFO - Epoch(val) [110][350/407] eta: 0:00:10 time: 0.177611 data_time: 0.007643 memory: 1657 2022/09/13 02:29:53 - mmengine - INFO - Epoch(val) [110][400/407] eta: 0:00:01 time: 0.177677 data_time: 0.009120 memory: 1657 2022/09/13 02:30:28 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 02:30:42 - mmengine - INFO - Epoch(val) [110][407/407] coco/AP: 0.751092 coco/AP .5: 0.898764 coco/AP .75: 0.817459 coco/AP (M): 0.715338 coco/AP (L): 0.820388 coco/AR: 0.801669 coco/AR .5: 0.937343 coco/AR .75: 0.861304 coco/AR (M): 0.758918 coco/AR (L): 0.864400 2022/09/13 02:30:42 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_100.pth is removed 2022/09/13 02:30:46 - mmengine - INFO - The best checkpoint with 0.7511 coco/AP at 110 epoch is saved to best_coco/AP_epoch_110.pth. 2022/09/13 02:31:03 - mmengine - INFO - Epoch(train) [111][50/586] lr: 5.000000e-04 eta: 5:12:30 time: 0.339317 data_time: 0.026429 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.826394 loss: 0.000544 2022/09/13 02:31:20 - mmengine - INFO - Epoch(train) [111][100/586] lr: 5.000000e-04 eta: 5:12:15 time: 0.343861 data_time: 0.022298 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.842854 loss: 0.000556 2022/09/13 02:31:37 - mmengine - INFO - Epoch(train) [111][150/586] lr: 5.000000e-04 eta: 5:11:59 time: 0.332823 data_time: 0.027792 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.855908 loss: 0.000539 2022/09/13 02:31:54 - mmengine - INFO - Epoch(train) [111][200/586] lr: 5.000000e-04 eta: 5:11:44 time: 0.337270 data_time: 0.022065 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.791956 loss: 0.000564 2022/09/13 02:32:10 - mmengine - INFO - Epoch(train) [111][250/586] lr: 5.000000e-04 eta: 5:11:29 time: 0.335147 data_time: 0.022661 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.839759 loss: 0.000568 2022/09/13 02:32:27 - mmengine - INFO - Epoch(train) [111][300/586] lr: 5.000000e-04 eta: 5:11:13 time: 0.331519 data_time: 0.025432 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.912464 loss: 0.000553 2022/09/13 02:32:44 - mmengine - INFO - Epoch(train) [111][350/586] lr: 5.000000e-04 eta: 5:10:58 time: 0.336658 data_time: 0.023422 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.910208 loss: 0.000544 2022/09/13 02:33:01 - mmengine - INFO - Epoch(train) [111][400/586] lr: 5.000000e-04 eta: 5:10:43 time: 0.338703 data_time: 0.022161 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.813682 loss: 0.000534 2022/09/13 02:33:17 - mmengine - INFO - Epoch(train) [111][450/586] lr: 5.000000e-04 eta: 5:10:27 time: 0.328163 data_time: 0.021773 memory: 7489 loss_kpt: 0.000568 acc_pose: 0.776046 loss: 0.000568 2022/09/13 02:33:34 - mmengine - INFO - Epoch(train) [111][500/586] lr: 5.000000e-04 eta: 5:10:12 time: 0.340452 data_time: 0.022335 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.792209 loss: 0.000558 2022/09/13 02:33:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:33:51 - mmengine - INFO - Epoch(train) [111][550/586] lr: 5.000000e-04 eta: 5:09:57 time: 0.338600 data_time: 0.023026 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.756550 loss: 0.000558 2022/09/13 02:34:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:34:03 - mmengine - INFO - Saving checkpoint at 111 epochs 2022/09/13 02:34:27 - mmengine - INFO - Epoch(train) [112][50/586] lr: 5.000000e-04 eta: 5:09:20 time: 0.339207 data_time: 0.026914 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.868385 loss: 0.000560 2022/09/13 02:34:45 - mmengine - INFO - Epoch(train) [112][100/586] lr: 5.000000e-04 eta: 5:09:05 time: 0.352037 data_time: 0.027132 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.860916 loss: 0.000540 2022/09/13 02:35:01 - mmengine - INFO - Epoch(train) [112][150/586] lr: 5.000000e-04 eta: 5:08:50 time: 0.330940 data_time: 0.022939 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.846571 loss: 0.000555 2022/09/13 02:35:18 - mmengine - INFO - Epoch(train) [112][200/586] lr: 5.000000e-04 eta: 5:08:34 time: 0.337150 data_time: 0.023879 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.868229 loss: 0.000563 2022/09/13 02:35:35 - mmengine - INFO - Epoch(train) [112][250/586] lr: 5.000000e-04 eta: 5:08:19 time: 0.342928 data_time: 0.025710 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.828828 loss: 0.000553 2022/09/13 02:35:52 - mmengine - INFO - Epoch(train) [112][300/586] lr: 5.000000e-04 eta: 5:08:04 time: 0.331591 data_time: 0.023448 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.839498 loss: 0.000531 2022/09/13 02:36:09 - mmengine - INFO - Epoch(train) [112][350/586] lr: 5.000000e-04 eta: 5:07:48 time: 0.330141 data_time: 0.022261 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.847083 loss: 0.000547 2022/09/13 02:36:25 - mmengine - INFO - Epoch(train) [112][400/586] lr: 5.000000e-04 eta: 5:07:33 time: 0.336632 data_time: 0.021973 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.827883 loss: 0.000541 2022/09/13 02:36:42 - mmengine - INFO - Epoch(train) [112][450/586] lr: 5.000000e-04 eta: 5:07:17 time: 0.327776 data_time: 0.022976 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.875875 loss: 0.000557 2022/09/13 02:36:59 - mmengine - INFO - Epoch(train) [112][500/586] lr: 5.000000e-04 eta: 5:07:02 time: 0.341471 data_time: 0.023010 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.809795 loss: 0.000559 2022/09/13 02:37:16 - mmengine - INFO - Epoch(train) [112][550/586] lr: 5.000000e-04 eta: 5:06:47 time: 0.341077 data_time: 0.026104 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.866258 loss: 0.000557 2022/09/13 02:37:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:37:28 - mmengine - INFO - Saving checkpoint at 112 epochs 2022/09/13 02:37:52 - mmengine - INFO - Epoch(train) [113][50/586] lr: 5.000000e-04 eta: 5:06:11 time: 0.345697 data_time: 0.031263 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.875231 loss: 0.000561 2022/09/13 02:38:09 - mmengine - INFO - Epoch(train) [113][100/586] lr: 5.000000e-04 eta: 5:05:55 time: 0.340864 data_time: 0.023625 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.818682 loss: 0.000548 2022/09/13 02:38:26 - mmengine - INFO - Epoch(train) [113][150/586] lr: 5.000000e-04 eta: 5:05:40 time: 0.332762 data_time: 0.023038 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.873324 loss: 0.000560 2022/09/13 02:38:42 - mmengine - INFO - Epoch(train) [113][200/586] lr: 5.000000e-04 eta: 5:05:25 time: 0.336960 data_time: 0.022764 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.870085 loss: 0.000539 2022/09/13 02:38:59 - mmengine - INFO - Epoch(train) [113][250/586] lr: 5.000000e-04 eta: 5:05:09 time: 0.338663 data_time: 0.022375 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.891877 loss: 0.000560 2022/09/13 02:39:16 - mmengine - INFO - Epoch(train) [113][300/586] lr: 5.000000e-04 eta: 5:04:54 time: 0.331367 data_time: 0.022513 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.849512 loss: 0.000554 2022/09/13 02:39:33 - mmengine - INFO - Epoch(train) [113][350/586] lr: 5.000000e-04 eta: 5:04:39 time: 0.340190 data_time: 0.026339 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.831781 loss: 0.000565 2022/09/13 02:39:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:39:50 - mmengine - INFO - Epoch(train) [113][400/586] lr: 5.000000e-04 eta: 5:04:24 time: 0.340484 data_time: 0.022748 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.845405 loss: 0.000550 2022/09/13 02:40:07 - mmengine - INFO - Epoch(train) [113][450/586] lr: 5.000000e-04 eta: 5:04:08 time: 0.329670 data_time: 0.022509 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.850045 loss: 0.000552 2022/09/13 02:40:24 - mmengine - INFO - Epoch(train) [113][500/586] lr: 5.000000e-04 eta: 5:03:53 time: 0.338616 data_time: 0.021968 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.828399 loss: 0.000547 2022/09/13 02:40:40 - mmengine - INFO - Epoch(train) [113][550/586] lr: 5.000000e-04 eta: 5:03:37 time: 0.336711 data_time: 0.023045 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.809846 loss: 0.000556 2022/09/13 02:40:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:40:52 - mmengine - INFO - Saving checkpoint at 113 epochs 2022/09/13 02:41:16 - mmengine - INFO - Epoch(train) [114][50/586] lr: 5.000000e-04 eta: 5:03:01 time: 0.345066 data_time: 0.030487 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.860603 loss: 0.000546 2022/09/13 02:41:34 - mmengine - INFO - Epoch(train) [114][100/586] lr: 5.000000e-04 eta: 5:02:46 time: 0.342413 data_time: 0.022264 memory: 7489 loss_kpt: 0.000564 acc_pose: 0.775182 loss: 0.000564 2022/09/13 02:41:50 - mmengine - INFO - Epoch(train) [114][150/586] lr: 5.000000e-04 eta: 5:02:30 time: 0.327023 data_time: 0.022702 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.834833 loss: 0.000552 2022/09/13 02:42:07 - mmengine - INFO - Epoch(train) [114][200/586] lr: 5.000000e-04 eta: 5:02:15 time: 0.337291 data_time: 0.021804 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.855288 loss: 0.000561 2022/09/13 02:42:24 - mmengine - INFO - Epoch(train) [114][250/586] lr: 5.000000e-04 eta: 5:02:00 time: 0.337584 data_time: 0.021761 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.807175 loss: 0.000554 2022/09/13 02:42:40 - mmengine - INFO - Epoch(train) [114][300/586] lr: 5.000000e-04 eta: 5:01:44 time: 0.330993 data_time: 0.022242 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.895487 loss: 0.000558 2022/09/13 02:42:57 - mmengine - INFO - Epoch(train) [114][350/586] lr: 5.000000e-04 eta: 5:01:29 time: 0.336401 data_time: 0.021674 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.806358 loss: 0.000545 2022/09/13 02:43:15 - mmengine - INFO - Epoch(train) [114][400/586] lr: 5.000000e-04 eta: 5:01:14 time: 0.347851 data_time: 0.022270 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.883946 loss: 0.000541 2022/09/13 02:43:31 - mmengine - INFO - Epoch(train) [114][450/586] lr: 5.000000e-04 eta: 5:00:59 time: 0.331731 data_time: 0.023465 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.824755 loss: 0.000555 2022/09/13 02:43:48 - mmengine - INFO - Epoch(train) [114][500/586] lr: 5.000000e-04 eta: 5:00:43 time: 0.329293 data_time: 0.021796 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.793653 loss: 0.000559 2022/09/13 02:44:05 - mmengine - INFO - Epoch(train) [114][550/586] lr: 5.000000e-04 eta: 5:00:28 time: 0.342435 data_time: 0.021874 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.849230 loss: 0.000578 2022/09/13 02:44:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:44:16 - mmengine - INFO - Saving checkpoint at 114 epochs 2022/09/13 02:44:40 - mmengine - INFO - Epoch(train) [115][50/586] lr: 5.000000e-04 eta: 4:59:51 time: 0.335362 data_time: 0.025933 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.859254 loss: 0.000558 2022/09/13 02:44:58 - mmengine - INFO - Epoch(train) [115][100/586] lr: 5.000000e-04 eta: 4:59:36 time: 0.347986 data_time: 0.022491 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.784873 loss: 0.000560 2022/09/13 02:45:14 - mmengine - INFO - Epoch(train) [115][150/586] lr: 5.000000e-04 eta: 4:59:21 time: 0.327381 data_time: 0.022988 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.839550 loss: 0.000562 2022/09/13 02:45:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:45:31 - mmengine - INFO - Epoch(train) [115][200/586] lr: 5.000000e-04 eta: 4:59:06 time: 0.346906 data_time: 0.026288 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.785001 loss: 0.000556 2022/09/13 02:45:48 - mmengine - INFO - Epoch(train) [115][250/586] lr: 5.000000e-04 eta: 4:58:51 time: 0.341318 data_time: 0.023279 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.841412 loss: 0.000558 2022/09/13 02:46:05 - mmengine - INFO - Epoch(train) [115][300/586] lr: 5.000000e-04 eta: 4:58:35 time: 0.326110 data_time: 0.022352 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.831963 loss: 0.000563 2022/09/13 02:46:22 - mmengine - INFO - Epoch(train) [115][350/586] lr: 5.000000e-04 eta: 4:58:20 time: 0.339682 data_time: 0.022955 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.864139 loss: 0.000556 2022/09/13 02:46:39 - mmengine - INFO - Epoch(train) [115][400/586] lr: 5.000000e-04 eta: 4:58:05 time: 0.346428 data_time: 0.022964 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.850415 loss: 0.000547 2022/09/13 02:46:56 - mmengine - INFO - Epoch(train) [115][450/586] lr: 5.000000e-04 eta: 4:57:49 time: 0.333491 data_time: 0.024049 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.864794 loss: 0.000548 2022/09/13 02:47:12 - mmengine - INFO - Epoch(train) [115][500/586] lr: 5.000000e-04 eta: 4:57:34 time: 0.331972 data_time: 0.026820 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.839307 loss: 0.000547 2022/09/13 02:47:29 - mmengine - INFO - Epoch(train) [115][550/586] lr: 5.000000e-04 eta: 4:57:19 time: 0.341032 data_time: 0.022100 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.772906 loss: 0.000539 2022/09/13 02:47:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:47:41 - mmengine - INFO - Saving checkpoint at 115 epochs 2022/09/13 02:48:05 - mmengine - INFO - Epoch(train) [116][50/586] lr: 5.000000e-04 eta: 4:56:42 time: 0.336130 data_time: 0.028561 memory: 7489 loss_kpt: 0.000573 acc_pose: 0.870488 loss: 0.000573 2022/09/13 02:48:22 - mmengine - INFO - Epoch(train) [116][100/586] lr: 5.000000e-04 eta: 4:56:27 time: 0.344551 data_time: 0.023747 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.824075 loss: 0.000550 2022/09/13 02:48:39 - mmengine - INFO - Epoch(train) [116][150/586] lr: 5.000000e-04 eta: 4:56:12 time: 0.333227 data_time: 0.022845 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.806212 loss: 0.000548 2022/09/13 02:48:56 - mmengine - INFO - Epoch(train) [116][200/586] lr: 5.000000e-04 eta: 4:55:57 time: 0.337633 data_time: 0.022413 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.817844 loss: 0.000543 2022/09/13 02:49:13 - mmengine - INFO - Epoch(train) [116][250/586] lr: 5.000000e-04 eta: 4:55:41 time: 0.337475 data_time: 0.021766 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.878806 loss: 0.000539 2022/09/13 02:49:30 - mmengine - INFO - Epoch(train) [116][300/586] lr: 5.000000e-04 eta: 4:55:26 time: 0.337950 data_time: 0.022106 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.874844 loss: 0.000539 2022/09/13 02:49:47 - mmengine - INFO - Epoch(train) [116][350/586] lr: 5.000000e-04 eta: 4:55:11 time: 0.343524 data_time: 0.022451 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.799012 loss: 0.000544 2022/09/13 02:50:04 - mmengine - INFO - Epoch(train) [116][400/586] lr: 5.000000e-04 eta: 4:54:56 time: 0.339126 data_time: 0.022938 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.825942 loss: 0.000539 2022/09/13 02:50:21 - mmengine - INFO - Epoch(train) [116][450/586] lr: 5.000000e-04 eta: 4:54:41 time: 0.340571 data_time: 0.022280 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.865568 loss: 0.000534 2022/09/13 02:50:38 - mmengine - INFO - Epoch(train) [116][500/586] lr: 5.000000e-04 eta: 4:54:25 time: 0.335900 data_time: 0.023137 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.839339 loss: 0.000558 2022/09/13 02:50:55 - mmengine - INFO - Epoch(train) [116][550/586] lr: 5.000000e-04 eta: 4:54:10 time: 0.336992 data_time: 0.021714 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.878723 loss: 0.000549 2022/09/13 02:51:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:51:07 - mmengine - INFO - Saving checkpoint at 116 epochs 2022/09/13 02:51:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:51:31 - mmengine - INFO - Epoch(train) [117][50/586] lr: 5.000000e-04 eta: 4:53:34 time: 0.337041 data_time: 0.030333 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.817939 loss: 0.000548 2022/09/13 02:51:48 - mmengine - INFO - Epoch(train) [117][100/586] lr: 5.000000e-04 eta: 4:53:18 time: 0.339448 data_time: 0.025622 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.835056 loss: 0.000532 2022/09/13 02:52:05 - mmengine - INFO - Epoch(train) [117][150/586] lr: 5.000000e-04 eta: 4:53:03 time: 0.337777 data_time: 0.022212 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.846574 loss: 0.000553 2022/09/13 02:52:22 - mmengine - INFO - Epoch(train) [117][200/586] lr: 5.000000e-04 eta: 4:52:48 time: 0.330243 data_time: 0.027194 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.857643 loss: 0.000550 2022/09/13 02:52:39 - mmengine - INFO - Epoch(train) [117][250/586] lr: 5.000000e-04 eta: 4:52:32 time: 0.343926 data_time: 0.021948 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.838943 loss: 0.000543 2022/09/13 02:52:56 - mmengine - INFO - Epoch(train) [117][300/586] lr: 5.000000e-04 eta: 4:52:17 time: 0.335160 data_time: 0.023523 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.818469 loss: 0.000540 2022/09/13 02:53:12 - mmengine - INFO - Epoch(train) [117][350/586] lr: 5.000000e-04 eta: 4:52:02 time: 0.337104 data_time: 0.026503 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.878127 loss: 0.000569 2022/09/13 02:53:30 - mmengine - INFO - Epoch(train) [117][400/586] lr: 5.000000e-04 eta: 4:51:47 time: 0.340986 data_time: 0.022640 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.859160 loss: 0.000578 2022/09/13 02:53:46 - mmengine - INFO - Epoch(train) [117][450/586] lr: 5.000000e-04 eta: 4:51:31 time: 0.332947 data_time: 0.022416 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.766838 loss: 0.000554 2022/09/13 02:54:03 - mmengine - INFO - Epoch(train) [117][500/586] lr: 5.000000e-04 eta: 4:51:16 time: 0.339497 data_time: 0.022162 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.849120 loss: 0.000561 2022/09/13 02:54:20 - mmengine - INFO - Epoch(train) [117][550/586] lr: 5.000000e-04 eta: 4:51:01 time: 0.337038 data_time: 0.021885 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.853215 loss: 0.000556 2022/09/13 02:54:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:54:32 - mmengine - INFO - Saving checkpoint at 117 epochs 2022/09/13 02:54:56 - mmengine - INFO - Epoch(train) [118][50/586] lr: 5.000000e-04 eta: 4:50:24 time: 0.336183 data_time: 0.027643 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.883607 loss: 0.000536 2022/09/13 02:55:13 - mmengine - INFO - Epoch(train) [118][100/586] lr: 5.000000e-04 eta: 4:50:09 time: 0.342619 data_time: 0.023790 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.848907 loss: 0.000537 2022/09/13 02:55:30 - mmengine - INFO - Epoch(train) [118][150/586] lr: 5.000000e-04 eta: 4:49:54 time: 0.340458 data_time: 0.021875 memory: 7489 loss_kpt: 0.000563 acc_pose: 0.795119 loss: 0.000563 2022/09/13 02:55:47 - mmengine - INFO - Epoch(train) [118][200/586] lr: 5.000000e-04 eta: 4:49:39 time: 0.328731 data_time: 0.022741 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.926046 loss: 0.000552 2022/09/13 02:56:04 - mmengine - INFO - Epoch(train) [118][250/586] lr: 5.000000e-04 eta: 4:49:23 time: 0.339460 data_time: 0.021749 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.834025 loss: 0.000545 2022/09/13 02:56:20 - mmengine - INFO - Epoch(train) [118][300/586] lr: 5.000000e-04 eta: 4:49:08 time: 0.335544 data_time: 0.021689 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.812530 loss: 0.000554 2022/09/13 02:56:37 - mmengine - INFO - Epoch(train) [118][350/586] lr: 5.000000e-04 eta: 4:48:52 time: 0.334778 data_time: 0.022388 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.809113 loss: 0.000555 2022/09/13 02:56:54 - mmengine - INFO - Epoch(train) [118][400/586] lr: 5.000000e-04 eta: 4:48:37 time: 0.337124 data_time: 0.022474 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.857971 loss: 0.000546 2022/09/13 02:57:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:57:11 - mmengine - INFO - Epoch(train) [118][450/586] lr: 5.000000e-04 eta: 4:48:22 time: 0.336316 data_time: 0.025939 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.836522 loss: 0.000554 2022/09/13 02:57:28 - mmengine - INFO - Epoch(train) [118][500/586] lr: 5.000000e-04 eta: 4:48:06 time: 0.334375 data_time: 0.022798 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.817748 loss: 0.000565 2022/09/13 02:57:44 - mmengine - INFO - Epoch(train) [118][550/586] lr: 5.000000e-04 eta: 4:47:51 time: 0.335379 data_time: 0.021846 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.836487 loss: 0.000553 2022/09/13 02:57:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 02:57:57 - mmengine - INFO - Saving checkpoint at 118 epochs 2022/09/13 02:58:21 - mmengine - INFO - Epoch(train) [119][50/586] lr: 5.000000e-04 eta: 4:47:15 time: 0.344606 data_time: 0.029507 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.899943 loss: 0.000545 2022/09/13 02:58:38 - mmengine - INFO - Epoch(train) [119][100/586] lr: 5.000000e-04 eta: 4:47:00 time: 0.341332 data_time: 0.021913 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.870445 loss: 0.000539 2022/09/13 02:58:55 - mmengine - INFO - Epoch(train) [119][150/586] lr: 5.000000e-04 eta: 4:46:45 time: 0.330410 data_time: 0.022246 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.865961 loss: 0.000553 2022/09/13 02:59:11 - mmengine - INFO - Epoch(train) [119][200/586] lr: 5.000000e-04 eta: 4:46:29 time: 0.333172 data_time: 0.022718 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.811968 loss: 0.000565 2022/09/13 02:59:28 - mmengine - INFO - Epoch(train) [119][250/586] lr: 5.000000e-04 eta: 4:46:14 time: 0.340397 data_time: 0.022486 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.886062 loss: 0.000543 2022/09/13 02:59:45 - mmengine - INFO - Epoch(train) [119][300/586] lr: 5.000000e-04 eta: 4:45:59 time: 0.338962 data_time: 0.022271 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.837395 loss: 0.000560 2022/09/13 03:00:02 - mmengine - INFO - Epoch(train) [119][350/586] lr: 5.000000e-04 eta: 4:45:43 time: 0.336124 data_time: 0.023154 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.810362 loss: 0.000543 2022/09/13 03:00:19 - mmengine - INFO - Epoch(train) [119][400/586] lr: 5.000000e-04 eta: 4:45:28 time: 0.335889 data_time: 0.026057 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.817819 loss: 0.000532 2022/09/13 03:00:36 - mmengine - INFO - Epoch(train) [119][450/586] lr: 5.000000e-04 eta: 4:45:13 time: 0.344620 data_time: 0.022013 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.829100 loss: 0.000543 2022/09/13 03:00:53 - mmengine - INFO - Epoch(train) [119][500/586] lr: 5.000000e-04 eta: 4:44:57 time: 0.331548 data_time: 0.023137 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.864149 loss: 0.000545 2022/09/13 03:01:10 - mmengine - INFO - Epoch(train) [119][550/586] lr: 5.000000e-04 eta: 4:44:42 time: 0.337513 data_time: 0.022297 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.812970 loss: 0.000534 2022/09/13 03:01:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:01:22 - mmengine - INFO - Saving checkpoint at 119 epochs 2022/09/13 03:01:46 - mmengine - INFO - Epoch(train) [120][50/586] lr: 5.000000e-04 eta: 4:44:07 time: 0.349172 data_time: 0.029021 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.832860 loss: 0.000547 2022/09/13 03:02:03 - mmengine - INFO - Epoch(train) [120][100/586] lr: 5.000000e-04 eta: 4:43:51 time: 0.332940 data_time: 0.022789 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.869607 loss: 0.000549 2022/09/13 03:02:20 - mmengine - INFO - Epoch(train) [120][150/586] lr: 5.000000e-04 eta: 4:43:36 time: 0.338977 data_time: 0.021876 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.899847 loss: 0.000550 2022/09/13 03:02:36 - mmengine - INFO - Epoch(train) [120][200/586] lr: 5.000000e-04 eta: 4:43:20 time: 0.336439 data_time: 0.028576 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.847478 loss: 0.000561 2022/09/13 03:02:53 - mmengine - INFO - Epoch(train) [120][250/586] lr: 5.000000e-04 eta: 4:43:05 time: 0.339687 data_time: 0.022727 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.889135 loss: 0.000553 2022/09/13 03:02:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:03:10 - mmengine - INFO - Epoch(train) [120][300/586] lr: 5.000000e-04 eta: 4:42:50 time: 0.334977 data_time: 0.022635 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.854461 loss: 0.000560 2022/09/13 03:03:27 - mmengine - INFO - Epoch(train) [120][350/586] lr: 5.000000e-04 eta: 4:42:35 time: 0.340577 data_time: 0.022450 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.886392 loss: 0.000557 2022/09/13 03:03:44 - mmengine - INFO - Epoch(train) [120][400/586] lr: 5.000000e-04 eta: 4:42:19 time: 0.337977 data_time: 0.023364 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.820414 loss: 0.000561 2022/09/13 03:04:01 - mmengine - INFO - Epoch(train) [120][450/586] lr: 5.000000e-04 eta: 4:42:04 time: 0.333005 data_time: 0.021749 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.890185 loss: 0.000534 2022/09/13 03:04:18 - mmengine - INFO - Epoch(train) [120][500/586] lr: 5.000000e-04 eta: 4:41:48 time: 0.337062 data_time: 0.022485 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.880313 loss: 0.000535 2022/09/13 03:04:35 - mmengine - INFO - Epoch(train) [120][550/586] lr: 5.000000e-04 eta: 4:41:33 time: 0.336182 data_time: 0.022442 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.867979 loss: 0.000558 2022/09/13 03:04:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:04:47 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/09/13 03:05:03 - mmengine - INFO - Epoch(val) [120][50/407] eta: 0:01:05 time: 0.184628 data_time: 0.012816 memory: 7489 2022/09/13 03:05:12 - mmengine - INFO - Epoch(val) [120][100/407] eta: 0:00:55 time: 0.179508 data_time: 0.007840 memory: 1657 2022/09/13 03:05:21 - mmengine - INFO - Epoch(val) [120][150/407] eta: 0:00:45 time: 0.177880 data_time: 0.007470 memory: 1657 2022/09/13 03:05:30 - mmengine - INFO - Epoch(val) [120][200/407] eta: 0:00:37 time: 0.179179 data_time: 0.007764 memory: 1657 2022/09/13 03:05:39 - mmengine - INFO - Epoch(val) [120][250/407] eta: 0:00:28 time: 0.179585 data_time: 0.007615 memory: 1657 2022/09/13 03:05:48 - mmengine - INFO - Epoch(val) [120][300/407] eta: 0:00:19 time: 0.180195 data_time: 0.008306 memory: 1657 2022/09/13 03:05:57 - mmengine - INFO - Epoch(val) [120][350/407] eta: 0:00:10 time: 0.177987 data_time: 0.007740 memory: 1657 2022/09/13 03:06:06 - mmengine - INFO - Epoch(val) [120][400/407] eta: 0:00:01 time: 0.180656 data_time: 0.012010 memory: 1657 2022/09/13 03:06:41 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 03:06:54 - mmengine - INFO - Epoch(val) [120][407/407] coco/AP: 0.753496 coco/AP .5: 0.899292 coco/AP .75: 0.819687 coco/AP (M): 0.717363 coco/AP (L): 0.822548 coco/AR: 0.805668 coco/AR .5: 0.939389 coco/AR .75: 0.864924 coco/AR (M): 0.762305 coco/AR (L): 0.868302 2022/09/13 03:06:54 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_110.pth is removed 2022/09/13 03:06:58 - mmengine - INFO - The best checkpoint with 0.7535 coco/AP at 120 epoch is saved to best_coco/AP_epoch_120.pth. 2022/09/13 03:07:15 - mmengine - INFO - Epoch(train) [121][50/586] lr: 5.000000e-04 eta: 4:40:57 time: 0.336948 data_time: 0.031809 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.895774 loss: 0.000530 2022/09/13 03:07:32 - mmengine - INFO - Epoch(train) [121][100/586] lr: 5.000000e-04 eta: 4:40:42 time: 0.339973 data_time: 0.023296 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.888118 loss: 0.000540 2022/09/13 03:07:49 - mmengine - INFO - Epoch(train) [121][150/586] lr: 5.000000e-04 eta: 4:40:27 time: 0.334140 data_time: 0.023059 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.864820 loss: 0.000549 2022/09/13 03:08:06 - mmengine - INFO - Epoch(train) [121][200/586] lr: 5.000000e-04 eta: 4:40:11 time: 0.338109 data_time: 0.022739 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.822106 loss: 0.000555 2022/09/13 03:08:23 - mmengine - INFO - Epoch(train) [121][250/586] lr: 5.000000e-04 eta: 4:39:56 time: 0.338746 data_time: 0.021752 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.880833 loss: 0.000536 2022/09/13 03:08:39 - mmengine - INFO - Epoch(train) [121][300/586] lr: 5.000000e-04 eta: 4:39:41 time: 0.335799 data_time: 0.021886 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.877219 loss: 0.000526 2022/09/13 03:08:56 - mmengine - INFO - Epoch(train) [121][350/586] lr: 5.000000e-04 eta: 4:39:25 time: 0.336603 data_time: 0.024047 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.882127 loss: 0.000544 2022/09/13 03:09:13 - mmengine - INFO - Epoch(train) [121][400/586] lr: 5.000000e-04 eta: 4:39:10 time: 0.343530 data_time: 0.025770 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.844727 loss: 0.000558 2022/09/13 03:09:30 - mmengine - INFO - Epoch(train) [121][450/586] lr: 5.000000e-04 eta: 4:38:55 time: 0.332675 data_time: 0.021949 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.840374 loss: 0.000532 2022/09/13 03:09:47 - mmengine - INFO - Epoch(train) [121][500/586] lr: 5.000000e-04 eta: 4:38:40 time: 0.347278 data_time: 0.023331 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.882889 loss: 0.000548 2022/09/13 03:10:04 - mmengine - INFO - Epoch(train) [121][550/586] lr: 5.000000e-04 eta: 4:38:24 time: 0.336883 data_time: 0.023078 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.858690 loss: 0.000543 2022/09/13 03:10:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:10:16 - mmengine - INFO - Saving checkpoint at 121 epochs 2022/09/13 03:10:40 - mmengine - INFO - Epoch(train) [122][50/586] lr: 5.000000e-04 eta: 4:37:49 time: 0.337954 data_time: 0.027269 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.867781 loss: 0.000541 2022/09/13 03:10:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:10:58 - mmengine - INFO - Epoch(train) [122][100/586] lr: 5.000000e-04 eta: 4:37:34 time: 0.345814 data_time: 0.022590 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.880186 loss: 0.000538 2022/09/13 03:11:14 - mmengine - INFO - Epoch(train) [122][150/586] lr: 5.000000e-04 eta: 4:37:18 time: 0.330396 data_time: 0.023005 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.858671 loss: 0.000549 2022/09/13 03:11:31 - mmengine - INFO - Epoch(train) [122][200/586] lr: 5.000000e-04 eta: 4:37:03 time: 0.333067 data_time: 0.023550 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.815273 loss: 0.000555 2022/09/13 03:11:48 - mmengine - INFO - Epoch(train) [122][250/586] lr: 5.000000e-04 eta: 4:36:47 time: 0.339959 data_time: 0.022041 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.823234 loss: 0.000552 2022/09/13 03:12:04 - mmengine - INFO - Epoch(train) [122][300/586] lr: 5.000000e-04 eta: 4:36:32 time: 0.329746 data_time: 0.023409 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.873168 loss: 0.000548 2022/09/13 03:12:21 - mmengine - INFO - Epoch(train) [122][350/586] lr: 5.000000e-04 eta: 4:36:16 time: 0.337398 data_time: 0.022640 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.813081 loss: 0.000559 2022/09/13 03:12:38 - mmengine - INFO - Epoch(train) [122][400/586] lr: 5.000000e-04 eta: 4:36:01 time: 0.340631 data_time: 0.023507 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.843468 loss: 0.000548 2022/09/13 03:12:55 - mmengine - INFO - Epoch(train) [122][450/586] lr: 5.000000e-04 eta: 4:35:46 time: 0.333617 data_time: 0.026794 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.854420 loss: 0.000549 2022/09/13 03:13:12 - mmengine - INFO - Epoch(train) [122][500/586] lr: 5.000000e-04 eta: 4:35:30 time: 0.337250 data_time: 0.023396 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.888205 loss: 0.000542 2022/09/13 03:13:29 - mmengine - INFO - Epoch(train) [122][550/586] lr: 5.000000e-04 eta: 4:35:15 time: 0.338809 data_time: 0.022747 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.826958 loss: 0.000537 2022/09/13 03:13:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:13:41 - mmengine - INFO - Saving checkpoint at 122 epochs 2022/09/13 03:14:05 - mmengine - INFO - Epoch(train) [123][50/586] lr: 5.000000e-04 eta: 4:34:40 time: 0.341750 data_time: 0.030905 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.885139 loss: 0.000542 2022/09/13 03:14:22 - mmengine - INFO - Epoch(train) [123][100/586] lr: 5.000000e-04 eta: 4:34:25 time: 0.336063 data_time: 0.023806 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.810566 loss: 0.000559 2022/09/13 03:14:38 - mmengine - INFO - Epoch(train) [123][150/586] lr: 5.000000e-04 eta: 4:34:09 time: 0.335619 data_time: 0.023169 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.782113 loss: 0.000562 2022/09/13 03:14:55 - mmengine - INFO - Epoch(train) [123][200/586] lr: 5.000000e-04 eta: 4:33:53 time: 0.331455 data_time: 0.022937 memory: 7489 loss_kpt: 0.000578 acc_pose: 0.809224 loss: 0.000578 2022/09/13 03:15:12 - mmengine - INFO - Epoch(train) [123][250/586] lr: 5.000000e-04 eta: 4:33:38 time: 0.342393 data_time: 0.025019 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.848075 loss: 0.000539 2022/09/13 03:15:29 - mmengine - INFO - Epoch(train) [123][300/586] lr: 5.000000e-04 eta: 4:33:23 time: 0.328775 data_time: 0.022416 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.817908 loss: 0.000518 2022/09/13 03:15:45 - mmengine - INFO - Epoch(train) [123][350/586] lr: 5.000000e-04 eta: 4:33:07 time: 0.333685 data_time: 0.023203 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.860391 loss: 0.000560 2022/09/13 03:16:03 - mmengine - INFO - Epoch(train) [123][400/586] lr: 5.000000e-04 eta: 4:32:52 time: 0.342702 data_time: 0.025278 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.898738 loss: 0.000531 2022/09/13 03:16:19 - mmengine - INFO - Epoch(train) [123][450/586] lr: 5.000000e-04 eta: 4:32:36 time: 0.333470 data_time: 0.023074 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.901975 loss: 0.000531 2022/09/13 03:16:36 - mmengine - INFO - Epoch(train) [123][500/586] lr: 5.000000e-04 eta: 4:32:21 time: 0.334600 data_time: 0.022869 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.844130 loss: 0.000534 2022/09/13 03:16:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:16:53 - mmengine - INFO - Epoch(train) [123][550/586] lr: 5.000000e-04 eta: 4:32:06 time: 0.340852 data_time: 0.026152 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.801934 loss: 0.000536 2022/09/13 03:17:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:17:05 - mmengine - INFO - Saving checkpoint at 123 epochs 2022/09/13 03:17:29 - mmengine - INFO - Epoch(train) [124][50/586] lr: 5.000000e-04 eta: 4:31:31 time: 0.346181 data_time: 0.030150 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.856415 loss: 0.000541 2022/09/13 03:17:46 - mmengine - INFO - Epoch(train) [124][100/586] lr: 5.000000e-04 eta: 4:31:15 time: 0.330570 data_time: 0.022402 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.859745 loss: 0.000537 2022/09/13 03:18:03 - mmengine - INFO - Epoch(train) [124][150/586] lr: 5.000000e-04 eta: 4:31:00 time: 0.337573 data_time: 0.022822 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.841656 loss: 0.000562 2022/09/13 03:18:20 - mmengine - INFO - Epoch(train) [124][200/586] lr: 5.000000e-04 eta: 4:30:45 time: 0.340299 data_time: 0.023968 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.850202 loss: 0.000557 2022/09/13 03:18:37 - mmengine - INFO - Epoch(train) [124][250/586] lr: 5.000000e-04 eta: 4:30:29 time: 0.335723 data_time: 0.022546 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.805117 loss: 0.000561 2022/09/13 03:18:54 - mmengine - INFO - Epoch(train) [124][300/586] lr: 5.000000e-04 eta: 4:30:14 time: 0.337836 data_time: 0.022567 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.875162 loss: 0.000546 2022/09/13 03:19:10 - mmengine - INFO - Epoch(train) [124][350/586] lr: 5.000000e-04 eta: 4:29:58 time: 0.333780 data_time: 0.023365 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.891548 loss: 0.000530 2022/09/13 03:19:27 - mmengine - INFO - Epoch(train) [124][400/586] lr: 5.000000e-04 eta: 4:29:43 time: 0.342823 data_time: 0.022092 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.732513 loss: 0.000538 2022/09/13 03:19:44 - mmengine - INFO - Epoch(train) [124][450/586] lr: 5.000000e-04 eta: 4:29:28 time: 0.330950 data_time: 0.022827 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.842989 loss: 0.000537 2022/09/13 03:20:01 - mmengine - INFO - Epoch(train) [124][500/586] lr: 5.000000e-04 eta: 4:29:12 time: 0.335758 data_time: 0.024708 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.856739 loss: 0.000544 2022/09/13 03:20:18 - mmengine - INFO - Epoch(train) [124][550/586] lr: 5.000000e-04 eta: 4:28:57 time: 0.337067 data_time: 0.021900 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.836118 loss: 0.000537 2022/09/13 03:20:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:20:30 - mmengine - INFO - Saving checkpoint at 124 epochs 2022/09/13 03:20:53 - mmengine - INFO - Epoch(train) [125][50/586] lr: 5.000000e-04 eta: 4:28:22 time: 0.338312 data_time: 0.029344 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.855697 loss: 0.000539 2022/09/13 03:21:11 - mmengine - INFO - Epoch(train) [125][100/586] lr: 5.000000e-04 eta: 4:28:07 time: 0.342632 data_time: 0.025576 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.836810 loss: 0.000547 2022/09/13 03:21:27 - mmengine - INFO - Epoch(train) [125][150/586] lr: 5.000000e-04 eta: 4:27:51 time: 0.327945 data_time: 0.021948 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.858690 loss: 0.000544 2022/09/13 03:21:44 - mmengine - INFO - Epoch(train) [125][200/586] lr: 5.000000e-04 eta: 4:27:35 time: 0.333357 data_time: 0.022482 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.744790 loss: 0.000517 2022/09/13 03:22:01 - mmengine - INFO - Epoch(train) [125][250/586] lr: 5.000000e-04 eta: 4:27:20 time: 0.343944 data_time: 0.025447 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.815941 loss: 0.000525 2022/09/13 03:22:18 - mmengine - INFO - Epoch(train) [125][300/586] lr: 5.000000e-04 eta: 4:27:05 time: 0.339874 data_time: 0.022224 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.842294 loss: 0.000547 2022/09/13 03:22:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:22:34 - mmengine - INFO - Epoch(train) [125][350/586] lr: 5.000000e-04 eta: 4:26:49 time: 0.329213 data_time: 0.022582 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.830545 loss: 0.000531 2022/09/13 03:22:52 - mmengine - INFO - Epoch(train) [125][400/586] lr: 5.000000e-04 eta: 4:26:34 time: 0.343909 data_time: 0.022210 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.812297 loss: 0.000544 2022/09/13 03:23:08 - mmengine - INFO - Epoch(train) [125][450/586] lr: 5.000000e-04 eta: 4:26:18 time: 0.330138 data_time: 0.023386 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.849676 loss: 0.000528 2022/09/13 03:23:24 - mmengine - INFO - Epoch(train) [125][500/586] lr: 5.000000e-04 eta: 4:26:03 time: 0.325580 data_time: 0.021892 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.811066 loss: 0.000544 2022/09/13 03:23:42 - mmengine - INFO - Epoch(train) [125][550/586] lr: 5.000000e-04 eta: 4:25:48 time: 0.347004 data_time: 0.022753 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.915071 loss: 0.000547 2022/09/13 03:23:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:23:54 - mmengine - INFO - Saving checkpoint at 125 epochs 2022/09/13 03:24:18 - mmengine - INFO - Epoch(train) [126][50/586] lr: 5.000000e-04 eta: 4:25:13 time: 0.337401 data_time: 0.030912 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.862108 loss: 0.000529 2022/09/13 03:24:35 - mmengine - INFO - Epoch(train) [126][100/586] lr: 5.000000e-04 eta: 4:24:58 time: 0.346863 data_time: 0.022283 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.808991 loss: 0.000522 2022/09/13 03:24:52 - mmengine - INFO - Epoch(train) [126][150/586] lr: 5.000000e-04 eta: 4:24:42 time: 0.334571 data_time: 0.023882 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.802533 loss: 0.000534 2022/09/13 03:25:09 - mmengine - INFO - Epoch(train) [126][200/586] lr: 5.000000e-04 eta: 4:24:27 time: 0.338566 data_time: 0.022608 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.851771 loss: 0.000554 2022/09/13 03:25:26 - mmengine - INFO - Epoch(train) [126][250/586] lr: 5.000000e-04 eta: 4:24:12 time: 0.339219 data_time: 0.021596 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.857710 loss: 0.000552 2022/09/13 03:25:42 - mmengine - INFO - Epoch(train) [126][300/586] lr: 5.000000e-04 eta: 4:23:56 time: 0.335724 data_time: 0.025444 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.843055 loss: 0.000550 2022/09/13 03:25:59 - mmengine - INFO - Epoch(train) [126][350/586] lr: 5.000000e-04 eta: 4:23:40 time: 0.331514 data_time: 0.024284 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.836079 loss: 0.000552 2022/09/13 03:26:16 - mmengine - INFO - Epoch(train) [126][400/586] lr: 5.000000e-04 eta: 4:23:25 time: 0.342666 data_time: 0.022590 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.862751 loss: 0.000545 2022/09/13 03:26:33 - mmengine - INFO - Epoch(train) [126][450/586] lr: 5.000000e-04 eta: 4:23:10 time: 0.327625 data_time: 0.022489 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.907418 loss: 0.000551 2022/09/13 03:26:49 - mmengine - INFO - Epoch(train) [126][500/586] lr: 5.000000e-04 eta: 4:22:54 time: 0.333869 data_time: 0.025646 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.847620 loss: 0.000550 2022/09/13 03:27:06 - mmengine - INFO - Epoch(train) [126][550/586] lr: 5.000000e-04 eta: 4:22:39 time: 0.340447 data_time: 0.022746 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.878231 loss: 0.000560 2022/09/13 03:27:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:27:18 - mmengine - INFO - Saving checkpoint at 126 epochs 2022/09/13 03:27:42 - mmengine - INFO - Epoch(train) [127][50/586] lr: 5.000000e-04 eta: 4:22:04 time: 0.332122 data_time: 0.026123 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.894128 loss: 0.000543 2022/09/13 03:27:59 - mmengine - INFO - Epoch(train) [127][100/586] lr: 5.000000e-04 eta: 4:21:49 time: 0.339282 data_time: 0.021741 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.822831 loss: 0.000556 2022/09/13 03:28:16 - mmengine - INFO - Epoch(train) [127][150/586] lr: 5.000000e-04 eta: 4:21:33 time: 0.332981 data_time: 0.022657 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.886587 loss: 0.000525 2022/09/13 03:28:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:28:32 - mmengine - INFO - Epoch(train) [127][200/586] lr: 5.000000e-04 eta: 4:21:17 time: 0.332681 data_time: 0.022166 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.775760 loss: 0.000553 2022/09/13 03:28:49 - mmengine - INFO - Epoch(train) [127][250/586] lr: 5.000000e-04 eta: 4:21:02 time: 0.337968 data_time: 0.021997 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.899156 loss: 0.000551 2022/09/13 03:29:06 - mmengine - INFO - Epoch(train) [127][300/586] lr: 5.000000e-04 eta: 4:20:47 time: 0.334966 data_time: 0.023590 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.835450 loss: 0.000547 2022/09/13 03:29:23 - mmengine - INFO - Epoch(train) [127][350/586] lr: 5.000000e-04 eta: 4:20:31 time: 0.333259 data_time: 0.022729 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.844786 loss: 0.000525 2022/09/13 03:29:40 - mmengine - INFO - Epoch(train) [127][400/586] lr: 5.000000e-04 eta: 4:20:16 time: 0.342203 data_time: 0.022845 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.842547 loss: 0.000549 2022/09/13 03:29:56 - mmengine - INFO - Epoch(train) [127][450/586] lr: 5.000000e-04 eta: 4:20:00 time: 0.332273 data_time: 0.022116 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.901075 loss: 0.000554 2022/09/13 03:30:13 - mmengine - INFO - Epoch(train) [127][500/586] lr: 5.000000e-04 eta: 4:19:45 time: 0.336513 data_time: 0.022207 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.792057 loss: 0.000550 2022/09/13 03:30:30 - mmengine - INFO - Epoch(train) [127][550/586] lr: 5.000000e-04 eta: 4:19:29 time: 0.341501 data_time: 0.025320 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.822346 loss: 0.000541 2022/09/13 03:30:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:30:42 - mmengine - INFO - Saving checkpoint at 127 epochs 2022/09/13 03:31:06 - mmengine - INFO - Epoch(train) [128][50/586] lr: 5.000000e-04 eta: 4:18:55 time: 0.338420 data_time: 0.029990 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.889764 loss: 0.000556 2022/09/13 03:31:23 - mmengine - INFO - Epoch(train) [128][100/586] lr: 5.000000e-04 eta: 4:18:40 time: 0.340305 data_time: 0.023249 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.879462 loss: 0.000545 2022/09/13 03:31:40 - mmengine - INFO - Epoch(train) [128][150/586] lr: 5.000000e-04 eta: 4:18:24 time: 0.333132 data_time: 0.022711 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.887710 loss: 0.000537 2022/09/13 03:31:56 - mmengine - INFO - Epoch(train) [128][200/586] lr: 5.000000e-04 eta: 4:18:08 time: 0.326723 data_time: 0.026967 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.869713 loss: 0.000535 2022/09/13 03:32:13 - mmengine - INFO - Epoch(train) [128][250/586] lr: 5.000000e-04 eta: 4:17:53 time: 0.335073 data_time: 0.022679 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.879245 loss: 0.000538 2022/09/13 03:32:30 - mmengine - INFO - Epoch(train) [128][300/586] lr: 5.000000e-04 eta: 4:17:38 time: 0.339511 data_time: 0.028230 memory: 7489 loss_kpt: 0.000558 acc_pose: 0.797100 loss: 0.000558 2022/09/13 03:32:47 - mmengine - INFO - Epoch(train) [128][350/586] lr: 5.000000e-04 eta: 4:17:22 time: 0.333092 data_time: 0.022329 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.812485 loss: 0.000526 2022/09/13 03:33:04 - mmengine - INFO - Epoch(train) [128][400/586] lr: 5.000000e-04 eta: 4:17:07 time: 0.337985 data_time: 0.022260 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.783523 loss: 0.000537 2022/09/13 03:33:21 - mmengine - INFO - Epoch(train) [128][450/586] lr: 5.000000e-04 eta: 4:16:51 time: 0.339952 data_time: 0.026543 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.836645 loss: 0.000556 2022/09/13 03:33:37 - mmengine - INFO - Epoch(train) [128][500/586] lr: 5.000000e-04 eta: 4:16:36 time: 0.330252 data_time: 0.023313 memory: 7489 loss_kpt: 0.000562 acc_pose: 0.902525 loss: 0.000562 2022/09/13 03:33:54 - mmengine - INFO - Epoch(train) [128][550/586] lr: 5.000000e-04 eta: 4:16:21 time: 0.348389 data_time: 0.022453 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.852325 loss: 0.000555 2022/09/13 03:34:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:34:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:34:06 - mmengine - INFO - Saving checkpoint at 128 epochs 2022/09/13 03:34:31 - mmengine - INFO - Epoch(train) [129][50/586] lr: 5.000000e-04 eta: 4:15:46 time: 0.337728 data_time: 0.032710 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.846140 loss: 0.000542 2022/09/13 03:34:48 - mmengine - INFO - Epoch(train) [129][100/586] lr: 5.000000e-04 eta: 4:15:31 time: 0.337916 data_time: 0.025749 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.837001 loss: 0.000534 2022/09/13 03:35:05 - mmengine - INFO - Epoch(train) [129][150/586] lr: 5.000000e-04 eta: 4:15:16 time: 0.346894 data_time: 0.024498 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.811143 loss: 0.000540 2022/09/13 03:35:22 - mmengine - INFO - Epoch(train) [129][200/586] lr: 5.000000e-04 eta: 4:15:00 time: 0.332227 data_time: 0.022909 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.774818 loss: 0.000549 2022/09/13 03:35:39 - mmengine - INFO - Epoch(train) [129][250/586] lr: 5.000000e-04 eta: 4:14:45 time: 0.339842 data_time: 0.025907 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.859531 loss: 0.000542 2022/09/13 03:35:55 - mmengine - INFO - Epoch(train) [129][300/586] lr: 5.000000e-04 eta: 4:14:29 time: 0.335193 data_time: 0.023500 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.817092 loss: 0.000534 2022/09/13 03:36:12 - mmengine - INFO - Epoch(train) [129][350/586] lr: 5.000000e-04 eta: 4:14:14 time: 0.333363 data_time: 0.024047 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.852565 loss: 0.000538 2022/09/13 03:36:29 - mmengine - INFO - Epoch(train) [129][400/586] lr: 5.000000e-04 eta: 4:13:59 time: 0.345346 data_time: 0.025929 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.786744 loss: 0.000526 2022/09/13 03:36:46 - mmengine - INFO - Epoch(train) [129][450/586] lr: 5.000000e-04 eta: 4:13:43 time: 0.325733 data_time: 0.022024 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.800130 loss: 0.000557 2022/09/13 03:37:02 - mmengine - INFO - Epoch(train) [129][500/586] lr: 5.000000e-04 eta: 4:13:27 time: 0.333623 data_time: 0.022446 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.779787 loss: 0.000532 2022/09/13 03:37:19 - mmengine - INFO - Epoch(train) [129][550/586] lr: 5.000000e-04 eta: 4:13:12 time: 0.339813 data_time: 0.026093 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.842929 loss: 0.000534 2022/09/13 03:37:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:37:31 - mmengine - INFO - Saving checkpoint at 129 epochs 2022/09/13 03:37:55 - mmengine - INFO - Epoch(train) [130][50/586] lr: 5.000000e-04 eta: 4:12:37 time: 0.332883 data_time: 0.031666 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.906319 loss: 0.000538 2022/09/13 03:38:12 - mmengine - INFO - Epoch(train) [130][100/586] lr: 5.000000e-04 eta: 4:12:22 time: 0.336553 data_time: 0.024852 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.894961 loss: 0.000532 2022/09/13 03:38:29 - mmengine - INFO - Epoch(train) [130][150/586] lr: 5.000000e-04 eta: 4:12:07 time: 0.338432 data_time: 0.022376 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.858859 loss: 0.000537 2022/09/13 03:38:45 - mmengine - INFO - Epoch(train) [130][200/586] lr: 5.000000e-04 eta: 4:11:51 time: 0.331112 data_time: 0.022190 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.885069 loss: 0.000520 2022/09/13 03:39:02 - mmengine - INFO - Epoch(train) [130][250/586] lr: 5.000000e-04 eta: 4:11:36 time: 0.337968 data_time: 0.023054 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.862407 loss: 0.000554 2022/09/13 03:39:19 - mmengine - INFO - Epoch(train) [130][300/586] lr: 5.000000e-04 eta: 4:11:20 time: 0.337320 data_time: 0.021887 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.850303 loss: 0.000545 2022/09/13 03:39:36 - mmengine - INFO - Epoch(train) [130][350/586] lr: 5.000000e-04 eta: 4:11:05 time: 0.333691 data_time: 0.021994 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.759162 loss: 0.000549 2022/09/13 03:39:53 - mmengine - INFO - Epoch(train) [130][400/586] lr: 5.000000e-04 eta: 4:10:49 time: 0.338327 data_time: 0.022005 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.827387 loss: 0.000551 2022/09/13 03:39:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:40:09 - mmengine - INFO - Epoch(train) [130][450/586] lr: 5.000000e-04 eta: 4:10:34 time: 0.334261 data_time: 0.022174 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.846226 loss: 0.000538 2022/09/13 03:40:26 - mmengine - INFO - Epoch(train) [130][500/586] lr: 5.000000e-04 eta: 4:10:18 time: 0.328731 data_time: 0.023952 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.870879 loss: 0.000540 2022/09/13 03:40:43 - mmengine - INFO - Epoch(train) [130][550/586] lr: 5.000000e-04 eta: 4:10:03 time: 0.343536 data_time: 0.023183 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.794204 loss: 0.000540 2022/09/13 03:40:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:40:55 - mmengine - INFO - Saving checkpoint at 130 epochs 2022/09/13 03:41:11 - mmengine - INFO - Epoch(val) [130][50/407] eta: 0:01:06 time: 0.185592 data_time: 0.012771 memory: 7489 2022/09/13 03:41:20 - mmengine - INFO - Epoch(val) [130][100/407] eta: 0:00:54 time: 0.178500 data_time: 0.007736 memory: 1657 2022/09/13 03:41:29 - mmengine - INFO - Epoch(val) [130][150/407] eta: 0:00:45 time: 0.177560 data_time: 0.007487 memory: 1657 2022/09/13 03:41:38 - mmengine - INFO - Epoch(val) [130][200/407] eta: 0:00:36 time: 0.178627 data_time: 0.007543 memory: 1657 2022/09/13 03:41:47 - mmengine - INFO - Epoch(val) [130][250/407] eta: 0:00:28 time: 0.179416 data_time: 0.007947 memory: 1657 2022/09/13 03:41:56 - mmengine - INFO - Epoch(val) [130][300/407] eta: 0:00:19 time: 0.184687 data_time: 0.011844 memory: 1657 2022/09/13 03:42:05 - mmengine - INFO - Epoch(val) [130][350/407] eta: 0:00:10 time: 0.178790 data_time: 0.008086 memory: 1657 2022/09/13 03:42:14 - mmengine - INFO - Epoch(val) [130][400/407] eta: 0:00:01 time: 0.176328 data_time: 0.007066 memory: 1657 2022/09/13 03:42:49 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 03:43:03 - mmengine - INFO - Epoch(val) [130][407/407] coco/AP: 0.753941 coco/AP .5: 0.899775 coco/AP .75: 0.820975 coco/AP (M): 0.716716 coco/AP (L): 0.822646 coco/AR: 0.805305 coco/AR .5: 0.938287 coco/AR .75: 0.864924 coco/AR (M): 0.762333 coco/AR (L): 0.868079 2022/09/13 03:43:03 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_120.pth is removed 2022/09/13 03:43:07 - mmengine - INFO - The best checkpoint with 0.7539 coco/AP at 130 epoch is saved to best_coco/AP_epoch_130.pth. 2022/09/13 03:43:24 - mmengine - INFO - Epoch(train) [131][50/586] lr: 5.000000e-04 eta: 4:09:29 time: 0.337884 data_time: 0.027584 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.821766 loss: 0.000544 2022/09/13 03:43:41 - mmengine - INFO - Epoch(train) [131][100/586] lr: 5.000000e-04 eta: 4:09:13 time: 0.341858 data_time: 0.022611 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.891496 loss: 0.000524 2022/09/13 03:43:58 - mmengine - INFO - Epoch(train) [131][150/586] lr: 5.000000e-04 eta: 4:08:58 time: 0.334488 data_time: 0.022965 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.860262 loss: 0.000518 2022/09/13 03:44:15 - mmengine - INFO - Epoch(train) [131][200/586] lr: 5.000000e-04 eta: 4:08:42 time: 0.332351 data_time: 0.022498 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.846365 loss: 0.000557 2022/09/13 03:44:32 - mmengine - INFO - Epoch(train) [131][250/586] lr: 5.000000e-04 eta: 4:08:27 time: 0.343382 data_time: 0.022151 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.819226 loss: 0.000553 2022/09/13 03:44:49 - mmengine - INFO - Epoch(train) [131][300/586] lr: 5.000000e-04 eta: 4:08:12 time: 0.337866 data_time: 0.023418 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.890551 loss: 0.000536 2022/09/13 03:45:05 - mmengine - INFO - Epoch(train) [131][350/586] lr: 5.000000e-04 eta: 4:07:56 time: 0.336711 data_time: 0.022870 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.916543 loss: 0.000538 2022/09/13 03:45:23 - mmengine - INFO - Epoch(train) [131][400/586] lr: 5.000000e-04 eta: 4:07:41 time: 0.348403 data_time: 0.022283 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.892344 loss: 0.000536 2022/09/13 03:45:40 - mmengine - INFO - Epoch(train) [131][450/586] lr: 5.000000e-04 eta: 4:07:26 time: 0.338371 data_time: 0.025079 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.833909 loss: 0.000542 2022/09/13 03:45:56 - mmengine - INFO - Epoch(train) [131][500/586] lr: 5.000000e-04 eta: 4:07:10 time: 0.329616 data_time: 0.023497 memory: 7489 loss_kpt: 0.000560 acc_pose: 0.874627 loss: 0.000560 2022/09/13 03:46:13 - mmengine - INFO - Epoch(train) [131][550/586] lr: 5.000000e-04 eta: 4:06:54 time: 0.336179 data_time: 0.022845 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.842482 loss: 0.000542 2022/09/13 03:46:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:46:25 - mmengine - INFO - Saving checkpoint at 131 epochs 2022/09/13 03:46:50 - mmengine - INFO - Epoch(train) [132][50/586] lr: 5.000000e-04 eta: 4:06:21 time: 0.340156 data_time: 0.030458 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.837012 loss: 0.000542 2022/09/13 03:47:07 - mmengine - INFO - Epoch(train) [132][100/586] lr: 5.000000e-04 eta: 4:06:05 time: 0.341086 data_time: 0.022985 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.804607 loss: 0.000537 2022/09/13 03:47:24 - mmengine - INFO - Epoch(train) [132][150/586] lr: 5.000000e-04 eta: 4:05:50 time: 0.340064 data_time: 0.023488 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.840441 loss: 0.000545 2022/09/13 03:47:40 - mmengine - INFO - Epoch(train) [132][200/586] lr: 5.000000e-04 eta: 4:05:34 time: 0.329424 data_time: 0.022763 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.811223 loss: 0.000559 2022/09/13 03:47:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:47:57 - mmengine - INFO - Epoch(train) [132][250/586] lr: 5.000000e-04 eta: 4:05:19 time: 0.344698 data_time: 0.022607 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.912459 loss: 0.000531 2022/09/13 03:48:14 - mmengine - INFO - Epoch(train) [132][300/586] lr: 5.000000e-04 eta: 4:05:03 time: 0.334703 data_time: 0.023762 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.839753 loss: 0.000531 2022/09/13 03:48:31 - mmengine - INFO - Epoch(train) [132][350/586] lr: 5.000000e-04 eta: 4:04:48 time: 0.336269 data_time: 0.023159 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.859541 loss: 0.000540 2022/09/13 03:48:48 - mmengine - INFO - Epoch(train) [132][400/586] lr: 5.000000e-04 eta: 4:04:33 time: 0.342981 data_time: 0.026555 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.835032 loss: 0.000545 2022/09/13 03:49:05 - mmengine - INFO - Epoch(train) [132][450/586] lr: 5.000000e-04 eta: 4:04:17 time: 0.342982 data_time: 0.022945 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.777518 loss: 0.000541 2022/09/13 03:49:22 - mmengine - INFO - Epoch(train) [132][500/586] lr: 5.000000e-04 eta: 4:04:02 time: 0.327753 data_time: 0.023312 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.836154 loss: 0.000553 2022/09/13 03:49:39 - mmengine - INFO - Epoch(train) [132][550/586] lr: 5.000000e-04 eta: 4:03:46 time: 0.344870 data_time: 0.027193 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.826146 loss: 0.000511 2022/09/13 03:49:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:49:51 - mmengine - INFO - Saving checkpoint at 132 epochs 2022/09/13 03:50:15 - mmengine - INFO - Epoch(train) [133][50/586] lr: 5.000000e-04 eta: 4:03:13 time: 0.339936 data_time: 0.028699 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.842843 loss: 0.000533 2022/09/13 03:50:32 - mmengine - INFO - Epoch(train) [133][100/586] lr: 5.000000e-04 eta: 4:02:57 time: 0.334488 data_time: 0.021914 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.865871 loss: 0.000525 2022/09/13 03:50:49 - mmengine - INFO - Epoch(train) [133][150/586] lr: 5.000000e-04 eta: 4:02:42 time: 0.339656 data_time: 0.022952 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.904597 loss: 0.000539 2022/09/13 03:51:05 - mmengine - INFO - Epoch(train) [133][200/586] lr: 5.000000e-04 eta: 4:02:26 time: 0.331162 data_time: 0.022059 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.806856 loss: 0.000536 2022/09/13 03:51:22 - mmengine - INFO - Epoch(train) [133][250/586] lr: 5.000000e-04 eta: 4:02:11 time: 0.339952 data_time: 0.021923 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.844642 loss: 0.000517 2022/09/13 03:51:39 - mmengine - INFO - Epoch(train) [133][300/586] lr: 5.000000e-04 eta: 4:01:55 time: 0.334828 data_time: 0.025338 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.894808 loss: 0.000522 2022/09/13 03:51:56 - mmengine - INFO - Epoch(train) [133][350/586] lr: 5.000000e-04 eta: 4:01:40 time: 0.335980 data_time: 0.022752 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.839417 loss: 0.000537 2022/09/13 03:52:13 - mmengine - INFO - Epoch(train) [133][400/586] lr: 5.000000e-04 eta: 4:01:24 time: 0.337527 data_time: 0.022751 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.861697 loss: 0.000537 2022/09/13 03:52:29 - mmengine - INFO - Epoch(train) [133][450/586] lr: 5.000000e-04 eta: 4:01:09 time: 0.337285 data_time: 0.023442 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.827954 loss: 0.000552 2022/09/13 03:52:46 - mmengine - INFO - Epoch(train) [133][500/586] lr: 5.000000e-04 eta: 4:00:53 time: 0.337357 data_time: 0.022074 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.816534 loss: 0.000533 2022/09/13 03:53:03 - mmengine - INFO - Epoch(train) [133][550/586] lr: 5.000000e-04 eta: 4:00:38 time: 0.338525 data_time: 0.022282 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.859909 loss: 0.000552 2022/09/13 03:53:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:53:15 - mmengine - INFO - Saving checkpoint at 133 epochs 2022/09/13 03:53:40 - mmengine - INFO - Epoch(train) [134][50/586] lr: 5.000000e-04 eta: 4:00:04 time: 0.343384 data_time: 0.034717 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.840036 loss: 0.000539 2022/09/13 03:53:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:53:57 - mmengine - INFO - Epoch(train) [134][100/586] lr: 5.000000e-04 eta: 3:59:49 time: 0.350389 data_time: 0.025399 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.813017 loss: 0.000530 2022/09/13 03:54:14 - mmengine - INFO - Epoch(train) [134][150/586] lr: 5.000000e-04 eta: 3:59:33 time: 0.329570 data_time: 0.022178 memory: 7489 loss_kpt: 0.000565 acc_pose: 0.868334 loss: 0.000565 2022/09/13 03:54:30 - mmengine - INFO - Epoch(train) [134][200/586] lr: 5.000000e-04 eta: 3:59:18 time: 0.338831 data_time: 0.025393 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.804456 loss: 0.000559 2022/09/13 03:54:48 - mmengine - INFO - Epoch(train) [134][250/586] lr: 5.000000e-04 eta: 3:59:03 time: 0.340402 data_time: 0.022486 memory: 7489 loss_kpt: 0.000553 acc_pose: 0.814003 loss: 0.000553 2022/09/13 03:55:04 - mmengine - INFO - Epoch(train) [134][300/586] lr: 5.000000e-04 eta: 3:58:47 time: 0.335114 data_time: 0.022325 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.836098 loss: 0.000542 2022/09/13 03:55:21 - mmengine - INFO - Epoch(train) [134][350/586] lr: 5.000000e-04 eta: 3:58:31 time: 0.332377 data_time: 0.021542 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.879161 loss: 0.000533 2022/09/13 03:55:38 - mmengine - INFO - Epoch(train) [134][400/586] lr: 5.000000e-04 eta: 3:58:16 time: 0.344435 data_time: 0.022464 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.820010 loss: 0.000541 2022/09/13 03:55:55 - mmengine - INFO - Epoch(train) [134][450/586] lr: 5.000000e-04 eta: 3:58:01 time: 0.331816 data_time: 0.021608 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.853784 loss: 0.000534 2022/09/13 03:56:12 - mmengine - INFO - Epoch(train) [134][500/586] lr: 5.000000e-04 eta: 3:57:45 time: 0.338093 data_time: 0.025363 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.869372 loss: 0.000541 2022/09/13 03:56:28 - mmengine - INFO - Epoch(train) [134][550/586] lr: 5.000000e-04 eta: 3:57:30 time: 0.335320 data_time: 0.022415 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.885544 loss: 0.000542 2022/09/13 03:56:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:56:40 - mmengine - INFO - Saving checkpoint at 134 epochs 2022/09/13 03:57:05 - mmengine - INFO - Epoch(train) [135][50/586] lr: 5.000000e-04 eta: 3:56:56 time: 0.341043 data_time: 0.027549 memory: 7489 loss_kpt: 0.000557 acc_pose: 0.800232 loss: 0.000557 2022/09/13 03:57:21 - mmengine - INFO - Epoch(train) [135][100/586] lr: 5.000000e-04 eta: 3:56:41 time: 0.337455 data_time: 0.022738 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.823985 loss: 0.000538 2022/09/13 03:57:38 - mmengine - INFO - Epoch(train) [135][150/586] lr: 5.000000e-04 eta: 3:56:25 time: 0.334808 data_time: 0.023356 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.806133 loss: 0.000536 2022/09/13 03:57:55 - mmengine - INFO - Epoch(train) [135][200/586] lr: 5.000000e-04 eta: 3:56:10 time: 0.336913 data_time: 0.022860 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.873557 loss: 0.000526 2022/09/13 03:58:12 - mmengine - INFO - Epoch(train) [135][250/586] lr: 5.000000e-04 eta: 3:55:54 time: 0.330933 data_time: 0.023134 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.834666 loss: 0.000555 2022/09/13 03:58:28 - mmengine - INFO - Epoch(train) [135][300/586] lr: 5.000000e-04 eta: 3:55:38 time: 0.336600 data_time: 0.025576 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.843748 loss: 0.000543 2022/09/13 03:58:45 - mmengine - INFO - Epoch(train) [135][350/586] lr: 5.000000e-04 eta: 3:55:23 time: 0.334839 data_time: 0.022146 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.812811 loss: 0.000523 2022/09/13 03:59:02 - mmengine - INFO - Epoch(train) [135][400/586] lr: 5.000000e-04 eta: 3:55:08 time: 0.339187 data_time: 0.024090 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.847119 loss: 0.000550 2022/09/13 03:59:19 - mmengine - INFO - Epoch(train) [135][450/586] lr: 5.000000e-04 eta: 3:54:52 time: 0.333291 data_time: 0.022504 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.836499 loss: 0.000547 2022/09/13 03:59:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 03:59:36 - mmengine - INFO - Epoch(train) [135][500/586] lr: 5.000000e-04 eta: 3:54:37 time: 0.341713 data_time: 0.022383 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.908821 loss: 0.000528 2022/09/13 03:59:53 - mmengine - INFO - Epoch(train) [135][550/586] lr: 5.000000e-04 eta: 3:54:21 time: 0.347189 data_time: 0.023523 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.825317 loss: 0.000548 2022/09/13 04:00:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:00:05 - mmengine - INFO - Saving checkpoint at 135 epochs 2022/09/13 04:00:29 - mmengine - INFO - Epoch(train) [136][50/586] lr: 5.000000e-04 eta: 3:53:48 time: 0.344459 data_time: 0.035168 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.854089 loss: 0.000532 2022/09/13 04:00:46 - mmengine - INFO - Epoch(train) [136][100/586] lr: 5.000000e-04 eta: 3:53:33 time: 0.339899 data_time: 0.022797 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.826784 loss: 0.000549 2022/09/13 04:01:03 - mmengine - INFO - Epoch(train) [136][150/586] lr: 5.000000e-04 eta: 3:53:17 time: 0.340697 data_time: 0.022224 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.828516 loss: 0.000528 2022/09/13 04:01:20 - mmengine - INFO - Epoch(train) [136][200/586] lr: 5.000000e-04 eta: 3:53:02 time: 0.329550 data_time: 0.022179 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.870368 loss: 0.000534 2022/09/13 04:01:37 - mmengine - INFO - Epoch(train) [136][250/586] lr: 5.000000e-04 eta: 3:52:46 time: 0.340048 data_time: 0.023053 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.900556 loss: 0.000530 2022/09/13 04:01:54 - mmengine - INFO - Epoch(train) [136][300/586] lr: 5.000000e-04 eta: 3:52:31 time: 0.331451 data_time: 0.022987 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.821727 loss: 0.000524 2022/09/13 04:02:10 - mmengine - INFO - Epoch(train) [136][350/586] lr: 5.000000e-04 eta: 3:52:15 time: 0.334133 data_time: 0.022833 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.849457 loss: 0.000533 2022/09/13 04:02:27 - mmengine - INFO - Epoch(train) [136][400/586] lr: 5.000000e-04 eta: 3:51:59 time: 0.334725 data_time: 0.025362 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.890444 loss: 0.000550 2022/09/13 04:02:44 - mmengine - INFO - Epoch(train) [136][450/586] lr: 5.000000e-04 eta: 3:51:44 time: 0.338009 data_time: 0.022639 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.898728 loss: 0.000536 2022/09/13 04:03:00 - mmengine - INFO - Epoch(train) [136][500/586] lr: 5.000000e-04 eta: 3:51:28 time: 0.330551 data_time: 0.022785 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.851244 loss: 0.000530 2022/09/13 04:03:18 - mmengine - INFO - Epoch(train) [136][550/586] lr: 5.000000e-04 eta: 3:51:13 time: 0.344594 data_time: 0.022559 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.891407 loss: 0.000561 2022/09/13 04:03:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:03:30 - mmengine - INFO - Saving checkpoint at 136 epochs 2022/09/13 04:03:54 - mmengine - INFO - Epoch(train) [137][50/586] lr: 5.000000e-04 eta: 3:50:40 time: 0.337187 data_time: 0.030381 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.824459 loss: 0.000541 2022/09/13 04:04:11 - mmengine - INFO - Epoch(train) [137][100/586] lr: 5.000000e-04 eta: 3:50:24 time: 0.335605 data_time: 0.022008 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.819044 loss: 0.000541 2022/09/13 04:04:27 - mmengine - INFO - Epoch(train) [137][150/586] lr: 5.000000e-04 eta: 3:50:09 time: 0.332908 data_time: 0.022492 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.827816 loss: 0.000518 2022/09/13 04:04:45 - mmengine - INFO - Epoch(train) [137][200/586] lr: 5.000000e-04 eta: 3:49:53 time: 0.345231 data_time: 0.022370 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.874014 loss: 0.000532 2022/09/13 04:05:01 - mmengine - INFO - Epoch(train) [137][250/586] lr: 5.000000e-04 eta: 3:49:38 time: 0.335846 data_time: 0.023663 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.885469 loss: 0.000534 2022/09/13 04:05:18 - mmengine - INFO - Epoch(train) [137][300/586] lr: 5.000000e-04 eta: 3:49:22 time: 0.335606 data_time: 0.027959 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.857109 loss: 0.000556 2022/09/13 04:05:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:05:35 - mmengine - INFO - Epoch(train) [137][350/586] lr: 5.000000e-04 eta: 3:49:07 time: 0.336743 data_time: 0.023520 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.873972 loss: 0.000519 2022/09/13 04:05:52 - mmengine - INFO - Epoch(train) [137][400/586] lr: 5.000000e-04 eta: 3:48:51 time: 0.334104 data_time: 0.022715 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.802177 loss: 0.000542 2022/09/13 04:06:08 - mmengine - INFO - Epoch(train) [137][450/586] lr: 5.000000e-04 eta: 3:48:35 time: 0.333455 data_time: 0.023623 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.857929 loss: 0.000525 2022/09/13 04:06:25 - mmengine - INFO - Epoch(train) [137][500/586] lr: 5.000000e-04 eta: 3:48:20 time: 0.334285 data_time: 0.022321 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.836888 loss: 0.000536 2022/09/13 04:06:42 - mmengine - INFO - Epoch(train) [137][550/586] lr: 5.000000e-04 eta: 3:48:04 time: 0.334223 data_time: 0.021936 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.815146 loss: 0.000520 2022/09/13 04:06:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:06:54 - mmengine - INFO - Saving checkpoint at 137 epochs 2022/09/13 04:07:18 - mmengine - INFO - Epoch(train) [138][50/586] lr: 5.000000e-04 eta: 3:47:31 time: 0.337102 data_time: 0.026400 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.850814 loss: 0.000533 2022/09/13 04:07:35 - mmengine - INFO - Epoch(train) [138][100/586] lr: 5.000000e-04 eta: 3:47:16 time: 0.341078 data_time: 0.023648 memory: 7489 loss_kpt: 0.000556 acc_pose: 0.847326 loss: 0.000556 2022/09/13 04:07:52 - mmengine - INFO - Epoch(train) [138][150/586] lr: 5.000000e-04 eta: 3:47:00 time: 0.334160 data_time: 0.022944 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.830539 loss: 0.000512 2022/09/13 04:08:09 - mmengine - INFO - Epoch(train) [138][200/586] lr: 5.000000e-04 eta: 3:46:45 time: 0.336970 data_time: 0.022892 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.853839 loss: 0.000530 2022/09/13 04:08:26 - mmengine - INFO - Epoch(train) [138][250/586] lr: 5.000000e-04 eta: 3:46:29 time: 0.339584 data_time: 0.023207 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.837351 loss: 0.000548 2022/09/13 04:08:42 - mmengine - INFO - Epoch(train) [138][300/586] lr: 5.000000e-04 eta: 3:46:13 time: 0.331299 data_time: 0.023196 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.870917 loss: 0.000538 2022/09/13 04:08:59 - mmengine - INFO - Epoch(train) [138][350/586] lr: 5.000000e-04 eta: 3:45:58 time: 0.336859 data_time: 0.022670 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.789363 loss: 0.000541 2022/09/13 04:09:16 - mmengine - INFO - Epoch(train) [138][400/586] lr: 5.000000e-04 eta: 3:45:42 time: 0.338431 data_time: 0.022534 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.861875 loss: 0.000521 2022/09/13 04:09:33 - mmengine - INFO - Epoch(train) [138][450/586] lr: 5.000000e-04 eta: 3:45:27 time: 0.333523 data_time: 0.022020 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.756255 loss: 0.000537 2022/09/13 04:09:50 - mmengine - INFO - Epoch(train) [138][500/586] lr: 5.000000e-04 eta: 3:45:11 time: 0.337901 data_time: 0.022818 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.865428 loss: 0.000537 2022/09/13 04:10:07 - mmengine - INFO - Epoch(train) [138][550/586] lr: 5.000000e-04 eta: 3:44:56 time: 0.342025 data_time: 0.023341 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.803734 loss: 0.000555 2022/09/13 04:10:19 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:10:19 - mmengine - INFO - Saving checkpoint at 138 epochs 2022/09/13 04:10:43 - mmengine - INFO - Epoch(train) [139][50/586] lr: 5.000000e-04 eta: 3:44:23 time: 0.344409 data_time: 0.029700 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.859389 loss: 0.000551 2022/09/13 04:11:00 - mmengine - INFO - Epoch(train) [139][100/586] lr: 5.000000e-04 eta: 3:44:07 time: 0.326943 data_time: 0.022612 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.844260 loss: 0.000530 2022/09/13 04:11:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:11:17 - mmengine - INFO - Epoch(train) [139][150/586] lr: 5.000000e-04 eta: 3:43:52 time: 0.347686 data_time: 0.022294 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.899612 loss: 0.000545 2022/09/13 04:11:33 - mmengine - INFO - Epoch(train) [139][200/586] lr: 5.000000e-04 eta: 3:43:36 time: 0.329688 data_time: 0.022712 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.864163 loss: 0.000533 2022/09/13 04:11:50 - mmengine - INFO - Epoch(train) [139][250/586] lr: 5.000000e-04 eta: 3:43:21 time: 0.338602 data_time: 0.023052 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.889234 loss: 0.000515 2022/09/13 04:12:08 - mmengine - INFO - Epoch(train) [139][300/586] lr: 5.000000e-04 eta: 3:43:06 time: 0.342370 data_time: 0.022263 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.832165 loss: 0.000525 2022/09/13 04:12:24 - mmengine - INFO - Epoch(train) [139][350/586] lr: 5.000000e-04 eta: 3:42:50 time: 0.337479 data_time: 0.022404 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.830802 loss: 0.000537 2022/09/13 04:12:41 - mmengine - INFO - Epoch(train) [139][400/586] lr: 5.000000e-04 eta: 3:42:34 time: 0.335180 data_time: 0.022468 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.857583 loss: 0.000547 2022/09/13 04:12:58 - mmengine - INFO - Epoch(train) [139][450/586] lr: 5.000000e-04 eta: 3:42:19 time: 0.336739 data_time: 0.027220 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.849737 loss: 0.000551 2022/09/13 04:13:15 - mmengine - INFO - Epoch(train) [139][500/586] lr: 5.000000e-04 eta: 3:42:03 time: 0.335086 data_time: 0.022204 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.838992 loss: 0.000533 2022/09/13 04:13:32 - mmengine - INFO - Epoch(train) [139][550/586] lr: 5.000000e-04 eta: 3:41:48 time: 0.334550 data_time: 0.022532 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.875425 loss: 0.000538 2022/09/13 04:13:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:13:44 - mmengine - INFO - Saving checkpoint at 139 epochs 2022/09/13 04:14:08 - mmengine - INFO - Epoch(train) [140][50/586] lr: 5.000000e-04 eta: 3:41:15 time: 0.342261 data_time: 0.029647 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.886218 loss: 0.000537 2022/09/13 04:14:24 - mmengine - INFO - Epoch(train) [140][100/586] lr: 5.000000e-04 eta: 3:40:59 time: 0.330624 data_time: 0.022365 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.888833 loss: 0.000513 2022/09/13 04:14:42 - mmengine - INFO - Epoch(train) [140][150/586] lr: 5.000000e-04 eta: 3:40:44 time: 0.344618 data_time: 0.022519 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.874923 loss: 0.000541 2022/09/13 04:14:58 - mmengine - INFO - Epoch(train) [140][200/586] lr: 5.000000e-04 eta: 3:40:28 time: 0.335010 data_time: 0.022785 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.852146 loss: 0.000512 2022/09/13 04:15:15 - mmengine - INFO - Epoch(train) [140][250/586] lr: 5.000000e-04 eta: 3:40:13 time: 0.339009 data_time: 0.025177 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.893162 loss: 0.000531 2022/09/13 04:15:32 - mmengine - INFO - Epoch(train) [140][300/586] lr: 5.000000e-04 eta: 3:39:57 time: 0.333517 data_time: 0.022527 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.876271 loss: 0.000533 2022/09/13 04:15:48 - mmengine - INFO - Epoch(train) [140][350/586] lr: 5.000000e-04 eta: 3:39:42 time: 0.329443 data_time: 0.023245 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.893063 loss: 0.000537 2022/09/13 04:16:05 - mmengine - INFO - Epoch(train) [140][400/586] lr: 5.000000e-04 eta: 3:39:26 time: 0.338915 data_time: 0.023801 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.891948 loss: 0.000538 2022/09/13 04:16:22 - mmengine - INFO - Epoch(train) [140][450/586] lr: 5.000000e-04 eta: 3:39:10 time: 0.335951 data_time: 0.023931 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.885164 loss: 0.000534 2022/09/13 04:16:39 - mmengine - INFO - Epoch(train) [140][500/586] lr: 5.000000e-04 eta: 3:38:55 time: 0.331775 data_time: 0.022775 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.837454 loss: 0.000548 2022/09/13 04:16:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:16:56 - mmengine - INFO - Epoch(train) [140][550/586] lr: 5.000000e-04 eta: 3:38:39 time: 0.341626 data_time: 0.022403 memory: 7489 loss_kpt: 0.000561 acc_pose: 0.833334 loss: 0.000561 2022/09/13 04:17:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:17:08 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/09/13 04:17:25 - mmengine - INFO - Epoch(val) [140][50/407] eta: 0:01:06 time: 0.185464 data_time: 0.013227 memory: 7489 2022/09/13 04:17:34 - mmengine - INFO - Epoch(val) [140][100/407] eta: 0:00:55 time: 0.181772 data_time: 0.010545 memory: 1657 2022/09/13 04:17:43 - mmengine - INFO - Epoch(val) [140][150/407] eta: 0:00:45 time: 0.178429 data_time: 0.007944 memory: 1657 2022/09/13 04:17:52 - mmengine - INFO - Epoch(val) [140][200/407] eta: 0:00:37 time: 0.178999 data_time: 0.007937 memory: 1657 2022/09/13 04:18:00 - mmengine - INFO - Epoch(val) [140][250/407] eta: 0:00:27 time: 0.178223 data_time: 0.007559 memory: 1657 2022/09/13 04:18:09 - mmengine - INFO - Epoch(val) [140][300/407] eta: 0:00:19 time: 0.178318 data_time: 0.007820 memory: 1657 2022/09/13 04:18:18 - mmengine - INFO - Epoch(val) [140][350/407] eta: 0:00:10 time: 0.177832 data_time: 0.007410 memory: 1657 2022/09/13 04:18:27 - mmengine - INFO - Epoch(val) [140][400/407] eta: 0:00:01 time: 0.174569 data_time: 0.006819 memory: 1657 2022/09/13 04:19:03 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 04:19:16 - mmengine - INFO - Epoch(val) [140][407/407] coco/AP: 0.756542 coco/AP .5: 0.902475 coco/AP .75: 0.823683 coco/AP (M): 0.719079 coco/AP (L): 0.825484 coco/AR: 0.808486 coco/AR .5: 0.940176 coco/AR .75: 0.869647 coco/AR (M): 0.765474 coco/AR (L): 0.870494 2022/09/13 04:19:16 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_130.pth is removed 2022/09/13 04:19:20 - mmengine - INFO - The best checkpoint with 0.7565 coco/AP at 140 epoch is saved to best_coco/AP_epoch_140.pth. 2022/09/13 04:19:37 - mmengine - INFO - Epoch(train) [141][50/586] lr: 5.000000e-04 eta: 3:38:07 time: 0.334400 data_time: 0.028350 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.854417 loss: 0.000540 2022/09/13 04:19:54 - mmengine - INFO - Epoch(train) [141][100/586] lr: 5.000000e-04 eta: 3:37:51 time: 0.337758 data_time: 0.022136 memory: 7489 loss_kpt: 0.000559 acc_pose: 0.918218 loss: 0.000559 2022/09/13 04:20:10 - mmengine - INFO - Epoch(train) [141][150/586] lr: 5.000000e-04 eta: 3:37:35 time: 0.333368 data_time: 0.023106 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.874986 loss: 0.000545 2022/09/13 04:20:27 - mmengine - INFO - Epoch(train) [141][200/586] lr: 5.000000e-04 eta: 3:37:20 time: 0.342574 data_time: 0.022869 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.847369 loss: 0.000519 2022/09/13 04:20:44 - mmengine - INFO - Epoch(train) [141][250/586] lr: 5.000000e-04 eta: 3:37:04 time: 0.331196 data_time: 0.022583 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.838656 loss: 0.000547 2022/09/13 04:21:01 - mmengine - INFO - Epoch(train) [141][300/586] lr: 5.000000e-04 eta: 3:36:49 time: 0.337648 data_time: 0.023277 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.884811 loss: 0.000526 2022/09/13 04:21:18 - mmengine - INFO - Epoch(train) [141][350/586] lr: 5.000000e-04 eta: 3:36:33 time: 0.337216 data_time: 0.021907 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.819509 loss: 0.000530 2022/09/13 04:21:34 - mmengine - INFO - Epoch(train) [141][400/586] lr: 5.000000e-04 eta: 3:36:18 time: 0.333387 data_time: 0.022188 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.828036 loss: 0.000530 2022/09/13 04:21:51 - mmengine - INFO - Epoch(train) [141][450/586] lr: 5.000000e-04 eta: 3:36:02 time: 0.335607 data_time: 0.027004 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.887625 loss: 0.000543 2022/09/13 04:22:08 - mmengine - INFO - Epoch(train) [141][500/586] lr: 5.000000e-04 eta: 3:35:46 time: 0.331129 data_time: 0.022494 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.817804 loss: 0.000546 2022/09/13 04:22:25 - mmengine - INFO - Epoch(train) [141][550/586] lr: 5.000000e-04 eta: 3:35:31 time: 0.336743 data_time: 0.022115 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.891395 loss: 0.000533 2022/09/13 04:22:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:22:37 - mmengine - INFO - Saving checkpoint at 141 epochs 2022/09/13 04:23:01 - mmengine - INFO - Epoch(train) [142][50/586] lr: 5.000000e-04 eta: 3:34:58 time: 0.337815 data_time: 0.028747 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.824561 loss: 0.000532 2022/09/13 04:23:18 - mmengine - INFO - Epoch(train) [142][100/586] lr: 5.000000e-04 eta: 3:34:43 time: 0.343594 data_time: 0.025955 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.805489 loss: 0.000533 2022/09/13 04:23:34 - mmengine - INFO - Epoch(train) [142][150/586] lr: 5.000000e-04 eta: 3:34:27 time: 0.331177 data_time: 0.021819 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.818565 loss: 0.000520 2022/09/13 04:23:51 - mmengine - INFO - Epoch(train) [142][200/586] lr: 5.000000e-04 eta: 3:34:11 time: 0.332323 data_time: 0.022083 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.837557 loss: 0.000548 2022/09/13 04:24:08 - mmengine - INFO - Epoch(train) [142][250/586] lr: 5.000000e-04 eta: 3:33:56 time: 0.345460 data_time: 0.022623 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.874280 loss: 0.000511 2022/09/13 04:24:25 - mmengine - INFO - Epoch(train) [142][300/586] lr: 5.000000e-04 eta: 3:33:40 time: 0.332618 data_time: 0.021912 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.830569 loss: 0.000519 2022/09/13 04:24:42 - mmengine - INFO - Epoch(train) [142][350/586] lr: 5.000000e-04 eta: 3:33:25 time: 0.344673 data_time: 0.022954 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.791949 loss: 0.000547 2022/09/13 04:24:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:24:59 - mmengine - INFO - Epoch(train) [142][400/586] lr: 5.000000e-04 eta: 3:33:10 time: 0.338712 data_time: 0.022840 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.814802 loss: 0.000535 2022/09/13 04:25:16 - mmengine - INFO - Epoch(train) [142][450/586] lr: 5.000000e-04 eta: 3:32:54 time: 0.336754 data_time: 0.022605 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.829371 loss: 0.000527 2022/09/13 04:25:33 - mmengine - INFO - Epoch(train) [142][500/586] lr: 5.000000e-04 eta: 3:32:38 time: 0.333709 data_time: 0.022640 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.864239 loss: 0.000541 2022/09/13 04:25:50 - mmengine - INFO - Epoch(train) [142][550/586] lr: 5.000000e-04 eta: 3:32:23 time: 0.338046 data_time: 0.026308 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.791103 loss: 0.000525 2022/09/13 04:26:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:26:02 - mmengine - INFO - Saving checkpoint at 142 epochs 2022/09/13 04:26:26 - mmengine - INFO - Epoch(train) [143][50/586] lr: 5.000000e-04 eta: 3:31:50 time: 0.342845 data_time: 0.035424 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.863216 loss: 0.000527 2022/09/13 04:26:42 - mmengine - INFO - Epoch(train) [143][100/586] lr: 5.000000e-04 eta: 3:31:35 time: 0.333826 data_time: 0.022391 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.841262 loss: 0.000533 2022/09/13 04:26:59 - mmengine - INFO - Epoch(train) [143][150/586] lr: 5.000000e-04 eta: 3:31:19 time: 0.335633 data_time: 0.023171 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.856168 loss: 0.000525 2022/09/13 04:27:16 - mmengine - INFO - Epoch(train) [143][200/586] lr: 5.000000e-04 eta: 3:31:04 time: 0.336983 data_time: 0.022411 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.899262 loss: 0.000554 2022/09/13 04:27:33 - mmengine - INFO - Epoch(train) [143][250/586] lr: 5.000000e-04 eta: 3:30:48 time: 0.332597 data_time: 0.022321 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.879162 loss: 0.000527 2022/09/13 04:27:49 - mmengine - INFO - Epoch(train) [143][300/586] lr: 5.000000e-04 eta: 3:30:32 time: 0.334270 data_time: 0.022199 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.839809 loss: 0.000547 2022/09/13 04:28:06 - mmengine - INFO - Epoch(train) [143][350/586] lr: 5.000000e-04 eta: 3:30:17 time: 0.338858 data_time: 0.022175 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.879370 loss: 0.000512 2022/09/13 04:28:23 - mmengine - INFO - Epoch(train) [143][400/586] lr: 5.000000e-04 eta: 3:30:01 time: 0.334253 data_time: 0.023240 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.855728 loss: 0.000527 2022/09/13 04:28:40 - mmengine - INFO - Epoch(train) [143][450/586] lr: 5.000000e-04 eta: 3:29:46 time: 0.334061 data_time: 0.022216 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.830492 loss: 0.000524 2022/09/13 04:28:57 - mmengine - INFO - Epoch(train) [143][500/586] lr: 5.000000e-04 eta: 3:29:30 time: 0.340232 data_time: 0.025454 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.840063 loss: 0.000536 2022/09/13 04:29:14 - mmengine - INFO - Epoch(train) [143][550/586] lr: 5.000000e-04 eta: 3:29:15 time: 0.336350 data_time: 0.022227 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.862268 loss: 0.000534 2022/09/13 04:29:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:29:25 - mmengine - INFO - Saving checkpoint at 143 epochs 2022/09/13 04:29:50 - mmengine - INFO - Epoch(train) [144][50/586] lr: 5.000000e-04 eta: 3:28:42 time: 0.337502 data_time: 0.028137 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.763660 loss: 0.000536 2022/09/13 04:30:07 - mmengine - INFO - Epoch(train) [144][100/586] lr: 5.000000e-04 eta: 3:28:27 time: 0.343850 data_time: 0.023078 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.815999 loss: 0.000533 2022/09/13 04:30:24 - mmengine - INFO - Epoch(train) [144][150/586] lr: 5.000000e-04 eta: 3:28:11 time: 0.341230 data_time: 0.022666 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.851342 loss: 0.000550 2022/09/13 04:30:41 - mmengine - INFO - Epoch(train) [144][200/586] lr: 5.000000e-04 eta: 3:27:56 time: 0.337239 data_time: 0.022764 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.836115 loss: 0.000550 2022/09/13 04:30:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:30:58 - mmengine - INFO - Epoch(train) [144][250/586] lr: 5.000000e-04 eta: 3:27:40 time: 0.337256 data_time: 0.025174 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.895294 loss: 0.000537 2022/09/13 04:31:14 - mmengine - INFO - Epoch(train) [144][300/586] lr: 5.000000e-04 eta: 3:27:25 time: 0.337389 data_time: 0.023057 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.889314 loss: 0.000549 2022/09/13 04:31:31 - mmengine - INFO - Epoch(train) [144][350/586] lr: 5.000000e-04 eta: 3:27:09 time: 0.338638 data_time: 0.022883 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.870458 loss: 0.000544 2022/09/13 04:31:48 - mmengine - INFO - Epoch(train) [144][400/586] lr: 5.000000e-04 eta: 3:26:53 time: 0.335359 data_time: 0.022992 memory: 7489 loss_kpt: 0.000569 acc_pose: 0.850744 loss: 0.000569 2022/09/13 04:32:05 - mmengine - INFO - Epoch(train) [144][450/586] lr: 5.000000e-04 eta: 3:26:38 time: 0.340195 data_time: 0.025349 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.836086 loss: 0.000545 2022/09/13 04:32:22 - mmengine - INFO - Epoch(train) [144][500/586] lr: 5.000000e-04 eta: 3:26:22 time: 0.334078 data_time: 0.022930 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.845479 loss: 0.000535 2022/09/13 04:32:39 - mmengine - INFO - Epoch(train) [144][550/586] lr: 5.000000e-04 eta: 3:26:07 time: 0.344842 data_time: 0.025467 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.843589 loss: 0.000520 2022/09/13 04:32:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:32:51 - mmengine - INFO - Saving checkpoint at 144 epochs 2022/09/13 04:33:15 - mmengine - INFO - Epoch(train) [145][50/586] lr: 5.000000e-04 eta: 3:25:35 time: 0.347935 data_time: 0.031348 memory: 7489 loss_kpt: 0.000549 acc_pose: 0.855022 loss: 0.000549 2022/09/13 04:33:32 - mmengine - INFO - Epoch(train) [145][100/586] lr: 5.000000e-04 eta: 3:25:19 time: 0.327543 data_time: 0.022047 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.856461 loss: 0.000535 2022/09/13 04:33:49 - mmengine - INFO - Epoch(train) [145][150/586] lr: 5.000000e-04 eta: 3:25:04 time: 0.342978 data_time: 0.023178 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.862072 loss: 0.000540 2022/09/13 04:34:05 - mmengine - INFO - Epoch(train) [145][200/586] lr: 5.000000e-04 eta: 3:24:48 time: 0.334506 data_time: 0.022517 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.879228 loss: 0.000528 2022/09/13 04:34:22 - mmengine - INFO - Epoch(train) [145][250/586] lr: 5.000000e-04 eta: 3:24:32 time: 0.327399 data_time: 0.021954 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.866891 loss: 0.000525 2022/09/13 04:34:39 - mmengine - INFO - Epoch(train) [145][300/586] lr: 5.000000e-04 eta: 3:24:17 time: 0.345571 data_time: 0.022588 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.882081 loss: 0.000517 2022/09/13 04:34:56 - mmengine - INFO - Epoch(train) [145][350/586] lr: 5.000000e-04 eta: 3:24:01 time: 0.338263 data_time: 0.027148 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.830582 loss: 0.000527 2022/09/13 04:35:13 - mmengine - INFO - Epoch(train) [145][400/586] lr: 5.000000e-04 eta: 3:23:46 time: 0.333320 data_time: 0.022982 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.856820 loss: 0.000554 2022/09/13 04:35:30 - mmengine - INFO - Epoch(train) [145][450/586] lr: 5.000000e-04 eta: 3:23:30 time: 0.339045 data_time: 0.023710 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.867097 loss: 0.000531 2022/09/13 04:35:47 - mmengine - INFO - Epoch(train) [145][500/586] lr: 5.000000e-04 eta: 3:23:15 time: 0.336407 data_time: 0.022307 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.844738 loss: 0.000535 2022/09/13 04:36:03 - mmengine - INFO - Epoch(train) [145][550/586] lr: 5.000000e-04 eta: 3:22:59 time: 0.335620 data_time: 0.023723 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.908983 loss: 0.000529 2022/09/13 04:36:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:36:16 - mmengine - INFO - Saving checkpoint at 145 epochs 2022/09/13 04:36:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:36:40 - mmengine - INFO - Epoch(train) [146][50/586] lr: 5.000000e-04 eta: 3:22:27 time: 0.338949 data_time: 0.026668 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.894776 loss: 0.000542 2022/09/13 04:36:57 - mmengine - INFO - Epoch(train) [146][100/586] lr: 5.000000e-04 eta: 3:22:11 time: 0.336663 data_time: 0.023692 memory: 7489 loss_kpt: 0.000554 acc_pose: 0.892365 loss: 0.000554 2022/09/13 04:37:13 - mmengine - INFO - Epoch(train) [146][150/586] lr: 5.000000e-04 eta: 3:21:56 time: 0.333423 data_time: 0.021780 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.836723 loss: 0.000546 2022/09/13 04:37:30 - mmengine - INFO - Epoch(train) [146][200/586] lr: 5.000000e-04 eta: 3:21:40 time: 0.338864 data_time: 0.022735 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.817249 loss: 0.000531 2022/09/13 04:37:47 - mmengine - INFO - Epoch(train) [146][250/586] lr: 5.000000e-04 eta: 3:21:25 time: 0.346214 data_time: 0.022553 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.834624 loss: 0.000531 2022/09/13 04:38:04 - mmengine - INFO - Epoch(train) [146][300/586] lr: 5.000000e-04 eta: 3:21:09 time: 0.334438 data_time: 0.022808 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.784876 loss: 0.000536 2022/09/13 04:38:21 - mmengine - INFO - Epoch(train) [146][350/586] lr: 5.000000e-04 eta: 3:20:53 time: 0.336222 data_time: 0.023393 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.880166 loss: 0.000527 2022/09/13 04:38:38 - mmengine - INFO - Epoch(train) [146][400/586] lr: 5.000000e-04 eta: 3:20:38 time: 0.332406 data_time: 0.022524 memory: 7489 loss_kpt: 0.000552 acc_pose: 0.852521 loss: 0.000552 2022/09/13 04:38:55 - mmengine - INFO - Epoch(train) [146][450/586] lr: 5.000000e-04 eta: 3:20:22 time: 0.342817 data_time: 0.022948 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.914286 loss: 0.000529 2022/09/13 04:39:12 - mmengine - INFO - Epoch(train) [146][500/586] lr: 5.000000e-04 eta: 3:20:07 time: 0.333768 data_time: 0.022738 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.902920 loss: 0.000524 2022/09/13 04:39:29 - mmengine - INFO - Epoch(train) [146][550/586] lr: 5.000000e-04 eta: 3:19:51 time: 0.346148 data_time: 0.023114 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.849946 loss: 0.000525 2022/09/13 04:39:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:39:41 - mmengine - INFO - Saving checkpoint at 146 epochs 2022/09/13 04:40:05 - mmengine - INFO - Epoch(train) [147][50/586] lr: 5.000000e-04 eta: 3:19:19 time: 0.342797 data_time: 0.027287 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.775862 loss: 0.000525 2022/09/13 04:40:22 - mmengine - INFO - Epoch(train) [147][100/586] lr: 5.000000e-04 eta: 3:19:04 time: 0.339321 data_time: 0.022957 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.919327 loss: 0.000534 2022/09/13 04:40:39 - mmengine - INFO - Epoch(train) [147][150/586] lr: 5.000000e-04 eta: 3:18:48 time: 0.331393 data_time: 0.022865 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.869194 loss: 0.000521 2022/09/13 04:40:55 - mmengine - INFO - Epoch(train) [147][200/586] lr: 5.000000e-04 eta: 3:18:32 time: 0.336259 data_time: 0.023060 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.881893 loss: 0.000522 2022/09/13 04:41:12 - mmengine - INFO - Epoch(train) [147][250/586] lr: 5.000000e-04 eta: 3:18:17 time: 0.337119 data_time: 0.023745 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.893990 loss: 0.000531 2022/09/13 04:41:29 - mmengine - INFO - Epoch(train) [147][300/586] lr: 5.000000e-04 eta: 3:18:01 time: 0.336901 data_time: 0.021891 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.884354 loss: 0.000535 2022/09/13 04:41:46 - mmengine - INFO - Epoch(train) [147][350/586] lr: 5.000000e-04 eta: 3:17:46 time: 0.334130 data_time: 0.022864 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.830133 loss: 0.000547 2022/09/13 04:42:03 - mmengine - INFO - Epoch(train) [147][400/586] lr: 5.000000e-04 eta: 3:17:30 time: 0.340779 data_time: 0.025542 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.863981 loss: 0.000528 2022/09/13 04:42:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:42:19 - mmengine - INFO - Epoch(train) [147][450/586] lr: 5.000000e-04 eta: 3:17:14 time: 0.331075 data_time: 0.023406 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.836138 loss: 0.000528 2022/09/13 04:42:36 - mmengine - INFO - Epoch(train) [147][500/586] lr: 5.000000e-04 eta: 3:16:59 time: 0.332742 data_time: 0.021707 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.816721 loss: 0.000546 2022/09/13 04:42:53 - mmengine - INFO - Epoch(train) [147][550/586] lr: 5.000000e-04 eta: 3:16:43 time: 0.343741 data_time: 0.025893 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.813607 loss: 0.000532 2022/09/13 04:43:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:43:05 - mmengine - INFO - Saving checkpoint at 147 epochs 2022/09/13 04:43:30 - mmengine - INFO - Epoch(train) [148][50/586] lr: 5.000000e-04 eta: 3:16:11 time: 0.339897 data_time: 0.027715 memory: 7489 loss_kpt: 0.000546 acc_pose: 0.840500 loss: 0.000546 2022/09/13 04:43:47 - mmengine - INFO - Epoch(train) [148][100/586] lr: 5.000000e-04 eta: 3:15:56 time: 0.341033 data_time: 0.022535 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.863225 loss: 0.000526 2022/09/13 04:44:03 - mmengine - INFO - Epoch(train) [148][150/586] lr: 5.000000e-04 eta: 3:15:40 time: 0.330349 data_time: 0.022425 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.855090 loss: 0.000538 2022/09/13 04:44:20 - mmengine - INFO - Epoch(train) [148][200/586] lr: 5.000000e-04 eta: 3:15:24 time: 0.333530 data_time: 0.021960 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.844190 loss: 0.000537 2022/09/13 04:44:37 - mmengine - INFO - Epoch(train) [148][250/586] lr: 5.000000e-04 eta: 3:15:09 time: 0.335907 data_time: 0.023574 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.888411 loss: 0.000538 2022/09/13 04:44:53 - mmengine - INFO - Epoch(train) [148][300/586] lr: 5.000000e-04 eta: 3:14:53 time: 0.333578 data_time: 0.022804 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.861554 loss: 0.000544 2022/09/13 04:45:10 - mmengine - INFO - Epoch(train) [148][350/586] lr: 5.000000e-04 eta: 3:14:38 time: 0.338728 data_time: 0.022387 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.902051 loss: 0.000517 2022/09/13 04:45:27 - mmengine - INFO - Epoch(train) [148][400/586] lr: 5.000000e-04 eta: 3:14:22 time: 0.338539 data_time: 0.023191 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.834452 loss: 0.000519 2022/09/13 04:45:44 - mmengine - INFO - Epoch(train) [148][450/586] lr: 5.000000e-04 eta: 3:14:06 time: 0.342396 data_time: 0.025764 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.901967 loss: 0.000518 2022/09/13 04:46:01 - mmengine - INFO - Epoch(train) [148][500/586] lr: 5.000000e-04 eta: 3:13:51 time: 0.339180 data_time: 0.023257 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.890406 loss: 0.000536 2022/09/13 04:46:18 - mmengine - INFO - Epoch(train) [148][550/586] lr: 5.000000e-04 eta: 3:13:35 time: 0.335733 data_time: 0.023644 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.871742 loss: 0.000518 2022/09/13 04:46:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:46:31 - mmengine - INFO - Saving checkpoint at 148 epochs 2022/09/13 04:46:55 - mmengine - INFO - Epoch(train) [149][50/586] lr: 5.000000e-04 eta: 3:13:04 time: 0.343633 data_time: 0.031394 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.897784 loss: 0.000543 2022/09/13 04:47:12 - mmengine - INFO - Epoch(train) [149][100/586] lr: 5.000000e-04 eta: 3:12:48 time: 0.340005 data_time: 0.026646 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.812686 loss: 0.000532 2022/09/13 04:47:29 - mmengine - INFO - Epoch(train) [149][150/586] lr: 5.000000e-04 eta: 3:12:32 time: 0.332448 data_time: 0.023440 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.821418 loss: 0.000535 2022/09/13 04:47:45 - mmengine - INFO - Epoch(train) [149][200/586] lr: 5.000000e-04 eta: 3:12:17 time: 0.333608 data_time: 0.022376 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.839044 loss: 0.000524 2022/09/13 04:48:03 - mmengine - INFO - Epoch(train) [149][250/586] lr: 5.000000e-04 eta: 3:12:01 time: 0.344165 data_time: 0.025221 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.899069 loss: 0.000511 2022/09/13 04:48:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:48:19 - mmengine - INFO - Epoch(train) [149][300/586] lr: 5.000000e-04 eta: 3:11:45 time: 0.331344 data_time: 0.022028 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.759006 loss: 0.000550 2022/09/13 04:48:36 - mmengine - INFO - Epoch(train) [149][350/586] lr: 5.000000e-04 eta: 3:11:30 time: 0.336259 data_time: 0.022668 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.884158 loss: 0.000548 2022/09/13 04:48:53 - mmengine - INFO - Epoch(train) [149][400/586] lr: 5.000000e-04 eta: 3:11:14 time: 0.336711 data_time: 0.022996 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.896976 loss: 0.000539 2022/09/13 04:49:10 - mmengine - INFO - Epoch(train) [149][450/586] lr: 5.000000e-04 eta: 3:10:59 time: 0.339665 data_time: 0.022108 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.851721 loss: 0.000542 2022/09/13 04:49:27 - mmengine - INFO - Epoch(train) [149][500/586] lr: 5.000000e-04 eta: 3:10:43 time: 0.333810 data_time: 0.023503 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.891881 loss: 0.000516 2022/09/13 04:49:43 - mmengine - INFO - Epoch(train) [149][550/586] lr: 5.000000e-04 eta: 3:10:27 time: 0.337870 data_time: 0.022185 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.908786 loss: 0.000543 2022/09/13 04:49:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:49:55 - mmengine - INFO - Saving checkpoint at 149 epochs 2022/09/13 04:50:20 - mmengine - INFO - Epoch(train) [150][50/586] lr: 5.000000e-04 eta: 3:09:56 time: 0.341876 data_time: 0.032428 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.854899 loss: 0.000514 2022/09/13 04:50:36 - mmengine - INFO - Epoch(train) [150][100/586] lr: 5.000000e-04 eta: 3:09:40 time: 0.335284 data_time: 0.022291 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.886230 loss: 0.000543 2022/09/13 04:50:53 - mmengine - INFO - Epoch(train) [150][150/586] lr: 5.000000e-04 eta: 3:09:24 time: 0.330891 data_time: 0.022243 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.867257 loss: 0.000523 2022/09/13 04:51:10 - mmengine - INFO - Epoch(train) [150][200/586] lr: 5.000000e-04 eta: 3:09:09 time: 0.341932 data_time: 0.022883 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.790339 loss: 0.000523 2022/09/13 04:51:27 - mmengine - INFO - Epoch(train) [150][250/586] lr: 5.000000e-04 eta: 3:08:53 time: 0.335747 data_time: 0.022577 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.899924 loss: 0.000524 2022/09/13 04:51:44 - mmengine - INFO - Epoch(train) [150][300/586] lr: 5.000000e-04 eta: 3:08:38 time: 0.332801 data_time: 0.022942 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.855278 loss: 0.000517 2022/09/13 04:52:01 - mmengine - INFO - Epoch(train) [150][350/586] lr: 5.000000e-04 eta: 3:08:22 time: 0.348472 data_time: 0.022319 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.848222 loss: 0.000540 2022/09/13 04:52:18 - mmengine - INFO - Epoch(train) [150][400/586] lr: 5.000000e-04 eta: 3:08:06 time: 0.331212 data_time: 0.021754 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.891417 loss: 0.000537 2022/09/13 04:52:34 - mmengine - INFO - Epoch(train) [150][450/586] lr: 5.000000e-04 eta: 3:07:51 time: 0.333619 data_time: 0.022513 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.856751 loss: 0.000531 2022/09/13 04:52:51 - mmengine - INFO - Epoch(train) [150][500/586] lr: 5.000000e-04 eta: 3:07:35 time: 0.343174 data_time: 0.024890 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.818911 loss: 0.000529 2022/09/13 04:53:08 - mmengine - INFO - Epoch(train) [150][550/586] lr: 5.000000e-04 eta: 3:07:20 time: 0.335479 data_time: 0.022573 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.828132 loss: 0.000540 2022/09/13 04:53:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:53:20 - mmengine - INFO - Saving checkpoint at 150 epochs 2022/09/13 04:53:37 - mmengine - INFO - Epoch(val) [150][50/407] eta: 0:01:05 time: 0.184269 data_time: 0.012298 memory: 7489 2022/09/13 04:53:46 - mmengine - INFO - Epoch(val) [150][100/407] eta: 0:00:54 time: 0.178839 data_time: 0.007604 memory: 1657 2022/09/13 04:53:54 - mmengine - INFO - Epoch(val) [150][150/407] eta: 0:00:45 time: 0.177272 data_time: 0.007606 memory: 1657 2022/09/13 04:54:03 - mmengine - INFO - Epoch(val) [150][200/407] eta: 0:00:37 time: 0.179448 data_time: 0.008487 memory: 1657 2022/09/13 04:54:12 - mmengine - INFO - Epoch(val) [150][250/407] eta: 0:00:28 time: 0.178811 data_time: 0.008116 memory: 1657 2022/09/13 04:54:22 - mmengine - INFO - Epoch(val) [150][300/407] eta: 0:00:19 time: 0.184168 data_time: 0.013300 memory: 1657 2022/09/13 04:54:30 - mmengine - INFO - Epoch(val) [150][350/407] eta: 0:00:10 time: 0.176931 data_time: 0.007261 memory: 1657 2022/09/13 04:54:39 - mmengine - INFO - Epoch(val) [150][400/407] eta: 0:00:01 time: 0.175628 data_time: 0.007262 memory: 1657 2022/09/13 04:55:14 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 04:55:28 - mmengine - INFO - Epoch(val) [150][407/407] coco/AP: 0.756032 coco/AP .5: 0.900864 coco/AP .75: 0.821566 coco/AP (M): 0.720197 coco/AP (L): 0.823908 coco/AR: 0.806848 coco/AR .5: 0.938917 coco/AR .75: 0.865082 coco/AR (M): 0.764736 coco/AR (L): 0.868673 2022/09/13 04:55:46 - mmengine - INFO - Epoch(train) [151][50/586] lr: 5.000000e-04 eta: 3:06:48 time: 0.356842 data_time: 0.028485 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.872316 loss: 0.000520 2022/09/13 04:56:02 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:56:02 - mmengine - INFO - Epoch(train) [151][100/586] lr: 5.000000e-04 eta: 3:06:33 time: 0.329419 data_time: 0.024047 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.915902 loss: 0.000521 2022/09/13 04:56:19 - mmengine - INFO - Epoch(train) [151][150/586] lr: 5.000000e-04 eta: 3:06:17 time: 0.337939 data_time: 0.023509 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.743102 loss: 0.000523 2022/09/13 04:56:36 - mmengine - INFO - Epoch(train) [151][200/586] lr: 5.000000e-04 eta: 3:06:01 time: 0.338906 data_time: 0.026462 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.878489 loss: 0.000533 2022/09/13 04:56:53 - mmengine - INFO - Epoch(train) [151][250/586] lr: 5.000000e-04 eta: 3:05:46 time: 0.331434 data_time: 0.021933 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.831805 loss: 0.000533 2022/09/13 04:57:09 - mmengine - INFO - Epoch(train) [151][300/586] lr: 5.000000e-04 eta: 3:05:30 time: 0.333424 data_time: 0.022410 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.853746 loss: 0.000535 2022/09/13 04:57:27 - mmengine - INFO - Epoch(train) [151][350/586] lr: 5.000000e-04 eta: 3:05:15 time: 0.344047 data_time: 0.027612 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.857221 loss: 0.000526 2022/09/13 04:57:43 - mmengine - INFO - Epoch(train) [151][400/586] lr: 5.000000e-04 eta: 3:04:59 time: 0.326984 data_time: 0.022304 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.872587 loss: 0.000525 2022/09/13 04:58:00 - mmengine - INFO - Epoch(train) [151][450/586] lr: 5.000000e-04 eta: 3:04:43 time: 0.335197 data_time: 0.022246 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.824071 loss: 0.000536 2022/09/13 04:58:17 - mmengine - INFO - Epoch(train) [151][500/586] lr: 5.000000e-04 eta: 3:04:27 time: 0.338634 data_time: 0.022267 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.861785 loss: 0.000532 2022/09/13 04:58:33 - mmengine - INFO - Epoch(train) [151][550/586] lr: 5.000000e-04 eta: 3:04:12 time: 0.333802 data_time: 0.022611 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.860876 loss: 0.000524 2022/09/13 04:58:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 04:58:45 - mmengine - INFO - Saving checkpoint at 151 epochs 2022/09/13 04:59:10 - mmengine - INFO - Epoch(train) [152][50/586] lr: 5.000000e-04 eta: 3:03:40 time: 0.347687 data_time: 0.032360 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.892885 loss: 0.000515 2022/09/13 04:59:27 - mmengine - INFO - Epoch(train) [152][100/586] lr: 5.000000e-04 eta: 3:03:25 time: 0.338145 data_time: 0.022530 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.853028 loss: 0.000521 2022/09/13 04:59:44 - mmengine - INFO - Epoch(train) [152][150/586] lr: 5.000000e-04 eta: 3:03:09 time: 0.338184 data_time: 0.022638 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.890461 loss: 0.000519 2022/09/13 05:00:00 - mmengine - INFO - Epoch(train) [152][200/586] lr: 5.000000e-04 eta: 3:02:54 time: 0.336489 data_time: 0.022600 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.853667 loss: 0.000498 2022/09/13 05:00:17 - mmengine - INFO - Epoch(train) [152][250/586] lr: 5.000000e-04 eta: 3:02:38 time: 0.332016 data_time: 0.023227 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.802835 loss: 0.000528 2022/09/13 05:00:34 - mmengine - INFO - Epoch(train) [152][300/586] lr: 5.000000e-04 eta: 3:02:22 time: 0.341322 data_time: 0.025793 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.797119 loss: 0.000530 2022/09/13 05:00:51 - mmengine - INFO - Epoch(train) [152][350/586] lr: 5.000000e-04 eta: 3:02:07 time: 0.332010 data_time: 0.022400 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.941857 loss: 0.000522 2022/09/13 05:01:08 - mmengine - INFO - Epoch(train) [152][400/586] lr: 5.000000e-04 eta: 3:01:51 time: 0.344191 data_time: 0.021744 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.887089 loss: 0.000534 2022/09/13 05:01:24 - mmengine - INFO - Epoch(train) [152][450/586] lr: 5.000000e-04 eta: 3:01:35 time: 0.330263 data_time: 0.022872 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.903460 loss: 0.000540 2022/09/13 05:01:41 - mmengine - INFO - Epoch(train) [152][500/586] lr: 5.000000e-04 eta: 3:01:20 time: 0.333361 data_time: 0.022330 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.809424 loss: 0.000514 2022/09/13 05:01:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:01:58 - mmengine - INFO - Epoch(train) [152][550/586] lr: 5.000000e-04 eta: 3:01:04 time: 0.341726 data_time: 0.023216 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.807232 loss: 0.000528 2022/09/13 05:02:10 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:02:10 - mmengine - INFO - Saving checkpoint at 152 epochs 2022/09/13 05:02:35 - mmengine - INFO - Epoch(train) [153][50/586] lr: 5.000000e-04 eta: 3:00:33 time: 0.345365 data_time: 0.028852 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.819951 loss: 0.000518 2022/09/13 05:02:52 - mmengine - INFO - Epoch(train) [153][100/586] lr: 5.000000e-04 eta: 3:00:17 time: 0.339541 data_time: 0.022725 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.881925 loss: 0.000539 2022/09/13 05:03:08 - mmengine - INFO - Epoch(train) [153][150/586] lr: 5.000000e-04 eta: 3:00:01 time: 0.331482 data_time: 0.022660 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.828388 loss: 0.000535 2022/09/13 05:03:25 - mmengine - INFO - Epoch(train) [153][200/586] lr: 5.000000e-04 eta: 2:59:46 time: 0.341359 data_time: 0.022021 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.799907 loss: 0.000532 2022/09/13 05:03:42 - mmengine - INFO - Epoch(train) [153][250/586] lr: 5.000000e-04 eta: 2:59:30 time: 0.341268 data_time: 0.022932 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.855904 loss: 0.000550 2022/09/13 05:03:59 - mmengine - INFO - Epoch(train) [153][300/586] lr: 5.000000e-04 eta: 2:59:15 time: 0.340070 data_time: 0.021700 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.870834 loss: 0.000530 2022/09/13 05:04:16 - mmengine - INFO - Epoch(train) [153][350/586] lr: 5.000000e-04 eta: 2:58:59 time: 0.340830 data_time: 0.025082 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.848403 loss: 0.000531 2022/09/13 05:04:33 - mmengine - INFO - Epoch(train) [153][400/586] lr: 5.000000e-04 eta: 2:58:44 time: 0.336799 data_time: 0.022555 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.816840 loss: 0.000530 2022/09/13 05:04:50 - mmengine - INFO - Epoch(train) [153][450/586] lr: 5.000000e-04 eta: 2:58:28 time: 0.332783 data_time: 0.022208 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.814723 loss: 0.000539 2022/09/13 05:05:07 - mmengine - INFO - Epoch(train) [153][500/586] lr: 5.000000e-04 eta: 2:58:12 time: 0.338090 data_time: 0.022813 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.800542 loss: 0.000540 2022/09/13 05:05:23 - mmengine - INFO - Epoch(train) [153][550/586] lr: 5.000000e-04 eta: 2:57:57 time: 0.333655 data_time: 0.022980 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.875532 loss: 0.000535 2022/09/13 05:05:35 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:05:35 - mmengine - INFO - Saving checkpoint at 153 epochs 2022/09/13 05:05:59 - mmengine - INFO - Epoch(train) [154][50/586] lr: 5.000000e-04 eta: 2:57:25 time: 0.344231 data_time: 0.030977 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.882083 loss: 0.000529 2022/09/13 05:06:16 - mmengine - INFO - Epoch(train) [154][100/586] lr: 5.000000e-04 eta: 2:57:10 time: 0.333201 data_time: 0.022905 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.911155 loss: 0.000524 2022/09/13 05:06:33 - mmengine - INFO - Epoch(train) [154][150/586] lr: 5.000000e-04 eta: 2:56:54 time: 0.338621 data_time: 0.026210 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.838117 loss: 0.000534 2022/09/13 05:06:50 - mmengine - INFO - Epoch(train) [154][200/586] lr: 5.000000e-04 eta: 2:56:38 time: 0.336628 data_time: 0.022465 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.823423 loss: 0.000510 2022/09/13 05:07:06 - mmengine - INFO - Epoch(train) [154][250/586] lr: 5.000000e-04 eta: 2:56:23 time: 0.330501 data_time: 0.021741 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.781317 loss: 0.000519 2022/09/13 05:07:23 - mmengine - INFO - Epoch(train) [154][300/586] lr: 5.000000e-04 eta: 2:56:07 time: 0.338678 data_time: 0.022361 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.865041 loss: 0.000534 2022/09/13 05:07:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:07:41 - mmengine - INFO - Epoch(train) [154][350/586] lr: 5.000000e-04 eta: 2:55:52 time: 0.343643 data_time: 0.027575 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.810619 loss: 0.000526 2022/09/13 05:07:57 - mmengine - INFO - Epoch(train) [154][400/586] lr: 5.000000e-04 eta: 2:55:36 time: 0.331286 data_time: 0.022390 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.820248 loss: 0.000529 2022/09/13 05:08:14 - mmengine - INFO - Epoch(train) [154][450/586] lr: 5.000000e-04 eta: 2:55:20 time: 0.341458 data_time: 0.022700 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.859550 loss: 0.000532 2022/09/13 05:08:31 - mmengine - INFO - Epoch(train) [154][500/586] lr: 5.000000e-04 eta: 2:55:05 time: 0.341435 data_time: 0.028598 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.881918 loss: 0.000527 2022/09/13 05:08:48 - mmengine - INFO - Epoch(train) [154][550/586] lr: 5.000000e-04 eta: 2:54:49 time: 0.339779 data_time: 0.023882 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.821591 loss: 0.000531 2022/09/13 05:09:00 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:09:00 - mmengine - INFO - Saving checkpoint at 154 epochs 2022/09/13 05:09:24 - mmengine - INFO - Epoch(train) [155][50/586] lr: 5.000000e-04 eta: 2:54:18 time: 0.337811 data_time: 0.030219 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.919655 loss: 0.000531 2022/09/13 05:09:41 - mmengine - INFO - Epoch(train) [155][100/586] lr: 5.000000e-04 eta: 2:54:02 time: 0.338501 data_time: 0.022532 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.805444 loss: 0.000518 2022/09/13 05:09:58 - mmengine - INFO - Epoch(train) [155][150/586] lr: 5.000000e-04 eta: 2:53:47 time: 0.336687 data_time: 0.022360 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.850312 loss: 0.000513 2022/09/13 05:10:15 - mmengine - INFO - Epoch(train) [155][200/586] lr: 5.000000e-04 eta: 2:53:31 time: 0.336957 data_time: 0.023533 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.902136 loss: 0.000508 2022/09/13 05:10:32 - mmengine - INFO - Epoch(train) [155][250/586] lr: 5.000000e-04 eta: 2:53:15 time: 0.336731 data_time: 0.022897 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.836176 loss: 0.000526 2022/09/13 05:10:49 - mmengine - INFO - Epoch(train) [155][300/586] lr: 5.000000e-04 eta: 2:53:00 time: 0.337028 data_time: 0.022517 memory: 7489 loss_kpt: 0.000539 acc_pose: 0.843728 loss: 0.000539 2022/09/13 05:11:06 - mmengine - INFO - Epoch(train) [155][350/586] lr: 5.000000e-04 eta: 2:52:44 time: 0.336933 data_time: 0.022359 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.880534 loss: 0.000522 2022/09/13 05:11:22 - mmengine - INFO - Epoch(train) [155][400/586] lr: 5.000000e-04 eta: 2:52:28 time: 0.330747 data_time: 0.022863 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.802652 loss: 0.000530 2022/09/13 05:11:39 - mmengine - INFO - Epoch(train) [155][450/586] lr: 5.000000e-04 eta: 2:52:13 time: 0.337477 data_time: 0.023377 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.880184 loss: 0.000534 2022/09/13 05:11:56 - mmengine - INFO - Epoch(train) [155][500/586] lr: 5.000000e-04 eta: 2:51:57 time: 0.333902 data_time: 0.022799 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.823404 loss: 0.000530 2022/09/13 05:12:13 - mmengine - INFO - Epoch(train) [155][550/586] lr: 5.000000e-04 eta: 2:51:41 time: 0.340679 data_time: 0.027907 memory: 7489 loss_kpt: 0.000548 acc_pose: 0.897911 loss: 0.000548 2022/09/13 05:12:25 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:12:25 - mmengine - INFO - Saving checkpoint at 155 epochs 2022/09/13 05:12:50 - mmengine - INFO - Epoch(train) [156][50/586] lr: 5.000000e-04 eta: 2:51:10 time: 0.346438 data_time: 0.030820 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.772453 loss: 0.000545 2022/09/13 05:13:07 - mmengine - INFO - Epoch(train) [156][100/586] lr: 5.000000e-04 eta: 2:50:55 time: 0.336740 data_time: 0.023314 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.838767 loss: 0.000533 2022/09/13 05:13:23 - mmengine - INFO - Epoch(train) [156][150/586] lr: 5.000000e-04 eta: 2:50:39 time: 0.337178 data_time: 0.024767 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.801743 loss: 0.000515 2022/09/13 05:13:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:13:41 - mmengine - INFO - Epoch(train) [156][200/586] lr: 5.000000e-04 eta: 2:50:23 time: 0.341094 data_time: 0.022337 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.818624 loss: 0.000537 2022/09/13 05:13:57 - mmengine - INFO - Epoch(train) [156][250/586] lr: 5.000000e-04 eta: 2:50:08 time: 0.331471 data_time: 0.022003 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.869514 loss: 0.000531 2022/09/13 05:14:14 - mmengine - INFO - Epoch(train) [156][300/586] lr: 5.000000e-04 eta: 2:49:52 time: 0.340590 data_time: 0.022083 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.872935 loss: 0.000529 2022/09/13 05:14:31 - mmengine - INFO - Epoch(train) [156][350/586] lr: 5.000000e-04 eta: 2:49:36 time: 0.342523 data_time: 0.023342 memory: 7489 loss_kpt: 0.000543 acc_pose: 0.842171 loss: 0.000543 2022/09/13 05:14:48 - mmengine - INFO - Epoch(train) [156][400/586] lr: 5.000000e-04 eta: 2:49:21 time: 0.326708 data_time: 0.022758 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.822800 loss: 0.000538 2022/09/13 05:15:05 - mmengine - INFO - Epoch(train) [156][450/586] lr: 5.000000e-04 eta: 2:49:05 time: 0.342120 data_time: 0.026799 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.861246 loss: 0.000529 2022/09/13 05:15:22 - mmengine - INFO - Epoch(train) [156][500/586] lr: 5.000000e-04 eta: 2:48:50 time: 0.342303 data_time: 0.022142 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.824618 loss: 0.000542 2022/09/13 05:15:38 - mmengine - INFO - Epoch(train) [156][550/586] lr: 5.000000e-04 eta: 2:48:34 time: 0.326696 data_time: 0.022105 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.829164 loss: 0.000537 2022/09/13 05:15:50 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:15:50 - mmengine - INFO - Saving checkpoint at 156 epochs 2022/09/13 05:16:14 - mmengine - INFO - Epoch(train) [157][50/586] lr: 5.000000e-04 eta: 2:48:03 time: 0.339702 data_time: 0.027390 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.900399 loss: 0.000528 2022/09/13 05:16:31 - mmengine - INFO - Epoch(train) [157][100/586] lr: 5.000000e-04 eta: 2:47:47 time: 0.340207 data_time: 0.028370 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.883509 loss: 0.000534 2022/09/13 05:16:48 - mmengine - INFO - Epoch(train) [157][150/586] lr: 5.000000e-04 eta: 2:47:31 time: 0.331783 data_time: 0.023231 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.821295 loss: 0.000523 2022/09/13 05:17:05 - mmengine - INFO - Epoch(train) [157][200/586] lr: 5.000000e-04 eta: 2:47:16 time: 0.342415 data_time: 0.022404 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.853915 loss: 0.000520 2022/09/13 05:17:22 - mmengine - INFO - Epoch(train) [157][250/586] lr: 5.000000e-04 eta: 2:47:00 time: 0.336436 data_time: 0.025320 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.853429 loss: 0.000527 2022/09/13 05:17:39 - mmengine - INFO - Epoch(train) [157][300/586] lr: 5.000000e-04 eta: 2:46:44 time: 0.331637 data_time: 0.022749 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.841680 loss: 0.000521 2022/09/13 05:17:56 - mmengine - INFO - Epoch(train) [157][350/586] lr: 5.000000e-04 eta: 2:46:29 time: 0.345599 data_time: 0.022711 memory: 7489 loss_kpt: 0.000538 acc_pose: 0.863284 loss: 0.000538 2022/09/13 05:18:13 - mmengine - INFO - Epoch(train) [157][400/586] lr: 5.000000e-04 eta: 2:46:13 time: 0.334193 data_time: 0.022692 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.892984 loss: 0.000540 2022/09/13 05:18:29 - mmengine - INFO - Epoch(train) [157][450/586] lr: 5.000000e-04 eta: 2:45:57 time: 0.335363 data_time: 0.023520 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.846669 loss: 0.000521 2022/09/13 05:18:46 - mmengine - INFO - Epoch(train) [157][500/586] lr: 5.000000e-04 eta: 2:45:42 time: 0.339798 data_time: 0.023490 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.874241 loss: 0.000528 2022/09/13 05:19:03 - mmengine - INFO - Epoch(train) [157][550/586] lr: 5.000000e-04 eta: 2:45:26 time: 0.333434 data_time: 0.023654 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.897330 loss: 0.000519 2022/09/13 05:19:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:19:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:19:15 - mmengine - INFO - Saving checkpoint at 157 epochs 2022/09/13 05:19:40 - mmengine - INFO - Epoch(train) [158][50/586] lr: 5.000000e-04 eta: 2:44:55 time: 0.353529 data_time: 0.035395 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.825629 loss: 0.000526 2022/09/13 05:19:56 - mmengine - INFO - Epoch(train) [158][100/586] lr: 5.000000e-04 eta: 2:44:40 time: 0.331465 data_time: 0.023373 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.883015 loss: 0.000532 2022/09/13 05:20:13 - mmengine - INFO - Epoch(train) [158][150/586] lr: 5.000000e-04 eta: 2:44:24 time: 0.334416 data_time: 0.023148 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.828430 loss: 0.000515 2022/09/13 05:20:30 - mmengine - INFO - Epoch(train) [158][200/586] lr: 5.000000e-04 eta: 2:44:08 time: 0.341128 data_time: 0.026167 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.844086 loss: 0.000526 2022/09/13 05:20:47 - mmengine - INFO - Epoch(train) [158][250/586] lr: 5.000000e-04 eta: 2:43:53 time: 0.333935 data_time: 0.022364 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.874065 loss: 0.000541 2022/09/13 05:21:03 - mmengine - INFO - Epoch(train) [158][300/586] lr: 5.000000e-04 eta: 2:43:37 time: 0.332140 data_time: 0.022603 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.820172 loss: 0.000508 2022/09/13 05:21:21 - mmengine - INFO - Epoch(train) [158][350/586] lr: 5.000000e-04 eta: 2:43:21 time: 0.346558 data_time: 0.022688 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.845721 loss: 0.000533 2022/09/13 05:21:37 - mmengine - INFO - Epoch(train) [158][400/586] lr: 5.000000e-04 eta: 2:43:05 time: 0.326953 data_time: 0.022625 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.910530 loss: 0.000510 2022/09/13 05:21:54 - mmengine - INFO - Epoch(train) [158][450/586] lr: 5.000000e-04 eta: 2:42:50 time: 0.333316 data_time: 0.022238 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.856880 loss: 0.000512 2022/09/13 05:22:11 - mmengine - INFO - Epoch(train) [158][500/586] lr: 5.000000e-04 eta: 2:42:34 time: 0.346452 data_time: 0.026667 memory: 7489 loss_kpt: 0.000544 acc_pose: 0.841942 loss: 0.000544 2022/09/13 05:22:28 - mmengine - INFO - Epoch(train) [158][550/586] lr: 5.000000e-04 eta: 2:42:18 time: 0.328024 data_time: 0.023014 memory: 7489 loss_kpt: 0.000545 acc_pose: 0.801534 loss: 0.000545 2022/09/13 05:22:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:22:40 - mmengine - INFO - Saving checkpoint at 158 epochs 2022/09/13 05:23:05 - mmengine - INFO - Epoch(train) [159][50/586] lr: 5.000000e-04 eta: 2:41:48 time: 0.352780 data_time: 0.026322 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.787329 loss: 0.000522 2022/09/13 05:23:21 - mmengine - INFO - Epoch(train) [159][100/586] lr: 5.000000e-04 eta: 2:41:32 time: 0.330623 data_time: 0.022208 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.806440 loss: 0.000522 2022/09/13 05:23:38 - mmengine - INFO - Epoch(train) [159][150/586] lr: 5.000000e-04 eta: 2:41:16 time: 0.338992 data_time: 0.026768 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.885833 loss: 0.000510 2022/09/13 05:23:55 - mmengine - INFO - Epoch(train) [159][200/586] lr: 5.000000e-04 eta: 2:41:01 time: 0.336403 data_time: 0.022610 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.871473 loss: 0.000540 2022/09/13 05:24:12 - mmengine - INFO - Epoch(train) [159][250/586] lr: 5.000000e-04 eta: 2:40:45 time: 0.337142 data_time: 0.022787 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.805762 loss: 0.000533 2022/09/13 05:24:28 - mmengine - INFO - Epoch(train) [159][300/586] lr: 5.000000e-04 eta: 2:40:29 time: 0.332767 data_time: 0.021913 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.852153 loss: 0.000513 2022/09/13 05:24:45 - mmengine - INFO - Epoch(train) [159][350/586] lr: 5.000000e-04 eta: 2:40:14 time: 0.338464 data_time: 0.022258 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.883295 loss: 0.000536 2022/09/13 05:25:02 - mmengine - INFO - Epoch(train) [159][400/586] lr: 5.000000e-04 eta: 2:39:58 time: 0.341249 data_time: 0.022780 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.889626 loss: 0.000534 2022/09/13 05:25:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:25:19 - mmengine - INFO - Epoch(train) [159][450/586] lr: 5.000000e-04 eta: 2:39:42 time: 0.332401 data_time: 0.025750 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.864708 loss: 0.000519 2022/09/13 05:25:36 - mmengine - INFO - Epoch(train) [159][500/586] lr: 5.000000e-04 eta: 2:39:27 time: 0.345015 data_time: 0.022080 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.822653 loss: 0.000521 2022/09/13 05:25:53 - mmengine - INFO - Epoch(train) [159][550/586] lr: 5.000000e-04 eta: 2:39:11 time: 0.330765 data_time: 0.022929 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.886433 loss: 0.000521 2022/09/13 05:26:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:26:05 - mmengine - INFO - Saving checkpoint at 159 epochs 2022/09/13 05:26:29 - mmengine - INFO - Epoch(train) [160][50/586] lr: 5.000000e-04 eta: 2:38:40 time: 0.343321 data_time: 0.033094 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.805729 loss: 0.000519 2022/09/13 05:26:46 - mmengine - INFO - Epoch(train) [160][100/586] lr: 5.000000e-04 eta: 2:38:25 time: 0.337202 data_time: 0.022985 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.897185 loss: 0.000525 2022/09/13 05:27:03 - mmengine - INFO - Epoch(train) [160][150/586] lr: 5.000000e-04 eta: 2:38:09 time: 0.341073 data_time: 0.022844 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.859671 loss: 0.000518 2022/09/13 05:27:20 - mmengine - INFO - Epoch(train) [160][200/586] lr: 5.000000e-04 eta: 2:37:53 time: 0.340220 data_time: 0.026078 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.891548 loss: 0.000537 2022/09/13 05:27:37 - mmengine - INFO - Epoch(train) [160][250/586] lr: 5.000000e-04 eta: 2:37:38 time: 0.332473 data_time: 0.022317 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.858492 loss: 0.000515 2022/09/13 05:27:54 - mmengine - INFO - Epoch(train) [160][300/586] lr: 5.000000e-04 eta: 2:37:22 time: 0.334933 data_time: 0.022677 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.859077 loss: 0.000524 2022/09/13 05:28:11 - mmengine - INFO - Epoch(train) [160][350/586] lr: 5.000000e-04 eta: 2:37:06 time: 0.343488 data_time: 0.027329 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.912957 loss: 0.000514 2022/09/13 05:28:28 - mmengine - INFO - Epoch(train) [160][400/586] lr: 5.000000e-04 eta: 2:36:51 time: 0.341504 data_time: 0.021911 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.898959 loss: 0.000534 2022/09/13 05:28:44 - mmengine - INFO - Epoch(train) [160][450/586] lr: 5.000000e-04 eta: 2:36:35 time: 0.328680 data_time: 0.021894 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.856504 loss: 0.000524 2022/09/13 05:29:01 - mmengine - INFO - Epoch(train) [160][500/586] lr: 5.000000e-04 eta: 2:36:19 time: 0.339872 data_time: 0.026695 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.813090 loss: 0.000530 2022/09/13 05:29:18 - mmengine - INFO - Epoch(train) [160][550/586] lr: 5.000000e-04 eta: 2:36:04 time: 0.335004 data_time: 0.022698 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.874328 loss: 0.000528 2022/09/13 05:29:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:29:30 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/09/13 05:29:47 - mmengine - INFO - Epoch(val) [160][50/407] eta: 0:01:06 time: 0.184890 data_time: 0.012566 memory: 7489 2022/09/13 05:29:56 - mmengine - INFO - Epoch(val) [160][100/407] eta: 0:00:54 time: 0.178450 data_time: 0.007530 memory: 1657 2022/09/13 05:30:04 - mmengine - INFO - Epoch(val) [160][150/407] eta: 0:00:45 time: 0.177550 data_time: 0.007460 memory: 1657 2022/09/13 05:30:13 - mmengine - INFO - Epoch(val) [160][200/407] eta: 0:00:36 time: 0.178646 data_time: 0.007558 memory: 1657 2022/09/13 05:30:22 - mmengine - INFO - Epoch(val) [160][250/407] eta: 0:00:28 time: 0.179005 data_time: 0.007631 memory: 1657 2022/09/13 05:30:31 - mmengine - INFO - Epoch(val) [160][300/407] eta: 0:00:18 time: 0.177397 data_time: 0.007662 memory: 1657 2022/09/13 05:30:40 - mmengine - INFO - Epoch(val) [160][350/407] eta: 0:00:10 time: 0.181233 data_time: 0.007507 memory: 1657 2022/09/13 05:30:49 - mmengine - INFO - Epoch(val) [160][400/407] eta: 0:00:01 time: 0.175354 data_time: 0.007033 memory: 1657 2022/09/13 05:31:24 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 05:31:38 - mmengine - INFO - Epoch(val) [160][407/407] coco/AP: 0.754799 coco/AP .5: 0.899143 coco/AP .75: 0.821320 coco/AP (M): 0.718116 coco/AP (L): 0.826194 coco/AR: 0.806518 coco/AR .5: 0.939861 coco/AR .75: 0.864452 coco/AR (M): 0.762688 coco/AR (L): 0.870717 2022/09/13 05:31:56 - mmengine - INFO - Epoch(train) [161][50/586] lr: 5.000000e-04 eta: 2:35:33 time: 0.356590 data_time: 0.028841 memory: 7489 loss_kpt: 0.000542 acc_pose: 0.853679 loss: 0.000542 2022/09/13 05:32:13 - mmengine - INFO - Epoch(train) [161][100/586] lr: 5.000000e-04 eta: 2:35:17 time: 0.336831 data_time: 0.024176 memory: 7489 loss_kpt: 0.000550 acc_pose: 0.847612 loss: 0.000550 2022/09/13 05:32:29 - mmengine - INFO - Epoch(train) [161][150/586] lr: 5.000000e-04 eta: 2:35:02 time: 0.331783 data_time: 0.022913 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.917370 loss: 0.000547 2022/09/13 05:32:46 - mmengine - INFO - Epoch(train) [161][200/586] lr: 5.000000e-04 eta: 2:34:46 time: 0.336700 data_time: 0.022544 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.832543 loss: 0.000505 2022/09/13 05:32:59 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:33:03 - mmengine - INFO - Epoch(train) [161][250/586] lr: 5.000000e-04 eta: 2:34:30 time: 0.339352 data_time: 0.022944 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.789312 loss: 0.000526 2022/09/13 05:33:20 - mmengine - INFO - Epoch(train) [161][300/586] lr: 5.000000e-04 eta: 2:34:15 time: 0.329458 data_time: 0.023215 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.817188 loss: 0.000514 2022/09/13 05:33:37 - mmengine - INFO - Epoch(train) [161][350/586] lr: 5.000000e-04 eta: 2:33:59 time: 0.343941 data_time: 0.021741 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.841702 loss: 0.000530 2022/09/13 05:33:54 - mmengine - INFO - Epoch(train) [161][400/586] lr: 5.000000e-04 eta: 2:33:43 time: 0.339191 data_time: 0.022370 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.885001 loss: 0.000533 2022/09/13 05:34:11 - mmengine - INFO - Epoch(train) [161][450/586] lr: 5.000000e-04 eta: 2:33:28 time: 0.343024 data_time: 0.022607 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.840482 loss: 0.000510 2022/09/13 05:34:28 - mmengine - INFO - Epoch(train) [161][500/586] lr: 5.000000e-04 eta: 2:33:12 time: 0.341938 data_time: 0.022806 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.914379 loss: 0.000528 2022/09/13 05:34:45 - mmengine - INFO - Epoch(train) [161][550/586] lr: 5.000000e-04 eta: 2:32:57 time: 0.343721 data_time: 0.024442 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.915123 loss: 0.000525 2022/09/13 05:34:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:34:57 - mmengine - INFO - Saving checkpoint at 161 epochs 2022/09/13 05:35:21 - mmengine - INFO - Epoch(train) [162][50/586] lr: 5.000000e-04 eta: 2:32:26 time: 0.341057 data_time: 0.031119 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.853358 loss: 0.000521 2022/09/13 05:35:38 - mmengine - INFO - Epoch(train) [162][100/586] lr: 5.000000e-04 eta: 2:32:10 time: 0.336866 data_time: 0.023636 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.870973 loss: 0.000521 2022/09/13 05:35:55 - mmengine - INFO - Epoch(train) [162][150/586] lr: 5.000000e-04 eta: 2:31:55 time: 0.337613 data_time: 0.026789 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.814021 loss: 0.000521 2022/09/13 05:36:11 - mmengine - INFO - Epoch(train) [162][200/586] lr: 5.000000e-04 eta: 2:31:39 time: 0.334888 data_time: 0.023158 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.906472 loss: 0.000512 2022/09/13 05:36:28 - mmengine - INFO - Epoch(train) [162][250/586] lr: 5.000000e-04 eta: 2:31:23 time: 0.336369 data_time: 0.022981 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.907903 loss: 0.000511 2022/09/13 05:36:45 - mmengine - INFO - Epoch(train) [162][300/586] lr: 5.000000e-04 eta: 2:31:07 time: 0.334300 data_time: 0.023299 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.839108 loss: 0.000526 2022/09/13 05:37:02 - mmengine - INFO - Epoch(train) [162][350/586] lr: 5.000000e-04 eta: 2:30:52 time: 0.344428 data_time: 0.024085 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.870119 loss: 0.000524 2022/09/13 05:37:19 - mmengine - INFO - Epoch(train) [162][400/586] lr: 5.000000e-04 eta: 2:30:36 time: 0.334325 data_time: 0.022522 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.895172 loss: 0.000530 2022/09/13 05:37:36 - mmengine - INFO - Epoch(train) [162][450/586] lr: 5.000000e-04 eta: 2:30:20 time: 0.335458 data_time: 0.022198 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.877653 loss: 0.000530 2022/09/13 05:37:53 - mmengine - INFO - Epoch(train) [162][500/586] lr: 5.000000e-04 eta: 2:30:05 time: 0.340554 data_time: 0.023067 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.817507 loss: 0.000524 2022/09/13 05:38:09 - mmengine - INFO - Epoch(train) [162][550/586] lr: 5.000000e-04 eta: 2:29:49 time: 0.329118 data_time: 0.021756 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.869162 loss: 0.000525 2022/09/13 05:38:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:38:21 - mmengine - INFO - Saving checkpoint at 162 epochs 2022/09/13 05:38:46 - mmengine - INFO - Epoch(train) [163][50/586] lr: 5.000000e-04 eta: 2:29:19 time: 0.348892 data_time: 0.026970 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.919206 loss: 0.000523 2022/09/13 05:38:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:39:03 - mmengine - INFO - Epoch(train) [163][100/586] lr: 5.000000e-04 eta: 2:29:03 time: 0.335294 data_time: 0.026679 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.840341 loss: 0.000523 2022/09/13 05:39:20 - mmengine - INFO - Epoch(train) [163][150/586] lr: 5.000000e-04 eta: 2:28:47 time: 0.337073 data_time: 0.022128 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.910245 loss: 0.000516 2022/09/13 05:39:37 - mmengine - INFO - Epoch(train) [163][200/586] lr: 5.000000e-04 eta: 2:28:32 time: 0.344503 data_time: 0.025323 memory: 7489 loss_kpt: 0.000534 acc_pose: 0.904066 loss: 0.000534 2022/09/13 05:39:54 - mmengine - INFO - Epoch(train) [163][250/586] lr: 5.000000e-04 eta: 2:28:16 time: 0.335353 data_time: 0.023438 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.854828 loss: 0.000512 2022/09/13 05:40:11 - mmengine - INFO - Epoch(train) [163][300/586] lr: 5.000000e-04 eta: 2:28:00 time: 0.346848 data_time: 0.023769 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.876910 loss: 0.000532 2022/09/13 05:40:29 - mmengine - INFO - Epoch(train) [163][350/586] lr: 5.000000e-04 eta: 2:27:45 time: 0.342657 data_time: 0.022938 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.861173 loss: 0.000525 2022/09/13 05:40:45 - mmengine - INFO - Epoch(train) [163][400/586] lr: 5.000000e-04 eta: 2:27:29 time: 0.335328 data_time: 0.022430 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.863004 loss: 0.000524 2022/09/13 05:41:02 - mmengine - INFO - Epoch(train) [163][450/586] lr: 5.000000e-04 eta: 2:27:13 time: 0.339021 data_time: 0.022508 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.847086 loss: 0.000541 2022/09/13 05:41:19 - mmengine - INFO - Epoch(train) [163][500/586] lr: 5.000000e-04 eta: 2:26:58 time: 0.334629 data_time: 0.025571 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.789007 loss: 0.000514 2022/09/13 05:41:36 - mmengine - INFO - Epoch(train) [163][550/586] lr: 5.000000e-04 eta: 2:26:42 time: 0.336851 data_time: 0.021921 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.873456 loss: 0.000518 2022/09/13 05:41:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:41:48 - mmengine - INFO - Saving checkpoint at 163 epochs 2022/09/13 05:42:12 - mmengine - INFO - Epoch(train) [164][50/586] lr: 5.000000e-04 eta: 2:26:11 time: 0.337262 data_time: 0.029606 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.830953 loss: 0.000528 2022/09/13 05:42:29 - mmengine - INFO - Epoch(train) [164][100/586] lr: 5.000000e-04 eta: 2:25:56 time: 0.335687 data_time: 0.022520 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.864806 loss: 0.000509 2022/09/13 05:42:46 - mmengine - INFO - Epoch(train) [164][150/586] lr: 5.000000e-04 eta: 2:25:40 time: 0.338272 data_time: 0.022243 memory: 7489 loss_kpt: 0.000528 acc_pose: 0.812124 loss: 0.000528 2022/09/13 05:43:03 - mmengine - INFO - Epoch(train) [164][200/586] lr: 5.000000e-04 eta: 2:25:24 time: 0.340082 data_time: 0.027521 memory: 7489 loss_kpt: 0.000540 acc_pose: 0.819620 loss: 0.000540 2022/09/13 05:43:20 - mmengine - INFO - Epoch(train) [164][250/586] lr: 5.000000e-04 eta: 2:25:09 time: 0.334547 data_time: 0.023727 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.861275 loss: 0.000526 2022/09/13 05:43:37 - mmengine - INFO - Epoch(train) [164][300/586] lr: 5.000000e-04 eta: 2:24:53 time: 0.335746 data_time: 0.023346 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.830220 loss: 0.000523 2022/09/13 05:43:54 - mmengine - INFO - Epoch(train) [164][350/586] lr: 5.000000e-04 eta: 2:24:37 time: 0.335818 data_time: 0.027233 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.874970 loss: 0.000518 2022/09/13 05:44:10 - mmengine - INFO - Epoch(train) [164][400/586] lr: 5.000000e-04 eta: 2:24:21 time: 0.337337 data_time: 0.022272 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.831285 loss: 0.000519 2022/09/13 05:44:27 - mmengine - INFO - Epoch(train) [164][450/586] lr: 5.000000e-04 eta: 2:24:06 time: 0.337869 data_time: 0.022628 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.838666 loss: 0.000519 2022/09/13 05:44:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:44:45 - mmengine - INFO - Epoch(train) [164][500/586] lr: 5.000000e-04 eta: 2:23:50 time: 0.344308 data_time: 0.026079 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.826152 loss: 0.000517 2022/09/13 05:45:01 - mmengine - INFO - Epoch(train) [164][550/586] lr: 5.000000e-04 eta: 2:23:34 time: 0.336300 data_time: 0.022929 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.813948 loss: 0.000519 2022/09/13 05:45:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:45:14 - mmengine - INFO - Saving checkpoint at 164 epochs 2022/09/13 05:45:38 - mmengine - INFO - Epoch(train) [165][50/586] lr: 5.000000e-04 eta: 2:23:04 time: 0.341162 data_time: 0.030275 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.868400 loss: 0.000512 2022/09/13 05:45:55 - mmengine - INFO - Epoch(train) [165][100/586] lr: 5.000000e-04 eta: 2:22:48 time: 0.335645 data_time: 0.022323 memory: 7489 loss_kpt: 0.000537 acc_pose: 0.878979 loss: 0.000537 2022/09/13 05:46:12 - mmengine - INFO - Epoch(train) [165][150/586] lr: 5.000000e-04 eta: 2:22:33 time: 0.333939 data_time: 0.024826 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.893331 loss: 0.000535 2022/09/13 05:46:28 - mmengine - INFO - Epoch(train) [165][200/586] lr: 5.000000e-04 eta: 2:22:17 time: 0.332625 data_time: 0.023087 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.908015 loss: 0.000531 2022/09/13 05:46:45 - mmengine - INFO - Epoch(train) [165][250/586] lr: 5.000000e-04 eta: 2:22:01 time: 0.335852 data_time: 0.022492 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.875070 loss: 0.000526 2022/09/13 05:47:02 - mmengine - INFO - Epoch(train) [165][300/586] lr: 5.000000e-04 eta: 2:21:45 time: 0.334887 data_time: 0.025915 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.871899 loss: 0.000517 2022/09/13 05:47:19 - mmengine - INFO - Epoch(train) [165][350/586] lr: 5.000000e-04 eta: 2:21:30 time: 0.340611 data_time: 0.021704 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.874100 loss: 0.000512 2022/09/13 05:47:36 - mmengine - INFO - Epoch(train) [165][400/586] lr: 5.000000e-04 eta: 2:21:14 time: 0.338993 data_time: 0.022772 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.823549 loss: 0.000520 2022/09/13 05:47:53 - mmengine - INFO - Epoch(train) [165][450/586] lr: 5.000000e-04 eta: 2:20:58 time: 0.340663 data_time: 0.022925 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.854362 loss: 0.000524 2022/09/13 05:48:10 - mmengine - INFO - Epoch(train) [165][500/586] lr: 5.000000e-04 eta: 2:20:43 time: 0.334266 data_time: 0.021971 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.912284 loss: 0.000508 2022/09/13 05:48:26 - mmengine - INFO - Epoch(train) [165][550/586] lr: 5.000000e-04 eta: 2:20:27 time: 0.329851 data_time: 0.022559 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.868119 loss: 0.000527 2022/09/13 05:48:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:48:38 - mmengine - INFO - Saving checkpoint at 165 epochs 2022/09/13 05:49:03 - mmengine - INFO - Epoch(train) [166][50/586] lr: 5.000000e-04 eta: 2:19:57 time: 0.354482 data_time: 0.033078 memory: 7489 loss_kpt: 0.000531 acc_pose: 0.866027 loss: 0.000531 2022/09/13 05:49:20 - mmengine - INFO - Epoch(train) [166][100/586] lr: 5.000000e-04 eta: 2:19:41 time: 0.333433 data_time: 0.024647 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.879799 loss: 0.000526 2022/09/13 05:49:36 - mmengine - INFO - Epoch(train) [166][150/586] lr: 5.000000e-04 eta: 2:19:25 time: 0.334743 data_time: 0.023594 memory: 7489 loss_kpt: 0.000541 acc_pose: 0.862526 loss: 0.000541 2022/09/13 05:49:53 - mmengine - INFO - Epoch(train) [166][200/586] lr: 5.000000e-04 eta: 2:19:10 time: 0.337234 data_time: 0.024351 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.851205 loss: 0.000509 2022/09/13 05:50:10 - mmengine - INFO - Epoch(train) [166][250/586] lr: 5.000000e-04 eta: 2:18:54 time: 0.334071 data_time: 0.022165 memory: 7489 loss_kpt: 0.000536 acc_pose: 0.808822 loss: 0.000536 2022/09/13 05:50:27 - mmengine - INFO - Epoch(train) [166][300/586] lr: 5.000000e-04 eta: 2:18:38 time: 0.341811 data_time: 0.023156 memory: 7489 loss_kpt: 0.000547 acc_pose: 0.885122 loss: 0.000547 2022/09/13 05:50:30 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:50:44 - mmengine - INFO - Epoch(train) [166][350/586] lr: 5.000000e-04 eta: 2:18:22 time: 0.337809 data_time: 0.023777 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.849954 loss: 0.000523 2022/09/13 05:51:01 - mmengine - INFO - Epoch(train) [166][400/586] lr: 5.000000e-04 eta: 2:18:07 time: 0.335060 data_time: 0.021708 memory: 7489 loss_kpt: 0.000551 acc_pose: 0.826066 loss: 0.000551 2022/09/13 05:51:17 - mmengine - INFO - Epoch(train) [166][450/586] lr: 5.000000e-04 eta: 2:17:51 time: 0.333462 data_time: 0.025246 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.857487 loss: 0.000517 2022/09/13 05:51:34 - mmengine - INFO - Epoch(train) [166][500/586] lr: 5.000000e-04 eta: 2:17:35 time: 0.338834 data_time: 0.022722 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.913001 loss: 0.000522 2022/09/13 05:51:51 - mmengine - INFO - Epoch(train) [166][550/586] lr: 5.000000e-04 eta: 2:17:19 time: 0.333411 data_time: 0.021969 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.855773 loss: 0.000533 2022/09/13 05:52:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:52:03 - mmengine - INFO - Saving checkpoint at 166 epochs 2022/09/13 05:52:27 - mmengine - INFO - Epoch(train) [167][50/586] lr: 5.000000e-04 eta: 2:16:49 time: 0.340909 data_time: 0.028779 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.882656 loss: 0.000527 2022/09/13 05:52:44 - mmengine - INFO - Epoch(train) [167][100/586] lr: 5.000000e-04 eta: 2:16:34 time: 0.335180 data_time: 0.024507 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.876233 loss: 0.000523 2022/09/13 05:53:01 - mmengine - INFO - Epoch(train) [167][150/586] lr: 5.000000e-04 eta: 2:16:18 time: 0.333408 data_time: 0.022571 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.832970 loss: 0.000523 2022/09/13 05:53:18 - mmengine - INFO - Epoch(train) [167][200/586] lr: 5.000000e-04 eta: 2:16:02 time: 0.337617 data_time: 0.021702 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.851972 loss: 0.000524 2022/09/13 05:53:35 - mmengine - INFO - Epoch(train) [167][250/586] lr: 5.000000e-04 eta: 2:15:46 time: 0.337906 data_time: 0.025211 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.819054 loss: 0.000514 2022/09/13 05:53:51 - mmengine - INFO - Epoch(train) [167][300/586] lr: 5.000000e-04 eta: 2:15:31 time: 0.329142 data_time: 0.022854 memory: 7489 loss_kpt: 0.000533 acc_pose: 0.834929 loss: 0.000533 2022/09/13 05:54:08 - mmengine - INFO - Epoch(train) [167][350/586] lr: 5.000000e-04 eta: 2:15:15 time: 0.332542 data_time: 0.022639 memory: 7489 loss_kpt: 0.000522 acc_pose: 0.874615 loss: 0.000522 2022/09/13 05:54:25 - mmengine - INFO - Epoch(train) [167][400/586] lr: 5.000000e-04 eta: 2:14:59 time: 0.342317 data_time: 0.023097 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.885653 loss: 0.000521 2022/09/13 05:54:42 - mmengine - INFO - Epoch(train) [167][450/586] lr: 5.000000e-04 eta: 2:14:43 time: 0.333464 data_time: 0.023738 memory: 7489 loss_kpt: 0.000523 acc_pose: 0.862108 loss: 0.000523 2022/09/13 05:54:59 - mmengine - INFO - Epoch(train) [167][500/586] lr: 5.000000e-04 eta: 2:14:28 time: 0.344206 data_time: 0.023562 memory: 7489 loss_kpt: 0.000527 acc_pose: 0.872571 loss: 0.000527 2022/09/13 05:55:16 - mmengine - INFO - Epoch(train) [167][550/586] lr: 5.000000e-04 eta: 2:14:12 time: 0.341868 data_time: 0.022332 memory: 7489 loss_kpt: 0.000525 acc_pose: 0.836196 loss: 0.000525 2022/09/13 05:55:28 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:55:28 - mmengine - INFO - Saving checkpoint at 167 epochs 2022/09/13 05:55:52 - mmengine - INFO - Epoch(train) [168][50/586] lr: 5.000000e-04 eta: 2:13:42 time: 0.355747 data_time: 0.027749 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.803881 loss: 0.000520 2022/09/13 05:56:09 - mmengine - INFO - Epoch(train) [168][100/586] lr: 5.000000e-04 eta: 2:13:26 time: 0.334813 data_time: 0.022543 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.833800 loss: 0.000503 2022/09/13 05:56:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:56:25 - mmengine - INFO - Epoch(train) [168][150/586] lr: 5.000000e-04 eta: 2:13:11 time: 0.330168 data_time: 0.023167 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.918287 loss: 0.000513 2022/09/13 05:56:43 - mmengine - INFO - Epoch(train) [168][200/586] lr: 5.000000e-04 eta: 2:12:55 time: 0.346220 data_time: 0.027315 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.861912 loss: 0.000512 2022/09/13 05:56:59 - mmengine - INFO - Epoch(train) [168][250/586] lr: 5.000000e-04 eta: 2:12:39 time: 0.335636 data_time: 0.022794 memory: 7489 loss_kpt: 0.000517 acc_pose: 0.876399 loss: 0.000517 2022/09/13 05:57:16 - mmengine - INFO - Epoch(train) [168][300/586] lr: 5.000000e-04 eta: 2:12:23 time: 0.338891 data_time: 0.025685 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.841422 loss: 0.000515 2022/09/13 05:57:33 - mmengine - INFO - Epoch(train) [168][350/586] lr: 5.000000e-04 eta: 2:12:08 time: 0.338152 data_time: 0.023196 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.868217 loss: 0.000530 2022/09/13 05:57:50 - mmengine - INFO - Epoch(train) [168][400/586] lr: 5.000000e-04 eta: 2:11:52 time: 0.333260 data_time: 0.023174 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.887173 loss: 0.000521 2022/09/13 05:58:07 - mmengine - INFO - Epoch(train) [168][450/586] lr: 5.000000e-04 eta: 2:11:36 time: 0.338048 data_time: 0.026214 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.913500 loss: 0.000508 2022/09/13 05:58:24 - mmengine - INFO - Epoch(train) [168][500/586] lr: 5.000000e-04 eta: 2:11:21 time: 0.338046 data_time: 0.022578 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.847812 loss: 0.000512 2022/09/13 05:58:41 - mmengine - INFO - Epoch(train) [168][550/586] lr: 5.000000e-04 eta: 2:11:05 time: 0.339217 data_time: 0.022417 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.869748 loss: 0.000526 2022/09/13 05:58:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 05:58:53 - mmengine - INFO - Saving checkpoint at 168 epochs 2022/09/13 05:59:17 - mmengine - INFO - Epoch(train) [169][50/586] lr: 5.000000e-04 eta: 2:10:35 time: 0.348122 data_time: 0.026608 memory: 7489 loss_kpt: 0.000505 acc_pose: 0.840555 loss: 0.000505 2022/09/13 05:59:34 - mmengine - INFO - Epoch(train) [169][100/586] lr: 5.000000e-04 eta: 2:10:19 time: 0.337677 data_time: 0.027938 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.849406 loss: 0.000520 2022/09/13 05:59:51 - mmengine - INFO - Epoch(train) [169][150/586] lr: 5.000000e-04 eta: 2:10:03 time: 0.328699 data_time: 0.023964 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.882608 loss: 0.000510 2022/09/13 06:00:10 - mmengine - INFO - Epoch(train) [169][200/586] lr: 5.000000e-04 eta: 2:09:48 time: 0.381703 data_time: 0.025358 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.838437 loss: 0.000519 2022/09/13 06:00:27 - mmengine - INFO - Epoch(train) [169][250/586] lr: 5.000000e-04 eta: 2:09:32 time: 0.338378 data_time: 0.027826 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.876212 loss: 0.000500 2022/09/13 06:00:43 - mmengine - INFO - Epoch(train) [169][300/586] lr: 5.000000e-04 eta: 2:09:17 time: 0.332216 data_time: 0.021889 memory: 7489 loss_kpt: 0.000513 acc_pose: 0.911749 loss: 0.000513 2022/09/13 06:01:00 - mmengine - INFO - Epoch(train) [169][350/586] lr: 5.000000e-04 eta: 2:09:01 time: 0.337292 data_time: 0.022247 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.888520 loss: 0.000532 2022/09/13 06:01:17 - mmengine - INFO - Epoch(train) [169][400/586] lr: 5.000000e-04 eta: 2:08:45 time: 0.335543 data_time: 0.026366 memory: 7489 loss_kpt: 0.000515 acc_pose: 0.810773 loss: 0.000515 2022/09/13 06:01:34 - mmengine - INFO - Epoch(train) [169][450/586] lr: 5.000000e-04 eta: 2:08:29 time: 0.336461 data_time: 0.022589 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.808527 loss: 0.000509 2022/09/13 06:01:51 - mmengine - INFO - Epoch(train) [169][500/586] lr: 5.000000e-04 eta: 2:08:14 time: 0.340341 data_time: 0.022781 memory: 7489 loss_kpt: 0.000529 acc_pose: 0.856767 loss: 0.000529 2022/09/13 06:02:08 - mmengine - INFO - Epoch(train) [169][550/586] lr: 5.000000e-04 eta: 2:07:58 time: 0.340790 data_time: 0.026253 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.849406 loss: 0.000524 2022/09/13 06:02:09 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:02:20 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:02:20 - mmengine - INFO - Saving checkpoint at 169 epochs 2022/09/13 06:02:44 - mmengine - INFO - Epoch(train) [170][50/586] lr: 5.000000e-04 eta: 2:07:28 time: 0.347766 data_time: 0.037159 memory: 7489 loss_kpt: 0.000555 acc_pose: 0.735396 loss: 0.000555 2022/09/13 06:03:01 - mmengine - INFO - Epoch(train) [170][100/586] lr: 5.000000e-04 eta: 2:07:12 time: 0.332514 data_time: 0.024614 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.795859 loss: 0.000489 2022/09/13 06:03:17 - mmengine - INFO - Epoch(train) [170][150/586] lr: 5.000000e-04 eta: 2:06:57 time: 0.330121 data_time: 0.024562 memory: 7489 loss_kpt: 0.000526 acc_pose: 0.898491 loss: 0.000526 2022/09/13 06:03:34 - mmengine - INFO - Epoch(train) [170][200/586] lr: 5.000000e-04 eta: 2:06:41 time: 0.339114 data_time: 0.023672 memory: 7489 loss_kpt: 0.000519 acc_pose: 0.891566 loss: 0.000519 2022/09/13 06:03:51 - mmengine - INFO - Epoch(train) [170][250/586] lr: 5.000000e-04 eta: 2:06:25 time: 0.335103 data_time: 0.022250 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.876730 loss: 0.000507 2022/09/13 06:04:08 - mmengine - INFO - Epoch(train) [170][300/586] lr: 5.000000e-04 eta: 2:06:09 time: 0.335233 data_time: 0.022446 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.892140 loss: 0.000507 2022/09/13 06:04:25 - mmengine - INFO - Epoch(train) [170][350/586] lr: 5.000000e-04 eta: 2:05:54 time: 0.339963 data_time: 0.022110 memory: 7489 loss_kpt: 0.000532 acc_pose: 0.865971 loss: 0.000532 2022/09/13 06:04:42 - mmengine - INFO - Epoch(train) [170][400/586] lr: 5.000000e-04 eta: 2:05:38 time: 0.333483 data_time: 0.022642 memory: 7489 loss_kpt: 0.000524 acc_pose: 0.853393 loss: 0.000524 2022/09/13 06:04:58 - mmengine - INFO - Epoch(train) [170][450/586] lr: 5.000000e-04 eta: 2:05:22 time: 0.334637 data_time: 0.023277 memory: 7489 loss_kpt: 0.000516 acc_pose: 0.914933 loss: 0.000516 2022/09/13 06:05:15 - mmengine - INFO - Epoch(train) [170][500/586] lr: 5.000000e-04 eta: 2:05:06 time: 0.340997 data_time: 0.022385 memory: 7489 loss_kpt: 0.000535 acc_pose: 0.877262 loss: 0.000535 2022/09/13 06:05:32 - mmengine - INFO - Epoch(train) [170][550/586] lr: 5.000000e-04 eta: 2:04:51 time: 0.336860 data_time: 0.022141 memory: 7489 loss_kpt: 0.000530 acc_pose: 0.839940 loss: 0.000530 2022/09/13 06:05:44 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:05:44 - mmengine - INFO - Saving checkpoint at 170 epochs 2022/09/13 06:06:01 - mmengine - INFO - Epoch(val) [170][50/407] eta: 0:01:06 time: 0.186025 data_time: 0.013402 memory: 7489 2022/09/13 06:06:10 - mmengine - INFO - Epoch(val) [170][100/407] eta: 0:00:54 time: 0.178975 data_time: 0.007710 memory: 1657 2022/09/13 06:06:19 - mmengine - INFO - Epoch(val) [170][150/407] eta: 0:00:45 time: 0.178907 data_time: 0.007382 memory: 1657 2022/09/13 06:06:28 - mmengine - INFO - Epoch(val) [170][200/407] eta: 0:00:37 time: 0.178832 data_time: 0.008010 memory: 1657 2022/09/13 06:06:37 - mmengine - INFO - Epoch(val) [170][250/407] eta: 0:00:28 time: 0.181949 data_time: 0.007424 memory: 1657 2022/09/13 06:06:46 - mmengine - INFO - Epoch(val) [170][300/407] eta: 0:00:19 time: 0.177897 data_time: 0.007813 memory: 1657 2022/09/13 06:06:55 - mmengine - INFO - Epoch(val) [170][350/407] eta: 0:00:10 time: 0.179233 data_time: 0.008415 memory: 1657 2022/09/13 06:07:03 - mmengine - INFO - Epoch(val) [170][400/407] eta: 0:00:01 time: 0.175223 data_time: 0.007110 memory: 1657 2022/09/13 06:07:38 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 06:07:52 - mmengine - INFO - Epoch(val) [170][407/407] coco/AP: 0.757241 coco/AP .5: 0.902015 coco/AP .75: 0.820444 coco/AP (M): 0.722192 coco/AP (L): 0.825682 coco/AR: 0.807809 coco/AR .5: 0.940334 coco/AR .75: 0.864452 coco/AR (M): 0.766539 coco/AR (L): 0.868711 2022/09/13 06:07:52 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_140.pth is removed 2022/09/13 06:07:56 - mmengine - INFO - The best checkpoint with 0.7572 coco/AP at 170 epoch is saved to best_coco/AP_epoch_170.pth. 2022/09/13 06:08:13 - mmengine - INFO - Epoch(train) [171][50/586] lr: 5.000000e-05 eta: 2:04:21 time: 0.348965 data_time: 0.026990 memory: 7489 loss_kpt: 0.000521 acc_pose: 0.833263 loss: 0.000521 2022/09/13 06:08:30 - mmengine - INFO - Epoch(train) [171][100/586] lr: 5.000000e-05 eta: 2:04:05 time: 0.335896 data_time: 0.022377 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.874861 loss: 0.000496 2022/09/13 06:08:47 - mmengine - INFO - Epoch(train) [171][150/586] lr: 5.000000e-05 eta: 2:03:49 time: 0.335424 data_time: 0.022151 memory: 7489 loss_kpt: 0.000520 acc_pose: 0.858514 loss: 0.000520 2022/09/13 06:09:03 - mmengine - INFO - Epoch(train) [171][200/586] lr: 5.000000e-05 eta: 2:03:34 time: 0.337094 data_time: 0.022719 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.814592 loss: 0.000501 2022/09/13 06:09:20 - mmengine - INFO - Epoch(train) [171][250/586] lr: 5.000000e-05 eta: 2:03:18 time: 0.337498 data_time: 0.024206 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.850887 loss: 0.000514 2022/09/13 06:09:37 - mmengine - INFO - Epoch(train) [171][300/586] lr: 5.000000e-05 eta: 2:03:02 time: 0.331111 data_time: 0.023406 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.825061 loss: 0.000486 2022/09/13 06:09:54 - mmengine - INFO - Epoch(train) [171][350/586] lr: 5.000000e-05 eta: 2:02:46 time: 0.340243 data_time: 0.022600 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.873420 loss: 0.000496 2022/09/13 06:10:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:10:11 - mmengine - INFO - Epoch(train) [171][400/586] lr: 5.000000e-05 eta: 2:02:31 time: 0.335940 data_time: 0.022322 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.859446 loss: 0.000511 2022/09/13 06:10:28 - mmengine - INFO - Epoch(train) [171][450/586] lr: 5.000000e-05 eta: 2:02:15 time: 0.339206 data_time: 0.023049 memory: 7489 loss_kpt: 0.000507 acc_pose: 0.879008 loss: 0.000507 2022/09/13 06:10:45 - mmengine - INFO - Epoch(train) [171][500/586] lr: 5.000000e-05 eta: 2:01:59 time: 0.340210 data_time: 0.022463 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.812861 loss: 0.000508 2022/09/13 06:11:01 - mmengine - INFO - Epoch(train) [171][550/586] lr: 5.000000e-05 eta: 2:01:43 time: 0.333182 data_time: 0.026792 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.872992 loss: 0.000500 2022/09/13 06:11:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:11:14 - mmengine - INFO - Saving checkpoint at 171 epochs 2022/09/13 06:11:38 - mmengine - INFO - Epoch(train) [172][50/586] lr: 5.000000e-05 eta: 2:01:14 time: 0.350039 data_time: 0.030427 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.858578 loss: 0.000508 2022/09/13 06:11:55 - mmengine - INFO - Epoch(train) [172][100/586] lr: 5.000000e-05 eta: 2:00:58 time: 0.335884 data_time: 0.024947 memory: 7489 loss_kpt: 0.000518 acc_pose: 0.839369 loss: 0.000518 2022/09/13 06:12:12 - mmengine - INFO - Epoch(train) [172][150/586] lr: 5.000000e-05 eta: 2:00:42 time: 0.339992 data_time: 0.022088 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.896143 loss: 0.000491 2022/09/13 06:12:29 - mmengine - INFO - Epoch(train) [172][200/586] lr: 5.000000e-05 eta: 2:00:26 time: 0.334834 data_time: 0.026132 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.835873 loss: 0.000512 2022/09/13 06:12:45 - mmengine - INFO - Epoch(train) [172][250/586] lr: 5.000000e-05 eta: 2:00:11 time: 0.335258 data_time: 0.022122 memory: 7489 loss_kpt: 0.000510 acc_pose: 0.859278 loss: 0.000510 2022/09/13 06:13:02 - mmengine - INFO - Epoch(train) [172][300/586] lr: 5.000000e-05 eta: 1:59:55 time: 0.335609 data_time: 0.026638 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.851626 loss: 0.000489 2022/09/13 06:13:19 - mmengine - INFO - Epoch(train) [172][350/586] lr: 5.000000e-05 eta: 1:59:39 time: 0.338427 data_time: 0.022038 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.850816 loss: 0.000488 2022/09/13 06:13:36 - mmengine - INFO - Epoch(train) [172][400/586] lr: 5.000000e-05 eta: 1:59:23 time: 0.338446 data_time: 0.022791 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.875049 loss: 0.000506 2022/09/13 06:13:52 - mmengine - INFO - Epoch(train) [172][450/586] lr: 5.000000e-05 eta: 1:59:08 time: 0.328605 data_time: 0.023300 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.865702 loss: 0.000504 2022/09/13 06:14:10 - mmengine - INFO - Epoch(train) [172][500/586] lr: 5.000000e-05 eta: 1:58:52 time: 0.348721 data_time: 0.022103 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.848134 loss: 0.000492 2022/09/13 06:14:27 - mmengine - INFO - Epoch(train) [172][550/586] lr: 5.000000e-05 eta: 1:58:36 time: 0.333119 data_time: 0.022680 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.875815 loss: 0.000497 2022/09/13 06:14:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:14:38 - mmengine - INFO - Saving checkpoint at 172 epochs 2022/09/13 06:15:02 - mmengine - INFO - Epoch(train) [173][50/586] lr: 5.000000e-05 eta: 1:58:07 time: 0.347313 data_time: 0.028395 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.851705 loss: 0.000495 2022/09/13 06:15:19 - mmengine - INFO - Epoch(train) [173][100/586] lr: 5.000000e-05 eta: 1:57:51 time: 0.336486 data_time: 0.022618 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.893146 loss: 0.000508 2022/09/13 06:15:36 - mmengine - INFO - Epoch(train) [173][150/586] lr: 5.000000e-05 eta: 1:57:35 time: 0.332988 data_time: 0.023073 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.903353 loss: 0.000487 2022/09/13 06:15:53 - mmengine - INFO - Epoch(train) [173][200/586] lr: 5.000000e-05 eta: 1:57:19 time: 0.334083 data_time: 0.022367 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.904326 loss: 0.000502 2022/09/13 06:15:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:16:10 - mmengine - INFO - Epoch(train) [173][250/586] lr: 5.000000e-05 eta: 1:57:04 time: 0.345763 data_time: 0.021842 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.879954 loss: 0.000483 2022/09/13 06:16:26 - mmengine - INFO - Epoch(train) [173][300/586] lr: 5.000000e-05 eta: 1:56:48 time: 0.329611 data_time: 0.022816 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.823567 loss: 0.000509 2022/09/13 06:16:43 - mmengine - INFO - Epoch(train) [173][350/586] lr: 5.000000e-05 eta: 1:56:32 time: 0.340707 data_time: 0.022835 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.822487 loss: 0.000490 2022/09/13 06:17:00 - mmengine - INFO - Epoch(train) [173][400/586] lr: 5.000000e-05 eta: 1:56:16 time: 0.338009 data_time: 0.025128 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.830395 loss: 0.000475 2022/09/13 06:17:17 - mmengine - INFO - Epoch(train) [173][450/586] lr: 5.000000e-05 eta: 1:56:00 time: 0.336661 data_time: 0.022958 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.854741 loss: 0.000487 2022/09/13 06:17:34 - mmengine - INFO - Epoch(train) [173][500/586] lr: 5.000000e-05 eta: 1:55:45 time: 0.339554 data_time: 0.022623 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.844956 loss: 0.000501 2022/09/13 06:17:51 - mmengine - INFO - Epoch(train) [173][550/586] lr: 5.000000e-05 eta: 1:55:29 time: 0.340475 data_time: 0.023015 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.882612 loss: 0.000496 2022/09/13 06:18:03 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:18:03 - mmengine - INFO - Saving checkpoint at 173 epochs 2022/09/13 06:18:27 - mmengine - INFO - Epoch(train) [174][50/586] lr: 5.000000e-05 eta: 1:54:59 time: 0.344203 data_time: 0.028376 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.876108 loss: 0.000495 2022/09/13 06:18:44 - mmengine - INFO - Epoch(train) [174][100/586] lr: 5.000000e-05 eta: 1:54:44 time: 0.334440 data_time: 0.022326 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.869612 loss: 0.000495 2022/09/13 06:19:01 - mmengine - INFO - Epoch(train) [174][150/586] lr: 5.000000e-05 eta: 1:54:28 time: 0.330434 data_time: 0.022685 memory: 7489 loss_kpt: 0.000502 acc_pose: 0.851451 loss: 0.000502 2022/09/13 06:19:17 - mmengine - INFO - Epoch(train) [174][200/586] lr: 5.000000e-05 eta: 1:54:12 time: 0.335052 data_time: 0.021805 memory: 7489 loss_kpt: 0.000509 acc_pose: 0.877475 loss: 0.000509 2022/09/13 06:19:34 - mmengine - INFO - Epoch(train) [174][250/586] lr: 5.000000e-05 eta: 1:53:56 time: 0.337648 data_time: 0.021997 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.812274 loss: 0.000491 2022/09/13 06:19:51 - mmengine - INFO - Epoch(train) [174][300/586] lr: 5.000000e-05 eta: 1:53:40 time: 0.330028 data_time: 0.023157 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.892195 loss: 0.000487 2022/09/13 06:20:08 - mmengine - INFO - Epoch(train) [174][350/586] lr: 5.000000e-05 eta: 1:53:25 time: 0.338727 data_time: 0.027848 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.910742 loss: 0.000485 2022/09/13 06:20:25 - mmengine - INFO - Epoch(train) [174][400/586] lr: 5.000000e-05 eta: 1:53:09 time: 0.337821 data_time: 0.023064 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.854010 loss: 0.000493 2022/09/13 06:20:41 - mmengine - INFO - Epoch(train) [174][450/586] lr: 5.000000e-05 eta: 1:52:53 time: 0.332916 data_time: 0.023177 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.906671 loss: 0.000504 2022/09/13 06:20:58 - mmengine - INFO - Epoch(train) [174][500/586] lr: 5.000000e-05 eta: 1:52:37 time: 0.338077 data_time: 0.022953 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.861929 loss: 0.000480 2022/09/13 06:21:16 - mmengine - INFO - Epoch(train) [174][550/586] lr: 5.000000e-05 eta: 1:52:22 time: 0.345282 data_time: 0.021945 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.851099 loss: 0.000504 2022/09/13 06:21:27 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:21:27 - mmengine - INFO - Saving checkpoint at 174 epochs 2022/09/13 06:21:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:21:52 - mmengine - INFO - Epoch(train) [175][50/586] lr: 5.000000e-05 eta: 1:51:52 time: 0.346488 data_time: 0.027382 memory: 7489 loss_kpt: 0.000506 acc_pose: 0.855475 loss: 0.000506 2022/09/13 06:22:09 - mmengine - INFO - Epoch(train) [175][100/586] lr: 5.000000e-05 eta: 1:51:36 time: 0.337631 data_time: 0.026083 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.887297 loss: 0.000483 2022/09/13 06:22:25 - mmengine - INFO - Epoch(train) [175][150/586] lr: 5.000000e-05 eta: 1:51:21 time: 0.333876 data_time: 0.023177 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.854149 loss: 0.000485 2022/09/13 06:22:42 - mmengine - INFO - Epoch(train) [175][200/586] lr: 5.000000e-05 eta: 1:51:05 time: 0.334251 data_time: 0.022457 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.904138 loss: 0.000489 2022/09/13 06:22:59 - mmengine - INFO - Epoch(train) [175][250/586] lr: 5.000000e-05 eta: 1:50:49 time: 0.342921 data_time: 0.022543 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.838506 loss: 0.000487 2022/09/13 06:23:16 - mmengine - INFO - Epoch(train) [175][300/586] lr: 5.000000e-05 eta: 1:50:33 time: 0.333889 data_time: 0.021768 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.906438 loss: 0.000480 2022/09/13 06:23:33 - mmengine - INFO - Epoch(train) [175][350/586] lr: 5.000000e-05 eta: 1:50:18 time: 0.334973 data_time: 0.022799 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.891445 loss: 0.000482 2022/09/13 06:23:50 - mmengine - INFO - Epoch(train) [175][400/586] lr: 5.000000e-05 eta: 1:50:02 time: 0.340092 data_time: 0.022008 memory: 7489 loss_kpt: 0.000508 acc_pose: 0.837849 loss: 0.000508 2022/09/13 06:24:06 - mmengine - INFO - Epoch(train) [175][450/586] lr: 5.000000e-05 eta: 1:49:46 time: 0.331324 data_time: 0.021964 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.884977 loss: 0.000470 2022/09/13 06:24:23 - mmengine - INFO - Epoch(train) [175][500/586] lr: 5.000000e-05 eta: 1:49:30 time: 0.332475 data_time: 0.023485 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.841677 loss: 0.000493 2022/09/13 06:24:40 - mmengine - INFO - Epoch(train) [175][550/586] lr: 5.000000e-05 eta: 1:49:14 time: 0.337740 data_time: 0.021982 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.859491 loss: 0.000498 2022/09/13 06:24:52 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:24:52 - mmengine - INFO - Saving checkpoint at 175 epochs 2022/09/13 06:25:17 - mmengine - INFO - Epoch(train) [176][50/586] lr: 5.000000e-05 eta: 1:48:45 time: 0.345181 data_time: 0.034363 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.899953 loss: 0.000497 2022/09/13 06:25:34 - mmengine - INFO - Epoch(train) [176][100/586] lr: 5.000000e-05 eta: 1:48:29 time: 0.340394 data_time: 0.022518 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.840943 loss: 0.000489 2022/09/13 06:25:50 - mmengine - INFO - Epoch(train) [176][150/586] lr: 5.000000e-05 eta: 1:48:13 time: 0.333779 data_time: 0.023068 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.850337 loss: 0.000483 2022/09/13 06:26:07 - mmengine - INFO - Epoch(train) [176][200/586] lr: 5.000000e-05 eta: 1:47:58 time: 0.336063 data_time: 0.022026 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.881175 loss: 0.000503 2022/09/13 06:26:24 - mmengine - INFO - Epoch(train) [176][250/586] lr: 5.000000e-05 eta: 1:47:42 time: 0.339117 data_time: 0.022744 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.874878 loss: 0.000504 2022/09/13 06:26:41 - mmengine - INFO - Epoch(train) [176][300/586] lr: 5.000000e-05 eta: 1:47:26 time: 0.335799 data_time: 0.023239 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.848064 loss: 0.000491 2022/09/13 06:26:58 - mmengine - INFO - Epoch(train) [176][350/586] lr: 5.000000e-05 eta: 1:47:10 time: 0.335988 data_time: 0.026888 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.911875 loss: 0.000500 2022/09/13 06:27:15 - mmengine - INFO - Epoch(train) [176][400/586] lr: 5.000000e-05 eta: 1:46:55 time: 0.339323 data_time: 0.024030 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.879088 loss: 0.000493 2022/09/13 06:27:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:27:31 - mmengine - INFO - Epoch(train) [176][450/586] lr: 5.000000e-05 eta: 1:46:39 time: 0.332886 data_time: 0.021878 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.884234 loss: 0.000482 2022/09/13 06:27:48 - mmengine - INFO - Epoch(train) [176][500/586] lr: 5.000000e-05 eta: 1:46:23 time: 0.330153 data_time: 0.022503 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.845949 loss: 0.000491 2022/09/13 06:28:05 - mmengine - INFO - Epoch(train) [176][550/586] lr: 5.000000e-05 eta: 1:46:07 time: 0.339092 data_time: 0.021830 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.848273 loss: 0.000484 2022/09/13 06:28:17 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:28:17 - mmengine - INFO - Saving checkpoint at 176 epochs 2022/09/13 06:28:41 - mmengine - INFO - Epoch(train) [177][50/586] lr: 5.000000e-05 eta: 1:45:38 time: 0.335814 data_time: 0.029116 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.900559 loss: 0.000482 2022/09/13 06:28:58 - mmengine - INFO - Epoch(train) [177][100/586] lr: 5.000000e-05 eta: 1:45:22 time: 0.343133 data_time: 0.022533 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.885110 loss: 0.000498 2022/09/13 06:29:14 - mmengine - INFO - Epoch(train) [177][150/586] lr: 5.000000e-05 eta: 1:45:06 time: 0.334812 data_time: 0.025834 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.874497 loss: 0.000499 2022/09/13 06:29:31 - mmengine - INFO - Epoch(train) [177][200/586] lr: 5.000000e-05 eta: 1:44:50 time: 0.335259 data_time: 0.023012 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.833356 loss: 0.000497 2022/09/13 06:29:48 - mmengine - INFO - Epoch(train) [177][250/586] lr: 5.000000e-05 eta: 1:44:35 time: 0.343578 data_time: 0.025646 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.871374 loss: 0.000478 2022/09/13 06:30:05 - mmengine - INFO - Epoch(train) [177][300/586] lr: 5.000000e-05 eta: 1:44:19 time: 0.327483 data_time: 0.022096 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.903690 loss: 0.000484 2022/09/13 06:30:22 - mmengine - INFO - Epoch(train) [177][350/586] lr: 5.000000e-05 eta: 1:44:03 time: 0.339882 data_time: 0.023219 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.875077 loss: 0.000497 2022/09/13 06:30:39 - mmengine - INFO - Epoch(train) [177][400/586] lr: 5.000000e-05 eta: 1:43:47 time: 0.348566 data_time: 0.024819 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.835720 loss: 0.000475 2022/09/13 06:30:56 - mmengine - INFO - Epoch(train) [177][450/586] lr: 5.000000e-05 eta: 1:43:32 time: 0.332378 data_time: 0.022998 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.853534 loss: 0.000482 2022/09/13 06:31:12 - mmengine - INFO - Epoch(train) [177][500/586] lr: 5.000000e-05 eta: 1:43:16 time: 0.329545 data_time: 0.021740 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.888959 loss: 0.000486 2022/09/13 06:31:30 - mmengine - INFO - Epoch(train) [177][550/586] lr: 5.000000e-05 eta: 1:43:00 time: 0.344261 data_time: 0.022390 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.846133 loss: 0.000489 2022/09/13 06:31:42 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:31:42 - mmengine - INFO - Saving checkpoint at 177 epochs 2022/09/13 06:32:06 - mmengine - INFO - Epoch(train) [178][50/586] lr: 5.000000e-05 eta: 1:42:31 time: 0.350153 data_time: 0.032194 memory: 7489 loss_kpt: 0.000512 acc_pose: 0.867401 loss: 0.000512 2022/09/13 06:32:23 - mmengine - INFO - Epoch(train) [178][100/586] lr: 5.000000e-05 eta: 1:42:15 time: 0.337644 data_time: 0.022578 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.883483 loss: 0.000489 2022/09/13 06:32:40 - mmengine - INFO - Epoch(train) [178][150/586] lr: 5.000000e-05 eta: 1:41:59 time: 0.339546 data_time: 0.023002 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.850256 loss: 0.000483 2022/09/13 06:32:57 - mmengine - INFO - Epoch(train) [178][200/586] lr: 5.000000e-05 eta: 1:41:43 time: 0.335223 data_time: 0.026630 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.893227 loss: 0.000482 2022/09/13 06:33:14 - mmengine - INFO - Epoch(train) [178][250/586] lr: 5.000000e-05 eta: 1:41:28 time: 0.340786 data_time: 0.022591 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.861298 loss: 0.000493 2022/09/13 06:33:23 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:33:31 - mmengine - INFO - Epoch(train) [178][300/586] lr: 5.000000e-05 eta: 1:41:12 time: 0.333301 data_time: 0.023248 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.867049 loss: 0.000488 2022/09/13 06:33:47 - mmengine - INFO - Epoch(train) [178][350/586] lr: 5.000000e-05 eta: 1:40:56 time: 0.336931 data_time: 0.025675 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.865036 loss: 0.000501 2022/09/13 06:34:05 - mmengine - INFO - Epoch(train) [178][400/586] lr: 5.000000e-05 eta: 1:40:40 time: 0.342831 data_time: 0.023790 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.914551 loss: 0.000495 2022/09/13 06:34:21 - mmengine - INFO - Epoch(train) [178][450/586] lr: 5.000000e-05 eta: 1:40:24 time: 0.334166 data_time: 0.021998 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.803241 loss: 0.000484 2022/09/13 06:34:38 - mmengine - INFO - Epoch(train) [178][500/586] lr: 5.000000e-05 eta: 1:40:09 time: 0.335514 data_time: 0.027077 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.922950 loss: 0.000493 2022/09/13 06:34:55 - mmengine - INFO - Epoch(train) [178][550/586] lr: 5.000000e-05 eta: 1:39:53 time: 0.337814 data_time: 0.021821 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.843853 loss: 0.000487 2022/09/13 06:35:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:35:07 - mmengine - INFO - Saving checkpoint at 178 epochs 2022/09/13 06:35:31 - mmengine - INFO - Epoch(train) [179][50/586] lr: 5.000000e-05 eta: 1:39:24 time: 0.341914 data_time: 0.026327 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.875545 loss: 0.000492 2022/09/13 06:35:48 - mmengine - INFO - Epoch(train) [179][100/586] lr: 5.000000e-05 eta: 1:39:08 time: 0.338313 data_time: 0.023181 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.821043 loss: 0.000503 2022/09/13 06:36:05 - mmengine - INFO - Epoch(train) [179][150/586] lr: 5.000000e-05 eta: 1:38:52 time: 0.336955 data_time: 0.022484 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.790077 loss: 0.000495 2022/09/13 06:36:22 - mmengine - INFO - Epoch(train) [179][200/586] lr: 5.000000e-05 eta: 1:38:36 time: 0.336349 data_time: 0.022511 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.887763 loss: 0.000483 2022/09/13 06:36:39 - mmengine - INFO - Epoch(train) [179][250/586] lr: 5.000000e-05 eta: 1:38:20 time: 0.335586 data_time: 0.022431 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.846199 loss: 0.000483 2022/09/13 06:36:56 - mmengine - INFO - Epoch(train) [179][300/586] lr: 5.000000e-05 eta: 1:38:05 time: 0.341230 data_time: 0.027998 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.810177 loss: 0.000489 2022/09/13 06:37:12 - mmengine - INFO - Epoch(train) [179][350/586] lr: 5.000000e-05 eta: 1:37:49 time: 0.335342 data_time: 0.021679 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.917904 loss: 0.000499 2022/09/13 06:37:29 - mmengine - INFO - Epoch(train) [179][400/586] lr: 5.000000e-05 eta: 1:37:33 time: 0.333019 data_time: 0.022058 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.886780 loss: 0.000497 2022/09/13 06:37:46 - mmengine - INFO - Epoch(train) [179][450/586] lr: 5.000000e-05 eta: 1:37:17 time: 0.338289 data_time: 0.022491 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.828500 loss: 0.000495 2022/09/13 06:38:03 - mmengine - INFO - Epoch(train) [179][500/586] lr: 5.000000e-05 eta: 1:37:02 time: 0.337327 data_time: 0.023091 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.855540 loss: 0.000488 2022/09/13 06:38:20 - mmengine - INFO - Epoch(train) [179][550/586] lr: 5.000000e-05 eta: 1:36:46 time: 0.332022 data_time: 0.022970 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.860612 loss: 0.000473 2022/09/13 06:38:32 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:38:32 - mmengine - INFO - Saving checkpoint at 179 epochs 2022/09/13 06:38:55 - mmengine - INFO - Epoch(train) [180][50/586] lr: 5.000000e-05 eta: 1:36:16 time: 0.342337 data_time: 0.030222 memory: 7489 loss_kpt: 0.000504 acc_pose: 0.902565 loss: 0.000504 2022/09/13 06:39:13 - mmengine - INFO - Epoch(train) [180][100/586] lr: 5.000000e-05 eta: 1:36:01 time: 0.346613 data_time: 0.030284 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.873713 loss: 0.000472 2022/09/13 06:39:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:39:30 - mmengine - INFO - Epoch(train) [180][150/586] lr: 5.000000e-05 eta: 1:35:45 time: 0.342001 data_time: 0.025492 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.878010 loss: 0.000481 2022/09/13 06:39:47 - mmengine - INFO - Epoch(train) [180][200/586] lr: 5.000000e-05 eta: 1:35:29 time: 0.335845 data_time: 0.024254 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.869393 loss: 0.000496 2022/09/13 06:40:04 - mmengine - INFO - Epoch(train) [180][250/586] lr: 5.000000e-05 eta: 1:35:13 time: 0.338470 data_time: 0.022342 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.878355 loss: 0.000493 2022/09/13 06:40:20 - mmengine - INFO - Epoch(train) [180][300/586] lr: 5.000000e-05 eta: 1:34:58 time: 0.333692 data_time: 0.023032 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.871130 loss: 0.000466 2022/09/13 06:40:37 - mmengine - INFO - Epoch(train) [180][350/586] lr: 5.000000e-05 eta: 1:34:42 time: 0.335494 data_time: 0.025881 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.848198 loss: 0.000491 2022/09/13 06:40:54 - mmengine - INFO - Epoch(train) [180][400/586] lr: 5.000000e-05 eta: 1:34:26 time: 0.331789 data_time: 0.023029 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.891014 loss: 0.000482 2022/09/13 06:41:11 - mmengine - INFO - Epoch(train) [180][450/586] lr: 5.000000e-05 eta: 1:34:10 time: 0.335129 data_time: 0.022261 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.830501 loss: 0.000494 2022/09/13 06:41:27 - mmengine - INFO - Epoch(train) [180][500/586] lr: 5.000000e-05 eta: 1:33:54 time: 0.339394 data_time: 0.022679 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.871003 loss: 0.000490 2022/09/13 06:41:45 - mmengine - INFO - Epoch(train) [180][550/586] lr: 5.000000e-05 eta: 1:33:39 time: 0.340230 data_time: 0.021954 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.848294 loss: 0.000481 2022/09/13 06:41:57 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:41:57 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/09/13 06:42:13 - mmengine - INFO - Epoch(val) [180][50/407] eta: 0:01:06 time: 0.186650 data_time: 0.014957 memory: 7489 2022/09/13 06:42:22 - mmengine - INFO - Epoch(val) [180][100/407] eta: 0:00:55 time: 0.180585 data_time: 0.007869 memory: 1657 2022/09/13 06:42:31 - mmengine - INFO - Epoch(val) [180][150/407] eta: 0:00:45 time: 0.177749 data_time: 0.007873 memory: 1657 2022/09/13 06:42:40 - mmengine - INFO - Epoch(val) [180][200/407] eta: 0:00:36 time: 0.178280 data_time: 0.007695 memory: 1657 2022/09/13 06:42:49 - mmengine - INFO - Epoch(val) [180][250/407] eta: 0:00:27 time: 0.177947 data_time: 0.007950 memory: 1657 2022/09/13 06:42:58 - mmengine - INFO - Epoch(val) [180][300/407] eta: 0:00:19 time: 0.182570 data_time: 0.011092 memory: 1657 2022/09/13 06:43:07 - mmengine - INFO - Epoch(val) [180][350/407] eta: 0:00:10 time: 0.177975 data_time: 0.007199 memory: 1657 2022/09/13 06:43:16 - mmengine - INFO - Epoch(val) [180][400/407] eta: 0:00:01 time: 0.174885 data_time: 0.006819 memory: 1657 2022/09/13 06:43:51 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 06:44:04 - mmengine - INFO - Epoch(val) [180][407/407] coco/AP: 0.766776 coco/AP .5: 0.907264 coco/AP .75: 0.831588 coco/AP (M): 0.731124 coco/AP (L): 0.835744 coco/AR: 0.816121 coco/AR .5: 0.944427 coco/AR .75: 0.874213 coco/AR (M): 0.774488 coco/AR (L): 0.877592 2022/09/13 06:44:04 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_170.pth is removed 2022/09/13 06:44:08 - mmengine - INFO - The best checkpoint with 0.7668 coco/AP at 180 epoch is saved to best_coco/AP_epoch_180.pth. 2022/09/13 06:44:25 - mmengine - INFO - Epoch(train) [181][50/586] lr: 5.000000e-05 eta: 1:33:09 time: 0.337124 data_time: 0.026294 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.877702 loss: 0.000480 2022/09/13 06:44:42 - mmengine - INFO - Epoch(train) [181][100/586] lr: 5.000000e-05 eta: 1:32:54 time: 0.341265 data_time: 0.022578 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.897337 loss: 0.000482 2022/09/13 06:44:59 - mmengine - INFO - Epoch(train) [181][150/586] lr: 5.000000e-05 eta: 1:32:38 time: 0.335047 data_time: 0.022405 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.863108 loss: 0.000480 2022/09/13 06:45:16 - mmengine - INFO - Epoch(train) [181][200/586] lr: 5.000000e-05 eta: 1:32:22 time: 0.333060 data_time: 0.022079 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.857738 loss: 0.000476 2022/09/13 06:45:32 - mmengine - INFO - Epoch(train) [181][250/586] lr: 5.000000e-05 eta: 1:32:06 time: 0.336615 data_time: 0.025531 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.881782 loss: 0.000481 2022/09/13 06:45:49 - mmengine - INFO - Epoch(train) [181][300/586] lr: 5.000000e-05 eta: 1:31:50 time: 0.331808 data_time: 0.022114 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.866303 loss: 0.000500 2022/09/13 06:46:06 - mmengine - INFO - Epoch(train) [181][350/586] lr: 5.000000e-05 eta: 1:31:35 time: 0.333796 data_time: 0.022669 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.905718 loss: 0.000483 2022/09/13 06:46:23 - mmengine - INFO - Epoch(train) [181][400/586] lr: 5.000000e-05 eta: 1:31:19 time: 0.341723 data_time: 0.021859 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.890375 loss: 0.000490 2022/09/13 06:46:40 - mmengine - INFO - Epoch(train) [181][450/586] lr: 5.000000e-05 eta: 1:31:03 time: 0.335060 data_time: 0.022960 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.873085 loss: 0.000483 2022/09/13 06:46:57 - mmengine - INFO - Epoch(train) [181][500/586] lr: 5.000000e-05 eta: 1:30:47 time: 0.345383 data_time: 0.023343 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.889818 loss: 0.000464 2022/09/13 06:47:04 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:47:14 - mmengine - INFO - Epoch(train) [181][550/586] lr: 5.000000e-05 eta: 1:30:31 time: 0.343542 data_time: 0.023303 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.861374 loss: 0.000482 2022/09/13 06:47:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:47:26 - mmengine - INFO - Saving checkpoint at 181 epochs 2022/09/13 06:47:50 - mmengine - INFO - Epoch(train) [182][50/586] lr: 5.000000e-05 eta: 1:30:02 time: 0.349153 data_time: 0.034117 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.883605 loss: 0.000494 2022/09/13 06:48:07 - mmengine - INFO - Epoch(train) [182][100/586] lr: 5.000000e-05 eta: 1:29:47 time: 0.339042 data_time: 0.022638 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.814649 loss: 0.000478 2022/09/13 06:48:24 - mmengine - INFO - Epoch(train) [182][150/586] lr: 5.000000e-05 eta: 1:29:31 time: 0.331418 data_time: 0.022506 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.869535 loss: 0.000501 2022/09/13 06:48:41 - mmengine - INFO - Epoch(train) [182][200/586] lr: 5.000000e-05 eta: 1:29:15 time: 0.336465 data_time: 0.026645 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.881508 loss: 0.000490 2022/09/13 06:48:58 - mmengine - INFO - Epoch(train) [182][250/586] lr: 5.000000e-05 eta: 1:28:59 time: 0.342700 data_time: 0.022959 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.887088 loss: 0.000480 2022/09/13 06:49:14 - mmengine - INFO - Epoch(train) [182][300/586] lr: 5.000000e-05 eta: 1:28:43 time: 0.330490 data_time: 0.021893 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.887631 loss: 0.000489 2022/09/13 06:49:31 - mmengine - INFO - Epoch(train) [182][350/586] lr: 5.000000e-05 eta: 1:28:28 time: 0.333908 data_time: 0.023333 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.864559 loss: 0.000476 2022/09/13 06:49:48 - mmengine - INFO - Epoch(train) [182][400/586] lr: 5.000000e-05 eta: 1:28:12 time: 0.341212 data_time: 0.025719 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.899470 loss: 0.000482 2022/09/13 06:50:05 - mmengine - INFO - Epoch(train) [182][450/586] lr: 5.000000e-05 eta: 1:27:56 time: 0.333755 data_time: 0.023131 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.890990 loss: 0.000496 2022/09/13 06:50:22 - mmengine - INFO - Epoch(train) [182][500/586] lr: 5.000000e-05 eta: 1:27:40 time: 0.341583 data_time: 0.023626 memory: 7489 loss_kpt: 0.000498 acc_pose: 0.835717 loss: 0.000498 2022/09/13 06:50:39 - mmengine - INFO - Epoch(train) [182][550/586] lr: 5.000000e-05 eta: 1:27:24 time: 0.340438 data_time: 0.021959 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.849965 loss: 0.000467 2022/09/13 06:50:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:50:51 - mmengine - INFO - Saving checkpoint at 182 epochs 2022/09/13 06:51:15 - mmengine - INFO - Epoch(train) [183][50/586] lr: 5.000000e-05 eta: 1:26:55 time: 0.344277 data_time: 0.036866 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.924096 loss: 0.000480 2022/09/13 06:51:32 - mmengine - INFO - Epoch(train) [183][100/586] lr: 5.000000e-05 eta: 1:26:40 time: 0.341679 data_time: 0.023404 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.908079 loss: 0.000499 2022/09/13 06:51:49 - mmengine - INFO - Epoch(train) [183][150/586] lr: 5.000000e-05 eta: 1:26:24 time: 0.332948 data_time: 0.022335 memory: 7489 loss_kpt: 0.000457 acc_pose: 0.848246 loss: 0.000457 2022/09/13 06:52:06 - mmengine - INFO - Epoch(train) [183][200/586] lr: 5.000000e-05 eta: 1:26:08 time: 0.333035 data_time: 0.022928 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.905346 loss: 0.000475 2022/09/13 06:52:23 - mmengine - INFO - Epoch(train) [183][250/586] lr: 5.000000e-05 eta: 1:25:52 time: 0.334612 data_time: 0.022996 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.846394 loss: 0.000496 2022/09/13 06:52:39 - mmengine - INFO - Epoch(train) [183][300/586] lr: 5.000000e-05 eta: 1:25:36 time: 0.335064 data_time: 0.022232 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.843142 loss: 0.000485 2022/09/13 06:52:55 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:52:56 - mmengine - INFO - Epoch(train) [183][350/586] lr: 5.000000e-05 eta: 1:25:21 time: 0.333788 data_time: 0.022115 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.888048 loss: 0.000499 2022/09/13 06:53:13 - mmengine - INFO - Epoch(train) [183][400/586] lr: 5.000000e-05 eta: 1:25:05 time: 0.337130 data_time: 0.022724 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.927593 loss: 0.000475 2022/09/13 06:53:29 - mmengine - INFO - Epoch(train) [183][450/586] lr: 5.000000e-05 eta: 1:24:49 time: 0.330335 data_time: 0.022382 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.814599 loss: 0.000478 2022/09/13 06:53:46 - mmengine - INFO - Epoch(train) [183][500/586] lr: 5.000000e-05 eta: 1:24:33 time: 0.332933 data_time: 0.022368 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.908213 loss: 0.000476 2022/09/13 06:54:03 - mmengine - INFO - Epoch(train) [183][550/586] lr: 5.000000e-05 eta: 1:24:17 time: 0.338995 data_time: 0.022870 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.851197 loss: 0.000477 2022/09/13 06:54:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:54:15 - mmengine - INFO - Saving checkpoint at 183 epochs 2022/09/13 06:54:39 - mmengine - INFO - Epoch(train) [184][50/586] lr: 5.000000e-05 eta: 1:23:48 time: 0.342925 data_time: 0.032291 memory: 7489 loss_kpt: 0.000511 acc_pose: 0.889063 loss: 0.000511 2022/09/13 06:54:56 - mmengine - INFO - Epoch(train) [184][100/586] lr: 5.000000e-05 eta: 1:23:32 time: 0.340557 data_time: 0.026156 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.883441 loss: 0.000485 2022/09/13 06:55:13 - mmengine - INFO - Epoch(train) [184][150/586] lr: 5.000000e-05 eta: 1:23:17 time: 0.334434 data_time: 0.022655 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.876906 loss: 0.000475 2022/09/13 06:55:30 - mmengine - INFO - Epoch(train) [184][200/586] lr: 5.000000e-05 eta: 1:23:01 time: 0.338022 data_time: 0.026437 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.884209 loss: 0.000470 2022/09/13 06:55:47 - mmengine - INFO - Epoch(train) [184][250/586] lr: 5.000000e-05 eta: 1:22:45 time: 0.340007 data_time: 0.022735 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.852573 loss: 0.000477 2022/09/13 06:56:04 - mmengine - INFO - Epoch(train) [184][300/586] lr: 5.000000e-05 eta: 1:22:29 time: 0.331661 data_time: 0.022813 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.840651 loss: 0.000500 2022/09/13 06:56:20 - mmengine - INFO - Epoch(train) [184][350/586] lr: 5.000000e-05 eta: 1:22:13 time: 0.337480 data_time: 0.026205 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.857900 loss: 0.000464 2022/09/13 06:56:38 - mmengine - INFO - Epoch(train) [184][400/586] lr: 5.000000e-05 eta: 1:21:58 time: 0.343358 data_time: 0.022438 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.838054 loss: 0.000495 2022/09/13 06:56:54 - mmengine - INFO - Epoch(train) [184][450/586] lr: 5.000000e-05 eta: 1:21:42 time: 0.330944 data_time: 0.023112 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.871164 loss: 0.000480 2022/09/13 06:57:11 - mmengine - INFO - Epoch(train) [184][500/586] lr: 5.000000e-05 eta: 1:21:26 time: 0.336975 data_time: 0.023217 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.890834 loss: 0.000490 2022/09/13 06:57:28 - mmengine - INFO - Epoch(train) [184][550/586] lr: 5.000000e-05 eta: 1:21:10 time: 0.339749 data_time: 0.022354 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.801567 loss: 0.000473 2022/09/13 06:57:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:57:40 - mmengine - INFO - Saving checkpoint at 184 epochs 2022/09/13 06:58:04 - mmengine - INFO - Epoch(train) [185][50/586] lr: 5.000000e-05 eta: 1:20:41 time: 0.337417 data_time: 0.026211 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.875544 loss: 0.000469 2022/09/13 06:58:21 - mmengine - INFO - Epoch(train) [185][100/586] lr: 5.000000e-05 eta: 1:20:25 time: 0.339786 data_time: 0.022411 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.874422 loss: 0.000470 2022/09/13 06:58:38 - mmengine - INFO - Epoch(train) [185][150/586] lr: 5.000000e-05 eta: 1:20:10 time: 0.336915 data_time: 0.022807 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.837681 loss: 0.000494 2022/09/13 06:58:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 06:58:54 - mmengine - INFO - Epoch(train) [185][200/586] lr: 5.000000e-05 eta: 1:19:54 time: 0.336065 data_time: 0.022988 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.872555 loss: 0.000487 2022/09/13 06:59:11 - mmengine - INFO - Epoch(train) [185][250/586] lr: 5.000000e-05 eta: 1:19:38 time: 0.338571 data_time: 0.023229 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.872834 loss: 0.000495 2022/09/13 06:59:28 - mmengine - INFO - Epoch(train) [185][300/586] lr: 5.000000e-05 eta: 1:19:22 time: 0.340785 data_time: 0.022259 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.849050 loss: 0.000486 2022/09/13 06:59:45 - mmengine - INFO - Epoch(train) [185][350/586] lr: 5.000000e-05 eta: 1:19:06 time: 0.330969 data_time: 0.023268 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.817136 loss: 0.000473 2022/09/13 07:00:02 - mmengine - INFO - Epoch(train) [185][400/586] lr: 5.000000e-05 eta: 1:18:51 time: 0.339574 data_time: 0.022031 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.863465 loss: 0.000477 2022/09/13 07:00:19 - mmengine - INFO - Epoch(train) [185][450/586] lr: 5.000000e-05 eta: 1:18:35 time: 0.337755 data_time: 0.027364 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.844665 loss: 0.000485 2022/09/13 07:00:36 - mmengine - INFO - Epoch(train) [185][500/586] lr: 5.000000e-05 eta: 1:18:19 time: 0.331921 data_time: 0.022761 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.827177 loss: 0.000491 2022/09/13 07:00:52 - mmengine - INFO - Epoch(train) [185][550/586] lr: 5.000000e-05 eta: 1:18:03 time: 0.336863 data_time: 0.023393 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.891073 loss: 0.000485 2022/09/13 07:01:05 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:01:05 - mmengine - INFO - Saving checkpoint at 185 epochs 2022/09/13 07:01:28 - mmengine - INFO - Epoch(train) [186][50/586] lr: 5.000000e-05 eta: 1:17:34 time: 0.334686 data_time: 0.028575 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.900954 loss: 0.000480 2022/09/13 07:01:46 - mmengine - INFO - Epoch(train) [186][100/586] lr: 5.000000e-05 eta: 1:17:18 time: 0.352296 data_time: 0.022569 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.798225 loss: 0.000483 2022/09/13 07:02:02 - mmengine - INFO - Epoch(train) [186][150/586] lr: 5.000000e-05 eta: 1:17:03 time: 0.336547 data_time: 0.022356 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.874727 loss: 0.000492 2022/09/13 07:02:19 - mmengine - INFO - Epoch(train) [186][200/586] lr: 5.000000e-05 eta: 1:16:47 time: 0.331446 data_time: 0.022604 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.876985 loss: 0.000484 2022/09/13 07:02:36 - mmengine - INFO - Epoch(train) [186][250/586] lr: 5.000000e-05 eta: 1:16:31 time: 0.339077 data_time: 0.022741 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.885151 loss: 0.000476 2022/09/13 07:02:53 - mmengine - INFO - Epoch(train) [186][300/586] lr: 5.000000e-05 eta: 1:16:15 time: 0.336374 data_time: 0.021658 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.910300 loss: 0.000496 2022/09/13 07:03:10 - mmengine - INFO - Epoch(train) [186][350/586] lr: 5.000000e-05 eta: 1:15:59 time: 0.334710 data_time: 0.021986 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.930872 loss: 0.000490 2022/09/13 07:03:27 - mmengine - INFO - Epoch(train) [186][400/586] lr: 5.000000e-05 eta: 1:15:44 time: 0.342981 data_time: 0.022238 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.810893 loss: 0.000474 2022/09/13 07:03:44 - mmengine - INFO - Epoch(train) [186][450/586] lr: 5.000000e-05 eta: 1:15:28 time: 0.337133 data_time: 0.022101 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.854023 loss: 0.000480 2022/09/13 07:04:00 - mmengine - INFO - Epoch(train) [186][500/586] lr: 5.000000e-05 eta: 1:15:12 time: 0.333999 data_time: 0.022745 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.871327 loss: 0.000476 2022/09/13 07:04:17 - mmengine - INFO - Epoch(train) [186][550/586] lr: 5.000000e-05 eta: 1:14:56 time: 0.334512 data_time: 0.022997 memory: 7489 loss_kpt: 0.000499 acc_pose: 0.837981 loss: 0.000499 2022/09/13 07:04:29 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:04:29 - mmengine - INFO - Saving checkpoint at 186 epochs 2022/09/13 07:04:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:04:54 - mmengine - INFO - Epoch(train) [187][50/586] lr: 5.000000e-05 eta: 1:14:27 time: 0.343737 data_time: 0.031307 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.834036 loss: 0.000478 2022/09/13 07:05:10 - mmengine - INFO - Epoch(train) [187][100/586] lr: 5.000000e-05 eta: 1:14:11 time: 0.332801 data_time: 0.022753 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.832633 loss: 0.000472 2022/09/13 07:05:27 - mmengine - INFO - Epoch(train) [187][150/586] lr: 5.000000e-05 eta: 1:13:56 time: 0.337539 data_time: 0.022828 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.909575 loss: 0.000491 2022/09/13 07:05:44 - mmengine - INFO - Epoch(train) [187][200/586] lr: 5.000000e-05 eta: 1:13:40 time: 0.335771 data_time: 0.022260 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.879529 loss: 0.000480 2022/09/13 07:06:01 - mmengine - INFO - Epoch(train) [187][250/586] lr: 5.000000e-05 eta: 1:13:24 time: 0.336398 data_time: 0.022694 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.904913 loss: 0.000481 2022/09/13 07:06:18 - mmengine - INFO - Epoch(train) [187][300/586] lr: 5.000000e-05 eta: 1:13:08 time: 0.337810 data_time: 0.025751 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.823030 loss: 0.000472 2022/09/13 07:06:35 - mmengine - INFO - Epoch(train) [187][350/586] lr: 5.000000e-05 eta: 1:12:52 time: 0.334587 data_time: 0.021962 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.889993 loss: 0.000477 2022/09/13 07:06:51 - mmengine - INFO - Epoch(train) [187][400/586] lr: 5.000000e-05 eta: 1:12:36 time: 0.335557 data_time: 0.021946 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.905571 loss: 0.000492 2022/09/13 07:07:08 - mmengine - INFO - Epoch(train) [187][450/586] lr: 5.000000e-05 eta: 1:12:21 time: 0.339294 data_time: 0.022523 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.858762 loss: 0.000463 2022/09/13 07:07:26 - mmengine - INFO - Epoch(train) [187][500/586] lr: 5.000000e-05 eta: 1:12:05 time: 0.343512 data_time: 0.026749 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.840253 loss: 0.000476 2022/09/13 07:07:42 - mmengine - INFO - Epoch(train) [187][550/586] lr: 5.000000e-05 eta: 1:11:49 time: 0.333767 data_time: 0.022011 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.845290 loss: 0.000473 2022/09/13 07:07:54 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:07:54 - mmengine - INFO - Saving checkpoint at 187 epochs 2022/09/13 07:08:18 - mmengine - INFO - Epoch(train) [188][50/586] lr: 5.000000e-05 eta: 1:11:20 time: 0.341683 data_time: 0.031575 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.855595 loss: 0.000475 2022/09/13 07:08:36 - mmengine - INFO - Epoch(train) [188][100/586] lr: 5.000000e-05 eta: 1:11:04 time: 0.341015 data_time: 0.023709 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.901960 loss: 0.000481 2022/09/13 07:08:53 - mmengine - INFO - Epoch(train) [188][150/586] lr: 5.000000e-05 eta: 1:10:49 time: 0.345010 data_time: 0.022327 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.888045 loss: 0.000492 2022/09/13 07:09:09 - mmengine - INFO - Epoch(train) [188][200/586] lr: 5.000000e-05 eta: 1:10:33 time: 0.333878 data_time: 0.021793 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.859460 loss: 0.000480 2022/09/13 07:09:27 - mmengine - INFO - Epoch(train) [188][250/586] lr: 5.000000e-05 eta: 1:10:17 time: 0.339912 data_time: 0.022374 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.859634 loss: 0.000468 2022/09/13 07:09:43 - mmengine - INFO - Epoch(train) [188][300/586] lr: 5.000000e-05 eta: 1:10:01 time: 0.338071 data_time: 0.023306 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.923944 loss: 0.000471 2022/09/13 07:10:00 - mmengine - INFO - Epoch(train) [188][350/586] lr: 5.000000e-05 eta: 1:09:45 time: 0.340230 data_time: 0.022762 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.886513 loss: 0.000487 2022/09/13 07:10:17 - mmengine - INFO - Epoch(train) [188][400/586] lr: 5.000000e-05 eta: 1:09:30 time: 0.338297 data_time: 0.026178 memory: 7489 loss_kpt: 0.000514 acc_pose: 0.849992 loss: 0.000514 2022/09/13 07:10:24 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:10:34 - mmengine - INFO - Epoch(train) [188][450/586] lr: 5.000000e-05 eta: 1:09:14 time: 0.340449 data_time: 0.022823 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.866796 loss: 0.000461 2022/09/13 07:10:51 - mmengine - INFO - Epoch(train) [188][500/586] lr: 5.000000e-05 eta: 1:08:58 time: 0.335457 data_time: 0.022009 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.860398 loss: 0.000461 2022/09/13 07:11:08 - mmengine - INFO - Epoch(train) [188][550/586] lr: 5.000000e-05 eta: 1:08:42 time: 0.342977 data_time: 0.026225 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.880249 loss: 0.000481 2022/09/13 07:11:21 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:11:21 - mmengine - INFO - Saving checkpoint at 188 epochs 2022/09/13 07:11:45 - mmengine - INFO - Epoch(train) [189][50/586] lr: 5.000000e-05 eta: 1:08:13 time: 0.340707 data_time: 0.027839 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.795276 loss: 0.000472 2022/09/13 07:12:02 - mmengine - INFO - Epoch(train) [189][100/586] lr: 5.000000e-05 eta: 1:07:58 time: 0.340655 data_time: 0.023759 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.902740 loss: 0.000479 2022/09/13 07:12:18 - mmengine - INFO - Epoch(train) [189][150/586] lr: 5.000000e-05 eta: 1:07:42 time: 0.333290 data_time: 0.022378 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.856446 loss: 0.000486 2022/09/13 07:12:35 - mmengine - INFO - Epoch(train) [189][200/586] lr: 5.000000e-05 eta: 1:07:26 time: 0.335490 data_time: 0.022393 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.913210 loss: 0.000487 2022/09/13 07:12:52 - mmengine - INFO - Epoch(train) [189][250/586] lr: 5.000000e-05 eta: 1:07:10 time: 0.333479 data_time: 0.022789 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.859698 loss: 0.000470 2022/09/13 07:13:08 - mmengine - INFO - Epoch(train) [189][300/586] lr: 5.000000e-05 eta: 1:06:54 time: 0.331418 data_time: 0.022414 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.868937 loss: 0.000501 2022/09/13 07:13:26 - mmengine - INFO - Epoch(train) [189][350/586] lr: 5.000000e-05 eta: 1:06:38 time: 0.341463 data_time: 0.025427 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.880094 loss: 0.000474 2022/09/13 07:13:42 - mmengine - INFO - Epoch(train) [189][400/586] lr: 5.000000e-05 eta: 1:06:23 time: 0.337724 data_time: 0.023336 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.889709 loss: 0.000476 2022/09/13 07:13:59 - mmengine - INFO - Epoch(train) [189][450/586] lr: 5.000000e-05 eta: 1:06:07 time: 0.330997 data_time: 0.023605 memory: 7489 loss_kpt: 0.000462 acc_pose: 0.882712 loss: 0.000462 2022/09/13 07:14:16 - mmengine - INFO - Epoch(train) [189][500/586] lr: 5.000000e-05 eta: 1:05:51 time: 0.338774 data_time: 0.023029 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.825060 loss: 0.000489 2022/09/13 07:14:33 - mmengine - INFO - Epoch(train) [189][550/586] lr: 5.000000e-05 eta: 1:05:35 time: 0.334069 data_time: 0.022714 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.915621 loss: 0.000469 2022/09/13 07:14:45 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:14:45 - mmengine - INFO - Saving checkpoint at 189 epochs 2022/09/13 07:15:10 - mmengine - INFO - Epoch(train) [190][50/586] lr: 5.000000e-05 eta: 1:05:06 time: 0.349670 data_time: 0.031884 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.874078 loss: 0.000477 2022/09/13 07:15:27 - mmengine - INFO - Epoch(train) [190][100/586] lr: 5.000000e-05 eta: 1:04:51 time: 0.347579 data_time: 0.024098 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.860003 loss: 0.000469 2022/09/13 07:15:44 - mmengine - INFO - Epoch(train) [190][150/586] lr: 5.000000e-05 eta: 1:04:35 time: 0.335709 data_time: 0.022103 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.938448 loss: 0.000481 2022/09/13 07:16:01 - mmengine - INFO - Epoch(train) [190][200/586] lr: 5.000000e-05 eta: 1:04:19 time: 0.335628 data_time: 0.022671 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.906341 loss: 0.000480 2022/09/13 07:16:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:16:17 - mmengine - INFO - Epoch(train) [190][250/586] lr: 5.000000e-05 eta: 1:04:03 time: 0.337607 data_time: 0.027178 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.877118 loss: 0.000489 2022/09/13 07:16:34 - mmengine - INFO - Epoch(train) [190][300/586] lr: 5.000000e-05 eta: 1:03:47 time: 0.339338 data_time: 0.022697 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.935954 loss: 0.000490 2022/09/13 07:16:51 - mmengine - INFO - Epoch(train) [190][350/586] lr: 5.000000e-05 eta: 1:03:31 time: 0.334035 data_time: 0.023507 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.865682 loss: 0.000483 2022/09/13 07:17:08 - mmengine - INFO - Epoch(train) [190][400/586] lr: 5.000000e-05 eta: 1:03:16 time: 0.339326 data_time: 0.022726 memory: 7489 loss_kpt: 0.000497 acc_pose: 0.807900 loss: 0.000497 2022/09/13 07:17:25 - mmengine - INFO - Epoch(train) [190][450/586] lr: 5.000000e-05 eta: 1:03:00 time: 0.338174 data_time: 0.022482 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.814894 loss: 0.000477 2022/09/13 07:17:42 - mmengine - INFO - Epoch(train) [190][500/586] lr: 5.000000e-05 eta: 1:02:44 time: 0.339137 data_time: 0.022229 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.831428 loss: 0.000488 2022/09/13 07:17:59 - mmengine - INFO - Epoch(train) [190][550/586] lr: 5.000000e-05 eta: 1:02:28 time: 0.337138 data_time: 0.025113 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.868114 loss: 0.000476 2022/09/13 07:18:11 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:18:11 - mmengine - INFO - Saving checkpoint at 190 epochs 2022/09/13 07:18:27 - mmengine - INFO - Epoch(val) [190][50/407] eta: 0:01:07 time: 0.188240 data_time: 0.012152 memory: 7489 2022/09/13 07:18:36 - mmengine - INFO - Epoch(val) [190][100/407] eta: 0:00:54 time: 0.178566 data_time: 0.007388 memory: 1657 2022/09/13 07:18:45 - mmengine - INFO - Epoch(val) [190][150/407] eta: 0:00:45 time: 0.178335 data_time: 0.007724 memory: 1657 2022/09/13 07:18:54 - mmengine - INFO - Epoch(val) [190][200/407] eta: 0:00:36 time: 0.177840 data_time: 0.007498 memory: 1657 2022/09/13 07:19:03 - mmengine - INFO - Epoch(val) [190][250/407] eta: 0:00:28 time: 0.179519 data_time: 0.008660 memory: 1657 2022/09/13 07:19:12 - mmengine - INFO - Epoch(val) [190][300/407] eta: 0:00:19 time: 0.178165 data_time: 0.007474 memory: 1657 2022/09/13 07:19:21 - mmengine - INFO - Epoch(val) [190][350/407] eta: 0:00:10 time: 0.177913 data_time: 0.007753 memory: 1657 2022/09/13 07:19:29 - mmengine - INFO - Epoch(val) [190][400/407] eta: 0:00:01 time: 0.174390 data_time: 0.006963 memory: 1657 2022/09/13 07:20:05 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 07:20:18 - mmengine - INFO - Epoch(val) [190][407/407] coco/AP: 0.766639 coco/AP .5: 0.906435 coco/AP .75: 0.830621 coco/AP (M): 0.729397 coco/AP (L): 0.836745 coco/AR: 0.815806 coco/AR .5: 0.943482 coco/AR .75: 0.872481 coco/AR (M): 0.773368 coco/AR (L): 0.878224 2022/09/13 07:20:36 - mmengine - INFO - Epoch(train) [191][50/586] lr: 5.000000e-05 eta: 1:02:00 time: 0.351826 data_time: 0.027676 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.880790 loss: 0.000468 2022/09/13 07:20:52 - mmengine - INFO - Epoch(train) [191][100/586] lr: 5.000000e-05 eta: 1:01:44 time: 0.332334 data_time: 0.022215 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.864014 loss: 0.000470 2022/09/13 07:21:09 - mmengine - INFO - Epoch(train) [191][150/586] lr: 5.000000e-05 eta: 1:01:28 time: 0.329213 data_time: 0.022254 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.872412 loss: 0.000489 2022/09/13 07:21:26 - mmengine - INFO - Epoch(train) [191][200/586] lr: 5.000000e-05 eta: 1:01:12 time: 0.343350 data_time: 0.026044 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.856513 loss: 0.000474 2022/09/13 07:21:43 - mmengine - INFO - Epoch(train) [191][250/586] lr: 5.000000e-05 eta: 1:00:56 time: 0.335821 data_time: 0.023470 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.883689 loss: 0.000490 2022/09/13 07:21:59 - mmengine - INFO - Epoch(train) [191][300/586] lr: 5.000000e-05 eta: 1:00:40 time: 0.334554 data_time: 0.024082 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.849522 loss: 0.000483 2022/09/13 07:22:17 - mmengine - INFO - Epoch(train) [191][350/586] lr: 5.000000e-05 eta: 1:00:25 time: 0.347244 data_time: 0.022234 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.899534 loss: 0.000481 2022/09/13 07:22:33 - mmengine - INFO - Epoch(train) [191][400/586] lr: 5.000000e-05 eta: 1:00:09 time: 0.332439 data_time: 0.022052 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.923601 loss: 0.000476 2022/09/13 07:22:50 - mmengine - INFO - Epoch(train) [191][450/586] lr: 5.000000e-05 eta: 0:59:53 time: 0.331174 data_time: 0.023718 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.887688 loss: 0.000475 2022/09/13 07:23:07 - mmengine - INFO - Epoch(train) [191][500/586] lr: 5.000000e-05 eta: 0:59:37 time: 0.338632 data_time: 0.025648 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.908492 loss: 0.000483 2022/09/13 07:23:24 - mmengine - INFO - Epoch(train) [191][550/586] lr: 5.000000e-05 eta: 0:59:21 time: 0.334735 data_time: 0.022469 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.920302 loss: 0.000485 2022/09/13 07:23:36 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:23:36 - mmengine - INFO - Saving checkpoint at 191 epochs 2022/09/13 07:24:00 - mmengine - INFO - Epoch(train) [192][50/586] lr: 5.000000e-05 eta: 0:58:53 time: 0.347074 data_time: 0.030571 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.874275 loss: 0.000495 2022/09/13 07:24:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:24:18 - mmengine - INFO - Epoch(train) [192][100/586] lr: 5.000000e-05 eta: 0:58:37 time: 0.345705 data_time: 0.023692 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.890547 loss: 0.000463 2022/09/13 07:24:34 - mmengine - INFO - Epoch(train) [192][150/586] lr: 5.000000e-05 eta: 0:58:21 time: 0.335434 data_time: 0.022904 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.902066 loss: 0.000487 2022/09/13 07:24:52 - mmengine - INFO - Epoch(train) [192][200/586] lr: 5.000000e-05 eta: 0:58:05 time: 0.342314 data_time: 0.021950 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.875676 loss: 0.000471 2022/09/13 07:25:08 - mmengine - INFO - Epoch(train) [192][250/586] lr: 5.000000e-05 eta: 0:57:49 time: 0.336996 data_time: 0.023290 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.866395 loss: 0.000476 2022/09/13 07:25:25 - mmengine - INFO - Epoch(train) [192][300/586] lr: 5.000000e-05 eta: 0:57:34 time: 0.337197 data_time: 0.022702 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.935130 loss: 0.000492 2022/09/13 07:25:42 - mmengine - INFO - Epoch(train) [192][350/586] lr: 5.000000e-05 eta: 0:57:18 time: 0.334147 data_time: 0.022833 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.858346 loss: 0.000469 2022/09/13 07:25:59 - mmengine - INFO - Epoch(train) [192][400/586] lr: 5.000000e-05 eta: 0:57:02 time: 0.333286 data_time: 0.022332 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.797324 loss: 0.000479 2022/09/13 07:26:15 - mmengine - INFO - Epoch(train) [192][450/586] lr: 5.000000e-05 eta: 0:56:46 time: 0.335348 data_time: 0.025791 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.868429 loss: 0.000469 2022/09/13 07:26:32 - mmengine - INFO - Epoch(train) [192][500/586] lr: 5.000000e-05 eta: 0:56:30 time: 0.334290 data_time: 0.023880 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.936761 loss: 0.000470 2022/09/13 07:26:49 - mmengine - INFO - Epoch(train) [192][550/586] lr: 5.000000e-05 eta: 0:56:14 time: 0.334560 data_time: 0.023680 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.871652 loss: 0.000489 2022/09/13 07:27:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:27:01 - mmengine - INFO - Saving checkpoint at 192 epochs 2022/09/13 07:27:25 - mmengine - INFO - Epoch(train) [193][50/586] lr: 5.000000e-05 eta: 0:55:46 time: 0.338614 data_time: 0.027842 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.872393 loss: 0.000481 2022/09/13 07:27:42 - mmengine - INFO - Epoch(train) [193][100/586] lr: 5.000000e-05 eta: 0:55:30 time: 0.335354 data_time: 0.026334 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.853995 loss: 0.000483 2022/09/13 07:27:59 - mmengine - INFO - Epoch(train) [193][150/586] lr: 5.000000e-05 eta: 0:55:14 time: 0.335153 data_time: 0.023239 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.845665 loss: 0.000473 2022/09/13 07:28:16 - mmengine - INFO - Epoch(train) [193][200/586] lr: 5.000000e-05 eta: 0:54:58 time: 0.335430 data_time: 0.022685 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.869005 loss: 0.000490 2022/09/13 07:28:33 - mmengine - INFO - Epoch(train) [193][250/586] lr: 5.000000e-05 eta: 0:54:42 time: 0.343614 data_time: 0.022952 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.850053 loss: 0.000479 2022/09/13 07:28:49 - mmengine - INFO - Epoch(train) [193][300/586] lr: 5.000000e-05 eta: 0:54:26 time: 0.327830 data_time: 0.022388 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.935674 loss: 0.000469 2022/09/13 07:29:06 - mmengine - INFO - Epoch(train) [193][350/586] lr: 5.000000e-05 eta: 0:54:11 time: 0.340910 data_time: 0.021835 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.853273 loss: 0.000493 2022/09/13 07:29:23 - mmengine - INFO - Epoch(train) [193][400/586] lr: 5.000000e-05 eta: 0:53:55 time: 0.341197 data_time: 0.022752 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.872279 loss: 0.000481 2022/09/13 07:29:40 - mmengine - INFO - Epoch(train) [193][450/586] lr: 5.000000e-05 eta: 0:53:39 time: 0.334242 data_time: 0.022549 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.901016 loss: 0.000490 2022/09/13 07:29:53 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:29:57 - mmengine - INFO - Epoch(train) [193][500/586] lr: 5.000000e-05 eta: 0:53:23 time: 0.337560 data_time: 0.022334 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.887601 loss: 0.000479 2022/09/13 07:30:14 - mmengine - INFO - Epoch(train) [193][550/586] lr: 5.000000e-05 eta: 0:53:07 time: 0.341625 data_time: 0.026993 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.857618 loss: 0.000476 2022/09/13 07:30:26 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:30:26 - mmengine - INFO - Saving checkpoint at 193 epochs 2022/09/13 07:30:50 - mmengine - INFO - Epoch(train) [194][50/586] lr: 5.000000e-05 eta: 0:52:39 time: 0.339978 data_time: 0.029791 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.838882 loss: 0.000469 2022/09/13 07:31:07 - mmengine - INFO - Epoch(train) [194][100/586] lr: 5.000000e-05 eta: 0:52:23 time: 0.341418 data_time: 0.022890 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.899545 loss: 0.000496 2022/09/13 07:31:24 - mmengine - INFO - Epoch(train) [194][150/586] lr: 5.000000e-05 eta: 0:52:07 time: 0.337768 data_time: 0.026260 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.878720 loss: 0.000486 2022/09/13 07:31:40 - mmengine - INFO - Epoch(train) [194][200/586] lr: 5.000000e-05 eta: 0:51:51 time: 0.333498 data_time: 0.021953 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.896017 loss: 0.000466 2022/09/13 07:31:58 - mmengine - INFO - Epoch(train) [194][250/586] lr: 5.000000e-05 eta: 0:51:36 time: 0.343425 data_time: 0.022742 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.902540 loss: 0.000489 2022/09/13 07:32:15 - mmengine - INFO - Epoch(train) [194][300/586] lr: 5.000000e-05 eta: 0:51:20 time: 0.342370 data_time: 0.026829 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.866870 loss: 0.000478 2022/09/13 07:32:31 - mmengine - INFO - Epoch(train) [194][350/586] lr: 5.000000e-05 eta: 0:51:04 time: 0.330163 data_time: 0.023573 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.857020 loss: 0.000471 2022/09/13 07:32:48 - mmengine - INFO - Epoch(train) [194][400/586] lr: 5.000000e-05 eta: 0:50:48 time: 0.340362 data_time: 0.022384 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.918386 loss: 0.000477 2022/09/13 07:33:05 - mmengine - INFO - Epoch(train) [194][450/586] lr: 5.000000e-05 eta: 0:50:32 time: 0.342059 data_time: 0.025187 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.808965 loss: 0.000478 2022/09/13 07:33:22 - mmengine - INFO - Epoch(train) [194][500/586] lr: 5.000000e-05 eta: 0:50:16 time: 0.337053 data_time: 0.023874 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.894037 loss: 0.000474 2022/09/13 07:33:39 - mmengine - INFO - Epoch(train) [194][550/586] lr: 5.000000e-05 eta: 0:50:00 time: 0.331896 data_time: 0.022487 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.898652 loss: 0.000472 2022/09/13 07:33:51 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:33:51 - mmengine - INFO - Saving checkpoint at 194 epochs 2022/09/13 07:34:15 - mmengine - INFO - Epoch(train) [195][50/586] lr: 5.000000e-05 eta: 0:49:32 time: 0.340175 data_time: 0.031162 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.846911 loss: 0.000483 2022/09/13 07:34:33 - mmengine - INFO - Epoch(train) [195][100/586] lr: 5.000000e-05 eta: 0:49:16 time: 0.341781 data_time: 0.027260 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.803220 loss: 0.000482 2022/09/13 07:34:49 - mmengine - INFO - Epoch(train) [195][150/586] lr: 5.000000e-05 eta: 0:49:00 time: 0.332844 data_time: 0.023477 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.894867 loss: 0.000486 2022/09/13 07:35:06 - mmengine - INFO - Epoch(train) [195][200/586] lr: 5.000000e-05 eta: 0:48:44 time: 0.334371 data_time: 0.024284 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.869876 loss: 0.000470 2022/09/13 07:35:23 - mmengine - INFO - Epoch(train) [195][250/586] lr: 5.000000e-05 eta: 0:48:29 time: 0.341576 data_time: 0.023687 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.849074 loss: 0.000496 2022/09/13 07:35:40 - mmengine - INFO - Epoch(train) [195][300/586] lr: 5.000000e-05 eta: 0:48:13 time: 0.336211 data_time: 0.022479 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.912288 loss: 0.000484 2022/09/13 07:35:46 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:35:57 - mmengine - INFO - Epoch(train) [195][350/586] lr: 5.000000e-05 eta: 0:47:57 time: 0.334748 data_time: 0.023698 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.877142 loss: 0.000482 2022/09/13 07:36:14 - mmengine - INFO - Epoch(train) [195][400/586] lr: 5.000000e-05 eta: 0:47:41 time: 0.342051 data_time: 0.022127 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.869601 loss: 0.000488 2022/09/13 07:36:31 - mmengine - INFO - Epoch(train) [195][450/586] lr: 5.000000e-05 eta: 0:47:25 time: 0.334064 data_time: 0.022556 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.880698 loss: 0.000478 2022/09/13 07:36:47 - mmengine - INFO - Epoch(train) [195][500/586] lr: 5.000000e-05 eta: 0:47:09 time: 0.329155 data_time: 0.021603 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.839149 loss: 0.000485 2022/09/13 07:37:04 - mmengine - INFO - Epoch(train) [195][550/586] lr: 5.000000e-05 eta: 0:46:53 time: 0.344459 data_time: 0.027388 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.919784 loss: 0.000465 2022/09/13 07:37:16 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:37:16 - mmengine - INFO - Saving checkpoint at 195 epochs 2022/09/13 07:37:40 - mmengine - INFO - Epoch(train) [196][50/586] lr: 5.000000e-05 eta: 0:46:25 time: 0.344074 data_time: 0.031017 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.891673 loss: 0.000489 2022/09/13 07:37:58 - mmengine - INFO - Epoch(train) [196][100/586] lr: 5.000000e-05 eta: 0:46:09 time: 0.341834 data_time: 0.023091 memory: 7489 loss_kpt: 0.000500 acc_pose: 0.819006 loss: 0.000500 2022/09/13 07:38:15 - mmengine - INFO - Epoch(train) [196][150/586] lr: 5.000000e-05 eta: 0:45:53 time: 0.339970 data_time: 0.023742 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.895554 loss: 0.000467 2022/09/13 07:38:31 - mmengine - INFO - Epoch(train) [196][200/586] lr: 5.000000e-05 eta: 0:45:38 time: 0.331407 data_time: 0.023269 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.867279 loss: 0.000464 2022/09/13 07:38:48 - mmengine - INFO - Epoch(train) [196][250/586] lr: 5.000000e-05 eta: 0:45:22 time: 0.339197 data_time: 0.023079 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.873775 loss: 0.000467 2022/09/13 07:39:05 - mmengine - INFO - Epoch(train) [196][300/586] lr: 5.000000e-05 eta: 0:45:06 time: 0.332506 data_time: 0.023329 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.927361 loss: 0.000485 2022/09/13 07:39:22 - mmengine - INFO - Epoch(train) [196][350/586] lr: 5.000000e-05 eta: 0:44:50 time: 0.338963 data_time: 0.022638 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.863812 loss: 0.000478 2022/09/13 07:39:39 - mmengine - INFO - Epoch(train) [196][400/586] lr: 5.000000e-05 eta: 0:44:34 time: 0.336888 data_time: 0.022023 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.917710 loss: 0.000477 2022/09/13 07:39:56 - mmengine - INFO - Epoch(train) [196][450/586] lr: 5.000000e-05 eta: 0:44:18 time: 0.338780 data_time: 0.022659 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.849416 loss: 0.000478 2022/09/13 07:40:12 - mmengine - INFO - Epoch(train) [196][500/586] lr: 5.000000e-05 eta: 0:44:02 time: 0.333089 data_time: 0.023671 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.855094 loss: 0.000479 2022/09/13 07:40:29 - mmengine - INFO - Epoch(train) [196][550/586] lr: 5.000000e-05 eta: 0:43:46 time: 0.336599 data_time: 0.022153 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.888136 loss: 0.000475 2022/09/13 07:40:41 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:40:41 - mmengine - INFO - Saving checkpoint at 196 epochs 2022/09/13 07:41:05 - mmengine - INFO - Epoch(train) [197][50/586] lr: 5.000000e-05 eta: 0:43:18 time: 0.340315 data_time: 0.031494 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.881044 loss: 0.000494 2022/09/13 07:41:22 - mmengine - INFO - Epoch(train) [197][100/586] lr: 5.000000e-05 eta: 0:43:02 time: 0.336461 data_time: 0.023615 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.832369 loss: 0.000503 2022/09/13 07:41:37 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:41:39 - mmengine - INFO - Epoch(train) [197][150/586] lr: 5.000000e-05 eta: 0:42:47 time: 0.339615 data_time: 0.025610 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.885908 loss: 0.000476 2022/09/13 07:41:56 - mmengine - INFO - Epoch(train) [197][200/586] lr: 5.000000e-05 eta: 0:42:31 time: 0.337739 data_time: 0.023909 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.854246 loss: 0.000471 2022/09/13 07:42:13 - mmengine - INFO - Epoch(train) [197][250/586] lr: 5.000000e-05 eta: 0:42:15 time: 0.340670 data_time: 0.026614 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.814292 loss: 0.000485 2022/09/13 07:42:30 - mmengine - INFO - Epoch(train) [197][300/586] lr: 5.000000e-05 eta: 0:41:59 time: 0.336135 data_time: 0.022866 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.910529 loss: 0.000483 2022/09/13 07:42:47 - mmengine - INFO - Epoch(train) [197][350/586] lr: 5.000000e-05 eta: 0:41:43 time: 0.339941 data_time: 0.022720 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.855600 loss: 0.000483 2022/09/13 07:43:04 - mmengine - INFO - Epoch(train) [197][400/586] lr: 5.000000e-05 eta: 0:41:27 time: 0.334349 data_time: 0.022643 memory: 7489 loss_kpt: 0.000491 acc_pose: 0.852516 loss: 0.000491 2022/09/13 07:43:21 - mmengine - INFO - Epoch(train) [197][450/586] lr: 5.000000e-05 eta: 0:41:11 time: 0.339341 data_time: 0.022217 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.921307 loss: 0.000468 2022/09/13 07:43:38 - mmengine - INFO - Epoch(train) [197][500/586] lr: 5.000000e-05 eta: 0:40:55 time: 0.341155 data_time: 0.022578 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.835279 loss: 0.000482 2022/09/13 07:43:54 - mmengine - INFO - Epoch(train) [197][550/586] lr: 5.000000e-05 eta: 0:40:40 time: 0.327300 data_time: 0.022049 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.920946 loss: 0.000469 2022/09/13 07:44:07 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:44:07 - mmengine - INFO - Saving checkpoint at 197 epochs 2022/09/13 07:44:30 - mmengine - INFO - Epoch(train) [198][50/586] lr: 5.000000e-05 eta: 0:40:12 time: 0.346702 data_time: 0.031327 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.806664 loss: 0.000466 2022/09/13 07:44:47 - mmengine - INFO - Epoch(train) [198][100/586] lr: 5.000000e-05 eta: 0:39:56 time: 0.330737 data_time: 0.022553 memory: 7489 loss_kpt: 0.000454 acc_pose: 0.878268 loss: 0.000454 2022/09/13 07:45:04 - mmengine - INFO - Epoch(train) [198][150/586] lr: 5.000000e-05 eta: 0:39:40 time: 0.338209 data_time: 0.021922 memory: 7489 loss_kpt: 0.000465 acc_pose: 0.843847 loss: 0.000465 2022/09/13 07:45:21 - mmengine - INFO - Epoch(train) [198][200/586] lr: 5.000000e-05 eta: 0:39:24 time: 0.336345 data_time: 0.022551 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.934651 loss: 0.000461 2022/09/13 07:45:38 - mmengine - INFO - Epoch(train) [198][250/586] lr: 5.000000e-05 eta: 0:39:08 time: 0.335218 data_time: 0.022843 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.869081 loss: 0.000476 2022/09/13 07:45:54 - mmengine - INFO - Epoch(train) [198][300/586] lr: 5.000000e-05 eta: 0:38:52 time: 0.328541 data_time: 0.022442 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.847585 loss: 0.000476 2022/09/13 07:46:11 - mmengine - INFO - Epoch(train) [198][350/586] lr: 5.000000e-05 eta: 0:38:36 time: 0.344007 data_time: 0.021845 memory: 7489 loss_kpt: 0.000494 acc_pose: 0.869222 loss: 0.000494 2022/09/13 07:46:28 - mmengine - INFO - Epoch(train) [198][400/586] lr: 5.000000e-05 eta: 0:38:20 time: 0.331945 data_time: 0.022176 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.875288 loss: 0.000467 2022/09/13 07:46:45 - mmengine - INFO - Epoch(train) [198][450/586] lr: 5.000000e-05 eta: 0:38:04 time: 0.346088 data_time: 0.023167 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.868181 loss: 0.000474 2022/09/13 07:47:02 - mmengine - INFO - Epoch(train) [198][500/586] lr: 5.000000e-05 eta: 0:37:49 time: 0.337180 data_time: 0.022737 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.888044 loss: 0.000478 2022/09/13 07:47:19 - mmengine - INFO - Epoch(train) [198][550/586] lr: 5.000000e-05 eta: 0:37:33 time: 0.343400 data_time: 0.022656 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.864081 loss: 0.000474 2022/09/13 07:47:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:47:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:47:31 - mmengine - INFO - Saving checkpoint at 198 epochs 2022/09/13 07:47:56 - mmengine - INFO - Epoch(train) [199][50/586] lr: 5.000000e-05 eta: 0:37:05 time: 0.343590 data_time: 0.027447 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.817871 loss: 0.000480 2022/09/13 07:48:12 - mmengine - INFO - Epoch(train) [199][100/586] lr: 5.000000e-05 eta: 0:36:49 time: 0.333430 data_time: 0.022749 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.852516 loss: 0.000474 2022/09/13 07:48:29 - mmengine - INFO - Epoch(train) [199][150/586] lr: 5.000000e-05 eta: 0:36:33 time: 0.340143 data_time: 0.026259 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.866823 loss: 0.000479 2022/09/13 07:48:46 - mmengine - INFO - Epoch(train) [199][200/586] lr: 5.000000e-05 eta: 0:36:17 time: 0.338517 data_time: 0.023601 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.882938 loss: 0.000481 2022/09/13 07:49:03 - mmengine - INFO - Epoch(train) [199][250/586] lr: 5.000000e-05 eta: 0:36:01 time: 0.337547 data_time: 0.022566 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.906853 loss: 0.000486 2022/09/13 07:49:20 - mmengine - INFO - Epoch(train) [199][300/586] lr: 5.000000e-05 eta: 0:35:45 time: 0.336714 data_time: 0.022687 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.855635 loss: 0.000478 2022/09/13 07:49:37 - mmengine - INFO - Epoch(train) [199][350/586] lr: 5.000000e-05 eta: 0:35:29 time: 0.336820 data_time: 0.022074 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.831938 loss: 0.000482 2022/09/13 07:49:54 - mmengine - INFO - Epoch(train) [199][400/586] lr: 5.000000e-05 eta: 0:35:14 time: 0.340402 data_time: 0.022293 memory: 7489 loss_kpt: 0.000458 acc_pose: 0.908930 loss: 0.000458 2022/09/13 07:50:11 - mmengine - INFO - Epoch(train) [199][450/586] lr: 5.000000e-05 eta: 0:34:58 time: 0.329959 data_time: 0.022618 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.850208 loss: 0.000472 2022/09/13 07:50:28 - mmengine - INFO - Epoch(train) [199][500/586] lr: 5.000000e-05 eta: 0:34:42 time: 0.344489 data_time: 0.022276 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.932320 loss: 0.000470 2022/09/13 07:50:44 - mmengine - INFO - Epoch(train) [199][550/586] lr: 5.000000e-05 eta: 0:34:26 time: 0.333979 data_time: 0.023925 memory: 7489 loss_kpt: 0.000448 acc_pose: 0.871059 loss: 0.000448 2022/09/13 07:50:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:50:56 - mmengine - INFO - Saving checkpoint at 199 epochs 2022/09/13 07:51:21 - mmengine - INFO - Epoch(train) [200][50/586] lr: 5.000000e-05 eta: 0:33:58 time: 0.348295 data_time: 0.031259 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.896683 loss: 0.000480 2022/09/13 07:51:38 - mmengine - INFO - Epoch(train) [200][100/586] lr: 5.000000e-05 eta: 0:33:42 time: 0.335898 data_time: 0.023733 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.846799 loss: 0.000471 2022/09/13 07:51:55 - mmengine - INFO - Epoch(train) [200][150/586] lr: 5.000000e-05 eta: 0:33:26 time: 0.338535 data_time: 0.022414 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.906977 loss: 0.000478 2022/09/13 07:52:11 - mmengine - INFO - Epoch(train) [200][200/586] lr: 5.000000e-05 eta: 0:33:10 time: 0.335739 data_time: 0.022428 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.892137 loss: 0.000481 2022/09/13 07:52:29 - mmengine - INFO - Epoch(train) [200][250/586] lr: 5.000000e-05 eta: 0:32:54 time: 0.341926 data_time: 0.023206 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.891951 loss: 0.000485 2022/09/13 07:52:46 - mmengine - INFO - Epoch(train) [200][300/586] lr: 5.000000e-05 eta: 0:32:38 time: 0.339840 data_time: 0.022070 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.885352 loss: 0.000469 2022/09/13 07:53:03 - mmengine - INFO - Epoch(train) [200][350/586] lr: 5.000000e-05 eta: 0:32:23 time: 0.339092 data_time: 0.026749 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.895300 loss: 0.000481 2022/09/13 07:53:15 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:53:19 - mmengine - INFO - Epoch(train) [200][400/586] lr: 5.000000e-05 eta: 0:32:07 time: 0.336466 data_time: 0.023244 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.880956 loss: 0.000473 2022/09/13 07:53:36 - mmengine - INFO - Epoch(train) [200][450/586] lr: 5.000000e-05 eta: 0:31:51 time: 0.332689 data_time: 0.022983 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.875966 loss: 0.000488 2022/09/13 07:53:53 - mmengine - INFO - Epoch(train) [200][500/586] lr: 5.000000e-05 eta: 0:31:35 time: 0.333736 data_time: 0.022412 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.836143 loss: 0.000488 2022/09/13 07:54:10 - mmengine - INFO - Epoch(train) [200][550/586] lr: 5.000000e-05 eta: 0:31:19 time: 0.342245 data_time: 0.022434 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.924231 loss: 0.000473 2022/09/13 07:54:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:54:22 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/09/13 07:54:39 - mmengine - INFO - Epoch(val) [200][50/407] eta: 0:01:07 time: 0.188712 data_time: 0.014749 memory: 7489 2022/09/13 07:54:48 - mmengine - INFO - Epoch(val) [200][100/407] eta: 0:00:54 time: 0.178133 data_time: 0.007916 memory: 1657 2022/09/13 07:54:56 - mmengine - INFO - Epoch(val) [200][150/407] eta: 0:00:45 time: 0.178053 data_time: 0.007898 memory: 1657 2022/09/13 07:55:05 - mmengine - INFO - Epoch(val) [200][200/407] eta: 0:00:36 time: 0.177805 data_time: 0.007550 memory: 1657 2022/09/13 07:55:14 - mmengine - INFO - Epoch(val) [200][250/407] eta: 0:00:27 time: 0.178123 data_time: 0.007652 memory: 1657 2022/09/13 07:55:23 - mmengine - INFO - Epoch(val) [200][300/407] eta: 0:00:19 time: 0.177908 data_time: 0.007259 memory: 1657 2022/09/13 07:55:32 - mmengine - INFO - Epoch(val) [200][350/407] eta: 0:00:10 time: 0.182469 data_time: 0.007309 memory: 1657 2022/09/13 07:55:41 - mmengine - INFO - Epoch(val) [200][400/407] eta: 0:00:01 time: 0.175269 data_time: 0.006804 memory: 1657 2022/09/13 07:56:16 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 07:56:30 - mmengine - INFO - Epoch(val) [200][407/407] coco/AP: 0.766723 coco/AP .5: 0.906751 coco/AP .75: 0.831564 coco/AP (M): 0.729783 coco/AP (L): 0.836593 coco/AR: 0.816152 coco/AR .5: 0.943168 coco/AR .75: 0.873111 coco/AR (M): 0.773423 coco/AR (L): 0.878595 2022/09/13 07:56:48 - mmengine - INFO - Epoch(train) [201][50/586] lr: 5.000000e-06 eta: 0:30:51 time: 0.353100 data_time: 0.027619 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.891547 loss: 0.000478 2022/09/13 07:57:05 - mmengine - INFO - Epoch(train) [201][100/586] lr: 5.000000e-06 eta: 0:30:35 time: 0.339273 data_time: 0.024772 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.867120 loss: 0.000464 2022/09/13 07:57:21 - mmengine - INFO - Epoch(train) [201][150/586] lr: 5.000000e-06 eta: 0:30:19 time: 0.332023 data_time: 0.025717 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.851339 loss: 0.000476 2022/09/13 07:57:38 - mmengine - INFO - Epoch(train) [201][200/586] lr: 5.000000e-06 eta: 0:30:03 time: 0.340666 data_time: 0.030562 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.852211 loss: 0.000495 2022/09/13 07:57:55 - mmengine - INFO - Epoch(train) [201][250/586] lr: 5.000000e-06 eta: 0:29:48 time: 0.340855 data_time: 0.022045 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.867321 loss: 0.000479 2022/09/13 07:58:12 - mmengine - INFO - Epoch(train) [201][300/586] lr: 5.000000e-06 eta: 0:29:32 time: 0.334223 data_time: 0.022843 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.888708 loss: 0.000484 2022/09/13 07:58:29 - mmengine - INFO - Epoch(train) [201][350/586] lr: 5.000000e-06 eta: 0:29:16 time: 0.335781 data_time: 0.022349 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.890986 loss: 0.000464 2022/09/13 07:58:46 - mmengine - INFO - Epoch(train) [201][400/586] lr: 5.000000e-06 eta: 0:29:00 time: 0.340164 data_time: 0.022895 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.894633 loss: 0.000478 2022/09/13 07:59:03 - mmengine - INFO - Epoch(train) [201][450/586] lr: 5.000000e-06 eta: 0:28:44 time: 0.333661 data_time: 0.022236 memory: 7489 loss_kpt: 0.000501 acc_pose: 0.892181 loss: 0.000501 2022/09/13 07:59:20 - mmengine - INFO - Epoch(train) [201][500/586] lr: 5.000000e-06 eta: 0:28:28 time: 0.343377 data_time: 0.022305 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.905406 loss: 0.000464 2022/09/13 07:59:36 - mmengine - INFO - Epoch(train) [201][550/586] lr: 5.000000e-06 eta: 0:28:12 time: 0.335026 data_time: 0.022093 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.828829 loss: 0.000467 2022/09/13 07:59:48 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 07:59:48 - mmengine - INFO - Saving checkpoint at 201 epochs 2022/09/13 08:00:13 - mmengine - INFO - Epoch(train) [202][50/586] lr: 5.000000e-06 eta: 0:27:44 time: 0.340457 data_time: 0.027337 memory: 7489 loss_kpt: 0.000488 acc_pose: 0.855100 loss: 0.000488 2022/09/13 08:00:29 - mmengine - INFO - Epoch(train) [202][100/586] lr: 5.000000e-06 eta: 0:27:28 time: 0.335503 data_time: 0.022502 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.890758 loss: 0.000466 2022/09/13 08:00:46 - mmengine - INFO - Epoch(train) [202][150/586] lr: 5.000000e-06 eta: 0:27:13 time: 0.340798 data_time: 0.023056 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.853781 loss: 0.000475 2022/09/13 08:01:03 - mmengine - INFO - Epoch(train) [202][200/586] lr: 5.000000e-06 eta: 0:26:57 time: 0.330444 data_time: 0.021884 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.863293 loss: 0.000470 2022/09/13 08:01:08 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:01:20 - mmengine - INFO - Epoch(train) [202][250/586] lr: 5.000000e-06 eta: 0:26:41 time: 0.340711 data_time: 0.022298 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.874355 loss: 0.000479 2022/09/13 08:01:37 - mmengine - INFO - Epoch(train) [202][300/586] lr: 5.000000e-06 eta: 0:26:25 time: 0.337065 data_time: 0.022506 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.836163 loss: 0.000483 2022/09/13 08:01:53 - mmengine - INFO - Epoch(train) [202][350/586] lr: 5.000000e-06 eta: 0:26:09 time: 0.330074 data_time: 0.022315 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.879646 loss: 0.000479 2022/09/13 08:02:10 - mmengine - INFO - Epoch(train) [202][400/586] lr: 5.000000e-06 eta: 0:25:53 time: 0.340021 data_time: 0.022447 memory: 7489 loss_kpt: 0.000503 acc_pose: 0.831917 loss: 0.000503 2022/09/13 08:02:28 - mmengine - INFO - Epoch(train) [202][450/586] lr: 5.000000e-06 eta: 0:25:37 time: 0.344033 data_time: 0.022770 memory: 7489 loss_kpt: 0.000495 acc_pose: 0.837748 loss: 0.000495 2022/09/13 08:02:44 - mmengine - INFO - Epoch(train) [202][500/586] lr: 5.000000e-06 eta: 0:25:21 time: 0.330990 data_time: 0.022945 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.888688 loss: 0.000461 2022/09/13 08:03:01 - mmengine - INFO - Epoch(train) [202][550/586] lr: 5.000000e-06 eta: 0:25:05 time: 0.345596 data_time: 0.022616 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.846770 loss: 0.000484 2022/09/13 08:03:14 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:03:14 - mmengine - INFO - Saving checkpoint at 202 epochs 2022/09/13 08:03:38 - mmengine - INFO - Epoch(train) [203][50/586] lr: 5.000000e-06 eta: 0:24:38 time: 0.343958 data_time: 0.032281 memory: 7489 loss_kpt: 0.000490 acc_pose: 0.804833 loss: 0.000490 2022/09/13 08:03:55 - mmengine - INFO - Epoch(train) [203][100/586] lr: 5.000000e-06 eta: 0:24:22 time: 0.341875 data_time: 0.021640 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.833419 loss: 0.000471 2022/09/13 08:04:12 - mmengine - INFO - Epoch(train) [203][150/586] lr: 5.000000e-06 eta: 0:24:06 time: 0.338964 data_time: 0.021785 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.827261 loss: 0.000486 2022/09/13 08:04:29 - mmengine - INFO - Epoch(train) [203][200/586] lr: 5.000000e-06 eta: 0:23:50 time: 0.336743 data_time: 0.025682 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.877479 loss: 0.000475 2022/09/13 08:04:46 - mmengine - INFO - Epoch(train) [203][250/586] lr: 5.000000e-06 eta: 0:23:34 time: 0.337846 data_time: 0.022649 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.907893 loss: 0.000474 2022/09/13 08:05:03 - mmengine - INFO - Epoch(train) [203][300/586] lr: 5.000000e-06 eta: 0:23:18 time: 0.335637 data_time: 0.023080 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.831058 loss: 0.000483 2022/09/13 08:05:20 - mmengine - INFO - Epoch(train) [203][350/586] lr: 5.000000e-06 eta: 0:23:02 time: 0.344143 data_time: 0.022850 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.941498 loss: 0.000473 2022/09/13 08:05:37 - mmengine - INFO - Epoch(train) [203][400/586] lr: 5.000000e-06 eta: 0:22:46 time: 0.334883 data_time: 0.023026 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.853158 loss: 0.000477 2022/09/13 08:05:53 - mmengine - INFO - Epoch(train) [203][450/586] lr: 5.000000e-06 eta: 0:22:30 time: 0.331335 data_time: 0.022858 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.882877 loss: 0.000466 2022/09/13 08:06:11 - mmengine - INFO - Epoch(train) [203][500/586] lr: 5.000000e-06 eta: 0:22:14 time: 0.343282 data_time: 0.022621 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.887363 loss: 0.000466 2022/09/13 08:06:28 - mmengine - INFO - Epoch(train) [203][550/586] lr: 5.000000e-06 eta: 0:21:59 time: 0.341181 data_time: 0.022873 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.907065 loss: 0.000467 2022/09/13 08:06:39 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:06:39 - mmengine - INFO - Saving checkpoint at 203 epochs 2022/09/13 08:07:01 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:07:04 - mmengine - INFO - Epoch(train) [204][50/586] lr: 5.000000e-06 eta: 0:21:31 time: 0.344405 data_time: 0.029417 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.901053 loss: 0.000477 2022/09/13 08:07:21 - mmengine - INFO - Epoch(train) [204][100/586] lr: 5.000000e-06 eta: 0:21:15 time: 0.343634 data_time: 0.022960 memory: 7489 loss_kpt: 0.000486 acc_pose: 0.813595 loss: 0.000486 2022/09/13 08:07:38 - mmengine - INFO - Epoch(train) [204][150/586] lr: 5.000000e-06 eta: 0:20:59 time: 0.339134 data_time: 0.022321 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.826717 loss: 0.000479 2022/09/13 08:07:55 - mmengine - INFO - Epoch(train) [204][200/586] lr: 5.000000e-06 eta: 0:20:43 time: 0.337253 data_time: 0.021870 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.890007 loss: 0.000481 2022/09/13 08:08:12 - mmengine - INFO - Epoch(train) [204][250/586] lr: 5.000000e-06 eta: 0:20:27 time: 0.336913 data_time: 0.023341 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.887157 loss: 0.000485 2022/09/13 08:08:29 - mmengine - INFO - Epoch(train) [204][300/586] lr: 5.000000e-06 eta: 0:20:11 time: 0.339185 data_time: 0.022229 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.899013 loss: 0.000468 2022/09/13 08:08:46 - mmengine - INFO - Epoch(train) [204][350/586] lr: 5.000000e-06 eta: 0:19:55 time: 0.338333 data_time: 0.022681 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.895404 loss: 0.000469 2022/09/13 08:09:02 - mmengine - INFO - Epoch(train) [204][400/586] lr: 5.000000e-06 eta: 0:19:39 time: 0.336597 data_time: 0.022818 memory: 7489 loss_kpt: 0.000461 acc_pose: 0.896875 loss: 0.000461 2022/09/13 08:09:20 - mmengine - INFO - Epoch(train) [204][450/586] lr: 5.000000e-06 eta: 0:19:24 time: 0.343123 data_time: 0.026421 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.846976 loss: 0.000475 2022/09/13 08:09:37 - mmengine - INFO - Epoch(train) [204][500/586] lr: 5.000000e-06 eta: 0:19:08 time: 0.337185 data_time: 0.023675 memory: 7489 loss_kpt: 0.000460 acc_pose: 0.866155 loss: 0.000460 2022/09/13 08:09:54 - mmengine - INFO - Epoch(train) [204][550/586] lr: 5.000000e-06 eta: 0:18:52 time: 0.339734 data_time: 0.022325 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.865457 loss: 0.000477 2022/09/13 08:10:06 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:10:06 - mmengine - INFO - Saving checkpoint at 204 epochs 2022/09/13 08:10:30 - mmengine - INFO - Epoch(train) [205][50/586] lr: 5.000000e-06 eta: 0:18:24 time: 0.335786 data_time: 0.027869 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.941411 loss: 0.000478 2022/09/13 08:10:47 - mmengine - INFO - Epoch(train) [205][100/586] lr: 5.000000e-06 eta: 0:18:08 time: 0.342748 data_time: 0.026221 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.844306 loss: 0.000476 2022/09/13 08:11:03 - mmengine - INFO - Epoch(train) [205][150/586] lr: 5.000000e-06 eta: 0:17:52 time: 0.331893 data_time: 0.022817 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.910966 loss: 0.000471 2022/09/13 08:11:20 - mmengine - INFO - Epoch(train) [205][200/586] lr: 5.000000e-06 eta: 0:17:36 time: 0.335938 data_time: 0.022277 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.814594 loss: 0.000475 2022/09/13 08:11:37 - mmengine - INFO - Epoch(train) [205][250/586] lr: 5.000000e-06 eta: 0:17:20 time: 0.338603 data_time: 0.025354 memory: 7489 loss_kpt: 0.000489 acc_pose: 0.887314 loss: 0.000489 2022/09/13 08:11:54 - mmengine - INFO - Epoch(train) [205][300/586] lr: 5.000000e-06 eta: 0:17:04 time: 0.336383 data_time: 0.022281 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.897833 loss: 0.000480 2022/09/13 08:12:11 - mmengine - INFO - Epoch(train) [205][350/586] lr: 5.000000e-06 eta: 0:16:49 time: 0.339767 data_time: 0.023510 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.844148 loss: 0.000480 2022/09/13 08:12:28 - mmengine - INFO - Epoch(train) [205][400/586] lr: 5.000000e-06 eta: 0:16:33 time: 0.338370 data_time: 0.022204 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.861886 loss: 0.000481 2022/09/13 08:12:45 - mmengine - INFO - Epoch(train) [205][450/586] lr: 5.000000e-06 eta: 0:16:17 time: 0.335743 data_time: 0.022877 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.910005 loss: 0.000483 2022/09/13 08:12:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:13:02 - mmengine - INFO - Epoch(train) [205][500/586] lr: 5.000000e-06 eta: 0:16:01 time: 0.336119 data_time: 0.022812 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.868698 loss: 0.000473 2022/09/13 08:13:18 - mmengine - INFO - Epoch(train) [205][550/586] lr: 5.000000e-06 eta: 0:15:45 time: 0.334739 data_time: 0.022032 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.875154 loss: 0.000470 2022/09/13 08:13:31 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:13:31 - mmengine - INFO - Saving checkpoint at 205 epochs 2022/09/13 08:13:55 - mmengine - INFO - Epoch(train) [206][50/586] lr: 5.000000e-06 eta: 0:15:17 time: 0.350650 data_time: 0.032920 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.866390 loss: 0.000468 2022/09/13 08:14:12 - mmengine - INFO - Epoch(train) [206][100/586] lr: 5.000000e-06 eta: 0:15:01 time: 0.338046 data_time: 0.023942 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.897569 loss: 0.000483 2022/09/13 08:14:29 - mmengine - INFO - Epoch(train) [206][150/586] lr: 5.000000e-06 eta: 0:14:45 time: 0.339560 data_time: 0.022065 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.897190 loss: 0.000476 2022/09/13 08:14:46 - mmengine - INFO - Epoch(train) [206][200/586] lr: 5.000000e-06 eta: 0:14:30 time: 0.333313 data_time: 0.022821 memory: 7489 loss_kpt: 0.000485 acc_pose: 0.881663 loss: 0.000485 2022/09/13 08:15:03 - mmengine - INFO - Epoch(train) [206][250/586] lr: 5.000000e-06 eta: 0:14:14 time: 0.339887 data_time: 0.022893 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.883194 loss: 0.000482 2022/09/13 08:15:20 - mmengine - INFO - Epoch(train) [206][300/586] lr: 5.000000e-06 eta: 0:13:58 time: 0.334958 data_time: 0.022393 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.851525 loss: 0.000470 2022/09/13 08:15:37 - mmengine - INFO - Epoch(train) [206][350/586] lr: 5.000000e-06 eta: 0:13:42 time: 0.338649 data_time: 0.022824 memory: 7489 loss_kpt: 0.000496 acc_pose: 0.876686 loss: 0.000496 2022/09/13 08:15:54 - mmengine - INFO - Epoch(train) [206][400/586] lr: 5.000000e-06 eta: 0:13:26 time: 0.338199 data_time: 0.021770 memory: 7489 loss_kpt: 0.000457 acc_pose: 0.928600 loss: 0.000457 2022/09/13 08:16:10 - mmengine - INFO - Epoch(train) [206][450/586] lr: 5.000000e-06 eta: 0:13:10 time: 0.336982 data_time: 0.021926 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.867082 loss: 0.000476 2022/09/13 08:16:27 - mmengine - INFO - Epoch(train) [206][500/586] lr: 5.000000e-06 eta: 0:12:54 time: 0.333793 data_time: 0.025977 memory: 7489 loss_kpt: 0.000482 acc_pose: 0.902100 loss: 0.000482 2022/09/13 08:16:44 - mmengine - INFO - Epoch(train) [206][550/586] lr: 5.000000e-06 eta: 0:12:38 time: 0.338974 data_time: 0.022816 memory: 7489 loss_kpt: 0.000473 acc_pose: 0.923356 loss: 0.000473 2022/09/13 08:16:56 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:16:56 - mmengine - INFO - Saving checkpoint at 206 epochs 2022/09/13 08:17:20 - mmengine - INFO - Epoch(train) [207][50/586] lr: 5.000000e-06 eta: 0:12:11 time: 0.339335 data_time: 0.027698 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.897295 loss: 0.000478 2022/09/13 08:17:38 - mmengine - INFO - Epoch(train) [207][100/586] lr: 5.000000e-06 eta: 0:11:55 time: 0.343445 data_time: 0.022595 memory: 7489 loss_kpt: 0.000492 acc_pose: 0.887534 loss: 0.000492 2022/09/13 08:17:55 - mmengine - INFO - Epoch(train) [207][150/586] lr: 5.000000e-06 eta: 0:11:39 time: 0.342922 data_time: 0.022469 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.899633 loss: 0.000484 2022/09/13 08:18:11 - mmengine - INFO - Epoch(train) [207][200/586] lr: 5.000000e-06 eta: 0:11:23 time: 0.335417 data_time: 0.022432 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.918309 loss: 0.000477 2022/09/13 08:18:29 - mmengine - INFO - Epoch(train) [207][250/586] lr: 5.000000e-06 eta: 0:11:07 time: 0.348420 data_time: 0.023424 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.908684 loss: 0.000475 2022/09/13 08:18:40 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:18:46 - mmengine - INFO - Epoch(train) [207][300/586] lr: 5.000000e-06 eta: 0:10:51 time: 0.339269 data_time: 0.022422 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.877086 loss: 0.000470 2022/09/13 08:19:03 - mmengine - INFO - Epoch(train) [207][350/586] lr: 5.000000e-06 eta: 0:10:35 time: 0.336014 data_time: 0.022938 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.912887 loss: 0.000467 2022/09/13 08:19:20 - mmengine - INFO - Epoch(train) [207][400/586] lr: 5.000000e-06 eta: 0:10:19 time: 0.336569 data_time: 0.022602 memory: 7489 loss_kpt: 0.000479 acc_pose: 0.852263 loss: 0.000479 2022/09/13 08:19:36 - mmengine - INFO - Epoch(train) [207][450/586] lr: 5.000000e-06 eta: 0:10:03 time: 0.335287 data_time: 0.022016 memory: 7489 loss_kpt: 0.000476 acc_pose: 0.854257 loss: 0.000476 2022/09/13 08:19:53 - mmengine - INFO - Epoch(train) [207][500/586] lr: 5.000000e-06 eta: 0:09:47 time: 0.332120 data_time: 0.022132 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.816402 loss: 0.000472 2022/09/13 08:20:10 - mmengine - INFO - Epoch(train) [207][550/586] lr: 5.000000e-06 eta: 0:09:31 time: 0.339502 data_time: 0.021738 memory: 7489 loss_kpt: 0.000466 acc_pose: 0.894127 loss: 0.000466 2022/09/13 08:20:22 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:20:22 - mmengine - INFO - Saving checkpoint at 207 epochs 2022/09/13 08:20:46 - mmengine - INFO - Epoch(train) [208][50/586] lr: 5.000000e-06 eta: 0:09:04 time: 0.341919 data_time: 0.031924 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.904421 loss: 0.000474 2022/09/13 08:21:03 - mmengine - INFO - Epoch(train) [208][100/586] lr: 5.000000e-06 eta: 0:08:48 time: 0.341012 data_time: 0.023687 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.881193 loss: 0.000477 2022/09/13 08:21:20 - mmengine - INFO - Epoch(train) [208][150/586] lr: 5.000000e-06 eta: 0:08:32 time: 0.335642 data_time: 0.023023 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.856661 loss: 0.000472 2022/09/13 08:21:37 - mmengine - INFO - Epoch(train) [208][200/586] lr: 5.000000e-06 eta: 0:08:16 time: 0.333291 data_time: 0.023403 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.889241 loss: 0.000464 2022/09/13 08:21:54 - mmengine - INFO - Epoch(train) [208][250/586] lr: 5.000000e-06 eta: 0:08:00 time: 0.344090 data_time: 0.021909 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.871955 loss: 0.000474 2022/09/13 08:22:10 - mmengine - INFO - Epoch(train) [208][300/586] lr: 5.000000e-06 eta: 0:07:44 time: 0.328598 data_time: 0.022971 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.892914 loss: 0.000471 2022/09/13 08:22:27 - mmengine - INFO - Epoch(train) [208][350/586] lr: 5.000000e-06 eta: 0:07:28 time: 0.334937 data_time: 0.021831 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.896897 loss: 0.000483 2022/09/13 08:22:44 - mmengine - INFO - Epoch(train) [208][400/586] lr: 5.000000e-06 eta: 0:07:12 time: 0.345633 data_time: 0.024192 memory: 7489 loss_kpt: 0.000483 acc_pose: 0.908290 loss: 0.000483 2022/09/13 08:23:01 - mmengine - INFO - Epoch(train) [208][450/586] lr: 5.000000e-06 eta: 0:06:56 time: 0.331850 data_time: 0.022482 memory: 7489 loss_kpt: 0.000472 acc_pose: 0.894933 loss: 0.000472 2022/09/13 08:23:18 - mmengine - INFO - Epoch(train) [208][500/586] lr: 5.000000e-06 eta: 0:06:40 time: 0.337336 data_time: 0.022631 memory: 7489 loss_kpt: 0.000493 acc_pose: 0.877851 loss: 0.000493 2022/09/13 08:23:35 - mmengine - INFO - Epoch(train) [208][550/586] lr: 5.000000e-06 eta: 0:06:25 time: 0.341007 data_time: 0.025835 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.888552 loss: 0.000480 2022/09/13 08:23:47 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:23:47 - mmengine - INFO - Saving checkpoint at 208 epochs 2022/09/13 08:24:12 - mmengine - INFO - Epoch(train) [209][50/586] lr: 5.000000e-06 eta: 0:05:57 time: 0.347600 data_time: 0.028630 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.914128 loss: 0.000470 2022/09/13 08:24:29 - mmengine - INFO - Epoch(train) [209][100/586] lr: 5.000000e-06 eta: 0:05:41 time: 0.341835 data_time: 0.023557 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.819749 loss: 0.000474 2022/09/13 08:24:33 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:24:46 - mmengine - INFO - Epoch(train) [209][150/586] lr: 5.000000e-06 eta: 0:05:25 time: 0.338707 data_time: 0.022789 memory: 7489 loss_kpt: 0.000469 acc_pose: 0.886402 loss: 0.000469 2022/09/13 08:25:03 - mmengine - INFO - Epoch(train) [209][200/586] lr: 5.000000e-06 eta: 0:05:09 time: 0.339314 data_time: 0.022671 memory: 7489 loss_kpt: 0.000471 acc_pose: 0.867934 loss: 0.000471 2022/09/13 08:25:20 - mmengine - INFO - Epoch(train) [209][250/586] lr: 5.000000e-06 eta: 0:04:53 time: 0.340247 data_time: 0.021934 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.885097 loss: 0.000470 2022/09/13 08:25:36 - mmengine - INFO - Epoch(train) [209][300/586] lr: 5.000000e-06 eta: 0:04:37 time: 0.331642 data_time: 0.022421 memory: 7489 loss_kpt: 0.000475 acc_pose: 0.871410 loss: 0.000475 2022/09/13 08:25:53 - mmengine - INFO - Epoch(train) [209][350/586] lr: 5.000000e-06 eta: 0:04:21 time: 0.333121 data_time: 0.025531 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.859946 loss: 0.000484 2022/09/13 08:26:10 - mmengine - INFO - Epoch(train) [209][400/586] lr: 5.000000e-06 eta: 0:04:06 time: 0.336215 data_time: 0.022517 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.876996 loss: 0.000468 2022/09/13 08:26:27 - mmengine - INFO - Epoch(train) [209][450/586] lr: 5.000000e-06 eta: 0:03:50 time: 0.338676 data_time: 0.021757 memory: 7489 loss_kpt: 0.000474 acc_pose: 0.925260 loss: 0.000474 2022/09/13 08:26:44 - mmengine - INFO - Epoch(train) [209][500/586] lr: 5.000000e-06 eta: 0:03:34 time: 0.337091 data_time: 0.022771 memory: 7489 loss_kpt: 0.000463 acc_pose: 0.833274 loss: 0.000463 2022/09/13 08:27:01 - mmengine - INFO - Epoch(train) [209][550/586] lr: 5.000000e-06 eta: 0:03:18 time: 0.340902 data_time: 0.022428 memory: 7489 loss_kpt: 0.000459 acc_pose: 0.916260 loss: 0.000459 2022/09/13 08:27:13 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:27:13 - mmengine - INFO - Saving checkpoint at 209 epochs 2022/09/13 08:27:37 - mmengine - INFO - Epoch(train) [210][50/586] lr: 5.000000e-06 eta: 0:02:50 time: 0.346988 data_time: 0.035639 memory: 7489 loss_kpt: 0.000464 acc_pose: 0.873956 loss: 0.000464 2022/09/13 08:27:54 - mmengine - INFO - Epoch(train) [210][100/586] lr: 5.000000e-06 eta: 0:02:34 time: 0.337379 data_time: 0.022926 memory: 7489 loss_kpt: 0.000478 acc_pose: 0.832557 loss: 0.000478 2022/09/13 08:28:11 - mmengine - INFO - Epoch(train) [210][150/586] lr: 5.000000e-06 eta: 0:02:18 time: 0.341944 data_time: 0.026099 memory: 7489 loss_kpt: 0.000484 acc_pose: 0.859129 loss: 0.000484 2022/09/13 08:28:28 - mmengine - INFO - Epoch(train) [210][200/586] lr: 5.000000e-06 eta: 0:02:03 time: 0.332092 data_time: 0.022919 memory: 7489 loss_kpt: 0.000467 acc_pose: 0.853121 loss: 0.000467 2022/09/13 08:28:44 - mmengine - INFO - Epoch(train) [210][250/586] lr: 5.000000e-06 eta: 0:01:47 time: 0.334742 data_time: 0.022736 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.873566 loss: 0.000470 2022/09/13 08:29:01 - mmengine - INFO - Epoch(train) [210][300/586] lr: 5.000000e-06 eta: 0:01:31 time: 0.335897 data_time: 0.022184 memory: 7489 loss_kpt: 0.000481 acc_pose: 0.867906 loss: 0.000481 2022/09/13 08:29:18 - mmengine - INFO - Epoch(train) [210][350/586] lr: 5.000000e-06 eta: 0:01:15 time: 0.337939 data_time: 0.021972 memory: 7489 loss_kpt: 0.000468 acc_pose: 0.833651 loss: 0.000468 2022/09/13 08:29:35 - mmengine - INFO - Epoch(train) [210][400/586] lr: 5.000000e-06 eta: 0:00:59 time: 0.338703 data_time: 0.022052 memory: 7489 loss_kpt: 0.000470 acc_pose: 0.879350 loss: 0.000470 2022/09/13 08:29:52 - mmengine - INFO - Epoch(train) [210][450/586] lr: 5.000000e-06 eta: 0:00:43 time: 0.342211 data_time: 0.025901 memory: 7489 loss_kpt: 0.000487 acc_pose: 0.869215 loss: 0.000487 2022/09/13 08:30:09 - mmengine - INFO - Epoch(train) [210][500/586] lr: 5.000000e-06 eta: 0:00:27 time: 0.333719 data_time: 0.022668 memory: 7489 loss_kpt: 0.000477 acc_pose: 0.841180 loss: 0.000477 2022/09/13 08:30:18 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:30:26 - mmengine - INFO - Epoch(train) [210][550/586] lr: 5.000000e-06 eta: 0:00:11 time: 0.338593 data_time: 0.022403 memory: 7489 loss_kpt: 0.000480 acc_pose: 0.835288 loss: 0.000480 2022/09/13 08:30:38 - mmengine - INFO - Exp name: td-hm_hrnet-w48_udp-8xb32-210e_coco-256x192_20220912_194702 2022/09/13 08:30:38 - mmengine - INFO - Saving checkpoint at 210 epochs 2022/09/13 08:30:54 - mmengine - INFO - Epoch(val) [210][50/407] eta: 0:01:05 time: 0.183948 data_time: 0.012136 memory: 7489 2022/09/13 08:31:03 - mmengine - INFO - Epoch(val) [210][100/407] eta: 0:00:54 time: 0.177937 data_time: 0.007695 memory: 1657 2022/09/13 08:31:12 - mmengine - INFO - Epoch(val) [210][150/407] eta: 0:00:45 time: 0.177740 data_time: 0.007459 memory: 1657 2022/09/13 08:31:21 - mmengine - INFO - Epoch(val) [210][200/407] eta: 0:00:37 time: 0.181294 data_time: 0.007623 memory: 1657 2022/09/13 08:31:30 - mmengine - INFO - Epoch(val) [210][250/407] eta: 0:00:27 time: 0.178164 data_time: 0.007321 memory: 1657 2022/09/13 08:31:39 - mmengine - INFO - Epoch(val) [210][300/407] eta: 0:00:19 time: 0.178429 data_time: 0.008177 memory: 1657 2022/09/13 08:31:48 - mmengine - INFO - Epoch(val) [210][350/407] eta: 0:00:10 time: 0.178222 data_time: 0.007944 memory: 1657 2022/09/13 08:31:57 - mmengine - INFO - Epoch(val) [210][400/407] eta: 0:00:01 time: 0.176485 data_time: 0.008044 memory: 1657 2022/09/13 08:32:32 - mmengine - INFO - Evaluating CocoMetric... 2022/09/13 08:32:45 - mmengine - INFO - Epoch(val) [210][407/407] coco/AP: 0.767989 coco/AP .5: 0.907992 coco/AP .75: 0.832867 coco/AP (M): 0.731162 coco/AP (L): 0.837428 coco/AR: 0.817128 coco/AR .5: 0.944584 coco/AR .75: 0.874528 coco/AR (M): 0.775007 coco/AR (L): 0.878967 2022/09/13 08:32:45 - mmengine - INFO - The previous best checkpoint /mnt/lustre/liqikai/work_dirs/20220912/udp_w48_256_v1/best_coco/AP_epoch_180.pth is removed 2022/09/13 08:32:49 - mmengine - INFO - The best checkpoint with 0.7680 coco/AP at 210 epoch is saved to best_coco/AP_epoch_210.pth.